diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..7393429f764158e9ffb96b968a27940203471ac4 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,14 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +*.jpg filter=lfs diff=lfs merge=lfs -text +*.whl filter=lfs diff=lfs merge=lfs -text +*.pdf filter=lfs diff=lfs merge=lfs -text +*.png filter=lfs diff=lfs merge=lfs -text +*.rknn filter=lfs diff=lfs merge=lfs -text +*.caffemodel filter=lfs diff=lfs merge=lfs -text +*.JPEG filter=lfs diff=lfs merge=lfs -text +*.so filter=lfs diff=lfs merge=lfs -text +*.so.* filter=lfs diff=lfs merge=lfs -text +*.a filter=lfs diff=lfs merge=lfs -text +opencv_version filter=lfs diff=lfs merge=lfs -text diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 0000000000000000000000000000000000000000..5aafa5e946b799bd997ec81205a7c55630b73678 --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,152 @@ +# CHANGELOG + +## v2.1.0 + +- Support RV1103B (Beta) +- Support RK2118 (Beta) +- Support Flash Attention (Only RK3562 and RK3576) +- Improve MatMul API +- Improve support for int32 and int64 +- Support more operators and operator fusion + +## v2.0.0-beta0 + + - Support RK3576 (Beta) + - Support RK2118 (Beta) + - Support SDPA (Scaled Dot Product Attention) to improve transformer performance + - Improve custom operators support + - Improve MatMul API + - Improve support for Reshape,Transpose,BatchLayernorm,Softmax,Deconv,Matmul,ScatterND etc. + - Support pytorch 2.1 + - Improve support for QAT models of pytorch and onnx + - Optimize automatic generation of C++ code + + + +## v1.6.0 + + - Support ONNX model of OPSET 12~19 + - Support custom operators (including CPU and GPU) + - Improve support for dynamic weight convolution, Layernorm, RoiAlign, Softmax, ReduceL2, Gelu, GLU, etc. + - Added support for python3.7/3.9/3.11 + - Add rknn_convert function + - Improve transformer support + - Improve MatMul API, such as increasing the K limit length, RK3588 adding int4 * int4 -> int16 support, etc. + - Reduce RV1106 rknn_init initialization time, memory consumption, etc. + - RV1106 adds int16 support for some operators + - Fixed the problem that the convolution operator of RV1106 platform may make random errors in some cases. + - Improve user manual + - Reconstruct the rknn model zoo and add support for multiple models such as detection, segmentation, OCR, and license plate recognition. + + + +## v1.5.2 + +- Improve dynamic shape support +- Improve matmul api support +- Add GPU back-end implementations for some operators such as matmul +- Improve transformer support +- Reduce rknn_init memory usage +- Optimize rknn_init time-consuming + + + +## v1.5.0 + +- Support RK3562 +- Support more NPU operator fuse, such as Conv-Silu/Conv-Swish/Conv-Hardswish/Conv-sigmoid/Conv-HardSwish/Conv-Gelu .. +- Improve support for NHWC output layout +- RK3568/RK3588：The maximum input resolution up to 8192 +- Improve support for Swish/DataConvert/Softmax/Lstm/LayerNorm/Gather/Transpose/Mul/Maxpool/Sigmoid/Pad +- Improve support for CPU operators (Cast, Sin, Cos, RMSNorm, ScalerND, GRU) +- Limited support for dynamic resolution +- Provide MATMUL API +- Add RV1103/RV1106 rknn_server application as proxy between PC and board +- Add more examples such as rknn_dynamic_shape_input_demo and video demo for yolov5 +- Bug fix + + + +## v1.4.0 + +- Support more NPU operators, such as Reshape、Transpose、MatMul、 Max、Min、exGelu、exSoftmax13、Resize etc. + +- Add **Weight Share** function, reduce memory usage. + +- Add **Weight Compression** function, reduce memory and bandwidth usage.(RK3588/RV1103/RV1106) + +- RK3588 supports storing weights or feature maps on SRAM, reducing system bandwidth consumption. + +- RK3588 adds the function of running a single model on multiple cores at the same time. + +- Add new output layout NHWC (C has alignment restrictions) . + +- Improve support for non-4D input. + +- Add more examples such as rknn_yolov5_android_apk_demo and rknn_internal_mem_reuse_demo. + +- Bug fix. + + + +## v1.3.0 + +- Support RV1103/RV1106（Beta SDK） +- rknn_tensor_attr support w_stride(rename from stride) and h_stride +- Rename rknn_destroy_mem() +- Support more NPU operators, such as Where, Resize, Pad, Reshape, Transpose etc. +- RK3588 support multi-batch multi-core mode +- When RKNN_LOG_LEVEL=4, it supports to display the MACs utilization and bandwidth occupation of each layer. +- Bug fix + + + +## v1.2.0 + +- Support RK3588 +- Support more operators, such as GRU、Swish、LayerNorm etc. +- Reduce memory usage +- Improve zero-copy interface implementation +- Bug fix + + + +## v1.1.0 + +- Support INT8+FP16 mixed quantization to improve model accuracy +- Support specifying input and output dtype, which can be solidified into the model +- Support multiple inputs of the model with different channel mean/std +- Improve the stability of multi-thread + multi-process runtime +- Support flashing cache for fd pointed to internal tensor memory which are allocated by users +- Improve dumping internal layer results of the model +- Add rknn_server application as proxy between PC and board +- Support more operators, such as HardSigmoid、HardSwish、Gather、ReduceMax、Elu +- Add LSTM support (structure cifg and peephole are not supported, function: layernormal, clip is not supported) +- Bug fix + + + +## v1.0 + +- Optimize the performance of rknn_inputs_set() +- Add more functions for zero-copy +- Add new OP support, see OP support list document for details. +- Add multi-process support +- Support per-channel quantitative model +- Bug fix + + + +## v0.7 + +- Optimize the performance of rknn_inputs_set(), especially for models whose input width is 8-byte aligned. + +- Add new OP support, see OP support list document for details. + +- Bug fix + + + +## v0.6 +- Initial version + diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..e784451492a8cfa786654047e7165c355fc48838 --- /dev/null +++ b/LICENSE @@ -0,0 +1,5 @@ +**Copyright Statement** + +Copyright(C) 2024 Rockchip Electronics Co., Ltd. All rights reserved. + +BY OPENING OR USING THIS FILE, RECEIVER HEREBY ACKNOWLEDGES AND AGREES THAT THE SOFTWARE/FIRMWARE AND ITS DOCUMENTATIONS ("ROCKCHIP SOFTWARE") RECEIVED FROM ROCKCHIP ON AN "AS-IS" BASIS ONLY WITHOUT ANY AND ALL WARRANTIES, EITHER EXPRESS, IMPLIED OR STATUTORY, INCLUDING, WITHOUT LIMITATION, ANY WARRANTY OR CONDITION WITH RESPECT TO TITLE, MERCHANTABILITY, FITNESS FOR ANY PARTICULAR PURPOSE, OR NON-INFRINGEMENT. NEITHER DOES ROCKCHIP PROVIDE ANY WARRANTY WHATSOEVER WITH RESPECT TO ANY OPEN SOURCE TECHNOLOGIES, THIRD-PARTY TECHNOLOGIES OR ANY STANDARD TECHNOLOGIES WHICH MAY BE SUPPORTED BY, INCORPORATED IN, OR SUPPLIED WITH THE ROCKCHIP SOFTWARE. RECEIVER EXPRESSLY ACKNOWLEDGES THAT IT IS RECEIVER'S SOLE RESPONSIBILITY TO OBTAIN AND MAINTAIN ALL NECESSARY LICENSES AND RIGHTS FROM THEIR RESPECTIVE OWNERS TO USE ANY SUCH THIRD-PARTY TECHNOLOGIES OR ANY STANDARD TECHNOLOGIES. RECEIVER'S SOLE AND EXCLUSIVE REMEDY AND ROCKCHIP'S ENTIRE AND CUMULATIVE LIABILITY WITH RESPECT TO THE ROCKCHIP SOFTWARE RELEASED HEREUNDER WILL BE, AT ROCKCHIP 'S OPTION, TO REVISE OR REPLACE THE ROCKCHIP SOFTWARE AT ISSUE, OR REFUND ANY FEES OR CHARGE PAID BY RECEIVER TO ROCKCHIP FOR SUCH ROCKCHIP SOFTWARE AT ISSUE. diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..ddbd82bdf27230d7c7633e19fac6cc68d7ef689a --- /dev/null +++ b/README.md @@ -0,0 +1,84 @@ +# Description + RKNN software stack can help users to quickly deploy AI models to Rockchip chips. The overall framework is as follows: +
+
+ +
+ + In order to use RKNPU, users need to first run the RKNN-Toolkit2 tool on the computer, convert the trained model into an RKNN format model, and then inference on the development board using the RKNN C API or Python API. + +- RKNN-Toolkit2 is a software development kit for users to perform model conversion, inference and performance evaluation on PC and Rockchip NPU platforms. + +- RKNN-Toolkit-Lite2 provides Python programming interfaces for Rockchip NPU platform to help users deploy RKNN models and accelerate the implementation of AI applications. + +- RKNN Runtime provides C/C++ programming interfaces for Rockchip NPU platform to help users deploy RKNN models and accelerate the implementation of AI applications. + +- RKNPU kernel driver is responsible for interacting with NPU hardware. It has been open source and can be found in the Rockchip kernel code. + +# Support Platform + - RK3588 Series + - RK3576 Series + - RK3566/RK3568 Series + - RK3562 Series + - RV1103/RV1106 + - RV1103B + - RK2118 + + +Note: + +​ **For RK1808/RV1109/RV1126/RK3399Pro, please refer to :** + +​ https://github.com/airockchip/rknn-toolkit + +​ https://github.com/airockchip/rknpu + +​ https://github.com/airockchip/RK3399Pro_npu + + +# Download +- You can also download all packages, docker image, examples, docs and platform-tools from [RKNPU2_SDK](https://console.zbox.filez.com/l/I00fc3), fetch code: rknn +- You can get more examples from [rknn mode zoo](https://github.com/airockchip/rknn_model_zoo) + +# Notes +- RKNN-Toolkit2 is not compatible with [RKNN-Toolkit](https://github.com/airockchip/rknn-toolkit) +- Currently only support on: + - Ubuntu 18.04 python 3.6/3.7 + - Ubuntu 20.04 python 3.8/3.9 + - Ubuntu 22.04 python 3.10/3.11 +- Latest version:v2.1.0 + + + +# RKNN LLM + +If you want to deploy LLM (Large Language Model), we have introduced a new SDK called RKNN-LLM. For details, please refer to: + +https://github.com/airockchip/rknn-llm + + + +# CHANGELOG + +## v2.1.0 + - Support RV1103B (Beta) +- Support RK2118 (Beta) +- Support Flash Attention (Only RK3562 and RK3576) +- Improve MatMul API +- Improve support for int32 and int64 +- Support more operators and operator fusion + + for older version, please refer [CHANGELOG](CHANGELOG.md) + +# Feedback and Community Support +- [Redmine](https://redmine.rock-chips.com) (**Feedback recommended, Please consult our sales or FAE for the redmine account**) +- QQ Group Chat: 1025468710 (full, please join group 3) +- QQ Group Chat2: 547021958 (full, please join group 3) +- QQ Group Chat3: 469385426 +
+ + + +
+ + diff --git a/autosparsity/README.md b/autosparsity/README.md new file mode 100644 index 0000000000000000000000000000000000000000..95cf0142967fcd88c1104c3cc56eb11b128cdd28 --- /dev/null +++ b/autosparsity/README.md @@ -0,0 +1,58 @@ +## AutoSparsity + +Enables sparse training and inference for PyTorch models. + + +## Usage + +### Step 1 + +Install autosparsity package + +```bash +pip install packages/autosparsity-1.0-cp38-cp38m-linux_x86_64.whl +``` + +### Step 2 + +Taking ResNet50 in torchvision as an example to generate the sparse model. + +```bash +python examples/autosparsity.py +``` +To sparsity a custom model, just add the sparsity_model functionwhen model training, as follows: + +```python +# insert model autosparsity code before training +import torch +import torchvision.models as models +from autosparsity.sparsity import sparsity_model + +... + +model = models.resnet34(pretrained=True).cuda() +mode = 0 +sparsity_model(model, optimizer, mode) + +# normal training +x, y = DataLoader(args) +for epoch in range(epochs): + y_pred = model(x) + loss = loss_func(y_pred, y) + loss.backward() + optimizer.step() + ... +``` + +- Note: Make sure CUDA is available + + +### Step3 + +Use RKNN-Toolkite to perfom sparse inference + +```bash +python examples/test.py +``` +- Note: Only supports RK3576 target platform + diff --git a/autosparsity/examples/README.md b/autosparsity/examples/README.md new file mode 100644 index 0000000000000000000000000000000000000000..de0e79f6cd2eff873dff305db2049c4f1ec5eead --- /dev/null +++ b/autosparsity/examples/README.md @@ -0,0 +1,68 @@ +# Sparse Infer + +This tool is used for the Torch model to autosparsity the weights during the training, which can save model storage and reduce model inference time in RKNN sparse inference. + +## Usage + +### Step 1 + +Install autosparsity package + +```bash +pip install ../packages/autosparsity-1.0-cp38-cp38m-linux_x86_64.whl +``` + +### Step 2 + +Taking ResNet50 in torchvision as an example to generate the sparse model. + +```bash +python autosparsity.py +``` +To sparsity a custom model, just add the sparsity_model functionwhen model training, as follows: + +```python +# insert model autosparsity code before training +import torch +import torchvision.models as models +from autosparsity.sparsity import sparsity_model + +... + +model = models.resnet34(pretrained=True).cuda() +mode = 0 +sparsity_model(model, optimizer, mode) + +# normal training +x, y = DataLoader(args) +for epoch in range(epochs): + y_pred = model(x) + loss = loss_func(y_pred, y) + loss.backward() + optimizer.step() + ... +``` + +- Note: Make sure CUDA is available + + +### Step3 + +Perfom sparse inference + +```bash +python test.py +``` +- Note: Only supports RK3576 target platform + +### Expected Results: + +This will print the , as follows: +``` +-----TOP 5----- +[155] score:0.877372 class:"Shih-Tzu" +[283] score:0.042477 class:"Persian cat" +[ 82] score:0.006625 class:"ruffed grouse, partridge, Bonasa umbellus" +[154] score:0.006625 class:"Pekinese, Pekingese, Peke" +[204] score:0.004696 class:"Lhasa, Lhasa apso" +``` diff --git a/autosparsity/examples/autosparsity.py b/autosparsity/examples/autosparsity.py new file mode 100644 index 0000000000000000000000000000000000000000..6618ec14d38f3f6113f9a11aa88f8989ac88c266 --- /dev/null +++ b/autosparsity/examples/autosparsity.py @@ -0,0 +1,17 @@ +import torch +import torchvision.models as models +from autosparsity.sparsity import sparsity_model + + +if __name__ == "__main__": + + model = models.resnet50(pretrained=True).cuda() + optimizer = None + mode = 0 + sparsity_model(model, optimizer, mode) + + model.eval() + x = torch.randn((1,3,224,224)).cuda() + torch.onnx.export( + model, x, 'resnet50.onnx', input_names=['inputs'], output_names=['outputs'] + ) \ No newline at end of file diff --git a/autosparsity/examples/datasets.txt b/autosparsity/examples/datasets.txt new file mode 100644 index 0000000000000000000000000000000000000000..e9f106640be9c286473dc8b5d66b87bb2d361aeb --- /dev/null +++ b/autosparsity/examples/datasets.txt @@ -0,0 +1 @@ +./dog_224x224.jpg \ No newline at end of file diff --git a/autosparsity/examples/dog_224x224.jpg b/autosparsity/examples/dog_224x224.jpg new file mode 100644 index 0000000000000000000000000000000000000000..004b0416e23454a12eb06eaba238c7e2d5f2b0fc --- /dev/null +++ b/autosparsity/examples/dog_224x224.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c350299c6283d5f62fecf1f845b6b3be9aafec8dff528ca09a129990f0a584b0 +size 18889 diff --git a/autosparsity/examples/labels.txt b/autosparsity/examples/labels.txt new file mode 100644 index 0000000000000000000000000000000000000000..0993780f99088349e2c156a5a8c57ce1013eb493 --- /dev/null +++ b/autosparsity/examples/labels.txt @@ -0,0 +1,1000 @@ +0:tench, Tinca tinca +1:goldfish, Carassius auratus +2:great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias +3:tiger shark, Galeocerdo cuvieri +4:hammerhead, hammerhead shark +5:electric ray, crampfish, numbfish, torpedo +6:stingray +7:cock +8:hen +9:ostrich, Struthio camelus +10:brambling, Fringilla montifringilla +11:goldfinch, Carduelis carduelis +12:house finch, linnet, Carpodacus mexicanus +13:junco, snowbird +14:indigo bunting, indigo finch, indigo bird, Passerina cyanea +15:robin, American robin, Turdus migratorius +16:bulbul +17:jay +18:magpie +19:chickadee +20:water ouzel, dipper +21:kite +22:bald eagle, American eagle, Haliaeetus leucocephalus +23:vulture +24:great grey owl, great gray owl, Strix nebulosa +25:European fire salamander, Salamandra salamandra +26:common newt, Triturus vulgaris +27:eft +28:spotted salamander, Ambystoma maculatum +29:axolotl, mud puppy, Ambystoma mexicanum +30:bullfrog, Rana catesbeiana +31:tree frog, tree-frog +32:tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui +33:loggerhead, loggerhead turtle, Caretta caretta +34:leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea +35:mud turtle +36:terrapin +37:box turtle, box tortoise +38:banded gecko +39:common iguana, iguana, Iguana iguana +40:American chameleon, anole, Anolis carolinensis +41:whiptail, whiptail lizard +42:agama +43:frilled lizard, Chlamydosaurus kingi +44:alligator lizard +45:Gila monster, Heloderma suspectum +46:green lizard, Lacerta viridis +47:African chameleon, Chamaeleo chamaeleon +48:Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis +49:African crocodile, Nile crocodile, Crocodylus niloticus +50:American alligator, Alligator mississipiensis +51:triceratops +52:thunder snake, worm snake, Carphophis amoenus +53:ringneck snake, ring-necked snake, ring snake +54:hognose snake, puff adder, sand viper +55:green snake, grass snake +56:king snake, kingsnake +57:garter snake, grass snake +58:water snake +59:vine snake +60:night snake, Hypsiglena torquata +61:boa constrictor, Constrictor constrictor +62:rock python, rock snake, Python sebae +63:Indian cobra, Naja naja +64:green mamba +65:sea snake +66:horned viper, cerastes, sand viper, horned asp, Cerastes cornutus +67:diamondback, diamondback rattlesnake, Crotalus adamanteus +68:sidewinder, horned rattlesnake, Crotalus cerastes +69:trilobite +70:harvestman, daddy longlegs, Phalangium opilio +71:scorpion +72:black and gold garden spider, Argiope aurantia +73:barn spider, Araneus cavaticus +74:garden spider, Aranea diademata +75:black widow, Latrodectus mactans +76:tarantula +77:wolf spider, hunting spider +78:tick +79:centipede +80:black grouse +81:ptarmigan +82:ruffed grouse, partridge, Bonasa umbellus +83:prairie chicken, prairie grouse, prairie fowl +84:peacock +85:quail +86:partridge +87:African grey, African gray, Psittacus erithacus +88:macaw +89:sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita +90:lorikeet +91:coucal +92:bee eater +93:hornbill +94:hummingbird +95:jacamar +96:toucan +97:drake +98:red-breasted merganser, Mergus serrator +99:goose +100:black swan, Cygnus atratus +101:tusker +102:echidna, spiny anteater, anteater +103:platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus +104:wallaby, brush kangaroo +105:koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus +106:wombat +107:jellyfish +108:sea anemone, anemone +109:brain coral +110:flatworm, platyhelminth +111:nematode, nematode worm, roundworm +112:conch +113:snail +114:slug +115:sea slug, nudibranch +116:chiton, coat-of-mail shell, sea cradle, polyplacophore +117:chambered nautilus, pearly nautilus, nautilus +118:Dungeness crab, Cancer magister +119:rock crab, Cancer irroratus +120:fiddler crab +121:king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica +122:American lobster, Northern lobster, Maine lobster, Homarus americanus +123:spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish +124:crayfish, crawfish, crawdad, crawdaddy +125:hermit crab +126:isopod +127:white stork, Ciconia ciconia +128:black stork, Ciconia nigra +129:spoonbill +130:flamingo +131:little blue heron, Egretta caerulea +132:American egret, great white heron, Egretta albus +133:bittern +134:crane +135:limpkin, Aramus pictus +136:European gallinule, Porphyrio porphyrio +137:American coot, marsh hen, mud hen, water hen, Fulica americana +138:bustard +139:ruddy turnstone, Arenaria interpres +140:red-backed sandpiper, dunlin, Erolia alpina +141:redshank, Tringa totanus +142:dowitcher +143:oystercatcher, oyster catcher +144:pelican +145:king penguin, Aptenodytes patagonica +146:albatross, mollymawk +147:grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus +148:killer whale, killer, orca, grampus, sea wolf, Orcinus orca +149:dugong, Dugong dugon +150:sea lion +151:Chihuahua +152:Japanese spaniel +153:Maltese dog, Maltese terrier, Maltese +154:Pekinese, Pekingese, Peke +155:Shih-Tzu +156:Blenheim spaniel +157:papillon +158:toy terrier +159:Rhodesian ridgeback +160:Afghan hound, Afghan +161:basset, basset hound +162:beagle +163:bloodhound, sleuthhound +164:bluetick +165:black-and-tan coonhound +166:Walker hound, Walker foxhound +167:English foxhound +168:redbone +169:borzoi, Russian wolfhound +170:Irish wolfhound +171:Italian greyhound +172:whippet +173:Ibizan hound, Ibizan Podenco +174:Norwegian elkhound, elkhound +175:otterhound, otter hound +176:Saluki, gazelle hound +177:Scottish deerhound, deerhound +178:Weimaraner +179:Staffordshire bullterrier, Staffordshire bull terrier +180:American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier +181:Bedlington terrier +182:Border terrier +183:Kerry blue terrier +184:Irish terrier +185:Norfolk terrier +186:Norwich terrier +187:Yorkshire terrier +188:wire-haired fox terrier +189:Lakeland terrier +190:Sealyham terrier, Sealyham +191:Airedale, Airedale terrier +192:cairn, cairn terrier +193:Australian terrier +194:Dandie Dinmont, Dandie Dinmont terrier +195:Boston bull, Boston terrier +196:miniature schnauzer +197:giant schnauzer +198:standard schnauzer +199:Scotch terrier, Scottish terrier, Scottie +200:Tibetan terrier, chrysanthemum dog +201:silky terrier, Sydney silky +202:soft-coated wheaten terrier +203:West Highland white terrier +204:Lhasa, Lhasa apso +205:flat-coated retriever +206:curly-coated retriever +207:golden retriever +208:Labrador retriever +209:Chesapeake Bay retriever +210:German short-haired pointer +211:vizsla, Hungarian pointer +212:English setter +213:Irish setter, red setter +214:Gordon setter +215:Brittany spaniel +216:clumber, clumber spaniel +217:English springer, English springer spaniel +218:Welsh springer spaniel +219:cocker spaniel, English cocker spaniel, cocker +220:Sussex spaniel +221:Irish water spaniel +222:kuvasz +223:schipperke +224:groenendael +225:malinois +226:briard +227:kelpie +228:komondor +229:Old English sheepdog, bobtail +230:Shetland sheepdog, Shetland sheep dog, Shetland +231:collie +232:Border collie +233:Bouvier des Flandres, Bouviers des Flandres +234:Rottweiler +235:German shepherd, German shepherd dog, German police dog, alsatian +236:Doberman, Doberman pinscher +237:miniature pinscher +238:Greater Swiss Mountain dog +239:Bernese mountain dog +240:Appenzeller +241:EntleBucher +242:boxer +243:bull mastiff +244:Tibetan mastiff +245:French bulldog +246:Great Dane +247:Saint Bernard, St Bernard +248:Eskimo dog, husky +249:malamute, malemute, Alaskan malamute +250:Siberian husky +251:dalmatian, coach dog, carriage dog +252:affenpinscher, monkey pinscher, monkey dog +253:basenji +254:pug, pug-dog +255:Leonberg +256:Newfoundland, Newfoundland dog +257:Great Pyrenees +258:Samoyed, Samoyede +259:Pomeranian +260:chow, chow chow +261:keeshond +262:Brabancon griffon +263:Pembroke, Pembroke Welsh corgi +264:Cardigan, Cardigan Welsh corgi +265:toy poodle +266:miniature poodle +267:standard poodle +268:Mexican hairless +269:timber wolf, grey wolf, gray wolf, Canis lupus +270:white wolf, Arctic wolf, Canis lupus tundrarum +271:red wolf, maned wolf, Canis rufus, Canis niger +272:coyote, prairie wolf, brush wolf, Canis latrans +273:dingo, warrigal, warragal, Canis dingo +274:dhole, Cuon alpinus +275:African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus +276:hyena, hyaena +277:red fox, Vulpes vulpes +278:kit fox, Vulpes macrotis +279:Arctic fox, white fox, Alopex lagopus +280:grey fox, gray fox, Urocyon cinereoargenteus +281:tabby, tabby cat +282:tiger cat +283:Persian cat +284:Siamese cat, Siamese +285:Egyptian cat +286:cougar, puma, catamount, mountain lion, painter, panther, Felis concolor +287:lynx, catamount +288:leopard, Panthera pardus +289:snow leopard, ounce, Panthera uncia +290:jaguar, panther, Panthera onca, Felis onca +291:lion, king of beasts, Panthera leo +292:tiger, Panthera tigris +293:cheetah, chetah, Acinonyx jubatus +294:brown bear, bruin, Ursus arctos +295:American 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cream, icecream +929:ice lolly, lolly, lollipop, popsicle +930:French loaf +931:bagel, beigel +932:pretzel +933:cheeseburger +934:hotdog, hot dog, red hot +935:mashed potato +936:head cabbage +937:broccoli +938:cauliflower +939:zucchini, courgette +940:spaghetti squash +941:acorn squash +942:butternut squash +943:cucumber, cuke +944:artichoke, globe artichoke +945:bell pepper +946:cardoon +947:mushroom +948:Granny Smith +949:strawberry +950:orange +951:lemon +952:fig +953:pineapple, ananas +954:banana +955:jackfruit, jak, jack +956:custard apple +957:pomegranate +958:hay +959:carbonara +960:chocolate sauce, chocolate syrup +961:dough +962:meat loaf, meatloaf +963:pizza, pizza pie +964:potpie +965:burrito +966:red wine +967:espresso +968:cup +969:eggnog +970:alp +971:bubble +972:cliff, drop, drop-off +973:coral reef +974:geyser +975:lakeside, lakeshore +976:promontory, headland, head, foreland +977:sandbar, sand bar +978:seashore, coast, seacoast, sea-coast +979:valley, vale +980:volcano +981:ballplayer, baseball player +982:groom, bridegroom +983:scuba diver +984:rapeseed +985:daisy +986:yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum +987:corn +988:acorn +989:hip, rose hip, rosehip +990:buckeye, horse chestnut, conker +991:coral fungus +992:agaric +993:gyromitra +994:stinkhorn, carrion fungus +995:earthstar +996:hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa +997:bolete +998:ear, spike, capitulum +999:toilet tissue, toilet paper, bathroom tissue \ No newline at end of file diff --git a/autosparsity/examples/resnet50.onnx b/autosparsity/examples/resnet50.onnx new file mode 100644 index 0000000000000000000000000000000000000000..913f41183cdd090b64cd5273b1a97e638dcdcf03 --- /dev/null +++ b/autosparsity/examples/resnet50.onnx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4428727ffe996c87fcb871d7e21093c34c64b76f978f9de3c813322d61419470 +size 102146376 diff --git a/autosparsity/examples/test.py b/autosparsity/examples/test.py new file mode 100644 index 0000000000000000000000000000000000000000..6ed73325dde6b7237d9d41970362981656bbd8ff --- /dev/null +++ b/autosparsity/examples/test.py @@ -0,0 +1,81 @@ +import numpy as np +import cv2 +from rknn.api import RKNN + + +ONNX_MODEL = 'resnet50.onnx' +RKNN_MODEL = 'resnet50.rknn' + +def show_outputs(outputs): + output = outputs[0][0] + index = sorted(range(len(output)), key=lambda k : output[k], reverse=True) + fp = open('./labels.txt', 'r') + labels = fp.readlines() + top5_str = 'resnet50_sparse_infer\n-----TOP 5-----\n' + for i in range(5): + value = output[index[i]] + if value > 0: + topi = '[{:>3d}] score:{:.6f} class:"{}"\n'.format(index[i], value, labels[index[i]].strip().split(':')[-1]) + else: + topi = '[ -1]: 0.0\n' + top5_str += topi + print(top5_str.strip()) + + +if __name__ == '__main__': + + # Create RKNN object + rknn = RKNN(verbose=True) + + # Pre-process config + print('--> Config model') + rknn.config(mean_values=[123.675, 116.28, 103.53], std_values=[58.395, 57.12, 57.375], target_platform='rk3576', sparse_infer=True) + print('done') + + # Load model + print('--> Loading model') + ret = rknn.load_onnx(model=ONNX_MODEL) + if ret != 0: + print('Load model failed!') + exit(ret) + print('done') + + # Build model + print('--> Building model') + ret = rknn.build(do_quantization=True, dataset='./datasets.txt') + if ret != 0: + print('Build model failed!') + exit(ret) + print('done') + + # Export rknn model + print('--> Export rknn model') + ret = rknn.export_rknn(RKNN_MODEL) + if ret != 0: + print('Export rknn model failed!') + exit(ret) + print('done') + + # Set inputs + img = cv2.imread('./dog_224x224.jpg') + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + img = np.expand_dims(img, 0) + + # Init runtime environment + print('--> Init runtime environment') + ret = rknn.init_runtime(target='rk3576') + if ret != 0: + print('Init runtime environment failed!') + exit(ret) + print('done') + + # Inference + print('--> Running model') + outputs = rknn.inference(inputs=[img], data_format=['nhwc']) + x = outputs[0] + output = np.exp(x)/np.sum(np.exp(x)) + outputs = [output] + show_outputs(outputs) + print('done') + + rknn.release() diff --git a/autosparsity/packages/autosparsity-1.0-cp310-cp310-linux_x86_64.whl b/autosparsity/packages/autosparsity-1.0-cp310-cp310-linux_x86_64.whl new file mode 100644 index 0000000000000000000000000000000000000000..2cd97ffb9001d32aca86c9cada7ed2d3a7751d7e --- /dev/null +++ b/autosparsity/packages/autosparsity-1.0-cp310-cp310-linux_x86_64.whl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:91738c554c28a71a17c799d9e8c7e600212c34a8a6cd36a178f13ef005f0b925 +size 1726486 diff --git a/autosparsity/packages/autosparsity-1.0-cp311-cp311-linux_x86_64.whl b/autosparsity/packages/autosparsity-1.0-cp311-cp311-linux_x86_64.whl new file mode 100644 index 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b/doc/RKNNToolKit2_OP_Support-v2.1.0.md @@ -0,0 +1,519 @@ +# RKNNToolkit2 OPs Support + +## ONNX OPs supported by RKNN Toolkit2 + +According to [ONNX official instructions](https://github.com/microsoft/onnxruntime/blob/master/docs/Versioning.md 'ONNX Version Description'), the corresponding ONNX opset version is 19. +The list of ONNX OPs supported by RKNN Toolkit2 is as follows: +
(For more restrictions, please refer to ) + +| **Operators** | **Remarks** | +| ------------------------- | --------------------------------------- | +| Abs | Not Supported | +| Acos | Not Supported | +| Acosh | Not Supported | +| Add | | +| And | | +| ArgMax | | +| ArgMin | | +| Asin | Not Supported | +| Asinh | Not Supported | +| Atan | Not Supported | +| Atanh | Not Supported | +| AveragePool | | +| BatchNormalization | | +| Bernoulli | Not Supported | +| BitShift | Not Supported | +| BitwiseAnd | Not Supported | +| BitwiseNot | Not Supported | +| BitwiseOr | Not Supported | +| BitwiseXor | Not Supported | +| BlackmanWindow | Not Supported | +| Cast | | +| CastLike | Not Supported | +| Ceil | Not Supported | +| Celu | Not Supported | +| CenterCropPad | Not Supported | +| Clip | | +| Col2Im | Not Supported | +| Compress | Not Supported | +| Concat | | +| ConcatFromSequence | Not Supported | +| Constant | | +| ConstantOfShape | | +| Conv | | +| ConvInteger | Not Supported | +| ConvTranspose | | +| Cos | | +| Cosh | Not Supported | +| CumSum | Not Supported | +| DeformConv | Not Supported | +| DepthToSpace | | +| DequantizeLinear | | +| Det | Not Supported | +| DFT | Not Supported | +| Div | | +| Dropout | | +| DynamicQuantizeLinear | Not Supported | +| Einsum | Not Supported | +| Elu | | +| Equal | | +| Erf | | +| Exp | | +| Expand | | +| EyeLike | only support constant input | +| Flatten | | +| Floor | | +| Gather | | +| GatherElements | | +| GatherND | Not Supported | +| Gemm | | +| GlobalAveragePool | | +| GlobalLpPool | Not Supported | +| GlobalMaxPool | | +| Greater | | +| GreaterOrEqual | | +| GridSample | Not Supported | +| GroupNormalization | Not Supported | +| GRU | batchsize: 1 | +| HammingWindow | Not Supported | +| HannWindow | Not Supported | +| Hardmax | Not Supported | +| HardSigmoid | | +| HardSwish | | +| Identity | | +| If | only support constant input | +| InstanceNormalization | | +| IsInf | Not Supported | +| IsNaN | Not Supported | +| LayerNormalization | | +| LeakyRelu | | +| Less | | +| LessOrEqual | | +| Log | | +| LogSoftmax | batchsize: 1 | +| Loop | Not Supported | +| LpNormalization | | +| LpPool | Not Supported | +| LRN | | +| LSTM | | +| MatMul | | +| MatMulInteger | Not Supported | +| Max | | +| MaxPool | | +| MaxRoiPool | | +| MaxUnpool | | +| Mean | Not Supported | +| MeanVarianceNormalization | | +| MelWeightMatrix | Not Supported | +| Min | | +| Mish | | +| Mod | | +| Mul | | +| Multinomial | Not Supported | +| Neg | Not Supported | +| NegativeLogLikelihoodLoss | Not Supported | +| NonMaxSuppression | Not Supported | +| NonZero | Not Supported | +| Not | Not Supported | +| OneHot | Not Supported | +| Optional | Not Supported | +| OptionalGetElement | Not Supported | +| OptionalHasElement | Not Supported | +| Or | Not Supported | +| Pad | | +| Pow | | +| PRelu | | +| QLinearConv | Not Supported | +| QLinearMatMul | Not Supported | +| QuantizeLinear | | +| RandomNormal | Not Supported | +| RandomNormalLike | Not Supported | +| RandomUniform | Not Supported | +| RandomUniformLike | Not Supported | +| Range | Not Supported | +| Reciprocal | Not Supported | +| ReduceL1 | Not Supported | +| ReduceL2 | Not Supported | +| ReduceLogSum | Not Supported | +| ReduceLogSumExp | Not Supported | +| ReduceMax | | +| ReduceMean | | +| ReduceMin | | +| ReduceProd | Not Supported | +| ReduceSum | | +| ReduceSumSquare | Not Supported | +| Relu | | +| Reshape | | +| Resize | mode: nearest2d/bilinear | +| ReverseSequence | | +| RNN | Not Supported | +| RoiAlign | pool type: average
batchsize: 1 | +| Round | Not Supported | +| Scan | Not Supported | +| ScatterElements | Not Supported | +| ScatterND | | +| Selu | Not Supported | +| SequenceAt | Not Supported | +| SequenceConstruct | Not Supported | +| SequenceEmpty | Not Supported | +| SequenceErase | Not Supported | +| SequenceInsert | Not Supported | +| SequenceLength | Not Supported | +| SequenceMap | Not Supported | +| Shape | | +| Shrink | Not Supported | +| Sigmoid | | +| Sign | Not Supported | +| Sin | | +| Sinh | Not Supported | +| Size | | +| Slice | batchsize: 1 | +| Softmax | batchsize: 1 | +| SoftmaxCrossEntropyLoss | Not Supported | +| Softplus | | +| Softsign | Not Supported | +| SpaceToDepth | | +| Split | | +| SplitToSequence | Not Supported | +| Sqrt | | +| Squeeze | | +| STFT | Not Supported | +| StringNormalizer | Not Supported | +| Sub | | +| Sum | Not Supported | +| Tan | Not Supported | +| Tanh | | +| TfIdfVectorizer | Not Supported | +| ThresholdedRelu | Not Supported | +| Tile | batchsize: 1
not support broadcast | +| TopK | Not Supported | +| Transpose | | +| Trilu | Not Supported | +| Unique | Not Supported | +| Unsqueeze | | +| Where | | +| Xor | Not Supported | + +## Pytorch OPs supported by RKNN Toolkit2 + +The Pytorch version supported by RKNN Toolkit2 is >1.6.0, models generated by other versions may not support. +The list of Pytorch OPs supported by RKNN Toolkit2 is as follows: + +| **Operators** | **Remarks** | +| ----------------------------- | ---------------------------------- | +| aten::_convolution | same as onnx Conv | +| aten::abs | Not supported | +| aten::abs_ | Not supported | +| aten::adaptive_avg_pool1d | Not supported | +| aten::adaptive_avg_pool2d | same as onnx AveragePool | +| aten::adaptive_max_pool1d | Not supported | +| aten::adaptive_max_pool2d | same as onnx MaxPool | +| aten::add | same as onnx Add | +| aten::add_ | | +| aten::addmm | same as onnx Gemm | +| aten::affine_grid_generator | Not supported | +| aten::alpha_dropout | | +| aten::alpha_dropout_ | Not supported | +| aten::arange | Not supported | +| aten::avg_pool1d | Not supported | +| aten::avg_pool2d | same as onnx AveragePool | +| aten::avg_pool3d | Not supported | +| aten::batch_norm | same as onnx BatchNormalization | +| aten::bmm | same as onnx MatMul | +| aten::cat | same as onnx Concat | +| aten::celu | Not supported | +| aten::celu_ | Not supported | +| aten::chunk | | +| aten::clamp | | +| aten::clamp_ | | +| aten::clamp_max | Not supported | +| aten::clamp_max_ | Not supported | +| aten::clamp_min | | +| aten::clamp_min_ | Not supported | +| aten::clone | | +| aten::constant_pad_nd | same as onnx Pad | +| aten::contiguous | | +| aten::copy | | +| aten::cos | Not supported | +| aten::cos_ | Not supported | +| aten::cumsum | Not supported | +| aten::detach | | +| aten::detach_ | Not supported | +| aten::div | same as onnx Div | +| aten::div_ | | +| aten::dropout | | +| aten::dropout_ | | +| aten::einsum | Not supported | +| aten::elu | same as onnx Elu | +| aten::elu_ | | +| aten::embedding | same as onnx Gather | +| aten::empty | | +| aten::eq | Not supported | +| aten::eq_ | Not supported | +| aten::erf | Not supported | +| aten::erf_ | Not supported | +| aten::erfc | Not supported | +| aten::erfc_ | Not supported | +| aten::exp | | +| aten::exp_ | | +| aten::expand | | +| aten::expand_as | Not supported | +| aten::expm1 | Not supported | +| aten::expm1_ | Not supported | +| aten::feature_dropout | | +| aten::feature_dropout_ | Not supported | +| aten::flatten | | +| aten::flip | Not supported | +| aten::floor | Not supported | +| aten::floor_ | Not supported | +| aten::floor_divide | Not supported | +| aten::floor_divide_ | Not supported | +| aten::gather | Not supported | +| aten::ge | Not supported | +| aten::ge_ | Not supported | +| aten::gelu | | +| aten::gelu_ | Not supported | +| aten::grid_sampler | Not supported | +| aten::gru | | +| aten::gt | | +| aten::gt_ | Not supported | +| aten::hardshrink | Not supported | +| aten::hardshrink_ | Not supported | +| aten::hardswish | same as onnx HardSwish | +| aten::hardswish_ | | +| aten::hardtanh | | +| aten::hardtanh_ | | +| aten::index | Not supported | +| aten::index_put | Not supported | +| aten::index_put_ | Not supported | +| aten::instance_norm | same as onnx InstanceNormalization | +| aten::Int | | +| aten::layer_norm | | +| aten::le | Not supported | +| aten::le_ | Not supported | +| aten::leaky_relu | same as onnx LeakyRelu | +| aten::leaky_relu_ | | +| aten::lerp | Not supported | +| aten::lerp_ | Not supported | +| aten::log | Not supported | +| aten::log_ | Not supported | +| aten::log10 | Not supported | +| aten::log10_ | Not supported | +| aten::log1p | Not supported | +| aten::log1p_ | Not supported | +| aten::log2 | Not supported | +| aten::log2_ | Not supported | +| aten::log_sigmoid | Not supported | +| aten::log_softmax | Not supported | +| aten::linear | same as onnx Gemm | +| aten::lstm | same as onnx LSTM | +| aten::lt | | +| aten::lt_ | Not supported | +| aten::matmul | same as onnx MatMul | +| aten::max | | +| aten::maximum | | +| aten::max_ | Not supported | +| aten::max_pool1d | same as onnx MaxPool | +| aten::max_pool1d_with_indices | | +| aten::max_pool2d | same as onnx MaxPool | +| aten::max_pool2d_with_indices | | +| aten::mean | same as onnx ReduceMean | +| aten::meshgrid | Not supported | +| aten::min | | +| aten::minimum | | +| aten::min_ | Not supported | +| aten::mish | | +| aten::mm | same as onnx MatMul | +| aten::mul | same as onnx Mul | +| aten::mul_ | | +| aten::narrow | same as onnx Slice | +| aten::ne | | +| aten::ne_ | Not supported | +| aten::neg | Not supported | +| aten::neg_ | Not supported | +| aten::new_full | Not supported | +| aten::new_zeros | Not supported | +| aten::nonzero | Not supported | +| aten::norm | Not supported | +| aten::ones | | +| aten::ones_like | | +| aten::pad | Not supported | +| aten::permute | same as onnx Transpose | +| aten::pow | | +| aten::pow_ | Not supported | +| aten::prelu | same as onnx PRelu | +| aten::prelu_ | Not supported | +| aten::prod | | +| aten::reciprocal | | +| aten::reciprocal_ | Not supported | +| aten::reflection_pad1d | | +| aten::reflection_pad2d | | +| aten::relu | same as onnx Relu | +| aten::relu6 | same as onnx Relu | +| aten::relu_ | | +| aten::relu6_ | | +| aten::repeat | | +| aten::reshape | | +| aten::reshape_ | Not supported | +| torchvision::roi_align | Not supported | +| aten::rsqrt | Not supported | +| aten::rsqrt_ | Not supported | +| aten::ScalarImplicit | | +| aten::select | | +| aten::selu | Not supported | +| aten::selu_ | Not supported | +| aten::sigmoid | same as onnx Sigmoid | +| aten::sigmoid_ | | +| aten::silu | | +| aten::silu_ | | +| aten::sin | Not supported | +| aten::sin_ | Not supported | +| aten::size | | +| aten::slice | same as onnx Slice | +| aten::softmax | same as onnx Softmax | +| aten::softplus | | +| aten::softshrink | Not supported | +| aten::sort | Not supported | +| aten::split | same as onnx Split | +| aten::split_with_sizes | | +| aten::sqrt | Not supported | +| aten::sqrt_ | Not supported | +| aten::squeeze | | +| aten::squeeze_ | Not supported | +| aten::stack | | +| aten::sub | same as onnx Sub | +| aten::sub_ | | +| aten::sum | same as onnx ReduceSum | +| aten::t | | +| aten::t_ | Not supported | +| aten::tanh | | +| aten::tanh_ | | +| aten::threshold | | +| aten::threshold_ | | +| aten::to | | +| aten::topk | Not supported | +| aten::transpose | | +| aten::transpose_ | | +| aten::true_divide | same as onnx Div | +| aten::true_divide_ | Not supported | +| aten::type_as | | +| aten::unfold | Not supported | +| aten::unsqueeze | | +| aten::upsample_bilinear2d | | +| aten::upsample_nearest2d | | +| aten::view | | +| aten::view_ | Not supported | +| aten::view_as | Not supported | +| aten::view_as_ | Not supported | +| aten::where | | +| aten::zero_ | Not supported | +| aten::zeros | | +| aten::zeros_like | | + + + + +## Caffe OPs supported by RKNN Toolkit2 + +Caffe protocols RKNN Toolkit2 uses only based on the officially modified protocol of berkeley. +The protocol based on the official revision of berkeley comes from [berkeley caffe](https://github.com/BVLC/caffe/tree/master/src/caffe/proto 'Berkeley Caffe'), commit hash is 21d0608. On this basis RKNN Toolkit2 have added some OPs. +Based on this protocol, the list of Caffe OPs supported by RKNN Toolkit2 is as follows: + +| **Operators** | **Remarks** | +| ---------------------- | ------------------------------------------------------------------------------------------------------------- | +| BatchNorm | same as onnx BatchNormalization | +| bn (BatchNorm + Scale) | same as onnx BatchNormalization according to https://github.com/TimoSaemann/caffe-segnet-cudnn5 | +| BNLL | | +| Concat | same as onnx Concat | +| Convolution | same as onnx Conv | +| ConvolutionDepthwise | kernel height/width: [1, 8]
others same as onnx Conv | +| Crop | | +| Deconvolution | same as ConvTranspose | +| Dropout | | +| Eltwise | | +| Flatten | | +| HardSigmoid | | +| InnerProduct | same as onnx Gemm | +| LRN | same as onnx LRN | +| Lstm | same as onnx LSTM according to https://github.com/xmfbit/warpctc-caffe | +| Normalize | | +| Permute | same as onnx Transpose | +| Power | | +| Pooling | same as onnx pooling | +| PRelu | same as onnx PRelu | +| Proposal | batch: 1 | +| Reduction | output dims <= 4 | +| Relu | same as onnx Relu | +| Relu6 | same as onnx Clip | +| Reorg | | +| Reshape | same as onnx Reshape | +| Resize | bilinear; nearest | +| Reverse | | +| ROIPooling | same as MaxRoiPool according to https://github.com/twmht/caffe-pva-faster-rcnn | +| Scale | same as onnx Mul | +| Sigmoid | same as onnx Sigmoid | +| Slice | same as onnx Split | +| Softmax | same as onnx Softmax | +| Split | same as onnx Slice | +| TanH | same as onnx TanH | +| Tile | same as onnx Tile | +| Transpose | same as onnx Transpose | +| Upsample | according to https://github.com/SeanQ88/caffe_upsample and https://github.com/TimoSaemann/caffe-segnet-cudnn5 | + + +## TensorFlow OPs supported by RKNN Toolkit2 + +The pb files (contain OPs belows) generated by TensorFlow version 1.12 - 1.15 for 1.x and 2.3 - 2.5 for 2.x are supported by RKNN Toolkit2. For more information on TensorFlow version compatibility, please refer to [tensorflow official instructions on OP version](https://www.tensorflow.org/guide/versions 'Tensorflow official instructions on OP version') . +The list of TensorFlow OPs supported by RKNN Toolkit2 is as follows: + +| **Operators** | **Remarks** | +| --------------------- | --------------------------------------------------------- | +| Add | same as onnx Add | +| AvgPool | same as onnx AveragePool | +| Concat | same as onnx Concat | +| Conv2D | same as onnx Conv | +| DepthToSpace | | +| DepthwiseConv2d | kernel height/width: [1, 8]
others same as onnx Conv | +| Div | same as onnx Div | +| Dropout | | +| Flatten | | +| LeakyRelu | same as onnx LeakyRelu | +| Less | same as onnx Less | +| LRN | | +| MatMul | | +| MaxPool | same as onnx MaxPool | +| Mean | output dims <= 4 | +| Pad | same as onnx Pad | +| Relu | same as onnx Relu | +| Reshape | | +| ResizeBilinear | | +| ResizeNearestNeighbor | | +| Sigmoid | | +| Slice | | +| Softmax | | +| Softplus | same as onnx Softplus | +| SpaceToDepth | | +| Split | | +| Squeeze | | +| StridedSlice | | +| Tanh | same as onnx TanH | +| Transpose | | + +## Darknet OPs supported by RKNN Toolkit2 +The list of Darknet OPs supported by RKNN Toolkit2 is as follows: + +| **Operators** | **Remarks** | +| ----------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| add | same as onnx Add | +| batchnormalize | same as onnx BatchNormalization | +| concat | same as onnx Concat | +| convolutional | same as onnx Conv | +| depthwise_convolutional | kernel height/width: [1, 8]
others same as onnx Conv | +| fullconnect | | +| leakyrelu | same as onnx LeakyRelu | +| mish | | +| pooling | **AveragePool**: same as onnx AveragePool
**GlobalAveragePool**: same as onnx GlobalAveragePool
**MaxPool/GlobalMaxPool**: same as onnx MaxPool/GlobalMaxPool | +| route | | +| shortcut | | +| softmax | | +| upsampling | | \ No newline at end of file diff --git a/doc/Using RKNN-ToolKit2 in WSL.md b/doc/Using RKNN-ToolKit2 in WSL.md new file mode 100644 index 0000000000000000000000000000000000000000..663d703e0da21c6b97a896fd4d11bdbfd9b33ee4 --- /dev/null +++ b/doc/Using RKNN-ToolKit2 in WSL.md @@ -0,0 +1,63 @@ +# Using RKNN-ToolKit2 in WSL + +## 1. Installing WSL on Windows Host +- You can refer to the following link for installing WSL: https://learn.microsoft.com/en-us/windows/wsl/install + +## 2. Using RKNN-Toolkit2 in WSL +1. Refer to the document[《Rockchip_RKNPU_Quick_Start》](https://github.com/airockchip/rknn-toolkit2/blob/master/doc/01_Rockchip_RKNPU_Quick_Start_RKNN_SDK_V2.0.0beta0_EN.pdf) to install the RKNN-ToolKit2 environment in WSL +2. Refer to the document[《Rockchip_RKNPU_User_Guide》](https://github.com/airockchip/rknn-toolkit2/blob/master/doc/02_Rockchip_RKNPU_User_Guide_RKNN_SDK_V2.0.0beta0_EN.pdf) for model conversion, quantization, and other operations in WSL. + +## 3. Using RKNN-Toolkit2 for Board Debugging in WSL + +### 3.1. Installing adb in WSL Terminal +``` +sudo apt update +sudo apt install adb +``` + +### 3.2 Connecting the Device in WSL +You can choose to connect the device through Ethernet or USB. + +#### 3.2.1 Ethernet Connection +``` +# 1. Connect the device using Ethernet cable. + +# 2. In WSL, use adb connect to connect to the device. +adb connect +``` +`IP address` is the IP address of the board. + + +#### 3.2.2 USB Connection +``` +# 1. Connect the device to Windows via USB. + +# 2. Use the adbkit tool in Windows to convert USB to TCP. +npm install --save adbkit +adbkit usb-device-to-tcp -p + +# Note: Configure WSL to access Windows network. + +# 3. In WSL, use adb connect to connect to the device. +adb connect +``` +- `device_id` can be obtained by running the `adb devices` command in Windows (adb tool must be installed in Windows). +- `IP address` is the IP address of the Windows host. + +### 3.3 Using RKNN-Toolkit2 for Board Debugging +Refer to the document [《Rockchip_RKNPU_User_Guide》](https://github.com/airockchip/rknn-toolkit2/blob/master/doc/02_Rockchip_RKNPU_User_Guide_RKNN_SDK_V2.0.0beta0_EN.pdf) for board inference, accuracy analysis, and other operations in WSL. + + + +## NOte: +1. It is recommended to install WSL2 with Ubuntu version 22.04, which has been verified to work (other versions are theoretically feasible but not tested). +2. If you encounter the "ImportError: libGL.so.1: cannot open shared object file: No such file or directory" error while using RKNN-ToolKit2 in WSL, execute the following code to resolve it: +``` +1. Install the required library: +sudo apt update +sudo apt install libgl1-mesa-glx + +2. Set the environment variable: +echo 'export LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu/mesa' >> ~/.bashrc +source ~/.bashrc +``` \ No newline at end of file diff --git "a/doc/WSL\344\270\255\344\275\277\347\224\250RKNN_ToolKit2.md" "b/doc/WSL\344\270\255\344\275\277\347\224\250RKNN_ToolKit2.md" new file mode 100644 index 0000000000000000000000000000000000000000..dc41d2186a9abea49666a60fdcce9c3e9ac6f623 --- /dev/null +++ "b/doc/WSL\344\270\255\344\275\277\347\224\250RKNN_ToolKit2.md" @@ -0,0 +1,63 @@ +# WSL 中 使用RKNN-ToolKit2 + +## 1. Windows主机安装WSL +- 可参考 https://learn.microsoft.com/zh-cn/windows/wsl/install + +## 2. WSL 中使用RKNN-Toolkit2 +1. 参考[《Rockchip_RKNPU_Quick_Start手册》](https://github.com/airockchip/rknn-toolkit2/blob/master/doc/01_Rockchip_RKNPU_Quick_Start_RKNN_SDK_V2.0.0beta0_CN.pdf)在 WSL 中安装RKNN-ToolKit2环境 +2. 参考[《Rockchip_RKNPU_User_Guide手册》](https://github.com/airockchip/rknn-toolkit2/blob/master/doc/02_Rockchip_RKNPU_User_Guide_RKNN_SDK_V2.0.0beta0_CN.pdf)在 WSL 进行模型转换、量化等操作 + +## 3. WSL 中使用RKNN-Toolkit2进行连板调试 + +### 3.1. WSL 终端安装adb +``` +sudo apt update +sudo apt install adb +``` + +### 3.2 WSL 连接设备 +可选择通过网线或USB进行设备连接 + +#### 3.2.1 通过网线连接 +``` +# 1. 使用网线连接设备 + +# 2. 在 WSL 中使用 adb connect 连接设备 +adb connect +``` +`IP地址` 为板子的IP地址 + + +#### 3.2.2 通过USB连接 +``` +# 1. 在 Windows 上通过USB连接设备 + +# 2. 在 Windows 上使用adbkit工具，将USB转为TCP +npm install --save adbkit +adbkit usb-device-to-tcp -p <端口号> + +# 注：配置WSL可以访问Windows的网络 + +# 3. 在 WSL 中使用 adb connect 连接设备 +adb connect +``` +- `device_id`可通过在Windows中使用`adb devices`命令查看 (需要Windows中已安装adb工具) +- `IP地址` 为 Windows主机 的IP地址 + +### 3.3 使用RKNN-ToolKit2进行连板调试 +参考[《Rockchip_RKNPU_User_Guide手册》](https://github.com/airockchip/rknn-toolkit2/blob/master/doc/02_Rockchip_RKNPU_User_Guide_RKNN_SDK_V2.0.0beta0_CN.pdf)在 WSL 进行连板推理、连板精度分析等操作 + + + +## 注： +1. 推荐安装 WSL2，Ubuntu版本号为22.04 已验证可行(其余版本未验证，理论可行) +2. 在WSL使用RKNN-ToolKit2中若出现 "ImportError: libGL.so.1: cannot open shared object file: No such file or directory"，请执行以下代码解决 +``` +1. 安装对应库 +sudo apt update +sudo apt install libgl1-mesa-glx + +2. 设置环境变量 +echo 'export LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu/mesa' >> ~/.bashrc +source ~/.bashrc +``` \ No newline at end of file diff --git a/doc/rknn_server_proxy.md b/doc/rknn_server_proxy.md new file mode 100644 index 0000000000000000000000000000000000000000..2eff17c2483fc50565d9834a53124f76b40746d2 --- /dev/null +++ b/doc/rknn_server_proxy.md @@ -0,0 +1,349 @@ +**目录** + +[TOC] + +## 1. 连板调试简介 +RKNN Toolkit2的连板功能一般需要更新板端的 rknn_server 和 librknnrt.so/librknnmrt.so，并且手动启动 rknn_server 才能正常工作。 +rknn_server: 是一个运行在板子上的后台代理服务，用于接收PC通过USB传输过来的协议，然后执行板端runtime对应的接口，并返回结果给PC。 + +- librknnrt.so: 是一个板端的RKNPU Runtime库(非RV1103/RV1106/RV1103B平台不适用)。 +- librknnmrt.so: 是专用于RV1103/RV1106/RV1103B平台的RKNPU Runtime库。 + +开机后通过ps命令查看rknn_server进程是否已存在，如果已存在，则不需要手动启动，否则需要手动启动。 + +## 2. 环境要求 +### 2.1 硬件环境 +本文档适用如下硬件平台： + +- RV1103 +- RV1103B +- RV1106 +- RK3562 +- RK3566系列 +- RK3568系列 +- RK3576系列 +- RK3588系列 + +### 2.2 软件环境 +- 若使用动态形状输入RKNN模型，要求rknn_server和RKNPU Runtime库版本>=1.5.0。 +- 若使用大于2GB的模型，要求rknn_server版本>=2.0.0b0。 +- 在RV1103/RV1106/RV1103B等小内存平台上，建议rknn_server版本>=2.1.0。 + + +## 3. rknn_server存放目录 +rknn_server存放在runtime目录下, 请根据板子上的系统选择相应版本的rknn_server,不同芯片和系统对应的rknn_server路径如下： +### 3.1 Android平台 +|系统|路径| +|-----|-----| +|32-bit Android|runtime/Android/rknn_server/arm/rknn_server| +|64-bit Android|runtime/Android/rknn_server/arm64/rknn_server| + + +### 3.2 Linux平台 + +|芯片|系统|路径| +|-----|-----|-----| +|RV1103/RV1106/RV1103B|32-bit Linux|runtime/Linux/rknn_server/armhf-uclibc/usr/bin/rknn_server| +|其他芯片|32-bit Linux|runtime/Linux/rknn_server/armhf/usr/bin/rknn_server| +|其他芯片|64-bit Linux|runtime/Linux/rknn_server/aarch64/usr/bin/rknn_server| + +## 4. 启动步骤 +### 4.1 Android平台 +进入rknpu2工程的根目录，在PC端执行下列命令启动代理服务: +1. 重新挂载系统分区,使系统分区重新可写 +``` +adb root && adb remount +``` +2. 更新代理程序 +``` +// 64-bit Android系统 +adb push runtime/Android/rknn_server/arm64/rknn_server /vendor/bin/ +// 32-bit Android系统 +adb push runtime/Android/rknn_server/arm/rknn_server /vendor/bin/ +``` +3. 更新RKNPU runtime库 +``` +// 64-bit Android系统 +adb push runtime/Android/librknn_api/arm64-v8a/librknnrt.so /vendor/lib64 +// 32-bit Android系统 +adb push runtime/Android/librknn_api/armeabi-v7a/librknnrt.so /vendor/lib +``` +4. 修改代理程序权限 +``` +adb shell chmod +x /vendor/bin/rknn_server +``` +5. 启动代理服务 +``` +adb shell "killall rknn_server" +adb shell "nohup /vendor/bin/rknn_server >/dev/null"& +``` +6. 检查代理服务是否启动成功： +``` +adb shell ps -ef|grep rknn_server +``` +查看是否有`rknn_server`的进程id,如果存在表示代理服务启动成功;否则请在板子上手动启动代理服务,步骤如下: +adb shell命令**进入板子shell界面**后,执行下列命令启动代理服务 + +``` +nohup /vendor/bin/rknn_server > /dev/null& +``` + +### 4.2 Linux平台(非RV1103/RV1106/RV1103B) +1. 更新代理程序 +``` +// 64-bit Linux系统 +adb push runtime/Linux/rknn_server/aarch64/usr/bin/rknn_server /usr/bin/ +// 32-bit Linux系统 +adb push runtime/Linux/rknn_server/armhf/usr/bin/rknn_server /usr/bin/ +``` +2. 更新RKNPU runtime库 +``` +// 64-bit Linux系统 +adb push runtime/Linux/librknn_api/aarch64/librknnrt.so /usr/lib +// 32-bit Linux系统 +adb push runtime/Linux/librknn_api/armhf/librknnrt.so /usr/lib +``` +3. 修改代理程序权限 +``` +adb shell chmod +x /usr/bin/rknn_server +``` +4. 启动代理服务 +``` +adb shell "killall rknn_server" +adb shell "nohup /usr/bin/rknn_server >/dev/null"& +``` +5. 检查代理服务是否启动成功： +``` +adb shell ps -ef|grep rknn_server +``` +查看是否有`rknn_server`的进程id,如果存在表示代理服务启动成功;否则请在板子上手动启动代理服务,方法如下: +adb shell命令**进入板子shell界面**后,执行下列命令启动代理服务 + +``` +nohup /usr/bin/rknn_server > /dev/null& +``` + +### 4.3Linux平台(RV1103/RV1106/RV1103B) +RV1103/RV1106/RV1103B上使用的RKNPU Runtime库是librknnmrt.so，使用armhf-uclibc目录下的rknn_server,启动步骤如下： +1. 更新代理程序 +``` +adb push runtime/Linux/rknn_server/armhf-uclibc/usr/bin/rknn_server /oem/usr/bin +``` +2. 更新RKNPU runtime库 +``` +adb push runtime/Linux/librknn_api/armhf-uclibc/librknnmrt.so /oem/usr/lib +``` +3. 修改代理程序权限 +``` +adb shell chmod +x /oem/usr/bin/rknn_server +``` +4. 启动代理服务 +``` +adb shell "killall rknn_server" +adb shell "nohup /oem/usr/bin/rknn_server >/dev/null"& +``` +5. 检查代理服务是否启动成功： +``` +adb shell ps |grep rknn_server +``` +查看是否有`rknn_server`的进程id,如果存在表示代理服务启动成功;否则请在板子上手动启动代理服务,方法如下: +adb shell命令**进入板子shell界面**后,执行下列命令启动代理服务 + +``` +nohup /oem/usr/bin/rknn_server > /dev/null& +``` + +## 5. 查看rknn_server详细日志 +代理服务默认不开启详细日志，如遇到连板过程报错，需开启详细日志来定位错误原因，请参考相应平台执行步骤： +### 5.1 Android平台 +1. 设置环境变量开启详细日志,并启动代理: +``` +adb logcat -c +adb shell "killall rknn_server" +adb shell "setprop persist.vendor.rknn.server.log.level 5 && nohup /vendor/bin/rknn_server >/dev/null"& +``` +2. 检查代理服务是否启动成功： + +``` +adb shell ps -ef|grep rknn_server +``` +3. 运行PC上python推理程序 +4. 查看运行日志 +``` +adb logcat +``` + +5. 开启详细日志会导致推理速度变慢，恢复默认日志等级的命令如下： + +```sh +adb shell "killall rknn_server" +adb shell "setprop persist.vendor.rknn.server.log.level 0 && nohup /vendor/bin/rknn_server >/dev/null"& +``` + +### 5.2 Linux平台(非RV1103/RV1106/RV1103B) + +1. 创建目录,用于保存代理服务的详细日志 +``` +adb shell mkdir -p /userdata +``` +2. 设置环境变量开启详细日志,并启动代理: +``` +adb shell "killall rknn_server" +adb shell "export RKNN_SERVER_LOGLEVEL=5 && nohup /usr/bin/rknn_server >/userdata/server.log"& +``` +3. 检查代理服务是否启动成功： +``` +adb shell ps -ef|grep rknn_server +``` +查看是否有`rknn_server`的进程id,如果存在表示代理服务启动成功;否则请在板子上手动启动代理服务,方法如下: +adb shell命令进入板子shell界面后,执行下列命令启动代理服务 +``` +export RKNN_SERVER_LOGLEVEL=5 +nohup /usr/bin/rknn_server >/userdata/server.log& +exit +``` +4. 运行PC上python推理程序 +5. 查看日志 +``` +adb shell cat /userdata/server.log +``` + +6. 开启详细日志会导致推理速度变慢，恢复默认日志等级的命令如下： + +```sh + adb shell "killall rknn_server" + adb shell "export RKNN_SERVER_LOGLEVEL=0 && nohup /usr/bin/rknn_server >/userdata/server.log"& +``` + +### 5.3 Linux平台(RV1103/RV1106/RV1103B) + +1. 创建目录,用于保存代理服务的详细日志 +``` +adb shell mkdir -p /userdata +``` +2. 设置环境变量开启详细日志,并启动代理: +``` +adb shell "killall rknn_server" +adb shell "export RKNN_SERVER_LOGLEVEL=5 && nohup /oem/usr/bin/rknn_server >/userdata/server.log"& +``` +3. 检查代理服务是否启动成功： +``` +adb shell ps|grep rknn_server +``` +查看是否有`rknn_server`的进程id,如果存在表示代理服务启动成功;否则请在板子上手动启动代理服务,方法如下: +adb shell命令进入板子shell界面后,执行下列命令启动代理服务 +``` +export RKNN_SERVER_LOGLEVEL=5 +nohup /oem/usr/bin/rknn_server >/userdata/server.log& +exit +``` +4. 运行PC上python推理程序 +5. 查看日志 +``` +adb shell cat /userdata/server.log +``` + +6. 开启详细日志会导致推理速度变慢，恢复默认日志等级的命令如下： + +```sh +adb shell "killall rknn_server" +adb shell "export RKNN_SERVER_LOGLEVEL=0 && nohup /oem/usr/bin/rknn_server >/userdata/server.log"& +``` + +## 6. 常见问题 +### 问题1 +Debian系统上rknn_server服务已经后台启动, 但是连板推理时依旧有如下报错： +``` +D NPUTransfer: ERROR: socket read fd = 4, n = -1: Connection reset by peer +D NPUTransfer: Transfer client closed, fd = 4 +E RKNNAPI: rknn_init, server connect fail! ret = -9(ERROR_PIPE)! +E build_graph: The rknn_server on the concected device is abnormal, please start the rknn_server on the device according to: + https://github.com/airockchip/rknn-toolkit2/blob/master/doc/rknn_server_proxy.md +``` + +**解决方法：** +这通常是由于Debian固件上的adbd程序没有监听5037端口导致的，可以在板子上执行以下命令来判断: +``` +netstat -n -t -u -a +``` +如果输出结果中没有5037端口,则执行下列命令下载和更新adbd程序, 并重启板子;否则,跳过下列步骤。 +``` +wget -O adbd.zip https://ftzr.zbox.filez.com/v2/delivery/data/7f0ac30dfa474892841fcb2cd29ad924/adbd.zip +unzip adbd.zip +adb push adbd/linux-aarch64/adbd /usr/bin/adbd +``` +进入设备shell命令，增加adbd的可执行权限 + +```sh +adb shell "chmod +x /usr/bin/adbd" +adb reboot +``` + +重启设备后，按照启动步骤启动rknn_server服务，再次尝试连板推理。 + + +### 问题2 +在RV1103/RV1106/RV1103B设备上遇到"E RKNN: failed to allocate fd, ret: -1, errno: 12"或者OOM报错。 +**解决方法：** +在RV1103/RV1106/RV1103B设备上运行RkLunch-stop.sh，关闭其他占用内存的应用，并将rknn_server升级到>=2.1.0版本后再连板推理。 + +### 问题3 +RV1103/RV1106/RV1103B使用init_runtime python接口时，没有逐层的耗时。 +原因：RV1103/RV1106/RV1103B上**不支持**perf_debug=True参数。 + +### 问题4 +RV1103/RV1106/RV1103B使用accuracy_analysis python接口使用时，因为模型太大，板子上存储容量不够导致运行失败。 + +**解决方法：** + +在设备端df -h查看存储空间情况，假设设备存储情况如下： + +```sh +Filesystem Size Used Available Use% Mounted on +ubi0:rootfs 17.0M 14.1M 2.9M 83% / +devtmpfs 27.4M 0 27.4M 0% /dev +tmpfs 27.5M 0 27.5M 0% /dev/shm +tmpfs 27.5M 8.0K 27.5M 0% /tmp +tmpfs 27.5M 84.0K 27.4M 0% /run +/dev/ubi5_0 20.6M 14.5M 6.1M 70% /oem +/dev/ubi6_0 52.8M 24.5M 28.2M 46% /userdata + +``` + +从上面看出/userdata/目录的可用空间最大，故将/userdata/目录作为精度分析时保存中间结果文件的目录，再重启rknn_server，PC上执行命令如下： + +``` +adb shell "killall rknn_server" +adb shell "export RKNN_DUMP_DIR=/userdata/dumps && nohup /usr/bin/rknn_server >/dev/null"& +``` + +RV1103/RV1106/RV1103B的rknn_server默认的保存中间结果路径是/tmp/dumps，如果需要恢复默认路径，请执行下列命令： + +```adb shell "killall rknn_server" +adb shell "killall rknn_server" +adb shell "unset RKNN_DUMP_DIR && nohup /usr/bin/rknn_server >/dev/null"& +``` + +如果设备存储空间不足，并且RV1103/RV1106/RV1103B固件支持NFS挂载，则可以将dump目录挂载到NFS目录后，再执行精度分析。假设目标服务器的IP为192.168.1.1，服务器NFS目录是/data0/nfs_shared，则在设备上执行下列命令将/userdata/dumps挂载到NFS目录 + +``` +# 以241为例 +mkdir -p /userdata/dumps +mount -t nfs -o nolock 192.168.1.1:/data0/nfs_shared /userdata/dumps +``` + +### 问题5 + +使用adb命令更新Runtime库后没有生效。 + +**解决方法**： + +更新库后要重新启动rknn_server，下次连板推理才能生效。如果是RV1103/RV1106/RV1103B平台，确保将librknnmrt.so放在/oem/usr/lib目录，如果/usr/lib/目录下有同名的库，要删除/usr/lib/下的librknnmrt.so，并重启rknn_server。 + +### 问题6 + +Android系统启动代理服务遇到权限问题。 + +**解决方法**： + +使用adb shell setenforce 0命令来关闭selinux。 diff --git a/res/QQGroup2QRCode.png b/res/QQGroup2QRCode.png new file mode 100755 index 0000000000000000000000000000000000000000..4013b6079f6323cb59c06800790427166a629869 --- /dev/null +++ b/res/QQGroup2QRCode.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d40267b1f8cc050987feb4c871f2fba4b5005c8ff0cbb5da471b62879518cec6 +size 96408 diff --git a/res/QQGroup3QRCode.png b/res/QQGroup3QRCode.png new file mode 100755 index 0000000000000000000000000000000000000000..482b0bdd55c5a07d5814669e32c3565ed4992957 --- /dev/null +++ b/res/QQGroup3QRCode.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0bc9629b703bfaaefe4eafc80297869edee79772f421f000709d9c8ea26ec10a +size 97404 diff --git 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sha256:c2992ee2c61da554f39a24f34e90a1db5f28838bed15fd298708655d72b03576 +size 5811 diff --git a/rknn-toolkit-lite2/CHANGELOG.txt b/rknn-toolkit-lite2/CHANGELOG.txt new file mode 100644 index 0000000000000000000000000000000000000000..2048eb3f01b6099dd50167efcbda44c19606c62e --- /dev/null +++ b/rknn-toolkit-lite2/CHANGELOG.txt @@ -0,0 +1,45 @@ +2024-08-08 +版本: v2.1.0 +1. 修复已知BUG。 + +2024-03-14 +版本: v2.0.0b0 +1. 功能完善： + 1.1 新增对RK3576的支持； + 1.2 init_runtime core_mask参数增加新的可选值：RKNNLite.NPU_CORE_ALL。 + +2023-12-04 +版本: v1.6.0 +1. 功能完善： + 1.1 新增适配AARCH64 Python3.11的安装包； + 1.2 推理接口增加对动态shape模型的支持； + 1.3 推理接口增加对4维NCHW格式输入的支持。 + +2023-08-21 +版本: v1.5.2 +1. 更新版本号 + +版本: v1.5.0 +1. 功能完善： + 1.1 新增对RK3562的支持； + 1.2 新增适配AARCH64 Python3.8, Python3.10的安装包； + 1.3 适配1.5.0版本NPU驱动。 + +2022-08-31 +版本: v1.4.0 +1. 功能完善： + 1.1 init_runtime core_mask支持NPU_CORE_0_1和NPU_CORE_0_1_2； + 1.2 适配1.4.0版本NPU驱动。 + +2022-04-27 +版本: v1.3.0 +1. 功能完善： + 1.1 完善init_runtime失败的提示信息； + 1.2 适配1.3.0版本NPU驱动。 + +2022-01-14 +版本：v1.2.0 +1. 新功能： + 1.1 RKNN模型推理； + 1.2 SDK版本查询； + 1.3 模型可运行平台查询。 diff --git a/rknn-toolkit-lite2/examples/dynamic_shape/README.md b/rknn-toolkit-lite2/examples/dynamic_shape/README.md new file mode 100644 index 0000000000000000000000000000000000000000..63edd3a6a55bf37917b16d8bd1d047eb2f6b866f --- /dev/null +++ b/rknn-toolkit-lite2/examples/dynamic_shape/README.md @@ -0,0 +1,49 @@ +# How to use dynamic shape function + +## Model Source +The model used in this example come from the following open source projects: +https://github.com/shicai/MobileNet-Caffe + +### Convert to RKNN model +Please refer to the example in the RKNN Toolkit2 project to generate the RKNN model: +https://github.com/rockchip-linux/rknn-toolkit2/tree/master/examples/functions/dynamic_shape + +## Script Usage +*Usage:* +``` +python test.py +``` + +## Expected Results +This example will print the TOP5 labels and corresponding scores of the test image classification results for each different input shape, as follows: +``` +model: mobilenet_v2 + +input shape: 1,3,224,224 +W The input[0] need NHWC data format, but NCHW set, the data format and data buffer will be changed to NHWC. +-----TOP 5----- +[155] score:0.992188 class:"Shih-Tzu" +[154] score:0.002636 class:"Pekinese, Pekingese, Peke" +[204] score:0.002636 class:"Lhasa, Lhasa apso" +[283] score:0.001698 class:"Persian cat" +[896] score:0.000273 class:"washbasin, handbasin, washbowl, lavabo, wash-hand basin" + +input shape: 1,3,160,160 +W The input[0] need NHWC data format, but NCHW set, the data format and data buffer will be changed to NHWC. +-----TOP 5----- +[155] score:0.558594 class:"Shih-Tzu" +[154] score:0.408447 class:"Pekinese, Pekingese, Peke" +[204] score:0.031036 class:"Lhasa, Lhasa apso" +[194] score:0.000956 class:"Dandie Dinmont, Dandie Dinmont terrier" +[219] score:0.000256 class:"cocker spaniel, English cocker spaniel, cocker" + +input shape: 1,3,256,256 +W The input[0] need NHWC data format, but NCHW set, the data format and data buffer will be changed to NHWC. +-----TOP 5----- +[155] score:0.980957 class:"Shih-Tzu" +[154] score:0.008835 class:"Pekinese, Pekingese, Peke" +[204] score:0.004883 class:"Lhasa, Lhasa apso" +[193] score:0.000929 class:"Australian terrier" +[200] score:0.000509 class:"Tibetan terrier, chrysanthemum dog" +``` +- Note: Different platforms, different versions of tools and drivers may have slightly different results. diff --git a/rknn-toolkit-lite2/examples/dynamic_shape/dog_224x224.jpg b/rknn-toolkit-lite2/examples/dynamic_shape/dog_224x224.jpg new file mode 100644 index 0000000000000000000000000000000000000000..004b0416e23454a12eb06eaba238c7e2d5f2b0fc --- /dev/null +++ b/rknn-toolkit-lite2/examples/dynamic_shape/dog_224x224.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c350299c6283d5f62fecf1f845b6b3be9aafec8dff528ca09a129990f0a584b0 +size 18889 diff --git a/rknn-toolkit-lite2/examples/dynamic_shape/mobilenet_v2_for_rk3562.rknn b/rknn-toolkit-lite2/examples/dynamic_shape/mobilenet_v2_for_rk3562.rknn new file mode 100644 index 0000000000000000000000000000000000000000..53fdef162efa5984b7b4d657bb59ec83e6ff0928 --- /dev/null +++ b/rknn-toolkit-lite2/examples/dynamic_shape/mobilenet_v2_for_rk3562.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f71efa3af9e664e79a3508663f02038e3fdc081cb3f3e4f2be646f6588b622bb +size 4955210 diff --git a/rknn-toolkit-lite2/examples/dynamic_shape/mobilenet_v2_for_rk3566_rk3568.rknn b/rknn-toolkit-lite2/examples/dynamic_shape/mobilenet_v2_for_rk3566_rk3568.rknn new file mode 100644 index 0000000000000000000000000000000000000000..79b5929c538f09e60848ae18f3dc40463dfe36f9 --- /dev/null +++ b/rknn-toolkit-lite2/examples/dynamic_shape/mobilenet_v2_for_rk3566_rk3568.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f591b57b93f76226aed9741ecc4910bb7d69c88fe397fdeaedd57fa0f1904373 +size 4623050 diff --git a/rknn-toolkit-lite2/examples/dynamic_shape/mobilenet_v2_for_rk3576.rknn b/rknn-toolkit-lite2/examples/dynamic_shape/mobilenet_v2_for_rk3576.rknn new file mode 100644 index 0000000000000000000000000000000000000000..feff11470bdb2c7db799f181e671f4fbb226f67e --- /dev/null +++ b/rknn-toolkit-lite2/examples/dynamic_shape/mobilenet_v2_for_rk3576.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e659fee1c0f0e2a9d22fe9e6dc85ea29c6a2dca8a222995c0838a3ece5e9ad49 +size 6006922 diff --git a/rknn-toolkit-lite2/examples/dynamic_shape/mobilenet_v2_for_rk3588.rknn b/rknn-toolkit-lite2/examples/dynamic_shape/mobilenet_v2_for_rk3588.rknn new file mode 100644 index 0000000000000000000000000000000000000000..6b7607adcfeadf8a4faa40cf3022de94afd158d1 --- /dev/null +++ b/rknn-toolkit-lite2/examples/dynamic_shape/mobilenet_v2_for_rk3588.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b866cf43678a7bec1df7b002d222f369f9a63d5197e0c1eaa9d0a407b1deb433 +size 5856394 diff --git a/rknn-toolkit-lite2/examples/dynamic_shape/synset_label.py b/rknn-toolkit-lite2/examples/dynamic_shape/synset_label.py new file mode 100644 index 0000000000000000000000000000000000000000..b53fbf603e968333b3e101d8cbf4267a12b3ab5b --- /dev/null +++ b/rknn-toolkit-lite2/examples/dynamic_shape/synset_label.py @@ -0,0 +1,1003 @@ +# the labels come from synset.txt, download link: https://s3.amazonaws.com/onnx-model-zoo/synset.txt + +labels = \ +{0: 'tench, Tinca tinca', + 1: 'goldfish, Carassius auratus', + 2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias', + 3: 'tiger shark, Galeocerdo cuvieri', + 4: 'hammerhead, hammerhead shark', + 5: 'electric ray, crampfish, numbfish, torpedo', + 6: 'stingray', + 7: 'cock', + 8: 'hen', + 9: 'ostrich, Struthio camelus', + 10: 'brambling, Fringilla montifringilla', + 11: 'goldfinch, Carduelis carduelis', + 12: 'house finch, linnet, Carpodacus mexicanus', + 13: 'junco, snowbird', + 14: 'indigo bunting, indigo finch, indigo bird, Passerina cyanea', + 15: 'robin, American robin, Turdus migratorius', + 16: 'bulbul', + 17: 'jay', + 18: 'magpie', + 19: 'chickadee', + 20: 'water ouzel, dipper', + 21: 'kite', + 22: 'bald eagle, American eagle, Haliaeetus leucocephalus', + 23: 'vulture', + 24: 'great grey owl, great gray owl, Strix nebulosa', + 25: 'European fire salamander, Salamandra salamandra', + 26: 'common newt, Triturus vulgaris', + 27: 'eft', + 28: 'spotted salamander, Ambystoma maculatum', + 29: 'axolotl, mud puppy, Ambystoma mexicanum', + 30: 'bullfrog, Rana catesbeiana', + 31: 'tree frog, tree-frog', + 32: 'tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui', + 33: 'loggerhead, loggerhead turtle, Caretta caretta', + 34: 'leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea', + 35: 'mud turtle', + 36: 'terrapin', + 37: 'box turtle, box tortoise', + 38: 'banded gecko', + 39: 'common iguana, iguana, Iguana iguana', + 40: 'American chameleon, anole, Anolis carolinensis', + 41: 'whiptail, whiptail lizard', + 42: 'agama', + 43: 'frilled lizard, Chlamydosaurus kingi', + 44: 'alligator lizard', + 45: 'Gila monster, Heloderma suspectum', + 46: 'green lizard, Lacerta viridis', + 47: 'African chameleon, Chamaeleo chamaeleon', + 48: 'Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis', + 49: 'African crocodile, Nile crocodile, Crocodylus niloticus', + 50: 'American alligator, Alligator mississipiensis', + 51: 'triceratops', + 52: 'thunder snake, worm snake, Carphophis amoenus', + 53: 'ringneck snake, ring-necked snake, ring snake', + 54: 'hognose snake, puff adder, sand viper', + 55: 'green snake, grass snake', + 56: 'king snake, kingsnake', + 57: 'garter snake, grass snake', + 58: 'water snake', + 59: 'vine snake', + 60: 'night snake, Hypsiglena torquata', + 61: 'boa constrictor, Constrictor constrictor', + 62: 'rock python, rock snake, Python sebae', + 63: 'Indian cobra, Naja naja', + 64: 'green mamba', + 65: 'sea snake', + 66: 'horned viper, cerastes, sand viper, horned asp, Cerastes cornutus', + 67: 'diamondback, diamondback rattlesnake, Crotalus adamanteus', + 68: 'sidewinder, horned rattlesnake, Crotalus cerastes', + 69: 'trilobite', + 70: 'harvestman, daddy longlegs, Phalangium opilio', + 71: 'scorpion', + 72: 'black and gold garden spider, Argiope aurantia', + 73: 'barn spider, Araneus cavaticus', + 74: 'garden spider, Aranea diademata', + 75: 'black widow, Latrodectus mactans', + 76: 'tarantula', + 77: 'wolf spider, hunting spider', + 78: 'tick', + 79: 'centipede', + 80: 'black grouse', + 81: 'ptarmigan', + 82: 'ruffed grouse, partridge, Bonasa umbellus', + 83: 'prairie chicken, prairie grouse, prairie fowl', + 84: 'peacock', + 85: 'quail', + 86: 'partridge', + 87: 'African grey, African gray, Psittacus erithacus', + 88: 'macaw', + 89: 'sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita', + 90: 'lorikeet', + 91: 'coucal', + 92: 'bee eater', + 93: 'hornbill', + 94: 'hummingbird', + 95: 'jacamar', + 96: 'toucan', + 97: 'drake', + 98: 'red-breasted merganser, Mergus serrator', + 99: 'goose', + 100: 'black swan, Cygnus atratus', + 101: 'tusker', + 102: 'echidna, spiny anteater, anteater', + 103: 'platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus', + 104: 'wallaby, brush kangaroo', + 105: 'koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus', + 106: 'wombat', + 107: 'jellyfish', + 108: 'sea anemone, anemone', + 109: 'brain coral', + 110: 'flatworm, platyhelminth', + 111: 'nematode, nematode worm, roundworm', + 112: 'conch', + 113: 'snail', + 114: 'slug', + 115: 'sea slug, nudibranch', + 116: 'chiton, coat-of-mail shell, sea cradle, polyplacophore', + 117: 'chambered nautilus, pearly nautilus, nautilus', + 118: 'Dungeness crab, Cancer magister', + 119: 'rock crab, Cancer irroratus', + 120: 'fiddler crab', + 121: 'king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica', + 122: 'American lobster, Northern lobster, Maine lobster, Homarus americanus', + 123: 'spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish', + 124: 'crayfish, crawfish, crawdad, crawdaddy', + 125: 'hermit crab', + 126: 'isopod', + 127: 'white stork, Ciconia ciconia', + 128: 'black stork, Ciconia nigra', + 129: 'spoonbill', + 130: 'flamingo', + 131: 'little blue heron, Egretta caerulea', + 132: 'American egret, great white heron, Egretta albus', + 133: 'bittern', + 134: 'crane', + 135: 'limpkin, Aramus pictus', + 136: 'European gallinule, Porphyrio porphyrio', + 137: 'American coot, marsh hen, mud hen, water hen, Fulica americana', + 138: 'bustard', + 139: 'ruddy turnstone, Arenaria interpres', + 140: 'red-backed sandpiper, dunlin, Erolia alpina', + 141: 'redshank, Tringa totanus', + 142: 'dowitcher', + 143: 'oystercatcher, oyster catcher', + 144: 'pelican', + 145: 'king penguin, Aptenodytes patagonica', + 146: 'albatross, mollymawk', + 147: 'grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus', + 148: 'killer whale, killer, orca, grampus, sea wolf, Orcinus orca', + 149: 'dugong, Dugong dugon', + 150: 'sea lion', + 151: 'Chihuahua', + 152: 'Japanese spaniel', + 153: 'Maltese dog, Maltese terrier, Maltese', + 154: 'Pekinese, Pekingese, Peke', + 155: 'Shih-Tzu', + 156: 'Blenheim spaniel', + 157: 'papillon', + 158: 'toy terrier', + 159: 'Rhodesian ridgeback', + 160: 'Afghan hound, Afghan', + 161: 'basset, basset hound', + 162: 'beagle', + 163: 'bloodhound, sleuthhound', + 164: 'bluetick', + 165: 'black-and-tan coonhound', + 166: 'Walker hound, Walker foxhound', + 167: 'English foxhound', + 168: 'redbone', + 169: 'borzoi, Russian wolfhound', + 170: 'Irish wolfhound', + 171: 'Italian greyhound', + 172: 'whippet', + 173: 'Ibizan hound, Ibizan Podenco', + 174: 'Norwegian elkhound, elkhound', + 175: 'otterhound, otter hound', + 176: 'Saluki, gazelle hound', + 177: 'Scottish deerhound, deerhound', + 178: 'Weimaraner', + 179: 'Staffordshire bullterrier, Staffordshire bull terrier', + 180: 'American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier', + 181: 'Bedlington terrier', + 182: 'Border terrier', + 183: 'Kerry blue terrier', + 184: 'Irish terrier', + 185: 'Norfolk terrier', + 186: 'Norwich terrier', + 187: 'Yorkshire terrier', + 188: 'wire-haired fox terrier', + 189: 'Lakeland terrier', + 190: 'Sealyham terrier, Sealyham', + 191: 'Airedale, Airedale terrier', + 192: 'cairn, cairn terrier', + 193: 'Australian terrier', + 194: 'Dandie Dinmont, Dandie Dinmont terrier', + 195: 'Boston bull, Boston terrier', + 196: 'miniature schnauzer', + 197: 'giant schnauzer', + 198: 'standard schnauzer', + 199: 'Scotch terrier, Scottish terrier, Scottie', + 200: 'Tibetan terrier, chrysanthemum dog', + 201: 'silky terrier, Sydney silky', + 202: 'soft-coated wheaten terrier', + 203: 'West Highland white terrier', + 204: 'Lhasa, Lhasa apso', + 205: 'flat-coated retriever', + 206: 'curly-coated retriever', + 207: 'golden retriever', + 208: 'Labrador retriever', + 209: 'Chesapeake Bay retriever', + 210: 'German short-haired pointer', + 211: 'vizsla, Hungarian pointer', + 212: 'English setter', + 213: 'Irish setter, red setter', + 214: 'Gordon setter', + 215: 'Brittany spaniel', + 216: 'clumber, clumber spaniel', + 217: 'English springer, English springer spaniel', + 218: 'Welsh springer spaniel', + 219: 'cocker spaniel, English cocker spaniel, cocker', + 220: 'Sussex spaniel', + 221: 'Irish water spaniel', + 222: 'kuvasz', + 223: 'schipperke', + 224: 'groenendael', + 225: 'malinois', + 226: 'briard', + 227: 'kelpie', + 228: 'komondor', + 229: 'Old English sheepdog, bobtail', + 230: 'Shetland sheepdog, Shetland sheep dog, Shetland', + 231: 'collie', + 232: 'Border collie', + 233: 'Bouvier des Flandres, Bouviers des Flandres', + 234: 'Rottweiler', + 235: 'German shepherd, German shepherd dog, German police dog, alsatian', + 236: 'Doberman, Doberman pinscher', + 237: 'miniature pinscher', + 238: 'Greater Swiss Mountain dog', + 239: 'Bernese mountain dog', + 240: 'Appenzeller', + 241: 'EntleBucher', + 242: 'boxer', + 243: 'bull mastiff', + 244: 'Tibetan mastiff', + 245: 'French bulldog', + 246: 'Great Dane', + 247: 'Saint Bernard, St Bernard', + 248: 'Eskimo dog, husky', + 249: 'malamute, malemute, Alaskan malamute', + 250: 'Siberian husky', + 251: 'dalmatian, coach dog, carriage dog', + 252: 'affenpinscher, monkey pinscher, monkey dog', + 253: 'basenji', + 254: 'pug, pug-dog', + 255: 'Leonberg', + 256: 'Newfoundland, Newfoundland dog', + 257: 'Great Pyrenees', + 258: 'Samoyed, Samoyede', + 259: 'Pomeranian', + 260: 'chow, chow chow', + 261: 'keeshond', + 262: 'Brabancon griffon', + 263: 'Pembroke, Pembroke Welsh corgi', + 264: 'Cardigan, Cardigan Welsh corgi', + 265: 'toy poodle', + 266: 'miniature poodle', + 267: 'standard poodle', + 268: 'Mexican hairless', + 269: 'timber wolf, grey wolf, gray wolf, Canis lupus', + 270: 'white wolf, Arctic wolf, Canis lupus tundrarum', + 271: 'red wolf, maned wolf, Canis rufus, Canis niger', + 272: 'coyote, prairie wolf, brush wolf, Canis latrans', + 273: 'dingo, warrigal, warragal, Canis dingo', + 274: 'dhole, Cuon alpinus', + 275: 'African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus', + 276: 'hyena, hyaena', + 277: 'red fox, Vulpes vulpes', + 278: 'kit fox, Vulpes macrotis', + 279: 'Arctic fox, white fox, Alopex lagopus', + 280: 'grey fox, gray fox, Urocyon cinereoargenteus', + 281: 'tabby, tabby cat', + 282: 'tiger cat', + 283: 'Persian cat', + 284: 'Siamese cat, Siamese', + 285: 'Egyptian cat', + 286: 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor', + 287: 'lynx, catamount', + 288: 'leopard, Panthera pardus', + 289: 'snow leopard, ounce, Panthera uncia', + 290: 'jaguar, panther, Panthera onca, Felis onca', + 291: 'lion, king of beasts, Panthera leo', + 292: 'tiger, Panthera tigris', + 293: 'cheetah, chetah, Acinonyx jubatus', + 294: 'brown bear, bruin, Ursus arctos', + 295: 'American black bear, black bear, Ursus americanus, Euarctos americanus', + 296: 'ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus', + 297: 'sloth bear, Melursus ursinus, Ursus ursinus', + 298: 'mongoose', + 299: 'meerkat, mierkat', + 300: 'tiger beetle', + 301: 'ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle', + 302: 'ground beetle, carabid beetle', + 303: 'long-horned beetle, longicorn, longicorn beetle', + 304: 'leaf beetle, chrysomelid', + 305: 'dung beetle', + 306: 'rhinoceros beetle', + 307: 'weevil', + 308: 'fly', + 309: 'bee', + 310: 'ant, emmet, pismire', + 311: 'grasshopper, hopper', + 312: 'cricket', + 313: 'walking stick, walkingstick, stick insect', + 314: 'cockroach, roach', + 315: 'mantis, mantid', + 316: 'cicada, cicala', + 317: 'leafhopper', + 318: 'lacewing, lacewing fly', + 319: "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", + 320: 'damselfly', + 321: 'admiral', + 322: 'ringlet, ringlet butterfly', + 323: 'monarch, monarch butterfly, milkweed butterfly, Danaus plexippus', + 324: 'cabbage butterfly', + 325: 'sulphur butterfly, sulfur butterfly', + 326: 'lycaenid, lycaenid butterfly', + 327: 'starfish, sea star', + 328: 'sea urchin', + 329: 'sea cucumber, holothurian', + 330: 'wood rabbit, cottontail, cottontail rabbit', + 331: 'hare', + 332: 'Angora, Angora rabbit', + 333: 'hamster', + 334: 'porcupine, hedgehog', + 335: 'fox squirrel, eastern fox squirrel, Sciurus niger', + 336: 'marmot', + 337: 'beaver', + 338: 'guinea pig, Cavia cobaya', + 339: 'sorrel', + 340: 'zebra', + 341: 'hog, pig, grunter, squealer, Sus scrofa', + 342: 'wild boar, boar, Sus scrofa', + 343: 'warthog', + 344: 'hippopotamus, hippo, river horse, Hippopotamus amphibius', + 345: 'ox', + 346: 'water buffalo, water ox, Asiatic buffalo, Bubalus bubalis', + 347: 'bison', + 348: 'ram, tup', + 349: 'bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis', + 350: 'ibex, Capra ibex', + 351: 'hartebeest', + 352: 'impala, Aepyceros melampus', + 353: 'gazelle', + 354: 'Arabian camel, dromedary, Camelus dromedarius', + 355: 'llama', + 356: 'weasel', + 357: 'mink', + 358: 'polecat, fitch, foulmart, foumart, Mustela putorius', + 359: 'black-footed ferret, ferret, Mustela nigripes', + 360: 'otter', + 361: 'skunk, polecat, wood pussy', + 362: 'badger', + 363: 'armadillo', + 364: 'three-toed sloth, ai, Bradypus tridactylus', + 365: 'orangutan, orang, orangutang, Pongo pygmaeus', + 366: 'gorilla, Gorilla gorilla', + 367: 'chimpanzee, chimp, Pan troglodytes', + 368: 'gibbon, Hylobates lar', + 369: 'siamang, Hylobates syndactylus, Symphalangus syndactylus', + 370: 'guenon, guenon monkey', + 371: 'patas, hussar monkey, Erythrocebus patas', + 372: 'baboon', + 373: 'macaque', + 374: 'langur', + 375: 'colobus, colobus monkey', + 376: 'proboscis monkey, Nasalis larvatus', + 377: 'marmoset', + 378: 'capuchin, ringtail, Cebus capucinus', + 379: 'howler monkey, howler', + 380: 'titi, titi monkey', + 381: 'spider monkey, Ateles geoffroyi', + 382: 'squirrel monkey, Saimiri sciureus', + 383: 'Madagascar cat, ring-tailed lemur, Lemur catta', + 384: 'indri, indris, Indri indri, Indri brevicaudatus', + 385: 'Indian elephant, Elephas maximus', + 386: 'African elephant, Loxodonta africana', + 387: 'lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens', + 388: 'giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca', + 389: 'barracouta, snoek', + 390: 'eel', + 391: 'coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch', + 392: 'rock beauty, Holocanthus tricolor', + 393: 'anemone fish', + 394: 'sturgeon', + 395: 'gar, garfish, garpike, billfish, Lepisosteus osseus', + 396: 'lionfish', + 397: 'puffer, pufferfish, blowfish, globefish', + 398: 'abacus', + 399: 'abaya', + 400: "academic gown, academic robe, judge's robe", + 401: 'accordion, piano accordion, squeeze box', + 402: 'acoustic guitar', + 403: 'aircraft carrier, carrier, flattop, attack aircraft carrier', + 404: 'airliner', + 405: 'airship, dirigible', + 406: 'altar', + 407: 'ambulance', + 408: 'amphibian, amphibious vehicle', + 409: 'analog clock', + 410: 'apiary, bee house', + 411: 'apron', + 412: 'ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin', + 413: 'assault rifle, assault gun', + 414: 'backpack, back pack, knapsack, packsack, rucksack, haversack', + 415: 'bakery, bakeshop, bakehouse', + 416: 'balance beam, beam', + 417: 'balloon', + 418: 'ballpoint, ballpoint pen, ballpen, Biro', + 419: 'Band Aid', + 420: 'banjo', + 421: 'bannister, banister, balustrade, balusters, handrail', + 422: 'barbell', + 423: 'barber chair', + 424: 'barbershop', + 425: 'barn', + 426: 'barometer', + 427: 'barrel, cask', + 428: 'barrow, garden cart, lawn cart, wheelbarrow', + 429: 'baseball', + 430: 'basketball', + 431: 'bassinet', + 432: 'bassoon', + 433: 'bathing cap, swimming cap', + 434: 'bath towel', + 435: 'bathtub, bathing tub, bath, tub', + 436: 'beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon', + 437: 'beacon, lighthouse, beacon light, pharos', + 438: 'beaker', + 439: 'bearskin, busby, shako', + 440: 'beer bottle', + 441: 'beer glass', + 442: 'bell cote, bell cot', + 443: 'bib', + 444: 'bicycle-built-for-two, tandem bicycle, tandem', + 445: 'bikini, two-piece', + 446: 'binder, ring-binder', + 447: 'binoculars, field glasses, opera glasses', + 448: 'birdhouse', + 449: 'boathouse', + 450: 'bobsled, bobsleigh, bob', + 451: 'bolo tie, bolo, bola tie, bola', + 452: 'bonnet, poke bonnet', + 453: 'bookcase', + 454: 'bookshop, bookstore, bookstall', + 455: 'bottlecap', + 456: 'bow', + 457: 'bow tie, bow-tie, bowtie', + 458: 'brass, memorial tablet, plaque', + 459: 'brassiere, bra, bandeau', + 460: 'breakwater, groin, groyne, mole, bulwark, seawall, jetty', + 461: 'breastplate, aegis, egis', + 462: 'broom', + 463: 'bucket, pail', + 464: 'buckle', + 465: 'bulletproof vest', + 466: 'bullet train, bullet', + 467: 'butcher shop, meat market', + 468: 'cab, hack, taxi, taxicab', + 469: 'caldron, cauldron', + 470: 'candle, taper, wax light', + 471: 'cannon', + 472: 'canoe', + 473: 'can opener, tin opener', + 474: 'cardigan', + 475: 'car mirror', + 476: 'carousel, carrousel, merry-go-round, roundabout, whirligig', + 477: "carpenter's kit, tool kit", + 478: 'carton', + 479: 'car wheel', + 480: 'cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM', + 481: 'cassette', + 482: 'cassette player', + 483: 'castle', + 484: 'catamaran', + 485: 'CD player', + 486: 'cello, violoncello', + 487: 'cellular telephone, cellular phone, cellphone, cell, mobile phone', + 488: 'chain', + 489: 'chainlink fence', + 490: 'chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour', + 491: 'chain saw, chainsaw', + 492: 'chest', + 493: 'chiffonier, commode', + 494: 'chime, bell, gong', + 495: 'china cabinet, china closet', + 496: 'Christmas stocking', + 497: 'church, church building', + 498: 'cinema, movie theater, movie theatre, movie house, picture palace', + 499: 'cleaver, meat cleaver, chopper', + 500: 'cliff dwelling', + 501: 'cloak', + 502: 'clog, geta, patten, sabot', + 503: 'cocktail shaker', + 504: 'coffee mug', + 505: 'coffeepot', + 506: 'coil, spiral, volute, whorl, helix', + 507: 'combination lock', + 508: 'computer keyboard, keypad', + 509: 'confectionery, confectionary, candy store', + 510: 'container ship, containership, container vessel', + 511: 'convertible', + 512: 'corkscrew, bottle screw', + 513: 'cornet, horn, trumpet, trump', + 514: 'cowboy boot', + 515: 'cowboy hat, ten-gallon hat', + 516: 'cradle', + 517: 'crane', + 518: 'crash helmet', + 519: 'crate', + 520: 'crib, cot', + 521: 'Crock Pot', + 522: 'croquet ball', + 523: 'crutch', + 524: 'cuirass', + 525: 'dam, dike, dyke', + 526: 'desk', + 527: 'desktop computer', + 528: 'dial telephone, dial phone', + 529: 'diaper, nappy, napkin', + 530: 'digital clock', + 531: 'digital watch', + 532: 'dining table, board', + 533: 'dishrag, dishcloth', + 534: 'dishwasher, dish washer, dishwashing machine', + 535: 'disk brake, disc brake', + 536: 'dock, dockage, docking facility', + 537: 'dogsled, dog sled, dog sleigh', + 538: 'dome', + 539: 'doormat, welcome mat', + 540: 'drilling platform, offshore rig', + 541: 'drum, membranophone, tympan', + 542: 'drumstick', + 543: 'dumbbell', + 544: 'Dutch oven', + 545: 'electric fan, blower', + 546: 'electric guitar', + 547: 'electric locomotive', + 548: 'entertainment center', + 549: 'envelope', + 550: 'espresso maker', + 551: 'face powder', + 552: 'feather boa, boa', + 553: 'file, file cabinet, filing cabinet', + 554: 'fireboat', + 555: 'fire engine, fire truck', + 556: 'fire screen, fireguard', + 557: 'flagpole, flagstaff', + 558: 'flute, transverse flute', + 559: 'folding chair', + 560: 'football helmet', + 561: 'forklift', + 562: 'fountain', + 563: 'fountain pen', + 564: 'four-poster', + 565: 'freight car', + 566: 'French horn, horn', + 567: 'frying pan, frypan, skillet', + 568: 'fur coat', + 569: 'garbage truck, dustcart', + 570: 'gasmask, respirator, gas helmet', + 571: 'gas pump, gasoline pump, petrol pump, island dispenser', + 572: 'goblet', + 573: 'go-kart', + 574: 'golf ball', + 575: 'golfcart, golf cart', + 576: 'gondola', + 577: 'gong, tam-tam', + 578: 'gown', + 579: 'grand piano, grand', + 580: 'greenhouse, nursery, glasshouse', + 581: 'grille, radiator grille', + 582: 'grocery store, grocery, food market, market', + 583: 'guillotine', + 584: 'hair slide', + 585: 'hair spray', + 586: 'half track', + 587: 'hammer', + 588: 'hamper', + 589: 'hand blower, blow dryer, blow drier, hair dryer, hair drier', + 590: 'hand-held computer, hand-held microcomputer', + 591: 'handkerchief, hankie, hanky, hankey', + 592: 'hard disc, hard disk, fixed disk', + 593: 'harmonica, mouth organ, harp, mouth harp', + 594: 'harp', + 595: 'harvester, reaper', + 596: 'hatchet', + 597: 'holster', + 598: 'home theater, home theatre', + 599: 'honeycomb', + 600: 'hook, claw', + 601: 'hoopskirt, crinoline', + 602: 'horizontal bar, high bar', + 603: 'horse cart, horse-cart', + 604: 'hourglass', + 605: 'iPod', + 606: 'iron, smoothing iron', + 607: "jack-o'-lantern", + 608: 'jean, blue jean, denim', + 609: 'jeep, landrover', + 610: 'jersey, T-shirt, tee shirt', + 611: 'jigsaw puzzle', + 612: 'jinrikisha, ricksha, rickshaw', + 613: 'joystick', + 614: 'kimono', + 615: 'knee pad', + 616: 'knot', + 617: 'lab coat, laboratory coat', + 618: 'ladle', + 619: 'lampshade, lamp shade', + 620: 'laptop, laptop computer', + 621: 'lawn mower, mower', + 622: 'lens cap, lens cover', + 623: 'letter opener, paper knife, paperknife', + 624: 'library', + 625: 'lifeboat', + 626: 'lighter, light, igniter, ignitor', + 627: 'limousine, limo', + 628: 'liner, ocean liner', + 629: 'lipstick, lip rouge', + 630: 'Loafer', + 631: 'lotion', + 632: 'loudspeaker, speaker, speaker unit, loudspeaker system, speaker system', + 633: "loupe, jeweler's loupe", + 634: 'lumbermill, sawmill', + 635: 'magnetic compass', + 636: 'mailbag, postbag', + 637: 'mailbox, letter box', + 638: 'maillot', + 639: 'maillot, tank suit', + 640: 'manhole cover', + 641: 'maraca', + 642: 'marimba, xylophone', + 643: 'mask', + 644: 'matchstick', + 645: 'maypole', + 646: 'maze, labyrinth', + 647: 'measuring cup', + 648: 'medicine chest, medicine cabinet', + 649: 'megalith, megalithic structure', + 650: 'microphone, mike', + 651: 'microwave, microwave oven', + 652: 'military uniform', + 653: 'milk can', + 654: 'minibus', + 655: 'miniskirt, mini', + 656: 'minivan', + 657: 'missile', + 658: 'mitten', + 659: 'mixing bowl', + 660: 'mobile home, manufactured home', + 661: 'Model T', + 662: 'modem', + 663: 'monastery', + 664: 'monitor', + 665: 'moped', + 666: 'mortar', + 667: 'mortarboard', + 668: 'mosque', + 669: 'mosquito net', + 670: 'motor scooter, scooter', + 671: 'mountain bike, all-terrain bike, off-roader', + 672: 'mountain tent', + 673: 'mouse, computer mouse', + 674: 'mousetrap', + 675: 'moving van', + 676: 'muzzle', + 677: 'nail', + 678: 'neck brace', + 679: 'necklace', + 680: 'nipple', + 681: 'notebook, notebook computer', + 682: 'obelisk', + 683: 'oboe, hautboy, hautbois', + 684: 'ocarina, sweet potato', + 685: 'odometer, hodometer, mileometer, milometer', + 686: 'oil filter', + 687: 'organ, pipe organ', + 688: 'oscilloscope, scope, cathode-ray oscilloscope, CRO', + 689: 'overskirt', + 690: 'oxcart', + 691: 'oxygen mask', + 692: 'packet', + 693: 'paddle, boat paddle', + 694: 'paddlewheel, paddle wheel', + 695: 'padlock', + 696: 'paintbrush', + 697: "pajama, pyjama, pj's, jammies", + 698: 'palace', + 699: 'panpipe, pandean pipe, syrinx', + 700: 'paper towel', + 701: 'parachute, chute', + 702: 'parallel bars, bars', + 703: 'park bench', + 704: 'parking meter', + 705: 'passenger car, coach, carriage', + 706: 'patio, terrace', + 707: 'pay-phone, pay-station', + 708: 'pedestal, plinth, footstall', + 709: 'pencil box, pencil case', + 710: 'pencil sharpener', + 711: 'perfume, essence', + 712: 'Petri dish', + 713: 'photocopier', + 714: 'pick, plectrum, plectron', + 715: 'pickelhaube', + 716: 'picket fence, paling', + 717: 'pickup, pickup truck', + 718: 'pier', + 719: 'piggy bank, penny bank', + 720: 'pill bottle', + 721: 'pillow', + 722: 'ping-pong ball', + 723: 'pinwheel', + 724: 'pirate, pirate ship', + 725: 'pitcher, ewer', + 726: "plane, carpenter's plane, woodworking plane", + 727: 'planetarium', + 728: 'plastic bag', + 729: 'plate rack', + 730: 'plow, plough', + 731: "plunger, plumber's helper", + 732: 'Polaroid camera, Polaroid Land camera', + 733: 'pole', + 734: 'police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria', + 735: 'poncho', + 736: 'pool table, billiard table, snooker table', + 737: 'pop bottle, soda bottle', + 738: 'pot, flowerpot', + 739: "potter's wheel", + 740: 'power drill', + 741: 'prayer rug, prayer mat', + 742: 'printer', + 743: 'prison, prison house', + 744: 'projectile, missile', + 745: 'projector', + 746: 'puck, hockey puck', + 747: 'punching bag, punch bag, punching ball, punchball', + 748: 'purse', + 749: 'quill, quill pen', + 750: 'quilt, comforter, comfort, puff', + 751: 'racer, race car, racing car', + 752: 'racket, racquet', + 753: 'radiator', + 754: 'radio, wireless', + 755: 'radio telescope, radio reflector', + 756: 'rain barrel', + 757: 'recreational vehicle, RV, R.V.', + 758: 'reel', + 759: 'reflex camera', + 760: 'refrigerator, icebox', + 761: 'remote control, remote', + 762: 'restaurant, eating house, eating place, eatery', + 763: 'revolver, six-gun, six-shooter', + 764: 'rifle', + 765: 'rocking chair, rocker', + 766: 'rotisserie', + 767: 'rubber eraser, rubber, pencil eraser', + 768: 'rugby ball', + 769: 'rule, ruler', + 770: 'running shoe', + 771: 'safe', + 772: 'safety pin', + 773: 'saltshaker, salt shaker', + 774: 'sandal', + 775: 'sarong', + 776: 'sax, saxophone', + 777: 'scabbard', + 778: 'scale, weighing machine', + 779: 'school bus', + 780: 'schooner', + 781: 'scoreboard', + 782: 'screen, CRT screen', + 783: 'screw', + 784: 'screwdriver', + 785: 'seat belt, seatbelt', + 786: 'sewing machine', + 787: 'shield, buckler', + 788: 'shoe shop, shoe-shop, shoe store', + 789: 'shoji', + 790: 'shopping basket', + 791: 'shopping cart', + 792: 'shovel', + 793: 'shower cap', + 794: 'shower curtain', + 795: 'ski', + 796: 'ski mask', + 797: 'sleeping bag', + 798: 'slide rule, slipstick', + 799: 'sliding door', + 800: 'slot, one-armed bandit', + 801: 'snorkel', + 802: 'snowmobile', + 803: 'snowplow, snowplough', + 804: 'soap dispenser', + 805: 'soccer ball', + 806: 'sock', + 807: 'solar dish, solar collector, solar furnace', + 808: 'sombrero', + 809: 'soup bowl', + 810: 'space bar', + 811: 'space heater', + 812: 'space shuttle', + 813: 'spatula', + 814: 'speedboat', + 815: "spider web, spider's web", + 816: 'spindle', + 817: 'sports car, sport car', + 818: 'spotlight, spot', + 819: 'stage', + 820: 'steam locomotive', + 821: 'steel arch bridge', + 822: 'steel drum', + 823: 'stethoscope', + 824: 'stole', + 825: 'stone wall', + 826: 'stopwatch, stop watch', + 827: 'stove', + 828: 'strainer', + 829: 'streetcar, tram, tramcar, trolley, trolley car', + 830: 'stretcher', + 831: 'studio couch, day bed', + 832: 'stupa, tope', + 833: 'submarine, pigboat, sub, U-boat', + 834: 'suit, suit of clothes', + 835: 'sundial', + 836: 'sunglass', + 837: 'sunglasses, dark glasses, shades', + 838: 'sunscreen, sunblock, sun blocker', + 839: 'suspension bridge', + 840: 'swab, swob, mop', + 841: 'sweatshirt', + 842: 'swimming trunks, bathing trunks', + 843: 'swing', + 844: 'switch, electric switch, electrical switch', + 845: 'syringe', + 846: 'table lamp', + 847: 'tank, army tank, armored combat vehicle, armoured combat vehicle', + 848: 'tape player', + 849: 'teapot', + 850: 'teddy, teddy bear', + 851: 'television, television system', + 852: 'tennis ball', + 853: 'thatch, thatched roof', + 854: 'theater curtain, theatre curtain', + 855: 'thimble', + 856: 'thresher, thrasher, threshing machine', + 857: 'throne', + 858: 'tile roof', + 859: 'toaster', + 860: 'tobacco shop, tobacconist shop, tobacconist', + 861: 'toilet seat', + 862: 'torch', + 863: 'totem pole', + 864: 'tow truck, tow car, wrecker', + 865: 'toyshop', + 866: 'tractor', + 867: 'trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi', + 868: 'tray', + 869: 'trench coat', + 870: 'tricycle, trike, velocipede', + 871: 'trimaran', + 872: 'tripod', + 873: 'triumphal arch', + 874: 'trolleybus, trolley coach, trackless trolley', + 875: 'trombone', + 876: 'tub, vat', + 877: 'turnstile', + 878: 'typewriter keyboard', + 879: 'umbrella', + 880: 'unicycle, monocycle', + 881: 'upright, upright piano', + 882: 'vacuum, vacuum cleaner', + 883: 'vase', + 884: 'vault', + 885: 'velvet', + 886: 'vending machine', + 887: 'vestment', + 888: 'viaduct', + 889: 'violin, fiddle', + 890: 'volleyball', + 891: 'waffle iron', + 892: 'wall clock', + 893: 'wallet, billfold, notecase, pocketbook', + 894: 'wardrobe, closet, press', + 895: 'warplane, military plane', + 896: 'washbasin, handbasin, washbowl, lavabo, wash-hand basin', + 897: 'washer, automatic washer, washing machine', + 898: 'water bottle', + 899: 'water jug', + 900: 'water tower', + 901: 'whiskey jug', + 902: 'whistle', + 903: 'wig', + 904: 'window screen', + 905: 'window shade', + 906: 'Windsor tie', + 907: 'wine bottle', + 908: 'wing', + 909: 'wok', + 910: 'wooden spoon', + 911: 'wool, woolen, woollen', + 912: 'worm fence, snake fence, snake-rail fence, Virginia fence', + 913: 'wreck', + 914: 'yawl', + 915: 'yurt', + 916: 'web site, website, internet site, site', + 917: 'comic book', + 918: 'crossword puzzle, crossword', + 919: 'street sign', + 920: 'traffic light, traffic signal, stoplight', + 921: 'book jacket, dust cover, dust jacket, dust wrapper', + 922: 'menu', + 923: 'plate', + 924: 'guacamole', + 925: 'consomme', + 926: 'hot pot, hotpot', + 927: 'trifle', + 928: 'ice cream, icecream', + 929: 'ice lolly, lolly, lollipop, popsicle', + 930: 'French loaf', + 931: 'bagel, beigel', + 932: 'pretzel', + 933: 'cheeseburger', + 934: 'hotdog, hot dog, red hot', + 935: 'mashed potato', + 936: 'head cabbage', + 937: 'broccoli', + 938: 'cauliflower', + 939: 'zucchini, courgette', + 940: 'spaghetti squash', + 941: 'acorn squash', + 942: 'butternut squash', + 943: 'cucumber, cuke', + 944: 'artichoke, globe artichoke', + 945: 'bell pepper', + 946: 'cardoon', + 947: 'mushroom', + 948: 'Granny Smith', + 949: 'strawberry', + 950: 'orange', + 951: 'lemon', + 952: 'fig', + 953: 'pineapple, ananas', + 954: 'banana', + 955: 'jackfruit, jak, jack', + 956: 'custard apple', + 957: 'pomegranate', + 958: 'hay', + 959: 'carbonara', + 960: 'chocolate sauce, chocolate syrup', + 961: 'dough', + 962: 'meat loaf, meatloaf', + 963: 'pizza, pizza pie', + 964: 'potpie', + 965: 'burrito', + 966: 'red wine', + 967: 'espresso', + 968: 'cup', + 969: 'eggnog', + 970: 'alp', + 971: 'bubble', + 972: 'cliff, drop, drop-off', + 973: 'coral reef', + 974: 'geyser', + 975: 'lakeside, lakeshore', + 976: 'promontory, headland, head, foreland', + 977: 'sandbar, sand bar', + 978: 'seashore, coast, seacoast, sea-coast', + 979: 'valley, vale', + 980: 'volcano', + 981: 'ballplayer, baseball player', + 982: 'groom, bridegroom', + 983: 'scuba diver', + 984: 'rapeseed', + 985: 'daisy', + 986: "yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum", + 987: 'corn', + 988: 'acorn', + 989: 'hip, rose hip, rosehip', + 990: 'buckeye, horse chestnut, conker', + 991: 'coral fungus', + 992: 'agaric', + 993: 'gyromitra', + 994: 'stinkhorn, carrion fungus', + 995: 'earthstar', + 996: 'hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa', + 997: 'bolete', + 998: 'ear, spike, capitulum', + 999: 'toilet tissue, toilet paper, bathroom tissue'} \ No newline at end of file diff --git a/rknn-toolkit-lite2/examples/dynamic_shape/test.py b/rknn-toolkit-lite2/examples/dynamic_shape/test.py new file mode 100644 index 0000000000000000000000000000000000000000..62fa0e5b5bb019c863e1bf14d7cb9dfa48d4fa65 --- /dev/null +++ b/rknn-toolkit-lite2/examples/dynamic_shape/test.py @@ -0,0 +1,135 @@ +import cv2 +import numpy as np +import platform +from synset_label import labels +from rknnlite.api import RKNNLite + +# decice tree for RK356x/RK3576/RK3588 +DEVICE_COMPATIBLE_NODE = '/proc/device-tree/compatible' + +def get_host(): + # get platform and device type + system = platform.system() + machine = platform.machine() + os_machine = system + '-' + machine + if os_machine == 'Linux-aarch64': + try: + with open(DEVICE_COMPATIBLE_NODE) as f: + device_compatible_str = f.read() + if 'rk3588' in device_compatible_str: + host = 'RK3588' + elif 'rk3562' in device_compatible_str: + host = 'RK3562' + elif 'rk3576' in device_compatible_str: + host = 'RK3576' + else: + host = 'RK3566_RK3568' + except IOError: + print('Read device node {} failed.'.format(DEVICE_COMPATIBLE_NODE)) + exit(-1) + else: + host = os_machine + return host + +INPUT_SIZE = 224 + +RK3566_RK3568_RKNN_MODEL = 'mobilenet_v2_for_rk3566_rk3568.rknn' +RK3588_RKNN_MODEL = 'mobilenet_v2_for_rk3588.rknn' +RK3562_RKNN_MODEL = 'mobilenet_v2_for_rk3562.rknn' +RK3576_RKNN_MODEL = 'mobilenet_v2_for_rk3576.rknn' + + +def show_top5(result): + output = result[0].reshape(-1) + # Get the indices of the top 5 largest values + output_sorted_indices = np.argsort(output)[::-1][:5] + top5_str = '-----TOP 5-----\n' + for i, index in enumerate(output_sorted_indices): + value = output[index] + if value > 0: + topi = '[{:>3d}] score:{:.6f} class:"{}"\n'.format( + index, value, labels[index]) + else: + topi = '-1: 0.0\n' + top5_str += topi + print(top5_str) + + +if __name__ == '__main__': + + # Get device information + host_name = get_host() + if host_name == 'RK3566_RK3568': + rknn_model = RK3566_RK3568_RKNN_MODEL + elif host_name == 'RK3562': + rknn_model = RK3562_RKNN_MODEL + elif host_name == 'RK3576': + rknn_model = RK3576_RKNN_MODEL + elif host_name == 'RK3588': + rknn_model = RK3588_RKNN_MODEL + else: + print("This demo cannot run on the current platform: {}".format(host_name)) + exit(-1) + + dynamic_input = [ + [[1, 3, 192, 192]], + [[1, 3, 256, 256]], + [[1, 3, 160, 160]], + [[1, 3, 224, 224]] + ] + + rknn_lite = RKNNLite() + + # Load RKNN model + print('--> Load RKNN model') + ret = rknn_lite.load_rknn(rknn_model) + if ret != 0: + print('Load RKNN model failed') + exit(ret) + print('done') + + img = cv2.imread('./dog_224x224.jpg') + + # Init runtime environment + print('--> Init runtime environment') + # Run on RK356x / RK3576 / RK3588 with Debian OS, do not need specify target. + if host_name in ['RK3576', 'RK3588']: + # For RK3576 / RK3588, specify which NPU core the model runs on through the core_mask parameter. + ret = rknn_lite.init_runtime(core_mask=RKNNLite.NPU_CORE_0) + else: + ret = rknn_lite.init_runtime() + if ret != 0: + print('Init runtime environment failed') + exit(ret) + print('done') + + # Inference + print('--> Running model') + print('model: mobilenet_v2\n') + print('input shape: 1,3,224,224') + real_img = cv2.resize(img, (224, 224)) + real_img = np.expand_dims(real_img, 0) + real_img = np.transpose(real_img, (0, 3, 1, 2)) + outputs = rknn_lite.inference(inputs=[real_img], data_format=['nchw']) + # Show the classification results + show_top5(outputs) + + print('input shape: 1,3,160,160') + real_img = cv2.resize(img, (160, 160)) + real_img = np.expand_dims(real_img, 0) + real_img = np.transpose(real_img, (0, 3, 1, 2)) + outputs = rknn_lite.inference(inputs=[real_img], data_format=['nchw']) + # Show the classification results + show_top5(outputs) + + print('input shape: 1,3,256,256') + real_img = cv2.resize(img, (256, 256)) + real_img = np.expand_dims(real_img, 0) + real_img = np.transpose(real_img, (0, 3, 1, 2)) + outputs = rknn_lite.inference(inputs=[real_img], data_format=['nchw']) + # Show the classification results + show_top5(outputs) + + print('done') + + rknn_lite.release() diff --git a/rknn-toolkit-lite2/examples/resnet18/README.md b/rknn-toolkit-lite2/examples/resnet18/README.md new file mode 100644 index 0000000000000000000000000000000000000000..2af7d5edd4cc12907edf6b1fe88bd2a5d2aaeee8 --- /dev/null +++ b/rknn-toolkit-lite2/examples/resnet18/README.md @@ -0,0 +1,34 @@ +# Example of resnet18 + +## Model Source + +### Original model +The models used in this example come from the torchvision project: +https://github.com/pytorch/vision/tree/main/torchvision/models + +### Convert to RKNN model +Please refer to the example in the RKNN Toolkit2 project to generate the RKNN model: +https://github.com/rockchip-linux/rknn-toolkit2/tree/master/examples/pytorch/resnet18 + +## Script Usage + +Usage + +``` +python test.py +``` + +## Expected results + +This example will print the TOP5 labels and corresponding scores of the test image classification results. For example, the inference results of this example are as follows: +``` +-----TOP 5----- +[812] score:0.999680 class:"space shuttle" +[404] score:0.000249 class:"airliner" +[657] score:0.000013 class:"missile" +[466] score:0.000009 class:"bullet train, bullet" +[895] score:0.000008 class:"warplane, military plane" +``` + +1. The label index with the highest score is 812, the corresponding label is `space shuttle`. +2. Different platforms, different versions of tools and drivers may have slightly different results. diff --git a/rknn-toolkit-lite2/examples/resnet18/resnet18_for_rk3562.rknn b/rknn-toolkit-lite2/examples/resnet18/resnet18_for_rk3562.rknn new file mode 100644 index 0000000000000000000000000000000000000000..7b806bd6f6e0f8d0e3ce761207307abde3239ab8 --- /dev/null +++ b/rknn-toolkit-lite2/examples/resnet18/resnet18_for_rk3562.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:97b6fe0bf2e9879001831b1cd32bdeda6868e8079304c84c49b2463c8046e3bd +size 11849493 diff --git a/rknn-toolkit-lite2/examples/resnet18/resnet18_for_rk3566_rk3568.rknn b/rknn-toolkit-lite2/examples/resnet18/resnet18_for_rk3566_rk3568.rknn new file mode 100644 index 0000000000000000000000000000000000000000..3c4eb95c83ef15c8e835cb35434180c90b134261 --- /dev/null +++ b/rknn-toolkit-lite2/examples/resnet18/resnet18_for_rk3566_rk3568.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3a37ea8a4a49278db0d47db5817ccb9f03127d69edfddf709f7998d05b377f03 +size 11834645 diff --git a/rknn-toolkit-lite2/examples/resnet18/resnet18_for_rk3576.rknn b/rknn-toolkit-lite2/examples/resnet18/resnet18_for_rk3576.rknn new file mode 100644 index 0000000000000000000000000000000000000000..a7546e7706187e934a17817e90cd028b75efe5b0 --- /dev/null +++ b/rknn-toolkit-lite2/examples/resnet18/resnet18_for_rk3576.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e32069165d149da30810a2436837fcc3b50a8582d0d420877bfbfb26e787780d +size 11986965 diff --git a/rknn-toolkit-lite2/examples/resnet18/resnet18_for_rk3588.rknn b/rknn-toolkit-lite2/examples/resnet18/resnet18_for_rk3588.rknn new file mode 100644 index 0000000000000000000000000000000000000000..729c56c3de07d9177e74682c3d0c5ae1cbb91cbb --- /dev/null +++ b/rknn-toolkit-lite2/examples/resnet18/resnet18_for_rk3588.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9546823b3fc80ef6ba506ebee42aa16a5b8e8fe3fa8e13139552a83737538021 +size 11996181 diff --git a/rknn-toolkit-lite2/examples/resnet18/space_shuttle_224.jpg b/rknn-toolkit-lite2/examples/resnet18/space_shuttle_224.jpg new file mode 100644 index 0000000000000000000000000000000000000000..412a855c379648d80d5b4267ba1da58188cdee1c --- /dev/null +++ b/rknn-toolkit-lite2/examples/resnet18/space_shuttle_224.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a827cc9060d15cd34b1b5ac512f4d6d8366416a5874a2075951991cbacef78d4 +size 23472 diff --git a/rknn-toolkit-lite2/examples/resnet18/synset_label.py b/rknn-toolkit-lite2/examples/resnet18/synset_label.py new file mode 100644 index 0000000000000000000000000000000000000000..b53fbf603e968333b3e101d8cbf4267a12b3ab5b --- /dev/null +++ b/rknn-toolkit-lite2/examples/resnet18/synset_label.py @@ -0,0 +1,1003 @@ +# the labels come from synset.txt, download link: https://s3.amazonaws.com/onnx-model-zoo/synset.txt + +labels = \ +{0: 'tench, Tinca tinca', + 1: 'goldfish, Carassius auratus', + 2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias', + 3: 'tiger shark, Galeocerdo cuvieri', + 4: 'hammerhead, hammerhead shark', + 5: 'electric ray, crampfish, numbfish, torpedo', + 6: 'stingray', + 7: 'cock', + 8: 'hen', + 9: 'ostrich, Struthio camelus', + 10: 'brambling, Fringilla montifringilla', + 11: 'goldfinch, Carduelis carduelis', + 12: 'house finch, linnet, Carpodacus mexicanus', + 13: 'junco, snowbird', + 14: 'indigo bunting, indigo finch, indigo bird, Passerina cyanea', + 15: 'robin, American robin, Turdus migratorius', + 16: 'bulbul', + 17: 'jay', + 18: 'magpie', + 19: 'chickadee', + 20: 'water ouzel, dipper', + 21: 'kite', + 22: 'bald eagle, American eagle, Haliaeetus leucocephalus', + 23: 'vulture', + 24: 'great grey owl, great gray owl, Strix nebulosa', + 25: 'European fire salamander, Salamandra salamandra', + 26: 'common newt, Triturus vulgaris', + 27: 'eft', + 28: 'spotted salamander, Ambystoma maculatum', + 29: 'axolotl, mud puppy, Ambystoma mexicanum', + 30: 'bullfrog, Rana catesbeiana', + 31: 'tree frog, tree-frog', + 32: 'tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui', + 33: 'loggerhead, loggerhead turtle, Caretta caretta', + 34: 'leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea', + 35: 'mud turtle', + 36: 'terrapin', + 37: 'box turtle, box tortoise', + 38: 'banded gecko', + 39: 'common iguana, iguana, Iguana iguana', + 40: 'American chameleon, anole, Anolis carolinensis', + 41: 'whiptail, whiptail lizard', + 42: 'agama', + 43: 'frilled lizard, Chlamydosaurus kingi', + 44: 'alligator lizard', + 45: 'Gila monster, Heloderma suspectum', + 46: 'green lizard, Lacerta viridis', + 47: 'African chameleon, Chamaeleo chamaeleon', + 48: 'Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis', + 49: 'African crocodile, Nile crocodile, Crocodylus niloticus', + 50: 'American alligator, Alligator mississipiensis', + 51: 'triceratops', + 52: 'thunder snake, worm snake, Carphophis amoenus', + 53: 'ringneck snake, ring-necked snake, ring snake', + 54: 'hognose snake, puff adder, sand viper', + 55: 'green snake, grass snake', + 56: 'king snake, kingsnake', + 57: 'garter snake, grass snake', + 58: 'water snake', + 59: 'vine snake', + 60: 'night snake, Hypsiglena torquata', + 61: 'boa constrictor, Constrictor constrictor', + 62: 'rock python, rock snake, Python sebae', + 63: 'Indian cobra, Naja naja', + 64: 'green mamba', + 65: 'sea snake', + 66: 'horned viper, cerastes, sand viper, horned asp, Cerastes cornutus', + 67: 'diamondback, diamondback rattlesnake, Crotalus adamanteus', + 68: 'sidewinder, horned rattlesnake, Crotalus cerastes', + 69: 'trilobite', + 70: 'harvestman, daddy longlegs, Phalangium opilio', + 71: 'scorpion', + 72: 'black and gold garden spider, Argiope aurantia', + 73: 'barn spider, Araneus cavaticus', + 74: 'garden spider, Aranea diademata', + 75: 'black widow, Latrodectus mactans', + 76: 'tarantula', + 77: 'wolf spider, hunting spider', + 78: 'tick', + 79: 'centipede', + 80: 'black grouse', + 81: 'ptarmigan', + 82: 'ruffed grouse, partridge, Bonasa umbellus', + 83: 'prairie chicken, prairie grouse, prairie fowl', + 84: 'peacock', + 85: 'quail', + 86: 'partridge', + 87: 'African grey, African gray, Psittacus erithacus', + 88: 'macaw', + 89: 'sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita', + 90: 'lorikeet', + 91: 'coucal', + 92: 'bee eater', + 93: 'hornbill', + 94: 'hummingbird', + 95: 'jacamar', + 96: 'toucan', + 97: 'drake', + 98: 'red-breasted merganser, Mergus serrator', + 99: 'goose', + 100: 'black swan, Cygnus atratus', + 101: 'tusker', + 102: 'echidna, spiny anteater, anteater', + 103: 'platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus', + 104: 'wallaby, brush kangaroo', + 105: 'koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus', + 106: 'wombat', + 107: 'jellyfish', + 108: 'sea anemone, anemone', + 109: 'brain coral', + 110: 'flatworm, platyhelminth', + 111: 'nematode, nematode worm, roundworm', + 112: 'conch', + 113: 'snail', + 114: 'slug', + 115: 'sea slug, nudibranch', + 116: 'chiton, coat-of-mail shell, sea cradle, polyplacophore', + 117: 'chambered nautilus, pearly nautilus, nautilus', + 118: 'Dungeness crab, Cancer magister', + 119: 'rock crab, Cancer irroratus', + 120: 'fiddler crab', + 121: 'king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica', + 122: 'American lobster, Northern lobster, Maine lobster, Homarus americanus', + 123: 'spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish', + 124: 'crayfish, crawfish, crawdad, crawdaddy', + 125: 'hermit crab', + 126: 'isopod', + 127: 'white stork, Ciconia ciconia', + 128: 'black stork, Ciconia nigra', + 129: 'spoonbill', + 130: 'flamingo', + 131: 'little blue heron, Egretta caerulea', + 132: 'American egret, great white heron, Egretta albus', + 133: 'bittern', + 134: 'crane', + 135: 'limpkin, Aramus pictus', + 136: 'European gallinule, Porphyrio porphyrio', + 137: 'American coot, marsh hen, mud hen, water hen, Fulica americana', + 138: 'bustard', + 139: 'ruddy turnstone, Arenaria interpres', + 140: 'red-backed sandpiper, dunlin, Erolia alpina', + 141: 'redshank, Tringa totanus', + 142: 'dowitcher', + 143: 'oystercatcher, oyster catcher', + 144: 'pelican', + 145: 'king penguin, Aptenodytes patagonica', + 146: 'albatross, mollymawk', + 147: 'grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus', + 148: 'killer whale, killer, orca, grampus, sea wolf, Orcinus orca', + 149: 'dugong, Dugong dugon', + 150: 'sea lion', + 151: 'Chihuahua', + 152: 'Japanese spaniel', + 153: 'Maltese dog, Maltese terrier, Maltese', + 154: 'Pekinese, Pekingese, Peke', + 155: 'Shih-Tzu', + 156: 'Blenheim spaniel', + 157: 'papillon', + 158: 'toy terrier', + 159: 'Rhodesian ridgeback', + 160: 'Afghan hound, Afghan', + 161: 'basset, basset hound', + 162: 'beagle', + 163: 'bloodhound, sleuthhound', + 164: 'bluetick', + 165: 'black-and-tan coonhound', + 166: 'Walker hound, Walker foxhound', + 167: 'English foxhound', + 168: 'redbone', + 169: 'borzoi, Russian wolfhound', + 170: 'Irish wolfhound', + 171: 'Italian greyhound', + 172: 'whippet', + 173: 'Ibizan hound, Ibizan Podenco', + 174: 'Norwegian elkhound, elkhound', + 175: 'otterhound, otter hound', + 176: 'Saluki, gazelle hound', + 177: 'Scottish deerhound, deerhound', + 178: 'Weimaraner', + 179: 'Staffordshire bullterrier, Staffordshire bull terrier', + 180: 'American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier', + 181: 'Bedlington terrier', + 182: 'Border terrier', + 183: 'Kerry blue terrier', + 184: 'Irish terrier', + 185: 'Norfolk terrier', + 186: 'Norwich terrier', + 187: 'Yorkshire terrier', + 188: 'wire-haired fox terrier', + 189: 'Lakeland terrier', + 190: 'Sealyham terrier, Sealyham', + 191: 'Airedale, Airedale terrier', + 192: 'cairn, cairn terrier', + 193: 'Australian terrier', + 194: 'Dandie Dinmont, Dandie Dinmont terrier', + 195: 'Boston bull, Boston terrier', + 196: 'miniature schnauzer', + 197: 'giant schnauzer', + 198: 'standard schnauzer', + 199: 'Scotch terrier, Scottish terrier, Scottie', + 200: 'Tibetan terrier, chrysanthemum dog', + 201: 'silky terrier, Sydney silky', + 202: 'soft-coated wheaten terrier', + 203: 'West Highland white terrier', + 204: 'Lhasa, Lhasa apso', + 205: 'flat-coated retriever', + 206: 'curly-coated retriever', + 207: 'golden retriever', + 208: 'Labrador retriever', + 209: 'Chesapeake Bay retriever', + 210: 'German short-haired pointer', + 211: 'vizsla, Hungarian pointer', + 212: 'English setter', + 213: 'Irish setter, red setter', + 214: 'Gordon setter', + 215: 'Brittany spaniel', + 216: 'clumber, clumber spaniel', + 217: 'English springer, English springer spaniel', + 218: 'Welsh springer spaniel', + 219: 'cocker spaniel, English cocker spaniel, cocker', + 220: 'Sussex spaniel', + 221: 'Irish water spaniel', + 222: 'kuvasz', + 223: 'schipperke', + 224: 'groenendael', + 225: 'malinois', + 226: 'briard', + 227: 'kelpie', + 228: 'komondor', + 229: 'Old English sheepdog, bobtail', + 230: 'Shetland sheepdog, Shetland sheep dog, Shetland', + 231: 'collie', + 232: 'Border collie', + 233: 'Bouvier des Flandres, Bouviers des Flandres', + 234: 'Rottweiler', + 235: 'German shepherd, German shepherd dog, German police dog, alsatian', + 236: 'Doberman, Doberman pinscher', + 237: 'miniature pinscher', + 238: 'Greater Swiss Mountain dog', + 239: 'Bernese mountain dog', + 240: 'Appenzeller', + 241: 'EntleBucher', + 242: 'boxer', + 243: 'bull mastiff', + 244: 'Tibetan mastiff', + 245: 'French bulldog', + 246: 'Great Dane', + 247: 'Saint Bernard, St Bernard', + 248: 'Eskimo dog, husky', + 249: 'malamute, malemute, Alaskan malamute', + 250: 'Siberian husky', + 251: 'dalmatian, coach dog, carriage dog', + 252: 'affenpinscher, monkey pinscher, monkey dog', + 253: 'basenji', + 254: 'pug, pug-dog', + 255: 'Leonberg', + 256: 'Newfoundland, Newfoundland dog', + 257: 'Great Pyrenees', + 258: 'Samoyed, Samoyede', + 259: 'Pomeranian', + 260: 'chow, chow chow', + 261: 'keeshond', + 262: 'Brabancon griffon', + 263: 'Pembroke, Pembroke Welsh corgi', + 264: 'Cardigan, Cardigan Welsh corgi', + 265: 'toy poodle', + 266: 'miniature poodle', + 267: 'standard poodle', + 268: 'Mexican hairless', + 269: 'timber wolf, grey wolf, gray wolf, Canis lupus', + 270: 'white wolf, Arctic wolf, Canis lupus tundrarum', + 271: 'red wolf, maned wolf, Canis rufus, Canis niger', + 272: 'coyote, prairie wolf, brush wolf, Canis latrans', + 273: 'dingo, warrigal, warragal, Canis dingo', + 274: 'dhole, Cuon alpinus', + 275: 'African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus', + 276: 'hyena, hyaena', + 277: 'red fox, Vulpes vulpes', + 278: 'kit fox, Vulpes macrotis', + 279: 'Arctic fox, white fox, Alopex lagopus', + 280: 'grey fox, gray fox, Urocyon cinereoargenteus', + 281: 'tabby, tabby cat', + 282: 'tiger cat', + 283: 'Persian cat', + 284: 'Siamese cat, Siamese', + 285: 'Egyptian cat', + 286: 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor', + 287: 'lynx, catamount', + 288: 'leopard, Panthera pardus', + 289: 'snow leopard, ounce, Panthera uncia', + 290: 'jaguar, panther, Panthera onca, Felis onca', + 291: 'lion, king of beasts, Panthera leo', + 292: 'tiger, Panthera tigris', + 293: 'cheetah, chetah, Acinonyx jubatus', + 294: 'brown bear, bruin, Ursus arctos', + 295: 'American black bear, black bear, Ursus americanus, Euarctos americanus', + 296: 'ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus', + 297: 'sloth bear, Melursus ursinus, Ursus ursinus', + 298: 'mongoose', + 299: 'meerkat, mierkat', + 300: 'tiger beetle', + 301: 'ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle', + 302: 'ground beetle, carabid beetle', + 303: 'long-horned beetle, longicorn, longicorn beetle', + 304: 'leaf beetle, chrysomelid', + 305: 'dung beetle', + 306: 'rhinoceros beetle', + 307: 'weevil', + 308: 'fly', + 309: 'bee', + 310: 'ant, emmet, pismire', + 311: 'grasshopper, hopper', + 312: 'cricket', + 313: 'walking stick, walkingstick, stick insect', + 314: 'cockroach, roach', + 315: 'mantis, mantid', + 316: 'cicada, cicala', + 317: 'leafhopper', + 318: 'lacewing, lacewing fly', + 319: "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", + 320: 'damselfly', + 321: 'admiral', + 322: 'ringlet, ringlet butterfly', + 323: 'monarch, monarch butterfly, milkweed butterfly, Danaus plexippus', + 324: 'cabbage butterfly', + 325: 'sulphur butterfly, sulfur butterfly', + 326: 'lycaenid, lycaenid butterfly', + 327: 'starfish, sea star', + 328: 'sea urchin', + 329: 'sea cucumber, holothurian', + 330: 'wood rabbit, cottontail, cottontail rabbit', + 331: 'hare', + 332: 'Angora, Angora rabbit', + 333: 'hamster', + 334: 'porcupine, hedgehog', + 335: 'fox squirrel, eastern fox squirrel, Sciurus niger', + 336: 'marmot', + 337: 'beaver', + 338: 'guinea pig, Cavia cobaya', + 339: 'sorrel', + 340: 'zebra', + 341: 'hog, pig, grunter, squealer, Sus scrofa', + 342: 'wild boar, boar, Sus scrofa', + 343: 'warthog', + 344: 'hippopotamus, hippo, river horse, Hippopotamus amphibius', + 345: 'ox', + 346: 'water buffalo, water ox, Asiatic buffalo, Bubalus bubalis', + 347: 'bison', + 348: 'ram, tup', + 349: 'bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis', + 350: 'ibex, Capra ibex', + 351: 'hartebeest', + 352: 'impala, Aepyceros melampus', + 353: 'gazelle', + 354: 'Arabian camel, dromedary, Camelus dromedarius', + 355: 'llama', + 356: 'weasel', + 357: 'mink', + 358: 'polecat, fitch, foulmart, foumart, Mustela putorius', + 359: 'black-footed ferret, ferret, Mustela nigripes', + 360: 'otter', + 361: 'skunk, polecat, wood pussy', + 362: 'badger', + 363: 'armadillo', + 364: 'three-toed sloth, ai, Bradypus tridactylus', + 365: 'orangutan, orang, orangutang, Pongo pygmaeus', + 366: 'gorilla, Gorilla gorilla', + 367: 'chimpanzee, chimp, Pan troglodytes', + 368: 'gibbon, Hylobates lar', + 369: 'siamang, Hylobates syndactylus, Symphalangus syndactylus', + 370: 'guenon, guenon monkey', + 371: 'patas, hussar monkey, Erythrocebus patas', + 372: 'baboon', + 373: 'macaque', + 374: 'langur', + 375: 'colobus, colobus monkey', + 376: 'proboscis monkey, Nasalis larvatus', + 377: 'marmoset', + 378: 'capuchin, ringtail, Cebus capucinus', + 379: 'howler monkey, howler', + 380: 'titi, titi monkey', + 381: 'spider monkey, Ateles geoffroyi', + 382: 'squirrel monkey, Saimiri sciureus', + 383: 'Madagascar cat, ring-tailed lemur, Lemur catta', + 384: 'indri, indris, Indri indri, Indri brevicaudatus', + 385: 'Indian elephant, Elephas maximus', + 386: 'African elephant, Loxodonta africana', + 387: 'lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens', + 388: 'giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca', + 389: 'barracouta, snoek', + 390: 'eel', + 391: 'coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch', + 392: 'rock beauty, Holocanthus tricolor', + 393: 'anemone fish', + 394: 'sturgeon', + 395: 'gar, garfish, garpike, billfish, Lepisosteus osseus', + 396: 'lionfish', + 397: 'puffer, pufferfish, blowfish, globefish', + 398: 'abacus', + 399: 'abaya', + 400: "academic gown, academic robe, judge's robe", + 401: 'accordion, piano accordion, squeeze box', + 402: 'acoustic guitar', + 403: 'aircraft carrier, carrier, flattop, attack aircraft carrier', + 404: 'airliner', + 405: 'airship, dirigible', + 406: 'altar', + 407: 'ambulance', + 408: 'amphibian, amphibious vehicle', + 409: 'analog clock', + 410: 'apiary, bee house', + 411: 'apron', + 412: 'ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin', + 413: 'assault rifle, assault gun', + 414: 'backpack, back pack, knapsack, packsack, rucksack, haversack', + 415: 'bakery, bakeshop, bakehouse', + 416: 'balance beam, beam', + 417: 'balloon', + 418: 'ballpoint, ballpoint pen, ballpen, Biro', + 419: 'Band Aid', + 420: 'banjo', + 421: 'bannister, banister, balustrade, balusters, handrail', + 422: 'barbell', + 423: 'barber chair', + 424: 'barbershop', + 425: 'barn', + 426: 'barometer', + 427: 'barrel, cask', + 428: 'barrow, garden cart, lawn cart, wheelbarrow', + 429: 'baseball', + 430: 'basketball', + 431: 'bassinet', + 432: 'bassoon', + 433: 'bathing cap, swimming cap', + 434: 'bath towel', + 435: 'bathtub, bathing tub, bath, tub', + 436: 'beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon', + 437: 'beacon, lighthouse, beacon light, pharos', + 438: 'beaker', + 439: 'bearskin, busby, shako', + 440: 'beer bottle', + 441: 'beer glass', + 442: 'bell cote, bell cot', + 443: 'bib', + 444: 'bicycle-built-for-two, tandem bicycle, tandem', + 445: 'bikini, two-piece', + 446: 'binder, ring-binder', + 447: 'binoculars, field glasses, opera glasses', + 448: 'birdhouse', + 449: 'boathouse', + 450: 'bobsled, bobsleigh, bob', + 451: 'bolo tie, bolo, bola tie, bola', + 452: 'bonnet, poke bonnet', + 453: 'bookcase', + 454: 'bookshop, bookstore, bookstall', + 455: 'bottlecap', + 456: 'bow', + 457: 'bow tie, bow-tie, bowtie', + 458: 'brass, memorial tablet, plaque', + 459: 'brassiere, bra, bandeau', + 460: 'breakwater, groin, groyne, mole, bulwark, seawall, jetty', + 461: 'breastplate, aegis, egis', + 462: 'broom', + 463: 'bucket, pail', + 464: 'buckle', + 465: 'bulletproof vest', + 466: 'bullet train, bullet', + 467: 'butcher shop, meat market', + 468: 'cab, hack, taxi, taxicab', + 469: 'caldron, cauldron', + 470: 'candle, taper, wax light', + 471: 'cannon', + 472: 'canoe', + 473: 'can opener, tin opener', + 474: 'cardigan', + 475: 'car mirror', + 476: 'carousel, carrousel, merry-go-round, roundabout, whirligig', + 477: "carpenter's kit, tool kit", + 478: 'carton', + 479: 'car wheel', + 480: 'cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM', + 481: 'cassette', + 482: 'cassette player', + 483: 'castle', + 484: 'catamaran', + 485: 'CD player', + 486: 'cello, violoncello', + 487: 'cellular telephone, cellular phone, cellphone, cell, mobile phone', + 488: 'chain', + 489: 'chainlink fence', + 490: 'chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour', + 491: 'chain saw, chainsaw', + 492: 'chest', + 493: 'chiffonier, commode', + 494: 'chime, bell, gong', + 495: 'china cabinet, china closet', + 496: 'Christmas stocking', + 497: 'church, church building', + 498: 'cinema, movie theater, movie theatre, movie house, picture palace', + 499: 'cleaver, meat cleaver, chopper', + 500: 'cliff dwelling', + 501: 'cloak', + 502: 'clog, geta, patten, sabot', + 503: 'cocktail shaker', + 504: 'coffee mug', + 505: 'coffeepot', + 506: 'coil, spiral, volute, whorl, helix', + 507: 'combination lock', + 508: 'computer keyboard, keypad', + 509: 'confectionery, confectionary, candy store', + 510: 'container ship, containership, container vessel', + 511: 'convertible', + 512: 'corkscrew, bottle screw', + 513: 'cornet, horn, trumpet, trump', + 514: 'cowboy boot', + 515: 'cowboy hat, ten-gallon hat', + 516: 'cradle', + 517: 'crane', + 518: 'crash helmet', + 519: 'crate', + 520: 'crib, cot', + 521: 'Crock Pot', + 522: 'croquet ball', + 523: 'crutch', + 524: 'cuirass', + 525: 'dam, dike, dyke', + 526: 'desk', + 527: 'desktop computer', + 528: 'dial telephone, dial phone', + 529: 'diaper, nappy, napkin', + 530: 'digital clock', + 531: 'digital watch', + 532: 'dining table, board', + 533: 'dishrag, dishcloth', + 534: 'dishwasher, dish washer, dishwashing machine', + 535: 'disk brake, disc brake', + 536: 'dock, dockage, docking facility', + 537: 'dogsled, dog sled, dog sleigh', + 538: 'dome', + 539: 'doormat, welcome mat', + 540: 'drilling platform, offshore rig', + 541: 'drum, membranophone, tympan', + 542: 'drumstick', + 543: 'dumbbell', + 544: 'Dutch oven', + 545: 'electric fan, blower', + 546: 'electric guitar', + 547: 'electric locomotive', + 548: 'entertainment center', + 549: 'envelope', + 550: 'espresso maker', + 551: 'face powder', + 552: 'feather boa, boa', + 553: 'file, file cabinet, filing cabinet', + 554: 'fireboat', + 555: 'fire engine, fire truck', + 556: 'fire screen, fireguard', + 557: 'flagpole, flagstaff', + 558: 'flute, transverse flute', + 559: 'folding chair', + 560: 'football helmet', + 561: 'forklift', + 562: 'fountain', + 563: 'fountain pen', + 564: 'four-poster', + 565: 'freight car', + 566: 'French horn, horn', + 567: 'frying pan, frypan, skillet', + 568: 'fur coat', + 569: 'garbage truck, dustcart', + 570: 'gasmask, respirator, gas helmet', + 571: 'gas pump, gasoline pump, petrol pump, island dispenser', + 572: 'goblet', + 573: 'go-kart', + 574: 'golf ball', + 575: 'golfcart, golf cart', + 576: 'gondola', + 577: 'gong, tam-tam', + 578: 'gown', + 579: 'grand piano, grand', + 580: 'greenhouse, nursery, glasshouse', + 581: 'grille, radiator grille', + 582: 'grocery store, grocery, food market, market', + 583: 'guillotine', + 584: 'hair slide', + 585: 'hair spray', + 586: 'half track', + 587: 'hammer', + 588: 'hamper', + 589: 'hand blower, blow dryer, blow drier, hair dryer, hair drier', + 590: 'hand-held computer, hand-held microcomputer', + 591: 'handkerchief, hankie, hanky, hankey', + 592: 'hard disc, hard disk, fixed disk', + 593: 'harmonica, mouth organ, harp, mouth harp', + 594: 'harp', + 595: 'harvester, reaper', + 596: 'hatchet', + 597: 'holster', + 598: 'home theater, home theatre', + 599: 'honeycomb', + 600: 'hook, claw', + 601: 'hoopskirt, crinoline', + 602: 'horizontal bar, high bar', + 603: 'horse cart, horse-cart', + 604: 'hourglass', + 605: 'iPod', + 606: 'iron, smoothing iron', + 607: "jack-o'-lantern", + 608: 'jean, blue jean, denim', + 609: 'jeep, landrover', + 610: 'jersey, T-shirt, tee shirt', + 611: 'jigsaw puzzle', + 612: 'jinrikisha, ricksha, rickshaw', + 613: 'joystick', + 614: 'kimono', + 615: 'knee pad', + 616: 'knot', + 617: 'lab coat, laboratory coat', + 618: 'ladle', + 619: 'lampshade, lamp shade', + 620: 'laptop, laptop computer', + 621: 'lawn mower, mower', + 622: 'lens cap, lens cover', + 623: 'letter opener, paper knife, paperknife', + 624: 'library', + 625: 'lifeboat', + 626: 'lighter, light, igniter, ignitor', + 627: 'limousine, limo', + 628: 'liner, ocean liner', + 629: 'lipstick, lip rouge', + 630: 'Loafer', + 631: 'lotion', + 632: 'loudspeaker, speaker, speaker unit, loudspeaker system, speaker system', + 633: "loupe, jeweler's loupe", + 634: 'lumbermill, sawmill', + 635: 'magnetic compass', + 636: 'mailbag, postbag', + 637: 'mailbox, letter box', + 638: 'maillot', + 639: 'maillot, tank suit', + 640: 'manhole cover', + 641: 'maraca', + 642: 'marimba, xylophone', + 643: 'mask', + 644: 'matchstick', + 645: 'maypole', + 646: 'maze, labyrinth', + 647: 'measuring cup', + 648: 'medicine chest, medicine cabinet', + 649: 'megalith, megalithic structure', + 650: 'microphone, mike', + 651: 'microwave, microwave oven', + 652: 'military uniform', + 653: 'milk can', + 654: 'minibus', + 655: 'miniskirt, mini', + 656: 'minivan', + 657: 'missile', + 658: 'mitten', + 659: 'mixing bowl', + 660: 'mobile home, manufactured home', + 661: 'Model T', + 662: 'modem', + 663: 'monastery', + 664: 'monitor', + 665: 'moped', + 666: 'mortar', + 667: 'mortarboard', + 668: 'mosque', + 669: 'mosquito net', + 670: 'motor scooter, scooter', + 671: 'mountain bike, all-terrain bike, off-roader', + 672: 'mountain tent', + 673: 'mouse, computer mouse', + 674: 'mousetrap', + 675: 'moving van', + 676: 'muzzle', + 677: 'nail', + 678: 'neck brace', + 679: 'necklace', + 680: 'nipple', + 681: 'notebook, notebook computer', + 682: 'obelisk', + 683: 'oboe, hautboy, hautbois', + 684: 'ocarina, sweet potato', + 685: 'odometer, hodometer, mileometer, milometer', + 686: 'oil filter', + 687: 'organ, pipe organ', + 688: 'oscilloscope, scope, cathode-ray oscilloscope, CRO', + 689: 'overskirt', + 690: 'oxcart', + 691: 'oxygen mask', + 692: 'packet', + 693: 'paddle, boat paddle', + 694: 'paddlewheel, paddle wheel', + 695: 'padlock', + 696: 'paintbrush', + 697: "pajama, pyjama, pj's, jammies", + 698: 'palace', + 699: 'panpipe, pandean pipe, syrinx', + 700: 'paper towel', + 701: 'parachute, chute', + 702: 'parallel bars, bars', + 703: 'park bench', + 704: 'parking meter', + 705: 'passenger car, coach, carriage', + 706: 'patio, terrace', + 707: 'pay-phone, pay-station', + 708: 'pedestal, plinth, footstall', + 709: 'pencil box, pencil case', + 710: 'pencil sharpener', + 711: 'perfume, essence', + 712: 'Petri dish', + 713: 'photocopier', + 714: 'pick, plectrum, plectron', + 715: 'pickelhaube', + 716: 'picket fence, paling', + 717: 'pickup, pickup truck', + 718: 'pier', + 719: 'piggy bank, penny bank', + 720: 'pill bottle', + 721: 'pillow', + 722: 'ping-pong ball', + 723: 'pinwheel', + 724: 'pirate, pirate ship', + 725: 'pitcher, ewer', + 726: "plane, carpenter's plane, woodworking plane", + 727: 'planetarium', + 728: 'plastic bag', + 729: 'plate rack', + 730: 'plow, plough', + 731: "plunger, plumber's helper", + 732: 'Polaroid camera, Polaroid Land camera', + 733: 'pole', + 734: 'police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria', + 735: 'poncho', + 736: 'pool table, billiard table, snooker table', + 737: 'pop bottle, soda bottle', + 738: 'pot, flowerpot', + 739: "potter's wheel", + 740: 'power drill', + 741: 'prayer rug, prayer mat', + 742: 'printer', + 743: 'prison, prison house', + 744: 'projectile, missile', + 745: 'projector', + 746: 'puck, hockey puck', + 747: 'punching bag, punch bag, punching ball, punchball', + 748: 'purse', + 749: 'quill, quill pen', + 750: 'quilt, comforter, comfort, puff', + 751: 'racer, race car, racing car', + 752: 'racket, racquet', + 753: 'radiator', + 754: 'radio, wireless', + 755: 'radio telescope, radio reflector', + 756: 'rain barrel', + 757: 'recreational vehicle, RV, R.V.', + 758: 'reel', + 759: 'reflex camera', + 760: 'refrigerator, icebox', + 761: 'remote control, remote', + 762: 'restaurant, eating house, eating place, eatery', + 763: 'revolver, six-gun, six-shooter', + 764: 'rifle', + 765: 'rocking chair, rocker', + 766: 'rotisserie', + 767: 'rubber eraser, rubber, pencil eraser', + 768: 'rugby ball', + 769: 'rule, ruler', + 770: 'running shoe', + 771: 'safe', + 772: 'safety pin', + 773: 'saltshaker, salt shaker', + 774: 'sandal', + 775: 'sarong', + 776: 'sax, saxophone', + 777: 'scabbard', + 778: 'scale, weighing machine', + 779: 'school bus', + 780: 'schooner', + 781: 'scoreboard', + 782: 'screen, CRT screen', + 783: 'screw', + 784: 'screwdriver', + 785: 'seat belt, seatbelt', + 786: 'sewing machine', + 787: 'shield, buckler', + 788: 'shoe shop, shoe-shop, shoe store', + 789: 'shoji', + 790: 'shopping basket', + 791: 'shopping cart', + 792: 'shovel', + 793: 'shower cap', + 794: 'shower curtain', + 795: 'ski', + 796: 'ski mask', + 797: 'sleeping bag', + 798: 'slide rule, slipstick', + 799: 'sliding door', + 800: 'slot, one-armed bandit', + 801: 'snorkel', + 802: 'snowmobile', + 803: 'snowplow, snowplough', + 804: 'soap dispenser', + 805: 'soccer ball', + 806: 'sock', + 807: 'solar dish, solar collector, solar furnace', + 808: 'sombrero', + 809: 'soup bowl', + 810: 'space bar', + 811: 'space heater', + 812: 'space shuttle', + 813: 'spatula', + 814: 'speedboat', + 815: "spider web, spider's web", + 816: 'spindle', + 817: 'sports car, sport car', + 818: 'spotlight, spot', + 819: 'stage', + 820: 'steam locomotive', + 821: 'steel arch bridge', + 822: 'steel drum', + 823: 'stethoscope', + 824: 'stole', + 825: 'stone wall', + 826: 'stopwatch, stop watch', + 827: 'stove', + 828: 'strainer', + 829: 'streetcar, tram, tramcar, trolley, trolley car', + 830: 'stretcher', + 831: 'studio couch, day bed', + 832: 'stupa, tope', + 833: 'submarine, pigboat, sub, U-boat', + 834: 'suit, suit of clothes', + 835: 'sundial', + 836: 'sunglass', + 837: 'sunglasses, dark glasses, shades', + 838: 'sunscreen, sunblock, sun blocker', + 839: 'suspension bridge', + 840: 'swab, swob, mop', + 841: 'sweatshirt', + 842: 'swimming trunks, bathing trunks', + 843: 'swing', + 844: 'switch, electric switch, electrical switch', + 845: 'syringe', + 846: 'table lamp', + 847: 'tank, army tank, armored combat vehicle, armoured combat vehicle', + 848: 'tape player', + 849: 'teapot', + 850: 'teddy, teddy bear', + 851: 'television, television system', + 852: 'tennis ball', + 853: 'thatch, thatched roof', + 854: 'theater curtain, theatre curtain', + 855: 'thimble', + 856: 'thresher, thrasher, threshing machine', + 857: 'throne', + 858: 'tile roof', + 859: 'toaster', + 860: 'tobacco shop, tobacconist shop, tobacconist', + 861: 'toilet seat', + 862: 'torch', + 863: 'totem pole', + 864: 'tow truck, tow car, wrecker', + 865: 'toyshop', + 866: 'tractor', + 867: 'trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi', + 868: 'tray', + 869: 'trench coat', + 870: 'tricycle, trike, velocipede', + 871: 'trimaran', + 872: 'tripod', + 873: 'triumphal arch', + 874: 'trolleybus, trolley coach, trackless trolley', + 875: 'trombone', + 876: 'tub, vat', + 877: 'turnstile', + 878: 'typewriter keyboard', + 879: 'umbrella', + 880: 'unicycle, monocycle', + 881: 'upright, upright piano', + 882: 'vacuum, vacuum cleaner', + 883: 'vase', + 884: 'vault', + 885: 'velvet', + 886: 'vending machine', + 887: 'vestment', + 888: 'viaduct', + 889: 'violin, fiddle', + 890: 'volleyball', + 891: 'waffle iron', + 892: 'wall clock', + 893: 'wallet, billfold, notecase, pocketbook', + 894: 'wardrobe, closet, press', + 895: 'warplane, military plane', + 896: 'washbasin, handbasin, washbowl, lavabo, wash-hand basin', + 897: 'washer, automatic washer, washing machine', + 898: 'water bottle', + 899: 'water jug', + 900: 'water tower', + 901: 'whiskey jug', + 902: 'whistle', + 903: 'wig', + 904: 'window screen', + 905: 'window shade', + 906: 'Windsor tie', + 907: 'wine bottle', + 908: 'wing', + 909: 'wok', + 910: 'wooden spoon', + 911: 'wool, woolen, woollen', + 912: 'worm fence, snake fence, snake-rail fence, Virginia fence', + 913: 'wreck', + 914: 'yawl', + 915: 'yurt', + 916: 'web site, website, internet site, site', + 917: 'comic book', + 918: 'crossword puzzle, crossword', + 919: 'street sign', + 920: 'traffic light, traffic signal, stoplight', + 921: 'book jacket, dust cover, dust jacket, dust wrapper', + 922: 'menu', + 923: 'plate', + 924: 'guacamole', + 925: 'consomme', + 926: 'hot pot, hotpot', + 927: 'trifle', + 928: 'ice cream, icecream', + 929: 'ice lolly, lolly, lollipop, popsicle', + 930: 'French loaf', + 931: 'bagel, beigel', + 932: 'pretzel', + 933: 'cheeseburger', + 934: 'hotdog, hot dog, red hot', + 935: 'mashed potato', + 936: 'head cabbage', + 937: 'broccoli', + 938: 'cauliflower', + 939: 'zucchini, courgette', + 940: 'spaghetti squash', + 941: 'acorn squash', + 942: 'butternut squash', + 943: 'cucumber, cuke', + 944: 'artichoke, globe artichoke', + 945: 'bell pepper', + 946: 'cardoon', + 947: 'mushroom', + 948: 'Granny Smith', + 949: 'strawberry', + 950: 'orange', + 951: 'lemon', + 952: 'fig', + 953: 'pineapple, ananas', + 954: 'banana', + 955: 'jackfruit, jak, jack', + 956: 'custard apple', + 957: 'pomegranate', + 958: 'hay', + 959: 'carbonara', + 960: 'chocolate sauce, chocolate syrup', + 961: 'dough', + 962: 'meat loaf, meatloaf', + 963: 'pizza, pizza pie', + 964: 'potpie', + 965: 'burrito', + 966: 'red wine', + 967: 'espresso', + 968: 'cup', + 969: 'eggnog', + 970: 'alp', + 971: 'bubble', + 972: 'cliff, drop, drop-off', + 973: 'coral reef', + 974: 'geyser', + 975: 'lakeside, lakeshore', + 976: 'promontory, headland, head, foreland', + 977: 'sandbar, sand bar', + 978: 'seashore, coast, seacoast, sea-coast', + 979: 'valley, vale', + 980: 'volcano', + 981: 'ballplayer, baseball player', + 982: 'groom, bridegroom', + 983: 'scuba diver', + 984: 'rapeseed', + 985: 'daisy', + 986: "yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum", + 987: 'corn', + 988: 'acorn', + 989: 'hip, rose hip, rosehip', + 990: 'buckeye, horse chestnut, conker', + 991: 'coral fungus', + 992: 'agaric', + 993: 'gyromitra', + 994: 'stinkhorn, carrion fungus', + 995: 'earthstar', + 996: 'hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa', + 997: 'bolete', + 998: 'ear, spike, capitulum', + 999: 'toilet tissue, toilet paper, bathroom tissue'} \ No newline at end of file diff --git a/rknn-toolkit-lite2/examples/resnet18/test.py b/rknn-toolkit-lite2/examples/resnet18/test.py new file mode 100644 index 0000000000000000000000000000000000000000..08c24d629ca72da812ec88cd25c7cde95bd5371e --- /dev/null +++ b/rknn-toolkit-lite2/examples/resnet18/test.py @@ -0,0 +1,110 @@ +import cv2 +import numpy as np +import platform +from synset_label import labels +from rknnlite.api import RKNNLite + +# decice tree for RK356x/RK3576/RK3588 +DEVICE_COMPATIBLE_NODE = '/proc/device-tree/compatible' + +def get_host(): + # get platform and device type + system = platform.system() + machine = platform.machine() + os_machine = system + '-' + machine + if os_machine == 'Linux-aarch64': + try: + with open(DEVICE_COMPATIBLE_NODE) as f: + device_compatible_str = f.read() + if 'rk3562' in device_compatible_str: + host = 'RK3562' + elif 'rk3576' in device_compatible_str: + host = 'RK3576' + elif 'rk3588' in device_compatible_str: + host = 'RK3588' + else: + host = 'RK3566_RK3568' + except IOError: + print('Read device node {} failed.'.format(DEVICE_COMPATIBLE_NODE)) + exit(-1) + else: + host = os_machine + return host + +INPUT_SIZE = 224 + +RK3566_RK3568_RKNN_MODEL = 'resnet18_for_rk3566_rk3568.rknn' +RK3588_RKNN_MODEL = 'resnet18_for_rk3588.rknn' +RK3562_RKNN_MODEL = 'resnet18_for_rk3562.rknn' +RK3576_RKNN_MODEL = 'resnet18_for_rk3576.rknn' + + +def show_top5(result): + output = result[0].reshape(-1) + # Softmax + output = np.exp(output) / np.sum(np.exp(output)) + # Get the indices of the top 5 largest values + output_sorted_indices = np.argsort(output)[::-1][:5] + top5_str = 'resnet18\n-----TOP 5-----\n' + for i, index in enumerate(output_sorted_indices): + value = output[index] + if value > 0: + topi = '[{:>3d}] score:{:.6f} class:"{}"\n'.format(index, value, labels[index]) + else: + topi = '-1: 0.0\n' + top5_str += topi + print(top5_str) + + +if __name__ == '__main__': + + # Get device information + host_name = get_host() + if host_name == 'RK3566_RK3568': + rknn_model = RK3566_RK3568_RKNN_MODEL + elif host_name == 'RK3562': + rknn_model = RK3562_RKNN_MODEL + elif host_name == 'RK3576': + rknn_model = RK3576_RKNN_MODEL + elif host_name == 'RK3588': + rknn_model = RK3588_RKNN_MODEL + else: + print("This demo cannot run on the current platform: {}".format(host_name)) + exit(-1) + + rknn_lite = RKNNLite() + + # Load RKNN model + print('--> Load RKNN model') + ret = rknn_lite.load_rknn(rknn_model) + if ret != 0: + print('Load RKNN model failed') + exit(ret) + print('done') + + ori_img = cv2.imread('./space_shuttle_224.jpg') + img = cv2.cvtColor(ori_img, cv2.COLOR_BGR2RGB) + img = np.expand_dims(img, 0) + + # Init runtime environment + print('--> Init runtime environment') + # Run on RK356x / RK3576 / RK3588 with Debian OS, do not need specify target. + if host_name in ['RK3576', 'RK3588']: + # For RK3576 / RK3588, specify which NPU core the model runs on through the core_mask parameter. + ret = rknn_lite.init_runtime(core_mask=RKNNLite.NPU_CORE_0) + else: + ret = rknn_lite.init_runtime() + if ret != 0: + print('Init runtime environment failed') + exit(ret) + print('done') + + # Inference + print('--> Running model') + outputs = rknn_lite.inference(inputs=[img]) + + # Show the classification results + show_top5(outputs) + print('done') + + rknn_lite.release() diff --git a/rknn-toolkit-lite2/packages/rknn_toolkit_lite2-2.1.0-cp310-cp310-linux_aarch64.whl b/rknn-toolkit-lite2/packages/rknn_toolkit_lite2-2.1.0-cp310-cp310-linux_aarch64.whl new file mode 100644 index 0000000000000000000000000000000000000000..fb9c709947d9a8dbaea46ef0c471cc7e18911640 --- /dev/null +++ b/rknn-toolkit-lite2/packages/rknn_toolkit_lite2-2.1.0-cp310-cp310-linux_aarch64.whl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c972398c5c4deaba31a7a847e94de2cdec30533f610a0be44eed9979a8bbd1bb +size 765634 diff --git a/rknn-toolkit-lite2/packages/rknn_toolkit_lite2-2.1.0-cp311-cp311-linux_aarch64.whl b/rknn-toolkit-lite2/packages/rknn_toolkit_lite2-2.1.0-cp311-cp311-linux_aarch64.whl new file mode 100644 index 0000000000000000000000000000000000000000..0c542b5eb4b5a873e6692372f83e09dee674be38 --- /dev/null +++ b/rknn-toolkit-lite2/packages/rknn_toolkit_lite2-2.1.0-cp311-cp311-linux_aarch64.whl @@ -0,0 +1,3 @@ +version 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删除rknn.config的target_sub_class参数 +4. 修复mmse存在的报错问题 +5. 增加对paddle导出的onnx模型的支持 + +2024-7-15 +版本: v2.0.0b22: +更新内容: +1. 修复Runtime不兼容<=0.8.0版本rknpu驱动的问题 +2. 完善exWindow/reducemean/cumsum支持 +3. 修复图优化进度条显示问题 +4. 修复scipy库报错问题 + +2024-7-12 +版本: v2.0.0b21: +更新内容: +1. [修复] + 1) 修复输入出现4维undefine layout等若干Bug + 2) 修复1103b板子信息上错误并增加rknn_server版本检查 + 3) 修复sparse_infer开启时的报错问题 +2. [特性] + 1) 增加one-hot/top-k算子支持 + 2) 增加exWindow mode=partition_num_first支持 + 3) 增加ConvTranspose+Sigmoid融合支持 + 4) 增加无效ReduceMean/ReduceSum消除规则 + 5) 增加1103b等小内存平台的分段传输功能 +3. 优化SDPA的仿真器推理速度 +4. 完善对带有Constant的external_data的支持 +5. 完善图优化规则优先级逻辑, 支持多级优先级 + +2024-6-28 +版本: v2.0.0b20: +更新内容: +1. 增加exNorm的OP量化支持 +2. 优化模型转换中shape推理的耗时 +3. 修复Slice去除无效参数导致的错误 +4. 修复自定义OP的shape推理异常问题 +5. 修复softmax因axis为负值导致的异常 + +2024-6-26 +版本: v2.0.0b19: +更新内容: +1. 修复prelu算子错误 +2. 增加Matmul API B_layout=TP_NORM功能以支持B输入是NxK的数据排布 +3. RK3588增强大channel的exNorm算子支持 +4. 增加图优化进度条显示 +5. 完善w4a8的量化计算方式 +6. 修复exmatmul/deconv/Reduce类OP异常问题 +7. 修复Concat/Add/Reshape的消融问题 +8. 修复模型裁剪type错误和shape推理的问题 + +2024-6-20 +版本: v2.0.0b18: +更新内容: +1. 修复部分激活函数下溢出和Pad浮点算子出现NaN的Bug +2. 增加更多Transpose算子规格的NPU支持;优化非对齐Concat的效率 +3. 更新exglu/exnorm/Elu/Log/Sub/Div/avgpool和多级split/concat支持 +4. 增加rv1103b的平台支持 +5. 更新Relu/Clip/LeakyRelu/Elu/TopK的5维支持 +6. 优化swap合并性能和bypass特性支持 +7. 完善reduce类OP的图优化支持 +8. 修复多slice/gather/scatternd的合并问题 + +2024-6-13 +版本: v2.0.0b17: +更新内容: +1. 更新Einsum/Softmax/ReduceSum/Gather支持 +2. 增加BN/rmsnorm/Floor等消融规则 +3. 修复exmatmul/GreaterOrEqual问题 + +2024-6-12 +版本: v2.0.0b16: +更新内容: +1. 完善BN与deconv的融合规则 +2. 修复Slice的交换规则问题 + +2024-6-8 +版本: v2.0.0b15: +更新内容: +1. 增加config的quantized_hybrid_level配置参数 +2. 增加bn和conv/deconv的合并规则 +3. 更新Gather/TP/并行OP相关支持 +4. 增加对sequence类型的部分支持 +5. 修复w8a16下mmse可能报错的问题 + +2024-5-31 +版本: v2.0.0b14: +更新内容: +1. Runtime对部分CPU算子模型解析参数错误的兼容性问题 +2. 增加convtranspose+relu/leakyrelu的融合 +3. 优化常量折叠对空tensor的支持 +4. 完善avgpool的图优化处理 + +2024-5-29 +版本: v2.0.0b13: +更新内容: +1. 修复多batch SDPA算子结果错误和Runtime兼容老版本rknn模型问题 +2. 增加rv1103平台的SDPA支持 +3. 修改quantized_dtype定义, 增加部分w4a16/w4a8的量化支持 +4. 修复混合量化推荐功能异常问题 + +2024-5-24 +版本: v2.0.0b12: +更新内容: +1. 修复RK3562 exNorm NPU算子中间结果溢出问题 +2. 增加对大kernel_size卷积支持 +3. 完善Glu子图合并支持和非4D支持 +4. 修复1维Dropout问题 +5. 修复CodeGen输入文件异常问题 + +2024-5-20 +版本: v2.0.0b11: +更新内容: +1. 增加exNorm CPU算子支持 +2. 增加scalar常量/reducel2/Gelu子图支持 +3. 优化axis-OP附近Reshape消除规则 +4. 优化exMatMul的执行性能和精度问题 +5. 修复精度分析core_mask参数失效问题 +6. 修复带Constant节点的常量折叠问题 +7. 修复超大模型的内存问题 +8. 修复gather子图/SDPA/swap规则问题 + +2024-5-16 +版本: v2.0.0b10: +更新内容: +1. RK3576增加exSDPA算子多核支持 +2. 修复Reshape/Transpose算子Bug/部分exNorm结果不正确问题 +3. 优化exWindow/Gather子图融合问题 +4. 修复并行Slice消除问题 +5. 修复where转Clip的报错问题 + +2024-5-7 +版本: v2.0.0b9: +更新内容: +1. 将exLayerNorm/exMeanVarianceNorm/RMSNorm统一转换成exNrom +2. 完善exMatMul算子功能 +3. 完善Transpose/Reshape图优化功能 +4. 增加Transformer模型性能 +5. 完善Reshape/TP/Mul/Gather消融功能 +6. 优化Concat/Split/Slice的性能 +7. 增加exWindow/SDPA/大尺寸Conv的支持 +8. 优化动态shape的图优化处理流程 + +2024-4-28 +版本: v2.0.0b8: +更新内容: +1. 优化Concat/Split的对齐规则 +2. 修复CodeGen在没有设置输入时的报错 +3. 修复错误捕抓模块的问题 +4. 修复shape推理的死循环问题 +5. 修复inference接口在传入大写的data_format时的报错问题 + +2024-4-23 +版本: v2.0.0b7: +更新内容: +1. 修复gather融合在indices为scalar值时的错误 + +2024-4-19 +版本: v2.0.0b5: +更新内容: +1. 增加对rv1103的i16的混合量化支持 +2. 优化rknn.config错误日志 +3. 优化大尺寸stride的conv的支持 +4. 优化rknnlog异常捕抓模块日志太长、不清晰的问题 +5. 修复静态图动态化时reshape参数错误的问题 + +2024-4-15 +版本: v2.0.0b4: +更新内容: +1. 增加对TopK的部分转换支持 + +2024-4-12 +版本: v2.0.0b3: +更新内容: +1. [RK3588]增加更多Transpose规格的NPU算子支持 +2. 修复fp16的模型加载报错问题 + +2024-4-9 +版本: v2.0.0b2: +更新内容: +1. 增加exSwish的beta属性支持 +2. 修复input_size_list解析错误的问题 + +2024-4-3 +版本: v2.0.0b1: +更新内容: +1. 修复Div算子height/width方向broadcast情况错误 +2. 增加RKNN_FLAG_MODEL_BUFFER_ZERO_COPY标志，用于NPU分配的model +buffer初始化上下文 +3. 修复部分Transpose和Reshape错误问题 +4. 增加Split/TP/Relu的消除规则 +5. 增加动态shape的模型裁剪和CodeGen支持 +6. 更新对MeanVarNorm/GLU的支持 +7. 修复EinSum/Slice转换问题 +8. 修复量化模型Clip的min值问题 +9. 更新QAT对输入&输出量化参数不一致的支持 +10. 修复常量折叠可能存在的遗留无效常量问题 + +2024-3-22 +版本: v2.0.0b0: +更新内容: +1. 修复部分图优化问题 +2. 更新Matmul支持 +3. 更新稀疏推理功能 +4. 更新codegen功能 +5. 更新onnx_edit功能 + +2024-2-26 +版本: v1.6.2b0: +更新内容: +1. 增加onnx_edit接口 +2. 更新gen_cpp_demo功能 +3. 更新torch 2.1.0的支持 +4. 更新elu/cat支持 +5. 更新量化onnx模型的支持 +6. 更新自定义op用例和文档 +7. 修复部分dilations问题 + +2024-2-19 +版本: v1.6.1b13: +更新内容: +1. [RK3576] 增加target支持 +2. 修复rknn_convert问题 +3. 更新sigmoid支持 +4. 优化transpose/reshape性能 +5. 优化工具包大小 +6. 修复量化参数错误问题 +7. 添加cat的量化支持 +8. 修复reshape优化问题 +9. 更新自定义op的用法 + +2024-1-28 +版本: v1.6.1b11: +更新内容: +1. [RK3588] 增加更多规格Reshape算子NPU支持 +2. 添加rk3576部分支持 + +2024-1-23 +版本: v1.6.1b9: +更新内容: +1. [RK3588] 修复BEVformer模型融合错误问题 +2. 修复bias量化参数问题 +3. 更新MatMul转换支持 +4. 修复图优化可能存在的死循环问题 +5. 更新MeanVariance支持 + +2024-1-19 +版本: v1.6.1b8: +更新内容: +1. [RK3588] 修复Pad算子错误问题 +2. 更新pt的linear/deconv/bn/mul量化op支持 +3. 优化conv+reshape/deconv/性能 +4. 更新量化转换节点的支持 +5. 添加tranpose消除优化 + +2024-1-15 +版本: v1.6.1b7: +更新内容: +1. [RK3588] 增加exMeanVarianceNorm算子支持 + +2024-1-12 +版本: v1.6.1b6: +更新内容: +1. [RK3588] 优化rknn模型内部的layout转换性能 +2. 修复部分adb问题 +3. 修复pt模型的量化dtype问题 +4. 修复opset转换问题 +5. 修复pt模型的部分op支持 +6. 更新自定义op的支持 +7. 更新ScatterND/DeConv/Gather的支持 +8. 添加Concat/Scatternd相关优化 +9. 修复auto_pads与pads相关逻辑 + +2023-12-26 +版本: v1.6.1b3: +更新内容: +1. [RK3566/RK3562] 增加动态权重卷积支持 +2. [Matmul] 修改Matmul API的rknn_matmul_info结构体定义,rknn_matmul_api.h兼容1.5.2版本的头文件 +3. [OP] 修复Not算子出错的Bug +4. 修复图优化部分bug + +2023-12-22 +版本: v1.6.1b1: +更新内容: +1. [RK3566] 修复float16 Conv+Activation层融合的错误 +2. [Matmul] 增加Matmul API添加量化参数的功能,rknn_matmul_api.h头文件更新 +3. 更新layernorm支持 +4. 修复图优化部分bug +5. 添加部分图优化规则 +6. 添加QAT模型的channel支持 +7. 添加float64模型支持 + +2023-12-19 +版本: v1.6.1b0: +更新内容: +1. 修改hybrid_quantization_step1接口生成推荐层逻辑.proposal=False不生成推荐层 +2. 增加部分OP的2纬~6维的图优化支持 +3. 修复图优化部分bug +4. 添加部分图优化规则 +5. 更新日志提示 +6. 修复部分pb模型问题 + +2023-11-30 +版本: v1.6.0: +更新内容: +1. 优化部分图优化规则 +2. 更新docker镜像为ubuntu20.04/cp38 +3. 更新对cp37/cp39/cp311版本的支持 +4. 添加自定义OP功能 + +2023-12-11 +版本: v1.5.3b21: +更新内容: +1. 修复非对齐channel的Expand算子结果错误问题 +2. [RK3588]修复start=-1, end=-w-1, step=-1的slice算子结果错误问题 +3. [RV1106]: + 1) 增加Transpose perm=[1,0,2,3]优化 + 2) 修复RKNN_QUERY_OUTPUT_ATTR查询fmt错误问题 +4. Matmul API头文件数据结构优化,新app需更新头文件并编译程序 + +2023-11-24 +版本: v1.5.3b17: +更新内容: +1. 修复动态权重卷积的bug +2. 添加部分结构图优化，如Transformer FFN +3. 修复部分图优化报错问题 +4. 更新eval_perf&eval_memory功能 +5. 添加部分pytorch的OP支持 + +2023-11-13 +版本: v1.5.3b15: +更新内容: +1. 添加rknn_convert的功能 +2. 更新GLU的支持 +3. 修复部分图优化失效报错 +4. 优化Transformer的FFN结构 +5. 更新对ONNX的opset 12~19的支持 + +2023-11-03 +版本: v1.5.3b14: +更新内容: +1. 支持opset 12~19 的ONNX模型. +2. 优化RV1103/RV1106 Runtime初始化模型速度. +3. 优化eval_mem接口显示内容. +4. 其他若干Bug修复. +5. 更新dynamic_input对只存在一组shape的支持 +6. 修复常量折叠可能导致的问题 +7. 添加对If的部分支持 +8. 修复onnx模型裁剪导致的问题 +9. 更新rknn.eval_memory功能 +10. 修复新torch版本导致的部分错误 +11. 更新pb/tflite模型的支持 + +2023-10-27 +版本: v1.5.3b13: +更新内容: +1. 修复rknn_set_io_mem接口输入输出内存地址相同导致的错误。 +2. 修复”unknown target error“报错日志。 +3. 其他若干bug修复。 +4. 修复DFP量化问题 +5. 去除对torchvision包的依赖 +6. 修复新版本numpy导致的包错 + +2023-10-21 +版本: v1.5.3b11: +更新内容: +1. 修复部分Slice层生成的rknn模型输出形状不对的Bug. +2. 修复Conv开启quantize weight后结果错误的Bug. +3. 修复Runtime显示ddr cycle错误等Bug. +4. 添加config.quantize_weight的功能 +5. 优化RoiAlign/Softmax/ReduceL2/Gelu的支持 +6. 修复带external权重的fp16模型加载问题 +7. 添加部分Conv融合功能, 提高性能 +8. 更新requirements.txt的部分包依赖 +9. 更新模型剪枝支持 +10. 更新部分功能的用户提示 +11. 更新examples示例 +12. 修复部分量化信息错误导致的精度问题 +13. 添加对cp37/cp39/cp311版本的支持 +14. 更新精度分析功能 + +2023-9-21 +版本: v1.5.3b7: +更新内容: +1. 支持自定义LayerNorm参数,避免float16类型输入上溢出. +2. 添加config.remove_reshape的功能 +3. 优化Where的支持 + +2023-9-12 +版本: v1.5.3b6: +更新内容: +1. 支持输入类型是int64的Add. +2. 支持使用子图定义来做混合量化 +3. 修复部分dynamic_input功能报错 +4. 修复部分图优化报错 +5. 优化GEMM的支持 +6. 修复部分caffe模型解析错误问题 + +2023-9-5 +版本: v1.5.3b2: +更新内容: +1. 修复rknn_batch_size>1的动态shape模型特定情况随机错误问题 +2. 更新QAT模型支持 +3. 修复ubuntu16.04存在的报错问题 + +2023-9-1 +版本: v1.5.3b1(功能未合并到主分支): +更新内容: +1. 动态权重的普通卷积支持 +2. 添加cocnat/split/slice/reshape的图优化 +3. 添加导出常量输出的功能 +4. 修复mmse可能存在的报错问题 + +2023-8-28 +版本: v1.5.3b0(功能未合并到主分支): +更新内容: +1. 优化Transpose+Matmul结构的GPU运算效率 +2. 优化dynamic_input功能 +3. split并行节点的优化 +4. 优化部分库对python版本的依赖 +5. 添加自定义op支持 + +2023-8-25 +版本: v1.5.2: +更新内容: +1. 添加glu支持 +2. 更新examples +3. 完善dynamic_input功能 +4. 优化transformer模型支持 +5. 添加torch的部分op支持 +6. 更新rknn_batch_size支持 + +2023-7-14 +版本: v1.5.1b19: +更新内容: +1. 优化quantize_weight功能,优化部分网络生成的rknn模型大小. + +2023-7-3 +版本: v1.5.1b18: +更新内容: +1. 增加Matmul层GPU target支持 +2. 增加where+softmax子图优化 +3. 修复load_rknn在dynamic_input启用时的部分问题 +4. 添加新的图优化规则 +5. 更新对大模型动态输入的支持 + +2023-6-29 +版本: v1.5.1b17: +更新内容: +1. 修复LSTM的重复调用rknn_inputs_set导致卡死问题 +2. 修复Transpose输出内存分配过大问题 +3. 优化部分单通道输入大分辨率模型效率 +4. 修复图优化规则报错问题 +5. 修复原生动态模型的大部分问题 + +2023-6-25 +版本: v1.5.1b16: +更新内容: +1. 修复动态shape LSTM的错误问题 +2. 更新config.target_platform的默认值 +3. 修复原生动态模型的支持问题 + +2023-6-16 +版本: v1.5.1b13: +更新内容: +1. 动态shape rknn_batch_size > 1的支持 +2. 更新动态输入的支持 +3. 更新部分图优化支持 +4. 添加对原生动态模型的初步支持 + +2023-6-9 +版本: v1.5.1b10: +更新内容: +1. reshape/transpose等胶水算子的layout优化 + +2023-6-9 +版本: v1.5.1b9: +更新内容: +1. 修复动态shape最高维度非1情况结果错误的bug + +2023-6-9 +版本: v1.5.1b8: +更新内容: +1. [C API]修复多核多batch模式运行结果错误的Bug +2. [C API]优化rknn_init耗时 +3. [C API]增加CURRENT_NATIVE_INPUT/OUTPUT_ATTR属性查询 +4. 修复部分模型internal 内存偏大的问题 +5. 修复动态模型支持问题 +6. 动态模型添加对rknn_batch_size的支持 +7. 更新部分图优化支持 + +2023-6-1 +版本: v1.5.1b3: +更新内容: +1. 修复动态shape性能分析/内存分析失败的Bug +2. 降低导出RKNN模型大小 +3. 添加通过加载多个onnx模型来仿真动态模型的实验性功能 + +2023-5-30 +版本: v1.5.1b2: +更新内容: +1. [新特性]采用预编译模型，提高Runtime初始化RKNN模型效率。要求重新导出RKNN模型并更新Runtime，若不使用新特性无需重新导出 +2. 修复稀疏化的报错和部分精度问题 +3. 更新cp38/cp310的依赖库版本 +4. 混合量化添加对子图进行混合量化的功能 + +2023-5-27 +版本: v1.5.1b1: +更新内容: +1. 修复Pad算子的错误 +2. 添加Mul的广播部分支持 + +2023-5-18 +版本: v1.5.0: +更新内容: +1. 更新config.dynamic_input的接口定义 +2. 修复部分op属性获取失败的问题 + +2023-5-17 +版本: v1.4.6b2: +更新内容: +1. 修复RK3562 多batch rknn模型C API运行错误的Bug + +2023-5-15 +版本: v1.4.6b1: +更新内容: +1. 修复普通API多输入多输出模型动态shape出错的bug +2. 增加RK3562 Matmul API支持 +3. 修复第一层为Reshape时dynamic_input失败的问题 +4. 修复opset12~15可能存在的问题 + +2023-5-11 +版本: v1.4.6b0: +更新内容: +1. 优化RKNN_FLAG_COLLECT_MODEL_INFO_ONLY初始化效率 +2. 修复1x1x1x1两个feature Add算子转换Bug +3. 修复load_rknn加载老版本模型出错的兼容性问题 +4. 添加opset13/14/15的部分支持 (试验性质) +5. 修复eval_perf导出csv时可能会报错的问题 +6. 修复load_rknn报错问题 +7. 添加非4维的ReduceXXX支持 + +2023-5-6 +版本: v1.4.5b3: +更新内容: +1. 增加RKNN_MIN_TIMEOUT_MS环境变量设置NPU提交任务超时的阈值 +2. 添加一维Where的支持 +3. 修复大模型包含Constant节点报错的问题 +4. 优化权重稀疏化的性能 + +2023-4-28 +版本: v1.4.5b2: +更新内容: +1. 修复dynamic_input普通api结果错误问题 +2. 修复非4维输入连板推理报错问题 + +2023-4-27 +版本: v1.4.5b1: +更新内容: +1. 修复dynamic_input连板推理输出shape报错问题 +2. 添加matmul前后transpose的消除规则, 并优化matmul性能 +3. 修复大模型编译报错问题 +4. 添加load_rknn的dynamic_input支持 +5. 修复代码生产时resize出错的问题 + +2023-4-26 +版本: v1.4.5b0: +更新内容: +1. [RK3562] 优化Transformer模型中的transpose/reshape多算子级联的性能 +2. 增加后缀为.torchscript的pytorch文件格式支持 + +2023-4-25 +版本: v1.4.4b5: +更新内容: +1. 修复dynamic_input在存在Reshape下的推理报错问题 +2. 增加dynamic_input多轴动态支持 +3. 更新cpp部署代码生成功能 + +2023-4-23 +版本: v1.4.4b3: +更新内容: +1. 添加dynamic_input功能 +2. 修复3维deconv报错问题 +3. 更新大模型转换支持 +4. 优化模拟器推理性能 +5. 添加cpp部署代码生成功能 +6. 修复load_rknn的推理问题 + +2023-4-14 +版本: v1.4.3b12: +更新内容: +1. [RK3562]增加指定层跑CPU/GPU/NPU特性 +2. 修复concat优化规则 +3. 添加op_target功能 + +2023-4-11 +版本: v1.4.3b10: +更新内容: +1. 更新rknn编译器 + +2023-4-10 +版本: v1.4.3b9: +更新内容: +1. 更新tensorflow QAT支持 +2. 优化大模型的转换内存和性能 +3. 修复图优化问题，并添加部分新规则 +4. 修复mmse报错问题 +5. 优化conv的拆分规则 +6. 修复混合量化问题 +7. 添加RMSNorm支持 +8. load_onnx添加input_initial_val参数 +9. 修复onnxoptimizer报错问题 + +2023-3-28 +版本: v1.4.3b4: +更新内容: +1. 修复5维slice的问题 + +2023-3-27 +版本: v1.4.3b3: +更新内容: +1. [RK3566]优化CNN+LSTM结构模型的内存 +2. 优化Concat性能 +3. load_tflite/load_tensorflow添加input_is_nchw参数 + +2023-3-23 +版本: v1.4.3b2: +更新内容: +1. mul/add/div/sub算子优化 +2. 修复多级maxpool量化问题 + +2023-3-21 +版本: v1.4.3b1: +更新内容: +1. 修复Expand算子Bug + +2023-3-21 +版本: v1.4.3b0: +更新内容: +1. [RK3562]增加内部Buffer循环复用功能 +2. [RK3562]优化多batch layerNorm算子精度 +3. [RK3566]int8 Matmul CPU算子优化 +4. [全平台]expand NPU OP支持 +5. [全平台]fp16模型输入耗时优化 +6. 完善Cast算子的支持 +7. 修复remove_weight/多输入归一化参数匹配错误等Bug +8. 更新常量折叠支持 +9. 更新eval_perf功能 +10. 增加float16模型的支持 +11. 优化常量共享的模型 + +2023-3-9 +版本: v1.4.2b6: +更新内容: +1. RK3562平台Bug修复 +2. 增加model_pruning控制，并支持deconv，以及Bug修复 +3. 增加If/Loop的部分转换支持 +4. 修复MMSE部分模型失败的问题 +5. 优化仿真器的结果 +6. 增加python3.10的支持 +7. 优化转换内存占用 +8. 增加部分非4维Op支持 + +2023-2-15 +版本: v1.4.2b1: +更新内容: +1. 修复RK3562查询的size_with_stride大小错误问题 + +2023-2-14 +版本: v1.4.2b0: +更新内容: +1. 更新neg支持 +2. 增加min/max的融合优化 +3. 增加了RK3562平台支持 + +2023-2-8 +版本: v1.4.1b23: +更新内容: +1. 修复特定stride反卷积算子的Bug +2. 更新MatMul的perchannel量化支持 +3. 更新动态图检测功能 +4. 优化where的量化支持 + +2023-2-2 +版本: v1.4.1b22: +更新内容: +1. 增加Equal算子对Bool类型支持 +2. 修复Matmul算子/exLayerNorm算子的Bug +3. 更新equal/slice/cast/pad/ConvTranspose支持 +4. 更新QAT模型支持 +5. 移除bfloat16包依赖 + +2023-1-13 +版本: v1.4.1b21: +更新内容: +1. 修复RK3588 Matmul接口错误 +2. 修复4通道输入float16类型模型在RK356X平台查询虚宽错误问题 +3. 模型不填写量化信息情况下，默认Tensor量化类型为float16 +4. 增加unk__xxx无效shape支持 +5. 更新abs/dataconvert支持 +6. 优化模型剪枝功能 + +2023-1-6 +版本: v1.4.1b19: +更新内容: +1. [功能]增加Conv+Add+Relu子图融合。 +2. 修复Conv+Add在量化参数不一致情况下融合的Bug。 +3. 修复RK3588 大kernel卷积的Bug。 +4. 增加模型剪枝功能 +5. 优化Sigmoid的量化参数 +6. 增加rk3562的支持 + +2022-12-17 +版本: v1.4.1b17: +更新内容: +1. [优化]增加NPU输出NCHW数据支持。 +2. [功能]增加conv+add+relu融合支持。 +3. 修复最高维度非1模型MaxPool算子错误的Bug。 +4. 修复最高维度非1模型首层Conv错误的Bug。 +5. 修改4维npy的layout定义 +6. 优化dataconvert/gather/transpose/mul/maxpool/sigmoid/pad/conv/relu/softmax支持 +7. 增加aten::upsample_nearest2d支持 +8. 修复仿真器在perchannel下可能的溢出问题 +9. 增加更多的转换错误提示 +10. 更新混合量化支持 + +2022-11-26 +版本: v1.4.1b14: +更新内容: +1. 修复寄存器位宽限制警告。 +2. 优化Concat CPU算子效率。 +3. 增加2维layernorm支持 +4. 更新MatMul支持 + +2022-11-19 +版本: v1.4.1b13: +更新内容: +1. [重要]Android NDK编译器升级到r23b版本，APP建议使用该版本NDK重新编译。 +2. LSTM结构更新升级，需要重新转换模型。 +3. RK356X增加Transpose优化。 +4. RK356X模型非对齐通道的float类型NCHW输出效率优化。 +5. 增加常量输出节点删除功能 +6. MMSE支持无法batch扩维的模型 +7. 修复resize/clip缺失属性的问题 +8. 增加swish/dataconvert/softmax/lstm/layernorm相关优化 +9. 增加离群值检测功能 +10. 优化非4维OP的性能 + +2022-11-01 +版本: v1.4.1b12: +更新内容: +1.修复LSTM模型多次转换结果不一致问题。 +2.改进onnx模型裁剪功能 + +2022-10-29 +版本: v1.4.1b11: +更新内容: +1.修复Runtime外部分配内接口运行LSTM错误问题。 +2.修复Runtime rknn_dup_context接口运行LSTM错误问题。 +3.优化大模型转换性能 +4.添加Loop/Scatter转换支持 + +2022-10-24 +版本: v1.4.1b10: +更新内容: +1.修复LSTM兼容性问题。 +2.修复RK3588输入自动填充虚宽值的重复运行错误的bug。 +3.修复出现size=0的中间tensor刷cache失败的问题(模型需重新生成)。 +4.增加IN、Swish非4维支持 +5.添加tflite支持perchannel的QAT模型 + +2022-10-19 +版本: v1.4.1b9: +更新内容: +1.修复RV1106 rknn_detroy接口内存泄漏问题。 + +2022-10-18 +版本: v1.4.1b8: +更新内容: +1.修复非LSTM模型共享权重时rknn_init失败的bug。 + +2022-10-17 +版本: v1.4.1b7: +更新内容: +1.修复RK3588分支合并后的bug。 + +2022-10-17 +版本: v1.4.1b6: +更新内容: +1.修复大分辨率输入的bug。 +2.优化无效pad + +2022-10-13 +版本: v1.4.1b5: +更新内容: +1.修复32-bit库matmul错误的bug。 +2.添加FAQ文档 +3.更新图优化规则 +4.调节MatMul量化方式 + +2022-10-12 +版本: v1.4.1b4: +更新内容: +1.修复LSTM共享权重失败问题。 +2.更新图优化规则 + +2022-10-10 +版本: v1.4.1b3: +更新内容: +1. LSTM寄存器配置内存占用的优化。 +2. 优化MMSE量化算法 +3. 优化KL量化算法 + +2022-9-30 +版本: v1.4.1b2: +更新内容: +1. 关闭寄存器差量支持 +2. 增加Batchnorm+Relu融合支持 +3. 增加32-bit Runtime库Neon优化支持。 +4. 优化rknn_init空初始化性能。 +5. 更新精度分析功能 +6. 修复QAT模型的hardsigmoid等问题 +7. 修复lstm/gru图优化问题 +8. 更新图优化规则 + +2022-9-14 +版本: v1.4.1b1: +更新内容: +1. 增加寄存器差量支持 +2. 修复lstm的bug + +2022-9-14 +版本: v1.4.1b0: +更新内容: +1. 增加rknn.config接口增加npu_do_output_nhwc配置，开启或关闭NPU直接输出NHWC的特性 +2. 修复QAT模型解析问题 + + +------------------------------------------------------------ +2022-8-20 +版本: v1.4.0: +更新内容: +1. 升级相关依赖包到主流版本 +2. 添加更多2/3/5维度的Op支持 +3. 更新config/init_runtime等接口 +4. 更新LSTM等Op支持 +5. 添加yuv输入支持 +6. 更新QAT模型支持 + +2022-7-2 +版本: v1.3.4b5: +更新内容: +1. rknn-toolkit2: + 1) optimize_onnx接口 + a. 在设置optimization_level=2时，关闭conv+add融合。 + b. 保留BatchNormalize算子带的量化参数。 + 2) RK3588屏蔽NPU直接输出NHWC layout的支持， RK3566/RV1106保留该功能。 +2. C API: + 1) RK3588/RK3566/RV1106支持传入一个包含rknn模型的大文件路径，rknn_init接口设置包含偏移和真实rknn模型大小的rknn_init_extend结构体指针。 + + +------------------------------------------------------------ +2021-4-22 +版本: v1.3.0: +更新内容: +1. 新功能: python3.8/ubuntu20.04 平台支持 +2. 修复一些已知的bug: + 1) 修复了一些图优化和量化bug + +2021-4-7 +版本: v1.2.5: +更新内容: +1. 新功能: rv1103/rv1109平台的支持. +2. 修复一些已知的bug: + 1) 修复了一些QAT模型转换问题 + 2) 修复了一些图优化bug + + +2021-1-27 +版本: v1.2.1-beta: +更新内容: +1. 新功能: 多batch的NHWC格式输入时,在H维度,有效元素个数与实际内存中的元素个数不一致时,支持H方向实际元素个数按照h_stride设置. +2. 修复一些已知的bug: + 1) LSTM算子内部变量重名的问题. + + +------------------------------------------------------------ +2021-1-12 +版本：v1.2.0 +更新内容: +1. 新功能: rk3588平台的支持; rknn模型加密支持; tensorflow/tflite/pytorch量化感知模型支持; 增加了一些新的 op 支持: InstanceNormalization, Swish, Conv1D等（详见 op support list）；增加了参数量计算以及算力分析 +2. examples 更新：增加了从 pytorch 转 onnx 的转换 demo：resnet18_export_onnx ；增加了pytorch量化感知模型的加载demo：resnet18_qat demo；增加了模型加密功能：添加了3588平台 rknn 转换 demo +3. 接口更改：移除了 config，load_caffe，load_tensorflow等接口的一些不必要的参数设置，更新了 eval_perf 接口，详细改动见Uer_Guide文档 +4. 修复一些已知的bug: + 1) 修复了一些模型无法转换rknn的问题 + 2) 修复了一些图优化bug + 3) 修复了一些模型推理结果错误的问题 + 4) 修复了 pytorch、tflite 某些 op 转换失败的问题 +5. 优化: 精度分析耗时优化; 模型转换和量化耗时优化 + + +------------------------------------------------------------ +2021-8-12 +版本：v1.1.0 +更新内容: +1. 新功能: LSTM，GRU的支持；增加了accuracy_analysis对比项目；增加了一些op支持：caffe hardswish；onnx gather,reduceMax等op；更新了更全面的图优化规则。 +2. examples更新：增加了yolov5的demo +3. 修复一些已知的bug： + 1）修复了一些模拟器的推理结果错误问题 + 2）修复了一些图优化bug + 3）修复了一些大模型无法转换rknn的问题 + 4）修复了多输入的转换和推理bug +4. 更新了文档，更新了OP支持列表 + +2021-6-30 +版本：v1.1.0beta +更新内容: +1. 新功能: 混合量化功能(支持自定义是否量化以及量化参数修改)；完善了 accuracy_analysis 对比功能（包括连板对比结果) +2. examples更新：增加了常用接口的demo示例：accuracy_analysis、batch_size、hybrid_quant、load_quantized_model、mmse、multi_input_test +3. 修复一些已知的bug： + 1）修复了一些int8/fp16模型的转换问题以及op精度问题 + 2）修复了一些图优化bug，修复了一些依赖的版本问题 +4. 更新了文档，更新了OP支持列表 + + +------------------------------------------------------------ +2021-4-30 +版本：v1.0.0 +更新内容: +1. 新功能: 卷积类的per channel量化功能；添加了config中custom_inf的模型信息设置、img_quant_RGB2BGR设置；添加了eval performance的性能测试接口；增加了版本打印功能 +2. OP支持：1) 添加了Caffe新OP支持：Power/Tile/Eltwise(Max)/去除了normalize维度的限制; 2) 添加了onnx新OP支持:HardSigmoid/Pow/Tile +3. 修复一些已知的bug： + 1) 修复了caffe FC的输出shape以及name的错误 + 2) 优化了mmse的量化性能 + 3）修复caffe的Pooling层的输出shape计算错误 + 4）修复了caffe slice丢弃了其中一个输出的inference bug + 5）修复了一些模型优化的bug +4. 弃置了reorder_channel的config设置，由用户自行保证inference输入数据的channel正确性 +5. 更新了文档，更新了OP支持列表 + + +------------------------------------------------------------ +2021-4-2 +版本：v0.7.0 +更新内容: +1. 新功能: 新的量化算法支持(mmse), 添加支持tensorflow的预量化模型导入 +2. 添加了Caffe新OP支持：relu6/ConvolutionDepthwise/Transpose/reorg +3. 修复一些已知的bug: + 1) 增加concat的非channel维度，非4维输入的支持 + 2) 修复了第一层是scale的预处理bug + 3）更新了onnxruntime==1.7.0的版本 +4. 更新了文档，更新了OP支持列表 + + +------------------------------------------------------------ +2021-3-1 +版本：v0.6.0 +更新内容: +1. 新功能: caffe load API添加指定输入name的接口；添加了caffe lrn(WithinChannel)的支持 +2. 添加了Caffe新OP支持：crop/flatten/normalize/proposal/reduction +3. 添加了onnx/pytorch/tensorflow/darknet/tflite新OP支持 +4. 移除了aciq以及Kl散度量化功能 +5. 修复一些已知的bug: + 1) 最后一层是reshape转换bug； + 2) 修复了caffe中InnerProduct随机生成blob的bug； + 3) 修复了过大的size导致GlobalAvgPool GlobalMaxPool crash的问题; + 4) 修复了第一层是RoIpooling的维度错误； + 5) 修复了SSD设备端推理错误的问题等。 +6. 更新了文档，增加了OP支持列表 diff --git a/rknn-toolkit2/docker/docker_file/ubuntu_20_04_cp38/Dockerfile_ubuntu_20_04_for_cp38 b/rknn-toolkit2/docker/docker_file/ubuntu_20_04_cp38/Dockerfile_ubuntu_20_04_for_cp38 new file mode 100644 index 0000000000000000000000000000000000000000..24d1dcb72bd8f90898c911881a0b1582f2b09785 --- /dev/null +++ b/rknn-toolkit2/docker/docker_file/ubuntu_20_04_cp38/Dockerfile_ubuntu_20_04_for_cp38 @@ -0,0 +1,27 @@ +FROM ubuntu:20.04 + +COPY sources_bionic.list /etc/apt/sources.list + +ENV DEBIAN_FRONTEND=noninteractive + +RUN apt-get update \ + && apt-get install -y python3 python3-dev python3-pip gcc vim libprotobuf-dev zlib1g zlib1g-dev libsm6 \ + && apt-get install -y libgl1 libglib2.0-0 android-tools-adb + +RUN cd /usr/bin \ + && ln -sfn idle3 idle \ + && ln -sfn pydoc3 pydoc \ + && ln -sfn python3 python \ + && ln -sfn python3-config python-config \ + && ln -sfn pip3 pip \ + && ls -al + +RUN python -m pip install --upgrade pip -i https://pypi.tuna.tsinghua.edu.cn/simple --trusted-host=pypi.tuna.tsinghua.edu.cn +RUN pip3 config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple +RUN pip3 config set install.trusted-host pypi.tuna.tsinghua.edu.cn + +RUN python3 --version +RUN pip3 --version +COPY rknn_toolkit2-2.1.0+708089d1-cp38-cp38-linux_x86_64.whl rknn_toolkit2-2.1.0+708089d1-cp38-cp38-linux_x86_64.whl +RUN pip3 install torch==1.10.1 +RUN pip3 install rknn_toolkit2-2.1.0+708089d1-cp38-cp38-linux_x86_64.whl diff --git a/rknn-toolkit2/docker/docker_file/ubuntu_20_04_cp38/rknn_toolkit2-2.1.0+708089d1-cp38-cp38-linux_x86_64.whl b/rknn-toolkit2/docker/docker_file/ubuntu_20_04_cp38/rknn_toolkit2-2.1.0+708089d1-cp38-cp38-linux_x86_64.whl new file mode 100644 index 0000000000000000000000000000000000000000..db60a57870ff1ade743420d18f9b641fc62e47eb --- /dev/null +++ b/rknn-toolkit2/docker/docker_file/ubuntu_20_04_cp38/rknn_toolkit2-2.1.0+708089d1-cp38-cp38-linux_x86_64.whl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ce5f162e56e4e0117b939b4dd59e12fbf69d4d8503b7d4f17d88cba1a9492aab +size 48556997 diff --git a/rknn-toolkit2/docker/docker_file/ubuntu_20_04_cp38/sources_bionic.list b/rknn-toolkit2/docker/docker_file/ubuntu_20_04_cp38/sources_bionic.list new file mode 100644 index 0000000000000000000000000000000000000000..dab0a2ea2c399c594d67353463ce72a3501196fa --- /dev/null +++ b/rknn-toolkit2/docker/docker_file/ubuntu_20_04_cp38/sources_bionic.list @@ -0,0 +1,14 @@ +deb http://mirrors.aliyun.com/ubuntu/ focal main restricted universe multiverse +deb-src http://mirrors.aliyun.com/ubuntu/ focal main restricted universe multiverse + +deb http://mirrors.aliyun.com/ubuntu/ focal-security main restricted universe multiverse +deb-src http://mirrors.aliyun.com/ubuntu/ focal-security main restricted universe multiverse + +deb http://mirrors.aliyun.com/ubuntu/ focal-updates main restricted universe multiverse +deb-src http://mirrors.aliyun.com/ubuntu/ focal-updates main restricted universe multiverse + +deb http://mirrors.aliyun.com/ubuntu/ focal-proposed main restricted universe multiverse +deb-src http://mirrors.aliyun.com/ubuntu/ focal-proposed main restricted universe multiverse + +deb http://mirrors.aliyun.com/ubuntu/ focal-backports main restricted universe multiverse +deb-src http://mirrors.aliyun.com/ubuntu/ focal-backports main restricted universe multiverse \ No newline at end of file diff --git a/rknn-toolkit2/examples/caffe/mobilenet_v2/README.md b/rknn-toolkit2/examples/caffe/mobilenet_v2/README.md new file mode 100644 index 0000000000000000000000000000000000000000..d5d5be6fa53cc657edaf7a30655b1d8a446c5e64 --- /dev/null +++ b/rknn-toolkit2/examples/caffe/mobilenet_v2/README.md @@ -0,0 +1,30 @@ +# Caffe MobileNet V2 + +## Model Source +The model used in this example come from the following open source projects: +https://github.com/shicai/MobileNet-Caffe + +## Script Usage +*Usage:* +``` +python test.py +``` +*rknn_convert usage:* +``` +python3 -m rknn.api.rknn_convert -t rk3566 -i ./model_config.yml -o ./ +``` +*Description:* +- The default target platform in script is 'rk3566', please modify the 'target_platform' parameter of 'rknn.config' according to the actual platform. +- If connecting board is required, please add the 'target' parameter in 'rknn.init_runtime'. + +## Expected Results +This example will print the TOP5 labels and corresponding scores of the test image classification results, as follows: +``` +-----TOP 5----- +[155] score:0.994629 class:"Shih-Tzu" +[154] score:0.001950 class:"Pekinese, Pekingese, Peke" +[204] score:0.001950 class:"Lhasa, Lhasa apso" +[283] score:0.000674 class:"Persian cat" +[196] score:0.000109 class:"miniature schnauzer" +``` +- Note: Different platforms, different versions of tools and drivers may have slightly different results. \ No newline at end of file diff --git a/rknn-toolkit2/examples/caffe/mobilenet_v2/dataset.txt b/rknn-toolkit2/examples/caffe/mobilenet_v2/dataset.txt new file mode 100644 index 0000000000000000000000000000000000000000..5ff21133b1a082f10275eb8b12595e270635d308 --- /dev/null +++ b/rknn-toolkit2/examples/caffe/mobilenet_v2/dataset.txt @@ -0,0 +1,20 @@ +./imgs/ILSVRC2012_val_00000665.JPEG +./imgs/ILSVRC2012_val_00001123.JPEG +./imgs/ILSVRC2012_val_00001129.JPEG +./imgs/ILSVRC2012_val_00001284.JPEG +./imgs/ILSVRC2012_val_00003026.JPEG +./imgs/ILSVRC2012_val_00005276.JPEG +./imgs/ILSVRC2012_val_00006178.JPEG +./imgs/ILSVRC2012_val_00007829.JPEG +./imgs/ILSVRC2012_val_00009338.JPEG +./imgs/ILSVRC2012_val_00010420.JPEG +./imgs/ILSVRC2012_val_00011878.JPEG +./imgs/ILSVRC2012_val_00011240.JPEG +./imgs/ILSVRC2012_val_00012037.JPEG +./imgs/ILSVRC2012_val_00012351.JPEG +./imgs/ILSVRC2012_val_00012470.JPEG +./imgs/ILSVRC2012_val_00020169.JPEG +./imgs/ILSVRC2012_val_00020173.JPEG 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Euarctos americanus +296:ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus +297:sloth bear, Melursus ursinus, Ursus ursinus +298:mongoose +299:meerkat, mierkat +300:tiger beetle +301:ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle +302:ground beetle, carabid beetle +303:long-horned beetle, longicorn, longicorn beetle +304:leaf beetle, chrysomelid +305:dung beetle +306:rhinoceros beetle +307:weevil +308:fly +309:bee +310:ant, emmet, pismire +311:grasshopper, hopper +312:cricket +313:walking stick, walkingstick, stick insect +314:cockroach, roach +315:mantis, mantid +316:cicada, cicala +317:leafhopper +318:lacewing, lacewing fly +319:dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk +320:damselfly +321:admiral +322:ringlet, ringlet butterfly +323:monarch, monarch butterfly, milkweed butterfly, Danaus plexippus +324:cabbage butterfly +325:sulphur butterfly, sulfur butterfly +326:lycaenid, lycaenid butterfly +327:starfish, sea star +328:sea urchin +329:sea cucumber, holothurian +330:wood rabbit, cottontail, cottontail rabbit +331:hare +332:Angora, Angora rabbit +333:hamster +334:porcupine, hedgehog +335:fox squirrel, eastern fox squirrel, Sciurus niger +336:marmot +337:beaver +338:guinea pig, Cavia cobaya +339:sorrel +340:zebra +341:hog, pig, grunter, squealer, Sus scrofa +342:wild boar, boar, Sus scrofa +343:warthog +344:hippopotamus, hippo, river horse, Hippopotamus amphibius +345:ox +346:water buffalo, water ox, Asiatic buffalo, Bubalus bubalis +347:bison +348:ram, tup +349:bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis +350:ibex, Capra ibex +351:hartebeest +352:impala, Aepyceros melampus +353:gazelle +354:Arabian camel, dromedary, Camelus dromedarius +355:llama +356:weasel +357:mink +358:polecat, fitch, foulmart, foumart, Mustela putorius +359:black-footed ferret, ferret, Mustela nigripes +360:otter +361:skunk, polecat, wood pussy +362:badger +363:armadillo +364:three-toed sloth, ai, Bradypus tridactylus +365:orangutan, orang, orangutang, Pongo pygmaeus +366:gorilla, Gorilla gorilla +367:chimpanzee, chimp, Pan troglodytes +368:gibbon, Hylobates lar +369:siamang, Hylobates syndactylus, Symphalangus syndactylus +370:guenon, guenon monkey +371:patas, hussar monkey, Erythrocebus patas +372:baboon +373:macaque +374:langur +375:colobus, colobus monkey +376:proboscis monkey, Nasalis larvatus +377:marmoset +378:capuchin, ringtail, Cebus capucinus +379:howler monkey, howler +380:titi, titi monkey +381:spider monkey, Ateles geoffroyi +382:squirrel monkey, Saimiri sciureus +383:Madagascar cat, ring-tailed lemur, Lemur catta +384:indri, indris, Indri indri, Indri brevicaudatus +385:Indian elephant, Elephas maximus +386:African elephant, Loxodonta africana +387:lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens +388:giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca 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bra, bandeau +460:breakwater, groin, groyne, mole, bulwark, seawall, jetty +461:breastplate, aegis, egis +462:broom +463:bucket, pail +464:buckle +465:bulletproof vest +466:bullet train, bullet +467:butcher shop, meat market +468:cab, hack, taxi, taxicab +469:caldron, cauldron +470:candle, taper, wax light +471:cannon +472:canoe +473:can opener, tin opener +474:cardigan +475:car mirror +476:carousel, carrousel, merry-go-round, roundabout, whirligig +477:carpenter's kit, tool kit +478:carton +479:car wheel +480:cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM +481:cassette +482:cassette player +483:castle +484:catamaran +485:CD player +486:cello, violoncello +487:cellular telephone, cellular phone, cellphone, cell, mobile phone +488:chain +489:chainlink fence +490:chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour +491:chain saw, chainsaw +492:chest +493:chiffonier, commode +494:chime, bell, gong +495:china cabinet, china closet +496:Christmas stocking +497:church, church building +498:cinema, movie theater, movie theatre, movie house, picture palace +499:cleaver, meat cleaver, chopper +500:cliff dwelling +501:cloak +502:clog, geta, patten, sabot +503:cocktail shaker +504:coffee mug +505:coffeepot +506:coil, spiral, volute, whorl, helix +507:combination lock +508:computer keyboard, keypad +509:confectionery, confectionary, candy store +510:container ship, containership, container vessel +511:convertible +512:corkscrew, bottle screw +513:cornet, horn, trumpet, trump +514:cowboy boot +515:cowboy hat, ten-gallon hat +516:cradle +517:crane +518:crash helmet +519:crate +520:crib, cot +521:Crock Pot +522:croquet ball +523:crutch +524:cuirass +525:dam, dike, dyke +526:desk +527:desktop computer +528:dial telephone, dial phone +529:diaper, nappy, napkin +530:digital clock +531:digital watch +532:dining table, board +533:dishrag, dishcloth +534:dishwasher, dish washer, dishwashing machine +535:disk brake, disc brake +536:dock, dockage, docking facility +537:dogsled, dog sled, dog sleigh +538:dome +539:doormat, welcome mat +540:drilling platform, offshore rig +541:drum, membranophone, tympan +542:drumstick +543:dumbbell +544:Dutch oven +545:electric fan, blower +546:electric guitar +547:electric locomotive +548:entertainment center +549:envelope +550:espresso maker +551:face powder +552:feather boa, boa +553:file, file cabinet, filing cabinet +554:fireboat +555:fire engine, fire truck +556:fire screen, fireguard +557:flagpole, flagstaff +558:flute, transverse flute +559:folding chair +560:football helmet +561:forklift +562:fountain +563:fountain pen +564:four-poster +565:freight car +566:French horn, horn +567:frying pan, frypan, skillet +568:fur coat +569:garbage truck, dustcart +570:gasmask, respirator, gas helmet +571:gas pump, gasoline pump, petrol pump, island dispenser +572:goblet +573:go-kart +574:golf ball +575:golfcart, golf cart +576:gondola +577:gong, tam-tam +578:gown +579:grand piano, grand +580:greenhouse, nursery, glasshouse +581:grille, radiator grille +582:grocery store, grocery, food market, market +583:guillotine +584:hair slide +585:hair spray +586:half track +587:hammer +588:hamper +589:hand blower, blow dryer, blow drier, hair dryer, hair drier +590:hand-held computer, hand-held microcomputer +591:handkerchief, hankie, hanky, hankey +592:hard disc, hard disk, fixed disk +593:harmonica, mouth organ, harp, mouth harp +594:harp +595:harvester, reaper +596:hatchet +597:holster +598:home theater, home theatre +599:honeycomb +600:hook, claw +601:hoopskirt, crinoline +602:horizontal bar, high bar +603:horse cart, horse-cart +604:hourglass +605:iPod +606:iron, smoothing iron +607:jack-o'-lantern +608:jean, blue jean, denim +609:jeep, landrover +610:jersey, T-shirt, tee shirt +611:jigsaw puzzle +612:jinrikisha, ricksha, rickshaw +613:joystick +614:kimono +615:knee pad +616:knot +617:lab coat, laboratory coat +618:ladle +619:lampshade, lamp shade +620:laptop, laptop computer +621:lawn mower, mower +622:lens cap, lens cover +623:letter opener, paper knife, paperknife +624:library +625:lifeboat +626:lighter, light, igniter, ignitor +627:limousine, limo +628:liner, ocean liner +629:lipstick, lip rouge +630:Loafer +631:lotion +632:loudspeaker, speaker, speaker unit, loudspeaker system, speaker system +633:loupe, jeweler's loupe +634:lumbermill, sawmill +635:magnetic compass +636:mailbag, postbag +637:mailbox, letter box +638:maillot +639:maillot, tank suit +640:manhole cover +641:maraca +642:marimba, xylophone +643:mask +644:matchstick +645:maypole +646:maze, labyrinth +647:measuring cup +648:medicine chest, medicine cabinet +649:megalith, megalithic structure +650:microphone, mike +651:microwave, microwave oven +652:military uniform +653:milk can +654:minibus +655:miniskirt, mini +656:minivan +657:missile +658:mitten +659:mixing bowl +660:mobile home, manufactured home +661:Model T +662:modem +663:monastery +664:monitor +665:moped +666:mortar +667:mortarboard +668:mosque +669:mosquito net +670:motor scooter, scooter +671:mountain bike, all-terrain bike, off-roader +672:mountain tent +673:mouse, computer mouse +674:mousetrap +675:moving van +676:muzzle +677:nail +678:neck brace +679:necklace +680:nipple +681:notebook, notebook computer +682:obelisk +683:oboe, hautboy, hautbois +684:ocarina, sweet potato +685:odometer, hodometer, mileometer, milometer +686:oil filter +687:organ, pipe organ +688:oscilloscope, scope, cathode-ray oscilloscope, CRO +689:overskirt +690:oxcart +691:oxygen mask +692:packet +693:paddle, boat paddle +694:paddlewheel, paddle wheel +695:padlock +696:paintbrush +697:pajama, pyjama, pj's, jammies +698:palace +699:panpipe, pandean pipe, syrinx +700:paper towel +701:parachute, chute +702:parallel bars, bars +703:park bench +704:parking meter +705:passenger car, coach, carriage +706:patio, terrace +707:pay-phone, pay-station +708:pedestal, plinth, footstall 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+844:switch, electric switch, electrical switch +845:syringe +846:table lamp +847:tank, army tank, armored combat vehicle, armoured combat vehicle +848:tape player +849:teapot +850:teddy, teddy bear +851:television, television system +852:tennis ball +853:thatch, thatched roof +854:theater curtain, theatre curtain +855:thimble +856:thresher, thrasher, threshing machine +857:throne +858:tile roof +859:toaster +860:tobacco shop, tobacconist shop, tobacconist +861:toilet seat +862:torch +863:totem pole +864:tow truck, tow car, wrecker +865:toyshop +866:tractor +867:trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi +868:tray +869:trench coat +870:tricycle, trike, velocipede +871:trimaran +872:tripod +873:triumphal arch +874:trolleybus, trolley coach, trackless trolley +875:trombone +876:tub, vat +877:turnstile +878:typewriter keyboard +879:umbrella +880:unicycle, monocycle +881:upright, upright piano +882:vacuum, vacuum cleaner +883:vase +884:vault +885:velvet +886:vending machine +887:vestment +888:viaduct +889:violin, fiddle +890:volleyball +891:waffle iron +892:wall clock +893:wallet, billfold, notecase, pocketbook +894:wardrobe, closet, press +895:warplane, military plane +896:washbasin, handbasin, washbowl, lavabo, wash-hand basin +897:washer, automatic washer, washing machine +898:water bottle +899:water jug +900:water tower +901:whiskey jug +902:whistle +903:wig +904:window screen +905:window shade +906:Windsor tie +907:wine bottle +908:wing +909:wok +910:wooden spoon +911:wool, woolen, woollen +912:worm fence, snake fence, snake-rail fence, Virginia fence +913:wreck +914:yawl +915:yurt +916:web site, website, internet site, site +917:comic book +918:crossword puzzle, crossword +919:street sign +920:traffic light, traffic signal, stoplight +921:book jacket, dust cover, dust jacket, dust wrapper +922:menu +923:plate +924:guacamole +925:consomme +926:hot pot, hotpot +927:trifle +928:ice cream, icecream +929:ice lolly, lolly, lollipop, popsicle +930:French loaf +931:bagel, beigel +932:pretzel +933:cheeseburger +934:hotdog, hot dog, red hot +935:mashed potato +936:head cabbage +937:broccoli +938:cauliflower +939:zucchini, courgette +940:spaghetti squash +941:acorn squash +942:butternut squash +943:cucumber, cuke +944:artichoke, globe artichoke +945:bell pepper +946:cardoon +947:mushroom +948:Granny Smith +949:strawberry +950:orange +951:lemon +952:fig +953:pineapple, ananas +954:banana +955:jackfruit, jak, jack +956:custard apple +957:pomegranate +958:hay +959:carbonara +960:chocolate sauce, chocolate syrup +961:dough +962:meat loaf, meatloaf +963:pizza, pizza pie +964:potpie +965:burrito +966:red wine +967:espresso +968:cup +969:eggnog +970:alp +971:bubble +972:cliff, drop, drop-off +973:coral reef +974:geyser +975:lakeside, lakeshore +976:promontory, headland, head, foreland +977:sandbar, sand bar +978:seashore, coast, seacoast, sea-coast +979:valley, vale +980:volcano +981:ballplayer, baseball player +982:groom, bridegroom +983:scuba diver +984:rapeseed +985:daisy +986:yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum +987:corn +988:acorn +989:hip, rose hip, rosehip +990:buckeye, horse chestnut, conker +991:coral fungus +992:agaric +993:gyromitra +994:stinkhorn, carrion fungus +995:earthstar +996:hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa +997:bolete +998:ear, spike, capitulum +999:toilet tissue, toilet paper, bathroom tissue \ No newline at end of file diff --git a/rknn-toolkit2/examples/caffe/mobilenet_v2/mobilenet_v2.caffemodel b/rknn-toolkit2/examples/caffe/mobilenet_v2/mobilenet_v2.caffemodel new file mode 100755 index 0000000000000000000000000000000000000000..584b45b3b709b2b5f10db821eb5ab1183f1ba075 --- /dev/null +++ b/rknn-toolkit2/examples/caffe/mobilenet_v2/mobilenet_v2.caffemodel @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a3124ce7abd258c7f35a2c586576bee8116934fa2a8556e4e528041859dd753f +size 14186496 diff --git a/rknn-toolkit2/examples/caffe/mobilenet_v2/mobilenet_v2_deploy.prototxt b/rknn-toolkit2/examples/caffe/mobilenet_v2/mobilenet_v2_deploy.prototxt new file mode 100755 index 0000000000000000000000000000000000000000..a6465e8c50ce1d90ee870db2ece75752e0a41bf3 --- /dev/null +++ b/rknn-toolkit2/examples/caffe/mobilenet_v2/mobilenet_v2_deploy.prototxt @@ -0,0 +1,3417 @@ +name: "MOBILENET_V2" +# transform_param { +# scale: 0.017 +# mirror: false +# crop_size: 224 +# mean_value: [103.94,116.78,123.68] +# } +input: "data" +input_dim: 1 +input_dim: 3 +input_dim: 224 +input_dim: 224 +layer { + name: "conv1" + type: "Convolution" + bottom: "data" + top: "conv1" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 32 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 2 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv1/bn" + type: "BatchNorm" + bottom: "conv1" + top: "conv1/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv1/scale" + type: "Scale" + bottom: "conv1/bn" + top: "conv1/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "relu1" + type: "ReLU" + bottom: "conv1/bn" + top: "conv1/bn" +} +layer { + name: "conv2_1/expand" + type: "Convolution" + bottom: "conv1/bn" + top: "conv2_1/expand" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 32 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv2_1/expand/bn" + type: "BatchNorm" + bottom: "conv2_1/expand" + top: "conv2_1/expand/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv2_1/expand/scale" + type: "Scale" + bottom: "conv2_1/expand/bn" + top: "conv2_1/expand/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "relu2_1/expand" + type: "ReLU" + bottom: "conv2_1/expand/bn" + top: "conv2_1/expand/bn" +} +layer { + name: "conv2_1/dwise" + type: "Convolution" + bottom: "conv2_1/expand/bn" + top: "conv2_1/dwise" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 32 + bias_term: false + pad: 1 + kernel_size: 3 + group: 32 + weight_filler { + type: "msra" + } + engine: CAFFE + } +} +layer { + name: "conv2_1/dwise/bn" + type: "BatchNorm" + bottom: "conv2_1/dwise" + top: "conv2_1/dwise/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv2_1/dwise/scale" + type: "Scale" + bottom: "conv2_1/dwise/bn" + top: "conv2_1/dwise/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "relu2_1/dwise" + type: "ReLU" + bottom: "conv2_1/dwise/bn" + top: "conv2_1/dwise/bn" +} +layer { + name: "conv2_1/linear" + type: "Convolution" + bottom: "conv2_1/dwise/bn" + top: "conv2_1/linear" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 16 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv2_1/linear/bn" + type: "BatchNorm" + bottom: "conv2_1/linear" + top: "conv2_1/linear/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv2_1/linear/scale" + type: "Scale" + bottom: "conv2_1/linear/bn" + top: "conv2_1/linear/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "conv2_2/expand" + type: "Convolution" + bottom: "conv2_1/linear/bn" + top: "conv2_2/expand" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 96 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv2_2/expand/bn" + type: "BatchNorm" + bottom: "conv2_2/expand" + top: "conv2_2/expand/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv2_2/expand/scale" + type: "Scale" + bottom: "conv2_2/expand/bn" + top: "conv2_2/expand/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "relu2_2/expand" + type: "ReLU" + bottom: "conv2_2/expand/bn" + top: "conv2_2/expand/bn" +} +layer { + name: "conv2_2/dwise" + type: "Convolution" + bottom: "conv2_2/expand/bn" + top: "conv2_2/dwise" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 96 + bias_term: false + pad: 1 + kernel_size: 3 + group: 96 + stride: 2 + weight_filler { + type: "msra" + } + engine: CAFFE + } +} +layer { + name: "conv2_2/dwise/bn" + type: "BatchNorm" + bottom: "conv2_2/dwise" + top: "conv2_2/dwise/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv2_2/dwise/scale" + type: "Scale" + bottom: "conv2_2/dwise/bn" + top: "conv2_2/dwise/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "relu2_2/dwise" + type: "ReLU" + bottom: "conv2_2/dwise/bn" + top: "conv2_2/dwise/bn" +} +layer { + name: "conv2_2/linear" + type: "Convolution" + bottom: "conv2_2/dwise/bn" + top: "conv2_2/linear" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 24 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv2_2/linear/bn" + type: "BatchNorm" + bottom: "conv2_2/linear" + top: "conv2_2/linear/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv2_2/linear/scale" + type: "Scale" + bottom: "conv2_2/linear/bn" + top: "conv2_2/linear/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "conv3_1/expand" + type: "Convolution" + bottom: "conv2_2/linear/bn" + top: "conv3_1/expand" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 144 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv3_1/expand/bn" + type: "BatchNorm" + bottom: "conv3_1/expand" + top: "conv3_1/expand/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv3_1/expand/scale" + type: "Scale" + bottom: "conv3_1/expand/bn" + top: "conv3_1/expand/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "relu3_1/expand" + type: "ReLU" + bottom: "conv3_1/expand/bn" + top: "conv3_1/expand/bn" +} +layer { + name: "conv3_1/dwise" + type: "Convolution" + bottom: "conv3_1/expand/bn" + top: "conv3_1/dwise" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 144 + bias_term: false + pad: 1 + kernel_size: 3 + group: 144 + weight_filler { + type: "msra" + } + engine: CAFFE + } +} +layer { + name: "conv3_1/dwise/bn" + type: "BatchNorm" + bottom: "conv3_1/dwise" + top: "conv3_1/dwise/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv3_1/dwise/scale" + type: "Scale" + bottom: "conv3_1/dwise/bn" + top: "conv3_1/dwise/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "relu3_1/dwise" + type: "ReLU" + bottom: "conv3_1/dwise/bn" + top: "conv3_1/dwise/bn" +} +layer { + name: "conv3_1/linear" + type: "Convolution" + bottom: "conv3_1/dwise/bn" + top: "conv3_1/linear" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 24 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv3_1/linear/bn" + type: "BatchNorm" + bottom: "conv3_1/linear" + top: "conv3_1/linear/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv3_1/linear/scale" + type: "Scale" + bottom: "conv3_1/linear/bn" + top: "conv3_1/linear/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "block_3_1" + type: "Eltwise" + bottom: "conv2_2/linear/bn" + bottom: "conv3_1/linear/bn" + top: "block_3_1" +} +layer { + name: "conv3_2/expand" + type: "Convolution" + bottom: "block_3_1" + top: "conv3_2/expand" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 144 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv3_2/expand/bn" + type: "BatchNorm" + bottom: "conv3_2/expand" + top: "conv3_2/expand/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv3_2/expand/scale" + type: "Scale" + bottom: "conv3_2/expand/bn" + top: "conv3_2/expand/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "relu3_2/expand" + type: "ReLU" + bottom: "conv3_2/expand/bn" + top: "conv3_2/expand/bn" +} +layer { + name: "conv3_2/dwise" + type: "Convolution" + bottom: "conv3_2/expand/bn" + top: "conv3_2/dwise" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 144 + bias_term: false + pad: 1 + kernel_size: 3 + group: 144 + stride: 2 + weight_filler { + type: "msra" + } + engine: CAFFE + } +} +layer { + name: "conv3_2/dwise/bn" + type: "BatchNorm" + bottom: "conv3_2/dwise" + top: "conv3_2/dwise/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv3_2/dwise/scale" + type: "Scale" + bottom: "conv3_2/dwise/bn" + top: "conv3_2/dwise/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "relu3_2/dwise" + type: "ReLU" + bottom: "conv3_2/dwise/bn" + top: "conv3_2/dwise/bn" +} +layer { + name: "conv3_2/linear" + type: "Convolution" + bottom: "conv3_2/dwise/bn" + top: "conv3_2/linear" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 32 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv3_2/linear/bn" + type: "BatchNorm" + bottom: "conv3_2/linear" + top: "conv3_2/linear/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv3_2/linear/scale" + type: "Scale" + bottom: "conv3_2/linear/bn" + top: "conv3_2/linear/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "conv4_1/expand" + type: "Convolution" + bottom: "conv3_2/linear/bn" + top: "conv4_1/expand" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 192 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv4_1/expand/bn" + type: "BatchNorm" + bottom: "conv4_1/expand" + top: "conv4_1/expand/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv4_1/expand/scale" + type: "Scale" + bottom: "conv4_1/expand/bn" + top: "conv4_1/expand/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "relu4_1/expand" + type: "ReLU" + bottom: "conv4_1/expand/bn" + top: "conv4_1/expand/bn" +} +layer { + name: "conv4_1/dwise" + type: "Convolution" + bottom: "conv4_1/expand/bn" + top: "conv4_1/dwise" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 1 + kernel_size: 3 + group: 192 + weight_filler { + type: "msra" + } + engine: CAFFE + } +} +layer { + name: "conv4_1/dwise/bn" + type: "BatchNorm" + bottom: "conv4_1/dwise" + top: "conv4_1/dwise/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv4_1/dwise/scale" + type: "Scale" + bottom: "conv4_1/dwise/bn" + top: "conv4_1/dwise/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "relu4_1/dwise" + type: "ReLU" + bottom: "conv4_1/dwise/bn" + top: "conv4_1/dwise/bn" +} +layer { + name: "conv4_1/linear" + type: "Convolution" + bottom: "conv4_1/dwise/bn" + top: "conv4_1/linear" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 32 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv4_1/linear/bn" + type: "BatchNorm" + bottom: "conv4_1/linear" + top: "conv4_1/linear/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv4_1/linear/scale" + type: "Scale" + bottom: "conv4_1/linear/bn" + top: "conv4_1/linear/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "block_4_1" + type: "Eltwise" + bottom: "conv3_2/linear/bn" + bottom: "conv4_1/linear/bn" + top: "block_4_1" +} +layer { + name: "conv4_2/expand" + type: "Convolution" + bottom: "block_4_1" + top: "conv4_2/expand" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 192 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv4_2/expand/bn" + type: "BatchNorm" + bottom: "conv4_2/expand" + top: "conv4_2/expand/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv4_2/expand/scale" + type: "Scale" + bottom: "conv4_2/expand/bn" + top: "conv4_2/expand/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "relu4_2/expand" + type: "ReLU" + bottom: "conv4_2/expand/bn" + top: "conv4_2/expand/bn" +} +layer { + name: "conv4_2/dwise" + type: "Convolution" + bottom: "conv4_2/expand/bn" + top: "conv4_2/dwise" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 1 + kernel_size: 3 + group: 192 + weight_filler { + type: "msra" + } + engine: CAFFE + } +} +layer { + name: "conv4_2/dwise/bn" + type: "BatchNorm" + bottom: "conv4_2/dwise" + top: "conv4_2/dwise/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv4_2/dwise/scale" + type: "Scale" + bottom: "conv4_2/dwise/bn" + top: "conv4_2/dwise/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "relu4_2/dwise" + type: "ReLU" + bottom: "conv4_2/dwise/bn" + top: "conv4_2/dwise/bn" +} +layer { + name: "conv4_2/linear" + type: "Convolution" + bottom: "conv4_2/dwise/bn" + top: "conv4_2/linear" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 32 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv4_2/linear/bn" + type: "BatchNorm" + bottom: "conv4_2/linear" + top: "conv4_2/linear/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv4_2/linear/scale" + type: "Scale" + bottom: "conv4_2/linear/bn" + top: "conv4_2/linear/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "block_4_2" + type: "Eltwise" + bottom: "block_4_1" + bottom: "conv4_2/linear/bn" + top: "block_4_2" +} +layer { + name: "conv4_3/expand" + type: "Convolution" + bottom: "block_4_2" + top: "conv4_3/expand" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 192 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv4_3/expand/bn" + type: "BatchNorm" + bottom: "conv4_3/expand" + top: "conv4_3/expand/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv4_3/expand/scale" + type: "Scale" + bottom: "conv4_3/expand/bn" + top: "conv4_3/expand/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "relu4_3/expand" + type: "ReLU" + bottom: "conv4_3/expand/bn" + top: "conv4_3/expand/bn" +} +layer { + name: "conv4_3/dwise" + type: "Convolution" + bottom: "conv4_3/expand/bn" + top: "conv4_3/dwise" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 1 + kernel_size: 3 + group: 192 + weight_filler { + type: "msra" + } + engine: CAFFE + } +} +layer { + name: "conv4_3/dwise/bn" + type: "BatchNorm" + bottom: "conv4_3/dwise" + top: "conv4_3/dwise/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv4_3/dwise/scale" + type: "Scale" + bottom: "conv4_3/dwise/bn" + top: "conv4_3/dwise/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "relu4_3/dwise" + type: "ReLU" + bottom: "conv4_3/dwise/bn" + top: "conv4_3/dwise/bn" +} +layer { + name: "conv4_3/linear" + type: "Convolution" + bottom: "conv4_3/dwise/bn" + top: "conv4_3/linear" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 64 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv4_3/linear/bn" + type: "BatchNorm" + bottom: "conv4_3/linear" + top: "conv4_3/linear/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv4_3/linear/scale" + type: "Scale" + bottom: "conv4_3/linear/bn" + top: "conv4_3/linear/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "conv4_4/expand" + type: "Convolution" + bottom: "conv4_3/linear/bn" + top: "conv4_4/expand" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 384 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv4_4/expand/bn" + type: "BatchNorm" + bottom: "conv4_4/expand" + top: "conv4_4/expand/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv4_4/expand/scale" + type: "Scale" + bottom: "conv4_4/expand/bn" + top: "conv4_4/expand/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "relu4_4/expand" + type: "ReLU" + bottom: "conv4_4/expand/bn" + top: "conv4_4/expand/bn" +} +layer { + name: "conv4_4/dwise" + type: "Convolution" + bottom: "conv4_4/expand/bn" + top: "conv4_4/dwise" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 384 + bias_term: false + pad: 1 + kernel_size: 3 + group: 384 + weight_filler { + type: "msra" + } + engine: CAFFE + } +} +layer { + name: "conv4_4/dwise/bn" + type: "BatchNorm" + bottom: "conv4_4/dwise" + top: "conv4_4/dwise/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv4_4/dwise/scale" + type: "Scale" + bottom: "conv4_4/dwise/bn" + top: "conv4_4/dwise/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "relu4_4/dwise" + type: "ReLU" + bottom: "conv4_4/dwise/bn" + top: "conv4_4/dwise/bn" +} +layer { + name: "conv4_4/linear" + type: "Convolution" + bottom: "conv4_4/dwise/bn" + top: "conv4_4/linear" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 64 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv4_4/linear/bn" + type: "BatchNorm" + bottom: "conv4_4/linear" + top: "conv4_4/linear/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv4_4/linear/scale" + type: "Scale" + bottom: "conv4_4/linear/bn" + top: "conv4_4/linear/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "block_4_4" + type: "Eltwise" + bottom: "conv4_3/linear/bn" + bottom: "conv4_4/linear/bn" + top: "block_4_4" +} +layer { + name: "conv4_5/expand" + type: "Convolution" + bottom: "block_4_4" + top: "conv4_5/expand" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 384 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv4_5/expand/bn" + type: "BatchNorm" + bottom: "conv4_5/expand" + top: "conv4_5/expand/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv4_5/expand/scale" + type: "Scale" + bottom: "conv4_5/expand/bn" + top: "conv4_5/expand/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "relu4_5/expand" + type: "ReLU" + bottom: "conv4_5/expand/bn" + top: "conv4_5/expand/bn" +} +layer { + name: "conv4_5/dwise" + type: "Convolution" + bottom: "conv4_5/expand/bn" + top: "conv4_5/dwise" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 384 + bias_term: false + pad: 1 + kernel_size: 3 + group: 384 + weight_filler { + type: "msra" + } + engine: CAFFE + } +} +layer { + name: "conv4_5/dwise/bn" + type: "BatchNorm" + bottom: "conv4_5/dwise" + top: "conv4_5/dwise/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv4_5/dwise/scale" + type: "Scale" + bottom: "conv4_5/dwise/bn" + top: "conv4_5/dwise/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "relu4_5/dwise" + type: "ReLU" + bottom: "conv4_5/dwise/bn" + top: "conv4_5/dwise/bn" +} +layer { + name: "conv4_5/linear" + type: "Convolution" + bottom: "conv4_5/dwise/bn" + top: "conv4_5/linear" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 64 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv4_5/linear/bn" + type: "BatchNorm" + bottom: "conv4_5/linear" + top: "conv4_5/linear/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv4_5/linear/scale" + type: "Scale" + bottom: "conv4_5/linear/bn" + top: "conv4_5/linear/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "block_4_5" + type: "Eltwise" + bottom: "block_4_4" + bottom: "conv4_5/linear/bn" + top: "block_4_5" +} +layer { + name: "conv4_6/expand" + type: "Convolution" + bottom: "block_4_5" + top: "conv4_6/expand" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 384 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv4_6/expand/bn" + type: "BatchNorm" + bottom: "conv4_6/expand" + top: "conv4_6/expand/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv4_6/expand/scale" + type: "Scale" + bottom: "conv4_6/expand/bn" + top: "conv4_6/expand/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "relu4_6/expand" + type: "ReLU" + bottom: "conv4_6/expand/bn" + top: "conv4_6/expand/bn" +} +layer { + name: "conv4_6/dwise" + type: "Convolution" + bottom: "conv4_6/expand/bn" + top: "conv4_6/dwise" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 384 + bias_term: false + pad: 1 + kernel_size: 3 + group: 384 + weight_filler { + type: "msra" + } + engine: CAFFE + } +} +layer { + name: "conv4_6/dwise/bn" + type: "BatchNorm" + bottom: "conv4_6/dwise" + top: "conv4_6/dwise/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv4_6/dwise/scale" + type: "Scale" + bottom: "conv4_6/dwise/bn" + top: "conv4_6/dwise/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "relu4_6/dwise" + type: "ReLU" + bottom: "conv4_6/dwise/bn" + top: "conv4_6/dwise/bn" +} +layer { + name: "conv4_6/linear" + type: "Convolution" + bottom: "conv4_6/dwise/bn" + top: "conv4_6/linear" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 64 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv4_6/linear/bn" + type: "BatchNorm" + bottom: "conv4_6/linear" + top: "conv4_6/linear/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv4_6/linear/scale" + type: "Scale" + bottom: "conv4_6/linear/bn" + top: "conv4_6/linear/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "block_4_6" + type: "Eltwise" + bottom: "block_4_5" + bottom: "conv4_6/linear/bn" + top: "block_4_6" +} +layer { + name: "conv4_7/expand" + type: "Convolution" + bottom: "block_4_6" + top: "conv4_7/expand" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 384 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv4_7/expand/bn" + type: "BatchNorm" + bottom: "conv4_7/expand" + top: "conv4_7/expand/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv4_7/expand/scale" + type: "Scale" + bottom: "conv4_7/expand/bn" + top: "conv4_7/expand/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "relu4_7/expand" + type: "ReLU" + bottom: "conv4_7/expand/bn" + top: "conv4_7/expand/bn" +} +layer { + name: "conv4_7/dwise" + type: "Convolution" + bottom: "conv4_7/expand/bn" + top: "conv4_7/dwise" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 384 + bias_term: false + pad: 1 + kernel_size: 3 + group: 384 + stride: 2 + weight_filler { + type: "msra" + } + engine: CAFFE + } +} +layer { + name: "conv4_7/dwise/bn" + type: "BatchNorm" + bottom: "conv4_7/dwise" + top: "conv4_7/dwise/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv4_7/dwise/scale" + type: "Scale" + bottom: "conv4_7/dwise/bn" + top: "conv4_7/dwise/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "relu4_7/dwise" + type: "ReLU" + bottom: "conv4_7/dwise/bn" + top: "conv4_7/dwise/bn" +} +layer { + name: "conv4_7/linear" + type: "Convolution" + bottom: "conv4_7/dwise/bn" + top: "conv4_7/linear" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 96 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv4_7/linear/bn" + type: "BatchNorm" + bottom: "conv4_7/linear" + top: "conv4_7/linear/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv4_7/linear/scale" + type: "Scale" + bottom: "conv4_7/linear/bn" + top: "conv4_7/linear/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "conv5_1/expand" + type: "Convolution" + bottom: "conv4_7/linear/bn" + top: "conv5_1/expand" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 576 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv5_1/expand/bn" + type: "BatchNorm" + bottom: "conv5_1/expand" + top: "conv5_1/expand/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv5_1/expand/scale" + type: "Scale" + bottom: "conv5_1/expand/bn" + top: "conv5_1/expand/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "relu5_1/expand" + type: "ReLU" + bottom: "conv5_1/expand/bn" + top: "conv5_1/expand/bn" +} +layer { + name: "conv5_1/dwise" + type: "Convolution" + bottom: "conv5_1/expand/bn" + top: "conv5_1/dwise" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 576 + bias_term: false + pad: 1 + kernel_size: 3 + group: 576 + weight_filler { + type: "msra" + } + engine: CAFFE + } +} +layer { + name: "conv5_1/dwise/bn" + type: "BatchNorm" + bottom: "conv5_1/dwise" + top: "conv5_1/dwise/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv5_1/dwise/scale" + type: "Scale" + bottom: "conv5_1/dwise/bn" + top: "conv5_1/dwise/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "relu5_1/dwise" + type: "ReLU" + bottom: "conv5_1/dwise/bn" + top: "conv5_1/dwise/bn" +} +layer { + name: "conv5_1/linear" + type: "Convolution" + bottom: "conv5_1/dwise/bn" + top: "conv5_1/linear" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 96 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv5_1/linear/bn" + type: "BatchNorm" + bottom: "conv5_1/linear" + top: "conv5_1/linear/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv5_1/linear/scale" + type: "Scale" + bottom: "conv5_1/linear/bn" + top: "conv5_1/linear/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "block_5_1" + type: "Eltwise" + bottom: "conv4_7/linear/bn" + bottom: "conv5_1/linear/bn" + top: "block_5_1" +} +layer { + name: "conv5_2/expand" + type: "Convolution" + bottom: "block_5_1" + top: "conv5_2/expand" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 576 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv5_2/expand/bn" + type: "BatchNorm" + bottom: "conv5_2/expand" + top: "conv5_2/expand/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv5_2/expand/scale" + type: "Scale" + bottom: "conv5_2/expand/bn" + top: "conv5_2/expand/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "relu5_2/expand" + type: "ReLU" + bottom: "conv5_2/expand/bn" + top: "conv5_2/expand/bn" +} +layer { + name: "conv5_2/dwise" + type: "Convolution" + bottom: "conv5_2/expand/bn" + top: "conv5_2/dwise" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 576 + bias_term: false + pad: 1 + kernel_size: 3 + group: 576 + weight_filler { + type: "msra" + } + engine: CAFFE + } +} +layer { + name: "conv5_2/dwise/bn" + type: "BatchNorm" + bottom: "conv5_2/dwise" + top: "conv5_2/dwise/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv5_2/dwise/scale" + type: "Scale" + bottom: "conv5_2/dwise/bn" + top: "conv5_2/dwise/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "relu5_2/dwise" + type: "ReLU" + bottom: "conv5_2/dwise/bn" + top: "conv5_2/dwise/bn" +} +layer { + name: "conv5_2/linear" + type: "Convolution" + bottom: "conv5_2/dwise/bn" + top: "conv5_2/linear" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 96 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv5_2/linear/bn" + type: "BatchNorm" + bottom: "conv5_2/linear" + top: "conv5_2/linear/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv5_2/linear/scale" + type: "Scale" + bottom: "conv5_2/linear/bn" + top: "conv5_2/linear/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "block_5_2" + type: "Eltwise" + bottom: "block_5_1" + bottom: "conv5_2/linear/bn" + top: "block_5_2" +} +layer { + name: "conv5_3/expand" + type: "Convolution" + bottom: "block_5_2" + top: "conv5_3/expand" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 576 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv5_3/expand/bn" + type: "BatchNorm" + bottom: "conv5_3/expand" + top: "conv5_3/expand/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv5_3/expand/scale" + type: "Scale" + bottom: "conv5_3/expand/bn" + top: "conv5_3/expand/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "relu5_3/expand" + type: "ReLU" + bottom: "conv5_3/expand/bn" + top: "conv5_3/expand/bn" +} +layer { + name: "conv5_3/dwise" + type: "Convolution" + bottom: "conv5_3/expand/bn" + top: "conv5_3/dwise" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 576 + bias_term: false + pad: 1 + kernel_size: 3 + group: 576 + stride: 2 + weight_filler { + type: "msra" + } + engine: CAFFE + } +} +layer { + name: "conv5_3/dwise/bn" + type: "BatchNorm" + bottom: "conv5_3/dwise" + top: "conv5_3/dwise/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv5_3/dwise/scale" + type: "Scale" + bottom: "conv5_3/dwise/bn" + top: "conv5_3/dwise/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "relu5_3/dwise" + type: "ReLU" + bottom: "conv5_3/dwise/bn" + top: "conv5_3/dwise/bn" +} +layer { + name: "conv5_3/linear" + type: "Convolution" + bottom: "conv5_3/dwise/bn" + top: "conv5_3/linear" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 160 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv5_3/linear/bn" + type: "BatchNorm" + bottom: "conv5_3/linear" + top: "conv5_3/linear/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv5_3/linear/scale" + type: "Scale" + bottom: "conv5_3/linear/bn" + top: "conv5_3/linear/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "conv6_1/expand" + type: "Convolution" + bottom: "conv5_3/linear/bn" + top: "conv6_1/expand" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 960 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv6_1/expand/bn" + type: "BatchNorm" + bottom: "conv6_1/expand" + top: "conv6_1/expand/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv6_1/expand/scale" + type: "Scale" + bottom: "conv6_1/expand/bn" + top: "conv6_1/expand/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "relu6_1/expand" + type: "ReLU" + bottom: "conv6_1/expand/bn" + top: "conv6_1/expand/bn" +} +layer { + name: "conv6_1/dwise" + type: "Convolution" + bottom: "conv6_1/expand/bn" + top: "conv6_1/dwise" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 960 + bias_term: false + pad: 1 + kernel_size: 3 + group: 960 + weight_filler { + type: "msra" + } + engine: CAFFE + } +} +layer { + name: "conv6_1/dwise/bn" + type: "BatchNorm" + bottom: "conv6_1/dwise" + top: "conv6_1/dwise/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv6_1/dwise/scale" + type: "Scale" + bottom: "conv6_1/dwise/bn" + top: "conv6_1/dwise/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "relu6_1/dwise" + type: "ReLU" + bottom: "conv6_1/dwise/bn" + top: "conv6_1/dwise/bn" +} +layer { + name: "conv6_1/linear" + type: "Convolution" + bottom: "conv6_1/dwise/bn" + top: "conv6_1/linear" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 160 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv6_1/linear/bn" + type: "BatchNorm" + bottom: "conv6_1/linear" + top: "conv6_1/linear/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv6_1/linear/scale" + type: "Scale" + bottom: "conv6_1/linear/bn" + top: "conv6_1/linear/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "block_6_1" + type: "Eltwise" + bottom: "conv5_3/linear/bn" + bottom: "conv6_1/linear/bn" + top: "block_6_1" +} +layer { + name: "conv6_2/expand" + type: "Convolution" + bottom: "block_6_1" + top: "conv6_2/expand" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 960 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv6_2/expand/bn" + type: "BatchNorm" + bottom: "conv6_2/expand" + top: "conv6_2/expand/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv6_2/expand/scale" + type: "Scale" + bottom: "conv6_2/expand/bn" + top: "conv6_2/expand/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "relu6_2/expand" + type: "ReLU" + bottom: "conv6_2/expand/bn" + top: "conv6_2/expand/bn" +} +layer { + name: "conv6_2/dwise" + type: "Convolution" + bottom: "conv6_2/expand/bn" + top: "conv6_2/dwise" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 960 + bias_term: false + pad: 1 + kernel_size: 3 + group: 960 + weight_filler { + type: "msra" + } + engine: CAFFE + } +} +layer { + name: "conv6_2/dwise/bn" + type: "BatchNorm" + bottom: "conv6_2/dwise" + top: "conv6_2/dwise/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv6_2/dwise/scale" + type: "Scale" + bottom: "conv6_2/dwise/bn" + top: "conv6_2/dwise/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "relu6_2/dwise" + type: "ReLU" + bottom: "conv6_2/dwise/bn" + top: "conv6_2/dwise/bn" +} +layer { + name: "conv6_2/linear" + type: "Convolution" + bottom: "conv6_2/dwise/bn" + top: "conv6_2/linear" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 160 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv6_2/linear/bn" + type: "BatchNorm" + bottom: "conv6_2/linear" + top: "conv6_2/linear/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv6_2/linear/scale" + type: "Scale" + bottom: "conv6_2/linear/bn" + top: "conv6_2/linear/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "block_6_2" + type: "Eltwise" + bottom: "block_6_1" + bottom: "conv6_2/linear/bn" + top: "block_6_2" +} +layer { + name: "conv6_3/expand" + type: "Convolution" + bottom: "block_6_2" + top: "conv6_3/expand" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 960 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv6_3/expand/bn" + type: "BatchNorm" + bottom: "conv6_3/expand" + top: "conv6_3/expand/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv6_3/expand/scale" + type: "Scale" + bottom: "conv6_3/expand/bn" + top: "conv6_3/expand/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "relu6_3/expand" + type: "ReLU" + bottom: "conv6_3/expand/bn" + top: "conv6_3/expand/bn" +} +layer { + name: "conv6_3/dwise" + type: "Convolution" + bottom: "conv6_3/expand/bn" + top: "conv6_3/dwise" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 960 + bias_term: false + pad: 1 + kernel_size: 3 + group: 960 + weight_filler { + type: "msra" + } + engine: CAFFE + } +} +layer { + name: "conv6_3/dwise/bn" + type: "BatchNorm" + bottom: "conv6_3/dwise" + top: "conv6_3/dwise/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv6_3/dwise/scale" + type: "Scale" + bottom: "conv6_3/dwise/bn" + top: "conv6_3/dwise/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "relu6_3/dwise" + type: "ReLU" + bottom: "conv6_3/dwise/bn" + top: "conv6_3/dwise/bn" +} +layer { + name: "conv6_3/linear" + type: "Convolution" + bottom: "conv6_3/dwise/bn" + top: "conv6_3/linear" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 320 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv6_3/linear/bn" + type: "BatchNorm" + bottom: "conv6_3/linear" + top: "conv6_3/linear/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv6_3/linear/scale" + type: "Scale" + bottom: "conv6_3/linear/bn" + top: "conv6_3/linear/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "conv6_4" + type: "Convolution" + bottom: "conv6_3/linear/bn" + top: "conv6_4" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 1280 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv6_4/bn" + type: "BatchNorm" + bottom: "conv6_4" + top: "conv6_4/bn" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv6_4/scale" + type: "Scale" + bottom: "conv6_4/bn" + top: "conv6_4/bn" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + bias_term: true + } +} +layer { + name: "relu6_4" + type: "ReLU" + bottom: "conv6_4/bn" + top: "conv6_4/bn" +} +layer { + name: "pool6" + type: "Pooling" + bottom: "conv6_4/bn" + top: "pool6" + pooling_param { + pool: AVE + global_pooling: true + } +} +layer { + name: "fc7" + type: "Convolution" + bottom: "pool6" + top: "fc7" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 1000 + kernel_size: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "prob" + type: "Softmax" + bottom: "fc7" + top: "prob" +} \ No newline at end of file diff --git a/rknn-toolkit2/examples/caffe/mobilenet_v2/model_config.yml b/rknn-toolkit2/examples/caffe/mobilenet_v2/model_config.yml new file mode 100755 index 0000000000000000000000000000000000000000..51c569ca9c0e5db118242dbed7efff2265cf77e2 --- /dev/null +++ b/rknn-toolkit2/examples/caffe/mobilenet_v2/model_config.yml @@ -0,0 +1,17 @@ +models: + name: mobilenet_v2 # 模型输出名称 + platform: caffe # 原始模型使用的框架 + prototxt_file_path: ./mobilenet_v2_deploy.prototxt # caffe的prototxt文件 + caffemodel_file_path: ./mobilenet_v2.caffemodel # caffe的caffemode文件 + subgraphs: # 描述输入输出shape等信息，模型单输入可不写 + input_size_list: + - 1,3,224,224 + quantize: true # 是否量化 + dataset: ./dataset.txt # 量化dataset文件路径（相对yml路径） + configs: + quantized_dtype: asymmetric_quantized-8 # 量化类型 + mean_values: [103.94, 116.78, 123.68] # rknn.config的mean_values参数 + std_values: [58.82, 58.82, 58.82] # rknn.config的std_values参数 + quant_img_RGB2BGR: true # 进行RGB2BGR转换 + quantized_algorithm: normal # 量化算法 + quantized_method: channel # 量化方法 diff --git a/rknn-toolkit2/examples/caffe/mobilenet_v2/test.py b/rknn-toolkit2/examples/caffe/mobilenet_v2/test.py new file mode 100755 index 0000000000000000000000000000000000000000..62564cc3f4cd65196105fc003125b5fe489372fe --- /dev/null +++ b/rknn-toolkit2/examples/caffe/mobilenet_v2/test.py @@ -0,0 +1,76 @@ +import numpy as np +import cv2 +from rknn.api import RKNN + + +def show_outputs(outputs): + np.save('./caffe_mobilenet_v2_0.npy', outputs[0]) + output = outputs[0].reshape(-1) + index = sorted(range(len(output)), key=lambda k : output[k], reverse=True) + fp = open('./labels.txt', 'r') + labels = fp.readlines() + top5_str = 'mobilenet_v2\n-----TOP 5-----\n' + for i in range(5): + value = output[index[i]] + if value > 0: + topi = '[{:>3d}] score:{:.6f} class:"{}"\n'.format(index[i], value, labels[index[i]].strip().split(':')[-1]) + else: + topi = '[ -1]: 0.0\n' + top5_str += topi + print(top5_str.strip()) + + +if __name__ == '__main__': + + # Create RKNN object + rknn = RKNN(verbose=True) + + # Pre-process config + print('--> Config model') + rknn.config(mean_values=[103.94, 116.78, 123.68], std_values=[58.82, 58.82, 58.82], quant_img_RGB2BGR=True, target_platform='rk3566') + print('done') + + # Load model (from https://github.com/shicai/MobileNet-Caffe) + print('--> Loading model') + ret = rknn.load_caffe(model='./mobilenet_v2_deploy.prototxt', + blobs='./mobilenet_v2.caffemodel') + if ret != 0: + print('Load model failed!') + exit(ret) + print('done') + + # Build model + print('--> Building model') + ret = rknn.build(do_quantization=True, dataset='./dataset.txt') + if ret != 0: + print('Build model failed!') + exit(ret) + print('done') + + # Export rknn model + print('--> Export rknn model') + ret = rknn.export_rknn('./mobilenet_v2.rknn') + if ret != 0: + print('Export rknn model failed!') + exit(ret) + print('done') + + # Set inputs + img = cv2.imread('./dog_224x224.jpg') + img = np.expand_dims(img, 0) + + # Init runtime environment + print('--> Init runtime environment') + ret = rknn.init_runtime() + if ret != 0: + print('Init runtime environment failed!') + exit(ret) + print('done') + + # Inference + print('--> Running model') + outputs = rknn.inference(inputs=[img], data_format=['nhwc']) + show_outputs(outputs) + print('done') + + rknn.release() diff --git a/rknn-toolkit2/examples/caffe/vgg-ssd/README.md b/rknn-toolkit2/examples/caffe/vgg-ssd/README.md new file mode 100644 index 0000000000000000000000000000000000000000..2a2c1baf63da4b57c7fb4278fa8c988dec5e736c --- /dev/null +++ b/rknn-toolkit2/examples/caffe/vgg-ssd/README.md @@ -0,0 +1,24 @@ +# Caffe VGG-SSD + +## Model Source +The model used in this example come from the following open source projects: +https://github.com/weiliu89/caffe/tree/ssd#models +But the model download link has expired, please download from [VGG_VOC0712_SSD_300x300_iter_120000.caffemodel](https://ftzr.zbox.filez.com/v2/delivery/data/95f00b0fc900458ba134f8b180b3f7a1/asset/vgg-ssd/VGG_VOC0712_SSD_300x300_iter_120000.caffemodel). + +## Script Usage +*Usage:* +``` +python test.py +``` +*rknn_convert usage:* +``` +python3 -m rknn.api.rknn_convert -t rk3566 -i ./model_config.yml -o ./ +``` +*Description:* +- The default target platform in script is 'rk3566', please modify the 'target_platform' parameter of 'rknn.config' according to the actual platform. +- If connecting board is required, please add the 'target' parameter in 'rknn.init_runtime'. + +## Expected Results +This example will save the result of object detection to the 'result.jpg', as follows: +![result](result_truth.jpg) +- Note: Different platforms, different versions of tools and drivers may have slightly different results. \ No newline at end of file diff --git a/rknn-toolkit2/examples/caffe/vgg-ssd/dataset.txt b/rknn-toolkit2/examples/caffe/vgg-ssd/dataset.txt new file mode 100644 index 0000000000000000000000000000000000000000..8caaf85d1ca3ae2db2a96aafe248208dd250c065 --- /dev/null +++ b/rknn-toolkit2/examples/caffe/vgg-ssd/dataset.txt @@ -0,0 +1 @@ +road_300x300.jpg diff --git a/rknn-toolkit2/examples/caffe/vgg-ssd/deploy_rm_detection_output.prototxt b/rknn-toolkit2/examples/caffe/vgg-ssd/deploy_rm_detection_output.prototxt new file mode 100755 index 0000000000000000000000000000000000000000..6e42430c363c0e5027d1676c1baecb8436236864 --- /dev/null +++ b/rknn-toolkit2/examples/caffe/vgg-ssd/deploy_rm_detection_output.prototxt @@ -0,0 +1,1605 @@ +name: "VGG_VOC0712_SSD_300x300_deploy" +layer { + name: "input" + type: "Input" + top: "data" + input_param { + shape { + dim: 1 + dim: 3 + dim: 300 + dim: 300 + } + } +} +layer { + name: "conv1_1" + type: "Convolution" + bottom: "data" + top: "conv1_1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 64 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "relu1_1" + type: "ReLU" + bottom: "conv1_1" + top: "conv1_1" +} +layer { + name: "conv1_2" + type: "Convolution" + bottom: "conv1_1" + top: "conv1_2" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 64 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "relu1_2" + type: "ReLU" + bottom: "conv1_2" + top: "conv1_2" +} +layer { + name: "pool1" + type: "Pooling" + bottom: "conv1_2" + top: "pool1" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv2_1" + type: "Convolution" + bottom: "pool1" + top: "conv2_1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "relu2_1" + type: "ReLU" + bottom: "conv2_1" + top: "conv2_1" +} +layer { + name: "conv2_2" + type: "Convolution" + bottom: "conv2_1" + top: "conv2_2" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "relu2_2" + type: "ReLU" + bottom: "conv2_2" + top: "conv2_2" +} +layer { + name: "pool2" + type: "Pooling" + bottom: "conv2_2" + top: "pool2" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv3_1" + type: "Convolution" + bottom: "pool2" + top: "conv3_1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "relu3_1" + type: "ReLU" + bottom: "conv3_1" + top: "conv3_1" +} +layer { + name: "conv3_2" + type: "Convolution" + bottom: "conv3_1" + top: "conv3_2" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "relu3_2" + type: "ReLU" + bottom: "conv3_2" + top: "conv3_2" +} +layer { + name: "conv3_3" + type: "Convolution" + bottom: "conv3_2" + top: "conv3_3" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "relu3_3" + type: "ReLU" + bottom: "conv3_3" + top: "conv3_3" +} +layer { + name: "pool3" + type: "Pooling" + bottom: "conv3_3" + top: "pool3" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv4_1" + type: "Convolution" + bottom: "pool3" + top: "conv4_1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "relu4_1" + type: "ReLU" + bottom: "conv4_1" + top: "conv4_1" +} +layer { + name: "conv4_2" + type: "Convolution" + bottom: "conv4_1" + top: "conv4_2" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "relu4_2" + type: "ReLU" + bottom: "conv4_2" + top: "conv4_2" +} +layer { + name: "conv4_3" + type: "Convolution" + bottom: "conv4_2" + top: "conv4_3" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "relu4_3" + type: "ReLU" + bottom: "conv4_3" + top: "conv4_3" +} +layer { + name: "pool4" + type: "Pooling" + bottom: "conv4_3" + top: "pool4" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv5_1" + type: "Convolution" + bottom: "pool4" + top: "conv5_1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + dilation: 1 + } +} +layer { + name: "relu5_1" + type: "ReLU" + bottom: "conv5_1" + top: "conv5_1" +} +layer { + name: "conv5_2" + type: "Convolution" + bottom: "conv5_1" + top: "conv5_2" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + dilation: 1 + } +} +layer { + name: "relu5_2" + type: "ReLU" + bottom: "conv5_2" + top: "conv5_2" +} +layer { + name: "conv5_3" + type: "Convolution" + bottom: "conv5_2" + top: "conv5_3" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + dilation: 1 + } +} +layer { + name: "relu5_3" + type: "ReLU" + bottom: "conv5_3" + top: "conv5_3" +} +layer { + name: "pool5" + type: "Pooling" + bottom: "conv5_3" + top: "pool5" + pooling_param { + pool: MAX + kernel_size: 3 + stride: 1 + pad: 1 + } +} +layer { + name: "fc6" + type: "Convolution" + bottom: "pool5" + top: "fc6" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 1024 + pad: 6 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + dilation: 6 + } +} +layer { + name: "relu6" + type: "ReLU" + bottom: "fc6" + top: "fc6" +} +layer { + name: "fc7" + type: "Convolution" + bottom: "fc6" + top: "fc7" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 1024 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "relu7" + type: "ReLU" + bottom: "fc7" + top: "fc7" +} +layer { + name: "conv6_1" + type: "Convolution" + bottom: "fc7" + top: "conv6_1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv6_1_relu" + type: "ReLU" + bottom: "conv6_1" + top: "conv6_1" +} +layer { + name: "conv6_2" + type: "Convolution" + bottom: "conv6_1" + top: "conv6_2" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + stride: 2 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv6_2_relu" + type: "ReLU" + bottom: "conv6_2" + top: "conv6_2" +} +layer { + name: "conv7_1" + type: "Convolution" + bottom: "conv6_2" + top: "conv7_1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv7_1_relu" + type: "ReLU" + bottom: "conv7_1" + top: "conv7_1" +} +layer { + name: "conv7_2" + type: "Convolution" + bottom: "conv7_1" + top: "conv7_2" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 1 + kernel_size: 3 + stride: 2 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv7_2_relu" + type: "ReLU" + bottom: "conv7_2" + top: "conv7_2" +} +layer { + name: "conv8_1" + type: "Convolution" + bottom: "conv7_2" + top: "conv8_1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv8_1_relu" + type: "ReLU" + bottom: "conv8_1" + top: "conv8_1" +} +layer { + name: "conv8_2" + type: "Convolution" + bottom: "conv8_1" + top: "conv8_2" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 0 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv8_2_relu" + type: "ReLU" + bottom: "conv8_2" + top: "conv8_2" +} +layer { + name: "conv9_1" + type: "Convolution" + bottom: "conv8_2" + top: "conv9_1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv9_1_relu" + type: "ReLU" + bottom: "conv9_1" + top: "conv9_1" +} +layer { + name: "conv9_2" + type: "Convolution" + bottom: "conv9_1" + top: "conv9_2" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 0 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv9_2_relu" + type: "ReLU" + bottom: "conv9_2" + top: "conv9_2" +} +layer { + name: "conv4_3_norm" + type: "Normalize" + bottom: "conv4_3" + top: "conv4_3_norm" + norm_param { + across_spatial: false + scale_filler { + type: "constant" + value: 20 + } + channel_shared: false + } +} +layer { + name: "conv4_3_norm_mbox_loc" + type: "Convolution" + bottom: "conv4_3_norm" + top: "conv4_3_norm_mbox_loc" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 16 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv4_3_norm_mbox_loc_perm" + type: "Permute" + bottom: "conv4_3_norm_mbox_loc" + top: "conv4_3_norm_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv4_3_norm_mbox_loc_flat" + type: "Flatten" + bottom: "conv4_3_norm_mbox_loc_perm" + top: "conv4_3_norm_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv4_3_norm_mbox_conf" + type: "Convolution" + bottom: "conv4_3_norm" + top: "conv4_3_norm_mbox_conf" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 84 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv4_3_norm_mbox_conf_perm" + type: "Permute" + bottom: "conv4_3_norm_mbox_conf" + top: "conv4_3_norm_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv4_3_norm_mbox_conf_flat" + type: "Flatten" + bottom: "conv4_3_norm_mbox_conf_perm" + top: "conv4_3_norm_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv4_3_norm_mbox_priorbox" + type: "PriorBox" + bottom: "conv4_3_norm" + bottom: "data" + top: "conv4_3_norm_mbox_priorbox" + prior_box_param { + min_size: 30.0 + max_size: 60.0 + aspect_ratio: 2 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + step: 8 + offset: 0.5 + } +} +layer { + name: "fc7_mbox_loc" + type: "Convolution" + bottom: "fc7" + top: "fc7_mbox_loc" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 24 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "fc7_mbox_loc_perm" + type: "Permute" + bottom: "fc7_mbox_loc" + top: "fc7_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "fc7_mbox_loc_flat" + type: "Flatten" + bottom: "fc7_mbox_loc_perm" + top: "fc7_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "fc7_mbox_conf" + type: "Convolution" + bottom: "fc7" + top: "fc7_mbox_conf" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 126 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "fc7_mbox_conf_perm" + type: "Permute" + bottom: "fc7_mbox_conf" + top: "fc7_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "fc7_mbox_conf_flat" + type: "Flatten" + bottom: "fc7_mbox_conf_perm" + top: "fc7_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "fc7_mbox_priorbox" + type: "PriorBox" + bottom: "fc7" + bottom: "data" + top: "fc7_mbox_priorbox" + prior_box_param { + min_size: 60.0 + max_size: 111.0 + aspect_ratio: 2 + aspect_ratio: 3 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + step: 16 + offset: 0.5 + } +} +layer { + name: "conv6_2_mbox_loc" + type: "Convolution" + bottom: "conv6_2" + top: "conv6_2_mbox_loc" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 24 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv6_2_mbox_loc_perm" + type: "Permute" + bottom: "conv6_2_mbox_loc" + top: "conv6_2_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv6_2_mbox_loc_flat" + type: "Flatten" + bottom: "conv6_2_mbox_loc_perm" + top: "conv6_2_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv6_2_mbox_conf" + type: "Convolution" + bottom: "conv6_2" + top: "conv6_2_mbox_conf" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 126 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv6_2_mbox_conf_perm" + type: "Permute" + bottom: "conv6_2_mbox_conf" + top: "conv6_2_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv6_2_mbox_conf_flat" + type: "Flatten" + bottom: "conv6_2_mbox_conf_perm" + top: "conv6_2_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv6_2_mbox_priorbox" + type: "PriorBox" + bottom: "conv6_2" + bottom: "data" + top: "conv6_2_mbox_priorbox" + prior_box_param { + min_size: 111.0 + max_size: 162.0 + aspect_ratio: 2 + aspect_ratio: 3 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + step: 32 + offset: 0.5 + } +} +layer { + name: "conv7_2_mbox_loc" + type: "Convolution" + bottom: "conv7_2" + top: "conv7_2_mbox_loc" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 24 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv7_2_mbox_loc_perm" + type: "Permute" + bottom: "conv7_2_mbox_loc" + top: "conv7_2_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv7_2_mbox_loc_flat" + type: "Flatten" + bottom: "conv7_2_mbox_loc_perm" + top: "conv7_2_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv7_2_mbox_conf" + type: "Convolution" + bottom: "conv7_2" + top: "conv7_2_mbox_conf" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 126 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv7_2_mbox_conf_perm" + type: "Permute" + bottom: "conv7_2_mbox_conf" + top: "conv7_2_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv7_2_mbox_conf_flat" + type: "Flatten" + bottom: "conv7_2_mbox_conf_perm" + top: "conv7_2_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv7_2_mbox_priorbox" + type: "PriorBox" + bottom: "conv7_2" + bottom: "data" + top: "conv7_2_mbox_priorbox" + prior_box_param { + min_size: 162.0 + max_size: 213.0 + aspect_ratio: 2 + aspect_ratio: 3 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + step: 64 + offset: 0.5 + } +} +layer { + name: "conv8_2_mbox_loc" + type: "Convolution" + bottom: "conv8_2" + top: "conv8_2_mbox_loc" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 16 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv8_2_mbox_loc_perm" + type: "Permute" + bottom: "conv8_2_mbox_loc" + top: "conv8_2_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv8_2_mbox_loc_flat" + type: "Flatten" + bottom: "conv8_2_mbox_loc_perm" + top: "conv8_2_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv8_2_mbox_conf" + type: "Convolution" + bottom: "conv8_2" + top: "conv8_2_mbox_conf" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 84 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv8_2_mbox_conf_perm" + type: "Permute" + bottom: "conv8_2_mbox_conf" + top: "conv8_2_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv8_2_mbox_conf_flat" + type: "Flatten" + bottom: "conv8_2_mbox_conf_perm" + top: "conv8_2_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv8_2_mbox_priorbox" + type: "PriorBox" + bottom: "conv8_2" + bottom: "data" + top: "conv8_2_mbox_priorbox" + prior_box_param { + min_size: 213.0 + max_size: 264.0 + aspect_ratio: 2 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + step: 100 + offset: 0.5 + } +} +layer { + name: "conv9_2_mbox_loc" + type: "Convolution" + bottom: "conv9_2" + top: "conv9_2_mbox_loc" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 16 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv9_2_mbox_loc_perm" + type: "Permute" + bottom: "conv9_2_mbox_loc" + top: "conv9_2_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv9_2_mbox_loc_flat" + type: "Flatten" + bottom: "conv9_2_mbox_loc_perm" + top: "conv9_2_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv9_2_mbox_conf" + type: "Convolution" + bottom: "conv9_2" + top: "conv9_2_mbox_conf" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 84 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv9_2_mbox_conf_perm" + type: "Permute" + bottom: "conv9_2_mbox_conf" + top: "conv9_2_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv9_2_mbox_conf_flat" + type: "Flatten" + bottom: "conv9_2_mbox_conf_perm" + top: "conv9_2_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv9_2_mbox_priorbox" + type: "PriorBox" + bottom: "conv9_2" + bottom: "data" + top: "conv9_2_mbox_priorbox" + prior_box_param { + min_size: 264.0 + max_size: 315.0 + aspect_ratio: 2 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + step: 300 + offset: 0.5 + } +} +layer { + name: "mbox_loc" + type: "Concat" + bottom: "conv4_3_norm_mbox_loc_flat" + bottom: "fc7_mbox_loc_flat" + bottom: "conv6_2_mbox_loc_flat" + bottom: "conv7_2_mbox_loc_flat" + bottom: "conv8_2_mbox_loc_flat" + bottom: "conv9_2_mbox_loc_flat" + top: "mbox_loc" + concat_param { + axis: 1 + } +} +layer { + name: "mbox_conf" + type: "Concat" + bottom: "conv4_3_norm_mbox_conf_flat" + bottom: "fc7_mbox_conf_flat" + bottom: "conv6_2_mbox_conf_flat" + bottom: "conv7_2_mbox_conf_flat" + bottom: "conv8_2_mbox_conf_flat" + bottom: "conv9_2_mbox_conf_flat" + top: "mbox_conf" + concat_param { + axis: 1 + } +} +layer { + name: "mbox_priorbox" + type: "Concat" + bottom: "conv4_3_norm_mbox_priorbox" + bottom: "fc7_mbox_priorbox" + bottom: "conv6_2_mbox_priorbox" + bottom: "conv7_2_mbox_priorbox" + bottom: "conv8_2_mbox_priorbox" + bottom: "conv9_2_mbox_priorbox" + top: "mbox_priorbox" + concat_param { + axis: 2 + } +} +layer { + name: "mbox_conf_reshape" + type: "Reshape" + bottom: "mbox_conf" + top: "mbox_conf_reshape" + reshape_param { + shape { + dim: 0 + dim: -1 + dim: 21 + } + } +} +layer { + name: "mbox_conf_softmax" + type: "Softmax" + bottom: "mbox_conf_reshape" + top: "mbox_conf_softmax" + softmax_param { + axis: 2 + } +} +layer { + name: "mbox_conf_flatten" + type: "Flatten" + bottom: "mbox_conf_softmax" + top: "mbox_conf_flatten" + flatten_param { + axis: 1 + } +} + + diff --git a/rknn-toolkit2/examples/caffe/vgg-ssd/mbox_priorbox_97.txt b/rknn-toolkit2/examples/caffe/vgg-ssd/mbox_priorbox_97.txt new file mode 100644 index 0000000000000000000000000000000000000000..3a2b49a5cedafe87f1f9a0f8417aba466651f223 --- /dev/null +++ b/rknn-toolkit2/examples/caffe/vgg-ssd/mbox_priorbox_97.txt @@ -0,0 +1,69856 @@ +-0.036667 +-0.036667 +0.063333 +0.063333 +-0.057377 +-0.057377 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caffemodel_file_path: ./VGG_VOC0712_SSD_300x300_iter_120000.caffemodel # caffe的caffemode文件 + quantize: true # 是否量化 + dataset: ./dataset.txt # 量化dataset文件路径（相对yml路径） + configs: + quantized_dtype: asymmetric_quantized-8 # 量化类型 + mean_values: [103.94, 116.78, 123.68] # rknn.config的mean_values参数 + std_values: [1, 1, 1] # rknn.config的std_values参数 + quant_img_RGB2BGR: true # 进行RGB2BGR转换 + quantized_algorithm: normal # 量化算法 + quantized_method: channel # 量化方法 diff --git a/rknn-toolkit2/examples/caffe/vgg-ssd/result_truth.jpg b/rknn-toolkit2/examples/caffe/vgg-ssd/result_truth.jpg new file mode 100644 index 0000000000000000000000000000000000000000..8410eec972d7f076e68248827c6765118a2a1dd7 --- /dev/null +++ b/rknn-toolkit2/examples/caffe/vgg-ssd/result_truth.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:666110fe2ff4b430feeb26d5f7ae81ffa1e89e0476aee1bc584cd7b15bc3f604 +size 58894 diff --git a/rknn-toolkit2/examples/caffe/vgg-ssd/road_300x300.jpg b/rknn-toolkit2/examples/caffe/vgg-ssd/road_300x300.jpg new file mode 100644 index 0000000000000000000000000000000000000000..c4f34763ae6b96776fc34141854c4f3f148776e8 --- /dev/null +++ b/rknn-toolkit2/examples/caffe/vgg-ssd/road_300x300.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8cbf222148e95c001336ef65ad69749460547f03e1255384b4cc7fa960cb60a1 +size 47291 diff --git a/rknn-toolkit2/examples/caffe/vgg-ssd/test.py b/rknn-toolkit2/examples/caffe/vgg-ssd/test.py new file mode 100644 index 0000000000000000000000000000000000000000..fb33da7c230ae67bdf2699e0d3439ef6ad04e7b4 --- /dev/null +++ b/rknn-toolkit2/examples/caffe/vgg-ssd/test.py @@ -0,0 +1,229 @@ +import os +import math +import numpy as np +import cv2 +from rknn.api import RKNN + +import PIL.Image as Image +import PIL.ImageDraw as ImageDraw +import PIL.ImageFont as ImageFont + +np.set_printoptions(threshold=np.inf) + +CLASSES = ('__background__', + 'aeroplane', 'bicycle', 'bird', 'boat', + 'bottle', 'bus', 'car', 'cat', 'chair', + 'cow', 'diningtable', 'dog', 'horse', + 'motorbike', 'person', 'pottedplant', + 'sheep', 'sofa', 'train', 'tvmonitor') + +NUM_CLS = 21 + +CONF_THRESH = 0.5 +NMS_THRESH = 0.45 + + +def IntersectBBox(box1, box2): + if box1[0] > box2[2] or box1[2] < box2[0] or box1[1] > box2[3] or box1[3] < box2[1]: + return 0 + else: + area1 = (box1[2] - box1[0]) * (box1[3] - box1[1]) + area2 = (box2[2] - box2[0]) * (box2[3] - box2[1]) + + xx1 = max(box1[0], box2[0]) + yy1 = max(box1[1], box2[1]) + xx2 = min(box1[2], box2[2]) + yy2 = min(box1[3], box2[3]) + + w = max(0, xx2-xx1) + h = max(0, yy2-yy1) + + ovr = w*h / (area1 + area2 - w*h) + return ovr + + +def ssd_post_process(conf_data, loc_data): + prior_data = np.loadtxt('mbox_priorbox_97.txt', dtype=np.float32) + + prior_bboxes = prior_data[:len(loc_data)] + prior_variances = prior_data[len(loc_data):] + + prior_num = int(len(loc_data) / 4) # 8732 + + conf_data = conf_data.reshape(-1, 21) + + idx_class_conf = [] + bboxes = [] + + # conf + for prior_idx in range(0, prior_num): + max_val = np.max(conf_data[prior_idx]) + max_idx = np.argmax(conf_data[prior_idx]) + if max_val > CONF_THRESH and max_idx != 0: + idx_class_conf.append([prior_idx, max_idx, max_val]) + + # print(len(idx_class_conf)) + + # boxes + for i in range(0, prior_num): + prior_w = prior_bboxes[4*i+2] - prior_bboxes[4*i] + prior_h = prior_bboxes[4*i+3] - prior_bboxes[4*i+1] + prior_center_x = (prior_bboxes[4*i+2] + prior_bboxes[4*i]) / 2 + prior_center_y = (prior_bboxes[4*i+3] + prior_bboxes[4*i+1]) / 2 + + bbox_center_x = prior_variances[4*i+0] * loc_data[4*i+0][0] * prior_w + prior_center_x + bbox_center_y = prior_variances[4*i+1] * loc_data[4*i+1][0] * prior_h + prior_center_y + bbox_w = math.exp(prior_variances[4*i+2] * loc_data[4*i+2][0]) * prior_w + bbox_h = math.exp(prior_variances[4*i+3] * loc_data[4*i+3][0]) * prior_h + + tmp = [] + tmp.append(max(min(bbox_center_x - bbox_w / 2., 1), 0)) + tmp.append(max(min(bbox_center_y - bbox_h / 2., 1), 0)) + tmp.append(max(min(bbox_center_x + bbox_w / 2., 1), 0)) + tmp.append(max(min(bbox_center_y + bbox_h / 2., 1), 0)) + bboxes.append(tmp) + + # print(len(idx_class_conf)) + + # nms + cur_class_num = 0 + idx_class_conf_ = [] + for i in range(0, len(idx_class_conf)): + keep = True + k = 0 + while k < cur_class_num: + if keep: + ovr = IntersectBBox(bboxes[idx_class_conf[i][0]], bboxes[idx_class_conf_[k][0]]) + if idx_class_conf_[k][1] == idx_class_conf[i][1] and ovr > NMS_THRESH: + if idx_class_conf_[k][2] < idx_class_conf[i][2]: + idx_class_conf_.pop(k) + idx_class_conf_.append(idx_class_conf[i]) + keep = False + break + k += 1 + else: + break + if keep: + idx_class_conf_.append(idx_class_conf[i]) + cur_class_num += 1 + + # print(idx_class_conf_) + + box_class_score = [] + + for i in range(0, len(idx_class_conf_)): + bboxes[idx_class_conf_[i][0]].append(idx_class_conf_[i][1]) + bboxes[idx_class_conf_[i][0]].append(idx_class_conf_[i][2]) + box_class_score.append(bboxes[idx_class_conf_[i][0]]) + + img = cv2.imread('./road_300x300.jpg') + img_pil = Image.fromarray(img) + draw = ImageDraw.Draw(img_pil) + + font = ImageFont.load_default() + + if len(box_class_score) != 0: + print("{:^12} {:^12} {}".format('class', 'score', 'xmin, ymin, xmax, ymax')) + print('-' * 50) + + for i in range(0, len(box_class_score)): + x1 = int(box_class_score[i][0]*img.shape[1]) + y1 = int(box_class_score[i][1]*img.shape[0]) + x2 = int(box_class_score[i][2]*img.shape[1]) + y2 = int(box_class_score[i][3]*img.shape[0]) + color = (0, int(box_class_score[i][4]/20.0*255), 255) + draw.line([(x1, y1), (x1, y2), (x2, y2), + (x2, y1), (x1, y1)], width=2, fill=color) + display_str = CLASSES[box_class_score[i][4]] + ":" + str(box_class_score[i][5]) + try: + display_str_height = np.ceil((1 + 2 * 0.05) * font.getbbox(display_str)[3])+1 + except: + display_str_height = np.ceil((1 + 2 * 0.05) * font.getsize(display_str)[1])+1 + + if y1 > display_str_height: + text_bottom = y1 + else: + text_bottom = y1 + display_str_height + + try: + _, _, text_width, text_height = font.getbbox(display_str) + except: + text_width, text_height = font.getsize(display_str) + margin = np.ceil(0.05 * text_height) + draw.rectangle([(x1, text_bottom-text_height-2*margin), (x1+text_width, text_bottom)], fill=color) + draw.text((x1+margin, text_bottom-text_height-margin), display_str, fill='black', font=font) + + print("{:^12} {:^12.3f} [{:>4}, {:>4}, {:>4}, {:>4}]".format(CLASSES[box_class_score[i][4]], box_class_score[i][5], + x1, y1, x2, y2)) + + np.copyto(img, np.array(img_pil)) + cv2.imwrite("result.jpg", img) + print('Save results to result.jpg!') + + +if __name__ == '__main__': + + if not os.path.exists('./VGG_VOC0712_SSD_300x300_iter_120000.caffemodel'): + print('!!! Missing VGG_VOC0712_SSD_300x300_iter_120000.caffemodel !!!\n' + '1. Download models_VGGNet_VOC0712_SSD_300x300.tar.gz from https://drive.google.com/file/d/0BzKzrI_SkD1_WVVTSmQxU0dVRzA/view\n' + '2. Extract the VGG_VOC0712_SSD_300x300_iter_120000.caffemodel from models_VGGNet_VOC0712_SSD_300x300.tar.gz\n' + '3. Or you can also download caffemodel from https://ftzr.zbox.filez.com/v2/delivery/data/95f00b0fc900458ba134f8b180b3f7a1/asset/vgg-ssd/VGG_VOC0712_SSD_300x300_iter_120000.caffemodel\n') + exit(-1) + + # Create RKNN object + rknn = RKNN(verbose=True) + + # Pre-process config + print('--> Config model') + rknn.config(mean_values=[103.94, 116.78, 123.68], std_values=[1, 1, 1], quant_img_RGB2BGR=True, target_platform='rk3566') + print('done') + + # Load model + print('--> Loading model') + ret = rknn.load_caffe(model='./deploy_rm_detection_output.prototxt', + blobs='./VGG_VOC0712_SSD_300x300_iter_120000.caffemodel') + if ret != 0: + print('Load model failed!') + exit(ret) + print('done') + + # Build model + print('--> Building model') + ret = rknn.build(do_quantization=True, dataset='./dataset.txt') + if ret != 0: + print('Build model failed!') + exit(ret) + print('done') + + # Export rknn model + print('--> Export rknn model') + ret = rknn.export_rknn('./deploy_rm_detection_output.rknn') + if ret != 0: + print('Export rknn model failed!') + exit(ret) + print('done') + + # Set inputs + img = cv2.imread('./road_300x300.jpg') + img = np.expand_dims(img, 0) + + # Init runtime environment + print('--> Init runtime environment') + ret = rknn.init_runtime() + if ret != 0: + print('Init runtime environment failed!') + exit(ret) + print('done') + + # Inference + print('--> Running model') + outputs = rknn.inference(inputs=[img], data_format=['nhwc']) + print('done') + + outputs[0] = outputs[0].reshape((-1, 1)) + outputs[1] = outputs[1].reshape((-1, 1)) + np.save('./caffe_vgg-ssd_0.npy', outputs[0]) + np.save('./caffe_vgg-ssd_1.npy', outputs[1]) + ssd_post_process(outputs[1], outputs[0]) + + rknn.release() diff --git a/rknn-toolkit2/examples/darknet/yolov3_416x416/README.md b/rknn-toolkit2/examples/darknet/yolov3_416x416/README.md new file mode 100644 index 0000000000000000000000000000000000000000..29b2a0c066e24b05ccae69a9b7b7bbbcd371af0f --- /dev/null +++ b/rknn-toolkit2/examples/darknet/yolov3_416x416/README.md @@ -0,0 +1,24 @@ +# Darknet YOLO V3 + +## Model Source +The model used in this example come from the following open source projects: +https://pjreddie.com/darknet/yolo/ +The 'yolov3.cfg' changed the width & height from 608 to 416 on the basis of https://github.com/pjreddie/darknet/blob/master/cfg/yolov3.cfg + +## Script Usage +*Usage:* +``` +python test.py +``` +*rknn_convert usage:* +``` +python3 -m rknn.api.rknn_convert -t rk3568 -i ./model_config.yml -o ./ +``` +*Description:* +- The default target platform in script is 'rk3566', please modify the 'target_platform' parameter of 'rknn.config' according to the actual platform. +- If connecting board is required, please add the 'target' parameter in 'rknn.init_runtime'. + +## Expected Results +This example will save the result of object detection to the 'result.jpg', as follows: +![result](result_truth.jpg) +- Note: Different platforms, different versions of tools and drivers may have slightly different results. \ No newline at end of file diff --git a/rknn-toolkit2/examples/darknet/yolov3_416x416/dataset.txt b/rknn-toolkit2/examples/darknet/yolov3_416x416/dataset.txt new file mode 100644 index 0000000000000000000000000000000000000000..adb6857c5bff8e1d810cf6b0925a00147a193c9b --- /dev/null +++ b/rknn-toolkit2/examples/darknet/yolov3_416x416/dataset.txt @@ -0,0 +1 @@ +dog_bike_car_416x416.jpg diff --git a/rknn-toolkit2/examples/darknet/yolov3_416x416/dog_bike_car_416x416.jpg b/rknn-toolkit2/examples/darknet/yolov3_416x416/dog_bike_car_416x416.jpg new file mode 100644 index 0000000000000000000000000000000000000000..e7231e80a6727f62cdebeb673975c827b55f15cf --- /dev/null +++ b/rknn-toolkit2/examples/darknet/yolov3_416x416/dog_bike_car_416x416.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dd1a3ffbd7d938aac30b64ed523e6512aeee8dc8671abadc2206a0b821903b72 +size 85898 diff --git a/rknn-toolkit2/examples/darknet/yolov3_416x416/model_config.yml b/rknn-toolkit2/examples/darknet/yolov3_416x416/model_config.yml new file mode 100755 index 0000000000000000000000000000000000000000..0bb3c025568af48448620679553927d700d279bf --- /dev/null +++ b/rknn-toolkit2/examples/darknet/yolov3_416x416/model_config.yml @@ -0,0 +1,14 @@ +models: + name: yolov3_416 # 模型输出名称 + platform: darknet # 原始模型使用的框架 + darknet_cfg_path: ./yolov3.cfg # darknet cfg文件 + darknet_weights_path: ./yolov3.weights # darknet weights文件 + quantize: true # 是否量化 + dataset: ./dataset.txt # 量化dataset文件路径（相对yml路径） + configs: + quantized_dtype: asymmetric_quantized-8 # 量化类型 + mean_values: [0, 0, 0] # rknn.config的mean_values参数 + std_values: [255, 255, 255] # rknn.config的std_values参数 + quant_img_RGB2BGR: false # 不进行RGB2BGR转换 + quantized_algorithm: normal # 量化算法 + quantized_method: channel # 量化方法 diff --git a/rknn-toolkit2/examples/darknet/yolov3_416x416/result_truth.jpg b/rknn-toolkit2/examples/darknet/yolov3_416x416/result_truth.jpg new file mode 100644 index 0000000000000000000000000000000000000000..6242d5ce2cca26e8cb552b24bb5d354861c3a7ca --- /dev/null +++ b/rknn-toolkit2/examples/darknet/yolov3_416x416/result_truth.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:47ac9fb7206b0469cbe7244c15b094b2edbb8c1b2573159262c666fe850ca16f +size 90033 diff --git a/rknn-toolkit2/examples/darknet/yolov3_416x416/test.py b/rknn-toolkit2/examples/darknet/yolov3_416x416/test.py new file mode 100755 index 0000000000000000000000000000000000000000..e1560512453c20301c8d37b608fae82016c7b22a --- /dev/null +++ b/rknn-toolkit2/examples/darknet/yolov3_416x416/test.py @@ -0,0 +1,102 @@ +import numpy as np +import cv2 +from rknn.api import RKNN + +from yolov3_utils import yolov3_post_process, draw, download_yolov3_weight + +GRID0 = 13 +GRID1 = 26 +GRID2 = 52 +LISTSIZE = 85 +SPAN = 3 + +if __name__ == '__main__': + + # Model from https://pjreddie.com/darknet/yolo/ + # The yolov3.cfg changed the width & height from 608 to 416 on the basis of + # https://github.com/pjreddie/darknet/blob/master/cfg/yolov3.cfg + MODEL_PATH = './yolov3.cfg' + WEIGHT_PATH = './yolov3.weights' + RKNN_MODEL_PATH = './yolov3_416.rknn' + im_file = './dog_bike_car_416x416.jpg' + DATASET = './dataset.txt' + + # Download yolov3.weight + download_yolov3_weight(WEIGHT_PATH) + + # Create RKNN object + rknn = RKNN(verbose=True) + + # Pre-process config + print('--> Config model') + rknn.config(mean_values=[0, 0, 0], std_values=[255, 255, 255], target_platform='rk3566') + print('done') + + # Load model + print('--> Loading model') + ret = rknn.load_darknet(model=MODEL_PATH, weight=WEIGHT_PATH) + if ret != 0: + print('Load model failed!') + exit(ret) + print('done') + + # Build model + print('--> Building model') + ret = rknn.build(do_quantization=True, dataset=DATASET) + if ret != 0: + print('Build model failed!') + exit(ret) + print('done') + + # Export rknn model + print('--> Export rknn model') + ret = rknn.export_rknn(RKNN_MODEL_PATH) + if ret != 0: + print('Export rknn model failed!') + exit(ret) + print('done') + + # Set inputs + img = cv2.imread(im_file) + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + img = np.expand_dims(img, 0) + + # Init runtime environment + print('--> Init runtime environment') + ret = rknn.init_runtime() + if ret != 0: + print('Init runtime environment failed!') + exit(ret) + print('done') + + # Inference + print('--> Running model') + outputs = rknn.inference(inputs=[img], data_format=['nhwc']) + print('done') + + input0_data = outputs[0] + np.save('./darknet_yolov3_416x416_0.npy', input0_data) + input1_data = outputs[1] + np.save('./darknet_yolov3_416x416_1.npy', input1_data) + input2_data = outputs[2] + np.save('./darknet_yolov3_416x416_2.npy', input1_data) + + input0_data = input0_data.reshape(SPAN, LISTSIZE, GRID0, GRID0) + input1_data = input1_data.reshape(SPAN, LISTSIZE, GRID1, GRID1) + input2_data = input2_data.reshape(SPAN, LISTSIZE, GRID2, GRID2) + + input_data = [] + input_data.append(np.transpose(input0_data, (2, 3, 0, 1))) + input_data.append(np.transpose(input1_data, (2, 3, 0, 1))) + input_data.append(np.transpose(input2_data, (2, 3, 0, 1))) + + boxes, classes, scores = yolov3_post_process(input_data) + + image = cv2.imread(im_file) + if boxes is not None: + draw(image, boxes, scores, classes) + + print('Save results to result.jpg!') + cv2.imwrite('result.jpg', image) + + rknn.release() diff --git a/rknn-toolkit2/examples/darknet/yolov3_416x416/yolov3.cfg b/rknn-toolkit2/examples/darknet/yolov3_416x416/yolov3.cfg new file mode 100755 index 0000000000000000000000000000000000000000..cb4c8695bc2e7584b62f95fed03d1b3e239aeaf8 --- /dev/null +++ b/rknn-toolkit2/examples/darknet/yolov3_416x416/yolov3.cfg @@ -0,0 +1,789 @@ +[net] +# Testing +# batch=1 +# subdivisions=1 +# Training +batch=64 +subdivisions=16 +width=416 +height=416 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500200 +policy=steps +steps=400000,450000 +scales=.1,.1 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +###################### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 6,7,8 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 61 + + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 3,4,5 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 36 + + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 0,1,2 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + diff --git a/rknn-toolkit2/examples/darknet/yolov3_416x416/yolov3_utils.py b/rknn-toolkit2/examples/darknet/yolov3_416x416/yolov3_utils.py new file mode 100755 index 0000000000000000000000000000000000000000..d612e088ecb99b0860f8db267219e19a5040c9de --- /dev/null +++ b/rknn-toolkit2/examples/darknet/yolov3_416x416/yolov3_utils.py @@ -0,0 +1,202 @@ +import numpy as np +import cv2 +import os +import urllib.request + +NUM_CLS = 80 +MAX_BOXES = 500 +OBJ_THRESH = 0.5 +NMS_THRESH = 0.6 + +CLASSES = ("person", "bicycle", "car","motorbike ","aeroplane ","bus ","train","truck ","boat","traffic light", + "fire hydrant","stop sign ","parking meter","bench","bird","cat","dog ","horse ","sheep","cow","elephant", + "bear","zebra ","giraffe","backpack","umbrella","handbag","tie","suitcase","frisbee","skis","snowboard","sports ball","kite", + "baseball bat","baseball glove","skateboard","surfboard","tennis racket","bottle","wine glass","cup","fork","knife ", + "spoon","bowl","banana","apple","sandwich","orange","broccoli","carrot","hot dog","pizza ","donut","cake","chair","sofa", + "pottedplant","bed","diningtable","toilet ","tvmonitor","laptop ","mouse ","remote ","keyboard ","cell phone","microwave ", + "oven ","toaster","sink","refrigerator ","book","clock","vase","scissors ","teddy bear ","hair drier", "toothbrush ") + +def sigmoid(x): + return 1 / (1 + np.exp(-x)) + +def process(input, mask, anchors): + + anchors = [anchors[i] for i in mask] + grid_h, grid_w = map(int, input.shape[0:2]) + + box_confidence = sigmoid(input[..., 4]) + box_confidence = np.expand_dims(box_confidence, axis=-1) + + box_class_probs = sigmoid(input[..., 5:]) + + box_xy = sigmoid(input[..., :2]) + box_wh = np.exp(input[..., 2:4]) + box_wh = box_wh * anchors + + col = np.tile(np.arange(0, grid_w), grid_w).reshape(-1, grid_w) + row = np.tile(np.arange(0, grid_h).reshape(-1, 1), grid_h) + + col = col.reshape(grid_h, grid_w, 1, 1).repeat(3, axis=-2) + row = row.reshape(grid_h, grid_w, 1, 1).repeat(3, axis=-2) + grid = np.concatenate((col, row), axis=-1) + + box_xy += grid + box_xy /= (grid_w, grid_h) + box_wh /= (416, 416) + box_xy -= (box_wh / 2.) + box = np.concatenate((box_xy, box_wh), axis=-1) + + return box, box_confidence, box_class_probs + +def filter_boxes(boxes, box_confidences, box_class_probs): + """Filter boxes with object threshold. + + # Arguments + boxes: ndarray, boxes of objects. + box_confidences: ndarray, confidences of objects. + box_class_probs: ndarray, class_probs of objects. + + # Returns + boxes: ndarray, filtered boxes. + classes: ndarray, classes for boxes. + scores: ndarray, scores for boxes. + """ + box_scores = box_confidences * box_class_probs + box_classes = np.argmax(box_scores, axis=-1) + box_class_scores = np.max(box_scores, axis=-1) + pos = np.where(box_class_scores >= OBJ_THRESH) + + boxes = boxes[pos] + classes = box_classes[pos] + scores = box_class_scores[pos] + + return boxes, classes, scores + +def nms_boxes(boxes, scores): + """Suppress non-maximal boxes. + + # Arguments + boxes: ndarray, boxes of objects. + scores: ndarray, scores of objects. + + # Returns + keep: ndarray, index of effective boxes. + """ + x = boxes[:, 0] + y = boxes[:, 1] + w = boxes[:, 2] + h = boxes[:, 3] + + areas = w * h + order = scores.argsort()[::-1] + + keep = [] + while order.size > 0: + i = order[0] + keep.append(i) + + xx1 = np.maximum(x[i], x[order[1:]]) + yy1 = np.maximum(y[i], y[order[1:]]) + xx2 = np.minimum(x[i] + w[i], x[order[1:]] + w[order[1:]]) + yy2 = np.minimum(y[i] + h[i], y[order[1:]] + h[order[1:]]) + + w1 = np.maximum(0.0, xx2 - xx1 + 0.00001) + h1 = np.maximum(0.0, yy2 - yy1 + 0.00001) + inter = w1 * h1 + + ovr = inter / (areas[i] + areas[order[1:]] - inter) + inds = np.where(ovr <= NMS_THRESH)[0] + order = order[inds + 1] + keep = np.array(keep) + return keep + +def yolov3_post_process(input_data): + # yolov3 + masks = [[6, 7, 8], [3, 4, 5], [0, 1, 2]] + anchors = [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45], + [59, 119], [116, 90], [156, 198], [373, 326]] + # yolov3-tiny + # masks = [[3, 4, 5], [0, 1, 2]] + # anchors = [[10, 14], [23, 27], [37, 58], [81, 82], [135, 169], [344, 319]] + + boxes, classes, scores = [], [], [] + for input,mask in zip(input_data, masks): + b, c, s = process(input, mask, anchors) + b, c, s = filter_boxes(b, c, s) + boxes.append(b) + classes.append(c) + scores.append(s) + + boxes = np.concatenate(boxes) + classes = np.concatenate(classes) + scores = np.concatenate(scores) + + nboxes, nclasses, nscores = [], [], [] + for c in set(classes): + inds = np.where(classes == c) + b = boxes[inds] + c = classes[inds] + s = scores[inds] + + keep = nms_boxes(b, s) + + nboxes.append(b[keep]) + nclasses.append(c[keep]) + nscores.append(s[keep]) + + if not nclasses and not nscores: + return None, None, None + + boxes = np.concatenate(nboxes) + classes = np.concatenate(nclasses) + scores = np.concatenate(nscores) + + return boxes, classes, scores + +def draw(image, boxes, scores, classes): + """Draw the boxes on the image. + + # Argument: + image: original image. + boxes: ndarray, boxes of objects. + classes: ndarray, classes of objects. + scores: ndarray, scores of objects. + all_classes: all classes name. + """ + print("{:^12} {:^12} {}".format('class', 'score', 'xmin, ymin, xmax, ymax')) + print('-' * 50) + for box, score, cl in zip(boxes, scores, classes): + x, y, w, h = box + x *= image.shape[1] + y *= image.shape[0] + w *= image.shape[1] + h *= image.shape[0] + top = max(0, np.floor(x + 0.5).astype(int)) + left = max(0, np.floor(y + 0.5).astype(int)) + right = min(image.shape[1], np.floor(x + w + 0.5).astype(int)) + bottom = min(image.shape[0], np.floor(y + h + 0.5).astype(int)) + + cv2.rectangle(image, (top, left), (right, bottom), (255, 0, 0), 2) + cv2.putText(image, '{0} {1:.2f}'.format(CLASSES[cl], score), + (top, left - 6), + cv2.FONT_HERSHEY_SIMPLEX, + 0.6, (0, 0, 255), 2) + + print("{:^12} {:^12.3f} [{:>4}, {:>4}, {:>4}, {:>4}]".format(CLASSES[cl], score, top, left, right, bottom)) + +def download_yolov3_weight(dst_path): + if os.path.exists(dst_path): + print('yolov3.weight exist.') + return + print('Downloading yolov3.weights...') + url = 'https://pjreddie.com/media/files/yolov3.weights' + try: + urllib.request.urlretrieve(url, dst_path) + except urllib.error.HTTPError as e: + print('HTTPError code: ', e.code) + print('HTTPError reason: ', e.reason) + exit(-1) + except urllib.error.URLError as e: + print('URLError reason: ', e.reason) + else: + print('Download yolov3.weight success.') diff --git a/rknn-toolkit2/examples/functions/accuracy_analysis/README.md b/rknn-toolkit2/examples/functions/accuracy_analysis/README.md new file mode 100644 index 0000000000000000000000000000000000000000..493056647580f82fc8eceae7afc9619f41bc7e5d --- /dev/null +++ b/rknn-toolkit2/examples/functions/accuracy_analysis/README.md @@ -0,0 +1,39 @@ +# How to use accuracy-analysis function + +## Model Source +The model used in this example come from: +https://s3.amazonaws.com/onnx-model-zoo/resnet/resnet50v2/resnet50v2.onnx + +## Script Usage +*Usage:* +``` +python test.py +``` +*Description:* +- The default target platform in script is 'rk3566', please modify the 'target_platform' parameter of 'rknn.config' according to the actual platform. +- If connecting board is required, please add the 'target' parameter in 'rknn.accuracy_analysis'. + +## Expected Results +This example will outputs the results of the accuracy analysis and store all the results in the snapshot directory, as follows: +``` +# simulator_error: calculate the output error of each layer of the simulator (compared to the 'golden' value). +# entire: output error of each layer between 'golden' and 'simulator', these errors will accumulate layer by layer. +# single: single-layer output error between 'golden' and 'simulator', can better reflect the single-layer accuracy of the simulator. + +layer_name simulator_error + entire single + cos euc cos euc +-------------------------------------------------------------------------------------------------- +[Input] data 1.00000 | 0.0 1.00000 | 0.0 +[exDataConvert] data_int8 0.99997 | 2.3275 0.99997 | 2.3275 +[BatchNormalization] resnetv24_batchnorm0_fwd 0.99995 | 2.4514 0.99995 | 2.4514 + +... + +[Relu] resnetv24_relu1_fwd 0.98352 | 41.498 0.99989 | 3.3598 +[Conv] resnetv24_pool1_fwd 0.99545 | 2.2519 0.99999 | 0.1243 +[Conv] resnetv24_dense0_fwd_conv 0.99450 | 6.8046 0.99993 | 0.7382 +[Reshape] resnetv24_dense0_fwd_int8 0.99450 | 6.8046 0.99994 | 0.6719 +[exDataConvert] resnetv24_dense0_fwd 0.99450 | 6.8046 0.99994 | 0.6719 +``` +- Note: Different platforms, different versions of tools and drivers may have slightly different results. \ No newline at end of file diff --git a/rknn-toolkit2/examples/functions/accuracy_analysis/dataset.txt b/rknn-toolkit2/examples/functions/accuracy_analysis/dataset.txt new file mode 100644 index 0000000000000000000000000000000000000000..9078c68db586a0251bdd41282d67f4d256b3abc3 --- /dev/null +++ b/rknn-toolkit2/examples/functions/accuracy_analysis/dataset.txt @@ -0,0 +1 @@ +dog_224x224.jpg diff --git a/rknn-toolkit2/examples/functions/accuracy_analysis/dog_224x224.jpg b/rknn-toolkit2/examples/functions/accuracy_analysis/dog_224x224.jpg new file mode 100644 index 0000000000000000000000000000000000000000..004b0416e23454a12eb06eaba238c7e2d5f2b0fc --- /dev/null +++ b/rknn-toolkit2/examples/functions/accuracy_analysis/dog_224x224.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c350299c6283d5f62fecf1f845b6b3be9aafec8dff528ca09a129990f0a584b0 +size 18889 diff --git a/rknn-toolkit2/examples/functions/accuracy_analysis/labels.txt b/rknn-toolkit2/examples/functions/accuracy_analysis/labels.txt new file mode 100644 index 0000000000000000000000000000000000000000..0993780f99088349e2c156a5a8c57ce1013eb493 --- /dev/null +++ b/rknn-toolkit2/examples/functions/accuracy_analysis/labels.txt @@ -0,0 +1,1000 @@ +0:tench, Tinca tinca +1:goldfish, Carassius auratus +2:great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias +3:tiger shark, Galeocerdo cuvieri +4:hammerhead, hammerhead shark +5:electric ray, crampfish, numbfish, torpedo +6:stingray +7:cock +8:hen +9:ostrich, Struthio camelus +10:brambling, Fringilla montifringilla +11:goldfinch, Carduelis carduelis +12:house finch, linnet, Carpodacus mexicanus +13:junco, snowbird +14:indigo bunting, indigo finch, indigo bird, Passerina cyanea +15:robin, American robin, Turdus migratorius +16:bulbul +17:jay +18:magpie +19:chickadee +20:water ouzel, dipper +21:kite +22:bald eagle, American eagle, Haliaeetus leucocephalus +23:vulture +24:great grey owl, great gray owl, Strix nebulosa +25:European fire salamander, Salamandra salamandra +26:common newt, Triturus vulgaris +27:eft +28:spotted salamander, Ambystoma maculatum +29:axolotl, mud puppy, Ambystoma mexicanum +30:bullfrog, Rana catesbeiana +31:tree frog, tree-frog +32:tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui +33:loggerhead, loggerhead turtle, Caretta caretta +34:leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea +35:mud turtle +36:terrapin +37:box turtle, box tortoise +38:banded gecko +39:common iguana, iguana, Iguana iguana +40:American chameleon, anole, Anolis carolinensis +41:whiptail, whiptail lizard +42:agama +43:frilled lizard, Chlamydosaurus kingi +44:alligator lizard +45:Gila monster, Heloderma suspectum +46:green lizard, Lacerta viridis +47:African chameleon, Chamaeleo chamaeleon +48:Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis +49:African crocodile, Nile crocodile, Crocodylus niloticus +50:American alligator, Alligator mississipiensis +51:triceratops +52:thunder snake, worm snake, Carphophis amoenus +53:ringneck snake, ring-necked snake, ring snake +54:hognose snake, puff adder, sand viper +55:green snake, grass snake +56:king snake, kingsnake +57:garter snake, grass snake +58:water snake +59:vine snake +60:night snake, Hypsiglena torquata +61:boa constrictor, Constrictor constrictor +62:rock python, rock snake, Python sebae +63:Indian cobra, Naja naja +64:green mamba +65:sea snake +66:horned viper, cerastes, sand viper, horned asp, Cerastes cornutus +67:diamondback, diamondback rattlesnake, Crotalus adamanteus +68:sidewinder, horned rattlesnake, Crotalus cerastes +69:trilobite +70:harvestman, daddy longlegs, Phalangium opilio +71:scorpion +72:black and gold garden spider, Argiope aurantia +73:barn spider, Araneus cavaticus +74:garden spider, Aranea diademata +75:black widow, Latrodectus mactans +76:tarantula +77:wolf spider, hunting spider +78:tick +79:centipede +80:black grouse +81:ptarmigan +82:ruffed grouse, partridge, Bonasa umbellus +83:prairie chicken, prairie grouse, prairie fowl +84:peacock +85:quail +86:partridge +87:African grey, African gray, Psittacus erithacus +88:macaw +89:sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita +90:lorikeet +91:coucal +92:bee eater +93:hornbill +94:hummingbird +95:jacamar +96:toucan +97:drake +98:red-breasted merganser, Mergus serrator +99:goose +100:black swan, Cygnus atratus +101:tusker +102:echidna, spiny anteater, anteater +103:platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus +104:wallaby, brush kangaroo +105:koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus +106:wombat +107:jellyfish +108:sea anemone, anemone +109:brain coral +110:flatworm, platyhelminth +111:nematode, nematode worm, roundworm +112:conch +113:snail +114:slug +115:sea slug, nudibranch +116:chiton, coat-of-mail shell, sea cradle, polyplacophore +117:chambered nautilus, pearly nautilus, nautilus +118:Dungeness crab, Cancer magister +119:rock crab, Cancer irroratus +120:fiddler crab +121:king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica +122:American lobster, Northern lobster, Maine lobster, Homarus americanus +123:spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish +124:crayfish, crawfish, crawdad, crawdaddy +125:hermit crab +126:isopod +127:white stork, Ciconia ciconia +128:black stork, Ciconia nigra +129:spoonbill +130:flamingo +131:little blue heron, Egretta caerulea +132:American egret, great white heron, Egretta albus +133:bittern +134:crane +135:limpkin, Aramus pictus +136:European gallinule, Porphyrio porphyrio +137:American coot, marsh hen, mud hen, water hen, Fulica americana +138:bustard +139:ruddy turnstone, Arenaria interpres +140:red-backed sandpiper, dunlin, Erolia alpina +141:redshank, Tringa totanus +142:dowitcher +143:oystercatcher, oyster catcher +144:pelican +145:king penguin, Aptenodytes patagonica +146:albatross, mollymawk +147:grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus +148:killer whale, killer, orca, grampus, sea wolf, Orcinus orca +149:dugong, Dugong dugon +150:sea lion +151:Chihuahua +152:Japanese spaniel +153:Maltese dog, Maltese terrier, Maltese +154:Pekinese, Pekingese, Peke +155:Shih-Tzu +156:Blenheim spaniel +157:papillon +158:toy terrier +159:Rhodesian ridgeback +160:Afghan hound, Afghan +161:basset, basset hound +162:beagle +163:bloodhound, sleuthhound +164:bluetick +165:black-and-tan coonhound +166:Walker hound, Walker foxhound +167:English foxhound +168:redbone +169:borzoi, Russian wolfhound +170:Irish wolfhound +171:Italian greyhound +172:whippet +173:Ibizan hound, Ibizan Podenco +174:Norwegian elkhound, elkhound +175:otterhound, otter hound +176:Saluki, gazelle hound +177:Scottish deerhound, deerhound +178:Weimaraner +179:Staffordshire bullterrier, Staffordshire bull terrier +180:American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier +181:Bedlington terrier +182:Border terrier +183:Kerry blue terrier +184:Irish terrier +185:Norfolk terrier +186:Norwich terrier +187:Yorkshire terrier +188:wire-haired fox terrier +189:Lakeland terrier +190:Sealyham terrier, Sealyham +191:Airedale, Airedale terrier +192:cairn, cairn terrier +193:Australian terrier +194:Dandie Dinmont, Dandie Dinmont terrier +195:Boston bull, Boston terrier +196:miniature schnauzer +197:giant schnauzer +198:standard schnauzer +199:Scotch terrier, Scottish terrier, Scottie +200:Tibetan terrier, chrysanthemum dog +201:silky terrier, Sydney silky +202:soft-coated wheaten terrier +203:West Highland white terrier +204:Lhasa, Lhasa apso +205:flat-coated retriever +206:curly-coated retriever +207:golden retriever +208:Labrador retriever +209:Chesapeake Bay retriever +210:German short-haired pointer +211:vizsla, Hungarian pointer +212:English setter +213:Irish setter, red setter +214:Gordon setter +215:Brittany spaniel +216:clumber, clumber spaniel +217:English springer, English springer spaniel +218:Welsh springer spaniel +219:cocker spaniel, English cocker spaniel, cocker +220:Sussex spaniel +221:Irish water spaniel +222:kuvasz +223:schipperke +224:groenendael +225:malinois +226:briard +227:kelpie +228:komondor +229:Old English sheepdog, bobtail +230:Shetland sheepdog, Shetland sheep dog, Shetland +231:collie +232:Border collie +233:Bouvier des Flandres, Bouviers des Flandres +234:Rottweiler +235:German shepherd, German shepherd dog, German police dog, alsatian +236:Doberman, Doberman pinscher +237:miniature pinscher +238:Greater Swiss Mountain dog +239:Bernese mountain dog +240:Appenzeller +241:EntleBucher +242:boxer +243:bull mastiff +244:Tibetan mastiff +245:French bulldog +246:Great Dane +247:Saint Bernard, St Bernard +248:Eskimo dog, husky +249:malamute, malemute, Alaskan malamute +250:Siberian husky +251:dalmatian, coach dog, carriage dog +252:affenpinscher, monkey pinscher, monkey dog +253:basenji +254:pug, pug-dog +255:Leonberg +256:Newfoundland, Newfoundland dog +257:Great Pyrenees +258:Samoyed, Samoyede +259:Pomeranian +260:chow, chow chow +261:keeshond +262:Brabancon griffon +263:Pembroke, Pembroke Welsh corgi +264:Cardigan, Cardigan Welsh corgi +265:toy poodle +266:miniature poodle +267:standard poodle +268:Mexican hairless +269:timber wolf, grey wolf, gray wolf, Canis lupus +270:white wolf, Arctic wolf, Canis lupus tundrarum +271:red wolf, maned wolf, Canis rufus, Canis niger +272:coyote, prairie wolf, brush wolf, Canis latrans +273:dingo, warrigal, warragal, Canis dingo +274:dhole, Cuon alpinus +275:African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus +276:hyena, hyaena +277:red fox, Vulpes vulpes +278:kit fox, Vulpes macrotis +279:Arctic fox, white fox, Alopex lagopus +280:grey fox, gray fox, Urocyon cinereoargenteus +281:tabby, tabby cat +282:tiger cat +283:Persian cat +284:Siamese cat, Siamese +285:Egyptian cat +286:cougar, puma, catamount, mountain lion, painter, panther, Felis concolor +287:lynx, catamount +288:leopard, Panthera pardus +289:snow leopard, ounce, Panthera uncia +290:jaguar, panther, Panthera onca, Felis onca +291:lion, king of beasts, Panthera leo +292:tiger, Panthera tigris +293:cheetah, chetah, Acinonyx jubatus +294:brown bear, bruin, Ursus arctos +295:American black bear, black bear, Ursus americanus, Euarctos americanus +296:ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus +297:sloth bear, Melursus ursinus, Ursus ursinus +298:mongoose +299:meerkat, mierkat +300:tiger beetle +301:ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle +302:ground beetle, carabid beetle +303:long-horned beetle, longicorn, longicorn beetle +304:leaf beetle, chrysomelid +305:dung beetle +306:rhinoceros beetle +307:weevil +308:fly +309:bee +310:ant, emmet, pismire +311:grasshopper, hopper +312:cricket +313:walking stick, walkingstick, stick insect +314:cockroach, roach +315:mantis, mantid +316:cicada, cicala +317:leafhopper +318:lacewing, lacewing fly +319:dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk +320:damselfly +321:admiral +322:ringlet, ringlet butterfly +323:monarch, monarch butterfly, milkweed butterfly, Danaus plexippus +324:cabbage butterfly +325:sulphur butterfly, sulfur butterfly +326:lycaenid, lycaenid butterfly +327:starfish, sea star +328:sea urchin +329:sea cucumber, holothurian +330:wood rabbit, cottontail, cottontail rabbit +331:hare +332:Angora, Angora rabbit +333:hamster +334:porcupine, hedgehog +335:fox squirrel, eastern fox squirrel, Sciurus niger +336:marmot +337:beaver +338:guinea pig, Cavia cobaya +339:sorrel +340:zebra +341:hog, pig, grunter, squealer, Sus scrofa +342:wild boar, boar, Sus scrofa +343:warthog +344:hippopotamus, hippo, river horse, Hippopotamus amphibius +345:ox +346:water buffalo, water ox, Asiatic buffalo, Bubalus bubalis +347:bison +348:ram, tup +349:bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis +350:ibex, Capra ibex +351:hartebeest +352:impala, Aepyceros melampus +353:gazelle +354:Arabian camel, dromedary, Camelus dromedarius +355:llama +356:weasel +357:mink +358:polecat, fitch, foulmart, foumart, Mustela putorius +359:black-footed ferret, ferret, Mustela nigripes +360:otter +361:skunk, polecat, wood pussy +362:badger +363:armadillo +364:three-toed sloth, ai, Bradypus tridactylus +365:orangutan, orang, orangutang, Pongo pygmaeus +366:gorilla, Gorilla gorilla +367:chimpanzee, chimp, Pan troglodytes +368:gibbon, Hylobates lar +369:siamang, Hylobates syndactylus, Symphalangus syndactylus +370:guenon, guenon monkey +371:patas, hussar monkey, Erythrocebus patas +372:baboon +373:macaque +374:langur +375:colobus, colobus monkey +376:proboscis monkey, Nasalis larvatus +377:marmoset +378:capuchin, ringtail, Cebus capucinus +379:howler monkey, howler +380:titi, titi monkey +381:spider monkey, Ateles geoffroyi +382:squirrel monkey, Saimiri sciureus +383:Madagascar cat, ring-tailed lemur, Lemur catta +384:indri, indris, Indri indri, Indri brevicaudatus +385:Indian elephant, Elephas maximus +386:African elephant, Loxodonta africana +387:lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens +388:giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca +389:barracouta, snoek +390:eel +391:coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch +392:rock beauty, Holocanthus tricolor +393:anemone fish +394:sturgeon +395:gar, garfish, garpike, billfish, Lepisosteus osseus +396:lionfish +397:puffer, pufferfish, blowfish, globefish +398:abacus +399:abaya +400:academic gown, academic robe, judge's robe +401:accordion, piano accordion, squeeze box +402:acoustic guitar +403:aircraft carrier, carrier, flattop, attack aircraft carrier +404:airliner +405:airship, dirigible +406:altar +407:ambulance +408:amphibian, amphibious vehicle +409:analog clock +410:apiary, bee house +411:apron +412:ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin +413:assault rifle, assault gun +414:backpack, back pack, knapsack, packsack, rucksack, haversack +415:bakery, bakeshop, bakehouse +416:balance beam, beam +417:balloon +418:ballpoint, ballpoint pen, ballpen, Biro +419:Band Aid +420:banjo +421:bannister, banister, balustrade, balusters, handrail +422:barbell +423:barber chair +424:barbershop +425:barn +426:barometer +427:barrel, cask +428:barrow, garden cart, lawn cart, wheelbarrow +429:baseball +430:basketball +431:bassinet +432:bassoon +433:bathing cap, swimming cap +434:bath towel +435:bathtub, bathing tub, bath, tub +436:beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon +437:beacon, lighthouse, beacon light, pharos +438:beaker +439:bearskin, busby, shako +440:beer bottle +441:beer glass +442:bell cote, bell cot +443:bib +444:bicycle-built-for-two, tandem bicycle, tandem +445:bikini, two-piece +446:binder, ring-binder +447:binoculars, field glasses, opera glasses +448:birdhouse +449:boathouse +450:bobsled, bobsleigh, bob +451:bolo tie, bolo, bola tie, bola +452:bonnet, poke bonnet +453:bookcase +454:bookshop, bookstore, bookstall +455:bottlecap +456:bow +457:bow tie, bow-tie, bowtie +458:brass, memorial tablet, plaque +459:brassiere, bra, bandeau +460:breakwater, groin, groyne, mole, bulwark, seawall, jetty +461:breastplate, aegis, egis +462:broom +463:bucket, pail +464:buckle +465:bulletproof vest +466:bullet train, bullet +467:butcher shop, meat market +468:cab, hack, taxi, taxicab +469:caldron, cauldron +470:candle, taper, wax light +471:cannon +472:canoe +473:can opener, tin opener +474:cardigan +475:car mirror +476:carousel, carrousel, merry-go-round, roundabout, whirligig +477:carpenter's kit, tool kit +478:carton +479:car wheel +480:cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM +481:cassette +482:cassette player +483:castle +484:catamaran +485:CD player +486:cello, violoncello +487:cellular telephone, cellular phone, cellphone, cell, mobile phone +488:chain +489:chainlink fence +490:chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour +491:chain saw, chainsaw +492:chest +493:chiffonier, commode +494:chime, bell, gong +495:china cabinet, china closet +496:Christmas stocking +497:church, church building +498:cinema, movie theater, movie theatre, movie house, picture palace +499:cleaver, meat cleaver, chopper +500:cliff dwelling +501:cloak +502:clog, geta, patten, sabot +503:cocktail shaker +504:coffee mug +505:coffeepot +506:coil, spiral, volute, whorl, helix +507:combination lock +508:computer keyboard, keypad +509:confectionery, confectionary, candy store +510:container ship, containership, container vessel +511:convertible +512:corkscrew, bottle screw +513:cornet, horn, trumpet, trump +514:cowboy boot +515:cowboy hat, ten-gallon hat +516:cradle +517:crane +518:crash helmet +519:crate +520:crib, cot +521:Crock Pot +522:croquet ball +523:crutch +524:cuirass +525:dam, dike, dyke +526:desk +527:desktop computer +528:dial telephone, dial phone +529:diaper, nappy, napkin +530:digital clock +531:digital watch +532:dining table, board +533:dishrag, dishcloth +534:dishwasher, dish washer, dishwashing machine +535:disk brake, disc brake +536:dock, dockage, docking facility +537:dogsled, dog sled, dog sleigh +538:dome +539:doormat, welcome mat +540:drilling platform, offshore rig +541:drum, membranophone, tympan +542:drumstick +543:dumbbell +544:Dutch oven +545:electric fan, blower +546:electric guitar +547:electric locomotive +548:entertainment center +549:envelope +550:espresso maker +551:face powder +552:feather boa, boa +553:file, file cabinet, filing cabinet +554:fireboat +555:fire engine, fire truck +556:fire screen, fireguard +557:flagpole, flagstaff +558:flute, transverse flute +559:folding chair +560:football helmet +561:forklift +562:fountain +563:fountain pen +564:four-poster +565:freight car +566:French horn, horn +567:frying pan, frypan, skillet +568:fur coat +569:garbage truck, dustcart +570:gasmask, respirator, gas helmet +571:gas pump, gasoline pump, petrol pump, island dispenser +572:goblet +573:go-kart +574:golf ball +575:golfcart, golf cart +576:gondola +577:gong, tam-tam +578:gown +579:grand piano, grand +580:greenhouse, nursery, glasshouse +581:grille, radiator grille +582:grocery store, grocery, food market, market +583:guillotine +584:hair slide +585:hair spray +586:half track +587:hammer +588:hamper +589:hand blower, blow dryer, blow drier, hair dryer, hair drier +590:hand-held computer, hand-held microcomputer +591:handkerchief, hankie, hanky, hankey +592:hard disc, hard disk, fixed disk +593:harmonica, mouth organ, harp, mouth harp +594:harp +595:harvester, reaper +596:hatchet +597:holster +598:home theater, home theatre +599:honeycomb +600:hook, claw +601:hoopskirt, crinoline +602:horizontal bar, high bar +603:horse cart, horse-cart +604:hourglass +605:iPod +606:iron, smoothing iron +607:jack-o'-lantern +608:jean, blue jean, denim +609:jeep, landrover +610:jersey, T-shirt, tee shirt +611:jigsaw puzzle +612:jinrikisha, ricksha, rickshaw +613:joystick +614:kimono +615:knee pad +616:knot +617:lab coat, laboratory coat +618:ladle +619:lampshade, lamp shade +620:laptop, laptop computer +621:lawn mower, mower +622:lens cap, lens cover +623:letter opener, paper knife, paperknife +624:library +625:lifeboat +626:lighter, light, igniter, ignitor +627:limousine, limo +628:liner, ocean liner +629:lipstick, lip rouge +630:Loafer +631:lotion +632:loudspeaker, speaker, speaker unit, loudspeaker system, speaker system +633:loupe, jeweler's loupe +634:lumbermill, sawmill +635:magnetic compass +636:mailbag, postbag +637:mailbox, letter box +638:maillot +639:maillot, tank suit +640:manhole cover +641:maraca +642:marimba, xylophone +643:mask +644:matchstick +645:maypole +646:maze, labyrinth +647:measuring cup +648:medicine chest, medicine cabinet +649:megalith, megalithic structure +650:microphone, mike +651:microwave, microwave oven +652:military uniform +653:milk can +654:minibus +655:miniskirt, mini +656:minivan +657:missile +658:mitten +659:mixing bowl +660:mobile home, manufactured home +661:Model T +662:modem +663:monastery +664:monitor +665:moped +666:mortar +667:mortarboard +668:mosque +669:mosquito net +670:motor scooter, scooter +671:mountain bike, all-terrain bike, off-roader +672:mountain tent +673:mouse, computer mouse +674:mousetrap +675:moving van +676:muzzle +677:nail +678:neck brace +679:necklace +680:nipple +681:notebook, notebook computer +682:obelisk +683:oboe, hautboy, hautbois +684:ocarina, sweet potato +685:odometer, hodometer, mileometer, milometer +686:oil filter +687:organ, pipe organ +688:oscilloscope, scope, cathode-ray oscilloscope, CRO +689:overskirt +690:oxcart +691:oxygen mask +692:packet +693:paddle, boat paddle +694:paddlewheel, paddle wheel +695:padlock +696:paintbrush +697:pajama, pyjama, pj's, jammies +698:palace +699:panpipe, pandean pipe, syrinx +700:paper towel +701:parachute, chute +702:parallel bars, bars +703:park bench +704:parking meter +705:passenger car, coach, carriage +706:patio, terrace +707:pay-phone, pay-station +708:pedestal, plinth, footstall +709:pencil box, pencil case +710:pencil sharpener +711:perfume, essence +712:Petri dish +713:photocopier +714:pick, plectrum, plectron +715:pickelhaube +716:picket fence, paling +717:pickup, pickup truck +718:pier +719:piggy bank, penny bank +720:pill bottle +721:pillow +722:ping-pong ball +723:pinwheel +724:pirate, pirate ship +725:pitcher, ewer +726:plane, carpenter's plane, woodworking plane +727:planetarium +728:plastic bag +729:plate rack +730:plow, plough +731:plunger, plumber's helper +732:Polaroid camera, Polaroid Land camera +733:pole +734:police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria +735:poncho +736:pool table, billiard table, snooker table +737:pop bottle, soda bottle +738:pot, flowerpot +739:potter's wheel +740:power drill +741:prayer rug, prayer mat +742:printer +743:prison, prison house +744:projectile, missile +745:projector +746:puck, hockey puck +747:punching bag, punch bag, punching ball, punchball +748:purse +749:quill, quill pen +750:quilt, comforter, comfort, puff +751:racer, race car, racing car +752:racket, racquet +753:radiator +754:radio, wireless +755:radio telescope, radio reflector +756:rain barrel +757:recreational vehicle, RV, R.V. +758:reel +759:reflex camera +760:refrigerator, icebox +761:remote control, remote +762:restaurant, eating house, eating place, eatery +763:revolver, six-gun, six-shooter +764:rifle +765:rocking chair, rocker +766:rotisserie +767:rubber eraser, rubber, pencil eraser +768:rugby ball +769:rule, ruler +770:running shoe +771:safe +772:safety pin +773:saltshaker, salt shaker +774:sandal +775:sarong +776:sax, saxophone +777:scabbard +778:scale, weighing machine +779:school bus +780:schooner +781:scoreboard +782:screen, CRT screen +783:screw +784:screwdriver +785:seat belt, seatbelt +786:sewing machine +787:shield, buckler +788:shoe shop, shoe-shop, shoe store +789:shoji +790:shopping basket +791:shopping cart +792:shovel +793:shower cap +794:shower curtain +795:ski +796:ski mask +797:sleeping bag +798:slide rule, slipstick +799:sliding door +800:slot, one-armed bandit +801:snorkel +802:snowmobile +803:snowplow, snowplough +804:soap dispenser +805:soccer ball +806:sock +807:solar dish, solar collector, solar furnace +808:sombrero +809:soup bowl +810:space bar +811:space heater +812:space shuttle +813:spatula +814:speedboat +815:spider web, spider's web +816:spindle +817:sports car, sport car +818:spotlight, spot +819:stage +820:steam locomotive +821:steel arch bridge +822:steel drum +823:stethoscope +824:stole +825:stone wall +826:stopwatch, stop watch +827:stove +828:strainer +829:streetcar, tram, tramcar, trolley, trolley car +830:stretcher +831:studio couch, day bed +832:stupa, tope +833:submarine, pigboat, sub, U-boat +834:suit, suit of clothes +835:sundial +836:sunglass +837:sunglasses, dark glasses, shades +838:sunscreen, sunblock, sun blocker +839:suspension bridge +840:swab, swob, mop +841:sweatshirt +842:swimming trunks, bathing trunks +843:swing +844:switch, electric switch, electrical switch +845:syringe +846:table lamp +847:tank, army tank, armored combat vehicle, armoured combat vehicle +848:tape player +849:teapot +850:teddy, teddy bear +851:television, television system +852:tennis ball +853:thatch, thatched roof +854:theater curtain, theatre curtain +855:thimble +856:thresher, thrasher, threshing machine +857:throne +858:tile roof +859:toaster +860:tobacco shop, tobacconist shop, tobacconist +861:toilet seat +862:torch +863:totem pole +864:tow truck, tow car, wrecker +865:toyshop +866:tractor +867:trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi +868:tray +869:trench coat +870:tricycle, trike, velocipede +871:trimaran +872:tripod +873:triumphal arch +874:trolleybus, trolley coach, trackless trolley +875:trombone +876:tub, vat +877:turnstile +878:typewriter keyboard +879:umbrella +880:unicycle, monocycle +881:upright, upright piano +882:vacuum, vacuum cleaner +883:vase +884:vault +885:velvet +886:vending machine +887:vestment +888:viaduct +889:violin, fiddle +890:volleyball +891:waffle iron +892:wall clock +893:wallet, billfold, notecase, pocketbook +894:wardrobe, closet, press +895:warplane, military plane +896:washbasin, handbasin, washbowl, lavabo, wash-hand basin +897:washer, automatic washer, washing machine +898:water bottle +899:water jug +900:water tower +901:whiskey jug +902:whistle +903:wig +904:window screen +905:window shade +906:Windsor tie +907:wine bottle +908:wing +909:wok +910:wooden spoon +911:wool, woolen, woollen +912:worm fence, snake fence, snake-rail fence, Virginia fence +913:wreck +914:yawl +915:yurt +916:web site, website, internet site, site +917:comic book +918:crossword puzzle, crossword +919:street sign +920:traffic light, traffic signal, stoplight +921:book jacket, dust cover, dust jacket, dust wrapper +922:menu +923:plate +924:guacamole +925:consomme +926:hot pot, hotpot +927:trifle +928:ice cream, icecream +929:ice lolly, lolly, lollipop, popsicle +930:French loaf +931:bagel, beigel +932:pretzel +933:cheeseburger +934:hotdog, hot dog, red hot +935:mashed potato +936:head cabbage +937:broccoli +938:cauliflower +939:zucchini, courgette +940:spaghetti squash +941:acorn squash +942:butternut squash +943:cucumber, cuke +944:artichoke, globe artichoke +945:bell pepper +946:cardoon +947:mushroom +948:Granny Smith +949:strawberry +950:orange +951:lemon +952:fig +953:pineapple, ananas +954:banana +955:jackfruit, jak, jack +956:custard apple +957:pomegranate +958:hay +959:carbonara +960:chocolate sauce, chocolate syrup +961:dough +962:meat loaf, meatloaf +963:pizza, pizza pie +964:potpie +965:burrito +966:red wine +967:espresso +968:cup +969:eggnog +970:alp +971:bubble +972:cliff, drop, drop-off +973:coral reef +974:geyser +975:lakeside, lakeshore +976:promontory, headland, head, foreland +977:sandbar, sand bar +978:seashore, coast, seacoast, sea-coast +979:valley, vale +980:volcano +981:ballplayer, baseball player +982:groom, bridegroom +983:scuba diver +984:rapeseed +985:daisy +986:yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum +987:corn +988:acorn +989:hip, rose hip, rosehip +990:buckeye, horse chestnut, conker +991:coral fungus +992:agaric +993:gyromitra +994:stinkhorn, carrion fungus +995:earthstar +996:hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa +997:bolete +998:ear, spike, capitulum +999:toilet tissue, toilet paper, bathroom tissue \ No newline at end of file diff --git a/rknn-toolkit2/examples/functions/accuracy_analysis/test.py b/rknn-toolkit2/examples/functions/accuracy_analysis/test.py new file mode 100755 index 0000000000000000000000000000000000000000..10e008bd4060cd817bf7e76daf2aac59110d9a45 --- /dev/null +++ b/rknn-toolkit2/examples/functions/accuracy_analysis/test.py @@ -0,0 +1,120 @@ +import os +import urllib +import traceback +import time +import sys +import numpy as np +import cv2 +from rknn.api import RKNN +import urllib.request + +ONNX_MODEL = 'resnet50v2.onnx' +RKNN_MODEL = 'resnet50v2.rknn' + + +def show_outputs(outputs): + output = outputs + index = sorted(range(len(output)), key=lambda k : output[k], reverse=True) + fp = open('./labels.txt', 'r') + labels = fp.readlines() + top5_str = 'resnet50v2\n-----TOP 5-----\n' + for i in range(5): + value = output[index[i]] + if value > 0: + topi = '[{:>3d}] score:{:.6f} class:"{}"\n'.format(index[i], value, labels[index[i]].strip().split(':')[-1]) + else: + topi = '[ -1]: 0.0\n' + top5_str += topi + print(top5_str.strip()) + +def softmax(output): + return np.exp(output)/np.sum(np.exp(output)) + +def readable_speed(speed): + speed_bytes = float(speed) + speed_kbytes = speed_bytes / 1024 + if speed_kbytes > 1024: + speed_mbytes = speed_kbytes / 1024 + if speed_mbytes > 1024: + speed_gbytes = speed_mbytes / 1024 + return "{:.2f} GB/s".format(speed_gbytes) + else: + return "{:.2f} MB/s".format(speed_mbytes) + else: + return "{:.2f} KB/s".format(speed_kbytes) + + +def show_progress(blocknum, blocksize, totalsize): + speed = (blocknum * blocksize) / (time.time() - start_time) + speed_str = " Speed: {}".format(readable_speed(speed)) + recv_size = blocknum * blocksize + + f = sys.stdout + progress = (recv_size / totalsize) + progress_str = "{:.2f}%".format(progress * 100) + n = round(progress * 50) + s = ('#' * n).ljust(50, '-') + f.write(progress_str.ljust(8, ' ') + '[' + s + ']' + speed_str) + f.flush() + f.write('\r\n') + + +if __name__ == '__main__': + + # Create RKNN object + rknn = RKNN(verbose=True) + + # If resnet50v2 does not exist, download it. + # Download address: + # https://s3.amazonaws.com/onnx-model-zoo/resnet/resnet50v2/resnet50v2.onnx + if not os.path.exists(ONNX_MODEL): + print('--> Download {}'.format(ONNX_MODEL)) + url = 'https://s3.amazonaws.com/onnx-model-zoo/resnet/resnet50v2/resnet50v2.onnx' + download_file = ONNX_MODEL + try: + start_time = time.time() + urllib.request.urlretrieve(url, download_file, show_progress) + except: + print('Download {} failed.'.format(download_file)) + print(traceback.format_exc()) + exit(-1) + print('done') + + # Pre-process config + print('--> Config model') + rknn.config(mean_values=[123.68, 116.28, 103.53], std_values=[57.38, 57.38, 57.38], target_platform='rk3566') + print('done') + + # Load model + print('--> Loading model') + ret = rknn.load_onnx(model=ONNX_MODEL) + if ret != 0: + print('Load model failed!') + exit(ret) + print('done') + + # Build model + print('--> Building model') + ret = rknn.build(do_quantization=True, dataset='./dataset.txt') + if ret != 0: + print('Build model failed!') + exit(ret) + print('done') + + # Accuracy analysis + print('--> Accuracy analysis') + ret = rknn.accuracy_analysis(inputs=['./dog_224x224.jpg'], output_dir='./snapshot') + if ret != 0: + print('Accuracy analysis failed!') + exit(ret) + print('done') + + print('float32:') + output = np.genfromtxt('./snapshot/golden/resnetv24_dense0_fwd.txt') + show_outputs(softmax(output)) + + print('quantized:') + output = np.genfromtxt('./snapshot/simulator/resnetv24_dense0_fwd.txt') + show_outputs(softmax(output)) + + rknn.release() diff --git a/rknn-toolkit2/examples/functions/codegen/README.md b/rknn-toolkit2/examples/functions/codegen/README.md new file mode 100644 index 0000000000000000000000000000000000000000..bf462bb1c1c8f3b7eacc937063027f290b91ae91 --- /dev/null +++ b/rknn-toolkit2/examples/functions/codegen/README.md @@ -0,0 +1,25 @@ +## Codegen + +### 1.Introduction + +RKNN-Toolkit2 support using `rknn.codegen` to generate cpp deploy demo. + + + +### 2.How to use + +1.After "**export_rknn**", calling "**codegen**" interface to generate cpp demo as followed: + +```python +rknn.codegen(output_path='./rknn_app_demo', inputs=['./dog_224x224.jpg'], overwrite=False) +``` + +- If overwrite set False and './deploy_demo' already exists, skip generation. +- Inputs feed a list of input file. File could be **jpg/png/bmp/npy**. + + + +2.After calling "**codegen**", a folder named "**./rknn_app_demo**" should be created. Then follow the introduction of "**./rknn_app_demo/README.md**" to execute the demo. + + + diff --git a/rknn-toolkit2/examples/functions/codegen/test.py b/rknn-toolkit2/examples/functions/codegen/test.py new file mode 100755 index 0000000000000000000000000000000000000000..06a0390c54bfc0198b1b0dbfb17ac923e0628e76 --- /dev/null +++ b/rknn-toolkit2/examples/functions/codegen/test.py @@ -0,0 +1,64 @@ +import numpy as np +from rknn.api import RKNN + + +def show_outputs(outputs): + np.save('./caffe_mobilenet_v2_0.npy', outputs[0]) + output = outputs[0].reshape(-1) + index = sorted(range(len(output)), key=lambda k : output[k], reverse=True) + fp = open('./labels.txt', 'r') + labels = fp.readlines() + top5_str = 'mobilenet_v2\n-----TOP 5-----\n' + for i in range(5): + value = output[index[i]] + if value > 0: + topi = '[{:>3d}] score:{:.6f} class:"{}"\n'.format(index[i], value, labels[index[i]].strip().split(':')[-1]) + else: + topi = '[ -1]: 0.0\n' + top5_str += topi + print(top5_str.strip()) + + +if __name__ == '__main__': + + # Create RKNN object + rknn = RKNN(verbose=False) + + # Pre-process config + print('--> Config model') + rknn.config(mean_values=[103.94, 116.78, 123.68], std_values=[58.82, 58.82, 58.82], quant_img_RGB2BGR=True, target_platform='rk3562') + print('done') + + # Load model + print('--> Loading model') + ret = rknn.load_caffe(model='../../caffe/mobilenet_v2/mobilenet_v2_deploy.prototxt', + blobs='../../caffe/mobilenet_v2/mobilenet_v2.caffemodel') + if ret != 0: + print('Load model failed!') + exit(ret) + print('done') + + # Build model + print('--> Building model') + ret = rknn.build(do_quantization=True, dataset='../../caffe/mobilenet_v2/dataset.txt') + if ret != 0: + print('Build model failed!') + exit(ret) + print('done') + + # Export rknn model + print('--> Export rknn model') + ret = rknn.export_rknn('./mobilenet_v2.rknn') + if ret != 0: + print('Export rknn model failed!') + exit(ret) + print('done') + + print('--> Generate cpp demo') + ret = rknn.codegen(output_path='./rknn_app_demo', inputs=['../../caffe/mobilenet_v2/dog_224x224.jpg'], overwrite=True) + if ret != 0: + print('Generate cpp demo failed!') + exit(ret) + print('done') + + rknn.release() diff --git a/rknn-toolkit2/examples/functions/custom_op/convert_custom_onnx_to_rknn/dual_residual.onnx b/rknn-toolkit2/examples/functions/custom_op/convert_custom_onnx_to_rknn/dual_residual.onnx new file mode 100644 index 0000000000000000000000000000000000000000..98222e21a73ecde75fc1c4142c65a64f666611a1 --- /dev/null +++ b/rknn-toolkit2/examples/functions/custom_op/convert_custom_onnx_to_rknn/dual_residual.onnx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8c117a54d9166307face0cfcb34e31c3d66c8e5cfcb93ae82f03657bd519c6ba +size 425 diff --git a/rknn-toolkit2/examples/functions/custom_op/convert_custom_onnx_to_rknn/dual_residual_input_0.npy b/rknn-toolkit2/examples/functions/custom_op/convert_custom_onnx_to_rknn/dual_residual_input_0.npy new file mode 100644 index 0000000000000000000000000000000000000000..e5ea5b74acc1dc79cf21e17aa2a95deadfd29261 --- /dev/null +++ b/rknn-toolkit2/examples/functions/custom_op/convert_custom_onnx_to_rknn/dual_residual_input_0.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d406c365fe11c3c5bfaf0b97fcab0b28285ff3f7f1aef40abcdbab75e647a57f +size 1328 diff --git a/rknn-toolkit2/examples/functions/custom_op/convert_custom_onnx_to_rknn/dual_residual_input_1.npy b/rknn-toolkit2/examples/functions/custom_op/convert_custom_onnx_to_rknn/dual_residual_input_1.npy new file mode 100644 index 0000000000000000000000000000000000000000..9ce97e44f947119b5bbf4ef9fb7be227ff21e3dc --- /dev/null +++ b/rknn-toolkit2/examples/functions/custom_op/convert_custom_onnx_to_rknn/dual_residual_input_1.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c2fc49f22db3826bbc907ec912b0d4bedd39237f718bc9a5478d22e96f8b2a4f +size 1328 diff --git a/rknn-toolkit2/examples/functions/custom_op/convert_custom_onnx_to_rknn/dual_residual_output_0.npy b/rknn-toolkit2/examples/functions/custom_op/convert_custom_onnx_to_rknn/dual_residual_output_0.npy new file mode 100644 index 0000000000000000000000000000000000000000..60439b6f4a96f22d221c4533e572dc6f72ed329c --- /dev/null +++ b/rknn-toolkit2/examples/functions/custom_op/convert_custom_onnx_to_rknn/dual_residual_output_0.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:248263542ee11f6df0cd8f85641685ade840eb21df41b2a9d39bab5e4c6a985c +size 1328 diff --git a/rknn-toolkit2/examples/functions/custom_op/convert_custom_onnx_to_rknn/dual_residual_output_1.npy b/rknn-toolkit2/examples/functions/custom_op/convert_custom_onnx_to_rknn/dual_residual_output_1.npy new file mode 100644 index 0000000000000000000000000000000000000000..2343127a3a32f0c51b9cb3036972fada8a6656c4 --- /dev/null +++ b/rknn-toolkit2/examples/functions/custom_op/convert_custom_onnx_to_rknn/dual_residual_output_1.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:40a42add9ad6c8f6057b913cc560441bac434e9b117adc92c61900a89d6aa244 +size 1328 diff --git a/rknn-toolkit2/examples/functions/custom_op/convert_custom_onnx_to_rknn/test.py b/rknn-toolkit2/examples/functions/custom_op/convert_custom_onnx_to_rknn/test.py new file mode 100644 index 0000000000000000000000000000000000000000..28ce1b8a50f3bb759f074c3abebca56a77047a95 --- /dev/null +++ b/rknn-toolkit2/examples/functions/custom_op/convert_custom_onnx_to_rknn/test.py @@ -0,0 +1,91 @@ +import numpy as np +from rknn.api import RKNN +from rknn.api.custom_op import get_node_attr +from scipy.spatial import distance + + +class cstDualResidual: + # custom operator cstDualResidual + op_type = 'cstDualResidual' + def shape_infer(self, node, in_shapes, in_dtypes): + return in_shapes.copy(), in_dtypes.copy() + def compute(self, node, inputs): + x = inputs[0] + y = inputs[1] + alpha = get_node_attr(node, 'alpha') + tmp_1 = x*alpha - y + tmp_2 = y*alpha - x + return [tmp_1, tmp_2] + + +if __name__ == '__main__': + + custom_model_path = 'dual_residual.onnx' + + # Create RKNN object + rknn = RKNN(verbose=True) + + # Pre-process config + print('--> Config model') + rknn.config(target_platform='rk3588') + print('done') + + # Register cstSigmoid op + print('--> Register cstDualResidual op') + ret = rknn.reg_custom_op(cstDualResidual()) + if ret != 0: + print('Register cstDualResidual op failed!') + exit(ret) + print('done') + + # Load model + print('--> Loading model') + ret = rknn.load_onnx(model=custom_model_path) + if ret != 0: + print('Load model failed!') + exit(ret) + print('done') + + # Build model + print('--> Building model') + ret = rknn.build(do_quantization=False) + if ret != 0: + print('Build model failed!') + exit(ret) + print('done') + + # Export rknn model + print('--> Export rknn model') + ret = rknn.export_rknn('dual_residual_custom.rknn') + if ret != 0: + print('Export rknn model failed!') + exit(ret) + print('done') + + # Init runtime + print('--> Init runtime') + ret = rknn.init_runtime() + if ret != 0: + print('Init runtime failed!') + exit(ret) + print('done') + + in_data_0 = np.load('dual_residual_input_0.npy') + in_data_1 = np.load('dual_residual_input_1.npy') + + in_data_0 = np.transpose(in_data_0, (0,2,3,1)) + in_data_1 = np.transpose(in_data_1, (0,2,3,1)) + + outputs = rknn.inference(inputs=[in_data_0, in_data_1]) + + # verifying + golden_data_0 = np.load('dual_residual_output_0.npy').astype(np.float32).ravel() + golden_data_1 = np.load('dual_residual_output_1.npy').astype(np.float32).ravel() + + cosine_dst = 1 - distance.cosine(outputs[0].astype(np.float32).ravel(), golden_data_0) + print(f"cos distance for 0th output: {cosine_dst:.5f}") + cosine_dst = 1 - distance.cosine(outputs[1].astype(np.float32).ravel(), golden_data_1) + print(f"cos distance for 1th output: {cosine_dst:.5f}") + + # Release + rknn.release() diff --git a/rknn-toolkit2/examples/functions/custom_op/gen_custom_onnx_from_pytorch/README.md b/rknn-toolkit2/examples/functions/custom_op/gen_custom_onnx_from_pytorch/README.md new file mode 100644 index 0000000000000000000000000000000000000000..826222246c90ae9f19da1b52dc5b3b97427eaeff --- /dev/null +++ b/rknn-toolkit2/examples/functions/custom_op/gen_custom_onnx_from_pytorch/README.md @@ -0,0 +1,24 @@ +# 基于 Pytorch 导出带 custom op onnx 模型 + +本示例主要说明如何在 Pytorch 的框架下，导出带有自定义op的ONNX模型 + + + +## 适用场景 + +1.解决Pytorch op无法转成 onnx op的问题，例如 torch.nn.threshold 算子 + +2.为了性能，将多个零碎的Pytorch op转成单个 onnx op，并使用 neon/gpu 构建代码实现 + +3.将任意函数转为单个 onnx op，例如将后处理转为 op + + + +## 原理介绍 + +使用 torch.onnx.export 导出 onnx 模型时，会先对 model 做一次 torch.jit.trace 操作，转成 jit 模型，随后对 jit 格式定义的 op 进行映射，转成 onnx 模型。故实现自定义算子，存在以下两种情况: + +- **自定义 onnx op**。当 pytorch 已有的算子不支持转到 onnx，比如 torch.nn.threshold 算子，我们可以采取这种方式，此时只涉及 jit model -> onnx model 的链路，比较简单。请参考[文档](./register_onnx_symbolic/README.md) + +- **同时自定义 jit op 以及 onnx op**。正常情况下，由于 pytorch 算子的完备性很高，复杂操作都可以被拆成零碎的底层 op 进行支持，几乎不存在需要自己自定义 jit op 的情况。但缺点是模型有可能包含了非常多零碎的 jit op定义，性能较差，若想把某几个 jit op 合并成一个，并转为一个单一的 onnx op，请参考[文档](./register_pytorch_op/README.md) + diff --git a/rknn-toolkit2/examples/functions/custom_op/gen_custom_onnx_from_pytorch/README_EN.md b/rknn-toolkit2/examples/functions/custom_op/gen_custom_onnx_from_pytorch/README_EN.md new file mode 100644 index 0000000000000000000000000000000000000000..7fc67e7a3cbe640945bb5ef485babcd9a15da6d5 --- /dev/null +++ b/rknn-toolkit2/examples/functions/custom_op/gen_custom_onnx_from_pytorch/README_EN.md @@ -0,0 +1,24 @@ +# Export custom onnx based on Pytorch + +This example mainly explains how to export an ONNX model with a custom op under the Pytorch framework. + + + +## Applicable scene + +1. Solve the problem that Pytorch op cannot be converted into onnx op, such as torch.nn.threshold operator + +2. For performance, convert multiple fragmented Pytorch ops into a single onnx op, and use neon/gpu to build the code implementation + +3. Convert any function into a single onnx op, such as converting post-processing into an onnx op + + + +## Introduction + +When using torch.onnx.export to export an onnx model, a torch.jit.trace operation will be performed on the model first to convert it into a jit model, and then the op defined in the jit format will be mapped and converted into an onnx model. Therefore, there are two situations when implementing a custom operator: + +- **Custom onnx op**. When the existing operators of pytorch do not support switching to onnx, such as the torch.nn.threshold operator, we can adopt this method. At this time, it only involves the process of converting jit model to onnx model, which is relatively simple. Please refer to [doc](./register_onnx_symbolic/README_EN.md) + +- **Custom jit op and onnx op both**. Under normal circumstances, due to the high completeness of pytorch operators, complex operations can be split into fragmented underlying ops for support. However, the disadvantage is that the model may contain a lot of fragmented jit op definitions, resulting in poor performance. If you want to merge several jit ops into one and convert them into a single onnx op, please refer to [doc](./register_pytorch_op/README_EN.md) + diff --git a/rknn-toolkit2/examples/functions/custom_op/gen_custom_onnx_from_pytorch/register_onnx_symbolic/README.md b/rknn-toolkit2/examples/functions/custom_op/gen_custom_onnx_from_pytorch/register_onnx_symbolic/README.md new file mode 100644 index 0000000000000000000000000000000000000000..dd206d94dfa16271f3822f95c247209b47d71084 --- /dev/null +++ b/rknn-toolkit2/examples/functions/custom_op/gen_custom_onnx_from_pytorch/register_onnx_symbolic/README.md @@ -0,0 +1,139 @@ +# Register onnx symbolic + +本教程基于 pytorch-2.2.0，torchvision-0.16.0 版本进行测试验证。使用不同的版本可能会有差异。 + + + +## 1.使用场景 + +当 pytorch 已有算子转 onnx 失败时，例如 torch.nn.threshold(https://pytorch.org/docs/stable/generated/torch.nn.Threshold.html#torch.nn.Threshold)，可以参考本例程，为 pytorch op 添加转 onnx 的支持。 + +若需要自定义 pytorch op，请参考[文档](../register_pytorch_op/README.md)。 + + + +## 2.执行例子 + +``` +python test.py +``` + + + +## 3. 代码解释 + +当模型存在 torch.nn.Threshold 算子，尝试导出onnx模型时会报错，具体情况如下: + +```python +import torch + +class Model(torch.nn.Module): + def __init__(self, *args, **kwargs) -> None: + super().__init__(*args, **kwargs) + self.m = torch.nn.Threshold(0.1, 20) + + def forward(self, x): + y = self.m(x) + return y + +#导出 onnx 模型 +x = torch.randn(1, 3, 10, 10) +m = Model() +torch.onnx.export(m, (x,), "threshold.onnx", opset_version=12) +``` + +此时会碰到报错: + +```python +raise errors.SymbolicValueError( +torch.onnx.errors.SymbolicValueError: Unsupported: ONNX export of operator threshold, non-zero threshold. Please feel free to request support or submit a pull request on PyTorch GitHub: https://github.com/pytorch/pytorch/issues +``` + + + +可以看到当前 torch2onnx 不支持 **threshold** 算子到 onnx 导出，导致转模型失败。对于这种情况，可以在 torch.onnx.export 前，注册 onnx 的导出规则。 + +- 首先我们找到 **torch.nn.threshold** 的输入定义，后续需要使用 parse_args 为每个输入配置对应的数据类型。 + +```python +# 在 torch.nn.functional.py 文件可以找到 threshold 的输入定义 +result = _VF.threshold(input, threshold, value) +return result +``` + +- 定义一个 op 映射函数(对应下面代码中的 threshold_symbolic)，构建 pytorch、onnx 算子输入的对应关系，以下代码分别展示了三种注册方式，以及对应的区别 + +```python +from torch.onnx.symbolic_helper import parse_args + +''' +需要注意，该op映射函数必须使用 parse_args 进行修饰，修饰每一个输入的数据类型，根据torch的代码，有以下定义: +Args: + arg_descriptors: list of str, where each element is + a string that specifies the type to convert to. Valid descriptors: + "v": no conversion, keep torch._C.Value. + "i": int + "is": list of int + "f": float + "fs": list of float + "b": bool + "s": str + "t": torch.Tensor + "none": the variable is unused +''' + +''' +自定义op的命名规范为: <生成组织>:<自定义op的名称>，例如以下例子是 rknn_cst::cst_threshold +''' + +''' +例子一 +不需要记录op属性时，可以忽略这些输入，在 g.op 函数中只记录 tensor输入 +''' +@parse_args('v', 'f', 'f') +def threshold_symbolic(g, x, threshold, value): + output = g.op("rknn_cst::cstThreshold", x) + return output + + +''' +例子二 +当需要记录op属性时，必须在 g.op 中声明，声明方式为添加参数关键字，请注意参数关键字的结尾需要用 _{type} 声明数据类型，这里由于每一个参数都是浮点型，故末尾加上 _f 的数据类型声明 +''' +@parse_args('v', 'f', 'f') +def threshold_symbolic(g, x, threshold, value): + output = g.op("rknn_cst::cstThreshold", x, threshold_f=threshold, value_f=value) + return output + +''' +例子三 +若想要将属性记录为list，而不是单值，可采用下面方式 +''' +@parse_args('v', 'f', 'f') +def threshold_symbolic(g, x, threshold, value): + output = g.op("rknn_cst::cstThreshold", x, param_f=[threshold,value]) + return output +``` + +- 声明以上函数后，将此函数注册到 torch2onnx 功能 + +```python +register_custom_op_symbolic("aten::threshold", threshold_symbolic, 12) +#第一个参数，"aten::threshold" ，必须和pytorch已有的定义匹配上，不可以随意变更 +#第二个参数是前面定义的op映射函数 +#第三个参数是配置的 onnx-opset 版本，目前可不管，填入常用的 opset version 即可 +``` + + + +## 4.拓展 + +目前 torch2onnx 的自定义功能，不仅支持定义单个 torch op 到 单个 onnx op 的映射，理论上也支持下面形式 + +- 覆盖已有的 torch2onnx 导出规则 +- 导出带有子图形式的自定义op + +由于使用场景较少，该教程暂不提供这类复杂用法，若有需要请参考 onnx 的官方文档自行实现 + + + diff --git a/rknn-toolkit2/examples/functions/custom_op/gen_custom_onnx_from_pytorch/register_onnx_symbolic/README_EN.md b/rknn-toolkit2/examples/functions/custom_op/gen_custom_onnx_from_pytorch/register_onnx_symbolic/README_EN.md new file mode 100644 index 0000000000000000000000000000000000000000..ecb192b5f000094ccbc71b85b959f492786e85f2 --- /dev/null +++ b/rknn-toolkit2/examples/functions/custom_op/gen_custom_onnx_from_pytorch/register_onnx_symbolic/README_EN.md @@ -0,0 +1,138 @@ +# Register onnx symbolic + +This tutorial is based on pytorch-2.2.0 and torchvision-0.16.0 versions for testing and verification. There may be differences using different versions. + + + +## 1.Usage scenarios + +When pytorch fails to convert existing operators to onnx, such as torch.nn.threshold (https://pytorch.org/docs/stable/generated/torch.nn.Threshold.html#torch.nn.Threshold), you can refer to this tutorial, adds support for converting pytorch op to onnx op. + +If you need to customize pytorch op, please refer to [doc](../register_pytorch_op/README_EN.md). + + + +## 2.Usage + +``` +python test.py +``` + + + +## 3.Code explanation + +When the model has the torch.nn.Threshold operator, an error will be reported when trying to export the onnx model. The specific situation is as follows: + +```python +import torch + +class Model(torch.nn.Module): + def __init__(self, *args, **kwargs) -> None: + super().__init__(*args, **kwargs) + self.m = torch.nn.Threshold(0.1, 20) + + def forward(self, x): + y = self.m(x) + return y + +x = torch.randn(1, 3, 10, 10) +m = Model() +torch.onnx.export(m, (x,), "threshold.onnx", opset_version=12) +``` + +At this time we encounter an error: + +```python +raise errors.SymbolicValueError( +torch.onnx.errors.SymbolicValueError: Unsupported: ONNX export of operator threshold, non-zero threshold. Please feel free to request support or submit a pull request on PyTorch GitHub: https://github.com/pytorch/pytorch/issues +``` + + + +It can be seen that currently torch2onnx does not support the export of the **threshold** operator to onnx, causing the model transfer to fail. For this case, you can register the export rules of onnx before torch.onnx.export. + +- First we find the input definition of **torch.nn.threshold**, and then we need to use parse_args to configure the corresponding data type for each input. + +```python +# The input definition of threshold can be found in the torch.nn.functional.py +result = _VF.threshold(input, threshold, value) +return result +``` + +- Define an op mapping function (corresponding to threshold_symbolic in the code below) and construct the corresponding relationship between pytorch and onnx operator input. The following code shows the three registration methods and the corresponding differences. + +```python +from torch.onnx.symbolic_helper import parse_args + +''' +It should be noted that the op mapping function must be modified with parse_args to modify each input data type. According to the torch code, it has the following definition: +Args: + arg_descriptors: list of str, where each element is + a string that specifies the type to convert to. Valid descriptors: + "v": no conversion, keep torch._C.Value. + "i": int + "is": list of int + "f": float + "fs": list of float + "b": bool + "s": str + "t": torch.Tensor + "none": the variable is unused +''' + +''' +The naming convention for custom ops is: :, for example, the following example is rknn_cst::cst_threshold +''' + +''' +Case-1 +When you do not need to record the op attribute, you can ignore these inputs and only record the tensor input in the g.op function. +''' +@parse_args('v', 'f', 'f') +def threshold_symbolic(g, x, threshold, value): + output = g.op("rknn_cst::cstThreshold", x) + return output + + +''' +Case-2 +When the op attribute needs to be recorded, it must be declared in g.op. The declaration method is to add the parameter keyword. Please note that the data type needs to be declared with _{type} at the end of the parameter keyword. Since each parameter is a floating point type , so the data type declaration of _f is added at the end +''' +@parse_args('v', 'f', 'f') +def threshold_symbolic(g, x, threshold, value): + output = g.op("rknn_cst::cstThreshold", x, threshold_f=threshold, value_f=value) + return output + +''' +Case-3 +If you want to record attributes as a list instead of a single value, you can use the following method +''' +@parse_args('v', 'f', 'f') +def threshold_symbolic(g, x, threshold, value): + output = g.op("rknn_cst::cstThreshold", x, param_f=[threshold,value]) + return output +``` + +- After declaring the above function, register this function to the torch2onnx function + +```python +register_custom_op_symbolic("aten::threshold", threshold_symbolic, 12) +#The first parameter, "aten::threshold", must match the existing op definition of pytorch and cannot be changed at will. +#The second parameter is the op mapping function defined earlier +#The third parameter is the configured onnx-opset version. You can ignore it for now. Just fill in the commonly used opset version. +``` + + + +## 4.Extra + +Currently, the custom function of torch2onnx not only supports defining the mapping from a single torch op to a single onnx op, but also theoretically supports the following forms + +- Overwrite existing torch2onnx export rules +- Export custom ops with subgraph form + +Due to the small number of usage scenarios, this tutorial does not provide such complex usage. If necessary, please refer to the official documentation of onnx to implement it yourself. + + + diff --git a/rknn-toolkit2/examples/functions/custom_op/gen_custom_onnx_from_pytorch/register_onnx_symbolic/test.py b/rknn-toolkit2/examples/functions/custom_op/gen_custom_onnx_from_pytorch/register_onnx_symbolic/test.py new file mode 100644 index 0000000000000000000000000000000000000000..257e1e7320696c93c41c7c2c178d9b5581fa4868 --- /dev/null +++ b/rknn-toolkit2/examples/functions/custom_op/gen_custom_onnx_from_pytorch/register_onnx_symbolic/test.py @@ -0,0 +1,32 @@ +import torch +import numpy as np + +torch.manual_seed(0) + +class Model(torch.nn.Module): + def __init__(self, *args, **kwargs) -> None: + super().__init__(*args, **kwargs) + self.m = torch.nn.Threshold(0.1, 20) + + def forward(self, x): + y = self.m(x) + return y + +from torch.onnx import register_custom_op_symbolic +from torch.onnx.symbolic_helper import parse_args + +@parse_args('v', 'f', 'f') +def threshold_symbolic(g, x, threshold, value): + # output = g.op("rknn_cst::cst_threshold", x) # ignore threshold and value definition + output = g.op("rknn_cst::cstThreshold", x, threshold_f=threshold, value_f=value) # define threshold and value individually + # output = g.op("rknn_cst::cst_threshold", x, param_f=[threshold,value]) # combine threshold and value into a list + return output +register_custom_op_symbolic("aten::threshold", threshold_symbolic, 12) + +x = torch.randn(1, 3, 10, 10) +m = Model() +result = m(x) +torch.onnx.export(m, (x,), "threshold.onnx", opset_version=12) + +np.save("threshold_input.npy", x.numpy()) +np.save("threshold_output.npy", result.numpy()) diff --git a/rknn-toolkit2/examples/functions/custom_op/gen_custom_onnx_from_pytorch/register_pytorch_op/README.md b/rknn-toolkit2/examples/functions/custom_op/gen_custom_onnx_from_pytorch/register_pytorch_op/README.md new file mode 100644 index 0000000000000000000000000000000000000000..54a313f036c4b1dde6f554bfc8afb17182a3974a --- /dev/null +++ b/rknn-toolkit2/examples/functions/custom_op/gen_custom_onnx_from_pytorch/register_pytorch_op/README.md @@ -0,0 +1,114 @@ +# Register Pytorch op + +建议使用 Pytorch 1.13 或 2.x 版本执行本示例，且 python 环境中安装有 ninja 库 + + + +## 1.使用场景 + +当我们需要把模型中的一整块结构/复杂函数进行合并，并转换成 ONNX 的一个 op 时，可以参考本示例实现。 + +- 此功能的实现依赖于 torch.utils.cpp_extension 模块，目前参考文档较少，可以参考 + - 自定义op的文档： https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/core/op_registration/README.md + - 自定义op单元测试：https://github.com/pytorch/pytorch/blob/main/test/test_cpp_extensions_jit.py +- 请注意，pytorch 自定义 op 有多种实现方式，这里仅介绍其中一种较为方便的实现方式 + + + +完整使用流程如下： + +1）定义算子的 cpp 代码（若只用于导出模型，不在推理/反向传播中使用自定义功能，则cpp代码的计算结果正确与否不影响后续流程，只要输入输出的定义、shape能正确即可） + +2）使用 torch.utils.cpp_extension 加载 cpp 定义的自定义算子 + +3）使用自定义算子构造模型（或替换原模型中对应的结构） + +4）添加算子到 onnx 的映射函数 + +5）导出 onnx 模型 + + + +## 2.执行例子 + +``` +python test.py +``` + + + +## 3.代码解释 + +1）定义算子的 cpp 代码 + +假设有 tensor A 和 tensor B，它们的 shape 相同，构建一个自定义 op ，其输入为（A，B，alpha），输出为（A*alpha -B，B\*alpha - A），此时 cpp 定义代码如下 + +```cpp +cpp_source = """ +#include +std::tuple dual_residual(torch::Tensor x, torch::Tensor y, double_t alpha) { + torch::Tensor tmp1 = x* alpha - y; + torch::Tensor tmp2 = y* alpha - x; + return std::tuple(tmp1, tmp2);} +static auto registry = torch::RegisterOperators() + .op("cst::dual_residual", &dual_residual); +""" +``` + +其中的`static auto registry = torch::RegisterOperators().op("cst::dual_residual", &dual_residual);` 语句，作用是声明自定义op在 python 接口中的名称，例如示例代码执行后，对应的 python 接口为 torch.ops.cst.dual_residual + + + +2）使用 torch.utils.cpp_extension 接口加载上一步骤定义的 cpp 代码 + +```python +torch.utils.cpp_extension.load_inline( + name="test", + cpp_sources=cpp_source, + functions="dual_residual", + verbose=True, + is_python_module=True, + ) +# functions的字符串输入 dual_residual 需要和 cpp 定义中的函数名字一致 +``` + + + +3）使用自定义算子构造模型 + +```python +class Fake_model(torch.nn.Module): + def __init__(self, *args, **kwargs) -> None: + super().__init__(*args, **kwargs) + self.const = .5 + + def forward(self, x, y): + r1, r2 = torch.ops.cst.dual_residual(x, y, self.const) + return r1, r2 +``` + + + +4）参考[文档](../register_onnx_symbolic/README.md)，添加自定义 torch op 到 onnx op 的映射 + +```python +from torch.onnx import register_custom_op_symbolic +from torch.onnx.symbolic_helper import parse_args + +@parse_args('v', 'v', 'f') +def dual_residual_symbolic(g, x, y, alpha): + output = g.op("rknn_cst::cstDualResidual", x, y, alpha_f=alpha, + outputs=2) + return output + +register_custom_op_symbolic("cst::dual_residual", dual_residual_symbolic, 12) +``` + + + +5）导出模型 + +```python +torch.onnx.export(m, (x, y), "dual_residual.onnx", opset_version=12) +``` + diff --git a/rknn-toolkit2/examples/functions/custom_op/gen_custom_onnx_from_pytorch/register_pytorch_op/README_EN.md b/rknn-toolkit2/examples/functions/custom_op/gen_custom_onnx_from_pytorch/register_pytorch_op/README_EN.md new file mode 100644 index 0000000000000000000000000000000000000000..91985cb6abd7b35e5b92222e2db85cbd106bb71f --- /dev/null +++ b/rknn-toolkit2/examples/functions/custom_op/gen_custom_onnx_from_pytorch/register_pytorch_op/README_EN.md @@ -0,0 +1,114 @@ +# Register Pytorch op + +It is recommended to use Pytorch 1.13 or 2.x version to execute this example, and the ninja library is installed in the python environment + + + +## 1.Usage scenarios + +When we need to merge an entire structure/complex function in the model and convert it into an ONNX op, we can refer to this example implementation. + +- The implementation of this function depends on the torch.utils.cpp_extension module. There are currently few reference documents, so you can refer to + - Custom op documentation: https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/core/op_registration/README.md + - Custom op unit test: https://github.com/pytorch/pytorch/blob/main/test/test_cpp_extensions_jit.py +- Please note that there are many ways to implement pytorch custom op. Here we only introduce one of the more convenient ways to implement it. + + + +The complete usage process is as follows: + +1) Define the cpp code of the operator (if it is only used to export the model and does not use custom functions in inference/backpropagation, the correctness of the calculation results of the cpp code will not affect the subsequent process, as long as the definition and shape of the input and output are correct) + +2) Use torch.utils.cpp_extension to load the custom operator defined in cpp + +3) Use custom operators to construct the model (or replace the corresponding structure in the original model) + +4) Add operator to onnx mapping function + +5) Export onnx model + + + +## 2.Usage + +``` +python test.py +``` + + + +## 3.Code explanation + +1）Define operator in cpp code. + +Assume that there are tensor A and tensor B with the same shape. Build a custom op wiht input as (A, B, alpha) and the output as (A*alpha -B, B\*alpha - A). The definition code is as follows + +```cpp +cpp_source = """ +#include +std::tuple dual_residual(torch::Tensor x, torch::Tensor y, double_t alpha) { + torch::Tensor tmp1 = x* alpha - y; + torch::Tensor tmp2 = y* alpha - x; + return std::tuple(tmp1, tmp2);} +static auto registry = torch::RegisterOperators() + .op("cst::dual_residual", &dual_residual); +""" +``` + +The `static auto registry = torch::RegisterOperators().op("cst::dual_residual", &dual_residual);` statement is used to declare the name of the custom op in the python interface. For example, after the example code is executed, the corresponding The python interface is torch.ops.cst.dual_residual + + + +2）Use the torch.utils.cpp_extension interface to load the cpp code defined in the previous step + +```python +torch.utils.cpp_extension.load_inline( + name="test", + cpp_sources=cpp_source, + functions="dual_residual", + verbose=True, + is_python_module=True, + ) +# The string input dual_residual of functions needs to be consistent with the function name in the cpp definition +``` + + + +3）Construct a model using custom operators + +```python +class Model(torch.nn.Module): + def __init__(self, *args, **kwargs) -> None: + super().__init__(*args, **kwargs) + self.const = .5 + + def forward(self, x, y): + r1, r2 = torch.ops.cst.dual_residual(x, y, self.const) + return r1, r2 +``` + + + +4）Refer to [doc](../register_onnx_symbolic/README_EN.md) to add a custom torch op to onnx op mapping + +```python +from torch.onnx import register_custom_op_symbolic +from torch.onnx.symbolic_helper import parse_args + +@parse_args('v', 'v', 'f') +def dual_residual_symbolic(g, x, y, alpha): + output = g.op("rknn_cst::cstDualResidual", x, y, alpha_f=alpha, + outputs=2) + return output + +register_custom_op_symbolic("cst::dual_residual", dual_residual_symbolic, 12) +``` + + + +5）export onnx model + +```python +torch.onnx.export(m, (x, y), "dual_residual.onnx", opset_version=12) +``` + diff --git a/rknn-toolkit2/examples/functions/custom_op/gen_custom_onnx_from_pytorch/register_pytorch_op/test.py b/rknn-toolkit2/examples/functions/custom_op/gen_custom_onnx_from_pytorch/register_pytorch_op/test.py new file mode 100644 index 0000000000000000000000000000000000000000..52b1df13473145e72ee7cb61023851593cb2847d --- /dev/null +++ b/rknn-toolkit2/examples/functions/custom_op/gen_custom_onnx_from_pytorch/register_pytorch_op/test.py @@ -0,0 +1,60 @@ +import torch +import torch.utils.cpp_extension +import numpy as np + +cpp_source = """ +#include +std::tuple dual_residual(torch::Tensor x, torch::Tensor y, double_t alpha) { + torch::Tensor tmp1 = x* alpha - y; + torch::Tensor tmp2 = y* alpha - x; + return std::tuple(tmp1, tmp2);} +static auto registry = torch::RegisterOperators() + .op("cst::dual_residual", &dual_residual); +""" + +torch.utils.cpp_extension.load_inline( + name="test", + cpp_sources=cpp_source, + functions="dual_residual", + verbose=True, + is_python_module=True, +) + +class Model(torch.nn.Module): + def __init__(self, *args, **kwargs) -> None: + super().__init__(*args, **kwargs) + self.const = .5 + + def forward(self, x, y): + r1, r2 = torch.ops.cst.dual_residual(x, y, self.const) + return r1, r2 + +x = torch.randn(1, 3, 10, 10) +y = torch.randn(1, 3, 10, 10) +m = Model() +z = m(x, y) + +# target_result1 = x * .5 - y +# target_result2 = y * .5 - x +# print('result1 is equal:', torch.equal(z[0], target_result1)) +# print('result2 is equal:', torch.equal(z[1], target_result2)) + +# jit_m = torch.jit.trace(m, (x, y)) +# jit_m.save("dual_residual.pt") + +from torch.onnx import register_custom_op_symbolic +from torch.onnx.symbolic_helper import parse_args + +@parse_args('v', 'v', 'f') +def dual_residual_symbolic(g, x, y, alpha): + output = g.op("rknn_cst::cstDualResidual", x, y, alpha_f=alpha, + outputs=2) + return output + +register_custom_op_symbolic("cst::dual_residual", dual_residual_symbolic, 12) +torch.onnx.export(m, (x, y), "dual_residual.onnx", opset_version=12) + +np.save("dual_residual_input_0.npy", x.numpy()) +np.save("dual_residual_input_1.npy", y.numpy()) +np.save("dual_residual_output_0.npy", z[0].numpy()) +np.save("dual_residual_output_1.npy", z[1].numpy()) \ No newline at end of file diff --git a/rknn-toolkit2/examples/functions/custom_op/non-onnx_standard/README.md b/rknn-toolkit2/examples/functions/custom_op/non-onnx_standard/README.md new file mode 100644 index 0000000000000000000000000000000000000000..bdace5b098021891c0e756e9070919a7ff2ece99 --- /dev/null +++ b/rknn-toolkit2/examples/functions/custom_op/non-onnx_standard/README.md @@ -0,0 +1,32 @@ +# How to use custom OP function + +## Model Source +The model and YOLOX version used in this example come from the following open source project: https://github.com/airockchip/YOLOX + +Yolox_s Download link: [yolox_s.onnx](https://ftrg.zbox.filez.com/v2/delivery/data/ec1c6f44f8c24155875ac5bce7aa6b3c/examples/yolox/yolox_s.onnx) + +## Script Usage +*Usage:* +``` +python test.py +``` +*Description:* +- The default target platform in script is 'RK3588', please modify the 'target_platform' parameter of 'rknn.config()' according to the actual platform. + +## Processes +The process of this example is as follows: +1. Edit the original ONNX model and save the custom ONNX model. Take Sigmoid OP as an example. Users can replace any OP with their own custom OP in ONNX model. +2. Init RKNN. +3. Register custom OP cstSigmoid. +4. Convert custom ONNX model to RKNN model. +5. Infer RKNN model in simulator. +6. Save the picture of results. +7. Release RKNN resource. + +## Expected Results +1. Get an ONNX model named yolox_s_custom.onnx with cstSigmiod OP. +2. Get a RKNN model named yolox_s_custom.rknn. +3. Get a picture of detection results named result.jpg. +![Detection Results](result_truth.jpg) + +- Note: Different platforms, different versions of tools and drivers may have slightly different results. diff --git a/rknn-toolkit2/examples/functions/custom_op/non-onnx_standard/bus.jpg b/rknn-toolkit2/examples/functions/custom_op/non-onnx_standard/bus.jpg new file mode 100644 index 0000000000000000000000000000000000000000..19172b78dbbcf34de2b875ac72eb0b4b0651ee66 --- /dev/null +++ b/rknn-toolkit2/examples/functions/custom_op/non-onnx_standard/bus.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fb4914d123d97c440cd127ef0e98d4bdc68cd88e2683657528928b4a34014e16 +size 181374 diff --git a/rknn-toolkit2/examples/functions/custom_op/non-onnx_standard/dataset.txt b/rknn-toolkit2/examples/functions/custom_op/non-onnx_standard/dataset.txt new file mode 100644 index 0000000000000000000000000000000000000000..c32354e18fb66d7fcb2e031729a6f064823f3ba3 --- /dev/null +++ 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204829 diff --git a/rknn-toolkit2/examples/functions/custom_op/non-onnx_standard/result_truth.jpg b/rknn-toolkit2/examples/functions/custom_op/non-onnx_standard/result_truth.jpg new file mode 100644 index 0000000000000000000000000000000000000000..e90e812e48614acac0870a254a876e5628c88492 --- /dev/null +++ b/rknn-toolkit2/examples/functions/custom_op/non-onnx_standard/result_truth.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e957f233798900e9aa2fda0109da10a5acccd0264c2f40a206e06ed084023262 +size 192334 diff --git a/rknn-toolkit2/examples/functions/custom_op/non-onnx_standard/test.py b/rknn-toolkit2/examples/functions/custom_op/non-onnx_standard/test.py new file mode 100644 index 0000000000000000000000000000000000000000..daed81563927915bec03384615cb475c0e0153c3 --- /dev/null +++ b/rknn-toolkit2/examples/functions/custom_op/non-onnx_standard/test.py @@ -0,0 +1,302 @@ +import numpy as np +from rknn.api import RKNN + +OBJ_THRESH = 0.25 +NMS_THRESH = 0.45 +MODEL_IN_SIZE = (640, 640) + +CLASSES = ("person", "bicycle", "car","motorbike ","aeroplane ","bus ","train","truck ","boat","traffic light", + "fire hydrant","stop sign ","parking meter","bench","bird","cat","dog ","horse ","sheep","cow","elephant", + "bear","zebra ","giraffe","backpack","umbrella","handbag","tie","suitcase","frisbee","skis","snowboard","sports ball","kite", + "baseball bat","baseball glove","skateboard","surfboard","tennis racket","bottle","wine glass","cup","fork","knife ", + "spoon","bowl","banana","apple","sandwich","orange","broccoli","carrot","hot dog","pizza ","donut","cake","chair","sofa", + "pottedplant","bed","diningtable","toilet ","tvmonitor","laptop ","mouse ","remote ","keyboard ","cell phone","microwave ", + "oven ","toaster","sink","refrigerator ","book","clock","vase","scissors ","teddy bear ","hair drier", "toothbrush ") + +coco_id_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34, + 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, + 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90] + +class cstSigmoid: + # custom operator cstSigmoid + op_type = 'cstSigmoid' + def shape_infer(self, node, in_shapes, in_dtypes): + return in_shapes.copy(), in_dtypes.copy() + def compute(self, node, inputs): + return [1.0 / (1.0 + np.exp(np.negative(inputs[0])))] + +class Letter_Box_Info(): + def __init__(self, shape, new_shape, w_ratio, h_ratio, dw, dh, pad_color) -> None: + self.origin_shape = shape + self.new_shape = new_shape + self.w_ratio = w_ratio + self.h_ratio = h_ratio + self.dw = dw + self.dh = dh + self.pad_color = pad_color + +def filter_boxes(boxes, box_confidences, box_class_probs): + """Filter boxes with object threshold. + """ + box_confidences = box_confidences.reshape(-1) + candidate, class_num = box_class_probs.shape + + class_max_score = np.max(box_class_probs, axis=-1) + classes = np.argmax(box_class_probs, axis=-1) + + _class_pos = np.where(class_max_score* box_confidences >= OBJ_THRESH) + scores = (class_max_score* box_confidences)[_class_pos] + + boxes = boxes[_class_pos] + classes = classes[_class_pos] + + return boxes, classes, scores + +def nms_boxes(boxes, scores): + """Suppress non-maximal boxes. + # Returns + keep: ndarray, index of effective boxes. + """ + x = boxes[:, 0] + y = boxes[:, 1] + w = boxes[:, 2] - boxes[:, 0] + h = boxes[:, 3] - boxes[:, 1] + + areas = w * h + order = scores.argsort()[::-1] + + keep = [] + while order.size > 0: + i = order[0] + keep.append(i) + + xx1 = np.maximum(x[i], x[order[1:]]) + yy1 = np.maximum(y[i], y[order[1:]]) + xx2 = np.minimum(x[i] + w[i], x[order[1:]] + w[order[1:]]) + yy2 = np.minimum(y[i] + h[i], y[order[1:]] + h[order[1:]]) + + w1 = np.maximum(0.0, xx2 - xx1 + 0.00001) + h1 = np.maximum(0.0, yy2 - yy1 + 0.00001) + inter = w1 * h1 + + ovr = inter / (areas[i] + areas[order[1:]] - inter) + inds = np.where(ovr <= NMS_THRESH)[0] + order = order[inds + 1] + keep = np.array(keep) + return keep + +def box_process(position): + grid_h, grid_w = position.shape[2:4] + col, row = np.meshgrid(np.arange(0, grid_w), np.arange(0, grid_h)) + col = col.reshape(1, 1, grid_h, grid_w) + row = row.reshape(1, 1, grid_h, grid_w) + grid = np.concatenate((col, row), axis=1) + stride = np.array([MODEL_IN_SIZE[1]//grid_h, MODEL_IN_SIZE[0]//grid_w]).reshape(1,2,1,1) + + box_xy = position[:,:2,:,:] + box_wh = np.exp(position[:,2:4,:,:]) * stride + + box_xy += grid + box_xy *= stride + box = np.concatenate((box_xy, box_wh), axis=1) + + # Convert [c_x, c_y, w, h] to [x1, y1, x2, y2] + xyxy = np.copy(box) + xyxy[:, 0, :, :] = box[:, 0, :, :] - box[:, 2, :, :]/ 2 # top left x + xyxy[:, 1, :, :] = box[:, 1, :, :] - box[:, 3, :, :]/ 2 # top left y + xyxy[:, 2, :, :] = box[:, 0, :, :] + box[:, 2, :, :]/ 2 # bottom right x + xyxy[:, 3, :, :] = box[:, 1, :, :] + box[:, 3, :, :]/ 2 # bottom right y + + return xyxy + +def post_process(input_data): + boxes, scores, classes_conf = [], [], [] + + input_data = [_in.reshape([1, -1]+list(_in.shape[-2:])) for _in in input_data] + for i in range(len(input_data)): + boxes.append(box_process(input_data[i][:,:4,:,:])) + scores.append(input_data[i][:,4:5,:,:]) + classes_conf.append(input_data[i][:,5:,:,:]) + + def sp_flatten(_in): + ch = _in.shape[1] + _in = _in.transpose(0,2,3,1) + return _in.reshape(-1, ch) + + boxes = [sp_flatten(_v) for _v in boxes] + classes_conf = [sp_flatten(_v) for _v in classes_conf] + scores = [sp_flatten(_v) for _v in scores] + + boxes = np.concatenate(boxes) + classes_conf = np.concatenate(classes_conf) + scores = np.concatenate(scores) + + # filter according to threshold + boxes, classes, scores = filter_boxes(boxes, scores, classes_conf) + + # nms + nboxes, nclasses, nscores = [], [], [] + keep = nms_boxes(boxes, scores) + if len(keep) != 0: + nboxes.append(boxes[keep]) + nclasses.append(classes[keep]) + nscores.append(scores[keep]) + + if not nclasses and not nscores: + return None, None, None + + boxes = np.concatenate(nboxes) + classes = np.concatenate(nclasses) + scores = np.concatenate(nscores) + + return boxes, classes, scores + +def draw(image, boxes, scores, classes): + for box, score, cl in zip(boxes, scores, classes): + top, left, right, bottom = [int(_b) for _b in box] + print('class: {}, score: {}'.format(CLASSES[cl], score)) + print('box coordinate left,top,right,down: [{}, {}, {}, {}]'.format(top, left, right, bottom)) + + cv2.rectangle(image, (top, left), (right, bottom), (255, 0, 0), 2) + cv2.putText(image, '{0} {1:.2f}'.format(CLASSES[cl], score), + (top, left - 6), + cv2.FONT_HERSHEY_SIMPLEX, + 0.6, (0, 0, 255), 2) + +def letter_box(im, new_shape, pad_color=(0,0,0)): + # Resize and pad image while meeting stride-multiple constraints + shape = im.shape[:2] # current shape [height, width] + if isinstance(new_shape, int): + new_shape = (new_shape, new_shape) + + # Scale ratio + h_ratio = new_shape[0] / shape[0] + w_ratio = new_shape[1] / shape[1] + r = min(h_ratio, w_ratio) + + # Compute padding + new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) + dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding + + dw /= 2 # divide padding into 2 sides + dh /= 2 + + if shape[::-1] != new_unpad: # resize + im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR) + top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) + left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) + im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=pad_color) # add border + + letter_box_info = Letter_Box_Info(shape, new_shape, w_ratio, h_ratio, dw, dh, pad_color) + return im, letter_box_info + +def get_real_box(box, letter_box_info, in_format='xyxy'): + from copy import copy + bbox = copy(box) + # unletter_box result + bbox[:,0] -= letter_box_info.dw + bbox[:,0] /= letter_box_info.w_ratio + bbox[:,0] = np.clip(bbox[:,0], 0, letter_box_info.origin_shape[1]) + + bbox[:,1] -= letter_box_info.dh + bbox[:,1] /= letter_box_info.h_ratio + bbox[:,1] = np.clip(bbox[:,1], 0, letter_box_info.origin_shape[0]) + + bbox[:,2] -= letter_box_info.dw + bbox[:,2] /= letter_box_info.w_ratio + bbox[:,2] = np.clip(bbox[:,2], 0, letter_box_info.origin_shape[1]) + + bbox[:,3] -= letter_box_info.dh + bbox[:,3] /= letter_box_info.h_ratio + bbox[:,3] = np.clip(bbox[:,3], 0, letter_box_info.origin_shape[0]) + + return bbox + +def edit_onnx(in_model, output_model): + # Here, take OP Sigmoid as an example. Users can replace any OP with their own custom OP in ONNX model. + import onnx + model = onnx.load(in_model) + for node in model.graph.node: + if node.op_type == "Sigmoid": + node.op_type = "cstSigmoid" + onnx.save(model, output_model) + + +if __name__ == '__main__': + + model_path = 'yolox_s.onnx' + custom_model_path = 'yolox_s_custom.onnx' + edit_onnx(model_path, custom_model_path) + + # Create RKNN object + rknn = RKNN(verbose=True) + + # Pre-process config + print('--> Config model') + rknn.config(mean_values=[[0, 0, 0]], std_values=[[1, 1, 1]], target_platform='rk3588') + print('done') + + # Register cstSigmoid op + print('--> Register cstSigmoid op') + ret = rknn.reg_custom_op(cstSigmoid()) + if ret != 0: + print('Register cstSigmoid op failed!') + exit(ret) + print('done') + + # Load model + print('--> Loading model') + ret = rknn.load_onnx(model=custom_model_path, input_size_list=[[1, 3, MODEL_IN_SIZE[1], MODEL_IN_SIZE[0]]]) + if ret != 0: + print('Load model failed!') + exit(ret) + print('done') + + # Build model + print('--> Building model') + ret = rknn.build(do_quantization=True, dataset='dataset.txt') + if ret != 0: + print('Build model failed!') + exit(ret) + print('done') + + # Export rknn model + print('--> Export rknn model') + ret = rknn.export_rknn('yolox_s_custom.rknn') + if ret != 0: + print('Export rknn model failed!') + exit(ret) + print('done') + + # Init runtime + print('--> Init runtime') + ret = rknn.init_runtime() + if ret != 0: + print('Init runtime failed!') + exit(ret) + print('done') + + # Get input data + import cv2 + img_src = cv2.imread('bus.jpg') + img, letter_box_info = letter_box(im=img_src.copy(), new_shape=(MODEL_IN_SIZE[1], MODEL_IN_SIZE[0])) + print(letter_box_info) + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + + # Simulator inference + print('--> Inference model') + outputs = rknn.inference(inputs=[img]) + np.save('./functions_custom_op_non-onnx_standard_0.npy', outputs[0]) + np.save('./functions_custom_op_non-onnx_standard_1.npy', outputs[1]) + np.save('./functions_custom_op_non-onnx_standard_2.npy', outputs[2]) + + boxes, classes, scores = post_process(outputs) + + img_p = img_src.copy() + if boxes is not None: + draw(img_p, get_real_box(boxes, letter_box_info), scores, classes) + cv2.imwrite('result.jpg', img_p) + print('Save results to result.jpg!') + + # Release + rknn.release() diff --git a/rknn-toolkit2/examples/functions/custom_op/non-onnx_standard/yolox_s.onnx b/rknn-toolkit2/examples/functions/custom_op/non-onnx_standard/yolox_s.onnx new file mode 100644 index 0000000000000000000000000000000000000000..be02d3b39fc9241b2b5b3317256895c2bd8bf5e3 --- /dev/null +++ b/rknn-toolkit2/examples/functions/custom_op/non-onnx_standard/yolox_s.onnx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fc7f06de8ea114a58ef2eca91d0595277adc92062f12d8055fd676e39f7ac1bf +size 35859274 diff --git a/rknn-toolkit2/examples/functions/dynamic_shape/README.md b/rknn-toolkit2/examples/functions/dynamic_shape/README.md new file mode 100644 index 0000000000000000000000000000000000000000..e24b2026c9e6b94f6a1634d2836157d310608487 --- /dev/null +++ b/rknn-toolkit2/examples/functions/dynamic_shape/README.md @@ -0,0 +1,56 @@ +# How to use dynamic shape function + +## Model Source +The model used in this example come from the following open source projects: +https://github.com/shicai/MobileNet-Caffe + +## Script Usage +*Usage:* +``` +python test.py +``` +*Description:* +- The default target platform in script is 'rk3566', please modify the 'target_platform' parameter of 'rknn.config' according to the actual platform. +- If connecting board is required, please add the 'target' parameter in 'rknn.init_runtime'. +- The 'dynamic_input' parameter of 'rknn.config' is set to: + ``` + dynamic_input = [ + [[1,3,256,256]], # set 1: [input0_256] + [[1,3,160,160]], # set 2: [input0_160] + [[1,3,224,224]], # set 3: [input0_224] + ] + ``` + to simulate models with dynamic input shapes. + +## Expected Results +This example will print the TOP5 labels and corresponding scores of the test image classification results for each different input shape, as follows: +``` +--> Running model with input shape [1,3,224,224] +GraphPreparing : 100%|██████████████████████████████████████████| 104/104 [00:00<00:00, 4764.33it/s] +SessionPreparing : 100%|█████████████████████████████████████████| 104/104 [00:00<00:00, 366.73it/s] +-----TOP 5----- +[155] score:0.993652 class:"Shih-Tzu" +[154] score:0.002277 class:"Pekinese, Pekingese, Peke" +[204] score:0.002277 class:"Lhasa, Lhasa apso" +[283] score:0.000673 class:"Persian cat" +[196] score:0.000109 class:"miniature schnauzer" +--> Running model with input shape [1,3,160,160] +GraphPreparing : 100%|██████████████████████████████████████████| 104/104 [00:00<00:00, 9800.88it/s] +SessionPreparing : 100%|████████████████████████████████████████| 104/104 [00:00<00:00, 1296.61it/s] +-----TOP 5----- +[155] score:0.963867 class:"Shih-Tzu" +[154] score:0.029099 class:"Pekinese, Pekingese, Peke" +[204] score:0.006393 class:"Lhasa, Lhasa apso" +[194] score:0.000415 class:"Dandie Dinmont, Dandie Dinmont terrier" +[219] score:0.000090 class:"cocker spaniel, English cocker spaniel, cocker" +--> Running model with input shape [1,3,256,256] +GraphPreparing : 100%|██████████████████████████████████████████| 104/104 [00:00<00:00, 8788.48it/s] +SessionPreparing : 100%|████████████████████████████████████████| 104/104 [00:00<00:00, 1404.91it/s] +-----TOP 5----- +[155] score:0.981445 class:"Shih-Tzu" +[154] score:0.008896 class:"Pekinese, Pekingese, Peke" +[204] score:0.004169 class:"Lhasa, Lhasa apso" +[193] score:0.000783 class:"Australian terrier" +[283] score:0.000783 class:"Persian cat" +``` +- Note: Different platforms, different versions of tools and drivers may have slightly different results. \ No newline at end of file diff --git a/rknn-toolkit2/examples/functions/dynamic_shape/dog_224x224.jpg b/rknn-toolkit2/examples/functions/dynamic_shape/dog_224x224.jpg new file mode 100644 index 0000000000000000000000000000000000000000..004b0416e23454a12eb06eaba238c7e2d5f2b0fc --- /dev/null +++ b/rknn-toolkit2/examples/functions/dynamic_shape/dog_224x224.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c350299c6283d5f62fecf1f845b6b3be9aafec8dff528ca09a129990f0a584b0 +size 18889 diff --git a/rknn-toolkit2/examples/functions/dynamic_shape/labels.txt b/rknn-toolkit2/examples/functions/dynamic_shape/labels.txt new file mode 100644 index 0000000000000000000000000000000000000000..0993780f99088349e2c156a5a8c57ce1013eb493 --- /dev/null +++ b/rknn-toolkit2/examples/functions/dynamic_shape/labels.txt @@ -0,0 +1,1000 @@ +0:tench, Tinca tinca +1:goldfish, Carassius auratus +2:great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias +3:tiger shark, Galeocerdo cuvieri +4:hammerhead, hammerhead shark +5:electric ray, crampfish, numbfish, torpedo +6:stingray +7:cock +8:hen +9:ostrich, Struthio camelus +10:brambling, Fringilla montifringilla +11:goldfinch, Carduelis carduelis +12:house finch, linnet, Carpodacus mexicanus +13:junco, snowbird +14:indigo bunting, indigo finch, indigo bird, Passerina cyanea +15:robin, American robin, Turdus migratorius +16:bulbul +17:jay +18:magpie +19:chickadee +20:water ouzel, dipper +21:kite +22:bald eagle, American eagle, Haliaeetus leucocephalus +23:vulture +24:great grey owl, great gray owl, Strix nebulosa +25:European fire salamander, Salamandra salamandra +26:common newt, Triturus vulgaris +27:eft +28:spotted salamander, Ambystoma maculatum +29:axolotl, mud puppy, Ambystoma mexicanum +30:bullfrog, Rana catesbeiana +31:tree frog, tree-frog +32:tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui +33:loggerhead, loggerhead turtle, Caretta caretta +34:leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea +35:mud turtle +36:terrapin +37:box turtle, box tortoise +38:banded gecko +39:common iguana, iguana, Iguana iguana +40:American chameleon, anole, Anolis carolinensis +41:whiptail, whiptail lizard +42:agama +43:frilled lizard, Chlamydosaurus kingi +44:alligator lizard +45:Gila monster, Heloderma suspectum +46:green lizard, Lacerta viridis +47:African chameleon, Chamaeleo chamaeleon +48:Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis +49:African crocodile, Nile crocodile, Crocodylus niloticus +50:American alligator, Alligator mississipiensis +51:triceratops +52:thunder snake, worm snake, Carphophis amoenus +53:ringneck snake, ring-necked snake, ring snake +54:hognose snake, puff adder, sand viper +55:green snake, grass snake +56:king snake, kingsnake +57:garter snake, grass snake +58:water snake +59:vine snake +60:night snake, Hypsiglena torquata +61:boa constrictor, Constrictor constrictor +62:rock python, rock snake, Python sebae +63:Indian cobra, Naja naja +64:green mamba +65:sea snake +66:horned viper, cerastes, sand viper, horned asp, Cerastes cornutus +67:diamondback, diamondback rattlesnake, Crotalus adamanteus +68:sidewinder, horned rattlesnake, Crotalus cerastes +69:trilobite +70:harvestman, daddy longlegs, Phalangium opilio +71:scorpion +72:black and gold garden spider, Argiope aurantia +73:barn spider, Araneus cavaticus +74:garden spider, Aranea diademata +75:black widow, Latrodectus mactans +76:tarantula +77:wolf spider, hunting spider +78:tick +79:centipede +80:black grouse +81:ptarmigan +82:ruffed grouse, partridge, Bonasa umbellus +83:prairie chicken, prairie grouse, prairie fowl +84:peacock +85:quail +86:partridge +87:African grey, African gray, Psittacus erithacus +88:macaw +89:sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita +90:lorikeet +91:coucal +92:bee eater +93:hornbill +94:hummingbird +95:jacamar +96:toucan +97:drake +98:red-breasted merganser, Mergus serrator +99:goose +100:black swan, Cygnus atratus +101:tusker +102:echidna, spiny anteater, anteater +103:platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus +104:wallaby, brush kangaroo +105:koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus +106:wombat +107:jellyfish +108:sea anemone, anemone +109:brain coral +110:flatworm, platyhelminth +111:nematode, nematode worm, roundworm +112:conch +113:snail +114:slug +115:sea slug, nudibranch +116:chiton, coat-of-mail shell, sea cradle, polyplacophore +117:chambered nautilus, pearly nautilus, nautilus +118:Dungeness crab, Cancer magister +119:rock crab, Cancer irroratus +120:fiddler crab +121:king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica +122:American lobster, Northern lobster, Maine lobster, Homarus americanus +123:spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish +124:crayfish, crawfish, crawdad, crawdaddy +125:hermit crab +126:isopod +127:white stork, Ciconia ciconia +128:black stork, Ciconia nigra +129:spoonbill +130:flamingo +131:little blue heron, Egretta caerulea +132:American egret, great white heron, Egretta albus +133:bittern +134:crane +135:limpkin, Aramus pictus +136:European gallinule, Porphyrio porphyrio +137:American coot, marsh hen, mud hen, water hen, Fulica americana +138:bustard +139:ruddy turnstone, Arenaria interpres +140:red-backed sandpiper, dunlin, Erolia alpina +141:redshank, Tringa totanus +142:dowitcher +143:oystercatcher, oyster catcher +144:pelican +145:king penguin, Aptenodytes patagonica +146:albatross, mollymawk +147:grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus +148:killer whale, killer, orca, grampus, sea wolf, Orcinus orca +149:dugong, Dugong dugon +150:sea lion +151:Chihuahua +152:Japanese spaniel +153:Maltese dog, Maltese terrier, Maltese +154:Pekinese, Pekingese, Peke +155:Shih-Tzu +156:Blenheim spaniel +157:papillon +158:toy terrier +159:Rhodesian ridgeback +160:Afghan hound, Afghan +161:basset, basset hound +162:beagle +163:bloodhound, sleuthhound +164:bluetick +165:black-and-tan coonhound +166:Walker hound, Walker foxhound +167:English foxhound +168:redbone +169:borzoi, Russian wolfhound +170:Irish wolfhound +171:Italian greyhound +172:whippet +173:Ibizan hound, Ibizan Podenco +174:Norwegian elkhound, elkhound +175:otterhound, otter hound +176:Saluki, gazelle hound +177:Scottish deerhound, deerhound +178:Weimaraner +179:Staffordshire bullterrier, Staffordshire bull terrier +180:American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier +181:Bedlington terrier +182:Border terrier +183:Kerry blue terrier +184:Irish terrier +185:Norfolk terrier +186:Norwich terrier +187:Yorkshire terrier +188:wire-haired fox terrier +189:Lakeland terrier +190:Sealyham terrier, Sealyham +191:Airedale, Airedale terrier +192:cairn, cairn terrier +193:Australian terrier +194:Dandie Dinmont, Dandie Dinmont terrier +195:Boston bull, Boston terrier +196:miniature schnauzer +197:giant schnauzer +198:standard schnauzer +199:Scotch terrier, Scottish terrier, Scottie +200:Tibetan terrier, chrysanthemum dog +201:silky terrier, Sydney silky +202:soft-coated wheaten terrier +203:West Highland white terrier +204:Lhasa, Lhasa apso +205:flat-coated retriever +206:curly-coated retriever +207:golden retriever +208:Labrador retriever +209:Chesapeake Bay retriever +210:German short-haired pointer +211:vizsla, Hungarian pointer +212:English setter +213:Irish setter, red setter +214:Gordon setter +215:Brittany spaniel +216:clumber, clumber spaniel +217:English springer, English springer spaniel +218:Welsh springer spaniel +219:cocker spaniel, English cocker spaniel, cocker +220:Sussex spaniel +221:Irish water spaniel +222:kuvasz +223:schipperke +224:groenendael +225:malinois +226:briard +227:kelpie +228:komondor +229:Old English sheepdog, bobtail +230:Shetland sheepdog, Shetland sheep dog, Shetland +231:collie +232:Border collie +233:Bouvier des Flandres, Bouviers des Flandres +234:Rottweiler +235:German shepherd, German shepherd dog, German police dog, alsatian +236:Doberman, Doberman pinscher +237:miniature pinscher +238:Greater Swiss Mountain dog +239:Bernese mountain dog +240:Appenzeller +241:EntleBucher +242:boxer +243:bull mastiff +244:Tibetan mastiff +245:French bulldog +246:Great Dane +247:Saint Bernard, St Bernard +248:Eskimo dog, husky +249:malamute, malemute, Alaskan malamute +250:Siberian husky +251:dalmatian, coach dog, carriage dog +252:affenpinscher, monkey pinscher, monkey dog +253:basenji +254:pug, pug-dog +255:Leonberg +256:Newfoundland, Newfoundland dog +257:Great Pyrenees +258:Samoyed, Samoyede +259:Pomeranian +260:chow, chow chow +261:keeshond +262:Brabancon griffon +263:Pembroke, Pembroke Welsh corgi +264:Cardigan, Cardigan Welsh corgi +265:toy poodle +266:miniature poodle +267:standard poodle +268:Mexican hairless +269:timber wolf, grey wolf, gray wolf, Canis lupus +270:white wolf, Arctic wolf, Canis lupus tundrarum +271:red wolf, maned wolf, Canis rufus, Canis niger +272:coyote, prairie wolf, brush wolf, Canis latrans +273:dingo, warrigal, warragal, Canis dingo +274:dhole, Cuon alpinus +275:African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus +276:hyena, hyaena +277:red fox, Vulpes vulpes +278:kit fox, Vulpes macrotis +279:Arctic fox, white fox, Alopex lagopus +280:grey fox, gray fox, Urocyon cinereoargenteus +281:tabby, tabby cat +282:tiger cat +283:Persian cat +284:Siamese cat, Siamese +285:Egyptian cat +286:cougar, puma, catamount, mountain lion, painter, panther, Felis concolor +287:lynx, catamount +288:leopard, Panthera pardus +289:snow leopard, ounce, Panthera uncia +290:jaguar, panther, Panthera onca, Felis onca +291:lion, king of beasts, Panthera leo +292:tiger, Panthera tigris +293:cheetah, chetah, Acinonyx jubatus +294:brown bear, bruin, Ursus arctos +295:American black bear, black bear, Ursus americanus, Euarctos americanus +296:ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus +297:sloth bear, Melursus ursinus, Ursus ursinus +298:mongoose +299:meerkat, mierkat +300:tiger beetle +301:ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle +302:ground beetle, carabid beetle +303:long-horned beetle, longicorn, longicorn beetle +304:leaf beetle, chrysomelid +305:dung beetle +306:rhinoceros beetle +307:weevil +308:fly +309:bee +310:ant, emmet, pismire +311:grasshopper, hopper +312:cricket +313:walking stick, walkingstick, stick insect +314:cockroach, roach +315:mantis, mantid +316:cicada, cicala +317:leafhopper +318:lacewing, lacewing fly +319:dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk +320:damselfly +321:admiral +322:ringlet, ringlet butterfly +323:monarch, monarch butterfly, milkweed butterfly, Danaus plexippus +324:cabbage butterfly +325:sulphur butterfly, sulfur butterfly +326:lycaenid, lycaenid butterfly +327:starfish, sea star +328:sea urchin +329:sea cucumber, holothurian +330:wood rabbit, cottontail, cottontail rabbit +331:hare +332:Angora, Angora rabbit +333:hamster +334:porcupine, hedgehog +335:fox squirrel, eastern fox squirrel, Sciurus niger +336:marmot +337:beaver +338:guinea pig, Cavia cobaya +339:sorrel +340:zebra +341:hog, pig, grunter, squealer, Sus scrofa +342:wild boar, boar, Sus scrofa +343:warthog +344:hippopotamus, hippo, river horse, Hippopotamus amphibius +345:ox +346:water buffalo, water ox, Asiatic buffalo, Bubalus bubalis +347:bison +348:ram, tup +349:bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis +350:ibex, Capra ibex +351:hartebeest +352:impala, Aepyceros melampus +353:gazelle +354:Arabian camel, dromedary, Camelus dromedarius +355:llama +356:weasel +357:mink +358:polecat, fitch, foulmart, foumart, Mustela putorius +359:black-footed ferret, ferret, Mustela nigripes +360:otter +361:skunk, polecat, wood pussy +362:badger +363:armadillo +364:three-toed sloth, ai, Bradypus tridactylus +365:orangutan, orang, orangutang, Pongo pygmaeus +366:gorilla, Gorilla gorilla +367:chimpanzee, chimp, Pan troglodytes +368:gibbon, Hylobates lar +369:siamang, Hylobates syndactylus, Symphalangus syndactylus +370:guenon, guenon monkey +371:patas, hussar monkey, Erythrocebus patas +372:baboon +373:macaque +374:langur +375:colobus, colobus monkey +376:proboscis monkey, Nasalis larvatus +377:marmoset +378:capuchin, ringtail, Cebus capucinus +379:howler monkey, howler +380:titi, titi monkey +381:spider monkey, Ateles geoffroyi +382:squirrel monkey, Saimiri sciureus +383:Madagascar cat, ring-tailed lemur, Lemur catta +384:indri, indris, Indri indri, Indri brevicaudatus +385:Indian elephant, Elephas maximus +386:African elephant, Loxodonta africana +387:lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens +388:giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca +389:barracouta, snoek +390:eel +391:coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch +392:rock beauty, Holocanthus tricolor +393:anemone fish +394:sturgeon +395:gar, garfish, garpike, billfish, Lepisosteus osseus +396:lionfish +397:puffer, pufferfish, blowfish, globefish +398:abacus +399:abaya +400:academic gown, academic robe, judge's robe +401:accordion, piano accordion, squeeze box +402:acoustic guitar +403:aircraft carrier, carrier, flattop, attack aircraft carrier +404:airliner +405:airship, dirigible +406:altar +407:ambulance +408:amphibian, amphibious vehicle +409:analog clock +410:apiary, bee house +411:apron +412:ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin +413:assault rifle, assault gun +414:backpack, back pack, knapsack, packsack, rucksack, haversack +415:bakery, bakeshop, bakehouse +416:balance beam, beam +417:balloon +418:ballpoint, ballpoint pen, ballpen, Biro +419:Band Aid +420:banjo +421:bannister, banister, balustrade, balusters, handrail +422:barbell +423:barber chair +424:barbershop +425:barn +426:barometer +427:barrel, cask +428:barrow, garden cart, lawn cart, wheelbarrow +429:baseball +430:basketball +431:bassinet +432:bassoon +433:bathing cap, swimming cap +434:bath towel +435:bathtub, bathing tub, bath, tub +436:beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon +437:beacon, lighthouse, beacon light, pharos +438:beaker +439:bearskin, busby, shako +440:beer bottle +441:beer glass +442:bell cote, bell cot +443:bib +444:bicycle-built-for-two, tandem bicycle, tandem +445:bikini, two-piece +446:binder, ring-binder +447:binoculars, field glasses, opera glasses +448:birdhouse +449:boathouse +450:bobsled, bobsleigh, bob +451:bolo tie, bolo, bola tie, bola +452:bonnet, poke bonnet +453:bookcase +454:bookshop, bookstore, bookstall +455:bottlecap +456:bow +457:bow tie, bow-tie, bowtie +458:brass, memorial tablet, plaque +459:brassiere, bra, bandeau +460:breakwater, groin, groyne, mole, bulwark, seawall, jetty +461:breastplate, aegis, egis +462:broom +463:bucket, pail +464:buckle +465:bulletproof vest +466:bullet train, bullet +467:butcher shop, meat market +468:cab, hack, taxi, taxicab +469:caldron, cauldron +470:candle, taper, wax light +471:cannon +472:canoe +473:can opener, tin opener +474:cardigan +475:car mirror +476:carousel, carrousel, merry-go-round, roundabout, whirligig +477:carpenter's kit, tool kit +478:carton +479:car wheel +480:cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM +481:cassette +482:cassette player +483:castle +484:catamaran +485:CD player +486:cello, violoncello +487:cellular telephone, cellular phone, cellphone, cell, mobile phone +488:chain +489:chainlink fence +490:chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour +491:chain saw, chainsaw +492:chest +493:chiffonier, commode +494:chime, bell, gong +495:china cabinet, china closet +496:Christmas stocking +497:church, church building +498:cinema, movie theater, movie theatre, movie house, picture palace +499:cleaver, meat cleaver, chopper +500:cliff dwelling +501:cloak +502:clog, geta, patten, sabot +503:cocktail shaker +504:coffee mug +505:coffeepot +506:coil, spiral, volute, whorl, helix +507:combination lock +508:computer keyboard, keypad +509:confectionery, confectionary, candy store +510:container ship, containership, container vessel +511:convertible +512:corkscrew, bottle screw +513:cornet, horn, trumpet, trump +514:cowboy boot +515:cowboy hat, ten-gallon hat +516:cradle +517:crane +518:crash helmet +519:crate +520:crib, cot +521:Crock Pot +522:croquet ball +523:crutch +524:cuirass +525:dam, dike, dyke +526:desk +527:desktop computer +528:dial telephone, dial phone +529:diaper, nappy, napkin +530:digital clock +531:digital watch +532:dining table, board +533:dishrag, dishcloth +534:dishwasher, dish washer, dishwashing machine +535:disk brake, disc brake +536:dock, dockage, docking facility +537:dogsled, dog sled, dog sleigh +538:dome +539:doormat, welcome mat +540:drilling platform, offshore rig +541:drum, membranophone, tympan +542:drumstick +543:dumbbell +544:Dutch oven +545:electric fan, blower +546:electric guitar +547:electric locomotive +548:entertainment center +549:envelope +550:espresso maker +551:face powder +552:feather boa, boa +553:file, file cabinet, filing cabinet +554:fireboat +555:fire engine, fire truck +556:fire screen, fireguard +557:flagpole, flagstaff +558:flute, transverse flute +559:folding chair +560:football helmet +561:forklift +562:fountain +563:fountain pen +564:four-poster +565:freight car +566:French horn, horn +567:frying pan, frypan, skillet +568:fur coat +569:garbage truck, dustcart +570:gasmask, respirator, gas helmet +571:gas pump, gasoline pump, petrol pump, island dispenser +572:goblet +573:go-kart +574:golf ball +575:golfcart, golf cart +576:gondola +577:gong, tam-tam +578:gown +579:grand piano, grand +580:greenhouse, nursery, glasshouse +581:grille, radiator grille +582:grocery store, grocery, food market, market +583:guillotine +584:hair slide +585:hair spray +586:half track +587:hammer +588:hamper +589:hand blower, blow dryer, blow drier, hair dryer, hair drier +590:hand-held computer, hand-held microcomputer +591:handkerchief, hankie, hanky, hankey +592:hard disc, hard disk, fixed disk +593:harmonica, mouth organ, harp, mouth harp +594:harp +595:harvester, reaper +596:hatchet +597:holster +598:home theater, home theatre +599:honeycomb +600:hook, claw +601:hoopskirt, crinoline +602:horizontal bar, high bar +603:horse cart, horse-cart +604:hourglass +605:iPod +606:iron, smoothing iron +607:jack-o'-lantern +608:jean, blue jean, denim +609:jeep, landrover +610:jersey, T-shirt, tee shirt +611:jigsaw puzzle +612:jinrikisha, ricksha, rickshaw +613:joystick +614:kimono +615:knee pad +616:knot +617:lab coat, laboratory coat +618:ladle +619:lampshade, lamp shade +620:laptop, laptop computer +621:lawn mower, mower +622:lens cap, lens cover +623:letter opener, paper knife, paperknife +624:library +625:lifeboat +626:lighter, light, igniter, ignitor +627:limousine, limo +628:liner, ocean liner +629:lipstick, lip rouge +630:Loafer +631:lotion +632:loudspeaker, speaker, speaker unit, loudspeaker system, speaker system +633:loupe, jeweler's loupe +634:lumbermill, sawmill +635:magnetic compass +636:mailbag, postbag +637:mailbox, letter box +638:maillot +639:maillot, tank suit +640:manhole cover +641:maraca +642:marimba, xylophone +643:mask +644:matchstick +645:maypole +646:maze, labyrinth +647:measuring cup +648:medicine chest, medicine cabinet +649:megalith, megalithic structure +650:microphone, mike +651:microwave, microwave oven +652:military uniform +653:milk can +654:minibus +655:miniskirt, mini +656:minivan +657:missile +658:mitten +659:mixing bowl +660:mobile home, manufactured home +661:Model T +662:modem +663:monastery +664:monitor +665:moped +666:mortar 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racing car +752:racket, racquet +753:radiator +754:radio, wireless +755:radio telescope, radio reflector +756:rain barrel +757:recreational vehicle, RV, R.V. +758:reel +759:reflex camera +760:refrigerator, icebox +761:remote control, remote +762:restaurant, eating house, eating place, eatery +763:revolver, six-gun, six-shooter +764:rifle +765:rocking chair, rocker +766:rotisserie +767:rubber eraser, rubber, pencil eraser +768:rugby ball +769:rule, ruler +770:running shoe +771:safe +772:safety pin +773:saltshaker, salt shaker +774:sandal +775:sarong +776:sax, saxophone +777:scabbard +778:scale, weighing machine +779:school bus +780:schooner +781:scoreboard +782:screen, CRT screen +783:screw +784:screwdriver +785:seat belt, seatbelt +786:sewing machine +787:shield, buckler +788:shoe shop, shoe-shop, shoe store +789:shoji +790:shopping basket +791:shopping cart +792:shovel +793:shower cap +794:shower curtain +795:ski +796:ski mask +797:sleeping bag +798:slide rule, slipstick +799:sliding door +800:slot, one-armed bandit +801:snorkel +802:snowmobile +803:snowplow, snowplough +804:soap dispenser +805:soccer ball +806:sock +807:solar dish, solar collector, solar furnace +808:sombrero +809:soup bowl +810:space bar +811:space heater +812:space shuttle +813:spatula +814:speedboat +815:spider web, spider's web +816:spindle +817:sports car, sport car +818:spotlight, spot +819:stage +820:steam locomotive +821:steel arch bridge +822:steel drum +823:stethoscope +824:stole +825:stone wall +826:stopwatch, stop watch +827:stove +828:strainer +829:streetcar, tram, tramcar, trolley, trolley car +830:stretcher +831:studio couch, day bed +832:stupa, tope +833:submarine, pigboat, sub, U-boat +834:suit, suit of clothes +835:sundial +836:sunglass +837:sunglasses, dark glasses, shades +838:sunscreen, sunblock, sun blocker +839:suspension bridge +840:swab, swob, mop +841:sweatshirt +842:swimming trunks, bathing trunks +843:swing +844:switch, electric switch, electrical switch +845:syringe +846:table lamp +847:tank, army tank, armored combat vehicle, armoured combat vehicle +848:tape player +849:teapot +850:teddy, teddy bear +851:television, television system +852:tennis ball +853:thatch, thatched roof +854:theater curtain, theatre curtain +855:thimble +856:thresher, thrasher, threshing machine +857:throne +858:tile roof +859:toaster +860:tobacco shop, tobacconist shop, tobacconist +861:toilet seat +862:torch +863:totem pole +864:tow truck, tow car, wrecker +865:toyshop +866:tractor +867:trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi +868:tray +869:trench coat +870:tricycle, trike, velocipede +871:trimaran +872:tripod +873:triumphal arch +874:trolleybus, trolley coach, trackless trolley +875:trombone +876:tub, vat +877:turnstile +878:typewriter keyboard +879:umbrella +880:unicycle, monocycle +881:upright, upright piano +882:vacuum, vacuum cleaner +883:vase +884:vault +885:velvet +886:vending machine +887:vestment +888:viaduct +889:violin, fiddle +890:volleyball +891:waffle iron +892:wall clock +893:wallet, billfold, notecase, pocketbook +894:wardrobe, closet, press +895:warplane, military plane +896:washbasin, handbasin, washbowl, lavabo, wash-hand basin +897:washer, automatic washer, washing machine +898:water bottle +899:water jug +900:water tower +901:whiskey jug +902:whistle +903:wig +904:window screen +905:window shade +906:Windsor tie +907:wine bottle +908:wing +909:wok +910:wooden spoon +911:wool, woolen, woollen +912:worm fence, snake fence, snake-rail fence, Virginia fence +913:wreck +914:yawl +915:yurt +916:web site, website, internet site, site +917:comic book +918:crossword puzzle, crossword +919:street sign +920:traffic light, traffic signal, stoplight +921:book jacket, dust cover, dust jacket, dust wrapper +922:menu +923:plate +924:guacamole +925:consomme +926:hot pot, hotpot +927:trifle +928:ice cream, icecream +929:ice lolly, lolly, lollipop, popsicle +930:French loaf +931:bagel, beigel +932:pretzel +933:cheeseburger +934:hotdog, hot dog, red hot +935:mashed potato +936:head cabbage +937:broccoli +938:cauliflower +939:zucchini, courgette +940:spaghetti squash +941:acorn squash +942:butternut squash +943:cucumber, cuke +944:artichoke, globe artichoke +945:bell pepper +946:cardoon +947:mushroom +948:Granny Smith +949:strawberry +950:orange +951:lemon +952:fig +953:pineapple, ananas +954:banana +955:jackfruit, jak, jack +956:custard apple +957:pomegranate +958:hay +959:carbonara +960:chocolate sauce, chocolate syrup +961:dough +962:meat loaf, meatloaf +963:pizza, pizza pie +964:potpie +965:burrito +966:red wine +967:espresso +968:cup +969:eggnog +970:alp +971:bubble +972:cliff, drop, drop-off +973:coral reef +974:geyser +975:lakeside, lakeshore +976:promontory, headland, head, foreland +977:sandbar, sand bar +978:seashore, coast, seacoast, sea-coast +979:valley, vale +980:volcano +981:ballplayer, baseball player +982:groom, bridegroom +983:scuba diver +984:rapeseed +985:daisy +986:yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum +987:corn +988:acorn +989:hip, rose hip, rosehip +990:buckeye, horse chestnut, conker +991:coral fungus +992:agaric +993:gyromitra +994:stinkhorn, carrion fungus +995:earthstar +996:hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa +997:bolete +998:ear, spike, capitulum +999:toilet tissue, toilet paper, bathroom tissue \ No newline at end of file diff --git a/rknn-toolkit2/examples/functions/dynamic_shape/test.py b/rknn-toolkit2/examples/functions/dynamic_shape/test.py new file mode 100755 index 0000000000000000000000000000000000000000..25841b3deeb2bec35af2114999c4125b6fcebe37 --- /dev/null +++ b/rknn-toolkit2/examples/functions/dynamic_shape/test.py @@ -0,0 +1,109 @@ +import numpy as np +import cv2 +from rknn.api import RKNN + + +def show_outputs(outputs): + output_ = outputs[0].reshape((-1, 1000)) + fp = open('./labels.txt', 'r') + labels = fp.readlines() + for output in output_: + index = sorted(range(len(output)), key=lambda k : output[k], reverse=True) + top5_str = '-----TOP 5-----\n' + for i in range(5): + value = output[index[i]] + if value > 0: + topi = '[{:>3d}] score:{:.6f} class:"{}"\n'.format(index[i], value, labels[index[i]].strip().split(':')[-1]) + else: + topi = '[ -1]: 0.0\n' + top5_str += topi + print(top5_str.strip()) + + +def show_perfs(perfs): + perfs = 'perfs: {}\n'.format(outputs) + print(perfs) + + +if __name__ == '__main__': + + # Create RKNN object + rknn = RKNN(verbose=True) + + # The multiple sets of input shapes specified by the user, to simulate the function of dynamic input. + # Please make sure the model can be dynamic when enable 'config.dynamic_input', and shape in dynamic_input are correctly! + dynamic_input = [ + [[1,3,256,256]], # set 1: [input0_256] + [[1,3,160,160]], # set 2: [input0_160] + [[1,3,224,224]], # set 3: [input0_224] + ] + + # Pre-process config + print('--> Config model') + rknn.config(mean_values=[103.94, 116.78, 123.68], std_values=[58.82, 58.82, 58.82], quant_img_RGB2BGR=True, target_platform='rk3566', dynamic_input=dynamic_input) + print('done') + + # Load model (from https://github.com/shicai/MobileNet-Caffe) + print('--> Loading model') + ret = rknn.load_caffe(model='../../caffe/mobilenet_v2/mobilenet_v2_deploy.prototxt', + blobs='../../caffe/mobilenet_v2/mobilenet_v2.caffemodel') + if ret != 0: + print('Load model failed!') + exit(ret) + print('done') + + # Build model + print('--> Building model') + ret = rknn.build(do_quantization=True, dataset='../../caffe/mobilenet_v2/dataset.txt') + if ret != 0: + print('Build model failed!') + exit(ret) + print('done') + + # Export rknn model + print('--> Export rknn model') + ret = rknn.export_rknn('./mobilenet_v2.rknn') + if ret != 0: + print('Export rknn model failed!') + exit(ret) + print('done') + + # Init runtime environment + print('--> Init runtime environment') + ret = rknn.init_runtime() + if ret != 0: + print('Init runtime environment failed!') + exit(ret) + print('done') + + # Set inputs + img = cv2.imread('./dog_224x224.jpg') + + # Inference + print('\n--> Running model with input shape [1,3,224,224]') + img2 = cv2.resize(img, (224,224)) + img2 = np.expand_dims(img2, 0) + img2 = np.transpose(img2, (0,3,1,2)) # [1,3,224,224] + outputs = rknn.inference(inputs=[img2], data_format=['nchw']) + np.save('./functions_dynamic_shape_0.npy', outputs[0]) + show_outputs(outputs) + + print('--> Running model with input shape [1,3,160,160]') + img3 = cv2.resize(img, (160,160)) + img3 = np.expand_dims(img3, 0) + img3 = np.transpose(img3, (0,3,1,2)) # [1,3,160,160] + outputs = rknn.inference(inputs=[img3], data_format=['nchw']) + np.save('./functions_dynamic_shape_1.npy', outputs[0]) + show_outputs(outputs) + + print('--> Running model with input shape [1,3,256,256]') + img4 = cv2.resize(img, (256,256)) + img4 = np.expand_dims(img4, 0) + img4 = np.transpose(img4, (0,3,1,2)) # [1,3,256,256] + outputs = rknn.inference(inputs=[img4], data_format=['nchw']) + np.save('./functions_dynamic_shape_2.npy', outputs[0]) + show_outputs(outputs) + + print('done') + + rknn.release() diff --git a/rknn-toolkit2/examples/functions/hybrid_quant/README.md b/rknn-toolkit2/examples/functions/hybrid_quant/README.md new file mode 100644 index 0000000000000000000000000000000000000000..5f88bc0ab5f63157bc3bba118d29a2a77b7a42e3 --- /dev/null +++ b/rknn-toolkit2/examples/functions/hybrid_quant/README.md @@ -0,0 +1,40 @@ +# How to use hybrid-quantization function + +## Model Source +The model used in this example come from: +https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf1_detection_zoo.md ssd_mobilenet_v2_coco + +## Script Usage +*Usage:* +``` +1. python step1.py +2. modify ssd_mobilenet_v2.quantization.cfg according to the prompt of step1.py +3. python step2.py +``` +*Description:* +- The default target platform in script is 'rk3566', please modify the 'target_platform' parameter of 'rknn.config' according to the actual platform. +- If connecting board is required, please add the 'target' parameter in 'rknn.init_runtime'. + +## Expected Results +This example will outputs the results of the accuracy analysis and save the result of object detection to the 'result.jpg', as follows: +``` +layer_name simulator_error + entire single + cos euc cos euc +----------------------------------------------------------------------------------------------------------------------------------------------------- +[Input] FeatureExtractor/MobilenetV2/MobilenetV2/input:0 1.00000 | 0.0 1.00000 | 0.0 +[exDataConvert] FeatureExtractor/MobilenetV2/MobilenetV2/input:0_int8 0.99996 | 2.0377 0.99996 | 2.0377 +[Conv] Conv__350:0 +[Clip] FeatureExtractor/MobilenetV2/Conv/Relu6:0 0.99998 | 9.5952 0.99998 | 9.5952 +[Conv] FeatureExtractor/MobilenetV2/expanded_conv/depthwise/BatchNorm/batchnorm/add_1:0 +[Clip] FeatureExtractor/MobilenetV2/expanded_conv/depthwise/Relu6:0 0.99951 | 65.269 0.99957 | 61.673 + +.... + +[Concat] concat:0_before_conv 0.99817 | 9.3381 1.00000 | 0.0317 +[exDataConvert] concat:0_before_conv__int8 0.99812 | 9.4634 0.99994 | 1.6116 +[Conv] concat:0_int8 0.99812 | 9.4634 0.99994 | 1.6115 +[exDataConvert] concat:0 0.99812 | 9.4634 0.99994 | 1.6115 +``` +![result](result_truth.jpg) +- Note: Different platforms, different versions of tools and drivers may have slightly different results. \ No newline at end of file diff --git a/rknn-toolkit2/examples/functions/hybrid_quant/box_priors.txt b/rknn-toolkit2/examples/functions/hybrid_quant/box_priors.txt new file mode 100644 index 0000000000000000000000000000000000000000..7246b073fe7fd8b1d1340536457c8aeac24cd5a3 --- /dev/null +++ b/rknn-toolkit2/examples/functions/hybrid_quant/box_priors.txt @@ -0,0 +1,5 @@ + 0.02631579 0.02631579 0.026315793 0.02631579 0.02631579 0.026315793 0.02631579 0.02631579 0.026315793 0.02631579 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0.6717514 1.6454482 0.54845536 0.97467947 + diff --git a/rknn-toolkit2/examples/functions/hybrid_quant/dataset.txt b/rknn-toolkit2/examples/functions/hybrid_quant/dataset.txt new file mode 100644 index 0000000000000000000000000000000000000000..28fe4c85a2ec80cd5001d18a993621bda1f2d0d6 --- /dev/null +++ b/rknn-toolkit2/examples/functions/hybrid_quant/dataset.txt @@ -0,0 +1 @@ +./dog_bike_car_300x300.jpg diff --git a/rknn-toolkit2/examples/functions/hybrid_quant/dog_bike_car_300x300.jpg b/rknn-toolkit2/examples/functions/hybrid_quant/dog_bike_car_300x300.jpg new file mode 100644 index 0000000000000000000000000000000000000000..66de31b966efb351e587b68a62b27bfa229f563e --- /dev/null +++ b/rknn-toolkit2/examples/functions/hybrid_quant/dog_bike_car_300x300.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dfbb9952a01786f18adc1aea83915556d3762292d790e223bf73aa9a006b337d +size 50078 diff --git a/rknn-toolkit2/examples/functions/hybrid_quant/result_truth.jpg b/rknn-toolkit2/examples/functions/hybrid_quant/result_truth.jpg new file mode 100644 index 0000000000000000000000000000000000000000..07f81949262f9da550bc4850e0295de894c59191 --- /dev/null +++ b/rknn-toolkit2/examples/functions/hybrid_quant/result_truth.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:79bd5cb0c6fa01cbf8c3b003f095377014cfca3d15cf714d0f1720cfb15c5d5c +size 48869 diff --git a/rknn-toolkit2/examples/functions/hybrid_quant/ssd_mobilenet_v2.pb b/rknn-toolkit2/examples/functions/hybrid_quant/ssd_mobilenet_v2.pb new file mode 100755 index 0000000000000000000000000000000000000000..0b2a1bacddfd1344020aab892d2c0030d2521c8d --- /dev/null +++ b/rknn-toolkit2/examples/functions/hybrid_quant/ssd_mobilenet_v2.pb @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2a8d8a89d695842e60d8c6d144181100555563e21acf2fa1e8f561fec5c3c6ad +size 69688296 diff --git a/rknn-toolkit2/examples/functions/hybrid_quant/ssd_post_process.py b/rknn-toolkit2/examples/functions/hybrid_quant/ssd_post_process.py new file mode 100755 index 0000000000000000000000000000000000000000..c0036f65a4cf21ad762027c21a977855999dd588 --- /dev/null +++ b/rknn-toolkit2/examples/functions/hybrid_quant/ssd_post_process.py @@ -0,0 +1,184 @@ +import numpy as np +import cv2 +from rknn.api import RKNN +import math +import PIL.Image as Image +import PIL.ImageDraw as ImageDraw +import PIL.ImageFont as ImageFont +import re + +np.set_printoptions(threshold=np.inf) + +CLASSES = ('__background__', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', + 'traffic light', 'fire hydrant', '???', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', + 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', '???', 'backpack', 'umbrella', '???', '???', + 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', + 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', '???', 'wine glass', 'cup', 'fork', + 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', + 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', '???', 'dining table', '???', '???', 'toilet', + '???', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', + 'refrigerator', '???', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush') + +NUM_CLS = 91 + +CONF_THRESH = 0.5 +NMS_THRESH = 0.45 +TOP_BOXES = 100 +max_boxes_to_draw = 100 + +Y_SCALE = 10.0 +X_SCALE = 10.0 +H_SCALE = 5.0 +W_SCALE = 5.0 + +prior_file = './box_priors.txt' + +box_priors_ = [] +fp = open(prior_file, 'r') +ls = fp.readlines() +for s in ls: + aList = re.findall('([-+]?\d+(\.\d*)?|\.\d+)([eE][-+]?\d+)?', s) + for ss in aList: + aNum = float((ss[0] + ss[2])) + box_priors_.append(aNum) +fp.close() + + +def softmax(x): + return np.exp(x) / np.sum(np.exp(x), axis=0) + + +def IntersectBBox(box1, box2): + if box1[0] > box2[2] or box1[2] < box2[0] or box1[1] > box2[3] or box1[3] < box2[1]: + return 0 + else: + area1 = (box1[2] - box1[0]) * (box1[3] - box1[1]) + area2 = (box2[2] - box2[0]) * (box2[3] - box2[1]) + + xx1 = max(box1[0], box2[0]) + yy1 = max(box1[1], box2[1]) + xx2 = min(box1[2], box2[2]) + yy2 = min(box1[3], box2[3]) + + w = max(0, xx2 - xx1) + h = max(0, yy2 - yy1) + + ovr = w * h / (area1 + area2 - w * h + 0.000001) + return ovr + + +def ssd_post_process(conf_data, loc_data, imgpath, output_dir='.'): + prior_num = int(len(loc_data) / 4) # num prior boxes + + prior_bboxes = np.array(box_priors_) + prior_bboxes = prior_bboxes.reshape(4, prior_num) + + conf_data = conf_data.reshape(-1, NUM_CLS) + + for i in range(prior_num): + conf_data[i] = softmax(conf_data[i]) + + idx_class_conf = [] + bboxes = [] + + # conf + for prior_idx in range(0, prior_num): + conf_data[prior_idx][0] = 0 + max_val = np.max(conf_data[prior_idx]) + max_idx = np.argmax(conf_data[prior_idx]) + if max_val > CONF_THRESH: + idx_class_conf.append([prior_idx, max_idx, max_val]) + + idx_class_conf_sorted = sorted(idx_class_conf, key=lambda x: x[2], reverse=True) + + idx_class_conf = idx_class_conf_sorted[:min(TOP_BOXES, len(idx_class_conf_sorted))] + + # boxes + for i in range(0, prior_num): + bbox_center_x = loc_data[4 * i + 1] / X_SCALE * prior_bboxes[3][i] + prior_bboxes[1][i] + bbox_center_y = loc_data[4 * i + 0] / Y_SCALE * prior_bboxes[2][i] + prior_bboxes[0][i] + bbox_w = math.exp(loc_data[4 * i + 3] / W_SCALE) * prior_bboxes[3][i] + bbox_h = math.exp(loc_data[4 * i + 2] / H_SCALE) * prior_bboxes[2][i] + + tmp = [] + tmp.append(max(min(bbox_center_x - bbox_w / 2., 1), 0)) + tmp.append(max(min(bbox_center_y - bbox_h / 2., 1), 0)) + tmp.append(max(min(bbox_center_x + bbox_w / 2., 1), 0)) + tmp.append(max(min(bbox_center_y + bbox_h / 2., 1), 0)) + bboxes.append(tmp) + + # nms + cur_class_num = 0 + idx_class_conf_ = [] + for i in range(0, len(idx_class_conf)): + keep = True + k = 0 + while k < cur_class_num: + if keep: + ovr = IntersectBBox(bboxes[idx_class_conf[i][0]], bboxes[idx_class_conf_[k][0]]) + if idx_class_conf_[k][1] == idx_class_conf[i][1] and ovr > NMS_THRESH: + keep = False + break + k += 1 + else: + break + if keep: + idx_class_conf_.append(idx_class_conf[i]) + cur_class_num += 1 + idx_class_conf_ = idx_class_conf_[:min(len(idx_class_conf_), max_boxes_to_draw)] + + box_class_score = [] + + for i in range(0, len(idx_class_conf_)): + bboxes[idx_class_conf_[i][0]].append(idx_class_conf_[i][1]) + bboxes[idx_class_conf_[i][0]].append(idx_class_conf_[i][2]) + box_class_score.append(bboxes[idx_class_conf_[i][0]]) + + img = cv2.imread(imgpath) + img_pil = Image.fromarray(img) + draw = ImageDraw.Draw(img_pil) + + font = ImageFont.load_default() + + name = imgpath.split("/")[-1][:-4] + + if len(box_class_score) != 0: + print("{:^12} {:^12} {}".format('class', 'score', 'xmin, ymin, xmax, ymax')) + print('-' * 50) + + for i in range(0, len(box_class_score)): + x1 = box_class_score[i][0] * img.shape[1] + y1 = box_class_score[i][1] * img.shape[0] + x2 = box_class_score[i][2] * img.shape[1] + y2 = box_class_score[i][3] * img.shape[0] + + # draw rect + color = (0, int(box_class_score[i][4] / 20.0 * 255), 255) + draw.line([(x1, y1), (x1, y2), (x2, y2), + (x2, y1), (x1, y1)], width=2, fill=color) + + display_str = CLASSES[box_class_score[i][4]] + ":" + str('%.2f' % box_class_score[i][5]) + try: + display_str_height = np.ceil((1 + 2 * 0.05) * font.getbbox(display_str)[3]) + 1 + except: + display_str_height = np.ceil((1 + 2 * 0.05) * font.getsize(display_str)[1]) + 1 + + if y1 > display_str_height: + text_bottom = y1 + else: + text_bottom = y1 + display_str_height + + try: + _, _, text_width, text_height = font.getbbox(display_str) + except: + text_width, text_height = font.getsize(display_str) + margin = np.ceil(0.05 * text_height) + draw.rectangle([(x1, text_bottom - text_height - 2 * margin), (x1 + text_width, text_bottom)], fill=color) + draw.text((x1 + margin, text_bottom - text_height - margin), display_str, fill='black', font=font) + + print("{:^12} {:^12.3f} [{:>4}, {:>4}, {:>4}, {:>4}]".format(CLASSES[box_class_score[i][4]], box_class_score[i][5], + int(x1), int(y1), int(x2), int(y2))) + + np.copyto(img, np.array(img_pil)) + cv2.imwrite("result.jpg", img) + print('Save results to result.jpg!') \ No newline at end of file diff --git a/rknn-toolkit2/examples/functions/hybrid_quant/step1.py b/rknn-toolkit2/examples/functions/hybrid_quant/step1.py new file mode 100755 index 0000000000000000000000000000000000000000..d395d9a94c68df0f76f23e799e09aaf98393f4ac --- /dev/null +++ b/rknn-toolkit2/examples/functions/hybrid_quant/step1.py @@ -0,0 +1,60 @@ +import numpy as np +import cv2 +from rknn.api import RKNN + +if __name__ == '__main__': + + # Create RKNN object + rknn = RKNN(verbose=True) + + # Pre-process config + print('--> Config model') + rknn.config(mean_values=[127.5, 127.5, 127.5], std_values=[127.5, 127.5, 127.5], target_platform='rk3566') + print('done') + + # Load model (from https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf1_detection_zoo.md ssd_mobilenet_v2_coco) + print('--> Loading model') + ret = rknn.load_tensorflow(tf_pb='./ssd_mobilenet_v2.pb', + inputs=['FeatureExtractor/MobilenetV2/MobilenetV2/input'], + outputs=['concat_1', 'concat'], + input_size_list=[[1,300,300,3]]) + if ret != 0: + print('Load model failed!') + exit(ret) + print('done') + + # Build model + print('--> hybrid_quantization_step1') + ret = rknn.hybrid_quantization_step1(dataset='./dataset.txt', proposal=False) + if ret != 0: + print('hybrid_quantization_step1 failed!') + exit(ret) + print('done') + + # Tips + print("======================================== Tips ==========================================================") + print("After the execution of 'rknn.hybrid_quantization_step1' is completed, a configuration file") + print("'ssd_mobilenet_v2.quantization.cfg' will be generated. This configuration file is used to set the layer") + print("that needs to be hybrid quantized. ") + print("") + print("Users can set the 'output name' and 'data type' of the layer that needs to be configured into") + print("'custom_quantize_layers', as follows:") + print("") + print(" custom_quantize_layers:") + print(" FeatureExtractor/MobilenetV2/expanded_conv/depthwise/BatchNorm/batchnorm/add_1:0: float16") + print(" FeatureExtractor/MobilenetV2/expanded_conv/depthwise/Relu6:0: float16") + print("") + print("Currently supported data types are: 'int8', 'int16', 'float16'.") + print("") + print("In addition, the configuration file will automatically fill in some layers that may cause a decrease in") + print("accuracy (may be inaccurate). Users can consider these layers together with the results of accuracy") + print("analysis to obtain the layers that actually cause a decrease in accuracy and set them into") + print("'custom_quantize_layers'.") + print("") + print("After modifying 'ssd_mobilenet_v2.quantization.cfg', you can continue to execute 'step2.py' to complete") + print("the model conversion.") + print("========================================================================================================") + print("") + + rknn.release() + diff --git a/rknn-toolkit2/examples/functions/hybrid_quant/step2.py b/rknn-toolkit2/examples/functions/hybrid_quant/step2.py new file mode 100755 index 0000000000000000000000000000000000000000..920f7096298d480a0d087069ea04179210764dac --- /dev/null +++ b/rknn-toolkit2/examples/functions/hybrid_quant/step2.py @@ -0,0 +1,59 @@ +import numpy as np +import cv2 +from rknn.api import RKNN +from ssd_post_process import ssd_post_process + +if __name__ == '__main__': + + # Create RKNN object + rknn = RKNN(verbose=True) + + # Build model + print('--> hybrid_quantization_step2') + ret = rknn.hybrid_quantization_step2(model_input='./ssd_mobilenet_v2.model', + data_input='./ssd_mobilenet_v2.data', + model_quantization_cfg='./ssd_mobilenet_v2.quantization.cfg') + if ret != 0: + print('hybrid_quantization_step2 failed!') + exit(ret) + print('done') + + # Export rknn model + print('--> Export rknn model') + ret = rknn.export_rknn('./ssd_mobilenet_v2.rknn') + if ret != 0: + print('Export rknn model failed!') + exit(ret) + print('done') + + # Accuracy analysis + print('--> Accuracy analysis') + ret = rknn.accuracy_analysis(inputs=['./dog_bike_car_300x300.jpg'], output_dir=None) + if ret != 0: + print('Accuracy analysis failed!') + exit(ret) + print('done') + + # Set inputs + img = cv2.imread('./dog_bike_car_300x300.jpg') + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + img = np.expand_dims(img, 0) + + # Init runtime environment + print('--> Init runtime environment') + ret = rknn.init_runtime() + if ret != 0: + print('Init runtime environment failed!') + exit(ret) + print('done') + + # Inference + print('--> Running model') + outputs = rknn.inference(inputs=[img], data_format=['nhwc']) + print('done') + + np.save('./functions_hybrid_quant_0.npy', outputs[0]) + np.save('./functions_hybrid_quant_1.npy', outputs[1]) + ssd_post_process(np.reshape(outputs[0], (-1)), np.reshape(outputs[1], (-1)), './dog_bike_car_300x300.jpg', './') + + rknn.release() diff --git a/rknn-toolkit2/examples/functions/model_pruning/README.md b/rknn-toolkit2/examples/functions/model_pruning/README.md new file mode 100644 index 0000000000000000000000000000000000000000..0d49aa734fc0ebe315e68a081e9b8e56f0650de2 --- /dev/null +++ b/rknn-toolkit2/examples/functions/model_pruning/README.md @@ -0,0 +1,38 @@ +# How to use model_pruning function + +## Model Source +The model used in this example come from the following open source projects: +https://github.com/shicai/MobileNet-Caffe + +## Script Usage +*Usage:* +``` +python test.py +``` +*Description:* +- The default target platform in script is 'rk3566', please modify the 'target_platform' parameter of 'rknn.config' according to the actual platform. +- If connecting board is required, please add the 'target' parameter in 'rknn.init_runtime'. +- The 'model_pruning' parameter of 'rknn.config' is set to True. +- When verbose is set to True, the following similar prompts will appear during the build process, +indicating that model pruning has been effective for this model. (This means that approximately 6.9% +of the weights have been removed, resulting in a saving of about 13.4% of the computational workload.) +Please note that not all models can be pruned, only models with sparse weights are likely to benefit from pruning. + ``` + I model_pruning ... + I model_pruning results: + I -1.12144 MB (-6.9%) + I -0.00016 T (-13.4%) + I model_pruning done. + ``` + +## Expected Results +This example will print the TOP5 labels and corresponding scores of the test image classification results, as follows: +``` +-----TOP 5----- +[155] score:0.734863 class:"Shih-Tzu" +[154] score:0.180176 class:"Pekinese, Pekingese, Peke" +[204] score:0.051605 class:"Lhasa, Lhasa apso" +[284] score:0.004234 class:"Siamese cat, Siamese" +[252] score:0.003918 class:"affenpinscher, monkey pinscher, monkey dog" +``` +- Note: Different platforms, different versions of tools and drivers may have slightly different results. \ No newline at end of file diff --git a/rknn-toolkit2/examples/functions/model_pruning/dataset.txt b/rknn-toolkit2/examples/functions/model_pruning/dataset.txt new file mode 100644 index 0000000000000000000000000000000000000000..aa215cb4c2477015b3288893d9afb73f0e7905a3 --- /dev/null +++ b/rknn-toolkit2/examples/functions/model_pruning/dataset.txt @@ -0,0 +1 @@ +dog_224x224.jpg \ No newline at end of file diff --git a/rknn-toolkit2/examples/functions/model_pruning/dog_224x224.jpg b/rknn-toolkit2/examples/functions/model_pruning/dog_224x224.jpg new file mode 100644 index 0000000000000000000000000000000000000000..004b0416e23454a12eb06eaba238c7e2d5f2b0fc --- /dev/null +++ b/rknn-toolkit2/examples/functions/model_pruning/dog_224x224.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c350299c6283d5f62fecf1f845b6b3be9aafec8dff528ca09a129990f0a584b0 +size 18889 diff --git a/rknn-toolkit2/examples/functions/model_pruning/labels.txt b/rknn-toolkit2/examples/functions/model_pruning/labels.txt new file mode 100644 index 0000000000000000000000000000000000000000..0993780f99088349e2c156a5a8c57ce1013eb493 --- /dev/null +++ b/rknn-toolkit2/examples/functions/model_pruning/labels.txt @@ -0,0 +1,1000 @@ +0:tench, Tinca tinca +1:goldfish, Carassius auratus +2:great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias +3:tiger shark, Galeocerdo cuvieri +4:hammerhead, hammerhead shark +5:electric ray, crampfish, numbfish, torpedo +6:stingray +7:cock +8:hen +9:ostrich, Struthio camelus +10:brambling, Fringilla montifringilla +11:goldfinch, Carduelis carduelis +12:house finch, linnet, Carpodacus mexicanus +13:junco, snowbird +14:indigo bunting, indigo finch, indigo bird, Passerina cyanea +15:robin, American robin, Turdus migratorius +16:bulbul +17:jay +18:magpie +19:chickadee +20:water ouzel, dipper +21:kite +22:bald eagle, American eagle, Haliaeetus leucocephalus +23:vulture +24:great grey owl, great gray owl, Strix nebulosa +25:European fire salamander, Salamandra salamandra +26:common newt, Triturus vulgaris +27:eft +28:spotted salamander, Ambystoma maculatum +29:axolotl, mud puppy, Ambystoma mexicanum +30:bullfrog, Rana catesbeiana +31:tree frog, tree-frog +32:tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui +33:loggerhead, loggerhead turtle, Caretta caretta +34:leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea +35:mud turtle +36:terrapin +37:box turtle, box tortoise +38:banded gecko +39:common iguana, iguana, Iguana iguana +40:American chameleon, anole, Anolis carolinensis +41:whiptail, whiptail lizard +42:agama +43:frilled lizard, Chlamydosaurus kingi +44:alligator lizard +45:Gila monster, Heloderma suspectum +46:green lizard, Lacerta viridis +47:African chameleon, Chamaeleo chamaeleon +48:Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis +49:African crocodile, Nile crocodile, Crocodylus niloticus +50:American alligator, Alligator mississipiensis +51:triceratops +52:thunder snake, worm snake, Carphophis amoenus +53:ringneck snake, ring-necked snake, ring snake +54:hognose snake, puff adder, sand viper +55:green snake, grass snake +56:king snake, kingsnake +57:garter snake, grass snake +58:water snake +59:vine snake +60:night snake, Hypsiglena torquata +61:boa constrictor, Constrictor constrictor +62:rock python, rock snake, Python sebae +63:Indian cobra, Naja naja +64:green mamba +65:sea snake +66:horned viper, cerastes, sand viper, horned asp, Cerastes cornutus +67:diamondback, diamondback rattlesnake, Crotalus adamanteus +68:sidewinder, horned rattlesnake, Crotalus cerastes +69:trilobite +70:harvestman, daddy longlegs, Phalangium opilio +71:scorpion +72:black and gold garden spider, Argiope aurantia +73:barn spider, Araneus cavaticus +74:garden spider, Aranea diademata +75:black widow, Latrodectus mactans +76:tarantula +77:wolf spider, hunting spider +78:tick +79:centipede +80:black grouse +81:ptarmigan +82:ruffed grouse, partridge, Bonasa umbellus +83:prairie chicken, prairie grouse, prairie fowl +84:peacock +85:quail +86:partridge +87:African grey, African gray, Psittacus erithacus +88:macaw +89:sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita +90:lorikeet +91:coucal +92:bee eater +93:hornbill +94:hummingbird +95:jacamar +96:toucan +97:drake +98:red-breasted merganser, Mergus serrator +99:goose +100:black swan, Cygnus atratus +101:tusker +102:echidna, spiny anteater, anteater +103:platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus +104:wallaby, brush kangaroo +105:koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus +106:wombat +107:jellyfish +108:sea anemone, anemone +109:brain coral +110:flatworm, platyhelminth +111:nematode, nematode worm, roundworm +112:conch +113:snail +114:slug +115:sea slug, nudibranch +116:chiton, coat-of-mail shell, sea cradle, polyplacophore +117:chambered nautilus, pearly nautilus, nautilus +118:Dungeness crab, Cancer magister +119:rock crab, Cancer irroratus +120:fiddler crab +121:king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica +122:American lobster, Northern lobster, Maine lobster, Homarus americanus +123:spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish +124:crayfish, crawfish, crawdad, crawdaddy +125:hermit crab +126:isopod +127:white stork, Ciconia ciconia +128:black stork, Ciconia nigra +129:spoonbill +130:flamingo +131:little blue heron, Egretta caerulea +132:American egret, great white heron, Egretta albus +133:bittern +134:crane +135:limpkin, Aramus pictus +136:European gallinule, Porphyrio porphyrio +137:American coot, marsh hen, mud hen, water hen, Fulica americana +138:bustard +139:ruddy turnstone, Arenaria interpres +140:red-backed sandpiper, dunlin, Erolia alpina +141:redshank, Tringa totanus +142:dowitcher +143:oystercatcher, oyster catcher +144:pelican +145:king penguin, Aptenodytes patagonica +146:albatross, mollymawk +147:grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus +148:killer whale, killer, orca, grampus, sea wolf, Orcinus orca +149:dugong, Dugong dugon +150:sea lion +151:Chihuahua +152:Japanese spaniel +153:Maltese dog, Maltese terrier, Maltese +154:Pekinese, Pekingese, Peke +155:Shih-Tzu +156:Blenheim spaniel +157:papillon +158:toy terrier +159:Rhodesian ridgeback +160:Afghan hound, Afghan +161:basset, basset hound +162:beagle +163:bloodhound, sleuthhound +164:bluetick +165:black-and-tan coonhound +166:Walker hound, Walker foxhound +167:English foxhound +168:redbone +169:borzoi, Russian wolfhound +170:Irish wolfhound +171:Italian greyhound +172:whippet +173:Ibizan hound, Ibizan Podenco +174:Norwegian elkhound, elkhound +175:otterhound, otter hound +176:Saluki, gazelle hound +177:Scottish deerhound, deerhound +178:Weimaraner +179:Staffordshire bullterrier, Staffordshire bull terrier +180:American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier +181:Bedlington terrier +182:Border terrier +183:Kerry blue terrier +184:Irish terrier +185:Norfolk terrier +186:Norwich terrier +187:Yorkshire terrier +188:wire-haired fox terrier +189:Lakeland terrier +190:Sealyham terrier, Sealyham +191:Airedale, Airedale terrier +192:cairn, cairn terrier +193:Australian terrier +194:Dandie Dinmont, Dandie Dinmont terrier +195:Boston bull, Boston terrier +196:miniature schnauzer +197:giant schnauzer +198:standard schnauzer +199:Scotch terrier, Scottish terrier, Scottie +200:Tibetan terrier, chrysanthemum dog +201:silky terrier, Sydney silky +202:soft-coated wheaten terrier +203:West Highland white terrier +204:Lhasa, Lhasa apso +205:flat-coated retriever +206:curly-coated retriever +207:golden retriever +208:Labrador retriever +209:Chesapeake Bay retriever +210:German short-haired pointer +211:vizsla, Hungarian pointer +212:English setter +213:Irish setter, red setter +214:Gordon setter +215:Brittany spaniel +216:clumber, clumber spaniel +217:English springer, English springer spaniel +218:Welsh springer spaniel +219:cocker spaniel, English cocker spaniel, cocker +220:Sussex spaniel +221:Irish water spaniel +222:kuvasz +223:schipperke +224:groenendael +225:malinois +226:briard +227:kelpie +228:komondor +229:Old English sheepdog, bobtail +230:Shetland sheepdog, Shetland sheep dog, Shetland +231:collie +232:Border collie +233:Bouvier des Flandres, Bouviers des Flandres +234:Rottweiler +235:German shepherd, German shepherd dog, German police dog, alsatian +236:Doberman, Doberman pinscher +237:miniature pinscher +238:Greater Swiss Mountain dog +239:Bernese mountain dog +240:Appenzeller +241:EntleBucher +242:boxer +243:bull mastiff +244:Tibetan mastiff +245:French bulldog +246:Great Dane +247:Saint Bernard, St Bernard +248:Eskimo dog, husky +249:malamute, malemute, Alaskan malamute +250:Siberian husky +251:dalmatian, coach dog, carriage dog +252:affenpinscher, monkey pinscher, monkey dog +253:basenji +254:pug, pug-dog +255:Leonberg +256:Newfoundland, Newfoundland dog +257:Great Pyrenees +258:Samoyed, Samoyede +259:Pomeranian +260:chow, chow chow +261:keeshond +262:Brabancon griffon +263:Pembroke, Pembroke Welsh corgi +264:Cardigan, Cardigan Welsh corgi +265:toy poodle +266:miniature poodle +267:standard poodle +268:Mexican hairless +269:timber wolf, grey wolf, gray wolf, Canis lupus +270:white wolf, Arctic wolf, Canis lupus tundrarum +271:red wolf, maned wolf, Canis rufus, Canis niger +272:coyote, prairie wolf, brush wolf, Canis latrans +273:dingo, warrigal, warragal, Canis dingo +274:dhole, Cuon alpinus +275:African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus +276:hyena, hyaena +277:red fox, Vulpes vulpes +278:kit fox, Vulpes macrotis +279:Arctic fox, white fox, Alopex lagopus +280:grey fox, gray fox, Urocyon cinereoargenteus +281:tabby, tabby cat +282:tiger cat +283:Persian cat +284:Siamese cat, Siamese +285:Egyptian cat +286:cougar, puma, catamount, mountain lion, painter, panther, Felis concolor +287:lynx, catamount +288:leopard, Panthera pardus +289:snow leopard, ounce, Panthera uncia +290:jaguar, panther, Panthera onca, Felis onca +291:lion, king of beasts, Panthera leo +292:tiger, Panthera tigris +293:cheetah, chetah, Acinonyx jubatus +294:brown bear, bruin, Ursus arctos +295:American black bear, black bear, Ursus americanus, Euarctos americanus +296:ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus +297:sloth bear, Melursus ursinus, Ursus ursinus +298:mongoose +299:meerkat, mierkat +300:tiger beetle +301:ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle +302:ground beetle, carabid beetle +303:long-horned beetle, longicorn, longicorn beetle +304:leaf beetle, chrysomelid +305:dung beetle +306:rhinoceros beetle +307:weevil +308:fly +309:bee +310:ant, emmet, pismire +311:grasshopper, hopper +312:cricket +313:walking stick, walkingstick, stick insect +314:cockroach, roach +315:mantis, mantid +316:cicada, cicala +317:leafhopper +318:lacewing, lacewing fly +319:dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk +320:damselfly +321:admiral +322:ringlet, ringlet butterfly +323:monarch, monarch butterfly, milkweed butterfly, Danaus plexippus +324:cabbage butterfly +325:sulphur butterfly, sulfur butterfly +326:lycaenid, lycaenid butterfly +327:starfish, sea star +328:sea urchin +329:sea cucumber, holothurian +330:wood rabbit, cottontail, cottontail rabbit +331:hare +332:Angora, Angora rabbit +333:hamster +334:porcupine, hedgehog +335:fox squirrel, eastern fox squirrel, Sciurus niger +336:marmot +337:beaver +338:guinea pig, Cavia cobaya +339:sorrel +340:zebra +341:hog, pig, grunter, squealer, Sus scrofa +342:wild boar, boar, Sus scrofa +343:warthog +344:hippopotamus, hippo, river horse, Hippopotamus amphibius +345:ox +346:water buffalo, water ox, Asiatic buffalo, Bubalus bubalis +347:bison +348:ram, tup +349:bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis +350:ibex, Capra ibex +351:hartebeest +352:impala, Aepyceros melampus +353:gazelle +354:Arabian camel, dromedary, Camelus dromedarius +355:llama +356:weasel +357:mink +358:polecat, fitch, foulmart, foumart, Mustela putorius +359:black-footed ferret, ferret, Mustela nigripes +360:otter +361:skunk, polecat, wood pussy +362:badger +363:armadillo +364:three-toed sloth, ai, Bradypus tridactylus +365:orangutan, orang, orangutang, Pongo pygmaeus +366:gorilla, Gorilla gorilla +367:chimpanzee, chimp, Pan troglodytes +368:gibbon, Hylobates lar +369:siamang, Hylobates syndactylus, Symphalangus syndactylus +370:guenon, guenon monkey +371:patas, hussar monkey, Erythrocebus patas +372:baboon +373:macaque +374:langur +375:colobus, colobus monkey +376:proboscis monkey, Nasalis larvatus +377:marmoset +378:capuchin, ringtail, Cebus capucinus +379:howler monkey, howler +380:titi, titi monkey +381:spider monkey, Ateles geoffroyi +382:squirrel monkey, Saimiri sciureus +383:Madagascar cat, ring-tailed lemur, Lemur catta +384:indri, indris, Indri indri, Indri brevicaudatus +385:Indian elephant, Elephas maximus +386:African elephant, Loxodonta africana +387:lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens +388:giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca +389:barracouta, snoek +390:eel +391:coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch +392:rock beauty, Holocanthus tricolor +393:anemone fish +394:sturgeon +395:gar, garfish, garpike, billfish, Lepisosteus osseus +396:lionfish +397:puffer, pufferfish, blowfish, globefish +398:abacus +399:abaya +400:academic gown, academic robe, judge's robe +401:accordion, piano accordion, squeeze box +402:acoustic guitar +403:aircraft carrier, carrier, flattop, attack aircraft carrier +404:airliner +405:airship, dirigible +406:altar +407:ambulance +408:amphibian, amphibious vehicle +409:analog clock +410:apiary, bee house +411:apron +412:ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin +413:assault rifle, assault gun +414:backpack, back pack, knapsack, packsack, rucksack, haversack +415:bakery, bakeshop, bakehouse +416:balance beam, beam +417:balloon +418:ballpoint, ballpoint pen, ballpen, Biro +419:Band Aid +420:banjo +421:bannister, banister, balustrade, balusters, handrail +422:barbell +423:barber chair +424:barbershop +425:barn +426:barometer +427:barrel, cask +428:barrow, garden cart, lawn cart, wheelbarrow +429:baseball +430:basketball +431:bassinet +432:bassoon +433:bathing cap, swimming cap +434:bath towel +435:bathtub, bathing tub, bath, tub +436:beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon +437:beacon, lighthouse, beacon light, pharos +438:beaker +439:bearskin, busby, shako +440:beer bottle +441:beer glass +442:bell cote, bell cot +443:bib +444:bicycle-built-for-two, tandem bicycle, tandem +445:bikini, two-piece +446:binder, ring-binder +447:binoculars, field glasses, opera glasses +448:birdhouse +449:boathouse +450:bobsled, bobsleigh, bob +451:bolo tie, bolo, bola tie, bola +452:bonnet, poke bonnet +453:bookcase +454:bookshop, bookstore, bookstall +455:bottlecap +456:bow +457:bow tie, bow-tie, bowtie +458:brass, memorial tablet, plaque +459:brassiere, bra, bandeau +460:breakwater, groin, groyne, mole, bulwark, seawall, jetty +461:breastplate, aegis, egis +462:broom +463:bucket, pail +464:buckle +465:bulletproof vest +466:bullet train, bullet +467:butcher shop, meat market +468:cab, hack, taxi, taxicab +469:caldron, cauldron +470:candle, taper, wax light +471:cannon +472:canoe +473:can opener, tin opener +474:cardigan +475:car mirror +476:carousel, carrousel, merry-go-round, roundabout, whirligig +477:carpenter's kit, tool kit +478:carton +479:car wheel +480:cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM +481:cassette +482:cassette player +483:castle +484:catamaran +485:CD player +486:cello, violoncello +487:cellular telephone, cellular phone, cellphone, cell, mobile phone +488:chain +489:chainlink fence +490:chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour +491:chain saw, chainsaw +492:chest +493:chiffonier, commode +494:chime, bell, gong +495:china cabinet, china closet +496:Christmas stocking +497:church, church building +498:cinema, movie theater, movie theatre, movie house, picture palace +499:cleaver, meat cleaver, chopper +500:cliff dwelling +501:cloak +502:clog, geta, patten, sabot +503:cocktail shaker +504:coffee mug +505:coffeepot +506:coil, spiral, volute, whorl, helix +507:combination lock +508:computer keyboard, keypad +509:confectionery, confectionary, candy store +510:container ship, containership, container vessel +511:convertible +512:corkscrew, bottle screw +513:cornet, horn, trumpet, trump +514:cowboy boot +515:cowboy hat, ten-gallon hat +516:cradle +517:crane +518:crash helmet +519:crate +520:crib, cot +521:Crock Pot +522:croquet ball +523:crutch +524:cuirass +525:dam, dike, dyke +526:desk +527:desktop computer +528:dial telephone, dial phone +529:diaper, nappy, napkin +530:digital clock +531:digital watch +532:dining table, board +533:dishrag, dishcloth +534:dishwasher, dish washer, dishwashing machine +535:disk brake, disc brake +536:dock, dockage, docking facility +537:dogsled, dog sled, dog sleigh +538:dome +539:doormat, welcome mat +540:drilling platform, offshore rig +541:drum, membranophone, tympan +542:drumstick +543:dumbbell +544:Dutch oven +545:electric fan, blower +546:electric guitar +547:electric locomotive +548:entertainment center +549:envelope +550:espresso maker +551:face powder +552:feather boa, boa +553:file, file cabinet, filing cabinet +554:fireboat +555:fire engine, fire truck +556:fire screen, fireguard +557:flagpole, flagstaff +558:flute, transverse flute +559:folding chair +560:football helmet +561:forklift +562:fountain +563:fountain pen +564:four-poster +565:freight car +566:French horn, horn +567:frying pan, frypan, skillet +568:fur coat +569:garbage truck, dustcart +570:gasmask, respirator, gas helmet +571:gas pump, gasoline pump, petrol pump, island dispenser +572:goblet +573:go-kart +574:golf ball +575:golfcart, golf cart +576:gondola +577:gong, tam-tam +578:gown +579:grand piano, grand +580:greenhouse, nursery, glasshouse +581:grille, radiator grille +582:grocery store, grocery, food market, market +583:guillotine +584:hair slide +585:hair spray +586:half track +587:hammer +588:hamper +589:hand blower, blow dryer, blow drier, hair dryer, hair drier +590:hand-held computer, hand-held microcomputer +591:handkerchief, hankie, hanky, hankey +592:hard disc, hard disk, fixed disk +593:harmonica, mouth organ, harp, mouth harp +594:harp +595:harvester, reaper +596:hatchet +597:holster +598:home theater, home theatre +599:honeycomb +600:hook, claw +601:hoopskirt, crinoline +602:horizontal bar, high bar +603:horse cart, horse-cart +604:hourglass +605:iPod +606:iron, smoothing iron +607:jack-o'-lantern +608:jean, blue jean, denim +609:jeep, landrover +610:jersey, T-shirt, tee shirt +611:jigsaw puzzle +612:jinrikisha, ricksha, rickshaw +613:joystick +614:kimono +615:knee pad +616:knot +617:lab coat, laboratory coat +618:ladle +619:lampshade, lamp shade +620:laptop, laptop computer +621:lawn mower, mower +622:lens cap, lens cover +623:letter opener, paper knife, paperknife +624:library +625:lifeboat +626:lighter, light, igniter, ignitor +627:limousine, limo +628:liner, ocean liner +629:lipstick, lip rouge +630:Loafer +631:lotion +632:loudspeaker, speaker, speaker unit, loudspeaker system, speaker system +633:loupe, jeweler's loupe +634:lumbermill, sawmill +635:magnetic compass +636:mailbag, postbag +637:mailbox, letter box +638:maillot +639:maillot, tank suit +640:manhole cover +641:maraca +642:marimba, xylophone +643:mask +644:matchstick +645:maypole +646:maze, labyrinth +647:measuring cup +648:medicine chest, medicine cabinet +649:megalith, megalithic structure +650:microphone, mike +651:microwave, microwave oven +652:military uniform +653:milk can +654:minibus +655:miniskirt, mini +656:minivan +657:missile +658:mitten +659:mixing bowl +660:mobile home, manufactured home +661:Model T +662:modem +663:monastery +664:monitor +665:moped +666:mortar +667:mortarboard +668:mosque +669:mosquito net +670:motor scooter, scooter +671:mountain bike, all-terrain bike, off-roader +672:mountain tent +673:mouse, computer mouse +674:mousetrap +675:moving van +676:muzzle +677:nail +678:neck brace +679:necklace +680:nipple +681:notebook, notebook computer +682:obelisk +683:oboe, hautboy, hautbois +684:ocarina, sweet potato +685:odometer, hodometer, mileometer, milometer +686:oil filter +687:organ, pipe organ +688:oscilloscope, scope, cathode-ray oscilloscope, CRO +689:overskirt +690:oxcart +691:oxygen mask +692:packet +693:paddle, boat paddle +694:paddlewheel, paddle wheel +695:padlock +696:paintbrush +697:pajama, pyjama, pj's, jammies +698:palace +699:panpipe, pandean pipe, syrinx +700:paper towel +701:parachute, chute +702:parallel bars, bars +703:park bench +704:parking meter +705:passenger car, coach, carriage +706:patio, terrace +707:pay-phone, pay-station +708:pedestal, plinth, footstall +709:pencil box, pencil case +710:pencil sharpener +711:perfume, essence +712:Petri dish +713:photocopier +714:pick, plectrum, plectron +715:pickelhaube +716:picket fence, paling +717:pickup, pickup truck +718:pier +719:piggy bank, penny bank +720:pill bottle +721:pillow +722:ping-pong ball +723:pinwheel +724:pirate, pirate ship +725:pitcher, ewer +726:plane, carpenter's plane, woodworking plane +727:planetarium +728:plastic bag +729:plate rack +730:plow, plough +731:plunger, plumber's helper +732:Polaroid camera, Polaroid Land camera +733:pole +734:police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria +735:poncho +736:pool table, billiard table, snooker table +737:pop bottle, soda bottle +738:pot, flowerpot +739:potter's wheel +740:power drill +741:prayer rug, prayer mat +742:printer +743:prison, prison house +744:projectile, missile +745:projector +746:puck, hockey puck +747:punching bag, punch bag, punching ball, punchball +748:purse +749:quill, quill pen +750:quilt, comforter, comfort, puff +751:racer, race car, racing car +752:racket, racquet +753:radiator +754:radio, wireless +755:radio telescope, radio reflector +756:rain barrel +757:recreational vehicle, RV, R.V. +758:reel +759:reflex camera +760:refrigerator, icebox +761:remote control, remote +762:restaurant, eating house, eating place, eatery +763:revolver, six-gun, six-shooter +764:rifle +765:rocking chair, rocker +766:rotisserie +767:rubber eraser, rubber, pencil eraser +768:rugby ball +769:rule, ruler +770:running shoe +771:safe +772:safety pin +773:saltshaker, salt shaker +774:sandal +775:sarong +776:sax, saxophone +777:scabbard +778:scale, weighing machine +779:school bus +780:schooner +781:scoreboard +782:screen, CRT screen +783:screw +784:screwdriver +785:seat belt, seatbelt +786:sewing machine +787:shield, buckler +788:shoe shop, shoe-shop, shoe store +789:shoji +790:shopping basket +791:shopping cart +792:shovel +793:shower cap +794:shower curtain +795:ski +796:ski mask +797:sleeping bag +798:slide rule, slipstick +799:sliding door +800:slot, one-armed bandit +801:snorkel +802:snowmobile +803:snowplow, snowplough +804:soap dispenser +805:soccer ball +806:sock +807:solar dish, solar collector, solar furnace +808:sombrero +809:soup bowl +810:space bar +811:space heater +812:space shuttle +813:spatula +814:speedboat +815:spider web, spider's web +816:spindle +817:sports car, sport car +818:spotlight, spot +819:stage +820:steam locomotive +821:steel arch bridge +822:steel drum +823:stethoscope +824:stole +825:stone wall +826:stopwatch, stop watch +827:stove +828:strainer +829:streetcar, tram, tramcar, trolley, trolley car +830:stretcher +831:studio couch, day bed +832:stupa, tope +833:submarine, pigboat, sub, U-boat +834:suit, suit of clothes +835:sundial +836:sunglass +837:sunglasses, dark glasses, shades +838:sunscreen, sunblock, sun blocker +839:suspension bridge +840:swab, swob, mop +841:sweatshirt +842:swimming trunks, bathing trunks +843:swing +844:switch, electric switch, electrical switch +845:syringe +846:table lamp +847:tank, army tank, armored combat vehicle, armoured combat vehicle +848:tape player +849:teapot +850:teddy, teddy bear +851:television, television system +852:tennis ball +853:thatch, thatched roof +854:theater curtain, theatre curtain +855:thimble +856:thresher, thrasher, threshing machine +857:throne +858:tile roof +859:toaster +860:tobacco shop, tobacconist shop, tobacconist +861:toilet seat +862:torch +863:totem pole +864:tow truck, tow car, wrecker +865:toyshop +866:tractor +867:trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi +868:tray +869:trench coat +870:tricycle, trike, velocipede +871:trimaran +872:tripod +873:triumphal arch +874:trolleybus, trolley coach, trackless trolley +875:trombone +876:tub, vat +877:turnstile +878:typewriter keyboard +879:umbrella +880:unicycle, monocycle +881:upright, upright piano +882:vacuum, vacuum cleaner +883:vase +884:vault +885:velvet +886:vending machine +887:vestment +888:viaduct +889:violin, fiddle +890:volleyball +891:waffle iron +892:wall clock +893:wallet, billfold, notecase, pocketbook +894:wardrobe, closet, press +895:warplane, military plane +896:washbasin, handbasin, washbowl, lavabo, wash-hand basin +897:washer, automatic washer, washing machine +898:water bottle +899:water jug +900:water tower +901:whiskey jug +902:whistle +903:wig +904:window screen +905:window shade +906:Windsor tie +907:wine bottle +908:wing +909:wok +910:wooden spoon +911:wool, woolen, woollen +912:worm fence, snake fence, snake-rail fence, Virginia fence +913:wreck +914:yawl +915:yurt +916:web site, website, internet site, site +917:comic book +918:crossword puzzle, crossword +919:street sign +920:traffic light, traffic signal, stoplight +921:book jacket, dust cover, dust jacket, dust wrapper +922:menu +923:plate +924:guacamole +925:consomme +926:hot pot, hotpot +927:trifle +928:ice cream, icecream +929:ice lolly, lolly, lollipop, popsicle +930:French loaf +931:bagel, beigel +932:pretzel +933:cheeseburger +934:hotdog, hot dog, red hot +935:mashed potato +936:head cabbage +937:broccoli +938:cauliflower +939:zucchini, courgette +940:spaghetti squash +941:acorn squash +942:butternut squash +943:cucumber, cuke +944:artichoke, globe artichoke +945:bell pepper +946:cardoon +947:mushroom +948:Granny Smith +949:strawberry +950:orange +951:lemon +952:fig +953:pineapple, ananas +954:banana +955:jackfruit, jak, jack +956:custard apple +957:pomegranate +958:hay +959:carbonara +960:chocolate sauce, chocolate syrup +961:dough +962:meat loaf, meatloaf +963:pizza, pizza pie +964:potpie +965:burrito +966:red wine +967:espresso +968:cup +969:eggnog +970:alp +971:bubble +972:cliff, drop, drop-off +973:coral reef +974:geyser +975:lakeside, lakeshore +976:promontory, headland, head, foreland +977:sandbar, sand bar +978:seashore, coast, seacoast, sea-coast +979:valley, vale +980:volcano +981:ballplayer, baseball player +982:groom, bridegroom +983:scuba diver +984:rapeseed +985:daisy +986:yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum +987:corn +988:acorn +989:hip, rose hip, rosehip +990:buckeye, horse chestnut, conker +991:coral fungus +992:agaric +993:gyromitra +994:stinkhorn, carrion fungus +995:earthstar +996:hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa +997:bolete +998:ear, spike, capitulum +999:toilet tissue, toilet paper, bathroom tissue \ No newline at end of file diff --git a/rknn-toolkit2/examples/functions/model_pruning/mobilenet.caffemodel b/rknn-toolkit2/examples/functions/model_pruning/mobilenet.caffemodel new file mode 100755 index 0000000000000000000000000000000000000000..9c7c739d6dfc2d33c870a2f3d19a46c80fcc24a8 --- /dev/null +++ b/rknn-toolkit2/examples/functions/model_pruning/mobilenet.caffemodel @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8d6edcd3dbd1356f2f19dd220c362c2ba8f44233a9b6c12ca6d0351cb0c446b6 +size 17027058 diff --git a/rknn-toolkit2/examples/functions/model_pruning/mobilenet_deploy.prototxt b/rknn-toolkit2/examples/functions/model_pruning/mobilenet_deploy.prototxt new file mode 100755 index 0000000000000000000000000000000000000000..f8c906f5175e0d591998342341656f4ed9c5ab20 --- /dev/null +++ b/rknn-toolkit2/examples/functions/model_pruning/mobilenet_deploy.prototxt @@ -0,0 +1,1996 @@ +name: "MOBILENET" +# transform_param { +# scale: 0.017 +# mirror: false +# crop_size: 224 +# mean_value: [103.94,116.78,123.68] +# } +input: "data" +input_dim: 1 +input_dim: 3 +input_dim: 224 +input_dim: 224 +layer { + name: "conv1" + type: "Convolution" + bottom: "data" + top: "conv1" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 32 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 2 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv1/bn" + type: "BatchNorm" + bottom: "conv1" + top: "conv1" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv1/scale" + type: "Scale" + bottom: "conv1" + top: "conv1" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "relu1" + type: "ReLU" + bottom: "conv1" + top: "conv1" +} +layer { + name: "conv2_1/dw" + type: "Convolution" + bottom: "conv1" + top: "conv2_1/dw" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 32 + bias_term: false + pad: 1 + kernel_size: 3 + group: 32 + engine: CAFFE + stride: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv2_1/dw/bn" + type: "BatchNorm" + bottom: "conv2_1/dw" + top: "conv2_1/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv2_1/dw/scale" + type: "Scale" + bottom: "conv2_1/dw" + top: "conv2_1/dw" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "relu2_1/dw" + type: "ReLU" + bottom: "conv2_1/dw" + top: "conv2_1/dw" +} +layer { + name: "conv2_1/sep" + type: "Convolution" + bottom: "conv2_1/dw" + top: "conv2_1/sep" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv2_1/sep/bn" + type: "BatchNorm" + bottom: "conv2_1/sep" + top: "conv2_1/sep" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv2_1/sep/scale" + type: "Scale" + bottom: "conv2_1/sep" + top: "conv2_1/sep" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "relu2_1/sep" + type: "ReLU" + bottom: "conv2_1/sep" + top: "conv2_1/sep" +} +layer { + name: "conv2_2/dw" + type: "Convolution" + bottom: "conv2_1/sep" + top: "conv2_2/dw" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 1 + kernel_size: 3 + group: 64 + engine: CAFFE + stride: 2 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv2_2/dw/bn" + type: "BatchNorm" + bottom: "conv2_2/dw" + top: "conv2_2/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv2_2/dw/scale" + type: "Scale" + bottom: "conv2_2/dw" + top: "conv2_2/dw" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "relu2_2/dw" + type: "ReLU" + bottom: "conv2_2/dw" + top: "conv2_2/dw" +} +layer { + name: "conv2_2/sep" + type: "Convolution" + bottom: "conv2_2/dw" + top: "conv2_2/sep" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 128 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv2_2/sep/bn" + type: "BatchNorm" + bottom: "conv2_2/sep" + top: "conv2_2/sep" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv2_2/sep/scale" + type: "Scale" + bottom: "conv2_2/sep" + top: "conv2_2/sep" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "relu2_2/sep" + type: "ReLU" + bottom: "conv2_2/sep" + top: "conv2_2/sep" +} +layer { + name: "conv3_1/dw" + type: "Convolution" + bottom: "conv2_2/sep" + top: "conv3_1/dw" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 128 + bias_term: false + pad: 1 + kernel_size: 3 + group: 128 + engine: CAFFE + stride: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv3_1/dw/bn" + type: "BatchNorm" + bottom: "conv3_1/dw" + top: "conv3_1/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv3_1/dw/scale" + type: "Scale" + bottom: "conv3_1/dw" + top: "conv3_1/dw" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "relu3_1/dw" + type: "ReLU" + bottom: "conv3_1/dw" + top: "conv3_1/dw" +} +layer { + name: "conv3_1/sep" + type: "Convolution" + bottom: "conv3_1/dw" + top: "conv3_1/sep" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 128 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv3_1/sep/bn" + type: "BatchNorm" + bottom: "conv3_1/sep" + top: "conv3_1/sep" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv3_1/sep/scale" + type: "Scale" + bottom: "conv3_1/sep" + top: "conv3_1/sep" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "relu3_1/sep" + type: "ReLU" + bottom: "conv3_1/sep" + top: "conv3_1/sep" +} +layer { + name: "conv3_2/dw" + type: "Convolution" + bottom: "conv3_1/sep" + top: "conv3_2/dw" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 128 + bias_term: false + pad: 1 + kernel_size: 3 + group: 128 + engine: CAFFE + stride: 2 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv3_2/dw/bn" + type: "BatchNorm" + bottom: "conv3_2/dw" + top: "conv3_2/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv3_2/dw/scale" + type: "Scale" + bottom: "conv3_2/dw" + top: "conv3_2/dw" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "relu3_2/dw" + type: "ReLU" + bottom: "conv3_2/dw" + top: "conv3_2/dw" +} +layer { + name: "conv3_2/sep" + type: "Convolution" + bottom: "conv3_2/dw" + top: "conv3_2/sep" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 256 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv3_2/sep/bn" + type: "BatchNorm" + bottom: "conv3_2/sep" + top: "conv3_2/sep" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv3_2/sep/scale" + type: "Scale" + bottom: "conv3_2/sep" + top: "conv3_2/sep" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "relu3_2/sep" + type: "ReLU" + bottom: "conv3_2/sep" + top: "conv3_2/sep" +} +layer { + name: "conv4_1/dw" + type: "Convolution" + bottom: "conv3_2/sep" + top: "conv4_1/dw" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 256 + bias_term: false + pad: 1 + kernel_size: 3 + group: 256 + engine: CAFFE + stride: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv4_1/dw/bn" + type: "BatchNorm" + bottom: "conv4_1/dw" + top: "conv4_1/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv4_1/dw/scale" + type: "Scale" + bottom: "conv4_1/dw" + top: "conv4_1/dw" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "relu4_1/dw" + type: "ReLU" + bottom: "conv4_1/dw" + top: "conv4_1/dw" +} +layer { + name: "conv4_1/sep" + type: "Convolution" + bottom: "conv4_1/dw" + top: "conv4_1/sep" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 256 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv4_1/sep/bn" + type: "BatchNorm" + bottom: "conv4_1/sep" + top: "conv4_1/sep" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv4_1/sep/scale" + type: "Scale" + bottom: "conv4_1/sep" + top: "conv4_1/sep" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "relu4_1/sep" + type: "ReLU" + bottom: "conv4_1/sep" + top: "conv4_1/sep" +} +layer { + name: "conv4_2/dw" + type: "Convolution" + bottom: "conv4_1/sep" + top: "conv4_2/dw" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 256 + bias_term: false + pad: 1 + kernel_size: 3 + group: 256 + engine: CAFFE + stride: 2 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv4_2/dw/bn" + type: "BatchNorm" + bottom: "conv4_2/dw" + top: "conv4_2/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv4_2/dw/scale" + type: "Scale" + bottom: "conv4_2/dw" + top: "conv4_2/dw" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "relu4_2/dw" + type: "ReLU" + bottom: "conv4_2/dw" + top: "conv4_2/dw" +} +layer { + name: "conv4_2/sep" + type: "Convolution" + bottom: "conv4_2/dw" + top: "conv4_2/sep" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 512 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv4_2/sep/bn" + type: "BatchNorm" + bottom: "conv4_2/sep" + top: "conv4_2/sep" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv4_2/sep/scale" + type: "Scale" + bottom: "conv4_2/sep" + top: "conv4_2/sep" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "relu4_2/sep" + type: "ReLU" + bottom: "conv4_2/sep" + top: "conv4_2/sep" +} +layer { + name: "conv5_1/dw" + type: "Convolution" + bottom: "conv4_2/sep" + top: "conv5_1/dw" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 512 + bias_term: false + pad: 1 + kernel_size: 3 + group: 512 + engine: CAFFE + stride: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv5_1/dw/bn" + type: "BatchNorm" + bottom: "conv5_1/dw" + top: "conv5_1/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv5_1/dw/scale" + type: "Scale" + bottom: "conv5_1/dw" + top: "conv5_1/dw" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "relu5_1/dw" + type: "ReLU" + bottom: "conv5_1/dw" + top: "conv5_1/dw" +} +layer { + name: "conv5_1/sep" + type: "Convolution" + bottom: "conv5_1/dw" + top: "conv5_1/sep" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 512 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv5_1/sep/bn" + type: "BatchNorm" + bottom: "conv5_1/sep" + top: "conv5_1/sep" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv5_1/sep/scale" + type: "Scale" + bottom: "conv5_1/sep" + top: "conv5_1/sep" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "relu5_1/sep" + type: "ReLU" + bottom: "conv5_1/sep" + top: "conv5_1/sep" +} +layer { + name: "conv5_2/dw" + type: "Convolution" + bottom: "conv5_1/sep" + top: "conv5_2/dw" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 512 + bias_term: false + pad: 1 + kernel_size: 3 + group: 512 + engine: CAFFE + stride: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv5_2/dw/bn" + type: "BatchNorm" + bottom: "conv5_2/dw" + top: "conv5_2/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv5_2/dw/scale" + type: "Scale" + bottom: "conv5_2/dw" + top: "conv5_2/dw" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "relu5_2/dw" + type: "ReLU" + bottom: "conv5_2/dw" + top: "conv5_2/dw" +} +layer { + name: "conv5_2/sep" + type: "Convolution" + bottom: "conv5_2/dw" + top: "conv5_2/sep" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 512 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv5_2/sep/bn" + type: "BatchNorm" + bottom: "conv5_2/sep" + top: "conv5_2/sep" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv5_2/sep/scale" + type: "Scale" + bottom: "conv5_2/sep" + top: "conv5_2/sep" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "relu5_2/sep" + type: "ReLU" + bottom: "conv5_2/sep" + top: "conv5_2/sep" +} +layer { + name: "conv5_3/dw" + type: "Convolution" + bottom: "conv5_2/sep" + top: "conv5_3/dw" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 512 + bias_term: false + pad: 1 + kernel_size: 3 + group: 512 + engine: CAFFE + stride: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv5_3/dw/bn" + type: "BatchNorm" + bottom: "conv5_3/dw" + top: "conv5_3/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv5_3/dw/scale" + type: "Scale" + bottom: "conv5_3/dw" + top: "conv5_3/dw" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "relu5_3/dw" + type: "ReLU" + bottom: "conv5_3/dw" + top: "conv5_3/dw" +} +layer { + name: "conv5_3/sep" + type: "Convolution" + bottom: "conv5_3/dw" + top: "conv5_3/sep" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 512 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv5_3/sep/bn" + type: "BatchNorm" + bottom: "conv5_3/sep" + top: "conv5_3/sep" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv5_3/sep/scale" + type: "Scale" + bottom: "conv5_3/sep" + top: "conv5_3/sep" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "relu5_3/sep" + type: "ReLU" + bottom: "conv5_3/sep" + top: "conv5_3/sep" +} +layer { + name: "conv5_4/dw" + type: "Convolution" + bottom: "conv5_3/sep" + top: "conv5_4/dw" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 512 + bias_term: false + pad: 1 + kernel_size: 3 + group: 512 + engine: CAFFE + stride: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv5_4/dw/bn" + type: "BatchNorm" + bottom: "conv5_4/dw" + top: "conv5_4/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv5_4/dw/scale" + type: "Scale" + bottom: "conv5_4/dw" + top: "conv5_4/dw" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "relu5_4/dw" + type: "ReLU" + bottom: "conv5_4/dw" + top: "conv5_4/dw" +} +layer { + name: "conv5_4/sep" + type: "Convolution" + bottom: "conv5_4/dw" + top: "conv5_4/sep" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 512 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv5_4/sep/bn" + type: "BatchNorm" + bottom: "conv5_4/sep" + top: "conv5_4/sep" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv5_4/sep/scale" + type: "Scale" + bottom: "conv5_4/sep" + top: "conv5_4/sep" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "relu5_4/sep" + type: "ReLU" + bottom: "conv5_4/sep" + top: "conv5_4/sep" +} +layer { + name: "conv5_5/dw" + type: "Convolution" + bottom: "conv5_4/sep" + top: "conv5_5/dw" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 512 + bias_term: false + pad: 1 + kernel_size: 3 + group: 512 + engine: CAFFE + stride: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv5_5/dw/bn" + type: "BatchNorm" + bottom: "conv5_5/dw" + top: "conv5_5/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv5_5/dw/scale" + type: "Scale" + bottom: "conv5_5/dw" + top: "conv5_5/dw" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "relu5_5/dw" + type: "ReLU" + bottom: "conv5_5/dw" + top: "conv5_5/dw" +} +layer { + name: "conv5_5/sep" + type: "Convolution" + bottom: "conv5_5/dw" + top: "conv5_5/sep" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 512 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv5_5/sep/bn" + type: "BatchNorm" + bottom: "conv5_5/sep" + top: "conv5_5/sep" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv5_5/sep/scale" + type: "Scale" + bottom: "conv5_5/sep" + top: "conv5_5/sep" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "relu5_5/sep" + type: "ReLU" + bottom: "conv5_5/sep" + top: "conv5_5/sep" +} +layer { + name: "conv5_6/dw" + type: "Convolution" + bottom: "conv5_5/sep" + top: "conv5_6/dw" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 512 + bias_term: false + pad: 1 + kernel_size: 3 + group: 512 + engine: CAFFE + stride: 2 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv5_6/dw/bn" + type: "BatchNorm" + bottom: "conv5_6/dw" + top: "conv5_6/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv5_6/dw/scale" + type: "Scale" + bottom: "conv5_6/dw" + top: "conv5_6/dw" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "relu5_6/dw" + type: "ReLU" + bottom: "conv5_6/dw" + top: "conv5_6/dw" +} +layer { + name: "conv5_6/sep" + type: "Convolution" + bottom: "conv5_6/dw" + top: "conv5_6/sep" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 1024 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv5_6/sep/bn" + type: "BatchNorm" + bottom: "conv5_6/sep" + top: "conv5_6/sep" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv5_6/sep/scale" + type: "Scale" + bottom: "conv5_6/sep" + top: "conv5_6/sep" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "relu5_6/sep" + type: "ReLU" + bottom: "conv5_6/sep" + top: "conv5_6/sep" +} +layer { + name: "conv6/dw" + type: "Convolution" + bottom: "conv5_6/sep" + top: "conv6/dw" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 1024 + bias_term: false + pad: 1 + kernel_size: 3 + group: 1024 + engine: CAFFE + stride: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv6/dw/bn" + type: "BatchNorm" + bottom: "conv6/dw" + top: "conv6/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv6/dw/scale" + type: "Scale" + bottom: "conv6/dw" + top: "conv6/dw" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "relu6/dw" + type: "ReLU" + bottom: "conv6/dw" + top: "conv6/dw" +} +layer { + name: "conv6/sep" + type: "Convolution" + bottom: "conv6/dw" + top: "conv6/sep" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 1024 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv6/sep/bn" + type: "BatchNorm" + bottom: "conv6/sep" + top: "conv6/sep" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + batch_norm_param { + use_global_stats: true + eps: 1e-5 + } +} +layer { + name: "conv6/sep/scale" + type: "Scale" + bottom: "conv6/sep" + top: "conv6/sep" + param { + lr_mult: 1 + decay_mult: 0 + } + param { + lr_mult: 1 + decay_mult: 0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "relu6/sep" + type: "ReLU" + bottom: "conv6/sep" + top: "conv6/sep" +} +layer { + name: "pool6" + type: "Pooling" + bottom: "conv6/sep" + top: "pool6" + pooling_param { + pool: AVE + global_pooling: true + } +} +layer { + name: "fc7" + type: "Convolution" + bottom: "pool6" + top: "fc7" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 1000 + kernel_size: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "prob" + type: "Softmax" + bottom: "fc7" + top: "prob" +} diff --git a/rknn-toolkit2/examples/functions/model_pruning/test.py b/rknn-toolkit2/examples/functions/model_pruning/test.py new file mode 100755 index 0000000000000000000000000000000000000000..05e97c8cc203358c501faf0d475f341149f6f4de --- /dev/null +++ b/rknn-toolkit2/examples/functions/model_pruning/test.py @@ -0,0 +1,94 @@ +import numpy as np +import cv2 +from rknn.api import RKNN + + +def show_outputs(outputs): + np.save('./functions_model_pruning_0.npy', outputs[0]) + output = outputs[0].reshape(-1) + print(output.shape) + index = sorted(range(len(output)), key=lambda k : output[k], reverse=True) + fp = open('./labels.txt', 'r') + labels = fp.readlines() + top5_str = 'mobilenet\n-----TOP 5-----\n' + for i in range(5): + value = output[index[i]] + if value > 0: + topi = '[{:>3d}] score:{:.6f} class:"{}"\n'.format(index[i], value, labels[index[i]].strip().split(':')[-1]) + else: + topi = '[ -1]: 0.0\n' + top5_str += topi + print(top5_str.strip()) + + +if __name__ == '__main__': + + # Create RKNN object + rknn = RKNN(verbose=True) + + # Pre-process config + print('--> Config model') + rknn.config(mean_values=[103.94, 116.78, 123.68], std_values=[58.82, 58.82, 58.82], quant_img_RGB2BGR=True, target_platform='rk3566', model_pruning=True) + print('done') + + # Load model (from https://github.com/shicai/MobileNet-Caffe) + print('--> Loading model') + ret = rknn.load_caffe(model='./mobilenet_deploy.prototxt', + blobs='./mobilenet.caffemodel') + if ret != 0: + print('Load model failed!') + exit(ret) + print('done') + + # Build model + print('--> Building model') + ret = rknn.build(do_quantization=True, dataset='./dataset.txt') + if ret != 0: + print('Build model failed!') + exit(ret) + print('done') + + # Tips + print('') + print('======================================== Tips =================================================================') + print('When verbose = True, the following similar prompts will appear during the build process, indicating that ') + print('model pruning has been effective for this model:') + print('') + print(' I model_pruning ...') + print(' I model_pruning results:') + print(' I Weight: -1.12145 MB (-6.9%)') + print(' I GFLOPs: -0.15563 (-13.4%)') + print(' I model_pruning done.') + print('') + print('The meaning of this prompts is that 6.9% of the weight was removed, and approximately 13.4% of computility were saved.') + print('Please note that not all models can be pruned, only models with sparse weights can be pruned.') + print('===============================================================================================================') + print('') + + # Export rknn model + print('--> Export rknn model') + ret = rknn.export_rknn('./mobilenet.rknn') + if ret != 0: + print('Export rknn model failed!') + exit(ret) + print('done') + + # Set inputs + img = cv2.imread('./dog_224x224.jpg') + img = np.expand_dims(img, 0) + + # Init runtime environment + print('--> Init runtime environment') + ret = rknn.init_runtime() + if ret != 0: + print('Init runtime environment failed!') + exit(ret) + print('done') + + # Inference + print('--> Running model') + outputs = rknn.inference(inputs=[img], data_format=['nhwc']) + show_outputs(outputs) + print('done') + + rknn.release() diff --git a/rknn-toolkit2/examples/functions/multi_batch/README.md b/rknn-toolkit2/examples/functions/multi_batch/README.md new file mode 100644 index 0000000000000000000000000000000000000000..2fa3d15b72e6b6f94794009688397e66cf8d6c07 --- /dev/null +++ b/rknn-toolkit2/examples/functions/multi_batch/README.md @@ -0,0 +1,45 @@ +# How to expand batch for use multi-batch function + +## Model Source +The model used in this example come from the following open source projects: +https://github.com/shicai/MobileNet-Caffe + +## Script Usage +*Usage:* +``` +python test.py +``` +*Description:* +- The default target platform in script is 'rk3566', please modify the 'target_platform' parameter of 'rknn.config' according to the actual platform. +- If connecting board is required, pl'ease add the 'target' parameter in 'rknn.init_runtime'. +- You can modify the 'rknn_batch_size' parameter of 'rknn.build' to achieve the effect of multi-batch. + +## Expected Results +This example will print the TOP5 labels and corresponding scores of the test image classification results for every batch, as follows: +``` +----- Batch 0: TOP 5 ----- +[155] score:0.993652 class:"Shih-Tzu" +[154] score:0.002834 class:"Pekinese, Pekingese, Peke" +[204] score:0.002172 class:"Lhasa, Lhasa apso" +[283] score:0.000571 class:"Persian cat" +[284] score:0.000133 class:"Siamese cat, Siamese" +----- Batch 1: TOP 5 ----- +[ 1] score:0.598145 class:"goldfish, Carassius auratus" +[794] score:0.062073 class:"shower curtain" +[996] score:0.062073 class:"hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa" +[927] score:0.041687 class:"trifle" +[115] score:0.027863 class:"sea slug, nudibranch" +----- Batch 2: TOP 5 ----- +[812] score:0.999023 class:"space shuttle" +[148] score:0.000293 class:"killer whale, killer, orca, grampus, sea wolf, Orcinus orca" +[517] score:0.000132 class:"crane" +[833] score:0.000132 class:"submarine, pigboat, sub, U-boat" +[525] score:0.000089 class:"dam, dike, dyke" +----- Batch 3: TOP 5 ----- +[155] score:0.993652 class:"Shih-Tzu" +[154] score:0.002834 class:"Pekinese, Pekingese, Peke" +[204] score:0.002172 class:"Lhasa, Lhasa apso" +[283] score:0.000571 class:"Persian cat" +[284] score:0.000133 class:"Siamese cat, Siamese" +``` +- Note: Different platforms, different versions of tools and drivers may have slightly different results. \ No newline at end of file diff --git a/rknn-toolkit2/examples/functions/multi_batch/dataset.txt b/rknn-toolkit2/examples/functions/multi_batch/dataset.txt new file mode 100644 index 0000000000000000000000000000000000000000..c5e5388f906f59488dc7f0b25dd87a81048b468e --- /dev/null +++ b/rknn-toolkit2/examples/functions/multi_batch/dataset.txt @@ -0,0 +1,3 @@ +dog_224x224.jpg +goldfish_224x224.jpg +space_shuttle_224.jpg \ No newline at end of file diff --git a/rknn-toolkit2/examples/functions/multi_batch/dog_224x224.jpg b/rknn-toolkit2/examples/functions/multi_batch/dog_224x224.jpg new file mode 100644 index 0000000000000000000000000000000000000000..004b0416e23454a12eb06eaba238c7e2d5f2b0fc --- /dev/null +++ b/rknn-toolkit2/examples/functions/multi_batch/dog_224x224.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c350299c6283d5f62fecf1f845b6b3be9aafec8dff528ca09a129990f0a584b0 +size 18889 diff --git a/rknn-toolkit2/examples/functions/multi_batch/goldfish_224x224.jpg b/rknn-toolkit2/examples/functions/multi_batch/goldfish_224x224.jpg new file mode 100644 index 0000000000000000000000000000000000000000..cc48ae72082b079027ce726a8e0fabfa65f538e0 --- /dev/null +++ b/rknn-toolkit2/examples/functions/multi_batch/goldfish_224x224.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d3001d36c811f8d3c5cccbed3db00932eccac235e037b96dd303b95f13b61b1d +size 61574 diff --git a/rknn-toolkit2/examples/functions/multi_batch/labels.txt b/rknn-toolkit2/examples/functions/multi_batch/labels.txt new file mode 100644 index 0000000000000000000000000000000000000000..0993780f99088349e2c156a5a8c57ce1013eb493 --- /dev/null +++ b/rknn-toolkit2/examples/functions/multi_batch/labels.txt @@ -0,0 +1,1000 @@ +0:tench, Tinca tinca +1:goldfish, Carassius auratus +2:great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias +3:tiger shark, Galeocerdo cuvieri +4:hammerhead, hammerhead shark +5:electric ray, crampfish, numbfish, torpedo +6:stingray +7:cock +8:hen +9:ostrich, Struthio camelus +10:brambling, Fringilla montifringilla +11:goldfinch, Carduelis carduelis +12:house finch, linnet, Carpodacus mexicanus +13:junco, snowbird +14:indigo bunting, indigo finch, indigo bird, Passerina cyanea +15:robin, American robin, Turdus migratorius +16:bulbul +17:jay +18:magpie +19:chickadee +20:water ouzel, dipper +21:kite +22:bald eagle, American eagle, Haliaeetus leucocephalus +23:vulture +24:great grey owl, great gray owl, Strix nebulosa +25:European fire salamander, Salamandra salamandra +26:common newt, Triturus vulgaris +27:eft +28:spotted salamander, Ambystoma maculatum +29:axolotl, mud puppy, Ambystoma mexicanum +30:bullfrog, Rana catesbeiana +31:tree frog, tree-frog +32:tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui +33:loggerhead, loggerhead turtle, Caretta caretta +34:leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea +35:mud turtle +36:terrapin +37:box turtle, box tortoise +38:banded gecko +39:common iguana, iguana, Iguana iguana +40:American chameleon, anole, Anolis carolinensis +41:whiptail, whiptail lizard +42:agama +43:frilled lizard, Chlamydosaurus kingi +44:alligator lizard +45:Gila monster, Heloderma suspectum +46:green lizard, Lacerta viridis +47:African chameleon, Chamaeleo chamaeleon +48:Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis +49:African crocodile, Nile crocodile, Crocodylus niloticus +50:American alligator, Alligator mississipiensis +51:triceratops +52:thunder snake, worm snake, Carphophis amoenus +53:ringneck snake, ring-necked snake, ring snake +54:hognose snake, puff adder, sand viper +55:green snake, grass snake +56:king snake, kingsnake +57:garter snake, grass snake +58:water snake +59:vine snake +60:night snake, Hypsiglena torquata +61:boa constrictor, Constrictor constrictor +62:rock python, rock snake, Python sebae +63:Indian cobra, Naja naja +64:green mamba +65:sea snake +66:horned viper, cerastes, sand viper, horned asp, Cerastes cornutus +67:diamondback, diamondback rattlesnake, Crotalus adamanteus +68:sidewinder, horned rattlesnake, Crotalus cerastes +69:trilobite +70:harvestman, daddy longlegs, Phalangium opilio +71:scorpion +72:black and gold garden spider, Argiope aurantia +73:barn spider, Araneus cavaticus +74:garden spider, Aranea diademata +75:black widow, Latrodectus mactans +76:tarantula +77:wolf spider, hunting spider +78:tick +79:centipede +80:black grouse +81:ptarmigan +82:ruffed grouse, partridge, Bonasa umbellus +83:prairie chicken, prairie grouse, prairie fowl +84:peacock +85:quail +86:partridge +87:African grey, African gray, Psittacus erithacus +88:macaw +89:sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita +90:lorikeet +91:coucal +92:bee eater +93:hornbill +94:hummingbird +95:jacamar +96:toucan +97:drake +98:red-breasted merganser, Mergus serrator +99:goose +100:black swan, Cygnus atratus +101:tusker +102:echidna, spiny anteater, anteater +103:platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus +104:wallaby, brush kangaroo +105:koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus +106:wombat +107:jellyfish +108:sea anemone, anemone +109:brain coral +110:flatworm, platyhelminth +111:nematode, nematode worm, roundworm +112:conch +113:snail +114:slug +115:sea slug, nudibranch +116:chiton, coat-of-mail shell, sea cradle, polyplacophore +117:chambered nautilus, pearly nautilus, nautilus +118:Dungeness crab, Cancer magister +119:rock crab, Cancer irroratus +120:fiddler crab +121:king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica +122:American lobster, Northern lobster, Maine lobster, Homarus americanus +123:spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish +124:crayfish, crawfish, crawdad, crawdaddy +125:hermit crab +126:isopod +127:white stork, Ciconia ciconia +128:black stork, Ciconia nigra +129:spoonbill +130:flamingo +131:little blue heron, Egretta caerulea +132:American egret, great white heron, Egretta albus +133:bittern +134:crane +135:limpkin, Aramus pictus +136:European gallinule, Porphyrio porphyrio +137:American coot, marsh hen, mud hen, water hen, Fulica americana +138:bustard +139:ruddy turnstone, Arenaria interpres +140:red-backed sandpiper, dunlin, Erolia alpina +141:redshank, Tringa totanus +142:dowitcher +143:oystercatcher, oyster catcher +144:pelican +145:king penguin, Aptenodytes patagonica +146:albatross, mollymawk +147:grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus +148:killer whale, killer, orca, grampus, sea wolf, Orcinus orca +149:dugong, Dugong dugon +150:sea lion +151:Chihuahua +152:Japanese spaniel +153:Maltese dog, Maltese terrier, Maltese +154:Pekinese, Pekingese, Peke +155:Shih-Tzu +156:Blenheim spaniel +157:papillon +158:toy terrier +159:Rhodesian ridgeback +160:Afghan hound, Afghan +161:basset, basset hound +162:beagle +163:bloodhound, sleuthhound +164:bluetick +165:black-and-tan coonhound +166:Walker hound, Walker foxhound +167:English foxhound +168:redbone +169:borzoi, Russian wolfhound +170:Irish wolfhound +171:Italian greyhound +172:whippet +173:Ibizan hound, Ibizan Podenco +174:Norwegian elkhound, elkhound +175:otterhound, otter hound +176:Saluki, gazelle hound +177:Scottish deerhound, deerhound +178:Weimaraner +179:Staffordshire bullterrier, Staffordshire bull terrier +180:American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier +181:Bedlington terrier +182:Border terrier +183:Kerry blue terrier +184:Irish terrier +185:Norfolk terrier +186:Norwich terrier +187:Yorkshire terrier +188:wire-haired fox terrier +189:Lakeland terrier +190:Sealyham terrier, Sealyham +191:Airedale, Airedale terrier +192:cairn, cairn terrier +193:Australian terrier +194:Dandie Dinmont, Dandie Dinmont terrier +195:Boston bull, Boston terrier +196:miniature schnauzer +197:giant schnauzer +198:standard schnauzer +199:Scotch terrier, Scottish terrier, Scottie +200:Tibetan terrier, chrysanthemum dog +201:silky terrier, Sydney silky +202:soft-coated wheaten terrier +203:West Highland white terrier +204:Lhasa, Lhasa apso +205:flat-coated retriever +206:curly-coated retriever +207:golden retriever +208:Labrador retriever +209:Chesapeake Bay retriever +210:German short-haired pointer +211:vizsla, Hungarian pointer +212:English setter +213:Irish setter, red setter +214:Gordon setter +215:Brittany spaniel +216:clumber, clumber spaniel +217:English springer, English springer spaniel +218:Welsh springer spaniel +219:cocker spaniel, English cocker spaniel, cocker +220:Sussex spaniel +221:Irish water spaniel +222:kuvasz +223:schipperke +224:groenendael +225:malinois +226:briard +227:kelpie +228:komondor +229:Old English sheepdog, bobtail +230:Shetland sheepdog, Shetland sheep dog, Shetland +231:collie +232:Border collie +233:Bouvier des Flandres, Bouviers des Flandres +234:Rottweiler +235:German shepherd, German shepherd dog, German police dog, alsatian +236:Doberman, Doberman pinscher +237:miniature pinscher +238:Greater Swiss Mountain dog +239:Bernese mountain dog +240:Appenzeller +241:EntleBucher +242:boxer +243:bull mastiff +244:Tibetan mastiff +245:French bulldog +246:Great Dane +247:Saint Bernard, St Bernard +248:Eskimo dog, husky +249:malamute, malemute, Alaskan malamute +250:Siberian husky +251:dalmatian, coach dog, carriage dog +252:affenpinscher, monkey pinscher, monkey dog +253:basenji +254:pug, pug-dog +255:Leonberg +256:Newfoundland, Newfoundland dog +257:Great Pyrenees +258:Samoyed, Samoyede +259:Pomeranian +260:chow, chow chow +261:keeshond +262:Brabancon griffon +263:Pembroke, Pembroke Welsh corgi +264:Cardigan, Cardigan Welsh corgi +265:toy poodle +266:miniature poodle +267:standard poodle +268:Mexican hairless +269:timber wolf, grey wolf, gray wolf, Canis lupus +270:white wolf, Arctic wolf, Canis lupus tundrarum +271:red wolf, maned wolf, Canis rufus, Canis niger +272:coyote, prairie wolf, brush wolf, Canis latrans +273:dingo, warrigal, warragal, Canis dingo +274:dhole, Cuon alpinus +275:African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus +276:hyena, hyaena +277:red fox, Vulpes vulpes +278:kit fox, Vulpes macrotis +279:Arctic fox, white fox, Alopex lagopus +280:grey fox, gray fox, Urocyon cinereoargenteus +281:tabby, tabby cat +282:tiger cat +283:Persian cat +284:Siamese cat, Siamese +285:Egyptian cat +286:cougar, puma, catamount, mountain lion, painter, panther, Felis concolor +287:lynx, catamount +288:leopard, Panthera pardus +289:snow leopard, ounce, Panthera uncia +290:jaguar, panther, Panthera onca, Felis onca +291:lion, king of beasts, Panthera leo +292:tiger, Panthera tigris +293:cheetah, chetah, Acinonyx jubatus +294:brown bear, bruin, Ursus arctos +295:American black bear, black bear, Ursus americanus, Euarctos americanus +296:ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus +297:sloth bear, Melursus ursinus, Ursus ursinus +298:mongoose +299:meerkat, mierkat +300:tiger beetle +301:ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle +302:ground beetle, carabid beetle +303:long-horned beetle, longicorn, longicorn beetle +304:leaf beetle, chrysomelid +305:dung beetle +306:rhinoceros beetle +307:weevil +308:fly +309:bee +310:ant, emmet, pismire +311:grasshopper, hopper +312:cricket +313:walking stick, walkingstick, stick insect +314:cockroach, roach +315:mantis, mantid +316:cicada, cicala +317:leafhopper +318:lacewing, lacewing fly +319:dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk +320:damselfly +321:admiral +322:ringlet, ringlet butterfly +323:monarch, monarch butterfly, milkweed butterfly, Danaus plexippus +324:cabbage butterfly +325:sulphur butterfly, sulfur butterfly +326:lycaenid, lycaenid butterfly +327:starfish, sea star +328:sea urchin +329:sea cucumber, holothurian +330:wood rabbit, cottontail, cottontail rabbit +331:hare +332:Angora, Angora rabbit +333:hamster +334:porcupine, hedgehog +335:fox squirrel, eastern fox squirrel, Sciurus niger +336:marmot +337:beaver +338:guinea pig, Cavia cobaya +339:sorrel +340:zebra +341:hog, pig, grunter, squealer, Sus scrofa +342:wild boar, boar, Sus scrofa +343:warthog +344:hippopotamus, hippo, river horse, Hippopotamus amphibius +345:ox +346:water buffalo, water ox, Asiatic buffalo, Bubalus bubalis +347:bison +348:ram, tup +349:bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis +350:ibex, Capra ibex +351:hartebeest +352:impala, Aepyceros melampus +353:gazelle +354:Arabian camel, dromedary, Camelus dromedarius +355:llama +356:weasel +357:mink +358:polecat, fitch, foulmart, foumart, Mustela putorius +359:black-footed ferret, ferret, Mustela nigripes +360:otter +361:skunk, polecat, wood pussy +362:badger +363:armadillo +364:three-toed sloth, ai, Bradypus tridactylus +365:orangutan, orang, orangutang, Pongo pygmaeus +366:gorilla, Gorilla gorilla +367:chimpanzee, chimp, Pan troglodytes +368:gibbon, Hylobates lar +369:siamang, Hylobates syndactylus, Symphalangus syndactylus +370:guenon, guenon monkey +371:patas, hussar monkey, Erythrocebus patas +372:baboon +373:macaque +374:langur +375:colobus, colobus monkey +376:proboscis monkey, Nasalis larvatus +377:marmoset +378:capuchin, ringtail, Cebus capucinus +379:howler monkey, howler +380:titi, titi monkey +381:spider monkey, Ateles geoffroyi +382:squirrel monkey, Saimiri sciureus +383:Madagascar cat, ring-tailed lemur, Lemur catta +384:indri, indris, Indri indri, Indri brevicaudatus +385:Indian elephant, Elephas maximus +386:African elephant, Loxodonta africana +387:lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens +388:giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca +389:barracouta, snoek +390:eel +391:coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch +392:rock beauty, Holocanthus tricolor +393:anemone fish +394:sturgeon +395:gar, garfish, garpike, billfish, Lepisosteus osseus +396:lionfish +397:puffer, pufferfish, blowfish, globefish +398:abacus +399:abaya +400:academic gown, academic robe, judge's robe +401:accordion, piano accordion, squeeze box +402:acoustic guitar +403:aircraft carrier, carrier, flattop, attack aircraft carrier +404:airliner +405:airship, dirigible +406:altar +407:ambulance +408:amphibian, amphibious vehicle +409:analog clock +410:apiary, bee house +411:apron +412:ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin +413:assault rifle, assault gun +414:backpack, back pack, knapsack, packsack, rucksack, haversack +415:bakery, bakeshop, bakehouse +416:balance beam, beam +417:balloon +418:ballpoint, ballpoint pen, ballpen, Biro +419:Band Aid +420:banjo +421:bannister, banister, balustrade, balusters, handrail +422:barbell +423:barber chair +424:barbershop +425:barn +426:barometer +427:barrel, cask +428:barrow, garden cart, lawn cart, wheelbarrow +429:baseball +430:basketball +431:bassinet +432:bassoon +433:bathing cap, swimming cap +434:bath towel +435:bathtub, bathing tub, bath, tub +436:beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon +437:beacon, lighthouse, beacon light, pharos +438:beaker +439:bearskin, busby, shako +440:beer bottle +441:beer glass +442:bell cote, bell cot +443:bib +444:bicycle-built-for-two, tandem bicycle, tandem +445:bikini, two-piece +446:binder, ring-binder +447:binoculars, field glasses, opera glasses +448:birdhouse +449:boathouse +450:bobsled, bobsleigh, bob +451:bolo tie, bolo, bola tie, bola +452:bonnet, poke bonnet +453:bookcase +454:bookshop, bookstore, bookstall +455:bottlecap +456:bow +457:bow tie, bow-tie, bowtie +458:brass, memorial tablet, plaque +459:brassiere, bra, bandeau +460:breakwater, groin, groyne, mole, bulwark, seawall, jetty +461:breastplate, aegis, egis +462:broom +463:bucket, pail +464:buckle +465:bulletproof vest +466:bullet train, bullet +467:butcher shop, meat market +468:cab, hack, taxi, taxicab +469:caldron, cauldron +470:candle, taper, wax light +471:cannon +472:canoe +473:can opener, tin opener +474:cardigan +475:car mirror +476:carousel, carrousel, merry-go-round, roundabout, whirligig +477:carpenter's kit, tool kit +478:carton +479:car wheel +480:cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM +481:cassette +482:cassette player +483:castle +484:catamaran +485:CD player +486:cello, violoncello +487:cellular telephone, cellular phone, cellphone, cell, mobile phone +488:chain +489:chainlink fence +490:chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour +491:chain saw, chainsaw +492:chest +493:chiffonier, commode +494:chime, bell, gong +495:china cabinet, china closet +496:Christmas stocking +497:church, church building +498:cinema, movie theater, movie theatre, movie house, picture palace +499:cleaver, meat cleaver, chopper +500:cliff dwelling +501:cloak +502:clog, geta, patten, sabot +503:cocktail shaker +504:coffee mug +505:coffeepot +506:coil, spiral, volute, whorl, helix +507:combination lock +508:computer keyboard, keypad +509:confectionery, confectionary, candy store +510:container ship, containership, container vessel +511:convertible +512:corkscrew, bottle screw +513:cornet, horn, trumpet, trump +514:cowboy boot +515:cowboy hat, ten-gallon hat +516:cradle +517:crane +518:crash helmet +519:crate +520:crib, cot +521:Crock Pot +522:croquet ball +523:crutch +524:cuirass +525:dam, dike, dyke +526:desk +527:desktop computer +528:dial telephone, dial phone +529:diaper, nappy, napkin +530:digital clock +531:digital watch +532:dining table, board +533:dishrag, dishcloth +534:dishwasher, dish washer, dishwashing machine +535:disk brake, disc brake +536:dock, dockage, docking facility +537:dogsled, dog sled, dog sleigh +538:dome +539:doormat, welcome mat +540:drilling platform, offshore rig +541:drum, membranophone, tympan +542:drumstick +543:dumbbell +544:Dutch oven +545:electric fan, blower +546:electric guitar +547:electric locomotive +548:entertainment center +549:envelope +550:espresso maker +551:face powder +552:feather boa, boa +553:file, file cabinet, filing cabinet +554:fireboat +555:fire engine, fire truck +556:fire screen, fireguard +557:flagpole, flagstaff +558:flute, transverse flute +559:folding chair +560:football helmet +561:forklift +562:fountain +563:fountain pen +564:four-poster +565:freight car +566:French horn, horn +567:frying pan, frypan, skillet +568:fur coat +569:garbage truck, dustcart +570:gasmask, respirator, gas helmet +571:gas pump, gasoline pump, petrol pump, island dispenser +572:goblet +573:go-kart +574:golf ball +575:golfcart, golf cart +576:gondola +577:gong, tam-tam +578:gown +579:grand piano, grand +580:greenhouse, nursery, glasshouse +581:grille, radiator grille +582:grocery store, grocery, food market, market +583:guillotine +584:hair slide +585:hair spray +586:half track +587:hammer +588:hamper +589:hand blower, blow dryer, blow drier, hair dryer, hair drier +590:hand-held computer, hand-held microcomputer +591:handkerchief, hankie, hanky, hankey +592:hard disc, hard disk, fixed disk +593:harmonica, mouth organ, harp, mouth harp +594:harp +595:harvester, reaper +596:hatchet +597:holster +598:home theater, home theatre +599:honeycomb +600:hook, claw +601:hoopskirt, crinoline +602:horizontal bar, high bar +603:horse cart, horse-cart +604:hourglass +605:iPod +606:iron, smoothing iron +607:jack-o'-lantern +608:jean, blue jean, denim +609:jeep, landrover +610:jersey, T-shirt, tee shirt +611:jigsaw puzzle +612:jinrikisha, ricksha, rickshaw +613:joystick +614:kimono +615:knee pad +616:knot +617:lab coat, laboratory coat +618:ladle +619:lampshade, lamp shade +620:laptop, laptop computer +621:lawn mower, mower +622:lens cap, lens cover +623:letter opener, paper knife, paperknife +624:library +625:lifeboat +626:lighter, light, igniter, ignitor +627:limousine, limo +628:liner, ocean liner +629:lipstick, lip rouge +630:Loafer +631:lotion +632:loudspeaker, speaker, speaker unit, loudspeaker system, speaker system +633:loupe, jeweler's loupe +634:lumbermill, sawmill +635:magnetic compass +636:mailbag, postbag +637:mailbox, letter box +638:maillot +639:maillot, tank suit +640:manhole cover +641:maraca +642:marimba, xylophone +643:mask +644:matchstick +645:maypole +646:maze, labyrinth +647:measuring cup +648:medicine chest, medicine cabinet +649:megalith, megalithic structure +650:microphone, mike +651:microwave, microwave oven +652:military uniform +653:milk can +654:minibus +655:miniskirt, mini +656:minivan +657:missile +658:mitten +659:mixing bowl +660:mobile home, manufactured home +661:Model T +662:modem +663:monastery +664:monitor +665:moped +666:mortar +667:mortarboard +668:mosque +669:mosquito net +670:motor scooter, scooter +671:mountain bike, all-terrain bike, off-roader +672:mountain tent +673:mouse, computer mouse +674:mousetrap +675:moving van +676:muzzle +677:nail +678:neck brace +679:necklace +680:nipple +681:notebook, notebook computer +682:obelisk +683:oboe, hautboy, hautbois +684:ocarina, sweet potato +685:odometer, hodometer, mileometer, milometer +686:oil filter +687:organ, pipe organ +688:oscilloscope, scope, cathode-ray oscilloscope, CRO +689:overskirt +690:oxcart +691:oxygen mask +692:packet +693:paddle, boat paddle +694:paddlewheel, paddle wheel +695:padlock +696:paintbrush +697:pajama, pyjama, pj's, jammies +698:palace +699:panpipe, pandean pipe, syrinx +700:paper towel +701:parachute, chute +702:parallel bars, bars +703:park bench +704:parking meter +705:passenger car, coach, carriage +706:patio, terrace +707:pay-phone, pay-station +708:pedestal, plinth, footstall +709:pencil box, pencil case +710:pencil sharpener +711:perfume, essence +712:Petri dish +713:photocopier +714:pick, plectrum, plectron +715:pickelhaube +716:picket fence, paling +717:pickup, pickup truck +718:pier +719:piggy bank, penny bank +720:pill bottle +721:pillow +722:ping-pong ball +723:pinwheel +724:pirate, pirate ship +725:pitcher, ewer +726:plane, carpenter's plane, woodworking plane +727:planetarium +728:plastic bag +729:plate rack +730:plow, plough +731:plunger, plumber's helper +732:Polaroid camera, Polaroid Land camera +733:pole +734:police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria +735:poncho +736:pool table, billiard table, snooker table +737:pop bottle, soda bottle +738:pot, flowerpot +739:potter's wheel +740:power drill +741:prayer rug, prayer mat +742:printer +743:prison, prison house +744:projectile, missile +745:projector +746:puck, hockey puck +747:punching bag, punch bag, punching ball, punchball +748:purse +749:quill, quill pen +750:quilt, comforter, comfort, puff +751:racer, race car, racing car +752:racket, racquet +753:radiator +754:radio, wireless +755:radio telescope, radio reflector +756:rain barrel +757:recreational vehicle, RV, R.V. +758:reel +759:reflex camera +760:refrigerator, icebox +761:remote control, remote +762:restaurant, eating house, eating place, eatery +763:revolver, six-gun, six-shooter +764:rifle +765:rocking chair, rocker +766:rotisserie +767:rubber eraser, rubber, pencil eraser +768:rugby ball +769:rule, ruler +770:running shoe +771:safe +772:safety pin +773:saltshaker, salt shaker +774:sandal +775:sarong +776:sax, saxophone +777:scabbard +778:scale, weighing machine +779:school bus +780:schooner +781:scoreboard +782:screen, CRT screen +783:screw +784:screwdriver +785:seat belt, seatbelt +786:sewing machine +787:shield, buckler +788:shoe shop, shoe-shop, shoe store +789:shoji +790:shopping basket +791:shopping cart +792:shovel +793:shower cap +794:shower curtain +795:ski +796:ski mask +797:sleeping bag +798:slide rule, slipstick +799:sliding door +800:slot, one-armed bandit +801:snorkel +802:snowmobile +803:snowplow, snowplough +804:soap dispenser +805:soccer ball +806:sock +807:solar dish, solar collector, solar furnace +808:sombrero +809:soup bowl +810:space bar +811:space heater +812:space shuttle +813:spatula +814:speedboat +815:spider web, spider's web +816:spindle +817:sports car, sport car +818:spotlight, spot +819:stage +820:steam locomotive +821:steel arch bridge +822:steel drum +823:stethoscope +824:stole +825:stone wall +826:stopwatch, stop watch +827:stove +828:strainer +829:streetcar, tram, tramcar, trolley, trolley car +830:stretcher +831:studio couch, day bed +832:stupa, tope +833:submarine, pigboat, sub, U-boat +834:suit, suit of clothes +835:sundial +836:sunglass +837:sunglasses, dark glasses, shades +838:sunscreen, sunblock, sun blocker +839:suspension bridge +840:swab, swob, mop +841:sweatshirt +842:swimming trunks, bathing trunks +843:swing +844:switch, electric switch, electrical switch +845:syringe +846:table lamp +847:tank, army tank, armored combat vehicle, armoured combat vehicle +848:tape player +849:teapot +850:teddy, teddy bear +851:television, television system +852:tennis ball +853:thatch, thatched roof +854:theater curtain, theatre curtain +855:thimble +856:thresher, thrasher, threshing machine +857:throne +858:tile roof +859:toaster +860:tobacco shop, tobacconist shop, tobacconist +861:toilet seat +862:torch +863:totem pole +864:tow truck, tow car, wrecker +865:toyshop +866:tractor +867:trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi +868:tray +869:trench coat +870:tricycle, trike, velocipede +871:trimaran +872:tripod +873:triumphal arch +874:trolleybus, trolley coach, trackless trolley +875:trombone +876:tub, vat +877:turnstile +878:typewriter keyboard +879:umbrella +880:unicycle, monocycle +881:upright, upright piano +882:vacuum, vacuum cleaner +883:vase +884:vault +885:velvet +886:vending machine +887:vestment +888:viaduct +889:violin, fiddle +890:volleyball +891:waffle iron +892:wall clock +893:wallet, billfold, notecase, pocketbook +894:wardrobe, closet, press +895:warplane, military plane +896:washbasin, handbasin, washbowl, lavabo, wash-hand basin +897:washer, automatic washer, washing machine +898:water bottle +899:water jug +900:water tower +901:whiskey jug +902:whistle +903:wig +904:window screen +905:window shade +906:Windsor tie +907:wine bottle +908:wing +909:wok +910:wooden spoon +911:wool, woolen, woollen +912:worm fence, snake fence, snake-rail fence, Virginia fence +913:wreck +914:yawl +915:yurt +916:web site, website, internet site, site +917:comic book +918:crossword puzzle, crossword +919:street sign +920:traffic light, traffic signal, stoplight +921:book jacket, dust cover, dust jacket, dust wrapper +922:menu +923:plate +924:guacamole +925:consomme +926:hot pot, hotpot +927:trifle +928:ice cream, icecream +929:ice lolly, lolly, lollipop, popsicle +930:French loaf +931:bagel, beigel +932:pretzel +933:cheeseburger +934:hotdog, hot dog, red hot +935:mashed potato +936:head cabbage +937:broccoli +938:cauliflower +939:zucchini, courgette +940:spaghetti squash +941:acorn squash +942:butternut squash +943:cucumber, cuke +944:artichoke, globe artichoke +945:bell pepper +946:cardoon +947:mushroom +948:Granny Smith +949:strawberry +950:orange +951:lemon +952:fig +953:pineapple, ananas +954:banana +955:jackfruit, jak, jack +956:custard apple +957:pomegranate +958:hay +959:carbonara +960:chocolate sauce, chocolate syrup +961:dough +962:meat loaf, meatloaf +963:pizza, pizza pie +964:potpie +965:burrito +966:red wine +967:espresso +968:cup +969:eggnog +970:alp +971:bubble +972:cliff, drop, drop-off +973:coral reef +974:geyser +975:lakeside, lakeshore +976:promontory, headland, head, foreland +977:sandbar, sand bar +978:seashore, coast, seacoast, sea-coast +979:valley, vale +980:volcano +981:ballplayer, baseball player +982:groom, bridegroom +983:scuba diver +984:rapeseed +985:daisy +986:yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum +987:corn +988:acorn +989:hip, rose hip, rosehip +990:buckeye, horse chestnut, conker +991:coral fungus +992:agaric +993:gyromitra +994:stinkhorn, carrion fungus +995:earthstar +996:hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa +997:bolete +998:ear, spike, capitulum +999:toilet tissue, toilet paper, bathroom tissue \ No newline at end of file diff --git a/rknn-toolkit2/examples/functions/multi_batch/space_shuttle_224.jpg b/rknn-toolkit2/examples/functions/multi_batch/space_shuttle_224.jpg new file mode 100644 index 0000000000000000000000000000000000000000..412a855c379648d80d5b4267ba1da58188cdee1c --- /dev/null +++ b/rknn-toolkit2/examples/functions/multi_batch/space_shuttle_224.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a827cc9060d15cd34b1b5ac512f4d6d8366416a5874a2075951991cbacef78d4 +size 23472 diff --git a/rknn-toolkit2/examples/functions/multi_batch/test.py b/rknn-toolkit2/examples/functions/multi_batch/test.py new file mode 100755 index 0000000000000000000000000000000000000000..16095262bcad4e72d73dfb089478e6cac2d16a70 --- /dev/null +++ b/rknn-toolkit2/examples/functions/multi_batch/test.py @@ -0,0 +1,88 @@ +import numpy as np +import cv2 +from rknn.api import RKNN + + +def show_outputs(outputs): + output_ = outputs[0].reshape((-1, 1000)) + fp = open('./labels.txt', 'r') + labels = fp.readlines() + for batch, output in enumerate(output_): + index = sorted(range(len(output)), key=lambda k : output[k], reverse=True) + top5_str = '----- Batch {}: TOP 5 -----\n'.format(batch) + for i in range(5): + value = output[index[i]] + if value > 0: + topi = '[{:>3d}] score:{:.6f} class:"{}"\n'.format(index[i], value, labels[index[i]].strip().split(':')[-1]) + else: + topi = '[ -1]: 0.0\n' + top5_str += topi + print(top5_str.strip()) + + +def show_perfs(perfs): + perfs = 'perfs: {}\n'.format(outputs) + print(perfs) + + +if __name__ == '__main__': + + # Create RKNN object + rknn = RKNN(verbose=True) + + # Pre-process config + print('--> Config model') + rknn.config(mean_values=[103.94, 116.78, 123.68], std_values=[58.82, 58.82, 58.82], quant_img_RGB2BGR=True, target_platform='rk3566') + print('done') + + # Load model (from https://github.com/shicai/MobileNet-Caffe) + print('--> Loading model') + ret = rknn.load_caffe(model='../../caffe/mobilenet_v2/mobilenet_v2_deploy.prototxt', + blobs='../../caffe/mobilenet_v2/mobilenet_v2.caffemodel') + if ret != 0: + print('Load model failed!') + exit(ret) + print('done') + + # Build model + print('--> Building model') + ret = rknn.build(do_quantization=True, dataset='./dataset.txt', rknn_batch_size=4) + if ret != 0: + print('Build model failed!') + exit(ret) + print('done') + + # Export rknn model + print('--> Export rknn model') + ret = rknn.export_rknn('./mobilenet_v2.rknn') + if ret != 0: + print('Export rknn model failed!') + exit(ret) + print('done') + + # Set inputs + img_0 = cv2.imread('./dog_224x224.jpg') + img_0 = np.expand_dims(img_0, 0) + img_1 = cv2.imread('./goldfish_224x224.jpg') + img_1 = np.expand_dims(img_1, 0) + img_2 = cv2.imread('./space_shuttle_224.jpg') + img_2 = np.expand_dims(img_2, 0) + + img = np.concatenate((img_0, img_1, img_2, img_0), axis=0) # the inputs data need to be merged together using np.concatenate. + + # Init runtime environment + print('--> Init runtime environment') + ret = rknn.init_runtime() + if ret != 0: + print('Init runtime environment failed!') + exit(ret) + print('done') + + # Inference + print('--> Running model') + outputs = rknn.inference(inputs=[img], data_format=['nhwc']) + np.save('./functions_multi_batch_0.npy', outputs[0]) + show_outputs(outputs) + print('done') + + rknn.release() diff --git a/rknn-toolkit2/examples/functions/multi_input/README.md b/rknn-toolkit2/examples/functions/multi_input/README.md new file mode 100644 index 0000000000000000000000000000000000000000..963ae18a32465ae8e7f47656003c956fdff8f4f2 --- /dev/null +++ b/rknn-toolkit2/examples/functions/multi_input/README.md @@ -0,0 +1,28 @@ +# How to convert multi-input model + +## Model Source +The model used in this example come from 'gen_pb.py'. + +## Script Usage +*Usage:* +``` +python test.py +``` +*Description:* +- The default target platform in script is 'rk3566', please modify the 'target_platform' parameter of 'rknn.config' according to the actual platform. +- If connecting board is required, please add the 'target' parameter in 'rknn.init_runtime'. +- The model has multiple inputs, so multiple input files need to be set in 'dataset.txt': + ``` + dog_128x128.jpg input2.npy input3.npy dog_128x128_gray.png + ``` +- The 'inputs' and 'data_format' of 'rknn.inference' should also be set accordingly, as follows: + ``` + rknn.inference(inputs=[img, input2, input3, img_gray], data_format=['nhwc', 'nchw', 'nchw', 'nhwc']) + ``` +## Expected Results +This example will print outputs of inference, as follows: +``` +inference result: [array([[ -3.1041162, -7.0548096, -5.361655 , ..., -14.391811 , + -15.802773 , -11.287695 ]], dtype=float32)] +``` +- Note: Different platforms, different versions of tools and drivers may have slightly different results. \ No newline at end of file diff --git a/rknn-toolkit2/examples/functions/multi_input/conv_128.pb b/rknn-toolkit2/examples/functions/multi_input/conv_128.pb new file mode 100755 index 0000000000000000000000000000000000000000..7a3f29afbe768e276e5ff80e542ca36a7f5b0702 --- /dev/null +++ b/rknn-toolkit2/examples/functions/multi_input/conv_128.pb @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:13d078cc43b0253d921f21e3137e9f3b9f387188fb885c41dd29dbce12586366 +size 2108 diff --git a/rknn-toolkit2/examples/functions/multi_input/dataset.txt b/rknn-toolkit2/examples/functions/multi_input/dataset.txt new file mode 100644 index 0000000000000000000000000000000000000000..3c075bb8435f15ca6afbf215f05d27614bd21ddf --- /dev/null +++ b/rknn-toolkit2/examples/functions/multi_input/dataset.txt @@ -0,0 +1 @@ +dog_128x128.jpg input2.npy input3.npy dog_128x128_gray.png \ No newline at end of file diff --git a/rknn-toolkit2/examples/functions/multi_input/dog_128x128.jpg b/rknn-toolkit2/examples/functions/multi_input/dog_128x128.jpg new file mode 100644 index 0000000000000000000000000000000000000000..1a2082b0f7c2dcb8766428644394fcbf5ca394e4 --- /dev/null +++ b/rknn-toolkit2/examples/functions/multi_input/dog_128x128.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:edeaf387d7eef6e1b6338dc8bc4afead542f049cbd58b11aafc4ccd1e566f690 +size 7954 diff --git a/rknn-toolkit2/examples/functions/multi_input/dog_128x128_gray.png b/rknn-toolkit2/examples/functions/multi_input/dog_128x128_gray.png new file mode 100755 index 0000000000000000000000000000000000000000..59a042379fd8c12b6e542c6298710f6881165cfb --- /dev/null +++ b/rknn-toolkit2/examples/functions/multi_input/dog_128x128_gray.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d7241f3f207764b8d8e06299859fefd63b96a8567cf155ef08b8480204c70908 +size 7046 diff --git a/rknn-toolkit2/examples/functions/multi_input/gen_pb.py b/rknn-toolkit2/examples/functions/multi_input/gen_pb.py new file mode 100755 index 0000000000000000000000000000000000000000..392f31dcf1e3b6dc4528b9fa5225cef66fa5c3f2 --- /dev/null +++ b/rknn-toolkit2/examples/functions/multi_input/gen_pb.py @@ -0,0 +1,112 @@ +#encoding=utf-8 +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf +import numpy as np +from numpy import random as nr +import cv2 +from tensorflow.python.framework import graph_util + +tf.logging.set_verbosity(tf.logging.INFO) + + + +outputShape={1:[8,8,3]} + + + + +def newtork(x1, x2, x3, x4): + #weights = tf.get_variable("w1",shape=[3,3,3,1],initializer=tf.truncated_normal_initializer(stddev=1.0)) + weights1 = tf.Variable(tf.ones([3, 3, 3, 1])) + conv1 = tf.nn.conv2d(x1,weights1,strides=[1, 1, 1, 1],padding='SAME') + + weights2 = tf.Variable(tf.ones([3, 3, 3, 1])) + conv2 = tf.nn.conv2d(x2, weights2, strides=[1, 1, 1, 1], padding='SAME') + + weights3 = tf.Variable(tf.ones([3, 3, 3, 1])) + conv3 = tf.nn.conv2d(x3, weights3, strides=[1, 1, 1, 1], padding='SAME') + + weights4 = tf.Variable(tf.ones([3, 3, 1, 1])) + conv4 = tf.nn.conv2d(x4, weights4, strides=[1, 1, 1, 1], padding='SAME') + + add2 = tf.add(conv1, conv2) + add3 = tf.add(add2, conv3) + add4 = tf.add(add3, conv4, name="output") + return add4 + + +def train(): + inWidth = 128 + inHeight = 128 + inChannel = 3 + + img = cv2.imread('./dog_128x128.jpg') + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + + img = img.astype(np.float32) + img[:,:,:] -= [127.5, 127.5, 127.5] + img[:,:,:] /= 128.0 + img = img.reshape((1,128,128,3)) + + img_gray = cv2.imread('./dog_128x128_gray.png', cv2.IMREAD_GRAYSCALE) + img_gray = img_gray.astype(np.float32) + img_gray[:, :] -= [127.5] + img_gray[:, :] /= 128.0 + img_gray = img_gray.reshape((1, 128, 128, 1)) + + x1 = tf.placeholder(tf.float32,[1,inHeight,inWidth,inChannel],name="input1") + my_input1 = img + + x2 = tf.placeholder(tf.float32, [1, inHeight, inWidth, 3], name="input2") + #my_input2 = np.ones([1, inHeight, inWidth, 5],dtype=np.float) + #my_input2 = np.zeros([1, inHeight, inWidth, 5], dtype=np.float) + my_input2 = nr.rand(1, inHeight, inWidth, 3) # np.ones([1, inHeight, inWidth, 5],dtype=np.float) + my_input2 = (my_input2 - 0.5) * 2 + np.save('input2.npy', my_input2) + + x3 = tf.placeholder(tf.float32, [1, inHeight, inWidth, 3], name="input3") + #my_input3 = np.ones([1, inHeight, inWidth, 4], dtype=np.float) + #my_input3 = np.zeros([1, inHeight, inWidth, 4], dtype=np.float) + my_input3 = nr.rand(1, inHeight, inWidth, 3) + my_input3 = (my_input3 - 0.5) * 2 + np.save('input3.npy', my_input3) + + x4 = tf.placeholder(tf.float32,[1,inHeight,inWidth,1],name="input4") + my_input4 = img_gray + + layerNum = 1 + outShape=outputShape[layerNum] + y = tf.placeholder(tf.float32,[1,outShape[2],outShape[1],outShape[0]]) + my_label = np.ones([1,outShape[2],outShape[1],outShape[0]],dtype=np.float) + + + predict_res = newtork(x1, x2, x3, x4) + + #set loss func + # loss = tf.reduce_mean(predict_res-y) + # train_step = tf.train.AdamOptimizer(0.0005).minimize(loss) + + #iterate for 10 + pb_name='conv_128.pb' + with tf.Session() as sess: + sess.run(tf.global_variables_initializer()) + print(sess.run(predict_res,feed_dict={x1:my_input1, x2:my_input2, x3:my_input3, x4:my_input4, y:my_label})) + + print(predict_res) + print([n.name for n in tf.get_default_graph().as_graph_def().node]) + + constant_graph = graph_util.convert_variables_to_constants(sess, sess.graph_def, output_node_names=['output']) + with tf.gfile.GFile(pb_name, mode='wb') as f: + f.write(constant_graph.SerializeToString()) + + tf.train.Saver().save(sess,"model.ckpt") + +def main(): + train() + +if __name__=="__main__": + main() + diff --git a/rknn-toolkit2/examples/functions/multi_input/input2.npy b/rknn-toolkit2/examples/functions/multi_input/input2.npy new file mode 100755 index 0000000000000000000000000000000000000000..368ff524179885f8cefa4d4c99247c68290fb275 --- /dev/null +++ b/rknn-toolkit2/examples/functions/multi_input/input2.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a94af61a4f76910d7c281880bd2a88fcb939362797816ec7945df4a87f687e31 +size 393344 diff --git a/rknn-toolkit2/examples/functions/multi_input/input3.npy b/rknn-toolkit2/examples/functions/multi_input/input3.npy new file mode 100755 index 0000000000000000000000000000000000000000..acd84eebb23f05351fa4fb428cb91df55ac7b17f --- /dev/null +++ b/rknn-toolkit2/examples/functions/multi_input/input3.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:885a1e0f9d7e50cb32d843dd38e7868ae4a4eff7985b2338e322ce21f24d6f2d +size 393344 diff --git a/rknn-toolkit2/examples/functions/multi_input/test.py b/rknn-toolkit2/examples/functions/multi_input/test.py new file mode 100755 index 0000000000000000000000000000000000000000..213f75915a1c85325ed1524d4be2351144762ad7 --- /dev/null +++ b/rknn-toolkit2/examples/functions/multi_input/test.py @@ -0,0 +1,72 @@ +import numpy as np +import cv2 +from rknn.api import RKNN + +if __name__ == '__main__': + + # Create RKNN object + rknn = RKNN(verbose=True) + + # Pre-process config + print('--> Config model') + rknn.config(mean_values=[[127.5, 127.5, 127.5], [0, 0, 0], [0, 0, 0], [127.5]], + std_values=[[128, 128, 128], [1, 1, 1], [1, 1, 1], [128]], + target_platform='rk3566') + print('done') + + # Load model + print('--> Loading model') + ret = rknn.load_tensorflow(tf_pb='./conv_128.pb', + inputs=['input1', 'input2', 'input3', 'input4'], + outputs=['output'], + input_size_list=[[1, 128, 128, 3], [1, 128, 128, 3], [1, 128, 128, 3], [1, 128, 128, 1]]) + if ret != 0: + print('Load model failed!') + exit(ret) + print('done') + + # Build model + print('--> Building model') + ret = rknn.build(do_quantization=True, dataset='./dataset.txt') + if ret != 0: + print('Build model failed!') + exit(ret) + print('done') + + # Export rknn model + print('--> Export rknn model') + ret = rknn.export_rknn('./conv_128.rknn') + if ret != 0: + print('Export rknn model failed!') + exit(ret) + print('done') + + # Init runtime environment + print('--> Init runtime environment') + ret = rknn.init_runtime() + if ret != 0: + print('Init runtime environment failed!') + exit(ret) + print('done') + + # Set inputs + img = cv2.imread('./dog_128x128.jpg') + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # nhwc + img = np.expand_dims(img, 0) + + img_gray = cv2.imread('./dog_128x128_gray.png', cv2.IMREAD_GRAYSCALE) + img_gray = np.expand_dims(img_gray, -1) # nhwc + + input2 = np.load('input2.npy').astype('float32') # nchw + + input3 = np.load('input3.npy').astype('float32') # nchw + + # Inference + print('--> Running model') + outputs = rknn.inference(inputs=[img, input2, input3, img_gray], data_format=['nhwc', 'nchw', 'nchw', 'nhwc']) + np.save('./functions_multi_input_0.npy', outputs[0]) + print('done') + outputs[0] = outputs[0].reshape((1, -1)) + print('inference result: ', outputs) + + rknn.release() diff --git a/rknn-toolkit2/examples/functions/npu_device_test/README.md b/rknn-toolkit2/examples/functions/npu_device_test/README.md new file mode 100644 index 0000000000000000000000000000000000000000..a54a65af84f3567f69d5c6e39f5961cbbb7f64c6 --- /dev/null +++ b/rknn-toolkit2/examples/functions/npu_device_test/README.md @@ -0,0 +1,116 @@ +# How to connect the board for debugging + +## Model Source +The model used in this example come from the following open source projects: +https://github.com/shicai/MobileNet-Caffe + +## Script Usage +*Usage:* +``` +python test.py +``` +*Description:* +- The default target platform in script is 'rk3588', please modify the 'target_platform' parameter of 'rknn.config' according to the actual platform. +- The 'target' parameter in 'rknn.init_runtime'/'rknn.accuracy_analysis' is set to 'rk3588'. +- The 'perf_debug' and 'eval_mem' parameter of 'rknn.config' is set to True. + +## Expected Results +This example would output the following results: +- Output encrypted rknn model: mobilenet_v2.crypt.rknn +- List devices that connect to host + ``` + ************************* + all device(s) with adb mode: + rk3588 + ************************* + ``` +- Print version + ``` + ============================================== + RKNN VERSION: + API: librknn_api version: 1.5.2 (8babfea build@2023-08-25T02:31:12) + DRV: rknn_server: 1.5.2 (8babfea build@2023-08-25T10:30:12) + rknnrt: 1.5.3b9 (ba0047cf1@2023-10-11T19:55:40) + ============================================== + ``` +- Print perf information + ``` + =================================================================================================================== + Performance + #### The performance result is just for debugging, #### + #### may worse than actual performance! #### + =================================================================================================================== + ID OpType DataType Target InputShape OutputShape DDR Cycles NPU Cycles Total Cycles Time(us) MacUsage(%) WorkLoad(0/1/2)-ImproveTherical Task Number Lut Number RW(KB) FullName + 1 InputOperator UINT8 CPU \ (1,3,224,224) 0 0 0 7 \ 0.0%/0.0%/0.0% - Up:0.0% 0 0 147.00 InputOperator:data + 2 ConvRelu UINT8 NPU (1,3,224,224),(32,3,3,3),(32) (1,32,112,112) 94150 10584 94150 428 2.47 100.0%/0.0%/0.0% - Up:0.0% 3 0 543.75 + + ... + + Total Operator Elapsed Time(us): 14147 + Total Memory RW Amount(MB): 0 + Operator Time-Consuming Ranking: + OpType Call Number CPU Time(us) NPU Time(us) Total Time(us) Time Ratio(%) + ConvRelu 36 0 9338 9338 66.0 + ConvAdd 10 0 2155 2155 15.23 + Conv 9 0 2097 2097 14.82 + exDataConvert 1 0 269 269 1.9 + Softmax 1 0 248 248 1.75 + OutputOperator 1 33 0 33 0.23 + InputOperator 1 7 0 7 0.04 + + =================================================================================================================== + ``` +- Print memory information + ``` + ====================================================== + Memory Profile Info Dump + ====================================================== + NPU model memory detail(bytes): + Total Weight Memory: 3.53 MiB + Total Internal Tensor Memory: 1.67 MiB + Total Memory: 5.66 MiB + + INFO: When evaluating memory usage, we need consider + the size of model, current model size is: 4.08 MiB + ====================================================== + ``` +- Print the TOP5 labels and corresponding scores of the test image classification results + ``` + -----TOP 5----- + [155] score:0.936523 class:"Shih-Tzu" + [154] score:0.002403 class:"Pekinese, Pekingese, Peke" + [204] score:0.002403 class:"Lhasa, Lhasa apso" + [283] score:0.000658 class:"Persian cat" + [284] score:0.000229 class:"Siamese cat, Siamese" + ``` +- Output the results of the accuracy analysis: + ``` + # simulator_error: calculate the output error of each layer of the simulator (compared to the 'golden' value). + # entire: output error of each layer between 'golden' and 'simulator', these errors will accumulate layer by layer. + # single: single-layer output error between 'golden' and 'simulator', can better reflect the single-layer accuracy of the simulator. + # runtime_error: calculate the output error of each layer of the runtime. + # entire: output error of each layer between 'golden' and 'runtime', these errors will accumulate layer by layer. + # single_sim: single-layer output error between 'simulator' and 'runtime', can better reflect the single-layer accuracy of runtime. + + layer_name simulator_error runtime_error + entire single entire single_sim + cos euc cos euc cos euc cos euc + ----------------------------------------------------------------------------------------------------------------- + [Input] data 1.00000 | 0.0 1.00000 | 0.0 + [exDataConvert] data_int8 0.99999 | 2.0599 0.99999 | 2.0599 + [Conv] conv1/scale + [Relu] relu1 0.99997 | 4.7572 0.99997 | 4.7572 0.99996 | 5.2593 0.99994 | 6.3273 + [Conv] conv2_1/expand/scale + [Relu] relu2_1/expand 0.99993 | 6.8963 0.99994 | 6.3494 0.99992 | 7.1564 1.00000 | 1.0312 + [Conv] conv2_1/dwise/scale + [Relu] relu2_1/dwise 0.99949 | 22.639 0.99958 | 20.480 0.99949 | 22.665 1.00000 | 1.4891 + + ... + + [Relu] relu6_4 0.97924 | 64.548 0.99989 | 4.7152 0.98151 | 60.976 1.00000 | 0.2890 + [Conv] pool6 0.98537 | 4.8954 0.99997 | 0.2290 0.98766 | 4.5005 1.00000 | 0.0 + [Conv] fc7 0.98323 | 21.938 0.99990 | 1.6519 0.98521 | 20.567 1.00000 | 0.0 + [exDataConvert] fc7__float16 0.98323 | 21.939 0.99996 | 1.1046 0.98521 | 20.570 1.00000 | 0.0512 + [Softmax] prob 1.00000 | 0.0010 1.00000 | 0.0001 1.00000 | 0.0569 1.00000 | 0.0566 + ``` +- Note: Different platforms, different versions of tools and drivers may have slightly different results. \ No newline at end of file diff --git a/rknn-toolkit2/examples/functions/npu_device_test/dataset.txt b/rknn-toolkit2/examples/functions/npu_device_test/dataset.txt new file mode 100644 index 0000000000000000000000000000000000000000..9078c68db586a0251bdd41282d67f4d256b3abc3 --- /dev/null +++ b/rknn-toolkit2/examples/functions/npu_device_test/dataset.txt @@ -0,0 +1 @@ +dog_224x224.jpg diff --git a/rknn-toolkit2/examples/functions/npu_device_test/dog_224x224.jpg b/rknn-toolkit2/examples/functions/npu_device_test/dog_224x224.jpg new file mode 100644 index 0000000000000000000000000000000000000000..004b0416e23454a12eb06eaba238c7e2d5f2b0fc --- /dev/null +++ b/rknn-toolkit2/examples/functions/npu_device_test/dog_224x224.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c350299c6283d5f62fecf1f845b6b3be9aafec8dff528ca09a129990f0a584b0 +size 18889 diff --git a/rknn-toolkit2/examples/functions/npu_device_test/labels.txt b/rknn-toolkit2/examples/functions/npu_device_test/labels.txt new file mode 100644 index 0000000000000000000000000000000000000000..0993780f99088349e2c156a5a8c57ce1013eb493 --- /dev/null +++ b/rknn-toolkit2/examples/functions/npu_device_test/labels.txt @@ -0,0 +1,1000 @@ +0:tench, Tinca tinca +1:goldfish, Carassius auratus +2:great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias +3:tiger shark, Galeocerdo cuvieri +4:hammerhead, hammerhead shark +5:electric ray, crampfish, numbfish, torpedo +6:stingray +7:cock +8:hen +9:ostrich, Struthio camelus +10:brambling, Fringilla montifringilla +11:goldfinch, Carduelis carduelis +12:house finch, linnet, Carpodacus mexicanus +13:junco, snowbird +14:indigo bunting, indigo finch, indigo bird, Passerina cyanea +15:robin, American robin, Turdus migratorius +16:bulbul +17:jay +18:magpie +19:chickadee +20:water ouzel, dipper +21:kite +22:bald eagle, American eagle, Haliaeetus leucocephalus +23:vulture +24:great grey owl, great gray owl, Strix nebulosa +25:European fire salamander, Salamandra salamandra +26:common newt, Triturus vulgaris +27:eft +28:spotted salamander, Ambystoma maculatum +29:axolotl, mud puppy, Ambystoma mexicanum +30:bullfrog, Rana catesbeiana +31:tree frog, tree-frog +32:tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui +33:loggerhead, loggerhead turtle, Caretta caretta +34:leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea +35:mud turtle +36:terrapin +37:box turtle, box tortoise +38:banded gecko +39:common iguana, iguana, Iguana iguana +40:American chameleon, anole, Anolis carolinensis +41:whiptail, whiptail lizard +42:agama +43:frilled lizard, Chlamydosaurus kingi +44:alligator lizard +45:Gila monster, Heloderma suspectum +46:green lizard, Lacerta viridis +47:African chameleon, Chamaeleo chamaeleon +48:Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis +49:African crocodile, Nile crocodile, Crocodylus niloticus +50:American alligator, Alligator mississipiensis +51:triceratops +52:thunder snake, worm snake, Carphophis amoenus +53:ringneck snake, ring-necked snake, ring snake +54:hognose snake, puff adder, sand viper +55:green snake, grass snake +56:king snake, kingsnake +57:garter snake, grass snake +58:water snake +59:vine snake +60:night snake, Hypsiglena torquata +61:boa constrictor, Constrictor constrictor +62:rock python, rock snake, Python sebae +63:Indian cobra, Naja naja +64:green mamba +65:sea snake +66:horned viper, cerastes, sand viper, horned asp, Cerastes cornutus +67:diamondback, diamondback rattlesnake, Crotalus adamanteus +68:sidewinder, horned rattlesnake, Crotalus cerastes +69:trilobite +70:harvestman, daddy longlegs, Phalangium opilio +71:scorpion +72:black and gold garden spider, Argiope aurantia +73:barn spider, Araneus cavaticus +74:garden spider, Aranea diademata +75:black widow, Latrodectus mactans +76:tarantula +77:wolf spider, hunting spider +78:tick +79:centipede +80:black grouse +81:ptarmigan +82:ruffed grouse, partridge, Bonasa umbellus +83:prairie chicken, prairie grouse, prairie fowl +84:peacock +85:quail +86:partridge +87:African grey, African gray, Psittacus erithacus +88:macaw +89:sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita +90:lorikeet +91:coucal +92:bee eater +93:hornbill +94:hummingbird +95:jacamar +96:toucan +97:drake +98:red-breasted merganser, Mergus serrator +99:goose +100:black swan, Cygnus atratus +101:tusker +102:echidna, spiny anteater, anteater +103:platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus +104:wallaby, brush kangaroo +105:koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus +106:wombat +107:jellyfish +108:sea anemone, anemone +109:brain coral +110:flatworm, platyhelminth +111:nematode, nematode worm, roundworm +112:conch +113:snail +114:slug +115:sea slug, nudibranch +116:chiton, coat-of-mail shell, sea cradle, polyplacophore +117:chambered nautilus, pearly nautilus, nautilus +118:Dungeness crab, Cancer magister +119:rock crab, Cancer irroratus +120:fiddler crab +121:king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica +122:American lobster, Northern lobster, Maine lobster, Homarus americanus +123:spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish +124:crayfish, crawfish, crawdad, crawdaddy +125:hermit crab +126:isopod +127:white stork, Ciconia ciconia +128:black stork, Ciconia nigra +129:spoonbill +130:flamingo +131:little blue heron, Egretta caerulea +132:American egret, great white heron, Egretta albus +133:bittern +134:crane +135:limpkin, Aramus pictus +136:European gallinule, Porphyrio porphyrio +137:American coot, marsh hen, mud hen, water hen, Fulica americana +138:bustard +139:ruddy turnstone, Arenaria interpres +140:red-backed sandpiper, dunlin, Erolia alpina +141:redshank, Tringa totanus +142:dowitcher +143:oystercatcher, oyster catcher +144:pelican +145:king penguin, Aptenodytes patagonica +146:albatross, mollymawk +147:grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus +148:killer whale, killer, orca, grampus, sea wolf, Orcinus orca +149:dugong, Dugong dugon +150:sea lion +151:Chihuahua +152:Japanese spaniel +153:Maltese dog, Maltese terrier, Maltese +154:Pekinese, Pekingese, Peke +155:Shih-Tzu +156:Blenheim spaniel +157:papillon +158:toy terrier +159:Rhodesian ridgeback +160:Afghan hound, Afghan +161:basset, basset hound +162:beagle +163:bloodhound, sleuthhound +164:bluetick +165:black-and-tan coonhound +166:Walker hound, Walker foxhound +167:English foxhound +168:redbone +169:borzoi, Russian wolfhound +170:Irish wolfhound +171:Italian greyhound +172:whippet +173:Ibizan hound, Ibizan Podenco +174:Norwegian elkhound, elkhound +175:otterhound, otter hound +176:Saluki, gazelle hound +177:Scottish deerhound, deerhound +178:Weimaraner +179:Staffordshire bullterrier, Staffordshire bull terrier +180:American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier +181:Bedlington terrier +182:Border terrier +183:Kerry blue terrier +184:Irish terrier +185:Norfolk terrier +186:Norwich terrier +187:Yorkshire terrier +188:wire-haired fox terrier +189:Lakeland terrier +190:Sealyham terrier, Sealyham +191:Airedale, Airedale terrier +192:cairn, cairn terrier +193:Australian terrier +194:Dandie Dinmont, Dandie Dinmont terrier +195:Boston bull, Boston terrier +196:miniature schnauzer +197:giant schnauzer +198:standard schnauzer +199:Scotch terrier, Scottish terrier, Scottie +200:Tibetan terrier, chrysanthemum dog +201:silky terrier, Sydney silky +202:soft-coated wheaten terrier +203:West Highland white terrier +204:Lhasa, Lhasa apso +205:flat-coated retriever +206:curly-coated retriever +207:golden retriever +208:Labrador retriever +209:Chesapeake Bay retriever +210:German short-haired pointer +211:vizsla, Hungarian pointer +212:English setter +213:Irish setter, red setter +214:Gordon setter +215:Brittany spaniel +216:clumber, clumber spaniel +217:English springer, English springer spaniel +218:Welsh springer spaniel +219:cocker spaniel, English cocker spaniel, cocker +220:Sussex spaniel +221:Irish water spaniel +222:kuvasz +223:schipperke +224:groenendael +225:malinois +226:briard +227:kelpie +228:komondor +229:Old English sheepdog, bobtail +230:Shetland sheepdog, Shetland sheep dog, Shetland +231:collie +232:Border collie +233:Bouvier des Flandres, Bouviers des Flandres +234:Rottweiler +235:German shepherd, German shepherd dog, German police dog, alsatian +236:Doberman, Doberman pinscher +237:miniature pinscher +238:Greater Swiss Mountain dog +239:Bernese mountain dog +240:Appenzeller +241:EntleBucher +242:boxer +243:bull mastiff +244:Tibetan mastiff +245:French bulldog +246:Great Dane +247:Saint Bernard, St Bernard +248:Eskimo dog, husky +249:malamute, malemute, Alaskan malamute +250:Siberian husky +251:dalmatian, coach dog, carriage dog +252:affenpinscher, monkey pinscher, monkey dog +253:basenji +254:pug, pug-dog +255:Leonberg +256:Newfoundland, Newfoundland dog +257:Great Pyrenees +258:Samoyed, Samoyede +259:Pomeranian +260:chow, chow chow +261:keeshond +262:Brabancon griffon +263:Pembroke, Pembroke Welsh corgi +264:Cardigan, Cardigan Welsh corgi +265:toy poodle +266:miniature poodle +267:standard poodle +268:Mexican hairless +269:timber wolf, grey wolf, gray wolf, Canis lupus +270:white wolf, Arctic wolf, Canis lupus tundrarum +271:red wolf, maned wolf, Canis rufus, Canis niger +272:coyote, prairie wolf, brush wolf, Canis latrans +273:dingo, warrigal, warragal, Canis dingo +274:dhole, Cuon alpinus +275:African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus +276:hyena, hyaena +277:red fox, Vulpes vulpes +278:kit fox, Vulpes macrotis +279:Arctic fox, white fox, Alopex lagopus +280:grey fox, gray fox, Urocyon cinereoargenteus +281:tabby, tabby cat +282:tiger cat +283:Persian cat +284:Siamese cat, Siamese +285:Egyptian cat +286:cougar, puma, catamount, mountain lion, painter, panther, Felis concolor +287:lynx, catamount +288:leopard, Panthera pardus +289:snow leopard, ounce, Panthera uncia +290:jaguar, panther, Panthera onca, Felis onca +291:lion, king of beasts, Panthera leo +292:tiger, Panthera tigris +293:cheetah, chetah, Acinonyx jubatus +294:brown bear, bruin, Ursus arctos +295:American black bear, black bear, Ursus americanus, Euarctos americanus +296:ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus +297:sloth bear, Melursus ursinus, Ursus ursinus +298:mongoose +299:meerkat, mierkat +300:tiger beetle +301:ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle +302:ground beetle, carabid beetle +303:long-horned beetle, longicorn, longicorn beetle +304:leaf beetle, chrysomelid +305:dung beetle +306:rhinoceros beetle +307:weevil +308:fly +309:bee +310:ant, emmet, pismire +311:grasshopper, hopper +312:cricket +313:walking stick, walkingstick, stick insect +314:cockroach, roach +315:mantis, mantid +316:cicada, cicala +317:leafhopper +318:lacewing, lacewing fly +319:dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk +320:damselfly +321:admiral +322:ringlet, ringlet butterfly +323:monarch, monarch butterfly, milkweed butterfly, Danaus plexippus +324:cabbage butterfly +325:sulphur butterfly, sulfur butterfly +326:lycaenid, lycaenid butterfly +327:starfish, sea star +328:sea urchin +329:sea cucumber, holothurian +330:wood rabbit, cottontail, cottontail rabbit +331:hare +332:Angora, Angora rabbit +333:hamster +334:porcupine, hedgehog +335:fox squirrel, eastern fox squirrel, Sciurus niger +336:marmot +337:beaver +338:guinea pig, Cavia cobaya +339:sorrel +340:zebra +341:hog, pig, grunter, squealer, Sus scrofa +342:wild boar, boar, Sus scrofa +343:warthog +344:hippopotamus, hippo, river horse, Hippopotamus amphibius +345:ox +346:water buffalo, water ox, Asiatic buffalo, Bubalus bubalis +347:bison +348:ram, tup +349:bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis +350:ibex, Capra ibex +351:hartebeest +352:impala, Aepyceros melampus +353:gazelle +354:Arabian camel, dromedary, Camelus dromedarius +355:llama +356:weasel +357:mink +358:polecat, fitch, foulmart, foumart, Mustela putorius +359:black-footed ferret, ferret, Mustela nigripes +360:otter +361:skunk, polecat, wood pussy +362:badger +363:armadillo +364:three-toed sloth, ai, Bradypus tridactylus +365:orangutan, orang, orangutang, Pongo pygmaeus +366:gorilla, Gorilla gorilla +367:chimpanzee, chimp, Pan troglodytes +368:gibbon, Hylobates lar +369:siamang, Hylobates syndactylus, Symphalangus syndactylus +370:guenon, guenon monkey +371:patas, hussar monkey, Erythrocebus patas +372:baboon +373:macaque +374:langur +375:colobus, colobus monkey +376:proboscis monkey, Nasalis larvatus +377:marmoset +378:capuchin, ringtail, Cebus capucinus +379:howler monkey, howler +380:titi, titi monkey +381:spider monkey, Ateles geoffroyi +382:squirrel monkey, Saimiri sciureus +383:Madagascar cat, ring-tailed lemur, Lemur catta +384:indri, indris, Indri indri, Indri brevicaudatus +385:Indian elephant, Elephas maximus +386:African elephant, Loxodonta africana +387:lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens +388:giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca +389:barracouta, snoek +390:eel +391:coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch +392:rock beauty, Holocanthus tricolor +393:anemone fish +394:sturgeon +395:gar, garfish, garpike, billfish, Lepisosteus osseus +396:lionfish +397:puffer, pufferfish, blowfish, globefish +398:abacus +399:abaya +400:academic gown, academic robe, judge's robe +401:accordion, piano accordion, squeeze box +402:acoustic guitar +403:aircraft carrier, carrier, flattop, attack aircraft carrier +404:airliner +405:airship, dirigible +406:altar +407:ambulance +408:amphibian, amphibious vehicle +409:analog clock +410:apiary, bee house +411:apron +412:ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin +413:assault rifle, assault gun +414:backpack, back pack, knapsack, packsack, rucksack, haversack +415:bakery, bakeshop, bakehouse +416:balance beam, beam +417:balloon +418:ballpoint, ballpoint pen, ballpen, Biro +419:Band Aid +420:banjo +421:bannister, banister, balustrade, balusters, handrail +422:barbell +423:barber chair +424:barbershop +425:barn +426:barometer +427:barrel, cask +428:barrow, garden cart, lawn cart, wheelbarrow +429:baseball +430:basketball +431:bassinet +432:bassoon +433:bathing cap, swimming cap +434:bath towel +435:bathtub, bathing tub, bath, tub +436:beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon +437:beacon, lighthouse, beacon light, pharos +438:beaker +439:bearskin, busby, shako +440:beer bottle +441:beer glass +442:bell cote, bell cot +443:bib +444:bicycle-built-for-two, tandem bicycle, tandem +445:bikini, two-piece +446:binder, ring-binder +447:binoculars, field glasses, opera glasses +448:birdhouse +449:boathouse +450:bobsled, bobsleigh, bob +451:bolo tie, bolo, bola tie, bola +452:bonnet, poke bonnet +453:bookcase +454:bookshop, bookstore, bookstall +455:bottlecap +456:bow +457:bow tie, bow-tie, bowtie +458:brass, memorial tablet, plaque +459:brassiere, bra, bandeau +460:breakwater, groin, groyne, mole, bulwark, seawall, jetty +461:breastplate, aegis, egis +462:broom +463:bucket, pail +464:buckle +465:bulletproof vest +466:bullet train, bullet +467:butcher shop, meat market +468:cab, hack, taxi, taxicab +469:caldron, cauldron +470:candle, taper, wax light +471:cannon +472:canoe +473:can opener, tin opener +474:cardigan +475:car mirror +476:carousel, carrousel, merry-go-round, roundabout, whirligig +477:carpenter's kit, tool kit +478:carton +479:car wheel +480:cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM +481:cassette +482:cassette player +483:castle +484:catamaran +485:CD player +486:cello, violoncello +487:cellular telephone, cellular phone, cellphone, cell, mobile phone +488:chain +489:chainlink fence +490:chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour +491:chain saw, chainsaw +492:chest +493:chiffonier, commode +494:chime, bell, gong +495:china cabinet, china closet +496:Christmas stocking +497:church, church building +498:cinema, movie theater, movie theatre, movie house, picture palace +499:cleaver, meat cleaver, chopper +500:cliff dwelling +501:cloak +502:clog, geta, patten, sabot +503:cocktail shaker +504:coffee mug +505:coffeepot +506:coil, spiral, volute, whorl, helix +507:combination lock +508:computer keyboard, keypad +509:confectionery, confectionary, candy store +510:container ship, containership, container vessel +511:convertible +512:corkscrew, bottle screw +513:cornet, horn, trumpet, trump +514:cowboy boot +515:cowboy hat, ten-gallon hat +516:cradle +517:crane +518:crash helmet +519:crate +520:crib, cot +521:Crock Pot +522:croquet ball +523:crutch +524:cuirass +525:dam, dike, dyke +526:desk +527:desktop computer +528:dial telephone, dial phone +529:diaper, nappy, napkin +530:digital clock +531:digital watch +532:dining table, board +533:dishrag, dishcloth +534:dishwasher, dish washer, dishwashing machine +535:disk brake, disc brake +536:dock, dockage, docking facility +537:dogsled, dog sled, dog sleigh +538:dome +539:doormat, welcome mat +540:drilling platform, offshore rig +541:drum, membranophone, tympan +542:drumstick +543:dumbbell +544:Dutch oven +545:electric fan, blower +546:electric guitar +547:electric locomotive +548:entertainment center +549:envelope +550:espresso maker +551:face powder +552:feather boa, boa +553:file, file cabinet, filing cabinet +554:fireboat +555:fire engine, fire truck +556:fire screen, fireguard +557:flagpole, flagstaff +558:flute, transverse flute +559:folding chair +560:football helmet +561:forklift +562:fountain +563:fountain pen +564:four-poster +565:freight car +566:French horn, horn +567:frying pan, frypan, skillet +568:fur coat +569:garbage truck, dustcart +570:gasmask, respirator, gas helmet +571:gas pump, gasoline pump, petrol pump, island dispenser +572:goblet +573:go-kart +574:golf ball +575:golfcart, golf cart +576:gondola +577:gong, tam-tam +578:gown +579:grand piano, grand +580:greenhouse, nursery, glasshouse +581:grille, radiator grille +582:grocery store, grocery, food market, market +583:guillotine +584:hair slide +585:hair spray +586:half track +587:hammer +588:hamper +589:hand blower, blow dryer, blow drier, hair dryer, hair drier +590:hand-held computer, hand-held microcomputer +591:handkerchief, hankie, hanky, hankey +592:hard disc, hard disk, fixed disk +593:harmonica, mouth organ, harp, mouth harp +594:harp +595:harvester, reaper +596:hatchet +597:holster +598:home theater, home theatre +599:honeycomb +600:hook, claw +601:hoopskirt, crinoline +602:horizontal bar, high bar +603:horse cart, horse-cart +604:hourglass +605:iPod +606:iron, smoothing iron +607:jack-o'-lantern +608:jean, blue jean, denim +609:jeep, landrover +610:jersey, T-shirt, tee shirt +611:jigsaw puzzle +612:jinrikisha, ricksha, rickshaw +613:joystick +614:kimono +615:knee pad +616:knot +617:lab coat, laboratory coat +618:ladle +619:lampshade, lamp shade +620:laptop, laptop computer +621:lawn mower, mower +622:lens cap, lens cover +623:letter opener, paper knife, paperknife +624:library +625:lifeboat +626:lighter, light, igniter, ignitor +627:limousine, limo +628:liner, ocean liner +629:lipstick, lip rouge +630:Loafer +631:lotion +632:loudspeaker, speaker, speaker unit, loudspeaker system, speaker system +633:loupe, jeweler's loupe +634:lumbermill, sawmill +635:magnetic compass +636:mailbag, postbag +637:mailbox, letter box +638:maillot +639:maillot, tank suit +640:manhole cover +641:maraca +642:marimba, xylophone +643:mask +644:matchstick +645:maypole +646:maze, labyrinth +647:measuring cup +648:medicine chest, medicine cabinet +649:megalith, megalithic structure +650:microphone, mike +651:microwave, microwave oven +652:military uniform +653:milk can +654:minibus +655:miniskirt, mini +656:minivan +657:missile +658:mitten +659:mixing bowl +660:mobile home, manufactured home +661:Model T +662:modem +663:monastery +664:monitor +665:moped +666:mortar +667:mortarboard +668:mosque +669:mosquito net +670:motor scooter, scooter +671:mountain bike, all-terrain bike, off-roader +672:mountain tent +673:mouse, computer mouse +674:mousetrap +675:moving van +676:muzzle +677:nail +678:neck brace +679:necklace +680:nipple +681:notebook, notebook computer +682:obelisk +683:oboe, hautboy, hautbois +684:ocarina, sweet potato +685:odometer, hodometer, mileometer, milometer +686:oil filter +687:organ, pipe organ +688:oscilloscope, scope, cathode-ray oscilloscope, CRO +689:overskirt +690:oxcart +691:oxygen mask +692:packet +693:paddle, boat paddle +694:paddlewheel, paddle wheel +695:padlock +696:paintbrush +697:pajama, pyjama, pj's, jammies +698:palace +699:panpipe, pandean pipe, syrinx +700:paper towel +701:parachute, chute +702:parallel bars, bars +703:park bench +704:parking meter +705:passenger car, coach, carriage +706:patio, terrace +707:pay-phone, pay-station +708:pedestal, plinth, footstall +709:pencil box, pencil case +710:pencil sharpener +711:perfume, essence +712:Petri dish +713:photocopier +714:pick, plectrum, plectron +715:pickelhaube +716:picket fence, paling +717:pickup, pickup truck +718:pier +719:piggy bank, penny bank +720:pill bottle +721:pillow +722:ping-pong ball +723:pinwheel +724:pirate, pirate ship +725:pitcher, ewer +726:plane, carpenter's plane, woodworking plane +727:planetarium +728:plastic bag +729:plate rack +730:plow, plough +731:plunger, plumber's helper +732:Polaroid camera, Polaroid Land camera +733:pole +734:police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria +735:poncho +736:pool table, billiard table, snooker table +737:pop bottle, soda bottle +738:pot, flowerpot +739:potter's wheel +740:power drill +741:prayer rug, prayer mat +742:printer +743:prison, prison house +744:projectile, missile +745:projector +746:puck, hockey puck +747:punching bag, punch bag, punching ball, punchball +748:purse +749:quill, quill pen +750:quilt, comforter, comfort, puff +751:racer, race car, racing car +752:racket, racquet +753:radiator +754:radio, wireless +755:radio telescope, radio reflector +756:rain barrel +757:recreational vehicle, RV, R.V. +758:reel +759:reflex camera +760:refrigerator, icebox +761:remote control, remote +762:restaurant, eating house, eating place, eatery +763:revolver, six-gun, six-shooter +764:rifle +765:rocking chair, rocker +766:rotisserie +767:rubber eraser, rubber, pencil eraser +768:rugby ball +769:rule, ruler +770:running shoe +771:safe +772:safety pin +773:saltshaker, salt shaker +774:sandal +775:sarong +776:sax, saxophone +777:scabbard +778:scale, weighing machine +779:school bus +780:schooner +781:scoreboard +782:screen, CRT screen +783:screw +784:screwdriver +785:seat belt, seatbelt +786:sewing machine +787:shield, buckler +788:shoe shop, shoe-shop, shoe store +789:shoji +790:shopping basket +791:shopping cart +792:shovel +793:shower cap +794:shower curtain +795:ski +796:ski mask +797:sleeping bag +798:slide rule, slipstick +799:sliding door +800:slot, one-armed bandit +801:snorkel +802:snowmobile +803:snowplow, snowplough +804:soap dispenser +805:soccer ball +806:sock +807:solar dish, solar collector, solar furnace +808:sombrero +809:soup bowl +810:space bar +811:space heater +812:space shuttle +813:spatula +814:speedboat +815:spider web, spider's web +816:spindle +817:sports car, sport car +818:spotlight, spot +819:stage +820:steam locomotive +821:steel arch bridge +822:steel drum +823:stethoscope +824:stole +825:stone wall +826:stopwatch, stop watch +827:stove +828:strainer +829:streetcar, tram, tramcar, trolley, trolley car +830:stretcher +831:studio couch, day bed +832:stupa, tope +833:submarine, pigboat, sub, U-boat +834:suit, suit of clothes +835:sundial +836:sunglass +837:sunglasses, dark glasses, shades +838:sunscreen, sunblock, sun blocker +839:suspension bridge +840:swab, swob, mop +841:sweatshirt +842:swimming trunks, bathing trunks +843:swing +844:switch, electric switch, electrical switch +845:syringe +846:table lamp +847:tank, army tank, armored combat vehicle, armoured combat vehicle +848:tape player +849:teapot +850:teddy, teddy bear +851:television, television system +852:tennis ball +853:thatch, thatched roof +854:theater curtain, theatre curtain +855:thimble +856:thresher, thrasher, threshing machine +857:throne +858:tile roof +859:toaster +860:tobacco shop, tobacconist shop, tobacconist +861:toilet seat +862:torch +863:totem pole +864:tow truck, tow car, wrecker +865:toyshop +866:tractor +867:trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi +868:tray +869:trench coat +870:tricycle, trike, velocipede +871:trimaran +872:tripod +873:triumphal arch +874:trolleybus, trolley coach, trackless trolley +875:trombone +876:tub, vat +877:turnstile +878:typewriter keyboard +879:umbrella +880:unicycle, monocycle +881:upright, upright piano +882:vacuum, vacuum cleaner +883:vase +884:vault +885:velvet +886:vending machine +887:vestment +888:viaduct +889:violin, fiddle +890:volleyball +891:waffle iron +892:wall clock +893:wallet, billfold, notecase, pocketbook +894:wardrobe, closet, press +895:warplane, military plane +896:washbasin, handbasin, washbowl, lavabo, wash-hand basin +897:washer, automatic washer, washing machine +898:water bottle +899:water jug +900:water tower +901:whiskey jug +902:whistle +903:wig +904:window screen +905:window shade +906:Windsor tie +907:wine bottle +908:wing +909:wok +910:wooden spoon +911:wool, woolen, woollen +912:worm fence, snake fence, snake-rail fence, Virginia fence +913:wreck +914:yawl +915:yurt +916:web site, website, internet site, site +917:comic book +918:crossword puzzle, crossword +919:street sign +920:traffic light, traffic signal, stoplight +921:book jacket, dust cover, dust jacket, dust wrapper +922:menu +923:plate +924:guacamole +925:consomme +926:hot pot, hotpot +927:trifle +928:ice cream, icecream +929:ice lolly, lolly, lollipop, popsicle +930:French loaf +931:bagel, beigel +932:pretzel +933:cheeseburger +934:hotdog, hot dog, red hot +935:mashed potato +936:head cabbage +937:broccoli +938:cauliflower +939:zucchini, courgette +940:spaghetti squash +941:acorn squash +942:butternut squash +943:cucumber, cuke +944:artichoke, globe artichoke +945:bell pepper +946:cardoon +947:mushroom +948:Granny Smith +949:strawberry +950:orange +951:lemon +952:fig +953:pineapple, ananas +954:banana +955:jackfruit, jak, jack +956:custard apple +957:pomegranate +958:hay +959:carbonara +960:chocolate sauce, chocolate syrup +961:dough +962:meat loaf, meatloaf +963:pizza, pizza pie +964:potpie +965:burrito +966:red wine +967:espresso +968:cup +969:eggnog +970:alp +971:bubble +972:cliff, drop, drop-off +973:coral reef +974:geyser +975:lakeside, lakeshore +976:promontory, headland, head, foreland +977:sandbar, sand bar +978:seashore, coast, seacoast, sea-coast +979:valley, vale +980:volcano +981:ballplayer, baseball player +982:groom, bridegroom +983:scuba diver +984:rapeseed +985:daisy +986:yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum +987:corn +988:acorn +989:hip, rose hip, rosehip +990:buckeye, horse chestnut, conker +991:coral fungus +992:agaric +993:gyromitra +994:stinkhorn, carrion fungus +995:earthstar +996:hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa +997:bolete +998:ear, spike, capitulum +999:toilet tissue, toilet paper, bathroom tissue \ No newline at end of file diff --git a/rknn-toolkit2/examples/functions/npu_device_test/test.py b/rknn-toolkit2/examples/functions/npu_device_test/test.py new file mode 100644 index 0000000000000000000000000000000000000000..859bfcdd0229e6ef7be940babaaff4f0a2a0f111 --- /dev/null +++ b/rknn-toolkit2/examples/functions/npu_device_test/test.py @@ -0,0 +1,118 @@ +import numpy as np +import cv2 +from rknn.api import RKNN + + +def show_outputs(outputs): + output = outputs[0].reshape(-1) + index = sorted(range(len(output)), key=lambda k : output[k], reverse=True) + fp = open('./labels.txt', 'r') + labels = fp.readlines() + top5_str = 'mobilenet_v2\n-----TOP 5-----\n' + for i in range(5): + value = output[index[i]] + if value > 0: + topi = '[{:>3d}] score:{:.6f} class:"{}"\n'.format(index[i], value, labels[index[i]].strip().split(':')[-1]) + else: + topi = '[ -1]: 0.0\n' + top5_str += topi + print(top5_str.strip()) + + +if __name__ == '__main__': + + # Create RKNN object + rknn = RKNN(verbose=True) + + # Pre-process config + print('--> Config model') + rknn.config(mean_values=[103.94, 116.78, 123.68], std_values=[58.82, 58.82, 58.82], + quant_img_RGB2BGR=True, target_platform='rk3588') + print('done') + + # Load model (from https://github.com/shicai/MobileNet-Caffe) + print('--> Loading model') + ret = rknn.load_caffe(model='./../../caffe/mobilenet_v2/mobilenet_v2_deploy.prototxt', + blobs='./../../caffe/mobilenet_v2/mobilenet_v2.caffemodel') + if ret != 0: + print('Load model failed!') + exit(ret) + print('done') + + # Build model + print('--> Building model') + ret = rknn.build(do_quantization=True, dataset='./dataset.txt') + if ret != 0: + print('Build model failed!') + exit(ret) + print('done') + + # Accuracy analysis + print('--> Accuracy analysis') + ret = rknn.accuracy_analysis(inputs=['./dog_224x224.jpg'], output_dir='./snapshot', target='rk3588') + if ret != 0: + print('Accuracy analysis failed!') + exit(ret) + print('done') + + # Export rknn model + print('--> Export rknn model') + ret = rknn.export_rknn('./mobilenet_v2.rknn') + if ret != 0: + print('Export rknn model failed!') + exit(ret) + print('done') + + # Export encrypted RKNN model + print('--> Export encrypted rknn model') + ret = rknn.export_encrypted_rknn_model('./mobilenet_v2.rknn', None, 3) + + rknn.release() + + + # Create RKNN object + rknn = RKNN(verbose=True) + + # load rknn model + print('--> Load rknn model') + ret = rknn.load_rknn('./mobilenet_v2.rknn') + if ret != 0: + print('Load rknn model failed!') + exit(ret) + print('done') + + # Set inputs + img = cv2.imread('./dog_224x224.jpg') + img = np.expand_dims(img, 0) + + print('--> List devices') + rknn.list_devices() + + # Init runtime environment + print('--> Init runtime environment') + ret = rknn.init_runtime(target='rk3588', perf_debug=True, eval_mem=True) + if ret != 0: + print('Init runtime environment failed!') + exit(ret) + print('done') + + print('--> Get sdk version') + sdk_version = rknn.get_sdk_version() + print(sdk_version) + + # eval perf + print('--> Eval perf') + rknn.eval_perf() + + # eval perf + print('--> Eval memory') + rknn.eval_memory() + + # Inference + print('--> Running model') + outputs = rknn.inference(inputs=[img], data_format=['nhwc']) + np.save('./functions_npu_device_test_0.npy', outputs[0]) + show_outputs(outputs) + print('done') + + rknn.release() diff --git a/rknn-toolkit2/examples/functions/onnx_edit/README.md b/rknn-toolkit2/examples/functions/onnx_edit/README.md new file mode 100644 index 0000000000000000000000000000000000000000..0b3103f5c0418219d38719c9cfb2459a42673630 --- /dev/null +++ b/rknn-toolkit2/examples/functions/onnx_edit/README.md @@ -0,0 +1,72 @@ +# ONNX Edit + +rknn.utils.onnx_edit 是 RKNN-Toolkit2 的内置工具，用于调整onnx模型。对于一些具有特殊结构的onnx模型，调整后的模型在转换为RKNN后可以获得更好的推理性能。接口定义如下： + +```python +from rknn.utils import onnx_edit +onnx_edit(model: str, + export_path : str, + inputs_transform : dict = {}, + outputs_transform : dict = {}, + dataset: Optional[str] = None): +""" + Args: + model: The path to the onnx file. + export_path: The path to the exported onnx file. + inputs_transform: The dictionary of inputs transform equation. Feed like {'input_0': 'a,b,c->1,ab,1,c', ...} + outputs_transform: The dictionary of outputs transform equation. Feed like {'output_2': 'a,b,c,->1,b,1,ac', ...} + dataset: The path to the dataset file. dataset file should meet rknn.build requirements. If given, transform of inputs will also apply on dataset and generate a new dataset on the same folder of export_path. +""" +``` + +目前可以使用以下功能： + +- 对输入和输出进行 reshape 和 transpose 调整（目前暂不支持对动态shape进行修改） + + + +## 1.对输入和输出进行 reshape 和 transpose 调整 + +如果某些模型在转换为 RKNN 之前，能够对输入输出进行特定的 reshape 和 transpose 操作，则可以利用 RKNN-Toolkit2 的图优化功能，使最终的 RKNN 模型更加简洁，具有更好的推理性能。例如，通过执行该目录下的 test.py 脚本，可以比较 onnx_edit 前后生成的 RKNN 模型的差异。 + +变换方程书写规则： + +- 必须有两部分字符组成，并用 '->' 隔开，左边为原始字符shape，右边是变换后字符shape +- 字符shape里面的符号只允许是 [a-z] 或 ',' '1'，除了'1'以外，不支持其他数字shape字符，原因是相同数字字符无法判断 transpose 的前后关系 +- 字符[a-z]认为是独立的，没有顺序关系，即 `a,b->b,a` 和 `c,a->a,c` 表示的是一样的变换 +- 等式左边的原始字符shape，被 ',' 分隔成 n 份，n的个数必须和模型的维度匹配，例如原始输入定义是 [32,4,1,64], 输入字符可以是 'a,b,c,d' 或 'a,b,1,d' +- 原始字符shape的每一个符号，除了'1'，都必须存在于变换后字符shape，例如 `a,1,c,d->ac,d,1` 是有效的，`a,1,c,d->ac,1` 是无效的，缺了 'd' +- 变换后字符shape，允许插入任意个数的 '1'，达成扩维效果，例如 `a,b,c-> a,1,cb,1,1` +- 原始字符shape，允许用多个字母以及赋值公式来表示对shape进行拆分，例如原始输入定义是 [32,4,1,64]，`ab,c,d,qk[a=2,k=8]->aq,cd,1,kb`，表示将 32 拆分成 2x16，将 64 拆分成 8x8，再进行 transpose, reshape 操作。其中 '[ ]' 的部分称为赋值公式，多个公式用 ',' 符号分隔。此外，允许拆分中的某个字符没有赋值，此时会自动推断对应的shape，例如赋值公式只给了 a=2，已知在模型中 ab=32，则自动推断出 b=16；若推断出的shape异常会直接报错，比如 ab=32，若赋值 a=5，则 b=6.4，又维度必须是整数，此时会抛出异常错误。 +- 支持动态shape +- 若对动态shape的维度进行拆分，拆分后的维度只允许其中一个是动态shape，假设有3维度动态shape，则 `ab,c,d->a,c,d,b` 是无效的，因为a,b均是未知维度，无法进行拆分操作。而 `ab,c,d[a=2]->a,c,d,b` 是有效的 +- 允许对动态shape的维度进行赋值，此时对应维度会变成固定shape。假设有3维度动态shape，`ab,c,d[a=2]->a,c,d,b` 会使变更后的维度为 [2,c,d,b]，第一维为2，而 `ab,c,d[a=2,b=5,c=7,d=11]->a,c,d,b` 会使变更后的维度变成 [2,7,11,5] + + + +以下是一些变换例子： + +```shell +1. Reshape 3D to 4D: 'a,b,c->a,b,1,c' or 'a,b,c->a,1,b,c' +2. Reshape 5D to 4D: 'a,b,c,d,e->ab,c,d,e' or 'a,b,c,d,e->a,bce,1,1' +3. Transpose(0,3,1,2): 'a,b,c,d->a,d,b,c' +4. Transpose, merge dim: 'a,b,c,d->ad,c,b' or 'a,b,c,d->d,acb,1' +5. Split dim, transpose, merge dim: 'a,bc,de,f[b=2,d=4]->ab,fe,dc,1', # 'c','e' will be auto infered +6. Split dim, transpose, merge dim: 'a,bc,de,f[b=2,c=24,d=4]->ab,fe,dc,1', # 'bc' = 24 shuold be equal to tensor's 2nd-dim, 'e' will be auto infered +``` + + + +执行 `test.py` 后，onnx_edit 接口会打印出以下变换的公式： + +```shell +I For 'k_cache.1':'a,b,c,d->1,ad,b,c' + Input:'k_cache.1' was reset as shape-[1, 32, 48, 1].(Origin shape is [4, 48, 1, 8]) + Insert ops to transform [1, 32, 48, 1] to [4, 48, 1, 8]: + - Insert reshape op. [1, 32, 48, 1] reshape to [4, 8, 48, 1]. + - Insert transpose op. [4, 8, 48, 1] transpose(0, 2, 3, 1) to [4, 48, 1, 8]. +I For 'k_cache':'a,b,c,d->1,ab,c,d' + Output:'k_cache' was reset as shape-[1, 32, 48, 1].(Origin shape is [4, 8, 48, 1]) + Insert ops to transform [4, 8, 48, 1] to [1, 32, 48, 1]: + - Insert reshape op. [4, 8, 48, 1] reshape to [1, 32, 48, 1]. +``` diff --git a/rknn-toolkit2/examples/functions/onnx_edit/README_EN.md b/rknn-toolkit2/examples/functions/onnx_edit/README_EN.md new file mode 100644 index 0000000000000000000000000000000000000000..730c98df6e762b85dd6259ef3172f3943a306ecf --- /dev/null +++ b/rknn-toolkit2/examples/functions/onnx_edit/README_EN.md @@ -0,0 +1,80 @@ +# ONNX Edit + +RKNN-Toolkit2 has a built-in `rknn.utils.onnx_edit` tool. Used to adjust the onnx model. For some **onnx models with special structures**, **better inference performance** can be obtained after the adjusted model is converted to RKNN. The interface is defined as follow: + +```python +onnx_edit(model: str, + export_path : str, + inputs_transform : dict = {}, + outputs_transform : dict = {}, + dataset: Optional[str] = None): +""" + Args: + model: The path to the onnx file. + export_path: The path to the exported onnx file. + inputs_transform: The dictionary of inputs transform equation. + outputs_transform: The dictionary of outputs transform equation. + dataset: The path to the dataset file. dataset file should meet rknn.build requirements. If given, transform of inputs will also apply on dataset and generate a new dataset on the same folder of export_path. +""" +``` + +Currently the following functions are available: + +- Reshape and transpose adjustments to input and output（Modification of dynamic shapes is currently not supported.） + + + +## 1.Reshape and transpose adjustments to input and output + +If some models can reshape and transpose the input and output to a certain extent before converting to RKNN, the graph optimization function of RKNN-Toolkit2 can be used to make the final RKNN model more concise and **have better inference performance**. For example, by executing the `test.py` script in this directory, you can compare the differences in the RKNN models generated before and after onnx_edit. + +Transformation equation rules: + +- It must be composed of two parts of symbolic shape, separated by **'->'**. The **left side is the original input symbolic shape**, and the **right side is the transformed symbolic shape**. + +- The character in symbolic shape are only allowed to be **[a-z]** or **','** **'1'**. Other numeric shape characters are not supported except for '1'. The reason is that the same numeric characters cannot determine the context of transpose. + +- Characters **[a-z]** are considered independent and have no order relationship, that means, **'a,b->b,a'** and **'c,a->a,c'** represent the same transformation. + +- Separated by **','**, **original input symbolic shape** on the left side of the equation must **match the dimensions of the input**. For example, the original input definition is **[32,4,1,64]**, and the input characters can be **'a,b,c,d'** or **'a,b,1,d'** + +- **Every character** of the original input symbolic shape, except '1', **must exist in the transformed symbolic shape**. For example, **'a,1,c,d->ac,d,1'** is valid, **'a,1, c,d->ac,1'** is invalid because **'d'** is missing. + +- The transformed symbolic shape allows the insertion of **any number of '1'** to achieve a **dimension expansion**, such as **'a,b,c-> a,1,cb,1,1'** + +- The original input symbolic shape allows using **multiple characters** and **assignment formulas** to split the shape. For example, the original input definition is **[32,4,1,64]**, **'ab,c,d,qk[a=2,k =8]->aq,cd,1,kb'**, which means split 32 into 2x16, split 64 into 8x8, and then perform transpose and reshape operations. The part '[ ]' is called the assignment formula, and multiple formulas are separated by **','**. It is allowed that one character in the split is not assigned a value. At this time, the corresponding shape will be automatically inferred. For example, the assignment formula only gives **a=2**, and **ab=32** is known, **then b=16 is automatically inferred**; if the inferred shape is abnormal An error will be reported directly, for example, ab=32. If a=5 is assigned, then b=6.4. The dimension must be an integer, and an exception error will be thrown. + +- Support dynamic shape +- If the dimensions of a dynamic shape are split, only one of the split dimensions is allowed to be a dynamic shape. Assuming there are 3-dimensional dynamic shapes, `ab,c,d->a,c,d,b` is invalid , because both a and b are unknown dimensions and cannot be split. And `ab,c,d[a=2]->a,c,d,b` is valid +- Allows assignment of dimensions to dynamic shapes, in which case the corresponding dimensions will become fixed shapes. Assuming there is a 3-dimensional dynamic shape, `ab,c,d[a=2]->a,c,d,b` will cause the changed dimensions to be [2,c,d,b], and the first dimension will be 2, and `ab,c,d[a=2,b=5,c=7,d=11]->a,c,d,b` will make the dimension fixed to [2,7,11,5]. + + + + +Example of transform equation: + +```shell +1. Reshape 3D to 4D: 'a,b,c->a,b,1,c' or 'a,b,c->a,1,b,c' +2. Reshape 5D to 4D: 'a,b,c,d,e->ab,c,d,e' or 'a,b,c,d,e->a,bce,1,1' +3. Transpose(0,3,1,2): 'a,b,c,d->a,d,b,c' +4. Transpose, merge dim: 'a,b,c,d->ad,c,b' or 'a,b,c,d->d,acb,1' +5. Split dim, transpose, merge dim: 'a,bc,de,f[b=2,d=4]->ab,fe,dc,1', # 'c','e' will be auto infered +6. Split dim, transpose, merge dim: 'a,bc,de,f[b=2,c=24,d=4]->ab,fe,dc,1', # 'bc' = 24 shuold be equal to tensor's 2nd-dim, 'e' will be auto infered +``` + + + +After running `test.py`, onnx_edit interface will show transform log as follow: + +```shell +I For 'k_cache.1':'a,b,c,d->1,ad,b,c' + Input:'k_cache.1' was reset as shape-[1, 32, 48, 1].(Origin shape is [4, 48, 1, 8]) + Insert ops to transform [1, 32, 48, 1] to [4, 48, 1, 8]: + - Insert reshape op. [1, 32, 48, 1] reshape to [4, 8, 48, 1]. + - Insert transpose op. [4, 8, 48, 1] transpose(0, 2, 3, 1) to [4, 48, 1, 8]. +I For 'k_cache':'a,b,c,d->1,ab,c,d' + Output:'k_cache' was reset as shape-[1, 32, 48, 1].(Origin shape is [4, 8, 48, 1]) + Insert ops to transform [4, 8, 48, 1] to [1, 32, 48, 1]: + - Insert reshape op. [4, 8, 48, 1] reshape to [1, 32, 48, 1]. +``` + diff --git a/rknn-toolkit2/examples/functions/onnx_edit/concat_block.onnx b/rknn-toolkit2/examples/functions/onnx_edit/concat_block.onnx new file mode 100644 index 0000000000000000000000000000000000000000..98aeddccdc90c83c1ef63a2c4f63ba2c8645894a --- /dev/null +++ b/rknn-toolkit2/examples/functions/onnx_edit/concat_block.onnx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4924f6503a98194a75a32adc82ac4b9ff3016ac7e6cffa798b627c9ab4a063d4 +size 1727 diff --git a/rknn-toolkit2/examples/functions/onnx_edit/concat_block_input_0.npy b/rknn-toolkit2/examples/functions/onnx_edit/concat_block_input_0.npy new file mode 100644 index 0000000000000000000000000000000000000000..03038f8a0f7f09c311f672b6a898191b6680eba6 --- /dev/null +++ b/rknn-toolkit2/examples/functions/onnx_edit/concat_block_input_0.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:56a2219543243e5d2222d6ae34b907c0bf492adc969ab07cbb6bcc1f539aa6e0 +size 12224 diff --git a/rknn-toolkit2/examples/functions/onnx_edit/concat_block_input_1.npy b/rknn-toolkit2/examples/functions/onnx_edit/concat_block_input_1.npy new file mode 100644 index 0000000000000000000000000000000000000000..04b10efcd4737a2edaf05cd1738a82527b9fb7e6 --- /dev/null +++ b/rknn-toolkit2/examples/functions/onnx_edit/concat_block_input_1.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1682ec66e2802abefcda21edc301d0cd8336da995a301a5bd1ca6768cc931557 +size 6272 diff --git a/rknn-toolkit2/examples/functions/onnx_edit/dataset.txt b/rknn-toolkit2/examples/functions/onnx_edit/dataset.txt new file mode 100644 index 0000000000000000000000000000000000000000..95d6e0afdeaf7f05be278a350f4fab5e4e413c42 --- /dev/null +++ b/rknn-toolkit2/examples/functions/onnx_edit/dataset.txt @@ -0,0 +1 @@ +concat_block_input_0.npy concat_block_input_1.npy diff --git a/rknn-toolkit2/examples/functions/onnx_edit/test.py b/rknn-toolkit2/examples/functions/onnx_edit/test.py new file mode 100644 index 0000000000000000000000000000000000000000..4c0ba26adfefa20554c1b9ad1ae69db357dbe77a --- /dev/null +++ b/rknn-toolkit2/examples/functions/onnx_edit/test.py @@ -0,0 +1,24 @@ +from rknn.api import RKNN + +# Test on the original onnx model +rknn = RKNN(verbose=True) +rknn.config(target_platform='rk3588') +rknn.load_onnx("./concat_block.onnx") +rknn.build(do_quantization=True, dataset='./dataset.txt') +rknn.export_rknn('./concat_block.rknn') + +# use rknn.utils.onnx_edit to edit the onnx model +from rknn.utils import onnx_edit +ret = onnx_edit(model = './concat_block.onnx', + export_path = './concat_block_edited.onnx', + inputs_transform = { 'k_cache.1': 'a,b,c,d->1,ad,b,c'}, + outputs_transform = {'k_cache': 'a,b,c,d->1,ab,c,d'}, + dataset = './dataset.txt' + ) + +# Test on the edited onnx model +rknn = RKNN(verbose=True) +rknn.config(target_platform='rk3588') +rknn.load_onnx("./concat_block_edited.onnx") +rknn.build(do_quantization=True, dataset='./dataset_for_concat_block_edited.txt') +rknn.export_rknn('./concat_block_edited.rknn') diff --git a/rknn-toolkit2/examples/functions/quantize_algorithm_mmse/README.md b/rknn-toolkit2/examples/functions/quantize_algorithm_mmse/README.md new file mode 100644 index 0000000000000000000000000000000000000000..1592fc7e6068c10ddb31ae6a82d1d0d36aeaa52e --- /dev/null +++ b/rknn-toolkit2/examples/functions/quantize_algorithm_mmse/README.md @@ -0,0 +1,46 @@ +# How to use MMSE quantize algorithm + +## Model Source +The model used in this example come from the following open source projects: +https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md + +## Script Usage +*Usage:* +``` +python test.py +``` +*Description:* +- The default target platform in script is 'rk3566', please modify the 'target_platform' parameter of 'rknn.config' according to the actual platform. +- If connecting board is required, please add the 'target' parameter in 'rknn.init_runtime'. +- The 'quantized_algorithm' parameter of 'rknn.config' is set to 'mmse'. and a 'MmseQuant2' progress bar can be seen during the conversion process, indicating the execution progress of MMSE. + + +## Expected Results +This example will outputs the results of the accuracy analysis and print the TOP5 labels and corresponding scores of the test image classification results, as follows: +``` +layer_name simulator_error + entire single + cos euc cos euc +-------------------------------------------------------------------------------------------------------------------------- +[Input] input:0 1.00000 | 0.0 1.00000 | 0.0 +[exDataConvert] input:0_int8 0.99999 | 0.8565 0.99999 | 0.8565 +[Conv] MobilenetV1/MobilenetV1/Conv2d_0/BatchNorm/FusedBatchNorm:0 +[Clip] MobilenetV1/MobilenetV1/Conv2d_0/Relu6:0 0.99999 | 4.5562 0.99999 | 4.5562 + +... + +[Clip] MobilenetV1/MobilenetV1/Conv2d_13_pointwise/Relu6:0 0.99681 | 29.000 0.99979 | 7.3940 +[Conv] MobilenetV1/Logits/AvgPool_1a/AvgPool:0 0.99903 | 1.4259 0.99997 | 0.2520 +[Conv] MobilenetV1/Logits/Conv2d_1c_1x1/BiasAdd:0 0.99914 | 4.6217 0.99992 | 1.4081 +[Reshape] MobilenetV1/Logits/SpatialSqueeze:0_int8 0.99914 | 4.6217 0.99995 | 1.0506 +[exDataConvert] MobilenetV1/Logits/SpatialSqueeze:0 0.99914 | 4.6217 0.99995 | 1.0506 +``` +``` +-----TOP 5----- +[ 156] score:0.945152 class:"Shih-Tzu" +[ 155] score:0.050125 class:"Pekinese, Pekingese, Peke" +[ 205] score:0.003332 class:"Lhasa, Lhasa apso" +[ 284] score:0.000685 class:"Persian cat" +[ 260] score:0.000090 class:"Pomeranian" +``` +- Note: Different platforms, different versions of tools and drivers may have slightly different results. \ No newline at end of file diff --git a/rknn-toolkit2/examples/functions/quantize_algorithm_mmse/dataset.txt b/rknn-toolkit2/examples/functions/quantize_algorithm_mmse/dataset.txt new file mode 100644 index 0000000000000000000000000000000000000000..9078c68db586a0251bdd41282d67f4d256b3abc3 --- /dev/null +++ b/rknn-toolkit2/examples/functions/quantize_algorithm_mmse/dataset.txt @@ -0,0 +1 @@ +dog_224x224.jpg diff --git a/rknn-toolkit2/examples/functions/quantize_algorithm_mmse/dog_224x224.jpg b/rknn-toolkit2/examples/functions/quantize_algorithm_mmse/dog_224x224.jpg new file mode 100644 index 0000000000000000000000000000000000000000..004b0416e23454a12eb06eaba238c7e2d5f2b0fc --- /dev/null +++ b/rknn-toolkit2/examples/functions/quantize_algorithm_mmse/dog_224x224.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c350299c6283d5f62fecf1f845b6b3be9aafec8dff528ca09a129990f0a584b0 +size 18889 diff --git a/rknn-toolkit2/examples/functions/quantize_algorithm_mmse/labels.txt b/rknn-toolkit2/examples/functions/quantize_algorithm_mmse/labels.txt new file mode 100644 index 0000000000000000000000000000000000000000..dcd01c14b4635096ae295a8e81de0f65188bc1f8 --- /dev/null +++ b/rknn-toolkit2/examples/functions/quantize_algorithm_mmse/labels.txt @@ -0,0 +1,1001 @@ +0:__background__ +1:tench, Tinca tinca +2:goldfish, Carassius auratus +3:great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias +4:tiger shark, Galeocerdo cuvieri +5:hammerhead, hammerhead shark +6:electric ray, crampfish, numbfish, torpedo +7:stingray +8:cock +9:hen +10:ostrich, Struthio camelus +11:brambling, Fringilla montifringilla +12:goldfinch, Carduelis carduelis +13:house finch, linnet, Carpodacus mexicanus +14:junco, snowbird +15:indigo bunting, indigo finch, indigo bird, Passerina cyanea +16:robin, American robin, Turdus migratorius +17:bulbul +18:jay +19:magpie +20:chickadee +21:water ouzel, dipper +22:kite +23:bald eagle, American eagle, Haliaeetus leucocephalus +24:vulture +25:great grey owl, great gray owl, Strix nebulosa +26:European fire salamander, Salamandra salamandra +27:common newt, Triturus vulgaris +28:eft +29:spotted salamander, Ambystoma maculatum +30:axolotl, mud puppy, Ambystoma mexicanum +31:bullfrog, Rana catesbeiana +32:tree frog, tree-frog +33:tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui +34:loggerhead, loggerhead turtle, Caretta caretta +35:leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea +36:mud turtle +37:terrapin +38:box turtle, box tortoise +39:banded gecko +40:common iguana, iguana, Iguana iguana +41:American chameleon, anole, Anolis carolinensis +42:whiptail, whiptail lizard +43:agama +44:frilled lizard, Chlamydosaurus kingi +45:alligator lizard +46:Gila monster, Heloderma suspectum +47:green lizard, Lacerta viridis +48:African chameleon, Chamaeleo chamaeleon +49:Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis +50:African crocodile, Nile crocodile, Crocodylus niloticus +51:American alligator, Alligator mississipiensis +52:triceratops +53:thunder snake, worm snake, Carphophis amoenus +54:ringneck snake, ring-necked snake, ring snake +55:hognose snake, puff adder, sand viper +56:green snake, grass snake +57:king snake, kingsnake +58:garter snake, grass snake +59:water snake +60:vine snake +61:night snake, Hypsiglena torquata +62:boa constrictor, Constrictor constrictor +63:rock python, rock snake, Python sebae +64:Indian cobra, Naja naja +65:green mamba +66:sea snake +67:horned viper, cerastes, sand viper, horned asp, Cerastes cornutus +68:diamondback, diamondback rattlesnake, Crotalus adamanteus +69:sidewinder, horned rattlesnake, Crotalus cerastes +70:trilobite +71:harvestman, daddy longlegs, Phalangium opilio +72:scorpion +73:black and gold garden spider, Argiope aurantia +74:barn spider, Araneus cavaticus +75:garden spider, Aranea diademata +76:black widow, Latrodectus mactans +77:tarantula +78:wolf spider, hunting spider +79:tick +80:centipede +81:black grouse +82:ptarmigan +83:ruffed grouse, partridge, Bonasa umbellus +84:prairie chicken, prairie grouse, prairie fowl +85:peacock +86:quail +87:partridge +88:African grey, African gray, Psittacus erithacus +89:macaw +90:sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita +91:lorikeet +92:coucal +93:bee eater +94:hornbill +95:hummingbird +96:jacamar +97:toucan +98:drake +99:red-breasted merganser, Mergus serrator +100:goose +101:black swan, Cygnus atratus +102:tusker +103:echidna, spiny anteater, anteater +104:platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus +105:wallaby, brush kangaroo +106:koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus +107:wombat +108:jellyfish +109:sea anemone, anemone +110:brain coral +111:flatworm, platyhelminth +112:nematode, nematode worm, roundworm +113:conch +114:snail +115:slug +116:sea slug, nudibranch +117:chiton, coat-of-mail shell, sea cradle, polyplacophore +118:chambered nautilus, pearly nautilus, nautilus +119:Dungeness crab, Cancer magister +120:rock crab, Cancer irroratus +121:fiddler crab +122:king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica +123:American lobster, Northern lobster, Maine lobster, Homarus americanus +124:spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish +125:crayfish, crawfish, crawdad, crawdaddy +126:hermit crab +127:isopod +128:white stork, Ciconia ciconia +129:black stork, Ciconia nigra +130:spoonbill +131:flamingo +132:little blue heron, Egretta caerulea +133:American egret, great white heron, Egretta albus +134:bittern +135:crane +136:limpkin, Aramus pictus +137:European gallinule, Porphyrio porphyrio +138:American coot, marsh hen, mud hen, water hen, Fulica americana +139:bustard +140:ruddy turnstone, Arenaria interpres +141:red-backed sandpiper, dunlin, Erolia alpina +142:redshank, Tringa totanus +143:dowitcher +144:oystercatcher, oyster catcher +145:pelican +146:king penguin, Aptenodytes patagonica +147:albatross, mollymawk +148:grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus +149:killer whale, killer, orca, grampus, sea wolf, Orcinus orca +150:dugong, Dugong dugon +151:sea lion +152:Chihuahua +153:Japanese spaniel +154:Maltese dog, Maltese terrier, Maltese +155:Pekinese, Pekingese, Peke +156:Shih-Tzu +157:Blenheim spaniel +158:papillon +159:toy terrier +160:Rhodesian ridgeback +161:Afghan hound, Afghan +162:basset, basset hound +163:beagle +164:bloodhound, sleuthhound +165:bluetick +166:black-and-tan coonhound +167:Walker hound, Walker foxhound +168:English foxhound +169:redbone +170:borzoi, Russian wolfhound +171:Irish wolfhound +172:Italian greyhound +173:whippet +174:Ibizan hound, Ibizan Podenco +175:Norwegian elkhound, elkhound +176:otterhound, otter hound +177:Saluki, gazelle hound +178:Scottish deerhound, deerhound +179:Weimaraner +180:Staffordshire bullterrier, Staffordshire bull terrier +181:American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier +182:Bedlington terrier +183:Border terrier +184:Kerry blue terrier +185:Irish terrier +186:Norfolk terrier +187:Norwich terrier +188:Yorkshire terrier +189:wire-haired fox terrier +190:Lakeland terrier +191:Sealyham terrier, Sealyham +192:Airedale, Airedale terrier +193:cairn, cairn terrier +194:Australian terrier +195:Dandie Dinmont, Dandie Dinmont terrier +196:Boston bull, Boston terrier +197:miniature schnauzer +198:giant schnauzer +199:standard schnauzer +200:Scotch terrier, Scottish terrier, Scottie +201:Tibetan terrier, chrysanthemum dog +202:silky terrier, Sydney silky +203:soft-coated wheaten terrier +204:West Highland white terrier +205:Lhasa, Lhasa apso +206:flat-coated retriever +207:curly-coated retriever +208:golden retriever +209:Labrador retriever +210:Chesapeake Bay retriever +211:German short-haired pointer +212:vizsla, Hungarian pointer +213:English setter +214:Irish setter, red setter +215:Gordon setter +216:Brittany spaniel +217:clumber, clumber spaniel +218:English springer, English springer spaniel +219:Welsh springer spaniel +220:cocker spaniel, English cocker spaniel, cocker +221:Sussex spaniel +222:Irish water spaniel +223:kuvasz +224:schipperke +225:groenendael +226:malinois +227:briard +228:kelpie +229:komondor +230:Old English sheepdog, bobtail +231:Shetland sheepdog, Shetland sheep dog, Shetland +232:collie +233:Border collie +234:Bouvier des Flandres, Bouviers des Flandres +235:Rottweiler +236:German shepherd, German shepherd dog, German police dog, alsatian +237:Doberman, Doberman pinscher +238:miniature pinscher +239:Greater Swiss Mountain dog +240:Bernese mountain dog +241:Appenzeller +242:EntleBucher +243:boxer +244:bull mastiff +245:Tibetan mastiff +246:French bulldog +247:Great Dane +248:Saint Bernard, St Bernard +249:Eskimo dog, husky +250:malamute, malemute, Alaskan malamute +251:Siberian husky +252:dalmatian, coach dog, carriage dog +253:affenpinscher, monkey pinscher, monkey dog +254:basenji +255:pug, pug-dog +256:Leonberg +257:Newfoundland, Newfoundland dog +258:Great Pyrenees +259:Samoyed, Samoyede +260:Pomeranian +261:chow, chow chow +262:keeshond +263:Brabancon griffon +264:Pembroke, Pembroke Welsh corgi +265:Cardigan, Cardigan Welsh corgi +266:toy poodle +267:miniature poodle +268:standard poodle +269:Mexican hairless +270:timber wolf, grey wolf, gray wolf, Canis lupus +271:white wolf, Arctic wolf, Canis lupus tundrarum +272:red wolf, maned wolf, Canis rufus, Canis niger +273:coyote, prairie wolf, brush wolf, Canis latrans +274:dingo, warrigal, warragal, Canis dingo +275:dhole, Cuon alpinus +276:African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus +277:hyena, hyaena +278:red fox, Vulpes vulpes +279:kit fox, Vulpes macrotis +280:Arctic fox, white fox, Alopex lagopus +281:grey fox, gray fox, Urocyon cinereoargenteus +282:tabby, tabby cat +283:tiger cat +284:Persian cat +285:Siamese cat, Siamese +286:Egyptian cat +287:cougar, puma, catamount, mountain lion, painter, panther, Felis concolor +288:lynx, catamount +289:leopard, Panthera pardus +290:snow leopard, ounce, Panthera uncia +291:jaguar, panther, Panthera onca, Felis onca +292:lion, king of beasts, Panthera leo +293:tiger, Panthera tigris +294:cheetah, chetah, Acinonyx jubatus +295:brown bear, bruin, Ursus arctos +296:American black bear, black bear, Ursus americanus, Euarctos americanus +297:ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus +298:sloth bear, Melursus ursinus, Ursus ursinus +299:mongoose +300:meerkat, mierkat +301:tiger beetle +302:ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle +303:ground beetle, carabid beetle +304:long-horned beetle, longicorn, longicorn beetle +305:leaf beetle, chrysomelid +306:dung beetle +307:rhinoceros beetle +308:weevil +309:fly +310:bee +311:ant, emmet, pismire +312:grasshopper, hopper +313:cricket +314:walking stick, walkingstick, stick insect +315:cockroach, roach +316:mantis, mantid +317:cicada, cicala +318:leafhopper +319:lacewing, lacewing fly +320:dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk +321:damselfly +322:admiral +323:ringlet, ringlet butterfly +324:monarch, monarch butterfly, milkweed butterfly, Danaus plexippus +325:cabbage butterfly +326:sulphur butterfly, sulfur butterfly +327:lycaenid, lycaenid butterfly +328:starfish, sea star +329:sea urchin +330:sea cucumber, holothurian +331:wood rabbit, cottontail, cottontail rabbit +332:hare +333:Angora, Angora rabbit +334:hamster +335:porcupine, hedgehog +336:fox squirrel, eastern fox squirrel, Sciurus niger +337:marmot +338:beaver +339:guinea pig, Cavia cobaya +340:sorrel +341:zebra +342:hog, pig, grunter, squealer, Sus scrofa +343:wild boar, boar, Sus scrofa +344:warthog +345:hippopotamus, hippo, river horse, Hippopotamus amphibius +346:ox +347:water buffalo, water ox, Asiatic buffalo, Bubalus bubalis +348:bison +349:ram, tup +350:bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis +351:ibex, Capra ibex +352:hartebeest +353:impala, Aepyceros melampus +354:gazelle +355:Arabian camel, dromedary, Camelus dromedarius +356:llama +357:weasel +358:mink +359:polecat, fitch, foulmart, foumart, Mustela putorius +360:black-footed ferret, ferret, Mustela nigripes +361:otter +362:skunk, polecat, wood pussy +363:badger +364:armadillo +365:three-toed sloth, ai, Bradypus tridactylus +366:orangutan, orang, orangutang, Pongo pygmaeus +367:gorilla, Gorilla gorilla +368:chimpanzee, chimp, Pan troglodytes +369:gibbon, Hylobates lar +370:siamang, Hylobates syndactylus, Symphalangus syndactylus +371:guenon, guenon monkey +372:patas, hussar monkey, Erythrocebus patas +373:baboon +374:macaque +375:langur +376:colobus, colobus monkey +377:proboscis monkey, Nasalis larvatus +378:marmoset +379:capuchin, ringtail, Cebus capucinus +380:howler monkey, howler +381:titi, titi monkey +382:spider monkey, Ateles geoffroyi +383:squirrel monkey, Saimiri sciureus +384:Madagascar cat, ring-tailed lemur, Lemur catta +385:indri, indris, Indri indri, Indri brevicaudatus +386:Indian elephant, Elephas maximus +387:African elephant, Loxodonta africana +388:lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens +389:giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca +390:barracouta, snoek +391:eel +392:coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch +393:rock beauty, Holocanthus tricolor +394:anemone fish +395:sturgeon +396:gar, garfish, garpike, billfish, Lepisosteus osseus +397:lionfish +398:puffer, pufferfish, blowfish, globefish +399:abacus +400:abaya +401:academic gown, academic robe, judge's robe +402:accordion, piano accordion, squeeze box +403:acoustic guitar +404:aircraft carrier, carrier, flattop, attack aircraft carrier +405:airliner +406:airship, dirigible +407:altar +408:ambulance +409:amphibian, amphibious vehicle +410:analog clock +411:apiary, bee house +412:apron +413:ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin +414:assault rifle, assault gun +415:backpack, back pack, knapsack, packsack, rucksack, haversack +416:bakery, bakeshop, bakehouse +417:balance beam, beam +418:balloon +419:ballpoint, ballpoint pen, ballpen, Biro +420:Band Aid +421:banjo +422:bannister, banister, balustrade, balusters, handrail +423:barbell +424:barber chair +425:barbershop +426:barn +427:barometer +428:barrel, cask +429:barrow, garden cart, lawn cart, wheelbarrow +430:baseball +431:basketball +432:bassinet +433:bassoon +434:bathing cap, swimming cap +435:bath towel +436:bathtub, bathing tub, bath, tub +437:beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon +438:beacon, lighthouse, beacon light, pharos +439:beaker +440:bearskin, busby, shako +441:beer bottle +442:beer glass +443:bell cote, bell cot +444:bib +445:bicycle-built-for-two, tandem bicycle, tandem +446:bikini, two-piece +447:binder, ring-binder +448:binoculars, field glasses, opera glasses +449:birdhouse +450:boathouse +451:bobsled, bobsleigh, bob +452:bolo tie, bolo, bola tie, bola +453:bonnet, poke bonnet +454:bookcase +455:bookshop, bookstore, bookstall +456:bottlecap +457:bow +458:bow tie, bow-tie, bowtie +459:brass, memorial tablet, plaque +460:brassiere, bra, bandeau +461:breakwater, groin, groyne, mole, bulwark, seawall, jetty +462:breastplate, aegis, egis +463:broom +464:bucket, pail +465:buckle +466:bulletproof vest +467:bullet train, bullet +468:butcher shop, meat market +469:cab, hack, taxi, taxicab +470:caldron, cauldron +471:candle, taper, wax light +472:cannon +473:canoe +474:can opener, tin opener +475:cardigan +476:car mirror +477:carousel, carrousel, merry-go-round, roundabout, whirligig +478:carpenter's kit, tool kit +479:carton +480:car wheel +481:cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM +482:cassette +483:cassette player +484:castle +485:catamaran +486:CD player +487:cello, violoncello +488:cellular telephone, cellular phone, cellphone, cell, mobile phone +489:chain +490:chainlink fence +491:chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour +492:chain saw, chainsaw +493:chest +494:chiffonier, commode +495:chime, bell, gong +496:china cabinet, china closet +497:Christmas stocking +498:church, church building +499:cinema, movie theater, movie theatre, movie house, picture palace +500:cleaver, meat cleaver, chopper +501:cliff dwelling +502:cloak +503:clog, geta, patten, sabot +504:cocktail shaker +505:coffee mug +506:coffeepot +507:coil, spiral, volute, whorl, helix +508:combination lock +509:computer keyboard, keypad +510:confectionery, confectionary, candy store +511:container ship, containership, container vessel +512:convertible +513:corkscrew, bottle screw +514:cornet, horn, trumpet, trump +515:cowboy boot +516:cowboy hat, ten-gallon hat +517:cradle +518:crane +519:crash helmet +520:crate +521:crib, cot +522:Crock Pot +523:croquet ball +524:crutch +525:cuirass +526:dam, dike, dyke +527:desk +528:desktop computer +529:dial telephone, dial phone +530:diaper, nappy, napkin +531:digital clock +532:digital watch +533:dining table, board +534:dishrag, dishcloth +535:dishwasher, dish washer, dishwashing machine +536:disk brake, disc brake +537:dock, dockage, docking facility +538:dogsled, dog sled, dog sleigh +539:dome +540:doormat, welcome mat +541:drilling platform, offshore rig +542:drum, membranophone, tympan +543:drumstick +544:dumbbell +545:Dutch oven +546:electric fan, blower +547:electric guitar +548:electric locomotive +549:entertainment center +550:envelope +551:espresso maker +552:face powder +553:feather boa, boa +554:file, file cabinet, filing cabinet +555:fireboat +556:fire engine, fire truck +557:fire screen, fireguard +558:flagpole, flagstaff +559:flute, transverse flute +560:folding chair +561:football helmet +562:forklift +563:fountain +564:fountain pen +565:four-poster +566:freight car +567:French horn, horn +568:frying pan, frypan, skillet +569:fur coat +570:garbage truck, dustcart +571:gasmask, respirator, gas helmet +572:gas pump, gasoline pump, petrol pump, island dispenser +573:goblet +574:go-kart +575:golf ball +576:golfcart, golf cart +577:gondola +578:gong, tam-tam +579:gown +580:grand piano, grand +581:greenhouse, nursery, glasshouse +582:grille, radiator grille +583:grocery store, grocery, food market, market +584:guillotine +585:hair slide +586:hair spray +587:half track +588:hammer +589:hamper +590:hand blower, blow dryer, blow drier, hair dryer, hair drier +591:hand-held computer, hand-held microcomputer +592:handkerchief, hankie, hanky, hankey +593:hard disc, hard disk, fixed disk +594:harmonica, mouth organ, harp, mouth harp +595:harp +596:harvester, reaper +597:hatchet +598:holster +599:home theater, home theatre +600:honeycomb +601:hook, claw +602:hoopskirt, crinoline +603:horizontal bar, high bar +604:horse cart, horse-cart +605:hourglass +606:iPod +607:iron, smoothing iron +608:jack-o'-lantern +609:jean, blue jean, denim +610:jeep, landrover +611:jersey, T-shirt, tee shirt +612:jigsaw puzzle +613:jinrikisha, ricksha, rickshaw +614:joystick +615:kimono +616:knee pad +617:knot +618:lab coat, laboratory coat +619:ladle +620:lampshade, lamp shade +621:laptop, laptop computer +622:lawn mower, mower +623:lens cap, lens cover +624:letter opener, paper knife, paperknife +625:library +626:lifeboat +627:lighter, light, igniter, ignitor +628:limousine, limo +629:liner, ocean liner +630:lipstick, lip rouge +631:Loafer +632:lotion +633:loudspeaker, speaker, speaker unit, loudspeaker system, speaker system +634:loupe, jeweler's loupe +635:lumbermill, sawmill +636:magnetic compass +637:mailbag, postbag +638:mailbox, letter box +639:maillot +640:maillot, tank suit +641:manhole cover +642:maraca +643:marimba, xylophone +644:mask +645:matchstick +646:maypole +647:maze, labyrinth +648:measuring cup +649:medicine chest, medicine cabinet +650:megalith, megalithic structure +651:microphone, mike +652:microwave, microwave oven +653:military uniform +654:milk can +655:minibus +656:miniskirt, mini +657:minivan +658:missile +659:mitten +660:mixing bowl +661:mobile home, manufactured home +662:Model T +663:modem +664:monastery +665:monitor +666:moped +667:mortar +668:mortarboard +669:mosque +670:mosquito net +671:motor scooter, scooter +672:mountain bike, all-terrain bike, off-roader +673:mountain tent +674:mouse, computer mouse +675:mousetrap +676:moving van +677:muzzle +678:nail +679:neck brace +680:necklace +681:nipple +682:notebook, notebook computer +683:obelisk +684:oboe, hautboy, hautbois +685:ocarina, sweet potato +686:odometer, hodometer, mileometer, milometer +687:oil filter +688:organ, pipe organ +689:oscilloscope, scope, cathode-ray oscilloscope, CRO +690:overskirt +691:oxcart +692:oxygen mask +693:packet +694:paddle, boat paddle +695:paddlewheel, paddle wheel +696:padlock +697:paintbrush +698:pajama, pyjama, pj's, jammies 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+740:potter's wheel +741:power drill +742:prayer rug, prayer mat +743:printer +744:prison, prison house +745:projectile, missile +746:projector +747:puck, hockey puck +748:punching bag, punch bag, punching ball, punchball +749:purse +750:quill, quill pen +751:quilt, comforter, comfort, puff +752:racer, race car, racing car +753:racket, racquet +754:radiator +755:radio, wireless +756:radio telescope, radio reflector +757:rain barrel +758:recreational vehicle, RV, R.V. +759:reel +760:reflex camera +761:refrigerator, icebox +762:remote control, remote +763:restaurant, eating house, eating place, eatery +764:revolver, six-gun, six-shooter +765:rifle +766:rocking chair, rocker +767:rotisserie +768:rubber eraser, rubber, pencil eraser +769:rugby ball +770:rule, ruler +771:running shoe +772:safe +773:safety pin +774:saltshaker, salt shaker +775:sandal +776:sarong +777:sax, saxophone +778:scabbard +779:scale, weighing machine +780:school bus +781:schooner +782:scoreboard +783:screen, CRT 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pigboat, sub, U-boat +835:suit, suit of clothes +836:sundial +837:sunglass +838:sunglasses, dark glasses, shades +839:sunscreen, sunblock, sun blocker +840:suspension bridge +841:swab, swob, mop +842:sweatshirt +843:swimming trunks, bathing trunks +844:swing +845:switch, electric switch, electrical switch +846:syringe +847:table lamp +848:tank, army tank, armored combat vehicle, armoured combat vehicle +849:tape player +850:teapot +851:teddy, teddy bear +852:television, television system +853:tennis ball +854:thatch, thatched roof +855:theater curtain, theatre curtain +856:thimble +857:thresher, thrasher, threshing machine +858:throne +859:tile roof +860:toaster +861:tobacco shop, tobacconist shop, tobacconist +862:toilet seat +863:torch +864:totem pole +865:tow truck, tow car, wrecker +866:toyshop +867:tractor +868:trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi +869:tray +870:trench coat +871:tricycle, trike, velocipede +872:trimaran +873:tripod +874:triumphal arch +875:trolleybus, trolley coach, trackless trolley +876:trombone +877:tub, vat +878:turnstile +879:typewriter keyboard +880:umbrella +881:unicycle, monocycle +882:upright, upright piano +883:vacuum, vacuum cleaner +884:vase +885:vault +886:velvet +887:vending machine +888:vestment +889:viaduct +890:violin, fiddle +891:volleyball +892:waffle iron +893:wall clock +894:wallet, billfold, notecase, pocketbook +895:wardrobe, closet, press +896:warplane, military plane +897:washbasin, handbasin, washbowl, lavabo, wash-hand basin +898:washer, automatic washer, washing machine +899:water bottle +900:water jug +901:water tower +902:whiskey jug +903:whistle +904:wig +905:window screen +906:window shade +907:Windsor tie +908:wine bottle +909:wing +910:wok +911:wooden spoon +912:wool, woolen, woollen +913:worm fence, snake fence, snake-rail fence, Virginia fence +914:wreck +915:yawl +916:yurt +917:web site, website, internet site, site +918:comic book +919:crossword puzzle, crossword +920:street sign +921:traffic light, traffic signal, stoplight +922:book jacket, dust cover, dust jacket, dust wrapper +923:menu +924:plate +925:guacamole +926:consomme +927:hot pot, hotpot +928:trifle +929:ice cream, icecream +930:ice lolly, lolly, lollipop, popsicle +931:French loaf +932:bagel, beigel +933:pretzel +934:cheeseburger +935:hotdog, hot dog, red hot +936:mashed potato +937:head cabbage +938:broccoli +939:cauliflower +940:zucchini, courgette +941:spaghetti squash +942:acorn squash +943:butternut squash +944:cucumber, cuke +945:artichoke, globe artichoke +946:bell pepper +947:cardoon +948:mushroom +949:Granny Smith +950:strawberry +951:orange +952:lemon +953:fig +954:pineapple, ananas +955:banana +956:jackfruit, jak, jack +957:custard apple +958:pomegranate +959:hay +960:carbonara +961:chocolate sauce, chocolate syrup +962:dough +963:meat loaf, meatloaf +964:pizza, pizza pie +965:potpie +966:burrito +967:red wine +968:espresso +969:cup +970:eggnog +971:alp +972:bubble +973:cliff, drop, drop-off +974:coral reef +975:geyser +976:lakeside, lakeshore +977:promontory, headland, head, foreland +978:sandbar, sand bar +979:seashore, coast, seacoast, sea-coast +980:valley, vale +981:volcano +982:ballplayer, baseball player +983:groom, bridegroom +984:scuba diver +985:rapeseed +986:daisy +987:yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum +988:corn +989:acorn +990:hip, rose hip, rosehip +991:buckeye, horse chestnut, conker +992:coral fungus +993:agaric +994:gyromitra +995:stinkhorn, carrion fungus +996:earthstar +997:hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa +998:bolete +999:ear, spike, capitulum +1000:toilet tissue, toilet paper, bathroom tissue \ No newline at end of file diff --git a/rknn-toolkit2/examples/functions/quantize_algorithm_mmse/mobilenet_v1_1.0_224_frozen.pb b/rknn-toolkit2/examples/functions/quantize_algorithm_mmse/mobilenet_v1_1.0_224_frozen.pb new file mode 100755 index 0000000000000000000000000000000000000000..94d69ce1e82a5ecd874943e1c7f9d928a9836e8b --- /dev/null +++ b/rknn-toolkit2/examples/functions/quantize_algorithm_mmse/mobilenet_v1_1.0_224_frozen.pb @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3ed7703cccb55920e37a95ece9b2cbead4ffcd8081f2cfa9309060590aa2239c +size 17085200 diff --git a/rknn-toolkit2/examples/functions/quantize_algorithm_mmse/test.py b/rknn-toolkit2/examples/functions/quantize_algorithm_mmse/test.py new file mode 100755 index 0000000000000000000000000000000000000000..aea809a31711b392cd2ba97dd0cd378054d6adf1 --- /dev/null +++ b/rknn-toolkit2/examples/functions/quantize_algorithm_mmse/test.py @@ -0,0 +1,83 @@ +import numpy as np +import cv2 +from rknn.api import RKNN + + +def show_outputs(outputs): + output = outputs[0][0] + index = sorted(range(len(output)), key=lambda k : output[k], reverse=True) + fp = open('./labels.txt', 'r') + labels = fp.readlines() + top5_str = 'mobilenet_v1\n-----TOP 5-----\n' + for i in range(5): + value = output[index[i]] + if value > 0: + topi = '[{:>4d}] score:{:.6f} class:"{}"\n'.format(index[i], value, labels[index[i]].strip().split(':')[-1]) + else: + topi = '[ -1]: 0.0\n' + top5_str += topi + print(top5_str.strip()) + +def softmax(outputs): + outputs[0] = np.exp(outputs[0])/np.sum(np.exp(outputs[0])) + return outputs + +if __name__ == '__main__': + + # Create RKNN object + rknn = RKNN(verbose=True) + + # Pre-process config + print('--> Config model') + rknn.config(mean_values=[128, 128, 128], std_values=[128, 128, 128], target_platform='rk3566', + quantized_method='channel', quantized_algorithm='mmse') + print('done') + + # Load model (from https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md) + print('--> Loading model') + ret = rknn.load_tensorflow(tf_pb='mobilenet_v1_1.0_224_frozen.pb', + inputs=['input'], + input_size_list=[[1, 224, 224, 3]], + outputs=['MobilenetV1/Logits/SpatialSqueeze']) + if ret != 0: + print('Load model failed!') + exit(ret) + print('done') + + # Build model + print('--> Building model') + ret = rknn.build(do_quantization=True, dataset='./dataset.txt') + if ret != 0: + print('Build model failed!') + exit(ret) + print('done') + + # Accuracy analysis + print('--> Accuracy analysis') + ret = rknn.accuracy_analysis(inputs=['dog_224x224.jpg'], output_dir=None) + if ret != 0: + print('Accuracy analysis failed!') + exit(ret) + print('done') + + # Set inputs + img = cv2.imread('./dog_224x224.jpg') + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + img = np.expand_dims(img, 0) + + # Init runtime environment + print('--> Init runtime environment') + ret = rknn.init_runtime() + if ret != 0: + print('Init runtime environment failed!') + exit(ret) + print('done') + + # Inference + print('--> Running model') + outputs = rknn.inference(inputs=[img], data_format=['nhwc']) + np.save('./functions_quantize_algorithm_mmse_0.npy', outputs[0]) + show_outputs(softmax(outputs)) + print('done') + + rknn.release() diff --git a/rknn-toolkit2/examples/onnx/resnet50v2/README.md b/rknn-toolkit2/examples/onnx/resnet50v2/README.md new file mode 100644 index 0000000000000000000000000000000000000000..12f0056f917a2495d3a6c55c6da4b056ad3aec45 --- /dev/null +++ b/rknn-toolkit2/examples/onnx/resnet50v2/README.md @@ -0,0 +1,30 @@ +# ONNX ResNet50 V2 + +## Model Source +The model used in this example come from: +https://s3.amazonaws.com/onnx-model-zoo/resnet/resnet50v2/resnet50v2.onnx + +## Script Usage +*Usage:* +``` +python test.py +``` +*rknn_convert usage:* +``` +python3 -m rknn.api.rknn_convert -t rk3568 -i ./model_config.yml -o ./ +``` +*Description:* +- The default target platform in script is 'rk3566', please modify the 'target_platform' parameter of 'rknn.config' according to the actual platform. +- If connecting board is required, please add the 'target' parameter in 'rknn.init_runtime'. + +## Expected Results +This example will print the TOP5 labels and corresponding scores of the test image classification results, as follows: +``` +-----TOP 5----- +[155] score:0.742885 class:"Shih-Tzu" +[154] score:0.225878 class:"Pekinese, Pekingese, Peke" +[262] score:0.015507 class:"Brabancon griffon" +[152] score:0.003501 class:"Japanese spaniel" +[254] score:0.002801 class:"pug, pug-dog" +``` +- Note: Different platforms, different versions of tools and drivers may have slightly different results. \ No newline at end of file diff --git a/rknn-toolkit2/examples/onnx/resnet50v2/dataset.txt b/rknn-toolkit2/examples/onnx/resnet50v2/dataset.txt new file mode 100644 index 0000000000000000000000000000000000000000..9078c68db586a0251bdd41282d67f4d256b3abc3 --- /dev/null +++ b/rknn-toolkit2/examples/onnx/resnet50v2/dataset.txt @@ -0,0 +1 @@ +dog_224x224.jpg diff --git a/rknn-toolkit2/examples/onnx/resnet50v2/dog_224x224.jpg b/rknn-toolkit2/examples/onnx/resnet50v2/dog_224x224.jpg new file mode 100644 index 0000000000000000000000000000000000000000..004b0416e23454a12eb06eaba238c7e2d5f2b0fc --- /dev/null +++ b/rknn-toolkit2/examples/onnx/resnet50v2/dog_224x224.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c350299c6283d5f62fecf1f845b6b3be9aafec8dff528ca09a129990f0a584b0 +size 18889 diff --git a/rknn-toolkit2/examples/onnx/resnet50v2/labels.txt b/rknn-toolkit2/examples/onnx/resnet50v2/labels.txt new file mode 100644 index 0000000000000000000000000000000000000000..0993780f99088349e2c156a5a8c57ce1013eb493 --- /dev/null +++ b/rknn-toolkit2/examples/onnx/resnet50v2/labels.txt @@ -0,0 +1,1000 @@ +0:tench, Tinca tinca +1:goldfish, Carassius auratus +2:great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias +3:tiger shark, Galeocerdo cuvieri +4:hammerhead, hammerhead shark +5:electric ray, crampfish, numbfish, torpedo +6:stingray +7:cock +8:hen +9:ostrich, Struthio camelus +10:brambling, Fringilla montifringilla +11:goldfinch, Carduelis carduelis +12:house finch, linnet, Carpodacus mexicanus +13:junco, snowbird +14:indigo bunting, indigo finch, indigo bird, Passerina cyanea +15:robin, American robin, Turdus migratorius +16:bulbul +17:jay +18:magpie +19:chickadee +20:water ouzel, dipper +21:kite +22:bald eagle, American eagle, Haliaeetus leucocephalus +23:vulture +24:great grey owl, great gray owl, Strix nebulosa +25:European fire salamander, Salamandra salamandra +26:common newt, Triturus vulgaris +27:eft +28:spotted salamander, Ambystoma maculatum +29:axolotl, mud puppy, Ambystoma mexicanum +30:bullfrog, Rana catesbeiana +31:tree frog, tree-frog +32:tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui +33:loggerhead, loggerhead turtle, Caretta caretta +34:leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea +35:mud turtle +36:terrapin +37:box turtle, box tortoise +38:banded gecko +39:common iguana, iguana, Iguana iguana +40:American chameleon, anole, Anolis carolinensis +41:whiptail, whiptail lizard +42:agama +43:frilled lizard, Chlamydosaurus kingi +44:alligator lizard +45:Gila monster, Heloderma suspectum +46:green lizard, Lacerta viridis +47:African chameleon, Chamaeleo chamaeleon +48:Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis +49:African crocodile, Nile crocodile, Crocodylus niloticus +50:American alligator, Alligator mississipiensis +51:triceratops +52:thunder snake, worm snake, Carphophis amoenus +53:ringneck snake, ring-necked snake, ring snake +54:hognose snake, puff adder, sand viper +55:green snake, grass snake +56:king snake, kingsnake +57:garter snake, grass snake +58:water snake +59:vine snake +60:night snake, Hypsiglena torquata +61:boa constrictor, Constrictor constrictor +62:rock python, rock snake, Python sebae +63:Indian cobra, Naja naja +64:green mamba +65:sea snake +66:horned viper, cerastes, sand viper, horned asp, Cerastes cornutus +67:diamondback, diamondback rattlesnake, Crotalus adamanteus +68:sidewinder, horned rattlesnake, Crotalus cerastes +69:trilobite +70:harvestman, daddy longlegs, Phalangium opilio +71:scorpion +72:black and gold garden spider, Argiope aurantia +73:barn spider, Araneus cavaticus +74:garden spider, Aranea diademata +75:black widow, Latrodectus mactans +76:tarantula +77:wolf spider, hunting spider +78:tick +79:centipede +80:black grouse +81:ptarmigan +82:ruffed grouse, partridge, Bonasa umbellus +83:prairie chicken, prairie grouse, prairie fowl +84:peacock +85:quail +86:partridge +87:African grey, African gray, Psittacus erithacus +88:macaw +89:sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita +90:lorikeet +91:coucal +92:bee eater +93:hornbill +94:hummingbird +95:jacamar +96:toucan +97:drake +98:red-breasted merganser, Mergus serrator +99:goose +100:black swan, Cygnus atratus +101:tusker +102:echidna, spiny anteater, anteater +103:platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus +104:wallaby, brush kangaroo +105:koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus +106:wombat +107:jellyfish +108:sea anemone, anemone +109:brain coral +110:flatworm, platyhelminth +111:nematode, nematode worm, roundworm +112:conch +113:snail +114:slug +115:sea slug, nudibranch +116:chiton, coat-of-mail shell, sea cradle, polyplacophore +117:chambered nautilus, pearly nautilus, nautilus +118:Dungeness crab, Cancer magister +119:rock crab, Cancer irroratus +120:fiddler crab +121:king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica +122:American lobster, Northern lobster, Maine lobster, Homarus americanus +123:spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish +124:crayfish, crawfish, crawdad, crawdaddy +125:hermit crab +126:isopod +127:white stork, Ciconia ciconia +128:black stork, Ciconia nigra +129:spoonbill +130:flamingo +131:little blue heron, Egretta caerulea +132:American egret, great white heron, Egretta albus +133:bittern +134:crane +135:limpkin, Aramus pictus +136:European gallinule, Porphyrio porphyrio +137:American coot, marsh hen, mud hen, water hen, Fulica americana +138:bustard +139:ruddy turnstone, Arenaria interpres +140:red-backed sandpiper, dunlin, Erolia alpina +141:redshank, Tringa totanus +142:dowitcher +143:oystercatcher, oyster catcher +144:pelican +145:king penguin, Aptenodytes patagonica +146:albatross, mollymawk +147:grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus +148:killer whale, killer, orca, grampus, sea wolf, Orcinus orca +149:dugong, Dugong dugon +150:sea lion +151:Chihuahua +152:Japanese spaniel +153:Maltese dog, Maltese terrier, Maltese +154:Pekinese, Pekingese, Peke +155:Shih-Tzu +156:Blenheim spaniel +157:papillon +158:toy terrier +159:Rhodesian ridgeback +160:Afghan hound, Afghan +161:basset, basset hound +162:beagle +163:bloodhound, sleuthhound +164:bluetick +165:black-and-tan coonhound +166:Walker hound, Walker foxhound +167:English foxhound +168:redbone +169:borzoi, Russian wolfhound +170:Irish wolfhound +171:Italian greyhound +172:whippet +173:Ibizan hound, Ibizan Podenco +174:Norwegian elkhound, elkhound +175:otterhound, otter hound +176:Saluki, gazelle hound +177:Scottish deerhound, deerhound +178:Weimaraner +179:Staffordshire bullterrier, Staffordshire bull terrier +180:American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier +181:Bedlington terrier +182:Border terrier +183:Kerry blue terrier +184:Irish terrier +185:Norfolk terrier +186:Norwich terrier +187:Yorkshire terrier +188:wire-haired fox terrier +189:Lakeland terrier +190:Sealyham terrier, Sealyham +191:Airedale, Airedale terrier +192:cairn, cairn terrier +193:Australian terrier +194:Dandie Dinmont, Dandie Dinmont terrier +195:Boston bull, Boston terrier +196:miniature schnauzer +197:giant schnauzer +198:standard schnauzer +199:Scotch terrier, Scottish terrier, Scottie +200:Tibetan terrier, chrysanthemum dog +201:silky terrier, Sydney silky +202:soft-coated wheaten terrier +203:West Highland white terrier +204:Lhasa, Lhasa apso +205:flat-coated retriever +206:curly-coated retriever +207:golden retriever +208:Labrador retriever +209:Chesapeake Bay retriever +210:German short-haired pointer +211:vizsla, Hungarian pointer +212:English setter +213:Irish setter, red setter +214:Gordon setter +215:Brittany spaniel +216:clumber, clumber spaniel +217:English springer, English springer spaniel +218:Welsh springer spaniel +219:cocker spaniel, English cocker spaniel, cocker +220:Sussex spaniel +221:Irish water spaniel +222:kuvasz +223:schipperke +224:groenendael +225:malinois +226:briard +227:kelpie +228:komondor +229:Old English sheepdog, bobtail +230:Shetland sheepdog, Shetland sheep dog, Shetland +231:collie +232:Border collie +233:Bouvier des Flandres, Bouviers des Flandres +234:Rottweiler +235:German shepherd, German shepherd dog, German police dog, alsatian +236:Doberman, Doberman pinscher +237:miniature pinscher +238:Greater Swiss Mountain dog +239:Bernese mountain dog +240:Appenzeller +241:EntleBucher +242:boxer +243:bull mastiff +244:Tibetan mastiff +245:French bulldog +246:Great Dane +247:Saint Bernard, St Bernard +248:Eskimo dog, husky +249:malamute, malemute, Alaskan malamute +250:Siberian husky +251:dalmatian, coach dog, carriage dog +252:affenpinscher, monkey pinscher, monkey dog +253:basenji +254:pug, pug-dog +255:Leonberg +256:Newfoundland, Newfoundland dog +257:Great Pyrenees +258:Samoyed, Samoyede +259:Pomeranian +260:chow, chow chow +261:keeshond +262:Brabancon griffon +263:Pembroke, Pembroke Welsh corgi +264:Cardigan, Cardigan Welsh corgi +265:toy poodle +266:miniature poodle +267:standard poodle +268:Mexican hairless +269:timber wolf, grey wolf, gray wolf, Canis lupus +270:white wolf, Arctic wolf, Canis lupus tundrarum +271:red wolf, maned wolf, Canis rufus, Canis niger +272:coyote, prairie wolf, brush wolf, Canis latrans +273:dingo, warrigal, warragal, Canis dingo +274:dhole, Cuon alpinus +275:African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus +276:hyena, hyaena +277:red fox, Vulpes vulpes +278:kit fox, Vulpes macrotis +279:Arctic fox, white fox, Alopex lagopus +280:grey fox, gray fox, Urocyon cinereoargenteus +281:tabby, tabby cat +282:tiger cat +283:Persian cat +284:Siamese cat, Siamese +285:Egyptian cat +286:cougar, puma, catamount, mountain lion, painter, panther, Felis concolor +287:lynx, catamount +288:leopard, Panthera pardus +289:snow leopard, ounce, Panthera uncia +290:jaguar, panther, Panthera onca, Felis onca +291:lion, king of beasts, Panthera leo +292:tiger, Panthera tigris +293:cheetah, chetah, Acinonyx jubatus +294:brown bear, bruin, Ursus arctos +295:American black bear, black bear, Ursus americanus, Euarctos americanus +296:ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus +297:sloth bear, Melursus ursinus, Ursus ursinus +298:mongoose +299:meerkat, mierkat +300:tiger beetle +301:ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle +302:ground beetle, carabid beetle +303:long-horned beetle, longicorn, longicorn beetle +304:leaf beetle, chrysomelid +305:dung beetle +306:rhinoceros beetle +307:weevil +308:fly +309:bee +310:ant, emmet, pismire +311:grasshopper, hopper +312:cricket +313:walking stick, walkingstick, stick insect +314:cockroach, roach +315:mantis, mantid +316:cicada, cicala +317:leafhopper +318:lacewing, lacewing fly +319:dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk +320:damselfly +321:admiral +322:ringlet, ringlet butterfly +323:monarch, monarch butterfly, milkweed butterfly, Danaus plexippus +324:cabbage butterfly +325:sulphur butterfly, sulfur butterfly +326:lycaenid, lycaenid butterfly +327:starfish, sea star +328:sea urchin +329:sea cucumber, holothurian +330:wood rabbit, cottontail, cottontail rabbit +331:hare +332:Angora, Angora rabbit +333:hamster +334:porcupine, hedgehog +335:fox squirrel, eastern fox squirrel, Sciurus niger +336:marmot +337:beaver +338:guinea pig, Cavia cobaya +339:sorrel +340:zebra +341:hog, pig, grunter, squealer, Sus scrofa +342:wild boar, boar, Sus scrofa +343:warthog +344:hippopotamus, hippo, river horse, Hippopotamus amphibius +345:ox +346:water buffalo, water ox, Asiatic buffalo, Bubalus bubalis +347:bison +348:ram, tup +349:bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis +350:ibex, Capra ibex +351:hartebeest +352:impala, Aepyceros melampus +353:gazelle +354:Arabian camel, dromedary, Camelus dromedarius +355:llama +356:weasel +357:mink +358:polecat, fitch, foulmart, foumart, Mustela putorius +359:black-footed ferret, ferret, Mustela nigripes +360:otter +361:skunk, polecat, wood pussy +362:badger +363:armadillo +364:three-toed sloth, ai, Bradypus tridactylus +365:orangutan, orang, orangutang, Pongo pygmaeus +366:gorilla, Gorilla gorilla +367:chimpanzee, chimp, Pan troglodytes +368:gibbon, Hylobates lar +369:siamang, Hylobates syndactylus, Symphalangus syndactylus +370:guenon, guenon monkey +371:patas, hussar monkey, Erythrocebus patas +372:baboon +373:macaque +374:langur +375:colobus, colobus monkey +376:proboscis monkey, Nasalis larvatus +377:marmoset +378:capuchin, ringtail, Cebus capucinus +379:howler monkey, howler +380:titi, titi monkey +381:spider monkey, Ateles geoffroyi +382:squirrel monkey, Saimiri sciureus +383:Madagascar cat, ring-tailed lemur, Lemur catta +384:indri, indris, Indri indri, Indri brevicaudatus +385:Indian elephant, Elephas maximus +386:African elephant, Loxodonta africana +387:lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens +388:giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca +389:barracouta, snoek +390:eel +391:coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch +392:rock beauty, Holocanthus tricolor +393:anemone fish +394:sturgeon +395:gar, garfish, garpike, billfish, Lepisosteus osseus +396:lionfish +397:puffer, pufferfish, blowfish, globefish +398:abacus +399:abaya +400:academic gown, academic robe, judge's robe +401:accordion, piano accordion, squeeze box +402:acoustic guitar +403:aircraft carrier, carrier, flattop, attack aircraft carrier +404:airliner +405:airship, dirigible +406:altar +407:ambulance +408:amphibian, amphibious vehicle +409:analog clock +410:apiary, bee house +411:apron +412:ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin +413:assault rifle, assault gun +414:backpack, back pack, knapsack, packsack, rucksack, haversack +415:bakery, bakeshop, bakehouse +416:balance beam, beam +417:balloon +418:ballpoint, ballpoint pen, ballpen, Biro +419:Band Aid +420:banjo +421:bannister, banister, balustrade, balusters, handrail +422:barbell +423:barber chair +424:barbershop +425:barn +426:barometer +427:barrel, cask +428:barrow, garden cart, lawn cart, wheelbarrow +429:baseball +430:basketball +431:bassinet +432:bassoon +433:bathing cap, swimming cap +434:bath towel +435:bathtub, bathing tub, bath, tub +436:beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon +437:beacon, lighthouse, beacon light, pharos +438:beaker +439:bearskin, busby, shako +440:beer bottle +441:beer glass +442:bell cote, bell cot +443:bib +444:bicycle-built-for-two, tandem bicycle, tandem +445:bikini, two-piece +446:binder, ring-binder +447:binoculars, field glasses, opera glasses +448:birdhouse +449:boathouse +450:bobsled, bobsleigh, bob +451:bolo tie, bolo, bola tie, bola +452:bonnet, poke bonnet +453:bookcase +454:bookshop, bookstore, bookstall +455:bottlecap +456:bow +457:bow tie, bow-tie, bowtie +458:brass, memorial tablet, plaque +459:brassiere, bra, bandeau +460:breakwater, groin, groyne, mole, bulwark, seawall, jetty +461:breastplate, aegis, egis +462:broom +463:bucket, pail +464:buckle +465:bulletproof vest +466:bullet train, bullet +467:butcher shop, meat market +468:cab, hack, taxi, taxicab +469:caldron, cauldron +470:candle, taper, wax light +471:cannon +472:canoe +473:can opener, tin opener +474:cardigan +475:car mirror +476:carousel, carrousel, merry-go-round, roundabout, whirligig +477:carpenter's kit, tool kit +478:carton +479:car wheel +480:cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM +481:cassette +482:cassette player +483:castle +484:catamaran +485:CD player +486:cello, violoncello +487:cellular telephone, cellular phone, cellphone, cell, mobile phone +488:chain +489:chainlink fence +490:chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour +491:chain saw, chainsaw +492:chest +493:chiffonier, commode +494:chime, bell, gong +495:china cabinet, china closet +496:Christmas stocking +497:church, church building +498:cinema, movie theater, movie theatre, movie house, picture palace +499:cleaver, meat cleaver, chopper +500:cliff dwelling +501:cloak +502:clog, geta, patten, sabot +503:cocktail shaker +504:coffee mug +505:coffeepot +506:coil, spiral, volute, whorl, helix +507:combination lock +508:computer keyboard, keypad +509:confectionery, confectionary, candy store +510:container ship, containership, container vessel +511:convertible +512:corkscrew, bottle screw +513:cornet, horn, trumpet, trump +514:cowboy boot +515:cowboy hat, ten-gallon hat +516:cradle +517:crane +518:crash helmet +519:crate +520:crib, cot +521:Crock Pot +522:croquet ball +523:crutch +524:cuirass +525:dam, dike, dyke +526:desk +527:desktop computer +528:dial telephone, dial phone +529:diaper, nappy, napkin +530:digital clock +531:digital watch +532:dining table, board +533:dishrag, dishcloth +534:dishwasher, dish washer, dishwashing machine +535:disk brake, disc brake +536:dock, dockage, docking facility +537:dogsled, dog sled, dog sleigh +538:dome +539:doormat, welcome mat +540:drilling platform, offshore rig +541:drum, membranophone, tympan +542:drumstick +543:dumbbell +544:Dutch oven +545:electric fan, blower +546:electric guitar +547:electric locomotive +548:entertainment center +549:envelope +550:espresso maker +551:face powder +552:feather boa, boa +553:file, file cabinet, filing cabinet +554:fireboat +555:fire engine, fire truck +556:fire screen, fireguard +557:flagpole, flagstaff +558:flute, transverse flute +559:folding chair +560:football helmet +561:forklift +562:fountain +563:fountain pen +564:four-poster +565:freight car +566:French horn, horn +567:frying pan, frypan, skillet +568:fur coat +569:garbage truck, dustcart +570:gasmask, respirator, gas helmet +571:gas pump, gasoline pump, petrol pump, island dispenser +572:goblet +573:go-kart +574:golf ball +575:golfcart, golf cart +576:gondola +577:gong, tam-tam +578:gown +579:grand piano, grand +580:greenhouse, nursery, glasshouse +581:grille, radiator grille +582:grocery store, grocery, food market, market +583:guillotine +584:hair slide +585:hair spray +586:half track +587:hammer +588:hamper +589:hand blower, blow dryer, blow drier, hair dryer, hair drier +590:hand-held computer, hand-held microcomputer +591:handkerchief, hankie, hanky, hankey +592:hard disc, hard disk, fixed disk +593:harmonica, mouth organ, harp, mouth harp +594:harp +595:harvester, reaper +596:hatchet +597:holster +598:home theater, home theatre +599:honeycomb +600:hook, claw +601:hoopskirt, crinoline +602:horizontal bar, high bar +603:horse cart, horse-cart +604:hourglass +605:iPod +606:iron, smoothing iron +607:jack-o'-lantern +608:jean, blue jean, denim +609:jeep, landrover +610:jersey, T-shirt, tee shirt +611:jigsaw puzzle +612:jinrikisha, ricksha, rickshaw +613:joystick +614:kimono +615:knee pad +616:knot +617:lab coat, laboratory coat +618:ladle +619:lampshade, lamp shade +620:laptop, laptop computer +621:lawn mower, mower +622:lens cap, lens cover +623:letter opener, paper knife, paperknife +624:library +625:lifeboat +626:lighter, light, igniter, ignitor +627:limousine, limo +628:liner, ocean liner +629:lipstick, lip rouge +630:Loafer +631:lotion +632:loudspeaker, speaker, speaker unit, loudspeaker system, speaker system +633:loupe, jeweler's loupe +634:lumbermill, sawmill +635:magnetic compass +636:mailbag, postbag +637:mailbox, letter box +638:maillot +639:maillot, tank suit +640:manhole cover +641:maraca +642:marimba, xylophone +643:mask +644:matchstick +645:maypole +646:maze, labyrinth +647:measuring cup +648:medicine chest, medicine cabinet +649:megalith, megalithic structure +650:microphone, mike +651:microwave, microwave oven +652:military uniform +653:milk can +654:minibus +655:miniskirt, mini +656:minivan +657:missile +658:mitten +659:mixing bowl +660:mobile home, manufactured home +661:Model T +662:modem +663:monastery +664:monitor +665:moped +666:mortar +667:mortarboard +668:mosque +669:mosquito net +670:motor scooter, scooter +671:mountain bike, all-terrain bike, off-roader +672:mountain tent +673:mouse, computer mouse +674:mousetrap +675:moving van +676:muzzle +677:nail +678:neck brace +679:necklace +680:nipple +681:notebook, notebook computer +682:obelisk +683:oboe, hautboy, hautbois +684:ocarina, sweet potato +685:odometer, hodometer, mileometer, milometer +686:oil filter +687:organ, pipe organ +688:oscilloscope, scope, cathode-ray oscilloscope, CRO +689:overskirt +690:oxcart +691:oxygen mask +692:packet +693:paddle, boat paddle +694:paddlewheel, paddle wheel +695:padlock +696:paintbrush +697:pajama, pyjama, pj's, jammies +698:palace +699:panpipe, pandean pipe, syrinx +700:paper towel +701:parachute, chute +702:parallel bars, bars +703:park bench +704:parking meter +705:passenger car, coach, carriage +706:patio, terrace +707:pay-phone, pay-station +708:pedestal, plinth, footstall +709:pencil box, pencil case +710:pencil sharpener +711:perfume, essence +712:Petri dish +713:photocopier +714:pick, plectrum, plectron +715:pickelhaube +716:picket fence, paling +717:pickup, pickup truck +718:pier +719:piggy bank, penny bank +720:pill bottle +721:pillow +722:ping-pong ball +723:pinwheel +724:pirate, pirate ship +725:pitcher, ewer +726:plane, carpenter's plane, woodworking plane +727:planetarium +728:plastic bag +729:plate rack +730:plow, plough +731:plunger, plumber's helper +732:Polaroid camera, Polaroid Land camera +733:pole +734:police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria +735:poncho +736:pool table, billiard table, snooker table +737:pop bottle, soda bottle +738:pot, flowerpot +739:potter's wheel +740:power drill +741:prayer rug, prayer mat +742:printer +743:prison, prison house +744:projectile, missile +745:projector +746:puck, hockey puck +747:punching bag, punch bag, punching ball, punchball +748:purse +749:quill, quill pen +750:quilt, comforter, comfort, puff +751:racer, race car, racing car +752:racket, racquet +753:radiator +754:radio, wireless +755:radio telescope, radio reflector +756:rain barrel +757:recreational vehicle, RV, R.V. +758:reel +759:reflex camera +760:refrigerator, icebox +761:remote control, remote +762:restaurant, eating house, eating place, eatery +763:revolver, six-gun, six-shooter +764:rifle +765:rocking chair, rocker +766:rotisserie +767:rubber eraser, rubber, pencil eraser +768:rugby ball +769:rule, ruler +770:running shoe +771:safe +772:safety pin +773:saltshaker, salt shaker +774:sandal +775:sarong +776:sax, saxophone +777:scabbard +778:scale, weighing machine +779:school bus +780:schooner +781:scoreboard +782:screen, CRT screen +783:screw +784:screwdriver +785:seat belt, seatbelt +786:sewing machine +787:shield, buckler +788:shoe shop, shoe-shop, shoe store +789:shoji +790:shopping basket +791:shopping cart +792:shovel +793:shower cap +794:shower curtain +795:ski +796:ski mask +797:sleeping bag +798:slide rule, slipstick +799:sliding door +800:slot, one-armed bandit +801:snorkel +802:snowmobile +803:snowplow, snowplough +804:soap dispenser +805:soccer ball +806:sock +807:solar dish, solar collector, solar furnace +808:sombrero +809:soup bowl +810:space bar +811:space heater +812:space shuttle +813:spatula +814:speedboat +815:spider web, spider's web +816:spindle +817:sports car, sport car +818:spotlight, spot +819:stage +820:steam locomotive +821:steel arch bridge +822:steel drum +823:stethoscope +824:stole +825:stone wall +826:stopwatch, stop watch +827:stove +828:strainer +829:streetcar, tram, tramcar, trolley, trolley car +830:stretcher +831:studio couch, day bed +832:stupa, tope +833:submarine, pigboat, sub, U-boat +834:suit, suit of clothes +835:sundial +836:sunglass +837:sunglasses, dark glasses, shades +838:sunscreen, sunblock, sun blocker +839:suspension bridge +840:swab, swob, mop +841:sweatshirt +842:swimming trunks, bathing trunks +843:swing +844:switch, electric switch, electrical switch +845:syringe +846:table lamp +847:tank, army tank, armored combat vehicle, armoured combat vehicle +848:tape player +849:teapot +850:teddy, teddy bear +851:television, television system +852:tennis ball +853:thatch, thatched roof +854:theater curtain, theatre curtain +855:thimble +856:thresher, thrasher, threshing machine +857:throne +858:tile roof +859:toaster +860:tobacco shop, tobacconist shop, tobacconist +861:toilet seat +862:torch +863:totem pole +864:tow truck, tow car, wrecker +865:toyshop +866:tractor +867:trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi +868:tray +869:trench coat +870:tricycle, trike, velocipede +871:trimaran +872:tripod +873:triumphal arch +874:trolleybus, trolley coach, trackless trolley +875:trombone +876:tub, vat +877:turnstile +878:typewriter keyboard +879:umbrella +880:unicycle, monocycle +881:upright, upright piano +882:vacuum, vacuum cleaner +883:vase +884:vault +885:velvet +886:vending machine +887:vestment +888:viaduct +889:violin, fiddle +890:volleyball +891:waffle iron +892:wall clock +893:wallet, billfold, notecase, pocketbook +894:wardrobe, closet, press +895:warplane, military plane +896:washbasin, handbasin, washbowl, lavabo, wash-hand basin +897:washer, automatic washer, washing machine +898:water bottle +899:water jug +900:water tower +901:whiskey jug +902:whistle +903:wig +904:window screen +905:window shade +906:Windsor tie +907:wine bottle +908:wing +909:wok +910:wooden spoon +911:wool, woolen, woollen +912:worm fence, snake fence, snake-rail fence, Virginia fence +913:wreck +914:yawl +915:yurt +916:web site, website, internet site, site +917:comic book +918:crossword puzzle, crossword +919:street sign +920:traffic light, traffic signal, stoplight +921:book jacket, dust cover, dust jacket, dust wrapper +922:menu +923:plate +924:guacamole +925:consomme +926:hot pot, hotpot +927:trifle +928:ice cream, icecream +929:ice lolly, lolly, lollipop, popsicle +930:French loaf +931:bagel, beigel +932:pretzel +933:cheeseburger +934:hotdog, hot dog, red hot +935:mashed potato +936:head cabbage +937:broccoli +938:cauliflower +939:zucchini, courgette +940:spaghetti squash +941:acorn squash +942:butternut squash +943:cucumber, cuke +944:artichoke, globe artichoke +945:bell pepper +946:cardoon +947:mushroom +948:Granny Smith +949:strawberry +950:orange +951:lemon +952:fig +953:pineapple, ananas +954:banana +955:jackfruit, jak, jack +956:custard apple +957:pomegranate +958:hay +959:carbonara +960:chocolate sauce, chocolate syrup +961:dough +962:meat loaf, meatloaf +963:pizza, pizza pie +964:potpie +965:burrito +966:red wine +967:espresso +968:cup +969:eggnog +970:alp +971:bubble +972:cliff, drop, drop-off +973:coral reef +974:geyser +975:lakeside, lakeshore +976:promontory, headland, head, foreland +977:sandbar, sand bar +978:seashore, coast, seacoast, sea-coast +979:valley, vale +980:volcano +981:ballplayer, baseball player +982:groom, bridegroom +983:scuba diver +984:rapeseed +985:daisy +986:yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum +987:corn +988:acorn +989:hip, rose hip, rosehip +990:buckeye, horse chestnut, conker +991:coral fungus +992:agaric +993:gyromitra +994:stinkhorn, carrion fungus +995:earthstar +996:hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa +997:bolete +998:ear, spike, capitulum +999:toilet tissue, toilet paper, bathroom tissue \ No newline at end of file diff --git a/rknn-toolkit2/examples/onnx/resnet50v2/model_config.yml b/rknn-toolkit2/examples/onnx/resnet50v2/model_config.yml new file mode 100755 index 0000000000000000000000000000000000000000..467849e501b335c89e3ad3a45168d064a439f596 --- /dev/null +++ b/rknn-toolkit2/examples/onnx/resnet50v2/model_config.yml @@ -0,0 +1,13 @@ +models: + name: resnet50v2 # 模型输出名称 + platform: onnx # 原始模型使用的框架 + model_file_path: ./resnet50v2.onnx #原模型路径 + quantize: true # 是否量化 + dataset: ./dataset.txt # 量化dataset文件路径（相对yml路径） + configs: + quantized_dtype: asymmetric_quantized-8 # 量化类型 + mean_values: [123.675, 116.28, 103.53] # rknn.config的mean_values参数 + std_values: [58.82, 58.82, 58.82] # rknn.config的std_values参数 + quant_img_RGB2BGR: false # 不进行RGB2BGR转换 + quantized_algorithm: normal # 量化算法 + quantized_method: channel # 量化方法 diff --git a/rknn-toolkit2/examples/onnx/resnet50v2/test.py b/rknn-toolkit2/examples/onnx/resnet50v2/test.py new file mode 100755 index 0000000000000000000000000000000000000000..b05795543a5e65e108564caea1f8e45a6a35c763 --- /dev/null +++ b/rknn-toolkit2/examples/onnx/resnet50v2/test.py @@ -0,0 +1,132 @@ +import os +import urllib +import traceback +import time +import sys +import numpy as np +import cv2 +from rknn.api import RKNN + +ONNX_MODEL = 'resnet50v2.onnx' +RKNN_MODEL = 'resnet50v2.rknn' + + +def show_outputs(outputs): + output = outputs[0][0] + index = sorted(range(len(output)), key=lambda k : output[k], reverse=True) + fp = open('./labels.txt', 'r') + labels = fp.readlines() + top5_str = 'resnet50v2\n-----TOP 5-----\n' + for i in range(5): + value = output[index[i]] + if value > 0: + topi = '[{:>3d}] score:{:.6f} class:"{}"\n'.format(index[i], value, labels[index[i]].strip().split(':')[-1]) + else: + topi = '[ -1]: 0.0\n' + top5_str += topi + print(top5_str.strip()) + + +def readable_speed(speed): + speed_bytes = float(speed) + speed_kbytes = speed_bytes / 1024 + if speed_kbytes > 1024: + speed_mbytes = speed_kbytes / 1024 + if speed_mbytes > 1024: + speed_gbytes = speed_mbytes / 1024 + return "{:.2f} GB/s".format(speed_gbytes) + else: + return "{:.2f} MB/s".format(speed_mbytes) + else: + return "{:.2f} KB/s".format(speed_kbytes) + + +def show_progress(blocknum, blocksize, totalsize): + speed = (blocknum * blocksize) / (time.time() - start_time) + speed_str = " Speed: {}".format(readable_speed(speed)) + recv_size = blocknum * blocksize + + f = sys.stdout + progress = (recv_size / totalsize) + progress_str = "{:.2f}%".format(progress * 100) + n = round(progress * 50) + s = ('#' * n).ljust(50, '-') + f.write(progress_str.ljust(8, ' ') + '[' + s + ']' + speed_str) + f.flush() + f.write('\r\n') + + +if __name__ == '__main__': + + # Create RKNN object + rknn = RKNN(verbose=True) + + # If resnet50v2 does not exist, download it. + # Download address: + # https://s3.amazonaws.com/onnx-model-zoo/resnet/resnet50v2/resnet50v2.onnx + if not os.path.exists(ONNX_MODEL): + print('--> Download {}'.format(ONNX_MODEL)) + url = 'https://s3.amazonaws.com/onnx-model-zoo/resnet/resnet50v2/resnet50v2.onnx' + download_file = ONNX_MODEL + try: + start_time = time.time() + urllib.request.urlretrieve(url, download_file, show_progress) + except: + print('Download {} failed.'.format(download_file)) + print(traceback.format_exc()) + exit(-1) + print('done') + + # pre-process config + print('--> config model') + rknn.config(mean_values=[123.675, 116.28, 103.53], std_values=[58.82, 58.82, 58.82], target_platform='rk3566') + print('done') + + # Load model + print('--> Loading model') + ret = rknn.load_onnx(model=ONNX_MODEL) + if ret != 0: + print('Load model failed!') + exit(ret) + print('done') + + # Build model + print('--> Building model') + ret = rknn.build(do_quantization=True, dataset='./dataset.txt') + if ret != 0: + print('Build model failed!') + exit(ret) + print('done') + + # Export rknn model + print('--> Export rknn model') + ret = rknn.export_rknn(RKNN_MODEL) + if ret != 0: + print('Export rknn model failed!') + exit(ret) + print('done') + + # Set inputs + img = cv2.imread('./dog_224x224.jpg') + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + img = np.expand_dims(img, 0) + + # Init runtime environment + print('--> Init runtime environment') + ret = rknn.init_runtime() + if ret != 0: + print('Init runtime environment failed!') + exit(ret) + print('done') + + # Inference + print('--> Running model') + outputs = rknn.inference(inputs=[img], data_format=['nhwc']) + np.save('./onnx_resnet50v2_0.npy', outputs[0]) + x = outputs[0] + output = np.exp(x)/np.sum(np.exp(x)) + outputs = [output] + show_outputs(outputs) + print('done') + + rknn.release() diff --git a/rknn-toolkit2/examples/onnx/yolov5/README.md b/rknn-toolkit2/examples/onnx/yolov5/README.md new file mode 100644 index 0000000000000000000000000000000000000000..2b4df924a3d1434529daa9690378aa96740b08f9 --- /dev/null +++ b/rknn-toolkit2/examples/onnx/yolov5/README.md @@ -0,0 +1,23 @@ +# ONNX Yolo V5s + +## Model Source +The model used in this example come from: +https://github.com/airockchip/rknn_model_zoo + +## Script Usage +*Usage:* +``` +python test.py +``` +*rknn_convert usage:* +``` +python3 -m rknn.api.rknn_convert -t rk3568 -i ./model_config.yml -o ./ +``` +*Description:* +- The default target platform in script is 'rk3566', please modify the 'target_platform' parameter of 'rknn.config' according to the actual platform. +- If connecting board is required, please add the 'target' parameter in 'rknn.init_runtime'. + +## Expected Results +This example will save the result of object detection to the 'result.jpg', as follows: +![result](result_truth.jpg) +- Note: Different platforms, different versions of tools and drivers may have slightly different results. \ No newline at end of file diff --git a/rknn-toolkit2/examples/onnx/yolov5/bus.jpg b/rknn-toolkit2/examples/onnx/yolov5/bus.jpg new file mode 100644 index 0000000000000000000000000000000000000000..19172b78dbbcf34de2b875ac72eb0b4b0651ee66 --- /dev/null +++ b/rknn-toolkit2/examples/onnx/yolov5/bus.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fb4914d123d97c440cd127ef0e98d4bdc68cd88e2683657528928b4a34014e16 +size 181374 diff --git a/rknn-toolkit2/examples/onnx/yolov5/dataset.txt b/rknn-toolkit2/examples/onnx/yolov5/dataset.txt new file mode 100644 index 0000000000000000000000000000000000000000..a81b1f855159be64b372f8927d6733de2a6e595c --- /dev/null +++ b/rknn-toolkit2/examples/onnx/yolov5/dataset.txt @@ -0,0 +1 @@ +bus.jpg diff --git a/rknn-toolkit2/examples/onnx/yolov5/model_config.yml b/rknn-toolkit2/examples/onnx/yolov5/model_config.yml new file mode 100755 index 0000000000000000000000000000000000000000..0f1b6667974bcac2346239c822f10c5468e6c07f --- /dev/null +++ b/rknn-toolkit2/examples/onnx/yolov5/model_config.yml @@ -0,0 +1,13 @@ +models: + name: yolov5s_relu # 模型输出名称 + platform: onnx # 原始模型使用的框架 + model_file_path: ./yolov5s_relu.onnx #原模型路径 + quantize: true # 是否量化 + dataset: ./dataset.txt # 量化dataset文件路径（相对yml路径） + configs: + quantized_dtype: asymmetric_quantized-8 # 量化类型 + mean_values: [0, 0, 0] # rknn.config的mean_values参数 + std_values: [255, 255, 255] # rknn.config的std_values参数 + quant_img_RGB2BGR: false # 不进行RGB2BGR转换 + quantized_algorithm: normal # 量化算法 + quantized_method: channel # 量化方法 diff --git a/rknn-toolkit2/examples/onnx/yolov5/result_truth.jpg b/rknn-toolkit2/examples/onnx/yolov5/result_truth.jpg new file mode 100644 index 0000000000000000000000000000000000000000..55114e08e9f605f8dff8db52519404c8a4c916b7 --- /dev/null +++ b/rknn-toolkit2/examples/onnx/yolov5/result_truth.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:eb9b5728afd5a6c70877d83900d38c34ad12af1ddb98345348553bd1d43c0da5 +size 192971 diff --git a/rknn-toolkit2/examples/onnx/yolov5/test.py b/rknn-toolkit2/examples/onnx/yolov5/test.py new file mode 100755 index 0000000000000000000000000000000000000000..1e0e302e3492d46782f9a772a8d64514a021a335 --- /dev/null +++ b/rknn-toolkit2/examples/onnx/yolov5/test.py @@ -0,0 +1,314 @@ +import os +import urllib +import traceback +import time +import sys +import numpy as np +import cv2 +from rknn.api import RKNN + +# Model from https://github.com/airockchip/rknn_model_zoo +ONNX_MODEL = 'yolov5s_relu.onnx' +RKNN_MODEL = 'yolov5s_relu.rknn' +IMG_PATH = './bus.jpg' +DATASET = './dataset.txt' + +QUANTIZE_ON = True + +OBJ_THRESH = 0.25 +NMS_THRESH = 0.45 +IMG_SIZE = 640 + +CLASSES = ("person", "bicycle", "car", "motorbike ", "aeroplane ", "bus ", "train", "truck ", "boat", "traffic light", + "fire hydrant", "stop sign ", "parking meter", "bench", "bird", "cat", "dog ", "horse ", "sheep", "cow", "elephant", + "bear", "zebra ", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", + "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife ", + "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza ", "donut", "cake", "chair", "sofa", + "pottedplant", "bed", "diningtable", "toilet ", "tvmonitor", "laptop ", "mouse ", "remote ", "keyboard ", "cell phone", "microwave ", + "oven ", "toaster", "sink", "refrigerator ", "book", "clock", "vase", "scissors ", "teddy bear ", "hair drier", "toothbrush ") + + + +def xywh2xyxy(x): + # Convert [x, y, w, h] to [x1, y1, x2, y2] + y = np.copy(x) + y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x + y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y + y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x + y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y + return y + + +def process(input, mask, anchors): + + anchors = [anchors[i] for i in mask] + grid_h, grid_w = map(int, input.shape[0:2]) + + box_confidence = input[..., 4] + box_confidence = np.expand_dims(box_confidence, axis=-1) + + box_class_probs = input[..., 5:] + + box_xy = input[..., :2]*2 - 0.5 + + col = np.tile(np.arange(0, grid_w), grid_w).reshape(-1, grid_w) + row = np.tile(np.arange(0, grid_h).reshape(-1, 1), grid_h) + col = col.reshape(grid_h, grid_w, 1, 1).repeat(3, axis=-2) + row = row.reshape(grid_h, grid_w, 1, 1).repeat(3, axis=-2) + grid = np.concatenate((col, row), axis=-1) + box_xy += grid + box_xy *= int(IMG_SIZE/grid_h) + + box_wh = pow(input[..., 2:4]*2, 2) + box_wh = box_wh * anchors + + box = np.concatenate((box_xy, box_wh), axis=-1) + + return box, box_confidence, box_class_probs + + +def filter_boxes(boxes, box_confidences, box_class_probs): + """Filter boxes with box threshold. It's a bit different with origin yolov5 post process! + + # Arguments + boxes: ndarray, boxes of objects. + box_confidences: ndarray, confidences of objects. + box_class_probs: ndarray, class_probs of objects. + + # Returns + boxes: ndarray, filtered boxes. + classes: ndarray, classes for boxes. + scores: ndarray, scores for boxes. + """ + boxes = boxes.reshape(-1, 4) + box_confidences = box_confidences.reshape(-1) + box_class_probs = box_class_probs.reshape(-1, box_class_probs.shape[-1]) + + _box_pos = np.where(box_confidences >= OBJ_THRESH) + boxes = boxes[_box_pos] + box_confidences = box_confidences[_box_pos] + box_class_probs = box_class_probs[_box_pos] + + class_max_score = np.max(box_class_probs, axis=-1) + classes = np.argmax(box_class_probs, axis=-1) + _class_pos = np.where(class_max_score >= OBJ_THRESH) + + boxes = boxes[_class_pos] + classes = classes[_class_pos] + scores = (class_max_score* box_confidences)[_class_pos] + + return boxes, classes, scores + + +def nms_boxes(boxes, scores): + """Suppress non-maximal boxes. + + # Arguments + boxes: ndarray, boxes of objects. + scores: ndarray, scores of objects. + + # Returns + keep: ndarray, index of effective boxes. + """ + x = boxes[:, 0] + y = boxes[:, 1] + w = boxes[:, 2] - boxes[:, 0] + h = boxes[:, 3] - boxes[:, 1] + + areas = w * h + order = scores.argsort()[::-1] + + keep = [] + while order.size > 0: + i = order[0] + keep.append(i) + + xx1 = np.maximum(x[i], x[order[1:]]) + yy1 = np.maximum(y[i], y[order[1:]]) + xx2 = np.minimum(x[i] + w[i], x[order[1:]] + w[order[1:]]) + yy2 = np.minimum(y[i] + h[i], y[order[1:]] + h[order[1:]]) + + w1 = np.maximum(0.0, xx2 - xx1 + 0.00001) + h1 = np.maximum(0.0, yy2 - yy1 + 0.00001) + inter = w1 * h1 + + ovr = inter / (areas[i] + areas[order[1:]] - inter) + inds = np.where(ovr <= NMS_THRESH)[0] + order = order[inds + 1] + keep = np.array(keep) + return keep + + +def yolov5_post_process(input_data): + masks = [[0, 1, 2], [3, 4, 5], [6, 7, 8]] + anchors = [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45], + [59, 119], [116, 90], [156, 198], [373, 326]] + + boxes, classes, scores = [], [], [] + for input, mask in zip(input_data, masks): + b, c, s = process(input, mask, anchors) + b, c, s = filter_boxes(b, c, s) + boxes.append(b) + classes.append(c) + scores.append(s) + + boxes = np.concatenate(boxes) + boxes = xywh2xyxy(boxes) + classes = np.concatenate(classes) + scores = np.concatenate(scores) + + nboxes, nclasses, nscores = [], [], [] + for c in set(classes): + inds = np.where(classes == c) + b = boxes[inds] + c = classes[inds] + s = scores[inds] + + keep = nms_boxes(b, s) + + nboxes.append(b[keep]) + nclasses.append(c[keep]) + nscores.append(s[keep]) + + if not nclasses and not nscores: + return None, None, None + + boxes = np.concatenate(nboxes) + classes = np.concatenate(nclasses) + scores = np.concatenate(nscores) + + return boxes, classes, scores + + +def draw(image, boxes, scores, classes): + """Draw the boxes on the image. + + # Argument: + image: original image. + boxes: ndarray, boxes of objects. + classes: ndarray, classes of objects. + scores: ndarray, scores of objects. + all_classes: all classes name. + """ + print("{:^12} {:^12} {}".format('class', 'score', 'xmin, ymin, xmax, ymax')) + print('-' * 50) + for box, score, cl in zip(boxes, scores, classes): + top, left, right, bottom = box + top = int(top) + left = int(left) + right = int(right) + bottom = int(bottom) + + cv2.rectangle(image, (top, left), (right, bottom), (255, 0, 0), 2) + cv2.putText(image, '{0} {1:.2f}'.format(CLASSES[cl], score), + (top, left - 6), + cv2.FONT_HERSHEY_SIMPLEX, + 0.6, (0, 0, 255), 2) + + print("{:^12} {:^12.3f} [{:>4}, {:>4}, {:>4}, {:>4}]".format(CLASSES[cl], score, top, left, right, bottom)) + +def letterbox(im, new_shape=(640, 640), color=(0, 0, 0)): + # Resize and pad image while meeting stride-multiple constraints + shape = im.shape[:2] # current shape [height, width] + if isinstance(new_shape, int): + new_shape = (new_shape, new_shape) + + # Scale ratio (new / old) + r = min(new_shape[0] / shape[0], new_shape[1] / shape[1]) + + # Compute padding + ratio = r, r # width, height ratios + new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) + dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding + + dw /= 2 # divide padding into 2 sides + dh /= 2 + + if shape[::-1] != new_unpad: # resize + im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR) + top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) + left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) + im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border + return im, ratio, (dw, dh) + + +if __name__ == '__main__': + + # Create RKNN object + rknn = RKNN(verbose=True) + + # pre-process config + print('--> Config model') + rknn.config(mean_values=[[0, 0, 0]], std_values=[[255, 255, 255]], target_platform='rk3566') + print('done') + + # Load ONNX model + print('--> Loading model') + ret = rknn.load_onnx(model=ONNX_MODEL) + if ret != 0: + print('Load model failed!') + exit(ret) + print('done') + + # Build model + print('--> Building model') + ret = rknn.build(do_quantization=QUANTIZE_ON, dataset=DATASET) + if ret != 0: + print('Build model failed!') + exit(ret) + print('done') + + # Export RKNN model + print('--> Export rknn model') + ret = rknn.export_rknn(RKNN_MODEL) + if ret != 0: + print('Export rknn model failed!') + exit(ret) + print('done') + + # Init runtime environment + print('--> Init runtime environment') + ret = rknn.init_runtime() + if ret != 0: + print('Init runtime environment failed!') + exit(ret) + print('done') + + # Set inputs + img = cv2.imread(IMG_PATH) + # img, ratio, (dw, dh) = letterbox(img, new_shape=(IMG_SIZE, IMG_SIZE)) + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + img = cv2.resize(img, (IMG_SIZE, IMG_SIZE)) + + # Inference + print('--> Running model') + img2 = np.expand_dims(img, 0) + outputs = rknn.inference(inputs=[img2], data_format=['nhwc']) + np.save('./onnx_yolov5_0.npy', outputs[0]) + np.save('./onnx_yolov5_1.npy', outputs[1]) + np.save('./onnx_yolov5_2.npy', outputs[2]) + print('done') + + # post process + input0_data = outputs[0] + input1_data = outputs[1] + input2_data = outputs[2] + + input0_data = input0_data.reshape([3, -1]+list(input0_data.shape[-2:])) + input1_data = input1_data.reshape([3, -1]+list(input1_data.shape[-2:])) + input2_data = input2_data.reshape([3, -1]+list(input2_data.shape[-2:])) + + input_data = list() + input_data.append(np.transpose(input0_data, (2, 3, 0, 1))) + input_data.append(np.transpose(input1_data, (2, 3, 0, 1))) + input_data.append(np.transpose(input2_data, (2, 3, 0, 1))) + + boxes, classes, scores = yolov5_post_process(input_data) + + img_1 = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + if boxes is not None: + draw(img_1, boxes, scores, classes) + cv2.imwrite('result.jpg', img_1) + print('Save results to result.jpg!') + + rknn.release() diff --git a/rknn-toolkit2/examples/onnx/yolov5/yolov5s_relu.onnx b/rknn-toolkit2/examples/onnx/yolov5/yolov5s_relu.onnx new file mode 100644 index 0000000000000000000000000000000000000000..7c0fea5bd06cf3940948870c11fd86763dfedea2 --- /dev/null +++ b/rknn-toolkit2/examples/onnx/yolov5/yolov5s_relu.onnx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b532116d87b115490b2c5ff309ef5441e372877122eee7ef7f8c50079509beba +size 28924147 diff --git a/rknn-toolkit2/examples/pytorch/resnet18/README.md b/rknn-toolkit2/examples/pytorch/resnet18/README.md new file mode 100644 index 0000000000000000000000000000000000000000..37eed052ad3e579a2f12a769fc9773f53fb01a3a --- /dev/null +++ b/rknn-toolkit2/examples/pytorch/resnet18/README.md @@ -0,0 +1,29 @@ +# Pytorch ResNet18 + +## Model Source +The model used in this example come from the 'torchvision', more details in the 'export_pytorch_model' function of the script. + +## Script Usage +*Usage:* +``` +python test.py +``` +*rknn_convert usage:* +``` +python3 -m rknn.api.rknn_convert -t rk3568 -i ./model_config.yml -o ./ +``` +*Description:* +- The default target platform in script is 'rk3566', please modify the 'target_platform' parameter of 'rknn.config' according to the actual platform. +- If connecting board is required, please add the 'target' parameter in 'rknn.init_runtime'. + +## Expected Results +This example will print the TOP5 labels and corresponding scores of the test image classification results, as follows: +``` +-----TOP 5----- +[812] score:0.999739 class:"space shuttle" +[404] score:0.000197 class:"airliner" +[657] score:0.000013 class:"missile" +[833] score:0.000009 class:"submarine, pigboat, sub, U-boat" +[744] score:0.000007 class:"projectile, missile" +``` +- Note: Different platforms, different versions of tools and drivers may have slightly different results. \ No newline at end of file diff --git a/rknn-toolkit2/examples/pytorch/resnet18/dataset.txt b/rknn-toolkit2/examples/pytorch/resnet18/dataset.txt new file mode 100644 index 0000000000000000000000000000000000000000..14518b31a5f6eeccb9b68aceeb51daef648ff4d9 --- /dev/null +++ b/rknn-toolkit2/examples/pytorch/resnet18/dataset.txt @@ -0,0 +1 @@ +space_shuttle_224.jpg diff --git a/rknn-toolkit2/examples/pytorch/resnet18/labels.txt b/rknn-toolkit2/examples/pytorch/resnet18/labels.txt new file mode 100644 index 0000000000000000000000000000000000000000..0993780f99088349e2c156a5a8c57ce1013eb493 --- /dev/null +++ b/rknn-toolkit2/examples/pytorch/resnet18/labels.txt @@ -0,0 +1,1000 @@ +0:tench, Tinca tinca +1:goldfish, Carassius auratus +2:great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias +3:tiger shark, Galeocerdo cuvieri +4:hammerhead, hammerhead shark +5:electric ray, crampfish, numbfish, torpedo +6:stingray +7:cock +8:hen +9:ostrich, Struthio camelus +10:brambling, Fringilla montifringilla +11:goldfinch, Carduelis carduelis +12:house finch, linnet, Carpodacus mexicanus +13:junco, snowbird +14:indigo bunting, indigo finch, indigo bird, Passerina cyanea +15:robin, American robin, Turdus migratorius +16:bulbul +17:jay +18:magpie +19:chickadee +20:water ouzel, dipper +21:kite +22:bald eagle, American eagle, Haliaeetus leucocephalus +23:vulture +24:great grey owl, great gray owl, Strix nebulosa +25:European fire salamander, Salamandra salamandra +26:common newt, Triturus vulgaris +27:eft +28:spotted salamander, Ambystoma maculatum +29:axolotl, mud puppy, Ambystoma mexicanum +30:bullfrog, Rana catesbeiana +31:tree frog, tree-frog +32:tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui +33:loggerhead, loggerhead turtle, Caretta caretta +34:leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea +35:mud turtle +36:terrapin +37:box turtle, box tortoise +38:banded gecko +39:common iguana, iguana, Iguana iguana +40:American chameleon, anole, Anolis carolinensis +41:whiptail, whiptail lizard +42:agama +43:frilled lizard, Chlamydosaurus kingi +44:alligator lizard +45:Gila monster, Heloderma suspectum +46:green lizard, Lacerta viridis +47:African chameleon, Chamaeleo chamaeleon +48:Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis +49:African crocodile, Nile crocodile, Crocodylus niloticus +50:American alligator, Alligator mississipiensis +51:triceratops +52:thunder snake, worm snake, Carphophis amoenus +53:ringneck snake, ring-necked snake, ring snake +54:hognose snake, puff adder, sand viper +55:green snake, grass snake +56:king snake, kingsnake +57:garter snake, grass snake +58:water snake +59:vine snake +60:night snake, Hypsiglena torquata +61:boa constrictor, Constrictor constrictor +62:rock python, rock snake, Python sebae +63:Indian cobra, Naja naja +64:green mamba +65:sea snake +66:horned viper, cerastes, sand viper, horned asp, Cerastes cornutus +67:diamondback, diamondback rattlesnake, Crotalus adamanteus +68:sidewinder, horned rattlesnake, Crotalus cerastes +69:trilobite +70:harvestman, daddy longlegs, Phalangium opilio +71:scorpion +72:black and gold garden spider, Argiope aurantia +73:barn spider, Araneus cavaticus +74:garden spider, Aranea diademata +75:black widow, Latrodectus mactans +76:tarantula +77:wolf spider, hunting spider +78:tick +79:centipede +80:black grouse +81:ptarmigan +82:ruffed grouse, partridge, Bonasa umbellus +83:prairie chicken, prairie grouse, prairie fowl +84:peacock +85:quail +86:partridge +87:African grey, African gray, Psittacus erithacus +88:macaw +89:sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita +90:lorikeet +91:coucal +92:bee eater +93:hornbill +94:hummingbird +95:jacamar +96:toucan +97:drake +98:red-breasted merganser, Mergus serrator +99:goose +100:black swan, Cygnus atratus +101:tusker +102:echidna, spiny anteater, anteater +103:platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus +104:wallaby, brush kangaroo +105:koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus +106:wombat +107:jellyfish +108:sea anemone, anemone +109:brain coral +110:flatworm, platyhelminth +111:nematode, nematode worm, roundworm +112:conch +113:snail +114:slug +115:sea slug, nudibranch +116:chiton, coat-of-mail shell, sea cradle, polyplacophore +117:chambered nautilus, pearly nautilus, nautilus +118:Dungeness crab, Cancer magister +119:rock crab, Cancer irroratus +120:fiddler crab +121:king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica +122:American lobster, Northern lobster, Maine lobster, Homarus americanus +123:spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish +124:crayfish, crawfish, crawdad, crawdaddy +125:hermit crab +126:isopod +127:white stork, Ciconia ciconia +128:black stork, Ciconia nigra +129:spoonbill +130:flamingo +131:little blue heron, Egretta caerulea +132:American egret, great white heron, Egretta albus +133:bittern +134:crane +135:limpkin, Aramus pictus +136:European gallinule, Porphyrio porphyrio +137:American coot, marsh hen, mud hen, water hen, Fulica americana +138:bustard +139:ruddy turnstone, Arenaria interpres +140:red-backed sandpiper, dunlin, Erolia alpina +141:redshank, Tringa totanus +142:dowitcher +143:oystercatcher, oyster catcher +144:pelican +145:king penguin, Aptenodytes patagonica +146:albatross, mollymawk +147:grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus +148:killer whale, killer, orca, grampus, sea wolf, Orcinus orca +149:dugong, Dugong dugon +150:sea lion +151:Chihuahua +152:Japanese spaniel +153:Maltese dog, Maltese terrier, Maltese +154:Pekinese, Pekingese, Peke +155:Shih-Tzu +156:Blenheim spaniel +157:papillon +158:toy terrier +159:Rhodesian ridgeback +160:Afghan hound, Afghan +161:basset, basset hound +162:beagle +163:bloodhound, sleuthhound +164:bluetick +165:black-and-tan coonhound +166:Walker hound, Walker foxhound +167:English foxhound +168:redbone +169:borzoi, Russian wolfhound +170:Irish wolfhound +171:Italian greyhound +172:whippet +173:Ibizan hound, Ibizan Podenco +174:Norwegian elkhound, elkhound +175:otterhound, otter hound +176:Saluki, gazelle hound +177:Scottish deerhound, deerhound +178:Weimaraner +179:Staffordshire bullterrier, Staffordshire bull terrier +180:American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier +181:Bedlington terrier +182:Border terrier +183:Kerry blue terrier +184:Irish terrier +185:Norfolk terrier +186:Norwich terrier +187:Yorkshire terrier +188:wire-haired fox terrier +189:Lakeland terrier +190:Sealyham terrier, Sealyham +191:Airedale, Airedale terrier +192:cairn, cairn terrier +193:Australian terrier +194:Dandie Dinmont, Dandie Dinmont terrier +195:Boston bull, Boston terrier +196:miniature schnauzer +197:giant schnauzer +198:standard schnauzer +199:Scotch terrier, Scottish terrier, Scottie +200:Tibetan terrier, chrysanthemum dog +201:silky terrier, Sydney silky +202:soft-coated wheaten terrier +203:West Highland white terrier +204:Lhasa, Lhasa apso +205:flat-coated retriever +206:curly-coated retriever +207:golden retriever +208:Labrador retriever +209:Chesapeake Bay retriever +210:German short-haired pointer +211:vizsla, Hungarian pointer +212:English setter +213:Irish setter, red setter +214:Gordon setter +215:Brittany spaniel +216:clumber, clumber spaniel +217:English springer, English springer spaniel +218:Welsh springer spaniel +219:cocker spaniel, English cocker spaniel, cocker +220:Sussex spaniel +221:Irish water spaniel +222:kuvasz +223:schipperke +224:groenendael +225:malinois +226:briard +227:kelpie +228:komondor +229:Old English sheepdog, bobtail +230:Shetland sheepdog, Shetland sheep dog, Shetland +231:collie +232:Border collie +233:Bouvier des Flandres, Bouviers des Flandres +234:Rottweiler +235:German shepherd, German shepherd dog, German police dog, alsatian +236:Doberman, Doberman pinscher +237:miniature pinscher +238:Greater Swiss Mountain dog +239:Bernese mountain dog +240:Appenzeller +241:EntleBucher +242:boxer +243:bull mastiff +244:Tibetan mastiff +245:French bulldog +246:Great Dane +247:Saint Bernard, St Bernard +248:Eskimo dog, husky +249:malamute, malemute, Alaskan malamute +250:Siberian husky +251:dalmatian, coach dog, carriage dog +252:affenpinscher, monkey pinscher, monkey dog +253:basenji +254:pug, pug-dog +255:Leonberg +256:Newfoundland, Newfoundland dog +257:Great Pyrenees +258:Samoyed, Samoyede +259:Pomeranian +260:chow, chow chow +261:keeshond +262:Brabancon griffon +263:Pembroke, Pembroke Welsh corgi +264:Cardigan, Cardigan Welsh corgi +265:toy poodle +266:miniature poodle +267:standard poodle +268:Mexican hairless +269:timber wolf, grey wolf, gray wolf, Canis lupus +270:white wolf, Arctic wolf, Canis lupus tundrarum +271:red wolf, maned wolf, Canis rufus, Canis niger +272:coyote, prairie wolf, brush wolf, Canis latrans +273:dingo, warrigal, warragal, Canis dingo +274:dhole, Cuon alpinus +275:African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus +276:hyena, hyaena +277:red fox, Vulpes vulpes +278:kit fox, Vulpes macrotis +279:Arctic fox, white fox, Alopex lagopus +280:grey fox, gray fox, Urocyon cinereoargenteus +281:tabby, tabby cat +282:tiger cat +283:Persian cat +284:Siamese cat, Siamese +285:Egyptian cat +286:cougar, puma, catamount, mountain lion, painter, panther, Felis concolor +287:lynx, catamount +288:leopard, Panthera pardus +289:snow leopard, ounce, Panthera uncia +290:jaguar, panther, Panthera onca, Felis onca +291:lion, king of beasts, Panthera leo +292:tiger, Panthera tigris +293:cheetah, chetah, Acinonyx jubatus +294:brown bear, bruin, Ursus arctos +295:American black bear, black bear, Ursus americanus, Euarctos americanus +296:ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus +297:sloth bear, Melursus ursinus, Ursus ursinus +298:mongoose +299:meerkat, mierkat +300:tiger beetle +301:ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle 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Sciurus niger +336:marmot +337:beaver +338:guinea pig, Cavia cobaya +339:sorrel +340:zebra +341:hog, pig, grunter, squealer, Sus scrofa +342:wild boar, boar, Sus scrofa +343:warthog +344:hippopotamus, hippo, river horse, Hippopotamus amphibius +345:ox +346:water buffalo, water ox, Asiatic buffalo, Bubalus bubalis +347:bison +348:ram, tup +349:bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis +350:ibex, Capra ibex +351:hartebeest +352:impala, Aepyceros melampus +353:gazelle +354:Arabian camel, dromedary, Camelus dromedarius +355:llama +356:weasel +357:mink +358:polecat, fitch, foulmart, foumart, Mustela putorius +359:black-footed ferret, ferret, Mustela nigripes +360:otter +361:skunk, polecat, wood pussy +362:badger +363:armadillo +364:three-toed sloth, ai, Bradypus tridactylus +365:orangutan, orang, orangutang, Pongo pygmaeus +366:gorilla, Gorilla gorilla +367:chimpanzee, chimp, Pan troglodytes +368:gibbon, Hylobates lar +369:siamang, Hylobates syndactylus, Symphalangus syndactylus +370:guenon, guenon monkey +371:patas, hussar monkey, Erythrocebus patas +372:baboon +373:macaque +374:langur +375:colobus, colobus monkey +376:proboscis monkey, Nasalis larvatus +377:marmoset +378:capuchin, ringtail, Cebus capucinus +379:howler monkey, howler +380:titi, titi monkey +381:spider monkey, Ateles geoffroyi +382:squirrel monkey, Saimiri sciureus +383:Madagascar cat, ring-tailed lemur, Lemur catta +384:indri, indris, Indri indri, Indri brevicaudatus +385:Indian elephant, Elephas maximus +386:African elephant, Loxodonta africana +387:lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens +388:giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca +389:barracouta, snoek +390:eel +391:coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch +392:rock beauty, Holocanthus tricolor +393:anemone fish +394:sturgeon +395:gar, garfish, garpike, billfish, Lepisosteus osseus +396:lionfish +397:puffer, pufferfish, blowfish, globefish +398:abacus +399:abaya +400:academic gown, academic robe, judge's robe +401:accordion, piano accordion, squeeze box +402:acoustic guitar +403:aircraft carrier, carrier, flattop, attack aircraft carrier +404:airliner +405:airship, dirigible +406:altar +407:ambulance +408:amphibian, amphibious vehicle +409:analog clock +410:apiary, bee house +411:apron +412:ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin +413:assault rifle, assault gun +414:backpack, back pack, knapsack, packsack, rucksack, haversack +415:bakery, bakeshop, bakehouse +416:balance beam, beam +417:balloon +418:ballpoint, ballpoint pen, ballpen, Biro +419:Band Aid +420:banjo +421:bannister, banister, balustrade, balusters, handrail +422:barbell +423:barber chair +424:barbershop +425:barn +426:barometer +427:barrel, cask +428:barrow, garden cart, lawn cart, wheelbarrow +429:baseball +430:basketball +431:bassinet +432:bassoon 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taxicab +469:caldron, cauldron +470:candle, taper, wax light +471:cannon +472:canoe +473:can opener, tin opener +474:cardigan +475:car mirror +476:carousel, carrousel, merry-go-round, roundabout, whirligig +477:carpenter's kit, tool kit +478:carton +479:car wheel +480:cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM +481:cassette +482:cassette player +483:castle +484:catamaran +485:CD player +486:cello, violoncello +487:cellular telephone, cellular phone, cellphone, cell, mobile phone +488:chain +489:chainlink fence +490:chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour +491:chain saw, chainsaw +492:chest +493:chiffonier, commode +494:chime, bell, gong +495:china cabinet, china closet +496:Christmas stocking +497:church, church building +498:cinema, movie theater, movie theatre, movie house, picture palace +499:cleaver, meat cleaver, chopper +500:cliff dwelling +501:cloak +502:clog, geta, patten, sabot +503:cocktail shaker +504:coffee mug +505:coffeepot +506:coil, spiral, volute, whorl, helix +507:combination lock +508:computer keyboard, keypad +509:confectionery, confectionary, candy store +510:container ship, containership, container vessel +511:convertible +512:corkscrew, bottle screw +513:cornet, horn, trumpet, trump +514:cowboy boot +515:cowboy hat, ten-gallon hat +516:cradle +517:crane +518:crash helmet +519:crate +520:crib, cot +521:Crock Pot +522:croquet ball +523:crutch +524:cuirass +525:dam, dike, dyke +526:desk +527:desktop computer +528:dial telephone, dial phone +529:diaper, nappy, napkin +530:digital clock +531:digital watch +532:dining table, board +533:dishrag, dishcloth +534:dishwasher, dish washer, dishwashing machine +535:disk brake, disc brake +536:dock, dockage, docking facility +537:dogsled, dog sled, dog sleigh +538:dome +539:doormat, welcome mat +540:drilling platform, offshore rig +541:drum, membranophone, tympan +542:drumstick 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limo +628:liner, ocean liner +629:lipstick, lip rouge +630:Loafer +631:lotion +632:loudspeaker, speaker, speaker unit, loudspeaker system, speaker system +633:loupe, jeweler's loupe +634:lumbermill, sawmill +635:magnetic compass +636:mailbag, postbag +637:mailbox, letter box +638:maillot +639:maillot, tank suit +640:manhole cover +641:maraca +642:marimba, xylophone +643:mask +644:matchstick +645:maypole +646:maze, labyrinth +647:measuring cup +648:medicine chest, medicine cabinet +649:megalith, megalithic structure +650:microphone, mike +651:microwave, microwave oven +652:military uniform +653:milk can +654:minibus +655:miniskirt, mini +656:minivan +657:missile +658:mitten +659:mixing bowl +660:mobile home, manufactured home +661:Model T +662:modem +663:monastery +664:monitor +665:moped +666:mortar +667:mortarboard +668:mosque +669:mosquito net +670:motor scooter, scooter +671:mountain bike, all-terrain bike, off-roader +672:mountain tent +673:mouse, computer mouse +674:mousetrap 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penny bank +720:pill bottle +721:pillow +722:ping-pong ball +723:pinwheel +724:pirate, pirate ship +725:pitcher, ewer +726:plane, carpenter's plane, woodworking plane +727:planetarium +728:plastic bag +729:plate rack +730:plow, plough +731:plunger, plumber's helper +732:Polaroid camera, Polaroid Land camera +733:pole +734:police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria +735:poncho +736:pool table, billiard table, snooker table +737:pop bottle, soda bottle +738:pot, flowerpot +739:potter's wheel +740:power drill +741:prayer rug, prayer mat +742:printer +743:prison, prison house +744:projectile, missile +745:projector +746:puck, hockey puck +747:punching bag, punch bag, punching ball, punchball +748:purse +749:quill, quill pen +750:quilt, comforter, comfort, puff +751:racer, race car, racing car +752:racket, racquet +753:radiator +754:radio, wireless +755:radio telescope, radio reflector +756:rain barrel +757:recreational vehicle, RV, R.V. +758:reel +759:reflex camera +760:refrigerator, icebox +761:remote control, remote +762:restaurant, eating house, eating place, eatery +763:revolver, six-gun, six-shooter +764:rifle +765:rocking chair, rocker +766:rotisserie +767:rubber eraser, rubber, pencil eraser +768:rugby ball +769:rule, ruler +770:running shoe +771:safe +772:safety pin +773:saltshaker, salt shaker +774:sandal +775:sarong +776:sax, saxophone +777:scabbard +778:scale, weighing machine +779:school bus +780:schooner +781:scoreboard +782:screen, CRT screen +783:screw +784:screwdriver +785:seat belt, seatbelt +786:sewing machine +787:shield, buckler +788:shoe shop, shoe-shop, shoe store +789:shoji +790:shopping basket +791:shopping cart +792:shovel +793:shower cap +794:shower curtain +795:ski +796:ski mask +797:sleeping bag +798:slide rule, slipstick +799:sliding door +800:slot, one-armed bandit +801:snorkel +802:snowmobile +803:snowplow, snowplough +804:soap dispenser +805:soccer ball +806:sock +807:solar dish, solar collector, solar furnace +808:sombrero +809:soup bowl +810:space bar +811:space heater +812:space shuttle +813:spatula +814:speedboat +815:spider web, spider's web +816:spindle +817:sports car, sport car +818:spotlight, spot +819:stage +820:steam locomotive +821:steel arch bridge +822:steel drum +823:stethoscope +824:stole +825:stone wall +826:stopwatch, stop watch +827:stove +828:strainer +829:streetcar, tram, tramcar, trolley, trolley car +830:stretcher +831:studio couch, day bed +832:stupa, tope +833:submarine, pigboat, sub, U-boat +834:suit, suit of clothes +835:sundial +836:sunglass +837:sunglasses, dark glasses, shades +838:sunscreen, sunblock, sun blocker +839:suspension bridge +840:swab, swob, mop +841:sweatshirt +842:swimming trunks, bathing trunks +843:swing +844:switch, electric switch, electrical switch +845:syringe +846:table lamp +847:tank, army tank, armored combat vehicle, armoured combat vehicle +848:tape player +849:teapot +850:teddy, teddy bear +851:television, television system 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handbasin, washbowl, lavabo, wash-hand basin +897:washer, automatic washer, washing machine +898:water bottle +899:water jug +900:water tower +901:whiskey jug +902:whistle +903:wig +904:window screen +905:window shade +906:Windsor tie +907:wine bottle +908:wing +909:wok +910:wooden spoon +911:wool, woolen, woollen +912:worm fence, snake fence, snake-rail fence, Virginia fence +913:wreck +914:yawl +915:yurt +916:web site, website, internet site, site +917:comic book +918:crossword puzzle, crossword +919:street sign +920:traffic light, traffic signal, stoplight +921:book jacket, dust cover, dust jacket, dust wrapper +922:menu +923:plate +924:guacamole +925:consomme +926:hot pot, hotpot +927:trifle +928:ice cream, icecream +929:ice lolly, lolly, lollipop, popsicle +930:French loaf +931:bagel, beigel +932:pretzel +933:cheeseburger +934:hotdog, hot dog, red hot +935:mashed potato +936:head cabbage +937:broccoli +938:cauliflower +939:zucchini, courgette +940:spaghetti squash +941:acorn squash +942:butternut squash +943:cucumber, cuke +944:artichoke, globe artichoke +945:bell pepper +946:cardoon +947:mushroom +948:Granny Smith +949:strawberry +950:orange +951:lemon +952:fig +953:pineapple, ananas +954:banana +955:jackfruit, jak, jack +956:custard apple +957:pomegranate +958:hay +959:carbonara +960:chocolate sauce, chocolate syrup +961:dough +962:meat loaf, meatloaf +963:pizza, pizza pie +964:potpie +965:burrito +966:red wine +967:espresso +968:cup +969:eggnog +970:alp +971:bubble +972:cliff, drop, drop-off +973:coral reef +974:geyser +975:lakeside, lakeshore +976:promontory, headland, head, foreland +977:sandbar, sand bar +978:seashore, coast, seacoast, sea-coast +979:valley, vale +980:volcano +981:ballplayer, baseball player +982:groom, bridegroom +983:scuba diver +984:rapeseed +985:daisy +986:yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum +987:corn +988:acorn +989:hip, rose hip, rosehip +990:buckeye, horse chestnut, conker +991:coral fungus +992:agaric +993:gyromitra +994:stinkhorn, carrion fungus +995:earthstar +996:hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa +997:bolete +998:ear, spike, capitulum +999:toilet tissue, toilet paper, bathroom tissue \ No newline at end of file diff --git a/rknn-toolkit2/examples/pytorch/resnet18/model_config.yml b/rknn-toolkit2/examples/pytorch/resnet18/model_config.yml new file mode 100755 index 0000000000000000000000000000000000000000..712ebc0ffb8414e5ca58a1a08fd58e2d5a8de24f --- /dev/null +++ b/rknn-toolkit2/examples/pytorch/resnet18/model_config.yml @@ -0,0 +1,16 @@ +models: + name: resnet_18 # 模型输出名称 + platform: pytorch # 原始模型使用的框架 + model_file_path: ./resnet18.pt # 原模型路径 + subgraphs: # 描述输入输出shape等信息 + input_size_list: + - 1, 3, 224, 224 + quantize: true # 是否量化 + dataset: ./dataset.txt # 量化dataset文件路径（相对yml路径） + configs: + quantized_dtype: asymmetric_quantized-8 # 量化类型 + mean_values: [123.675, 116.28, 103.53] # rknn.config的mean_values参数 + std_values: [58.395, 58.395, 58.395] # rknn.config的std_values参数 + quant_img_RGB2BGR: false # 不进行RGB2BGR转换 + quantized_algorithm: normal # 量化算法 + quantized_method: channel # 量化方法 diff --git a/rknn-toolkit2/examples/pytorch/resnet18/space_shuttle_224.jpg b/rknn-toolkit2/examples/pytorch/resnet18/space_shuttle_224.jpg new file mode 100644 index 0000000000000000000000000000000000000000..412a855c379648d80d5b4267ba1da58188cdee1c --- /dev/null +++ b/rknn-toolkit2/examples/pytorch/resnet18/space_shuttle_224.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a827cc9060d15cd34b1b5ac512f4d6d8366416a5874a2075951991cbacef78d4 +size 23472 diff --git a/rknn-toolkit2/examples/pytorch/resnet18/test.py b/rknn-toolkit2/examples/pytorch/resnet18/test.py new file mode 100755 index 0000000000000000000000000000000000000000..962bdea0444d2fda5e66c296e2724955ea524906 --- /dev/null +++ b/rknn-toolkit2/examples/pytorch/resnet18/test.py @@ -0,0 +1,100 @@ +import numpy as np +import cv2 +from rknn.api import RKNN +import os + + +def export_pytorch_model(): + import torch + import torchvision.models as models + net = models.resnet18(pretrained=True) + net.eval() + trace_model = torch.jit.trace(net, torch.Tensor(1, 3, 224, 224)) + trace_model.save('./resnet18.pt') + + +def show_outputs(output): + index = sorted(range(len(output)), key=lambda k : output[k], reverse=True) + fp = open('./labels.txt', 'r') + labels = fp.readlines() + top5_str = 'resnet18\n-----TOP 5-----\n' + for i in range(5): + value = output[index[i]] + if value > 0: + topi = '[{:>3d}] score:{:.6f} class:"{}"\n'.format(index[i], value, labels[index[i]].strip().split(':')[-1]) + else: + topi = '[ -1]: 0.0\n' + top5_str += topi + print(top5_str.strip()) + + +def show_perfs(perfs): + perfs = 'perfs: {}\n'.format(perfs) + print(perfs) + + +def softmax(x): + return np.exp(x)/sum(np.exp(x)) + + +if __name__ == '__main__': + + model = './resnet18.pt' + if not os.path.exists(model): + export_pytorch_model() + + input_size_list = [[1, 3, 224, 224]] + + # Create RKNN object + rknn = RKNN(verbose=True) + + # Pre-process config + print('--> Config model') + rknn.config(mean_values=[123.675, 116.28, 103.53], std_values=[58.395, 58.395, 58.395], target_platform='rk3566') + print('done') + + # Load model + print('--> Loading model') + ret = rknn.load_pytorch(model=model, input_size_list=input_size_list) + if ret != 0: + print('Load model failed!') + exit(ret) + print('done') + + # Build model + print('--> Building model') + ret = rknn.build(do_quantization=True, dataset='./dataset.txt') + if ret != 0: + print('Build model failed!') + exit(ret) + print('done') + + # Export rknn model + print('--> Export rknn model') + ret = rknn.export_rknn('./resnet_18.rknn') + if ret != 0: + print('Export rknn model failed!') + exit(ret) + print('done') + + # Set inputs + img = cv2.imread('./space_shuttle_224.jpg') + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + img = np.expand_dims(img, 0) + + # Init runtime environment + print('--> Init runtime environment') + ret = rknn.init_runtime() + if ret != 0: + print('Init runtime environment failed!') + exit(ret) + print('done') + + # Inference + print('--> Running model') + outputs = rknn.inference(inputs=[img], data_format=['nhwc']) + np.save('./pytorch_resnet18_0.npy', outputs[0]) + show_outputs(softmax(np.array(outputs[0][0]))) + print('done') + + rknn.release() diff --git a/rknn-toolkit2/examples/pytorch/resnet18_qat/README.md b/rknn-toolkit2/examples/pytorch/resnet18_qat/README.md new file mode 100644 index 0000000000000000000000000000000000000000..fd564cd129ac56952b9c950203af60ea2c783301 --- /dev/null +++ b/rknn-toolkit2/examples/pytorch/resnet18_qat/README.md @@ -0,0 +1,30 @@ +# Pytorch ResNet18 QAT + +## Model Source +The model used in this example come from the 'torchvision', more details in the 'export_pytorch_model' function of the script. + +## Script Usage +*Usage:* +``` +python test.py +``` +*rknn_convert usage:* +``` +python3 -m rknn.api.rknn_convert -t rk3568 -i ./model_config.yml -o ./ +``` +*Description:* +- The default target platform in script is 'rk3566', please modify the 'target_platform' parameter of 'rknn.config' according to the actual platform. +- If connecting board is required, please add the 'target' parameter in 'rknn.init_runtime'. +- This is a QAT model, and the do_quantization of rknn.build needs to be set to False. + +## Expected Results +This example will print the TOP5 labels and corresponding scores of the test image classification results, as follows: +``` +-----TOP 5----- +[812] score:0.999741 class:"space shuttle" +[404] score:0.000194 class:"airliner" +[657] score:0.000015 class:"missile" +[466] score:0.000008 class:"bullet train, bullet" +[744] score:0.000008 class:"projectile, missile" +``` +- Note: Different platforms, different versions of tools and drivers may have slightly different results. \ No newline at end of file diff --git a/rknn-toolkit2/examples/pytorch/resnet18_qat/labels.txt b/rknn-toolkit2/examples/pytorch/resnet18_qat/labels.txt new file mode 100644 index 0000000000000000000000000000000000000000..0993780f99088349e2c156a5a8c57ce1013eb493 --- /dev/null +++ b/rknn-toolkit2/examples/pytorch/resnet18_qat/labels.txt @@ -0,0 +1,1000 @@ +0:tench, Tinca tinca +1:goldfish, Carassius auratus +2:great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias +3:tiger shark, Galeocerdo cuvieri +4:hammerhead, hammerhead shark +5:electric ray, crampfish, numbfish, torpedo +6:stingray +7:cock +8:hen +9:ostrich, Struthio camelus +10:brambling, Fringilla montifringilla +11:goldfinch, Carduelis carduelis +12:house finch, linnet, Carpodacus mexicanus +13:junco, snowbird +14:indigo bunting, indigo finch, indigo bird, Passerina cyanea +15:robin, American robin, Turdus migratorius +16:bulbul +17:jay +18:magpie +19:chickadee +20:water ouzel, dipper +21:kite +22:bald eagle, American eagle, Haliaeetus leucocephalus +23:vulture +24:great grey owl, great gray owl, Strix nebulosa +25:European fire salamander, Salamandra salamandra +26:common newt, Triturus vulgaris +27:eft +28:spotted salamander, Ambystoma maculatum +29:axolotl, mud puppy, Ambystoma mexicanum +30:bullfrog, Rana catesbeiana +31:tree frog, tree-frog +32:tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui +33:loggerhead, loggerhead turtle, Caretta caretta +34:leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea +35:mud turtle +36:terrapin +37:box turtle, box tortoise +38:banded gecko +39:common iguana, iguana, Iguana iguana +40:American chameleon, anole, Anolis carolinensis +41:whiptail, whiptail lizard +42:agama +43:frilled lizard, Chlamydosaurus kingi +44:alligator lizard +45:Gila monster, Heloderma suspectum +46:green lizard, Lacerta viridis +47:African chameleon, Chamaeleo chamaeleon +48:Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis +49:African crocodile, Nile crocodile, Crocodylus niloticus +50:American alligator, Alligator mississipiensis +51:triceratops +52:thunder snake, worm snake, Carphophis amoenus +53:ringneck snake, ring-necked snake, ring snake +54:hognose snake, puff adder, sand viper +55:green snake, grass snake +56:king snake, kingsnake +57:garter snake, grass snake +58:water snake +59:vine snake +60:night snake, Hypsiglena torquata +61:boa constrictor, Constrictor constrictor +62:rock python, rock snake, Python sebae +63:Indian cobra, Naja naja +64:green mamba +65:sea snake +66:horned viper, cerastes, sand viper, horned asp, Cerastes cornutus +67:diamondback, diamondback rattlesnake, Crotalus adamanteus +68:sidewinder, horned rattlesnake, Crotalus cerastes +69:trilobite +70:harvestman, daddy longlegs, Phalangium opilio +71:scorpion +72:black and gold garden spider, Argiope aurantia +73:barn spider, Araneus cavaticus +74:garden spider, Aranea diademata +75:black widow, Latrodectus mactans +76:tarantula +77:wolf spider, hunting spider +78:tick +79:centipede +80:black grouse +81:ptarmigan +82:ruffed grouse, partridge, Bonasa umbellus +83:prairie chicken, prairie grouse, prairie fowl +84:peacock +85:quail +86:partridge +87:African grey, African gray, Psittacus erithacus +88:macaw +89:sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita +90:lorikeet +91:coucal +92:bee eater +93:hornbill +94:hummingbird +95:jacamar +96:toucan +97:drake +98:red-breasted merganser, Mergus serrator +99:goose +100:black swan, Cygnus atratus +101:tusker +102:echidna, spiny anteater, anteater +103:platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus +104:wallaby, brush kangaroo +105:koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus +106:wombat +107:jellyfish +108:sea anemone, anemone +109:brain coral +110:flatworm, platyhelminth +111:nematode, nematode worm, roundworm +112:conch +113:snail +114:slug +115:sea slug, nudibranch +116:chiton, coat-of-mail shell, sea cradle, polyplacophore +117:chambered nautilus, pearly nautilus, nautilus +118:Dungeness crab, Cancer magister +119:rock crab, Cancer irroratus +120:fiddler crab +121:king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica +122:American lobster, Northern lobster, Maine lobster, Homarus americanus +123:spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish +124:crayfish, crawfish, crawdad, crawdaddy +125:hermit crab +126:isopod +127:white stork, Ciconia ciconia +128:black stork, Ciconia nigra +129:spoonbill +130:flamingo +131:little blue heron, Egretta caerulea +132:American egret, great white heron, Egretta albus +133:bittern +134:crane +135:limpkin, Aramus pictus +136:European gallinule, Porphyrio porphyrio +137:American coot, marsh hen, mud hen, water hen, Fulica americana +138:bustard +139:ruddy turnstone, Arenaria interpres +140:red-backed sandpiper, dunlin, Erolia alpina +141:redshank, Tringa totanus +142:dowitcher +143:oystercatcher, oyster catcher +144:pelican +145:king penguin, Aptenodytes patagonica +146:albatross, mollymawk +147:grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus +148:killer whale, killer, orca, grampus, sea wolf, Orcinus orca +149:dugong, Dugong dugon +150:sea lion +151:Chihuahua +152:Japanese spaniel +153:Maltese dog, Maltese terrier, Maltese +154:Pekinese, Pekingese, Peke +155:Shih-Tzu +156:Blenheim spaniel +157:papillon +158:toy terrier +159:Rhodesian ridgeback +160:Afghan hound, Afghan +161:basset, basset hound +162:beagle +163:bloodhound, sleuthhound +164:bluetick +165:black-and-tan coonhound +166:Walker hound, Walker foxhound +167:English foxhound +168:redbone +169:borzoi, Russian wolfhound +170:Irish wolfhound +171:Italian greyhound +172:whippet +173:Ibizan hound, Ibizan Podenco +174:Norwegian elkhound, elkhound +175:otterhound, otter hound +176:Saluki, gazelle hound +177:Scottish deerhound, deerhound +178:Weimaraner +179:Staffordshire bullterrier, Staffordshire bull terrier +180:American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier +181:Bedlington terrier +182:Border terrier +183:Kerry blue terrier +184:Irish terrier +185:Norfolk terrier +186:Norwich terrier +187:Yorkshire terrier +188:wire-haired fox terrier +189:Lakeland terrier +190:Sealyham terrier, Sealyham +191:Airedale, Airedale terrier +192:cairn, cairn terrier +193:Australian terrier +194:Dandie Dinmont, Dandie Dinmont terrier +195:Boston bull, Boston terrier +196:miniature schnauzer +197:giant schnauzer +198:standard schnauzer +199:Scotch terrier, Scottish terrier, Scottie +200:Tibetan terrier, chrysanthemum dog +201:silky terrier, Sydney silky +202:soft-coated wheaten terrier +203:West Highland white terrier +204:Lhasa, Lhasa apso +205:flat-coated retriever +206:curly-coated retriever +207:golden retriever +208:Labrador retriever +209:Chesapeake Bay retriever +210:German short-haired pointer +211:vizsla, Hungarian pointer +212:English setter +213:Irish setter, red setter +214:Gordon setter +215:Brittany spaniel +216:clumber, clumber spaniel +217:English springer, English springer spaniel +218:Welsh springer spaniel +219:cocker spaniel, English cocker spaniel, cocker +220:Sussex spaniel +221:Irish water spaniel +222:kuvasz +223:schipperke +224:groenendael +225:malinois +226:briard +227:kelpie +228:komondor +229:Old English sheepdog, bobtail +230:Shetland sheepdog, Shetland sheep dog, Shetland +231:collie +232:Border collie +233:Bouvier des Flandres, Bouviers des Flandres +234:Rottweiler +235:German shepherd, German shepherd dog, German police dog, alsatian +236:Doberman, Doberman pinscher +237:miniature pinscher +238:Greater Swiss Mountain dog +239:Bernese mountain dog +240:Appenzeller +241:EntleBucher +242:boxer +243:bull mastiff +244:Tibetan mastiff +245:French bulldog +246:Great Dane +247:Saint Bernard, St Bernard +248:Eskimo dog, husky +249:malamute, malemute, Alaskan malamute +250:Siberian husky +251:dalmatian, coach dog, carriage dog +252:affenpinscher, monkey pinscher, monkey dog +253:basenji +254:pug, pug-dog +255:Leonberg +256:Newfoundland, Newfoundland dog +257:Great Pyrenees +258:Samoyed, Samoyede +259:Pomeranian +260:chow, chow chow +261:keeshond +262:Brabancon griffon +263:Pembroke, Pembroke Welsh corgi +264:Cardigan, Cardigan Welsh corgi +265:toy poodle +266:miniature poodle +267:standard poodle +268:Mexican hairless +269:timber wolf, grey wolf, gray wolf, Canis lupus +270:white wolf, Arctic wolf, Canis lupus tundrarum +271:red wolf, maned wolf, Canis rufus, Canis niger +272:coyote, prairie wolf, brush wolf, Canis latrans +273:dingo, warrigal, warragal, Canis dingo +274:dhole, Cuon alpinus +275:African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus +276:hyena, hyaena +277:red fox, Vulpes vulpes +278:kit fox, Vulpes macrotis +279:Arctic fox, white fox, Alopex lagopus +280:grey fox, gray fox, Urocyon cinereoargenteus +281:tabby, tabby cat +282:tiger cat +283:Persian cat +284:Siamese cat, Siamese +285:Egyptian cat +286:cougar, puma, catamount, mountain lion, painter, panther, Felis concolor +287:lynx, catamount +288:leopard, Panthera pardus +289:snow leopard, ounce, Panthera uncia +290:jaguar, panther, Panthera onca, Felis onca +291:lion, king of beasts, Panthera leo +292:tiger, Panthera tigris +293:cheetah, chetah, Acinonyx jubatus +294:brown bear, bruin, Ursus arctos +295:American black bear, black bear, Ursus americanus, Euarctos americanus +296:ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus +297:sloth bear, Melursus ursinus, Ursus ursinus +298:mongoose +299:meerkat, mierkat +300:tiger beetle +301:ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle +302:ground beetle, carabid beetle +303:long-horned beetle, longicorn, longicorn beetle +304:leaf beetle, chrysomelid +305:dung beetle +306:rhinoceros beetle +307:weevil +308:fly +309:bee +310:ant, emmet, pismire +311:grasshopper, hopper +312:cricket +313:walking stick, walkingstick, stick insect +314:cockroach, roach +315:mantis, mantid +316:cicada, cicala +317:leafhopper +318:lacewing, lacewing fly +319:dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk +320:damselfly +321:admiral +322:ringlet, ringlet butterfly +323:monarch, monarch butterfly, milkweed butterfly, Danaus plexippus +324:cabbage butterfly +325:sulphur butterfly, sulfur butterfly +326:lycaenid, lycaenid butterfly +327:starfish, sea star +328:sea urchin +329:sea cucumber, holothurian +330:wood rabbit, cottontail, cottontail rabbit +331:hare +332:Angora, Angora rabbit +333:hamster +334:porcupine, hedgehog +335:fox squirrel, eastern fox squirrel, Sciurus niger +336:marmot +337:beaver +338:guinea pig, Cavia cobaya +339:sorrel +340:zebra +341:hog, pig, grunter, squealer, Sus scrofa +342:wild boar, boar, Sus scrofa +343:warthog +344:hippopotamus, hippo, river horse, Hippopotamus amphibius +345:ox +346:water buffalo, water ox, Asiatic buffalo, Bubalus bubalis +347:bison +348:ram, tup +349:bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis +350:ibex, Capra ibex +351:hartebeest +352:impala, Aepyceros melampus +353:gazelle +354:Arabian camel, dromedary, Camelus dromedarius +355:llama +356:weasel +357:mink +358:polecat, fitch, foulmart, foumart, Mustela putorius +359:black-footed ferret, ferret, Mustela nigripes +360:otter +361:skunk, polecat, wood pussy +362:badger +363:armadillo +364:three-toed sloth, ai, Bradypus tridactylus +365:orangutan, orang, orangutang, Pongo pygmaeus +366:gorilla, Gorilla gorilla +367:chimpanzee, chimp, Pan troglodytes +368:gibbon, Hylobates lar +369:siamang, Hylobates syndactylus, Symphalangus syndactylus +370:guenon, guenon monkey +371:patas, hussar monkey, Erythrocebus patas +372:baboon +373:macaque +374:langur +375:colobus, colobus monkey +376:proboscis monkey, Nasalis larvatus +377:marmoset +378:capuchin, ringtail, Cebus capucinus +379:howler monkey, howler +380:titi, titi monkey +381:spider monkey, Ateles geoffroyi +382:squirrel monkey, Saimiri sciureus +383:Madagascar cat, ring-tailed lemur, Lemur catta +384:indri, indris, Indri indri, Indri brevicaudatus +385:Indian elephant, Elephas maximus +386:African elephant, Loxodonta africana +387:lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens +388:giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca +389:barracouta, snoek +390:eel +391:coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch +392:rock beauty, Holocanthus tricolor +393:anemone fish +394:sturgeon +395:gar, garfish, garpike, billfish, Lepisosteus osseus +396:lionfish +397:puffer, pufferfish, blowfish, globefish +398:abacus +399:abaya +400:academic gown, academic robe, judge's robe +401:accordion, piano accordion, squeeze box +402:acoustic guitar +403:aircraft carrier, carrier, flattop, attack aircraft carrier +404:airliner +405:airship, dirigible +406:altar +407:ambulance +408:amphibian, amphibious vehicle +409:analog clock +410:apiary, bee house +411:apron +412:ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin +413:assault rifle, assault gun +414:backpack, back pack, knapsack, packsack, rucksack, haversack +415:bakery, bakeshop, bakehouse +416:balance beam, beam +417:balloon +418:ballpoint, ballpoint pen, ballpen, Biro +419:Band Aid +420:banjo +421:bannister, banister, balustrade, balusters, handrail +422:barbell +423:barber chair +424:barbershop +425:barn +426:barometer +427:barrel, cask +428:barrow, garden cart, lawn cart, wheelbarrow +429:baseball +430:basketball +431:bassinet +432:bassoon +433:bathing cap, swimming cap +434:bath towel +435:bathtub, bathing tub, bath, tub +436:beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon +437:beacon, lighthouse, beacon light, pharos +438:beaker +439:bearskin, busby, shako +440:beer bottle +441:beer glass +442:bell cote, bell cot +443:bib +444:bicycle-built-for-two, tandem bicycle, tandem +445:bikini, two-piece +446:binder, ring-binder +447:binoculars, field glasses, opera glasses +448:birdhouse +449:boathouse +450:bobsled, bobsleigh, bob +451:bolo tie, bolo, bola tie, bola +452:bonnet, poke bonnet +453:bookcase +454:bookshop, bookstore, bookstall +455:bottlecap +456:bow +457:bow tie, bow-tie, bowtie +458:brass, memorial tablet, plaque +459:brassiere, bra, bandeau +460:breakwater, groin, groyne, mole, bulwark, seawall, jetty +461:breastplate, aegis, egis +462:broom +463:bucket, pail +464:buckle +465:bulletproof vest +466:bullet train, bullet +467:butcher shop, meat market +468:cab, hack, taxi, taxicab +469:caldron, cauldron +470:candle, taper, wax light +471:cannon +472:canoe +473:can opener, tin opener +474:cardigan +475:car mirror +476:carousel, carrousel, merry-go-round, roundabout, whirligig +477:carpenter's kit, tool kit +478:carton +479:car wheel +480:cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM +481:cassette +482:cassette player +483:castle +484:catamaran +485:CD player +486:cello, violoncello +487:cellular telephone, cellular phone, cellphone, cell, mobile phone +488:chain +489:chainlink fence +490:chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour +491:chain saw, chainsaw +492:chest +493:chiffonier, commode +494:chime, bell, gong +495:china cabinet, china closet +496:Christmas stocking +497:church, church building +498:cinema, movie theater, movie theatre, movie house, picture palace +499:cleaver, meat cleaver, chopper +500:cliff dwelling +501:cloak +502:clog, geta, patten, sabot +503:cocktail shaker +504:coffee mug +505:coffeepot +506:coil, spiral, volute, whorl, helix +507:combination lock +508:computer keyboard, keypad +509:confectionery, confectionary, candy store +510:container ship, containership, container vessel +511:convertible +512:corkscrew, bottle screw +513:cornet, horn, trumpet, trump +514:cowboy boot +515:cowboy hat, ten-gallon hat +516:cradle +517:crane +518:crash helmet +519:crate +520:crib, cot +521:Crock Pot +522:croquet ball +523:crutch +524:cuirass +525:dam, dike, dyke +526:desk +527:desktop computer +528:dial telephone, dial phone +529:diaper, nappy, napkin +530:digital clock +531:digital watch +532:dining table, board +533:dishrag, dishcloth +534:dishwasher, dish washer, dishwashing machine +535:disk brake, disc brake +536:dock, dockage, docking facility +537:dogsled, dog sled, dog sleigh +538:dome +539:doormat, welcome mat +540:drilling platform, offshore rig +541:drum, membranophone, tympan +542:drumstick +543:dumbbell +544:Dutch oven +545:electric fan, blower +546:electric guitar +547:electric locomotive +548:entertainment center +549:envelope +550:espresso maker +551:face powder +552:feather boa, boa +553:file, file cabinet, filing cabinet +554:fireboat +555:fire engine, fire truck +556:fire screen, fireguard +557:flagpole, flagstaff +558:flute, transverse flute +559:folding chair +560:football helmet +561:forklift +562:fountain +563:fountain pen +564:four-poster +565:freight car +566:French horn, horn +567:frying pan, frypan, skillet +568:fur coat +569:garbage truck, dustcart +570:gasmask, respirator, gas helmet +571:gas pump, gasoline pump, petrol pump, island dispenser +572:goblet +573:go-kart +574:golf ball +575:golfcart, golf cart +576:gondola +577:gong, tam-tam +578:gown +579:grand piano, grand +580:greenhouse, nursery, glasshouse +581:grille, radiator grille +582:grocery store, grocery, food market, market +583:guillotine +584:hair slide +585:hair spray +586:half track +587:hammer +588:hamper +589:hand blower, blow dryer, blow drier, hair dryer, hair drier +590:hand-held computer, hand-held microcomputer +591:handkerchief, hankie, hanky, hankey +592:hard disc, hard disk, fixed disk +593:harmonica, mouth organ, harp, mouth harp +594:harp +595:harvester, reaper +596:hatchet +597:holster +598:home theater, home theatre +599:honeycomb +600:hook, claw +601:hoopskirt, crinoline +602:horizontal bar, high bar +603:horse cart, horse-cart +604:hourglass +605:iPod +606:iron, smoothing iron +607:jack-o'-lantern +608:jean, blue jean, denim +609:jeep, landrover +610:jersey, T-shirt, tee shirt +611:jigsaw puzzle +612:jinrikisha, ricksha, rickshaw +613:joystick +614:kimono +615:knee pad +616:knot +617:lab coat, laboratory coat +618:ladle +619:lampshade, lamp shade +620:laptop, laptop computer +621:lawn mower, mower +622:lens cap, lens cover +623:letter opener, paper knife, paperknife +624:library +625:lifeboat +626:lighter, light, igniter, ignitor +627:limousine, limo +628:liner, ocean liner +629:lipstick, lip rouge +630:Loafer +631:lotion +632:loudspeaker, speaker, speaker unit, loudspeaker system, speaker system +633:loupe, jeweler's loupe +634:lumbermill, sawmill +635:magnetic compass +636:mailbag, postbag +637:mailbox, letter box +638:maillot +639:maillot, tank suit +640:manhole cover +641:maraca +642:marimba, xylophone +643:mask +644:matchstick +645:maypole +646:maze, labyrinth +647:measuring cup +648:medicine chest, medicine cabinet +649:megalith, megalithic structure +650:microphone, mike +651:microwave, microwave oven +652:military uniform +653:milk can +654:minibus +655:miniskirt, mini +656:minivan +657:missile +658:mitten +659:mixing bowl +660:mobile home, manufactured home +661:Model T +662:modem +663:monastery +664:monitor +665:moped +666:mortar +667:mortarboard +668:mosque +669:mosquito net +670:motor scooter, scooter +671:mountain bike, all-terrain bike, off-roader +672:mountain tent +673:mouse, computer mouse +674:mousetrap +675:moving van +676:muzzle +677:nail +678:neck brace +679:necklace +680:nipple +681:notebook, notebook computer +682:obelisk +683:oboe, hautboy, hautbois +684:ocarina, sweet potato +685:odometer, hodometer, mileometer, milometer +686:oil filter +687:organ, pipe organ +688:oscilloscope, scope, cathode-ray oscilloscope, CRO +689:overskirt +690:oxcart +691:oxygen mask +692:packet +693:paddle, boat paddle +694:paddlewheel, paddle wheel +695:padlock +696:paintbrush +697:pajama, pyjama, pj's, jammies +698:palace +699:panpipe, pandean pipe, syrinx +700:paper towel +701:parachute, chute +702:parallel bars, bars +703:park bench +704:parking meter +705:passenger car, coach, carriage +706:patio, terrace +707:pay-phone, pay-station +708:pedestal, plinth, footstall +709:pencil box, pencil case +710:pencil sharpener +711:perfume, essence +712:Petri dish +713:photocopier +714:pick, plectrum, plectron +715:pickelhaube +716:picket fence, paling +717:pickup, pickup truck +718:pier +719:piggy bank, penny bank +720:pill bottle +721:pillow +722:ping-pong ball +723:pinwheel +724:pirate, pirate ship +725:pitcher, ewer +726:plane, carpenter's plane, woodworking plane +727:planetarium +728:plastic bag +729:plate rack +730:plow, plough +731:plunger, plumber's helper +732:Polaroid camera, Polaroid Land camera +733:pole +734:police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria +735:poncho +736:pool table, billiard table, snooker table +737:pop bottle, soda bottle +738:pot, flowerpot +739:potter's wheel +740:power drill +741:prayer rug, prayer mat +742:printer +743:prison, prison house +744:projectile, missile +745:projector +746:puck, hockey puck +747:punching bag, punch bag, punching ball, punchball +748:purse +749:quill, quill pen +750:quilt, comforter, comfort, puff +751:racer, race car, racing car +752:racket, racquet +753:radiator +754:radio, wireless +755:radio telescope, radio reflector +756:rain barrel +757:recreational vehicle, RV, R.V. +758:reel +759:reflex camera +760:refrigerator, icebox +761:remote control, remote +762:restaurant, eating house, eating place, eatery +763:revolver, six-gun, six-shooter +764:rifle +765:rocking chair, rocker +766:rotisserie +767:rubber eraser, rubber, pencil eraser +768:rugby ball +769:rule, ruler +770:running shoe +771:safe +772:safety pin +773:saltshaker, salt shaker +774:sandal +775:sarong +776:sax, saxophone +777:scabbard +778:scale, weighing machine +779:school bus +780:schooner +781:scoreboard +782:screen, CRT screen +783:screw +784:screwdriver +785:seat belt, seatbelt +786:sewing machine +787:shield, buckler +788:shoe shop, shoe-shop, shoe store +789:shoji +790:shopping basket +791:shopping cart +792:shovel +793:shower cap +794:shower curtain +795:ski +796:ski mask +797:sleeping bag +798:slide rule, slipstick +799:sliding door +800:slot, one-armed bandit +801:snorkel +802:snowmobile +803:snowplow, snowplough +804:soap dispenser +805:soccer ball +806:sock +807:solar dish, solar collector, solar furnace +808:sombrero +809:soup bowl +810:space bar +811:space heater +812:space shuttle +813:spatula +814:speedboat +815:spider web, spider's web +816:spindle +817:sports car, sport car +818:spotlight, spot +819:stage +820:steam locomotive +821:steel arch bridge +822:steel drum +823:stethoscope +824:stole +825:stone wall +826:stopwatch, stop watch +827:stove +828:strainer +829:streetcar, tram, tramcar, trolley, trolley car +830:stretcher +831:studio couch, day bed +832:stupa, tope +833:submarine, pigboat, sub, U-boat +834:suit, suit of clothes +835:sundial +836:sunglass +837:sunglasses, dark glasses, shades +838:sunscreen, sunblock, sun blocker +839:suspension bridge +840:swab, swob, mop +841:sweatshirt +842:swimming trunks, bathing trunks +843:swing +844:switch, electric switch, electrical switch +845:syringe +846:table lamp +847:tank, army tank, armored combat vehicle, armoured combat vehicle +848:tape player +849:teapot +850:teddy, teddy bear +851:television, television system +852:tennis ball +853:thatch, thatched roof +854:theater curtain, theatre curtain +855:thimble +856:thresher, thrasher, threshing machine +857:throne +858:tile roof +859:toaster +860:tobacco shop, tobacconist shop, tobacconist +861:toilet seat +862:torch +863:totem pole +864:tow truck, tow car, wrecker +865:toyshop +866:tractor +867:trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi +868:tray +869:trench coat +870:tricycle, trike, velocipede +871:trimaran +872:tripod +873:triumphal arch +874:trolleybus, trolley coach, trackless trolley +875:trombone +876:tub, vat +877:turnstile +878:typewriter keyboard +879:umbrella +880:unicycle, monocycle +881:upright, upright piano +882:vacuum, vacuum cleaner +883:vase +884:vault +885:velvet +886:vending machine +887:vestment +888:viaduct +889:violin, fiddle +890:volleyball +891:waffle iron +892:wall clock +893:wallet, billfold, notecase, pocketbook +894:wardrobe, closet, press +895:warplane, military plane +896:washbasin, handbasin, washbowl, lavabo, wash-hand basin +897:washer, automatic washer, washing machine +898:water bottle +899:water jug +900:water tower +901:whiskey jug +902:whistle +903:wig +904:window screen +905:window shade +906:Windsor tie +907:wine bottle +908:wing +909:wok +910:wooden spoon +911:wool, woolen, woollen +912:worm fence, snake fence, snake-rail fence, Virginia fence +913:wreck +914:yawl +915:yurt +916:web site, website, internet site, site +917:comic book +918:crossword puzzle, crossword +919:street sign +920:traffic light, traffic signal, stoplight +921:book jacket, dust cover, dust jacket, dust wrapper +922:menu +923:plate +924:guacamole +925:consomme +926:hot pot, hotpot +927:trifle +928:ice cream, icecream +929:ice lolly, lolly, lollipop, popsicle +930:French loaf +931:bagel, beigel +932:pretzel +933:cheeseburger +934:hotdog, hot dog, red hot +935:mashed potato +936:head cabbage +937:broccoli +938:cauliflower +939:zucchini, courgette +940:spaghetti squash +941:acorn squash +942:butternut squash +943:cucumber, cuke +944:artichoke, globe artichoke +945:bell pepper +946:cardoon +947:mushroom +948:Granny Smith +949:strawberry +950:orange +951:lemon +952:fig +953:pineapple, ananas +954:banana +955:jackfruit, jak, jack +956:custard apple +957:pomegranate +958:hay +959:carbonara +960:chocolate sauce, chocolate syrup +961:dough +962:meat loaf, meatloaf +963:pizza, pizza pie +964:potpie +965:burrito +966:red wine +967:espresso +968:cup +969:eggnog +970:alp +971:bubble +972:cliff, drop, drop-off +973:coral reef +974:geyser +975:lakeside, lakeshore +976:promontory, headland, head, foreland +977:sandbar, sand bar +978:seashore, coast, seacoast, sea-coast +979:valley, vale +980:volcano +981:ballplayer, baseball player +982:groom, bridegroom +983:scuba diver +984:rapeseed +985:daisy +986:yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum +987:corn +988:acorn +989:hip, rose hip, rosehip +990:buckeye, horse chestnut, conker +991:coral fungus +992:agaric +993:gyromitra +994:stinkhorn, carrion fungus +995:earthstar +996:hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa +997:bolete +998:ear, spike, capitulum +999:toilet tissue, toilet paper, bathroom tissue \ No newline at end of file diff --git a/rknn-toolkit2/examples/pytorch/resnet18_qat/model_config.yml b/rknn-toolkit2/examples/pytorch/resnet18_qat/model_config.yml new file mode 100755 index 0000000000000000000000000000000000000000..a6899360e5b07071acc686baa8a7a65a7e231d12 --- /dev/null +++ b/rknn-toolkit2/examples/pytorch/resnet18_qat/model_config.yml @@ -0,0 +1,12 @@ +models: + name: resnet_18 # 模型输出名称 + platform: pytorch # 原始模型使用的框架 + model_file_path: ./resnet18_i8.pt # 原模型路径 + subgraphs: # 描述输入输出shape等信息 + input_size_list: + - 1, 3, 224, 224 + quantize: False # QAT已经量化过，不需要再量化 + configs: + mean_values: [123.675, 116.28, 103.53] # rknn.config的mean_values参数 + std_values: [58.395, 58.395, 58.395] # rknn.config的std_values参数 + quant_img_RGB2BGR: false # 不进行RGB2BGR转换 diff --git a/rknn-toolkit2/examples/pytorch/resnet18_qat/resnet18_i8.pt b/rknn-toolkit2/examples/pytorch/resnet18_qat/resnet18_i8.pt new file mode 100755 index 0000000000000000000000000000000000000000..6c2024a18e526fd98b28eb5bfb9c80312e5b92f0 --- /dev/null +++ b/rknn-toolkit2/examples/pytorch/resnet18_qat/resnet18_i8.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:47b314875bb4b2d175bf744123eb517fedb23431cbb04612cad82e7fb33af674 +size 11939926 diff --git a/rknn-toolkit2/examples/pytorch/resnet18_qat/space_shuttle_224.jpg b/rknn-toolkit2/examples/pytorch/resnet18_qat/space_shuttle_224.jpg new file mode 100644 index 0000000000000000000000000000000000000000..412a855c379648d80d5b4267ba1da58188cdee1c --- /dev/null +++ b/rknn-toolkit2/examples/pytorch/resnet18_qat/space_shuttle_224.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a827cc9060d15cd34b1b5ac512f4d6d8366416a5874a2075951991cbacef78d4 +size 23472 diff --git a/rknn-toolkit2/examples/pytorch/resnet18_qat/test.py b/rknn-toolkit2/examples/pytorch/resnet18_qat/test.py new file mode 100755 index 0000000000000000000000000000000000000000..17e75e3ea9b8c1218c6574fe809318cadd950c00 --- /dev/null +++ b/rknn-toolkit2/examples/pytorch/resnet18_qat/test.py @@ -0,0 +1,110 @@ +import numpy as np +import cv2 +from rknn.api import RKNN +import os + + +def export_pytorch_model(): + import torch + import torchvision.models as models + net = models.quantization.resnet18(pretrained=True, quantize=True) + net.eval() + trace_model = torch.jit.trace(net, torch.Tensor(1, 3, 224, 224)) + trace_model.save('./resnet18_i8.pt') + +def show_outputs(output): + index = sorted(range(len(output)), key=lambda k : output[k], reverse=True) + fp = open('./labels.txt', 'r') + labels = fp.readlines() + top5_str = 'resnet18\n-----TOP 5-----\n' + for i in range(5): + value = output[index[i]] + if value > 0: + topi = '[{:>3d}] score:{:.6f} class:"{}"\n'.format(index[i], value, labels[index[i]].strip().split(':')[-1]) + else: + topi = '[ -1]: 0.0\n' + top5_str += topi + print(top5_str.strip()) + + +def show_perfs(perfs): + perfs = 'perfs: {}\n'.format(perfs) + print(perfs) + + +def softmax(x): + return np.exp(x)/sum(np.exp(x)) + +def torch_version(): + import torch + torch_ver = torch.__version__.split('.') + torch_ver[2] = torch_ver[2].split('+')[0] + return [int(v) for v in torch_ver] + +if __name__ == '__main__': + + if torch_version() < [1, 9, 0]: + import torch + print("Your torch version is '{}', in order to better support the Quantization Aware Training (QAT) model,\n" + "Please update the torch version to '1.9.0' or higher!".format(torch.__version__)) + exit(0) + + model = './resnet18_i8.pt' + if not os.path.exists(model): + export_pytorch_model() + + input_size_list = [[1, 3, 224, 224]] + + # Create RKNN object + rknn = RKNN(verbose=True) + + # Pre-process config + print('--> Config model') + rknn.config(mean_values=[123.675, 116.28, 103.53], std_values=[58.395, 58.395, 58.395], target_platform='rk3566') + print('done') + + # Load model + print('--> Loading model') + ret = rknn.load_pytorch(model=model, input_size_list=input_size_list) + if ret != 0: + print('Load model failed!') + exit(ret) + print('done') + + # Build model + print('--> Building model') + ret = rknn.build(do_quantization=False) + if ret != 0: + print('Build model failed!') + exit(ret) + print('done') + + # Export rknn model + print('--> Export rknn model') + ret = rknn.export_rknn('./resnet_18.rknn') + if ret != 0: + print('Export rknn model failed!') + exit(ret) + print('done') + + # Set inputs + img = cv2.imread('./space_shuttle_224.jpg') + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + img = np.expand_dims(img, 0) + + # Init runtime environment + print('--> Init runtime environment') + ret = rknn.init_runtime() + if ret != 0: + print('Init runtime environment failed!') + exit(ret) + print('done') + + # Inference + print('--> Running model') + outputs = rknn.inference(inputs=[img], data_format=['nhwc']) + np.save('./pytorch_resnet18_qat_0.npy', outputs[0]) + show_outputs(softmax(np.array(outputs[0][0]))) + print('done') + + rknn.release() diff --git a/rknn-toolkit2/examples/pytorch/yolov5/README.md b/rknn-toolkit2/examples/pytorch/yolov5/README.md new file mode 100644 index 0000000000000000000000000000000000000000..18b88f8575b8071d07ce77e6025fa80b474416a0 --- /dev/null +++ b/rknn-toolkit2/examples/pytorch/yolov5/README.md @@ -0,0 +1,3 @@ +# Description of example changes + +At present, the models of the YOLO series have been transferred to the [rknn_model_zoo](https://github.com/airockchip/rknn_model_zoo "rknn model zoo") project, please refer to the instructions in this project to export the TorchScript model, and use the scripts provided by the project for model conversion, model evaluation, and deployment. \ No newline at end of file diff --git a/rknn-toolkit2/examples/readme.txt b/rknn-toolkit2/examples/readme.txt new file mode 100644 index 0000000000000000000000000000000000000000..a08549c239030c363cf90d465ee1ca3e90111874 --- /dev/null +++ b/rknn-toolkit2/examples/readme.txt @@ -0,0 +1,33 @@ +The directory structure of examples is as follows: +. +├── caffe +│ ├── mobilenet_v2 # mobilenet_v2 float model +│ └── vgg-ssd # vgg-ssd float model +├── onnx +│ ├── resnet50v2 # resnet50v2 float model +│ └── yolov5 # yolov5 float model +├── pytorch +│ ├── resnet18 # resnet18 float model +│ ├── resnet18_qat # resnet18 QAT model +│ ├── resnet18_export_onnx # how to export onnx model from pytorch +│ └── yolov5 # yolov5 float model +├── tensorflow +│ ├── ssd_mobilenet_v1 # ssd_mobilenet_v1 float model +│ └── inception_v3_qat # inception_v3 QAT model +├── tflite +│ ├── mobilenet_v1 # mobilenet_v1 float model +│ └── mobilenet_v1_qat # mobilenet_v1 QAT model +├── darknet +│ └── yolov3_416x416 # yolov3 float model +└── functions + ├── accuracy_analysis # how to use accuracy-analysis function + ├── codegen # how to generate c++ deployment demo when converting model + ├── custom_op # How to use custom_op function + ├── dynamic_shape # how to use dynamic shape function + ├── hybrid_quant # how to use hybrid-quantization function + ├── model_pruning # how to use model_pruning function + ├── multi_batch # how to expand batch for use multi-batch function + ├── multi_input # How to convert multi-input model + ├── npu_device_test # how to test npu device by connect the board + ├── onnx_edit # how to use onnx_edit function + └── quantize_algorithm_mmse # how to use MMSE quantize algorithm \ No newline at end of file diff --git a/rknn-toolkit2/examples/tensorflow/inception_v3_qat/README.md b/rknn-toolkit2/examples/tensorflow/inception_v3_qat/README.md new file mode 100644 index 0000000000000000000000000000000000000000..d1a268302fea79588e0e509edb6d1204a47d431d --- /dev/null +++ b/rknn-toolkit2/examples/tensorflow/inception_v3_qat/README.md @@ -0,0 +1,31 @@ +# TensorFlow Inception V3 QAT + +## Model Source +The model used in this example come from: +https://storage.googleapis.com/download.tensorflow.org/models/tflite_11_05_08/inception_v3_quant.tgz + +## Script Usage +*Usage:* +``` +python test.py +``` +*rknn_convert usage:* +``` +python3 -m rknn.api.rknn_convert -t rk3568 -i ./model_config.yml -o ./ +``` +*Description:* +- The default target platform in script is 'rk3566', please modify the 'target_platform' parameter of 'rknn.config' according to the actual platform. +- If connecting board is required, please add the 'target' parameter in 'rknn.init_runtime'. +- This is a QAT model, and the do_quantization of rknn.build needs to be set to False. + +## Expected Results +This example will print the TOP5 labels and corresponding scores of the test image classification results, as follows: +``` +-----TOP 5----- +[ 2] score:0.996572 class:"goldfish, Carassius auratus" +[ 408] score:0.000464 class:"ambulance" +[ 795] score:0.000201 class:"shower curtain" +[ 352] score:0.000070 class:"hartebeest" +[ 974] score:0.000051 class:"coral reef" +``` +- Note: Different platforms, different versions of tools and drivers may have slightly different results. \ No newline at end of file diff --git a/rknn-toolkit2/examples/tensorflow/inception_v3_qat/goldfish_299x299.jpg b/rknn-toolkit2/examples/tensorflow/inception_v3_qat/goldfish_299x299.jpg new file mode 100644 index 0000000000000000000000000000000000000000..b3dd3b09fb02b37db695c36ee33eb548bfaaa373 --- /dev/null +++ b/rknn-toolkit2/examples/tensorflow/inception_v3_qat/goldfish_299x299.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:368a42831521cd46ceed64164771051eed9e43ebff78eeb6fa9ad948e3c1838b +size 87254 diff --git a/rknn-toolkit2/examples/tensorflow/inception_v3_qat/labels.txt b/rknn-toolkit2/examples/tensorflow/inception_v3_qat/labels.txt new file mode 100644 index 0000000000000000000000000000000000000000..dcd01c14b4635096ae295a8e81de0f65188bc1f8 --- /dev/null +++ b/rknn-toolkit2/examples/tensorflow/inception_v3_qat/labels.txt @@ -0,0 +1,1001 @@ +0:__background__ +1:tench, Tinca tinca +2:goldfish, Carassius auratus +3:great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias +4:tiger shark, Galeocerdo cuvieri +5:hammerhead, hammerhead shark +6:electric ray, crampfish, numbfish, torpedo +7:stingray +8:cock +9:hen +10:ostrich, Struthio camelus +11:brambling, Fringilla montifringilla +12:goldfinch, Carduelis carduelis +13:house finch, linnet, Carpodacus mexicanus +14:junco, snowbird +15:indigo bunting, indigo finch, indigo bird, Passerina cyanea +16:robin, American robin, Turdus migratorius +17:bulbul +18:jay +19:magpie +20:chickadee +21:water ouzel, dipper +22:kite +23:bald eagle, American eagle, Haliaeetus leucocephalus +24:vulture +25:great grey owl, great gray owl, Strix nebulosa +26:European fire salamander, Salamandra salamandra +27:common newt, Triturus vulgaris +28:eft +29:spotted salamander, Ambystoma maculatum +30:axolotl, mud puppy, Ambystoma mexicanum +31:bullfrog, Rana catesbeiana 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kingsnake +58:garter snake, grass snake +59:water snake +60:vine snake +61:night snake, Hypsiglena torquata +62:boa constrictor, Constrictor constrictor +63:rock python, rock snake, Python sebae +64:Indian cobra, Naja naja +65:green mamba +66:sea snake +67:horned viper, cerastes, sand viper, horned asp, Cerastes cornutus +68:diamondback, diamondback rattlesnake, Crotalus adamanteus +69:sidewinder, horned rattlesnake, Crotalus cerastes +70:trilobite +71:harvestman, daddy longlegs, Phalangium opilio +72:scorpion +73:black and gold garden spider, Argiope aurantia +74:barn spider, Araneus cavaticus +75:garden spider, Aranea diademata +76:black widow, Latrodectus mactans +77:tarantula +78:wolf spider, hunting spider +79:tick +80:centipede +81:black grouse +82:ptarmigan +83:ruffed grouse, partridge, Bonasa umbellus +84:prairie chicken, prairie grouse, prairie fowl +85:peacock +86:quail +87:partridge +88:African grey, African gray, Psittacus erithacus +89:macaw +90:sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita +91:lorikeet +92:coucal +93:bee eater +94:hornbill +95:hummingbird +96:jacamar +97:toucan +98:drake +99:red-breasted merganser, Mergus serrator +100:goose +101:black swan, Cygnus atratus +102:tusker +103:echidna, spiny anteater, anteater +104:platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus +105:wallaby, brush kangaroo +106:koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus +107:wombat +108:jellyfish +109:sea anemone, anemone +110:brain coral +111:flatworm, platyhelminth +112:nematode, nematode worm, roundworm +113:conch +114:snail +115:slug +116:sea slug, nudibranch +117:chiton, coat-of-mail shell, sea cradle, polyplacophore +118:chambered nautilus, pearly nautilus, nautilus +119:Dungeness crab, Cancer magister +120:rock crab, Cancer irroratus +121:fiddler crab +122:king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica +123:American lobster, Northern lobster, Maine lobster, Homarus americanus +124:spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish +125:crayfish, crawfish, crawdad, crawdaddy +126:hermit crab +127:isopod +128:white stork, Ciconia ciconia +129:black stork, Ciconia nigra +130:spoonbill +131:flamingo +132:little blue heron, Egretta caerulea +133:American egret, great white heron, Egretta albus +134:bittern +135:crane +136:limpkin, Aramus pictus +137:European gallinule, Porphyrio porphyrio +138:American coot, marsh hen, mud hen, water hen, Fulica americana +139:bustard +140:ruddy turnstone, Arenaria interpres +141:red-backed sandpiper, dunlin, Erolia alpina +142:redshank, Tringa totanus +143:dowitcher +144:oystercatcher, oyster catcher +145:pelican +146:king penguin, Aptenodytes patagonica +147:albatross, mollymawk +148:grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus +149:killer whale, killer, orca, grampus, sea wolf, Orcinus orca +150:dugong, Dugong dugon +151:sea lion 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water spaniel +223:kuvasz +224:schipperke +225:groenendael +226:malinois +227:briard +228:kelpie +229:komondor +230:Old English sheepdog, bobtail +231:Shetland sheepdog, Shetland sheep dog, Shetland +232:collie +233:Border collie +234:Bouvier des Flandres, Bouviers des Flandres +235:Rottweiler +236:German shepherd, German shepherd dog, German police dog, alsatian +237:Doberman, Doberman pinscher +238:miniature pinscher +239:Greater Swiss Mountain dog +240:Bernese mountain dog +241:Appenzeller +242:EntleBucher +243:boxer +244:bull mastiff +245:Tibetan mastiff +246:French bulldog +247:Great Dane +248:Saint Bernard, St Bernard +249:Eskimo dog, husky +250:malamute, malemute, Alaskan malamute +251:Siberian husky +252:dalmatian, coach dog, carriage dog +253:affenpinscher, monkey pinscher, monkey dog +254:basenji +255:pug, pug-dog +256:Leonberg +257:Newfoundland, Newfoundland dog +258:Great Pyrenees +259:Samoyed, Samoyede +260:Pomeranian +261:chow, chow chow +262:keeshond +263:Brabancon 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computer +529:dial telephone, dial phone +530:diaper, nappy, napkin +531:digital clock +532:digital watch +533:dining table, board +534:dishrag, dishcloth +535:dishwasher, dish washer, dishwashing machine +536:disk brake, disc brake +537:dock, dockage, docking facility +538:dogsled, dog sled, dog sleigh +539:dome +540:doormat, welcome mat +541:drilling platform, offshore rig +542:drum, membranophone, tympan +543:drumstick +544:dumbbell +545:Dutch oven +546:electric fan, blower +547:electric guitar +548:electric locomotive +549:entertainment center +550:envelope +551:espresso maker +552:face powder +553:feather boa, boa +554:file, file cabinet, filing cabinet +555:fireboat +556:fire engine, fire truck +557:fire screen, fireguard +558:flagpole, flagstaff +559:flute, transverse flute +560:folding chair +561:football helmet +562:forklift +563:fountain +564:fountain pen +565:four-poster +566:freight car +567:French horn, horn +568:frying pan, frypan, skillet +569:fur coat +570:garbage truck, dustcart +571:gasmask, respirator, gas helmet +572:gas pump, gasoline pump, petrol pump, island dispenser +573:goblet +574:go-kart +575:golf ball +576:golfcart, golf cart +577:gondola +578:gong, tam-tam +579:gown +580:grand piano, grand +581:greenhouse, nursery, glasshouse +582:grille, radiator grille +583:grocery store, grocery, food market, market +584:guillotine +585:hair slide +586:hair spray +587:half track +588:hammer +589:hamper +590:hand blower, blow dryer, blow drier, hair dryer, hair drier +591:hand-held computer, hand-held microcomputer +592:handkerchief, hankie, hanky, hankey +593:hard disc, hard disk, fixed disk +594:harmonica, mouth organ, harp, mouth harp +595:harp +596:harvester, reaper +597:hatchet +598:holster +599:home theater, home theatre +600:honeycomb +601:hook, claw +602:hoopskirt, crinoline +603:horizontal bar, high bar +604:horse cart, horse-cart +605:hourglass +606:iPod +607:iron, smoothing iron +608:jack-o'-lantern +609:jean, blue jean, denim +610:jeep, landrover +611:jersey, T-shirt, tee shirt +612:jigsaw puzzle +613:jinrikisha, ricksha, rickshaw +614:joystick +615:kimono +616:knee pad +617:knot +618:lab coat, laboratory coat +619:ladle +620:lampshade, lamp shade +621:laptop, laptop computer +622:lawn mower, mower +623:lens cap, lens cover +624:letter opener, paper knife, paperknife +625:library +626:lifeboat +627:lighter, light, igniter, ignitor +628:limousine, limo +629:liner, ocean liner +630:lipstick, lip rouge +631:Loafer +632:lotion +633:loudspeaker, speaker, speaker unit, loudspeaker system, speaker system +634:loupe, jeweler's loupe +635:lumbermill, sawmill +636:magnetic compass +637:mailbag, postbag +638:mailbox, letter box +639:maillot +640:maillot, tank suit +641:manhole cover +642:maraca +643:marimba, xylophone +644:mask +645:matchstick +646:maypole +647:maze, labyrinth +648:measuring cup +649:medicine chest, medicine cabinet +650:megalith, megalithic structure +651:microphone, mike +652:microwave, microwave oven +653:military uniform +654:milk can +655:minibus +656:miniskirt, mini +657:minivan +658:missile +659:mitten +660:mixing bowl +661:mobile home, manufactured home +662:Model T +663:modem +664:monastery +665:monitor +666:moped +667:mortar +668:mortarboard +669:mosque +670:mosquito net +671:motor scooter, scooter +672:mountain bike, all-terrain bike, off-roader +673:mountain tent +674:mouse, computer mouse +675:mousetrap +676:moving van +677:muzzle +678:nail +679:neck brace +680:necklace +681:nipple +682:notebook, notebook computer +683:obelisk +684:oboe, hautboy, hautbois +685:ocarina, sweet potato +686:odometer, hodometer, mileometer, milometer +687:oil filter +688:organ, pipe organ +689:oscilloscope, scope, cathode-ray oscilloscope, CRO +690:overskirt +691:oxcart +692:oxygen mask +693:packet +694:paddle, boat paddle +695:paddlewheel, paddle wheel +696:padlock +697:paintbrush +698:pajama, pyjama, pj's, jammies +699:palace +700:panpipe, pandean pipe, syrinx +701:paper towel 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+743:printer +744:prison, prison house +745:projectile, missile +746:projector +747:puck, hockey puck +748:punching bag, punch bag, punching ball, punchball +749:purse +750:quill, quill pen +751:quilt, comforter, comfort, puff +752:racer, race car, racing car +753:racket, racquet +754:radiator +755:radio, wireless +756:radio telescope, radio reflector +757:rain barrel +758:recreational vehicle, RV, R.V. +759:reel +760:reflex camera +761:refrigerator, icebox +762:remote control, remote +763:restaurant, eating house, eating place, eatery +764:revolver, six-gun, six-shooter +765:rifle +766:rocking chair, rocker +767:rotisserie +768:rubber eraser, rubber, pencil eraser +769:rugby ball +770:rule, ruler +771:running shoe +772:safe +773:safety pin +774:saltshaker, salt shaker +775:sandal +776:sarong +777:sax, saxophone +778:scabbard +779:scale, weighing machine +780:school bus +781:schooner +782:scoreboard +783:screen, CRT screen +784:screw +785:screwdriver +786:seat belt, seatbelt +787:sewing machine +788:shield, buckler +789:shoe shop, shoe-shop, shoe store +790:shoji +791:shopping basket +792:shopping cart +793:shovel +794:shower cap +795:shower curtain +796:ski +797:ski mask +798:sleeping bag +799:slide rule, slipstick +800:sliding door +801:slot, one-armed bandit +802:snorkel +803:snowmobile +804:snowplow, snowplough +805:soap dispenser +806:soccer ball +807:sock +808:solar dish, solar collector, solar furnace +809:sombrero +810:soup bowl +811:space bar +812:space heater +813:space shuttle +814:spatula +815:speedboat +816:spider web, spider's web +817:spindle +818:sports car, sport car +819:spotlight, spot +820:stage +821:steam locomotive +822:steel arch bridge +823:steel drum +824:stethoscope +825:stole +826:stone wall +827:stopwatch, stop watch +828:stove +829:strainer +830:streetcar, tram, tramcar, trolley, trolley car +831:stretcher +832:studio couch, day bed +833:stupa, tope +834:submarine, pigboat, sub, U-boat +835:suit, suit of clothes +836:sundial +837:sunglass +838:sunglasses, dark glasses, shades +839:sunscreen, sunblock, sun blocker +840:suspension bridge +841:swab, swob, mop +842:sweatshirt +843:swimming trunks, bathing trunks +844:swing +845:switch, electric switch, electrical switch +846:syringe +847:table lamp +848:tank, army tank, armored combat vehicle, armoured combat vehicle +849:tape player +850:teapot +851:teddy, teddy bear +852:television, television system +853:tennis ball +854:thatch, thatched roof +855:theater curtain, theatre curtain +856:thimble +857:thresher, thrasher, threshing machine +858:throne +859:tile roof +860:toaster +861:tobacco shop, tobacconist shop, tobacconist +862:toilet seat +863:torch +864:totem pole +865:tow truck, tow car, wrecker +866:toyshop +867:tractor +868:trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi +869:tray +870:trench coat +871:tricycle, trike, velocipede +872:trimaran +873:tripod +874:triumphal arch +875:trolleybus, trolley coach, trackless trolley +876:trombone +877:tub, vat +878:turnstile +879:typewriter keyboard +880:umbrella +881:unicycle, monocycle +882:upright, upright piano +883:vacuum, vacuum cleaner +884:vase +885:vault +886:velvet +887:vending machine +888:vestment +889:viaduct +890:violin, fiddle +891:volleyball +892:waffle iron +893:wall clock +894:wallet, billfold, notecase, pocketbook +895:wardrobe, closet, press +896:warplane, military plane +897:washbasin, handbasin, washbowl, lavabo, wash-hand basin +898:washer, automatic washer, washing machine +899:water bottle +900:water jug +901:water tower +902:whiskey jug +903:whistle +904:wig +905:window screen +906:window shade +907:Windsor tie +908:wine bottle +909:wing +910:wok +911:wooden spoon +912:wool, woolen, woollen +913:worm fence, snake fence, snake-rail fence, Virginia fence +914:wreck +915:yawl +916:yurt +917:web site, website, internet site, site +918:comic book +919:crossword puzzle, crossword +920:street sign +921:traffic light, traffic signal, stoplight +922:book jacket, dust cover, dust jacket, dust wrapper +923:menu +924:plate +925:guacamole +926:consomme +927:hot pot, hotpot +928:trifle +929:ice cream, icecream +930:ice lolly, lolly, lollipop, popsicle +931:French loaf +932:bagel, beigel +933:pretzel +934:cheeseburger +935:hotdog, hot dog, red hot +936:mashed potato +937:head cabbage +938:broccoli +939:cauliflower +940:zucchini, courgette +941:spaghetti squash +942:acorn squash +943:butternut squash +944:cucumber, cuke +945:artichoke, globe artichoke +946:bell pepper +947:cardoon +948:mushroom +949:Granny Smith +950:strawberry +951:orange +952:lemon +953:fig +954:pineapple, ananas +955:banana +956:jackfruit, jak, jack +957:custard apple +958:pomegranate +959:hay +960:carbonara +961:chocolate sauce, chocolate syrup +962:dough +963:meat loaf, meatloaf +964:pizza, pizza pie +965:potpie +966:burrito +967:red wine +968:espresso +969:cup +970:eggnog +971:alp +972:bubble +973:cliff, drop, drop-off +974:coral reef +975:geyser +976:lakeside, lakeshore +977:promontory, headland, head, foreland +978:sandbar, sand bar +979:seashore, coast, seacoast, sea-coast +980:valley, vale +981:volcano +982:ballplayer, baseball player +983:groom, bridegroom +984:scuba diver +985:rapeseed +986:daisy +987:yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum +988:corn +989:acorn +990:hip, rose hip, rosehip +991:buckeye, horse chestnut, conker +992:coral fungus +993:agaric +994:gyromitra +995:stinkhorn, carrion fungus +996:earthstar +997:hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa +998:bolete +999:ear, spike, capitulum +1000:toilet tissue, toilet paper, bathroom tissue \ No newline at end of file diff --git a/rknn-toolkit2/examples/tensorflow/inception_v3_qat/model_config.yml b/rknn-toolkit2/examples/tensorflow/inception_v3_qat/model_config.yml new file mode 100755 index 0000000000000000000000000000000000000000..ccc496a4ffc1300ec6532f80175a75fc0de7590b --- /dev/null +++ b/rknn-toolkit2/examples/tensorflow/inception_v3_qat/model_config.yml @@ -0,0 +1,16 @@ +models: + name: inception_v3_quant_frozen # 模型输出名称 + platform: tensorflow # 原始模型使用的框架 + model_file_path: ./inception_v3_quant_frozen.pb # 原模型路径 + subgraphs: # 描述输入输出shape等信息 + input_size_list: + - 1, 299, 299, 3 + inputs: # 输入tensor名称 + - input + outputs: # 输出tensor名称 + - InceptionV3/Logits/SpatialSqueeze + quantize: false # QAT已经量化过，不需要再量化 + configs: + mean_values: [104, 117, 123] # rknn.config的mean_values参数 + std_values: [128, 128, 128] # rknn.config的std_values参数 + quant_img_RGB2BGR: false # 不进行RGB2BGR转换 diff --git a/rknn-toolkit2/examples/tensorflow/inception_v3_qat/test.py b/rknn-toolkit2/examples/tensorflow/inception_v3_qat/test.py new file mode 100755 index 0000000000000000000000000000000000000000..2fd0539adc8cbe226034663d0e258463eda8f304 --- /dev/null +++ b/rknn-toolkit2/examples/tensorflow/inception_v3_qat/test.py @@ -0,0 +1,159 @@ +import numpy as np +import cv2 +import os +import urllib +import tarfile +import shutil +import traceback +import time +import sys +from rknn.api import RKNN + +PB_FILE = './inception_v3_quant_frozen.pb' +RKNN_MODEL_PATH = './inception_v3_quant_frozen.rknn' +INPUTS = ['input'] +OUTPUTS = ['InceptionV3/Logits/SpatialSqueeze'] +IMG_PATH = './goldfish_299x299.jpg' +INPUT_SIZE = 299 + + +def show_outputs(outputs): + output = outputs[0][0] + index = sorted(range(len(output)), key=lambda k : output[k], reverse=True) + fp = open('./labels.txt', 'r') + labels = fp.readlines() + top5_str = 'inception_v3\n-----TOP 5-----\n' + for i in range(5): + value = output[index[i]] + if value > 0: + topi = '[{:>4d}] score:{:.6f} class:"{}"\n'.format(index[i], value, labels[index[i]].strip().split(':')[-1]) + else: + topi = '[ -1]: 0.0\n' + top5_str += topi + print(top5_str.strip()) + + +def readable_speed(speed): + speed_bytes = float(speed) + speed_kbytes = speed_bytes / 1024 + if speed_kbytes > 1024: + speed_mbytes = speed_kbytes / 1024 + if speed_mbytes > 1024: + speed_gbytes = speed_mbytes / 1024 + return "{:.2f} GB/s".format(speed_gbytes) + else: + return "{:.2f} MB/s".format(speed_mbytes) + else: + return "{:.2f} KB/s".format(speed_kbytes) + + +def show_progress(blocknum, blocksize, totalsize): + speed = (blocknum * blocksize) / (time.time() - start_time) + speed_str = " Speed: {}".format(readable_speed(speed)) + recv_size = blocknum * blocksize + + f = sys.stdout + progress = (recv_size / totalsize) + progress_str = "{:.2f}%".format(progress * 100) + n = round(progress * 50) + s = ('#' * n).ljust(50, '-') + f.write(progress_str.ljust(8, ' ') + '[' + s + ']' + speed_str) + f.flush() + f.write('\r\n') + + +if __name__ == '__main__': + + # Create RKNN object + rknn = RKNN(verbose=True) + + # If inception_v3_quant_frozen.pb does not exist, download it. + # Download address: + # https://storage.googleapis.com/download.tensorflow.org/models/tflite_11_05_08/inception_v3_quant.tgz + # https://ftzr.zbox.filez.com/v2/delivery/data/95f00b0fc900458ba134f8b180b3f7a1/asset/inception_v3_qat/inception_v3_quant_frozen.pb + if not os.path.exists(PB_FILE): + print('--> Download {}'.format(PB_FILE)) + url = 'https://storage.googleapis.com/download.tensorflow.org/models/tflite_11_05_08/inception_v3_quant.tgz' + download_file = 'inception_v3_quant.tgz' + try: + start_time = time.time() + urllib.request.urlretrieve(url, download_file, show_progress) + except: + print('Download {} failed.'.format(download_file)) + print(traceback.format_exc()) + exit(-1) + try: + tar = tarfile.open(download_file) + target_dir = os.path.splitext(download_file)[0] + if os.path.isdir(target_dir): + pass + else: + os.mkdir(target_dir) + tar.extractall(target_dir) + tar.close() + except: + print('Extract {} failed.'.format(download_file)) + exit(-1) + pb_file = os.path.join(target_dir, PB_FILE) + if os.path.exists(pb_file): + shutil.copyfile(pb_file, './inception_v3_quant_frozen.pb') + shutil.rmtree(target_dir) + os.remove(download_file) + print('done') + + # Pre-process config + print('--> Config model') + rknn.config(mean_values=[104, 117, 123], std_values=[128, 128, 128], target_platform='rk3566') + print('done') + + # Load model + print('--> Loading model') + ret = rknn.load_tensorflow(tf_pb=PB_FILE, + inputs=INPUTS, + outputs=OUTPUTS, + input_size_list=[[1, INPUT_SIZE, INPUT_SIZE, 3]]) + if ret != 0: + print('Load model failed!') + exit(ret) + print('done') + + # Build model + print('--> Building model') + ret = rknn.build(do_quantization=False) + if ret != 0: + print('Build model failed!') + exit(ret) + print('done') + + # Export rknn model + print('--> Export rknn model') + ret = rknn.export_rknn(RKNN_MODEL_PATH) + if ret != 0: + print('Export rknn model failed!') + exit(ret) + print('done') + + # Set inputs + img = cv2.imread(IMG_PATH) + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + img = np.expand_dims(img, 0) + + # Init runtime environment + print('--> Init runtime environment') + ret = rknn.init_runtime() + if ret != 0: + print('Init runtime environment failed!') + exit(ret) + print('done') + + # Inference + print('--> Running model') + outputs = rknn.inference(inputs=[img], data_format=['nhwc']) + np.save('./tensorflow_inception_v3_qat_0.npy', outputs[0]) + x = outputs[0] + output = np.exp(x)/np.sum(np.exp(x)) + outputs = [output] + show_outputs(outputs) + print('done') + + rknn.release() diff --git a/rknn-toolkit2/examples/tensorflow/ssd_mobilenet_v1/README.md b/rknn-toolkit2/examples/tensorflow/ssd_mobilenet_v1/README.md new file mode 100644 index 0000000000000000000000000000000000000000..f96e5de066b591cadf47b53b836e3ac230ebf518 --- /dev/null +++ b/rknn-toolkit2/examples/tensorflow/ssd_mobilenet_v1/README.md @@ -0,0 +1,23 @@ +# TensorFlow SSD Mobilenet V1 + +## Model Source +The model used in this example come from the following open source projects: +https://github.com/fvmassoli/Deep-Learning-SSD-Object-Detection + +## Script Usage +*Usage:* +``` +python test.py +``` +*rknn_convert usage:* +``` +python3 -m rknn.api.rknn_convert -t rk3568 -i ./model_config.yml -o ./ +``` +*Description:* +- The default target platform in script is 'rk3566', please modify the 'target_platform' parameter of 'rknn.config' according to the actual platform. +- If connecting board is required, please add the 'target' parameter in 'rknn.init_runtime'. + +## Expected Results +This example will save the result of object detection to the 'result.jpg', as follows: +![result](result_truth.jpg) +- Note: Different platforms, different versions of tools and drivers may have slightly different results. \ No newline at end of file diff --git a/rknn-toolkit2/examples/tensorflow/ssd_mobilenet_v1/box_priors.txt b/rknn-toolkit2/examples/tensorflow/ssd_mobilenet_v1/box_priors.txt new file mode 100644 index 0000000000000000000000000000000000000000..7246b073fe7fd8b1d1340536457c8aeac24cd5a3 --- /dev/null +++ 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0.86602545 0.28866076 0.5700877 0.49999994 0.7071067 0.3535534 0.8660253 0.28866065 0.5700878 0.49999997 0.7071067 0.35355335 0.8660254 0.2886607 0.57008773 0.5 0.7071067 0.35355335 0.8660253 0.2886607 0.5700878 0.5 0.7071067 0.35355332 0.86602545 0.28866073 0.5700877 0.5 0.70710665 0.3535533 0.86602545 0.28866076 0.5700877 0.49999994 0.7071067 0.3535534 0.8660253 0.28866065 0.5700878 0.6499999 0.9192387 0.45961934 1.1258329 0.3752589 0.7211102 0.64999986 0.9192387 0.4596193 1.125833 0.37525892 0.7211102 0.64999986 0.91923875 0.45961928 1.1258328 0.37525892 0.72111017 0.6499999 0.9192387 0.45961934 1.1258329 0.3752589 0.7211102 0.64999986 0.9192387 0.4596193 1.125833 0.37525892 0.7211102 0.64999986 0.91923875 0.45961928 1.1258328 0.37525892 0.72111017 0.6499999 0.9192387 0.45961934 1.1258329 0.3752589 0.7211102 0.64999986 0.9192387 0.4596193 1.125833 0.37525892 0.7211102 0.64999986 0.91923875 0.45961928 1.1258328 0.37525892 0.72111017 0.79999995 1.1313708 0.5656854 1.3856406 0.46185714 0.8717798 0.79999995 1.1313708 0.56568533 1.3856406 0.46185708 0.87177986 0.79999995 1.1313708 0.5656854 1.3856406 0.46185714 0.8717798 0.79999995 1.1313708 0.56568533 1.3856406 0.46185708 0.87177986 0.9499999 1.3435028 0.6717514 1.6454482 0.54845536 0.97467947 + diff --git a/rknn-toolkit2/examples/tensorflow/ssd_mobilenet_v1/coco_labels_list.txt b/rknn-toolkit2/examples/tensorflow/ssd_mobilenet_v1/coco_labels_list.txt new file mode 100644 index 0000000000000000000000000000000000000000..5a70ff82aa7b0fa7315ca591820e4cf7d2f5ad18 --- /dev/null +++ b/rknn-toolkit2/examples/tensorflow/ssd_mobilenet_v1/coco_labels_list.txt @@ -0,0 +1,91 @@ +??? +person +bicycle +car +motorcycle +airplane +bus +train +truck +boat +traffic light +fire hydrant +??? +stop sign +parking meter +bench +bird +cat +dog +horse +sheep +cow +elephant +bear +zebra +giraffe +??? +backpack +umbrella +??? +??? +handbag +tie +suitcase +frisbee +skis +snowboard +sports ball +kite +baseball bat +baseball glove +skateboard +surfboard +tennis racket +bottle +??? +wine glass +cup +fork +knife +spoon +bowl +banana +apple +sandwich +orange +broccoli +carrot +hot dog +pizza +donut +cake +chair +couch +potted plant +bed +??? +dining table +??? +??? +toilet +??? +tv +laptop +mouse +remote +keyboard +cell phone +microwave +oven +toaster +sink +refrigerator +??? +book +clock +vase +scissors +teddy bear +hair drier +toothbrush diff --git a/rknn-toolkit2/examples/tensorflow/ssd_mobilenet_v1/dataset.txt b/rknn-toolkit2/examples/tensorflow/ssd_mobilenet_v1/dataset.txt new file mode 100644 index 0000000000000000000000000000000000000000..d873b170508af32cc7c6fe8c8788d7c6b6a64363 --- /dev/null +++ b/rknn-toolkit2/examples/tensorflow/ssd_mobilenet_v1/dataset.txt @@ -0,0 +1 @@ +road.bmp diff --git a/rknn-toolkit2/examples/tensorflow/ssd_mobilenet_v1/model_config.yml b/rknn-toolkit2/examples/tensorflow/ssd_mobilenet_v1/model_config.yml new file mode 100755 index 0000000000000000000000000000000000000000..7e8c8acff5cc13b03eda5d26fd539446817fc648 --- /dev/null +++ b/rknn-toolkit2/examples/tensorflow/ssd_mobilenet_v1/model_config.yml @@ -0,0 +1,18 @@ +models: + name: ssd_mobilenet_v1_coco # 模型输出名称 + platform: tensorflow # 原始模型使用的框架 + model_file_path: ./ssd_mobilenet_v1_coco_2017_11_17.pb # 原模型路径 + subgraphs: # 描述输入输出shape等信息 + input_size_list: + - 1, 300, 300, 3 + inputs: # 输入tensor名称 + - Preprocessor/sub + outputs: # 输出tensor名称 + - concat + - concat_1 + quantize: True # 开启量化 + dataset: ./dataset.txt # 量化dataset文件路径（相对yml路径） + configs: + mean_values: [127.5, 127.5, 127.5] # rknn.config的mean_values参数 + std_values: [127.5, 127.5, 127.5] # rknn.config的std_values参数 + quant_img_RGB2BGR: false # 不进行RGB2BGR转换 diff --git a/rknn-toolkit2/examples/tensorflow/ssd_mobilenet_v1/result_truth.jpg b/rknn-toolkit2/examples/tensorflow/ssd_mobilenet_v1/result_truth.jpg new file mode 100644 index 0000000000000000000000000000000000000000..1013a036a09f0972ade974ac37aeb1c92de9a459 --- /dev/null +++ b/rknn-toolkit2/examples/tensorflow/ssd_mobilenet_v1/result_truth.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2871f841c637cf62a380e414cb0ee8854c9b59d14a5748e902543c31a940519c +size 54338 diff --git a/rknn-toolkit2/examples/tensorflow/ssd_mobilenet_v1/road.bmp b/rknn-toolkit2/examples/tensorflow/ssd_mobilenet_v1/road.bmp new file mode 100755 index 0000000000000000000000000000000000000000..1254b968a88a9532921666b3efa6990039f1b6bf Binary files /dev/null and b/rknn-toolkit2/examples/tensorflow/ssd_mobilenet_v1/road.bmp differ diff --git a/rknn-toolkit2/examples/tensorflow/ssd_mobilenet_v1/ssd_mobilenet_v1_coco_2017_11_17.pb b/rknn-toolkit2/examples/tensorflow/ssd_mobilenet_v1/ssd_mobilenet_v1_coco_2017_11_17.pb new file mode 100755 index 0000000000000000000000000000000000000000..6fa96a3604c72e47eea3ff69b2d8b68348001b17 --- /dev/null +++ b/rknn-toolkit2/examples/tensorflow/ssd_mobilenet_v1/ssd_mobilenet_v1_coco_2017_11_17.pb @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cb3ce31b95a54162c25d951780a740a8767e6f9987298ec53d3146f0a5506858 +size 29112121 diff --git a/rknn-toolkit2/examples/tensorflow/ssd_mobilenet_v1/test.py b/rknn-toolkit2/examples/tensorflow/ssd_mobilenet_v1/test.py new file mode 100755 index 0000000000000000000000000000000000000000..710e9a1ccbc8b490f8030437b2bb91ac22449298 --- /dev/null +++ b/rknn-toolkit2/examples/tensorflow/ssd_mobilenet_v1/test.py @@ -0,0 +1,224 @@ +import numpy as np + +import re +import math +import random +import cv2 + +from rknn.api import RKNN + +INPUT_SIZE = 300 + +NUM_RESULTS = 1917 +NUM_CLASSES = 91 + +Y_SCALE = 10.0 +X_SCALE = 10.0 +H_SCALE = 5.0 +W_SCALE = 5.0 + +CLASSES = ('__background__', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', + 'traffic light', 'fire hydrant', '???', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', + 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', '???', 'backpack', 'umbrella', '???', '???', + 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', + 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', '???', 'wine glass', 'cup', 'fork', + 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', + 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', '???', 'dining table', '???', '???', 'toilet', + '???', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', + 'refrigerator', '???', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush') + +def expit(x): + return 1. / (1. + math.exp(-x)) + + +def unexpit(y): + return -1.0 * math.log((1.0 / y) - 1.0) + + +def CalculateOverlap(xmin0, ymin0, xmax0, ymax0, xmin1, ymin1, xmax1, ymax1): + w = max(0.0, min(xmax0, xmax1) - max(xmin0, xmin1)) + h = max(0.0, min(ymax0, ymax1) - max(ymin0, ymin1)) + i = w * h + u = (xmax0 - xmin0) * (ymax0 - ymin0) + (xmax1 - xmin1) * (ymax1 - ymin1) - i + + if u <= 0.0: + return 0.0 + + return i / u + + +def load_box_priors(): + box_priors_ = [] + fp = open('./box_priors.txt', 'r') + ls = fp.readlines() + for s in ls: + aList = re.findall('([-+]?\d+(\.\d*)?|\.\d+)([eE][-+]?\d+)?', s) + for ss in aList: + aNum = float((ss[0]+ss[2])) + box_priors_.append(aNum) + fp.close() + + box_priors = np.array(box_priors_) + box_priors = box_priors.reshape(4, NUM_RESULTS) + + return box_priors + + +if __name__ == '__main__': + + # Create RKNN object + rknn = RKNN(verbose=True) + + # Pre-process config + print('--> Config model') + rknn.config(mean_values=[127.5, 127.5, 127.5], std_values=[127.5, 127.5, 127.5], target_platform='rk3566') + print('done') + + # Load model (from https://github.com/fvmassoli/Deep-Learning-SSD-Object-Detection) + print('--> Loading model') + ret = rknn.load_tensorflow(tf_pb='./ssd_mobilenet_v1_coco_2017_11_17.pb', + inputs=['Preprocessor/sub'], + outputs=['concat', 'concat_1'], + input_size_list=[[1, INPUT_SIZE, INPUT_SIZE, 3]]) + if ret != 0: + print('Load model failed!') + exit(ret) + print('done') + + # Build Model + print('--> Building model') + ret = rknn.build(do_quantization=True, dataset='./dataset.txt') + if ret != 0: + print('Build model failed!') + exit(ret) + print('done') + + # Export rknn model + print('--> Export rknn model') + ret = rknn.export_rknn('./ssd_mobilenet_v1_coco.rknn') + if ret != 0: + print('Export rknn model failed!') + exit(ret) + print('done') + + # Set inputs + orig_img = cv2.imread('./road.bmp') + img = cv2.cvtColor(orig_img, cv2.COLOR_BGR2RGB) + img = cv2.resize(img, (INPUT_SIZE, INPUT_SIZE), interpolation=cv2.INTER_CUBIC) + img = np.expand_dims(img, 0) + + # Init runtime environment + print('--> Init runtime environment') + ret = rknn.init_runtime() + if ret != 0: + print('Init runtime environment failed!') + exit(ret) + print('done') + + # Inference + print('--> Running model') + outputs = rknn.inference(inputs=[img], data_format=['nhwc']) + print('done') + + predictions = outputs[0].reshape((1, NUM_RESULTS, 4)) + np.save('./tensorflow_ssd_mobilenet_v1_0.npy', outputs[0]) + outputClasses = outputs[1].reshape((1, NUM_RESULTS, NUM_CLASSES)) + np.save('./tensorflow_ssd_mobilenet_v1_1.npy', outputs[0]) + candidateBox = np.zeros([2, NUM_RESULTS], dtype=int) + classScore = [-1000.0] * NUM_RESULTS + vaildCnt = 0 + + box_priors = load_box_priors() + + # Post Process + # got valid candidate box + for i in range(0, NUM_RESULTS): + topClassScore = -1000 + topClassScoreIndex = -1 + + # Skip the first catch-all class. + for j in range(1, NUM_CLASSES): + score = expit(outputClasses[0][i][j]) + + if score > topClassScore: + topClassScoreIndex = j + topClassScore = score + + if topClassScore > 0.4: + candidateBox[0][vaildCnt] = i + candidateBox[1][vaildCnt] = topClassScoreIndex + classScore[vaildCnt] = topClassScore + vaildCnt += 1 + + # calc position + for i in range(0, vaildCnt): + if candidateBox[0][i] == -1: + continue + + n = candidateBox[0][i] + ycenter = predictions[0][n][0] / Y_SCALE * box_priors[2][n] + box_priors[0][n] + xcenter = predictions[0][n][1] / X_SCALE * box_priors[3][n] + box_priors[1][n] + h = math.exp(predictions[0][n][2] / H_SCALE) * box_priors[2][n] + w = math.exp(predictions[0][n][3] / W_SCALE) * box_priors[3][n] + + ymin = ycenter - h / 2. + xmin = xcenter - w / 2. + ymax = ycenter + h / 2. + xmax = xcenter + w / 2. + + predictions[0][n][0] = ymin + predictions[0][n][1] = xmin + predictions[0][n][2] = ymax + predictions[0][n][3] = xmax + + # NMS + for i in range(0, vaildCnt): + if candidateBox[0][i] == -1: + continue + + n = candidateBox[0][i] + xmin0 = predictions[0][n][1] + ymin0 = predictions[0][n][0] + xmax0 = predictions[0][n][3] + ymax0 = predictions[0][n][2] + + for j in range(i+1, vaildCnt): + m = candidateBox[0][j] + + if m == -1: + continue + + xmin1 = predictions[0][m][1] + ymin1 = predictions[0][m][0] + xmax1 = predictions[0][m][3] + ymax1 = predictions[0][m][2] + + iou = CalculateOverlap(xmin0, ymin0, xmax0, ymax0, xmin1, ymin1, xmax1, ymax1) + + if iou >= 0.45: + candidateBox[0][j] = -1 + + # Draw result + if vaildCnt != 0: + print("{:^12} {:^12} {}".format('class', 'score', 'xmin, ymin, xmax, ymax')) + print('-' * 50) + for i in range(0, vaildCnt): + if candidateBox[0][i] == -1: + continue + + n = candidateBox[0][i] + + xmin = max(0.0, min(1.0, predictions[0][n][1])) * INPUT_SIZE + ymin = max(0.0, min(1.0, predictions[0][n][0])) * INPUT_SIZE + xmax = max(0.0, min(1.0, predictions[0][n][3])) * INPUT_SIZE + ymax = max(0.0, min(1.0, predictions[0][n][2])) * INPUT_SIZE + + print("{:^12} {:^12.3f} [{:>4}, {:>4}, {:>4}, {:>4}]".format(CLASSES[candidateBox[1][i]], classScore[i], + int(xmin), int(ymin), int(xmax), int(ymax))) + cv2.rectangle(orig_img, (int(xmin), int(ymin)), (int(xmax), int(ymax)), + (random.random()*255, random.random()*255, random.random()*255), 3) + + cv2.imwrite("result.jpg", orig_img) + print('Save results to result.jpg!') + + rknn.release() diff --git a/rknn-toolkit2/examples/tflite/mobilenet_v1/README.md b/rknn-toolkit2/examples/tflite/mobilenet_v1/README.md new file mode 100644 index 0000000000000000000000000000000000000000..fc7467087f745dc2f93c951c22aff03fc50935cc --- /dev/null +++ b/rknn-toolkit2/examples/tflite/mobilenet_v1/README.md @@ -0,0 +1,30 @@ +# TFLite MobileNet V1 + +## Model Source +The model used in this example come from the following open source projects: +https://www.tensorflow.org/lite/guide/hosted_models?hl=zh-cn + +## Script Usage +*Usage:* +``` +python test.py +``` +*rknn_convert usage:* +``` +python3 -m rknn.api.rknn_convert -t rk3568 -i ./model_config.yml -o ./ +``` +*Description:* +- The default target platform in script is 'rk3566', please modify the 'target_platform' parameter of 'rknn.config' according to the actual platform. +- If connecting board is required, please add the 'target' parameter in 'rknn.init_runtime'. + +## Expected Results +This example will print the TOP5 labels and corresponding scores of the test image classification results, as follows: +``` +-----TOP 5----- +[ 156] score:0.928223 class:"Shih-Tzu" +[ 155] score:0.063171 class:"Pekinese, Pekingese, Peke" +[ 205] score:0.004299 class:"Lhasa, Lhasa apso" +[ 284] score:0.003096 class:"Persian cat" +[ 285] score:0.000171 class:"Siamese cat, Siamese" +``` +- Note: Different platforms, different versions of tools and drivers may have slightly different results. diff --git a/rknn-toolkit2/examples/tflite/mobilenet_v1/dataset.txt b/rknn-toolkit2/examples/tflite/mobilenet_v1/dataset.txt new file mode 100644 index 0000000000000000000000000000000000000000..9078c68db586a0251bdd41282d67f4d256b3abc3 --- /dev/null +++ b/rknn-toolkit2/examples/tflite/mobilenet_v1/dataset.txt @@ -0,0 +1 @@ +dog_224x224.jpg diff --git a/rknn-toolkit2/examples/tflite/mobilenet_v1/dog_224x224.jpg b/rknn-toolkit2/examples/tflite/mobilenet_v1/dog_224x224.jpg new file mode 100644 index 0000000000000000000000000000000000000000..004b0416e23454a12eb06eaba238c7e2d5f2b0fc --- /dev/null +++ b/rknn-toolkit2/examples/tflite/mobilenet_v1/dog_224x224.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c350299c6283d5f62fecf1f845b6b3be9aafec8dff528ca09a129990f0a584b0 +size 18889 diff --git a/rknn-toolkit2/examples/tflite/mobilenet_v1/labels.txt b/rknn-toolkit2/examples/tflite/mobilenet_v1/labels.txt new file mode 100644 index 0000000000000000000000000000000000000000..dcd01c14b4635096ae295a8e81de0f65188bc1f8 --- /dev/null +++ b/rknn-toolkit2/examples/tflite/mobilenet_v1/labels.txt @@ -0,0 +1,1001 @@ +0:__background__ +1:tench, Tinca tinca +2:goldfish, Carassius auratus +3:great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias +4:tiger shark, Galeocerdo cuvieri +5:hammerhead, hammerhead shark +6:electric ray, crampfish, numbfish, torpedo +7:stingray +8:cock +9:hen +10:ostrich, Struthio camelus +11:brambling, Fringilla montifringilla +12:goldfinch, Carduelis carduelis +13:house finch, linnet, Carpodacus mexicanus +14:junco, snowbird +15:indigo bunting, indigo finch, indigo bird, Passerina cyanea +16:robin, American robin, Turdus migratorius +17:bulbul +18:jay +19:magpie +20:chickadee +21:water ouzel, dipper +22:kite +23:bald eagle, American eagle, Haliaeetus leucocephalus +24:vulture +25:great grey owl, great gray owl, Strix nebulosa +26:European fire salamander, Salamandra salamandra +27:common newt, Triturus vulgaris +28:eft +29:spotted salamander, Ambystoma maculatum +30:axolotl, mud puppy, Ambystoma mexicanum +31:bullfrog, Rana catesbeiana +32:tree frog, tree-frog +33:tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui +34:loggerhead, loggerhead turtle, Caretta caretta +35:leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea +36:mud turtle +37:terrapin +38:box turtle, box tortoise +39:banded gecko +40:common iguana, iguana, Iguana iguana +41:American chameleon, anole, Anolis carolinensis +42:whiptail, whiptail lizard +43:agama +44:frilled lizard, Chlamydosaurus kingi +45:alligator lizard +46:Gila monster, Heloderma suspectum +47:green lizard, Lacerta viridis +48:African chameleon, Chamaeleo chamaeleon +49:Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis +50:African crocodile, Nile crocodile, Crocodylus niloticus +51:American alligator, Alligator mississipiensis +52:triceratops +53:thunder snake, worm snake, Carphophis amoenus +54:ringneck snake, ring-necked snake, ring snake +55:hognose snake, puff adder, sand viper +56:green snake, grass snake +57:king snake, kingsnake +58:garter snake, grass snake +59:water snake +60:vine snake +61:night snake, Hypsiglena torquata +62:boa constrictor, Constrictor constrictor +63:rock python, rock snake, Python sebae +64:Indian cobra, Naja naja +65:green mamba +66:sea snake +67:horned viper, cerastes, sand viper, horned asp, Cerastes cornutus +68:diamondback, diamondback rattlesnake, Crotalus adamanteus +69:sidewinder, horned rattlesnake, Crotalus cerastes +70:trilobite +71:harvestman, daddy longlegs, Phalangium opilio +72:scorpion +73:black and gold garden spider, Argiope aurantia +74:barn spider, Araneus cavaticus +75:garden spider, Aranea diademata +76:black widow, Latrodectus mactans +77:tarantula +78:wolf spider, hunting spider +79:tick +80:centipede +81:black grouse +82:ptarmigan +83:ruffed grouse, partridge, Bonasa umbellus +84:prairie chicken, prairie grouse, prairie fowl +85:peacock +86:quail +87:partridge +88:African grey, African gray, Psittacus erithacus +89:macaw +90:sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita +91:lorikeet +92:coucal +93:bee eater +94:hornbill +95:hummingbird +96:jacamar +97:toucan +98:drake +99:red-breasted merganser, Mergus serrator +100:goose +101:black swan, Cygnus atratus +102:tusker +103:echidna, spiny anteater, anteater +104:platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus +105:wallaby, brush kangaroo +106:koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus +107:wombat +108:jellyfish +109:sea anemone, anemone +110:brain coral +111:flatworm, platyhelminth +112:nematode, nematode worm, roundworm +113:conch +114:snail +115:slug +116:sea slug, nudibranch +117:chiton, coat-of-mail shell, sea cradle, polyplacophore +118:chambered nautilus, pearly nautilus, nautilus +119:Dungeness crab, Cancer magister +120:rock crab, Cancer irroratus +121:fiddler crab +122:king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica +123:American lobster, Northern lobster, Maine lobster, Homarus americanus +124:spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish +125:crayfish, crawfish, crawdad, crawdaddy +126:hermit crab +127:isopod +128:white stork, Ciconia ciconia +129:black stork, Ciconia nigra +130:spoonbill +131:flamingo +132:little blue heron, Egretta caerulea +133:American egret, great white heron, Egretta albus +134:bittern +135:crane +136:limpkin, Aramus pictus +137:European gallinule, Porphyrio porphyrio +138:American coot, marsh hen, mud hen, water hen, Fulica americana +139:bustard +140:ruddy turnstone, Arenaria interpres +141:red-backed sandpiper, dunlin, Erolia alpina +142:redshank, Tringa totanus +143:dowitcher +144:oystercatcher, oyster catcher +145:pelican +146:king penguin, Aptenodytes patagonica +147:albatross, mollymawk +148:grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus +149:killer whale, killer, orca, grampus, sea wolf, Orcinus orca +150:dugong, Dugong dugon +151:sea lion +152:Chihuahua +153:Japanese spaniel +154:Maltese dog, Maltese terrier, Maltese +155:Pekinese, Pekingese, Peke +156:Shih-Tzu +157:Blenheim spaniel +158:papillon +159:toy terrier +160:Rhodesian ridgeback +161:Afghan hound, Afghan +162:basset, basset hound +163:beagle +164:bloodhound, sleuthhound +165:bluetick +166:black-and-tan coonhound +167:Walker hound, Walker foxhound +168:English foxhound +169:redbone +170:borzoi, Russian wolfhound +171:Irish wolfhound +172:Italian greyhound +173:whippet +174:Ibizan hound, Ibizan Podenco +175:Norwegian elkhound, elkhound +176:otterhound, otter hound +177:Saluki, gazelle hound +178:Scottish deerhound, deerhound +179:Weimaraner +180:Staffordshire bullterrier, Staffordshire bull terrier +181:American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier +182:Bedlington terrier +183:Border terrier +184:Kerry blue terrier +185:Irish terrier +186:Norfolk terrier +187:Norwich terrier +188:Yorkshire terrier +189:wire-haired fox terrier +190:Lakeland terrier +191:Sealyham terrier, Sealyham +192:Airedale, Airedale terrier +193:cairn, cairn terrier +194:Australian terrier +195:Dandie Dinmont, Dandie Dinmont terrier +196:Boston bull, Boston terrier +197:miniature schnauzer +198:giant schnauzer +199:standard schnauzer +200:Scotch terrier, Scottish terrier, Scottie +201:Tibetan terrier, chrysanthemum dog +202:silky terrier, Sydney silky +203:soft-coated wheaten terrier +204:West Highland white terrier +205:Lhasa, Lhasa apso +206:flat-coated retriever +207:curly-coated retriever +208:golden retriever +209:Labrador retriever +210:Chesapeake Bay retriever +211:German short-haired pointer +212:vizsla, Hungarian pointer +213:English setter +214:Irish setter, red setter +215:Gordon setter +216:Brittany spaniel +217:clumber, clumber spaniel +218:English springer, English springer spaniel +219:Welsh springer spaniel +220:cocker spaniel, English cocker spaniel, cocker +221:Sussex spaniel +222:Irish water spaniel +223:kuvasz +224:schipperke +225:groenendael +226:malinois +227:briard +228:kelpie +229:komondor +230:Old English sheepdog, bobtail +231:Shetland sheepdog, Shetland sheep dog, Shetland +232:collie +233:Border collie +234:Bouvier des Flandres, Bouviers des Flandres +235:Rottweiler +236:German shepherd, German shepherd dog, German police dog, alsatian +237:Doberman, Doberman pinscher +238:miniature pinscher +239:Greater Swiss Mountain dog +240:Bernese mountain dog +241:Appenzeller +242:EntleBucher +243:boxer +244:bull mastiff +245:Tibetan mastiff +246:French bulldog +247:Great Dane +248:Saint Bernard, St Bernard +249:Eskimo dog, husky +250:malamute, malemute, Alaskan malamute +251:Siberian husky +252:dalmatian, coach dog, carriage dog +253:affenpinscher, monkey pinscher, monkey dog +254:basenji +255:pug, pug-dog +256:Leonberg +257:Newfoundland, Newfoundland dog +258:Great Pyrenees +259:Samoyed, Samoyede +260:Pomeranian +261:chow, chow chow +262:keeshond +263:Brabancon griffon +264:Pembroke, Pembroke Welsh corgi +265:Cardigan, Cardigan Welsh corgi +266:toy poodle +267:miniature poodle +268:standard poodle +269:Mexican hairless +270:timber wolf, grey wolf, gray wolf, Canis lupus +271:white wolf, Arctic wolf, Canis lupus tundrarum +272:red wolf, maned wolf, Canis rufus, Canis niger +273:coyote, prairie wolf, brush wolf, Canis latrans +274:dingo, warrigal, warragal, Canis dingo +275:dhole, Cuon alpinus +276:African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus +277:hyena, hyaena +278:red fox, Vulpes vulpes +279:kit fox, Vulpes macrotis +280:Arctic fox, white fox, Alopex lagopus +281:grey fox, gray fox, Urocyon cinereoargenteus +282:tabby, tabby cat +283:tiger cat +284:Persian cat +285:Siamese cat, Siamese +286:Egyptian cat +287:cougar, puma, catamount, mountain lion, painter, panther, Felis concolor +288:lynx, catamount +289:leopard, Panthera pardus +290:snow leopard, ounce, Panthera uncia +291:jaguar, panther, Panthera onca, Felis onca +292:lion, king of beasts, Panthera leo +293:tiger, Panthera tigris +294:cheetah, chetah, Acinonyx jubatus +295:brown bear, bruin, Ursus arctos +296:American black bear, black bear, Ursus americanus, Euarctos americanus +297:ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus +298:sloth bear, Melursus ursinus, Ursus ursinus +299:mongoose +300:meerkat, mierkat +301:tiger beetle +302:ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle +303:ground beetle, carabid beetle +304:long-horned beetle, longicorn, longicorn beetle +305:leaf beetle, chrysomelid +306:dung beetle +307:rhinoceros beetle +308:weevil +309:fly +310:bee +311:ant, emmet, pismire +312:grasshopper, hopper +313:cricket +314:walking stick, walkingstick, stick insect +315:cockroach, roach +316:mantis, mantid +317:cicada, cicala +318:leafhopper +319:lacewing, lacewing fly +320:dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk +321:damselfly +322:admiral +323:ringlet, ringlet butterfly +324:monarch, monarch butterfly, milkweed butterfly, Danaus plexippus +325:cabbage butterfly +326:sulphur butterfly, sulfur butterfly +327:lycaenid, lycaenid butterfly +328:starfish, sea star +329:sea urchin +330:sea cucumber, holothurian +331:wood rabbit, cottontail, cottontail rabbit +332:hare +333:Angora, Angora rabbit +334:hamster +335:porcupine, hedgehog +336:fox squirrel, eastern fox squirrel, Sciurus niger +337:marmot +338:beaver +339:guinea pig, Cavia cobaya +340:sorrel +341:zebra +342:hog, pig, grunter, squealer, Sus scrofa +343:wild boar, boar, Sus scrofa +344:warthog +345:hippopotamus, hippo, river horse, Hippopotamus amphibius +346:ox +347:water buffalo, water ox, Asiatic buffalo, Bubalus bubalis +348:bison +349:ram, tup +350:bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis +351:ibex, Capra ibex +352:hartebeest +353:impala, Aepyceros melampus +354:gazelle +355:Arabian camel, dromedary, Camelus dromedarius +356:llama +357:weasel +358:mink +359:polecat, fitch, foulmart, foumart, Mustela putorius +360:black-footed ferret, ferret, Mustela nigripes +361:otter +362:skunk, polecat, wood pussy +363:badger +364:armadillo +365:three-toed sloth, ai, Bradypus tridactylus +366:orangutan, orang, orangutang, Pongo pygmaeus +367:gorilla, Gorilla gorilla +368:chimpanzee, chimp, Pan troglodytes +369:gibbon, Hylobates lar +370:siamang, Hylobates syndactylus, Symphalangus syndactylus +371:guenon, guenon monkey +372:patas, hussar monkey, Erythrocebus patas +373:baboon +374:macaque +375:langur +376:colobus, colobus monkey +377:proboscis monkey, Nasalis larvatus +378:marmoset +379:capuchin, ringtail, Cebus capucinus +380:howler monkey, howler +381:titi, titi monkey +382:spider monkey, Ateles geoffroyi +383:squirrel monkey, Saimiri sciureus +384:Madagascar cat, ring-tailed lemur, Lemur catta +385:indri, indris, Indri indri, Indri brevicaudatus +386:Indian elephant, Elephas maximus +387:African elephant, Loxodonta africana +388:lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens +389:giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca +390:barracouta, snoek +391:eel +392:coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch +393:rock beauty, Holocanthus tricolor +394:anemone fish +395:sturgeon +396:gar, garfish, garpike, billfish, Lepisosteus osseus +397:lionfish +398:puffer, pufferfish, blowfish, globefish +399:abacus +400:abaya +401:academic gown, academic robe, judge's robe +402:accordion, piano accordion, squeeze box +403:acoustic guitar +404:aircraft carrier, carrier, flattop, attack aircraft carrier +405:airliner +406:airship, dirigible +407:altar +408:ambulance +409:amphibian, amphibious vehicle +410:analog clock +411:apiary, bee house +412:apron +413:ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin +414:assault rifle, assault gun +415:backpack, back pack, knapsack, packsack, rucksack, haversack +416:bakery, bakeshop, bakehouse +417:balance beam, beam +418:balloon +419:ballpoint, ballpoint pen, ballpen, Biro +420:Band Aid +421:banjo +422:bannister, banister, balustrade, balusters, handrail +423:barbell +424:barber chair +425:barbershop +426:barn +427:barometer +428:barrel, cask +429:barrow, garden cart, lawn cart, wheelbarrow +430:baseball +431:basketball +432:bassinet +433:bassoon +434:bathing cap, swimming cap +435:bath towel +436:bathtub, bathing tub, bath, tub +437:beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon +438:beacon, lighthouse, beacon light, pharos +439:beaker +440:bearskin, busby, shako +441:beer bottle +442:beer glass +443:bell cote, bell cot +444:bib +445:bicycle-built-for-two, tandem bicycle, tandem +446:bikini, two-piece +447:binder, ring-binder +448:binoculars, field glasses, opera glasses +449:birdhouse +450:boathouse +451:bobsled, bobsleigh, bob +452:bolo tie, bolo, bola tie, bola +453:bonnet, poke bonnet +454:bookcase +455:bookshop, bookstore, bookstall +456:bottlecap +457:bow +458:bow tie, bow-tie, bowtie +459:brass, memorial tablet, plaque +460:brassiere, bra, bandeau +461:breakwater, groin, groyne, mole, bulwark, seawall, jetty +462:breastplate, aegis, egis +463:broom +464:bucket, pail +465:buckle +466:bulletproof vest +467:bullet train, bullet +468:butcher shop, meat market +469:cab, hack, taxi, taxicab +470:caldron, cauldron +471:candle, taper, wax light +472:cannon +473:canoe +474:can opener, tin opener +475:cardigan +476:car mirror +477:carousel, carrousel, merry-go-round, roundabout, whirligig +478:carpenter's kit, tool kit +479:carton +480:car wheel +481:cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM +482:cassette +483:cassette player +484:castle +485:catamaran +486:CD player +487:cello, violoncello +488:cellular telephone, cellular phone, cellphone, cell, mobile phone +489:chain +490:chainlink fence +491:chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour +492:chain saw, chainsaw +493:chest +494:chiffonier, commode +495:chime, bell, gong +496:china cabinet, china closet +497:Christmas stocking +498:church, church building +499:cinema, movie theater, movie theatre, movie house, picture palace +500:cleaver, meat cleaver, chopper +501:cliff dwelling +502:cloak +503:clog, geta, patten, sabot +504:cocktail shaker +505:coffee mug +506:coffeepot +507:coil, spiral, volute, whorl, helix +508:combination lock +509:computer keyboard, keypad +510:confectionery, confectionary, candy store +511:container ship, containership, container vessel +512:convertible +513:corkscrew, bottle screw +514:cornet, horn, trumpet, trump +515:cowboy boot +516:cowboy hat, ten-gallon hat +517:cradle +518:crane +519:crash helmet +520:crate +521:crib, cot +522:Crock Pot +523:croquet ball +524:crutch +525:cuirass +526:dam, dike, dyke +527:desk +528:desktop computer +529:dial telephone, dial phone +530:diaper, nappy, napkin +531:digital clock +532:digital watch +533:dining table, board +534:dishrag, dishcloth +535:dishwasher, dish washer, dishwashing machine +536:disk brake, disc brake +537:dock, dockage, docking facility +538:dogsled, dog sled, dog sleigh +539:dome +540:doormat, welcome mat +541:drilling platform, offshore rig +542:drum, membranophone, tympan +543:drumstick +544:dumbbell +545:Dutch oven +546:electric fan, blower +547:electric guitar +548:electric locomotive +549:entertainment center +550:envelope +551:espresso maker +552:face powder +553:feather boa, boa +554:file, file cabinet, filing cabinet +555:fireboat +556:fire engine, fire truck +557:fire screen, fireguard +558:flagpole, flagstaff +559:flute, transverse flute +560:folding chair +561:football helmet +562:forklift +563:fountain +564:fountain pen +565:four-poster +566:freight car +567:French horn, horn +568:frying pan, frypan, skillet +569:fur coat +570:garbage truck, dustcart +571:gasmask, respirator, gas helmet +572:gas pump, gasoline pump, petrol pump, island dispenser +573:goblet +574:go-kart +575:golf ball +576:golfcart, golf cart +577:gondola +578:gong, tam-tam +579:gown +580:grand piano, grand +581:greenhouse, nursery, glasshouse +582:grille, radiator grille +583:grocery store, grocery, food market, market +584:guillotine +585:hair slide +586:hair spray +587:half track +588:hammer +589:hamper +590:hand blower, blow dryer, blow drier, hair dryer, hair drier +591:hand-held computer, hand-held microcomputer +592:handkerchief, hankie, hanky, hankey +593:hard disc, hard disk, fixed disk +594:harmonica, mouth organ, harp, mouth harp +595:harp +596:harvester, reaper +597:hatchet +598:holster +599:home theater, home theatre +600:honeycomb +601:hook, claw +602:hoopskirt, crinoline +603:horizontal bar, high bar +604:horse cart, horse-cart +605:hourglass +606:iPod +607:iron, smoothing iron +608:jack-o'-lantern +609:jean, blue jean, denim +610:jeep, landrover +611:jersey, T-shirt, tee shirt +612:jigsaw puzzle +613:jinrikisha, ricksha, rickshaw +614:joystick +615:kimono +616:knee pad +617:knot +618:lab coat, laboratory coat +619:ladle +620:lampshade, lamp shade +621:laptop, laptop computer +622:lawn mower, mower +623:lens cap, lens cover +624:letter opener, paper knife, paperknife +625:library +626:lifeboat +627:lighter, light, igniter, ignitor +628:limousine, limo +629:liner, ocean liner +630:lipstick, lip rouge +631:Loafer +632:lotion +633:loudspeaker, speaker, speaker unit, loudspeaker system, speaker system +634:loupe, jeweler's loupe +635:lumbermill, sawmill +636:magnetic compass +637:mailbag, postbag +638:mailbox, letter box +639:maillot +640:maillot, tank suit +641:manhole cover +642:maraca +643:marimba, xylophone +644:mask +645:matchstick +646:maypole +647:maze, labyrinth +648:measuring cup +649:medicine chest, medicine cabinet +650:megalith, megalithic structure +651:microphone, mike +652:microwave, microwave oven +653:military uniform +654:milk can +655:minibus +656:miniskirt, mini +657:minivan +658:missile +659:mitten +660:mixing bowl +661:mobile home, manufactured home +662:Model T +663:modem +664:monastery +665:monitor +666:moped +667:mortar +668:mortarboard +669:mosque +670:mosquito net +671:motor scooter, scooter +672:mountain bike, all-terrain bike, off-roader +673:mountain tent +674:mouse, computer mouse +675:mousetrap +676:moving van +677:muzzle +678:nail +679:neck brace +680:necklace +681:nipple +682:notebook, notebook computer +683:obelisk +684:oboe, hautboy, hautbois +685:ocarina, sweet potato +686:odometer, hodometer, mileometer, milometer +687:oil filter +688:organ, pipe organ +689:oscilloscope, scope, cathode-ray oscilloscope, CRO +690:overskirt +691:oxcart +692:oxygen mask +693:packet +694:paddle, boat paddle +695:paddlewheel, paddle wheel +696:padlock +697:paintbrush +698:pajama, pyjama, pj's, jammies +699:palace +700:panpipe, pandean pipe, syrinx +701:paper towel +702:parachute, chute +703:parallel bars, bars +704:park bench +705:parking meter +706:passenger car, coach, carriage +707:patio, terrace +708:pay-phone, pay-station +709:pedestal, plinth, footstall +710:pencil box, pencil case +711:pencil sharpener +712:perfume, essence +713:Petri dish +714:photocopier +715:pick, plectrum, plectron +716:pickelhaube +717:picket fence, paling +718:pickup, pickup truck +719:pier +720:piggy bank, penny bank +721:pill bottle +722:pillow +723:ping-pong ball +724:pinwheel +725:pirate, pirate ship +726:pitcher, ewer +727:plane, carpenter's plane, woodworking plane +728:planetarium +729:plastic bag +730:plate rack +731:plow, plough +732:plunger, plumber's helper +733:Polaroid camera, Polaroid Land camera +734:pole +735:police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria +736:poncho +737:pool table, billiard table, snooker table +738:pop bottle, soda bottle +739:pot, flowerpot +740:potter's wheel +741:power drill +742:prayer rug, prayer mat +743:printer +744:prison, prison house +745:projectile, missile +746:projector +747:puck, hockey puck +748:punching bag, punch bag, punching ball, punchball +749:purse +750:quill, quill pen +751:quilt, comforter, comfort, puff +752:racer, race car, racing car +753:racket, racquet +754:radiator +755:radio, wireless +756:radio telescope, radio reflector +757:rain barrel +758:recreational vehicle, RV, R.V. +759:reel +760:reflex camera +761:refrigerator, icebox +762:remote control, remote +763:restaurant, eating house, eating place, eatery +764:revolver, six-gun, six-shooter +765:rifle +766:rocking chair, rocker +767:rotisserie +768:rubber eraser, rubber, pencil eraser +769:rugby ball +770:rule, ruler +771:running shoe +772:safe +773:safety pin +774:saltshaker, salt shaker +775:sandal +776:sarong +777:sax, saxophone +778:scabbard +779:scale, weighing machine +780:school bus +781:schooner +782:scoreboard +783:screen, CRT screen +784:screw +785:screwdriver +786:seat belt, seatbelt +787:sewing machine +788:shield, buckler +789:shoe shop, shoe-shop, shoe store +790:shoji +791:shopping basket +792:shopping cart +793:shovel +794:shower cap +795:shower curtain +796:ski +797:ski mask +798:sleeping bag +799:slide rule, slipstick +800:sliding door +801:slot, one-armed bandit +802:snorkel +803:snowmobile +804:snowplow, snowplough +805:soap dispenser +806:soccer ball +807:sock +808:solar dish, solar collector, solar furnace +809:sombrero +810:soup bowl +811:space bar +812:space heater +813:space shuttle +814:spatula +815:speedboat +816:spider web, spider's web +817:spindle +818:sports car, sport car +819:spotlight, spot +820:stage +821:steam locomotive +822:steel arch bridge +823:steel drum +824:stethoscope +825:stole +826:stone wall +827:stopwatch, stop watch +828:stove +829:strainer +830:streetcar, tram, tramcar, trolley, trolley car +831:stretcher +832:studio couch, day bed +833:stupa, tope +834:submarine, pigboat, sub, U-boat +835:suit, suit of clothes +836:sundial +837:sunglass +838:sunglasses, dark glasses, shades +839:sunscreen, sunblock, sun blocker +840:suspension bridge +841:swab, swob, mop +842:sweatshirt +843:swimming trunks, bathing trunks +844:swing +845:switch, electric switch, electrical switch +846:syringe +847:table lamp +848:tank, army tank, armored combat vehicle, armoured combat vehicle +849:tape player +850:teapot +851:teddy, teddy bear +852:television, television system +853:tennis ball +854:thatch, thatched roof +855:theater curtain, theatre curtain +856:thimble +857:thresher, thrasher, threshing machine +858:throne +859:tile roof +860:toaster +861:tobacco shop, tobacconist shop, tobacconist +862:toilet seat +863:torch +864:totem pole +865:tow truck, tow car, wrecker +866:toyshop +867:tractor +868:trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi +869:tray +870:trench coat +871:tricycle, trike, velocipede +872:trimaran +873:tripod +874:triumphal arch +875:trolleybus, trolley coach, trackless trolley +876:trombone +877:tub, vat +878:turnstile +879:typewriter keyboard +880:umbrella +881:unicycle, monocycle +882:upright, upright piano +883:vacuum, vacuum cleaner +884:vase +885:vault +886:velvet +887:vending machine +888:vestment +889:viaduct +890:violin, fiddle +891:volleyball +892:waffle iron +893:wall clock +894:wallet, billfold, notecase, pocketbook +895:wardrobe, closet, press +896:warplane, military plane +897:washbasin, handbasin, washbowl, lavabo, wash-hand basin +898:washer, automatic washer, washing machine +899:water bottle +900:water jug +901:water tower +902:whiskey jug +903:whistle +904:wig +905:window screen +906:window shade +907:Windsor tie +908:wine bottle +909:wing +910:wok +911:wooden spoon +912:wool, woolen, woollen +913:worm fence, snake fence, snake-rail fence, Virginia fence +914:wreck +915:yawl +916:yurt +917:web site, website, internet site, site +918:comic book +919:crossword puzzle, crossword +920:street sign +921:traffic light, traffic signal, stoplight +922:book jacket, dust cover, dust jacket, dust wrapper +923:menu +924:plate +925:guacamole +926:consomme +927:hot pot, hotpot +928:trifle +929:ice cream, icecream +930:ice lolly, lolly, lollipop, popsicle +931:French loaf +932:bagel, beigel +933:pretzel +934:cheeseburger +935:hotdog, hot dog, red hot +936:mashed potato +937:head cabbage +938:broccoli +939:cauliflower +940:zucchini, courgette +941:spaghetti squash +942:acorn squash +943:butternut squash +944:cucumber, cuke +945:artichoke, globe artichoke +946:bell pepper +947:cardoon +948:mushroom +949:Granny Smith +950:strawberry +951:orange +952:lemon +953:fig +954:pineapple, ananas +955:banana +956:jackfruit, jak, jack +957:custard apple +958:pomegranate +959:hay +960:carbonara +961:chocolate sauce, chocolate syrup +962:dough +963:meat loaf, meatloaf +964:pizza, pizza pie +965:potpie +966:burrito +967:red wine +968:espresso +969:cup +970:eggnog +971:alp +972:bubble +973:cliff, drop, drop-off +974:coral reef +975:geyser +976:lakeside, lakeshore +977:promontory, headland, head, foreland +978:sandbar, sand bar +979:seashore, coast, seacoast, sea-coast +980:valley, vale +981:volcano +982:ballplayer, baseball player +983:groom, bridegroom +984:scuba diver +985:rapeseed +986:daisy +987:yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum +988:corn +989:acorn +990:hip, rose hip, rosehip +991:buckeye, horse chestnut, conker +992:coral fungus +993:agaric +994:gyromitra +995:stinkhorn, carrion fungus +996:earthstar +997:hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa +998:bolete +999:ear, spike, capitulum +1000:toilet tissue, toilet paper, bathroom tissue \ No newline at end of file diff --git a/rknn-toolkit2/examples/tflite/mobilenet_v1/mobilenet_v1_1.0_224.tflite b/rknn-toolkit2/examples/tflite/mobilenet_v1/mobilenet_v1_1.0_224.tflite new file mode 100755 index 0000000000000000000000000000000000000000..5c3561024027f68cbed35a2ae0e4784f81934860 --- /dev/null +++ b/rknn-toolkit2/examples/tflite/mobilenet_v1/mobilenet_v1_1.0_224.tflite @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a6e60eb9b50ca82aedbe12f1340b3bfbc57d1e771b71d2f98bfed3cd01e339f9 +size 16900760 diff --git a/rknn-toolkit2/examples/tflite/mobilenet_v1/model_config.yml b/rknn-toolkit2/examples/tflite/mobilenet_v1/model_config.yml new file mode 100755 index 0000000000000000000000000000000000000000..ca39033b6d9e4efac27497ccb067503d761e2d54 --- /dev/null +++ b/rknn-toolkit2/examples/tflite/mobilenet_v1/model_config.yml @@ -0,0 +1,10 @@ +models: + name: mobilenet_v1 # 模型输出名称 + platform: tflite # 原始模型使用的框架 + model_file_path: ./mobilenet_v1_1.0_224.tflite # 原模型路径 + quantize: True # 开启量化 + dataset: ./dataset.txt # 量化dataset文件路径（相对yml路径） + configs: + mean_values: [128, 128, 128] # rknn.config的mean_values参数 + std_values: [128, 128, 128] # rknn.config的std_values参数 + quant_img_RGB2BGR: false # 不进行RGB2BGR转换 diff --git a/rknn-toolkit2/examples/tflite/mobilenet_v1/test.py b/rknn-toolkit2/examples/tflite/mobilenet_v1/test.py new file mode 100755 index 0000000000000000000000000000000000000000..23a8ee57b8ebace3e034a24d81ab4c9b85715298 --- /dev/null +++ b/rknn-toolkit2/examples/tflite/mobilenet_v1/test.py @@ -0,0 +1,76 @@ +import numpy as np +import cv2 +from rknn.api import RKNN + + +def show_outputs(outputs): + output = outputs[0][0] + index = sorted(range(len(output)), key=lambda k : output[k], reverse=True) + fp = open('./labels.txt', 'r') + labels = fp.readlines() + top5_str = 'mobilenet_v1\n-----TOP 5-----\n' + for i in range(5): + value = output[index[i]] + if value > 0: + topi = '[{:>4d}] score:{:.6f} class:"{}"\n'.format(index[i], value, labels[index[i]].strip().split(':')[-1]) + else: + topi = '[ -1]: 0.0\n' + top5_str += topi + print(top5_str.strip()) + + +if __name__ == '__main__': + + # Create RKNN object + rknn = RKNN(verbose=True) + + # Pre-process config + print('--> Config model') + rknn.config(mean_values=[128, 128, 128], std_values=[128, 128, 128], target_platform='rk3566') + print('done') + + # Load model (from https://www.tensorflow.org/lite/guide/hosted_models?hl=zh-cn) + print('--> Loading model') + ret = rknn.load_tflite(model='mobilenet_v1_1.0_224.tflite') + if ret != 0: + print('Load model failed!') + exit(ret) + print('done') + + # Build model + print('--> Building model') + ret = rknn.build(do_quantization=True, dataset='./dataset.txt') + if ret != 0: + print('Build model failed!') + exit(ret) + print('done') + + # Export rknn model + print('--> Export rknn model') + ret = rknn.export_rknn('./mobilenet_v1.rknn') + if ret != 0: + print('Export rknn model failed!') + exit(ret) + print('done') + + # Set inputs + img = cv2.imread('./dog_224x224.jpg') + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + img = np.expand_dims(img, 0) + + # Init runtime environment + print('--> Init runtime environment') + ret = rknn.init_runtime() + if ret != 0: + print('Init runtime environment failed!') + exit(ret) + print('done') + + # Inference + print('--> Running model') + outputs = rknn.inference(inputs=[img], data_format=['nhwc']) + np.save('./tflite_mobilenet_v1_0.npy', outputs[0]) + show_outputs(outputs) + print('done') + + rknn.release() diff --git a/rknn-toolkit2/examples/tflite/mobilenet_v1_qat/README.md b/rknn-toolkit2/examples/tflite/mobilenet_v1_qat/README.md new file mode 100644 index 0000000000000000000000000000000000000000..a11a1d150c8594db00f74672b8f958fff0118d91 --- /dev/null +++ b/rknn-toolkit2/examples/tflite/mobilenet_v1_qat/README.md @@ -0,0 +1,31 @@ +# TFLite MobileNet V1 QAT + +## Model Source +The model used in this example come from the following open source projects: +https://www.tensorflow.org/lite/examples/image_classification/overview?hl=zh-cn + +## Script Usage +*Usage:* +``` +python test.py +``` +*rknn_convert usage:* +``` +python3 -m rknn.api.rknn_convert -t rk3568 -i ./model_config.yml -o ./ +``` +*Description:* +- The default target platform in script is 'rk3566', please modify the 'target_platform' parameter of 'rknn.config' according to the actual platform. +- If connecting board is required, please add the 'target' parameter in 'rknn.init_runtime'. +- This is a QAT model, and the do_quantization of rknn.build needs to be set to False. + +## Expected Results +This example will print the TOP5 labels and corresponding scores of the test image classification results, as follows: +``` +-----TOP 5----- +[ 156] score:0.984375 class:"Shih-Tzu" +[ 155] score:0.007812 class:"Pekinese, Pekingese, Peke" +[ 205] score:0.003906 class:"Lhasa, Lhasa apso" +[ -1]: 0.0 +[ -1]: 0.0 +``` +- Note: Different platforms, different versions of tools and drivers may have slightly different results. \ No newline at end of file diff --git a/rknn-toolkit2/examples/tflite/mobilenet_v1_qat/dog_224x224.jpg b/rknn-toolkit2/examples/tflite/mobilenet_v1_qat/dog_224x224.jpg new file mode 100644 index 0000000000000000000000000000000000000000..004b0416e23454a12eb06eaba238c7e2d5f2b0fc --- /dev/null +++ b/rknn-toolkit2/examples/tflite/mobilenet_v1_qat/dog_224x224.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c350299c6283d5f62fecf1f845b6b3be9aafec8dff528ca09a129990f0a584b0 +size 18889 diff --git a/rknn-toolkit2/examples/tflite/mobilenet_v1_qat/labels.txt b/rknn-toolkit2/examples/tflite/mobilenet_v1_qat/labels.txt new file mode 100644 index 0000000000000000000000000000000000000000..dcd01c14b4635096ae295a8e81de0f65188bc1f8 --- /dev/null +++ b/rknn-toolkit2/examples/tflite/mobilenet_v1_qat/labels.txt @@ -0,0 +1,1001 @@ +0:__background__ +1:tench, Tinca tinca +2:goldfish, Carassius auratus +3:great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias +4:tiger shark, Galeocerdo cuvieri +5:hammerhead, hammerhead shark +6:electric ray, crampfish, numbfish, torpedo +7:stingray +8:cock +9:hen +10:ostrich, Struthio camelus +11:brambling, Fringilla montifringilla +12:goldfinch, Carduelis carduelis +13:house finch, linnet, Carpodacus mexicanus +14:junco, snowbird +15:indigo bunting, indigo finch, indigo bird, Passerina cyanea +16:robin, American robin, Turdus migratorius +17:bulbul +18:jay +19:magpie +20:chickadee +21:water ouzel, dipper +22:kite +23:bald eagle, American eagle, Haliaeetus leucocephalus +24:vulture +25:great grey owl, great gray owl, Strix nebulosa +26:European fire salamander, Salamandra salamandra +27:common newt, Triturus vulgaris +28:eft +29:spotted salamander, Ambystoma maculatum +30:axolotl, mud puppy, Ambystoma mexicanum +31:bullfrog, Rana catesbeiana +32:tree frog, tree-frog +33:tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui +34:loggerhead, loggerhead turtle, Caretta caretta +35:leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea +36:mud turtle +37:terrapin +38:box turtle, box tortoise +39:banded gecko +40:common iguana, iguana, Iguana iguana +41:American chameleon, anole, Anolis carolinensis +42:whiptail, whiptail lizard +43:agama +44:frilled lizard, Chlamydosaurus kingi +45:alligator lizard +46:Gila monster, Heloderma suspectum +47:green lizard, Lacerta viridis +48:African chameleon, Chamaeleo chamaeleon +49:Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis +50:African crocodile, Nile crocodile, Crocodylus niloticus +51:American alligator, Alligator mississipiensis +52:triceratops +53:thunder snake, worm snake, Carphophis amoenus +54:ringneck snake, ring-necked snake, ring snake +55:hognose snake, puff adder, sand viper +56:green snake, grass snake +57:king snake, kingsnake +58:garter snake, grass snake +59:water snake +60:vine snake +61:night snake, Hypsiglena torquata +62:boa constrictor, Constrictor constrictor +63:rock python, rock snake, Python sebae +64:Indian cobra, Naja naja +65:green mamba +66:sea snake +67:horned viper, cerastes, sand viper, horned asp, Cerastes cornutus +68:diamondback, diamondback rattlesnake, Crotalus adamanteus +69:sidewinder, horned rattlesnake, Crotalus cerastes +70:trilobite +71:harvestman, daddy longlegs, Phalangium opilio +72:scorpion +73:black and gold garden spider, Argiope aurantia +74:barn spider, Araneus cavaticus +75:garden spider, Aranea diademata +76:black widow, Latrodectus mactans +77:tarantula +78:wolf spider, hunting spider +79:tick +80:centipede +81:black grouse +82:ptarmigan +83:ruffed grouse, partridge, Bonasa umbellus +84:prairie chicken, prairie grouse, prairie fowl +85:peacock +86:quail +87:partridge +88:African grey, African gray, Psittacus erithacus +89:macaw +90:sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita +91:lorikeet +92:coucal +93:bee eater +94:hornbill +95:hummingbird +96:jacamar +97:toucan +98:drake +99:red-breasted merganser, Mergus serrator +100:goose +101:black swan, Cygnus atratus +102:tusker +103:echidna, spiny anteater, anteater +104:platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus +105:wallaby, brush kangaroo +106:koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus +107:wombat +108:jellyfish +109:sea anemone, anemone +110:brain coral +111:flatworm, platyhelminth +112:nematode, nematode worm, roundworm +113:conch +114:snail +115:slug +116:sea slug, nudibranch +117:chiton, coat-of-mail shell, sea cradle, polyplacophore +118:chambered nautilus, pearly nautilus, nautilus +119:Dungeness crab, Cancer magister +120:rock crab, Cancer irroratus +121:fiddler crab +122:king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica +123:American lobster, Northern lobster, Maine lobster, Homarus americanus +124:spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish +125:crayfish, crawfish, crawdad, crawdaddy +126:hermit crab +127:isopod +128:white stork, Ciconia ciconia +129:black stork, Ciconia nigra +130:spoonbill +131:flamingo +132:little blue heron, Egretta caerulea +133:American egret, great white heron, Egretta albus +134:bittern +135:crane +136:limpkin, Aramus pictus +137:European gallinule, Porphyrio porphyrio +138:American coot, marsh hen, mud hen, water hen, Fulica americana +139:bustard +140:ruddy turnstone, Arenaria interpres +141:red-backed sandpiper, dunlin, Erolia alpina +142:redshank, Tringa totanus +143:dowitcher +144:oystercatcher, oyster catcher +145:pelican +146:king penguin, Aptenodytes patagonica +147:albatross, mollymawk +148:grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus +149:killer whale, killer, orca, grampus, sea wolf, Orcinus orca +150:dugong, Dugong dugon +151:sea lion +152:Chihuahua +153:Japanese spaniel +154:Maltese dog, Maltese terrier, Maltese +155:Pekinese, Pekingese, Peke +156:Shih-Tzu +157:Blenheim spaniel +158:papillon +159:toy terrier +160:Rhodesian ridgeback +161:Afghan hound, Afghan +162:basset, basset hound +163:beagle +164:bloodhound, sleuthhound +165:bluetick +166:black-and-tan coonhound +167:Walker hound, Walker foxhound +168:English foxhound +169:redbone +170:borzoi, Russian wolfhound +171:Irish wolfhound +172:Italian greyhound +173:whippet +174:Ibizan hound, Ibizan Podenco +175:Norwegian elkhound, elkhound +176:otterhound, otter hound +177:Saluki, gazelle hound +178:Scottish deerhound, deerhound +179:Weimaraner +180:Staffordshire bullterrier, Staffordshire bull terrier +181:American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier +182:Bedlington terrier +183:Border terrier +184:Kerry blue terrier +185:Irish terrier +186:Norfolk terrier +187:Norwich terrier +188:Yorkshire terrier +189:wire-haired fox terrier +190:Lakeland terrier +191:Sealyham terrier, Sealyham +192:Airedale, Airedale terrier +193:cairn, cairn terrier +194:Australian terrier +195:Dandie Dinmont, Dandie Dinmont terrier +196:Boston bull, Boston terrier +197:miniature schnauzer +198:giant schnauzer +199:standard schnauzer +200:Scotch terrier, Scottish terrier, Scottie +201:Tibetan terrier, chrysanthemum dog +202:silky terrier, Sydney silky +203:soft-coated wheaten terrier +204:West Highland white terrier +205:Lhasa, Lhasa apso +206:flat-coated retriever +207:curly-coated retriever +208:golden retriever +209:Labrador retriever +210:Chesapeake Bay retriever +211:German short-haired pointer +212:vizsla, Hungarian pointer +213:English setter +214:Irish setter, red setter +215:Gordon setter +216:Brittany spaniel +217:clumber, clumber spaniel +218:English springer, English springer spaniel +219:Welsh springer spaniel +220:cocker spaniel, English cocker spaniel, cocker +221:Sussex spaniel +222:Irish water spaniel +223:kuvasz +224:schipperke +225:groenendael +226:malinois +227:briard +228:kelpie +229:komondor +230:Old English sheepdog, bobtail +231:Shetland sheepdog, Shetland sheep dog, Shetland +232:collie +233:Border collie +234:Bouvier des Flandres, Bouviers des Flandres +235:Rottweiler +236:German shepherd, German shepherd dog, German police dog, alsatian +237:Doberman, Doberman pinscher +238:miniature pinscher +239:Greater Swiss Mountain dog +240:Bernese mountain dog +241:Appenzeller +242:EntleBucher +243:boxer +244:bull mastiff +245:Tibetan mastiff +246:French bulldog +247:Great Dane +248:Saint Bernard, St Bernard +249:Eskimo dog, husky +250:malamute, malemute, Alaskan malamute +251:Siberian husky +252:dalmatian, coach dog, carriage dog +253:affenpinscher, monkey pinscher, monkey dog +254:basenji +255:pug, pug-dog +256:Leonberg +257:Newfoundland, Newfoundland dog +258:Great Pyrenees +259:Samoyed, Samoyede +260:Pomeranian +261:chow, chow chow +262:keeshond +263:Brabancon griffon +264:Pembroke, Pembroke Welsh corgi +265:Cardigan, Cardigan Welsh corgi +266:toy poodle +267:miniature poodle +268:standard poodle +269:Mexican hairless +270:timber wolf, grey wolf, gray wolf, Canis lupus +271:white wolf, Arctic wolf, Canis lupus tundrarum +272:red wolf, maned wolf, Canis rufus, Canis niger +273:coyote, prairie wolf, brush wolf, Canis latrans +274:dingo, warrigal, warragal, Canis dingo +275:dhole, Cuon alpinus +276:African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus +277:hyena, hyaena +278:red fox, Vulpes vulpes +279:kit fox, Vulpes macrotis +280:Arctic fox, white fox, Alopex lagopus +281:grey fox, gray fox, Urocyon cinereoargenteus +282:tabby, tabby cat +283:tiger cat +284:Persian cat +285:Siamese cat, Siamese +286:Egyptian cat +287:cougar, puma, catamount, mountain lion, painter, panther, Felis concolor +288:lynx, catamount +289:leopard, Panthera pardus +290:snow leopard, ounce, Panthera uncia +291:jaguar, panther, Panthera onca, Felis onca +292:lion, king of beasts, Panthera leo +293:tiger, Panthera tigris +294:cheetah, chetah, Acinonyx jubatus +295:brown bear, bruin, Ursus arctos +296:American black bear, black bear, Ursus americanus, Euarctos americanus +297:ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus +298:sloth bear, Melursus ursinus, Ursus ursinus +299:mongoose +300:meerkat, mierkat +301:tiger beetle +302:ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle +303:ground beetle, carabid beetle +304:long-horned beetle, longicorn, longicorn beetle +305:leaf beetle, chrysomelid +306:dung beetle +307:rhinoceros beetle +308:weevil +309:fly +310:bee +311:ant, emmet, pismire +312:grasshopper, hopper +313:cricket +314:walking stick, walkingstick, stick insect +315:cockroach, roach +316:mantis, mantid +317:cicada, cicala +318:leafhopper +319:lacewing, lacewing fly +320:dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk +321:damselfly +322:admiral +323:ringlet, ringlet butterfly +324:monarch, monarch butterfly, milkweed butterfly, Danaus plexippus +325:cabbage butterfly +326:sulphur butterfly, sulfur butterfly +327:lycaenid, lycaenid butterfly +328:starfish, sea star +329:sea urchin +330:sea cucumber, holothurian +331:wood rabbit, cottontail, cottontail rabbit +332:hare +333:Angora, Angora rabbit +334:hamster +335:porcupine, hedgehog +336:fox squirrel, eastern fox squirrel, Sciurus niger +337:marmot +338:beaver +339:guinea pig, Cavia cobaya +340:sorrel +341:zebra +342:hog, pig, grunter, squealer, Sus scrofa +343:wild boar, boar, Sus scrofa +344:warthog +345:hippopotamus, hippo, river horse, Hippopotamus amphibius +346:ox +347:water buffalo, water ox, Asiatic buffalo, Bubalus bubalis +348:bison +349:ram, tup +350:bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis +351:ibex, Capra ibex +352:hartebeest +353:impala, Aepyceros melampus +354:gazelle +355:Arabian camel, dromedary, Camelus dromedarius +356:llama +357:weasel +358:mink +359:polecat, fitch, foulmart, foumart, Mustela putorius +360:black-footed ferret, ferret, Mustela nigripes +361:otter +362:skunk, polecat, wood pussy +363:badger +364:armadillo +365:three-toed sloth, ai, Bradypus tridactylus +366:orangutan, orang, orangutang, Pongo pygmaeus +367:gorilla, Gorilla gorilla +368:chimpanzee, chimp, Pan troglodytes +369:gibbon, Hylobates lar +370:siamang, Hylobates syndactylus, Symphalangus syndactylus +371:guenon, guenon monkey +372:patas, hussar monkey, Erythrocebus patas +373:baboon +374:macaque +375:langur +376:colobus, colobus monkey +377:proboscis monkey, Nasalis larvatus +378:marmoset 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+405:airliner +406:airship, dirigible +407:altar +408:ambulance +409:amphibian, amphibious vehicle +410:analog clock +411:apiary, bee house +412:apron +413:ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin +414:assault rifle, assault gun +415:backpack, back pack, knapsack, packsack, rucksack, haversack +416:bakery, bakeshop, bakehouse +417:balance beam, beam +418:balloon +419:ballpoint, ballpoint pen, ballpen, Biro +420:Band Aid +421:banjo +422:bannister, banister, balustrade, balusters, handrail +423:barbell +424:barber chair +425:barbershop +426:barn +427:barometer +428:barrel, cask +429:barrow, garden cart, lawn cart, wheelbarrow +430:baseball +431:basketball +432:bassinet +433:bassoon +434:bathing cap, swimming cap +435:bath towel +436:bathtub, bathing tub, bath, tub +437:beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon +438:beacon, lighthouse, beacon light, pharos +439:beaker +440:bearskin, busby, shako +441:beer bottle +442:beer glass +443:bell cote, bell cot +444:bib +445:bicycle-built-for-two, tandem bicycle, tandem +446:bikini, two-piece +447:binder, ring-binder +448:binoculars, field glasses, opera glasses +449:birdhouse +450:boathouse +451:bobsled, bobsleigh, bob +452:bolo tie, bolo, bola tie, bola +453:bonnet, poke bonnet +454:bookcase +455:bookshop, bookstore, bookstall +456:bottlecap +457:bow +458:bow tie, bow-tie, bowtie +459:brass, memorial tablet, plaque +460:brassiere, bra, bandeau +461:breakwater, groin, groyne, mole, bulwark, seawall, jetty +462:breastplate, aegis, egis +463:broom +464:bucket, pail +465:buckle +466:bulletproof vest +467:bullet train, bullet +468:butcher shop, meat market +469:cab, hack, taxi, taxicab +470:caldron, cauldron +471:candle, taper, wax light +472:cannon +473:canoe +474:can opener, tin opener +475:cardigan +476:car mirror +477:carousel, carrousel, merry-go-round, roundabout, whirligig +478:carpenter's kit, tool kit +479:carton +480:car wheel +481:cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM +482:cassette +483:cassette player +484:castle +485:catamaran +486:CD player +487:cello, violoncello +488:cellular telephone, cellular phone, cellphone, cell, mobile phone +489:chain +490:chainlink fence +491:chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour +492:chain saw, chainsaw +493:chest +494:chiffonier, commode +495:chime, bell, gong +496:china cabinet, china closet +497:Christmas stocking +498:church, church building +499:cinema, movie theater, movie theatre, movie house, picture palace +500:cleaver, meat cleaver, chopper +501:cliff dwelling +502:cloak +503:clog, geta, patten, sabot +504:cocktail shaker +505:coffee mug +506:coffeepot +507:coil, spiral, volute, whorl, helix +508:combination lock +509:computer keyboard, keypad +510:confectionery, confectionary, candy store +511:container ship, containership, container vessel +512:convertible +513:corkscrew, bottle screw +514:cornet, horn, trumpet, trump +515:cowboy boot +516:cowboy hat, ten-gallon hat +517:cradle +518:crane +519:crash helmet +520:crate +521:crib, cot +522:Crock Pot +523:croquet ball +524:crutch +525:cuirass +526:dam, dike, dyke +527:desk +528:desktop computer +529:dial telephone, dial phone +530:diaper, nappy, napkin +531:digital clock +532:digital watch +533:dining table, board +534:dishrag, dishcloth +535:dishwasher, dish washer, dishwashing machine +536:disk brake, disc brake +537:dock, dockage, docking facility +538:dogsled, dog sled, dog sleigh +539:dome +540:doormat, welcome mat +541:drilling platform, offshore rig +542:drum, membranophone, tympan +543:drumstick +544:dumbbell +545:Dutch oven +546:electric fan, blower +547:electric guitar +548:electric locomotive +549:entertainment center +550:envelope +551:espresso maker +552:face powder +553:feather boa, boa +554:file, file cabinet, filing cabinet +555:fireboat +556:fire engine, fire truck +557:fire screen, fireguard +558:flagpole, flagstaff +559:flute, transverse flute +560:folding chair +561:football helmet +562:forklift +563:fountain +564:fountain pen +565:four-poster +566:freight car +567:French horn, horn +568:frying pan, frypan, skillet +569:fur coat +570:garbage truck, dustcart +571:gasmask, respirator, gas helmet +572:gas pump, gasoline pump, petrol pump, island dispenser +573:goblet +574:go-kart +575:golf ball +576:golfcart, golf cart +577:gondola +578:gong, tam-tam +579:gown +580:grand piano, grand +581:greenhouse, nursery, glasshouse +582:grille, radiator grille +583:grocery store, grocery, food market, market +584:guillotine +585:hair slide +586:hair spray +587:half track +588:hammer +589:hamper +590:hand blower, blow dryer, blow drier, hair dryer, hair drier +591:hand-held computer, hand-held microcomputer +592:handkerchief, hankie, hanky, hankey +593:hard disc, hard disk, fixed disk +594:harmonica, mouth organ, harp, mouth harp 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letter box +639:maillot +640:maillot, tank suit +641:manhole cover +642:maraca +643:marimba, xylophone +644:mask +645:matchstick +646:maypole +647:maze, labyrinth +648:measuring cup +649:medicine chest, medicine cabinet +650:megalith, megalithic structure +651:microphone, mike +652:microwave, microwave oven +653:military uniform +654:milk can +655:minibus +656:miniskirt, mini +657:minivan +658:missile +659:mitten +660:mixing bowl +661:mobile home, manufactured home +662:Model T +663:modem +664:monastery +665:monitor +666:moped +667:mortar +668:mortarboard +669:mosque +670:mosquito net +671:motor scooter, scooter +672:mountain bike, all-terrain bike, off-roader +673:mountain tent +674:mouse, computer mouse +675:mousetrap +676:moving van +677:muzzle +678:nail +679:neck brace +680:necklace +681:nipple +682:notebook, notebook computer +683:obelisk +684:oboe, hautboy, hautbois +685:ocarina, sweet potato +686:odometer, hodometer, mileometer, milometer +687:oil filter +688:organ, pipe organ +689:oscilloscope, scope, cathode-ray oscilloscope, CRO +690:overskirt +691:oxcart +692:oxygen mask +693:packet +694:paddle, boat paddle +695:paddlewheel, paddle wheel +696:padlock +697:paintbrush +698:pajama, pyjama, pj's, jammies +699:palace +700:panpipe, pandean pipe, syrinx +701:paper towel +702:parachute, chute +703:parallel bars, bars +704:park bench +705:parking meter +706:passenger car, coach, carriage +707:patio, terrace +708:pay-phone, pay-station +709:pedestal, plinth, footstall +710:pencil box, pencil case +711:pencil sharpener +712:perfume, essence +713:Petri dish +714:photocopier +715:pick, plectrum, plectron +716:pickelhaube +717:picket fence, paling +718:pickup, pickup truck +719:pier +720:piggy bank, penny bank +721:pill bottle +722:pillow +723:ping-pong ball +724:pinwheel +725:pirate, pirate ship +726:pitcher, ewer +727:plane, carpenter's plane, woodworking plane +728:planetarium +729:plastic bag +730:plate rack +731:plow, plough +732:plunger, plumber's helper +733:Polaroid camera, Polaroid Land camera +734:pole +735:police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria +736:poncho +737:pool table, billiard table, snooker table +738:pop bottle, soda bottle +739:pot, flowerpot +740:potter's wheel +741:power drill +742:prayer rug, prayer mat +743:printer +744:prison, prison house +745:projectile, missile +746:projector +747:puck, hockey puck +748:punching bag, punch bag, punching ball, punchball +749:purse +750:quill, quill pen +751:quilt, comforter, comfort, puff +752:racer, race car, racing car +753:racket, racquet +754:radiator +755:radio, wireless +756:radio telescope, radio reflector +757:rain barrel +758:recreational vehicle, RV, R.V. +759:reel +760:reflex camera +761:refrigerator, icebox +762:remote control, remote +763:restaurant, eating house, eating place, eatery +764:revolver, six-gun, six-shooter +765:rifle +766:rocking chair, rocker +767:rotisserie +768:rubber eraser, rubber, pencil eraser +769:rugby ball 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+823:steel drum +824:stethoscope +825:stole +826:stone wall +827:stopwatch, stop watch +828:stove +829:strainer +830:streetcar, tram, tramcar, trolley, trolley car +831:stretcher +832:studio couch, day bed +833:stupa, tope +834:submarine, pigboat, sub, U-boat +835:suit, suit of clothes +836:sundial +837:sunglass +838:sunglasses, dark glasses, shades +839:sunscreen, sunblock, sun blocker +840:suspension bridge +841:swab, swob, mop +842:sweatshirt +843:swimming trunks, bathing trunks +844:swing +845:switch, electric switch, electrical switch +846:syringe +847:table lamp +848:tank, army tank, armored combat vehicle, armoured combat vehicle +849:tape player +850:teapot +851:teddy, teddy bear +852:television, television system +853:tennis ball +854:thatch, thatched roof +855:theater curtain, theatre curtain +856:thimble +857:thresher, thrasher, threshing machine +858:throne +859:tile roof +860:toaster +861:tobacco shop, tobacconist shop, tobacconist +862:toilet seat +863:torch +864:totem pole +865:tow truck, tow car, wrecker +866:toyshop +867:tractor +868:trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi +869:tray +870:trench coat +871:tricycle, trike, velocipede +872:trimaran +873:tripod +874:triumphal arch +875:trolleybus, trolley coach, trackless trolley +876:trombone +877:tub, vat +878:turnstile +879:typewriter keyboard +880:umbrella +881:unicycle, monocycle +882:upright, upright piano +883:vacuum, vacuum cleaner +884:vase +885:vault +886:velvet +887:vending machine +888:vestment +889:viaduct +890:violin, fiddle +891:volleyball +892:waffle iron +893:wall clock +894:wallet, billfold, notecase, pocketbook +895:wardrobe, closet, press +896:warplane, military plane +897:washbasin, handbasin, washbowl, lavabo, wash-hand basin +898:washer, automatic washer, washing machine +899:water bottle +900:water jug +901:water tower +902:whiskey jug +903:whistle +904:wig +905:window screen +906:window shade +907:Windsor tie +908:wine bottle +909:wing +910:wok +911:wooden spoon +912:wool, woolen, woollen +913:worm fence, snake fence, snake-rail fence, Virginia fence +914:wreck +915:yawl +916:yurt +917:web site, website, internet site, site +918:comic book +919:crossword puzzle, crossword +920:street sign +921:traffic light, traffic signal, stoplight +922:book jacket, dust cover, dust jacket, dust wrapper +923:menu +924:plate +925:guacamole +926:consomme +927:hot pot, hotpot +928:trifle +929:ice cream, icecream +930:ice lolly, lolly, lollipop, popsicle +931:French loaf +932:bagel, beigel +933:pretzel +934:cheeseburger +935:hotdog, hot dog, red hot +936:mashed potato +937:head cabbage +938:broccoli +939:cauliflower +940:zucchini, courgette +941:spaghetti squash +942:acorn squash +943:butternut squash +944:cucumber, cuke +945:artichoke, globe artichoke +946:bell pepper +947:cardoon +948:mushroom +949:Granny Smith +950:strawberry +951:orange +952:lemon +953:fig +954:pineapple, ananas +955:banana +956:jackfruit, jak, jack +957:custard apple +958:pomegranate +959:hay +960:carbonara +961:chocolate sauce, chocolate syrup +962:dough +963:meat loaf, meatloaf +964:pizza, pizza pie +965:potpie +966:burrito +967:red wine +968:espresso +969:cup +970:eggnog +971:alp +972:bubble +973:cliff, drop, drop-off +974:coral reef +975:geyser +976:lakeside, lakeshore +977:promontory, headland, head, foreland +978:sandbar, sand bar +979:seashore, coast, seacoast, sea-coast +980:valley, vale +981:volcano +982:ballplayer, baseball player +983:groom, bridegroom +984:scuba diver +985:rapeseed +986:daisy +987:yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum +988:corn +989:acorn +990:hip, rose hip, rosehip +991:buckeye, horse chestnut, conker +992:coral fungus +993:agaric +994:gyromitra +995:stinkhorn, carrion fungus +996:earthstar +997:hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa +998:bolete +999:ear, spike, capitulum +1000:toilet tissue, toilet paper, bathroom tissue \ No newline at end of file diff --git a/rknn-toolkit2/examples/tflite/mobilenet_v1_qat/mobilenet_v1_1.0_224_quant.tflite b/rknn-toolkit2/examples/tflite/mobilenet_v1_qat/mobilenet_v1_1.0_224_quant.tflite new file mode 100755 index 0000000000000000000000000000000000000000..b5807f4d1ef84d4a646f46ec578dbcee751643e5 --- /dev/null +++ b/rknn-toolkit2/examples/tflite/mobilenet_v1_qat/mobilenet_v1_1.0_224_quant.tflite @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ecc3a67c47c5a609ec35f6a58a7d97532834e43df4cb7d3f1204a8164b7d20dd +size 4276352 diff --git a/rknn-toolkit2/examples/tflite/mobilenet_v1_qat/model_config.yml b/rknn-toolkit2/examples/tflite/mobilenet_v1_qat/model_config.yml new file mode 100755 index 0000000000000000000000000000000000000000..ad8e93495a3f4dcab23eeee916370228fb2388f6 --- /dev/null +++ b/rknn-toolkit2/examples/tflite/mobilenet_v1_qat/model_config.yml @@ -0,0 +1,9 @@ +models: + name: mobilenet_v1 # 模型输出名称 + platform: tflite # 原始模型使用的框架 + model_file_path: ./mobilenet_v1_1.0_224_quant.tflite # 原模型路径 + quantize: False # QAT量化过 + configs: + mean_values: [128, 128, 128] # rknn.config的mean_values参数 + std_values: [128, 128, 128] # rknn.config的std_values参数 + quant_img_RGB2BGR: false # 不进行RGB2BGR转换 diff --git a/rknn-toolkit2/examples/tflite/mobilenet_v1_qat/test.py b/rknn-toolkit2/examples/tflite/mobilenet_v1_qat/test.py new file mode 100755 index 0000000000000000000000000000000000000000..61a44ad2d61ef2d342f203757f85d7ad31fd8dc5 --- /dev/null +++ b/rknn-toolkit2/examples/tflite/mobilenet_v1_qat/test.py @@ -0,0 +1,79 @@ +import numpy as np +import cv2 +from rknn.api import RKNN + + +def show_outputs(outputs): + output = outputs[0][0] + index = sorted(range(len(output)), key=lambda k : output[k], reverse=True) + fp = open('./labels.txt', 'r') + labels = fp.readlines() + top5_str = 'mobilenet_v1\n-----TOP 5-----\n' + for i in range(5): + value = output[index[i]] + if value > 0: + topi = '[{:>4d}] score:{:.6f} class:"{}"\n'.format(index[i], value, labels[index[i]].strip().split(':')[-1]) + else: + topi = '[ -1]: 0.0\n' + top5_str += topi + print(top5_str.strip()) + +def dequantize(outputs, scale, zp): + outputs[0] = (outputs[0] - zp) * scale + return outputs + +if __name__ == '__main__': + + # Create RKNN object + rknn = RKNN(verbose=True) + + # Pre-process config + print('--> Config model') + rknn.config(mean_values=[128, 128, 128], std_values=[128, 128, 128], target_platform='rk3566') + print('done') + + # Load model (from https://www.tensorflow.org/lite/examples/image_classification/overview?hl=zh-cn) + print('--> Loading model') + ret = rknn.load_tflite(model='mobilenet_v1_1.0_224_quant.tflite') + if ret != 0: + print('Load model failed!') + exit(ret) + print('done') + + # Build model + print('--> Building model') + ret = rknn.build(do_quantization=False) + if ret != 0: + print('Build model failed!') + exit(ret) + print('done') + + # Export rknn model + print('--> Export rknn model') + ret = rknn.export_rknn('./mobilenet_v1.rknn') + if ret != 0: + print('Export rknn model failed!') + exit(ret) + print('done') + + # Set inputs + img = cv2.imread('./dog_224x224.jpg') + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + img = np.expand_dims(img, 0) + + # Init runtime environment + print('--> Init runtime environment') + ret = rknn.init_runtime() + if ret != 0: + print('Init runtime environment failed!') + exit(ret) + print('done') + + # Inference + print('--> Running model') + outputs = rknn.inference(inputs=[img], data_format=['nhwc']) + np.save('./tflite_mobilenet_v1_qat_0.npy', outputs[0]) + show_outputs(dequantize(outputs, scale=0.00390625, zp=0)) + print('done') + + rknn.release() diff --git a/rknn-toolkit2/packages/md5sum.txt b/rknn-toolkit2/packages/md5sum.txt new file mode 100644 index 0000000000000000000000000000000000000000..734a77e9cc2ba8f9704433c37439449b1830f7f0 --- /dev/null +++ b/rknn-toolkit2/packages/md5sum.txt @@ -0,0 +1,6 @@ +49dc6e0b6c276db1db44ba38e6f4a277 rknn_toolkit2-2.1.0+708089d1-cp310-cp310-linux_x86_64.whl +3ed72827476a2b2905471c8dddd33a04 rknn_toolkit2-2.1.0+708089d1-cp311-cp311-linux_x86_64.whl +5d8441cb5951cff671cb678584c83d93 rknn_toolkit2-2.1.0+708089d1-cp36-cp36m-linux_x86_64.whl +004c4d1eebc3f55572858020145ed147 rknn_toolkit2-2.1.0+708089d1-cp37-cp37m-linux_x86_64.whl +f819ebb2a3badbc44b21924969c7f588 rknn_toolkit2-2.1.0+708089d1-cp38-cp38-linux_x86_64.whl +f84006e40bf8039757a2ae67013c8a11 rknn_toolkit2-2.1.0+708089d1-cp39-cp39-linux_x86_64.whl diff --git a/rknn-toolkit2/packages/requirements_cp310-2.1.0.txt b/rknn-toolkit2/packages/requirements_cp310-2.1.0.txt new file mode 100644 index 0000000000000000000000000000000000000000..6b22f2aef8306c8d751380249239fb0558a2619d --- /dev/null +++ b/rknn-toolkit2/packages/requirements_cp310-2.1.0.txt @@ -0,0 +1,20 @@ +# if install failed, please change the pip source to 'https://pypi.tuna.tsinghua.edu.cn/simple/' + +# base deps +protobuf==3.20.3 + +# utils +psutil>=5.9.0 +ruamel.yaml>=0.17.21 +scipy>=1.9.3 +tqdm>=4.64.1 +opencv-python>=4.5.5.64 +fast-histogram>=0.11 +numpy<=1.26.4 + +# base +onnx==1.14.1 +onnxoptimizer==0.3.8 +onnxruntime==1.16.0 +torch>=1.13.1,<=2.1.0 +tensorflow==2.8.1 \ No newline at end of file diff --git a/rknn-toolkit2/packages/requirements_cp311-2.1.0.txt b/rknn-toolkit2/packages/requirements_cp311-2.1.0.txt new file mode 100644 index 0000000000000000000000000000000000000000..b4320ac76fcbd1fb00aac3f6e23429c5400c216b --- /dev/null +++ b/rknn-toolkit2/packages/requirements_cp311-2.1.0.txt @@ -0,0 +1,21 @@ +# if install failed, please change the pip source to 'https://pypi.tuna.tsinghua.edu.cn/simple/' + +# base deps +protobuf==3.20.3 + +# utils +psutil>=5.9.0 +ruamel.yaml>=0.17.21 +scipy>=1.9.3 +tqdm>=4.64.1 +opencv-python>=4.5.5.64 +fast-histogram>=0.11 +Pillow>=10.0.1 +numpy<=1.26.4 + +# base +onnx==1.14.1 +onnxoptimizer==0.3.8 +onnxruntime==1.16.0 +torch>=1.13.1,<=2.1.0 +tensorflow==2.14.0 \ No newline at end of file diff --git a/rknn-toolkit2/packages/requirements_cp36-2.1.0.txt b/rknn-toolkit2/packages/requirements_cp36-2.1.0.txt new file mode 100644 index 0000000000000000000000000000000000000000..e63f7bbdd05b39616c66dfd29c5652b09abec9ca --- /dev/null +++ b/rknn-toolkit2/packages/requirements_cp36-2.1.0.txt @@ -0,0 +1,20 @@ +# if install failed, please change the pip source to 'https://pypi.tuna.tsinghua.edu.cn/simple/' + +# base deps +protobuf==3.12.2 + +# utils +psutil>=5.9.0 +ruamel.yaml>=0.17.4 +scipy>=1.5.4 +tqdm>=4.64.0 +opencv-python>=4.5.5.64,<=4.6.0.66 +fast-histogram>=0.11 +numpy<=1.26.4 + +# base +onnx==1.10.0 +onnxoptimizer==0.2.7 +onnxruntime==1.10.0 +torch>=1.10.1,<=2.1.0 +tensorflow==2.6.2 \ No newline at end of file diff --git a/rknn-toolkit2/packages/requirements_cp37-2.1.0.txt b/rknn-toolkit2/packages/requirements_cp37-2.1.0.txt new file mode 100644 index 0000000000000000000000000000000000000000..14f2bc5159b52ac39925a68327d8967cf549d565 --- /dev/null +++ b/rknn-toolkit2/packages/requirements_cp37-2.1.0.txt @@ -0,0 +1,20 @@ +# if install failed, please change the pip source to 'https://pypi.tuna.tsinghua.edu.cn/simple/' + +# base deps +protobuf==3.20.3 + +# utils +psutil>=5.9.0 +ruamel.yaml>=0.17.4 +scipy>=1.5.4 +tqdm>=4.64.0 +opencv-python>=4.5.5.64 +fast-histogram>=0.11 +numpy<=1.26.4 + +# base +onnx==1.14.1 +onnxoptimizer==0.2.7 +onnxruntime==1.14.1 +torch>=1.10.1,<=2.1.0 +tensorflow==2.8.1 \ No newline at end of file diff --git a/rknn-toolkit2/packages/requirements_cp38-2.1.0.txt b/rknn-toolkit2/packages/requirements_cp38-2.1.0.txt new file mode 100644 index 0000000000000000000000000000000000000000..44b71bad88b7a412694dad34fd489e08ed709cca --- /dev/null +++ b/rknn-toolkit2/packages/requirements_cp38-2.1.0.txt @@ -0,0 +1,20 @@ +# if install failed, please change the pip source to 'https://pypi.tuna.tsinghua.edu.cn/simple/' + +# base deps +protobuf==3.20.3 + +# utils +psutil>=5.9.0 +ruamel.yaml>=0.17.4 +scipy>=1.5.4 +tqdm>=4.64.0 +opencv-python>=4.5.5.64 +fast-histogram>=0.11 +numpy<=1.26.4 + +# base +onnx==1.14.1 +onnxoptimizer==0.2.7 +onnxruntime==1.16.0 +torch>=1.10.1,<=2.1.0 +tensorflow==2.8.1 \ No newline at end of file diff --git a/rknn-toolkit2/packages/requirements_cp39-2.1.0.txt b/rknn-toolkit2/packages/requirements_cp39-2.1.0.txt new file mode 100644 index 0000000000000000000000000000000000000000..44b71bad88b7a412694dad34fd489e08ed709cca --- /dev/null +++ b/rknn-toolkit2/packages/requirements_cp39-2.1.0.txt @@ -0,0 +1,20 @@ +# if install failed, please change the pip source to 'https://pypi.tuna.tsinghua.edu.cn/simple/' + +# base deps +protobuf==3.20.3 + +# utils +psutil>=5.9.0 +ruamel.yaml>=0.17.4 +scipy>=1.5.4 +tqdm>=4.64.0 +opencv-python>=4.5.5.64 +fast-histogram>=0.11 +numpy<=1.26.4 + +# base +onnx==1.14.1 +onnxoptimizer==0.2.7 +onnxruntime==1.16.0 +torch>=1.10.1,<=2.1.0 +tensorflow==2.8.1 \ No newline at end of file diff --git a/rknn-toolkit2/packages/rknn_toolkit2-2.1.0+708089d1-cp310-cp310-linux_x86_64.whl b/rknn-toolkit2/packages/rknn_toolkit2-2.1.0+708089d1-cp310-cp310-linux_x86_64.whl new file mode 100644 index 0000000000000000000000000000000000000000..33c85905ba5c782e145634e5e217f56d6711c61c --- /dev/null +++ b/rknn-toolkit2/packages/rknn_toolkit2-2.1.0+708089d1-cp310-cp310-linux_x86_64.whl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d4f85d6a28666e39f5804e64ebdd830cc6399530842fe38e5754413c92f8f96b +size 44811343 diff --git a/rknn-toolkit2/packages/rknn_toolkit2-2.1.0+708089d1-cp311-cp311-linux_x86_64.whl b/rknn-toolkit2/packages/rknn_toolkit2-2.1.0+708089d1-cp311-cp311-linux_x86_64.whl new file mode 100644 index 0000000000000000000000000000000000000000..97ba6eaf2e866903d54dcd3b5da6d372b3853d97 --- /dev/null +++ b/rknn-toolkit2/packages/rknn_toolkit2-2.1.0+708089d1-cp311-cp311-linux_x86_64.whl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4705565323678930b9fc41961a553f6e4a6f93287dc7f55c53747a044b903509 +size 44830336 diff --git a/rknn-toolkit2/packages/rknn_toolkit2-2.1.0+708089d1-cp36-cp36m-linux_x86_64.whl b/rknn-toolkit2/packages/rknn_toolkit2-2.1.0+708089d1-cp36-cp36m-linux_x86_64.whl new file mode 100644 index 0000000000000000000000000000000000000000..0e5171f73f59915cc4d9691c6b8b7048e9a1ff3f --- /dev/null +++ b/rknn-toolkit2/packages/rknn_toolkit2-2.1.0+708089d1-cp36-cp36m-linux_x86_64.whl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7c7731c97f7676945365cfd19fca8f07b6efbe98dca1f6a2a514082eaca2ab89 +size 43939251 diff --git a/rknn-toolkit2/packages/rknn_toolkit2-2.1.0+708089d1-cp37-cp37m-linux_x86_64.whl b/rknn-toolkit2/packages/rknn_toolkit2-2.1.0+708089d1-cp37-cp37m-linux_x86_64.whl new file mode 100644 index 0000000000000000000000000000000000000000..1adad530a38f337a2d243c4deed548ff8e3be35c --- /dev/null +++ b/rknn-toolkit2/packages/rknn_toolkit2-2.1.0+708089d1-cp37-cp37m-linux_x86_64.whl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a978410143654a48b5d70344ceb89aaad706a04764d6b7e8c37b65115a6e6556 +size 45696542 diff --git a/rknn-toolkit2/packages/rknn_toolkit2-2.1.0+708089d1-cp38-cp38-linux_x86_64.whl b/rknn-toolkit2/packages/rknn_toolkit2-2.1.0+708089d1-cp38-cp38-linux_x86_64.whl new file mode 100644 index 0000000000000000000000000000000000000000..db60a57870ff1ade743420d18f9b641fc62e47eb --- /dev/null +++ b/rknn-toolkit2/packages/rknn_toolkit2-2.1.0+708089d1-cp38-cp38-linux_x86_64.whl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ce5f162e56e4e0117b939b4dd59e12fbf69d4d8503b7d4f17d88cba1a9492aab +size 48556997 diff --git a/rknn-toolkit2/packages/rknn_toolkit2-2.1.0+708089d1-cp39-cp39-linux_x86_64.whl b/rknn-toolkit2/packages/rknn_toolkit2-2.1.0+708089d1-cp39-cp39-linux_x86_64.whl new file mode 100644 index 0000000000000000000000000000000000000000..4e11ec1caf750a0b15eebe2d56af3aa97f0ec7db --- /dev/null +++ b/rknn-toolkit2/packages/rknn_toolkit2-2.1.0+708089d1-cp39-cp39-linux_x86_64.whl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:24b27f7bb96d4b81565c787bbc3154887327455e079c3df6ba2d22d168d52618 +size 48518610 diff --git a/rknpu2/examples/3rdparty/cnpy/cnpy.cpp b/rknpu2/examples/3rdparty/cnpy/cnpy.cpp new file mode 100644 index 0000000000000000000000000000000000000000..5754a681e9df3e988f6d13792c43de111c8cf12f --- /dev/null +++ b/rknpu2/examples/3rdparty/cnpy/cnpy.cpp @@ -0,0 +1,403 @@ +// Copyright (C) 2011 Carl Rogers +// Released under MIT License +// license available in LICENSE file, or at http://www.opensource.org/licenses/mit-license.php + +#include "cnpy.h" + +#include + +#include +#include +#include +#include +#include +#include +#include + +char cnpy::BigEndianTest(int size) +{ + if (size == 1) + return '|'; + int x = 1; + return (((char*)&x)[0]) ? '<' : '>'; +} + +char cnpy::map_type(const std::type_info& t) +{ + if (t == typeid(float)) + return 'f'; + if (t == typeid(double)) + return 'f'; + if (t == typeid(long double)) + return 'f'; + + if (t == typeid(int)) + return 'i'; + if (t == typeid(char)) + return 'i'; + if (t == typeid(signed char)) + return 'i'; + if (t == typeid(short)) + return 'i'; + if (t == typeid(long)) + return 'i'; + if (t == typeid(long long)) + return 'i'; + + if (t == typeid(unsigned char)) + return 'u'; + if (t == typeid(unsigned short)) + return 'u'; + if (t == typeid(unsigned long)) + return 'u'; + if (t == typeid(unsigned long long)) + return 'u'; + if (t == typeid(unsigned int)) + return 'u'; + + if (t == typeid(bool)) + return 'b'; + + if (t == typeid(std::complex)) + return 'c'; + if (t == typeid(std::complex)) + return 'c'; + if (t == typeid(std::complex)) + return 'c'; + + else + return '?'; +} + +template <> +std::vector& cnpy::operator+=(std::vector& lhs, const std::string rhs) +{ + lhs.insert(lhs.end(), rhs.begin(), rhs.end()); + return lhs; +} + +template <> +std::vector& cnpy::operator+=(std::vector& lhs, const char* rhs) +{ + // write in little endian + size_t len = strlen(rhs); + lhs.reserve(len); + for (size_t byte = 0; byte < len; byte++) { + lhs.push_back(rhs[byte]); + } + return lhs; +} + +void cnpy::parse_npy_header(unsigned char* buffer, size_t& word_size, std::vector& shape, bool& fortran_order, + std::string& typeName) +{ + // std::string magic_string(buffer,6); + uint8_t major_version = *reinterpret_cast(buffer + 6); + uint8_t minor_version = *reinterpret_cast(buffer + 7); + uint16_t header_len = *reinterpret_cast(buffer + 8); + std::string header(reinterpret_cast(buffer + 9), header_len); + + size_t loc1, loc2; + + // fortran order + loc1 = header.find("fortran_order") + 16; + fortran_order = (header.substr(loc1, 4) == "True" ? true : false); + if (fortran_order) + throw std::runtime_error("npy input file: 'fortran_order' must be false, use: arr2 = np.ascontiguousarray(arr1)"); + + // shape + loc1 = header.find("("); + loc2 = header.find(")"); + + std::regex num_regex("[0-9][0-9]*"); + std::smatch sm; + shape.clear(); + + std::string str_shape = header.substr(loc1 + 1, loc2 - loc1 - 1); + while (std::regex_search(str_shape, sm, num_regex)) { + shape.push_back(std::stoi(sm[0].str())); + str_shape = sm.suffix().str(); + } + + // endian, word size, data type + // byte order code | stands for not applicable. + // not sure when this applies except for byte array + loc1 = header.find("descr") + 9; + bool littleEndian = (header[loc1] == '<' || header[loc1] == '|' ? true : false); + assert(littleEndian); + + // char type = header[loc1+1]; + // assert(type == map_type(T)); + + std::string str_ws = header.substr(loc1 + 2); + loc2 = str_ws.find("'"); + word_size = atoi(str_ws.substr(0, loc2).c_str()); + if (header.substr(loc1 + 1, 1) == "i") { + typeName = "int"; + } else if (header.substr(loc1 + 1, 1) == "u") { + typeName = "uint"; + } else if (header.substr(loc1 + 1, 1) == "f") { + typeName = "float"; + } + typeName = typeName + std::to_string(word_size * 8); +} + +void cnpy::parse_npy_header(FILE* fp, size_t& word_size, std::vector& shape, bool& fortran_order, + std::string& typeName) +{ + char buffer[256]; + size_t res = fread(buffer, sizeof(char), 11, fp); + if (res != 11) + throw std::runtime_error("parse_npy_header: failed fread"); + std::string header = fgets(buffer, 256, fp); + assert(header[header.size() - 1] == '\n'); + + size_t loc1, loc2; + + // fortran order + loc1 = header.find("fortran_order"); + if (loc1 == std::string::npos) + throw std::runtime_error("parse_npy_header: failed to find header keyword: 'fortran_order'"); + loc1 += 16; + fortran_order = (header.substr(loc1, 4) == "True" ? true : false); + if (fortran_order) + throw std::runtime_error("npy input file: 'fortran_order' must be false, use: arr2 = np.ascontiguousarray(arr1)"); + + // shape + loc1 = header.find("("); + loc2 = header.find(")"); + if (loc1 == std::string::npos || loc2 == std::string::npos) + throw std::runtime_error("parse_npy_header: failed to find header keyword: '(' or ')'"); + + std::regex num_regex("[0-9][0-9]*"); + std::smatch sm; + shape.clear(); + + std::string str_shape = header.substr(loc1 + 1, loc2 - loc1 - 1); + while (std::regex_search(str_shape, sm, num_regex)) { + shape.push_back(std::stoi(sm[0].str())); + str_shape = sm.suffix().str(); + } + + // endian, word size, data type + // byte order code | stands for not applicable. + // not sure when this applies except for byte array + loc1 = header.find("descr"); + if (loc1 == std::string::npos) + throw std::runtime_error("parse_npy_header: failed to find header keyword: 'descr'"); + loc1 += 9; + bool littleEndian = (header[loc1] == '<' || header[loc1] == '|' ? true : false); + assert(littleEndian); + + // char type = header[loc1+1]; + // assert(type == map_type(T)); + + std::string str_ws = header.substr(loc1 + 2); + loc2 = str_ws.find("'"); + word_size = atoi(str_ws.substr(0, loc2).c_str()); + if (header.substr(loc1 + 1, 1) == "i") { + typeName = "int"; + } else if (header.substr(loc1 + 1, 1) == "u") { + typeName = "uint"; + } else if (header.substr(loc1 + 1, 1) == "f") { + typeName = "float"; + } + typeName = typeName + std::to_string(word_size * 8); +} + +void cnpy::parse_zip_footer(FILE* fp, uint16_t& nrecs, size_t& global_header_size, size_t& global_header_offset) +{ + std::vector footer(22); + fseek(fp, -22, SEEK_END); + size_t res = fread(&footer[0], sizeof(char), 22, fp); + if (res != 22) + throw std::runtime_error("parse_zip_footer: failed fread"); + + uint16_t disk_no, disk_start, nrecs_on_disk, comment_len; + disk_no = *(uint16_t*)&footer[4]; + disk_start = *(uint16_t*)&footer[6]; + nrecs_on_disk = *(uint16_t*)&footer[8]; + nrecs = *(uint16_t*)&footer[10]; + global_header_size = *(uint32_t*)&footer[12]; + global_header_offset = *(uint32_t*)&footer[16]; + comment_len = *(uint16_t*)&footer[20]; + + assert(disk_no == 0); + assert(disk_start == 0); + assert(nrecs_on_disk == nrecs); + assert(comment_len == 0); +} + +cnpy::NpyArray load_the_npy_file(FILE* fp) +{ + std::vector shape; + size_t word_size; + std::string typeName; + bool fortran_order; + cnpy::parse_npy_header(fp, word_size, shape, fortran_order, typeName); + + cnpy::NpyArray arr(shape, word_size, fortran_order, typeName); + size_t nread = fread(arr.data(), 1, arr.num_bytes(), fp); + if (nread != arr.num_bytes()) + throw std::runtime_error("load_the_npy_file: failed fread"); + return arr; +} + +cnpy::NpyArray load_the_npz_array(FILE* fp, uint32_t compr_bytes, uint32_t uncompr_bytes) +{ + std::vector buffer_compr(compr_bytes); + std::vector buffer_uncompr(uncompr_bytes); + size_t nread = fread(&buffer_compr[0], 1, compr_bytes, fp); + if (nread != compr_bytes) + throw std::runtime_error("load_the_npy_file: failed fread"); + +#if 0 + int err; + z_stream d_stream; + + d_stream.zalloc = Z_NULL; + d_stream.zfree = Z_NULL; + d_stream.opaque = Z_NULL; + d_stream.avail_in = 0; + d_stream.next_in = Z_NULL; + err = inflateInit2(&d_stream, -MAX_WBITS); + + d_stream.avail_in = compr_bytes; + d_stream.next_in = &buffer_compr[0]; + d_stream.avail_out = uncompr_bytes; + d_stream.next_out = &buffer_uncompr[0]; + + err = inflate(&d_stream, Z_FINISH); + err = inflateEnd(&d_stream); +#endif + + std::vector shape; + size_t word_size; + bool fortran_order; + std::string typeName; + cnpy::parse_npy_header(&buffer_uncompr[0], word_size, shape, fortran_order, typeName); + + cnpy::NpyArray array(shape, word_size, fortran_order, typeName); + + size_t offset = uncompr_bytes - array.num_bytes(); + memcpy(array.data(), &buffer_uncompr[0] + offset, array.num_bytes()); + + return array; +} + +cnpy::npz_t cnpy::npz_load(std::string fname) +{ + FILE* fp = fopen(fname.c_str(), "rb"); + + if (!fp) { + throw std::runtime_error("npz_load: Error! Unable to open file " + fname + "!"); + } + + cnpy::npz_t arrays; + + while (1) { + std::vector local_header(30); + size_t headerres = fread(&local_header[0], sizeof(char), 30, fp); + if (headerres != 30) + throw std::runtime_error("npz_load: failed fread"); + + // if we've reached the global header, stop reading + if (local_header[2] != 0x03 || local_header[3] != 0x04) + break; + + // read in the variable name + uint16_t name_len = *(uint16_t*)&local_header[26]; + std::string varname(name_len, ' '); + size_t vname_res = fread(&varname[0], sizeof(char), name_len, fp); + if (vname_res != name_len) + throw std::runtime_error("npz_load: failed fread"); + + // erase the lagging .npy + varname.erase(varname.end() - 4, varname.end()); + + // read in the extra field + uint16_t extra_field_len = *(uint16_t*)&local_header[28]; + if (extra_field_len > 0) { + std::vector buff(extra_field_len); + size_t efield_res = fread(&buff[0], sizeof(char), extra_field_len, fp); + if (efield_res != extra_field_len) + throw std::runtime_error("npz_load: failed fread"); + } + + uint16_t compr_method = *reinterpret_cast(&local_header[0] + 8); + uint32_t compr_bytes = *reinterpret_cast(&local_header[0] + 18); + uint32_t uncompr_bytes = *reinterpret_cast(&local_header[0] + 22); + + if (compr_method == 0) { + arrays[varname] = load_the_npy_file(fp); + } else { + arrays[varname] = load_the_npz_array(fp, compr_bytes, uncompr_bytes); + } + } + + fclose(fp); + return arrays; +} + +cnpy::NpyArray cnpy::npz_load(std::string fname, std::string varname) +{ + FILE* fp = fopen(fname.c_str(), "rb"); + + if (!fp) + throw std::runtime_error("npz_load: Unable to open file " + fname); + + while (1) { + std::vector local_header(30); + size_t header_res = fread(&local_header[0], sizeof(char), 30, fp); + if (header_res != 30) + throw std::runtime_error("npz_load: failed fread"); + + // if we've reached the global header, stop reading + if (local_header[2] != 0x03 || local_header[3] != 0x04) + break; + + // read in the variable name + uint16_t name_len = *(uint16_t*)&local_header[26]; + std::string vname(name_len, ' '); + size_t vname_res = fread(&vname[0], sizeof(char), name_len, fp); + if (vname_res != name_len) + throw std::runtime_error("npz_load: failed fread"); + vname.erase(vname.end() - 4, vname.end()); // erase the lagging .npy + + // read in the extra field + uint16_t extra_field_len = *(uint16_t*)&local_header[28]; + fseek(fp, extra_field_len, SEEK_CUR); // skip past the extra field + + uint16_t compr_method = *reinterpret_cast(&local_header[0] + 8); + uint32_t compr_bytes = *reinterpret_cast(&local_header[0] + 18); + uint32_t uncompr_bytes = *reinterpret_cast(&local_header[0] + 22); + + if (vname == varname) { + NpyArray array = (compr_method == 0) ? load_the_npy_file(fp) : load_the_npz_array(fp, compr_bytes, uncompr_bytes); + fclose(fp); + return array; + } else { + // skip past the data + uint32_t size = *(uint32_t*)&local_header[22]; + fseek(fp, size, SEEK_CUR); + } + } + + fclose(fp); + + // if we get here, we haven't found the variable in the file + throw std::runtime_error("npz_load: Variable name " + varname + " not found in " + fname); +} + +cnpy::NpyArray cnpy::npy_load(std::string fname) +{ + FILE* fp = fopen(fname.c_str(), "rb"); + + if (!fp) + throw std::runtime_error("npy_load: Unable to open file " + fname); + + NpyArray arr = load_the_npy_file(fp); + + fclose(fp); + return arr; +} diff --git a/rknpu2/examples/3rdparty/cnpy/cnpy.h b/rknpu2/examples/3rdparty/cnpy/cnpy.h new file mode 100644 index 0000000000000000000000000000000000000000..0252b4bf09a61472a37f9282f1c2b3c7efa72314 --- /dev/null +++ b/rknpu2/examples/3rdparty/cnpy/cnpy.h @@ -0,0 +1,321 @@ +// Copyright (C) 2011 Carl Rogers +// Released under MIT License +// license available in LICENSE file, or at http://www.opensource.org/licenses/mit-license.php + +#ifndef LIBCNPY_H_ +#define LIBCNPY_H_ + +#if 0 +#include +#endif + +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace cnpy { + +struct NpyArray +{ + NpyArray(const std::vector& _shape, size_t _word_size, bool _fortran_order, std::string _typeName) + : shape(_shape) + , word_size(_word_size) + , fortran_order(_fortran_order) + , typeName(_typeName) + { + num_vals = 1; + for (size_t i = 0; i < shape.size(); i++) + num_vals *= shape[i]; + data_holder = std::shared_ptr>(new std::vector(num_vals * word_size)); + } + + NpyArray() + : shape(0) + , word_size(0) + , fortran_order(0) + , num_vals(0) + {} + + template + T* data() + { + return reinterpret_cast(&(*data_holder)[0]); + } + + template + const T* data() const + { + return reinterpret_cast(&(*data_holder)[0]); + } + + template + std::vector as_vec() const + { + const T* p = data(); + return std::vector(p, p + num_vals); + } + + size_t num_bytes() const { return data_holder->size(); } + + std::shared_ptr> data_holder; + std::vector shape; + size_t word_size; + bool fortran_order; + size_t num_vals; + std::string typeName; +}; + +using npz_t = std::map; + +char BigEndianTest(int size); +char map_type(const std::type_info& t); +template +std::vector create_npy_header(const std::vector& shape); +void parse_npy_header(FILE* fp, size_t& word_size, std::vector& shape, bool& fortran_order, + std::string& typeName); +void parse_npy_header(unsigned char* buffer, size_t& word_size, std::vector& shape, bool& fortran_order, + std::string& typeName); +void parse_zip_footer(FILE* fp, uint16_t& nrecs, size_t& global_header_size, size_t& global_header_offset); +npz_t npz_load(std::string fname); +NpyArray npz_load(std::string fname, std::string varname); +NpyArray npy_load(std::string fname); + +template +std::vector& operator+=(std::vector& lhs, const T rhs) +{ + // write in little endian + for (size_t byte = 0; byte < sizeof(T); byte++) { + char val = *((char*)&rhs + byte); + lhs.push_back(val); + } + return lhs; +} + +template <> +std::vector& operator+=(std::vector& lhs, const std::string rhs); +template <> +std::vector& operator+=(std::vector& lhs, const char* rhs); + +template +int npy_save(std::string fname, const T* data, const std::vector shape, std::string mode = "w") +{ + std::ofstream ofs(fname, std::ios::out); + if (!ofs.is_open()) { + return -1; + } + ofs.close(); + FILE* fp = NULL; + std::vector true_data_shape; // if appending, the shape of existing + new data + + if (mode == "a") + fp = fopen(fname.c_str(), "r+b"); + + if (fp) { + // file exists. we need to append to it. read the header, modify the array size + size_t word_size; + bool fortran_order; + std::string typeName; + parse_npy_header(fp, word_size, true_data_shape, fortran_order, typeName); + assert(!fortran_order); + + if (word_size != sizeof(T)) { + std::cout << "libnpy error: " << fname << " has word size " << word_size << " but npy_save appending data sized " + << sizeof(T) << "\n"; + assert(word_size == sizeof(T)); + } + if (true_data_shape.size() != shape.size()) { + std::cout << "libnpy error: npy_save attempting to append misdimensioned data to " << fname << "\n"; + assert(true_data_shape.size() != shape.size()); + } + + for (size_t i = 1; i < shape.size(); i++) { + if (shape[i] != true_data_shape[i]) { + std::cout << "libnpy error: npy_save attempting to append misshaped data to " << fname << "\n"; + assert(shape[i] == true_data_shape[i]); + } + } + true_data_shape[0] += shape[0]; + } else { + fp = fopen(fname.c_str(), "wb"); + true_data_shape = shape; + } + + std::vector header = create_npy_header(true_data_shape); + size_t nels = std::accumulate(shape.begin(), shape.end(), 1, std::multiplies()); + + fseek(fp, 0, SEEK_SET); + fwrite(&header[0], sizeof(char), header.size(), fp); + fseek(fp, 0, SEEK_END); + fwrite(data, sizeof(T), nels, fp); + fclose(fp); + return 0; +} + +template +void npz_save(std::string zipname, std::string fname, const T* data, const std::vector& shape, + std::string mode = "w") +{ + // first, append a .npy to the fname + fname += ".npy"; + + // now, on with the show + FILE* fp = NULL; + uint16_t nrecs = 0; + size_t global_header_offset = 0; + std::vector global_header; + + if (mode == "a") + fp = fopen(zipname.c_str(), "r+b"); + + if (fp) { + // zip file exists. we need to add a new npy file to it. + // first read the footer. this gives us the offset and size of the global header + // then read and store the global header. + // below, we will write the the new data at the start of the global header then append the global header and footer + // below it + size_t global_header_size; + parse_zip_footer(fp, nrecs, global_header_size, global_header_offset); + fseek(fp, global_header_offset, SEEK_SET); + global_header.resize(global_header_size); + size_t res = fread(&global_header[0], sizeof(char), global_header_size, fp); + if (res != global_header_size) { + throw std::runtime_error("npz_save: header read error while adding to existing zip"); + } + fseek(fp, global_header_offset, SEEK_SET); + } else { + fp = fopen(zipname.c_str(), "wb"); + } + + std::vector npy_header = create_npy_header(shape); + + size_t nels = std::accumulate(shape.begin(), shape.end(), 1, std::multiplies()); + size_t nbytes = nels * sizeof(T) + npy_header.size(); + +#if 0 + // get the CRC of the data to be added + uint32_t crc = crc32(0L, (uint8_t*)&npy_header[0], npy_header.size()); + crc = crc32(crc, (uint8_t*)data, nels * sizeof(T)); +#else + uint32_t crc = 0; +#endif + + // build the local header + std::vector local_header; + local_header += "PK"; // first part of sig + local_header += (uint16_t)0x0403; // second part of sig + local_header += (uint16_t)20; // min version to extract + local_header += (uint16_t)0; // general purpose bit flag + local_header += (uint16_t)0; // compression method + local_header += (uint16_t)0; // file last mod time + local_header += (uint16_t)0; // file last mod date + local_header += (uint32_t)crc; // crc + local_header += (uint32_t)nbytes; // compressed size + local_header += (uint32_t)nbytes; // uncompressed size + local_header += (uint16_t)fname.size(); // fname length + local_header += (uint16_t)0; // extra field length + local_header += fname; + + // build global header + global_header += "PK"; // first part of sig + global_header += (uint16_t)0x0201; // second part of sig + global_header += (uint16_t)20; // version made by + global_header.insert(global_header.end(), local_header.begin() + 4, local_header.begin() + 30); + global_header += (uint16_t)0; // file comment length + global_header += (uint16_t)0; // disk number where file starts + global_header += (uint16_t)0; // internal file attributes + global_header += (uint32_t)0; // external file attributes + global_header += (uint32_t) + global_header_offset; // relative offset of local file header, since it begins where the global header used to begin + global_header += fname; + + // build footer + std::vector footer; + footer += "PK"; // first part of sig + footer += (uint16_t)0x0605; // second part of sig + footer += (uint16_t)0; // number of this disk + footer += (uint16_t)0; // disk where footer starts + footer += (uint16_t)(nrecs + 1); // number of records on this disk + footer += (uint16_t)(nrecs + 1); // total number of records + footer += (uint32_t)global_header.size(); // nbytes of global headers + footer += + (uint32_t)(global_header_offset + nbytes + local_header.size()); // offset of start of global headers, since global + // header now starts after newly written array + footer += (uint16_t)0; // zip file comment length + + // write everything + fwrite(&local_header[0], sizeof(char), local_header.size(), fp); + fwrite(&npy_header[0], sizeof(char), npy_header.size(), fp); + fwrite(data, sizeof(T), nels, fp); + fwrite(&global_header[0], sizeof(char), global_header.size(), fp); + fwrite(&footer[0], sizeof(char), footer.size(), fp); + fclose(fp); +} + +template +void npy_save(std::string fname, const std::vector data, std::string mode = "w") +{ + std::vector shape; + shape.push_back(data.size()); + npy_save(fname, &data[0], shape, mode); +} + +template +void npz_save(std::string zipname, std::string fname, const std::vector data, std::string mode = "w") +{ + std::vector shape; + shape.push_back(data.size()); + npz_save(zipname, fname, &data[0], shape, mode); +} + +template +std::vector create_npy_header(const std::vector& shape) +{ + const char* tpye_name = typeid(T).name(); + std::vector dict; + dict += "{'descr': '"; + dict += BigEndianTest(sizeof(T)); + if (std::string(tpye_name) == "N4rknn7float16E") { + dict += "f"; + } else { + dict += map_type(typeid(T)); + } + dict += std::to_string(sizeof(T)); + dict += "', 'fortran_order': False, 'shape': ("; + dict += std::to_string(shape[0]); + for (size_t i = 1; i < shape.size(); i++) { + dict += ", "; + dict += std::to_string(shape[i]); + } + if (shape.size() == 1) + dict += ","; + dict += "), }"; + // pad with spaces so that preamble+dict is modulo 16 bytes. preamble is 10 bytes. dict needs to end with \n + int remainder = 16 - (10 + dict.size()) % 16; + dict.insert(dict.end(), remainder, ' '); + dict.back() = '\n'; + + std::vector header; + header += (char)0x93; + header += "NUMPY"; + header += (char)0x01; // major version of numpy format + header += (char)0x00; // minor version of numpy format + header += (uint16_t)dict.size(); + header.insert(header.end(), dict.begin(), dict.end()); + + return header; +} + +} // namespace cnpy + +#endif diff --git a/rknpu2/examples/3rdparty/fp16/Float16.h b/rknpu2/examples/3rdparty/fp16/Float16.h new file mode 100644 index 0000000000000000000000000000000000000000..9fd7cd6078af3c70020e06aa3e53e4f720ce6d23 --- /dev/null +++ b/rknpu2/examples/3rdparty/fp16/Float16.h @@ -0,0 +1,185 @@ +#ifndef _RKNPU2_RKNN_MATMUL_API_DEMO_H_ +#define _RKNPU2_RKNN_MATMUL_API_DEMO_H_ + +namespace rknpu2 { + +using ushort = unsigned short; + +typedef union suf32 +{ + int i; + unsigned u; + float f; +} suf32; + +class float16 +{ +public: + float16() {} + explicit float16(float x) { w = bits(x); } + + operator float() const + { + suf32 out; + + unsigned t = ((w & 0x7fff) << 13) + 0x38000000; + unsigned sign = (w & 0x8000) << 16; + unsigned e = w & 0x7c00; + + out.u = t + (1 << 23); + out.u = (e >= 0x7c00 ? t + 0x38000000 : e == 0 ? (static_cast(out.f -= 6.103515625e-05f), out.u) : t) | sign; + return out.f; + } + + static float16 fromBits(ushort b) + { + float16 result; + result.w = b; + return result; + } + static float16 zero() + { + float16 result; + result.w = (ushort)0; + return result; + } + ushort bits() const { return w; } + + static ushort bits(float x) + { + suf32 in; + in.f = x; + unsigned sign = in.u & 0x80000000; + in.u ^= sign; + ushort w; + + if (in.u >= 0x47800000) + w = (ushort)(in.u > 0x7f800000 ? 0x7e00 : 0x7c00); + else { + if (in.u < 0x38800000) { + in.f += 0.5f; + w = (ushort)(in.u - 0x3f000000); + } else { + unsigned t = in.u + 0xc8000fff; + w = (ushort)((t + ((in.u >> 13) & 1)) >> 13); + } + } + + w = (ushort)(w | (sign >> 16)); + + return w; + } + + float16& operator=(float x) + { + w = bits(x); + return *this; + } + + float16& operator+=(float x) + { + w = bits(float() + x); + return *this; + } + + float16& operator/(float x) + { + w = bits(float() / x); + return *this; + } + + inline bool is_nan() const { return ((w & 0x7c00u) == 0x7c00u) && ((w & 0x03ffu) != 0x0000u); } + + inline bool greater(const float16& x) const + { + bool sign = w & 0x8000; + bool sign_x = x.w & 0x8000; + if (sign) { + if (sign_x) + return w < x.w; + else + return false; + } else { + if (sign_x) + /* Signed zeros are equal, have to check for it */ + return (w != 0 || x.w != 0x8000); + else + return w > x.w; + } + return false; + } + + inline bool less(const float16& x) const + { + bool sign = w & 0x8000; + bool sign_x = x.w & 0x8000; + if (sign) { + if (sign_x) + return w > x.w; + else + /* Signed zeros are equal, have to check for it */ + return (w != 0x8000 || x.w != 0); + } else { + if (sign_x) + return false; + else + return w < x.w; + } + return false; + } + + inline bool operator>(const float16& x) const + { + if (is_nan() || x.is_nan()) { + return false; + } + return greater(x); + } + + inline bool operator<(const float16& x) const + { + if (is_nan() || x.is_nan()) { + return false; + } + return less(x); + } + + inline bool operator>=(const float16& x) const + { + if (is_nan() || x.is_nan()) { + return false; + } + return !less(x); + } + + inline bool operator<=(const float16& x) const + { + if (is_nan() || x.is_nan()) { + return false; + } + return !greater(x); + } + + inline bool operator==(const float16& x) const + { + /* + * The equality cases are as follows: + * - If either value is NaN, never equal. + * - If the values are equal, equal. + * - If the values are both signed zeros, equal. + */ + if (is_nan() || x.is_nan()) { + return false; + } + return (w == x.w || ((w | x.w) & 0x7fff) == 0); + } + + inline bool operator!=(const float16& x) const { return !((*this) == x); } + +protected: + ushort w = 0; +}; + +} // namespace rknn + +#endif /* _RKNPU2_RKNN_MATMUL_API_DEMO_H_ */ \ No newline at end of file diff --git a/rknpu2/examples/3rdparty/mpp/Android/arm64-v8a/libmpp.so b/rknpu2/examples/3rdparty/mpp/Android/arm64-v8a/libmpp.so new file mode 100755 index 0000000000000000000000000000000000000000..812b23cdbd2f2570b92e3bc761500b61e87d670b --- /dev/null +++ b/rknpu2/examples/3rdparty/mpp/Android/arm64-v8a/libmpp.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8bf0d56634bd3927dbad704957b07776d153e4a9845989b062a48c2ab4b9be4b +size 3653592 diff --git a/rknpu2/examples/3rdparty/mpp/Android/armeabi-v7a/libmpp.so b/rknpu2/examples/3rdparty/mpp/Android/armeabi-v7a/libmpp.so new file mode 100755 index 0000000000000000000000000000000000000000..928da60cdca5048012094cd8ee0e8f5cdc123564 --- /dev/null +++ b/rknpu2/examples/3rdparty/mpp/Android/armeabi-v7a/libmpp.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1f7c2aecc2a1e7eed0e236e9a5dfcf4034c98a0c9f21567e56215e75001af3ca +size 3151976 diff --git a/rknpu2/examples/3rdparty/mpp/Linux/aarch64/librockchip_mpp.so b/rknpu2/examples/3rdparty/mpp/Linux/aarch64/librockchip_mpp.so new file mode 100755 index 0000000000000000000000000000000000000000..14ee06d2cd6e35a93d8e4ace75708ecba21f2a1a --- /dev/null +++ b/rknpu2/examples/3rdparty/mpp/Linux/aarch64/librockchip_mpp.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:44b4d0df401c57c9b2a48e1f4703348e786c2585adef0131e77e7863356d5ce8 +size 2321616 diff --git a/rknpu2/examples/3rdparty/mpp/Linux/aarch64/librockchip_mpp.so.0 b/rknpu2/examples/3rdparty/mpp/Linux/aarch64/librockchip_mpp.so.0 new file mode 100755 index 0000000000000000000000000000000000000000..14ee06d2cd6e35a93d8e4ace75708ecba21f2a1a --- /dev/null +++ b/rknpu2/examples/3rdparty/mpp/Linux/aarch64/librockchip_mpp.so.0 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:44b4d0df401c57c9b2a48e1f4703348e786c2585adef0131e77e7863356d5ce8 +size 2321616 diff --git a/rknpu2/examples/3rdparty/mpp/Linux/aarch64/librockchip_mpp.so.1 b/rknpu2/examples/3rdparty/mpp/Linux/aarch64/librockchip_mpp.so.1 new file mode 100755 index 0000000000000000000000000000000000000000..14ee06d2cd6e35a93d8e4ace75708ecba21f2a1a --- /dev/null +++ b/rknpu2/examples/3rdparty/mpp/Linux/aarch64/librockchip_mpp.so.1 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:44b4d0df401c57c9b2a48e1f4703348e786c2585adef0131e77e7863356d5ce8 +size 2321616 diff --git a/rknpu2/examples/3rdparty/mpp/include/rockchip/mpp_buffer.h b/rknpu2/examples/3rdparty/mpp/include/rockchip/mpp_buffer.h new file mode 100644 index 0000000000000000000000000000000000000000..652acc876804d44a02c0ef02904a2325b6e6d00c --- /dev/null +++ b/rknpu2/examples/3rdparty/mpp/include/rockchip/mpp_buffer.h @@ -0,0 +1,327 @@ +/* + * Copyright 2015 Rockchip Electronics Co. LTD + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __MPP_BUFFER_H__ +#define __MPP_BUFFER_H__ + +#include "rk_type.h" +#include "mpp_err.h" + +/* + * MppBuffer module has several functions: + * + * 1. buffer get / put / reference management / external commit / get info. + * this part is the basic user interface for MppBuffer. + * + * function: + * + * mpp_buffer_get + * mpp_buffer_put + * mpp_buffer_inc_ref + * mpp_buffer_commit + * mpp_buffer_info_get + * + * 2. user buffer working flow control abstraction. + * buffer should attach to certain group, and buffer mode control the buffer usage flow. + * this part is also a part of user interface. + * + * function: + * + * mpp_buffer_group_get + * mpp_buffer_group_normal_get + * mpp_buffer_group_limit_get + * mpp_buffer_group_put + * mpp_buffer_group_limit_config + * + * 3. buffer allocator management + * this part is for allocator on different os, it does not have user interface + * it will support normal buffer, Android ion buffer, Linux v4l2 vb2 buffer + * user can only use MppBufferType to choose. + * + */ + +/* + * mpp buffer group support two work flow mode: + * + * normal flow: all buffer are generated by MPP + * under this mode, buffer pool is maintained internally + * + * typical call flow: + * + * mpp_buffer_group_get() return A + * mpp_buffer_get(A) return a ref +1 -> used + * mpp_buffer_inc_ref(a) ref +1 + * mpp_buffer_put(a) ref -1 + * mpp_buffer_put(a) ref -1 -> unused + * mpp_buffer_group_put(A) + * + * commit flow: all buffer are commited out of MPP + * under this mode, buffers is commit by external api. + * normally MPP only use it but not generate it. + * + * typical call flow: + * + * ==== external allocator ==== + * mpp_buffer_group_get() return A + * mpp_buffer_commit(A, x) + * mpp_buffer_commit(A, y) + * + * ======= internal user ====== + * mpp_buffer_get(A) return a + * mpp_buffer_get(A) return b + * mpp_buffer_put(a) + * mpp_buffer_put(b) + * + * ==== external allocator ==== + * mpp_buffer_group_put(A) + * + * NOTE: commit interface required group handle to record group information + */ + +/* + * mpp buffer group has two buffer limit mode: normal and limit + * + * normal mode: allows any buffer size and always general new buffer is no unused buffer + * is available. + * This mode normally use with normal flow and is used for table / stream buffer + * + * limit mode : restrict the buffer's size and count in the buffer group. if try to calloc + * buffer with different size or extra count it will fail. + * This mode normally use with commit flow and is used for frame buffer + */ + +/* + * NOTE: normal mode is recommanded to work with normal flow, working with limit mode is not. + * limit mode is recommanded to work with commit flow, working with normal mode is not. + */ +typedef enum { + MPP_BUFFER_INTERNAL, + MPP_BUFFER_EXTERNAL, + MPP_BUFFER_MODE_BUTT, +} MppBufferMode; + +/* + * the mpp buffer has serval types: + * + * normal : normal malloc buffer for unit test or hardware simulation + * ion : use ion device under Android/Linux, MppBuffer will encapsulte ion file handle + * ext_dma : the DMABUF(DMA buffers) come from the application + * drm : use the drm device interface for memory management + */ +typedef enum { + MPP_BUFFER_TYPE_NORMAL, + MPP_BUFFER_TYPE_ION, + MPP_BUFFER_TYPE_EXT_DMA, + MPP_BUFFER_TYPE_DRM, + MPP_BUFFER_TYPE_DMA_HEAP, + MPP_BUFFER_TYPE_BUTT, +} MppBufferType; + +#define MPP_BUFFER_TYPE_MASK 0x0000FFFF + +/* + * MPP_BUFFER_FLAGS cooperate with MppBufferType + * 16 high bits of MppBufferType are used in flags + * + * eg: + * DRM CMA buffer : MPP_BUFFER_TYPE_DRM | MPP_BUFFER_FLAGS_CONTIG + * = 0x00010003 + * DRM SECURE buffer: MPP_BUFFER_TYPE_DRM | MPP_BUFFER_FLAGS_SECURE + * = 0x00080003 + * + * The dma buffer source can also be set by format: flags | type. + * dma buffer source flags: + * MPP_BUFFER_FLAGS_CONTIG means cma + * MPP_BUFFER_FLAGS_CACHABLE means cachable + * MPP_BUFFER_FLAGS_DMA32 means dma32 + * + * flags originate from drm_rockchip_gem_mem_type + */ +#define MPP_BUFFER_FLAGS_MASK 0x003f0000 //ROCKCHIP_BO_MASK << 16 +#define MPP_BUFFER_FLAGS_CONTIG 0x00010000 //ROCKCHIP_BO_CONTIG << 16 +#define MPP_BUFFER_FLAGS_CACHABLE 0x00020000 //ROCKCHIP_BO_CACHABLE << 16 +#define MPP_BUFFER_FLAGS_WC 0x00040000 //ROCKCHIP_BO_WC << 16 +#define MPP_BUFFER_FLAGS_SECURE 0x00080000 //ROCKCHIP_BO_SECURE << 16 +#define MPP_BUFFER_FLAGS_ALLOC_KMAP 0x00100000 //ROCKCHIP_BO_ALLOC_KMAP << 16 +#define MPP_BUFFER_FLAGS_DMA32 0x00200000 //ROCKCHIP_BO_DMA32 << 16 + +/* + * MppBufferInfo variable's meaning is different in different MppBufferType + * + * Common + * index - the buffer index used to track buffer in buffer pool + * size - the buffer size + * + * MPP_BUFFER_TYPE_NORMAL + * + * ptr - virtual address of normal malloced buffer + * fd - unused and set to -1, the allocator would return its + * internal buffer counter number + * + * MPP_BUFFER_TYPE_ION + * + * ptr - virtual address of ion buffer in user space + * hnd - ion handle in user space + * fd - ion buffer file handle for map / unmap + * + */ +typedef struct MppBufferInfo_t { + MppBufferType type; + size_t size; + void *ptr; + void *hnd; + int fd; + int index; +} MppBufferInfo; + +#define BUFFER_GROUP_SIZE_DEFAULT (SZ_1M*80) + +/* + * mpp_buffer_import_with_tag(MppBufferGroup group, MppBufferInfo *info, MppBuffer *buffer) + * + * 1. group - specified the MppBuffer to be attached to. + * group can be NULL then this buffer will attached to default legecy group + * Default to NULL on mpp_buffer_import case + * + * 2. info - input information for the output MppBuffer + * info can NOT be NULL. It must contain at least one of ptr/fd. + * + * 3. buffer - generated MppBuffer from MppBufferInfo. + * buffer can be NULL then the buffer is commit to group with unused status. + * Otherwise generated buffer will be directly got and ref_count increased. + * Default to NULL on mpp_buffer_commit case + * + * mpp_buffer_commit usage: + * + * Add a external buffer info to group. This buffer will be on unused status. + * Typical usage is on Android. MediaPlayer gralloc Graphic buffer then commit these buffer + * to decoder's buffer group. Then decoder will recycle these buffer and return buffer reference + * to MediaPlayer for display. + * + * mpp_buffer_import usage: + * + * Transfer a external buffer info to MppBuffer but it is not expected to attached to certain + * buffer group. So the group is set to NULL. Then this buffer can be used for MppFrame/MppPacket. + * Typical usage is for image processing. Image processing normally will be a oneshot operation + * It does not need complicated group management. But in other hand mpp still need to know the + * imported buffer is leak or not and trace its usage inside mpp process. So we attach this kind + * of buffer to default misc buffer group for management. + */ +#define mpp_buffer_commit(group, info) \ + mpp_buffer_import_with_tag(group, info, NULL, MODULE_TAG, __FUNCTION__) + +#define mpp_buffer_import(buffer, info) \ + mpp_buffer_import_with_tag(NULL, info, buffer, MODULE_TAG, __FUNCTION__) + +#define mpp_buffer_get(group, buffer, size) \ + mpp_buffer_get_with_tag(group, buffer, size, MODULE_TAG, __FUNCTION__) + +#define mpp_buffer_put(buffer) \ + mpp_buffer_put_with_caller(buffer, __FUNCTION__) + +#define mpp_buffer_inc_ref(buffer) \ + mpp_buffer_inc_ref_with_caller(buffer, __FUNCTION__) + +#define mpp_buffer_info_get(buffer, info) \ + mpp_buffer_info_get_with_caller(buffer, info, __FUNCTION__) + +#define mpp_buffer_read(buffer, offset, data, size) \ + mpp_buffer_read_with_caller(buffer, offset, data, size, __FUNCTION__) + +#define mpp_buffer_write(buffer, offset, data, size) \ + mpp_buffer_write_with_caller(buffer, offset, data, size, __FUNCTION__) + +#define mpp_buffer_get_ptr(buffer) \ + mpp_buffer_get_ptr_with_caller(buffer, __FUNCTION__) + +#define mpp_buffer_get_fd(buffer) \ + mpp_buffer_get_fd_with_caller(buffer, __FUNCTION__) + +#define mpp_buffer_get_size(buffer) \ + mpp_buffer_get_size_with_caller(buffer, __FUNCTION__) + +#define mpp_buffer_get_index(buffer) \ + mpp_buffer_get_index_with_caller(buffer, __FUNCTION__) + +#define mpp_buffer_set_index(buffer, index) \ + mpp_buffer_set_index_with_caller(buffer, index, __FUNCTION__) + +#define mpp_buffer_get_offset(buffer) \ + mpp_buffer_get_offset_with_caller(buffer, __FUNCTION__) + +#define mpp_buffer_set_offset(buffer, offset) \ + mpp_buffer_set_offset_with_caller(buffer, offset, __FUNCTION__) + +#define mpp_buffer_group_get_internal(group, type, ...) \ + mpp_buffer_group_get(group, type, MPP_BUFFER_INTERNAL, MODULE_TAG, __FUNCTION__) + +#define mpp_buffer_group_get_external(group, type, ...) \ + mpp_buffer_group_get(group, type, MPP_BUFFER_EXTERNAL, MODULE_TAG, __FUNCTION__) + +#ifdef __cplusplus +extern "C" { +#endif + +/* + * MppBuffer interface + * these interface will change value of group and buffer so before calling functions + * parameter need to be checked. + * + * IMPORTANT: + * mpp_buffer_import_with_tag - compounded interface for commit and import + * + */ +MPP_RET mpp_buffer_import_with_tag(MppBufferGroup group, MppBufferInfo *info, MppBuffer *buffer, + const char *tag, const char *caller); +MPP_RET mpp_buffer_get_with_tag(MppBufferGroup group, MppBuffer *buffer, size_t size, + const char *tag, const char *caller); +MPP_RET mpp_buffer_put_with_caller(MppBuffer buffer, const char *caller); +MPP_RET mpp_buffer_inc_ref_with_caller(MppBuffer buffer, const char *caller); + +MPP_RET mpp_buffer_info_get_with_caller(MppBuffer buffer, MppBufferInfo *info, const char *caller); +MPP_RET mpp_buffer_read_with_caller(MppBuffer buffer, size_t offset, void *data, size_t size, const char *caller); +MPP_RET mpp_buffer_write_with_caller(MppBuffer buffer, size_t offset, void *data, size_t size, const char *caller); +void *mpp_buffer_get_ptr_with_caller(MppBuffer buffer, const char *caller); +int mpp_buffer_get_fd_with_caller(MppBuffer buffer, const char *caller); +size_t mpp_buffer_get_size_with_caller(MppBuffer buffer, const char *caller); +int mpp_buffer_get_index_with_caller(MppBuffer buffer, const char *caller); +MPP_RET mpp_buffer_set_index_with_caller(MppBuffer buffer, int index, const char *caller); +size_t mpp_buffer_get_offset_with_caller(MppBuffer buffer, const char *caller); +MPP_RET mpp_buffer_set_offset_with_caller(MppBuffer buffer, size_t offset, const char *caller); + +MPP_RET mpp_buffer_group_get(MppBufferGroup *group, MppBufferType type, MppBufferMode mode, + const char *tag, const char *caller); +MPP_RET mpp_buffer_group_put(MppBufferGroup group); +MPP_RET mpp_buffer_group_clear(MppBufferGroup group); +RK_S32 mpp_buffer_group_unused(MppBufferGroup group); +size_t mpp_buffer_group_usage(MppBufferGroup group); +MppBufferMode mpp_buffer_group_mode(MppBufferGroup group); +MppBufferType mpp_buffer_group_type(MppBufferGroup group); + +/* + * size : 0 - no limit, other - max buffer size + * count : 0 - no limit, other - max buffer count + */ +MPP_RET mpp_buffer_group_limit_config(MppBufferGroup group, size_t size, RK_S32 count); + +RK_U32 mpp_buffer_total_now(); +RK_U32 mpp_buffer_total_max(); + +#ifdef __cplusplus +} +#endif + +#endif /*__MPP_BUFFER_H__*/ diff --git a/rknpu2/examples/3rdparty/mpp/include/rockchip/mpp_compat.h b/rknpu2/examples/3rdparty/mpp/include/rockchip/mpp_compat.h new file mode 100644 index 0000000000000000000000000000000000000000..22a3f17dad6c13041f0554b97c1000690884c913 --- /dev/null +++ b/rknpu2/examples/3rdparty/mpp/include/rockchip/mpp_compat.h @@ -0,0 +1,62 @@ +/* + * Copyright 2021 Rockchip Electronics Co. LTD + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __MPP_COMPAT_H__ +#define __MPP_COMPAT_H__ + +#include "rk_type.h" +#include "mpp_err.h" + +typedef enum MppCompatId_e { + MPP_COMPAT_INC_FBC_BUF_SIZE, + MPP_COMPAT_ENC_ASYNC_INPUT, + MPP_COMPAT_DEC_FBC_HDR_256_ODD, + MPP_COMPAT_BUTT, +} MppCompatId; + +typedef enum MppCompatType_e { + MPP_COMPAT_BOOL, + MPP_COMPAT_S32, + MPP_COMPAT_TYPE_BUTT, +} MppCompatType; + +typedef struct MppCompat_t MppCompat; + +/* external user can only update value_ext to notify mpp to change its behavior */ +struct MppCompat_t { + const MppCompatId feature_id; + const MppCompatType feature_type; + const RK_S32 value_mpp; + RK_S32 value_usr; + const char *name; + MppCompat * const next; +}; + +#ifdef __cplusplus +extern "C" { +#endif + +MppCompat *mpp_compat_query(void); +MppCompat *mpp_compat_query_by_id(MppCompatId id); +MPP_RET mpp_compat_update(MppCompat *compat, RK_S32 value); + +void mpp_compat_show(void); + +#ifdef __cplusplus +} +#endif + +#endif /*__MPP_COMPAT_H__*/ diff --git a/rknpu2/examples/3rdparty/mpp/include/rockchip/mpp_err.h b/rknpu2/examples/3rdparty/mpp/include/rockchip/mpp_err.h new file mode 100644 index 0000000000000000000000000000000000000000..469c6811a06ae91e56f4b60d68246eda56c5b33e --- /dev/null +++ b/rknpu2/examples/3rdparty/mpp/include/rockchip/mpp_err.h @@ -0,0 +1,54 @@ +/* + * Copyright 2015 Rockchip Electronics Co. LTD + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __MPP_ERR_H__ +#define __MPP_ERR_H__ + +#define RK_OK 0 +#define RK_SUCCESS 0 + +typedef enum { + MPP_SUCCESS = RK_SUCCESS, + MPP_OK = RK_OK, + + MPP_NOK = -1, + MPP_ERR_UNKNOW = -2, + MPP_ERR_NULL_PTR = -3, + MPP_ERR_MALLOC = -4, + MPP_ERR_OPEN_FILE = -5, + MPP_ERR_VALUE = -6, + MPP_ERR_READ_BIT = -7, + MPP_ERR_TIMEOUT = -8, + MPP_ERR_PERM = -9, + + MPP_ERR_BASE = -1000, + + /* The error in stream processing */ + MPP_ERR_LIST_STREAM = MPP_ERR_BASE - 1, + MPP_ERR_INIT = MPP_ERR_BASE - 2, + MPP_ERR_VPU_CODEC_INIT = MPP_ERR_BASE - 3, + MPP_ERR_STREAM = MPP_ERR_BASE - 4, + MPP_ERR_FATAL_THREAD = MPP_ERR_BASE - 5, + MPP_ERR_NOMEM = MPP_ERR_BASE - 6, + MPP_ERR_PROTOL = MPP_ERR_BASE - 7, + MPP_FAIL_SPLIT_FRAME = MPP_ERR_BASE - 8, + MPP_ERR_VPUHW = MPP_ERR_BASE - 9, + MPP_EOS_STREAM_REACHED = MPP_ERR_BASE - 11, + MPP_ERR_BUFFER_FULL = MPP_ERR_BASE - 12, + MPP_ERR_DISPLAY_FULL = MPP_ERR_BASE - 13, +} MPP_RET; + +#endif /*__MPP_ERR_H__*/ diff --git a/rknpu2/examples/3rdparty/mpp/include/rockchip/mpp_frame.h b/rknpu2/examples/3rdparty/mpp/include/rockchip/mpp_frame.h new file mode 100644 index 0000000000000000000000000000000000000000..7bb0043a50222c2e6d4c4f40562c6746b19714cc --- /dev/null +++ b/rknpu2/examples/3rdparty/mpp/include/rockchip/mpp_frame.h @@ -0,0 +1,432 @@ +/* + * Copyright 2015 Rockchip Electronics Co. LTD + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __MPP_FRAME_H__ +#define __MPP_FRAME_H__ + +#include "mpp_buffer.h" +#include "mpp_meta.h" + +/* + * bit definition for mode flag in MppFrame + */ +/* progressive frame */ +#define MPP_FRAME_FLAG_FRAME (0x00000000) +/* top field only */ +#define MPP_FRAME_FLAG_TOP_FIELD (0x00000001) +/* bottom field only */ +#define MPP_FRAME_FLAG_BOT_FIELD (0x00000002) +/* paired field */ +#define MPP_FRAME_FLAG_PAIRED_FIELD (MPP_FRAME_FLAG_TOP_FIELD|MPP_FRAME_FLAG_BOT_FIELD) +/* paired field with field order of top first */ +#define MPP_FRAME_FLAG_TOP_FIRST (0x00000004) +/* paired field with field order of bottom first */ +#define MPP_FRAME_FLAG_BOT_FIRST (0x00000008) +/* paired field with unknown field order (MBAFF) */ +#define MPP_FRAME_FLAG_DEINTERLACED (MPP_FRAME_FLAG_TOP_FIRST|MPP_FRAME_FLAG_BOT_FIRST) +#define MPP_FRAME_FLAG_FIELD_ORDER_MASK (0x0000000C) +// for multiview stream +#define MPP_FRAME_FLAG_VIEW_ID_MASK (0x000000f0) + +#define MPP_FRAME_FLAG_IEP_DEI_MASK (0x00000f00) +#define MPP_FRAME_FLAG_IEP_DEI_I2O1 (0x00000100) +#define MPP_FRAME_FLAG_IEP_DEI_I4O2 (0x00000200) +#define MPP_FRAME_FLAG_IEP_DEI_I4O1 (0x00000300) + +/* + * MPEG vs JPEG YUV range. + */ +typedef enum { + MPP_FRAME_RANGE_UNSPECIFIED = 0, + MPP_FRAME_RANGE_MPEG = 1, ///< the normal 219*2^(n-8) "MPEG" YUV ranges + MPP_FRAME_RANGE_JPEG = 2, ///< the normal 2^n-1 "JPEG" YUV ranges + MPP_FRAME_RANGE_NB, ///< Not part of ABI +} MppFrameColorRange; + +typedef enum { + MPP_FRAME_VIDEO_FMT_COMPONEMT = 0, + MPP_FRAME_VIDEO_FMT_PAL = 1, + MPP_FRAME_VIDEO_FMT_NTSC = 2, + MPP_FRAME_VIDEO_FMT_SECAM = 3, + MPP_FRAME_VIDEO_FMT_MAC = 4, + MPP_FRAME_VIDEO_FMT_UNSPECIFIED = 5, + MPP_FRAME_VIDEO_FMT_RESERVED0 = 6, + MPP_FRAME_VIDEO_FMT_RESERVED1 = 7, +} MppFrameVideoFormat; + +/* + * Chromaticity coordinates of the source primaries. + */ +typedef enum { + MPP_FRAME_PRI_RESERVED0 = 0, + MPP_FRAME_PRI_BT709 = 1, ///< also ITU-R BT1361 / IEC 61966-2-4 / SMPTE RP177 Annex B + MPP_FRAME_PRI_UNSPECIFIED = 2, + MPP_FRAME_PRI_RESERVED = 3, + MPP_FRAME_PRI_BT470M = 4, ///< also FCC Title 47 Code of Federal Regulations 73.682 (a)(20) + + MPP_FRAME_PRI_BT470BG = 5, ///< also ITU-R BT601-6 625 / ITU-R BT1358 625 / ITU-R BT1700 625 PAL & SECAM + MPP_FRAME_PRI_SMPTE170M = 6, ///< also ITU-R BT601-6 525 / ITU-R BT1358 525 / ITU-R BT1700 NTSC/SMPTE ST 170 (2004) + MPP_FRAME_PRI_SMPTE240M = 7, ///< functionally identical to above/SMPTE ST 240 + MPP_FRAME_PRI_FILM = 8, ///< colour filters using Illuminant C + MPP_FRAME_PRI_BT2020 = 9, ///< ITU-R BT2020 / ITU-R BT.2100-2 + MPP_FRAME_PRI_SMPTEST428_1 = 10, ///< SMPTE ST 428-1 (CIE 1931 XYZ) + MPP_FRAME_PRI_SMPTE431 = 11, ///< SMPTE ST 431-2 (2011) / DCI P3 + MPP_FRAME_PRI_SMPTE432 = 12, ///< SMPTE ST 432-1 (2010) / P3 D65 / Display P3 + MPP_FRAME_PRI_JEDEC_P22 = 22, ///< JEDEC P22 phosphors + MPP_FRAME_PRI_NB, ///< Not part of ABI +} MppFrameColorPrimaries; + +/* + * Color Transfer Characteristic. + */ +typedef enum { + MPP_FRAME_TRC_RESERVED0 = 0, + MPP_FRAME_TRC_BT709 = 1, ///< also ITU-R BT1361 + MPP_FRAME_TRC_UNSPECIFIED = 2, + MPP_FRAME_TRC_RESERVED = 3, + MPP_FRAME_TRC_GAMMA22 = 4, ///< also ITU-R BT470M / ITU-R BT1700 625 PAL & SECAM + MPP_FRAME_TRC_GAMMA28 = 5, ///< also ITU-R BT470BG + MPP_FRAME_TRC_SMPTE170M = 6, ///< also ITU-R BT601-6 525 or 625 / ITU-R BT1358 525 or 625 / ITU-R BT1700 NTSC + MPP_FRAME_TRC_SMPTE240M = 7, + MPP_FRAME_TRC_LINEAR = 8, ///< "Linear transfer characteristics" + MPP_FRAME_TRC_LOG = 9, ///< "Logarithmic transfer characteristic (100:1 range)" + MPP_FRAME_TRC_LOG_SQRT = 10, ///< "Logarithmic transfer characteristic (100 * Sqrt(10) : 1 range)" + MPP_FRAME_TRC_IEC61966_2_4 = 11, ///< IEC 61966-2-4 + MPP_FRAME_TRC_BT1361_ECG = 12, ///< ITU-R BT1361 Extended Colour Gamut + MPP_FRAME_TRC_IEC61966_2_1 = 13, ///< IEC 61966-2-1 (sRGB or sYCC) + MPP_FRAME_TRC_BT2020_10 = 14, ///< ITU-R BT2020 for 10 bit system + MPP_FRAME_TRC_BT2020_12 = 15, ///< ITU-R BT2020 for 12 bit system + MPP_FRAME_TRC_SMPTEST2084 = 16, ///< SMPTE ST 2084 for 10-, 12-, 14- and 16-bit systems + MPP_FRAME_TRC_SMPTEST428_1 = 17, ///< SMPTE ST 428-1 + MPP_FRAME_TRC_ARIB_STD_B67 = 18, ///< ARIB STD-B67, known as "Hybrid log-gamma" + MPP_FRAME_TRC_NB, ///< Not part of ABI +} MppFrameColorTransferCharacteristic; + +/* + * YUV colorspace type. + */ +typedef enum { + MPP_FRAME_SPC_RGB = 0, ///< order of coefficients is actually GBR, also IEC 61966-2-1 (sRGB) + MPP_FRAME_SPC_BT709 = 1, ///< also ITU-R BT1361 / IEC 61966-2-4 xvYCC709 / SMPTE RP177 Annex B + MPP_FRAME_SPC_UNSPECIFIED = 2, + MPP_FRAME_SPC_RESERVED = 3, + MPP_FRAME_SPC_FCC = 4, ///< FCC Title 47 Code of Federal Regulations 73.682 (a)(20) + MPP_FRAME_SPC_BT470BG = 5, ///< also ITU-R BT601-6 625 / ITU-R BT1358 625 / ITU-R BT1700 625 PAL & SECAM / IEC 61966-2-4 xvYCC601 + MPP_FRAME_SPC_SMPTE170M = 6, ///< also ITU-R BT601-6 525 / ITU-R BT1358 525 / ITU-R BT1700 NTSC / functionally identical to above + MPP_FRAME_SPC_SMPTE240M = 7, + MPP_FRAME_SPC_YCOCG = 8, ///< Used by Dirac / VC-2 and H.264 FRext, see ITU-T SG16 + MPP_FRAME_SPC_BT2020_NCL = 9, ///< ITU-R BT2020 non-constant luminance system + MPP_FRAME_SPC_BT2020_CL = 10, ///< ITU-R BT2020 constant luminance system + MPP_FRAME_SPC_SMPTE2085 = 11, ///< SMPTE 2085, Y'D'zD'x + MPP_FRAME_SPC_CHROMA_DERIVED_NCL = 12, ///< Chromaticity-derived non-constant luminance system + MPP_FRAME_SPC_CHROMA_DERIVED_CL = 13, ///< Chromaticity-derived constant luminance system + MPP_FRAME_SPC_ICTCP = 14, ///< ITU-R BT.2100-0, ICtCp + MPP_FRAME_SPC_NB, ///< Not part of ABI +} MppFrameColorSpace; + +/* + * Location of chroma samples. + * + * Illustration showing the location of the first (top left) chroma sample of the + * image, the left shows only luma, the right + * shows the location of the chroma sample, the 2 could be imagined to overlay + * each other but are drawn separately due to limitations of ASCII + * + * 1st 2nd 1st 2nd horizontal luma sample positions + * v v v v + * ______ ______ + *1st luma line > |X X ... |3 4 X ... X are luma samples, + * | |1 2 1-6 are possible chroma positions + *2nd luma line > |X X ... |5 6 X ... 0 is undefined/unknown position + */ +typedef enum { + MPP_CHROMA_LOC_UNSPECIFIED = 0, + MPP_CHROMA_LOC_LEFT = 1, ///< mpeg2/4 4:2:0, h264 default for 4:2:0 + MPP_CHROMA_LOC_CENTER = 2, ///< mpeg1 4:2:0, jpeg 4:2:0, h263 4:2:0 + MPP_CHROMA_LOC_TOPLEFT = 3, ///< ITU-R 601, SMPTE 274M 296M S314M(DV 4:1:1), mpeg2 4:2:2 + MPP_CHROMA_LOC_TOP = 4, + MPP_CHROMA_LOC_BOTTOMLEFT = 5, + MPP_CHROMA_LOC_BOTTOM = 6, + MPP_CHROMA_LOC_NB, ///< Not part of ABI +} MppFrameChromaLocation; + +#define MPP_FRAME_FMT_MASK (0x000fffff) + +#define MPP_FRAME_FMT_COLOR_MASK (0x000f0000) +#define MPP_FRAME_FMT_YUV (0x00000000) +#define MPP_FRAME_FMT_RGB (0x00010000) + +#define MPP_FRAME_FBC_MASK (0x00f00000) +#define MPP_FRAME_FBC_NONE (0x00000000) + +#define MPP_FRAME_HDR_MASK (0x0f000000) +#define MPP_FRAME_HDR_NONE (0x00000000) + +#define MPP_FRAME_HDR (0x01000000) + +/* + * AFBC_V1 is for ISP output. + * It has default payload offset to be calculated * from width and height: + * Payload offset = MPP_ALIGN(MPP_ALIGN(width, 16) * MPP_ALIGN(height, 16) / 16, SZ_4K) + */ +#define MPP_FRAME_FBC_AFBC_V1 (0x00100000) +/* + * AFBC_V2 is for video decoder output. + * It stores payload offset in first 32-bit in header address + * Payload offset is always set to zero. + */ +#define MPP_FRAME_FBC_AFBC_V2 (0x00200000) + +#define MPP_FRAME_FMT_LE_MASK (0x01000000) + +#define MPP_FRAME_FMT_IS_YUV(fmt) (((fmt & MPP_FRAME_FMT_COLOR_MASK) == MPP_FRAME_FMT_YUV) && \ + ((fmt & MPP_FRAME_FMT_MASK) < MPP_FMT_YUV_BUTT)) +#define MPP_FRAME_FMT_IS_YUV_10BIT(fmt) ((fmt & MPP_FRAME_FMT_MASK) == MPP_FMT_YUV420SP_10BIT || \ + (fmt & MPP_FRAME_FMT_MASK) == MPP_FMT_YUV422SP_10BIT) +#define MPP_FRAME_FMT_IS_RGB(fmt) (((fmt & MPP_FRAME_FMT_COLOR_MASK) == MPP_FRAME_FMT_RGB) && \ + ((fmt & MPP_FRAME_FMT_MASK) < MPP_FMT_RGB_BUTT)) + +/* + * For MPP_FRAME_FBC_AFBC_V1 the 16byte aligned stride is used. + */ +#define MPP_FRAME_FMT_IS_FBC(fmt) (fmt & MPP_FRAME_FBC_MASK) + +#define MPP_FRAME_FMT_IS_HDR(fmt) (fmt & MPP_FRAME_HDR_MASK) + +#define MPP_FRAME_FMT_IS_LE(fmt) ((fmt & MPP_FRAME_FMT_LE_MASK) == MPP_FRAME_FMT_LE_MASK) +#define MPP_FRAME_FMT_IS_BE(fmt) ((fmt & MPP_FRAME_FMT_LE_MASK) == 0) + +/* mpp color format index definition */ +typedef enum { + MPP_FMT_YUV420SP = (MPP_FRAME_FMT_YUV + 0), /* YYYY... UV... (NV12) */ + /* + * A rockchip specific pixel format, without gap between pixel aganist + * the P010_10LE/P010_10BE + */ + MPP_FMT_YUV420SP_10BIT = (MPP_FRAME_FMT_YUV + 1), + MPP_FMT_YUV422SP = (MPP_FRAME_FMT_YUV + 2), /* YYYY... UVUV... (NV16) */ + MPP_FMT_YUV422SP_10BIT = (MPP_FRAME_FMT_YUV + 3), ///< Not part of ABI + MPP_FMT_YUV420P = (MPP_FRAME_FMT_YUV + 4), /* YYYY... U...V... (I420) */ + MPP_FMT_YUV420SP_VU = (MPP_FRAME_FMT_YUV + 5), /* YYYY... VUVUVU... (NV21) */ + MPP_FMT_YUV422P = (MPP_FRAME_FMT_YUV + 6), /* YYYY... UU...VV...(422P) */ + MPP_FMT_YUV422SP_VU = (MPP_FRAME_FMT_YUV + 7), /* YYYY... VUVUVU... (NV61) */ + MPP_FMT_YUV422_YUYV = (MPP_FRAME_FMT_YUV + 8), /* YUYVYUYV... (YUY2) */ + MPP_FMT_YUV422_YVYU = (MPP_FRAME_FMT_YUV + 9), /* YVYUYVYU... (YVY2) */ + MPP_FMT_YUV422_UYVY = (MPP_FRAME_FMT_YUV + 10), /* UYVYUYVY... (UYVY) */ + MPP_FMT_YUV422_VYUY = (MPP_FRAME_FMT_YUV + 11), /* VYUYVYUY... (VYUY) */ + MPP_FMT_YUV400 = (MPP_FRAME_FMT_YUV + 12), /* YYYY... */ + MPP_FMT_YUV440SP = (MPP_FRAME_FMT_YUV + 13), /* YYYY... UVUV... */ + MPP_FMT_YUV411SP = (MPP_FRAME_FMT_YUV + 14), /* YYYY... UV... */ + MPP_FMT_YUV444SP = (MPP_FRAME_FMT_YUV + 15), /* YYYY... UVUVUVUV... */ + MPP_FMT_YUV444P = (MPP_FRAME_FMT_YUV + 16), /* YYYY... UUUU... VVVV... */ + MPP_FMT_YUV_BUTT, + + MPP_FMT_RGB565 = (MPP_FRAME_FMT_RGB + 0), /* 16-bit RGB */ + MPP_FMT_BGR565 = (MPP_FRAME_FMT_RGB + 1), /* 16-bit RGB */ + MPP_FMT_RGB555 = (MPP_FRAME_FMT_RGB + 2), /* 15-bit RGB */ + MPP_FMT_BGR555 = (MPP_FRAME_FMT_RGB + 3), /* 15-bit RGB */ + MPP_FMT_RGB444 = (MPP_FRAME_FMT_RGB + 4), /* 12-bit RGB */ + MPP_FMT_BGR444 = (MPP_FRAME_FMT_RGB + 5), /* 12-bit RGB */ + MPP_FMT_RGB888 = (MPP_FRAME_FMT_RGB + 6), /* 24-bit RGB */ + MPP_FMT_BGR888 = (MPP_FRAME_FMT_RGB + 7), /* 24-bit RGB */ + MPP_FMT_RGB101010 = (MPP_FRAME_FMT_RGB + 8), /* 30-bit RGB */ + MPP_FMT_BGR101010 = (MPP_FRAME_FMT_RGB + 9), /* 30-bit RGB */ + MPP_FMT_ARGB8888 = (MPP_FRAME_FMT_RGB + 10), /* 32-bit RGB */ + MPP_FMT_ABGR8888 = (MPP_FRAME_FMT_RGB + 11), /* 32-bit RGB */ + MPP_FMT_BGRA8888 = (MPP_FRAME_FMT_RGB + 12), /* 32-bit RGB */ + MPP_FMT_RGBA8888 = (MPP_FRAME_FMT_RGB + 13), /* 32-bit RGB */ + MPP_FMT_RGB_BUTT, + + MPP_FMT_BUTT, +} MppFrameFormat; + +/** + * Rational number (pair of numerator and denominator). + */ +typedef struct MppFrameRational { + RK_S32 num; ///< Numerator + RK_S32 den; ///< Denominator +} MppFrameRational; + +typedef struct MppFrameMasteringDisplayMetadata { + RK_U16 display_primaries[3][2]; + RK_U16 white_point[2]; + RK_U32 max_luminance; + RK_U32 min_luminance; +} MppFrameMasteringDisplayMetadata; + +typedef struct MppFrameContentLightMetadata { + RK_U16 MaxCLL; + RK_U16 MaxFALL; +} MppFrameContentLightMetadata; + +typedef struct MppFrameHdrDynamicMeta { + RK_U32 hdr_fmt; + RK_U32 size; + RK_U8 data[]; +} MppFrameHdrDynamicMeta; + +typedef enum MppFrameError { + /* General error not specified */ + MPP_FRAME_ERR_UNKNOW = 0x0001, + + /* Critical error for decoder not support error */ + MPP_FRAME_ERR_UNSUPPORT = 0x0002, + + /* + * Fatal error for decoder can not parse a valid frame for hardware. + * the pixel data is all invalid. + */ + MPP_FRAME_ERR_DEC_INVALID = 0x0010, + + /* + * Normal error for decoder found hardware error on decoding. + */ + MPP_FRAME_ERR_DEC_HW_ERR = 0x0100, + + /* + * Normal error for decoder found missing reference frame on decoding. + */ + MPP_FRAME_ERR_DEC_MISS_REF = 0x0200, +} MppFrameError; + +#ifdef __cplusplus +extern "C" { +#endif + +/* + * MppFrame interface + */ +MPP_RET mpp_frame_init(MppFrame *frame); +MPP_RET mpp_frame_deinit(MppFrame *frame); + +/* + * normal parameter + * + * offset_x + * <--------> + * + * <---------------+ hor_stride +---------------> + * + * +------------------------------------------------------+ ^ ^ + * | | | | + * | | | | offset_y + * | | | | + * | +--------------------------------+ ^ | | v + * | | | | | | + * | | | + | + + * | | | | + * | | valid data area | height | ver_stride + * | | | | + * | | | + | + + * | | | | | | + * | +--------------------------------+ v | | + * | | | + * | <----------+ width +---------> | | + * | | | + * +------------------------------------------------------+ v + * + */ +RK_U32 mpp_frame_get_width(const MppFrame frame); +void mpp_frame_set_width(MppFrame frame, RK_U32 width); +RK_U32 mpp_frame_get_height(const MppFrame frame); +void mpp_frame_set_height(MppFrame frame, RK_U32 height); +RK_U32 mpp_frame_get_hor_stride(const MppFrame frame); +void mpp_frame_set_hor_stride(MppFrame frame, RK_U32 hor_stride); +RK_U32 mpp_frame_get_ver_stride(const MppFrame frame); +void mpp_frame_set_ver_stride(MppFrame frame, RK_U32 ver_stride); +void mpp_frame_set_hor_stride_pixel(MppFrame frame, RK_U32 hor_stride_pixel); +RK_U32 mpp_frame_get_hor_stride_pixel(const MppFrame frame); +void mpp_frame_set_fbc_hdr_stride(MppFrame frame, RK_U32 fbc_hdr_stride); +RK_U32 mpp_frame_get_fbc_hdr_stride(const MppFrame frame); + +RK_U32 mpp_frame_get_offset_x(const MppFrame frame); +void mpp_frame_set_offset_x(MppFrame frame, RK_U32 offset_x); +RK_U32 mpp_frame_get_offset_y(const MppFrame frame); +void mpp_frame_set_offset_y(MppFrame frame, RK_U32 offset_y); +RK_U32 mpp_frame_get_mode(const MppFrame frame); +void mpp_frame_set_mode(MppFrame frame, RK_U32 mode); +RK_U32 mpp_frame_get_discard(const MppFrame frame); +void mpp_frame_set_discard(MppFrame frame, RK_U32 discard); +RK_U32 mpp_frame_get_viewid(const MppFrame frame); +void mpp_frame_set_viewid(MppFrame frame, RK_U32 viewid); +RK_U32 mpp_frame_get_poc(const MppFrame frame); +void mpp_frame_set_poc(MppFrame frame, RK_U32 poc); +RK_S64 mpp_frame_get_pts(const MppFrame frame); +void mpp_frame_set_pts(MppFrame frame, RK_S64 pts); +RK_S64 mpp_frame_get_dts(const MppFrame frame); +void mpp_frame_set_dts(MppFrame frame, RK_S64 dts); +RK_U32 mpp_frame_get_errinfo(const MppFrame frame); +void mpp_frame_set_errinfo(MppFrame frame, RK_U32 errinfo); +size_t mpp_frame_get_buf_size(const MppFrame frame); +void mpp_frame_set_buf_size(MppFrame frame, size_t buf_size); +void mpp_frame_set_thumbnail_en(MppFrame frame, RK_U32 thumbnail_en); +RK_U32 mpp_frame_get_thumbnail_en(const MppFrame frame); + +/* + * flow control parmeter + */ +RK_U32 mpp_frame_get_eos(const MppFrame frame); +void mpp_frame_set_eos(MppFrame frame, RK_U32 eos); +RK_U32 mpp_frame_get_info_change(const MppFrame frame); +void mpp_frame_set_info_change(MppFrame frame, RK_U32 info_change); + +/* + * buffer parameter + */ +MppBuffer mpp_frame_get_buffer(const MppFrame frame); +void mpp_frame_set_buffer(MppFrame frame, MppBuffer buffer); + +/* + * meta data parameter + */ +RK_S32 mpp_frame_has_meta(const MppFrame frame); +MppMeta mpp_frame_get_meta(const MppFrame frame); +void mpp_frame_set_meta(MppFrame frame, MppMeta meta); + +/* + * color related parameter + */ +MppFrameColorRange mpp_frame_get_color_range(const MppFrame frame); +void mpp_frame_set_color_range(MppFrame frame, MppFrameColorRange color_range); +MppFrameColorPrimaries mpp_frame_get_color_primaries(const MppFrame frame); +void mpp_frame_set_color_primaries(MppFrame frame, MppFrameColorPrimaries color_primaries); +MppFrameColorTransferCharacteristic mpp_frame_get_color_trc(const MppFrame frame); +void mpp_frame_set_color_trc(MppFrame frame, MppFrameColorTransferCharacteristic color_trc); +MppFrameColorSpace mpp_frame_get_colorspace(const MppFrame frame); +void mpp_frame_set_colorspace(MppFrame frame, MppFrameColorSpace colorspace); +MppFrameChromaLocation mpp_frame_get_chroma_location(const MppFrame frame); +void mpp_frame_set_chroma_location(MppFrame frame, MppFrameChromaLocation chroma_location); +MppFrameFormat mpp_frame_get_fmt(MppFrame frame); +void mpp_frame_set_fmt(MppFrame frame, MppFrameFormat fmt); +MppFrameRational mpp_frame_get_sar(const MppFrame frame); +void mpp_frame_set_sar(MppFrame frame, MppFrameRational sar); +MppFrameMasteringDisplayMetadata mpp_frame_get_mastering_display(const MppFrame frame); +void mpp_frame_set_mastering_display(MppFrame frame, MppFrameMasteringDisplayMetadata mastering_display); +MppFrameContentLightMetadata mpp_frame_get_content_light(const MppFrame frame); +void mpp_frame_set_content_light(MppFrame frame, MppFrameContentLightMetadata content_light); +MppFrameHdrDynamicMeta* mpp_frame_get_hdr_dynamic_meta(const MppFrame frame); +void mpp_frame_set_hdr_dynamic_meta(MppFrame frame, MppFrameHdrDynamicMeta *vivi_data); + +/* + * HDR parameter + */ + +#ifdef __cplusplus +} +#endif + +#endif /*__MPP_FRAME_H__*/ diff --git a/rknpu2/examples/3rdparty/mpp/include/rockchip/mpp_log.h b/rknpu2/examples/3rdparty/mpp/include/rockchip/mpp_log.h new file mode 100644 index 0000000000000000000000000000000000000000..2b38eab2de09d97757b87997233abdbc18d8aee8 --- /dev/null +++ b/rknpu2/examples/3rdparty/mpp/include/rockchip/mpp_log.h @@ -0,0 +1,99 @@ +/* + * Copyright 2022 Rockchip Electronics Co. LTD + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __MPP_LOG_H__ +#define __MPP_LOG_H__ + +#include "rk_type.h" +#include "mpp_log_def.h" + +/* + * _c function will add condition check + * _f function will add function name to the log + * _cf function will add both function name and condition check + */ + +/* + * mpp runtime log system usage: + * mpp_logf is for fatal logging. For use when aborting + * mpp_loge is for error logging. For use with unrecoverable failures. + * mpp_logw is for warning logging. For use with recoverable failures. + * mpp_logi is for informational logging. + * mpp_logd is for debug logging. + * mpp_logv is for verbose logging + */ + +#define mpp_logf(fmt, ...) _mpp_log_l(MPP_LOG_FATAL, MODULE_TAG, fmt, NULL, ## __VA_ARGS__) +#define mpp_loge(fmt, ...) _mpp_log_l(MPP_LOG_ERROR, MODULE_TAG, fmt, NULL, ## __VA_ARGS__) +#define mpp_logw(fmt, ...) _mpp_log_l(MPP_LOG_WARN, MODULE_TAG, fmt, NULL, ## __VA_ARGS__) +#define mpp_logi(fmt, ...) _mpp_log_l(MPP_LOG_INFO, MODULE_TAG, fmt, NULL, ## __VA_ARGS__) +#define mpp_logd(fmt, ...) _mpp_log_l(MPP_LOG_DEBUG, MODULE_TAG, fmt, NULL, ## __VA_ARGS__) +#define mpp_logv(fmt, ...) _mpp_log_l(MPP_LOG_VERBOSE, MODULE_TAG, fmt, NULL, ## __VA_ARGS__) + +#define mpp_logf_f(fmt, ...) _mpp_log_l(MPP_LOG_FATAL, MODULE_TAG, fmt, __FUNCTION__, ## __VA_ARGS__) +#define mpp_loge_f(fmt, ...) _mpp_log_l(MPP_LOG_ERROR, MODULE_TAG, fmt, __FUNCTION__, ## __VA_ARGS__) +#define mpp_logw_f(fmt, ...) _mpp_log_l(MPP_LOG_WARN, MODULE_TAG, fmt, __FUNCTION__, ## __VA_ARGS__) +#define mpp_logi_f(fmt, ...) _mpp_log_l(MPP_LOG_INFO, MODULE_TAG, fmt, __FUNCTION__, ## __VA_ARGS__) +#define mpp_logd_f(fmt, ...) _mpp_log_l(MPP_LOG_DEBUG, MODULE_TAG, fmt, __FUNCTION__, ## __VA_ARGS__) +#define mpp_logv_f(fmt, ...) _mpp_log_l(MPP_LOG_VERBOSE, MODULE_TAG, fmt, __FUNCTION__, ## __VA_ARGS__) + +#define mpp_logf_c(cond, fmt, ...) do { if (cond) mpp_logf(fmt, ## __VA_ARGS__); } while (0) +#define mpp_loge_c(cond, fmt, ...) do { if (cond) mpp_loge(fmt, ## __VA_ARGS__); } while (0) +#define mpp_logw_c(cond, fmt, ...) do { if (cond) mpp_logw(fmt, ## __VA_ARGS__); } while (0) +#define mpp_logi_c(cond, fmt, ...) do { if (cond) mpp_logi(fmt, ## __VA_ARGS__); } while (0) +#define mpp_logd_c(cond, fmt, ...) do { if (cond) mpp_logd(fmt, ## __VA_ARGS__); } while (0) +#define mpp_logv_c(cond, fmt, ...) do { if (cond) mpp_logv(fmt, ## __VA_ARGS__); } while (0) + +#define mpp_logf_cf(cond, fmt, ...) do { if (cond) mpp_logf_f(fmt, ## __VA_ARGS__); } while (0) +#define mpp_loge_cf(cond, fmt, ...) do { if (cond) mpp_loge_f(fmt, ## __VA_ARGS__); } while (0) +#define mpp_logw_cf(cond, fmt, ...) do { if (cond) mpp_logw_f(fmt, ## __VA_ARGS__); } while (0) +#define mpp_logi_cf(cond, fmt, ...) do { if (cond) mpp_logi_f(fmt, ## __VA_ARGS__); } while (0) +#define mpp_logd_cf(cond, fmt, ...) do { if (cond) mpp_logd_f(fmt, ## __VA_ARGS__); } while (0) +#define mpp_logv_cf(cond, fmt, ...) do { if (cond) mpp_logv_f(fmt, ## __VA_ARGS__); } while (0) + +/* + * mpp runtime log system usage: + * mpp_err is for error status message, it will print for sure. + * mpp_log is for important message like open/close/reset/flush, it will print too. + */ + +#define mpp_log(fmt, ...) mpp_logi(fmt, ## __VA_ARGS__) +#define mpp_err(fmt, ...) mpp_loge(fmt, ## __VA_ARGS__) + +#define mpp_log_f(fmt, ...) mpp_logi_f(fmt, ## __VA_ARGS__) +#define mpp_err_f(fmt, ...) mpp_loge_f(fmt, ## __VA_ARGS__) + +#define mpp_log_c(cond, fmt, ...) do { if (cond) mpp_log(fmt, ## __VA_ARGS__); } while (0) +#define mpp_log_cf(cond, fmt, ...) do { if (cond) mpp_log_f(fmt, ## __VA_ARGS__); } while (0) + +#ifdef __cplusplus +extern "C" { +#endif + +void _mpp_log_l(int level, const char *tag, const char *fmt, const char *func, ...); + +void mpp_set_log_level(int level); +int mpp_get_log_level(void); + +/* deprecated function */ +void _mpp_log(const char *tag, const char *fmt, const char *func, ...); +void _mpp_err(const char *tag, const char *fmt, const char *func, ...); + +#ifdef __cplusplus +} +#endif + +#endif /*__MPP_LOG_H__*/ diff --git a/rknpu2/examples/3rdparty/mpp/include/rockchip/mpp_log_def.h b/rknpu2/examples/3rdparty/mpp/include/rockchip/mpp_log_def.h new file mode 100644 index 0000000000000000000000000000000000000000..0b17c1b9126193cbd05a9d9a2a65205c3eeba953 --- /dev/null +++ b/rknpu2/examples/3rdparty/mpp/include/rockchip/mpp_log_def.h @@ -0,0 +1,37 @@ +/* + * Copyright 2022 Rockchip Electronics Co. LTD + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __MPP_LOG_DEF_H__ +#define __MPP_LOG_DEF_H__ + +#ifdef __cplusplus +extern "C" { +#endif + +#define MPP_LOG_UNKNOWN 0 /* internal use only */ +#define MPP_LOG_FATAL 1 /* fatal error on aborting */ +#define MPP_LOG_ERROR 2 /* error log on unrecoverable failures */ +#define MPP_LOG_WARN 3 /* warning log on recoverable failures */ +#define MPP_LOG_INFO 4 /* Informational log */ +#define MPP_LOG_DEBUG 5 /* Debug log */ +#define MPP_LOG_VERBOSE 6 /* Verbose log */ +#define MPP_LOG_SILENT 7 /* internal use only */ + +#ifdef __cplusplus +} +#endif + +#endif /*__MPP_LOG_DEF_H__*/ diff --git a/rknpu2/examples/3rdparty/mpp/include/rockchip/mpp_meta.h b/rknpu2/examples/3rdparty/mpp/include/rockchip/mpp_meta.h new file mode 100644 index 0000000000000000000000000000000000000000..5aea91dad6d1b89754086ba77d6ca7f00df58d10 --- /dev/null +++ b/rknpu2/examples/3rdparty/mpp/include/rockchip/mpp_meta.h @@ -0,0 +1,179 @@ +/* + * Copyright 2015 Rockchip Electronics Co. LTD + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __MPP_META_H__ +#define __MPP_META_H__ + +#include +#include "rk_type.h" + +#define FOURCC_META(a, b, c, d) ((RK_U32)(a) << 24 | \ + ((RK_U32)(b) << 16) | \ + ((RK_U32)(c) << 8) | \ + ((RK_U32)(d) << 0)) + +/* + * Mpp Metadata definition + * + * Metadata is for information transmision in mpp. + * Mpp task will contain two meta data: + * + * 1. Data flow metadata + * This metadata contains information of input / output data flow. For example + * A. decoder input side task the input packet must be defined and output frame + * may not be defined. Then decoder will try malloc or use committed buffer to + * complete decoding. + * B. decoder output side task + * + * + * 2. Flow control metadata + * + */ +typedef enum MppMetaDataType_e { + /* + * mpp meta data of data flow + * reference counter will be used for these meta data type + */ + TYPE_FRAME = FOURCC_META('m', 'f', 'r', 'm'), + TYPE_PACKET = FOURCC_META('m', 'p', 'k', 't'), + TYPE_BUFFER = FOURCC_META('m', 'b', 'u', 'f'), + + /* mpp meta data of normal data type */ + TYPE_S32 = FOURCC_META('s', '3', '2', ' '), + TYPE_S64 = FOURCC_META('s', '6', '4', ' '), + TYPE_PTR = FOURCC_META('p', 't', 'r', ' '), +} MppMetaType; + +typedef enum MppMetaKey_e { + /* data flow key */ + KEY_INPUT_FRAME = FOURCC_META('i', 'f', 'r', 'm'), + KEY_INPUT_PACKET = FOURCC_META('i', 'p', 'k', 't'), + KEY_OUTPUT_FRAME = FOURCC_META('o', 'f', 'r', 'm'), + KEY_OUTPUT_PACKET = FOURCC_META('o', 'p', 'k', 't'), + /* output motion information for motion detection */ + KEY_MOTION_INFO = FOURCC_META('m', 'v', 'i', 'f'), + KEY_HDR_INFO = FOURCC_META('h', 'd', 'r', ' '), + KEY_HDR_META_OFFSET = FOURCC_META('h', 'd', 'r', 'o'), + KEY_HDR_META_SIZE = FOURCC_META('h', 'd', 'r', 'l'), + + /* flow control key */ + KEY_INPUT_BLOCK = FOURCC_META('i', 'b', 'l', 'k'), + KEY_OUTPUT_BLOCK = FOURCC_META('o', 'b', 'l', 'k'), + KEY_INPUT_IDR_REQ = FOURCC_META('i', 'i', 'd', 'r'), /* input idr frame request flag */ + KEY_OUTPUT_INTRA = FOURCC_META('o', 'i', 'd', 'r'), /* output intra frame indicator */ + + /* mpp_frame / mpp_packet meta data info key */ + KEY_TEMPORAL_ID = FOURCC_META('t', 'l', 'i', 'd'), + KEY_LONG_REF_IDX = FOURCC_META('l', 't', 'i', 'd'), + KEY_ENC_AVERAGE_QP = FOURCC_META('a', 'v', 'g', 'q'), + KEY_ROI_DATA = FOURCC_META('r', 'o', 'i', ' '), + KEY_OSD_DATA = FOURCC_META('o', 's', 'd', ' '), + KEY_OSD_DATA2 = FOURCC_META('o', 's', 'd', '2'), + KEY_USER_DATA = FOURCC_META('u', 's', 'r', 'd'), + KEY_USER_DATAS = FOURCC_META('u', 'r', 'd', 's'), + + /* + * For vepu580 roi buffer config mode + * The encoder roi structure is so complex that we should provide a buffer + * tunnel for externl user to config encoder hardware by direct sending + * roi data buffer. + * This way can reduce the config parsing and roi buffer data generating + * overhead in mpp. + */ + KEY_ROI_DATA2 = FOURCC_META('r', 'o', 'i', '2'), + + /* + * qpmap for rv1109/1126 encoder qpmap config + * Input data is a MppBuffer which contains an array of 16bit Vepu541RoiCfg. + * And each 16bit represents a 16x16 block qp info. + * + * H.264 - 16x16 block qp is arranged in raster order: + * each value is a 16bit data + * 00 01 02 03 04 05 06 07 -> 00 01 02 03 04 05 06 07 + * 10 11 12 13 14 15 16 17 10 11 12 13 14 15 16 17 + * 20 21 22 23 24 25 26 27 20 21 22 23 24 25 26 27 + * 30 31 32 33 34 35 36 37 30 31 32 33 34 35 36 37 + * + * H.265 - 16x16 block qp is reorder to 64x64/32x32 ctu order then 64x64 / 32x32 ctu raster order + * 64x64 ctu + * 00 01 02 03 04 05 06 07 -> 00 01 02 03 10 11 12 13 20 21 22 23 30 31 32 33 04 05 06 07 14 15 16 17 24 25 26 27 34 35 36 37 + * 10 11 12 13 14 15 16 17 + * 20 21 22 23 24 25 26 27 + * 30 31 32 33 34 35 36 37 + * 32x32 ctu + * 00 01 02 03 04 05 06 07 -> 00 01 10 11 02 03 12 13 04 05 14 15 06 07 16 17 + * 10 11 12 13 14 15 16 17 20 21 30 31 22 23 32 33 24 25 34 35 26 27 36 37 + * 20 21 22 23 24 25 26 27 + * 30 31 32 33 34 35 36 37 + */ + KEY_QPMAP0 = FOURCC_META('e', 'q', 'm', '0'), + + /* input motion list for smart p rate control */ + KEY_MV_LIST = FOURCC_META('m', 'v', 'l', 't'), + + /* frame long-term reference frame operation */ + KEY_ENC_MARK_LTR = FOURCC_META('m', 'l', 't', 'r'), + KEY_ENC_USE_LTR = FOURCC_META('u', 'l', 't', 'r'), + + /* MLVEC specified encoder feature */ + KEY_ENC_FRAME_QP = FOURCC_META('f', 'r', 'm', 'q'), + KEY_ENC_BASE_LAYER_PID = FOURCC_META('b', 'p', 'i', 'd'), + + /* Thumbnail info for decoder output frame */ + KEY_DEC_TBN_EN = FOURCC_META('t', 'b', 'e', 'n'), + KEY_DEC_TBN_Y_OFFSET = FOURCC_META('t', 'b', 'y', 'o'), + KEY_DEC_TBN_UV_OFFSET = FOURCC_META('t', 'b', 'c', 'o'), +} MppMetaKey; + +#define mpp_meta_get(meta) mpp_meta_get_with_tag(meta, MODULE_TAG, __FUNCTION__) + +#include "mpp_frame.h" +#include "mpp_packet.h" + +#ifdef __cplusplus +extern "C" { +#endif + +MPP_RET mpp_meta_get_with_tag(MppMeta *meta, const char *tag, const char *caller); +MPP_RET mpp_meta_put(MppMeta meta); +RK_S32 mpp_meta_size(MppMeta meta); + +MPP_RET mpp_meta_set_s32(MppMeta meta, MppMetaKey key, RK_S32 val); +MPP_RET mpp_meta_set_s64(MppMeta meta, MppMetaKey key, RK_S64 val); +MPP_RET mpp_meta_set_ptr(MppMeta meta, MppMetaKey key, void *val); +MPP_RET mpp_meta_get_s32(MppMeta meta, MppMetaKey key, RK_S32 *val); +MPP_RET mpp_meta_get_s64(MppMeta meta, MppMetaKey key, RK_S64 *val); +MPP_RET mpp_meta_get_ptr(MppMeta meta, MppMetaKey key, void **val); + +MPP_RET mpp_meta_set_frame (MppMeta meta, MppMetaKey key, MppFrame frame); +MPP_RET mpp_meta_set_packet(MppMeta meta, MppMetaKey key, MppPacket packet); +MPP_RET mpp_meta_set_buffer(MppMeta meta, MppMetaKey key, MppBuffer buffer); +MPP_RET mpp_meta_get_frame (MppMeta meta, MppMetaKey key, MppFrame *frame); +MPP_RET mpp_meta_get_packet(MppMeta meta, MppMetaKey key, MppPacket *packet); +MPP_RET mpp_meta_get_buffer(MppMeta meta, MppMetaKey key, MppBuffer *buffer); + +MPP_RET mpp_meta_get_s32_d(MppMeta meta, MppMetaKey key, RK_S32 *val, RK_S32 def); +MPP_RET mpp_meta_get_s64_d(MppMeta meta, MppMetaKey key, RK_S64 *val, RK_S64 def); +MPP_RET mpp_meta_get_ptr_d(MppMeta meta, MppMetaKey key, void **val, void *def); +MPP_RET mpp_meta_get_frame_d(MppMeta meta, MppMetaKey key, MppFrame *frame, MppFrame def); +MPP_RET mpp_meta_get_packet_d(MppMeta meta, MppMetaKey key, MppPacket *packet, MppPacket def); +MPP_RET mpp_meta_get_buffer_d(MppMeta meta, MppMetaKey key, MppBuffer *buffer, MppBuffer def); + +#ifdef __cplusplus +} +#endif + +#endif /*__MPP_META_H__*/ diff --git a/rknpu2/examples/3rdparty/mpp/include/rockchip/mpp_packet.h b/rknpu2/examples/3rdparty/mpp/include/rockchip/mpp_packet.h new file mode 100644 index 0000000000000000000000000000000000000000..9d2768b8d498bd30a07b3491b7c47583c7a7ee83 --- /dev/null +++ b/rknpu2/examples/3rdparty/mpp/include/rockchip/mpp_packet.h @@ -0,0 +1,117 @@ +/* + * Copyright 2015 Rockchip Electronics Co. LTD + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __MPP_PACKET_H__ +#define __MPP_PACKET_H__ + +#include "mpp_meta.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/* + * MppPacket interface + * + * mpp_packet_init = mpp_packet_new + mpp_packet_set_data + mpp_packet_set_size + * mpp_packet_copy_init = mpp_packet_init + memcpy + */ +MPP_RET mpp_packet_new(MppPacket *packet); +MPP_RET mpp_packet_init(MppPacket *packet, void *data, size_t size); +MPP_RET mpp_packet_init_with_buffer(MppPacket *packet, MppBuffer buffer); +MPP_RET mpp_packet_copy_init(MppPacket *packet, const MppPacket src); +MPP_RET mpp_packet_deinit(MppPacket *packet); + +/* + * data : ( R/W ) start address of the whole packet memory + * size : ( R/W ) total size of the whole packet memory + * pos : ( R/W ) current access position of the whole packet memory, used for buffer read/write + * length : ( R/W ) the rest length from current position to end of buffer + * NOTE: normally length is updated only by set_pos, + * so set length must be used carefully for special usage + */ +void mpp_packet_set_data(MppPacket packet, void *data); +void mpp_packet_set_size(MppPacket packet, size_t size); +void mpp_packet_set_pos(MppPacket packet, void *pos); +void mpp_packet_set_length(MppPacket packet, size_t size); + +void* mpp_packet_get_data(const MppPacket packet); +void* mpp_packet_get_pos(const MppPacket packet); +size_t mpp_packet_get_size(const MppPacket packet); +size_t mpp_packet_get_length(const MppPacket packet); + + +void mpp_packet_set_pts(MppPacket packet, RK_S64 pts); +RK_S64 mpp_packet_get_pts(const MppPacket packet); +void mpp_packet_set_dts(MppPacket packet, RK_S64 dts); +RK_S64 mpp_packet_get_dts(const MppPacket packet); + +void mpp_packet_set_flag(MppPacket packet, RK_U32 flag); +RK_U32 mpp_packet_get_flag(const MppPacket packet); + +MPP_RET mpp_packet_set_eos(MppPacket packet); +MPP_RET mpp_packet_clr_eos(MppPacket packet); +RK_U32 mpp_packet_get_eos(MppPacket packet); +MPP_RET mpp_packet_set_extra_data(MppPacket packet); + +void mpp_packet_set_buffer(MppPacket packet, MppBuffer buffer); +MppBuffer mpp_packet_get_buffer(const MppPacket packet); + +/* + * data access interface + */ +MPP_RET mpp_packet_read(MppPacket packet, size_t offset, void *data, size_t size); +MPP_RET mpp_packet_write(MppPacket packet, size_t offset, void *data, size_t size); + +/* + * meta data access interface + */ +RK_S32 mpp_packet_has_meta(const MppPacket packet); +MppMeta mpp_packet_get_meta(const MppPacket packet); + +/* + * multi packet sequence interface for slice/split encoding/decoding + * partition - the packet is a part of a while image + * soi - Start Of Image + * eoi - End Of Image + */ +RK_U32 mpp_packet_is_partition(const MppPacket packet); +RK_U32 mpp_packet_is_soi(const MppPacket packet); +RK_U32 mpp_packet_is_eoi(const MppPacket packet); + +/* + * packet segement pack info for + * segment number - number of segment + * segment info - base address of segment info + */ +typedef struct MppPktSeg_t MppPktSeg; + +struct MppPktSeg_t { + RK_S32 index; + RK_S32 type; + RK_U32 offset; + RK_U32 len; + const MppPktSeg *next; +}; + +RK_U32 mpp_packet_get_segment_nb(const MppPacket packet); +const MppPktSeg *mpp_packet_get_segment_info(const MppPacket packet); + +#ifdef __cplusplus +} +#endif + +#endif /*__MPP_PACKET_H__*/ diff --git a/rknpu2/examples/3rdparty/mpp/include/rockchip/mpp_rc_api.h b/rknpu2/examples/3rdparty/mpp/include/rockchip/mpp_rc_api.h new file mode 100644 index 0000000000000000000000000000000000000000..71e845d1f7509c12b2c44d9692a341c84c531190 --- /dev/null +++ b/rknpu2/examples/3rdparty/mpp/include/rockchip/mpp_rc_api.h @@ -0,0 +1,257 @@ +/* + * Copyright 2016 Rockchip Electronics Co. LTD + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __MPP_RC_API_H__ +#define __MPP_RC_API_H__ + +#include "mpp_err.h" +#include "rk_venc_rc.h" +#include "mpp_rc_defs.h" + +/* + * Mpp rate control has three parts: + * + * 1. MPI user config module + * MppEncRcCfg structure is provided to user for overall rate control config + * Mpp will receive MppEncRcCfg from user, check parameter and set it to + * encoder. + * + * 2. Encoder rate control module + * Encoder will implement the rate control strategy required by users + * including CBR, VBR, AVBR and so on. + * This module only implement the target bit calculation behavior and + * quality restriction. And the quality level will be controlled by hal. + * + * 3. Hal rate control module + * Hal will implement the rate control on hardware. Hal will calculate the + * QP parameter for hardware according to the frame level target bit + * specified by the encoder. And the report the real bitrate and quality to + * encoder. + * + * The header defines the communication interfaces and structures used between + * MPI, encoder and hal. + */ + +typedef enum RcMode_e { + RC_VBR, + RC_CBR, + RC_FIXQP, + RC_AVBR, + RC_CVBR, + RC_QVBR, + RC_LEARNING, + RC_MODE_BUTT, +} RcMode; + +typedef enum GopMode_e { + NORMAL_P, + SMART_P, +} GopMode; + +/* + * frame rate parameters have great effect on rate control + * + * fps_in_flex + * 0 - fix input frame rate + * 1 - variable input frame rate + * + * fps_in_num + * input frame rate numerator, if 0 then default 30 + * + * fps_in_denorm + * input frame rate denorminator, if 0 then default 1 + * + * fps_out_flex + * 0 - fix output frame rate + * 1 - variable output frame rate + * + * fps_out_num + * output frame rate numerator, if 0 then default 30 + * + * fps_out_denorm + * output frame rate denorminator, if 0 then default 1 + */ +typedef struct RcFpsCfg_t { + RK_S32 fps_in_flex; + RK_S32 fps_in_num; + RK_S32 fps_in_denorm; + RK_S32 fps_out_flex; + RK_S32 fps_out_num; + RK_S32 fps_out_denorm; +} RcFpsCfg; + +typedef struct RcSuperframeCfg_t { + MppEncRcSuperFrameMode super_mode; + RK_U32 super_i_thd; + RK_U32 super_p_thd; + MppEncRcPriority rc_priority; +} RcSuperframeCfg; + +typedef struct RcDebreathCfg_t { + RK_U32 enable; + RK_U32 strength; +} RcDebreathCfg; + +typedef struct RcHierQPCfg_t { + RK_S32 hier_qp_en; + RK_S32 hier_qp_delta[4]; + RK_S32 hier_frame_num[4]; +} RcHierQPCfg; + +/* + * Control parameter from external config + * + * It will be updated on rc/prep/gopref config changed. + */ +typedef struct RcCfg_s { + /* encode image size */ + RK_S32 width; + RK_S32 height; + + /* Use rc_mode to find different api */ + RcMode mode; + + RcFpsCfg fps; + + GopMode gop_mode; + /* I frame gop len */ + RK_S32 igop; + /* visual gop len */ + RK_S32 vgop; + + /* bitrate parameter */ + RK_S32 bps_min; + RK_S32 bps_target; + RK_S32 bps_max; + RK_S32 stats_time; + + /* max I frame bit ratio to P frame bit */ + RK_S32 max_i_bit_prop; + RK_S32 min_i_bit_prop; + RK_S32 init_ip_ratio; + /* layer bitrate proportion */ + RK_S32 layer_bit_prop[4]; + + /* quality parameter */ + RK_S32 init_quality; + RK_S32 max_quality; + RK_S32 min_quality; + RK_S32 max_i_quality; + RK_S32 min_i_quality; + RK_S32 i_quality_delta; + RK_S32 vi_quality_delta; + /* layer quality proportion */ + RK_S32 layer_quality_delta[4]; + + /* reencode parameter */ + RK_S32 max_reencode_times; + + /* still / motion desision parameter */ + RK_S32 min_still_prop; + RK_S32 max_still_quality; + + /* + * vbr parameter + * + * vbr_hi_prop - high proportion bitrate for reduce quality + * vbr_lo_prop - low proportion bitrate for increase quality + */ + RK_S32 vbr_hi_prop; + RK_S32 vbr_lo_prop; + + MppEncRcDropFrmMode drop_mode; + RK_U32 drop_thd; + RK_U32 drop_gap; + + RcSuperframeCfg super_cfg; + RcDebreathCfg debreath_cfg; + RcHierQPCfg hier_qp_cfg; + RK_U32 refresh_len; +} RcCfg; + +/* + * Different rate control strategy will be implemented by different API config + */ +typedef struct RcImplApi_t { + char *name; + MppCodingType type; + RK_U32 ctx_size; + + MPP_RET (*init)(void *ctx, RcCfg *cfg); + MPP_RET (*deinit)(void *ctx); + + MPP_RET (*check_drop)(void *ctx, EncRcTask *task); + MPP_RET (*check_reenc)(void *ctx, EncRcTask *task); + + /* + * frm_start - frame level rate control frm_start. + * The EncRcTaskInfo will be output to hal for hardware to implement. + * frm_end - frame level rate control frm_end. + * The EncRcTaskInfo is returned for real quality and bitrate. + */ + MPP_RET (*frm_start)(void *ctx, EncRcTask *task); + MPP_RET (*frm_end)(void *ctx, EncRcTask *task); + + /* + * hal_start - hardware level rate control start. + * The EncRcTaskInfo will be output to hal for hardware to implement. + * hal_end - hardware level rate control end. + * The EncRcTaskInfo is returned for real quality and bitrate. + */ + MPP_RET (*hal_start)(void *ctx, EncRcTask *task); + MPP_RET (*hal_end)(void *ctx, EncRcTask *task); +} RcImplApi; + +/* + * structures for RC API register and query + */ +typedef struct RcApiBrief_t { + const char *name; + MppCodingType type; +} RcApiBrief; + +typedef struct RcApiQueryAll_t { + /* input param for query */ + RcApiBrief *brief; + RK_S32 max_count; + + /* output query count */ + RK_S32 count; +} RcApiQueryAll; + +typedef struct RcApiQueryType_t { + /* input param for query */ + RcApiBrief *brief; + RK_S32 max_count; + MppCodingType type; + + /* output query count */ + RK_S32 count; +} RcApiQueryType; + +#ifdef __cplusplus +extern "C" { +#endif + +MPP_RET rc_api_add(const RcImplApi *api); +MPP_RET rc_brief_get_all(RcApiQueryAll *query); +MPP_RET rc_brief_get_by_type(RcApiQueryType *query); + +#ifdef __cplusplus +} +#endif + +#endif /* __MPP_RC_API_H__ */ diff --git a/rknpu2/examples/3rdparty/mpp/include/rockchip/mpp_rc_defs.h b/rknpu2/examples/3rdparty/mpp/include/rockchip/mpp_rc_defs.h new file mode 100644 index 0000000000000000000000000000000000000000..b61fafd1c041f29b8b0496fae206421384d95bcb --- /dev/null +++ b/rknpu2/examples/3rdparty/mpp/include/rockchip/mpp_rc_defs.h @@ -0,0 +1,212 @@ +/* + * Copyright 2016 Rockchip Electronics Co. LTD + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __MPP_RC_DEFS_H__ +#define __MPP_RC_DEFS_H__ + +#include "rk_venc_ref.h" + +#define MAX_CPB_REFS (8) + +typedef enum EncFrmType_e { + INTER_P_FRAME = 0, + INTER_B_FRAME = 1, + INTRA_FRAME = 2, + INTER_VI_FRAME = 3, + INTRA_RFH_FRAME = 4, +} EncFrmType; + +/* + * EncFrmStatus controls record the encoding frame status and also control + * work flow of encoder. It is the communicat channel between encoder implement + * module, rate control module and hardware module. + * + * bit 0 ~ 31 frame status + * 0 ~ 15 current frame status + * 16 ~ 31 reference frame status + * bit 32 ~ 63 encoding flow control + */ +typedef union EncFrmStatus_u { + struct { + /* + * bit 0 ~ 31 frame status + */ + /* status flag */ + RK_U32 valid : 1; + /* + * 0 - write the reconstructed frame pixel to memory + * 1 - do not write the reconstructed frame pixel to memory + */ + RK_U32 non_recn : 1; + + /* + * 0 - normal frame and normal dpb management + * 1 - save recon frame as first pass extra frame. Used in two pass mode + */ + RK_U32 save_pass1 : 1; + + /* + * 0 - use normal input source frame as input + * 1 - use the previously stored first pass recon frame as input frame + */ + RK_U32 use_pass1 : 1; + + /* reference status flag */ + /* + * 0 - inter frame + * 1 - intra frame + */ + RK_U32 is_intra : 1; + + /* + * Valid when is_intra is true + * 0 - normal intra frame + * 1 - IDR frame + */ + RK_U32 is_idr : 1; + + /* + * 0 - mark as reference frame + * 1 - mark as non-refernce frame + */ + RK_U32 is_non_ref : 1; + + /* + * Valid when is_non_ref is false + * 0 - mark as short-term reference frame + * 1 - mark as long-term refernce frame + */ + RK_U32 is_lt_ref : 1; + + /* bit 8 - 15 */ + RK_U32 lt_idx : 4; + RK_U32 temporal_id : 4; + + /* distance between current frame and reference frame */ + MppEncRefMode ref_mode : 6; + RK_S32 ref_arg : 8; + RK_S32 ref_dist : 2; + + /* + * bit 32 ~ 63 encoder flow control flags + */ + /* + * 0 - normal frame encoding + * 1 - current frame will be dropped + */ + RK_U32 drop : 1; + + /* + * 0 - rate control module does not change frame type parameter + * 1 - rate control module changes frame type parameter reencode is needed + * to reprocess the dpb process. Also this means dpb module will follow + * the frame status parameter provided by rate control module. + */ + RK_U32 re_dpb_proc : 1; + + /* + * 0 - current frame encoding is in normal flow + * 1 - current frame encoding is in reencode flow + */ + RK_U32 reencode : 1; + + /* + * When true current frame size is super large then the frame should be reencoded. + */ + RK_U32 super_frame : 1; + + /* + * When true currnet frame is force to encoded as software skip frame + */ + RK_U32 force_pskip : 1; + + /* + * Current frame is intra refresh frame + */ + RK_U32 is_i_refresh : 1; + /* + * Current frame needs add recovery point prefix + */ + RK_U32 is_i_recovery : 1; + RK_U32 reserved1 : 1; + + /* reencode times */ + RK_U32 reencode_times : 8; + + /* sequential index for each frame */ + RK_U32 seq_idx : 16; + }; + RK_U64 val; +} EncFrmStatus; + +typedef struct EncCpbStatus_t { + RK_S32 seq_idx; + + EncFrmStatus curr; + EncFrmStatus refr; + + /* initial cpb status for current frame encoding */ + EncFrmStatus init[MAX_CPB_REFS]; + /* final cpb status after current frame encoding */ + EncFrmStatus final[MAX_CPB_REFS]; +} EncCpbStatus; + +#define ENC_RC_FORCE_QP (0x00000001) + +typedef struct EncRcForceCfg_t { + RK_U32 force_flag; + RK_S32 force_qp; + RK_U32 reserve[6]; +} EncRcForceCfg; + +/* + * communication channel between rc / hal / hardware + * + * rc -> hal bit_target / bit_max / bit_min + * hal -> hw quality_target / quality_max / quality_min + * hw -> rc / hal bit_real / quality_real / madi / madp + */ +typedef struct EncRcCommonInfo_t { + EncFrmType frame_type; + + /* rc to hal */ + RK_S32 bit_target; + RK_S32 bit_max; + RK_S32 bit_min; + + RK_S32 quality_target; + RK_S32 quality_max; + RK_S32 quality_min; + + /* rc from hardware */ + RK_S32 bit_real; + RK_S32 quality_real; + RK_S32 madi; + RK_S32 madp; + + RK_U32 iblk4_prop; // scale 256 + RK_S32 reserve[15]; +} EncRcTaskInfo; + +typedef struct EncRcTask_s { + EncCpbStatus cpb; + EncFrmStatus frm; + EncRcTaskInfo info; + EncRcForceCfg force; + MppFrame frame; +} EncRcTask; + +#endif /* __MPP_RC_DEFS_H__ */ diff --git a/rknpu2/examples/3rdparty/mpp/include/rockchip/mpp_task.h b/rknpu2/examples/3rdparty/mpp/include/rockchip/mpp_task.h new file mode 100644 index 0000000000000000000000000000000000000000..d526e3a0855daa7593aa1d6bcb4ffe99a90d849c --- /dev/null +++ b/rknpu2/examples/3rdparty/mpp/include/rockchip/mpp_task.h @@ -0,0 +1,237 @@ +/* + * Copyright 2015 Rockchip Electronics Co. LTD + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __MPP_TASK_H__ +#define __MPP_TASK_H__ + +#include "mpp_meta.h" + +/* + * Advanced task flow + * Advanced task flow introduces three concepts: port, task and item + * + * Port is from OpenMAX + * Port has two type: input port and output port which are all for data transaction. + * Port work like a queue. task will be dequeue from or enqueue to one port. + * On input side user will dequeue task from input port, setup task and enqueue task + * back to input port. + * On output side user will dequeue task from output port, get the information from + * and then enqueue task back to output port. + * + * Task indicates one transaction on the port. + * Task has two working mode: async mode and sync mode + * If mpp is work in sync mode on task enqueue function return the task has been done + * If mpp is work in async mode on task enqueue function return the task is just put + * on the task queue for process. + * Task can carry different items. Task just like a container of items + * + * Item indicates MppPacket or MppFrame which is contained in one task + */ + +/* + * One mpp task queue has two ports: input and output + * + * The whole picture is: + * Top layer mpp has two ports: mpp_input_port and mpp_output_port + * But internally these two ports belongs to two task queue. + * The mpp_input_port is the mpp_input_task_queue's input port. + * The mpp_output_port is the mpp_output_task_queue's output port. + * + * Each port uses its task queue to communication + */ +typedef enum { + MPP_PORT_INPUT, + MPP_PORT_OUTPUT, + MPP_PORT_BUTT, +} MppPortType; + +/* + * Advance task work flow mode: + ****************************************************************************** + * 1. async mode (default_val) + * + * mpp_init(type, coding, MPP_WORK_ASYNC) + * + * input thread + * a - poll(input) + * b - dequeue(input, *task) + * c - task_set_item(packet/frame) + * d - enqueue(input, task) // when enqueue return the task is not done yet + * + * output thread + * a - poll(output) + * b - dequeue(output, *task) + * c - task_get_item(frame/packet) + * d - enqueue(output, task) + ****************************************************************************** + * 2. sync mode + * + * mpp_init(type, coding, MPP_WORK_SYNC) + * + * a - poll(input) + * b - dequeue(input, *task) + * c - task_set_item(packet/frame) + * d - enqueue(task) // when enqueue return the task is finished + ****************************************************************************** + */ +typedef enum { + MPP_TASK_ASYNC, + MPP_TASK_SYNC, + MPP_TASK_WORK_MODE_BUTT, +} MppTaskWorkMode; + +/* + * Mpp port poll type + * + * MPP_POLL_BLOCK - for block poll + * MPP_POLL_NON_BLOCK - for non-block poll + * small than MPP_POLL_MAX - for poll with timeout in ms + * small than MPP_POLL_BUTT or larger than MPP_POLL_MAX is invalid value + */ +typedef enum { + MPP_POLL_BUTT = -2, + MPP_POLL_BLOCK = -1, + MPP_POLL_NON_BLOCK = 0, + MPP_POLL_MAX = 8000, +} MppPollType; + +/* + * Mpp timeout define + * MPP_TIMEOUT_BLOCK - for block poll + * MPP_TIMEOUT_NON_BLOCK - for non-block poll + * small than MPP_TIMEOUT_MAX - for poll with timeout in ms + * small than MPP_TIMEOUT_BUTT or larger than MPP_TIMEOUT_MAX is invalid value + */ +#define MPP_TIMEOUT_BUTT (-2L) +#define MPP_TIMEOUT_BLOCK (-1L) +#define MPP_TIMEOUT_NON_BLOCK (0L) +#define MPP_TIMEOUT_MAX (8000L) + +/* + * MppTask is descriptor of a task which send to mpp for process + * mpp can support different type of work mode, for example: + * + * decoder: + * + * 1. typical decoder mode: + * input - MppPacket (normal cpu buffer, need cpu copy) + * output - MppFrame (ion/drm buffer in external/internal mode) + * 2. secure decoder mode: + * input - MppPacket (externel ion/drm buffer, cpu can not access) + * output - MppFrame (ion/drm buffer in external/internal mode, cpu can not access) + * + * interface usage: + * + * typical flow + * input side: + * task_dequeue(ctx, PORT_INPUT, &task); + * task_put_item(task, MODE_INPUT, packet) + * task_enqueue(ctx, PORT_INPUT, task); + * output side: + * task_dequeue(ctx, PORT_OUTPUT, &task); + * task_get_item(task, MODE_OUTPUT, &frame) + * task_enqueue(ctx, PORT_OUTPUT, task); + * + * secure flow + * input side: + * task_dequeue(ctx, PORT_INPUT, &task); + * task_put_item(task, MODE_INPUT, packet) + * task_put_item(task, MODE_OUTPUT, frame) // buffer will be specified here + * task_enqueue(ctx, PORT_INPUT, task); + * output side: + * task_dequeue(ctx, PORT_OUTPUT, &task); + * task_get_item(task, MODE_OUTPUT, &frame) + * task_enqueue(ctx, PORT_OUTPUT, task); + * + * encoder: + * + * 1. typical encoder mode: + * input - MppFrame (ion/drm buffer in external mode) + * output - MppPacket (normal cpu buffer, need cpu copy) + * 2. user input encoder mode: + * input - MppFrame (normal cpu buffer, need to build hardware table for this buffer) + * output - MppPacket (normal cpu buffer, need cpu copy) + * 3. secure encoder mode: + * input - MppFrame (ion/drm buffer in external mode, cpu can not access) + * output - MppPacket (externel ion/drm buffer, cpu can not access) + * + * typical / user input flow + * input side: + * task_dequeue(ctx, PORT_INPUT, &task); + * task_put_item(task, MODE_INPUT, frame) + * task_enqueue(ctx, PORT_INPUT, task); + * output side: + * task_dequeue(ctx, PORT_OUTPUT, &task); + * task_get_item(task, MODE_OUTPUT, &packet) + * task_enqueue(ctx, PORT_OUTPUT, task); + * + * secure flow + * input side: + * task_dequeue(ctx, PORT_INPUT, &task); + * task_put_item(task, MODE_OUTPUT, packet) // buffer will be specified here + * task_put_item(task, MODE_INPUT, frame) + * task_enqueue(ctx, PORT_INPUT, task); + * output side: + * task_dequeue(ctx, PORT_OUTPUT, &task); + * task_get_item(task, MODE_OUTPUT, &packet) + * task_get_item(task, MODE_OUTPUT, &frame) + * task_enqueue(ctx, PORT_OUTPUT, task); + * + * NOTE: this flow can specify the output frame. User will setup both intput frame and output packet + * buffer at the input side. Then at output side when user gets a finished task user can get the output + * packet and corresponding released input frame. + * + * image processing + * + * 1. typical image process mode: + * input - MppFrame (ion/drm buffer in external mode) + * output - MppFrame (ion/drm buffer in external mode) + * + * typical / user input flow + * input side: + * task_dequeue(ctx, PORT_INPUT, &task); + * task_put_item(task, MODE_INPUT, frame) + * task_enqueue(ctx, PORT_INPUT, task); + * output side: + * task_dequeue(ctx, PORT_OUTPUT, &task); + * task_get_item(task, MODE_OUTPUT, &frame) + * task_enqueue(ctx, PORT_OUTPUT, task); + */ +/* NOTE: use index rather then handle to descripbe task */ + +#ifdef __cplusplus +extern "C" { +#endif + +MPP_RET mpp_task_meta_set_s32(MppTask task, MppMetaKey key, RK_S32 val); +MPP_RET mpp_task_meta_set_s64(MppTask task, MppMetaKey key, RK_S64 val); +MPP_RET mpp_task_meta_set_ptr(MppTask task, MppMetaKey key, void *val); +MPP_RET mpp_task_meta_set_frame (MppTask task, MppMetaKey key, MppFrame frame); +MPP_RET mpp_task_meta_set_packet(MppTask task, MppMetaKey key, MppPacket packet); +MPP_RET mpp_task_meta_set_buffer(MppTask task, MppMetaKey key, MppBuffer buffer); + +MPP_RET mpp_task_meta_get_s32(MppTask task, MppMetaKey key, RK_S32 *val, RK_S32 default_val); +MPP_RET mpp_task_meta_get_s64(MppTask task, MppMetaKey key, RK_S64 *val, RK_S64 default_val); +MPP_RET mpp_task_meta_get_ptr(MppTask task, MppMetaKey key, void **val, void *default_val); +MPP_RET mpp_task_meta_get_frame (MppTask task, MppMetaKey key, MppFrame *frame); +MPP_RET mpp_task_meta_get_packet(MppTask task, MppMetaKey key, MppPacket *packet); +MPP_RET mpp_task_meta_get_buffer(MppTask task, MppMetaKey key, MppBuffer *buffer); + +#ifdef __cplusplus +} +#endif + +#endif /*__MPP_QUEUE_H__*/ diff --git a/rknpu2/examples/3rdparty/mpp/include/rockchip/rk_hdr_meta_com.h b/rknpu2/examples/3rdparty/mpp/include/rockchip/rk_hdr_meta_com.h new file mode 100644 index 0000000000000000000000000000000000000000..3f466386fd1aed06edb9a86e6c9739bbeb93215f --- /dev/null +++ b/rknpu2/examples/3rdparty/mpp/include/rockchip/rk_hdr_meta_com.h @@ -0,0 +1,118 @@ +/* + * Copyright 2022 Rockchip Electronics Co. LTD + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __RK_HDR_META_COM_H__ +#define __RK_HDR_META_COM_H__ + +#include "rk_type.h" + +typedef enum HdrCodecType_e { + HDR_AVS2 = 0, + HDR_HEVC = 1, + HDR_H264 = 2, + HDR_AV1 = 3, + HDR_CODEC_BUT, +} HdrCodecType; + +typedef enum HdrFormat_e { + HDR_NONE = 0, + HDR10 = 1, + HLG = 2, +// RESERVED3 = 3, //reserved for more future static hdr format +// RESERVED4 = 4, //reserved for more future static hdr format + HDRVIVID = 5, +// RESERVED6 = 6, //reserved for hdr vivid +// RESERVED7 = 7, //reserved for hdr vivid + HDR10PLUS = 8, +// RESERVED9 = 9, //reserved for hdr10+ +// RESERVED10 = 10,//reserved for hdr10+ + DOLBY = 11, +// RESERVED12 = 12, //reserved for other dynamic hdr format +// RESERVED13 = 13, //reserved for other dynamic hdr format + HDR_FORMAT_MAX, +} HdrFormat; + +typedef enum HdrPayloadFormat_e { + STATIC = 0, + DYNAMIC = 1, + HDR_PAYLOAD_FORMAT_MAX, +} HdrPayloadFormat; + +typedef struct HdrStaticMeta_t { + RK_U32 color_space; + RK_U32 color_primaries; + RK_U32 color_trc; + RK_U32 red_x; + RK_U32 red_y; + RK_U32 green_x; + RK_U32 green_y; + RK_U32 blue_x; + RK_U32 blue_y; + RK_U32 white_point_x; + RK_U32 white_point_y; + RK_U32 min_luminance; + RK_U32 max_luminance; + RK_U32 max_cll; + RK_U32 max_fall; + RK_U32 reserved[4]; +} HdrStaticMeta; + +/* + * HDR metadata format from codec + * + * +----------+ + * | header1 | + * +----------+ + * | | + * | payload | + * | | + * +----------+ + * | header2 | + * +----------+ + * | | + * | payload | + * | | + * +----------+ + * | header3 | + * +----------+ + * | | + * | payload | + * | | + * +----------+ + */ +typedef struct RkMetaHdrHeader_t { + /* For transmission */ + RK_U16 magic; /* magic word for checking overwrite error */ + RK_U16 size; /* total header+payload length including header */ + RK_U16 message_total; /* total message count in current transmission */ + RK_U16 message_index; /* current message index in the transmission */ + + /* For payload identification */ + RK_U16 version; /* payload structure version */ + RK_U16 hdr_format; /* HDR protocol: HDR10, HLG, Dolby, HDRVivid ... */ + RK_U16 hdr_payload_type; /* HDR data type: static data, dynamic data ... */ + RK_U16 video_format; /* video format: H.264, H.265, AVS2 ... */ + + /* For extenstion usage */ + RK_U32 reserve[4]; + + /* payload data aligned to 32bits */ + RK_U32 payload[]; +} RkMetaHdrHeader; + +void fill_hdr_meta_to_frame(MppFrame frame, HdrCodecType codec_type); + +#endif diff --git a/rknpu2/examples/3rdparty/mpp/include/rockchip/rk_mpi.h b/rknpu2/examples/3rdparty/mpp/include/rockchip/rk_mpi.h new file mode 100644 index 0000000000000000000000000000000000000000..2cce8973e22cdc771e0b86b619965632b9e260d8 --- /dev/null +++ b/rknpu2/examples/3rdparty/mpp/include/rockchip/rk_mpi.h @@ -0,0 +1,274 @@ +/* + * Copyright 2015 Rockchip Electronics Co. LTD + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __RK_MPI_H__ +#define __RK_MPI_H__ + +/** + * @addtogroup rk_mpi + * @brief Rockchip Media Process Interface + * @details Media Process Platform(MPP) provides application programming + * interface for the application layer, by which applications can + * call hardware encode and decode. Current MPP fully supports + * chipset RK3288/RK3228/RK3229/RK3399/RK3328/RV1108. Old chipset + * like RK29xx/RK30xx/RK31XX/RK3368 is partly supported due to lack + * of some hardware register generation module. + */ + +#include "rk_mpi_cmd.h" +#include "mpp_task.h" + +/** + * @ingroup rk_mpi + * @brief MPP main work function set + * @details all api function are seperated into two sets: data io api set + * and control api set + * + * (1). the data api set is for data input/output flow including: + * + * (1.1) simple data api set: + * + * decode : both send video stream packet to decoder and get video frame from + * decoder at the same time. + * + * encode : both send video frame to encoder and get encoded video stream from + * encoder at the same time. + * + * decode_put_packet: send video stream packet to decoder only, async interface + * + * decode_get_frame : get video frame from decoder only, async interface + * + * encode_put_frame : send video frame to encoder only, async interface + * + * encode_get_packet: get encoded video packet from encoder only, async interface + * + * (1.2) advanced task api set: + * + * poll : poll port for dequeue + * + * dequeue : pop a task from mpp task queue + * + * enqueue : push a task to mpp task queue + * + * (2). the control api set is for mpp context control including: + * + * control : similiar to ioctl in kernel driver, setup or get mpp internal parameter + * + * reset : clear all data in mpp context, discard all packet and frame, + * reset all components to initialized status + */ +typedef struct MppApi_t { + /** + * @brief size of struct MppApi + */ + RK_U32 size; + /** + * @brief mpp api version, generated by Git + */ + RK_U32 version; + + // simple data flow interface + /** + * @brief both send video stream packet to decoder and get video frame from + * decoder at the same time + * @param[in] ctx The context of mpp, created by mpp_create() and initiated + * by mpp_init(). + * @param[in] packet The input video stream, its usage can refer mpp_packet.h. + * @param[out] frame The output picture, its usage can refer mpp_frame.h. + * @return 0 and positive for success, negative for failure. The return + * value is an error code. For details, please refer mpp_err.h. + */ + MPP_RET (*decode)(MppCtx ctx, MppPacket packet, MppFrame *frame); + /** + * @brief send video stream packet to decoder only, async interface + * @param[in] ctx The context of mpp, created by mpp_create() and initiated + * by mpp_init(). + * @param[in] packet The input video stream, its usage can refer mpp_packet.h. + * @return 0 and positive for success, negative for failure. The return + * value is an error code. For details, please refer mpp_err.h. + */ + MPP_RET (*decode_put_packet)(MppCtx ctx, MppPacket packet); + /** + * @brief get video frame from decoder only, async interface + * @param[in] ctx The context of mpp, created by mpp_create() and initiated + * by mpp_init(). + * @param[out] frame The output picture, its usage can refer mpp_frame.h. + * @return 0 and positive for success, negative for failure. The return + * value is an error code. For details, please refer mpp_err.h. + */ + MPP_RET (*decode_get_frame)(MppCtx ctx, MppFrame *frame); + /** + * @brief both send video frame to encoder and get encoded video stream from + * encoder at the same time + * @param[in] ctx The context of mpp, created by mpp_create() and initiated + * by mpp_init(). + * @param[in] frame The input video data, its usage can refer mpp_frame.h. + * @param[out] packet The output compressed data, its usage can refer mpp_packet.h. + * @return 0 and positive for success, negative for failure. The return + * value is an error code. For details, please refer mpp_err.h. + */ + MPP_RET (*encode)(MppCtx ctx, MppFrame frame, MppPacket *packet); + /** + * @brief send video frame to encoder only, async interface + * @param[in] ctx The context of mpp, created by mpp_create() and initiated + * by mpp_init(). + * @param[in] frame The input video data, its usage can refer mpp_frame.h. + * @return 0 and positive for success, negative for failure. The return + * value is an error code. For details, please refer mpp_err.h. + */ + MPP_RET (*encode_put_frame)(MppCtx ctx, MppFrame frame); + /** + * @brief get encoded video packet from encoder only, async interface + * @param[in] ctx The context of mpp, created by mpp_create() and initiated + * by mpp_init(). + * @param[out] packet The output compressed data, its usage can refer mpp_packet.h. + * @return 0 and positive for success, negative for failure. The return + * value is an error code. For details, please refer mpp_err.h. + */ + MPP_RET (*encode_get_packet)(MppCtx ctx, MppPacket *packet); + + /** + * @brief ISP interface, will be supported in the future. + */ + MPP_RET (*isp)(MppCtx ctx, MppFrame dst, MppFrame src); + /** + * @brief ISP interface, will be supported in the future. + */ + MPP_RET (*isp_put_frame)(MppCtx ctx, MppFrame frame); + /** + * @brief ISP interface, will be supported in the future. + */ + MPP_RET (*isp_get_frame)(MppCtx ctx, MppFrame *frame); + + // advance data flow interface + /** + * @brief poll port for dequeue + * @param[in] ctx The context of mpp, created by mpp_create() and initiated + * by mpp_init(). + * @param[in] type input port or output port which are both for data transaction + * @param[in] timeout mpp poll type, its usage can refer mpp_task.h. + * @return 0 and positive for success, negative for failure. The return + * value is an error code. For details, please refer mpp_err.h. + */ + MPP_RET (*poll)(MppCtx ctx, MppPortType type, MppPollType timeout); + /** + * @brief dequeue MppTask, pop a task from mpp task queue + * @param[in] ctx The context of mpp, created by mpp_create() and initiated + * by mpp_init(). + * @param[in] type input port or output port which are both for data transaction + * @param[out] task MppTask popped from mpp task queue, its usage can refer mpp_task.h. + * @return 0 and positive for success, negative for failure. The return + * value is an error code. For details, please refer mpp_err.h. + */ + MPP_RET (*dequeue)(MppCtx ctx, MppPortType type, MppTask *task); + /** + * @brief enqueue MppTask, push a task to mpp task queue + * @param[in] ctx The context of mpp, created by mpp_create() and initiated + * by mpp_init(). + * @param[in] type input port or output port which are both for data transaction + * @param[in] task MppTask which is sent to mpp for process, its usage can refer mpp_task.h. + * @return 0 and positive for success, negative for failure. The return + * value is an error code. For details, please refer mpp_err.h. + */ + MPP_RET (*enqueue)(MppCtx ctx, MppPortType type, MppTask task); + + // control interface + /** + * @brief discard all packet and frame, reset all component, + * for both decoder and encoder + * @param[in] ctx The context of mpp, created by mpp_create() and initiated + * by mpp_init(). + * @return 0 for success, others for failure. The return value is an + * error code. For details, please refer mpp_err.h. + */ + MPP_RET (*reset)(MppCtx ctx); + /** + * @brief control function for mpp property setting + * @param[in] ctx The context of mpp, created by mpp_create() and initiated + * by mpp_init(). + * @param[in] cmd The mpi command, its definition can refer rk_mpi_cmd.h. + * @param[in,out] param The mpi command parameter + * @return 0 for success, others for failure. The return value is an + * error code. For details, please refer mpp_err.h. + */ + MPP_RET (*control)(MppCtx ctx, MpiCmd cmd, MppParam param); + + /** + * @brief The reserved segment, may be used in the future + */ + RK_U32 reserv[16]; +} MppApi; + + +#ifdef __cplusplus +extern "C" { +#endif + +/** + * @ingroup rk_mpi + * @brief Create empty context structure and mpi function pointers. + * Use functions in MppApi to access mpp services. + * @param[in,out] ctx pointer of the mpp context, refer to MpiImpl_t. + * @param[in,out] mpi pointer of mpi function, refer to MppApi. + * @return 0 for success, others for failure. The return value is an + * error code. For details, please refer mpp_err.h. + * @note This interface creates base flow context, all function calls + * are based on it. + */ +MPP_RET mpp_create(MppCtx *ctx, MppApi **mpi); +/** + * @ingroup rk_mpi + * @brief Call after mpp_create to setup mpp type and video format. + * This function will call internal context init function. + * @param[in] ctx The context of mpp, created by mpp_create(). + * @param[in] type specify decoder or encoder, refer to MppCtxType. + * @param[in] coding specify video compression coding, refer to MppCodingType. + * @return 0 for success, others for failure. The return value is an + * error code. For details, please refer mpp_err.h. + */ +MPP_RET mpp_init(MppCtx ctx, MppCtxType type, MppCodingType coding); +/** + * @ingroup rk_mpi + * @brief Destroy mpp context and free both context and mpi structure, + * it matches with mpp_init(). + * @param[in] ctx The context of mpp, created by mpp_create(). + * @return 0 for success, others for failure. The return value is an + * error code. For details, please refer mpp_err.h. + */ +MPP_RET mpp_destroy(MppCtx ctx); +/** + * @ingroup rk_mpi + * @brief judge given format is supported or not by MPP. + * @param[in] type specify decoder or encoder, refer to MppCtxType. + * @param[in] coding specify video compression coding, refer to MppCodingType. + * @return 0 for support, -1 for unsupported. + */ +MPP_RET mpp_check_support_format(MppCtxType type, MppCodingType coding); +/** + * @ingroup rk_mpi + * @brief List all formats supported by MPP + * @param NULL no need to input parameter + * @return No return value. This function just prints format information supported + * by MPP on standard output. + */ +void mpp_show_support_format(void); +void mpp_show_color_format(void); + +#ifdef __cplusplus +} +#endif + +#endif /*__RK_MPI_H__*/ diff --git a/rknpu2/examples/3rdparty/mpp/include/rockchip/rk_mpi_cmd.h b/rknpu2/examples/3rdparty/mpp/include/rockchip/rk_mpi_cmd.h new file mode 100644 index 0000000000000000000000000000000000000000..391df8e597108392cc1c84e892001c5f98f762aa --- /dev/null +++ b/rknpu2/examples/3rdparty/mpp/include/rockchip/rk_mpi_cmd.h @@ -0,0 +1,209 @@ +/* + * Copyright 2015 Rockchip Electronics Co. LTD + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __RK_MPI_CMD_H__ +#define __RK_MPI_CMD_H__ + +/* + * Command id bit usage is defined as follows: + * bit 20 - 23 - module id + * bit 16 - 19 - contex id + * bit 0 - 15 - command id + */ +#define CMD_MODULE_ID_MASK (0x00F00000) +#define CMD_MODULE_OSAL (0x00100000) +#define CMD_MODULE_MPP (0x00200000) +#define CMD_MODULE_CODEC (0x00300000) +#define CMD_MODULE_HAL (0x00400000) + +#define CMD_CTX_ID_MASK (0x000F0000) +#define CMD_CTX_ID_DEC (0x00010000) +#define CMD_CTX_ID_ENC (0x00020000) +#define CMD_CTX_ID_ISP (0x00030000) + +/* separate encoder / decoder control command to different segment */ +#define CMD_CFG_ID_MASK (0x0000FF00) + +/* mpp status control command */ +#define CMD_STATE_OPS (0x00000100) + +/* decoder control command */ +#define CMD_DEC_CFG_ALL (0x00000000) +#define CMD_DEC_QUERY (0x00000100) +#define CMD_DEC_CFG (0x00000200) + +/* encoder control command */ +#define CMD_ENC_CFG_ALL (0x00000000) +#define CMD_ENC_QUERY (0x00000100) +#define CMD_ENC_CFG_RC_API (0x00000200) + +#define CMD_ENC_CFG_MISC (0x00008000) +#define CMD_ENC_CFG_SPLIT (0x00008100) +#define CMD_ENC_CFG_REF (0x00008200) +#define CMD_ENC_CFG_ROI (0x00008300) +#define CMD_ENC_CFG_OSD (0x00008400) + +typedef enum { + MPP_OSAL_CMD_BASE = CMD_MODULE_OSAL, + MPP_OSAL_CMD_END, + + MPP_CMD_BASE = CMD_MODULE_MPP, + MPP_ENABLE_DEINTERLACE, + MPP_SET_INPUT_BLOCK, /* deprecated */ + MPP_SET_INTPUT_BLOCK_TIMEOUT, /* deprecated */ + MPP_SET_OUTPUT_BLOCK, /* deprecated */ + MPP_SET_OUTPUT_BLOCK_TIMEOUT, /* deprecated */ + /* + * timeout setup, refer to MPP_TIMEOUT_XXX + * zero - non block + * negative - block with no timeout + * positive - timeout in milisecond + */ + MPP_SET_INPUT_TIMEOUT, /* parameter type RK_S64 */ + MPP_SET_OUTPUT_TIMEOUT, /* parameter type RK_S64 */ + MPP_SET_DISABLE_THREAD, /* MPP no thread mode and use external thread to decode */ + + MPP_STATE_CMD_BASE = CMD_MODULE_MPP | CMD_STATE_OPS, + MPP_START, + MPP_STOP, + MPP_PAUSE, + MPP_RESUME, + + MPP_CMD_END, + + MPP_CODEC_CMD_BASE = CMD_MODULE_CODEC, + MPP_CODEC_GET_FRAME_INFO, + MPP_CODEC_CMD_END, + + MPP_DEC_CMD_BASE = CMD_MODULE_CODEC | CMD_CTX_ID_DEC, + MPP_DEC_SET_FRAME_INFO, /* vpu api legacy control for buffer slot dimension init */ + MPP_DEC_SET_EXT_BUF_GROUP, /* IMPORTANT: set external buffer group to mpp decoder */ + MPP_DEC_SET_INFO_CHANGE_READY, + MPP_DEC_SET_PRESENT_TIME_ORDER, /* use input time order for output */ + MPP_DEC_SET_PARSER_SPLIT_MODE, /* Need to setup before init */ + MPP_DEC_SET_PARSER_FAST_MODE, /* Need to setup before init */ + MPP_DEC_GET_STREAM_COUNT, + MPP_DEC_GET_VPUMEM_USED_COUNT, + MPP_DEC_SET_VC1_EXTRA_DATA, + MPP_DEC_SET_OUTPUT_FORMAT, + MPP_DEC_SET_DISABLE_ERROR, /* When set it will disable sw/hw error (H.264 / H.265) */ + MPP_DEC_SET_IMMEDIATE_OUT, + MPP_DEC_SET_ENABLE_DEINTERLACE, /* MPP enable deinterlace by default. Vpuapi can disable it */ + MPP_DEC_SET_ENABLE_FAST_PLAY, /* enable idr output immediately */ + MPP_DEC_SET_DISABLE_THREAD, /* MPP no thread mode and use external thread to decode */ + MPP_DEC_SET_MAX_USE_BUFFER_SIZE, + MPP_DEC_SET_ENABLE_MVC, /* enable MVC decoding*/ + + MPP_DEC_CMD_QUERY = CMD_MODULE_CODEC | CMD_CTX_ID_DEC | CMD_DEC_QUERY, + /* query decoder runtime information for decode stage */ + MPP_DEC_QUERY, /* set and get MppDecQueryCfg structure */ + + CMD_DEC_CMD_CFG = CMD_MODULE_CODEC | CMD_CTX_ID_DEC | CMD_DEC_CFG, + MPP_DEC_SET_CFG, /* set MppDecCfg structure */ + MPP_DEC_GET_CFG, /* get MppDecCfg structure */ + + MPP_DEC_CMD_END, + + MPP_ENC_CMD_BASE = CMD_MODULE_CODEC | CMD_CTX_ID_ENC, + /* basic encoder setup control */ + MPP_ENC_SET_CFG, /* set MppEncCfg structure */ + MPP_ENC_GET_CFG, /* get MppEncCfg structure */ + MPP_ENC_SET_PREP_CFG, /* deprecated set MppEncPrepCfg structure, use MPP_ENC_SET_CFG instead */ + MPP_ENC_GET_PREP_CFG, /* deprecated get MppEncPrepCfg structure, use MPP_ENC_GET_CFG instead */ + MPP_ENC_SET_RC_CFG, /* deprecated set MppEncRcCfg structure, use MPP_ENC_SET_CFG instead */ + MPP_ENC_GET_RC_CFG, /* deprecated get MppEncRcCfg structure, use MPP_ENC_GET_CFG instead */ + MPP_ENC_SET_CODEC_CFG, /* deprecated set MppEncCodecCfg structure, use MPP_ENC_SET_CFG instead */ + MPP_ENC_GET_CODEC_CFG, /* deprecated get MppEncCodecCfg structure, use MPP_ENC_GET_CFG instead */ + /* runtime encoder setup control */ + MPP_ENC_SET_IDR_FRAME, /* next frame will be encoded as intra frame */ + MPP_ENC_SET_OSD_LEGACY_0, /* deprecated */ + MPP_ENC_SET_OSD_LEGACY_1, /* deprecated */ + MPP_ENC_SET_OSD_LEGACY_2, /* deprecated */ + MPP_ENC_GET_HDR_SYNC, /* get vps / sps / pps which has better sync behavior parameter is MppPacket */ + MPP_ENC_GET_EXTRA_INFO, /* deprecated */ + MPP_ENC_SET_SEI_CFG, /* SEI: Supplement Enhancemant Information, parameter is MppSeiMode */ + MPP_ENC_GET_SEI_DATA, /* SEI: Supplement Enhancemant Information, parameter is MppPacket */ + MPP_ENC_PRE_ALLOC_BUFF, /* deprecated */ + MPP_ENC_SET_QP_RANGE, /* used for adjusting qp range, the parameter can be 1 or 2 */ + MPP_ENC_SET_ROI_CFG, /* set MppEncROICfg structure */ + MPP_ENC_SET_CTU_QP, /* for H265 Encoder,set CTU's size and QP */ + + MPP_ENC_CMD_QUERY = CMD_MODULE_CODEC | CMD_CTX_ID_ENC | CMD_ENC_QUERY, + /* query encoder runtime information for encode stage */ + MPP_ENC_QUERY, /* set and get MppEncQueryCfg structure */ + + /* User define rate control stategy API control */ + MPP_ENC_CFG_RC_API = CMD_MODULE_CODEC | CMD_CTX_ID_ENC | CMD_ENC_CFG_RC_API, + /* + * Get RcApiQueryAll structure + * Get all available rate control stategy string and count + */ + MPP_ENC_GET_RC_API_ALL = MPP_ENC_CFG_RC_API + 1, + /* + * Get RcApiQueryType structure + * Get available rate control stategy string with certain type + */ + MPP_ENC_GET_RC_API_BY_TYPE = MPP_ENC_CFG_RC_API + 2, + /* + * Set RcImplApi structure + * Add new or update rate control stategy function pointers + */ + MPP_ENC_SET_RC_API_CFG = MPP_ENC_CFG_RC_API + 3, + /* + * Get RcApiBrief structure + * Get current used rate control stategy brief information (type and name) + */ + MPP_ENC_GET_RC_API_CURRENT = MPP_ENC_CFG_RC_API + 4, + /* + * Set RcApiBrief structure + * Set current used rate control stategy brief information (type and name) + */ + MPP_ENC_SET_RC_API_CURRENT = MPP_ENC_CFG_RC_API + 5, + + MPP_ENC_CFG_MISC = CMD_MODULE_CODEC | CMD_CTX_ID_ENC | CMD_ENC_CFG_MISC, + MPP_ENC_SET_HEADER_MODE, /* set MppEncHeaderMode */ + MPP_ENC_GET_HEADER_MODE, /* get MppEncHeaderMode */ + + MPP_ENC_CFG_SPLIT = CMD_MODULE_CODEC | CMD_CTX_ID_ENC | CMD_ENC_CFG_SPLIT, + MPP_ENC_SET_SPLIT, /* set MppEncSliceSplit structure */ + MPP_ENC_GET_SPLIT, /* get MppEncSliceSplit structure */ + + MPP_ENC_CFG_REF = CMD_MODULE_CODEC | CMD_CTX_ID_ENC | CMD_ENC_CFG_REF, + MPP_ENC_SET_REF_CFG, /* set MppEncRefCfg structure */ + + MPP_ENC_CFG_OSD = CMD_MODULE_CODEC | CMD_CTX_ID_ENC | CMD_ENC_CFG_OSD, + MPP_ENC_SET_OSD_PLT_CFG, /* set OSD palette, parameter should be pointer to MppEncOSDPltCfg */ + MPP_ENC_GET_OSD_PLT_CFG, /* get OSD palette, parameter should be pointer to MppEncOSDPltCfg */ + MPP_ENC_SET_OSD_DATA_CFG, /* set OSD data with at most 8 regions, parameter should be pointer to MppEncOSDData */ + + MPP_ENC_CMD_END, + + MPP_ISP_CMD_BASE = CMD_MODULE_CODEC | CMD_CTX_ID_ISP, + MPP_ISP_CMD_END, + + MPP_HAL_CMD_BASE = CMD_MODULE_HAL, + MPP_HAL_CMD_END, + + MPI_CMD_BUTT, +} MpiCmd; + +#include "rk_vdec_cmd.h" +#include "rk_vdec_cfg.h" +#include "rk_venc_cmd.h" +#include "rk_venc_cfg.h" +#include "rk_venc_ref.h" + +#endif /*__RK_MPI_CMD_H__*/ diff --git a/rknpu2/examples/3rdparty/mpp/include/rockchip/rk_type.h b/rknpu2/examples/3rdparty/mpp/include/rockchip/rk_type.h new file mode 100644 index 0000000000000000000000000000000000000000..cb1666b90cf3fa0ea8a575943ab35db55be4bc91 --- /dev/null +++ b/rknpu2/examples/3rdparty/mpp/include/rockchip/rk_type.h @@ -0,0 +1,142 @@ +/* + * Copyright 2015 Rockchip Electronics Co. LTD + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __RK_TYPE_H__ +#define __RK_TYPE_H__ + +#include + +#if defined(_WIN32) && !defined(__MINGW32CE__) + +typedef unsigned char RK_U8; +typedef unsigned short RK_U16; +typedef unsigned int RK_U32; +typedef unsigned long RK_ULONG; +typedef unsigned __int64 RK_U64; + +typedef signed char RK_S8; +typedef signed short RK_S16; +typedef signed int RK_S32; +typedef signed long RK_LONG; +typedef signed __int64 RK_S64; + +#else + +typedef unsigned char RK_U8; +typedef unsigned short RK_U16; +typedef unsigned int RK_U32; +typedef unsigned long RK_ULONG; +typedef unsigned long long int RK_U64; + + +typedef signed char RK_S8; +typedef signed short RK_S16; +typedef signed int RK_S32; +typedef signed long RK_LONG; +typedef signed long long int RK_S64; + +#endif + +#ifndef MODULE_TAG +#define MODULE_TAG NULL +#endif + +/** + * @ingroup rk_mpi + * @brief The type of mpp context + * @details This type is used when calling mpp_init(), which including decoder, + * encoder and Image Signal Process(ISP). So far decoder and encoder + * are supported perfectly, and ISP will be supported in the future. + */ +typedef enum { + MPP_CTX_DEC, /**< decoder */ + MPP_CTX_ENC, /**< encoder */ + MPP_CTX_ISP, /**< isp */ + MPP_CTX_BUTT, /**< undefined */ +} MppCtxType; + +/** + * @ingroup rk_mpi + * @brief Enumeration used to define the possible video compression codings. + * sync with the omx_video.h + * + * @note This essentially refers to file extensions. If the coding is + * being used to specify the ENCODE type, then additional work + * must be done to configure the exact flavor of the compression + * to be used. For decode cases where the user application can + * not differentiate between MPEG-4 and H.264 bit streams, it is + * up to the codec to handle this. + */ +typedef enum { + MPP_VIDEO_CodingUnused, /**< Value when coding is N/A */ + MPP_VIDEO_CodingAutoDetect, /**< Autodetection of coding type */ + MPP_VIDEO_CodingMPEG2, /**< AKA: H.262 */ + MPP_VIDEO_CodingH263, /**< H.263 */ + MPP_VIDEO_CodingMPEG4, /**< MPEG-4 */ + MPP_VIDEO_CodingWMV, /**< Windows Media Video (WMV1,WMV2,WMV3)*/ + MPP_VIDEO_CodingRV, /**< all versions of Real Video */ + MPP_VIDEO_CodingAVC, /**< H.264/AVC */ + MPP_VIDEO_CodingMJPEG, /**< Motion JPEG */ + MPP_VIDEO_CodingVP8, /**< VP8 */ + MPP_VIDEO_CodingVP9, /**< VP9 */ + MPP_VIDEO_CodingVC1 = 0x01000000, /**< Windows Media Video (WMV1,WMV2,WMV3)*/ + MPP_VIDEO_CodingFLV1, /**< Sorenson H.263 */ + MPP_VIDEO_CodingDIVX3, /**< DIVX3 */ + MPP_VIDEO_CodingVP6, + MPP_VIDEO_CodingHEVC, /**< H.265/HEVC */ + MPP_VIDEO_CodingAVSPLUS, /**< AVS+ */ + MPP_VIDEO_CodingAVS, /**< AVS profile=0x20 */ + MPP_VIDEO_CodingAVS2, /**< AVS2 */ + MPP_VIDEO_CodingAV1, /**< av1 */ + MPP_VIDEO_CodingKhronosExtensions = 0x6F000000, /**< Reserved region for introducing Khronos Standard Extensions */ + MPP_VIDEO_CodingVendorStartUnused = 0x7F000000, /**< Reserved region for introducing Vendor Extensions */ + MPP_VIDEO_CodingMax = 0x7FFFFFFF +} MppCodingType; + +/* + * All external interface object list here. + * The interface object is defined as void * for expandability + * The cross include between these objects will introduce extra + * compiling difficulty. So we move them together in this header. + * + * Object interface header list: + * + * MppCtx - rk_mpi.h + * MppParam - rk_mpi.h + * + * MppFrame - mpp_frame.h + * MppPacket - mpp_packet.h + * + * MppBuffer - mpp_buffer.h + * MppBufferGroup - mpp_buffer.h + * + * MppTask - mpp_task.h + * MppMeta - mpp_meta.h + */ + +typedef void* MppCtx; +typedef void* MppParam; + +typedef void* MppFrame; +typedef void* MppPacket; + +typedef void* MppBuffer; +typedef void* MppBufferGroup; + +typedef void* MppTask; +typedef void* MppMeta; + +#endif /*__RK_TYPE_H__*/ diff --git a/rknpu2/examples/3rdparty/mpp/include/rockchip/rk_vdec_cfg.h b/rknpu2/examples/3rdparty/mpp/include/rockchip/rk_vdec_cfg.h new file mode 100644 index 0000000000000000000000000000000000000000..30dc93221c91b7423dcc0b9aea39c253cfa1c4c2 --- /dev/null +++ b/rknpu2/examples/3rdparty/mpp/include/rockchip/rk_vdec_cfg.h @@ -0,0 +1,50 @@ +/* + * Copyright 2020 Rockchip Electronics Co. LTD + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __RK_VDEC_CFG_H__ +#define __RK_VDEC_CFG_H__ + +#include "rk_type.h" +#include "mpp_err.h" + +typedef void* MppDecCfg; + +#ifdef __cplusplus +extern "C" { +#endif + +MPP_RET mpp_dec_cfg_init(MppDecCfg *cfg); +MPP_RET mpp_dec_cfg_deinit(MppDecCfg cfg); + +MPP_RET mpp_dec_cfg_set_s32(MppDecCfg cfg, const char *name, RK_S32 val); +MPP_RET mpp_dec_cfg_set_u32(MppDecCfg cfg, const char *name, RK_U32 val); +MPP_RET mpp_dec_cfg_set_s64(MppDecCfg cfg, const char *name, RK_S64 val); +MPP_RET mpp_dec_cfg_set_u64(MppDecCfg cfg, const char *name, RK_U64 val); +MPP_RET mpp_dec_cfg_set_ptr(MppDecCfg cfg, const char *name, void *val); + +MPP_RET mpp_dec_cfg_get_s32(MppDecCfg cfg, const char *name, RK_S32 *val); +MPP_RET mpp_dec_cfg_get_u32(MppDecCfg cfg, const char *name, RK_U32 *val); +MPP_RET mpp_dec_cfg_get_s64(MppDecCfg cfg, const char *name, RK_S64 *val); +MPP_RET mpp_dec_cfg_get_u64(MppDecCfg cfg, const char *name, RK_U64 *val); +MPP_RET mpp_dec_cfg_get_ptr(MppDecCfg cfg, const char *name, void **val); + +void mpp_dec_cfg_show(void); + +#ifdef __cplusplus +} +#endif + +#endif /*__RK_VDEC_CFG_H__*/ diff --git a/rknpu2/examples/3rdparty/mpp/include/rockchip/rk_vdec_cmd.h b/rknpu2/examples/3rdparty/mpp/include/rockchip/rk_vdec_cmd.h new file mode 100644 index 0000000000000000000000000000000000000000..adeeb35d8243d841a2cbbfcd71cdc457852045a3 --- /dev/null +++ b/rknpu2/examples/3rdparty/mpp/include/rockchip/rk_vdec_cmd.h @@ -0,0 +1,69 @@ +/* + * Copyright 2015 Rockchip Electronics Co. LTD + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __RK_VDEC_CMD_H__ +#define __RK_VDEC_CMD_H__ + +#include "rk_type.h" +#include "mpp_err.h" + +/* + * decoder query interface is only for debug usage + */ +#define MPP_DEC_QUERY_STATUS (0x00000001) +#define MPP_DEC_QUERY_WAIT (0x00000002) +#define MPP_DEC_QUERY_FPS (0x00000004) +#define MPP_DEC_QUERY_BPS (0x00000008) +#define MPP_DEC_QUERY_DEC_IN_PKT (0x00000010) +#define MPP_DEC_QUERY_DEC_WORK (0x00000020) +#define MPP_DEC_QUERY_DEC_OUT_FRM (0x00000040) + +#define MPP_DEC_QUERY_ALL (MPP_DEC_QUERY_STATUS | \ + MPP_DEC_QUERY_WAIT | \ + MPP_DEC_QUERY_FPS | \ + MPP_DEC_QUERY_BPS | \ + MPP_DEC_QUERY_DEC_IN_PKT | \ + MPP_DEC_QUERY_DEC_WORK | \ + MPP_DEC_QUERY_DEC_OUT_FRM) + +typedef struct MppDecQueryCfg_t { + /* + * 32 bit query flag for query data check + * Each bit represent a query data switch. + * bit 0 - for querying decoder runtime status + * bit 1 - for querying decoder runtime waiting status + * bit 2 - for querying decoder realtime decode fps + * bit 3 - for querying decoder realtime input bps + * bit 4 - for querying decoder input packet count + * bit 5 - for querying decoder start hardware times + * bit 6 - for querying decoder output frame count + */ + RK_U32 query_flag; + + /* 64 bit query data output */ + RK_U32 rt_status; + RK_U32 rt_wait; + RK_U32 rt_fps; + RK_U32 rt_bps; + RK_U32 dec_in_pkt_cnt; + RK_U32 dec_hw_run_cnt; + RK_U32 dec_out_frm_cnt; +} MppDecQueryCfg; + +typedef void* MppExtCbCtx; +typedef MPP_RET (*MppExtCbFunc)(MppExtCbCtx cb_ctx, MppCtx mpp, RK_S32 cmd, void *arg); + +#endif /*__RK_VDEC_CMD_H__*/ diff --git a/rknpu2/examples/3rdparty/mpp/include/rockchip/rk_venc_cfg.h b/rknpu2/examples/3rdparty/mpp/include/rockchip/rk_venc_cfg.h new file mode 100644 index 0000000000000000000000000000000000000000..9f84952af3ab7ab52f47064faf642cc54d87b2bf --- /dev/null +++ b/rknpu2/examples/3rdparty/mpp/include/rockchip/rk_venc_cfg.h @@ -0,0 +1,52 @@ +/* + * Copyright 2015 Rockchip Electronics Co. LTD + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __RK_VENC_CFG_H__ +#define __RK_VENC_CFG_H__ + +#include "rk_type.h" +#include "mpp_err.h" + +typedef void* MppEncCfg; + +#ifdef __cplusplus +extern "C" { +#endif + +MPP_RET mpp_enc_cfg_init(MppEncCfg *cfg); +MPP_RET mpp_enc_cfg_deinit(MppEncCfg cfg); + +MPP_RET mpp_enc_cfg_set_s32(MppEncCfg cfg, const char *name, RK_S32 val); +MPP_RET mpp_enc_cfg_set_u32(MppEncCfg cfg, const char *name, RK_U32 val); +MPP_RET mpp_enc_cfg_set_s64(MppEncCfg cfg, const char *name, RK_S64 val); +MPP_RET mpp_enc_cfg_set_u64(MppEncCfg cfg, const char *name, RK_U64 val); +MPP_RET mpp_enc_cfg_set_ptr(MppEncCfg cfg, const char *name, void *val); +MPP_RET mpp_enc_cfg_set_st(MppEncCfg cfg, const char *name, void *val); + +MPP_RET mpp_enc_cfg_get_s32(MppEncCfg cfg, const char *name, RK_S32 *val); +MPP_RET mpp_enc_cfg_get_u32(MppEncCfg cfg, const char *name, RK_U32 *val); +MPP_RET mpp_enc_cfg_get_s64(MppEncCfg cfg, const char *name, RK_S64 *val); +MPP_RET mpp_enc_cfg_get_u64(MppEncCfg cfg, const char *name, RK_U64 *val); +MPP_RET mpp_enc_cfg_get_ptr(MppEncCfg cfg, const char *name, void **val); +MPP_RET mpp_enc_cfg_get_st(MppEncCfg cfg, const char *name, void *val); + +void mpp_enc_cfg_show(void); + +#ifdef __cplusplus +} +#endif + +#endif /*__RK_VENC_CFG_H__*/ diff --git a/rknpu2/examples/3rdparty/mpp/include/rockchip/rk_venc_cmd.h b/rknpu2/examples/3rdparty/mpp/include/rockchip/rk_venc_cmd.h new file mode 100644 index 0000000000000000000000000000000000000000..53e1d9f360e37e014a7b8e0e23c0d71b2fe0c6fe --- /dev/null +++ b/rknpu2/examples/3rdparty/mpp/include/rockchip/rk_venc_cmd.h @@ -0,0 +1,1394 @@ +/* + * Copyright 2015 Rockchip Electronics Co. LTD + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __RK_VENC_CMD_H__ +#define __RK_VENC_CMD_H__ + +#include "mpp_frame.h" +#include "rk_venc_rc.h" + +/* + * Configure of encoder is very complicated. So we divide configures into + * four parts: + * + * 1. Rate control parameter + * This is quality and bitrate request from user. + * + * 2. Data source MppFrame parameter + * This is data source buffer information. + * Now it is PreP config + * PreP : Encoder Preprocess configuration + * + * 3. Video codec infomation + * This is user custormized stream information. + * including: + * H.264 / H.265 / vp8 / mjpeg + * + * 4. Misc parameter + * including: + * Split : Slice split configuration + * GopRef: Reference gop configuration + * ROI : Region Of Interest + * OSD : On Screen Display + * MD : Motion Detection + * + * The module transcation flow is as follows: + * + * + + + * User | Mpi/Mpp | EncImpl + * | | Hal + * | | + * +----------+ | +---------+ | +-----------+ + * | | | | +-----RcCfg-----> | + * | RcCfg +---------> | | | EncImpl | + * | | | | | +-Frame-----> | + * +----------+ | | | | | +--+-----^--+ + * | | | | | | | + * | | | | | | | + * +----------+ | | | | | syntax | + * | | | | | | | | | + * | MppFrame +---------> MppEnc +---+ | | result + * | | | | | | | | | + * +----------+ | | | | | | | + * | | | | | +--v-----+--+ + * | | | +-Frame-----> | + * +----------+ | | | | | | + * | | | | +---CodecCfg----> Hal | + * | CodecCfg +---------> | | | | + * | | | | <-----Extra-----> | + * +----------+ | +---------+ | +-----------+ + * | | + * | | + * + + + * + * The function call flow is shown below: + * + * mpi mpp_enc controller hal + * + + + + + * | | | | + * | | | | + * +----------init------------> | | + * | | | | + * | | | | + * | PrepCfg | | | + * +---------control----------> PrepCfg | | + * | +-----control-----> | + * | | | PrepCfg | + * | +--------------------------control--------> + * | | | allocate + * | | | buffer + * | | | | + * | RcCfg | | | + * +---------control----------> RcCfg | | + * | +-----control-----> | + * | | rc_init | + * | | | | + * | | | | + * | CodecCfg | | | + * +---------control----------> | CodecCfg | + * | +--------------------------control--------> + * | | | generate + * | | | sps/pps + * | | | Get extra info | + * | +--------------------------control--------> + * | Get extra info | | | + * +---------control----------> | | + * | | | | + * | | | | + * | ROICfg | | | + * +---------control----------> | ROICfg | + * | +--------------------------control--------> + * | | | | + * | OSDCfg | | | + * +---------control----------> | OSDCfg | + * | +--------------------------control--------> + * | | | | + * | MDCfg | | | + * +---------control----------> | MDCfg | + * | +--------------------------control--------> + * | | | | + * | Set extra info | | | + * +---------control----------> | Set extra info | + * | +--------------------------control--------> + * | | | | + * | task | | | + * +----------encode----------> task | | + * | +-----encode------> | + * | | encode | + * | | | syntax | + * | +--------------------------gen_reg--------> + * | | | | + * | | | | + * | +---------------------------start---------> + * | | | | + * | | | | + * | +---------------------------wait----------> + * | | | | + * | | callback | | + * | +-----------------> | + * +--OSD-MD--encode----------> | | + * | . | | | + * | . | | | + * | . | | | + * +--OSD-MD--encode----------> | | + * | | | | + * +----------deinit----------> | | + * + + + + + */ + +/* + * encoder query interface is only for debug usage + */ +#define MPP_ENC_QUERY_STATUS (0x00000001) +#define MPP_ENC_QUERY_WAIT (0x00000002) +#define MPP_ENC_QUERY_FPS (0x00000004) +#define MPP_ENC_QUERY_BPS (0x00000008) +#define MPP_ENC_QUERY_ENC_IN_FRM (0x00000010) +#define MPP_ENC_QUERY_ENC_WORK (0x00000020) +#define MPP_ENC_QUERY_ENC_OUT_PKT (0x00000040) + +#define MPP_ENC_QUERY_ALL (MPP_ENC_QUERY_STATUS | \ + MPP_ENC_QUERY_WAIT | \ + MPP_ENC_QUERY_FPS | \ + MPP_ENC_QUERY_BPS | \ + MPP_ENC_QUERY_ENC_IN_FRM | \ + MPP_ENC_QUERY_ENC_WORK | \ + MPP_ENC_QUERY_ENC_OUT_PKT) + +typedef struct MppEncQueryCfg_t { + /* + * 32 bit query flag for query data check + * Each bit represent a query data switch. + * bit 0 - for querying encoder runtime status + * bit 1 - for querying encoder runtime waiting status + * bit 2 - for querying encoder realtime encode fps + * bit 3 - for querying encoder realtime output bps + * bit 4 - for querying encoder input frame count + * bit 5 - for querying encoder start hardware times + * bit 6 - for querying encoder output packet count + */ + RK_U32 query_flag; + + /* 64 bit query data output */ + RK_U32 rt_status; + RK_U32 rt_wait; + RK_U32 rt_fps; + RK_U32 rt_bps; + RK_U32 enc_in_frm_cnt; + RK_U32 enc_hw_run_cnt; + RK_U32 enc_out_pkt_cnt; +} MppEncQueryCfg; + +/* + * base working mode parameter + */ +typedef enum MppEncBaseCfgChange_e { + MPP_ENC_BASE_CFG_CHANGE_LOW_DELAY = (1 << 0), + MPP_ENC_BASE_CFG_CHANGE_ALL = (0xFFFFFFFF), +} MppEncBaseCfgChange; + +typedef struct MppEncBaseCfg_t { + RK_U32 change; + + RK_S32 low_delay; +} MppEncBaseCfg; + +/* + * Rate control parameter + */ +typedef enum MppEncRcCfgChange_e { + MPP_ENC_RC_CFG_CHANGE_RC_MODE = (1 << 0), + MPP_ENC_RC_CFG_CHANGE_QUALITY = (1 << 1), + MPP_ENC_RC_CFG_CHANGE_BPS = (1 << 2), /* change on bps target / max / min */ + MPP_ENC_RC_CFG_CHANGE_FPS_IN = (1 << 5), /* change on fps in flex / numerator / denorminator */ + MPP_ENC_RC_CFG_CHANGE_FPS_OUT = (1 << 6), /* change on fps out flex / numerator / denorminator */ + MPP_ENC_RC_CFG_CHANGE_GOP = (1 << 7), + MPP_ENC_RC_CFG_CHANGE_SKIP_CNT = (1 << 8), + MPP_ENC_RC_CFG_CHANGE_MAX_REENC = (1 << 9), + MPP_ENC_RC_CFG_CHANGE_DROP_FRM = (1 << 10), + MPP_ENC_RC_CFG_CHANGE_MAX_I_PROP = (1 << 11), + MPP_ENC_RC_CFG_CHANGE_MIN_I_PROP = (1 << 12), + MPP_ENC_RC_CFG_CHANGE_INIT_IP_RATIO = (1 << 13), + MPP_ENC_RC_CFG_CHANGE_PRIORITY = (1 << 14), + MPP_ENC_RC_CFG_CHANGE_SUPER_FRM = (1 << 15), + /* qp related change flag */ + MPP_ENC_RC_CFG_CHANGE_QP_INIT = (1 << 16), + MPP_ENC_RC_CFG_CHANGE_QP_RANGE = (1 << 17), + MPP_ENC_RC_CFG_CHANGE_QP_RANGE_I = (1 << 18), + MPP_ENC_RC_CFG_CHANGE_QP_MAX_STEP = (1 << 19), + MPP_ENC_RC_CFG_CHANGE_QP_IP = (1 << 20), + MPP_ENC_RC_CFG_CHANGE_QP_VI = (1 << 21), + MPP_ENC_RC_CFG_CHANGE_QP_ROW = (1 << 22), + MPP_ENC_RC_CFG_CHANGE_QP_ROW_I = (1 << 23), + MPP_ENC_RC_CFG_CHANGE_DEBREATH = (1 << 24), + MPP_ENC_RC_CFG_CHANGE_HIER_QP = (1 << 25), + MPP_ENC_RC_CFG_CHANGE_ST_TIME = (1 << 26), + MPP_ENC_RC_CFG_CHANGE_REFRESH = (1 << 27), + MPP_ENC_RC_CFG_CHANGE_GOP_REF_CFG = (1 << 28), + MPP_ENC_RC_CFG_CHANGE_ALL = (0xFFFFFFFF), +} MppEncRcCfgChange; + +typedef enum MppEncRcQuality_e { + MPP_ENC_RC_QUALITY_WORST, + MPP_ENC_RC_QUALITY_WORSE, + MPP_ENC_RC_QUALITY_MEDIUM, + MPP_ENC_RC_QUALITY_BETTER, + MPP_ENC_RC_QUALITY_BEST, + MPP_ENC_RC_QUALITY_CQP, + MPP_ENC_RC_QUALITY_AQ_ONLY, + MPP_ENC_RC_QUALITY_BUTT +} MppEncRcQuality; + +typedef struct MppEncRcCfg_t { + RK_U32 change; + + /* + * rc_mode - rate control mode + * + * mpp provide two rate control mode: + * + * Constant Bit Rate (CBR) mode + * - paramter 'bps*' define target bps + * - paramter quality and qp will not take effect + * + * Variable Bit Rate (VBR) mode + * - paramter 'quality' define 5 quality levels + * - paramter 'bps*' is used as reference but not strict condition + * - special Constant QP (CQP) mode is under VBR mode + * CQP mode will work with qp in CodecCfg. But only use for test + * + * default: CBR + */ + MppEncRcMode rc_mode; + + /* + * quality - quality parameter, only takes effect in VBR mode + * + * Mpp does not give the direct parameter in different protocol. + * + * Mpp provide total 5 quality level: + * Worst - worse - Medium - better - best + * + * extra CQP level means special constant-qp (CQP) mode + * + * default value: Medium + */ + MppEncRcQuality quality; + + /* + * bit rate parameters + * mpp gives three bit rate control parameter for control + * bps_target - target bit rate, unit: bit per second + * bps_max - maximun bit rate, unit: bit per second + * bps_min - minimun bit rate, unit: bit per second + * if user need constant bit rate set parameters to the similar value + * if user need variable bit rate set parameters as they need + */ + RK_S32 bps_target; + RK_S32 bps_max; + RK_S32 bps_min; + + /* + * frame rate parameters have great effect on rate control + * + * fps_in_flex + * 0 - fix input frame rate + * 1 - variable input frame rate + * + * fps_in_num + * input frame rate numerator, if 0 then default 30 + * + * fps_in_denorm + * input frame rate denorminator, if 0 then default 1 + * + * fps_out_flex + * 0 - fix output frame rate + * 1 - variable output frame rate + * + * fps_out_num + * output frame rate numerator, if 0 then default 30 + * + * fps_out_denorm + * output frame rate denorminator, if 0 then default 1 + */ + RK_S32 fps_in_flex; + RK_S32 fps_in_num; + RK_S32 fps_in_denorm; + RK_S32 fps_out_flex; + RK_S32 fps_out_num; + RK_S32 fps_out_denorm; + + /* + * gop - group of picture, gap between Intra frame + * 0 for only 1 I frame the rest are all P frames + * 1 for all I frame + * 2 for I P I P I P + * 3 for I P P I P P + * etc... + */ + RK_S32 gop; + void *ref_cfg; + + /* + * skip_cnt - max continuous frame skip count + * 0 - frame skip is not allow + */ + RK_S32 skip_cnt; + + /* + * max_reenc_times - max reencode time for one frame + * 0 - reencode is not allowed + * 1~3 max reencode time is limited to 3 + */ + RK_U32 max_reenc_times; + + /* + * stats_time - the time of bitrate statistics + */ + RK_S32 stats_time; + + /* + * drop frame parameters + * used on bitrate is far over the max bitrate + * + * drop_mode + * + * MPP_ENC_RC_DROP_FRM_DISABLED + * - do not drop frame when bitrate overflow. + * MPP_ENC_RC_DROP_FRM_NORMAL + * - do not encode the dropped frame when bitrate overflow. + * MPP_ENC_RC_DROP_FRM_PSKIP + * - encode a all skip frame when bitrate overflow. + * + * drop_threshold + * + * The percentage threshold over max_bitrate for trigger frame drop. + * + * drop_gap + * The max continuous frame drop number + */ + MppEncRcDropFrmMode drop_mode; + RK_U32 drop_threshold; + RK_U32 drop_gap; + + MppEncRcSuperFrameMode super_mode; + RK_U32 super_i_thd; + RK_U32 super_p_thd; + + MppEncRcPriority rc_priority; + + RK_U32 debreath_en; + RK_U32 debre_strength; + RK_S32 max_i_prop; + RK_S32 min_i_prop; + RK_S32 init_ip_ratio; + + /* general qp control */ + RK_S32 qp_init; + RK_S32 qp_max; + RK_S32 qp_max_i; + RK_S32 qp_min; + RK_S32 qp_min_i; + RK_S32 qp_max_step; /* delta qp between each two P frame */ + RK_S32 qp_delta_ip; /* delta qp between I and P */ + RK_S32 qp_delta_vi; /* delta qp between vi and P */ + + RK_S32 hier_qp_en; + RK_S32 hier_qp_delta[4]; + RK_S32 hier_frame_num[4]; + + RK_U32 refresh_en; + MppEncRcRefreshMode refresh_mode; + RK_U32 refresh_num; + RK_S32 refresh_length; +} MppEncRcCfg; + + +typedef enum MppEncHwCfgChange_e { + /* qp related hardware config flag */ + MPP_ENC_HW_CFG_CHANGE_QP_ROW = (1 << 0), + MPP_ENC_HW_CFG_CHANGE_QP_ROW_I = (1 << 1), + MPP_ENC_HW_CFG_CHANGE_AQ_THRD_I = (1 << 2), + MPP_ENC_HW_CFG_CHANGE_AQ_THRD_P = (1 << 3), + MPP_ENC_HW_CFG_CHANGE_AQ_STEP_I = (1 << 4), + MPP_ENC_HW_CFG_CHANGE_AQ_STEP_P = (1 << 5), + MPP_ENC_HW_CFG_CHANGE_MB_RC = (1 << 6), + MPP_ENC_HW_CFG_CHANGE_CU_MODE_BIAS = (1 << 8), + MPP_ENC_HW_CFG_CHANGE_CU_SKIP_BIAS = (1 << 9), + MPP_ENC_HW_CFG_CHANGE_ALL = (0xFFFFFFFF), +} MppEncHwCfgChange; + +/* + * Hardware related rate control config + * + * This config will open some detail feature to external user to control + * hardware behavior directly. + */ +typedef struct MppEncHwCfg_t { + RK_U32 change; + + /* vepu541/vepu540 */ + RK_S32 qp_delta_row; /* delta qp between two row in P frame */ + RK_S32 qp_delta_row_i; /* delta qp between two row in I frame */ + RK_U32 aq_thrd_i[16]; + RK_U32 aq_thrd_p[16]; + RK_S32 aq_step_i[16]; + RK_S32 aq_step_p[16]; + + /* vepu1/2 */ + RK_S32 mb_rc_disable; + + /* vepu580 */ + RK_S32 extra_buf; + + /* + * block mode decision bias config + * 0 - intra32x32 + * 1 - intra16x16 + * 2 - intra8x8 + * 3 - intra4x4 + * 4 - inter64x64 + * 5 - inter32x32 + * 6 - inter16x16 + * 7 - inter8x8 + * value range 0 ~ 15, default : 8 + * If the value is smaller then encoder will be more likely to encode corresponding block mode. + */ + RK_S32 mode_bias[8]; + + /* + * skip mode bias config + * skip_bias_en - enable flag for skip bias config + * skip_sad - sad threshold for skip / non-skip + * skip_bias - tendency for skip, value range 0 ~ 15, default : 8 + * If the value is smaller then encoder will be more likely to encode skip block. + */ + RK_S32 skip_bias_en; + RK_S32 skip_sad; + RK_S32 skip_bias; +} MppEncHwCfg; + +/* + * Mpp preprocess parameter + */ +typedef enum MppEncPrepCfgChange_e { + MPP_ENC_PREP_CFG_CHANGE_INPUT = (1 << 0), /* change on input config */ + MPP_ENC_PREP_CFG_CHANGE_FORMAT = (1 << 2), /* change on format */ + /* transform parameter */ + MPP_ENC_PREP_CFG_CHANGE_ROTATION = (1 << 4), /* change on rotation */ + MPP_ENC_PREP_CFG_CHANGE_MIRRORING = (1 << 5), /* change on mirroring */ + MPP_ENC_PREP_CFG_CHANGE_FLIP = (1 << 6), /* change on flip */ + /* enhancement parameter */ + MPP_ENC_PREP_CFG_CHANGE_DENOISE = (1 << 8), /* change on denoise */ + MPP_ENC_PREP_CFG_CHANGE_SHARPEN = (1 << 9), /* change on denoise */ + /* color related parameter */ + MPP_ENC_PREP_CFG_CHANGE_COLOR_RANGE = (1 << 16), /* change on color range */ + MPP_ENC_PREP_CFG_CHANGE_COLOR_SPACE = (1 << 17), /* change on color range */ + MPP_ENC_PREP_CFG_CHANGE_COLOR_PRIME = (1 << 18), /* change on color primaries */ + MPP_ENC_PREP_CFG_CHANGE_COLOR_TRC = (1 << 19), /* change on color transfer */ + + MPP_ENC_PREP_CFG_CHANGE_ALL = (0xFFFFFFFF), +} MppEncPrepCfgChange; + +/* + * Preprocess sharpen parameter + * + * 5x5 sharpen core + * + * enable_y - enable luma sharpen + * enable_uv - enable chroma sharpen + */ +typedef struct { + RK_U32 enable_y; + RK_U32 enable_uv; + RK_S32 coef[5]; + RK_S32 div; + RK_S32 threshold; +} MppEncPrepSharpenCfg; + +/* + * input frame rotation parameter + * 0 - disable rotation + * 1 - 90 degree + * 2 - 180 degree + * 3 - 270 degree + */ +typedef enum MppEncRotationCfg_e { + MPP_ENC_ROT_0, + MPP_ENC_ROT_90, + MPP_ENC_ROT_180, + MPP_ENC_ROT_270, + MPP_ENC_ROT_BUTT +} MppEncRotationCfg; + +typedef struct MppEncPrepCfg_t { + RK_U32 change; + + /* + * Mpp encoder input data dimension config + * + * width / height / hor_stride / ver_stride / format + * These information will be used for buffer allocation and rc config init + * The output format is always YUV420. So if input is RGB then color + * conversion will be done internally + */ + RK_S32 width; + RK_S32 height; + RK_S32 hor_stride; + RK_S32 ver_stride; + + /* + * Mpp encoder input data format config + */ + MppFrameFormat format; + MppFrameColorSpace color; + MppFrameColorPrimaries colorprim; + MppFrameColorTransferCharacteristic colortrc; + MppFrameColorRange range; + + /* suffix ext means the user set config externally */ + MppEncRotationCfg rotation; + MppEncRotationCfg rotation_ext; + + /* + * input frame mirroring parameter + * 0 - disable mirroring + * 1 - horizontal mirroring + */ + RK_S32 mirroring; + RK_S32 mirroring_ext; + + /* + * input frame flip parameter + * 0 - disable flip + * 1 - flip, vertical mirror transformation + */ + RK_S32 flip; + + /* + * TODO: + */ + RK_S32 denoise; + + MppEncPrepSharpenCfg sharpen; +} MppEncPrepCfg; + +/* + * Mpp Motion Detection parameter + * + * Mpp can output Motion Detection infomation for each frame. + * If user euqueue a encode task with KEY_MOTION_INFO by following function + * then encoder will output Motion Detection information to the buffer. + * + * mpp_task_meta_set_buffer(task, KEY_MOTION_INFO, buffer); + * + * Motion Detection information will be organized in this way: + * 1. Each 16x16 block will have a 32 bit block information which contains + * 15 bit SAD(Sum of Abstract Difference value + * 9 bit signed horizontal motion vector + * 8 bit signed vertical motion vector + * 2. The sequence of MD information in the buffer is corresponding to the + * block position in the frame, left-to right, top-to-bottom. + * 3. If the width of the frame is not a multiple of 256 pixels (16 macro + * blocks), DMA would extend the frame to a multiple of 256 pixels and + * the extended blocks' MD information are 32'h0000_0000. + * 4. Buffer must be ion buffer and 1024 byte aligned. + */ +typedef struct MppEncMDBlkInfo_t { + RK_U32 sad : 15; /* bit 0~14 - SAD */ + RK_S32 mvx : 9; /* bit 15~23 - signed horizontal mv */ + RK_S32 mvy : 8; /* bit 24~31 - signed vertical mv */ +} MppEncMDBlkInfo; + +typedef enum MppEncHeaderMode_e { + /* default mode: attach vps/sps/pps only on first frame */ + MPP_ENC_HEADER_MODE_DEFAULT, + /* IDR mode: attach vps/sps/pps on each IDR frame */ + MPP_ENC_HEADER_MODE_EACH_IDR, + MPP_ENC_HEADER_MODE_BUTT, +} MppEncHeaderMode; + +typedef enum MppEncSeiMode_e { + MPP_ENC_SEI_MODE_DISABLE, /* default mode, SEI writing is disabled */ + MPP_ENC_SEI_MODE_ONE_SEQ, /* one sequence has only one SEI */ + MPP_ENC_SEI_MODE_ONE_FRAME /* one frame may have one SEI, if SEI info has changed */ +} MppEncSeiMode; + +/* + * Mpp codec parameter + * parameter is defined from here + */ + +/* + * H.264 configurable parameter + */ +typedef enum MppEncH264CfgChange_e { + /* change on stream type */ + MPP_ENC_H264_CFG_STREAM_TYPE = (1 << 0), + /* change on svc / profile / level */ + MPP_ENC_H264_CFG_CHANGE_PROFILE = (1 << 1), + /* change on entropy_coding_mode / cabac_init_idc */ + MPP_ENC_H264_CFG_CHANGE_ENTROPY = (1 << 2), + + /* change on transform8x8_mode */ + MPP_ENC_H264_CFG_CHANGE_TRANS_8x8 = (1 << 4), + /* change on constrained_intra_pred_mode */ + MPP_ENC_H264_CFG_CHANGE_CONST_INTRA = (1 << 5), + /* change on chroma_cb_qp_offset/ chroma_cr_qp_offset */ + MPP_ENC_H264_CFG_CHANGE_CHROMA_QP = (1 << 6), + /* change on deblock_disable / deblock_offset_alpha / deblock_offset_beta */ + MPP_ENC_H264_CFG_CHANGE_DEBLOCKING = (1 << 7), + /* change on use_longterm */ + MPP_ENC_H264_CFG_CHANGE_LONG_TERM = (1 << 8), + /* change on scaling_list_mode */ + MPP_ENC_H264_CFG_CHANGE_SCALING_LIST = (1 << 9), + /* change on poc type */ + MPP_ENC_H264_CFG_CHANGE_POC_TYPE = (1 << 10), + /* change on log2 max poc lsb minus 4 */ + MPP_ENC_H264_CFG_CHANGE_MAX_POC_LSB = (1 << 11), + /* change on log2 max frame number minus 4 */ + MPP_ENC_H264_CFG_CHANGE_MAX_FRM_NUM = (1 << 12), + /* change on gaps_in_frame_num_value_allowed_flag */ + MPP_ENC_H264_CFG_CHANGE_GAPS_IN_FRM_NUM = (1 << 13), + + /* change on max_qp / min_qp */ + MPP_ENC_H264_CFG_CHANGE_QP_LIMIT = (1 << 16), + /* change on max_qp_i / min_qp_i */ + MPP_ENC_H264_CFG_CHANGE_QP_LIMIT_I = (1 << 17), + /* change on max_qp_step */ + MPP_ENC_H264_CFG_CHANGE_MAX_QP_STEP = (1 << 18), + /* change on qp_delta_ip */ + MPP_ENC_H264_CFG_CHANGE_QP_DELTA = (1 << 19), + /* change on intra_refresh_mode / intra_refresh_arg */ + MPP_ENC_H264_CFG_CHANGE_INTRA_REFRESH = (1 << 20), + /* change on max long-term reference frame count */ + MPP_ENC_H264_CFG_CHANGE_MAX_LTR = (1 << 21), + /* change on max temporal id */ + MPP_ENC_H264_CFG_CHANGE_MAX_TID = (1 << 22), + /* change on adding prefix nal */ + MPP_ENC_H264_CFG_CHANGE_ADD_PREFIX = (1 << 23), + /* change on base layer priority id */ + MPP_ENC_H264_CFG_CHANGE_BASE_LAYER_PID = (1 << 24), + + /* change on vui */ + MPP_ENC_H264_CFG_CHANGE_VUI = (1 << 28), + + /* change on constraint */ + MPP_ENC_H264_CFG_CHANGE_CONSTRAINT_SET = (1 << 29), + + MPP_ENC_H264_CFG_CHANGE_ALL = (0xFFFFFFFF), +} MppEncH264CfgChange; + +typedef struct MppEncH264Cfg_t { + RK_U32 change; + + /* + * H.264 stream format + * 0 - H.264 Annex B: NAL unit starts with '00 00 00 01' + * 1 - Plain NAL units without startcode + */ + RK_S32 stream_type; + + /* + * H.264 codec syntax config + * + * do NOT setup the three option below unless you are familiar with encoder detail + * poc_type - picture order count type 0 ~ 2 + * log2_max_poc_lsb - used in sps with poc_type 0, + * log2_max_frame_num - used in sps + */ + RK_U32 poc_type; + RK_U32 hw_poc_type; + RK_U32 log2_max_poc_lsb; + RK_U32 log2_max_frame_num; + RK_U32 gaps_not_allowed; + + /* + * H.264 profile_idc parameter + * 66 - Baseline profile + * 77 - Main profile + * 100 - High profile + */ + RK_S32 profile; + + /* + * H.264 level_idc parameter + * 10 / 11 / 12 / 13 - qcif@15fps / cif@7.5fps / cif@15fps / cif@30fps + * 20 / 21 / 22 - cif@30fps / half-D1@@25fps / D1@12.5fps + * 30 / 31 / 32 - D1@25fps / 720p@30fps / 720p@60fps + * 40 / 41 / 42 - 1080p@30fps / 1080p@30fps / 1080p@60fps + * 50 / 51 / 52 - 4K@30fps + */ + RK_S32 level; + + /* + * H.264 entropy coding method + * 0 - CAVLC + * 1 - CABAC + * When CABAC is select cabac_init_idc can be range 0~2 + */ + RK_S32 entropy_coding_mode; + RK_S32 entropy_coding_mode_ex; + RK_S32 cabac_init_idc; + RK_S32 cabac_init_idc_ex; + + /* + * 8x8 intra prediction and 8x8 transform enable flag + * This flag can only be enable under High profile + * 0 : disable (BP/MP) + * 1 : enable (HP) + */ + RK_S32 transform8x8_mode; + RK_S32 transform8x8_mode_ex; + + /* + * 0 : disable + * 1 : enable + */ + RK_S32 constrained_intra_pred_mode; + + /* + * 0 : flat scaling list + * 1 : default scaling list for all cases + * 2 : customized scaling list (not supported) + */ + RK_S32 scaling_list_mode; + + /* + * chroma qp offset (-12 - 12) + */ + RK_S32 chroma_cb_qp_offset; + RK_S32 chroma_cr_qp_offset; + + /* + * H.264 deblock filter mode flag + * 0 : enable + * 1 : disable + * 2 : disable deblocking filter at slice boundaries + * + * deblock filter offset alpha (-6 - 6) + * deblock filter offset beta (-6 - 6) + */ + RK_S32 deblock_disable; + RK_S32 deblock_offset_alpha; + RK_S32 deblock_offset_beta; + + /* + * H.264 long term reference picture enable flag + * 0 - disable + * 1 - enable + */ + RK_S32 use_longterm; + + /* + * quality config + * qp_max - 8 ~ 51 + * qp_max_i - 10 ~ 40 + * qp_min - 8 ~ 48 + * qp_min_i - 10 ~ 40 + * qp_max_step - max delta qp step between two frames + */ + RK_S32 qp_init; + RK_S16 qp_max; + RK_S16 qp_max_i; + RK_S16 qp_min; + RK_S16 qp_min_i; + RK_S16 qp_max_step; + RK_S16 qp_delta_ip; + + /* + * intra fresh config + * + * intra_refresh_mode + * 0 - no intra refresh + * 1 - intra refresh by MB row + * 2 - intra refresh by MB column + * 3 - intra refresh by MB gap + * + * intra_refresh_arg + * mode 0 - no effect + * mode 1 - refresh MB row number + * mode 2 - refresh MB colmn number + * mode 3 - refresh MB gap count + */ + RK_S32 intra_refresh_mode; + RK_S32 intra_refresh_arg; + + /* extra mode config */ + RK_S32 max_ltr_frames; + RK_S32 max_tid; + RK_S32 prefix_mode; + RK_S32 base_layer_pid; + /* + * Mpp encoder constraint_set parameter + * Mpp encoder constraint_set controls constraint_setx_flag in AVC. + * Mpp encoder constraint_set uses type RK_U32 to store force_flag and constraint_force as followed. + * | 00 | force_flag | 00 | constraint_force | + * As for force_flag and constraint_force, only low 6 bits are valid, + * corresponding to constraint_setx_flag from 5 to 0. + * If force_flag bit is enabled, constraint_setx_flag will be set correspondingly. + * Otherwise, constraint_setx_flag will use default value. + */ + RK_U32 constraint_set; +} MppEncH264Cfg; + +#define H265E_MAX_ROI_NUMBER 64 + +typedef struct H265eRect_t { + RK_S32 left; + RK_S32 right; + RK_S32 top; + RK_S32 bottom; +} H265eRect; + +typedef struct H265eRoi_Region_t { + RK_U8 level; + H265eRect rect; +} H265eRoiRegion; + +/* + * roi region only can be setting when rc_enable = 1 + */ +typedef struct MppEncH265RoiCfg_t { + /* + * the value is defined by H265eCtuMethod + */ + + RK_U8 method; + /* + * the number of roi,the value must less than H265E_MAX_ROI_NUMBER + */ + RK_S32 num; + + /* delat qp using in roi region*/ + RK_U32 delta_qp; + + /* roi region */ + H265eRoiRegion region[H265E_MAX_ROI_NUMBER]; +} MppEncH265RoiCfg; + +typedef struct H265eCtuQp_t { + /* the qp value using in ctu region */ + RK_U32 qp; + + /* + * define the ctu region + * method = H265E_METHOD_CUT_SIZE, the value of rect is in ctu size + * method = H264E_METHOD_COORDINATE,the value of rect is in coordinates + */ + H265eRect rect; +} H265eCtu; + +typedef struct H265eCtuRegion_t { + /* + * the value is defined by H265eCtuMethod + */ + RK_U8 method; + + /* + * the number of ctu,the value must less than H265E_MAX_ROI_NUMBER + */ + RK_S32 num; + + /* ctu region */ + H265eCtu ctu[H265E_MAX_ROI_NUMBER]; +} MppEncH265CtuCfg; + +/* + * define the method when set CTU/ROI parameters + * this value is using by method in H265eCtuRegion or H265eRoi struct + */ +typedef enum { + H265E_METHOD_CTU_SIZE, + H264E_METHOD_COORDINATE, +} H265eCtuMethod; + +/* + * H.265 configurable parameter + */ +typedef struct MppEncH265VuiCfg_t { + RK_U32 change; + RK_S32 vui_present; + RK_S32 vui_aspect_ratio; + RK_S32 vui_sar_size; + RK_S32 full_range; + RK_S32 time_scale; +} MppEncH265VuiCfg; + +typedef enum MppEncH265CfgChange_e { + /* change on stream type */ + MPP_ENC_H265_CFG_PROFILE_LEVEL_TILER_CHANGE = (1 << 0), + MPP_ENC_H265_CFG_INTRA_QP_CHANGE = (1 << 1), + MPP_ENC_H265_CFG_FRAME_RATE_CHANGE = (1 << 2), + MPP_ENC_H265_CFG_BITRATE_CHANGE = (1 << 3), + MPP_ENC_H265_CFG_GOP_SIZE = (1 << 4), + MPP_ENC_H265_CFG_RC_QP_CHANGE = (1 << 5), + MPP_ENC_H265_CFG_INTRA_REFRESH_CHANGE = (1 << 6), + MPP_ENC_H265_CFG_INDEPEND_SLICE_CHANGE = (1 << 7), + MPP_ENC_H265_CFG_DEPEND_SLICE_CHANGE = (1 << 8), + MPP_ENC_H265_CFG_CTU_CHANGE = (1 << 9), + MPP_ENC_H265_CFG_ROI_CHANGE = (1 << 10), + MPP_ENC_H265_CFG_CU_CHANGE = (1 << 11), + MPP_ENC_H265_CFG_DBLK_CHANGE = (1 << 12), + MPP_ENC_H265_CFG_SAO_CHANGE = (1 << 13), + MPP_ENC_H265_CFG_TRANS_CHANGE = (1 << 14), + MPP_ENC_H265_CFG_SLICE_CHANGE = (1 << 15), + MPP_ENC_H265_CFG_ENTROPY_CHANGE = (1 << 16), + MPP_ENC_H265_CFG_MERGE_CHANGE = (1 << 17), + MPP_ENC_H265_CFG_CHANGE_VUI = (1 << 18), + MPP_ENC_H265_CFG_RC_I_QP_CHANGE = (1 << 19), + MPP_ENC_H265_CFG_RC_MAX_QP_STEP_CHANGE = (1 << 21), + MPP_ENC_H265_CFG_RC_IP_DELTA_QP_CHANGE = (1 << 20), + MPP_ENC_H265_CFG_TILE_CHANGE = (1 << 22), + MPP_ENC_H265_CFG_SLICE_LPFACS_CHANGE = (1 << 23), + MPP_ENC_H265_CFG_TILE_LPFACS_CHANGE = (1 << 24), + MPP_ENC_H265_CFG_CHANGE_ALL = (0xFFFFFFFF), +} MppEncH265CfgChange; + +typedef struct MppEncH265SliceCfg_t { + /* default value: 0, means no slice split*/ + RK_U32 split_enable; + + /* 0: by bits number; 1: by lcu line number*/ + RK_U32 split_mode; + + /* + * when splitmode is 0, this value presents bits number, + * when splitmode is 1, this value presents lcu line number + */ + RK_U32 slice_size; + RK_U32 slice_out; +} MppEncH265SliceCfg; + +typedef struct MppEncH265CuCfg_t { + RK_U32 cu32x32_en; /*default: 1 */ + RK_U32 cu16x16_en; /*default: 1 */ + RK_U32 cu8x8_en; /*default: 1 */ + RK_U32 cu4x4_en; /*default: 1 */ + + // intra pred + RK_U32 constrained_intra_pred_flag; /*default: 0 */ + RK_U32 strong_intra_smoothing_enabled_flag; /*INTRA_SMOOTH*/ + RK_U32 pcm_enabled_flag; /*default: 0, enable ipcm*/ + RK_U32 pcm_loop_filter_disabled_flag; + +} MppEncH265CuCfg; + +typedef struct MppEncH265RefCfg_t { + RK_U32 num_lt_ref_pic; /*default: 0*/ +} MppEncH265RefCfg; + + +typedef struct MppEncH265DblkCfg_t { + RK_U32 slice_deblocking_filter_disabled_flag; /* default value: 0. {0,1} */ + RK_S32 slice_beta_offset_div2; /* default value: 0. [-6,+6] */ + RK_S32 slice_tc_offset_div2; /* default value: 0. [-6,+6] */ +} MppEncH265DblkCfg_t; + +typedef struct MppEncH265SaoCfg_t { + RK_U32 slice_sao_luma_disable; + RK_U32 slice_sao_chroma_disable; +} MppEncH265SaoCfg; + +typedef struct MppEncH265TransCfg_t { + RK_U32 transquant_bypass_enabled_flag; + RK_U32 transform_skip_enabled_flag; + RK_U32 defalut_ScalingList_enable; /* default: 0 */ + RK_S32 cb_qp_offset; + RK_S32 cr_qp_offset; +} MppEncH265TransCfg; + +typedef struct MppEncH265MergeCfg_t { + RK_U32 max_mrg_cnd; + RK_U32 merge_up_flag; + RK_U32 merge_left_flag; +} MppEncH265MergesCfg; + +typedef struct MppEncH265EntropyCfg_t { + RK_U32 cabac_init_flag; /* default: 0 */ +} MppEncH265EntropyCfg; + +typedef struct MppEncH265Cfg_t { + RK_U32 change; + + /* H.265 codec syntax config */ + RK_S32 profile; + RK_S32 level; + RK_S32 tier; + + /* constraint intra prediction flag */ + RK_S32 const_intra_pred; + RK_S32 ctu_size; + RK_S32 max_cu_size; + RK_S32 tmvp_enable; + RK_S32 amp_enable; + RK_S32 wpp_enable; + RK_S32 merge_range; + RK_S32 sao_enable; + RK_U32 num_ref; + + /* quality config */ + RK_S32 max_qp; + RK_S32 min_qp; + RK_S32 max_i_qp; + RK_S32 min_i_qp; + RK_S32 ip_qp_delta; + RK_S32 max_delta_qp; + RK_S32 intra_qp; + RK_S32 gop_delta_qp; + RK_S32 qp_init; + RK_S32 qp_max_step; + RK_S32 raw_dealt_qp; + RK_U8 qpmax_map[8]; + RK_U8 qpmin_map[8]; + RK_S32 qpmap_mode; + + /* intra fresh config */ + RK_S32 intra_refresh_mode; + RK_S32 intra_refresh_arg; + + /* slice mode config */ + RK_S32 independ_slice_mode; + RK_S32 independ_slice_arg; + RK_S32 depend_slice_mode; + RK_S32 depend_slice_arg; + + MppEncH265CuCfg cu_cfg; + MppEncH265SliceCfg slice_cfg; + MppEncH265EntropyCfg entropy_cfg; + MppEncH265TransCfg trans_cfg; + MppEncH265SaoCfg sao_cfg; + MppEncH265DblkCfg_t dblk_cfg; + MppEncH265RefCfg ref_cfg; + MppEncH265MergesCfg merge_cfg; + RK_S32 auto_tile; + RK_U32 lpf_acs_sli_en; + RK_U32 lpf_acs_tile_disable; + + /* extra info */ + MppEncH265VuiCfg vui; + + MppEncH265CtuCfg ctu; + MppEncH265RoiCfg roi; +} MppEncH265Cfg; + +/* + * motion jpeg configurable parameter + */ +typedef enum MppEncJpegCfgChange_e { + /* change on quant parameter */ + MPP_ENC_JPEG_CFG_CHANGE_QP = (1 << 0), + MPP_ENC_JPEG_CFG_CHANGE_QTABLE = (1 << 1), + MPP_ENC_JPEG_CFG_CHANGE_QFACTOR = (1 << 2), + MPP_ENC_JPEG_CFG_CHANGE_ALL = (0xFFFFFFFF), +} MppEncJpegCfgChange; + +typedef struct MppEncJpegCfg_t { + RK_U32 change; + RK_S32 quant; + /* + * quality factor config + * + * q_factor - 1 ~ 99 + * qf_max - 1 ~ 99 + * qf_min - 1 ~ 99 + * qtable_y: qtable for luma + * qtable_u: qtable for chroma + * qtable_v: default equal qtable_u + */ + RK_S32 q_factor; + RK_S32 qf_max; + RK_S32 qf_min; + RK_U8 *qtable_y; + RK_U8 *qtable_u; + RK_U8 *qtable_v; +} MppEncJpegCfg; + +/* + * vp8 configurable parameter + */ +typedef enum MppEncVP8CfgChange_e { + MPP_ENC_VP8_CFG_CHANGE_QP = (1 << 0), + MPP_ENC_VP8_CFG_CHANGE_DIS_IVF = (1 << 1), + MPP_ENC_VP8_CFG_CHANGE_ALL = (0xFFFFFFFF), +} MppEncVP8CfgChange; + +typedef struct MppEncVp8Cfg_t { + RK_U32 change; + RK_S32 quant; + + RK_S32 qp_init; + RK_S32 qp_max; + RK_S32 qp_max_i; + RK_S32 qp_min; + RK_S32 qp_min_i; + RK_S32 qp_max_step; + RK_S32 disable_ivf; +} MppEncVp8Cfg; + +/** + * @ingroup rk_mpi + * @brief MPP encoder codec configuration parameters + * @details The encoder codec configuration parameters are different for each + * compression codings. For example, H.264 encoder can configure + * profile, level, qp, etc. while jpeg encoder can configure qp + * only. The detailed parameters can refer the corresponding data + * structure such as MppEncH264Cfg and MppEncJpegCfg. This data + * structure is associated with MPP_ENC_SET_CODEC_CFG command. + */ +typedef struct MppEncCodecCfg_t { + MppCodingType coding; + + union { + RK_U32 change; + MppEncH264Cfg h264; + MppEncH265Cfg h265; + MppEncJpegCfg jpeg; + MppEncVp8Cfg vp8; + }; +} MppEncCodecCfg; + +typedef enum MppEncSliceSplit_e { + /* change on quant parameter */ + MPP_ENC_SPLIT_CFG_CHANGE_MODE = (1 << 0), + MPP_ENC_SPLIT_CFG_CHANGE_ARG = (1 << 1), + MPP_ENC_SPLIT_CFG_CHANGE_OUTPUT = (1 << 2), + MPP_ENC_SPLIT_CFG_CHANGE_ALL = (0xFFFFFFFF), +} MppEncSliceSplitChange; + +typedef enum MppEncSplitMode_e { + MPP_ENC_SPLIT_NONE, + MPP_ENC_SPLIT_BY_BYTE, + MPP_ENC_SPLIT_BY_CTU, +} MppEncSplitMode; + +typedef enum MppEncSplitOutMode_e { + MPP_ENC_SPLIT_OUT_LOWDELAY = (1 << 0), + MPP_ENC_SPLIT_OUT_SEGMENT = (1 << 1), +} MppEncSplitOutMode; + +typedef struct MppEncSliceSplit_t { + RK_U32 change; + + /* + * slice split mode + * + * MPP_ENC_SPLIT_NONE - No slice is split + * MPP_ENC_SPLIT_BY_BYTE - Slice is split by byte number + * MPP_ENC_SPLIT_BY_CTU - Slice is split by macroblock / ctu number + */ + RK_U32 split_mode; + + /* + * slice split size parameter + * + * When split by byte number this value is the max byte number for each + * slice. + * When split by macroblock / ctu number this value is the MB/CTU number + * for each slice. + */ + RK_U32 split_arg; + + /* + * slice split output mode + * + * MPP_ENC_SPLIT_OUT_LOWDELAY + * - When enabled encoder will lowdelay output each slice in a single packet + * MPP_ENC_SPLIT_OUT_SEGMENT + * - When enabled encoder will packet with segment info for each slice + */ + RK_U32 split_out; +} MppEncSliceSplit; + +/** + * @brief Mpp ROI parameter + * Region configure define a rectangle as ROI + * @note x, y, w, h are calculated in pixels, which had better be 16-pixel aligned. + * These parameters MUST retain in memory when encoder is running. + * Both absolute qp and relative qp are supported in vepu541. + * Only absolute qp is supported in rv1108 + */ +typedef struct MppEncROIRegion_t { + RK_U16 x; /**< horizontal position of top left corner */ + RK_U16 y; /**< vertical position of top left corner */ + RK_U16 w; /**< width of ROI rectangle */ + RK_U16 h; /**< height of ROI rectangle */ + RK_U16 intra; /**< flag of forced intra macroblock */ + RK_S16 quality; /**< absolute / relative qp of macroblock */ + RK_U16 qp_area_idx; /**< qp min max area select*/ + RK_U8 area_map_en; /**< enable area map */ + RK_U8 abs_qp_en; /**< absolute qp enable flag*/ +} MppEncROIRegion; + +/** + * @brief MPP encoder's ROI configuration + */ +typedef struct MppEncROICfg_t { + RK_U32 number; /**< ROI rectangle number */ + MppEncROIRegion *regions; /**< ROI parameters */ +} MppEncROICfg; + +/** + * @brief Mpp ROI parameter for vepu54x / vepu58x + * @note These encoders have more complex roi configure structure. + * User need to generate roi structure data for different soc. + * And send buffers to encoder through metadata. + */ +typedef struct MppEncROICfg2_t { + MppBuffer base_cfg_buf; + MppBuffer qp_cfg_buf; + MppBuffer amv_cfg_buf; + MppBuffer mv_cfg_buf; + + RK_U32 roi_qp_en : 1; + RK_U32 roi_amv_en : 1; + RK_U32 roi_mv_en : 1; + RK_U32 reserve_bits : 29; + RK_U32 reserve[3]; +} MppEncROICfg2; + +/* + * Mpp OSD parameter + * + * Mpp OSD support total 8 regions + * Mpp OSD support 256-color palette two mode palette: + * 1. Configurable OSD palette + * When palette is set. + * 2. fixed OSD palette + * When palette is NULL. + * + * if MppEncOSDPlt.buf != NULL , palette includes maximun 256 levels, + * every level composed of 32 bits defined below: + * Y : 8 bits + * U : 8 bits + * V : 8 bits + * alpha : 8 bits + */ +#define MPP_ENC_OSD_PLT_WHITE ((255<<24)|(128<<16)|(128<<8)|235) +#define MPP_ENC_OSD_PLT_YELLOW ((255<<24)|(146<<16)|( 16<<8)|210) +#define MPP_ENC_OSD_PLT_CYAN ((255<<24)|( 16<<16)|(166<<8)|170) +#define MPP_ENC_OSD_PLT_GREEN ((255<<24)|( 34<<16)|( 54<<8)|145) +#define MPP_ENC_OSD_PLT_TRANS (( 0<<24)|(222<<16)|(202<<8)|106) +#define MPP_ENC_OSD_PLT_RED ((255<<24)|(240<<16)|( 90<<8)| 81) +#define MPP_ENC_OSD_PLT_BLUE ((255<<24)|(110<<16)|(240<<8)| 41) +#define MPP_ENC_OSD_PLT_BLACK ((255<<24)|(128<<16)|(128<<8)| 16) + +typedef enum MppEncOSDPltType_e { + MPP_ENC_OSD_PLT_TYPE_DEFAULT, + MPP_ENC_OSD_PLT_TYPE_USERDEF, + MPP_ENC_OSD_PLT_TYPE_BUTT, +} MppEncOSDPltType; + +/* OSD palette value define */ +typedef union MppEncOSDPltVal_u { + struct { + RK_U32 v : 8; + RK_U32 u : 8; + RK_U32 y : 8; + RK_U32 alpha : 8; + }; + RK_U32 val; +} MppEncOSDPltVal; + +typedef struct MppEncOSDPlt_t { + MppEncOSDPltVal data[256]; +} MppEncOSDPlt; + +typedef enum MppEncOSDPltCfgChange_e { + MPP_ENC_OSD_PLT_CFG_CHANGE_MODE = (1 << 0), /* change osd plt type */ + MPP_ENC_OSD_PLT_CFG_CHANGE_PLT_VAL = (1 << 1), /* change osd plt table value */ + MPP_ENC_OSD_PLT_CFG_CHANGE_ALL = (0xFFFFFFFF), +} MppEncOSDPltCfgChange; + +typedef struct MppEncOSDPltCfg_t { + RK_U32 change; + MppEncOSDPltType type; + MppEncOSDPlt *plt; +} MppEncOSDPltCfg; + +/* position info is unit in 16 pixels(one MB), and + * x-directon range in pixels = (rd_pos_x - lt_pos_x + 1) * 16; + * y-directon range in pixels = (rd_pos_y - lt_pos_y + 1) * 16; + */ +typedef struct MppEncOSDRegion_t { + RK_U32 enable; + RK_U32 inverse; + RK_U32 start_mb_x; + RK_U32 start_mb_y; + RK_U32 num_mb_x; + RK_U32 num_mb_y; + RK_U32 buf_offset; +} MppEncOSDRegion; + +/* if num_region > 0 && region==NULL + * use old osd data + */ +typedef struct MppEncOSDData_t { + MppBuffer buf; + RK_U32 num_region; + MppEncOSDRegion region[8]; +} MppEncOSDData; + +typedef struct MppEncOSDRegion2_t { + RK_U32 enable; + RK_U32 inverse; + RK_U32 start_mb_x; + RK_U32 start_mb_y; + RK_U32 num_mb_x; + RK_U32 num_mb_y; + RK_U32 buf_offset; + MppBuffer buf; +} MppEncOSDRegion2; + +typedef struct MppEncOSDData2_t { + RK_U32 num_region; + MppEncOSDRegion2 region[8]; +} MppEncOSDData2; + +typedef struct MppEncUserData_t { + RK_U32 len; + void *pdata; +} MppEncUserData; + +typedef struct MppEncUserDataFull_t { + RK_U32 len; + RK_U8 *uuid; + void *pdata; +} MppEncUserDataFull; + +typedef struct MppEncUserDataSet_t { + RK_U32 count; + MppEncUserDataFull *datas; +} MppEncUserDataSet; + +typedef enum MppEncSceneMode_e { + MPP_ENC_SCENE_MODE_DEFAULT, + MPP_ENC_SCENE_MODE_IPC, + MPP_ENC_SCENE_MODE_BUTT, +} MppEncSceneMode; + +typedef enum MppEncFineTuneCfgChange_e { + /* change on scene mode */ + MPP_ENC_TUNE_CFG_CHANGE_SCENE_MODE = (1 << 0), +} MppEncFineTuneCfgChange; + +typedef struct MppEncFineTuneCfg_t { + RK_U32 change; + + MppEncSceneMode scene_mode; +} MppEncFineTuneCfg; + +#endif /*__RK_VENC_CMD_H__*/ diff --git a/rknpu2/examples/3rdparty/mpp/include/rockchip/rk_venc_rc.h b/rknpu2/examples/3rdparty/mpp/include/rockchip/rk_venc_rc.h new file mode 100644 index 0000000000000000000000000000000000000000..b03289c17d9187ce8928c706dafad59da5d7dc6a --- /dev/null +++ b/rknpu2/examples/3rdparty/mpp/include/rockchip/rk_venc_rc.h @@ -0,0 +1,66 @@ +/* + * Copyright 2015 Rockchip Electronics Co. LTD + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __RK_VENC_RC_H__ +#define __RK_VENC_RC_H__ + +#include "rk_type.h" + +#define MPP_ENC_MIN_BPS (SZ_1K) +#define MPP_ENC_MAX_BPS (SZ_1M * 200) + +/* Rate control parameter */ +typedef enum MppEncRcMode_e { + MPP_ENC_RC_MODE_VBR, + MPP_ENC_RC_MODE_CBR, + MPP_ENC_RC_MODE_FIXQP, + MPP_ENC_RC_MODE_AVBR, + MPP_ENC_RC_MODE_BUTT +} MppEncRcMode; + +typedef enum MppEncRcPriority_e { + MPP_ENC_RC_BY_BITRATE_FIRST, + MPP_ENC_RC_BY_FRM_SIZE_FIRST, + MPP_ENC_RC_PRIORITY_BUTT +} MppEncRcPriority; + +typedef enum MppEncRcDropFrmMode_e { + MPP_ENC_RC_DROP_FRM_DISABLED, + MPP_ENC_RC_DROP_FRM_NORMAL, + MPP_ENC_RC_DROP_FRM_PSKIP, + MPP_ENC_RC_DROP_FRM_BUTT +} MppEncRcDropFrmMode; + +typedef enum MppEncRcSuperFrameMode_t { + MPP_ENC_RC_SUPER_FRM_NONE, + MPP_ENC_RC_SUPER_FRM_DROP, + MPP_ENC_RC_SUPER_FRM_REENC, + MPP_ENC_RC_SUPER_FRM_BUTT +} MppEncRcSuperFrameMode; + +typedef enum MppEncRcGopMode_e { + MPP_ENC_RC_NORMAL_P, + MPP_ENC_RC_SMART_P, + MPP_ENC_RC_GOP_MODE_BUTT, +} MppEncRcGopMode; + +typedef enum MppEncRcIntraRefreshMode_e { + MPP_ENC_RC_INTRA_REFRESH_ROW = 0, + MPP_ENC_RC_INTRA_REFRESH_COL, + MPP_ENC_RC_INTRA_REFRESH_BUTT +} MppEncRcRefreshMode; + +#endif /*__RK_VENC_RC_H__*/ diff --git a/rknpu2/examples/3rdparty/mpp/include/rockchip/rk_venc_ref.h b/rknpu2/examples/3rdparty/mpp/include/rockchip/rk_venc_ref.h new file mode 100644 index 0000000000000000000000000000000000000000..8a117f30f86cd31f857e7d72aa062cad2d30aab4 --- /dev/null +++ b/rknpu2/examples/3rdparty/mpp/include/rockchip/rk_venc_ref.h @@ -0,0 +1,242 @@ +/* + * Copyright 2015 Rockchip Electronics Co. LTD + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __RK_VENC_REF_H__ +#define __RK_VENC_REF_H__ + +#include "rk_type.h" +#include "mpp_err.h" + +/* + * MPP reference management system follows the model of H.264/H.265 reference + * frame mangement. + * + * The reference frame is defined into two type: long-term reference frame and + * short-refernce frame (lt_ref and st_ref). + * + * The lt_ref can be only indexed by long-term reference frame index (lt_idx). + * The st_ref can be indexed by its temporal id (tid) and previous count. + * + * MppEncRefMode defined the way for user to reference the required frame. + * + * Normal reference mode without argument + * REF_TO_PREV_REF_FRM - refer to previous reference frame in encode order (No matter Lt or St) + * REF_TO_PREV_ST_REF - refer to previous short-term reference frame + * REF_TO_PREV_LT_REF - refer to previous long-term reference frame + * REF_TO_PREV_INTRA - refer to previous Intra / IDR frame + * REF_TO_ST_REF_SETUP - refer to refernce frame defined in StRefSetup + * + * Normal reference mode with argument + * REF_TO_TEMPORAL_LAYER - refer to previous reference frame with temporal id argument + * REF_TO_LT_REF_IDX - refer to long-term reference frame with lt_ref_idx argument + * REF_TO_ST_PREV_N_REF - refer to short-term reference frame with diff frame_num argument + * + * Long-term reference only mode + * REF_TO_ST_REF_SETUP - use corresponding mode of original short-term reference frame + * + * Short-term reference only mode + * REF_TO_LT_REF_SETUP - indicate that this frame will be overwrited by long-term config + * + * By combining frames with these modes user can define many kinds of reference hierarchy + * structure. But normally user should use simplified preset hierarchy pattern. + * + * The rules for virtual cpb management is similiar to H.264/H.265 + * 1. When one frame is marked as long-term reference frame it will be kept in cpb until + * it is replaced by other frame with the same lt_idx or IDR frame. + * 2. When one frame is marked as short-term reference frame it will be inert into cpb when + * there is enough storage space. When the number of total sum of long-term and short-term + * reference frame excess the cpb size limit the oldest short-term frame will be removed. + * This is call sliding window in H.264. + */ + +/* max 4 temporal layer */ +#define MPP_ENC_MAX_TEMPORAL_LAYER_NUM 4 +/* max 4 long-term reference frame */ +#define MPP_ENC_MAX_LT_REF_NUM 16 + +/* + * Group Of Picture (GOP) config is separated into three parts: + * + * 1. Intra / IDR frame config + * igop - the interval of two intra / IDR frames + * + * 2. Long-term reference config (MppEncRefLtFrmCfg) + * + * Setup long-term reference index max lt_idx, loop interval and reference + * mode for auto long-term reference frame generation. The encoder will + * mark frame to be long-term reference frame with given interval. + * + * 2.1 lt_idx + * The long-term reference frame index is unique identifier for a long-term + * reference frame. + * The max long-term reference frame index should NOT larger than + * max_num_ref_frames in sps. + * + * 2.2 lt_gap + * When lt_gap is zero the long-term reference frame generation is disabled. + * When lt_gap is non-zero (usually 2~3 second interval) then the long-term + * reference frame will be generated for error recovery or smart hierarchy. + * + * 2.2 lt_delay + * The lt_delay is the delay time for generation of long-term reference frame. + * The start point of lt_delay is the IDR/intra frame genertaed by igop. + * + * 2.4 ref_mode: Long-term refernce frame reference mode + * NOTE: temporal id of longterm reference frame is always zero. + * + * Examples: + * Sequence has only one lt_ref 0 and setup one long-term reference frame + * every 300 frame. + * { + * .lt_idx = 0, + * .lt_gap = 300, + * .lt_delay = 0, + * } + * result: + * frame 0 ...... 299 300 301 ...... 599 600 601 + * lt_idx 0 xxxxxx x 0 x xxxxxx x 0 x + * + * Sequence has lt_ref from 0 to 2 and setup a long-term reference frame + * every 100 frame. + * { + * .lt_idx = 0, + * .lt_gap = 300, + * .lt_delay = 0, + * } + * { + * .lt_idx = 1, + * .lt_gap = 300, + * .lt_delay = 100, + * } + * { + * .lt_idx = 2, + * .lt_gap = 300, + * .lt_delay = 200, + * } + * result: + * frame 0 ... 99 100 101 ... 199 200 201 ... 299 300 301 + * lt_idx 0 xxx x 1 x xxx x 2 x xxx x 0 x + * + * 3. Short-term reference config (MppEncStRefSetup) + * + * 3.1 is_non_ref + * The is_non_ref indicated the current frame is reference frame or not. + * + * 3.2 temporal_id + * The temporal id of the current frame configure. + * + * 3.3 ref_mode: short-term refernce frame reference mode + * + * 3.4 repeat + * The repeat time of the short-term reference frame configure. + * The overall frame count with the same config is repeat + 1. + * + * Examples: + * + */ + +#define REF_MODE_MODE_MASK (0x1F) +#define REF_MODE_ARG_MASK (0xFFFF0000) + +typedef enum MppEncRefMode_e { + /* max 32 mode in 32-bit */ + /* for default ref global config */ + REF_MODE_GLOBAL, + REF_TO_PREV_REF_FRM = REF_MODE_GLOBAL, + REF_TO_PREV_ST_REF, + REF_TO_PREV_LT_REF, + REF_TO_PREV_INTRA, + + /* for global config with args */ + REF_MODE_GLOBAL_WITH_ARG = 0x4, + /* with ref arg as temporal layer id */ + REF_TO_TEMPORAL_LAYER = REF_MODE_GLOBAL_WITH_ARG, + /* with ref arg as long-term reference picture index */ + REF_TO_LT_REF_IDX, + /* with ref arg as short-term reference picture difference frame_num */ + REF_TO_ST_PREV_N_REF, + REF_MODE_GLOBAL_BUTT, + + /* for lt-ref */ + REF_MODE_LT = 0x18, + REF_TO_ST_REF_SETUP, + REF_MODE_LT_BUTT, + + /* for st-ref */ + REF_MODE_ST = 0x1C, + REF_TO_LT_REF_SETUP, + REF_MODE_ST_BUTT, +} MppEncRefMode; + +typedef struct MppEncRefLtFrmCfg_t { + RK_S32 lt_idx; /* lt_idx of the reference frame */ + RK_S32 temporal_id; /* temporal_id of the reference frame */ + MppEncRefMode ref_mode; + RK_S32 ref_arg; + RK_S32 lt_gap; /* gap between two lt-ref with same lt_idx */ + RK_S32 lt_delay; /* delay offset to igop start frame */ +} MppEncRefLtFrmCfg; + +typedef struct MppEncRefStFrmCfg_t { + RK_S32 is_non_ref; + RK_S32 temporal_id; + MppEncRefMode ref_mode; + RK_S32 ref_arg; + RK_S32 repeat; /* repeat times */ +} MppEncRefStFrmCfg; + +typedef struct MppEncRefPreset_t { + /* input parameter for query */ + const char *name; + RK_S32 max_lt_cnt; + RK_S32 max_st_cnt; + MppEncRefLtFrmCfg *lt_cfg; + MppEncRefStFrmCfg *st_cfg; + + /* output parameter */ + RK_S32 lt_cnt; + RK_S32 st_cnt; +} MppEncRefPreset; + +typedef void* MppEncRefCfg; + +#ifdef __cplusplus +extern "C" { +#endif + +MPP_RET mpp_enc_ref_cfg_init(MppEncRefCfg *ref); +MPP_RET mpp_enc_ref_cfg_deinit(MppEncRefCfg *ref); + +MPP_RET mpp_enc_ref_cfg_reset(MppEncRefCfg ref); +MPP_RET mpp_enc_ref_cfg_set_cfg_cnt(MppEncRefCfg ref, RK_S32 lt_cnt, RK_S32 st_cnt); +MPP_RET mpp_enc_ref_cfg_add_lt_cfg(MppEncRefCfg ref, RK_S32 cnt, MppEncRefLtFrmCfg *frm); +MPP_RET mpp_enc_ref_cfg_add_st_cfg(MppEncRefCfg ref, RK_S32 cnt, MppEncRefStFrmCfg *frm); +MPP_RET mpp_enc_ref_cfg_check(MppEncRefCfg ref); + +/* + * A new reference configure will restart a new gop and clear cpb by default. + * The keep cpb function will let encoder keeps the current cpb status and do NOT + * reset all the reference frame in cpb. + */ +MPP_RET mpp_enc_ref_cfg_set_keep_cpb(MppEncRefCfg ref, RK_S32 keep); +MPP_RET mpp_enc_ref_cfg_get_preset(MppEncRefPreset *preset); +MPP_RET mpp_enc_ref_cfg_show(MppEncRefCfg ref); + +#ifdef __cplusplus +} +#endif + +#endif /*__RK_VENC_REF_H__*/ diff --git a/rknpu2/examples/3rdparty/mpp/include/rockchip/vpu.h b/rknpu2/examples/3rdparty/mpp/include/rockchip/vpu.h new file mode 100644 index 0000000000000000000000000000000000000000..5ba3579562ae4384cdbd5839c2963f8a823e6c6b --- /dev/null +++ b/rknpu2/examples/3rdparty/mpp/include/rockchip/vpu.h @@ -0,0 +1,123 @@ +/* + * Copyright 2015 Rockchip Electronics Co. LTD + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __VPU_H__ +#define __VPU_H__ + +#ifdef __cplusplus +extern "C" { +#endif + +#include "rk_type.h" + +#define VPU_SUCCESS (0) +#define VPU_FAILURE (-1) + +#define VPU_HW_WAIT_OK VPU_SUCCESS +#define VPU_HW_WAIT_ERROR VPU_FAILURE +#define VPU_HW_WAIT_TIMEOUT 1 + +// vpu decoder 60 registers, size 240B +#define VPU_REG_NUM_DEC (60) +// vpu post processor 41 registers, size 164B +#define VPU_REG_NUM_PP (41) +// vpu decoder + post processor 101 registers, size 404B +#define VPU_REG_NUM_DEC_PP (VPU_REG_NUM_DEC+VPU_REG_NUM_PP) +// vpu encoder 96 registers, size 384B +#define VPU_REG_NUM_ENC (96) + +typedef enum { + VPU_ENC = 0x0, + VPU_DEC = 0x1, + VPU_PP = 0x2, + VPU_DEC_PP = 0x3, + VPU_DEC_HEVC = 0x4, + VPU_DEC_RKV = 0x5, + VPU_ENC_RKV = 0x6, + VPU_DEC_AVSPLUS = 0x7, + VPU_ENC_VEPU22 = 0x8, + VPU_TYPE_BUTT , +} VPU_CLIENT_TYPE; + +/* Hardware decoder configuration description */ + +typedef struct VPUHwDecConfig { + RK_U32 maxDecPicWidth; /* Maximum video decoding width supported */ + RK_U32 maxPpOutPicWidth; /* Maximum output width of Post-Processor */ + RK_U32 h264Support; /* HW supports h.264 */ + RK_U32 jpegSupport; /* HW supports JPEG */ + RK_U32 mpeg4Support; /* HW supports MPEG-4 */ + RK_U32 customMpeg4Support; /* HW supports custom MPEG-4 features */ + RK_U32 vc1Support; /* HW supports VC-1 Simple */ + RK_U32 mpeg2Support; /* HW supports MPEG-2 */ + RK_U32 ppSupport; /* HW supports post-processor */ + RK_U32 ppConfig; /* HW post-processor functions bitmask */ + RK_U32 sorensonSparkSupport; /* HW supports Sorenson Spark */ + RK_U32 refBufSupport; /* HW supports reference picture buffering */ + RK_U32 vp6Support; /* HW supports VP6 */ + RK_U32 vp7Support; /* HW supports VP7 */ + RK_U32 vp8Support; /* HW supports VP8 */ + RK_U32 avsSupport; /* HW supports AVS */ + RK_U32 jpegESupport; /* HW supports JPEG extensions */ + RK_U32 rvSupport; /* HW supports REAL */ + RK_U32 mvcSupport; /* HW supports H264 MVC extension */ +} VPUHwDecConfig_t; + +/* Hardware encoder configuration description */ + +typedef struct VPUHwEndConfig { + RK_U32 maxEncodedWidth; /* Maximum supported width for video encoding (not JPEG) */ + RK_U32 h264Enabled; /* HW supports H.264 */ + RK_U32 jpegEnabled; /* HW supports JPEG */ + RK_U32 mpeg4Enabled; /* HW supports MPEG-4 */ + RK_U32 vsEnabled; /* HW supports video stabilization */ + RK_U32 rgbEnabled; /* HW supports RGB input */ + RK_U32 reg_size; /* HW bus type in use */ + RK_U32 reserv[2]; +} VPUHwEncConfig_t; + +typedef enum { + // common command + VPU_CMD_REGISTER , + VPU_CMD_REGISTER_ACK_OK , + VPU_CMD_REGISTER_ACK_FAIL , + VPU_CMD_UNREGISTER , + + VPU_SEND_CONFIG , + VPU_SEND_CONFIG_ACK_OK , + VPU_SEND_CONFIG_ACK_FAIL , + + VPU_GET_HW_INFO , + VPU_GET_HW_INFO_ACK_OK , + VPU_GET_HW_INFO_ACK_FAIL , + + VPU_CMD_BUTT , +} VPU_CMD_TYPE; + +int VPUClientInit(VPU_CLIENT_TYPE type); +RK_S32 VPUClientRelease(int socket); +RK_S32 VPUClientSendReg(int socket, RK_U32 *regs, RK_U32 nregs); +RK_S32 VPUClientSendReg2(RK_S32 socket, RK_S32 offset, RK_S32 size, void *param); +RK_S32 VPUClientWaitResult(int socket, RK_U32 *regs, RK_U32 nregs, VPU_CMD_TYPE *cmd, RK_S32 *len); +RK_S32 VPUClientGetHwCfg(int socket, RK_U32 *cfg, RK_U32 cfg_size); +RK_S32 VPUClientGetIOMMUStatus(); +RK_U32 VPUCheckSupportWidth(); + +#ifdef __cplusplus +} +#endif + +#endif /* __VPU_H__ */ diff --git a/rknpu2/examples/3rdparty/mpp/include/rockchip/vpu_api.h b/rknpu2/examples/3rdparty/mpp/include/rockchip/vpu_api.h new file mode 100644 index 0000000000000000000000000000000000000000..6f891cfb4e6ed1eec17734f1e26bc9623f2749bb --- /dev/null +++ b/rknpu2/examples/3rdparty/mpp/include/rockchip/vpu_api.h @@ -0,0 +1,504 @@ +/* + * Copyright 2015 Rockchip Electronics Co. LTD + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __VPU_API_H__ +#define __VPU_API_H__ + +#include "rk_type.h" +#include "mpp_err.h" + +/** + * @brief rockchip media process interface + */ + +#define VPU_API_NOPTS_VALUE (0x8000000000000000LL) + +/* + * bit definition of ColorType in structure VPU_FRAME + */ +#define VPU_OUTPUT_FORMAT_TYPE_MASK (0x0000ffff) +#define VPU_OUTPUT_FORMAT_ARGB8888 (0x00000000) +#define VPU_OUTPUT_FORMAT_ABGR8888 (0x00000001) +#define VPU_OUTPUT_FORMAT_RGB888 (0x00000002) +#define VPU_OUTPUT_FORMAT_RGB565 (0x00000003) +#define VPU_OUTPUT_FORMAT_RGB555 (0x00000004) +#define VPU_OUTPUT_FORMAT_YUV420_SEMIPLANAR (0x00000005) +#define VPU_OUTPUT_FORMAT_YUV420_PLANAR (0x00000006) +#define VPU_OUTPUT_FORMAT_YUV422 (0x00000007) +#define VPU_OUTPUT_FORMAT_YUV444 (0x00000008) +#define VPU_OUTPUT_FORMAT_YCH420 (0x00000009) +#define VPU_OUTPUT_FORMAT_BIT_MASK (0x000f0000) +#define VPU_OUTPUT_FORMAT_BIT_8 (0x00000000) +#define VPU_OUTPUT_FORMAT_BIT_10 (0x00010000) +#define VPU_OUTPUT_FORMAT_BIT_12 (0x00020000) +#define VPU_OUTPUT_FORMAT_BIT_14 (0x00030000) +#define VPU_OUTPUT_FORMAT_BIT_16 (0x00040000) +#define VPU_OUTPUT_FORMAT_FBC_MASK (0x00f00000) +#define VPU_OUTPUT_FORMAT_FBC_AFBC_V1 (0x00100000) +#define VPU_OUTPUT_FORMAT_FBC_AFBC_V2 (0x00200000) +#define VPU_OUTPUT_FORMAT_DYNCRANGE_MASK (0x0f000000) +#define VPU_OUTPUT_FORMAT_DYNCRANGE_SDR (0x00000000) +#define VPU_OUTPUT_FORMAT_DYNCRANGE_HDR10 (0x01000000) +#define VPU_OUTPUT_FORMAT_DYNCRANGE_HDR_HLG (0x02000000) +#define VPU_OUTPUT_FORMAT_DYNCRANGE_HDR_DOLBY (0x03000000) + +/** + * @brief input picture type + */ +typedef enum { + ENC_INPUT_YUV420_PLANAR = 0, /**< YYYY... UUUU... VVVV */ + ENC_INPUT_YUV420_SEMIPLANAR = 1, /**< YYYY... UVUVUV... */ + ENC_INPUT_YUV422_INTERLEAVED_YUYV = 2, /**< YUYVYUYV... */ + ENC_INPUT_YUV422_INTERLEAVED_UYVY = 3, /**< UYVYUYVY... */ + ENC_INPUT_RGB565 = 4, /**< 16-bit RGB */ + ENC_INPUT_BGR565 = 5, /**< 16-bit RGB */ + ENC_INPUT_RGB555 = 6, /**< 15-bit RGB */ + ENC_INPUT_BGR555 = 7, /**< 15-bit RGB */ + ENC_INPUT_RGB444 = 8, /**< 12-bit RGB */ + ENC_INPUT_BGR444 = 9, /**< 12-bit RGB */ + ENC_INPUT_RGB888 = 10, /**< 24-bit RGB */ + ENC_INPUT_BGR888 = 11, /**< 24-bit RGB */ + ENC_INPUT_RGB101010 = 12, /**< 30-bit RGB */ + ENC_INPUT_BGR101010 = 13 /**< 30-bit RGB */ +} EncInputPictureType; + +typedef enum VPU_API_CMD { + VPU_API_ENC_SETCFG, + VPU_API_ENC_GETCFG, + VPU_API_ENC_SETFORMAT, + VPU_API_ENC_SETIDRFRAME, + + VPU_API_ENABLE_DEINTERLACE, + VPU_API_SET_VPUMEM_CONTEXT, + VPU_API_USE_PRESENT_TIME_ORDER, + VPU_API_SET_DEFAULT_WIDTH_HEIGH, + VPU_API_SET_INFO_CHANGE, + VPU_API_USE_FAST_MODE, + VPU_API_DEC_GET_STREAM_COUNT, + VPU_API_GET_VPUMEM_USED_COUNT, + VPU_API_GET_FRAME_INFO, + VPU_API_SET_OUTPUT_BLOCK, + VPU_API_GET_EOS_STATUS, + VPU_API_SET_OUTPUT_MODE, + + /* get sps/pps header */ + VPU_API_GET_EXTRA_INFO = 0x200, + + VPU_API_SET_IMMEDIATE_OUT = 0x1000, + VPU_API_SET_PARSER_SPLIT_MODE, /* NOTE: should control before init */ + VPU_API_DEC_OUT_FRM_STRUCT_TYPE, + VPU_API_DEC_EN_THUMBNAIL, + VPU_API_DEC_EN_HDR_META, + VPU_API_DEC_EN_MVC, + VPU_API_DEC_EN_FBC_HDR_256_ODD, + + VPU_API_ENC_VEPU22_START = 0x2000, + VPU_API_ENC_SET_VEPU22_CFG, + VPU_API_ENC_GET_VEPU22_CFG, + VPU_API_ENC_SET_VEPU22_CTU_QP, + VPU_API_ENC_SET_VEPU22_ROI, + + VPU_API_ENC_MPP = 0x3000, + VPU_API_ENC_MPP_SETCFG, + VPU_API_ENC_MPP_GETCFG, + + /* mlvec dynamic configure */ + VPU_API_ENC_MLVEC_CFG = 0x4000, + VPU_API_ENC_SET_MAX_TID, + VPU_API_ENC_SET_MARK_LTR, + VPU_API_ENC_SET_USE_LTR, + VPU_API_ENC_SET_FRAME_QP, + VPU_API_ENC_SET_BASE_LAYER_PID, +} VPU_API_CMD; + +typedef struct { + RK_U32 TimeLow; + RK_U32 TimeHigh; +} TIME_STAMP; + +typedef struct { + RK_U32 CodecType; + RK_U32 ImgWidth; + RK_U32 ImgHeight; + RK_U32 ImgHorStride; + RK_U32 ImgVerStride; + RK_U32 BufSize; +} VPU_GENERIC; + +typedef struct VPUMem { + RK_U32 phy_addr; + RK_U32 *vir_addr; + RK_U32 size; + RK_U32 *offset; +} VPUMemLinear_t; + +typedef struct tVPU_FRAME { + RK_U32 FrameBusAddr[2]; // 0: Y address; 1: UV address; + RK_U32 FrameWidth; // buffer horizontal stride + RK_U32 FrameHeight; // buffer vertical stride + RK_U32 OutputWidth; // deprecated + RK_U32 OutputHeight; // deprecated + RK_U32 DisplayWidth; // valid width for display + RK_U32 DisplayHeight; // valid height for display + RK_U32 CodingType; + RK_U32 FrameType; // frame; top_field_first; bot_field_first + RK_U32 ColorType; + RK_U32 DecodeFrmNum; + TIME_STAMP ShowTime; + RK_U32 ErrorInfo; // error information + RK_U32 employ_cnt; + VPUMemLinear_t vpumem; + struct tVPU_FRAME *next_frame; + union { + struct { + RK_U32 Res0[2]; + struct { + RK_U32 ColorPrimaries : 8; + RK_U32 ColorTransfer : 8; + RK_U32 ColorCoeffs : 8; + RK_U32 ColorRange : 1; + RK_U32 Res1 : 7; + }; + + RK_U32 Res2; + }; + + RK_U32 Res[4]; + }; +} VPU_FRAME; + +typedef struct FrameThumbInfo { + RK_U32 enable; + RK_U32 yOffset; + RK_U32 uvOffset; +} FrameThumbInfo_t; + +typedef struct FrameHdrInfo { + RK_U32 isHdr; + RK_U32 offset; + RK_U32 size; +} FrameHdrInfo_t; + +typedef struct VideoFrame { + VPU_FRAME vpuFrame; + FrameThumbInfo_t thumbInfo; + FrameHdrInfo_t hdrInfo; + RK_U32 viewId; + RK_U32 reserved[16]; +} VideoFrame_t; + +typedef struct VideoPacket { + RK_S64 pts; /* with unit of us*/ + RK_S64 dts; /* with unit of us*/ + RK_U8 *data; + RK_S32 size; + RK_U32 capability; + RK_U32 nFlags; +} VideoPacket_t; + +typedef struct DecoderOut { + RK_U8 *data; + RK_U32 size; + RK_S64 timeUs; + RK_S32 nFlags; +} DecoderOut_t; + +typedef struct ParserOut { + RK_U8 *data; + RK_U32 size; + RK_S64 timeUs; + RK_U32 nFlags; + RK_U32 width; + RK_U32 height; +} ParserOut_t; + +typedef struct EncInputStream { + RK_U8 *buf; + RK_S32 size; + RK_U32 bufPhyAddr; + RK_S64 timeUs; + RK_U32 nFlags; +} EncInputStream_t; + +typedef struct EncoderOut { + RK_U8 *data; + RK_S32 size; + RK_S64 timeUs; + RK_S32 keyFrame; + +} EncoderOut_t; + +/* + * @brief Enumeration used to define the possible video compression codings. + * @note This essentially refers to file extensions. If the coding is + * being used to specify the ENCODE type, then additional work + * must be done to configure the exact flavor of the compression + * to be used. For decode cases where the user application can + * not differentiate between MPEG-4 and H.264 bit streams, it is + * up to the codec to handle this. + * + * sync with the omx_video.h + */ +typedef enum OMX_RK_VIDEO_CODINGTYPE { + OMX_RK_VIDEO_CodingUnused, /**< Value when coding is N/A */ + OMX_RK_VIDEO_CodingAutoDetect, /**< Autodetection of coding type */ + OMX_RK_VIDEO_CodingMPEG2, /**< AKA: H.262 */ + OMX_RK_VIDEO_CodingH263, /**< H.263 */ + OMX_RK_VIDEO_CodingMPEG4, /**< MPEG-4 */ + OMX_RK_VIDEO_CodingWMV, /**< Windows Media Video (WMV1,WMV2,WMV3)*/ + OMX_RK_VIDEO_CodingRV, /**< all versions of Real Video */ + OMX_RK_VIDEO_CodingAVC, /**< H.264/AVC */ + OMX_RK_VIDEO_CodingMJPEG, /**< Motion JPEG */ + OMX_RK_VIDEO_CodingVP8, /**< VP8 */ + OMX_RK_VIDEO_CodingVP9, /**< VP9 */ + OMX_RK_VIDEO_CodingVC1 = 0x01000000, /**< Windows Media Video (WMV1,WMV2,WMV3)*/ + OMX_RK_VIDEO_CodingFLV1, /**< Sorenson H.263 */ + OMX_RK_VIDEO_CodingDIVX3, /**< DIVX3 */ + OMX_RK_VIDEO_CodingVP6, + OMX_RK_VIDEO_CodingHEVC, /**< H.265/HEVC */ + OMX_RK_VIDEO_CodingAVSPLUS, /**< AVS+ profile 0x48 */ + OMX_RK_VIDEO_CodingAVS, /**< AVS profile 0x20 */ + OMX_RK_VIDEO_CodingAVS2, /**< AVS2 */ + OMX_RK_VIDEO_CodingAV1, /**< av1 */ + OMX_RK_VIDEO_CodingKhronosExtensions = 0x6F000000, /**< Reserved region for introducing Khronos Standard Extensions */ + OMX_RK_VIDEO_CodingVendorStartUnused = 0x7F000000, /**< Reserved region for introducing Vendor Extensions */ + OMX_RK_VIDEO_CodingMax = 0x7FFFFFFF +} OMX_RK_VIDEO_CODINGTYPE; + +typedef enum CODEC_TYPE { + CODEC_NONE, + CODEC_DECODER, + CODEC_ENCODER, + CODEC_BUTT, +} CODEC_TYPE; + +typedef enum VPU_API_ERR { + VPU_API_OK = 0, + VPU_API_ERR_UNKNOW = -1, + VPU_API_ERR_BASE = -1000, + VPU_API_ERR_LIST_STREAM = VPU_API_ERR_BASE - 1, + VPU_API_ERR_INIT = VPU_API_ERR_BASE - 2, + VPU_API_ERR_VPU_CODEC_INIT = VPU_API_ERR_BASE - 3, + VPU_API_ERR_STREAM = VPU_API_ERR_BASE - 4, + VPU_API_ERR_FATAL_THREAD = VPU_API_ERR_BASE - 5, + VPU_API_EOS_STREAM_REACHED = VPU_API_ERR_BASE - 11, + + VPU_API_ERR_BUTT, +} VPU_API_ERR; + +typedef enum VPU_FRAME_ERR { + VPU_FRAME_ERR_UNKNOW = 0x0001, + VPU_FRAME_ERR_UNSUPPORT = 0x0002, + +} VPU_FRAME_ERR; + +typedef struct EncParameter { + RK_S32 width; + RK_S32 height; + RK_S32 rc_mode; /* 0 - CQP mode; 1 - CBR mode; 2 - FIXQP mode*/ + RK_S32 bitRate; /* target bitrate */ + RK_S32 framerate; + RK_S32 qp; + RK_S32 enableCabac; + RK_S32 cabacInitIdc; + RK_S32 format; + RK_S32 intraPicRate; + RK_S32 framerateout; + RK_S32 profileIdc; + RK_S32 levelIdc; + RK_S32 reserved[3]; +} EncParameter_t; + +typedef struct EXtraCfg { + RK_S32 vc1extra_size; + RK_S32 vp6codeid; + RK_S32 tsformat; + RK_U32 ori_vpu; /* use origin vpu framework */ + /* below used in decode */ + RK_U32 mpp_mode; /* use mpp framework */ + RK_U32 bit_depth; /* 8 or 10 bit */ + RK_U32 yuv_format; /* 0:420 1:422 2:444 */ + RK_U32 reserved[16]; +} EXtraCfg_t; + +/** + * @brief vpu function interface + */ +typedef struct VpuCodecContext { + void* vpuApiObj; + + CODEC_TYPE codecType; + OMX_RK_VIDEO_CODINGTYPE videoCoding; + + RK_U32 width; + RK_U32 height; + void *extradata; + RK_S32 extradata_size; + + RK_U8 enableparsing; + + RK_S32 no_thread; + EXtraCfg_t extra_cfg; + + void* private_data; + + /* + ** 1: error state(not working) 0: working + */ + RK_S32 decoder_err; + + + /** + * Allocate and initialize an VpuCodecContext. + * + * @param ctx The context of vpu api, allocated in this function. + * @param extraData The extra data of codec, some codecs need / can + * use extradata like Huffman tables, also live VC1 codec can + * use extradata to initialize itself. + * @param extra_size The size of extra data. + * + * @return 0 for init success, others for failure. + * @note check whether ctx has been allocated success after you do init. + */ + RK_S32 (*init)(struct VpuCodecContext *ctx, RK_U8 *extraData, RK_U32 extra_size); + /** + * @brief both send video stream packet to decoder and get video frame from + * decoder at the same time + * @param ctx The context of vpu codec + * @param pkt[in] Stream to be decoded + * @param aDecOut[out] Decoding frame + * @return 0 for decode success, others for failure. + */ + RK_S32 (*decode)(struct VpuCodecContext *ctx, VideoPacket_t *pkt, DecoderOut_t *aDecOut); + /** + * @brief both send video frame to encoder and get encoded video stream from + * encoder at the same time. + * @param ctx The context of vpu codec + * @param aEncInStrm[in] Frame to be encoded + * @param aEncOut[out] Encoding stream + * @return 0 for encode success, others for failure. + */ + RK_S32 (*encode)(struct VpuCodecContext *ctx, EncInputStream_t *aEncInStrm, EncoderOut_t *aEncOut); + /** + * @brief flush codec while do fast forward playing. + * @param ctx The context of vpu codec + * @return 0 for flush success, others for failure. + */ + RK_S32 (*flush)(struct VpuCodecContext *ctx); + RK_S32 (*control)(struct VpuCodecContext *ctx, VPU_API_CMD cmdType, void* param); + /** + * @brief send video stream packet to decoder only, async interface + * @param ctx The context of vpu codec + * @param pkt Stream to be decoded + * @return 0 for success, others for failure. + */ + RK_S32 (*decode_sendstream)(struct VpuCodecContext *ctx, VideoPacket_t *pkt); + /** + * @brief get video frame from decoder only, async interface + * @param ctx The context of vpu codec + * @param aDecOut Decoding frame + * @return 0 for success, others for failure. + */ + RK_S32 (*decode_getframe)(struct VpuCodecContext *ctx, DecoderOut_t *aDecOut); + /** + * @brief send video frame to encoder only, async interface + * @param ctx The context of vpu codec + * @param aEncInStrm Frame to be encoded + * @return 0 for success, others for failure. + */ + RK_S32 (*encoder_sendframe)(struct VpuCodecContext *ctx, EncInputStream_t *aEncInStrm); + /** + * @brief get encoded video packet from encoder only, async interface + * @param ctx The context of vpu codec + * @param aEncOut Encoding stream + * @return 0 for success, others for failure. + */ + RK_S32 (*encoder_getstream)(struct VpuCodecContext *ctx, EncoderOut_t *aEncOut); +} VpuCodecContext_t; + +/* allocated vpu codec context */ +#ifdef __cplusplus +extern "C" +{ +#endif + +/** + * @brief open context of vpu + * @param ctx pointer of vpu codec context + */ +RK_S32 vpu_open_context(struct VpuCodecContext **ctx); +/** + * @brief close context of vpu + * @param ctx pointer of vpu codec context + */ +RK_S32 vpu_close_context(struct VpuCodecContext **ctx); + +#ifdef __cplusplus +} +#endif + +/* + * vpu_mem api + */ +#define vpu_display_mem_pool_FIELDS \ + RK_S32 (*commit_hdl)(vpu_display_mem_pool *p, RK_S32 hdl, RK_S32 size); \ + void* (*get_free)(vpu_display_mem_pool *p); \ + RK_S32 (*inc_used)(vpu_display_mem_pool *p, void *hdl); \ + RK_S32 (*put_used)(vpu_display_mem_pool *p, void *hdl); \ + RK_S32 (*reset)(vpu_display_mem_pool *p); \ + RK_S32 (*get_unused_num)(vpu_display_mem_pool *p); \ + RK_S32 buff_size;\ + float version; \ + RK_S32 res[18]; + +typedef struct vpu_display_mem_pool vpu_display_mem_pool; + +struct vpu_display_mem_pool { + vpu_display_mem_pool_FIELDS +}; + +#ifdef __cplusplus +extern "C" +{ +#endif + +/* + * vpu memory handle interface + */ +RK_S32 VPUMemJudgeIommu(void); +RK_S32 VPUMallocLinear(VPUMemLinear_t *p, RK_U32 size); +RK_S32 VPUFreeLinear(VPUMemLinear_t *p); +RK_S32 VPUMemDuplicate(VPUMemLinear_t *dst, VPUMemLinear_t *src); +RK_S32 VPUMemLink(VPUMemLinear_t *p); +RK_S32 VPUMemFlush(VPUMemLinear_t *p); +RK_S32 VPUMemClean(VPUMemLinear_t *p); +RK_S32 VPUMemInvalidate(VPUMemLinear_t *p); +RK_S32 VPUMemGetFD(VPUMemLinear_t *p); +RK_S32 VPUMallocLinearFromRender(VPUMemLinear_t *p, RK_U32 size, void *ctx); + +/* + * vpu memory allocator and manager interface + */ +vpu_display_mem_pool* open_vpu_memory_pool(void); +void close_vpu_memory_pool(vpu_display_mem_pool *p); +int create_vpu_memory_pool_allocator(vpu_display_mem_pool **ipool, int num, int size); +void release_vpu_memory_pool_allocator(vpu_display_mem_pool *ipool); + +#ifdef __cplusplus +} +#endif + +#endif /*__VPU_API_H__*/ diff --git a/rknpu2/examples/3rdparty/opencl/libopencl-stub/Android.mk b/rknpu2/examples/3rdparty/opencl/libopencl-stub/Android.mk new file mode 100644 index 0000000000000000000000000000000000000000..482c5ab63a8426545f399df73873d83c9ce44a06 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencl/libopencl-stub/Android.mk @@ -0,0 +1,17 @@ +# Android makefile +# Build this using ndk as +# ndk-build NDK_PROJECT_PATH=. APP_BUILD_SCRIPT=Android.mk +# + +LOCAL_PATH := $(call my-dir) +include $(CLEAR_VARS) + +LOCAL_MODULE := libOpenCL +LOCAL_C_INCLUDES := $(LOCAL_PATH)/include/ +LOCAL_SRC_FILES := src/libopencl.cc +LOCAL_CFLAGS = -fPIC -O2 + +#include $(BUILD_SHARED_LIBRARY) + +include $(PREBUILT_SHARED_LIBRARY) + diff --git a/rknpu2/examples/3rdparty/opencl/libopencl-stub/LICENSE b/rknpu2/examples/3rdparty/opencl/libopencl-stub/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..6bb8a291517d679d962db079ade50adfaf39ae2a --- /dev/null +++ b/rknpu2/examples/3rdparty/opencl/libopencl-stub/LICENSE @@ -0,0 +1,24 @@ +This is free and unencumbered software released into the public domain. + +Anyone is free to copy, modify, publish, use, compile, sell, or +distribute this software, either in source code form or as a compiled +binary, for any purpose, commercial or non-commercial, and by any +means. + +In jurisdictions that recognize copyright laws, the author or authors +of this software dedicate any and all copyright interest in the +software to the public domain. We make this dedication for the benefit +of the public at large and to the detriment of our heirs and +successors. We intend this dedication to be an overt act of +relinquishment in perpetuity of all present and future rights to this +software under copyright law. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, +EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. +IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR +OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, +ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR +OTHER DEALINGS IN THE SOFTWARE. + +For more information, please refer to \ No newline at end of file diff --git a/rknpu2/examples/3rdparty/opencl/libopencl-stub/Makefile b/rknpu2/examples/3rdparty/opencl/libopencl-stub/Makefile new file mode 100644 index 0000000000000000000000000000000000000000..494e1990277131cc98df4101da95574c110c6070 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencl/libopencl-stub/Makefile @@ -0,0 +1,24 @@ +CC = gcc +RANLIB = ranlib + +LIBSRC = src/libopencl.c +LIBOBJ=$(LIBSRC:.c=.o) + +CFLAGS = -O2 -fPIC -I ./include -Wall + +LIBOPENCL = libOpenCL.a +TARGETS = $(LIBOPENCL) + +all: $(TARGETS) + +libopencl.o: libopencl.c + $(CC) $(CFLAGS) -c libopencl.c -o libopencl.o + +$(TARGETS): $(LIBOBJ) + ar rcs $(LIBOPENCL) src/libopencl.o + $(RANLIB) $(LIBOPENCL) + gcc -shared -fPIC -o libOpenCL.so src/libopencl.o + +clean: + rm -f $(TARGETS) $(LIBOBJ) + diff --git a/rknpu2/examples/3rdparty/opencl/libopencl-stub/README.md b/rknpu2/examples/3rdparty/opencl/libopencl-stub/README.md new file mode 100644 index 0000000000000000000000000000000000000000..20b3dafa8095f379371431dd5e8582cfc65ee659 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencl/libopencl-stub/README.md @@ -0,0 +1,20 @@ +libopencl-stub +============== + +A stub opecl library that dynamically dlopen/dlsyms opencl implementations at runtime based on environment variables. Will be useful when opencl implementations are installed in non-standard paths (say pocl on android) + + + + LIBOPENCL_SO_PATH -- Path to opencl so that will be searched first + + LIBOPENCL_SO_PATH_2 -- Searched second + + LIBOPENCL_SO_PATH_3 -- Searched third + + LIBOPENCL_SO_PATH_4 -- Searched fourth + + + + +Default paths will be searched otherwise + diff --git a/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/cl.h b/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/cl.h new file mode 100755 index 0000000000000000000000000000000000000000..eb9e6b0131ce56b75e95b144ea223b05e1da0390 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/cl.h @@ -0,0 +1,1921 @@ +/******************************************************************************* + * Copyright (c) 2008-2020 The Khronos Group Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + ******************************************************************************/ + +#ifndef __OPENCL_CL_H +#define __OPENCL_CL_H + +#include +#include + +#ifdef __cplusplus +extern "C" { +#endif + +/******************************************************************************/ + +typedef struct _cl_platform_id * cl_platform_id; +typedef struct _cl_device_id * cl_device_id; +typedef struct _cl_context * cl_context; +typedef struct _cl_command_queue * cl_command_queue; +typedef struct _cl_mem * cl_mem; +typedef struct _cl_program * cl_program; +typedef struct _cl_kernel * cl_kernel; +typedef struct _cl_event * cl_event; +typedef struct _cl_sampler * cl_sampler; + +typedef cl_uint cl_bool; /* WARNING! Unlike cl_ types in cl_platform.h, cl_bool is not guaranteed to be the same size as the bool in kernels. */ +typedef cl_ulong cl_bitfield; +typedef cl_ulong cl_properties; +typedef cl_bitfield cl_device_type; +typedef cl_uint cl_platform_info; +typedef cl_uint cl_device_info; +typedef cl_bitfield cl_device_fp_config; +typedef cl_uint cl_device_mem_cache_type; +typedef cl_uint cl_device_local_mem_type; +typedef cl_bitfield cl_device_exec_capabilities; +#ifdef CL_VERSION_2_0 +typedef cl_bitfield cl_device_svm_capabilities; +#endif +typedef cl_bitfield cl_command_queue_properties; +#ifdef CL_VERSION_1_2 +typedef intptr_t cl_device_partition_property; +typedef cl_bitfield cl_device_affinity_domain; +#endif + +typedef intptr_t cl_context_properties; +typedef cl_uint cl_context_info; +#ifdef CL_VERSION_2_0 +typedef cl_properties cl_queue_properties; +#endif +typedef cl_uint cl_command_queue_info; +typedef cl_uint cl_channel_order; +typedef cl_uint cl_channel_type; +typedef cl_bitfield cl_mem_flags; +#ifdef CL_VERSION_2_0 +typedef cl_bitfield cl_svm_mem_flags; +#endif +typedef cl_uint cl_mem_object_type; +typedef cl_uint cl_mem_info; +#ifdef CL_VERSION_1_2 +typedef cl_bitfield cl_mem_migration_flags; +#endif +typedef cl_uint cl_image_info; +#ifdef CL_VERSION_1_1 +typedef cl_uint cl_buffer_create_type; +#endif +typedef cl_uint cl_addressing_mode; +typedef cl_uint cl_filter_mode; +typedef cl_uint cl_sampler_info; +typedef cl_bitfield cl_map_flags; +#ifdef CL_VERSION_2_0 +typedef intptr_t cl_pipe_properties; +typedef cl_uint cl_pipe_info; +#endif +typedef cl_uint cl_program_info; +typedef cl_uint cl_program_build_info; +#ifdef CL_VERSION_1_2 +typedef cl_uint cl_program_binary_type; +#endif +typedef cl_int cl_build_status; +typedef cl_uint cl_kernel_info; +#ifdef CL_VERSION_1_2 +typedef cl_uint cl_kernel_arg_info; +typedef cl_uint cl_kernel_arg_address_qualifier; +typedef cl_uint cl_kernel_arg_access_qualifier; +typedef cl_bitfield cl_kernel_arg_type_qualifier; +#endif +typedef cl_uint cl_kernel_work_group_info; +#ifdef CL_VERSION_2_1 +typedef cl_uint cl_kernel_sub_group_info; +#endif +typedef cl_uint cl_event_info; +typedef cl_uint cl_command_type; +typedef cl_uint cl_profiling_info; +#ifdef CL_VERSION_2_0 +typedef cl_properties cl_sampler_properties; +typedef cl_uint cl_kernel_exec_info; +#endif +#ifdef CL_VERSION_3_0 +typedef cl_bitfield cl_device_atomic_capabilities; +typedef cl_bitfield cl_device_device_enqueue_capabilities; +typedef cl_uint cl_khronos_vendor_id; +typedef cl_properties cl_mem_properties; +typedef cl_uint cl_version; +#endif + +typedef struct _cl_image_format { + cl_channel_order image_channel_order; + cl_channel_type image_channel_data_type; +} cl_image_format; + +#ifdef CL_VERSION_1_2 + +typedef struct _cl_image_desc { + cl_mem_object_type image_type; + size_t image_width; + size_t image_height; + size_t image_depth; + size_t image_array_size; + size_t image_row_pitch; + size_t image_slice_pitch; + cl_uint num_mip_levels; + cl_uint num_samples; +#ifdef CL_VERSION_2_0 +#ifdef __GNUC__ + __extension__ /* Prevents warnings about anonymous union in -pedantic builds */ +#endif +#ifdef _MSC_VER +#pragma warning( push ) +#pragma warning( disable : 4201 ) /* Prevents warning about nameless struct/union in /W4 /Za builds */ +#endif + union { +#endif + cl_mem buffer; +#ifdef CL_VERSION_2_0 + cl_mem mem_object; + }; +#ifdef _MSC_VER +#pragma warning( pop ) +#endif +#endif +} cl_image_desc; + +#endif + +#ifdef CL_VERSION_1_1 + +typedef struct _cl_buffer_region { + size_t origin; + size_t size; +} cl_buffer_region; + +#endif + +#ifdef CL_VERSION_3_0 + +#define CL_NAME_VERSION_MAX_NAME_SIZE 64 + +typedef struct _cl_name_version { + cl_version version; + char name[CL_NAME_VERSION_MAX_NAME_SIZE]; +} cl_name_version; + +#endif + +/******************************************************************************/ + +/* Error Codes */ +#define CL_SUCCESS 0 +#define CL_DEVICE_NOT_FOUND -1 +#define CL_DEVICE_NOT_AVAILABLE -2 +#define CL_COMPILER_NOT_AVAILABLE -3 +#define CL_MEM_OBJECT_ALLOCATION_FAILURE -4 +#define CL_OUT_OF_RESOURCES -5 +#define CL_OUT_OF_HOST_MEMORY -6 +#define CL_PROFILING_INFO_NOT_AVAILABLE -7 +#define CL_MEM_COPY_OVERLAP -8 +#define CL_IMAGE_FORMAT_MISMATCH -9 +#define CL_IMAGE_FORMAT_NOT_SUPPORTED -10 +#define CL_BUILD_PROGRAM_FAILURE -11 +#define CL_MAP_FAILURE -12 +#ifdef CL_VERSION_1_1 +#define CL_MISALIGNED_SUB_BUFFER_OFFSET -13 +#define CL_EXEC_STATUS_ERROR_FOR_EVENTS_IN_WAIT_LIST -14 +#endif +#ifdef CL_VERSION_1_2 +#define CL_COMPILE_PROGRAM_FAILURE -15 +#define CL_LINKER_NOT_AVAILABLE -16 +#define CL_LINK_PROGRAM_FAILURE -17 +#define CL_DEVICE_PARTITION_FAILED -18 +#define CL_KERNEL_ARG_INFO_NOT_AVAILABLE -19 +#endif + +#define CL_INVALID_VALUE -30 +#define CL_INVALID_DEVICE_TYPE -31 +#define CL_INVALID_PLATFORM -32 +#define CL_INVALID_DEVICE -33 +#define CL_INVALID_CONTEXT -34 +#define CL_INVALID_QUEUE_PROPERTIES -35 +#define CL_INVALID_COMMAND_QUEUE -36 +#define CL_INVALID_HOST_PTR -37 +#define CL_INVALID_MEM_OBJECT -38 +#define CL_INVALID_IMAGE_FORMAT_DESCRIPTOR -39 +#define CL_INVALID_IMAGE_SIZE -40 +#define CL_INVALID_SAMPLER -41 +#define CL_INVALID_BINARY -42 +#define CL_INVALID_BUILD_OPTIONS -43 +#define CL_INVALID_PROGRAM -44 +#define CL_INVALID_PROGRAM_EXECUTABLE -45 +#define CL_INVALID_KERNEL_NAME -46 +#define CL_INVALID_KERNEL_DEFINITION -47 +#define CL_INVALID_KERNEL -48 +#define CL_INVALID_ARG_INDEX -49 +#define CL_INVALID_ARG_VALUE -50 +#define CL_INVALID_ARG_SIZE -51 +#define CL_INVALID_KERNEL_ARGS -52 +#define CL_INVALID_WORK_DIMENSION -53 +#define CL_INVALID_WORK_GROUP_SIZE -54 +#define CL_INVALID_WORK_ITEM_SIZE -55 +#define CL_INVALID_GLOBAL_OFFSET -56 +#define CL_INVALID_EVENT_WAIT_LIST -57 +#define CL_INVALID_EVENT -58 +#define CL_INVALID_OPERATION -59 +#define CL_INVALID_GL_OBJECT -60 +#define CL_INVALID_BUFFER_SIZE -61 +#define CL_INVALID_MIP_LEVEL -62 +#define CL_INVALID_GLOBAL_WORK_SIZE -63 +#ifdef CL_VERSION_1_1 +#define CL_INVALID_PROPERTY -64 +#endif +#ifdef CL_VERSION_1_2 +#define CL_INVALID_IMAGE_DESCRIPTOR -65 +#define CL_INVALID_COMPILER_OPTIONS -66 +#define CL_INVALID_LINKER_OPTIONS -67 +#define CL_INVALID_DEVICE_PARTITION_COUNT -68 +#endif +#ifdef CL_VERSION_2_0 +#define CL_INVALID_PIPE_SIZE -69 +#define CL_INVALID_DEVICE_QUEUE -70 +#endif +#ifdef CL_VERSION_2_2 +#define CL_INVALID_SPEC_ID -71 +#define CL_MAX_SIZE_RESTRICTION_EXCEEDED -72 +#endif + + +/* cl_bool */ +#define CL_FALSE 0 +#define CL_TRUE 1 +#ifdef CL_VERSION_1_2 +#define CL_BLOCKING CL_TRUE +#define CL_NON_BLOCKING CL_FALSE +#endif + +/* cl_platform_info */ +#define CL_PLATFORM_PROFILE 0x0900 +#define CL_PLATFORM_VERSION 0x0901 +#define CL_PLATFORM_NAME 0x0902 +#define CL_PLATFORM_VENDOR 0x0903 +#define CL_PLATFORM_EXTENSIONS 0x0904 +#ifdef CL_VERSION_2_1 +#define CL_PLATFORM_HOST_TIMER_RESOLUTION 0x0905 +#endif +#ifdef CL_VERSION_3_0 +#define CL_PLATFORM_NUMERIC_VERSION 0x0906 +#define CL_PLATFORM_EXTENSIONS_WITH_VERSION 0x0907 +#endif + +/* cl_device_type - bitfield */ +#define CL_DEVICE_TYPE_DEFAULT (1 << 0) +#define CL_DEVICE_TYPE_CPU (1 << 1) +#define CL_DEVICE_TYPE_GPU (1 << 2) +#define CL_DEVICE_TYPE_ACCELERATOR (1 << 3) +#ifdef CL_VERSION_1_2 +#define CL_DEVICE_TYPE_CUSTOM (1 << 4) +#endif +#define CL_DEVICE_TYPE_ALL 0xFFFFFFFF + +/* cl_device_info */ +#define CL_DEVICE_TYPE 0x1000 +#define CL_DEVICE_VENDOR_ID 0x1001 +#define CL_DEVICE_MAX_COMPUTE_UNITS 0x1002 +#define CL_DEVICE_MAX_WORK_ITEM_DIMENSIONS 0x1003 +#define CL_DEVICE_MAX_WORK_GROUP_SIZE 0x1004 +#define CL_DEVICE_MAX_WORK_ITEM_SIZES 0x1005 +#define CL_DEVICE_PREFERRED_VECTOR_WIDTH_CHAR 0x1006 +#define CL_DEVICE_PREFERRED_VECTOR_WIDTH_SHORT 0x1007 +#define CL_DEVICE_PREFERRED_VECTOR_WIDTH_INT 0x1008 +#define CL_DEVICE_PREFERRED_VECTOR_WIDTH_LONG 0x1009 +#define CL_DEVICE_PREFERRED_VECTOR_WIDTH_FLOAT 0x100A +#define CL_DEVICE_PREFERRED_VECTOR_WIDTH_DOUBLE 0x100B +#define CL_DEVICE_MAX_CLOCK_FREQUENCY 0x100C +#define CL_DEVICE_ADDRESS_BITS 0x100D +#define CL_DEVICE_MAX_READ_IMAGE_ARGS 0x100E +#define CL_DEVICE_MAX_WRITE_IMAGE_ARGS 0x100F +#define CL_DEVICE_MAX_MEM_ALLOC_SIZE 0x1010 +#define CL_DEVICE_IMAGE2D_MAX_WIDTH 0x1011 +#define CL_DEVICE_IMAGE2D_MAX_HEIGHT 0x1012 +#define CL_DEVICE_IMAGE3D_MAX_WIDTH 0x1013 +#define CL_DEVICE_IMAGE3D_MAX_HEIGHT 0x1014 +#define CL_DEVICE_IMAGE3D_MAX_DEPTH 0x1015 +#define CL_DEVICE_IMAGE_SUPPORT 0x1016 +#define CL_DEVICE_MAX_PARAMETER_SIZE 0x1017 +#define CL_DEVICE_MAX_SAMPLERS 0x1018 +#define CL_DEVICE_MEM_BASE_ADDR_ALIGN 0x1019 +#define CL_DEVICE_MIN_DATA_TYPE_ALIGN_SIZE 0x101A +#define CL_DEVICE_SINGLE_FP_CONFIG 0x101B +#define CL_DEVICE_GLOBAL_MEM_CACHE_TYPE 0x101C +#define CL_DEVICE_GLOBAL_MEM_CACHELINE_SIZE 0x101D +#define CL_DEVICE_GLOBAL_MEM_CACHE_SIZE 0x101E +#define CL_DEVICE_GLOBAL_MEM_SIZE 0x101F +#define CL_DEVICE_MAX_CONSTANT_BUFFER_SIZE 0x1020 +#define CL_DEVICE_MAX_CONSTANT_ARGS 0x1021 +#define CL_DEVICE_LOCAL_MEM_TYPE 0x1022 +#define CL_DEVICE_LOCAL_MEM_SIZE 0x1023 +#define CL_DEVICE_ERROR_CORRECTION_SUPPORT 0x1024 +#define CL_DEVICE_PROFILING_TIMER_RESOLUTION 0x1025 +#define CL_DEVICE_ENDIAN_LITTLE 0x1026 +#define CL_DEVICE_AVAILABLE 0x1027 +#define CL_DEVICE_COMPILER_AVAILABLE 0x1028 +#define CL_DEVICE_EXECUTION_CAPABILITIES 0x1029 +#define CL_DEVICE_QUEUE_PROPERTIES 0x102A /* deprecated */ +#ifdef CL_VERSION_2_0 +#define CL_DEVICE_QUEUE_ON_HOST_PROPERTIES 0x102A +#endif +#define CL_DEVICE_NAME 0x102B +#define CL_DEVICE_VENDOR 0x102C +#define CL_DRIVER_VERSION 0x102D +#define CL_DEVICE_PROFILE 0x102E +#define CL_DEVICE_VERSION 0x102F +#define CL_DEVICE_EXTENSIONS 0x1030 +#define CL_DEVICE_PLATFORM 0x1031 +#ifdef CL_VERSION_1_2 +#define CL_DEVICE_DOUBLE_FP_CONFIG 0x1032 +#endif +/* 0x1033 reserved for CL_DEVICE_HALF_FP_CONFIG which is already defined in "cl_ext.h" */ +#ifdef CL_VERSION_1_1 +#define CL_DEVICE_PREFERRED_VECTOR_WIDTH_HALF 0x1034 +#define CL_DEVICE_HOST_UNIFIED_MEMORY 0x1035 /* deprecated */ +#define CL_DEVICE_NATIVE_VECTOR_WIDTH_CHAR 0x1036 +#define CL_DEVICE_NATIVE_VECTOR_WIDTH_SHORT 0x1037 +#define CL_DEVICE_NATIVE_VECTOR_WIDTH_INT 0x1038 +#define CL_DEVICE_NATIVE_VECTOR_WIDTH_LONG 0x1039 +#define CL_DEVICE_NATIVE_VECTOR_WIDTH_FLOAT 0x103A +#define CL_DEVICE_NATIVE_VECTOR_WIDTH_DOUBLE 0x103B +#define CL_DEVICE_NATIVE_VECTOR_WIDTH_HALF 0x103C +#define CL_DEVICE_OPENCL_C_VERSION 0x103D +#endif +#ifdef CL_VERSION_1_2 +#define CL_DEVICE_LINKER_AVAILABLE 0x103E +#define CL_DEVICE_BUILT_IN_KERNELS 0x103F +#define CL_DEVICE_IMAGE_MAX_BUFFER_SIZE 0x1040 +#define CL_DEVICE_IMAGE_MAX_ARRAY_SIZE 0x1041 +#define CL_DEVICE_PARENT_DEVICE 0x1042 +#define CL_DEVICE_PARTITION_MAX_SUB_DEVICES 0x1043 +#define CL_DEVICE_PARTITION_PROPERTIES 0x1044 +#define CL_DEVICE_PARTITION_AFFINITY_DOMAIN 0x1045 +#define CL_DEVICE_PARTITION_TYPE 0x1046 +#define CL_DEVICE_REFERENCE_COUNT 0x1047 +#define CL_DEVICE_PREFERRED_INTEROP_USER_SYNC 0x1048 +#define CL_DEVICE_PRINTF_BUFFER_SIZE 0x1049 +#endif +#ifdef CL_VERSION_2_0 +#define CL_DEVICE_IMAGE_PITCH_ALIGNMENT 0x104A +#define CL_DEVICE_IMAGE_BASE_ADDRESS_ALIGNMENT 0x104B +#define CL_DEVICE_MAX_READ_WRITE_IMAGE_ARGS 0x104C +#define CL_DEVICE_MAX_GLOBAL_VARIABLE_SIZE 0x104D +#define CL_DEVICE_QUEUE_ON_DEVICE_PROPERTIES 0x104E +#define CL_DEVICE_QUEUE_ON_DEVICE_PREFERRED_SIZE 0x104F +#define CL_DEVICE_QUEUE_ON_DEVICE_MAX_SIZE 0x1050 +#define CL_DEVICE_MAX_ON_DEVICE_QUEUES 0x1051 +#define CL_DEVICE_MAX_ON_DEVICE_EVENTS 0x1052 +#define CL_DEVICE_SVM_CAPABILITIES 0x1053 +#define CL_DEVICE_GLOBAL_VARIABLE_PREFERRED_TOTAL_SIZE 0x1054 +#define CL_DEVICE_MAX_PIPE_ARGS 0x1055 +#define CL_DEVICE_PIPE_MAX_ACTIVE_RESERVATIONS 0x1056 +#define CL_DEVICE_PIPE_MAX_PACKET_SIZE 0x1057 +#define CL_DEVICE_PREFERRED_PLATFORM_ATOMIC_ALIGNMENT 0x1058 +#define CL_DEVICE_PREFERRED_GLOBAL_ATOMIC_ALIGNMENT 0x1059 +#define CL_DEVICE_PREFERRED_LOCAL_ATOMIC_ALIGNMENT 0x105A +#endif +#ifdef CL_VERSION_2_1 +#define CL_DEVICE_IL_VERSION 0x105B +#define CL_DEVICE_MAX_NUM_SUB_GROUPS 0x105C +#define CL_DEVICE_SUB_GROUP_INDEPENDENT_FORWARD_PROGRESS 0x105D +#endif +#ifdef CL_VERSION_3_0 +#define CL_DEVICE_NUMERIC_VERSION 0x105E +#define CL_DEVICE_EXTENSIONS_WITH_VERSION 0x1060 +#define CL_DEVICE_ILS_WITH_VERSION 0x1061 +#define CL_DEVICE_BUILT_IN_KERNELS_WITH_VERSION 0x1062 +#define CL_DEVICE_ATOMIC_MEMORY_CAPABILITIES 0x1063 +#define CL_DEVICE_ATOMIC_FENCE_CAPABILITIES 0x1064 +#define CL_DEVICE_NON_UNIFORM_WORK_GROUP_SUPPORT 0x1065 +#define CL_DEVICE_OPENCL_C_ALL_VERSIONS 0x1066 +#define CL_DEVICE_PREFERRED_WORK_GROUP_SIZE_MULTIPLE 0x1067 +#define CL_DEVICE_WORK_GROUP_COLLECTIVE_FUNCTIONS_SUPPORT 0x1068 +#define CL_DEVICE_GENERIC_ADDRESS_SPACE_SUPPORT 0x1069 +/* 0x106A to 0x106E - Reserved for upcoming KHR extension */ +#define CL_DEVICE_OPENCL_C_FEATURES 0x106F +#define CL_DEVICE_DEVICE_ENQUEUE_CAPABILITIES 0x1070 +#define CL_DEVICE_PIPE_SUPPORT 0x1071 +#define CL_DEVICE_LATEST_CONFORMANCE_VERSION_PASSED 0x1072 +#endif + +/* cl_device_fp_config - bitfield */ +#define CL_FP_DENORM (1 << 0) +#define CL_FP_INF_NAN (1 << 1) +#define CL_FP_ROUND_TO_NEAREST (1 << 2) +#define CL_FP_ROUND_TO_ZERO (1 << 3) +#define CL_FP_ROUND_TO_INF (1 << 4) +#define CL_FP_FMA (1 << 5) +#ifdef CL_VERSION_1_1 +#define CL_FP_SOFT_FLOAT (1 << 6) +#endif +#ifdef CL_VERSION_1_2 +#define CL_FP_CORRECTLY_ROUNDED_DIVIDE_SQRT (1 << 7) +#endif + +/* cl_device_mem_cache_type */ +#define CL_NONE 0x0 +#define CL_READ_ONLY_CACHE 0x1 +#define CL_READ_WRITE_CACHE 0x2 + +/* cl_device_local_mem_type */ +#define CL_LOCAL 0x1 +#define CL_GLOBAL 0x2 + +/* cl_device_exec_capabilities - bitfield */ +#define CL_EXEC_KERNEL (1 << 0) +#define CL_EXEC_NATIVE_KERNEL (1 << 1) + +/* cl_command_queue_properties - bitfield */ +#define CL_QUEUE_OUT_OF_ORDER_EXEC_MODE_ENABLE (1 << 0) +#define CL_QUEUE_PROFILING_ENABLE (1 << 1) +#ifdef CL_VERSION_2_0 +#define CL_QUEUE_ON_DEVICE (1 << 2) +#define CL_QUEUE_ON_DEVICE_DEFAULT (1 << 3) +#endif + +/* cl_context_info */ +#define CL_CONTEXT_REFERENCE_COUNT 0x1080 +#define CL_CONTEXT_DEVICES 0x1081 +#define CL_CONTEXT_PROPERTIES 0x1082 +#ifdef CL_VERSION_1_1 +#define CL_CONTEXT_NUM_DEVICES 0x1083 +#endif + +/* cl_context_properties */ +#define CL_CONTEXT_PLATFORM 0x1084 +#ifdef CL_VERSION_1_2 +#define CL_CONTEXT_INTEROP_USER_SYNC 0x1085 +#endif + +#ifdef CL_VERSION_1_2 + +/* cl_device_partition_property */ +#define CL_DEVICE_PARTITION_EQUALLY 0x1086 +#define CL_DEVICE_PARTITION_BY_COUNTS 0x1087 +#define CL_DEVICE_PARTITION_BY_COUNTS_LIST_END 0x0 +#define CL_DEVICE_PARTITION_BY_AFFINITY_DOMAIN 0x1088 + +#endif + +#ifdef CL_VERSION_1_2 + +/* cl_device_affinity_domain */ +#define CL_DEVICE_AFFINITY_DOMAIN_NUMA (1 << 0) +#define CL_DEVICE_AFFINITY_DOMAIN_L4_CACHE (1 << 1) +#define CL_DEVICE_AFFINITY_DOMAIN_L3_CACHE (1 << 2) +#define CL_DEVICE_AFFINITY_DOMAIN_L2_CACHE (1 << 3) +#define CL_DEVICE_AFFINITY_DOMAIN_L1_CACHE (1 << 4) +#define CL_DEVICE_AFFINITY_DOMAIN_NEXT_PARTITIONABLE (1 << 5) + +#endif + +#ifdef CL_VERSION_2_0 + +/* cl_device_svm_capabilities */ +#define CL_DEVICE_SVM_COARSE_GRAIN_BUFFER (1 << 0) +#define CL_DEVICE_SVM_FINE_GRAIN_BUFFER (1 << 1) +#define CL_DEVICE_SVM_FINE_GRAIN_SYSTEM (1 << 2) +#define CL_DEVICE_SVM_ATOMICS (1 << 3) + +#endif + +/* cl_command_queue_info */ +#define CL_QUEUE_CONTEXT 0x1090 +#define CL_QUEUE_DEVICE 0x1091 +#define CL_QUEUE_REFERENCE_COUNT 0x1092 +#define CL_QUEUE_PROPERTIES 0x1093 +#ifdef CL_VERSION_2_0 +#define CL_QUEUE_SIZE 0x1094 +#endif +#ifdef CL_VERSION_2_1 +#define CL_QUEUE_DEVICE_DEFAULT 0x1095 +#endif +#ifdef CL_VERSION_3_0 +#define CL_QUEUE_PROPERTIES_ARRAY 0x1098 +#endif + +/* cl_mem_flags and cl_svm_mem_flags - bitfield */ +#define CL_MEM_READ_WRITE (1 << 0) +#define CL_MEM_WRITE_ONLY (1 << 1) +#define CL_MEM_READ_ONLY (1 << 2) +#define CL_MEM_USE_HOST_PTR (1 << 3) +#define CL_MEM_ALLOC_HOST_PTR (1 << 4) +#define CL_MEM_COPY_HOST_PTR (1 << 5) +/* reserved (1 << 6) */ +#ifdef CL_VERSION_1_2 +#define CL_MEM_HOST_WRITE_ONLY (1 << 7) +#define CL_MEM_HOST_READ_ONLY (1 << 8) +#define CL_MEM_HOST_NO_ACCESS (1 << 9) +#endif +#ifdef CL_VERSION_2_0 +#define CL_MEM_SVM_FINE_GRAIN_BUFFER (1 << 10) /* used by cl_svm_mem_flags only */ +#define CL_MEM_SVM_ATOMICS (1 << 11) /* used by cl_svm_mem_flags only */ +#define CL_MEM_KERNEL_READ_AND_WRITE (1 << 12) +#endif + +#ifdef CL_VERSION_1_2 + +/* cl_mem_migration_flags - bitfield */ +#define CL_MIGRATE_MEM_OBJECT_HOST (1 << 0) +#define CL_MIGRATE_MEM_OBJECT_CONTENT_UNDEFINED (1 << 1) + +#endif + +/* cl_channel_order */ +#define CL_R 0x10B0 +#define CL_A 0x10B1 +#define CL_RG 0x10B2 +#define CL_RA 0x10B3 +#define CL_RGB 0x10B4 +#define CL_RGBA 0x10B5 +#define CL_BGRA 0x10B6 +#define CL_ARGB 0x10B7 +#define CL_INTENSITY 0x10B8 +#define CL_LUMINANCE 0x10B9 +#ifdef CL_VERSION_1_1 +#define CL_Rx 0x10BA +#define CL_RGx 0x10BB +#define CL_RGBx 0x10BC +#endif +#ifdef CL_VERSION_1_2 +#define CL_DEPTH 0x10BD +#define CL_DEPTH_STENCIL 0x10BE +#endif +#ifdef CL_VERSION_2_0 +#define CL_sRGB 0x10BF +#define CL_sRGBx 0x10C0 +#define CL_sRGBA 0x10C1 +#define CL_sBGRA 0x10C2 +#define CL_ABGR 0x10C3 +#endif + +/* cl_channel_type */ +#define CL_SNORM_INT8 0x10D0 +#define CL_SNORM_INT16 0x10D1 +#define CL_UNORM_INT8 0x10D2 +#define CL_UNORM_INT16 0x10D3 +#define CL_UNORM_SHORT_565 0x10D4 +#define CL_UNORM_SHORT_555 0x10D5 +#define CL_UNORM_INT_101010 0x10D6 +#define CL_SIGNED_INT8 0x10D7 +#define CL_SIGNED_INT16 0x10D8 +#define CL_SIGNED_INT32 0x10D9 +#define CL_UNSIGNED_INT8 0x10DA +#define CL_UNSIGNED_INT16 0x10DB +#define CL_UNSIGNED_INT32 0x10DC +#define CL_HALF_FLOAT 0x10DD +#define CL_FLOAT 0x10DE +#ifdef CL_VERSION_1_2 +#define CL_UNORM_INT24 0x10DF +#endif +#ifdef CL_VERSION_2_1 +#define CL_UNORM_INT_101010_2 0x10E0 +#endif + +/* cl_mem_object_type */ +#define CL_MEM_OBJECT_BUFFER 0x10F0 +#define CL_MEM_OBJECT_IMAGE2D 0x10F1 +#define CL_MEM_OBJECT_IMAGE3D 0x10F2 +#ifdef CL_VERSION_1_2 +#define CL_MEM_OBJECT_IMAGE2D_ARRAY 0x10F3 +#define CL_MEM_OBJECT_IMAGE1D 0x10F4 +#define CL_MEM_OBJECT_IMAGE1D_ARRAY 0x10F5 +#define CL_MEM_OBJECT_IMAGE1D_BUFFER 0x10F6 +#endif +#ifdef CL_VERSION_2_0 +#define CL_MEM_OBJECT_PIPE 0x10F7 +#endif + +/* cl_mem_info */ +#define CL_MEM_TYPE 0x1100 +#define CL_MEM_FLAGS 0x1101 +#define CL_MEM_SIZE 0x1102 +#define CL_MEM_HOST_PTR 0x1103 +#define CL_MEM_MAP_COUNT 0x1104 +#define CL_MEM_REFERENCE_COUNT 0x1105 +#define CL_MEM_CONTEXT 0x1106 +#ifdef CL_VERSION_1_1 +#define CL_MEM_ASSOCIATED_MEMOBJECT 0x1107 +#define CL_MEM_OFFSET 0x1108 +#endif +#ifdef CL_VERSION_2_0 +#define CL_MEM_USES_SVM_POINTER 0x1109 +#endif +#ifdef CL_VERSION_3_0 +#define CL_MEM_PROPERTIES 0x110A +#endif + +/* cl_image_info */ +#define CL_IMAGE_FORMAT 0x1110 +#define CL_IMAGE_ELEMENT_SIZE 0x1111 +#define CL_IMAGE_ROW_PITCH 0x1112 +#define CL_IMAGE_SLICE_PITCH 0x1113 +#define CL_IMAGE_WIDTH 0x1114 +#define CL_IMAGE_HEIGHT 0x1115 +#define CL_IMAGE_DEPTH 0x1116 +#ifdef CL_VERSION_1_2 +#define CL_IMAGE_ARRAY_SIZE 0x1117 +#define CL_IMAGE_BUFFER 0x1118 +#define CL_IMAGE_NUM_MIP_LEVELS 0x1119 +#define CL_IMAGE_NUM_SAMPLES 0x111A +#endif + + +/* cl_pipe_info */ +#ifdef CL_VERSION_2_0 +#define CL_PIPE_PACKET_SIZE 0x1120 +#define CL_PIPE_MAX_PACKETS 0x1121 +#endif +#ifdef CL_VERSION_3_0 +#define CL_PIPE_PROPERTIES 0x1122 +#endif + +/* cl_addressing_mode */ +#define CL_ADDRESS_NONE 0x1130 +#define CL_ADDRESS_CLAMP_TO_EDGE 0x1131 +#define CL_ADDRESS_CLAMP 0x1132 +#define CL_ADDRESS_REPEAT 0x1133 +#ifdef CL_VERSION_1_1 +#define CL_ADDRESS_MIRRORED_REPEAT 0x1134 +#endif + +/* cl_filter_mode */ +#define CL_FILTER_NEAREST 0x1140 +#define CL_FILTER_LINEAR 0x1141 + +/* cl_sampler_info */ +#define CL_SAMPLER_REFERENCE_COUNT 0x1150 +#define CL_SAMPLER_CONTEXT 0x1151 +#define CL_SAMPLER_NORMALIZED_COORDS 0x1152 +#define CL_SAMPLER_ADDRESSING_MODE 0x1153 +#define CL_SAMPLER_FILTER_MODE 0x1154 +#ifdef CL_VERSION_2_0 +/* These enumerants are for the cl_khr_mipmap_image extension. + They have since been added to cl_ext.h with an appropriate + KHR suffix, but are left here for backwards compatibility. */ +#define CL_SAMPLER_MIP_FILTER_MODE 0x1155 +#define CL_SAMPLER_LOD_MIN 0x1156 +#define CL_SAMPLER_LOD_MAX 0x1157 +#endif +#ifdef CL_VERSION_3_0 +#define CL_SAMPLER_PROPERTIES 0x1158 +#endif + +/* cl_map_flags - bitfield */ +#define CL_MAP_READ (1 << 0) +#define CL_MAP_WRITE (1 << 1) +#ifdef CL_VERSION_1_2 +#define CL_MAP_WRITE_INVALIDATE_REGION (1 << 2) +#endif + +/* cl_program_info */ +#define CL_PROGRAM_REFERENCE_COUNT 0x1160 +#define CL_PROGRAM_CONTEXT 0x1161 +#define CL_PROGRAM_NUM_DEVICES 0x1162 +#define CL_PROGRAM_DEVICES 0x1163 +#define CL_PROGRAM_SOURCE 0x1164 +#define CL_PROGRAM_BINARY_SIZES 0x1165 +#define CL_PROGRAM_BINARIES 0x1166 +#ifdef CL_VERSION_1_2 +#define CL_PROGRAM_NUM_KERNELS 0x1167 +#define CL_PROGRAM_KERNEL_NAMES 0x1168 +#endif +#ifdef CL_VERSION_2_1 +#define CL_PROGRAM_IL 0x1169 +#endif +#ifdef CL_VERSION_2_2 +#define CL_PROGRAM_SCOPE_GLOBAL_CTORS_PRESENT 0x116A +#define CL_PROGRAM_SCOPE_GLOBAL_DTORS_PRESENT 0x116B +#endif + +/* cl_program_build_info */ +#define CL_PROGRAM_BUILD_STATUS 0x1181 +#define CL_PROGRAM_BUILD_OPTIONS 0x1182 +#define CL_PROGRAM_BUILD_LOG 0x1183 +#ifdef CL_VERSION_1_2 +#define CL_PROGRAM_BINARY_TYPE 0x1184 +#endif +#ifdef CL_VERSION_2_0 +#define CL_PROGRAM_BUILD_GLOBAL_VARIABLE_TOTAL_SIZE 0x1185 +#endif + +#ifdef CL_VERSION_1_2 + +/* cl_program_binary_type */ +#define CL_PROGRAM_BINARY_TYPE_NONE 0x0 +#define CL_PROGRAM_BINARY_TYPE_COMPILED_OBJECT 0x1 +#define CL_PROGRAM_BINARY_TYPE_LIBRARY 0x2 +#define CL_PROGRAM_BINARY_TYPE_EXECUTABLE 0x4 + +#endif + +/* cl_build_status */ +#define CL_BUILD_SUCCESS 0 +#define CL_BUILD_NONE -1 +#define CL_BUILD_ERROR -2 +#define CL_BUILD_IN_PROGRESS -3 + +/* cl_kernel_info */ +#define CL_KERNEL_FUNCTION_NAME 0x1190 +#define CL_KERNEL_NUM_ARGS 0x1191 +#define CL_KERNEL_REFERENCE_COUNT 0x1192 +#define CL_KERNEL_CONTEXT 0x1193 +#define CL_KERNEL_PROGRAM 0x1194 +#ifdef CL_VERSION_1_2 +#define CL_KERNEL_ATTRIBUTES 0x1195 +#endif + +#ifdef CL_VERSION_1_2 + +/* cl_kernel_arg_info */ +#define CL_KERNEL_ARG_ADDRESS_QUALIFIER 0x1196 +#define CL_KERNEL_ARG_ACCESS_QUALIFIER 0x1197 +#define CL_KERNEL_ARG_TYPE_NAME 0x1198 +#define CL_KERNEL_ARG_TYPE_QUALIFIER 0x1199 +#define CL_KERNEL_ARG_NAME 0x119A + +#endif + +#ifdef CL_VERSION_1_2 + +/* cl_kernel_arg_address_qualifier */ +#define CL_KERNEL_ARG_ADDRESS_GLOBAL 0x119B +#define CL_KERNEL_ARG_ADDRESS_LOCAL 0x119C +#define CL_KERNEL_ARG_ADDRESS_CONSTANT 0x119D +#define CL_KERNEL_ARG_ADDRESS_PRIVATE 0x119E + +#endif + +#ifdef CL_VERSION_1_2 + +/* cl_kernel_arg_access_qualifier */ +#define CL_KERNEL_ARG_ACCESS_READ_ONLY 0x11A0 +#define CL_KERNEL_ARG_ACCESS_WRITE_ONLY 0x11A1 +#define CL_KERNEL_ARG_ACCESS_READ_WRITE 0x11A2 +#define CL_KERNEL_ARG_ACCESS_NONE 0x11A3 + +#endif + +#ifdef CL_VERSION_1_2 + +/* cl_kernel_arg_type_qualifier */ +#define CL_KERNEL_ARG_TYPE_NONE 0 +#define CL_KERNEL_ARG_TYPE_CONST (1 << 0) +#define CL_KERNEL_ARG_TYPE_RESTRICT (1 << 1) +#define CL_KERNEL_ARG_TYPE_VOLATILE (1 << 2) +#ifdef CL_VERSION_2_0 +#define CL_KERNEL_ARG_TYPE_PIPE (1 << 3) +#endif + +#endif + +/* cl_kernel_work_group_info */ +#define CL_KERNEL_WORK_GROUP_SIZE 0x11B0 +#define CL_KERNEL_COMPILE_WORK_GROUP_SIZE 0x11B1 +#define CL_KERNEL_LOCAL_MEM_SIZE 0x11B2 +#define CL_KERNEL_PREFERRED_WORK_GROUP_SIZE_MULTIPLE 0x11B3 +#define CL_KERNEL_PRIVATE_MEM_SIZE 0x11B4 +#ifdef CL_VERSION_1_2 +#define CL_KERNEL_GLOBAL_WORK_SIZE 0x11B5 +#endif + +#ifdef CL_VERSION_2_1 + +/* cl_kernel_sub_group_info */ +#define CL_KERNEL_MAX_SUB_GROUP_SIZE_FOR_NDRANGE 0x2033 +#define CL_KERNEL_SUB_GROUP_COUNT_FOR_NDRANGE 0x2034 +#define CL_KERNEL_LOCAL_SIZE_FOR_SUB_GROUP_COUNT 0x11B8 +#define CL_KERNEL_MAX_NUM_SUB_GROUPS 0x11B9 +#define CL_KERNEL_COMPILE_NUM_SUB_GROUPS 0x11BA + +#endif + +#ifdef CL_VERSION_2_0 + +/* cl_kernel_exec_info */ +#define CL_KERNEL_EXEC_INFO_SVM_PTRS 0x11B6 +#define CL_KERNEL_EXEC_INFO_SVM_FINE_GRAIN_SYSTEM 0x11B7 + +#endif + +/* cl_event_info */ +#define CL_EVENT_COMMAND_QUEUE 0x11D0 +#define CL_EVENT_COMMAND_TYPE 0x11D1 +#define CL_EVENT_REFERENCE_COUNT 0x11D2 +#define CL_EVENT_COMMAND_EXECUTION_STATUS 0x11D3 +#ifdef CL_VERSION_1_1 +#define CL_EVENT_CONTEXT 0x11D4 +#endif + +/* cl_command_type */ +#define CL_COMMAND_NDRANGE_KERNEL 0x11F0 +#define CL_COMMAND_TASK 0x11F1 +#define CL_COMMAND_NATIVE_KERNEL 0x11F2 +#define CL_COMMAND_READ_BUFFER 0x11F3 +#define CL_COMMAND_WRITE_BUFFER 0x11F4 +#define CL_COMMAND_COPY_BUFFER 0x11F5 +#define CL_COMMAND_READ_IMAGE 0x11F6 +#define CL_COMMAND_WRITE_IMAGE 0x11F7 +#define CL_COMMAND_COPY_IMAGE 0x11F8 +#define CL_COMMAND_COPY_IMAGE_TO_BUFFER 0x11F9 +#define CL_COMMAND_COPY_BUFFER_TO_IMAGE 0x11FA +#define CL_COMMAND_MAP_BUFFER 0x11FB +#define CL_COMMAND_MAP_IMAGE 0x11FC +#define CL_COMMAND_UNMAP_MEM_OBJECT 0x11FD +#define CL_COMMAND_MARKER 0x11FE +#define CL_COMMAND_ACQUIRE_GL_OBJECTS 0x11FF +#define CL_COMMAND_RELEASE_GL_OBJECTS 0x1200 +#ifdef CL_VERSION_1_1 +#define CL_COMMAND_READ_BUFFER_RECT 0x1201 +#define CL_COMMAND_WRITE_BUFFER_RECT 0x1202 +#define CL_COMMAND_COPY_BUFFER_RECT 0x1203 +#define CL_COMMAND_USER 0x1204 +#endif +#ifdef CL_VERSION_1_2 +#define CL_COMMAND_BARRIER 0x1205 +#define CL_COMMAND_MIGRATE_MEM_OBJECTS 0x1206 +#define CL_COMMAND_FILL_BUFFER 0x1207 +#define CL_COMMAND_FILL_IMAGE 0x1208 +#endif +#ifdef CL_VERSION_2_0 +#define CL_COMMAND_SVM_FREE 0x1209 +#define CL_COMMAND_SVM_MEMCPY 0x120A +#define CL_COMMAND_SVM_MEMFILL 0x120B +#define CL_COMMAND_SVM_MAP 0x120C +#define CL_COMMAND_SVM_UNMAP 0x120D +#endif +#ifdef CL_VERSION_3_0 +#define CL_COMMAND_SVM_MIGRATE_MEM 0x120E +#endif + +/* command execution status */ +#define CL_COMPLETE 0x0 +#define CL_RUNNING 0x1 +#define CL_SUBMITTED 0x2 +#define CL_QUEUED 0x3 + +/* cl_buffer_create_type */ +#ifdef CL_VERSION_1_1 +#define CL_BUFFER_CREATE_TYPE_REGION 0x1220 +#endif + +/* cl_profiling_info */ +#define CL_PROFILING_COMMAND_QUEUED 0x1280 +#define CL_PROFILING_COMMAND_SUBMIT 0x1281 +#define CL_PROFILING_COMMAND_START 0x1282 +#define CL_PROFILING_COMMAND_END 0x1283 +#ifdef CL_VERSION_2_0 +#define CL_PROFILING_COMMAND_COMPLETE 0x1284 +#endif + +/* cl_device_atomic_capabilities - bitfield */ +#ifdef CL_VERSION_3_0 +#define CL_DEVICE_ATOMIC_ORDER_RELAXED (1 << 0) +#define CL_DEVICE_ATOMIC_ORDER_ACQ_REL (1 << 1) +#define CL_DEVICE_ATOMIC_ORDER_SEQ_CST (1 << 2) +#define CL_DEVICE_ATOMIC_SCOPE_WORK_ITEM (1 << 3) +#define CL_DEVICE_ATOMIC_SCOPE_WORK_GROUP (1 << 4) +#define CL_DEVICE_ATOMIC_SCOPE_DEVICE (1 << 5) +#define CL_DEVICE_ATOMIC_SCOPE_ALL_DEVICES (1 << 6) +#endif + +/* cl_device_device_enqueue_capabilities - bitfield */ +#ifdef CL_VERSION_3_0 +#define CL_DEVICE_QUEUE_SUPPORTED (1 << 0) +#define CL_DEVICE_QUEUE_REPLACEABLE_DEFAULT (1 << 1) +#endif + +/* cl_khronos_vendor_id */ +#define CL_KHRONOS_VENDOR_ID_CODEPLAY 0x10004 + +#ifdef CL_VERSION_3_0 + +/* cl_version */ +#define CL_VERSION_MAJOR_BITS (10) +#define CL_VERSION_MINOR_BITS (10) +#define CL_VERSION_PATCH_BITS (12) + +#define CL_VERSION_MAJOR_MASK ((1 << CL_VERSION_MAJOR_BITS) - 1) +#define CL_VERSION_MINOR_MASK ((1 << CL_VERSION_MINOR_BITS) - 1) +#define CL_VERSION_PATCH_MASK ((1 << CL_VERSION_PATCH_BITS) - 1) + +#define CL_VERSION_MAJOR(version) \ + ((version) >> (CL_VERSION_MINOR_BITS + CL_VERSION_PATCH_BITS)) + +#define CL_VERSION_MINOR(version) \ + (((version) >> CL_VERSION_PATCH_BITS) & CL_VERSION_MINOR_MASK) + +#define CL_VERSION_PATCH(version) ((version) & CL_VERSION_PATCH_MASK) + +#define CL_MAKE_VERSION(major, minor, patch) \ + ((((major) & CL_VERSION_MAJOR_MASK) \ + << (CL_VERSION_MINOR_BITS + CL_VERSION_PATCH_BITS)) | \ + (((minor) & CL_VERSION_MINOR_MASK) << CL_VERSION_PATCH_BITS) | \ + ((patch) & CL_VERSION_PATCH_MASK)) + +#endif + +/********************************************************************************************************/ + +/* Platform API */ +extern CL_API_ENTRY cl_int CL_API_CALL +clGetPlatformIDs(cl_uint num_entries, + cl_platform_id * platforms, + cl_uint * num_platforms) CL_API_SUFFIX__VERSION_1_0; + +extern CL_API_ENTRY cl_int CL_API_CALL +clGetPlatformInfo(cl_platform_id platform, + cl_platform_info param_name, + size_t param_value_size, + void * param_value, + size_t * param_value_size_ret) CL_API_SUFFIX__VERSION_1_0; + +/* Device APIs */ +extern CL_API_ENTRY cl_int CL_API_CALL +clGetDeviceIDs(cl_platform_id platform, + cl_device_type device_type, + cl_uint num_entries, + cl_device_id * devices, + cl_uint * num_devices) CL_API_SUFFIX__VERSION_1_0; + +extern CL_API_ENTRY cl_int CL_API_CALL +clGetDeviceInfo(cl_device_id device, + cl_device_info param_name, + size_t param_value_size, + void * param_value, + size_t * param_value_size_ret) CL_API_SUFFIX__VERSION_1_0; + +#ifdef CL_VERSION_1_2 + +extern CL_API_ENTRY cl_int CL_API_CALL +clCreateSubDevices(cl_device_id in_device, + const cl_device_partition_property * properties, + cl_uint num_devices, + cl_device_id * out_devices, + cl_uint * num_devices_ret) CL_API_SUFFIX__VERSION_1_2; + +extern CL_API_ENTRY cl_int CL_API_CALL +clRetainDevice(cl_device_id device) CL_API_SUFFIX__VERSION_1_2; + +extern CL_API_ENTRY cl_int CL_API_CALL +clReleaseDevice(cl_device_id device) CL_API_SUFFIX__VERSION_1_2; + +#endif + +#ifdef CL_VERSION_2_1 + +extern CL_API_ENTRY cl_int CL_API_CALL +clSetDefaultDeviceCommandQueue(cl_context context, + cl_device_id device, + cl_command_queue command_queue) CL_API_SUFFIX__VERSION_2_1; + +extern CL_API_ENTRY cl_int CL_API_CALL +clGetDeviceAndHostTimer(cl_device_id device, + cl_ulong* device_timestamp, + cl_ulong* host_timestamp) CL_API_SUFFIX__VERSION_2_1; + +extern CL_API_ENTRY cl_int CL_API_CALL +clGetHostTimer(cl_device_id device, + cl_ulong * host_timestamp) CL_API_SUFFIX__VERSION_2_1; + +#endif + +/* Context APIs */ +extern CL_API_ENTRY cl_context CL_API_CALL +clCreateContext(const cl_context_properties * properties, + cl_uint num_devices, + const cl_device_id * devices, + void (CL_CALLBACK * pfn_notify)(const char * errinfo, + const void * private_info, + size_t cb, + void * user_data), + void * user_data, + cl_int * errcode_ret) CL_API_SUFFIX__VERSION_1_0; + +extern CL_API_ENTRY cl_context CL_API_CALL +clCreateContextFromType(const cl_context_properties * properties, + cl_device_type device_type, + void (CL_CALLBACK * pfn_notify)(const char * errinfo, + const void * private_info, + size_t cb, + void * user_data), + void * user_data, + cl_int * errcode_ret) CL_API_SUFFIX__VERSION_1_0; + +extern CL_API_ENTRY cl_int CL_API_CALL +clRetainContext(cl_context context) CL_API_SUFFIX__VERSION_1_0; + +extern CL_API_ENTRY cl_int CL_API_CALL +clReleaseContext(cl_context context) CL_API_SUFFIX__VERSION_1_0; + +extern CL_API_ENTRY cl_int CL_API_CALL +clGetContextInfo(cl_context context, + cl_context_info param_name, + size_t param_value_size, + void * param_value, + size_t * param_value_size_ret) CL_API_SUFFIX__VERSION_1_0; + +#ifdef CL_VERSION_3_0 + +extern CL_API_ENTRY cl_int CL_API_CALL +clSetContextDestructorCallback(cl_context context, + void (CL_CALLBACK* pfn_notify)(cl_context context, + void* user_data), + void* user_data) CL_API_SUFFIX__VERSION_3_0; + +#endif + +/* Command Queue APIs */ + +#ifdef CL_VERSION_2_0 + +extern CL_API_ENTRY cl_command_queue CL_API_CALL +clCreateCommandQueueWithProperties(cl_context context, + cl_device_id device, + const cl_queue_properties * properties, + cl_int * errcode_ret) CL_API_SUFFIX__VERSION_2_0; + +#endif + +extern CL_API_ENTRY cl_int CL_API_CALL +clRetainCommandQueue(cl_command_queue command_queue) CL_API_SUFFIX__VERSION_1_0; + +extern CL_API_ENTRY cl_int CL_API_CALL +clReleaseCommandQueue(cl_command_queue command_queue) CL_API_SUFFIX__VERSION_1_0; + +extern CL_API_ENTRY cl_int CL_API_CALL +clGetCommandQueueInfo(cl_command_queue command_queue, + cl_command_queue_info param_name, + size_t param_value_size, + void * param_value, + size_t * param_value_size_ret) CL_API_SUFFIX__VERSION_1_0; + +/* Memory Object APIs */ +extern CL_API_ENTRY cl_mem CL_API_CALL +clCreateBuffer(cl_context context, + cl_mem_flags flags, + size_t size, + void * host_ptr, + cl_int * errcode_ret) CL_API_SUFFIX__VERSION_1_0; + +#ifdef CL_VERSION_1_1 + +extern CL_API_ENTRY cl_mem CL_API_CALL +clCreateSubBuffer(cl_mem buffer, + cl_mem_flags flags, + cl_buffer_create_type buffer_create_type, + const void * buffer_create_info, + cl_int * errcode_ret) CL_API_SUFFIX__VERSION_1_1; + +#endif + +#ifdef CL_VERSION_1_2 + +extern CL_API_ENTRY cl_mem CL_API_CALL +clCreateImage(cl_context context, + cl_mem_flags flags, + const cl_image_format * image_format, + const cl_image_desc * image_desc, + void * host_ptr, + cl_int * errcode_ret) CL_API_SUFFIX__VERSION_1_2; + +#endif + +#ifdef CL_VERSION_2_0 + +extern CL_API_ENTRY cl_mem CL_API_CALL +clCreatePipe(cl_context context, + cl_mem_flags flags, + cl_uint pipe_packet_size, + cl_uint pipe_max_packets, + const cl_pipe_properties * properties, + cl_int * errcode_ret) CL_API_SUFFIX__VERSION_2_0; + +#endif + +#ifdef CL_VERSION_3_0 + +extern CL_API_ENTRY cl_mem CL_API_CALL +clCreateBufferWithProperties(cl_context context, + const cl_mem_properties * properties, + cl_mem_flags flags, + size_t size, + void * host_ptr, + cl_int * errcode_ret) CL_API_SUFFIX__VERSION_3_0; + +extern CL_API_ENTRY cl_mem CL_API_CALL +clCreateImageWithProperties(cl_context context, + const cl_mem_properties * properties, + cl_mem_flags flags, + const cl_image_format * image_format, + const cl_image_desc * image_desc, + void * host_ptr, + cl_int * errcode_ret) CL_API_SUFFIX__VERSION_3_0; + +#endif + +extern CL_API_ENTRY cl_int CL_API_CALL +clRetainMemObject(cl_mem memobj) CL_API_SUFFIX__VERSION_1_0; + +extern CL_API_ENTRY cl_int CL_API_CALL +clReleaseMemObject(cl_mem memobj) CL_API_SUFFIX__VERSION_1_0; + +extern CL_API_ENTRY cl_int CL_API_CALL +clGetSupportedImageFormats(cl_context context, + cl_mem_flags flags, + cl_mem_object_type image_type, + cl_uint num_entries, + cl_image_format * image_formats, + cl_uint * num_image_formats) CL_API_SUFFIX__VERSION_1_0; + +extern CL_API_ENTRY cl_int CL_API_CALL +clGetMemObjectInfo(cl_mem memobj, + cl_mem_info param_name, + size_t param_value_size, + void * param_value, + size_t * param_value_size_ret) CL_API_SUFFIX__VERSION_1_0; + +extern CL_API_ENTRY cl_int CL_API_CALL +clGetImageInfo(cl_mem image, + cl_image_info param_name, + size_t param_value_size, + void * param_value, + size_t * param_value_size_ret) CL_API_SUFFIX__VERSION_1_0; + +#ifdef CL_VERSION_2_0 + +extern CL_API_ENTRY cl_int CL_API_CALL +clGetPipeInfo(cl_mem pipe, + cl_pipe_info param_name, + size_t param_value_size, + void * param_value, + size_t * param_value_size_ret) CL_API_SUFFIX__VERSION_2_0; + +#endif + +#ifdef CL_VERSION_1_1 + +extern CL_API_ENTRY cl_int CL_API_CALL +clSetMemObjectDestructorCallback(cl_mem memobj, + void (CL_CALLBACK * pfn_notify)(cl_mem memobj, + void * user_data), + void * user_data) CL_API_SUFFIX__VERSION_1_1; + +#endif + +/* SVM Allocation APIs */ + +#ifdef CL_VERSION_2_0 + +extern CL_API_ENTRY void * CL_API_CALL +clSVMAlloc(cl_context context, + cl_svm_mem_flags flags, + size_t size, + cl_uint alignment) CL_API_SUFFIX__VERSION_2_0; + +extern CL_API_ENTRY void CL_API_CALL +clSVMFree(cl_context context, + void * svm_pointer) CL_API_SUFFIX__VERSION_2_0; + +#endif + +/* Sampler APIs */ + +#ifdef CL_VERSION_2_0 + +extern CL_API_ENTRY cl_sampler CL_API_CALL +clCreateSamplerWithProperties(cl_context context, + const cl_sampler_properties * sampler_properties, + cl_int * errcode_ret) CL_API_SUFFIX__VERSION_2_0; + +#endif + +extern CL_API_ENTRY cl_int CL_API_CALL +clRetainSampler(cl_sampler sampler) CL_API_SUFFIX__VERSION_1_0; + +extern CL_API_ENTRY cl_int CL_API_CALL +clReleaseSampler(cl_sampler sampler) CL_API_SUFFIX__VERSION_1_0; + +extern CL_API_ENTRY cl_int CL_API_CALL +clGetSamplerInfo(cl_sampler sampler, + cl_sampler_info param_name, + size_t param_value_size, + void * param_value, + size_t * param_value_size_ret) CL_API_SUFFIX__VERSION_1_0; + +/* Program Object APIs */ +extern CL_API_ENTRY cl_program CL_API_CALL +clCreateProgramWithSource(cl_context context, + cl_uint count, + const char ** strings, + const size_t * lengths, + cl_int * errcode_ret) CL_API_SUFFIX__VERSION_1_0; + +extern CL_API_ENTRY cl_program CL_API_CALL +clCreateProgramWithBinary(cl_context context, + cl_uint num_devices, + const cl_device_id * device_list, + const size_t * lengths, + const unsigned char ** binaries, + cl_int * binary_status, + cl_int * errcode_ret) CL_API_SUFFIX__VERSION_1_0; + +#ifdef CL_VERSION_1_2 + +extern CL_API_ENTRY cl_program CL_API_CALL +clCreateProgramWithBuiltInKernels(cl_context context, + cl_uint num_devices, + const cl_device_id * device_list, + const char * kernel_names, + cl_int * errcode_ret) CL_API_SUFFIX__VERSION_1_2; + +#endif + +#ifdef CL_VERSION_2_1 + +extern CL_API_ENTRY cl_program CL_API_CALL +clCreateProgramWithIL(cl_context context, + const void* il, + size_t length, + cl_int* errcode_ret) CL_API_SUFFIX__VERSION_2_1; + +#endif + +extern CL_API_ENTRY cl_int CL_API_CALL +clRetainProgram(cl_program program) CL_API_SUFFIX__VERSION_1_0; + +extern CL_API_ENTRY cl_int CL_API_CALL +clReleaseProgram(cl_program program) CL_API_SUFFIX__VERSION_1_0; + +extern CL_API_ENTRY cl_int CL_API_CALL +clBuildProgram(cl_program program, + cl_uint num_devices, + const cl_device_id * device_list, + const char * options, + void (CL_CALLBACK * pfn_notify)(cl_program program, + void * user_data), + void * user_data) CL_API_SUFFIX__VERSION_1_0; + +#ifdef CL_VERSION_1_2 + +extern CL_API_ENTRY cl_int CL_API_CALL +clCompileProgram(cl_program program, + cl_uint num_devices, + const cl_device_id * device_list, + const char * options, + cl_uint num_input_headers, + const cl_program * input_headers, + const char ** header_include_names, + void (CL_CALLBACK * pfn_notify)(cl_program program, + void * user_data), + void * user_data) CL_API_SUFFIX__VERSION_1_2; + +extern CL_API_ENTRY cl_program CL_API_CALL +clLinkProgram(cl_context context, + cl_uint num_devices, + const cl_device_id * device_list, + const char * options, + cl_uint num_input_programs, + const cl_program * input_programs, + void (CL_CALLBACK * pfn_notify)(cl_program program, + void * user_data), + void * user_data, + cl_int * errcode_ret) CL_API_SUFFIX__VERSION_1_2; + +#endif + +#ifdef CL_VERSION_2_2 + +extern CL_API_ENTRY CL_EXT_PREFIX__VERSION_2_2_DEPRECATED cl_int CL_API_CALL +clSetProgramReleaseCallback(cl_program program, + void (CL_CALLBACK * pfn_notify)(cl_program program, + void * user_data), + void * user_data) CL_EXT_SUFFIX__VERSION_2_2_DEPRECATED; + +extern CL_API_ENTRY cl_int CL_API_CALL +clSetProgramSpecializationConstant(cl_program program, + cl_uint spec_id, + size_t spec_size, + const void* spec_value) CL_API_SUFFIX__VERSION_2_2; + +#endif + +#ifdef CL_VERSION_1_2 + +extern CL_API_ENTRY cl_int CL_API_CALL +clUnloadPlatformCompiler(cl_platform_id platform) CL_API_SUFFIX__VERSION_1_2; + +#endif + +extern CL_API_ENTRY cl_int CL_API_CALL +clGetProgramInfo(cl_program program, + cl_program_info param_name, + size_t param_value_size, + void * param_value, + size_t * param_value_size_ret) CL_API_SUFFIX__VERSION_1_0; + +extern CL_API_ENTRY cl_int CL_API_CALL +clGetProgramBuildInfo(cl_program program, + cl_device_id device, + cl_program_build_info param_name, + size_t param_value_size, + void * param_value, + size_t * param_value_size_ret) CL_API_SUFFIX__VERSION_1_0; + +/* Kernel Object APIs */ +extern CL_API_ENTRY cl_kernel CL_API_CALL +clCreateKernel(cl_program program, + const char * kernel_name, + cl_int * errcode_ret) CL_API_SUFFIX__VERSION_1_0; + +extern CL_API_ENTRY cl_int CL_API_CALL +clCreateKernelsInProgram(cl_program program, + cl_uint num_kernels, + cl_kernel * kernels, + cl_uint * num_kernels_ret) CL_API_SUFFIX__VERSION_1_0; + +#ifdef CL_VERSION_2_1 + +extern CL_API_ENTRY cl_kernel CL_API_CALL +clCloneKernel(cl_kernel source_kernel, + cl_int* errcode_ret) CL_API_SUFFIX__VERSION_2_1; + +#endif + +extern CL_API_ENTRY cl_int CL_API_CALL +clRetainKernel(cl_kernel kernel) CL_API_SUFFIX__VERSION_1_0; + +extern CL_API_ENTRY cl_int CL_API_CALL +clReleaseKernel(cl_kernel kernel) CL_API_SUFFIX__VERSION_1_0; + +extern CL_API_ENTRY cl_int CL_API_CALL +clSetKernelArg(cl_kernel kernel, + cl_uint arg_index, + size_t arg_size, + const void * arg_value) CL_API_SUFFIX__VERSION_1_0; + +#ifdef CL_VERSION_2_0 + +extern CL_API_ENTRY cl_int CL_API_CALL +clSetKernelArgSVMPointer(cl_kernel kernel, + cl_uint arg_index, + const void * arg_value) CL_API_SUFFIX__VERSION_2_0; + +extern CL_API_ENTRY cl_int CL_API_CALL +clSetKernelExecInfo(cl_kernel kernel, + cl_kernel_exec_info param_name, + size_t param_value_size, + const void * param_value) CL_API_SUFFIX__VERSION_2_0; + +#endif + +extern CL_API_ENTRY cl_int CL_API_CALL +clGetKernelInfo(cl_kernel kernel, + cl_kernel_info param_name, + size_t param_value_size, + void * param_value, + size_t * param_value_size_ret) CL_API_SUFFIX__VERSION_1_0; + +#ifdef CL_VERSION_1_2 + +extern CL_API_ENTRY cl_int CL_API_CALL +clGetKernelArgInfo(cl_kernel kernel, + cl_uint arg_indx, + cl_kernel_arg_info param_name, + size_t param_value_size, + void * param_value, + size_t * param_value_size_ret) CL_API_SUFFIX__VERSION_1_2; + +#endif + +extern CL_API_ENTRY cl_int CL_API_CALL +clGetKernelWorkGroupInfo(cl_kernel kernel, + cl_device_id device, + cl_kernel_work_group_info param_name, + size_t param_value_size, + void * param_value, + size_t * param_value_size_ret) CL_API_SUFFIX__VERSION_1_0; + +#ifdef CL_VERSION_2_1 + +extern CL_API_ENTRY cl_int CL_API_CALL +clGetKernelSubGroupInfo(cl_kernel kernel, + cl_device_id device, + cl_kernel_sub_group_info param_name, + size_t input_value_size, + const void* input_value, + size_t param_value_size, + void* param_value, + size_t* param_value_size_ret) CL_API_SUFFIX__VERSION_2_1; + +#endif + +/* Event Object APIs */ +extern CL_API_ENTRY cl_int CL_API_CALL +clWaitForEvents(cl_uint num_events, + const cl_event * event_list) CL_API_SUFFIX__VERSION_1_0; + +extern CL_API_ENTRY cl_int CL_API_CALL +clGetEventInfo(cl_event event, + cl_event_info param_name, + size_t param_value_size, + void * param_value, + size_t * param_value_size_ret) CL_API_SUFFIX__VERSION_1_0; + +#ifdef CL_VERSION_1_1 + +extern CL_API_ENTRY cl_event CL_API_CALL +clCreateUserEvent(cl_context context, + cl_int * errcode_ret) CL_API_SUFFIX__VERSION_1_1; + +#endif + +extern CL_API_ENTRY cl_int CL_API_CALL +clRetainEvent(cl_event event) CL_API_SUFFIX__VERSION_1_0; + +extern CL_API_ENTRY cl_int CL_API_CALL +clReleaseEvent(cl_event event) CL_API_SUFFIX__VERSION_1_0; + +#ifdef CL_VERSION_1_1 + +extern CL_API_ENTRY cl_int CL_API_CALL +clSetUserEventStatus(cl_event event, + cl_int execution_status) CL_API_SUFFIX__VERSION_1_1; + +extern CL_API_ENTRY cl_int CL_API_CALL +clSetEventCallback(cl_event event, + cl_int command_exec_callback_type, + void (CL_CALLBACK * pfn_notify)(cl_event event, + cl_int event_command_status, + void * user_data), + void * user_data) CL_API_SUFFIX__VERSION_1_1; + +#endif + +/* Profiling APIs */ +extern CL_API_ENTRY cl_int CL_API_CALL +clGetEventProfilingInfo(cl_event event, + cl_profiling_info param_name, + size_t param_value_size, + void * param_value, + size_t * param_value_size_ret) CL_API_SUFFIX__VERSION_1_0; + +/* Flush and Finish APIs */ +extern CL_API_ENTRY cl_int CL_API_CALL +clFlush(cl_command_queue command_queue) CL_API_SUFFIX__VERSION_1_0; + +extern CL_API_ENTRY cl_int CL_API_CALL +clFinish(cl_command_queue command_queue) CL_API_SUFFIX__VERSION_1_0; + +/* Enqueued Commands APIs */ +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueReadBuffer(cl_command_queue command_queue, + cl_mem buffer, + cl_bool blocking_read, + size_t offset, + size_t size, + void * ptr, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) CL_API_SUFFIX__VERSION_1_0; + +#ifdef CL_VERSION_1_1 + +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueReadBufferRect(cl_command_queue command_queue, + cl_mem buffer, + cl_bool blocking_read, + const size_t * buffer_offset, + const size_t * host_offset, + const size_t * region, + size_t buffer_row_pitch, + size_t buffer_slice_pitch, + size_t host_row_pitch, + size_t host_slice_pitch, + void * ptr, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) CL_API_SUFFIX__VERSION_1_1; + +#endif + +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueWriteBuffer(cl_command_queue command_queue, + cl_mem buffer, + cl_bool blocking_write, + size_t offset, + size_t size, + const void * ptr, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) CL_API_SUFFIX__VERSION_1_0; + +#ifdef CL_VERSION_1_1 + +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueWriteBufferRect(cl_command_queue command_queue, + cl_mem buffer, + cl_bool blocking_write, + const size_t * buffer_offset, + const size_t * host_offset, + const size_t * region, + size_t buffer_row_pitch, + size_t buffer_slice_pitch, + size_t host_row_pitch, + size_t host_slice_pitch, + const void * ptr, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) CL_API_SUFFIX__VERSION_1_1; + +#endif + +#ifdef CL_VERSION_1_2 + +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueFillBuffer(cl_command_queue command_queue, + cl_mem buffer, + const void * pattern, + size_t pattern_size, + size_t offset, + size_t size, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) CL_API_SUFFIX__VERSION_1_2; + +#endif + +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueCopyBuffer(cl_command_queue command_queue, + cl_mem src_buffer, + cl_mem dst_buffer, + size_t src_offset, + size_t dst_offset, + size_t size, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) CL_API_SUFFIX__VERSION_1_0; + +#ifdef CL_VERSION_1_1 + +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueCopyBufferRect(cl_command_queue command_queue, + cl_mem src_buffer, + cl_mem dst_buffer, + const size_t * src_origin, + const size_t * dst_origin, + const size_t * region, + size_t src_row_pitch, + size_t src_slice_pitch, + size_t dst_row_pitch, + size_t dst_slice_pitch, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) CL_API_SUFFIX__VERSION_1_1; + +#endif + +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueReadImage(cl_command_queue command_queue, + cl_mem image, + cl_bool blocking_read, + const size_t * origin, + const size_t * region, + size_t row_pitch, + size_t slice_pitch, + void * ptr, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) CL_API_SUFFIX__VERSION_1_0; + +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueWriteImage(cl_command_queue command_queue, + cl_mem image, + cl_bool blocking_write, + const size_t * origin, + const size_t * region, + size_t input_row_pitch, + size_t input_slice_pitch, + const void * ptr, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) CL_API_SUFFIX__VERSION_1_0; + +#ifdef CL_VERSION_1_2 + +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueFillImage(cl_command_queue command_queue, + cl_mem image, + const void * fill_color, + const size_t * origin, + const size_t * region, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) CL_API_SUFFIX__VERSION_1_2; + +#endif + +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueCopyImage(cl_command_queue command_queue, + cl_mem src_image, + cl_mem dst_image, + const size_t * src_origin, + const size_t * dst_origin, + const size_t * region, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) CL_API_SUFFIX__VERSION_1_0; + +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueCopyImageToBuffer(cl_command_queue command_queue, + cl_mem src_image, + cl_mem dst_buffer, + const size_t * src_origin, + const size_t * region, + size_t dst_offset, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) CL_API_SUFFIX__VERSION_1_0; + +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueCopyBufferToImage(cl_command_queue command_queue, + cl_mem src_buffer, + cl_mem dst_image, + size_t src_offset, + const size_t * dst_origin, + const size_t * region, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) CL_API_SUFFIX__VERSION_1_0; + +extern CL_API_ENTRY void * CL_API_CALL +clEnqueueMapBuffer(cl_command_queue command_queue, + cl_mem buffer, + cl_bool blocking_map, + cl_map_flags map_flags, + size_t offset, + size_t size, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event, + cl_int * errcode_ret) CL_API_SUFFIX__VERSION_1_0; + +extern CL_API_ENTRY void * CL_API_CALL +clEnqueueMapImage(cl_command_queue command_queue, + cl_mem image, + cl_bool blocking_map, + cl_map_flags map_flags, + const size_t * origin, + const size_t * region, + size_t * image_row_pitch, + size_t * image_slice_pitch, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event, + cl_int * errcode_ret) CL_API_SUFFIX__VERSION_1_0; + +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueUnmapMemObject(cl_command_queue command_queue, + cl_mem memobj, + void * mapped_ptr, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) CL_API_SUFFIX__VERSION_1_0; + +#ifdef CL_VERSION_1_2 + +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueMigrateMemObjects(cl_command_queue command_queue, + cl_uint num_mem_objects, + const cl_mem * mem_objects, + cl_mem_migration_flags flags, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) CL_API_SUFFIX__VERSION_1_2; + +#endif + +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueNDRangeKernel(cl_command_queue command_queue, + cl_kernel kernel, + cl_uint work_dim, + const size_t * global_work_offset, + const size_t * global_work_size, + const size_t * local_work_size, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) CL_API_SUFFIX__VERSION_1_0; + +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueNativeKernel(cl_command_queue command_queue, + void (CL_CALLBACK * user_func)(void *), + void * args, + size_t cb_args, + cl_uint num_mem_objects, + const cl_mem * mem_list, + const void ** args_mem_loc, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) CL_API_SUFFIX__VERSION_1_0; + +#ifdef CL_VERSION_1_2 + +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueMarkerWithWaitList(cl_command_queue command_queue, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) CL_API_SUFFIX__VERSION_1_2; + +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueBarrierWithWaitList(cl_command_queue command_queue, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) CL_API_SUFFIX__VERSION_1_2; + +#endif + +#ifdef CL_VERSION_2_0 + +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueSVMFree(cl_command_queue command_queue, + cl_uint num_svm_pointers, + void * svm_pointers[], + void (CL_CALLBACK * pfn_free_func)(cl_command_queue queue, + cl_uint num_svm_pointers, + void * svm_pointers[], + void * user_data), + void * user_data, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) CL_API_SUFFIX__VERSION_2_0; + +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueSVMMemcpy(cl_command_queue command_queue, + cl_bool blocking_copy, + void * dst_ptr, + const void * src_ptr, + size_t size, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) CL_API_SUFFIX__VERSION_2_0; + +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueSVMMemFill(cl_command_queue command_queue, + void * svm_ptr, + const void * pattern, + size_t pattern_size, + size_t size, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) CL_API_SUFFIX__VERSION_2_0; + +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueSVMMap(cl_command_queue command_queue, + cl_bool blocking_map, + cl_map_flags flags, + void * svm_ptr, + size_t size, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) CL_API_SUFFIX__VERSION_2_0; + +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueSVMUnmap(cl_command_queue command_queue, + void * svm_ptr, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) CL_API_SUFFIX__VERSION_2_0; + +#endif + +#ifdef CL_VERSION_2_1 + +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueSVMMigrateMem(cl_command_queue command_queue, + cl_uint num_svm_pointers, + const void ** svm_pointers, + const size_t * sizes, + cl_mem_migration_flags flags, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) CL_API_SUFFIX__VERSION_2_1; + +#endif + +#ifdef CL_VERSION_1_2 + +/* Extension function access + * + * Returns the extension function address for the given function name, + * or NULL if a valid function can not be found. The client must + * check to make sure the address is not NULL, before using or + * calling the returned function address. + */ +extern CL_API_ENTRY void * CL_API_CALL +clGetExtensionFunctionAddressForPlatform(cl_platform_id platform, + const char * func_name) CL_API_SUFFIX__VERSION_1_2; + +#endif + +#ifdef CL_USE_DEPRECATED_OPENCL_1_0_APIS + /* + * WARNING: + * This API introduces mutable state into the OpenCL implementation. It has been REMOVED + * to better facilitate thread safety. The 1.0 API is not thread safe. It is not tested by the + * OpenCL 1.1 conformance test, and consequently may not work or may not work dependably. + * It is likely to be non-performant. Use of this API is not advised. Use at your own risk. + * + * Software developers previously relying on this API are instructed to set the command queue + * properties when creating the queue, instead. + */ + extern CL_API_ENTRY cl_int CL_API_CALL + clSetCommandQueueProperty(cl_command_queue command_queue, + cl_command_queue_properties properties, + cl_bool enable, + cl_command_queue_properties * old_properties) CL_EXT_SUFFIX__VERSION_1_0_DEPRECATED; +#endif /* CL_USE_DEPRECATED_OPENCL_1_0_APIS */ + +/* Deprecated OpenCL 1.1 APIs */ +extern CL_API_ENTRY CL_EXT_PREFIX__VERSION_1_1_DEPRECATED cl_mem CL_API_CALL +clCreateImage2D(cl_context context, + cl_mem_flags flags, + const cl_image_format * image_format, + size_t image_width, + size_t image_height, + size_t image_row_pitch, + void * host_ptr, + cl_int * errcode_ret) CL_EXT_SUFFIX__VERSION_1_1_DEPRECATED; + +extern CL_API_ENTRY CL_EXT_PREFIX__VERSION_1_1_DEPRECATED cl_mem CL_API_CALL +clCreateImage3D(cl_context context, + cl_mem_flags flags, + const cl_image_format * image_format, + size_t image_width, + size_t image_height, + size_t image_depth, + size_t image_row_pitch, + size_t image_slice_pitch, + void * host_ptr, + cl_int * errcode_ret) CL_EXT_SUFFIX__VERSION_1_1_DEPRECATED; + +extern CL_API_ENTRY CL_EXT_PREFIX__VERSION_1_1_DEPRECATED cl_int CL_API_CALL +clEnqueueMarker(cl_command_queue command_queue, + cl_event * event) CL_EXT_SUFFIX__VERSION_1_1_DEPRECATED; + +extern CL_API_ENTRY CL_EXT_PREFIX__VERSION_1_1_DEPRECATED cl_int CL_API_CALL +clEnqueueWaitForEvents(cl_command_queue command_queue, + cl_uint num_events, + const cl_event * event_list) CL_EXT_SUFFIX__VERSION_1_1_DEPRECATED; + +extern CL_API_ENTRY CL_EXT_PREFIX__VERSION_1_1_DEPRECATED cl_int CL_API_CALL +clEnqueueBarrier(cl_command_queue command_queue) CL_EXT_SUFFIX__VERSION_1_1_DEPRECATED; + +extern CL_API_ENTRY CL_EXT_PREFIX__VERSION_1_1_DEPRECATED cl_int CL_API_CALL +clUnloadCompiler(void) CL_EXT_SUFFIX__VERSION_1_1_DEPRECATED; + +extern CL_API_ENTRY CL_EXT_PREFIX__VERSION_1_1_DEPRECATED void * CL_API_CALL +clGetExtensionFunctionAddress(const char * func_name) CL_EXT_SUFFIX__VERSION_1_1_DEPRECATED; + +/* Deprecated OpenCL 2.0 APIs */ +extern CL_API_ENTRY CL_EXT_PREFIX__VERSION_1_2_DEPRECATED cl_command_queue CL_API_CALL +clCreateCommandQueue(cl_context context, + cl_device_id device, + cl_command_queue_properties properties, + cl_int * errcode_ret) CL_EXT_SUFFIX__VERSION_1_2_DEPRECATED; + +extern CL_API_ENTRY CL_EXT_PREFIX__VERSION_1_2_DEPRECATED cl_sampler CL_API_CALL +clCreateSampler(cl_context context, + cl_bool normalized_coords, + cl_addressing_mode addressing_mode, + cl_filter_mode filter_mode, + cl_int * errcode_ret) CL_EXT_SUFFIX__VERSION_1_2_DEPRECATED; + +extern CL_API_ENTRY CL_EXT_PREFIX__VERSION_1_2_DEPRECATED cl_int CL_API_CALL +clEnqueueTask(cl_command_queue command_queue, + cl_kernel kernel, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) CL_EXT_SUFFIX__VERSION_1_2_DEPRECATED; + +#ifdef __cplusplus +} +#endif + +#endif /* __OPENCL_CL_H */ diff --git a/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/cl_d3d10.h b/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/cl_d3d10.h new file mode 100755 index 0000000000000000000000000000000000000000..cda5469e384133a9171a38a915cd222b80603a9d --- /dev/null +++ b/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/cl_d3d10.h @@ -0,0 +1,117 @@ +/******************************************************************************* + * Copyright (c) 2008-2020 The Khronos Group Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + ******************************************************************************/ + +#ifndef __OPENCL_CL_D3D10_H +#define __OPENCL_CL_D3D10_H + +#include +#include +#include + +#ifdef __cplusplus +extern "C" { +#endif + +/****************************************************************************** + * cl_khr_d3d10_sharing */ +#define cl_khr_d3d10_sharing 1 + +typedef cl_uint cl_d3d10_device_source_khr; +typedef cl_uint cl_d3d10_device_set_khr; + +/******************************************************************************/ + +/* Error Codes */ +#define CL_INVALID_D3D10_DEVICE_KHR -1002 +#define CL_INVALID_D3D10_RESOURCE_KHR -1003 +#define CL_D3D10_RESOURCE_ALREADY_ACQUIRED_KHR -1004 +#define CL_D3D10_RESOURCE_NOT_ACQUIRED_KHR -1005 + +/* cl_d3d10_device_source_nv */ +#define CL_D3D10_DEVICE_KHR 0x4010 +#define CL_D3D10_DXGI_ADAPTER_KHR 0x4011 + +/* cl_d3d10_device_set_nv */ +#define CL_PREFERRED_DEVICES_FOR_D3D10_KHR 0x4012 +#define CL_ALL_DEVICES_FOR_D3D10_KHR 0x4013 + +/* cl_context_info */ +#define CL_CONTEXT_D3D10_DEVICE_KHR 0x4014 +#define CL_CONTEXT_D3D10_PREFER_SHARED_RESOURCES_KHR 0x402C + +/* cl_mem_info */ +#define CL_MEM_D3D10_RESOURCE_KHR 0x4015 + +/* cl_image_info */ +#define CL_IMAGE_D3D10_SUBRESOURCE_KHR 0x4016 + +/* cl_command_type */ +#define CL_COMMAND_ACQUIRE_D3D10_OBJECTS_KHR 0x4017 +#define CL_COMMAND_RELEASE_D3D10_OBJECTS_KHR 0x4018 + +/******************************************************************************/ + +typedef CL_API_ENTRY cl_int (CL_API_CALL *clGetDeviceIDsFromD3D10KHR_fn)( + cl_platform_id platform, + cl_d3d10_device_source_khr d3d_device_source, + void * d3d_object, + cl_d3d10_device_set_khr d3d_device_set, + cl_uint num_entries, + cl_device_id * devices, + cl_uint * num_devices) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_mem (CL_API_CALL *clCreateFromD3D10BufferKHR_fn)( + cl_context context, + cl_mem_flags flags, + ID3D10Buffer * resource, + cl_int * errcode_ret) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_mem (CL_API_CALL *clCreateFromD3D10Texture2DKHR_fn)( + cl_context context, + cl_mem_flags flags, + ID3D10Texture2D * resource, + UINT subresource, + cl_int * errcode_ret) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_mem (CL_API_CALL *clCreateFromD3D10Texture3DKHR_fn)( + cl_context context, + cl_mem_flags flags, + ID3D10Texture3D * resource, + UINT subresource, + cl_int * errcode_ret) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_int (CL_API_CALL *clEnqueueAcquireD3D10ObjectsKHR_fn)( + cl_command_queue command_queue, + cl_uint num_objects, + const cl_mem * mem_objects, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_int (CL_API_CALL *clEnqueueReleaseD3D10ObjectsKHR_fn)( + cl_command_queue command_queue, + cl_uint num_objects, + const cl_mem * mem_objects, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) CL_API_SUFFIX__VERSION_1_0; + +#ifdef __cplusplus +} +#endif + +#endif /* __OPENCL_CL_D3D10_H */ + diff --git a/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/cl_d3d11.h b/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/cl_d3d11.h new file mode 100755 index 0000000000000000000000000000000000000000..6b7e2e97f554e7aaf8ca14bbba9e7865061c726d --- /dev/null +++ b/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/cl_d3d11.h @@ -0,0 +1,117 @@ +/******************************************************************************* + * Copyright (c) 2008-2020 The Khronos Group Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + ******************************************************************************/ + +#ifndef __OPENCL_CL_D3D11_H +#define __OPENCL_CL_D3D11_H + +#include +#include +#include + +#ifdef __cplusplus +extern "C" { +#endif + +/****************************************************************************** + * cl_khr_d3d11_sharing */ +#define cl_khr_d3d11_sharing 1 + +typedef cl_uint cl_d3d11_device_source_khr; +typedef cl_uint cl_d3d11_device_set_khr; + +/******************************************************************************/ + +/* Error Codes */ +#define CL_INVALID_D3D11_DEVICE_KHR -1006 +#define CL_INVALID_D3D11_RESOURCE_KHR -1007 +#define CL_D3D11_RESOURCE_ALREADY_ACQUIRED_KHR -1008 +#define CL_D3D11_RESOURCE_NOT_ACQUIRED_KHR -1009 + +/* cl_d3d11_device_source */ +#define CL_D3D11_DEVICE_KHR 0x4019 +#define CL_D3D11_DXGI_ADAPTER_KHR 0x401A + +/* cl_d3d11_device_set */ +#define CL_PREFERRED_DEVICES_FOR_D3D11_KHR 0x401B +#define CL_ALL_DEVICES_FOR_D3D11_KHR 0x401C + +/* cl_context_info */ +#define CL_CONTEXT_D3D11_DEVICE_KHR 0x401D +#define CL_CONTEXT_D3D11_PREFER_SHARED_RESOURCES_KHR 0x402D + +/* cl_mem_info */ +#define CL_MEM_D3D11_RESOURCE_KHR 0x401E + +/* cl_image_info */ +#define CL_IMAGE_D3D11_SUBRESOURCE_KHR 0x401F + +/* cl_command_type */ +#define CL_COMMAND_ACQUIRE_D3D11_OBJECTS_KHR 0x4020 +#define CL_COMMAND_RELEASE_D3D11_OBJECTS_KHR 0x4021 + +/******************************************************************************/ + +typedef CL_API_ENTRY cl_int (CL_API_CALL *clGetDeviceIDsFromD3D11KHR_fn)( + cl_platform_id platform, + cl_d3d11_device_source_khr d3d_device_source, + void * d3d_object, + cl_d3d11_device_set_khr d3d_device_set, + cl_uint num_entries, + cl_device_id * devices, + cl_uint * num_devices) CL_API_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_mem (CL_API_CALL *clCreateFromD3D11BufferKHR_fn)( + cl_context context, + cl_mem_flags flags, + ID3D11Buffer * resource, + cl_int * errcode_ret) CL_API_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_mem (CL_API_CALL *clCreateFromD3D11Texture2DKHR_fn)( + cl_context context, + cl_mem_flags flags, + ID3D11Texture2D * resource, + UINT subresource, + cl_int * errcode_ret) CL_API_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_mem (CL_API_CALL *clCreateFromD3D11Texture3DKHR_fn)( + cl_context context, + cl_mem_flags flags, + ID3D11Texture3D * resource, + UINT subresource, + cl_int * errcode_ret) CL_API_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_int (CL_API_CALL *clEnqueueAcquireD3D11ObjectsKHR_fn)( + cl_command_queue command_queue, + cl_uint num_objects, + const cl_mem * mem_objects, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) CL_API_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_int (CL_API_CALL *clEnqueueReleaseD3D11ObjectsKHR_fn)( + cl_command_queue command_queue, + cl_uint num_objects, + const cl_mem * mem_objects, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) CL_API_SUFFIX__VERSION_1_2; + +#ifdef __cplusplus +} +#endif + +#endif /* __OPENCL_CL_D3D11_H */ + diff --git a/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/cl_dx9_media_sharing.h b/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/cl_dx9_media_sharing.h new file mode 100755 index 0000000000000000000000000000000000000000..048937005353a8fb8c4fee624d3f36b9c894fe3e --- /dev/null +++ b/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/cl_dx9_media_sharing.h @@ -0,0 +1,118 @@ +/******************************************************************************* + * Copyright (c) 2008-2020 The Khronos Group Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + ******************************************************************************/ + +#ifndef __OPENCL_CL_DX9_MEDIA_SHARING_H +#define __OPENCL_CL_DX9_MEDIA_SHARING_H + +#include +#include + +#ifdef __cplusplus +extern "C" { +#endif + +/******************************************************************************/ +/* cl_khr_dx9_media_sharing */ +#define cl_khr_dx9_media_sharing 1 + +typedef cl_uint cl_dx9_media_adapter_type_khr; +typedef cl_uint cl_dx9_media_adapter_set_khr; + +#if defined(_WIN32) +#include +typedef struct _cl_dx9_surface_info_khr +{ + IDirect3DSurface9 *resource; + HANDLE shared_handle; +} cl_dx9_surface_info_khr; +#endif + + +/******************************************************************************/ + +/* Error Codes */ +#define CL_INVALID_DX9_MEDIA_ADAPTER_KHR -1010 +#define CL_INVALID_DX9_MEDIA_SURFACE_KHR -1011 +#define CL_DX9_MEDIA_SURFACE_ALREADY_ACQUIRED_KHR -1012 +#define CL_DX9_MEDIA_SURFACE_NOT_ACQUIRED_KHR -1013 + +/* cl_media_adapter_type_khr */ +#define CL_ADAPTER_D3D9_KHR 0x2020 +#define CL_ADAPTER_D3D9EX_KHR 0x2021 +#define CL_ADAPTER_DXVA_KHR 0x2022 + +/* cl_media_adapter_set_khr */ +#define CL_PREFERRED_DEVICES_FOR_DX9_MEDIA_ADAPTER_KHR 0x2023 +#define CL_ALL_DEVICES_FOR_DX9_MEDIA_ADAPTER_KHR 0x2024 + +/* cl_context_info */ +#define CL_CONTEXT_ADAPTER_D3D9_KHR 0x2025 +#define CL_CONTEXT_ADAPTER_D3D9EX_KHR 0x2026 +#define CL_CONTEXT_ADAPTER_DXVA_KHR 0x2027 + +/* cl_mem_info */ +#define CL_MEM_DX9_MEDIA_ADAPTER_TYPE_KHR 0x2028 +#define CL_MEM_DX9_MEDIA_SURFACE_INFO_KHR 0x2029 + +/* cl_image_info */ +#define CL_IMAGE_DX9_MEDIA_PLANE_KHR 0x202A + +/* cl_command_type */ +#define CL_COMMAND_ACQUIRE_DX9_MEDIA_SURFACES_KHR 0x202B +#define CL_COMMAND_RELEASE_DX9_MEDIA_SURFACES_KHR 0x202C + +/******************************************************************************/ + +typedef CL_API_ENTRY cl_int (CL_API_CALL *clGetDeviceIDsFromDX9MediaAdapterKHR_fn)( + cl_platform_id platform, + cl_uint num_media_adapters, + cl_dx9_media_adapter_type_khr * media_adapter_type, + void * media_adapters, + cl_dx9_media_adapter_set_khr media_adapter_set, + cl_uint num_entries, + cl_device_id * devices, + cl_uint * num_devices) CL_API_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_mem (CL_API_CALL *clCreateFromDX9MediaSurfaceKHR_fn)( + cl_context context, + cl_mem_flags flags, + cl_dx9_media_adapter_type_khr adapter_type, + void * surface_info, + cl_uint plane, + cl_int * errcode_ret) CL_API_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_int (CL_API_CALL *clEnqueueAcquireDX9MediaSurfacesKHR_fn)( + cl_command_queue command_queue, + cl_uint num_objects, + const cl_mem * mem_objects, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) CL_API_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_int (CL_API_CALL *clEnqueueReleaseDX9MediaSurfacesKHR_fn)( + cl_command_queue command_queue, + cl_uint num_objects, + const cl_mem * mem_objects, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) CL_API_SUFFIX__VERSION_1_2; + +#ifdef __cplusplus +} +#endif + +#endif /* __OPENCL_CL_DX9_MEDIA_SHARING_H */ + diff --git a/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/cl_dx9_media_sharing_intel.h b/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/cl_dx9_media_sharing_intel.h new file mode 100755 index 0000000000000000000000000000000000000000..4525a175e636c783969c56fb90fdb2cb45a16307 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/cl_dx9_media_sharing_intel.h @@ -0,0 +1,170 @@ +/******************************************************************************* + * Copyright (c) 2008-2020 The Khronos Group Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + ******************************************************************************/ +/*****************************************************************************\ + +Copyright (c) 2013-2019 Intel Corporation All Rights Reserved. + +THESE MATERIALS ARE PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL INTEL OR ITS +CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, +EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, +PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR +PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY +OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY OR TORT (INCLUDING +NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THESE +MATERIALS, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +File Name: cl_dx9_media_sharing_intel.h + +Abstract: + +Notes: + +\*****************************************************************************/ + +#ifndef __OPENCL_CL_DX9_MEDIA_SHARING_INTEL_H +#define __OPENCL_CL_DX9_MEDIA_SHARING_INTEL_H + +#include +#include +#include +#include +#include +#include + +#ifdef __cplusplus +extern "C" { +#endif + +/*************************************** +* cl_intel_dx9_media_sharing extension * +****************************************/ + +#define cl_intel_dx9_media_sharing 1 + +typedef cl_uint cl_dx9_device_source_intel; +typedef cl_uint cl_dx9_device_set_intel; + +/* error codes */ +#define CL_INVALID_DX9_DEVICE_INTEL -1010 +#define CL_INVALID_DX9_RESOURCE_INTEL -1011 +#define CL_DX9_RESOURCE_ALREADY_ACQUIRED_INTEL -1012 +#define CL_DX9_RESOURCE_NOT_ACQUIRED_INTEL -1013 + +/* cl_dx9_device_source_intel */ +#define CL_D3D9_DEVICE_INTEL 0x4022 +#define CL_D3D9EX_DEVICE_INTEL 0x4070 +#define CL_DXVA_DEVICE_INTEL 0x4071 + +/* cl_dx9_device_set_intel */ +#define CL_PREFERRED_DEVICES_FOR_DX9_INTEL 0x4024 +#define CL_ALL_DEVICES_FOR_DX9_INTEL 0x4025 + +/* cl_context_info */ +#define CL_CONTEXT_D3D9_DEVICE_INTEL 0x4026 +#define CL_CONTEXT_D3D9EX_DEVICE_INTEL 0x4072 +#define CL_CONTEXT_DXVA_DEVICE_INTEL 0x4073 + +/* cl_mem_info */ +#define CL_MEM_DX9_RESOURCE_INTEL 0x4027 +#define CL_MEM_DX9_SHARED_HANDLE_INTEL 0x4074 + +/* cl_image_info */ +#define CL_IMAGE_DX9_PLANE_INTEL 0x4075 + +/* cl_command_type */ +#define CL_COMMAND_ACQUIRE_DX9_OBJECTS_INTEL 0x402A +#define CL_COMMAND_RELEASE_DX9_OBJECTS_INTEL 0x402B +/******************************************************************************/ + +extern CL_API_ENTRY cl_int CL_API_CALL +clGetDeviceIDsFromDX9INTEL( + cl_platform_id platform, + cl_dx9_device_source_intel dx9_device_source, + void* dx9_object, + cl_dx9_device_set_intel dx9_device_set, + cl_uint num_entries, + cl_device_id* devices, + cl_uint* num_devices) CL_EXT_SUFFIX__VERSION_1_1; + +typedef CL_API_ENTRY cl_int (CL_API_CALL* clGetDeviceIDsFromDX9INTEL_fn)( + cl_platform_id platform, + cl_dx9_device_source_intel dx9_device_source, + void* dx9_object, + cl_dx9_device_set_intel dx9_device_set, + cl_uint num_entries, + cl_device_id* devices, + cl_uint* num_devices) CL_EXT_SUFFIX__VERSION_1_1; + +extern CL_API_ENTRY cl_mem CL_API_CALL +clCreateFromDX9MediaSurfaceINTEL( + cl_context context, + cl_mem_flags flags, + IDirect3DSurface9* resource, + HANDLE sharedHandle, + UINT plane, + cl_int* errcode_ret) CL_EXT_SUFFIX__VERSION_1_1; + +typedef CL_API_ENTRY cl_mem (CL_API_CALL *clCreateFromDX9MediaSurfaceINTEL_fn)( + cl_context context, + cl_mem_flags flags, + IDirect3DSurface9* resource, + HANDLE sharedHandle, + UINT plane, + cl_int* errcode_ret) CL_EXT_SUFFIX__VERSION_1_1; + +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueAcquireDX9ObjectsINTEL( + cl_command_queue command_queue, + cl_uint num_objects, + const cl_mem* mem_objects, + cl_uint num_events_in_wait_list, + const cl_event* event_wait_list, + cl_event* event) CL_EXT_SUFFIX__VERSION_1_1; + +typedef CL_API_ENTRY cl_int (CL_API_CALL *clEnqueueAcquireDX9ObjectsINTEL_fn)( + cl_command_queue command_queue, + cl_uint num_objects, + const cl_mem* mem_objects, + cl_uint num_events_in_wait_list, + const cl_event* event_wait_list, + cl_event* event) CL_EXT_SUFFIX__VERSION_1_1; + +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueReleaseDX9ObjectsINTEL( + cl_command_queue command_queue, + cl_uint num_objects, + cl_mem* mem_objects, + cl_uint num_events_in_wait_list, + const cl_event* event_wait_list, + cl_event* event) CL_EXT_SUFFIX__VERSION_1_1; + +typedef CL_API_ENTRY cl_int (CL_API_CALL *clEnqueueReleaseDX9ObjectsINTEL_fn)( + cl_command_queue command_queue, + cl_uint num_objects, + cl_mem* mem_objects, + cl_uint num_events_in_wait_list, + const cl_event* event_wait_list, + cl_event* event) CL_EXT_SUFFIX__VERSION_1_1; + +#ifdef __cplusplus +} +#endif + +#endif /* __OPENCL_CL_DX9_MEDIA_SHARING_INTEL_H */ + diff --git a/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/cl_egl.h b/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/cl_egl.h new file mode 100755 index 0000000000000000000000000000000000000000..c8bde80e15d1a22dec5939f3c3643eea13e59546 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/cl_egl.h @@ -0,0 +1,120 @@ +/******************************************************************************* + * Copyright (c) 2008-2020 The Khronos Group Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + ******************************************************************************/ + +#ifndef __OPENCL_CL_EGL_H +#define __OPENCL_CL_EGL_H + +#include + +#ifdef __cplusplus +extern "C" { +#endif + + +/* Command type for events created with clEnqueueAcquireEGLObjectsKHR */ +#define CL_COMMAND_EGL_FENCE_SYNC_OBJECT_KHR 0x202F +#define CL_COMMAND_ACQUIRE_EGL_OBJECTS_KHR 0x202D +#define CL_COMMAND_RELEASE_EGL_OBJECTS_KHR 0x202E + +/* Error type for clCreateFromEGLImageKHR */ +#define CL_INVALID_EGL_OBJECT_KHR -1093 +#define CL_EGL_RESOURCE_NOT_ACQUIRED_KHR -1092 + +/* CLeglImageKHR is an opaque handle to an EGLImage */ +typedef void* CLeglImageKHR; + +/* CLeglDisplayKHR is an opaque handle to an EGLDisplay */ +typedef void* CLeglDisplayKHR; + +/* CLeglSyncKHR is an opaque handle to an EGLSync object */ +typedef void* CLeglSyncKHR; + +/* properties passed to clCreateFromEGLImageKHR */ +typedef intptr_t cl_egl_image_properties_khr; + + +#define cl_khr_egl_image 1 + +extern CL_API_ENTRY cl_mem CL_API_CALL +clCreateFromEGLImageKHR(cl_context context, + CLeglDisplayKHR egldisplay, + CLeglImageKHR eglimage, + cl_mem_flags flags, + const cl_egl_image_properties_khr * properties, + cl_int * errcode_ret) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_mem (CL_API_CALL *clCreateFromEGLImageKHR_fn)( + cl_context context, + CLeglDisplayKHR egldisplay, + CLeglImageKHR eglimage, + cl_mem_flags flags, + const cl_egl_image_properties_khr * properties, + cl_int * errcode_ret); + + +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueAcquireEGLObjectsKHR(cl_command_queue command_queue, + cl_uint num_objects, + const cl_mem * mem_objects, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_int (CL_API_CALL *clEnqueueAcquireEGLObjectsKHR_fn)( + cl_command_queue command_queue, + cl_uint num_objects, + const cl_mem * mem_objects, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event); + + +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueReleaseEGLObjectsKHR(cl_command_queue command_queue, + cl_uint num_objects, + const cl_mem * mem_objects, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_int (CL_API_CALL *clEnqueueReleaseEGLObjectsKHR_fn)( + cl_command_queue command_queue, + cl_uint num_objects, + const cl_mem * mem_objects, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event); + + +#define cl_khr_egl_event 1 + +extern CL_API_ENTRY cl_event CL_API_CALL +clCreateEventFromEGLSyncKHR(cl_context context, + CLeglSyncKHR sync, + CLeglDisplayKHR display, + cl_int * errcode_ret) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_event (CL_API_CALL *clCreateEventFromEGLSyncKHR_fn)( + cl_context context, + CLeglSyncKHR sync, + CLeglDisplayKHR display, + cl_int * errcode_ret); + +#ifdef __cplusplus +} +#endif + +#endif /* __OPENCL_CL_EGL_H */ diff --git a/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/cl_ext.h b/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/cl_ext.h new file mode 100755 index 0000000000000000000000000000000000000000..34f6c3a1990dcef7ba15b7c6341f28ce8e7f0ab7 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/cl_ext.h @@ -0,0 +1,861 @@ +/******************************************************************************* + * Copyright (c) 2008-2020 The Khronos Group Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + ******************************************************************************/ + +/* cl_ext.h contains OpenCL extensions which don't have external */ +/* (OpenGL, D3D) dependencies. */ + +#ifndef __CL_EXT_H +#define __CL_EXT_H + +#ifdef __cplusplus +extern "C" { +#endif + +#include + +/* cl_khr_fp64 extension - no extension #define since it has no functions */ +/* CL_DEVICE_DOUBLE_FP_CONFIG is defined in CL.h for OpenCL >= 120 */ + +#if CL_TARGET_OPENCL_VERSION <= 110 +#define CL_DEVICE_DOUBLE_FP_CONFIG 0x1032 +#endif + +/* cl_khr_fp16 extension - no extension #define since it has no functions */ +#define CL_DEVICE_HALF_FP_CONFIG 0x1033 + +/* Memory object destruction + * + * Apple extension for use to manage externally allocated buffers used with cl_mem objects with CL_MEM_USE_HOST_PTR + * + * Registers a user callback function that will be called when the memory object is deleted and its resources + * freed. Each call to clSetMemObjectCallbackFn registers the specified user callback function on a callback + * stack associated with memobj. The registered user callback functions are called in the reverse order in + * which they were registered. The user callback functions are called and then the memory object is deleted + * and its resources freed. This provides a mechanism for the application (and libraries) using memobj to be + * notified when the memory referenced by host_ptr, specified when the memory object is created and used as + * the storage bits for the memory object, can be reused or freed. + * + * The application may not call CL api's with the cl_mem object passed to the pfn_notify. + * + * Please check for the "cl_APPLE_SetMemObjectDestructor" extension using clGetDeviceInfo(CL_DEVICE_EXTENSIONS) + * before using. + */ +#define cl_APPLE_SetMemObjectDestructor 1 +cl_int CL_API_ENTRY clSetMemObjectDestructorAPPLE( cl_mem memobj, + void (* pfn_notify)(cl_mem memobj, void * user_data), + void * user_data) CL_EXT_SUFFIX__VERSION_1_0; + + +/* Context Logging Functions + * + * The next three convenience functions are intended to be used as the pfn_notify parameter to clCreateContext(). + * Please check for the "cl_APPLE_ContextLoggingFunctions" extension using clGetDeviceInfo(CL_DEVICE_EXTENSIONS) + * before using. + * + * clLogMessagesToSystemLog forwards on all log messages to the Apple System Logger + */ +#define cl_APPLE_ContextLoggingFunctions 1 +extern void CL_API_ENTRY clLogMessagesToSystemLogAPPLE( const char * errstr, + const void * private_info, + size_t cb, + void * user_data) CL_EXT_SUFFIX__VERSION_1_0; + +/* clLogMessagesToStdout sends all log messages to the file descriptor stdout */ +extern void CL_API_ENTRY clLogMessagesToStdoutAPPLE( const char * errstr, + const void * private_info, + size_t cb, + void * user_data) CL_EXT_SUFFIX__VERSION_1_0; + +/* clLogMessagesToStderr sends all log messages to the file descriptor stderr */ +extern void CL_API_ENTRY clLogMessagesToStderrAPPLE( const char * errstr, + const void * private_info, + size_t cb, + void * user_data) CL_EXT_SUFFIX__VERSION_1_0; + + +/************************ +* cl_khr_icd extension * +************************/ +#define cl_khr_icd 1 + +/* cl_platform_info */ +#define CL_PLATFORM_ICD_SUFFIX_KHR 0x0920 + +/* Additional Error Codes */ +#define CL_PLATFORM_NOT_FOUND_KHR -1001 + +extern CL_API_ENTRY cl_int CL_API_CALL +clIcdGetPlatformIDsKHR(cl_uint num_entries, + cl_platform_id * platforms, + cl_uint * num_platforms); + +typedef CL_API_ENTRY cl_int +(CL_API_CALL *clIcdGetPlatformIDsKHR_fn)(cl_uint num_entries, + cl_platform_id * platforms, + cl_uint * num_platforms); + + +/******************************* + * cl_khr_il_program extension * + *******************************/ +#define cl_khr_il_program 1 + +/* New property to clGetDeviceInfo for retrieving supported intermediate + * languages + */ +#define CL_DEVICE_IL_VERSION_KHR 0x105B + +/* New property to clGetProgramInfo for retrieving for retrieving the IL of a + * program + */ +#define CL_PROGRAM_IL_KHR 0x1169 + +extern CL_API_ENTRY cl_program CL_API_CALL +clCreateProgramWithILKHR(cl_context context, + const void * il, + size_t length, + cl_int * errcode_ret); + +typedef CL_API_ENTRY cl_program +(CL_API_CALL *clCreateProgramWithILKHR_fn)(cl_context context, + const void * il, + size_t length, + cl_int * errcode_ret) CL_EXT_SUFFIX__VERSION_1_2; + +/* Extension: cl_khr_image2d_from_buffer + * + * This extension allows a 2D image to be created from a cl_mem buffer without + * a copy. The type associated with a 2D image created from a buffer in an + * OpenCL program is image2d_t. Both the sampler and sampler-less read_image + * built-in functions are supported for 2D images and 2D images created from + * a buffer. Similarly, the write_image built-ins are also supported for 2D + * images created from a buffer. + * + * When the 2D image from buffer is created, the client must specify the + * width, height, image format (i.e. channel order and channel data type) + * and optionally the row pitch. + * + * The pitch specified must be a multiple of + * CL_DEVICE_IMAGE_PITCH_ALIGNMENT_KHR pixels. + * The base address of the buffer must be aligned to + * CL_DEVICE_IMAGE_BASE_ADDRESS_ALIGNMENT_KHR pixels. + */ + +#define CL_DEVICE_IMAGE_PITCH_ALIGNMENT_KHR 0x104A +#define CL_DEVICE_IMAGE_BASE_ADDRESS_ALIGNMENT_KHR 0x104B + + +/************************************** + * cl_khr_initialize_memory extension * + **************************************/ + +#define CL_CONTEXT_MEMORY_INITIALIZE_KHR 0x2030 + + +/************************************** + * cl_khr_terminate_context extension * + **************************************/ + +#define CL_CONTEXT_TERMINATED_KHR -1121 + +#define CL_DEVICE_TERMINATE_CAPABILITY_KHR 0x2031 +#define CL_CONTEXT_TERMINATE_KHR 0x2032 + +#define cl_khr_terminate_context 1 +extern CL_API_ENTRY cl_int CL_API_CALL +clTerminateContextKHR(cl_context context) CL_EXT_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_int +(CL_API_CALL *clTerminateContextKHR_fn)(cl_context context) CL_EXT_SUFFIX__VERSION_1_2; + + +/* + * Extension: cl_khr_spir + * + * This extension adds support to create an OpenCL program object from a + * Standard Portable Intermediate Representation (SPIR) instance + */ + +#define CL_DEVICE_SPIR_VERSIONS 0x40E0 +#define CL_PROGRAM_BINARY_TYPE_INTERMEDIATE 0x40E1 + + +/***************************************** + * cl_khr_create_command_queue extension * + *****************************************/ +#define cl_khr_create_command_queue 1 + +typedef cl_properties cl_queue_properties_khr; + +extern CL_API_ENTRY cl_command_queue CL_API_CALL +clCreateCommandQueueWithPropertiesKHR(cl_context context, + cl_device_id device, + const cl_queue_properties_khr* properties, + cl_int* errcode_ret) CL_EXT_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_command_queue +(CL_API_CALL *clCreateCommandQueueWithPropertiesKHR_fn)(cl_context context, + cl_device_id device, + const cl_queue_properties_khr* properties, + cl_int* errcode_ret) CL_EXT_SUFFIX__VERSION_1_2; + + +/****************************************** +* cl_nv_device_attribute_query extension * +******************************************/ + +/* cl_nv_device_attribute_query extension - no extension #define since it has no functions */ +#define CL_DEVICE_COMPUTE_CAPABILITY_MAJOR_NV 0x4000 +#define CL_DEVICE_COMPUTE_CAPABILITY_MINOR_NV 0x4001 +#define CL_DEVICE_REGISTERS_PER_BLOCK_NV 0x4002 +#define CL_DEVICE_WARP_SIZE_NV 0x4003 +#define CL_DEVICE_GPU_OVERLAP_NV 0x4004 +#define CL_DEVICE_KERNEL_EXEC_TIMEOUT_NV 0x4005 +#define CL_DEVICE_INTEGRATED_MEMORY_NV 0x4006 + + +/********************************* +* cl_amd_device_attribute_query * +*********************************/ + +#define CL_DEVICE_PROFILING_TIMER_OFFSET_AMD 0x4036 +#define CL_DEVICE_TOPOLOGY_AMD 0x4037 +#define CL_DEVICE_BOARD_NAME_AMD 0x4038 +#define CL_DEVICE_GLOBAL_FREE_MEMORY_AMD 0x4039 +#define CL_DEVICE_SIMD_PER_COMPUTE_UNIT_AMD 0x4040 +#define CL_DEVICE_SIMD_WIDTH_AMD 0x4041 +#define CL_DEVICE_SIMD_INSTRUCTION_WIDTH_AMD 0x4042 +#define CL_DEVICE_WAVEFRONT_WIDTH_AMD 0x4043 +#define CL_DEVICE_GLOBAL_MEM_CHANNELS_AMD 0x4044 +#define CL_DEVICE_GLOBAL_MEM_CHANNEL_BANKS_AMD 0x4045 +#define CL_DEVICE_GLOBAL_MEM_CHANNEL_BANK_WIDTH_AMD 0x4046 +#define CL_DEVICE_LOCAL_MEM_SIZE_PER_COMPUTE_UNIT_AMD 0x4047 +#define CL_DEVICE_LOCAL_MEM_BANKS_AMD 0x4048 +#define CL_DEVICE_THREAD_TRACE_SUPPORTED_AMD 0x4049 +#define CL_DEVICE_GFXIP_MAJOR_AMD 0x404A +#define CL_DEVICE_GFXIP_MINOR_AMD 0x404B +#define CL_DEVICE_AVAILABLE_ASYNC_QUEUES_AMD 0x404C +#define CL_DEVICE_PREFERRED_WORK_GROUP_SIZE_AMD 0x4030 +#define CL_DEVICE_MAX_WORK_GROUP_SIZE_AMD 0x4031 +#define CL_DEVICE_PREFERRED_CONSTANT_BUFFER_SIZE_AMD 0x4033 +#define CL_DEVICE_PCIE_ID_AMD 0x4034 + + +/********************************* +* cl_arm_printf extension +*********************************/ + +#define CL_PRINTF_CALLBACK_ARM 0x40B0 +#define CL_PRINTF_BUFFERSIZE_ARM 0x40B1 + + +/*********************************** +* cl_ext_device_fission extension +***********************************/ +#define cl_ext_device_fission 1 + +extern CL_API_ENTRY cl_int CL_API_CALL +clReleaseDeviceEXT(cl_device_id device) CL_EXT_SUFFIX__VERSION_1_1; + +typedef CL_API_ENTRY cl_int +(CL_API_CALL *clReleaseDeviceEXT_fn)(cl_device_id device) CL_EXT_SUFFIX__VERSION_1_1; + +extern CL_API_ENTRY cl_int CL_API_CALL +clRetainDeviceEXT(cl_device_id device) CL_EXT_SUFFIX__VERSION_1_1; + +typedef CL_API_ENTRY cl_int +(CL_API_CALL *clRetainDeviceEXT_fn)(cl_device_id device) CL_EXT_SUFFIX__VERSION_1_1; + +typedef cl_ulong cl_device_partition_property_ext; +extern CL_API_ENTRY cl_int CL_API_CALL +clCreateSubDevicesEXT(cl_device_id in_device, + const cl_device_partition_property_ext * properties, + cl_uint num_entries, + cl_device_id * out_devices, + cl_uint * num_devices) CL_EXT_SUFFIX__VERSION_1_1; + +typedef CL_API_ENTRY cl_int +(CL_API_CALL * clCreateSubDevicesEXT_fn)(cl_device_id in_device, + const cl_device_partition_property_ext * properties, + cl_uint num_entries, + cl_device_id * out_devices, + cl_uint * num_devices) CL_EXT_SUFFIX__VERSION_1_1; + +/* cl_device_partition_property_ext */ +#define CL_DEVICE_PARTITION_EQUALLY_EXT 0x4050 +#define CL_DEVICE_PARTITION_BY_COUNTS_EXT 0x4051 +#define CL_DEVICE_PARTITION_BY_NAMES_EXT 0x4052 +#define CL_DEVICE_PARTITION_BY_AFFINITY_DOMAIN_EXT 0x4053 + +/* clDeviceGetInfo selectors */ +#define CL_DEVICE_PARENT_DEVICE_EXT 0x4054 +#define CL_DEVICE_PARTITION_TYPES_EXT 0x4055 +#define CL_DEVICE_AFFINITY_DOMAINS_EXT 0x4056 +#define CL_DEVICE_REFERENCE_COUNT_EXT 0x4057 +#define CL_DEVICE_PARTITION_STYLE_EXT 0x4058 + +/* error codes */ +#define CL_DEVICE_PARTITION_FAILED_EXT -1057 +#define CL_INVALID_PARTITION_COUNT_EXT -1058 +#define CL_INVALID_PARTITION_NAME_EXT -1059 + +/* CL_AFFINITY_DOMAINs */ +#define CL_AFFINITY_DOMAIN_L1_CACHE_EXT 0x1 +#define CL_AFFINITY_DOMAIN_L2_CACHE_EXT 0x2 +#define CL_AFFINITY_DOMAIN_L3_CACHE_EXT 0x3 +#define CL_AFFINITY_DOMAIN_L4_CACHE_EXT 0x4 +#define CL_AFFINITY_DOMAIN_NUMA_EXT 0x10 +#define CL_AFFINITY_DOMAIN_NEXT_FISSIONABLE_EXT 0x100 + +/* cl_device_partition_property_ext list terminators */ +#define CL_PROPERTIES_LIST_END_EXT ((cl_device_partition_property_ext) 0) +#define CL_PARTITION_BY_COUNTS_LIST_END_EXT ((cl_device_partition_property_ext) 0) +#define CL_PARTITION_BY_NAMES_LIST_END_EXT ((cl_device_partition_property_ext) 0 - 1) + + +/*********************************** + * cl_ext_migrate_memobject extension definitions + ***********************************/ +#define cl_ext_migrate_memobject 1 + +typedef cl_bitfield cl_mem_migration_flags_ext; + +#define CL_MIGRATE_MEM_OBJECT_HOST_EXT 0x1 + +#define CL_COMMAND_MIGRATE_MEM_OBJECT_EXT 0x4040 + +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueMigrateMemObjectEXT(cl_command_queue command_queue, + cl_uint num_mem_objects, + const cl_mem * mem_objects, + cl_mem_migration_flags_ext flags, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event); + +typedef CL_API_ENTRY cl_int +(CL_API_CALL *clEnqueueMigrateMemObjectEXT_fn)(cl_command_queue command_queue, + cl_uint num_mem_objects, + const cl_mem * mem_objects, + cl_mem_migration_flags_ext flags, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event); + + +/********************************* +* cl_qcom_ext_host_ptr extension +*********************************/ +#define cl_qcom_ext_host_ptr 1 + +#define CL_MEM_EXT_HOST_PTR_QCOM (1 << 29) + +#define CL_DEVICE_EXT_MEM_PADDING_IN_BYTES_QCOM 0x40A0 +#define CL_DEVICE_PAGE_SIZE_QCOM 0x40A1 +#define CL_IMAGE_ROW_ALIGNMENT_QCOM 0x40A2 +#define CL_IMAGE_SLICE_ALIGNMENT_QCOM 0x40A3 +#define CL_MEM_HOST_UNCACHED_QCOM 0x40A4 +#define CL_MEM_HOST_WRITEBACK_QCOM 0x40A5 +#define CL_MEM_HOST_WRITETHROUGH_QCOM 0x40A6 +#define CL_MEM_HOST_WRITE_COMBINING_QCOM 0x40A7 + +typedef cl_uint cl_image_pitch_info_qcom; + +extern CL_API_ENTRY cl_int CL_API_CALL +clGetDeviceImageInfoQCOM(cl_device_id device, + size_t image_width, + size_t image_height, + const cl_image_format *image_format, + cl_image_pitch_info_qcom param_name, + size_t param_value_size, + void *param_value, + size_t *param_value_size_ret); + +typedef struct _cl_mem_ext_host_ptr +{ + /* Type of external memory allocation. */ + /* Legal values will be defined in layered extensions. */ + cl_uint allocation_type; + + /* Host cache policy for this external memory allocation. */ + cl_uint host_cache_policy; + +} cl_mem_ext_host_ptr; + + +/******************************************* +* cl_qcom_ext_host_ptr_iocoherent extension +********************************************/ + +/* Cache policy specifying io-coherence */ +#define CL_MEM_HOST_IOCOHERENT_QCOM 0x40A9 + + +/********************************* +* cl_qcom_ion_host_ptr extension +*********************************/ + +#define CL_MEM_ION_HOST_PTR_QCOM 0x40A8 + +typedef struct _cl_mem_ion_host_ptr +{ + /* Type of external memory allocation. */ + /* Must be CL_MEM_ION_HOST_PTR_QCOM for ION allocations. */ + cl_mem_ext_host_ptr ext_host_ptr; + + /* ION file descriptor */ + int ion_filedesc; + + /* Host pointer to the ION allocated memory */ + void* ion_hostptr; + +} cl_mem_ion_host_ptr; + + +/********************************* +* cl_qcom_android_native_buffer_host_ptr extension +*********************************/ + +#define CL_MEM_ANDROID_NATIVE_BUFFER_HOST_PTR_QCOM 0x40C6 + +typedef struct _cl_mem_android_native_buffer_host_ptr +{ + /* Type of external memory allocation. */ + /* Must be CL_MEM_ANDROID_NATIVE_BUFFER_HOST_PTR_QCOM for Android native buffers. */ + cl_mem_ext_host_ptr ext_host_ptr; + + /* Virtual pointer to the android native buffer */ + void* anb_ptr; + +} cl_mem_android_native_buffer_host_ptr; + + +/****************************************** + * cl_img_yuv_image extension * + ******************************************/ + +/* Image formats used in clCreateImage */ +#define CL_NV21_IMG 0x40D0 +#define CL_YV12_IMG 0x40D1 + + +/****************************************** + * cl_img_cached_allocations extension * + ******************************************/ + +/* Flag values used by clCreateBuffer */ +#define CL_MEM_USE_UNCACHED_CPU_MEMORY_IMG (1 << 26) +#define CL_MEM_USE_CACHED_CPU_MEMORY_IMG (1 << 27) + + +/****************************************** + * cl_img_use_gralloc_ptr extension * + ******************************************/ +#define cl_img_use_gralloc_ptr 1 + +/* Flag values used by clCreateBuffer */ +#define CL_MEM_USE_GRALLOC_PTR_IMG (1 << 28) + +/* To be used by clGetEventInfo: */ +#define CL_COMMAND_ACQUIRE_GRALLOC_OBJECTS_IMG 0x40D2 +#define CL_COMMAND_RELEASE_GRALLOC_OBJECTS_IMG 0x40D3 + +/* Error code from clEnqueueReleaseGrallocObjectsIMG */ +#define CL_GRALLOC_RESOURCE_NOT_ACQUIRED_IMG 0x40D4 + +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueAcquireGrallocObjectsIMG(cl_command_queue command_queue, + cl_uint num_objects, + const cl_mem * mem_objects, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) CL_EXT_SUFFIX__VERSION_1_2; + +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueReleaseGrallocObjectsIMG(cl_command_queue command_queue, + cl_uint num_objects, + const cl_mem * mem_objects, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) CL_EXT_SUFFIX__VERSION_1_2; + + +/********************************* +* cl_khr_subgroups extension +*********************************/ +#define cl_khr_subgroups 1 + +#if !defined(CL_VERSION_2_1) +/* For OpenCL 2.1 and newer, cl_kernel_sub_group_info is declared in CL.h. + In hindsight, there should have been a khr suffix on this type for + the extension, but keeping it un-suffixed to maintain backwards + compatibility. */ +typedef cl_uint cl_kernel_sub_group_info; +#endif + +/* cl_kernel_sub_group_info */ +#define CL_KERNEL_MAX_SUB_GROUP_SIZE_FOR_NDRANGE_KHR 0x2033 +#define CL_KERNEL_SUB_GROUP_COUNT_FOR_NDRANGE_KHR 0x2034 + +extern CL_API_ENTRY cl_int CL_API_CALL +clGetKernelSubGroupInfoKHR(cl_kernel in_kernel, + cl_device_id in_device, + cl_kernel_sub_group_info param_name, + size_t input_value_size, + const void * input_value, + size_t param_value_size, + void * param_value, + size_t * param_value_size_ret) CL_EXT_SUFFIX__VERSION_2_0_DEPRECATED; + +typedef CL_API_ENTRY cl_int +(CL_API_CALL * clGetKernelSubGroupInfoKHR_fn)(cl_kernel in_kernel, + cl_device_id in_device, + cl_kernel_sub_group_info param_name, + size_t input_value_size, + const void * input_value, + size_t param_value_size, + void * param_value, + size_t * param_value_size_ret) CL_EXT_SUFFIX__VERSION_2_0_DEPRECATED; + + +/********************************* +* cl_khr_mipmap_image extension +*********************************/ + +/* cl_sampler_properties */ +#define CL_SAMPLER_MIP_FILTER_MODE_KHR 0x1155 +#define CL_SAMPLER_LOD_MIN_KHR 0x1156 +#define CL_SAMPLER_LOD_MAX_KHR 0x1157 + + +/********************************* +* cl_khr_priority_hints extension +*********************************/ +/* This extension define is for backwards compatibility. + It shouldn't be required since this extension has no new functions. */ +#define cl_khr_priority_hints 1 + +typedef cl_uint cl_queue_priority_khr; + +/* cl_command_queue_properties */ +#define CL_QUEUE_PRIORITY_KHR 0x1096 + +/* cl_queue_priority_khr */ +#define CL_QUEUE_PRIORITY_HIGH_KHR (1<<0) +#define CL_QUEUE_PRIORITY_MED_KHR (1<<1) +#define CL_QUEUE_PRIORITY_LOW_KHR (1<<2) + + +/********************************* +* cl_khr_throttle_hints extension +*********************************/ +/* This extension define is for backwards compatibility. + It shouldn't be required since this extension has no new functions. */ +#define cl_khr_throttle_hints 1 + +typedef cl_uint cl_queue_throttle_khr; + +/* cl_command_queue_properties */ +#define CL_QUEUE_THROTTLE_KHR 0x1097 + +/* cl_queue_throttle_khr */ +#define CL_QUEUE_THROTTLE_HIGH_KHR (1<<0) +#define CL_QUEUE_THROTTLE_MED_KHR (1<<1) +#define CL_QUEUE_THROTTLE_LOW_KHR (1<<2) + + +/********************************* +* cl_khr_subgroup_named_barrier +*********************************/ +/* This extension define is for backwards compatibility. + It shouldn't be required since this extension has no new functions. */ +#define cl_khr_subgroup_named_barrier 1 + +/* cl_device_info */ +#define CL_DEVICE_MAX_NAMED_BARRIER_COUNT_KHR 0x2035 + + +/********************************* +* cl_khr_extended_versioning +*********************************/ + +#define cl_khr_extended_versioning 1 + +#define CL_VERSION_MAJOR_BITS_KHR (10) +#define CL_VERSION_MINOR_BITS_KHR (10) +#define CL_VERSION_PATCH_BITS_KHR (12) + +#define CL_VERSION_MAJOR_MASK_KHR ((1 << CL_VERSION_MAJOR_BITS_KHR) - 1) +#define CL_VERSION_MINOR_MASK_KHR ((1 << CL_VERSION_MINOR_BITS_KHR) - 1) +#define CL_VERSION_PATCH_MASK_KHR ((1 << CL_VERSION_PATCH_BITS_KHR) - 1) + +#define CL_VERSION_MAJOR_KHR(version) ((version) >> (CL_VERSION_MINOR_BITS_KHR + CL_VERSION_PATCH_BITS_KHR)) +#define CL_VERSION_MINOR_KHR(version) (((version) >> CL_VERSION_PATCH_BITS_KHR) & CL_VERSION_MINOR_MASK_KHR) +#define CL_VERSION_PATCH_KHR(version) ((version) & CL_VERSION_PATCH_MASK_KHR) + +#define CL_MAKE_VERSION_KHR(major, minor, patch) \ + ((((major) & CL_VERSION_MAJOR_MASK_KHR) << (CL_VERSION_MINOR_BITS_KHR + CL_VERSION_PATCH_BITS_KHR)) | \ + (((minor) & CL_VERSION_MINOR_MASK_KHR) << CL_VERSION_PATCH_BITS_KHR) | \ + ((patch) & CL_VERSION_PATCH_MASK_KHR)) + +typedef cl_uint cl_version_khr; + +#define CL_NAME_VERSION_MAX_NAME_SIZE_KHR 64 + +typedef struct _cl_name_version_khr +{ + cl_version_khr version; + char name[CL_NAME_VERSION_MAX_NAME_SIZE_KHR]; +} cl_name_version_khr; + +/* cl_platform_info */ +#define CL_PLATFORM_NUMERIC_VERSION_KHR 0x0906 +#define CL_PLATFORM_EXTENSIONS_WITH_VERSION_KHR 0x0907 + +/* cl_device_info */ +#define CL_DEVICE_NUMERIC_VERSION_KHR 0x105E +#define CL_DEVICE_OPENCL_C_NUMERIC_VERSION_KHR 0x105F +#define CL_DEVICE_EXTENSIONS_WITH_VERSION_KHR 0x1060 +#define CL_DEVICE_ILS_WITH_VERSION_KHR 0x1061 +#define CL_DEVICE_BUILT_IN_KERNELS_WITH_VERSION_KHR 0x1062 + + +/********************************* +* cl_khr_device_uuid extension +*********************************/ +#define cl_khr_device_uuid 1 + +#define CL_UUID_SIZE_KHR 16 +#define CL_LUID_SIZE_KHR 8 + +#define CL_DEVICE_UUID_KHR 0x106A +#define CL_DRIVER_UUID_KHR 0x106B +#define CL_DEVICE_LUID_VALID_KHR 0x106C +#define CL_DEVICE_LUID_KHR 0x106D +#define CL_DEVICE_NODE_MASK_KHR 0x106E + + +/********************************** + * cl_arm_import_memory extension * + **********************************/ +#define cl_arm_import_memory 1 + +typedef intptr_t cl_import_properties_arm; + +/* Default and valid proporties name for cl_arm_import_memory */ +#define CL_IMPORT_TYPE_ARM 0x40B2 + +/* Host process memory type default value for CL_IMPORT_TYPE_ARM property */ +#define CL_IMPORT_TYPE_HOST_ARM 0x40B3 + +/* DMA BUF memory type value for CL_IMPORT_TYPE_ARM property */ +#define CL_IMPORT_TYPE_DMA_BUF_ARM 0x40B4 + +/* Protected memory property */ +#define CL_IMPORT_TYPE_PROTECTED_ARM 0x40B5 + +/* Android hardware buffer type value for CL_IMPORT_TYPE_ARM property */ +#define CL_IMPORT_TYPE_ANDROID_HARDWARE_BUFFER_ARM 0x41E2 + +/* Data consistency with host property */ +#define CL_IMPORT_DMA_BUF_DATA_CONSISTENCY_WITH_HOST_ARM 0x41E3 + +/* Import memory size value to indicate a size for the whole buffer */ +#define CL_IMPORT_MEMORY_WHOLE_ALLOCATION_ARM SIZE_MAX + +/* This extension adds a new function that allows for direct memory import into + * OpenCL via the clImportMemoryARM function. + * + * Memory imported through this interface will be mapped into the device's page + * tables directly, providing zero copy access. It will never fall back to copy + * operations and aliased buffers. + * + * Types of memory supported for import are specified as additional extension + * strings. + * + * This extension produces cl_mem allocations which are compatible with all other + * users of cl_mem in the standard API. + * + * This extension maps pages with the same properties as the normal buffer creation + * function clCreateBuffer. + */ +extern CL_API_ENTRY cl_mem CL_API_CALL +clImportMemoryARM( cl_context context, + cl_mem_flags flags, + const cl_import_properties_arm *properties, + void *memory, + size_t size, + cl_int *errcode_ret) CL_EXT_SUFFIX__VERSION_1_0; + + +/****************************************** + * cl_arm_shared_virtual_memory extension * + ******************************************/ +#define cl_arm_shared_virtual_memory 1 + +/* Used by clGetDeviceInfo */ +#define CL_DEVICE_SVM_CAPABILITIES_ARM 0x40B6 + +/* Used by clGetMemObjectInfo */ +#define CL_MEM_USES_SVM_POINTER_ARM 0x40B7 + +/* Used by clSetKernelExecInfoARM: */ +#define CL_KERNEL_EXEC_INFO_SVM_PTRS_ARM 0x40B8 +#define CL_KERNEL_EXEC_INFO_SVM_FINE_GRAIN_SYSTEM_ARM 0x40B9 + +/* To be used by clGetEventInfo: */ +#define CL_COMMAND_SVM_FREE_ARM 0x40BA +#define CL_COMMAND_SVM_MEMCPY_ARM 0x40BB +#define CL_COMMAND_SVM_MEMFILL_ARM 0x40BC +#define CL_COMMAND_SVM_MAP_ARM 0x40BD +#define CL_COMMAND_SVM_UNMAP_ARM 0x40BE + +/* Flag values returned by clGetDeviceInfo with CL_DEVICE_SVM_CAPABILITIES_ARM as the param_name. */ +#define CL_DEVICE_SVM_COARSE_GRAIN_BUFFER_ARM (1 << 0) +#define CL_DEVICE_SVM_FINE_GRAIN_BUFFER_ARM (1 << 1) +#define CL_DEVICE_SVM_FINE_GRAIN_SYSTEM_ARM (1 << 2) +#define CL_DEVICE_SVM_ATOMICS_ARM (1 << 3) + +/* Flag values used by clSVMAllocARM: */ +#define CL_MEM_SVM_FINE_GRAIN_BUFFER_ARM (1 << 10) +#define CL_MEM_SVM_ATOMICS_ARM (1 << 11) + +typedef cl_bitfield cl_svm_mem_flags_arm; +typedef cl_uint cl_kernel_exec_info_arm; +typedef cl_bitfield cl_device_svm_capabilities_arm; + +extern CL_API_ENTRY void * CL_API_CALL +clSVMAllocARM(cl_context context, + cl_svm_mem_flags_arm flags, + size_t size, + cl_uint alignment) CL_EXT_SUFFIX__VERSION_1_2; + +extern CL_API_ENTRY void CL_API_CALL +clSVMFreeARM(cl_context context, + void * svm_pointer) CL_EXT_SUFFIX__VERSION_1_2; + +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueSVMFreeARM(cl_command_queue command_queue, + cl_uint num_svm_pointers, + void * svm_pointers[], + void (CL_CALLBACK * pfn_free_func)(cl_command_queue queue, + cl_uint num_svm_pointers, + void * svm_pointers[], + void * user_data), + void * user_data, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) CL_EXT_SUFFIX__VERSION_1_2; + +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueSVMMemcpyARM(cl_command_queue command_queue, + cl_bool blocking_copy, + void * dst_ptr, + const void * src_ptr, + size_t size, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) CL_EXT_SUFFIX__VERSION_1_2; + +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueSVMMemFillARM(cl_command_queue command_queue, + void * svm_ptr, + const void * pattern, + size_t pattern_size, + size_t size, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) CL_EXT_SUFFIX__VERSION_1_2; + +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueSVMMapARM(cl_command_queue command_queue, + cl_bool blocking_map, + cl_map_flags flags, + void * svm_ptr, + size_t size, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) CL_EXT_SUFFIX__VERSION_1_2; + +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueSVMUnmapARM(cl_command_queue command_queue, + void * svm_ptr, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) CL_EXT_SUFFIX__VERSION_1_2; + +extern CL_API_ENTRY cl_int CL_API_CALL +clSetKernelArgSVMPointerARM(cl_kernel kernel, + cl_uint arg_index, + const void * arg_value) CL_EXT_SUFFIX__VERSION_1_2; + +extern CL_API_ENTRY cl_int CL_API_CALL +clSetKernelExecInfoARM(cl_kernel kernel, + cl_kernel_exec_info_arm param_name, + size_t param_value_size, + const void * param_value) CL_EXT_SUFFIX__VERSION_1_2; + +/******************************** + * cl_arm_get_core_id extension * + ********************************/ + +#ifdef CL_VERSION_1_2 + +#define cl_arm_get_core_id 1 + +/* Device info property for bitfield of cores present */ +#define CL_DEVICE_COMPUTE_UNITS_BITFIELD_ARM 0x40BF + +#endif /* CL_VERSION_1_2 */ + +/********************************* +* cl_arm_job_slot_selection +*********************************/ + +#define cl_arm_job_slot_selection 1 + +/* cl_device_info */ +#define CL_DEVICE_JOB_SLOTS_ARM 0x41E0 + +/* cl_command_queue_properties */ +#define CL_QUEUE_JOB_SLOT_ARM 0x41E1 + +/********************************* +* cl_arm_scheduling_controls +*********************************/ + +#define cl_arm_scheduling_controls 1 + +/* cl_device_info */ +#define CL_DEVICE_SCHEDULING_CONTROLS_CAPABILITIES_ARM 0x41E4 + +#define CL_DEVICE_SCHEDULING_KERNEL_BATCHING_ARM (1 << 0) +#define CL_DEVICE_SCHEDULING_WORKGROUP_BATCH_SIZE_ARM (1 << 1) +#define CL_DEVICE_SCHEDULING_WORKGROUP_BATCH_SIZE_MODIFIER_ARM (1 << 2) + +/* cl_kernel_info */ +#define CL_KERNEL_EXEC_INFO_WORKGROUP_BATCH_SIZE_ARM 0x41E5 +#define CL_KERNEL_EXEC_INFO_WORKGROUP_BATCH_SIZE_MODIFIER_ARM 0x41E6 + +/* cl_queue_properties */ +#define CL_QUEUE_KERNEL_BATCHING_ARM 0x41E7 + +#ifdef __cplusplus +} +#endif + + +#endif /* __CL_EXT_H */ diff --git a/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/cl_ext_intel.h b/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/cl_ext_intel.h new file mode 100755 index 0000000000000000000000000000000000000000..e43a0c6915d6300a3f6efb6818f8c611c077aa86 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/cl_ext_intel.h @@ -0,0 +1,712 @@ +/******************************************************************************* + * Copyright (c) 2008-2020 The Khronos Group Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * + ******************************************************************************/ +/*****************************************************************************\ + +Copyright (c) 2013-2020 Intel Corporation All Rights Reserved. + +THESE MATERIALS ARE PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL INTEL OR ITS +CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, +EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, +PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR +PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY +OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY OR TORT (INCLUDING +NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THESE +MATERIALS, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +File Name: cl_ext_intel.h + +Abstract: + +Notes: + +\*****************************************************************************/ + +#ifndef __CL_EXT_INTEL_H +#define __CL_EXT_INTEL_H + +#include +#include + +#ifdef __cplusplus +extern "C" { +#endif + +/*************************************** +* cl_intel_thread_local_exec extension * +****************************************/ + +#define cl_intel_thread_local_exec 1 + +#define CL_QUEUE_THREAD_LOCAL_EXEC_ENABLE_INTEL (((cl_bitfield)1) << 31) + +/*********************************************** +* cl_intel_device_partition_by_names extension * +************************************************/ + +#define cl_intel_device_partition_by_names 1 + +#define CL_DEVICE_PARTITION_BY_NAMES_INTEL 0x4052 +#define CL_PARTITION_BY_NAMES_LIST_END_INTEL -1 + +/************************************************ +* cl_intel_accelerator extension * +* cl_intel_motion_estimation extension * +* cl_intel_advanced_motion_estimation extension * +*************************************************/ + +#define cl_intel_accelerator 1 +#define cl_intel_motion_estimation 1 +#define cl_intel_advanced_motion_estimation 1 + +typedef struct _cl_accelerator_intel* cl_accelerator_intel; +typedef cl_uint cl_accelerator_type_intel; +typedef cl_uint cl_accelerator_info_intel; + +typedef struct _cl_motion_estimation_desc_intel { + cl_uint mb_block_type; + cl_uint subpixel_mode; + cl_uint sad_adjust_mode; + cl_uint search_path_type; +} cl_motion_estimation_desc_intel; + +/* error codes */ +#define CL_INVALID_ACCELERATOR_INTEL -1094 +#define CL_INVALID_ACCELERATOR_TYPE_INTEL -1095 +#define CL_INVALID_ACCELERATOR_DESCRIPTOR_INTEL -1096 +#define CL_ACCELERATOR_TYPE_NOT_SUPPORTED_INTEL -1097 + +/* cl_accelerator_type_intel */ +#define CL_ACCELERATOR_TYPE_MOTION_ESTIMATION_INTEL 0x0 + +/* cl_accelerator_info_intel */ +#define CL_ACCELERATOR_DESCRIPTOR_INTEL 0x4090 +#define CL_ACCELERATOR_REFERENCE_COUNT_INTEL 0x4091 +#define CL_ACCELERATOR_CONTEXT_INTEL 0x4092 +#define CL_ACCELERATOR_TYPE_INTEL 0x4093 + +/* cl_motion_detect_desc_intel flags */ +#define CL_ME_MB_TYPE_16x16_INTEL 0x0 +#define CL_ME_MB_TYPE_8x8_INTEL 0x1 +#define CL_ME_MB_TYPE_4x4_INTEL 0x2 + +#define CL_ME_SUBPIXEL_MODE_INTEGER_INTEL 0x0 +#define CL_ME_SUBPIXEL_MODE_HPEL_INTEL 0x1 +#define CL_ME_SUBPIXEL_MODE_QPEL_INTEL 0x2 + +#define CL_ME_SAD_ADJUST_MODE_NONE_INTEL 0x0 +#define CL_ME_SAD_ADJUST_MODE_HAAR_INTEL 0x1 + +#define CL_ME_SEARCH_PATH_RADIUS_2_2_INTEL 0x0 +#define CL_ME_SEARCH_PATH_RADIUS_4_4_INTEL 0x1 +#define CL_ME_SEARCH_PATH_RADIUS_16_12_INTEL 0x5 + +#define CL_ME_SKIP_BLOCK_TYPE_16x16_INTEL 0x0 +#define CL_ME_CHROMA_INTRA_PREDICT_ENABLED_INTEL 0x1 +#define CL_ME_LUMA_INTRA_PREDICT_ENABLED_INTEL 0x2 +#define CL_ME_SKIP_BLOCK_TYPE_8x8_INTEL 0x4 + +#define CL_ME_FORWARD_INPUT_MODE_INTEL 0x1 +#define CL_ME_BACKWARD_INPUT_MODE_INTEL 0x2 +#define CL_ME_BIDIRECTION_INPUT_MODE_INTEL 0x3 + +#define CL_ME_BIDIR_WEIGHT_QUARTER_INTEL 16 +#define CL_ME_BIDIR_WEIGHT_THIRD_INTEL 21 +#define CL_ME_BIDIR_WEIGHT_HALF_INTEL 32 +#define CL_ME_BIDIR_WEIGHT_TWO_THIRD_INTEL 43 +#define CL_ME_BIDIR_WEIGHT_THREE_QUARTER_INTEL 48 + +#define CL_ME_COST_PENALTY_NONE_INTEL 0x0 +#define CL_ME_COST_PENALTY_LOW_INTEL 0x1 +#define CL_ME_COST_PENALTY_NORMAL_INTEL 0x2 +#define CL_ME_COST_PENALTY_HIGH_INTEL 0x3 + +#define CL_ME_COST_PRECISION_QPEL_INTEL 0x0 +#define CL_ME_COST_PRECISION_HPEL_INTEL 0x1 +#define CL_ME_COST_PRECISION_PEL_INTEL 0x2 +#define CL_ME_COST_PRECISION_DPEL_INTEL 0x3 + +#define CL_ME_LUMA_PREDICTOR_MODE_VERTICAL_INTEL 0x0 +#define CL_ME_LUMA_PREDICTOR_MODE_HORIZONTAL_INTEL 0x1 +#define CL_ME_LUMA_PREDICTOR_MODE_DC_INTEL 0x2 +#define CL_ME_LUMA_PREDICTOR_MODE_DIAGONAL_DOWN_LEFT_INTEL 0x3 + +#define CL_ME_LUMA_PREDICTOR_MODE_DIAGONAL_DOWN_RIGHT_INTEL 0x4 +#define CL_ME_LUMA_PREDICTOR_MODE_PLANE_INTEL 0x4 +#define CL_ME_LUMA_PREDICTOR_MODE_VERTICAL_RIGHT_INTEL 0x5 +#define CL_ME_LUMA_PREDICTOR_MODE_HORIZONTAL_DOWN_INTEL 0x6 +#define CL_ME_LUMA_PREDICTOR_MODE_VERTICAL_LEFT_INTEL 0x7 +#define CL_ME_LUMA_PREDICTOR_MODE_HORIZONTAL_UP_INTEL 0x8 + +#define CL_ME_CHROMA_PREDICTOR_MODE_DC_INTEL 0x0 +#define CL_ME_CHROMA_PREDICTOR_MODE_HORIZONTAL_INTEL 0x1 +#define CL_ME_CHROMA_PREDICTOR_MODE_VERTICAL_INTEL 0x2 +#define CL_ME_CHROMA_PREDICTOR_MODE_PLANE_INTEL 0x3 + +/* cl_device_info */ +#define CL_DEVICE_ME_VERSION_INTEL 0x407E + +#define CL_ME_VERSION_LEGACY_INTEL 0x0 +#define CL_ME_VERSION_ADVANCED_VER_1_INTEL 0x1 +#define CL_ME_VERSION_ADVANCED_VER_2_INTEL 0x2 + +extern CL_API_ENTRY cl_accelerator_intel CL_API_CALL +clCreateAcceleratorINTEL( + cl_context context, + cl_accelerator_type_intel accelerator_type, + size_t descriptor_size, + const void* descriptor, + cl_int* errcode_ret) CL_EXT_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_accelerator_intel (CL_API_CALL *clCreateAcceleratorINTEL_fn)( + cl_context context, + cl_accelerator_type_intel accelerator_type, + size_t descriptor_size, + const void* descriptor, + cl_int* errcode_ret) CL_EXT_SUFFIX__VERSION_1_2; + +extern CL_API_ENTRY cl_int CL_API_CALL +clGetAcceleratorInfoINTEL( + cl_accelerator_intel accelerator, + cl_accelerator_info_intel param_name, + size_t param_value_size, + void* param_value, + size_t* param_value_size_ret) CL_EXT_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_int (CL_API_CALL *clGetAcceleratorInfoINTEL_fn)( + cl_accelerator_intel accelerator, + cl_accelerator_info_intel param_name, + size_t param_value_size, + void* param_value, + size_t* param_value_size_ret) CL_EXT_SUFFIX__VERSION_1_2; + +extern CL_API_ENTRY cl_int CL_API_CALL +clRetainAcceleratorINTEL( + cl_accelerator_intel accelerator) CL_EXT_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_int (CL_API_CALL *clRetainAcceleratorINTEL_fn)( + cl_accelerator_intel accelerator) CL_EXT_SUFFIX__VERSION_1_2; + +extern CL_API_ENTRY cl_int CL_API_CALL +clReleaseAcceleratorINTEL( + cl_accelerator_intel accelerator) CL_EXT_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_int (CL_API_CALL *clReleaseAcceleratorINTEL_fn)( + cl_accelerator_intel accelerator) CL_EXT_SUFFIX__VERSION_1_2; + +/****************************************** +* cl_intel_simultaneous_sharing extension * +*******************************************/ + +#define cl_intel_simultaneous_sharing 1 + +#define CL_DEVICE_SIMULTANEOUS_INTEROPS_INTEL 0x4104 +#define CL_DEVICE_NUM_SIMULTANEOUS_INTEROPS_INTEL 0x4105 + +/*********************************** +* cl_intel_egl_image_yuv extension * +************************************/ + +#define cl_intel_egl_image_yuv 1 + +#define CL_EGL_YUV_PLANE_INTEL 0x4107 + +/******************************** +* cl_intel_packed_yuv extension * +*********************************/ + +#define cl_intel_packed_yuv 1 + +#define CL_YUYV_INTEL 0x4076 +#define CL_UYVY_INTEL 0x4077 +#define CL_YVYU_INTEL 0x4078 +#define CL_VYUY_INTEL 0x4079 + +/******************************************** +* cl_intel_required_subgroup_size extension * +*********************************************/ + +#define cl_intel_required_subgroup_size 1 + +#define CL_DEVICE_SUB_GROUP_SIZES_INTEL 0x4108 +#define CL_KERNEL_SPILL_MEM_SIZE_INTEL 0x4109 +#define CL_KERNEL_COMPILE_SUB_GROUP_SIZE_INTEL 0x410A + +/**************************************** +* cl_intel_driver_diagnostics extension * +*****************************************/ + +#define cl_intel_driver_diagnostics 1 + +typedef cl_uint cl_diagnostics_verbose_level; + +#define CL_CONTEXT_SHOW_DIAGNOSTICS_INTEL 0x4106 + +#define CL_CONTEXT_DIAGNOSTICS_LEVEL_ALL_INTEL ( 0xff ) +#define CL_CONTEXT_DIAGNOSTICS_LEVEL_GOOD_INTEL ( 1 ) +#define CL_CONTEXT_DIAGNOSTICS_LEVEL_BAD_INTEL ( 1 << 1 ) +#define CL_CONTEXT_DIAGNOSTICS_LEVEL_NEUTRAL_INTEL ( 1 << 2 ) + +/******************************** +* cl_intel_planar_yuv extension * +*********************************/ + +#define CL_NV12_INTEL 0x410E + +#define CL_MEM_NO_ACCESS_INTEL ( 1 << 24 ) +#define CL_MEM_ACCESS_FLAGS_UNRESTRICTED_INTEL ( 1 << 25 ) + +#define CL_DEVICE_PLANAR_YUV_MAX_WIDTH_INTEL 0x417E +#define CL_DEVICE_PLANAR_YUV_MAX_HEIGHT_INTEL 0x417F + +/******************************************************* +* cl_intel_device_side_avc_motion_estimation extension * +********************************************************/ + +#define CL_DEVICE_AVC_ME_VERSION_INTEL 0x410B +#define CL_DEVICE_AVC_ME_SUPPORTS_TEXTURE_SAMPLER_USE_INTEL 0x410C +#define CL_DEVICE_AVC_ME_SUPPORTS_PREEMPTION_INTEL 0x410D + +#define CL_AVC_ME_VERSION_0_INTEL 0x0 /* No support. */ +#define CL_AVC_ME_VERSION_1_INTEL 0x1 /* First supported version. */ + +#define CL_AVC_ME_MAJOR_16x16_INTEL 0x0 +#define CL_AVC_ME_MAJOR_16x8_INTEL 0x1 +#define CL_AVC_ME_MAJOR_8x16_INTEL 0x2 +#define CL_AVC_ME_MAJOR_8x8_INTEL 0x3 + +#define CL_AVC_ME_MINOR_8x8_INTEL 0x0 +#define CL_AVC_ME_MINOR_8x4_INTEL 0x1 +#define CL_AVC_ME_MINOR_4x8_INTEL 0x2 +#define CL_AVC_ME_MINOR_4x4_INTEL 0x3 + +#define CL_AVC_ME_MAJOR_FORWARD_INTEL 0x0 +#define CL_AVC_ME_MAJOR_BACKWARD_INTEL 0x1 +#define CL_AVC_ME_MAJOR_BIDIRECTIONAL_INTEL 0x2 + +#define CL_AVC_ME_PARTITION_MASK_ALL_INTEL 0x0 +#define CL_AVC_ME_PARTITION_MASK_16x16_INTEL 0x7E +#define CL_AVC_ME_PARTITION_MASK_16x8_INTEL 0x7D +#define CL_AVC_ME_PARTITION_MASK_8x16_INTEL 0x7B +#define CL_AVC_ME_PARTITION_MASK_8x8_INTEL 0x77 +#define CL_AVC_ME_PARTITION_MASK_8x4_INTEL 0x6F +#define CL_AVC_ME_PARTITION_MASK_4x8_INTEL 0x5F +#define CL_AVC_ME_PARTITION_MASK_4x4_INTEL 0x3F + +#define CL_AVC_ME_SEARCH_WINDOW_EXHAUSTIVE_INTEL 0x0 +#define CL_AVC_ME_SEARCH_WINDOW_SMALL_INTEL 0x1 +#define CL_AVC_ME_SEARCH_WINDOW_TINY_INTEL 0x2 +#define CL_AVC_ME_SEARCH_WINDOW_EXTRA_TINY_INTEL 0x3 +#define CL_AVC_ME_SEARCH_WINDOW_DIAMOND_INTEL 0x4 +#define CL_AVC_ME_SEARCH_WINDOW_LARGE_DIAMOND_INTEL 0x5 +#define CL_AVC_ME_SEARCH_WINDOW_RESERVED0_INTEL 0x6 +#define CL_AVC_ME_SEARCH_WINDOW_RESERVED1_INTEL 0x7 +#define CL_AVC_ME_SEARCH_WINDOW_CUSTOM_INTEL 0x8 +#define CL_AVC_ME_SEARCH_WINDOW_16x12_RADIUS_INTEL 0x9 +#define CL_AVC_ME_SEARCH_WINDOW_4x4_RADIUS_INTEL 0x2 +#define CL_AVC_ME_SEARCH_WINDOW_2x2_RADIUS_INTEL 0xa + +#define CL_AVC_ME_SAD_ADJUST_MODE_NONE_INTEL 0x0 +#define CL_AVC_ME_SAD_ADJUST_MODE_HAAR_INTEL 0x2 + +#define CL_AVC_ME_SUBPIXEL_MODE_INTEGER_INTEL 0x0 +#define CL_AVC_ME_SUBPIXEL_MODE_HPEL_INTEL 0x1 +#define CL_AVC_ME_SUBPIXEL_MODE_QPEL_INTEL 0x3 + +#define CL_AVC_ME_COST_PRECISION_QPEL_INTEL 0x0 +#define CL_AVC_ME_COST_PRECISION_HPEL_INTEL 0x1 +#define CL_AVC_ME_COST_PRECISION_PEL_INTEL 0x2 +#define CL_AVC_ME_COST_PRECISION_DPEL_INTEL 0x3 + +#define CL_AVC_ME_BIDIR_WEIGHT_QUARTER_INTEL 0x10 +#define CL_AVC_ME_BIDIR_WEIGHT_THIRD_INTEL 0x15 +#define CL_AVC_ME_BIDIR_WEIGHT_HALF_INTEL 0x20 +#define CL_AVC_ME_BIDIR_WEIGHT_TWO_THIRD_INTEL 0x2B +#define CL_AVC_ME_BIDIR_WEIGHT_THREE_QUARTER_INTEL 0x30 + +#define CL_AVC_ME_BORDER_REACHED_LEFT_INTEL 0x0 +#define CL_AVC_ME_BORDER_REACHED_RIGHT_INTEL 0x2 +#define CL_AVC_ME_BORDER_REACHED_TOP_INTEL 0x4 +#define CL_AVC_ME_BORDER_REACHED_BOTTOM_INTEL 0x8 + +#define CL_AVC_ME_SKIP_BLOCK_PARTITION_16x16_INTEL 0x0 +#define CL_AVC_ME_SKIP_BLOCK_PARTITION_8x8_INTEL 0x4000 + +#define CL_AVC_ME_SKIP_BLOCK_16x16_FORWARD_ENABLE_INTEL ( 0x1 << 24 ) +#define CL_AVC_ME_SKIP_BLOCK_16x16_BACKWARD_ENABLE_INTEL ( 0x2 << 24 ) +#define CL_AVC_ME_SKIP_BLOCK_16x16_DUAL_ENABLE_INTEL ( 0x3 << 24 ) +#define CL_AVC_ME_SKIP_BLOCK_8x8_FORWARD_ENABLE_INTEL ( 0x55 << 24 ) +#define CL_AVC_ME_SKIP_BLOCK_8x8_BACKWARD_ENABLE_INTEL ( 0xAA << 24 ) +#define CL_AVC_ME_SKIP_BLOCK_8x8_DUAL_ENABLE_INTEL ( 0xFF << 24 ) +#define CL_AVC_ME_SKIP_BLOCK_8x8_0_FORWARD_ENABLE_INTEL ( 0x1 << 24 ) +#define CL_AVC_ME_SKIP_BLOCK_8x8_0_BACKWARD_ENABLE_INTEL ( 0x2 << 24 ) +#define CL_AVC_ME_SKIP_BLOCK_8x8_1_FORWARD_ENABLE_INTEL ( 0x1 << 26 ) +#define CL_AVC_ME_SKIP_BLOCK_8x8_1_BACKWARD_ENABLE_INTEL ( 0x2 << 26 ) +#define CL_AVC_ME_SKIP_BLOCK_8x8_2_FORWARD_ENABLE_INTEL ( 0x1 << 28 ) +#define CL_AVC_ME_SKIP_BLOCK_8x8_2_BACKWARD_ENABLE_INTEL ( 0x2 << 28 ) +#define CL_AVC_ME_SKIP_BLOCK_8x8_3_FORWARD_ENABLE_INTEL ( 0x1 << 30 ) +#define CL_AVC_ME_SKIP_BLOCK_8x8_3_BACKWARD_ENABLE_INTEL ( 0x2 << 30 ) + +#define CL_AVC_ME_BLOCK_BASED_SKIP_4x4_INTEL 0x00 +#define CL_AVC_ME_BLOCK_BASED_SKIP_8x8_INTEL 0x80 + +#define CL_AVC_ME_INTRA_16x16_INTEL 0x0 +#define CL_AVC_ME_INTRA_8x8_INTEL 0x1 +#define CL_AVC_ME_INTRA_4x4_INTEL 0x2 + +#define CL_AVC_ME_INTRA_LUMA_PARTITION_MASK_16x16_INTEL 0x6 +#define CL_AVC_ME_INTRA_LUMA_PARTITION_MASK_8x8_INTEL 0x5 +#define CL_AVC_ME_INTRA_LUMA_PARTITION_MASK_4x4_INTEL 0x3 + +#define CL_AVC_ME_INTRA_NEIGHBOR_LEFT_MASK_ENABLE_INTEL 0x60 +#define CL_AVC_ME_INTRA_NEIGHBOR_UPPER_MASK_ENABLE_INTEL 0x10 +#define CL_AVC_ME_INTRA_NEIGHBOR_UPPER_RIGHT_MASK_ENABLE_INTEL 0x8 +#define CL_AVC_ME_INTRA_NEIGHBOR_UPPER_LEFT_MASK_ENABLE_INTEL 0x4 + +#define CL_AVC_ME_LUMA_PREDICTOR_MODE_VERTICAL_INTEL 0x0 +#define CL_AVC_ME_LUMA_PREDICTOR_MODE_HORIZONTAL_INTEL 0x1 +#define CL_AVC_ME_LUMA_PREDICTOR_MODE_DC_INTEL 0x2 +#define CL_AVC_ME_LUMA_PREDICTOR_MODE_DIAGONAL_DOWN_LEFT_INTEL 0x3 +#define CL_AVC_ME_LUMA_PREDICTOR_MODE_DIAGONAL_DOWN_RIGHT_INTEL 0x4 +#define CL_AVC_ME_LUMA_PREDICTOR_MODE_PLANE_INTEL 0x4 +#define CL_AVC_ME_LUMA_PREDICTOR_MODE_VERTICAL_RIGHT_INTEL 0x5 +#define CL_AVC_ME_LUMA_PREDICTOR_MODE_HORIZONTAL_DOWN_INTEL 0x6 +#define CL_AVC_ME_LUMA_PREDICTOR_MODE_VERTICAL_LEFT_INTEL 0x7 +#define CL_AVC_ME_LUMA_PREDICTOR_MODE_HORIZONTAL_UP_INTEL 0x8 +#define CL_AVC_ME_CHROMA_PREDICTOR_MODE_DC_INTEL 0x0 +#define CL_AVC_ME_CHROMA_PREDICTOR_MODE_HORIZONTAL_INTEL 0x1 +#define CL_AVC_ME_CHROMA_PREDICTOR_MODE_VERTICAL_INTEL 0x2 +#define CL_AVC_ME_CHROMA_PREDICTOR_MODE_PLANE_INTEL 0x3 + +#define CL_AVC_ME_FRAME_FORWARD_INTEL 0x1 +#define CL_AVC_ME_FRAME_BACKWARD_INTEL 0x2 +#define CL_AVC_ME_FRAME_DUAL_INTEL 0x3 + +#define CL_AVC_ME_SLICE_TYPE_PRED_INTEL 0x0 +#define CL_AVC_ME_SLICE_TYPE_BPRED_INTEL 0x1 +#define CL_AVC_ME_SLICE_TYPE_INTRA_INTEL 0x2 + +#define CL_AVC_ME_INTERLACED_SCAN_TOP_FIELD_INTEL 0x0 +#define CL_AVC_ME_INTERLACED_SCAN_BOTTOM_FIELD_INTEL 0x1 + +/******************************************* +* cl_intel_unified_shared_memory extension * +********************************************/ + +/* These APIs are in sync with Revision O of the cl_intel_unified_shared_memory spec! */ + +#define cl_intel_unified_shared_memory 1 + +/* cl_device_info */ +#define CL_DEVICE_HOST_MEM_CAPABILITIES_INTEL 0x4190 +#define CL_DEVICE_DEVICE_MEM_CAPABILITIES_INTEL 0x4191 +#define CL_DEVICE_SINGLE_DEVICE_SHARED_MEM_CAPABILITIES_INTEL 0x4192 +#define CL_DEVICE_CROSS_DEVICE_SHARED_MEM_CAPABILITIES_INTEL 0x4193 +#define CL_DEVICE_SHARED_SYSTEM_MEM_CAPABILITIES_INTEL 0x4194 + +typedef cl_bitfield cl_device_unified_shared_memory_capabilities_intel; + +/* cl_device_unified_shared_memory_capabilities_intel - bitfield */ +#define CL_UNIFIED_SHARED_MEMORY_ACCESS_INTEL (1 << 0) +#define CL_UNIFIED_SHARED_MEMORY_ATOMIC_ACCESS_INTEL (1 << 1) +#define CL_UNIFIED_SHARED_MEMORY_CONCURRENT_ACCESS_INTEL (1 << 2) +#define CL_UNIFIED_SHARED_MEMORY_CONCURRENT_ATOMIC_ACCESS_INTEL (1 << 3) + +typedef cl_properties cl_mem_properties_intel; + +/* cl_mem_properties_intel */ +#define CL_MEM_ALLOC_FLAGS_INTEL 0x4195 + +typedef cl_bitfield cl_mem_alloc_flags_intel; + +/* cl_mem_alloc_flags_intel - bitfield */ +#define CL_MEM_ALLOC_WRITE_COMBINED_INTEL (1 << 0) + +typedef cl_uint cl_mem_info_intel; + +/* cl_mem_alloc_info_intel */ +#define CL_MEM_ALLOC_TYPE_INTEL 0x419A +#define CL_MEM_ALLOC_BASE_PTR_INTEL 0x419B +#define CL_MEM_ALLOC_SIZE_INTEL 0x419C +#define CL_MEM_ALLOC_DEVICE_INTEL 0x419D +/* Enum values 0x419E-0x419F are reserved for future queries. */ + +typedef cl_uint cl_unified_shared_memory_type_intel; + +/* cl_unified_shared_memory_type_intel */ +#define CL_MEM_TYPE_UNKNOWN_INTEL 0x4196 +#define CL_MEM_TYPE_HOST_INTEL 0x4197 +#define CL_MEM_TYPE_DEVICE_INTEL 0x4198 +#define CL_MEM_TYPE_SHARED_INTEL 0x4199 + +typedef cl_uint cl_mem_advice_intel; + +/* cl_mem_advice_intel */ +/* Enum values 0x4208-0x420F are reserved for future memory advices. */ + +/* cl_kernel_exec_info */ +#define CL_KERNEL_EXEC_INFO_INDIRECT_HOST_ACCESS_INTEL 0x4200 +#define CL_KERNEL_EXEC_INFO_INDIRECT_DEVICE_ACCESS_INTEL 0x4201 +#define CL_KERNEL_EXEC_INFO_INDIRECT_SHARED_ACCESS_INTEL 0x4202 +#define CL_KERNEL_EXEC_INFO_USM_PTRS_INTEL 0x4203 + +/* cl_command_type */ +#define CL_COMMAND_MEMFILL_INTEL 0x4204 +#define CL_COMMAND_MEMCPY_INTEL 0x4205 +#define CL_COMMAND_MIGRATEMEM_INTEL 0x4206 +#define CL_COMMAND_MEMADVISE_INTEL 0x4207 + +extern CL_API_ENTRY void* CL_API_CALL +clHostMemAllocINTEL( + cl_context context, + const cl_mem_properties_intel* properties, + size_t size, + cl_uint alignment, + cl_int* errcode_ret); + +typedef CL_API_ENTRY void* (CL_API_CALL * +clHostMemAllocINTEL_fn)( + cl_context context, + const cl_mem_properties_intel* properties, + size_t size, + cl_uint alignment, + cl_int* errcode_ret); + +extern CL_API_ENTRY void* CL_API_CALL +clDeviceMemAllocINTEL( + cl_context context, + cl_device_id device, + const cl_mem_properties_intel* properties, + size_t size, + cl_uint alignment, + cl_int* errcode_ret); + +typedef CL_API_ENTRY void* (CL_API_CALL * +clDeviceMemAllocINTEL_fn)( + cl_context context, + cl_device_id device, + const cl_mem_properties_intel* properties, + size_t size, + cl_uint alignment, + cl_int* errcode_ret); + +extern CL_API_ENTRY void* CL_API_CALL +clSharedMemAllocINTEL( + cl_context context, + cl_device_id device, + const cl_mem_properties_intel* properties, + size_t size, + cl_uint alignment, + cl_int* errcode_ret); + +typedef CL_API_ENTRY void* (CL_API_CALL * +clSharedMemAllocINTEL_fn)( + cl_context context, + cl_device_id device, + const cl_mem_properties_intel* properties, + size_t size, + cl_uint alignment, + cl_int* errcode_ret); + +extern CL_API_ENTRY cl_int CL_API_CALL +clMemFreeINTEL( + cl_context context, + void* ptr); + +typedef CL_API_ENTRY cl_int (CL_API_CALL * +clMemFreeINTEL_fn)( + cl_context context, + void* ptr); + +extern CL_API_ENTRY cl_int CL_API_CALL +clGetMemAllocInfoINTEL( + cl_context context, + const void* ptr, + cl_mem_info_intel param_name, + size_t param_value_size, + void* param_value, + size_t* param_value_size_ret); + +typedef CL_API_ENTRY cl_int (CL_API_CALL * +clGetMemAllocInfoINTEL_fn)( + cl_context context, + const void* ptr, + cl_mem_info_intel param_name, + size_t param_value_size, + void* param_value, + size_t* param_value_size_ret); + +extern CL_API_ENTRY cl_int CL_API_CALL +clSetKernelArgMemPointerINTEL( + cl_kernel kernel, + cl_uint arg_index, + const void* arg_value); + +typedef CL_API_ENTRY cl_int (CL_API_CALL * +clSetKernelArgMemPointerINTEL_fn)( + cl_kernel kernel, + cl_uint arg_index, + const void* arg_value); + +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueMemsetINTEL( /* Deprecated */ + cl_command_queue command_queue, + void* dst_ptr, + cl_int value, + size_t size, + cl_uint num_events_in_wait_list, + const cl_event* event_wait_list, + cl_event* event); + +typedef CL_API_ENTRY cl_int (CL_API_CALL * +clEnqueueMemsetINTEL_fn)( /* Deprecated */ + cl_command_queue command_queue, + void* dst_ptr, + cl_int value, + size_t size, + cl_uint num_events_in_wait_list, + const cl_event* event_wait_list, + cl_event* event); + +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueMemFillINTEL( + cl_command_queue command_queue, + void* dst_ptr, + const void* pattern, + size_t pattern_size, + size_t size, + cl_uint num_events_in_wait_list, + const cl_event* event_wait_list, + cl_event* event); + +typedef CL_API_ENTRY cl_int (CL_API_CALL * +clEnqueueMemFillINTEL_fn)( + cl_command_queue command_queue, + void* dst_ptr, + const void* pattern, + size_t pattern_size, + size_t size, + cl_uint num_events_in_wait_list, + const cl_event* event_wait_list, + cl_event* event); + +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueMemcpyINTEL( + cl_command_queue command_queue, + cl_bool blocking, + void* dst_ptr, + const void* src_ptr, + size_t size, + cl_uint num_events_in_wait_list, + const cl_event* event_wait_list, + cl_event* event); + +typedef CL_API_ENTRY cl_int (CL_API_CALL * +clEnqueueMemcpyINTEL_fn)( + cl_command_queue command_queue, + cl_bool blocking, + void* dst_ptr, + const void* src_ptr, + size_t size, + cl_uint num_events_in_wait_list, + const cl_event* event_wait_list, + cl_event* event); + +#ifdef CL_VERSION_1_2 + +/* Because these APIs use cl_mem_migration_flags, they require + OpenCL 1.2: */ + +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueMigrateMemINTEL( + cl_command_queue command_queue, + const void* ptr, + size_t size, + cl_mem_migration_flags flags, + cl_uint num_events_in_wait_list, + const cl_event* event_wait_list, + cl_event* event); + +typedef CL_API_ENTRY cl_int (CL_API_CALL * +clEnqueueMigrateMemINTEL_fn)( + cl_command_queue command_queue, + const void* ptr, + size_t size, + cl_mem_migration_flags flags, + cl_uint num_events_in_wait_list, + const cl_event* event_wait_list, + cl_event* event); + +#endif + +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueMemAdviseINTEL( + cl_command_queue command_queue, + const void* ptr, + size_t size, + cl_mem_advice_intel advice, + cl_uint num_events_in_wait_list, + const cl_event* event_wait_list, + cl_event* event); + +typedef CL_API_ENTRY cl_int (CL_API_CALL * +clEnqueueMemAdviseINTEL_fn)( + cl_command_queue command_queue, + const void* ptr, + size_t size, + cl_mem_advice_intel advice, + cl_uint num_events_in_wait_list, + const cl_event* event_wait_list, + cl_event* event); + +/*************************************************** +* cl_intel_create_buffer_with_properties extension * +****************************************************/ + +#define cl_intel_create_buffer_with_properties 1 + +extern CL_API_ENTRY cl_mem CL_API_CALL +clCreateBufferWithPropertiesINTEL( + cl_context context, + const cl_mem_properties_intel* properties, + cl_mem_flags flags, + size_t size, + void * host_ptr, + cl_int * errcode_ret) CL_EXT_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_mem (CL_API_CALL * +clCreateBufferWithPropertiesINTEL_fn)( + cl_context context, + const cl_mem_properties_intel* properties, + cl_mem_flags flags, + size_t size, + void * host_ptr, + cl_int * errcode_ret) CL_EXT_SUFFIX__VERSION_1_0; + +/****************************************** +* cl_intel_mem_channel_property extension * +*******************************************/ + +#define CL_MEM_CHANNEL_INTEL 0x4213 + +#ifdef __cplusplus +} +#endif + +#endif /* __CL_EXT_INTEL_H */ diff --git a/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/cl_gl.h b/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/cl_gl.h new file mode 100755 index 0000000000000000000000000000000000000000..b587f02a986e9bf4af93f286beefa3d6ba64daa2 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/cl_gl.h @@ -0,0 +1,159 @@ +/******************************************************************************* + * Copyright (c) 2008-2020 The Khronos Group Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + ******************************************************************************/ + +#ifndef __OPENCL_CL_GL_H +#define __OPENCL_CL_GL_H + +#include + +#ifdef __cplusplus +extern "C" { +#endif + +typedef cl_uint cl_gl_object_type; +typedef cl_uint cl_gl_texture_info; +typedef cl_uint cl_gl_platform_info; +typedef struct __GLsync *cl_GLsync; + +/* cl_gl_object_type = 0x2000 - 0x200F enum values are currently taken */ +#define CL_GL_OBJECT_BUFFER 0x2000 +#define CL_GL_OBJECT_TEXTURE2D 0x2001 +#define CL_GL_OBJECT_TEXTURE3D 0x2002 +#define CL_GL_OBJECT_RENDERBUFFER 0x2003 +#ifdef CL_VERSION_1_2 +#define CL_GL_OBJECT_TEXTURE2D_ARRAY 0x200E +#define CL_GL_OBJECT_TEXTURE1D 0x200F +#define CL_GL_OBJECT_TEXTURE1D_ARRAY 0x2010 +#define CL_GL_OBJECT_TEXTURE_BUFFER 0x2011 +#endif + +/* cl_gl_texture_info */ +#define CL_GL_TEXTURE_TARGET 0x2004 +#define CL_GL_MIPMAP_LEVEL 0x2005 +#ifdef CL_VERSION_1_2 +#define CL_GL_NUM_SAMPLES 0x2012 +#endif + + +extern CL_API_ENTRY cl_mem CL_API_CALL +clCreateFromGLBuffer(cl_context context, + cl_mem_flags flags, + cl_GLuint bufobj, + cl_int * errcode_ret) CL_API_SUFFIX__VERSION_1_0; + +#ifdef CL_VERSION_1_2 + +extern CL_API_ENTRY cl_mem CL_API_CALL +clCreateFromGLTexture(cl_context context, + cl_mem_flags flags, + cl_GLenum target, + cl_GLint miplevel, + cl_GLuint texture, + cl_int * errcode_ret) CL_API_SUFFIX__VERSION_1_2; + +#endif + +extern CL_API_ENTRY cl_mem CL_API_CALL +clCreateFromGLRenderbuffer(cl_context context, + cl_mem_flags flags, + cl_GLuint renderbuffer, + cl_int * errcode_ret) CL_API_SUFFIX__VERSION_1_0; + +extern CL_API_ENTRY cl_int CL_API_CALL +clGetGLObjectInfo(cl_mem memobj, + cl_gl_object_type * gl_object_type, + cl_GLuint * gl_object_name) CL_API_SUFFIX__VERSION_1_0; + +extern CL_API_ENTRY cl_int CL_API_CALL +clGetGLTextureInfo(cl_mem memobj, + cl_gl_texture_info param_name, + size_t param_value_size, + void * param_value, + size_t * param_value_size_ret) CL_API_SUFFIX__VERSION_1_0; + +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueAcquireGLObjects(cl_command_queue command_queue, + cl_uint num_objects, + const cl_mem * mem_objects, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) CL_API_SUFFIX__VERSION_1_0; + +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueReleaseGLObjects(cl_command_queue command_queue, + cl_uint num_objects, + const cl_mem * mem_objects, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) CL_API_SUFFIX__VERSION_1_0; + + +/* Deprecated OpenCL 1.1 APIs */ +extern CL_API_ENTRY CL_EXT_PREFIX__VERSION_1_1_DEPRECATED cl_mem CL_API_CALL +clCreateFromGLTexture2D(cl_context context, + cl_mem_flags flags, + cl_GLenum target, + cl_GLint miplevel, + cl_GLuint texture, + cl_int * errcode_ret) CL_EXT_SUFFIX__VERSION_1_1_DEPRECATED; + +extern CL_API_ENTRY CL_EXT_PREFIX__VERSION_1_1_DEPRECATED cl_mem CL_API_CALL +clCreateFromGLTexture3D(cl_context context, + cl_mem_flags flags, + cl_GLenum target, + cl_GLint miplevel, + cl_GLuint texture, + cl_int * errcode_ret) CL_EXT_SUFFIX__VERSION_1_1_DEPRECATED; + +/* cl_khr_gl_sharing extension */ + +#define cl_khr_gl_sharing 1 + +typedef cl_uint cl_gl_context_info; + +/* Additional Error Codes */ +#define CL_INVALID_GL_SHAREGROUP_REFERENCE_KHR -1000 + +/* cl_gl_context_info */ +#define CL_CURRENT_DEVICE_FOR_GL_CONTEXT_KHR 0x2006 +#define CL_DEVICES_FOR_GL_CONTEXT_KHR 0x2007 + +/* Additional cl_context_properties */ +#define CL_GL_CONTEXT_KHR 0x2008 +#define CL_EGL_DISPLAY_KHR 0x2009 +#define CL_GLX_DISPLAY_KHR 0x200A +#define CL_WGL_HDC_KHR 0x200B +#define CL_CGL_SHAREGROUP_KHR 0x200C + +extern CL_API_ENTRY cl_int CL_API_CALL +clGetGLContextInfoKHR(const cl_context_properties * properties, + cl_gl_context_info param_name, + size_t param_value_size, + void * param_value, + size_t * param_value_size_ret) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_int (CL_API_CALL *clGetGLContextInfoKHR_fn)( + const cl_context_properties * properties, + cl_gl_context_info param_name, + size_t param_value_size, + void * param_value, + size_t * param_value_size_ret); + +#ifdef __cplusplus +} +#endif + +#endif /* __OPENCL_CL_GL_H */ diff --git a/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/cl_gl_ext.h b/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/cl_gl_ext.h new file mode 100755 index 0000000000000000000000000000000000000000..9bb75405bc9611385c0013ca296ebcf69c810e9c --- /dev/null +++ b/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/cl_gl_ext.h @@ -0,0 +1,40 @@ +/******************************************************************************* + * Copyright (c) 2008-2020 The Khronos Group Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + ******************************************************************************/ + +#ifndef __OPENCL_CL_GL_EXT_H +#define __OPENCL_CL_GL_EXT_H + +#ifdef __cplusplus +extern "C" { +#endif + +#include + +/* + * cl_khr_gl_event extension + */ +#define CL_COMMAND_GL_FENCE_SYNC_OBJECT_KHR 0x200D + +extern CL_API_ENTRY cl_event CL_API_CALL +clCreateEventFromGLsyncKHR(cl_context context, + cl_GLsync cl_GLsync, + cl_int * errcode_ret) CL_EXT_SUFFIX__VERSION_1_1; + +#ifdef __cplusplus +} +#endif + +#endif /* __OPENCL_CL_GL_EXT_H */ diff --git a/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/cl_half.h b/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/cl_half.h new file mode 100755 index 0000000000000000000000000000000000000000..f748d9ed4cffbc83eb13b049b134a5e4b397eeb6 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/cl_half.h @@ -0,0 +1,440 @@ +/******************************************************************************* + * Copyright (c) 2019-2020 The Khronos Group Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + ******************************************************************************/ + +/** + * This is a header-only utility library that provides OpenCL host code with + * routines for converting to/from cl_half values. + * + * Example usage: + * + * #include + * ... + * cl_half h = cl_half_from_float(0.5f, CL_HALF_RTE); + * cl_float f = cl_half_to_float(h); + */ + +#ifndef OPENCL_CL_HALF_H +#define OPENCL_CL_HALF_H + +#include + +#include + +#ifdef __cplusplus +extern "C" { +#endif + + +/** + * Rounding mode used when converting to cl_half. + */ +typedef enum +{ + CL_HALF_RTE, // round to nearest even + CL_HALF_RTZ, // round towards zero + CL_HALF_RTP, // round towards positive infinity + CL_HALF_RTN, // round towards negative infinity +} cl_half_rounding_mode; + + +/* Private utility macros. */ +#define CL_HALF_EXP_MASK 0x7C00 +#define CL_HALF_MAX_FINITE_MAG 0x7BFF + + +/* + * Utility to deal with values that overflow when converting to half precision. + */ +static inline cl_half cl_half_handle_overflow(cl_half_rounding_mode rounding_mode, + uint16_t sign) +{ + if (rounding_mode == CL_HALF_RTZ) + { + // Round overflow towards zero -> largest finite number (preserving sign) + return (sign << 15) | CL_HALF_MAX_FINITE_MAG; + } + else if (rounding_mode == CL_HALF_RTP && sign) + { + // Round negative overflow towards positive infinity -> most negative finite number + return (1 << 15) | CL_HALF_MAX_FINITE_MAG; + } + else if (rounding_mode == CL_HALF_RTN && !sign) + { + // Round positive overflow towards negative infinity -> largest finite number + return CL_HALF_MAX_FINITE_MAG; + } + + // Overflow to infinity + return (sign << 15) | CL_HALF_EXP_MASK; +} + +/* + * Utility to deal with values that underflow when converting to half precision. + */ +static inline cl_half cl_half_handle_underflow(cl_half_rounding_mode rounding_mode, + uint16_t sign) +{ + if (rounding_mode == CL_HALF_RTP && !sign) + { + // Round underflow towards positive infinity -> smallest positive value + return (sign << 15) | 1; + } + else if (rounding_mode == CL_HALF_RTN && sign) + { + // Round underflow towards negative infinity -> largest negative value + return (sign << 15) | 1; + } + + // Flush to zero + return (sign << 15); +} + + +/** + * Convert a cl_float to a cl_half. + */ +static inline cl_half cl_half_from_float(cl_float f, cl_half_rounding_mode rounding_mode) +{ + // Type-punning to get direct access to underlying bits + union + { + cl_float f; + uint32_t i; + } f32; + f32.f = f; + + // Extract sign bit + uint16_t sign = f32.i >> 31; + + // Extract FP32 exponent and mantissa + uint32_t f_exp = (f32.i >> (CL_FLT_MANT_DIG - 1)) & 0xFF; + uint32_t f_mant = f32.i & ((1 << (CL_FLT_MANT_DIG - 1)) - 1); + + // Remove FP32 exponent bias + int32_t exp = f_exp - CL_FLT_MAX_EXP + 1; + + // Add FP16 exponent bias + uint16_t h_exp = exp + CL_HALF_MAX_EXP - 1; + + // Position of the bit that will become the FP16 mantissa LSB + uint32_t lsb_pos = CL_FLT_MANT_DIG - CL_HALF_MANT_DIG; + + // Check for NaN / infinity + if (f_exp == 0xFF) + { + if (f_mant) + { + // NaN -> propagate mantissa and silence it + uint16_t h_mant = f_mant >> lsb_pos; + h_mant |= 0x200; + return (sign << 15) | CL_HALF_EXP_MASK | h_mant; + } + else + { + // Infinity -> zero mantissa + return (sign << 15) | CL_HALF_EXP_MASK; + } + } + + // Check for zero + if (!f_exp && !f_mant) + { + return (sign << 15); + } + + // Check for overflow + if (exp >= CL_HALF_MAX_EXP) + { + return cl_half_handle_overflow(rounding_mode, sign); + } + + // Check for underflow + if (exp < (CL_HALF_MIN_EXP - CL_HALF_MANT_DIG - 1)) + { + return cl_half_handle_underflow(rounding_mode, sign); + } + + // Check for value that will become denormal + if (exp < -14) + { + // Denormal -> include the implicit 1 from the FP32 mantissa + h_exp = 0; + f_mant |= 1 << (CL_FLT_MANT_DIG - 1); + + // Mantissa shift amount depends on exponent + lsb_pos = -exp + (CL_FLT_MANT_DIG - 25); + } + + // Generate FP16 mantissa by shifting FP32 mantissa + uint16_t h_mant = f_mant >> lsb_pos; + + // Check whether we need to round + uint32_t halfway = 1 << (lsb_pos - 1); + uint32_t mask = (halfway << 1) - 1; + switch (rounding_mode) + { + case CL_HALF_RTE: + if ((f_mant & mask) > halfway) + { + // More than halfway -> round up + h_mant += 1; + } + else if ((f_mant & mask) == halfway) + { + // Exactly halfway -> round to nearest even + if (h_mant & 0x1) + h_mant += 1; + } + break; + case CL_HALF_RTZ: + // Mantissa has already been truncated -> do nothing + break; + case CL_HALF_RTP: + if ((f_mant & mask) && !sign) + { + // Round positive numbers up + h_mant += 1; + } + break; + case CL_HALF_RTN: + if ((f_mant & mask) && sign) + { + // Round negative numbers down + h_mant += 1; + } + break; + } + + // Check for mantissa overflow + if (h_mant & 0x400) + { + h_exp += 1; + h_mant = 0; + } + + return (sign << 15) | (h_exp << 10) | h_mant; +} + + +/** + * Convert a cl_double to a cl_half. + */ +static inline cl_half cl_half_from_double(cl_double d, cl_half_rounding_mode rounding_mode) +{ + // Type-punning to get direct access to underlying bits + union + { + cl_double d; + uint64_t i; + } f64; + f64.d = d; + + // Extract sign bit + uint16_t sign = f64.i >> 63; + + // Extract FP64 exponent and mantissa + uint64_t d_exp = (f64.i >> (CL_DBL_MANT_DIG - 1)) & 0x7FF; + uint64_t d_mant = f64.i & (((uint64_t)1 << (CL_DBL_MANT_DIG - 1)) - 1); + + // Remove FP64 exponent bias + int64_t exp = d_exp - CL_DBL_MAX_EXP + 1; + + // Add FP16 exponent bias + uint16_t h_exp = (uint16_t)(exp + CL_HALF_MAX_EXP - 1); + + // Position of the bit that will become the FP16 mantissa LSB + uint32_t lsb_pos = CL_DBL_MANT_DIG - CL_HALF_MANT_DIG; + + // Check for NaN / infinity + if (d_exp == 0x7FF) + { + if (d_mant) + { + // NaN -> propagate mantissa and silence it + uint16_t h_mant = (uint16_t)(d_mant >> lsb_pos); + h_mant |= 0x200; + return (sign << 15) | CL_HALF_EXP_MASK | h_mant; + } + else + { + // Infinity -> zero mantissa + return (sign << 15) | CL_HALF_EXP_MASK; + } + } + + // Check for zero + if (!d_exp && !d_mant) + { + return (sign << 15); + } + + // Check for overflow + if (exp >= CL_HALF_MAX_EXP) + { + return cl_half_handle_overflow(rounding_mode, sign); + } + + // Check for underflow + if (exp < (CL_HALF_MIN_EXP - CL_HALF_MANT_DIG - 1)) + { + return cl_half_handle_underflow(rounding_mode, sign); + } + + // Check for value that will become denormal + if (exp < -14) + { + // Include the implicit 1 from the FP64 mantissa + h_exp = 0; + d_mant |= (uint64_t)1 << (CL_DBL_MANT_DIG - 1); + + // Mantissa shift amount depends on exponent + lsb_pos = (uint32_t)(-exp + (CL_DBL_MANT_DIG - 25)); + } + + // Generate FP16 mantissa by shifting FP64 mantissa + uint16_t h_mant = (uint16_t)(d_mant >> lsb_pos); + + // Check whether we need to round + uint64_t halfway = (uint64_t)1 << (lsb_pos - 1); + uint64_t mask = (halfway << 1) - 1; + switch (rounding_mode) + { + case CL_HALF_RTE: + if ((d_mant & mask) > halfway) + { + // More than halfway -> round up + h_mant += 1; + } + else if ((d_mant & mask) == halfway) + { + // Exactly halfway -> round to nearest even + if (h_mant & 0x1) + h_mant += 1; + } + break; + case CL_HALF_RTZ: + // Mantissa has already been truncated -> do nothing + break; + case CL_HALF_RTP: + if ((d_mant & mask) && !sign) + { + // Round positive numbers up + h_mant += 1; + } + break; + case CL_HALF_RTN: + if ((d_mant & mask) && sign) + { + // Round negative numbers down + h_mant += 1; + } + break; + } + + // Check for mantissa overflow + if (h_mant & 0x400) + { + h_exp += 1; + h_mant = 0; + } + + return (sign << 15) | (h_exp << 10) | h_mant; +} + + +/** + * Convert a cl_half to a cl_float. + */ +static inline cl_float cl_half_to_float(cl_half h) +{ + // Type-punning to get direct access to underlying bits + union + { + cl_float f; + uint32_t i; + } f32; + + // Extract sign bit + uint16_t sign = h >> 15; + + // Extract FP16 exponent and mantissa + uint16_t h_exp = (h >> (CL_HALF_MANT_DIG - 1)) & 0x1F; + uint16_t h_mant = h & 0x3FF; + + // Remove FP16 exponent bias + int32_t exp = h_exp - CL_HALF_MAX_EXP + 1; + + // Add FP32 exponent bias + uint32_t f_exp = exp + CL_FLT_MAX_EXP - 1; + + // Check for NaN / infinity + if (h_exp == 0x1F) + { + if (h_mant) + { + // NaN -> propagate mantissa and silence it + uint32_t f_mant = h_mant << (CL_FLT_MANT_DIG - CL_HALF_MANT_DIG); + f_mant |= 0x400000; + f32.i = (sign << 31) | 0x7F800000 | f_mant; + return f32.f; + } + else + { + // Infinity -> zero mantissa + f32.i = (sign << 31) | 0x7F800000; + return f32.f; + } + } + + // Check for zero / denormal + if (h_exp == 0) + { + if (h_mant == 0) + { + // Zero -> zero exponent + f_exp = 0; + } + else + { + // Denormal -> normalize it + // - Shift mantissa to make most-significant 1 implicit + // - Adjust exponent accordingly + uint32_t shift = 0; + while ((h_mant & 0x400) == 0) + { + h_mant <<= 1; + shift++; + } + h_mant &= 0x3FF; + f_exp -= shift - 1; + } + } + + f32.i = (sign << 31) | (f_exp << 23) | (h_mant << 13); + return f32.f; +} + + +#undef CL_HALF_EXP_MASK +#undef CL_HALF_MAX_FINITE_MAG + + +#ifdef __cplusplus +} +#endif + + +#endif /* OPENCL_CL_HALF_H */ diff --git a/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/cl_icd.h b/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/cl_icd.h new file mode 100755 index 0000000000000000000000000000000000000000..8ff8b94f9d74dc831301818043b716e112c09ca7 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/cl_icd.h @@ -0,0 +1,1294 @@ +/******************************************************************************* + * Copyright (c) 2019-2020 The Khronos Group Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + ******************************************************************************/ + +#ifndef OPENCL_CL_ICD_H +#define OPENCL_CL_ICD_H + +#include +#include +#include +#include + +#if defined(_WIN32) +#include +#include +#include +#endif + +#ifdef __cplusplus +extern "C" { +#endif + +/* + * This file contains pointer type definitions for each of the CL API calls as + * well as a type definition for the dispatch table used by the Khronos ICD + * loader (see cl_khr_icd extension specification for background). + */ + +/* API function pointer definitions */ + +// Platform APIs +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clGetPlatformIDs)( + cl_uint num_entries, cl_platform_id *platforms, + cl_uint *num_platforms) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clGetPlatformInfo)( + cl_platform_id platform, cl_platform_info param_name, + size_t param_value_size, void *param_value, + size_t *param_value_size_ret) CL_API_SUFFIX__VERSION_1_0; + +// Device APIs +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clGetDeviceIDs)( + cl_platform_id platform, cl_device_type device_type, cl_uint num_entries, + cl_device_id *devices, cl_uint *num_devices) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clGetDeviceInfo)( + cl_device_id device, cl_device_info param_name, size_t param_value_size, + void *param_value, size_t *param_value_size_ret) CL_API_SUFFIX__VERSION_1_0; + +#ifdef CL_VERSION_1_2 + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clCreateSubDevices)( + cl_device_id in_device, + const cl_device_partition_property *partition_properties, + cl_uint num_entries, cl_device_id *out_devices, cl_uint *num_devices); + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clRetainDevice)( + cl_device_id device) CL_API_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clReleaseDevice)( + cl_device_id device) CL_API_SUFFIX__VERSION_1_2; + +#else + +typedef void *cl_api_clCreateSubDevices; +typedef void *cl_api_clRetainDevice; +typedef void *cl_api_clReleaseDevice; + +#endif + +// Context APIs +typedef CL_API_ENTRY cl_context(CL_API_CALL *cl_api_clCreateContext)( + const cl_context_properties *properties, cl_uint num_devices, + const cl_device_id *devices, + void(CL_CALLBACK *pfn_notify)(const char *, const void *, size_t, void *), + void *user_data, cl_int *errcode_ret) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_context(CL_API_CALL *cl_api_clCreateContextFromType)( + const cl_context_properties *properties, cl_device_type device_type, + void(CL_CALLBACK *pfn_notify)(const char *, const void *, size_t, void *), + void *user_data, cl_int *errcode_ret) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clRetainContext)( + cl_context context) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clReleaseContext)( + cl_context context) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clGetContextInfo)( + cl_context context, cl_context_info param_name, size_t param_value_size, + void *param_value, size_t *param_value_size_ret) CL_API_SUFFIX__VERSION_1_0; + +// Command Queue APIs +typedef CL_API_ENTRY cl_command_queue(CL_API_CALL *cl_api_clCreateCommandQueue)( + cl_context context, cl_device_id device, + cl_command_queue_properties properties, + cl_int *errcode_ret) CL_API_SUFFIX__VERSION_1_0; + +#ifdef CL_VERSION_2_0 + +typedef CL_API_ENTRY +cl_command_queue(CL_API_CALL *cl_api_clCreateCommandQueueWithProperties)( + cl_context /* context */, cl_device_id /* device */, + const cl_queue_properties * /* properties */, + cl_int * /* errcode_ret */) CL_API_SUFFIX__VERSION_2_0; + +#else + +typedef void *cl_api_clCreateCommandQueueWithProperties; + +#endif + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clRetainCommandQueue)( + cl_command_queue command_queue) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clReleaseCommandQueue)( + cl_command_queue command_queue) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clGetCommandQueueInfo)( + cl_command_queue command_queue, cl_command_queue_info param_name, + size_t param_value_size, void *param_value, + size_t *param_value_size_ret) CL_API_SUFFIX__VERSION_1_0; + +// Memory Object APIs +typedef CL_API_ENTRY cl_mem(CL_API_CALL *cl_api_clCreateBuffer)( + cl_context context, cl_mem_flags flags, size_t size, void *host_ptr, + cl_int *errcode_ret) CL_API_SUFFIX__VERSION_1_0; + +#ifdef CL_VERSION_1_2 + +typedef CL_API_ENTRY cl_mem(CL_API_CALL *cl_api_clCreateImage)( + cl_context context, cl_mem_flags flags, const cl_image_format *image_format, + const cl_image_desc *image_desc, void *host_ptr, + cl_int *errcode_ret) CL_API_SUFFIX__VERSION_1_2; + +#else + +typedef void *cl_api_clCreateImage; + +#endif + +#ifdef CL_VERSION_3_0 + +typedef CL_API_ENTRY cl_mem(CL_API_CALL *cl_api_clCreateBufferWithProperties)( + cl_context context, const cl_mem_properties *properties, cl_mem_flags flags, + size_t size, void *host_ptr, + cl_int *errcode_ret) CL_API_SUFFIX__VERSION_3_0; + +typedef CL_API_ENTRY cl_mem(CL_API_CALL *cl_api_clCreateImageWithProperties)( + cl_context context, const cl_mem_properties *properties, cl_mem_flags flags, + const cl_image_format *image_format, const cl_image_desc *image_desc, + void *host_ptr, cl_int *errcode_ret) CL_API_SUFFIX__VERSION_3_0; + +typedef CL_API_ENTRY cl_int(CL_API_CALL* cl_api_clSetContextDestructorCallback)( + cl_context context, + void(CL_CALLBACK* pfn_notify)(cl_context context, void* user_data), + void* user_data) CL_API_SUFFIX__VERSION_3_0; + +#else + +typedef void *cl_api_clCreateBufferWithProperties; +typedef void *cl_api_clCreateImageWithProperties; +typedef void *cl_api_clSetContextDestructorCallback; + +#endif + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clRetainMemObject)( + cl_mem memobj) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clReleaseMemObject)( + cl_mem memobj) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clGetSupportedImageFormats)( + cl_context context, cl_mem_flags flags, cl_mem_object_type image_type, + cl_uint num_entries, cl_image_format *image_formats, + cl_uint *num_image_formats) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clGetMemObjectInfo)( + cl_mem memobj, cl_mem_info param_name, size_t param_value_size, + void *param_value, size_t *param_value_size_ret) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clGetImageInfo)( + cl_mem image, cl_image_info param_name, size_t param_value_size, + void *param_value, size_t *param_value_size_ret) CL_API_SUFFIX__VERSION_1_0; + +#ifdef CL_VERSION_2_0 + +typedef CL_API_ENTRY cl_mem(CL_API_CALL *cl_api_clCreatePipe)( + cl_context /* context */, cl_mem_flags /* flags */, + cl_uint /* pipe_packet_size */, cl_uint /* pipe_max_packets */, + const cl_pipe_properties * /* properties */, + cl_int * /* errcode_ret */) CL_API_SUFFIX__VERSION_2_0; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clGetPipeInfo)( + cl_mem /* pipe */, cl_pipe_info /* param_name */, + size_t /* param_value_size */, void * /* param_value */, + size_t * /* param_value_size_ret */) CL_API_SUFFIX__VERSION_2_0; + +typedef CL_API_ENTRY void *(CL_API_CALL *cl_api_clSVMAlloc)( + cl_context /* context */, cl_svm_mem_flags /* flags */, size_t /* size */, + unsigned int /* alignment */)CL_API_SUFFIX__VERSION_2_0; + +typedef CL_API_ENTRY void(CL_API_CALL *cl_api_clSVMFree)( + cl_context /* context */, + void * /* svm_pointer */) CL_API_SUFFIX__VERSION_2_0; + +#else + +typedef void *cl_api_clCreatePipe; +typedef void *cl_api_clGetPipeInfo; +typedef void *cl_api_clSVMAlloc; +typedef void *cl_api_clSVMFree; + +#endif + +// Sampler APIs +typedef CL_API_ENTRY cl_sampler(CL_API_CALL *cl_api_clCreateSampler)( + cl_context context, cl_bool normalized_coords, + cl_addressing_mode addressing_mode, cl_filter_mode filter_mode, + cl_int *errcode_ret) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clRetainSampler)( + cl_sampler sampler) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clReleaseSampler)( + cl_sampler sampler) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clGetSamplerInfo)( + cl_sampler sampler, cl_sampler_info param_name, size_t param_value_size, + void *param_value, size_t *param_value_size_ret) CL_API_SUFFIX__VERSION_1_0; + +#ifdef CL_VERSION_2_0 + +typedef CL_API_ENTRY +cl_sampler(CL_API_CALL *cl_api_clCreateSamplerWithProperties)( + cl_context /* context */, + const cl_sampler_properties * /* sampler_properties */, + cl_int * /* errcode_ret */) CL_API_SUFFIX__VERSION_2_0; + +#else + +typedef void *cl_api_clCreateSamplerWithProperties; + +#endif + +// Program Object APIs +typedef CL_API_ENTRY cl_program(CL_API_CALL *cl_api_clCreateProgramWithSource)( + cl_context context, cl_uint count, const char **strings, + const size_t *lengths, cl_int *errcode_ret) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_program(CL_API_CALL *cl_api_clCreateProgramWithBinary)( + cl_context context, cl_uint num_devices, const cl_device_id *device_list, + const size_t *lengths, const unsigned char **binaries, + cl_int *binary_status, cl_int *errcode_ret) CL_API_SUFFIX__VERSION_1_0; + +#ifdef CL_VERSION_1_2 + +typedef CL_API_ENTRY +cl_program(CL_API_CALL *cl_api_clCreateProgramWithBuiltInKernels)( + cl_context context, cl_uint num_devices, const cl_device_id *device_list, + const char *kernel_names, cl_int *errcode_ret) CL_API_SUFFIX__VERSION_1_2; + +#else + +typedef void *cl_api_clCreateProgramWithBuiltInKernels; + +#endif + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clRetainProgram)( + cl_program program) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clReleaseProgram)( + cl_program program) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clBuildProgram)( + cl_program program, cl_uint num_devices, const cl_device_id *device_list, + const char *options, + void(CL_CALLBACK *pfn_notify)(cl_program program, void *user_data), + void *user_data) CL_API_SUFFIX__VERSION_1_0; + +#ifdef CL_VERSION_1_2 + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clCompileProgram)( + cl_program program, cl_uint num_devices, const cl_device_id *device_list, + const char *options, cl_uint num_input_headers, + const cl_program *input_headers, const char **header_include_names, + void(CL_CALLBACK *pfn_notify)(cl_program program, void *user_data), + void *user_data) CL_API_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_program(CL_API_CALL *cl_api_clLinkProgram)( + cl_context context, cl_uint num_devices, const cl_device_id *device_list, + const char *options, cl_uint num_input_programs, + const cl_program *input_programs, + void(CL_CALLBACK *pfn_notify)(cl_program program, void *user_data), + void *user_data, cl_int *errcode_ret) CL_API_SUFFIX__VERSION_1_2; + +#else + +typedef void *cl_api_clCompileProgram; +typedef void *cl_api_clLinkProgram; + +#endif + +#ifdef CL_VERSION_2_2 + +typedef CL_API_ENTRY +cl_int(CL_API_CALL *cl_api_clSetProgramSpecializationConstant)( + cl_program program, cl_uint spec_id, size_t spec_size, + const void *spec_value) CL_API_SUFFIX__VERSION_2_2; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clSetProgramReleaseCallback)( + cl_program program, + void(CL_CALLBACK *pfn_notify)(cl_program program, void *user_data), + void *user_data) CL_API_SUFFIX__VERSION_2_2; + +#else + +typedef void *cl_api_clSetProgramSpecializationConstant; +typedef void *cl_api_clSetProgramReleaseCallback; + +#endif + +#ifdef CL_VERSION_1_2 + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clUnloadPlatformCompiler)( + cl_platform_id platform) CL_API_SUFFIX__VERSION_1_2; + +#else + +typedef void *cl_api_clUnloadPlatformCompiler; + +#endif + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clGetProgramInfo)( + cl_program program, cl_program_info param_name, size_t param_value_size, + void *param_value, size_t *param_value_size_ret) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clGetProgramBuildInfo)( + cl_program program, cl_device_id device, cl_program_build_info param_name, + size_t param_value_size, void *param_value, + size_t *param_value_size_ret) CL_API_SUFFIX__VERSION_1_0; + +// Kernel Object APIs +typedef CL_API_ENTRY cl_kernel(CL_API_CALL *cl_api_clCreateKernel)( + cl_program program, const char *kernel_name, + cl_int *errcode_ret) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clCreateKernelsInProgram)( + cl_program program, cl_uint num_kernels, cl_kernel *kernels, + cl_uint *num_kernels_ret) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clRetainKernel)( + cl_kernel kernel) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clReleaseKernel)( + cl_kernel kernel) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clSetKernelArg)( + cl_kernel kernel, cl_uint arg_index, size_t arg_size, + const void *arg_value) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clGetKernelInfo)( + cl_kernel kernel, cl_kernel_info param_name, size_t param_value_size, + void *param_value, size_t *param_value_size_ret) CL_API_SUFFIX__VERSION_1_0; + +#ifdef CL_VERSION_1_2 + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clGetKernelArgInfo)( + cl_kernel kernel, cl_uint arg_indx, cl_kernel_arg_info param_name, + size_t param_value_size, void *param_value, + size_t *param_value_size_ret) CL_API_SUFFIX__VERSION_1_2; + +#else + +typedef void *cl_api_clGetKernelArgInfo; + +#endif + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clGetKernelWorkGroupInfo)( + cl_kernel kernel, cl_device_id device, cl_kernel_work_group_info param_name, + size_t param_value_size, void *param_value, + size_t *param_value_size_ret) CL_API_SUFFIX__VERSION_1_0; + +#ifdef CL_VERSION_2_0 + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clSetKernelArgSVMPointer)( + cl_kernel /* kernel */, cl_uint /* arg_index */, + const void * /* arg_value */) CL_API_SUFFIX__VERSION_2_0; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clSetKernelExecInfo)( + cl_kernel /* kernel */, cl_kernel_exec_info /* param_name */, + size_t /* param_value_size */, + const void * /* param_value */) CL_API_SUFFIX__VERSION_2_0; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clGetKernelSubGroupInfoKHR)( + cl_kernel /* in_kernel */, cl_device_id /*in_device*/, + cl_kernel_sub_group_info /* param_name */, size_t /*input_value_size*/, + const void * /*input_value*/, size_t /*param_value_size*/, + void * /*param_value*/, + size_t * /*param_value_size_ret*/) CL_EXT_SUFFIX__VERSION_2_0; + +#else + +typedef void *cl_api_clSetKernelArgSVMPointer; +typedef void *cl_api_clSetKernelExecInfo; +typedef void *cl_api_clGetKernelSubGroupInfoKHR; + +#endif + +// Event Object APIs +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clWaitForEvents)( + cl_uint num_events, const cl_event *event_list) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clGetEventInfo)( + cl_event event, cl_event_info param_name, size_t param_value_size, + void *param_value, size_t *param_value_size_ret) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clRetainEvent)(cl_event event) + CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clReleaseEvent)(cl_event event) + CL_API_SUFFIX__VERSION_1_0; + +// Profiling APIs +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clGetEventProfilingInfo)( + cl_event event, cl_profiling_info param_name, size_t param_value_size, + void *param_value, size_t *param_value_size_ret) CL_API_SUFFIX__VERSION_1_0; + +// Flush and Finish APIs +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clFlush)( + cl_command_queue command_queue) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clFinish)( + cl_command_queue command_queue) CL_API_SUFFIX__VERSION_1_0; + +// Enqueued Commands APIs +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clEnqueueReadBuffer)( + cl_command_queue command_queue, cl_mem buffer, cl_bool blocking_read, + size_t offset, size_t cb, void *ptr, cl_uint num_events_in_wait_list, + const cl_event *event_wait_list, + cl_event *event) CL_API_SUFFIX__VERSION_1_0; + +#ifdef CL_VERSION_1_1 + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clEnqueueReadBufferRect)( + cl_command_queue command_queue, cl_mem buffer, cl_bool blocking_read, + const size_t *buffer_origin, const size_t *host_origin, + const size_t *region, size_t buffer_row_pitch, size_t buffer_slice_pitch, + size_t host_row_pitch, size_t host_slice_pitch, void *ptr, + cl_uint num_events_in_wait_list, const cl_event *event_wait_list, + cl_event *event) CL_API_SUFFIX__VERSION_1_1; + +#else + +typedef void *cl_api_clEnqueueReadBufferRect; + +#endif + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clEnqueueWriteBuffer)( + cl_command_queue command_queue, cl_mem buffer, cl_bool blocking_write, + size_t offset, size_t cb, const void *ptr, cl_uint num_events_in_wait_list, + const cl_event *event_wait_list, + cl_event *event) CL_API_SUFFIX__VERSION_1_0; + +#ifdef CL_VERSION_1_1 + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clEnqueueWriteBufferRect)( + cl_command_queue command_queue, cl_mem buffer, cl_bool blocking_read, + const size_t *buffer_origin, const size_t *host_origin, + const size_t *region, size_t buffer_row_pitch, size_t buffer_slice_pitch, + size_t host_row_pitch, size_t host_slice_pitch, const void *ptr, + cl_uint num_events_in_wait_list, const cl_event *event_wait_list, + cl_event *event) CL_API_SUFFIX__VERSION_1_1; + +#else + +typedef void *cl_api_clEnqueueWriteBufferRect; + +#endif + +#ifdef CL_VERSION_1_2 + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clEnqueueFillBuffer)( + cl_command_queue command_queue, cl_mem buffer, const void *pattern, + size_t pattern_size, size_t offset, size_t cb, + cl_uint num_events_in_wait_list, const cl_event *event_wait_list, + cl_event *event) CL_API_SUFFIX__VERSION_1_2; + +#else + +typedef void *cl_api_clEnqueueFillBuffer; + +#endif + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clEnqueueCopyBuffer)( + cl_command_queue command_queue, cl_mem src_buffer, cl_mem dst_buffer, + size_t src_offset, size_t dst_offset, size_t cb, + cl_uint num_events_in_wait_list, const cl_event *event_wait_list, + cl_event *event) CL_API_SUFFIX__VERSION_1_0; + +#ifdef CL_VERSION_1_1 + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clEnqueueCopyBufferRect)( + cl_command_queue command_queue, cl_mem src_buffer, cl_mem dst_buffer, + const size_t *src_origin, const size_t *dst_origin, const size_t *region, + size_t src_row_pitch, size_t src_slice_pitch, size_t dst_row_pitch, + size_t dst_slice_pitch, cl_uint num_events_in_wait_list, + const cl_event *event_wait_list, + cl_event *event) CL_API_SUFFIX__VERSION_1_1; + +#else + +typedef void *cl_api_clEnqueueCopyBufferRect; + +#endif + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clEnqueueReadImage)( + cl_command_queue command_queue, cl_mem image, cl_bool blocking_read, + const size_t *origin, const size_t *region, size_t row_pitch, + size_t slice_pitch, void *ptr, cl_uint num_events_in_wait_list, + const cl_event *event_wait_list, + cl_event *event) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clEnqueueWriteImage)( + cl_command_queue command_queue, cl_mem image, cl_bool blocking_write, + const size_t *origin, const size_t *region, size_t input_row_pitch, + size_t input_slice_pitch, const void *ptr, cl_uint num_events_in_wait_list, + const cl_event *event_wait_list, + cl_event *event) CL_API_SUFFIX__VERSION_1_0; + +#ifdef CL_VERSION_1_2 + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clEnqueueFillImage)( + cl_command_queue command_queue, cl_mem image, const void *fill_color, + const size_t origin[3], const size_t region[3], + cl_uint num_events_in_wait_list, const cl_event *event_wait_list, + cl_event *event) CL_API_SUFFIX__VERSION_1_2; + +#else + +typedef void *cl_api_clEnqueueFillImage; + +#endif + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clEnqueueCopyImage)( + cl_command_queue command_queue, cl_mem src_image, cl_mem dst_image, + const size_t *src_origin, const size_t *dst_origin, const size_t *region, + cl_uint num_events_in_wait_list, const cl_event *event_wait_list, + cl_event *event) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clEnqueueCopyImageToBuffer)( + cl_command_queue command_queue, cl_mem src_image, cl_mem dst_buffer, + const size_t *src_origin, const size_t *region, size_t dst_offset, + cl_uint num_events_in_wait_list, const cl_event *event_wait_list, + cl_event *event) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clEnqueueCopyBufferToImage)( + cl_command_queue command_queue, cl_mem src_buffer, cl_mem dst_image, + size_t src_offset, const size_t *dst_origin, const size_t *region, + cl_uint num_events_in_wait_list, const cl_event *event_wait_list, + cl_event *event) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY void *(CL_API_CALL *cl_api_clEnqueueMapBuffer)( + cl_command_queue command_queue, cl_mem buffer, cl_bool blocking_map, + cl_map_flags map_flags, size_t offset, size_t cb, + cl_uint num_events_in_wait_list, const cl_event *event_wait_list, + cl_event *event, cl_int *errcode_ret)CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY void *(CL_API_CALL *cl_api_clEnqueueMapImage)( + cl_command_queue command_queue, cl_mem image, cl_bool blocking_map, + cl_map_flags map_flags, const size_t *origin, const size_t *region, + size_t *image_row_pitch, size_t *image_slice_pitch, + cl_uint num_events_in_wait_list, const cl_event *event_wait_list, + cl_event *event, cl_int *errcode_ret)CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clEnqueueUnmapMemObject)( + cl_command_queue command_queue, cl_mem memobj, void *mapped_ptr, + cl_uint num_events_in_wait_list, const cl_event *event_wait_list, + cl_event *event) CL_API_SUFFIX__VERSION_1_0; + +#ifdef CL_VERSION_1_2 + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clEnqueueMigrateMemObjects)( + cl_command_queue command_queue, cl_uint num_mem_objects, + const cl_mem *mem_objects, cl_mem_migration_flags flags, + cl_uint num_events_in_wait_list, const cl_event *event_wait_list, + cl_event *event) CL_API_SUFFIX__VERSION_1_2; + +#else + +typedef void *cl_api_clEnqueueMigrateMemObjects; + +#endif + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clEnqueueNDRangeKernel)( + cl_command_queue command_queue, cl_kernel kernel, cl_uint work_dim, + const size_t *global_work_offset, const size_t *global_work_size, + const size_t *local_work_size, cl_uint num_events_in_wait_list, + const cl_event *event_wait_list, + cl_event *event) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clEnqueueTask)( + cl_command_queue command_queue, cl_kernel kernel, + cl_uint num_events_in_wait_list, const cl_event *event_wait_list, + cl_event *event) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clEnqueueNativeKernel)( + cl_command_queue command_queue, void(CL_CALLBACK *user_func)(void *), + void *args, size_t cb_args, cl_uint num_mem_objects, const cl_mem *mem_list, + const void **args_mem_loc, cl_uint num_events_in_wait_list, + const cl_event *event_wait_list, + cl_event *event) CL_API_SUFFIX__VERSION_1_0; + +#ifdef CL_VERSION_1_2 + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clEnqueueMarkerWithWaitList)( + cl_command_queue command_queue, cl_uint num_events_in_wait_list, + const cl_event *event_wait_list, + cl_event *event) CL_API_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clEnqueueBarrierWithWaitList)( + cl_command_queue command_queue, cl_uint num_events_in_wait_list, + const cl_event *event_wait_list, + cl_event *event) CL_API_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY void *( + CL_API_CALL *cl_api_clGetExtensionFunctionAddressForPlatform)( + cl_platform_id platform, + const char *function_name)CL_API_SUFFIX__VERSION_1_2; + +#else + +typedef void *cl_api_clEnqueueMarkerWithWaitList; +typedef void *cl_api_clEnqueueBarrierWithWaitList; +typedef void *cl_api_clGetExtensionFunctionAddressForPlatform; + +#endif + +// Shared Virtual Memory APIs + +#ifdef CL_VERSION_2_0 + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clEnqueueSVMFree)( + cl_command_queue /* command_queue */, cl_uint /* num_svm_pointers */, + void ** /* svm_pointers */, + void(CL_CALLBACK *pfn_free_func)(cl_command_queue /* queue */, + cl_uint /* num_svm_pointers */, + void ** /* svm_pointers[] */, + void * /* user_data */), + void * /* user_data */, cl_uint /* num_events_in_wait_list */, + const cl_event * /* event_wait_list */, + cl_event * /* event */) CL_API_SUFFIX__VERSION_2_0; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clEnqueueSVMMemcpy)( + cl_command_queue /* command_queue */, cl_bool /* blocking_copy */, + void * /* dst_ptr */, const void * /* src_ptr */, size_t /* size */, + cl_uint /* num_events_in_wait_list */, + const cl_event * /* event_wait_list */, + cl_event * /* event */) CL_API_SUFFIX__VERSION_2_0; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clEnqueueSVMMemFill)( + cl_command_queue /* command_queue */, void * /* svm_ptr */, + const void * /* pattern */, size_t /* pattern_size */, size_t /* size */, + cl_uint /* num_events_in_wait_list */, + const cl_event * /* event_wait_list */, + cl_event * /* event */) CL_API_SUFFIX__VERSION_2_0; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clEnqueueSVMMap)( + cl_command_queue /* command_queue */, cl_bool /* blocking_map */, + cl_map_flags /* map_flags */, void * /* svm_ptr */, size_t /* size */, + cl_uint /* num_events_in_wait_list */, + const cl_event * /* event_wait_list */, + cl_event * /* event */) CL_API_SUFFIX__VERSION_2_0; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clEnqueueSVMUnmap)( + cl_command_queue /* command_queue */, void * /* svm_ptr */, + cl_uint /* num_events_in_wait_list */, + const cl_event * /* event_wait_list */, + cl_event * /* event */) CL_API_SUFFIX__VERSION_2_0; + +#else + +typedef void *cl_api_clEnqueueSVMFree; +typedef void *cl_api_clEnqueueSVMMemcpy; +typedef void *cl_api_clEnqueueSVMMemFill; +typedef void *cl_api_clEnqueueSVMMap; +typedef void *cl_api_clEnqueueSVMUnmap; + +#endif + +// Deprecated APIs +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clSetCommandQueueProperty)( + cl_command_queue command_queue, cl_command_queue_properties properties, + cl_bool enable, cl_command_queue_properties *old_properties) + CL_EXT_SUFFIX__VERSION_1_0_DEPRECATED; + +typedef CL_API_ENTRY cl_mem(CL_API_CALL *cl_api_clCreateImage2D)( + cl_context context, cl_mem_flags flags, const cl_image_format *image_format, + size_t image_width, size_t image_height, size_t image_row_pitch, + void *host_ptr, cl_int *errcode_ret) CL_EXT_SUFFIX__VERSION_1_1_DEPRECATED; + +typedef CL_API_ENTRY cl_mem(CL_API_CALL *cl_api_clCreateImage3D)( + cl_context context, cl_mem_flags flags, const cl_image_format *image_format, + size_t image_width, size_t image_height, size_t image_depth, + size_t image_row_pitch, size_t image_slice_pitch, void *host_ptr, + cl_int *errcode_ret) CL_EXT_SUFFIX__VERSION_1_1_DEPRECATED; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clUnloadCompiler)(void) + CL_EXT_SUFFIX__VERSION_1_1_DEPRECATED; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clEnqueueMarker)( + cl_command_queue command_queue, + cl_event *event) CL_EXT_SUFFIX__VERSION_1_1_DEPRECATED; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clEnqueueWaitForEvents)( + cl_command_queue command_queue, cl_uint num_events, + const cl_event *event_list) CL_EXT_SUFFIX__VERSION_1_1_DEPRECATED; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clEnqueueBarrier)( + cl_command_queue command_queue) CL_EXT_SUFFIX__VERSION_1_1_DEPRECATED; + +typedef CL_API_ENTRY void *(CL_API_CALL *cl_api_clGetExtensionFunctionAddress)( + const char *function_name)CL_EXT_SUFFIX__VERSION_1_1_DEPRECATED; + +// GL and other APIs +typedef CL_API_ENTRY cl_mem(CL_API_CALL *cl_api_clCreateFromGLBuffer)( + cl_context context, cl_mem_flags flags, cl_GLuint bufobj, + int *errcode_ret) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_mem(CL_API_CALL *cl_api_clCreateFromGLTexture)( + cl_context context, cl_mem_flags flags, cl_GLenum target, cl_GLint miplevel, + cl_GLuint texture, cl_int *errcode_ret) CL_API_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_mem(CL_API_CALL *cl_api_clCreateFromGLTexture2D)( + cl_context context, cl_mem_flags flags, cl_GLenum target, cl_GLint miplevel, + cl_GLuint texture, cl_int *errcode_ret) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_mem(CL_API_CALL *cl_api_clCreateFromGLTexture3D)( + cl_context context, cl_mem_flags flags, cl_GLenum target, cl_GLint miplevel, + cl_GLuint texture, cl_int *errcode_ret) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_mem(CL_API_CALL *cl_api_clCreateFromGLRenderbuffer)( + cl_context context, cl_mem_flags flags, cl_GLuint renderbuffer, + cl_int *errcode_ret) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clGetGLObjectInfo)( + cl_mem memobj, cl_gl_object_type *gl_object_type, + cl_GLuint *gl_object_name) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clGetGLTextureInfo)( + cl_mem memobj, cl_gl_texture_info param_name, size_t param_value_size, + void *param_value, size_t *param_value_size_ret) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clEnqueueAcquireGLObjects)( + cl_command_queue command_queue, cl_uint num_objects, + const cl_mem *mem_objects, cl_uint num_events_in_wait_list, + const cl_event *event_wait_list, + cl_event *event) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clEnqueueReleaseGLObjects)( + cl_command_queue command_queue, cl_uint num_objects, + const cl_mem *mem_objects, cl_uint num_events_in_wait_list, + const cl_event *event_wait_list, + cl_event *event) CL_API_SUFFIX__VERSION_1_0; + +/* cl_khr_gl_sharing */ +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clGetGLContextInfoKHR)( + const cl_context_properties *properties, cl_gl_context_info param_name, + size_t param_value_size, void *param_value, size_t *param_value_size_ret); + +/* cl_khr_gl_event */ +typedef CL_API_ENTRY cl_event(CL_API_CALL *cl_api_clCreateEventFromGLsyncKHR)( + cl_context context, cl_GLsync sync, cl_int *errcode_ret); + +#if defined(_WIN32) + +/* cl_khr_d3d10_sharing */ + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clGetDeviceIDsFromD3D10KHR)( + cl_platform_id platform, cl_d3d10_device_source_khr d3d_device_source, + void *d3d_object, cl_d3d10_device_set_khr d3d_device_set, + cl_uint num_entries, cl_device_id *devices, + cl_uint *num_devices) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_mem(CL_API_CALL *cl_api_clCreateFromD3D10BufferKHR)( + cl_context context, cl_mem_flags flags, ID3D10Buffer *resource, + cl_int *errcode_ret) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_mem(CL_API_CALL *cl_api_clCreateFromD3D10Texture2DKHR)( + cl_context context, cl_mem_flags flags, ID3D10Texture2D *resource, + UINT subresource, cl_int *errcode_ret) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_mem(CL_API_CALL *cl_api_clCreateFromD3D10Texture3DKHR)( + cl_context context, cl_mem_flags flags, ID3D10Texture3D *resource, + UINT subresource, cl_int *errcode_ret) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY +cl_int(CL_API_CALL *cl_api_clEnqueueAcquireD3D10ObjectsKHR)( + cl_command_queue command_queue, cl_uint num_objects, + const cl_mem *mem_objects, cl_uint num_events_in_wait_list, + const cl_event *event_wait_list, + cl_event *event) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY +cl_int(CL_API_CALL *cl_api_clEnqueueReleaseD3D10ObjectsKHR)( + cl_command_queue command_queue, cl_uint num_objects, + const cl_mem *mem_objects, cl_uint num_events_in_wait_list, + const cl_event *event_wait_list, + cl_event *event) CL_API_SUFFIX__VERSION_1_0; + +extern CL_API_ENTRY cl_int CL_API_CALL clGetDeviceIDsFromD3D10KHR( + cl_platform_id platform, cl_d3d10_device_source_khr d3d_device_source, + void *d3d_object, cl_d3d10_device_set_khr d3d_device_set, + cl_uint num_entries, cl_device_id *devices, cl_uint *num_devices); + +extern CL_API_ENTRY cl_mem CL_API_CALL +clCreateFromD3D10BufferKHR(cl_context context, cl_mem_flags flags, + ID3D10Buffer *resource, cl_int *errcode_ret); + +extern CL_API_ENTRY cl_mem CL_API_CALL clCreateFromD3D10Texture2DKHR( + cl_context context, cl_mem_flags flags, ID3D10Texture2D *resource, + UINT subresource, cl_int *errcode_ret); + +extern CL_API_ENTRY cl_mem CL_API_CALL clCreateFromD3D10Texture3DKHR( + cl_context context, cl_mem_flags flags, ID3D10Texture3D *resource, + UINT subresource, cl_int *errcode_ret); + +extern CL_API_ENTRY cl_int CL_API_CALL clEnqueueAcquireD3D10ObjectsKHR( + cl_command_queue command_queue, cl_uint num_objects, + const cl_mem *mem_objects, cl_uint num_events_in_wait_list, + const cl_event *event_wait_list, cl_event *event); + +extern CL_API_ENTRY cl_int CL_API_CALL clEnqueueReleaseD3D10ObjectsKHR( + cl_command_queue command_queue, cl_uint num_objects, + const cl_mem *mem_objects, cl_uint num_events_in_wait_list, + const cl_event *event_wait_list, cl_event *event); + +/* cl_khr_d3d11_sharing */ +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clGetDeviceIDsFromD3D11KHR)( + cl_platform_id platform, cl_d3d11_device_source_khr d3d_device_source, + void *d3d_object, cl_d3d11_device_set_khr d3d_device_set, + cl_uint num_entries, cl_device_id *devices, + cl_uint *num_devices) CL_API_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_mem(CL_API_CALL *cl_api_clCreateFromD3D11BufferKHR)( + cl_context context, cl_mem_flags flags, ID3D11Buffer *resource, + cl_int *errcode_ret) CL_API_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_mem(CL_API_CALL *cl_api_clCreateFromD3D11Texture2DKHR)( + cl_context context, cl_mem_flags flags, ID3D11Texture2D *resource, + UINT subresource, cl_int *errcode_ret) CL_API_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_mem(CL_API_CALL *cl_api_clCreateFromD3D11Texture3DKHR)( + cl_context context, cl_mem_flags flags, ID3D11Texture3D *resource, + UINT subresource, cl_int *errcode_ret) CL_API_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY +cl_int(CL_API_CALL *cl_api_clEnqueueAcquireD3D11ObjectsKHR)( + cl_command_queue command_queue, cl_uint num_objects, + const cl_mem *mem_objects, cl_uint num_events_in_wait_list, + const cl_event *event_wait_list, + cl_event *event) CL_API_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY +cl_int(CL_API_CALL *cl_api_clEnqueueReleaseD3D11ObjectsKHR)( + cl_command_queue command_queue, cl_uint num_objects, + const cl_mem *mem_objects, cl_uint num_events_in_wait_list, + const cl_event *event_wait_list, + cl_event *event) CL_API_SUFFIX__VERSION_1_2; + +/* cl_khr_dx9_media_sharing */ +typedef CL_API_ENTRY +cl_int(CL_API_CALL *cl_api_clGetDeviceIDsFromDX9MediaAdapterKHR)( + cl_platform_id platform, cl_uint num_media_adapters, + cl_dx9_media_adapter_type_khr *media_adapters_type, void *media_adapters, + cl_dx9_media_adapter_set_khr media_adapter_set, cl_uint num_entries, + cl_device_id *devices, cl_uint *num_devices) CL_API_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_mem(CL_API_CALL *cl_api_clCreateFromDX9MediaSurfaceKHR)( + cl_context context, cl_mem_flags flags, + cl_dx9_media_adapter_type_khr adapter_type, void *surface_info, + cl_uint plane, cl_int *errcode_ret) CL_API_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY +cl_int(CL_API_CALL *cl_api_clEnqueueAcquireDX9MediaSurfacesKHR)( + cl_command_queue command_queue, cl_uint num_objects, + const cl_mem *mem_objects, cl_uint num_events_in_wait_list, + const cl_event *event_wait_list, + cl_event *event) CL_API_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY +cl_int(CL_API_CALL *cl_api_clEnqueueReleaseDX9MediaSurfacesKHR)( + cl_command_queue command_queue, cl_uint num_objects, + const cl_mem *mem_objects, cl_uint num_events_in_wait_list, + const cl_event *event_wait_list, + cl_event *event) CL_API_SUFFIX__VERSION_1_2; + +/* cl_khr_d3d11_sharing */ +extern CL_API_ENTRY cl_int CL_API_CALL clGetDeviceIDsFromD3D11KHR( + cl_platform_id platform, cl_d3d11_device_source_khr d3d_device_source, + void *d3d_object, cl_d3d11_device_set_khr d3d_device_set, + cl_uint num_entries, cl_device_id *devices, cl_uint *num_devices); + +extern CL_API_ENTRY cl_mem CL_API_CALL +clCreateFromD3D11BufferKHR(cl_context context, cl_mem_flags flags, + ID3D11Buffer *resource, cl_int *errcode_ret); + +extern CL_API_ENTRY cl_mem CL_API_CALL clCreateFromD3D11Texture2DKHR( + cl_context context, cl_mem_flags flags, ID3D11Texture2D *resource, + UINT subresource, cl_int *errcode_ret); + +extern CL_API_ENTRY cl_mem CL_API_CALL clCreateFromD3D11Texture3DKHR( + cl_context context, cl_mem_flags flags, ID3D11Texture3D *resource, + UINT subresource, cl_int *errcode_ret); + +extern CL_API_ENTRY cl_int CL_API_CALL clEnqueueAcquireD3D11ObjectsKHR( + cl_command_queue command_queue, cl_uint num_objects, + const cl_mem *mem_objects, cl_uint num_events_in_wait_list, + const cl_event *event_wait_list, cl_event *event); + +extern CL_API_ENTRY cl_int CL_API_CALL clEnqueueReleaseD3D11ObjectsKHR( + cl_command_queue command_queue, cl_uint num_objects, + const cl_mem *mem_objects, cl_uint num_events_in_wait_list, + const cl_event *event_wait_list, cl_event *event); + +/* cl_khr_dx9_media_sharing */ +extern CL_API_ENTRY cl_int CL_API_CALL clGetDeviceIDsFromDX9MediaAdapterKHR( + cl_platform_id platform, cl_uint num_media_adapters, + cl_dx9_media_adapter_type_khr *media_adapter_type, void *media_adapters, + cl_dx9_media_adapter_set_khr media_adapter_set, cl_uint num_entries, + cl_device_id *devices, cl_uint *num_devices); + +extern CL_API_ENTRY cl_mem CL_API_CALL clCreateFromDX9MediaSurfaceKHR( + cl_context context, cl_mem_flags flags, + cl_dx9_media_adapter_type_khr adapter_type, void *surface_info, + cl_uint plane, cl_int *errcode_ret); + +extern CL_API_ENTRY cl_int CL_API_CALL clEnqueueAcquireDX9MediaSurfacesKHR( + cl_command_queue command_queue, cl_uint num_objects, + const cl_mem *mem_objects, cl_uint num_events_in_wait_list, + const cl_event *event_wait_list, cl_event *event); + +extern CL_API_ENTRY cl_int CL_API_CALL clEnqueueReleaseDX9MediaSurfacesKHR( + cl_command_queue command_queue, cl_uint num_objects, + const cl_mem *mem_objects, cl_uint num_events_in_wait_list, + const cl_event *event_wait_list, cl_event *event); + +#else + +/* cl_khr_d3d10_sharing */ +typedef void *cl_api_clGetDeviceIDsFromD3D10KHR; +typedef void *cl_api_clCreateFromD3D10BufferKHR; +typedef void *cl_api_clCreateFromD3D10Texture2DKHR; +typedef void *cl_api_clCreateFromD3D10Texture3DKHR; +typedef void *cl_api_clEnqueueAcquireD3D10ObjectsKHR; +typedef void *cl_api_clEnqueueReleaseD3D10ObjectsKHR; + +/* cl_khr_d3d11_sharing */ +typedef void *cl_api_clGetDeviceIDsFromD3D11KHR; +typedef void *cl_api_clCreateFromD3D11BufferKHR; +typedef void *cl_api_clCreateFromD3D11Texture2DKHR; +typedef void *cl_api_clCreateFromD3D11Texture3DKHR; +typedef void *cl_api_clEnqueueAcquireD3D11ObjectsKHR; +typedef void *cl_api_clEnqueueReleaseD3D11ObjectsKHR; + +/* cl_khr_dx9_media_sharing */ +typedef void *cl_api_clCreateFromDX9MediaSurfaceKHR; +typedef void *cl_api_clEnqueueAcquireDX9MediaSurfacesKHR; +typedef void *cl_api_clEnqueueReleaseDX9MediaSurfacesKHR; +typedef void *cl_api_clGetDeviceIDsFromDX9MediaAdapterKHR; + +#endif + +/* OpenCL 1.1 */ + +#ifdef CL_VERSION_1_1 + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clSetEventCallback)( + cl_event /* event */, cl_int /* command_exec_callback_type */, + void(CL_CALLBACK * /* pfn_notify */)(cl_event, cl_int, void *), + void * /* user_data */) CL_API_SUFFIX__VERSION_1_1; + +typedef CL_API_ENTRY cl_mem(CL_API_CALL *cl_api_clCreateSubBuffer)( + cl_mem /* buffer */, cl_mem_flags /* flags */, + cl_buffer_create_type /* buffer_create_type */, + const void * /* buffer_create_info */, + cl_int * /* errcode_ret */) CL_API_SUFFIX__VERSION_1_1; + +typedef CL_API_ENTRY +cl_int(CL_API_CALL *cl_api_clSetMemObjectDestructorCallback)( + cl_mem /* memobj */, + void(CL_CALLBACK * /*pfn_notify*/)(cl_mem /* memobj */, + void * /*user_data*/), + void * /*user_data */) CL_API_SUFFIX__VERSION_1_1; + +typedef CL_API_ENTRY cl_event(CL_API_CALL *cl_api_clCreateUserEvent)( + cl_context /* context */, + cl_int * /* errcode_ret */) CL_API_SUFFIX__VERSION_1_1; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clSetUserEventStatus)( + cl_event /* event */, + cl_int /* execution_status */) CL_API_SUFFIX__VERSION_1_1; + +#else + +typedef void *cl_api_clSetEventCallback; +typedef void *cl_api_clCreateSubBuffer; +typedef void *cl_api_clSetMemObjectDestructorCallback; +typedef void *cl_api_clCreateUserEvent; +typedef void *cl_api_clSetUserEventStatus; + +#endif + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clCreateSubDevicesEXT)( + cl_device_id in_device, + const cl_device_partition_property_ext *partition_properties, + cl_uint num_entries, cl_device_id *out_devices, cl_uint *num_devices); + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clRetainDeviceEXT)( + cl_device_id device) CL_API_SUFFIX__VERSION_1_0; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clReleaseDeviceEXT)( + cl_device_id device) CL_API_SUFFIX__VERSION_1_0; + +/* cl_khr_egl_image */ +typedef CL_API_ENTRY cl_mem(CL_API_CALL *cl_api_clCreateFromEGLImageKHR)( + cl_context context, CLeglDisplayKHR display, CLeglImageKHR image, + cl_mem_flags flags, const cl_egl_image_properties_khr *properties, + cl_int *errcode_ret); + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clEnqueueAcquireEGLObjectsKHR)( + cl_command_queue command_queue, cl_uint num_objects, + const cl_mem *mem_objects, cl_uint num_events_in_wait_list, + const cl_event *event_wait_list, cl_event *event); + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clEnqueueReleaseEGLObjectsKHR)( + cl_command_queue command_queue, cl_uint num_objects, + const cl_mem *mem_objects, cl_uint num_events_in_wait_list, + const cl_event *event_wait_list, cl_event *event); + +/* cl_khr_egl_event */ +typedef CL_API_ENTRY cl_event(CL_API_CALL *cl_api_clCreateEventFromEGLSyncKHR)( + cl_context context, CLeglSyncKHR sync, CLeglDisplayKHR display, + cl_int *errcode_ret); + +#ifdef CL_VERSION_2_1 + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clSetDefaultDeviceCommandQueue)( + cl_context context, cl_device_id device, + cl_command_queue command_queue) CL_API_SUFFIX__VERSION_2_1; + +typedef CL_API_ENTRY cl_program(CL_API_CALL *cl_api_clCreateProgramWithIL)( + cl_context context, const void *il, size_t length, + cl_int *errcode_ret) CL_API_SUFFIX__VERSION_2_1; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clGetKernelSubGroupInfo)( + cl_kernel kernel, cl_device_id device, cl_kernel_sub_group_info param_name, + size_t input_value_size, const void *input_value, size_t param_value_size, + void *param_value, size_t *param_value_size_ret) CL_API_SUFFIX__VERSION_2_1; + +typedef CL_API_ENTRY cl_kernel(CL_API_CALL *cl_api_clCloneKernel)( + cl_kernel source_kernel, cl_int *errcode_ret) CL_API_SUFFIX__VERSION_2_1; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clEnqueueSVMMigrateMem)( + cl_command_queue command_queue, cl_uint num_svm_pointers, + const void **svm_pointers, const size_t *sizes, + cl_mem_migration_flags flags, cl_uint num_events_in_wait_list, + const cl_event *event_wait_list, + cl_event *event) CL_API_SUFFIX__VERSION_2_1; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clGetDeviceAndHostTimer)( + cl_device_id device, cl_ulong *device_timestamp, + cl_ulong *host_timestamp) CL_API_SUFFIX__VERSION_2_1; + +typedef CL_API_ENTRY cl_int(CL_API_CALL *cl_api_clGetHostTimer)( + cl_device_id device, cl_ulong *host_timestamp) CL_API_SUFFIX__VERSION_2_1; + +#else + +typedef void *cl_api_clSetDefaultDeviceCommandQueue; +typedef void *cl_api_clCreateProgramWithIL; +typedef void *cl_api_clGetKernelSubGroupInfo; +typedef void *cl_api_clCloneKernel; +typedef void *cl_api_clEnqueueSVMMigrateMem; +typedef void *cl_api_clGetDeviceAndHostTimer; +typedef void *cl_api_clGetHostTimer; + +#endif + +/* Vendor dispatch table struture */ + +typedef struct _cl_icd_dispatch { + /* OpenCL 1.0 */ + cl_api_clGetPlatformIDs clGetPlatformIDs; + cl_api_clGetPlatformInfo clGetPlatformInfo; + cl_api_clGetDeviceIDs clGetDeviceIDs; + cl_api_clGetDeviceInfo clGetDeviceInfo; + cl_api_clCreateContext clCreateContext; + cl_api_clCreateContextFromType clCreateContextFromType; + cl_api_clRetainContext clRetainContext; + cl_api_clReleaseContext clReleaseContext; + cl_api_clGetContextInfo clGetContextInfo; + cl_api_clCreateCommandQueue clCreateCommandQueue; + cl_api_clRetainCommandQueue clRetainCommandQueue; + cl_api_clReleaseCommandQueue clReleaseCommandQueue; + cl_api_clGetCommandQueueInfo clGetCommandQueueInfo; + cl_api_clSetCommandQueueProperty clSetCommandQueueProperty; + cl_api_clCreateBuffer clCreateBuffer; + cl_api_clCreateImage2D clCreateImage2D; + cl_api_clCreateImage3D clCreateImage3D; + cl_api_clRetainMemObject clRetainMemObject; + cl_api_clReleaseMemObject clReleaseMemObject; + cl_api_clGetSupportedImageFormats clGetSupportedImageFormats; + cl_api_clGetMemObjectInfo clGetMemObjectInfo; + cl_api_clGetImageInfo clGetImageInfo; + cl_api_clCreateSampler clCreateSampler; + cl_api_clRetainSampler clRetainSampler; + cl_api_clReleaseSampler clReleaseSampler; + cl_api_clGetSamplerInfo clGetSamplerInfo; + cl_api_clCreateProgramWithSource clCreateProgramWithSource; + cl_api_clCreateProgramWithBinary clCreateProgramWithBinary; + cl_api_clRetainProgram clRetainProgram; + cl_api_clReleaseProgram clReleaseProgram; + cl_api_clBuildProgram clBuildProgram; + cl_api_clUnloadCompiler clUnloadCompiler; + cl_api_clGetProgramInfo clGetProgramInfo; + cl_api_clGetProgramBuildInfo clGetProgramBuildInfo; + cl_api_clCreateKernel clCreateKernel; + cl_api_clCreateKernelsInProgram clCreateKernelsInProgram; + cl_api_clRetainKernel clRetainKernel; + cl_api_clReleaseKernel clReleaseKernel; + cl_api_clSetKernelArg clSetKernelArg; + cl_api_clGetKernelInfo clGetKernelInfo; + cl_api_clGetKernelWorkGroupInfo clGetKernelWorkGroupInfo; + cl_api_clWaitForEvents clWaitForEvents; + cl_api_clGetEventInfo clGetEventInfo; + cl_api_clRetainEvent clRetainEvent; + cl_api_clReleaseEvent clReleaseEvent; + cl_api_clGetEventProfilingInfo clGetEventProfilingInfo; + cl_api_clFlush clFlush; + cl_api_clFinish clFinish; + cl_api_clEnqueueReadBuffer clEnqueueReadBuffer; + cl_api_clEnqueueWriteBuffer clEnqueueWriteBuffer; + cl_api_clEnqueueCopyBuffer clEnqueueCopyBuffer; + cl_api_clEnqueueReadImage clEnqueueReadImage; + cl_api_clEnqueueWriteImage clEnqueueWriteImage; + cl_api_clEnqueueCopyImage clEnqueueCopyImage; + cl_api_clEnqueueCopyImageToBuffer clEnqueueCopyImageToBuffer; + cl_api_clEnqueueCopyBufferToImage clEnqueueCopyBufferToImage; + cl_api_clEnqueueMapBuffer clEnqueueMapBuffer; + cl_api_clEnqueueMapImage clEnqueueMapImage; + cl_api_clEnqueueUnmapMemObject clEnqueueUnmapMemObject; + cl_api_clEnqueueNDRangeKernel clEnqueueNDRangeKernel; + cl_api_clEnqueueTask clEnqueueTask; + cl_api_clEnqueueNativeKernel clEnqueueNativeKernel; + cl_api_clEnqueueMarker clEnqueueMarker; + cl_api_clEnqueueWaitForEvents clEnqueueWaitForEvents; + cl_api_clEnqueueBarrier clEnqueueBarrier; + cl_api_clGetExtensionFunctionAddress clGetExtensionFunctionAddress; + cl_api_clCreateFromGLBuffer clCreateFromGLBuffer; + cl_api_clCreateFromGLTexture2D clCreateFromGLTexture2D; + cl_api_clCreateFromGLTexture3D clCreateFromGLTexture3D; + cl_api_clCreateFromGLRenderbuffer clCreateFromGLRenderbuffer; + cl_api_clGetGLObjectInfo clGetGLObjectInfo; + cl_api_clGetGLTextureInfo clGetGLTextureInfo; + cl_api_clEnqueueAcquireGLObjects clEnqueueAcquireGLObjects; + cl_api_clEnqueueReleaseGLObjects clEnqueueReleaseGLObjects; + cl_api_clGetGLContextInfoKHR clGetGLContextInfoKHR; + + /* cl_khr_d3d10_sharing */ + cl_api_clGetDeviceIDsFromD3D10KHR clGetDeviceIDsFromD3D10KHR; + cl_api_clCreateFromD3D10BufferKHR clCreateFromD3D10BufferKHR; + cl_api_clCreateFromD3D10Texture2DKHR clCreateFromD3D10Texture2DKHR; + cl_api_clCreateFromD3D10Texture3DKHR clCreateFromD3D10Texture3DKHR; + cl_api_clEnqueueAcquireD3D10ObjectsKHR clEnqueueAcquireD3D10ObjectsKHR; + cl_api_clEnqueueReleaseD3D10ObjectsKHR clEnqueueReleaseD3D10ObjectsKHR; + + /* OpenCL 1.1 */ + cl_api_clSetEventCallback clSetEventCallback; + cl_api_clCreateSubBuffer clCreateSubBuffer; + cl_api_clSetMemObjectDestructorCallback clSetMemObjectDestructorCallback; + cl_api_clCreateUserEvent clCreateUserEvent; + cl_api_clSetUserEventStatus clSetUserEventStatus; + cl_api_clEnqueueReadBufferRect clEnqueueReadBufferRect; + cl_api_clEnqueueWriteBufferRect clEnqueueWriteBufferRect; + cl_api_clEnqueueCopyBufferRect clEnqueueCopyBufferRect; + + /* cl_ext_device_fission */ + cl_api_clCreateSubDevicesEXT clCreateSubDevicesEXT; + cl_api_clRetainDeviceEXT clRetainDeviceEXT; + cl_api_clReleaseDeviceEXT clReleaseDeviceEXT; + + /* cl_khr_gl_event */ + cl_api_clCreateEventFromGLsyncKHR clCreateEventFromGLsyncKHR; + + /* OpenCL 1.2 */ + cl_api_clCreateSubDevices clCreateSubDevices; + cl_api_clRetainDevice clRetainDevice; + cl_api_clReleaseDevice clReleaseDevice; + cl_api_clCreateImage clCreateImage; + cl_api_clCreateProgramWithBuiltInKernels clCreateProgramWithBuiltInKernels; + cl_api_clCompileProgram clCompileProgram; + cl_api_clLinkProgram clLinkProgram; + cl_api_clUnloadPlatformCompiler clUnloadPlatformCompiler; + cl_api_clGetKernelArgInfo clGetKernelArgInfo; + cl_api_clEnqueueFillBuffer clEnqueueFillBuffer; + cl_api_clEnqueueFillImage clEnqueueFillImage; + cl_api_clEnqueueMigrateMemObjects clEnqueueMigrateMemObjects; + cl_api_clEnqueueMarkerWithWaitList clEnqueueMarkerWithWaitList; + cl_api_clEnqueueBarrierWithWaitList clEnqueueBarrierWithWaitList; + cl_api_clGetExtensionFunctionAddressForPlatform + clGetExtensionFunctionAddressForPlatform; + cl_api_clCreateFromGLTexture clCreateFromGLTexture; + + /* cl_khr_d3d11_sharing */ + cl_api_clGetDeviceIDsFromD3D11KHR clGetDeviceIDsFromD3D11KHR; + cl_api_clCreateFromD3D11BufferKHR clCreateFromD3D11BufferKHR; + cl_api_clCreateFromD3D11Texture2DKHR clCreateFromD3D11Texture2DKHR; + cl_api_clCreateFromD3D11Texture3DKHR clCreateFromD3D11Texture3DKHR; + cl_api_clCreateFromDX9MediaSurfaceKHR clCreateFromDX9MediaSurfaceKHR; + cl_api_clEnqueueAcquireD3D11ObjectsKHR clEnqueueAcquireD3D11ObjectsKHR; + cl_api_clEnqueueReleaseD3D11ObjectsKHR clEnqueueReleaseD3D11ObjectsKHR; + + /* cl_khr_dx9_media_sharing */ + cl_api_clGetDeviceIDsFromDX9MediaAdapterKHR + clGetDeviceIDsFromDX9MediaAdapterKHR; + cl_api_clEnqueueAcquireDX9MediaSurfacesKHR + clEnqueueAcquireDX9MediaSurfacesKHR; + cl_api_clEnqueueReleaseDX9MediaSurfacesKHR + clEnqueueReleaseDX9MediaSurfacesKHR; + + /* cl_khr_egl_image */ + cl_api_clCreateFromEGLImageKHR clCreateFromEGLImageKHR; + cl_api_clEnqueueAcquireEGLObjectsKHR clEnqueueAcquireEGLObjectsKHR; + cl_api_clEnqueueReleaseEGLObjectsKHR clEnqueueReleaseEGLObjectsKHR; + + /* cl_khr_egl_event */ + cl_api_clCreateEventFromEGLSyncKHR clCreateEventFromEGLSyncKHR; + + /* OpenCL 2.0 */ + cl_api_clCreateCommandQueueWithProperties clCreateCommandQueueWithProperties; + cl_api_clCreatePipe clCreatePipe; + cl_api_clGetPipeInfo clGetPipeInfo; + cl_api_clSVMAlloc clSVMAlloc; + cl_api_clSVMFree clSVMFree; + cl_api_clEnqueueSVMFree clEnqueueSVMFree; + cl_api_clEnqueueSVMMemcpy clEnqueueSVMMemcpy; + cl_api_clEnqueueSVMMemFill clEnqueueSVMMemFill; + cl_api_clEnqueueSVMMap clEnqueueSVMMap; + cl_api_clEnqueueSVMUnmap clEnqueueSVMUnmap; + cl_api_clCreateSamplerWithProperties clCreateSamplerWithProperties; + cl_api_clSetKernelArgSVMPointer clSetKernelArgSVMPointer; + cl_api_clSetKernelExecInfo clSetKernelExecInfo; + + /* cl_khr_sub_groups */ + cl_api_clGetKernelSubGroupInfoKHR clGetKernelSubGroupInfoKHR; + + /* OpenCL 2.1 */ + cl_api_clCloneKernel clCloneKernel; + cl_api_clCreateProgramWithIL clCreateProgramWithIL; + cl_api_clEnqueueSVMMigrateMem clEnqueueSVMMigrateMem; + cl_api_clGetDeviceAndHostTimer clGetDeviceAndHostTimer; + cl_api_clGetHostTimer clGetHostTimer; + cl_api_clGetKernelSubGroupInfo clGetKernelSubGroupInfo; + cl_api_clSetDefaultDeviceCommandQueue clSetDefaultDeviceCommandQueue; + + /* OpenCL 2.2 */ + cl_api_clSetProgramReleaseCallback clSetProgramReleaseCallback; + cl_api_clSetProgramSpecializationConstant clSetProgramSpecializationConstant; + + /* OpenCL 3.0 */ + cl_api_clCreateBufferWithProperties clCreateBufferWithProperties; + cl_api_clCreateImageWithProperties clCreateImageWithProperties; + cl_api_clSetContextDestructorCallback clSetContextDestructorCallback; + +} cl_icd_dispatch; + +#ifdef __cplusplus +} +#endif + +#endif /* #ifndef OPENCL_CL_ICD_H */ diff --git a/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/cl_platform.h b/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/cl_platform.h new file mode 100755 index 0000000000000000000000000000000000000000..3f7d4dae203f30acdbb3fedd46ff2e106417d637 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/cl_platform.h @@ -0,0 +1,1386 @@ +/******************************************************************************* + * Copyright (c) 2008-2020 The Khronos Group Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + ******************************************************************************/ + +#ifndef __CL_PLATFORM_H +#define __CL_PLATFORM_H + +#include + +#ifdef __cplusplus +extern "C" { +#endif + +#if defined(_WIN32) + #define CL_API_ENTRY + #define CL_API_CALL __stdcall + #define CL_CALLBACK __stdcall +#else + #define CL_API_ENTRY + #define CL_API_CALL + #define CL_CALLBACK +#endif + +/* + * Deprecation flags refer to the last version of the header in which the + * feature was not deprecated. + * + * E.g. VERSION_1_1_DEPRECATED means the feature is present in 1.1 without + * deprecation but is deprecated in versions later than 1.1. + */ + +#define CL_EXTENSION_WEAK_LINK +#define CL_API_SUFFIX__VERSION_1_0 +#define CL_EXT_SUFFIX__VERSION_1_0 +#define CL_API_SUFFIX__VERSION_1_1 +#define CL_EXT_SUFFIX__VERSION_1_1 +#define CL_API_SUFFIX__VERSION_1_2 +#define CL_EXT_SUFFIX__VERSION_1_2 +#define CL_API_SUFFIX__VERSION_2_0 +#define CL_EXT_SUFFIX__VERSION_2_0 +#define CL_API_SUFFIX__VERSION_2_1 +#define CL_EXT_SUFFIX__VERSION_2_1 +#define CL_API_SUFFIX__VERSION_2_2 +#define CL_EXT_SUFFIX__VERSION_2_2 +#define CL_API_SUFFIX__VERSION_3_0 +#define CL_EXT_SUFFIX__VERSION_3_0 +#define CL_API_SUFFIX__EXPERIMENTAL +#define CL_EXT_SUFFIX__EXPERIMENTAL + + +#ifdef __GNUC__ + #define CL_EXT_SUFFIX_DEPRECATED __attribute__((deprecated)) + #define CL_EXT_PREFIX_DEPRECATED +#elif defined(_WIN32) + #define CL_EXT_SUFFIX_DEPRECATED + #define CL_EXT_PREFIX_DEPRECATED __declspec(deprecated) +#else + #define CL_EXT_SUFFIX_DEPRECATED + #define CL_EXT_PREFIX_DEPRECATED +#endif + +#ifdef CL_USE_DEPRECATED_OPENCL_1_0_APIS + #define CL_EXT_SUFFIX__VERSION_1_0_DEPRECATED + #define CL_EXT_PREFIX__VERSION_1_0_DEPRECATED +#else + #define CL_EXT_SUFFIX__VERSION_1_0_DEPRECATED CL_EXT_SUFFIX_DEPRECATED + #define CL_EXT_PREFIX__VERSION_1_0_DEPRECATED CL_EXT_PREFIX_DEPRECATED +#endif + +#ifdef CL_USE_DEPRECATED_OPENCL_1_1_APIS + #define CL_EXT_SUFFIX__VERSION_1_1_DEPRECATED + #define CL_EXT_PREFIX__VERSION_1_1_DEPRECATED +#else + #define CL_EXT_SUFFIX__VERSION_1_1_DEPRECATED CL_EXT_SUFFIX_DEPRECATED + #define CL_EXT_PREFIX__VERSION_1_1_DEPRECATED CL_EXT_PREFIX_DEPRECATED +#endif + +#ifdef CL_USE_DEPRECATED_OPENCL_1_2_APIS + #define CL_EXT_SUFFIX__VERSION_1_2_DEPRECATED + #define CL_EXT_PREFIX__VERSION_1_2_DEPRECATED +#else + #define CL_EXT_SUFFIX__VERSION_1_2_DEPRECATED CL_EXT_SUFFIX_DEPRECATED + #define CL_EXT_PREFIX__VERSION_1_2_DEPRECATED CL_EXT_PREFIX_DEPRECATED + #endif + +#ifdef CL_USE_DEPRECATED_OPENCL_2_0_APIS + #define CL_EXT_SUFFIX__VERSION_2_0_DEPRECATED + #define CL_EXT_PREFIX__VERSION_2_0_DEPRECATED +#else + #define CL_EXT_SUFFIX__VERSION_2_0_DEPRECATED CL_EXT_SUFFIX_DEPRECATED + #define CL_EXT_PREFIX__VERSION_2_0_DEPRECATED CL_EXT_PREFIX_DEPRECATED +#endif + +#ifdef CL_USE_DEPRECATED_OPENCL_2_1_APIS + #define CL_EXT_SUFFIX__VERSION_2_1_DEPRECATED + #define CL_EXT_PREFIX__VERSION_2_1_DEPRECATED +#else + #define CL_EXT_SUFFIX__VERSION_2_1_DEPRECATED CL_EXT_SUFFIX_DEPRECATED + #define CL_EXT_PREFIX__VERSION_2_1_DEPRECATED CL_EXT_PREFIX_DEPRECATED +#endif + +#ifdef CL_USE_DEPRECATED_OPENCL_2_2_APIS + #define CL_EXT_SUFFIX__VERSION_2_2_DEPRECATED + #define CL_EXT_PREFIX__VERSION_2_2_DEPRECATED +#else + #define CL_EXT_SUFFIX__VERSION_2_2_DEPRECATED CL_EXT_SUFFIX_DEPRECATED + #define CL_EXT_PREFIX__VERSION_2_2_DEPRECATED CL_EXT_PREFIX_DEPRECATED +#endif + +#if (defined (_WIN32) && defined(_MSC_VER)) + +/* scalar types */ +typedef signed __int8 cl_char; +typedef unsigned __int8 cl_uchar; +typedef signed __int16 cl_short; +typedef unsigned __int16 cl_ushort; +typedef signed __int32 cl_int; +typedef unsigned __int32 cl_uint; +typedef signed __int64 cl_long; +typedef unsigned __int64 cl_ulong; + +typedef unsigned __int16 cl_half; +typedef float cl_float; +typedef double cl_double; + +/* Macro names and corresponding values defined by OpenCL */ +#define CL_CHAR_BIT 8 +#define CL_SCHAR_MAX 127 +#define CL_SCHAR_MIN (-127-1) +#define CL_CHAR_MAX CL_SCHAR_MAX +#define CL_CHAR_MIN CL_SCHAR_MIN +#define CL_UCHAR_MAX 255 +#define CL_SHRT_MAX 32767 +#define CL_SHRT_MIN (-32767-1) +#define CL_USHRT_MAX 65535 +#define CL_INT_MAX 2147483647 +#define CL_INT_MIN (-2147483647-1) +#define CL_UINT_MAX 0xffffffffU +#define CL_LONG_MAX ((cl_long) 0x7FFFFFFFFFFFFFFFLL) +#define CL_LONG_MIN ((cl_long) -0x7FFFFFFFFFFFFFFFLL - 1LL) +#define CL_ULONG_MAX ((cl_ulong) 0xFFFFFFFFFFFFFFFFULL) + +#define CL_FLT_DIG 6 +#define CL_FLT_MANT_DIG 24 +#define CL_FLT_MAX_10_EXP +38 +#define CL_FLT_MAX_EXP +128 +#define CL_FLT_MIN_10_EXP -37 +#define CL_FLT_MIN_EXP -125 +#define CL_FLT_RADIX 2 +#define CL_FLT_MAX 340282346638528859811704183484516925440.0f +#define CL_FLT_MIN 1.175494350822287507969e-38f +#define CL_FLT_EPSILON 1.1920928955078125e-7f + +#define CL_HALF_DIG 3 +#define CL_HALF_MANT_DIG 11 +#define CL_HALF_MAX_10_EXP +4 +#define CL_HALF_MAX_EXP +16 +#define CL_HALF_MIN_10_EXP -4 +#define CL_HALF_MIN_EXP -13 +#define CL_HALF_RADIX 2 +#define CL_HALF_MAX 65504.0f +#define CL_HALF_MIN 6.103515625e-05f +#define CL_HALF_EPSILON 9.765625e-04f + +#define CL_DBL_DIG 15 +#define CL_DBL_MANT_DIG 53 +#define CL_DBL_MAX_10_EXP +308 +#define CL_DBL_MAX_EXP +1024 +#define CL_DBL_MIN_10_EXP -307 +#define CL_DBL_MIN_EXP -1021 +#define CL_DBL_RADIX 2 +#define CL_DBL_MAX 1.7976931348623158e+308 +#define CL_DBL_MIN 2.225073858507201383090e-308 +#define CL_DBL_EPSILON 2.220446049250313080847e-16 + +#define CL_M_E 2.7182818284590452354 +#define CL_M_LOG2E 1.4426950408889634074 +#define CL_M_LOG10E 0.43429448190325182765 +#define CL_M_LN2 0.69314718055994530942 +#define CL_M_LN10 2.30258509299404568402 +#define CL_M_PI 3.14159265358979323846 +#define CL_M_PI_2 1.57079632679489661923 +#define CL_M_PI_4 0.78539816339744830962 +#define CL_M_1_PI 0.31830988618379067154 +#define CL_M_2_PI 0.63661977236758134308 +#define CL_M_2_SQRTPI 1.12837916709551257390 +#define CL_M_SQRT2 1.41421356237309504880 +#define CL_M_SQRT1_2 0.70710678118654752440 + +#define CL_M_E_F 2.718281828f +#define CL_M_LOG2E_F 1.442695041f +#define CL_M_LOG10E_F 0.434294482f +#define CL_M_LN2_F 0.693147181f +#define CL_M_LN10_F 2.302585093f +#define CL_M_PI_F 3.141592654f +#define CL_M_PI_2_F 1.570796327f +#define CL_M_PI_4_F 0.785398163f +#define CL_M_1_PI_F 0.318309886f +#define CL_M_2_PI_F 0.636619772f +#define CL_M_2_SQRTPI_F 1.128379167f +#define CL_M_SQRT2_F 1.414213562f +#define CL_M_SQRT1_2_F 0.707106781f + +#define CL_NAN (CL_INFINITY - CL_INFINITY) +#define CL_HUGE_VALF ((cl_float) 1e50) +#define CL_HUGE_VAL ((cl_double) 1e500) +#define CL_MAXFLOAT CL_FLT_MAX +#define CL_INFINITY CL_HUGE_VALF + +#else + +#include + +/* scalar types */ +typedef int8_t cl_char; +typedef uint8_t cl_uchar; +typedef int16_t cl_short; +typedef uint16_t cl_ushort; +typedef int32_t cl_int; +typedef uint32_t cl_uint; +typedef int64_t cl_long; +typedef uint64_t cl_ulong; + +typedef uint16_t cl_half; +typedef float cl_float; +typedef double cl_double; + +/* Macro names and corresponding values defined by OpenCL */ +#define CL_CHAR_BIT 8 +#define CL_SCHAR_MAX 127 +#define CL_SCHAR_MIN (-127-1) +#define CL_CHAR_MAX CL_SCHAR_MAX +#define CL_CHAR_MIN CL_SCHAR_MIN +#define CL_UCHAR_MAX 255 +#define CL_SHRT_MAX 32767 +#define CL_SHRT_MIN (-32767-1) +#define CL_USHRT_MAX 65535 +#define CL_INT_MAX 2147483647 +#define CL_INT_MIN (-2147483647-1) +#define CL_UINT_MAX 0xffffffffU +#define CL_LONG_MAX ((cl_long) 0x7FFFFFFFFFFFFFFFLL) +#define CL_LONG_MIN ((cl_long) -0x7FFFFFFFFFFFFFFFLL - 1LL) +#define CL_ULONG_MAX ((cl_ulong) 0xFFFFFFFFFFFFFFFFULL) + +#define CL_FLT_DIG 6 +#define CL_FLT_MANT_DIG 24 +#define CL_FLT_MAX_10_EXP +38 +#define CL_FLT_MAX_EXP +128 +#define CL_FLT_MIN_10_EXP -37 +#define CL_FLT_MIN_EXP -125 +#define CL_FLT_RADIX 2 +#define CL_FLT_MAX 340282346638528859811704183484516925440.0f +#define CL_FLT_MIN 1.175494350822287507969e-38f +#define CL_FLT_EPSILON 1.1920928955078125e-7f + +#define CL_HALF_DIG 3 +#define CL_HALF_MANT_DIG 11 +#define CL_HALF_MAX_10_EXP +4 +#define CL_HALF_MAX_EXP +16 +#define CL_HALF_MIN_10_EXP -4 +#define CL_HALF_MIN_EXP -13 +#define CL_HALF_RADIX 2 +#define CL_HALF_MAX 65504.0f +#define CL_HALF_MIN 6.103515625e-05f +#define CL_HALF_EPSILON 9.765625e-04f + +#define CL_DBL_DIG 15 +#define CL_DBL_MANT_DIG 53 +#define CL_DBL_MAX_10_EXP +308 +#define CL_DBL_MAX_EXP +1024 +#define CL_DBL_MIN_10_EXP -307 +#define CL_DBL_MIN_EXP -1021 +#define CL_DBL_RADIX 2 +#define CL_DBL_MAX 179769313486231570814527423731704356798070567525844996598917476803157260780028538760589558632766878171540458953514382464234321326889464182768467546703537516986049910576551282076245490090389328944075868508455133942304583236903222948165808559332123348274797826204144723168738177180919299881250404026184124858368.0 +#define CL_DBL_MIN 2.225073858507201383090e-308 +#define CL_DBL_EPSILON 2.220446049250313080847e-16 + +#define CL_M_E 2.7182818284590452354 +#define CL_M_LOG2E 1.4426950408889634074 +#define CL_M_LOG10E 0.43429448190325182765 +#define CL_M_LN2 0.69314718055994530942 +#define CL_M_LN10 2.30258509299404568402 +#define CL_M_PI 3.14159265358979323846 +#define CL_M_PI_2 1.57079632679489661923 +#define CL_M_PI_4 0.78539816339744830962 +#define CL_M_1_PI 0.31830988618379067154 +#define CL_M_2_PI 0.63661977236758134308 +#define CL_M_2_SQRTPI 1.12837916709551257390 +#define CL_M_SQRT2 1.41421356237309504880 +#define CL_M_SQRT1_2 0.70710678118654752440 + +#define CL_M_E_F 2.718281828f +#define CL_M_LOG2E_F 1.442695041f +#define CL_M_LOG10E_F 0.434294482f +#define CL_M_LN2_F 0.693147181f +#define CL_M_LN10_F 2.302585093f +#define CL_M_PI_F 3.141592654f +#define CL_M_PI_2_F 1.570796327f +#define CL_M_PI_4_F 0.785398163f +#define CL_M_1_PI_F 0.318309886f +#define CL_M_2_PI_F 0.636619772f +#define CL_M_2_SQRTPI_F 1.128379167f +#define CL_M_SQRT2_F 1.414213562f +#define CL_M_SQRT1_2_F 0.707106781f + +#if defined( __GNUC__ ) + #define CL_HUGE_VALF __builtin_huge_valf() + #define CL_HUGE_VAL __builtin_huge_val() + #define CL_NAN __builtin_nanf( "" ) +#else + #define CL_HUGE_VALF ((cl_float) 1e50) + #define CL_HUGE_VAL ((cl_double) 1e500) + float nanf( const char * ); + #define CL_NAN nanf( "" ) +#endif +#define CL_MAXFLOAT CL_FLT_MAX +#define CL_INFINITY CL_HUGE_VALF + +#endif + +#include + +/* Mirror types to GL types. Mirror types allow us to avoid deciding which 87s to load based on whether we are using GL or GLES here. */ +typedef unsigned int cl_GLuint; +typedef int cl_GLint; +typedef unsigned int cl_GLenum; + +/* + * Vector types + * + * Note: OpenCL requires that all types be naturally aligned. + * This means that vector types must be naturally aligned. + * For example, a vector of four floats must be aligned to + * a 16 byte boundary (calculated as 4 * the natural 4-byte + * alignment of the float). The alignment qualifiers here + * will only function properly if your compiler supports them + * and if you don't actively work to defeat them. For example, + * in order for a cl_float4 to be 16 byte aligned in a struct, + * the start of the struct must itself be 16-byte aligned. + * + * Maintaining proper alignment is the user's responsibility. + */ + +/* Define basic vector types */ +#if defined( __VEC__ ) + #if !defined(__clang__) + #include /* may be omitted depending on compiler. AltiVec spec provides no way to detect whether the header is required. */ + #endif + typedef __vector unsigned char __cl_uchar16; + typedef __vector signed char __cl_char16; + typedef __vector unsigned short __cl_ushort8; + typedef __vector signed short __cl_short8; + typedef __vector unsigned int __cl_uint4; + typedef __vector signed int __cl_int4; + typedef __vector float __cl_float4; + #define __CL_UCHAR16__ 1 + #define __CL_CHAR16__ 1 + #define __CL_USHORT8__ 1 + #define __CL_SHORT8__ 1 + #define __CL_UINT4__ 1 + #define __CL_INT4__ 1 + #define __CL_FLOAT4__ 1 +#endif + +#if defined( __SSE__ ) + #if defined( __MINGW64__ ) + #include + #else + #include + #endif + #if defined( __GNUC__ ) + typedef float __cl_float4 __attribute__((vector_size(16))); + #else + typedef __m128 __cl_float4; + #endif + #define __CL_FLOAT4__ 1 +#endif + +#if defined( __SSE2__ ) + #if defined( __MINGW64__ ) + #include + #else + #include + #endif + #if defined( __GNUC__ ) + typedef cl_uchar __cl_uchar16 __attribute__((vector_size(16))); + typedef cl_char __cl_char16 __attribute__((vector_size(16))); + typedef cl_ushort __cl_ushort8 __attribute__((vector_size(16))); + typedef cl_short __cl_short8 __attribute__((vector_size(16))); + typedef cl_uint __cl_uint4 __attribute__((vector_size(16))); + typedef cl_int __cl_int4 __attribute__((vector_size(16))); + typedef cl_ulong __cl_ulong2 __attribute__((vector_size(16))); + typedef cl_long __cl_long2 __attribute__((vector_size(16))); + typedef cl_double __cl_double2 __attribute__((vector_size(16))); + #else + typedef __m128i __cl_uchar16; + typedef __m128i __cl_char16; + typedef __m128i __cl_ushort8; + typedef __m128i __cl_short8; + typedef __m128i __cl_uint4; + typedef __m128i __cl_int4; + typedef __m128i __cl_ulong2; + typedef __m128i __cl_long2; + typedef __m128d __cl_double2; + #endif + #define __CL_UCHAR16__ 1 + #define __CL_CHAR16__ 1 + #define __CL_USHORT8__ 1 + #define __CL_SHORT8__ 1 + #define __CL_INT4__ 1 + #define __CL_UINT4__ 1 + #define __CL_ULONG2__ 1 + #define __CL_LONG2__ 1 + #define __CL_DOUBLE2__ 1 +#endif + +#if defined( __MMX__ ) + #include + #if defined( __GNUC__ ) + typedef cl_uchar __cl_uchar8 __attribute__((vector_size(8))); + typedef cl_char __cl_char8 __attribute__((vector_size(8))); + typedef cl_ushort __cl_ushort4 __attribute__((vector_size(8))); + typedef cl_short __cl_short4 __attribute__((vector_size(8))); + typedef cl_uint __cl_uint2 __attribute__((vector_size(8))); + typedef cl_int __cl_int2 __attribute__((vector_size(8))); + typedef cl_ulong __cl_ulong1 __attribute__((vector_size(8))); + typedef cl_long __cl_long1 __attribute__((vector_size(8))); + typedef cl_float __cl_float2 __attribute__((vector_size(8))); + #else + typedef __m64 __cl_uchar8; + typedef __m64 __cl_char8; + typedef __m64 __cl_ushort4; + typedef __m64 __cl_short4; + typedef __m64 __cl_uint2; + typedef __m64 __cl_int2; + typedef __m64 __cl_ulong1; + typedef __m64 __cl_long1; + typedef __m64 __cl_float2; + #endif + #define __CL_UCHAR8__ 1 + #define __CL_CHAR8__ 1 + #define __CL_USHORT4__ 1 + #define __CL_SHORT4__ 1 + #define __CL_INT2__ 1 + #define __CL_UINT2__ 1 + #define __CL_ULONG1__ 1 + #define __CL_LONG1__ 1 + #define __CL_FLOAT2__ 1 +#endif + +#if defined( __AVX__ ) + #if defined( __MINGW64__ ) + #include + #else + #include + #endif + #if defined( __GNUC__ ) + typedef cl_float __cl_float8 __attribute__((vector_size(32))); + typedef cl_double __cl_double4 __attribute__((vector_size(32))); + #else + typedef __m256 __cl_float8; + typedef __m256d __cl_double4; + #endif + #define __CL_FLOAT8__ 1 + #define __CL_DOUBLE4__ 1 +#endif + +/* Define capabilities for anonymous struct members. */ +#if !defined(__cplusplus) && defined(__STDC_VERSION__) && __STDC_VERSION__ >= 201112L +#define __CL_HAS_ANON_STRUCT__ 1 +#define __CL_ANON_STRUCT__ +#elif defined( __GNUC__) && ! defined( __STRICT_ANSI__ ) +#define __CL_HAS_ANON_STRUCT__ 1 +#define __CL_ANON_STRUCT__ __extension__ +#elif defined( _WIN32) && defined(_MSC_VER) + #if _MSC_VER >= 1500 + /* Microsoft Developer Studio 2008 supports anonymous structs, but + * complains by default. */ + #define __CL_HAS_ANON_STRUCT__ 1 + #define __CL_ANON_STRUCT__ + /* Disable warning C4201: nonstandard extension used : nameless + * struct/union */ + #pragma warning( push ) + #pragma warning( disable : 4201 ) + #endif +#else +#define __CL_HAS_ANON_STRUCT__ 0 +#define __CL_ANON_STRUCT__ +#endif + +/* Define alignment keys */ +#if defined( __GNUC__ ) + #define CL_ALIGNED(_x) __attribute__ ((aligned(_x))) +#elif defined( _WIN32) && (_MSC_VER) + /* Alignment keys neutered on windows because MSVC can't swallow function arguments with alignment requirements */ + /* http://msdn.microsoft.com/en-us/library/373ak2y1%28VS.71%29.aspx */ + /* #include */ + /* #define CL_ALIGNED(_x) _CRT_ALIGN(_x) */ + #define CL_ALIGNED(_x) +#else + #warning Need to implement some method to align data here + #define CL_ALIGNED(_x) +#endif + +/* Indicate whether .xyzw, .s0123 and .hi.lo are supported */ +#if __CL_HAS_ANON_STRUCT__ + /* .xyzw and .s0123...{f|F} are supported */ + #define CL_HAS_NAMED_VECTOR_FIELDS 1 + /* .hi and .lo are supported */ + #define CL_HAS_HI_LO_VECTOR_FIELDS 1 +#endif + +/* Define cl_vector types */ + +/* ---- cl_charn ---- */ +typedef union +{ + cl_char CL_ALIGNED(2) s[2]; +#if __CL_HAS_ANON_STRUCT__ + __CL_ANON_STRUCT__ struct{ cl_char x, y; }; + __CL_ANON_STRUCT__ struct{ cl_char s0, s1; }; + __CL_ANON_STRUCT__ struct{ cl_char lo, hi; }; +#endif +#if defined( __CL_CHAR2__) + __cl_char2 v2; +#endif +}cl_char2; + +typedef union +{ + cl_char CL_ALIGNED(4) s[4]; +#if __CL_HAS_ANON_STRUCT__ + __CL_ANON_STRUCT__ struct{ cl_char x, y, z, w; }; + __CL_ANON_STRUCT__ struct{ cl_char s0, s1, s2, s3; }; + __CL_ANON_STRUCT__ struct{ cl_char2 lo, hi; }; +#endif +#if defined( __CL_CHAR2__) + __cl_char2 v2[2]; +#endif +#if defined( __CL_CHAR4__) + __cl_char4 v4; +#endif +}cl_char4; + +/* cl_char3 is identical in size, alignment and behavior to cl_char4. See section 6.1.5. */ +typedef cl_char4 cl_char3; + +typedef union +{ + cl_char CL_ALIGNED(8) s[8]; +#if __CL_HAS_ANON_STRUCT__ + __CL_ANON_STRUCT__ struct{ cl_char x, y, z, w; }; + __CL_ANON_STRUCT__ struct{ cl_char s0, s1, s2, s3, s4, s5, s6, s7; }; + __CL_ANON_STRUCT__ struct{ cl_char4 lo, hi; }; +#endif +#if defined( __CL_CHAR2__) + __cl_char2 v2[4]; +#endif +#if defined( __CL_CHAR4__) + __cl_char4 v4[2]; +#endif +#if defined( __CL_CHAR8__ ) + __cl_char8 v8; +#endif +}cl_char8; + +typedef union +{ + cl_char CL_ALIGNED(16) s[16]; +#if __CL_HAS_ANON_STRUCT__ + __CL_ANON_STRUCT__ struct{ cl_char x, y, z, w, __spacer4, __spacer5, __spacer6, __spacer7, __spacer8, __spacer9, sa, sb, sc, sd, se, sf; }; + __CL_ANON_STRUCT__ struct{ cl_char s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, sA, sB, sC, sD, sE, sF; }; + __CL_ANON_STRUCT__ struct{ cl_char8 lo, hi; }; +#endif +#if defined( __CL_CHAR2__) + __cl_char2 v2[8]; +#endif +#if defined( __CL_CHAR4__) + __cl_char4 v4[4]; +#endif +#if defined( __CL_CHAR8__ ) + __cl_char8 v8[2]; +#endif +#if defined( __CL_CHAR16__ ) + __cl_char16 v16; +#endif +}cl_char16; + + +/* ---- cl_ucharn ---- */ +typedef union +{ + cl_uchar CL_ALIGNED(2) s[2]; +#if __CL_HAS_ANON_STRUCT__ + __CL_ANON_STRUCT__ struct{ cl_uchar x, y; }; + __CL_ANON_STRUCT__ struct{ cl_uchar s0, s1; }; + __CL_ANON_STRUCT__ struct{ cl_uchar lo, hi; }; +#endif +#if defined( __cl_uchar2__) + __cl_uchar2 v2; +#endif +}cl_uchar2; + +typedef union +{ + cl_uchar CL_ALIGNED(4) s[4]; +#if __CL_HAS_ANON_STRUCT__ + __CL_ANON_STRUCT__ struct{ cl_uchar x, y, z, w; }; + __CL_ANON_STRUCT__ struct{ cl_uchar s0, s1, s2, s3; }; + __CL_ANON_STRUCT__ struct{ cl_uchar2 lo, hi; }; +#endif +#if defined( __CL_UCHAR2__) + __cl_uchar2 v2[2]; +#endif +#if defined( __CL_UCHAR4__) + __cl_uchar4 v4; +#endif +}cl_uchar4; + +/* cl_uchar3 is identical in size, alignment and behavior to cl_uchar4. See section 6.1.5. */ +typedef cl_uchar4 cl_uchar3; + +typedef union +{ + cl_uchar CL_ALIGNED(8) s[8]; +#if __CL_HAS_ANON_STRUCT__ + __CL_ANON_STRUCT__ struct{ cl_uchar x, y, z, w; }; + __CL_ANON_STRUCT__ struct{ cl_uchar s0, s1, s2, s3, s4, s5, s6, s7; }; + __CL_ANON_STRUCT__ struct{ cl_uchar4 lo, hi; }; +#endif +#if defined( __CL_UCHAR2__) + __cl_uchar2 v2[4]; +#endif +#if defined( __CL_UCHAR4__) + __cl_uchar4 v4[2]; +#endif +#if defined( __CL_UCHAR8__ ) + __cl_uchar8 v8; +#endif +}cl_uchar8; + +typedef union +{ + cl_uchar CL_ALIGNED(16) s[16]; +#if __CL_HAS_ANON_STRUCT__ + __CL_ANON_STRUCT__ struct{ cl_uchar x, y, z, w, __spacer4, __spacer5, __spacer6, __spacer7, __spacer8, __spacer9, sa, sb, sc, sd, se, sf; }; + __CL_ANON_STRUCT__ struct{ cl_uchar s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, sA, sB, sC, sD, sE, sF; }; + __CL_ANON_STRUCT__ struct{ cl_uchar8 lo, hi; }; +#endif +#if defined( __CL_UCHAR2__) + __cl_uchar2 v2[8]; +#endif +#if defined( __CL_UCHAR4__) + __cl_uchar4 v4[4]; +#endif +#if defined( __CL_UCHAR8__ ) + __cl_uchar8 v8[2]; +#endif +#if defined( __CL_UCHAR16__ ) + __cl_uchar16 v16; +#endif +}cl_uchar16; + + +/* ---- cl_shortn ---- */ +typedef union +{ + cl_short CL_ALIGNED(4) s[2]; +#if __CL_HAS_ANON_STRUCT__ + __CL_ANON_STRUCT__ struct{ cl_short x, y; }; + __CL_ANON_STRUCT__ struct{ cl_short s0, s1; }; + __CL_ANON_STRUCT__ struct{ cl_short lo, hi; }; +#endif +#if defined( __CL_SHORT2__) + __cl_short2 v2; +#endif +}cl_short2; + +typedef union +{ + cl_short CL_ALIGNED(8) s[4]; +#if __CL_HAS_ANON_STRUCT__ + __CL_ANON_STRUCT__ struct{ cl_short x, y, z, w; }; + __CL_ANON_STRUCT__ struct{ cl_short s0, s1, s2, s3; }; + __CL_ANON_STRUCT__ struct{ cl_short2 lo, hi; }; +#endif +#if defined( __CL_SHORT2__) + __cl_short2 v2[2]; +#endif +#if defined( __CL_SHORT4__) + __cl_short4 v4; +#endif +}cl_short4; + +/* cl_short3 is identical in size, alignment and behavior to cl_short4. See section 6.1.5. */ +typedef cl_short4 cl_short3; + +typedef union +{ + cl_short CL_ALIGNED(16) s[8]; +#if __CL_HAS_ANON_STRUCT__ + __CL_ANON_STRUCT__ struct{ cl_short x, y, z, w; }; + __CL_ANON_STRUCT__ struct{ cl_short s0, s1, s2, s3, s4, s5, s6, s7; }; + __CL_ANON_STRUCT__ struct{ cl_short4 lo, hi; }; +#endif +#if defined( __CL_SHORT2__) + __cl_short2 v2[4]; +#endif +#if defined( __CL_SHORT4__) + __cl_short4 v4[2]; +#endif +#if defined( __CL_SHORT8__ ) + __cl_short8 v8; +#endif +}cl_short8; + +typedef union +{ + cl_short CL_ALIGNED(32) s[16]; +#if __CL_HAS_ANON_STRUCT__ + __CL_ANON_STRUCT__ struct{ cl_short x, y, z, w, __spacer4, __spacer5, __spacer6, __spacer7, __spacer8, __spacer9, sa, sb, sc, sd, se, sf; }; + __CL_ANON_STRUCT__ struct{ cl_short s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, sA, sB, sC, sD, sE, sF; }; + __CL_ANON_STRUCT__ struct{ cl_short8 lo, hi; }; +#endif +#if defined( __CL_SHORT2__) + __cl_short2 v2[8]; +#endif +#if defined( __CL_SHORT4__) + __cl_short4 v4[4]; +#endif +#if defined( __CL_SHORT8__ ) + __cl_short8 v8[2]; +#endif +#if defined( __CL_SHORT16__ ) + __cl_short16 v16; +#endif +}cl_short16; + + +/* ---- cl_ushortn ---- */ +typedef union +{ + cl_ushort CL_ALIGNED(4) s[2]; +#if __CL_HAS_ANON_STRUCT__ + __CL_ANON_STRUCT__ struct{ cl_ushort x, y; }; + __CL_ANON_STRUCT__ struct{ cl_ushort s0, s1; }; + __CL_ANON_STRUCT__ struct{ cl_ushort lo, hi; }; +#endif +#if defined( __CL_USHORT2__) + __cl_ushort2 v2; +#endif +}cl_ushort2; + +typedef union +{ + cl_ushort CL_ALIGNED(8) s[4]; +#if __CL_HAS_ANON_STRUCT__ + __CL_ANON_STRUCT__ struct{ cl_ushort x, y, z, w; }; + __CL_ANON_STRUCT__ struct{ cl_ushort s0, s1, s2, s3; }; + __CL_ANON_STRUCT__ struct{ cl_ushort2 lo, hi; }; +#endif +#if defined( __CL_USHORT2__) + __cl_ushort2 v2[2]; +#endif +#if defined( __CL_USHORT4__) + __cl_ushort4 v4; +#endif +}cl_ushort4; + +/* cl_ushort3 is identical in size, alignment and behavior to cl_ushort4. See section 6.1.5. */ +typedef cl_ushort4 cl_ushort3; + +typedef union +{ + cl_ushort CL_ALIGNED(16) s[8]; +#if __CL_HAS_ANON_STRUCT__ + __CL_ANON_STRUCT__ struct{ cl_ushort x, y, z, w; }; + __CL_ANON_STRUCT__ struct{ cl_ushort s0, s1, s2, s3, s4, s5, s6, s7; }; + __CL_ANON_STRUCT__ struct{ cl_ushort4 lo, hi; }; +#endif +#if defined( __CL_USHORT2__) + __cl_ushort2 v2[4]; +#endif +#if defined( __CL_USHORT4__) + __cl_ushort4 v4[2]; +#endif +#if defined( __CL_USHORT8__ ) + __cl_ushort8 v8; +#endif +}cl_ushort8; + +typedef union +{ + cl_ushort CL_ALIGNED(32) s[16]; +#if __CL_HAS_ANON_STRUCT__ + __CL_ANON_STRUCT__ struct{ cl_ushort x, y, z, w, __spacer4, __spacer5, __spacer6, __spacer7, __spacer8, __spacer9, sa, sb, sc, sd, se, sf; }; + __CL_ANON_STRUCT__ struct{ cl_ushort s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, sA, sB, sC, sD, sE, sF; }; + __CL_ANON_STRUCT__ struct{ cl_ushort8 lo, hi; }; +#endif +#if defined( __CL_USHORT2__) + __cl_ushort2 v2[8]; +#endif +#if defined( __CL_USHORT4__) + __cl_ushort4 v4[4]; +#endif +#if defined( __CL_USHORT8__ ) + __cl_ushort8 v8[2]; +#endif +#if defined( __CL_USHORT16__ ) + __cl_ushort16 v16; +#endif +}cl_ushort16; + + +/* ---- cl_halfn ---- */ +typedef union +{ + cl_half CL_ALIGNED(4) s[2]; +#if __CL_HAS_ANON_STRUCT__ + __CL_ANON_STRUCT__ struct{ cl_half x, y; }; + __CL_ANON_STRUCT__ struct{ cl_half s0, s1; }; + __CL_ANON_STRUCT__ struct{ cl_half lo, hi; }; +#endif +#if defined( __CL_HALF2__) + __cl_half2 v2; +#endif +}cl_half2; + +typedef union +{ + cl_half CL_ALIGNED(8) s[4]; +#if __CL_HAS_ANON_STRUCT__ + __CL_ANON_STRUCT__ struct{ cl_half x, y, z, w; }; + __CL_ANON_STRUCT__ struct{ cl_half s0, s1, s2, s3; }; + __CL_ANON_STRUCT__ struct{ cl_half2 lo, hi; }; +#endif +#if defined( __CL_HALF2__) + __cl_half2 v2[2]; +#endif +#if defined( __CL_HALF4__) + __cl_half4 v4; +#endif +}cl_half4; + +/* cl_half3 is identical in size, alignment and behavior to cl_half4. See section 6.1.5. */ +typedef cl_half4 cl_half3; + +typedef union +{ + cl_half CL_ALIGNED(16) s[8]; +#if __CL_HAS_ANON_STRUCT__ + __CL_ANON_STRUCT__ struct{ cl_half x, y, z, w; }; + __CL_ANON_STRUCT__ struct{ cl_half s0, s1, s2, s3, s4, s5, s6, s7; }; + __CL_ANON_STRUCT__ struct{ cl_half4 lo, hi; }; +#endif +#if defined( __CL_HALF2__) + __cl_half2 v2[4]; +#endif +#if defined( __CL_HALF4__) + __cl_half4 v4[2]; +#endif +#if defined( __CL_HALF8__ ) + __cl_half8 v8; +#endif +}cl_half8; + +typedef union +{ + cl_half CL_ALIGNED(32) s[16]; +#if __CL_HAS_ANON_STRUCT__ + __CL_ANON_STRUCT__ struct{ cl_half x, y, z, w, __spacer4, __spacer5, __spacer6, __spacer7, __spacer8, __spacer9, sa, sb, sc, sd, se, sf; }; + __CL_ANON_STRUCT__ struct{ cl_half s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, sA, sB, sC, sD, sE, sF; }; + __CL_ANON_STRUCT__ struct{ cl_half8 lo, hi; }; +#endif +#if defined( __CL_HALF2__) + __cl_half2 v2[8]; +#endif +#if defined( __CL_HALF4__) + __cl_half4 v4[4]; +#endif +#if defined( __CL_HALF8__ ) + __cl_half8 v8[2]; +#endif +#if defined( __CL_HALF16__ ) + __cl_half16 v16; +#endif +}cl_half16; + +/* ---- cl_intn ---- */ +typedef union +{ + cl_int CL_ALIGNED(8) s[2]; +#if __CL_HAS_ANON_STRUCT__ + __CL_ANON_STRUCT__ struct{ cl_int x, y; }; + __CL_ANON_STRUCT__ struct{ cl_int s0, s1; }; + __CL_ANON_STRUCT__ struct{ cl_int lo, hi; }; +#endif +#if defined( __CL_INT2__) + __cl_int2 v2; +#endif +}cl_int2; + +typedef union +{ + cl_int CL_ALIGNED(16) s[4]; +#if __CL_HAS_ANON_STRUCT__ + __CL_ANON_STRUCT__ struct{ cl_int x, y, z, w; }; + __CL_ANON_STRUCT__ struct{ cl_int s0, s1, s2, s3; }; + __CL_ANON_STRUCT__ struct{ cl_int2 lo, hi; }; +#endif +#if defined( __CL_INT2__) + __cl_int2 v2[2]; +#endif +#if defined( __CL_INT4__) + __cl_int4 v4; +#endif +}cl_int4; + +/* cl_int3 is identical in size, alignment and behavior to cl_int4. See section 6.1.5. */ +typedef cl_int4 cl_int3; + +typedef union +{ + cl_int CL_ALIGNED(32) s[8]; +#if __CL_HAS_ANON_STRUCT__ + __CL_ANON_STRUCT__ struct{ cl_int x, y, z, w; }; + __CL_ANON_STRUCT__ struct{ cl_int s0, s1, s2, s3, s4, s5, s6, s7; }; + __CL_ANON_STRUCT__ struct{ cl_int4 lo, hi; }; +#endif +#if defined( __CL_INT2__) + __cl_int2 v2[4]; +#endif +#if defined( __CL_INT4__) + __cl_int4 v4[2]; +#endif +#if defined( __CL_INT8__ ) + __cl_int8 v8; +#endif +}cl_int8; + +typedef union +{ + cl_int CL_ALIGNED(64) s[16]; +#if __CL_HAS_ANON_STRUCT__ + __CL_ANON_STRUCT__ struct{ cl_int x, y, z, w, __spacer4, __spacer5, __spacer6, __spacer7, __spacer8, __spacer9, sa, sb, sc, sd, se, sf; }; + __CL_ANON_STRUCT__ struct{ cl_int s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, sA, sB, sC, sD, sE, sF; }; + __CL_ANON_STRUCT__ struct{ cl_int8 lo, hi; }; +#endif +#if defined( __CL_INT2__) + __cl_int2 v2[8]; +#endif +#if defined( __CL_INT4__) + __cl_int4 v4[4]; +#endif +#if defined( __CL_INT8__ ) + __cl_int8 v8[2]; +#endif +#if defined( __CL_INT16__ ) + __cl_int16 v16; +#endif +}cl_int16; + + +/* ---- cl_uintn ---- */ +typedef union +{ + cl_uint CL_ALIGNED(8) s[2]; +#if __CL_HAS_ANON_STRUCT__ + __CL_ANON_STRUCT__ struct{ cl_uint x, y; }; + __CL_ANON_STRUCT__ struct{ cl_uint s0, s1; }; + __CL_ANON_STRUCT__ struct{ cl_uint lo, hi; }; +#endif +#if defined( __CL_UINT2__) + __cl_uint2 v2; +#endif +}cl_uint2; + +typedef union +{ + cl_uint CL_ALIGNED(16) s[4]; +#if __CL_HAS_ANON_STRUCT__ + __CL_ANON_STRUCT__ struct{ cl_uint x, y, z, w; }; + __CL_ANON_STRUCT__ struct{ cl_uint s0, s1, s2, s3; }; + __CL_ANON_STRUCT__ struct{ cl_uint2 lo, hi; }; +#endif +#if defined( __CL_UINT2__) + __cl_uint2 v2[2]; +#endif +#if defined( __CL_UINT4__) + __cl_uint4 v4; +#endif +}cl_uint4; + +/* cl_uint3 is identical in size, alignment and behavior to cl_uint4. See section 6.1.5. */ +typedef cl_uint4 cl_uint3; + +typedef union +{ + cl_uint CL_ALIGNED(32) s[8]; +#if __CL_HAS_ANON_STRUCT__ + __CL_ANON_STRUCT__ struct{ cl_uint x, y, z, w; }; + __CL_ANON_STRUCT__ struct{ cl_uint s0, s1, s2, s3, s4, s5, s6, s7; }; + __CL_ANON_STRUCT__ struct{ cl_uint4 lo, hi; }; +#endif +#if defined( __CL_UINT2__) + __cl_uint2 v2[4]; +#endif +#if defined( __CL_UINT4__) + __cl_uint4 v4[2]; +#endif +#if defined( __CL_UINT8__ ) + __cl_uint8 v8; +#endif +}cl_uint8; + +typedef union +{ + cl_uint CL_ALIGNED(64) s[16]; +#if __CL_HAS_ANON_STRUCT__ + __CL_ANON_STRUCT__ struct{ cl_uint x, y, z, w, __spacer4, __spacer5, __spacer6, __spacer7, __spacer8, __spacer9, sa, sb, sc, sd, se, sf; }; + __CL_ANON_STRUCT__ struct{ cl_uint s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, sA, sB, sC, sD, sE, sF; }; + __CL_ANON_STRUCT__ struct{ cl_uint8 lo, hi; }; +#endif +#if defined( __CL_UINT2__) + __cl_uint2 v2[8]; +#endif +#if defined( __CL_UINT4__) + __cl_uint4 v4[4]; +#endif +#if defined( __CL_UINT8__ ) + __cl_uint8 v8[2]; +#endif +#if defined( __CL_UINT16__ ) + __cl_uint16 v16; +#endif +}cl_uint16; + +/* ---- cl_longn ---- */ +typedef union +{ + cl_long CL_ALIGNED(16) s[2]; +#if __CL_HAS_ANON_STRUCT__ + __CL_ANON_STRUCT__ struct{ cl_long x, y; }; + __CL_ANON_STRUCT__ struct{ cl_long s0, s1; }; + __CL_ANON_STRUCT__ struct{ cl_long lo, hi; }; +#endif +#if defined( __CL_LONG2__) + __cl_long2 v2; +#endif +}cl_long2; + +typedef union +{ + cl_long CL_ALIGNED(32) s[4]; +#if __CL_HAS_ANON_STRUCT__ + __CL_ANON_STRUCT__ struct{ cl_long x, y, z, w; }; + __CL_ANON_STRUCT__ struct{ cl_long s0, s1, s2, s3; }; + __CL_ANON_STRUCT__ struct{ cl_long2 lo, hi; }; +#endif +#if defined( __CL_LONG2__) + __cl_long2 v2[2]; +#endif +#if defined( __CL_LONG4__) + __cl_long4 v4; +#endif +}cl_long4; + +/* cl_long3 is identical in size, alignment and behavior to cl_long4. See section 6.1.5. */ +typedef cl_long4 cl_long3; + +typedef union +{ + cl_long CL_ALIGNED(64) s[8]; +#if __CL_HAS_ANON_STRUCT__ + __CL_ANON_STRUCT__ struct{ cl_long x, y, z, w; }; + __CL_ANON_STRUCT__ struct{ cl_long s0, s1, s2, s3, s4, s5, s6, s7; }; + __CL_ANON_STRUCT__ struct{ cl_long4 lo, hi; }; +#endif +#if defined( __CL_LONG2__) + __cl_long2 v2[4]; +#endif +#if defined( __CL_LONG4__) + __cl_long4 v4[2]; +#endif +#if defined( __CL_LONG8__ ) + __cl_long8 v8; +#endif +}cl_long8; + +typedef union +{ + cl_long CL_ALIGNED(128) s[16]; +#if __CL_HAS_ANON_STRUCT__ + __CL_ANON_STRUCT__ struct{ cl_long x, y, z, w, __spacer4, __spacer5, __spacer6, __spacer7, __spacer8, __spacer9, sa, sb, sc, sd, se, sf; }; + __CL_ANON_STRUCT__ struct{ cl_long s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, sA, sB, sC, sD, sE, sF; }; + __CL_ANON_STRUCT__ struct{ cl_long8 lo, hi; }; +#endif +#if defined( __CL_LONG2__) + __cl_long2 v2[8]; +#endif +#if defined( __CL_LONG4__) + __cl_long4 v4[4]; +#endif +#if defined( __CL_LONG8__ ) + __cl_long8 v8[2]; +#endif +#if defined( __CL_LONG16__ ) + __cl_long16 v16; +#endif +}cl_long16; + + +/* ---- cl_ulongn ---- */ +typedef union +{ + cl_ulong CL_ALIGNED(16) s[2]; +#if __CL_HAS_ANON_STRUCT__ + __CL_ANON_STRUCT__ struct{ cl_ulong x, y; }; + __CL_ANON_STRUCT__ struct{ cl_ulong s0, s1; }; + __CL_ANON_STRUCT__ struct{ cl_ulong lo, hi; }; +#endif +#if defined( __CL_ULONG2__) + __cl_ulong2 v2; +#endif +}cl_ulong2; + +typedef union +{ + cl_ulong CL_ALIGNED(32) s[4]; +#if __CL_HAS_ANON_STRUCT__ + __CL_ANON_STRUCT__ struct{ cl_ulong x, y, z, w; }; + __CL_ANON_STRUCT__ struct{ cl_ulong s0, s1, s2, s3; }; + __CL_ANON_STRUCT__ struct{ cl_ulong2 lo, hi; }; +#endif +#if defined( __CL_ULONG2__) + __cl_ulong2 v2[2]; +#endif +#if defined( __CL_ULONG4__) + __cl_ulong4 v4; +#endif +}cl_ulong4; + +/* cl_ulong3 is identical in size, alignment and behavior to cl_ulong4. See section 6.1.5. */ +typedef cl_ulong4 cl_ulong3; + +typedef union +{ + cl_ulong CL_ALIGNED(64) s[8]; +#if __CL_HAS_ANON_STRUCT__ + __CL_ANON_STRUCT__ struct{ cl_ulong x, y, z, w; }; + __CL_ANON_STRUCT__ struct{ cl_ulong s0, s1, s2, s3, s4, s5, s6, s7; }; + __CL_ANON_STRUCT__ struct{ cl_ulong4 lo, hi; }; +#endif +#if defined( __CL_ULONG2__) + __cl_ulong2 v2[4]; +#endif +#if defined( __CL_ULONG4__) + __cl_ulong4 v4[2]; +#endif +#if defined( __CL_ULONG8__ ) + __cl_ulong8 v8; +#endif +}cl_ulong8; + +typedef union +{ + cl_ulong CL_ALIGNED(128) s[16]; +#if __CL_HAS_ANON_STRUCT__ + __CL_ANON_STRUCT__ struct{ cl_ulong x, y, z, w, __spacer4, __spacer5, __spacer6, __spacer7, __spacer8, __spacer9, sa, sb, sc, sd, se, sf; }; + __CL_ANON_STRUCT__ struct{ cl_ulong s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, sA, sB, sC, sD, sE, sF; }; + __CL_ANON_STRUCT__ struct{ cl_ulong8 lo, hi; }; +#endif +#if defined( __CL_ULONG2__) + __cl_ulong2 v2[8]; +#endif +#if defined( __CL_ULONG4__) + __cl_ulong4 v4[4]; +#endif +#if defined( __CL_ULONG8__ ) + __cl_ulong8 v8[2]; +#endif +#if defined( __CL_ULONG16__ ) + __cl_ulong16 v16; +#endif +}cl_ulong16; + + +/* --- cl_floatn ---- */ + +typedef union +{ + cl_float CL_ALIGNED(8) s[2]; +#if __CL_HAS_ANON_STRUCT__ + __CL_ANON_STRUCT__ struct{ cl_float x, y; }; + __CL_ANON_STRUCT__ struct{ cl_float s0, s1; }; + __CL_ANON_STRUCT__ struct{ cl_float lo, hi; }; +#endif +#if defined( __CL_FLOAT2__) + __cl_float2 v2; +#endif +}cl_float2; + +typedef union +{ + cl_float CL_ALIGNED(16) s[4]; +#if __CL_HAS_ANON_STRUCT__ + __CL_ANON_STRUCT__ struct{ cl_float x, y, z, w; }; + __CL_ANON_STRUCT__ struct{ cl_float s0, s1, s2, s3; }; + __CL_ANON_STRUCT__ struct{ cl_float2 lo, hi; }; +#endif +#if defined( __CL_FLOAT2__) + __cl_float2 v2[2]; +#endif +#if defined( __CL_FLOAT4__) + __cl_float4 v4; +#endif +}cl_float4; + +/* cl_float3 is identical in size, alignment and behavior to cl_float4. See section 6.1.5. */ +typedef cl_float4 cl_float3; + +typedef union +{ + cl_float CL_ALIGNED(32) s[8]; +#if __CL_HAS_ANON_STRUCT__ + __CL_ANON_STRUCT__ struct{ cl_float x, y, z, w; }; + __CL_ANON_STRUCT__ struct{ cl_float s0, s1, s2, s3, s4, s5, s6, s7; }; + __CL_ANON_STRUCT__ struct{ cl_float4 lo, hi; }; +#endif +#if defined( __CL_FLOAT2__) + __cl_float2 v2[4]; +#endif +#if defined( __CL_FLOAT4__) + __cl_float4 v4[2]; +#endif +#if defined( __CL_FLOAT8__ ) + __cl_float8 v8; +#endif +}cl_float8; + +typedef union +{ + cl_float CL_ALIGNED(64) s[16]; +#if __CL_HAS_ANON_STRUCT__ + __CL_ANON_STRUCT__ struct{ cl_float x, y, z, w, __spacer4, __spacer5, __spacer6, __spacer7, __spacer8, __spacer9, sa, sb, sc, sd, se, sf; }; + __CL_ANON_STRUCT__ struct{ cl_float s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, sA, sB, sC, sD, sE, sF; }; + __CL_ANON_STRUCT__ struct{ cl_float8 lo, hi; }; +#endif +#if defined( __CL_FLOAT2__) + __cl_float2 v2[8]; +#endif +#if defined( __CL_FLOAT4__) + __cl_float4 v4[4]; +#endif +#if defined( __CL_FLOAT8__ ) + __cl_float8 v8[2]; +#endif +#if defined( __CL_FLOAT16__ ) + __cl_float16 v16; +#endif +}cl_float16; + +/* --- cl_doublen ---- */ + +typedef union +{ + cl_double CL_ALIGNED(16) s[2]; +#if __CL_HAS_ANON_STRUCT__ + __CL_ANON_STRUCT__ struct{ cl_double x, y; }; + __CL_ANON_STRUCT__ struct{ cl_double s0, s1; }; + __CL_ANON_STRUCT__ struct{ cl_double lo, hi; }; +#endif +#if defined( __CL_DOUBLE2__) + __cl_double2 v2; +#endif +}cl_double2; + +typedef union +{ + cl_double CL_ALIGNED(32) s[4]; +#if __CL_HAS_ANON_STRUCT__ + __CL_ANON_STRUCT__ struct{ cl_double x, y, z, w; }; + __CL_ANON_STRUCT__ struct{ cl_double s0, s1, s2, s3; }; + __CL_ANON_STRUCT__ struct{ cl_double2 lo, hi; }; +#endif +#if defined( __CL_DOUBLE2__) + __cl_double2 v2[2]; +#endif +#if defined( __CL_DOUBLE4__) + __cl_double4 v4; +#endif +}cl_double4; + +/* cl_double3 is identical in size, alignment and behavior to cl_double4. See section 6.1.5. */ +typedef cl_double4 cl_double3; + +typedef union +{ + cl_double CL_ALIGNED(64) s[8]; +#if __CL_HAS_ANON_STRUCT__ + __CL_ANON_STRUCT__ struct{ cl_double x, y, z, w; }; + __CL_ANON_STRUCT__ struct{ cl_double s0, s1, s2, s3, s4, s5, s6, s7; }; + __CL_ANON_STRUCT__ struct{ cl_double4 lo, hi; }; +#endif +#if defined( __CL_DOUBLE2__) + __cl_double2 v2[4]; +#endif +#if defined( __CL_DOUBLE4__) + __cl_double4 v4[2]; +#endif +#if defined( __CL_DOUBLE8__ ) + __cl_double8 v8; +#endif +}cl_double8; + +typedef union +{ + cl_double CL_ALIGNED(128) s[16]; +#if __CL_HAS_ANON_STRUCT__ + __CL_ANON_STRUCT__ struct{ cl_double x, y, z, w, __spacer4, __spacer5, __spacer6, __spacer7, __spacer8, __spacer9, sa, sb, sc, sd, se, sf; }; + __CL_ANON_STRUCT__ struct{ cl_double s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, sA, sB, sC, sD, sE, sF; }; + __CL_ANON_STRUCT__ struct{ cl_double8 lo, hi; }; +#endif +#if defined( __CL_DOUBLE2__) + __cl_double2 v2[8]; +#endif +#if defined( __CL_DOUBLE4__) + __cl_double4 v4[4]; +#endif +#if defined( __CL_DOUBLE8__ ) + __cl_double8 v8[2]; +#endif +#if defined( __CL_DOUBLE16__ ) + __cl_double16 v16; +#endif +}cl_double16; + +/* Macro to facilitate debugging + * Usage: + * Place CL_PROGRAM_STRING_DEBUG_INFO on the line before the first line of your source. + * The first line ends with: CL_PROGRAM_STRING_DEBUG_INFO \" + * Each line thereafter of OpenCL C source must end with: \n\ + * The last line ends in "; + * + * Example: + * + * const char *my_program = CL_PROGRAM_STRING_DEBUG_INFO "\ + * kernel void foo( int a, float * b ) \n\ + * { \n\ + * // my comment \n\ + * *b[ get_global_id(0)] = a; \n\ + * } \n\ + * "; + * + * This should correctly set up the line, (column) and file information for your source + * string so you can do source level debugging. + */ +#define __CL_STRINGIFY( _x ) # _x +#define _CL_STRINGIFY( _x ) __CL_STRINGIFY( _x ) +#define CL_PROGRAM_STRING_DEBUG_INFO "#line " _CL_STRINGIFY(__LINE__) " \"" __FILE__ "\" \n\n" + +#ifdef __cplusplus +} +#endif + +#undef __CL_HAS_ANON_STRUCT__ +#undef __CL_ANON_STRUCT__ +#if defined( _WIN32) && defined(_MSC_VER) + #if _MSC_VER >=1500 + #pragma warning( pop ) + #endif +#endif + +#endif /* __CL_PLATFORM_H */ diff --git a/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/cl_va_api_media_sharing_intel.h b/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/cl_va_api_media_sharing_intel.h new file mode 100755 index 0000000000000000000000000000000000000000..0e7cd4d6f8321f15c0fab08df055f93b0739f0e0 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/cl_va_api_media_sharing_intel.h @@ -0,0 +1,160 @@ +/******************************************************************************* + * Copyright (c) 2008-2020 The Khronos Group Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + ******************************************************************************/ +/*****************************************************************************\ + +Copyright (c) 2013-2019 Intel Corporation All Rights Reserved. + +THESE MATERIALS ARE PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL INTEL OR ITS +CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, +EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, +PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR +PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY +OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY OR TORT (INCLUDING +NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THESE +MATERIALS, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +File Name: cl_va_api_media_sharing_intel.h + +Abstract: + +Notes: + +\*****************************************************************************/ + + +#ifndef __OPENCL_CL_VA_API_MEDIA_SHARING_INTEL_H +#define __OPENCL_CL_VA_API_MEDIA_SHARING_INTEL_H + +#include +#include +#include + +#ifdef __cplusplus +extern "C" { +#endif + +/****************************************** +* cl_intel_va_api_media_sharing extension * +*******************************************/ + +#define cl_intel_va_api_media_sharing 1 + +/* error codes */ +#define CL_INVALID_VA_API_MEDIA_ADAPTER_INTEL -1098 +#define CL_INVALID_VA_API_MEDIA_SURFACE_INTEL -1099 +#define CL_VA_API_MEDIA_SURFACE_ALREADY_ACQUIRED_INTEL -1100 +#define CL_VA_API_MEDIA_SURFACE_NOT_ACQUIRED_INTEL -1101 + +/* cl_va_api_device_source_intel */ +#define CL_VA_API_DISPLAY_INTEL 0x4094 + +/* cl_va_api_device_set_intel */ +#define CL_PREFERRED_DEVICES_FOR_VA_API_INTEL 0x4095 +#define CL_ALL_DEVICES_FOR_VA_API_INTEL 0x4096 + +/* cl_context_info */ +#define CL_CONTEXT_VA_API_DISPLAY_INTEL 0x4097 + +/* cl_mem_info */ +#define CL_MEM_VA_API_MEDIA_SURFACE_INTEL 0x4098 + +/* cl_image_info */ +#define CL_IMAGE_VA_API_PLANE_INTEL 0x4099 + +/* cl_command_type */ +#define CL_COMMAND_ACQUIRE_VA_API_MEDIA_SURFACES_INTEL 0x409A +#define CL_COMMAND_RELEASE_VA_API_MEDIA_SURFACES_INTEL 0x409B + +typedef cl_uint cl_va_api_device_source_intel; +typedef cl_uint cl_va_api_device_set_intel; + +extern CL_API_ENTRY cl_int CL_API_CALL +clGetDeviceIDsFromVA_APIMediaAdapterINTEL( + cl_platform_id platform, + cl_va_api_device_source_intel media_adapter_type, + void* media_adapter, + cl_va_api_device_set_intel media_adapter_set, + cl_uint num_entries, + cl_device_id* devices, + cl_uint* num_devices) CL_EXT_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_int (CL_API_CALL * clGetDeviceIDsFromVA_APIMediaAdapterINTEL_fn)( + cl_platform_id platform, + cl_va_api_device_source_intel media_adapter_type, + void* media_adapter, + cl_va_api_device_set_intel media_adapter_set, + cl_uint num_entries, + cl_device_id* devices, + cl_uint* num_devices) CL_EXT_SUFFIX__VERSION_1_2; + +extern CL_API_ENTRY cl_mem CL_API_CALL +clCreateFromVA_APIMediaSurfaceINTEL( + cl_context context, + cl_mem_flags flags, + VASurfaceID* surface, + cl_uint plane, + cl_int* errcode_ret) CL_EXT_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_mem (CL_API_CALL * clCreateFromVA_APIMediaSurfaceINTEL_fn)( + cl_context context, + cl_mem_flags flags, + VASurfaceID* surface, + cl_uint plane, + cl_int* errcode_ret) CL_EXT_SUFFIX__VERSION_1_2; + +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueAcquireVA_APIMediaSurfacesINTEL( + cl_command_queue command_queue, + cl_uint num_objects, + const cl_mem* mem_objects, + cl_uint num_events_in_wait_list, + const cl_event* event_wait_list, + cl_event* event) CL_EXT_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_int (CL_API_CALL *clEnqueueAcquireVA_APIMediaSurfacesINTEL_fn)( + cl_command_queue command_queue, + cl_uint num_objects, + const cl_mem* mem_objects, + cl_uint num_events_in_wait_list, + const cl_event* event_wait_list, + cl_event* event) CL_EXT_SUFFIX__VERSION_1_2; + +extern CL_API_ENTRY cl_int CL_API_CALL +clEnqueueReleaseVA_APIMediaSurfacesINTEL( + cl_command_queue command_queue, + cl_uint num_objects, + const cl_mem* mem_objects, + cl_uint num_events_in_wait_list, + const cl_event* event_wait_list, + cl_event* event) CL_EXT_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_int (CL_API_CALL *clEnqueueReleaseVA_APIMediaSurfacesINTEL_fn)( + cl_command_queue command_queue, + cl_uint num_objects, + const cl_mem* mem_objects, + cl_uint num_events_in_wait_list, + const cl_event* event_wait_list, + cl_event* event) CL_EXT_SUFFIX__VERSION_1_2; + +#ifdef __cplusplus +} +#endif + +#endif /* __OPENCL_CL_VA_API_MEDIA_SHARING_INTEL_H */ + diff --git a/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/cl_version.h b/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/cl_version.h new file mode 100755 index 0000000000000000000000000000000000000000..f38280a8a0b4c44b243d9769d04543a55cbd9dc8 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/cl_version.h @@ -0,0 +1,81 @@ +/******************************************************************************* + * Copyright (c) 2018-2020 The Khronos Group Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + ******************************************************************************/ + +#ifndef __CL_VERSION_H +#define __CL_VERSION_H + +/* Detect which version to target */ +#if !defined(CL_TARGET_OPENCL_VERSION) +#pragma message("cl_version.h: CL_TARGET_OPENCL_VERSION is not defined. Defaulting to 220 (OpenCL 2.2)") +#define CL_TARGET_OPENCL_VERSION 220 +#endif +#if CL_TARGET_OPENCL_VERSION != 100 && \ + CL_TARGET_OPENCL_VERSION != 110 && \ + CL_TARGET_OPENCL_VERSION != 120 && \ + CL_TARGET_OPENCL_VERSION != 200 && \ + CL_TARGET_OPENCL_VERSION != 210 && \ + CL_TARGET_OPENCL_VERSION != 220 && \ + CL_TARGET_OPENCL_VERSION != 300 +#pragma message("cl_version: CL_TARGET_OPENCL_VERSION is not a valid value (100, 110, 120, 200, 210, 220, 300). Defaulting to 220 (OpenCL 2.2)") +#undef CL_TARGET_OPENCL_VERSION +#define CL_TARGET_OPENCL_VERSION 220 +#endif + + +/* OpenCL Version */ +#if CL_TARGET_OPENCL_VERSION >= 300 && !defined(CL_VERSION_3_0) +#define CL_VERSION_3_0 1 +#endif +#if CL_TARGET_OPENCL_VERSION >= 220 && !defined(CL_VERSION_2_2) +#define CL_VERSION_2_2 1 +#endif +#if CL_TARGET_OPENCL_VERSION >= 210 && !defined(CL_VERSION_2_1) +#define CL_VERSION_2_1 1 +#endif +#if CL_TARGET_OPENCL_VERSION >= 200 && !defined(CL_VERSION_2_0) +#define CL_VERSION_2_0 1 +#endif +#if CL_TARGET_OPENCL_VERSION >= 120 && !defined(CL_VERSION_1_2) +#define CL_VERSION_1_2 1 +#endif +#if CL_TARGET_OPENCL_VERSION >= 110 && !defined(CL_VERSION_1_1) +#define CL_VERSION_1_1 1 +#endif +#if CL_TARGET_OPENCL_VERSION >= 100 && !defined(CL_VERSION_1_0) +#define CL_VERSION_1_0 1 +#endif + +/* Allow deprecated APIs for older OpenCL versions. */ +#if CL_TARGET_OPENCL_VERSION <= 220 && !defined(CL_USE_DEPRECATED_OPENCL_2_2_APIS) +#define CL_USE_DEPRECATED_OPENCL_2_2_APIS +#endif +#if CL_TARGET_OPENCL_VERSION <= 210 && !defined(CL_USE_DEPRECATED_OPENCL_2_1_APIS) +#define CL_USE_DEPRECATED_OPENCL_2_1_APIS +#endif +#if CL_TARGET_OPENCL_VERSION <= 200 && !defined(CL_USE_DEPRECATED_OPENCL_2_0_APIS) +#define CL_USE_DEPRECATED_OPENCL_2_0_APIS +#endif +#if CL_TARGET_OPENCL_VERSION <= 120 && !defined(CL_USE_DEPRECATED_OPENCL_1_2_APIS) +#define CL_USE_DEPRECATED_OPENCL_1_2_APIS +#endif +#if CL_TARGET_OPENCL_VERSION <= 110 && !defined(CL_USE_DEPRECATED_OPENCL_1_1_APIS) +#define CL_USE_DEPRECATED_OPENCL_1_1_APIS +#endif +#if CL_TARGET_OPENCL_VERSION <= 100 && !defined(CL_USE_DEPRECATED_OPENCL_1_0_APIS) +#define CL_USE_DEPRECATED_OPENCL_1_0_APIS +#endif + +#endif /* __CL_VERSION_H */ diff --git a/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/opencl.h b/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/opencl.h new file mode 100755 index 0000000000000000000000000000000000000000..1c4e10c88afaa1487600ac41edb77459b208bee7 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/CL/opencl.h @@ -0,0 +1,33 @@ +/******************************************************************************* + * Copyright (c) 2008-2020 The Khronos Group Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + ******************************************************************************/ + +#ifndef __OPENCL_H +#define __OPENCL_H + +#ifdef __cplusplus +extern "C" { +#endif + +#include +#include +#include +#include + +#ifdef __cplusplus +} +#endif + +#endif /* __OPENCL_H */ diff --git a/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/libopencl.h b/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/libopencl.h new file mode 100644 index 0000000000000000000000000000000000000000..823d9be9010a3d8f99972a097dba8d5ef0c3edae --- /dev/null +++ b/rknpu2/examples/3rdparty/opencl/libopencl-stub/include/libopencl.h @@ -0,0 +1,240 @@ +#ifndef LIBOPENCL_STUB_H +#define LIBOPENCL_STUB_H + +#include +#include +#include +#include +#include +#include + + +typedef void (*f_pfn_notify)(const char *, const void *, size_t, void *); + +typedef cl_int (*f_clGetPlatformIDs) (cl_uint, cl_platform_id *, cl_uint *); + +typedef cl_int (*f_clGetPlatformInfo) (cl_platform_id, cl_platform_info, size_t, void *, size_t *); + +typedef cl_int (*f_clGetDeviceIDs) (cl_platform_id, cl_device_type, cl_uint, cl_device_id *, cl_uint *); + +typedef cl_int (*f_clGetDeviceInfo) (cl_device_id, cl_device_info, size_t, void *, size_t *); + +typedef cl_int (*f_clCreateSubDevices) (cl_device_id, const cl_device_partition_property *, + cl_uint, cl_device_id *, cl_uint *); + +typedef cl_int (*f_clRetainDevice) (cl_device_id); + +typedef cl_int (*f_clReleaseDevice) (cl_device_id); + +typedef cl_context (*f_clCreateContext) (const cl_context_properties *, cl_uint, const cl_device_id *, + f_pfn_notify, void *, cl_int *); + +typedef cl_context (*f_clCreateContextFromType) (const cl_context_properties *, cl_device_type, + f_pfn_notify, void *, cl_int *); + +typedef cl_int (*f_clRetainContext) (cl_context); + +typedef cl_int (*f_clReleaseContext) (cl_context); + +typedef cl_int (*f_clGetContextInfo) (cl_context, cl_context_info, size_t, void *, size_t *); + +typedef cl_command_queue (*f_clCreateCommandQueue) (cl_context, cl_device_id, cl_command_queue_properties, cl_int *); + +typedef cl_int (*f_clRetainCommandQueue) (cl_command_queue); + +typedef cl_int (*f_clReleaseCommandQueue) (cl_command_queue); + +typedef cl_int (*f_clGetCommandQueueInfo) (cl_command_queue, cl_command_queue_info, size_t, void *, size_t *); + +typedef cl_mem (*f_clCreateBuffer) (cl_context, cl_mem_flags, size_t, void *, cl_int *); + +typedef cl_mem (*f_clCreateSubBuffer) (cl_mem, cl_mem_flags, cl_buffer_create_type, const void *, cl_int *); + +typedef cl_mem (*f_clCreateImage) (cl_context, cl_mem_flags, const cl_image_format *, const cl_image_desc *, void *, cl_int *); + +typedef cl_int (*f_clRetainMemObject) (cl_mem); + +typedef cl_int (*f_clReleaseMemObject) (cl_mem); + +typedef cl_int (*f_clGetMemObjectInfo) (cl_mem, cl_mem_info, size_t, void *, size_t *); + +typedef cl_int (*f_clGetImageInfo) (cl_mem, cl_image_info, size_t, void *, size_t *); + +typedef cl_int (*f_clSetMemObjectDestructorCallback) (cl_mem, void (*pfn_notify)( cl_mem memobj, void* user_data), void *); + +typedef cl_int (*f_clGetSupportedImageFormats) (cl_context, cl_mem_flags, cl_mem_object_type, cl_uint, cl_image_format *, cl_uint *); + +typedef cl_sampler (*f_clCreateSampler) (cl_context, cl_bool, cl_addressing_mode, cl_filter_mode, cl_int *); + +typedef cl_int (*f_clRetainSampler) (cl_sampler); + +typedef cl_int (*f_clReleaseSampler) (cl_sampler); + +typedef cl_int (*f_clGetSamplerInfo) (cl_sampler, cl_sampler_info, size_t, void *, size_t *); + +typedef cl_program (*f_clCreateProgramWithSource) (cl_context, cl_uint, const char **, const size_t *, cl_int *); + +typedef cl_program (*f_clCreateProgramWithBinary) (cl_context, cl_uint, const cl_device_id *, + const size_t *, const unsigned char **, cl_int *, cl_int *); + +typedef cl_program (*f_clCreateProgramWithBuiltInKernels) (cl_context, cl_uint, const cl_device_id *, const char *, cl_int *); + +typedef cl_int (*f_clRetainProgram) (cl_program); + +typedef cl_int (*f_clReleaseProgram) (cl_program); + +typedef cl_int (*f_clBuildProgram) (cl_program, cl_uint, const cl_device_id *, const char *, + void (*pfn_notify)(cl_program program, void * user_data), void *); + +typedef cl_int (*f_clCompileProgram) (cl_program, cl_uint, const cl_device_id *, const char *, cl_uint, const cl_program *, + const char **, void (*pfn_notify)(cl_program program, void * user_data), void *); + +typedef cl_program (*f_clLinkProgram) (cl_context, cl_uint, const cl_device_id *, const char *, cl_uint, const cl_program *, + void (*pfn_notify)(cl_program program, void * user_data), void *, cl_int *); + +typedef cl_int (*f_clUnloadPlatformCompiler)(cl_platform_id); + +typedef cl_int (*f_clGetProgramInfo) (cl_program, cl_program_info, size_t, void *, size_t *); + +typedef cl_int (*f_clGetProgramBuildInfo) (cl_program, cl_device_id, cl_program_build_info, size_t, void *, size_t *); + +typedef cl_kernel (*f_clCreateKernel) (cl_program, const char *, cl_int *); + +typedef cl_int (*f_clCreateKernelsInProgram) (cl_program, cl_uint, cl_kernel *, cl_uint *); + +typedef cl_int (*f_clRetainKernel) (cl_kernel); + +typedef cl_int (*f_clReleaseKernel) (cl_kernel); + +typedef cl_int (*f_clSetKernelArg) (cl_kernel, cl_uint, size_t,const void *); + +typedef cl_int (*f_clGetKernelInfo) (cl_kernel, cl_kernel_info, size_t, void *, size_t *); + +typedef cl_int (*f_clGetKernelArgInfo) (cl_kernel, cl_uint, cl_kernel_arg_info, size_t, void *, size_t *); + +typedef cl_int (*f_clGetKernelWorkGroupInfo) (cl_kernel, cl_device_id, cl_kernel_work_group_info, size_t, void *, size_t *); + +typedef cl_int (*f_clWaitForEvents) (cl_uint, const cl_event *); + +typedef cl_int (*f_clGetEventInfo) (cl_event, cl_event_info, size_t, void *, size_t *); + +typedef cl_event (*f_clCreateUserEvent) (cl_context, cl_int *); + +typedef cl_int (*f_clRetainEvent) (cl_event); + +typedef cl_int (*f_clReleaseEvent) (cl_event); + +typedef cl_int (*f_clSetUserEventStatus) (cl_event, cl_int); + +typedef cl_int (*f_clSetEventCallback) (cl_event, cl_int, void (*pfn_notify)(cl_event, cl_int, void *), void *); + +typedef cl_int (*f_clGetEventProfilingInfo) (cl_event, cl_profiling_info, size_t, void *, size_t *); + +typedef cl_int (*f_clFlush) (cl_command_queue); + +typedef cl_int (*f_clFinish) (cl_command_queue); + +typedef cl_int (*f_clEnqueueReadBuffer) (cl_command_queue, cl_mem, cl_bool, size_t, size_t, void *, cl_uint, const cl_event *, cl_event *); + +typedef cl_int (*f_clEnqueueReadBufferRect) (cl_command_queue, cl_mem, cl_bool, const size_t *, const size_t *, const size_t *, + size_t, size_t, size_t, size_t, void *, cl_uint, const cl_event *, cl_event *); + +typedef cl_int (*f_clEnqueueWriteBuffer) (cl_command_queue, cl_mem, cl_bool, size_t, size_t, const void *, cl_uint, const cl_event *, cl_event *); + +typedef cl_int (*f_clEnqueueWriteBufferRect) (cl_command_queue, cl_mem, cl_bool, const size_t *, const size_t *, const size_t *, + size_t, size_t, size_t, size_t, const void *, cl_uint, const cl_event *, cl_event *); + +typedef cl_int (*f_clEnqueueFillBuffer) (cl_command_queue, cl_mem, const void *, size_t, size_t, size_t, cl_uint, const cl_event *, cl_event *); + +typedef cl_int (*f_clEnqueueCopyBuffer) (cl_command_queue, cl_mem, cl_mem, size_t, size_t, size_t, cl_uint, const cl_event *, cl_event *); + +typedef cl_int (*f_clEnqueueCopyBufferRect) (cl_command_queue, cl_mem, cl_mem, const size_t *, const size_t *, const size_t *, + size_t, size_t, size_t, size_t, cl_uint, const cl_event *, cl_event *); + +typedef cl_int (*f_clEnqueueReadImage) (cl_command_queue, cl_mem, cl_bool, const size_t *, const size_t *, + size_t, size_t, void *, cl_uint, const cl_event *, cl_event *); + +typedef cl_int (*f_clEnqueueWriteImage) (cl_command_queue, cl_mem, cl_bool, const size_t *, const size_t *, + size_t, size_t, const void *, cl_uint, const cl_event *, cl_event *); + +typedef cl_int (*f_clEnqueueFillImage) (cl_command_queue, cl_mem, const void *, const size_t *, const size_t *, cl_uint, const cl_event *, cl_event *); + +typedef cl_int (*f_clEnqueueCopyImage) (cl_command_queue, cl_mem, cl_mem, const size_t *, const size_t *, const size_t *, + cl_uint, const cl_event *, cl_event *); + +typedef cl_int (*f_clEnqueueCopyImageToBuffer) (cl_command_queue, cl_mem, cl_mem, const size_t *, const size_t *, size_t, cl_uint, const cl_event *, cl_event *); + +typedef cl_int (*f_clEnqueueCopyBufferToImage) (cl_command_queue, cl_mem, cl_mem, size_t, const size_t *, const size_t *, cl_uint, const cl_event *, cl_event *); + +typedef void * (*f_clEnqueueMapBuffer) (cl_command_queue, cl_mem, cl_bool, cl_map_flags, size_t, + size_t, cl_uint, const cl_event *, cl_event *, cl_int *); + +typedef void * (*f_clEnqueueMapImage) (cl_command_queue, cl_mem, cl_bool, cl_map_flags, const size_t *, const size_t *, + size_t *, size_t *, cl_uint, const cl_event *, cl_event *, cl_int *); + +typedef cl_int (*f_clEnqueueUnmapMemObject) (cl_command_queue, cl_mem, void *, cl_uint, const cl_event *, cl_event *); + +typedef cl_int (*f_clEnqueueMigrateMemObjects)(cl_command_queue, cl_uint, const cl_mem *, cl_mem_migration_flags, + cl_uint, const cl_event *, cl_event *); + +typedef cl_int (*f_clEnqueueNDRangeKernel)(cl_command_queue, cl_kernel, cl_uint, const size_t *, const size_t *, + const size_t *, cl_uint, const cl_event *, cl_event *); + +typedef cl_int (*f_clEnqueueTask)(cl_command_queue, cl_kernel, cl_uint, const cl_event *, cl_event *); + +typedef cl_int (*f_clEnqueueNativeKernel)(cl_command_queue, void (*user_func)(void *), void *, size_t, + cl_uint, const cl_mem *, const void **, cl_uint, const cl_event *, cl_event *); + +typedef cl_int (*f_clEnqueueMarkerWithWaitList)(cl_command_queue, cl_uint, const cl_event *, cl_event *); + +typedef cl_int (*f_clEnqueueBarrierWithWaitList)(cl_command_queue, cl_uint, const cl_event *, cl_event *); + +typedef void * (*f_clGetExtensionFunctionAddressForPlatform)(cl_platform_id, const char *); + +typedef cl_mem (*f_clCreateImage2D)(cl_context, cl_mem_flags,const cl_image_format *, size_t, size_t, + size_t, void *, cl_int *); + +typedef cl_mem (*f_clCreateImage3D)(cl_context, cl_mem_flags, const cl_image_format *, size_t, + size_t, size_t, size_t, size_t, void *, cl_int *); + +typedef cl_int (*f_clEnqueueMarker)(cl_command_queue, cl_event *); + +typedef cl_int(*f_clEnqueueWaitForEvents)(cl_command_queue, cl_uint, const cl_event *); + +typedef cl_int (*f_clEnqueueBarrier)(cl_command_queue); + +typedef cl_int (*f_clUnloadCompiler)(void); + +typedef void * (*f_clGetExtensionFunctionAddress)(const char *); + +typedef cl_mem (*f_clCreateFromGLBuffer) (cl_context, cl_mem_flags, cl_GLuint, int *); + +typedef cl_mem (*f_clCreateFromGLTexture) (cl_context, cl_mem_flags, cl_GLenum, cl_GLint, cl_GLuint, cl_int *); + +typedef cl_mem (*f_clCreateFromGLRenderbuffer) (cl_context, cl_mem_flags, cl_GLuint, cl_int *); + +typedef cl_int (*f_clGetGLObjectInfo) (cl_mem memobj, cl_gl_object_type *, cl_GLuint *); + +typedef cl_int (*f_clGetGLTextureInfo) (cl_mem, cl_gl_texture_info, size_t, void *, size_t *); + +typedef cl_int (*f_clEnqueueAcquireGLObjects) (cl_command_queue, cl_uint, const cl_mem *, cl_uint, + const cl_event *, cl_event *); + +typedef cl_int (*f_clEnqueueReleaseGLObjects) (cl_command_queue, cl_uint, const cl_mem *, cl_uint, + const cl_event *, cl_event *); + +typedef cl_mem (*f_clCreateFromGLTexture2D) (cl_context, cl_mem_flags, cl_GLenum, cl_GLint, cl_GLuint, cl_int *); + +typedef cl_mem (*f_clCreateFromGLTexture3D) (cl_context, cl_mem_flags, cl_GLenum, cl_GLint, cl_GLuint, cl_int *); + +typedef cl_int (*f_clGetGLContextInfoKHR) (const cl_context_properties *, cl_gl_context_info, size_t, + void *, size_t *); + +typedef cl_mem (*f_clImportMemoryARM) ( cl_context, cl_mem_flags, const cl_import_properties_arm *, + void *, size_t, cl_int *); + +// Additional api to reset currently opened opencl shared-object +// Subsequent calls will use newly set environment variables +void stubOpenclReset(); + +#endif // LIBOPENCL_STUB_H diff --git a/rknpu2/examples/3rdparty/opencl/libopencl-stub/src/libopencl.cc b/rknpu2/examples/3rdparty/opencl/libopencl-stub/src/libopencl.cc new file mode 100644 index 0000000000000000000000000000000000000000..fb81ac0418670c3a62d6eb813806699af001127e --- /dev/null +++ b/rknpu2/examples/3rdparty/opencl/libopencl-stub/src/libopencl.cc @@ -0,0 +1,2143 @@ +/* + * Stub libopencl that dlsyms into actual library based on environment variable + * + * LIBOPENCL_SO_PATH -- Path to opencl so that will be searched first + * LIBOPENCL_SO_PATH_2 -- Searched second + * LIBOPENCL_SO_PATH_3 -- Searched third + * LIBOPENCL_SO_PATH_4 -- Searched fourth + * + * If none of these are set, default system paths will be considered +**/ + +#include "libopencl.h" +#include + +#if defined(__APPLE__) || defined(__MACOSX) +static const char *default_so_paths[] = { + "libOpenCL.so", + "/System/Library/Frameworks/OpenCL.framework/OpenCL" +}; +#elif defined(__ANDROID__) +static const char *default_so_paths[] = { +#if 1 + "/vendor/lib64/libOpenCL.so" +#else + "/system/vendor/lib64/egl/libGLES_mali.so", + "/system/vendor/lib/egl/libGLES_mali.so" +#endif +}; +#elif defined(_WIN32) +static const char *default_so_paths[] = { + "OpenCL.dll" +}; +#elif defined(__linux__) +static const char *default_so_paths[] = { + "/usr/lib/libOpenCL.so", + "/usr/local/lib/libOpenCL.so", + "/usr/local/lib/libpocl.so", + "/usr/lib64/libOpenCL.so", + "/usr/lib32/libOpenCL.so", + "libOpenCL.so" +}; +#endif + +static void *so_handle = NULL; + + +static int access_file(const char *filename) +{ + struct stat buffer; + return (stat(filename, &buffer) == 0); +} + +static int open_libopencl_so() +{ + char *path = NULL, *str = NULL; + int i; + + if((str=getenv("LIBOPENCL_SO_PATH")) && access_file(str)) { + path = str; + } + else if((str=getenv("LIBOPENCL_SO_PATH_2")) && access_file(str)) { + path = str; + } + else if((str=getenv("LIBOPENCL_SO_PATH_3")) && access_file(str)) { + path = str; + } + else if((str=getenv("LIBOPENCL_SO_PATH_4")) && access_file(str)) { + path = str; + } + + if(!path) + { + for(i=0; i<(sizeof(default_so_paths) / sizeof(char*)); i++) + { + if(access_file(default_so_paths[i])) + { + path = (char *) default_so_paths[i]; + break; + } + } + } + + if(path) + { + printf("Loading OpenCL library %s\n", path); + so_handle = dlopen(path, RTLD_LAZY); + if (!so_handle) { + fprintf(stderr, "%s\n", dlerror()); + exit(EXIT_FAILURE); + } + + return 0; + } + else + { + return -1; + } +} + +void stubOpenclReset() +{ + if(so_handle) + dlclose(so_handle); + + so_handle = NULL; +} + +cl_int +clGetPlatformIDs(cl_uint num_entries, + cl_platform_id * platforms, + cl_uint * num_platforms) +{ + f_clGetPlatformIDs func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clGetPlatformIDs) dlsym(so_handle, "clGetPlatformIDs"); + if(func) { + return func(num_entries, platforms, num_platforms); + } else { + return CL_INVALID_PLATFORM; + } +} + + +cl_int +clGetPlatformInfo(cl_platform_id platform, + cl_platform_info param_name, + size_t param_value_size, + void * param_value, + size_t * param_value_size_ret) +{ + f_clGetPlatformInfo func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clGetPlatformInfo) dlsym(so_handle, "clGetPlatformInfo"); + if(func) { + return func(platform, param_name, param_value_size, param_value, param_value_size_ret); + } else { + return CL_INVALID_PLATFORM; + } +} + + +cl_int +clGetDeviceIDs(cl_platform_id platform, + cl_device_type device_type, + cl_uint num_entries, + cl_device_id * devices, + cl_uint * num_devices) +{ + f_clGetDeviceIDs func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clGetDeviceIDs) dlsym(so_handle, "clGetDeviceIDs"); + if(func) { + return func(platform, device_type, num_entries, devices, num_devices); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clGetDeviceInfo(cl_device_id device, + cl_device_info param_name, + size_t param_value_size, + void * param_value, + size_t * param_value_size_ret) +{ + f_clGetDeviceInfo func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clGetDeviceInfo) dlsym(so_handle, "clGetDeviceInfo"); + if(func) { + return func(device, param_name, param_value_size, param_value, param_value_size_ret); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clCreateSubDevices(cl_device_id in_device, + const cl_device_partition_property * properties, + cl_uint num_devices, + cl_device_id * out_devices, + cl_uint * num_devices_ret) +{ + f_clCreateSubDevices func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clCreateSubDevices) dlsym(so_handle, "clCreateSubDevices"); + if(func) { + return func(in_device, properties, num_devices, out_devices, num_devices_ret); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clRetainDevice(cl_device_id device) +{ + f_clRetainDevice func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clRetainDevice) dlsym(so_handle, "clRetainDevice"); + if(func) { + return func(device); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clReleaseDevice(cl_device_id device) +{ + f_clReleaseDevice func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clReleaseDevice) dlsym(so_handle, "clReleaseDevice"); + if(func) { + return func(device); + } else { + return CL_INVALID_PLATFORM; + } +} + + +cl_context +clCreateContext(const cl_context_properties * properties, + cl_uint num_devices, + const cl_device_id * devices, + void (*pfn_notify)(const char *, const void *, size_t, void *), + void * user_data, + cl_int * errcode_ret) +{ + f_clCreateContext func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clCreateContext) dlsym(so_handle, "clCreateContext"); + if(func) { + return func(properties, num_devices, devices, pfn_notify, user_data, errcode_ret); + } else { + return NULL; + } +} + +cl_context +clCreateContextFromType(const cl_context_properties * properties, + cl_device_type device_type, + void (*pfn_notify )(const char *, const void *, size_t, void *), + void * user_data, + cl_int * errcode_ret) +{ + f_clCreateContextFromType func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clCreateContextFromType) dlsym(so_handle, "clCreateContextFromType"); + if(func) { + return func(properties, device_type, pfn_notify, user_data, errcode_ret); + } else { + return NULL; + } +} + +cl_int +clRetainContext(cl_context context) +{ + f_clRetainContext func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clRetainContext) dlsym(so_handle, "clRetainContext"); + if(func) { + return func(context); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clReleaseContext(cl_context context) +{ + f_clReleaseContext func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clReleaseContext) dlsym(so_handle, "clReleaseContext"); + if(func) { + return func(context); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clGetContextInfo(cl_context context, + cl_context_info param_name, + size_t param_value_size, + void * param_value, + size_t * param_value_size_ret) +{ + f_clGetContextInfo func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clGetContextInfo) dlsym(so_handle, "clGetContextInfo"); + if(func) { + return func(context, param_name, param_value_size, + param_value, param_value_size_ret); + } else { + return CL_INVALID_PLATFORM; + } +} + + +cl_command_queue +clCreateCommandQueue(cl_context context, + cl_device_id device, + cl_command_queue_properties properties, + cl_int * errcode_ret) +{ + f_clCreateCommandQueue func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clCreateCommandQueue) dlsym(so_handle, "clCreateCommandQueue"); + if(func) { + return func(context, device, properties, errcode_ret); + } else { + return NULL; + } +} + +cl_int +clRetainCommandQueue(cl_command_queue command_queue) +{ + f_clRetainCommandQueue func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clRetainCommandQueue) dlsym(so_handle, "clRetainCommandQueue"); + if(func) { + return func(command_queue); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clReleaseCommandQueue(cl_command_queue command_queue) +{ + f_clReleaseCommandQueue func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clReleaseCommandQueue) dlsym(so_handle, "clReleaseCommandQueue"); + if(func) { + return func(command_queue); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clGetCommandQueueInfo(cl_command_queue command_queue, + cl_command_queue_info param_name, + size_t param_value_size, + void * param_value, + size_t * param_value_size_ret) +{ + f_clGetCommandQueueInfo func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clGetCommandQueueInfo) dlsym(so_handle, "clGetCommandQueueInfo"); + if(func) { + return func(command_queue, param_name, param_value_size, + param_value, param_value_size_ret); + } else { + return CL_INVALID_PLATFORM; + } +} + + +cl_mem +clCreateBuffer(cl_context context, + cl_mem_flags flags, + size_t size, + void * host_ptr, + cl_int * errcode_ret) +{ + f_clCreateBuffer func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clCreateBuffer) dlsym(so_handle, "clCreateBuffer"); + if(func) { + return func(context, flags, size, host_ptr, errcode_ret); + } else { + return NULL; + } +} + +cl_mem +clCreateSubBuffer(cl_mem buffer, + cl_mem_flags flags, + cl_buffer_create_type buffer_create_type, + const void * buffer_create_info, + cl_int * errcode_ret) +{ + f_clCreateSubBuffer func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clCreateSubBuffer) dlsym(so_handle, "clCreateSubBuffer"); + if(func) { + return func(buffer, flags, buffer_create_type, + buffer_create_info, errcode_ret); + } else { + return NULL; + } +} + +cl_mem +clCreateImage(cl_context context, + cl_mem_flags flags, + const cl_image_format * image_format, + const cl_image_desc * image_desc, + void * host_ptr, + cl_int * errcode_ret) +{ + f_clCreateImage func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clCreateImage) dlsym(so_handle, "clCreateImage"); + if(func) { + return func(context, flags, image_format, image_desc, + host_ptr, errcode_ret); + } else { + return NULL; + } +} + +cl_int +clRetainMemObject(cl_mem memobj) +{ + f_clRetainMemObject func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clRetainMemObject) dlsym(so_handle, "clRetainMemObject"); + if(func) { + return func(memobj); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clReleaseMemObject(cl_mem memobj) +{ + f_clReleaseMemObject func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clReleaseMemObject) dlsym(so_handle, "clReleaseMemObject"); + if(func) { + return func(memobj); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clGetSupportedImageFormats(cl_context context, + cl_mem_flags flags, + cl_mem_object_type image_type, + cl_uint num_entries, + cl_image_format * image_formats, + cl_uint * num_image_formats) +{ + f_clGetSupportedImageFormats func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clGetSupportedImageFormats) dlsym(so_handle, "clGetSupportedImageFormats"); + if(func) { + return func(context, flags, image_type, num_entries, + image_formats, num_image_formats); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clGetMemObjectInfo(cl_mem memobj, + cl_mem_info param_name, + size_t param_value_size, + void * param_value, + size_t * param_value_size_ret) +{ + f_clGetMemObjectInfo func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clGetMemObjectInfo) dlsym(so_handle, "clGetMemObjectInfo"); + if(func) { + return func(memobj, param_name, param_value_size, + param_value, param_value_size_ret); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clGetImageInfo(cl_mem image, + cl_image_info param_name, + size_t param_value_size, + void * param_value, + size_t * param_value_size_ret) +{ + f_clGetImageInfo func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clGetImageInfo) dlsym(so_handle, "clGetImageInfo"); + if(func) { + return func(image, param_name, param_value_size, + param_value, param_value_size_ret); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clSetMemObjectDestructorCallback( cl_mem memobj, + void (*pfn_notify)( cl_mem memobj, void* user_data), + void * user_data ) +{ + f_clSetMemObjectDestructorCallback func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clSetMemObjectDestructorCallback) dlsym(so_handle, "clSetMemObjectDestructorCallback"); + if(func) { + return func(memobj, pfn_notify, user_data); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_sampler +clCreateSampler(cl_context context, + cl_bool normalized_coords, + cl_addressing_mode addressing_mode, + cl_filter_mode filter_mode, + cl_int * errcode_ret) +{ + f_clCreateSampler func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clCreateSampler) dlsym(so_handle, "clCreateSampler"); + if(func) { + return func(context, normalized_coords, addressing_mode, filter_mode, errcode_ret); + } else { + return NULL; + } +} + +cl_int +clRetainSampler(cl_sampler sampler) +{ + f_clRetainSampler func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clRetainSampler) dlsym(so_handle, "clRetainSampler"); + if(func) { + return func(sampler); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clReleaseSampler(cl_sampler sampler) +{ + f_clReleaseSampler func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clReleaseSampler) dlsym(so_handle, "clReleaseSampler"); + if(func) { + return func(sampler); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clGetSamplerInfo(cl_sampler sampler, + cl_sampler_info param_name, + size_t param_value_size, + void * param_value, + size_t * param_value_size_ret) +{ + f_clGetSamplerInfo func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clGetSamplerInfo) dlsym(so_handle, "clGetSamplerInfo"); + if(func) { + return func(sampler, param_name, param_value_size, param_value, param_value_size_ret); + } else { + return CL_INVALID_PLATFORM; + } +} + + +cl_program +clCreateProgramWithSource(cl_context context, + cl_uint count, + const char ** strings, + const size_t * lengths, + cl_int * errcode_ret) +{ + f_clCreateProgramWithSource func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clCreateProgramWithSource) dlsym(so_handle, "clCreateProgramWithSource"); + if(func) { + return func(context, count, strings, lengths, errcode_ret); + } else { + return NULL; + } +} + + + +cl_program +clCreateProgramWithBinary(cl_context context, + cl_uint num_devices, + const cl_device_id * device_list, + const size_t * lengths, + const unsigned char ** binaries, + cl_int * binary_status, + cl_int * errcode_ret) +{ + f_clCreateProgramWithBinary func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clCreateProgramWithBinary) dlsym(so_handle, "clCreateProgramWithBinary"); + if(func) { + return func(context, num_devices, device_list, lengths, binaries, binary_status, errcode_ret); + } else { + return NULL; + } +} + +cl_program +clCreateProgramWithBuiltInKernels(cl_context context, + cl_uint num_devices, + const cl_device_id * device_list, + const char * kernel_names, + cl_int * errcode_ret) +{ + f_clCreateProgramWithBuiltInKernels func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clCreateProgramWithBuiltInKernels) dlsym(so_handle, "clCreateProgramWithBuiltInKernels"); + if(func) { + return func(context, num_devices, device_list, kernel_names, errcode_ret); + } else { + return NULL; + } +} + +cl_int +clRetainProgram(cl_program program) +{ + f_clRetainProgram func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clRetainProgram) dlsym(so_handle, "clRetainProgram"); + if(func) { + return func(program); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clReleaseProgram(cl_program program) +{ + f_clReleaseProgram func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clReleaseProgram) dlsym(so_handle, "clReleaseProgram"); + if(func) { + return func(program); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clBuildProgram(cl_program program, + cl_uint num_devices, + const cl_device_id * device_list, + const char * options, + void (*pfn_notify)(cl_program program, void * user_data), + void * user_data) +{ + f_clBuildProgram func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clBuildProgram) dlsym(so_handle, "clBuildProgram"); + if(func) { + return func(program, num_devices, device_list, options, pfn_notify, user_data); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clCompileProgram(cl_program program, + cl_uint num_devices, + const cl_device_id * device_list, + const char * options, + cl_uint num_input_headers, + const cl_program * input_headers, + const char ** header_include_names, + void (*pfn_notify)(cl_program program, void * user_data), + void * user_data) +{ + f_clCompileProgram func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clCompileProgram) dlsym(so_handle, "clCompileProgram"); + if(func) { + return func(program, num_devices, device_list, options, num_input_headers, input_headers, + header_include_names, pfn_notify, user_data); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_program +clLinkProgram(cl_context context, + cl_uint num_devices, + const cl_device_id * device_list, + const char * options, + cl_uint num_input_programs, + const cl_program * input_programs, + void (*pfn_notify)(cl_program program, void * user_data), + void * user_data, + cl_int * errcode_ret) +{ + f_clLinkProgram func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clLinkProgram) dlsym(so_handle, "clLinkProgram"); + if(func) { + return func(context, num_devices, device_list, options, num_input_programs, + input_programs, pfn_notify, user_data, errcode_ret); + } else { + return NULL; + } +} + + +cl_int +clUnloadPlatformCompiler(cl_platform_id platform) +{ + f_clUnloadPlatformCompiler func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clUnloadPlatformCompiler) dlsym(so_handle, "clUnloadPlatformCompiler"); + if(func) { + return func(platform); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clGetProgramInfo(cl_program program, + cl_program_info param_name, + size_t param_value_size, + void * param_value, + size_t * param_value_size_ret) +{ + f_clGetProgramInfo func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clGetProgramInfo) dlsym(so_handle, "clGetProgramInfo"); + if(func) { + return func(program, param_name, param_value_size, + param_value, param_value_size_ret); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clGetProgramBuildInfo(cl_program program, + cl_device_id device, + cl_program_build_info param_name, + size_t param_value_size, + void * param_value, + size_t * param_value_size_ret) +{ + f_clGetProgramBuildInfo func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clGetProgramBuildInfo) dlsym(so_handle, "clGetProgramBuildInfo"); + if(func) { + return func(program, device, param_name, param_value_size, + param_value, param_value_size_ret); + } else { + return CL_INVALID_PLATFORM; + } +} + + +cl_kernel +clCreateKernel(cl_program program, + const char * kernel_name, + cl_int * errcode_ret) +{ + f_clCreateKernel func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clCreateKernel) dlsym(so_handle, "clCreateKernel"); + if(func) { + return func(program, kernel_name, errcode_ret); + } else { + return NULL; + } +} + +cl_int +clCreateKernelsInProgram(cl_program program, + cl_uint num_kernels, + cl_kernel * kernels, + cl_uint * num_kernels_ret) +{ + f_clCreateKernelsInProgram func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clCreateKernelsInProgram) dlsym(so_handle, "clCreateKernelsInProgram"); + if(func) { + return func(program, num_kernels, kernels, num_kernels_ret); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clRetainKernel(cl_kernel kernel) +{ + f_clRetainKernel func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clRetainKernel) dlsym(so_handle, "clRetainKernel"); + if(func) { + return func(kernel); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clReleaseKernel(cl_kernel kernel) +{ + f_clReleaseKernel func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clReleaseKernel) dlsym(so_handle, "clReleaseKernel"); + if(func) { + return func(kernel); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clSetKernelArg(cl_kernel kernel, + cl_uint arg_index, + size_t arg_size, + const void * arg_value) +{ + f_clSetKernelArg func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clSetKernelArg) dlsym(so_handle, "clSetKernelArg"); + if(func) { + return func(kernel, arg_index, arg_size, arg_value); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clGetKernelInfo(cl_kernel kernel, + cl_kernel_info param_name, + size_t param_value_size, + void * param_value, + size_t * param_value_size_ret) +{ + f_clGetKernelInfo func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clGetKernelInfo) dlsym(so_handle, "clGetKernelInfo"); + if(func) { + return func(kernel, param_name, param_value_size, param_value, param_value_size_ret); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clGetKernelArgInfo(cl_kernel kernel, + cl_uint arg_indx, + cl_kernel_arg_info param_name, + size_t param_value_size, + void * param_value, + size_t * param_value_size_ret) +{ + f_clGetKernelArgInfo func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clGetKernelArgInfo) dlsym(so_handle, "clGetKernelArgInfo"); + if(func) { + return func(kernel, arg_indx, param_name, param_value_size, + param_value, param_value_size_ret); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clGetKernelWorkGroupInfo(cl_kernel kernel, + cl_device_id device, + cl_kernel_work_group_info param_name, + size_t param_value_size, + void * param_value, + size_t * param_value_size_ret) +{ + f_clGetKernelWorkGroupInfo func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clGetKernelWorkGroupInfo) dlsym(so_handle, "clGetKernelWorkGroupInfo"); + if(func) { + return func(kernel, device, param_name, param_value_size, param_value, param_value_size_ret); + } else { + return CL_INVALID_PLATFORM; + } +} + + +cl_int +clWaitForEvents(cl_uint num_events, + const cl_event * event_list) +{ + f_clWaitForEvents func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clWaitForEvents) dlsym(so_handle, "clWaitForEvents"); + if(func) { + return func(num_events, event_list); + } else { + return CL_INVALID_PLATFORM; + } +} + + +cl_int +clGetEventInfo(cl_event event, + cl_event_info param_name, + size_t param_value_size, + void * param_value, + size_t * param_value_size_ret) +{ + f_clGetEventInfo func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clGetEventInfo) dlsym(so_handle, "clGetEventInfo"); + if(func) { + return func(event, param_name, param_value_size, param_value, param_value_size_ret); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_event +clCreateUserEvent(cl_context context, + cl_int * errcode_ret) +{ + f_clCreateUserEvent func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clCreateUserEvent) dlsym(so_handle, "clCreateUserEvent"); + if(func) { + return func(context, errcode_ret); + } else { + return NULL; + } +} + +cl_int +clRetainEvent(cl_event event) +{ + f_clRetainEvent func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clRetainEvent) dlsym(so_handle, "clRetainEvent"); + if(func) { + return func(event); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clReleaseEvent(cl_event event) +{ + f_clReleaseEvent func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clReleaseEvent) dlsym(so_handle, "clReleaseEvent"); + if(func) { + return func(event); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clSetUserEventStatus(cl_event event, + cl_int execution_status) +{ + f_clSetUserEventStatus func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clSetUserEventStatus) dlsym(so_handle, "clSetUserEventStatus"); + if(func) { + return func(event, execution_status); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clSetEventCallback( cl_event event, + cl_int command_exec_callback_type, + void (*pfn_notify)(cl_event, cl_int, void *), + void * user_data) +{ + f_clSetEventCallback func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clSetEventCallback) dlsym(so_handle, "clSetEventCallback"); + if(func) { + return func(event, command_exec_callback_type, pfn_notify, user_data); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clGetEventProfilingInfo(cl_event event, + cl_profiling_info param_name, + size_t param_value_size, + void * param_value, + size_t * param_value_size_ret) +{ + f_clGetEventProfilingInfo func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clGetEventProfilingInfo) dlsym(so_handle, "clGetEventProfilingInfo"); + if(func) { + return func(event, param_name, param_value_size, param_value, param_value_size_ret); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clFlush(cl_command_queue command_queue) +{ + f_clFlush func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clFlush) dlsym(so_handle, "clFlush"); + if(func) { + return func(command_queue); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clFinish(cl_command_queue command_queue) +{ + f_clFinish func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clFinish) dlsym(so_handle, "clFinish"); + if(func) { + return func(command_queue); + } else { + return CL_INVALID_PLATFORM; + } +} + + +cl_int +clEnqueueReadBuffer(cl_command_queue command_queue, + cl_mem buffer, + cl_bool blocking_read, + size_t offset, + size_t size, + void * ptr, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) +{ + f_clEnqueueReadBuffer func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clEnqueueReadBuffer) dlsym(so_handle, "clEnqueueReadBuffer"); + if(func) { + return func(command_queue, buffer, blocking_read, offset, size, ptr, + num_events_in_wait_list, event_wait_list, event); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clEnqueueReadBufferRect(cl_command_queue command_queue, + cl_mem buffer, + cl_bool blocking_read, + const size_t * buffer_offset, + const size_t * host_offset, + const size_t * region, + size_t buffer_row_pitch, + size_t buffer_slice_pitch, + size_t host_row_pitch, + size_t host_slice_pitch, + void * ptr, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) +{ + f_clEnqueueReadBufferRect func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clEnqueueReadBufferRect) dlsym(so_handle, "clEnqueueReadBufferRect"); + if(func) { + return func(command_queue, buffer, blocking_read, buffer_offset, host_offset, region, + buffer_row_pitch, buffer_slice_pitch, host_row_pitch, host_slice_pitch, ptr, + num_events_in_wait_list, event_wait_list, event); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clEnqueueWriteBuffer(cl_command_queue command_queue, + cl_mem buffer, + cl_bool blocking_write, + size_t offset, + size_t size, + const void * ptr, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) +{ + f_clEnqueueWriteBuffer func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clEnqueueWriteBuffer) dlsym(so_handle, "clEnqueueWriteBuffer"); + if(func) { + return func(command_queue, buffer, blocking_write, offset, size, ptr, + num_events_in_wait_list, event_wait_list, event); + } else { + return CL_INVALID_PLATFORM; + } +} + + +cl_int +clEnqueueWriteBufferRect(cl_command_queue command_queue, + cl_mem buffer, + cl_bool blocking_write, + const size_t * buffer_offset, + const size_t * host_offset, + const size_t * region, + size_t buffer_row_pitch, + size_t buffer_slice_pitch, + size_t host_row_pitch, + size_t host_slice_pitch, + const void * ptr, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) +{ + f_clEnqueueWriteBufferRect func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clEnqueueWriteBufferRect) dlsym(so_handle, "clEnqueueWriteBufferRect"); + if(func) { + return func(command_queue, buffer, blocking_write, buffer_offset, host_offset, region, + buffer_row_pitch, buffer_slice_pitch, host_row_pitch, host_slice_pitch, + ptr, num_events_in_wait_list, event_wait_list, event); + } else { + return CL_INVALID_PLATFORM; + } +} + + +cl_int +clEnqueueFillBuffer(cl_command_queue command_queue, + cl_mem buffer, + const void * pattern, + size_t pattern_size, + size_t offset, + size_t size, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) +{ + f_clEnqueueFillBuffer func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clEnqueueFillBuffer) dlsym(so_handle, "clEnqueueFillBuffer"); + if(func) { + return func(command_queue, buffer, pattern, pattern_size, offset, size, + num_events_in_wait_list, event_wait_list, event); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clEnqueueCopyBuffer(cl_command_queue command_queue, + cl_mem src_buffer, + cl_mem dst_buffer, + size_t src_offset, + size_t dst_offset, + size_t size, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) +{ + f_clEnqueueCopyBuffer func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clEnqueueCopyBuffer) dlsym(so_handle, "clEnqueueCopyBuffer"); + if(func) { + return func(command_queue, src_buffer, dst_buffer, src_offset, dst_offset, size, + num_events_in_wait_list, event_wait_list, event); + } else { + return CL_INVALID_PLATFORM; + } +} + + + +cl_int +clEnqueueCopyBufferRect(cl_command_queue command_queue, + cl_mem src_buffer, + cl_mem dst_buffer, + const size_t * src_origin, + const size_t * dst_origin, + const size_t * region, + size_t src_row_pitch, + size_t src_slice_pitch, + size_t dst_row_pitch, + size_t dst_slice_pitch, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) +{ + f_clEnqueueCopyBufferRect func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clEnqueueCopyBufferRect) dlsym(so_handle, "clEnqueueCopyBufferRect"); + if(func) { + return func(command_queue, src_buffer, dst_buffer, src_origin, dst_origin, region, src_row_pitch, + src_slice_pitch, dst_row_pitch, dst_slice_pitch, num_events_in_wait_list, event_wait_list, event); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clEnqueueReadImage(cl_command_queue command_queue, + cl_mem image, + cl_bool blocking_read, + const size_t * origin, + const size_t * region, + size_t row_pitch, + size_t slice_pitch, + void * ptr, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) +{ + f_clEnqueueReadImage func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clEnqueueReadImage) dlsym(so_handle, "clEnqueueReadImage"); + if(func) { + return func(command_queue, image, blocking_read, origin, region, row_pitch, slice_pitch, + ptr, num_events_in_wait_list, event_wait_list, event); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clEnqueueWriteImage(cl_command_queue command_queue, + cl_mem image, + cl_bool blocking_write, + const size_t * origin, + const size_t * region, + size_t input_row_pitch, + size_t input_slice_pitch, + const void * ptr, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) +{ + f_clEnqueueWriteImage func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clEnqueueWriteImage) dlsym(so_handle, "clEnqueueWriteImage"); + if(func) { + return func(command_queue, image, blocking_write, origin, region, input_row_pitch, input_slice_pitch, ptr, + num_events_in_wait_list, event_wait_list, event); + } else { + return CL_INVALID_PLATFORM; + } +} + + +cl_int +clEnqueueFillImage(cl_command_queue command_queue, + cl_mem image, + const void * fill_color, + const size_t * origin, + const size_t * region, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) +{ + f_clEnqueueFillImage func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clEnqueueFillImage) dlsym(so_handle, "clEnqueueFillImage"); + if(func) { + return func(command_queue, image, fill_color, origin, region, num_events_in_wait_list, event_wait_list, event); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clEnqueueCopyImage(cl_command_queue command_queue, + cl_mem src_image, + cl_mem dst_image, + const size_t * src_origin, + const size_t * dst_origin, + const size_t * region, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) +{ + f_clEnqueueCopyImage func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clEnqueueCopyImage) dlsym(so_handle, "clEnqueueCopyImage"); + if(func) { + return func(command_queue, src_image, dst_image, src_origin, dst_origin, region, + num_events_in_wait_list, event_wait_list, event); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clEnqueueCopyImageToBuffer(cl_command_queue command_queue, + cl_mem src_image, + cl_mem dst_buffer, + const size_t * src_origin, + const size_t * region, + size_t dst_offset, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) +{ + f_clEnqueueCopyImageToBuffer func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clEnqueueCopyImageToBuffer) dlsym(so_handle, "clEnqueueCopyImageToBuffer"); + if(func) { + return func(command_queue, src_image, dst_buffer, src_origin, region, dst_offset, + num_events_in_wait_list, event_wait_list, event); + } else { + return CL_INVALID_PLATFORM; + } +} + + +cl_int +clEnqueueCopyBufferToImage(cl_command_queue command_queue, + cl_mem src_buffer, + cl_mem dst_image, + size_t src_offset, + const size_t * dst_origin, + const size_t * region, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) +{ + f_clEnqueueCopyBufferToImage func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clEnqueueCopyBufferToImage) dlsym(so_handle, "clEnqueueCopyBufferToImage"); + if(func) { + return func(command_queue, src_buffer, dst_image, src_offset, dst_origin, region, + num_events_in_wait_list, event_wait_list, event); + } else { + return CL_INVALID_PLATFORM; + } +} + +void * +clEnqueueMapBuffer(cl_command_queue command_queue, + cl_mem buffer, + cl_bool blocking_map, + cl_map_flags map_flags, + size_t offset, + size_t size, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event, + cl_int * errcode_ret) +{ + f_clEnqueueMapBuffer func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clEnqueueMapBuffer) dlsym(so_handle, "clEnqueueMapBuffer"); + if(func) { + return func(command_queue, buffer, blocking_map, map_flags, offset, size, + num_events_in_wait_list, event_wait_list, event, errcode_ret); + } else { + return NULL; + } +} + +void * +clEnqueueMapImage(cl_command_queue command_queue, + cl_mem image, + cl_bool blocking_map, + cl_map_flags map_flags, + const size_t * origin, + const size_t * region, + size_t * image_row_pitch, + size_t * image_slice_pitch, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event, + cl_int * errcode_ret) +{ + f_clEnqueueMapImage func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clEnqueueMapImage) dlsym(so_handle, "clEnqueueMapImage"); + if(func) { + return func(command_queue, image, blocking_map, map_flags, origin, region, image_row_pitch, + image_slice_pitch, num_events_in_wait_list, event_wait_list, event, errcode_ret); + } else { + return NULL; + } +} + +cl_int +clEnqueueUnmapMemObject(cl_command_queue command_queue, + cl_mem memobj, + void * mapped_ptr, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) +{ + f_clEnqueueUnmapMemObject func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clEnqueueUnmapMemObject) dlsym(so_handle, "clEnqueueUnmapMemObject"); + if(func) { + return func(command_queue, memobj, mapped_ptr, num_events_in_wait_list, event_wait_list, event); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clEnqueueMigrateMemObjects(cl_command_queue command_queue, + cl_uint num_mem_objects, + const cl_mem * mem_objects, + cl_mem_migration_flags flags, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) +{ + f_clEnqueueMigrateMemObjects func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clEnqueueMigrateMemObjects) dlsym(so_handle, "clEnqueueMigrateMemObjects"); + if(func) { + return func(command_queue, num_mem_objects, mem_objects, flags, num_events_in_wait_list, event_wait_list, event); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clEnqueueNDRangeKernel(cl_command_queue command_queue, + cl_kernel kernel, + cl_uint work_dim, + const size_t * global_work_offset, + const size_t * global_work_size, + const size_t * local_work_size, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) +{ + f_clEnqueueNDRangeKernel func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clEnqueueNDRangeKernel) dlsym(so_handle, "clEnqueueNDRangeKernel"); + if(func) { + return func(command_queue, kernel, work_dim, global_work_offset, global_work_size, local_work_size, + num_events_in_wait_list, event_wait_list, event); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clEnqueueTask(cl_command_queue command_queue, + cl_kernel kernel, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) +{ + f_clEnqueueTask func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clEnqueueTask) dlsym(so_handle, "clEnqueueTask"); + if(func) { + return func(command_queue, kernel, num_events_in_wait_list, event_wait_list, event); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clEnqueueNativeKernel(cl_command_queue command_queue, + void (*user_func)(void *), + void * args, + size_t cb_args, + cl_uint num_mem_objects, + const cl_mem * mem_list, + const void ** args_mem_loc, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) +{ + f_clEnqueueNativeKernel func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clEnqueueNativeKernel) dlsym(so_handle, "clEnqueueNativeKernel"); + if(func) { + return func(command_queue, user_func, args, cb_args, num_mem_objects, mem_list, + args_mem_loc, num_events_in_wait_list, event_wait_list, event); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clEnqueueMarkerWithWaitList(cl_command_queue command_queue, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) +{ + f_clEnqueueMarkerWithWaitList func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clEnqueueMarkerWithWaitList) dlsym(so_handle, "clEnqueueMarkerWithWaitList"); + if(func) { + return func(command_queue, num_events_in_wait_list, event_wait_list, event); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clEnqueueBarrierWithWaitList(cl_command_queue command_queue, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) +{ + f_clEnqueueBarrierWithWaitList func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clEnqueueBarrierWithWaitList) dlsym(so_handle, "clEnqueueBarrierWithWaitList"); + if(func) { + return func(command_queue, num_events_in_wait_list, event_wait_list, event); + } else { + return CL_INVALID_PLATFORM; + } +} + +void * +clGetExtensionFunctionAddressForPlatform(cl_platform_id platform, + const char * func_name) +{ + f_clGetExtensionFunctionAddressForPlatform func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clGetExtensionFunctionAddressForPlatform) dlsym(so_handle, "clGetExtensionFunctionAddressForPlatform"); + if(func) { + return func(platform, func_name); + } else { + return NULL; + } +} + + +cl_mem +clCreateImage2D(cl_context context, + cl_mem_flags flags, + const cl_image_format * image_format, + size_t image_width, + size_t image_height, + size_t image_row_pitch, + void * host_ptr, + cl_int * errcode_ret) +{ + f_clCreateImage2D func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clCreateImage2D) dlsym(so_handle, "clCreateImage2D"); + if(func) { + return func(context, flags, image_format, image_width, image_height, + image_row_pitch, host_ptr, errcode_ret); + } else { + return NULL; + } +} + +cl_mem +clCreateImage3D(cl_context context, + cl_mem_flags flags, + const cl_image_format * image_format, + size_t image_width, + size_t image_height, + size_t image_depth, + size_t image_row_pitch, + size_t image_slice_pitch, + void * host_ptr, + cl_int * errcode_ret) +{ + f_clCreateImage3D func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clCreateImage3D) dlsym(so_handle, "clCreateImage3D"); + if(func) { + return func(context, flags, image_format, image_width, image_height, image_depth, + image_row_pitch, image_slice_pitch, host_ptr, errcode_ret); + } else { + return NULL; + } +} + +cl_int +clEnqueueMarker(cl_command_queue command_queue, + cl_event * event) +{ + f_clEnqueueMarker func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clEnqueueMarker) dlsym(so_handle, "clEnqueueMarker"); + if(func) { + return func(command_queue, event); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clEnqueueWaitForEvents(cl_command_queue command_queue, + cl_uint num_events, + const cl_event * event_list) +{ + f_clEnqueueWaitForEvents func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clEnqueueWaitForEvents) dlsym(so_handle, "clEnqueueWaitForEvents"); + if(func) { + return func(command_queue, num_events, event_list); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clEnqueueBarrier(cl_command_queue command_queue) +{ + f_clEnqueueBarrier func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clEnqueueBarrier) dlsym(so_handle, "clEnqueueBarrier"); + if(func) { + return func(command_queue); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clUnloadCompiler(void) +{ + f_clUnloadCompiler func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clUnloadCompiler) dlsym(so_handle, "clUnloadCompiler"); + if(func) { + return func(); + } else { + return CL_INVALID_PLATFORM; + } +} + +void * +clGetExtensionFunctionAddress(const char * func_name) +{ + f_clGetExtensionFunctionAddress func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clGetExtensionFunctionAddress) dlsym(so_handle, "clGetExtensionFunctionAddress"); + if(func) { + return func(func_name); + } else { + return NULL; + } +} + + +cl_mem +clCreateFromGLBuffer(cl_context context, + cl_mem_flags flags, + cl_GLuint bufobj, + int * errcode_ret) +{ + f_clCreateFromGLBuffer func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clCreateFromGLBuffer) dlsym(so_handle, "clCreateFromGLBuffer"); + if(func) { + return func(context, flags, bufobj, errcode_ret); + } else { + return NULL; + } +} + +cl_mem +clCreateFromGLTexture(cl_context context, + cl_mem_flags flags, + cl_GLenum target, + cl_GLint miplevel, + cl_GLuint texture, + cl_int * errcode_ret) +{ + f_clCreateFromGLTexture func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clCreateFromGLTexture) dlsym(so_handle, "clCreateFromGLTexture"); + if(func) { + return func(context, flags, target, miplevel, texture, errcode_ret); + } else { + return NULL; + } +} + +cl_mem +clCreateFromGLRenderbuffer(cl_context context, + cl_mem_flags flags, + cl_GLuint renderbuffer, + cl_int * errcode_ret) +{ + f_clCreateFromGLRenderbuffer func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clCreateFromGLRenderbuffer) dlsym(so_handle, "clCreateFromGLRenderbuffer"); + if(func) { + return func(context, flags, renderbuffer, errcode_ret); + } else { + return NULL; + } +} + +cl_int +clGetGLObjectInfo(cl_mem memobj, + cl_gl_object_type * gl_object_type, + cl_GLuint * gl_object_name) +{ + f_clGetGLObjectInfo func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clGetGLObjectInfo) dlsym(so_handle, "clGetGLObjectInfo"); + if(func) { + return func(memobj, gl_object_type, gl_object_name); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clGetGLTextureInfo(cl_mem memobj, + cl_gl_texture_info param_name, + size_t param_value_size, + void * param_value, + size_t * param_value_size_ret) +{ + f_clGetGLTextureInfo func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clGetGLTextureInfo) dlsym(so_handle, "clGetGLTextureInfo"); + if(func) { + return func(memobj, param_name, param_value_size, param_value, param_value_size_ret); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clEnqueueAcquireGLObjects(cl_command_queue command_queue, + cl_uint num_objects, + const cl_mem * mem_objects, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) +{ + f_clEnqueueAcquireGLObjects func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clEnqueueAcquireGLObjects) dlsym(so_handle, "clEnqueueAcquireGLObjects"); + if(func) { + return func(command_queue, num_objects, mem_objects, num_events_in_wait_list, event_wait_list, event); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_int +clEnqueueReleaseGLObjects(cl_command_queue command_queue, + cl_uint num_objects, + const cl_mem * mem_objects, + cl_uint num_events_in_wait_list, + const cl_event * event_wait_list, + cl_event * event) +{ + f_clEnqueueReleaseGLObjects func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clEnqueueReleaseGLObjects) dlsym(so_handle, "clEnqueueReleaseGLObjects"); + if(func) { + return func(command_queue, num_objects, mem_objects, num_events_in_wait_list, event_wait_list, event); + } else { + return CL_INVALID_PLATFORM; + } +} + + +cl_mem +clCreateFromGLTexture2D(cl_context context, + cl_mem_flags flags, + cl_GLenum target, + cl_GLint miplevel, + cl_GLuint texture, + cl_int * errcode_ret) +{ + f_clCreateFromGLTexture2D func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clCreateFromGLTexture2D) dlsym(so_handle, "clCreateFromGLTexture2D"); + if(func) { + return func(context, flags, target, miplevel, texture, errcode_ret); + } else { + return NULL; + } +} + +cl_mem +clCreateFromGLTexture3D(cl_context context, + cl_mem_flags flags, + cl_GLenum target, + cl_GLint miplevel, + cl_GLuint texture, + cl_int * errcode_ret) +{ + f_clCreateFromGLTexture3D func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clCreateFromGLTexture3D) dlsym(so_handle, "clCreateFromGLTexture3D"); + if(func) { + return func(context, flags, target, miplevel, texture, errcode_ret); + } else { + return NULL; + } +} + +cl_int +clGetGLContextInfoKHR(const cl_context_properties * properties, + cl_gl_context_info param_name, + size_t param_value_size, + void * param_value, + size_t * param_value_size_ret) +{ + f_clGetGLContextInfoKHR func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clGetGLContextInfoKHR) dlsym(so_handle, "clGetGLContextInfoKHR"); + if(func) { + return func(properties, param_name, param_value_size, param_value, param_value_size_ret); + } else { + return CL_INVALID_PLATFORM; + } +} + +cl_mem +clImportMemoryARM( cl_context context, + cl_mem_flags flags, + const cl_import_properties_arm *properties, + void *memory, + size_t size, + cl_int *errcode_ret) { + + f_clImportMemoryARM func; + + if(!so_handle) + open_libopencl_so(); + + func = (f_clImportMemoryARM) dlsym(so_handle, "clImportMemoryARM"); + if(func) { + return func(context, flags, properties, memory, size, errcode_ret); + } else { + return NULL; + } +} \ No newline at end of file diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/haarcascades/haarcascade_eye.xml b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/haarcascades/haarcascade_eye.xml new file mode 100644 index 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+ <_> + + 6 44 2 2 + <_> + + 6 51 2 6 + <_> + + 6 77 2 1 + <_> + + 7 0 1 1 + <_> + + 7 9 1 2 + <_> + + 7 20 1 9 + <_> + + 7 23 1 4 + <_> + + 7 45 1 7 + <_> + + 7 77 1 1 + <_> + + 8 0 1 1 + <_> + + 8 47 1 11 + <_> + + 8 53 1 4 + <_> + + 8 77 1 1 + <_> + + 9 0 1 2 + <_> + + 9 0 1 15 + <_> + + 9 0 1 20 + <_> + + 9 2 1 3 + <_> + + 9 3 1 2 + <_> + + 9 6 1 3 + <_> + + 9 9 1 13 + <_> + + 9 13 1 2 + <_> + + 9 13 1 8 + <_> + + 9 19 1 16 + <_> + + 9 20 1 4 + <_> + + 9 25 1 4 + <_> + + 9 43 1 5 + <_> + + 9 48 1 4 + <_> + + 9 59 1 3 + <_> + + 9 61 1 5 + diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/SoftFloat-COPYING.txt b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/SoftFloat-COPYING.txt new file mode 100644 index 0000000000000000000000000000000000000000..e16aa0fd58c12d2da71a95650be9a23b87ef46e0 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/SoftFloat-COPYING.txt @@ -0,0 +1,36 @@ + +License for Berkeley SoftFloat Release 3c + +John R. Hauser +2017 February 10 + +The following applies to the whole of SoftFloat Release 3c as well as to +each source file individually. + +Copyright 2011, 2012, 2013, 2014, 2015, 2016, 2017 The Regents of the +University of California. All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + + 1. Redistributions of source code must retain the above copyright notice, + this list of conditions, and the following disclaimer. + + 2. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions, and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + 3. Neither the name of the University nor the names of its contributors + may be used to endorse or promote products derived from this software + without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE REGENTS AND CONTRIBUTORS "AS IS", AND ANY +EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE, ARE +DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR ANY +DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND +ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF +THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/cpufeatures-LICENSE b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/cpufeatures-LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..d6c092292c614ce80204df312cfb89cdbb946035 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/cpufeatures-LICENSE @@ -0,0 +1,13 @@ +Copyright (C) 2016 The Android Open Source Project + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/cpufeatures-README.md b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/cpufeatures-README.md new file mode 100644 index 0000000000000000000000000000000000000000..d643c1c4a04b07af4d02edb47be6dfeeb2010eb8 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/cpufeatures-README.md @@ -0,0 +1,4 @@ +The Android NDK provides a small library named cpufeatures that your app can use at runtime to detect the target device's CPU family and the optional features it supports. +It is designed to work as-is on all official Android platform versions. + +https://developer.android.com/ndk/guides/cpu-features.html diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/jasper-LICENSE b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/jasper-LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..f817ef43a55a70eb1646b07bf254218fee97ee20 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/jasper-LICENSE @@ -0,0 +1,51 @@ +JasPer License Version 2.0 + +Copyright (c) 2001-2006 Michael David Adams +Copyright (c) 1999-2000 Image Power, Inc. +Copyright (c) 1999-2000 The University of British Columbia + +All rights reserved. + +Permission is hereby granted, free of charge, to any person (the +"User") obtaining a copy of this software and associated documentation +files (the "Software"), to deal in the Software without restriction, +including without limitation the rights to use, copy, modify, merge, +publish, distribute, and/or sell copies of the Software, and to permit +persons to whom the Software is furnished to do so, subject to the +following conditions: + +1. The above copyright notices and this permission notice (which +includes the disclaimer below) shall be included in all copies or +substantial portions of the Software. + +2. The name of a copyright holder shall not be used to endorse or +promote products derived from the Software without specific prior +written permission. + +THIS DISCLAIMER OF WARRANTY CONSTITUTES AN ESSENTIAL PART OF THIS +LICENSE. NO USE OF THE SOFTWARE IS AUTHORIZED HEREUNDER EXCEPT UNDER +THIS DISCLAIMER. THE SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS +"AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING +BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A +PARTICULAR PURPOSE AND NONINFRINGEMENT OF THIRD PARTY RIGHTS. IN NO +EVENT SHALL THE COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, OR ANY SPECIAL +INDIRECT OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING +FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, +NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION +WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. NO ASSURANCES ARE +PROVIDED BY THE COPYRIGHT HOLDERS THAT THE SOFTWARE DOES NOT INFRINGE +THE PATENT OR OTHER INTELLECTUAL PROPERTY RIGHTS OF ANY OTHER ENTITY. +EACH COPYRIGHT HOLDER DISCLAIMS ANY LIABILITY TO THE USER FOR CLAIMS +BROUGHT BY ANY OTHER ENTITY BASED ON INFRINGEMENT OF INTELLECTUAL +PROPERTY RIGHTS OR OTHERWISE. AS A CONDITION TO EXERCISING THE RIGHTS +GRANTED HEREUNDER, EACH USER HEREBY ASSUMES SOLE RESPONSIBILITY TO SECURE +ANY OTHER INTELLECTUAL PROPERTY RIGHTS NEEDED, IF ANY. THE SOFTWARE +IS NOT FAULT-TOLERANT AND IS NOT INTENDED FOR USE IN MISSION-CRITICAL +SYSTEMS, SUCH AS THOSE USED IN THE OPERATION OF NUCLEAR FACILITIES, +AIRCRAFT NAVIGATION OR COMMUNICATION SYSTEMS, AIR TRAFFIC CONTROL +SYSTEMS, DIRECT LIFE SUPPORT MACHINES, OR WEAPONS SYSTEMS, IN WHICH +THE FAILURE OF THE SOFTWARE OR SYSTEM COULD LEAD DIRECTLY TO DEATH, +PERSONAL INJURY, OR SEVERE PHYSICAL OR ENVIRONMENTAL DAMAGE ("HIGH +RISK ACTIVITIES"). THE COPYRIGHT HOLDERS SPECIFICALLY DISCLAIM ANY +EXPRESS OR IMPLIED WARRANTY OF FITNESS FOR HIGH RISK ACTIVITIES. + diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/jasper-README b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/jasper-README new file mode 100644 index 0000000000000000000000000000000000000000..88fd2f10bd4d4809a53cc8076668d71829c249ad --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/jasper-README @@ -0,0 +1,22 @@ +JasPer Readme +************* + +This is the source code distribution for JasPer. JasPer is a collection +of software (i.e., a library and application programs) for the coding +and manipulation of images. This software can handle image data in a +variety of formats. One such format supported by JasPer is the JPEG-2000 +format defined in ISO/IEC 15444-1. + +The complete licensing terms for the JasPer software can be found in +the file named "LICENSE" in the top level directory of this software +distribution. Any use of this software contrary to the terms of the +license is strictly prohibited. The changes made to the software +since the last release are described in the file "NEWS". Detailed +documentation on the JasPer software can be found in the JasPer Software +Reference Manual. This manual is located in the "doc" directory, and +includes useful information such as: 1) how to build, install, and use +the software, 2) how to submit report bugs, and 3) where to find +additional information about the software. + +Enjoy! :) + diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/jasper-copyright b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/jasper-copyright new file mode 100644 index 0000000000000000000000000000000000000000..88a55abcad68c9a21328907ca6b64f816f2d7265 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/jasper-copyright @@ -0,0 +1,62 @@ +This package was debianized by Christopher L Cheney on +Fri, 22 Aug 2003 01:33:34 -0500. + +The current maintainer is Roland Stigge + +It was downloaded from http://www.ece.uvic.ca/~mdadams/jasper/ + +Upstream Author: Michael Adams + +License: + +JasPer License Version 2.0 + +Copyright (c) 1999-2000 Image Power, Inc. +Copyright (c) 1999-2000 The University of British Columbia +Copyright (c) 2001-2003 Michael David Adams + +All rights reserved. + +Permission is hereby granted, free of charge, to any person (the +"User") obtaining a copy of this software and associated documentation +files (the "Software"), to deal in the Software without restriction, +including without limitation the rights to use, copy, modify, merge, +publish, distribute, and/or sell copies of the Software, and to permit +persons to whom the Software is furnished to do so, subject to the +following conditions: + +1. The above copyright notices and this permission notice (which +includes the disclaimer below) shall be included in all copies or +substantial portions of the Software. + +2. The name of a copyright holder shall not be used to endorse or +promote products derived from the Software without specific prior +written permission. + +THIS DISCLAIMER OF WARRANTY CONSTITUTES AN ESSENTIAL PART OF THIS +LICENSE. NO USE OF THE SOFTWARE IS AUTHORIZED HEREUNDER EXCEPT UNDER +THIS DISCLAIMER. THE SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS +"AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING +BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A +PARTICULAR PURPOSE AND NONINFRINGEMENT OF THIRD PARTY RIGHTS. IN NO +EVENT SHALL THE COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, OR ANY SPECIAL +INDIRECT OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING +FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, +NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION +WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. NO ASSURANCES ARE +PROVIDED BY THE COPYRIGHT HOLDERS THAT THE SOFTWARE DOES NOT INFRINGE +THE PATENT OR OTHER INTELLECTUAL PROPERTY RIGHTS OF ANY OTHER ENTITY. +EACH COPYRIGHT HOLDER DISCLAIMS ANY LIABILITY TO THE USER FOR CLAIMS +BROUGHT BY ANY OTHER ENTITY BASED ON INFRINGEMENT OF INTELLECTUAL +PROPERTY RIGHTS OR OTHERWISE. AS A CONDITION TO EXERCISING THE RIGHTS +GRANTED HEREUNDER, EACH USER HEREBY ASSUMES SOLE RESPONSIBILITY TO SECURE +ANY OTHER INTELLECTUAL PROPERTY RIGHTS NEEDED, IF ANY. THE SOFTWARE +IS NOT FAULT-TOLERANT AND IS NOT INTENDED FOR USE IN MISSION-CRITICAL +SYSTEMS, SUCH AS THOSE USED IN THE OPERATION OF NUCLEAR FACILITIES, +AIRCRAFT NAVIGATION OR COMMUNICATION SYSTEMS, AIR TRAFFIC CONTROL +SYSTEMS, DIRECT LIFE SUPPORT MACHINES, OR WEAPONS SYSTEMS, IN WHICH +THE FAILURE OF THE SOFTWARE OR SYSTEM COULD LEAD DIRECTLY TO DEATH, +PERSONAL INJURY, OR SEVERE PHYSICAL OR ENVIRONMENTAL DAMAGE ("HIGH +RISK ACTIVITIES"). THE COPYRIGHT HOLDERS SPECIFICALLY DISCLAIM ANY +EXPRESS OR IMPLIED WARRANTY OF FITNESS FOR HIGH RISK ACTIVITIES. + diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/libjpeg-turbo-LICENSE.md b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/libjpeg-turbo-LICENSE.md new file mode 100644 index 0000000000000000000000000000000000000000..0572390635d944af818ebe132019722650e3ac0b --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/libjpeg-turbo-LICENSE.md @@ -0,0 +1,139 @@ +libjpeg-turbo Licenses +====================== + +libjpeg-turbo is covered by three compatible BSD-style open source licenses: + +- The IJG (Independent JPEG Group) License, which is listed in + [README.ijg](README.ijg) + + This license applies to the libjpeg API library and associated programs + (any code inherited from libjpeg, and any modifications to that code.) + +- The Modified (3-clause) BSD License, which is listed below + + This license covers the TurboJPEG API library and associated programs. + +- The zlib License, which is listed below + + This license is a subset of the other two, and it covers the libjpeg-turbo + SIMD extensions. + + +Complying with the libjpeg-turbo Licenses +========================================= + +This section provides a roll-up of the libjpeg-turbo licensing terms, to the +best of our understanding. + +1. If you are distributing a modified version of the libjpeg-turbo source, + then: + + 1. You cannot alter or remove any existing copyright or license notices + from the source. + + **Origin** + - Clause 1 of the IJG License + - Clause 1 of the Modified BSD License + - Clauses 1 and 3 of the zlib License + + 2. You must add your own copyright notice to the header of each source + file you modified, so others can tell that you modified that file (if + there is not an existing copyright header in that file, then you can + simply add a notice stating that you modified the file.) + + **Origin** + - Clause 1 of the IJG License + - Clause 2 of the zlib License + + 3. You must include the IJG README file, and you must not alter any of the + copyright or license text in that file. + + **Origin** + - Clause 1 of the IJG License + +2. If you are distributing only libjpeg-turbo binaries without the source, or + if you are distributing an application that statically links with + libjpeg-turbo, then: + + 1. Your product documentation must include a message stating: + + This software is based in part on the work of the Independent JPEG + Group. + + **Origin** + - Clause 2 of the IJG license + + 2. If your binary distribution includes or uses the TurboJPEG API, then + your product documentation must include the text of the Modified BSD + License. + + **Origin** + - Clause 2 of the Modified BSD License + +3. You cannot use the name of the IJG or The libjpeg-turbo Project or the + contributors thereof in advertising, publicity, etc. + + **Origin** + - IJG License + - Clause 3 of the Modified BSD License + +4. The IJG and The libjpeg-turbo Project do not warrant libjpeg-turbo to be + free of defects, nor do we accept any liability for undesirable + consequences resulting from your use of the software. + + **Origin** + - IJG License + - Modified BSD License + - zlib License + + +The Modified (3-clause) BSD License +=================================== + +Copyright (C)\ \. All Rights Reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +- Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. +- Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. +- Neither the name of the libjpeg-turbo Project nor the names of its + contributors may be used to endorse or promote products derived from this + software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS", +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE +LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +POSSIBILITY OF SUCH DAMAGE. + + +The zlib License +================ + +Copyright (C) \, \. + +This software is provided 'as-is', without any express or implied +warranty. In no event will the authors be held liable for any damages +arising from the use of this software. + +Permission is granted to anyone to use this software for any purpose, +including commercial applications, and to alter it and redistribute it +freely, subject to the following restrictions: + +1. The origin of this software must not be misrepresented; you must not + claim that you wrote the original software. If you use this software + in a product, an acknowledgment in the product documentation would be + appreciated but is not required. +2. Altered source versions must be plainly marked as such, and must not be + misrepresented as being the original software. +3. This notice may not be removed or altered from any source distribution. diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/libjpeg-turbo-README.ijg b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/libjpeg-turbo-README.ijg new file mode 100644 index 0000000000000000000000000000000000000000..9c450ceb07ce9a43325e6524b0b2da017fea8db3 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/libjpeg-turbo-README.ijg @@ -0,0 +1,279 @@ +libjpeg-turbo note: This file has been modified by The libjpeg-turbo Project +to include only information relevant to libjpeg-turbo, to wordsmith certain +sections, and to remove impolitic language that existed in the libjpeg v8 +README. It is included only for reference. Please see README.md for +information specific to libjpeg-turbo. + + +The Independent JPEG Group's JPEG software +========================================== + +This distribution contains a release of the Independent JPEG Group's free JPEG +software. You are welcome to redistribute this software and to use it for any +purpose, subject to the conditions under LEGAL ISSUES, below. + +This software is the work of Tom Lane, Guido Vollbeding, Philip Gladstone, +Bill Allombert, Jim Boucher, Lee Crocker, Bob Friesenhahn, Ben Jackson, +Julian Minguillon, Luis Ortiz, George Phillips, Davide Rossi, Ge' Weijers, +and other members of the Independent JPEG Group. + +IJG is not affiliated with the ISO/IEC JTC1/SC29/WG1 standards committee +(also known as JPEG, together with ITU-T SG16). + + +DOCUMENTATION ROADMAP +===================== + +This file contains the following sections: + +OVERVIEW General description of JPEG and the IJG software. +LEGAL ISSUES Copyright, lack of warranty, terms of distribution. +REFERENCES Where to learn more about JPEG. +ARCHIVE LOCATIONS Where to find newer versions of this software. +FILE FORMAT WARS Software *not* to get. +TO DO Plans for future IJG releases. + +Other documentation files in the distribution are: + +User documentation: + usage.txt Usage instructions for cjpeg, djpeg, jpegtran, + rdjpgcom, and wrjpgcom. + *.1 Unix-style man pages for programs (same info as usage.txt). + wizard.txt Advanced usage instructions for JPEG wizards only. + change.log Version-to-version change highlights. +Programmer and internal documentation: + libjpeg.txt How to use the JPEG library in your own programs. + example.c Sample code for calling the JPEG library. + structure.txt Overview of the JPEG library's internal structure. + coderules.txt Coding style rules --- please read if you contribute code. + +Please read at least usage.txt. Some information can also be found in the JPEG +FAQ (Frequently Asked Questions) article. See ARCHIVE LOCATIONS below to find +out where to obtain the FAQ article. + +If you want to understand how the JPEG code works, we suggest reading one or +more of the REFERENCES, then looking at the documentation files (in roughly +the order listed) before diving into the code. + + +OVERVIEW +======== + +This package contains C software to implement JPEG image encoding, decoding, +and transcoding. JPEG (pronounced "jay-peg") is a standardized compression +method for full-color and grayscale images. JPEG's strong suit is compressing +photographic images or other types of images that have smooth color and +brightness transitions between neighboring pixels. Images with sharp lines or +other abrupt features may not compress well with JPEG, and a higher JPEG +quality may have to be used to avoid visible compression artifacts with such +images. + +JPEG is lossy, meaning that the output pixels are not necessarily identical to +the input pixels. However, on photographic content and other "smooth" images, +very good compression ratios can be obtained with no visible compression +artifacts, and extremely high compression ratios are possible if you are +willing to sacrifice image quality (by reducing the "quality" setting in the +compressor.) + +This software implements JPEG baseline, extended-sequential, and progressive +compression processes. Provision is made for supporting all variants of these +processes, although some uncommon parameter settings aren't implemented yet. +We have made no provision for supporting the hierarchical or lossless +processes defined in the standard. + +We provide a set of library routines for reading and writing JPEG image files, +plus two sample applications "cjpeg" and "djpeg", which use the library to +perform conversion between JPEG and some other popular image file formats. +The library is intended to be reused in other applications. + +In order to support file conversion and viewing software, we have included +considerable functionality beyond the bare JPEG coding/decoding capability; +for example, the color quantization modules are not strictly part of JPEG +decoding, but they are essential for output to colormapped file formats or +colormapped displays. These extra functions can be compiled out of the +library if not required for a particular application. + +We have also included "jpegtran", a utility for lossless transcoding between +different JPEG processes, and "rdjpgcom" and "wrjpgcom", two simple +applications for inserting and extracting textual comments in JFIF files. + +The emphasis in designing this software has been on achieving portability and +flexibility, while also making it fast enough to be useful. In particular, +the software is not intended to be read as a tutorial on JPEG. (See the +REFERENCES section for introductory material.) Rather, it is intended to +be reliable, portable, industrial-strength code. We do not claim to have +achieved that goal in every aspect of the software, but we strive for it. + +We welcome the use of this software as a component of commercial products. +No royalty is required, but we do ask for an acknowledgement in product +documentation, as described under LEGAL ISSUES. + + +LEGAL ISSUES +============ + +In plain English: + +1. We don't promise that this software works. (But if you find any bugs, + please let us know!) +2. You can use this software for whatever you want. You don't have to pay us. +3. You may not pretend that you wrote this software. If you use it in a + program, you must acknowledge somewhere in your documentation that + you've used the IJG code. + +In legalese: + +The authors make NO WARRANTY or representation, either express or implied, +with respect to this software, its quality, accuracy, merchantability, or +fitness for a particular purpose. This software is provided "AS IS", and you, +its user, assume the entire risk as to its quality and accuracy. + +This software is copyright (C) 1991-2016, Thomas G. Lane, Guido Vollbeding. +All Rights Reserved except as specified below. + +Permission is hereby granted to use, copy, modify, and distribute this +software (or portions thereof) for any purpose, without fee, subject to these +conditions: +(1) If any part of the source code for this software is distributed, then this +README file must be included, with this copyright and no-warranty notice +unaltered; and any additions, deletions, or changes to the original files +must be clearly indicated in accompanying documentation. +(2) If only executable code is distributed, then the accompanying +documentation must state that "this software is based in part on the work of +the Independent JPEG Group". +(3) Permission for use of this software is granted only if the user accepts +full responsibility for any undesirable consequences; the authors accept +NO LIABILITY for damages of any kind. + +These conditions apply to any software derived from or based on the IJG code, +not just to the unmodified library. If you use our work, you ought to +acknowledge us. + +Permission is NOT granted for the use of any IJG author's name or company name +in advertising or publicity relating to this software or products derived from +it. This software may be referred to only as "the Independent JPEG Group's +software". + +We specifically permit and encourage the use of this software as the basis of +commercial products, provided that all warranty or liability claims are +assumed by the product vendor. + + +The Unix configuration script "configure" was produced with GNU Autoconf. +It is copyright by the Free Software Foundation but is freely distributable. +The same holds for its supporting scripts (config.guess, config.sub, +ltmain.sh). Another support script, install-sh, is copyright by X Consortium +but is also freely distributable. + +The IJG distribution formerly included code to read and write GIF files. +To avoid entanglement with the Unisys LZW patent (now expired), GIF reading +support has been removed altogether, and the GIF writer has been simplified +to produce "uncompressed GIFs". This technique does not use the LZW +algorithm; the resulting GIF files are larger than usual, but are readable +by all standard GIF decoders. + +We are required to state that + "The Graphics Interchange Format(c) is the Copyright property of + CompuServe Incorporated. GIF(sm) is a Service Mark property of + CompuServe Incorporated." + + +REFERENCES +========== + +We recommend reading one or more of these references before trying to +understand the innards of the JPEG software. + +The best short technical introduction to the JPEG compression algorithm is + Wallace, Gregory K. "The JPEG Still Picture Compression Standard", + Communications of the ACM, April 1991 (vol. 34 no. 4), pp. 30-44. +(Adjacent articles in that issue discuss MPEG motion picture compression, +applications of JPEG, and related topics.) If you don't have the CACM issue +handy, a PDF file containing a revised version of Wallace's article is +available at http://www.ijg.org/files/Wallace.JPEG.pdf. The file (actually +a preprint for an article that appeared in IEEE Trans. Consumer Electronics) +omits the sample images that appeared in CACM, but it includes corrections +and some added material. Note: the Wallace article is copyright ACM and IEEE, +and it may not be used for commercial purposes. + +A somewhat less technical, more leisurely introduction to JPEG can be found in +"The Data Compression Book" by Mark Nelson and Jean-loup Gailly, published by +M&T Books (New York), 2nd ed. 1996, ISBN 1-55851-434-1. This book provides +good explanations and example C code for a multitude of compression methods +including JPEG. It is an excellent source if you are comfortable reading C +code but don't know much about data compression in general. The book's JPEG +sample code is far from industrial-strength, but when you are ready to look +at a full implementation, you've got one here... + +The best currently available description of JPEG is the textbook "JPEG Still +Image Data Compression Standard" by William B. Pennebaker and Joan L. +Mitchell, published by Van Nostrand Reinhold, 1993, ISBN 0-442-01272-1. +Price US$59.95, 638 pp. The book includes the complete text of the ISO JPEG +standards (DIS 10918-1 and draft DIS 10918-2). + +The original JPEG standard is divided into two parts, Part 1 being the actual +specification, while Part 2 covers compliance testing methods. Part 1 is +titled "Digital Compression and Coding of Continuous-tone Still Images, +Part 1: Requirements and guidelines" and has document numbers ISO/IEC IS +10918-1, ITU-T T.81. Part 2 is titled "Digital Compression and Coding of +Continuous-tone Still Images, Part 2: Compliance testing" and has document +numbers ISO/IEC IS 10918-2, ITU-T T.83. + +The JPEG standard does not specify all details of an interchangeable file +format. For the omitted details we follow the "JFIF" conventions, revision +1.02. JFIF 1.02 has been adopted as an Ecma International Technical Report +and thus received a formal publication status. It is available as a free +download in PDF format from +http://www.ecma-international.org/publications/techreports/E-TR-098.htm. +A PostScript version of the JFIF document is available at +http://www.ijg.org/files/jfif.ps.gz. There is also a plain text version at +http://www.ijg.org/files/jfif.txt.gz, but it is missing the figures. + +The TIFF 6.0 file format specification can be obtained by FTP from +ftp://ftp.sgi.com/graphics/tiff/TIFF6.ps.gz. The JPEG incorporation scheme +found in the TIFF 6.0 spec of 3-June-92 has a number of serious problems. +IJG does not recommend use of the TIFF 6.0 design (TIFF Compression tag 6). +Instead, we recommend the JPEG design proposed by TIFF Technical Note #2 +(Compression tag 7). Copies of this Note can be obtained from +http://www.ijg.org/files/. It is expected that the next revision +of the TIFF spec will replace the 6.0 JPEG design with the Note's design. +Although IJG's own code does not support TIFF/JPEG, the free libtiff library +uses our library to implement TIFF/JPEG per the Note. + + +ARCHIVE LOCATIONS +================= + +The "official" archive site for this software is www.ijg.org. +The most recent released version can always be found there in +directory "files". + +The JPEG FAQ (Frequently Asked Questions) article is a source of some +general information about JPEG. +It is available on the World Wide Web at http://www.faqs.org/faqs/jpeg-faq/ +and other news.answers archive sites, including the official news.answers +archive at rtfm.mit.edu: ftp://rtfm.mit.edu/pub/usenet/news.answers/jpeg-faq/. +If you don't have Web or FTP access, send e-mail to mail-server@rtfm.mit.edu +with body + send usenet/news.answers/jpeg-faq/part1 + send usenet/news.answers/jpeg-faq/part2 + + +FILE FORMAT WARS +================ + +The ISO/IEC JTC1/SC29/WG1 standards committee (also known as JPEG, together +with ITU-T SG16) currently promotes different formats containing the name +"JPEG" which are incompatible with original DCT-based JPEG. IJG therefore does +not support these formats (see REFERENCES). Indeed, one of the original +reasons for developing this free software was to help force convergence on +common, interoperable format standards for JPEG files. +Don't use an incompatible file format! +(In any case, our decoder will remain capable of reading existing JPEG +image files indefinitely.) + + +TO DO +===== + +Please send bug reports, offers of help, etc. to jpeg-info@jpegclub.org. diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/libjpeg-turbo-README.md b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/libjpeg-turbo-README.md new file mode 100644 index 0000000000000000000000000000000000000000..74e6eac45a78b7c4817b2c85373c1673dfb3d5a2 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/libjpeg-turbo-README.md @@ -0,0 +1,341 @@ +Background +========== + +libjpeg-turbo is a JPEG image codec that uses SIMD instructions (MMX, SSE2, +NEON, AltiVec) to accelerate baseline JPEG compression and decompression on +x86, x86-64, ARM, and PowerPC systems. On such systems, libjpeg-turbo is +generally 2-6x as fast as libjpeg, all else being equal. On other types of +systems, libjpeg-turbo can still outperform libjpeg by a significant amount, by +virtue of its highly-optimized Huffman coding routines. In many cases, the +performance of libjpeg-turbo rivals that of proprietary high-speed JPEG codecs. + +libjpeg-turbo implements both the traditional libjpeg API as well as the less +powerful but more straightforward TurboJPEG API. libjpeg-turbo also features +colorspace extensions that allow it to compress from/decompress to 32-bit and +big-endian pixel buffers (RGBX, XBGR, etc.), as well as a full-featured Java +interface. + +libjpeg-turbo was originally based on libjpeg/SIMD, an MMX-accelerated +derivative of libjpeg v6b developed by Miyasaka Masaru. The TigerVNC and +VirtualGL projects made numerous enhancements to the codec in 2009, and in +early 2010, libjpeg-turbo spun off into an independent project, with the goal +of making high-speed JPEG compression/decompression technology available to a +broader range of users and developers. + + +License +======= + +libjpeg-turbo is covered by three compatible BSD-style open source licenses. +Refer to [LICENSE.md](LICENSE.md) for a roll-up of license terms. + + +Building libjpeg-turbo +====================== + +Refer to [BUILDING.md](BUILDING.md) for complete instructions. + + +Using libjpeg-turbo +=================== + +libjpeg-turbo includes two APIs that can be used to compress and decompress +JPEG images: + +- **TurboJPEG API**
+ This API provides an easy-to-use interface for compressing and decompressing + JPEG images in memory. It also provides some functionality that would not be + straightforward to achieve using the underlying libjpeg API, such as + generating planar YUV images and performing multiple simultaneous lossless + transforms on an image. The Java interface for libjpeg-turbo is written on + top of the TurboJPEG API. + +- **libjpeg API**
+ This is the de facto industry-standard API for compressing and decompressing + JPEG images. It is more difficult to use than the TurboJPEG API but also + more powerful. The libjpeg API implementation in libjpeg-turbo is both + API/ABI-compatible and mathematically compatible with libjpeg v6b. It can + also optionally be configured to be API/ABI-compatible with libjpeg v7 and v8 + (see below.) + +There is no significant performance advantage to either API when both are used +to perform similar operations. + +Colorspace Extensions +--------------------- + +libjpeg-turbo includes extensions that allow JPEG images to be compressed +directly from (and decompressed directly to) buffers that use BGR, BGRX, +RGBX, XBGR, and XRGB pixel ordering. This is implemented with ten new +colorspace constants: + + JCS_EXT_RGB /* red/green/blue */ + JCS_EXT_RGBX /* red/green/blue/x */ + JCS_EXT_BGR /* blue/green/red */ + JCS_EXT_BGRX /* blue/green/red/x */ + JCS_EXT_XBGR /* x/blue/green/red */ + JCS_EXT_XRGB /* x/red/green/blue */ + JCS_EXT_RGBA /* red/green/blue/alpha */ + JCS_EXT_BGRA /* blue/green/red/alpha */ + JCS_EXT_ABGR /* alpha/blue/green/red */ + JCS_EXT_ARGB /* alpha/red/green/blue */ + +Setting `cinfo.in_color_space` (compression) or `cinfo.out_color_space` +(decompression) to one of these values will cause libjpeg-turbo to read the +red, green, and blue values from (or write them to) the appropriate position in +the pixel when compressing from/decompressing to an RGB buffer. + +Your application can check for the existence of these extensions at compile +time with: + + #ifdef JCS_EXTENSIONS + +At run time, attempting to use these extensions with a libjpeg implementation +that does not support them will result in a "Bogus input colorspace" error. +Applications can trap this error in order to test whether run-time support is +available for the colorspace extensions. + +When using the RGBX, BGRX, XBGR, and XRGB colorspaces during decompression, the +X byte is undefined, and in order to ensure the best performance, libjpeg-turbo +can set that byte to whatever value it wishes. If an application expects the X +byte to be used as an alpha channel, then it should specify `JCS_EXT_RGBA`, +`JCS_EXT_BGRA`, `JCS_EXT_ABGR`, or `JCS_EXT_ARGB`. When these colorspace +constants are used, the X byte is guaranteed to be 0xFF, which is interpreted +as opaque. + +Your application can check for the existence of the alpha channel colorspace +extensions at compile time with: + + #ifdef JCS_ALPHA_EXTENSIONS + +[jcstest.c](jcstest.c), located in the libjpeg-turbo source tree, demonstrates +how to check for the existence of the colorspace extensions at compile time and +run time. + +libjpeg v7 and v8 API/ABI Emulation +----------------------------------- + +With libjpeg v7 and v8, new features were added that necessitated extending the +compression and decompression structures. Unfortunately, due to the exposed +nature of those structures, extending them also necessitated breaking backward +ABI compatibility with previous libjpeg releases. Thus, programs that were +built to use libjpeg v7 or v8 did not work with libjpeg-turbo, since it is +based on the libjpeg v6b code base. Although libjpeg v7 and v8 are not +as widely used as v6b, enough programs (including a few Linux distros) made +the switch that there was a demand to emulate the libjpeg v7 and v8 ABIs +in libjpeg-turbo. It should be noted, however, that this feature was added +primarily so that applications that had already been compiled to use libjpeg +v7+ could take advantage of accelerated baseline JPEG encoding/decoding +without recompiling. libjpeg-turbo does not claim to support all of the +libjpeg v7+ features, nor to produce identical output to libjpeg v7+ in all +cases (see below.) + +By passing an argument of `--with-jpeg7` or `--with-jpeg8` to `configure`, or +an argument of `-DWITH_JPEG7=1` or `-DWITH_JPEG8=1` to `cmake`, you can build a +version of libjpeg-turbo that emulates the libjpeg v7 or v8 ABI, so that +programs that are built against libjpeg v7 or v8 can be run with libjpeg-turbo. +The following section describes which libjpeg v7+ features are supported and +which aren't. + +### Support for libjpeg v7 and v8 Features + +#### Fully supported + +- **libjpeg: IDCT scaling extensions in decompressor**
+ libjpeg-turbo supports IDCT scaling with scaling factors of 1/8, 1/4, 3/8, + 1/2, 5/8, 3/4, 7/8, 9/8, 5/4, 11/8, 3/2, 13/8, 7/4, 15/8, and 2/1 (only 1/4 + and 1/2 are SIMD-accelerated.) + +- **libjpeg: Arithmetic coding** + +- **libjpeg: In-memory source and destination managers**
+ See notes below. + +- **cjpeg: Separate quality settings for luminance and chrominance**
+ Note that the libpjeg v7+ API was extended to accommodate this feature only + for convenience purposes. It has always been possible to implement this + feature with libjpeg v6b (see rdswitch.c for an example.) + +- **cjpeg: 32-bit BMP support** + +- **cjpeg: `-rgb` option** + +- **jpegtran: Lossless cropping** + +- **jpegtran: `-perfect` option** + +- **jpegtran: Forcing width/height when performing lossless crop** + +- **rdjpgcom: `-raw` option** + +- **rdjpgcom: Locale awareness** + + +#### Not supported + +NOTE: As of this writing, extensive research has been conducted into the +usefulness of DCT scaling as a means of data reduction and SmartScale as a +means of quality improvement. The reader is invited to peruse the research at + and draw his/her own conclusions, +but it is the general belief of our project that these features have not +demonstrated sufficient usefulness to justify inclusion in libjpeg-turbo. + +- **libjpeg: DCT scaling in compressor**
+ `cinfo.scale_num` and `cinfo.scale_denom` are silently ignored. + There is no technical reason why DCT scaling could not be supported when + emulating the libjpeg v7+ API/ABI, but without the SmartScale extension (see + below), only scaling factors of 1/2, 8/15, 4/7, 8/13, 2/3, 8/11, 4/5, and + 8/9 would be available, which is of limited usefulness. + +- **libjpeg: SmartScale**
+ `cinfo.block_size` is silently ignored. + SmartScale is an extension to the JPEG format that allows for DCT block + sizes other than 8x8. Providing support for this new format would be + feasible (particularly without full acceleration.) However, until/unless + the format becomes either an official industry standard or, at minimum, an + accepted solution in the community, we are hesitant to implement it, as + there is no sense of whether or how it might change in the future. It is + our belief that SmartScale has not demonstrated sufficient usefulness as a + lossless format nor as a means of quality enhancement, and thus our primary + interest in providing this feature would be as a means of supporting + additional DCT scaling factors. + +- **libjpeg: Fancy downsampling in compressor**
+ `cinfo.do_fancy_downsampling` is silently ignored. + This requires the DCT scaling feature, which is not supported. + +- **jpegtran: Scaling**
+ This requires both the DCT scaling and SmartScale features, which are not + supported. + +- **Lossless RGB JPEG files**
+ This requires the SmartScale feature, which is not supported. + +### What About libjpeg v9? + +libjpeg v9 introduced yet another field to the JPEG compression structure +(`color_transform`), thus making the ABI backward incompatible with that of +libjpeg v8. This new field was introduced solely for the purpose of supporting +lossless SmartScale encoding. Furthermore, there was actually no reason to +extend the API in this manner, as the color transform could have just as easily +been activated by way of a new JPEG colorspace constant, thus preserving +backward ABI compatibility. + +Our research (see link above) has shown that lossless SmartScale does not +generally accomplish anything that can't already be accomplished better with +existing, standard lossless formats. Therefore, at this time it is our belief +that there is not sufficient technical justification for software projects to +upgrade from libjpeg v8 to libjpeg v9, and thus there is not sufficient +technical justification for us to emulate the libjpeg v9 ABI. + +In-Memory Source/Destination Managers +------------------------------------- + +By default, libjpeg-turbo 1.3 and later includes the `jpeg_mem_src()` and +`jpeg_mem_dest()` functions, even when not emulating the libjpeg v8 API/ABI. +Previously, it was necessary to build libjpeg-turbo from source with libjpeg v8 +API/ABI emulation in order to use the in-memory source/destination managers, +but several projects requested that those functions be included when emulating +the libjpeg v6b API/ABI as well. This allows the use of those functions by +programs that need them, without breaking ABI compatibility for programs that +don't, and it allows those functions to be provided in the "official" +libjpeg-turbo binaries. + +Those who are concerned about maintaining strict conformance with the libjpeg +v6b or v7 API can pass an argument of `--without-mem-srcdst` to `configure` or +an argument of `-DWITH_MEM_SRCDST=0` to `cmake` prior to building +libjpeg-turbo. This will restore the pre-1.3 behavior, in which +`jpeg_mem_src()` and `jpeg_mem_dest()` are only included when emulating the +libjpeg v8 API/ABI. + +On Un*x systems, including the in-memory source/destination managers changes +the dynamic library version from 62.1.0 to 62.2.0 if using libjpeg v6b API/ABI +emulation and from 7.1.0 to 7.2.0 if using libjpeg v7 API/ABI emulation. + +Note that, on most Un*x systems, the dynamic linker will not look for a +function in a library until that function is actually used. Thus, if a program +is built against libjpeg-turbo 1.3+ and uses `jpeg_mem_src()` or +`jpeg_mem_dest()`, that program will not fail if run against an older version +of libjpeg-turbo or against libjpeg v7- until the program actually tries to +call `jpeg_mem_src()` or `jpeg_mem_dest()`. Such is not the case on Windows. +If a program is built against the libjpeg-turbo 1.3+ DLL and uses +`jpeg_mem_src()` or `jpeg_mem_dest()`, then it must use the libjpeg-turbo 1.3+ +DLL at run time. + +Both cjpeg and djpeg have been extended to allow testing the in-memory +source/destination manager functions. See their respective man pages for more +details. + + +Mathematical Compatibility +========================== + +For the most part, libjpeg-turbo should produce identical output to libjpeg +v6b. The one exception to this is when using the floating point DCT/IDCT, in +which case the outputs of libjpeg v6b and libjpeg-turbo can differ for the +following reasons: + +- The SSE/SSE2 floating point DCT implementation in libjpeg-turbo is ever so + slightly more accurate than the implementation in libjpeg v6b, but not by + any amount perceptible to human vision (generally in the range of 0.01 to + 0.08 dB gain in PNSR.) + +- When not using the SIMD extensions, libjpeg-turbo uses the more accurate + (and slightly faster) floating point IDCT algorithm introduced in libjpeg + v8a as opposed to the algorithm used in libjpeg v6b. It should be noted, + however, that this algorithm basically brings the accuracy of the floating + point IDCT in line with the accuracy of the slow integer IDCT. The floating + point DCT/IDCT algorithms are mainly a legacy feature, and they do not + produce significantly more accuracy than the slow integer algorithms (to put + numbers on this, the typical difference in PNSR between the two algorithms + is less than 0.10 dB, whereas changing the quality level by 1 in the upper + range of the quality scale is typically more like a 1.0 dB difference.) + +- If the floating point algorithms in libjpeg-turbo are not implemented using + SIMD instructions on a particular platform, then the accuracy of the + floating point DCT/IDCT can depend on the compiler settings. + +While libjpeg-turbo does emulate the libjpeg v8 API/ABI, under the hood it is +still using the same algorithms as libjpeg v6b, so there are several specific +cases in which libjpeg-turbo cannot be expected to produce the same output as +libjpeg v8: + +- When decompressing using scaling factors of 1/2 and 1/4, because libjpeg v8 + implements those scaling algorithms differently than libjpeg v6b does, and + libjpeg-turbo's SIMD extensions are based on the libjpeg v6b behavior. + +- When using chrominance subsampling, because libjpeg v8 implements this + with its DCT/IDCT scaling algorithms rather than with a separate + downsampling/upsampling algorithm. In our testing, the subsampled/upsampled + output of libjpeg v8 is less accurate than that of libjpeg v6b for this + reason. + +- When decompressing using a scaling factor > 1 and merged (AKA "non-fancy" or + "non-smooth") chrominance upsampling, because libjpeg v8 does not support + merged upsampling with scaling factors > 1. + + +Performance Pitfalls +==================== + +Restart Markers +--------------- + +The optimized Huffman decoder in libjpeg-turbo does not handle restart markers +in a way that makes the rest of the libjpeg infrastructure happy, so it is +necessary to use the slow Huffman decoder when decompressing a JPEG image that +has restart markers. This can cause the decompression performance to drop by +as much as 20%, but the performance will still be much greater than that of +libjpeg. Many consumer packages, such as PhotoShop, use restart markers when +generating JPEG images, so images generated by those programs will experience +this issue. + +Fast Integer Forward DCT at High Quality Levels +----------------------------------------------- + +The algorithm used by the SIMD-accelerated quantization function cannot produce +correct results whenever the fast integer forward DCT is used along with a JPEG +quality of 98-100. Thus, libjpeg-turbo must use the non-SIMD quantization +function in those cases. This causes performance to drop by as much as 40%. +It is therefore strongly advised that you use the slow integer forward DCT +whenever encoding images with a JPEG quality of 98 or higher. diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/libpng-LICENSE b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/libpng-LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..6ee9c8f5548223ab91d4600840d2597bd5cdbe9f --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/libpng-LICENSE @@ -0,0 +1,133 @@ + +This copy of the libpng notices is provided for your convenience. In case of +any discrepancy between this copy and the notices in the file png.h that is +included in the libpng distribution, the latter shall prevail. + +COPYRIGHT NOTICE, DISCLAIMER, and LICENSE: + +If you modify libpng you may insert additional notices immediately following +this sentence. + +This code is released under the libpng license. + +libpng versions 1.0.7, July 1, 2000 through 1.6.35, July 15, 2018 are +Copyright (c) 2000-2002, 2004, 2006-2018 Glenn Randers-Pehrson, are +derived from libpng-1.0.6, and are distributed according to the same +disclaimer and license as libpng-1.0.6 with the following individuals +added to the list of Contributing Authors: + + Simon-Pierre Cadieux + Eric S. Raymond + Mans Rullgard + Cosmin Truta + Gilles Vollant + James Yu + Mandar Sahastrabuddhe + Google Inc. + Vadim Barkov + +and with the following additions to the disclaimer: + + There is no warranty against interference with your enjoyment of the + library or against infringement. There is no warranty that our + efforts or the library will fulfill any of your particular purposes + or needs. This library is provided with all faults, and the entire + risk of satisfactory quality, performance, accuracy, and effort is with + the user. + +Some files in the "contrib" directory and some configure-generated +files that are distributed with libpng have other copyright owners and +are released under other open source licenses. + +libpng versions 0.97, January 1998, through 1.0.6, March 20, 2000, are +Copyright (c) 1998-2000 Glenn Randers-Pehrson, are derived from +libpng-0.96, and are distributed according to the same disclaimer and +license as libpng-0.96, with the following individuals added to the list +of Contributing Authors: + + Tom Lane + Glenn Randers-Pehrson + Willem van Schaik + +libpng versions 0.89, June 1996, through 0.96, May 1997, are +Copyright (c) 1996-1997 Andreas Dilger, are derived from libpng-0.88, +and are distributed according to the same disclaimer and license as +libpng-0.88, with the following individuals added to the list of +Contributing Authors: + + John Bowler + Kevin Bracey + Sam Bushell + Magnus Holmgren + Greg Roelofs + Tom Tanner + +Some files in the "scripts" directory have other copyright owners +but are released under this license. + +libpng versions 0.5, May 1995, through 0.88, January 1996, are +Copyright (c) 1995-1996 Guy Eric Schalnat, Group 42, Inc. + +For the purposes of this copyright and license, "Contributing Authors" +is defined as the following set of individuals: + + Andreas Dilger + Dave Martindale + Guy Eric Schalnat + Paul Schmidt + Tim Wegner + +The PNG Reference Library is supplied "AS IS". The Contributing Authors +and Group 42, Inc. disclaim all warranties, expressed or implied, +including, without limitation, the warranties of merchantability and of +fitness for any purpose. The Contributing Authors and Group 42, Inc. +assume no liability for direct, indirect, incidental, special, exemplary, +or consequential damages, which may result from the use of the PNG +Reference Library, even if advised of the possibility of such damage. + +Permission is hereby granted to use, copy, modify, and distribute this +source code, or portions hereof, for any purpose, without fee, subject +to the following restrictions: + + 1. The origin of this source code must not be misrepresented. + + 2. Altered versions must be plainly marked as such and must not + be misrepresented as being the original source. + + 3. This Copyright notice may not be removed or altered from any + source or altered source distribution. + +The Contributing Authors and Group 42, Inc. specifically permit, without +fee, and encourage the use of this source code as a component to +supporting the PNG file format in commercial products. If you use this +source code in a product, acknowledgment is not required but would be +appreciated. + +END OF COPYRIGHT NOTICE, DISCLAIMER, and LICENSE. + +TRADEMARK: + +The name "libpng" has not been registered by the Copyright owner +as a trademark in any jurisdiction. However, because libpng has +been distributed and maintained world-wide, continually since 1995, +the Copyright owner claims "common-law trademark protection" in any +jurisdiction where common-law trademark is recognized. + +OSI CERTIFICATION: + +Libpng is OSI Certified Open Source Software. OSI Certified Open Source is +a certification mark of the Open Source Initiative. OSI has not addressed +the additional disclaimers inserted at version 1.0.7. + +EXPORT CONTROL: + +The Copyright owner believes that the Export Control Classification +Number (ECCN) for libpng is EAR99, which means not subject to export +controls or International Traffic in Arms Regulations (ITAR) because +it is open source, publicly available software, that does not contain +any encryption software. See the EAR, paragraphs 734.3(b)(3) and +734.7(b). + +Glenn Randers-Pehrson +glennrp at users.sourceforge.net +July 15, 2018 diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/libpng-README b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/libpng-README new file mode 100644 index 0000000000000000000000000000000000000000..f098b27f3971aec2dd120678d09c5aac707704d1 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/libpng-README @@ -0,0 +1,222 @@ +README for libpng version 1.6.35 - July 15, 2018 (shared library 16.0) +See the note about version numbers near the top of png.h + +See INSTALL for instructions on how to install libpng. + +Libpng comes in several distribution formats. Get libpng-*.tar.gz or +libpng-*.tar.xz or if you want UNIX-style line endings in the text files, +or lpng*.7z or lpng*.zip if you want DOS-style line endings. + +Version 0.89 was the first official release of libpng. Don't let the +fact that it's the first release fool you. The libpng library has been in +extensive use and testing since mid-1995. By late 1997 it had +finally gotten to the stage where there hadn't been significant +changes to the API in some time, and people have a bad feeling about +libraries with versions < 1.0. Version 1.0.0 was released in +March 1998. + +**** +Note that some of the changes to the png_info structure render this +version of the library binary incompatible with libpng-0.89 or +earlier versions if you are using a shared library. The type of the +"filler" parameter for png_set_filler() has changed from png_byte to +png_uint_32, which will affect shared-library applications that use +this function. + +To avoid problems with changes to the internals of the png info_struct, +new APIs have been made available in 0.95 to avoid direct application +access to info_ptr. These functions are the png_set_ and +png_get_ functions. These functions should be used when +accessing/storing the info_struct data, rather than manipulating it +directly, to avoid such problems in the future. + +It is important to note that the APIs did not make current programs +that access the info struct directly incompatible with the new +library, through libpng-1.2.x. In libpng-1.4.x, which was meant to +be a transitional release, members of the png_struct and the +info_struct can still be accessed, but the compiler will issue a +warning about deprecated usage. Since libpng-1.5.0, direct access +to these structs is not allowed, and the definitions of the structs +reside in private pngstruct.h and pnginfo.h header files that are not +accessible to applications. It is strongly suggested that new +programs use the new APIs (as shown in example.c and pngtest.c), and +older programs be converted to the new format, to facilitate upgrades +in the future. +**** + +Additions since 0.90 include the ability to compile libpng as a +Windows DLL, and new APIs for accessing data in the info struct. +Experimental functions include the ability to set weighting and cost +factors for row filter selection, direct reads of integers from buffers +on big-endian processors that support misaligned data access, faster +methods of doing alpha composition, and more accurate 16->8 bit color +conversion. + +The additions since 0.89 include the ability to read from a PNG stream +which has had some (or all) of the signature bytes read by the calling +application. This also allows the reading of embedded PNG streams that +do not have the PNG file signature. As well, it is now possible to set +the library action on the detection of chunk CRC errors. It is possible +to set different actions based on whether the CRC error occurred in a +critical or an ancillary chunk. + +The changes made to the library, and bugs fixed are based on discussions +on the PNG-implement mailing list and not on material submitted +privately to Guy, Andreas, or Glenn. They will forward any good +suggestions to the list. + +For a detailed description on using libpng, read libpng-manual.txt. For +examples of libpng in a program, see example.c and pngtest.c. For usage +information and restrictions (what little they are) on libpng, see +png.h. For a description on using zlib (the compression library used by +libpng) and zlib's restrictions, see zlib.h + +I have included a general makefile, as well as several machine and +compiler specific ones, but you may have to modify one for your own needs. + +You should use zlib 1.0.4 or later to run this, but it MAY work with +versions as old as zlib 0.95. Even so, there are bugs in older zlib +versions which can cause the output of invalid compression streams for +some images. You will definitely need zlib 1.0.4 or later if you are +taking advantage of the MS-DOS "far" structure allocation for the small +and medium memory models. You should also note that zlib is a +compression library that is useful for more things than just PNG files. +You can use zlib as a drop-in replacement for fread() and fwrite() if +you are so inclined. + +zlib should be available at the same place that libpng is, or at zlib.net. + +You may also want a copy of the PNG specification. It is available +as an RFC, a W3C Recommendation, and an ISO/IEC Standard. You can find +these at http://www.libpng.org/pub/png/pngdocs.html . + +This code is currently being archived at libpng.sourceforge.io in the +[DOWNLOAD] area, and at http://libpng.download/src . If you +can't find it in any of those places, e-mail me, and I'll help you find it. + +I am not a lawyer, but I believe that the Export Control Classification +Number (ECCN) for libpng is EAR99, which means not subject to export +controls or International Traffic in Arms Regulations (ITAR) because it +is open source, publicly available software, that does not contain any +encryption software. See the EAR, paragraphs 734.3(b)(3) and 734.7(b). + +If you have any code changes, requests, problems, etc., please e-mail +them to me. Also, I'd appreciate any make files or project files, +and any modifications you needed to make to get libpng to compile, +along with a #define variable to tell what compiler/system you are on. +If you needed to add transformations to libpng, or wish libpng would +provide the image in a different way, drop me a note (and code, if +possible), so I can consider supporting the transformation. +Finally, if you get any warning messages when compiling libpng +(note: not zlib), and they are easy to fix, I'd appreciate the +fix. Please mention "libpng" somewhere in the subject line. Thanks. + +This release was created and will be supported by myself (of course +based in a large way on Guy's and Andreas' earlier work), and the PNG +development group. + +Send comments/corrections/commendations to png-mng-implement at +lists.sourceforge.net (subscription required; visit +https://lists.sourceforge.net/lists/listinfo/png-mng-implement +to subscribe). + +You can't reach Guy, the original libpng author, at the addresses +given in previous versions of this document. He and Andreas will +read mail addressed to the png-implement list, however. + +Please do not send general questions about PNG. Send them to +png-mng-misc at lists.sf.net (subscription required; visit +https://lists.sourceforge.net/lists/listinfo/png-mng-misc to +subscribe). If you have a question about something +in the PNG specification that is related to using libpng, send it +to me. Send me any questions that start with "I was using libpng, +and ...". If in doubt, send questions to me. I'll bounce them +to others, if necessary. + +Please do not send suggestions on how to change PNG. We have +been discussing PNG for twenty years now, and it is official and +finished. If you have suggestions for libpng, however, I'll +gladly listen. Even if your suggestion is not used immediately, +it may be used later. + +Files in this distribution: + + ANNOUNCE => Announcement of this version, with recent changes + CHANGES => Description of changes between libpng versions + KNOWNBUG => List of known bugs and deficiencies + LICENSE => License to use and redistribute libpng + README => This file + TODO => Things not implemented in the current library + Y2KINFO => Statement of Y2K compliance + example.c => Example code for using libpng functions + libpng.3 => manual page for libpng (includes libpng-manual.txt) + libpng-manual.txt => Description of libpng and its functions + libpngpf.3 => manual page for libpng's private functions + png.5 => manual page for the PNG format + png.c => Basic interface functions common to library + png.h => Library function and interface declarations (public) + pngpriv.h => Library function and interface declarations (private) + pngconf.h => System specific library configuration (public) + pngstruct.h => png_struct declaration (private) + pnginfo.h => png_info struct declaration (private) + pngdebug.h => debugging macros (private) + pngerror.c => Error/warning message I/O functions + pngget.c => Functions for retrieving info from struct + pngmem.c => Memory handling functions + pngbar.png => PNG logo, 88x31 + pngnow.png => PNG logo, 98x31 + pngpread.c => Progressive reading functions + pngread.c => Read data/helper high-level functions + pngrio.c => Lowest-level data read I/O functions + pngrtran.c => Read data transformation functions + pngrutil.c => Read data utility functions + pngset.c => Functions for storing data into the info_struct + pngtest.c => Library test program + pngtest.png => Library test sample image + pngtrans.c => Common data transformation functions + pngwio.c => Lowest-level write I/O functions + pngwrite.c => High-level write functions + pngwtran.c => Write data transformations + pngwutil.c => Write utility functions + arm => Contains optimized code for the ARM platform + powerpc => Contains optimized code for the PowerPC platform + contrib => Contributions + arm-neon => Optimized code for ARM-NEON platform + powerpc-vsx => Optimized code for POWERPC-VSX platform + examples => Example programs + gregbook => source code for PNG reading and writing, from + Greg Roelofs' "PNG: The Definitive Guide", + O'Reilly, 1999 + libtests => Test programs + mips-msa => Optimized code for MIPS-MSA platform + pngminim => Minimal decoder, encoder, and progressive decoder + programs demonstrating use of pngusr.dfa + pngminus => Simple pnm2png and png2pnm programs + pngsuite => Test images + testpngs + tools => Various tools + visupng => Contains a MSVC workspace for VisualPng + intel => Optimized code for INTEL-SSE2 platform + mips => Optimized code for MIPS platform + projects => Contains project files and workspaces for + building a DLL + owatcom => Contains a WATCOM project for building libpng + visualc71 => Contains a Microsoft Visual C++ (MSVC) + workspace for building libpng and zlib + vstudio => Contains a Microsoft Visual C++ (MSVC) + workspace for building libpng and zlib + scripts => Directory containing scripts for building libpng: + (see scripts/README.txt for the list of scripts) + +Good luck, and happy coding. + +-Glenn Randers-Pehrson (current maintainer, since 1998) + Internet: glennrp at users.sourceforge.net + +-Andreas Eric Dilger (former maintainer, 1996-1997) + Internet: adilger at enel.ucalgary.ca + Web: http://www-mddsp.enel.ucalgary.ca/People/adilger/ + +-Guy Eric Schalnat (original author and former maintainer, 1995-1996) + (formerly of Group 42, Inc) + Internet: gschal at infinet.com diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/libtiff-COPYRIGHT b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/libtiff-COPYRIGHT new file mode 100644 index 0000000000000000000000000000000000000000..8282186151e224ac83d9990805062ff7b8effa00 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/libtiff-COPYRIGHT @@ -0,0 +1,21 @@ +Copyright (c) 1988-1997 Sam Leffler +Copyright (c) 1991-1997 Silicon Graphics, Inc. + +Permission to use, copy, modify, distribute, and sell this software and +its documentation for any purpose is hereby granted without fee, provided +that (i) the above copyright notices and this permission notice appear in +all copies of the software and related documentation, and (ii) the names of +Sam Leffler and Silicon Graphics may not be used in any advertising or +publicity relating to the software without the specific, prior written +permission of Sam Leffler and Silicon Graphics. + +THE SOFTWARE IS PROVIDED "AS-IS" AND WITHOUT WARRANTY OF ANY KIND, +EXPRESS, IMPLIED OR OTHERWISE, INCLUDING WITHOUT LIMITATION, ANY +WARRANTY OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. + +IN NO EVENT SHALL SAM LEFFLER OR SILICON GRAPHICS BE LIABLE FOR +ANY SPECIAL, INCIDENTAL, INDIRECT OR CONSEQUENTIAL DAMAGES OF ANY KIND, +OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, +WHETHER OR NOT ADVISED OF THE POSSIBILITY OF DAMAGE, AND ON ANY THEORY OF +LIABILITY, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE +OF THIS SOFTWARE. diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/openexr-AUTHORS.ilmbase b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/openexr-AUTHORS.ilmbase new file mode 100644 index 0000000000000000000000000000000000000000..51c1f9cea5329ed8e8cd4ce118a1a6a16db49b7a --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/openexr-AUTHORS.ilmbase @@ -0,0 +1,21 @@ +Developers: +----------- + +Florian Kainz +Rod Bogart +Drew Hess +Bill Anderson +Wojciech Jarosz + +Contributors: +------------- + +Rito Trevino +Josh Pines +Christian Rouet + +Win32 build system: +------------------- + +Nick Porcino +Kimball Thurston diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/openexr-AUTHORS.openexr b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/openexr-AUTHORS.openexr new file mode 100644 index 0000000000000000000000000000000000000000..2926b13e837b2a84dfa827dcc222d495185e2ae0 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/openexr-AUTHORS.openexr @@ -0,0 +1,41 @@ +Developers: +----------- + +Florian Kainz +Rod Bogart +Drew Hess +Paul Schneider +Bill Anderson +Wojciech Jarosz +Andrew Kunz + +Contributors: +------------- + +Simon Green +Rito Trevino +Josh Pines +Christian Rouet +Rodrigo Damazio +Greg Ward +Joseph Goldstone +Loren Carpenter, Pixar Animation Studios + +Win32 build system: +------------------- + +Nick Porcino +Kimball Thurston + +Win32 port contributors: +------------------------ + +Dustin Graves +Jukka Liimatta +Baumann Konstantin +Daniel Koch +E. Scott Larsen +stephan mantler +Andreas Kahler +Frank Jargstorff +Lutz Latta diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/openexr-LICENSE b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/openexr-LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..6372750d3cc40ba4dd12c57b2ac9026a2d7d54b9 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/openexr-LICENSE @@ -0,0 +1,34 @@ +Copyright (c) 2006, Industrial Light & Magic, a division of Lucasfilm +Entertainment Company Ltd. Portions contributed and copyright held by +others as indicated. All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are +met: + + * Redistributions of source code must retain the above + copyright notice, this list of conditions and the following + disclaimer. + + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided with + the distribution. + + * Neither the name of Industrial Light & Magic nor the names of + any other contributors to this software may be used to endorse or + promote products derived from this software without specific prior + written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS +IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, +THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR +PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR +CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, +EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, +PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR +PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF +LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING +NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/openexr-fix_msvc2013_errors.patch b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/openexr-fix_msvc2013_errors.patch new file mode 100644 index 0000000000000000000000000000000000000000..0ce106f13b5778d8cdb364074309c6d18ff61e29 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/openexr-fix_msvc2013_errors.patch @@ -0,0 +1,72 @@ +diff --git a/3rdparty/openexr/IlmImf/ImfAcesFile.cpp b/3rdparty/openexr/IlmImf/ImfAcesFile.cpp +index de4bf83..9418b9d 100644 +--- a/3rdparty/openexr/IlmImf/ImfAcesFile.cpp ++++ b/3rdparty/openexr/IlmImf/ImfAcesFile.cpp +@@ -42,6 +42,7 @@ + #include + #include + #include ++#include // for std::max() + + using namespace std; + using namespace Imath; +diff --git a/3rdparty/openexr/IlmImf/ImfOutputFile.cpp b/3rdparty/openexr/IlmImf/ImfOutputFile.cpp +index 8831ec9..e69b92b 100644 +--- a/3rdparty/openexr/IlmImf/ImfOutputFile.cpp ++++ b/3rdparty/openexr/IlmImf/ImfOutputFile.cpp +@@ -58,6 +58,7 @@ + #include + #include + #include ++#include // for std::max() + + + namespace Imf { +diff --git a/3rdparty/openexr/IlmImf/ImfScanLineInputFile.cpp b/3rdparty/openexr/IlmImf/ImfScanLineInputFile.cpp +index f7a12a3..5d8b522 100644 +--- a/3rdparty/openexr/IlmImf/ImfScanLineInputFile.cpp ++++ b/3rdparty/openexr/IlmImf/ImfScanLineInputFile.cpp +@@ -56,6 +56,7 @@ + #include + #include + #include ++#include // for std::max() + + + namespace Imf { +diff --git a/3rdparty/openexr/IlmImf/ImfTiledMisc.cpp b/3rdparty/openexr/IlmImf/ImfTiledMisc.cpp +index 57f52f1..9588e78 100644 +--- a/3rdparty/openexr/IlmImf/ImfTiledMisc.cpp ++++ b/3rdparty/openexr/IlmImf/ImfTiledMisc.cpp +@@ -43,6 +43,7 @@ + #include "Iex.h" + #include + #include ++#include // for std::max() + + + namespace Imf { +diff --git a/3rdparty/openexr/IlmImf/ImfTiledOutputFile.cpp b/3rdparty/openexr/IlmImf/ImfTiledOutputFile.cpp +index 0882106..0bc3cb3 100644 +--- a/3rdparty/openexr/IlmImf/ImfTiledOutputFile.cpp ++++ b/3rdparty/openexr/IlmImf/ImfTiledOutputFile.cpp +@@ -63,6 +63,7 @@ + #include + #include + #include ++#include // for std::max() + + + namespace Imf { +diff --git a/3rdparty/openexr/Imath/ImathMatrixAlgo.cpp b/3rdparty/openexr/Imath/ImathMatrixAlgo.cpp +index f0d2ed6..7ddc649 100644 +--- a/3rdparty/openexr/Imath/ImathMatrixAlgo.cpp ++++ b/3rdparty/openexr/Imath/ImathMatrixAlgo.cpp +@@ -44,6 +44,7 @@ + + #include "ImathMatrixAlgo.h" + #include ++#include // for std::max() + + #if defined(OPENEXR_DLL) + #define EXPORT_CONST __declspec(dllexport) diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/protobuf-LICENSE b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/protobuf-LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..f028c82324202241b99522ed311d1c369c1eee66 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/protobuf-LICENSE @@ -0,0 +1,42 @@ +This license applies to all parts of Protocol Buffers except the following: + + - Atomicops support for generic gcc, located in + src/google/protobuf/stubs/atomicops_internals_generic_gcc.h. + This file is copyrighted by Red Hat Inc. + + - Atomicops support for AIX/POWER, located in + src/google/protobuf/stubs/atomicops_internals_power.h. + This file is copyrighted by Bloomberg Finance LP. + +Copyright 2014, Google Inc. All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are +met: + + * Redistributions of source code must retain the above copyright +notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above +copyright notice, this list of conditions and the following disclaimer +in the documentation and/or other materials provided with the +distribution. + * Neither the name of Google Inc. nor the names of its +contributors may be used to endorse or promote products derived from +this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +Code generated by the Protocol Buffer compiler is owned by the owner +of the input file used when generating it. This code is not +standalone and requires a support library to be linked with it. This +support library is itself covered by the above license. diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/protobuf-README.md b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/protobuf-README.md new file mode 100644 index 0000000000000000000000000000000000000000..e0edb75f580ec43c1e7abf2aa99aeb6a8d01001f --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/protobuf-README.md @@ -0,0 +1,3 @@ +Project: Protocol Buffers - Google's data interchange format +Source code: https://github.com/google/protobuf +Version: 3.5.1 diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/quirc-LICENSE b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/quirc-LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..d47c0262c2f2a785b6b0f5a87f940251352decc9 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/quirc-LICENSE @@ -0,0 +1,16 @@ +quirc -- QR-code recognition library +Copyright (C) 2010-2012 Daniel Beer + +Permission to use, copy, modify, and/or distribute this software for +any purpose with or without fee is hereby granted, provided that the +above copyright notice and this permission notice appear in all +copies. + +THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL +WARRANTIES WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE +AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL +DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR +PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER +TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR +PERFORMANCE OF THIS SOFTWARE. diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/zlib-README b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/zlib-README new file mode 100644 index 0000000000000000000000000000000000000000..51106de4753292ad59de03de9e634e6814eeb7a2 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/licenses/zlib-README @@ -0,0 +1,115 @@ +ZLIB DATA COMPRESSION LIBRARY + +zlib 1.2.11 is a general purpose data compression library. All the code is +thread safe. The data format used by the zlib library is described by RFCs +(Request for Comments) 1950 to 1952 in the files +http://tools.ietf.org/html/rfc1950 (zlib format), rfc1951 (deflate format) and +rfc1952 (gzip format). + +All functions of the compression library are documented in the file zlib.h +(volunteer to write man pages welcome, contact zlib@gzip.org). A usage example +of the library is given in the file test/example.c which also tests that +the library is working correctly. Another example is given in the file +test/minigzip.c. The compression library itself is composed of all source +files in the root directory. + +To compile all files and run the test program, follow the instructions given at +the top of Makefile.in. In short "./configure; make test", and if that goes +well, "make install" should work for most flavors of Unix. For Windows, use +one of the special makefiles in win32/ or contrib/vstudio/ . For VMS, use +make_vms.com. + +Questions about zlib should be sent to , or to Gilles Vollant + for the Windows DLL version. The zlib home page is +http://zlib.net/ . Before reporting a problem, please check this site to +verify that you have the latest version of zlib; otherwise get the latest +version and check whether the problem still exists or not. + +PLEASE read the zlib FAQ http://zlib.net/zlib_faq.html before asking for help. + +Mark Nelson wrote an article about zlib for the Jan. 1997 +issue of Dr. Dobb's Journal; a copy of the article is available at +http://marknelson.us/1997/01/01/zlib-engine/ . + +The changes made in version 1.2.11 are documented in the file ChangeLog. + +Unsupported third party contributions are provided in directory contrib/ . + +zlib is available in Java using the java.util.zip package, documented at +http://java.sun.com/developer/technicalArticles/Programming/compression/ . + +A Perl interface to zlib written by Paul Marquess is available +at CPAN (Comprehensive Perl Archive Network) sites, including +http://search.cpan.org/~pmqs/IO-Compress-Zlib/ . + +A Python interface to zlib written by A.M. Kuchling is +available in Python 1.5 and later versions, see +http://docs.python.org/library/zlib.html . + +zlib is built into tcl: http://wiki.tcl.tk/4610 . + +An experimental package to read and write files in .zip format, written on top +of zlib by Gilles Vollant , is available in the +contrib/minizip directory of zlib. + + +Notes for some targets: + +- For Windows DLL versions, please see win32/DLL_FAQ.txt + +- For 64-bit Irix, deflate.c must be compiled without any optimization. With + -O, one libpng test fails. The test works in 32 bit mode (with the -n32 + compiler flag). The compiler bug has been reported to SGI. + +- zlib doesn't work with gcc 2.6.3 on a DEC 3000/300LX under OSF/1 2.1 it works + when compiled with cc. + +- On Digital Unix 4.0D (formely OSF/1) on AlphaServer, the cc option -std1 is + necessary to get gzprintf working correctly. This is done by configure. + +- zlib doesn't work on HP-UX 9.05 with some versions of /bin/cc. It works with + other compilers. Use "make test" to check your compiler. + +- gzdopen is not supported on RISCOS or BEOS. + +- For PalmOs, see http://palmzlib.sourceforge.net/ + + +Acknowledgments: + + The deflate format used by zlib was defined by Phil Katz. The deflate and + zlib specifications were written by L. Peter Deutsch. Thanks to all the + people who reported problems and suggested various improvements in zlib; they + are too numerous to cite here. + +Copyright notice: + + (C) 1995-2017 Jean-loup Gailly and Mark Adler + + This software is provided 'as-is', without any express or implied + warranty. In no event will the authors be held liable for any damages + arising from the use of this software. + + Permission is granted to anyone to use this software for any purpose, + including commercial applications, and to alter it and redistribute it + freely, subject to the following restrictions: + + 1. The origin of this software must not be misrepresented; you must not + claim that you wrote the original software. If you use this software + in a product, an acknowledgment in the product documentation would be + appreciated but is not required. + 2. Altered source versions must be plainly marked as such, and must not be + misrepresented as being the original software. + 3. This notice may not be removed or altered from any source distribution. + + Jean-loup Gailly Mark Adler + jloup@gzip.org madler@alumni.caltech.edu + +If you use the zlib library in a product, we would appreciate *not* receiving +lengthy legal documents to sign. The sources are provided for free but without +warranty of any kind. The library has been entirely written by Jean-loup +Gailly and Mark Adler; it does not include third-party code. + +If you redistribute modified sources, we would appreciate that you include in +the file ChangeLog history information documenting your changes. Please read +the FAQ for more information on the distribution of modified source versions. diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/valgrind.supp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/valgrind.supp new file mode 100644 index 0000000000000000000000000000000000000000..7a4341467f2fd9beda7f5ac006ba5ff2d126c75f --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/valgrind.supp @@ -0,0 +1,221 @@ +{ + OpenCV-IPP static init + Memcheck:Cond + fun:ippicvGetCpuFeatures + fun:ippicvStaticInit +} + +{ + OpenCV-getInitializationMutex + Memcheck:Leak + ... + fun:_ZN2cv22getInitializationMutexEv +} + +{ + OpenCV-SingletonBuffer + Memcheck:Leak + ... + fun:_ZN2cv20allocSingletonBufferEm +} + +{ + OpenCV-getStdAllocator + Memcheck:Leak + ... + fun:_ZN2cv3Mat15getStdAllocatorEv +} + +{ + OpenCV-getOpenCLAllocator + Memcheck:Leak + ... + fun:_ZN2cv3ocl18getOpenCLAllocatorEv +} + +{ + OpenCV-getCoreTlsData + Memcheck:Leak + fun:_Znwm + fun:_ZN2cv14getCoreTlsDataEv +} + +{ + OpenCV-TLS-getTlsStorage + Memcheck:Leak + ... + fun:_ZN2cvL13getTlsStorageEv +} + +{ + OpenCV-TLS-getData() + Memcheck:Leak + ... + fun:*setData* + fun:_ZNK2cv16TLSDataContainer7getDataEv +} + +{ + OpenCV-parallel_for-reconfigure + Memcheck:Leak + ... + fun:_ZN2cv10ThreadPool12reconfigure_Ej +} + +{ + OpenCV-parallel_for-instance + Memcheck:Leak + fun:_Znwm + fun:*instance* + ... + fun:_ZN2cv13parallel_for_ERKNS_5RangeERKNS_16ParallelLoopBodyEd +} + +{ + OpenCV-parallel_for-setNumThreads() + Memcheck:Leak + ... + fun:_ZN2cv13setNumThreadsEi +} + +{ + OpenCV-parallel_for-getNumThreads() + Memcheck:Leak + ... + fun:_ZN2cv13getNumThreadsEv +} + +{ + OpenCV-getIPPSingelton + Memcheck:Leak + ... + fun:_ZN2cv3ippL15getIPPSingeltonEv +} + +{ + OpenCV-getGlobalMatOpInitializer + Memcheck:Leak + fun:_Znwm + fun:_ZN2cvL25getGlobalMatOpInitializerEv +} + +{ + OpenCV-CoreTLSData + Memcheck:Leak + ... + fun:_ZNK2cv7TLSDataINS_11CoreTLSDataEE3getEv +} + +{ + OpenCV-getThreadID() + Memcheck:Leak + ... + fun:_ZN2cv5utils11getThreadIDEv +} + +{ + OpenCV-ThreadID + Memcheck:Leak + fun:_Znwm + fun:_ZNK2cv7TLSDataINS_12_GLOBAL__N_18ThreadIDEE18createDataInstanceEv +} + +{ + OpenCV-ThreadID-TLS + Memcheck:Leak + fun:_Znwm + fun:getThreadIDTLS +} + +{ + OpenCV-CoreTLS + Memcheck:Leak + fun:_Znwm + fun:_ZNK2cv7TLSDataINS_11CoreTLSDataEE18createDataInstanceEv +} + +{ + OpenCV-UMatDataAutoLockerTLS + Memcheck:Leak + ... + fun:_ZN2cvL21getUMatDataAutoLockerEv +} + +{ + OpenCV-haveOpenCL + Memcheck:Leak + ... + fun:_ZN2cv3ocl10haveOpenCLEv +} + +{ + OpenCV-DNN-getLayerFactoryMutex + Memcheck:Leak + ... + fun:_ZN2cv3dnn*L20getLayerFactoryMutexEv +} + +{ + OpenCV-ocl::Context + Memcheck:Leak + ... + fun:_ZN2cv3ocl7Context10getDefaultEb +} + +{ + OpenCV-ocl::Device + Memcheck:Leak + ... + fun:_ZN2cv3ocl6Device10getDefaultEv +} + +{ + OpenCV-ocl::Queue + Memcheck:Leak + ... + fun:_ZN2cv3ocl5Queue6createERKNS0_7ContextERKNS0_6DeviceE +} + +{ + OpenCV-ocl::Program + Memcheck:Leak + ... + fun:_ZN2cv3ocl6Kernel6createEPKcRKNS0_7ProgramE +} + +{ + OpenCV-ocl::ProgramEntry + Memcheck:Leak + ... + fun:_ZNK2cv3ocl8internal12ProgramEntrycvRNS0_13ProgramSourceEEv +} + +{ + OpenCV-ocl::Context::getProg + Memcheck:Leak + ... + fun:_ZN2cv3ocl7Context7getProgERKNS0_13ProgramSourceERKNS_6StringERS5_ +} + +{ + OpenCV-getTraceManager() + Memcheck:Leak + ... + fun:getTraceManagerCallOnce +} + +{ + OpenCV-ITT + Memcheck:Leak + ... + fun:__itt_*create* +} + +{ + OpenCV-FFmpeg-swsscale + Memcheck:Addr16 + ... + fun:sws_scale + fun:_ZN20CvVideoWriter_FFMPEG10writeFrameEPKhiiiii + fun:cvWriteFrame_FFMPEG +} diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/valgrind_3rdparty.supp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/valgrind_3rdparty.supp new file mode 100644 index 0000000000000000000000000000000000000000..50811d112d37a8ae6007bfc81a7b16fb9a980f9b --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/valgrind_3rdparty.supp @@ -0,0 +1,122 @@ +{ + IPP static init + Memcheck:Cond + fun:ippicvGetCpuFeatures + fun:ippicvStaticInit +} + +{ + TBB - allocate_via_handler_v3 issue + Memcheck:Leak + fun:malloc + fun:_ZN3tbb8internal23allocate_via_handler_v3Em +} + +{ + GTest + Memcheck:Cond + fun:_ZN7testing8internal11CmpHelperLEIddEENS_15AssertionResultEPKcS4_RKT_RKT0_ +} + +{ + OpenCL + Memcheck:Cond + ... + obj:**/libOpenCL.so* +} + +{ + OpenCL-Intel + Memcheck:Cond + ... + obj:**/libigdrcl.so +} + +{ + OpenCL-Intel + Memcheck:Leak + ... + obj:*/libigdrcl.so* +} + +{ + OpenCL + Memcheck:Param + ioctl(generic) + ... + fun:clGetPlatformIDs +} + +{ + OpenCL-Init + Memcheck:Leak + ... + fun:clGetPlatformIDs +} + +{ + glib + Memcheck:Leak + fun:*alloc + obj:*/libglib* +} + +{ + gcrypt + Memcheck:Leak + ... + obj:*/libgcrypt* +} + +{ + p11-kit + Memcheck:Leak + fun:*alloc + obj:*/libp11-kit* +} + +{ + gobject + Memcheck:Leak + fun:*alloc + ... + obj:*/libgobject* +} + +{ + tasn + Memcheck:Leak + fun:*alloc + obj:*/libtasn*.so* +} + +{ + dl_init + Memcheck:Leak + ... + fun:_dl_init +} + +{ + dl_open + Memcheck:Leak + ... + fun:_dl_open +} + +{ + GDAL + Memcheck:Leak + fun:*alloc + ... + obj:/usr/lib/libgdal.so.1.17.1 +} + +{ + FFMPEG-sws_scale + Memcheck:Addr16 + ... + fun:sws_scale + ... + fun:cvWriteFrame_FFMPEG +} diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/3rdparty/libs/arm64-v8a/libIlmImf.a b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/3rdparty/libs/arm64-v8a/libIlmImf.a new file mode 100644 index 0000000000000000000000000000000000000000..7a3dca19bf8a1e906a90393dc5cf76b502130f09 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/3rdparty/libs/arm64-v8a/libIlmImf.a @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:34ba1ffb902cd7b1537e8733ca6b846da225ef05366b3c11b5f831aebaec08a8 +size 22095896 diff --git 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a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/3rdparty/libs/armeabi-v7a/liblibtiff.a b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/3rdparty/libs/armeabi-v7a/liblibtiff.a new file mode 100644 index 0000000000000000000000000000000000000000..734f5c67b221a6825811f5ee089fb9bc1d0eb082 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/3rdparty/libs/armeabi-v7a/liblibtiff.a @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e6c2ad84c7cf31b8b245bc713f11c83a1d9261ee4a89c869e2dfb36c8538fe7c +size 2026550 diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/3rdparty/libs/armeabi-v7a/liblibwebp.a b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/3rdparty/libs/armeabi-v7a/liblibwebp.a new file mode 100644 index 0000000000000000000000000000000000000000..faca22ba2aeb32b43944ca761de37207c2f5127d --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/3rdparty/libs/armeabi-v7a/liblibwebp.a @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:47fa039edf960e90618241db2d702826e5c56e6a214244342cc420bc5a9ef82e +size 3081826 diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/3rdparty/libs/armeabi-v7a/libquirc.a b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/3rdparty/libs/armeabi-v7a/libquirc.a new file mode 100644 index 0000000000000000000000000000000000000000..2d4f762b8d3ecb3204149851f4df292cdd476ab5 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/3rdparty/libs/armeabi-v7a/libquirc.a @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fe31bdc249fcee9a9a7b2b77f3a25e9bae35ba4cada11eebafadbf67e059093d +size 58992 diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/3rdparty/libs/armeabi-v7a/libtegra_hal.a b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/3rdparty/libs/armeabi-v7a/libtegra_hal.a new file mode 100644 index 0000000000000000000000000000000000000000..a5fed1198588450754e1b0353caeb4acb9a09d53 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/3rdparty/libs/armeabi-v7a/libtegra_hal.a @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c13e22dca9162bf8eeb8e32202defab02482ff434720267ea1250e1deabf2e4 +size 6921736 diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/3rdparty/libs/armeabi-v7a/libzlib.a b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/3rdparty/libs/armeabi-v7a/libzlib.a new file mode 100644 index 0000000000000000000000000000000000000000..4cad102f065b0858bafbc9da0a4a58a8e0e98b26 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/3rdparty/libs/armeabi-v7a/libzlib.a @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0ae80ea4ba8b85894f2f42fcbe5b30bd928d18aa7faf8c7bbca06705d61837b7 +size 347858 diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/OpenCV-arm64-v8a.mk b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/OpenCV-arm64-v8a.mk new file mode 100644 index 0000000000000000000000000000000000000000..2ffd783a00816e685fd2c9d319f1c9f3055667c5 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/OpenCV-arm64-v8a.mk @@ -0,0 +1,2 @@ +OPENCV_3RDPARTY_COMPONENTS:=cpufeatures zlib libjpeg-turbo libwebp libpng libtiff libjasper IlmImf tegra_hal +OPENCV_EXTRA_COMPONENTS:=dl m log diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/OpenCV-armeabi-v7a.mk b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/OpenCV-armeabi-v7a.mk new file mode 100644 index 0000000000000000000000000000000000000000..2ffd783a00816e685fd2c9d319f1c9f3055667c5 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/OpenCV-armeabi-v7a.mk @@ -0,0 +1,2 @@ +OPENCV_3RDPARTY_COMPONENTS:=cpufeatures zlib libjpeg-turbo libwebp libpng libtiff libjasper IlmImf tegra_hal +OPENCV_EXTRA_COMPONENTS:=dl m log diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/OpenCV.mk b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/OpenCV.mk new file mode 100644 index 0000000000000000000000000000000000000000..c72242429eb076cf759671840db2c9509f880a3a --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/OpenCV.mk @@ -0,0 +1,120 @@ +# In order to compile your application under cygwin +# you might need to define NDK_USE_CYGPATH=1 before calling the ndk-build + +USER_LOCAL_PATH:=$(LOCAL_PATH) + +USER_LOCAL_C_INCLUDES:=$(LOCAL_C_INCLUDES) +USER_LOCAL_CFLAGS:=$(LOCAL_CFLAGS) +USER_LOCAL_STATIC_LIBRARIES:=$(LOCAL_STATIC_LIBRARIES) +USER_LOCAL_SHARED_LIBRARIES:=$(LOCAL_SHARED_LIBRARIES) +USER_LOCAL_LDLIBS:=$(LOCAL_LDLIBS) + +LOCAL_PATH:=$(subst ?,,$(firstword ?$(subst \, ,$(subst /, ,$(call my-dir))))) + +OPENCV_TARGET_ARCH_ABI:=$(TARGET_ARCH_ABI) +OPENCV_THIS_DIR:=$(patsubst $(LOCAL_PATH)\\%,%,$(patsubst $(LOCAL_PATH)/%,%,$(call my-dir))) +OPENCV_MK_DIR:=$(dir $(lastword $(MAKEFILE_LIST))) +OPENCV_3RDPARTY_LIBS_DIR:=$(OPENCV_THIS_DIR)/../3rdparty/libs/$(OPENCV_TARGET_ARCH_ABI) +OPENCV_BASEDIR:= +OPENCV_LOCAL_C_INCLUDES:="$(LOCAL_PATH)/$(OPENCV_THIS_DIR)/include/opencv" "$(LOCAL_PATH)/$(OPENCV_THIS_DIR)/include" +OPENCV_MODULES:=calib3d features2d imgcodecs imgproc core +OPENCV_SUB_MK:=$(call my-dir)/OpenCV-$(TARGET_ARCH_ABI).mk + +ifeq ($(OPENCV_LIB_TYPE),) + OPENCV_LIB_TYPE:=SHARED +endif + +ifeq ($(OPENCV_LIB_TYPE),SHARED) + OPENCV_LIBS:=java3 + OPENCV_LIB_TYPE:=SHARED +else + OPENCV_LIBS:=$(OPENCV_MODULES) + OPENCV_LIB_TYPE:=STATIC +endif + +ifeq ($(OPENCV_LIB_TYPE),SHARED) + OPENCV_3RDPARTY_COMPONENTS:= + OPENCV_EXTRA_COMPONENTS:= +else + include $(OPENCV_SUB_MK) +endif + +ifeq ($(OPENCV_LIB_TYPE),SHARED) + OPENCV_LIBS_DIR:=$(OPENCV_THIS_DIR)/../libs/$(OPENCV_TARGET_ARCH_ABI) + OPENCV_LIB_SUFFIX:=so +else + OPENCV_LIBS_DIR:=$(OPENCV_THIS_DIR)/../staticlibs/$(OPENCV_TARGET_ARCH_ABI) + OPENCV_LIB_SUFFIX:=a + OPENCV_INSTALL_MODULES:=on +endif + +define add_opencv_module + include $(CLEAR_VARS) + LOCAL_MODULE:=opencv_$1 + LOCAL_SRC_FILES:=$(OPENCV_LIBS_DIR)/libopencv_$1.$(OPENCV_LIB_SUFFIX) + include $(PREBUILT_$(OPENCV_LIB_TYPE)_LIBRARY) +endef + +define add_opencv_3rdparty_component + include $(CLEAR_VARS) + LOCAL_MODULE:=$1 + LOCAL_SRC_FILES:=$(OPENCV_3RDPARTY_LIBS_DIR)/lib$1.a + include $(PREBUILT_STATIC_LIBRARY) +endef + +ifeq ($(OPENCV_MK_$(OPENCV_TARGET_ARCH_ABI)_ALREADY_INCLUDED),) + ifeq ($(OPENCV_INSTALL_MODULES),on) + $(foreach module,$(OPENCV_LIBS),$(eval $(call add_opencv_module,$(module)))) + endif + + $(foreach module,$(OPENCV_3RDPARTY_COMPONENTS),$(eval $(call add_opencv_3rdparty_component,$(module)))) + + ifneq ($(OPENCV_BASEDIR),) + OPENCV_LOCAL_C_INCLUDES += $(foreach mod, $(OPENCV_MODULES), $(OPENCV_BASEDIR)/modules/$(mod)/include) + endif + + #turn off module installation to prevent their redefinition + OPENCV_MK_$(OPENCV_TARGET_ARCH_ABI)_ALREADY_INCLUDED:=on +endif + +ifeq ($(OPENCV_LOCAL_CFLAGS),) + OPENCV_LOCAL_CFLAGS := -fPIC -DANDROID -fsigned-char +endif + +include $(CLEAR_VARS) + +LOCAL_C_INCLUDES:=$(USER_LOCAL_C_INCLUDES) +LOCAL_CFLAGS:=$(USER_LOCAL_CFLAGS) +LOCAL_STATIC_LIBRARIES:=$(USER_LOCAL_STATIC_LIBRARIES) +LOCAL_SHARED_LIBRARIES:=$(USER_LOCAL_SHARED_LIBRARIES) +LOCAL_LDLIBS:=$(USER_LOCAL_LDLIBS) + +# Details: #10229 +ifeq ($(OPENCV_SKIP_ANDROID_IPP_FIX_1),) + LOCAL_LDFLAGS += -Wl,--exclude-libs,libippicv.a + LOCAL_LDFLAGS += -Wl,--exclude-libs,libippiw.a +else + ifeq ($(OPENCV_SKIP_ANDROID_IPP_FIX_2),) + LOCAL_LDFLAGS += -Wl,-Bsymbolic + endif +endif + +LOCAL_C_INCLUDES += $(OPENCV_LOCAL_C_INCLUDES) +LOCAL_CFLAGS += $(OPENCV_LOCAL_CFLAGS) + +ifeq ($(OPENCV_INSTALL_MODULES),on) + LOCAL_$(OPENCV_LIB_TYPE)_LIBRARIES += $(foreach mod, $(OPENCV_LIBS), opencv_$(mod)) +else + $(call __ndk_info,OpenCV: You should ignore warning about 'non-system libraries in linker flags' and 'opencv_java' library.) + $(call __ndk_info, 'OPENCV_INSTALL_MODULES:=on' can be used to build APK with included OpenCV binaries) + LOCAL_LDLIBS += -L$(call host-path,$(LOCAL_PATH)/$(OPENCV_LIBS_DIR)) $(foreach lib, $(OPENCV_LIBS), -lopencv_$(lib)) +endif + +ifeq ($(OPENCV_LIB_TYPE),STATIC) + LOCAL_STATIC_LIBRARIES += $(OPENCV_3RDPARTY_COMPONENTS) +endif + +LOCAL_LDLIBS += $(foreach lib,$(OPENCV_EXTRA_COMPONENTS), -l$(lib)) + +#restore the LOCAL_PATH +LOCAL_PATH:=$(USER_LOCAL_PATH) diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/OpenCVConfig-version.cmake b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/OpenCVConfig-version.cmake new file mode 100644 index 0000000000000000000000000000000000000000..f8be90d0b64ad0ff98b823e89d10f5399908a262 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/OpenCVConfig-version.cmake @@ -0,0 +1,15 @@ +set(OpenCV_VERSION 3.4.5) +set(PACKAGE_VERSION ${OpenCV_VERSION}) + +set(PACKAGE_VERSION_EXACT False) +set(PACKAGE_VERSION_COMPATIBLE False) + +if(PACKAGE_FIND_VERSION VERSION_EQUAL PACKAGE_VERSION) + set(PACKAGE_VERSION_EXACT True) + set(PACKAGE_VERSION_COMPATIBLE True) +endif() + +if(PACKAGE_FIND_VERSION_MAJOR EQUAL 3 + AND PACKAGE_FIND_VERSION VERSION_LESS PACKAGE_VERSION) + set(PACKAGE_VERSION_COMPATIBLE True) +endif() diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/OpenCVConfig.cmake b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/OpenCVConfig.cmake new file mode 100644 index 0000000000000000000000000000000000000000..eae4651c2eec8719020b520fa536f3fbad1fcd9a --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/OpenCVConfig.cmake @@ -0,0 +1,50 @@ +# =================================================================================== +# The OpenCV CMake configuration file +# +# ** File generated automatically, do not modify ** +# +# Usage from an external project: +# In your CMakeLists.txt, add these lines: +# +# find_package(OpenCV REQUIRED) +# include_directories(${OpenCV_INCLUDE_DIRS}) # Not needed for CMake >= 2.8.11 +# target_link_libraries(MY_TARGET_NAME ${OpenCV_LIBS}) +# +# Or you can search for specific OpenCV modules: +# +# find_package(OpenCV REQUIRED core videoio) +# +# If the module is found then OPENCV__FOUND is set to TRUE. +# +# This file will define the following variables: +# - OpenCV_LIBS : The list of all imported targets for OpenCV modules. +# - OpenCV_INCLUDE_DIRS : The OpenCV include directories. +# - OpenCV_ANDROID_NATIVE_API_LEVEL : Minimum required level of Android API. +# - OpenCV_VERSION : The version of this OpenCV build: "3.4.5" +# - OpenCV_VERSION_MAJOR : Major version part of OpenCV_VERSION: "3" +# - OpenCV_VERSION_MINOR : Minor version part of OpenCV_VERSION: "4" +# - OpenCV_VERSION_PATCH : Patch version part of OpenCV_VERSION: "5" +# - OpenCV_VERSION_STATUS : Development status of this build: "" +# +# =================================================================================== + +# Extract directory name from full path of the file currently being processed. +# Note that CMake 2.8.3 introduced CMAKE_CURRENT_LIST_DIR. We reimplement it +# for older versions of CMake to support these as well. +if(CMAKE_VERSION VERSION_LESS "2.8.3") + get_filename_component(CMAKE_CURRENT_LIST_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH) +endif() + +if(NOT DEFINED OpenCV_CONFIG_SUBDIR) + set(OpenCV_CONFIG_SUBDIR "/abi-${ANDROID_NDK_ABI_NAME}") +endif() + +set(OpenCV_CONFIG_PATH "${CMAKE_CURRENT_LIST_DIR}${OpenCV_CONFIG_SUBDIR}") +if(EXISTS "${OpenCV_CONFIG_PATH}/OpenCVConfig.cmake") + include("${OpenCV_CONFIG_PATH}/OpenCVConfig.cmake") +else() + if(NOT OpenCV_FIND_QUIETLY) + message(WARNING "Found OpenCV Android Pack but it has no binaries compatible with your ABI (can't find: ${OpenCV_CONFIG_SUBDIR})") + endif() + set(OpenCV_FOUND FALSE) +endif() diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/abi-arm64-v8a/OpenCVConfig-version.cmake b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/abi-arm64-v8a/OpenCVConfig-version.cmake new file mode 100644 index 0000000000000000000000000000000000000000..f8be90d0b64ad0ff98b823e89d10f5399908a262 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/abi-arm64-v8a/OpenCVConfig-version.cmake @@ -0,0 +1,15 @@ +set(OpenCV_VERSION 3.4.5) +set(PACKAGE_VERSION ${OpenCV_VERSION}) + +set(PACKAGE_VERSION_EXACT False) +set(PACKAGE_VERSION_COMPATIBLE False) + +if(PACKAGE_FIND_VERSION VERSION_EQUAL PACKAGE_VERSION) + set(PACKAGE_VERSION_EXACT True) + set(PACKAGE_VERSION_COMPATIBLE True) +endif() + +if(PACKAGE_FIND_VERSION_MAJOR EQUAL 3 + AND PACKAGE_FIND_VERSION VERSION_LESS PACKAGE_VERSION) + set(PACKAGE_VERSION_COMPATIBLE True) +endif() diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/abi-arm64-v8a/OpenCVConfig.cmake b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/abi-arm64-v8a/OpenCVConfig.cmake new file mode 100644 index 0000000000000000000000000000000000000000..e211709bd6ab71ae513490b33d92be3b7d5b9e82 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/abi-arm64-v8a/OpenCVConfig.cmake @@ -0,0 +1,328 @@ +# =================================================================================== +# The OpenCV CMake configuration file +# +# ** File generated automatically, do not modify ** +# +# Usage from an external project: +# In your CMakeLists.txt, add these lines: +# +# find_package(OpenCV REQUIRED) +# include_directories(${OpenCV_INCLUDE_DIRS}) # Not needed for CMake >= 2.8.11 +# target_link_libraries(MY_TARGET_NAME ${OpenCV_LIBS}) +# +# Or you can search for specific OpenCV modules: +# +# find_package(OpenCV REQUIRED core videoio) +# +# You can also mark OpenCV components as optional: + +# find_package(OpenCV REQUIRED core OPTIONAL_COMPONENTS viz) +# +# If the module is found then OPENCV__FOUND is set to TRUE. +# +# This file will define the following variables: +# - OpenCV_LIBS : The list of all imported targets for OpenCV modules. +# - OpenCV_INCLUDE_DIRS : The OpenCV include directories. +# - OpenCV_COMPUTE_CAPABILITIES : The version of compute capability. +# - OpenCV_ANDROID_NATIVE_API_LEVEL : Minimum required level of Android API. +# - OpenCV_VERSION : The version of this OpenCV build: "3.4.5" +# - OpenCV_VERSION_MAJOR : Major version part of OpenCV_VERSION: "3" +# - OpenCV_VERSION_MINOR : Minor version part of OpenCV_VERSION: "4" +# - OpenCV_VERSION_PATCH : Patch version part of OpenCV_VERSION: "5" +# - OpenCV_VERSION_STATUS : Development status of this build: "" +# +# Advanced variables: +# - OpenCV_SHARED : Use OpenCV as shared library +# - OpenCV_INSTALL_PATH : OpenCV location +# - OpenCV_LIB_COMPONENTS : Present OpenCV modules list +# - OpenCV_USE_MANGLED_PATHS : Mangled OpenCV path flag +# +# Deprecated variables: +# - OpenCV_VERSION_TWEAK : Always "0" +# +# =================================================================================== + +# ====================================================== +# Version variables: +# ====================================================== +SET(OpenCV_VERSION 3.4.5) +SET(OpenCV_VERSION_MAJOR 3) +SET(OpenCV_VERSION_MINOR 4) +SET(OpenCV_VERSION_PATCH 5) +SET(OpenCV_VERSION_TWEAK 0) +SET(OpenCV_VERSION_STATUS "") + +include(FindPackageHandleStandardArgs) + +if(NOT CMAKE_VERSION VERSION_LESS 2.8.8 + AND OpenCV_FIND_COMPONENTS # prevent excessive output +) + # HANDLE_COMPONENTS was introduced in CMake 2.8.8 + list(APPEND _OpenCV_FPHSA_ARGS HANDLE_COMPONENTS) + # The missing components will be handled by the FindPackageHandleStandardArgs + # module. + set(_OpenCV_HANDLE_COMPONENTS_MANUALLY FALSE) +else() + # The missing components will be handled by this config. + set(_OpenCV_HANDLE_COMPONENTS_MANUALLY TRUE) +endif() + +# Extract directory name from full path of the file currently being processed. +# Note that CMake 2.8.3 introduced CMAKE_CURRENT_LIST_DIR. We reimplement it +# for older versions of CMake to support these as well. +if(CMAKE_VERSION VERSION_LESS "2.8.3") + get_filename_component(CMAKE_CURRENT_LIST_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH) +endif() + +# Extract the directory where *this* file has been installed (determined at cmake run-time) +# Get the absolute path with no ../.. relative marks, to eliminate implicit linker warnings +set(OpenCV_CONFIG_PATH "${CMAKE_CURRENT_LIST_DIR}") +get_filename_component(OpenCV_INSTALL_PATH "${OpenCV_CONFIG_PATH}/../../../../" REALPATH) + +# Search packages for host system instead of packages for target system. +# in case of cross compilation this macro should be defined by toolchain file +if(NOT COMMAND find_host_package) + macro(find_host_package) + find_package(${ARGN}) + endmacro() +endif() +if(NOT COMMAND find_host_program) + macro(find_host_program) + find_program(${ARGN}) + endmacro() +endif() + + + +# Android API level from which OpenCV has been compiled is remembered +set(OpenCV_ANDROID_NATIVE_API_LEVEL "24") + +# ============================================================== +# Check OpenCV availability +# ============================================================== +if(OpenCV_ANDROID_NATIVE_API_LEVEL GREATER ANDROID_NATIVE_API_LEVEL) + if(NOT OpenCV_FIND_QUIETLY) + message(WARNING "Minimum required by OpenCV API level is android-${OpenCV_ANDROID_NATIVE_API_LEVEL}") + endif() + set(OpenCV_FOUND 0) + return() +endif() + + + + + +# Some additional settings are required if OpenCV is built as static libs +set(OpenCV_SHARED OFF) + +# Enables mangled install paths, that help with side by side installs +set(OpenCV_USE_MANGLED_PATHS FALSE) + +set(OpenCV_LIB_COMPONENTS opencv_calib3d;opencv_core;opencv_features2d;opencv_imgcodecs;opencv_imgproc) +set(OpenCV_INCLUDE_DIRS "${OpenCV_INSTALL_PATH}/sdk/native/jni/include" "${OpenCV_INSTALL_PATH}/sdk/native/jni/include/opencv") + +if(NOT TARGET opencv_core) + include(${CMAKE_CURRENT_LIST_DIR}/OpenCVModules${OpenCV_MODULES_SUFFIX}.cmake) +endif() + +if(NOT CMAKE_VERSION VERSION_LESS "2.8.11") + # Target property INTERFACE_INCLUDE_DIRECTORIES available since 2.8.11: + # * http://www.cmake.org/cmake/help/v2.8.11/cmake.html#prop_tgt:INTERFACE_INCLUDE_DIRECTORIES + foreach(__component ${OpenCV_LIB_COMPONENTS}) + if(TARGET ${__component}) + set_target_properties( + ${__component} + PROPERTIES + INTERFACE_INCLUDE_DIRECTORIES "${OpenCV_INCLUDE_DIRS}" + ) + endif() + endforeach() +endif() + + +if(NOT DEFINED OPENCV_MAP_IMPORTED_CONFIG) + if(CMAKE_GENERATOR MATCHES "Visual Studio" OR MSVC) + # OpenCV supports Debug and Release builds only. + # But MSVS has 'RelWithDebInfo' 'MinSizeRel' configurations for applications. + # By default CMake maps these configuration on the first available (Debug) which is wrong. + # Non-Debug build of Application can't be used with OpenCV Debug build (ABI mismatch problem) + # Add mapping of RelWithDebInfo and MinSizeRel to Release here + set(OPENCV_MAP_IMPORTED_CONFIG "RELWITHDEBINFO=!Release;MINSIZEREL=!Release") + endif() +endif() +set(__remap_warnings "") +macro(ocv_map_imported_config target) + if(DEFINED OPENCV_MAP_IMPORTED_CONFIG) # list, "RELWITHDEBINFO=Release;MINSIZEREL=Release" + get_target_property(__available_configurations ${target} IMPORTED_CONFIGURATIONS) + foreach(remap ${OPENCV_MAP_IMPORTED_CONFIG}) + if(remap MATCHES "^(.+)=(!?)([^!]+)$") + set(__remap_config "${CMAKE_MATCH_1}") + set(__final_config "${CMAKE_MATCH_3}") + set(__force_flag "${CMAKE_MATCH_2}") + string(TOUPPER "${__final_config}" __final_config_upper) + string(TOUPPER "${__remap_config}" __remap_config_upper) + if(";${__available_configurations};" MATCHES ";${__remap_config_upper};" AND NOT "${__force_flag}" STREQUAL "!") + # configuration already exists, skip remap + set(__remap_warnings "${__remap_warnings}... Configuration already exists ${__remap_config} (skip mapping ${__remap_config} => ${__final_config}) (available configurations: ${__available_configurations})\n") + continue() + endif() + if(__available_configurations AND NOT ";${__available_configurations};" MATCHES ";${__final_config_upper};") + # skip, configuration is not available + if(NOT "${__force_flag}" STREQUAL "!") + set(__remap_warnings "${__remap_warnings}... Configuration is not available '${__final_config}' for ${target}, build may fail (available configurations: ${__available_configurations})\n") + endif() + endif() + set_target_properties(${target} PROPERTIES + MAP_IMPORTED_CONFIG_${__remap_config} "${__final_config}" + ) + else() + message(WARNING "Invalid entry of OPENCV_MAP_IMPORTED_CONFIG: '${remap}' (${OPENCV_MAP_IMPORTED_CONFIG})") + endif() + endforeach() + endif() +endmacro() + + +# ============================================================== +# Form list of modules (components) to find +# ============================================================== +if(NOT OpenCV_FIND_COMPONENTS) + set(OpenCV_FIND_COMPONENTS ${OpenCV_LIB_COMPONENTS}) + list(REMOVE_ITEM OpenCV_FIND_COMPONENTS opencv_java) + if(GTest_FOUND OR GTEST_FOUND) + list(REMOVE_ITEM OpenCV_FIND_COMPONENTS opencv_ts) + endif() +endif() + +set(OpenCV_WORLD_COMPONENTS ) + +# expand short module names and see if requested components exist +foreach(__cvcomponent ${OpenCV_FIND_COMPONENTS}) + # Store the name of the original component so we can set the + # OpenCV__FOUND variable which can be checked by the user. + set (__original_cvcomponent ${__cvcomponent}) + if(NOT __cvcomponent MATCHES "^opencv_") + set(__cvcomponent opencv_${__cvcomponent}) + endif() + list(FIND OpenCV_LIB_COMPONENTS ${__cvcomponent} __cvcomponentIdx) + if(__cvcomponentIdx LESS 0) + if(_OpenCV_HANDLE_COMPONENTS_MANUALLY) + # Either the component is required or the user did not set any components at + # all. In the latter case, the OpenCV_FIND_REQUIRED_ variable + # will not be defined since it is not set by this config. So let's assume + # the implicitly set components are always required. + if(NOT DEFINED OpenCV_FIND_REQUIRED_${__original_cvcomponent} OR + OpenCV_FIND_REQUIRED_${__original_cvcomponent}) + message(FATAL_ERROR "${__cvcomponent} is required but was not found") + elseif(NOT OpenCV_FIND_QUIETLY) + # The component was marked as optional using OPTIONAL_COMPONENTS + message(WARNING "Optional component ${__cvcomponent} was not found") + endif() + endif(_OpenCV_HANDLE_COMPONENTS_MANUALLY) + #indicate that module is NOT found + string(TOUPPER "${__cvcomponent}" __cvcomponentUP) + set(${__cvcomponentUP}_FOUND "${__cvcomponentUP}_FOUND-NOTFOUND") + set(OpenCV_${__original_cvcomponent}_FOUND FALSE) + else() + # Not using list(APPEND) here, because OpenCV_LIBS may not exist yet. + # Also not clearing OpenCV_LIBS anywhere, so that multiple calls + # to find_package(OpenCV) with different component lists add up. + set(OpenCV_LIBS ${OpenCV_LIBS} "${__cvcomponent}") + #indicate that module is found + string(TOUPPER "${__cvcomponent}" __cvcomponentUP) + set(${__cvcomponentUP}_FOUND 1) + set(OpenCV_${__original_cvcomponent}_FOUND TRUE) + endif() + if(OpenCV_SHARED AND ";${OpenCV_WORLD_COMPONENTS};" MATCHES ";${__cvcomponent};" AND NOT TARGET ${__cvcomponent}) + get_target_property(__implib_dbg opencv_world IMPORTED_IMPLIB_DEBUG) + get_target_property(__implib_release opencv_world IMPORTED_IMPLIB_RELEASE) + get_target_property(__location_dbg opencv_world IMPORTED_LOCATION_DEBUG) + get_target_property(__location_release opencv_world IMPORTED_LOCATION_RELEASE) + get_target_property(__include_dir opencv_world INTERFACE_INCLUDE_DIRECTORIES) + add_library(${__cvcomponent} SHARED IMPORTED) + set_target_properties(${__cvcomponent} PROPERTIES INTERFACE_INCLUDE_DIRECTORIES "${__include_dir}") + if(__location_dbg) + set_property(TARGET ${__cvcomponent} APPEND PROPERTY IMPORTED_CONFIGURATIONS DEBUG) + set_target_properties(${__cvcomponent} PROPERTIES + IMPORTED_IMPLIB_DEBUG "${__implib_dbg}" + IMPORTED_LINK_INTERFACE_LIBRARIES_DEBUG "" + IMPORTED_LOCATION_DEBUG "${__location_dbg}" + ) + endif() + if(__location_release) + set_property(TARGET ${__cvcomponent} APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) + set_target_properties(${__cvcomponent} PROPERTIES + IMPORTED_IMPLIB_RELEASE "${__implib_release}" + IMPORTED_LINK_INTERFACE_LIBRARIES_RELEASE "" + IMPORTED_LOCATION_RELEASE "${__location_release}" + ) + endif() + endif() + if(TARGET ${__cvcomponent}) + ocv_map_imported_config(${__cvcomponent}) + endif() +endforeach() + +if(__remap_warnings AND NOT OpenCV_FIND_QUIETLY) + message("OpenCV: configurations remap warnings:\n${__remap_warnings}OpenCV: Check variable OPENCV_MAP_IMPORTED_CONFIG=${OPENCV_MAP_IMPORTED_CONFIG}") +endif() + +# ============================================================== +# Compatibility stuff +# ============================================================== +set(OpenCV_LIBRARIES ${OpenCV_LIBS}) + +# +# Some macros for samples +# +macro(ocv_check_dependencies) + set(OCV_DEPENDENCIES_FOUND TRUE) + foreach(d ${ARGN}) + if(NOT TARGET ${d}) + message(WARNING "OpenCV: Can't resolve dependency: ${d}") + set(OCV_DEPENDENCIES_FOUND FALSE) + break() + endif() + endforeach() +endmacro() + +# adds include directories in such way that directories from the OpenCV source tree go first +function(ocv_include_directories) + set(__add_before "") + file(TO_CMAKE_PATH "${OpenCV_INSTALL_PATH}" __baseDir) + foreach(dir ${ARGN}) + get_filename_component(__abs_dir "${dir}" ABSOLUTE) + if("${__abs_dir}" MATCHES "^${__baseDir}") + list(APPEND __add_before "${dir}") + else() + include_directories(AFTER SYSTEM "${dir}") + endif() + endforeach() + include_directories(BEFORE ${__add_before}) +endfunction() + +macro(ocv_include_modules) + include_directories(BEFORE "${OpenCV_INCLUDE_DIRS}") +endmacro() + +macro(ocv_include_modules_recurse) + include_directories(BEFORE "${OpenCV_INCLUDE_DIRS}") +endmacro() + +macro(ocv_target_link_libraries) + target_link_libraries(${ARGN}) +endmacro() + +# remove all matching elements from the list +macro(ocv_list_filterout lst regex) + foreach(item ${${lst}}) + if(item MATCHES "${regex}") + list(REMOVE_ITEM ${lst} "${item}") + endif() + endforeach() +endmacro() + +# We do not actually need REQUIRED_VARS to be checked for. Just use the +# installation directory for the status. +find_package_handle_standard_args(OpenCV REQUIRED_VARS OpenCV_INSTALL_PATH + VERSION_VAR OpenCV_VERSION ${_OpenCV_FPHSA_ARGS}) diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/abi-arm64-v8a/OpenCVModules-release.cmake b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/abi-arm64-v8a/OpenCVModules-release.cmake new file mode 100644 index 0000000000000000000000000000000000000000..8a771165c10281df7a139bb81ab4da519585b5e0 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/abi-arm64-v8a/OpenCVModules-release.cmake @@ -0,0 +1,169 @@ +#---------------------------------------------------------------- +# Generated CMake target import file for configuration "Release". +#---------------------------------------------------------------- + +# Commands may need to know the format version. +set(CMAKE_IMPORT_FILE_VERSION 1) + +# Import target "libcpufeatures" for configuration "Release" +set_property(TARGET libcpufeatures APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(libcpufeatures PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "C" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/arm64-v8a/libcpufeatures.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS libcpufeatures ) +list(APPEND _IMPORT_CHECK_FILES_FOR_libcpufeatures "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/arm64-v8a/libcpufeatures.a" ) + +# Import target "zlib" for configuration "Release" +set_property(TARGET zlib APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(zlib PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "C" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/arm64-v8a/libzlib.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS zlib ) +list(APPEND _IMPORT_CHECK_FILES_FOR_zlib "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/arm64-v8a/libzlib.a" ) + +# Import target "libjpeg-turbo" for configuration "Release" +set_property(TARGET libjpeg-turbo APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(libjpeg-turbo PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "C" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/arm64-v8a/liblibjpeg-turbo.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS libjpeg-turbo ) +list(APPEND _IMPORT_CHECK_FILES_FOR_libjpeg-turbo "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/arm64-v8a/liblibjpeg-turbo.a" ) + +# Import target "libtiff" for configuration "Release" +set_property(TARGET libtiff APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(libtiff PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "C;CXX" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/arm64-v8a/liblibtiff.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS libtiff ) +list(APPEND _IMPORT_CHECK_FILES_FOR_libtiff "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/arm64-v8a/liblibtiff.a" ) + +# Import target "libwebp" for configuration "Release" +set_property(TARGET libwebp APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(libwebp PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "C" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/arm64-v8a/liblibwebp.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS libwebp ) +list(APPEND _IMPORT_CHECK_FILES_FOR_libwebp "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/arm64-v8a/liblibwebp.a" ) + +# Import target "libjasper" for configuration "Release" +set_property(TARGET libjasper APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(libjasper PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "C" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/arm64-v8a/liblibjasper.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS libjasper ) +list(APPEND _IMPORT_CHECK_FILES_FOR_libjasper "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/arm64-v8a/liblibjasper.a" ) + +# Import target "libpng" for configuration "Release" +set_property(TARGET libpng APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(libpng PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "C" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/arm64-v8a/liblibpng.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS libpng ) +list(APPEND _IMPORT_CHECK_FILES_FOR_libpng "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/arm64-v8a/liblibpng.a" ) + +# Import target "IlmImf" for configuration "Release" +set_property(TARGET IlmImf APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(IlmImf PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/arm64-v8a/libIlmImf.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS IlmImf ) +list(APPEND _IMPORT_CHECK_FILES_FOR_IlmImf "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/arm64-v8a/libIlmImf.a" ) + +# Import target "libprotobuf" for configuration "Release" +set_property(TARGET libprotobuf APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(libprotobuf PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/arm64-v8a/liblibprotobuf.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS libprotobuf ) +list(APPEND _IMPORT_CHECK_FILES_FOR_libprotobuf "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/arm64-v8a/liblibprotobuf.a" ) + +# Import target "quirc" for configuration "Release" +set_property(TARGET quirc APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(quirc PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "C" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/arm64-v8a/libquirc.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS quirc ) +list(APPEND _IMPORT_CHECK_FILES_FOR_quirc "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/arm64-v8a/libquirc.a" ) + +# Import target "tegra_hal" for configuration "Release" +set_property(TARGET tegra_hal APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(tegra_hal PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/arm64-v8a/libtegra_hal.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS tegra_hal ) +list(APPEND _IMPORT_CHECK_FILES_FOR_tegra_hal "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/arm64-v8a/libtegra_hal.a" ) + +# Import target "opencv_core" for configuration "Release" +set_property(TARGET opencv_core APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(opencv_core PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/staticlibs/arm64-v8a/libopencv_core.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS opencv_core ) +list(APPEND _IMPORT_CHECK_FILES_FOR_opencv_core "${_IMPORT_PREFIX}/sdk/native/staticlibs/arm64-v8a/libopencv_core.a" ) + +# Import target "opencv_imgproc" for configuration "Release" +set_property(TARGET opencv_imgproc APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(opencv_imgproc PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/staticlibs/arm64-v8a/libopencv_imgproc.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS opencv_imgproc ) +list(APPEND _IMPORT_CHECK_FILES_FOR_opencv_imgproc "${_IMPORT_PREFIX}/sdk/native/staticlibs/arm64-v8a/libopencv_imgproc.a" ) + +# Import target "opencv_features2d" for configuration "Release" +set_property(TARGET opencv_features2d APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(opencv_features2d PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/staticlibs/arm64-v8a/libopencv_features2d.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS opencv_features2d ) +list(APPEND _IMPORT_CHECK_FILES_FOR_opencv_features2d "${_IMPORT_PREFIX}/sdk/native/staticlibs/arm64-v8a/libopencv_features2d.a" ) + +# Import target "opencv_imgcodecs" for configuration "Release" +set_property(TARGET opencv_imgcodecs APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(opencv_imgcodecs PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/staticlibs/arm64-v8a/libopencv_imgcodecs.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS opencv_imgcodecs ) +list(APPEND _IMPORT_CHECK_FILES_FOR_opencv_imgcodecs "${_IMPORT_PREFIX}/sdk/native/staticlibs/arm64-v8a/libopencv_imgcodecs.a" ) + +# Import target "opencv_calib3d" for configuration "Release" +set_property(TARGET opencv_calib3d APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(opencv_calib3d PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/staticlibs/arm64-v8a/libopencv_calib3d.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS opencv_calib3d ) +list(APPEND _IMPORT_CHECK_FILES_FOR_opencv_calib3d "${_IMPORT_PREFIX}/sdk/native/staticlibs/arm64-v8a/libopencv_calib3d.a" ) + +# Commands beyond this point should not need to know the version. +set(CMAKE_IMPORT_FILE_VERSION) diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/abi-arm64-v8a/OpenCVModules.cmake b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/abi-arm64-v8a/OpenCVModules.cmake new file mode 100644 index 0000000000000000000000000000000000000000..db3777a5e3db6bf4b2ed21eaf52ad0029f1ab3ad --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/abi-arm64-v8a/OpenCVModules.cmake @@ -0,0 +1,176 @@ +# Generated by CMake + +if("${CMAKE_MAJOR_VERSION}.${CMAKE_MINOR_VERSION}" LESS 2.5) + message(FATAL_ERROR "CMake >= 2.6.0 required") +endif() +cmake_policy(PUSH) +cmake_policy(VERSION 2.6) +#---------------------------------------------------------------- +# Generated CMake target import file. +#---------------------------------------------------------------- + +# Commands may need to know the format version. +set(CMAKE_IMPORT_FILE_VERSION 1) + +# Protect against multiple inclusion, which would fail when already imported targets are added once more. +set(_targetsDefined) +set(_targetsNotDefined) +set(_expectedTargets) +foreach(_expectedTarget libcpufeatures zlib libjpeg-turbo libtiff libwebp libjasper libpng IlmImf libprotobuf quirc tegra_hal opencv_core opencv_imgproc opencv_features2d opencv_imgcodecs opencv_calib3d) + list(APPEND _expectedTargets ${_expectedTarget}) + if(NOT TARGET ${_expectedTarget}) + list(APPEND _targetsNotDefined ${_expectedTarget}) + endif() + if(TARGET ${_expectedTarget}) + list(APPEND _targetsDefined ${_expectedTarget}) + endif() +endforeach() +if("${_targetsDefined}" STREQUAL "${_expectedTargets}") + unset(_targetsDefined) + unset(_targetsNotDefined) + unset(_expectedTargets) + set(CMAKE_IMPORT_FILE_VERSION) + cmake_policy(POP) + return() +endif() +if(NOT "${_targetsDefined}" STREQUAL "") + message(FATAL_ERROR "Some (but not all) targets in this export set were already defined.\nTargets Defined: ${_targetsDefined}\nTargets not yet defined: ${_targetsNotDefined}\n") +endif() +unset(_targetsDefined) +unset(_targetsNotDefined) +unset(_expectedTargets) + + +# Compute the installation prefix relative to this file. +get_filename_component(_IMPORT_PREFIX "${CMAKE_CURRENT_LIST_FILE}" PATH) +get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH) +get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH) +get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH) +get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH) +if(_IMPORT_PREFIX STREQUAL "/") + set(_IMPORT_PREFIX "") +endif() + +# Create imported target libcpufeatures +add_library(libcpufeatures STATIC IMPORTED) + +# Create imported target zlib +add_library(zlib STATIC IMPORTED) + +# Create imported target libjpeg-turbo +add_library(libjpeg-turbo STATIC IMPORTED) + +# Create imported target libtiff +add_library(libtiff STATIC IMPORTED) + +set_target_properties(libtiff PROPERTIES + INTERFACE_LINK_LIBRARIES "zlib" +) + +# Create imported target libwebp +add_library(libwebp STATIC IMPORTED) + +set_target_properties(libwebp PROPERTIES + INTERFACE_LINK_LIBRARIES "libcpufeatures" +) + +# Create imported target libjasper +add_library(libjasper STATIC IMPORTED) + +# Create imported target libpng +add_library(libpng STATIC IMPORTED) + +set_target_properties(libpng PROPERTIES + INTERFACE_LINK_LIBRARIES "zlib" +) + +# Create imported target IlmImf +add_library(IlmImf STATIC IMPORTED) + +set_target_properties(IlmImf PROPERTIES + INTERFACE_LINK_LIBRARIES "zlib" +) + +# Create imported target libprotobuf +add_library(libprotobuf STATIC IMPORTED) + +# Create imported target quirc +add_library(quirc STATIC IMPORTED) + +# Create imported target tegra_hal +add_library(tegra_hal STATIC IMPORTED) + +# Create imported target opencv_core +add_library(opencv_core STATIC IMPORTED) + +set_target_properties(opencv_core PROPERTIES + INTERFACE_LINK_LIBRARIES "\$;\$;\$;\$;\$;\$;\$" +) + +# Create imported target opencv_imgproc +add_library(opencv_imgproc STATIC IMPORTED) + +set_target_properties(opencv_imgproc PROPERTIES + INTERFACE_LINK_LIBRARIES "opencv_core;\$;\$;\$;\$" +) + +# Create imported target opencv_features2d +add_library(opencv_features2d STATIC IMPORTED) + +set_target_properties(opencv_features2d PROPERTIES + INTERFACE_LINK_LIBRARIES "opencv_core;opencv_imgproc;\$;\$;\$;\$" +) + +# Create imported target opencv_imgcodecs +add_library(opencv_imgcodecs STATIC IMPORTED) + +set_target_properties(opencv_imgcodecs PROPERTIES + INTERFACE_LINK_LIBRARIES "opencv_core;opencv_imgproc;\$;\$;\$;\$;\$;\$;\$;\$;\$;\$;\$" +) + +# Create imported target opencv_calib3d +add_library(opencv_calib3d STATIC IMPORTED) + +set_target_properties(opencv_calib3d PROPERTIES + INTERFACE_LINK_LIBRARIES "opencv_core;opencv_imgproc;opencv_features2d;\$;\$;\$;\$" +) + +if(CMAKE_VERSION VERSION_LESS 2.8.12) + message(FATAL_ERROR "This file relies on consumers using CMake 2.8.12 or greater.") +endif() + +# Load information for each installed configuration. +get_filename_component(_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH) +file(GLOB CONFIG_FILES "${_DIR}/OpenCVModules-*.cmake") +foreach(f ${CONFIG_FILES}) + include(${f}) +endforeach() + +# Cleanup temporary variables. +set(_IMPORT_PREFIX) + +# Loop over all imported files and verify that they actually exist +foreach(target ${_IMPORT_CHECK_TARGETS} ) + foreach(file ${_IMPORT_CHECK_FILES_FOR_${target}} ) + if(NOT EXISTS "${file}" ) + message(FATAL_ERROR "The imported target \"${target}\" references the file + \"${file}\" +but this file does not exist. Possible reasons include: +* The file was deleted, renamed, or moved to another location. +* An install or uninstall procedure did not complete successfully. +* The installation package was faulty and contained + \"${CMAKE_CURRENT_LIST_FILE}\" +but not all the files it references. +") + endif() + endforeach() + unset(_IMPORT_CHECK_FILES_FOR_${target}) +endforeach() +unset(_IMPORT_CHECK_TARGETS) + +# This file does not depend on other imported targets which have +# been exported from the same project but in a separate export set. + +# Commands beyond this point should not need to know the version. +set(CMAKE_IMPORT_FILE_VERSION) +cmake_policy(POP) diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/abi-armeabi-v7a/OpenCVConfig-version.cmake b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/abi-armeabi-v7a/OpenCVConfig-version.cmake new file mode 100644 index 0000000000000000000000000000000000000000..f8be90d0b64ad0ff98b823e89d10f5399908a262 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/abi-armeabi-v7a/OpenCVConfig-version.cmake @@ -0,0 +1,15 @@ +set(OpenCV_VERSION 3.4.5) +set(PACKAGE_VERSION ${OpenCV_VERSION}) + +set(PACKAGE_VERSION_EXACT False) +set(PACKAGE_VERSION_COMPATIBLE False) + +if(PACKAGE_FIND_VERSION VERSION_EQUAL PACKAGE_VERSION) + set(PACKAGE_VERSION_EXACT True) + set(PACKAGE_VERSION_COMPATIBLE True) +endif() + +if(PACKAGE_FIND_VERSION_MAJOR EQUAL 3 + AND PACKAGE_FIND_VERSION VERSION_LESS PACKAGE_VERSION) + set(PACKAGE_VERSION_COMPATIBLE True) +endif() diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/abi-armeabi-v7a/OpenCVConfig.cmake b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/abi-armeabi-v7a/OpenCVConfig.cmake new file mode 100644 index 0000000000000000000000000000000000000000..e211709bd6ab71ae513490b33d92be3b7d5b9e82 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/abi-armeabi-v7a/OpenCVConfig.cmake @@ -0,0 +1,328 @@ +# =================================================================================== +# The OpenCV CMake configuration file +# +# ** File generated automatically, do not modify ** +# +# Usage from an external project: +# In your CMakeLists.txt, add these lines: +# +# find_package(OpenCV REQUIRED) +# include_directories(${OpenCV_INCLUDE_DIRS}) # Not needed for CMake >= 2.8.11 +# target_link_libraries(MY_TARGET_NAME ${OpenCV_LIBS}) +# +# Or you can search for specific OpenCV modules: +# +# find_package(OpenCV REQUIRED core videoio) +# +# You can also mark OpenCV components as optional: + +# find_package(OpenCV REQUIRED core OPTIONAL_COMPONENTS viz) +# +# If the module is found then OPENCV__FOUND is set to TRUE. +# +# This file will define the following variables: +# - OpenCV_LIBS : The list of all imported targets for OpenCV modules. +# - OpenCV_INCLUDE_DIRS : The OpenCV include directories. +# - OpenCV_COMPUTE_CAPABILITIES : The version of compute capability. +# - OpenCV_ANDROID_NATIVE_API_LEVEL : Minimum required level of Android API. +# - OpenCV_VERSION : The version of this OpenCV build: "3.4.5" +# - OpenCV_VERSION_MAJOR : Major version part of OpenCV_VERSION: "3" +# - OpenCV_VERSION_MINOR : Minor version part of OpenCV_VERSION: "4" +# - OpenCV_VERSION_PATCH : Patch version part of OpenCV_VERSION: "5" +# - OpenCV_VERSION_STATUS : Development status of this build: "" +# +# Advanced variables: +# - OpenCV_SHARED : Use OpenCV as shared library +# - OpenCV_INSTALL_PATH : OpenCV location +# - OpenCV_LIB_COMPONENTS : Present OpenCV modules list +# - OpenCV_USE_MANGLED_PATHS : Mangled OpenCV path flag +# +# Deprecated variables: +# - OpenCV_VERSION_TWEAK : Always "0" +# +# =================================================================================== + +# ====================================================== +# Version variables: +# ====================================================== +SET(OpenCV_VERSION 3.4.5) +SET(OpenCV_VERSION_MAJOR 3) +SET(OpenCV_VERSION_MINOR 4) +SET(OpenCV_VERSION_PATCH 5) +SET(OpenCV_VERSION_TWEAK 0) +SET(OpenCV_VERSION_STATUS "") + +include(FindPackageHandleStandardArgs) + +if(NOT CMAKE_VERSION VERSION_LESS 2.8.8 + AND OpenCV_FIND_COMPONENTS # prevent excessive output +) + # HANDLE_COMPONENTS was introduced in CMake 2.8.8 + list(APPEND _OpenCV_FPHSA_ARGS HANDLE_COMPONENTS) + # The missing components will be handled by the FindPackageHandleStandardArgs + # module. + set(_OpenCV_HANDLE_COMPONENTS_MANUALLY FALSE) +else() + # The missing components will be handled by this config. + set(_OpenCV_HANDLE_COMPONENTS_MANUALLY TRUE) +endif() + +# Extract directory name from full path of the file currently being processed. +# Note that CMake 2.8.3 introduced CMAKE_CURRENT_LIST_DIR. We reimplement it +# for older versions of CMake to support these as well. +if(CMAKE_VERSION VERSION_LESS "2.8.3") + get_filename_component(CMAKE_CURRENT_LIST_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH) +endif() + +# Extract the directory where *this* file has been installed (determined at cmake run-time) +# Get the absolute path with no ../.. relative marks, to eliminate implicit linker warnings +set(OpenCV_CONFIG_PATH "${CMAKE_CURRENT_LIST_DIR}") +get_filename_component(OpenCV_INSTALL_PATH "${OpenCV_CONFIG_PATH}/../../../../" REALPATH) + +# Search packages for host system instead of packages for target system. +# in case of cross compilation this macro should be defined by toolchain file +if(NOT COMMAND find_host_package) + macro(find_host_package) + find_package(${ARGN}) + endmacro() +endif() +if(NOT COMMAND find_host_program) + macro(find_host_program) + find_program(${ARGN}) + endmacro() +endif() + + + +# Android API level from which OpenCV has been compiled is remembered +set(OpenCV_ANDROID_NATIVE_API_LEVEL "24") + +# ============================================================== +# Check OpenCV availability +# ============================================================== +if(OpenCV_ANDROID_NATIVE_API_LEVEL GREATER ANDROID_NATIVE_API_LEVEL) + if(NOT OpenCV_FIND_QUIETLY) + message(WARNING "Minimum required by OpenCV API level is android-${OpenCV_ANDROID_NATIVE_API_LEVEL}") + endif() + set(OpenCV_FOUND 0) + return() +endif() + + + + + +# Some additional settings are required if OpenCV is built as static libs +set(OpenCV_SHARED OFF) + +# Enables mangled install paths, that help with side by side installs +set(OpenCV_USE_MANGLED_PATHS FALSE) + +set(OpenCV_LIB_COMPONENTS opencv_calib3d;opencv_core;opencv_features2d;opencv_imgcodecs;opencv_imgproc) +set(OpenCV_INCLUDE_DIRS "${OpenCV_INSTALL_PATH}/sdk/native/jni/include" "${OpenCV_INSTALL_PATH}/sdk/native/jni/include/opencv") + +if(NOT TARGET opencv_core) + include(${CMAKE_CURRENT_LIST_DIR}/OpenCVModules${OpenCV_MODULES_SUFFIX}.cmake) +endif() + +if(NOT CMAKE_VERSION VERSION_LESS "2.8.11") + # Target property INTERFACE_INCLUDE_DIRECTORIES available since 2.8.11: + # * http://www.cmake.org/cmake/help/v2.8.11/cmake.html#prop_tgt:INTERFACE_INCLUDE_DIRECTORIES + foreach(__component ${OpenCV_LIB_COMPONENTS}) + if(TARGET ${__component}) + set_target_properties( + ${__component} + PROPERTIES + INTERFACE_INCLUDE_DIRECTORIES "${OpenCV_INCLUDE_DIRS}" + ) + endif() + endforeach() +endif() + + +if(NOT DEFINED OPENCV_MAP_IMPORTED_CONFIG) + if(CMAKE_GENERATOR MATCHES "Visual Studio" OR MSVC) + # OpenCV supports Debug and Release builds only. + # But MSVS has 'RelWithDebInfo' 'MinSizeRel' configurations for applications. + # By default CMake maps these configuration on the first available (Debug) which is wrong. + # Non-Debug build of Application can't be used with OpenCV Debug build (ABI mismatch problem) + # Add mapping of RelWithDebInfo and MinSizeRel to Release here + set(OPENCV_MAP_IMPORTED_CONFIG "RELWITHDEBINFO=!Release;MINSIZEREL=!Release") + endif() +endif() +set(__remap_warnings "") +macro(ocv_map_imported_config target) + if(DEFINED OPENCV_MAP_IMPORTED_CONFIG) # list, "RELWITHDEBINFO=Release;MINSIZEREL=Release" + get_target_property(__available_configurations ${target} IMPORTED_CONFIGURATIONS) + foreach(remap ${OPENCV_MAP_IMPORTED_CONFIG}) + if(remap MATCHES "^(.+)=(!?)([^!]+)$") + set(__remap_config "${CMAKE_MATCH_1}") + set(__final_config "${CMAKE_MATCH_3}") + set(__force_flag "${CMAKE_MATCH_2}") + string(TOUPPER "${__final_config}" __final_config_upper) + string(TOUPPER "${__remap_config}" __remap_config_upper) + if(";${__available_configurations};" MATCHES ";${__remap_config_upper};" AND NOT "${__force_flag}" STREQUAL "!") + # configuration already exists, skip remap + set(__remap_warnings "${__remap_warnings}... Configuration already exists ${__remap_config} (skip mapping ${__remap_config} => ${__final_config}) (available configurations: ${__available_configurations})\n") + continue() + endif() + if(__available_configurations AND NOT ";${__available_configurations};" MATCHES ";${__final_config_upper};") + # skip, configuration is not available + if(NOT "${__force_flag}" STREQUAL "!") + set(__remap_warnings "${__remap_warnings}... Configuration is not available '${__final_config}' for ${target}, build may fail (available configurations: ${__available_configurations})\n") + endif() + endif() + set_target_properties(${target} PROPERTIES + MAP_IMPORTED_CONFIG_${__remap_config} "${__final_config}" + ) + else() + message(WARNING "Invalid entry of OPENCV_MAP_IMPORTED_CONFIG: '${remap}' (${OPENCV_MAP_IMPORTED_CONFIG})") + endif() + endforeach() + endif() +endmacro() + + +# ============================================================== +# Form list of modules (components) to find +# ============================================================== +if(NOT OpenCV_FIND_COMPONENTS) + set(OpenCV_FIND_COMPONENTS ${OpenCV_LIB_COMPONENTS}) + list(REMOVE_ITEM OpenCV_FIND_COMPONENTS opencv_java) + if(GTest_FOUND OR GTEST_FOUND) + list(REMOVE_ITEM OpenCV_FIND_COMPONENTS opencv_ts) + endif() +endif() + +set(OpenCV_WORLD_COMPONENTS ) + +# expand short module names and see if requested components exist +foreach(__cvcomponent ${OpenCV_FIND_COMPONENTS}) + # Store the name of the original component so we can set the + # OpenCV__FOUND variable which can be checked by the user. + set (__original_cvcomponent ${__cvcomponent}) + if(NOT __cvcomponent MATCHES "^opencv_") + set(__cvcomponent opencv_${__cvcomponent}) + endif() + list(FIND OpenCV_LIB_COMPONENTS ${__cvcomponent} __cvcomponentIdx) + if(__cvcomponentIdx LESS 0) + if(_OpenCV_HANDLE_COMPONENTS_MANUALLY) + # Either the component is required or the user did not set any components at + # all. In the latter case, the OpenCV_FIND_REQUIRED_ variable + # will not be defined since it is not set by this config. So let's assume + # the implicitly set components are always required. + if(NOT DEFINED OpenCV_FIND_REQUIRED_${__original_cvcomponent} OR + OpenCV_FIND_REQUIRED_${__original_cvcomponent}) + message(FATAL_ERROR "${__cvcomponent} is required but was not found") + elseif(NOT OpenCV_FIND_QUIETLY) + # The component was marked as optional using OPTIONAL_COMPONENTS + message(WARNING "Optional component ${__cvcomponent} was not found") + endif() + endif(_OpenCV_HANDLE_COMPONENTS_MANUALLY) + #indicate that module is NOT found + string(TOUPPER "${__cvcomponent}" __cvcomponentUP) + set(${__cvcomponentUP}_FOUND "${__cvcomponentUP}_FOUND-NOTFOUND") + set(OpenCV_${__original_cvcomponent}_FOUND FALSE) + else() + # Not using list(APPEND) here, because OpenCV_LIBS may not exist yet. + # Also not clearing OpenCV_LIBS anywhere, so that multiple calls + # to find_package(OpenCV) with different component lists add up. + set(OpenCV_LIBS ${OpenCV_LIBS} "${__cvcomponent}") + #indicate that module is found + string(TOUPPER "${__cvcomponent}" __cvcomponentUP) + set(${__cvcomponentUP}_FOUND 1) + set(OpenCV_${__original_cvcomponent}_FOUND TRUE) + endif() + if(OpenCV_SHARED AND ";${OpenCV_WORLD_COMPONENTS};" MATCHES ";${__cvcomponent};" AND NOT TARGET ${__cvcomponent}) + get_target_property(__implib_dbg opencv_world IMPORTED_IMPLIB_DEBUG) + get_target_property(__implib_release opencv_world IMPORTED_IMPLIB_RELEASE) + get_target_property(__location_dbg opencv_world IMPORTED_LOCATION_DEBUG) + get_target_property(__location_release opencv_world IMPORTED_LOCATION_RELEASE) + get_target_property(__include_dir opencv_world INTERFACE_INCLUDE_DIRECTORIES) + add_library(${__cvcomponent} SHARED IMPORTED) + set_target_properties(${__cvcomponent} PROPERTIES INTERFACE_INCLUDE_DIRECTORIES "${__include_dir}") + if(__location_dbg) + set_property(TARGET ${__cvcomponent} APPEND PROPERTY IMPORTED_CONFIGURATIONS DEBUG) + set_target_properties(${__cvcomponent} PROPERTIES + IMPORTED_IMPLIB_DEBUG "${__implib_dbg}" + IMPORTED_LINK_INTERFACE_LIBRARIES_DEBUG "" + IMPORTED_LOCATION_DEBUG "${__location_dbg}" + ) + endif() + if(__location_release) + set_property(TARGET ${__cvcomponent} APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) + set_target_properties(${__cvcomponent} PROPERTIES + IMPORTED_IMPLIB_RELEASE "${__implib_release}" + IMPORTED_LINK_INTERFACE_LIBRARIES_RELEASE "" + IMPORTED_LOCATION_RELEASE "${__location_release}" + ) + endif() + endif() + if(TARGET ${__cvcomponent}) + ocv_map_imported_config(${__cvcomponent}) + endif() +endforeach() + +if(__remap_warnings AND NOT OpenCV_FIND_QUIETLY) + message("OpenCV: configurations remap warnings:\n${__remap_warnings}OpenCV: Check variable OPENCV_MAP_IMPORTED_CONFIG=${OPENCV_MAP_IMPORTED_CONFIG}") +endif() + +# ============================================================== +# Compatibility stuff +# ============================================================== +set(OpenCV_LIBRARIES ${OpenCV_LIBS}) + +# +# Some macros for samples +# +macro(ocv_check_dependencies) + set(OCV_DEPENDENCIES_FOUND TRUE) + foreach(d ${ARGN}) + if(NOT TARGET ${d}) + message(WARNING "OpenCV: Can't resolve dependency: ${d}") + set(OCV_DEPENDENCIES_FOUND FALSE) + break() + endif() + endforeach() +endmacro() + +# adds include directories in such way that directories from the OpenCV source tree go first +function(ocv_include_directories) + set(__add_before "") + file(TO_CMAKE_PATH "${OpenCV_INSTALL_PATH}" __baseDir) + foreach(dir ${ARGN}) + get_filename_component(__abs_dir "${dir}" ABSOLUTE) + if("${__abs_dir}" MATCHES "^${__baseDir}") + list(APPEND __add_before "${dir}") + else() + include_directories(AFTER SYSTEM "${dir}") + endif() + endforeach() + include_directories(BEFORE ${__add_before}) +endfunction() + +macro(ocv_include_modules) + include_directories(BEFORE "${OpenCV_INCLUDE_DIRS}") +endmacro() + +macro(ocv_include_modules_recurse) + include_directories(BEFORE "${OpenCV_INCLUDE_DIRS}") +endmacro() + +macro(ocv_target_link_libraries) + target_link_libraries(${ARGN}) +endmacro() + +# remove all matching elements from the list +macro(ocv_list_filterout lst regex) + foreach(item ${${lst}}) + if(item MATCHES "${regex}") + list(REMOVE_ITEM ${lst} "${item}") + endif() + endforeach() +endmacro() + +# We do not actually need REQUIRED_VARS to be checked for. Just use the +# installation directory for the status. +find_package_handle_standard_args(OpenCV REQUIRED_VARS OpenCV_INSTALL_PATH + VERSION_VAR OpenCV_VERSION ${_OpenCV_FPHSA_ARGS}) diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/abi-armeabi-v7a/OpenCVModules-release.cmake b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/abi-armeabi-v7a/OpenCVModules-release.cmake new file mode 100644 index 0000000000000000000000000000000000000000..e98a73a0a1a7f7b63021a8d5f2129fc45db8d74e --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/abi-armeabi-v7a/OpenCVModules-release.cmake @@ -0,0 +1,169 @@ +#---------------------------------------------------------------- +# Generated CMake target import file for configuration "Release". +#---------------------------------------------------------------- + +# Commands may need to know the format version. +set(CMAKE_IMPORT_FILE_VERSION 1) + +# Import target "libcpufeatures" for configuration "Release" +set_property(TARGET libcpufeatures APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(libcpufeatures PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "C" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/armeabi-v7a/libcpufeatures.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS libcpufeatures ) +list(APPEND _IMPORT_CHECK_FILES_FOR_libcpufeatures "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/armeabi-v7a/libcpufeatures.a" ) + +# Import target "zlib" for configuration "Release" +set_property(TARGET zlib APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(zlib PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "C" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/armeabi-v7a/libzlib.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS zlib ) +list(APPEND _IMPORT_CHECK_FILES_FOR_zlib "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/armeabi-v7a/libzlib.a" ) + +# Import target "libjpeg-turbo" for configuration "Release" +set_property(TARGET libjpeg-turbo APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(libjpeg-turbo PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "C" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/armeabi-v7a/liblibjpeg-turbo.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS libjpeg-turbo ) +list(APPEND _IMPORT_CHECK_FILES_FOR_libjpeg-turbo "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/armeabi-v7a/liblibjpeg-turbo.a" ) + +# Import target "libtiff" for configuration "Release" +set_property(TARGET libtiff APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(libtiff PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "C;CXX" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/armeabi-v7a/liblibtiff.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS libtiff ) +list(APPEND _IMPORT_CHECK_FILES_FOR_libtiff "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/armeabi-v7a/liblibtiff.a" ) + +# Import target "libwebp" for configuration "Release" +set_property(TARGET libwebp APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(libwebp PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "C" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/armeabi-v7a/liblibwebp.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS libwebp ) +list(APPEND _IMPORT_CHECK_FILES_FOR_libwebp "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/armeabi-v7a/liblibwebp.a" ) + +# Import target "libjasper" for configuration "Release" +set_property(TARGET libjasper APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(libjasper PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "C" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/armeabi-v7a/liblibjasper.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS libjasper ) +list(APPEND _IMPORT_CHECK_FILES_FOR_libjasper "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/armeabi-v7a/liblibjasper.a" ) + +# Import target "libpng" for configuration "Release" +set_property(TARGET libpng APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(libpng PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "ASM;C" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/armeabi-v7a/liblibpng.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS libpng ) +list(APPEND _IMPORT_CHECK_FILES_FOR_libpng "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/armeabi-v7a/liblibpng.a" ) + +# Import target "IlmImf" for configuration "Release" +set_property(TARGET IlmImf APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(IlmImf PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/armeabi-v7a/libIlmImf.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS IlmImf ) +list(APPEND _IMPORT_CHECK_FILES_FOR_IlmImf "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/armeabi-v7a/libIlmImf.a" ) + +# Import target "libprotobuf" for configuration "Release" +set_property(TARGET libprotobuf APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(libprotobuf PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/armeabi-v7a/liblibprotobuf.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS libprotobuf ) +list(APPEND _IMPORT_CHECK_FILES_FOR_libprotobuf "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/armeabi-v7a/liblibprotobuf.a" ) + +# Import target "quirc" for configuration "Release" +set_property(TARGET quirc APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(quirc PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "C" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/armeabi-v7a/libquirc.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS quirc ) +list(APPEND _IMPORT_CHECK_FILES_FOR_quirc "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/armeabi-v7a/libquirc.a" ) + +# Import target "tegra_hal" for configuration "Release" +set_property(TARGET tegra_hal APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(tegra_hal PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/armeabi-v7a/libtegra_hal.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS tegra_hal ) +list(APPEND _IMPORT_CHECK_FILES_FOR_tegra_hal "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/armeabi-v7a/libtegra_hal.a" ) + +# Import target "opencv_core" for configuration "Release" +set_property(TARGET opencv_core APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(opencv_core PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/staticlibs/armeabi-v7a/libopencv_core.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS opencv_core ) +list(APPEND _IMPORT_CHECK_FILES_FOR_opencv_core "${_IMPORT_PREFIX}/sdk/native/staticlibs/armeabi-v7a/libopencv_core.a" ) + +# Import target "opencv_imgproc" for configuration "Release" +set_property(TARGET opencv_imgproc APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(opencv_imgproc PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/staticlibs/armeabi-v7a/libopencv_imgproc.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS opencv_imgproc ) +list(APPEND _IMPORT_CHECK_FILES_FOR_opencv_imgproc "${_IMPORT_PREFIX}/sdk/native/staticlibs/armeabi-v7a/libopencv_imgproc.a" ) + +# Import target "opencv_features2d" for configuration "Release" +set_property(TARGET opencv_features2d APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(opencv_features2d PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/staticlibs/armeabi-v7a/libopencv_features2d.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS opencv_features2d ) +list(APPEND _IMPORT_CHECK_FILES_FOR_opencv_features2d "${_IMPORT_PREFIX}/sdk/native/staticlibs/armeabi-v7a/libopencv_features2d.a" ) + +# Import target "opencv_imgcodecs" for configuration "Release" +set_property(TARGET opencv_imgcodecs APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(opencv_imgcodecs PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/staticlibs/armeabi-v7a/libopencv_imgcodecs.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS opencv_imgcodecs ) +list(APPEND _IMPORT_CHECK_FILES_FOR_opencv_imgcodecs "${_IMPORT_PREFIX}/sdk/native/staticlibs/armeabi-v7a/libopencv_imgcodecs.a" ) + +# Import target "opencv_calib3d" for configuration "Release" +set_property(TARGET opencv_calib3d APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(opencv_calib3d PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/staticlibs/armeabi-v7a/libopencv_calib3d.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS opencv_calib3d ) +list(APPEND _IMPORT_CHECK_FILES_FOR_opencv_calib3d "${_IMPORT_PREFIX}/sdk/native/staticlibs/armeabi-v7a/libopencv_calib3d.a" ) + +# Commands beyond this point should not need to know the version. +set(CMAKE_IMPORT_FILE_VERSION) diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/abi-armeabi-v7a/OpenCVModules.cmake b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/abi-armeabi-v7a/OpenCVModules.cmake new file mode 100644 index 0000000000000000000000000000000000000000..db3777a5e3db6bf4b2ed21eaf52ad0029f1ab3ad --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/abi-armeabi-v7a/OpenCVModules.cmake @@ -0,0 +1,176 @@ +# Generated by CMake + +if("${CMAKE_MAJOR_VERSION}.${CMAKE_MINOR_VERSION}" LESS 2.5) + message(FATAL_ERROR "CMake >= 2.6.0 required") +endif() +cmake_policy(PUSH) +cmake_policy(VERSION 2.6) +#---------------------------------------------------------------- +# Generated CMake target import file. +#---------------------------------------------------------------- + +# Commands may need to know the format version. +set(CMAKE_IMPORT_FILE_VERSION 1) + +# Protect against multiple inclusion, which would fail when already imported targets are added once more. +set(_targetsDefined) +set(_targetsNotDefined) +set(_expectedTargets) +foreach(_expectedTarget libcpufeatures zlib libjpeg-turbo libtiff libwebp libjasper libpng IlmImf libprotobuf quirc tegra_hal opencv_core opencv_imgproc opencv_features2d opencv_imgcodecs opencv_calib3d) + list(APPEND _expectedTargets ${_expectedTarget}) + if(NOT TARGET ${_expectedTarget}) + list(APPEND _targetsNotDefined ${_expectedTarget}) + endif() + if(TARGET ${_expectedTarget}) + list(APPEND _targetsDefined ${_expectedTarget}) + endif() +endforeach() +if("${_targetsDefined}" STREQUAL "${_expectedTargets}") + unset(_targetsDefined) + unset(_targetsNotDefined) + unset(_expectedTargets) + set(CMAKE_IMPORT_FILE_VERSION) + cmake_policy(POP) + return() +endif() +if(NOT "${_targetsDefined}" STREQUAL "") + message(FATAL_ERROR "Some (but not all) targets in this export set were already defined.\nTargets Defined: ${_targetsDefined}\nTargets not yet defined: ${_targetsNotDefined}\n") +endif() +unset(_targetsDefined) +unset(_targetsNotDefined) +unset(_expectedTargets) + + +# Compute the installation prefix relative to this file. +get_filename_component(_IMPORT_PREFIX "${CMAKE_CURRENT_LIST_FILE}" PATH) +get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH) +get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH) +get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH) +get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH) +if(_IMPORT_PREFIX STREQUAL "/") + set(_IMPORT_PREFIX "") +endif() + +# Create imported target libcpufeatures +add_library(libcpufeatures STATIC IMPORTED) + +# Create imported target zlib +add_library(zlib STATIC IMPORTED) + +# Create imported target libjpeg-turbo +add_library(libjpeg-turbo STATIC IMPORTED) + +# Create imported target libtiff +add_library(libtiff STATIC IMPORTED) + +set_target_properties(libtiff PROPERTIES + INTERFACE_LINK_LIBRARIES "zlib" +) + +# Create imported target libwebp +add_library(libwebp STATIC IMPORTED) + +set_target_properties(libwebp PROPERTIES + INTERFACE_LINK_LIBRARIES "libcpufeatures" +) + +# Create imported target libjasper +add_library(libjasper STATIC IMPORTED) + +# Create imported target libpng +add_library(libpng STATIC IMPORTED) + +set_target_properties(libpng PROPERTIES + INTERFACE_LINK_LIBRARIES "zlib" +) + +# Create imported target IlmImf +add_library(IlmImf STATIC IMPORTED) + +set_target_properties(IlmImf PROPERTIES + INTERFACE_LINK_LIBRARIES "zlib" +) + +# Create imported target libprotobuf +add_library(libprotobuf STATIC IMPORTED) + +# Create imported target quirc +add_library(quirc STATIC IMPORTED) + +# Create imported target tegra_hal +add_library(tegra_hal STATIC IMPORTED) + +# Create imported target opencv_core +add_library(opencv_core STATIC IMPORTED) + +set_target_properties(opencv_core PROPERTIES + INTERFACE_LINK_LIBRARIES "\$;\$;\$;\$;\$;\$;\$" +) + +# Create imported target opencv_imgproc +add_library(opencv_imgproc STATIC IMPORTED) + +set_target_properties(opencv_imgproc PROPERTIES + INTERFACE_LINK_LIBRARIES "opencv_core;\$;\$;\$;\$" +) + +# Create imported target opencv_features2d +add_library(opencv_features2d STATIC IMPORTED) + +set_target_properties(opencv_features2d PROPERTIES + INTERFACE_LINK_LIBRARIES "opencv_core;opencv_imgproc;\$;\$;\$;\$" +) + +# Create imported target opencv_imgcodecs +add_library(opencv_imgcodecs STATIC IMPORTED) + +set_target_properties(opencv_imgcodecs PROPERTIES + INTERFACE_LINK_LIBRARIES "opencv_core;opencv_imgproc;\$;\$;\$;\$;\$;\$;\$;\$;\$;\$;\$" +) + +# Create imported target opencv_calib3d +add_library(opencv_calib3d STATIC IMPORTED) + +set_target_properties(opencv_calib3d PROPERTIES + INTERFACE_LINK_LIBRARIES "opencv_core;opencv_imgproc;opencv_features2d;\$;\$;\$;\$" +) + +if(CMAKE_VERSION VERSION_LESS 2.8.12) + message(FATAL_ERROR "This file relies on consumers using CMake 2.8.12 or greater.") +endif() + +# Load information for each installed configuration. +get_filename_component(_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH) +file(GLOB CONFIG_FILES "${_DIR}/OpenCVModules-*.cmake") +foreach(f ${CONFIG_FILES}) + include(${f}) +endforeach() + +# Cleanup temporary variables. +set(_IMPORT_PREFIX) + +# Loop over all imported files and verify that they actually exist +foreach(target ${_IMPORT_CHECK_TARGETS} ) + foreach(file ${_IMPORT_CHECK_FILES_FOR_${target}} ) + if(NOT EXISTS "${file}" ) + message(FATAL_ERROR "The imported target \"${target}\" references the file + \"${file}\" +but this file does not exist. Possible reasons include: +* The file was deleted, renamed, or moved to another location. +* An install or uninstall procedure did not complete successfully. +* The installation package was faulty and contained + \"${CMAKE_CURRENT_LIST_FILE}\" +but not all the files it references. +") + endif() + endforeach() + unset(_IMPORT_CHECK_FILES_FOR_${target}) +endforeach() +unset(_IMPORT_CHECK_TARGETS) + +# This file does not depend on other imported targets which have +# been exported from the same project but in a separate export set. + +# Commands beyond this point should not need to know the version. +set(CMAKE_IMPORT_FILE_VERSION) +cmake_policy(POP) diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/android.toolchain.cmake b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/android.toolchain.cmake new file mode 100644 index 0000000000000000000000000000000000000000..b37dea01ae57079fba4327ef5dfaaab30a7c91b0 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/android.toolchain.cmake @@ -0,0 +1,1792 @@ +# Copyright (c) 2010-2011, Ethan Rublee +# Copyright (c) 2011-2014, Andrey Kamaev +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright notice, +# this list of conditions and the following disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# 3. Neither the name of the copyright holder nor the names of its +# contributors may be used to endorse or promote products derived from this +# software without specific prior written permission. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE +# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +# POSSIBILITY OF SUCH DAMAGE. + +# ------------------------------------------------------------------------------ +# Android CMake toolchain file, for use with the Android NDK r5-r10d +# Requires cmake 2.6.3 or newer (2.8.9 or newer is recommended). +# See home page: https://github.com/taka-no-me/android-cmake +# +# Usage Linux: +# $ export ANDROID_NDK=/absolute/path/to/the/android-ndk +# $ mkdir build && cd build +# $ cmake -DCMAKE_TOOLCHAIN_FILE=path/to/the/android.toolchain.cmake .. +# $ make -j8 +# +# Usage Windows: +# You need native port of make to build your project. +# Android NDK r7 (and newer) already has make.exe on board. +# For older NDK you have to install it separately. +# For example, this one: http://gnuwin32.sourceforge.net/packages/make.htm +# +# $ SET ANDROID_NDK=C:\absolute\path\to\the\android-ndk +# $ mkdir build && cd build +# $ cmake.exe -G"MinGW Makefiles" +# -DCMAKE_TOOLCHAIN_FILE=path\to\the\android.toolchain.cmake +# -DCMAKE_MAKE_PROGRAM="%ANDROID_NDK%\prebuilt\windows\bin\make.exe" .. +# $ cmake.exe --build . +# +# +# Options (can be set as cmake parameters: -D=): +# ANDROID_NDK=/opt/android-ndk - path to the NDK root. +# Can be set as environment variable. Can be set only at first cmake run. +# +# ANDROID_ABI=armeabi-v7a - specifies the target Application Binary +# Interface (ABI). This option nearly matches to the APP_ABI variable +# used by ndk-build tool from Android NDK. +# +# Possible targets are: +# "armeabi" - ARMv5TE based CPU with software floating point operations +# "armeabi-v7a" - ARMv7 based devices with hardware FPU instructions +# this ABI target is used by default +# "armeabi-v7a-hard with NEON" - ARMv7 based devices with hardware FPU instructions and hardfp +# "armeabi-v7a with NEON" - same as armeabi-v7a, but +# sets NEON as floating-point unit +# "armeabi-v7a with VFPV3" - same as armeabi-v7a, but +# sets VFPV3 as floating-point unit (has 32 registers instead of 16) +# "armeabi-v6 with VFP" - tuned for ARMv6 processors having VFP +# "x86" - IA-32 instruction set +# "mips" - MIPS32 instruction set +# +# 64-bit ABIs for NDK r10 and newer: +# "arm64-v8a" - ARMv8 AArch64 instruction set +# "x86_64" - Intel64 instruction set (r1) +# "mips64" - MIPS64 instruction set (r6) +# +# ANDROID_NATIVE_API_LEVEL=android-8 - level of Android API compile for. +# Option is read-only when standalone toolchain is used. +# Note: building for "android-L" requires explicit configuration. +# +# ANDROID_TOOLCHAIN_NAME=arm-linux-androideabi-4.9 - the name of compiler +# toolchain to be used. The list of possible values depends on the NDK +# version. For NDK r10c the possible values are: +# +# * aarch64-linux-android-4.9 +# * aarch64-linux-android-clang3.4 +# * aarch64-linux-android-clang3.5 +# * arm-linux-androideabi-4.6 +# * arm-linux-androideabi-4.8 +# * arm-linux-androideabi-4.9 (default) +# * arm-linux-androideabi-clang3.4 +# * arm-linux-androideabi-clang3.5 +# * mips64el-linux-android-4.9 +# * mips64el-linux-android-clang3.4 +# * mips64el-linux-android-clang3.5 +# * mipsel-linux-android-4.6 +# * mipsel-linux-android-4.8 +# * mipsel-linux-android-4.9 +# * mipsel-linux-android-clang3.4 +# * mipsel-linux-android-clang3.5 +# * x86-4.6 +# * x86-4.8 +# * x86-4.9 +# * x86-clang3.4 +# * x86-clang3.5 +# * x86_64-4.9 +# * x86_64-clang3.4 +# * x86_64-clang3.5 +# +# ANDROID_FORCE_ARM_BUILD=OFF - set ON to generate 32-bit ARM instructions +# instead of Thumb. Is not available for "armeabi-v6 with VFP" +# (is forced to be ON) ABI. +# +# ANDROID_NO_UNDEFINED=ON - set ON to show all undefined symbols as linker +# errors even if they are not used. +# +# ANDROID_SO_UNDEFINED=OFF - set ON to allow undefined symbols in shared +# libraries. Automatically turned for NDK r5x and r6x due to GLESv2 +# problems. +# +# ANDROID_STL=gnustl_static - specify the runtime to use. +# +# Possible values are: +# none -> Do not configure the runtime. +# system -> Use the default minimal system C++ runtime library. +# Implies -fno-rtti -fno-exceptions. +# Is not available for standalone toolchain. +# system_re -> Use the default minimal system C++ runtime library. +# Implies -frtti -fexceptions. +# Is not available for standalone toolchain. +# gabi++_static -> Use the GAbi++ runtime as a static library. +# Implies -frtti -fno-exceptions. +# Available for NDK r7 and newer. +# Is not available for standalone toolchain. +# gabi++_shared -> Use the GAbi++ runtime as a shared library. +# Implies -frtti -fno-exceptions. +# Available for NDK r7 and newer. +# Is not available for standalone toolchain. +# stlport_static -> Use the STLport runtime as a static library. +# Implies -fno-rtti -fno-exceptions for NDK before r7. +# Implies -frtti -fno-exceptions for NDK r7 and newer. +# Is not available for standalone toolchain. +# stlport_shared -> Use the STLport runtime as a shared library. +# Implies -fno-rtti -fno-exceptions for NDK before r7. +# Implies -frtti -fno-exceptions for NDK r7 and newer. +# Is not available for standalone toolchain. +# gnustl_static -> Use the GNU STL as a static library. +# Implies -frtti -fexceptions. +# gnustl_shared -> Use the GNU STL as a shared library. +# Implies -frtti -fno-exceptions. +# Available for NDK r7b and newer. +# Silently degrades to gnustl_static if not available. +# c++_static -> Use the LLVM libc++ runtime as a static library. +# Implies -frtti -fexceptions. +# c++_shared -> Use the LLVM libc++ runtime as a shared library. +# Implies -frtti -fno-exceptions. +# +# ANDROID_STL_FORCE_FEATURES=ON - turn rtti and exceptions support based on +# chosen runtime. If disabled, then the user is responsible for settings +# these options. +# +# What?: +# android-cmake toolchain searches for NDK/toolchain in the following order: +# ANDROID_NDK - cmake parameter +# ANDROID_NDK - environment variable +# ANDROID_STANDALONE_TOOLCHAIN - cmake parameter +# ANDROID_STANDALONE_TOOLCHAIN - environment variable +# ANDROID_NDK - default locations +# ANDROID_STANDALONE_TOOLCHAIN - default locations +# +# Make sure to do the following in your scripts: +# SET( CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${my_cxx_flags}" ) +# SET( CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${my_cxx_flags}" ) +# The flags will be prepopulated with critical flags, so don't loose them. +# Also be aware that toolchain also sets configuration-specific compiler +# flags and linker flags. +# +# ANDROID and BUILD_ANDROID will be set to true, you may test any of these +# variables to make necessary Android-specific configuration changes. +# +# Also ARMEABI or ARMEABI_V7A or ARMEABI_V7A_HARD or X86 or MIPS or ARM64_V8A or X86_64 or MIPS64 +# will be set true, mutually exclusive. NEON option will be set true +# if VFP is set to NEON. +# +# ------------------------------------------------------------------------------ + +cmake_minimum_required( VERSION 2.6.3 ) + +if( DEFINED CMAKE_CROSSCOMPILING ) + # subsequent toolchain loading is not really needed + return() +endif() + +if( CMAKE_TOOLCHAIN_FILE ) + # touch toolchain variable to suppress "unused variable" warning +endif() + +# inherit settings in recursive loads +get_property( _CMAKE_IN_TRY_COMPILE GLOBAL PROPERTY IN_TRY_COMPILE ) +if( _CMAKE_IN_TRY_COMPILE ) + include( "${CMAKE_CURRENT_SOURCE_DIR}/../android.toolchain.config.cmake" OPTIONAL ) +endif() + +# this one is important +if( CMAKE_VERSION VERSION_GREATER "3.0.99" ) + set( CMAKE_SYSTEM_NAME Android ) +else() + set( CMAKE_SYSTEM_NAME Linux ) +endif() + +# this one not so much +set( CMAKE_SYSTEM_VERSION 1 ) + +# rpath makes low sense for Android +set( CMAKE_SHARED_LIBRARY_RUNTIME_C_FLAG "" ) +set( CMAKE_SKIP_RPATH TRUE CACHE BOOL "If set, runtime paths are not added when using shared libraries." ) + +# NDK search paths +set( ANDROID_SUPPORTED_NDK_VERSIONS ${ANDROID_EXTRA_NDK_VERSIONS} -r10d -r10c -r10b -r10 -r9d -r9c -r9b -r9 -r8e -r8d -r8c -r8b -r8 -r7c -r7b -r7 -r6b -r6 -r5c -r5b -r5 "" ) +if( NOT DEFINED ANDROID_NDK_SEARCH_PATHS ) + if( CMAKE_HOST_WIN32 ) + file( TO_CMAKE_PATH "$ENV{PROGRAMFILES}" ANDROID_NDK_SEARCH_PATHS ) + set( ANDROID_NDK_SEARCH_PATHS "${ANDROID_NDK_SEARCH_PATHS}" "$ENV{SystemDrive}/NVPACK" ) + else() + file( TO_CMAKE_PATH "$ENV{HOME}" ANDROID_NDK_SEARCH_PATHS ) + set( ANDROID_NDK_SEARCH_PATHS /opt "${ANDROID_NDK_SEARCH_PATHS}/NVPACK" ) + endif() +endif() +if( NOT DEFINED ANDROID_STANDALONE_TOOLCHAIN_SEARCH_PATH ) + set( ANDROID_STANDALONE_TOOLCHAIN_SEARCH_PATH /opt/android-toolchain ) +endif() + +# known ABIs +set( ANDROID_SUPPORTED_ABIS_arm "armeabi-v7a;armeabi;armeabi-v7a with NEON;armeabi-v7a-hard with NEON;armeabi-v7a with VFPV3;armeabi-v6 with VFP" ) +set( ANDROID_SUPPORTED_ABIS_arm64 "arm64-v8a" ) +set( ANDROID_SUPPORTED_ABIS_x86 "x86" ) +set( ANDROID_SUPPORTED_ABIS_x86_64 "x86_64" ) +set( ANDROID_SUPPORTED_ABIS_mips "mips" ) +set( ANDROID_SUPPORTED_ABIS_mips64 "mips64" ) + +# API level defaults +set( ANDROID_DEFAULT_NDK_API_LEVEL 9 ) +set( ANDROID_DEFAULT_NDK_API_LEVEL_arm64 21 ) +set( ANDROID_DEFAULT_NDK_API_LEVEL_x86 9 ) +set( ANDROID_DEFAULT_NDK_API_LEVEL_x86_64 21 ) +set( ANDROID_DEFAULT_NDK_API_LEVEL_mips 9 ) +set( ANDROID_DEFAULT_NDK_API_LEVEL_mips64 21 ) + + +macro( __LIST_FILTER listvar regex ) + if( ${listvar} ) + foreach( __val ${${listvar}} ) + if( __val MATCHES "${regex}" ) + list( REMOVE_ITEM ${listvar} "${__val}" ) + endif() + endforeach() + endif() +endmacro() + +macro( __INIT_VARIABLE var_name ) + set( __test_path 0 ) + foreach( __var ${ARGN} ) + if( __var STREQUAL "PATH" ) + set( __test_path 1 ) + break() + endif() + endforeach() + + if( __test_path AND NOT EXISTS "${${var_name}}" ) + unset( ${var_name} CACHE ) + endif() + + if( " ${${var_name}}" STREQUAL " " ) + set( __values 0 ) + foreach( __var ${ARGN} ) + if( __var STREQUAL "VALUES" ) + set( __values 1 ) + elseif( NOT __var STREQUAL "PATH" ) + if( __var MATCHES "^ENV_.*$" ) + string( REPLACE "ENV_" "" __var "${__var}" ) + set( __value "$ENV{${__var}}" ) + elseif( DEFINED ${__var} ) + set( __value "${${__var}}" ) + elseif( __values ) + set( __value "${__var}" ) + else() + set( __value "" ) + endif() + + if( NOT " ${__value}" STREQUAL " " AND (NOT __test_path OR EXISTS "${__value}") ) + set( ${var_name} "${__value}" ) + break() + endif() + endif() + endforeach() + unset( __value ) + unset( __values ) + endif() + + if( __test_path ) + file( TO_CMAKE_PATH "${${var_name}}" ${var_name} ) + endif() + unset( __test_path ) +endmacro() + +macro( __DETECT_NATIVE_API_LEVEL _var _path ) + set( __ndkApiLevelRegex "^[\t ]*#define[\t ]+__ANDROID_API__[\t ]+([0-9]+)[\t ]*.*$" ) + file( STRINGS ${_path} __apiFileContent REGEX "${__ndkApiLevelRegex}" ) + if( NOT __apiFileContent ) + set( __ndkApiLevelRegex "^[\t ]*#define[\t ]+__ANDROID_API__[\t ]+__ANDROID_API_FUTURE__[\t ]*$" ) + file( STRINGS ${_path} __apiFileContent REGEX "${__ndkApiLevelRegex}" ) + if( __apiFileContent ) + set(${_var} 10000) + else() + message( SEND_ERROR "Could not get Android native API level. Probably you have specified invalid level value, or your copy of NDK/toolchain is broken." ) + endif() + else() + string( REGEX REPLACE "${__ndkApiLevelRegex}" "\\1" ${_var} "${__apiFileContent}" ) + endif() + unset( __apiFileContent ) + unset( __ndkApiLevelRegex ) +endmacro() + +macro( __DETECT_TOOLCHAIN_MACHINE_NAME _var _root ) + if( EXISTS "${_root}" ) + file( GLOB __gccExePath RELATIVE "${_root}/bin/" "${_root}/bin/*-gcc${TOOL_OS_SUFFIX}" ) + __LIST_FILTER( __gccExePath "^[.].*" ) + list( LENGTH __gccExePath __gccExePathsCount ) + if( NOT __gccExePathsCount EQUAL 1 AND NOT _CMAKE_IN_TRY_COMPILE ) + message( WARNING "Could not determine machine name for compiler from ${_root}" ) + set( ${_var} "" ) + else() + get_filename_component( __gccExeName "${__gccExePath}" NAME_WE ) + string( REPLACE "-gcc" "" ${_var} "${__gccExeName}" ) + endif() + unset( __gccExePath ) + unset( __gccExePathsCount ) + unset( __gccExeName ) + else() + set( ${_var} "" ) + endif() +endmacro() + + +# fight against cygwin +set( ANDROID_FORBID_SYGWIN TRUE CACHE BOOL "Prevent cmake from working under cygwin and using cygwin tools") +mark_as_advanced( ANDROID_FORBID_SYGWIN ) +if( ANDROID_FORBID_SYGWIN ) + if( CYGWIN ) + message( FATAL_ERROR "Android NDK and android-cmake toolchain are not welcome Cygwin. It is unlikely that this cmake toolchain will work under cygwin. But if you want to try then you can set cmake variable ANDROID_FORBID_SYGWIN to FALSE and rerun cmake." ) + endif() + + if( CMAKE_HOST_WIN32 ) + # remove cygwin from PATH + set( __new_path "$ENV{PATH}") + __LIST_FILTER( __new_path "cygwin" ) + set(ENV{PATH} "${__new_path}") + unset(__new_path) + endif() +endif() + + +# detect current host platform +if( NOT DEFINED ANDROID_NDK_HOST_X64 AND (CMAKE_HOST_SYSTEM_PROCESSOR MATCHES "amd64|x86_64|AMD64" OR CMAKE_HOST_APPLE) ) + set( ANDROID_NDK_HOST_X64 1 CACHE BOOL "Try to use 64-bit compiler toolchain" ) + mark_as_advanced( ANDROID_NDK_HOST_X64 ) +endif() + +set( TOOL_OS_SUFFIX "" ) +if( CMAKE_HOST_APPLE ) + set( ANDROID_NDK_HOST_SYSTEM_NAME "darwin-x86_64" ) + set( ANDROID_NDK_HOST_SYSTEM_NAME2 "darwin-x86" ) +elseif( CMAKE_HOST_WIN32 ) + set( ANDROID_NDK_HOST_SYSTEM_NAME "windows-x86_64" ) + set( ANDROID_NDK_HOST_SYSTEM_NAME2 "windows" ) + set( TOOL_OS_SUFFIX ".exe" ) +elseif( CMAKE_HOST_UNIX ) + set( ANDROID_NDK_HOST_SYSTEM_NAME "linux-x86_64" ) + set( ANDROID_NDK_HOST_SYSTEM_NAME2 "linux-x86" ) +else() + message( FATAL_ERROR "Cross-compilation on your platform is not supported by this cmake toolchain" ) +endif() + +if( NOT ANDROID_NDK_HOST_X64 ) + set( ANDROID_NDK_HOST_SYSTEM_NAME ${ANDROID_NDK_HOST_SYSTEM_NAME2} ) +endif() + +# see if we have path to Android NDK +if( NOT ANDROID_NDK AND NOT ANDROID_STANDALONE_TOOLCHAIN ) + __INIT_VARIABLE( ANDROID_NDK PATH ENV_ANDROID_NDK ) +endif() +if( NOT ANDROID_NDK ) + # see if we have path to Android standalone toolchain + __INIT_VARIABLE( ANDROID_STANDALONE_TOOLCHAIN PATH ENV_ANDROID_STANDALONE_TOOLCHAIN ) + + if( NOT ANDROID_STANDALONE_TOOLCHAIN ) + #try to find Android NDK in one of the the default locations + set( __ndkSearchPaths ) + foreach( __ndkSearchPath ${ANDROID_NDK_SEARCH_PATHS} ) + foreach( suffix ${ANDROID_SUPPORTED_NDK_VERSIONS} ) + list( APPEND __ndkSearchPaths "${__ndkSearchPath}/android-ndk${suffix}" ) + endforeach() + endforeach() + __INIT_VARIABLE( ANDROID_NDK PATH VALUES ${__ndkSearchPaths} ) + unset( __ndkSearchPaths ) + + if( ANDROID_NDK ) + message( STATUS "Using default path for Android NDK: ${ANDROID_NDK}" ) + message( STATUS " If you prefer to use a different location, please define a cmake or environment variable: ANDROID_NDK" ) + else() + #try to find Android standalone toolchain in one of the the default locations + __INIT_VARIABLE( ANDROID_STANDALONE_TOOLCHAIN PATH ANDROID_STANDALONE_TOOLCHAIN_SEARCH_PATH ) + + if( ANDROID_STANDALONE_TOOLCHAIN ) + message( STATUS "Using default path for standalone toolchain ${ANDROID_STANDALONE_TOOLCHAIN}" ) + message( STATUS " If you prefer to use a different location, please define the variable: ANDROID_STANDALONE_TOOLCHAIN" ) + endif( ANDROID_STANDALONE_TOOLCHAIN ) + endif( ANDROID_NDK ) + endif( NOT ANDROID_STANDALONE_TOOLCHAIN ) +endif( NOT ANDROID_NDK ) + +# remember found paths +if( ANDROID_NDK ) + get_filename_component( ANDROID_NDK "${ANDROID_NDK}" ABSOLUTE ) + set( ANDROID_NDK "${ANDROID_NDK}" CACHE INTERNAL "Path of the Android NDK" FORCE ) + set( BUILD_WITH_ANDROID_NDK True ) + if( EXISTS "${ANDROID_NDK}/RELEASE.TXT" ) + file( STRINGS "${ANDROID_NDK}/RELEASE.TXT" ANDROID_NDK_RELEASE_FULL LIMIT_COUNT 1 REGEX "r[0-9]+[a-z]?" ) + string( REGEX MATCH "r([0-9]+)([a-z]?)" ANDROID_NDK_RELEASE "${ANDROID_NDK_RELEASE_FULL}" ) + else() + set( ANDROID_NDK_RELEASE "r1x" ) + set( ANDROID_NDK_RELEASE_FULL "unreleased" ) + endif() + string( REGEX REPLACE "r([0-9]+)([a-z]?)" "\\1*1000" ANDROID_NDK_RELEASE_NUM "${ANDROID_NDK_RELEASE}" ) + string( FIND " abcdefghijklmnopqastuvwxyz" "${CMAKE_MATCH_2}" __ndkReleaseLetterNum ) + math( EXPR ANDROID_NDK_RELEASE_NUM "${ANDROID_NDK_RELEASE_NUM}+${__ndkReleaseLetterNum}" ) +elseif( ANDROID_STANDALONE_TOOLCHAIN ) + get_filename_component( ANDROID_STANDALONE_TOOLCHAIN "${ANDROID_STANDALONE_TOOLCHAIN}" ABSOLUTE ) + # try to detect change + if( CMAKE_AR ) + string( LENGTH "${ANDROID_STANDALONE_TOOLCHAIN}" __length ) + string( SUBSTRING "${CMAKE_AR}" 0 ${__length} __androidStandaloneToolchainPreviousPath ) + if( NOT __androidStandaloneToolchainPreviousPath STREQUAL ANDROID_STANDALONE_TOOLCHAIN ) + message( FATAL_ERROR "It is not possible to change path to the Android standalone toolchain on subsequent run." ) + endif() + unset( __androidStandaloneToolchainPreviousPath ) + unset( __length ) + endif() + set( ANDROID_STANDALONE_TOOLCHAIN "${ANDROID_STANDALONE_TOOLCHAIN}" CACHE INTERNAL "Path of the Android standalone toolchain" FORCE ) + set( BUILD_WITH_STANDALONE_TOOLCHAIN True ) +else() + list(GET ANDROID_NDK_SEARCH_PATHS 0 ANDROID_NDK_SEARCH_PATH) + message( FATAL_ERROR "Could not find neither Android NDK nor Android standalone toolchain. + You should either set an environment variable: + export ANDROID_NDK=~/my-android-ndk + or + export ANDROID_STANDALONE_TOOLCHAIN=~/my-android-toolchain + or put the toolchain or NDK in the default path: + sudo ln -s ~/my-android-ndk ${ANDROID_NDK_SEARCH_PATH}/android-ndk + sudo ln -s ~/my-android-toolchain ${ANDROID_STANDALONE_TOOLCHAIN_SEARCH_PATH}" ) +endif() + +# android NDK layout +if( BUILD_WITH_ANDROID_NDK ) + if( NOT DEFINED ANDROID_NDK_LAYOUT ) + # try to automatically detect the layout + if( EXISTS "${ANDROID_NDK}/RELEASE.TXT") + set( ANDROID_NDK_LAYOUT "RELEASE" ) + elseif( EXISTS "${ANDROID_NDK}/../../linux-x86/toolchain/" ) + set( ANDROID_NDK_LAYOUT "LINARO" ) + elseif( EXISTS "${ANDROID_NDK}/../../gcc/" ) + set( ANDROID_NDK_LAYOUT "ANDROID" ) + endif() + endif() + set( ANDROID_NDK_LAYOUT "${ANDROID_NDK_LAYOUT}" CACHE STRING "The inner layout of NDK" ) + mark_as_advanced( ANDROID_NDK_LAYOUT ) + if( ANDROID_NDK_LAYOUT STREQUAL "LINARO" ) + set( ANDROID_NDK_HOST_SYSTEM_NAME ${ANDROID_NDK_HOST_SYSTEM_NAME2} ) # only 32-bit at the moment + set( ANDROID_NDK_TOOLCHAINS_PATH "${ANDROID_NDK}/../../${ANDROID_NDK_HOST_SYSTEM_NAME}/toolchain" ) + set( ANDROID_NDK_TOOLCHAINS_SUBPATH "" ) + set( ANDROID_NDK_TOOLCHAINS_SUBPATH2 "" ) + elseif( ANDROID_NDK_LAYOUT STREQUAL "ANDROID" ) + set( ANDROID_NDK_HOST_SYSTEM_NAME ${ANDROID_NDK_HOST_SYSTEM_NAME2} ) # only 32-bit at the moment + set( ANDROID_NDK_TOOLCHAINS_PATH "${ANDROID_NDK}/../../gcc/${ANDROID_NDK_HOST_SYSTEM_NAME}/arm" ) + set( ANDROID_NDK_TOOLCHAINS_SUBPATH "" ) + set( ANDROID_NDK_TOOLCHAINS_SUBPATH2 "" ) + else() # ANDROID_NDK_LAYOUT STREQUAL "RELEASE" + set( ANDROID_NDK_TOOLCHAINS_PATH "${ANDROID_NDK}/toolchains" ) + set( ANDROID_NDK_TOOLCHAINS_SUBPATH "/prebuilt/${ANDROID_NDK_HOST_SYSTEM_NAME}" ) + set( ANDROID_NDK_TOOLCHAINS_SUBPATH2 "/prebuilt/${ANDROID_NDK_HOST_SYSTEM_NAME2}" ) + endif() + get_filename_component( ANDROID_NDK_TOOLCHAINS_PATH "${ANDROID_NDK_TOOLCHAINS_PATH}" ABSOLUTE ) + + # try to detect change of NDK + if( CMAKE_AR ) + string( LENGTH "${ANDROID_NDK_TOOLCHAINS_PATH}" __length ) + string( SUBSTRING "${CMAKE_AR}" 0 ${__length} __androidNdkPreviousPath ) + if( NOT __androidNdkPreviousPath STREQUAL ANDROID_NDK_TOOLCHAINS_PATH ) + message( FATAL_ERROR "It is not possible to change the path to the NDK on subsequent CMake run. You must remove all generated files from your build folder first. + " ) + endif() + unset( __androidNdkPreviousPath ) + unset( __length ) + endif() +endif() + + +# get all the details about standalone toolchain +if( BUILD_WITH_STANDALONE_TOOLCHAIN ) + __DETECT_NATIVE_API_LEVEL( ANDROID_SUPPORTED_NATIVE_API_LEVELS "${ANDROID_STANDALONE_TOOLCHAIN}/sysroot/usr/include/android/api-level.h" ) + set( ANDROID_STANDALONE_TOOLCHAIN_API_LEVEL ${ANDROID_SUPPORTED_NATIVE_API_LEVELS} ) + set( __availableToolchains "standalone" ) + __DETECT_TOOLCHAIN_MACHINE_NAME( __availableToolchainMachines "${ANDROID_STANDALONE_TOOLCHAIN}" ) + if( NOT __availableToolchainMachines ) + message( FATAL_ERROR "Could not determine machine name of your toolchain. Probably your Android standalone toolchain is broken." ) + endif() + if( __availableToolchainMachines MATCHES x86_64 ) + set( __availableToolchainArchs "x86_64" ) + elseif( __availableToolchainMachines MATCHES i686 ) + set( __availableToolchainArchs "x86" ) + elseif( __availableToolchainMachines MATCHES aarch64 ) + set( __availableToolchainArchs "arm64" ) + elseif( __availableToolchainMachines MATCHES arm ) + set( __availableToolchainArchs "arm" ) + elseif( __availableToolchainMachines MATCHES mips64el ) + set( __availableToolchainArchs "mips64" ) + elseif( __availableToolchainMachines MATCHES mipsel ) + set( __availableToolchainArchs "mips" ) + endif() + execute_process( COMMAND "${ANDROID_STANDALONE_TOOLCHAIN}/bin/${__availableToolchainMachines}-gcc${TOOL_OS_SUFFIX}" -dumpversion + OUTPUT_VARIABLE __availableToolchainCompilerVersions OUTPUT_STRIP_TRAILING_WHITESPACE ) + string( REGEX MATCH "[0-9]+[.][0-9]+([.][0-9]+)?" __availableToolchainCompilerVersions "${__availableToolchainCompilerVersions}" ) + if( EXISTS "${ANDROID_STANDALONE_TOOLCHAIN}/bin/clang${TOOL_OS_SUFFIX}" ) + list( APPEND __availableToolchains "standalone-clang" ) + list( APPEND __availableToolchainMachines ${__availableToolchainMachines} ) + list( APPEND __availableToolchainArchs ${__availableToolchainArchs} ) + list( APPEND __availableToolchainCompilerVersions ${__availableToolchainCompilerVersions} ) + endif() +endif() + +macro( __GLOB_NDK_TOOLCHAINS __availableToolchainsVar __availableToolchainsLst __toolchain_subpath ) + foreach( __toolchain ${${__availableToolchainsLst}} ) + if( "${__toolchain}" MATCHES "-clang3[.][0-9]$" AND NOT EXISTS "${ANDROID_NDK_TOOLCHAINS_PATH}/${__toolchain}${__toolchain_subpath}" ) + SET( __toolchainVersionRegex "^TOOLCHAIN_VERSION[\t ]+:=[\t ]+(.*)$" ) + FILE( STRINGS "${ANDROID_NDK_TOOLCHAINS_PATH}/${__toolchain}/setup.mk" __toolchainVersionStr REGEX "${__toolchainVersionRegex}" ) + if( __toolchainVersionStr ) + string( REGEX REPLACE "${__toolchainVersionRegex}" "\\1" __toolchainVersionStr "${__toolchainVersionStr}" ) + string( REGEX REPLACE "-clang3[.][0-9]$" "-${__toolchainVersionStr}" __gcc_toolchain "${__toolchain}" ) + else() + string( REGEX REPLACE "-clang3[.][0-9]$" "-4.6" __gcc_toolchain "${__toolchain}" ) + endif() + unset( __toolchainVersionStr ) + unset( __toolchainVersionRegex ) + else() + set( __gcc_toolchain "${__toolchain}" ) + endif() + __DETECT_TOOLCHAIN_MACHINE_NAME( __machine "${ANDROID_NDK_TOOLCHAINS_PATH}/${__gcc_toolchain}${__toolchain_subpath}" ) + if( __machine ) + string( REGEX MATCH "[0-9]+[.][0-9]+([.][0-9x]+)?$" __version "${__gcc_toolchain}" ) + if( __machine MATCHES x86_64 ) + set( __arch "x86_64" ) + elseif( __machine MATCHES i686 ) + set( __arch "x86" ) + elseif( __machine MATCHES aarch64 ) + set( __arch "arm64" ) + elseif( __machine MATCHES arm ) + set( __arch "arm" ) + elseif( __machine MATCHES mips64el ) + set( __arch "mips64" ) + elseif( __machine MATCHES mipsel ) + set( __arch "mips" ) + else() + set( __arch "" ) + endif() + #message("machine: !${__machine}!\narch: !${__arch}!\nversion: !${__version}!\ntoolchain: !${__toolchain}!\n") + if (__arch) + list( APPEND __availableToolchainMachines "${__machine}" ) + list( APPEND __availableToolchainArchs "${__arch}" ) + list( APPEND __availableToolchainCompilerVersions "${__version}" ) + list( APPEND ${__availableToolchainsVar} "${__toolchain}" ) + endif() + endif() + unset( __gcc_toolchain ) + endforeach() +endmacro() + +# get all the details about NDK +if( BUILD_WITH_ANDROID_NDK ) + file( GLOB ANDROID_SUPPORTED_NATIVE_API_LEVELS RELATIVE "${ANDROID_NDK}/platforms" "${ANDROID_NDK}/platforms/android-*" ) + string( REPLACE "android-" "" ANDROID_SUPPORTED_NATIVE_API_LEVELS "${ANDROID_SUPPORTED_NATIVE_API_LEVELS}" ) + set( __availableToolchains "" ) + set( __availableToolchainMachines "" ) + set( __availableToolchainArchs "" ) + set( __availableToolchainCompilerVersions "" ) + if( ANDROID_TOOLCHAIN_NAME AND EXISTS "${ANDROID_NDK_TOOLCHAINS_PATH}/${ANDROID_TOOLCHAIN_NAME}/" ) + # do not go through all toolchains if we know the name + set( __availableToolchainsLst "${ANDROID_TOOLCHAIN_NAME}" ) + __GLOB_NDK_TOOLCHAINS( __availableToolchains __availableToolchainsLst "${ANDROID_NDK_TOOLCHAINS_SUBPATH}" ) + if( NOT __availableToolchains AND NOT ANDROID_NDK_TOOLCHAINS_SUBPATH STREQUAL ANDROID_NDK_TOOLCHAINS_SUBPATH2 ) + __GLOB_NDK_TOOLCHAINS( __availableToolchains __availableToolchainsLst "${ANDROID_NDK_TOOLCHAINS_SUBPATH2}" ) + if( __availableToolchains ) + set( ANDROID_NDK_TOOLCHAINS_SUBPATH ${ANDROID_NDK_TOOLCHAINS_SUBPATH2} ) + endif() + endif() + endif() + if( NOT __availableToolchains ) + file( GLOB __availableToolchainsLst RELATIVE "${ANDROID_NDK_TOOLCHAINS_PATH}" "${ANDROID_NDK_TOOLCHAINS_PATH}/*" ) + if( __availableToolchainsLst ) + list(SORT __availableToolchainsLst) # we need clang to go after gcc + endif() + __LIST_FILTER( __availableToolchainsLst "^[.]" ) + __LIST_FILTER( __availableToolchainsLst "llvm" ) + __LIST_FILTER( __availableToolchainsLst "renderscript" ) + __GLOB_NDK_TOOLCHAINS( __availableToolchains __availableToolchainsLst "${ANDROID_NDK_TOOLCHAINS_SUBPATH}" ) + if( NOT __availableToolchains AND NOT ANDROID_NDK_TOOLCHAINS_SUBPATH STREQUAL ANDROID_NDK_TOOLCHAINS_SUBPATH2 ) + __GLOB_NDK_TOOLCHAINS( __availableToolchains __availableToolchainsLst "${ANDROID_NDK_TOOLCHAINS_SUBPATH2}" ) + if( __availableToolchains ) + set( ANDROID_NDK_TOOLCHAINS_SUBPATH ${ANDROID_NDK_TOOLCHAINS_SUBPATH2} ) + endif() + endif() + endif() + if( NOT __availableToolchains ) + message( FATAL_ERROR "Could not find any working toolchain in the NDK. Probably your Android NDK is broken." ) + endif() +endif() + +# build list of available ABIs +set( ANDROID_SUPPORTED_ABIS "" ) +set( __uniqToolchainArchNames ${__availableToolchainArchs} ) +list( REMOVE_DUPLICATES __uniqToolchainArchNames ) +list( SORT __uniqToolchainArchNames ) +foreach( __arch ${__uniqToolchainArchNames} ) + list( APPEND ANDROID_SUPPORTED_ABIS ${ANDROID_SUPPORTED_ABIS_${__arch}} ) +endforeach() +unset( __uniqToolchainArchNames ) +if( NOT ANDROID_SUPPORTED_ABIS ) + message( FATAL_ERROR "No one of known Android ABIs is supported by this cmake toolchain." ) +endif() + +# choose target ABI +__INIT_VARIABLE( ANDROID_ABI VALUES ${ANDROID_SUPPORTED_ABIS} ) +# verify that target ABI is supported +list( FIND ANDROID_SUPPORTED_ABIS "${ANDROID_ABI}" __androidAbiIdx ) +if( __androidAbiIdx EQUAL -1 ) + string( REPLACE ";" "\", \"" PRINTABLE_ANDROID_SUPPORTED_ABIS "${ANDROID_SUPPORTED_ABIS}" ) + message( FATAL_ERROR "Specified ANDROID_ABI = \"${ANDROID_ABI}\" is not supported by this cmake toolchain or your NDK/toolchain. + Supported values are: \"${PRINTABLE_ANDROID_SUPPORTED_ABIS}\" + " ) +endif() +unset( __androidAbiIdx ) + +# set target ABI options +if( ANDROID_ABI STREQUAL "x86" ) + set( X86 true ) + set( ANDROID_NDK_ABI_NAME "x86" ) + set( ANDROID_ARCH_NAME "x86" ) + set( ANDROID_LLVM_TRIPLE "i686-none-linux-android" ) + set( CMAKE_SYSTEM_PROCESSOR "i686" ) +elseif( ANDROID_ABI STREQUAL "x86_64" ) + set( X86 true ) + set( X86_64 true ) + set( ANDROID_NDK_ABI_NAME "x86_64" ) + set( ANDROID_ARCH_NAME "x86_64" ) + set( CMAKE_SYSTEM_PROCESSOR "x86_64" ) + set( ANDROID_LLVM_TRIPLE "x86_64-none-linux-android" ) +elseif( ANDROID_ABI STREQUAL "mips64" ) + set( MIPS64 true ) + set( ANDROID_NDK_ABI_NAME "mips64" ) + set( ANDROID_ARCH_NAME "mips64" ) + set( ANDROID_LLVM_TRIPLE "mips64el-none-linux-android" ) + set( CMAKE_SYSTEM_PROCESSOR "mips64" ) +elseif( ANDROID_ABI STREQUAL "mips" ) + set( MIPS true ) + set( ANDROID_NDK_ABI_NAME "mips" ) + set( ANDROID_ARCH_NAME "mips" ) + set( ANDROID_LLVM_TRIPLE "mipsel-none-linux-android" ) + set( CMAKE_SYSTEM_PROCESSOR "mips" ) +elseif( ANDROID_ABI STREQUAL "arm64-v8a" ) + set( ARM64_V8A true ) + set( ANDROID_NDK_ABI_NAME "arm64-v8a" ) + set( ANDROID_ARCH_NAME "arm64" ) + set( ANDROID_LLVM_TRIPLE "aarch64-none-linux-android" ) + set( CMAKE_SYSTEM_PROCESSOR "aarch64" ) + set( VFPV3 true ) + set( NEON true ) +elseif( ANDROID_ABI STREQUAL "armeabi" ) + set( ARMEABI true ) + set( ANDROID_NDK_ABI_NAME "armeabi" ) + set( ANDROID_ARCH_NAME "arm" ) + set( ANDROID_LLVM_TRIPLE "armv5te-none-linux-androideabi" ) + set( CMAKE_SYSTEM_PROCESSOR "armv5te" ) +elseif( ANDROID_ABI STREQUAL "armeabi-v6 with VFP" ) + set( ARMEABI_V6 true ) + set( ANDROID_NDK_ABI_NAME "armeabi" ) + set( ANDROID_ARCH_NAME "arm" ) + set( ANDROID_LLVM_TRIPLE "armv5te-none-linux-androideabi" ) + set( CMAKE_SYSTEM_PROCESSOR "armv6" ) + # need always fallback to older platform + set( ARMEABI true ) +elseif( ANDROID_ABI STREQUAL "armeabi-v7a") + set( ARMEABI_V7A true ) + set( ANDROID_NDK_ABI_NAME "armeabi-v7a" ) + set( ANDROID_ARCH_NAME "arm" ) + set( ANDROID_LLVM_TRIPLE "armv7-none-linux-androideabi" ) + set( CMAKE_SYSTEM_PROCESSOR "armv7-a" ) +elseif( ANDROID_ABI STREQUAL "armeabi-v7a with VFPV3" ) + set( ARMEABI_V7A true ) + set( ANDROID_NDK_ABI_NAME "armeabi-v7a" ) + set( ANDROID_ARCH_NAME "arm" ) + set( ANDROID_LLVM_TRIPLE "armv7-none-linux-androideabi" ) + set( CMAKE_SYSTEM_PROCESSOR "armv7-a" ) + set( VFPV3 true ) +elseif( ANDROID_ABI STREQUAL "armeabi-v7a with NEON" ) + set( ARMEABI_V7A true ) + set( ANDROID_NDK_ABI_NAME "armeabi-v7a" ) + set( ANDROID_ARCH_NAME "arm" ) + set( ANDROID_LLVM_TRIPLE "armv7-none-linux-androideabi" ) + set( CMAKE_SYSTEM_PROCESSOR "armv7-a" ) + set( VFPV3 true ) + set( NEON true ) +elseif( ANDROID_ABI STREQUAL "armeabi-v7a-hard with NEON" ) + set( ARMEABI_V7A_HARD true ) + set( ANDROID_NDK_ABI_NAME "armeabi-v7a-hard" ) + set( ANDROID_ARCH_NAME "arm" ) + set( ANDROID_LLVM_TRIPLE "armv7-none-linux-androideabi" ) + set( CMAKE_SYSTEM_PROCESSOR "armv7-a" ) + set( VFPV3 true ) + set( NEON true ) +else() + message( SEND_ERROR "Unknown ANDROID_ABI=\"${ANDROID_ABI}\" is specified." ) +endif() + +if( CMAKE_BINARY_DIR AND EXISTS "${CMAKE_BINARY_DIR}${CMAKE_FILES_DIRECTORY}/CMakeSystem.cmake" ) + # really dirty hack + # it is not possible to change CMAKE_SYSTEM_PROCESSOR after the first run... + file( APPEND "${CMAKE_BINARY_DIR}${CMAKE_FILES_DIRECTORY}/CMakeSystem.cmake" "SET(CMAKE_SYSTEM_PROCESSOR \"${CMAKE_SYSTEM_PROCESSOR}\")\n" ) +endif() + +if( ANDROID_ARCH_NAME STREQUAL "arm" AND NOT ARMEABI_V6 ) + __INIT_VARIABLE( ANDROID_FORCE_ARM_BUILD VALUES OFF ) + set( ANDROID_FORCE_ARM_BUILD ${ANDROID_FORCE_ARM_BUILD} CACHE BOOL "Use 32-bit ARM instructions instead of Thumb-1" FORCE ) + mark_as_advanced( ANDROID_FORCE_ARM_BUILD ) +else() + unset( ANDROID_FORCE_ARM_BUILD CACHE ) +endif() + +# choose toolchain +if( ANDROID_TOOLCHAIN_NAME ) + list( FIND __availableToolchains "${ANDROID_TOOLCHAIN_NAME}" __toolchainIdx ) + if( __toolchainIdx EQUAL -1 ) + list( SORT __availableToolchains ) + string( REPLACE ";" "\n * " toolchains_list "${__availableToolchains}" ) + set( toolchains_list " * ${toolchains_list}") + message( FATAL_ERROR "Specified toolchain \"${ANDROID_TOOLCHAIN_NAME}\" is missing in your NDK or broken. Please verify that your NDK is working or select another compiler toolchain. +To configure the toolchain set CMake variable ANDROID_TOOLCHAIN_NAME to one of the following values:\n${toolchains_list}\n" ) + endif() + list( GET __availableToolchainArchs ${__toolchainIdx} __toolchainArch ) + if( NOT __toolchainArch STREQUAL ANDROID_ARCH_NAME ) + message( SEND_ERROR "Selected toolchain \"${ANDROID_TOOLCHAIN_NAME}\" is not able to compile binaries for the \"${ANDROID_ARCH_NAME}\" platform." ) + endif() +else() + set( __toolchainIdx -1 ) + set( __applicableToolchains "" ) + set( __toolchainMaxVersion "0.0.0" ) + list( LENGTH __availableToolchains __availableToolchainsCount ) + math( EXPR __availableToolchainsCount "${__availableToolchainsCount}-1" ) + foreach( __idx RANGE ${__availableToolchainsCount} ) + list( GET __availableToolchainArchs ${__idx} __toolchainArch ) + if( __toolchainArch STREQUAL ANDROID_ARCH_NAME ) + list( GET __availableToolchainCompilerVersions ${__idx} __toolchainVersion ) + string( REPLACE "x" "99" __toolchainVersion "${__toolchainVersion}") + if( __toolchainVersion VERSION_GREATER __toolchainMaxVersion ) + set( __toolchainMaxVersion "${__toolchainVersion}" ) + set( __toolchainIdx ${__idx} ) + endif() + endif() + endforeach() + unset( __availableToolchainsCount ) + unset( __toolchainMaxVersion ) + unset( __toolchainVersion ) +endif() +unset( __toolchainArch ) +if( __toolchainIdx EQUAL -1 ) + message( FATAL_ERROR "No one of available compiler toolchains is able to compile for ${ANDROID_ARCH_NAME} platform." ) +endif() +list( GET __availableToolchains ${__toolchainIdx} ANDROID_TOOLCHAIN_NAME ) +list( GET __availableToolchainMachines ${__toolchainIdx} ANDROID_TOOLCHAIN_MACHINE_NAME ) +list( GET __availableToolchainCompilerVersions ${__toolchainIdx} ANDROID_COMPILER_VERSION ) + +unset( __toolchainIdx ) +unset( __availableToolchains ) +unset( __availableToolchainMachines ) +unset( __availableToolchainArchs ) +unset( __availableToolchainCompilerVersions ) + +# choose native API level +__INIT_VARIABLE( ANDROID_NATIVE_API_LEVEL ENV_ANDROID_NATIVE_API_LEVEL ANDROID_API_LEVEL ENV_ANDROID_API_LEVEL ANDROID_STANDALONE_TOOLCHAIN_API_LEVEL ANDROID_DEFAULT_NDK_API_LEVEL_${ANDROID_ARCH_NAME} ANDROID_DEFAULT_NDK_API_LEVEL ) +string( REPLACE "android-" "" ANDROID_NATIVE_API_LEVEL "${ANDROID_NATIVE_API_LEVEL}" ) +string( STRIP "${ANDROID_NATIVE_API_LEVEL}" ANDROID_NATIVE_API_LEVEL ) +# adjust API level +set( __real_api_level ${ANDROID_DEFAULT_NDK_API_LEVEL_${ANDROID_ARCH_NAME}} ) +foreach( __level ${ANDROID_SUPPORTED_NATIVE_API_LEVELS} ) + if( (__level LESS ANDROID_NATIVE_API_LEVEL OR __level STREQUAL ANDROID_NATIVE_API_LEVEL) AND NOT __level LESS __real_api_level ) + set( __real_api_level ${__level} ) + endif() +endforeach() +if( __real_api_level AND NOT ANDROID_NATIVE_API_LEVEL STREQUAL __real_api_level ) + message( STATUS "Adjusting Android API level 'android-${ANDROID_NATIVE_API_LEVEL}' to 'android-${__real_api_level}'") + set( ANDROID_NATIVE_API_LEVEL ${__real_api_level} ) +endif() +unset(__real_api_level) +# validate +list( FIND ANDROID_SUPPORTED_NATIVE_API_LEVELS "${ANDROID_NATIVE_API_LEVEL}" __levelIdx ) +if( __levelIdx EQUAL -1 ) + message( SEND_ERROR "Specified Android native API level 'android-${ANDROID_NATIVE_API_LEVEL}' is not supported by your NDK/toolchain.\nSupported values of ANDROID_NATIVE_API_LEVEL: ${ANDROID_SUPPORTED_NATIVE_API_LEVELS}" ) +else() + if( BUILD_WITH_ANDROID_NDK ) + if(EXISTS "${ANDROID_NDK}/platforms/android-${ANDROID_NATIVE_API_LEVEL}/arch-${ANDROID_ARCH_NAME}/usr/include/android/api-level.h") + __DETECT_NATIVE_API_LEVEL( __realApiLevel "${ANDROID_NDK}/platforms/android-${ANDROID_NATIVE_API_LEVEL}/arch-${ANDROID_ARCH_NAME}/usr/include/android/api-level.h" ) + else() + __DETECT_NATIVE_API_LEVEL( __realApiLevel "${ANDROID_NDK}/sysroot/usr/include/android/api-level.h") + endif() + + if( NOT __realApiLevel EQUAL ANDROID_NATIVE_API_LEVEL AND NOT __realApiLevel GREATER 9000 ) + message( SEND_ERROR "Specified Android API level (${ANDROID_NATIVE_API_LEVEL}) does not match to the level found (${__realApiLevel}). Probably your copy of NDK is broken." ) + endif() + unset( __realApiLevel ) + endif() + set( ANDROID_NATIVE_API_LEVEL "${ANDROID_NATIVE_API_LEVEL}" CACHE STRING "Android API level for native code" FORCE ) + set( CMAKE_ANDROID_API ${ANDROID_NATIVE_API_LEVEL} ) + if( CMAKE_VERSION VERSION_GREATER "2.8" ) + list( SORT ANDROID_SUPPORTED_NATIVE_API_LEVELS ) + set_property( CACHE ANDROID_NATIVE_API_LEVEL PROPERTY STRINGS ${ANDROID_SUPPORTED_NATIVE_API_LEVELS} ) + endif() +endif() +unset( __levelIdx ) + + +# remember target ABI +set( ANDROID_ABI "${ANDROID_ABI}" CACHE STRING "The target ABI for Android. If arm, then armeabi-v7a is recommended for hardware floating point." FORCE ) +if( CMAKE_VERSION VERSION_GREATER "2.8" ) + list( SORT ANDROID_SUPPORTED_ABIS_${ANDROID_ARCH_NAME} ) + set_property( CACHE ANDROID_ABI PROPERTY STRINGS ${ANDROID_SUPPORTED_ABIS_${ANDROID_ARCH_NAME}} ) +endif() + + +# runtime choice (STL, rtti, exceptions) +if( NOT ANDROID_STL ) + set( ANDROID_STL gnustl_static ) +endif() +set( ANDROID_STL "${ANDROID_STL}" CACHE STRING "C++ runtime" ) +set( ANDROID_STL_FORCE_FEATURES ON CACHE BOOL "automatically configure rtti and exceptions support based on C++ runtime" ) +mark_as_advanced( ANDROID_STL ANDROID_STL_FORCE_FEATURES ) + +if( BUILD_WITH_ANDROID_NDK ) + if( NOT "${ANDROID_STL}" MATCHES "^(none|system|system_re|gabi\\+\\+_static|gabi\\+\\+_shared|stlport_static|stlport_shared|gnustl_static|gnustl_shared|c\\+\\+_static|c\\+\\+_shared)$") + message( FATAL_ERROR "ANDROID_STL is set to invalid value \"${ANDROID_STL}\". +The possible values are: + none -> Do not configure the runtime. + system -> Use the default minimal system C++ runtime library. + system_re -> Same as system but with rtti and exceptions. + gabi++_static -> Use the GAbi++ runtime as a static library. + gabi++_shared -> Use the GAbi++ runtime as a shared library. + stlport_static -> Use the STLport runtime as a static library. + stlport_shared -> Use the STLport runtime as a shared library. + gnustl_static -> (default) Use the GNU STL as a static library. + gnustl_shared -> Use the GNU STL as a shared library. + c++_shared -> Use the LLVM libc++ runtime as a shared library. + c++_static -> Use the LLVM libc++ runtime as a static library. +" ) + endif() +elseif( BUILD_WITH_STANDALONE_TOOLCHAIN ) + if( NOT "${ANDROID_STL}" MATCHES "^(none|gnustl_static|gnustl_shared|c\\+\\+_static|c\\+\\+_shared)$") + message( FATAL_ERROR "ANDROID_STL is set to invalid value \"${ANDROID_STL}\". +The possible values are: + none -> Do not configure the runtime. + gnustl_static -> (default) Use the GNU STL as a static library. + gnustl_shared -> Use the GNU STL as a shared library. + c++_shared -> Use the LLVM libc++ runtime as a shared library. + c++_static -> Use the LLVM libc++ runtime as a static library. +" ) + endif() +endif() + +unset( ANDROID_RTTI ) +unset( ANDROID_EXCEPTIONS ) +unset( ANDROID_STL_INCLUDE_DIRS ) +unset( __libstl ) +unset( __libsupcxx ) + +if( NOT _CMAKE_IN_TRY_COMPILE AND ANDROID_NDK_RELEASE STREQUAL "r7b" AND ARMEABI_V7A AND NOT VFPV3 AND ANDROID_STL MATCHES "gnustl" ) + message( WARNING "The GNU STL armeabi-v7a binaries from NDK r7b can crash non-NEON devices. The files provided with NDK r7b were not configured properly, resulting in crashes on Tegra2-based devices and others when trying to use certain floating-point functions (e.g., cosf, sinf, expf). +You are strongly recommended to switch to another NDK release. +" ) +endif() + +if( NOT _CMAKE_IN_TRY_COMPILE AND X86 AND ANDROID_STL MATCHES "gnustl" AND ANDROID_NDK_RELEASE STREQUAL "r6" ) + message( WARNING "The x86 system header file from NDK r6 has incorrect definition for ptrdiff_t. You are recommended to upgrade to a newer NDK release or manually patch the header: +See https://android.googlesource.com/platform/development.git f907f4f9d4e56ccc8093df6fee54454b8bcab6c2 + diff --git a/ndk/platforms/android-9/arch-x86/include/machine/_types.h b/ndk/platforms/android-9/arch-x86/include/machine/_types.h + index 5e28c64..65892a1 100644 + --- a/ndk/platforms/android-9/arch-x86/include/machine/_types.h + +++ b/ndk/platforms/android-9/arch-x86/include/machine/_types.h + @@ -51,7 +51,11 @@ typedef long int ssize_t; + #endif + #ifndef _PTRDIFF_T + #define _PTRDIFF_T + -typedef long ptrdiff_t; + +# ifdef __ANDROID__ + + typedef int ptrdiff_t; + +# else + + typedef long ptrdiff_t; + +# endif + #endif +" ) +endif() + + +# setup paths and STL for standalone toolchain +if( BUILD_WITH_STANDALONE_TOOLCHAIN ) + set( ANDROID_TOOLCHAIN_ROOT "${ANDROID_STANDALONE_TOOLCHAIN}" ) + set( ANDROID_CLANG_TOOLCHAIN_ROOT "${ANDROID_STANDALONE_TOOLCHAIN}" ) + set( ANDROID_SYSROOT "${ANDROID_STANDALONE_TOOLCHAIN}/sysroot" ) + set( ANDROID_SYSROOT_INCLUDE "${ANDROID_STANDALONE_TOOLCHAIN}/sysroot/usr/include" ) + + if( NOT ANDROID_STL STREQUAL "none" ) + set( ANDROID_STL_INCLUDE_DIRS "${ANDROID_STANDALONE_TOOLCHAIN}/include/c++/${ANDROID_COMPILER_VERSION}" ) + if( NOT EXISTS "${ANDROID_STL_INCLUDE_DIRS}" ) + # old location ( pre r8c ) + set( ANDROID_STL_INCLUDE_DIRS "${ANDROID_STANDALONE_TOOLCHAIN}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/include/c++/${ANDROID_COMPILER_VERSION}" ) + endif() + if( (ARMEABI_V7A OR ARMEABI_V7A_HARD) AND EXISTS "${ANDROID_STL_INCLUDE_DIRS}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/${CMAKE_SYSTEM_PROCESSOR}/bits" ) + list( APPEND ANDROID_STL_INCLUDE_DIRS "${ANDROID_STL_INCLUDE_DIRS}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/${CMAKE_SYSTEM_PROCESSOR}" ) + elseif( ARMEABI AND NOT ANDROID_FORCE_ARM_BUILD AND EXISTS "${ANDROID_STL_INCLUDE_DIRS}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/thumb/bits" ) + list( APPEND ANDROID_STL_INCLUDE_DIRS "${ANDROID_STL_INCLUDE_DIRS}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/thumb" ) + else() + list( APPEND ANDROID_STL_INCLUDE_DIRS "${ANDROID_STL_INCLUDE_DIRS}/${ANDROID_TOOLCHAIN_MACHINE_NAME}" ) + endif() + # always search static GNU STL to get the location of libsupc++.a + if( (ARMEABI_V7A OR ARMEABI_V7A_HARD) AND NOT ANDROID_FORCE_ARM_BUILD AND EXISTS "${ANDROID_STANDALONE_TOOLCHAIN}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/lib/${CMAKE_SYSTEM_PROCESSOR}/thumb/libstdc++.a" ) + set( __libstl "${ANDROID_STANDALONE_TOOLCHAIN}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/lib/${CMAKE_SYSTEM_PROCESSOR}/thumb" ) + elseif( (ARMEABI_V7A OR ARMEABI_V7A_HARD) AND EXISTS "${ANDROID_STANDALONE_TOOLCHAIN}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/lib/${CMAKE_SYSTEM_PROCESSOR}/libstdc++.a" ) + set( __libstl "${ANDROID_STANDALONE_TOOLCHAIN}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/lib/${CMAKE_SYSTEM_PROCESSOR}" ) + elseif( ARMEABI AND NOT ANDROID_FORCE_ARM_BUILD AND EXISTS "${ANDROID_STANDALONE_TOOLCHAIN}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/lib/thumb/libstdc++.a" ) + set( __libstl "${ANDROID_STANDALONE_TOOLCHAIN}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/lib/thumb" ) + elseif( EXISTS "${ANDROID_STANDALONE_TOOLCHAIN}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/lib/libstdc++.a" ) + set( __libstl "${ANDROID_STANDALONE_TOOLCHAIN}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/lib" ) + endif() + if( __libstl ) + set( __libsupcxx "${__libstl}/libsupc++.a" ) + set( __libstl "${__libstl}/libstdc++.a" ) + endif() + if( NOT EXISTS "${__libsupcxx}" ) + message( FATAL_ERROR "The required libstdsupc++.a is missing in your standalone toolchain. + Usually it happens because of bug in make-standalone-toolchain.sh script from NDK r7, r7b and r7c. + You need to either upgrade to newer NDK or manually copy + $ANDROID_NDK/sources/cxx-stl/gnu-libstdc++/libs/${ANDROID_NDK_ABI_NAME}/libsupc++.a + to + ${__libsupcxx} + " ) + endif() + if( ANDROID_STL STREQUAL "gnustl_shared" ) + if( (ARMEABI_V7A OR ARMEABI_V7A_HARD) AND EXISTS "${ANDROID_STANDALONE_TOOLCHAIN}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/lib/${CMAKE_SYSTEM_PROCESSOR}/libgnustl_shared.so" ) + set( __libstl "${ANDROID_STANDALONE_TOOLCHAIN}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/lib/${CMAKE_SYSTEM_PROCESSOR}/libgnustl_shared.so" ) + elseif( ARMEABI AND NOT ANDROID_FORCE_ARM_BUILD AND EXISTS "${ANDROID_STANDALONE_TOOLCHAIN}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/lib/thumb/libgnustl_shared.so" ) + set( __libstl "${ANDROID_STANDALONE_TOOLCHAIN}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/lib/thumb/libgnustl_shared.so" ) + elseif( EXISTS "${ANDROID_STANDALONE_TOOLCHAIN}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/lib/libgnustl_shared.so" ) + set( __libstl "${ANDROID_STANDALONE_TOOLCHAIN}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/lib/libgnustl_shared.so" ) + endif() + endif() + endif() +endif() + +# clang +if( "${ANDROID_TOOLCHAIN_NAME}" STREQUAL "standalone-clang" ) + set( ANDROID_COMPILER_IS_CLANG 1 ) + execute_process( COMMAND "${ANDROID_CLANG_TOOLCHAIN_ROOT}/bin/clang${TOOL_OS_SUFFIX}" --version OUTPUT_VARIABLE ANDROID_CLANG_VERSION OUTPUT_STRIP_TRAILING_WHITESPACE ) + string( REGEX MATCH "[0-9]+[.][0-9]+" ANDROID_CLANG_VERSION "${ANDROID_CLANG_VERSION}") +elseif( "${ANDROID_TOOLCHAIN_NAME}" MATCHES "-clang3[.][0-9]?$" ) + string( REGEX MATCH "3[.][0-9]$" ANDROID_CLANG_VERSION "${ANDROID_TOOLCHAIN_NAME}") + string( REGEX REPLACE "-clang${ANDROID_CLANG_VERSION}$" "-${ANDROID_COMPILER_VERSION}" ANDROID_GCC_TOOLCHAIN_NAME "${ANDROID_TOOLCHAIN_NAME}" ) + if( NOT EXISTS "${ANDROID_NDK_TOOLCHAINS_PATH}/llvm-${ANDROID_CLANG_VERSION}${ANDROID_NDK_TOOLCHAINS_SUBPATH}/bin/clang${TOOL_OS_SUFFIX}" ) + message( FATAL_ERROR "Could not find the Clang compiler driver" ) + endif() + set( ANDROID_COMPILER_IS_CLANG 1 ) + set( ANDROID_CLANG_TOOLCHAIN_ROOT "${ANDROID_NDK_TOOLCHAINS_PATH}/llvm-${ANDROID_CLANG_VERSION}${ANDROID_NDK_TOOLCHAINS_SUBPATH}" ) +else() + set( ANDROID_GCC_TOOLCHAIN_NAME "${ANDROID_TOOLCHAIN_NAME}" ) + unset( ANDROID_COMPILER_IS_CLANG CACHE ) +endif() + +string( REPLACE "." "" _clang_name "clang${ANDROID_CLANG_VERSION}" ) +if( NOT EXISTS "${ANDROID_CLANG_TOOLCHAIN_ROOT}/bin/${_clang_name}${TOOL_OS_SUFFIX}" ) + set( _clang_name "clang" ) +endif() + + +# setup paths and STL for NDK +if( BUILD_WITH_ANDROID_NDK ) + set( ANDROID_TOOLCHAIN_ROOT "${ANDROID_NDK_TOOLCHAINS_PATH}/${ANDROID_GCC_TOOLCHAIN_NAME}${ANDROID_NDK_TOOLCHAINS_SUBPATH}" ) + set( ANDROID_SYSROOT "${ANDROID_NDK}/platforms/android-${ANDROID_NATIVE_API_LEVEL}/arch-${ANDROID_ARCH_NAME}" ) + if( EXISTS "${ANDROID_SYSROOT}/usr/include" ) + set( ANDROID_SYSROOT_INCLUDE "${ANDROID_SYSROOT}/usr/include" ) + else() + set( ANDROID_SYSROOT_INCLUDE "${ANDROID_NDK}/sysroot/usr/include" "${ANDROID_NDK}/sysroot/usr/include/${ANDROID_TOOLCHAIN_MACHINE_NAME}" ) + endif() + + if( ANDROID_STL STREQUAL "none" ) + # do nothing + elseif( ANDROID_STL STREQUAL "system" ) + set( ANDROID_RTTI OFF ) + set( ANDROID_EXCEPTIONS OFF ) + set( ANDROID_STL_INCLUDE_DIRS "${ANDROID_NDK}/sources/cxx-stl/system/include" ) + elseif( ANDROID_STL STREQUAL "system_re" ) + set( ANDROID_RTTI ON ) + set( ANDROID_EXCEPTIONS ON ) + set( ANDROID_STL_INCLUDE_DIRS "${ANDROID_NDK}/sources/cxx-stl/system/include" ) + elseif( ANDROID_STL MATCHES "gabi" ) + if( ANDROID_NDK_RELEASE_NUM LESS 7000 ) # before r7 + message( FATAL_ERROR "gabi++ is not available in your NDK. You have to upgrade to NDK r7 or newer to use gabi++.") + endif() + set( ANDROID_RTTI ON ) + set( ANDROID_EXCEPTIONS OFF ) + set( ANDROID_STL_INCLUDE_DIRS "${ANDROID_NDK}/sources/cxx-stl/gabi++/include" ) + set( __libstl "${ANDROID_NDK}/sources/cxx-stl/gabi++/libs/${ANDROID_NDK_ABI_NAME}/libgabi++_static.a" ) + elseif( ANDROID_STL MATCHES "stlport" ) + if( NOT ANDROID_NDK_RELEASE_NUM LESS 8004 ) # before r8d + set( ANDROID_EXCEPTIONS ON ) + else() + set( ANDROID_EXCEPTIONS OFF ) + endif() + if( ANDROID_NDK_RELEASE_NUM LESS 7000 ) # before r7 + set( ANDROID_RTTI OFF ) + else() + set( ANDROID_RTTI ON ) + endif() + set( ANDROID_STL_INCLUDE_DIRS "${ANDROID_NDK}/sources/cxx-stl/stlport/stlport" ) + set( __libstl "${ANDROID_NDK}/sources/cxx-stl/stlport/libs/${ANDROID_NDK_ABI_NAME}/libstlport_static.a" ) + elseif( ANDROID_STL MATCHES "gnustl" ) + set( ANDROID_EXCEPTIONS ON ) + set( ANDROID_RTTI ON ) + if( EXISTS "${ANDROID_NDK}/sources/cxx-stl/gnu-libstdc++/${ANDROID_COMPILER_VERSION}" ) + if( ARMEABI_V7A AND ANDROID_COMPILER_VERSION VERSION_EQUAL "4.7" AND ANDROID_NDK_RELEASE STREQUAL "r8d" ) + # gnustl binary for 4.7 compiler is buggy :( + # TODO: look for right fix + set( __libstl "${ANDROID_NDK}/sources/cxx-stl/gnu-libstdc++/4.6" ) + else() + set( __libstl "${ANDROID_NDK}/sources/cxx-stl/gnu-libstdc++/${ANDROID_COMPILER_VERSION}" ) + endif() + else() + set( __libstl "${ANDROID_NDK}/sources/cxx-stl/gnu-libstdc++" ) + endif() + set( ANDROID_STL_INCLUDE_DIRS "${__libstl}/include" "${__libstl}/libs/${ANDROID_NDK_ABI_NAME}/include" "${__libstl}/include/backward" ) + if( EXISTS "${__libstl}/libs/${ANDROID_NDK_ABI_NAME}/libgnustl_static.a" ) + set( __libstl "${__libstl}/libs/${ANDROID_NDK_ABI_NAME}/libgnustl_static.a" ) + else() + set( __libstl "${__libstl}/libs/${ANDROID_NDK_ABI_NAME}/libstdc++.a" ) + endif() + elseif( ANDROID_STL MATCHES "c\\+\\+" ) + set( ANDROID_EXCEPTIONS ON ) + set( ANDROID_RTTI ON ) + set( __libstl "${ANDROID_NDK}/sources/cxx-stl/llvm-libc++" ) + set( __libstl "${__libstl}/libs/${ANDROID_NDK_ABI_NAME}/libc++_static.a" ) + set( ANDROID_STL_INCLUDE_DIRS "${ANDROID_NDK}/sources/android/support/include" "${ANDROID_NDK}/sources/cxx-stl/llvm-libc++/libcxx/include" "${ANDROID_NDK}/sources/cxx-stl/llvm-libc++abi/libcxxabi/include" ) + else() + message( FATAL_ERROR "Unknown runtime: ${ANDROID_STL}" ) + endif() + + # find libsupc++.a - rtti & exceptions + if( ANDROID_STL STREQUAL "system_re" OR ANDROID_STL MATCHES "gnustl" ) + set( __libsupcxx "${ANDROID_NDK}/sources/cxx-stl/gnu-libstdc++/${ANDROID_COMPILER_VERSION}/libs/${ANDROID_NDK_ABI_NAME}/libsupc++.a" ) # r8b or newer + if( NOT EXISTS "${__libsupcxx}" ) + set( __libsupcxx "${ANDROID_NDK}/sources/cxx-stl/gnu-libstdc++/libs/${ANDROID_NDK_ABI_NAME}/libsupc++.a" ) # r7-r8 + endif() + if( NOT EXISTS "${__libsupcxx}" ) # before r7 + if( ARMEABI_V7A ) + if( ANDROID_FORCE_ARM_BUILD ) + set( __libsupcxx "${ANDROID_TOOLCHAIN_ROOT}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/lib/${CMAKE_SYSTEM_PROCESSOR}/libsupc++.a" ) + else() + set( __libsupcxx "${ANDROID_TOOLCHAIN_ROOT}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/lib/${CMAKE_SYSTEM_PROCESSOR}/thumb/libsupc++.a" ) + endif() + elseif( ARMEABI AND NOT ANDROID_FORCE_ARM_BUILD ) + set( __libsupcxx "${ANDROID_TOOLCHAIN_ROOT}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/lib/thumb/libsupc++.a" ) + else() + set( __libsupcxx "${ANDROID_TOOLCHAIN_ROOT}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/lib/libsupc++.a" ) + endif() + endif() + if( NOT EXISTS "${__libsupcxx}") + message( ERROR "Could not find libsupc++.a for a chosen platform. Either your NDK is not supported or is broken.") + endif() + endif() +endif() + + +# case of shared STL linkage +if( ANDROID_STL MATCHES "shared" AND DEFINED __libstl ) + string( REPLACE "_static.a" "_shared.so" __libstl "${__libstl}" ) + if( NOT EXISTS "${__libstl}" ) + message( FATAL_ERROR "Unable to find shared library ${__libstl}" ) + endif() +endif() + + +# ccache support +__INIT_VARIABLE( _ndk_ccache NDK_CCACHE ENV_NDK_CCACHE ) +if( _ndk_ccache ) + if( DEFINED NDK_CCACHE AND NOT EXISTS NDK_CCACHE ) + unset( NDK_CCACHE CACHE ) + endif() + find_program( NDK_CCACHE "${_ndk_ccache}" DOC "The path to ccache binary") +else() + unset( NDK_CCACHE CACHE ) +endif() +unset( _ndk_ccache ) + + +# setup the cross-compiler +if( NOT CMAKE_C_COMPILER ) + if( NDK_CCACHE AND NOT ANDROID_SYSROOT MATCHES "[ ;\"]" ) + set( CMAKE_C_COMPILER "${NDK_CCACHE}" CACHE PATH "ccache as C compiler" ) + set( CMAKE_CXX_COMPILER "${NDK_CCACHE}" CACHE PATH "ccache as C++ compiler" ) + if( ANDROID_COMPILER_IS_CLANG ) + set( CMAKE_C_COMPILER_ARG1 "${ANDROID_CLANG_TOOLCHAIN_ROOT}/bin/${_clang_name}${TOOL_OS_SUFFIX}" CACHE PATH "C compiler") + set( CMAKE_CXX_COMPILER_ARG1 "${ANDROID_CLANG_TOOLCHAIN_ROOT}/bin/${_clang_name}++${TOOL_OS_SUFFIX}" CACHE PATH "C++ compiler") + else() + set( CMAKE_C_COMPILER_ARG1 "${ANDROID_TOOLCHAIN_ROOT}/bin/${ANDROID_TOOLCHAIN_MACHINE_NAME}-gcc${TOOL_OS_SUFFIX}" CACHE PATH "C compiler") + set( CMAKE_CXX_COMPILER_ARG1 "${ANDROID_TOOLCHAIN_ROOT}/bin/${ANDROID_TOOLCHAIN_MACHINE_NAME}-g++${TOOL_OS_SUFFIX}" CACHE PATH "C++ compiler") + endif() + else() + if( ANDROID_COMPILER_IS_CLANG ) + set( CMAKE_C_COMPILER "${ANDROID_CLANG_TOOLCHAIN_ROOT}/bin/${_clang_name}${TOOL_OS_SUFFIX}" CACHE PATH "C compiler") + set( CMAKE_CXX_COMPILER "${ANDROID_CLANG_TOOLCHAIN_ROOT}/bin/${_clang_name}++${TOOL_OS_SUFFIX}" CACHE PATH "C++ compiler") + else() + set( CMAKE_C_COMPILER "${ANDROID_TOOLCHAIN_ROOT}/bin/${ANDROID_TOOLCHAIN_MACHINE_NAME}-gcc${TOOL_OS_SUFFIX}" CACHE PATH "C compiler" ) + set( CMAKE_CXX_COMPILER "${ANDROID_TOOLCHAIN_ROOT}/bin/${ANDROID_TOOLCHAIN_MACHINE_NAME}-g++${TOOL_OS_SUFFIX}" CACHE PATH "C++ compiler" ) + endif() + endif() + set( CMAKE_ASM_COMPILER "${ANDROID_TOOLCHAIN_ROOT}/bin/${ANDROID_TOOLCHAIN_MACHINE_NAME}-gcc${TOOL_OS_SUFFIX}" CACHE PATH "assembler" ) + set( CMAKE_STRIP "${ANDROID_TOOLCHAIN_ROOT}/bin/${ANDROID_TOOLCHAIN_MACHINE_NAME}-strip${TOOL_OS_SUFFIX}" CACHE PATH "strip" ) + if( EXISTS "${ANDROID_TOOLCHAIN_ROOT}/bin/${ANDROID_TOOLCHAIN_MACHINE_NAME}-gcc-ar${TOOL_OS_SUFFIX}" ) + # Use gcc-ar if we have it for better LTO support. + set( CMAKE_AR "${ANDROID_TOOLCHAIN_ROOT}/bin/${ANDROID_TOOLCHAIN_MACHINE_NAME}-gcc-ar${TOOL_OS_SUFFIX}" CACHE PATH "archive" ) + else() + set( CMAKE_AR "${ANDROID_TOOLCHAIN_ROOT}/bin/${ANDROID_TOOLCHAIN_MACHINE_NAME}-ar${TOOL_OS_SUFFIX}" CACHE PATH "archive" ) + endif() + set( CMAKE_LINKER "${ANDROID_TOOLCHAIN_ROOT}/bin/${ANDROID_TOOLCHAIN_MACHINE_NAME}-ld${TOOL_OS_SUFFIX}" CACHE PATH "linker" ) + set( CMAKE_NM "${ANDROID_TOOLCHAIN_ROOT}/bin/${ANDROID_TOOLCHAIN_MACHINE_NAME}-nm${TOOL_OS_SUFFIX}" CACHE PATH "nm" ) + set( CMAKE_OBJCOPY "${ANDROID_TOOLCHAIN_ROOT}/bin/${ANDROID_TOOLCHAIN_MACHINE_NAME}-objcopy${TOOL_OS_SUFFIX}" CACHE PATH "objcopy" ) + set( CMAKE_OBJDUMP "${ANDROID_TOOLCHAIN_ROOT}/bin/${ANDROID_TOOLCHAIN_MACHINE_NAME}-objdump${TOOL_OS_SUFFIX}" CACHE PATH "objdump" ) + set( CMAKE_RANLIB "${ANDROID_TOOLCHAIN_ROOT}/bin/${ANDROID_TOOLCHAIN_MACHINE_NAME}-ranlib${TOOL_OS_SUFFIX}" CACHE PATH "ranlib" ) +endif() + +set( _CMAKE_TOOLCHAIN_PREFIX "${ANDROID_TOOLCHAIN_MACHINE_NAME}-" ) +if( CMAKE_VERSION VERSION_LESS 2.8.5 ) + set( CMAKE_ASM_COMPILER_ARG1 "-c" ) +endif() +if( APPLE ) + find_program( CMAKE_INSTALL_NAME_TOOL NAMES install_name_tool ) + if( NOT CMAKE_INSTALL_NAME_TOOL ) + message( FATAL_ERROR "Could not find install_name_tool, please check your installation." ) + endif() + mark_as_advanced( CMAKE_INSTALL_NAME_TOOL ) +endif() + +# Force set compilers because standard identification works badly for us +if( CMAKE_VERSION VERSION_LESS 3.5.0 ) + include( CMakeForceCompiler ) + CMAKE_FORCE_C_COMPILER( "${CMAKE_C_COMPILER}" GNU ) +endif() +if( ANDROID_COMPILER_IS_CLANG ) + set( CMAKE_C_COMPILER_ID Clang ) +endif() +set( CMAKE_C_PLATFORM_ID Linux ) +if( X86_64 OR MIPS64 OR ARM64_V8A ) + set( CMAKE_C_SIZEOF_DATA_PTR 8 ) +else() + set( CMAKE_C_SIZEOF_DATA_PTR 4 ) +endif() +set( CMAKE_C_HAS_ISYSROOT 1 ) +set( CMAKE_C_COMPILER_ABI ELF ) +if( CMAKE_VERSION VERSION_LESS 3.5.0 ) + CMAKE_FORCE_CXX_COMPILER( "${CMAKE_CXX_COMPILER}" GNU ) +endif() +if( ANDROID_COMPILER_IS_CLANG ) + set( CMAKE_CXX_COMPILER_ID Clang) +endif() +set( CMAKE_CXX_PLATFORM_ID Linux ) +set( CMAKE_CXX_SIZEOF_DATA_PTR ${CMAKE_C_SIZEOF_DATA_PTR} ) +set( CMAKE_CXX_HAS_ISYSROOT 1 ) +set( CMAKE_CXX_COMPILER_ABI ELF ) +set( CMAKE_CXX_SOURCE_FILE_EXTENSIONS cc cp cxx cpp CPP c++ C ) +# force ASM compiler (required for CMake < 2.8.5) +set( CMAKE_ASM_COMPILER_ID_RUN TRUE ) +set( CMAKE_ASM_COMPILER_ID GNU ) +set( CMAKE_ASM_COMPILER_WORKS TRUE ) +set( CMAKE_ASM_COMPILER_FORCED TRUE ) +set( CMAKE_COMPILER_IS_GNUASM 1) +set( CMAKE_ASM_SOURCE_FILE_EXTENSIONS s S asm ) + +foreach( lang C CXX ASM ) + if( ANDROID_COMPILER_IS_CLANG ) + set( CMAKE_${lang}_COMPILER_VERSION ${ANDROID_CLANG_VERSION} ) + else() + set( CMAKE_${lang}_COMPILER_VERSION ${ANDROID_COMPILER_VERSION} ) + endif() +endforeach() + +# flags and definitions +remove_definitions( -DANDROID ) +add_definitions( -DANDROID ) + +if( ANDROID_SYSROOT MATCHES "[ ;\"]" ) + if( CMAKE_HOST_WIN32 ) + # try to convert path to 8.3 form + file( WRITE "${CMAKE_BINARY_DIR}${CMAKE_FILES_DIRECTORY}/cvt83.cmd" "@echo %~s1" ) + execute_process( COMMAND "$ENV{ComSpec}" /c "${CMAKE_BINARY_DIR}${CMAKE_FILES_DIRECTORY}/cvt83.cmd" "${ANDROID_SYSROOT}" + OUTPUT_VARIABLE __path OUTPUT_STRIP_TRAILING_WHITESPACE + RESULT_VARIABLE __result ERROR_QUIET ) + if( __result EQUAL 0 ) + file( TO_CMAKE_PATH "${__path}" ANDROID_SYSROOT ) + set( ANDROID_CXX_FLAGS "--sysroot=${ANDROID_SYSROOT}" ) + else() + set( ANDROID_CXX_FLAGS "--sysroot=\"${ANDROID_SYSROOT}\"" ) + endif() + else() + set( ANDROID_CXX_FLAGS "'--sysroot=${ANDROID_SYSROOT}'" ) + endif() + if( NOT _CMAKE_IN_TRY_COMPILE ) + # quotes can break try_compile and compiler identification + message(WARNING "Path to your Android NDK (or toolchain) has non-alphanumeric symbols.\nThe build might be broken.\n") + endif() +else() + set( ANDROID_CXX_FLAGS "--sysroot=${ANDROID_SYSROOT}" ) +endif() + +# NDK flags +if (ARM64_V8A ) + set( ANDROID_CXX_FLAGS "${ANDROID_CXX_FLAGS} -funwind-tables" ) + set( ANDROID_CXX_FLAGS_RELEASE "-fomit-frame-pointer -fstrict-aliasing" ) + set( ANDROID_CXX_FLAGS_DEBUG "-fno-omit-frame-pointer -fno-strict-aliasing" ) + if( NOT ANDROID_COMPILER_IS_CLANG ) + set( ANDROID_CXX_FLAGS_RELEASE "${ANDROID_CXX_FLAGS_RELEASE} -funswitch-loops -finline-limit=300" ) + endif() +elseif( ARMEABI OR ARMEABI_V7A OR ARMEABI_V7A_HARD) + set( ANDROID_CXX_FLAGS "${ANDROID_CXX_FLAGS} -funwind-tables" ) + if( NOT ANDROID_FORCE_ARM_BUILD AND NOT ARMEABI_V6 ) + set( ANDROID_CXX_FLAGS_RELEASE "-mthumb -fomit-frame-pointer -fno-strict-aliasing" ) + set( ANDROID_CXX_FLAGS_DEBUG "-marm -fno-omit-frame-pointer -fno-strict-aliasing" ) + if( NOT ANDROID_COMPILER_IS_CLANG ) + set( ANDROID_CXX_FLAGS "${ANDROID_CXX_FLAGS} -finline-limit=64" ) + endif() + else() + # always compile ARMEABI_V6 in arm mode; otherwise there is no difference from ARMEABI + set( ANDROID_CXX_FLAGS_RELEASE "-marm -fomit-frame-pointer -fstrict-aliasing" ) + set( ANDROID_CXX_FLAGS_DEBUG "-marm -fno-omit-frame-pointer -fno-strict-aliasing" ) + if( NOT ANDROID_COMPILER_IS_CLANG ) + set( ANDROID_CXX_FLAGS "${ANDROID_CXX_FLAGS} -funswitch-loops -finline-limit=300" ) + endif() + endif() +elseif( X86 OR X86_64 ) + set( ANDROID_CXX_FLAGS "${ANDROID_CXX_FLAGS} -funwind-tables" ) + if( NOT ANDROID_COMPILER_IS_CLANG ) + set( ANDROID_CXX_FLAGS "${ANDROID_CXX_FLAGS} -funswitch-loops -finline-limit=300" ) + endif() + set( ANDROID_CXX_FLAGS_RELEASE "-fomit-frame-pointer -fstrict-aliasing" ) + set( ANDROID_CXX_FLAGS_DEBUG "-fno-omit-frame-pointer -fno-strict-aliasing" ) +elseif( MIPS OR MIPS64 ) + set( ANDROID_CXX_FLAGS "${ANDROID_CXX_FLAGS} -fno-strict-aliasing -finline-functions -funwind-tables -fmessage-length=0" ) + set( ANDROID_CXX_FLAGS_RELEASE "-fomit-frame-pointer" ) + set( ANDROID_CXX_FLAGS_DEBUG "-fno-omit-frame-pointer" ) + if( NOT ANDROID_COMPILER_IS_CLANG ) + set( ANDROID_CXX_FLAGS "${ANDROID_CXX_FLAGS} -fno-inline-functions-called-once -fgcse-after-reload -frerun-cse-after-loop -frename-registers" ) + set( ANDROID_CXX_FLAGS_RELEASE "${ANDROID_CXX_FLAGS_RELEASE} -funswitch-loops -finline-limit=300" ) + endif() +elseif() + set( ANDROID_CXX_FLAGS_RELEASE "" ) + set( ANDROID_CXX_FLAGS_DEBUG "" ) +endif() + +set( ANDROID_CXX_FLAGS "${ANDROID_CXX_FLAGS} -fsigned-char" ) # good/necessary when porting desktop libraries + +if( NOT X86 AND NOT ANDROID_COMPILER_IS_CLANG ) + set( ANDROID_CXX_FLAGS "-Wno-psabi ${ANDROID_CXX_FLAGS}" ) +endif() + +if( NOT ANDROID_COMPILER_VERSION VERSION_LESS "4.6" ) + set( ANDROID_CXX_FLAGS "${ANDROID_CXX_FLAGS} -no-canonical-prefixes" ) # see https://android-review.googlesource.com/#/c/47564/ +endif() + +# ABI-specific flags +if( ARMEABI_V7A_HARD ) + set( ANDROID_CXX_FLAGS "${ANDROID_CXX_FLAGS} -march=armv7-a -mfloat-abi=hard -mhard-float -D_NDK_MATH_NO_SOFTFP=1" ) + if( NEON ) + set( ANDROID_CXX_FLAGS "${ANDROID_CXX_FLAGS} -mfpu=neon" ) + elseif( VFPV3 ) + set( ANDROID_CXX_FLAGS "${ANDROID_CXX_FLAGS} -mfpu=vfpv3" ) + else() + set( ANDROID_CXX_FLAGS "${ANDROID_CXX_FLAGS} -mfpu=vfpv3-d16" ) + endif() +elseif( ARMEABI_V7A ) + set( ANDROID_CXX_FLAGS "${ANDROID_CXX_FLAGS} -march=armv7-a -mfloat-abi=softfp" ) + if( NEON ) + set( ANDROID_CXX_FLAGS "${ANDROID_CXX_FLAGS} -mfpu=neon" ) + elseif( VFPV3 ) + set( ANDROID_CXX_FLAGS "${ANDROID_CXX_FLAGS} -mfpu=vfpv3" ) + else() + set( ANDROID_CXX_FLAGS "${ANDROID_CXX_FLAGS} -mfpu=vfpv3-d16" ) + endif() + +elseif( ARMEABI_V6 ) + set( ANDROID_CXX_FLAGS "${ANDROID_CXX_FLAGS} -march=armv6 -mfloat-abi=softfp -mfpu=vfp" ) # vfp == vfpv2 +elseif( ARMEABI ) + set( ANDROID_CXX_FLAGS "${ANDROID_CXX_FLAGS} -march=armv5te -mtune=xscale -msoft-float" ) +endif() + +if( ANDROID_STL MATCHES "gnustl" AND (EXISTS "${__libstl}" OR EXISTS "${__libsupcxx}") ) + set( CMAKE_CXX_CREATE_SHARED_LIBRARY " -o " ) + set( CMAKE_CXX_CREATE_SHARED_MODULE " -o " ) + set( CMAKE_CXX_LINK_EXECUTABLE " -o " ) +else() + set( CMAKE_CXX_CREATE_SHARED_LIBRARY " -o " ) + set( CMAKE_CXX_CREATE_SHARED_MODULE " -o " ) + set( CMAKE_CXX_LINK_EXECUTABLE " -o " ) +endif() + +# STL +if( EXISTS "${__libstl}" OR EXISTS "${__libsupcxx}" ) + if( EXISTS "${__libstl}" ) + set( CMAKE_CXX_CREATE_SHARED_LIBRARY "${CMAKE_CXX_CREATE_SHARED_LIBRARY} \"${__libstl}\"" ) + set( CMAKE_CXX_CREATE_SHARED_MODULE "${CMAKE_CXX_CREATE_SHARED_MODULE} \"${__libstl}\"" ) + set( CMAKE_CXX_LINK_EXECUTABLE "${CMAKE_CXX_LINK_EXECUTABLE} \"${__libstl}\"" ) + endif() + if( EXISTS "${__libsupcxx}" ) + set( CMAKE_CXX_CREATE_SHARED_LIBRARY "${CMAKE_CXX_CREATE_SHARED_LIBRARY} \"${__libsupcxx}\"" ) + set( CMAKE_CXX_CREATE_SHARED_MODULE "${CMAKE_CXX_CREATE_SHARED_MODULE} \"${__libsupcxx}\"" ) + set( CMAKE_CXX_LINK_EXECUTABLE "${CMAKE_CXX_LINK_EXECUTABLE} \"${__libsupcxx}\"" ) + # C objects: + set( CMAKE_C_CREATE_SHARED_LIBRARY " -o " ) + set( CMAKE_C_CREATE_SHARED_MODULE " -o " ) + set( CMAKE_C_LINK_EXECUTABLE " -o " ) + set( CMAKE_C_CREATE_SHARED_LIBRARY "${CMAKE_C_CREATE_SHARED_LIBRARY} \"${__libsupcxx}\"" ) + set( CMAKE_C_CREATE_SHARED_MODULE "${CMAKE_C_CREATE_SHARED_MODULE} \"${__libsupcxx}\"" ) + set( CMAKE_C_LINK_EXECUTABLE "${CMAKE_C_LINK_EXECUTABLE} \"${__libsupcxx}\"" ) + endif() + if( ANDROID_STL MATCHES "gnustl" ) + if( NOT EXISTS "${ANDROID_LIBM_PATH}" ) + set( ANDROID_LIBM_PATH -lm ) + endif() + set( CMAKE_CXX_CREATE_SHARED_LIBRARY "${CMAKE_CXX_CREATE_SHARED_LIBRARY} ${ANDROID_LIBM_PATH}" ) + set( CMAKE_CXX_CREATE_SHARED_MODULE "${CMAKE_CXX_CREATE_SHARED_MODULE} ${ANDROID_LIBM_PATH}" ) + set( CMAKE_CXX_LINK_EXECUTABLE "${CMAKE_CXX_LINK_EXECUTABLE} ${ANDROID_LIBM_PATH}" ) + endif() +endif() + +# variables controlling optional build flags +if( ANDROID_NDK_RELEASE_NUM LESS 7000 ) # before r7 + # libGLESv2.so in NDK's prior to r7 refers to missing external symbols. + # So this flag option is required for all projects using OpenGL from native. + __INIT_VARIABLE( ANDROID_SO_UNDEFINED VALUES ON ) +else() + __INIT_VARIABLE( ANDROID_SO_UNDEFINED VALUES OFF ) +endif() +__INIT_VARIABLE( ANDROID_NO_UNDEFINED VALUES ON ) +__INIT_VARIABLE( ANDROID_FUNCTION_LEVEL_LINKING VALUES ON ) +__INIT_VARIABLE( ANDROID_GOLD_LINKER VALUES ON ) +__INIT_VARIABLE( ANDROID_NOEXECSTACK VALUES ON ) +__INIT_VARIABLE( ANDROID_RELRO VALUES ON ) + +set( ANDROID_NO_UNDEFINED ${ANDROID_NO_UNDEFINED} CACHE BOOL "Show all undefined symbols as linker errors" ) +set( ANDROID_SO_UNDEFINED ${ANDROID_SO_UNDEFINED} CACHE BOOL "Allows or disallows undefined symbols in shared libraries" ) +set( ANDROID_FUNCTION_LEVEL_LINKING ${ANDROID_FUNCTION_LEVEL_LINKING} CACHE BOOL "Put each function in separate section and enable garbage collection of unused input sections at link time" ) +set( ANDROID_GOLD_LINKER ${ANDROID_GOLD_LINKER} CACHE BOOL "Enables gold linker" ) +set( ANDROID_NOEXECSTACK ${ANDROID_NOEXECSTACK} CACHE BOOL "Allows or disallows undefined symbols in shared libraries" ) +set( ANDROID_RELRO ${ANDROID_RELRO} CACHE BOOL "Enables RELRO - a memory corruption mitigation technique" ) +mark_as_advanced( ANDROID_NO_UNDEFINED ANDROID_SO_UNDEFINED ANDROID_FUNCTION_LEVEL_LINKING ANDROID_GOLD_LINKER ANDROID_NOEXECSTACK ANDROID_RELRO ) + +# linker flags +set( ANDROID_LINKER_FLAGS "" ) + +if( ARMEABI_V7A ) + # this is *required* to use the following linker flags that routes around + # a CPU bug in some Cortex-A8 implementations: + set( ANDROID_LINKER_FLAGS "${ANDROID_LINKER_FLAGS} -Wl,--fix-cortex-a8" ) +endif() + +if( ARMEABI_V7A_HARD ) + set( ANDROID_LINKER_FLAGS "${ANDROID_LINKER_FLAGS} -Wl,--no-warn-mismatch -lm_hard" ) +endif() + +if( ANDROID_NO_UNDEFINED ) + if( MIPS ) + # there is some sysroot-related problem in mips linker... + if( NOT ANDROID_SYSROOT MATCHES "[ ;\"]" ) + set( ANDROID_LINKER_FLAGS "${ANDROID_LINKER_FLAGS} -Wl,--no-undefined -Wl,-rpath-link,${ANDROID_SYSROOT}/usr/lib" ) + endif() + else() + set( ANDROID_LINKER_FLAGS "${ANDROID_LINKER_FLAGS} -Wl,--no-undefined" ) + endif() +endif() + +if( ANDROID_SO_UNDEFINED ) + set( ANDROID_LINKER_FLAGS "${ANDROID_LINKER_FLAGS} -Wl,-allow-shlib-undefined" ) +endif() + +if( ANDROID_FUNCTION_LEVEL_LINKING ) + set( ANDROID_CXX_FLAGS "${ANDROID_CXX_FLAGS} -fdata-sections -ffunction-sections" ) + set( ANDROID_LINKER_FLAGS "${ANDROID_LINKER_FLAGS} -Wl,--gc-sections" ) +endif() + +if( ANDROID_COMPILER_VERSION VERSION_EQUAL "4.6" ) + if( ANDROID_GOLD_LINKER AND (CMAKE_HOST_UNIX OR ANDROID_NDK_RELEASE_NUM GREATER 8002) AND (ARMEABI OR ARMEABI_V7A OR ARMEABI_V7A_HARD OR X86) ) + set( ANDROID_LINKER_FLAGS "${ANDROID_LINKER_FLAGS} -fuse-ld=gold" ) + elseif( ANDROID_NDK_RELEASE_NUM GREATER 8002 ) # after r8b + set( ANDROID_LINKER_FLAGS "${ANDROID_LINKER_FLAGS} -fuse-ld=bfd" ) + elseif( ANDROID_NDK_RELEASE STREQUAL "r8b" AND ARMEABI AND NOT _CMAKE_IN_TRY_COMPILE ) + message( WARNING "The default bfd linker from arm GCC 4.6 toolchain can fail with 'unresolvable R_ARM_THM_CALL relocation' error message. See https://code.google.com/p/android/issues/detail?id=35342 + On Linux and OS X host platform you can workaround this problem using gold linker (default). + Rerun cmake with -DANDROID_GOLD_LINKER=ON option in case of problems. +" ) + endif() +endif() # version 4.6 + +if( ANDROID_NOEXECSTACK ) + if( ANDROID_COMPILER_IS_CLANG ) + set( ANDROID_CXX_FLAGS "${ANDROID_CXX_FLAGS} -Xclang -mnoexecstack" ) + else() + set( ANDROID_CXX_FLAGS "${ANDROID_CXX_FLAGS} -Wa,--noexecstack" ) + endif() + set( ANDROID_LINKER_FLAGS "${ANDROID_LINKER_FLAGS} -Wl,-z,noexecstack" ) +endif() + +if( ANDROID_RELRO ) + set( ANDROID_LINKER_FLAGS "${ANDROID_LINKER_FLAGS} -Wl,-z,relro -Wl,-z,now" ) +endif() + +if( ANDROID_COMPILER_IS_CLANG ) + set( ANDROID_CXX_FLAGS "-target ${ANDROID_LLVM_TRIPLE} -Qunused-arguments ${ANDROID_CXX_FLAGS}" ) + if( BUILD_WITH_ANDROID_NDK ) + set( ANDROID_CXX_FLAGS "-gcc-toolchain ${ANDROID_TOOLCHAIN_ROOT} ${ANDROID_CXX_FLAGS}" ) + endif() +endif() + +# cache flags +set( CMAKE_CXX_FLAGS "" CACHE STRING "c++ flags" ) +set( CMAKE_C_FLAGS "" CACHE STRING "c flags" ) +set( CMAKE_CXX_FLAGS_RELEASE "-O3 -DNDEBUG" CACHE STRING "c++ Release flags" ) +set( CMAKE_C_FLAGS_RELEASE "-O3 -DNDEBUG" CACHE STRING "c Release flags" ) +set( CMAKE_CXX_FLAGS_DEBUG "-O0 -g -DDEBUG -D_DEBUG" CACHE STRING "c++ Debug flags" ) +set( CMAKE_C_FLAGS_DEBUG "-O0 -g -DDEBUG -D_DEBUG" CACHE STRING "c Debug flags" ) +set( CMAKE_SHARED_LINKER_FLAGS "" CACHE STRING "shared linker flags" ) +set( CMAKE_MODULE_LINKER_FLAGS "" CACHE STRING "module linker flags" ) +set( CMAKE_EXE_LINKER_FLAGS "-Wl,-z,nocopyreloc" CACHE STRING "executable linker flags" ) + +# put flags to cache (for debug purpose only) +set( ANDROID_CXX_FLAGS "${ANDROID_CXX_FLAGS}" CACHE INTERNAL "Android specific c/c++ flags" ) +set( ANDROID_CXX_FLAGS_RELEASE "${ANDROID_CXX_FLAGS_RELEASE}" CACHE INTERNAL "Android specific c/c++ Release flags" ) +set( ANDROID_CXX_FLAGS_DEBUG "${ANDROID_CXX_FLAGS_DEBUG}" CACHE INTERNAL "Android specific c/c++ Debug flags" ) +set( ANDROID_LINKER_FLAGS "${ANDROID_LINKER_FLAGS}" CACHE INTERNAL "Android specific c/c++ linker flags" ) + +# finish flags +set( CMAKE_CXX_FLAGS "${ANDROID_CXX_FLAGS} ${CMAKE_CXX_FLAGS}" ) +set( CMAKE_C_FLAGS "${ANDROID_CXX_FLAGS} ${CMAKE_C_FLAGS}" ) +set( CMAKE_CXX_FLAGS_RELEASE "${ANDROID_CXX_FLAGS_RELEASE} ${CMAKE_CXX_FLAGS_RELEASE}" ) +set( CMAKE_C_FLAGS_RELEASE "${ANDROID_CXX_FLAGS_RELEASE} ${CMAKE_C_FLAGS_RELEASE}" ) +set( CMAKE_CXX_FLAGS_DEBUG "${ANDROID_CXX_FLAGS_DEBUG} ${CMAKE_CXX_FLAGS_DEBUG}" ) +set( CMAKE_C_FLAGS_DEBUG "${ANDROID_CXX_FLAGS_DEBUG} ${CMAKE_C_FLAGS_DEBUG}" ) +set( CMAKE_SHARED_LINKER_FLAGS "${ANDROID_LINKER_FLAGS} ${CMAKE_SHARED_LINKER_FLAGS}" ) +set( CMAKE_MODULE_LINKER_FLAGS "${ANDROID_LINKER_FLAGS} ${CMAKE_MODULE_LINKER_FLAGS}" ) +set( CMAKE_EXE_LINKER_FLAGS "${ANDROID_LINKER_FLAGS} ${CMAKE_EXE_LINKER_FLAGS}" ) + +if( MIPS AND BUILD_WITH_ANDROID_NDK AND ANDROID_NDK_RELEASE STREQUAL "r8" ) + set( CMAKE_SHARED_LINKER_FLAGS "-Wl,-T,${ANDROID_NDK_TOOLCHAINS_PATH}/${ANDROID_GCC_TOOLCHAIN_NAME}/mipself.xsc ${CMAKE_SHARED_LINKER_FLAGS}" ) + set( CMAKE_MODULE_LINKER_FLAGS "-Wl,-T,${ANDROID_NDK_TOOLCHAINS_PATH}/${ANDROID_GCC_TOOLCHAIN_NAME}/mipself.xsc ${CMAKE_MODULE_LINKER_FLAGS}" ) + set( CMAKE_EXE_LINKER_FLAGS "-Wl,-T,${ANDROID_NDK_TOOLCHAINS_PATH}/${ANDROID_GCC_TOOLCHAIN_NAME}/mipself.x ${CMAKE_EXE_LINKER_FLAGS}" ) +endif() + +# pie/pic +if( NOT (ANDROID_NATIVE_API_LEVEL LESS 16) AND (NOT DEFINED ANDROID_APP_PIE OR ANDROID_APP_PIE) AND (CMAKE_VERSION VERSION_GREATER 2.8.8) ) + set( CMAKE_POSITION_INDEPENDENT_CODE TRUE ) + set( CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} -fPIE -pie") +else() + set( CMAKE_POSITION_INDEPENDENT_CODE FALSE ) + set( CMAKE_CXX_FLAGS "-fpic ${CMAKE_CXX_FLAGS}" ) + set( CMAKE_C_FLAGS "-fpic ${CMAKE_C_FLAGS}" ) +endif() + +# configure rtti +if( DEFINED ANDROID_RTTI AND ANDROID_STL_FORCE_FEATURES ) + if( ANDROID_RTTI ) + set( CMAKE_CXX_FLAGS "-frtti ${CMAKE_CXX_FLAGS}" ) + else() + set( CMAKE_CXX_FLAGS "-fno-rtti ${CMAKE_CXX_FLAGS}" ) + endif() +endif() + +# configure exceptions +if( DEFINED ANDROID_EXCEPTIONS AND ANDROID_STL_FORCE_FEATURES ) + if( ANDROID_EXCEPTIONS ) + set( CMAKE_CXX_FLAGS "-fexceptions ${CMAKE_CXX_FLAGS}" ) + set( CMAKE_C_FLAGS "-fexceptions ${CMAKE_C_FLAGS}" ) + else() + set( CMAKE_CXX_FLAGS "-fno-exceptions ${CMAKE_CXX_FLAGS}" ) + set( CMAKE_C_FLAGS "-fno-exceptions ${CMAKE_C_FLAGS}" ) + endif() +endif() + +# global includes and link directories +include_directories( SYSTEM "${ANDROID_SYSROOT_INCLUDE}" ${ANDROID_STL_INCLUDE_DIRS} ) +get_filename_component(__android_install_path "${CMAKE_INSTALL_PREFIX}/libs/${ANDROID_NDK_ABI_NAME}" ABSOLUTE) # avoid CMP0015 policy warning +link_directories( "${__android_install_path}" ) +set( CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DANDROID -D__ANDROID_API__=${ANDROID_NATIVE_API_LEVEL}" ) +set( CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -DANDROID -D__ANDROID_API__=${ANDROID_NATIVE_API_LEVEL}" ) + +# detect if need link crtbegin_so.o explicitly +if( NOT DEFINED ANDROID_EXPLICIT_CRT_LINK ) + set( __cmd "${CMAKE_CXX_CREATE_SHARED_LIBRARY}" ) + string( REPLACE "" "${CMAKE_CXX_COMPILER} ${CMAKE_CXX_COMPILER_ARG1}" __cmd "${__cmd}" ) + string( REPLACE "" "${CMAKE_C_COMPILER} ${CMAKE_C_COMPILER_ARG1}" __cmd "${__cmd}" ) + string( REPLACE "" "${CMAKE_CXX_FLAGS}" __cmd "${__cmd}" ) + string( REPLACE "" "" __cmd "${__cmd}" ) + string( REPLACE "" "${CMAKE_SHARED_LINKER_FLAGS}" __cmd "${__cmd}" ) + string( REPLACE "" "-shared" __cmd "${__cmd}" ) + string( REPLACE "" "" __cmd "${__cmd}" ) + string( REPLACE "" "" __cmd "${__cmd}" ) + string( REPLACE "" "${CMAKE_BINARY_DIR}${CMAKE_FILES_DIRECTORY}/toolchain_crtlink_test.so" __cmd "${__cmd}" ) + string( REPLACE "" "\"${ANDROID_SYSROOT}/usr/lib/crtbegin_so.o\"" __cmd "${__cmd}" ) + string( REPLACE "" "" __cmd "${__cmd}" ) + separate_arguments( __cmd ) + foreach( __var ANDROID_NDK ANDROID_NDK_TOOLCHAINS_PATH ANDROID_STANDALONE_TOOLCHAIN ) + if( ${__var} ) + set( __tmp "${${__var}}" ) + separate_arguments( __tmp ) + string( REPLACE "${__tmp}" "${${__var}}" __cmd "${__cmd}") + endif() + endforeach() + string( REPLACE "'" "" __cmd "${__cmd}" ) + string( REPLACE "\"" "" __cmd "${__cmd}" ) + execute_process( COMMAND ${__cmd} RESULT_VARIABLE __cmd_result OUTPUT_QUIET ERROR_QUIET ) + if( __cmd_result EQUAL 0 ) + set( ANDROID_EXPLICIT_CRT_LINK ON ) + else() + set( ANDROID_EXPLICIT_CRT_LINK OFF ) + endif() +endif() + +if( ANDROID_EXPLICIT_CRT_LINK ) + set( CMAKE_CXX_CREATE_SHARED_LIBRARY "${CMAKE_CXX_CREATE_SHARED_LIBRARY} \"${ANDROID_SYSROOT}/usr/lib/crtbegin_so.o\"" ) + set( CMAKE_CXX_CREATE_SHARED_MODULE "${CMAKE_CXX_CREATE_SHARED_MODULE} \"${ANDROID_SYSROOT}/usr/lib/crtbegin_so.o\"" ) +endif() + +# setup output directories +set( CMAKE_INSTALL_PREFIX "${ANDROID_TOOLCHAIN_ROOT}/user" CACHE STRING "path for installing" ) + +if( DEFINED LIBRARY_OUTPUT_PATH_ROOT + OR EXISTS "${CMAKE_SOURCE_DIR}/AndroidManifest.xml" + OR (EXISTS "${CMAKE_SOURCE_DIR}/../AndroidManifest.xml" AND EXISTS "${CMAKE_SOURCE_DIR}/../jni/") ) + set( LIBRARY_OUTPUT_PATH_ROOT ${CMAKE_SOURCE_DIR} CACHE PATH "Root for binaries output, set this to change where Android libs are installed to" ) + if( NOT _CMAKE_IN_TRY_COMPILE ) + if( EXISTS "${CMAKE_SOURCE_DIR}/jni/CMakeLists.txt" ) + set( EXECUTABLE_OUTPUT_PATH "${LIBRARY_OUTPUT_PATH_ROOT}/bin/${ANDROID_NDK_ABI_NAME}" CACHE PATH "Output directory for applications" ) + else() + set( EXECUTABLE_OUTPUT_PATH "${LIBRARY_OUTPUT_PATH_ROOT}/bin" CACHE PATH "Output directory for applications" ) + endif() + set( LIBRARY_OUTPUT_PATH "${LIBRARY_OUTPUT_PATH_ROOT}/libs/${ANDROID_NDK_ABI_NAME}" CACHE PATH "Output directory for Android libs" ) + endif() +endif() + +# copy shaed stl library to build directory +if( NOT _CMAKE_IN_TRY_COMPILE AND __libstl MATCHES "[.]so$" AND DEFINED LIBRARY_OUTPUT_PATH ) + get_filename_component( __libstlname "${__libstl}" NAME ) + execute_process( COMMAND "${CMAKE_COMMAND}" -E copy_if_different "${__libstl}" "${LIBRARY_OUTPUT_PATH}/${__libstlname}" RESULT_VARIABLE __fileCopyProcess ) + if( NOT __fileCopyProcess EQUAL 0 OR NOT EXISTS "${LIBRARY_OUTPUT_PATH}/${__libstlname}") + message( SEND_ERROR "Failed copying of ${__libstl} to the ${LIBRARY_OUTPUT_PATH}/${__libstlname}" ) + endif() + unset( __fileCopyProcess ) + unset( __libstlname ) +endif() + + +# set these global flags for cmake client scripts to change behavior +set( ANDROID True ) +set( BUILD_ANDROID True ) + +# where is the target environment +set( CMAKE_FIND_ROOT_PATH + "${ANDROID_TOOLCHAIN_ROOT}/bin" + "${ANDROID_TOOLCHAIN_ROOT}/${ANDROID_TOOLCHAIN_MACHINE_NAME}" + "${ANDROID_SYSROOT}" + "${ANDROID_NDK}/sysroot" # NDK16+ + "${CMAKE_INSTALL_PREFIX}" + "${CMAKE_INSTALL_PREFIX}/share" ) + +# only search for libraries and includes in the ndk toolchain +if(NOT CMAKE_FIND_ROOT_PATH_MODE_LIBRARY) + set( CMAKE_FIND_ROOT_PATH_MODE_LIBRARY ONLY ) +endif() + +if(NOT CMAKE_FIND_ROOT_PATH_MODE_INCLUDE) + set( CMAKE_FIND_ROOT_PATH_MODE_INCLUDE ONLY ) +endif() + +if(NOT CMAKE_FIND_ROOT_PATH_MODE_PACKAGE) + set( CMAKE_FIND_ROOT_PATH_MODE_PACKAGE ONLY ) +endif() + +if(NOT CMAKE_FIND_ROOT_PATH_MODE_PROGRAM) + set( CMAKE_FIND_ROOT_PATH_MODE_PROGRAM NEVER ) +endif() + +macro(__cmake_find_root_save_and_reset) + foreach(v + CMAKE_FIND_ROOT_PATH_MODE_LIBRARY + CMAKE_FIND_ROOT_PATH_MODE_INCLUDE + CMAKE_FIND_ROOT_PATH_MODE_PACKAGE + CMAKE_FIND_ROOT_PATH_MODE_PROGRAM + ) + set(__save_${v} ${${v}}) + set(${v} NEVER) + endforeach() +endmacro() + +macro(__cmake_find_root_restore) + foreach(v + CMAKE_FIND_ROOT_PATH_MODE_LIBRARY + CMAKE_FIND_ROOT_PATH_MODE_INCLUDE + CMAKE_FIND_ROOT_PATH_MODE_PACKAGE + CMAKE_FIND_ROOT_PATH_MODE_PROGRAM + ) + set(${v} ${__save_${v}}) + unset(__save_${v}) + endforeach() +endmacro() + +# macro to find packages on the host OS +macro( find_host_package ) + __cmake_find_root_save_and_reset() + if( CMAKE_HOST_WIN32 ) + SET( WIN32 1 ) + SET( UNIX ) + elseif( CMAKE_HOST_APPLE ) + SET( APPLE 1 ) + SET( UNIX ) + endif() + find_package( ${ARGN} ) + SET( WIN32 ) + SET( APPLE ) + SET( UNIX 1 ) + __cmake_find_root_restore() +endmacro() + + +# macro to find programs on the host OS +macro( find_host_program ) + __cmake_find_root_save_and_reset() + if( CMAKE_HOST_WIN32 ) + SET( WIN32 1 ) + SET( UNIX ) + elseif( CMAKE_HOST_APPLE ) + SET( APPLE 1 ) + SET( UNIX ) + endif() + find_program( ${ARGN} ) + SET( WIN32 ) + SET( APPLE ) + SET( UNIX 1 ) + __cmake_find_root_restore() +endmacro() + + +# export toolchain settings for the try_compile() command +if( NOT _CMAKE_IN_TRY_COMPILE ) + set( __toolchain_config "") + foreach( __var NDK_CCACHE LIBRARY_OUTPUT_PATH_ROOT ANDROID_FORBID_SYGWIN + ANDROID_NDK_HOST_X64 + ANDROID_NDK + ANDROID_NDK_LAYOUT + ANDROID_STANDALONE_TOOLCHAIN + ANDROID_TOOLCHAIN_NAME + ANDROID_ABI + ANDROID_NATIVE_API_LEVEL + ANDROID_STL + ANDROID_STL_FORCE_FEATURES + ANDROID_FORCE_ARM_BUILD + ANDROID_NO_UNDEFINED + ANDROID_SO_UNDEFINED + ANDROID_FUNCTION_LEVEL_LINKING + ANDROID_GOLD_LINKER + ANDROID_NOEXECSTACK + ANDROID_RELRO + ANDROID_LIBM_PATH + ANDROID_EXPLICIT_CRT_LINK + ANDROID_APP_PIE + ) + if( DEFINED ${__var} ) + if( ${__var} MATCHES " ") + set( __toolchain_config "${__toolchain_config}set( ${__var} \"${${__var}}\" CACHE INTERNAL \"\" )\n" ) + else() + set( __toolchain_config "${__toolchain_config}set( ${__var} ${${__var}} CACHE INTERNAL \"\" )\n" ) + endif() + endif() + endforeach() + file( WRITE "${CMAKE_BINARY_DIR}${CMAKE_FILES_DIRECTORY}/android.toolchain.config.cmake" "${__toolchain_config}" ) + unset( __toolchain_config ) +endif() + + +# force cmake to produce / instead of \ in build commands for Ninja generator +if( CMAKE_GENERATOR MATCHES "Ninja" AND CMAKE_HOST_WIN32 ) + # it is a bad hack after all + # CMake generates Ninja makefiles with UNIX paths only if it thinks that we are going to build with MinGW + set( CMAKE_COMPILER_IS_MINGW TRUE ) # tell CMake that we are MinGW + set( CMAKE_CROSSCOMPILING TRUE ) # stop recursion + enable_language( C ) + enable_language( CXX ) + # unset( CMAKE_COMPILER_IS_MINGW ) # can't unset because CMake does not convert back-slashes in response files without it + unset( MINGW ) +endif() + + +# Variables controlling behavior or set by cmake toolchain: +# ANDROID_ABI : "armeabi-v7a" (default), "armeabi", "armeabi-v7a with NEON", "armeabi-v7a-hard with NEON", "armeabi-v7a with VFPV3", "armeabi-v6 with VFP", "x86", "mips", "arm64-v8a", "x86_64", "mips64" +# ANDROID_NATIVE_API_LEVEL : 3,4,5,8,9,14,15,16,17,18,19,21 (depends on NDK version) +# ANDROID_STL : gnustl_static/gnustl_shared/stlport_static/stlport_shared/gabi++_static/gabi++_shared/system_re/system/none +# ANDROID_FORBID_SYGWIN : ON/OFF +# ANDROID_NO_UNDEFINED : ON/OFF +# ANDROID_SO_UNDEFINED : OFF/ON (default depends on NDK version) +# ANDROID_FUNCTION_LEVEL_LINKING : ON/OFF +# ANDROID_GOLD_LINKER : ON/OFF +# ANDROID_NOEXECSTACK : ON/OFF +# ANDROID_RELRO : ON/OFF +# ANDROID_FORCE_ARM_BUILD : ON/OFF +# ANDROID_STL_FORCE_FEATURES : ON/OFF +# ANDROID_LIBM_PATH : path to libm.so (set to something like $(TOP)/out/target/product//obj/lib/libm.so) to workaround unresolved `sincos` +# Can be set only at the first run: +# ANDROID_NDK : path to your NDK install +# NDK_CCACHE : path to your ccache executable +# ANDROID_TOOLCHAIN_NAME : the NDK name of compiler toolchain +# ANDROID_NDK_HOST_X64 : try to use x86_64 toolchain (default for x64 host systems) +# ANDROID_NDK_LAYOUT : the inner NDK structure (RELEASE, LINARO, ANDROID) +# LIBRARY_OUTPUT_PATH_ROOT : +# ANDROID_STANDALONE_TOOLCHAIN +# +# Primary read-only variables: +# ANDROID : always TRUE +# ARMEABI : TRUE for arm v6 and older devices +# ARMEABI_V6 : TRUE for arm v6 +# ARMEABI_V7A : TRUE for arm v7a +# ARMEABI_V7A_HARD : TRUE for arm v7a with hardfp +# ARM64_V8A : TRUE for arm64-v8a +# NEON : TRUE if NEON unit is enabled +# VFPV3 : TRUE if VFP version 3 is enabled +# X86 : TRUE if configured for x86 +# X86_64 : TRUE if configured for x86_64 +# MIPS : TRUE if configured for mips +# MIPS64 : TRUE if configured for mips64 +# BUILD_WITH_ANDROID_NDK : TRUE if NDK is used +# BUILD_WITH_STANDALONE_TOOLCHAIN : TRUE if standalone toolchain is used +# ANDROID_NDK_HOST_SYSTEM_NAME : "windows", "linux-x86" or "darwin-x86" depending on host platform +# ANDROID_NDK_ABI_NAME : "armeabi", "armeabi-v7a", "armeabi-v7a-hard", "x86", "mips", "arm64-v8a", "x86_64", "mips64" depending on ANDROID_ABI +# ANDROID_NDK_RELEASE : from r5 to r10d; set only for NDK +# ANDROID_NDK_RELEASE_NUM : numeric ANDROID_NDK_RELEASE version (1000*major+minor) +# ANDROID_ARCH_NAME : "arm", "x86", "mips", "arm64", "x86_64", "mips64" depending on ANDROID_ABI +# ANDROID_SYSROOT : path to the compiler sysroot +# ANDROID_SYSROOT_INCLUDE : paths to system include paths +# TOOL_OS_SUFFIX : "" or ".exe" depending on host platform +# ANDROID_COMPILER_IS_CLANG : TRUE if clang compiler is used +# +# Secondary (less stable) read-only variables: +# ANDROID_COMPILER_VERSION : GCC version used (not Clang version) +# ANDROID_CLANG_VERSION : version of clang compiler if clang is used +# ANDROID_CXX_FLAGS : C/C++ compiler flags required by Android platform +# ANDROID_SUPPORTED_ABIS : list of currently allowed values for ANDROID_ABI +# ANDROID_TOOLCHAIN_MACHINE_NAME : "arm-linux-androideabi", "arm-eabi" or "i686-android-linux" +# ANDROID_TOOLCHAIN_ROOT : path to the top level of toolchain (standalone or placed inside NDK) +# ANDROID_CLANG_TOOLCHAIN_ROOT : path to clang tools +# ANDROID_SUPPORTED_NATIVE_API_LEVELS : list of native API levels found inside NDK +# ANDROID_STL_INCLUDE_DIRS : stl include paths +# ANDROID_RTTI : if rtti is enabled by the runtime +# ANDROID_EXCEPTIONS : if exceptions are enabled by the runtime +# ANDROID_GCC_TOOLCHAIN_NAME : read-only, differs from ANDROID_TOOLCHAIN_NAME only if clang is used +# +# Defaults: +# ANDROID_DEFAULT_NDK_API_LEVEL +# ANDROID_DEFAULT_NDK_API_LEVEL_${ARCH} +# ANDROID_NDK_SEARCH_PATHS +# ANDROID_SUPPORTED_ABIS_${ARCH} +# ANDROID_SUPPORTED_NDK_VERSIONS diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv/cv.h b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv/cv.h new file mode 100644 index 0000000000000000000000000000000000000000..19a74e297cfb58d8bfd0d2d5bbec9de46e55f975 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv/cv.h @@ -0,0 +1,73 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OLD_CV_H +#define OPENCV_OLD_CV_H + +#if defined(_MSC_VER) + #define CV_DO_PRAGMA(x) __pragma(x) + #define __CVSTR2__(x) #x + #define __CVSTR1__(x) __CVSTR2__(x) + #define __CVMSVCLOC__ __FILE__ "("__CVSTR1__(__LINE__)") : " + #define CV_MSG_PRAGMA(_msg) CV_DO_PRAGMA(message (__CVMSVCLOC__ _msg)) +#elif defined(__GNUC__) + #define CV_DO_PRAGMA(x) _Pragma (#x) + #define CV_MSG_PRAGMA(_msg) CV_DO_PRAGMA(message (_msg)) +#else + #define CV_DO_PRAGMA(x) + #define CV_MSG_PRAGMA(_msg) +#endif +#define CV_WARNING(x) CV_MSG_PRAGMA("Warning: " #x) + +//CV_WARNING("This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module") + +#include "opencv2/core/core_c.h" +#include "opencv2/imgproc/imgproc_c.h" +#include "opencv2/photo/photo_c.h" +#include "opencv2/video/tracking_c.h" +#include "opencv2/objdetect/objdetect_c.h" + +#if !defined(CV_IMPL) +#define CV_IMPL extern "C" +#endif //CV_IMPL + +#endif // __OPENCV_OLD_CV_H_ diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv/cv.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv/cv.hpp new file mode 100644 index 0000000000000000000000000000000000000000..867395644b4f44d0c1290de73c5285e71d3a1e70 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv/cv.hpp @@ -0,0 +1,60 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OLD_CV_HPP +#define OPENCV_OLD_CV_HPP + +//#if defined(__GNUC__) +//#warning "This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module" +//#endif + +#include "cv.h" +#include "opencv2/core.hpp" +#include "opencv2/imgproc.hpp" +#include "opencv2/photo.hpp" +#include "opencv2/video.hpp" +#include "opencv2/highgui.hpp" +#include "opencv2/features2d.hpp" +#include "opencv2/calib3d.hpp" +#include "opencv2/objdetect.hpp" + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv/cvaux.h b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv/cvaux.h new file mode 100644 index 0000000000000000000000000000000000000000..c0367cc25b765d545ab12e463295d34793c2f7e9 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv/cvaux.h @@ -0,0 +1,57 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// Intel License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000, Intel Corporation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of Intel Corporation may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OLD_AUX_H +#define OPENCV_OLD_AUX_H + +//#if defined(__GNUC__) +//#warning "This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module" +//#endif + +#include "opencv2/core/core_c.h" +#include "opencv2/imgproc/imgproc_c.h" +#include "opencv2/photo/photo_c.h" +#include "opencv2/video/tracking_c.h" +#include "opencv2/objdetect/objdetect_c.h" + +#endif + +/* End of file. */ diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv/cvaux.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv/cvaux.hpp new file mode 100644 index 0000000000000000000000000000000000000000..4888eef282ecb2ef5e0e5ebf88b745aec3eb70c7 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv/cvaux.hpp @@ -0,0 +1,52 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// Intel License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000, Intel Corporation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of Intel Corporation may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OLD_AUX_HPP +#define OPENCV_OLD_AUX_HPP + +//#if defined(__GNUC__) +//#warning "This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module" +//#endif + +#include "cvaux.h" +#include "opencv2/core/utility.hpp" + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv/cvwimage.h b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv/cvwimage.h new file mode 100644 index 0000000000000000000000000000000000000000..ec0ab141d9974866f29ff1a84961a50b67176b1c --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv/cvwimage.h @@ -0,0 +1,46 @@ +/////////////////////////////////////////////////////////////////////////////// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to +// this license. If you do not agree to this license, do not download, +// install, copy or use the software. +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2008, Google, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of Intel Corporation or contributors may not be used to endorse +// or promote products derived from this software without specific +// prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" +// and any express or implied warranties, including, but not limited to, the +// implied warranties of merchantability and fitness for a particular purpose +// are disclaimed. In no event shall the Intel Corporation or contributors be +// liable for any direct, indirect, incidental, special, exemplary, or +// consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. + + +#ifndef OPENCV_OLD_WIMAGE_HPP +#define OPENCV_OLD_WIMAGE_HPP + +#include "opencv2/core/wimage.hpp" + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv/cxcore.h b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv/cxcore.h new file mode 100644 index 0000000000000000000000000000000000000000..dc070c77d2890a4443ebcb776ac978712c92e0ec --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv/cxcore.h @@ -0,0 +1,52 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OLD_CXCORE_H +#define OPENCV_OLD_CXCORE_H + +//#if defined(__GNUC__) +//#warning "This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module" +//#endif + +#include "opencv2/core/core_c.h" + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv/cxcore.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv/cxcore.hpp new file mode 100644 index 0000000000000000000000000000000000000000..c371677c933fdd520af741bc95825c71d14a5508 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv/cxcore.hpp @@ -0,0 +1,53 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OLD_CXCORE_HPP +#define OPENCV_OLD_CXCORE_HPP + +//#if defined(__GNUC__) +//#warning "This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module" +//#endif + +#include "cxcore.h" +#include "opencv2/core.hpp" + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv/cxeigen.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv/cxeigen.hpp new file mode 100644 index 0000000000000000000000000000000000000000..1d3df914ac95f387775587a692b1fc06e01da953 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv/cxeigen.hpp @@ -0,0 +1,48 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OLD_EIGEN_HPP +#define OPENCV_OLD_EIGEN_HPP + +#include "opencv2/core/eigen.hpp" + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv/cxmisc.h b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv/cxmisc.h new file mode 100644 index 0000000000000000000000000000000000000000..9b9bc8204bd810f5c57bc6c93552c4684af63b93 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv/cxmisc.h @@ -0,0 +1,8 @@ +#ifndef OPENCV_OLD_CXMISC_H +#define OPENCV_OLD_CXMISC_H + +#ifdef __cplusplus +# include "opencv2/core/utility.hpp" +#endif + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv/highgui.h b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv/highgui.h new file mode 100644 index 0000000000000000000000000000000000000000..69b394e08ace38c92a2ea35a5e9f7c5eca22ae80 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv/highgui.h @@ -0,0 +1,48 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// Intel License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000, Intel Corporation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of Intel Corporation may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OLD_HIGHGUI_H +#define OPENCV_OLD_HIGHGUI_H + +#include "opencv2/core/core_c.h" +#include "opencv2/highgui/highgui_c.h" + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv/ml.h b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv/ml.h new file mode 100644 index 0000000000000000000000000000000000000000..0c376bacb9ab5ef9167f54b688ce474f2a92ac2e --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv/ml.h @@ -0,0 +1,47 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// Intel License Agreement +// +// Copyright (C) 2000, Intel Corporation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of Intel Corporation may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OLD_ML_H +#define OPENCV_OLD_ML_H + +#include "opencv2/core/core_c.h" +#include "opencv2/ml.hpp" + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/calib3d.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/calib3d.hpp new file mode 100644 index 0000000000000000000000000000000000000000..c5b3ffd4d3ca5aa5a3dbedd1472a771e1dc9662f --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/calib3d.hpp @@ -0,0 +1,2479 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CALIB3D_HPP +#define OPENCV_CALIB3D_HPP + +#include "opencv2/core.hpp" +#include "opencv2/features2d.hpp" +#include "opencv2/core/affine.hpp" + +/** + @defgroup calib3d Camera Calibration and 3D Reconstruction + +The functions in this section use a so-called pinhole camera model. In this model, a scene view is +formed by projecting 3D points into the image plane using a perspective transformation. + +\f[s \; m' = A [R|t] M'\f] + +or + +\f[s \vecthree{u}{v}{1} = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1} +\begin{bmatrix} +r_{11} & r_{12} & r_{13} & t_1 \\ +r_{21} & r_{22} & r_{23} & t_2 \\ +r_{31} & r_{32} & r_{33} & t_3 +\end{bmatrix} +\begin{bmatrix} +X \\ +Y \\ +Z \\ +1 +\end{bmatrix}\f] + +where: + +- \f$(X, Y, Z)\f$ are the coordinates of a 3D point in the world coordinate space +- \f$(u, v)\f$ are the coordinates of the projection point in pixels +- \f$A\f$ is a camera matrix, or a matrix of intrinsic parameters +- \f$(cx, cy)\f$ is a principal point that is usually at the image center +- \f$fx, fy\f$ are the focal lengths expressed in pixel units. + +Thus, if an image from the camera is scaled by a factor, all of these parameters should be scaled +(multiplied/divided, respectively) by the same factor. The matrix of intrinsic parameters does not +depend on the scene viewed. So, once estimated, it can be re-used as long as the focal length is +fixed (in case of zoom lens). The joint rotation-translation matrix \f$[R|t]\f$ is called a matrix of +extrinsic parameters. It is used to describe the camera motion around a static scene, or vice versa, +rigid motion of an object in front of a still camera. That is, \f$[R|t]\f$ translates coordinates of a +point \f$(X, Y, Z)\f$ to a coordinate system, fixed with respect to the camera. The transformation above +is equivalent to the following (when \f$z \ne 0\f$ ): + +\f[\begin{array}{l} +\vecthree{x}{y}{z} = R \vecthree{X}{Y}{Z} + t \\ +x' = x/z \\ +y' = y/z \\ +u = f_x*x' + c_x \\ +v = f_y*y' + c_y +\end{array}\f] + +The following figure illustrates the pinhole camera model. + +![Pinhole camera model](pics/pinhole_camera_model.png) + +Real lenses usually have some distortion, mostly radial distortion and slight tangential distortion. +So, the above model is extended as: + +\f[\begin{array}{l} +\vecthree{x}{y}{z} = R \vecthree{X}{Y}{Z} + t \\ +x' = x/z \\ +y' = y/z \\ +x'' = x' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} + 2 p_1 x' y' + p_2(r^2 + 2 x'^2) + s_1 r^2 + s_2 r^4 \\ +y'' = y' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} + p_1 (r^2 + 2 y'^2) + 2 p_2 x' y' + s_3 r^2 + s_4 r^4 \\ +\text{where} \quad r^2 = x'^2 + y'^2 \\ +u = f_x*x'' + c_x \\ +v = f_y*y'' + c_y +\end{array}\f] + +\f$k_1\f$, \f$k_2\f$, \f$k_3\f$, \f$k_4\f$, \f$k_5\f$, and \f$k_6\f$ are radial distortion coefficients. \f$p_1\f$ and \f$p_2\f$ are +tangential distortion coefficients. \f$s_1\f$, \f$s_2\f$, \f$s_3\f$, and \f$s_4\f$, are the thin prism distortion +coefficients. Higher-order coefficients are not considered in OpenCV. + +The next figures show two common types of radial distortion: barrel distortion (typically \f$ k_1 < 0 \f$) and pincushion distortion (typically \f$ k_1 > 0 \f$). + +![](pics/distortion_examples.png) +![](pics/distortion_examples2.png) + +In some cases the image sensor may be tilted in order to focus an oblique plane in front of the +camera (Scheimpfug condition). This can be useful for particle image velocimetry (PIV) or +triangulation with a laser fan. The tilt causes a perspective distortion of \f$x''\f$ and +\f$y''\f$. This distortion can be modelled in the following way, see e.g. @cite Louhichi07. + +\f[\begin{array}{l} +s\vecthree{x'''}{y'''}{1} = +\vecthreethree{R_{33}(\tau_x, \tau_y)}{0}{-R_{13}(\tau_x, \tau_y)} +{0}{R_{33}(\tau_x, \tau_y)}{-R_{23}(\tau_x, \tau_y)} +{0}{0}{1} R(\tau_x, \tau_y) \vecthree{x''}{y''}{1}\\ +u = f_x*x''' + c_x \\ +v = f_y*y''' + c_y +\end{array}\f] + +where the matrix \f$R(\tau_x, \tau_y)\f$ is defined by two rotations with angular parameter \f$\tau_x\f$ +and \f$\tau_y\f$, respectively, + +\f[ +R(\tau_x, \tau_y) = +\vecthreethree{\cos(\tau_y)}{0}{-\sin(\tau_y)}{0}{1}{0}{\sin(\tau_y)}{0}{\cos(\tau_y)} +\vecthreethree{1}{0}{0}{0}{\cos(\tau_x)}{\sin(\tau_x)}{0}{-\sin(\tau_x)}{\cos(\tau_x)} = +\vecthreethree{\cos(\tau_y)}{\sin(\tau_y)\sin(\tau_x)}{-\sin(\tau_y)\cos(\tau_x)} +{0}{\cos(\tau_x)}{\sin(\tau_x)} +{\sin(\tau_y)}{-\cos(\tau_y)\sin(\tau_x)}{\cos(\tau_y)\cos(\tau_x)}. +\f] + +In the functions below the coefficients are passed or returned as + +\f[(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f] + +vector. That is, if the vector contains four elements, it means that \f$k_3=0\f$ . The distortion +coefficients do not depend on the scene viewed. Thus, they also belong to the intrinsic camera +parameters. And they remain the same regardless of the captured image resolution. If, for example, a +camera has been calibrated on images of 320 x 240 resolution, absolutely the same distortion +coefficients can be used for 640 x 480 images from the same camera while \f$f_x\f$, \f$f_y\f$, \f$c_x\f$, and +\f$c_y\f$ need to be scaled appropriately. + +The functions below use the above model to do the following: + +- Project 3D points to the image plane given intrinsic and extrinsic parameters. +- Compute extrinsic parameters given intrinsic parameters, a few 3D points, and their +projections. +- Estimate intrinsic and extrinsic camera parameters from several views of a known calibration +pattern (every view is described by several 3D-2D point correspondences). +- Estimate the relative position and orientation of the stereo camera "heads" and compute the +*rectification* transformation that makes the camera optical axes parallel. + +@note + - A calibration sample for 3 cameras in horizontal position can be found at + opencv_source_code/samples/cpp/3calibration.cpp + - A calibration sample based on a sequence of images can be found at + opencv_source_code/samples/cpp/calibration.cpp + - A calibration sample in order to do 3D reconstruction can be found at + opencv_source_code/samples/cpp/build3dmodel.cpp + - A calibration sample of an artificially generated camera and chessboard patterns can be + found at opencv_source_code/samples/cpp/calibration_artificial.cpp + - A calibration example on stereo calibration can be found at + opencv_source_code/samples/cpp/stereo_calib.cpp + - A calibration example on stereo matching can be found at + opencv_source_code/samples/cpp/stereo_match.cpp + - (Python) A camera calibration sample can be found at + opencv_source_code/samples/python/calibrate.py + + @{ + @defgroup calib3d_fisheye Fisheye camera model + + Definitions: Let P be a point in 3D of coordinates X in the world reference frame (stored in the + matrix X) The coordinate vector of P in the camera reference frame is: + + \f[Xc = R X + T\f] + + where R is the rotation matrix corresponding to the rotation vector om: R = rodrigues(om); call x, y + and z the 3 coordinates of Xc: + + \f[x = Xc_1 \\ y = Xc_2 \\ z = Xc_3\f] + + The pinhole projection coordinates of P is [a; b] where + + \f[a = x / z \ and \ b = y / z \\ r^2 = a^2 + b^2 \\ \theta = atan(r)\f] + + Fisheye distortion: + + \f[\theta_d = \theta (1 + k_1 \theta^2 + k_2 \theta^4 + k_3 \theta^6 + k_4 \theta^8)\f] + + The distorted point coordinates are [x'; y'] where + + \f[x' = (\theta_d / r) a \\ y' = (\theta_d / r) b \f] + + Finally, conversion into pixel coordinates: The final pixel coordinates vector [u; v] where: + + \f[u = f_x (x' + \alpha y') + c_x \\ + v = f_y y' + c_y\f] + + @defgroup calib3d_c C API + + @} + */ + +namespace cv +{ + +//! @addtogroup calib3d +//! @{ + +//! type of the robust estimation algorithm +enum { LMEDS = 4, //!< least-median of squares algorithm + RANSAC = 8, //!< RANSAC algorithm + RHO = 16 //!< RHO algorithm + }; + +enum { SOLVEPNP_ITERATIVE = 0, + SOLVEPNP_EPNP = 1, //!< EPnP: Efficient Perspective-n-Point Camera Pose Estimation @cite lepetit2009epnp + SOLVEPNP_P3P = 2, //!< Complete Solution Classification for the Perspective-Three-Point Problem @cite gao2003complete + SOLVEPNP_DLS = 3, //!< A Direct Least-Squares (DLS) Method for PnP @cite hesch2011direct + SOLVEPNP_UPNP = 4, //!< Exhaustive Linearization for Robust Camera Pose and Focal Length Estimation @cite penate2013exhaustive + SOLVEPNP_AP3P = 5, //!< An Efficient Algebraic Solution to the Perspective-Three-Point Problem @cite Ke17 + SOLVEPNP_MAX_COUNT //!< Used for count +}; + +enum { CALIB_CB_ADAPTIVE_THRESH = 1, + CALIB_CB_NORMALIZE_IMAGE = 2, + CALIB_CB_FILTER_QUADS = 4, + CALIB_CB_FAST_CHECK = 8 + }; + +enum { CALIB_CB_SYMMETRIC_GRID = 1, + CALIB_CB_ASYMMETRIC_GRID = 2, + CALIB_CB_CLUSTERING = 4 + }; + +enum { CALIB_USE_INTRINSIC_GUESS = 0x00001, + CALIB_FIX_ASPECT_RATIO = 0x00002, + CALIB_FIX_PRINCIPAL_POINT = 0x00004, + CALIB_ZERO_TANGENT_DIST = 0x00008, + CALIB_FIX_FOCAL_LENGTH = 0x00010, + CALIB_FIX_K1 = 0x00020, + CALIB_FIX_K2 = 0x00040, + CALIB_FIX_K3 = 0x00080, + CALIB_FIX_K4 = 0x00800, + CALIB_FIX_K5 = 0x01000, + CALIB_FIX_K6 = 0x02000, + CALIB_RATIONAL_MODEL = 0x04000, + CALIB_THIN_PRISM_MODEL = 0x08000, + CALIB_FIX_S1_S2_S3_S4 = 0x10000, + CALIB_TILTED_MODEL = 0x40000, + CALIB_FIX_TAUX_TAUY = 0x80000, + CALIB_USE_QR = 0x100000, //!< use QR instead of SVD decomposition for solving. Faster but potentially less precise + CALIB_FIX_TANGENT_DIST = 0x200000, + // only for stereo + CALIB_FIX_INTRINSIC = 0x00100, + CALIB_SAME_FOCAL_LENGTH = 0x00200, + // for stereo rectification + CALIB_ZERO_DISPARITY = 0x00400, + CALIB_USE_LU = (1 << 17), //!< use LU instead of SVD decomposition for solving. much faster but potentially less precise + CALIB_USE_EXTRINSIC_GUESS = (1 << 22), //!< for stereoCalibrate + }; + +//! the algorithm for finding fundamental matrix +enum { FM_7POINT = 1, //!< 7-point algorithm + FM_8POINT = 2, //!< 8-point algorithm + FM_LMEDS = 4, //!< least-median algorithm. 7-point algorithm is used. + FM_RANSAC = 8 //!< RANSAC algorithm. It needs at least 15 points. 7-point algorithm is used. + }; + + + +/** @brief Converts a rotation matrix to a rotation vector or vice versa. + +@param src Input rotation vector (3x1 or 1x3) or rotation matrix (3x3). +@param dst Output rotation matrix (3x3) or rotation vector (3x1 or 1x3), respectively. +@param jacobian Optional output Jacobian matrix, 3x9 or 9x3, which is a matrix of partial +derivatives of the output array components with respect to the input array components. + +\f[\begin{array}{l} \theta \leftarrow norm(r) \\ r \leftarrow r/ \theta \\ R = \cos{\theta} I + (1- \cos{\theta} ) r r^T + \sin{\theta} \vecthreethree{0}{-r_z}{r_y}{r_z}{0}{-r_x}{-r_y}{r_x}{0} \end{array}\f] + +Inverse transformation can be also done easily, since + +\f[\sin ( \theta ) \vecthreethree{0}{-r_z}{r_y}{r_z}{0}{-r_x}{-r_y}{r_x}{0} = \frac{R - R^T}{2}\f] + +A rotation vector is a convenient and most compact representation of a rotation matrix (since any +rotation matrix has just 3 degrees of freedom). The representation is used in the global 3D geometry +optimization procedures like calibrateCamera, stereoCalibrate, or solvePnP . + */ +CV_EXPORTS_W void Rodrigues( InputArray src, OutputArray dst, OutputArray jacobian = noArray() ); + +/** @example samples/cpp/tutorial_code/features2D/Homography/pose_from_homography.cpp +An example program about pose estimation from coplanar points + +Check @ref tutorial_homography "the corresponding tutorial" for more details +*/ + +/** @brief Finds a perspective transformation between two planes. + +@param srcPoints Coordinates of the points in the original plane, a matrix of the type CV_32FC2 +or vector\ . +@param dstPoints Coordinates of the points in the target plane, a matrix of the type CV_32FC2 or +a vector\ . +@param method Method used to compute a homography matrix. The following methods are possible: +- **0** - a regular method using all the points, i.e., the least squares method +- **RANSAC** - RANSAC-based robust method +- **LMEDS** - Least-Median robust method +- **RHO** - PROSAC-based robust method +@param ransacReprojThreshold Maximum allowed reprojection error to treat a point pair as an inlier +(used in the RANSAC and RHO methods only). That is, if +\f[\| \texttt{dstPoints} _i - \texttt{convertPointsHomogeneous} ( \texttt{H} * \texttt{srcPoints} _i) \|_2 > \texttt{ransacReprojThreshold}\f] +then the point \f$i\f$ is considered as an outlier. If srcPoints and dstPoints are measured in pixels, +it usually makes sense to set this parameter somewhere in the range of 1 to 10. +@param mask Optional output mask set by a robust method ( RANSAC or LMEDS ). Note that the input +mask values are ignored. +@param maxIters The maximum number of RANSAC iterations. +@param confidence Confidence level, between 0 and 1. + +The function finds and returns the perspective transformation \f$H\f$ between the source and the +destination planes: + +\f[s_i \vecthree{x'_i}{y'_i}{1} \sim H \vecthree{x_i}{y_i}{1}\f] + +so that the back-projection error + +\f[\sum _i \left ( x'_i- \frac{h_{11} x_i + h_{12} y_i + h_{13}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2+ \left ( y'_i- \frac{h_{21} x_i + h_{22} y_i + h_{23}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2\f] + +is minimized. If the parameter method is set to the default value 0, the function uses all the point +pairs to compute an initial homography estimate with a simple least-squares scheme. + +However, if not all of the point pairs ( \f$srcPoints_i\f$, \f$dstPoints_i\f$ ) fit the rigid perspective +transformation (that is, there are some outliers), this initial estimate will be poor. In this case, +you can use one of the three robust methods. The methods RANSAC, LMeDS and RHO try many different +random subsets of the corresponding point pairs (of four pairs each, collinear pairs are discarded), estimate the homography matrix +using this subset and a simple least-squares algorithm, and then compute the quality/goodness of the +computed homography (which is the number of inliers for RANSAC or the least median re-projection error for +LMeDS). The best subset is then used to produce the initial estimate of the homography matrix and +the mask of inliers/outliers. + +Regardless of the method, robust or not, the computed homography matrix is refined further (using +inliers only in case of a robust method) with the Levenberg-Marquardt method to reduce the +re-projection error even more. + +The methods RANSAC and RHO can handle practically any ratio of outliers but need a threshold to +distinguish inliers from outliers. The method LMeDS does not need any threshold but it works +correctly only when there are more than 50% of inliers. Finally, if there are no outliers and the +noise is rather small, use the default method (method=0). + +The function is used to find initial intrinsic and extrinsic matrices. Homography matrix is +determined up to a scale. Thus, it is normalized so that \f$h_{33}=1\f$. Note that whenever an \f$H\f$ matrix +cannot be estimated, an empty one will be returned. + +@sa +getAffineTransform, estimateAffine2D, estimateAffinePartial2D, getPerspectiveTransform, warpPerspective, +perspectiveTransform + */ +CV_EXPORTS_W Mat findHomography( InputArray srcPoints, InputArray dstPoints, + int method = 0, double ransacReprojThreshold = 3, + OutputArray mask=noArray(), const int maxIters = 2000, + const double confidence = 0.995); + +/** @overload */ +CV_EXPORTS Mat findHomography( InputArray srcPoints, InputArray dstPoints, + OutputArray mask, int method = 0, double ransacReprojThreshold = 3 ); + +/** @brief Computes an RQ decomposition of 3x3 matrices. + +@param src 3x3 input matrix. +@param mtxR Output 3x3 upper-triangular matrix. +@param mtxQ Output 3x3 orthogonal matrix. +@param Qx Optional output 3x3 rotation matrix around x-axis. +@param Qy Optional output 3x3 rotation matrix around y-axis. +@param Qz Optional output 3x3 rotation matrix around z-axis. + +The function computes a RQ decomposition using the given rotations. This function is used in +decomposeProjectionMatrix to decompose the left 3x3 submatrix of a projection matrix into a camera +and a rotation matrix. + +It optionally returns three rotation matrices, one for each axis, and the three Euler angles in +degrees (as the return value) that could be used in OpenGL. Note, there is always more than one +sequence of rotations about the three principal axes that results in the same orientation of an +object, e.g. see @cite Slabaugh . Returned tree rotation matrices and corresponding three Euler angles +are only one of the possible solutions. + */ +CV_EXPORTS_W Vec3d RQDecomp3x3( InputArray src, OutputArray mtxR, OutputArray mtxQ, + OutputArray Qx = noArray(), + OutputArray Qy = noArray(), + OutputArray Qz = noArray()); + +/** @brief Decomposes a projection matrix into a rotation matrix and a camera matrix. + +@param projMatrix 3x4 input projection matrix P. +@param cameraMatrix Output 3x3 camera matrix K. +@param rotMatrix Output 3x3 external rotation matrix R. +@param transVect Output 4x1 translation vector T. +@param rotMatrixX Optional 3x3 rotation matrix around x-axis. +@param rotMatrixY Optional 3x3 rotation matrix around y-axis. +@param rotMatrixZ Optional 3x3 rotation matrix around z-axis. +@param eulerAngles Optional three-element vector containing three Euler angles of rotation in +degrees. + +The function computes a decomposition of a projection matrix into a calibration and a rotation +matrix and the position of a camera. + +It optionally returns three rotation matrices, one for each axis, and three Euler angles that could +be used in OpenGL. Note, there is always more than one sequence of rotations about the three +principal axes that results in the same orientation of an object, e.g. see @cite Slabaugh . Returned +tree rotation matrices and corresponding three Euler angles are only one of the possible solutions. + +The function is based on RQDecomp3x3 . + */ +CV_EXPORTS_W void decomposeProjectionMatrix( InputArray projMatrix, OutputArray cameraMatrix, + OutputArray rotMatrix, OutputArray transVect, + OutputArray rotMatrixX = noArray(), + OutputArray rotMatrixY = noArray(), + OutputArray rotMatrixZ = noArray(), + OutputArray eulerAngles =noArray() ); + +/** @brief Computes partial derivatives of the matrix product for each multiplied matrix. + +@param A First multiplied matrix. +@param B Second multiplied matrix. +@param dABdA First output derivative matrix d(A\*B)/dA of size +\f$\texttt{A.rows*B.cols} \times {A.rows*A.cols}\f$ . +@param dABdB Second output derivative matrix d(A\*B)/dB of size +\f$\texttt{A.rows*B.cols} \times {B.rows*B.cols}\f$ . + +The function computes partial derivatives of the elements of the matrix product \f$A*B\f$ with regard to +the elements of each of the two input matrices. The function is used to compute the Jacobian +matrices in stereoCalibrate but can also be used in any other similar optimization function. + */ +CV_EXPORTS_W void matMulDeriv( InputArray A, InputArray B, OutputArray dABdA, OutputArray dABdB ); + +/** @brief Combines two rotation-and-shift transformations. + +@param rvec1 First rotation vector. +@param tvec1 First translation vector. +@param rvec2 Second rotation vector. +@param tvec2 Second translation vector. +@param rvec3 Output rotation vector of the superposition. +@param tvec3 Output translation vector of the superposition. +@param dr3dr1 +@param dr3dt1 +@param dr3dr2 +@param dr3dt2 +@param dt3dr1 +@param dt3dt1 +@param dt3dr2 +@param dt3dt2 Optional output derivatives of rvec3 or tvec3 with regard to rvec1, rvec2, tvec1 and +tvec2, respectively. + +The functions compute: + +\f[\begin{array}{l} \texttt{rvec3} = \mathrm{rodrigues} ^{-1} \left ( \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \mathrm{rodrigues} ( \texttt{rvec1} ) \right ) \\ \texttt{tvec3} = \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \texttt{tvec1} + \texttt{tvec2} \end{array} ,\f] + +where \f$\mathrm{rodrigues}\f$ denotes a rotation vector to a rotation matrix transformation, and +\f$\mathrm{rodrigues}^{-1}\f$ denotes the inverse transformation. See Rodrigues for details. + +Also, the functions can compute the derivatives of the output vectors with regards to the input +vectors (see matMulDeriv ). The functions are used inside stereoCalibrate but can also be used in +your own code where Levenberg-Marquardt or another gradient-based solver is used to optimize a +function that contains a matrix multiplication. + */ +CV_EXPORTS_W void composeRT( InputArray rvec1, InputArray tvec1, + InputArray rvec2, InputArray tvec2, + OutputArray rvec3, OutputArray tvec3, + OutputArray dr3dr1 = noArray(), OutputArray dr3dt1 = noArray(), + OutputArray dr3dr2 = noArray(), OutputArray dr3dt2 = noArray(), + OutputArray dt3dr1 = noArray(), OutputArray dt3dt1 = noArray(), + OutputArray dt3dr2 = noArray(), OutputArray dt3dt2 = noArray() ); + +/** @brief Projects 3D points to an image plane. + +@param objectPoints Array of object points, 3xN/Nx3 1-channel or 1xN/Nx1 3-channel (or +vector\ ), where N is the number of points in the view. +@param rvec Rotation vector. See Rodrigues for details. +@param tvec Translation vector. +@param cameraMatrix Camera matrix \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$ . +@param distCoeffs Input vector of distortion coefficients +\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of +4, 5, 8, 12 or 14 elements. If the vector is empty, the zero distortion coefficients are assumed. +@param imagePoints Output array of image points, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel, or +vector\ . +@param jacobian Optional output 2Nx(10+\) jacobian matrix of derivatives of image +points with respect to components of the rotation vector, translation vector, focal lengths, +coordinates of the principal point and the distortion coefficients. In the old interface different +components of the jacobian are returned via different output parameters. +@param aspectRatio Optional "fixed aspect ratio" parameter. If the parameter is not 0, the +function assumes that the aspect ratio (*fx/fy*) is fixed and correspondingly adjusts the jacobian +matrix. + +The function computes projections of 3D points to the image plane given intrinsic and extrinsic +camera parameters. Optionally, the function computes Jacobians - matrices of partial derivatives of +image points coordinates (as functions of all the input parameters) with respect to the particular +parameters, intrinsic and/or extrinsic. The Jacobians are used during the global optimization in +calibrateCamera, solvePnP, and stereoCalibrate . The function itself can also be used to compute a +re-projection error given the current intrinsic and extrinsic parameters. + +@note By setting rvec=tvec=(0,0,0) or by setting cameraMatrix to a 3x3 identity matrix, or by +passing zero distortion coefficients, you can get various useful partial cases of the function. This +means that you can compute the distorted coordinates for a sparse set of points or apply a +perspective transformation (and also compute the derivatives) in the ideal zero-distortion setup. + */ +CV_EXPORTS_W void projectPoints( InputArray objectPoints, + InputArray rvec, InputArray tvec, + InputArray cameraMatrix, InputArray distCoeffs, + OutputArray imagePoints, + OutputArray jacobian = noArray(), + double aspectRatio = 0 ); + +/** @example samples/cpp/tutorial_code/features2D/Homography/homography_from_camera_displacement.cpp +An example program about homography from the camera displacement + +Check @ref tutorial_homography "the corresponding tutorial" for more details +*/ + +/** @brief Finds an object pose from 3D-2D point correspondences. + +@param objectPoints Array of object points in the object coordinate space, Nx3 1-channel or +1xN/Nx1 3-channel, where N is the number of points. vector\ can be also passed here. +@param imagePoints Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, +where N is the number of points. vector\ can be also passed here. +@param cameraMatrix Input camera matrix \f$A = \vecthreethree{fx}{0}{cx}{0}{fy}{cy}{0}{0}{1}\f$ . +@param distCoeffs Input vector of distortion coefficients +\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of +4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are +assumed. +@param rvec Output rotation vector (see @ref Rodrigues ) that, together with tvec , brings points from +the model coordinate system to the camera coordinate system. +@param tvec Output translation vector. +@param useExtrinsicGuess Parameter used for #SOLVEPNP_ITERATIVE. If true (1), the function uses +the provided rvec and tvec values as initial approximations of the rotation and translation +vectors, respectively, and further optimizes them. +@param flags Method for solving a PnP problem: +- **SOLVEPNP_ITERATIVE** Iterative method is based on Levenberg-Marquardt optimization. In +this case the function finds such a pose that minimizes reprojection error, that is the sum +of squared distances between the observed projections imagePoints and the projected (using +projectPoints ) objectPoints . +- **SOLVEPNP_P3P** Method is based on the paper of X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang +"Complete Solution Classification for the Perspective-Three-Point Problem" (@cite gao2003complete). +In this case the function requires exactly four object and image points. +- **SOLVEPNP_AP3P** Method is based on the paper of T. Ke, S. Roumeliotis +"An Efficient Algebraic Solution to the Perspective-Three-Point Problem" (@cite Ke17). +In this case the function requires exactly four object and image points. +- **SOLVEPNP_EPNP** Method has been introduced by F.Moreno-Noguer, V.Lepetit and P.Fua in the +paper "EPnP: Efficient Perspective-n-Point Camera Pose Estimation" (@cite lepetit2009epnp). +- **SOLVEPNP_DLS** Method is based on the paper of Joel A. Hesch and Stergios I. Roumeliotis. +"A Direct Least-Squares (DLS) Method for PnP" (@cite hesch2011direct). +- **SOLVEPNP_UPNP** Method is based on the paper of A.Penate-Sanchez, J.Andrade-Cetto, +F.Moreno-Noguer. "Exhaustive Linearization for Robust Camera Pose and Focal Length +Estimation" (@cite penate2013exhaustive). In this case the function also estimates the parameters \f$f_x\f$ and \f$f_y\f$ +assuming that both have the same value. Then the cameraMatrix is updated with the estimated +focal length. +- **SOLVEPNP_AP3P** Method is based on the paper of Tong Ke and Stergios I. Roumeliotis. +"An Efficient Algebraic Solution to the Perspective-Three-Point Problem" (@cite Ke17). In this case the +function requires exactly four object and image points. + +The function estimates the object pose given a set of object points, their corresponding image +projections, as well as the camera matrix and the distortion coefficients, see the figure below +(more precisely, the X-axis of the camera frame is pointing to the right, the Y-axis downward +and the Z-axis forward). + +![](pnp.jpg) + +Points expressed in the world frame \f$ \bf{X}_w \f$ are projected into the image plane \f$ \left[ u, v \right] \f$ +using the perspective projection model \f$ \Pi \f$ and the camera intrinsic parameters matrix \f$ \bf{A} \f$: + +\f[ + \begin{align*} + \begin{bmatrix} + u \\ + v \\ + 1 + \end{bmatrix} &= + \bf{A} \hspace{0.1em} \Pi \hspace{0.2em} ^{c}\bf{M}_w + \begin{bmatrix} + X_{w} \\ + Y_{w} \\ + Z_{w} \\ + 1 + \end{bmatrix} \\ + \begin{bmatrix} + u \\ + v \\ + 1 + \end{bmatrix} &= + \begin{bmatrix} + f_x & 0 & c_x \\ + 0 & f_y & c_y \\ + 0 & 0 & 1 + \end{bmatrix} + \begin{bmatrix} + 1 & 0 & 0 & 0 \\ + 0 & 1 & 0 & 0 \\ + 0 & 0 & 1 & 0 + \end{bmatrix} + \begin{bmatrix} + r_{11} & r_{12} & r_{13} & t_x \\ + r_{21} & r_{22} & r_{23} & t_y \\ + r_{31} & r_{32} & r_{33} & t_z \\ + 0 & 0 & 0 & 1 + \end{bmatrix} + \begin{bmatrix} + X_{w} \\ + Y_{w} \\ + Z_{w} \\ + 1 + \end{bmatrix} + \end{align*} +\f] + +The estimated pose is thus the rotation (`rvec`) and the translation (`tvec`) vectors that allow to transform +a 3D point expressed in the world frame into the camera frame: + +\f[ + \begin{align*} + \begin{bmatrix} + X_c \\ + Y_c \\ + Z_c \\ + 1 + \end{bmatrix} &= + \hspace{0.2em} ^{c}\bf{M}_w + \begin{bmatrix} + X_{w} \\ + Y_{w} \\ + Z_{w} \\ + 1 + \end{bmatrix} \\ + \begin{bmatrix} + X_c \\ + Y_c \\ + Z_c \\ + 1 + \end{bmatrix} &= + \begin{bmatrix} + r_{11} & r_{12} & r_{13} & t_x \\ + r_{21} & r_{22} & r_{23} & t_y \\ + r_{31} & r_{32} & r_{33} & t_z \\ + 0 & 0 & 0 & 1 + \end{bmatrix} + \begin{bmatrix} + X_{w} \\ + Y_{w} \\ + Z_{w} \\ + 1 + \end{bmatrix} + \end{align*} +\f] + +@note + - An example of how to use solvePnP for planar augmented reality can be found at + opencv_source_code/samples/python/plane_ar.py + - If you are using Python: + - Numpy array slices won't work as input because solvePnP requires contiguous + arrays (enforced by the assertion using cv::Mat::checkVector() around line 55 of + modules/calib3d/src/solvepnp.cpp version 2.4.9) + - The P3P algorithm requires image points to be in an array of shape (N,1,2) due + to its calling of cv::undistortPoints (around line 75 of modules/calib3d/src/solvepnp.cpp version 2.4.9) + which requires 2-channel information. + - Thus, given some data D = np.array(...) where D.shape = (N,M), in order to use a subset of + it as, e.g., imagePoints, one must effectively copy it into a new array: imagePoints = + np.ascontiguousarray(D[:,:2]).reshape((N,1,2)) + - The methods **SOLVEPNP_DLS** and **SOLVEPNP_UPNP** cannot be used as the current implementations are + unstable and sometimes give completely wrong results. If you pass one of these two + flags, **SOLVEPNP_EPNP** method will be used instead. + - The minimum number of points is 4 in the general case. In the case of **SOLVEPNP_P3P** and **SOLVEPNP_AP3P** + methods, it is required to use exactly 4 points (the first 3 points are used to estimate all the solutions + of the P3P problem, the last one is used to retain the best solution that minimizes the reprojection error). + - With **SOLVEPNP_ITERATIVE** method and `useExtrinsicGuess=true`, the minimum number of points is 3 (3 points + are sufficient to compute a pose but there are up to 4 solutions). The initial solution should be close to the + global solution to converge. + */ +CV_EXPORTS_W bool solvePnP( InputArray objectPoints, InputArray imagePoints, + InputArray cameraMatrix, InputArray distCoeffs, + OutputArray rvec, OutputArray tvec, + bool useExtrinsicGuess = false, int flags = SOLVEPNP_ITERATIVE ); + +/** @brief Finds an object pose from 3D-2D point correspondences using the RANSAC scheme. + +@param objectPoints Array of object points in the object coordinate space, Nx3 1-channel or +1xN/Nx1 3-channel, where N is the number of points. vector\ can be also passed here. +@param imagePoints Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, +where N is the number of points. vector\ can be also passed here. +@param cameraMatrix Input camera matrix \f$A = \vecthreethree{fx}{0}{cx}{0}{fy}{cy}{0}{0}{1}\f$ . +@param distCoeffs Input vector of distortion coefficients +\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of +4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are +assumed. +@param rvec Output rotation vector (see Rodrigues ) that, together with tvec , brings points from +the model coordinate system to the camera coordinate system. +@param tvec Output translation vector. +@param useExtrinsicGuess Parameter used for SOLVEPNP_ITERATIVE. If true (1), the function uses +the provided rvec and tvec values as initial approximations of the rotation and translation +vectors, respectively, and further optimizes them. +@param iterationsCount Number of iterations. +@param reprojectionError Inlier threshold value used by the RANSAC procedure. The parameter value +is the maximum allowed distance between the observed and computed point projections to consider it +an inlier. +@param confidence The probability that the algorithm produces a useful result. +@param inliers Output vector that contains indices of inliers in objectPoints and imagePoints . +@param flags Method for solving a PnP problem (see solvePnP ). + +The function estimates an object pose given a set of object points, their corresponding image +projections, as well as the camera matrix and the distortion coefficients. This function finds such +a pose that minimizes reprojection error, that is, the sum of squared distances between the observed +projections imagePoints and the projected (using projectPoints ) objectPoints. The use of RANSAC +makes the function resistant to outliers. + +@note + - An example of how to use solvePNPRansac for object detection can be found at + opencv_source_code/samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/ + - The default method used to estimate the camera pose for the Minimal Sample Sets step + is #SOLVEPNP_EPNP. Exceptions are: + - if you choose #SOLVEPNP_P3P or #SOLVEPNP_AP3P, these methods will be used. + - if the number of input points is equal to 4, #SOLVEPNP_P3P is used. + - The method used to estimate the camera pose using all the inliers is defined by the + flags parameters unless it is equal to #SOLVEPNP_P3P or #SOLVEPNP_AP3P. In this case, + the method #SOLVEPNP_EPNP will be used instead. + */ +CV_EXPORTS_W bool solvePnPRansac( InputArray objectPoints, InputArray imagePoints, + InputArray cameraMatrix, InputArray distCoeffs, + OutputArray rvec, OutputArray tvec, + bool useExtrinsicGuess = false, int iterationsCount = 100, + float reprojectionError = 8.0, double confidence = 0.99, + OutputArray inliers = noArray(), int flags = SOLVEPNP_ITERATIVE ); +/** @brief Finds an object pose from 3 3D-2D point correspondences. + +@param objectPoints Array of object points in the object coordinate space, 3x3 1-channel or +1x3/3x1 3-channel. vector\ can be also passed here. +@param imagePoints Array of corresponding image points, 3x2 1-channel or 1x3/3x1 2-channel. + vector\ can be also passed here. +@param cameraMatrix Input camera matrix \f$A = \vecthreethree{fx}{0}{cx}{0}{fy}{cy}{0}{0}{1}\f$ . +@param distCoeffs Input vector of distortion coefficients +\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of +4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are +assumed. +@param rvecs Output rotation vectors (see Rodrigues ) that, together with tvecs , brings points from +the model coordinate system to the camera coordinate system. A P3P problem has up to 4 solutions. +@param tvecs Output translation vectors. +@param flags Method for solving a P3P problem: +- **SOLVEPNP_P3P** Method is based on the paper of X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang +"Complete Solution Classification for the Perspective-Three-Point Problem" (@cite gao2003complete). +- **SOLVEPNP_AP3P** Method is based on the paper of Tong Ke and Stergios I. Roumeliotis. +"An Efficient Algebraic Solution to the Perspective-Three-Point Problem" (@cite Ke17). + +The function estimates the object pose given 3 object points, their corresponding image +projections, as well as the camera matrix and the distortion coefficients. + */ +CV_EXPORTS_W int solveP3P( InputArray objectPoints, InputArray imagePoints, + InputArray cameraMatrix, InputArray distCoeffs, + OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, + int flags ); + +/** @brief Finds an initial camera matrix from 3D-2D point correspondences. + +@param objectPoints Vector of vectors of the calibration pattern points in the calibration pattern +coordinate space. In the old interface all the per-view vectors are concatenated. See +calibrateCamera for details. +@param imagePoints Vector of vectors of the projections of the calibration pattern points. In the +old interface all the per-view vectors are concatenated. +@param imageSize Image size in pixels used to initialize the principal point. +@param aspectRatio If it is zero or negative, both \f$f_x\f$ and \f$f_y\f$ are estimated independently. +Otherwise, \f$f_x = f_y * \texttt{aspectRatio}\f$ . + +The function estimates and returns an initial camera matrix for the camera calibration process. +Currently, the function only supports planar calibration patterns, which are patterns where each +object point has z-coordinate =0. + */ +CV_EXPORTS_W Mat initCameraMatrix2D( InputArrayOfArrays objectPoints, + InputArrayOfArrays imagePoints, + Size imageSize, double aspectRatio = 1.0 ); + +/** @brief Finds the positions of internal corners of the chessboard. + +@param image Source chessboard view. It must be an 8-bit grayscale or color image. +@param patternSize Number of inner corners per a chessboard row and column +( patternSize = cvSize(points_per_row,points_per_colum) = cvSize(columns,rows) ). +@param corners Output array of detected corners. +@param flags Various operation flags that can be zero or a combination of the following values: +- **CALIB_CB_ADAPTIVE_THRESH** Use adaptive thresholding to convert the image to black +and white, rather than a fixed threshold level (computed from the average image brightness). +- **CALIB_CB_NORMALIZE_IMAGE** Normalize the image gamma with equalizeHist before +applying fixed or adaptive thresholding. +- **CALIB_CB_FILTER_QUADS** Use additional criteria (like contour area, perimeter, +square-like shape) to filter out false quads extracted at the contour retrieval stage. +- **CALIB_CB_FAST_CHECK** Run a fast check on the image that looks for chessboard corners, +and shortcut the call if none is found. This can drastically speed up the call in the +degenerate condition when no chessboard is observed. + +The function attempts to determine whether the input image is a view of the chessboard pattern and +locate the internal chessboard corners. The function returns a non-zero value if all of the corners +are found and they are placed in a certain order (row by row, left to right in every row). +Otherwise, if the function fails to find all the corners or reorder them, it returns 0. For example, +a regular chessboard has 8 x 8 squares and 7 x 7 internal corners, that is, points where the black +squares touch each other. The detected coordinates are approximate, and to determine their positions +more accurately, the function calls cornerSubPix. You also may use the function cornerSubPix with +different parameters if returned coordinates are not accurate enough. + +Sample usage of detecting and drawing chessboard corners: : +@code + Size patternsize(8,6); //interior number of corners + Mat gray = ....; //source image + vector corners; //this will be filled by the detected corners + + //CALIB_CB_FAST_CHECK saves a lot of time on images + //that do not contain any chessboard corners + bool patternfound = findChessboardCorners(gray, patternsize, corners, + CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE + + CALIB_CB_FAST_CHECK); + + if(patternfound) + cornerSubPix(gray, corners, Size(11, 11), Size(-1, -1), + TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 30, 0.1)); + + drawChessboardCorners(img, patternsize, Mat(corners), patternfound); +@endcode +@note The function requires white space (like a square-thick border, the wider the better) around +the board to make the detection more robust in various environments. Otherwise, if there is no +border and the background is dark, the outer black squares cannot be segmented properly and so the +square grouping and ordering algorithm fails. + */ +CV_EXPORTS_W bool findChessboardCorners( InputArray image, Size patternSize, OutputArray corners, + int flags = CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE ); + +//! finds subpixel-accurate positions of the chessboard corners +CV_EXPORTS bool find4QuadCornerSubpix( InputArray img, InputOutputArray corners, Size region_size ); + +/** @brief Renders the detected chessboard corners. + +@param image Destination image. It must be an 8-bit color image. +@param patternSize Number of inner corners per a chessboard row and column +(patternSize = cv::Size(points_per_row,points_per_column)). +@param corners Array of detected corners, the output of findChessboardCorners. +@param patternWasFound Parameter indicating whether the complete board was found or not. The +return value of findChessboardCorners should be passed here. + +The function draws individual chessboard corners detected either as red circles if the board was not +found, or as colored corners connected with lines if the board was found. + */ +CV_EXPORTS_W void drawChessboardCorners( InputOutputArray image, Size patternSize, + InputArray corners, bool patternWasFound ); + +/** @brief Draw axes of the world/object coordinate system from pose estimation. @sa solvePnP + +@param image Input/output image. It must have 1 or 3 channels. The number of channels is not altered. +@param cameraMatrix Input 3x3 floating-point matrix of camera intrinsic parameters. +\f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ +@param distCoeffs Input vector of distortion coefficients +\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of +4, 5, 8, 12 or 14 elements. If the vector is empty, the zero distortion coefficients are assumed. +@param rvec Rotation vector (see @ref Rodrigues ) that, together with tvec , brings points from +the model coordinate system to the camera coordinate system. +@param tvec Translation vector. +@param length Length of the painted axes in the same unit than tvec (usually in meters). +@param thickness Line thickness of the painted axes. + +This function draws the axes of the world/object coordinate system w.r.t. to the camera frame. +OX is drawn in red, OY in green and OZ in blue. + */ +CV_EXPORTS_W void drawFrameAxes(InputOutputArray image, InputArray cameraMatrix, InputArray distCoeffs, + InputArray rvec, InputArray tvec, float length, int thickness=3); + +struct CV_EXPORTS_W_SIMPLE CirclesGridFinderParameters +{ + CV_WRAP CirclesGridFinderParameters(); + CV_PROP_RW cv::Size2f densityNeighborhoodSize; + CV_PROP_RW float minDensity; + CV_PROP_RW int kmeansAttempts; + CV_PROP_RW int minDistanceToAddKeypoint; + CV_PROP_RW int keypointScale; + CV_PROP_RW float minGraphConfidence; + CV_PROP_RW float vertexGain; + CV_PROP_RW float vertexPenalty; + CV_PROP_RW float existingVertexGain; + CV_PROP_RW float edgeGain; + CV_PROP_RW float edgePenalty; + CV_PROP_RW float convexHullFactor; + CV_PROP_RW float minRNGEdgeSwitchDist; + + enum GridType + { + SYMMETRIC_GRID, ASYMMETRIC_GRID + }; + GridType gridType; +}; + +struct CV_EXPORTS_W_SIMPLE CirclesGridFinderParameters2 : public CirclesGridFinderParameters +{ + CV_WRAP CirclesGridFinderParameters2(); + + CV_PROP_RW float squareSize; //!< Distance between two adjacent points. Used by CALIB_CB_CLUSTERING. + CV_PROP_RW float maxRectifiedDistance; //!< Max deviation from predicion. Used by CALIB_CB_CLUSTERING. +}; + +/** @brief Finds centers in the grid of circles. + +@param image grid view of input circles; it must be an 8-bit grayscale or color image. +@param patternSize number of circles per row and column +( patternSize = Size(points_per_row, points_per_colum) ). +@param centers output array of detected centers. +@param flags various operation flags that can be one of the following values: +- **CALIB_CB_SYMMETRIC_GRID** uses symmetric pattern of circles. +- **CALIB_CB_ASYMMETRIC_GRID** uses asymmetric pattern of circles. +- **CALIB_CB_CLUSTERING** uses a special algorithm for grid detection. It is more robust to +perspective distortions but much more sensitive to background clutter. +@param blobDetector feature detector that finds blobs like dark circles on light background. +@param parameters struct for finding circles in a grid pattern. + +The function attempts to determine whether the input image contains a grid of circles. If it is, the +function locates centers of the circles. The function returns a non-zero value if all of the centers +have been found and they have been placed in a certain order (row by row, left to right in every +row). Otherwise, if the function fails to find all the corners or reorder them, it returns 0. + +Sample usage of detecting and drawing the centers of circles: : +@code + Size patternsize(7,7); //number of centers + Mat gray = ....; //source image + vector centers; //this will be filled by the detected centers + + bool patternfound = findCirclesGrid(gray, patternsize, centers); + + drawChessboardCorners(img, patternsize, Mat(centers), patternfound); +@endcode +@note The function requires white space (like a square-thick border, the wider the better) around +the board to make the detection more robust in various environments. + */ +CV_EXPORTS_W bool findCirclesGrid( InputArray image, Size patternSize, + OutputArray centers, int flags, + const Ptr &blobDetector, + CirclesGridFinderParameters parameters); + +/** @overload */ +CV_EXPORTS_W bool findCirclesGrid2( InputArray image, Size patternSize, + OutputArray centers, int flags, + const Ptr &blobDetector, + CirclesGridFinderParameters2 parameters); + +/** @overload */ +CV_EXPORTS_W bool findCirclesGrid( InputArray image, Size patternSize, + OutputArray centers, int flags = CALIB_CB_SYMMETRIC_GRID, + const Ptr &blobDetector = SimpleBlobDetector::create()); + +/** @brief Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern. + +@param objectPoints In the new interface it is a vector of vectors of calibration pattern points in +the calibration pattern coordinate space (e.g. std::vector>). The outer +vector contains as many elements as the number of the pattern views. If the same calibration pattern +is shown in each view and it is fully visible, all the vectors will be the same. Although, it is +possible to use partially occluded patterns, or even different patterns in different views. Then, +the vectors will be different. The points are 3D, but since they are in a pattern coordinate system, +then, if the rig is planar, it may make sense to put the model to a XY coordinate plane so that +Z-coordinate of each input object point is 0. +In the old interface all the vectors of object points from different views are concatenated +together. +@param imagePoints In the new interface it is a vector of vectors of the projections of calibration +pattern points (e.g. std::vector>). imagePoints.size() and +objectPoints.size() and imagePoints[i].size() must be equal to objectPoints[i].size() for each i. +In the old interface all the vectors of object points from different views are concatenated +together. +@param imageSize Size of the image used only to initialize the intrinsic camera matrix. +@param cameraMatrix Output 3x3 floating-point camera matrix +\f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . If CV\_CALIB\_USE\_INTRINSIC\_GUESS +and/or CALIB_FIX_ASPECT_RATIO are specified, some or all of fx, fy, cx, cy must be +initialized before calling the function. +@param distCoeffs Output vector of distortion coefficients +\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of +4, 5, 8, 12 or 14 elements. +@param rvecs Output vector of rotation vectors (see Rodrigues ) estimated for each pattern view +(e.g. std::vector>). That is, each k-th rotation vector together with the corresponding +k-th translation vector (see the next output parameter description) brings the calibration pattern +from the model coordinate space (in which object points are specified) to the world coordinate +space, that is, a real position of the calibration pattern in the k-th pattern view (k=0.. *M* -1). +@param tvecs Output vector of translation vectors estimated for each pattern view. +@param stdDeviationsIntrinsics Output vector of standard deviations estimated for intrinsic parameters. + Order of deviations values: +\f$(f_x, f_y, c_x, c_y, k_1, k_2, p_1, p_2, k_3, k_4, k_5, k_6 , s_1, s_2, s_3, + s_4, \tau_x, \tau_y)\f$ If one of parameters is not estimated, it's deviation is equals to zero. +@param stdDeviationsExtrinsics Output vector of standard deviations estimated for extrinsic parameters. + Order of deviations values: \f$(R_1, T_1, \dotsc , R_M, T_M)\f$ where M is number of pattern views, + \f$R_i, T_i\f$ are concatenated 1x3 vectors. + @param perViewErrors Output vector of the RMS re-projection error estimated for each pattern view. +@param flags Different flags that may be zero or a combination of the following values: +- **CALIB_USE_INTRINSIC_GUESS** cameraMatrix contains valid initial values of +fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image +center ( imageSize is used), and focal distances are computed in a least-squares fashion. +Note, that if intrinsic parameters are known, there is no need to use this function just to +estimate extrinsic parameters. Use solvePnP instead. +- **CALIB_FIX_PRINCIPAL_POINT** The principal point is not changed during the global +optimization. It stays at the center or at a different location specified when +CALIB_USE_INTRINSIC_GUESS is set too. +- **CALIB_FIX_ASPECT_RATIO** The functions considers only fy as a free parameter. The +ratio fx/fy stays the same as in the input cameraMatrix . When +CALIB_USE_INTRINSIC_GUESS is not set, the actual input values of fx and fy are +ignored, only their ratio is computed and used further. +- **CALIB_ZERO_TANGENT_DIST** Tangential distortion coefficients \f$(p_1, p_2)\f$ are set +to zeros and stay zero. +- **CALIB_FIX_K1,...,CALIB_FIX_K6** The corresponding radial distortion +coefficient is not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is +set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. +- **CALIB_RATIONAL_MODEL** Coefficients k4, k5, and k6 are enabled. To provide the +backward compatibility, this extra flag should be explicitly specified to make the +calibration function use the rational model and return 8 coefficients. If the flag is not +set, the function computes and returns only 5 distortion coefficients. +- **CALIB_THIN_PRISM_MODEL** Coefficients s1, s2, s3 and s4 are enabled. To provide the +backward compatibility, this extra flag should be explicitly specified to make the +calibration function use the thin prism model and return 12 coefficients. If the flag is not +set, the function computes and returns only 5 distortion coefficients. +- **CALIB_FIX_S1_S2_S3_S4** The thin prism distortion coefficients are not changed during +the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the +supplied distCoeffs matrix is used. Otherwise, it is set to 0. +- **CALIB_TILTED_MODEL** Coefficients tauX and tauY are enabled. To provide the +backward compatibility, this extra flag should be explicitly specified to make the +calibration function use the tilted sensor model and return 14 coefficients. If the flag is not +set, the function computes and returns only 5 distortion coefficients. +- **CALIB_FIX_TAUX_TAUY** The coefficients of the tilted sensor model are not changed during +the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the +supplied distCoeffs matrix is used. Otherwise, it is set to 0. +@param criteria Termination criteria for the iterative optimization algorithm. + +@return the overall RMS re-projection error. + +The function estimates the intrinsic camera parameters and extrinsic parameters for each of the +views. The algorithm is based on @cite Zhang2000 and @cite BouguetMCT . The coordinates of 3D object +points and their corresponding 2D projections in each view must be specified. That may be achieved +by using an object with a known geometry and easily detectable feature points. Such an object is +called a calibration rig or calibration pattern, and OpenCV has built-in support for a chessboard as +a calibration rig (see findChessboardCorners ). Currently, initialization of intrinsic parameters +(when CALIB_USE_INTRINSIC_GUESS is not set) is only implemented for planar calibration +patterns (where Z-coordinates of the object points must be all zeros). 3D calibration rigs can also +be used as long as initial cameraMatrix is provided. + +The algorithm performs the following steps: + +- Compute the initial intrinsic parameters (the option only available for planar calibration + patterns) or read them from the input parameters. The distortion coefficients are all set to + zeros initially unless some of CALIB_FIX_K? are specified. + +- Estimate the initial camera pose as if the intrinsic parameters have been already known. This is + done using solvePnP . + +- Run the global Levenberg-Marquardt optimization algorithm to minimize the reprojection error, + that is, the total sum of squared distances between the observed feature points imagePoints and + the projected (using the current estimates for camera parameters and the poses) object points + objectPoints. See projectPoints for details. + +@note + If you use a non-square (=non-NxN) grid and findChessboardCorners for calibration, and + calibrateCamera returns bad values (zero distortion coefficients, an image center very far from + (w/2-0.5,h/2-0.5), and/or large differences between \f$f_x\f$ and \f$f_y\f$ (ratios of 10:1 or more)), + then you have probably used patternSize=cvSize(rows,cols) instead of using + patternSize=cvSize(cols,rows) in findChessboardCorners . + +@sa + findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate, undistort + */ +CV_EXPORTS_AS(calibrateCameraExtended) double calibrateCamera( InputArrayOfArrays objectPoints, + InputArrayOfArrays imagePoints, Size imageSize, + InputOutputArray cameraMatrix, InputOutputArray distCoeffs, + OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, + OutputArray stdDeviationsIntrinsics, + OutputArray stdDeviationsExtrinsics, + OutputArray perViewErrors, + int flags = 0, TermCriteria criteria = TermCriteria( + TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON) ); + +/** @overload double calibrateCamera( InputArrayOfArrays objectPoints, + InputArrayOfArrays imagePoints, Size imageSize, + InputOutputArray cameraMatrix, InputOutputArray distCoeffs, + OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, + OutputArray stdDeviations, OutputArray perViewErrors, + int flags = 0, TermCriteria criteria = TermCriteria( + TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON) ) + */ +CV_EXPORTS_W double calibrateCamera( InputArrayOfArrays objectPoints, + InputArrayOfArrays imagePoints, Size imageSize, + InputOutputArray cameraMatrix, InputOutputArray distCoeffs, + OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, + int flags = 0, TermCriteria criteria = TermCriteria( + TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON) ); + +/** @brief Computes useful camera characteristics from the camera matrix. + +@param cameraMatrix Input camera matrix that can be estimated by calibrateCamera or +stereoCalibrate . +@param imageSize Input image size in pixels. +@param apertureWidth Physical width in mm of the sensor. +@param apertureHeight Physical height in mm of the sensor. +@param fovx Output field of view in degrees along the horizontal sensor axis. +@param fovy Output field of view in degrees along the vertical sensor axis. +@param focalLength Focal length of the lens in mm. +@param principalPoint Principal point in mm. +@param aspectRatio \f$f_y/f_x\f$ + +The function computes various useful camera characteristics from the previously estimated camera +matrix. + +@note + Do keep in mind that the unity measure 'mm' stands for whatever unit of measure one chooses for + the chessboard pitch (it can thus be any value). + */ +CV_EXPORTS_W void calibrationMatrixValues( InputArray cameraMatrix, Size imageSize, + double apertureWidth, double apertureHeight, + CV_OUT double& fovx, CV_OUT double& fovy, + CV_OUT double& focalLength, CV_OUT Point2d& principalPoint, + CV_OUT double& aspectRatio ); + +/** @brief Calibrates the stereo camera. + +@param objectPoints Vector of vectors of the calibration pattern points. +@param imagePoints1 Vector of vectors of the projections of the calibration pattern points, +observed by the first camera. +@param imagePoints2 Vector of vectors of the projections of the calibration pattern points, +observed by the second camera. +@param cameraMatrix1 Input/output first camera matrix: +\f$\vecthreethree{f_x^{(j)}}{0}{c_x^{(j)}}{0}{f_y^{(j)}}{c_y^{(j)}}{0}{0}{1}\f$ , \f$j = 0,\, 1\f$ . If +any of CALIB_USE_INTRINSIC_GUESS , CALIB_FIX_ASPECT_RATIO , +CALIB_FIX_INTRINSIC , or CALIB_FIX_FOCAL_LENGTH are specified, some or all of the +matrix components must be initialized. See the flags description for details. +@param distCoeffs1 Input/output vector of distortion coefficients +\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of +4, 5, 8, 12 or 14 elements. The output vector length depends on the flags. +@param cameraMatrix2 Input/output second camera matrix. The parameter is similar to cameraMatrix1 +@param distCoeffs2 Input/output lens distortion coefficients for the second camera. The parameter +is similar to distCoeffs1 . +@param imageSize Size of the image used only to initialize intrinsic camera matrix. +@param R Output rotation matrix between the 1st and the 2nd camera coordinate systems. +@param T Output translation vector between the coordinate systems of the cameras. +@param E Output essential matrix. +@param F Output fundamental matrix. +@param perViewErrors Output vector of the RMS re-projection error estimated for each pattern view. +@param flags Different flags that may be zero or a combination of the following values: +- **CALIB_FIX_INTRINSIC** Fix cameraMatrix? and distCoeffs? so that only R, T, E , and F +matrices are estimated. +- **CALIB_USE_INTRINSIC_GUESS** Optimize some or all of the intrinsic parameters +according to the specified flags. Initial values are provided by the user. +- **CALIB_USE_EXTRINSIC_GUESS** R, T contain valid initial values that are optimized further. +Otherwise R, T are initialized to the median value of the pattern views (each dimension separately). +- **CALIB_FIX_PRINCIPAL_POINT** Fix the principal points during the optimization. +- **CALIB_FIX_FOCAL_LENGTH** Fix \f$f^{(j)}_x\f$ and \f$f^{(j)}_y\f$ . +- **CALIB_FIX_ASPECT_RATIO** Optimize \f$f^{(j)}_y\f$ . Fix the ratio \f$f^{(j)}_x/f^{(j)}_y\f$ +. +- **CALIB_SAME_FOCAL_LENGTH** Enforce \f$f^{(0)}_x=f^{(1)}_x\f$ and \f$f^{(0)}_y=f^{(1)}_y\f$ . +- **CALIB_ZERO_TANGENT_DIST** Set tangential distortion coefficients for each camera to +zeros and fix there. +- **CALIB_FIX_K1,...,CALIB_FIX_K6** Do not change the corresponding radial +distortion coefficient during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, +the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. +- **CALIB_RATIONAL_MODEL** Enable coefficients k4, k5, and k6. To provide the backward +compatibility, this extra flag should be explicitly specified to make the calibration +function use the rational model and return 8 coefficients. If the flag is not set, the +function computes and returns only 5 distortion coefficients. +- **CALIB_THIN_PRISM_MODEL** Coefficients s1, s2, s3 and s4 are enabled. To provide the +backward compatibility, this extra flag should be explicitly specified to make the +calibration function use the thin prism model and return 12 coefficients. If the flag is not +set, the function computes and returns only 5 distortion coefficients. +- **CALIB_FIX_S1_S2_S3_S4** The thin prism distortion coefficients are not changed during +the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the +supplied distCoeffs matrix is used. Otherwise, it is set to 0. +- **CALIB_TILTED_MODEL** Coefficients tauX and tauY are enabled. To provide the +backward compatibility, this extra flag should be explicitly specified to make the +calibration function use the tilted sensor model and return 14 coefficients. If the flag is not +set, the function computes and returns only 5 distortion coefficients. +- **CALIB_FIX_TAUX_TAUY** The coefficients of the tilted sensor model are not changed during +the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the +supplied distCoeffs matrix is used. Otherwise, it is set to 0. +@param criteria Termination criteria for the iterative optimization algorithm. + +The function estimates transformation between two cameras making a stereo pair. If you have a stereo +camera where the relative position and orientation of two cameras is fixed, and if you computed +poses of an object relative to the first camera and to the second camera, (R1, T1) and (R2, T2), +respectively (this can be done with solvePnP ), then those poses definitely relate to each other. +This means that, given ( \f$R_1\f$,\f$T_1\f$ ), it should be possible to compute ( \f$R_2\f$,\f$T_2\f$ ). You only +need to know the position and orientation of the second camera relative to the first camera. This is +what the described function does. It computes ( \f$R\f$,\f$T\f$ ) so that: + +\f[R_2=R*R_1\f] +\f[T_2=R*T_1 + T,\f] + +Optionally, it computes the essential matrix E: + +\f[E= \vecthreethree{0}{-T_2}{T_1}{T_2}{0}{-T_0}{-T_1}{T_0}{0} *R\f] + +where \f$T_i\f$ are components of the translation vector \f$T\f$ : \f$T=[T_0, T_1, T_2]^T\f$ . And the function +can also compute the fundamental matrix F: + +\f[F = cameraMatrix2^{-T} E cameraMatrix1^{-1}\f] + +Besides the stereo-related information, the function can also perform a full calibration of each of +two cameras. However, due to the high dimensionality of the parameter space and noise in the input +data, the function can diverge from the correct solution. If the intrinsic parameters can be +estimated with high accuracy for each of the cameras individually (for example, using +calibrateCamera ), you are recommended to do so and then pass CALIB_FIX_INTRINSIC flag to the +function along with the computed intrinsic parameters. Otherwise, if all the parameters are +estimated at once, it makes sense to restrict some parameters, for example, pass +CALIB_SAME_FOCAL_LENGTH and CALIB_ZERO_TANGENT_DIST flags, which is usually a +reasonable assumption. + +Similarly to calibrateCamera , the function minimizes the total re-projection error for all the +points in all the available views from both cameras. The function returns the final value of the +re-projection error. + */ +CV_EXPORTS_AS(stereoCalibrateExtended) double stereoCalibrate( InputArrayOfArrays objectPoints, + InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2, + InputOutputArray cameraMatrix1, InputOutputArray distCoeffs1, + InputOutputArray cameraMatrix2, InputOutputArray distCoeffs2, + Size imageSize, InputOutputArray R,InputOutputArray T, OutputArray E, OutputArray F, + OutputArray perViewErrors, int flags = CALIB_FIX_INTRINSIC, + TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 1e-6) ); + +/// @overload +CV_EXPORTS_W double stereoCalibrate( InputArrayOfArrays objectPoints, + InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2, + InputOutputArray cameraMatrix1, InputOutputArray distCoeffs1, + InputOutputArray cameraMatrix2, InputOutputArray distCoeffs2, + Size imageSize, OutputArray R,OutputArray T, OutputArray E, OutputArray F, + int flags = CALIB_FIX_INTRINSIC, + TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 1e-6) ); + +/** @brief Computes rectification transforms for each head of a calibrated stereo camera. + +@param cameraMatrix1 First camera matrix. +@param distCoeffs1 First camera distortion parameters. +@param cameraMatrix2 Second camera matrix. +@param distCoeffs2 Second camera distortion parameters. +@param imageSize Size of the image used for stereo calibration. +@param R Rotation matrix between the coordinate systems of the first and the second cameras. +@param T Translation vector between coordinate systems of the cameras. +@param R1 Output 3x3 rectification transform (rotation matrix) for the first camera. +@param R2 Output 3x3 rectification transform (rotation matrix) for the second camera. +@param P1 Output 3x4 projection matrix in the new (rectified) coordinate systems for the first +camera. +@param P2 Output 3x4 projection matrix in the new (rectified) coordinate systems for the second +camera. +@param Q Output \f$4 \times 4\f$ disparity-to-depth mapping matrix (see reprojectImageTo3D ). +@param flags Operation flags that may be zero or CALIB_ZERO_DISPARITY . If the flag is set, +the function makes the principal points of each camera have the same pixel coordinates in the +rectified views. And if the flag is not set, the function may still shift the images in the +horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the +useful image area. +@param alpha Free scaling parameter. If it is -1 or absent, the function performs the default +scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified +images are zoomed and shifted so that only valid pixels are visible (no black areas after +rectification). alpha=1 means that the rectified image is decimated and shifted so that all the +pixels from the original images from the cameras are retained in the rectified images (no source +image pixels are lost). Obviously, any intermediate value yields an intermediate result between +those two extreme cases. +@param newImageSize New image resolution after rectification. The same size should be passed to +initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) +is passed (default), it is set to the original imageSize . Setting it to larger value can help you +preserve details in the original image, especially when there is a big radial distortion. +@param validPixROI1 Optional output rectangles inside the rectified images where all the pixels +are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller +(see the picture below). +@param validPixROI2 Optional output rectangles inside the rectified images where all the pixels +are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller +(see the picture below). + +The function computes the rotation matrices for each camera that (virtually) make both camera image +planes the same plane. Consequently, this makes all the epipolar lines parallel and thus simplifies +the dense stereo correspondence problem. The function takes the matrices computed by stereoCalibrate +as input. As output, it provides two rotation matrices and also two projection matrices in the new +coordinates. The function distinguishes the following two cases: + +- **Horizontal stereo**: the first and the second camera views are shifted relative to each other + mainly along the x axis (with possible small vertical shift). In the rectified images, the + corresponding epipolar lines in the left and right cameras are horizontal and have the same + y-coordinate. P1 and P2 look like: + + \f[\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\f] + + \f[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x*f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\f] + + where \f$T_x\f$ is a horizontal shift between the cameras and \f$cx_1=cx_2\f$ if + CALIB_ZERO_DISPARITY is set. + +- **Vertical stereo**: the first and the second camera views are shifted relative to each other + mainly in vertical direction (and probably a bit in the horizontal direction too). The epipolar + lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like: + + \f[\texttt{P1} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\f] + + \f[\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y*f \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\f] + + where \f$T_y\f$ is a vertical shift between the cameras and \f$cy_1=cy_2\f$ if CALIB_ZERO_DISPARITY is + set. + +As you can see, the first three columns of P1 and P2 will effectively be the new "rectified" camera +matrices. The matrices, together with R1 and R2 , can then be passed to initUndistortRectifyMap to +initialize the rectification map for each camera. + +See below the screenshot from the stereo_calib.cpp sample. Some red horizontal lines pass through +the corresponding image regions. This means that the images are well rectified, which is what most +stereo correspondence algorithms rely on. The green rectangles are roi1 and roi2 . You see that +their interiors are all valid pixels. + +![image](pics/stereo_undistort.jpg) + */ +CV_EXPORTS_W void stereoRectify( InputArray cameraMatrix1, InputArray distCoeffs1, + InputArray cameraMatrix2, InputArray distCoeffs2, + Size imageSize, InputArray R, InputArray T, + OutputArray R1, OutputArray R2, + OutputArray P1, OutputArray P2, + OutputArray Q, int flags = CALIB_ZERO_DISPARITY, + double alpha = -1, Size newImageSize = Size(), + CV_OUT Rect* validPixROI1 = 0, CV_OUT Rect* validPixROI2 = 0 ); + +/** @brief Computes a rectification transform for an uncalibrated stereo camera. + +@param points1 Array of feature points in the first image. +@param points2 The corresponding points in the second image. The same formats as in +findFundamentalMat are supported. +@param F Input fundamental matrix. It can be computed from the same set of point pairs using +findFundamentalMat . +@param imgSize Size of the image. +@param H1 Output rectification homography matrix for the first image. +@param H2 Output rectification homography matrix for the second image. +@param threshold Optional threshold used to filter out the outliers. If the parameter is greater +than zero, all the point pairs that do not comply with the epipolar geometry (that is, the points +for which \f$|\texttt{points2[i]}^T*\texttt{F}*\texttt{points1[i]}|>\texttt{threshold}\f$ ) are +rejected prior to computing the homographies. Otherwise, all the points are considered inliers. + +The function computes the rectification transformations without knowing intrinsic parameters of the +cameras and their relative position in the space, which explains the suffix "uncalibrated". Another +related difference from stereoRectify is that the function outputs not the rectification +transformations in the object (3D) space, but the planar perspective transformations encoded by the +homography matrices H1 and H2 . The function implements the algorithm @cite Hartley99 . + +@note + While the algorithm does not need to know the intrinsic parameters of the cameras, it heavily + depends on the epipolar geometry. Therefore, if the camera lenses have a significant distortion, + it would be better to correct it before computing the fundamental matrix and calling this + function. For example, distortion coefficients can be estimated for each head of stereo camera + separately by using calibrateCamera . Then, the images can be corrected using undistort , or + just the point coordinates can be corrected with undistortPoints . + */ +CV_EXPORTS_W bool stereoRectifyUncalibrated( InputArray points1, InputArray points2, + InputArray F, Size imgSize, + OutputArray H1, OutputArray H2, + double threshold = 5 ); + +//! computes the rectification transformations for 3-head camera, where all the heads are on the same line. +CV_EXPORTS_W float rectify3Collinear( InputArray cameraMatrix1, InputArray distCoeffs1, + InputArray cameraMatrix2, InputArray distCoeffs2, + InputArray cameraMatrix3, InputArray distCoeffs3, + InputArrayOfArrays imgpt1, InputArrayOfArrays imgpt3, + Size imageSize, InputArray R12, InputArray T12, + InputArray R13, InputArray T13, + OutputArray R1, OutputArray R2, OutputArray R3, + OutputArray P1, OutputArray P2, OutputArray P3, + OutputArray Q, double alpha, Size newImgSize, + CV_OUT Rect* roi1, CV_OUT Rect* roi2, int flags ); + +/** @brief Returns the new camera matrix based on the free scaling parameter. + +@param cameraMatrix Input camera matrix. +@param distCoeffs Input vector of distortion coefficients +\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of +4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are +assumed. +@param imageSize Original image size. +@param alpha Free scaling parameter between 0 (when all the pixels in the undistorted image are +valid) and 1 (when all the source image pixels are retained in the undistorted image). See +stereoRectify for details. +@param newImgSize Image size after rectification. By default, it is set to imageSize . +@param validPixROI Optional output rectangle that outlines all-good-pixels region in the +undistorted image. See roi1, roi2 description in stereoRectify . +@param centerPrincipalPoint Optional flag that indicates whether in the new camera matrix the +principal point should be at the image center or not. By default, the principal point is chosen to +best fit a subset of the source image (determined by alpha) to the corrected image. +@return new_camera_matrix Output new camera matrix. + +The function computes and returns the optimal new camera matrix based on the free scaling parameter. +By varying this parameter, you may retrieve only sensible pixels alpha=0 , keep all the original +image pixels if there is valuable information in the corners alpha=1 , or get something in between. +When alpha\>0 , the undistorted result is likely to have some black pixels corresponding to +"virtual" pixels outside of the captured distorted image. The original camera matrix, distortion +coefficients, the computed new camera matrix, and newImageSize should be passed to +initUndistortRectifyMap to produce the maps for remap . + */ +CV_EXPORTS_W Mat getOptimalNewCameraMatrix( InputArray cameraMatrix, InputArray distCoeffs, + Size imageSize, double alpha, Size newImgSize = Size(), + CV_OUT Rect* validPixROI = 0, + bool centerPrincipalPoint = false); + +/** @brief Converts points from Euclidean to homogeneous space. + +@param src Input vector of N-dimensional points. +@param dst Output vector of N+1-dimensional points. + +The function converts points from Euclidean to homogeneous space by appending 1's to the tuple of +point coordinates. That is, each point (x1, x2, ..., xn) is converted to (x1, x2, ..., xn, 1). + */ +CV_EXPORTS_W void convertPointsToHomogeneous( InputArray src, OutputArray dst ); + +/** @brief Converts points from homogeneous to Euclidean space. + +@param src Input vector of N-dimensional points. +@param dst Output vector of N-1-dimensional points. + +The function converts points homogeneous to Euclidean space using perspective projection. That is, +each point (x1, x2, ... x(n-1), xn) is converted to (x1/xn, x2/xn, ..., x(n-1)/xn). When xn=0, the +output point coordinates will be (0,0,0,...). + */ +CV_EXPORTS_W void convertPointsFromHomogeneous( InputArray src, OutputArray dst ); + +/** @brief Converts points to/from homogeneous coordinates. + +@param src Input array or vector of 2D, 3D, or 4D points. +@param dst Output vector of 2D, 3D, or 4D points. + +The function converts 2D or 3D points from/to homogeneous coordinates by calling either +convertPointsToHomogeneous or convertPointsFromHomogeneous. + +@note The function is obsolete. Use one of the previous two functions instead. + */ +CV_EXPORTS void convertPointsHomogeneous( InputArray src, OutputArray dst ); + +/** @brief Calculates a fundamental matrix from the corresponding points in two images. + +@param points1 Array of N points from the first image. The point coordinates should be +floating-point (single or double precision). +@param points2 Array of the second image points of the same size and format as points1 . +@param method Method for computing a fundamental matrix. +- **CV_FM_7POINT** for a 7-point algorithm. \f$N = 7\f$ +- **CV_FM_8POINT** for an 8-point algorithm. \f$N \ge 8\f$ +- **CV_FM_RANSAC** for the RANSAC algorithm. \f$N \ge 8\f$ +- **CV_FM_LMEDS** for the LMedS algorithm. \f$N \ge 8\f$ +@param ransacReprojThreshold Parameter used only for RANSAC. It is the maximum distance from a point to an epipolar +line in pixels, beyond which the point is considered an outlier and is not used for computing the +final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the +point localization, image resolution, and the image noise. +@param confidence Parameter used for the RANSAC and LMedS methods only. It specifies a desirable level +of confidence (probability) that the estimated matrix is correct. +@param mask + +The epipolar geometry is described by the following equation: + +\f[[p_2; 1]^T F [p_1; 1] = 0\f] + +where \f$F\f$ is a fundamental matrix, \f$p_1\f$ and \f$p_2\f$ are corresponding points in the first and the +second images, respectively. + +The function calculates the fundamental matrix using one of four methods listed above and returns +the found fundamental matrix. Normally just one matrix is found. But in case of the 7-point +algorithm, the function may return up to 3 solutions ( \f$9 \times 3\f$ matrix that stores all 3 +matrices sequentially). + +The calculated fundamental matrix may be passed further to computeCorrespondEpilines that finds the +epipolar lines corresponding to the specified points. It can also be passed to +stereoRectifyUncalibrated to compute the rectification transformation. : +@code + // Example. Estimation of fundamental matrix using the RANSAC algorithm + int point_count = 100; + vector points1(point_count); + vector points2(point_count); + + // initialize the points here ... + for( int i = 0; i < point_count; i++ ) + { + points1[i] = ...; + points2[i] = ...; + } + + Mat fundamental_matrix = + findFundamentalMat(points1, points2, FM_RANSAC, 3, 0.99); +@endcode + */ +CV_EXPORTS_W Mat findFundamentalMat( InputArray points1, InputArray points2, + int method = FM_RANSAC, + double ransacReprojThreshold = 3., double confidence = 0.99, + OutputArray mask = noArray() ); + +/** @overload */ +CV_EXPORTS Mat findFundamentalMat( InputArray points1, InputArray points2, + OutputArray mask, int method = FM_RANSAC, + double ransacReprojThreshold = 3., double confidence = 0.99 ); + +/** @brief Calculates an essential matrix from the corresponding points in two images. + +@param points1 Array of N (N \>= 5) 2D points from the first image. The point coordinates should +be floating-point (single or double precision). +@param points2 Array of the second image points of the same size and format as points1 . +@param cameraMatrix Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . +Note that this function assumes that points1 and points2 are feature points from cameras with the +same camera matrix. +@param method Method for computing an essential matrix. +- **RANSAC** for the RANSAC algorithm. +- **LMEDS** for the LMedS algorithm. +@param prob Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of +confidence (probability) that the estimated matrix is correct. +@param threshold Parameter used for RANSAC. It is the maximum distance from a point to an epipolar +line in pixels, beyond which the point is considered an outlier and is not used for computing the +final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the +point localization, image resolution, and the image noise. +@param mask Output array of N elements, every element of which is set to 0 for outliers and to 1 +for the other points. The array is computed only in the RANSAC and LMedS methods. + +This function estimates essential matrix based on the five-point algorithm solver in @cite Nister03 . +@cite SteweniusCFS is also a related. The epipolar geometry is described by the following equation: + +\f[[p_2; 1]^T K^{-T} E K^{-1} [p_1; 1] = 0\f] + +where \f$E\f$ is an essential matrix, \f$p_1\f$ and \f$p_2\f$ are corresponding points in the first and the +second images, respectively. The result of this function may be passed further to +decomposeEssentialMat or recoverPose to recover the relative pose between cameras. + */ +CV_EXPORTS_W Mat findEssentialMat( InputArray points1, InputArray points2, + InputArray cameraMatrix, int method = RANSAC, + double prob = 0.999, double threshold = 1.0, + OutputArray mask = noArray() ); + +/** @overload +@param points1 Array of N (N \>= 5) 2D points from the first image. The point coordinates should +be floating-point (single or double precision). +@param points2 Array of the second image points of the same size and format as points1 . +@param focal focal length of the camera. Note that this function assumes that points1 and points2 +are feature points from cameras with same focal length and principal point. +@param pp principal point of the camera. +@param method Method for computing a fundamental matrix. +- **RANSAC** for the RANSAC algorithm. +- **LMEDS** for the LMedS algorithm. +@param threshold Parameter used for RANSAC. It is the maximum distance from a point to an epipolar +line in pixels, beyond which the point is considered an outlier and is not used for computing the +final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the +point localization, image resolution, and the image noise. +@param prob Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of +confidence (probability) that the estimated matrix is correct. +@param mask Output array of N elements, every element of which is set to 0 for outliers and to 1 +for the other points. The array is computed only in the RANSAC and LMedS methods. + +This function differs from the one above that it computes camera matrix from focal length and +principal point: + +\f[K = +\begin{bmatrix} +f & 0 & x_{pp} \\ +0 & f & y_{pp} \\ +0 & 0 & 1 +\end{bmatrix}\f] + */ +CV_EXPORTS_W Mat findEssentialMat( InputArray points1, InputArray points2, + double focal = 1.0, Point2d pp = Point2d(0, 0), + int method = RANSAC, double prob = 0.999, + double threshold = 1.0, OutputArray mask = noArray() ); + +/** @brief Decompose an essential matrix to possible rotations and translation. + +@param E The input essential matrix. +@param R1 One possible rotation matrix. +@param R2 Another possible rotation matrix. +@param t One possible translation. + +This function decompose an essential matrix E using svd decomposition @cite HartleyZ00 . Generally 4 +possible poses exists for a given E. They are \f$[R_1, t]\f$, \f$[R_1, -t]\f$, \f$[R_2, t]\f$, \f$[R_2, -t]\f$. By +decomposing E, you can only get the direction of the translation, so the function returns unit t. + */ +CV_EXPORTS_W void decomposeEssentialMat( InputArray E, OutputArray R1, OutputArray R2, OutputArray t ); + +/** @brief Recover relative camera rotation and translation from an estimated essential matrix and the +corresponding points in two images, using cheirality check. Returns the number of inliers which pass +the check. + +@param E The input essential matrix. +@param points1 Array of N 2D points from the first image. The point coordinates should be +floating-point (single or double precision). +@param points2 Array of the second image points of the same size and format as points1 . +@param cameraMatrix Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . +Note that this function assumes that points1 and points2 are feature points from cameras with the +same camera matrix. +@param R Recovered relative rotation. +@param t Recovered relative translation. +@param mask Input/output mask for inliers in points1 and points2. +: If it is not empty, then it marks inliers in points1 and points2 for then given essential +matrix E. Only these inliers will be used to recover pose. In the output mask only inliers +which pass the cheirality check. +This function decomposes an essential matrix using decomposeEssentialMat and then verifies possible +pose hypotheses by doing cheirality check. The cheirality check basically means that the +triangulated 3D points should have positive depth. Some details can be found in @cite Nister03 . + +This function can be used to process output E and mask from findEssentialMat. In this scenario, +points1 and points2 are the same input for findEssentialMat. : +@code + // Example. Estimation of fundamental matrix using the RANSAC algorithm + int point_count = 100; + vector points1(point_count); + vector points2(point_count); + + // initialize the points here ... + for( int i = 0; i < point_count; i++ ) + { + points1[i] = ...; + points2[i] = ...; + } + + // cametra matrix with both focal lengths = 1, and principal point = (0, 0) + Mat cameraMatrix = Mat::eye(3, 3, CV_64F); + + Mat E, R, t, mask; + + E = findEssentialMat(points1, points2, cameraMatrix, RANSAC, 0.999, 1.0, mask); + recoverPose(E, points1, points2, cameraMatrix, R, t, mask); +@endcode + */ +CV_EXPORTS_W int recoverPose( InputArray E, InputArray points1, InputArray points2, + InputArray cameraMatrix, OutputArray R, OutputArray t, + InputOutputArray mask = noArray() ); + +/** @overload +@param E The input essential matrix. +@param points1 Array of N 2D points from the first image. The point coordinates should be +floating-point (single or double precision). +@param points2 Array of the second image points of the same size and format as points1 . +@param R Recovered relative rotation. +@param t Recovered relative translation. +@param focal Focal length of the camera. Note that this function assumes that points1 and points2 +are feature points from cameras with same focal length and principal point. +@param pp principal point of the camera. +@param mask Input/output mask for inliers in points1 and points2. +: If it is not empty, then it marks inliers in points1 and points2 for then given essential +matrix E. Only these inliers will be used to recover pose. In the output mask only inliers +which pass the cheirality check. + +This function differs from the one above that it computes camera matrix from focal length and +principal point: + +\f[K = +\begin{bmatrix} +f & 0 & x_{pp} \\ +0 & f & y_{pp} \\ +0 & 0 & 1 +\end{bmatrix}\f] + */ +CV_EXPORTS_W int recoverPose( InputArray E, InputArray points1, InputArray points2, + OutputArray R, OutputArray t, + double focal = 1.0, Point2d pp = Point2d(0, 0), + InputOutputArray mask = noArray() ); + +/** @overload +@param E The input essential matrix. +@param points1 Array of N 2D points from the first image. The point coordinates should be +floating-point (single or double precision). +@param points2 Array of the second image points of the same size and format as points1. +@param cameraMatrix Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . +Note that this function assumes that points1 and points2 are feature points from cameras with the +same camera matrix. +@param R Recovered relative rotation. +@param t Recovered relative translation. +@param distanceThresh threshold distance which is used to filter out far away points (i.e. infinite points). +@param mask Input/output mask for inliers in points1 and points2. +: If it is not empty, then it marks inliers in points1 and points2 for then given essential +matrix E. Only these inliers will be used to recover pose. In the output mask only inliers +which pass the cheirality check. +@param triangulatedPoints 3d points which were reconstructed by triangulation. + */ + +CV_EXPORTS_W int recoverPose( InputArray E, InputArray points1, InputArray points2, + InputArray cameraMatrix, OutputArray R, OutputArray t, double distanceThresh, InputOutputArray mask = noArray(), + OutputArray triangulatedPoints = noArray()); + +/** @brief For points in an image of a stereo pair, computes the corresponding epilines in the other image. + +@param points Input points. \f$N \times 1\f$ or \f$1 \times N\f$ matrix of type CV_32FC2 or +vector\ . +@param whichImage Index of the image (1 or 2) that contains the points . +@param F Fundamental matrix that can be estimated using findFundamentalMat or stereoRectify . +@param lines Output vector of the epipolar lines corresponding to the points in the other image. +Each line \f$ax + by + c=0\f$ is encoded by 3 numbers \f$(a, b, c)\f$ . + +For every point in one of the two images of a stereo pair, the function finds the equation of the +corresponding epipolar line in the other image. + +From the fundamental matrix definition (see findFundamentalMat ), line \f$l^{(2)}_i\f$ in the second +image for the point \f$p^{(1)}_i\f$ in the first image (when whichImage=1 ) is computed as: + +\f[l^{(2)}_i = F p^{(1)}_i\f] + +And vice versa, when whichImage=2, \f$l^{(1)}_i\f$ is computed from \f$p^{(2)}_i\f$ as: + +\f[l^{(1)}_i = F^T p^{(2)}_i\f] + +Line coefficients are defined up to a scale. They are normalized so that \f$a_i^2+b_i^2=1\f$ . + */ +CV_EXPORTS_W void computeCorrespondEpilines( InputArray points, int whichImage, + InputArray F, OutputArray lines ); + +/** @brief Reconstructs points by triangulation. + +@param projMatr1 3x4 projection matrix of the first camera. +@param projMatr2 3x4 projection matrix of the second camera. +@param projPoints1 2xN array of feature points in the first image. In case of c++ version it can +be also a vector of feature points or two-channel matrix of size 1xN or Nx1. +@param projPoints2 2xN array of corresponding points in the second image. In case of c++ version +it can be also a vector of feature points or two-channel matrix of size 1xN or Nx1. +@param points4D 4xN array of reconstructed points in homogeneous coordinates. + +The function reconstructs 3-dimensional points (in homogeneous coordinates) by using their +observations with a stereo camera. Projections matrices can be obtained from stereoRectify. + +@note + Keep in mind that all input data should be of float type in order for this function to work. + +@sa + reprojectImageTo3D + */ +CV_EXPORTS_W void triangulatePoints( InputArray projMatr1, InputArray projMatr2, + InputArray projPoints1, InputArray projPoints2, + OutputArray points4D ); + +/** @brief Refines coordinates of corresponding points. + +@param F 3x3 fundamental matrix. +@param points1 1xN array containing the first set of points. +@param points2 1xN array containing the second set of points. +@param newPoints1 The optimized points1. +@param newPoints2 The optimized points2. + +The function implements the Optimal Triangulation Method (see Multiple View Geometry for details). +For each given point correspondence points1[i] \<-\> points2[i], and a fundamental matrix F, it +computes the corrected correspondences newPoints1[i] \<-\> newPoints2[i] that minimize the geometric +error \f$d(points1[i], newPoints1[i])^2 + d(points2[i],newPoints2[i])^2\f$ (where \f$d(a,b)\f$ is the +geometric distance between points \f$a\f$ and \f$b\f$ ) subject to the epipolar constraint +\f$newPoints2^T * F * newPoints1 = 0\f$ . + */ +CV_EXPORTS_W void correctMatches( InputArray F, InputArray points1, InputArray points2, + OutputArray newPoints1, OutputArray newPoints2 ); + +/** @brief Filters off small noise blobs (speckles) in the disparity map + +@param img The input 16-bit signed disparity image +@param newVal The disparity value used to paint-off the speckles +@param maxSpeckleSize The maximum speckle size to consider it a speckle. Larger blobs are not +affected by the algorithm +@param maxDiff Maximum difference between neighbor disparity pixels to put them into the same +blob. Note that since StereoBM, StereoSGBM and may be other algorithms return a fixed-point +disparity map, where disparity values are multiplied by 16, this scale factor should be taken into +account when specifying this parameter value. +@param buf The optional temporary buffer to avoid memory allocation within the function. + */ +CV_EXPORTS_W void filterSpeckles( InputOutputArray img, double newVal, + int maxSpeckleSize, double maxDiff, + InputOutputArray buf = noArray() ); + +//! computes valid disparity ROI from the valid ROIs of the rectified images (that are returned by cv::stereoRectify()) +CV_EXPORTS_W Rect getValidDisparityROI( Rect roi1, Rect roi2, + int minDisparity, int numberOfDisparities, + int SADWindowSize ); + +//! validates disparity using the left-right check. The matrix "cost" should be computed by the stereo correspondence algorithm +CV_EXPORTS_W void validateDisparity( InputOutputArray disparity, InputArray cost, + int minDisparity, int numberOfDisparities, + int disp12MaxDisp = 1 ); + +/** @brief Reprojects a disparity image to 3D space. + +@param disparity Input single-channel 8-bit unsigned, 16-bit signed, 32-bit signed or 32-bit +floating-point disparity image. If 16-bit signed format is used, the values are assumed to have no +fractional bits. +@param _3dImage Output 3-channel floating-point image of the same size as disparity . Each +element of _3dImage(x,y) contains 3D coordinates of the point (x,y) computed from the disparity +map. +@param Q \f$4 \times 4\f$ perspective transformation matrix that can be obtained with stereoRectify. +@param handleMissingValues Indicates, whether the function should handle missing values (i.e. +points where the disparity was not computed). If handleMissingValues=true, then pixels with the +minimal disparity that corresponds to the outliers (see StereoMatcher::compute ) are transformed +to 3D points with a very large Z value (currently set to 10000). +@param ddepth The optional output array depth. If it is -1, the output image will have CV_32F +depth. ddepth can also be set to CV_16S, CV_32S or CV_32F. + +The function transforms a single-channel disparity map to a 3-channel image representing a 3D +surface. That is, for each pixel (x,y) and the corresponding disparity d=disparity(x,y) , it +computes: + +\f[\begin{array}{l} [X \; Y \; Z \; W]^T = \texttt{Q} *[x \; y \; \texttt{disparity} (x,y) \; 1]^T \\ \texttt{\_3dImage} (x,y) = (X/W, \; Y/W, \; Z/W) \end{array}\f] + +The matrix Q can be an arbitrary \f$4 \times 4\f$ matrix (for example, the one computed by +stereoRectify). To reproject a sparse set of points {(x,y,d),...} to 3D space, use +perspectiveTransform . + */ +CV_EXPORTS_W void reprojectImageTo3D( InputArray disparity, + OutputArray _3dImage, InputArray Q, + bool handleMissingValues = false, + int ddepth = -1 ); + +/** @brief Calculates the Sampson Distance between two points. + +The function cv::sampsonDistance calculates and returns the first order approximation of the geometric error as: +\f[ +sd( \texttt{pt1} , \texttt{pt2} )= +\frac{(\texttt{pt2}^t \cdot \texttt{F} \cdot \texttt{pt1})^2} +{((\texttt{F} \cdot \texttt{pt1})(0))^2 + +((\texttt{F} \cdot \texttt{pt1})(1))^2 + +((\texttt{F}^t \cdot \texttt{pt2})(0))^2 + +((\texttt{F}^t \cdot \texttt{pt2})(1))^2} +\f] +The fundamental matrix may be calculated using the cv::findFundamentalMat function. See @cite HartleyZ00 11.4.3 for details. +@param pt1 first homogeneous 2d point +@param pt2 second homogeneous 2d point +@param F fundamental matrix +@return The computed Sampson distance. +*/ +CV_EXPORTS_W double sampsonDistance(InputArray pt1, InputArray pt2, InputArray F); + +/** @brief Computes an optimal affine transformation between two 3D point sets. + +It computes +\f[ +\begin{bmatrix} +x\\ +y\\ +z\\ +\end{bmatrix} += +\begin{bmatrix} +a_{11} & a_{12} & a_{13}\\ +a_{21} & a_{22} & a_{23}\\ +a_{31} & a_{32} & a_{33}\\ +\end{bmatrix} +\begin{bmatrix} +X\\ +Y\\ +Z\\ +\end{bmatrix} ++ +\begin{bmatrix} +b_1\\ +b_2\\ +b_3\\ +\end{bmatrix} +\f] + +@param src First input 3D point set containing \f$(X,Y,Z)\f$. +@param dst Second input 3D point set containing \f$(x,y,z)\f$. +@param out Output 3D affine transformation matrix \f$3 \times 4\f$ of the form +\f[ +\begin{bmatrix} +a_{11} & a_{12} & a_{13} & b_1\\ +a_{21} & a_{22} & a_{23} & b_2\\ +a_{31} & a_{32} & a_{33} & b_3\\ +\end{bmatrix} +\f] +@param inliers Output vector indicating which points are inliers (1-inlier, 0-outlier). +@param ransacThreshold Maximum reprojection error in the RANSAC algorithm to consider a point as +an inlier. +@param confidence Confidence level, between 0 and 1, for the estimated transformation. Anything +between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation +significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation. + +The function estimates an optimal 3D affine transformation between two 3D point sets using the +RANSAC algorithm. + */ +CV_EXPORTS_W int estimateAffine3D(InputArray src, InputArray dst, + OutputArray out, OutputArray inliers, + double ransacThreshold = 3, double confidence = 0.99); + +/** @brief Computes an optimal affine transformation between two 2D point sets. + +It computes +\f[ +\begin{bmatrix} +x\\ +y\\ +\end{bmatrix} += +\begin{bmatrix} +a_{11} & a_{12}\\ +a_{21} & a_{22}\\ +\end{bmatrix} +\begin{bmatrix} +X\\ +Y\\ +\end{bmatrix} ++ +\begin{bmatrix} +b_1\\ +b_2\\ +\end{bmatrix} +\f] + +@param from First input 2D point set containing \f$(X,Y)\f$. +@param to Second input 2D point set containing \f$(x,y)\f$. +@param inliers Output vector indicating which points are inliers (1-inlier, 0-outlier). +@param method Robust method used to compute transformation. The following methods are possible: +- cv::RANSAC - RANSAC-based robust method +- cv::LMEDS - Least-Median robust method +RANSAC is the default method. +@param ransacReprojThreshold Maximum reprojection error in the RANSAC algorithm to consider +a point as an inlier. Applies only to RANSAC. +@param maxIters The maximum number of robust method iterations. +@param confidence Confidence level, between 0 and 1, for the estimated transformation. Anything +between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation +significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation. +@param refineIters Maximum number of iterations of refining algorithm (Levenberg-Marquardt). +Passing 0 will disable refining, so the output matrix will be output of robust method. + +@return Output 2D affine transformation matrix \f$2 \times 3\f$ or empty matrix if transformation +could not be estimated. The returned matrix has the following form: +\f[ +\begin{bmatrix} +a_{11} & a_{12} & b_1\\ +a_{21} & a_{22} & b_2\\ +\end{bmatrix} +\f] + +The function estimates an optimal 2D affine transformation between two 2D point sets using the +selected robust algorithm. + +The computed transformation is then refined further (using only inliers) with the +Levenberg-Marquardt method to reduce the re-projection error even more. + +@note +The RANSAC method can handle practically any ratio of outliers but needs a threshold to +distinguish inliers from outliers. The method LMeDS does not need any threshold but it works +correctly only when there are more than 50% of inliers. + +@sa estimateAffinePartial2D, getAffineTransform +*/ +CV_EXPORTS_W cv::Mat estimateAffine2D(InputArray from, InputArray to, OutputArray inliers = noArray(), + int method = RANSAC, double ransacReprojThreshold = 3, + size_t maxIters = 2000, double confidence = 0.99, + size_t refineIters = 10); + +/** @brief Computes an optimal limited affine transformation with 4 degrees of freedom between +two 2D point sets. + +@param from First input 2D point set. +@param to Second input 2D point set. +@param inliers Output vector indicating which points are inliers. +@param method Robust method used to compute transformation. The following methods are possible: +- cv::RANSAC - RANSAC-based robust method +- cv::LMEDS - Least-Median robust method +RANSAC is the default method. +@param ransacReprojThreshold Maximum reprojection error in the RANSAC algorithm to consider +a point as an inlier. Applies only to RANSAC. +@param maxIters The maximum number of robust method iterations. +@param confidence Confidence level, between 0 and 1, for the estimated transformation. Anything +between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation +significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation. +@param refineIters Maximum number of iterations of refining algorithm (Levenberg-Marquardt). +Passing 0 will disable refining, so the output matrix will be output of robust method. + +@return Output 2D affine transformation (4 degrees of freedom) matrix \f$2 \times 3\f$ or +empty matrix if transformation could not be estimated. + +The function estimates an optimal 2D affine transformation with 4 degrees of freedom limited to +combinations of translation, rotation, and uniform scaling. Uses the selected algorithm for robust +estimation. + +The computed transformation is then refined further (using only inliers) with the +Levenberg-Marquardt method to reduce the re-projection error even more. + +Estimated transformation matrix is: +\f[ \begin{bmatrix} \cos(\theta) \cdot s & -\sin(\theta) \cdot s & t_x \\ + \sin(\theta) \cdot s & \cos(\theta) \cdot s & t_y +\end{bmatrix} \f] +Where \f$ \theta \f$ is the rotation angle, \f$ s \f$ the scaling factor and \f$ t_x, t_y \f$ are +translations in \f$ x, y \f$ axes respectively. + +@note +The RANSAC method can handle practically any ratio of outliers but need a threshold to +distinguish inliers from outliers. The method LMeDS does not need any threshold but it works +correctly only when there are more than 50% of inliers. + +@sa estimateAffine2D, getAffineTransform +*/ +CV_EXPORTS_W cv::Mat estimateAffinePartial2D(InputArray from, InputArray to, OutputArray inliers = noArray(), + int method = RANSAC, double ransacReprojThreshold = 3, + size_t maxIters = 2000, double confidence = 0.99, + size_t refineIters = 10); + +/** @example samples/cpp/tutorial_code/features2D/Homography/decompose_homography.cpp +An example program with homography decomposition. + +Check @ref tutorial_homography "the corresponding tutorial" for more details. +*/ + +/** @brief Decompose a homography matrix to rotation(s), translation(s) and plane normal(s). + +@param H The input homography matrix between two images. +@param K The input intrinsic camera calibration matrix. +@param rotations Array of rotation matrices. +@param translations Array of translation matrices. +@param normals Array of plane normal matrices. + +This function extracts relative camera motion between two views observing a planar object from the +homography H induced by the plane. The intrinsic camera matrix K must also be provided. The function +may return up to four mathematical solution sets. At least two of the solutions may further be +invalidated if point correspondences are available by applying positive depth constraint (all points +must be in front of the camera). The decomposition method is described in detail in @cite Malis . + */ +CV_EXPORTS_W int decomposeHomographyMat(InputArray H, + InputArray K, + OutputArrayOfArrays rotations, + OutputArrayOfArrays translations, + OutputArrayOfArrays normals); + +/** @brief Filters homography decompositions based on additional information. + +@param rotations Vector of rotation matrices. +@param normals Vector of plane normal matrices. +@param beforePoints Vector of (rectified) visible reference points before the homography is applied +@param afterPoints Vector of (rectified) visible reference points after the homography is applied +@param possibleSolutions Vector of int indices representing the viable solution set after filtering +@param pointsMask optional Mat/Vector of 8u type representing the mask for the inliers as given by the findHomography function + +This function is intended to filter the output of the decomposeHomographyMat based on additional +information as described in @cite Malis . The summary of the method: the decomposeHomographyMat function +returns 2 unique solutions and their "opposites" for a total of 4 solutions. If we have access to the +sets of points visible in the camera frame before and after the homography transformation is applied, +we can determine which are the true potential solutions and which are the opposites by verifying which +homographies are consistent with all visible reference points being in front of the camera. The inputs +are left unchanged; the filtered solution set is returned as indices into the existing one. + +*/ +CV_EXPORTS_W void filterHomographyDecompByVisibleRefpoints(InputArrayOfArrays rotations, + InputArrayOfArrays normals, + InputArray beforePoints, + InputArray afterPoints, + OutputArray possibleSolutions, + InputArray pointsMask = noArray()); + +/** @brief The base class for stereo correspondence algorithms. + */ +class CV_EXPORTS_W StereoMatcher : public Algorithm +{ +public: + enum { DISP_SHIFT = 4, + DISP_SCALE = (1 << DISP_SHIFT) + }; + + /** @brief Computes disparity map for the specified stereo pair + + @param left Left 8-bit single-channel image. + @param right Right image of the same size and the same type as the left one. + @param disparity Output disparity map. It has the same size as the input images. Some algorithms, + like StereoBM or StereoSGBM compute 16-bit fixed-point disparity map (where each disparity value + has 4 fractional bits), whereas other algorithms output 32-bit floating-point disparity map. + */ + CV_WRAP virtual void compute( InputArray left, InputArray right, + OutputArray disparity ) = 0; + + CV_WRAP virtual int getMinDisparity() const = 0; + CV_WRAP virtual void setMinDisparity(int minDisparity) = 0; + + CV_WRAP virtual int getNumDisparities() const = 0; + CV_WRAP virtual void setNumDisparities(int numDisparities) = 0; + + CV_WRAP virtual int getBlockSize() const = 0; + CV_WRAP virtual void setBlockSize(int blockSize) = 0; + + CV_WRAP virtual int getSpeckleWindowSize() const = 0; + CV_WRAP virtual void setSpeckleWindowSize(int speckleWindowSize) = 0; + + CV_WRAP virtual int getSpeckleRange() const = 0; + CV_WRAP virtual void setSpeckleRange(int speckleRange) = 0; + + CV_WRAP virtual int getDisp12MaxDiff() const = 0; + CV_WRAP virtual void setDisp12MaxDiff(int disp12MaxDiff) = 0; +}; + + +/** @brief Class for computing stereo correspondence using the block matching algorithm, introduced and +contributed to OpenCV by K. Konolige. + */ +class CV_EXPORTS_W StereoBM : public StereoMatcher +{ +public: + enum { PREFILTER_NORMALIZED_RESPONSE = 0, + PREFILTER_XSOBEL = 1 + }; + + CV_WRAP virtual int getPreFilterType() const = 0; + CV_WRAP virtual void setPreFilterType(int preFilterType) = 0; + + CV_WRAP virtual int getPreFilterSize() const = 0; + CV_WRAP virtual void setPreFilterSize(int preFilterSize) = 0; + + CV_WRAP virtual int getPreFilterCap() const = 0; + CV_WRAP virtual void setPreFilterCap(int preFilterCap) = 0; + + CV_WRAP virtual int getTextureThreshold() const = 0; + CV_WRAP virtual void setTextureThreshold(int textureThreshold) = 0; + + CV_WRAP virtual int getUniquenessRatio() const = 0; + CV_WRAP virtual void setUniquenessRatio(int uniquenessRatio) = 0; + + CV_WRAP virtual int getSmallerBlockSize() const = 0; + CV_WRAP virtual void setSmallerBlockSize(int blockSize) = 0; + + CV_WRAP virtual Rect getROI1() const = 0; + CV_WRAP virtual void setROI1(Rect roi1) = 0; + + CV_WRAP virtual Rect getROI2() const = 0; + CV_WRAP virtual void setROI2(Rect roi2) = 0; + + /** @brief Creates StereoBM object + + @param numDisparities the disparity search range. For each pixel algorithm will find the best + disparity from 0 (default minimum disparity) to numDisparities. The search range can then be + shifted by changing the minimum disparity. + @param blockSize the linear size of the blocks compared by the algorithm. The size should be odd + (as the block is centered at the current pixel). Larger block size implies smoother, though less + accurate disparity map. Smaller block size gives more detailed disparity map, but there is higher + chance for algorithm to find a wrong correspondence. + + The function create StereoBM object. You can then call StereoBM::compute() to compute disparity for + a specific stereo pair. + */ + CV_WRAP static Ptr create(int numDisparities = 0, int blockSize = 21); +}; + +/** @brief The class implements the modified H. Hirschmuller algorithm @cite HH08 that differs from the original +one as follows: + +- By default, the algorithm is single-pass, which means that you consider only 5 directions +instead of 8. Set mode=StereoSGBM::MODE_HH in createStereoSGBM to run the full variant of the +algorithm but beware that it may consume a lot of memory. +- The algorithm matches blocks, not individual pixels. Though, setting blockSize=1 reduces the +blocks to single pixels. +- Mutual information cost function is not implemented. Instead, a simpler Birchfield-Tomasi +sub-pixel metric from @cite BT98 is used. Though, the color images are supported as well. +- Some pre- and post- processing steps from K. Konolige algorithm StereoBM are included, for +example: pre-filtering (StereoBM::PREFILTER_XSOBEL type) and post-filtering (uniqueness +check, quadratic interpolation and speckle filtering). + +@note + - (Python) An example illustrating the use of the StereoSGBM matching algorithm can be found + at opencv_source_code/samples/python/stereo_match.py + */ +class CV_EXPORTS_W StereoSGBM : public StereoMatcher +{ +public: + enum + { + MODE_SGBM = 0, + MODE_HH = 1, + MODE_SGBM_3WAY = 2, + MODE_HH4 = 3 + }; + + CV_WRAP virtual int getPreFilterCap() const = 0; + CV_WRAP virtual void setPreFilterCap(int preFilterCap) = 0; + + CV_WRAP virtual int getUniquenessRatio() const = 0; + CV_WRAP virtual void setUniquenessRatio(int uniquenessRatio) = 0; + + CV_WRAP virtual int getP1() const = 0; + CV_WRAP virtual void setP1(int P1) = 0; + + CV_WRAP virtual int getP2() const = 0; + CV_WRAP virtual void setP2(int P2) = 0; + + CV_WRAP virtual int getMode() const = 0; + CV_WRAP virtual void setMode(int mode) = 0; + + /** @brief Creates StereoSGBM object + + @param minDisparity Minimum possible disparity value. Normally, it is zero but sometimes + rectification algorithms can shift images, so this parameter needs to be adjusted accordingly. + @param numDisparities Maximum disparity minus minimum disparity. The value is always greater than + zero. In the current implementation, this parameter must be divisible by 16. + @param blockSize Matched block size. It must be an odd number \>=1 . Normally, it should be + somewhere in the 3..11 range. + @param P1 The first parameter controlling the disparity smoothness. See below. + @param P2 The second parameter controlling the disparity smoothness. The larger the values are, + the smoother the disparity is. P1 is the penalty on the disparity change by plus or minus 1 + between neighbor pixels. P2 is the penalty on the disparity change by more than 1 between neighbor + pixels. The algorithm requires P2 \> P1 . See stereo_match.cpp sample where some reasonably good + P1 and P2 values are shown (like 8\*number_of_image_channels\*SADWindowSize\*SADWindowSize and + 32\*number_of_image_channels\*SADWindowSize\*SADWindowSize , respectively). + @param disp12MaxDiff Maximum allowed difference (in integer pixel units) in the left-right + disparity check. Set it to a non-positive value to disable the check. + @param preFilterCap Truncation value for the prefiltered image pixels. The algorithm first + computes x-derivative at each pixel and clips its value by [-preFilterCap, preFilterCap] interval. + The result values are passed to the Birchfield-Tomasi pixel cost function. + @param uniquenessRatio Margin in percentage by which the best (minimum) computed cost function + value should "win" the second best value to consider the found match correct. Normally, a value + within the 5-15 range is good enough. + @param speckleWindowSize Maximum size of smooth disparity regions to consider their noise speckles + and invalidate. Set it to 0 to disable speckle filtering. Otherwise, set it somewhere in the + 50-200 range. + @param speckleRange Maximum disparity variation within each connected component. If you do speckle + filtering, set the parameter to a positive value, it will be implicitly multiplied by 16. + Normally, 1 or 2 is good enough. + @param mode Set it to StereoSGBM::MODE_HH to run the full-scale two-pass dynamic programming + algorithm. It will consume O(W\*H\*numDisparities) bytes, which is large for 640x480 stereo and + huge for HD-size pictures. By default, it is set to false . + + The first constructor initializes StereoSGBM with all the default parameters. So, you only have to + set StereoSGBM::numDisparities at minimum. The second constructor enables you to set each parameter + to a custom value. + */ + CV_WRAP static Ptr create(int minDisparity = 0, int numDisparities = 16, int blockSize = 3, + int P1 = 0, int P2 = 0, int disp12MaxDiff = 0, + int preFilterCap = 0, int uniquenessRatio = 0, + int speckleWindowSize = 0, int speckleRange = 0, + int mode = StereoSGBM::MODE_SGBM); +}; + +//! @} calib3d + +/** @brief The methods in this namespace use a so-called fisheye camera model. + @ingroup calib3d_fisheye +*/ +namespace fisheye +{ +//! @addtogroup calib3d_fisheye +//! @{ + + enum{ + CALIB_USE_INTRINSIC_GUESS = 1 << 0, + CALIB_RECOMPUTE_EXTRINSIC = 1 << 1, + CALIB_CHECK_COND = 1 << 2, + CALIB_FIX_SKEW = 1 << 3, + CALIB_FIX_K1 = 1 << 4, + CALIB_FIX_K2 = 1 << 5, + CALIB_FIX_K3 = 1 << 6, + CALIB_FIX_K4 = 1 << 7, + CALIB_FIX_INTRINSIC = 1 << 8, + CALIB_FIX_PRINCIPAL_POINT = 1 << 9 + }; + + /** @brief Projects points using fisheye model + + @param objectPoints Array of object points, 1xN/Nx1 3-channel (or vector\ ), where N is + the number of points in the view. + @param imagePoints Output array of image points, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel, or + vector\. + @param affine + @param K Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$. + @param D Input vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$. + @param alpha The skew coefficient. + @param jacobian Optional output 2Nx15 jacobian matrix of derivatives of image points with respect + to components of the focal lengths, coordinates of the principal point, distortion coefficients, + rotation vector, translation vector, and the skew. In the old interface different components of + the jacobian are returned via different output parameters. + + The function computes projections of 3D points to the image plane given intrinsic and extrinsic + camera parameters. Optionally, the function computes Jacobians - matrices of partial derivatives of + image points coordinates (as functions of all the input parameters) with respect to the particular + parameters, intrinsic and/or extrinsic. + */ + CV_EXPORTS void projectPoints(InputArray objectPoints, OutputArray imagePoints, const Affine3d& affine, + InputArray K, InputArray D, double alpha = 0, OutputArray jacobian = noArray()); + + /** @overload */ + CV_EXPORTS_W void projectPoints(InputArray objectPoints, OutputArray imagePoints, InputArray rvec, InputArray tvec, + InputArray K, InputArray D, double alpha = 0, OutputArray jacobian = noArray()); + + /** @brief Distorts 2D points using fisheye model. + + @param undistorted Array of object points, 1xN/Nx1 2-channel (or vector\ ), where N is + the number of points in the view. + @param K Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$. + @param D Input vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$. + @param alpha The skew coefficient. + @param distorted Output array of image points, 1xN/Nx1 2-channel, or vector\ . + + Note that the function assumes the camera matrix of the undistorted points to be identity. + This means if you want to transform back points undistorted with undistortPoints() you have to + multiply them with \f$P^{-1}\f$. + */ + CV_EXPORTS_W void distortPoints(InputArray undistorted, OutputArray distorted, InputArray K, InputArray D, double alpha = 0); + + /** @brief Undistorts 2D points using fisheye model + + @param distorted Array of object points, 1xN/Nx1 2-channel (or vector\ ), where N is the + number of points in the view. + @param K Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$. + @param D Input vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$. + @param R Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 + 1-channel or 1x1 3-channel + @param P New camera matrix (3x3) or new projection matrix (3x4) + @param undistorted Output array of image points, 1xN/Nx1 2-channel, or vector\ . + */ + CV_EXPORTS_W void undistortPoints(InputArray distorted, OutputArray undistorted, + InputArray K, InputArray D, InputArray R = noArray(), InputArray P = noArray()); + + /** @brief Computes undistortion and rectification maps for image transform by cv::remap(). If D is empty zero + distortion is used, if R or P is empty identity matrixes are used. + + @param K Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$. + @param D Input vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$. + @param R Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 + 1-channel or 1x1 3-channel + @param P New camera matrix (3x3) or new projection matrix (3x4) + @param size Undistorted image size. + @param m1type Type of the first output map that can be CV_32FC1 or CV_16SC2 . See convertMaps() + for details. + @param map1 The first output map. + @param map2 The second output map. + */ + CV_EXPORTS_W void initUndistortRectifyMap(InputArray K, InputArray D, InputArray R, InputArray P, + const cv::Size& size, int m1type, OutputArray map1, OutputArray map2); + + /** @brief Transforms an image to compensate for fisheye lens distortion. + + @param distorted image with fisheye lens distortion. + @param undistorted Output image with compensated fisheye lens distortion. + @param K Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$. + @param D Input vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$. + @param Knew Camera matrix of the distorted image. By default, it is the identity matrix but you + may additionally scale and shift the result by using a different matrix. + @param new_size + + The function transforms an image to compensate radial and tangential lens distortion. + + The function is simply a combination of fisheye::initUndistortRectifyMap (with unity R ) and remap + (with bilinear interpolation). See the former function for details of the transformation being + performed. + + See below the results of undistortImage. + - a\) result of undistort of perspective camera model (all possible coefficients (k_1, k_2, k_3, + k_4, k_5, k_6) of distortion were optimized under calibration) + - b\) result of fisheye::undistortImage of fisheye camera model (all possible coefficients (k_1, k_2, + k_3, k_4) of fisheye distortion were optimized under calibration) + - c\) original image was captured with fisheye lens + + Pictures a) and b) almost the same. But if we consider points of image located far from the center + of image, we can notice that on image a) these points are distorted. + + ![image](pics/fisheye_undistorted.jpg) + */ + CV_EXPORTS_W void undistortImage(InputArray distorted, OutputArray undistorted, + InputArray K, InputArray D, InputArray Knew = cv::noArray(), const Size& new_size = Size()); + + /** @brief Estimates new camera matrix for undistortion or rectification. + + @param K Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$. + @param image_size + @param D Input vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$. + @param R Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 + 1-channel or 1x1 3-channel + @param P New camera matrix (3x3) or new projection matrix (3x4) + @param balance Sets the new focal length in range between the min focal length and the max focal + length. Balance is in range of [0, 1]. + @param new_size + @param fov_scale Divisor for new focal length. + */ + CV_EXPORTS_W void estimateNewCameraMatrixForUndistortRectify(InputArray K, InputArray D, const Size &image_size, InputArray R, + OutputArray P, double balance = 0.0, const Size& new_size = Size(), double fov_scale = 1.0); + + /** @brief Performs camera calibaration + + @param objectPoints vector of vectors of calibration pattern points in the calibration pattern + coordinate space. + @param imagePoints vector of vectors of the projections of calibration pattern points. + imagePoints.size() and objectPoints.size() and imagePoints[i].size() must be equal to + objectPoints[i].size() for each i. + @param image_size Size of the image used only to initialize the intrinsic camera matrix. + @param K Output 3x3 floating-point camera matrix + \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . If + fisheye::CALIB_USE_INTRINSIC_GUESS/ is specified, some or all of fx, fy, cx, cy must be + initialized before calling the function. + @param D Output vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$. + @param rvecs Output vector of rotation vectors (see Rodrigues ) estimated for each pattern view. + That is, each k-th rotation vector together with the corresponding k-th translation vector (see + the next output parameter description) brings the calibration pattern from the model coordinate + space (in which object points are specified) to the world coordinate space, that is, a real + position of the calibration pattern in the k-th pattern view (k=0.. *M* -1). + @param tvecs Output vector of translation vectors estimated for each pattern view. + @param flags Different flags that may be zero or a combination of the following values: + - **fisheye::CALIB_USE_INTRINSIC_GUESS** cameraMatrix contains valid initial values of + fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image + center ( imageSize is used), and focal distances are computed in a least-squares fashion. + - **fisheye::CALIB_RECOMPUTE_EXTRINSIC** Extrinsic will be recomputed after each iteration + of intrinsic optimization. + - **fisheye::CALIB_CHECK_COND** The functions will check validity of condition number. + - **fisheye::CALIB_FIX_SKEW** Skew coefficient (alpha) is set to zero and stay zero. + - **fisheye::CALIB_FIX_K1..fisheye::CALIB_FIX_K4** Selected distortion coefficients + are set to zeros and stay zero. + - **fisheye::CALIB_FIX_PRINCIPAL_POINT** The principal point is not changed during the global +optimization. It stays at the center or at a different location specified when CALIB_USE_INTRINSIC_GUESS is set too. + @param criteria Termination criteria for the iterative optimization algorithm. + */ + CV_EXPORTS_W double calibrate(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, const Size& image_size, + InputOutputArray K, InputOutputArray D, OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, int flags = 0, + TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 100, DBL_EPSILON)); + + /** @brief Stereo rectification for fisheye camera model + + @param K1 First camera matrix. + @param D1 First camera distortion parameters. + @param K2 Second camera matrix. + @param D2 Second camera distortion parameters. + @param imageSize Size of the image used for stereo calibration. + @param R Rotation matrix between the coordinate systems of the first and the second + cameras. + @param tvec Translation vector between coordinate systems of the cameras. + @param R1 Output 3x3 rectification transform (rotation matrix) for the first camera. + @param R2 Output 3x3 rectification transform (rotation matrix) for the second camera. + @param P1 Output 3x4 projection matrix in the new (rectified) coordinate systems for the first + camera. + @param P2 Output 3x4 projection matrix in the new (rectified) coordinate systems for the second + camera. + @param Q Output \f$4 \times 4\f$ disparity-to-depth mapping matrix (see reprojectImageTo3D ). + @param flags Operation flags that may be zero or CALIB_ZERO_DISPARITY . If the flag is set, + the function makes the principal points of each camera have the same pixel coordinates in the + rectified views. And if the flag is not set, the function may still shift the images in the + horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the + useful image area. + @param newImageSize New image resolution after rectification. The same size should be passed to + initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) + is passed (default), it is set to the original imageSize . Setting it to larger value can help you + preserve details in the original image, especially when there is a big radial distortion. + @param balance Sets the new focal length in range between the min focal length and the max focal + length. Balance is in range of [0, 1]. + @param fov_scale Divisor for new focal length. + */ + CV_EXPORTS_W void stereoRectify(InputArray K1, InputArray D1, InputArray K2, InputArray D2, const Size &imageSize, InputArray R, InputArray tvec, + OutputArray R1, OutputArray R2, OutputArray P1, OutputArray P2, OutputArray Q, int flags, const Size &newImageSize = Size(), + double balance = 0.0, double fov_scale = 1.0); + + /** @brief Performs stereo calibration + + @param objectPoints Vector of vectors of the calibration pattern points. + @param imagePoints1 Vector of vectors of the projections of the calibration pattern points, + observed by the first camera. + @param imagePoints2 Vector of vectors of the projections of the calibration pattern points, + observed by the second camera. + @param K1 Input/output first camera matrix: + \f$\vecthreethree{f_x^{(j)}}{0}{c_x^{(j)}}{0}{f_y^{(j)}}{c_y^{(j)}}{0}{0}{1}\f$ , \f$j = 0,\, 1\f$ . If + any of fisheye::CALIB_USE_INTRINSIC_GUESS , fisheye::CALIB_FIX_INTRINSIC are specified, + some or all of the matrix components must be initialized. + @param D1 Input/output vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$ of 4 elements. + @param K2 Input/output second camera matrix. The parameter is similar to K1 . + @param D2 Input/output lens distortion coefficients for the second camera. The parameter is + similar to D1 . + @param imageSize Size of the image used only to initialize intrinsic camera matrix. + @param R Output rotation matrix between the 1st and the 2nd camera coordinate systems. + @param T Output translation vector between the coordinate systems of the cameras. + @param flags Different flags that may be zero or a combination of the following values: + - **fisheye::CALIB_FIX_INTRINSIC** Fix K1, K2? and D1, D2? so that only R, T matrices + are estimated. + - **fisheye::CALIB_USE_INTRINSIC_GUESS** K1, K2 contains valid initial values of + fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image + center (imageSize is used), and focal distances are computed in a least-squares fashion. + - **fisheye::CALIB_RECOMPUTE_EXTRINSIC** Extrinsic will be recomputed after each iteration + of intrinsic optimization. + - **fisheye::CALIB_CHECK_COND** The functions will check validity of condition number. + - **fisheye::CALIB_FIX_SKEW** Skew coefficient (alpha) is set to zero and stay zero. + - **fisheye::CALIB_FIX_K1..4** Selected distortion coefficients are set to zeros and stay + zero. + @param criteria Termination criteria for the iterative optimization algorithm. + */ + CV_EXPORTS_W double stereoCalibrate(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2, + InputOutputArray K1, InputOutputArray D1, InputOutputArray K2, InputOutputArray D2, Size imageSize, + OutputArray R, OutputArray T, int flags = fisheye::CALIB_FIX_INTRINSIC, + TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 100, DBL_EPSILON)); + +//! @} calib3d_fisheye +} // end namespace fisheye + +} //end namespace cv + +#ifndef DISABLE_OPENCV_24_COMPATIBILITY +#include "opencv2/calib3d/calib3d_c.h" +#endif + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/calib3d/calib3d.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/calib3d/calib3d.hpp new file mode 100644 index 0000000000000000000000000000000000000000..b3da45edd59c0a0807abb834e0e4216522316443 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/calib3d/calib3d.hpp @@ -0,0 +1,48 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifdef __OPENCV_BUILD +#error this is a compatibility header which should not be used inside the OpenCV library +#endif + +#include "opencv2/calib3d.hpp" diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/calib3d/calib3d_c.h b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/calib3d/calib3d_c.h new file mode 100644 index 0000000000000000000000000000000000000000..8ec6390d70e3064debb62ad7301ba31707f6339c --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/calib3d/calib3d_c.h @@ -0,0 +1,427 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CALIB3D_C_H +#define OPENCV_CALIB3D_C_H + +#include "opencv2/core/core_c.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/** @addtogroup calib3d_c + @{ + */ + +/****************************************************************************************\ +* Camera Calibration, Pose Estimation and Stereo * +\****************************************************************************************/ + +typedef struct CvPOSITObject CvPOSITObject; + +/* Allocates and initializes CvPOSITObject structure before doing cvPOSIT */ +CVAPI(CvPOSITObject*) cvCreatePOSITObject( CvPoint3D32f* points, int point_count ); + + +/* Runs POSIT (POSe from ITeration) algorithm for determining 3d position of + an object given its model and projection in a weak-perspective case */ +CVAPI(void) cvPOSIT( CvPOSITObject* posit_object, CvPoint2D32f* image_points, + double focal_length, CvTermCriteria criteria, + float* rotation_matrix, float* translation_vector); + +/* Releases CvPOSITObject structure */ +CVAPI(void) cvReleasePOSITObject( CvPOSITObject** posit_object ); + +/* updates the number of RANSAC iterations */ +CVAPI(int) cvRANSACUpdateNumIters( double p, double err_prob, + int model_points, int max_iters ); + +CVAPI(void) cvConvertPointsHomogeneous( const CvMat* src, CvMat* dst ); + +/* Calculates fundamental matrix given a set of corresponding points */ +#define CV_FM_7POINT 1 +#define CV_FM_8POINT 2 + +#define CV_LMEDS 4 +#define CV_RANSAC 8 + +#define CV_FM_LMEDS_ONLY CV_LMEDS +#define CV_FM_RANSAC_ONLY CV_RANSAC +#define CV_FM_LMEDS CV_LMEDS +#define CV_FM_RANSAC CV_RANSAC + +enum +{ + CV_ITERATIVE = 0, + CV_EPNP = 1, // F.Moreno-Noguer, V.Lepetit and P.Fua "EPnP: Efficient Perspective-n-Point Camera Pose Estimation" + CV_P3P = 2, // X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang; "Complete Solution Classification for the Perspective-Three-Point Problem" + CV_DLS = 3 // Joel A. Hesch and Stergios I. Roumeliotis. "A Direct Least-Squares (DLS) Method for PnP" +}; + +CVAPI(int) cvFindFundamentalMat( const CvMat* points1, const CvMat* points2, + CvMat* fundamental_matrix, + int method CV_DEFAULT(CV_FM_RANSAC), + double param1 CV_DEFAULT(3.), double param2 CV_DEFAULT(0.99), + CvMat* status CV_DEFAULT(NULL) ); + +/* For each input point on one of images + computes parameters of the corresponding + epipolar line on the other image */ +CVAPI(void) cvComputeCorrespondEpilines( const CvMat* points, + int which_image, + const CvMat* fundamental_matrix, + CvMat* correspondent_lines ); + +/* Triangulation functions */ + +CVAPI(void) cvTriangulatePoints(CvMat* projMatr1, CvMat* projMatr2, + CvMat* projPoints1, CvMat* projPoints2, + CvMat* points4D); + +CVAPI(void) cvCorrectMatches(CvMat* F, CvMat* points1, CvMat* points2, + CvMat* new_points1, CvMat* new_points2); + + +/* Computes the optimal new camera matrix according to the free scaling parameter alpha: + alpha=0 - only valid pixels will be retained in the undistorted image + alpha=1 - all the source image pixels will be retained in the undistorted image +*/ +CVAPI(void) cvGetOptimalNewCameraMatrix( const CvMat* camera_matrix, + const CvMat* dist_coeffs, + CvSize image_size, double alpha, + CvMat* new_camera_matrix, + CvSize new_imag_size CV_DEFAULT(cvSize(0,0)), + CvRect* valid_pixel_ROI CV_DEFAULT(0), + int center_principal_point CV_DEFAULT(0)); + +/* Converts rotation vector to rotation matrix or vice versa */ +CVAPI(int) cvRodrigues2( const CvMat* src, CvMat* dst, + CvMat* jacobian CV_DEFAULT(0) ); + +/* Finds perspective transformation between the object plane and image (view) plane */ +CVAPI(int) cvFindHomography( const CvMat* src_points, + const CvMat* dst_points, + CvMat* homography, + int method CV_DEFAULT(0), + double ransacReprojThreshold CV_DEFAULT(3), + CvMat* mask CV_DEFAULT(0), + int maxIters CV_DEFAULT(2000), + double confidence CV_DEFAULT(0.995)); + +/* Computes RQ decomposition for 3x3 matrices */ +CVAPI(void) cvRQDecomp3x3( const CvMat *matrixM, CvMat *matrixR, CvMat *matrixQ, + CvMat *matrixQx CV_DEFAULT(NULL), + CvMat *matrixQy CV_DEFAULT(NULL), + CvMat *matrixQz CV_DEFAULT(NULL), + CvPoint3D64f *eulerAngles CV_DEFAULT(NULL)); + +/* Computes projection matrix decomposition */ +CVAPI(void) cvDecomposeProjectionMatrix( const CvMat *projMatr, CvMat *calibMatr, + CvMat *rotMatr, CvMat *posVect, + CvMat *rotMatrX CV_DEFAULT(NULL), + CvMat *rotMatrY CV_DEFAULT(NULL), + CvMat *rotMatrZ CV_DEFAULT(NULL), + CvPoint3D64f *eulerAngles CV_DEFAULT(NULL)); + +/* Computes d(AB)/dA and d(AB)/dB */ +CVAPI(void) cvCalcMatMulDeriv( const CvMat* A, const CvMat* B, CvMat* dABdA, CvMat* dABdB ); + +/* Computes r3 = rodrigues(rodrigues(r2)*rodrigues(r1)), + t3 = rodrigues(r2)*t1 + t2 and the respective derivatives */ +CVAPI(void) cvComposeRT( const CvMat* _rvec1, const CvMat* _tvec1, + const CvMat* _rvec2, const CvMat* _tvec2, + CvMat* _rvec3, CvMat* _tvec3, + CvMat* dr3dr1 CV_DEFAULT(0), CvMat* dr3dt1 CV_DEFAULT(0), + CvMat* dr3dr2 CV_DEFAULT(0), CvMat* dr3dt2 CV_DEFAULT(0), + CvMat* dt3dr1 CV_DEFAULT(0), CvMat* dt3dt1 CV_DEFAULT(0), + CvMat* dt3dr2 CV_DEFAULT(0), CvMat* dt3dt2 CV_DEFAULT(0) ); + +/* Projects object points to the view plane using + the specified extrinsic and intrinsic camera parameters */ +CVAPI(void) cvProjectPoints2( const CvMat* object_points, const CvMat* rotation_vector, + const CvMat* translation_vector, const CvMat* camera_matrix, + const CvMat* distortion_coeffs, CvMat* image_points, + CvMat* dpdrot CV_DEFAULT(NULL), CvMat* dpdt CV_DEFAULT(NULL), + CvMat* dpdf CV_DEFAULT(NULL), CvMat* dpdc CV_DEFAULT(NULL), + CvMat* dpddist CV_DEFAULT(NULL), + double aspect_ratio CV_DEFAULT(0)); + +/* Finds extrinsic camera parameters from + a few known corresponding point pairs and intrinsic parameters */ +CVAPI(void) cvFindExtrinsicCameraParams2( const CvMat* object_points, + const CvMat* image_points, + const CvMat* camera_matrix, + const CvMat* distortion_coeffs, + CvMat* rotation_vector, + CvMat* translation_vector, + int use_extrinsic_guess CV_DEFAULT(0) ); + +/* Computes initial estimate of the intrinsic camera parameters + in case of planar calibration target (e.g. chessboard) */ +CVAPI(void) cvInitIntrinsicParams2D( const CvMat* object_points, + const CvMat* image_points, + const CvMat* npoints, CvSize image_size, + CvMat* camera_matrix, + double aspect_ratio CV_DEFAULT(1.) ); + +#define CV_CALIB_CB_ADAPTIVE_THRESH 1 +#define CV_CALIB_CB_NORMALIZE_IMAGE 2 +#define CV_CALIB_CB_FILTER_QUADS 4 +#define CV_CALIB_CB_FAST_CHECK 8 + +// Performs a fast check if a chessboard is in the input image. This is a workaround to +// a problem of cvFindChessboardCorners being slow on images with no chessboard +// - src: input image +// - size: chessboard size +// Returns 1 if a chessboard can be in this image and findChessboardCorners should be called, +// 0 if there is no chessboard, -1 in case of error +CVAPI(int) cvCheckChessboard(IplImage* src, CvSize size); + + /* Detects corners on a chessboard calibration pattern */ +CVAPI(int) cvFindChessboardCorners( const void* image, CvSize pattern_size, + CvPoint2D32f* corners, + int* corner_count CV_DEFAULT(NULL), + int flags CV_DEFAULT(CV_CALIB_CB_ADAPTIVE_THRESH+CV_CALIB_CB_NORMALIZE_IMAGE) ); + +/* Draws individual chessboard corners or the whole chessboard detected */ +CVAPI(void) cvDrawChessboardCorners( CvArr* image, CvSize pattern_size, + CvPoint2D32f* corners, + int count, int pattern_was_found ); + +#define CV_CALIB_USE_INTRINSIC_GUESS 1 +#define CV_CALIB_FIX_ASPECT_RATIO 2 +#define CV_CALIB_FIX_PRINCIPAL_POINT 4 +#define CV_CALIB_ZERO_TANGENT_DIST 8 +#define CV_CALIB_FIX_FOCAL_LENGTH 16 +#define CV_CALIB_FIX_K1 32 +#define CV_CALIB_FIX_K2 64 +#define CV_CALIB_FIX_K3 128 +#define CV_CALIB_FIX_K4 2048 +#define CV_CALIB_FIX_K5 4096 +#define CV_CALIB_FIX_K6 8192 +#define CV_CALIB_RATIONAL_MODEL 16384 +#define CV_CALIB_THIN_PRISM_MODEL 32768 +#define CV_CALIB_FIX_S1_S2_S3_S4 65536 +#define CV_CALIB_TILTED_MODEL 262144 +#define CV_CALIB_FIX_TAUX_TAUY 524288 +#define CV_CALIB_FIX_TANGENT_DIST 2097152 + +#define CV_CALIB_NINTRINSIC 18 + +/* Finds intrinsic and extrinsic camera parameters + from a few views of known calibration pattern */ +CVAPI(double) cvCalibrateCamera2( const CvMat* object_points, + const CvMat* image_points, + const CvMat* point_counts, + CvSize image_size, + CvMat* camera_matrix, + CvMat* distortion_coeffs, + CvMat* rotation_vectors CV_DEFAULT(NULL), + CvMat* translation_vectors CV_DEFAULT(NULL), + int flags CV_DEFAULT(0), + CvTermCriteria term_crit CV_DEFAULT(cvTermCriteria( + CV_TERMCRIT_ITER+CV_TERMCRIT_EPS,30,DBL_EPSILON)) ); + +/* Computes various useful characteristics of the camera from the data computed by + cvCalibrateCamera2 */ +CVAPI(void) cvCalibrationMatrixValues( const CvMat *camera_matrix, + CvSize image_size, + double aperture_width CV_DEFAULT(0), + double aperture_height CV_DEFAULT(0), + double *fovx CV_DEFAULT(NULL), + double *fovy CV_DEFAULT(NULL), + double *focal_length CV_DEFAULT(NULL), + CvPoint2D64f *principal_point CV_DEFAULT(NULL), + double *pixel_aspect_ratio CV_DEFAULT(NULL)); + +#define CV_CALIB_FIX_INTRINSIC 256 +#define CV_CALIB_SAME_FOCAL_LENGTH 512 + +/* Computes the transformation from one camera coordinate system to another one + from a few correspondent views of the same calibration target. Optionally, calibrates + both cameras */ +CVAPI(double) cvStereoCalibrate( const CvMat* object_points, const CvMat* image_points1, + const CvMat* image_points2, const CvMat* npoints, + CvMat* camera_matrix1, CvMat* dist_coeffs1, + CvMat* camera_matrix2, CvMat* dist_coeffs2, + CvSize image_size, CvMat* R, CvMat* T, + CvMat* E CV_DEFAULT(0), CvMat* F CV_DEFAULT(0), + int flags CV_DEFAULT(CV_CALIB_FIX_INTRINSIC), + CvTermCriteria term_crit CV_DEFAULT(cvTermCriteria( + CV_TERMCRIT_ITER+CV_TERMCRIT_EPS,30,1e-6)) ); + +#define CV_CALIB_ZERO_DISPARITY 1024 + +/* Computes 3D rotations (+ optional shift) for each camera coordinate system to make both + views parallel (=> to make all the epipolar lines horizontal or vertical) */ +CVAPI(void) cvStereoRectify( const CvMat* camera_matrix1, const CvMat* camera_matrix2, + const CvMat* dist_coeffs1, const CvMat* dist_coeffs2, + CvSize image_size, const CvMat* R, const CvMat* T, + CvMat* R1, CvMat* R2, CvMat* P1, CvMat* P2, + CvMat* Q CV_DEFAULT(0), + int flags CV_DEFAULT(CV_CALIB_ZERO_DISPARITY), + double alpha CV_DEFAULT(-1), + CvSize new_image_size CV_DEFAULT(cvSize(0,0)), + CvRect* valid_pix_ROI1 CV_DEFAULT(0), + CvRect* valid_pix_ROI2 CV_DEFAULT(0)); + +/* Computes rectification transformations for uncalibrated pair of images using a set + of point correspondences */ +CVAPI(int) cvStereoRectifyUncalibrated( const CvMat* points1, const CvMat* points2, + const CvMat* F, CvSize img_size, + CvMat* H1, CvMat* H2, + double threshold CV_DEFAULT(5)); + + + +/* stereo correspondence parameters and functions */ + +#define CV_STEREO_BM_NORMALIZED_RESPONSE 0 +#define CV_STEREO_BM_XSOBEL 1 + +/* Block matching algorithm structure */ +typedef struct CvStereoBMState +{ + // pre-filtering (normalization of input images) + int preFilterType; // =CV_STEREO_BM_NORMALIZED_RESPONSE now + int preFilterSize; // averaging window size: ~5x5..21x21 + int preFilterCap; // the output of pre-filtering is clipped by [-preFilterCap,preFilterCap] + + // correspondence using Sum of Absolute Difference (SAD) + int SADWindowSize; // ~5x5..21x21 + int minDisparity; // minimum disparity (can be negative) + int numberOfDisparities; // maximum disparity - minimum disparity (> 0) + + // post-filtering + int textureThreshold; // the disparity is only computed for pixels + // with textured enough neighborhood + int uniquenessRatio; // accept the computed disparity d* only if + // SAD(d) >= SAD(d*)*(1 + uniquenessRatio/100.) + // for any d != d*+/-1 within the search range. + int speckleWindowSize; // disparity variation window + int speckleRange; // acceptable range of variation in window + + int trySmallerWindows; // if 1, the results may be more accurate, + // at the expense of slower processing + CvRect roi1, roi2; + int disp12MaxDiff; + + // temporary buffers + CvMat* preFilteredImg0; + CvMat* preFilteredImg1; + CvMat* slidingSumBuf; + CvMat* cost; + CvMat* disp; +} CvStereoBMState; + +#define CV_STEREO_BM_BASIC 0 +#define CV_STEREO_BM_FISH_EYE 1 +#define CV_STEREO_BM_NARROW 2 + +CVAPI(CvStereoBMState*) cvCreateStereoBMState(int preset CV_DEFAULT(CV_STEREO_BM_BASIC), + int numberOfDisparities CV_DEFAULT(0)); + +CVAPI(void) cvReleaseStereoBMState( CvStereoBMState** state ); + +CVAPI(void) cvFindStereoCorrespondenceBM( const CvArr* left, const CvArr* right, + CvArr* disparity, CvStereoBMState* state ); + +CVAPI(CvRect) cvGetValidDisparityROI( CvRect roi1, CvRect roi2, int minDisparity, + int numberOfDisparities, int SADWindowSize ); + +CVAPI(void) cvValidateDisparity( CvArr* disparity, const CvArr* cost, + int minDisparity, int numberOfDisparities, + int disp12MaxDiff CV_DEFAULT(1) ); + +/* Reprojects the computed disparity image to the 3D space using the specified 4x4 matrix */ +CVAPI(void) cvReprojectImageTo3D( const CvArr* disparityImage, + CvArr* _3dImage, const CvMat* Q, + int handleMissingValues CV_DEFAULT(0) ); + +/** @} calib3d_c */ + +#ifdef __cplusplus +} // extern "C" + +////////////////////////////////////////////////////////////////////////////////////////// +class CV_EXPORTS CvLevMarq +{ +public: + CvLevMarq(); + CvLevMarq( int nparams, int nerrs, CvTermCriteria criteria= + cvTermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER,30,DBL_EPSILON), + bool completeSymmFlag=false ); + ~CvLevMarq(); + void init( int nparams, int nerrs, CvTermCriteria criteria= + cvTermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER,30,DBL_EPSILON), + bool completeSymmFlag=false ); + bool update( const CvMat*& param, CvMat*& J, CvMat*& err ); + bool updateAlt( const CvMat*& param, CvMat*& JtJ, CvMat*& JtErr, double*& errNorm ); + + void clear(); + void step(); + enum { DONE=0, STARTED=1, CALC_J=2, CHECK_ERR=3 }; + + cv::Ptr mask; + cv::Ptr prevParam; + cv::Ptr param; + cv::Ptr J; + cv::Ptr err; + cv::Ptr JtJ; + cv::Ptr JtJN; + cv::Ptr JtErr; + cv::Ptr JtJV; + cv::Ptr JtJW; + double prevErrNorm, errNorm; + int lambdaLg10; + CvTermCriteria criteria; + int state; + int iters; + bool completeSymmFlag; + int solveMethod; +}; + +#endif + +#endif /* OPENCV_CALIB3D_C_H */ diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core.hpp new file mode 100644 index 0000000000000000000000000000000000000000..089c0db3342048c008487c00b8125e070d75f6a0 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core.hpp @@ -0,0 +1,3285 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2015, Intel Corporation, all rights reserved. +// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved. +// Copyright (C) 2015, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_HPP +#define OPENCV_CORE_HPP + +#ifndef __cplusplus +# error core.hpp header must be compiled as C++ +#endif + +#include "opencv2/core/cvdef.h" +#include "opencv2/core/version.hpp" +#include "opencv2/core/base.hpp" +#include "opencv2/core/cvstd.hpp" +#include "opencv2/core/traits.hpp" +#include "opencv2/core/matx.hpp" +#include "opencv2/core/types.hpp" +#include "opencv2/core/mat.hpp" +#include "opencv2/core/persistence.hpp" + +/** +@defgroup core Core functionality +@{ + @defgroup core_basic Basic structures + @defgroup core_c C structures and operations + @{ + @defgroup core_c_glue Connections with C++ + @} + @defgroup core_array Operations on arrays + @defgroup core_xml XML/YAML Persistence + @defgroup core_cluster Clustering + @defgroup core_utils Utility and system functions and macros + @{ + @defgroup core_utils_sse SSE utilities + @defgroup core_utils_neon NEON utilities + @defgroup core_utils_softfloat Softfloat support + @defgroup core_utils_samples Utility functions for OpenCV samples + @} + @defgroup core_opengl OpenGL interoperability + @defgroup core_ipp Intel IPP Asynchronous C/C++ Converters + @defgroup core_optim Optimization Algorithms + @defgroup core_directx DirectX interoperability + @defgroup core_eigen Eigen support + @defgroup core_opencl OpenCL support + @defgroup core_va_intel Intel VA-API/OpenCL (CL-VA) interoperability + @defgroup core_hal Hardware Acceleration Layer + @{ + @defgroup core_hal_functions Functions + @defgroup core_hal_interface Interface + @defgroup core_hal_intrin Universal intrinsics + @{ + @defgroup core_hal_intrin_impl Private implementation helpers + @} + @} +@} + */ + +namespace cv { + +//! @addtogroup core_utils +//! @{ + +/*! @brief Class passed to an error. + +This class encapsulates all or almost all necessary +information about the error happened in the program. The exception is +usually constructed and thrown implicitly via CV_Error and CV_Error_ macros. +@see error + */ +class CV_EXPORTS Exception : public std::exception +{ +public: + /*! + Default constructor + */ + Exception(); + /*! + Full constructor. Normally the constructor is not called explicitly. + Instead, the macros CV_Error(), CV_Error_() and CV_Assert() are used. + */ + Exception(int _code, const String& _err, const String& _func, const String& _file, int _line); + virtual ~Exception() throw(); + + /*! + \return the error description and the context as a text string. + */ + virtual const char *what() const throw() CV_OVERRIDE; + void formatMessage(); + + String msg; ///< the formatted error message + + int code; ///< error code @see CVStatus + String err; ///< error description + String func; ///< function name. Available only when the compiler supports getting it + String file; ///< source file name where the error has occurred + int line; ///< line number in the source file where the error has occurred +}; + +/*! @brief Signals an error and raises the exception. + +By default the function prints information about the error to stderr, +then it either stops if cv::setBreakOnError() had been called before or raises the exception. +It is possible to alternate error processing by using #redirectError(). +@param exc the exception raisen. +@deprecated drop this version + */ +CV_EXPORTS void error( const Exception& exc ); + +enum SortFlags { SORT_EVERY_ROW = 0, //!< each matrix row is sorted independently + SORT_EVERY_COLUMN = 1, //!< each matrix column is sorted + //!< independently; this flag and the previous one are + //!< mutually exclusive. + SORT_ASCENDING = 0, //!< each matrix row is sorted in the ascending + //!< order. + SORT_DESCENDING = 16 //!< each matrix row is sorted in the + //!< descending order; this flag and the previous one are also + //!< mutually exclusive. + }; + +//! @} core_utils + +//! @addtogroup core +//! @{ + +//! Covariation flags +enum CovarFlags { + /** The output covariance matrix is calculated as: + \f[\texttt{scale} \cdot [ \texttt{vects} [0]- \texttt{mean} , \texttt{vects} [1]- \texttt{mean} ,...]^T \cdot [ \texttt{vects} [0]- \texttt{mean} , \texttt{vects} [1]- \texttt{mean} ,...],\f] + The covariance matrix will be nsamples x nsamples. Such an unusual covariance matrix is used + for fast PCA of a set of very large vectors (see, for example, the EigenFaces technique for + face recognition). Eigenvalues of this "scrambled" matrix match the eigenvalues of the true + covariance matrix. The "true" eigenvectors can be easily calculated from the eigenvectors of + the "scrambled" covariance matrix. */ + COVAR_SCRAMBLED = 0, + /**The output covariance matrix is calculated as: + \f[\texttt{scale} \cdot [ \texttt{vects} [0]- \texttt{mean} , \texttt{vects} [1]- \texttt{mean} ,...] \cdot [ \texttt{vects} [0]- \texttt{mean} , \texttt{vects} [1]- \texttt{mean} ,...]^T,\f] + covar will be a square matrix of the same size as the total number of elements in each input + vector. One and only one of #COVAR_SCRAMBLED and #COVAR_NORMAL must be specified.*/ + COVAR_NORMAL = 1, + /** If the flag is specified, the function does not calculate mean from + the input vectors but, instead, uses the passed mean vector. This is useful if mean has been + pre-calculated or known in advance, or if the covariance matrix is calculated by parts. In + this case, mean is not a mean vector of the input sub-set of vectors but rather the mean + vector of the whole set.*/ + COVAR_USE_AVG = 2, + /** If the flag is specified, the covariance matrix is scaled. In the + "normal" mode, scale is 1./nsamples . In the "scrambled" mode, scale is the reciprocal of the + total number of elements in each input vector. By default (if the flag is not specified), the + covariance matrix is not scaled ( scale=1 ).*/ + COVAR_SCALE = 4, + /** If the flag is + specified, all the input vectors are stored as rows of the samples matrix. mean should be a + single-row vector in this case.*/ + COVAR_ROWS = 8, + /** If the flag is + specified, all the input vectors are stored as columns of the samples matrix. mean should be a + single-column vector in this case.*/ + COVAR_COLS = 16 +}; + +//! k-Means flags +enum KmeansFlags { + /** Select random initial centers in each attempt.*/ + KMEANS_RANDOM_CENTERS = 0, + /** Use kmeans++ center initialization by Arthur and Vassilvitskii [Arthur2007].*/ + KMEANS_PP_CENTERS = 2, + /** During the first (and possibly the only) attempt, use the + user-supplied labels instead of computing them from the initial centers. For the second and + further attempts, use the random or semi-random centers. Use one of KMEANS_\*_CENTERS flag + to specify the exact method.*/ + KMEANS_USE_INITIAL_LABELS = 1 +}; + +//! type of line +enum LineTypes { + FILLED = -1, + LINE_4 = 4, //!< 4-connected line + LINE_8 = 8, //!< 8-connected line + LINE_AA = 16 //!< antialiased line +}; + +//! Only a subset of Hershey fonts are supported +enum HersheyFonts { + FONT_HERSHEY_SIMPLEX = 0, //!< normal size sans-serif font + FONT_HERSHEY_PLAIN = 1, //!< small size sans-serif font + FONT_HERSHEY_DUPLEX = 2, //!< normal size sans-serif font (more complex than FONT_HERSHEY_SIMPLEX) + FONT_HERSHEY_COMPLEX = 3, //!< normal size serif font + FONT_HERSHEY_TRIPLEX = 4, //!< normal size serif font (more complex than FONT_HERSHEY_COMPLEX) + FONT_HERSHEY_COMPLEX_SMALL = 5, //!< smaller version of FONT_HERSHEY_COMPLEX + FONT_HERSHEY_SCRIPT_SIMPLEX = 6, //!< hand-writing style font + FONT_HERSHEY_SCRIPT_COMPLEX = 7, //!< more complex variant of FONT_HERSHEY_SCRIPT_SIMPLEX + FONT_ITALIC = 16 //!< flag for italic font +}; + +enum ReduceTypes { REDUCE_SUM = 0, //!< the output is the sum of all rows/columns of the matrix. + REDUCE_AVG = 1, //!< the output is the mean vector of all rows/columns of the matrix. + REDUCE_MAX = 2, //!< the output is the maximum (column/row-wise) of all rows/columns of the matrix. + REDUCE_MIN = 3 //!< the output is the minimum (column/row-wise) of all rows/columns of the matrix. + }; + + +/** @brief Swaps two matrices +*/ +CV_EXPORTS void swap(Mat& a, Mat& b); +/** @overload */ +CV_EXPORTS void swap( UMat& a, UMat& b ); + +//! @} core + +//! @addtogroup core_array +//! @{ + +/** @brief Computes the source location of an extrapolated pixel. + +The function computes and returns the coordinate of a donor pixel corresponding to the specified +extrapolated pixel when using the specified extrapolation border mode. For example, if you use +cv::BORDER_WRAP mode in the horizontal direction, cv::BORDER_REFLECT_101 in the vertical direction and +want to compute value of the "virtual" pixel Point(-5, 100) in a floating-point image img , it +looks like: +@code{.cpp} + float val = img.at(borderInterpolate(100, img.rows, cv::BORDER_REFLECT_101), + borderInterpolate(-5, img.cols, cv::BORDER_WRAP)); +@endcode +Normally, the function is not called directly. It is used inside filtering functions and also in +copyMakeBorder. +@param p 0-based coordinate of the extrapolated pixel along one of the axes, likely \<0 or \>= len +@param len Length of the array along the corresponding axis. +@param borderType Border type, one of the #BorderTypes, except for #BORDER_TRANSPARENT and +#BORDER_ISOLATED . When borderType==#BORDER_CONSTANT , the function always returns -1, regardless +of p and len. + +@sa copyMakeBorder +*/ +CV_EXPORTS_W int borderInterpolate(int p, int len, int borderType); + +/** @example samples/cpp/tutorial_code/ImgTrans/copyMakeBorder_demo.cpp +An example using copyMakeBorder function. +Check @ref tutorial_copyMakeBorder "the corresponding tutorial" for more details +*/ + +/** @brief Forms a border around an image. + +The function copies the source image into the middle of the destination image. The areas to the +left, to the right, above and below the copied source image will be filled with extrapolated +pixels. This is not what filtering functions based on it do (they extrapolate pixels on-fly), but +what other more complex functions, including your own, may do to simplify image boundary handling. + +The function supports the mode when src is already in the middle of dst . In this case, the +function does not copy src itself but simply constructs the border, for example: + +@code{.cpp} + // let border be the same in all directions + int border=2; + // constructs a larger image to fit both the image and the border + Mat gray_buf(rgb.rows + border*2, rgb.cols + border*2, rgb.depth()); + // select the middle part of it w/o copying data + Mat gray(gray_canvas, Rect(border, border, rgb.cols, rgb.rows)); + // convert image from RGB to grayscale + cvtColor(rgb, gray, COLOR_RGB2GRAY); + // form a border in-place + copyMakeBorder(gray, gray_buf, border, border, + border, border, BORDER_REPLICATE); + // now do some custom filtering ... + ... +@endcode +@note When the source image is a part (ROI) of a bigger image, the function will try to use the +pixels outside of the ROI to form a border. To disable this feature and always do extrapolation, as +if src was not a ROI, use borderType | #BORDER_ISOLATED. + +@param src Source image. +@param dst Destination image of the same type as src and the size Size(src.cols+left+right, +src.rows+top+bottom) . +@param top +@param bottom +@param left +@param right Parameter specifying how many pixels in each direction from the source image rectangle +to extrapolate. For example, top=1, bottom=1, left=1, right=1 mean that 1 pixel-wide border needs +to be built. +@param borderType Border type. See borderInterpolate for details. +@param value Border value if borderType==BORDER_CONSTANT . + +@sa borderInterpolate +*/ +CV_EXPORTS_W void copyMakeBorder(InputArray src, OutputArray dst, + int top, int bottom, int left, int right, + int borderType, const Scalar& value = Scalar() ); + +/** @brief Calculates the per-element sum of two arrays or an array and a scalar. + +The function add calculates: +- Sum of two arrays when both input arrays have the same size and the same number of channels: +\f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) + \texttt{src2}(I)) \quad \texttt{if mask}(I) \ne0\f] +- Sum of an array and a scalar when src2 is constructed from Scalar or has the same number of +elements as `src1.channels()`: +\f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) + \texttt{src2} ) \quad \texttt{if mask}(I) \ne0\f] +- Sum of a scalar and an array when src1 is constructed from Scalar or has the same number of +elements as `src2.channels()`: +\f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1} + \texttt{src2}(I) ) \quad \texttt{if mask}(I) \ne0\f] +where `I` is a multi-dimensional index of array elements. In case of multi-channel arrays, each +channel is processed independently. + +The first function in the list above can be replaced with matrix expressions: +@code{.cpp} + dst = src1 + src2; + dst += src1; // equivalent to add(dst, src1, dst); +@endcode +The input arrays and the output array can all have the same or different depths. For example, you +can add a 16-bit unsigned array to a 8-bit signed array and store the sum as a 32-bit +floating-point array. Depth of the output array is determined by the dtype parameter. In the second +and third cases above, as well as in the first case, when src1.depth() == src2.depth(), dtype can +be set to the default -1. In this case, the output array will have the same depth as the input +array, be it src1, src2 or both. +@note Saturation is not applied when the output array has the depth CV_32S. You may even get +result of an incorrect sign in the case of overflow. +@param src1 first input array or a scalar. +@param src2 second input array or a scalar. +@param dst output array that has the same size and number of channels as the input array(s); the +depth is defined by dtype or src1/src2. +@param mask optional operation mask - 8-bit single channel array, that specifies elements of the +output array to be changed. +@param dtype optional depth of the output array (see the discussion below). +@sa subtract, addWeighted, scaleAdd, Mat::convertTo +*/ +CV_EXPORTS_W void add(InputArray src1, InputArray src2, OutputArray dst, + InputArray mask = noArray(), int dtype = -1); + +/** @brief Calculates the per-element difference between two arrays or array and a scalar. + +The function subtract calculates: +- Difference between two arrays, when both input arrays have the same size and the same number of +channels: + \f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) - \texttt{src2}(I)) \quad \texttt{if mask}(I) \ne0\f] +- Difference between an array and a scalar, when src2 is constructed from Scalar or has the same +number of elements as `src1.channels()`: + \f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) - \texttt{src2} ) \quad \texttt{if mask}(I) \ne0\f] +- Difference between a scalar and an array, when src1 is constructed from Scalar or has the same +number of elements as `src2.channels()`: + \f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1} - \texttt{src2}(I) ) \quad \texttt{if mask}(I) \ne0\f] +- The reverse difference between a scalar and an array in the case of `SubRS`: + \f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src2} - \texttt{src1}(I) ) \quad \texttt{if mask}(I) \ne0\f] +where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each +channel is processed independently. + +The first function in the list above can be replaced with matrix expressions: +@code{.cpp} + dst = src1 - src2; + dst -= src1; // equivalent to subtract(dst, src1, dst); +@endcode +The input arrays and the output array can all have the same or different depths. For example, you +can subtract to 8-bit unsigned arrays and store the difference in a 16-bit signed array. Depth of +the output array is determined by dtype parameter. In the second and third cases above, as well as +in the first case, when src1.depth() == src2.depth(), dtype can be set to the default -1. In this +case the output array will have the same depth as the input array, be it src1, src2 or both. +@note Saturation is not applied when the output array has the depth CV_32S. You may even get +result of an incorrect sign in the case of overflow. +@param src1 first input array or a scalar. +@param src2 second input array or a scalar. +@param dst output array of the same size and the same number of channels as the input array. +@param mask optional operation mask; this is an 8-bit single channel array that specifies elements +of the output array to be changed. +@param dtype optional depth of the output array +@sa add, addWeighted, scaleAdd, Mat::convertTo + */ +CV_EXPORTS_W void subtract(InputArray src1, InputArray src2, OutputArray dst, + InputArray mask = noArray(), int dtype = -1); + + +/** @brief Calculates the per-element scaled product of two arrays. + +The function multiply calculates the per-element product of two arrays: + +\f[\texttt{dst} (I)= \texttt{saturate} ( \texttt{scale} \cdot \texttt{src1} (I) \cdot \texttt{src2} (I))\f] + +There is also a @ref MatrixExpressions -friendly variant of the first function. See Mat::mul . + +For a not-per-element matrix product, see gemm . + +@note Saturation is not applied when the output array has the depth +CV_32S. You may even get result of an incorrect sign in the case of +overflow. +@param src1 first input array. +@param src2 second input array of the same size and the same type as src1. +@param dst output array of the same size and type as src1. +@param scale optional scale factor. +@param dtype optional depth of the output array +@sa add, subtract, divide, scaleAdd, addWeighted, accumulate, accumulateProduct, accumulateSquare, +Mat::convertTo +*/ +CV_EXPORTS_W void multiply(InputArray src1, InputArray src2, + OutputArray dst, double scale = 1, int dtype = -1); + +/** @brief Performs per-element division of two arrays or a scalar by an array. + +The function cv::divide divides one array by another: +\f[\texttt{dst(I) = saturate(src1(I)*scale/src2(I))}\f] +or a scalar by an array when there is no src1 : +\f[\texttt{dst(I) = saturate(scale/src2(I))}\f] + +When src2(I) is zero, dst(I) will also be zero. Different channels of +multi-channel arrays are processed independently. + +@note Saturation is not applied when the output array has the depth CV_32S. You may even get +result of an incorrect sign in the case of overflow. +@param src1 first input array. +@param src2 second input array of the same size and type as src1. +@param scale scalar factor. +@param dst output array of the same size and type as src2. +@param dtype optional depth of the output array; if -1, dst will have depth src2.depth(), but in +case of an array-by-array division, you can only pass -1 when src1.depth()==src2.depth(). +@sa multiply, add, subtract +*/ +CV_EXPORTS_W void divide(InputArray src1, InputArray src2, OutputArray dst, + double scale = 1, int dtype = -1); + +/** @overload */ +CV_EXPORTS_W void divide(double scale, InputArray src2, + OutputArray dst, int dtype = -1); + +/** @brief Calculates the sum of a scaled array and another array. + +The function scaleAdd is one of the classical primitive linear algebra operations, known as DAXPY +or SAXPY in [BLAS](http://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms). It calculates +the sum of a scaled array and another array: +\f[\texttt{dst} (I)= \texttt{scale} \cdot \texttt{src1} (I) + \texttt{src2} (I)\f] +The function can also be emulated with a matrix expression, for example: +@code{.cpp} + Mat A(3, 3, CV_64F); + ... + A.row(0) = A.row(1)*2 + A.row(2); +@endcode +@param src1 first input array. +@param alpha scale factor for the first array. +@param src2 second input array of the same size and type as src1. +@param dst output array of the same size and type as src1. +@sa add, addWeighted, subtract, Mat::dot, Mat::convertTo +*/ +CV_EXPORTS_W void scaleAdd(InputArray src1, double alpha, InputArray src2, OutputArray dst); + +/** @example samples/cpp/tutorial_code/HighGUI/AddingImagesTrackbar.cpp +Check @ref tutorial_trackbar "the corresponding tutorial" for more details +*/ + +/** @brief Calculates the weighted sum of two arrays. + +The function addWeighted calculates the weighted sum of two arrays as follows: +\f[\texttt{dst} (I)= \texttt{saturate} ( \texttt{src1} (I)* \texttt{alpha} + \texttt{src2} (I)* \texttt{beta} + \texttt{gamma} )\f] +where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each +channel is processed independently. +The function can be replaced with a matrix expression: +@code{.cpp} + dst = src1*alpha + src2*beta + gamma; +@endcode +@note Saturation is not applied when the output array has the depth CV_32S. You may even get +result of an incorrect sign in the case of overflow. +@param src1 first input array. +@param alpha weight of the first array elements. +@param src2 second input array of the same size and channel number as src1. +@param beta weight of the second array elements. +@param gamma scalar added to each sum. +@param dst output array that has the same size and number of channels as the input arrays. +@param dtype optional depth of the output array; when both input arrays have the same depth, dtype +can be set to -1, which will be equivalent to src1.depth(). +@sa add, subtract, scaleAdd, Mat::convertTo +*/ +CV_EXPORTS_W void addWeighted(InputArray src1, double alpha, InputArray src2, + double beta, double gamma, OutputArray dst, int dtype = -1); + +/** @brief Scales, calculates absolute values, and converts the result to 8-bit. + +On each element of the input array, the function convertScaleAbs +performs three operations sequentially: scaling, taking an absolute +value, conversion to an unsigned 8-bit type: +\f[\texttt{dst} (I)= \texttt{saturate\_cast} (| \texttt{src} (I)* \texttt{alpha} + \texttt{beta} |)\f] +In case of multi-channel arrays, the function processes each channel +independently. When the output is not 8-bit, the operation can be +emulated by calling the Mat::convertTo method (or by using matrix +expressions) and then by calculating an absolute value of the result. +For example: +@code{.cpp} + Mat_ A(30,30); + randu(A, Scalar(-100), Scalar(100)); + Mat_ B = A*5 + 3; + B = abs(B); + // Mat_ B = abs(A*5+3) will also do the job, + // but it will allocate a temporary matrix +@endcode +@param src input array. +@param dst output array. +@param alpha optional scale factor. +@param beta optional delta added to the scaled values. +@sa Mat::convertTo, cv::abs(const Mat&) +*/ +CV_EXPORTS_W void convertScaleAbs(InputArray src, OutputArray dst, + double alpha = 1, double beta = 0); + +/** @brief Converts an array to half precision floating number. + +This function converts FP32 (single precision floating point) from/to FP16 (half precision floating point). CV_16S format is used to represent FP16 data. +There are two use modes (src -> dst): CV_32F -> CV_16S and CV_16S -> CV_32F. The input array has to have type of CV_32F or +CV_16S to represent the bit depth. If the input array is neither of them, the function will raise an error. +The format of half precision floating point is defined in IEEE 754-2008. + +@param src input array. +@param dst output array. +*/ +CV_EXPORTS_W void convertFp16(InputArray src, OutputArray dst); + +/** @brief Performs a look-up table transform of an array. + +The function LUT fills the output array with values from the look-up table. Indices of the entries +are taken from the input array. That is, the function processes each element of src as follows: +\f[\texttt{dst} (I) \leftarrow \texttt{lut(src(I) + d)}\f] +where +\f[d = \fork{0}{if \(\texttt{src}\) has depth \(\texttt{CV_8U}\)}{128}{if \(\texttt{src}\) has depth \(\texttt{CV_8S}\)}\f] +@param src input array of 8-bit elements. +@param lut look-up table of 256 elements; in case of multi-channel input array, the table should +either have a single channel (in this case the same table is used for all channels) or the same +number of channels as in the input array. +@param dst output array of the same size and number of channels as src, and the same depth as lut. +@sa convertScaleAbs, Mat::convertTo +*/ +CV_EXPORTS_W void LUT(InputArray src, InputArray lut, OutputArray dst); + +/** @brief Calculates the sum of array elements. + +The function cv::sum calculates and returns the sum of array elements, +independently for each channel. +@param src input array that must have from 1 to 4 channels. +@sa countNonZero, mean, meanStdDev, norm, minMaxLoc, reduce +*/ +CV_EXPORTS_AS(sumElems) Scalar sum(InputArray src); + +/** @brief Counts non-zero array elements. + +The function returns the number of non-zero elements in src : +\f[\sum _{I: \; \texttt{src} (I) \ne0 } 1\f] +@param src single-channel array. +@sa mean, meanStdDev, norm, minMaxLoc, calcCovarMatrix +*/ +CV_EXPORTS_W int countNonZero( InputArray src ); + +/** @brief Returns the list of locations of non-zero pixels + +Given a binary matrix (likely returned from an operation such +as threshold(), compare(), >, ==, etc, return all of +the non-zero indices as a cv::Mat or std::vector (x,y) +For example: +@code{.cpp} + cv::Mat binaryImage; // input, binary image + cv::Mat locations; // output, locations of non-zero pixels + cv::findNonZero(binaryImage, locations); + + // access pixel coordinates + Point pnt = locations.at(i); +@endcode +or +@code{.cpp} + cv::Mat binaryImage; // input, binary image + vector locations; // output, locations of non-zero pixels + cv::findNonZero(binaryImage, locations); + + // access pixel coordinates + Point pnt = locations[i]; +@endcode +@param src single-channel array (type CV_8UC1) +@param idx the output array, type of cv::Mat or std::vector, corresponding to non-zero indices in the input +*/ +CV_EXPORTS_W void findNonZero( InputArray src, OutputArray idx ); + +/** @brief Calculates an average (mean) of array elements. + +The function cv::mean calculates the mean value M of array elements, +independently for each channel, and return it: +\f[\begin{array}{l} N = \sum _{I: \; \texttt{mask} (I) \ne 0} 1 \\ M_c = \left ( \sum _{I: \; \texttt{mask} (I) \ne 0}{ \texttt{mtx} (I)_c} \right )/N \end{array}\f] +When all the mask elements are 0's, the function returns Scalar::all(0) +@param src input array that should have from 1 to 4 channels so that the result can be stored in +Scalar_ . +@param mask optional operation mask. +@sa countNonZero, meanStdDev, norm, minMaxLoc +*/ +CV_EXPORTS_W Scalar mean(InputArray src, InputArray mask = noArray()); + +/** Calculates a mean and standard deviation of array elements. + +The function cv::meanStdDev calculates the mean and the standard deviation M +of array elements independently for each channel and returns it via the +output parameters: +\f[\begin{array}{l} N = \sum _{I, \texttt{mask} (I) \ne 0} 1 \\ \texttt{mean} _c = \frac{\sum_{ I: \; \texttt{mask}(I) \ne 0} \texttt{src} (I)_c}{N} \\ \texttt{stddev} _c = \sqrt{\frac{\sum_{ I: \; \texttt{mask}(I) \ne 0} \left ( \texttt{src} (I)_c - \texttt{mean} _c \right )^2}{N}} \end{array}\f] +When all the mask elements are 0's, the function returns +mean=stddev=Scalar::all(0). +@note The calculated standard deviation is only the diagonal of the +complete normalized covariance matrix. If the full matrix is needed, you +can reshape the multi-channel array M x N to the single-channel array +M\*N x mtx.channels() (only possible when the matrix is continuous) and +then pass the matrix to calcCovarMatrix . +@param src input array that should have from 1 to 4 channels so that the results can be stored in +Scalar_ 's. +@param mean output parameter: calculated mean value. +@param stddev output parameter: calculated standard deviation. +@param mask optional operation mask. +@sa countNonZero, mean, norm, minMaxLoc, calcCovarMatrix +*/ +CV_EXPORTS_W void meanStdDev(InputArray src, OutputArray mean, OutputArray stddev, + InputArray mask=noArray()); + +/** @brief Calculates the absolute norm of an array. + +This version of #norm calculates the absolute norm of src1. The type of norm to calculate is specified using #NormTypes. + +As example for one array consider the function \f$r(x)= \begin{pmatrix} x \\ 1-x \end{pmatrix}, x \in [-1;1]\f$. +The \f$ L_{1}, L_{2} \f$ and \f$ L_{\infty} \f$ norm for the sample value \f$r(-1) = \begin{pmatrix} -1 \\ 2 \end{pmatrix}\f$ +is calculated as follows +\f{align*} + \| r(-1) \|_{L_1} &= |-1| + |2| = 3 \\ + \| r(-1) \|_{L_2} &= \sqrt{(-1)^{2} + (2)^{2}} = \sqrt{5} \\ + \| r(-1) \|_{L_\infty} &= \max(|-1|,|2|) = 2 +\f} +and for \f$r(0.5) = \begin{pmatrix} 0.5 \\ 0.5 \end{pmatrix}\f$ the calculation is +\f{align*} + \| r(0.5) \|_{L_1} &= |0.5| + |0.5| = 1 \\ + \| r(0.5) \|_{L_2} &= \sqrt{(0.5)^{2} + (0.5)^{2}} = \sqrt{0.5} \\ + \| r(0.5) \|_{L_\infty} &= \max(|0.5|,|0.5|) = 0.5. +\f} +The following graphic shows all values for the three norm functions \f$\| r(x) \|_{L_1}, \| r(x) \|_{L_2}\f$ and \f$\| r(x) \|_{L_\infty}\f$. +It is notable that the \f$ L_{1} \f$ norm forms the upper and the \f$ L_{\infty} \f$ norm forms the lower border for the example function \f$ r(x) \f$. +![Graphs for the different norm functions from the above example](pics/NormTypes_OneArray_1-2-INF.png) + +When the mask parameter is specified and it is not empty, the norm is + +If normType is not specified, #NORM_L2 is used. +calculated only over the region specified by the mask. + +Multi-channel input arrays are treated as single-channel arrays, that is, +the results for all channels are combined. + +Hamming norms can only be calculated with CV_8U depth arrays. + +@param src1 first input array. +@param normType type of the norm (see #NormTypes). +@param mask optional operation mask; it must have the same size as src1 and CV_8UC1 type. +*/ +CV_EXPORTS_W double norm(InputArray src1, int normType = NORM_L2, InputArray mask = noArray()); + +/** @brief Calculates an absolute difference norm or a relative difference norm. + +This version of cv::norm calculates the absolute difference norm +or the relative difference norm of arrays src1 and src2. +The type of norm to calculate is specified using #NormTypes. + +@param src1 first input array. +@param src2 second input array of the same size and the same type as src1. +@param normType type of the norm (see #NormTypes). +@param mask optional operation mask; it must have the same size as src1 and CV_8UC1 type. +*/ +CV_EXPORTS_W double norm(InputArray src1, InputArray src2, + int normType = NORM_L2, InputArray mask = noArray()); +/** @overload +@param src first input array. +@param normType type of the norm (see #NormTypes). +*/ +CV_EXPORTS double norm( const SparseMat& src, int normType ); + +/** @brief Computes the Peak Signal-to-Noise Ratio (PSNR) image quality metric. + +This function calculates the Peak Signal-to-Noise Ratio (PSNR) image quality metric in decibels (dB), between two input arrays src1 and src2. Arrays must have depth CV_8U. + +The PSNR is calculated as follows: + +\f[ +\texttt{PSNR} = 10 \cdot \log_{10}{\left( \frac{R^2}{MSE} \right) } +\f] + +where R is the maximum integer value of depth CV_8U (255) and MSE is the mean squared error between the two arrays. + +@param src1 first input array. +@param src2 second input array of the same size as src1. + + */ +CV_EXPORTS_W double PSNR(InputArray src1, InputArray src2); + +/** @brief naive nearest neighbor finder + +see http://en.wikipedia.org/wiki/Nearest_neighbor_search +@todo document + */ +CV_EXPORTS_W void batchDistance(InputArray src1, InputArray src2, + OutputArray dist, int dtype, OutputArray nidx, + int normType = NORM_L2, int K = 0, + InputArray mask = noArray(), int update = 0, + bool crosscheck = false); + +/** @brief Normalizes the norm or value range of an array. + +The function cv::normalize normalizes scale and shift the input array elements so that +\f[\| \texttt{dst} \| _{L_p}= \texttt{alpha}\f] +(where p=Inf, 1 or 2) when normType=NORM_INF, NORM_L1, or NORM_L2, respectively; or so that +\f[\min _I \texttt{dst} (I)= \texttt{alpha} , \, \, \max _I \texttt{dst} (I)= \texttt{beta}\f] + +when normType=NORM_MINMAX (for dense arrays only). The optional mask specifies a sub-array to be +normalized. This means that the norm or min-n-max are calculated over the sub-array, and then this +sub-array is modified to be normalized. If you want to only use the mask to calculate the norm or +min-max but modify the whole array, you can use norm and Mat::convertTo. + +In case of sparse matrices, only the non-zero values are analyzed and transformed. Because of this, +the range transformation for sparse matrices is not allowed since it can shift the zero level. + +Possible usage with some positive example data: +@code{.cpp} + vector positiveData = { 2.0, 8.0, 10.0 }; + vector normalizedData_l1, normalizedData_l2, normalizedData_inf, normalizedData_minmax; + + // Norm to probability (total count) + // sum(numbers) = 20.0 + // 2.0 0.1 (2.0/20.0) + // 8.0 0.4 (8.0/20.0) + // 10.0 0.5 (10.0/20.0) + normalize(positiveData, normalizedData_l1, 1.0, 0.0, NORM_L1); + + // Norm to unit vector: ||positiveData|| = 1.0 + // 2.0 0.15 + // 8.0 0.62 + // 10.0 0.77 + normalize(positiveData, normalizedData_l2, 1.0, 0.0, NORM_L2); + + // Norm to max element + // 2.0 0.2 (2.0/10.0) + // 8.0 0.8 (8.0/10.0) + // 10.0 1.0 (10.0/10.0) + normalize(positiveData, normalizedData_inf, 1.0, 0.0, NORM_INF); + + // Norm to range [0.0;1.0] + // 2.0 0.0 (shift to left border) + // 8.0 0.75 (6.0/8.0) + // 10.0 1.0 (shift to right border) + normalize(positiveData, normalizedData_minmax, 1.0, 0.0, NORM_MINMAX); +@endcode + +@param src input array. +@param dst output array of the same size as src . +@param alpha norm value to normalize to or the lower range boundary in case of the range +normalization. +@param beta upper range boundary in case of the range normalization; it is not used for the norm +normalization. +@param norm_type normalization type (see cv::NormTypes). +@param dtype when negative, the output array has the same type as src; otherwise, it has the same +number of channels as src and the depth =CV_MAT_DEPTH(dtype). +@param mask optional operation mask. +@sa norm, Mat::convertTo, SparseMat::convertTo +*/ +CV_EXPORTS_W void normalize( InputArray src, InputOutputArray dst, double alpha = 1, double beta = 0, + int norm_type = NORM_L2, int dtype = -1, InputArray mask = noArray()); + +/** @overload +@param src input array. +@param dst output array of the same size as src . +@param alpha norm value to normalize to or the lower range boundary in case of the range +normalization. +@param normType normalization type (see cv::NormTypes). +*/ +CV_EXPORTS void normalize( const SparseMat& src, SparseMat& dst, double alpha, int normType ); + +/** @brief Finds the global minimum and maximum in an array. + +The function cv::minMaxLoc finds the minimum and maximum element values and their positions. The +extremums are searched across the whole array or, if mask is not an empty array, in the specified +array region. + +The function do not work with multi-channel arrays. If you need to find minimum or maximum +elements across all the channels, use Mat::reshape first to reinterpret the array as +single-channel. Or you may extract the particular channel using either extractImageCOI , or +mixChannels , or split . +@param src input single-channel array. +@param minVal pointer to the returned minimum value; NULL is used if not required. +@param maxVal pointer to the returned maximum value; NULL is used if not required. +@param minLoc pointer to the returned minimum location (in 2D case); NULL is used if not required. +@param maxLoc pointer to the returned maximum location (in 2D case); NULL is used if not required. +@param mask optional mask used to select a sub-array. +@sa max, min, compare, inRange, extractImageCOI, mixChannels, split, Mat::reshape +*/ +CV_EXPORTS_W void minMaxLoc(InputArray src, CV_OUT double* minVal, + CV_OUT double* maxVal = 0, CV_OUT Point* minLoc = 0, + CV_OUT Point* maxLoc = 0, InputArray mask = noArray()); + + +/** @brief Finds the global minimum and maximum in an array + +The function cv::minMaxIdx finds the minimum and maximum element values and their positions. The +extremums are searched across the whole array or, if mask is not an empty array, in the specified +array region. The function does not work with multi-channel arrays. If you need to find minimum or +maximum elements across all the channels, use Mat::reshape first to reinterpret the array as +single-channel. Or you may extract the particular channel using either extractImageCOI , or +mixChannels , or split . In case of a sparse matrix, the minimum is found among non-zero elements +only. +@note When minIdx is not NULL, it must have at least 2 elements (as well as maxIdx), even if src is +a single-row or single-column matrix. In OpenCV (following MATLAB) each array has at least 2 +dimensions, i.e. single-column matrix is Mx1 matrix (and therefore minIdx/maxIdx will be +(i1,0)/(i2,0)) and single-row matrix is 1xN matrix (and therefore minIdx/maxIdx will be +(0,j1)/(0,j2)). +@param src input single-channel array. +@param minVal pointer to the returned minimum value; NULL is used if not required. +@param maxVal pointer to the returned maximum value; NULL is used if not required. +@param minIdx pointer to the returned minimum location (in nD case); NULL is used if not required; +Otherwise, it must point to an array of src.dims elements, the coordinates of the minimum element +in each dimension are stored there sequentially. +@param maxIdx pointer to the returned maximum location (in nD case). NULL is used if not required. +@param mask specified array region +*/ +CV_EXPORTS void minMaxIdx(InputArray src, double* minVal, double* maxVal = 0, + int* minIdx = 0, int* maxIdx = 0, InputArray mask = noArray()); + +/** @overload +@param a input single-channel array. +@param minVal pointer to the returned minimum value; NULL is used if not required. +@param maxVal pointer to the returned maximum value; NULL is used if not required. +@param minIdx pointer to the returned minimum location (in nD case); NULL is used if not required; +Otherwise, it must point to an array of src.dims elements, the coordinates of the minimum element +in each dimension are stored there sequentially. +@param maxIdx pointer to the returned maximum location (in nD case). NULL is used if not required. +*/ +CV_EXPORTS void minMaxLoc(const SparseMat& a, double* minVal, + double* maxVal, int* minIdx = 0, int* maxIdx = 0); + +/** @brief Reduces a matrix to a vector. + +The function #reduce reduces the matrix to a vector by treating the matrix rows/columns as a set of +1D vectors and performing the specified operation on the vectors until a single row/column is +obtained. For example, the function can be used to compute horizontal and vertical projections of a +raster image. In case of #REDUCE_MAX and #REDUCE_MIN , the output image should have the same type as the source one. +In case of #REDUCE_SUM and #REDUCE_AVG , the output may have a larger element bit-depth to preserve accuracy. +And multi-channel arrays are also supported in these two reduction modes. + +The following code demonstrates its usage for a single channel matrix. +@snippet snippets/core_reduce.cpp example + +And the following code demonstrates its usage for a two-channel matrix. +@snippet snippets/core_reduce.cpp example2 + +@param src input 2D matrix. +@param dst output vector. Its size and type is defined by dim and dtype parameters. +@param dim dimension index along which the matrix is reduced. 0 means that the matrix is reduced to +a single row. 1 means that the matrix is reduced to a single column. +@param rtype reduction operation that could be one of #ReduceTypes +@param dtype when negative, the output vector will have the same type as the input matrix, +otherwise, its type will be CV_MAKE_TYPE(CV_MAT_DEPTH(dtype), src.channels()). +@sa repeat +*/ +CV_EXPORTS_W void reduce(InputArray src, OutputArray dst, int dim, int rtype, int dtype = -1); + +/** @brief Creates one multi-channel array out of several single-channel ones. + +The function cv::merge merges several arrays to make a single multi-channel array. That is, each +element of the output array will be a concatenation of the elements of the input arrays, where +elements of i-th input array are treated as mv[i].channels()-element vectors. + +The function cv::split does the reverse operation. If you need to shuffle channels in some other +advanced way, use cv::mixChannels. + +The following example shows how to merge 3 single channel matrices into a single 3-channel matrix. +@snippet snippets/core_merge.cpp example + +@param mv input array of matrices to be merged; all the matrices in mv must have the same +size and the same depth. +@param count number of input matrices when mv is a plain C array; it must be greater than zero. +@param dst output array of the same size and the same depth as mv[0]; The number of channels will +be equal to the parameter count. +@sa mixChannels, split, Mat::reshape +*/ +CV_EXPORTS void merge(const Mat* mv, size_t count, OutputArray dst); + +/** @overload +@param mv input vector of matrices to be merged; all the matrices in mv must have the same +size and the same depth. +@param dst output array of the same size and the same depth as mv[0]; The number of channels will +be the total number of channels in the matrix array. + */ +CV_EXPORTS_W void merge(InputArrayOfArrays mv, OutputArray dst); + +/** @brief Divides a multi-channel array into several single-channel arrays. + +The function cv::split splits a multi-channel array into separate single-channel arrays: +\f[\texttt{mv} [c](I) = \texttt{src} (I)_c\f] +If you need to extract a single channel or do some other sophisticated channel permutation, use +mixChannels . + +The following example demonstrates how to split a 3-channel matrix into 3 single channel matrices. +@snippet snippets/core_split.cpp example + +@param src input multi-channel array. +@param mvbegin output array; the number of arrays must match src.channels(); the arrays themselves are +reallocated, if needed. +@sa merge, mixChannels, cvtColor +*/ +CV_EXPORTS void split(const Mat& src, Mat* mvbegin); + +/** @overload +@param m input multi-channel array. +@param mv output vector of arrays; the arrays themselves are reallocated, if needed. +*/ +CV_EXPORTS_W void split(InputArray m, OutputArrayOfArrays mv); + +/** @brief Copies specified channels from input arrays to the specified channels of +output arrays. + +The function cv::mixChannels provides an advanced mechanism for shuffling image channels. + +cv::split,cv::merge,cv::extractChannel,cv::insertChannel and some forms of cv::cvtColor are partial cases of cv::mixChannels. + +In the example below, the code splits a 4-channel BGRA image into a 3-channel BGR (with B and R +channels swapped) and a separate alpha-channel image: +@code{.cpp} + Mat bgra( 100, 100, CV_8UC4, Scalar(255,0,0,255) ); + Mat bgr( bgra.rows, bgra.cols, CV_8UC3 ); + Mat alpha( bgra.rows, bgra.cols, CV_8UC1 ); + + // forming an array of matrices is a quite efficient operation, + // because the matrix data is not copied, only the headers + Mat out[] = { bgr, alpha }; + // bgra[0] -> bgr[2], bgra[1] -> bgr[1], + // bgra[2] -> bgr[0], bgra[3] -> alpha[0] + int from_to[] = { 0,2, 1,1, 2,0, 3,3 }; + mixChannels( &bgra, 1, out, 2, from_to, 4 ); +@endcode +@note Unlike many other new-style C++ functions in OpenCV (see the introduction section and +Mat::create ), cv::mixChannels requires the output arrays to be pre-allocated before calling the +function. +@param src input array or vector of matrices; all of the matrices must have the same size and the +same depth. +@param nsrcs number of matrices in `src`. +@param dst output array or vector of matrices; all the matrices **must be allocated**; their size and +depth must be the same as in `src[0]`. +@param ndsts number of matrices in `dst`. +@param fromTo array of index pairs specifying which channels are copied and where; fromTo[k\*2] is +a 0-based index of the input channel in src, fromTo[k\*2+1] is an index of the output channel in +dst; the continuous channel numbering is used: the first input image channels are indexed from 0 to +src[0].channels()-1, the second input image channels are indexed from src[0].channels() to +src[0].channels() + src[1].channels()-1, and so on, the same scheme is used for the output image +channels; as a special case, when fromTo[k\*2] is negative, the corresponding output channel is +filled with zero . +@param npairs number of index pairs in `fromTo`. +@sa split, merge, extractChannel, insertChannel, cvtColor +*/ +CV_EXPORTS void mixChannels(const Mat* src, size_t nsrcs, Mat* dst, size_t ndsts, + const int* fromTo, size_t npairs); + +/** @overload +@param src input array or vector of matrices; all of the matrices must have the same size and the +same depth. +@param dst output array or vector of matrices; all the matrices **must be allocated**; their size and +depth must be the same as in src[0]. +@param fromTo array of index pairs specifying which channels are copied and where; fromTo[k\*2] is +a 0-based index of the input channel in src, fromTo[k\*2+1] is an index of the output channel in +dst; the continuous channel numbering is used: the first input image channels are indexed from 0 to +src[0].channels()-1, the second input image channels are indexed from src[0].channels() to +src[0].channels() + src[1].channels()-1, and so on, the same scheme is used for the output image +channels; as a special case, when fromTo[k\*2] is negative, the corresponding output channel is +filled with zero . +@param npairs number of index pairs in fromTo. +*/ +CV_EXPORTS void mixChannels(InputArrayOfArrays src, InputOutputArrayOfArrays dst, + const int* fromTo, size_t npairs); + +/** @overload +@param src input array or vector of matrices; all of the matrices must have the same size and the +same depth. +@param dst output array or vector of matrices; all the matrices **must be allocated**; their size and +depth must be the same as in src[0]. +@param fromTo array of index pairs specifying which channels are copied and where; fromTo[k\*2] is +a 0-based index of the input channel in src, fromTo[k\*2+1] is an index of the output channel in +dst; the continuous channel numbering is used: the first input image channels are indexed from 0 to +src[0].channels()-1, the second input image channels are indexed from src[0].channels() to +src[0].channels() + src[1].channels()-1, and so on, the same scheme is used for the output image +channels; as a special case, when fromTo[k\*2] is negative, the corresponding output channel is +filled with zero . +*/ +CV_EXPORTS_W void mixChannels(InputArrayOfArrays src, InputOutputArrayOfArrays dst, + const std::vector& fromTo); + +/** @brief Extracts a single channel from src (coi is 0-based index) +@param src input array +@param dst output array +@param coi index of channel to extract +@sa mixChannels, split +*/ +CV_EXPORTS_W void extractChannel(InputArray src, OutputArray dst, int coi); + +/** @brief Inserts a single channel to dst (coi is 0-based index) +@param src input array +@param dst output array +@param coi index of channel for insertion +@sa mixChannels, merge +*/ +CV_EXPORTS_W void insertChannel(InputArray src, InputOutputArray dst, int coi); + +/** @brief Flips a 2D array around vertical, horizontal, or both axes. + +The function cv::flip flips the array in one of three different ways (row +and column indices are 0-based): +\f[\texttt{dst} _{ij} = +\left\{ +\begin{array}{l l} +\texttt{src} _{\texttt{src.rows}-i-1,j} & if\; \texttt{flipCode} = 0 \\ +\texttt{src} _{i, \texttt{src.cols} -j-1} & if\; \texttt{flipCode} > 0 \\ +\texttt{src} _{ \texttt{src.rows} -i-1, \texttt{src.cols} -j-1} & if\; \texttt{flipCode} < 0 \\ +\end{array} +\right.\f] +The example scenarios of using the function are the following: +* Vertical flipping of the image (flipCode == 0) to switch between + top-left and bottom-left image origin. This is a typical operation + in video processing on Microsoft Windows\* OS. +* Horizontal flipping of the image with the subsequent horizontal + shift and absolute difference calculation to check for a + vertical-axis symmetry (flipCode \> 0). +* Simultaneous horizontal and vertical flipping of the image with + the subsequent shift and absolute difference calculation to check + for a central symmetry (flipCode \< 0). +* Reversing the order of point arrays (flipCode \> 0 or + flipCode == 0). +@param src input array. +@param dst output array of the same size and type as src. +@param flipCode a flag to specify how to flip the array; 0 means +flipping around the x-axis and positive value (for example, 1) means +flipping around y-axis. Negative value (for example, -1) means flipping +around both axes. +@sa transpose , repeat , completeSymm +*/ +CV_EXPORTS_W void flip(InputArray src, OutputArray dst, int flipCode); + +enum RotateFlags { + ROTATE_90_CLOCKWISE = 0, //! A = (cv::Mat_(3, 2) << 1, 4, + 2, 5, + 3, 6); + cv::Mat_ B = (cv::Mat_(3, 2) << 7, 10, + 8, 11, + 9, 12); + + cv::Mat C; + cv::hconcat(A, B, C); + //C: + //[1, 4, 7, 10; + // 2, 5, 8, 11; + // 3, 6, 9, 12] + @endcode + @param src1 first input array to be considered for horizontal concatenation. + @param src2 second input array to be considered for horizontal concatenation. + @param dst output array. It has the same number of rows and depth as the src1 and src2, and the sum of cols of the src1 and src2. + */ +CV_EXPORTS void hconcat(InputArray src1, InputArray src2, OutputArray dst); +/** @overload + @code{.cpp} + std::vector matrices = { cv::Mat(4, 1, CV_8UC1, cv::Scalar(1)), + cv::Mat(4, 1, CV_8UC1, cv::Scalar(2)), + cv::Mat(4, 1, CV_8UC1, cv::Scalar(3)),}; + + cv::Mat out; + cv::hconcat( matrices, out ); + //out: + //[1, 2, 3; + // 1, 2, 3; + // 1, 2, 3; + // 1, 2, 3] + @endcode + @param src input array or vector of matrices. all of the matrices must have the same number of rows and the same depth. + @param dst output array. It has the same number of rows and depth as the src, and the sum of cols of the src. +same depth. + */ +CV_EXPORTS_W void hconcat(InputArrayOfArrays src, OutputArray dst); + +/** @brief Applies vertical concatenation to given matrices. + +The function vertically concatenates two or more cv::Mat matrices (with the same number of cols). +@code{.cpp} + cv::Mat matArray[] = { cv::Mat(1, 4, CV_8UC1, cv::Scalar(1)), + cv::Mat(1, 4, CV_8UC1, cv::Scalar(2)), + cv::Mat(1, 4, CV_8UC1, cv::Scalar(3)),}; + + cv::Mat out; + cv::vconcat( matArray, 3, out ); + //out: + //[1, 1, 1, 1; + // 2, 2, 2, 2; + // 3, 3, 3, 3] +@endcode +@param src input array or vector of matrices. all of the matrices must have the same number of cols and the same depth. +@param nsrc number of matrices in src. +@param dst output array. It has the same number of cols and depth as the src, and the sum of rows of the src. +@sa cv::hconcat(const Mat*, size_t, OutputArray), @sa cv::hconcat(InputArrayOfArrays, OutputArray) and @sa cv::hconcat(InputArray, InputArray, OutputArray) +*/ +CV_EXPORTS void vconcat(const Mat* src, size_t nsrc, OutputArray dst); +/** @overload + @code{.cpp} + cv::Mat_ A = (cv::Mat_(3, 2) << 1, 7, + 2, 8, + 3, 9); + cv::Mat_ B = (cv::Mat_(3, 2) << 4, 10, + 5, 11, + 6, 12); + + cv::Mat C; + cv::vconcat(A, B, C); + //C: + //[1, 7; + // 2, 8; + // 3, 9; + // 4, 10; + // 5, 11; + // 6, 12] + @endcode + @param src1 first input array to be considered for vertical concatenation. + @param src2 second input array to be considered for vertical concatenation. + @param dst output array. It has the same number of cols and depth as the src1 and src2, and the sum of rows of the src1 and src2. + */ +CV_EXPORTS void vconcat(InputArray src1, InputArray src2, OutputArray dst); +/** @overload + @code{.cpp} + std::vector matrices = { cv::Mat(1, 4, CV_8UC1, cv::Scalar(1)), + cv::Mat(1, 4, CV_8UC1, cv::Scalar(2)), + cv::Mat(1, 4, CV_8UC1, cv::Scalar(3)),}; + + cv::Mat out; + cv::vconcat( matrices, out ); + //out: + //[1, 1, 1, 1; + // 2, 2, 2, 2; + // 3, 3, 3, 3] + @endcode + @param src input array or vector of matrices. all of the matrices must have the same number of cols and the same depth + @param dst output array. It has the same number of cols and depth as the src, and the sum of rows of the src. +same depth. + */ +CV_EXPORTS_W void vconcat(InputArrayOfArrays src, OutputArray dst); + +/** @brief computes bitwise conjunction of the two arrays (dst = src1 & src2) +Calculates the per-element bit-wise conjunction of two arrays or an +array and a scalar. + +The function cv::bitwise_and calculates the per-element bit-wise logical conjunction for: +* Two arrays when src1 and src2 have the same size: + \f[\texttt{dst} (I) = \texttt{src1} (I) \wedge \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f] +* An array and a scalar when src2 is constructed from Scalar or has + the same number of elements as `src1.channels()`: + \f[\texttt{dst} (I) = \texttt{src1} (I) \wedge \texttt{src2} \quad \texttt{if mask} (I) \ne0\f] +* A scalar and an array when src1 is constructed from Scalar or has + the same number of elements as `src2.channels()`: + \f[\texttt{dst} (I) = \texttt{src1} \wedge \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f] +In case of floating-point arrays, their machine-specific bit +representations (usually IEEE754-compliant) are used for the operation. +In case of multi-channel arrays, each channel is processed +independently. In the second and third cases above, the scalar is first +converted to the array type. +@param src1 first input array or a scalar. +@param src2 second input array or a scalar. +@param dst output array that has the same size and type as the input +arrays. +@param mask optional operation mask, 8-bit single channel array, that +specifies elements of the output array to be changed. +*/ +CV_EXPORTS_W void bitwise_and(InputArray src1, InputArray src2, + OutputArray dst, InputArray mask = noArray()); + +/** @brief Calculates the per-element bit-wise disjunction of two arrays or an +array and a scalar. + +The function cv::bitwise_or calculates the per-element bit-wise logical disjunction for: +* Two arrays when src1 and src2 have the same size: + \f[\texttt{dst} (I) = \texttt{src1} (I) \vee \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f] +* An array and a scalar when src2 is constructed from Scalar or has + the same number of elements as `src1.channels()`: + \f[\texttt{dst} (I) = \texttt{src1} (I) \vee \texttt{src2} \quad \texttt{if mask} (I) \ne0\f] +* A scalar and an array when src1 is constructed from Scalar or has + the same number of elements as `src2.channels()`: + \f[\texttt{dst} (I) = \texttt{src1} \vee \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f] +In case of floating-point arrays, their machine-specific bit +representations (usually IEEE754-compliant) are used for the operation. +In case of multi-channel arrays, each channel is processed +independently. In the second and third cases above, the scalar is first +converted to the array type. +@param src1 first input array or a scalar. +@param src2 second input array or a scalar. +@param dst output array that has the same size and type as the input +arrays. +@param mask optional operation mask, 8-bit single channel array, that +specifies elements of the output array to be changed. +*/ +CV_EXPORTS_W void bitwise_or(InputArray src1, InputArray src2, + OutputArray dst, InputArray mask = noArray()); + +/** @brief Calculates the per-element bit-wise "exclusive or" operation on two +arrays or an array and a scalar. + +The function cv::bitwise_xor calculates the per-element bit-wise logical "exclusive-or" +operation for: +* Two arrays when src1 and src2 have the same size: + \f[\texttt{dst} (I) = \texttt{src1} (I) \oplus \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f] +* An array and a scalar when src2 is constructed from Scalar or has + the same number of elements as `src1.channels()`: + \f[\texttt{dst} (I) = \texttt{src1} (I) \oplus \texttt{src2} \quad \texttt{if mask} (I) \ne0\f] +* A scalar and an array when src1 is constructed from Scalar or has + the same number of elements as `src2.channels()`: + \f[\texttt{dst} (I) = \texttt{src1} \oplus \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f] +In case of floating-point arrays, their machine-specific bit +representations (usually IEEE754-compliant) are used for the operation. +In case of multi-channel arrays, each channel is processed +independently. In the 2nd and 3rd cases above, the scalar is first +converted to the array type. +@param src1 first input array or a scalar. +@param src2 second input array or a scalar. +@param dst output array that has the same size and type as the input +arrays. +@param mask optional operation mask, 8-bit single channel array, that +specifies elements of the output array to be changed. +*/ +CV_EXPORTS_W void bitwise_xor(InputArray src1, InputArray src2, + OutputArray dst, InputArray mask = noArray()); + +/** @brief Inverts every bit of an array. + +The function cv::bitwise_not calculates per-element bit-wise inversion of the input +array: +\f[\texttt{dst} (I) = \neg \texttt{src} (I)\f] +In case of a floating-point input array, its machine-specific bit +representation (usually IEEE754-compliant) is used for the operation. In +case of multi-channel arrays, each channel is processed independently. +@param src input array. +@param dst output array that has the same size and type as the input +array. +@param mask optional operation mask, 8-bit single channel array, that +specifies elements of the output array to be changed. +*/ +CV_EXPORTS_W void bitwise_not(InputArray src, OutputArray dst, + InputArray mask = noArray()); + +/** @brief Calculates the per-element absolute difference between two arrays or between an array and a scalar. + +The function cv::absdiff calculates: +* Absolute difference between two arrays when they have the same + size and type: + \f[\texttt{dst}(I) = \texttt{saturate} (| \texttt{src1}(I) - \texttt{src2}(I)|)\f] +* Absolute difference between an array and a scalar when the second + array is constructed from Scalar or has as many elements as the + number of channels in `src1`: + \f[\texttt{dst}(I) = \texttt{saturate} (| \texttt{src1}(I) - \texttt{src2} |)\f] +* Absolute difference between a scalar and an array when the first + array is constructed from Scalar or has as many elements as the + number of channels in `src2`: + \f[\texttt{dst}(I) = \texttt{saturate} (| \texttt{src1} - \texttt{src2}(I) |)\f] + where I is a multi-dimensional index of array elements. In case of + multi-channel arrays, each channel is processed independently. +@note Saturation is not applied when the arrays have the depth CV_32S. +You may even get a negative value in the case of overflow. +@param src1 first input array or a scalar. +@param src2 second input array or a scalar. +@param dst output array that has the same size and type as input arrays. +@sa cv::abs(const Mat&) +*/ +CV_EXPORTS_W void absdiff(InputArray src1, InputArray src2, OutputArray dst); + +/** @brief Checks if array elements lie between the elements of two other arrays. + +The function checks the range as follows: +- For every element of a single-channel input array: + \f[\texttt{dst} (I)= \texttt{lowerb} (I)_0 \leq \texttt{src} (I)_0 \leq \texttt{upperb} (I)_0\f] +- For two-channel arrays: + \f[\texttt{dst} (I)= \texttt{lowerb} (I)_0 \leq \texttt{src} (I)_0 \leq \texttt{upperb} (I)_0 \land \texttt{lowerb} (I)_1 \leq \texttt{src} (I)_1 \leq \texttt{upperb} (I)_1\f] +- and so forth. + +That is, dst (I) is set to 255 (all 1 -bits) if src (I) is within the +specified 1D, 2D, 3D, ... box and 0 otherwise. + +When the lower and/or upper boundary parameters are scalars, the indexes +(I) at lowerb and upperb in the above formulas should be omitted. +@param src first input array. +@param lowerb inclusive lower boundary array or a scalar. +@param upperb inclusive upper boundary array or a scalar. +@param dst output array of the same size as src and CV_8U type. +*/ +CV_EXPORTS_W void inRange(InputArray src, InputArray lowerb, + InputArray upperb, OutputArray dst); + +/** @brief Performs the per-element comparison of two arrays or an array and scalar value. + +The function compares: +* Elements of two arrays when src1 and src2 have the same size: + \f[\texttt{dst} (I) = \texttt{src1} (I) \,\texttt{cmpop}\, \texttt{src2} (I)\f] +* Elements of src1 with a scalar src2 when src2 is constructed from + Scalar or has a single element: + \f[\texttt{dst} (I) = \texttt{src1}(I) \,\texttt{cmpop}\, \texttt{src2}\f] +* src1 with elements of src2 when src1 is constructed from Scalar or + has a single element: + \f[\texttt{dst} (I) = \texttt{src1} \,\texttt{cmpop}\, \texttt{src2} (I)\f] +When the comparison result is true, the corresponding element of output +array is set to 255. The comparison operations can be replaced with the +equivalent matrix expressions: +@code{.cpp} + Mat dst1 = src1 >= src2; + Mat dst2 = src1 < 8; + ... +@endcode +@param src1 first input array or a scalar; when it is an array, it must have a single channel. +@param src2 second input array or a scalar; when it is an array, it must have a single channel. +@param dst output array of type ref CV_8U that has the same size and the same number of channels as + the input arrays. +@param cmpop a flag, that specifies correspondence between the arrays (cv::CmpTypes) +@sa checkRange, min, max, threshold +*/ +CV_EXPORTS_W void compare(InputArray src1, InputArray src2, OutputArray dst, int cmpop); + +/** @brief Calculates per-element minimum of two arrays or an array and a scalar. + +The function cv::min calculates the per-element minimum of two arrays: +\f[\texttt{dst} (I)= \min ( \texttt{src1} (I), \texttt{src2} (I))\f] +or array and a scalar: +\f[\texttt{dst} (I)= \min ( \texttt{src1} (I), \texttt{value} )\f] +@param src1 first input array. +@param src2 second input array of the same size and type as src1. +@param dst output array of the same size and type as src1. +@sa max, compare, inRange, minMaxLoc +*/ +CV_EXPORTS_W void min(InputArray src1, InputArray src2, OutputArray dst); +/** @overload +needed to avoid conflicts with const _Tp& std::min(const _Tp&, const _Tp&, _Compare) +*/ +CV_EXPORTS void min(const Mat& src1, const Mat& src2, Mat& dst); +/** @overload +needed to avoid conflicts with const _Tp& std::min(const _Tp&, const _Tp&, _Compare) +*/ +CV_EXPORTS void min(const UMat& src1, const UMat& src2, UMat& dst); + +/** @brief Calculates per-element maximum of two arrays or an array and a scalar. + +The function cv::max calculates the per-element maximum of two arrays: +\f[\texttt{dst} (I)= \max ( \texttt{src1} (I), \texttt{src2} (I))\f] +or array and a scalar: +\f[\texttt{dst} (I)= \max ( \texttt{src1} (I), \texttt{value} )\f] +@param src1 first input array. +@param src2 second input array of the same size and type as src1 . +@param dst output array of the same size and type as src1. +@sa min, compare, inRange, minMaxLoc, @ref MatrixExpressions +*/ +CV_EXPORTS_W void max(InputArray src1, InputArray src2, OutputArray dst); +/** @overload +needed to avoid conflicts with const _Tp& std::min(const _Tp&, const _Tp&, _Compare) +*/ +CV_EXPORTS void max(const Mat& src1, const Mat& src2, Mat& dst); +/** @overload +needed to avoid conflicts with const _Tp& std::min(const _Tp&, const _Tp&, _Compare) +*/ +CV_EXPORTS void max(const UMat& src1, const UMat& src2, UMat& dst); + +/** @brief Calculates a square root of array elements. + +The function cv::sqrt calculates a square root of each input array element. +In case of multi-channel arrays, each channel is processed +independently. The accuracy is approximately the same as of the built-in +std::sqrt . +@param src input floating-point array. +@param dst output array of the same size and type as src. +*/ +CV_EXPORTS_W void sqrt(InputArray src, OutputArray dst); + +/** @brief Raises every array element to a power. + +The function cv::pow raises every element of the input array to power : +\f[\texttt{dst} (I) = \fork{\texttt{src}(I)^{power}}{if \(\texttt{power}\) is integer}{|\texttt{src}(I)|^{power}}{otherwise}\f] + +So, for a non-integer power exponent, the absolute values of input array +elements are used. However, it is possible to get true values for +negative values using some extra operations. In the example below, +computing the 5th root of array src shows: +@code{.cpp} + Mat mask = src < 0; + pow(src, 1./5, dst); + subtract(Scalar::all(0), dst, dst, mask); +@endcode +For some values of power, such as integer values, 0.5 and -0.5, +specialized faster algorithms are used. + +Special values (NaN, Inf) are not handled. +@param src input array. +@param power exponent of power. +@param dst output array of the same size and type as src. +@sa sqrt, exp, log, cartToPolar, polarToCart +*/ +CV_EXPORTS_W void pow(InputArray src, double power, OutputArray dst); + +/** @brief Calculates the exponent of every array element. + +The function cv::exp calculates the exponent of every element of the input +array: +\f[\texttt{dst} [I] = e^{ src(I) }\f] + +The maximum relative error is about 7e-6 for single-precision input and +less than 1e-10 for double-precision input. Currently, the function +converts denormalized values to zeros on output. Special values (NaN, +Inf) are not handled. +@param src input array. +@param dst output array of the same size and type as src. +@sa log , cartToPolar , polarToCart , phase , pow , sqrt , magnitude +*/ +CV_EXPORTS_W void exp(InputArray src, OutputArray dst); + +/** @brief Calculates the natural logarithm of every array element. + +The function cv::log calculates the natural logarithm of every element of the input array: +\f[\texttt{dst} (I) = \log (\texttt{src}(I)) \f] + +Output on zero, negative and special (NaN, Inf) values is undefined. + +@param src input array. +@param dst output array of the same size and type as src . +@sa exp, cartToPolar, polarToCart, phase, pow, sqrt, magnitude +*/ +CV_EXPORTS_W void log(InputArray src, OutputArray dst); + +/** @brief Calculates x and y coordinates of 2D vectors from their magnitude and angle. + +The function cv::polarToCart calculates the Cartesian coordinates of each 2D +vector represented by the corresponding elements of magnitude and angle: +\f[\begin{array}{l} \texttt{x} (I) = \texttt{magnitude} (I) \cos ( \texttt{angle} (I)) \\ \texttt{y} (I) = \texttt{magnitude} (I) \sin ( \texttt{angle} (I)) \\ \end{array}\f] + +The relative accuracy of the estimated coordinates is about 1e-6. +@param magnitude input floating-point array of magnitudes of 2D vectors; +it can be an empty matrix (=Mat()), in this case, the function assumes +that all the magnitudes are =1; if it is not empty, it must have the +same size and type as angle. +@param angle input floating-point array of angles of 2D vectors. +@param x output array of x-coordinates of 2D vectors; it has the same +size and type as angle. +@param y output array of y-coordinates of 2D vectors; it has the same +size and type as angle. +@param angleInDegrees when true, the input angles are measured in +degrees, otherwise, they are measured in radians. +@sa cartToPolar, magnitude, phase, exp, log, pow, sqrt +*/ +CV_EXPORTS_W void polarToCart(InputArray magnitude, InputArray angle, + OutputArray x, OutputArray y, bool angleInDegrees = false); + +/** @brief Calculates the magnitude and angle of 2D vectors. + +The function cv::cartToPolar calculates either the magnitude, angle, or both +for every 2D vector (x(I),y(I)): +\f[\begin{array}{l} \texttt{magnitude} (I)= \sqrt{\texttt{x}(I)^2+\texttt{y}(I)^2} , \\ \texttt{angle} (I)= \texttt{atan2} ( \texttt{y} (I), \texttt{x} (I))[ \cdot180 / \pi ] \end{array}\f] + +The angles are calculated with accuracy about 0.3 degrees. For the point +(0,0), the angle is set to 0. +@param x array of x-coordinates; this must be a single-precision or +double-precision floating-point array. +@param y array of y-coordinates, that must have the same size and same type as x. +@param magnitude output array of magnitudes of the same size and type as x. +@param angle output array of angles that has the same size and type as +x; the angles are measured in radians (from 0 to 2\*Pi) or in degrees (0 to 360 degrees). +@param angleInDegrees a flag, indicating whether the angles are measured +in radians (which is by default), or in degrees. +@sa Sobel, Scharr +*/ +CV_EXPORTS_W void cartToPolar(InputArray x, InputArray y, + OutputArray magnitude, OutputArray angle, + bool angleInDegrees = false); + +/** @brief Calculates the rotation angle of 2D vectors. + +The function cv::phase calculates the rotation angle of each 2D vector that +is formed from the corresponding elements of x and y : +\f[\texttt{angle} (I) = \texttt{atan2} ( \texttt{y} (I), \texttt{x} (I))\f] + +The angle estimation accuracy is about 0.3 degrees. When x(I)=y(I)=0 , +the corresponding angle(I) is set to 0. +@param x input floating-point array of x-coordinates of 2D vectors. +@param y input array of y-coordinates of 2D vectors; it must have the +same size and the same type as x. +@param angle output array of vector angles; it has the same size and +same type as x . +@param angleInDegrees when true, the function calculates the angle in +degrees, otherwise, they are measured in radians. +*/ +CV_EXPORTS_W void phase(InputArray x, InputArray y, OutputArray angle, + bool angleInDegrees = false); + +/** @brief Calculates the magnitude of 2D vectors. + +The function cv::magnitude calculates the magnitude of 2D vectors formed +from the corresponding elements of x and y arrays: +\f[\texttt{dst} (I) = \sqrt{\texttt{x}(I)^2 + \texttt{y}(I)^2}\f] +@param x floating-point array of x-coordinates of the vectors. +@param y floating-point array of y-coordinates of the vectors; it must +have the same size as x. +@param magnitude output array of the same size and type as x. +@sa cartToPolar, polarToCart, phase, sqrt +*/ +CV_EXPORTS_W void magnitude(InputArray x, InputArray y, OutputArray magnitude); + +/** @brief Checks every element of an input array for invalid values. + +The function cv::checkRange checks that every array element is neither NaN nor infinite. When minVal \> +-DBL_MAX and maxVal \< DBL_MAX, the function also checks that each value is between minVal and +maxVal. In case of multi-channel arrays, each channel is processed independently. If some values +are out of range, position of the first outlier is stored in pos (when pos != NULL). Then, the +function either returns false (when quiet=true) or throws an exception. +@param a input array. +@param quiet a flag, indicating whether the functions quietly return false when the array elements +are out of range or they throw an exception. +@param pos optional output parameter, when not NULL, must be a pointer to array of src.dims +elements. +@param minVal inclusive lower boundary of valid values range. +@param maxVal exclusive upper boundary of valid values range. +*/ +CV_EXPORTS_W bool checkRange(InputArray a, bool quiet = true, CV_OUT Point* pos = 0, + double minVal = -DBL_MAX, double maxVal = DBL_MAX); + +/** @brief converts NaN's to the given number +*/ +CV_EXPORTS_W void patchNaNs(InputOutputArray a, double val = 0); + +/** @brief Performs generalized matrix multiplication. + +The function cv::gemm performs generalized matrix multiplication similar to the +gemm functions in BLAS level 3. For example, +`gemm(src1, src2, alpha, src3, beta, dst, GEMM_1_T + GEMM_3_T)` +corresponds to +\f[\texttt{dst} = \texttt{alpha} \cdot \texttt{src1} ^T \cdot \texttt{src2} + \texttt{beta} \cdot \texttt{src3} ^T\f] + +In case of complex (two-channel) data, performed a complex matrix +multiplication. + +The function can be replaced with a matrix expression. For example, the +above call can be replaced with: +@code{.cpp} + dst = alpha*src1.t()*src2 + beta*src3.t(); +@endcode +@param src1 first multiplied input matrix that could be real(CV_32FC1, +CV_64FC1) or complex(CV_32FC2, CV_64FC2). +@param src2 second multiplied input matrix of the same type as src1. +@param alpha weight of the matrix product. +@param src3 third optional delta matrix added to the matrix product; it +should have the same type as src1 and src2. +@param beta weight of src3. +@param dst output matrix; it has the proper size and the same type as +input matrices. +@param flags operation flags (cv::GemmFlags) +@sa mulTransposed , transform +*/ +CV_EXPORTS_W void gemm(InputArray src1, InputArray src2, double alpha, + InputArray src3, double beta, OutputArray dst, int flags = 0); + +/** @brief Calculates the product of a matrix and its transposition. + +The function cv::mulTransposed calculates the product of src and its +transposition: +\f[\texttt{dst} = \texttt{scale} ( \texttt{src} - \texttt{delta} )^T ( \texttt{src} - \texttt{delta} )\f] +if aTa=true , and +\f[\texttt{dst} = \texttt{scale} ( \texttt{src} - \texttt{delta} ) ( \texttt{src} - \texttt{delta} )^T\f] +otherwise. The function is used to calculate the covariance matrix. With +zero delta, it can be used as a faster substitute for general matrix +product A\*B when B=A' +@param src input single-channel matrix. Note that unlike gemm, the +function can multiply not only floating-point matrices. +@param dst output square matrix. +@param aTa Flag specifying the multiplication ordering. See the +description below. +@param delta Optional delta matrix subtracted from src before the +multiplication. When the matrix is empty ( delta=noArray() ), it is +assumed to be zero, that is, nothing is subtracted. If it has the same +size as src , it is simply subtracted. Otherwise, it is "repeated" (see +repeat ) to cover the full src and then subtracted. Type of the delta +matrix, when it is not empty, must be the same as the type of created +output matrix. See the dtype parameter description below. +@param scale Optional scale factor for the matrix product. +@param dtype Optional type of the output matrix. When it is negative, +the output matrix will have the same type as src . Otherwise, it will be +type=CV_MAT_DEPTH(dtype) that should be either CV_32F or CV_64F . +@sa calcCovarMatrix, gemm, repeat, reduce +*/ +CV_EXPORTS_W void mulTransposed( InputArray src, OutputArray dst, bool aTa, + InputArray delta = noArray(), + double scale = 1, int dtype = -1 ); + +/** @brief Transposes a matrix. + +The function cv::transpose transposes the matrix src : +\f[\texttt{dst} (i,j) = \texttt{src} (j,i)\f] +@note No complex conjugation is done in case of a complex matrix. It +should be done separately if needed. +@param src input array. +@param dst output array of the same type as src. +*/ +CV_EXPORTS_W void transpose(InputArray src, OutputArray dst); + +/** @brief Performs the matrix transformation of every array element. + +The function cv::transform performs the matrix transformation of every +element of the array src and stores the results in dst : +\f[\texttt{dst} (I) = \texttt{m} \cdot \texttt{src} (I)\f] +(when m.cols=src.channels() ), or +\f[\texttt{dst} (I) = \texttt{m} \cdot [ \texttt{src} (I); 1]\f] +(when m.cols=src.channels()+1 ) + +Every element of the N -channel array src is interpreted as N -element +vector that is transformed using the M x N or M x (N+1) matrix m to +M-element vector - the corresponding element of the output array dst . + +The function may be used for geometrical transformation of +N -dimensional points, arbitrary linear color space transformation (such +as various kinds of RGB to YUV transforms), shuffling the image +channels, and so forth. +@param src input array that must have as many channels (1 to 4) as +m.cols or m.cols-1. +@param dst output array of the same size and depth as src; it has as +many channels as m.rows. +@param m transformation 2x2 or 2x3 floating-point matrix. +@sa perspectiveTransform, getAffineTransform, estimateAffine2D, warpAffine, warpPerspective +*/ +CV_EXPORTS_W void transform(InputArray src, OutputArray dst, InputArray m ); + +/** @brief Performs the perspective matrix transformation of vectors. + +The function cv::perspectiveTransform transforms every element of src by +treating it as a 2D or 3D vector, in the following way: +\f[(x, y, z) \rightarrow (x'/w, y'/w, z'/w)\f] +where +\f[(x', y', z', w') = \texttt{mat} \cdot \begin{bmatrix} x & y & z & 1 \end{bmatrix}\f] +and +\f[w = \fork{w'}{if \(w' \ne 0\)}{\infty}{otherwise}\f] + +Here a 3D vector transformation is shown. In case of a 2D vector +transformation, the z component is omitted. + +@note The function transforms a sparse set of 2D or 3D vectors. If you +want to transform an image using perspective transformation, use +warpPerspective . If you have an inverse problem, that is, you want to +compute the most probable perspective transformation out of several +pairs of corresponding points, you can use getPerspectiveTransform or +findHomography . +@param src input two-channel or three-channel floating-point array; each +element is a 2D/3D vector to be transformed. +@param dst output array of the same size and type as src. +@param m 3x3 or 4x4 floating-point transformation matrix. +@sa transform, warpPerspective, getPerspectiveTransform, findHomography +*/ +CV_EXPORTS_W void perspectiveTransform(InputArray src, OutputArray dst, InputArray m ); + +/** @brief Copies the lower or the upper half of a square matrix to its another half. + +The function cv::completeSymm copies the lower or the upper half of a square matrix to +its another half. The matrix diagonal remains unchanged: + - \f$\texttt{m}_{ij}=\texttt{m}_{ji}\f$ for \f$i > j\f$ if + lowerToUpper=false + - \f$\texttt{m}_{ij}=\texttt{m}_{ji}\f$ for \f$i < j\f$ if + lowerToUpper=true + +@param m input-output floating-point square matrix. +@param lowerToUpper operation flag; if true, the lower half is copied to +the upper half. Otherwise, the upper half is copied to the lower half. +@sa flip, transpose +*/ +CV_EXPORTS_W void completeSymm(InputOutputArray m, bool lowerToUpper = false); + +/** @brief Initializes a scaled identity matrix. + +The function cv::setIdentity initializes a scaled identity matrix: +\f[\texttt{mtx} (i,j)= \fork{\texttt{value}}{ if \(i=j\)}{0}{otherwise}\f] + +The function can also be emulated using the matrix initializers and the +matrix expressions: +@code + Mat A = Mat::eye(4, 3, CV_32F)*5; + // A will be set to [[5, 0, 0], [0, 5, 0], [0, 0, 5], [0, 0, 0]] +@endcode +@param mtx matrix to initialize (not necessarily square). +@param s value to assign to diagonal elements. +@sa Mat::zeros, Mat::ones, Mat::setTo, Mat::operator= +*/ +CV_EXPORTS_W void setIdentity(InputOutputArray mtx, const Scalar& s = Scalar(1)); + +/** @brief Returns the determinant of a square floating-point matrix. + +The function cv::determinant calculates and returns the determinant of the +specified matrix. For small matrices ( mtx.cols=mtx.rows\<=3 ), the +direct method is used. For larger matrices, the function uses LU +factorization with partial pivoting. + +For symmetric positively-determined matrices, it is also possible to use +eigen decomposition to calculate the determinant. +@param mtx input matrix that must have CV_32FC1 or CV_64FC1 type and +square size. +@sa trace, invert, solve, eigen, @ref MatrixExpressions +*/ +CV_EXPORTS_W double determinant(InputArray mtx); + +/** @brief Returns the trace of a matrix. + +The function cv::trace returns the sum of the diagonal elements of the +matrix mtx . +\f[\mathrm{tr} ( \texttt{mtx} ) = \sum _i \texttt{mtx} (i,i)\f] +@param mtx input matrix. +*/ +CV_EXPORTS_W Scalar trace(InputArray mtx); + +/** @brief Finds the inverse or pseudo-inverse of a matrix. + +The function cv::invert inverts the matrix src and stores the result in dst +. When the matrix src is singular or non-square, the function calculates +the pseudo-inverse matrix (the dst matrix) so that norm(src\*dst - I) is +minimal, where I is an identity matrix. + +In case of the #DECOMP_LU method, the function returns non-zero value if +the inverse has been successfully calculated and 0 if src is singular. + +In case of the #DECOMP_SVD method, the function returns the inverse +condition number of src (the ratio of the smallest singular value to the +largest singular value) and 0 if src is singular. The SVD method +calculates a pseudo-inverse matrix if src is singular. + +Similarly to #DECOMP_LU, the method #DECOMP_CHOLESKY works only with +non-singular square matrices that should also be symmetrical and +positively defined. In this case, the function stores the inverted +matrix in dst and returns non-zero. Otherwise, it returns 0. + +@param src input floating-point M x N matrix. +@param dst output matrix of N x M size and the same type as src. +@param flags inversion method (cv::DecompTypes) +@sa solve, SVD +*/ +CV_EXPORTS_W double invert(InputArray src, OutputArray dst, int flags = DECOMP_LU); + +/** @brief Solves one or more linear systems or least-squares problems. + +The function cv::solve solves a linear system or least-squares problem (the +latter is possible with SVD or QR methods, or by specifying the flag +#DECOMP_NORMAL ): +\f[\texttt{dst} = \arg \min _X \| \texttt{src1} \cdot \texttt{X} - \texttt{src2} \|\f] + +If #DECOMP_LU or #DECOMP_CHOLESKY method is used, the function returns 1 +if src1 (or \f$\texttt{src1}^T\texttt{src1}\f$ ) is non-singular. Otherwise, +it returns 0. In the latter case, dst is not valid. Other methods find a +pseudo-solution in case of a singular left-hand side part. + +@note If you want to find a unity-norm solution of an under-defined +singular system \f$\texttt{src1}\cdot\texttt{dst}=0\f$ , the function solve +will not do the work. Use SVD::solveZ instead. + +@param src1 input matrix on the left-hand side of the system. +@param src2 input matrix on the right-hand side of the system. +@param dst output solution. +@param flags solution (matrix inversion) method (#DecompTypes) +@sa invert, SVD, eigen +*/ +CV_EXPORTS_W bool solve(InputArray src1, InputArray src2, + OutputArray dst, int flags = DECOMP_LU); + +/** @brief Sorts each row or each column of a matrix. + +The function cv::sort sorts each matrix row or each matrix column in +ascending or descending order. So you should pass two operation flags to +get desired behaviour. If you want to sort matrix rows or columns +lexicographically, you can use STL std::sort generic function with the +proper comparison predicate. + +@param src input single-channel array. +@param dst output array of the same size and type as src. +@param flags operation flags, a combination of #SortFlags +@sa sortIdx, randShuffle +*/ +CV_EXPORTS_W void sort(InputArray src, OutputArray dst, int flags); + +/** @brief Sorts each row or each column of a matrix. + +The function cv::sortIdx sorts each matrix row or each matrix column in the +ascending or descending order. So you should pass two operation flags to +get desired behaviour. Instead of reordering the elements themselves, it +stores the indices of sorted elements in the output array. For example: +@code + Mat A = Mat::eye(3,3,CV_32F), B; + sortIdx(A, B, SORT_EVERY_ROW + SORT_ASCENDING); + // B will probably contain + // (because of equal elements in A some permutations are possible): + // [[1, 2, 0], [0, 2, 1], [0, 1, 2]] +@endcode +@param src input single-channel array. +@param dst output integer array of the same size as src. +@param flags operation flags that could be a combination of cv::SortFlags +@sa sort, randShuffle +*/ +CV_EXPORTS_W void sortIdx(InputArray src, OutputArray dst, int flags); + +/** @brief Finds the real roots of a cubic equation. + +The function solveCubic finds the real roots of a cubic equation: +- if coeffs is a 4-element vector: +\f[\texttt{coeffs} [0] x^3 + \texttt{coeffs} [1] x^2 + \texttt{coeffs} [2] x + \texttt{coeffs} [3] = 0\f] +- if coeffs is a 3-element vector: +\f[x^3 + \texttt{coeffs} [0] x^2 + \texttt{coeffs} [1] x + \texttt{coeffs} [2] = 0\f] + +The roots are stored in the roots array. +@param coeffs equation coefficients, an array of 3 or 4 elements. +@param roots output array of real roots that has 1 or 3 elements. +@return number of real roots. It can be 0, 1 or 2. +*/ +CV_EXPORTS_W int solveCubic(InputArray coeffs, OutputArray roots); + +/** @brief Finds the real or complex roots of a polynomial equation. + +The function cv::solvePoly finds real and complex roots of a polynomial equation: +\f[\texttt{coeffs} [n] x^{n} + \texttt{coeffs} [n-1] x^{n-1} + ... + \texttt{coeffs} [1] x + \texttt{coeffs} [0] = 0\f] +@param coeffs array of polynomial coefficients. +@param roots output (complex) array of roots. +@param maxIters maximum number of iterations the algorithm does. +*/ +CV_EXPORTS_W double solvePoly(InputArray coeffs, OutputArray roots, int maxIters = 300); + +/** @brief Calculates eigenvalues and eigenvectors of a symmetric matrix. + +The function cv::eigen calculates just eigenvalues, or eigenvalues and eigenvectors of the symmetric +matrix src: +@code + src*eigenvectors.row(i).t() = eigenvalues.at(i)*eigenvectors.row(i).t() +@endcode + +@note Use cv::eigenNonSymmetric for calculation of real eigenvalues and eigenvectors of non-symmetric matrix. + +@param src input matrix that must have CV_32FC1 or CV_64FC1 type, square size and be symmetrical +(src ^T^ == src). +@param eigenvalues output vector of eigenvalues of the same type as src; the eigenvalues are stored +in the descending order. +@param eigenvectors output matrix of eigenvectors; it has the same size and type as src; the +eigenvectors are stored as subsequent matrix rows, in the same order as the corresponding +eigenvalues. +@sa eigenNonSymmetric, completeSymm , PCA +*/ +CV_EXPORTS_W bool eigen(InputArray src, OutputArray eigenvalues, + OutputArray eigenvectors = noArray()); + +/** @brief Calculates eigenvalues and eigenvectors of a non-symmetric matrix (real eigenvalues only). + +@note Assumes real eigenvalues. + +The function calculates eigenvalues and eigenvectors (optional) of the square matrix src: +@code + src*eigenvectors.row(i).t() = eigenvalues.at(i)*eigenvectors.row(i).t() +@endcode + +@param src input matrix (CV_32FC1 or CV_64FC1 type). +@param eigenvalues output vector of eigenvalues (type is the same type as src). +@param eigenvectors output matrix of eigenvectors (type is the same type as src). The eigenvectors are stored as subsequent matrix rows, in the same order as the corresponding eigenvalues. +@sa eigen +*/ +CV_EXPORTS_W void eigenNonSymmetric(InputArray src, OutputArray eigenvalues, + OutputArray eigenvectors); + +/** @brief Calculates the covariance matrix of a set of vectors. + +The function cv::calcCovarMatrix calculates the covariance matrix and, optionally, the mean vector of +the set of input vectors. +@param samples samples stored as separate matrices +@param nsamples number of samples +@param covar output covariance matrix of the type ctype and square size. +@param mean input or output (depending on the flags) array as the average value of the input vectors. +@param flags operation flags as a combination of #CovarFlags +@param ctype type of the matrixl; it equals 'CV_64F' by default. +@sa PCA, mulTransposed, Mahalanobis +@todo InputArrayOfArrays +*/ +CV_EXPORTS void calcCovarMatrix( const Mat* samples, int nsamples, Mat& covar, Mat& mean, + int flags, int ctype = CV_64F); + +/** @overload +@note use #COVAR_ROWS or #COVAR_COLS flag +@param samples samples stored as rows/columns of a single matrix. +@param covar output covariance matrix of the type ctype and square size. +@param mean input or output (depending on the flags) array as the average value of the input vectors. +@param flags operation flags as a combination of #CovarFlags +@param ctype type of the matrixl; it equals 'CV_64F' by default. +*/ +CV_EXPORTS_W void calcCovarMatrix( InputArray samples, OutputArray covar, + InputOutputArray mean, int flags, int ctype = CV_64F); + +/** wrap PCA::operator() */ +CV_EXPORTS_W void PCACompute(InputArray data, InputOutputArray mean, + OutputArray eigenvectors, int maxComponents = 0); + +/** wrap PCA::operator() and add eigenvalues output parameter */ +CV_EXPORTS_AS(PCACompute2) void PCACompute(InputArray data, InputOutputArray mean, + OutputArray eigenvectors, OutputArray eigenvalues, + int maxComponents = 0); + +/** wrap PCA::operator() */ +CV_EXPORTS_W void PCACompute(InputArray data, InputOutputArray mean, + OutputArray eigenvectors, double retainedVariance); + +/** wrap PCA::operator() and add eigenvalues output parameter */ +CV_EXPORTS_AS(PCACompute2) void PCACompute(InputArray data, InputOutputArray mean, + OutputArray eigenvectors, OutputArray eigenvalues, + double retainedVariance); + +/** wrap PCA::project */ +CV_EXPORTS_W void PCAProject(InputArray data, InputArray mean, + InputArray eigenvectors, OutputArray result); + +/** wrap PCA::backProject */ +CV_EXPORTS_W void PCABackProject(InputArray data, InputArray mean, + InputArray eigenvectors, OutputArray result); + +/** wrap SVD::compute */ +CV_EXPORTS_W void SVDecomp( InputArray src, OutputArray w, OutputArray u, OutputArray vt, int flags = 0 ); + +/** wrap SVD::backSubst */ +CV_EXPORTS_W void SVBackSubst( InputArray w, InputArray u, InputArray vt, + InputArray rhs, OutputArray dst ); + +/** @brief Calculates the Mahalanobis distance between two vectors. + +The function cv::Mahalanobis calculates and returns the weighted distance between two vectors: +\f[d( \texttt{vec1} , \texttt{vec2} )= \sqrt{\sum_{i,j}{\texttt{icovar(i,j)}\cdot(\texttt{vec1}(I)-\texttt{vec2}(I))\cdot(\texttt{vec1(j)}-\texttt{vec2(j)})} }\f] +The covariance matrix may be calculated using the #calcCovarMatrix function and then inverted using +the invert function (preferably using the #DECOMP_SVD method, as the most accurate). +@param v1 first 1D input vector. +@param v2 second 1D input vector. +@param icovar inverse covariance matrix. +*/ +CV_EXPORTS_W double Mahalanobis(InputArray v1, InputArray v2, InputArray icovar); + +/** @brief Performs a forward or inverse Discrete Fourier transform of a 1D or 2D floating-point array. + +The function cv::dft performs one of the following: +- Forward the Fourier transform of a 1D vector of N elements: + \f[Y = F^{(N)} \cdot X,\f] + where \f$F^{(N)}_{jk}=\exp(-2\pi i j k/N)\f$ and \f$i=\sqrt{-1}\f$ +- Inverse the Fourier transform of a 1D vector of N elements: + \f[\begin{array}{l} X'= \left (F^{(N)} \right )^{-1} \cdot Y = \left (F^{(N)} \right )^* \cdot y \\ X = (1/N) \cdot X, \end{array}\f] + where \f$F^*=\left(\textrm{Re}(F^{(N)})-\textrm{Im}(F^{(N)})\right)^T\f$ +- Forward the 2D Fourier transform of a M x N matrix: + \f[Y = F^{(M)} \cdot X \cdot F^{(N)}\f] +- Inverse the 2D Fourier transform of a M x N matrix: + \f[\begin{array}{l} X'= \left (F^{(M)} \right )^* \cdot Y \cdot \left (F^{(N)} \right )^* \\ X = \frac{1}{M \cdot N} \cdot X' \end{array}\f] + +In case of real (single-channel) data, the output spectrum of the forward Fourier transform or input +spectrum of the inverse Fourier transform can be represented in a packed format called *CCS* +(complex-conjugate-symmetrical). It was borrowed from IPL (Intel\* Image Processing Library). Here +is how 2D *CCS* spectrum looks: +\f[\begin{bmatrix} Re Y_{0,0} & Re Y_{0,1} & Im Y_{0,1} & Re Y_{0,2} & Im Y_{0,2} & \cdots & Re Y_{0,N/2-1} & Im Y_{0,N/2-1} & Re Y_{0,N/2} \\ Re Y_{1,0} & Re Y_{1,1} & Im Y_{1,1} & Re Y_{1,2} & Im Y_{1,2} & \cdots & Re Y_{1,N/2-1} & Im Y_{1,N/2-1} & Re Y_{1,N/2} \\ Im Y_{1,0} & Re Y_{2,1} & Im Y_{2,1} & Re Y_{2,2} & Im Y_{2,2} & \cdots & Re Y_{2,N/2-1} & Im Y_{2,N/2-1} & Im Y_{1,N/2} \\ \hdotsfor{9} \\ Re Y_{M/2-1,0} & Re Y_{M-3,1} & Im Y_{M-3,1} & \hdotsfor{3} & Re Y_{M-3,N/2-1} & Im Y_{M-3,N/2-1}& Re Y_{M/2-1,N/2} \\ Im Y_{M/2-1,0} & Re Y_{M-2,1} & Im Y_{M-2,1} & \hdotsfor{3} & Re Y_{M-2,N/2-1} & Im Y_{M-2,N/2-1}& Im Y_{M/2-1,N/2} \\ Re Y_{M/2,0} & Re Y_{M-1,1} & Im Y_{M-1,1} & \hdotsfor{3} & Re Y_{M-1,N/2-1} & Im Y_{M-1,N/2-1}& Re Y_{M/2,N/2} \end{bmatrix}\f] + +In case of 1D transform of a real vector, the output looks like the first row of the matrix above. + +So, the function chooses an operation mode depending on the flags and size of the input array: +- If #DFT_ROWS is set or the input array has a single row or single column, the function + performs a 1D forward or inverse transform of each row of a matrix when #DFT_ROWS is set. + Otherwise, it performs a 2D transform. +- If the input array is real and #DFT_INVERSE is not set, the function performs a forward 1D or + 2D transform: + - When #DFT_COMPLEX_OUTPUT is set, the output is a complex matrix of the same size as + input. + - When #DFT_COMPLEX_OUTPUT is not set, the output is a real matrix of the same size as + input. In case of 2D transform, it uses the packed format as shown above. In case of a + single 1D transform, it looks like the first row of the matrix above. In case of + multiple 1D transforms (when using the #DFT_ROWS flag), each row of the output matrix + looks like the first row of the matrix above. +- If the input array is complex and either #DFT_INVERSE or #DFT_REAL_OUTPUT are not set, the + output is a complex array of the same size as input. The function performs a forward or + inverse 1D or 2D transform of the whole input array or each row of the input array + independently, depending on the flags DFT_INVERSE and DFT_ROWS. +- When #DFT_INVERSE is set and the input array is real, or it is complex but #DFT_REAL_OUTPUT + is set, the output is a real array of the same size as input. The function performs a 1D or 2D + inverse transformation of the whole input array or each individual row, depending on the flags + #DFT_INVERSE and #DFT_ROWS. + +If #DFT_SCALE is set, the scaling is done after the transformation. + +Unlike dct , the function supports arrays of arbitrary size. But only those arrays are processed +efficiently, whose sizes can be factorized in a product of small prime numbers (2, 3, and 5 in the +current implementation). Such an efficient DFT size can be calculated using the getOptimalDFTSize +method. + +The sample below illustrates how to calculate a DFT-based convolution of two 2D real arrays: +@code + void convolveDFT(InputArray A, InputArray B, OutputArray C) + { + // reallocate the output array if needed + C.create(abs(A.rows - B.rows)+1, abs(A.cols - B.cols)+1, A.type()); + Size dftSize; + // calculate the size of DFT transform + dftSize.width = getOptimalDFTSize(A.cols + B.cols - 1); + dftSize.height = getOptimalDFTSize(A.rows + B.rows - 1); + + // allocate temporary buffers and initialize them with 0's + Mat tempA(dftSize, A.type(), Scalar::all(0)); + Mat tempB(dftSize, B.type(), Scalar::all(0)); + + // copy A and B to the top-left corners of tempA and tempB, respectively + Mat roiA(tempA, Rect(0,0,A.cols,A.rows)); + A.copyTo(roiA); + Mat roiB(tempB, Rect(0,0,B.cols,B.rows)); + B.copyTo(roiB); + + // now transform the padded A & B in-place; + // use "nonzeroRows" hint for faster processing + dft(tempA, tempA, 0, A.rows); + dft(tempB, tempB, 0, B.rows); + + // multiply the spectrums; + // the function handles packed spectrum representations well + mulSpectrums(tempA, tempB, tempA); + + // transform the product back from the frequency domain. + // Even though all the result rows will be non-zero, + // you need only the first C.rows of them, and thus you + // pass nonzeroRows == C.rows + dft(tempA, tempA, DFT_INVERSE + DFT_SCALE, C.rows); + + // now copy the result back to C. + tempA(Rect(0, 0, C.cols, C.rows)).copyTo(C); + + // all the temporary buffers will be deallocated automatically + } +@endcode +To optimize this sample, consider the following approaches: +- Since nonzeroRows != 0 is passed to the forward transform calls and since A and B are copied to + the top-left corners of tempA and tempB, respectively, it is not necessary to clear the whole + tempA and tempB. It is only necessary to clear the tempA.cols - A.cols ( tempB.cols - B.cols) + rightmost columns of the matrices. +- This DFT-based convolution does not have to be applied to the whole big arrays, especially if B + is significantly smaller than A or vice versa. Instead, you can calculate convolution by parts. + To do this, you need to split the output array C into multiple tiles. For each tile, estimate + which parts of A and B are required to calculate convolution in this tile. If the tiles in C are + too small, the speed will decrease a lot because of repeated work. In the ultimate case, when + each tile in C is a single pixel, the algorithm becomes equivalent to the naive convolution + algorithm. If the tiles are too big, the temporary arrays tempA and tempB become too big and + there is also a slowdown because of bad cache locality. So, there is an optimal tile size + somewhere in the middle. +- If different tiles in C can be calculated in parallel and, thus, the convolution is done by + parts, the loop can be threaded. + +All of the above improvements have been implemented in #matchTemplate and #filter2D . Therefore, by +using them, you can get the performance even better than with the above theoretically optimal +implementation. Though, those two functions actually calculate cross-correlation, not convolution, +so you need to "flip" the second convolution operand B vertically and horizontally using flip . +@note +- An example using the discrete fourier transform can be found at + opencv_source_code/samples/cpp/dft.cpp +- (Python) An example using the dft functionality to perform Wiener deconvolution can be found + at opencv_source/samples/python/deconvolution.py +- (Python) An example rearranging the quadrants of a Fourier image can be found at + opencv_source/samples/python/dft.py +@param src input array that could be real or complex. +@param dst output array whose size and type depends on the flags . +@param flags transformation flags, representing a combination of the #DftFlags +@param nonzeroRows when the parameter is not zero, the function assumes that only the first +nonzeroRows rows of the input array (#DFT_INVERSE is not set) or only the first nonzeroRows of the +output array (#DFT_INVERSE is set) contain non-zeros, thus, the function can handle the rest of the +rows more efficiently and save some time; this technique is very useful for calculating array +cross-correlation or convolution using DFT. +@sa dct , getOptimalDFTSize , mulSpectrums, filter2D , matchTemplate , flip , cartToPolar , +magnitude , phase +*/ +CV_EXPORTS_W void dft(InputArray src, OutputArray dst, int flags = 0, int nonzeroRows = 0); + +/** @brief Calculates the inverse Discrete Fourier Transform of a 1D or 2D array. + +idft(src, dst, flags) is equivalent to dft(src, dst, flags | #DFT_INVERSE) . +@note None of dft and idft scales the result by default. So, you should pass #DFT_SCALE to one of +dft or idft explicitly to make these transforms mutually inverse. +@sa dft, dct, idct, mulSpectrums, getOptimalDFTSize +@param src input floating-point real or complex array. +@param dst output array whose size and type depend on the flags. +@param flags operation flags (see dft and #DftFlags). +@param nonzeroRows number of dst rows to process; the rest of the rows have undefined content (see +the convolution sample in dft description. +*/ +CV_EXPORTS_W void idft(InputArray src, OutputArray dst, int flags = 0, int nonzeroRows = 0); + +/** @brief Performs a forward or inverse discrete Cosine transform of 1D or 2D array. + +The function cv::dct performs a forward or inverse discrete Cosine transform (DCT) of a 1D or 2D +floating-point array: +- Forward Cosine transform of a 1D vector of N elements: + \f[Y = C^{(N)} \cdot X\f] + where + \f[C^{(N)}_{jk}= \sqrt{\alpha_j/N} \cos \left ( \frac{\pi(2k+1)j}{2N} \right )\f] + and + \f$\alpha_0=1\f$, \f$\alpha_j=2\f$ for *j \> 0*. +- Inverse Cosine transform of a 1D vector of N elements: + \f[X = \left (C^{(N)} \right )^{-1} \cdot Y = \left (C^{(N)} \right )^T \cdot Y\f] + (since \f$C^{(N)}\f$ is an orthogonal matrix, \f$C^{(N)} \cdot \left(C^{(N)}\right)^T = I\f$ ) +- Forward 2D Cosine transform of M x N matrix: + \f[Y = C^{(N)} \cdot X \cdot \left (C^{(N)} \right )^T\f] +- Inverse 2D Cosine transform of M x N matrix: + \f[X = \left (C^{(N)} \right )^T \cdot X \cdot C^{(N)}\f] + +The function chooses the mode of operation by looking at the flags and size of the input array: +- If (flags & #DCT_INVERSE) == 0 , the function does a forward 1D or 2D transform. Otherwise, it + is an inverse 1D or 2D transform. +- If (flags & #DCT_ROWS) != 0 , the function performs a 1D transform of each row. +- If the array is a single column or a single row, the function performs a 1D transform. +- If none of the above is true, the function performs a 2D transform. + +@note Currently dct supports even-size arrays (2, 4, 6 ...). For data analysis and approximation, you +can pad the array when necessary. +Also, the function performance depends very much, and not monotonically, on the array size (see +getOptimalDFTSize ). In the current implementation DCT of a vector of size N is calculated via DFT +of a vector of size N/2 . Thus, the optimal DCT size N1 \>= N can be calculated as: +@code + size_t getOptimalDCTSize(size_t N) { return 2*getOptimalDFTSize((N+1)/2); } + N1 = getOptimalDCTSize(N); +@endcode +@param src input floating-point array. +@param dst output array of the same size and type as src . +@param flags transformation flags as a combination of cv::DftFlags (DCT_*) +@sa dft , getOptimalDFTSize , idct +*/ +CV_EXPORTS_W void dct(InputArray src, OutputArray dst, int flags = 0); + +/** @brief Calculates the inverse Discrete Cosine Transform of a 1D or 2D array. + +idct(src, dst, flags) is equivalent to dct(src, dst, flags | DCT_INVERSE). +@param src input floating-point single-channel array. +@param dst output array of the same size and type as src. +@param flags operation flags. +@sa dct, dft, idft, getOptimalDFTSize +*/ +CV_EXPORTS_W void idct(InputArray src, OutputArray dst, int flags = 0); + +/** @brief Performs the per-element multiplication of two Fourier spectrums. + +The function cv::mulSpectrums performs the per-element multiplication of the two CCS-packed or complex +matrices that are results of a real or complex Fourier transform. + +The function, together with dft and idft , may be used to calculate convolution (pass conjB=false ) +or correlation (pass conjB=true ) of two arrays rapidly. When the arrays are complex, they are +simply multiplied (per element) with an optional conjugation of the second-array elements. When the +arrays are real, they are assumed to be CCS-packed (see dft for details). +@param a first input array. +@param b second input array of the same size and type as src1 . +@param c output array of the same size and type as src1 . +@param flags operation flags; currently, the only supported flag is cv::DFT_ROWS, which indicates that +each row of src1 and src2 is an independent 1D Fourier spectrum. If you do not want to use this flag, then simply add a `0` as value. +@param conjB optional flag that conjugates the second input array before the multiplication (true) +or not (false). +*/ +CV_EXPORTS_W void mulSpectrums(InputArray a, InputArray b, OutputArray c, + int flags, bool conjB = false); + +/** @brief Returns the optimal DFT size for a given vector size. + +DFT performance is not a monotonic function of a vector size. Therefore, when you calculate +convolution of two arrays or perform the spectral analysis of an array, it usually makes sense to +pad the input data with zeros to get a bit larger array that can be transformed much faster than the +original one. Arrays whose size is a power-of-two (2, 4, 8, 16, 32, ...) are the fastest to process. +Though, the arrays whose size is a product of 2's, 3's, and 5's (for example, 300 = 5\*5\*3\*2\*2) +are also processed quite efficiently. + +The function cv::getOptimalDFTSize returns the minimum number N that is greater than or equal to vecsize +so that the DFT of a vector of size N can be processed efficiently. In the current implementation N += 2 ^p^ \* 3 ^q^ \* 5 ^r^ for some integer p, q, r. + +The function returns a negative number if vecsize is too large (very close to INT_MAX ). + +While the function cannot be used directly to estimate the optimal vector size for DCT transform +(since the current DCT implementation supports only even-size vectors), it can be easily processed +as getOptimalDFTSize((vecsize+1)/2)\*2. +@param vecsize vector size. +@sa dft , dct , idft , idct , mulSpectrums +*/ +CV_EXPORTS_W int getOptimalDFTSize(int vecsize); + +/** @brief Returns the default random number generator. + +The function cv::theRNG returns the default random number generator. For each thread, there is a +separate random number generator, so you can use the function safely in multi-thread environments. +If you just need to get a single random number using this generator or initialize an array, you can +use randu or randn instead. But if you are going to generate many random numbers inside a loop, it +is much faster to use this function to retrieve the generator and then use RNG::operator _Tp() . +@sa RNG, randu, randn +*/ +CV_EXPORTS RNG& theRNG(); + +/** @brief Sets state of default random number generator. + +The function cv::setRNGSeed sets state of default random number generator to custom value. +@param seed new state for default random number generator +@sa RNG, randu, randn +*/ +CV_EXPORTS_W void setRNGSeed(int seed); + +/** @brief Generates a single uniformly-distributed random number or an array of random numbers. + +Non-template variant of the function fills the matrix dst with uniformly-distributed +random numbers from the specified range: +\f[\texttt{low} _c \leq \texttt{dst} (I)_c < \texttt{high} _c\f] +@param dst output array of random numbers; the array must be pre-allocated. +@param low inclusive lower boundary of the generated random numbers. +@param high exclusive upper boundary of the generated random numbers. +@sa RNG, randn, theRNG +*/ +CV_EXPORTS_W void randu(InputOutputArray dst, InputArray low, InputArray high); + +/** @brief Fills the array with normally distributed random numbers. + +The function cv::randn fills the matrix dst with normally distributed random numbers with the specified +mean vector and the standard deviation matrix. The generated random numbers are clipped to fit the +value range of the output array data type. +@param dst output array of random numbers; the array must be pre-allocated and have 1 to 4 channels. +@param mean mean value (expectation) of the generated random numbers. +@param stddev standard deviation of the generated random numbers; it can be either a vector (in +which case a diagonal standard deviation matrix is assumed) or a square matrix. +@sa RNG, randu +*/ +CV_EXPORTS_W void randn(InputOutputArray dst, InputArray mean, InputArray stddev); + +/** @brief Shuffles the array elements randomly. + +The function cv::randShuffle shuffles the specified 1D array by randomly choosing pairs of elements and +swapping them. The number of such swap operations will be dst.rows\*dst.cols\*iterFactor . +@param dst input/output numerical 1D array. +@param iterFactor scale factor that determines the number of random swap operations (see the details +below). +@param rng optional random number generator used for shuffling; if it is zero, theRNG () is used +instead. +@sa RNG, sort +*/ +CV_EXPORTS_W void randShuffle(InputOutputArray dst, double iterFactor = 1., RNG* rng = 0); + +/** @brief Principal Component Analysis + +The class is used to calculate a special basis for a set of vectors. The +basis will consist of eigenvectors of the covariance matrix calculated +from the input set of vectors. The class %PCA can also transform +vectors to/from the new coordinate space defined by the basis. Usually, +in this new coordinate system, each vector from the original set (and +any linear combination of such vectors) can be quite accurately +approximated by taking its first few components, corresponding to the +eigenvectors of the largest eigenvalues of the covariance matrix. +Geometrically it means that you calculate a projection of the vector to +a subspace formed by a few eigenvectors corresponding to the dominant +eigenvalues of the covariance matrix. And usually such a projection is +very close to the original vector. So, you can represent the original +vector from a high-dimensional space with a much shorter vector +consisting of the projected vector's coordinates in the subspace. Such a +transformation is also known as Karhunen-Loeve Transform, or KLT. +See http://en.wikipedia.org/wiki/Principal_component_analysis + +The sample below is the function that takes two matrices. The first +function stores a set of vectors (a row per vector) that is used to +calculate PCA. The second function stores another "test" set of vectors +(a row per vector). First, these vectors are compressed with PCA, then +reconstructed back, and then the reconstruction error norm is computed +and printed for each vector. : + +@code{.cpp} +using namespace cv; + +PCA compressPCA(const Mat& pcaset, int maxComponents, + const Mat& testset, Mat& compressed) +{ + PCA pca(pcaset, // pass the data + Mat(), // we do not have a pre-computed mean vector, + // so let the PCA engine to compute it + PCA::DATA_AS_ROW, // indicate that the vectors + // are stored as matrix rows + // (use PCA::DATA_AS_COL if the vectors are + // the matrix columns) + maxComponents // specify, how many principal components to retain + ); + // if there is no test data, just return the computed basis, ready-to-use + if( !testset.data ) + return pca; + CV_Assert( testset.cols == pcaset.cols ); + + compressed.create(testset.rows, maxComponents, testset.type()); + + Mat reconstructed; + for( int i = 0; i < testset.rows; i++ ) + { + Mat vec = testset.row(i), coeffs = compressed.row(i), reconstructed; + // compress the vector, the result will be stored + // in the i-th row of the output matrix + pca.project(vec, coeffs); + // and then reconstruct it + pca.backProject(coeffs, reconstructed); + // and measure the error + printf("%d. diff = %g\n", i, norm(vec, reconstructed, NORM_L2)); + } + return pca; +} +@endcode +@sa calcCovarMatrix, mulTransposed, SVD, dft, dct +*/ +class CV_EXPORTS PCA +{ +public: + enum Flags { DATA_AS_ROW = 0, //!< indicates that the input samples are stored as matrix rows + DATA_AS_COL = 1, //!< indicates that the input samples are stored as matrix columns + USE_AVG = 2 //! + }; + + /** @brief default constructor + + The default constructor initializes an empty %PCA structure. The other + constructors initialize the structure and call PCA::operator()(). + */ + PCA(); + + /** @overload + @param data input samples stored as matrix rows or matrix columns. + @param mean optional mean value; if the matrix is empty (@c noArray()), + the mean is computed from the data. + @param flags operation flags; currently the parameter is only used to + specify the data layout (PCA::Flags) + @param maxComponents maximum number of components that %PCA should + retain; by default, all the components are retained. + */ + PCA(InputArray data, InputArray mean, int flags, int maxComponents = 0); + + /** @overload + @param data input samples stored as matrix rows or matrix columns. + @param mean optional mean value; if the matrix is empty (noArray()), + the mean is computed from the data. + @param flags operation flags; currently the parameter is only used to + specify the data layout (PCA::Flags) + @param retainedVariance Percentage of variance that PCA should retain. + Using this parameter will let the PCA decided how many components to + retain but it will always keep at least 2. + */ + PCA(InputArray data, InputArray mean, int flags, double retainedVariance); + + /** @brief performs %PCA + + The operator performs %PCA of the supplied dataset. It is safe to reuse + the same PCA structure for multiple datasets. That is, if the structure + has been previously used with another dataset, the existing internal + data is reclaimed and the new @ref eigenvalues, @ref eigenvectors and @ref + mean are allocated and computed. + + The computed @ref eigenvalues are sorted from the largest to the smallest and + the corresponding @ref eigenvectors are stored as eigenvectors rows. + + @param data input samples stored as the matrix rows or as the matrix + columns. + @param mean optional mean value; if the matrix is empty (noArray()), + the mean is computed from the data. + @param flags operation flags; currently the parameter is only used to + specify the data layout. (Flags) + @param maxComponents maximum number of components that PCA should + retain; by default, all the components are retained. + */ + PCA& operator()(InputArray data, InputArray mean, int flags, int maxComponents = 0); + + /** @overload + @param data input samples stored as the matrix rows or as the matrix + columns. + @param mean optional mean value; if the matrix is empty (noArray()), + the mean is computed from the data. + @param flags operation flags; currently the parameter is only used to + specify the data layout. (PCA::Flags) + @param retainedVariance Percentage of variance that %PCA should retain. + Using this parameter will let the %PCA decided how many components to + retain but it will always keep at least 2. + */ + PCA& operator()(InputArray data, InputArray mean, int flags, double retainedVariance); + + /** @brief Projects vector(s) to the principal component subspace. + + The methods project one or more vectors to the principal component + subspace, where each vector projection is represented by coefficients in + the principal component basis. The first form of the method returns the + matrix that the second form writes to the result. So the first form can + be used as a part of expression while the second form can be more + efficient in a processing loop. + @param vec input vector(s); must have the same dimensionality and the + same layout as the input data used at %PCA phase, that is, if + DATA_AS_ROW are specified, then `vec.cols==data.cols` + (vector dimensionality) and `vec.rows` is the number of vectors to + project, and the same is true for the PCA::DATA_AS_COL case. + */ + Mat project(InputArray vec) const; + + /** @overload + @param vec input vector(s); must have the same dimensionality and the + same layout as the input data used at PCA phase, that is, if + DATA_AS_ROW are specified, then `vec.cols==data.cols` + (vector dimensionality) and `vec.rows` is the number of vectors to + project, and the same is true for the PCA::DATA_AS_COL case. + @param result output vectors; in case of PCA::DATA_AS_COL, the + output matrix has as many columns as the number of input vectors, this + means that `result.cols==vec.cols` and the number of rows match the + number of principal components (for example, `maxComponents` parameter + passed to the constructor). + */ + void project(InputArray vec, OutputArray result) const; + + /** @brief Reconstructs vectors from their PC projections. + + The methods are inverse operations to PCA::project. They take PC + coordinates of projected vectors and reconstruct the original vectors. + Unless all the principal components have been retained, the + reconstructed vectors are different from the originals. But typically, + the difference is small if the number of components is large enough (but + still much smaller than the original vector dimensionality). As a + result, PCA is used. + @param vec coordinates of the vectors in the principal component + subspace, the layout and size are the same as of PCA::project output + vectors. + */ + Mat backProject(InputArray vec) const; + + /** @overload + @param vec coordinates of the vectors in the principal component + subspace, the layout and size are the same as of PCA::project output + vectors. + @param result reconstructed vectors; the layout and size are the same as + of PCA::project input vectors. + */ + void backProject(InputArray vec, OutputArray result) const; + + /** @brief write PCA objects + + Writes @ref eigenvalues @ref eigenvectors and @ref mean to specified FileStorage + */ + void write(FileStorage& fs) const; + + /** @brief load PCA objects + + Loads @ref eigenvalues @ref eigenvectors and @ref mean from specified FileNode + */ + void read(const FileNode& fn); + + Mat eigenvectors; //!< eigenvectors of the covariation matrix + Mat eigenvalues; //!< eigenvalues of the covariation matrix + Mat mean; //!< mean value subtracted before the projection and added after the back projection +}; + +/** @example samples/cpp/pca.cpp +An example using %PCA for dimensionality reduction while maintaining an amount of variance +*/ + +/** @example samples/cpp/tutorial_code/ml/introduction_to_pca/introduction_to_pca.cpp +Check @ref tutorial_introduction_to_pca "the corresponding tutorial" for more details +*/ + +/** +@brief Linear Discriminant Analysis +@todo document this class +*/ +class CV_EXPORTS LDA +{ +public: + /** @brief constructor + Initializes a LDA with num_components (default 0). + */ + explicit LDA(int num_components = 0); + + /** Initializes and performs a Discriminant Analysis with Fisher's + Optimization Criterion on given data in src and corresponding labels + in labels. If 0 (or less) number of components are given, they are + automatically determined for given data in computation. + */ + LDA(InputArrayOfArrays src, InputArray labels, int num_components = 0); + + /** Serializes this object to a given filename. + */ + void save(const String& filename) const; + + /** Deserializes this object from a given filename. + */ + void load(const String& filename); + + /** Serializes this object to a given cv::FileStorage. + */ + void save(FileStorage& fs) const; + + /** Deserializes this object from a given cv::FileStorage. + */ + void load(const FileStorage& node); + + /** destructor + */ + ~LDA(); + + /** Compute the discriminants for data in src (row aligned) and labels. + */ + void compute(InputArrayOfArrays src, InputArray labels); + + /** Projects samples into the LDA subspace. + src may be one or more row aligned samples. + */ + Mat project(InputArray src); + + /** Reconstructs projections from the LDA subspace. + src may be one or more row aligned projections. + */ + Mat reconstruct(InputArray src); + + /** Returns the eigenvectors of this LDA. + */ + Mat eigenvectors() const { return _eigenvectors; } + + /** Returns the eigenvalues of this LDA. + */ + Mat eigenvalues() const { return _eigenvalues; } + + static Mat subspaceProject(InputArray W, InputArray mean, InputArray src); + static Mat subspaceReconstruct(InputArray W, InputArray mean, InputArray src); + +protected: + bool _dataAsRow; // unused, but needed for 3.0 ABI compatibility. + int _num_components; + Mat _eigenvectors; + Mat _eigenvalues; + void lda(InputArrayOfArrays src, InputArray labels); +}; + +/** @brief Singular Value Decomposition + +Class for computing Singular Value Decomposition of a floating-point +matrix. The Singular Value Decomposition is used to solve least-square +problems, under-determined linear systems, invert matrices, compute +condition numbers, and so on. + +If you want to compute a condition number of a matrix or an absolute value of +its determinant, you do not need `u` and `vt`. You can pass +flags=SVD::NO_UV|... . Another flag SVD::FULL_UV indicates that full-size u +and vt must be computed, which is not necessary most of the time. + +@sa invert, solve, eigen, determinant +*/ +class CV_EXPORTS SVD +{ +public: + enum Flags { + /** allow the algorithm to modify the decomposed matrix; it can save space and speed up + processing. currently ignored. */ + MODIFY_A = 1, + /** indicates that only a vector of singular values `w` is to be processed, while u and vt + will be set to empty matrices */ + NO_UV = 2, + /** when the matrix is not square, by default the algorithm produces u and vt matrices of + sufficiently large size for the further A reconstruction; if, however, FULL_UV flag is + specified, u and vt will be full-size square orthogonal matrices.*/ + FULL_UV = 4 + }; + + /** @brief the default constructor + + initializes an empty SVD structure + */ + SVD(); + + /** @overload + initializes an empty SVD structure and then calls SVD::operator() + @param src decomposed matrix. The depth has to be CV_32F or CV_64F. + @param flags operation flags (SVD::Flags) + */ + SVD( InputArray src, int flags = 0 ); + + /** @brief the operator that performs SVD. The previously allocated u, w and vt are released. + + The operator performs the singular value decomposition of the supplied + matrix. The u,`vt` , and the vector of singular values w are stored in + the structure. The same SVD structure can be reused many times with + different matrices. Each time, if needed, the previous u,`vt` , and w + are reclaimed and the new matrices are created, which is all handled by + Mat::create. + @param src decomposed matrix. The depth has to be CV_32F or CV_64F. + @param flags operation flags (SVD::Flags) + */ + SVD& operator ()( InputArray src, int flags = 0 ); + + /** @brief decomposes matrix and stores the results to user-provided matrices + + The methods/functions perform SVD of matrix. Unlike SVD::SVD constructor + and SVD::operator(), they store the results to the user-provided + matrices: + + @code{.cpp} + Mat A, w, u, vt; + SVD::compute(A, w, u, vt); + @endcode + + @param src decomposed matrix. The depth has to be CV_32F or CV_64F. + @param w calculated singular values + @param u calculated left singular vectors + @param vt transposed matrix of right singular vectors + @param flags operation flags - see SVD::Flags. + */ + static void compute( InputArray src, OutputArray w, + OutputArray u, OutputArray vt, int flags = 0 ); + + /** @overload + computes singular values of a matrix + @param src decomposed matrix. The depth has to be CV_32F or CV_64F. + @param w calculated singular values + @param flags operation flags - see SVD::Flags. + */ + static void compute( InputArray src, OutputArray w, int flags = 0 ); + + /** @brief performs back substitution + */ + static void backSubst( InputArray w, InputArray u, + InputArray vt, InputArray rhs, + OutputArray dst ); + + /** @brief solves an under-determined singular linear system + + The method finds a unit-length solution x of a singular linear system + A\*x = 0. Depending on the rank of A, there can be no solutions, a + single solution or an infinite number of solutions. In general, the + algorithm solves the following problem: + \f[dst = \arg \min _{x: \| x \| =1} \| src \cdot x \|\f] + @param src left-hand-side matrix. + @param dst found solution. + */ + static void solveZ( InputArray src, OutputArray dst ); + + /** @brief performs a singular value back substitution. + + The method calculates a back substitution for the specified right-hand + side: + + \f[\texttt{x} = \texttt{vt} ^T \cdot diag( \texttt{w} )^{-1} \cdot \texttt{u} ^T \cdot \texttt{rhs} \sim \texttt{A} ^{-1} \cdot \texttt{rhs}\f] + + Using this technique you can either get a very accurate solution of the + convenient linear system, or the best (in the least-squares terms) + pseudo-solution of an overdetermined linear system. + + @param rhs right-hand side of a linear system (u\*w\*v')\*dst = rhs to + be solved, where A has been previously decomposed. + + @param dst found solution of the system. + + @note Explicit SVD with the further back substitution only makes sense + if you need to solve many linear systems with the same left-hand side + (for example, src ). If all you need is to solve a single system + (possibly with multiple rhs immediately available), simply call solve + add pass #DECOMP_SVD there. It does absolutely the same thing. + */ + void backSubst( InputArray rhs, OutputArray dst ) const; + + /** @todo document */ + template static + void compute( const Matx<_Tp, m, n>& a, Matx<_Tp, nm, 1>& w, Matx<_Tp, m, nm>& u, Matx<_Tp, n, nm>& vt ); + + /** @todo document */ + template static + void compute( const Matx<_Tp, m, n>& a, Matx<_Tp, nm, 1>& w ); + + /** @todo document */ + template static + void backSubst( const Matx<_Tp, nm, 1>& w, const Matx<_Tp, m, nm>& u, const Matx<_Tp, n, nm>& vt, const Matx<_Tp, m, nb>& rhs, Matx<_Tp, n, nb>& dst ); + + Mat u, w, vt; +}; + +/** @brief Random Number Generator + +Random number generator. It encapsulates the state (currently, a 64-bit +integer) and has methods to return scalar random values and to fill +arrays with random values. Currently it supports uniform and Gaussian +(normal) distributions. The generator uses Multiply-With-Carry +algorithm, introduced by G. Marsaglia ( + ). +Gaussian-distribution random numbers are generated using the Ziggurat +algorithm ( ), +introduced by G. Marsaglia and W. W. Tsang. +*/ +class CV_EXPORTS RNG +{ +public: + enum { UNIFORM = 0, + NORMAL = 1 + }; + + /** @brief constructor + + These are the RNG constructors. The first form sets the state to some + pre-defined value, equal to 2\*\*32-1 in the current implementation. The + second form sets the state to the specified value. If you passed state=0 + , the constructor uses the above default value instead to avoid the + singular random number sequence, consisting of all zeros. + */ + RNG(); + /** @overload + @param state 64-bit value used to initialize the RNG. + */ + RNG(uint64 state); + /**The method updates the state using the MWC algorithm and returns the + next 32-bit random number.*/ + unsigned next(); + + /**Each of the methods updates the state using the MWC algorithm and + returns the next random number of the specified type. In case of integer + types, the returned number is from the available value range for the + specified type. In case of floating-point types, the returned value is + from [0,1) range. + */ + operator uchar(); + /** @overload */ + operator schar(); + /** @overload */ + operator ushort(); + /** @overload */ + operator short(); + /** @overload */ + operator unsigned(); + /** @overload */ + operator int(); + /** @overload */ + operator float(); + /** @overload */ + operator double(); + + /** @brief returns a random integer sampled uniformly from [0, N). + + The methods transform the state using the MWC algorithm and return the + next random number. The first form is equivalent to RNG::next . The + second form returns the random number modulo N , which means that the + result is in the range [0, N) . + */ + unsigned operator ()(); + /** @overload + @param N upper non-inclusive boundary of the returned random number. + */ + unsigned operator ()(unsigned N); + + /** @brief returns uniformly distributed integer random number from [a,b) range + + The methods transform the state using the MWC algorithm and return the + next uniformly-distributed random number of the specified type, deduced + from the input parameter type, from the range [a, b) . There is a nuance + illustrated by the following sample: + + @code{.cpp} + RNG rng; + + // always produces 0 + double a = rng.uniform(0, 1); + + // produces double from [0, 1) + double a1 = rng.uniform((double)0, (double)1); + + // produces float from [0, 1) + float b = rng.uniform(0.f, 1.f); + + // produces double from [0, 1) + double c = rng.uniform(0., 1.); + + // may cause compiler error because of ambiguity: + // RNG::uniform(0, (int)0.999999)? or RNG::uniform((double)0, 0.99999)? + double d = rng.uniform(0, 0.999999); + @endcode + + The compiler does not take into account the type of the variable to + which you assign the result of RNG::uniform . The only thing that + matters to the compiler is the type of a and b parameters. So, if you + want a floating-point random number, but the range boundaries are + integer numbers, either put dots in the end, if they are constants, or + use explicit type cast operators, as in the a1 initialization above. + @param a lower inclusive boundary of the returned random number. + @param b upper non-inclusive boundary of the returned random number. + */ + int uniform(int a, int b); + /** @overload */ + float uniform(float a, float b); + /** @overload */ + double uniform(double a, double b); + + /** @brief Fills arrays with random numbers. + + @param mat 2D or N-dimensional matrix; currently matrices with more than + 4 channels are not supported by the methods, use Mat::reshape as a + possible workaround. + @param distType distribution type, RNG::UNIFORM or RNG::NORMAL. + @param a first distribution parameter; in case of the uniform + distribution, this is an inclusive lower boundary, in case of the normal + distribution, this is a mean value. + @param b second distribution parameter; in case of the uniform + distribution, this is a non-inclusive upper boundary, in case of the + normal distribution, this is a standard deviation (diagonal of the + standard deviation matrix or the full standard deviation matrix). + @param saturateRange pre-saturation flag; for uniform distribution only; + if true, the method will first convert a and b to the acceptable value + range (according to the mat datatype) and then will generate uniformly + distributed random numbers within the range [saturate(a), saturate(b)), + if saturateRange=false, the method will generate uniformly distributed + random numbers in the original range [a, b) and then will saturate them, + it means, for example, that + theRNG().fill(mat_8u, RNG::UNIFORM, -DBL_MAX, DBL_MAX) will likely + produce array mostly filled with 0's and 255's, since the range (0, 255) + is significantly smaller than [-DBL_MAX, DBL_MAX). + + Each of the methods fills the matrix with the random values from the + specified distribution. As the new numbers are generated, the RNG state + is updated accordingly. In case of multiple-channel images, every + channel is filled independently, which means that RNG cannot generate + samples from the multi-dimensional Gaussian distribution with + non-diagonal covariance matrix directly. To do that, the method + generates samples from multi-dimensional standard Gaussian distribution + with zero mean and identity covariation matrix, and then transforms them + using transform to get samples from the specified Gaussian distribution. + */ + void fill( InputOutputArray mat, int distType, InputArray a, InputArray b, bool saturateRange = false ); + + /** @brief Returns the next random number sampled from the Gaussian distribution + @param sigma standard deviation of the distribution. + + The method transforms the state using the MWC algorithm and returns the + next random number from the Gaussian distribution N(0,sigma) . That is, + the mean value of the returned random numbers is zero and the standard + deviation is the specified sigma . + */ + double gaussian(double sigma); + + uint64 state; + + bool operator ==(const RNG& other) const; +}; + +/** @brief Mersenne Twister random number generator + +Inspired by http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/MT2002/CODES/mt19937ar.c +@todo document +*/ +class CV_EXPORTS RNG_MT19937 +{ +public: + RNG_MT19937(); + RNG_MT19937(unsigned s); + void seed(unsigned s); + + unsigned next(); + + operator int(); + operator unsigned(); + operator float(); + operator double(); + + unsigned operator ()(unsigned N); + unsigned operator ()(); + + /** @brief returns uniformly distributed integer random number from [a,b) range*/ + int uniform(int a, int b); + /** @brief returns uniformly distributed floating-point random number from [a,b) range*/ + float uniform(float a, float b); + /** @brief returns uniformly distributed double-precision floating-point random number from [a,b) range*/ + double uniform(double a, double b); + +private: + enum PeriodParameters {N = 624, M = 397}; + unsigned state[N]; + int mti; +}; + +//! @} core_array + +//! @addtogroup core_cluster +//! @{ + +/** @example samples/cpp/kmeans.cpp +An example on K-means clustering +*/ + +/** @brief Finds centers of clusters and groups input samples around the clusters. + +The function kmeans implements a k-means algorithm that finds the centers of cluster_count clusters +and groups the input samples around the clusters. As an output, \f$\texttt{bestLabels}_i\f$ contains a +0-based cluster index for the sample stored in the \f$i^{th}\f$ row of the samples matrix. + +@note +- (Python) An example on K-means clustering can be found at + opencv_source_code/samples/python/kmeans.py +@param data Data for clustering. An array of N-Dimensional points with float coordinates is needed. +Examples of this array can be: +- Mat points(count, 2, CV_32F); +- Mat points(count, 1, CV_32FC2); +- Mat points(1, count, CV_32FC2); +- std::vector\ points(sampleCount); +@param K Number of clusters to split the set by. +@param bestLabels Input/output integer array that stores the cluster indices for every sample. +@param criteria The algorithm termination criteria, that is, the maximum number of iterations and/or +the desired accuracy. The accuracy is specified as criteria.epsilon. As soon as each of the cluster +centers moves by less than criteria.epsilon on some iteration, the algorithm stops. +@param attempts Flag to specify the number of times the algorithm is executed using different +initial labellings. The algorithm returns the labels that yield the best compactness (see the last +function parameter). +@param flags Flag that can take values of cv::KmeansFlags +@param centers Output matrix of the cluster centers, one row per each cluster center. +@return The function returns the compactness measure that is computed as +\f[\sum _i \| \texttt{samples} _i - \texttt{centers} _{ \texttt{labels} _i} \| ^2\f] +after every attempt. The best (minimum) value is chosen and the corresponding labels and the +compactness value are returned by the function. Basically, you can use only the core of the +function, set the number of attempts to 1, initialize labels each time using a custom algorithm, +pass them with the ( flags = #KMEANS_USE_INITIAL_LABELS ) flag, and then choose the best +(most-compact) clustering. +*/ +CV_EXPORTS_W double kmeans( InputArray data, int K, InputOutputArray bestLabels, + TermCriteria criteria, int attempts, + int flags, OutputArray centers = noArray() ); + +//! @} core_cluster + +//! @addtogroup core_basic +//! @{ + +/////////////////////////////// Formatted output of cv::Mat /////////////////////////// + +/** @todo document */ +class CV_EXPORTS Formatted +{ +public: + virtual const char* next() = 0; + virtual void reset() = 0; + virtual ~Formatted(); +}; + +/** @todo document */ +class CV_EXPORTS Formatter +{ +public: + enum { FMT_DEFAULT = 0, + FMT_MATLAB = 1, + FMT_CSV = 2, + FMT_PYTHON = 3, + FMT_NUMPY = 4, + FMT_C = 5 + }; + + virtual ~Formatter(); + + virtual Ptr format(const Mat& mtx) const = 0; + + virtual void set32fPrecision(int p = 8) = 0; + virtual void set64fPrecision(int p = 16) = 0; + virtual void setMultiline(bool ml = true) = 0; + + static Ptr get(int fmt = FMT_DEFAULT); + +}; + +static inline +String& operator << (String& out, Ptr fmtd) +{ + fmtd->reset(); + for(const char* str = fmtd->next(); str; str = fmtd->next()) + out += cv::String(str); + return out; +} + +static inline +String& operator << (String& out, const Mat& mtx) +{ + return out << Formatter::get()->format(mtx); +} + +//////////////////////////////////////// Algorithm //////////////////////////////////// + +class CV_EXPORTS Algorithm; + +template struct ParamType {}; + + +/** @brief This is a base class for all more or less complex algorithms in OpenCV + +especially for classes of algorithms, for which there can be multiple implementations. The examples +are stereo correspondence (for which there are algorithms like block matching, semi-global block +matching, graph-cut etc.), background subtraction (which can be done using mixture-of-gaussians +models, codebook-based algorithm etc.), optical flow (block matching, Lucas-Kanade, Horn-Schunck +etc.). + +Here is example of SimpleBlobDetector use in your application via Algorithm interface: +@snippet snippets/core_various.cpp Algorithm +*/ +class CV_EXPORTS_W Algorithm +{ +public: + Algorithm(); + virtual ~Algorithm(); + + /** @brief Clears the algorithm state + */ + CV_WRAP virtual void clear() {} + + /** @brief Stores algorithm parameters in a file storage + */ + virtual void write(FileStorage& fs) const { CV_UNUSED(fs); } + + /** @brief simplified API for language bindings + * @overload + */ + CV_WRAP void write(const Ptr& fs, const String& name = String()) const; + + /** @brief Reads algorithm parameters from a file storage + */ + CV_WRAP virtual void read(const FileNode& fn) { CV_UNUSED(fn); } + + /** @brief Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read + */ + CV_WRAP virtual bool empty() const { return false; } + + /** @brief Reads algorithm from the file node + + This is static template method of Algorithm. It's usage is following (in the case of SVM): + @code + cv::FileStorage fsRead("example.xml", FileStorage::READ); + Ptr svm = Algorithm::read(fsRead.root()); + @endcode + In order to make this method work, the derived class must overwrite Algorithm::read(const + FileNode& fn) and also have static create() method without parameters + (or with all the optional parameters) + */ + template static Ptr<_Tp> read(const FileNode& fn) + { + Ptr<_Tp> obj = _Tp::create(); + obj->read(fn); + return !obj->empty() ? obj : Ptr<_Tp>(); + } + + /** @brief Loads algorithm from the file + + @param filename Name of the file to read. + @param objname The optional name of the node to read (if empty, the first top-level node will be used) + + This is static template method of Algorithm. It's usage is following (in the case of SVM): + @code + Ptr svm = Algorithm::load("my_svm_model.xml"); + @endcode + In order to make this method work, the derived class must overwrite Algorithm::read(const + FileNode& fn). + */ + template static Ptr<_Tp> load(const String& filename, const String& objname=String()) + { + FileStorage fs(filename, FileStorage::READ); + CV_Assert(fs.isOpened()); + FileNode fn = objname.empty() ? fs.getFirstTopLevelNode() : fs[objname]; + if (fn.empty()) return Ptr<_Tp>(); + Ptr<_Tp> obj = _Tp::create(); + obj->read(fn); + return !obj->empty() ? obj : Ptr<_Tp>(); + } + + /** @brief Loads algorithm from a String + + @param strModel The string variable containing the model you want to load. + @param objname The optional name of the node to read (if empty, the first top-level node will be used) + + This is static template method of Algorithm. It's usage is following (in the case of SVM): + @code + Ptr svm = Algorithm::loadFromString(myStringModel); + @endcode + */ + template static Ptr<_Tp> loadFromString(const String& strModel, const String& objname=String()) + { + FileStorage fs(strModel, FileStorage::READ + FileStorage::MEMORY); + FileNode fn = objname.empty() ? fs.getFirstTopLevelNode() : fs[objname]; + Ptr<_Tp> obj = _Tp::create(); + obj->read(fn); + return !obj->empty() ? obj : Ptr<_Tp>(); + } + + /** Saves the algorithm to a file. + In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs). */ + CV_WRAP virtual void save(const String& filename) const; + + /** Returns the algorithm string identifier. + This string is used as top level xml/yml node tag when the object is saved to a file or string. */ + CV_WRAP virtual String getDefaultName() const; + +protected: + void writeFormat(FileStorage& fs) const; +}; + +struct Param { + enum { INT=0, BOOLEAN=1, REAL=2, STRING=3, MAT=4, MAT_VECTOR=5, ALGORITHM=6, FLOAT=7, + UNSIGNED_INT=8, UINT64=9, UCHAR=11, SCALAR=12 }; +}; + + + +template<> struct ParamType +{ + typedef bool const_param_type; + typedef bool member_type; + + enum { type = Param::BOOLEAN }; +}; + +template<> struct ParamType +{ + typedef int const_param_type; + typedef int member_type; + + enum { type = Param::INT }; +}; + +template<> struct ParamType +{ + typedef double const_param_type; + typedef double member_type; + + enum { type = Param::REAL }; +}; + +template<> struct ParamType +{ + typedef const String& const_param_type; + typedef String member_type; + + enum { type = Param::STRING }; +}; + +template<> struct ParamType +{ + typedef const Mat& const_param_type; + typedef Mat member_type; + + enum { type = Param::MAT }; +}; + +template<> struct ParamType > +{ + typedef const std::vector& const_param_type; + typedef std::vector member_type; + + enum { type = Param::MAT_VECTOR }; +}; + +template<> struct ParamType +{ + typedef const Ptr& const_param_type; + typedef Ptr member_type; + + enum { type = Param::ALGORITHM }; +}; + +template<> struct ParamType +{ + typedef float const_param_type; + typedef float member_type; + + enum { type = Param::FLOAT }; +}; + +template<> struct ParamType +{ + typedef unsigned const_param_type; + typedef unsigned member_type; + + enum { type = Param::UNSIGNED_INT }; +}; + +template<> struct ParamType +{ + typedef uint64 const_param_type; + typedef uint64 member_type; + + enum { type = Param::UINT64 }; +}; + +template<> struct ParamType +{ + typedef uchar const_param_type; + typedef uchar member_type; + + enum { type = Param::UCHAR }; +}; + +template<> struct ParamType +{ + typedef const Scalar& const_param_type; + typedef Scalar member_type; + + enum { type = Param::SCALAR }; +}; + +//! @} core_basic + +} //namespace cv + +#include "opencv2/core/operations.hpp" +#include "opencv2/core/cvstd.inl.hpp" +#include "opencv2/core/utility.hpp" +#include "opencv2/core/optim.hpp" +#include "opencv2/core/ovx.hpp" + +#endif /*OPENCV_CORE_HPP*/ diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/affine.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/affine.hpp new file mode 100644 index 0000000000000000000000000000000000000000..7e2ed307858323ffbb4fa95c7c1275b9932c0574 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/affine.hpp @@ -0,0 +1,678 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_AFFINE3_HPP +#define OPENCV_CORE_AFFINE3_HPP + +#ifdef __cplusplus + +#include + +namespace cv +{ + +//! @addtogroup core +//! @{ + + /** @brief Affine transform + * + * It represents a 4x4 homogeneous transformation matrix \f$T\f$ + * + * \f[T = + * \begin{bmatrix} + * R & t\\ + * 0 & 1\\ + * \end{bmatrix} + * \f] + * + * where \f$R\f$ is a 3x3 rotation matrix and \f$t\f$ is a 3x1 translation vector. + * + * You can specify \f$R\f$ either by a 3x3 rotation matrix or by a 3x1 rotation vector, + * which is converted to a 3x3 rotation matrix by the Rodrigues formula. + * + * To construct a matrix \f$T\f$ representing first rotation around the axis \f$r\f$ with rotation + * angle \f$|r|\f$ in radian (right hand rule) and then translation by the vector \f$t\f$, you can use + * + * @code + * cv::Vec3f r, t; + * cv::Affine3f T(r, t); + * @endcode + * + * If you already have the rotation matrix \f$R\f$, then you can use + * + * @code + * cv::Matx33f R; + * cv::Affine3f T(R, t); + * @endcode + * + * To extract the rotation matrix \f$R\f$ from \f$T\f$, use + * + * @code + * cv::Matx33f R = T.rotation(); + * @endcode + * + * To extract the translation vector \f$t\f$ from \f$T\f$, use + * + * @code + * cv::Vec3f t = T.translation(); + * @endcode + * + * To extract the rotation vector \f$r\f$ from \f$T\f$, use + * + * @code + * cv::Vec3f r = T.rvec(); + * @endcode + * + * Note that since the mapping from rotation vectors to rotation matrices + * is many to one. The returned rotation vector is not necessarily the one + * you used before to set the matrix. + * + * If you have two transformations \f$T = T_1 * T_2\f$, use + * + * @code + * cv::Affine3f T, T1, T2; + * T = T2.concatenate(T1); + * @endcode + * + * To get the inverse transform of \f$T\f$, use + * + * @code + * cv::Affine3f T, T_inv; + * T_inv = T.inv(); + * @endcode + * + */ + template + class Affine3 + { + public: + typedef T float_type; + typedef Matx Mat3; + typedef Matx Mat4; + typedef Vec Vec3; + + //! Default constructor. It represents a 4x4 identity matrix. + Affine3(); + + //! Augmented affine matrix + Affine3(const Mat4& affine); + + /** + * The resulting 4x4 matrix is + * + * \f[ + * \begin{bmatrix} + * R & t\\ + * 0 & 1\\ + * \end{bmatrix} + * \f] + * + * @param R 3x3 rotation matrix. + * @param t 3x1 translation vector. + */ + Affine3(const Mat3& R, const Vec3& t = Vec3::all(0)); + + /** + * Rodrigues vector. + * + * The last row of the current matrix is set to [0,0,0,1]. + * + * @param rvec 3x1 rotation vector. Its direction indicates the rotation axis and its length + * indicates the rotation angle in radian (using right hand rule). + * @param t 3x1 translation vector. + */ + Affine3(const Vec3& rvec, const Vec3& t = Vec3::all(0)); + + /** + * Combines all constructors above. Supports 4x4, 3x4, 3x3, 1x3, 3x1 sizes of data matrix. + * + * The last row of the current matrix is set to [0,0,0,1] when data is not 4x4. + * + * @param data 1-channel matrix. + * when it is 4x4, it is copied to the current matrix and t is not used. + * When it is 3x4, it is copied to the upper part 3x4 of the current matrix and t is not used. + * When it is 3x3, it is copied to the upper left 3x3 part of the current matrix. + * When it is 3x1 or 1x3, it is treated as a rotation vector and the Rodrigues formula is used + * to compute a 3x3 rotation matrix. + * @param t 3x1 translation vector. It is used only when data is neither 4x4 nor 3x4. + */ + explicit Affine3(const Mat& data, const Vec3& t = Vec3::all(0)); + + //! From 16-element array + explicit Affine3(const float_type* vals); + + //! Create an 4x4 identity transform + static Affine3 Identity(); + + /** + * Rotation matrix. + * + * Copy the rotation matrix to the upper left 3x3 part of the current matrix. + * The remaining elements of the current matrix are not changed. + * + * @param R 3x3 rotation matrix. + * + */ + void rotation(const Mat3& R); + + /** + * Rodrigues vector. + * + * It sets the upper left 3x3 part of the matrix. The remaining part is unaffected. + * + * @param rvec 3x1 rotation vector. The direction indicates the rotation axis and + * its length indicates the rotation angle in radian (using the right thumb convention). + */ + void rotation(const Vec3& rvec); + + /** + * Combines rotation methods above. Supports 3x3, 1x3, 3x1 sizes of data matrix. + * + * It sets the upper left 3x3 part of the matrix. The remaining part is unaffected. + * + * @param data 1-channel matrix. + * When it is a 3x3 matrix, it sets the upper left 3x3 part of the current matrix. + * When it is a 1x3 or 3x1 matrix, it is used as a rotation vector. The Rodrigues formula + * is used to compute the rotation matrix and sets the upper left 3x3 part of the current matrix. + */ + void rotation(const Mat& data); + + /** + * Copy the 3x3 matrix L to the upper left part of the current matrix + * + * It sets the upper left 3x3 part of the matrix. The remaining part is unaffected. + * + * @param L 3x3 matrix. + */ + void linear(const Mat3& L); + + /** + * Copy t to the first three elements of the last column of the current matrix + * + * It sets the upper right 3x1 part of the matrix. The remaining part is unaffected. + * + * @param t 3x1 translation vector. + */ + void translation(const Vec3& t); + + //! @return the upper left 3x3 part + Mat3 rotation() const; + + //! @return the upper left 3x3 part + Mat3 linear() const; + + //! @return the upper right 3x1 part + Vec3 translation() const; + + //! Rodrigues vector. + //! @return a vector representing the upper left 3x3 rotation matrix of the current matrix. + //! @warning Since the mapping between rotation vectors and rotation matrices is many to one, + //! this function returns only one rotation vector that represents the current rotation matrix, + //! which is not necessarily the same one set by `rotation(const Vec3& rvec)`. + Vec3 rvec() const; + + //! @return the inverse of the current matrix. + Affine3 inv(int method = cv::DECOMP_SVD) const; + + //! a.rotate(R) is equivalent to Affine(R, 0) * a; + Affine3 rotate(const Mat3& R) const; + + //! a.rotate(rvec) is equivalent to Affine(rvec, 0) * a; + Affine3 rotate(const Vec3& rvec) const; + + //! a.translate(t) is equivalent to Affine(E, t) * a, where E is an identity matrix + Affine3 translate(const Vec3& t) const; + + //! a.concatenate(affine) is equivalent to affine * a; + Affine3 concatenate(const Affine3& affine) const; + + template operator Affine3() const; + + template Affine3 cast() const; + + Mat4 matrix; + +#if defined EIGEN_WORLD_VERSION && defined EIGEN_GEOMETRY_MODULE_H + Affine3(const Eigen::Transform& affine); + Affine3(const Eigen::Transform& affine); + operator Eigen::Transform() const; + operator Eigen::Transform() const; +#endif + }; + + template static + Affine3 operator*(const Affine3& affine1, const Affine3& affine2); + + //! V is a 3-element vector with member fields x, y and z + template static + V operator*(const Affine3& affine, const V& vector); + + typedef Affine3 Affine3f; + typedef Affine3 Affine3d; + + static Vec3f operator*(const Affine3f& affine, const Vec3f& vector); + static Vec3d operator*(const Affine3d& affine, const Vec3d& vector); + + template class DataType< Affine3<_Tp> > + { + public: + typedef Affine3<_Tp> value_type; + typedef Affine3::work_type> work_type; + typedef _Tp channel_type; + + enum { generic_type = 0, + channels = 16, + fmt = traits::SafeFmt::fmt + ((channels - 1) << 8) +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + ,depth = DataType::depth + ,type = CV_MAKETYPE(depth, channels) +#endif + }; + + typedef Vec vec_type; + }; + + namespace traits { + template + struct Depth< Affine3<_Tp> > { enum { value = Depth<_Tp>::value }; }; + template + struct Type< Affine3<_Tp> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, 16) }; }; + } // namespace + +//! @} core + +} + +//! @cond IGNORED + +/////////////////////////////////////////////////////////////////////////////////// +// Implementation + +template inline +cv::Affine3::Affine3() + : matrix(Mat4::eye()) +{} + +template inline +cv::Affine3::Affine3(const Mat4& affine) + : matrix(affine) +{} + +template inline +cv::Affine3::Affine3(const Mat3& R, const Vec3& t) +{ + rotation(R); + translation(t); + matrix.val[12] = matrix.val[13] = matrix.val[14] = 0; + matrix.val[15] = 1; +} + +template inline +cv::Affine3::Affine3(const Vec3& _rvec, const Vec3& t) +{ + rotation(_rvec); + translation(t); + matrix.val[12] = matrix.val[13] = matrix.val[14] = 0; + matrix.val[15] = 1; +} + +template inline +cv::Affine3::Affine3(const cv::Mat& data, const Vec3& t) +{ + CV_Assert(data.type() == cv::traits::Type::value); + CV_Assert(data.channels() == 1); + + if (data.cols == 4 && data.rows == 4) + { + data.copyTo(matrix); + return; + } + else if (data.cols == 4 && data.rows == 3) + { + rotation(data(Rect(0, 0, 3, 3))); + translation(data(Rect(3, 0, 1, 3))); + } + else + { + rotation(data); + translation(t); + } + + matrix.val[12] = matrix.val[13] = matrix.val[14] = 0; + matrix.val[15] = 1; +} + +template inline +cv::Affine3::Affine3(const float_type* vals) : matrix(vals) +{} + +template inline +cv::Affine3 cv::Affine3::Identity() +{ + return Affine3(cv::Affine3::Mat4::eye()); +} + +template inline +void cv::Affine3::rotation(const Mat3& R) +{ + linear(R); +} + +template inline +void cv::Affine3::rotation(const Vec3& _rvec) +{ + double theta = norm(_rvec); + + if (theta < DBL_EPSILON) + rotation(Mat3::eye()); + else + { + double c = std::cos(theta); + double s = std::sin(theta); + double c1 = 1. - c; + double itheta = (theta != 0) ? 1./theta : 0.; + + Point3_ r = _rvec*itheta; + + Mat3 rrt( r.x*r.x, r.x*r.y, r.x*r.z, r.x*r.y, r.y*r.y, r.y*r.z, r.x*r.z, r.y*r.z, r.z*r.z ); + Mat3 r_x( 0, -r.z, r.y, r.z, 0, -r.x, -r.y, r.x, 0 ); + + // R = cos(theta)*I + (1 - cos(theta))*r*rT + sin(theta)*[r_x] + // where [r_x] is [0 -rz ry; rz 0 -rx; -ry rx 0] + Mat3 R = c*Mat3::eye() + c1*rrt + s*r_x; + + rotation(R); + } +} + +//Combines rotation methods above. Supports 3x3, 1x3, 3x1 sizes of data matrix; +template inline +void cv::Affine3::rotation(const cv::Mat& data) +{ + CV_Assert(data.type() == cv::traits::Type::value); + CV_Assert(data.channels() == 1); + + if (data.cols == 3 && data.rows == 3) + { + Mat3 R; + data.copyTo(R); + rotation(R); + } + else if ((data.cols == 3 && data.rows == 1) || (data.cols == 1 && data.rows == 3)) + { + Vec3 _rvec; + data.reshape(1, 3).copyTo(_rvec); + rotation(_rvec); + } + else + CV_Error(Error::StsError, "Input matrix can only be 3x3, 1x3 or 3x1"); +} + +template inline +void cv::Affine3::linear(const Mat3& L) +{ + matrix.val[0] = L.val[0]; matrix.val[1] = L.val[1]; matrix.val[ 2] = L.val[2]; + matrix.val[4] = L.val[3]; matrix.val[5] = L.val[4]; matrix.val[ 6] = L.val[5]; + matrix.val[8] = L.val[6]; matrix.val[9] = L.val[7]; matrix.val[10] = L.val[8]; +} + +template inline +void cv::Affine3::translation(const Vec3& t) +{ + matrix.val[3] = t[0]; matrix.val[7] = t[1]; matrix.val[11] = t[2]; +} + +template inline +typename cv::Affine3::Mat3 cv::Affine3::rotation() const +{ + return linear(); +} + +template inline +typename cv::Affine3::Mat3 cv::Affine3::linear() const +{ + typename cv::Affine3::Mat3 R; + R.val[0] = matrix.val[0]; R.val[1] = matrix.val[1]; R.val[2] = matrix.val[ 2]; + R.val[3] = matrix.val[4]; R.val[4] = matrix.val[5]; R.val[5] = matrix.val[ 6]; + R.val[6] = matrix.val[8]; R.val[7] = matrix.val[9]; R.val[8] = matrix.val[10]; + return R; +} + +template inline +typename cv::Affine3::Vec3 cv::Affine3::translation() const +{ + return Vec3(matrix.val[3], matrix.val[7], matrix.val[11]); +} + +template inline +typename cv::Affine3::Vec3 cv::Affine3::rvec() const +{ + cv::Vec3d w; + cv::Matx33d u, vt, R = rotation(); + cv::SVD::compute(R, w, u, vt, cv::SVD::FULL_UV + cv::SVD::MODIFY_A); + R = u * vt; + + double rx = R.val[7] - R.val[5]; + double ry = R.val[2] - R.val[6]; + double rz = R.val[3] - R.val[1]; + + double s = std::sqrt((rx*rx + ry*ry + rz*rz)*0.25); + double c = (R.val[0] + R.val[4] + R.val[8] - 1) * 0.5; + c = c > 1.0 ? 1.0 : c < -1.0 ? -1.0 : c; + double theta = acos(c); + + if( s < 1e-5 ) + { + if( c > 0 ) + rx = ry = rz = 0; + else + { + double t; + t = (R.val[0] + 1) * 0.5; + rx = std::sqrt(std::max(t, 0.0)); + t = (R.val[4] + 1) * 0.5; + ry = std::sqrt(std::max(t, 0.0)) * (R.val[1] < 0 ? -1.0 : 1.0); + t = (R.val[8] + 1) * 0.5; + rz = std::sqrt(std::max(t, 0.0)) * (R.val[2] < 0 ? -1.0 : 1.0); + + if( fabs(rx) < fabs(ry) && fabs(rx) < fabs(rz) && (R.val[5] > 0) != (ry*rz > 0) ) + rz = -rz; + theta /= std::sqrt(rx*rx + ry*ry + rz*rz); + rx *= theta; + ry *= theta; + rz *= theta; + } + } + else + { + double vth = 1/(2*s); + vth *= theta; + rx *= vth; ry *= vth; rz *= vth; + } + + return cv::Vec3d(rx, ry, rz); +} + +template inline +cv::Affine3 cv::Affine3::inv(int method) const +{ + return matrix.inv(method); +} + +template inline +cv::Affine3 cv::Affine3::rotate(const Mat3& R) const +{ + Mat3 Lc = linear(); + Vec3 tc = translation(); + Mat4 result; + result.val[12] = result.val[13] = result.val[14] = 0; + result.val[15] = 1; + + for(int j = 0; j < 3; ++j) + { + for(int i = 0; i < 3; ++i) + { + float_type value = 0; + for(int k = 0; k < 3; ++k) + value += R(j, k) * Lc(k, i); + result(j, i) = value; + } + + result(j, 3) = R.row(j).dot(tc.t()); + } + return result; +} + +template inline +cv::Affine3 cv::Affine3::rotate(const Vec3& _rvec) const +{ + return rotate(Affine3f(_rvec).rotation()); +} + +template inline +cv::Affine3 cv::Affine3::translate(const Vec3& t) const +{ + Mat4 m = matrix; + m.val[ 3] += t[0]; + m.val[ 7] += t[1]; + m.val[11] += t[2]; + return m; +} + +template inline +cv::Affine3 cv::Affine3::concatenate(const Affine3& affine) const +{ + return (*this).rotate(affine.rotation()).translate(affine.translation()); +} + +template template inline +cv::Affine3::operator Affine3() const +{ + return Affine3(matrix); +} + +template template inline +cv::Affine3 cv::Affine3::cast() const +{ + return Affine3(matrix); +} + +template inline +cv::Affine3 cv::operator*(const cv::Affine3& affine1, const cv::Affine3& affine2) +{ + return affine2.concatenate(affine1); +} + +template inline +V cv::operator*(const cv::Affine3& affine, const V& v) +{ + const typename Affine3::Mat4& m = affine.matrix; + + V r; + r.x = m.val[0] * v.x + m.val[1] * v.y + m.val[ 2] * v.z + m.val[ 3]; + r.y = m.val[4] * v.x + m.val[5] * v.y + m.val[ 6] * v.z + m.val[ 7]; + r.z = m.val[8] * v.x + m.val[9] * v.y + m.val[10] * v.z + m.val[11]; + return r; +} + +static inline +cv::Vec3f cv::operator*(const cv::Affine3f& affine, const cv::Vec3f& v) +{ + const cv::Matx44f& m = affine.matrix; + cv::Vec3f r; + r.val[0] = m.val[0] * v[0] + m.val[1] * v[1] + m.val[ 2] * v[2] + m.val[ 3]; + r.val[1] = m.val[4] * v[0] + m.val[5] * v[1] + m.val[ 6] * v[2] + m.val[ 7]; + r.val[2] = m.val[8] * v[0] + m.val[9] * v[1] + m.val[10] * v[2] + m.val[11]; + return r; +} + +static inline +cv::Vec3d cv::operator*(const cv::Affine3d& affine, const cv::Vec3d& v) +{ + const cv::Matx44d& m = affine.matrix; + cv::Vec3d r; + r.val[0] = m.val[0] * v[0] + m.val[1] * v[1] + m.val[ 2] * v[2] + m.val[ 3]; + r.val[1] = m.val[4] * v[0] + m.val[5] * v[1] + m.val[ 6] * v[2] + m.val[ 7]; + r.val[2] = m.val[8] * v[0] + m.val[9] * v[1] + m.val[10] * v[2] + m.val[11]; + return r; +} + + + +#if defined EIGEN_WORLD_VERSION && defined EIGEN_GEOMETRY_MODULE_H + +template inline +cv::Affine3::Affine3(const Eigen::Transform& affine) +{ + cv::Mat(4, 4, cv::traits::Type::value, affine.matrix().data()).copyTo(matrix); +} + +template inline +cv::Affine3::Affine3(const Eigen::Transform& affine) +{ + Eigen::Transform a = affine; + cv::Mat(4, 4, cv::traits::Type::value, a.matrix().data()).copyTo(matrix); +} + +template inline +cv::Affine3::operator Eigen::Transform() const +{ + Eigen::Transform r; + cv::Mat hdr(4, 4, cv::traits::Type::value, r.matrix().data()); + cv::Mat(matrix, false).copyTo(hdr); + return r; +} + +template inline +cv::Affine3::operator Eigen::Transform() const +{ + return this->operator Eigen::Transform(); +} + +#endif /* defined EIGEN_WORLD_VERSION && defined EIGEN_GEOMETRY_MODULE_H */ + +//! @endcond + +#endif /* __cplusplus */ + +#endif /* OPENCV_CORE_AFFINE3_HPP */ diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/base.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/base.hpp new file mode 100644 index 0000000000000000000000000000000000000000..31cd7a8202acfb0e001ee0346caff7233f459e23 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/base.hpp @@ -0,0 +1,707 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2014, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_BASE_HPP +#define OPENCV_CORE_BASE_HPP + +#ifndef __cplusplus +# error base.hpp header must be compiled as C++ +#endif + +#include "opencv2/opencv_modules.hpp" + +#include +#include + +#include "opencv2/core/cvdef.h" +#include "opencv2/core/cvstd.hpp" + +namespace cv +{ + +//! @addtogroup core_utils +//! @{ + +namespace Error { +//! error codes +enum Code { + StsOk= 0, //!< everything is ok + StsBackTrace= -1, //!< pseudo error for back trace + StsError= -2, //!< unknown /unspecified error + StsInternal= -3, //!< internal error (bad state) + StsNoMem= -4, //!< insufficient memory + StsBadArg= -5, //!< function arg/param is bad + StsBadFunc= -6, //!< unsupported function + StsNoConv= -7, //!< iteration didn't converge + StsAutoTrace= -8, //!< tracing + HeaderIsNull= -9, //!< image header is NULL + BadImageSize= -10, //!< image size is invalid + BadOffset= -11, //!< offset is invalid + BadDataPtr= -12, //!< + BadStep= -13, //!< image step is wrong, this may happen for a non-continuous matrix. + BadModelOrChSeq= -14, //!< + BadNumChannels= -15, //!< bad number of channels, for example, some functions accept only single channel matrices. + BadNumChannel1U= -16, //!< + BadDepth= -17, //!< input image depth is not supported by the function + BadAlphaChannel= -18, //!< + BadOrder= -19, //!< number of dimensions is out of range + BadOrigin= -20, //!< incorrect input origin + BadAlign= -21, //!< incorrect input align + BadCallBack= -22, //!< + BadTileSize= -23, //!< + BadCOI= -24, //!< input COI is not supported + BadROISize= -25, //!< incorrect input roi + MaskIsTiled= -26, //!< + StsNullPtr= -27, //!< null pointer + StsVecLengthErr= -28, //!< incorrect vector length + StsFilterStructContentErr= -29, //!< incorrect filter structure content + StsKernelStructContentErr= -30, //!< incorrect transform kernel content + StsFilterOffsetErr= -31, //!< incorrect filter offset value + StsBadSize= -201, //!< the input/output structure size is incorrect + StsDivByZero= -202, //!< division by zero + StsInplaceNotSupported= -203, //!< in-place operation is not supported + StsObjectNotFound= -204, //!< request can't be completed + StsUnmatchedFormats= -205, //!< formats of input/output arrays differ + StsBadFlag= -206, //!< flag is wrong or not supported + StsBadPoint= -207, //!< bad CvPoint + StsBadMask= -208, //!< bad format of mask (neither 8uC1 nor 8sC1) + StsUnmatchedSizes= -209, //!< sizes of input/output structures do not match + StsUnsupportedFormat= -210, //!< the data format/type is not supported by the function + StsOutOfRange= -211, //!< some of parameters are out of range + StsParseError= -212, //!< invalid syntax/structure of the parsed file + StsNotImplemented= -213, //!< the requested function/feature is not implemented + StsBadMemBlock= -214, //!< an allocated block has been corrupted + StsAssert= -215, //!< assertion failed + GpuNotSupported= -216, //!< no CUDA support + GpuApiCallError= -217, //!< GPU API call error + OpenGlNotSupported= -218, //!< no OpenGL support + OpenGlApiCallError= -219, //!< OpenGL API call error + OpenCLApiCallError= -220, //!< OpenCL API call error + OpenCLDoubleNotSupported= -221, + OpenCLInitError= -222, //!< OpenCL initialization error + OpenCLNoAMDBlasFft= -223 +}; +} //Error + +//! @} core_utils + +//! @addtogroup core_array +//! @{ + +//! matrix decomposition types +enum DecompTypes { + /** Gaussian elimination with the optimal pivot element chosen. */ + DECOMP_LU = 0, + /** singular value decomposition (SVD) method; the system can be over-defined and/or the matrix + src1 can be singular */ + DECOMP_SVD = 1, + /** eigenvalue decomposition; the matrix src1 must be symmetrical */ + DECOMP_EIG = 2, + /** Cholesky \f$LL^T\f$ factorization; the matrix src1 must be symmetrical and positively + defined */ + DECOMP_CHOLESKY = 3, + /** QR factorization; the system can be over-defined and/or the matrix src1 can be singular */ + DECOMP_QR = 4, + /** while all the previous flags are mutually exclusive, this flag can be used together with + any of the previous; it means that the normal equations + \f$\texttt{src1}^T\cdot\texttt{src1}\cdot\texttt{dst}=\texttt{src1}^T\texttt{src2}\f$ are + solved instead of the original system + \f$\texttt{src1}\cdot\texttt{dst}=\texttt{src2}\f$ */ + DECOMP_NORMAL = 16 +}; + +/** norm types + +src1 and src2 denote input arrays. +*/ + +enum NormTypes { + /** + \f[ + norm = \forkthree + {\|\texttt{src1}\|_{L_{\infty}} = \max _I | \texttt{src1} (I)|}{if \(\texttt{normType} = \texttt{NORM_INF}\) } + {\|\texttt{src1}-\texttt{src2}\|_{L_{\infty}} = \max _I | \texttt{src1} (I) - \texttt{src2} (I)|}{if \(\texttt{normType} = \texttt{NORM_INF}\) } + {\frac{\|\texttt{src1}-\texttt{src2}\|_{L_{\infty}} }{\|\texttt{src2}\|_{L_{\infty}} }}{if \(\texttt{normType} = \texttt{NORM_RELATIVE | NORM_INF}\) } + \f] + */ + NORM_INF = 1, + /** + \f[ + norm = \forkthree + {\| \texttt{src1} \| _{L_1} = \sum _I | \texttt{src1} (I)|}{if \(\texttt{normType} = \texttt{NORM_L1}\)} + { \| \texttt{src1} - \texttt{src2} \| _{L_1} = \sum _I | \texttt{src1} (I) - \texttt{src2} (I)|}{if \(\texttt{normType} = \texttt{NORM_L1}\) } + { \frac{\|\texttt{src1}-\texttt{src2}\|_{L_1} }{\|\texttt{src2}\|_{L_1}} }{if \(\texttt{normType} = \texttt{NORM_RELATIVE | NORM_L1}\) } + \f]*/ + NORM_L1 = 2, + /** + \f[ + norm = \forkthree + { \| \texttt{src1} \| _{L_2} = \sqrt{\sum_I \texttt{src1}(I)^2} }{if \(\texttt{normType} = \texttt{NORM_L2}\) } + { \| \texttt{src1} - \texttt{src2} \| _{L_2} = \sqrt{\sum_I (\texttt{src1}(I) - \texttt{src2}(I))^2} }{if \(\texttt{normType} = \texttt{NORM_L2}\) } + { \frac{\|\texttt{src1}-\texttt{src2}\|_{L_2} }{\|\texttt{src2}\|_{L_2}} }{if \(\texttt{normType} = \texttt{NORM_RELATIVE | NORM_L2}\) } + \f] + */ + NORM_L2 = 4, + /** + \f[ + norm = \forkthree + { \| \texttt{src1} \| _{L_2} ^{2} = \sum_I \texttt{src1}(I)^2} {if \(\texttt{normType} = \texttt{NORM_L2SQR}\)} + { \| \texttt{src1} - \texttt{src2} \| _{L_2} ^{2} = \sum_I (\texttt{src1}(I) - \texttt{src2}(I))^2 }{if \(\texttt{normType} = \texttt{NORM_L2SQR}\) } + { \left(\frac{\|\texttt{src1}-\texttt{src2}\|_{L_2} }{\|\texttt{src2}\|_{L_2}}\right)^2 }{if \(\texttt{normType} = \texttt{NORM_RELATIVE | NORM_L2}\) } + \f] + */ + NORM_L2SQR = 5, + /** + In the case of one input array, calculates the Hamming distance of the array from zero, + In the case of two input arrays, calculates the Hamming distance between the arrays. + */ + NORM_HAMMING = 6, + /** + Similar to NORM_HAMMING, but in the calculation, each two bits of the input sequence will + be added and treated as a single bit to be used in the same calculation as NORM_HAMMING. + */ + NORM_HAMMING2 = 7, + NORM_TYPE_MASK = 7, //!< bit-mask which can be used to separate norm type from norm flags + NORM_RELATIVE = 8, //!< flag + NORM_MINMAX = 32 //!< flag + }; + +//! comparison types +enum CmpTypes { CMP_EQ = 0, //!< src1 is equal to src2. + CMP_GT = 1, //!< src1 is greater than src2. + CMP_GE = 2, //!< src1 is greater than or equal to src2. + CMP_LT = 3, //!< src1 is less than src2. + CMP_LE = 4, //!< src1 is less than or equal to src2. + CMP_NE = 5 //!< src1 is unequal to src2. + }; + +//! generalized matrix multiplication flags +enum GemmFlags { GEMM_1_T = 1, //!< transposes src1 + GEMM_2_T = 2, //!< transposes src2 + GEMM_3_T = 4 //!< transposes src3 + }; + +enum DftFlags { + /** performs an inverse 1D or 2D transform instead of the default forward + transform. */ + DFT_INVERSE = 1, + /** scales the result: divide it by the number of array elements. Normally, it is + combined with DFT_INVERSE. */ + DFT_SCALE = 2, + /** performs a forward or inverse transform of every individual row of the input + matrix; this flag enables you to transform multiple vectors simultaneously and can be used to + decrease the overhead (which is sometimes several times larger than the processing itself) to + perform 3D and higher-dimensional transformations and so forth.*/ + DFT_ROWS = 4, + /** performs a forward transformation of 1D or 2D real array; the result, + though being a complex array, has complex-conjugate symmetry (*CCS*, see the function + description below for details), and such an array can be packed into a real array of the same + size as input, which is the fastest option and which is what the function does by default; + however, you may wish to get a full complex array (for simpler spectrum analysis, and so on) - + pass the flag to enable the function to produce a full-size complex output array. */ + DFT_COMPLEX_OUTPUT = 16, + /** performs an inverse transformation of a 1D or 2D complex array; the + result is normally a complex array of the same size, however, if the input array has + conjugate-complex symmetry (for example, it is a result of forward transformation with + DFT_COMPLEX_OUTPUT flag), the output is a real array; while the function itself does not + check whether the input is symmetrical or not, you can pass the flag and then the function + will assume the symmetry and produce the real output array (note that when the input is packed + into a real array and inverse transformation is executed, the function treats the input as a + packed complex-conjugate symmetrical array, and the output will also be a real array). */ + DFT_REAL_OUTPUT = 32, + /** specifies that input is complex input. If this flag is set, the input must have 2 channels. + On the other hand, for backwards compatibility reason, if input has 2 channels, input is + already considered complex. */ + DFT_COMPLEX_INPUT = 64, + /** performs an inverse 1D or 2D transform instead of the default forward transform. */ + DCT_INVERSE = DFT_INVERSE, + /** performs a forward or inverse transform of every individual row of the input + matrix. This flag enables you to transform multiple vectors simultaneously and can be used to + decrease the overhead (which is sometimes several times larger than the processing itself) to + perform 3D and higher-dimensional transforms and so forth.*/ + DCT_ROWS = DFT_ROWS +}; + +//! Various border types, image boundaries are denoted with `|` +//! @see borderInterpolate, copyMakeBorder +enum BorderTypes { + BORDER_CONSTANT = 0, //!< `iiiiii|abcdefgh|iiiiiii` with some specified `i` + BORDER_REPLICATE = 1, //!< `aaaaaa|abcdefgh|hhhhhhh` + BORDER_REFLECT = 2, //!< `fedcba|abcdefgh|hgfedcb` + BORDER_WRAP = 3, //!< `cdefgh|abcdefgh|abcdefg` + BORDER_REFLECT_101 = 4, //!< `gfedcb|abcdefgh|gfedcba` + BORDER_TRANSPARENT = 5, //!< `uvwxyz|abcdefgh|ijklmno` + + BORDER_REFLECT101 = BORDER_REFLECT_101, //!< same as BORDER_REFLECT_101 + BORDER_DEFAULT = BORDER_REFLECT_101, //!< same as BORDER_REFLECT_101 + BORDER_ISOLATED = 16 //!< do not look outside of ROI +}; + +//! @} core_array + +//! @addtogroup core_utils +//! @{ + +/*! @brief Signals an error and raises the exception. + +By default the function prints information about the error to stderr, +then it either stops if setBreakOnError() had been called before or raises the exception. +It is possible to alternate error processing by using redirectError(). +@param _code - error code (Error::Code) +@param _err - error description +@param _func - function name. Available only when the compiler supports getting it +@param _file - source file name where the error has occurred +@param _line - line number in the source file where the error has occurred +@see CV_Error, CV_Error_, CV_Assert, CV_DbgAssert + */ +CV_EXPORTS void error(int _code, const String& _err, const char* _func, const char* _file, int _line); + +#ifdef __GNUC__ +# if defined __clang__ || defined __APPLE__ +# pragma GCC diagnostic push +# pragma GCC diagnostic ignored "-Winvalid-noreturn" +# endif +#endif + +/** same as cv::error, but does not return */ +CV_INLINE CV_NORETURN void errorNoReturn(int _code, const String& _err, const char* _func, const char* _file, int _line) +{ + error(_code, _err, _func, _file, _line); +#ifdef __GNUC__ +# if !defined __clang__ && !defined __APPLE__ + // this suppresses this warning: "noreturn" function does return [enabled by default] + __builtin_trap(); + // or use infinite loop: for (;;) {} +# endif +#endif +} +#ifdef __GNUC__ +# if defined __clang__ || defined __APPLE__ +# pragma GCC diagnostic pop +# endif +#endif + +#ifdef CV_STATIC_ANALYSIS + +// In practice, some macro are not processed correctly (noreturn is not detected). +// We need to use simplified definition for them. +#define CV_Error(...) do { abort(); } while (0) +#define CV_Error_( code, args ) do { cv::format args; abort(); } while (0) +#define CV_Assert( expr ) do { if (!(expr)) abort(); } while (0) +#define CV_ErrorNoReturn CV_Error +#define CV_ErrorNoReturn_ CV_Error_ + +#else // CV_STATIC_ANALYSIS + +/** @brief Call the error handler. + +Currently, the error handler prints the error code and the error message to the standard +error stream `stderr`. In the Debug configuration, it then provokes memory access violation, so that +the execution stack and all the parameters can be analyzed by the debugger. In the Release +configuration, the exception is thrown. + +@param code one of Error::Code +@param msg error message +*/ +#define CV_Error( code, msg ) cv::error( code, msg, CV_Func, __FILE__, __LINE__ ) + +/** @brief Call the error handler. + +This macro can be used to construct an error message on-fly to include some dynamic information, +for example: +@code + // note the extra parentheses around the formatted text message + CV_Error_(Error::StsOutOfRange, + ("the value at (%d, %d)=%g is out of range", badPt.x, badPt.y, badValue)); +@endcode +@param code one of Error::Code +@param args printf-like formatted error message in parentheses +*/ +#define CV_Error_( code, args ) cv::error( code, cv::format args, CV_Func, __FILE__, __LINE__ ) + +/** @brief Checks a condition at runtime and throws exception if it fails + +The macros CV_Assert (and CV_DbgAssert(expr)) evaluate the specified expression. If it is 0, the macros +raise an error (see cv::error). The macro CV_Assert checks the condition in both Debug and Release +configurations while CV_DbgAssert is only retained in the Debug configuration. +*/ +#define CV_Assert( expr ) do { if(!!(expr)) ; else cv::error( cv::Error::StsAssert, #expr, CV_Func, __FILE__, __LINE__ ); } while(0) + +//! @cond IGNORED +#define CV__ErrorNoReturn( code, msg ) cv::errorNoReturn( code, msg, CV_Func, __FILE__, __LINE__ ) +#define CV__ErrorNoReturn_( code, args ) cv::errorNoReturn( code, cv::format args, CV_Func, __FILE__, __LINE__ ) +#ifdef __OPENCV_BUILD +#undef CV_Error +#define CV_Error CV__ErrorNoReturn +#undef CV_Error_ +#define CV_Error_ CV__ErrorNoReturn_ +#undef CV_Assert +#define CV_Assert( expr ) do { if(!!(expr)) ; else cv::errorNoReturn( cv::Error::StsAssert, #expr, CV_Func, __FILE__, __LINE__ ); } while(0) +#else +// backward compatibility +#define CV_ErrorNoReturn CV__ErrorNoReturn +#define CV_ErrorNoReturn_ CV__ErrorNoReturn_ +#endif +//! @endcond + +#endif // CV_STATIC_ANALYSIS + +//! @cond IGNORED + +#if defined OPENCV_FORCE_MULTIARG_ASSERT_CHECK && defined CV_STATIC_ANALYSIS +#warning "OPENCV_FORCE_MULTIARG_ASSERT_CHECK can't be used with CV_STATIC_ANALYSIS" +#undef OPENCV_FORCE_MULTIARG_ASSERT_CHECK +#endif + +#ifdef OPENCV_FORCE_MULTIARG_ASSERT_CHECK +#define CV_Assert_1( expr ) do { if(!!(expr)) ; else cv::error( cv::Error::StsAssert, #expr, CV_Func, __FILE__, __LINE__ ); } while(0) +#else +#define CV_Assert_1 CV_Assert +#endif +#define CV_Assert_2( expr1, expr2 ) CV_Assert_1(expr1); CV_Assert_1(expr2) +#define CV_Assert_3( expr1, expr2, expr3 ) CV_Assert_2(expr1, expr2); CV_Assert_1(expr3) +#define CV_Assert_4( expr1, expr2, expr3, expr4 ) CV_Assert_3(expr1, expr2, expr3); CV_Assert_1(expr4) +#define CV_Assert_5( expr1, expr2, expr3, expr4, expr5 ) CV_Assert_4(expr1, expr2, expr3, expr4); CV_Assert_1(expr5) +#define CV_Assert_6( expr1, expr2, expr3, expr4, expr5, expr6 ) CV_Assert_5(expr1, expr2, expr3, expr4, expr5); CV_Assert_1(expr6) +#define CV_Assert_7( expr1, expr2, expr3, expr4, expr5, expr6, expr7 ) CV_Assert_6(expr1, expr2, expr3, expr4, expr5, expr6 ); CV_Assert_1(expr7) +#define CV_Assert_8( expr1, expr2, expr3, expr4, expr5, expr6, expr7, expr8 ) CV_Assert_7(expr1, expr2, expr3, expr4, expr5, expr6, expr7 ); CV_Assert_1(expr8) +#define CV_Assert_9( expr1, expr2, expr3, expr4, expr5, expr6, expr7, expr8, expr9 ) CV_Assert_8(expr1, expr2, expr3, expr4, expr5, expr6, expr7, expr8 ); CV_Assert_1(expr9) +#define CV_Assert_10( expr1, expr2, expr3, expr4, expr5, expr6, expr7, expr8, expr9, expr10 ) CV_Assert_9(expr1, expr2, expr3, expr4, expr5, expr6, expr7, expr8, expr9 ); CV_Assert_1(expr10) + +#define CV_Assert_N(...) do { __CV_CAT(CV_Assert_, __CV_VA_NUM_ARGS(__VA_ARGS__)) (__VA_ARGS__); } while(0) + +#ifdef OPENCV_FORCE_MULTIARG_ASSERT_CHECK +#undef CV_Assert +#define CV_Assert CV_Assert_N +#endif +//! @endcond + +#if defined _DEBUG || defined CV_STATIC_ANALYSIS +# define CV_DbgAssert(expr) CV_Assert(expr) +#else +/** replaced with CV_Assert(expr) in Debug configuration */ +# define CV_DbgAssert(expr) +#endif + +/* + * Hamming distance functor - counts the bit differences between two strings - useful for the Brief descriptor + * bit count of A exclusive XOR'ed with B + */ +struct CV_EXPORTS Hamming +{ + enum { normType = NORM_HAMMING }; + typedef unsigned char ValueType; + typedef int ResultType; + + /** this will count the bits in a ^ b + */ + ResultType operator()( const unsigned char* a, const unsigned char* b, int size ) const; +}; + +typedef Hamming HammingLUT; + +/////////////////////////////////// inline norms //////////////////////////////////// + +template inline _Tp cv_abs(_Tp x) { return std::abs(x); } +inline int cv_abs(uchar x) { return x; } +inline int cv_abs(schar x) { return std::abs(x); } +inline int cv_abs(ushort x) { return x; } +inline int cv_abs(short x) { return std::abs(x); } + +template static inline +_AccTp normL2Sqr(const _Tp* a, int n) +{ + _AccTp s = 0; + int i=0; +#if CV_ENABLE_UNROLLED + for( ; i <= n - 4; i += 4 ) + { + _AccTp v0 = a[i], v1 = a[i+1], v2 = a[i+2], v3 = a[i+3]; + s += v0*v0 + v1*v1 + v2*v2 + v3*v3; + } +#endif + for( ; i < n; i++ ) + { + _AccTp v = a[i]; + s += v*v; + } + return s; +} + +template static inline +_AccTp normL1(const _Tp* a, int n) +{ + _AccTp s = 0; + int i = 0; +#if CV_ENABLE_UNROLLED + for(; i <= n - 4; i += 4 ) + { + s += (_AccTp)cv_abs(a[i]) + (_AccTp)cv_abs(a[i+1]) + + (_AccTp)cv_abs(a[i+2]) + (_AccTp)cv_abs(a[i+3]); + } +#endif + for( ; i < n; i++ ) + s += cv_abs(a[i]); + return s; +} + +template static inline +_AccTp normInf(const _Tp* a, int n) +{ + _AccTp s = 0; + for( int i = 0; i < n; i++ ) + s = std::max(s, (_AccTp)cv_abs(a[i])); + return s; +} + +template static inline +_AccTp normL2Sqr(const _Tp* a, const _Tp* b, int n) +{ + _AccTp s = 0; + int i= 0; +#if CV_ENABLE_UNROLLED + for(; i <= n - 4; i += 4 ) + { + _AccTp v0 = _AccTp(a[i] - b[i]), v1 = _AccTp(a[i+1] - b[i+1]), v2 = _AccTp(a[i+2] - b[i+2]), v3 = _AccTp(a[i+3] - b[i+3]); + s += v0*v0 + v1*v1 + v2*v2 + v3*v3; + } +#endif + for( ; i < n; i++ ) + { + _AccTp v = _AccTp(a[i] - b[i]); + s += v*v; + } + return s; +} + +static inline float normL2Sqr(const float* a, const float* b, int n) +{ + float s = 0.f; + for( int i = 0; i < n; i++ ) + { + float v = a[i] - b[i]; + s += v*v; + } + return s; +} + +template static inline +_AccTp normL1(const _Tp* a, const _Tp* b, int n) +{ + _AccTp s = 0; + int i= 0; +#if CV_ENABLE_UNROLLED + for(; i <= n - 4; i += 4 ) + { + _AccTp v0 = _AccTp(a[i] - b[i]), v1 = _AccTp(a[i+1] - b[i+1]), v2 = _AccTp(a[i+2] - b[i+2]), v3 = _AccTp(a[i+3] - b[i+3]); + s += std::abs(v0) + std::abs(v1) + std::abs(v2) + std::abs(v3); + } +#endif + for( ; i < n; i++ ) + { + _AccTp v = _AccTp(a[i] - b[i]); + s += std::abs(v); + } + return s; +} + +inline float normL1(const float* a, const float* b, int n) +{ + float s = 0.f; + for( int i = 0; i < n; i++ ) + { + s += std::abs(a[i] - b[i]); + } + return s; +} + +inline int normL1(const uchar* a, const uchar* b, int n) +{ + int s = 0; + for( int i = 0; i < n; i++ ) + { + s += std::abs(a[i] - b[i]); + } + return s; +} + +template static inline +_AccTp normInf(const _Tp* a, const _Tp* b, int n) +{ + _AccTp s = 0; + for( int i = 0; i < n; i++ ) + { + _AccTp v0 = a[i] - b[i]; + s = std::max(s, std::abs(v0)); + } + return s; +} + +/** @brief Computes the cube root of an argument. + + The function cubeRoot computes \f$\sqrt[3]{\texttt{val}}\f$. Negative arguments are handled correctly. + NaN and Inf are not handled. The accuracy approaches the maximum possible accuracy for + single-precision data. + @param val A function argument. + */ +CV_EXPORTS_W float cubeRoot(float val); + +/** @brief Calculates the angle of a 2D vector in degrees. + + The function fastAtan2 calculates the full-range angle of an input 2D vector. The angle is measured + in degrees and varies from 0 to 360 degrees. The accuracy is about 0.3 degrees. + @param x x-coordinate of the vector. + @param y y-coordinate of the vector. + */ +CV_EXPORTS_W float fastAtan2(float y, float x); + +/** proxy for hal::LU */ +CV_EXPORTS int LU(float* A, size_t astep, int m, float* b, size_t bstep, int n); +/** proxy for hal::LU */ +CV_EXPORTS int LU(double* A, size_t astep, int m, double* b, size_t bstep, int n); +/** proxy for hal::Cholesky */ +CV_EXPORTS bool Cholesky(float* A, size_t astep, int m, float* b, size_t bstep, int n); +/** proxy for hal::Cholesky */ +CV_EXPORTS bool Cholesky(double* A, size_t astep, int m, double* b, size_t bstep, int n); + +////////////////// forward declarations for important OpenCV types ////////////////// + +//! @cond IGNORED + +template class Vec; +template class Matx; + +template class Complex; +template class Point_; +template class Point3_; +template class Size_; +template class Rect_; +template class Scalar_; + +class CV_EXPORTS RotatedRect; +class CV_EXPORTS Range; +class CV_EXPORTS TermCriteria; +class CV_EXPORTS KeyPoint; +class CV_EXPORTS DMatch; +class CV_EXPORTS RNG; + +class CV_EXPORTS Mat; +class CV_EXPORTS MatExpr; + +class CV_EXPORTS UMat; + +class CV_EXPORTS SparseMat; +typedef Mat MatND; + +template class Mat_; +template class SparseMat_; + +class CV_EXPORTS MatConstIterator; +class CV_EXPORTS SparseMatIterator; +class CV_EXPORTS SparseMatConstIterator; +template class MatIterator_; +template class MatConstIterator_; +template class SparseMatIterator_; +template class SparseMatConstIterator_; + +namespace ogl +{ + class CV_EXPORTS Buffer; + class CV_EXPORTS Texture2D; + class CV_EXPORTS Arrays; +} + +namespace cuda +{ + class CV_EXPORTS GpuMat; + class CV_EXPORTS HostMem; + class CV_EXPORTS Stream; + class CV_EXPORTS Event; +} + +namespace cudev +{ + template class GpuMat_; +} + +namespace ipp +{ +#if OPENCV_ABI_COMPATIBILITY > 300 +CV_EXPORTS unsigned long long getIppFeatures(); +#else +CV_EXPORTS int getIppFeatures(); +#endif +CV_EXPORTS void setIppStatus(int status, const char * const funcname = NULL, const char * const filename = NULL, + int line = 0); +CV_EXPORTS int getIppStatus(); +CV_EXPORTS String getIppErrorLocation(); +CV_EXPORTS_W bool useIPP(); +CV_EXPORTS_W void setUseIPP(bool flag); +CV_EXPORTS_W String getIppVersion(); + +// IPP Not-Exact mode. This function may force use of IPP then both IPP and OpenCV provide proper results +// but have internal accuracy differences which have too much direct or indirect impact on accuracy tests. +CV_EXPORTS_W bool useIPP_NotExact(); +CV_EXPORTS_W void setUseIPP_NotExact(bool flag); +#if OPENCV_ABI_COMPATIBILITY < 400 +CV_EXPORTS_W bool useIPP_NE(); +CV_EXPORTS_W void setUseIPP_NE(bool flag); +#endif + +} // ipp + +//! @endcond + +//! @} core_utils + + + + +} // cv + +#include "opencv2/core/neon_utils.hpp" +#include "opencv2/core/vsx_utils.hpp" +#include "opencv2/core/check.hpp" + +#endif //OPENCV_CORE_BASE_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/bindings_utils.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/bindings_utils.hpp new file mode 100644 index 0000000000000000000000000000000000000000..c1123f2b0e3fa8b712961e1bd818f8c37b9beaa6 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/bindings_utils.hpp @@ -0,0 +1,23 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#ifndef OPENCV_CORE_BINDINGS_UTILS_HPP +#define OPENCV_CORE_BINDINGS_UTILS_HPP + +namespace cv { namespace utils { +//! @addtogroup core_utils +//! @{ + +CV_EXPORTS_W String dumpInputArray(InputArray argument); + +CV_EXPORTS_W String dumpInputArrayOfArrays(InputArrayOfArrays argument); + +CV_EXPORTS_W String dumpInputOutputArray(InputOutputArray argument); + +CV_EXPORTS_W String dumpInputOutputArrayOfArrays(InputOutputArrayOfArrays argument); + +//! @} +}} // namespace + +#endif // OPENCV_CORE_BINDINGS_UTILS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/bufferpool.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/bufferpool.hpp new file mode 100644 index 0000000000000000000000000000000000000000..4698e5da167d6a3b6a9ef7672faa3542b23fa662 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/bufferpool.hpp @@ -0,0 +1,40 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. +// +// Copyright (C) 2014, Advanced Micro Devices, Inc., all rights reserved. + +#ifndef OPENCV_CORE_BUFFER_POOL_HPP +#define OPENCV_CORE_BUFFER_POOL_HPP + +#ifdef _MSC_VER +#pragma warning(push) +#pragma warning(disable: 4265) +#endif + +namespace cv +{ + +//! @addtogroup core +//! @{ + +class BufferPoolController +{ +protected: + ~BufferPoolController() { } +public: + virtual size_t getReservedSize() const = 0; + virtual size_t getMaxReservedSize() const = 0; + virtual void setMaxReservedSize(size_t size) = 0; + virtual void freeAllReservedBuffers() = 0; +}; + +//! @} + +} + +#ifdef _MSC_VER +#pragma warning(pop) +#endif + +#endif // OPENCV_CORE_BUFFER_POOL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/check.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/check.hpp new file mode 100644 index 0000000000000000000000000000000000000000..bf441383e84059ae41263e9208018ea976df0774 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/check.hpp @@ -0,0 +1,157 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#ifndef OPENCV_CORE_CHECK_HPP +#define OPENCV_CORE_CHECK_HPP + +#include + +namespace cv { + +/** Returns string of cv::Mat depth value: CV_8U -> "CV_8U" or "" */ +CV_EXPORTS const char* depthToString(int depth); + +/** Returns string of cv::Mat depth value: CV_8UC3 -> "CV_8UC3" or "" */ +CV_EXPORTS const String typeToString(int type); + + +//! @cond IGNORED +namespace detail { + +/** Returns string of cv::Mat depth value: CV_8U -> "CV_8U" or NULL */ +CV_EXPORTS const char* depthToString_(int depth); + +/** Returns string of cv::Mat depth value: CV_8UC3 -> "CV_8UC3" or cv::String() */ +CV_EXPORTS const cv::String typeToString_(int type); + +enum TestOp { + TEST_CUSTOM = 0, + TEST_EQ = 1, + TEST_NE = 2, + TEST_LE = 3, + TEST_LT = 4, + TEST_GE = 5, + TEST_GT = 6, + CV__LAST_TEST_OP +}; + +struct CheckContext { + const char* func; + const char* file; + int line; + enum TestOp testOp; + const char* message; + const char* p1_str; + const char* p2_str; +}; + +#ifndef CV__CHECK_FILENAME +# define CV__CHECK_FILENAME __FILE__ +#endif + +#ifndef CV__CHECK_FUNCTION +# if defined _MSC_VER +# define CV__CHECK_FUNCTION __FUNCSIG__ +# elif defined __GNUC__ +# define CV__CHECK_FUNCTION __PRETTY_FUNCTION__ +# else +# define CV__CHECK_FUNCTION "" +# endif +#endif + +#define CV__CHECK_LOCATION_VARNAME(id) CVAUX_CONCAT(CVAUX_CONCAT(__cv_check_, id), __LINE__) +#define CV__DEFINE_CHECK_CONTEXT(id, message, testOp, p1_str, p2_str) \ + static const cv::detail::CheckContext CV__CHECK_LOCATION_VARNAME(id) = \ + { CV__CHECK_FUNCTION, CV__CHECK_FILENAME, __LINE__, testOp, message, p1_str, p2_str } + +CV_EXPORTS void CV_NORETURN check_failed_auto(const int v1, const int v2, const CheckContext& ctx); +CV_EXPORTS void CV_NORETURN check_failed_auto(const size_t v1, const size_t v2, const CheckContext& ctx); +CV_EXPORTS void CV_NORETURN check_failed_auto(const float v1, const float v2, const CheckContext& ctx); +CV_EXPORTS void CV_NORETURN check_failed_auto(const double v1, const double v2, const CheckContext& ctx); +CV_EXPORTS void CV_NORETURN check_failed_MatDepth(const int v1, const int v2, const CheckContext& ctx); +CV_EXPORTS void CV_NORETURN check_failed_MatType(const int v1, const int v2, const CheckContext& ctx); +CV_EXPORTS void CV_NORETURN check_failed_MatChannels(const int v1, const int v2, const CheckContext& ctx); + +CV_EXPORTS void CV_NORETURN check_failed_auto(const int v, const CheckContext& ctx); +CV_EXPORTS void CV_NORETURN check_failed_auto(const size_t v, const CheckContext& ctx); +CV_EXPORTS void CV_NORETURN check_failed_auto(const float v, const CheckContext& ctx); +CV_EXPORTS void CV_NORETURN check_failed_auto(const double v, const CheckContext& ctx); +CV_EXPORTS void CV_NORETURN check_failed_MatDepth(const int v, const CheckContext& ctx); +CV_EXPORTS void CV_NORETURN check_failed_MatType(const int v, const CheckContext& ctx); +CV_EXPORTS void CV_NORETURN check_failed_MatChannels(const int v, const CheckContext& ctx); + + +#define CV__TEST_EQ(v1, v2) ((v1) == (v2)) +#define CV__TEST_NE(v1, v2) ((v1) != (v2)) +#define CV__TEST_LE(v1, v2) ((v1) <= (v2)) +#define CV__TEST_LT(v1, v2) ((v1) < (v2)) +#define CV__TEST_GE(v1, v2) ((v1) >= (v2)) +#define CV__TEST_GT(v1, v2) ((v1) > (v2)) + +#define CV__CHECK(id, op, type, v1, v2, v1_str, v2_str, msg_str) do { \ + if(CV__TEST_##op((v1), (v2))) ; else { \ + CV__DEFINE_CHECK_CONTEXT(id, msg_str, cv::detail::TEST_ ## op, v1_str, v2_str); \ + cv::detail::check_failed_ ## type((v1), (v2), CV__CHECK_LOCATION_VARNAME(id)); \ + } \ +} while (0) + +#define CV__CHECK_CUSTOM_TEST(id, type, v, test_expr, v_str, test_expr_str, msg_str) do { \ + if(!!(test_expr)) ; else { \ + CV__DEFINE_CHECK_CONTEXT(id, msg_str, cv::detail::TEST_CUSTOM, v_str, test_expr_str); \ + cv::detail::check_failed_ ## type((v), CV__CHECK_LOCATION_VARNAME(id)); \ + } \ +} while (0) + +} // namespace +//! @endcond + + +/// Supported values of these types: int, float, double +#define CV_CheckEQ(v1, v2, msg) CV__CHECK(_, EQ, auto, v1, v2, #v1, #v2, msg) +#define CV_CheckNE(v1, v2, msg) CV__CHECK(_, NE, auto, v1, v2, #v1, #v2, msg) +#define CV_CheckLE(v1, v2, msg) CV__CHECK(_, LE, auto, v1, v2, #v1, #v2, msg) +#define CV_CheckLT(v1, v2, msg) CV__CHECK(_, LT, auto, v1, v2, #v1, #v2, msg) +#define CV_CheckGE(v1, v2, msg) CV__CHECK(_, GE, auto, v1, v2, #v1, #v2, msg) +#define CV_CheckGT(v1, v2, msg) CV__CHECK(_, GT, auto, v1, v2, #v1, #v2, msg) + +/// Check with additional "decoding" of type values in error message +#define CV_CheckTypeEQ(t1, t2, msg) CV__CHECK(_, EQ, MatType, t1, t2, #t1, #t2, msg) +/// Check with additional "decoding" of depth values in error message +#define CV_CheckDepthEQ(d1, d2, msg) CV__CHECK(_, EQ, MatDepth, d1, d2, #d1, #d2, msg) + +#define CV_CheckChannelsEQ(c1, c2, msg) CV__CHECK(_, EQ, MatChannels, c1, c2, #c1, #c2, msg) + +/// Example: type == CV_8UC1 || type == CV_8UC3 +#define CV_CheckType(t, test_expr, msg) CV__CHECK_CUSTOM_TEST(_, MatType, t, (test_expr), #t, #test_expr, msg) + +/// Example: depth == CV_32F || depth == CV_64F +#define CV_CheckDepth(t, test_expr, msg) CV__CHECK_CUSTOM_TEST(_, MatDepth, t, (test_expr), #t, #test_expr, msg) + +/// Example: v == A || v == B +#define CV_Check(v, test_expr, msg) CV__CHECK_CUSTOM_TEST(_, auto, v, (test_expr), #v, #test_expr, msg) + +/// Some complex conditions: CV_Check(src2, src2.empty() || (src2.type() == src1.type() && src2.size() == src1.size()), "src2 should have same size/type as src1") +// TODO define pretty-printers + +#ifndef NDEBUG +#define CV_DbgCheck(v, test_expr, msg) CV__CHECK_CUSTOM_TEST(_, auto, v, (test_expr), #v, #test_expr, msg) +#define CV_DbgCheckEQ(v1, v2, msg) CV__CHECK(_, EQ, auto, v1, v2, #v1, #v2, msg) +#define CV_DbgCheckNE(v1, v2, msg) CV__CHECK(_, NE, auto, v1, v2, #v1, #v2, msg) +#define CV_DbgCheckLE(v1, v2, msg) CV__CHECK(_, LE, auto, v1, v2, #v1, #v2, msg) +#define CV_DbgCheckLT(v1, v2, msg) CV__CHECK(_, LT, auto, v1, v2, #v1, #v2, msg) +#define CV_DbgCheckGE(v1, v2, msg) CV__CHECK(_, GE, auto, v1, v2, #v1, #v2, msg) +#define CV_DbgCheckGT(v1, v2, msg) CV__CHECK(_, GT, auto, v1, v2, #v1, #v2, msg) +#else +#define CV_DbgCheck(v, test_expr, msg) do { } while (0) +#define CV_DbgCheckEQ(v1, v2, msg) do { } while (0) +#define CV_DbgCheckNE(v1, v2, msg) do { } while (0) +#define CV_DbgCheckLE(v1, v2, msg) do { } while (0) +#define CV_DbgCheckLT(v1, v2, msg) do { } while (0) +#define CV_DbgCheckGE(v1, v2, msg) do { } while (0) +#define CV_DbgCheckGT(v1, v2, msg) do { } while (0) +#endif + +} // namespace + +#endif // OPENCV_CORE_CHECK_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/core.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/core.hpp new file mode 100644 index 0000000000000000000000000000000000000000..438918359be44fcf2489dc4f10617385fe3770e9 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/core.hpp @@ -0,0 +1,48 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifdef __OPENCV_BUILD +#error this is a compatibility header which should not be used inside the OpenCV library +#endif + +#include "opencv2/core.hpp" diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/core_c.h b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/core_c.h new file mode 100644 index 0000000000000000000000000000000000000000..e5fe516d264e30060360087b472e1eaec6cd0e44 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/core_c.h @@ -0,0 +1,3175 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + + +#ifndef OPENCV_CORE_C_H +#define OPENCV_CORE_C_H + +#include "opencv2/core/types_c.h" + +#ifdef __cplusplus +# ifdef _MSC_VER +/* disable warning C4190: 'function' has C-linkage specified, but returns UDT 'typename' + which is incompatible with C + + It is OK to disable it because we only extend few plain structures with + C++ construrtors for simpler interoperability with C++ API of the library +*/ +# pragma warning(disable:4190) +# elif defined __clang__ && __clang_major__ >= 3 +# pragma GCC diagnostic ignored "-Wreturn-type-c-linkage" +# endif +#endif + +#ifdef __cplusplus +extern "C" { +#endif + +/** @addtogroup core_c + @{ +*/ + +/****************************************************************************************\ +* Array allocation, deallocation, initialization and access to elements * +\****************************************************************************************/ + +/** `malloc` wrapper. + If there is no enough memory, the function + (as well as other OpenCV functions that call cvAlloc) + raises an error. */ +CVAPI(void*) cvAlloc( size_t size ); + +/** `free` wrapper. + Here and further all the memory releasing functions + (that all call cvFree) take double pointer in order to + to clear pointer to the data after releasing it. + Passing pointer to NULL pointer is Ok: nothing happens in this case +*/ +CVAPI(void) cvFree_( void* ptr ); +#define cvFree(ptr) (cvFree_(*(ptr)), *(ptr)=0) + +/** @brief Creates an image header but does not allocate the image data. + +@param size Image width and height +@param depth Image depth (see cvCreateImage ) +@param channels Number of channels (see cvCreateImage ) + */ +CVAPI(IplImage*) cvCreateImageHeader( CvSize size, int depth, int channels ); + +/** @brief Initializes an image header that was previously allocated. + +The returned IplImage\* points to the initialized header. +@param image Image header to initialize +@param size Image width and height +@param depth Image depth (see cvCreateImage ) +@param channels Number of channels (see cvCreateImage ) +@param origin Top-left IPL_ORIGIN_TL or bottom-left IPL_ORIGIN_BL +@param align Alignment for image rows, typically 4 or 8 bytes + */ +CVAPI(IplImage*) cvInitImageHeader( IplImage* image, CvSize size, int depth, + int channels, int origin CV_DEFAULT(0), + int align CV_DEFAULT(4)); + +/** @brief Creates an image header and allocates the image data. + +This function call is equivalent to the following code: +@code + header = cvCreateImageHeader(size, depth, channels); + cvCreateData(header); +@endcode +@param size Image width and height +@param depth Bit depth of image elements. See IplImage for valid depths. +@param channels Number of channels per pixel. See IplImage for details. This function only creates +images with interleaved channels. + */ +CVAPI(IplImage*) cvCreateImage( CvSize size, int depth, int channels ); + +/** @brief Deallocates an image header. + +This call is an analogue of : +@code + if(image ) + { + iplDeallocate(*image, IPL_IMAGE_HEADER | IPL_IMAGE_ROI); + *image = 0; + } +@endcode +but it does not use IPL functions by default (see the CV_TURN_ON_IPL_COMPATIBILITY macro). +@param image Double pointer to the image header + */ +CVAPI(void) cvReleaseImageHeader( IplImage** image ); + +/** @brief Deallocates the image header and the image data. + +This call is a shortened form of : +@code + if(*image ) + { + cvReleaseData(*image); + cvReleaseImageHeader(image); + } +@endcode +@param image Double pointer to the image header +*/ +CVAPI(void) cvReleaseImage( IplImage** image ); + +/** Creates a copy of IPL image (widthStep may differ) */ +CVAPI(IplImage*) cvCloneImage( const IplImage* image ); + +/** @brief Sets the channel of interest in an IplImage. + +If the ROI is set to NULL and the coi is *not* 0, the ROI is allocated. Most OpenCV functions do +*not* support the COI setting, so to process an individual image/matrix channel one may copy (via +cvCopy or cvSplit) the channel to a separate image/matrix, process it and then copy the result +back (via cvCopy or cvMerge) if needed. +@param image A pointer to the image header +@param coi The channel of interest. 0 - all channels are selected, 1 - first channel is selected, +etc. Note that the channel indices become 1-based. + */ +CVAPI(void) cvSetImageCOI( IplImage* image, int coi ); + +/** @brief Returns the index of the channel of interest. + +Returns the channel of interest of in an IplImage. Returned values correspond to the coi in +cvSetImageCOI. +@param image A pointer to the image header + */ +CVAPI(int) cvGetImageCOI( const IplImage* image ); + +/** @brief Sets an image Region Of Interest (ROI) for a given rectangle. + +If the original image ROI was NULL and the rect is not the whole image, the ROI structure is +allocated. + +Most OpenCV functions support the use of ROI and treat the image rectangle as a separate image. For +example, all of the pixel coordinates are counted from the top-left (or bottom-left) corner of the +ROI, not the original image. +@param image A pointer to the image header +@param rect The ROI rectangle + */ +CVAPI(void) cvSetImageROI( IplImage* image, CvRect rect ); + +/** @brief Resets the image ROI to include the entire image and releases the ROI structure. + +This produces a similar result to the following, but in addition it releases the ROI structure. : +@code + cvSetImageROI(image, cvRect(0, 0, image->width, image->height )); + cvSetImageCOI(image, 0); +@endcode +@param image A pointer to the image header + */ +CVAPI(void) cvResetImageROI( IplImage* image ); + +/** @brief Returns the image ROI. + +If there is no ROI set, cvRect(0,0,image-\>width,image-\>height) is returned. +@param image A pointer to the image header + */ +CVAPI(CvRect) cvGetImageROI( const IplImage* image ); + +/** @brief Creates a matrix header but does not allocate the matrix data. + +The function allocates a new matrix header and returns a pointer to it. The matrix data can then be +allocated using cvCreateData or set explicitly to user-allocated data via cvSetData. +@param rows Number of rows in the matrix +@param cols Number of columns in the matrix +@param type Type of the matrix elements, see cvCreateMat + */ +CVAPI(CvMat*) cvCreateMatHeader( int rows, int cols, int type ); + +#define CV_AUTOSTEP 0x7fffffff + +/** @brief Initializes a pre-allocated matrix header. + +This function is often used to process raw data with OpenCV matrix functions. For example, the +following code computes the matrix product of two matrices, stored as ordinary arrays: +@code + double a[] = { 1, 2, 3, 4, + 5, 6, 7, 8, + 9, 10, 11, 12 }; + + double b[] = { 1, 5, 9, + 2, 6, 10, + 3, 7, 11, + 4, 8, 12 }; + + double c[9]; + CvMat Ma, Mb, Mc ; + + cvInitMatHeader(&Ma, 3, 4, CV_64FC1, a); + cvInitMatHeader(&Mb, 4, 3, CV_64FC1, b); + cvInitMatHeader(&Mc, 3, 3, CV_64FC1, c); + + cvMatMulAdd(&Ma, &Mb, 0, &Mc); + // the c array now contains the product of a (3x4) and b (4x3) +@endcode +@param mat A pointer to the matrix header to be initialized +@param rows Number of rows in the matrix +@param cols Number of columns in the matrix +@param type Type of the matrix elements, see cvCreateMat . +@param data Optional: data pointer assigned to the matrix header +@param step Optional: full row width in bytes of the assigned data. By default, the minimal +possible step is used which assumes there are no gaps between subsequent rows of the matrix. + */ +CVAPI(CvMat*) cvInitMatHeader( CvMat* mat, int rows, int cols, + int type, void* data CV_DEFAULT(NULL), + int step CV_DEFAULT(CV_AUTOSTEP) ); + +/** @brief Creates a matrix header and allocates the matrix data. + +The function call is equivalent to the following code: +@code + CvMat* mat = cvCreateMatHeader(rows, cols, type); + cvCreateData(mat); +@endcode +@param rows Number of rows in the matrix +@param cols Number of columns in the matrix +@param type The type of the matrix elements in the form +CV_\\C\ , where S=signed, U=unsigned, F=float. For +example, CV _ 8UC1 means the elements are 8-bit unsigned and the there is 1 channel, and CV _ +32SC2 means the elements are 32-bit signed and there are 2 channels. + */ +CVAPI(CvMat*) cvCreateMat( int rows, int cols, int type ); + +/** @brief Deallocates a matrix. + +The function decrements the matrix data reference counter and deallocates matrix header. If the data +reference counter is 0, it also deallocates the data. : +@code + if(*mat ) + cvDecRefData(*mat); + cvFree((void**)mat); +@endcode +@param mat Double pointer to the matrix + */ +CVAPI(void) cvReleaseMat( CvMat** mat ); + +/** @brief Decrements an array data reference counter. + +The function decrements the data reference counter in a CvMat or CvMatND if the reference counter + +pointer is not NULL. If the counter reaches zero, the data is deallocated. In the current +implementation the reference counter is not NULL only if the data was allocated using the +cvCreateData function. The counter will be NULL in other cases such as: external data was assigned +to the header using cvSetData, header is part of a larger matrix or image, or the header was +converted from an image or n-dimensional matrix header. +@param arr Pointer to an array header + */ +CV_INLINE void cvDecRefData( CvArr* arr ) +{ + if( CV_IS_MAT( arr )) + { + CvMat* mat = (CvMat*)arr; + mat->data.ptr = NULL; + if( mat->refcount != NULL && --*mat->refcount == 0 ) + cvFree( &mat->refcount ); + mat->refcount = NULL; + } + else if( CV_IS_MATND( arr )) + { + CvMatND* mat = (CvMatND*)arr; + mat->data.ptr = NULL; + if( mat->refcount != NULL && --*mat->refcount == 0 ) + cvFree( &mat->refcount ); + mat->refcount = NULL; + } +} + +/** @brief Increments array data reference counter. + +The function increments CvMat or CvMatND data reference counter and returns the new counter value if +the reference counter pointer is not NULL, otherwise it returns zero. +@param arr Array header + */ +CV_INLINE int cvIncRefData( CvArr* arr ) +{ + int refcount = 0; + if( CV_IS_MAT( arr )) + { + CvMat* mat = (CvMat*)arr; + if( mat->refcount != NULL ) + refcount = ++*mat->refcount; + } + else if( CV_IS_MATND( arr )) + { + CvMatND* mat = (CvMatND*)arr; + if( mat->refcount != NULL ) + refcount = ++*mat->refcount; + } + return refcount; +} + + +/** Creates an exact copy of the input matrix (except, may be, step value) */ +CVAPI(CvMat*) cvCloneMat( const CvMat* mat ); + + +/** @brief Returns matrix header corresponding to the rectangular sub-array of input image or matrix. + +The function returns header, corresponding to a specified rectangle of the input array. In other + +words, it allows the user to treat a rectangular part of input array as a stand-alone array. ROI is +taken into account by the function so the sub-array of ROI is actually extracted. +@param arr Input array +@param submat Pointer to the resultant sub-array header +@param rect Zero-based coordinates of the rectangle of interest + */ +CVAPI(CvMat*) cvGetSubRect( const CvArr* arr, CvMat* submat, CvRect rect ); +#define cvGetSubArr cvGetSubRect + +/** @brief Returns array row or row span. + +The function returns the header, corresponding to a specified row/row span of the input array. +cvGetRow(arr, submat, row) is a shortcut for cvGetRows(arr, submat, row, row+1). +@param arr Input array +@param submat Pointer to the resulting sub-array header +@param start_row Zero-based index of the starting row (inclusive) of the span +@param end_row Zero-based index of the ending row (exclusive) of the span +@param delta_row Index step in the row span. That is, the function extracts every delta_row -th +row from start_row and up to (but not including) end_row . + */ +CVAPI(CvMat*) cvGetRows( const CvArr* arr, CvMat* submat, + int start_row, int end_row, + int delta_row CV_DEFAULT(1)); + +/** @overload +@param arr Input array +@param submat Pointer to the resulting sub-array header +@param row Zero-based index of the selected row +*/ +CV_INLINE CvMat* cvGetRow( const CvArr* arr, CvMat* submat, int row ) +{ + return cvGetRows( arr, submat, row, row + 1, 1 ); +} + + +/** @brief Returns one of more array columns. + +The function returns the header, corresponding to a specified column span of the input array. That + +is, no data is copied. Therefore, any modifications of the submatrix will affect the original array. +If you need to copy the columns, use cvCloneMat. cvGetCol(arr, submat, col) is a shortcut for +cvGetCols(arr, submat, col, col+1). +@param arr Input array +@param submat Pointer to the resulting sub-array header +@param start_col Zero-based index of the starting column (inclusive) of the span +@param end_col Zero-based index of the ending column (exclusive) of the span + */ +CVAPI(CvMat*) cvGetCols( const CvArr* arr, CvMat* submat, + int start_col, int end_col ); + +/** @overload +@param arr Input array +@param submat Pointer to the resulting sub-array header +@param col Zero-based index of the selected column +*/ +CV_INLINE CvMat* cvGetCol( const CvArr* arr, CvMat* submat, int col ) +{ + return cvGetCols( arr, submat, col, col + 1 ); +} + +/** @brief Returns one of array diagonals. + +The function returns the header, corresponding to a specified diagonal of the input array. +@param arr Input array +@param submat Pointer to the resulting sub-array header +@param diag Index of the array diagonal. Zero value corresponds to the main diagonal, -1 +corresponds to the diagonal above the main, 1 corresponds to the diagonal below the main, and so +forth. + */ +CVAPI(CvMat*) cvGetDiag( const CvArr* arr, CvMat* submat, + int diag CV_DEFAULT(0)); + +/** low-level scalar <-> raw data conversion functions */ +CVAPI(void) cvScalarToRawData( const CvScalar* scalar, void* data, int type, + int extend_to_12 CV_DEFAULT(0) ); + +CVAPI(void) cvRawDataToScalar( const void* data, int type, CvScalar* scalar ); + +/** @brief Creates a new matrix header but does not allocate the matrix data. + +The function allocates a header for a multi-dimensional dense array. The array data can further be +allocated using cvCreateData or set explicitly to user-allocated data via cvSetData. +@param dims Number of array dimensions +@param sizes Array of dimension sizes +@param type Type of array elements, see cvCreateMat + */ +CVAPI(CvMatND*) cvCreateMatNDHeader( int dims, const int* sizes, int type ); + +/** @brief Creates the header and allocates the data for a multi-dimensional dense array. + +This function call is equivalent to the following code: +@code + CvMatND* mat = cvCreateMatNDHeader(dims, sizes, type); + cvCreateData(mat); +@endcode +@param dims Number of array dimensions. This must not exceed CV_MAX_DIM (32 by default, but can be +changed at build time). +@param sizes Array of dimension sizes. +@param type Type of array elements, see cvCreateMat . + */ +CVAPI(CvMatND*) cvCreateMatND( int dims, const int* sizes, int type ); + +/** @brief Initializes a pre-allocated multi-dimensional array header. + +@param mat A pointer to the array header to be initialized +@param dims The number of array dimensions +@param sizes An array of dimension sizes +@param type Type of array elements, see cvCreateMat +@param data Optional data pointer assigned to the matrix header + */ +CVAPI(CvMatND*) cvInitMatNDHeader( CvMatND* mat, int dims, const int* sizes, + int type, void* data CV_DEFAULT(NULL) ); + +/** @brief Deallocates a multi-dimensional array. + +The function decrements the array data reference counter and releases the array header. If the +reference counter reaches 0, it also deallocates the data. : +@code + if(*mat ) + cvDecRefData(*mat); + cvFree((void**)mat); +@endcode +@param mat Double pointer to the array + */ +CV_INLINE void cvReleaseMatND( CvMatND** mat ) +{ + cvReleaseMat( (CvMat**)mat ); +} + +/** Creates a copy of CvMatND (except, may be, steps) */ +CVAPI(CvMatND*) cvCloneMatND( const CvMatND* mat ); + +/** @brief Creates sparse array. + +The function allocates a multi-dimensional sparse array. Initially the array contain no elements, +that is PtrND and other related functions will return 0 for every index. +@param dims Number of array dimensions. In contrast to the dense matrix, the number of dimensions is +practically unlimited (up to \f$2^{16}\f$ ). +@param sizes Array of dimension sizes +@param type Type of array elements. The same as for CvMat + */ +CVAPI(CvSparseMat*) cvCreateSparseMat( int dims, const int* sizes, int type ); + +/** @brief Deallocates sparse array. + +The function releases the sparse array and clears the array pointer upon exit. +@param mat Double pointer to the array + */ +CVAPI(void) cvReleaseSparseMat( CvSparseMat** mat ); + +/** Creates a copy of CvSparseMat (except, may be, zero items) */ +CVAPI(CvSparseMat*) cvCloneSparseMat( const CvSparseMat* mat ); + +/** @brief Initializes sparse array elements iterator. + +The function initializes iterator of sparse array elements and returns pointer to the first element, +or NULL if the array is empty. +@param mat Input array +@param mat_iterator Initialized iterator + */ +CVAPI(CvSparseNode*) cvInitSparseMatIterator( const CvSparseMat* mat, + CvSparseMatIterator* mat_iterator ); + +/** @brief Returns the next sparse matrix element + +The function moves iterator to the next sparse matrix element and returns pointer to it. In the +current version there is no any particular order of the elements, because they are stored in the +hash table. The sample below demonstrates how to iterate through the sparse matrix: +@code + // print all the non-zero sparse matrix elements and compute their sum + double sum = 0; + int i, dims = cvGetDims(sparsemat); + CvSparseMatIterator it; + CvSparseNode* node = cvInitSparseMatIterator(sparsemat, &it); + + for(; node != 0; node = cvGetNextSparseNode(&it)) + { + int* idx = CV_NODE_IDX(array, node); + float val = *(float*)CV_NODE_VAL(array, node); + printf("M"); + for(i = 0; i < dims; i++ ) + printf("[%d]", idx[i]); + printf("=%g\n", val); + + sum += val; + } + + printf("nTotal sum = %g\n", sum); +@endcode +@param mat_iterator Sparse array iterator + */ +CV_INLINE CvSparseNode* cvGetNextSparseNode( CvSparseMatIterator* mat_iterator ) +{ + if( mat_iterator->node->next ) + return mat_iterator->node = mat_iterator->node->next; + else + { + int idx; + for( idx = ++mat_iterator->curidx; idx < mat_iterator->mat->hashsize; idx++ ) + { + CvSparseNode* node = (CvSparseNode*)mat_iterator->mat->hashtable[idx]; + if( node ) + { + mat_iterator->curidx = idx; + return mat_iterator->node = node; + } + } + return NULL; + } +} + + +#define CV_MAX_ARR 10 + +/** matrix iterator: used for n-ary operations on dense arrays */ +typedef struct CvNArrayIterator +{ + int count; /**< number of arrays */ + int dims; /**< number of dimensions to iterate */ + CvSize size; /**< maximal common linear size: { width = size, height = 1 } */ + uchar* ptr[CV_MAX_ARR]; /**< pointers to the array slices */ + int stack[CV_MAX_DIM]; /**< for internal use */ + CvMatND* hdr[CV_MAX_ARR]; /**< pointers to the headers of the + matrices that are processed */ +} +CvNArrayIterator; + +#define CV_NO_DEPTH_CHECK 1 +#define CV_NO_CN_CHECK 2 +#define CV_NO_SIZE_CHECK 4 + +/** initializes iterator that traverses through several arrays simulteneously + (the function together with cvNextArraySlice is used for + N-ari element-wise operations) */ +CVAPI(int) cvInitNArrayIterator( int count, CvArr** arrs, + const CvArr* mask, CvMatND* stubs, + CvNArrayIterator* array_iterator, + int flags CV_DEFAULT(0) ); + +/** returns zero value if iteration is finished, non-zero (slice length) otherwise */ +CVAPI(int) cvNextNArraySlice( CvNArrayIterator* array_iterator ); + + +/** @brief Returns type of array elements. + +The function returns type of the array elements. In the case of IplImage the type is converted to +CvMat-like representation. For example, if the image has been created as: +@code + IplImage* img = cvCreateImage(cvSize(640, 480), IPL_DEPTH_8U, 3); +@endcode +The code cvGetElemType(img) will return CV_8UC3. +@param arr Input array + */ +CVAPI(int) cvGetElemType( const CvArr* arr ); + +/** @brief Return number of array dimensions + +The function returns the array dimensionality and the array of dimension sizes. In the case of +IplImage or CvMat it always returns 2 regardless of number of image/matrix rows. For example, the +following code calculates total number of array elements: +@code + int sizes[CV_MAX_DIM]; + int i, total = 1; + int dims = cvGetDims(arr, size); + for(i = 0; i < dims; i++ ) + total *= sizes[i]; +@endcode +@param arr Input array +@param sizes Optional output vector of the array dimension sizes. For 2d arrays the number of rows +(height) goes first, number of columns (width) next. + */ +CVAPI(int) cvGetDims( const CvArr* arr, int* sizes CV_DEFAULT(NULL) ); + + +/** @brief Returns array size along the specified dimension. + +@param arr Input array +@param index Zero-based dimension index (for matrices 0 means number of rows, 1 means number of +columns; for images 0 means height, 1 means width) + */ +CVAPI(int) cvGetDimSize( const CvArr* arr, int index ); + + +/** @brief Return pointer to a particular array element. + +The functions return a pointer to a specific array element. Number of array dimension should match +to the number of indices passed to the function except for cvPtr1D function that can be used for +sequential access to 1D, 2D or nD dense arrays. + +The functions can be used for sparse arrays as well - if the requested node does not exist they +create it and set it to zero. + +All these as well as other functions accessing array elements ( cvGetND , cvGetRealND , cvSet +, cvSetND , cvSetRealND ) raise an error in case if the element index is out of range. +@param arr Input array +@param idx0 The first zero-based component of the element index +@param type Optional output parameter: type of matrix elements + */ +CVAPI(uchar*) cvPtr1D( const CvArr* arr, int idx0, int* type CV_DEFAULT(NULL)); +/** @overload */ +CVAPI(uchar*) cvPtr2D( const CvArr* arr, int idx0, int idx1, int* type CV_DEFAULT(NULL) ); +/** @overload */ +CVAPI(uchar*) cvPtr3D( const CvArr* arr, int idx0, int idx1, int idx2, + int* type CV_DEFAULT(NULL)); +/** @overload +@param arr Input array +@param idx Array of the element indices +@param type Optional output parameter: type of matrix elements +@param create_node Optional input parameter for sparse matrices. Non-zero value of the parameter +means that the requested element is created if it does not exist already. +@param precalc_hashval Optional input parameter for sparse matrices. If the pointer is not NULL, +the function does not recalculate the node hash value, but takes it from the specified location. +It is useful for speeding up pair-wise operations (TODO: provide an example) +*/ +CVAPI(uchar*) cvPtrND( const CvArr* arr, const int* idx, int* type CV_DEFAULT(NULL), + int create_node CV_DEFAULT(1), + unsigned* precalc_hashval CV_DEFAULT(NULL)); + +/** @brief Return a specific array element. + +The functions return a specific array element. In the case of a sparse array the functions return 0 +if the requested node does not exist (no new node is created by the functions). +@param arr Input array +@param idx0 The first zero-based component of the element index + */ +CVAPI(CvScalar) cvGet1D( const CvArr* arr, int idx0 ); +/** @overload */ +CVAPI(CvScalar) cvGet2D( const CvArr* arr, int idx0, int idx1 ); +/** @overload */ +CVAPI(CvScalar) cvGet3D( const CvArr* arr, int idx0, int idx1, int idx2 ); +/** @overload +@param arr Input array +@param idx Array of the element indices +*/ +CVAPI(CvScalar) cvGetND( const CvArr* arr, const int* idx ); + +/** @brief Return a specific element of single-channel 1D, 2D, 3D or nD array. + +Returns a specific element of a single-channel array. If the array has multiple channels, a runtime +error is raised. Note that Get?D functions can be used safely for both single-channel and +multiple-channel arrays though they are a bit slower. + +In the case of a sparse array the functions return 0 if the requested node does not exist (no new +node is created by the functions). +@param arr Input array. Must have a single channel. +@param idx0 The first zero-based component of the element index + */ +CVAPI(double) cvGetReal1D( const CvArr* arr, int idx0 ); +/** @overload */ +CVAPI(double) cvGetReal2D( const CvArr* arr, int idx0, int idx1 ); +/** @overload */ +CVAPI(double) cvGetReal3D( const CvArr* arr, int idx0, int idx1, int idx2 ); +/** @overload +@param arr Input array. Must have a single channel. +@param idx Array of the element indices +*/ +CVAPI(double) cvGetRealND( const CvArr* arr, const int* idx ); + +/** @brief Change the particular array element. + +The functions assign the new value to a particular array element. In the case of a sparse array the +functions create the node if it does not exist yet. +@param arr Input array +@param idx0 The first zero-based component of the element index +@param value The assigned value + */ +CVAPI(void) cvSet1D( CvArr* arr, int idx0, CvScalar value ); +/** @overload */ +CVAPI(void) cvSet2D( CvArr* arr, int idx0, int idx1, CvScalar value ); +/** @overload */ +CVAPI(void) cvSet3D( CvArr* arr, int idx0, int idx1, int idx2, CvScalar value ); +/** @overload +@param arr Input array +@param idx Array of the element indices +@param value The assigned value +*/ +CVAPI(void) cvSetND( CvArr* arr, const int* idx, CvScalar value ); + +/** @brief Change a specific array element. + +The functions assign a new value to a specific element of a single-channel array. If the array has +multiple channels, a runtime error is raised. Note that the Set\*D function can be used safely for +both single-channel and multiple-channel arrays, though they are a bit slower. + +In the case of a sparse array the functions create the node if it does not yet exist. +@param arr Input array +@param idx0 The first zero-based component of the element index +@param value The assigned value + */ +CVAPI(void) cvSetReal1D( CvArr* arr, int idx0, double value ); +/** @overload */ +CVAPI(void) cvSetReal2D( CvArr* arr, int idx0, int idx1, double value ); +/** @overload */ +CVAPI(void) cvSetReal3D( CvArr* arr, int idx0, + int idx1, int idx2, double value ); +/** @overload +@param arr Input array +@param idx Array of the element indices +@param value The assigned value +*/ +CVAPI(void) cvSetRealND( CvArr* arr, const int* idx, double value ); + +/** clears element of ND dense array, + in case of sparse arrays it deletes the specified node */ +CVAPI(void) cvClearND( CvArr* arr, const int* idx ); + +/** @brief Returns matrix header for arbitrary array. + +The function returns a matrix header for the input array that can be a matrix - CvMat, an image - +IplImage, or a multi-dimensional dense array - CvMatND (the third option is allowed only if +allowND != 0) . In the case of matrix the function simply returns the input pointer. In the case of +IplImage\* or CvMatND it initializes the header structure with parameters of the current image ROI +and returns &header. Because COI is not supported by CvMat, it is returned separately. + +The function provides an easy way to handle both types of arrays - IplImage and CvMat using the same +code. Input array must have non-zero data pointer, otherwise the function will report an error. + +@note If the input array is IplImage with planar data layout and COI set, the function returns the +pointer to the selected plane and COI == 0. This feature allows user to process IplImage structures +with planar data layout, even though OpenCV does not support such images. +@param arr Input array +@param header Pointer to CvMat structure used as a temporary buffer +@param coi Optional output parameter for storing COI +@param allowND If non-zero, the function accepts multi-dimensional dense arrays (CvMatND\*) and +returns 2D matrix (if CvMatND has two dimensions) or 1D matrix (when CvMatND has 1 dimension or +more than 2 dimensions). The CvMatND array must be continuous. +@sa cvGetImage, cvarrToMat. + */ +CVAPI(CvMat*) cvGetMat( const CvArr* arr, CvMat* header, + int* coi CV_DEFAULT(NULL), + int allowND CV_DEFAULT(0)); + +/** @brief Returns image header for arbitrary array. + +The function returns the image header for the input array that can be a matrix (CvMat) or image +(IplImage). In the case of an image the function simply returns the input pointer. In the case of +CvMat it initializes an image_header structure with the parameters of the input matrix. Note that +if we transform IplImage to CvMat using cvGetMat and then transform CvMat back to IplImage using +this function, we will get different headers if the ROI is set in the original image. +@param arr Input array +@param image_header Pointer to IplImage structure used as a temporary buffer + */ +CVAPI(IplImage*) cvGetImage( const CvArr* arr, IplImage* image_header ); + + +/** @brief Changes the shape of a multi-dimensional array without copying the data. + +The function is an advanced version of cvReshape that can work with multi-dimensional arrays as +well (though it can work with ordinary images and matrices) and change the number of dimensions. + +Below are the two samples from the cvReshape description rewritten using cvReshapeMatND: +@code + IplImage* color_img = cvCreateImage(cvSize(320,240), IPL_DEPTH_8U, 3); + IplImage gray_img_hdr, *gray_img; + gray_img = (IplImage*)cvReshapeMatND(color_img, sizeof(gray_img_hdr), &gray_img_hdr, 1, 0, 0); + ... + int size[] = { 2, 2, 2 }; + CvMatND* mat = cvCreateMatND(3, size, CV_32F); + CvMat row_header, *row; + row = (CvMat*)cvReshapeMatND(mat, sizeof(row_header), &row_header, 0, 1, 0); +@endcode +In C, the header file for this function includes a convenient macro cvReshapeND that does away with +the sizeof_header parameter. So, the lines containing the call to cvReshapeMatND in the examples +may be replaced as follow: +@code + gray_img = (IplImage*)cvReshapeND(color_img, &gray_img_hdr, 1, 0, 0); + ... + row = (CvMat*)cvReshapeND(mat, &row_header, 0, 1, 0); +@endcode +@param arr Input array +@param sizeof_header Size of output header to distinguish between IplImage, CvMat and CvMatND +output headers +@param header Output header to be filled +@param new_cn New number of channels. new_cn = 0 means that the number of channels remains +unchanged. +@param new_dims New number of dimensions. new_dims = 0 means that the number of dimensions +remains the same. +@param new_sizes Array of new dimension sizes. Only new_dims-1 values are used, because the +total number of elements must remain the same. Thus, if new_dims = 1, new_sizes array is not +used. + */ +CVAPI(CvArr*) cvReshapeMatND( const CvArr* arr, + int sizeof_header, CvArr* header, + int new_cn, int new_dims, int* new_sizes ); + +#define cvReshapeND( arr, header, new_cn, new_dims, new_sizes ) \ + cvReshapeMatND( (arr), sizeof(*(header)), (header), \ + (new_cn), (new_dims), (new_sizes)) + +/** @brief Changes shape of matrix/image without copying data. + +The function initializes the CvMat header so that it points to the same data as the original array +but has a different shape - different number of channels, different number of rows, or both. + +The following example code creates one image buffer and two image headers, the first is for a +320x240x3 image and the second is for a 960x240x1 image: +@code + IplImage* color_img = cvCreateImage(cvSize(320,240), IPL_DEPTH_8U, 3); + CvMat gray_mat_hdr; + IplImage gray_img_hdr, *gray_img; + cvReshape(color_img, &gray_mat_hdr, 1); + gray_img = cvGetImage(&gray_mat_hdr, &gray_img_hdr); +@endcode +And the next example converts a 3x3 matrix to a single 1x9 vector: +@code + CvMat* mat = cvCreateMat(3, 3, CV_32F); + CvMat row_header, *row; + row = cvReshape(mat, &row_header, 0, 1); +@endcode +@param arr Input array +@param header Output header to be filled +@param new_cn New number of channels. 'new_cn = 0' means that the number of channels remains +unchanged. +@param new_rows New number of rows. 'new_rows = 0' means that the number of rows remains +unchanged unless it needs to be changed according to new_cn value. +*/ +CVAPI(CvMat*) cvReshape( const CvArr* arr, CvMat* header, + int new_cn, int new_rows CV_DEFAULT(0) ); + +/** Repeats source 2d array several times in both horizontal and + vertical direction to fill destination array */ +CVAPI(void) cvRepeat( const CvArr* src, CvArr* dst ); + +/** @brief Allocates array data + +The function allocates image, matrix or multi-dimensional dense array data. Note that in the case of +matrix types OpenCV allocation functions are used. In the case of IplImage they are used unless +CV_TURN_ON_IPL_COMPATIBILITY() has been called before. In the latter case IPL functions are used +to allocate the data. +@param arr Array header + */ +CVAPI(void) cvCreateData( CvArr* arr ); + +/** @brief Releases array data. + +The function releases the array data. In the case of CvMat or CvMatND it simply calls +cvDecRefData(), that is the function can not deallocate external data. See also the note to +cvCreateData . +@param arr Array header + */ +CVAPI(void) cvReleaseData( CvArr* arr ); + +/** @brief Assigns user data to the array header. + +The function assigns user data to the array header. Header should be initialized before using +cvCreateMatHeader, cvCreateImageHeader, cvCreateMatNDHeader, cvInitMatHeader, +cvInitImageHeader or cvInitMatNDHeader. +@param arr Array header +@param data User data +@param step Full row length in bytes + */ +CVAPI(void) cvSetData( CvArr* arr, void* data, int step ); + +/** @brief Retrieves low-level information about the array. + +The function fills output variables with low-level information about the array data. All output + +parameters are optional, so some of the pointers may be set to NULL. If the array is IplImage with +ROI set, the parameters of ROI are returned. + +The following example shows how to get access to array elements. It computes absolute values of the +array elements : +@code + float* data; + int step; + CvSize size; + + cvGetRawData(array, (uchar**)&data, &step, &size); + step /= sizeof(data[0]); + + for(int y = 0; y < size.height; y++, data += step ) + for(int x = 0; x < size.width; x++ ) + data[x] = (float)fabs(data[x]); +@endcode +@param arr Array header +@param data Output pointer to the whole image origin or ROI origin if ROI is set +@param step Output full row length in bytes +@param roi_size Output ROI size + */ +CVAPI(void) cvGetRawData( const CvArr* arr, uchar** data, + int* step CV_DEFAULT(NULL), + CvSize* roi_size CV_DEFAULT(NULL)); + +/** @brief Returns size of matrix or image ROI. + +The function returns number of rows (CvSize::height) and number of columns (CvSize::width) of the +input matrix or image. In the case of image the size of ROI is returned. +@param arr array header + */ +CVAPI(CvSize) cvGetSize( const CvArr* arr ); + +/** @brief Copies one array to another. + +The function copies selected elements from an input array to an output array: + +\f[\texttt{dst} (I)= \texttt{src} (I) \quad \text{if} \quad \texttt{mask} (I) \ne 0.\f] + +If any of the passed arrays is of IplImage type, then its ROI and COI fields are used. Both arrays +must have the same type, the same number of dimensions, and the same size. The function can also +copy sparse arrays (mask is not supported in this case). +@param src The source array +@param dst The destination array +@param mask Operation mask, 8-bit single channel array; specifies elements of the destination array +to be changed + */ +CVAPI(void) cvCopy( const CvArr* src, CvArr* dst, + const CvArr* mask CV_DEFAULT(NULL) ); + +/** @brief Sets every element of an array to a given value. + +The function copies the scalar value to every selected element of the destination array: +\f[\texttt{arr} (I)= \texttt{value} \quad \text{if} \quad \texttt{mask} (I) \ne 0\f] +If array arr is of IplImage type, then is ROI used, but COI must not be set. +@param arr The destination array +@param value Fill value +@param mask Operation mask, 8-bit single channel array; specifies elements of the destination +array to be changed + */ +CVAPI(void) cvSet( CvArr* arr, CvScalar value, + const CvArr* mask CV_DEFAULT(NULL) ); + +/** @brief Clears the array. + +The function clears the array. In the case of dense arrays (CvMat, CvMatND or IplImage), +cvZero(array) is equivalent to cvSet(array,cvScalarAll(0),0). In the case of sparse arrays all the +elements are removed. +@param arr Array to be cleared + */ +CVAPI(void) cvSetZero( CvArr* arr ); +#define cvZero cvSetZero + + +/** Splits a multi-channel array into the set of single-channel arrays or + extracts particular [color] plane */ +CVAPI(void) cvSplit( const CvArr* src, CvArr* dst0, CvArr* dst1, + CvArr* dst2, CvArr* dst3 ); + +/** Merges a set of single-channel arrays into the single multi-channel array + or inserts one particular [color] plane to the array */ +CVAPI(void) cvMerge( const CvArr* src0, const CvArr* src1, + const CvArr* src2, const CvArr* src3, + CvArr* dst ); + +/** Copies several channels from input arrays to + certain channels of output arrays */ +CVAPI(void) cvMixChannels( const CvArr** src, int src_count, + CvArr** dst, int dst_count, + const int* from_to, int pair_count ); + +/** @brief Converts one array to another with optional linear transformation. + +The function has several different purposes, and thus has several different names. It copies one +array to another with optional scaling, which is performed first, and/or optional type conversion, +performed after: + +\f[\texttt{dst} (I) = \texttt{scale} \texttt{src} (I) + ( \texttt{shift} _0, \texttt{shift} _1,...)\f] + +All the channels of multi-channel arrays are processed independently. + +The type of conversion is done with rounding and saturation, that is if the result of scaling + +conversion can not be represented exactly by a value of the destination array element type, it is +set to the nearest representable value on the real axis. +@param src Source array +@param dst Destination array +@param scale Scale factor +@param shift Value added to the scaled source array elements + */ +CVAPI(void) cvConvertScale( const CvArr* src, CvArr* dst, + double scale CV_DEFAULT(1), + double shift CV_DEFAULT(0) ); +#define cvCvtScale cvConvertScale +#define cvScale cvConvertScale +#define cvConvert( src, dst ) cvConvertScale( (src), (dst), 1, 0 ) + + +/** Performs linear transformation on every source array element, + stores absolute value of the result: + dst(x,y,c) = abs(scale*src(x,y,c)+shift). + destination array must have 8u type. + In other cases one may use cvConvertScale + cvAbsDiffS */ +CVAPI(void) cvConvertScaleAbs( const CvArr* src, CvArr* dst, + double scale CV_DEFAULT(1), + double shift CV_DEFAULT(0) ); +#define cvCvtScaleAbs cvConvertScaleAbs + + +/** checks termination criteria validity and + sets eps to default_eps (if it is not set), + max_iter to default_max_iters (if it is not set) +*/ +CVAPI(CvTermCriteria) cvCheckTermCriteria( CvTermCriteria criteria, + double default_eps, + int default_max_iters ); + +/****************************************************************************************\ +* Arithmetic, logic and comparison operations * +\****************************************************************************************/ + +/** dst(mask) = src1(mask) + src2(mask) */ +CVAPI(void) cvAdd( const CvArr* src1, const CvArr* src2, CvArr* dst, + const CvArr* mask CV_DEFAULT(NULL)); + +/** dst(mask) = src(mask) + value */ +CVAPI(void) cvAddS( const CvArr* src, CvScalar value, CvArr* dst, + const CvArr* mask CV_DEFAULT(NULL)); + +/** dst(mask) = src1(mask) - src2(mask) */ +CVAPI(void) cvSub( const CvArr* src1, const CvArr* src2, CvArr* dst, + const CvArr* mask CV_DEFAULT(NULL)); + +/** dst(mask) = src(mask) - value = src(mask) + (-value) */ +CV_INLINE void cvSubS( const CvArr* src, CvScalar value, CvArr* dst, + const CvArr* mask CV_DEFAULT(NULL)) +{ + cvAddS( src, cvScalar( -value.val[0], -value.val[1], -value.val[2], -value.val[3]), + dst, mask ); +} + +/** dst(mask) = value - src(mask) */ +CVAPI(void) cvSubRS( const CvArr* src, CvScalar value, CvArr* dst, + const CvArr* mask CV_DEFAULT(NULL)); + +/** dst(idx) = src1(idx) * src2(idx) * scale + (scaled element-wise multiplication of 2 arrays) */ +CVAPI(void) cvMul( const CvArr* src1, const CvArr* src2, + CvArr* dst, double scale CV_DEFAULT(1) ); + +/** element-wise division/inversion with scaling: + dst(idx) = src1(idx) * scale / src2(idx) + or dst(idx) = scale / src2(idx) if src1 == 0 */ +CVAPI(void) cvDiv( const CvArr* src1, const CvArr* src2, + CvArr* dst, double scale CV_DEFAULT(1)); + +/** dst = src1 * scale + src2 */ +CVAPI(void) cvScaleAdd( const CvArr* src1, CvScalar scale, + const CvArr* src2, CvArr* dst ); +#define cvAXPY( A, real_scalar, B, C ) cvScaleAdd(A, cvRealScalar(real_scalar), B, C) + +/** dst = src1 * alpha + src2 * beta + gamma */ +CVAPI(void) cvAddWeighted( const CvArr* src1, double alpha, + const CvArr* src2, double beta, + double gamma, CvArr* dst ); + +/** @brief Calculates the dot product of two arrays in Euclidean metrics. + +The function calculates and returns the Euclidean dot product of two arrays. + +\f[src1 \bullet src2 = \sum _I ( \texttt{src1} (I) \texttt{src2} (I))\f] + +In the case of multiple channel arrays, the results for all channels are accumulated. In particular, +cvDotProduct(a,a) where a is a complex vector, will return \f$||\texttt{a}||^2\f$. The function can +process multi-dimensional arrays, row by row, layer by layer, and so on. +@param src1 The first source array +@param src2 The second source array + */ +CVAPI(double) cvDotProduct( const CvArr* src1, const CvArr* src2 ); + +/** dst(idx) = src1(idx) & src2(idx) */ +CVAPI(void) cvAnd( const CvArr* src1, const CvArr* src2, + CvArr* dst, const CvArr* mask CV_DEFAULT(NULL)); + +/** dst(idx) = src(idx) & value */ +CVAPI(void) cvAndS( const CvArr* src, CvScalar value, + CvArr* dst, const CvArr* mask CV_DEFAULT(NULL)); + +/** dst(idx) = src1(idx) | src2(idx) */ +CVAPI(void) cvOr( const CvArr* src1, const CvArr* src2, + CvArr* dst, const CvArr* mask CV_DEFAULT(NULL)); + +/** dst(idx) = src(idx) | value */ +CVAPI(void) cvOrS( const CvArr* src, CvScalar value, + CvArr* dst, const CvArr* mask CV_DEFAULT(NULL)); + +/** dst(idx) = src1(idx) ^ src2(idx) */ +CVAPI(void) cvXor( const CvArr* src1, const CvArr* src2, + CvArr* dst, const CvArr* mask CV_DEFAULT(NULL)); + +/** dst(idx) = src(idx) ^ value */ +CVAPI(void) cvXorS( const CvArr* src, CvScalar value, + CvArr* dst, const CvArr* mask CV_DEFAULT(NULL)); + +/** dst(idx) = ~src(idx) */ +CVAPI(void) cvNot( const CvArr* src, CvArr* dst ); + +/** dst(idx) = lower(idx) <= src(idx) < upper(idx) */ +CVAPI(void) cvInRange( const CvArr* src, const CvArr* lower, + const CvArr* upper, CvArr* dst ); + +/** dst(idx) = lower <= src(idx) < upper */ +CVAPI(void) cvInRangeS( const CvArr* src, CvScalar lower, + CvScalar upper, CvArr* dst ); + +#define CV_CMP_EQ 0 +#define CV_CMP_GT 1 +#define CV_CMP_GE 2 +#define CV_CMP_LT 3 +#define CV_CMP_LE 4 +#define CV_CMP_NE 5 + +/** The comparison operation support single-channel arrays only. + Destination image should be 8uC1 or 8sC1 */ + +/** dst(idx) = src1(idx) _cmp_op_ src2(idx) */ +CVAPI(void) cvCmp( const CvArr* src1, const CvArr* src2, CvArr* dst, int cmp_op ); + +/** dst(idx) = src1(idx) _cmp_op_ value */ +CVAPI(void) cvCmpS( const CvArr* src, double value, CvArr* dst, int cmp_op ); + +/** dst(idx) = min(src1(idx),src2(idx)) */ +CVAPI(void) cvMin( const CvArr* src1, const CvArr* src2, CvArr* dst ); + +/** dst(idx) = max(src1(idx),src2(idx)) */ +CVAPI(void) cvMax( const CvArr* src1, const CvArr* src2, CvArr* dst ); + +/** dst(idx) = min(src(idx),value) */ +CVAPI(void) cvMinS( const CvArr* src, double value, CvArr* dst ); + +/** dst(idx) = max(src(idx),value) */ +CVAPI(void) cvMaxS( const CvArr* src, double value, CvArr* dst ); + +/** dst(x,y,c) = abs(src1(x,y,c) - src2(x,y,c)) */ +CVAPI(void) cvAbsDiff( const CvArr* src1, const CvArr* src2, CvArr* dst ); + +/** dst(x,y,c) = abs(src(x,y,c) - value(c)) */ +CVAPI(void) cvAbsDiffS( const CvArr* src, CvArr* dst, CvScalar value ); +#define cvAbs( src, dst ) cvAbsDiffS( (src), (dst), cvScalarAll(0)) + +/****************************************************************************************\ +* Math operations * +\****************************************************************************************/ + +/** Does cartesian->polar coordinates conversion. + Either of output components (magnitude or angle) is optional */ +CVAPI(void) cvCartToPolar( const CvArr* x, const CvArr* y, + CvArr* magnitude, CvArr* angle CV_DEFAULT(NULL), + int angle_in_degrees CV_DEFAULT(0)); + +/** Does polar->cartesian coordinates conversion. + Either of output components (magnitude or angle) is optional. + If magnitude is missing it is assumed to be all 1's */ +CVAPI(void) cvPolarToCart( const CvArr* magnitude, const CvArr* angle, + CvArr* x, CvArr* y, + int angle_in_degrees CV_DEFAULT(0)); + +/** Does powering: dst(idx) = src(idx)^power */ +CVAPI(void) cvPow( const CvArr* src, CvArr* dst, double power ); + +/** Does exponention: dst(idx) = exp(src(idx)). + Overflow is not handled yet. Underflow is handled. + Maximal relative error is ~7e-6 for single-precision input */ +CVAPI(void) cvExp( const CvArr* src, CvArr* dst ); + +/** Calculates natural logarithms: dst(idx) = log(abs(src(idx))). + Logarithm of 0 gives large negative number(~-700) + Maximal relative error is ~3e-7 for single-precision output +*/ +CVAPI(void) cvLog( const CvArr* src, CvArr* dst ); + +/** Fast arctangent calculation */ +CVAPI(float) cvFastArctan( float y, float x ); + +/** Fast cubic root calculation */ +CVAPI(float) cvCbrt( float value ); + +#define CV_CHECK_RANGE 1 +#define CV_CHECK_QUIET 2 +/** Checks array values for NaNs, Infs or simply for too large numbers + (if CV_CHECK_RANGE is set). If CV_CHECK_QUIET is set, + no runtime errors is raised (function returns zero value in case of "bad" values). + Otherwise cvError is called */ +CVAPI(int) cvCheckArr( const CvArr* arr, int flags CV_DEFAULT(0), + double min_val CV_DEFAULT(0), double max_val CV_DEFAULT(0)); +#define cvCheckArray cvCheckArr + +#define CV_RAND_UNI 0 +#define CV_RAND_NORMAL 1 + +/** @brief Fills an array with random numbers and updates the RNG state. + +The function fills the destination array with uniformly or normally distributed random numbers. +@param rng CvRNG state initialized by cvRNG +@param arr The destination array +@param dist_type Distribution type +> - **CV_RAND_UNI** uniform distribution +> - **CV_RAND_NORMAL** normal or Gaussian distribution +@param param1 The first parameter of the distribution. In the case of a uniform distribution it is +the inclusive lower boundary of the random numbers range. In the case of a normal distribution it +is the mean value of the random numbers. +@param param2 The second parameter of the distribution. In the case of a uniform distribution it +is the exclusive upper boundary of the random numbers range. In the case of a normal distribution +it is the standard deviation of the random numbers. +@sa randu, randn, RNG::fill. + */ +CVAPI(void) cvRandArr( CvRNG* rng, CvArr* arr, int dist_type, + CvScalar param1, CvScalar param2 ); + +CVAPI(void) cvRandShuffle( CvArr* mat, CvRNG* rng, + double iter_factor CV_DEFAULT(1.)); + +#define CV_SORT_EVERY_ROW 0 +#define CV_SORT_EVERY_COLUMN 1 +#define CV_SORT_ASCENDING 0 +#define CV_SORT_DESCENDING 16 + +CVAPI(void) cvSort( const CvArr* src, CvArr* dst CV_DEFAULT(NULL), + CvArr* idxmat CV_DEFAULT(NULL), + int flags CV_DEFAULT(0)); + +/** Finds real roots of a cubic equation */ +CVAPI(int) cvSolveCubic( const CvMat* coeffs, CvMat* roots ); + +/** Finds all real and complex roots of a polynomial equation */ +CVAPI(void) cvSolvePoly(const CvMat* coeffs, CvMat *roots2, + int maxiter CV_DEFAULT(20), int fig CV_DEFAULT(100)); + +/****************************************************************************************\ +* Matrix operations * +\****************************************************************************************/ + +/** @brief Calculates the cross product of two 3D vectors. + +The function calculates the cross product of two 3D vectors: +\f[\texttt{dst} = \texttt{src1} \times \texttt{src2}\f] +or: +\f[\begin{array}{l} \texttt{dst} _1 = \texttt{src1} _2 \texttt{src2} _3 - \texttt{src1} _3 \texttt{src2} _2 \\ \texttt{dst} _2 = \texttt{src1} _3 \texttt{src2} _1 - \texttt{src1} _1 \texttt{src2} _3 \\ \texttt{dst} _3 = \texttt{src1} _1 \texttt{src2} _2 - \texttt{src1} _2 \texttt{src2} _1 \end{array}\f] +@param src1 The first source vector +@param src2 The second source vector +@param dst The destination vector + */ +CVAPI(void) cvCrossProduct( const CvArr* src1, const CvArr* src2, CvArr* dst ); + +/** Matrix transform: dst = A*B + C, C is optional */ +#define cvMatMulAdd( src1, src2, src3, dst ) cvGEMM( (src1), (src2), 1., (src3), 1., (dst), 0 ) +#define cvMatMul( src1, src2, dst ) cvMatMulAdd( (src1), (src2), NULL, (dst)) + +#define CV_GEMM_A_T 1 +#define CV_GEMM_B_T 2 +#define CV_GEMM_C_T 4 +/** Extended matrix transform: + dst = alpha*op(A)*op(B) + beta*op(C), where op(X) is X or X^T */ +CVAPI(void) cvGEMM( const CvArr* src1, const CvArr* src2, double alpha, + const CvArr* src3, double beta, CvArr* dst, + int tABC CV_DEFAULT(0)); +#define cvMatMulAddEx cvGEMM + +/** Transforms each element of source array and stores + resultant vectors in destination array */ +CVAPI(void) cvTransform( const CvArr* src, CvArr* dst, + const CvMat* transmat, + const CvMat* shiftvec CV_DEFAULT(NULL)); +#define cvMatMulAddS cvTransform + +/** Does perspective transform on every element of input array */ +CVAPI(void) cvPerspectiveTransform( const CvArr* src, CvArr* dst, + const CvMat* mat ); + +/** Calculates (A-delta)*(A-delta)^T (order=0) or (A-delta)^T*(A-delta) (order=1) */ +CVAPI(void) cvMulTransposed( const CvArr* src, CvArr* dst, int order, + const CvArr* delta CV_DEFAULT(NULL), + double scale CV_DEFAULT(1.) ); + +/** Tranposes matrix. Square matrices can be transposed in-place */ +CVAPI(void) cvTranspose( const CvArr* src, CvArr* dst ); +#define cvT cvTranspose + +/** Completes the symmetric matrix from the lower (LtoR=0) or from the upper (LtoR!=0) part */ +CVAPI(void) cvCompleteSymm( CvMat* matrix, int LtoR CV_DEFAULT(0) ); + +/** Mirror array data around horizontal (flip=0), + vertical (flip=1) or both(flip=-1) axises: + cvFlip(src) flips images vertically and sequences horizontally (inplace) */ +CVAPI(void) cvFlip( const CvArr* src, CvArr* dst CV_DEFAULT(NULL), + int flip_mode CV_DEFAULT(0)); +#define cvMirror cvFlip + + +#define CV_SVD_MODIFY_A 1 +#define CV_SVD_U_T 2 +#define CV_SVD_V_T 4 + +/** Performs Singular Value Decomposition of a matrix */ +CVAPI(void) cvSVD( CvArr* A, CvArr* W, CvArr* U CV_DEFAULT(NULL), + CvArr* V CV_DEFAULT(NULL), int flags CV_DEFAULT(0)); + +/** Performs Singular Value Back Substitution (solves A*X = B): + flags must be the same as in cvSVD */ +CVAPI(void) cvSVBkSb( const CvArr* W, const CvArr* U, + const CvArr* V, const CvArr* B, + CvArr* X, int flags ); + +#define CV_LU 0 +#define CV_SVD 1 +#define CV_SVD_SYM 2 +#define CV_CHOLESKY 3 +#define CV_QR 4 +#define CV_NORMAL 16 + +/** Inverts matrix */ +CVAPI(double) cvInvert( const CvArr* src, CvArr* dst, + int method CV_DEFAULT(CV_LU)); +#define cvInv cvInvert + +/** Solves linear system (src1)*(dst) = (src2) + (returns 0 if src1 is a singular and CV_LU method is used) */ +CVAPI(int) cvSolve( const CvArr* src1, const CvArr* src2, CvArr* dst, + int method CV_DEFAULT(CV_LU)); + +/** Calculates determinant of input matrix */ +CVAPI(double) cvDet( const CvArr* mat ); + +/** Calculates trace of the matrix (sum of elements on the main diagonal) */ +CVAPI(CvScalar) cvTrace( const CvArr* mat ); + +/** Finds eigen values and vectors of a symmetric matrix */ +CVAPI(void) cvEigenVV( CvArr* mat, CvArr* evects, CvArr* evals, + double eps CV_DEFAULT(0), + int lowindex CV_DEFAULT(-1), + int highindex CV_DEFAULT(-1)); + +///* Finds selected eigen values and vectors of a symmetric matrix */ +//CVAPI(void) cvSelectedEigenVV( CvArr* mat, CvArr* evects, CvArr* evals, +// int lowindex, int highindex ); + +/** Makes an identity matrix (mat_ij = i == j) */ +CVAPI(void) cvSetIdentity( CvArr* mat, CvScalar value CV_DEFAULT(cvRealScalar(1)) ); + +/** Fills matrix with given range of numbers */ +CVAPI(CvArr*) cvRange( CvArr* mat, double start, double end ); + +/** @anchor core_c_CovarFlags +@name Flags for cvCalcCovarMatrix +@see cvCalcCovarMatrix + @{ +*/ + +/** flag for cvCalcCovarMatrix, transpose([v1-avg, v2-avg,...]) * [v1-avg,v2-avg,...] */ +#define CV_COVAR_SCRAMBLED 0 + +/** flag for cvCalcCovarMatrix, [v1-avg, v2-avg,...] * transpose([v1-avg,v2-avg,...]) */ +#define CV_COVAR_NORMAL 1 + +/** flag for cvCalcCovarMatrix, do not calc average (i.e. mean vector) - use the input vector instead + (useful for calculating covariance matrix by parts) */ +#define CV_COVAR_USE_AVG 2 + +/** flag for cvCalcCovarMatrix, scale the covariance matrix coefficients by number of the vectors */ +#define CV_COVAR_SCALE 4 + +/** flag for cvCalcCovarMatrix, all the input vectors are stored in a single matrix, as its rows */ +#define CV_COVAR_ROWS 8 + +/** flag for cvCalcCovarMatrix, all the input vectors are stored in a single matrix, as its columns */ +#define CV_COVAR_COLS 16 + +/** @} */ + +/** Calculates covariation matrix for a set of vectors +@see @ref core_c_CovarFlags "flags" +*/ +CVAPI(void) cvCalcCovarMatrix( const CvArr** vects, int count, + CvArr* cov_mat, CvArr* avg, int flags ); + +#define CV_PCA_DATA_AS_ROW 0 +#define CV_PCA_DATA_AS_COL 1 +#define CV_PCA_USE_AVG 2 +CVAPI(void) cvCalcPCA( const CvArr* data, CvArr* mean, + CvArr* eigenvals, CvArr* eigenvects, int flags ); + +CVAPI(void) cvProjectPCA( const CvArr* data, const CvArr* mean, + const CvArr* eigenvects, CvArr* result ); + +CVAPI(void) cvBackProjectPCA( const CvArr* proj, const CvArr* mean, + const CvArr* eigenvects, CvArr* result ); + +/** Calculates Mahalanobis(weighted) distance */ +CVAPI(double) cvMahalanobis( const CvArr* vec1, const CvArr* vec2, const CvArr* mat ); +#define cvMahalonobis cvMahalanobis + +/****************************************************************************************\ +* Array Statistics * +\****************************************************************************************/ + +/** Finds sum of array elements */ +CVAPI(CvScalar) cvSum( const CvArr* arr ); + +/** Calculates number of non-zero pixels */ +CVAPI(int) cvCountNonZero( const CvArr* arr ); + +/** Calculates mean value of array elements */ +CVAPI(CvScalar) cvAvg( const CvArr* arr, const CvArr* mask CV_DEFAULT(NULL) ); + +/** Calculates mean and standard deviation of pixel values */ +CVAPI(void) cvAvgSdv( const CvArr* arr, CvScalar* mean, CvScalar* std_dev, + const CvArr* mask CV_DEFAULT(NULL) ); + +/** Finds global minimum, maximum and their positions */ +CVAPI(void) cvMinMaxLoc( const CvArr* arr, double* min_val, double* max_val, + CvPoint* min_loc CV_DEFAULT(NULL), + CvPoint* max_loc CV_DEFAULT(NULL), + const CvArr* mask CV_DEFAULT(NULL) ); + +/** @anchor core_c_NormFlags + @name Flags for cvNorm and cvNormalize + @{ +*/ +#define CV_C 1 +#define CV_L1 2 +#define CV_L2 4 +#define CV_NORM_MASK 7 +#define CV_RELATIVE 8 +#define CV_DIFF 16 +#define CV_MINMAX 32 + +#define CV_DIFF_C (CV_DIFF | CV_C) +#define CV_DIFF_L1 (CV_DIFF | CV_L1) +#define CV_DIFF_L2 (CV_DIFF | CV_L2) +#define CV_RELATIVE_C (CV_RELATIVE | CV_C) +#define CV_RELATIVE_L1 (CV_RELATIVE | CV_L1) +#define CV_RELATIVE_L2 (CV_RELATIVE | CV_L2) +/** @} */ + +/** Finds norm, difference norm or relative difference norm for an array (or two arrays) +@see ref core_c_NormFlags "flags" +*/ +CVAPI(double) cvNorm( const CvArr* arr1, const CvArr* arr2 CV_DEFAULT(NULL), + int norm_type CV_DEFAULT(CV_L2), + const CvArr* mask CV_DEFAULT(NULL) ); + +/** @see ref core_c_NormFlags "flags" */ +CVAPI(void) cvNormalize( const CvArr* src, CvArr* dst, + double a CV_DEFAULT(1.), double b CV_DEFAULT(0.), + int norm_type CV_DEFAULT(CV_L2), + const CvArr* mask CV_DEFAULT(NULL) ); + +/** @anchor core_c_ReduceFlags + @name Flags for cvReduce + @{ +*/ +#define CV_REDUCE_SUM 0 +#define CV_REDUCE_AVG 1 +#define CV_REDUCE_MAX 2 +#define CV_REDUCE_MIN 3 +/** @} */ + +/** @see @ref core_c_ReduceFlags "flags" */ +CVAPI(void) cvReduce( const CvArr* src, CvArr* dst, int dim CV_DEFAULT(-1), + int op CV_DEFAULT(CV_REDUCE_SUM) ); + +/****************************************************************************************\ +* Discrete Linear Transforms and Related Functions * +\****************************************************************************************/ + +/** @anchor core_c_DftFlags + @name Flags for cvDFT, cvDCT and cvMulSpectrums + @{ + */ +#define CV_DXT_FORWARD 0 +#define CV_DXT_INVERSE 1 +#define CV_DXT_SCALE 2 /**< divide result by size of array */ +#define CV_DXT_INV_SCALE (CV_DXT_INVERSE + CV_DXT_SCALE) +#define CV_DXT_INVERSE_SCALE CV_DXT_INV_SCALE +#define CV_DXT_ROWS 4 /**< transform each row individually */ +#define CV_DXT_MUL_CONJ 8 /**< conjugate the second argument of cvMulSpectrums */ +/** @} */ + +/** Discrete Fourier Transform: + complex->complex, + real->ccs (forward), + ccs->real (inverse) +@see core_c_DftFlags "flags" +*/ +CVAPI(void) cvDFT( const CvArr* src, CvArr* dst, int flags, + int nonzero_rows CV_DEFAULT(0) ); +#define cvFFT cvDFT + +/** Multiply results of DFTs: DFT(X)*DFT(Y) or DFT(X)*conj(DFT(Y)) +@see core_c_DftFlags "flags" +*/ +CVAPI(void) cvMulSpectrums( const CvArr* src1, const CvArr* src2, + CvArr* dst, int flags ); + +/** Finds optimal DFT vector size >= size0 */ +CVAPI(int) cvGetOptimalDFTSize( int size0 ); + +/** Discrete Cosine Transform +@see core_c_DftFlags "flags" +*/ +CVAPI(void) cvDCT( const CvArr* src, CvArr* dst, int flags ); + +/****************************************************************************************\ +* Dynamic data structures * +\****************************************************************************************/ + +/** Calculates length of sequence slice (with support of negative indices). */ +CVAPI(int) cvSliceLength( CvSlice slice, const CvSeq* seq ); + + +/** Creates new memory storage. + block_size == 0 means that default, + somewhat optimal size, is used (currently, it is 64K) */ +CVAPI(CvMemStorage*) cvCreateMemStorage( int block_size CV_DEFAULT(0)); + + +/** Creates a memory storage that will borrow memory blocks from parent storage */ +CVAPI(CvMemStorage*) cvCreateChildMemStorage( CvMemStorage* parent ); + + +/** Releases memory storage. All the children of a parent must be released before + the parent. A child storage returns all the blocks to parent when it is released */ +CVAPI(void) cvReleaseMemStorage( CvMemStorage** storage ); + + +/** Clears memory storage. This is the only way(!!!) (besides cvRestoreMemStoragePos) + to reuse memory allocated for the storage - cvClearSeq,cvClearSet ... + do not free any memory. + A child storage returns all the blocks to the parent when it is cleared */ +CVAPI(void) cvClearMemStorage( CvMemStorage* storage ); + +/** Remember a storage "free memory" position */ +CVAPI(void) cvSaveMemStoragePos( const CvMemStorage* storage, CvMemStoragePos* pos ); + +/** Restore a storage "free memory" position */ +CVAPI(void) cvRestoreMemStoragePos( CvMemStorage* storage, CvMemStoragePos* pos ); + +/** Allocates continuous buffer of the specified size in the storage */ +CVAPI(void*) cvMemStorageAlloc( CvMemStorage* storage, size_t size ); + +/** Allocates string in memory storage */ +CVAPI(CvString) cvMemStorageAllocString( CvMemStorage* storage, const char* ptr, + int len CV_DEFAULT(-1) ); + +/** Creates new empty sequence that will reside in the specified storage */ +CVAPI(CvSeq*) cvCreateSeq( int seq_flags, size_t header_size, + size_t elem_size, CvMemStorage* storage ); + +/** Changes default size (granularity) of sequence blocks. + The default size is ~1Kbyte */ +CVAPI(void) cvSetSeqBlockSize( CvSeq* seq, int delta_elems ); + + +/** Adds new element to the end of sequence. Returns pointer to the element */ +CVAPI(schar*) cvSeqPush( CvSeq* seq, const void* element CV_DEFAULT(NULL)); + + +/** Adds new element to the beginning of sequence. Returns pointer to it */ +CVAPI(schar*) cvSeqPushFront( CvSeq* seq, const void* element CV_DEFAULT(NULL)); + + +/** Removes the last element from sequence and optionally saves it */ +CVAPI(void) cvSeqPop( CvSeq* seq, void* element CV_DEFAULT(NULL)); + + +/** Removes the first element from sequence and optioanally saves it */ +CVAPI(void) cvSeqPopFront( CvSeq* seq, void* element CV_DEFAULT(NULL)); + + +#define CV_FRONT 1 +#define CV_BACK 0 +/** Adds several new elements to the end of sequence */ +CVAPI(void) cvSeqPushMulti( CvSeq* seq, const void* elements, + int count, int in_front CV_DEFAULT(0) ); + +/** Removes several elements from the end of sequence and optionally saves them */ +CVAPI(void) cvSeqPopMulti( CvSeq* seq, void* elements, + int count, int in_front CV_DEFAULT(0) ); + +/** Inserts a new element in the middle of sequence. + cvSeqInsert(seq,0,elem) == cvSeqPushFront(seq,elem) */ +CVAPI(schar*) cvSeqInsert( CvSeq* seq, int before_index, + const void* element CV_DEFAULT(NULL)); + +/** Removes specified sequence element */ +CVAPI(void) cvSeqRemove( CvSeq* seq, int index ); + + +/** Removes all the elements from the sequence. The freed memory + can be reused later only by the same sequence unless cvClearMemStorage + or cvRestoreMemStoragePos is called */ +CVAPI(void) cvClearSeq( CvSeq* seq ); + + +/** Retrieves pointer to specified sequence element. + Negative indices are supported and mean counting from the end + (e.g -1 means the last sequence element) */ +CVAPI(schar*) cvGetSeqElem( const CvSeq* seq, int index ); + +/** Calculates index of the specified sequence element. + Returns -1 if element does not belong to the sequence */ +CVAPI(int) cvSeqElemIdx( const CvSeq* seq, const void* element, + CvSeqBlock** block CV_DEFAULT(NULL) ); + +/** Initializes sequence writer. The new elements will be added to the end of sequence */ +CVAPI(void) cvStartAppendToSeq( CvSeq* seq, CvSeqWriter* writer ); + + +/** Combination of cvCreateSeq and cvStartAppendToSeq */ +CVAPI(void) cvStartWriteSeq( int seq_flags, int header_size, + int elem_size, CvMemStorage* storage, + CvSeqWriter* writer ); + +/** Closes sequence writer, updates sequence header and returns pointer + to the resultant sequence + (which may be useful if the sequence was created using cvStartWriteSeq)) +*/ +CVAPI(CvSeq*) cvEndWriteSeq( CvSeqWriter* writer ); + + +/** Updates sequence header. May be useful to get access to some of previously + written elements via cvGetSeqElem or sequence reader */ +CVAPI(void) cvFlushSeqWriter( CvSeqWriter* writer ); + + +/** Initializes sequence reader. + The sequence can be read in forward or backward direction */ +CVAPI(void) cvStartReadSeq( const CvSeq* seq, CvSeqReader* reader, + int reverse CV_DEFAULT(0) ); + + +/** Returns current sequence reader position (currently observed sequence element) */ +CVAPI(int) cvGetSeqReaderPos( CvSeqReader* reader ); + + +/** Changes sequence reader position. It may seek to an absolute or + to relative to the current position */ +CVAPI(void) cvSetSeqReaderPos( CvSeqReader* reader, int index, + int is_relative CV_DEFAULT(0)); + +/** Copies sequence content to a continuous piece of memory */ +CVAPI(void*) cvCvtSeqToArray( const CvSeq* seq, void* elements, + CvSlice slice CV_DEFAULT(CV_WHOLE_SEQ) ); + +/** Creates sequence header for array. + After that all the operations on sequences that do not alter the content + can be applied to the resultant sequence */ +CVAPI(CvSeq*) cvMakeSeqHeaderForArray( int seq_type, int header_size, + int elem_size, void* elements, int total, + CvSeq* seq, CvSeqBlock* block ); + +/** Extracts sequence slice (with or without copying sequence elements) */ +CVAPI(CvSeq*) cvSeqSlice( const CvSeq* seq, CvSlice slice, + CvMemStorage* storage CV_DEFAULT(NULL), + int copy_data CV_DEFAULT(0)); + +CV_INLINE CvSeq* cvCloneSeq( const CvSeq* seq, CvMemStorage* storage CV_DEFAULT(NULL)) +{ + return cvSeqSlice( seq, CV_WHOLE_SEQ, storage, 1 ); +} + +/** Removes sequence slice */ +CVAPI(void) cvSeqRemoveSlice( CvSeq* seq, CvSlice slice ); + +/** Inserts a sequence or array into another sequence */ +CVAPI(void) cvSeqInsertSlice( CvSeq* seq, int before_index, const CvArr* from_arr ); + +/** a < b ? -1 : a > b ? 1 : 0 */ +typedef int (CV_CDECL* CvCmpFunc)(const void* a, const void* b, void* userdata ); + +/** Sorts sequence in-place given element comparison function */ +CVAPI(void) cvSeqSort( CvSeq* seq, CvCmpFunc func, void* userdata CV_DEFAULT(NULL) ); + +/** Finds element in a [sorted] sequence */ +CVAPI(schar*) cvSeqSearch( CvSeq* seq, const void* elem, CvCmpFunc func, + int is_sorted, int* elem_idx, + void* userdata CV_DEFAULT(NULL) ); + +/** Reverses order of sequence elements in-place */ +CVAPI(void) cvSeqInvert( CvSeq* seq ); + +/** Splits sequence into one or more equivalence classes using the specified criteria */ +CVAPI(int) cvSeqPartition( const CvSeq* seq, CvMemStorage* storage, + CvSeq** labels, CvCmpFunc is_equal, void* userdata ); + +/************ Internal sequence functions ************/ +CVAPI(void) cvChangeSeqBlock( void* reader, int direction ); +CVAPI(void) cvCreateSeqBlock( CvSeqWriter* writer ); + + +/** Creates a new set */ +CVAPI(CvSet*) cvCreateSet( int set_flags, int header_size, + int elem_size, CvMemStorage* storage ); + +/** Adds new element to the set and returns pointer to it */ +CVAPI(int) cvSetAdd( CvSet* set_header, CvSetElem* elem CV_DEFAULT(NULL), + CvSetElem** inserted_elem CV_DEFAULT(NULL) ); + +/** Fast variant of cvSetAdd */ +CV_INLINE CvSetElem* cvSetNew( CvSet* set_header ) +{ + CvSetElem* elem = set_header->free_elems; + if( elem ) + { + set_header->free_elems = elem->next_free; + elem->flags = elem->flags & CV_SET_ELEM_IDX_MASK; + set_header->active_count++; + } + else + cvSetAdd( set_header, NULL, &elem ); + return elem; +} + +/** Removes set element given its pointer */ +CV_INLINE void cvSetRemoveByPtr( CvSet* set_header, void* elem ) +{ + CvSetElem* _elem = (CvSetElem*)elem; + assert( _elem->flags >= 0 /*&& (elem->flags & CV_SET_ELEM_IDX_MASK) < set_header->total*/ ); + _elem->next_free = set_header->free_elems; + _elem->flags = (_elem->flags & CV_SET_ELEM_IDX_MASK) | CV_SET_ELEM_FREE_FLAG; + set_header->free_elems = _elem; + set_header->active_count--; +} + +/** Removes element from the set by its index */ +CVAPI(void) cvSetRemove( CvSet* set_header, int index ); + +/** Returns a set element by index. If the element doesn't belong to the set, + NULL is returned */ +CV_INLINE CvSetElem* cvGetSetElem( const CvSet* set_header, int idx ) +{ + CvSetElem* elem = (CvSetElem*)(void *)cvGetSeqElem( (CvSeq*)set_header, idx ); + return elem && CV_IS_SET_ELEM( elem ) ? elem : 0; +} + +/** Removes all the elements from the set */ +CVAPI(void) cvClearSet( CvSet* set_header ); + +/** Creates new graph */ +CVAPI(CvGraph*) cvCreateGraph( int graph_flags, int header_size, + int vtx_size, int edge_size, + CvMemStorage* storage ); + +/** Adds new vertex to the graph */ +CVAPI(int) cvGraphAddVtx( CvGraph* graph, const CvGraphVtx* vtx CV_DEFAULT(NULL), + CvGraphVtx** inserted_vtx CV_DEFAULT(NULL) ); + + +/** Removes vertex from the graph together with all incident edges */ +CVAPI(int) cvGraphRemoveVtx( CvGraph* graph, int index ); +CVAPI(int) cvGraphRemoveVtxByPtr( CvGraph* graph, CvGraphVtx* vtx ); + + +/** Link two vertices specified by indices or pointers if they + are not connected or return pointer to already existing edge + connecting the vertices. + Functions return 1 if a new edge was created, 0 otherwise */ +CVAPI(int) cvGraphAddEdge( CvGraph* graph, + int start_idx, int end_idx, + const CvGraphEdge* edge CV_DEFAULT(NULL), + CvGraphEdge** inserted_edge CV_DEFAULT(NULL) ); + +CVAPI(int) cvGraphAddEdgeByPtr( CvGraph* graph, + CvGraphVtx* start_vtx, CvGraphVtx* end_vtx, + const CvGraphEdge* edge CV_DEFAULT(NULL), + CvGraphEdge** inserted_edge CV_DEFAULT(NULL) ); + +/** Remove edge connecting two vertices */ +CVAPI(void) cvGraphRemoveEdge( CvGraph* graph, int start_idx, int end_idx ); +CVAPI(void) cvGraphRemoveEdgeByPtr( CvGraph* graph, CvGraphVtx* start_vtx, + CvGraphVtx* end_vtx ); + +/** Find edge connecting two vertices */ +CVAPI(CvGraphEdge*) cvFindGraphEdge( const CvGraph* graph, int start_idx, int end_idx ); +CVAPI(CvGraphEdge*) cvFindGraphEdgeByPtr( const CvGraph* graph, + const CvGraphVtx* start_vtx, + const CvGraphVtx* end_vtx ); +#define cvGraphFindEdge cvFindGraphEdge +#define cvGraphFindEdgeByPtr cvFindGraphEdgeByPtr + +/** Remove all vertices and edges from the graph */ +CVAPI(void) cvClearGraph( CvGraph* graph ); + + +/** Count number of edges incident to the vertex */ +CVAPI(int) cvGraphVtxDegree( const CvGraph* graph, int vtx_idx ); +CVAPI(int) cvGraphVtxDegreeByPtr( const CvGraph* graph, const CvGraphVtx* vtx ); + + +/** Retrieves graph vertex by given index */ +#define cvGetGraphVtx( graph, idx ) (CvGraphVtx*)cvGetSetElem((CvSet*)(graph), (idx)) + +/** Retrieves index of a graph vertex given its pointer */ +#define cvGraphVtxIdx( graph, vtx ) ((vtx)->flags & CV_SET_ELEM_IDX_MASK) + +/** Retrieves index of a graph edge given its pointer */ +#define cvGraphEdgeIdx( graph, edge ) ((edge)->flags & CV_SET_ELEM_IDX_MASK) + +#define cvGraphGetVtxCount( graph ) ((graph)->active_count) +#define cvGraphGetEdgeCount( graph ) ((graph)->edges->active_count) + +#define CV_GRAPH_VERTEX 1 +#define CV_GRAPH_TREE_EDGE 2 +#define CV_GRAPH_BACK_EDGE 4 +#define CV_GRAPH_FORWARD_EDGE 8 +#define CV_GRAPH_CROSS_EDGE 16 +#define CV_GRAPH_ANY_EDGE 30 +#define CV_GRAPH_NEW_TREE 32 +#define CV_GRAPH_BACKTRACKING 64 +#define CV_GRAPH_OVER -1 + +#define CV_GRAPH_ALL_ITEMS -1 + +/** flags for graph vertices and edges */ +#define CV_GRAPH_ITEM_VISITED_FLAG (1 << 30) +#define CV_IS_GRAPH_VERTEX_VISITED(vtx) \ + (((CvGraphVtx*)(vtx))->flags & CV_GRAPH_ITEM_VISITED_FLAG) +#define CV_IS_GRAPH_EDGE_VISITED(edge) \ + (((CvGraphEdge*)(edge))->flags & CV_GRAPH_ITEM_VISITED_FLAG) +#define CV_GRAPH_SEARCH_TREE_NODE_FLAG (1 << 29) +#define CV_GRAPH_FORWARD_EDGE_FLAG (1 << 28) + +typedef struct CvGraphScanner +{ + CvGraphVtx* vtx; /* current graph vertex (or current edge origin) */ + CvGraphVtx* dst; /* current graph edge destination vertex */ + CvGraphEdge* edge; /* current edge */ + + CvGraph* graph; /* the graph */ + CvSeq* stack; /* the graph vertex stack */ + int index; /* the lower bound of certainly visited vertices */ + int mask; /* event mask */ +} +CvGraphScanner; + +/** Creates new graph scanner. */ +CVAPI(CvGraphScanner*) cvCreateGraphScanner( CvGraph* graph, + CvGraphVtx* vtx CV_DEFAULT(NULL), + int mask CV_DEFAULT(CV_GRAPH_ALL_ITEMS)); + +/** Releases graph scanner. */ +CVAPI(void) cvReleaseGraphScanner( CvGraphScanner** scanner ); + +/** Get next graph element */ +CVAPI(int) cvNextGraphItem( CvGraphScanner* scanner ); + +/** Creates a copy of graph */ +CVAPI(CvGraph*) cvCloneGraph( const CvGraph* graph, CvMemStorage* storage ); + + +/** Does look-up transformation. Elements of the source array + (that should be 8uC1 or 8sC1) are used as indexes in lutarr 256-element table */ +CVAPI(void) cvLUT( const CvArr* src, CvArr* dst, const CvArr* lut ); + + +/******************* Iteration through the sequence tree *****************/ +typedef struct CvTreeNodeIterator +{ + const void* node; + int level; + int max_level; +} +CvTreeNodeIterator; + +CVAPI(void) cvInitTreeNodeIterator( CvTreeNodeIterator* tree_iterator, + const void* first, int max_level ); +CVAPI(void*) cvNextTreeNode( CvTreeNodeIterator* tree_iterator ); +CVAPI(void*) cvPrevTreeNode( CvTreeNodeIterator* tree_iterator ); + +/** Inserts sequence into tree with specified "parent" sequence. + If parent is equal to frame (e.g. the most external contour), + then added contour will have null pointer to parent. */ +CVAPI(void) cvInsertNodeIntoTree( void* node, void* parent, void* frame ); + +/** Removes contour from tree (together with the contour children). */ +CVAPI(void) cvRemoveNodeFromTree( void* node, void* frame ); + +/** Gathers pointers to all the sequences, + accessible from the `first`, to the single sequence */ +CVAPI(CvSeq*) cvTreeToNodeSeq( const void* first, int header_size, + CvMemStorage* storage ); + +/** The function implements the K-means algorithm for clustering an array of sample + vectors in a specified number of classes */ +#define CV_KMEANS_USE_INITIAL_LABELS 1 +CVAPI(int) cvKMeans2( const CvArr* samples, int cluster_count, CvArr* labels, + CvTermCriteria termcrit, int attempts CV_DEFAULT(1), + CvRNG* rng CV_DEFAULT(0), int flags CV_DEFAULT(0), + CvArr* _centers CV_DEFAULT(0), double* compactness CV_DEFAULT(0) ); + +/****************************************************************************************\ +* System functions * +\****************************************************************************************/ + +/** Loads optimized functions from IPP, MKL etc. or switches back to pure C code */ +CVAPI(int) cvUseOptimized( int on_off ); + +typedef IplImage* (CV_STDCALL* Cv_iplCreateImageHeader) + (int,int,int,char*,char*,int,int,int,int,int, + IplROI*,IplImage*,void*,IplTileInfo*); +typedef void (CV_STDCALL* Cv_iplAllocateImageData)(IplImage*,int,int); +typedef void (CV_STDCALL* Cv_iplDeallocate)(IplImage*,int); +typedef IplROI* (CV_STDCALL* Cv_iplCreateROI)(int,int,int,int,int); +typedef IplImage* (CV_STDCALL* Cv_iplCloneImage)(const IplImage*); + +/** @brief Makes OpenCV use IPL functions for allocating IplImage and IplROI structures. + +Normally, the function is not called directly. Instead, a simple macro +CV_TURN_ON_IPL_COMPATIBILITY() is used that calls cvSetIPLAllocators and passes there pointers +to IPL allocation functions. : +@code + ... + CV_TURN_ON_IPL_COMPATIBILITY() + ... +@endcode +@param create_header pointer to a function, creating IPL image header. +@param allocate_data pointer to a function, allocating IPL image data. +@param deallocate pointer to a function, deallocating IPL image. +@param create_roi pointer to a function, creating IPL image ROI (i.e. Region of Interest). +@param clone_image pointer to a function, cloning an IPL image. + */ +CVAPI(void) cvSetIPLAllocators( Cv_iplCreateImageHeader create_header, + Cv_iplAllocateImageData allocate_data, + Cv_iplDeallocate deallocate, + Cv_iplCreateROI create_roi, + Cv_iplCloneImage clone_image ); + +#define CV_TURN_ON_IPL_COMPATIBILITY() \ + cvSetIPLAllocators( iplCreateImageHeader, iplAllocateImage, \ + iplDeallocate, iplCreateROI, iplCloneImage ) + +/****************************************************************************************\ +* Data Persistence * +\****************************************************************************************/ + +/********************************** High-level functions ********************************/ + +/** @brief Opens file storage for reading or writing data. + +The function opens file storage for reading or writing data. In the latter case, a new file is +created or an existing file is rewritten. The type of the read or written file is determined by the +filename extension: .xml for XML, .yml or .yaml for YAML and .json for JSON. + +At the same time, it also supports adding parameters like "example.xml?base64". + +The function returns a pointer to the CvFileStorage structure. +If the file cannot be opened then the function returns NULL. +@param filename Name of the file associated with the storage +@param memstorage Memory storage used for temporary data and for +: storing dynamic structures, such as CvSeq or CvGraph . If it is NULL, a temporary memory + storage is created and used. +@param flags Can be one of the following: +> - **CV_STORAGE_READ** the storage is open for reading +> - **CV_STORAGE_WRITE** the storage is open for writing + (use **CV_STORAGE_WRITE | CV_STORAGE_WRITE_BASE64** to write rawdata in Base64) +@param encoding + */ +CVAPI(CvFileStorage*) cvOpenFileStorage( const char* filename, CvMemStorage* memstorage, + int flags, const char* encoding CV_DEFAULT(NULL) ); + +/** @brief Releases file storage. + +The function closes the file associated with the storage and releases all the temporary structures. +It must be called after all I/O operations with the storage are finished. +@param fs Double pointer to the released file storage + */ +CVAPI(void) cvReleaseFileStorage( CvFileStorage** fs ); + +/** returns attribute value or 0 (NULL) if there is no such attribute */ +CVAPI(const char*) cvAttrValue( const CvAttrList* attr, const char* attr_name ); + +/** @brief Starts writing a new structure. + +The function starts writing a compound structure (collection) that can be a sequence or a map. After +all the structure fields, which can be scalars or structures, are written, cvEndWriteStruct should +be called. The function can be used to group some objects or to implement the write function for a +some user object (see CvTypeInfo). +@param fs File storage +@param name Name of the written structure. The structure can be accessed by this name when the +storage is read. +@param struct_flags A combination one of the following values: +- **CV_NODE_SEQ** the written structure is a sequence (see discussion of CvFileStorage ), + that is, its elements do not have a name. +- **CV_NODE_MAP** the written structure is a map (see discussion of CvFileStorage ), that + is, all its elements have names. +One and only one of the two above flags must be specified +- **CV_NODE_FLOW** the optional flag that makes sense only for YAML streams. It means that + the structure is written as a flow (not as a block), which is more compact. It is + recommended to use this flag for structures or arrays whose elements are all scalars. +@param type_name Optional parameter - the object type name. In + case of XML it is written as a type_id attribute of the structure opening tag. In the case of + YAML it is written after a colon following the structure name (see the example in + CvFileStorage description). In case of JSON it is written as a name/value pair. + Mainly it is used with user objects. When the storage is read, the + encoded type name is used to determine the object type (see CvTypeInfo and cvFindType ). +@param attributes This parameter is not used in the current implementation + */ +CVAPI(void) cvStartWriteStruct( CvFileStorage* fs, const char* name, + int struct_flags, const char* type_name CV_DEFAULT(NULL), + CvAttrList attributes CV_DEFAULT(cvAttrList())); + +/** @brief Finishes writing to a file node collection. +@param fs File storage +@sa cvStartWriteStruct. + */ +CVAPI(void) cvEndWriteStruct( CvFileStorage* fs ); + +/** @brief Writes an integer value. + +The function writes a single integer value (with or without a name) to the file storage. +@param fs File storage +@param name Name of the written value. Should be NULL if and only if the parent structure is a +sequence. +@param value The written value + */ +CVAPI(void) cvWriteInt( CvFileStorage* fs, const char* name, int value ); + +/** @brief Writes a floating-point value. + +The function writes a single floating-point value (with or without a name) to file storage. Special +values are encoded as follows: NaN (Not A Number) as .NaN, infinity as +.Inf or -.Inf. + +The following example shows how to use the low-level writing functions to store custom structures, +such as termination criteria, without registering a new type. : +@code + void write_termcriteria( CvFileStorage* fs, const char* struct_name, + CvTermCriteria* termcrit ) + { + cvStartWriteStruct( fs, struct_name, CV_NODE_MAP, NULL, cvAttrList(0,0)); + cvWriteComment( fs, "termination criteria", 1 ); // just a description + if( termcrit->type & CV_TERMCRIT_ITER ) + cvWriteInteger( fs, "max_iterations", termcrit->max_iter ); + if( termcrit->type & CV_TERMCRIT_EPS ) + cvWriteReal( fs, "accuracy", termcrit->epsilon ); + cvEndWriteStruct( fs ); + } +@endcode +@param fs File storage +@param name Name of the written value. Should be NULL if and only if the parent structure is a +sequence. +@param value The written value +*/ +CVAPI(void) cvWriteReal( CvFileStorage* fs, const char* name, double value ); + +/** @brief Writes a text string. + +The function writes a text string to file storage. +@param fs File storage +@param name Name of the written string . Should be NULL if and only if the parent structure is a +sequence. +@param str The written text string +@param quote If non-zero, the written string is put in quotes, regardless of whether they are +required. Otherwise, if the flag is zero, quotes are used only when they are required (e.g. when +the string starts with a digit or contains spaces). + */ +CVAPI(void) cvWriteString( CvFileStorage* fs, const char* name, + const char* str, int quote CV_DEFAULT(0) ); + +/** @brief Writes a comment. + +The function writes a comment into file storage. The comments are skipped when the storage is read. +@param fs File storage +@param comment The written comment, single-line or multi-line +@param eol_comment If non-zero, the function tries to put the comment at the end of current line. +If the flag is zero, if the comment is multi-line, or if it does not fit at the end of the current +line, the comment starts a new line. + */ +CVAPI(void) cvWriteComment( CvFileStorage* fs, const char* comment, + int eol_comment ); + +/** @brief Writes an object to file storage. + +The function writes an object to file storage. First, the appropriate type info is found using +cvTypeOf. Then, the write method associated with the type info is called. + +Attributes are used to customize the writing procedure. The standard types support the following +attributes (all the dt attributes have the same format as in cvWriteRawData): + +-# CvSeq + - **header_dt** description of user fields of the sequence header that follow CvSeq, or + CvChain (if the sequence is a Freeman chain) or CvContour (if the sequence is a contour or + point sequence) + - **dt** description of the sequence elements. + - **recursive** if the attribute is present and is not equal to "0" or "false", the whole + tree of sequences (contours) is stored. +-# CvGraph + - **header_dt** description of user fields of the graph header that follows CvGraph; + - **vertex_dt** description of user fields of graph vertices + - **edge_dt** description of user fields of graph edges (note that the edge weight is + always written, so there is no need to specify it explicitly) + +Below is the code that creates the YAML file shown in the CvFileStorage description: +@code + #include "cxcore.h" + + int main( int argc, char** argv ) + { + CvMat* mat = cvCreateMat( 3, 3, CV_32F ); + CvFileStorage* fs = cvOpenFileStorage( "example.yml", 0, CV_STORAGE_WRITE ); + + cvSetIdentity( mat ); + cvWrite( fs, "A", mat, cvAttrList(0,0) ); + + cvReleaseFileStorage( &fs ); + cvReleaseMat( &mat ); + return 0; + } +@endcode +@param fs File storage +@param name Name of the written object. Should be NULL if and only if the parent structure is a +sequence. +@param ptr Pointer to the object +@param attributes The attributes of the object. They are specific for each particular type (see +the discussion below). + */ +CVAPI(void) cvWrite( CvFileStorage* fs, const char* name, const void* ptr, + CvAttrList attributes CV_DEFAULT(cvAttrList())); + +/** @brief Starts the next stream. + +The function finishes the currently written stream and starts the next stream. In the case of XML +the file with multiple streams looks like this: +@code{.xml} + + + + + + + ... +@endcode +The YAML file will look like this: +@code{.yaml} + %YAML 1.0 + # stream #1 data + ... + --- + # stream #2 data +@endcode +This is useful for concatenating files or for resuming the writing process. +@param fs File storage + */ +CVAPI(void) cvStartNextStream( CvFileStorage* fs ); + +/** @brief Writes multiple numbers. + +The function writes an array, whose elements consist of single or multiple numbers. The function +call can be replaced with a loop containing a few cvWriteInt and cvWriteReal calls, but a single +call is more efficient. Note that because none of the elements have a name, they should be written +to a sequence rather than a map. +@param fs File storage +@param src Pointer to the written array +@param len Number of the array elements to write +@param dt Specification of each array element, see @ref format_spec "format specification" + */ +CVAPI(void) cvWriteRawData( CvFileStorage* fs, const void* src, + int len, const char* dt ); + +/** @brief Writes multiple numbers in Base64. + +If either CV_STORAGE_WRITE_BASE64 or cv::FileStorage::WRITE_BASE64 is used, +this function will be the same as cvWriteRawData. If neither, the main +difference is that it outputs a sequence in Base64 encoding rather than +in plain text. + +This function can only be used to write a sequence with a type "binary". + +@param fs File storage +@param src Pointer to the written array +@param len Number of the array elements to write +@param dt Specification of each array element, see @ref format_spec "format specification" +*/ +CVAPI(void) cvWriteRawDataBase64( CvFileStorage* fs, const void* src, + int len, const char* dt ); + +/** @brief Returns a unique pointer for a given name. + +The function returns a unique pointer for each particular file node name. This pointer can be then +passed to the cvGetFileNode function that is faster than cvGetFileNodeByName because it compares +text strings by comparing pointers rather than the strings' content. + +Consider the following example where an array of points is encoded as a sequence of 2-entry maps: +@code + points: + - { x: 10, y: 10 } + - { x: 20, y: 20 } + - { x: 30, y: 30 } + # ... +@endcode +Then, it is possible to get hashed "x" and "y" pointers to speed up decoding of the points. : +@code + #include "cxcore.h" + + int main( int argc, char** argv ) + { + CvFileStorage* fs = cvOpenFileStorage( "points.yml", 0, CV_STORAGE_READ ); + CvStringHashNode* x_key = cvGetHashedNode( fs, "x", -1, 1 ); + CvStringHashNode* y_key = cvGetHashedNode( fs, "y", -1, 1 ); + CvFileNode* points = cvGetFileNodeByName( fs, 0, "points" ); + + if( CV_NODE_IS_SEQ(points->tag) ) + { + CvSeq* seq = points->data.seq; + int i, total = seq->total; + CvSeqReader reader; + cvStartReadSeq( seq, &reader, 0 ); + for( i = 0; i < total; i++ ) + { + CvFileNode* pt = (CvFileNode*)reader.ptr; + #if 1 // faster variant + CvFileNode* xnode = cvGetFileNode( fs, pt, x_key, 0 ); + CvFileNode* ynode = cvGetFileNode( fs, pt, y_key, 0 ); + assert( xnode && CV_NODE_IS_INT(xnode->tag) && + ynode && CV_NODE_IS_INT(ynode->tag)); + int x = xnode->data.i; // or x = cvReadInt( xnode, 0 ); + int y = ynode->data.i; // or y = cvReadInt( ynode, 0 ); + #elif 1 // slower variant; does not use x_key & y_key + CvFileNode* xnode = cvGetFileNodeByName( fs, pt, "x" ); + CvFileNode* ynode = cvGetFileNodeByName( fs, pt, "y" ); + assert( xnode && CV_NODE_IS_INT(xnode->tag) && + ynode && CV_NODE_IS_INT(ynode->tag)); + int x = xnode->data.i; // or x = cvReadInt( xnode, 0 ); + int y = ynode->data.i; // or y = cvReadInt( ynode, 0 ); + #else // the slowest yet the easiest to use variant + int x = cvReadIntByName( fs, pt, "x", 0 ); + int y = cvReadIntByName( fs, pt, "y", 0 ); + #endif + CV_NEXT_SEQ_ELEM( seq->elem_size, reader ); + printf(" + } + } + cvReleaseFileStorage( &fs ); + return 0; + } +@endcode +Please note that whatever method of accessing a map you are using, it is still much slower than +using plain sequences; for example, in the above example, it is more efficient to encode the points +as pairs of integers in a single numeric sequence. +@param fs File storage +@param name Literal node name +@param len Length of the name (if it is known apriori), or -1 if it needs to be calculated +@param create_missing Flag that specifies, whether an absent key should be added into the hash table +*/ +CVAPI(CvStringHashNode*) cvGetHashedKey( CvFileStorage* fs, const char* name, + int len CV_DEFAULT(-1), + int create_missing CV_DEFAULT(0)); + +/** @brief Retrieves one of the top-level nodes of the file storage. + +The function returns one of the top-level file nodes. The top-level nodes do not have a name, they +correspond to the streams that are stored one after another in the file storage. If the index is out +of range, the function returns a NULL pointer, so all the top-level nodes can be iterated by +subsequent calls to the function with stream_index=0,1,..., until the NULL pointer is returned. +This function can be used as a base for recursive traversal of the file storage. +@param fs File storage +@param stream_index Zero-based index of the stream. See cvStartNextStream . In most cases, +there is only one stream in the file; however, there can be several. + */ +CVAPI(CvFileNode*) cvGetRootFileNode( const CvFileStorage* fs, + int stream_index CV_DEFAULT(0) ); + +/** @brief Finds a node in a map or file storage. + +The function finds a file node. It is a faster version of cvGetFileNodeByName (see +cvGetHashedKey discussion). Also, the function can insert a new node, if it is not in the map yet. +@param fs File storage +@param map The parent map. If it is NULL, the function searches a top-level node. If both map and +key are NULLs, the function returns the root file node - a map that contains top-level nodes. +@param key Unique pointer to the node name, retrieved with cvGetHashedKey +@param create_missing Flag that specifies whether an absent node should be added to the map + */ +CVAPI(CvFileNode*) cvGetFileNode( CvFileStorage* fs, CvFileNode* map, + const CvStringHashNode* key, + int create_missing CV_DEFAULT(0) ); + +/** @brief Finds a node in a map or file storage. + +The function finds a file node by name. The node is searched either in map or, if the pointer is +NULL, among the top-level file storage nodes. Using this function for maps and cvGetSeqElem (or +sequence reader) for sequences, it is possible to navigate through the file storage. To speed up +multiple queries for a certain key (e.g., in the case of an array of structures) one may use a +combination of cvGetHashedKey and cvGetFileNode. +@param fs File storage +@param map The parent map. If it is NULL, the function searches in all the top-level nodes +(streams), starting with the first one. +@param name The file node name + */ +CVAPI(CvFileNode*) cvGetFileNodeByName( const CvFileStorage* fs, + const CvFileNode* map, + const char* name ); + +/** @brief Retrieves an integer value from a file node. + +The function returns an integer that is represented by the file node. If the file node is NULL, the +default_value is returned (thus, it is convenient to call the function right after cvGetFileNode +without checking for a NULL pointer). If the file node has type CV_NODE_INT, then node-\>data.i is +returned. If the file node has type CV_NODE_REAL, then node-\>data.f is converted to an integer +and returned. Otherwise the error is reported. +@param node File node +@param default_value The value that is returned if node is NULL + */ +CV_INLINE int cvReadInt( const CvFileNode* node, int default_value CV_DEFAULT(0) ) +{ + return !node ? default_value : + CV_NODE_IS_INT(node->tag) ? node->data.i : + CV_NODE_IS_REAL(node->tag) ? cvRound(node->data.f) : 0x7fffffff; +} + +/** @brief Finds a file node and returns its value. + +The function is a simple superposition of cvGetFileNodeByName and cvReadInt. +@param fs File storage +@param map The parent map. If it is NULL, the function searches a top-level node. +@param name The node name +@param default_value The value that is returned if the file node is not found + */ +CV_INLINE int cvReadIntByName( const CvFileStorage* fs, const CvFileNode* map, + const char* name, int default_value CV_DEFAULT(0) ) +{ + return cvReadInt( cvGetFileNodeByName( fs, map, name ), default_value ); +} + +/** @brief Retrieves a floating-point value from a file node. + +The function returns a floating-point value that is represented by the file node. If the file node +is NULL, the default_value is returned (thus, it is convenient to call the function right after +cvGetFileNode without checking for a NULL pointer). If the file node has type CV_NODE_REAL , +then node-\>data.f is returned. If the file node has type CV_NODE_INT , then node-:math:\>data.f +is converted to floating-point and returned. Otherwise the result is not determined. +@param node File node +@param default_value The value that is returned if node is NULL + */ +CV_INLINE double cvReadReal( const CvFileNode* node, double default_value CV_DEFAULT(0.) ) +{ + return !node ? default_value : + CV_NODE_IS_INT(node->tag) ? (double)node->data.i : + CV_NODE_IS_REAL(node->tag) ? node->data.f : 1e300; +} + +/** @brief Finds a file node and returns its value. + +The function is a simple superposition of cvGetFileNodeByName and cvReadReal . +@param fs File storage +@param map The parent map. If it is NULL, the function searches a top-level node. +@param name The node name +@param default_value The value that is returned if the file node is not found + */ +CV_INLINE double cvReadRealByName( const CvFileStorage* fs, const CvFileNode* map, + const char* name, double default_value CV_DEFAULT(0.) ) +{ + return cvReadReal( cvGetFileNodeByName( fs, map, name ), default_value ); +} + +/** @brief Retrieves a text string from a file node. + +The function returns a text string that is represented by the file node. If the file node is NULL, +the default_value is returned (thus, it is convenient to call the function right after +cvGetFileNode without checking for a NULL pointer). If the file node has type CV_NODE_STR , then +node-:math:\>data.str.ptr is returned. Otherwise the result is not determined. +@param node File node +@param default_value The value that is returned if node is NULL + */ +CV_INLINE const char* cvReadString( const CvFileNode* node, + const char* default_value CV_DEFAULT(NULL) ) +{ + return !node ? default_value : CV_NODE_IS_STRING(node->tag) ? node->data.str.ptr : 0; +} + +/** @brief Finds a file node by its name and returns its value. + +The function is a simple superposition of cvGetFileNodeByName and cvReadString . +@param fs File storage +@param map The parent map. If it is NULL, the function searches a top-level node. +@param name The node name +@param default_value The value that is returned if the file node is not found + */ +CV_INLINE const char* cvReadStringByName( const CvFileStorage* fs, const CvFileNode* map, + const char* name, const char* default_value CV_DEFAULT(NULL) ) +{ + return cvReadString( cvGetFileNodeByName( fs, map, name ), default_value ); +} + + +/** @brief Decodes an object and returns a pointer to it. + +The function decodes a user object (creates an object in a native representation from the file +storage subtree) and returns it. The object to be decoded must be an instance of a registered type +that supports the read method (see CvTypeInfo). The type of the object is determined by the type +name that is encoded in the file. If the object is a dynamic structure, it is created either in +memory storage and passed to cvOpenFileStorage or, if a NULL pointer was passed, in temporary +memory storage, which is released when cvReleaseFileStorage is called. Otherwise, if the object is +not a dynamic structure, it is created in a heap and should be released with a specialized function +or by using the generic cvRelease. +@param fs File storage +@param node The root object node +@param attributes Unused parameter + */ +CVAPI(void*) cvRead( CvFileStorage* fs, CvFileNode* node, + CvAttrList* attributes CV_DEFAULT(NULL)); + +/** @brief Finds an object by name and decodes it. + +The function is a simple superposition of cvGetFileNodeByName and cvRead. +@param fs File storage +@param map The parent map. If it is NULL, the function searches a top-level node. +@param name The node name +@param attributes Unused parameter + */ +CV_INLINE void* cvReadByName( CvFileStorage* fs, const CvFileNode* map, + const char* name, CvAttrList* attributes CV_DEFAULT(NULL) ) +{ + return cvRead( fs, cvGetFileNodeByName( fs, map, name ), attributes ); +} + + +/** @brief Initializes the file node sequence reader. + +The function initializes the sequence reader to read data from a file node. The initialized reader +can be then passed to cvReadRawDataSlice. +@param fs File storage +@param src The file node (a sequence) to read numbers from +@param reader Pointer to the sequence reader + */ +CVAPI(void) cvStartReadRawData( const CvFileStorage* fs, const CvFileNode* src, + CvSeqReader* reader ); + +/** @brief Initializes file node sequence reader. + +The function reads one or more elements from the file node, representing a sequence, to a +user-specified array. The total number of read sequence elements is a product of total and the +number of components in each array element. For example, if dt=2if, the function will read total\*3 +sequence elements. As with any sequence, some parts of the file node sequence can be skipped or read +repeatedly by repositioning the reader using cvSetSeqReaderPos. +@param fs File storage +@param reader The sequence reader. Initialize it with cvStartReadRawData . +@param count The number of elements to read +@param dst Pointer to the destination array +@param dt Specification of each array element. It has the same format as in cvWriteRawData . + */ +CVAPI(void) cvReadRawDataSlice( const CvFileStorage* fs, CvSeqReader* reader, + int count, void* dst, const char* dt ); + +/** @brief Reads multiple numbers. + +The function reads elements from a file node that represents a sequence of scalars. +@param fs File storage +@param src The file node (a sequence) to read numbers from +@param dst Pointer to the destination array +@param dt Specification of each array element. It has the same format as in cvWriteRawData . + */ +CVAPI(void) cvReadRawData( const CvFileStorage* fs, const CvFileNode* src, + void* dst, const char* dt ); + +/** @brief Writes a file node to another file storage. + +The function writes a copy of a file node to file storage. Possible applications of the function are +merging several file storages into one and conversion between XML, YAML and JSON formats. +@param fs Destination file storage +@param new_node_name New name of the file node in the destination file storage. To keep the +existing name, use cvcvGetFileNodeName +@param node The written node +@param embed If the written node is a collection and this parameter is not zero, no extra level of +hierarchy is created. Instead, all the elements of node are written into the currently written +structure. Of course, map elements can only be embedded into another map, and sequence elements +can only be embedded into another sequence. + */ +CVAPI(void) cvWriteFileNode( CvFileStorage* fs, const char* new_node_name, + const CvFileNode* node, int embed ); + +/** @brief Returns the name of a file node. + +The function returns the name of a file node or NULL, if the file node does not have a name or if +node is NULL. +@param node File node + */ +CVAPI(const char*) cvGetFileNodeName( const CvFileNode* node ); + +/*********************************** Adding own types ***********************************/ + +/** @brief Registers a new type. + +The function registers a new type, which is described by info . The function creates a copy of the +structure, so the user should delete it after calling the function. +@param info Type info structure + */ +CVAPI(void) cvRegisterType( const CvTypeInfo* info ); + +/** @brief Unregisters the type. + +The function unregisters a type with a specified name. If the name is unknown, it is possible to +locate the type info by an instance of the type using cvTypeOf or by iterating the type list, +starting from cvFirstType, and then calling cvUnregisterType(info-\>typeName). +@param type_name Name of an unregistered type + */ +CVAPI(void) cvUnregisterType( const char* type_name ); + +/** @brief Returns the beginning of a type list. + +The function returns the first type in the list of registered types. Navigation through the list can +be done via the prev and next fields of the CvTypeInfo structure. + */ +CVAPI(CvTypeInfo*) cvFirstType(void); + +/** @brief Finds a type by its name. + +The function finds a registered type by its name. It returns NULL if there is no type with the +specified name. +@param type_name Type name + */ +CVAPI(CvTypeInfo*) cvFindType( const char* type_name ); + +/** @brief Returns the type of an object. + +The function finds the type of a given object. It iterates through the list of registered types and +calls the is_instance function/method for every type info structure with that object until one of +them returns non-zero or until the whole list has been traversed. In the latter case, the function +returns NULL. +@param struct_ptr The object pointer + */ +CVAPI(CvTypeInfo*) cvTypeOf( const void* struct_ptr ); + +/** @brief Releases an object. + +The function finds the type of a given object and calls release with the double pointer. +@param struct_ptr Double pointer to the object + */ +CVAPI(void) cvRelease( void** struct_ptr ); + +/** @brief Makes a clone of an object. + +The function finds the type of a given object and calls clone with the passed object. Of course, if +you know the object type, for example, struct_ptr is CvMat\*, it is faster to call the specific +function, like cvCloneMat. +@param struct_ptr The object to clone + */ +CVAPI(void*) cvClone( const void* struct_ptr ); + +/** @brief Saves an object to a file. + +The function saves an object to a file. It provides a simple interface to cvWrite . +@param filename File name +@param struct_ptr Object to save +@param name Optional object name. If it is NULL, the name will be formed from filename . +@param comment Optional comment to put in the beginning of the file +@param attributes Optional attributes passed to cvWrite + */ +CVAPI(void) cvSave( const char* filename, const void* struct_ptr, + const char* name CV_DEFAULT(NULL), + const char* comment CV_DEFAULT(NULL), + CvAttrList attributes CV_DEFAULT(cvAttrList())); + +/** @brief Loads an object from a file. + +The function loads an object from a file. It basically reads the specified file, find the first +top-level node and calls cvRead for that node. If the file node does not have type information or +the type information can not be found by the type name, the function returns NULL. After the object +is loaded, the file storage is closed and all the temporary buffers are deleted. Thus, to load a +dynamic structure, such as a sequence, contour, or graph, one should pass a valid memory storage +destination to the function. +@param filename File name +@param memstorage Memory storage for dynamic structures, such as CvSeq or CvGraph . It is not used +for matrices or images. +@param name Optional object name. If it is NULL, the first top-level object in the storage will be +loaded. +@param real_name Optional output parameter that will contain the name of the loaded object +(useful if name=NULL ) + */ +CVAPI(void*) cvLoad( const char* filename, + CvMemStorage* memstorage CV_DEFAULT(NULL), + const char* name CV_DEFAULT(NULL), + const char** real_name CV_DEFAULT(NULL) ); + +/*********************************** Measuring Execution Time ***************************/ + +/** helper functions for RNG initialization and accurate time measurement: + uses internal clock counter on x86 */ +CVAPI(int64) cvGetTickCount( void ); +CVAPI(double) cvGetTickFrequency( void ); + +/*********************************** CPU capabilities ***********************************/ + +CVAPI(int) cvCheckHardwareSupport(int feature); + +/*********************************** Multi-Threading ************************************/ + +/** retrieve/set the number of threads used in OpenMP implementations */ +CVAPI(int) cvGetNumThreads( void ); +CVAPI(void) cvSetNumThreads( int threads CV_DEFAULT(0) ); +/** get index of the thread being executed */ +CVAPI(int) cvGetThreadNum( void ); + + +/********************************** Error Handling **************************************/ + +/** Get current OpenCV error status */ +CVAPI(int) cvGetErrStatus( void ); + +/** Sets error status silently */ +CVAPI(void) cvSetErrStatus( int status ); + +#define CV_ErrModeLeaf 0 /* Print error and exit program */ +#define CV_ErrModeParent 1 /* Print error and continue */ +#define CV_ErrModeSilent 2 /* Don't print and continue */ + +/** Retrieves current error processing mode */ +CVAPI(int) cvGetErrMode( void ); + +/** Sets error processing mode, returns previously used mode */ +CVAPI(int) cvSetErrMode( int mode ); + +/** Sets error status and performs some additional actions (displaying message box, + writing message to stderr, terminating application etc.) + depending on the current error mode */ +CVAPI(void) cvError( int status, const char* func_name, + const char* err_msg, const char* file_name, int line ); + +/** Retrieves textual description of the error given its code */ +CVAPI(const char*) cvErrorStr( int status ); + +/** Retrieves detailed information about the last error occurred */ +CVAPI(int) cvGetErrInfo( const char** errcode_desc, const char** description, + const char** filename, int* line ); + +/** Maps IPP error codes to the counterparts from OpenCV */ +CVAPI(int) cvErrorFromIppStatus( int ipp_status ); + +typedef int (CV_CDECL *CvErrorCallback)( int status, const char* func_name, + const char* err_msg, const char* file_name, int line, void* userdata ); + +/** Assigns a new error-handling function */ +CVAPI(CvErrorCallback) cvRedirectError( CvErrorCallback error_handler, + void* userdata CV_DEFAULT(NULL), + void** prev_userdata CV_DEFAULT(NULL) ); + +/** Output nothing */ +CVAPI(int) cvNulDevReport( int status, const char* func_name, const char* err_msg, + const char* file_name, int line, void* userdata ); + +/** Output to console(fprintf(stderr,...)) */ +CVAPI(int) cvStdErrReport( int status, const char* func_name, const char* err_msg, + const char* file_name, int line, void* userdata ); + +/** Output to MessageBox(WIN32) */ +CVAPI(int) cvGuiBoxReport( int status, const char* func_name, const char* err_msg, + const char* file_name, int line, void* userdata ); + +#define OPENCV_ERROR(status,func,context) \ +cvError((status),(func),(context),__FILE__,__LINE__) + +#define OPENCV_ASSERT(expr,func,context) \ +{if (! (expr)) \ +{OPENCV_ERROR(CV_StsInternal,(func),(context));}} + +#define OPENCV_CALL( Func ) \ +{ \ +Func; \ +} + + +/** CV_FUNCNAME macro defines icvFuncName constant which is used by CV_ERROR macro */ +#ifdef CV_NO_FUNC_NAMES +#define CV_FUNCNAME( Name ) +#define cvFuncName "" +#else +#define CV_FUNCNAME( Name ) \ +static char cvFuncName[] = Name +#endif + + +/** + CV_ERROR macro unconditionally raises error with passed code and message. + After raising error, control will be transferred to the exit label. + */ +#define CV_ERROR( Code, Msg ) \ +{ \ + cvError( (Code), cvFuncName, Msg, __FILE__, __LINE__ ); \ + __CV_EXIT__; \ +} + +/** + CV_CHECK macro checks error status after CV (or IPL) + function call. If error detected, control will be transferred to the exit + label. + */ +#define CV_CHECK() \ +{ \ + if( cvGetErrStatus() < 0 ) \ + CV_ERROR( CV_StsBackTrace, "Inner function failed." ); \ +} + + +/** + CV_CALL macro calls CV (or IPL) function, checks error status and + signals a error if the function failed. Useful in "parent node" + error processing mode + */ +#define CV_CALL( Func ) \ +{ \ + Func; \ + CV_CHECK(); \ +} + + +/** Runtime assertion macro */ +#define CV_ASSERT( Condition ) \ +{ \ + if( !(Condition) ) \ + CV_ERROR( CV_StsInternal, "Assertion: " #Condition " failed" ); \ +} + +#define __CV_BEGIN__ { +#define __CV_END__ goto exit; exit: ; } +#define __CV_EXIT__ goto exit + +/** @} core_c */ + +#ifdef __cplusplus +} // extern "C" +#endif + +#ifdef __cplusplus + +//! @addtogroup core_c_glue +//! @{ + +//! class for automatic module/RTTI data registration/unregistration +struct CV_EXPORTS CvType +{ + CvType( const char* type_name, + CvIsInstanceFunc is_instance, CvReleaseFunc release=0, + CvReadFunc read=0, CvWriteFunc write=0, CvCloneFunc clone=0 ); + ~CvType(); + CvTypeInfo* info; + + static CvTypeInfo* first; + static CvTypeInfo* last; +}; + +//! @} + +#include "opencv2/core/utility.hpp" + +namespace cv +{ + +//! @addtogroup core_c_glue +//! @{ + +/////////////////////////////////////////// glue /////////////////////////////////////////// + +//! converts array (CvMat or IplImage) to cv::Mat +CV_EXPORTS Mat cvarrToMat(const CvArr* arr, bool copyData=false, + bool allowND=true, int coiMode=0, + AutoBuffer* buf=0); + +static inline Mat cvarrToMatND(const CvArr* arr, bool copyData=false, int coiMode=0) +{ + return cvarrToMat(arr, copyData, true, coiMode); +} + + +//! extracts Channel of Interest from CvMat or IplImage and makes cv::Mat out of it. +CV_EXPORTS void extractImageCOI(const CvArr* arr, OutputArray coiimg, int coi=-1); +//! inserts single-channel cv::Mat into a multi-channel CvMat or IplImage +CV_EXPORTS void insertImageCOI(InputArray coiimg, CvArr* arr, int coi=-1); + + + +////// specialized implementations of DefaultDeleter::operator() for classic OpenCV types ////// + +template<> CV_EXPORTS void DefaultDeleter::operator ()(CvMat* obj) const; +template<> CV_EXPORTS void DefaultDeleter::operator ()(IplImage* obj) const; +template<> CV_EXPORTS void DefaultDeleter::operator ()(CvMatND* obj) const; +template<> CV_EXPORTS void DefaultDeleter::operator ()(CvSparseMat* obj) const; +template<> CV_EXPORTS void DefaultDeleter::operator ()(CvMemStorage* obj) const; + +////////////// convenient wrappers for operating old-style dynamic structures ////////////// + +template class SeqIterator; + +typedef Ptr MemStorage; + +/*! + Template Sequence Class derived from CvSeq + + The class provides more convenient access to sequence elements, + STL-style operations and iterators. + + \note The class is targeted for simple data types, + i.e. no constructors or destructors + are called for the sequence elements. +*/ +template class Seq +{ +public: + typedef SeqIterator<_Tp> iterator; + typedef SeqIterator<_Tp> const_iterator; + + //! the default constructor + Seq(); + //! the constructor for wrapping CvSeq structure. The real element type in CvSeq should match _Tp. + Seq(const CvSeq* seq); + //! creates the empty sequence that resides in the specified storage + Seq(MemStorage& storage, int headerSize = sizeof(CvSeq)); + //! returns read-write reference to the specified element + _Tp& operator [](int idx); + //! returns read-only reference to the specified element + const _Tp& operator[](int idx) const; + //! returns iterator pointing to the beginning of the sequence + SeqIterator<_Tp> begin() const; + //! returns iterator pointing to the element following the last sequence element + SeqIterator<_Tp> end() const; + //! returns the number of elements in the sequence + size_t size() const; + //! returns the type of sequence elements (CV_8UC1 ... CV_64FC(CV_CN_MAX) ...) + int type() const; + //! returns the depth of sequence elements (CV_8U ... CV_64F) + int depth() const; + //! returns the number of channels in each sequence element + int channels() const; + //! returns the size of each sequence element + size_t elemSize() const; + //! returns index of the specified sequence element + size_t index(const _Tp& elem) const; + //! appends the specified element to the end of the sequence + void push_back(const _Tp& elem); + //! appends the specified element to the front of the sequence + void push_front(const _Tp& elem); + //! appends zero or more elements to the end of the sequence + void push_back(const _Tp* elems, size_t count); + //! appends zero or more elements to the front of the sequence + void push_front(const _Tp* elems, size_t count); + //! inserts the specified element to the specified position + void insert(int idx, const _Tp& elem); + //! inserts zero or more elements to the specified position + void insert(int idx, const _Tp* elems, size_t count); + //! removes element at the specified position + void remove(int idx); + //! removes the specified subsequence + void remove(const Range& r); + + //! returns reference to the first sequence element + _Tp& front(); + //! returns read-only reference to the first sequence element + const _Tp& front() const; + //! returns reference to the last sequence element + _Tp& back(); + //! returns read-only reference to the last sequence element + const _Tp& back() const; + //! returns true iff the sequence contains no elements + bool empty() const; + + //! removes all the elements from the sequence + void clear(); + //! removes the first element from the sequence + void pop_front(); + //! removes the last element from the sequence + void pop_back(); + //! removes zero or more elements from the beginning of the sequence + void pop_front(_Tp* elems, size_t count); + //! removes zero or more elements from the end of the sequence + void pop_back(_Tp* elems, size_t count); + + //! copies the whole sequence or the sequence slice to the specified vector + void copyTo(std::vector<_Tp>& vec, const Range& range=Range::all()) const; + //! returns the vector containing all the sequence elements + operator std::vector<_Tp>() const; + + CvSeq* seq; +}; + + +/*! + STL-style Sequence Iterator inherited from the CvSeqReader structure +*/ +template class SeqIterator : public CvSeqReader +{ +public: + //! the default constructor + SeqIterator(); + //! the constructor setting the iterator to the beginning or to the end of the sequence + SeqIterator(const Seq<_Tp>& seq, bool seekEnd=false); + //! positions the iterator within the sequence + void seek(size_t pos); + //! reports the current iterator position + size_t tell() const; + //! returns reference to the current sequence element + _Tp& operator *(); + //! returns read-only reference to the current sequence element + const _Tp& operator *() const; + //! moves iterator to the next sequence element + SeqIterator& operator ++(); + //! moves iterator to the next sequence element + SeqIterator operator ++(int) const; + //! moves iterator to the previous sequence element + SeqIterator& operator --(); + //! moves iterator to the previous sequence element + SeqIterator operator --(int) const; + + //! moves iterator forward by the specified offset (possibly negative) + SeqIterator& operator +=(int); + //! moves iterator backward by the specified offset (possibly negative) + SeqIterator& operator -=(int); + + // this is index of the current element module seq->total*2 + // (to distinguish between 0 and seq->total) + int index; +}; + + + +// bridge C++ => C Seq API +CV_EXPORTS schar* seqPush( CvSeq* seq, const void* element=0); +CV_EXPORTS schar* seqPushFront( CvSeq* seq, const void* element=0); +CV_EXPORTS void seqPop( CvSeq* seq, void* element=0); +CV_EXPORTS void seqPopFront( CvSeq* seq, void* element=0); +CV_EXPORTS void seqPopMulti( CvSeq* seq, void* elements, + int count, int in_front=0 ); +CV_EXPORTS void seqRemove( CvSeq* seq, int index ); +CV_EXPORTS void clearSeq( CvSeq* seq ); +CV_EXPORTS schar* getSeqElem( const CvSeq* seq, int index ); +CV_EXPORTS void seqRemoveSlice( CvSeq* seq, CvSlice slice ); +CV_EXPORTS void seqInsertSlice( CvSeq* seq, int before_index, const CvArr* from_arr ); + +template inline Seq<_Tp>::Seq() : seq(0) {} +template inline Seq<_Tp>::Seq( const CvSeq* _seq ) : seq((CvSeq*)_seq) +{ + CV_Assert(!_seq || _seq->elem_size == sizeof(_Tp)); +} + +template inline Seq<_Tp>::Seq( MemStorage& storage, + int headerSize ) +{ + CV_Assert(headerSize >= (int)sizeof(CvSeq)); + seq = cvCreateSeq(DataType<_Tp>::type, headerSize, sizeof(_Tp), storage); +} + +template inline _Tp& Seq<_Tp>::operator [](int idx) +{ return *(_Tp*)getSeqElem(seq, idx); } + +template inline const _Tp& Seq<_Tp>::operator [](int idx) const +{ return *(_Tp*)getSeqElem(seq, idx); } + +template inline SeqIterator<_Tp> Seq<_Tp>::begin() const +{ return SeqIterator<_Tp>(*this); } + +template inline SeqIterator<_Tp> Seq<_Tp>::end() const +{ return SeqIterator<_Tp>(*this, true); } + +template inline size_t Seq<_Tp>::size() const +{ return seq ? seq->total : 0; } + +template inline int Seq<_Tp>::type() const +{ return seq ? CV_MAT_TYPE(seq->flags) : 0; } + +template inline int Seq<_Tp>::depth() const +{ return seq ? CV_MAT_DEPTH(seq->flags) : 0; } + +template inline int Seq<_Tp>::channels() const +{ return seq ? CV_MAT_CN(seq->flags) : 0; } + +template inline size_t Seq<_Tp>::elemSize() const +{ return seq ? seq->elem_size : 0; } + +template inline size_t Seq<_Tp>::index(const _Tp& elem) const +{ return cvSeqElemIdx(seq, &elem); } + +template inline void Seq<_Tp>::push_back(const _Tp& elem) +{ cvSeqPush(seq, &elem); } + +template inline void Seq<_Tp>::push_front(const _Tp& elem) +{ cvSeqPushFront(seq, &elem); } + +template inline void Seq<_Tp>::push_back(const _Tp* elem, size_t count) +{ cvSeqPushMulti(seq, elem, (int)count, 0); } + +template inline void Seq<_Tp>::push_front(const _Tp* elem, size_t count) +{ cvSeqPushMulti(seq, elem, (int)count, 1); } + +template inline _Tp& Seq<_Tp>::back() +{ return *(_Tp*)getSeqElem(seq, -1); } + +template inline const _Tp& Seq<_Tp>::back() const +{ return *(const _Tp*)getSeqElem(seq, -1); } + +template inline _Tp& Seq<_Tp>::front() +{ return *(_Tp*)getSeqElem(seq, 0); } + +template inline const _Tp& Seq<_Tp>::front() const +{ return *(const _Tp*)getSeqElem(seq, 0); } + +template inline bool Seq<_Tp>::empty() const +{ return !seq || seq->total == 0; } + +template inline void Seq<_Tp>::clear() +{ if(seq) clearSeq(seq); } + +template inline void Seq<_Tp>::pop_back() +{ seqPop(seq); } + +template inline void Seq<_Tp>::pop_front() +{ seqPopFront(seq); } + +template inline void Seq<_Tp>::pop_back(_Tp* elem, size_t count) +{ seqPopMulti(seq, elem, (int)count, 0); } + +template inline void Seq<_Tp>::pop_front(_Tp* elem, size_t count) +{ seqPopMulti(seq, elem, (int)count, 1); } + +template inline void Seq<_Tp>::insert(int idx, const _Tp& elem) +{ seqInsert(seq, idx, &elem); } + +template inline void Seq<_Tp>::insert(int idx, const _Tp* elems, size_t count) +{ + CvMat m = cvMat(1, count, DataType<_Tp>::type, elems); + seqInsertSlice(seq, idx, &m); +} + +template inline void Seq<_Tp>::remove(int idx) +{ seqRemove(seq, idx); } + +template inline void Seq<_Tp>::remove(const Range& r) +{ seqRemoveSlice(seq, cvSlice(r.start, r.end)); } + +template inline void Seq<_Tp>::copyTo(std::vector<_Tp>& vec, const Range& range) const +{ + size_t len = !seq ? 0 : range == Range::all() ? seq->total : range.end - range.start; + vec.resize(len); + if( seq && len ) + cvCvtSeqToArray(seq, &vec[0], cvSlice(range)); +} + +template inline Seq<_Tp>::operator std::vector<_Tp>() const +{ + std::vector<_Tp> vec; + copyTo(vec); + return vec; +} + +template inline SeqIterator<_Tp>::SeqIterator() +{ memset(this, 0, sizeof(*this)); } + +template inline SeqIterator<_Tp>::SeqIterator(const Seq<_Tp>& _seq, bool seekEnd) +{ + cvStartReadSeq(_seq.seq, this); + index = seekEnd ? _seq.seq->total : 0; +} + +template inline void SeqIterator<_Tp>::seek(size_t pos) +{ + cvSetSeqReaderPos(this, (int)pos, false); + index = pos; +} + +template inline size_t SeqIterator<_Tp>::tell() const +{ return index; } + +template inline _Tp& SeqIterator<_Tp>::operator *() +{ return *(_Tp*)ptr; } + +template inline const _Tp& SeqIterator<_Tp>::operator *() const +{ return *(const _Tp*)ptr; } + +template inline SeqIterator<_Tp>& SeqIterator<_Tp>::operator ++() +{ + CV_NEXT_SEQ_ELEM(sizeof(_Tp), *this); + if( ++index >= seq->total*2 ) + index = 0; + return *this; +} + +template inline SeqIterator<_Tp> SeqIterator<_Tp>::operator ++(int) const +{ + SeqIterator<_Tp> it = *this; + ++*this; + return it; +} + +template inline SeqIterator<_Tp>& SeqIterator<_Tp>::operator --() +{ + CV_PREV_SEQ_ELEM(sizeof(_Tp), *this); + if( --index < 0 ) + index = seq->total*2-1; + return *this; +} + +template inline SeqIterator<_Tp> SeqIterator<_Tp>::operator --(int) const +{ + SeqIterator<_Tp> it = *this; + --*this; + return it; +} + +template inline SeqIterator<_Tp>& SeqIterator<_Tp>::operator +=(int delta) +{ + cvSetSeqReaderPos(this, delta, 1); + index += delta; + int n = seq->total*2; + if( index < 0 ) + index += n; + if( index >= n ) + index -= n; + return *this; +} + +template inline SeqIterator<_Tp>& SeqIterator<_Tp>::operator -=(int delta) +{ + return (*this += -delta); +} + +template inline ptrdiff_t operator - (const SeqIterator<_Tp>& a, + const SeqIterator<_Tp>& b) +{ + ptrdiff_t delta = a.index - b.index, n = a.seq->total; + if( delta > n || delta < -n ) + delta += delta < 0 ? n : -n; + return delta; +} + +template inline bool operator == (const SeqIterator<_Tp>& a, + const SeqIterator<_Tp>& b) +{ + return a.seq == b.seq && a.index == b.index; +} + +template inline bool operator != (const SeqIterator<_Tp>& a, + const SeqIterator<_Tp>& b) +{ + return !(a == b); +} + +//! @} + +} // cv + +#endif + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda.hpp new file mode 100644 index 0000000000000000000000000000000000000000..820aba71ecb7276fc6a08aff3d50598b85daa11e --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda.hpp @@ -0,0 +1,1049 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_CUDA_HPP +#define OPENCV_CORE_CUDA_HPP + +#ifndef __cplusplus +# error cuda.hpp header must be compiled as C++ +#endif + +#include "opencv2/core.hpp" +#include "opencv2/core/cuda_types.hpp" + +/** + @defgroup cuda CUDA-accelerated Computer Vision + @{ + @defgroup cudacore Core part + @{ + @defgroup cudacore_init Initialization and Information + @defgroup cudacore_struct Data Structures + @} + @} + */ + +namespace cv { namespace cuda { + +//! @addtogroup cudacore_struct +//! @{ + +//=================================================================================== +// GpuMat +//=================================================================================== + +/** @brief Base storage class for GPU memory with reference counting. + +Its interface matches the Mat interface with the following limitations: + +- no arbitrary dimensions support (only 2D) +- no functions that return references to their data (because references on GPU are not valid for + CPU) +- no expression templates technique support + +Beware that the latter limitation may lead to overloaded matrix operators that cause memory +allocations. The GpuMat class is convertible to cuda::PtrStepSz and cuda::PtrStep so it can be +passed directly to the kernel. + +@note In contrast with Mat, in most cases GpuMat::isContinuous() == false . This means that rows are +aligned to a size depending on the hardware. Single-row GpuMat is always a continuous matrix. + +@note You are not recommended to leave static or global GpuMat variables allocated, that is, to rely +on its destructor. The destruction order of such variables and CUDA context is undefined. GPU memory +release function returns error if the CUDA context has been destroyed before. + +Some member functions are described as a "Blocking Call" while some are described as a +"Non-Blocking Call". Blocking functions are synchronous to host. It is guaranteed that the GPU +operation is finished when the function returns. However, non-blocking functions are asynchronous to +host. Those functions may return even if the GPU operation is not finished. + +Compared to their blocking counterpart, non-blocking functions accept Stream as an additional +argument. If a non-default stream is passed, the GPU operation may overlap with operations in other +streams. + +@sa Mat + */ +class CV_EXPORTS GpuMat +{ +public: + class CV_EXPORTS Allocator + { + public: + virtual ~Allocator() {} + + // allocator must fill data, step and refcount fields + virtual bool allocate(GpuMat* mat, int rows, int cols, size_t elemSize) = 0; + virtual void free(GpuMat* mat) = 0; + }; + + //! default allocator + static Allocator* defaultAllocator(); + static void setDefaultAllocator(Allocator* allocator); + + //! default constructor + explicit GpuMat(Allocator* allocator = defaultAllocator()); + + //! constructs GpuMat of the specified size and type + GpuMat(int rows, int cols, int type, Allocator* allocator = defaultAllocator()); + GpuMat(Size size, int type, Allocator* allocator = defaultAllocator()); + + //! constucts GpuMat and fills it with the specified value _s + GpuMat(int rows, int cols, int type, Scalar s, Allocator* allocator = defaultAllocator()); + GpuMat(Size size, int type, Scalar s, Allocator* allocator = defaultAllocator()); + + //! copy constructor + GpuMat(const GpuMat& m); + + //! constructor for GpuMat headers pointing to user-allocated data + GpuMat(int rows, int cols, int type, void* data, size_t step = Mat::AUTO_STEP); + GpuMat(Size size, int type, void* data, size_t step = Mat::AUTO_STEP); + + //! creates a GpuMat header for a part of the bigger matrix + GpuMat(const GpuMat& m, Range rowRange, Range colRange); + GpuMat(const GpuMat& m, Rect roi); + + //! builds GpuMat from host memory (Blocking call) + explicit GpuMat(InputArray arr, Allocator* allocator = defaultAllocator()); + + //! destructor - calls release() + ~GpuMat(); + + //! assignment operators + GpuMat& operator =(const GpuMat& m); + + //! allocates new GpuMat data unless the GpuMat already has specified size and type + void create(int rows, int cols, int type); + void create(Size size, int type); + + //! decreases reference counter, deallocate the data when reference counter reaches 0 + void release(); + + //! swaps with other smart pointer + void swap(GpuMat& mat); + + /** @brief Performs data upload to GpuMat (Blocking call) + + This function copies data from host memory to device memory. As being a blocking call, it is + guaranteed that the copy operation is finished when this function returns. + */ + void upload(InputArray arr); + + /** @brief Performs data upload to GpuMat (Non-Blocking call) + + This function copies data from host memory to device memory. As being a non-blocking call, this + function may return even if the copy operation is not finished. + + The copy operation may be overlapped with operations in other non-default streams if \p stream is + not the default stream and \p dst is HostMem allocated with HostMem::PAGE_LOCKED option. + */ + void upload(InputArray arr, Stream& stream); + + /** @brief Performs data download from GpuMat (Blocking call) + + This function copies data from device memory to host memory. As being a blocking call, it is + guaranteed that the copy operation is finished when this function returns. + */ + void download(OutputArray dst) const; + + /** @brief Performs data download from GpuMat (Non-Blocking call) + + This function copies data from device memory to host memory. As being a non-blocking call, this + function may return even if the copy operation is not finished. + + The copy operation may be overlapped with operations in other non-default streams if \p stream is + not the default stream and \p dst is HostMem allocated with HostMem::PAGE_LOCKED option. + */ + void download(OutputArray dst, Stream& stream) const; + + //! returns deep copy of the GpuMat, i.e. the data is copied + GpuMat clone() const; + + //! copies the GpuMat content to device memory (Blocking call) + void copyTo(OutputArray dst) const; + + //! copies the GpuMat content to device memory (Non-Blocking call) + void copyTo(OutputArray dst, Stream& stream) const; + + //! copies those GpuMat elements to "m" that are marked with non-zero mask elements (Blocking call) + void copyTo(OutputArray dst, InputArray mask) const; + + //! copies those GpuMat elements to "m" that are marked with non-zero mask elements (Non-Blocking call) + void copyTo(OutputArray dst, InputArray mask, Stream& stream) const; + + //! sets some of the GpuMat elements to s (Blocking call) + GpuMat& setTo(Scalar s); + + //! sets some of the GpuMat elements to s (Non-Blocking call) + GpuMat& setTo(Scalar s, Stream& stream); + + //! sets some of the GpuMat elements to s, according to the mask (Blocking call) + GpuMat& setTo(Scalar s, InputArray mask); + + //! sets some of the GpuMat elements to s, according to the mask (Non-Blocking call) + GpuMat& setTo(Scalar s, InputArray mask, Stream& stream); + + //! converts GpuMat to another datatype (Blocking call) + void convertTo(OutputArray dst, int rtype) const; + + //! converts GpuMat to another datatype (Non-Blocking call) + void convertTo(OutputArray dst, int rtype, Stream& stream) const; + + //! converts GpuMat to another datatype with scaling (Blocking call) + void convertTo(OutputArray dst, int rtype, double alpha, double beta = 0.0) const; + + //! converts GpuMat to another datatype with scaling (Non-Blocking call) + void convertTo(OutputArray dst, int rtype, double alpha, Stream& stream) const; + + //! converts GpuMat to another datatype with scaling (Non-Blocking call) + void convertTo(OutputArray dst, int rtype, double alpha, double beta, Stream& stream) const; + + void assignTo(GpuMat& m, int type=-1) const; + + //! returns pointer to y-th row + uchar* ptr(int y = 0); + const uchar* ptr(int y = 0) const; + + //! template version of the above method + template _Tp* ptr(int y = 0); + template const _Tp* ptr(int y = 0) const; + + template operator PtrStepSz<_Tp>() const; + template operator PtrStep<_Tp>() const; + + //! returns a new GpuMat header for the specified row + GpuMat row(int y) const; + + //! returns a new GpuMat header for the specified column + GpuMat col(int x) const; + + //! ... for the specified row span + GpuMat rowRange(int startrow, int endrow) const; + GpuMat rowRange(Range r) const; + + //! ... for the specified column span + GpuMat colRange(int startcol, int endcol) const; + GpuMat colRange(Range r) const; + + //! extracts a rectangular sub-GpuMat (this is a generalized form of row, rowRange etc.) + GpuMat operator ()(Range rowRange, Range colRange) const; + GpuMat operator ()(Rect roi) const; + + //! creates alternative GpuMat header for the same data, with different + //! number of channels and/or different number of rows + GpuMat reshape(int cn, int rows = 0) const; + + //! locates GpuMat header within a parent GpuMat + void locateROI(Size& wholeSize, Point& ofs) const; + + //! moves/resizes the current GpuMat ROI inside the parent GpuMat + GpuMat& adjustROI(int dtop, int dbottom, int dleft, int dright); + + //! returns true iff the GpuMat data is continuous + //! (i.e. when there are no gaps between successive rows) + bool isContinuous() const; + + //! returns element size in bytes + size_t elemSize() const; + + //! returns the size of element channel in bytes + size_t elemSize1() const; + + //! returns element type + int type() const; + + //! returns element type + int depth() const; + + //! returns number of channels + int channels() const; + + //! returns step/elemSize1() + size_t step1() const; + + //! returns GpuMat size : width == number of columns, height == number of rows + Size size() const; + + //! returns true if GpuMat data is NULL + bool empty() const; + + //! internal use method: updates the continuity flag + void updateContinuityFlag(); + + /*! includes several bit-fields: + - the magic signature + - continuity flag + - depth + - number of channels + */ + int flags; + + //! the number of rows and columns + int rows, cols; + + //! a distance between successive rows in bytes; includes the gap if any + size_t step; + + //! pointer to the data + uchar* data; + + //! pointer to the reference counter; + //! when GpuMat points to user-allocated data, the pointer is NULL + int* refcount; + + //! helper fields used in locateROI and adjustROI + uchar* datastart; + const uchar* dataend; + + //! allocator + Allocator* allocator; +}; + +/** @brief Creates a continuous matrix. + +@param rows Row count. +@param cols Column count. +@param type Type of the matrix. +@param arr Destination matrix. This parameter changes only if it has a proper type and area ( +\f$\texttt{rows} \times \texttt{cols}\f$ ). + +Matrix is called continuous if its elements are stored continuously, that is, without gaps at the +end of each row. + */ +CV_EXPORTS void createContinuous(int rows, int cols, int type, OutputArray arr); + +/** @brief Ensures that the size of a matrix is big enough and the matrix has a proper type. + +@param rows Minimum desired number of rows. +@param cols Minimum desired number of columns. +@param type Desired matrix type. +@param arr Destination matrix. + +The function does not reallocate memory if the matrix has proper attributes already. + */ +CV_EXPORTS void ensureSizeIsEnough(int rows, int cols, int type, OutputArray arr); + +/** @brief BufferPool for use with CUDA streams + +BufferPool utilizes Stream's allocator to create new buffers for GpuMat's. It is +only useful when enabled with #setBufferPoolUsage. + +@code + setBufferPoolUsage(true); +@endcode + +@note #setBufferPoolUsage must be called \em before any Stream declaration. + +Users may specify custom allocator for Stream and may implement their own stream based +functions utilizing the same underlying GPU memory management. + +If custom allocator is not specified, BufferPool utilizes StackAllocator by +default. StackAllocator allocates a chunk of GPU device memory beforehand, +and when GpuMat is declared later on, it is given the pre-allocated memory. +This kind of strategy reduces the number of calls for memory allocating APIs +such as cudaMalloc or cudaMallocPitch. + +Below is an example that utilizes BufferPool with StackAllocator: + +@code + #include + + using namespace cv; + using namespace cv::cuda + + int main() + { + setBufferPoolUsage(true); // Tell OpenCV that we are going to utilize BufferPool + setBufferPoolConfig(getDevice(), 1024 * 1024 * 64, 2); // Allocate 64 MB, 2 stacks (default is 10 MB, 5 stacks) + + Stream stream1, stream2; // Each stream uses 1 stack + BufferPool pool1(stream1), pool2(stream2); + + GpuMat d_src1 = pool1.getBuffer(4096, 4096, CV_8UC1); // 16MB + GpuMat d_dst1 = pool1.getBuffer(4096, 4096, CV_8UC3); // 48MB, pool1 is now full + + GpuMat d_src2 = pool2.getBuffer(1024, 1024, CV_8UC1); // 1MB + GpuMat d_dst2 = pool2.getBuffer(1024, 1024, CV_8UC3); // 3MB + + cvtColor(d_src1, d_dst1, CV_GRAY2BGR, 0, stream1); + cvtColor(d_src2, d_dst2, CV_GRAY2BGR, 0, stream2); + } +@endcode + +If we allocate another GpuMat on pool1 in the above example, it will be carried out by +the DefaultAllocator since the stack for pool1 is full. + +@code + GpuMat d_add1 = pool1.getBuffer(1024, 1024, CV_8UC1); // Stack for pool1 is full, memory is allocated with DefaultAllocator +@endcode + +If a third stream is declared in the above example, allocating with #getBuffer +within that stream will also be carried out by the DefaultAllocator because we've run out of +stacks. + +@code + Stream stream3; // Only 2 stacks were allocated, we've run out of stacks + BufferPool pool3(stream3); + GpuMat d_src3 = pool3.getBuffer(1024, 1024, CV_8UC1); // Memory is allocated with DefaultAllocator +@endcode + +@warning When utilizing StackAllocator, deallocation order is important. + +Just like a stack, deallocation must be done in LIFO order. Below is an example of +erroneous usage that violates LIFO rule. If OpenCV is compiled in Debug mode, this +sample code will emit CV_Assert error. + +@code + int main() + { + setBufferPoolUsage(true); // Tell OpenCV that we are going to utilize BufferPool + Stream stream; // A default size (10 MB) stack is allocated to this stream + BufferPool pool(stream); + + GpuMat mat1 = pool.getBuffer(1024, 1024, CV_8UC1); // Allocate mat1 (1MB) + GpuMat mat2 = pool.getBuffer(1024, 1024, CV_8UC1); // Allocate mat2 (1MB) + + mat1.release(); // erroneous usage : mat2 must be deallocated before mat1 + } +@endcode + +Since C++ local variables are destroyed in the reverse order of construction, +the code sample below satisfies the LIFO rule. Local GpuMat's are deallocated +and the corresponding memory is automatically returned to the pool for later usage. + +@code + int main() + { + setBufferPoolUsage(true); // Tell OpenCV that we are going to utilize BufferPool + setBufferPoolConfig(getDevice(), 1024 * 1024 * 64, 2); // Allocate 64 MB, 2 stacks (default is 10 MB, 5 stacks) + + Stream stream1, stream2; // Each stream uses 1 stack + BufferPool pool1(stream1), pool2(stream2); + + for (int i = 0; i < 10; i++) + { + GpuMat d_src1 = pool1.getBuffer(4096, 4096, CV_8UC1); // 16MB + GpuMat d_dst1 = pool1.getBuffer(4096, 4096, CV_8UC3); // 48MB, pool1 is now full + + GpuMat d_src2 = pool2.getBuffer(1024, 1024, CV_8UC1); // 1MB + GpuMat d_dst2 = pool2.getBuffer(1024, 1024, CV_8UC3); // 3MB + + d_src1.setTo(Scalar(i), stream1); + d_src2.setTo(Scalar(i), stream2); + + cvtColor(d_src1, d_dst1, CV_GRAY2BGR, 0, stream1); + cvtColor(d_src2, d_dst2, CV_GRAY2BGR, 0, stream2); + // The order of destruction of the local variables is: + // d_dst2 => d_src2 => d_dst1 => d_src1 + // LIFO rule is satisfied, this code runs without error + } + } +@endcode + */ +class CV_EXPORTS BufferPool +{ +public: + + //! Gets the BufferPool for the given stream. + explicit BufferPool(Stream& stream); + + //! Allocates a new GpuMat of given size and type. + GpuMat getBuffer(int rows, int cols, int type); + + //! Allocates a new GpuMat of given size and type. + GpuMat getBuffer(Size size, int type) { return getBuffer(size.height, size.width, type); } + + //! Returns the allocator associated with the stream. + Ptr getAllocator() const { return allocator_; } + +private: + Ptr allocator_; +}; + +//! BufferPool management (must be called before Stream creation) +CV_EXPORTS void setBufferPoolUsage(bool on); +CV_EXPORTS void setBufferPoolConfig(int deviceId, size_t stackSize, int stackCount); + +//=================================================================================== +// HostMem +//=================================================================================== + +/** @brief Class with reference counting wrapping special memory type allocation functions from CUDA. + +Its interface is also Mat-like but with additional memory type parameters. + +- **PAGE_LOCKED** sets a page locked memory type used commonly for fast and asynchronous + uploading/downloading data from/to GPU. +- **SHARED** specifies a zero copy memory allocation that enables mapping the host memory to GPU + address space, if supported. +- **WRITE_COMBINED** sets the write combined buffer that is not cached by CPU. Such buffers are + used to supply GPU with data when GPU only reads it. The advantage is a better CPU cache + utilization. + +@note Allocation size of such memory types is usually limited. For more details, see *CUDA 2.2 +Pinned Memory APIs* document or *CUDA C Programming Guide*. + */ +class CV_EXPORTS HostMem +{ +public: + enum AllocType { PAGE_LOCKED = 1, SHARED = 2, WRITE_COMBINED = 4 }; + + static MatAllocator* getAllocator(AllocType alloc_type = PAGE_LOCKED); + + explicit HostMem(AllocType alloc_type = PAGE_LOCKED); + + HostMem(const HostMem& m); + + HostMem(int rows, int cols, int type, AllocType alloc_type = PAGE_LOCKED); + HostMem(Size size, int type, AllocType alloc_type = PAGE_LOCKED); + + //! creates from host memory with coping data + explicit HostMem(InputArray arr, AllocType alloc_type = PAGE_LOCKED); + + ~HostMem(); + + HostMem& operator =(const HostMem& m); + + //! swaps with other smart pointer + void swap(HostMem& b); + + //! returns deep copy of the matrix, i.e. the data is copied + HostMem clone() const; + + //! allocates new matrix data unless the matrix already has specified size and type. + void create(int rows, int cols, int type); + void create(Size size, int type); + + //! creates alternative HostMem header for the same data, with different + //! number of channels and/or different number of rows + HostMem reshape(int cn, int rows = 0) const; + + //! decrements reference counter and released memory if needed. + void release(); + + //! returns matrix header with disabled reference counting for HostMem data. + Mat createMatHeader() const; + + /** @brief Maps CPU memory to GPU address space and creates the cuda::GpuMat header without reference counting + for it. + + This can be done only if memory was allocated with the SHARED flag and if it is supported by the + hardware. Laptops often share video and CPU memory, so address spaces can be mapped, which + eliminates an extra copy. + */ + GpuMat createGpuMatHeader() const; + + // Please see cv::Mat for descriptions + bool isContinuous() const; + size_t elemSize() const; + size_t elemSize1() const; + int type() const; + int depth() const; + int channels() const; + size_t step1() const; + Size size() const; + bool empty() const; + + // Please see cv::Mat for descriptions + int flags; + int rows, cols; + size_t step; + + uchar* data; + int* refcount; + + uchar* datastart; + const uchar* dataend; + + AllocType alloc_type; +}; + +/** @brief Page-locks the memory of matrix and maps it for the device(s). + +@param m Input matrix. + */ +CV_EXPORTS void registerPageLocked(Mat& m); + +/** @brief Unmaps the memory of matrix and makes it pageable again. + +@param m Input matrix. + */ +CV_EXPORTS void unregisterPageLocked(Mat& m); + +//=================================================================================== +// Stream +//=================================================================================== + +/** @brief This class encapsulates a queue of asynchronous calls. + +@note Currently, you may face problems if an operation is enqueued twice with different data. Some +functions use the constant GPU memory, and next call may update the memory before the previous one +has been finished. But calling different operations asynchronously is safe because each operation +has its own constant buffer. Memory copy/upload/download/set operations to the buffers you hold are +also safe. + +@note The Stream class is not thread-safe. Please use different Stream objects for different CPU threads. + +@code +void thread1() +{ + cv::cuda::Stream stream1; + cv::cuda::func1(..., stream1); +} + +void thread2() +{ + cv::cuda::Stream stream2; + cv::cuda::func2(..., stream2); +} +@endcode + +@note By default all CUDA routines are launched in Stream::Null() object, if the stream is not specified by user. +In multi-threading environment the stream objects must be passed explicitly (see previous note). + */ +class CV_EXPORTS Stream +{ + typedef void (Stream::*bool_type)() const; + void this_type_does_not_support_comparisons() const {} + +public: + typedef void (*StreamCallback)(int status, void* userData); + + //! creates a new asynchronous stream + Stream(); + + //! creates a new asynchronous stream with custom allocator + Stream(const Ptr& allocator); + + /** @brief Returns true if the current stream queue is finished. Otherwise, it returns false. + */ + bool queryIfComplete() const; + + /** @brief Blocks the current CPU thread until all operations in the stream are complete. + */ + void waitForCompletion(); + + /** @brief Makes a compute stream wait on an event. + */ + void waitEvent(const Event& event); + + /** @brief Adds a callback to be called on the host after all currently enqueued items in the stream have + completed. + + @note Callbacks must not make any CUDA API calls. Callbacks must not perform any synchronization + that may depend on outstanding device work or other callbacks that are not mandated to run earlier. + Callbacks without a mandated order (in independent streams) execute in undefined order and may be + serialized. + */ + void enqueueHostCallback(StreamCallback callback, void* userData); + + //! return Stream object for default CUDA stream + static Stream& Null(); + + //! returns true if stream object is not default (!= 0) + operator bool_type() const; + + class Impl; + +private: + Ptr impl_; + Stream(const Ptr& impl); + + friend struct StreamAccessor; + friend class BufferPool; + friend class DefaultDeviceInitializer; +}; + +class CV_EXPORTS Event +{ +public: + enum CreateFlags + { + DEFAULT = 0x00, /**< Default event flag */ + BLOCKING_SYNC = 0x01, /**< Event uses blocking synchronization */ + DISABLE_TIMING = 0x02, /**< Event will not record timing data */ + INTERPROCESS = 0x04 /**< Event is suitable for interprocess use. DisableTiming must be set */ + }; + + explicit Event(CreateFlags flags = DEFAULT); + + //! records an event + void record(Stream& stream = Stream::Null()); + + //! queries an event's status + bool queryIfComplete() const; + + //! waits for an event to complete + void waitForCompletion(); + + //! computes the elapsed time between events + static float elapsedTime(const Event& start, const Event& end); + + class Impl; + +private: + Ptr impl_; + Event(const Ptr& impl); + + friend struct EventAccessor; +}; + +//! @} cudacore_struct + +//=================================================================================== +// Initialization & Info +//=================================================================================== + +//! @addtogroup cudacore_init +//! @{ + +/** @brief Returns the number of installed CUDA-enabled devices. + +Use this function before any other CUDA functions calls. If OpenCV is compiled without CUDA support, +this function returns 0. If the CUDA driver is not installed, or is incompatible, this function +returns -1. + */ +CV_EXPORTS int getCudaEnabledDeviceCount(); + +/** @brief Sets a device and initializes it for the current thread. + +@param device System index of a CUDA device starting with 0. + +If the call of this function is omitted, a default device is initialized at the fist CUDA usage. + */ +CV_EXPORTS void setDevice(int device); + +/** @brief Returns the current device index set by cuda::setDevice or initialized by default. + */ +CV_EXPORTS int getDevice(); + +/** @brief Explicitly destroys and cleans up all resources associated with the current device in the current +process. + +Any subsequent API call to this device will reinitialize the device. + */ +CV_EXPORTS void resetDevice(); + +/** @brief Enumeration providing CUDA computing features. + */ +enum FeatureSet +{ + FEATURE_SET_COMPUTE_10 = 10, + FEATURE_SET_COMPUTE_11 = 11, + FEATURE_SET_COMPUTE_12 = 12, + FEATURE_SET_COMPUTE_13 = 13, + FEATURE_SET_COMPUTE_20 = 20, + FEATURE_SET_COMPUTE_21 = 21, + FEATURE_SET_COMPUTE_30 = 30, + FEATURE_SET_COMPUTE_32 = 32, + FEATURE_SET_COMPUTE_35 = 35, + FEATURE_SET_COMPUTE_50 = 50, + + GLOBAL_ATOMICS = FEATURE_SET_COMPUTE_11, + SHARED_ATOMICS = FEATURE_SET_COMPUTE_12, + NATIVE_DOUBLE = FEATURE_SET_COMPUTE_13, + WARP_SHUFFLE_FUNCTIONS = FEATURE_SET_COMPUTE_30, + DYNAMIC_PARALLELISM = FEATURE_SET_COMPUTE_35 +}; + +//! checks whether current device supports the given feature +CV_EXPORTS bool deviceSupports(FeatureSet feature_set); + +/** @brief Class providing a set of static methods to check what NVIDIA\* card architecture the CUDA module was +built for. + +According to the CUDA C Programming Guide Version 3.2: "PTX code produced for some specific compute +capability can always be compiled to binary code of greater or equal compute capability". + */ +class CV_EXPORTS TargetArchs +{ +public: + /** @brief The following method checks whether the module was built with the support of the given feature: + + @param feature_set Features to be checked. See :ocvcuda::FeatureSet. + */ + static bool builtWith(FeatureSet feature_set); + + /** @brief There is a set of methods to check whether the module contains intermediate (PTX) or binary CUDA + code for the given architecture(s): + + @param major Major compute capability version. + @param minor Minor compute capability version. + */ + static bool has(int major, int minor); + static bool hasPtx(int major, int minor); + static bool hasBin(int major, int minor); + + static bool hasEqualOrLessPtx(int major, int minor); + static bool hasEqualOrGreater(int major, int minor); + static bool hasEqualOrGreaterPtx(int major, int minor); + static bool hasEqualOrGreaterBin(int major, int minor); +}; + +/** @brief Class providing functionality for querying the specified GPU properties. + */ +class CV_EXPORTS DeviceInfo +{ +public: + //! creates DeviceInfo object for the current GPU + DeviceInfo(); + + /** @brief The constructors. + + @param device_id System index of the CUDA device starting with 0. + + Constructs the DeviceInfo object for the specified device. If device_id parameter is missed, it + constructs an object for the current device. + */ + DeviceInfo(int device_id); + + /** @brief Returns system index of the CUDA device starting with 0. + */ + int deviceID() const; + + //! ASCII string identifying device + const char* name() const; + + //! global memory available on device in bytes + size_t totalGlobalMem() const; + + //! shared memory available per block in bytes + size_t sharedMemPerBlock() const; + + //! 32-bit registers available per block + int regsPerBlock() const; + + //! warp size in threads + int warpSize() const; + + //! maximum pitch in bytes allowed by memory copies + size_t memPitch() const; + + //! maximum number of threads per block + int maxThreadsPerBlock() const; + + //! maximum size of each dimension of a block + Vec3i maxThreadsDim() const; + + //! maximum size of each dimension of a grid + Vec3i maxGridSize() const; + + //! clock frequency in kilohertz + int clockRate() const; + + //! constant memory available on device in bytes + size_t totalConstMem() const; + + //! major compute capability + int majorVersion() const; + + //! minor compute capability + int minorVersion() const; + + //! alignment requirement for textures + size_t textureAlignment() const; + + //! pitch alignment requirement for texture references bound to pitched memory + size_t texturePitchAlignment() const; + + //! number of multiprocessors on device + int multiProcessorCount() const; + + //! specified whether there is a run time limit on kernels + bool kernelExecTimeoutEnabled() const; + + //! device is integrated as opposed to discrete + bool integrated() const; + + //! device can map host memory with cudaHostAlloc/cudaHostGetDevicePointer + bool canMapHostMemory() const; + + enum ComputeMode + { + ComputeModeDefault, /**< default compute mode (Multiple threads can use cudaSetDevice with this device) */ + ComputeModeExclusive, /**< compute-exclusive-thread mode (Only one thread in one process will be able to use cudaSetDevice with this device) */ + ComputeModeProhibited, /**< compute-prohibited mode (No threads can use cudaSetDevice with this device) */ + ComputeModeExclusiveProcess /**< compute-exclusive-process mode (Many threads in one process will be able to use cudaSetDevice with this device) */ + }; + + //! compute mode + ComputeMode computeMode() const; + + //! maximum 1D texture size + int maxTexture1D() const; + + //! maximum 1D mipmapped texture size + int maxTexture1DMipmap() const; + + //! maximum size for 1D textures bound to linear memory + int maxTexture1DLinear() const; + + //! maximum 2D texture dimensions + Vec2i maxTexture2D() const; + + //! maximum 2D mipmapped texture dimensions + Vec2i maxTexture2DMipmap() const; + + //! maximum dimensions (width, height, pitch) for 2D textures bound to pitched memory + Vec3i maxTexture2DLinear() const; + + //! maximum 2D texture dimensions if texture gather operations have to be performed + Vec2i maxTexture2DGather() const; + + //! maximum 3D texture dimensions + Vec3i maxTexture3D() const; + + //! maximum Cubemap texture dimensions + int maxTextureCubemap() const; + + //! maximum 1D layered texture dimensions + Vec2i maxTexture1DLayered() const; + + //! maximum 2D layered texture dimensions + Vec3i maxTexture2DLayered() const; + + //! maximum Cubemap layered texture dimensions + Vec2i maxTextureCubemapLayered() const; + + //! maximum 1D surface size + int maxSurface1D() const; + + //! maximum 2D surface dimensions + Vec2i maxSurface2D() const; + + //! maximum 3D surface dimensions + Vec3i maxSurface3D() const; + + //! maximum 1D layered surface dimensions + Vec2i maxSurface1DLayered() const; + + //! maximum 2D layered surface dimensions + Vec3i maxSurface2DLayered() const; + + //! maximum Cubemap surface dimensions + int maxSurfaceCubemap() const; + + //! maximum Cubemap layered surface dimensions + Vec2i maxSurfaceCubemapLayered() const; + + //! alignment requirements for surfaces + size_t surfaceAlignment() const; + + //! device can possibly execute multiple kernels concurrently + bool concurrentKernels() const; + + //! device has ECC support enabled + bool ECCEnabled() const; + + //! PCI bus ID of the device + int pciBusID() const; + + //! PCI device ID of the device + int pciDeviceID() const; + + //! PCI domain ID of the device + int pciDomainID() const; + + //! true if device is a Tesla device using TCC driver, false otherwise + bool tccDriver() const; + + //! number of asynchronous engines + int asyncEngineCount() const; + + //! device shares a unified address space with the host + bool unifiedAddressing() const; + + //! peak memory clock frequency in kilohertz + int memoryClockRate() const; + + //! global memory bus width in bits + int memoryBusWidth() const; + + //! size of L2 cache in bytes + int l2CacheSize() const; + + //! maximum resident threads per multiprocessor + int maxThreadsPerMultiProcessor() const; + + //! gets free and total device memory + void queryMemory(size_t& totalMemory, size_t& freeMemory) const; + size_t freeMemory() const; + size_t totalMemory() const; + + /** @brief Provides information on CUDA feature support. + + @param feature_set Features to be checked. See cuda::FeatureSet. + + This function returns true if the device has the specified CUDA feature. Otherwise, it returns false + */ + bool supports(FeatureSet feature_set) const; + + /** @brief Checks the CUDA module and device compatibility. + + This function returns true if the CUDA module can be run on the specified device. Otherwise, it + returns false . + */ + bool isCompatible() const; + +private: + int device_id_; +}; + +CV_EXPORTS void printCudaDeviceInfo(int device); +CV_EXPORTS void printShortCudaDeviceInfo(int device); + +/** @brief Converts an array to half precision floating number. + +@param _src input array. +@param _dst output array. +@param stream Stream for the asynchronous version. +@sa convertFp16 +*/ +CV_EXPORTS void convertFp16(InputArray _src, OutputArray _dst, Stream& stream = Stream::Null()); + +//! @} cudacore_init + +}} // namespace cv { namespace cuda { + + +#include "opencv2/core/cuda.inl.hpp" + +#endif /* OPENCV_CORE_CUDA_HPP */ diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda.inl.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda.inl.hpp new file mode 100644 index 0000000000000000000000000000000000000000..35ae2e49d77f331cca5d6cb13a48f3a6885c2ab7 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda.inl.hpp @@ -0,0 +1,631 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_CUDAINL_HPP +#define OPENCV_CORE_CUDAINL_HPP + +#include "opencv2/core/cuda.hpp" + +//! @cond IGNORED + +namespace cv { namespace cuda { + +//=================================================================================== +// GpuMat +//=================================================================================== + +inline +GpuMat::GpuMat(Allocator* allocator_) + : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_) +{} + +inline +GpuMat::GpuMat(int rows_, int cols_, int type_, Allocator* allocator_) + : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_) +{ + if (rows_ > 0 && cols_ > 0) + create(rows_, cols_, type_); +} + +inline +GpuMat::GpuMat(Size size_, int type_, Allocator* allocator_) + : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_) +{ + if (size_.height > 0 && size_.width > 0) + create(size_.height, size_.width, type_); +} + +inline +GpuMat::GpuMat(int rows_, int cols_, int type_, Scalar s_, Allocator* allocator_) + : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_) +{ + if (rows_ > 0 && cols_ > 0) + { + create(rows_, cols_, type_); + setTo(s_); + } +} + +inline +GpuMat::GpuMat(Size size_, int type_, Scalar s_, Allocator* allocator_) + : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_) +{ + if (size_.height > 0 && size_.width > 0) + { + create(size_.height, size_.width, type_); + setTo(s_); + } +} + +inline +GpuMat::GpuMat(const GpuMat& m) + : flags(m.flags), rows(m.rows), cols(m.cols), step(m.step), data(m.data), refcount(m.refcount), datastart(m.datastart), dataend(m.dataend), allocator(m.allocator) +{ + if (refcount) + CV_XADD(refcount, 1); +} + +inline +GpuMat::GpuMat(InputArray arr, Allocator* allocator_) : + flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_) +{ + upload(arr); +} + +inline +GpuMat::~GpuMat() +{ + release(); +} + +inline +GpuMat& GpuMat::operator =(const GpuMat& m) +{ + if (this != &m) + { + GpuMat temp(m); + swap(temp); + } + + return *this; +} + +inline +void GpuMat::create(Size size_, int type_) +{ + create(size_.height, size_.width, type_); +} + +inline +void GpuMat::swap(GpuMat& b) +{ + std::swap(flags, b.flags); + std::swap(rows, b.rows); + std::swap(cols, b.cols); + std::swap(step, b.step); + std::swap(data, b.data); + std::swap(datastart, b.datastart); + std::swap(dataend, b.dataend); + std::swap(refcount, b.refcount); + std::swap(allocator, b.allocator); +} + +inline +GpuMat GpuMat::clone() const +{ + GpuMat m; + copyTo(m); + return m; +} + +inline +void GpuMat::copyTo(OutputArray dst, InputArray mask) const +{ + copyTo(dst, mask, Stream::Null()); +} + +inline +GpuMat& GpuMat::setTo(Scalar s) +{ + return setTo(s, Stream::Null()); +} + +inline +GpuMat& GpuMat::setTo(Scalar s, InputArray mask) +{ + return setTo(s, mask, Stream::Null()); +} + +inline +void GpuMat::convertTo(OutputArray dst, int rtype) const +{ + convertTo(dst, rtype, Stream::Null()); +} + +inline +void GpuMat::convertTo(OutputArray dst, int rtype, double alpha, double beta) const +{ + convertTo(dst, rtype, alpha, beta, Stream::Null()); +} + +inline +void GpuMat::convertTo(OutputArray dst, int rtype, double alpha, Stream& stream) const +{ + convertTo(dst, rtype, alpha, 0.0, stream); +} + +inline +void GpuMat::assignTo(GpuMat& m, int _type) const +{ + if (_type < 0) + m = *this; + else + convertTo(m, _type); +} + +inline +uchar* GpuMat::ptr(int y) +{ + CV_DbgAssert( (unsigned)y < (unsigned)rows ); + return data + step * y; +} + +inline +const uchar* GpuMat::ptr(int y) const +{ + CV_DbgAssert( (unsigned)y < (unsigned)rows ); + return data + step * y; +} + +template inline +_Tp* GpuMat::ptr(int y) +{ + return (_Tp*)ptr(y); +} + +template inline +const _Tp* GpuMat::ptr(int y) const +{ + return (const _Tp*)ptr(y); +} + +template inline +GpuMat::operator PtrStepSz() const +{ + return PtrStepSz(rows, cols, (T*)data, step); +} + +template inline +GpuMat::operator PtrStep() const +{ + return PtrStep((T*)data, step); +} + +inline +GpuMat GpuMat::row(int y) const +{ + return GpuMat(*this, Range(y, y+1), Range::all()); +} + +inline +GpuMat GpuMat::col(int x) const +{ + return GpuMat(*this, Range::all(), Range(x, x+1)); +} + +inline +GpuMat GpuMat::rowRange(int startrow, int endrow) const +{ + return GpuMat(*this, Range(startrow, endrow), Range::all()); +} + +inline +GpuMat GpuMat::rowRange(Range r) const +{ + return GpuMat(*this, r, Range::all()); +} + +inline +GpuMat GpuMat::colRange(int startcol, int endcol) const +{ + return GpuMat(*this, Range::all(), Range(startcol, endcol)); +} + +inline +GpuMat GpuMat::colRange(Range r) const +{ + return GpuMat(*this, Range::all(), r); +} + +inline +GpuMat GpuMat::operator ()(Range rowRange_, Range colRange_) const +{ + return GpuMat(*this, rowRange_, colRange_); +} + +inline +GpuMat GpuMat::operator ()(Rect roi) const +{ + return GpuMat(*this, roi); +} + +inline +bool GpuMat::isContinuous() const +{ + return (flags & Mat::CONTINUOUS_FLAG) != 0; +} + +inline +size_t GpuMat::elemSize() const +{ + return CV_ELEM_SIZE(flags); +} + +inline +size_t GpuMat::elemSize1() const +{ + return CV_ELEM_SIZE1(flags); +} + +inline +int GpuMat::type() const +{ + return CV_MAT_TYPE(flags); +} + +inline +int GpuMat::depth() const +{ + return CV_MAT_DEPTH(flags); +} + +inline +int GpuMat::channels() const +{ + return CV_MAT_CN(flags); +} + +inline +size_t GpuMat::step1() const +{ + return step / elemSize1(); +} + +inline +Size GpuMat::size() const +{ + return Size(cols, rows); +} + +inline +bool GpuMat::empty() const +{ + return data == 0; +} + +static inline +GpuMat createContinuous(int rows, int cols, int type) +{ + GpuMat m; + createContinuous(rows, cols, type, m); + return m; +} + +static inline +void createContinuous(Size size, int type, OutputArray arr) +{ + createContinuous(size.height, size.width, type, arr); +} + +static inline +GpuMat createContinuous(Size size, int type) +{ + GpuMat m; + createContinuous(size, type, m); + return m; +} + +static inline +void ensureSizeIsEnough(Size size, int type, OutputArray arr) +{ + ensureSizeIsEnough(size.height, size.width, type, arr); +} + +static inline +void swap(GpuMat& a, GpuMat& b) +{ + a.swap(b); +} + +//=================================================================================== +// HostMem +//=================================================================================== + +inline +HostMem::HostMem(AllocType alloc_type_) + : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), alloc_type(alloc_type_) +{ +} + +inline +HostMem::HostMem(const HostMem& m) + : flags(m.flags), rows(m.rows), cols(m.cols), step(m.step), data(m.data), refcount(m.refcount), datastart(m.datastart), dataend(m.dataend), alloc_type(m.alloc_type) +{ + if( refcount ) + CV_XADD(refcount, 1); +} + +inline +HostMem::HostMem(int rows_, int cols_, int type_, AllocType alloc_type_) + : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), alloc_type(alloc_type_) +{ + if (rows_ > 0 && cols_ > 0) + create(rows_, cols_, type_); +} + +inline +HostMem::HostMem(Size size_, int type_, AllocType alloc_type_) + : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), alloc_type(alloc_type_) +{ + if (size_.height > 0 && size_.width > 0) + create(size_.height, size_.width, type_); +} + +inline +HostMem::HostMem(InputArray arr, AllocType alloc_type_) + : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), alloc_type(alloc_type_) +{ + arr.getMat().copyTo(*this); +} + +inline +HostMem::~HostMem() +{ + release(); +} + +inline +HostMem& HostMem::operator =(const HostMem& m) +{ + if (this != &m) + { + HostMem temp(m); + swap(temp); + } + + return *this; +} + +inline +void HostMem::swap(HostMem& b) +{ + std::swap(flags, b.flags); + std::swap(rows, b.rows); + std::swap(cols, b.cols); + std::swap(step, b.step); + std::swap(data, b.data); + std::swap(datastart, b.datastart); + std::swap(dataend, b.dataend); + std::swap(refcount, b.refcount); + std::swap(alloc_type, b.alloc_type); +} + +inline +HostMem HostMem::clone() const +{ + HostMem m(size(), type(), alloc_type); + createMatHeader().copyTo(m); + return m; +} + +inline +void HostMem::create(Size size_, int type_) +{ + create(size_.height, size_.width, type_); +} + +inline +Mat HostMem::createMatHeader() const +{ + return Mat(size(), type(), data, step); +} + +inline +bool HostMem::isContinuous() const +{ + return (flags & Mat::CONTINUOUS_FLAG) != 0; +} + +inline +size_t HostMem::elemSize() const +{ + return CV_ELEM_SIZE(flags); +} + +inline +size_t HostMem::elemSize1() const +{ + return CV_ELEM_SIZE1(flags); +} + +inline +int HostMem::type() const +{ + return CV_MAT_TYPE(flags); +} + +inline +int HostMem::depth() const +{ + return CV_MAT_DEPTH(flags); +} + +inline +int HostMem::channels() const +{ + return CV_MAT_CN(flags); +} + +inline +size_t HostMem::step1() const +{ + return step / elemSize1(); +} + +inline +Size HostMem::size() const +{ + return Size(cols, rows); +} + +inline +bool HostMem::empty() const +{ + return data == 0; +} + +static inline +void swap(HostMem& a, HostMem& b) +{ + a.swap(b); +} + +//=================================================================================== +// Stream +//=================================================================================== + +inline +Stream::Stream(const Ptr& impl) + : impl_(impl) +{ +} + +//=================================================================================== +// Event +//=================================================================================== + +inline +Event::Event(const Ptr& impl) + : impl_(impl) +{ +} + +//=================================================================================== +// Initialization & Info +//=================================================================================== + +inline +bool TargetArchs::has(int major, int minor) +{ + return hasPtx(major, minor) || hasBin(major, minor); +} + +inline +bool TargetArchs::hasEqualOrGreater(int major, int minor) +{ + return hasEqualOrGreaterPtx(major, minor) || hasEqualOrGreaterBin(major, minor); +} + +inline +DeviceInfo::DeviceInfo() +{ + device_id_ = getDevice(); +} + +inline +DeviceInfo::DeviceInfo(int device_id) +{ + CV_Assert( device_id >= 0 && device_id < getCudaEnabledDeviceCount() ); + device_id_ = device_id; +} + +inline +int DeviceInfo::deviceID() const +{ + return device_id_; +} + +inline +size_t DeviceInfo::freeMemory() const +{ + size_t _totalMemory = 0, _freeMemory = 0; + queryMemory(_totalMemory, _freeMemory); + return _freeMemory; +} + +inline +size_t DeviceInfo::totalMemory() const +{ + size_t _totalMemory = 0, _freeMemory = 0; + queryMemory(_totalMemory, _freeMemory); + return _totalMemory; +} + +inline +bool DeviceInfo::supports(FeatureSet feature_set) const +{ + int version = majorVersion() * 10 + minorVersion(); + return version >= feature_set; +} + + +}} // namespace cv { namespace cuda { + +//=================================================================================== +// Mat +//=================================================================================== + +namespace cv { + +inline +Mat::Mat(const cuda::GpuMat& m) + : flags(0), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows) +{ + m.download(*this); +} + +} + +//! @endcond + +#endif // OPENCV_CORE_CUDAINL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/block.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/block.hpp new file mode 100644 index 0000000000000000000000000000000000000000..c277f0ea9c411b6763ba04d6dec56644eb25479a --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/block.hpp @@ -0,0 +1,211 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_DEVICE_BLOCK_HPP +#define OPENCV_CUDA_DEVICE_BLOCK_HPP + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + struct Block + { + static __device__ __forceinline__ unsigned int id() + { + return blockIdx.x; + } + + static __device__ __forceinline__ unsigned int stride() + { + return blockDim.x * blockDim.y * blockDim.z; + } + + static __device__ __forceinline__ void sync() + { + __syncthreads(); + } + + static __device__ __forceinline__ int flattenedThreadId() + { + return threadIdx.z * blockDim.x * blockDim.y + threadIdx.y * blockDim.x + threadIdx.x; + } + + template + static __device__ __forceinline__ void fill(It beg, It end, const T& value) + { + int STRIDE = stride(); + It t = beg + flattenedThreadId(); + + for(; t < end; t += STRIDE) + *t = value; + } + + template + static __device__ __forceinline__ void yota(OutIt beg, OutIt end, T value) + { + int STRIDE = stride(); + int tid = flattenedThreadId(); + value += tid; + + for(OutIt t = beg + tid; t < end; t += STRIDE, value += STRIDE) + *t = value; + } + + template + static __device__ __forceinline__ void copy(InIt beg, InIt end, OutIt out) + { + int STRIDE = stride(); + InIt t = beg + flattenedThreadId(); + OutIt o = out + (t - beg); + + for(; t < end; t += STRIDE, o += STRIDE) + *o = *t; + } + + template + static __device__ __forceinline__ void transform(InIt beg, InIt end, OutIt out, UnOp op) + { + int STRIDE = stride(); + InIt t = beg + flattenedThreadId(); + OutIt o = out + (t - beg); + + for(; t < end; t += STRIDE, o += STRIDE) + *o = op(*t); + } + + template + static __device__ __forceinline__ void transform(InIt1 beg1, InIt1 end1, InIt2 beg2, OutIt out, BinOp op) + { + int STRIDE = stride(); + InIt1 t1 = beg1 + flattenedThreadId(); + InIt2 t2 = beg2 + flattenedThreadId(); + OutIt o = out + (t1 - beg1); + + for(; t1 < end1; t1 += STRIDE, t2 += STRIDE, o += STRIDE) + *o = op(*t1, *t2); + } + + template + static __device__ __forceinline__ void reduce(volatile T* buffer, BinOp op) + { + int tid = flattenedThreadId(); + T val = buffer[tid]; + + if (CTA_SIZE >= 1024) { if (tid < 512) buffer[tid] = val = op(val, buffer[tid + 512]); __syncthreads(); } + if (CTA_SIZE >= 512) { if (tid < 256) buffer[tid] = val = op(val, buffer[tid + 256]); __syncthreads(); } + if (CTA_SIZE >= 256) { if (tid < 128) buffer[tid] = val = op(val, buffer[tid + 128]); __syncthreads(); } + if (CTA_SIZE >= 128) { if (tid < 64) buffer[tid] = val = op(val, buffer[tid + 64]); __syncthreads(); } + + if (tid < 32) + { + if (CTA_SIZE >= 64) { buffer[tid] = val = op(val, buffer[tid + 32]); } + if (CTA_SIZE >= 32) { buffer[tid] = val = op(val, buffer[tid + 16]); } + if (CTA_SIZE >= 16) { buffer[tid] = val = op(val, buffer[tid + 8]); } + if (CTA_SIZE >= 8) { buffer[tid] = val = op(val, buffer[tid + 4]); } + if (CTA_SIZE >= 4) { buffer[tid] = val = op(val, buffer[tid + 2]); } + if (CTA_SIZE >= 2) { buffer[tid] = val = op(val, buffer[tid + 1]); } + } + } + + template + static __device__ __forceinline__ T reduce(volatile T* buffer, T init, BinOp op) + { + int tid = flattenedThreadId(); + T val = buffer[tid] = init; + __syncthreads(); + + if (CTA_SIZE >= 1024) { if (tid < 512) buffer[tid] = val = op(val, buffer[tid + 512]); __syncthreads(); } + if (CTA_SIZE >= 512) { if (tid < 256) buffer[tid] = val = op(val, buffer[tid + 256]); __syncthreads(); } + if (CTA_SIZE >= 256) { if (tid < 128) buffer[tid] = val = op(val, buffer[tid + 128]); __syncthreads(); } + if (CTA_SIZE >= 128) { if (tid < 64) buffer[tid] = val = op(val, buffer[tid + 64]); __syncthreads(); } + + if (tid < 32) + { + if (CTA_SIZE >= 64) { buffer[tid] = val = op(val, buffer[tid + 32]); } + if (CTA_SIZE >= 32) { buffer[tid] = val = op(val, buffer[tid + 16]); } + if (CTA_SIZE >= 16) { buffer[tid] = val = op(val, buffer[tid + 8]); } + if (CTA_SIZE >= 8) { buffer[tid] = val = op(val, buffer[tid + 4]); } + if (CTA_SIZE >= 4) { buffer[tid] = val = op(val, buffer[tid + 2]); } + if (CTA_SIZE >= 2) { buffer[tid] = val = op(val, buffer[tid + 1]); } + } + __syncthreads(); + return buffer[0]; + } + + template + static __device__ __forceinline__ void reduce_n(T* data, unsigned int n, BinOp op) + { + int ftid = flattenedThreadId(); + int sft = stride(); + + if (sft < n) + { + for (unsigned int i = sft + ftid; i < n; i += sft) + data[ftid] = op(data[ftid], data[i]); + + __syncthreads(); + + n = sft; + } + + while (n > 1) + { + unsigned int half = n/2; + + if (ftid < half) + data[ftid] = op(data[ftid], data[n - ftid - 1]); + + __syncthreads(); + + n = n - half; + } + } + }; +}}} + +//! @endcond + +#endif /* OPENCV_CUDA_DEVICE_BLOCK_HPP */ diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/border_interpolate.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/border_interpolate.hpp new file mode 100644 index 0000000000000000000000000000000000000000..874f705baf0249644f0b92ea31cfa2cb31b4ae19 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/border_interpolate.hpp @@ -0,0 +1,722 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_BORDER_INTERPOLATE_HPP +#define OPENCV_CUDA_BORDER_INTERPOLATE_HPP + +#include "saturate_cast.hpp" +#include "vec_traits.hpp" +#include "vec_math.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + ////////////////////////////////////////////////////////////// + // BrdConstant + + template struct BrdRowConstant + { + typedef D result_type; + + explicit __host__ __device__ __forceinline__ BrdRowConstant(int width_, const D& val_ = VecTraits::all(0)) : width(width_), val(val_) {} + + template __device__ __forceinline__ D at_low(int x, const T* data) const + { + return x >= 0 ? saturate_cast(data[x]) : val; + } + + template __device__ __forceinline__ D at_high(int x, const T* data) const + { + return x < width ? saturate_cast(data[x]) : val; + } + + template __device__ __forceinline__ D at(int x, const T* data) const + { + return (x >= 0 && x < width) ? saturate_cast(data[x]) : val; + } + + int width; + D val; + }; + + template struct BrdColConstant + { + typedef D result_type; + + explicit __host__ __device__ __forceinline__ BrdColConstant(int height_, const D& val_ = VecTraits::all(0)) : height(height_), val(val_) {} + + template __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const + { + return y >= 0 ? saturate_cast(*(const T*)((const char*)data + y * step)) : val; + } + + template __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const + { + return y < height ? saturate_cast(*(const T*)((const char*)data + y * step)) : val; + } + + template __device__ __forceinline__ D at(int y, const T* data, size_t step) const + { + return (y >= 0 && y < height) ? saturate_cast(*(const T*)((const char*)data + y * step)) : val; + } + + int height; + D val; + }; + + template struct BrdConstant + { + typedef D result_type; + + __host__ __device__ __forceinline__ BrdConstant(int height_, int width_, const D& val_ = VecTraits::all(0)) : height(height_), width(width_), val(val_) + { + } + + template __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const + { + return (x >= 0 && x < width && y >= 0 && y < height) ? saturate_cast(((const T*)((const uchar*)data + y * step))[x]) : val; + } + + template __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const + { + return (x >= 0 && x < width && y >= 0 && y < height) ? saturate_cast(src(y, x)) : val; + } + + int height; + int width; + D val; + }; + + ////////////////////////////////////////////////////////////// + // BrdReplicate + + template struct BrdRowReplicate + { + typedef D result_type; + + explicit __host__ __device__ __forceinline__ BrdRowReplicate(int width) : last_col(width - 1) {} + template __host__ __device__ __forceinline__ BrdRowReplicate(int width, U) : last_col(width - 1) {} + + __device__ __forceinline__ int idx_col_low(int x) const + { + return ::max(x, 0); + } + + __device__ __forceinline__ int idx_col_high(int x) const + { + return ::min(x, last_col); + } + + __device__ __forceinline__ int idx_col(int x) const + { + return idx_col_low(idx_col_high(x)); + } + + template __device__ __forceinline__ D at_low(int x, const T* data) const + { + return saturate_cast(data[idx_col_low(x)]); + } + + template __device__ __forceinline__ D at_high(int x, const T* data) const + { + return saturate_cast(data[idx_col_high(x)]); + } + + template __device__ __forceinline__ D at(int x, const T* data) const + { + return saturate_cast(data[idx_col(x)]); + } + + int last_col; + }; + + template struct BrdColReplicate + { + typedef D result_type; + + explicit __host__ __device__ __forceinline__ BrdColReplicate(int height) : last_row(height - 1) {} + template __host__ __device__ __forceinline__ BrdColReplicate(int height, U) : last_row(height - 1) {} + + __device__ __forceinline__ int idx_row_low(int y) const + { + return ::max(y, 0); + } + + __device__ __forceinline__ int idx_row_high(int y) const + { + return ::min(y, last_row); + } + + __device__ __forceinline__ int idx_row(int y) const + { + return idx_row_low(idx_row_high(y)); + } + + template __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const + { + return saturate_cast(*(const T*)((const char*)data + idx_row_low(y) * step)); + } + + template __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const + { + return saturate_cast(*(const T*)((const char*)data + idx_row_high(y) * step)); + } + + template __device__ __forceinline__ D at(int y, const T* data, size_t step) const + { + return saturate_cast(*(const T*)((const char*)data + idx_row(y) * step)); + } + + int last_row; + }; + + template struct BrdReplicate + { + typedef D result_type; + + __host__ __device__ __forceinline__ BrdReplicate(int height, int width) : last_row(height - 1), last_col(width - 1) {} + template __host__ __device__ __forceinline__ BrdReplicate(int height, int width, U) : last_row(height - 1), last_col(width - 1) {} + + __device__ __forceinline__ int idx_row_low(int y) const + { + return ::max(y, 0); + } + + __device__ __forceinline__ int idx_row_high(int y) const + { + return ::min(y, last_row); + } + + __device__ __forceinline__ int idx_row(int y) const + { + return idx_row_low(idx_row_high(y)); + } + + __device__ __forceinline__ int idx_col_low(int x) const + { + return ::max(x, 0); + } + + __device__ __forceinline__ int idx_col_high(int x) const + { + return ::min(x, last_col); + } + + __device__ __forceinline__ int idx_col(int x) const + { + return idx_col_low(idx_col_high(x)); + } + + template __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const + { + return saturate_cast(((const T*)((const char*)data + idx_row(y) * step))[idx_col(x)]); + } + + template __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const + { + return saturate_cast(src(idx_row(y), idx_col(x))); + } + + int last_row; + int last_col; + }; + + ////////////////////////////////////////////////////////////// + // BrdReflect101 + + template struct BrdRowReflect101 + { + typedef D result_type; + + explicit __host__ __device__ __forceinline__ BrdRowReflect101(int width) : last_col(width - 1) {} + template __host__ __device__ __forceinline__ BrdRowReflect101(int width, U) : last_col(width - 1) {} + + __device__ __forceinline__ int idx_col_low(int x) const + { + return ::abs(x) % (last_col + 1); + } + + __device__ __forceinline__ int idx_col_high(int x) const + { + return ::abs(last_col - ::abs(last_col - x)) % (last_col + 1); + } + + __device__ __forceinline__ int idx_col(int x) const + { + return idx_col_low(idx_col_high(x)); + } + + template __device__ __forceinline__ D at_low(int x, const T* data) const + { + return saturate_cast(data[idx_col_low(x)]); + } + + template __device__ __forceinline__ D at_high(int x, const T* data) const + { + return saturate_cast(data[idx_col_high(x)]); + } + + template __device__ __forceinline__ D at(int x, const T* data) const + { + return saturate_cast(data[idx_col(x)]); + } + + int last_col; + }; + + template struct BrdColReflect101 + { + typedef D result_type; + + explicit __host__ __device__ __forceinline__ BrdColReflect101(int height) : last_row(height - 1) {} + template __host__ __device__ __forceinline__ BrdColReflect101(int height, U) : last_row(height - 1) {} + + __device__ __forceinline__ int idx_row_low(int y) const + { + return ::abs(y) % (last_row + 1); + } + + __device__ __forceinline__ int idx_row_high(int y) const + { + return ::abs(last_row - ::abs(last_row - y)) % (last_row + 1); + } + + __device__ __forceinline__ int idx_row(int y) const + { + return idx_row_low(idx_row_high(y)); + } + + template __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const + { + return saturate_cast(*(const D*)((const char*)data + idx_row_low(y) * step)); + } + + template __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const + { + return saturate_cast(*(const D*)((const char*)data + idx_row_high(y) * step)); + } + + template __device__ __forceinline__ D at(int y, const T* data, size_t step) const + { + return saturate_cast(*(const D*)((const char*)data + idx_row(y) * step)); + } + + int last_row; + }; + + template struct BrdReflect101 + { + typedef D result_type; + + __host__ __device__ __forceinline__ BrdReflect101(int height, int width) : last_row(height - 1), last_col(width - 1) {} + template __host__ __device__ __forceinline__ BrdReflect101(int height, int width, U) : last_row(height - 1), last_col(width - 1) {} + + __device__ __forceinline__ int idx_row_low(int y) const + { + return ::abs(y) % (last_row + 1); + } + + __device__ __forceinline__ int idx_row_high(int y) const + { + return ::abs(last_row - ::abs(last_row - y)) % (last_row + 1); + } + + __device__ __forceinline__ int idx_row(int y) const + { + return idx_row_low(idx_row_high(y)); + } + + __device__ __forceinline__ int idx_col_low(int x) const + { + return ::abs(x) % (last_col + 1); + } + + __device__ __forceinline__ int idx_col_high(int x) const + { + return ::abs(last_col - ::abs(last_col - x)) % (last_col + 1); + } + + __device__ __forceinline__ int idx_col(int x) const + { + return idx_col_low(idx_col_high(x)); + } + + template __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const + { + return saturate_cast(((const T*)((const char*)data + idx_row(y) * step))[idx_col(x)]); + } + + template __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const + { + return saturate_cast(src(idx_row(y), idx_col(x))); + } + + int last_row; + int last_col; + }; + + ////////////////////////////////////////////////////////////// + // BrdReflect + + template struct BrdRowReflect + { + typedef D result_type; + + explicit __host__ __device__ __forceinline__ BrdRowReflect(int width) : last_col(width - 1) {} + template __host__ __device__ __forceinline__ BrdRowReflect(int width, U) : last_col(width - 1) {} + + __device__ __forceinline__ int idx_col_low(int x) const + { + return (::abs(x) - (x < 0)) % (last_col + 1); + } + + __device__ __forceinline__ int idx_col_high(int x) const + { + return ::abs(last_col - ::abs(last_col - x) + (x > last_col)) % (last_col + 1); + } + + __device__ __forceinline__ int idx_col(int x) const + { + return idx_col_high(::abs(x) - (x < 0)); + } + + template __device__ __forceinline__ D at_low(int x, const T* data) const + { + return saturate_cast(data[idx_col_low(x)]); + } + + template __device__ __forceinline__ D at_high(int x, const T* data) const + { + return saturate_cast(data[idx_col_high(x)]); + } + + template __device__ __forceinline__ D at(int x, const T* data) const + { + return saturate_cast(data[idx_col(x)]); + } + + int last_col; + }; + + template struct BrdColReflect + { + typedef D result_type; + + explicit __host__ __device__ __forceinline__ BrdColReflect(int height) : last_row(height - 1) {} + template __host__ __device__ __forceinline__ BrdColReflect(int height, U) : last_row(height - 1) {} + + __device__ __forceinline__ int idx_row_low(int y) const + { + return (::abs(y) - (y < 0)) % (last_row + 1); + } + + __device__ __forceinline__ int idx_row_high(int y) const + { + return ::abs(last_row - ::abs(last_row - y) + (y > last_row)) % (last_row + 1); + } + + __device__ __forceinline__ int idx_row(int y) const + { + return idx_row_high(::abs(y) - (y < 0)); + } + + template __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const + { + return saturate_cast(*(const D*)((const char*)data + idx_row_low(y) * step)); + } + + template __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const + { + return saturate_cast(*(const D*)((const char*)data + idx_row_high(y) * step)); + } + + template __device__ __forceinline__ D at(int y, const T* data, size_t step) const + { + return saturate_cast(*(const D*)((const char*)data + idx_row(y) * step)); + } + + int last_row; + }; + + template struct BrdReflect + { + typedef D result_type; + + __host__ __device__ __forceinline__ BrdReflect(int height, int width) : last_row(height - 1), last_col(width - 1) {} + template __host__ __device__ __forceinline__ BrdReflect(int height, int width, U) : last_row(height - 1), last_col(width - 1) {} + + __device__ __forceinline__ int idx_row_low(int y) const + { + return (::abs(y) - (y < 0)) % (last_row + 1); + } + + __device__ __forceinline__ int idx_row_high(int y) const + { + return /*::abs*/(last_row - ::abs(last_row - y) + (y > last_row)) /*% (last_row + 1)*/; + } + + __device__ __forceinline__ int idx_row(int y) const + { + return idx_row_low(idx_row_high(y)); + } + + __device__ __forceinline__ int idx_col_low(int x) const + { + return (::abs(x) - (x < 0)) % (last_col + 1); + } + + __device__ __forceinline__ int idx_col_high(int x) const + { + return (last_col - ::abs(last_col - x) + (x > last_col)); + } + + __device__ __forceinline__ int idx_col(int x) const + { + return idx_col_low(idx_col_high(x)); + } + + template __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const + { + return saturate_cast(((const T*)((const char*)data + idx_row(y) * step))[idx_col(x)]); + } + + template __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const + { + return saturate_cast(src(idx_row(y), idx_col(x))); + } + + int last_row; + int last_col; + }; + + ////////////////////////////////////////////////////////////// + // BrdWrap + + template struct BrdRowWrap + { + typedef D result_type; + + explicit __host__ __device__ __forceinline__ BrdRowWrap(int width_) : width(width_) {} + template __host__ __device__ __forceinline__ BrdRowWrap(int width_, U) : width(width_) {} + + __device__ __forceinline__ int idx_col_low(int x) const + { + return (x >= 0) * x + (x < 0) * (x - ((x - width + 1) / width) * width); + } + + __device__ __forceinline__ int idx_col_high(int x) const + { + return (x < width) * x + (x >= width) * (x % width); + } + + __device__ __forceinline__ int idx_col(int x) const + { + return idx_col_high(idx_col_low(x)); + } + + template __device__ __forceinline__ D at_low(int x, const T* data) const + { + return saturate_cast(data[idx_col_low(x)]); + } + + template __device__ __forceinline__ D at_high(int x, const T* data) const + { + return saturate_cast(data[idx_col_high(x)]); + } + + template __device__ __forceinline__ D at(int x, const T* data) const + { + return saturate_cast(data[idx_col(x)]); + } + + int width; + }; + + template struct BrdColWrap + { + typedef D result_type; + + explicit __host__ __device__ __forceinline__ BrdColWrap(int height_) : height(height_) {} + template __host__ __device__ __forceinline__ BrdColWrap(int height_, U) : height(height_) {} + + __device__ __forceinline__ int idx_row_low(int y) const + { + return (y >= 0) * y + (y < 0) * (y - ((y - height + 1) / height) * height); + } + + __device__ __forceinline__ int idx_row_high(int y) const + { + return (y < height) * y + (y >= height) * (y % height); + } + + __device__ __forceinline__ int idx_row(int y) const + { + return idx_row_high(idx_row_low(y)); + } + + template __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const + { + return saturate_cast(*(const D*)((const char*)data + idx_row_low(y) * step)); + } + + template __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const + { + return saturate_cast(*(const D*)((const char*)data + idx_row_high(y) * step)); + } + + template __device__ __forceinline__ D at(int y, const T* data, size_t step) const + { + return saturate_cast(*(const D*)((const char*)data + idx_row(y) * step)); + } + + int height; + }; + + template struct BrdWrap + { + typedef D result_type; + + __host__ __device__ __forceinline__ BrdWrap(int height_, int width_) : + height(height_), width(width_) + { + } + template + __host__ __device__ __forceinline__ BrdWrap(int height_, int width_, U) : + height(height_), width(width_) + { + } + + __device__ __forceinline__ int idx_row_low(int y) const + { + return (y >= 0) ? y : (y - ((y - height + 1) / height) * height); + } + + __device__ __forceinline__ int idx_row_high(int y) const + { + return (y < height) ? y : (y % height); + } + + __device__ __forceinline__ int idx_row(int y) const + { + return idx_row_high(idx_row_low(y)); + } + + __device__ __forceinline__ int idx_col_low(int x) const + { + return (x >= 0) ? x : (x - ((x - width + 1) / width) * width); + } + + __device__ __forceinline__ int idx_col_high(int x) const + { + return (x < width) ? x : (x % width); + } + + __device__ __forceinline__ int idx_col(int x) const + { + return idx_col_high(idx_col_low(x)); + } + + template __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const + { + return saturate_cast(((const T*)((const char*)data + idx_row(y) * step))[idx_col(x)]); + } + + template __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const + { + return saturate_cast(src(idx_row(y), idx_col(x))); + } + + int height; + int width; + }; + + ////////////////////////////////////////////////////////////// + // BorderReader + + template struct BorderReader + { + typedef typename B::result_type elem_type; + typedef typename Ptr2D::index_type index_type; + + __host__ __device__ __forceinline__ BorderReader(const Ptr2D& ptr_, const B& b_) : ptr(ptr_), b(b_) {} + + __device__ __forceinline__ elem_type operator ()(index_type y, index_type x) const + { + return b.at(y, x, ptr); + } + + Ptr2D ptr; + B b; + }; + + // under win32 there is some bug with templated types that passed as kernel parameters + // with this specialization all works fine + template struct BorderReader< Ptr2D, BrdConstant > + { + typedef typename BrdConstant::result_type elem_type; + typedef typename Ptr2D::index_type index_type; + + __host__ __device__ __forceinline__ BorderReader(const Ptr2D& src_, const BrdConstant& b) : + src(src_), height(b.height), width(b.width), val(b.val) + { + } + + __device__ __forceinline__ D operator ()(index_type y, index_type x) const + { + return (x >= 0 && x < width && y >= 0 && y < height) ? saturate_cast(src(y, x)) : val; + } + + Ptr2D src; + int height; + int width; + D val; + }; +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_BORDER_INTERPOLATE_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/color.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/color.hpp new file mode 100644 index 0000000000000000000000000000000000000000..dcce280214826d3495bb89699be3197822085090 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/color.hpp @@ -0,0 +1,309 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_COLOR_HPP +#define OPENCV_CUDA_COLOR_HPP + +#include "detail/color_detail.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + // All OPENCV_CUDA_IMPLEMENT_*_TRAITS(ColorSpace1_to_ColorSpace2, ...) macros implements + // template class ColorSpace1_to_ColorSpace2_traits + // { + // typedef ... functor_type; + // static __host__ __device__ functor_type create_functor(); + // }; + + OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgr_to_rgb, 3, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgr_to_bgra, 3, 4, 0) + OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgr_to_rgba, 3, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgra_to_bgr, 4, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgra_to_rgb, 4, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgra_to_rgba, 4, 4, 2) + + #undef OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS + + OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(bgr_to_bgr555, 3, 0, 5) + OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(bgr_to_bgr565, 3, 0, 6) + OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(rgb_to_bgr555, 3, 2, 5) + OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(rgb_to_bgr565, 3, 2, 6) + OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(bgra_to_bgr555, 4, 0, 5) + OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(bgra_to_bgr565, 4, 0, 6) + OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(rgba_to_bgr555, 4, 2, 5) + OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(rgba_to_bgr565, 4, 2, 6) + + #undef OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS + + OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr555_to_rgb, 3, 2, 5) + OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr565_to_rgb, 3, 2, 6) + OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr555_to_bgr, 3, 0, 5) + OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr565_to_bgr, 3, 0, 6) + OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr555_to_rgba, 4, 2, 5) + OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr565_to_rgba, 4, 2, 6) + OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr555_to_bgra, 4, 0, 5) + OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr565_to_bgra, 4, 0, 6) + + #undef OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS + + OPENCV_CUDA_IMPLEMENT_GRAY2RGB_TRAITS(gray_to_bgr, 3) + OPENCV_CUDA_IMPLEMENT_GRAY2RGB_TRAITS(gray_to_bgra, 4) + + #undef OPENCV_CUDA_IMPLEMENT_GRAY2RGB_TRAITS + + OPENCV_CUDA_IMPLEMENT_GRAY2RGB5x5_TRAITS(gray_to_bgr555, 5) + OPENCV_CUDA_IMPLEMENT_GRAY2RGB5x5_TRAITS(gray_to_bgr565, 6) + + #undef OPENCV_CUDA_IMPLEMENT_GRAY2RGB5x5_TRAITS + + OPENCV_CUDA_IMPLEMENT_RGB5x52GRAY_TRAITS(bgr555_to_gray, 5) + OPENCV_CUDA_IMPLEMENT_RGB5x52GRAY_TRAITS(bgr565_to_gray, 6) + + #undef OPENCV_CUDA_IMPLEMENT_RGB5x52GRAY_TRAITS + + OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS(rgb_to_gray, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS(bgr_to_gray, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS(rgba_to_gray, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS(bgra_to_gray, 4, 0) + + #undef OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS + + OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(rgb_to_yuv, 3, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(rgba_to_yuv, 4, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(rgb_to_yuv4, 3, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(rgba_to_yuv4, 4, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(bgr_to_yuv, 3, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(bgra_to_yuv, 4, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(bgr_to_yuv4, 3, 4, 0) + OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(bgra_to_yuv4, 4, 4, 0) + + #undef OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS + + OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv_to_rgb, 3, 3, 2) + OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv_to_rgba, 3, 4, 2) + OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv4_to_rgb, 4, 3, 2) + OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv4_to_rgba, 4, 4, 2) + OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv_to_bgr, 3, 3, 0) + OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv_to_bgra, 3, 4, 0) + OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv4_to_bgr, 4, 3, 0) + OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv4_to_bgra, 4, 4, 0) + + #undef OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS + + OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(rgb_to_YCrCb, 3, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(rgba_to_YCrCb, 4, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(rgb_to_YCrCb4, 3, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(rgba_to_YCrCb4, 4, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(bgr_to_YCrCb, 3, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(bgra_to_YCrCb, 4, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(bgr_to_YCrCb4, 3, 4, 0) + OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(bgra_to_YCrCb4, 4, 4, 0) + + #undef OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS + + OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb_to_rgb, 3, 3, 2) + OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb_to_rgba, 3, 4, 2) + OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb4_to_rgb, 4, 3, 2) + OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb4_to_rgba, 4, 4, 2) + OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb_to_bgr, 3, 3, 0) + OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb_to_bgra, 3, 4, 0) + OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb4_to_bgr, 4, 3, 0) + OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb4_to_bgra, 4, 4, 0) + + #undef OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS + + OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(rgb_to_xyz, 3, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(rgba_to_xyz, 4, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(rgb_to_xyz4, 3, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(rgba_to_xyz4, 4, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(bgr_to_xyz, 3, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(bgra_to_xyz, 4, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(bgr_to_xyz4, 3, 4, 0) + OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(bgra_to_xyz4, 4, 4, 0) + + #undef OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS + + OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz_to_rgb, 3, 3, 2) + OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz4_to_rgb, 4, 3, 2) + OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz_to_rgba, 3, 4, 2) + OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz4_to_rgba, 4, 4, 2) + OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz_to_bgr, 3, 3, 0) + OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz4_to_bgr, 4, 3, 0) + OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz_to_bgra, 3, 4, 0) + OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz4_to_bgra, 4, 4, 0) + + #undef OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS + + OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(rgb_to_hsv, 3, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(rgba_to_hsv, 4, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(rgb_to_hsv4, 3, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(rgba_to_hsv4, 4, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(bgr_to_hsv, 3, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(bgra_to_hsv, 4, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(bgr_to_hsv4, 3, 4, 0) + OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(bgra_to_hsv4, 4, 4, 0) + + #undef OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS + + OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv_to_rgb, 3, 3, 2) + OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv_to_rgba, 3, 4, 2) + OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv4_to_rgb, 4, 3, 2) + OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv4_to_rgba, 4, 4, 2) + OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv_to_bgr, 3, 3, 0) + OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv_to_bgra, 3, 4, 0) + OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv4_to_bgr, 4, 3, 0) + OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv4_to_bgra, 4, 4, 0) + + #undef OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS + + OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(rgb_to_hls, 3, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(rgba_to_hls, 4, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(rgb_to_hls4, 3, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(rgba_to_hls4, 4, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(bgr_to_hls, 3, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(bgra_to_hls, 4, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(bgr_to_hls4, 3, 4, 0) + OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(bgra_to_hls4, 4, 4, 0) + + #undef OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS + + OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls_to_rgb, 3, 3, 2) + OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls_to_rgba, 3, 4, 2) + OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls4_to_rgb, 4, 3, 2) + OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls4_to_rgba, 4, 4, 2) + OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls_to_bgr, 3, 3, 0) + OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls_to_bgra, 3, 4, 0) + OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls4_to_bgr, 4, 3, 0) + OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls4_to_bgra, 4, 4, 0) + + #undef OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS + + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(rgb_to_lab, 3, 3, true, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(rgba_to_lab, 4, 3, true, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(rgb_to_lab4, 3, 4, true, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(rgba_to_lab4, 4, 4, true, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(bgr_to_lab, 3, 3, true, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(bgra_to_lab, 4, 3, true, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(bgr_to_lab4, 3, 4, true, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(bgra_to_lab4, 4, 4, true, 0) + + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lrgb_to_lab, 3, 3, false, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lrgba_to_lab, 4, 3, false, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lrgb_to_lab4, 3, 4, false, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lrgba_to_lab4, 4, 4, false, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lbgr_to_lab, 3, 3, false, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lbgra_to_lab, 4, 3, false, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lbgr_to_lab4, 3, 4, false, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lbgra_to_lab4, 4, 4, false, 0) + + #undef OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS + + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_rgb, 3, 3, true, 2) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_rgb, 4, 3, true, 2) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_rgba, 3, 4, true, 2) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_rgba, 4, 4, true, 2) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_bgr, 3, 3, true, 0) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_bgr, 4, 3, true, 0) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_bgra, 3, 4, true, 0) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_bgra, 4, 4, true, 0) + + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_lrgb, 3, 3, false, 2) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_lrgb, 4, 3, false, 2) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_lrgba, 3, 4, false, 2) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_lrgba, 4, 4, false, 2) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_lbgr, 3, 3, false, 0) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_lbgr, 4, 3, false, 0) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_lbgra, 3, 4, false, 0) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_lbgra, 4, 4, false, 0) + + #undef OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS + + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(rgb_to_luv, 3, 3, true, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(rgba_to_luv, 4, 3, true, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(rgb_to_luv4, 3, 4, true, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(rgba_to_luv4, 4, 4, true, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(bgr_to_luv, 3, 3, true, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(bgra_to_luv, 4, 3, true, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(bgr_to_luv4, 3, 4, true, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(bgra_to_luv4, 4, 4, true, 0) + + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lrgb_to_luv, 3, 3, false, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lrgba_to_luv, 4, 3, false, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lrgb_to_luv4, 3, 4, false, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lrgba_to_luv4, 4, 4, false, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lbgr_to_luv, 3, 3, false, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lbgra_to_luv, 4, 3, false, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lbgr_to_luv4, 3, 4, false, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lbgra_to_luv4, 4, 4, false, 0) + + #undef OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS + + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_rgb, 3, 3, true, 2) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_rgb, 4, 3, true, 2) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_rgba, 3, 4, true, 2) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_rgba, 4, 4, true, 2) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_bgr, 3, 3, true, 0) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_bgr, 4, 3, true, 0) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_bgra, 3, 4, true, 0) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_bgra, 4, 4, true, 0) + + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_lrgb, 3, 3, false, 2) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_lrgb, 4, 3, false, 2) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_lrgba, 3, 4, false, 2) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_lrgba, 4, 4, false, 2) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_lbgr, 3, 3, false, 0) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_lbgr, 4, 3, false, 0) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_lbgra, 3, 4, false, 0) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_lbgra, 4, 4, false, 0) + + #undef OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_COLOR_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/common.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/common.hpp new file mode 100644 index 0000000000000000000000000000000000000000..14b1f3f5aecd40562c3b8b9ad06d05098fa3da30 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/common.hpp @@ -0,0 +1,109 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_COMMON_HPP +#define OPENCV_CUDA_COMMON_HPP + +#include +#include "opencv2/core/cuda_types.hpp" +#include "opencv2/core/cvdef.h" +#include "opencv2/core/base.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +#ifndef CV_PI_F + #ifndef CV_PI + #define CV_PI_F 3.14159265f + #else + #define CV_PI_F ((float)CV_PI) + #endif +#endif + +namespace cv { namespace cuda { + static inline void checkCudaError(cudaError_t err, const char* file, const int line, const char* func) + { + if (cudaSuccess != err) + cv::error(cv::Error::GpuApiCallError, cudaGetErrorString(err), func, file, line); + } +}} + +#ifndef cudaSafeCall + #define cudaSafeCall(expr) cv::cuda::checkCudaError(expr, __FILE__, __LINE__, CV_Func) +#endif + +namespace cv { namespace cuda +{ + template static inline bool isAligned(const T* ptr, size_t size) + { + return reinterpret_cast(ptr) % size == 0; + } + + static inline bool isAligned(size_t step, size_t size) + { + return step % size == 0; + } +}} + +namespace cv { namespace cuda +{ + namespace device + { + __host__ __device__ __forceinline__ int divUp(int total, int grain) + { + return (total + grain - 1) / grain; + } + + template inline void bindTexture(const textureReference* tex, const PtrStepSz& img) + { + cudaChannelFormatDesc desc = cudaCreateChannelDesc(); + cudaSafeCall( cudaBindTexture2D(0, tex, img.ptr(), &desc, img.cols, img.rows, img.step) ); + } + } +}} + +//! @endcond + +#endif // OPENCV_CUDA_COMMON_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/datamov_utils.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/datamov_utils.hpp new file mode 100644 index 0000000000000000000000000000000000000000..6820d0fd64de9090dd9f02a296ea3de0a3884ef1 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/datamov_utils.hpp @@ -0,0 +1,113 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_DATAMOV_UTILS_HPP +#define OPENCV_CUDA_DATAMOV_UTILS_HPP + +#include "common.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 200 + + // for Fermi memory space is detected automatically + template struct ForceGlob + { + __device__ __forceinline__ static void Load(const T* ptr, int offset, T& val) { val = ptr[offset]; } + }; + + #else // __CUDA_ARCH__ >= 200 + + #if defined(_WIN64) || defined(__LP64__) + // 64-bit register modifier for inlined asm + #define OPENCV_CUDA_ASM_PTR "l" + #else + // 32-bit register modifier for inlined asm + #define OPENCV_CUDA_ASM_PTR "r" + #endif + + template struct ForceGlob; + + #define OPENCV_CUDA_DEFINE_FORCE_GLOB(base_type, ptx_type, reg_mod) \ + template <> struct ForceGlob \ + { \ + __device__ __forceinline__ static void Load(const base_type* ptr, int offset, base_type& val) \ + { \ + asm("ld.global."#ptx_type" %0, [%1];" : "="#reg_mod(val) : OPENCV_CUDA_ASM_PTR(ptr + offset)); \ + } \ + }; + + #define OPENCV_CUDA_DEFINE_FORCE_GLOB_B(base_type, ptx_type) \ + template <> struct ForceGlob \ + { \ + __device__ __forceinline__ static void Load(const base_type* ptr, int offset, base_type& val) \ + { \ + asm("ld.global."#ptx_type" %0, [%1];" : "=r"(*reinterpret_cast(&val)) : OPENCV_CUDA_ASM_PTR(ptr + offset)); \ + } \ + }; + + OPENCV_CUDA_DEFINE_FORCE_GLOB_B(uchar, u8) + OPENCV_CUDA_DEFINE_FORCE_GLOB_B(schar, s8) + OPENCV_CUDA_DEFINE_FORCE_GLOB_B(char, b8) + OPENCV_CUDA_DEFINE_FORCE_GLOB (ushort, u16, h) + OPENCV_CUDA_DEFINE_FORCE_GLOB (short, s16, h) + OPENCV_CUDA_DEFINE_FORCE_GLOB (uint, u32, r) + OPENCV_CUDA_DEFINE_FORCE_GLOB (int, s32, r) + OPENCV_CUDA_DEFINE_FORCE_GLOB (float, f32, f) + OPENCV_CUDA_DEFINE_FORCE_GLOB (double, f64, d) + + #undef OPENCV_CUDA_DEFINE_FORCE_GLOB + #undef OPENCV_CUDA_DEFINE_FORCE_GLOB_B + #undef OPENCV_CUDA_ASM_PTR + + #endif // __CUDA_ARCH__ >= 200 +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_DATAMOV_UTILS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/detail/color_detail.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/detail/color_detail.hpp new file mode 100644 index 0000000000000000000000000000000000000000..bfb40550dcda7b48b2ebdf7342447468dfa350ce --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/detail/color_detail.hpp @@ -0,0 +1,1980 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_COLOR_DETAIL_HPP +#define OPENCV_CUDA_COLOR_DETAIL_HPP + +#include "../common.hpp" +#include "../vec_traits.hpp" +#include "../saturate_cast.hpp" +#include "../limits.hpp" +#include "../functional.hpp" + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + #ifndef CV_DESCALE + #define CV_DESCALE(x, n) (((x) + (1 << ((n)-1))) >> (n)) + #endif + + namespace color_detail + { + template struct ColorChannel + { + typedef float worktype_f; + static __device__ __forceinline__ T max() { return numeric_limits::max(); } + static __device__ __forceinline__ T half() { return (T)(max()/2 + 1); } + }; + + template<> struct ColorChannel + { + typedef float worktype_f; + static __device__ __forceinline__ float max() { return 1.f; } + static __device__ __forceinline__ float half() { return 0.5f; } + }; + + template static __device__ __forceinline__ void setAlpha(typename TypeVec::vec_type& vec, T val) + { + } + + template static __device__ __forceinline__ void setAlpha(typename TypeVec::vec_type& vec, T val) + { + vec.w = val; + } + + template static __device__ __forceinline__ T getAlpha(const typename TypeVec::vec_type& vec) + { + return ColorChannel::max(); + } + + template static __device__ __forceinline__ T getAlpha(const typename TypeVec::vec_type& vec) + { + return vec.w; + } + + enum + { + yuv_shift = 14, + xyz_shift = 12, + R2Y = 4899, + G2Y = 9617, + B2Y = 1868, + BLOCK_SIZE = 256 + }; + } + +////////////////// Various 3/4-channel to 3/4-channel RGB transformations ///////////////// + + namespace color_detail + { + template struct RGB2RGB + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + dst.x = (&src.x)[bidx]; + dst.y = src.y; + dst.z = (&src.x)[bidx^2]; + setAlpha(dst, getAlpha(src)); + + return dst; + } + + __host__ __device__ __forceinline__ RGB2RGB() {} + __host__ __device__ __forceinline__ RGB2RGB(const RGB2RGB&) {} + }; + + template <> struct RGB2RGB : unary_function + { + __device__ uint operator()(uint src) const + { + uint dst = 0; + + dst |= (0xffu & (src >> 16)); + dst |= (0xffu & (src >> 8)) << 8; + dst |= (0xffu & (src)) << 16; + dst |= (0xffu & (src >> 24)) << 24; + + return dst; + } + + __host__ __device__ __forceinline__ RGB2RGB() {} + __host__ __device__ __forceinline__ RGB2RGB(const RGB2RGB&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(name, scn, dcn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + +/////////// Transforming 16-bit (565 or 555) RGB to/from 24/32-bit (888[8]) RGB ////////// + + namespace color_detail + { + template struct RGB2RGB5x5Converter; + template struct RGB2RGB5x5Converter<6, bidx> + { + static __device__ __forceinline__ ushort cvt(const uchar3& src) + { + return (ushort)(((&src.x)[bidx] >> 3) | ((src.y & ~3) << 3) | (((&src.x)[bidx^2] & ~7) << 8)); + } + + static __device__ __forceinline__ ushort cvt(uint src) + { + uint b = 0xffu & (src >> (bidx * 8)); + uint g = 0xffu & (src >> 8); + uint r = 0xffu & (src >> ((bidx ^ 2) * 8)); + return (ushort)((b >> 3) | ((g & ~3) << 3) | ((r & ~7) << 8)); + } + }; + + template struct RGB2RGB5x5Converter<5, bidx> + { + static __device__ __forceinline__ ushort cvt(const uchar3& src) + { + return (ushort)(((&src.x)[bidx] >> 3) | ((src.y & ~7) << 2) | (((&src.x)[bidx^2] & ~7) << 7)); + } + + static __device__ __forceinline__ ushort cvt(uint src) + { + uint b = 0xffu & (src >> (bidx * 8)); + uint g = 0xffu & (src >> 8); + uint r = 0xffu & (src >> ((bidx ^ 2) * 8)); + uint a = 0xffu & (src >> 24); + return (ushort)((b >> 3) | ((g & ~7) << 2) | ((r & ~7) << 7) | (a * 0x8000)); + } + }; + + template struct RGB2RGB5x5; + + template struct RGB2RGB5x5<3, bidx,green_bits> : unary_function + { + __device__ __forceinline__ ushort operator()(const uchar3& src) const + { + return RGB2RGB5x5Converter::cvt(src); + } + + __host__ __device__ __forceinline__ RGB2RGB5x5() {} + __host__ __device__ __forceinline__ RGB2RGB5x5(const RGB2RGB5x5&) {} + }; + + template struct RGB2RGB5x5<4, bidx,green_bits> : unary_function + { + __device__ __forceinline__ ushort operator()(uint src) const + { + return RGB2RGB5x5Converter::cvt(src); + } + + __host__ __device__ __forceinline__ RGB2RGB5x5() {} + __host__ __device__ __forceinline__ RGB2RGB5x5(const RGB2RGB5x5&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(name, scn, bidx, green_bits) \ + struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2RGB5x5 functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + + namespace color_detail + { + template struct RGB5x52RGBConverter; + + template struct RGB5x52RGBConverter<5, bidx> + { + static __device__ __forceinline__ void cvt(uint src, uchar3& dst) + { + (&dst.x)[bidx] = src << 3; + dst.y = (src >> 2) & ~7; + (&dst.x)[bidx ^ 2] = (src >> 7) & ~7; + } + + static __device__ __forceinline__ void cvt(uint src, uint& dst) + { + dst = 0; + + dst |= (0xffu & (src << 3)) << (bidx * 8); + dst |= (0xffu & ((src >> 2) & ~7)) << 8; + dst |= (0xffu & ((src >> 7) & ~7)) << ((bidx ^ 2) * 8); + dst |= ((src & 0x8000) * 0xffu) << 24; + } + }; + + template struct RGB5x52RGBConverter<6, bidx> + { + static __device__ __forceinline__ void cvt(uint src, uchar3& dst) + { + (&dst.x)[bidx] = src << 3; + dst.y = (src >> 3) & ~3; + (&dst.x)[bidx ^ 2] = (src >> 8) & ~7; + } + + static __device__ __forceinline__ void cvt(uint src, uint& dst) + { + dst = 0xffu << 24; + + dst |= (0xffu & (src << 3)) << (bidx * 8); + dst |= (0xffu &((src >> 3) & ~3)) << 8; + dst |= (0xffu & ((src >> 8) & ~7)) << ((bidx ^ 2) * 8); + } + }; + + template struct RGB5x52RGB; + + template struct RGB5x52RGB<3, bidx, green_bits> : unary_function + { + __device__ __forceinline__ uchar3 operator()(ushort src) const + { + uchar3 dst; + RGB5x52RGBConverter::cvt(src, dst); + return dst; + } + __host__ __device__ __forceinline__ RGB5x52RGB() {} + __host__ __device__ __forceinline__ RGB5x52RGB(const RGB5x52RGB&) {} + + }; + + template struct RGB5x52RGB<4, bidx, green_bits> : unary_function + { + __device__ __forceinline__ uint operator()(ushort src) const + { + uint dst; + RGB5x52RGBConverter::cvt(src, dst); + return dst; + } + __host__ __device__ __forceinline__ RGB5x52RGB() {} + __host__ __device__ __forceinline__ RGB5x52RGB(const RGB5x52RGB&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(name, dcn, bidx, green_bits) \ + struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB5x52RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + +///////////////////////////////// Grayscale to Color //////////////////////////////// + + namespace color_detail + { + template struct Gray2RGB : unary_function::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator()(T src) const + { + typename TypeVec::vec_type dst; + + dst.z = dst.y = dst.x = src; + setAlpha(dst, ColorChannel::max()); + + return dst; + } + __host__ __device__ __forceinline__ Gray2RGB() {} + __host__ __device__ __forceinline__ Gray2RGB(const Gray2RGB&) {} + }; + + template <> struct Gray2RGB : unary_function + { + __device__ __forceinline__ uint operator()(uint src) const + { + uint dst = 0xffu << 24; + + dst |= src; + dst |= src << 8; + dst |= src << 16; + + return dst; + } + __host__ __device__ __forceinline__ Gray2RGB() {} + __host__ __device__ __forceinline__ Gray2RGB(const Gray2RGB&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_GRAY2RGB_TRAITS(name, dcn) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::Gray2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + + namespace color_detail + { + template struct Gray2RGB5x5Converter; + template<> struct Gray2RGB5x5Converter<6> + { + static __device__ __forceinline__ ushort cvt(uint t) + { + return (ushort)((t >> 3) | ((t & ~3) << 3) | ((t & ~7) << 8)); + } + }; + + template<> struct Gray2RGB5x5Converter<5> + { + static __device__ __forceinline__ ushort cvt(uint t) + { + t >>= 3; + return (ushort)(t | (t << 5) | (t << 10)); + } + }; + + template struct Gray2RGB5x5 : unary_function + { + __device__ __forceinline__ ushort operator()(uint src) const + { + return Gray2RGB5x5Converter::cvt(src); + } + + __host__ __device__ __forceinline__ Gray2RGB5x5() {} + __host__ __device__ __forceinline__ Gray2RGB5x5(const Gray2RGB5x5&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_GRAY2RGB5x5_TRAITS(name, green_bits) \ + struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::Gray2RGB5x5 functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + +///////////////////////////////// Color to Grayscale //////////////////////////////// + + namespace color_detail + { + template struct RGB5x52GrayConverter; + template <> struct RGB5x52GrayConverter<6> + { + static __device__ __forceinline__ uchar cvt(uint t) + { + return (uchar)CV_DESCALE(((t << 3) & 0xf8) * B2Y + ((t >> 3) & 0xfc) * G2Y + ((t >> 8) & 0xf8) * R2Y, yuv_shift); + } + }; + + template <> struct RGB5x52GrayConverter<5> + { + static __device__ __forceinline__ uchar cvt(uint t) + { + return (uchar)CV_DESCALE(((t << 3) & 0xf8) * B2Y + ((t >> 2) & 0xf8) * G2Y + ((t >> 7) & 0xf8) * R2Y, yuv_shift); + } + }; + + template struct RGB5x52Gray : unary_function + { + __device__ __forceinline__ uchar operator()(uint src) const + { + return RGB5x52GrayConverter::cvt(src); + } + __host__ __device__ __forceinline__ RGB5x52Gray() {} + __host__ __device__ __forceinline__ RGB5x52Gray(const RGB5x52Gray&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB5x52GRAY_TRAITS(name, green_bits) \ + struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB5x52Gray functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + + namespace color_detail + { + template static __device__ __forceinline__ T RGB2GrayConvert(const T* src) + { + return (T)CV_DESCALE((unsigned)(src[bidx] * B2Y + src[1] * G2Y + src[bidx^2] * R2Y), yuv_shift); + } + + template static __device__ __forceinline__ uchar RGB2GrayConvert(uint src) + { + uint b = 0xffu & (src >> (bidx * 8)); + uint g = 0xffu & (src >> 8); + uint r = 0xffu & (src >> ((bidx ^ 2) * 8)); + return CV_DESCALE((uint)(b * B2Y + g * G2Y + r * R2Y), yuv_shift); + } + + template static __device__ __forceinline__ float RGB2GrayConvert(const float* src) + { + return src[bidx] * 0.114f + src[1] * 0.587f + src[bidx^2] * 0.299f; + } + + template struct RGB2Gray : unary_function::vec_type, T> + { + __device__ __forceinline__ T operator()(const typename TypeVec::vec_type& src) const + { + return RGB2GrayConvert(&src.x); + } + __host__ __device__ __forceinline__ RGB2Gray() {} + __host__ __device__ __forceinline__ RGB2Gray(const RGB2Gray&) {} + }; + + template struct RGB2Gray : unary_function + { + __device__ __forceinline__ uchar operator()(uint src) const + { + return RGB2GrayConvert(src); + } + __host__ __device__ __forceinline__ RGB2Gray() {} + __host__ __device__ __forceinline__ RGB2Gray(const RGB2Gray&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS(name, scn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2Gray functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + +///////////////////////////////////// RGB <-> YUV ////////////////////////////////////// + + namespace color_detail + { + __constant__ float c_RGB2YUVCoeffs_f[5] = { 0.114f, 0.587f, 0.299f, 0.492f, 0.877f }; + __constant__ int c_RGB2YUVCoeffs_i[5] = { B2Y, G2Y, R2Y, 8061, 14369 }; + + template static __device__ void RGB2YUVConvert(const T* src, D& dst) + { + const int delta = ColorChannel::half() * (1 << yuv_shift); + + const int Y = CV_DESCALE(src[0] * c_RGB2YUVCoeffs_i[bidx^2] + src[1] * c_RGB2YUVCoeffs_i[1] + src[2] * c_RGB2YUVCoeffs_i[bidx], yuv_shift); + const int Cr = CV_DESCALE((src[bidx^2] - Y) * c_RGB2YUVCoeffs_i[3] + delta, yuv_shift); + const int Cb = CV_DESCALE((src[bidx] - Y) * c_RGB2YUVCoeffs_i[4] + delta, yuv_shift); + + dst.x = saturate_cast(Y); + dst.y = saturate_cast(Cr); + dst.z = saturate_cast(Cb); + } + + template static __device__ __forceinline__ void RGB2YUVConvert(const float* src, D& dst) + { + dst.x = src[0] * c_RGB2YUVCoeffs_f[bidx^2] + src[1] * c_RGB2YUVCoeffs_f[1] + src[2] * c_RGB2YUVCoeffs_f[bidx]; + dst.y = (src[bidx^2] - dst.x) * c_RGB2YUVCoeffs_f[3] + ColorChannel::half(); + dst.z = (src[bidx] - dst.x) * c_RGB2YUVCoeffs_f[4] + ColorChannel::half(); + } + + template struct RGB2YUV + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + RGB2YUVConvert(&src.x, dst); + return dst; + } + __host__ __device__ __forceinline__ RGB2YUV() {} + __host__ __device__ __forceinline__ RGB2YUV(const RGB2YUV&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(name, scn, dcn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2YUV functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + + namespace color_detail + { + __constant__ float c_YUV2RGBCoeffs_f[5] = { 2.032f, -0.395f, -0.581f, 1.140f }; + __constant__ int c_YUV2RGBCoeffs_i[5] = { 33292, -6472, -9519, 18678 }; + + template static __device__ void YUV2RGBConvert(const T& src, D* dst) + { + const int b = src.x + CV_DESCALE((src.z - ColorChannel::half()) * c_YUV2RGBCoeffs_i[3], yuv_shift); + + const int g = src.x + CV_DESCALE((src.z - ColorChannel::half()) * c_YUV2RGBCoeffs_i[2] + + (src.y - ColorChannel::half()) * c_YUV2RGBCoeffs_i[1], yuv_shift); + + const int r = src.x + CV_DESCALE((src.y - ColorChannel::half()) * c_YUV2RGBCoeffs_i[0], yuv_shift); + + dst[bidx] = saturate_cast(b); + dst[1] = saturate_cast(g); + dst[bidx^2] = saturate_cast(r); + } + + template static __device__ uint YUV2RGBConvert(uint src) + { + const int x = 0xff & (src); + const int y = 0xff & (src >> 8); + const int z = 0xff & (src >> 16); + + const int b = x + CV_DESCALE((z - ColorChannel::half()) * c_YUV2RGBCoeffs_i[3], yuv_shift); + + const int g = x + CV_DESCALE((z - ColorChannel::half()) * c_YUV2RGBCoeffs_i[2] + + (y - ColorChannel::half()) * c_YUV2RGBCoeffs_i[1], yuv_shift); + + const int r = x + CV_DESCALE((y - ColorChannel::half()) * c_YUV2RGBCoeffs_i[0], yuv_shift); + + uint dst = 0xffu << 24; + + dst |= saturate_cast(b) << (bidx * 8); + dst |= saturate_cast(g) << 8; + dst |= saturate_cast(r) << ((bidx ^ 2) * 8); + + return dst; + } + + template static __device__ __forceinline__ void YUV2RGBConvert(const T& src, float* dst) + { + dst[bidx] = src.x + (src.z - ColorChannel::half()) * c_YUV2RGBCoeffs_f[3]; + + dst[1] = src.x + (src.z - ColorChannel::half()) * c_YUV2RGBCoeffs_f[2] + + (src.y - ColorChannel::half()) * c_YUV2RGBCoeffs_f[1]; + + dst[bidx^2] = src.x + (src.y - ColorChannel::half()) * c_YUV2RGBCoeffs_f[0]; + } + + template struct YUV2RGB + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + YUV2RGBConvert(src, &dst.x); + setAlpha(dst, ColorChannel::max()); + + return dst; + } + __host__ __device__ __forceinline__ YUV2RGB() {} + __host__ __device__ __forceinline__ YUV2RGB(const YUV2RGB&) {} + }; + + template struct YUV2RGB : unary_function + { + __device__ __forceinline__ uint operator ()(uint src) const + { + return YUV2RGBConvert(src); + } + __host__ __device__ __forceinline__ YUV2RGB() {} + __host__ __device__ __forceinline__ YUV2RGB(const YUV2RGB&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(name, scn, dcn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::YUV2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + +///////////////////////////////////// RGB <-> YCrCb ////////////////////////////////////// + + namespace color_detail + { + __constant__ float c_RGB2YCrCbCoeffs_f[5] = {0.299f, 0.587f, 0.114f, 0.713f, 0.564f}; + __constant__ int c_RGB2YCrCbCoeffs_i[5] = {R2Y, G2Y, B2Y, 11682, 9241}; + + template static __device__ void RGB2YCrCbConvert(const T* src, D& dst) + { + const int delta = ColorChannel::half() * (1 << yuv_shift); + + const int Y = CV_DESCALE(src[0] * c_RGB2YCrCbCoeffs_i[bidx^2] + src[1] * c_RGB2YCrCbCoeffs_i[1] + src[2] * c_RGB2YCrCbCoeffs_i[bidx], yuv_shift); + const int Cr = CV_DESCALE((src[bidx^2] - Y) * c_RGB2YCrCbCoeffs_i[3] + delta, yuv_shift); + const int Cb = CV_DESCALE((src[bidx] - Y) * c_RGB2YCrCbCoeffs_i[4] + delta, yuv_shift); + + dst.x = saturate_cast(Y); + dst.y = saturate_cast(Cr); + dst.z = saturate_cast(Cb); + } + + template static __device__ uint RGB2YCrCbConvert(uint src) + { + const int delta = ColorChannel::half() * (1 << yuv_shift); + + const int Y = CV_DESCALE((0xffu & src) * c_RGB2YCrCbCoeffs_i[bidx^2] + (0xffu & (src >> 8)) * c_RGB2YCrCbCoeffs_i[1] + (0xffu & (src >> 16)) * c_RGB2YCrCbCoeffs_i[bidx], yuv_shift); + const int Cr = CV_DESCALE(((0xffu & (src >> ((bidx ^ 2) * 8))) - Y) * c_RGB2YCrCbCoeffs_i[3] + delta, yuv_shift); + const int Cb = CV_DESCALE(((0xffu & (src >> (bidx * 8))) - Y) * c_RGB2YCrCbCoeffs_i[4] + delta, yuv_shift); + + uint dst = 0; + + dst |= saturate_cast(Y); + dst |= saturate_cast(Cr) << 8; + dst |= saturate_cast(Cb) << 16; + + return dst; + } + + template static __device__ __forceinline__ void RGB2YCrCbConvert(const float* src, D& dst) + { + dst.x = src[0] * c_RGB2YCrCbCoeffs_f[bidx^2] + src[1] * c_RGB2YCrCbCoeffs_f[1] + src[2] * c_RGB2YCrCbCoeffs_f[bidx]; + dst.y = (src[bidx^2] - dst.x) * c_RGB2YCrCbCoeffs_f[3] + ColorChannel::half(); + dst.z = (src[bidx] - dst.x) * c_RGB2YCrCbCoeffs_f[4] + ColorChannel::half(); + } + + template struct RGB2YCrCb + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + RGB2YCrCbConvert(&src.x, dst); + return dst; + } + __host__ __device__ __forceinline__ RGB2YCrCb() {} + __host__ __device__ __forceinline__ RGB2YCrCb(const RGB2YCrCb&) {} + }; + + template struct RGB2YCrCb : unary_function + { + __device__ __forceinline__ uint operator ()(uint src) const + { + return RGB2YCrCbConvert(src); + } + + __host__ __device__ __forceinline__ RGB2YCrCb() {} + __host__ __device__ __forceinline__ RGB2YCrCb(const RGB2YCrCb&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(name, scn, dcn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2YCrCb functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + + namespace color_detail + { + __constant__ float c_YCrCb2RGBCoeffs_f[5] = {1.403f, -0.714f, -0.344f, 1.773f}; + __constant__ int c_YCrCb2RGBCoeffs_i[5] = {22987, -11698, -5636, 29049}; + + template static __device__ void YCrCb2RGBConvert(const T& src, D* dst) + { + const int b = src.x + CV_DESCALE((src.z - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[3], yuv_shift); + const int g = src.x + CV_DESCALE((src.z - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[2] + (src.y - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[1], yuv_shift); + const int r = src.x + CV_DESCALE((src.y - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[0], yuv_shift); + + dst[bidx] = saturate_cast(b); + dst[1] = saturate_cast(g); + dst[bidx^2] = saturate_cast(r); + } + + template static __device__ uint YCrCb2RGBConvert(uint src) + { + const int x = 0xff & (src); + const int y = 0xff & (src >> 8); + const int z = 0xff & (src >> 16); + + const int b = x + CV_DESCALE((z - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[3], yuv_shift); + const int g = x + CV_DESCALE((z - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[2] + (y - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[1], yuv_shift); + const int r = x + CV_DESCALE((y - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[0], yuv_shift); + + uint dst = 0xffu << 24; + + dst |= saturate_cast(b) << (bidx * 8); + dst |= saturate_cast(g) << 8; + dst |= saturate_cast(r) << ((bidx ^ 2) * 8); + + return dst; + } + + template __device__ __forceinline__ void YCrCb2RGBConvert(const T& src, float* dst) + { + dst[bidx] = src.x + (src.z - ColorChannel::half()) * c_YCrCb2RGBCoeffs_f[3]; + dst[1] = src.x + (src.z - ColorChannel::half()) * c_YCrCb2RGBCoeffs_f[2] + (src.y - ColorChannel::half()) * c_YCrCb2RGBCoeffs_f[1]; + dst[bidx^2] = src.x + (src.y - ColorChannel::half()) * c_YCrCb2RGBCoeffs_f[0]; + } + + template struct YCrCb2RGB + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + YCrCb2RGBConvert(src, &dst.x); + setAlpha(dst, ColorChannel::max()); + + return dst; + } + __host__ __device__ __forceinline__ YCrCb2RGB() {} + __host__ __device__ __forceinline__ YCrCb2RGB(const YCrCb2RGB&) {} + }; + + template struct YCrCb2RGB : unary_function + { + __device__ __forceinline__ uint operator ()(uint src) const + { + return YCrCb2RGBConvert(src); + } + __host__ __device__ __forceinline__ YCrCb2RGB() {} + __host__ __device__ __forceinline__ YCrCb2RGB(const YCrCb2RGB&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(name, scn, dcn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::YCrCb2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + +////////////////////////////////////// RGB <-> XYZ /////////////////////////////////////// + + namespace color_detail + { + __constant__ float c_RGB2XYZ_D65f[9] = { 0.412453f, 0.357580f, 0.180423f, 0.212671f, 0.715160f, 0.072169f, 0.019334f, 0.119193f, 0.950227f }; + __constant__ int c_RGB2XYZ_D65i[9] = { 1689, 1465, 739, 871, 2929, 296, 79, 488, 3892 }; + + template static __device__ __forceinline__ void RGB2XYZConvert(const T* src, D& dst) + { + dst.z = saturate_cast(CV_DESCALE(src[bidx^2] * c_RGB2XYZ_D65i[6] + src[1] * c_RGB2XYZ_D65i[7] + src[bidx] * c_RGB2XYZ_D65i[8], xyz_shift)); + dst.x = saturate_cast(CV_DESCALE(src[bidx^2] * c_RGB2XYZ_D65i[0] + src[1] * c_RGB2XYZ_D65i[1] + src[bidx] * c_RGB2XYZ_D65i[2], xyz_shift)); + dst.y = saturate_cast(CV_DESCALE(src[bidx^2] * c_RGB2XYZ_D65i[3] + src[1] * c_RGB2XYZ_D65i[4] + src[bidx] * c_RGB2XYZ_D65i[5], xyz_shift)); + } + + template static __device__ __forceinline__ uint RGB2XYZConvert(uint src) + { + const uint b = 0xffu & (src >> (bidx * 8)); + const uint g = 0xffu & (src >> 8); + const uint r = 0xffu & (src >> ((bidx ^ 2) * 8)); + + const uint x = saturate_cast(CV_DESCALE(r * c_RGB2XYZ_D65i[0] + g * c_RGB2XYZ_D65i[1] + b * c_RGB2XYZ_D65i[2], xyz_shift)); + const uint y = saturate_cast(CV_DESCALE(r * c_RGB2XYZ_D65i[3] + g * c_RGB2XYZ_D65i[4] + b * c_RGB2XYZ_D65i[5], xyz_shift)); + const uint z = saturate_cast(CV_DESCALE(r * c_RGB2XYZ_D65i[6] + g * c_RGB2XYZ_D65i[7] + b * c_RGB2XYZ_D65i[8], xyz_shift)); + + uint dst = 0; + + dst |= x; + dst |= y << 8; + dst |= z << 16; + + return dst; + } + + template static __device__ __forceinline__ void RGB2XYZConvert(const float* src, D& dst) + { + dst.x = src[bidx^2] * c_RGB2XYZ_D65f[0] + src[1] * c_RGB2XYZ_D65f[1] + src[bidx] * c_RGB2XYZ_D65f[2]; + dst.y = src[bidx^2] * c_RGB2XYZ_D65f[3] + src[1] * c_RGB2XYZ_D65f[4] + src[bidx] * c_RGB2XYZ_D65f[5]; + dst.z = src[bidx^2] * c_RGB2XYZ_D65f[6] + src[1] * c_RGB2XYZ_D65f[7] + src[bidx] * c_RGB2XYZ_D65f[8]; + } + + template struct RGB2XYZ + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + RGB2XYZConvert(&src.x, dst); + + return dst; + } + __host__ __device__ __forceinline__ RGB2XYZ() {} + __host__ __device__ __forceinline__ RGB2XYZ(const RGB2XYZ&) {} + }; + + template struct RGB2XYZ : unary_function + { + __device__ __forceinline__ uint operator()(uint src) const + { + return RGB2XYZConvert(src); + } + __host__ __device__ __forceinline__ RGB2XYZ() {} + __host__ __device__ __forceinline__ RGB2XYZ(const RGB2XYZ&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(name, scn, dcn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2XYZ functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + + namespace color_detail + { + __constant__ float c_XYZ2sRGB_D65f[9] = { 3.240479f, -1.53715f, -0.498535f, -0.969256f, 1.875991f, 0.041556f, 0.055648f, -0.204043f, 1.057311f }; + __constant__ int c_XYZ2sRGB_D65i[9] = { 13273, -6296, -2042, -3970, 7684, 170, 228, -836, 4331 }; + + template static __device__ __forceinline__ void XYZ2RGBConvert(const T& src, D* dst) + { + dst[bidx^2] = saturate_cast(CV_DESCALE(src.x * c_XYZ2sRGB_D65i[0] + src.y * c_XYZ2sRGB_D65i[1] + src.z * c_XYZ2sRGB_D65i[2], xyz_shift)); + dst[1] = saturate_cast(CV_DESCALE(src.x * c_XYZ2sRGB_D65i[3] + src.y * c_XYZ2sRGB_D65i[4] + src.z * c_XYZ2sRGB_D65i[5], xyz_shift)); + dst[bidx] = saturate_cast(CV_DESCALE(src.x * c_XYZ2sRGB_D65i[6] + src.y * c_XYZ2sRGB_D65i[7] + src.z * c_XYZ2sRGB_D65i[8], xyz_shift)); + } + + template static __device__ __forceinline__ uint XYZ2RGBConvert(uint src) + { + const int x = 0xff & src; + const int y = 0xff & (src >> 8); + const int z = 0xff & (src >> 16); + + const uint r = saturate_cast(CV_DESCALE(x * c_XYZ2sRGB_D65i[0] + y * c_XYZ2sRGB_D65i[1] + z * c_XYZ2sRGB_D65i[2], xyz_shift)); + const uint g = saturate_cast(CV_DESCALE(x * c_XYZ2sRGB_D65i[3] + y * c_XYZ2sRGB_D65i[4] + z * c_XYZ2sRGB_D65i[5], xyz_shift)); + const uint b = saturate_cast(CV_DESCALE(x * c_XYZ2sRGB_D65i[6] + y * c_XYZ2sRGB_D65i[7] + z * c_XYZ2sRGB_D65i[8], xyz_shift)); + + uint dst = 0xffu << 24; + + dst |= b << (bidx * 8); + dst |= g << 8; + dst |= r << ((bidx ^ 2) * 8); + + return dst; + } + + template static __device__ __forceinline__ void XYZ2RGBConvert(const T& src, float* dst) + { + dst[bidx^2] = src.x * c_XYZ2sRGB_D65f[0] + src.y * c_XYZ2sRGB_D65f[1] + src.z * c_XYZ2sRGB_D65f[2]; + dst[1] = src.x * c_XYZ2sRGB_D65f[3] + src.y * c_XYZ2sRGB_D65f[4] + src.z * c_XYZ2sRGB_D65f[5]; + dst[bidx] = src.x * c_XYZ2sRGB_D65f[6] + src.y * c_XYZ2sRGB_D65f[7] + src.z * c_XYZ2sRGB_D65f[8]; + } + + template struct XYZ2RGB + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + XYZ2RGBConvert(src, &dst.x); + setAlpha(dst, ColorChannel::max()); + + return dst; + } + __host__ __device__ __forceinline__ XYZ2RGB() {} + __host__ __device__ __forceinline__ XYZ2RGB(const XYZ2RGB&) {} + }; + + template struct XYZ2RGB : unary_function + { + __device__ __forceinline__ uint operator()(uint src) const + { + return XYZ2RGBConvert(src); + } + __host__ __device__ __forceinline__ XYZ2RGB() {} + __host__ __device__ __forceinline__ XYZ2RGB(const XYZ2RGB&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(name, scn, dcn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::XYZ2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + +////////////////////////////////////// RGB <-> HSV /////////////////////////////////////// + + namespace color_detail + { + __constant__ int c_HsvDivTable [256] = {0, 1044480, 522240, 348160, 261120, 208896, 174080, 149211, 130560, 116053, 104448, 94953, 87040, 80345, 74606, 69632, 65280, 61440, 58027, 54973, 52224, 49737, 47476, 45412, 43520, 41779, 40172, 38684, 37303, 36017, 34816, 33693, 32640, 31651, 30720, 29842, 29013, 28229, 27486, 26782, 26112, 25475, 24869, 24290, 23738, 23211, 22706, 22223, 21760, 21316, 20890, 20480, 20086, 19707, 19342, 18991, 18651, 18324, 18008, 17703, 17408, 17123, 16846, 16579, 16320, 16069, 15825, 15589, 15360, 15137, 14921, 14711, 14507, 14308, 14115, 13926, 13743, 13565, 13391, 13221, 13056, 12895, 12738, 12584, 12434, 12288, 12145, 12006, 11869, 11736, 11605, 11478, 11353, 11231, 11111, 10995, 10880, 10768, 10658, 10550, 10445, 10341, 10240, 10141, 10043, 9947, 9854, 9761, 9671, 9582, 9495, 9410, 9326, 9243, 9162, 9082, 9004, 8927, 8852, 8777, 8704, 8632, 8561, 8492, 8423, 8356, 8290, 8224, 8160, 8097, 8034, 7973, 7913, 7853, 7795, 7737, 7680, 7624, 7569, 7514, 7461, 7408, 7355, 7304, 7253, 7203, 7154, 7105, 7057, 7010, 6963, 6917, 6872, 6827, 6782, 6739, 6695, 6653, 6611, 6569, 6528, 6487, 6447, 6408, 6369, 6330, 6292, 6254, 6217, 6180, 6144, 6108, 6073, 6037, 6003, 5968, 5935, 5901, 5868, 5835, 5803, 5771, 5739, 5708, 5677, 5646, 5615, 5585, 5556, 5526, 5497, 5468, 5440, 5412, 5384, 5356, 5329, 5302, 5275, 5249, 5222, 5196, 5171, 5145, 5120, 5095, 5070, 5046, 5022, 4998, 4974, 4950, 4927, 4904, 4881, 4858, 4836, 4813, 4791, 4769, 4748, 4726, 4705, 4684, 4663, 4642, 4622, 4601, 4581, 4561, 4541, 4522, 4502, 4483, 4464, 4445, 4426, 4407, 4389, 4370, 4352, 4334, 4316, 4298, 4281, 4263, 4246, 4229, 4212, 4195, 4178, 4161, 4145, 4128, 4112, 4096}; + __constant__ int c_HsvDivTable180[256] = {0, 122880, 61440, 40960, 30720, 24576, 20480, 17554, 15360, 13653, 12288, 11171, 10240, 9452, 8777, 8192, 7680, 7228, 6827, 6467, 6144, 5851, 5585, 5343, 5120, 4915, 4726, 4551, 4389, 4237, 4096, 3964, 3840, 3724, 3614, 3511, 3413, 3321, 3234, 3151, 3072, 2997, 2926, 2858, 2793, 2731, 2671, 2614, 2560, 2508, 2458, 2409, 2363, 2318, 2276, 2234, 2194, 2156, 2119, 2083, 2048, 2014, 1982, 1950, 1920, 1890, 1862, 1834, 1807, 1781, 1755, 1731, 1707, 1683, 1661, 1638, 1617, 1596, 1575, 1555, 1536, 1517, 1499, 1480, 1463, 1446, 1429, 1412, 1396, 1381, 1365, 1350, 1336, 1321, 1307, 1293, 1280, 1267, 1254, 1241, 1229, 1217, 1205, 1193, 1182, 1170, 1159, 1148, 1138, 1127, 1117, 1107, 1097, 1087, 1078, 1069, 1059, 1050, 1041, 1033, 1024, 1016, 1007, 999, 991, 983, 975, 968, 960, 953, 945, 938, 931, 924, 917, 910, 904, 897, 890, 884, 878, 871, 865, 859, 853, 847, 842, 836, 830, 825, 819, 814, 808, 803, 798, 793, 788, 783, 778, 773, 768, 763, 759, 754, 749, 745, 740, 736, 731, 727, 723, 719, 714, 710, 706, 702, 698, 694, 690, 686, 683, 679, 675, 671, 668, 664, 661, 657, 654, 650, 647, 643, 640, 637, 633, 630, 627, 624, 621, 617, 614, 611, 608, 605, 602, 599, 597, 594, 591, 588, 585, 582, 580, 577, 574, 572, 569, 566, 564, 561, 559, 556, 554, 551, 549, 546, 544, 541, 539, 537, 534, 532, 530, 527, 525, 523, 521, 518, 516, 514, 512, 510, 508, 506, 504, 502, 500, 497, 495, 493, 492, 490, 488, 486, 484, 482}; + __constant__ int c_HsvDivTable256[256] = {0, 174763, 87381, 58254, 43691, 34953, 29127, 24966, 21845, 19418, 17476, 15888, 14564, 13443, 12483, 11651, 10923, 10280, 9709, 9198, 8738, 8322, 7944, 7598, 7282, 6991, 6722, 6473, 6242, 6026, 5825, 5638, 5461, 5296, 5140, 4993, 4855, 4723, 4599, 4481, 4369, 4263, 4161, 4064, 3972, 3884, 3799, 3718, 3641, 3567, 3495, 3427, 3361, 3297, 3236, 3178, 3121, 3066, 3013, 2962, 2913, 2865, 2819, 2774, 2731, 2689, 2648, 2608, 2570, 2533, 2497, 2461, 2427, 2394, 2362, 2330, 2300, 2270, 2241, 2212, 2185, 2158, 2131, 2106, 2081, 2056, 2032, 2009, 1986, 1964, 1942, 1920, 1900, 1879, 1859, 1840, 1820, 1802, 1783, 1765, 1748, 1730, 1713, 1697, 1680, 1664, 1649, 1633, 1618, 1603, 1589, 1574, 1560, 1547, 1533, 1520, 1507, 1494, 1481, 1469, 1456, 1444, 1432, 1421, 1409, 1398, 1387, 1376, 1365, 1355, 1344, 1334, 1324, 1314, 1304, 1295, 1285, 1276, 1266, 1257, 1248, 1239, 1231, 1222, 1214, 1205, 1197, 1189, 1181, 1173, 1165, 1157, 1150, 1142, 1135, 1128, 1120, 1113, 1106, 1099, 1092, 1085, 1079, 1072, 1066, 1059, 1053, 1046, 1040, 1034, 1028, 1022, 1016, 1010, 1004, 999, 993, 987, 982, 976, 971, 966, 960, 955, 950, 945, 940, 935, 930, 925, 920, 915, 910, 906, 901, 896, 892, 887, 883, 878, 874, 869, 865, 861, 857, 853, 848, 844, 840, 836, 832, 828, 824, 820, 817, 813, 809, 805, 802, 798, 794, 791, 787, 784, 780, 777, 773, 770, 767, 763, 760, 757, 753, 750, 747, 744, 741, 737, 734, 731, 728, 725, 722, 719, 716, 713, 710, 708, 705, 702, 699, 696, 694, 691, 688, 685}; + + template static __device__ void RGB2HSVConvert(const uchar* src, D& dst) + { + const int hsv_shift = 12; + const int* hdiv_table = hr == 180 ? c_HsvDivTable180 : c_HsvDivTable256; + + int b = src[bidx], g = src[1], r = src[bidx^2]; + int h, s, v = b; + int vmin = b, diff; + int vr, vg; + + v = ::max(v, g); + v = ::max(v, r); + vmin = ::min(vmin, g); + vmin = ::min(vmin, r); + + diff = v - vmin; + vr = (v == r) * -1; + vg = (v == g) * -1; + + s = (diff * c_HsvDivTable[v] + (1 << (hsv_shift-1))) >> hsv_shift; + h = (vr & (g - b)) + (~vr & ((vg & (b - r + 2 * diff)) + ((~vg) & (r - g + 4 * diff)))); + h = (h * hdiv_table[diff] + (1 << (hsv_shift-1))) >> hsv_shift; + h += (h < 0) * hr; + + dst.x = saturate_cast(h); + dst.y = (uchar)s; + dst.z = (uchar)v; + } + + template static __device__ uint RGB2HSVConvert(uint src) + { + const int hsv_shift = 12; + const int* hdiv_table = hr == 180 ? c_HsvDivTable180 : c_HsvDivTable256; + + const int b = 0xff & (src >> (bidx * 8)); + const int g = 0xff & (src >> 8); + const int r = 0xff & (src >> ((bidx ^ 2) * 8)); + + int h, s, v = b; + int vmin = b, diff; + int vr, vg; + + v = ::max(v, g); + v = ::max(v, r); + vmin = ::min(vmin, g); + vmin = ::min(vmin, r); + + diff = v - vmin; + vr = (v == r) * -1; + vg = (v == g) * -1; + + s = (diff * c_HsvDivTable[v] + (1 << (hsv_shift-1))) >> hsv_shift; + h = (vr & (g - b)) + (~vr & ((vg & (b - r + 2 * diff)) + ((~vg) & (r - g + 4 * diff)))); + h = (h * hdiv_table[diff] + (1 << (hsv_shift-1))) >> hsv_shift; + h += (h < 0) * hr; + + uint dst = 0; + + dst |= saturate_cast(h); + dst |= (0xffu & s) << 8; + dst |= (0xffu & v) << 16; + + return dst; + } + + template static __device__ void RGB2HSVConvert(const float* src, D& dst) + { + const float hscale = hr * (1.f / 360.f); + + float b = src[bidx], g = src[1], r = src[bidx^2]; + float h, s, v; + + float vmin, diff; + + v = vmin = r; + v = fmax(v, g); + v = fmax(v, b); + vmin = fmin(vmin, g); + vmin = fmin(vmin, b); + + diff = v - vmin; + s = diff / (float)(::fabs(v) + numeric_limits::epsilon()); + diff = (float)(60. / (diff + numeric_limits::epsilon())); + + h = (v == r) * (g - b) * diff; + h += (v != r && v == g) * ((b - r) * diff + 120.f); + h += (v != r && v != g) * ((r - g) * diff + 240.f); + h += (h < 0) * 360.f; + + dst.x = h * hscale; + dst.y = s; + dst.z = v; + } + + template struct RGB2HSV + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + RGB2HSVConvert(&src.x, dst); + + return dst; + } + __host__ __device__ __forceinline__ RGB2HSV() {} + __host__ __device__ __forceinline__ RGB2HSV(const RGB2HSV&) {} + }; + + template struct RGB2HSV : unary_function + { + __device__ __forceinline__ uint operator()(uint src) const + { + return RGB2HSVConvert(src); + } + __host__ __device__ __forceinline__ RGB2HSV() {} + __host__ __device__ __forceinline__ RGB2HSV(const RGB2HSV&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(name, scn, dcn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2HSV functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template struct name ## _full_traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2HSV functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template <> struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2HSV functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template <> struct name ## _full_traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2HSV functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + + namespace color_detail + { + __constant__ int c_HsvSectorData[6][3] = { {1,3,0}, {1,0,2}, {3,0,1}, {0,2,1}, {0,1,3}, {2,1,0} }; + + template static __device__ void HSV2RGBConvert(const T& src, float* dst) + { + const float hscale = 6.f / hr; + + float h = src.x, s = src.y, v = src.z; + float b = v, g = v, r = v; + + if (s != 0) + { + h *= hscale; + + if( h < 0 ) + do h += 6; while( h < 0 ); + else if( h >= 6 ) + do h -= 6; while( h >= 6 ); + + int sector = __float2int_rd(h); + h -= sector; + + if ( (unsigned)sector >= 6u ) + { + sector = 0; + h = 0.f; + } + + float tab[4]; + tab[0] = v; + tab[1] = v * (1.f - s); + tab[2] = v * (1.f - s * h); + tab[3] = v * (1.f - s * (1.f - h)); + + b = tab[c_HsvSectorData[sector][0]]; + g = tab[c_HsvSectorData[sector][1]]; + r = tab[c_HsvSectorData[sector][2]]; + } + + dst[bidx] = b; + dst[1] = g; + dst[bidx^2] = r; + } + + template static __device__ void HSV2RGBConvert(const T& src, uchar* dst) + { + float3 buf; + + buf.x = src.x; + buf.y = src.y * (1.f / 255.f); + buf.z = src.z * (1.f / 255.f); + + HSV2RGBConvert(buf, &buf.x); + + dst[0] = saturate_cast(buf.x * 255.f); + dst[1] = saturate_cast(buf.y * 255.f); + dst[2] = saturate_cast(buf.z * 255.f); + } + + template static __device__ uint HSV2RGBConvert(uint src) + { + float3 buf; + + buf.x = src & 0xff; + buf.y = ((src >> 8) & 0xff) * (1.f/255.f); + buf.z = ((src >> 16) & 0xff) * (1.f/255.f); + + HSV2RGBConvert(buf, &buf.x); + + uint dst = 0xffu << 24; + + dst |= saturate_cast(buf.x * 255.f); + dst |= saturate_cast(buf.y * 255.f) << 8; + dst |= saturate_cast(buf.z * 255.f) << 16; + + return dst; + } + + template struct HSV2RGB + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + HSV2RGBConvert(src, &dst.x); + setAlpha(dst, ColorChannel::max()); + + return dst; + } + __host__ __device__ __forceinline__ HSV2RGB() {} + __host__ __device__ __forceinline__ HSV2RGB(const HSV2RGB&) {} + }; + + template struct HSV2RGB : unary_function + { + __device__ __forceinline__ uint operator()(uint src) const + { + return HSV2RGBConvert(src); + } + __host__ __device__ __forceinline__ HSV2RGB() {} + __host__ __device__ __forceinline__ HSV2RGB(const HSV2RGB&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(name, scn, dcn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::HSV2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template struct name ## _full_traits \ + { \ + typedef ::cv::cuda::device::color_detail::HSV2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template <> struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::HSV2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template <> struct name ## _full_traits \ + { \ + typedef ::cv::cuda::device::color_detail::HSV2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + +/////////////////////////////////////// RGB <-> HLS //////////////////////////////////////// + + namespace color_detail + { + template static __device__ void RGB2HLSConvert(const float* src, D& dst) + { + const float hscale = hr * (1.f / 360.f); + + float b = src[bidx], g = src[1], r = src[bidx^2]; + float h = 0.f, s = 0.f, l; + float vmin, vmax, diff; + + vmax = vmin = r; + vmax = fmax(vmax, g); + vmax = fmax(vmax, b); + vmin = fmin(vmin, g); + vmin = fmin(vmin, b); + + diff = vmax - vmin; + l = (vmax + vmin) * 0.5f; + + if (diff > numeric_limits::epsilon()) + { + s = (l < 0.5f) * diff / (vmax + vmin); + s += (l >= 0.5f) * diff / (2.0f - vmax - vmin); + + diff = 60.f / diff; + + h = (vmax == r) * (g - b) * diff; + h += (vmax != r && vmax == g) * ((b - r) * diff + 120.f); + h += (vmax != r && vmax != g) * ((r - g) * diff + 240.f); + h += (h < 0.f) * 360.f; + } + + dst.x = h * hscale; + dst.y = l; + dst.z = s; + } + + template static __device__ void RGB2HLSConvert(const uchar* src, D& dst) + { + float3 buf; + + buf.x = src[0] * (1.f / 255.f); + buf.y = src[1] * (1.f / 255.f); + buf.z = src[2] * (1.f / 255.f); + + RGB2HLSConvert(&buf.x, buf); + + dst.x = saturate_cast(buf.x); + dst.y = saturate_cast(buf.y*255.f); + dst.z = saturate_cast(buf.z*255.f); + } + + template static __device__ uint RGB2HLSConvert(uint src) + { + float3 buf; + + buf.x = (0xff & src) * (1.f / 255.f); + buf.y = (0xff & (src >> 8)) * (1.f / 255.f); + buf.z = (0xff & (src >> 16)) * (1.f / 255.f); + + RGB2HLSConvert(&buf.x, buf); + + uint dst = 0xffu << 24; + + dst |= saturate_cast(buf.x); + dst |= saturate_cast(buf.y * 255.f) << 8; + dst |= saturate_cast(buf.z * 255.f) << 16; + + return dst; + } + + template struct RGB2HLS + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + RGB2HLSConvert(&src.x, dst); + + return dst; + } + __host__ __device__ __forceinline__ RGB2HLS() {} + __host__ __device__ __forceinline__ RGB2HLS(const RGB2HLS&) {} + }; + + template struct RGB2HLS : unary_function + { + __device__ __forceinline__ uint operator()(uint src) const + { + return RGB2HLSConvert(src); + } + __host__ __device__ __forceinline__ RGB2HLS() {} + __host__ __device__ __forceinline__ RGB2HLS(const RGB2HLS&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(name, scn, dcn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2HLS functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template struct name ## _full_traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2HLS functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template <> struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2HLS functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template <> struct name ## _full_traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2HLS functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + + namespace color_detail + { + __constant__ int c_HlsSectorData[6][3] = { {1,3,0}, {1,0,2}, {3,0,1}, {0,2,1}, {0,1,3}, {2,1,0} }; + + template static __device__ void HLS2RGBConvert(const T& src, float* dst) + { + const float hscale = 6.0f / hr; + + float h = src.x, l = src.y, s = src.z; + float b = l, g = l, r = l; + + if (s != 0) + { + float p2 = (l <= 0.5f) * l * (1 + s); + p2 += (l > 0.5f) * (l + s - l * s); + float p1 = 2 * l - p2; + + h *= hscale; + + if( h < 0 ) + do h += 6; while( h < 0 ); + else if( h >= 6 ) + do h -= 6; while( h >= 6 ); + + int sector; + sector = __float2int_rd(h); + + h -= sector; + + float tab[4]; + tab[0] = p2; + tab[1] = p1; + tab[2] = p1 + (p2 - p1) * (1 - h); + tab[3] = p1 + (p2 - p1) * h; + + b = tab[c_HlsSectorData[sector][0]]; + g = tab[c_HlsSectorData[sector][1]]; + r = tab[c_HlsSectorData[sector][2]]; + } + + dst[bidx] = b; + dst[1] = g; + dst[bidx^2] = r; + } + + template static __device__ void HLS2RGBConvert(const T& src, uchar* dst) + { + float3 buf; + + buf.x = src.x; + buf.y = src.y * (1.f / 255.f); + buf.z = src.z * (1.f / 255.f); + + HLS2RGBConvert(buf, &buf.x); + + dst[0] = saturate_cast(buf.x * 255.f); + dst[1] = saturate_cast(buf.y * 255.f); + dst[2] = saturate_cast(buf.z * 255.f); + } + + template static __device__ uint HLS2RGBConvert(uint src) + { + float3 buf; + + buf.x = 0xff & src; + buf.y = (0xff & (src >> 8)) * (1.f / 255.f); + buf.z = (0xff & (src >> 16)) * (1.f / 255.f); + + HLS2RGBConvert(buf, &buf.x); + + uint dst = 0xffu << 24; + + dst |= saturate_cast(buf.x * 255.f); + dst |= saturate_cast(buf.y * 255.f) << 8; + dst |= saturate_cast(buf.z * 255.f) << 16; + + return dst; + } + + template struct HLS2RGB + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + HLS2RGBConvert(src, &dst.x); + setAlpha(dst, ColorChannel::max()); + + return dst; + } + __host__ __device__ __forceinline__ HLS2RGB() {} + __host__ __device__ __forceinline__ HLS2RGB(const HLS2RGB&) {} + }; + + template struct HLS2RGB : unary_function + { + __device__ __forceinline__ uint operator()(uint src) const + { + return HLS2RGBConvert(src); + } + __host__ __device__ __forceinline__ HLS2RGB() {} + __host__ __device__ __forceinline__ HLS2RGB(const HLS2RGB&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(name, scn, dcn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::HLS2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template struct name ## _full_traits \ + { \ + typedef ::cv::cuda::device::color_detail::HLS2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template <> struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::HLS2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template <> struct name ## _full_traits \ + { \ + typedef ::cv::cuda::device::color_detail::HLS2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + +///////////////////////////////////// RGB <-> Lab ///////////////////////////////////// + + namespace color_detail + { + enum + { + LAB_CBRT_TAB_SIZE = 1024, + GAMMA_TAB_SIZE = 1024, + lab_shift = xyz_shift, + gamma_shift = 3, + lab_shift2 = (lab_shift + gamma_shift), + LAB_CBRT_TAB_SIZE_B = (256 * 3 / 2 * (1 << gamma_shift)) + }; + + __constant__ ushort c_sRGBGammaTab_b[] = {0,1,1,2,2,3,4,4,5,6,6,7,8,8,9,10,11,11,12,13,14,15,16,17,19,20,21,22,24,25,26,28,29,31,33,34,36,38,40,41,43,45,47,49,51,54,56,58,60,63,65,68,70,73,75,78,81,83,86,89,92,95,98,101,105,108,111,115,118,121,125,129,132,136,140,144,147,151,155,160,164,168,172,176,181,185,190,194,199,204,209,213,218,223,228,233,239,244,249,255,260,265,271,277,282,288,294,300,306,312,318,324,331,337,343,350,356,363,370,376,383,390,397,404,411,418,426,433,440,448,455,463,471,478,486,494,502,510,518,527,535,543,552,560,569,578,586,595,604,613,622,631,641,650,659,669,678,688,698,707,717,727,737,747,757,768,778,788,799,809,820,831,842,852,863,875,886,897,908,920,931,943,954,966,978,990,1002,1014,1026,1038,1050,1063,1075,1088,1101,1113,1126,1139,1152,1165,1178,1192,1205,1218,1232,1245,1259,1273,1287,1301,1315,1329,1343,1357,1372,1386,1401,1415,1430,1445,1460,1475,1490,1505,1521,1536,1551,1567,1583,1598,1614,1630,1646,1662,1678,1695,1711,1728,1744,1761,1778,1794,1811,1828,1846,1863,1880,1897,1915,1933,1950,1968,1986,2004,2022,2040}; + + __device__ __forceinline__ int LabCbrt_b(int i) + { + float x = i * (1.f / (255.f * (1 << gamma_shift))); + return (1 << lab_shift2) * (x < 0.008856f ? x * 7.787f + 0.13793103448275862f : ::cbrtf(x)); + } + + template + __device__ __forceinline__ void RGB2LabConvert_b(const T& src, D& dst) + { + const int Lscale = (116 * 255 + 50) / 100; + const int Lshift = -((16 * 255 * (1 << lab_shift2) + 50) / 100); + + int B = blueIdx == 0 ? src.x : src.z; + int G = src.y; + int R = blueIdx == 0 ? src.z : src.x; + + if (srgb) + { + B = c_sRGBGammaTab_b[B]; + G = c_sRGBGammaTab_b[G]; + R = c_sRGBGammaTab_b[R]; + } + else + { + B <<= 3; + G <<= 3; + R <<= 3; + } + + int fX = LabCbrt_b(CV_DESCALE(B * 778 + G * 1541 + R * 1777, lab_shift)); + int fY = LabCbrt_b(CV_DESCALE(B * 296 + G * 2929 + R * 871, lab_shift)); + int fZ = LabCbrt_b(CV_DESCALE(B * 3575 + G * 448 + R * 73, lab_shift)); + + int L = CV_DESCALE(Lscale * fY + Lshift, lab_shift2); + int a = CV_DESCALE(500 * (fX - fY) + 128 * (1 << lab_shift2), lab_shift2); + int b = CV_DESCALE(200 * (fY - fZ) + 128 * (1 << lab_shift2), lab_shift2); + + dst.x = saturate_cast(L); + dst.y = saturate_cast(a); + dst.z = saturate_cast(b); + } + + __device__ __forceinline__ float splineInterpolate(float x, const float* tab, int n) + { + int ix = ::min(::max(int(x), 0), n-1); + x -= ix; + tab += ix * 4; + return ((tab[3] * x + tab[2]) * x + tab[1]) * x + tab[0]; + } + + __constant__ float c_sRGBGammaTab[] = 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+ + template + __device__ __forceinline__ void RGB2LabConvert_f(const T& src, D& dst) + { + const float _1_3 = 1.0f / 3.0f; + const float _a = 16.0f / 116.0f; + + float B = blueIdx == 0 ? src.x : src.z; + float G = src.y; + float R = blueIdx == 0 ? src.z : src.x; + + if (srgb) + { + B = splineInterpolate(B * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE); + G = splineInterpolate(G * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE); + R = splineInterpolate(R * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE); + } + + float X = B * 0.189828f + G * 0.376219f + R * 0.433953f; + float Y = B * 0.072169f + G * 0.715160f + R * 0.212671f; + float Z = B * 0.872766f + G * 0.109477f + R * 0.017758f; + + float FX = X > 0.008856f ? ::powf(X, _1_3) : (7.787f * X + _a); + float FY = Y > 0.008856f ? ::powf(Y, _1_3) : (7.787f * Y + _a); + float FZ = Z > 0.008856f ? ::powf(Z, _1_3) : (7.787f * Z + _a); + + float L = Y > 0.008856f ? (116.f * FY - 16.f) : (903.3f * Y); + float a = 500.f * (FX - FY); + float b = 200.f * (FY - FZ); + + dst.x = L; + dst.y = a; + dst.z = b; + } + + template struct RGB2Lab; + template + struct RGB2Lab + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + RGB2LabConvert_b(src, dst); + + return dst; + } + __host__ __device__ __forceinline__ RGB2Lab() {} + __host__ __device__ __forceinline__ RGB2Lab(const RGB2Lab&) {} + }; + template + struct RGB2Lab + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + RGB2LabConvert_f(src, dst); + + return dst; + } + __host__ __device__ __forceinline__ RGB2Lab() {} + __host__ __device__ __forceinline__ RGB2Lab(const RGB2Lab&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(name, scn, dcn, srgb, blueIdx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2Lab functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + + namespace color_detail + { + __constant__ float c_sRGBInvGammaTab[] = 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+ + template + __device__ __forceinline__ void Lab2RGBConvert_f(const T& src, D& dst) + { + const float lThresh = 0.008856f * 903.3f; + const float fThresh = 7.787f * 0.008856f + 16.0f / 116.0f; + + float Y, fy; + + if (src.x <= lThresh) + { + Y = src.x / 903.3f; + fy = 7.787f * Y + 16.0f / 116.0f; + } + else + { + fy = (src.x + 16.0f) / 116.0f; + Y = fy * fy * fy; + } + + float X = src.y / 500.0f + fy; + float Z = fy - src.z / 200.0f; + + if (X <= fThresh) + X = (X - 16.0f / 116.0f) / 7.787f; + else + X = X * X * X; + + if (Z <= fThresh) + Z = (Z - 16.0f / 116.0f) / 7.787f; + else + Z = Z * Z * Z; + + float B = 0.052891f * X - 0.204043f * Y + 1.151152f * Z; + float G = -0.921235f * X + 1.875991f * Y + 0.045244f * Z; + float R = 3.079933f * X - 1.537150f * Y - 0.542782f * Z; + + if (srgb) + { + B = splineInterpolate(B * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE); + G = splineInterpolate(G * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE); + R = splineInterpolate(R * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE); + } + + dst.x = blueIdx == 0 ? B : R; + dst.y = G; + dst.z = blueIdx == 0 ? R : B; + setAlpha(dst, ColorChannel::max()); + } + + template + __device__ __forceinline__ void Lab2RGBConvert_b(const T& src, D& dst) + { + float3 srcf, dstf; + + srcf.x = src.x * (100.f / 255.f); + srcf.y = src.y - 128; + srcf.z = src.z - 128; + + Lab2RGBConvert_f(srcf, dstf); + + dst.x = saturate_cast(dstf.x * 255.f); + dst.y = saturate_cast(dstf.y * 255.f); + dst.z = saturate_cast(dstf.z * 255.f); + setAlpha(dst, ColorChannel::max()); + } + + template struct Lab2RGB; + template + struct Lab2RGB + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + Lab2RGBConvert_b(src, dst); + + return dst; + } + __host__ __device__ __forceinline__ Lab2RGB() {} + __host__ __device__ __forceinline__ Lab2RGB(const Lab2RGB&) {} + }; + template + struct Lab2RGB + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + Lab2RGBConvert_f(src, dst); + + return dst; + } + __host__ __device__ __forceinline__ Lab2RGB() {} + __host__ __device__ __forceinline__ Lab2RGB(const Lab2RGB&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(name, scn, dcn, srgb, blueIdx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::Lab2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + +///////////////////////////////////// RGB <-> Luv ///////////////////////////////////// + + namespace color_detail + { + __constant__ float c_LabCbrtTab[] = 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+ + template + __device__ __forceinline__ void RGB2LuvConvert_f(const T& src, D& dst) + { + const float _d = 1.f / (0.950456f + 15 + 1.088754f * 3); + const float _un = 13 * (4 * 0.950456f * _d); + const float _vn = 13 * (9 * _d); + + float B = blueIdx == 0 ? src.x : src.z; + float G = src.y; + float R = blueIdx == 0 ? src.z : src.x; + + if (srgb) + { + B = splineInterpolate(B * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE); + G = splineInterpolate(G * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE); + R = splineInterpolate(R * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE); + } + + float X = R * 0.412453f + G * 0.357580f + B * 0.180423f; + float Y = R * 0.212671f + G * 0.715160f + B * 0.072169f; + float Z = R * 0.019334f + G * 0.119193f + B * 0.950227f; + + float L = splineInterpolate(Y * (LAB_CBRT_TAB_SIZE / 1.5f), c_LabCbrtTab, LAB_CBRT_TAB_SIZE); + L = 116.f * L - 16.f; + + const float d = (4 * 13) / ::fmaxf(X + 15 * Y + 3 * Z, numeric_limits::epsilon()); + float u = L * (X * d - _un); + float v = L * ((9 * 0.25f) * Y * d - _vn); + + dst.x = L; + dst.y = u; + dst.z = v; + } + + template + __device__ __forceinline__ void RGB2LuvConvert_b(const T& src, D& dst) + { + float3 srcf, dstf; + + srcf.x = src.x * (1.f / 255.f); + srcf.y = src.y * (1.f / 255.f); + srcf.z = src.z * (1.f / 255.f); + + RGB2LuvConvert_f(srcf, dstf); + + dst.x = saturate_cast(dstf.x * 2.55f); + dst.y = saturate_cast(dstf.y * 0.72033898305084743f + 96.525423728813564f); + dst.z = saturate_cast(dstf.z * 0.9732824427480916f + 136.259541984732824f); + } + + template struct RGB2Luv; + template + struct RGB2Luv + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + RGB2LuvConvert_b(src, dst); + + return dst; + } + __host__ __device__ __forceinline__ RGB2Luv() {} + __host__ __device__ __forceinline__ RGB2Luv(const RGB2Luv&) {} + }; + template + struct RGB2Luv + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + RGB2LuvConvert_f(src, dst); + + return dst; + } + __host__ __device__ __forceinline__ RGB2Luv() {} + __host__ __device__ __forceinline__ RGB2Luv(const RGB2Luv&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(name, scn, dcn, srgb, blueIdx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2Luv functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + + namespace color_detail + { + template + __device__ __forceinline__ void Luv2RGBConvert_f(const T& src, D& dst) + { + const float _d = 1.f / (0.950456f + 15 + 1.088754f * 3); + const float _un = 4 * 0.950456f * _d; + const float _vn = 9 * _d; + + float L = src.x; + float u = src.y; + float v = src.z; + + float Y = (L + 16.f) * (1.f / 116.f); + Y = Y * Y * Y; + + float d = (1.f / 13.f) / L; + u = u * d + _un; + v = v * d + _vn; + + float iv = 1.f / v; + float X = 2.25f * u * Y * iv; + float Z = (12 - 3 * u - 20 * v) * Y * 0.25f * iv; + + float B = 0.055648f * X - 0.204043f * Y + 1.057311f * Z; + float G = -0.969256f * X + 1.875991f * Y + 0.041556f * Z; + float R = 3.240479f * X - 1.537150f * Y - 0.498535f * Z; + + if (srgb) + { + B = splineInterpolate(B * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE); + G = splineInterpolate(G * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE); + R = splineInterpolate(R * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE); + } + + dst.x = blueIdx == 0 ? B : R; + dst.y = G; + dst.z = blueIdx == 0 ? R : B; + setAlpha(dst, ColorChannel::max()); + } + + template + __device__ __forceinline__ void Luv2RGBConvert_b(const T& src, D& dst) + { + float3 srcf, dstf; + + srcf.x = src.x * (100.f / 255.f); + srcf.y = src.y * 1.388235294117647f - 134.f; + srcf.z = src.z * 1.027450980392157f - 140.f; + + Luv2RGBConvert_f(srcf, dstf); + + dst.x = saturate_cast(dstf.x * 255.f); + dst.y = saturate_cast(dstf.y * 255.f); + dst.z = saturate_cast(dstf.z * 255.f); + setAlpha(dst, ColorChannel::max()); + } + + template struct Luv2RGB; + template + struct Luv2RGB + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + Luv2RGBConvert_b(src, dst); + + return dst; + } + __host__ __device__ __forceinline__ Luv2RGB() {} + __host__ __device__ __forceinline__ Luv2RGB(const Luv2RGB&) {} + }; + template + struct Luv2RGB + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + Luv2RGBConvert_f(src, dst); + + return dst; + } + __host__ __device__ __forceinline__ Luv2RGB() {} + __host__ __device__ __forceinline__ Luv2RGB(const Luv2RGB&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(name, scn, dcn, srgb, blueIdx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::Luv2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + + #undef CV_DESCALE + +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_COLOR_DETAIL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/detail/reduce.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/detail/reduce.hpp new file mode 100644 index 0000000000000000000000000000000000000000..8af20b0dc86d76232220c5111bbdf5a2bc31748c --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/detail/reduce.hpp @@ -0,0 +1,365 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_REDUCE_DETAIL_HPP +#define OPENCV_CUDA_REDUCE_DETAIL_HPP + +#include +#include "../warp.hpp" +#include "../warp_shuffle.hpp" + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + namespace reduce_detail + { + template struct GetType; + template struct GetType + { + typedef T type; + }; + template struct GetType + { + typedef T type; + }; + template struct GetType + { + typedef T type; + }; + + template + struct For + { + template + static __device__ void loadToSmem(const PointerTuple& smem, const ValTuple& val, unsigned int tid) + { + thrust::get(smem)[tid] = thrust::get(val); + + For::loadToSmem(smem, val, tid); + } + template + static __device__ void loadFromSmem(const PointerTuple& smem, const ValTuple& val, unsigned int tid) + { + thrust::get(val) = thrust::get(smem)[tid]; + + For::loadFromSmem(smem, val, tid); + } + + template + static __device__ void merge(const PointerTuple& smem, const ValTuple& val, unsigned int tid, unsigned int delta, const OpTuple& op) + { + typename GetType::type>::type reg = thrust::get(smem)[tid + delta]; + thrust::get(smem)[tid] = thrust::get(val) = thrust::get(op)(thrust::get(val), reg); + + For::merge(smem, val, tid, delta, op); + } + template + static __device__ void mergeShfl(const ValTuple& val, unsigned int delta, unsigned int width, const OpTuple& op) + { + typename GetType::type>::type reg = shfl_down(thrust::get(val), delta, width); + thrust::get(val) = thrust::get(op)(thrust::get(val), reg); + + For::mergeShfl(val, delta, width, op); + } + }; + template + struct For + { + template + static __device__ void loadToSmem(const PointerTuple&, const ValTuple&, unsigned int) + { + } + template + static __device__ void loadFromSmem(const PointerTuple&, const ValTuple&, unsigned int) + { + } + + template + static __device__ void merge(const PointerTuple&, const ValTuple&, unsigned int, unsigned int, const OpTuple&) + { + } + template + static __device__ void mergeShfl(const ValTuple&, unsigned int, unsigned int, const OpTuple&) + { + } + }; + + template + __device__ __forceinline__ void loadToSmem(volatile T* smem, T& val, unsigned int tid) + { + smem[tid] = val; + } + template + __device__ __forceinline__ void loadFromSmem(volatile T* smem, T& val, unsigned int tid) + { + val = smem[tid]; + } + template + __device__ __forceinline__ void loadToSmem(const thrust::tuple& smem, + const thrust::tuple& val, + unsigned int tid) + { + For<0, thrust::tuple_size >::value>::loadToSmem(smem, val, tid); + } + template + __device__ __forceinline__ void loadFromSmem(const thrust::tuple& smem, + const thrust::tuple& val, + unsigned int tid) + { + For<0, thrust::tuple_size >::value>::loadFromSmem(smem, val, tid); + } + + template + __device__ __forceinline__ void merge(volatile T* smem, T& val, unsigned int tid, unsigned int delta, const Op& op) + { + T reg = smem[tid + delta]; + smem[tid] = val = op(val, reg); + } + template + __device__ __forceinline__ void mergeShfl(T& val, unsigned int delta, unsigned int width, const Op& op) + { + T reg = shfl_down(val, delta, width); + val = op(val, reg); + } + template + __device__ __forceinline__ void merge(const thrust::tuple& smem, + const thrust::tuple& val, + unsigned int tid, + unsigned int delta, + const thrust::tuple& op) + { + For<0, thrust::tuple_size >::value>::merge(smem, val, tid, delta, op); + } + template + __device__ __forceinline__ void mergeShfl(const thrust::tuple& val, + unsigned int delta, + unsigned int width, + const thrust::tuple& op) + { + For<0, thrust::tuple_size >::value>::mergeShfl(val, delta, width, op); + } + + template struct Generic + { + template + static __device__ void reduce(Pointer smem, Reference val, unsigned int tid, Op op) + { + loadToSmem(smem, val, tid); + if (N >= 32) + __syncthreads(); + + if (N >= 2048) + { + if (tid < 1024) + merge(smem, val, tid, 1024, op); + + __syncthreads(); + } + if (N >= 1024) + { + if (tid < 512) + merge(smem, val, tid, 512, op); + + __syncthreads(); + } + if (N >= 512) + { + if (tid < 256) + merge(smem, val, tid, 256, op); + + __syncthreads(); + } + if (N >= 256) + { + if (tid < 128) + merge(smem, val, tid, 128, op); + + __syncthreads(); + } + if (N >= 128) + { + if (tid < 64) + merge(smem, val, tid, 64, op); + + __syncthreads(); + } + if (N >= 64) + { + if (tid < 32) + merge(smem, val, tid, 32, op); + } + + if (tid < 16) + { + merge(smem, val, tid, 16, op); + merge(smem, val, tid, 8, op); + merge(smem, val, tid, 4, op); + merge(smem, val, tid, 2, op); + merge(smem, val, tid, 1, op); + } + } + }; + + template + struct Unroll + { + static __device__ void loopShfl(Reference val, Op op, unsigned int N) + { + mergeShfl(val, I, N, op); + Unroll::loopShfl(val, op, N); + } + static __device__ void loop(Pointer smem, Reference val, unsigned int tid, Op op) + { + merge(smem, val, tid, I, op); + Unroll::loop(smem, val, tid, op); + } + }; + template + struct Unroll<0, Pointer, Reference, Op> + { + static __device__ void loopShfl(Reference, Op, unsigned int) + { + } + static __device__ void loop(Pointer, Reference, unsigned int, Op) + { + } + }; + + template struct WarpOptimized + { + template + static __device__ void reduce(Pointer smem, Reference val, unsigned int tid, Op op) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + CV_UNUSED(smem); + CV_UNUSED(tid); + + Unroll::loopShfl(val, op, N); + #else + loadToSmem(smem, val, tid); + + if (tid < N / 2) + Unroll::loop(smem, val, tid, op); + #endif + } + }; + + template struct GenericOptimized32 + { + enum { M = N / 32 }; + + template + static __device__ void reduce(Pointer smem, Reference val, unsigned int tid, Op op) + { + const unsigned int laneId = Warp::laneId(); + + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + Unroll<16, Pointer, Reference, Op>::loopShfl(val, op, warpSize); + + if (laneId == 0) + loadToSmem(smem, val, tid / 32); + #else + loadToSmem(smem, val, tid); + + if (laneId < 16) + Unroll<16, Pointer, Reference, Op>::loop(smem, val, tid, op); + + __syncthreads(); + + if (laneId == 0) + loadToSmem(smem, val, tid / 32); + #endif + + __syncthreads(); + + loadFromSmem(smem, val, tid); + + if (tid < 32) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + Unroll::loopShfl(val, op, M); + #else + Unroll::loop(smem, val, tid, op); + #endif + } + } + }; + + template struct StaticIf; + template struct StaticIf + { + typedef T1 type; + }; + template struct StaticIf + { + typedef T2 type; + }; + + template struct IsPowerOf2 + { + enum { value = ((N != 0) && !(N & (N - 1))) }; + }; + + template struct Dispatcher + { + typedef typename StaticIf< + (N <= 32) && IsPowerOf2::value, + WarpOptimized, + typename StaticIf< + (N <= 1024) && IsPowerOf2::value, + GenericOptimized32, + Generic + >::type + >::type reductor; + }; + } +}}} + +//! @endcond + +#endif // OPENCV_CUDA_REDUCE_DETAIL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/detail/reduce_key_val.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/detail/reduce_key_val.hpp new file mode 100644 index 0000000000000000000000000000000000000000..df37c173be64bed1331f49260388acceea6de56b --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/detail/reduce_key_val.hpp @@ -0,0 +1,502 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_PRED_VAL_REDUCE_DETAIL_HPP +#define OPENCV_CUDA_PRED_VAL_REDUCE_DETAIL_HPP + +#include +#include "../warp.hpp" +#include "../warp_shuffle.hpp" + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + namespace reduce_key_val_detail + { + template struct GetType; + template struct GetType + { + typedef T type; + }; + template struct GetType + { + typedef T type; + }; + template struct GetType + { + typedef T type; + }; + + template + struct For + { + template + static __device__ void loadToSmem(const PointerTuple& smem, const ReferenceTuple& data, unsigned int tid) + { + thrust::get(smem)[tid] = thrust::get(data); + + For::loadToSmem(smem, data, tid); + } + template + static __device__ void loadFromSmem(const PointerTuple& smem, const ReferenceTuple& data, unsigned int tid) + { + thrust::get(data) = thrust::get(smem)[tid]; + + For::loadFromSmem(smem, data, tid); + } + + template + static __device__ void copyShfl(const ReferenceTuple& val, unsigned int delta, int width) + { + thrust::get(val) = shfl_down(thrust::get(val), delta, width); + + For::copyShfl(val, delta, width); + } + template + static __device__ void copy(const PointerTuple& svals, const ReferenceTuple& val, unsigned int tid, unsigned int delta) + { + thrust::get(svals)[tid] = thrust::get(val) = thrust::get(svals)[tid + delta]; + + For::copy(svals, val, tid, delta); + } + + template + static __device__ void mergeShfl(const KeyReferenceTuple& key, const ValReferenceTuple& val, const CmpTuple& cmp, unsigned int delta, int width) + { + typename GetType::type>::type reg = shfl_down(thrust::get(key), delta, width); + + if (thrust::get(cmp)(reg, thrust::get(key))) + { + thrust::get(key) = reg; + thrust::get(val) = shfl_down(thrust::get(val), delta, width); + } + + For::mergeShfl(key, val, cmp, delta, width); + } + template + static __device__ void merge(const KeyPointerTuple& skeys, const KeyReferenceTuple& key, + const ValPointerTuple& svals, const ValReferenceTuple& val, + const CmpTuple& cmp, + unsigned int tid, unsigned int delta) + { + typename GetType::type>::type reg = thrust::get(skeys)[tid + delta]; + + if (thrust::get(cmp)(reg, thrust::get(key))) + { + thrust::get(skeys)[tid] = thrust::get(key) = reg; + thrust::get(svals)[tid] = thrust::get(val) = thrust::get(svals)[tid + delta]; + } + + For::merge(skeys, key, svals, val, cmp, tid, delta); + } + }; + template + struct For + { + template + static __device__ void loadToSmem(const PointerTuple&, const ReferenceTuple&, unsigned int) + { + } + template + static __device__ void loadFromSmem(const PointerTuple&, const ReferenceTuple&, unsigned int) + { + } + + template + static __device__ void copyShfl(const ReferenceTuple&, unsigned int, int) + { + } + template + static __device__ void copy(const PointerTuple&, const ReferenceTuple&, unsigned int, unsigned int) + { + } + + template + static __device__ void mergeShfl(const KeyReferenceTuple&, const ValReferenceTuple&, const CmpTuple&, unsigned int, int) + { + } + template + static __device__ void merge(const KeyPointerTuple&, const KeyReferenceTuple&, + const ValPointerTuple&, const ValReferenceTuple&, + const CmpTuple&, + unsigned int, unsigned int) + { + } + }; + + ////////////////////////////////////////////////////// + // loadToSmem + + template + __device__ __forceinline__ void loadToSmem(volatile T* smem, T& data, unsigned int tid) + { + smem[tid] = data; + } + template + __device__ __forceinline__ void loadFromSmem(volatile T* smem, T& data, unsigned int tid) + { + data = smem[tid]; + } + template + __device__ __forceinline__ void loadToSmem(const thrust::tuple& smem, + const thrust::tuple& data, + unsigned int tid) + { + For<0, thrust::tuple_size >::value>::loadToSmem(smem, data, tid); + } + template + __device__ __forceinline__ void loadFromSmem(const thrust::tuple& smem, + const thrust::tuple& data, + unsigned int tid) + { + For<0, thrust::tuple_size >::value>::loadFromSmem(smem, data, tid); + } + + ////////////////////////////////////////////////////// + // copyVals + + template + __device__ __forceinline__ void copyValsShfl(V& val, unsigned int delta, int width) + { + val = shfl_down(val, delta, width); + } + template + __device__ __forceinline__ void copyVals(volatile V* svals, V& val, unsigned int tid, unsigned int delta) + { + svals[tid] = val = svals[tid + delta]; + } + template + __device__ __forceinline__ void copyValsShfl(const thrust::tuple& val, + unsigned int delta, + int width) + { + For<0, thrust::tuple_size >::value>::copyShfl(val, delta, width); + } + template + __device__ __forceinline__ void copyVals(const thrust::tuple& svals, + const thrust::tuple& val, + unsigned int tid, unsigned int delta) + { + For<0, thrust::tuple_size >::value>::copy(svals, val, tid, delta); + } + + ////////////////////////////////////////////////////// + // merge + + template + __device__ __forceinline__ void mergeShfl(K& key, V& val, const Cmp& cmp, unsigned int delta, int width) + { + K reg = shfl_down(key, delta, width); + + if (cmp(reg, key)) + { + key = reg; + copyValsShfl(val, delta, width); + } + } + template + __device__ __forceinline__ void merge(volatile K* skeys, K& key, volatile V* svals, V& val, const Cmp& cmp, unsigned int tid, unsigned int delta) + { + K reg = skeys[tid + delta]; + + if (cmp(reg, key)) + { + skeys[tid] = key = reg; + copyVals(svals, val, tid, delta); + } + } + template + __device__ __forceinline__ void mergeShfl(K& key, + const thrust::tuple& val, + const Cmp& cmp, + unsigned int delta, int width) + { + K reg = shfl_down(key, delta, width); + + if (cmp(reg, key)) + { + key = reg; + copyValsShfl(val, delta, width); + } + } + template + __device__ __forceinline__ void merge(volatile K* skeys, K& key, + const thrust::tuple& svals, + const thrust::tuple& val, + const Cmp& cmp, unsigned int tid, unsigned int delta) + { + K reg = skeys[tid + delta]; + + if (cmp(reg, key)) + { + skeys[tid] = key = reg; + copyVals(svals, val, tid, delta); + } + } + template + __device__ __forceinline__ void mergeShfl(const thrust::tuple& key, + const thrust::tuple& val, + const thrust::tuple& cmp, + unsigned int delta, int width) + { + For<0, thrust::tuple_size >::value>::mergeShfl(key, val, cmp, delta, width); + } + template + __device__ __forceinline__ void merge(const thrust::tuple& skeys, + const thrust::tuple& key, + const thrust::tuple& svals, + const thrust::tuple& val, + const thrust::tuple& cmp, + unsigned int tid, unsigned int delta) + { + For<0, thrust::tuple_size >::value>::merge(skeys, key, svals, val, cmp, tid, delta); + } + + ////////////////////////////////////////////////////// + // Generic + + template struct Generic + { + template + static __device__ void reduce(KP skeys, KR key, VP svals, VR val, unsigned int tid, Cmp cmp) + { + loadToSmem(skeys, key, tid); + loadValsToSmem(svals, val, tid); + if (N >= 32) + __syncthreads(); + + if (N >= 2048) + { + if (tid < 1024) + merge(skeys, key, svals, val, cmp, tid, 1024); + + __syncthreads(); + } + if (N >= 1024) + { + if (tid < 512) + merge(skeys, key, svals, val, cmp, tid, 512); + + __syncthreads(); + } + if (N >= 512) + { + if (tid < 256) + merge(skeys, key, svals, val, cmp, tid, 256); + + __syncthreads(); + } + if (N >= 256) + { + if (tid < 128) + merge(skeys, key, svals, val, cmp, tid, 128); + + __syncthreads(); + } + if (N >= 128) + { + if (tid < 64) + merge(skeys, key, svals, val, cmp, tid, 64); + + __syncthreads(); + } + if (N >= 64) + { + if (tid < 32) + merge(skeys, key, svals, val, cmp, tid, 32); + } + + if (tid < 16) + { + merge(skeys, key, svals, val, cmp, tid, 16); + merge(skeys, key, svals, val, cmp, tid, 8); + merge(skeys, key, svals, val, cmp, tid, 4); + merge(skeys, key, svals, val, cmp, tid, 2); + merge(skeys, key, svals, val, cmp, tid, 1); + } + } + }; + + template + struct Unroll + { + static __device__ void loopShfl(KR key, VR val, Cmp cmp, unsigned int N) + { + mergeShfl(key, val, cmp, I, N); + Unroll::loopShfl(key, val, cmp, N); + } + static __device__ void loop(KP skeys, KR key, VP svals, VR val, unsigned int tid, Cmp cmp) + { + merge(skeys, key, svals, val, cmp, tid, I); + Unroll::loop(skeys, key, svals, val, tid, cmp); + } + }; + template + struct Unroll<0, KP, KR, VP, VR, Cmp> + { + static __device__ void loopShfl(KR, VR, Cmp, unsigned int) + { + } + static __device__ void loop(KP, KR, VP, VR, unsigned int, Cmp) + { + } + }; + + template struct WarpOptimized + { + template + static __device__ void reduce(KP skeys, KR key, VP svals, VR val, unsigned int tid, Cmp cmp) + { + #if 0 // __CUDA_ARCH__ >= 300 + CV_UNUSED(skeys); + CV_UNUSED(svals); + CV_UNUSED(tid); + + Unroll::loopShfl(key, val, cmp, N); + #else + loadToSmem(skeys, key, tid); + loadToSmem(svals, val, tid); + + if (tid < N / 2) + Unroll::loop(skeys, key, svals, val, tid, cmp); + #endif + } + }; + + template struct GenericOptimized32 + { + enum { M = N / 32 }; + + template + static __device__ void reduce(KP skeys, KR key, VP svals, VR val, unsigned int tid, Cmp cmp) + { + const unsigned int laneId = Warp::laneId(); + + #if 0 // __CUDA_ARCH__ >= 300 + Unroll<16, KP, KR, VP, VR, Cmp>::loopShfl(key, val, cmp, warpSize); + + if (laneId == 0) + { + loadToSmem(skeys, key, tid / 32); + loadToSmem(svals, val, tid / 32); + } + #else + loadToSmem(skeys, key, tid); + loadToSmem(svals, val, tid); + + if (laneId < 16) + Unroll<16, KP, KR, VP, VR, Cmp>::loop(skeys, key, svals, val, tid, cmp); + + __syncthreads(); + + if (laneId == 0) + { + loadToSmem(skeys, key, tid / 32); + loadToSmem(svals, val, tid / 32); + } + #endif + + __syncthreads(); + + loadFromSmem(skeys, key, tid); + + if (tid < 32) + { + #if 0 // __CUDA_ARCH__ >= 300 + loadFromSmem(svals, val, tid); + + Unroll::loopShfl(key, val, cmp, M); + #else + Unroll::loop(skeys, key, svals, val, tid, cmp); + #endif + } + } + }; + + template struct StaticIf; + template struct StaticIf + { + typedef T1 type; + }; + template struct StaticIf + { + typedef T2 type; + }; + + template struct IsPowerOf2 + { + enum { value = ((N != 0) && !(N & (N - 1))) }; + }; + + template struct Dispatcher + { + typedef typename StaticIf< + (N <= 32) && IsPowerOf2::value, + WarpOptimized, + typename StaticIf< + (N <= 1024) && IsPowerOf2::value, + GenericOptimized32, + Generic + >::type + >::type reductor; + }; + } +}}} + +//! @endcond + +#endif // OPENCV_CUDA_PRED_VAL_REDUCE_DETAIL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/detail/transform_detail.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/detail/transform_detail.hpp new file mode 100644 index 0000000000000000000000000000000000000000..1919848827543461c62aeacc90b41c8c105fa5ea --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/detail/transform_detail.hpp @@ -0,0 +1,392 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_TRANSFORM_DETAIL_HPP +#define OPENCV_CUDA_TRANSFORM_DETAIL_HPP + +#include "../common.hpp" +#include "../vec_traits.hpp" +#include "../functional.hpp" + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + namespace transform_detail + { + //! Read Write Traits + + template struct UnaryReadWriteTraits + { + typedef typename TypeVec::vec_type read_type; + typedef typename TypeVec::vec_type write_type; + }; + + template struct BinaryReadWriteTraits + { + typedef typename TypeVec::vec_type read_type1; + typedef typename TypeVec::vec_type read_type2; + typedef typename TypeVec::vec_type write_type; + }; + + //! Transform kernels + + template struct OpUnroller; + template <> struct OpUnroller<1> + { + template + static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, UnOp& op, int x_shifted, int y) + { + if (mask(y, x_shifted)) + dst.x = op(src.x); + } + + template + static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, BinOp& op, int x_shifted, int y) + { + if (mask(y, x_shifted)) + dst.x = op(src1.x, src2.x); + } + }; + template <> struct OpUnroller<2> + { + template + static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, UnOp& op, int x_shifted, int y) + { + if (mask(y, x_shifted)) + dst.x = op(src.x); + if (mask(y, x_shifted + 1)) + dst.y = op(src.y); + } + + template + static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, BinOp& op, int x_shifted, int y) + { + if (mask(y, x_shifted)) + dst.x = op(src1.x, src2.x); + if (mask(y, x_shifted + 1)) + dst.y = op(src1.y, src2.y); + } + }; + template <> struct OpUnroller<3> + { + template + static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, const UnOp& op, int x_shifted, int y) + { + if (mask(y, x_shifted)) + dst.x = op(src.x); + if (mask(y, x_shifted + 1)) + dst.y = op(src.y); + if (mask(y, x_shifted + 2)) + dst.z = op(src.z); + } + + template + static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, const BinOp& op, int x_shifted, int y) + { + if (mask(y, x_shifted)) + dst.x = op(src1.x, src2.x); + if (mask(y, x_shifted + 1)) + dst.y = op(src1.y, src2.y); + if (mask(y, x_shifted + 2)) + dst.z = op(src1.z, src2.z); + } + }; + template <> struct OpUnroller<4> + { + template + static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, const UnOp& op, int x_shifted, int y) + { + if (mask(y, x_shifted)) + dst.x = op(src.x); + if (mask(y, x_shifted + 1)) + dst.y = op(src.y); + if (mask(y, x_shifted + 2)) + dst.z = op(src.z); + if (mask(y, x_shifted + 3)) + dst.w = op(src.w); + } + + template + static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, const BinOp& op, int x_shifted, int y) + { + if (mask(y, x_shifted)) + dst.x = op(src1.x, src2.x); + if (mask(y, x_shifted + 1)) + dst.y = op(src1.y, src2.y); + if (mask(y, x_shifted + 2)) + dst.z = op(src1.z, src2.z); + if (mask(y, x_shifted + 3)) + dst.w = op(src1.w, src2.w); + } + }; + template <> struct OpUnroller<8> + { + template + static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, const UnOp& op, int x_shifted, int y) + { + if (mask(y, x_shifted)) + dst.a0 = op(src.a0); + if (mask(y, x_shifted + 1)) + dst.a1 = op(src.a1); + if (mask(y, x_shifted + 2)) + dst.a2 = op(src.a2); + if (mask(y, x_shifted + 3)) + dst.a3 = op(src.a3); + if (mask(y, x_shifted + 4)) + dst.a4 = op(src.a4); + if (mask(y, x_shifted + 5)) + dst.a5 = op(src.a5); + if (mask(y, x_shifted + 6)) + dst.a6 = op(src.a6); + if (mask(y, x_shifted + 7)) + dst.a7 = op(src.a7); + } + + template + static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, const BinOp& op, int x_shifted, int y) + { + if (mask(y, x_shifted)) + dst.a0 = op(src1.a0, src2.a0); + if (mask(y, x_shifted + 1)) + dst.a1 = op(src1.a1, src2.a1); + if (mask(y, x_shifted + 2)) + dst.a2 = op(src1.a2, src2.a2); + if (mask(y, x_shifted + 3)) + dst.a3 = op(src1.a3, src2.a3); + if (mask(y, x_shifted + 4)) + dst.a4 = op(src1.a4, src2.a4); + if (mask(y, x_shifted + 5)) + dst.a5 = op(src1.a5, src2.a5); + if (mask(y, x_shifted + 6)) + dst.a6 = op(src1.a6, src2.a6); + if (mask(y, x_shifted + 7)) + dst.a7 = op(src1.a7, src2.a7); + } + }; + + template + static __global__ void transformSmart(const PtrStepSz src_, PtrStep dst_, const Mask mask, const UnOp op) + { + typedef TransformFunctorTraits ft; + typedef typename UnaryReadWriteTraits::read_type read_type; + typedef typename UnaryReadWriteTraits::write_type write_type; + + const int x = threadIdx.x + blockIdx.x * blockDim.x; + const int y = threadIdx.y + blockIdx.y * blockDim.y; + const int x_shifted = x * ft::smart_shift; + + if (y < src_.rows) + { + const T* src = src_.ptr(y); + D* dst = dst_.ptr(y); + + if (x_shifted + ft::smart_shift - 1 < src_.cols) + { + const read_type src_n_el = ((const read_type*)src)[x]; + OpUnroller::unroll(src_n_el, ((write_type*)dst)[x], mask, op, x_shifted, y); + } + else + { + for (int real_x = x_shifted; real_x < src_.cols; ++real_x) + { + if (mask(y, real_x)) + dst[real_x] = op(src[real_x]); + } + } + } + } + + template + __global__ static void transformSimple(const PtrStepSz src, PtrStep dst, const Mask mask, const UnOp op) + { + const int x = blockDim.x * blockIdx.x + threadIdx.x; + const int y = blockDim.y * blockIdx.y + threadIdx.y; + + if (x < src.cols && y < src.rows && mask(y, x)) + { + dst.ptr(y)[x] = op(src.ptr(y)[x]); + } + } + + template + static __global__ void transformSmart(const PtrStepSz src1_, const PtrStep src2_, PtrStep dst_, + const Mask mask, const BinOp op) + { + typedef TransformFunctorTraits ft; + typedef typename BinaryReadWriteTraits::read_type1 read_type1; + typedef typename BinaryReadWriteTraits::read_type2 read_type2; + typedef typename BinaryReadWriteTraits::write_type write_type; + + const int x = threadIdx.x + blockIdx.x * blockDim.x; + const int y = threadIdx.y + blockIdx.y * blockDim.y; + const int x_shifted = x * ft::smart_shift; + + if (y < src1_.rows) + { + const T1* src1 = src1_.ptr(y); + const T2* src2 = src2_.ptr(y); + D* dst = dst_.ptr(y); + + if (x_shifted + ft::smart_shift - 1 < src1_.cols) + { + const read_type1 src1_n_el = ((const read_type1*)src1)[x]; + const read_type2 src2_n_el = ((const read_type2*)src2)[x]; + + OpUnroller::unroll(src1_n_el, src2_n_el, ((write_type*)dst)[x], mask, op, x_shifted, y); + } + else + { + for (int real_x = x_shifted; real_x < src1_.cols; ++real_x) + { + if (mask(y, real_x)) + dst[real_x] = op(src1[real_x], src2[real_x]); + } + } + } + } + + template + static __global__ void transformSimple(const PtrStepSz src1, const PtrStep src2, PtrStep dst, + const Mask mask, const BinOp op) + { + const int x = blockDim.x * blockIdx.x + threadIdx.x; + const int y = blockDim.y * blockIdx.y + threadIdx.y; + + if (x < src1.cols && y < src1.rows && mask(y, x)) + { + const T1 src1_data = src1.ptr(y)[x]; + const T2 src2_data = src2.ptr(y)[x]; + dst.ptr(y)[x] = op(src1_data, src2_data); + } + } + + template struct TransformDispatcher; + template<> struct TransformDispatcher + { + template + static void call(PtrStepSz src, PtrStepSz dst, UnOp op, Mask mask, cudaStream_t stream) + { + typedef TransformFunctorTraits ft; + + const dim3 threads(ft::simple_block_dim_x, ft::simple_block_dim_y, 1); + const dim3 grid(divUp(src.cols, threads.x), divUp(src.rows, threads.y), 1); + + transformSimple<<>>(src, dst, mask, op); + cudaSafeCall( cudaGetLastError() ); + + if (stream == 0) + cudaSafeCall( cudaDeviceSynchronize() ); + } + + template + static void call(PtrStepSz src1, PtrStepSz src2, PtrStepSz dst, BinOp op, Mask mask, cudaStream_t stream) + { + typedef TransformFunctorTraits ft; + + const dim3 threads(ft::simple_block_dim_x, ft::simple_block_dim_y, 1); + const dim3 grid(divUp(src1.cols, threads.x), divUp(src1.rows, threads.y), 1); + + transformSimple<<>>(src1, src2, dst, mask, op); + cudaSafeCall( cudaGetLastError() ); + + if (stream == 0) + cudaSafeCall( cudaDeviceSynchronize() ); + } + }; + template<> struct TransformDispatcher + { + template + static void call(PtrStepSz src, PtrStepSz dst, UnOp op, Mask mask, cudaStream_t stream) + { + typedef TransformFunctorTraits ft; + + CV_StaticAssert(ft::smart_shift != 1, ""); + + if (!isAligned(src.data, ft::smart_shift * sizeof(T)) || !isAligned(src.step, ft::smart_shift * sizeof(T)) || + !isAligned(dst.data, ft::smart_shift * sizeof(D)) || !isAligned(dst.step, ft::smart_shift * sizeof(D))) + { + TransformDispatcher::call(src, dst, op, mask, stream); + return; + } + + const dim3 threads(ft::smart_block_dim_x, ft::smart_block_dim_y, 1); + const dim3 grid(divUp(src.cols, threads.x * ft::smart_shift), divUp(src.rows, threads.y), 1); + + transformSmart<<>>(src, dst, mask, op); + cudaSafeCall( cudaGetLastError() ); + + if (stream == 0) + cudaSafeCall( cudaDeviceSynchronize() ); + } + + template + static void call(PtrStepSz src1, PtrStepSz src2, PtrStepSz dst, BinOp op, Mask mask, cudaStream_t stream) + { + typedef TransformFunctorTraits ft; + + CV_StaticAssert(ft::smart_shift != 1, ""); + + if (!isAligned(src1.data, ft::smart_shift * sizeof(T1)) || !isAligned(src1.step, ft::smart_shift * sizeof(T1)) || + !isAligned(src2.data, ft::smart_shift * sizeof(T2)) || !isAligned(src2.step, ft::smart_shift * sizeof(T2)) || + !isAligned(dst.data, ft::smart_shift * sizeof(D)) || !isAligned(dst.step, ft::smart_shift * sizeof(D))) + { + TransformDispatcher::call(src1, src2, dst, op, mask, stream); + return; + } + + const dim3 threads(ft::smart_block_dim_x, ft::smart_block_dim_y, 1); + const dim3 grid(divUp(src1.cols, threads.x * ft::smart_shift), divUp(src1.rows, threads.y), 1); + + transformSmart<<>>(src1, src2, dst, mask, op); + cudaSafeCall( cudaGetLastError() ); + + if (stream == 0) + cudaSafeCall( cudaDeviceSynchronize() ); + } + }; + } // namespace transform_detail +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_TRANSFORM_DETAIL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/detail/type_traits_detail.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/detail/type_traits_detail.hpp new file mode 100644 index 0000000000000000000000000000000000000000..a78bd2c0d858f10d785cabef7258ac4ed656586f --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/detail/type_traits_detail.hpp @@ -0,0 +1,191 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_TYPE_TRAITS_DETAIL_HPP +#define OPENCV_CUDA_TYPE_TRAITS_DETAIL_HPP + +#include "../common.hpp" +#include "../vec_traits.hpp" + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + namespace type_traits_detail + { + template struct Select { typedef T1 type; }; + template struct Select { typedef T2 type; }; + + template struct IsSignedIntergral { enum {value = 0}; }; + template <> struct IsSignedIntergral { enum {value = 1}; }; + template <> struct IsSignedIntergral { enum {value = 1}; }; + template <> struct IsSignedIntergral { enum {value = 1}; }; + template <> struct IsSignedIntergral { enum {value = 1}; }; + template <> struct IsSignedIntergral { enum {value = 1}; }; + template <> struct IsSignedIntergral { enum {value = 1}; }; + + template struct IsUnsignedIntegral { enum {value = 0}; }; + template <> struct IsUnsignedIntegral { enum {value = 1}; }; + template <> struct IsUnsignedIntegral { enum {value = 1}; }; + template <> struct IsUnsignedIntegral { enum {value = 1}; }; + template <> struct IsUnsignedIntegral { enum {value = 1}; }; + template <> struct IsUnsignedIntegral { enum {value = 1}; }; + template <> struct IsUnsignedIntegral { enum {value = 1}; }; + + template struct IsIntegral { enum {value = IsSignedIntergral::value || IsUnsignedIntegral::value}; }; + template <> struct IsIntegral { enum {value = 1}; }; + template <> struct IsIntegral { enum {value = 1}; }; + + template struct IsFloat { enum {value = 0}; }; + template <> struct IsFloat { enum {value = 1}; }; + template <> struct IsFloat { enum {value = 1}; }; + + template struct IsVec { enum {value = 0}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + + template struct AddParameterType { typedef const U& type; }; + template struct AddParameterType { typedef U& type; }; + template <> struct AddParameterType { typedef void type; }; + + template struct ReferenceTraits + { + enum { value = false }; + typedef U type; + }; + template struct ReferenceTraits + { + enum { value = true }; + typedef U type; + }; + + template struct PointerTraits + { + enum { value = false }; + typedef void type; + }; + template struct PointerTraits + { + enum { value = true }; + typedef U type; + }; + template struct PointerTraits + { + enum { value = true }; + typedef U type; + }; + + template struct UnConst + { + typedef U type; + enum { value = 0 }; + }; + template struct UnConst + { + typedef U type; + enum { value = 1 }; + }; + template struct UnConst + { + typedef U& type; + enum { value = 1 }; + }; + + template struct UnVolatile + { + typedef U type; + enum { value = 0 }; + }; + template struct UnVolatile + { + typedef U type; + enum { value = 1 }; + }; + template struct UnVolatile + { + typedef U& type; + enum { value = 1 }; + }; + } // namespace type_traits_detail +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_TYPE_TRAITS_DETAIL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/detail/vec_distance_detail.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/detail/vec_distance_detail.hpp new file mode 100644 index 0000000000000000000000000000000000000000..8283a99560d995f34a768fb40b0c4a33d5bcfee2 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/detail/vec_distance_detail.hpp @@ -0,0 +1,121 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_VEC_DISTANCE_DETAIL_HPP +#define OPENCV_CUDA_VEC_DISTANCE_DETAIL_HPP + +#include "../datamov_utils.hpp" + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + namespace vec_distance_detail + { + template struct UnrollVecDiffCached + { + template + static __device__ void calcCheck(const T1* vecCached, const T2* vecGlob, int len, Dist& dist, int ind) + { + if (ind < len) + { + T1 val1 = *vecCached++; + + T2 val2; + ForceGlob::Load(vecGlob, ind, val2); + + dist.reduceIter(val1, val2); + + UnrollVecDiffCached::calcCheck(vecCached, vecGlob, len, dist, ind + THREAD_DIM); + } + } + + template + static __device__ void calcWithoutCheck(const T1* vecCached, const T2* vecGlob, Dist& dist) + { + T1 val1 = *vecCached++; + + T2 val2; + ForceGlob::Load(vecGlob, 0, val2); + vecGlob += THREAD_DIM; + + dist.reduceIter(val1, val2); + + UnrollVecDiffCached::calcWithoutCheck(vecCached, vecGlob, dist); + } + }; + template struct UnrollVecDiffCached + { + template + static __device__ __forceinline__ void calcCheck(const T1*, const T2*, int, Dist&, int) + { + } + + template + static __device__ __forceinline__ void calcWithoutCheck(const T1*, const T2*, Dist&) + { + } + }; + + template struct VecDiffCachedCalculator; + template struct VecDiffCachedCalculator + { + template + static __device__ __forceinline__ void calc(const T1* vecCached, const T2* vecGlob, int len, Dist& dist, int tid) + { + UnrollVecDiffCached::calcCheck(vecCached, vecGlob, len, dist, tid); + } + }; + template struct VecDiffCachedCalculator + { + template + static __device__ __forceinline__ void calc(const T1* vecCached, const T2* vecGlob, int len, Dist& dist, int tid) + { + UnrollVecDiffCached::calcWithoutCheck(vecCached, vecGlob + tid, dist); + } + }; + } // namespace vec_distance_detail +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_VEC_DISTANCE_DETAIL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/dynamic_smem.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/dynamic_smem.hpp new file mode 100644 index 0000000000000000000000000000000000000000..42570c6830c41bb53e7f9802f2a430f5181218c0 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/dynamic_smem.hpp @@ -0,0 +1,88 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_DYNAMIC_SMEM_HPP +#define OPENCV_CUDA_DYNAMIC_SMEM_HPP + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + template struct DynamicSharedMem + { + __device__ __forceinline__ operator T*() + { + extern __shared__ int __smem[]; + return (T*)__smem; + } + + __device__ __forceinline__ operator const T*() const + { + extern __shared__ int __smem[]; + return (T*)__smem; + } + }; + + // specialize for double to avoid unaligned memory access compile errors + template<> struct DynamicSharedMem + { + __device__ __forceinline__ operator double*() + { + extern __shared__ double __smem_d[]; + return (double*)__smem_d; + } + + __device__ __forceinline__ operator const double*() const + { + extern __shared__ double __smem_d[]; + return (double*)__smem_d; + } + }; +}}} + +//! @endcond + +#endif // OPENCV_CUDA_DYNAMIC_SMEM_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/emulation.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/emulation.hpp new file mode 100644 index 0000000000000000000000000000000000000000..17dc1171a237e2f1979ad2f782e79c3a9b7f9d53 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/emulation.hpp @@ -0,0 +1,269 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_EMULATION_HPP_ +#define OPENCV_CUDA_EMULATION_HPP_ + +#include "common.hpp" +#include "warp_reduce.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + struct Emulation + { + + static __device__ __forceinline__ int syncthreadsOr(int pred) + { +#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ < 200) + // just campilation stab + return 0; +#else + return __syncthreads_or(pred); +#endif + } + + template + static __forceinline__ __device__ int Ballot(int predicate) + { +#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ >= 200) + return __ballot(predicate); +#else + __shared__ volatile int cta_buffer[CTA_SIZE]; + + int tid = threadIdx.x; + cta_buffer[tid] = predicate ? (1 << (tid & 31)) : 0; + return warp_reduce(cta_buffer); +#endif + } + + struct smem + { + enum { TAG_MASK = (1U << ( (sizeof(unsigned int) << 3) - 5U)) - 1U }; + + template + static __device__ __forceinline__ T atomicInc(T* address, T val) + { +#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ < 120) + T count; + unsigned int tag = threadIdx.x << ( (sizeof(unsigned int) << 3) - 5U); + do + { + count = *address & TAG_MASK; + count = tag | (count + 1); + *address = count; + } while (*address != count); + + return (count & TAG_MASK) - 1; +#else + return ::atomicInc(address, val); +#endif + } + + template + static __device__ __forceinline__ T atomicAdd(T* address, T val) + { +#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ < 120) + T count; + unsigned int tag = threadIdx.x << ( (sizeof(unsigned int) << 3) - 5U); + do + { + count = *address & TAG_MASK; + count = tag | (count + val); + *address = count; + } while (*address != count); + + return (count & TAG_MASK) - val; +#else + return ::atomicAdd(address, val); +#endif + } + + template + static __device__ __forceinline__ T atomicMin(T* address, T val) + { +#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ < 120) + T count = ::min(*address, val); + do + { + *address = count; + } while (*address > count); + + return count; +#else + return ::atomicMin(address, val); +#endif + } + }; // struct cmem + + struct glob + { + static __device__ __forceinline__ int atomicAdd(int* address, int val) + { + return ::atomicAdd(address, val); + } + static __device__ __forceinline__ unsigned int atomicAdd(unsigned int* address, unsigned int val) + { + return ::atomicAdd(address, val); + } + static __device__ __forceinline__ float atomicAdd(float* address, float val) + { + #if __CUDA_ARCH__ >= 200 + return ::atomicAdd(address, val); + #else + int* address_as_i = (int*) address; + int old = *address_as_i, assumed; + do { + assumed = old; + old = ::atomicCAS(address_as_i, assumed, + __float_as_int(val + __int_as_float(assumed))); + } while (assumed != old); + return __int_as_float(old); + #endif + } + static __device__ __forceinline__ double atomicAdd(double* address, double val) + { + #if __CUDA_ARCH__ >= 130 + unsigned long long int* address_as_ull = (unsigned long long int*) address; + unsigned long long int old = *address_as_ull, assumed; + do { + assumed = old; + old = ::atomicCAS(address_as_ull, assumed, + __double_as_longlong(val + __longlong_as_double(assumed))); + } while (assumed != old); + return __longlong_as_double(old); + #else + CV_UNUSED(address); + CV_UNUSED(val); + return 0.0; + #endif + } + + static __device__ __forceinline__ int atomicMin(int* address, int val) + { + return ::atomicMin(address, val); + } + static __device__ __forceinline__ float atomicMin(float* address, float val) + { + #if __CUDA_ARCH__ >= 120 + int* address_as_i = (int*) address; + int old = *address_as_i, assumed; + do { + assumed = old; + old = ::atomicCAS(address_as_i, assumed, + __float_as_int(::fminf(val, __int_as_float(assumed)))); + } while (assumed != old); + return __int_as_float(old); + #else + CV_UNUSED(address); + CV_UNUSED(val); + return 0.0f; + #endif + } + static __device__ __forceinline__ double atomicMin(double* address, double val) + { + #if __CUDA_ARCH__ >= 130 + unsigned long long int* address_as_ull = (unsigned long long int*) address; + unsigned long long int old = *address_as_ull, assumed; + do { + assumed = old; + old = ::atomicCAS(address_as_ull, assumed, + __double_as_longlong(::fmin(val, __longlong_as_double(assumed)))); + } while (assumed != old); + return __longlong_as_double(old); + #else + CV_UNUSED(address); + CV_UNUSED(val); + return 0.0; + #endif + } + + static __device__ __forceinline__ int atomicMax(int* address, int val) + { + return ::atomicMax(address, val); + } + static __device__ __forceinline__ float atomicMax(float* address, float val) + { + #if __CUDA_ARCH__ >= 120 + int* address_as_i = (int*) address; + int old = *address_as_i, assumed; + do { + assumed = old; + old = ::atomicCAS(address_as_i, assumed, + __float_as_int(::fmaxf(val, __int_as_float(assumed)))); + } while (assumed != old); + return __int_as_float(old); + #else + CV_UNUSED(address); + CV_UNUSED(val); + return 0.0f; + #endif + } + static __device__ __forceinline__ double atomicMax(double* address, double val) + { + #if __CUDA_ARCH__ >= 130 + unsigned long long int* address_as_ull = (unsigned long long int*) address; + unsigned long long int old = *address_as_ull, assumed; + do { + assumed = old; + old = ::atomicCAS(address_as_ull, assumed, + __double_as_longlong(::fmax(val, __longlong_as_double(assumed)))); + } while (assumed != old); + return __longlong_as_double(old); + #else + CV_UNUSED(address); + CV_UNUSED(val); + return 0.0; + #endif + } + }; + }; //struct Emulation +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif /* OPENCV_CUDA_EMULATION_HPP_ */ diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/filters.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/filters.hpp new file mode 100644 index 0000000000000000000000000000000000000000..bb94212299a7f2ae5a2fa9fe7d38323adddfb3c3 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/filters.hpp @@ -0,0 +1,286 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_FILTERS_HPP +#define OPENCV_CUDA_FILTERS_HPP + +#include "saturate_cast.hpp" +#include "vec_traits.hpp" +#include "vec_math.hpp" +#include "type_traits.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + template struct PointFilter + { + typedef typename Ptr2D::elem_type elem_type; + typedef float index_type; + + explicit __host__ __device__ __forceinline__ PointFilter(const Ptr2D& src_, float fx = 0.f, float fy = 0.f) + : src(src_) + { + CV_UNUSED(fx); + CV_UNUSED(fy); + } + + __device__ __forceinline__ elem_type operator ()(float y, float x) const + { + return src(__float2int_rz(y), __float2int_rz(x)); + } + + Ptr2D src; + }; + + template struct LinearFilter + { + typedef typename Ptr2D::elem_type elem_type; + typedef float index_type; + + explicit __host__ __device__ __forceinline__ LinearFilter(const Ptr2D& src_, float fx = 0.f, float fy = 0.f) + : src(src_) + { + CV_UNUSED(fx); + CV_UNUSED(fy); + } + __device__ __forceinline__ elem_type operator ()(float y, float x) const + { + typedef typename TypeVec::cn>::vec_type work_type; + + work_type out = VecTraits::all(0); + + const int x1 = __float2int_rd(x); + const int y1 = __float2int_rd(y); + const int x2 = x1 + 1; + const int y2 = y1 + 1; + + elem_type src_reg = src(y1, x1); + out = out + src_reg * ((x2 - x) * (y2 - y)); + + src_reg = src(y1, x2); + out = out + src_reg * ((x - x1) * (y2 - y)); + + src_reg = src(y2, x1); + out = out + src_reg * ((x2 - x) * (y - y1)); + + src_reg = src(y2, x2); + out = out + src_reg * ((x - x1) * (y - y1)); + + return saturate_cast(out); + } + + Ptr2D src; + }; + + template struct CubicFilter + { + typedef typename Ptr2D::elem_type elem_type; + typedef float index_type; + typedef typename TypeVec::cn>::vec_type work_type; + + explicit __host__ __device__ __forceinline__ CubicFilter(const Ptr2D& src_, float fx = 0.f, float fy = 0.f) + : src(src_) + { + CV_UNUSED(fx); + CV_UNUSED(fy); + } + + static __device__ __forceinline__ float bicubicCoeff(float x_) + { + float x = fabsf(x_); + if (x <= 1.0f) + { + return x * x * (1.5f * x - 2.5f) + 1.0f; + } + else if (x < 2.0f) + { + return x * (x * (-0.5f * x + 2.5f) - 4.0f) + 2.0f; + } + else + { + return 0.0f; + } + } + + __device__ elem_type operator ()(float y, float x) const + { + const float xmin = ::ceilf(x - 2.0f); + const float xmax = ::floorf(x + 2.0f); + + const float ymin = ::ceilf(y - 2.0f); + const float ymax = ::floorf(y + 2.0f); + + work_type sum = VecTraits::all(0); + float wsum = 0.0f; + + for (float cy = ymin; cy <= ymax; cy += 1.0f) + { + for (float cx = xmin; cx <= xmax; cx += 1.0f) + { + const float w = bicubicCoeff(x - cx) * bicubicCoeff(y - cy); + sum = sum + w * src(__float2int_rd(cy), __float2int_rd(cx)); + wsum += w; + } + } + + work_type res = (!wsum)? VecTraits::all(0) : sum / wsum; + + return saturate_cast(res); + } + + Ptr2D src; + }; + // for integer scaling + template struct IntegerAreaFilter + { + typedef typename Ptr2D::elem_type elem_type; + typedef float index_type; + + explicit __host__ __device__ __forceinline__ IntegerAreaFilter(const Ptr2D& src_, float scale_x_, float scale_y_) + : src(src_), scale_x(scale_x_), scale_y(scale_y_), scale(1.f / (scale_x * scale_y)) {} + + __device__ __forceinline__ elem_type operator ()(float y, float x) const + { + float fsx1 = x * scale_x; + float fsx2 = fsx1 + scale_x; + + int sx1 = __float2int_ru(fsx1); + int sx2 = __float2int_rd(fsx2); + + float fsy1 = y * scale_y; + float fsy2 = fsy1 + scale_y; + + int sy1 = __float2int_ru(fsy1); + int sy2 = __float2int_rd(fsy2); + + typedef typename TypeVec::cn>::vec_type work_type; + work_type out = VecTraits::all(0.f); + + for(int dy = sy1; dy < sy2; ++dy) + for(int dx = sx1; dx < sx2; ++dx) + { + out = out + src(dy, dx) * scale; + } + + return saturate_cast(out); + } + + Ptr2D src; + float scale_x, scale_y ,scale; + }; + + template struct AreaFilter + { + typedef typename Ptr2D::elem_type elem_type; + typedef float index_type; + + explicit __host__ __device__ __forceinline__ AreaFilter(const Ptr2D& src_, float scale_x_, float scale_y_) + : src(src_), scale_x(scale_x_), scale_y(scale_y_){} + + __device__ __forceinline__ elem_type operator ()(float y, float x) const + { + float fsx1 = x * scale_x; + float fsx2 = fsx1 + scale_x; + + int sx1 = __float2int_ru(fsx1); + int sx2 = __float2int_rd(fsx2); + + float fsy1 = y * scale_y; + float fsy2 = fsy1 + scale_y; + + int sy1 = __float2int_ru(fsy1); + int sy2 = __float2int_rd(fsy2); + + float scale = 1.f / (fminf(scale_x, src.width - fsx1) * fminf(scale_y, src.height - fsy1)); + + typedef typename TypeVec::cn>::vec_type work_type; + work_type out = VecTraits::all(0.f); + + for (int dy = sy1; dy < sy2; ++dy) + { + for (int dx = sx1; dx < sx2; ++dx) + out = out + src(dy, dx) * scale; + + if (sx1 > fsx1) + out = out + src(dy, (sx1 -1) ) * ((sx1 - fsx1) * scale); + + if (sx2 < fsx2) + out = out + src(dy, sx2) * ((fsx2 -sx2) * scale); + } + + if (sy1 > fsy1) + for (int dx = sx1; dx < sx2; ++dx) + out = out + src( (sy1 - 1) , dx) * ((sy1 -fsy1) * scale); + + if (sy2 < fsy2) + for (int dx = sx1; dx < sx2; ++dx) + out = out + src(sy2, dx) * ((fsy2 -sy2) * scale); + + if ((sy1 > fsy1) && (sx1 > fsx1)) + out = out + src( (sy1 - 1) , (sx1 - 1)) * ((sy1 -fsy1) * (sx1 -fsx1) * scale); + + if ((sy1 > fsy1) && (sx2 < fsx2)) + out = out + src( (sy1 - 1) , sx2) * ((sy1 -fsy1) * (fsx2 -sx2) * scale); + + if ((sy2 < fsy2) && (sx2 < fsx2)) + out = out + src(sy2, sx2) * ((fsy2 -sy2) * (fsx2 -sx2) * scale); + + if ((sy2 < fsy2) && (sx1 > fsx1)) + out = out + src(sy2, (sx1 - 1)) * ((fsy2 -sy2) * (sx1 -fsx1) * scale); + + return saturate_cast(out); + } + + Ptr2D src; + float scale_x, scale_y; + int width, haight; + }; +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_FILTERS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/funcattrib.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/funcattrib.hpp new file mode 100644 index 0000000000000000000000000000000000000000..f58208048882076cb4748706491c46462833ad44 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/funcattrib.hpp @@ -0,0 +1,79 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_DEVICE_FUNCATTRIB_HPP +#define OPENCV_CUDA_DEVICE_FUNCATTRIB_HPP + +#include + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + template + void printFuncAttrib(Func& func) + { + + cudaFuncAttributes attrs; + cudaFuncGetAttributes(&attrs, func); + + printf("=== Function stats ===\n"); + printf("Name: \n"); + printf("sharedSizeBytes = %d\n", attrs.sharedSizeBytes); + printf("constSizeBytes = %d\n", attrs.constSizeBytes); + printf("localSizeBytes = %d\n", attrs.localSizeBytes); + printf("maxThreadsPerBlock = %d\n", attrs.maxThreadsPerBlock); + printf("numRegs = %d\n", attrs.numRegs); + printf("ptxVersion = %d\n", attrs.ptxVersion); + printf("binaryVersion = %d\n", attrs.binaryVersion); + printf("\n"); + fflush(stdout); + } +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif /* OPENCV_CUDA_DEVICE_FUNCATTRIB_HPP */ diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/functional.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/functional.hpp new file mode 100644 index 0000000000000000000000000000000000000000..4944381ddaa82591f158f87cd41b3784c2cf1787 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/functional.hpp @@ -0,0 +1,811 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_FUNCTIONAL_HPP +#define OPENCV_CUDA_FUNCTIONAL_HPP + +#include +#include "saturate_cast.hpp" +#include "vec_traits.hpp" +#include "type_traits.hpp" +#include "device_functions.h" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + // Function Objects +#ifdef CV_CXX11 + template struct unary_function + { + typedef Argument argument_type; + typedef Result result_type; + }; + template struct binary_function + { + typedef Argument1 first_argument_type; + typedef Argument2 second_argument_type; + typedef Result result_type; + }; +#else + template struct unary_function : public std::unary_function {}; + template struct binary_function : public std::binary_function {}; +#endif + + // Arithmetic Operations + template struct plus : binary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a + b; + } + __host__ __device__ __forceinline__ plus() {} + __host__ __device__ __forceinline__ plus(const plus&) {} + }; + + template struct minus : binary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a - b; + } + __host__ __device__ __forceinline__ minus() {} + __host__ __device__ __forceinline__ minus(const minus&) {} + }; + + template struct multiplies : binary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a * b; + } + __host__ __device__ __forceinline__ multiplies() {} + __host__ __device__ __forceinline__ multiplies(const multiplies&) {} + }; + + template struct divides : binary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a / b; + } + __host__ __device__ __forceinline__ divides() {} + __host__ __device__ __forceinline__ divides(const divides&) {} + }; + + template struct modulus : binary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a % b; + } + __host__ __device__ __forceinline__ modulus() {} + __host__ __device__ __forceinline__ modulus(const modulus&) {} + }; + + template struct negate : unary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a) const + { + return -a; + } + __host__ __device__ __forceinline__ negate() {} + __host__ __device__ __forceinline__ negate(const negate&) {} + }; + + // Comparison Operations + template struct equal_to : binary_function + { + __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a == b; + } + __host__ __device__ __forceinline__ equal_to() {} + __host__ __device__ __forceinline__ equal_to(const equal_to&) {} + }; + + template struct not_equal_to : binary_function + { + __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a != b; + } + __host__ __device__ __forceinline__ not_equal_to() {} + __host__ __device__ __forceinline__ not_equal_to(const not_equal_to&) {} + }; + + template struct greater : binary_function + { + __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a > b; + } + __host__ __device__ __forceinline__ greater() {} + __host__ __device__ __forceinline__ greater(const greater&) {} + }; + + template struct less : binary_function + { + __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a < b; + } + __host__ __device__ __forceinline__ less() {} + __host__ __device__ __forceinline__ less(const less&) {} + }; + + template struct greater_equal : binary_function + { + __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a >= b; + } + __host__ __device__ __forceinline__ greater_equal() {} + __host__ __device__ __forceinline__ greater_equal(const greater_equal&) {} + }; + + template struct less_equal : binary_function + { + __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a <= b; + } + __host__ __device__ __forceinline__ less_equal() {} + __host__ __device__ __forceinline__ less_equal(const less_equal&) {} + }; + + // Logical Operations + template struct logical_and : binary_function + { + __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a && b; + } + __host__ __device__ __forceinline__ logical_and() {} + __host__ __device__ __forceinline__ logical_and(const logical_and&) {} + }; + + template struct logical_or : binary_function + { + __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a || b; + } + __host__ __device__ __forceinline__ logical_or() {} + __host__ __device__ __forceinline__ logical_or(const logical_or&) {} + }; + + template struct logical_not : unary_function + { + __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a) const + { + return !a; + } + __host__ __device__ __forceinline__ logical_not() {} + __host__ __device__ __forceinline__ logical_not(const logical_not&) {} + }; + + // Bitwise Operations + template struct bit_and : binary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a & b; + } + __host__ __device__ __forceinline__ bit_and() {} + __host__ __device__ __forceinline__ bit_and(const bit_and&) {} + }; + + template struct bit_or : binary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a | b; + } + __host__ __device__ __forceinline__ bit_or() {} + __host__ __device__ __forceinline__ bit_or(const bit_or&) {} + }; + + template struct bit_xor : binary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a ^ b; + } + __host__ __device__ __forceinline__ bit_xor() {} + __host__ __device__ __forceinline__ bit_xor(const bit_xor&) {} + }; + + template struct bit_not : unary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType v) const + { + return ~v; + } + __host__ __device__ __forceinline__ bit_not() {} + __host__ __device__ __forceinline__ bit_not(const bit_not&) {} + }; + + // Generalized Identity Operations + template struct identity : unary_function + { + __device__ __forceinline__ typename TypeTraits::ParameterType operator()(typename TypeTraits::ParameterType x) const + { + return x; + } + __host__ __device__ __forceinline__ identity() {} + __host__ __device__ __forceinline__ identity(const identity&) {} + }; + + template struct project1st : binary_function + { + __device__ __forceinline__ typename TypeTraits::ParameterType operator()(typename TypeTraits::ParameterType lhs, typename TypeTraits::ParameterType rhs) const + { + return lhs; + } + __host__ __device__ __forceinline__ project1st() {} + __host__ __device__ __forceinline__ project1st(const project1st&) {} + }; + + template struct project2nd : binary_function + { + __device__ __forceinline__ typename TypeTraits::ParameterType operator()(typename TypeTraits::ParameterType lhs, typename TypeTraits::ParameterType rhs) const + { + return rhs; + } + __host__ __device__ __forceinline__ project2nd() {} + __host__ __device__ __forceinline__ project2nd(const project2nd&) {} + }; + + // Min/Max Operations + +#define OPENCV_CUDA_IMPLEMENT_MINMAX(name, type, op) \ + template <> struct name : binary_function \ + { \ + __device__ __forceinline__ type operator()(type lhs, type rhs) const {return op(lhs, rhs);} \ + __host__ __device__ __forceinline__ name() {}\ + __host__ __device__ __forceinline__ name(const name&) {}\ + }; + + template struct maximum : binary_function + { + __device__ __forceinline__ T operator()(typename TypeTraits::ParameterType lhs, typename TypeTraits::ParameterType rhs) const + { + return max(lhs, rhs); + } + __host__ __device__ __forceinline__ maximum() {} + __host__ __device__ __forceinline__ maximum(const maximum&) {} + }; + + OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, uchar, ::max) + OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, schar, ::max) + OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, char, ::max) + OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, ushort, ::max) + OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, short, ::max) + OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, int, ::max) + OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, uint, ::max) + OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, float, ::fmax) + OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, double, ::fmax) + + template struct minimum : binary_function + { + __device__ __forceinline__ T operator()(typename TypeTraits::ParameterType lhs, typename TypeTraits::ParameterType rhs) const + { + return min(lhs, rhs); + } + __host__ __device__ __forceinline__ minimum() {} + __host__ __device__ __forceinline__ minimum(const minimum&) {} + }; + + OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, uchar, ::min) + OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, schar, ::min) + OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, char, ::min) + OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, ushort, ::min) + OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, short, ::min) + OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, int, ::min) + OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, uint, ::min) + OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, float, ::fmin) + OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, double, ::fmin) + +#undef OPENCV_CUDA_IMPLEMENT_MINMAX + + // Math functions + + template struct abs_func : unary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType x) const + { + return abs(x); + } + + __host__ __device__ __forceinline__ abs_func() {} + __host__ __device__ __forceinline__ abs_func(const abs_func&) {} + }; + template <> struct abs_func : unary_function + { + __device__ __forceinline__ unsigned char operator ()(unsigned char x) const + { + return x; + } + + __host__ __device__ __forceinline__ abs_func() {} + __host__ __device__ __forceinline__ abs_func(const abs_func&) {} + }; + template <> struct abs_func : unary_function + { + __device__ __forceinline__ signed char operator ()(signed char x) const + { + return ::abs((int)x); + } + + __host__ __device__ __forceinline__ abs_func() {} + __host__ __device__ __forceinline__ abs_func(const abs_func&) {} + }; + template <> struct abs_func : unary_function + { + __device__ __forceinline__ char operator ()(char x) const + { + return ::abs((int)x); + } + + __host__ __device__ __forceinline__ abs_func() {} + __host__ __device__ __forceinline__ abs_func(const abs_func&) {} + }; + template <> struct abs_func : unary_function + { + __device__ __forceinline__ unsigned short operator ()(unsigned short x) const + { + return x; + } + + __host__ __device__ __forceinline__ abs_func() {} + __host__ __device__ __forceinline__ abs_func(const abs_func&) {} + }; + template <> struct abs_func : unary_function + { + __device__ __forceinline__ short operator ()(short x) const + { + return ::abs((int)x); + } + + __host__ __device__ __forceinline__ abs_func() {} + __host__ __device__ __forceinline__ abs_func(const abs_func&) {} + }; + template <> struct abs_func : unary_function + { + __device__ __forceinline__ unsigned int operator ()(unsigned int x) const + { + return x; + } + + __host__ __device__ __forceinline__ abs_func() {} + __host__ __device__ __forceinline__ abs_func(const abs_func&) {} + }; + template <> struct abs_func : unary_function + { + __device__ __forceinline__ int operator ()(int x) const + { + return ::abs(x); + } + + __host__ __device__ __forceinline__ abs_func() {} + __host__ __device__ __forceinline__ abs_func(const abs_func&) {} + }; + template <> struct abs_func : unary_function + { + __device__ __forceinline__ float operator ()(float x) const + { + return ::fabsf(x); + } + + __host__ __device__ __forceinline__ abs_func() {} + __host__ __device__ __forceinline__ abs_func(const abs_func&) {} + }; + template <> struct abs_func : unary_function + { + __device__ __forceinline__ double operator ()(double x) const + { + return ::fabs(x); + } + + __host__ __device__ __forceinline__ abs_func() {} + __host__ __device__ __forceinline__ abs_func(const abs_func&) {} + }; + +#define OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(name, func) \ + template struct name ## _func : unary_function \ + { \ + __device__ __forceinline__ float operator ()(typename TypeTraits::ParameterType v) const \ + { \ + return func ## f(v); \ + } \ + __host__ __device__ __forceinline__ name ## _func() {} \ + __host__ __device__ __forceinline__ name ## _func(const name ## _func&) {} \ + }; \ + template <> struct name ## _func : unary_function \ + { \ + __device__ __forceinline__ double operator ()(double v) const \ + { \ + return func(v); \ + } \ + __host__ __device__ __forceinline__ name ## _func() {} \ + __host__ __device__ __forceinline__ name ## _func(const name ## _func&) {} \ + }; + +#define OPENCV_CUDA_IMPLEMENT_BIN_FUNCTOR(name, func) \ + template struct name ## _func : binary_function \ + { \ + __device__ __forceinline__ float operator ()(typename TypeTraits::ParameterType v1, typename TypeTraits::ParameterType v2) const \ + { \ + return func ## f(v1, v2); \ + } \ + __host__ __device__ __forceinline__ name ## _func() {} \ + __host__ __device__ __forceinline__ name ## _func(const name ## _func&) {} \ + }; \ + template <> struct name ## _func : binary_function \ + { \ + __device__ __forceinline__ double operator ()(double v1, double v2) const \ + { \ + return func(v1, v2); \ + } \ + __host__ __device__ __forceinline__ name ## _func() {} \ + __host__ __device__ __forceinline__ name ## _func(const name ## _func&) {} \ + }; + + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(sqrt, ::sqrt) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(exp, ::exp) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(exp2, ::exp2) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(exp10, ::exp10) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(log, ::log) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(log2, ::log2) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(log10, ::log10) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(sin, ::sin) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(cos, ::cos) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(tan, ::tan) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(asin, ::asin) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(acos, ::acos) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(atan, ::atan) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(sinh, ::sinh) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(cosh, ::cosh) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(tanh, ::tanh) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(asinh, ::asinh) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(acosh, ::acosh) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(atanh, ::atanh) + + OPENCV_CUDA_IMPLEMENT_BIN_FUNCTOR(hypot, ::hypot) + OPENCV_CUDA_IMPLEMENT_BIN_FUNCTOR(atan2, ::atan2) + OPENCV_CUDA_IMPLEMENT_BIN_FUNCTOR(pow, ::pow) + + #undef OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR + #undef OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR_NO_DOUBLE + #undef OPENCV_CUDA_IMPLEMENT_BIN_FUNCTOR + + template struct hypot_sqr_func : binary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType src1, typename TypeTraits::ParameterType src2) const + { + return src1 * src1 + src2 * src2; + } + __host__ __device__ __forceinline__ hypot_sqr_func() {} + __host__ __device__ __forceinline__ hypot_sqr_func(const hypot_sqr_func&) {} + }; + + // Saturate Cast Functor + template struct saturate_cast_func : unary_function + { + __device__ __forceinline__ D operator ()(typename TypeTraits::ParameterType v) const + { + return saturate_cast(v); + } + __host__ __device__ __forceinline__ saturate_cast_func() {} + __host__ __device__ __forceinline__ saturate_cast_func(const saturate_cast_func&) {} + }; + + // Threshold Functors + template struct thresh_binary_func : unary_function + { + __host__ __device__ __forceinline__ thresh_binary_func(T thresh_, T maxVal_) : thresh(thresh_), maxVal(maxVal_) {} + + __device__ __forceinline__ T operator()(typename TypeTraits::ParameterType src) const + { + return (src > thresh) * maxVal; + } + + __host__ __device__ __forceinline__ thresh_binary_func() {} + __host__ __device__ __forceinline__ thresh_binary_func(const thresh_binary_func& other) + : thresh(other.thresh), maxVal(other.maxVal) {} + + T thresh; + T maxVal; + }; + + template struct thresh_binary_inv_func : unary_function + { + __host__ __device__ __forceinline__ thresh_binary_inv_func(T thresh_, T maxVal_) : thresh(thresh_), maxVal(maxVal_) {} + + __device__ __forceinline__ T operator()(typename TypeTraits::ParameterType src) const + { + return (src <= thresh) * maxVal; + } + + __host__ __device__ __forceinline__ thresh_binary_inv_func() {} + __host__ __device__ __forceinline__ thresh_binary_inv_func(const thresh_binary_inv_func& other) + : thresh(other.thresh), maxVal(other.maxVal) {} + + T thresh; + T maxVal; + }; + + template struct thresh_trunc_func : unary_function + { + explicit __host__ __device__ __forceinline__ thresh_trunc_func(T thresh_, T maxVal_ = 0) : thresh(thresh_) {CV_UNUSED(maxVal_);} + + __device__ __forceinline__ T operator()(typename TypeTraits::ParameterType src) const + { + return minimum()(src, thresh); + } + + __host__ __device__ __forceinline__ thresh_trunc_func() {} + __host__ __device__ __forceinline__ thresh_trunc_func(const thresh_trunc_func& other) + : thresh(other.thresh) {} + + T thresh; + }; + + template struct thresh_to_zero_func : unary_function + { + explicit __host__ __device__ __forceinline__ thresh_to_zero_func(T thresh_, T maxVal_ = 0) : thresh(thresh_) {CV_UNUSED(maxVal_);} + + __device__ __forceinline__ T operator()(typename TypeTraits::ParameterType src) const + { + return (src > thresh) * src; + } + + __host__ __device__ __forceinline__ thresh_to_zero_func() {} + __host__ __device__ __forceinline__ thresh_to_zero_func(const thresh_to_zero_func& other) + : thresh(other.thresh) {} + + T thresh; + }; + + template struct thresh_to_zero_inv_func : unary_function + { + explicit __host__ __device__ __forceinline__ thresh_to_zero_inv_func(T thresh_, T maxVal_ = 0) : thresh(thresh_) {CV_UNUSED(maxVal_);} + + __device__ __forceinline__ T operator()(typename TypeTraits::ParameterType src) const + { + return (src <= thresh) * src; + } + + __host__ __device__ __forceinline__ thresh_to_zero_inv_func() {} + __host__ __device__ __forceinline__ thresh_to_zero_inv_func(const thresh_to_zero_inv_func& other) + : thresh(other.thresh) {} + + T thresh; + }; + + // Function Object Adaptors + template struct unary_negate : unary_function + { + explicit __host__ __device__ __forceinline__ unary_negate(const Predicate& p) : pred(p) {} + + __device__ __forceinline__ bool operator()(typename TypeTraits::ParameterType x) const + { + return !pred(x); + } + + __host__ __device__ __forceinline__ unary_negate() {} + __host__ __device__ __forceinline__ unary_negate(const unary_negate& other) : pred(other.pred) {} + + Predicate pred; + }; + + template __host__ __device__ __forceinline__ unary_negate not1(const Predicate& pred) + { + return unary_negate(pred); + } + + template struct binary_negate : binary_function + { + explicit __host__ __device__ __forceinline__ binary_negate(const Predicate& p) : pred(p) {} + + __device__ __forceinline__ bool operator()(typename TypeTraits::ParameterType x, + typename TypeTraits::ParameterType y) const + { + return !pred(x,y); + } + + __host__ __device__ __forceinline__ binary_negate() {} + __host__ __device__ __forceinline__ binary_negate(const binary_negate& other) : pred(other.pred) {} + + Predicate pred; + }; + + template __host__ __device__ __forceinline__ binary_negate not2(const BinaryPredicate& pred) + { + return binary_negate(pred); + } + + template struct binder1st : unary_function + { + __host__ __device__ __forceinline__ binder1st(const Op& op_, const typename Op::first_argument_type& arg1_) : op(op_), arg1(arg1_) {} + + __device__ __forceinline__ typename Op::result_type operator ()(typename TypeTraits::ParameterType a) const + { + return op(arg1, a); + } + + __host__ __device__ __forceinline__ binder1st() {} + __host__ __device__ __forceinline__ binder1st(const binder1st& other) : op(other.op), arg1(other.arg1) {} + + Op op; + typename Op::first_argument_type arg1; + }; + + template __host__ __device__ __forceinline__ binder1st bind1st(const Op& op, const T& x) + { + return binder1st(op, typename Op::first_argument_type(x)); + } + + template struct binder2nd : unary_function + { + __host__ __device__ __forceinline__ binder2nd(const Op& op_, const typename Op::second_argument_type& arg2_) : op(op_), arg2(arg2_) {} + + __forceinline__ __device__ typename Op::result_type operator ()(typename TypeTraits::ParameterType a) const + { + return op(a, arg2); + } + + __host__ __device__ __forceinline__ binder2nd() {} + __host__ __device__ __forceinline__ binder2nd(const binder2nd& other) : op(other.op), arg2(other.arg2) {} + + Op op; + typename Op::second_argument_type arg2; + }; + + template __host__ __device__ __forceinline__ binder2nd bind2nd(const Op& op, const T& x) + { + return binder2nd(op, typename Op::second_argument_type(x)); + } + + // Functor Traits + template struct IsUnaryFunction + { + typedef char Yes; + struct No {Yes a[2];}; + + template static Yes check(unary_function); + static No check(...); + + static F makeF(); + + enum { value = (sizeof(check(makeF())) == sizeof(Yes)) }; + }; + + template struct IsBinaryFunction + { + typedef char Yes; + struct No {Yes a[2];}; + + template static Yes check(binary_function); + static No check(...); + + static F makeF(); + + enum { value = (sizeof(check(makeF())) == sizeof(Yes)) }; + }; + + namespace functional_detail + { + template struct UnOpShift { enum { shift = 1 }; }; + template struct UnOpShift { enum { shift = 4 }; }; + template struct UnOpShift { enum { shift = 2 }; }; + + template struct DefaultUnaryShift + { + enum { shift = UnOpShift::shift }; + }; + + template struct BinOpShift { enum { shift = 1 }; }; + template struct BinOpShift { enum { shift = 4 }; }; + template struct BinOpShift { enum { shift = 2 }; }; + + template struct DefaultBinaryShift + { + enum { shift = BinOpShift::shift }; + }; + + template ::value> struct ShiftDispatcher; + template struct ShiftDispatcher + { + enum { shift = DefaultUnaryShift::shift }; + }; + template struct ShiftDispatcher + { + enum { shift = DefaultBinaryShift::shift }; + }; + } + + template struct DefaultTransformShift + { + enum { shift = functional_detail::ShiftDispatcher::shift }; + }; + + template struct DefaultTransformFunctorTraits + { + enum { simple_block_dim_x = 16 }; + enum { simple_block_dim_y = 16 }; + + enum { smart_block_dim_x = 16 }; + enum { smart_block_dim_y = 16 }; + enum { smart_shift = DefaultTransformShift::shift }; + }; + + template struct TransformFunctorTraits : DefaultTransformFunctorTraits {}; + +#define OPENCV_CUDA_TRANSFORM_FUNCTOR_TRAITS(type) \ + template <> struct TransformFunctorTraits< type > : DefaultTransformFunctorTraits< type > +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_FUNCTIONAL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/limits.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/limits.hpp new file mode 100644 index 0000000000000000000000000000000000000000..7e15ed629a395ae7a79e46883bd8f0d67eebb8ee --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/limits.hpp @@ -0,0 +1,128 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_LIMITS_HPP +#define OPENCV_CUDA_LIMITS_HPP + +#include +#include +#include "common.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ +template struct numeric_limits; + +template <> struct numeric_limits +{ + __device__ __forceinline__ static bool min() { return false; } + __device__ __forceinline__ static bool max() { return true; } + static const bool is_signed = false; +}; + +template <> struct numeric_limits +{ + __device__ __forceinline__ static signed char min() { return SCHAR_MIN; } + __device__ __forceinline__ static signed char max() { return SCHAR_MAX; } + static const bool is_signed = true; +}; + +template <> struct numeric_limits +{ + __device__ __forceinline__ static unsigned char min() { return 0; } + __device__ __forceinline__ static unsigned char max() { return UCHAR_MAX; } + static const bool is_signed = false; +}; + +template <> struct numeric_limits +{ + __device__ __forceinline__ static short min() { return SHRT_MIN; } + __device__ __forceinline__ static short max() { return SHRT_MAX; } + static const bool is_signed = true; +}; + +template <> struct numeric_limits +{ + __device__ __forceinline__ static unsigned short min() { return 0; } + __device__ __forceinline__ static unsigned short max() { return USHRT_MAX; } + static const bool is_signed = false; +}; + +template <> struct numeric_limits +{ + __device__ __forceinline__ static int min() { return INT_MIN; } + __device__ __forceinline__ static int max() { return INT_MAX; } + static const bool is_signed = true; +}; + +template <> struct numeric_limits +{ + __device__ __forceinline__ static unsigned int min() { return 0; } + __device__ __forceinline__ static unsigned int max() { return UINT_MAX; } + static const bool is_signed = false; +}; + +template <> struct numeric_limits +{ + __device__ __forceinline__ static float min() { return FLT_MIN; } + __device__ __forceinline__ static float max() { return FLT_MAX; } + __device__ __forceinline__ static float epsilon() { return FLT_EPSILON; } + static const bool is_signed = true; +}; + +template <> struct numeric_limits +{ + __device__ __forceinline__ static double min() { return DBL_MIN; } + __device__ __forceinline__ static double max() { return DBL_MAX; } + __device__ __forceinline__ static double epsilon() { return DBL_EPSILON; } + static const bool is_signed = true; +}; +}}} // namespace cv { namespace cuda { namespace cudev { + +//! @endcond + +#endif // OPENCV_CUDA_LIMITS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/reduce.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/reduce.hpp new file mode 100644 index 0000000000000000000000000000000000000000..5de365081789d6be1b6e5b35f3648fa07e091650 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/reduce.hpp @@ -0,0 +1,209 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_REDUCE_HPP +#define OPENCV_CUDA_REDUCE_HPP + +#ifndef THRUST_DEBUG // eliminate -Wundef warning +#define THRUST_DEBUG 0 +#endif + +#include +#include "detail/reduce.hpp" +#include "detail/reduce_key_val.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + template + __device__ __forceinline__ void reduce(volatile T* smem, T& val, unsigned int tid, const Op& op) + { + reduce_detail::Dispatcher::reductor::template reduce(smem, val, tid, op); + } + template + __device__ __forceinline__ void reduce(const thrust::tuple& smem, + const thrust::tuple& val, + unsigned int tid, + const thrust::tuple& op) + { + reduce_detail::Dispatcher::reductor::template reduce< + const thrust::tuple&, + const thrust::tuple&, + const thrust::tuple&>(smem, val, tid, op); + } + + template + __device__ __forceinline__ void reduceKeyVal(volatile K* skeys, K& key, volatile V* svals, V& val, unsigned int tid, const Cmp& cmp) + { + reduce_key_val_detail::Dispatcher::reductor::template reduce(skeys, key, svals, val, tid, cmp); + } + template + __device__ __forceinline__ void reduceKeyVal(volatile K* skeys, K& key, + const thrust::tuple& svals, + const thrust::tuple& val, + unsigned int tid, const Cmp& cmp) + { + reduce_key_val_detail::Dispatcher::reductor::template reduce&, + const thrust::tuple&, + const Cmp&>(skeys, key, svals, val, tid, cmp); + } + template + __device__ __forceinline__ void reduceKeyVal(const thrust::tuple& skeys, + const thrust::tuple& key, + const thrust::tuple& svals, + const thrust::tuple& val, + unsigned int tid, + const thrust::tuple& cmp) + { + reduce_key_val_detail::Dispatcher::reductor::template reduce< + const thrust::tuple&, + const thrust::tuple&, + const thrust::tuple&, + const thrust::tuple&, + const thrust::tuple& + >(skeys, key, svals, val, tid, cmp); + } + + // smem_tuple + + template + __device__ __forceinline__ + thrust::tuple + smem_tuple(T0* t0) + { + return thrust::make_tuple((volatile T0*) t0); + } + + template + __device__ __forceinline__ + thrust::tuple + smem_tuple(T0* t0, T1* t1) + { + return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1); + } + + template + __device__ __forceinline__ + thrust::tuple + smem_tuple(T0* t0, T1* t1, T2* t2) + { + return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2); + } + + template + __device__ __forceinline__ + thrust::tuple + smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3) + { + return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3); + } + + template + __device__ __forceinline__ + thrust::tuple + smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4) + { + return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4); + } + + template + __device__ __forceinline__ + thrust::tuple + smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5) + { + return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5); + } + + template + __device__ __forceinline__ + thrust::tuple + smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5, T6* t6) + { + return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5, (volatile T6*) t6); + } + + template + __device__ __forceinline__ + thrust::tuple + smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5, T6* t6, T7* t7) + { + return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5, (volatile T6*) t6, (volatile T7*) t7); + } + + template + __device__ __forceinline__ + thrust::tuple + smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5, T6* t6, T7* t7, T8* t8) + { + return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5, (volatile T6*) t6, (volatile T7*) t7, (volatile T8*) t8); + } + + template + __device__ __forceinline__ + thrust::tuple + smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5, T6* t6, T7* t7, T8* t8, T9* t9) + { + return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5, (volatile T6*) t6, (volatile T7*) t7, (volatile T8*) t8, (volatile T9*) t9); + } +}}} + +//! @endcond + +#endif // OPENCV_CUDA_REDUCE_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/saturate_cast.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/saturate_cast.hpp new file mode 100644 index 0000000000000000000000000000000000000000..c3a3d1cb8338375118930fc7abf918e57a041ceb --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/saturate_cast.hpp @@ -0,0 +1,292 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_SATURATE_CAST_HPP +#define OPENCV_CUDA_SATURATE_CAST_HPP + +#include "common.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + template __device__ __forceinline__ _Tp saturate_cast(uchar v) { return _Tp(v); } + template __device__ __forceinline__ _Tp saturate_cast(schar v) { return _Tp(v); } + template __device__ __forceinline__ _Tp saturate_cast(ushort v) { return _Tp(v); } + template __device__ __forceinline__ _Tp saturate_cast(short v) { return _Tp(v); } + template __device__ __forceinline__ _Tp saturate_cast(uint v) { return _Tp(v); } + template __device__ __forceinline__ _Tp saturate_cast(int v) { return _Tp(v); } + template __device__ __forceinline__ _Tp saturate_cast(float v) { return _Tp(v); } + template __device__ __forceinline__ _Tp saturate_cast(double v) { return _Tp(v); } + + template<> __device__ __forceinline__ uchar saturate_cast(schar v) + { + uint res = 0; + int vi = v; + asm("cvt.sat.u8.s8 %0, %1;" : "=r"(res) : "r"(vi)); + return res; + } + template<> __device__ __forceinline__ uchar saturate_cast(short v) + { + uint res = 0; + asm("cvt.sat.u8.s16 %0, %1;" : "=r"(res) : "h"(v)); + return res; + } + template<> __device__ __forceinline__ uchar saturate_cast(ushort v) + { + uint res = 0; + asm("cvt.sat.u8.u16 %0, %1;" : "=r"(res) : "h"(v)); + return res; + } + template<> __device__ __forceinline__ uchar saturate_cast(int v) + { + uint res = 0; + asm("cvt.sat.u8.s32 %0, %1;" : "=r"(res) : "r"(v)); + return res; + } + template<> __device__ __forceinline__ uchar saturate_cast(uint v) + { + uint res = 0; + asm("cvt.sat.u8.u32 %0, %1;" : "=r"(res) : "r"(v)); + return res; + } + template<> __device__ __forceinline__ uchar saturate_cast(float v) + { + uint res = 0; + asm("cvt.rni.sat.u8.f32 %0, %1;" : "=r"(res) : "f"(v)); + return res; + } + template<> __device__ __forceinline__ uchar saturate_cast(double v) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130 + uint res = 0; + asm("cvt.rni.sat.u8.f64 %0, %1;" : "=r"(res) : "d"(v)); + return res; + #else + return saturate_cast((float)v); + #endif + } + + template<> __device__ __forceinline__ schar saturate_cast(uchar v) + { + uint res = 0; + uint vi = v; + asm("cvt.sat.s8.u8 %0, %1;" : "=r"(res) : "r"(vi)); + return res; + } + template<> __device__ __forceinline__ schar saturate_cast(short v) + { + uint res = 0; + asm("cvt.sat.s8.s16 %0, %1;" : "=r"(res) : "h"(v)); + return res; + } + template<> __device__ __forceinline__ schar saturate_cast(ushort v) + { + uint res = 0; + asm("cvt.sat.s8.u16 %0, %1;" : "=r"(res) : "h"(v)); + return res; + } + template<> __device__ __forceinline__ schar saturate_cast(int v) + { + uint res = 0; + asm("cvt.sat.s8.s32 %0, %1;" : "=r"(res) : "r"(v)); + return res; + } + template<> __device__ __forceinline__ schar saturate_cast(uint v) + { + uint res = 0; + asm("cvt.sat.s8.u32 %0, %1;" : "=r"(res) : "r"(v)); + return res; + } + template<> __device__ __forceinline__ schar saturate_cast(float v) + { + uint res = 0; + asm("cvt.rni.sat.s8.f32 %0, %1;" : "=r"(res) : "f"(v)); + return res; + } + template<> __device__ __forceinline__ schar saturate_cast(double v) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130 + uint res = 0; + asm("cvt.rni.sat.s8.f64 %0, %1;" : "=r"(res) : "d"(v)); + return res; + #else + return saturate_cast((float)v); + #endif + } + + template<> __device__ __forceinline__ ushort saturate_cast(schar v) + { + ushort res = 0; + int vi = v; + asm("cvt.sat.u16.s8 %0, %1;" : "=h"(res) : "r"(vi)); + return res; + } + template<> __device__ __forceinline__ ushort saturate_cast(short v) + { + ushort res = 0; + asm("cvt.sat.u16.s16 %0, %1;" : "=h"(res) : "h"(v)); + return res; + } + template<> __device__ __forceinline__ ushort saturate_cast(int v) + { + ushort res = 0; + asm("cvt.sat.u16.s32 %0, %1;" : "=h"(res) : "r"(v)); + return res; + } + template<> __device__ __forceinline__ ushort saturate_cast(uint v) + { + ushort res = 0; + asm("cvt.sat.u16.u32 %0, %1;" : "=h"(res) : "r"(v)); + return res; + } + template<> __device__ __forceinline__ ushort saturate_cast(float v) + { + ushort res = 0; + asm("cvt.rni.sat.u16.f32 %0, %1;" : "=h"(res) : "f"(v)); + return res; + } + template<> __device__ __forceinline__ ushort saturate_cast(double v) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130 + ushort res = 0; + asm("cvt.rni.sat.u16.f64 %0, %1;" : "=h"(res) : "d"(v)); + return res; + #else + return saturate_cast((float)v); + #endif + } + + template<> __device__ __forceinline__ short saturate_cast(ushort v) + { + short res = 0; + asm("cvt.sat.s16.u16 %0, %1;" : "=h"(res) : "h"(v)); + return res; + } + template<> __device__ __forceinline__ short saturate_cast(int v) + { + short res = 0; + asm("cvt.sat.s16.s32 %0, %1;" : "=h"(res) : "r"(v)); + return res; + } + template<> __device__ __forceinline__ short saturate_cast(uint v) + { + short res = 0; + asm("cvt.sat.s16.u32 %0, %1;" : "=h"(res) : "r"(v)); + return res; + } + template<> __device__ __forceinline__ short saturate_cast(float v) + { + short res = 0; + asm("cvt.rni.sat.s16.f32 %0, %1;" : "=h"(res) : "f"(v)); + return res; + } + template<> __device__ __forceinline__ short saturate_cast(double v) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130 + short res = 0; + asm("cvt.rni.sat.s16.f64 %0, %1;" : "=h"(res) : "d"(v)); + return res; + #else + return saturate_cast((float)v); + #endif + } + + template<> __device__ __forceinline__ int saturate_cast(uint v) + { + int res = 0; + asm("cvt.sat.s32.u32 %0, %1;" : "=r"(res) : "r"(v)); + return res; + } + template<> __device__ __forceinline__ int saturate_cast(float v) + { + return __float2int_rn(v); + } + template<> __device__ __forceinline__ int saturate_cast(double v) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130 + return __double2int_rn(v); + #else + return saturate_cast((float)v); + #endif + } + + template<> __device__ __forceinline__ uint saturate_cast(schar v) + { + uint res = 0; + int vi = v; + asm("cvt.sat.u32.s8 %0, %1;" : "=r"(res) : "r"(vi)); + return res; + } + template<> __device__ __forceinline__ uint saturate_cast(short v) + { + uint res = 0; + asm("cvt.sat.u32.s16 %0, %1;" : "=r"(res) : "h"(v)); + return res; + } + template<> __device__ __forceinline__ uint saturate_cast(int v) + { + uint res = 0; + asm("cvt.sat.u32.s32 %0, %1;" : "=r"(res) : "r"(v)); + return res; + } + template<> __device__ __forceinline__ uint saturate_cast(float v) + { + return __float2uint_rn(v); + } + template<> __device__ __forceinline__ uint saturate_cast(double v) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130 + return __double2uint_rn(v); + #else + return saturate_cast((float)v); + #endif + } +}}} + +//! @endcond + +#endif /* OPENCV_CUDA_SATURATE_CAST_HPP */ diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/scan.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/scan.hpp new file mode 100644 index 0000000000000000000000000000000000000000..e128fb0962efb20ecaa6b16c545b16d5917549f4 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/scan.hpp @@ -0,0 +1,258 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_SCAN_HPP +#define OPENCV_CUDA_SCAN_HPP + +#include "opencv2/core/cuda/common.hpp" +#include "opencv2/core/cuda/utility.hpp" +#include "opencv2/core/cuda/warp.hpp" +#include "opencv2/core/cuda/warp_shuffle.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + enum ScanKind { EXCLUSIVE = 0, INCLUSIVE = 1 }; + + template struct WarpScan + { + __device__ __forceinline__ WarpScan() {} + __device__ __forceinline__ WarpScan(const WarpScan& other) { CV_UNUSED(other); } + + __device__ __forceinline__ T operator()( volatile T *ptr , const unsigned int idx) + { + const unsigned int lane = idx & 31; + F op; + + if ( lane >= 1) ptr [idx ] = op(ptr [idx - 1], ptr [idx]); + if ( lane >= 2) ptr [idx ] = op(ptr [idx - 2], ptr [idx]); + if ( lane >= 4) ptr [idx ] = op(ptr [idx - 4], ptr [idx]); + if ( lane >= 8) ptr [idx ] = op(ptr [idx - 8], ptr [idx]); + if ( lane >= 16) ptr [idx ] = op(ptr [idx - 16], ptr [idx]); + + if( Kind == INCLUSIVE ) + return ptr [idx]; + else + return (lane > 0) ? ptr [idx - 1] : 0; + } + + __device__ __forceinline__ unsigned int index(const unsigned int tid) + { + return tid; + } + + __device__ __forceinline__ void init(volatile T *ptr){} + + static const int warp_offset = 0; + + typedef WarpScan merge; + }; + + template struct WarpScanNoComp + { + __device__ __forceinline__ WarpScanNoComp() {} + __device__ __forceinline__ WarpScanNoComp(const WarpScanNoComp& other) { CV_UNUSED(other); } + + __device__ __forceinline__ T operator()( volatile T *ptr , const unsigned int idx) + { + const unsigned int lane = threadIdx.x & 31; + F op; + + ptr [idx ] = op(ptr [idx - 1], ptr [idx]); + ptr [idx ] = op(ptr [idx - 2], ptr [idx]); + ptr [idx ] = op(ptr [idx - 4], ptr [idx]); + ptr [idx ] = op(ptr [idx - 8], ptr [idx]); + ptr [idx ] = op(ptr [idx - 16], ptr [idx]); + + if( Kind == INCLUSIVE ) + return ptr [idx]; + else + return (lane > 0) ? ptr [idx - 1] : 0; + } + + __device__ __forceinline__ unsigned int index(const unsigned int tid) + { + return (tid >> warp_log) * warp_smem_stride + 16 + (tid & warp_mask); + } + + __device__ __forceinline__ void init(volatile T *ptr) + { + ptr[threadIdx.x] = 0; + } + + static const int warp_smem_stride = 32 + 16 + 1; + static const int warp_offset = 16; + static const int warp_log = 5; + static const int warp_mask = 31; + + typedef WarpScanNoComp merge; + }; + + template struct BlockScan + { + __device__ __forceinline__ BlockScan() {} + __device__ __forceinline__ BlockScan(const BlockScan& other) { CV_UNUSED(other); } + + __device__ __forceinline__ T operator()(volatile T *ptr) + { + const unsigned int tid = threadIdx.x; + const unsigned int lane = tid & warp_mask; + const unsigned int warp = tid >> warp_log; + + Sc scan; + typename Sc::merge merge_scan; + const unsigned int idx = scan.index(tid); + + T val = scan(ptr, idx); + __syncthreads (); + + if( warp == 0) + scan.init(ptr); + __syncthreads (); + + if( lane == 31 ) + ptr [scan.warp_offset + warp ] = (Kind == INCLUSIVE) ? val : ptr [idx]; + __syncthreads (); + + if( warp == 0 ) + merge_scan(ptr, idx); + __syncthreads(); + + if ( warp > 0) + val = ptr [scan.warp_offset + warp - 1] + val; + __syncthreads (); + + ptr[idx] = val; + __syncthreads (); + + return val ; + } + + static const int warp_log = 5; + static const int warp_mask = 31; + }; + + template + __device__ T warpScanInclusive(T idata, volatile T* s_Data, unsigned int tid) + { + #if __CUDA_ARCH__ >= 300 + const unsigned int laneId = cv::cuda::device::Warp::laneId(); + + // scan on shuffl functions + #pragma unroll + for (int i = 1; i <= (OPENCV_CUDA_WARP_SIZE / 2); i *= 2) + { + const T n = cv::cuda::device::shfl_up(idata, i); + if (laneId >= i) + idata += n; + } + + return idata; + #else + unsigned int pos = 2 * tid - (tid & (OPENCV_CUDA_WARP_SIZE - 1)); + s_Data[pos] = 0; + pos += OPENCV_CUDA_WARP_SIZE; + s_Data[pos] = idata; + + s_Data[pos] += s_Data[pos - 1]; + s_Data[pos] += s_Data[pos - 2]; + s_Data[pos] += s_Data[pos - 4]; + s_Data[pos] += s_Data[pos - 8]; + s_Data[pos] += s_Data[pos - 16]; + + return s_Data[pos]; + #endif + } + + template + __device__ __forceinline__ T warpScanExclusive(T idata, volatile T* s_Data, unsigned int tid) + { + return warpScanInclusive(idata, s_Data, tid) - idata; + } + + template + __device__ T blockScanInclusive(T idata, volatile T* s_Data, unsigned int tid) + { + if (tiNumScanThreads > OPENCV_CUDA_WARP_SIZE) + { + //Bottom-level inclusive warp scan + T warpResult = warpScanInclusive(idata, s_Data, tid); + + //Save top elements of each warp for exclusive warp scan + //sync to wait for warp scans to complete (because s_Data is being overwritten) + __syncthreads(); + if ((tid & (OPENCV_CUDA_WARP_SIZE - 1)) == (OPENCV_CUDA_WARP_SIZE - 1)) + { + s_Data[tid >> OPENCV_CUDA_LOG_WARP_SIZE] = warpResult; + } + + //wait for warp scans to complete + __syncthreads(); + + if (tid < (tiNumScanThreads / OPENCV_CUDA_WARP_SIZE) ) + { + //grab top warp elements + T val = s_Data[tid]; + //calculate exclusive scan and write back to shared memory + s_Data[tid] = warpScanExclusive(val, s_Data, tid); + } + + //return updated warp scans with exclusive scan results + __syncthreads(); + + return warpResult + s_Data[tid >> OPENCV_CUDA_LOG_WARP_SIZE]; + } + else + { + return warpScanInclusive(idata, s_Data, tid); + } + } +}}} + +//! @endcond + +#endif // OPENCV_CUDA_SCAN_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/simd_functions.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/simd_functions.hpp new file mode 100644 index 0000000000000000000000000000000000000000..3d8c2e0d8e56ae109e1f7ab839aec46b492c556c --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/simd_functions.hpp @@ -0,0 +1,869 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +/* + * Copyright (c) 2013 NVIDIA Corporation. All rights reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * Redistributions of source code must retain the above copyright notice, + * this list of conditions and the following disclaimer. + * + * Redistributions in binary form must reproduce the above copyright notice, + * this list of conditions and the following disclaimer in the documentation + * and/or other materials provided with the distribution. + * + * Neither the name of NVIDIA Corporation nor the names of its contributors + * may be used to endorse or promote products derived from this software + * without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + * ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE + * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + * POSSIBILITY OF SUCH DAMAGE. + */ + +#ifndef OPENCV_CUDA_SIMD_FUNCTIONS_HPP +#define OPENCV_CUDA_SIMD_FUNCTIONS_HPP + +#include "common.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + // 2 + + static __device__ __forceinline__ unsigned int vadd2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vadd2.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #elif __CUDA_ARCH__ >= 200 + asm("vadd.u32.u32.u32.sat %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vadd.u32.u32.u32.sat %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int s; + s = a ^ b; // sum bits + r = a + b; // actual sum + s = s ^ r; // determine carry-ins for each bit position + s = s & 0x00010000; // carry-in to high word (= carry-out from low word) + r = r - s; // subtract out carry-out from low word + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vsub2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vsub2.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #elif __CUDA_ARCH__ >= 200 + asm("vsub.u32.u32.u32.sat %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vsub.u32.u32.u32.sat %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int s; + s = a ^ b; // sum bits + r = a - b; // actual sum + s = s ^ r; // determine carry-ins for each bit position + s = s & 0x00010000; // borrow to high word + r = r + s; // compensate for borrow from low word + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vabsdiff2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vabsdiff2.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #elif __CUDA_ARCH__ >= 200 + asm("vabsdiff.u32.u32.u32.sat %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vabsdiff.u32.u32.u32.sat %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int s, t, u, v; + s = a & 0x0000ffff; // extract low halfword + r = b & 0x0000ffff; // extract low halfword + u = ::max(r, s); // maximum of low halfwords + v = ::min(r, s); // minimum of low halfwords + s = a & 0xffff0000; // extract high halfword + r = b & 0xffff0000; // extract high halfword + t = ::max(r, s); // maximum of high halfwords + s = ::min(r, s); // minimum of high halfwords + r = u | t; // maximum of both halfwords + s = v | s; // minimum of both halfwords + r = r - s; // |a - b| = max(a,b) - min(a,b); + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vavg2(unsigned int a, unsigned int b) + { + unsigned int r, s; + + // HAKMEM #23: a + b = 2 * (a & b) + (a ^ b) ==> + // (a + b) / 2 = (a & b) + ((a ^ b) >> 1) + s = a ^ b; + r = a & b; + s = s & 0xfffefffe; // ensure shift doesn't cross halfword boundaries + s = s >> 1; + s = r + s; + + return s; + } + + static __device__ __forceinline__ unsigned int vavrg2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vavrg2.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + // HAKMEM #23: a + b = 2 * (a | b) - (a ^ b) ==> + // (a + b + 1) / 2 = (a | b) - ((a ^ b) >> 1) + unsigned int s; + s = a ^ b; + r = a | b; + s = s & 0xfffefffe; // ensure shift doesn't cross half-word boundaries + s = s >> 1; + r = r - s; + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vseteq2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vset2.u32.u32.eq %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + // inspired by Alan Mycroft's null-byte detection algorithm: + // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) + unsigned int c; + r = a ^ b; // 0x0000 if a == b + c = r | 0x80008000; // set msbs, to catch carry out + r = r ^ c; // extract msbs, msb = 1 if r < 0x8000 + c = c - 0x00010001; // msb = 0, if r was 0x0000 or 0x8000 + c = r & ~c; // msb = 1, if r was 0x0000 + r = c >> 15; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmpeq2(unsigned int a, unsigned int b) + { + unsigned int r, c; + + #if __CUDA_ARCH__ >= 300 + r = vseteq2(a, b); + c = r << 16; // convert bool + r = c - r; // into mask + #else + // inspired by Alan Mycroft's null-byte detection algorithm: + // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) + r = a ^ b; // 0x0000 if a == b + c = r | 0x80008000; // set msbs, to catch carry out + r = r ^ c; // extract msbs, msb = 1 if r < 0x8000 + c = c - 0x00010001; // msb = 0, if r was 0x0000 or 0x8000 + c = r & ~c; // msb = 1, if r was 0x0000 + r = c >> 15; // convert + r = c - r; // msbs to + r = c | r; // mask + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vsetge2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vset2.u32.u32.ge %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int c; + asm("not.b32 %0, %0;" : "+r"(b)); + c = vavrg2(a, b); // (a + ~b + 1) / 2 = (a - b) / 2 + c = c & 0x80008000; // msb = carry-outs + r = c >> 15; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmpge2(unsigned int a, unsigned int b) + { + unsigned int r, c; + + #if __CUDA_ARCH__ >= 300 + r = vsetge2(a, b); + c = r << 16; // convert bool + r = c - r; // into mask + #else + asm("not.b32 %0, %0;" : "+r"(b)); + c = vavrg2(a, b); // (a + ~b + 1) / 2 = (a - b) / 2 + c = c & 0x80008000; // msb = carry-outs + r = c >> 15; // convert + r = c - r; // msbs to + r = c | r; // mask + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vsetgt2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vset2.u32.u32.gt %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int c; + asm("not.b32 %0, %0;" : "+r"(b)); + c = vavg2(a, b); // (a + ~b) / 2 = (a - b) / 2 [rounded down] + c = c & 0x80008000; // msbs = carry-outs + r = c >> 15; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmpgt2(unsigned int a, unsigned int b) + { + unsigned int r, c; + + #if __CUDA_ARCH__ >= 300 + r = vsetgt2(a, b); + c = r << 16; // convert bool + r = c - r; // into mask + #else + asm("not.b32 %0, %0;" : "+r"(b)); + c = vavg2(a, b); // (a + ~b) / 2 = (a - b) / 2 [rounded down] + c = c & 0x80008000; // msbs = carry-outs + r = c >> 15; // convert + r = c - r; // msbs to + r = c | r; // mask + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vsetle2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vset2.u32.u32.le %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int c; + asm("not.b32 %0, %0;" : "+r"(a)); + c = vavrg2(a, b); // (b + ~a + 1) / 2 = (b - a) / 2 + c = c & 0x80008000; // msb = carry-outs + r = c >> 15; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmple2(unsigned int a, unsigned int b) + { + unsigned int r, c; + + #if __CUDA_ARCH__ >= 300 + r = vsetle2(a, b); + c = r << 16; // convert bool + r = c - r; // into mask + #else + asm("not.b32 %0, %0;" : "+r"(a)); + c = vavrg2(a, b); // (b + ~a + 1) / 2 = (b - a) / 2 + c = c & 0x80008000; // msb = carry-outs + r = c >> 15; // convert + r = c - r; // msbs to + r = c | r; // mask + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vsetlt2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vset2.u32.u32.lt %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int c; + asm("not.b32 %0, %0;" : "+r"(a)); + c = vavg2(a, b); // (b + ~a) / 2 = (b - a) / 2 [rounded down] + c = c & 0x80008000; // msb = carry-outs + r = c >> 15; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmplt2(unsigned int a, unsigned int b) + { + unsigned int r, c; + + #if __CUDA_ARCH__ >= 300 + r = vsetlt2(a, b); + c = r << 16; // convert bool + r = c - r; // into mask + #else + asm("not.b32 %0, %0;" : "+r"(a)); + c = vavg2(a, b); // (b + ~a) / 2 = (b - a) / 2 [rounded down] + c = c & 0x80008000; // msb = carry-outs + r = c >> 15; // convert + r = c - r; // msbs to + r = c | r; // mask + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vsetne2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm ("vset2.u32.u32.ne %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + // inspired by Alan Mycroft's null-byte detection algorithm: + // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) + unsigned int c; + r = a ^ b; // 0x0000 if a == b + c = r | 0x80008000; // set msbs, to catch carry out + c = c - 0x00010001; // msb = 0, if r was 0x0000 or 0x8000 + c = r | c; // msb = 1, if r was not 0x0000 + c = c & 0x80008000; // extract msbs + r = c >> 15; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmpne2(unsigned int a, unsigned int b) + { + unsigned int r, c; + + #if __CUDA_ARCH__ >= 300 + r = vsetne2(a, b); + c = r << 16; // convert bool + r = c - r; // into mask + #else + // inspired by Alan Mycroft's null-byte detection algorithm: + // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) + r = a ^ b; // 0x0000 if a == b + c = r | 0x80008000; // set msbs, to catch carry out + c = c - 0x00010001; // msb = 0, if r was 0x0000 or 0x8000 + c = r | c; // msb = 1, if r was not 0x0000 + c = c & 0x80008000; // extract msbs + r = c >> 15; // convert + r = c - r; // msbs to + r = c | r; // mask + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vmax2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vmax2.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #elif __CUDA_ARCH__ >= 200 + asm("vmax.u32.u32.u32 %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vmax.u32.u32.u32 %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int s, t, u; + r = a & 0x0000ffff; // extract low halfword + s = b & 0x0000ffff; // extract low halfword + t = ::max(r, s); // maximum of low halfwords + r = a & 0xffff0000; // extract high halfword + s = b & 0xffff0000; // extract high halfword + u = ::max(r, s); // maximum of high halfwords + r = t | u; // combine halfword maximums + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vmin2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vmin2.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #elif __CUDA_ARCH__ >= 200 + asm("vmin.u32.u32.u32 %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vmin.u32.u32.u32 %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int s, t, u; + r = a & 0x0000ffff; // extract low halfword + s = b & 0x0000ffff; // extract low halfword + t = ::min(r, s); // minimum of low halfwords + r = a & 0xffff0000; // extract high halfword + s = b & 0xffff0000; // extract high halfword + u = ::min(r, s); // minimum of high halfwords + r = t | u; // combine halfword minimums + #endif + + return r; + } + + // 4 + + static __device__ __forceinline__ unsigned int vadd4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vadd4.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #elif __CUDA_ARCH__ >= 200 + asm("vadd.u32.u32.u32.sat %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vadd.u32.u32.u32.sat %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vadd.u32.u32.u32.sat %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vadd.u32.u32.u32.sat %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int s, t; + s = a ^ b; // sum bits + r = a & 0x7f7f7f7f; // clear msbs + t = b & 0x7f7f7f7f; // clear msbs + s = s & 0x80808080; // msb sum bits + r = r + t; // add without msbs, record carry-out in msbs + r = r ^ s; // sum of msb sum and carry-in bits, w/o carry-out + #endif /* __CUDA_ARCH__ >= 300 */ + + return r; + } + + static __device__ __forceinline__ unsigned int vsub4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vsub4.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #elif __CUDA_ARCH__ >= 200 + asm("vsub.u32.u32.u32.sat %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vsub.u32.u32.u32.sat %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vsub.u32.u32.u32.sat %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vsub.u32.u32.u32.sat %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int s, t; + s = a ^ ~b; // inverted sum bits + r = a | 0x80808080; // set msbs + t = b & 0x7f7f7f7f; // clear msbs + s = s & 0x80808080; // inverted msb sum bits + r = r - t; // subtract w/o msbs, record inverted borrows in msb + r = r ^ s; // combine inverted msb sum bits and borrows + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vavg4(unsigned int a, unsigned int b) + { + unsigned int r, s; + + // HAKMEM #23: a + b = 2 * (a & b) + (a ^ b) ==> + // (a + b) / 2 = (a & b) + ((a ^ b) >> 1) + s = a ^ b; + r = a & b; + s = s & 0xfefefefe; // ensure following shift doesn't cross byte boundaries + s = s >> 1; + s = r + s; + + return s; + } + + static __device__ __forceinline__ unsigned int vavrg4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vavrg4.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + // HAKMEM #23: a + b = 2 * (a | b) - (a ^ b) ==> + // (a + b + 1) / 2 = (a | b) - ((a ^ b) >> 1) + unsigned int c; + c = a ^ b; + r = a | b; + c = c & 0xfefefefe; // ensure following shift doesn't cross byte boundaries + c = c >> 1; + r = r - c; + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vseteq4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vset4.u32.u32.eq %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + // inspired by Alan Mycroft's null-byte detection algorithm: + // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) + unsigned int c; + r = a ^ b; // 0x00 if a == b + c = r | 0x80808080; // set msbs, to catch carry out + r = r ^ c; // extract msbs, msb = 1 if r < 0x80 + c = c - 0x01010101; // msb = 0, if r was 0x00 or 0x80 + c = r & ~c; // msb = 1, if r was 0x00 + r = c >> 7; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmpeq4(unsigned int a, unsigned int b) + { + unsigned int r, t; + + #if __CUDA_ARCH__ >= 300 + r = vseteq4(a, b); + t = r << 8; // convert bool + r = t - r; // to mask + #else + // inspired by Alan Mycroft's null-byte detection algorithm: + // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) + t = a ^ b; // 0x00 if a == b + r = t | 0x80808080; // set msbs, to catch carry out + t = t ^ r; // extract msbs, msb = 1 if t < 0x80 + r = r - 0x01010101; // msb = 0, if t was 0x00 or 0x80 + r = t & ~r; // msb = 1, if t was 0x00 + t = r >> 7; // build mask + t = r - t; // from + r = t | r; // msbs + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vsetle4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vset4.u32.u32.le %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int c; + asm("not.b32 %0, %0;" : "+r"(a)); + c = vavrg4(a, b); // (b + ~a + 1) / 2 = (b - a) / 2 + c = c & 0x80808080; // msb = carry-outs + r = c >> 7; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmple4(unsigned int a, unsigned int b) + { + unsigned int r, c; + + #if __CUDA_ARCH__ >= 300 + r = vsetle4(a, b); + c = r << 8; // convert bool + r = c - r; // to mask + #else + asm("not.b32 %0, %0;" : "+r"(a)); + c = vavrg4(a, b); // (b + ~a + 1) / 2 = (b - a) / 2 + c = c & 0x80808080; // msbs = carry-outs + r = c >> 7; // convert + r = c - r; // msbs to + r = c | r; // mask + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vsetlt4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vset4.u32.u32.lt %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int c; + asm("not.b32 %0, %0;" : "+r"(a)); + c = vavg4(a, b); // (b + ~a) / 2 = (b - a) / 2 [rounded down] + c = c & 0x80808080; // msb = carry-outs + r = c >> 7; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmplt4(unsigned int a, unsigned int b) + { + unsigned int r, c; + + #if __CUDA_ARCH__ >= 300 + r = vsetlt4(a, b); + c = r << 8; // convert bool + r = c - r; // to mask + #else + asm("not.b32 %0, %0;" : "+r"(a)); + c = vavg4(a, b); // (b + ~a) / 2 = (b - a) / 2 [rounded down] + c = c & 0x80808080; // msbs = carry-outs + r = c >> 7; // convert + r = c - r; // msbs to + r = c | r; // mask + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vsetge4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vset4.u32.u32.ge %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int c; + asm("not.b32 %0, %0;" : "+r"(b)); + c = vavrg4(a, b); // (a + ~b + 1) / 2 = (a - b) / 2 + c = c & 0x80808080; // msb = carry-outs + r = c >> 7; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmpge4(unsigned int a, unsigned int b) + { + unsigned int r, s; + + #if __CUDA_ARCH__ >= 300 + r = vsetge4(a, b); + s = r << 8; // convert bool + r = s - r; // to mask + #else + asm ("not.b32 %0,%0;" : "+r"(b)); + r = vavrg4 (a, b); // (a + ~b + 1) / 2 = (a - b) / 2 + r = r & 0x80808080; // msb = carry-outs + s = r >> 7; // build mask + s = r - s; // from + r = s | r; // msbs + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vsetgt4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vset4.u32.u32.gt %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int c; + asm("not.b32 %0, %0;" : "+r"(b)); + c = vavg4(a, b); // (a + ~b) / 2 = (a - b) / 2 [rounded down] + c = c & 0x80808080; // msb = carry-outs + r = c >> 7; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmpgt4(unsigned int a, unsigned int b) + { + unsigned int r, c; + + #if __CUDA_ARCH__ >= 300 + r = vsetgt4(a, b); + c = r << 8; // convert bool + r = c - r; // to mask + #else + asm("not.b32 %0, %0;" : "+r"(b)); + c = vavg4(a, b); // (a + ~b) / 2 = (a - b) / 2 [rounded down] + c = c & 0x80808080; // msb = carry-outs + r = c >> 7; // convert + r = c - r; // msbs to + r = c | r; // mask + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vsetne4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vset4.u32.u32.ne %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + // inspired by Alan Mycroft's null-byte detection algorithm: + // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) + unsigned int c; + r = a ^ b; // 0x00 if a == b + c = r | 0x80808080; // set msbs, to catch carry out + c = c - 0x01010101; // msb = 0, if r was 0x00 or 0x80 + c = r | c; // msb = 1, if r was not 0x00 + c = c & 0x80808080; // extract msbs + r = c >> 7; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmpne4(unsigned int a, unsigned int b) + { + unsigned int r, c; + + #if __CUDA_ARCH__ >= 300 + r = vsetne4(a, b); + c = r << 8; // convert bool + r = c - r; // to mask + #else + // inspired by Alan Mycroft's null-byte detection algorithm: + // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) + r = a ^ b; // 0x00 if a == b + c = r | 0x80808080; // set msbs, to catch carry out + c = c - 0x01010101; // msb = 0, if r was 0x00 or 0x80 + c = r | c; // msb = 1, if r was not 0x00 + c = c & 0x80808080; // extract msbs + r = c >> 7; // convert + r = c - r; // msbs to + r = c | r; // mask + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vabsdiff4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vabsdiff4.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #elif __CUDA_ARCH__ >= 200 + asm("vabsdiff.u32.u32.u32.sat %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vabsdiff.u32.u32.u32.sat %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vabsdiff.u32.u32.u32.sat %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vabsdiff.u32.u32.u32.sat %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int s; + s = vcmpge4(a, b); // mask = 0xff if a >= b + r = a ^ b; // + s = (r & s) ^ b; // select a when a >= b, else select b => max(a,b) + r = s ^ r; // select a when b >= a, else select b => min(a,b) + r = s - r; // |a - b| = max(a,b) - min(a,b); + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vmax4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vmax4.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #elif __CUDA_ARCH__ >= 200 + asm("vmax.u32.u32.u32 %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vmax.u32.u32.u32 %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vmax.u32.u32.u32 %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vmax.u32.u32.u32 %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int s; + s = vcmpge4(a, b); // mask = 0xff if a >= b + r = a & s; // select a when b >= a + s = b & ~s; // select b when b < a + r = r | s; // combine byte selections + #endif + + return r; // byte-wise unsigned maximum + } + + static __device__ __forceinline__ unsigned int vmin4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vmin4.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #elif __CUDA_ARCH__ >= 200 + asm("vmin.u32.u32.u32 %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vmin.u32.u32.u32 %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vmin.u32.u32.u32 %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vmin.u32.u32.u32 %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int s; + s = vcmpge4(b, a); // mask = 0xff if a >= b + r = a & s; // select a when b >= a + s = b & ~s; // select b when b < a + r = r | s; // combine byte selections + #endif + + return r; + } +}}} + +//! @endcond + +#endif // OPENCV_CUDA_SIMD_FUNCTIONS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/transform.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/transform.hpp new file mode 100644 index 0000000000000000000000000000000000000000..42aa6ea170f21f95fc1e3702c2c410dbd3cc45e7 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/transform.hpp @@ -0,0 +1,75 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_TRANSFORM_HPP +#define OPENCV_CUDA_TRANSFORM_HPP + +#include "common.hpp" +#include "utility.hpp" +#include "detail/transform_detail.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + template + static inline void transform(PtrStepSz src, PtrStepSz dst, UnOp op, const Mask& mask, cudaStream_t stream) + { + typedef TransformFunctorTraits ft; + transform_detail::TransformDispatcher::cn == 1 && VecTraits::cn == 1 && ft::smart_shift != 1>::call(src, dst, op, mask, stream); + } + + template + static inline void transform(PtrStepSz src1, PtrStepSz src2, PtrStepSz dst, BinOp op, const Mask& mask, cudaStream_t stream) + { + typedef TransformFunctorTraits ft; + transform_detail::TransformDispatcher::cn == 1 && VecTraits::cn == 1 && VecTraits::cn == 1 && ft::smart_shift != 1>::call(src1, src2, dst, op, mask, stream); + } +}}} + +//! @endcond + +#endif // OPENCV_CUDA_TRANSFORM_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/type_traits.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/type_traits.hpp new file mode 100644 index 0000000000000000000000000000000000000000..8b7a3fd16832df41f6bdb47a06569f6265ccad9d --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/type_traits.hpp @@ -0,0 +1,90 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_TYPE_TRAITS_HPP +#define OPENCV_CUDA_TYPE_TRAITS_HPP + +#include "detail/type_traits_detail.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + template struct IsSimpleParameter + { + enum {value = type_traits_detail::IsIntegral::value || type_traits_detail::IsFloat::value || + type_traits_detail::PointerTraits::type>::value}; + }; + + template struct TypeTraits + { + typedef typename type_traits_detail::UnConst::type NonConstType; + typedef typename type_traits_detail::UnVolatile::type NonVolatileType; + typedef typename type_traits_detail::UnVolatile::type>::type UnqualifiedType; + typedef typename type_traits_detail::PointerTraits::type PointeeType; + typedef typename type_traits_detail::ReferenceTraits::type ReferredType; + + enum { isConst = type_traits_detail::UnConst::value }; + enum { isVolatile = type_traits_detail::UnVolatile::value }; + + enum { isReference = type_traits_detail::ReferenceTraits::value }; + enum { isPointer = type_traits_detail::PointerTraits::type>::value }; + + enum { isUnsignedInt = type_traits_detail::IsUnsignedIntegral::value }; + enum { isSignedInt = type_traits_detail::IsSignedIntergral::value }; + enum { isIntegral = type_traits_detail::IsIntegral::value }; + enum { isFloat = type_traits_detail::IsFloat::value }; + enum { isArith = isIntegral || isFloat }; + enum { isVec = type_traits_detail::IsVec::value }; + + typedef typename type_traits_detail::Select::value, + T, typename type_traits_detail::AddParameterType::type>::type ParameterType; + }; +}}} + +//! @endcond + +#endif // OPENCV_CUDA_TYPE_TRAITS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/utility.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/utility.hpp new file mode 100644 index 0000000000000000000000000000000000000000..7f5db48a500f6e8b0399c04ad05d79a0a0137741 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/utility.hpp @@ -0,0 +1,230 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_UTILITY_HPP +#define OPENCV_CUDA_UTILITY_HPP + +#include "saturate_cast.hpp" +#include "datamov_utils.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + struct CV_EXPORTS ThrustAllocator + { + typedef uchar value_type; + virtual ~ThrustAllocator(); + virtual __device__ __host__ uchar* allocate(size_t numBytes) = 0; + virtual __device__ __host__ void deallocate(uchar* ptr, size_t numBytes) = 0; + static ThrustAllocator& getAllocator(); + static void setAllocator(ThrustAllocator* allocator); + }; + #define OPENCV_CUDA_LOG_WARP_SIZE (5) + #define OPENCV_CUDA_WARP_SIZE (1 << OPENCV_CUDA_LOG_WARP_SIZE) + #define OPENCV_CUDA_LOG_MEM_BANKS ((__CUDA_ARCH__ >= 200) ? 5 : 4) // 32 banks on fermi, 16 on tesla + #define OPENCV_CUDA_MEM_BANKS (1 << OPENCV_CUDA_LOG_MEM_BANKS) + + /////////////////////////////////////////////////////////////////////////////// + // swap + + template void __device__ __host__ __forceinline__ swap(T& a, T& b) + { + const T temp = a; + a = b; + b = temp; + } + + /////////////////////////////////////////////////////////////////////////////// + // Mask Reader + + struct SingleMask + { + explicit __host__ __device__ __forceinline__ SingleMask(PtrStepb mask_) : mask(mask_) {} + __host__ __device__ __forceinline__ SingleMask(const SingleMask& mask_): mask(mask_.mask){} + + __device__ __forceinline__ bool operator()(int y, int x) const + { + return mask.ptr(y)[x] != 0; + } + + PtrStepb mask; + }; + + struct SingleMaskChannels + { + __host__ __device__ __forceinline__ SingleMaskChannels(PtrStepb mask_, int channels_) + : mask(mask_), channels(channels_) {} + __host__ __device__ __forceinline__ SingleMaskChannels(const SingleMaskChannels& mask_) + :mask(mask_.mask), channels(mask_.channels){} + + __device__ __forceinline__ bool operator()(int y, int x) const + { + return mask.ptr(y)[x / channels] != 0; + } + + PtrStepb mask; + int channels; + }; + + struct MaskCollection + { + explicit __host__ __device__ __forceinline__ MaskCollection(PtrStepb* maskCollection_) + : maskCollection(maskCollection_) {} + + __device__ __forceinline__ MaskCollection(const MaskCollection& masks_) + : maskCollection(masks_.maskCollection), curMask(masks_.curMask){} + + __device__ __forceinline__ void next() + { + curMask = *maskCollection++; + } + __device__ __forceinline__ void setMask(int z) + { + curMask = maskCollection[z]; + } + + __device__ __forceinline__ bool operator()(int y, int x) const + { + uchar val; + return curMask.data == 0 || (ForceGlob::Load(curMask.ptr(y), x, val), (val != 0)); + } + + const PtrStepb* maskCollection; + PtrStepb curMask; + }; + + struct WithOutMask + { + __host__ __device__ __forceinline__ WithOutMask(){} + __host__ __device__ __forceinline__ WithOutMask(const WithOutMask&){} + + __device__ __forceinline__ void next() const + { + } + __device__ __forceinline__ void setMask(int) const + { + } + + __device__ __forceinline__ bool operator()(int, int) const + { + return true; + } + + __device__ __forceinline__ bool operator()(int, int, int) const + { + return true; + } + + static __device__ __forceinline__ bool check(int, int) + { + return true; + } + + static __device__ __forceinline__ bool check(int, int, int) + { + return true; + } + }; + + /////////////////////////////////////////////////////////////////////////////// + // Solve linear system + + // solve 2x2 linear system Ax=b + template __device__ __forceinline__ bool solve2x2(const T A[2][2], const T b[2], T x[2]) + { + T det = A[0][0] * A[1][1] - A[1][0] * A[0][1]; + + if (det != 0) + { + double invdet = 1.0 / det; + + x[0] = saturate_cast(invdet * (b[0] * A[1][1] - b[1] * A[0][1])); + + x[1] = saturate_cast(invdet * (A[0][0] * b[1] - A[1][0] * b[0])); + + return true; + } + + return false; + } + + // solve 3x3 linear system Ax=b + template __device__ __forceinline__ bool solve3x3(const T A[3][3], const T b[3], T x[3]) + { + T det = A[0][0] * (A[1][1] * A[2][2] - A[1][2] * A[2][1]) + - A[0][1] * (A[1][0] * A[2][2] - A[1][2] * A[2][0]) + + A[0][2] * (A[1][0] * A[2][1] - A[1][1] * A[2][0]); + + if (det != 0) + { + double invdet = 1.0 / det; + + x[0] = saturate_cast(invdet * + (b[0] * (A[1][1] * A[2][2] - A[1][2] * A[2][1]) - + A[0][1] * (b[1] * A[2][2] - A[1][2] * b[2] ) + + A[0][2] * (b[1] * A[2][1] - A[1][1] * b[2] ))); + + x[1] = saturate_cast(invdet * + (A[0][0] * (b[1] * A[2][2] - A[1][2] * b[2] ) - + b[0] * (A[1][0] * A[2][2] - A[1][2] * A[2][0]) + + A[0][2] * (A[1][0] * b[2] - b[1] * A[2][0]))); + + x[2] = saturate_cast(invdet * + (A[0][0] * (A[1][1] * b[2] - b[1] * A[2][1]) - + A[0][1] * (A[1][0] * b[2] - b[1] * A[2][0]) + + b[0] * (A[1][0] * A[2][1] - A[1][1] * A[2][0]))); + + return true; + } + + return false; + } +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_UTILITY_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/vec_distance.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/vec_distance.hpp new file mode 100644 index 0000000000000000000000000000000000000000..ef6e51087d33e8fd859c63be04515a7e99fd6afb --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/vec_distance.hpp @@ -0,0 +1,232 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_VEC_DISTANCE_HPP +#define OPENCV_CUDA_VEC_DISTANCE_HPP + +#include "reduce.hpp" +#include "functional.hpp" +#include "detail/vec_distance_detail.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + template struct L1Dist + { + typedef int value_type; + typedef int result_type; + + __device__ __forceinline__ L1Dist() : mySum(0) {} + + __device__ __forceinline__ void reduceIter(int val1, int val2) + { + mySum = __sad(val1, val2, mySum); + } + + template __device__ __forceinline__ void reduceAll(int* smem, int tid) + { + reduce(smem, mySum, tid, plus()); + } + + __device__ __forceinline__ operator int() const + { + return mySum; + } + + int mySum; + }; + template <> struct L1Dist + { + typedef float value_type; + typedef float result_type; + + __device__ __forceinline__ L1Dist() : mySum(0.0f) {} + + __device__ __forceinline__ void reduceIter(float val1, float val2) + { + mySum += ::fabs(val1 - val2); + } + + template __device__ __forceinline__ void reduceAll(float* smem, int tid) + { + reduce(smem, mySum, tid, plus()); + } + + __device__ __forceinline__ operator float() const + { + return mySum; + } + + float mySum; + }; + + struct L2Dist + { + typedef float value_type; + typedef float result_type; + + __device__ __forceinline__ L2Dist() : mySum(0.0f) {} + + __device__ __forceinline__ void reduceIter(float val1, float val2) + { + float reg = val1 - val2; + mySum += reg * reg; + } + + template __device__ __forceinline__ void reduceAll(float* smem, int tid) + { + reduce(smem, mySum, tid, plus()); + } + + __device__ __forceinline__ operator float() const + { + return sqrtf(mySum); + } + + float mySum; + }; + + struct HammingDist + { + typedef int value_type; + typedef int result_type; + + __device__ __forceinline__ HammingDist() : mySum(0) {} + + __device__ __forceinline__ void reduceIter(int val1, int val2) + { + mySum += __popc(val1 ^ val2); + } + + template __device__ __forceinline__ void reduceAll(int* smem, int tid) + { + reduce(smem, mySum, tid, plus()); + } + + __device__ __forceinline__ operator int() const + { + return mySum; + } + + int mySum; + }; + + // calc distance between two vectors in global memory + template + __device__ void calcVecDiffGlobal(const T1* vec1, const T2* vec2, int len, Dist& dist, typename Dist::result_type* smem, int tid) + { + for (int i = tid; i < len; i += THREAD_DIM) + { + T1 val1; + ForceGlob::Load(vec1, i, val1); + + T2 val2; + ForceGlob::Load(vec2, i, val2); + + dist.reduceIter(val1, val2); + } + + dist.reduceAll(smem, tid); + } + + // calc distance between two vectors, first vector is cached in register or shared memory, second vector is in global memory + template + __device__ __forceinline__ void calcVecDiffCached(const T1* vecCached, const T2* vecGlob, int len, Dist& dist, typename Dist::result_type* smem, int tid) + { + vec_distance_detail::VecDiffCachedCalculator::calc(vecCached, vecGlob, len, dist, tid); + + dist.reduceAll(smem, tid); + } + + // calc distance between two vectors in global memory + template struct VecDiffGlobal + { + explicit __device__ __forceinline__ VecDiffGlobal(const T1* vec1_, int = 0, void* = 0, int = 0, int = 0) + { + vec1 = vec1_; + } + + template + __device__ __forceinline__ void calc(const T2* vec2, int len, Dist& dist, typename Dist::result_type* smem, int tid) const + { + calcVecDiffGlobal(vec1, vec2, len, dist, smem, tid); + } + + const T1* vec1; + }; + + // calc distance between two vectors, first vector is cached in register memory, second vector is in global memory + template struct VecDiffCachedRegister + { + template __device__ __forceinline__ VecDiffCachedRegister(const T1* vec1, int len, U* smem, int glob_tid, int tid) + { + if (glob_tid < len) + smem[glob_tid] = vec1[glob_tid]; + __syncthreads(); + + U* vec1ValsPtr = vec1Vals; + + #pragma unroll + for (int i = tid; i < MAX_LEN; i += THREAD_DIM) + *vec1ValsPtr++ = smem[i]; + + __syncthreads(); + } + + template + __device__ __forceinline__ void calc(const T2* vec2, int len, Dist& dist, typename Dist::result_type* smem, int tid) const + { + calcVecDiffCached(vec1Vals, vec2, len, dist, smem, tid); + } + + U vec1Vals[MAX_LEN / THREAD_DIM]; + }; +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_VEC_DISTANCE_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/vec_math.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/vec_math.hpp new file mode 100644 index 0000000000000000000000000000000000000000..9085b92397d8db583a3890d688a1fe8b7bf72152 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/vec_math.hpp @@ -0,0 +1,930 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_VECMATH_HPP +#define OPENCV_CUDA_VECMATH_HPP + +#include "vec_traits.hpp" +#include "saturate_cast.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + +// saturate_cast + +namespace vec_math_detail +{ + template struct SatCastHelper; + template struct SatCastHelper<1, VecD> + { + template static __device__ __forceinline__ VecD cast(const VecS& v) + { + typedef typename VecTraits::elem_type D; + return VecTraits::make(saturate_cast(v.x)); + } + }; + template struct SatCastHelper<2, VecD> + { + template static __device__ __forceinline__ VecD cast(const VecS& v) + { + typedef typename VecTraits::elem_type D; + return VecTraits::make(saturate_cast(v.x), saturate_cast(v.y)); + } + }; + template struct SatCastHelper<3, VecD> + { + template static __device__ __forceinline__ VecD cast(const VecS& v) + { + typedef typename VecTraits::elem_type D; + return VecTraits::make(saturate_cast(v.x), saturate_cast(v.y), saturate_cast(v.z)); + } + }; + template struct SatCastHelper<4, VecD> + { + template static __device__ __forceinline__ VecD cast(const VecS& v) + { + typedef typename VecTraits::elem_type D; + return VecTraits::make(saturate_cast(v.x), saturate_cast(v.y), saturate_cast(v.z), saturate_cast(v.w)); + } + }; + + template static __device__ __forceinline__ VecD saturate_cast_helper(const VecS& v) + { + return SatCastHelper::cn, VecD>::cast(v); + } +} + +template static __device__ __forceinline__ T saturate_cast(const uchar1& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const char1& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const ushort1& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const short1& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const uint1& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const int1& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const float1& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const double1& v) {return vec_math_detail::saturate_cast_helper(v);} + +template static __device__ __forceinline__ T saturate_cast(const uchar2& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const char2& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const ushort2& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const short2& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const uint2& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const int2& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const float2& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const double2& v) {return vec_math_detail::saturate_cast_helper(v);} + +template static __device__ __forceinline__ T saturate_cast(const uchar3& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const char3& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const ushort3& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const short3& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const uint3& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const int3& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const float3& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const double3& v) {return vec_math_detail::saturate_cast_helper(v);} + +template static __device__ __forceinline__ T saturate_cast(const uchar4& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const char4& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const ushort4& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const short4& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const uint4& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const int4& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const float4& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const double4& v) {return vec_math_detail::saturate_cast_helper(v);} + +// unary operators + +#define CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(op, input_type, output_type) \ + __device__ __forceinline__ output_type ## 1 operator op(const input_type ## 1 & a) \ + { \ + return VecTraits::make(op (a.x)); \ + } \ + __device__ __forceinline__ output_type ## 2 operator op(const input_type ## 2 & a) \ + { \ + return VecTraits::make(op (a.x), op (a.y)); \ + } \ + __device__ __forceinline__ output_type ## 3 operator op(const input_type ## 3 & a) \ + { \ + return VecTraits::make(op (a.x), op (a.y), op (a.z)); \ + } \ + __device__ __forceinline__ output_type ## 4 operator op(const input_type ## 4 & a) \ + { \ + return VecTraits::make(op (a.x), op (a.y), op (a.z), op (a.w)); \ + } + +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, char, char) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, short, short) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, int, int) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, char, uchar) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, ushort, uchar) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, short, uchar) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, int, uchar) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, uint, uchar) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, float, uchar) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, double, uchar) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, char, char) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, ushort, ushort) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, short, short) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, int, int) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, uint, uint) + +#undef CV_CUDEV_IMPLEMENT_VEC_UNARY_OP + +// unary functions + +#define CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(func_name, func, input_type, output_type) \ + __device__ __forceinline__ output_type ## 1 func_name(const input_type ## 1 & a) \ + { \ + return VecTraits::make(func (a.x)); \ + } \ + __device__ __forceinline__ output_type ## 2 func_name(const input_type ## 2 & a) \ + { \ + return VecTraits::make(func (a.x), func (a.y)); \ + } \ + __device__ __forceinline__ output_type ## 3 func_name(const input_type ## 3 & a) \ + { \ + return VecTraits::make(func (a.x), func (a.y), func (a.z)); \ + } \ + __device__ __forceinline__ output_type ## 4 func_name(const input_type ## 4 & a) \ + { \ + return VecTraits::make(func (a.x), func (a.y), func (a.z), func (a.w)); \ + } + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, /*::abs*/, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::abs, char, char) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, /*::abs*/, ushort, ushort) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::abs, short, short) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::abs, int, int) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, /*::abs*/, uint, uint) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::fabsf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::fabs, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrt, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::exp, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::log, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sin, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cos, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tan, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asin, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acos, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atan, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinh, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::cosh, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanh, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinh, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acosh, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanh, double, double) + +#undef CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC + +// binary operators (vec & vec) + +#define CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(op, input_type, output_type) \ + __device__ __forceinline__ output_type ## 1 operator op(const input_type ## 1 & a, const input_type ## 1 & b) \ + { \ + return VecTraits::make(a.x op b.x); \ + } \ + __device__ __forceinline__ output_type ## 2 operator op(const input_type ## 2 & a, const input_type ## 2 & b) \ + { \ + return VecTraits::make(a.x op b.x, a.y op b.y); \ + } \ + __device__ __forceinline__ output_type ## 3 operator op(const input_type ## 3 & a, const input_type ## 3 & b) \ + { \ + return VecTraits::make(a.x op b.x, a.y op b.y, a.z op b.z); \ + } \ + __device__ __forceinline__ output_type ## 4 operator op(const input_type ## 4 & a, const input_type ## 4 & b) \ + { \ + return VecTraits::make(a.x op b.x, a.y op b.y, a.z op b.z, a.w op b.w); \ + } + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, uchar, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, char, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, ushort, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, short, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, int, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, uint, uint) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, float, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, double, double) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, uchar, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, char, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, ushort, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, short, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, int, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, uint, uint) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, float, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, double, double) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, uchar, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, char, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, ushort, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, short, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, int, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, uint, uint) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, float, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, double, double) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, uchar, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, char, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, ushort, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, short, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, int, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, uint, uint) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, float, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, double, double) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, char, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, ushort, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, short, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, int, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, uint, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, float, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, double, uchar) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, char, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, ushort, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, short, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, int, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, uint, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, float, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, double, uchar) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, char, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, ushort, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, short, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, int, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, uint, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, float, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, double, uchar) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, char, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, ushort, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, short, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, int, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, uint, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, float, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, double, uchar) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, char, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, ushort, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, short, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, int, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, uint, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, float, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, double, uchar) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, char, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, ushort, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, short, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, int, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, uint, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, float, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, double, uchar) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, char, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, ushort, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, short, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, int, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, uint, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, float, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, double, uchar) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, char, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, ushort, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, short, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, int, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, uint, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, float, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, double, uchar) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, char, char) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, ushort, ushort) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, short, short) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, int, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, uint, uint) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, char, char) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, ushort, ushort) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, short, short) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, int, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, uint, uint) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, char, char) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, ushort, ushort) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, short, short) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, int, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, uint, uint) + +#undef CV_CUDEV_IMPLEMENT_VEC_BINARY_OP + +// binary operators (vec & scalar) + +#define CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(op, input_type, scalar_type, output_type) \ + __device__ __forceinline__ output_type ## 1 operator op(const input_type ## 1 & a, scalar_type s) \ + { \ + return VecTraits::make(a.x op s); \ + } \ + __device__ __forceinline__ output_type ## 1 operator op(scalar_type s, const input_type ## 1 & b) \ + { \ + return VecTraits::make(s op b.x); \ + } \ + __device__ __forceinline__ output_type ## 2 operator op(const input_type ## 2 & a, scalar_type s) \ + { \ + return VecTraits::make(a.x op s, a.y op s); \ + } \ + __device__ __forceinline__ output_type ## 2 operator op(scalar_type s, const input_type ## 2 & b) \ + { \ + return VecTraits::make(s op b.x, s op b.y); \ + } \ + __device__ __forceinline__ output_type ## 3 operator op(const input_type ## 3 & a, scalar_type s) \ + { \ + return VecTraits::make(a.x op s, a.y op s, a.z op s); \ + } \ + __device__ __forceinline__ output_type ## 3 operator op(scalar_type s, const input_type ## 3 & b) \ + { \ + return VecTraits::make(s op b.x, s op b.y, s op b.z); \ + } \ + __device__ __forceinline__ output_type ## 4 operator op(const input_type ## 4 & a, scalar_type s) \ + { \ + return VecTraits::make(a.x op s, a.y op s, a.z op s, a.w op s); \ + } \ + __device__ __forceinline__ output_type ## 4 operator op(scalar_type s, const input_type ## 4 & b) \ + { \ + return VecTraits::make(s op b.x, s op b.y, s op b.z, s op b.w); \ + } + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uchar, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uchar, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uchar, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, char, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, char, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, char, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, ushort, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, ushort, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, ushort, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, short, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, short, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, short, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, int, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, int, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, int, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uint, uint, uint) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uint, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uint, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, float, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, float, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, double, double, double) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uchar, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uchar, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uchar, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, char, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, char, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, char, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, ushort, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, ushort, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, ushort, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, short, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, short, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, short, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, int, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, int, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, int, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uint, uint, uint) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uint, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uint, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, float, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, float, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, double, double, double) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uchar, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uchar, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uchar, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, char, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, char, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, char, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, ushort, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, ushort, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, ushort, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, short, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, short, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, short, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, int, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, int, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, int, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uint, uint, uint) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uint, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uint, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, float, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, float, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, double, double, double) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uchar, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uchar, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uchar, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, char, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, char, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, char, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, ushort, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, ushort, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, ushort, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, short, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, short, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, short, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, int, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, int, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, int, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uint, uint, uint) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uint, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uint, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, float, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, float, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, double, double, double) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, char, char, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, ushort, ushort, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, short, short, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, int, int, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, uint, uint, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, float, float, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, double, double, uchar) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, char, char, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, ushort, ushort, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, short, short, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, int, int, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, uint, uint, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, float, float, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, double, double, uchar) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, char, char, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, ushort, ushort, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, short, short, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, int, int, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, uint, uint, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, float, float, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, double, double, uchar) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, char, char, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, ushort, ushort, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, short, short, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, int, int, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, uint, uint, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, float, float, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, double, double, uchar) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, char, char, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, ushort, ushort, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, short, short, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, int, int, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, uint, uint, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, float, float, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, double, double, uchar) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, char, char, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, ushort, ushort, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, short, short, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, int, int, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, uint, uint, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, float, float, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, double, double, uchar) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, char, char, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, ushort, ushort, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, short, short, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, int, int, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, uint, uint, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, float, float, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, double, double, uchar) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, char, char, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, ushort, ushort, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, short, short, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, int, int, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, uint, uint, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, float, float, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, double, double, uchar) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, char, char, char) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, ushort, ushort, ushort) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, short, short, short) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, int, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, uint, uint, uint) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, char, char, char) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, ushort, ushort, ushort) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, short, short, short) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, int, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, uint, uint, uint) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, char, char, char) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, ushort, ushort, ushort) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, short, short, short) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, int, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, uint, uint, uint) + +#undef CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP + +// binary function (vec & vec) + +#define CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(func_name, func, input_type, output_type) \ + __device__ __forceinline__ output_type ## 1 func_name(const input_type ## 1 & a, const input_type ## 1 & b) \ + { \ + return VecTraits::make(func (a.x, b.x)); \ + } \ + __device__ __forceinline__ output_type ## 2 func_name(const input_type ## 2 & a, const input_type ## 2 & b) \ + { \ + return VecTraits::make(func (a.x, b.x), func (a.y, b.y)); \ + } \ + __device__ __forceinline__ output_type ## 3 func_name(const input_type ## 3 & a, const input_type ## 3 & b) \ + { \ + return VecTraits::make(func (a.x, b.x), func (a.y, b.y), func (a.z, b.z)); \ + } \ + __device__ __forceinline__ output_type ## 4 func_name(const input_type ## 4 & a, const input_type ## 4 & b) \ + { \ + return VecTraits::make(func (a.x, b.x), func (a.y, b.y), func (a.z, b.z), func (a.w, b.w)); \ + } + +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, char, char) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, ushort, ushort) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, short, short) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, uint, uint) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, int, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::fmaxf, float, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::fmax, double, double) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, char, char) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, ushort, ushort) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, short, short) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, uint, uint) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, int, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::fminf, float, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::fmin, double, double) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, char, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, short, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, int, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, float, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypot, double, double) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, char, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, short, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, uint, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, int, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, float, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2, double, double) + +#undef CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC + +// binary function (vec & scalar) + +#define CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(func_name, func, input_type, scalar_type, output_type) \ + __device__ __forceinline__ output_type ## 1 func_name(const input_type ## 1 & a, scalar_type s) \ + { \ + return VecTraits::make(func ((output_type) a.x, (output_type) s)); \ + } \ + __device__ __forceinline__ output_type ## 1 func_name(scalar_type s, const input_type ## 1 & b) \ + { \ + return VecTraits::make(func ((output_type) s, (output_type) b.x)); \ + } \ + __device__ __forceinline__ output_type ## 2 func_name(const input_type ## 2 & a, scalar_type s) \ + { \ + return VecTraits::make(func ((output_type) a.x, (output_type) s), func ((output_type) a.y, (output_type) s)); \ + } \ + __device__ __forceinline__ output_type ## 2 func_name(scalar_type s, const input_type ## 2 & b) \ + { \ + return VecTraits::make(func ((output_type) s, (output_type) b.x), func ((output_type) s, (output_type) b.y)); \ + } \ + __device__ __forceinline__ output_type ## 3 func_name(const input_type ## 3 & a, scalar_type s) \ + { \ + return VecTraits::make(func ((output_type) a.x, (output_type) s), func ((output_type) a.y, (output_type) s), func ((output_type) a.z, (output_type) s)); \ + } \ + __device__ __forceinline__ output_type ## 3 func_name(scalar_type s, const input_type ## 3 & b) \ + { \ + return VecTraits::make(func ((output_type) s, (output_type) b.x), func ((output_type) s, (output_type) b.y), func ((output_type) s, (output_type) b.z)); \ + } \ + __device__ __forceinline__ output_type ## 4 func_name(const input_type ## 4 & a, scalar_type s) \ + { \ + return VecTraits::make(func ((output_type) a.x, (output_type) s), func ((output_type) a.y, (output_type) s), func ((output_type) a.z, (output_type) s), func ((output_type) a.w, (output_type) s)); \ + } \ + __device__ __forceinline__ output_type ## 4 func_name(scalar_type s, const input_type ## 4 & b) \ + { \ + return VecTraits::make(func ((output_type) s, (output_type) b.x), func ((output_type) s, (output_type) b.y), func ((output_type) s, (output_type) b.z), func ((output_type) s, (output_type) b.w)); \ + } + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, uchar, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, uchar, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, char, char, char) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, char, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, char, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, ushort, ushort, ushort) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, ushort, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, ushort, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, short, short, short) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, short, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, short, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, uint, uint, uint) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, uint, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, uint, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, int, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, int, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, int, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, float, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, float, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, double, double, double) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, uchar, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, uchar, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, char, char, char) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, char, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, char, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, ushort, ushort, ushort) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, ushort, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, ushort, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, short, short, short) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, short, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, short, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, uint, uint, uint) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, uint, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, uint, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, int, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, int, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, int, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, float, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, float, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, double, double, double) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, uchar, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, uchar, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, char, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, char, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, ushort, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, ushort, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, short, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, short, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, uint, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, uint, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, int, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, int, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, float, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, float, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, double, double, double) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, uchar, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, uchar, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, char, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, char, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, ushort, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, ushort, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, short, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, short, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, uint, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, uint, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, int, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, int, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, float, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, float, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, double, double, double) + +#undef CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC + +}}} // namespace cv { namespace cuda { namespace device + +//! @endcond + +#endif // OPENCV_CUDA_VECMATH_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/vec_traits.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/vec_traits.hpp new file mode 100644 index 0000000000000000000000000000000000000000..b5ff281a0b8a3b7c5a6a481fa7880b7f24e4786f --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/vec_traits.hpp @@ -0,0 +1,288 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_VEC_TRAITS_HPP +#define OPENCV_CUDA_VEC_TRAITS_HPP + +#include "common.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + template struct TypeVec; + + struct __align__(8) uchar8 + { + uchar a0, a1, a2, a3, a4, a5, a6, a7; + }; + static __host__ __device__ __forceinline__ uchar8 make_uchar8(uchar a0, uchar a1, uchar a2, uchar a3, uchar a4, uchar a5, uchar a6, uchar a7) + { + uchar8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; + return val; + } + struct __align__(8) char8 + { + schar a0, a1, a2, a3, a4, a5, a6, a7; + }; + static __host__ __device__ __forceinline__ char8 make_char8(schar a0, schar a1, schar a2, schar a3, schar a4, schar a5, schar a6, schar a7) + { + char8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; + return val; + } + struct __align__(16) ushort8 + { + ushort a0, a1, a2, a3, a4, a5, a6, a7; + }; + static __host__ __device__ __forceinline__ ushort8 make_ushort8(ushort a0, ushort a1, ushort a2, ushort a3, ushort a4, ushort a5, ushort a6, ushort a7) + { + ushort8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; + return val; + } + struct __align__(16) short8 + { + short a0, a1, a2, a3, a4, a5, a6, a7; + }; + static __host__ __device__ __forceinline__ short8 make_short8(short a0, short a1, short a2, short a3, short a4, short a5, short a6, short a7) + { + short8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; + return val; + } + struct __align__(32) uint8 + { + uint a0, a1, a2, a3, a4, a5, a6, a7; + }; + static __host__ __device__ __forceinline__ uint8 make_uint8(uint a0, uint a1, uint a2, uint a3, uint a4, uint a5, uint a6, uint a7) + { + uint8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; + return val; + } + struct __align__(32) int8 + { + int a0, a1, a2, a3, a4, a5, a6, a7; + }; + static __host__ __device__ __forceinline__ int8 make_int8(int a0, int a1, int a2, int a3, int a4, int a5, int a6, int a7) + { + int8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; + return val; + } + struct __align__(32) float8 + { + float a0, a1, a2, a3, a4, a5, a6, a7; + }; + static __host__ __device__ __forceinline__ float8 make_float8(float a0, float a1, float a2, float a3, float a4, float a5, float a6, float a7) + { + float8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; + return val; + } + struct double8 + { + double a0, a1, a2, a3, a4, a5, a6, a7; + }; + static __host__ __device__ __forceinline__ double8 make_double8(double a0, double a1, double a2, double a3, double a4, double a5, double a6, double a7) + { + double8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; + return val; + } + +#define OPENCV_CUDA_IMPLEMENT_TYPE_VEC(type) \ + template<> struct TypeVec { typedef type vec_type; }; \ + template<> struct TypeVec { typedef type ## 1 vec_type; }; \ + template<> struct TypeVec { typedef type ## 2 vec_type; }; \ + template<> struct TypeVec { typedef type ## 2 vec_type; }; \ + template<> struct TypeVec { typedef type ## 3 vec_type; }; \ + template<> struct TypeVec { typedef type ## 3 vec_type; }; \ + template<> struct TypeVec { typedef type ## 4 vec_type; }; \ + template<> struct TypeVec { typedef type ## 4 vec_type; }; \ + template<> struct TypeVec { typedef type ## 8 vec_type; }; \ + template<> struct TypeVec { typedef type ## 8 vec_type; }; + + OPENCV_CUDA_IMPLEMENT_TYPE_VEC(uchar) + OPENCV_CUDA_IMPLEMENT_TYPE_VEC(char) + OPENCV_CUDA_IMPLEMENT_TYPE_VEC(ushort) + OPENCV_CUDA_IMPLEMENT_TYPE_VEC(short) + OPENCV_CUDA_IMPLEMENT_TYPE_VEC(int) + OPENCV_CUDA_IMPLEMENT_TYPE_VEC(uint) + OPENCV_CUDA_IMPLEMENT_TYPE_VEC(float) + OPENCV_CUDA_IMPLEMENT_TYPE_VEC(double) + + #undef OPENCV_CUDA_IMPLEMENT_TYPE_VEC + + template<> struct TypeVec { typedef schar vec_type; }; + template<> struct TypeVec { typedef char2 vec_type; }; + template<> struct TypeVec { typedef char3 vec_type; }; + template<> struct TypeVec { typedef char4 vec_type; }; + template<> struct TypeVec { typedef char8 vec_type; }; + + template<> struct TypeVec { typedef uchar vec_type; }; + template<> struct TypeVec { typedef uchar2 vec_type; }; + template<> struct TypeVec { typedef uchar3 vec_type; }; + template<> struct TypeVec { typedef uchar4 vec_type; }; + template<> struct TypeVec { typedef uchar8 vec_type; }; + + template struct VecTraits; + +#define OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(type) \ + template<> struct VecTraits \ + { \ + typedef type elem_type; \ + enum {cn=1}; \ + static __device__ __host__ __forceinline__ type all(type v) {return v;} \ + static __device__ __host__ __forceinline__ type make(type x) {return x;} \ + static __device__ __host__ __forceinline__ type make(const type* v) {return *v;} \ + }; \ + template<> struct VecTraits \ + { \ + typedef type elem_type; \ + enum {cn=1}; \ + static __device__ __host__ __forceinline__ type ## 1 all(type v) {return make_ ## type ## 1(v);} \ + static __device__ __host__ __forceinline__ type ## 1 make(type x) {return make_ ## type ## 1(x);} \ + static __device__ __host__ __forceinline__ type ## 1 make(const type* v) {return make_ ## type ## 1(*v);} \ + }; \ + template<> struct VecTraits \ + { \ + typedef type elem_type; \ + enum {cn=2}; \ + static __device__ __host__ __forceinline__ type ## 2 all(type v) {return make_ ## type ## 2(v, v);} \ + static __device__ __host__ __forceinline__ type ## 2 make(type x, type y) {return make_ ## type ## 2(x, y);} \ + static __device__ __host__ __forceinline__ type ## 2 make(const type* v) {return make_ ## type ## 2(v[0], v[1]);} \ + }; \ + template<> struct VecTraits \ + { \ + typedef type elem_type; \ + enum {cn=3}; \ + static __device__ __host__ __forceinline__ type ## 3 all(type v) {return make_ ## type ## 3(v, v, v);} \ + static __device__ __host__ __forceinline__ type ## 3 make(type x, type y, type z) {return make_ ## type ## 3(x, y, z);} \ + static __device__ __host__ __forceinline__ type ## 3 make(const type* v) {return make_ ## type ## 3(v[0], v[1], v[2]);} \ + }; \ + template<> struct VecTraits \ + { \ + typedef type elem_type; \ + enum {cn=4}; \ + static __device__ __host__ __forceinline__ type ## 4 all(type v) {return make_ ## type ## 4(v, v, v, v);} \ + static __device__ __host__ __forceinline__ type ## 4 make(type x, type y, type z, type w) {return make_ ## type ## 4(x, y, z, w);} \ + static __device__ __host__ __forceinline__ type ## 4 make(const type* v) {return make_ ## type ## 4(v[0], v[1], v[2], v[3]);} \ + }; \ + template<> struct VecTraits \ + { \ + typedef type elem_type; \ + enum {cn=8}; \ + static __device__ __host__ __forceinline__ type ## 8 all(type v) {return make_ ## type ## 8(v, v, v, v, v, v, v, v);} \ + static __device__ __host__ __forceinline__ type ## 8 make(type a0, type a1, type a2, type a3, type a4, type a5, type a6, type a7) {return make_ ## type ## 8(a0, a1, a2, a3, a4, a5, a6, a7);} \ + static __device__ __host__ __forceinline__ type ## 8 make(const type* v) {return make_ ## type ## 8(v[0], v[1], v[2], v[3], v[4], v[5], v[6], v[7]);} \ + }; + + OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(uchar) + OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(ushort) + OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(short) + OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(int) + OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(uint) + OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(float) + OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(double) + + #undef OPENCV_CUDA_IMPLEMENT_VEC_TRAITS + + template<> struct VecTraits + { + typedef char elem_type; + enum {cn=1}; + static __device__ __host__ __forceinline__ char all(char v) {return v;} + static __device__ __host__ __forceinline__ char make(char x) {return x;} + static __device__ __host__ __forceinline__ char make(const char* x) {return *x;} + }; + template<> struct VecTraits + { + typedef schar elem_type; + enum {cn=1}; + static __device__ __host__ __forceinline__ schar all(schar v) {return v;} + static __device__ __host__ __forceinline__ schar make(schar x) {return x;} + static __device__ __host__ __forceinline__ schar make(const schar* x) {return *x;} + }; + template<> struct VecTraits + { + typedef schar elem_type; + enum {cn=1}; + static __device__ __host__ __forceinline__ char1 all(schar v) {return make_char1(v);} + static __device__ __host__ __forceinline__ char1 make(schar x) {return make_char1(x);} + static __device__ __host__ __forceinline__ char1 make(const schar* v) {return make_char1(v[0]);} + }; + template<> struct VecTraits + { + typedef schar elem_type; + enum {cn=2}; + static __device__ __host__ __forceinline__ char2 all(schar v) {return make_char2(v, v);} + static __device__ __host__ __forceinline__ char2 make(schar x, schar y) {return make_char2(x, y);} + static __device__ __host__ __forceinline__ char2 make(const schar* v) {return make_char2(v[0], v[1]);} + }; + template<> struct VecTraits + { + typedef schar elem_type; + enum {cn=3}; + static __device__ __host__ __forceinline__ char3 all(schar v) {return make_char3(v, v, v);} + static __device__ __host__ __forceinline__ char3 make(schar x, schar y, schar z) {return make_char3(x, y, z);} + static __device__ __host__ __forceinline__ char3 make(const schar* v) {return make_char3(v[0], v[1], v[2]);} + }; + template<> struct VecTraits + { + typedef schar elem_type; + enum {cn=4}; + static __device__ __host__ __forceinline__ char4 all(schar v) {return make_char4(v, v, v, v);} + static __device__ __host__ __forceinline__ char4 make(schar x, schar y, schar z, schar w) {return make_char4(x, y, z, w);} + static __device__ __host__ __forceinline__ char4 make(const schar* v) {return make_char4(v[0], v[1], v[2], v[3]);} + }; + template<> struct VecTraits + { + typedef schar elem_type; + enum {cn=8}; + static __device__ __host__ __forceinline__ char8 all(schar v) {return make_char8(v, v, v, v, v, v, v, v);} + static __device__ __host__ __forceinline__ char8 make(schar a0, schar a1, schar a2, schar a3, schar a4, schar a5, schar a6, schar a7) {return make_char8(a0, a1, a2, a3, a4, a5, a6, a7);} + static __device__ __host__ __forceinline__ char8 make(const schar* v) {return make_char8(v[0], v[1], v[2], v[3], v[4], v[5], v[6], v[7]);} + }; +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_VEC_TRAITS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/warp.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/warp.hpp new file mode 100644 index 0000000000000000000000000000000000000000..8af7e6a212afd45cc9ddd00c26010875f55d80bf --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/warp.hpp @@ -0,0 +1,139 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_DEVICE_WARP_HPP +#define OPENCV_CUDA_DEVICE_WARP_HPP + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + struct Warp + { + enum + { + LOG_WARP_SIZE = 5, + WARP_SIZE = 1 << LOG_WARP_SIZE, + STRIDE = WARP_SIZE + }; + + /** \brief Returns the warp lane ID of the calling thread. */ + static __device__ __forceinline__ unsigned int laneId() + { + unsigned int ret; + asm("mov.u32 %0, %%laneid;" : "=r"(ret) ); + return ret; + } + + template + static __device__ __forceinline__ void fill(It beg, It end, const T& value) + { + for(It t = beg + laneId(); t < end; t += STRIDE) + *t = value; + } + + template + static __device__ __forceinline__ OutIt copy(InIt beg, InIt end, OutIt out) + { + for(InIt t = beg + laneId(); t < end; t += STRIDE, out += STRIDE) + *out = *t; + return out; + } + + template + static __device__ __forceinline__ OutIt transform(InIt beg, InIt end, OutIt out, UnOp op) + { + for(InIt t = beg + laneId(); t < end; t += STRIDE, out += STRIDE) + *out = op(*t); + return out; + } + + template + static __device__ __forceinline__ OutIt transform(InIt1 beg1, InIt1 end1, InIt2 beg2, OutIt out, BinOp op) + { + unsigned int lane = laneId(); + + InIt1 t1 = beg1 + lane; + InIt2 t2 = beg2 + lane; + for(; t1 < end1; t1 += STRIDE, t2 += STRIDE, out += STRIDE) + *out = op(*t1, *t2); + return out; + } + + template + static __device__ __forceinline__ T reduce(volatile T *ptr, BinOp op) + { + const unsigned int lane = laneId(); + + if (lane < 16) + { + T partial = ptr[lane]; + + ptr[lane] = partial = op(partial, ptr[lane + 16]); + ptr[lane] = partial = op(partial, ptr[lane + 8]); + ptr[lane] = partial = op(partial, ptr[lane + 4]); + ptr[lane] = partial = op(partial, ptr[lane + 2]); + ptr[lane] = partial = op(partial, ptr[lane + 1]); + } + + return *ptr; + } + + template + static __device__ __forceinline__ void yota(OutIt beg, OutIt end, T value) + { + unsigned int lane = laneId(); + value += lane; + + for(OutIt t = beg + lane; t < end; t += STRIDE, value += STRIDE) + *t = value; + } + }; +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif /* OPENCV_CUDA_DEVICE_WARP_HPP */ diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/warp_reduce.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/warp_reduce.hpp new file mode 100644 index 0000000000000000000000000000000000000000..530303d2495fe878340b80bf1a4b330ed7aadc5f --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/warp_reduce.hpp @@ -0,0 +1,76 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_WARP_REDUCE_HPP__ +#define OPENCV_CUDA_WARP_REDUCE_HPP__ + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + template + __device__ __forceinline__ T warp_reduce(volatile T *ptr , const unsigned int tid = threadIdx.x) + { + const unsigned int lane = tid & 31; // index of thread in warp (0..31) + + if (lane < 16) + { + T partial = ptr[tid]; + + ptr[tid] = partial = partial + ptr[tid + 16]; + ptr[tid] = partial = partial + ptr[tid + 8]; + ptr[tid] = partial = partial + ptr[tid + 4]; + ptr[tid] = partial = partial + ptr[tid + 2]; + ptr[tid] = partial = partial + ptr[tid + 1]; + } + + return ptr[tid - lane]; + } +}}} // namespace cv { namespace cuda { namespace cudev { + +//! @endcond + +#endif /* OPENCV_CUDA_WARP_REDUCE_HPP__ */ diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/warp_shuffle.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/warp_shuffle.hpp new file mode 100644 index 0000000000000000000000000000000000000000..0da54aee99bf82f0946b64ed39d3bb9bceaf5d7b --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda/warp_shuffle.hpp @@ -0,0 +1,162 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_WARP_SHUFFLE_HPP +#define OPENCV_CUDA_WARP_SHUFFLE_HPP + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ +#if __CUDACC_VER_MAJOR__ >= 9 +# define __shfl(x, y, z) __shfl_sync(0xFFFFFFFFU, x, y, z) +# define __shfl_up(x, y, z) __shfl_up_sync(0xFFFFFFFFU, x, y, z) +# define __shfl_down(x, y, z) __shfl_down_sync(0xFFFFFFFFU, x, y, z) +#endif + template + __device__ __forceinline__ T shfl(T val, int srcLane, int width = warpSize) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + return __shfl(val, srcLane, width); + #else + return T(); + #endif + } + __device__ __forceinline__ unsigned int shfl(unsigned int val, int srcLane, int width = warpSize) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + return (unsigned int) __shfl((int) val, srcLane, width); + #else + return 0; + #endif + } + __device__ __forceinline__ double shfl(double val, int srcLane, int width = warpSize) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + int lo = __double2loint(val); + int hi = __double2hiint(val); + + lo = __shfl(lo, srcLane, width); + hi = __shfl(hi, srcLane, width); + + return __hiloint2double(hi, lo); + #else + return 0.0; + #endif + } + + template + __device__ __forceinline__ T shfl_down(T val, unsigned int delta, int width = warpSize) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + return __shfl_down(val, delta, width); + #else + return T(); + #endif + } + __device__ __forceinline__ unsigned int shfl_down(unsigned int val, unsigned int delta, int width = warpSize) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + return (unsigned int) __shfl_down((int) val, delta, width); + #else + return 0; + #endif + } + __device__ __forceinline__ double shfl_down(double val, unsigned int delta, int width = warpSize) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + int lo = __double2loint(val); + int hi = __double2hiint(val); + + lo = __shfl_down(lo, delta, width); + hi = __shfl_down(hi, delta, width); + + return __hiloint2double(hi, lo); + #else + return 0.0; + #endif + } + + template + __device__ __forceinline__ T shfl_up(T val, unsigned int delta, int width = warpSize) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + return __shfl_up(val, delta, width); + #else + return T(); + #endif + } + __device__ __forceinline__ unsigned int shfl_up(unsigned int val, unsigned int delta, int width = warpSize) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + return (unsigned int) __shfl_up((int) val, delta, width); + #else + return 0; + #endif + } + __device__ __forceinline__ double shfl_up(double val, unsigned int delta, int width = warpSize) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + int lo = __double2loint(val); + int hi = __double2hiint(val); + + lo = __shfl_up(lo, delta, width); + hi = __shfl_up(hi, delta, width); + + return __hiloint2double(hi, lo); + #else + return 0.0; + #endif + } +}}} + +# undef __shfl +# undef __shfl_up +# undef __shfl_down + +//! @endcond + +#endif // OPENCV_CUDA_WARP_SHUFFLE_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda_stream_accessor.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda_stream_accessor.hpp new file mode 100644 index 0000000000000000000000000000000000000000..deaf356fff4311ffddcc388ee28106d0b5459f3f --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda_stream_accessor.hpp @@ -0,0 +1,86 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_CUDA_STREAM_ACCESSOR_HPP +#define OPENCV_CORE_CUDA_STREAM_ACCESSOR_HPP + +#ifndef __cplusplus +# error cuda_stream_accessor.hpp header must be compiled as C++ +#endif + +/** @file cuda_stream_accessor.hpp + * This is only header file that depends on CUDA Runtime API. All other headers are independent. + */ + +#include +#include "opencv2/core/cuda.hpp" + +namespace cv +{ + namespace cuda + { + +//! @addtogroup cudacore_struct +//! @{ + + /** @brief Class that enables getting cudaStream_t from cuda::Stream + */ + struct StreamAccessor + { + CV_EXPORTS static cudaStream_t getStream(const Stream& stream); + CV_EXPORTS static Stream wrapStream(cudaStream_t stream); + }; + + /** @brief Class that enables getting cudaEvent_t from cuda::Event + */ + struct EventAccessor + { + CV_EXPORTS static cudaEvent_t getEvent(const Event& event); + CV_EXPORTS static Event wrapEvent(cudaEvent_t event); + }; + +//! @} + + } +} + +#endif /* OPENCV_CORE_CUDA_STREAM_ACCESSOR_HPP */ diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda_types.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda_types.hpp new file mode 100644 index 0000000000000000000000000000000000000000..e2647c0455b1e501cf96b7badee7cfc6c5e18eff --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cuda_types.hpp @@ -0,0 +1,142 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_CUDA_TYPES_HPP +#define OPENCV_CORE_CUDA_TYPES_HPP + +#ifndef __cplusplus +# error cuda_types.hpp header must be compiled as C++ +#endif + +#if defined(__OPENCV_BUILD) && defined(__clang__) +#pragma clang diagnostic ignored "-Winconsistent-missing-override" +#endif +#if defined(__OPENCV_BUILD) && defined(__GNUC__) && __GNUC__ >= 5 +#pragma GCC diagnostic ignored "-Wsuggest-override" +#endif + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +#ifdef __CUDACC__ + #define __CV_CUDA_HOST_DEVICE__ __host__ __device__ __forceinline__ +#else + #define __CV_CUDA_HOST_DEVICE__ +#endif + +namespace cv +{ + namespace cuda + { + + // Simple lightweight structures that encapsulates information about an image on device. + // It is intended to pass to nvcc-compiled code. GpuMat depends on headers that nvcc can't compile + + template struct DevPtr + { + typedef T elem_type; + typedef int index_type; + + enum { elem_size = sizeof(elem_type) }; + + T* data; + + __CV_CUDA_HOST_DEVICE__ DevPtr() : data(0) {} + __CV_CUDA_HOST_DEVICE__ DevPtr(T* data_) : data(data_) {} + + __CV_CUDA_HOST_DEVICE__ size_t elemSize() const { return elem_size; } + __CV_CUDA_HOST_DEVICE__ operator T*() { return data; } + __CV_CUDA_HOST_DEVICE__ operator const T*() const { return data; } + }; + + template struct PtrSz : public DevPtr + { + __CV_CUDA_HOST_DEVICE__ PtrSz() : size(0) {} + __CV_CUDA_HOST_DEVICE__ PtrSz(T* data_, size_t size_) : DevPtr(data_), size(size_) {} + + size_t size; + }; + + template struct PtrStep : public DevPtr + { + __CV_CUDA_HOST_DEVICE__ PtrStep() : step(0) {} + __CV_CUDA_HOST_DEVICE__ PtrStep(T* data_, size_t step_) : DevPtr(data_), step(step_) {} + + size_t step; + + __CV_CUDA_HOST_DEVICE__ T* ptr(int y = 0) { return ( T*)( ( char*)DevPtr::data + y * step); } + __CV_CUDA_HOST_DEVICE__ const T* ptr(int y = 0) const { return (const T*)( (const char*)DevPtr::data + y * step); } + + __CV_CUDA_HOST_DEVICE__ T& operator ()(int y, int x) { return ptr(y)[x]; } + __CV_CUDA_HOST_DEVICE__ const T& operator ()(int y, int x) const { return ptr(y)[x]; } + }; + + template struct PtrStepSz : public PtrStep + { + __CV_CUDA_HOST_DEVICE__ PtrStepSz() : cols(0), rows(0) {} + __CV_CUDA_HOST_DEVICE__ PtrStepSz(int rows_, int cols_, T* data_, size_t step_) + : PtrStep(data_, step_), cols(cols_), rows(rows_) {} + + template + explicit PtrStepSz(const PtrStepSz& d) : PtrStep((T*)d.data, d.step), cols(d.cols), rows(d.rows){} + + int cols; + int rows; + }; + + typedef PtrStepSz PtrStepSzb; + typedef PtrStepSz PtrStepSzf; + typedef PtrStepSz PtrStepSzi; + + typedef PtrStep PtrStepb; + typedef PtrStep PtrStepf; + typedef PtrStep PtrStepi; + + } +} + +//! @endcond + +#endif /* OPENCV_CORE_CUDA_TYPES_HPP */ diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cv_cpu_dispatch.h b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cv_cpu_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..57aa0ce2fb2b4978b59dbab2b6332f11b37128f6 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cv_cpu_dispatch.h @@ -0,0 +1,247 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#if defined __OPENCV_BUILD \ + +#include "cv_cpu_config.h" +#include "cv_cpu_helper.h" + +#ifdef CV_CPU_DISPATCH_MODE +#define CV_CPU_OPTIMIZATION_NAMESPACE __CV_CAT(opt_, CV_CPU_DISPATCH_MODE) +#define CV_CPU_OPTIMIZATION_NAMESPACE_BEGIN namespace __CV_CAT(opt_, CV_CPU_DISPATCH_MODE) { +#define CV_CPU_OPTIMIZATION_NAMESPACE_END } +#else +#define CV_CPU_OPTIMIZATION_NAMESPACE cpu_baseline +#define CV_CPU_OPTIMIZATION_NAMESPACE_BEGIN namespace cpu_baseline { +#define CV_CPU_OPTIMIZATION_NAMESPACE_END } +#endif + + +#define __CV_CPU_DISPATCH_CHAIN_END(fn, args, mode, ...) /* done */ +#define __CV_CPU_DISPATCH(fn, args, mode, ...) __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) +#define __CV_CPU_DISPATCH_EXPAND(fn, args, ...) __CV_EXPAND(__CV_CPU_DISPATCH(fn, args, __VA_ARGS__)) +#define CV_CPU_DISPATCH(fn, args, ...) __CV_CPU_DISPATCH_EXPAND(fn, args, __VA_ARGS__, END) // expand macros + + +#if defined CV_ENABLE_INTRINSICS \ + && !defined CV_DISABLE_OPTIMIZATION \ + && !defined __CUDACC__ /* do not include SSE/AVX/NEON headers for NVCC compiler */ \ + +#ifdef CV_CPU_COMPILE_SSE2 +# include +# define CV_MMX 1 +# define CV_SSE 1 +# define CV_SSE2 1 +#endif +#ifdef CV_CPU_COMPILE_SSE3 +# include +# define CV_SSE3 1 +#endif +#ifdef CV_CPU_COMPILE_SSSE3 +# include +# define CV_SSSE3 1 +#endif +#ifdef CV_CPU_COMPILE_SSE4_1 +# include +# define CV_SSE4_1 1 +#endif +#ifdef CV_CPU_COMPILE_SSE4_2 +# include +# define CV_SSE4_2 1 +#endif +#ifdef CV_CPU_COMPILE_POPCNT +# ifdef _MSC_VER +# include +# if defined(_M_X64) +# define CV_POPCNT_U64 _mm_popcnt_u64 +# endif +# define CV_POPCNT_U32 _mm_popcnt_u32 +# else +# include +# if defined(__x86_64__) +# define CV_POPCNT_U64 __builtin_popcountll +# endif +# define CV_POPCNT_U32 __builtin_popcount +# endif +# define CV_POPCNT 1 +#endif +#ifdef CV_CPU_COMPILE_AVX +# include +# define CV_AVX 1 +#endif +#ifdef CV_CPU_COMPILE_FP16 +# if defined(__arm__) || defined(__aarch64__) || defined(_M_ARM) +# include +# else +# include +# endif +# define CV_FP16 1 +#endif +#ifdef CV_CPU_COMPILE_AVX2 +# include +# define CV_AVX2 1 +#endif +#ifdef CV_CPU_COMPILE_AVX_512F +# include +# define CV_AVX_512F 1 +#endif +#ifdef CV_CPU_COMPILE_AVX512_SKX +# include +# define CV_AVX512_SKX 1 +#endif +#ifdef CV_CPU_COMPILE_FMA3 +# define CV_FMA3 1 +#endif + +#if defined _WIN32 && defined(_M_ARM) +# include +# include +# define CV_NEON 1 +#elif defined(__ARM_NEON__) || (defined (__ARM_NEON) && defined(__aarch64__)) +# include +# define CV_NEON 1 +#endif + +#if defined(__ARM_NEON__) || defined(__aarch64__) +# include +#endif + +#ifdef CV_CPU_COMPILE_VSX +# include +# undef vector +# undef pixel +# undef bool +# define CV_VSX 1 +#endif + +#ifdef CV_CPU_COMPILE_VSX3 +# define CV_VSX3 1 +#endif + +#endif // CV_ENABLE_INTRINSICS && !CV_DISABLE_OPTIMIZATION && !__CUDACC__ + +#if defined CV_CPU_COMPILE_AVX && !defined CV_CPU_BASELINE_COMPILE_AVX +struct VZeroUpperGuard { +#ifdef __GNUC__ + __attribute__((always_inline)) +#endif + inline ~VZeroUpperGuard() { _mm256_zeroupper(); } +}; +#define __CV_AVX_GUARD VZeroUpperGuard __vzeroupper_guard; CV_UNUSED(__vzeroupper_guard); +#endif + +#ifdef __CV_AVX_GUARD +#define CV_AVX_GUARD __CV_AVX_GUARD +#else +#define CV_AVX_GUARD +#endif + +#endif // __OPENCV_BUILD + + + +#if !defined __OPENCV_BUILD /* Compatibility code */ \ + && !defined __CUDACC__ /* do not include SSE/AVX/NEON headers for NVCC compiler */ +#if defined __SSE2__ || defined _M_X64 || (defined _M_IX86_FP && _M_IX86_FP >= 2) +# include +# define CV_MMX 1 +# define CV_SSE 1 +# define CV_SSE2 1 +#elif defined _WIN32 && defined(_M_ARM) +# include +# include +# define CV_NEON 1 +#elif defined(__ARM_NEON__) || (defined (__ARM_NEON) && defined(__aarch64__)) +# include +# define CV_NEON 1 +#elif defined(__VSX__) && defined(__PPC64__) && defined(__LITTLE_ENDIAN__) +# include +# undef vector +# undef pixel +# undef bool +# define CV_VSX 1 +#endif + +#endif // !__OPENCV_BUILD && !__CUDACC (Compatibility code) + + + +#ifndef CV_MMX +# define CV_MMX 0 +#endif +#ifndef CV_SSE +# define CV_SSE 0 +#endif +#ifndef CV_SSE2 +# define CV_SSE2 0 +#endif +#ifndef CV_SSE3 +# define CV_SSE3 0 +#endif +#ifndef CV_SSSE3 +# define CV_SSSE3 0 +#endif +#ifndef CV_SSE4_1 +# define CV_SSE4_1 0 +#endif +#ifndef CV_SSE4_2 +# define CV_SSE4_2 0 +#endif +#ifndef CV_POPCNT +# define CV_POPCNT 0 +#endif +#ifndef CV_AVX +# define CV_AVX 0 +#endif +#ifndef CV_FP16 +# define CV_FP16 0 +#endif +#ifndef CV_AVX2 +# define CV_AVX2 0 +#endif +#ifndef CV_FMA3 +# define CV_FMA3 0 +#endif +#ifndef CV_AVX_512F +# define CV_AVX_512F 0 +#endif +#ifndef CV_AVX_512BW +# define CV_AVX_512BW 0 +#endif +#ifndef CV_AVX_512CD +# define CV_AVX_512CD 0 +#endif +#ifndef CV_AVX_512DQ +# define CV_AVX_512DQ 0 +#endif +#ifndef CV_AVX_512ER +# define CV_AVX_512ER 0 +#endif +#ifndef CV_AVX_512IFMA512 +# define CV_AVX_512IFMA512 0 +#endif +#ifndef CV_AVX_512PF +# define CV_AVX_512PF 0 +#endif +#ifndef CV_AVX_512VBMI +# define CV_AVX_512VBMI 0 +#endif +#ifndef CV_AVX_512VL +# define CV_AVX_512VL 0 +#endif +#ifndef CV_AVX512_SKX +# define CV_AVX512_SKX 0 +#endif + +#ifndef CV_NEON +# define CV_NEON 0 +#endif + +#ifndef CV_VSX +# define CV_VSX 0 +#endif + +#ifndef CV_VSX3 +# define CV_VSX3 0 +#endif diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cv_cpu_helper.h b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cv_cpu_helper.h new file mode 100644 index 0000000000000000000000000000000000000000..ad1339796d8b4cccd8cd794ed4eb1d2ea59a9f4f --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cv_cpu_helper.h @@ -0,0 +1,340 @@ +// AUTOGENERATED, DO NOT EDIT + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSE +# define CV_TRY_SSE 1 +# define CV_CPU_FORCE_SSE 1 +# define CV_CPU_HAS_SUPPORT_SSE 1 +# define CV_CPU_CALL_SSE(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_SSE_(fn, args) return (opt_SSE::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSE +# define CV_TRY_SSE 1 +# define CV_CPU_FORCE_SSE 0 +# define CV_CPU_HAS_SUPPORT_SSE (cv::checkHardwareSupport(CV_CPU_SSE)) +# define CV_CPU_CALL_SSE(fn, args) if (CV_CPU_HAS_SUPPORT_SSE) return (opt_SSE::fn args) +# define CV_CPU_CALL_SSE_(fn, args) if (CV_CPU_HAS_SUPPORT_SSE) return (opt_SSE::fn args) +#else +# define CV_TRY_SSE 0 +# define CV_CPU_FORCE_SSE 0 +# define CV_CPU_HAS_SUPPORT_SSE 0 +# define CV_CPU_CALL_SSE(fn, args) +# define CV_CPU_CALL_SSE_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_SSE(fn, args, mode, ...) CV_CPU_CALL_SSE(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSE2 +# define CV_TRY_SSE2 1 +# define CV_CPU_FORCE_SSE2 1 +# define CV_CPU_HAS_SUPPORT_SSE2 1 +# define CV_CPU_CALL_SSE2(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_SSE2_(fn, args) return (opt_SSE2::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSE2 +# define CV_TRY_SSE2 1 +# define CV_CPU_FORCE_SSE2 0 +# define CV_CPU_HAS_SUPPORT_SSE2 (cv::checkHardwareSupport(CV_CPU_SSE2)) +# define CV_CPU_CALL_SSE2(fn, args) if (CV_CPU_HAS_SUPPORT_SSE2) return (opt_SSE2::fn args) +# define CV_CPU_CALL_SSE2_(fn, args) if (CV_CPU_HAS_SUPPORT_SSE2) return (opt_SSE2::fn args) +#else +# define CV_TRY_SSE2 0 +# define CV_CPU_FORCE_SSE2 0 +# define CV_CPU_HAS_SUPPORT_SSE2 0 +# define CV_CPU_CALL_SSE2(fn, args) +# define CV_CPU_CALL_SSE2_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_SSE2(fn, args, mode, ...) CV_CPU_CALL_SSE2(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSE3 +# define CV_TRY_SSE3 1 +# define CV_CPU_FORCE_SSE3 1 +# define CV_CPU_HAS_SUPPORT_SSE3 1 +# define CV_CPU_CALL_SSE3(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_SSE3_(fn, args) return (opt_SSE3::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSE3 +# define CV_TRY_SSE3 1 +# define CV_CPU_FORCE_SSE3 0 +# define CV_CPU_HAS_SUPPORT_SSE3 (cv::checkHardwareSupport(CV_CPU_SSE3)) +# define CV_CPU_CALL_SSE3(fn, args) if (CV_CPU_HAS_SUPPORT_SSE3) return (opt_SSE3::fn args) +# define CV_CPU_CALL_SSE3_(fn, args) if (CV_CPU_HAS_SUPPORT_SSE3) return (opt_SSE3::fn args) +#else +# define CV_TRY_SSE3 0 +# define CV_CPU_FORCE_SSE3 0 +# define CV_CPU_HAS_SUPPORT_SSE3 0 +# define CV_CPU_CALL_SSE3(fn, args) +# define CV_CPU_CALL_SSE3_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_SSE3(fn, args, mode, ...) CV_CPU_CALL_SSE3(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSSE3 +# define CV_TRY_SSSE3 1 +# define CV_CPU_FORCE_SSSE3 1 +# define CV_CPU_HAS_SUPPORT_SSSE3 1 +# define CV_CPU_CALL_SSSE3(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_SSSE3_(fn, args) return (opt_SSSE3::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSSE3 +# define CV_TRY_SSSE3 1 +# define CV_CPU_FORCE_SSSE3 0 +# define CV_CPU_HAS_SUPPORT_SSSE3 (cv::checkHardwareSupport(CV_CPU_SSSE3)) +# define CV_CPU_CALL_SSSE3(fn, args) if (CV_CPU_HAS_SUPPORT_SSSE3) return (opt_SSSE3::fn args) +# define CV_CPU_CALL_SSSE3_(fn, args) if (CV_CPU_HAS_SUPPORT_SSSE3) return (opt_SSSE3::fn args) +#else +# define CV_TRY_SSSE3 0 +# define CV_CPU_FORCE_SSSE3 0 +# define CV_CPU_HAS_SUPPORT_SSSE3 0 +# define CV_CPU_CALL_SSSE3(fn, args) +# define CV_CPU_CALL_SSSE3_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_SSSE3(fn, args, mode, ...) CV_CPU_CALL_SSSE3(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSE4_1 +# define CV_TRY_SSE4_1 1 +# define CV_CPU_FORCE_SSE4_1 1 +# define CV_CPU_HAS_SUPPORT_SSE4_1 1 +# define CV_CPU_CALL_SSE4_1(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_SSE4_1_(fn, args) return (opt_SSE4_1::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSE4_1 +# define CV_TRY_SSE4_1 1 +# define CV_CPU_FORCE_SSE4_1 0 +# define CV_CPU_HAS_SUPPORT_SSE4_1 (cv::checkHardwareSupport(CV_CPU_SSE4_1)) +# define CV_CPU_CALL_SSE4_1(fn, args) if (CV_CPU_HAS_SUPPORT_SSE4_1) return (opt_SSE4_1::fn args) +# define CV_CPU_CALL_SSE4_1_(fn, args) if (CV_CPU_HAS_SUPPORT_SSE4_1) return (opt_SSE4_1::fn args) +#else +# define CV_TRY_SSE4_1 0 +# define CV_CPU_FORCE_SSE4_1 0 +# define CV_CPU_HAS_SUPPORT_SSE4_1 0 +# define CV_CPU_CALL_SSE4_1(fn, args) +# define CV_CPU_CALL_SSE4_1_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_SSE4_1(fn, args, mode, ...) CV_CPU_CALL_SSE4_1(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSE4_2 +# define CV_TRY_SSE4_2 1 +# define CV_CPU_FORCE_SSE4_2 1 +# define CV_CPU_HAS_SUPPORT_SSE4_2 1 +# define CV_CPU_CALL_SSE4_2(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_SSE4_2_(fn, args) return (opt_SSE4_2::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSE4_2 +# define CV_TRY_SSE4_2 1 +# define CV_CPU_FORCE_SSE4_2 0 +# define CV_CPU_HAS_SUPPORT_SSE4_2 (cv::checkHardwareSupport(CV_CPU_SSE4_2)) +# define CV_CPU_CALL_SSE4_2(fn, args) if (CV_CPU_HAS_SUPPORT_SSE4_2) return (opt_SSE4_2::fn args) +# define CV_CPU_CALL_SSE4_2_(fn, args) if (CV_CPU_HAS_SUPPORT_SSE4_2) return (opt_SSE4_2::fn args) +#else +# define CV_TRY_SSE4_2 0 +# define CV_CPU_FORCE_SSE4_2 0 +# define CV_CPU_HAS_SUPPORT_SSE4_2 0 +# define CV_CPU_CALL_SSE4_2(fn, args) +# define CV_CPU_CALL_SSE4_2_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_SSE4_2(fn, args, mode, ...) CV_CPU_CALL_SSE4_2(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_POPCNT +# define CV_TRY_POPCNT 1 +# define CV_CPU_FORCE_POPCNT 1 +# define CV_CPU_HAS_SUPPORT_POPCNT 1 +# define CV_CPU_CALL_POPCNT(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_POPCNT_(fn, args) return (opt_POPCNT::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_POPCNT +# define CV_TRY_POPCNT 1 +# define CV_CPU_FORCE_POPCNT 0 +# define CV_CPU_HAS_SUPPORT_POPCNT (cv::checkHardwareSupport(CV_CPU_POPCNT)) +# define CV_CPU_CALL_POPCNT(fn, args) if (CV_CPU_HAS_SUPPORT_POPCNT) return (opt_POPCNT::fn args) +# define CV_CPU_CALL_POPCNT_(fn, args) if (CV_CPU_HAS_SUPPORT_POPCNT) return (opt_POPCNT::fn args) +#else +# define CV_TRY_POPCNT 0 +# define CV_CPU_FORCE_POPCNT 0 +# define CV_CPU_HAS_SUPPORT_POPCNT 0 +# define CV_CPU_CALL_POPCNT(fn, args) +# define CV_CPU_CALL_POPCNT_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_POPCNT(fn, args, mode, ...) CV_CPU_CALL_POPCNT(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX +# define CV_TRY_AVX 1 +# define CV_CPU_FORCE_AVX 1 +# define CV_CPU_HAS_SUPPORT_AVX 1 +# define CV_CPU_CALL_AVX(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_AVX_(fn, args) return (opt_AVX::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX +# define CV_TRY_AVX 1 +# define CV_CPU_FORCE_AVX 0 +# define CV_CPU_HAS_SUPPORT_AVX (cv::checkHardwareSupport(CV_CPU_AVX)) +# define CV_CPU_CALL_AVX(fn, args) if (CV_CPU_HAS_SUPPORT_AVX) return (opt_AVX::fn args) +# define CV_CPU_CALL_AVX_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX) return (opt_AVX::fn args) +#else +# define CV_TRY_AVX 0 +# define CV_CPU_FORCE_AVX 0 +# define CV_CPU_HAS_SUPPORT_AVX 0 +# define CV_CPU_CALL_AVX(fn, args) +# define CV_CPU_CALL_AVX_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_AVX(fn, args, mode, ...) CV_CPU_CALL_AVX(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_FP16 +# define CV_TRY_FP16 1 +# define CV_CPU_FORCE_FP16 1 +# define CV_CPU_HAS_SUPPORT_FP16 1 +# define CV_CPU_CALL_FP16(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_FP16_(fn, args) return (opt_FP16::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_FP16 +# define CV_TRY_FP16 1 +# define CV_CPU_FORCE_FP16 0 +# define CV_CPU_HAS_SUPPORT_FP16 (cv::checkHardwareSupport(CV_CPU_FP16)) +# define CV_CPU_CALL_FP16(fn, args) if (CV_CPU_HAS_SUPPORT_FP16) return (opt_FP16::fn args) +# define CV_CPU_CALL_FP16_(fn, args) if (CV_CPU_HAS_SUPPORT_FP16) return (opt_FP16::fn args) +#else +# define CV_TRY_FP16 0 +# define CV_CPU_FORCE_FP16 0 +# define CV_CPU_HAS_SUPPORT_FP16 0 +# define CV_CPU_CALL_FP16(fn, args) +# define CV_CPU_CALL_FP16_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_FP16(fn, args, mode, ...) CV_CPU_CALL_FP16(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX2 +# define CV_TRY_AVX2 1 +# define CV_CPU_FORCE_AVX2 1 +# define CV_CPU_HAS_SUPPORT_AVX2 1 +# define CV_CPU_CALL_AVX2(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_AVX2_(fn, args) return (opt_AVX2::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX2 +# define CV_TRY_AVX2 1 +# define CV_CPU_FORCE_AVX2 0 +# define CV_CPU_HAS_SUPPORT_AVX2 (cv::checkHardwareSupport(CV_CPU_AVX2)) +# define CV_CPU_CALL_AVX2(fn, args) if (CV_CPU_HAS_SUPPORT_AVX2) return (opt_AVX2::fn args) +# define CV_CPU_CALL_AVX2_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX2) return (opt_AVX2::fn args) +#else +# define CV_TRY_AVX2 0 +# define CV_CPU_FORCE_AVX2 0 +# define CV_CPU_HAS_SUPPORT_AVX2 0 +# define CV_CPU_CALL_AVX2(fn, args) +# define CV_CPU_CALL_AVX2_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_AVX2(fn, args, mode, ...) CV_CPU_CALL_AVX2(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_FMA3 +# define CV_TRY_FMA3 1 +# define CV_CPU_FORCE_FMA3 1 +# define CV_CPU_HAS_SUPPORT_FMA3 1 +# define CV_CPU_CALL_FMA3(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_FMA3_(fn, args) return (opt_FMA3::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_FMA3 +# define CV_TRY_FMA3 1 +# define CV_CPU_FORCE_FMA3 0 +# define CV_CPU_HAS_SUPPORT_FMA3 (cv::checkHardwareSupport(CV_CPU_FMA3)) +# define CV_CPU_CALL_FMA3(fn, args) if (CV_CPU_HAS_SUPPORT_FMA3) return (opt_FMA3::fn args) +# define CV_CPU_CALL_FMA3_(fn, args) if (CV_CPU_HAS_SUPPORT_FMA3) return (opt_FMA3::fn args) +#else +# define CV_TRY_FMA3 0 +# define CV_CPU_FORCE_FMA3 0 +# define CV_CPU_HAS_SUPPORT_FMA3 0 +# define CV_CPU_CALL_FMA3(fn, args) +# define CV_CPU_CALL_FMA3_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_FMA3(fn, args, mode, ...) CV_CPU_CALL_FMA3(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX_512F +# define CV_TRY_AVX_512F 1 +# define CV_CPU_FORCE_AVX_512F 1 +# define CV_CPU_HAS_SUPPORT_AVX_512F 1 +# define CV_CPU_CALL_AVX_512F(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_AVX_512F_(fn, args) return (opt_AVX_512F::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX_512F +# define CV_TRY_AVX_512F 1 +# define CV_CPU_FORCE_AVX_512F 0 +# define CV_CPU_HAS_SUPPORT_AVX_512F (cv::checkHardwareSupport(CV_CPU_AVX_512F)) +# define CV_CPU_CALL_AVX_512F(fn, args) if (CV_CPU_HAS_SUPPORT_AVX_512F) return (opt_AVX_512F::fn args) +# define CV_CPU_CALL_AVX_512F_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX_512F) return (opt_AVX_512F::fn args) +#else +# define CV_TRY_AVX_512F 0 +# define CV_CPU_FORCE_AVX_512F 0 +# define CV_CPU_HAS_SUPPORT_AVX_512F 0 +# define CV_CPU_CALL_AVX_512F(fn, args) +# define CV_CPU_CALL_AVX_512F_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_AVX_512F(fn, args, mode, ...) CV_CPU_CALL_AVX_512F(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX512_SKX +# define CV_TRY_AVX512_SKX 1 +# define CV_CPU_FORCE_AVX512_SKX 1 +# define CV_CPU_HAS_SUPPORT_AVX512_SKX 1 +# define CV_CPU_CALL_AVX512_SKX(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_AVX512_SKX_(fn, args) return (opt_AVX512_SKX::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX512_SKX +# define CV_TRY_AVX512_SKX 1 +# define CV_CPU_FORCE_AVX512_SKX 0 +# define CV_CPU_HAS_SUPPORT_AVX512_SKX (cv::checkHardwareSupport(CV_CPU_AVX512_SKX)) +# define CV_CPU_CALL_AVX512_SKX(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_SKX) return (opt_AVX512_SKX::fn args) +# define CV_CPU_CALL_AVX512_SKX_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_SKX) return (opt_AVX512_SKX::fn args) +#else +# define CV_TRY_AVX512_SKX 0 +# define CV_CPU_FORCE_AVX512_SKX 0 +# define CV_CPU_HAS_SUPPORT_AVX512_SKX 0 +# define CV_CPU_CALL_AVX512_SKX(fn, args) +# define CV_CPU_CALL_AVX512_SKX_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_AVX512_SKX(fn, args, mode, ...) CV_CPU_CALL_AVX512_SKX(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_NEON +# define CV_TRY_NEON 1 +# define CV_CPU_FORCE_NEON 1 +# define CV_CPU_HAS_SUPPORT_NEON 1 +# define CV_CPU_CALL_NEON(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_NEON_(fn, args) return (opt_NEON::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_NEON +# define CV_TRY_NEON 1 +# define CV_CPU_FORCE_NEON 0 +# define CV_CPU_HAS_SUPPORT_NEON (cv::checkHardwareSupport(CV_CPU_NEON)) +# define CV_CPU_CALL_NEON(fn, args) if (CV_CPU_HAS_SUPPORT_NEON) return (opt_NEON::fn args) +# define CV_CPU_CALL_NEON_(fn, args) if (CV_CPU_HAS_SUPPORT_NEON) return (opt_NEON::fn args) +#else +# define CV_TRY_NEON 0 +# define CV_CPU_FORCE_NEON 0 +# define CV_CPU_HAS_SUPPORT_NEON 0 +# define CV_CPU_CALL_NEON(fn, args) +# define CV_CPU_CALL_NEON_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_NEON(fn, args, mode, ...) CV_CPU_CALL_NEON(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_VSX +# define CV_TRY_VSX 1 +# define CV_CPU_FORCE_VSX 1 +# define CV_CPU_HAS_SUPPORT_VSX 1 +# define CV_CPU_CALL_VSX(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_VSX_(fn, args) return (opt_VSX::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_VSX +# define CV_TRY_VSX 1 +# define CV_CPU_FORCE_VSX 0 +# define CV_CPU_HAS_SUPPORT_VSX (cv::checkHardwareSupport(CV_CPU_VSX)) +# define CV_CPU_CALL_VSX(fn, args) if (CV_CPU_HAS_SUPPORT_VSX) return (opt_VSX::fn args) +# define CV_CPU_CALL_VSX_(fn, args) if (CV_CPU_HAS_SUPPORT_VSX) return (opt_VSX::fn args) +#else +# define CV_TRY_VSX 0 +# define CV_CPU_FORCE_VSX 0 +# define CV_CPU_HAS_SUPPORT_VSX 0 +# define CV_CPU_CALL_VSX(fn, args) +# define CV_CPU_CALL_VSX_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_VSX(fn, args, mode, ...) CV_CPU_CALL_VSX(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_VSX3 +# define CV_TRY_VSX3 1 +# define CV_CPU_FORCE_VSX3 1 +# define CV_CPU_HAS_SUPPORT_VSX3 1 +# define CV_CPU_CALL_VSX3(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_VSX3_(fn, args) return (opt_VSX3::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_VSX3 +# define CV_TRY_VSX3 1 +# define CV_CPU_FORCE_VSX3 0 +# define CV_CPU_HAS_SUPPORT_VSX3 (cv::checkHardwareSupport(CV_CPU_VSX3)) +# define CV_CPU_CALL_VSX3(fn, args) if (CV_CPU_HAS_SUPPORT_VSX3) return (opt_VSX3::fn args) +# define CV_CPU_CALL_VSX3_(fn, args) if (CV_CPU_HAS_SUPPORT_VSX3) return (opt_VSX3::fn args) +#else +# define CV_TRY_VSX3 0 +# define CV_CPU_FORCE_VSX3 0 +# define CV_CPU_HAS_SUPPORT_VSX3 0 +# define CV_CPU_CALL_VSX3(fn, args) +# define CV_CPU_CALL_VSX3_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_VSX3(fn, args, mode, ...) CV_CPU_CALL_VSX3(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#define CV_CPU_CALL_BASELINE(fn, args) return (cpu_baseline::fn args) +#define __CV_CPU_DISPATCH_CHAIN_BASELINE(fn, args, mode, ...) CV_CPU_CALL_BASELINE(fn, args) /* last in sequence */ diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cvdef.h b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cvdef.h new file mode 100644 index 0000000000000000000000000000000000000000..1e8a697e90ae28175928b3ebe6a4793457c144bf --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cvdef.h @@ -0,0 +1,753 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_CVDEF_H +#define OPENCV_CORE_CVDEF_H + +//! @addtogroup core_utils +//! @{ + +#if !defined CV_DOXYGEN && !defined CV_IGNORE_DEBUG_BUILD_GUARD +#if (defined(_MSC_VER) && (defined(DEBUG) || defined(_DEBUG))) || \ + (defined(_GLIBCXX_DEBUG) || defined(_GLIBCXX_DEBUG_PEDANTIC)) +// Guard to prevent using of binary incompatible binaries / runtimes +// https://github.com/opencv/opencv/pull/9161 +#define CV__DEBUG_NS_BEGIN namespace debug_build_guard { +#define CV__DEBUG_NS_END } +namespace cv { namespace debug_build_guard { } using namespace debug_build_guard; } +#endif +#endif + +#ifndef CV__DEBUG_NS_BEGIN +#define CV__DEBUG_NS_BEGIN +#define CV__DEBUG_NS_END +#endif + + +#ifdef __OPENCV_BUILD +#include "cvconfig.h" +#endif + +#ifndef __CV_EXPAND +#define __CV_EXPAND(x) x +#endif + +#ifndef __CV_CAT +#define __CV_CAT__(x, y) x ## y +#define __CV_CAT_(x, y) __CV_CAT__(x, y) +#define __CV_CAT(x, y) __CV_CAT_(x, y) +#endif + +#define __CV_VA_NUM_ARGS_HELPER(_1, _2, _3, _4, _5, _6, _7, _8, _9, _10, N, ...) N +#define __CV_VA_NUM_ARGS(...) __CV_VA_NUM_ARGS_HELPER(__VA_ARGS__, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0) + +#if defined __GNUC__ +#define CV_Func __func__ +#elif defined _MSC_VER +#define CV_Func __FUNCTION__ +#else +#define CV_Func "" +#endif + +//! @cond IGNORED + +//////////////// static assert ///////////////// +#define CVAUX_CONCAT_EXP(a, b) a##b +#define CVAUX_CONCAT(a, b) CVAUX_CONCAT_EXP(a,b) + +#if defined(__clang__) +# ifndef __has_extension +# define __has_extension __has_feature /* compatibility, for older versions of clang */ +# endif +# if __has_extension(cxx_static_assert) +# define CV_StaticAssert(condition, reason) static_assert((condition), reason " " #condition) +# elif __has_extension(c_static_assert) +# define CV_StaticAssert(condition, reason) _Static_assert((condition), reason " " #condition) +# endif +#elif defined(__GNUC__) +# if (defined(__GXX_EXPERIMENTAL_CXX0X__) || __cplusplus >= 201103L) +# define CV_StaticAssert(condition, reason) static_assert((condition), reason " " #condition) +# endif +#elif defined(_MSC_VER) +# if _MSC_VER >= 1600 /* MSVC 10 */ +# define CV_StaticAssert(condition, reason) static_assert((condition), reason " " #condition) +# endif +#endif +#ifndef CV_StaticAssert +# if !defined(__clang__) && defined(__GNUC__) && (__GNUC__*100 + __GNUC_MINOR__ > 302) +# define CV_StaticAssert(condition, reason) ({ extern int __attribute__((error("CV_StaticAssert: " reason " " #condition))) CV_StaticAssert(); ((condition) ? 0 : CV_StaticAssert()); }) +# else + template struct CV_StaticAssert_failed; + template <> struct CV_StaticAssert_failed { enum { val = 1 }; }; + template struct CV_StaticAssert_test {}; +# define CV_StaticAssert(condition, reason)\ + typedef cv::CV_StaticAssert_test< sizeof(cv::CV_StaticAssert_failed< static_cast(condition) >) > CVAUX_CONCAT(CV_StaticAssert_failed_at_, __LINE__) +# endif +#endif + +// Suppress warning "-Wdeprecated-declarations" / C4996 +#if defined(_MSC_VER) + #define CV_DO_PRAGMA(x) __pragma(x) +#elif defined(__GNUC__) + #define CV_DO_PRAGMA(x) _Pragma (#x) +#else + #define CV_DO_PRAGMA(x) +#endif + +#ifdef _MSC_VER +#define CV_SUPPRESS_DEPRECATED_START \ + CV_DO_PRAGMA(warning(push)) \ + CV_DO_PRAGMA(warning(disable: 4996)) +#define CV_SUPPRESS_DEPRECATED_END CV_DO_PRAGMA(warning(pop)) +#elif defined (__clang__) || ((__GNUC__) && (__GNUC__*100 + __GNUC_MINOR__ > 405)) +#define CV_SUPPRESS_DEPRECATED_START \ + CV_DO_PRAGMA(GCC diagnostic push) \ + CV_DO_PRAGMA(GCC diagnostic ignored "-Wdeprecated-declarations") +#define CV_SUPPRESS_DEPRECATED_END CV_DO_PRAGMA(GCC diagnostic pop) +#else +#define CV_SUPPRESS_DEPRECATED_START +#define CV_SUPPRESS_DEPRECATED_END +#endif + +#define CV_UNUSED(name) (void)name + +#if defined __GNUC__ && !defined __EXCEPTIONS +#define CV_TRY +#define CV_CATCH(A, B) for (A B; false; ) +#define CV_CATCH_ALL if (false) +#define CV_THROW(A) abort() +#define CV_RETHROW() abort() +#else +#define CV_TRY try +#define CV_CATCH(A, B) catch(const A & B) +#define CV_CATCH_ALL catch(...) +#define CV_THROW(A) throw A +#define CV_RETHROW() throw +#endif + +//! @endcond + +// undef problematic defines sometimes defined by system headers (windows.h in particular) +#undef small +#undef min +#undef max +#undef abs +#undef Complex + +#include +#include "opencv2/core/hal/interface.h" + +#if defined __ICL +# define CV_ICC __ICL +#elif defined __ICC +# define CV_ICC __ICC +#elif defined __ECL +# define CV_ICC __ECL +#elif defined __ECC +# define CV_ICC __ECC +#elif defined __INTEL_COMPILER +# define CV_ICC __INTEL_COMPILER +#endif + +#ifndef CV_INLINE +# if defined __cplusplus +# define CV_INLINE static inline +# elif defined _MSC_VER +# define CV_INLINE __inline +# else +# define CV_INLINE static +# endif +#endif + +#if defined CV_DISABLE_OPTIMIZATION || (defined CV_ICC && !defined CV_ENABLE_UNROLLED) +# define CV_ENABLE_UNROLLED 0 +#else +# define CV_ENABLE_UNROLLED 1 +#endif + +#ifdef __GNUC__ +# define CV_DECL_ALIGNED(x) __attribute__ ((aligned (x))) +#elif defined _MSC_VER +# define CV_DECL_ALIGNED(x) __declspec(align(x)) +#else +# define CV_DECL_ALIGNED(x) +#endif + +/* CPU features and intrinsics support */ +#define CV_CPU_NONE 0 +#define CV_CPU_MMX 1 +#define CV_CPU_SSE 2 +#define CV_CPU_SSE2 3 +#define CV_CPU_SSE3 4 +#define CV_CPU_SSSE3 5 +#define CV_CPU_SSE4_1 6 +#define CV_CPU_SSE4_2 7 +#define CV_CPU_POPCNT 8 +#define CV_CPU_FP16 9 +#define CV_CPU_AVX 10 +#define CV_CPU_AVX2 11 +#define CV_CPU_FMA3 12 + +#define CV_CPU_AVX_512F 13 +#define CV_CPU_AVX_512BW 14 +#define CV_CPU_AVX_512CD 15 +#define CV_CPU_AVX_512DQ 16 +#define CV_CPU_AVX_512ER 17 +#define CV_CPU_AVX_512IFMA512 18 // deprecated +#define CV_CPU_AVX_512IFMA 18 +#define CV_CPU_AVX_512PF 19 +#define CV_CPU_AVX_512VBMI 20 +#define CV_CPU_AVX_512VL 21 + +#define CV_CPU_NEON 100 + +#define CV_CPU_VSX 200 +#define CV_CPU_VSX3 201 + +// CPU features groups +#define CV_CPU_AVX512_SKX 256 + +// when adding to this list remember to update the following enum +#define CV_HARDWARE_MAX_FEATURE 512 + +/** @brief Available CPU features. +*/ +enum CpuFeatures { + CPU_MMX = 1, + CPU_SSE = 2, + CPU_SSE2 = 3, + CPU_SSE3 = 4, + CPU_SSSE3 = 5, + CPU_SSE4_1 = 6, + CPU_SSE4_2 = 7, + CPU_POPCNT = 8, + CPU_FP16 = 9, + CPU_AVX = 10, + CPU_AVX2 = 11, + CPU_FMA3 = 12, + + CPU_AVX_512F = 13, + CPU_AVX_512BW = 14, + CPU_AVX_512CD = 15, + CPU_AVX_512DQ = 16, + CPU_AVX_512ER = 17, + CPU_AVX_512IFMA512 = 18, // deprecated + CPU_AVX_512IFMA = 18, + CPU_AVX_512PF = 19, + CPU_AVX_512VBMI = 20, + CPU_AVX_512VL = 21, + + CPU_NEON = 100, + + CPU_VSX = 200, + CPU_VSX3 = 201, + + CPU_AVX512_SKX = 256, //!< Skylake-X with AVX-512F/CD/BW/DQ/VL + + CPU_MAX_FEATURE = 512 // see CV_HARDWARE_MAX_FEATURE +}; + + +#include "cv_cpu_dispatch.h" + + +/* fundamental constants */ +#define CV_PI 3.1415926535897932384626433832795 +#define CV_2PI 6.283185307179586476925286766559 +#define CV_LOG2 0.69314718055994530941723212145818 + +#if defined __ARM_FP16_FORMAT_IEEE \ + && !defined __CUDACC__ +# define CV_FP16_TYPE 1 +#else +# define CV_FP16_TYPE 0 +#endif + +typedef union Cv16suf +{ + short i; + ushort u; +#if CV_FP16_TYPE + __fp16 h; +#endif +} +Cv16suf; + +typedef union Cv32suf +{ + int i; + unsigned u; + float f; +} +Cv32suf; + +typedef union Cv64suf +{ + int64 i; + uint64 u; + double f; +} +Cv64suf; + +#define OPENCV_ABI_COMPATIBILITY 300 + +#ifdef __OPENCV_BUILD +# define DISABLE_OPENCV_24_COMPATIBILITY +# define OPENCV_DISABLE_DEPRECATED_COMPATIBILITY +#endif + +#ifdef CVAPI_EXPORTS +# if (defined _WIN32 || defined WINCE || defined __CYGWIN__) +# define CV_EXPORTS __declspec(dllexport) +# elif defined __GNUC__ && __GNUC__ >= 4 +# define CV_EXPORTS __attribute__ ((visibility ("default"))) +# endif +#endif + +#ifndef CV_EXPORTS +# define CV_EXPORTS +#endif + +#ifdef _MSC_VER +# define CV_EXPORTS_TEMPLATE +#else +# define CV_EXPORTS_TEMPLATE CV_EXPORTS +#endif + +#ifndef CV_DEPRECATED +# if defined(__GNUC__) +# define CV_DEPRECATED __attribute__ ((deprecated)) +# elif defined(_MSC_VER) +# define CV_DEPRECATED __declspec(deprecated) +# else +# define CV_DEPRECATED +# endif +#endif + +#ifndef CV_DEPRECATED_EXTERNAL +# if defined(__OPENCV_BUILD) +# define CV_DEPRECATED_EXTERNAL /* nothing */ +# else +# define CV_DEPRECATED_EXTERNAL CV_DEPRECATED +# endif +#endif + + +#ifndef CV_EXTERN_C +# ifdef __cplusplus +# define CV_EXTERN_C extern "C" +# else +# define CV_EXTERN_C +# endif +#endif + +/* special informative macros for wrapper generators */ +#define CV_EXPORTS_W CV_EXPORTS +#define CV_EXPORTS_W_SIMPLE CV_EXPORTS +#define CV_EXPORTS_AS(synonym) CV_EXPORTS +#define CV_EXPORTS_W_MAP CV_EXPORTS +#define CV_IN_OUT +#define CV_OUT +#define CV_PROP +#define CV_PROP_RW +#define CV_WRAP +#define CV_WRAP_AS(synonym) + +/****************************************************************************************\ +* Matrix type (Mat) * +\****************************************************************************************/ + +#define CV_MAT_CN_MASK ((CV_CN_MAX - 1) << CV_CN_SHIFT) +#define CV_MAT_CN(flags) ((((flags) & CV_MAT_CN_MASK) >> CV_CN_SHIFT) + 1) +#define CV_MAT_TYPE_MASK (CV_DEPTH_MAX*CV_CN_MAX - 1) +#define CV_MAT_TYPE(flags) ((flags) & CV_MAT_TYPE_MASK) +#define CV_MAT_CONT_FLAG_SHIFT 14 +#define CV_MAT_CONT_FLAG (1 << CV_MAT_CONT_FLAG_SHIFT) +#define CV_IS_MAT_CONT(flags) ((flags) & CV_MAT_CONT_FLAG) +#define CV_IS_CONT_MAT CV_IS_MAT_CONT +#define CV_SUBMAT_FLAG_SHIFT 15 +#define CV_SUBMAT_FLAG (1 << CV_SUBMAT_FLAG_SHIFT) +#define CV_IS_SUBMAT(flags) ((flags) & CV_MAT_SUBMAT_FLAG) + +/** Size of each channel item, + 0x8442211 = 1000 0100 0100 0010 0010 0001 0001 ~ array of sizeof(arr_type_elem) */ +#define CV_ELEM_SIZE1(type) \ + ((((sizeof(size_t)<<28)|0x8442211) >> CV_MAT_DEPTH(type)*4) & 15) + +/** 0x3a50 = 11 10 10 01 01 00 00 ~ array of log2(sizeof(arr_type_elem)) */ +#define CV_ELEM_SIZE(type) \ + (CV_MAT_CN(type) << ((((sizeof(size_t)/4+1)*16384|0x3a50) >> CV_MAT_DEPTH(type)*2) & 3)) + +#ifndef MIN +# define MIN(a,b) ((a) > (b) ? (b) : (a)) +#endif + +#ifndef MAX +# define MAX(a,b) ((a) < (b) ? (b) : (a)) +#endif + +/****************************************************************************************\ +* static analysys * +\****************************************************************************************/ + +// In practice, some macro are not processed correctly (noreturn is not detected). +// We need to use simplified definition for them. +#ifndef CV_STATIC_ANALYSIS +# if defined(__KLOCWORK__) || defined(__clang_analyzer__) || defined(__COVERITY__) +# define CV_STATIC_ANALYSIS 1 +# endif +#else +# if defined(CV_STATIC_ANALYSIS) && !(__CV_CAT(1, CV_STATIC_ANALYSIS) == 1) // defined and not empty +# if 0 == CV_STATIC_ANALYSIS +# undef CV_STATIC_ANALYSIS +# endif +# endif +#endif + +/****************************************************************************************\ +* Thread sanitizer * +\****************************************************************************************/ +#ifndef CV_THREAD_SANITIZER +# if defined(__has_feature) +# if __has_feature(thread_sanitizer) +# define CV_THREAD_SANITIZER +# endif +# endif +#endif + +/****************************************************************************************\ +* exchange-add operation for atomic operations on reference counters * +\****************************************************************************************/ + +#ifdef CV_XADD + // allow to use user-defined macro +#elif defined __GNUC__ || defined __clang__ +# if defined __clang__ && __clang_major__ >= 3 && !defined __ANDROID__ && !defined __EMSCRIPTEN__ && !defined(__CUDACC__) +# ifdef __ATOMIC_ACQ_REL +# define CV_XADD(addr, delta) __c11_atomic_fetch_add((_Atomic(int)*)(addr), delta, __ATOMIC_ACQ_REL) +# else +# define CV_XADD(addr, delta) __atomic_fetch_add((_Atomic(int)*)(addr), delta, 4) +# endif +# else +# if defined __ATOMIC_ACQ_REL && !defined __clang__ + // version for gcc >= 4.7 +# define CV_XADD(addr, delta) (int)__atomic_fetch_add((unsigned*)(addr), (unsigned)(delta), __ATOMIC_ACQ_REL) +# else +# define CV_XADD(addr, delta) (int)__sync_fetch_and_add((unsigned*)(addr), (unsigned)(delta)) +# endif +# endif +#elif defined _MSC_VER && !defined RC_INVOKED +# include +# define CV_XADD(addr, delta) (int)_InterlockedExchangeAdd((long volatile*)addr, delta) +#else + CV_INLINE CV_XADD(int* addr, int delta) { int tmp = *addr; *addr += delta; return tmp; } +#endif + + +/****************************************************************************************\ +* CV_NORETURN attribute * +\****************************************************************************************/ + +#ifndef CV_NORETURN +# if defined(__GNUC__) +# define CV_NORETURN __attribute__((__noreturn__)) +# elif defined(_MSC_VER) && (_MSC_VER >= 1300) +# define CV_NORETURN __declspec(noreturn) +# else +# define CV_NORETURN /* nothing by default */ +# endif +#endif + + +/****************************************************************************************\ +* CV_NODISCARD attribute * +* encourages the compiler to issue a warning if the return value is discarded (C++17) * +\****************************************************************************************/ +#ifndef CV_NODISCARD +# if defined(__GNUC__) +# define CV_NODISCARD __attribute__((__warn_unused_result__)) // at least available with GCC 3.4 +# elif defined(__clang__) && defined(__has_attribute) +# if __has_attribute(__warn_unused_result__) +# define CV_NODISCARD __attribute__((__warn_unused_result__)) +# endif +# endif +#endif +#ifndef CV_NODISCARD +# define CV_NODISCARD /* nothing by default */ +#endif + + +/****************************************************************************************\ +* C++ 11 * +\****************************************************************************************/ +#ifndef CV_CXX11 +# if __cplusplus >= 201103L || (defined(_MSC_VER) && _MSC_VER >= 1800) +# define CV_CXX11 1 +# endif +#else +# if CV_CXX11 == 0 +# undef CV_CXX11 +# endif +#endif + + +/****************************************************************************************\ +* C++ Move semantics * +\****************************************************************************************/ + +#ifndef CV_CXX_MOVE_SEMANTICS +# if __cplusplus >= 201103L || defined(__GXX_EXPERIMENTAL_CXX0X__) || (defined(_MSC_VER) && _MSC_VER >= 1600) +# define CV_CXX_MOVE_SEMANTICS 1 +# elif defined(__clang) +# if __has_feature(cxx_rvalue_references) +# define CV_CXX_MOVE_SEMANTICS 1 +# endif +# endif +#else +# if CV_CXX_MOVE_SEMANTICS == 0 +# undef CV_CXX_MOVE_SEMANTICS +# endif +#endif + +/****************************************************************************************\ +* C++11 std::array * +\****************************************************************************************/ + +#ifndef CV_CXX_STD_ARRAY +# if __cplusplus >= 201103L || (defined(_MSC_VER) && _MSC_VER >= 1900/*MSVS 2015*/) +# define CV_CXX_STD_ARRAY 1 +# include +# endif +#else +# if CV_CXX_STD_ARRAY == 0 +# undef CV_CXX_STD_ARRAY +# endif +#endif + + +/****************************************************************************************\ +* C++11 override / final * +\****************************************************************************************/ + +#ifndef CV_OVERRIDE +# ifdef CV_CXX11 +# define CV_OVERRIDE override +# endif +#endif +#ifndef CV_OVERRIDE +# define CV_OVERRIDE +#endif + +#ifndef CV_FINAL +# ifdef CV_CXX11 +# define CV_FINAL final +# endif +#endif +#ifndef CV_FINAL +# define CV_FINAL +#endif + + + +// Integer types portatibility +#ifdef OPENCV_STDINT_HEADER +#include OPENCV_STDINT_HEADER +#elif defined(__cplusplus) +#if defined(_MSC_VER) && _MSC_VER < 1600 /* MSVS 2010 */ +namespace cv { +typedef signed char int8_t; +typedef unsigned char uint8_t; +typedef signed short int16_t; +typedef unsigned short uint16_t; +typedef signed int int32_t; +typedef unsigned int uint32_t; +typedef signed __int64 int64_t; +typedef unsigned __int64 uint64_t; +} +#elif defined(_MSC_VER) || __cplusplus >= 201103L +#include +namespace cv { +using std::int8_t; +using std::uint8_t; +using std::int16_t; +using std::uint16_t; +using std::int32_t; +using std::uint32_t; +using std::int64_t; +using std::uint64_t; +} +#else +#include +namespace cv { +typedef ::int8_t int8_t; +typedef ::uint8_t uint8_t; +typedef ::int16_t int16_t; +typedef ::uint16_t uint16_t; +typedef ::int32_t int32_t; +typedef ::uint32_t uint32_t; +typedef ::int64_t int64_t; +typedef ::uint64_t uint64_t; +} +#endif +#else // pure C +#include +#endif + +#ifdef __cplusplus +namespace cv +{ + +class float16_t +{ +public: +#if CV_FP16_TYPE + + float16_t() {} + explicit float16_t(float x) { h = (__fp16)x; } + operator float() const { return (float)h; } + static float16_t fromBits(ushort w) + { + Cv16suf u; + u.u = w; + float16_t result; + result.h = u.h; + return result; + } + static float16_t zero() + { + float16_t result; + result.h = (__fp16)0; + return result; + } + ushort bits() const + { + Cv16suf u; + u.h = h; + return u.u; + } +protected: + __fp16 h; + +#else + float16_t() {} + explicit float16_t(float x) + { + #if CV_AVX2 + __m128 v = _mm_load_ss(&x); + w = (ushort)_mm_cvtsi128_si32(_mm_cvtps_ph(v, 0)); + #else + Cv32suf in; + in.f = x; + unsigned sign = in.u & 0x80000000; + in.u ^= sign; + + if( in.u >= 0x47800000 ) + w = (ushort)(in.u > 0x7f800000 ? 0x7e00 : 0x7c00); + else + { + if (in.u < 0x38800000) + { + in.f += 0.5f; + w = (ushort)(in.u - 0x3f000000); + } + else + { + unsigned t = in.u + 0xc8000fff; + w = (ushort)((t + ((in.u >> 13) & 1)) >> 13); + } + } + + w = (ushort)(w | (sign >> 16)); + #endif + } + + operator float() const + { + #if CV_AVX2 + float f; + _mm_store_ss(&f, _mm_cvtph_ps(_mm_cvtsi32_si128(w))); + return f; + #else + Cv32suf out; + + unsigned t = ((w & 0x7fff) << 13) + 0x38000000; + unsigned sign = (w & 0x8000) << 16; + unsigned e = w & 0x7c00; + + out.u = t + (1 << 23); + out.u = (e >= 0x7c00 ? t + 0x38000000 : + e == 0 ? (out.f -= 6.103515625e-05f, out.u) : t) | sign; + return out.f; + #endif + } + + static float16_t fromBits(ushort b) + { + float16_t result; + result.w = b; + return result; + } + static float16_t zero() + { + float16_t result; + result.w = (ushort)0; + return result; + } + ushort bits() const { return w; } +protected: + ushort w; + +#endif +}; + +} +#endif + +//! @} + +#ifndef __cplusplus +#include "opencv2/core/fast_math.hpp" // define cvRound(double) +#endif + +#endif // OPENCV_CORE_CVDEF_H diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cvstd.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cvstd.hpp new file mode 100644 index 0000000000000000000000000000000000000000..0a3f553ab89f986dac83bbdc46dbdb36e6c0a7c7 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cvstd.hpp @@ -0,0 +1,1040 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_CVSTD_HPP +#define OPENCV_CORE_CVSTD_HPP + +#ifndef __cplusplus +# error cvstd.hpp header must be compiled as C++ +#endif + +#include "opencv2/core/cvdef.h" +#include +#include +#include + +#include + +// import useful primitives from stl +# include +# include +# include //for abs(int) +# include + +namespace cv +{ + static inline uchar abs(uchar a) { return a; } + static inline ushort abs(ushort a) { return a; } + static inline unsigned abs(unsigned a) { return a; } + static inline uint64 abs(uint64 a) { return a; } + + using std::min; + using std::max; + using std::abs; + using std::swap; + using std::sqrt; + using std::exp; + using std::pow; + using std::log; +} + +namespace cv { + +//! @addtogroup core_utils +//! @{ + +//////////////////////////// memory management functions //////////////////////////// + +/** @brief Allocates an aligned memory buffer. + +The function allocates the buffer of the specified size and returns it. When the buffer size is 16 +bytes or more, the returned buffer is aligned to 16 bytes. +@param bufSize Allocated buffer size. + */ +CV_EXPORTS void* fastMalloc(size_t bufSize); + +/** @brief Deallocates a memory buffer. + +The function deallocates the buffer allocated with fastMalloc . If NULL pointer is passed, the +function does nothing. C version of the function clears the pointer *pptr* to avoid problems with +double memory deallocation. +@param ptr Pointer to the allocated buffer. + */ +CV_EXPORTS void fastFree(void* ptr); + +/*! + The STL-compilant memory Allocator based on cv::fastMalloc() and cv::fastFree() +*/ +template class Allocator +{ +public: + typedef _Tp value_type; + typedef value_type* pointer; + typedef const value_type* const_pointer; + typedef value_type& reference; + typedef const value_type& const_reference; + typedef size_t size_type; + typedef ptrdiff_t difference_type; + template class rebind { typedef Allocator other; }; + + explicit Allocator() {} + ~Allocator() {} + explicit Allocator(Allocator const&) {} + template + explicit Allocator(Allocator const&) {} + + // address + pointer address(reference r) { return &r; } + const_pointer address(const_reference r) { return &r; } + + pointer allocate(size_type count, const void* =0) { return reinterpret_cast(fastMalloc(count * sizeof (_Tp))); } + void deallocate(pointer p, size_type) { fastFree(p); } + + void construct(pointer p, const _Tp& v) { new(static_cast(p)) _Tp(v); } + void destroy(pointer p) { p->~_Tp(); } + + size_type max_size() const { return cv::max(static_cast<_Tp>(-1)/sizeof(_Tp), 1); } +}; + +//! @} core_utils + +//! @cond IGNORED + +namespace detail +{ + +// Metafunction to avoid taking a reference to void. +template +struct RefOrVoid { typedef T& type; }; + +template<> +struct RefOrVoid{ typedef void type; }; + +template<> +struct RefOrVoid{ typedef const void type; }; + +template<> +struct RefOrVoid{ typedef volatile void type; }; + +template<> +struct RefOrVoid{ typedef const volatile void type; }; + +// This class would be private to Ptr, if it didn't have to be a non-template. +struct PtrOwner; + +} + +template +struct DefaultDeleter +{ + void operator () (Y* p) const; +}; + +//! @endcond + +//! @addtogroup core_basic +//! @{ + +/** @brief Template class for smart pointers with shared ownership + +A Ptr\ pretends to be a pointer to an object of type T. Unlike an ordinary pointer, however, the +object will be automatically cleaned up once all Ptr instances pointing to it are destroyed. + +Ptr is similar to boost::shared_ptr that is part of the Boost library +() and std::shared_ptr from +the [C++11](http://en.wikipedia.org/wiki/C++11) standard. + +This class provides the following advantages: +- Default constructor, copy constructor, and assignment operator for an arbitrary C++ class or C + structure. For some objects, like files, windows, mutexes, sockets, and others, a copy + constructor or an assignment operator are difficult to define. For some other objects, like + complex classifiers in OpenCV, copy constructors are absent and not easy to implement. Finally, + some of complex OpenCV and your own data structures may be written in C. However, copy + constructors and default constructors can simplify programming a lot. Besides, they are often + required (for example, by STL containers). By using a Ptr to such an object instead of the + object itself, you automatically get all of the necessary constructors and the assignment + operator. +- *O(1)* complexity of the above-mentioned operations. While some structures, like std::vector, + provide a copy constructor and an assignment operator, the operations may take a considerable + amount of time if the data structures are large. But if the structures are put into a Ptr, the + overhead is small and independent of the data size. +- Automatic and customizable cleanup, even for C structures. See the example below with FILE\*. +- Heterogeneous collections of objects. The standard STL and most other C++ and OpenCV containers + can store only objects of the same type and the same size. The classical solution to store + objects of different types in the same container is to store pointers to the base class (Base\*) + instead but then you lose the automatic memory management. Again, by using Ptr\ instead + of raw pointers, you can solve the problem. + +A Ptr is said to *own* a pointer - that is, for each Ptr there is a pointer that will be deleted +once all Ptr instances that own it are destroyed. The owned pointer may be null, in which case +nothing is deleted. Each Ptr also *stores* a pointer. The stored pointer is the pointer the Ptr +pretends to be; that is, the one you get when you use Ptr::get or the conversion to T\*. It's +usually the same as the owned pointer, but if you use casts or the general shared-ownership +constructor, the two may diverge: the Ptr will still own the original pointer, but will itself point +to something else. + +The owned pointer is treated as a black box. The only thing Ptr needs to know about it is how to +delete it. This knowledge is encapsulated in the *deleter* - an auxiliary object that is associated +with the owned pointer and shared between all Ptr instances that own it. The default deleter is an +instance of DefaultDeleter, which uses the standard C++ delete operator; as such it will work with +any pointer allocated with the standard new operator. + +However, if the pointer must be deleted in a different way, you must specify a custom deleter upon +Ptr construction. A deleter is simply a callable object that accepts the pointer as its sole +argument. For example, if you want to wrap FILE, you may do so as follows: +@code + Ptr f(fopen("myfile.txt", "w"), fclose); + if(!f) throw ...; + fprintf(f, ....); + ... + // the file will be closed automatically by f's destructor. +@endcode +Alternatively, if you want all pointers of a particular type to be deleted the same way, you can +specialize DefaultDeleter::operator() for that type, like this: +@code + namespace cv { + template<> void DefaultDeleter::operator ()(FILE * obj) const + { + fclose(obj); + } + } +@endcode +For convenience, the following types from the OpenCV C API already have such a specialization that +calls the appropriate release function: +- CvCapture +- CvFileStorage +- CvHaarClassifierCascade +- CvMat +- CvMatND +- CvMemStorage +- CvSparseMat +- CvVideoWriter +- IplImage +@note The shared ownership mechanism is implemented with reference counting. As such, cyclic +ownership (e.g. when object a contains a Ptr to object b, which contains a Ptr to object a) will +lead to all involved objects never being cleaned up. Avoid such situations. +@note It is safe to concurrently read (but not write) a Ptr instance from multiple threads and +therefore it is normally safe to use it in multi-threaded applications. The same is true for Mat and +other C++ OpenCV classes that use internal reference counts. +*/ +template +struct Ptr +{ + /** Generic programming support. */ + typedef T element_type; + + /** The default constructor creates a null Ptr - one that owns and stores a null pointer. + */ + Ptr(); + + /** + If p is null, these are equivalent to the default constructor. + Otherwise, these constructors assume ownership of p - that is, the created Ptr owns and stores p + and assumes it is the sole owner of it. Don't use them if p is already owned by another Ptr, or + else p will get deleted twice. + With the first constructor, DefaultDeleter\() becomes the associated deleter (so p will + eventually be deleted with the standard delete operator). Y must be a complete type at the point + of invocation. + With the second constructor, d becomes the associated deleter. + Y\* must be convertible to T\*. + @param p Pointer to own. + @note It is often easier to use makePtr instead. + */ + template +#ifdef DISABLE_OPENCV_24_COMPATIBILITY + explicit +#endif + Ptr(Y* p); + + /** @overload + @param d Deleter to use for the owned pointer. + @param p Pointer to own. + */ + template + Ptr(Y* p, D d); + + /** + These constructors create a Ptr that shares ownership with another Ptr - that is, own the same + pointer as o. + With the first two, the same pointer is stored, as well; for the second, Y\* must be convertible + to T\*. + With the third, p is stored, and Y may be any type. This constructor allows to have completely + unrelated owned and stored pointers, and should be used with care to avoid confusion. A relatively + benign use is to create a non-owning Ptr, like this: + @code + ptr = Ptr(Ptr(), dont_delete_me); // owns nothing; will not delete the pointer. + @endcode + @param o Ptr to share ownership with. + */ + Ptr(const Ptr& o); + + /** @overload + @param o Ptr to share ownership with. + */ + template + Ptr(const Ptr& o); + + /** @overload + @param o Ptr to share ownership with. + @param p Pointer to store. + */ + template + Ptr(const Ptr& o, T* p); + + /** The destructor is equivalent to calling Ptr::release. */ + ~Ptr(); + + /** + Assignment replaces the current Ptr instance with one that owns and stores same pointers as o and + then destroys the old instance. + @param o Ptr to share ownership with. + */ + Ptr& operator = (const Ptr& o); + + /** @overload */ + template + Ptr& operator = (const Ptr& o); + + /** If no other Ptr instance owns the owned pointer, deletes it with the associated deleter. Then sets + both the owned and the stored pointers to NULL. + */ + void release(); + + /** + `ptr.reset(...)` is equivalent to `ptr = Ptr(...)`. + @param p Pointer to own. + */ + template + void reset(Y* p); + + /** @overload + @param d Deleter to use for the owned pointer. + @param p Pointer to own. + */ + template + void reset(Y* p, D d); + + /** + Swaps the owned and stored pointers (and deleters, if any) of this and o. + @param o Ptr to swap with. + */ + void swap(Ptr& o); + + /** Returns the stored pointer. */ + T* get() const; + + /** Ordinary pointer emulation. */ + typename detail::RefOrVoid::type operator * () const; + + /** Ordinary pointer emulation. */ + T* operator -> () const; + + /** Equivalent to get(). */ + operator T* () const; + + /** ptr.empty() is equivalent to `!ptr.get()`. */ + bool empty() const; + + /** Returns a Ptr that owns the same pointer as this, and stores the same + pointer as this, except converted via static_cast to Y*. + */ + template + Ptr staticCast() const; + + /** Ditto for const_cast. */ + template + Ptr constCast() const; + + /** Ditto for dynamic_cast. */ + template + Ptr dynamicCast() const; + +#ifdef CV_CXX_MOVE_SEMANTICS + Ptr(Ptr&& o); + Ptr& operator = (Ptr&& o); +#endif + +private: + detail::PtrOwner* owner; + T* stored; + + template + friend struct Ptr; // have to do this for the cross-type copy constructor +}; + +/** Equivalent to ptr1.swap(ptr2). Provided to help write generic algorithms. */ +template +void swap(Ptr& ptr1, Ptr& ptr2); + +/** Return whether ptr1.get() and ptr2.get() are equal and not equal, respectively. */ +template +bool operator == (const Ptr& ptr1, const Ptr& ptr2); +template +bool operator != (const Ptr& ptr1, const Ptr& ptr2); + +/** `makePtr(...)` is equivalent to `Ptr(new T(...))`. It is shorter than the latter, and it's +marginally safer than using a constructor or Ptr::reset, since it ensures that the owned pointer +is new and thus not owned by any other Ptr instance. +Unfortunately, perfect forwarding is impossible to implement in C++03, and so makePtr is limited +to constructors of T that have up to 10 arguments, none of which are non-const references. + */ +template +Ptr makePtr(); +/** @overload */ +template +Ptr makePtr(const A1& a1); +/** @overload */ +template +Ptr makePtr(const A1& a1, const A2& a2); +/** @overload */ +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3); +/** @overload */ +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4); +/** @overload */ +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5); +/** @overload */ +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6); +/** @overload */ +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7); +/** @overload */ +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8); +/** @overload */ +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8, const A9& a9); +/** @overload */ +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8, const A9& a9, const A10& a10); + +//////////////////////////////// string class //////////////////////////////// + +class CV_EXPORTS FileNode; //for string constructor from FileNode + +class CV_EXPORTS String +{ +public: + typedef char value_type; + typedef char& reference; + typedef const char& const_reference; + typedef char* pointer; + typedef const char* const_pointer; + typedef ptrdiff_t difference_type; + typedef size_t size_type; + typedef char* iterator; + typedef const char* const_iterator; + + static const size_t npos = size_t(-1); + + String(); + String(const String& str); + String(const String& str, size_t pos, size_t len = npos); + String(const char* s); + String(const char* s, size_t n); + String(size_t n, char c); + String(const char* first, const char* last); + template String(Iterator first, Iterator last); + explicit String(const FileNode& fn); + ~String(); + + String& operator=(const String& str); + String& operator=(const char* s); + String& operator=(char c); + + String& operator+=(const String& str); + String& operator+=(const char* s); + String& operator+=(char c); + + size_t size() const; + size_t length() const; + + char operator[](size_t idx) const; + char operator[](int idx) const; + + const char* begin() const; + const char* end() const; + + const char* c_str() const; + + bool empty() const; + void clear(); + + int compare(const char* s) const; + int compare(const String& str) const; + + void swap(String& str); + String substr(size_t pos = 0, size_t len = npos) const; + + size_t find(const char* s, size_t pos, size_t n) const; + size_t find(char c, size_t pos = 0) const; + size_t find(const String& str, size_t pos = 0) const; + size_t find(const char* s, size_t pos = 0) const; + + size_t rfind(const char* s, size_t pos, size_t n) const; + size_t rfind(char c, size_t pos = npos) const; + size_t rfind(const String& str, size_t pos = npos) const; + size_t rfind(const char* s, size_t pos = npos) const; + + size_t find_first_of(const char* s, size_t pos, size_t n) const; + size_t find_first_of(char c, size_t pos = 0) const; + size_t find_first_of(const String& str, size_t pos = 0) const; + size_t find_first_of(const char* s, size_t pos = 0) const; + + size_t find_last_of(const char* s, size_t pos, size_t n) const; + size_t find_last_of(char c, size_t pos = npos) const; + size_t find_last_of(const String& str, size_t pos = npos) const; + size_t find_last_of(const char* s, size_t pos = npos) const; + + friend String operator+ (const String& lhs, const String& rhs); + friend String operator+ (const String& lhs, const char* rhs); + friend String operator+ (const char* lhs, const String& rhs); + friend String operator+ (const String& lhs, char rhs); + friend String operator+ (char lhs, const String& rhs); + + String toLowerCase() const; + + String(const std::string& str); + String(const std::string& str, size_t pos, size_t len = npos); + String& operator=(const std::string& str); + String& operator+=(const std::string& str); + operator std::string() const; + + friend String operator+ (const String& lhs, const std::string& rhs); + friend String operator+ (const std::string& lhs, const String& rhs); + +private: + char* cstr_; + size_t len_; + + char* allocate(size_t len); // len without trailing 0 + void deallocate(); + + String(int); // disabled and invalid. Catch invalid usages like, commandLineParser.has(0) problem +}; + +//! @} core_basic + +////////////////////////// cv::String implementation ///////////////////////// + +//! @cond IGNORED + +inline +String::String() + : cstr_(0), len_(0) +{} + +inline +String::String(const String& str) + : cstr_(str.cstr_), len_(str.len_) +{ + if (cstr_) + CV_XADD(((int*)cstr_)-1, 1); +} + +inline +String::String(const String& str, size_t pos, size_t len) + : cstr_(0), len_(0) +{ + pos = min(pos, str.len_); + len = min(str.len_ - pos, len); + if (!len) return; + if (len == str.len_) + { + CV_XADD(((int*)str.cstr_)-1, 1); + cstr_ = str.cstr_; + len_ = str.len_; + return; + } + memcpy(allocate(len), str.cstr_ + pos, len); +} + +inline +String::String(const char* s) + : cstr_(0), len_(0) +{ + if (!s) return; + size_t len = strlen(s); + if (!len) return; + memcpy(allocate(len), s, len); +} + +inline +String::String(const char* s, size_t n) + : cstr_(0), len_(0) +{ + if (!n) return; + if (!s) return; + memcpy(allocate(n), s, n); +} + +inline +String::String(size_t n, char c) + : cstr_(0), len_(0) +{ + if (!n) return; + memset(allocate(n), c, n); +} + +inline +String::String(const char* first, const char* last) + : cstr_(0), len_(0) +{ + size_t len = (size_t)(last - first); + if (!len) return; + memcpy(allocate(len), first, len); +} + +template inline +String::String(Iterator first, Iterator last) + : cstr_(0), len_(0) +{ + size_t len = (size_t)(last - first); + if (!len) return; + char* str = allocate(len); + while (first != last) + { + *str++ = *first; + ++first; + } +} + +inline +String::~String() +{ + deallocate(); +} + +inline +String& String::operator=(const String& str) +{ + if (&str == this) return *this; + + deallocate(); + if (str.cstr_) CV_XADD(((int*)str.cstr_)-1, 1); + cstr_ = str.cstr_; + len_ = str.len_; + return *this; +} + +inline +String& String::operator=(const char* s) +{ + deallocate(); + if (!s) return *this; + size_t len = strlen(s); + if (len) memcpy(allocate(len), s, len); + return *this; +} + +inline +String& String::operator=(char c) +{ + deallocate(); + allocate(1)[0] = c; + return *this; +} + +inline +String& String::operator+=(const String& str) +{ + *this = *this + str; + return *this; +} + +inline +String& String::operator+=(const char* s) +{ + *this = *this + s; + return *this; +} + +inline +String& String::operator+=(char c) +{ + *this = *this + c; + return *this; +} + +inline +size_t String::size() const +{ + return len_; +} + +inline +size_t String::length() const +{ + return len_; +} + +inline +char String::operator[](size_t idx) const +{ + return cstr_[idx]; +} + +inline +char String::operator[](int idx) const +{ + return cstr_[idx]; +} + +inline +const char* String::begin() const +{ + return cstr_; +} + +inline +const char* String::end() const +{ + return len_ ? cstr_ + len_ : NULL; +} + +inline +bool String::empty() const +{ + return len_ == 0; +} + +inline +const char* String::c_str() const +{ + return cstr_ ? cstr_ : ""; +} + +inline +void String::swap(String& str) +{ + cv::swap(cstr_, str.cstr_); + cv::swap(len_, str.len_); +} + +inline +void String::clear() +{ + deallocate(); +} + +inline +int String::compare(const char* s) const +{ + if (cstr_ == s) return 0; + return strcmp(c_str(), s); +} + +inline +int String::compare(const String& str) const +{ + if (cstr_ == str.cstr_) return 0; + return strcmp(c_str(), str.c_str()); +} + +inline +String String::substr(size_t pos, size_t len) const +{ + return String(*this, pos, len); +} + +inline +size_t String::find(const char* s, size_t pos, size_t n) const +{ + if (n == 0 || pos + n > len_) return npos; + const char* lmax = cstr_ + len_ - n; + for (const char* i = cstr_ + pos; i <= lmax; ++i) + { + size_t j = 0; + while (j < n && s[j] == i[j]) ++j; + if (j == n) return (size_t)(i - cstr_); + } + return npos; +} + +inline +size_t String::find(char c, size_t pos) const +{ + return find(&c, pos, 1); +} + +inline +size_t String::find(const String& str, size_t pos) const +{ + return find(str.c_str(), pos, str.len_); +} + +inline +size_t String::find(const char* s, size_t pos) const +{ + if (pos >= len_ || !s[0]) return npos; + const char* lmax = cstr_ + len_; + for (const char* i = cstr_ + pos; i < lmax; ++i) + { + size_t j = 0; + while (s[j] && s[j] == i[j]) + { if(i + j >= lmax) return npos; + ++j; + } + if (!s[j]) return (size_t)(i - cstr_); + } + return npos; +} + +inline +size_t String::rfind(const char* s, size_t pos, size_t n) const +{ + if (n > len_) return npos; + if (pos > len_ - n) pos = len_ - n; + for (const char* i = cstr_ + pos; i >= cstr_; --i) + { + size_t j = 0; + while (j < n && s[j] == i[j]) ++j; + if (j == n) return (size_t)(i - cstr_); + } + return npos; +} + +inline +size_t String::rfind(char c, size_t pos) const +{ + return rfind(&c, pos, 1); +} + +inline +size_t String::rfind(const String& str, size_t pos) const +{ + return rfind(str.c_str(), pos, str.len_); +} + +inline +size_t String::rfind(const char* s, size_t pos) const +{ + return rfind(s, pos, strlen(s)); +} + +inline +size_t String::find_first_of(const char* s, size_t pos, size_t n) const +{ + if (n == 0 || pos + n > len_) return npos; + const char* lmax = cstr_ + len_; + for (const char* i = cstr_ + pos; i < lmax; ++i) + { + for (size_t j = 0; j < n; ++j) + if (s[j] == *i) + return (size_t)(i - cstr_); + } + return npos; +} + +inline +size_t String::find_first_of(char c, size_t pos) const +{ + return find_first_of(&c, pos, 1); +} + +inline +size_t String::find_first_of(const String& str, size_t pos) const +{ + return find_first_of(str.c_str(), pos, str.len_); +} + +inline +size_t String::find_first_of(const char* s, size_t pos) const +{ + if (len_ == 0) return npos; + if (pos >= len_ || !s[0]) return npos; + const char* lmax = cstr_ + len_; + for (const char* i = cstr_ + pos; i < lmax; ++i) + { + for (size_t j = 0; s[j]; ++j) + if (s[j] == *i) + return (size_t)(i - cstr_); + } + return npos; +} + +inline +size_t String::find_last_of(const char* s, size_t pos, size_t n) const +{ + if (len_ == 0) return npos; + if (pos >= len_) pos = len_ - 1; + for (const char* i = cstr_ + pos; i >= cstr_; --i) + { + for (size_t j = 0; j < n; ++j) + if (s[j] == *i) + return (size_t)(i - cstr_); + } + return npos; +} + +inline +size_t String::find_last_of(char c, size_t pos) const +{ + return find_last_of(&c, pos, 1); +} + +inline +size_t String::find_last_of(const String& str, size_t pos) const +{ + return find_last_of(str.c_str(), pos, str.len_); +} + +inline +size_t String::find_last_of(const char* s, size_t pos) const +{ + if (len_ == 0) return npos; + if (pos >= len_) pos = len_ - 1; + for (const char* i = cstr_ + pos; i >= cstr_; --i) + { + for (size_t j = 0; s[j]; ++j) + if (s[j] == *i) + return (size_t)(i - cstr_); + } + return npos; +} + +inline +String String::toLowerCase() const +{ + if (!cstr_) + return String(); + String res(cstr_, len_); + for (size_t i = 0; i < len_; ++i) + res.cstr_[i] = (char) ::tolower(cstr_[i]); + + return res; +} + +//! @endcond + +// ************************* cv::String non-member functions ************************* + +//! @relates cv::String +//! @{ + +inline +String operator + (const String& lhs, const String& rhs) +{ + String s; + s.allocate(lhs.len_ + rhs.len_); + if (lhs.len_) memcpy(s.cstr_, lhs.cstr_, lhs.len_); + if (rhs.len_) memcpy(s.cstr_ + lhs.len_, rhs.cstr_, rhs.len_); + return s; +} + +inline +String operator + (const String& lhs, const char* rhs) +{ + String s; + size_t rhslen = strlen(rhs); + s.allocate(lhs.len_ + rhslen); + if (lhs.len_) memcpy(s.cstr_, lhs.cstr_, lhs.len_); + if (rhslen) memcpy(s.cstr_ + lhs.len_, rhs, rhslen); + return s; +} + +inline +String operator + (const char* lhs, const String& rhs) +{ + String s; + size_t lhslen = strlen(lhs); + s.allocate(lhslen + rhs.len_); + if (lhslen) memcpy(s.cstr_, lhs, lhslen); + if (rhs.len_) memcpy(s.cstr_ + lhslen, rhs.cstr_, rhs.len_); + return s; +} + +inline +String operator + (const String& lhs, char rhs) +{ + String s; + s.allocate(lhs.len_ + 1); + if (lhs.len_) memcpy(s.cstr_, lhs.cstr_, lhs.len_); + s.cstr_[lhs.len_] = rhs; + return s; +} + +inline +String operator + (char lhs, const String& rhs) +{ + String s; + s.allocate(rhs.len_ + 1); + s.cstr_[0] = lhs; + if (rhs.len_) memcpy(s.cstr_ + 1, rhs.cstr_, rhs.len_); + return s; +} + +static inline bool operator== (const String& lhs, const String& rhs) { return 0 == lhs.compare(rhs); } +static inline bool operator== (const char* lhs, const String& rhs) { return 0 == rhs.compare(lhs); } +static inline bool operator== (const String& lhs, const char* rhs) { return 0 == lhs.compare(rhs); } +static inline bool operator!= (const String& lhs, const String& rhs) { return 0 != lhs.compare(rhs); } +static inline bool operator!= (const char* lhs, const String& rhs) { return 0 != rhs.compare(lhs); } +static inline bool operator!= (const String& lhs, const char* rhs) { return 0 != lhs.compare(rhs); } +static inline bool operator< (const String& lhs, const String& rhs) { return lhs.compare(rhs) < 0; } +static inline bool operator< (const char* lhs, const String& rhs) { return rhs.compare(lhs) > 0; } +static inline bool operator< (const String& lhs, const char* rhs) { return lhs.compare(rhs) < 0; } +static inline bool operator<= (const String& lhs, const String& rhs) { return lhs.compare(rhs) <= 0; } +static inline bool operator<= (const char* lhs, const String& rhs) { return rhs.compare(lhs) >= 0; } +static inline bool operator<= (const String& lhs, const char* rhs) { return lhs.compare(rhs) <= 0; } +static inline bool operator> (const String& lhs, const String& rhs) { return lhs.compare(rhs) > 0; } +static inline bool operator> (const char* lhs, const String& rhs) { return rhs.compare(lhs) < 0; } +static inline bool operator> (const String& lhs, const char* rhs) { return lhs.compare(rhs) > 0; } +static inline bool operator>= (const String& lhs, const String& rhs) { return lhs.compare(rhs) >= 0; } +static inline bool operator>= (const char* lhs, const String& rhs) { return rhs.compare(lhs) <= 0; } +static inline bool operator>= (const String& lhs, const char* rhs) { return lhs.compare(rhs) >= 0; } + +//! @} relates cv::String + +} // cv + +namespace std +{ + static inline void swap(cv::String& a, cv::String& b) { a.swap(b); } +} + +#include "opencv2/core/ptr.inl.hpp" + +#endif //OPENCV_CORE_CVSTD_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cvstd.inl.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cvstd.inl.hpp new file mode 100644 index 0000000000000000000000000000000000000000..ed37cacb300e13d17aed7d1441e7bb77eed365b5 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/cvstd.inl.hpp @@ -0,0 +1,285 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_CVSTDINL_HPP +#define OPENCV_CORE_CVSTDINL_HPP + +#include +#include + +//! @cond IGNORED + +#ifdef _MSC_VER +#pragma warning( push ) +#pragma warning( disable: 4127 ) +#endif + +namespace cv +{ + +template class DataType< std::complex<_Tp> > +{ +public: + typedef std::complex<_Tp> value_type; + typedef value_type work_type; + typedef _Tp channel_type; + + enum { generic_type = 0, + depth = DataType::depth, + channels = 2, + fmt = DataType::fmt + ((channels - 1) << 8), + type = CV_MAKETYPE(depth, channels) }; + + typedef Vec vec_type; +}; + +inline +String::String(const std::string& str) + : cstr_(0), len_(0) +{ + size_t len = str.size(); + if (len) memcpy(allocate(len), str.c_str(), len); +} + +inline +String::String(const std::string& str, size_t pos, size_t len) + : cstr_(0), len_(0) +{ + size_t strlen = str.size(); + pos = min(pos, strlen); + len = min(strlen - pos, len); + if (!len) return; + memcpy(allocate(len), str.c_str() + pos, len); +} + +inline +String& String::operator = (const std::string& str) +{ + deallocate(); + size_t len = str.size(); + if (len) memcpy(allocate(len), str.c_str(), len); + return *this; +} + +inline +String& String::operator += (const std::string& str) +{ + *this = *this + str; + return *this; +} + +inline +String::operator std::string() const +{ + return std::string(cstr_, len_); +} + +inline +String operator + (const String& lhs, const std::string& rhs) +{ + String s; + size_t rhslen = rhs.size(); + s.allocate(lhs.len_ + rhslen); + if (lhs.len_) memcpy(s.cstr_, lhs.cstr_, lhs.len_); + if (rhslen) memcpy(s.cstr_ + lhs.len_, rhs.c_str(), rhslen); + return s; +} + +inline +String operator + (const std::string& lhs, const String& rhs) +{ + String s; + size_t lhslen = lhs.size(); + s.allocate(lhslen + rhs.len_); + if (lhslen) memcpy(s.cstr_, lhs.c_str(), lhslen); + if (rhs.len_) memcpy(s.cstr_ + lhslen, rhs.cstr_, rhs.len_); + return s; +} + +inline +FileNode::operator std::string() const +{ + String value; + read(*this, value, value); + return value; +} + +template<> inline +void operator >> (const FileNode& n, std::string& value) +{ + read(n, value, std::string()); +} + +template<> inline +FileStorage& operator << (FileStorage& fs, const std::string& value) +{ + return fs << cv::String(value); +} + +static inline +std::ostream& operator << (std::ostream& os, const String& str) +{ + return os << str.c_str(); +} + +static inline +std::ostream& operator << (std::ostream& out, Ptr fmtd) +{ + fmtd->reset(); + for(const char* str = fmtd->next(); str; str = fmtd->next()) + out << str; + return out; +} + +static inline +std::ostream& operator << (std::ostream& out, const Mat& mtx) +{ + return out << Formatter::get()->format(mtx); +} + +static inline +std::ostream& operator << (std::ostream& out, const UMat& m) +{ + return out << m.getMat(ACCESS_READ); +} + +template static inline +std::ostream& operator << (std::ostream& out, const Complex<_Tp>& c) +{ + return out << "(" << c.re << "," << c.im << ")"; +} + +template static inline +std::ostream& operator << (std::ostream& out, const std::vector >& vec) +{ + return out << Formatter::get()->format(Mat(vec)); +} + + +template static inline +std::ostream& operator << (std::ostream& out, const std::vector >& vec) +{ + return out << Formatter::get()->format(Mat(vec)); +} + + +template static inline +std::ostream& operator << (std::ostream& out, const Matx<_Tp, m, n>& matx) +{ + return out << Formatter::get()->format(Mat(matx)); +} + +template static inline +std::ostream& operator << (std::ostream& out, const Point_<_Tp>& p) +{ + out << "[" << p.x << ", " << p.y << "]"; + return out; +} + +template static inline +std::ostream& operator << (std::ostream& out, const Point3_<_Tp>& p) +{ + out << "[" << p.x << ", " << p.y << ", " << p.z << "]"; + return out; +} + +template static inline +std::ostream& operator << (std::ostream& out, const Vec<_Tp, n>& vec) +{ + out << "["; + if (cv::traits::Depth<_Tp>::value <= CV_32S) + { + for (int i = 0; i < n - 1; ++i) { + out << (int)vec[i] << ", "; + } + out << (int)vec[n-1] << "]"; + } + else + { + for (int i = 0; i < n - 1; ++i) { + out << vec[i] << ", "; + } + out << vec[n-1] << "]"; + } + + return out; +} + +template static inline +std::ostream& operator << (std::ostream& out, const Size_<_Tp>& size) +{ + return out << "[" << size.width << " x " << size.height << "]"; +} + +template static inline +std::ostream& operator << (std::ostream& out, const Rect_<_Tp>& rect) +{ + return out << "[" << rect.width << " x " << rect.height << " from (" << rect.x << ", " << rect.y << ")]"; +} + +static inline std::ostream& operator << (std::ostream& out, const MatSize& msize) +{ + int i, dims = msize.dims(); + for( i = 0; i < dims; i++ ) + { + out << msize[i]; + if( i < dims-1 ) + out << " x "; + } + return out; +} + +static inline std::ostream &operator<< (std::ostream &s, cv::Range &r) +{ + return s << "[" << r.start << " : " << r.end << ")"; +} + +} // cv + +#ifdef _MSC_VER +#pragma warning( pop ) +#endif + +//! @endcond + +#endif // OPENCV_CORE_CVSTDINL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/directx.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/directx.hpp new file mode 100644 index 0000000000000000000000000000000000000000..056a85a1bc996f1483b681273887fc25840f7251 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/directx.hpp @@ -0,0 +1,184 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors as is and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the copyright holders or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_DIRECTX_HPP +#define OPENCV_CORE_DIRECTX_HPP + +#include "mat.hpp" +#include "ocl.hpp" + +#if !defined(__d3d11_h__) +struct ID3D11Device; +struct ID3D11Texture2D; +#endif + +#if !defined(__d3d10_h__) +struct ID3D10Device; +struct ID3D10Texture2D; +#endif + +#if !defined(_D3D9_H_) +struct IDirect3DDevice9; +struct IDirect3DDevice9Ex; +struct IDirect3DSurface9; +#endif + + +namespace cv { namespace directx { + +namespace ocl { +using namespace cv::ocl; + +//! @addtogroup core_directx +// This section describes OpenCL and DirectX interoperability. +// +// To enable DirectX support, configure OpenCV using CMake with WITH_DIRECTX=ON . Note, DirectX is +// supported only on Windows. +// +// To use OpenCL functionality you should first initialize OpenCL context from DirectX resource. +// +//! @{ + +// TODO static functions in the Context class +//! @brief Creates OpenCL context from D3D11 device +// +//! @param pD3D11Device - pointer to D3D11 device +//! @return Returns reference to OpenCL Context +CV_EXPORTS Context& initializeContextFromD3D11Device(ID3D11Device* pD3D11Device); + +//! @brief Creates OpenCL context from D3D10 device +// +//! @param pD3D10Device - pointer to D3D10 device +//! @return Returns reference to OpenCL Context +CV_EXPORTS Context& initializeContextFromD3D10Device(ID3D10Device* pD3D10Device); + +//! @brief Creates OpenCL context from Direct3DDevice9Ex device +// +//! @param pDirect3DDevice9Ex - pointer to Direct3DDevice9Ex device +//! @return Returns reference to OpenCL Context +CV_EXPORTS Context& initializeContextFromDirect3DDevice9Ex(IDirect3DDevice9Ex* pDirect3DDevice9Ex); + +//! @brief Creates OpenCL context from Direct3DDevice9 device +// +//! @param pDirect3DDevice9 - pointer to Direct3Device9 device +//! @return Returns reference to OpenCL Context +CV_EXPORTS Context& initializeContextFromDirect3DDevice9(IDirect3DDevice9* pDirect3DDevice9); + +//! @} + +} // namespace cv::directx::ocl + +//! @addtogroup core_directx +//! @{ + +//! @brief Converts InputArray to ID3D11Texture2D. If destination texture format is DXGI_FORMAT_NV12 then +//! input UMat expected to be in BGR format and data will be downsampled and color-converted to NV12. +// +//! @note Note: Destination texture must be allocated by application. Function does memory copy from src to +//! pD3D11Texture2D +// +//! @param src - source InputArray +//! @param pD3D11Texture2D - destination D3D11 texture +CV_EXPORTS void convertToD3D11Texture2D(InputArray src, ID3D11Texture2D* pD3D11Texture2D); + +//! @brief Converts ID3D11Texture2D to OutputArray. If input texture format is DXGI_FORMAT_NV12 then +//! data will be upsampled and color-converted to BGR format. +// +//! @note Note: Destination matrix will be re-allocated if it has not enough memory to match texture size. +//! function does memory copy from pD3D11Texture2D to dst +// +//! @param pD3D11Texture2D - source D3D11 texture +//! @param dst - destination OutputArray +CV_EXPORTS void convertFromD3D11Texture2D(ID3D11Texture2D* pD3D11Texture2D, OutputArray dst); + +//! @brief Converts InputArray to ID3D10Texture2D +// +//! @note Note: function does memory copy from src to +//! pD3D10Texture2D +// +//! @param src - source InputArray +//! @param pD3D10Texture2D - destination D3D10 texture +CV_EXPORTS void convertToD3D10Texture2D(InputArray src, ID3D10Texture2D* pD3D10Texture2D); + +//! @brief Converts ID3D10Texture2D to OutputArray +// +//! @note Note: function does memory copy from pD3D10Texture2D +//! to dst +// +//! @param pD3D10Texture2D - source D3D10 texture +//! @param dst - destination OutputArray +CV_EXPORTS void convertFromD3D10Texture2D(ID3D10Texture2D* pD3D10Texture2D, OutputArray dst); + +//! @brief Converts InputArray to IDirect3DSurface9 +// +//! @note Note: function does memory copy from src to +//! pDirect3DSurface9 +// +//! @param src - source InputArray +//! @param pDirect3DSurface9 - destination D3D10 texture +//! @param surfaceSharedHandle - shared handle +CV_EXPORTS void convertToDirect3DSurface9(InputArray src, IDirect3DSurface9* pDirect3DSurface9, void* surfaceSharedHandle = NULL); + +//! @brief Converts IDirect3DSurface9 to OutputArray +// +//! @note Note: function does memory copy from pDirect3DSurface9 +//! to dst +// +//! @param pDirect3DSurface9 - source D3D10 texture +//! @param dst - destination OutputArray +//! @param surfaceSharedHandle - shared handle +CV_EXPORTS void convertFromDirect3DSurface9(IDirect3DSurface9* pDirect3DSurface9, OutputArray dst, void* surfaceSharedHandle = NULL); + +//! @brief Get OpenCV type from DirectX type +//! @param iDXGI_FORMAT - enum DXGI_FORMAT for D3D10/D3D11 +//! @return OpenCV type or -1 if there is no equivalent +CV_EXPORTS int getTypeFromDXGI_FORMAT(const int iDXGI_FORMAT); // enum DXGI_FORMAT for D3D10/D3D11 + +//! @brief Get OpenCV type from DirectX type +//! @param iD3DFORMAT - enum D3DTYPE for D3D9 +//! @return OpenCV type or -1 if there is no equivalent +CV_EXPORTS int getTypeFromD3DFORMAT(const int iD3DFORMAT); // enum D3DTYPE for D3D9 + +//! @} + +} } // namespace cv::directx + +#endif // OPENCV_CORE_DIRECTX_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/eigen.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/eigen.hpp new file mode 100644 index 0000000000000000000000000000000000000000..741648edb8fb12e6597d75691fd0019f07ded093 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/eigen.hpp @@ -0,0 +1,280 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + + +#ifndef OPENCV_CORE_EIGEN_HPP +#define OPENCV_CORE_EIGEN_HPP + +#include "opencv2/core.hpp" + +#if defined _MSC_VER && _MSC_VER >= 1200 +#pragma warning( disable: 4714 ) //__forceinline is not inlined +#pragma warning( disable: 4127 ) //conditional expression is constant +#pragma warning( disable: 4244 ) //conversion from '__int64' to 'int', possible loss of data +#endif + +namespace cv +{ + +//! @addtogroup core_eigen +//! @{ + +template static inline +void eigen2cv( const Eigen::Matrix<_Tp, _rows, _cols, _options, _maxRows, _maxCols>& src, OutputArray dst ) +{ + if( !(src.Flags & Eigen::RowMajorBit) ) + { + Mat _src(src.cols(), src.rows(), traits::Type<_Tp>::value, + (void*)src.data(), src.outerStride()*sizeof(_Tp)); + transpose(_src, dst); + } + else + { + Mat _src(src.rows(), src.cols(), traits::Type<_Tp>::value, + (void*)src.data(), src.outerStride()*sizeof(_Tp)); + _src.copyTo(dst); + } +} + +// Matx case +template static inline +void eigen2cv( const Eigen::Matrix<_Tp, _rows, _cols, _options, _maxRows, _maxCols>& src, + Matx<_Tp, _rows, _cols>& dst ) +{ + if( !(src.Flags & Eigen::RowMajorBit) ) + { + dst = Matx<_Tp, _cols, _rows>(static_cast(src.data())).t(); + } + else + { + dst = Matx<_Tp, _rows, _cols>(static_cast(src.data())); + } +} + +template static inline +void cv2eigen( const Mat& src, + Eigen::Matrix<_Tp, _rows, _cols, _options, _maxRows, _maxCols>& dst ) +{ + CV_DbgAssert(src.rows == _rows && src.cols == _cols); + if( !(dst.Flags & Eigen::RowMajorBit) ) + { + const Mat _dst(src.cols, src.rows, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + if( src.type() == _dst.type() ) + transpose(src, _dst); + else if( src.cols == src.rows ) + { + src.convertTo(_dst, _dst.type()); + transpose(_dst, _dst); + } + else + Mat(src.t()).convertTo(_dst, _dst.type()); + } + else + { + const Mat _dst(src.rows, src.cols, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + src.convertTo(_dst, _dst.type()); + } +} + +// Matx case +template static inline +void cv2eigen( const Matx<_Tp, _rows, _cols>& src, + Eigen::Matrix<_Tp, _rows, _cols, _options, _maxRows, _maxCols>& dst ) +{ + if( !(dst.Flags & Eigen::RowMajorBit) ) + { + const Mat _dst(_cols, _rows, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + transpose(src, _dst); + } + else + { + const Mat _dst(_rows, _cols, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + Mat(src).copyTo(_dst); + } +} + +template static inline +void cv2eigen( const Mat& src, + Eigen::Matrix<_Tp, Eigen::Dynamic, Eigen::Dynamic>& dst ) +{ + dst.resize(src.rows, src.cols); + if( !(dst.Flags & Eigen::RowMajorBit) ) + { + const Mat _dst(src.cols, src.rows, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + if( src.type() == _dst.type() ) + transpose(src, _dst); + else if( src.cols == src.rows ) + { + src.convertTo(_dst, _dst.type()); + transpose(_dst, _dst); + } + else + Mat(src.t()).convertTo(_dst, _dst.type()); + } + else + { + const Mat _dst(src.rows, src.cols, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + src.convertTo(_dst, _dst.type()); + } +} + +// Matx case +template static inline +void cv2eigen( const Matx<_Tp, _rows, _cols>& src, + Eigen::Matrix<_Tp, Eigen::Dynamic, Eigen::Dynamic>& dst ) +{ + dst.resize(_rows, _cols); + if( !(dst.Flags & Eigen::RowMajorBit) ) + { + const Mat _dst(_cols, _rows, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + transpose(src, _dst); + } + else + { + const Mat _dst(_rows, _cols, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + Mat(src).copyTo(_dst); + } +} + +template static inline +void cv2eigen( const Mat& src, + Eigen::Matrix<_Tp, Eigen::Dynamic, 1>& dst ) +{ + CV_Assert(src.cols == 1); + dst.resize(src.rows); + + if( !(dst.Flags & Eigen::RowMajorBit) ) + { + const Mat _dst(src.cols, src.rows, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + if( src.type() == _dst.type() ) + transpose(src, _dst); + else + Mat(src.t()).convertTo(_dst, _dst.type()); + } + else + { + const Mat _dst(src.rows, src.cols, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + src.convertTo(_dst, _dst.type()); + } +} + +// Matx case +template static inline +void cv2eigen( const Matx<_Tp, _rows, 1>& src, + Eigen::Matrix<_Tp, Eigen::Dynamic, 1>& dst ) +{ + dst.resize(_rows); + + if( !(dst.Flags & Eigen::RowMajorBit) ) + { + const Mat _dst(1, _rows, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + transpose(src, _dst); + } + else + { + const Mat _dst(_rows, 1, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + src.copyTo(_dst); + } +} + + +template static inline +void cv2eigen( const Mat& src, + Eigen::Matrix<_Tp, 1, Eigen::Dynamic>& dst ) +{ + CV_Assert(src.rows == 1); + dst.resize(src.cols); + if( !(dst.Flags & Eigen::RowMajorBit) ) + { + const Mat _dst(src.cols, src.rows, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + if( src.type() == _dst.type() ) + transpose(src, _dst); + else + Mat(src.t()).convertTo(_dst, _dst.type()); + } + else + { + const Mat _dst(src.rows, src.cols, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + src.convertTo(_dst, _dst.type()); + } +} + +//Matx +template static inline +void cv2eigen( const Matx<_Tp, 1, _cols>& src, + Eigen::Matrix<_Tp, 1, Eigen::Dynamic>& dst ) +{ + dst.resize(_cols); + if( !(dst.Flags & Eigen::RowMajorBit) ) + { + const Mat _dst(_cols, 1, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + transpose(src, _dst); + } + else + { + const Mat _dst(1, _cols, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + Mat(src).copyTo(_dst); + } +} + +//! @} + +} // cv + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/fast_math.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/fast_math.hpp new file mode 100644 index 0000000000000000000000000000000000000000..d9ea28e6325a8e76df473d22371617b3ce324f64 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/fast_math.hpp @@ -0,0 +1,271 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_FAST_MATH_HPP +#define OPENCV_CORE_FAST_MATH_HPP + +#include "opencv2/core/cvdef.h" + +#if ((defined _MSC_VER && defined _M_X64) || (defined __GNUC__ && defined __x86_64__ \ + && defined __SSE2__ && !defined __APPLE__)) && !defined(__CUDACC__) +#include +#endif + + +//! @addtogroup core_utils +//! @{ + +/****************************************************************************************\ +* fast math * +\****************************************************************************************/ + +#ifdef __cplusplus +# include +#else +# ifdef __BORLANDC__ +# include +# else +# include +# endif +#endif + +#ifdef HAVE_TEGRA_OPTIMIZATION +# include "tegra_round.hpp" +#endif + +#if defined __GNUC__ && defined __arm__ && (defined __ARM_PCS_VFP || defined __ARM_VFPV3__ || defined __ARM_NEON__) && !defined __SOFTFP__ && !defined(__CUDACC__) + // 1. general scheme + #define ARM_ROUND(_value, _asm_string) \ + int res; \ + float temp; \ + CV_UNUSED(temp); \ + __asm__(_asm_string : [res] "=r" (res), [temp] "=w" (temp) : [value] "w" (_value)); \ + return res + // 2. version for double + #ifdef __clang__ + #define ARM_ROUND_DBL(value) ARM_ROUND(value, "vcvtr.s32.f64 %[temp], %[value] \n vmov %[res], %[temp]") + #else + #define ARM_ROUND_DBL(value) ARM_ROUND(value, "vcvtr.s32.f64 %[temp], %P[value] \n vmov %[res], %[temp]") + #endif + // 3. version for float + #define ARM_ROUND_FLT(value) ARM_ROUND(value, "vcvtr.s32.f32 %[temp], %[value]\n vmov %[res], %[temp]") +#endif + +/** @brief Rounds floating-point number to the nearest integer + + @param value floating-point number. If the value is outside of INT_MIN ... INT_MAX range, the + result is not defined. + */ +CV_INLINE int +cvRound( double value ) +{ +#if ((defined _MSC_VER && defined _M_X64) || (defined __GNUC__ && defined __x86_64__ \ + && defined __SSE2__ && !defined __APPLE__) || CV_SSE2) && !defined(__CUDACC__) + __m128d t = _mm_set_sd( value ); + return _mm_cvtsd_si32(t); +#elif defined _MSC_VER && defined _M_IX86 + int t; + __asm + { + fld value; + fistp t; + } + return t; +#elif ((defined _MSC_VER && defined _M_ARM) || defined CV_ICC || \ + defined __GNUC__) && defined HAVE_TEGRA_OPTIMIZATION + TEGRA_ROUND_DBL(value); +#elif defined CV_ICC || defined __GNUC__ +# if defined ARM_ROUND_DBL + ARM_ROUND_DBL(value); +# else + return (int)lrint(value); +# endif +#else + /* it's ok if round does not comply with IEEE754 standard; + the tests should allow +/-1 difference when the tested functions use round */ + return (int)(value + (value >= 0 ? 0.5 : -0.5)); +#endif +} + + +/** @brief Rounds floating-point number to the nearest integer not larger than the original. + + The function computes an integer i such that: + \f[i \le \texttt{value} < i+1\f] + @param value floating-point number. If the value is outside of INT_MIN ... INT_MAX range, the + result is not defined. + */ +CV_INLINE int cvFloor( double value ) +{ + int i = (int)value; + return i - (i > value); +} + +/** @brief Rounds floating-point number to the nearest integer not smaller than the original. + + The function computes an integer i such that: + \f[i \le \texttt{value} < i+1\f] + @param value floating-point number. If the value is outside of INT_MIN ... INT_MAX range, the + result is not defined. + */ +CV_INLINE int cvCeil( double value ) +{ + int i = (int)value; + return i + (i < value); +} + +/** @brief Determines if the argument is Not A Number. + + @param value The input floating-point value + + The function returns 1 if the argument is Not A Number (as defined by IEEE754 standard), 0 + otherwise. */ +CV_INLINE int cvIsNaN( double value ) +{ + Cv64suf ieee754; + ieee754.f = value; + return ((unsigned)(ieee754.u >> 32) & 0x7fffffff) + + ((unsigned)ieee754.u != 0) > 0x7ff00000; +} + +/** @brief Determines if the argument is Infinity. + + @param value The input floating-point value + + The function returns 1 if the argument is a plus or minus infinity (as defined by IEEE754 standard) + and 0 otherwise. */ +CV_INLINE int cvIsInf( double value ) +{ + Cv64suf ieee754; + ieee754.f = value; + return ((unsigned)(ieee754.u >> 32) & 0x7fffffff) == 0x7ff00000 && + (unsigned)ieee754.u == 0; +} + +#ifdef __cplusplus + +/** @overload */ +CV_INLINE int cvRound(float value) +{ +#if ((defined _MSC_VER && defined _M_X64) || (defined __GNUC__ && defined __x86_64__ \ + && defined __SSE2__ && !defined __APPLE__) || CV_SSE2) && !defined(__CUDACC__) + __m128 t = _mm_set_ss( value ); + return _mm_cvtss_si32(t); +#elif defined _MSC_VER && defined _M_IX86 + int t; + __asm + { + fld value; + fistp t; + } + return t; +#elif ((defined _MSC_VER && defined _M_ARM) || defined CV_ICC || \ + defined __GNUC__) && defined HAVE_TEGRA_OPTIMIZATION + TEGRA_ROUND_FLT(value); +#elif defined CV_ICC || defined __GNUC__ +# if defined ARM_ROUND_FLT + ARM_ROUND_FLT(value); +# else + return (int)lrintf(value); +# endif +#else + /* it's ok if round does not comply with IEEE754 standard; + the tests should allow +/-1 difference when the tested functions use round */ + return (int)(value + (value >= 0 ? 0.5f : -0.5f)); +#endif +} + +/** @overload */ +CV_INLINE int cvRound( int value ) +{ + return value; +} + +/** @overload */ +CV_INLINE int cvFloor( float value ) +{ + int i = (int)value; + return i - (i > value); +} + +/** @overload */ +CV_INLINE int cvFloor( int value ) +{ + return value; +} + +/** @overload */ +CV_INLINE int cvCeil( float value ) +{ + int i = (int)value; + return i + (i < value); +} + +/** @overload */ +CV_INLINE int cvCeil( int value ) +{ + return value; +} + +/** @overload */ +CV_INLINE int cvIsNaN( float value ) +{ + Cv32suf ieee754; + ieee754.f = value; + return (ieee754.u & 0x7fffffff) > 0x7f800000; +} + +/** @overload */ +CV_INLINE int cvIsInf( float value ) +{ + Cv32suf ieee754; + ieee754.f = value; + return (ieee754.u & 0x7fffffff) == 0x7f800000; +} + +#endif // __cplusplus + +//! @} core_utils + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/hal/hal.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/hal/hal.hpp new file mode 100644 index 0000000000000000000000000000000000000000..68900ec4281028f445b44b603790181d9731f7b3 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/hal/hal.hpp @@ -0,0 +1,250 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_HAL_HPP +#define OPENCV_HAL_HPP + +#include "opencv2/core/cvdef.h" +#include "opencv2/core/cvstd.hpp" +#include "opencv2/core/hal/interface.h" + +namespace cv { namespace hal { + +//! @addtogroup core_hal_functions +//! @{ + +CV_EXPORTS int normHamming(const uchar* a, int n); +CV_EXPORTS int normHamming(const uchar* a, const uchar* b, int n); + +CV_EXPORTS int normHamming(const uchar* a, int n, int cellSize); +CV_EXPORTS int normHamming(const uchar* a, const uchar* b, int n, int cellSize); + +CV_EXPORTS int LU32f(float* A, size_t astep, int m, float* b, size_t bstep, int n); +CV_EXPORTS int LU64f(double* A, size_t astep, int m, double* b, size_t bstep, int n); +CV_EXPORTS bool Cholesky32f(float* A, size_t astep, int m, float* b, size_t bstep, int n); +CV_EXPORTS bool Cholesky64f(double* A, size_t astep, int m, double* b, size_t bstep, int n); +CV_EXPORTS void SVD32f(float* At, size_t astep, float* W, float* U, size_t ustep, float* Vt, size_t vstep, int m, int n, int flags); +CV_EXPORTS void SVD64f(double* At, size_t astep, double* W, double* U, size_t ustep, double* Vt, size_t vstep, int m, int n, int flags); +CV_EXPORTS int QR32f(float* A, size_t astep, int m, int n, int k, float* b, size_t bstep, float* hFactors); +CV_EXPORTS int QR64f(double* A, size_t astep, int m, int n, int k, double* b, size_t bstep, double* hFactors); + +CV_EXPORTS void gemm32f(const float* src1, size_t src1_step, const float* src2, size_t src2_step, + float alpha, const float* src3, size_t src3_step, float beta, float* dst, size_t dst_step, + int m_a, int n_a, int n_d, int flags); +CV_EXPORTS void gemm64f(const double* src1, size_t src1_step, const double* src2, size_t src2_step, + double alpha, const double* src3, size_t src3_step, double beta, double* dst, size_t dst_step, + int m_a, int n_a, int n_d, int flags); +CV_EXPORTS void gemm32fc(const float* src1, size_t src1_step, const float* src2, size_t src2_step, + float alpha, const float* src3, size_t src3_step, float beta, float* dst, size_t dst_step, + int m_a, int n_a, int n_d, int flags); +CV_EXPORTS void gemm64fc(const double* src1, size_t src1_step, const double* src2, size_t src2_step, + double alpha, const double* src3, size_t src3_step, double beta, double* dst, size_t dst_step, + int m_a, int n_a, int n_d, int flags); + +CV_EXPORTS int normL1_(const uchar* a, const uchar* b, int n); +CV_EXPORTS float normL1_(const float* a, const float* b, int n); +CV_EXPORTS float normL2Sqr_(const float* a, const float* b, int n); + +CV_EXPORTS void exp32f(const float* src, float* dst, int n); +CV_EXPORTS void exp64f(const double* src, double* dst, int n); +CV_EXPORTS void log32f(const float* src, float* dst, int n); +CV_EXPORTS void log64f(const double* src, double* dst, int n); + +CV_EXPORTS void fastAtan32f(const float* y, const float* x, float* dst, int n, bool angleInDegrees); +CV_EXPORTS void fastAtan64f(const double* y, const double* x, double* dst, int n, bool angleInDegrees); +CV_EXPORTS void magnitude32f(const float* x, const float* y, float* dst, int n); +CV_EXPORTS void magnitude64f(const double* x, const double* y, double* dst, int n); +CV_EXPORTS void sqrt32f(const float* src, float* dst, int len); +CV_EXPORTS void sqrt64f(const double* src, double* dst, int len); +CV_EXPORTS void invSqrt32f(const float* src, float* dst, int len); +CV_EXPORTS void invSqrt64f(const double* src, double* dst, int len); + +CV_EXPORTS void split8u(const uchar* src, uchar** dst, int len, int cn ); +CV_EXPORTS void split16u(const ushort* src, ushort** dst, int len, int cn ); +CV_EXPORTS void split32s(const int* src, int** dst, int len, int cn ); +CV_EXPORTS void split64s(const int64* src, int64** dst, int len, int cn ); + +CV_EXPORTS void merge8u(const uchar** src, uchar* dst, int len, int cn ); +CV_EXPORTS void merge16u(const ushort** src, ushort* dst, int len, int cn ); +CV_EXPORTS void merge32s(const int** src, int* dst, int len, int cn ); +CV_EXPORTS void merge64s(const int64** src, int64* dst, int len, int cn ); + +CV_EXPORTS void add8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void add8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void add16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void add16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void add32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void add32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void add64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* ); + +CV_EXPORTS void sub8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void sub8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void sub16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void sub16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void sub32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void sub32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void sub64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* ); + +CV_EXPORTS void max8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void max8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void max16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void max16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void max32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void max32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void max64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* ); + +CV_EXPORTS void min8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void min8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void min16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void min16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void min32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void min32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void min64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* ); + +CV_EXPORTS void absdiff8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void absdiff8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void absdiff16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void absdiff16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void absdiff32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void absdiff32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void absdiff64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* ); + +CV_EXPORTS void and8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void or8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void xor8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void not8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); + +CV_EXPORTS void cmp8u(const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop); +CV_EXPORTS void cmp8s(const schar* src1, size_t step1, const schar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop); +CV_EXPORTS void cmp16u(const ushort* src1, size_t step1, const ushort* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop); +CV_EXPORTS void cmp16s(const short* src1, size_t step1, const short* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop); +CV_EXPORTS void cmp32s(const int* src1, size_t step1, const int* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop); +CV_EXPORTS void cmp32f(const float* src1, size_t step1, const float* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop); +CV_EXPORTS void cmp64f(const double* src1, size_t step1, const double* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop); + +CV_EXPORTS void mul8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void mul8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void mul16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void mul16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void mul32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void mul32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void mul64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* scale); + +CV_EXPORTS void div8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void div8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void div16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void div16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void div32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void div32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void div64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* scale); + +CV_EXPORTS void recip8u( const uchar *, size_t, const uchar * src2, size_t step2, uchar* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void recip8s( const schar *, size_t, const schar * src2, size_t step2, schar* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void recip16u( const ushort *, size_t, const ushort * src2, size_t step2, ushort* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void recip16s( const short *, size_t, const short * src2, size_t step2, short* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void recip32s( const int *, size_t, const int * src2, size_t step2, int* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void recip32f( const float *, size_t, const float * src2, size_t step2, float* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void recip64f( const double *, size_t, const double * src2, size_t step2, double* dst, size_t step, int width, int height, void* scale); + +CV_EXPORTS void addWeighted8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _scalars ); +CV_EXPORTS void addWeighted8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* scalars ); +CV_EXPORTS void addWeighted16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* scalars ); +CV_EXPORTS void addWeighted16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* scalars ); +CV_EXPORTS void addWeighted32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* scalars ); +CV_EXPORTS void addWeighted32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* scalars ); +CV_EXPORTS void addWeighted64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* scalars ); + +struct CV_EXPORTS DFT1D +{ + static Ptr create(int len, int count, int depth, int flags, bool * useBuffer = 0); + virtual void apply(const uchar *src, uchar *dst) = 0; + virtual ~DFT1D() {} +}; + +struct CV_EXPORTS DFT2D +{ + static Ptr create(int width, int height, int depth, + int src_channels, int dst_channels, + int flags, int nonzero_rows = 0); + virtual void apply(const uchar *src_data, size_t src_step, uchar *dst_data, size_t dst_step) = 0; + virtual ~DFT2D() {} +}; + +struct CV_EXPORTS DCT2D +{ + static Ptr create(int width, int height, int depth, int flags); + virtual void apply(const uchar *src_data, size_t src_step, uchar *dst_data, size_t dst_step) = 0; + virtual ~DCT2D() {} +}; + +//! @} core_hal + +//============================================================================= +// for binary compatibility with 3.0 + +//! @cond IGNORED + +CV_EXPORTS int LU(float* A, size_t astep, int m, float* b, size_t bstep, int n); +CV_EXPORTS int LU(double* A, size_t astep, int m, double* b, size_t bstep, int n); +CV_EXPORTS bool Cholesky(float* A, size_t astep, int m, float* b, size_t bstep, int n); +CV_EXPORTS bool Cholesky(double* A, size_t astep, int m, double* b, size_t bstep, int n); + +CV_EXPORTS void exp(const float* src, float* dst, int n); +CV_EXPORTS void exp(const double* src, double* dst, int n); +CV_EXPORTS void log(const float* src, float* dst, int n); +CV_EXPORTS void log(const double* src, double* dst, int n); + +CV_EXPORTS void fastAtan2(const float* y, const float* x, float* dst, int n, bool angleInDegrees); +CV_EXPORTS void magnitude(const float* x, const float* y, float* dst, int n); +CV_EXPORTS void magnitude(const double* x, const double* y, double* dst, int n); +CV_EXPORTS void sqrt(const float* src, float* dst, int len); +CV_EXPORTS void sqrt(const double* src, double* dst, int len); +CV_EXPORTS void invSqrt(const float* src, float* dst, int len); +CV_EXPORTS void invSqrt(const double* src, double* dst, int len); + +//! @endcond + +}} //cv::hal + +#endif //OPENCV_HAL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/hal/interface.h b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/hal/interface.h new file mode 100644 index 0000000000000000000000000000000000000000..8f640254c373728d46d3e4c32ebb15518c6f9b9a --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/hal/interface.h @@ -0,0 +1,182 @@ +#ifndef OPENCV_CORE_HAL_INTERFACE_H +#define OPENCV_CORE_HAL_INTERFACE_H + +//! @addtogroup core_hal_interface +//! @{ + +//! @name Return codes +//! @{ +#define CV_HAL_ERROR_OK 0 +#define CV_HAL_ERROR_NOT_IMPLEMENTED 1 +#define CV_HAL_ERROR_UNKNOWN -1 +//! @} + +#ifdef __cplusplus +#include +#else +#include +#include +#endif + +//! @name Data types +//! primitive types +//! - schar - signed 1 byte integer +//! - uchar - unsigned 1 byte integer +//! - short - signed 2 byte integer +//! - ushort - unsigned 2 byte integer +//! - int - signed 4 byte integer +//! - uint - unsigned 4 byte integer +//! - int64 - signed 8 byte integer +//! - uint64 - unsigned 8 byte integer +//! @{ +#if !defined _MSC_VER && !defined __BORLANDC__ +# if defined __cplusplus && __cplusplus >= 201103L && !defined __APPLE__ +# include +# ifdef __NEWLIB__ + typedef unsigned int uint; +# else + typedef std::uint32_t uint; +# endif +# else +# include + typedef uint32_t uint; +# endif +#else + typedef unsigned uint; +#endif + +typedef signed char schar; + +#ifndef __IPL_H__ + typedef unsigned char uchar; + typedef unsigned short ushort; +#endif + +#if defined _MSC_VER || defined __BORLANDC__ + typedef __int64 int64; + typedef unsigned __int64 uint64; +# define CV_BIG_INT(n) n##I64 +# define CV_BIG_UINT(n) n##UI64 +#else + typedef int64_t int64; + typedef uint64_t uint64; +# define CV_BIG_INT(n) n##LL +# define CV_BIG_UINT(n) n##ULL +#endif + +#define CV_CN_MAX 512 +#define CV_CN_SHIFT 3 +#define CV_DEPTH_MAX (1 << CV_CN_SHIFT) + +#define CV_8U 0 +#define CV_8S 1 +#define CV_16U 2 +#define CV_16S 3 +#define CV_32S 4 +#define CV_32F 5 +#define CV_64F 6 +#define CV_USRTYPE1 7 + +#define CV_MAT_DEPTH_MASK (CV_DEPTH_MAX - 1) +#define CV_MAT_DEPTH(flags) ((flags) & CV_MAT_DEPTH_MASK) + +#define CV_MAKETYPE(depth,cn) (CV_MAT_DEPTH(depth) + (((cn)-1) << CV_CN_SHIFT)) +#define CV_MAKE_TYPE CV_MAKETYPE + +#define CV_8UC1 CV_MAKETYPE(CV_8U,1) +#define CV_8UC2 CV_MAKETYPE(CV_8U,2) +#define CV_8UC3 CV_MAKETYPE(CV_8U,3) +#define CV_8UC4 CV_MAKETYPE(CV_8U,4) +#define CV_8UC(n) CV_MAKETYPE(CV_8U,(n)) + +#define CV_8SC1 CV_MAKETYPE(CV_8S,1) +#define CV_8SC2 CV_MAKETYPE(CV_8S,2) +#define CV_8SC3 CV_MAKETYPE(CV_8S,3) +#define CV_8SC4 CV_MAKETYPE(CV_8S,4) +#define CV_8SC(n) CV_MAKETYPE(CV_8S,(n)) + +#define CV_16UC1 CV_MAKETYPE(CV_16U,1) +#define CV_16UC2 CV_MAKETYPE(CV_16U,2) +#define CV_16UC3 CV_MAKETYPE(CV_16U,3) +#define CV_16UC4 CV_MAKETYPE(CV_16U,4) +#define CV_16UC(n) CV_MAKETYPE(CV_16U,(n)) + +#define CV_16SC1 CV_MAKETYPE(CV_16S,1) +#define CV_16SC2 CV_MAKETYPE(CV_16S,2) +#define CV_16SC3 CV_MAKETYPE(CV_16S,3) +#define CV_16SC4 CV_MAKETYPE(CV_16S,4) +#define CV_16SC(n) CV_MAKETYPE(CV_16S,(n)) + +#define CV_32SC1 CV_MAKETYPE(CV_32S,1) +#define CV_32SC2 CV_MAKETYPE(CV_32S,2) +#define CV_32SC3 CV_MAKETYPE(CV_32S,3) +#define CV_32SC4 CV_MAKETYPE(CV_32S,4) +#define CV_32SC(n) CV_MAKETYPE(CV_32S,(n)) + +#define CV_32FC1 CV_MAKETYPE(CV_32F,1) +#define CV_32FC2 CV_MAKETYPE(CV_32F,2) +#define CV_32FC3 CV_MAKETYPE(CV_32F,3) +#define CV_32FC4 CV_MAKETYPE(CV_32F,4) +#define CV_32FC(n) CV_MAKETYPE(CV_32F,(n)) + +#define CV_64FC1 CV_MAKETYPE(CV_64F,1) +#define CV_64FC2 CV_MAKETYPE(CV_64F,2) +#define CV_64FC3 CV_MAKETYPE(CV_64F,3) +#define CV_64FC4 CV_MAKETYPE(CV_64F,4) +#define CV_64FC(n) CV_MAKETYPE(CV_64F,(n)) +//! @} + +//! @name Comparison operation +//! @sa cv::CmpTypes +//! @{ +#define CV_HAL_CMP_EQ 0 +#define CV_HAL_CMP_GT 1 +#define CV_HAL_CMP_GE 2 +#define CV_HAL_CMP_LT 3 +#define CV_HAL_CMP_LE 4 +#define CV_HAL_CMP_NE 5 +//! @} + +//! @name Border processing modes +//! @sa cv::BorderTypes +//! @{ +#define CV_HAL_BORDER_CONSTANT 0 +#define CV_HAL_BORDER_REPLICATE 1 +#define CV_HAL_BORDER_REFLECT 2 +#define CV_HAL_BORDER_WRAP 3 +#define CV_HAL_BORDER_REFLECT_101 4 +#define CV_HAL_BORDER_TRANSPARENT 5 +#define CV_HAL_BORDER_ISOLATED 16 +//! @} + +//! @name DFT flags +//! @{ +#define CV_HAL_DFT_INVERSE 1 +#define CV_HAL_DFT_SCALE 2 +#define CV_HAL_DFT_ROWS 4 +#define CV_HAL_DFT_COMPLEX_OUTPUT 16 +#define CV_HAL_DFT_REAL_OUTPUT 32 +#define CV_HAL_DFT_TWO_STAGE 64 +#define CV_HAL_DFT_STAGE_COLS 128 +#define CV_HAL_DFT_IS_CONTINUOUS 512 +#define CV_HAL_DFT_IS_INPLACE 1024 +//! @} + +//! @name SVD flags +//! @{ +#define CV_HAL_SVD_NO_UV 1 +#define CV_HAL_SVD_SHORT_UV 2 +#define CV_HAL_SVD_MODIFY_A 4 +#define CV_HAL_SVD_FULL_UV 8 +//! @} + +//! @name Gemm flags +//! @{ +#define CV_HAL_GEMM_1_T 1 +#define CV_HAL_GEMM_2_T 2 +#define CV_HAL_GEMM_3_T 4 +//! @} + +//! @} + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/hal/intrin.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/hal/intrin.hpp new file mode 100644 index 0000000000000000000000000000000000000000..ef74176f3389a47644c92ea34fc65a1c8634e681 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/hal/intrin.hpp @@ -0,0 +1,420 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_HAL_INTRIN_HPP +#define OPENCV_HAL_INTRIN_HPP + +#include +#include +#include +#include "opencv2/core/cvdef.h" + +#define OPENCV_HAL_ADD(a, b) ((a) + (b)) +#define OPENCV_HAL_AND(a, b) ((a) & (b)) +#define OPENCV_HAL_NOP(a) (a) +#define OPENCV_HAL_1ST(a, b) (a) + +// unlike HAL API, which is in cv::hal, +// we put intrinsics into cv namespace to make its +// access from within opencv code more accessible +namespace cv { + +namespace hal { + +enum StoreMode +{ + STORE_UNALIGNED = 0, + STORE_ALIGNED = 1, + STORE_ALIGNED_NOCACHE = 2 +}; + +} + +template struct V_TypeTraits +{ +}; + +#define CV_INTRIN_DEF_TYPE_TRAITS(type, int_type_, uint_type_, abs_type_, w_type_, q_type_, sum_type_, nlanes128_) \ + template<> struct V_TypeTraits \ + { \ + typedef type value_type; \ + typedef int_type_ int_type; \ + typedef abs_type_ abs_type; \ + typedef uint_type_ uint_type; \ + typedef w_type_ w_type; \ + typedef q_type_ q_type; \ + typedef sum_type_ sum_type; \ + enum { nlanes128 = nlanes128_ }; \ + \ + static inline int_type reinterpret_int(type x) \ + { \ + union { type l; int_type i; } v; \ + v.l = x; \ + return v.i; \ + } \ + \ + static inline type reinterpret_from_int(int_type x) \ + { \ + union { type l; int_type i; } v; \ + v.i = x; \ + return v.l; \ + } \ + } + +CV_INTRIN_DEF_TYPE_TRAITS(uchar, schar, uchar, uchar, ushort, unsigned, unsigned, 16); +CV_INTRIN_DEF_TYPE_TRAITS(schar, schar, uchar, uchar, short, int, int, 16); +CV_INTRIN_DEF_TYPE_TRAITS(ushort, short, ushort, ushort, unsigned, uint64, unsigned, 8); +CV_INTRIN_DEF_TYPE_TRAITS(short, short, ushort, ushort, int, int64, int, 8); +CV_INTRIN_DEF_TYPE_TRAITS(unsigned, int, unsigned, unsigned, uint64, void, unsigned, 4); +CV_INTRIN_DEF_TYPE_TRAITS(int, int, unsigned, unsigned, int64, void, int, 4); +CV_INTRIN_DEF_TYPE_TRAITS(float, int, unsigned, float, double, void, float, 4); +CV_INTRIN_DEF_TYPE_TRAITS(uint64, int64, uint64, uint64, void, void, uint64, 2); +CV_INTRIN_DEF_TYPE_TRAITS(int64, int64, uint64, uint64, void, void, int64, 2); +CV_INTRIN_DEF_TYPE_TRAITS(double, int64, uint64, double, void, void, double, 2); + +#ifndef CV_DOXYGEN + +#ifdef CV_CPU_DISPATCH_MODE + #define CV_CPU_OPTIMIZATION_HAL_NAMESPACE __CV_CAT(hal_, CV_CPU_DISPATCH_MODE) + #define CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN namespace __CV_CAT(hal_, CV_CPU_DISPATCH_MODE) { + #define CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END } +#else + #define CV_CPU_OPTIMIZATION_HAL_NAMESPACE hal_baseline + #define CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN namespace hal_baseline { + #define CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END } +#endif + +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END +using namespace CV_CPU_OPTIMIZATION_HAL_NAMESPACE; +#endif +} + +#ifdef CV_DOXYGEN +# undef CV_AVX2 +# undef CV_SSE2 +# undef CV_NEON +# undef CV_VSX +# undef CV_FP16 +#endif + +#if CV_SSE2 || CV_NEON || CV_VSX +#define CV__SIMD_FORWARD 128 +#include "opencv2/core/hal/intrin_forward.hpp" +#endif + +#if CV_SSE2 + +#include "opencv2/core/hal/intrin_sse_em.hpp" +#include "opencv2/core/hal/intrin_sse.hpp" + +#elif CV_NEON + +#include "opencv2/core/hal/intrin_neon.hpp" + +#elif CV_VSX + +#include "opencv2/core/hal/intrin_vsx.hpp" + +#else + +#define CV_SIMD128_CPP 1 +#include "opencv2/core/hal/intrin_cpp.hpp" + +#endif + +// AVX2 can be used together with SSE2, so +// we define those two sets of intrinsics at once. +// Most of the intrinsics do not conflict (the proper overloaded variant is +// resolved by the argument types, e.g. v_float32x4 ~ SSE2, v_float32x8 ~ AVX2), +// but some of AVX2 intrinsics get v256_ prefix instead of v_, e.g. v256_load() vs v_load(). +// Correspondingly, the wide intrinsics (which are mapped to the "widest" +// available instruction set) will get vx_ prefix +// (and will be mapped to v256_ counterparts) (e.g. vx_load() => v256_load()) +#if CV_AVX2 + +#define CV__SIMD_FORWARD 256 +#include "opencv2/core/hal/intrin_forward.hpp" +#include "opencv2/core/hal/intrin_avx.hpp" + +#endif + +//! @cond IGNORED + +namespace cv { + +#ifndef CV_DOXYGEN +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN +#endif + +#ifndef CV_SIMD128 +#define CV_SIMD128 0 +#endif + +#ifndef CV_SIMD128_64F +#define CV_SIMD128_64F 0 +#endif + +#ifndef CV_SIMD256 +#define CV_SIMD256 0 +#endif + +#ifndef CV_SIMD256_64F +#define CV_SIMD256_64F 0 +#endif + +#ifndef CV_SIMD512 +#define CV_SIMD512 0 +#endif + +#ifndef CV_SIMD512_64F +#define CV_SIMD512_64F 0 +#endif + +#ifndef CV_SIMD128_FP16 +#define CV_SIMD128_FP16 0 +#endif + +#ifndef CV_SIMD256_FP16 +#define CV_SIMD256_FP16 0 +#endif + +#ifndef CV_SIMD512_FP16 +#define CV_SIMD512_FP16 0 +#endif + +//================================================================================================== + +#define CV_INTRIN_DEFINE_WIDE_INTRIN(typ, vtyp, short_typ, prefix, loadsfx) \ + inline vtyp vx_setall_##short_typ(typ v) { return prefix##_setall_##short_typ(v); } \ + inline vtyp vx_setzero_##short_typ() { return prefix##_setzero_##short_typ(); } \ + inline vtyp vx_##loadsfx(const typ* ptr) { return prefix##_##loadsfx(ptr); } \ + inline vtyp vx_##loadsfx##_aligned(const typ* ptr) { return prefix##_##loadsfx##_aligned(ptr); } \ + inline vtyp vx_##loadsfx##_low(const typ* ptr) { return prefix##_##loadsfx##_low(ptr); } \ + inline vtyp vx_##loadsfx##_halves(const typ* ptr0, const typ* ptr1) { return prefix##_##loadsfx##_halves(ptr0, ptr1); } \ + inline void vx_store(typ* ptr, const vtyp& v) { return v_store(ptr, v); } \ + inline void vx_store_aligned(typ* ptr, const vtyp& v) { return v_store_aligned(ptr, v); } + +#define CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND(typ, wtyp, prefix) \ + inline wtyp vx_load_expand(const typ* ptr) { return prefix##_load_expand(ptr); } + +#define CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND_Q(typ, qtyp, prefix) \ + inline qtyp vx_load_expand_q(const typ* ptr) { return prefix##_load_expand_q(ptr); } + +#define CV_INTRIN_DEFINE_WIDE_INTRIN_WITH_EXPAND(typ, vtyp, short_typ, wtyp, qtyp, prefix, loadsfx) \ + CV_INTRIN_DEFINE_WIDE_INTRIN(typ, vtyp, short_typ, prefix, loadsfx) \ + CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND(typ, wtyp, prefix) \ + CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND_Q(typ, qtyp, prefix) + +#define CV_INTRIN_DEFINE_WIDE_INTRIN_ALL_TYPES(prefix) \ + CV_INTRIN_DEFINE_WIDE_INTRIN_WITH_EXPAND(uchar, v_uint8, u8, v_uint16, v_uint32, prefix, load) \ + CV_INTRIN_DEFINE_WIDE_INTRIN_WITH_EXPAND(schar, v_int8, s8, v_int16, v_int32, prefix, load) \ + CV_INTRIN_DEFINE_WIDE_INTRIN(ushort, v_uint16, u16, prefix, load) \ + CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND(ushort, v_uint32, prefix) \ + CV_INTRIN_DEFINE_WIDE_INTRIN(short, v_int16, s16, prefix, load) \ + CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND(short, v_int32, prefix) \ + CV_INTRIN_DEFINE_WIDE_INTRIN(int, v_int32, s32, prefix, load) \ + CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND(int, v_int64, prefix) \ + CV_INTRIN_DEFINE_WIDE_INTRIN(unsigned, v_uint32, u32, prefix, load) \ + CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND(unsigned, v_uint64, prefix) \ + CV_INTRIN_DEFINE_WIDE_INTRIN(float, v_float32, f32, prefix, load) \ + CV_INTRIN_DEFINE_WIDE_INTRIN(int64, v_int64, s64, prefix, load) \ + CV_INTRIN_DEFINE_WIDE_INTRIN(uint64, v_uint64, u64, prefix, load) \ + CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND(float16_t, v_float32, prefix) + +template struct V_RegTraits +{ +}; + +#define CV_DEF_REG_TRAITS(prefix, _reg, lane_type, suffix, _u_reg, _w_reg, _q_reg, _int_reg, _round_reg) \ + template<> struct V_RegTraits<_reg> \ + { \ + typedef _reg reg; \ + typedef _u_reg u_reg; \ + typedef _w_reg w_reg; \ + typedef _q_reg q_reg; \ + typedef _int_reg int_reg; \ + typedef _round_reg round_reg; \ + } + +#if CV_SIMD128 || CV_SIMD128_CPP + CV_DEF_REG_TRAITS(v, v_uint8x16, uchar, u8, v_uint8x16, v_uint16x8, v_uint32x4, v_int8x16, void); + CV_DEF_REG_TRAITS(v, v_int8x16, schar, s8, v_uint8x16, v_int16x8, v_int32x4, v_int8x16, void); + CV_DEF_REG_TRAITS(v, v_uint16x8, ushort, u16, v_uint16x8, v_uint32x4, v_uint64x2, v_int16x8, void); + CV_DEF_REG_TRAITS(v, v_int16x8, short, s16, v_uint16x8, v_int32x4, v_int64x2, v_int16x8, void); + CV_DEF_REG_TRAITS(v, v_uint32x4, unsigned, u32, v_uint32x4, v_uint64x2, void, v_int32x4, void); + CV_DEF_REG_TRAITS(v, v_int32x4, int, s32, v_uint32x4, v_int64x2, void, v_int32x4, void); +#if CV_SIMD128_64F + CV_DEF_REG_TRAITS(v, v_float32x4, float, f32, v_float32x4, v_float64x2, void, v_int32x4, v_int32x4); +#else + CV_DEF_REG_TRAITS(v, v_float32x4, float, f32, v_float32x4, void, void, v_int32x4, v_int32x4); +#endif + CV_DEF_REG_TRAITS(v, v_uint64x2, uint64, u64, v_uint64x2, void, void, v_int64x2, void); + CV_DEF_REG_TRAITS(v, v_int64x2, int64, s64, v_uint64x2, void, void, v_int64x2, void); +#if CV_SIMD128_64F + CV_DEF_REG_TRAITS(v, v_float64x2, double, f64, v_float64x2, void, void, v_int64x2, v_int32x4); +#endif +#endif + +#if CV_SIMD256 + CV_DEF_REG_TRAITS(v256, v_uint8x32, uchar, u8, v_uint8x32, v_uint16x16, v_uint32x8, v_int8x32, void); + CV_DEF_REG_TRAITS(v256, v_int8x32, schar, s8, v_uint8x32, v_int16x16, v_int32x8, v_int8x32, void); + CV_DEF_REG_TRAITS(v256, v_uint16x16, ushort, u16, v_uint16x16, v_uint32x8, v_uint64x4, v_int16x16, void); + CV_DEF_REG_TRAITS(v256, v_int16x16, short, s16, v_uint16x16, v_int32x8, v_int64x4, v_int16x16, void); + CV_DEF_REG_TRAITS(v256, v_uint32x8, unsigned, u32, v_uint32x8, v_uint64x4, void, v_int32x8, void); + CV_DEF_REG_TRAITS(v256, v_int32x8, int, s32, v_uint32x8, v_int64x4, void, v_int32x8, void); + CV_DEF_REG_TRAITS(v256, v_float32x8, float, f32, v_float32x8, v_float64x4, void, v_int32x8, v_int32x8); + CV_DEF_REG_TRAITS(v256, v_uint64x4, uint64, u64, v_uint64x4, void, void, v_int64x4, void); + CV_DEF_REG_TRAITS(v256, v_int64x4, int64, s64, v_uint64x4, void, void, v_int64x4, void); + CV_DEF_REG_TRAITS(v256, v_float64x4, double, f64, v_float64x4, void, void, v_int64x4, v_int32x8); +#endif + +#if CV_SIMD512 && (!defined(CV__SIMD_FORCE_WIDTH) || CV__SIMD_FORCE_WIDTH == 512) +#define CV__SIMD_NAMESPACE simd512 +namespace CV__SIMD_NAMESPACE { + #define CV_SIMD 1 + #define CV_SIMD_64F CV_SIMD512_64F + #define CV_SIMD_WIDTH 64 + // TODO typedef v_uint8 / v_int32 / etc types here +} // namespace +using namespace CV__SIMD_NAMESPACE; +#elif CV_SIMD256 && (!defined(CV__SIMD_FORCE_WIDTH) || CV__SIMD_FORCE_WIDTH == 256) +#define CV__SIMD_NAMESPACE simd256 +namespace CV__SIMD_NAMESPACE { + #define CV_SIMD 1 + #define CV_SIMD_64F CV_SIMD256_64F + #define CV_SIMD_FP16 CV_SIMD256_FP16 + #define CV_SIMD_WIDTH 32 + typedef v_uint8x32 v_uint8; + typedef v_int8x32 v_int8; + typedef v_uint16x16 v_uint16; + typedef v_int16x16 v_int16; + typedef v_uint32x8 v_uint32; + typedef v_int32x8 v_int32; + typedef v_uint64x4 v_uint64; + typedef v_int64x4 v_int64; + typedef v_float32x8 v_float32; + #if CV_SIMD256_64F + typedef v_float64x4 v_float64; + #endif + CV_INTRIN_DEFINE_WIDE_INTRIN_ALL_TYPES(v256) + CV_INTRIN_DEFINE_WIDE_INTRIN(double, v_float64, f64, v256, load) + inline void vx_cleanup() { v256_cleanup(); } +} // namespace +using namespace CV__SIMD_NAMESPACE; +#elif (CV_SIMD128 || CV_SIMD128_CPP) && (!defined(CV__SIMD_FORCE_WIDTH) || CV__SIMD_FORCE_WIDTH == 128) +#define CV__SIMD_NAMESPACE simd128 +namespace CV__SIMD_NAMESPACE { + #define CV_SIMD CV_SIMD128 + #define CV_SIMD_64F CV_SIMD128_64F + #define CV_SIMD_WIDTH 16 + typedef v_uint8x16 v_uint8; + typedef v_int8x16 v_int8; + typedef v_uint16x8 v_uint16; + typedef v_int16x8 v_int16; + typedef v_uint32x4 v_uint32; + typedef v_int32x4 v_int32; + typedef v_uint64x2 v_uint64; + typedef v_int64x2 v_int64; + typedef v_float32x4 v_float32; + #if CV_SIMD128_64F + typedef v_float64x2 v_float64; + #endif + CV_INTRIN_DEFINE_WIDE_INTRIN_ALL_TYPES(v) + #if CV_SIMD128_64F + CV_INTRIN_DEFINE_WIDE_INTRIN(double, v_float64, f64, v, load) + #endif + inline void vx_cleanup() { v_cleanup(); } +} // namespace +using namespace CV__SIMD_NAMESPACE; +#endif + +inline unsigned int trailingZeros32(unsigned int value) { +#if defined(_MSC_VER) +#if (_MSC_VER < 1700) || defined(_M_ARM) + unsigned long index = 0; + _BitScanForward(&index, value); + return (unsigned int)index; +#elif defined(__clang__) + // clang-cl doesn't export _tzcnt_u32 for non BMI systems + return value ? __builtin_ctz(value) : 32; +#else + return _tzcnt_u32(value); +#endif +#elif defined(__GNUC__) || defined(__GNUG__) + return __builtin_ctz(value); +#elif defined(__ICC) || defined(__INTEL_COMPILER) + return _bit_scan_forward(value); +#elif defined(__clang__) + return llvm.cttz.i32(value, true); +#else + static const int MultiplyDeBruijnBitPosition[32] = { + 0, 1, 28, 2, 29, 14, 24, 3, 30, 22, 20, 15, 25, 17, 4, 8, + 31, 27, 13, 23, 21, 19, 16, 7, 26, 12, 18, 6, 11, 5, 10, 9 }; + return MultiplyDeBruijnBitPosition[((uint32_t)((value & -value) * 0x077CB531U)) >> 27]; +#endif +} + +#ifndef CV_DOXYGEN +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END +#endif + +#ifndef CV_SIMD_64F +#define CV_SIMD_64F 0 +#endif + +#ifndef CV_SIMD_FP16 +#define CV_SIMD_FP16 0 //!< Defined to 1 on native support of operations with float16x8_t / float16x16_t (SIMD256) types +#endif + + +#ifndef CV_SIMD +#define CV_SIMD 0 +#endif + +} // cv:: + +//! @endcond + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/hal/intrin_avx.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/hal/intrin_avx.hpp new file mode 100644 index 0000000000000000000000000000000000000000..c3797d67c1031bcc5d27f546b49734766a1e6e4d --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/hal/intrin_avx.hpp @@ -0,0 +1,2628 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html + +#ifndef OPENCV_HAL_INTRIN_AVX_HPP +#define OPENCV_HAL_INTRIN_AVX_HPP + +#define CV_SIMD256 1 +#define CV_SIMD256_64F 1 +#define CV_SIMD256_FP16 0 // no native operations with FP16 type. Only load/store from float32x8 are available (if CV_FP16 == 1) + +namespace cv +{ + +//! @cond IGNORED + +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN + +///////// Utils //////////// + +inline __m256i _v256_combine(const __m128i& lo, const __m128i& hi) +{ return _mm256_inserti128_si256(_mm256_castsi128_si256(lo), hi, 1); } + +inline __m256 _v256_combine(const __m128& lo, const __m128& hi) +{ return _mm256_insertf128_ps(_mm256_castps128_ps256(lo), hi, 1); } + +inline __m256d _v256_combine(const __m128d& lo, const __m128d& hi) +{ return _mm256_insertf128_pd(_mm256_castpd128_pd256(lo), hi, 1); } + +inline int _v_cvtsi256_si32(const __m256i& a) +{ return _mm_cvtsi128_si32(_mm256_castsi256_si128(a)); } + +inline __m256i _v256_shuffle_odd_64(const __m256i& v) +{ return _mm256_permute4x64_epi64(v, _MM_SHUFFLE(3, 1, 2, 0)); } + +inline __m256d _v256_shuffle_odd_64(const __m256d& v) +{ return _mm256_permute4x64_pd(v, _MM_SHUFFLE(3, 1, 2, 0)); } + +template +inline __m256i _v256_permute2x128(const __m256i& a, const __m256i& b) +{ return _mm256_permute2x128_si256(a, b, imm); } + +template +inline __m256 _v256_permute2x128(const __m256& a, const __m256& b) +{ return _mm256_permute2f128_ps(a, b, imm); } + +template +inline __m256d _v256_permute2x128(const __m256d& a, const __m256d& b) +{ return _mm256_permute2f128_pd(a, b, imm); } + +template +inline _Tpvec v256_permute2x128(const _Tpvec& a, const _Tpvec& b) +{ return _Tpvec(_v256_permute2x128(a.val, b.val)); } + +template +inline __m256i _v256_permute4x64(const __m256i& a) +{ return _mm256_permute4x64_epi64(a, imm); } + +template +inline __m256d _v256_permute4x64(const __m256d& a) +{ return _mm256_permute4x64_pd(a, imm); } + +template +inline _Tpvec v256_permute4x64(const _Tpvec& a) +{ return _Tpvec(_v256_permute4x64(a.val)); } + +inline __m128i _v256_extract_high(const __m256i& v) +{ return _mm256_extracti128_si256(v, 1); } + +inline __m128 _v256_extract_high(const __m256& v) +{ return _mm256_extractf128_ps(v, 1); } + +inline __m128d _v256_extract_high(const __m256d& v) +{ return _mm256_extractf128_pd(v, 1); } + +inline __m128i _v256_extract_low(const __m256i& v) +{ return _mm256_castsi256_si128(v); } + +inline __m128 _v256_extract_low(const __m256& v) +{ return _mm256_castps256_ps128(v); } + +inline __m128d _v256_extract_low(const __m256d& v) +{ return _mm256_castpd256_pd128(v); } + +inline __m256i _v256_packs_epu32(const __m256i& a, const __m256i& b) +{ + const __m256i m = _mm256_set1_epi32(65535); + __m256i am = _mm256_min_epu32(a, m); + __m256i bm = _mm256_min_epu32(b, m); + return _mm256_packus_epi32(am, bm); +} + +///////// Types //////////// + +struct v_uint8x32 +{ + typedef uchar lane_type; + enum { nlanes = 32 }; + __m256i val; + + explicit v_uint8x32(__m256i v) : val(v) {} + v_uint8x32(uchar v0, uchar v1, uchar v2, uchar v3, + uchar v4, uchar v5, uchar v6, uchar v7, + uchar v8, uchar v9, uchar v10, uchar v11, + uchar v12, uchar v13, uchar v14, uchar v15, + uchar v16, uchar v17, uchar v18, uchar v19, + uchar v20, uchar v21, uchar v22, uchar v23, + uchar v24, uchar v25, uchar v26, uchar v27, + uchar v28, uchar v29, uchar v30, uchar v31) + { + val = _mm256_setr_epi8((char)v0, (char)v1, (char)v2, (char)v3, + (char)v4, (char)v5, (char)v6 , (char)v7, (char)v8, (char)v9, + (char)v10, (char)v11, (char)v12, (char)v13, (char)v14, (char)v15, + (char)v16, (char)v17, (char)v18, (char)v19, (char)v20, (char)v21, + (char)v22, (char)v23, (char)v24, (char)v25, (char)v26, (char)v27, + (char)v28, (char)v29, (char)v30, (char)v31); + } + v_uint8x32() : val(_mm256_setzero_si256()) {} + uchar get0() const { return (uchar)_v_cvtsi256_si32(val); } +}; + +struct v_int8x32 +{ + typedef schar lane_type; + enum { nlanes = 32 }; + __m256i val; + + explicit v_int8x32(__m256i v) : val(v) {} + v_int8x32(schar v0, schar v1, schar v2, schar v3, + schar v4, schar v5, schar v6, schar v7, + schar v8, schar v9, schar v10, schar v11, + schar v12, schar v13, schar v14, schar v15, + schar v16, schar v17, schar v18, schar v19, + schar v20, schar v21, schar v22, schar v23, + schar v24, schar v25, schar v26, schar v27, + schar v28, schar v29, schar v30, schar v31) + { + val = _mm256_setr_epi8(v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, + v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, + v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31); + } + v_int8x32() : val(_mm256_setzero_si256()) {} + schar get0() const { return (schar)_v_cvtsi256_si32(val); } +}; + +struct v_uint16x16 +{ + typedef ushort lane_type; + enum { nlanes = 16 }; + __m256i val; + + explicit v_uint16x16(__m256i v) : val(v) {} + v_uint16x16(ushort v0, ushort v1, ushort v2, ushort v3, + ushort v4, ushort v5, ushort v6, ushort v7, + ushort v8, ushort v9, ushort v10, ushort v11, + ushort v12, ushort v13, ushort v14, ushort v15) + { + val = _mm256_setr_epi16((short)v0, (short)v1, (short)v2, (short)v3, + (short)v4, (short)v5, (short)v6, (short)v7, (short)v8, (short)v9, + (short)v10, (short)v11, (short)v12, (short)v13, (short)v14, (short)v15); + } + v_uint16x16() : val(_mm256_setzero_si256()) {} + ushort get0() const { return (ushort)_v_cvtsi256_si32(val); } +}; + +struct v_int16x16 +{ + typedef short lane_type; + enum { nlanes = 16 }; + __m256i val; + + explicit v_int16x16(__m256i v) : val(v) {} + v_int16x16(short v0, short v1, short v2, short v3, + short v4, short v5, short v6, short v7, + short v8, short v9, short v10, short v11, + short v12, short v13, short v14, short v15) + { + val = _mm256_setr_epi16(v0, v1, v2, v3, v4, v5, v6, v7, + v8, v9, v10, v11, v12, v13, v14, v15); + } + v_int16x16() : val(_mm256_setzero_si256()) {} + short get0() const { return (short)_v_cvtsi256_si32(val); } +}; + +struct v_uint32x8 +{ + typedef unsigned lane_type; + enum { nlanes = 8 }; + __m256i val; + + explicit v_uint32x8(__m256i v) : val(v) {} + v_uint32x8(unsigned v0, unsigned v1, unsigned v2, unsigned v3, + unsigned v4, unsigned v5, unsigned v6, unsigned v7) + { + val = _mm256_setr_epi32((unsigned)v0, (unsigned)v1, (unsigned)v2, + (unsigned)v3, (unsigned)v4, (unsigned)v5, (unsigned)v6, (unsigned)v7); + } + v_uint32x8() : val(_mm256_setzero_si256()) {} + unsigned get0() const { return (unsigned)_v_cvtsi256_si32(val); } +}; + +struct v_int32x8 +{ + typedef int lane_type; + enum { nlanes = 8 }; + __m256i val; + + explicit v_int32x8(__m256i v) : val(v) {} + v_int32x8(int v0, int v1, int v2, int v3, + int v4, int v5, int v6, int v7) + { + val = _mm256_setr_epi32(v0, v1, v2, v3, v4, v5, v6, v7); + } + v_int32x8() : val(_mm256_setzero_si256()) {} + int get0() const { return _v_cvtsi256_si32(val); } +}; + +struct v_float32x8 +{ + typedef float lane_type; + enum { nlanes = 8 }; + __m256 val; + + explicit v_float32x8(__m256 v) : val(v) {} + v_float32x8(float v0, float v1, float v2, float v3, + float v4, float v5, float v6, float v7) + { + val = _mm256_setr_ps(v0, v1, v2, v3, v4, v5, v6, v7); + } + v_float32x8() : val(_mm256_setzero_ps()) {} + float get0() const { return _mm_cvtss_f32(_mm256_castps256_ps128(val)); } +}; + +struct v_uint64x4 +{ + typedef uint64 lane_type; + enum { nlanes = 4 }; + __m256i val; + + explicit v_uint64x4(__m256i v) : val(v) {} + v_uint64x4(uint64 v0, uint64 v1, uint64 v2, uint64 v3) + { val = _mm256_setr_epi64x((int64)v0, (int64)v1, (int64)v2, (int64)v3); } + v_uint64x4() : val(_mm256_setzero_si256()) {} + uint64 get0() const + { + #if defined __x86_64__ || defined _M_X64 + return (uint64)_mm_cvtsi128_si64(_mm256_castsi256_si128(val)); + #else + int a = _mm_cvtsi128_si32(_mm256_castsi256_si128(val)); + int b = _mm_cvtsi128_si32(_mm256_castsi256_si128(_mm256_srli_epi64(val, 32))); + return (unsigned)a | ((uint64)(unsigned)b << 32); + #endif + } +}; + +struct v_int64x4 +{ + typedef int64 lane_type; + enum { nlanes = 4 }; + __m256i val; + + explicit v_int64x4(__m256i v) : val(v) {} + v_int64x4(int64 v0, int64 v1, int64 v2, int64 v3) + { val = _mm256_setr_epi64x(v0, v1, v2, v3); } + v_int64x4() : val(_mm256_setzero_si256()) {} + + int64 get0() const + { + #if defined __x86_64__ || defined _M_X64 + return (int64)_mm_cvtsi128_si64(_mm256_castsi256_si128(val)); + #else + int a = _mm_cvtsi128_si32(_mm256_castsi256_si128(val)); + int b = _mm_cvtsi128_si32(_mm256_castsi256_si128(_mm256_srli_epi64(val, 32))); + return (int64)((unsigned)a | ((uint64)(unsigned)b << 32)); + #endif + } +}; + +struct v_float64x4 +{ + typedef double lane_type; + enum { nlanes = 4 }; + __m256d val; + + explicit v_float64x4(__m256d v) : val(v) {} + v_float64x4(double v0, double v1, double v2, double v3) + { val = _mm256_setr_pd(v0, v1, v2, v3); } + v_float64x4() : val(_mm256_setzero_pd()) {} + double get0() const { return _mm_cvtsd_f64(_mm256_castpd256_pd128(val)); } +}; + +//////////////// Load and store operations /////////////// + +#define OPENCV_HAL_IMPL_AVX_LOADSTORE(_Tpvec, _Tp) \ + inline _Tpvec v256_load(const _Tp* ptr) \ + { return _Tpvec(_mm256_loadu_si256((const __m256i*)ptr)); } \ + inline _Tpvec v256_load_aligned(const _Tp* ptr) \ + { return _Tpvec(_mm256_load_si256((const __m256i*)ptr)); } \ + inline _Tpvec v256_load_low(const _Tp* ptr) \ + { \ + __m128i v128 = _mm_loadu_si128((const __m128i*)ptr); \ + return _Tpvec(_mm256_castsi128_si256(v128)); \ + } \ + inline _Tpvec v256_load_halves(const _Tp* ptr0, const _Tp* ptr1) \ + { \ + __m128i vlo = _mm_loadu_si128((const __m128i*)ptr0); \ + __m128i vhi = _mm_loadu_si128((const __m128i*)ptr1); \ + return _Tpvec(_v256_combine(vlo, vhi)); \ + } \ + inline void v_store(_Tp* ptr, const _Tpvec& a) \ + { _mm256_storeu_si256((__m256i*)ptr, a.val); } \ + inline void v_store_aligned(_Tp* ptr, const _Tpvec& a) \ + { _mm256_store_si256((__m256i*)ptr, a.val); } \ + inline void v_store_aligned_nocache(_Tp* ptr, const _Tpvec& a) \ + { _mm256_stream_si256((__m256i*)ptr, a.val); } \ + inline void v_store(_Tp* ptr, const _Tpvec& a, hal::StoreMode mode) \ + { \ + if( mode == hal::STORE_UNALIGNED ) \ + _mm256_storeu_si256((__m256i*)ptr, a.val); \ + else if( mode == hal::STORE_ALIGNED_NOCACHE ) \ + _mm256_stream_si256((__m256i*)ptr, a.val); \ + else \ + _mm256_store_si256((__m256i*)ptr, a.val); \ + } \ + inline void v_store_low(_Tp* ptr, const _Tpvec& a) \ + { _mm_storeu_si128((__m128i*)ptr, _v256_extract_low(a.val)); } \ + inline void v_store_high(_Tp* ptr, const _Tpvec& a) \ + { _mm_storeu_si128((__m128i*)ptr, _v256_extract_high(a.val)); } + +OPENCV_HAL_IMPL_AVX_LOADSTORE(v_uint8x32, uchar) +OPENCV_HAL_IMPL_AVX_LOADSTORE(v_int8x32, schar) +OPENCV_HAL_IMPL_AVX_LOADSTORE(v_uint16x16, ushort) +OPENCV_HAL_IMPL_AVX_LOADSTORE(v_int16x16, short) +OPENCV_HAL_IMPL_AVX_LOADSTORE(v_uint32x8, unsigned) +OPENCV_HAL_IMPL_AVX_LOADSTORE(v_int32x8, int) +OPENCV_HAL_IMPL_AVX_LOADSTORE(v_uint64x4, uint64) +OPENCV_HAL_IMPL_AVX_LOADSTORE(v_int64x4, int64) + +#define OPENCV_HAL_IMPL_AVX_LOADSTORE_FLT(_Tpvec, _Tp, suffix, halfreg) \ + inline _Tpvec v256_load(const _Tp* ptr) \ + { return _Tpvec(_mm256_loadu_##suffix(ptr)); } \ + inline _Tpvec v256_load_aligned(const _Tp* ptr) \ + { return _Tpvec(_mm256_load_##suffix(ptr)); } \ + inline _Tpvec v256_load_low(const _Tp* ptr) \ + { \ + return _Tpvec(_mm256_cast##suffix##128_##suffix##256 \ + (_mm_loadu_##suffix(ptr))); \ + } \ + inline _Tpvec v256_load_halves(const _Tp* ptr0, const _Tp* ptr1) \ + { \ + halfreg vlo = _mm_loadu_##suffix(ptr0); \ + halfreg vhi = _mm_loadu_##suffix(ptr1); \ + return _Tpvec(_v256_combine(vlo, vhi)); \ + } \ + inline void v_store(_Tp* ptr, const _Tpvec& a) \ + { _mm256_storeu_##suffix(ptr, a.val); } \ + inline void v_store_aligned(_Tp* ptr, const _Tpvec& a) \ + { _mm256_store_##suffix(ptr, a.val); } \ + inline void v_store_aligned_nocache(_Tp* ptr, const _Tpvec& a) \ + { _mm256_stream_##suffix(ptr, a.val); } \ + inline void v_store(_Tp* ptr, const _Tpvec& a, hal::StoreMode mode) \ + { \ + if( mode == hal::STORE_UNALIGNED ) \ + _mm256_storeu_##suffix(ptr, a.val); \ + else if( mode == hal::STORE_ALIGNED_NOCACHE ) \ + _mm256_stream_##suffix(ptr, a.val); \ + else \ + _mm256_store_##suffix(ptr, a.val); \ + } \ + inline void v_store_low(_Tp* ptr, const _Tpvec& a) \ + { _mm_storeu_##suffix(ptr, _v256_extract_low(a.val)); } \ + inline void v_store_high(_Tp* ptr, const _Tpvec& a) \ + { _mm_storeu_##suffix(ptr, _v256_extract_high(a.val)); } + +OPENCV_HAL_IMPL_AVX_LOADSTORE_FLT(v_float32x8, float, ps, __m128) +OPENCV_HAL_IMPL_AVX_LOADSTORE_FLT(v_float64x4, double, pd, __m128d) + +#define OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, _Tpvecf, suffix, cast) \ + inline _Tpvec v_reinterpret_as_##suffix(const _Tpvecf& a) \ + { return _Tpvec(cast(a.val)); } + +#define OPENCV_HAL_IMPL_AVX_INIT(_Tpvec, _Tp, suffix, ssuffix, ctype_s) \ + inline _Tpvec v256_setzero_##suffix() \ + { return _Tpvec(_mm256_setzero_si256()); } \ + inline _Tpvec v256_setall_##suffix(_Tp v) \ + { return _Tpvec(_mm256_set1_##ssuffix((ctype_s)v)); } \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_uint8x32, suffix, OPENCV_HAL_NOP) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_int8x32, suffix, OPENCV_HAL_NOP) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_uint16x16, suffix, OPENCV_HAL_NOP) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_int16x16, suffix, OPENCV_HAL_NOP) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_uint32x8, suffix, OPENCV_HAL_NOP) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_int32x8, suffix, OPENCV_HAL_NOP) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_uint64x4, suffix, OPENCV_HAL_NOP) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_int64x4, suffix, OPENCV_HAL_NOP) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_float32x8, suffix, _mm256_castps_si256) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_float64x4, suffix, _mm256_castpd_si256) + +OPENCV_HAL_IMPL_AVX_INIT(v_uint8x32, uchar, u8, epi8, char) +OPENCV_HAL_IMPL_AVX_INIT(v_int8x32, schar, s8, epi8, char) +OPENCV_HAL_IMPL_AVX_INIT(v_uint16x16, ushort, u16, epi16, short) +OPENCV_HAL_IMPL_AVX_INIT(v_int16x16, short, s16, epi16, short) +OPENCV_HAL_IMPL_AVX_INIT(v_uint32x8, unsigned, u32, epi32, int) +OPENCV_HAL_IMPL_AVX_INIT(v_int32x8, int, s32, epi32, int) +OPENCV_HAL_IMPL_AVX_INIT(v_uint64x4, uint64, u64, epi64x, int64) +OPENCV_HAL_IMPL_AVX_INIT(v_int64x4, int64, s64, epi64x, int64) + +#define OPENCV_HAL_IMPL_AVX_INIT_FLT(_Tpvec, _Tp, suffix, zsuffix, cast) \ + inline _Tpvec v256_setzero_##suffix() \ + { return _Tpvec(_mm256_setzero_##zsuffix()); } \ + inline _Tpvec v256_setall_##suffix(_Tp v) \ + { return _Tpvec(_mm256_set1_##zsuffix(v)); } \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_uint8x32, suffix, cast) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_int8x32, suffix, cast) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_uint16x16, suffix, cast) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_int16x16, suffix, cast) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_uint32x8, suffix, cast) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_int32x8, suffix, cast) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_uint64x4, suffix, cast) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_int64x4, suffix, cast) + +OPENCV_HAL_IMPL_AVX_INIT_FLT(v_float32x8, float, f32, ps, _mm256_castsi256_ps) +OPENCV_HAL_IMPL_AVX_INIT_FLT(v_float64x4, double, f64, pd, _mm256_castsi256_pd) + +inline v_float32x8 v_reinterpret_as_f32(const v_float32x8& a) +{ return a; } +inline v_float32x8 v_reinterpret_as_f32(const v_float64x4& a) +{ return v_float32x8(_mm256_castpd_ps(a.val)); } + +inline v_float64x4 v_reinterpret_as_f64(const v_float64x4& a) +{ return a; } +inline v_float64x4 v_reinterpret_as_f64(const v_float32x8& a) +{ return v_float64x4(_mm256_castps_pd(a.val)); } + +#if CV_FP16 +inline v_float32x8 v256_load_fp16_f32(const short* ptr) +{ + return v_float32x8(_mm256_cvtph_ps(_mm_loadu_si128((const __m128i*)ptr))); +} + +inline void v_store_fp16(short* ptr, const v_float32x8& a) +{ + __m128i fp16_value = _mm256_cvtps_ph(a.val, 0); + _mm_store_si128((__m128i*)ptr, fp16_value); +} +#endif + +/* Recombine */ +/*#define OPENCV_HAL_IMPL_AVX_COMBINE(_Tpvec, perm) \ + inline _Tpvec v_combine_low(const _Tpvec& a, const _Tpvec& b) \ + { return _Tpvec(perm(a.val, b.val, 0x20)); } \ + inline _Tpvec v_combine_high(const _Tpvec& a, const _Tpvec& b) \ + { return _Tpvec(perm(a.val, b.val, 0x31)); } \ + inline void v_recombine(const _Tpvec& a, const _Tpvec& b, \ + _Tpvec& c, _Tpvec& d) \ + { c = v_combine_low(a, b); d = v_combine_high(a, b); } + +#define OPENCV_HAL_IMPL_AVX_UNPACKS(_Tpvec, suffix) \ + OPENCV_HAL_IMPL_AVX_COMBINE(_Tpvec, _mm256_permute2x128_si256) \ + inline void v_zip(const _Tpvec& a0, const _Tpvec& a1, \ + _Tpvec& b0, _Tpvec& b1) \ + { \ + __m256i v0 = _v256_shuffle_odd_64(a0.val); \ + __m256i v1 = _v256_shuffle_odd_64(a1.val); \ + b0.val = _mm256_unpacklo_##suffix(v0, v1); \ + b1.val = _mm256_unpackhi_##suffix(v0, v1); \ + } + +OPENCV_HAL_IMPL_AVX_UNPACKS(v_uint8x32, epi8) +OPENCV_HAL_IMPL_AVX_UNPACKS(v_int8x32, epi8) +OPENCV_HAL_IMPL_AVX_UNPACKS(v_uint16x16, epi16) +OPENCV_HAL_IMPL_AVX_UNPACKS(v_int16x16, epi16) +OPENCV_HAL_IMPL_AVX_UNPACKS(v_uint32x8, epi32) +OPENCV_HAL_IMPL_AVX_UNPACKS(v_int32x8, epi32) +OPENCV_HAL_IMPL_AVX_UNPACKS(v_uint64x4, epi64) +OPENCV_HAL_IMPL_AVX_UNPACKS(v_int64x4, epi64) +OPENCV_HAL_IMPL_AVX_COMBINE(v_float32x8, _mm256_permute2f128_ps) +OPENCV_HAL_IMPL_AVX_COMBINE(v_float64x4, _mm256_permute2f128_pd) + +inline void v_zip(const v_float32x8& a0, const v_float32x8& a1, v_float32x8& b0, v_float32x8& b1) +{ + __m256 v0 = _mm256_unpacklo_ps(a0.val, a1.val); + __m256 v1 = _mm256_unpackhi_ps(a0.val, a1.val); + v_recombine(v_float32x8(v0), v_float32x8(v1), b0, b1); +} + +inline void v_zip(const v_float64x4& a0, const v_float64x4& a1, v_float64x4& b0, v_float64x4& b1) +{ + __m256d v0 = _v_shuffle_odd_64(a0.val); + __m256d v1 = _v_shuffle_odd_64(a1.val); + b0.val = _mm256_unpacklo_pd(v0, v1); + b1.val = _mm256_unpackhi_pd(v0, v1); +}*/ + +//////////////// Variant Value reordering /////////////// + +// unpacks +#define OPENCV_HAL_IMPL_AVX_UNPACK(_Tpvec, suffix) \ + inline _Tpvec v256_unpacklo(const _Tpvec& a, const _Tpvec& b) \ + { return _Tpvec(_mm256_unpacklo_##suffix(a.val, b.val)); } \ + inline _Tpvec v256_unpackhi(const _Tpvec& a, const _Tpvec& b) \ + { return _Tpvec(_mm256_unpackhi_##suffix(a.val, b.val)); } + +OPENCV_HAL_IMPL_AVX_UNPACK(v_uint8x32, epi8) +OPENCV_HAL_IMPL_AVX_UNPACK(v_int8x32, epi8) +OPENCV_HAL_IMPL_AVX_UNPACK(v_uint16x16, epi16) +OPENCV_HAL_IMPL_AVX_UNPACK(v_int16x16, epi16) +OPENCV_HAL_IMPL_AVX_UNPACK(v_uint32x8, epi32) +OPENCV_HAL_IMPL_AVX_UNPACK(v_int32x8, epi32) +OPENCV_HAL_IMPL_AVX_UNPACK(v_uint64x4, epi64) +OPENCV_HAL_IMPL_AVX_UNPACK(v_int64x4, epi64) +OPENCV_HAL_IMPL_AVX_UNPACK(v_float32x8, ps) +OPENCV_HAL_IMPL_AVX_UNPACK(v_float64x4, pd) + +// blend +#define OPENCV_HAL_IMPL_AVX_BLEND(_Tpvec, suffix) \ + template \ + inline _Tpvec v256_blend(const _Tpvec& a, const _Tpvec& b) \ + { return _Tpvec(_mm256_blend_##suffix(a.val, b.val, m)); } + +OPENCV_HAL_IMPL_AVX_BLEND(v_uint16x16, epi16) +OPENCV_HAL_IMPL_AVX_BLEND(v_int16x16, epi16) +OPENCV_HAL_IMPL_AVX_BLEND(v_uint32x8, epi32) +OPENCV_HAL_IMPL_AVX_BLEND(v_int32x8, epi32) +OPENCV_HAL_IMPL_AVX_BLEND(v_float32x8, ps) +OPENCV_HAL_IMPL_AVX_BLEND(v_float64x4, pd) + +template +inline v_uint64x4 v256_blend(const v_uint64x4& a, const v_uint64x4& b) +{ + enum {M0 = m}; + enum {M1 = (M0 | (M0 << 2)) & 0x33}; + enum {M2 = (M1 | (M1 << 1)) & 0x55}; + enum {MM = M2 | (M2 << 1)}; + return v_uint64x4(_mm256_blend_epi32(a.val, b.val, MM)); +} +template +inline v_int64x4 v256_blend(const v_int64x4& a, const v_int64x4& b) +{ return v_int64x4(v256_blend(v_uint64x4(a.val), v_uint64x4(b.val)).val); } + +// shuffle +// todo: emluate 64bit +#define OPENCV_HAL_IMPL_AVX_SHUFFLE(_Tpvec, intrin) \ + template \ + inline _Tpvec v256_shuffle(const _Tpvec& a) \ + { return _Tpvec(_mm256_##intrin(a.val, m)); } + +OPENCV_HAL_IMPL_AVX_SHUFFLE(v_uint32x8, shuffle_epi32) +OPENCV_HAL_IMPL_AVX_SHUFFLE(v_int32x8, shuffle_epi32) +OPENCV_HAL_IMPL_AVX_SHUFFLE(v_float32x8, permute_ps) +OPENCV_HAL_IMPL_AVX_SHUFFLE(v_float64x4, permute_pd) + +template +inline void v256_zip(const _Tpvec& a, const _Tpvec& b, _Tpvec& ab0, _Tpvec& ab1) +{ + ab0 = v256_unpacklo(a, b); + ab1 = v256_unpackhi(a, b); +} + +template +inline _Tpvec v256_combine_diagonal(const _Tpvec& a, const _Tpvec& b) +{ return _Tpvec(_mm256_blend_epi32(a.val, b.val, 0xf0)); } + +inline v_float32x8 v256_combine_diagonal(const v_float32x8& a, const v_float32x8& b) +{ return v256_blend<0xf0>(a, b); } + +inline v_float64x4 v256_combine_diagonal(const v_float64x4& a, const v_float64x4& b) +{ return v256_blend<0xc>(a, b); } + +template +inline _Tpvec v256_alignr_128(const _Tpvec& a, const _Tpvec& b) +{ return v256_permute2x128<0x21>(a, b); } + +template +inline _Tpvec v256_alignr_64(const _Tpvec& a, const _Tpvec& b) +{ return _Tpvec(_mm256_alignr_epi8(a.val, b.val, 8)); } +inline v_float64x4 v256_alignr_64(const v_float64x4& a, const v_float64x4& b) +{ return v_float64x4(_mm256_shuffle_pd(b.val, a.val, _MM_SHUFFLE(0, 0, 1, 1))); } +// todo: emulate float32 + +template +inline _Tpvec v256_swap_halves(const _Tpvec& a) +{ return v256_permute2x128<1>(a, a); } + +template +inline _Tpvec v256_reverse_64(const _Tpvec& a) +{ return v256_permute4x64<_MM_SHUFFLE(0, 1, 2, 3)>(a); } + +// ZIP +#define OPENCV_HAL_IMPL_AVX_ZIP(_Tpvec) \ + inline _Tpvec v_combine_low(const _Tpvec& a, const _Tpvec& b) \ + { return v256_permute2x128<0x20>(a, b); } \ + inline _Tpvec v_combine_high(const _Tpvec& a, const _Tpvec& b) \ + { return v256_permute2x128<0x31>(a, b); } \ + inline void v_recombine(const _Tpvec& a, const _Tpvec& b, \ + _Tpvec& c, _Tpvec& d) \ + { \ + _Tpvec a1b0 = v256_alignr_128(a, b); \ + c = v256_combine_diagonal(a, a1b0); \ + d = v256_combine_diagonal(a1b0, b); \ + } \ + inline void v_zip(const _Tpvec& a, const _Tpvec& b, \ + _Tpvec& ab0, _Tpvec& ab1) \ + { \ + _Tpvec ab0ab2, ab1ab3; \ + v256_zip(a, b, ab0ab2, ab1ab3); \ + v_recombine(ab0ab2, ab1ab3, ab0, ab1); \ + } + +OPENCV_HAL_IMPL_AVX_ZIP(v_uint8x32) +OPENCV_HAL_IMPL_AVX_ZIP(v_int8x32) +OPENCV_HAL_IMPL_AVX_ZIP(v_uint16x16) +OPENCV_HAL_IMPL_AVX_ZIP(v_int16x16) +OPENCV_HAL_IMPL_AVX_ZIP(v_uint32x8) +OPENCV_HAL_IMPL_AVX_ZIP(v_int32x8) +OPENCV_HAL_IMPL_AVX_ZIP(v_uint64x4) +OPENCV_HAL_IMPL_AVX_ZIP(v_int64x4) +OPENCV_HAL_IMPL_AVX_ZIP(v_float32x8) +OPENCV_HAL_IMPL_AVX_ZIP(v_float64x4) + +////////// Arithmetic, bitwise and comparison operations ///////// + +/* Element-wise binary and unary operations */ + +/** Arithmetics **/ +#define OPENCV_HAL_IMPL_AVX_BIN_OP(bin_op, _Tpvec, intrin) \ + inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b) \ + { return _Tpvec(intrin(a.val, b.val)); } \ + inline _Tpvec& operator bin_op##= (_Tpvec& a, const _Tpvec& b) \ + { a.val = intrin(a.val, b.val); return a; } + +OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_uint8x32, _mm256_adds_epu8) +OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_uint8x32, _mm256_subs_epu8) +OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_int8x32, _mm256_adds_epi8) +OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_int8x32, _mm256_subs_epi8) +OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_uint16x16, _mm256_adds_epu16) +OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_uint16x16, _mm256_subs_epu16) +OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_int16x16, _mm256_adds_epi16) +OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_int16x16, _mm256_subs_epi16) +OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_uint32x8, _mm256_add_epi32) +OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_uint32x8, _mm256_sub_epi32) +OPENCV_HAL_IMPL_AVX_BIN_OP(*, v_uint32x8, _mm256_mullo_epi32) +OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_int32x8, _mm256_add_epi32) +OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_int32x8, _mm256_sub_epi32) +OPENCV_HAL_IMPL_AVX_BIN_OP(*, v_int32x8, _mm256_mullo_epi32) +OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_uint64x4, _mm256_add_epi64) +OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_uint64x4, _mm256_sub_epi64) +OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_int64x4, _mm256_add_epi64) +OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_int64x4, _mm256_sub_epi64) + +OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_float32x8, _mm256_add_ps) +OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_float32x8, _mm256_sub_ps) +OPENCV_HAL_IMPL_AVX_BIN_OP(*, v_float32x8, _mm256_mul_ps) +OPENCV_HAL_IMPL_AVX_BIN_OP(/, v_float32x8, _mm256_div_ps) +OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_float64x4, _mm256_add_pd) +OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_float64x4, _mm256_sub_pd) +OPENCV_HAL_IMPL_AVX_BIN_OP(*, v_float64x4, _mm256_mul_pd) +OPENCV_HAL_IMPL_AVX_BIN_OP(/, v_float64x4, _mm256_div_pd) + +// saturating multiply 8-bit, 16-bit +inline v_uint8x32 operator * (const v_uint8x32& a, const v_uint8x32& b) +{ + v_uint16x16 c, d; + v_mul_expand(a, b, c, d); + return v_pack(c, d); +} +inline v_int8x32 operator * (const v_int8x32& a, const v_int8x32& b) +{ + v_int16x16 c, d; + v_mul_expand(a, b, c, d); + return v_pack(c, d); +} +inline v_uint16x16 operator * (const v_uint16x16& a, const v_uint16x16& b) +{ + __m256i pl = _mm256_mullo_epi16(a.val, b.val); + __m256i ph = _mm256_mulhi_epu16(a.val, b.val); + __m256i p0 = _mm256_unpacklo_epi16(pl, ph); + __m256i p1 = _mm256_unpackhi_epi16(pl, ph); + return v_uint16x16(_v256_packs_epu32(p0, p1)); +} +inline v_int16x16 operator * (const v_int16x16& a, const v_int16x16& b) +{ + __m256i pl = _mm256_mullo_epi16(a.val, b.val); + __m256i ph = _mm256_mulhi_epi16(a.val, b.val); + __m256i p0 = _mm256_unpacklo_epi16(pl, ph); + __m256i p1 = _mm256_unpackhi_epi16(pl, ph); + return v_int16x16(_mm256_packs_epi32(p0, p1)); +} +inline v_uint8x32& operator *= (v_uint8x32& a, const v_uint8x32& b) +{ a = a * b; return a; } +inline v_int8x32& operator *= (v_int8x32& a, const v_int8x32& b) +{ a = a * b; return a; } +inline v_uint16x16& operator *= (v_uint16x16& a, const v_uint16x16& b) +{ a = a * b; return a; } +inline v_int16x16& operator *= (v_int16x16& a, const v_int16x16& b) +{ a = a * b; return a; } + +/** Non-saturating arithmetics **/ +#define OPENCV_HAL_IMPL_AVX_BIN_FUNC(func, _Tpvec, intrin) \ + inline _Tpvec func(const _Tpvec& a, const _Tpvec& b) \ + { return _Tpvec(intrin(a.val, b.val)); } + +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_add_wrap, v_uint8x32, _mm256_add_epi8) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_add_wrap, v_int8x32, _mm256_add_epi8) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_add_wrap, v_uint16x16, _mm256_add_epi16) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_add_wrap, v_int16x16, _mm256_add_epi16) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_sub_wrap, v_uint8x32, _mm256_sub_epi8) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_sub_wrap, v_int8x32, _mm256_sub_epi8) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_sub_wrap, v_uint16x16, _mm256_sub_epi16) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_sub_wrap, v_int16x16, _mm256_sub_epi16) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_mul_wrap, v_uint16x16, _mm256_mullo_epi16) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_mul_wrap, v_int16x16, _mm256_mullo_epi16) + +inline v_uint8x32 v_mul_wrap(const v_uint8x32& a, const v_uint8x32& b) +{ + __m256i ad = _mm256_srai_epi16(a.val, 8); + __m256i bd = _mm256_srai_epi16(b.val, 8); + __m256i p0 = _mm256_mullo_epi16(a.val, b.val); // even + __m256i p1 = _mm256_slli_epi16(_mm256_mullo_epi16(ad, bd), 8); // odd + + const __m256i b01 = _mm256_set1_epi32(0xFF00FF00); + return v_uint8x32(_mm256_blendv_epi8(p0, p1, b01)); +} +inline v_int8x32 v_mul_wrap(const v_int8x32& a, const v_int8x32& b) +{ + return v_reinterpret_as_s8(v_mul_wrap(v_reinterpret_as_u8(a), v_reinterpret_as_u8(b))); +} + +// Multiply and expand +inline void v_mul_expand(const v_uint8x32& a, const v_uint8x32& b, + v_uint16x16& c, v_uint16x16& d) +{ + v_uint16x16 a0, a1, b0, b1; + v_expand(a, a0, a1); + v_expand(b, b0, b1); + c = v_mul_wrap(a0, b0); + d = v_mul_wrap(a1, b1); +} + +inline void v_mul_expand(const v_int8x32& a, const v_int8x32& b, + v_int16x16& c, v_int16x16& d) +{ + v_int16x16 a0, a1, b0, b1; + v_expand(a, a0, a1); + v_expand(b, b0, b1); + c = v_mul_wrap(a0, b0); + d = v_mul_wrap(a1, b1); +} + +inline void v_mul_expand(const v_int16x16& a, const v_int16x16& b, + v_int32x8& c, v_int32x8& d) +{ + v_int16x16 vhi = v_int16x16(_mm256_mulhi_epi16(a.val, b.val)); + + v_int16x16 v0, v1; + v_zip(v_mul_wrap(a, b), vhi, v0, v1); + + c = v_reinterpret_as_s32(v0); + d = v_reinterpret_as_s32(v1); +} + +inline void v_mul_expand(const v_uint16x16& a, const v_uint16x16& b, + v_uint32x8& c, v_uint32x8& d) +{ + v_uint16x16 vhi = v_uint16x16(_mm256_mulhi_epu16(a.val, b.val)); + + v_uint16x16 v0, v1; + v_zip(v_mul_wrap(a, b), vhi, v0, v1); + + c = v_reinterpret_as_u32(v0); + d = v_reinterpret_as_u32(v1); +} + +inline void v_mul_expand(const v_uint32x8& a, const v_uint32x8& b, + v_uint64x4& c, v_uint64x4& d) +{ + __m256i v0 = _mm256_mul_epu32(a.val, b.val); + __m256i v1 = _mm256_mul_epu32(_mm256_srli_epi64(a.val, 32), _mm256_srli_epi64(b.val, 32)); + v_zip(v_uint64x4(v0), v_uint64x4(v1), c, d); +} + +inline v_int16x16 v_mul_hi(const v_int16x16& a, const v_int16x16& b) { return v_int16x16(_mm256_mulhi_epi16(a.val, b.val)); } +inline v_uint16x16 v_mul_hi(const v_uint16x16& a, const v_uint16x16& b) { return v_uint16x16(_mm256_mulhi_epu16(a.val, b.val)); } + +/** Bitwise shifts **/ +#define OPENCV_HAL_IMPL_AVX_SHIFT_OP(_Tpuvec, _Tpsvec, suffix, srai) \ + inline _Tpuvec operator << (const _Tpuvec& a, int imm) \ + { return _Tpuvec(_mm256_slli_##suffix(a.val, imm)); } \ + inline _Tpsvec operator << (const _Tpsvec& a, int imm) \ + { return _Tpsvec(_mm256_slli_##suffix(a.val, imm)); } \ + inline _Tpuvec operator >> (const _Tpuvec& a, int imm) \ + { return _Tpuvec(_mm256_srli_##suffix(a.val, imm)); } \ + inline _Tpsvec operator >> (const _Tpsvec& a, int imm) \ + { return _Tpsvec(srai(a.val, imm)); } \ + template \ + inline _Tpuvec v_shl(const _Tpuvec& a) \ + { return _Tpuvec(_mm256_slli_##suffix(a.val, imm)); } \ + template \ + inline _Tpsvec v_shl(const _Tpsvec& a) \ + { return _Tpsvec(_mm256_slli_##suffix(a.val, imm)); } \ + template \ + inline _Tpuvec v_shr(const _Tpuvec& a) \ + { return _Tpuvec(_mm256_srli_##suffix(a.val, imm)); } \ + template \ + inline _Tpsvec v_shr(const _Tpsvec& a) \ + { return _Tpsvec(srai(a.val, imm)); } + +OPENCV_HAL_IMPL_AVX_SHIFT_OP(v_uint16x16, v_int16x16, epi16, _mm256_srai_epi16) +OPENCV_HAL_IMPL_AVX_SHIFT_OP(v_uint32x8, v_int32x8, epi32, _mm256_srai_epi32) + +inline __m256i _mm256_srai_epi64xx(const __m256i a, int imm) +{ + __m256i d = _mm256_set1_epi64x((int64)1 << 63); + __m256i r = _mm256_srli_epi64(_mm256_add_epi64(a, d), imm); + return _mm256_sub_epi64(r, _mm256_srli_epi64(d, imm)); +} +OPENCV_HAL_IMPL_AVX_SHIFT_OP(v_uint64x4, v_int64x4, epi64, _mm256_srai_epi64xx) + + +/** Bitwise logic **/ +#define OPENCV_HAL_IMPL_AVX_LOGIC_OP(_Tpvec, suffix, not_const) \ + OPENCV_HAL_IMPL_AVX_BIN_OP(&, _Tpvec, _mm256_and_##suffix) \ + OPENCV_HAL_IMPL_AVX_BIN_OP(|, _Tpvec, _mm256_or_##suffix) \ + OPENCV_HAL_IMPL_AVX_BIN_OP(^, _Tpvec, _mm256_xor_##suffix) \ + inline _Tpvec operator ~ (const _Tpvec& a) \ + { return _Tpvec(_mm256_xor_##suffix(a.val, not_const)); } + +OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_uint8x32, si256, _mm256_set1_epi32(-1)) +OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_int8x32, si256, _mm256_set1_epi32(-1)) +OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_uint16x16, si256, _mm256_set1_epi32(-1)) +OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_int16x16, si256, _mm256_set1_epi32(-1)) +OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_uint32x8, si256, _mm256_set1_epi32(-1)) +OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_int32x8, si256, _mm256_set1_epi32(-1)) +OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_uint64x4, si256, _mm256_set1_epi64x(-1)) +OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_int64x4, si256, _mm256_set1_epi64x(-1)) +OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_float32x8, ps, _mm256_castsi256_ps(_mm256_set1_epi32(-1))) +OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_float64x4, pd, _mm256_castsi256_pd(_mm256_set1_epi32(-1))) + +/** Select **/ +#define OPENCV_HAL_IMPL_AVX_SELECT(_Tpvec, suffix) \ + inline _Tpvec v_select(const _Tpvec& mask, const _Tpvec& a, const _Tpvec& b) \ + { return _Tpvec(_mm256_blendv_##suffix(b.val, a.val, mask.val)); } + +OPENCV_HAL_IMPL_AVX_SELECT(v_uint8x32, epi8) +OPENCV_HAL_IMPL_AVX_SELECT(v_int8x32, epi8) +OPENCV_HAL_IMPL_AVX_SELECT(v_uint16x16, epi8) +OPENCV_HAL_IMPL_AVX_SELECT(v_int16x16, epi8) +OPENCV_HAL_IMPL_AVX_SELECT(v_uint32x8, epi8) +OPENCV_HAL_IMPL_AVX_SELECT(v_int32x8, epi8) +OPENCV_HAL_IMPL_AVX_SELECT(v_float32x8, ps) +OPENCV_HAL_IMPL_AVX_SELECT(v_float64x4, pd) + +/** Comparison **/ +#define OPENCV_HAL_IMPL_AVX_CMP_OP_OV(_Tpvec) \ + inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \ + { return ~(a == b); } \ + inline _Tpvec operator < (const _Tpvec& a, const _Tpvec& b) \ + { return b > a; } \ + inline _Tpvec operator >= (const _Tpvec& a, const _Tpvec& b) \ + { return ~(a < b); } \ + inline _Tpvec operator <= (const _Tpvec& a, const _Tpvec& b) \ + { return b >= a; } + +#define OPENCV_HAL_IMPL_AVX_CMP_OP_INT(_Tpuvec, _Tpsvec, suffix, sbit) \ + inline _Tpuvec operator == (const _Tpuvec& a, const _Tpuvec& b) \ + { return _Tpuvec(_mm256_cmpeq_##suffix(a.val, b.val)); } \ + inline _Tpuvec operator > (const _Tpuvec& a, const _Tpuvec& b) \ + { \ + __m256i smask = _mm256_set1_##suffix(sbit); \ + return _Tpuvec(_mm256_cmpgt_##suffix( \ + _mm256_xor_si256(a.val, smask), \ + _mm256_xor_si256(b.val, smask))); \ + } \ + inline _Tpsvec operator == (const _Tpsvec& a, const _Tpsvec& b) \ + { return _Tpsvec(_mm256_cmpeq_##suffix(a.val, b.val)); } \ + inline _Tpsvec operator > (const _Tpsvec& a, const _Tpsvec& b) \ + { return _Tpsvec(_mm256_cmpgt_##suffix(a.val, b.val)); } \ + OPENCV_HAL_IMPL_AVX_CMP_OP_OV(_Tpuvec) \ + OPENCV_HAL_IMPL_AVX_CMP_OP_OV(_Tpsvec) + +OPENCV_HAL_IMPL_AVX_CMP_OP_INT(v_uint8x32, v_int8x32, epi8, (char)-128) +OPENCV_HAL_IMPL_AVX_CMP_OP_INT(v_uint16x16, v_int16x16, epi16, (short)-32768) +OPENCV_HAL_IMPL_AVX_CMP_OP_INT(v_uint32x8, v_int32x8, epi32, (int)0x80000000) + +#define OPENCV_HAL_IMPL_AVX_CMP_OP_64BIT(_Tpvec) \ + inline _Tpvec operator == (const _Tpvec& a, const _Tpvec& b) \ + { return _Tpvec(_mm256_cmpeq_epi64(a.val, b.val)); } \ + inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \ + { return ~(a == b); } + +OPENCV_HAL_IMPL_AVX_CMP_OP_64BIT(v_uint64x4) +OPENCV_HAL_IMPL_AVX_CMP_OP_64BIT(v_int64x4) + +#define OPENCV_HAL_IMPL_AVX_CMP_FLT(bin_op, imm8, _Tpvec, suffix) \ + inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b) \ + { return _Tpvec(_mm256_cmp_##suffix(a.val, b.val, imm8)); } + +#define OPENCV_HAL_IMPL_AVX_CMP_OP_FLT(_Tpvec, suffix) \ + OPENCV_HAL_IMPL_AVX_CMP_FLT(==, _CMP_EQ_OQ, _Tpvec, suffix) \ + OPENCV_HAL_IMPL_AVX_CMP_FLT(!=, _CMP_NEQ_OQ, _Tpvec, suffix) \ + OPENCV_HAL_IMPL_AVX_CMP_FLT(<, _CMP_LT_OQ, _Tpvec, suffix) \ + OPENCV_HAL_IMPL_AVX_CMP_FLT(>, _CMP_GT_OQ, _Tpvec, suffix) \ + OPENCV_HAL_IMPL_AVX_CMP_FLT(<=, _CMP_LE_OQ, _Tpvec, suffix) \ + OPENCV_HAL_IMPL_AVX_CMP_FLT(>=, _CMP_GE_OQ, _Tpvec, suffix) + +OPENCV_HAL_IMPL_AVX_CMP_OP_FLT(v_float32x8, ps) +OPENCV_HAL_IMPL_AVX_CMP_OP_FLT(v_float64x4, pd) + +inline v_float32x8 v_not_nan(const v_float32x8& a) +{ return v_float32x8(_mm256_cmp_ps(a.val, a.val, _CMP_ORD_Q)); } +inline v_float64x4 v_not_nan(const v_float64x4& a) +{ return v_float64x4(_mm256_cmp_pd(a.val, a.val, _CMP_ORD_Q)); } + +/** min/max **/ +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_min, v_uint8x32, _mm256_min_epu8) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_max, v_uint8x32, _mm256_max_epu8) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_min, v_int8x32, _mm256_min_epi8) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_max, v_int8x32, _mm256_max_epi8) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_min, v_uint16x16, _mm256_min_epu16) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_max, v_uint16x16, _mm256_max_epu16) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_min, v_int16x16, _mm256_min_epi16) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_max, v_int16x16, _mm256_max_epi16) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_min, v_uint32x8, _mm256_min_epu32) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_max, v_uint32x8, _mm256_max_epu32) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_min, v_int32x8, _mm256_min_epi32) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_max, v_int32x8, _mm256_max_epi32) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_min, v_float32x8, _mm256_min_ps) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_max, v_float32x8, _mm256_max_ps) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_min, v_float64x4, _mm256_min_pd) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_max, v_float64x4, _mm256_max_pd) + +/** Rotate **/ +template +inline v_uint8x32 v_rotate_left(const v_uint8x32& a, const v_uint8x32& b) +{ + enum {IMM_R = (16 - imm) & 0xFF}; + enum {IMM_R2 = (32 - imm) & 0xFF}; + + if (imm == 0) return a; + if (imm == 32) return b; + if (imm > 32) return v_uint8x32(); + + __m256i swap = _mm256_permute2x128_si256(a.val, b.val, 0x03); + if (imm == 16) return v_uint8x32(swap); + if (imm < 16) return v_uint8x32(_mm256_alignr_epi8(a.val, swap, IMM_R)); + return v_uint8x32(_mm256_alignr_epi8(swap, b.val, IMM_R2)); // imm < 32 +} + +template +inline v_uint8x32 v_rotate_right(const v_uint8x32& a, const v_uint8x32& b) +{ + enum {IMM_L = (imm - 16) & 0xFF}; + + if (imm == 0) return a; + if (imm == 32) return b; + if (imm > 32) return v_uint8x32(); + + __m256i swap = _mm256_permute2x128_si256(a.val, b.val, 0x21); + if (imm == 16) return v_uint8x32(swap); + if (imm < 16) return v_uint8x32(_mm256_alignr_epi8(swap, a.val, imm)); + return v_uint8x32(_mm256_alignr_epi8(b.val, swap, IMM_L)); +} + +template +inline v_uint8x32 v_rotate_left(const v_uint8x32& a) +{ + enum {IMM_L = (imm - 16) & 0xFF}; + enum {IMM_R = (16 - imm) & 0xFF}; + + if (imm == 0) return a; + if (imm > 32) return v_uint8x32(); + + // ESAC control[3] ? [127:0] = 0 + __m256i swapz = _mm256_permute2x128_si256(a.val, a.val, _MM_SHUFFLE(0, 0, 2, 0)); + if (imm == 16) return v_uint8x32(swapz); + if (imm < 16) return v_uint8x32(_mm256_alignr_epi8(a.val, swapz, IMM_R)); + return v_uint8x32(_mm256_slli_si256(swapz, IMM_L)); +} + +template +inline v_uint8x32 v_rotate_right(const v_uint8x32& a) +{ + enum {IMM_L = (imm - 16) & 0xFF}; + + if (imm == 0) return a; + if (imm > 32) return v_uint8x32(); + + // ESAC control[3] ? [127:0] = 0 + __m256i swapz = _mm256_permute2x128_si256(a.val, a.val, _MM_SHUFFLE(2, 0, 0, 1)); + if (imm == 16) return v_uint8x32(swapz); + if (imm < 16) return v_uint8x32(_mm256_alignr_epi8(swapz, a.val, imm)); + return v_uint8x32(_mm256_srli_si256(swapz, IMM_L)); +} + +#define OPENCV_HAL_IMPL_AVX_ROTATE_CAST(intrin, _Tpvec, cast) \ + template \ + inline _Tpvec intrin(const _Tpvec& a, const _Tpvec& b) \ + { \ + enum {IMMxW = imm * sizeof(typename _Tpvec::lane_type)}; \ + v_uint8x32 ret = intrin(v_reinterpret_as_u8(a), \ + v_reinterpret_as_u8(b)); \ + return _Tpvec(cast(ret.val)); \ + } \ + template \ + inline _Tpvec intrin(const _Tpvec& a) \ + { \ + enum {IMMxW = imm * sizeof(typename _Tpvec::lane_type)}; \ + v_uint8x32 ret = intrin(v_reinterpret_as_u8(a)); \ + return _Tpvec(cast(ret.val)); \ + } + +#define OPENCV_HAL_IMPL_AVX_ROTATE(_Tpvec) \ + OPENCV_HAL_IMPL_AVX_ROTATE_CAST(v_rotate_left, _Tpvec, OPENCV_HAL_NOP) \ + OPENCV_HAL_IMPL_AVX_ROTATE_CAST(v_rotate_right, _Tpvec, OPENCV_HAL_NOP) + +OPENCV_HAL_IMPL_AVX_ROTATE(v_int8x32) +OPENCV_HAL_IMPL_AVX_ROTATE(v_uint16x16) +OPENCV_HAL_IMPL_AVX_ROTATE(v_int16x16) +OPENCV_HAL_IMPL_AVX_ROTATE(v_uint32x8) +OPENCV_HAL_IMPL_AVX_ROTATE(v_int32x8) +OPENCV_HAL_IMPL_AVX_ROTATE(v_uint64x4) +OPENCV_HAL_IMPL_AVX_ROTATE(v_int64x4) + +OPENCV_HAL_IMPL_AVX_ROTATE_CAST(v_rotate_left, v_float32x8, _mm256_castsi256_ps) +OPENCV_HAL_IMPL_AVX_ROTATE_CAST(v_rotate_right, v_float32x8, _mm256_castsi256_ps) +OPENCV_HAL_IMPL_AVX_ROTATE_CAST(v_rotate_left, v_float64x4, _mm256_castsi256_pd) +OPENCV_HAL_IMPL_AVX_ROTATE_CAST(v_rotate_right, v_float64x4, _mm256_castsi256_pd) + +////////// Reduce and mask ///////// + +/** Reduce **/ +#define OPENCV_HAL_IMPL_AVX_REDUCE_16(_Tpvec, sctype, func, intrin) \ + inline sctype v_reduce_##func(const _Tpvec& a) \ + { \ + __m128i v0 = _v256_extract_low(a.val); \ + __m128i v1 = _v256_extract_high(a.val); \ + v0 = intrin(v0, v1); \ + v0 = intrin(v0, _mm_srli_si128(v0, 8)); \ + v0 = intrin(v0, _mm_srli_si128(v0, 4)); \ + v0 = intrin(v0, _mm_srli_si128(v0, 2)); \ + return (sctype) _mm_cvtsi128_si32(v0); \ + } + +OPENCV_HAL_IMPL_AVX_REDUCE_16(v_uint16x16, ushort, min, _mm_min_epu16) +OPENCV_HAL_IMPL_AVX_REDUCE_16(v_int16x16, short, min, _mm_min_epi16) +OPENCV_HAL_IMPL_AVX_REDUCE_16(v_uint16x16, ushort, max, _mm_max_epu16) +OPENCV_HAL_IMPL_AVX_REDUCE_16(v_int16x16, short, max, _mm_max_epi16) + +#define OPENCV_HAL_IMPL_AVX_REDUCE_8(_Tpvec, sctype, func, intrin) \ + inline sctype v_reduce_##func(const _Tpvec& a) \ + { \ + __m128i v0 = _v256_extract_low(a.val); \ + __m128i v1 = _v256_extract_high(a.val); \ + v0 = intrin(v0, v1); \ + v0 = intrin(v0, _mm_srli_si128(v0, 8)); \ + v0 = intrin(v0, _mm_srli_si128(v0, 4)); \ + return (sctype) _mm_cvtsi128_si32(v0); \ + } + +OPENCV_HAL_IMPL_AVX_REDUCE_8(v_uint32x8, unsigned, min, _mm_min_epu32) +OPENCV_HAL_IMPL_AVX_REDUCE_8(v_int32x8, int, min, _mm_min_epi32) +OPENCV_HAL_IMPL_AVX_REDUCE_8(v_uint32x8, unsigned, max, _mm_max_epu32) +OPENCV_HAL_IMPL_AVX_REDUCE_8(v_int32x8, int, max, _mm_max_epi32) + +#define OPENCV_HAL_IMPL_AVX_REDUCE_FLT(func, intrin) \ + inline float v_reduce_##func(const v_float32x8& a) \ + { \ + __m128 v0 = _v256_extract_low(a.val); \ + __m128 v1 = _v256_extract_high(a.val); \ + v0 = intrin(v0, v1); \ + v0 = intrin(v0, _mm_permute_ps(v0, _MM_SHUFFLE(0, 0, 3, 2))); \ + v0 = intrin(v0, _mm_permute_ps(v0, _MM_SHUFFLE(0, 0, 0, 3))); \ + return _mm_cvtss_f32(v0); \ + } + +OPENCV_HAL_IMPL_AVX_REDUCE_FLT(min, _mm_min_ps) +OPENCV_HAL_IMPL_AVX_REDUCE_FLT(max, _mm_max_ps) + +inline ushort v_reduce_sum(const v_uint16x16& a) +{ + __m128i a0 = _v256_extract_low(a.val); + __m128i a1 = _v256_extract_high(a.val); + + __m128i s0 = _mm_adds_epu16(a0, a1); + s0 = _mm_adds_epu16(s0, _mm_srli_si128(s0, 8)); + s0 = _mm_adds_epu16(s0, _mm_srli_si128(s0, 4)); + s0 = _mm_adds_epu16(s0, _mm_srli_si128(s0, 2)); + + return (ushort)_mm_cvtsi128_si32(s0); +} + +inline short v_reduce_sum(const v_int16x16& a) +{ + __m256i s0 = _mm256_hadds_epi16(a.val, a.val); + s0 = _mm256_hadds_epi16(s0, s0); + s0 = _mm256_hadds_epi16(s0, s0); + + __m128i s1 = _v256_extract_high(s0); + s1 = _mm_adds_epi16(_v256_extract_low(s0), s1); + + return (short)_mm_cvtsi128_si32(s1); +} + +inline int v_reduce_sum(const v_int32x8& a) +{ + __m256i s0 = _mm256_hadd_epi32(a.val, a.val); + s0 = _mm256_hadd_epi32(s0, s0); + + __m128i s1 = _v256_extract_high(s0); + s1 = _mm_add_epi32(_v256_extract_low(s0), s1); + + return _mm_cvtsi128_si32(s1); +} + +inline unsigned v_reduce_sum(const v_uint32x8& a) +{ return v_reduce_sum(v_reinterpret_as_s32(a)); } + +inline float v_reduce_sum(const v_float32x8& a) +{ + __m256 s0 = _mm256_hadd_ps(a.val, a.val); + s0 = _mm256_hadd_ps(s0, s0); + + __m128 s1 = _v256_extract_high(s0); + s1 = _mm_add_ps(_v256_extract_low(s0), s1); + + return _mm_cvtss_f32(s1); +} + +inline double v_reduce_sum(const v_float64x4& a) +{ + __m256d s0 = _mm256_hadd_pd(a.val, a.val); + return _mm_cvtsd_f64(_mm_add_pd(_v256_extract_low(s0), _v256_extract_high(s0))); +} + +inline v_float32x8 v_reduce_sum4(const v_float32x8& a, const v_float32x8& b, + const v_float32x8& c, const v_float32x8& d) +{ + __m256 ab = _mm256_hadd_ps(a.val, b.val); + __m256 cd = _mm256_hadd_ps(c.val, d.val); + return v_float32x8(_mm256_hadd_ps(ab, cd)); +} + +inline unsigned v_reduce_sad(const v_uint8x32& a, const v_uint8x32& b) +{ + return (unsigned)_v_cvtsi256_si32(_mm256_sad_epu8(a.val, b.val)); +} +inline unsigned v_reduce_sad(const v_int8x32& a, const v_int8x32& b) +{ + __m256i half = _mm256_set1_epi8(0x7f); + return (unsigned)_v_cvtsi256_si32(_mm256_sad_epu8(_mm256_add_epi8(a.val, half), _mm256_add_epi8(b.val, half))); +} +inline unsigned v_reduce_sad(const v_uint16x16& a, const v_uint16x16& b) +{ + v_uint32x8 l, h; + v_expand(v_add_wrap(a - b, b - a), l, h); + return v_reduce_sum(l + h); +} +inline unsigned v_reduce_sad(const v_int16x16& a, const v_int16x16& b) +{ + v_uint32x8 l, h; + v_expand(v_reinterpret_as_u16(v_sub_wrap(v_max(a, b), v_min(a, b))), l, h); + return v_reduce_sum(l + h); +} +inline unsigned v_reduce_sad(const v_uint32x8& a, const v_uint32x8& b) +{ + return v_reduce_sum(v_max(a, b) - v_min(a, b)); +} +inline unsigned v_reduce_sad(const v_int32x8& a, const v_int32x8& b) +{ + v_int32x8 m = a < b; + return v_reduce_sum(v_reinterpret_as_u32(((a - b) ^ m) - m)); +} +inline float v_reduce_sad(const v_float32x8& a, const v_float32x8& b) +{ + return v_reduce_sum((a - b) & v_float32x8(_mm256_castsi256_ps(_mm256_set1_epi32(0x7fffffff)))); +} + +/** Popcount **/ +#define OPENCV_HAL_IMPL_AVX_POPCOUNT(_Tpvec) \ + inline v_uint32x8 v_popcount(const _Tpvec& a) \ + { \ + const v_uint32x8 m1 = v256_setall_u32(0x55555555); \ + const v_uint32x8 m2 = v256_setall_u32(0x33333333); \ + const v_uint32x8 m4 = v256_setall_u32(0x0f0f0f0f); \ + v_uint32x8 p = v_reinterpret_as_u32(a); \ + p = ((p >> 1) & m1) + (p & m1); \ + p = ((p >> 2) & m2) + (p & m2); \ + p = ((p >> 4) & m4) + (p & m4); \ + p.val = _mm256_sad_epu8(p.val, _mm256_setzero_si256()); \ + return p; \ + } + +OPENCV_HAL_IMPL_AVX_POPCOUNT(v_uint8x32) +OPENCV_HAL_IMPL_AVX_POPCOUNT(v_int8x32) +OPENCV_HAL_IMPL_AVX_POPCOUNT(v_uint16x16) +OPENCV_HAL_IMPL_AVX_POPCOUNT(v_int16x16) +OPENCV_HAL_IMPL_AVX_POPCOUNT(v_uint32x8) +OPENCV_HAL_IMPL_AVX_POPCOUNT(v_int32x8) + +/** Mask **/ +inline int v_signmask(const v_int8x32& a) +{ return _mm256_movemask_epi8(a.val); } +inline int v_signmask(const v_uint8x32& a) +{ return v_signmask(v_reinterpret_as_s8(a)); } + +inline int v_signmask(const v_int16x16& a) +{ + v_int8x32 v = v_int8x32(_mm256_packs_epi16(a.val, a.val)); + return v_signmask(v) & 255; +} +inline int v_signmask(const v_uint16x16& a) +{ return v_signmask(v_reinterpret_as_s16(a)); } + +inline int v_signmask(const v_int32x8& a) +{ + __m256i a16 = _mm256_packs_epi32(a.val, a.val); + v_int8x32 v = v_int8x32(_mm256_packs_epi16(a16, a16)); + return v_signmask(v) & 15; +} +inline int v_signmask(const v_uint32x8& a) +{ return v_signmask(v_reinterpret_as_s32(a)); } + +inline int v_signmask(const v_float32x8& a) +{ return _mm256_movemask_ps(a.val); } +inline int v_signmask(const v_float64x4& a) +{ return _mm256_movemask_pd(a.val); } + +/** Checks **/ +#define OPENCV_HAL_IMPL_AVX_CHECK(_Tpvec, and_op, allmask) \ + inline bool v_check_all(const _Tpvec& a) \ + { \ + int mask = v_signmask(v_reinterpret_as_s8(a)); \ + return and_op(mask, allmask) == allmask; \ + } \ + inline bool v_check_any(const _Tpvec& a) \ + { \ + int mask = v_signmask(v_reinterpret_as_s8(a)); \ + return and_op(mask, allmask) != 0; \ + } + +OPENCV_HAL_IMPL_AVX_CHECK(v_uint8x32, OPENCV_HAL_1ST, -1) +OPENCV_HAL_IMPL_AVX_CHECK(v_int8x32, OPENCV_HAL_1ST, -1) +OPENCV_HAL_IMPL_AVX_CHECK(v_uint16x16, OPENCV_HAL_AND, (int)0xaaaa) +OPENCV_HAL_IMPL_AVX_CHECK(v_int16x16, OPENCV_HAL_AND, (int)0xaaaa) +OPENCV_HAL_IMPL_AVX_CHECK(v_uint32x8, OPENCV_HAL_AND, (int)0x8888) +OPENCV_HAL_IMPL_AVX_CHECK(v_int32x8, OPENCV_HAL_AND, (int)0x8888) + +#define OPENCV_HAL_IMPL_AVX_CHECK_FLT(_Tpvec, allmask) \ + inline bool v_check_all(const _Tpvec& a) \ + { \ + int mask = v_signmask(a); \ + return mask == allmask; \ + } \ + inline bool v_check_any(const _Tpvec& a) \ + { \ + int mask = v_signmask(a); \ + return mask != 0; \ + } + +OPENCV_HAL_IMPL_AVX_CHECK_FLT(v_float32x8, 255) +OPENCV_HAL_IMPL_AVX_CHECK_FLT(v_float64x4, 15) + + +////////// Other math ///////// + +/** Some frequent operations **/ +#define OPENCV_HAL_IMPL_AVX_MULADD(_Tpvec, suffix) \ + inline _Tpvec v_fma(const _Tpvec& a, const _Tpvec& b, const _Tpvec& c) \ + { return _Tpvec(_mm256_fmadd_##suffix(a.val, b.val, c.val)); } \ + inline _Tpvec v_muladd(const _Tpvec& a, const _Tpvec& b, const _Tpvec& c) \ + { return _Tpvec(_mm256_fmadd_##suffix(a.val, b.val, c.val)); } \ + inline _Tpvec v_sqrt(const _Tpvec& x) \ + { return _Tpvec(_mm256_sqrt_##suffix(x.val)); } \ + inline _Tpvec v_sqr_magnitude(const _Tpvec& a, const _Tpvec& b) \ + { return v_fma(a, a, b * b); } \ + inline _Tpvec v_magnitude(const _Tpvec& a, const _Tpvec& b) \ + { return v_sqrt(v_fma(a, a, b*b)); } + +OPENCV_HAL_IMPL_AVX_MULADD(v_float32x8, ps) +OPENCV_HAL_IMPL_AVX_MULADD(v_float64x4, pd) + +inline v_int32x8 v_fma(const v_int32x8& a, const v_int32x8& b, const v_int32x8& c) +{ + return a * b + c; +} + +inline v_int32x8 v_muladd(const v_int32x8& a, const v_int32x8& b, const v_int32x8& c) +{ + return v_fma(a, b, c); +} + +inline v_float32x8 v_invsqrt(const v_float32x8& x) +{ + v_float32x8 half = x * v256_setall_f32(0.5); + v_float32x8 t = v_float32x8(_mm256_rsqrt_ps(x.val)); + // todo: _mm256_fnmsub_ps + t *= v256_setall_f32(1.5) - ((t * t) * half); + return t; +} + +inline v_float64x4 v_invsqrt(const v_float64x4& x) +{ + return v256_setall_f64(1.) / v_sqrt(x); +} + +/** Absolute values **/ +#define OPENCV_HAL_IMPL_AVX_ABS(_Tpvec, suffix) \ + inline v_u##_Tpvec v_abs(const v_##_Tpvec& x) \ + { return v_u##_Tpvec(_mm256_abs_##suffix(x.val)); } + +OPENCV_HAL_IMPL_AVX_ABS(int8x32, epi8) +OPENCV_HAL_IMPL_AVX_ABS(int16x16, epi16) +OPENCV_HAL_IMPL_AVX_ABS(int32x8, epi32) + +inline v_float32x8 v_abs(const v_float32x8& x) +{ return x & v_float32x8(_mm256_castsi256_ps(_mm256_set1_epi32(0x7fffffff))); } +inline v_float64x4 v_abs(const v_float64x4& x) +{ return x & v_float64x4(_mm256_castsi256_pd(_mm256_srli_epi64(_mm256_set1_epi64x(-1), 1))); } + +/** Absolute difference **/ +inline v_uint8x32 v_absdiff(const v_uint8x32& a, const v_uint8x32& b) +{ return v_add_wrap(a - b, b - a); } +inline v_uint16x16 v_absdiff(const v_uint16x16& a, const v_uint16x16& b) +{ return v_add_wrap(a - b, b - a); } +inline v_uint32x8 v_absdiff(const v_uint32x8& a, const v_uint32x8& b) +{ return v_max(a, b) - v_min(a, b); } + +inline v_uint8x32 v_absdiff(const v_int8x32& a, const v_int8x32& b) +{ + v_int8x32 d = v_sub_wrap(a, b); + v_int8x32 m = a < b; + return v_reinterpret_as_u8(v_sub_wrap(d ^ m, m)); +} + +inline v_uint16x16 v_absdiff(const v_int16x16& a, const v_int16x16& b) +{ return v_reinterpret_as_u16(v_sub_wrap(v_max(a, b), v_min(a, b))); } + +inline v_uint32x8 v_absdiff(const v_int32x8& a, const v_int32x8& b) +{ + v_int32x8 d = a - b; + v_int32x8 m = a < b; + return v_reinterpret_as_u32((d ^ m) - m); +} + +inline v_float32x8 v_absdiff(const v_float32x8& a, const v_float32x8& b) +{ return v_abs(a - b); } + +inline v_float64x4 v_absdiff(const v_float64x4& a, const v_float64x4& b) +{ return v_abs(a - b); } + +/** Saturating absolute difference **/ +inline v_int8x32 v_absdiffs(const v_int8x32& a, const v_int8x32& b) +{ + v_int8x32 d = a - b; + v_int8x32 m = a < b; + return (d ^ m) - m; +} +inline v_int16x16 v_absdiffs(const v_int16x16& a, const v_int16x16& b) +{ return v_max(a, b) - v_min(a, b); } + +////////// Conversions ///////// + +/** Rounding **/ +inline v_int32x8 v_round(const v_float32x8& a) +{ return v_int32x8(_mm256_cvtps_epi32(a.val)); } + +inline v_int32x8 v_round(const v_float64x4& a) +{ return v_int32x8(_mm256_castsi128_si256(_mm256_cvtpd_epi32(a.val))); } + +inline v_int32x8 v_round(const v_float64x4& a, const v_float64x4& b) +{ + __m128i ai = _mm256_cvtpd_epi32(a.val), bi = _mm256_cvtpd_epi32(b.val); + return v_int32x8(_v256_combine(ai, bi)); +} + +inline v_int32x8 v_trunc(const v_float32x8& a) +{ return v_int32x8(_mm256_cvttps_epi32(a.val)); } + +inline v_int32x8 v_trunc(const v_float64x4& a) +{ return v_int32x8(_mm256_castsi128_si256(_mm256_cvttpd_epi32(a.val))); } + +inline v_int32x8 v_floor(const v_float32x8& a) +{ return v_int32x8(_mm256_cvttps_epi32(_mm256_floor_ps(a.val))); } + +inline v_int32x8 v_floor(const v_float64x4& a) +{ return v_trunc(v_float64x4(_mm256_floor_pd(a.val))); } + +inline v_int32x8 v_ceil(const v_float32x8& a) +{ return v_int32x8(_mm256_cvttps_epi32(_mm256_ceil_ps(a.val))); } + +inline v_int32x8 v_ceil(const v_float64x4& a) +{ return v_trunc(v_float64x4(_mm256_ceil_pd(a.val))); } + +/** To float **/ +inline v_float32x8 v_cvt_f32(const v_int32x8& a) +{ return v_float32x8(_mm256_cvtepi32_ps(a.val)); } + +inline v_float32x8 v_cvt_f32(const v_float64x4& a) +{ return v_float32x8(_mm256_castps128_ps256(_mm256_cvtpd_ps(a.val))); } + +inline v_float32x8 v_cvt_f32(const v_float64x4& a, const v_float64x4& b) +{ + __m128 af = _mm256_cvtpd_ps(a.val), bf = _mm256_cvtpd_ps(b.val); + return v_float32x8(_mm256_insertf128_ps(_mm256_castps128_ps256(af), bf, 1)); +} + +inline v_float64x4 v_cvt_f64(const v_int32x8& a) +{ return v_float64x4(_mm256_cvtepi32_pd(_v256_extract_low(a.val))); } + +inline v_float64x4 v_cvt_f64_high(const v_int32x8& a) +{ return v_float64x4(_mm256_cvtepi32_pd(_v256_extract_high(a.val))); } + +inline v_float64x4 v_cvt_f64(const v_float32x8& a) +{ return v_float64x4(_mm256_cvtps_pd(_v256_extract_low(a.val))); } + +inline v_float64x4 v_cvt_f64_high(const v_float32x8& a) +{ return v_float64x4(_mm256_cvtps_pd(_v256_extract_high(a.val))); } + +////////////// Lookup table access //////////////////// + +inline v_int32x8 v_lut(const int* tab, const v_int32x8& idxvec) +{ + return v_int32x8(_mm256_i32gather_epi32(tab, idxvec.val, 4)); +} + +inline v_uint32x8 v_lut(const unsigned* tab, const v_int32x8& idxvec) +{ + return v_reinterpret_as_u32(v_lut((const int *)tab, idxvec)); +} + +inline v_float32x8 v_lut(const float* tab, const v_int32x8& idxvec) +{ + return v_float32x8(_mm256_i32gather_ps(tab, idxvec.val, 4)); +} + +inline v_float64x4 v_lut(const double* tab, const v_int32x8& idxvec) +{ + return v_float64x4(_mm256_i32gather_pd(tab, _mm256_castsi256_si128(idxvec.val), 8)); +} + +inline void v_lut_deinterleave(const float* tab, const v_int32x8& idxvec, v_float32x8& x, v_float32x8& y) +{ + int CV_DECL_ALIGNED(32) idx[8]; + v_store_aligned(idx, idxvec); + __m128 z = _mm_setzero_ps(); + __m128 xy01, xy45, xy23, xy67; + xy01 = _mm_loadl_pi(z, (const __m64*)(tab + idx[0])); + xy01 = _mm_loadh_pi(xy01, (const __m64*)(tab + idx[1])); + xy45 = _mm_loadl_pi(z, (const __m64*)(tab + idx[4])); + xy45 = _mm_loadh_pi(xy45, (const __m64*)(tab + idx[5])); + __m256 xy0145 = _v256_combine(xy01, xy45); + xy23 = _mm_loadl_pi(z, (const __m64*)(tab + idx[2])); + xy23 = _mm_loadh_pi(xy23, (const __m64*)(tab + idx[3])); + xy67 = _mm_loadl_pi(z, (const __m64*)(tab + idx[6])); + xy67 = _mm_loadh_pi(xy67, (const __m64*)(tab + idx[7])); + __m256 xy2367 = _v256_combine(xy23, xy67); + + __m256 xxyy0145 = _mm256_unpacklo_ps(xy0145, xy2367); + __m256 xxyy2367 = _mm256_unpackhi_ps(xy0145, xy2367); + + x = v_float32x8(_mm256_unpacklo_ps(xxyy0145, xxyy2367)); + y = v_float32x8(_mm256_unpackhi_ps(xxyy0145, xxyy2367)); +} + +inline void v_lut_deinterleave(const double* tab, const v_int32x8& idxvec, v_float64x4& x, v_float64x4& y) +{ + int CV_DECL_ALIGNED(32) idx[4]; + v_store_low(idx, idxvec); + __m128d xy0 = _mm_loadu_pd(tab + idx[0]); + __m128d xy2 = _mm_loadu_pd(tab + idx[2]); + __m128d xy1 = _mm_loadu_pd(tab + idx[1]); + __m128d xy3 = _mm_loadu_pd(tab + idx[3]); + __m256d xy02 = _v256_combine(xy0, xy2); + __m256d xy13 = _v256_combine(xy1, xy3); + + x = v_float64x4(_mm256_unpacklo_pd(xy02, xy13)); + y = v_float64x4(_mm256_unpackhi_pd(xy02, xy13)); +} + +////////// Matrix operations ///////// + +inline v_int32x8 v_dotprod(const v_int16x16& a, const v_int16x16& b) +{ return v_int32x8(_mm256_madd_epi16(a.val, b.val)); } + +inline v_int32x8 v_dotprod(const v_int16x16& a, const v_int16x16& b, const v_int32x8& c) +{ return v_dotprod(a, b) + c; } + +#define OPENCV_HAL_AVX_SPLAT2_PS(a, im) \ + v_float32x8(_mm256_permute_ps(a.val, _MM_SHUFFLE(im, im, im, im))) + +inline v_float32x8 v_matmul(const v_float32x8& v, const v_float32x8& m0, + const v_float32x8& m1, const v_float32x8& m2, + const v_float32x8& m3) +{ + v_float32x8 v04 = OPENCV_HAL_AVX_SPLAT2_PS(v, 0); + v_float32x8 v15 = OPENCV_HAL_AVX_SPLAT2_PS(v, 1); + v_float32x8 v26 = OPENCV_HAL_AVX_SPLAT2_PS(v, 2); + v_float32x8 v37 = OPENCV_HAL_AVX_SPLAT2_PS(v, 3); + return v_fma(v04, m0, v_fma(v15, m1, v_fma(v26, m2, v37 * m3))); +} + +inline v_float32x8 v_matmuladd(const v_float32x8& v, const v_float32x8& m0, + const v_float32x8& m1, const v_float32x8& m2, + const v_float32x8& a) +{ + v_float32x8 v04 = OPENCV_HAL_AVX_SPLAT2_PS(v, 0); + v_float32x8 v15 = OPENCV_HAL_AVX_SPLAT2_PS(v, 1); + v_float32x8 v26 = OPENCV_HAL_AVX_SPLAT2_PS(v, 2); + return v_fma(v04, m0, v_fma(v15, m1, v_fma(v26, m2, a))); +} + +#define OPENCV_HAL_IMPL_AVX_TRANSPOSE4x4(_Tpvec, suffix, cast_from, cast_to) \ + inline void v_transpose4x4(const _Tpvec& a0, const _Tpvec& a1, \ + const _Tpvec& a2, const _Tpvec& a3, \ + _Tpvec& b0, _Tpvec& b1, _Tpvec& b2, _Tpvec& b3) \ + { \ + __m256i t0 = cast_from(_mm256_unpacklo_##suffix(a0.val, a1.val)); \ + __m256i t1 = cast_from(_mm256_unpacklo_##suffix(a2.val, a3.val)); \ + __m256i t2 = cast_from(_mm256_unpackhi_##suffix(a0.val, a1.val)); \ + __m256i t3 = cast_from(_mm256_unpackhi_##suffix(a2.val, a3.val)); \ + b0.val = cast_to(_mm256_unpacklo_epi64(t0, t1)); \ + b1.val = cast_to(_mm256_unpackhi_epi64(t0, t1)); \ + b2.val = cast_to(_mm256_unpacklo_epi64(t2, t3)); \ + b3.val = cast_to(_mm256_unpackhi_epi64(t2, t3)); \ + } + +OPENCV_HAL_IMPL_AVX_TRANSPOSE4x4(v_uint32x8, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_AVX_TRANSPOSE4x4(v_int32x8, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_AVX_TRANSPOSE4x4(v_float32x8, ps, _mm256_castps_si256, _mm256_castsi256_ps) + +//////////////// Value reordering /////////////// + +/* Expand */ +#define OPENCV_HAL_IMPL_AVX_EXPAND(_Tpvec, _Tpwvec, _Tp, intrin) \ + inline void v_expand(const _Tpvec& a, _Tpwvec& b0, _Tpwvec& b1) \ + { \ + b0.val = intrin(_v256_extract_low(a.val)); \ + b1.val = intrin(_v256_extract_high(a.val)); \ + } \ + inline _Tpwvec v_expand_low(const _Tpvec& a) \ + { return _Tpwvec(intrin(_v256_extract_low(a.val))); } \ + inline _Tpwvec v_expand_high(const _Tpvec& a) \ + { return _Tpwvec(intrin(_v256_extract_high(a.val))); } \ + inline _Tpwvec v256_load_expand(const _Tp* ptr) \ + { \ + __m128i a = _mm_loadu_si128((const __m128i*)ptr); \ + return _Tpwvec(intrin(a)); \ + } + +OPENCV_HAL_IMPL_AVX_EXPAND(v_uint8x32, v_uint16x16, uchar, _mm256_cvtepu8_epi16) +OPENCV_HAL_IMPL_AVX_EXPAND(v_int8x32, v_int16x16, schar, _mm256_cvtepi8_epi16) +OPENCV_HAL_IMPL_AVX_EXPAND(v_uint16x16, v_uint32x8, ushort, _mm256_cvtepu16_epi32) +OPENCV_HAL_IMPL_AVX_EXPAND(v_int16x16, v_int32x8, short, _mm256_cvtepi16_epi32) +OPENCV_HAL_IMPL_AVX_EXPAND(v_uint32x8, v_uint64x4, unsigned, _mm256_cvtepu32_epi64) +OPENCV_HAL_IMPL_AVX_EXPAND(v_int32x8, v_int64x4, int, _mm256_cvtepi32_epi64) + +#define OPENCV_HAL_IMPL_AVX_EXPAND_Q(_Tpvec, _Tp, intrin) \ + inline _Tpvec v256_load_expand_q(const _Tp* ptr) \ + { \ + __m128i a = _mm_loadl_epi64((const __m128i*)ptr); \ + return _Tpvec(intrin(a)); \ + } + +OPENCV_HAL_IMPL_AVX_EXPAND_Q(v_uint32x8, uchar, _mm256_cvtepu8_epi32) +OPENCV_HAL_IMPL_AVX_EXPAND_Q(v_int32x8, schar, _mm256_cvtepi8_epi32) + +/* pack */ +// 16 +inline v_int8x32 v_pack(const v_int16x16& a, const v_int16x16& b) +{ return v_int8x32(_v256_shuffle_odd_64(_mm256_packs_epi16(a.val, b.val))); } + +inline v_uint8x32 v_pack(const v_uint16x16& a, const v_uint16x16& b) +{ + __m256i t = _mm256_set1_epi16(255); + __m256i a1 = _mm256_min_epu16(a.val, t); + __m256i b1 = _mm256_min_epu16(b.val, t); + return v_uint8x32(_v256_shuffle_odd_64(_mm256_packus_epi16(a1, b1))); +} + +inline v_uint8x32 v_pack_u(const v_int16x16& a, const v_int16x16& b) +{ + return v_uint8x32(_v256_shuffle_odd_64(_mm256_packus_epi16(a.val, b.val))); +} + +inline void v_pack_store(schar* ptr, const v_int16x16& a) +{ v_store_low(ptr, v_pack(a, a)); } + +inline void v_pack_store(uchar* ptr, const v_uint16x16& a) +{ + const __m256i m = _mm256_set1_epi16(255); + __m256i am = _mm256_min_epu16(a.val, m); + am = _v256_shuffle_odd_64(_mm256_packus_epi16(am, am)); + v_store_low(ptr, v_uint8x32(am)); +} + +inline void v_pack_u_store(uchar* ptr, const v_int16x16& a) +{ v_store_low(ptr, v_pack_u(a, a)); } + +template inline +v_uint8x32 v_rshr_pack(const v_uint16x16& a, const v_uint16x16& b) +{ + // we assume that n > 0, and so the shifted 16-bit values can be treated as signed numbers. + v_uint16x16 delta = v256_setall_u16((short)(1 << (n-1))); + return v_pack_u(v_reinterpret_as_s16((a + delta) >> n), + v_reinterpret_as_s16((b + delta) >> n)); +} + +template inline +void v_rshr_pack_store(uchar* ptr, const v_uint16x16& a) +{ + v_uint16x16 delta = v256_setall_u16((short)(1 << (n-1))); + v_pack_u_store(ptr, v_reinterpret_as_s16((a + delta) >> n)); +} + +template inline +v_uint8x32 v_rshr_pack_u(const v_int16x16& a, const v_int16x16& b) +{ + v_int16x16 delta = v256_setall_s16((short)(1 << (n-1))); + return v_pack_u((a + delta) >> n, (b + delta) >> n); +} + +template inline +void v_rshr_pack_u_store(uchar* ptr, const v_int16x16& a) +{ + v_int16x16 delta = v256_setall_s16((short)(1 << (n-1))); + v_pack_u_store(ptr, (a + delta) >> n); +} + +template inline +v_int8x32 v_rshr_pack(const v_int16x16& a, const v_int16x16& b) +{ + v_int16x16 delta = v256_setall_s16((short)(1 << (n-1))); + return v_pack((a + delta) >> n, (b + delta) >> n); +} + +template inline +void v_rshr_pack_store(schar* ptr, const v_int16x16& a) +{ + v_int16x16 delta = v256_setall_s16((short)(1 << (n-1))); + v_pack_store(ptr, (a + delta) >> n); +} + +// 32 +inline v_int16x16 v_pack(const v_int32x8& a, const v_int32x8& b) +{ return v_int16x16(_v256_shuffle_odd_64(_mm256_packs_epi32(a.val, b.val))); } + +inline v_uint16x16 v_pack(const v_uint32x8& a, const v_uint32x8& b) +{ return v_uint16x16(_v256_shuffle_odd_64(_v256_packs_epu32(a.val, b.val))); } + +inline v_uint16x16 v_pack_u(const v_int32x8& a, const v_int32x8& b) +{ return v_uint16x16(_v256_shuffle_odd_64(_mm256_packus_epi32(a.val, b.val))); } + +inline void v_pack_store(short* ptr, const v_int32x8& a) +{ v_store_low(ptr, v_pack(a, a)); } + +inline void v_pack_store(ushort* ptr, const v_uint32x8& a) +{ + const __m256i m = _mm256_set1_epi32(65535); + __m256i am = _mm256_min_epu32(a.val, m); + am = _v256_shuffle_odd_64(_mm256_packus_epi32(am, am)); + v_store_low(ptr, v_uint16x16(am)); +} + +inline void v_pack_u_store(ushort* ptr, const v_int32x8& a) +{ v_store_low(ptr, v_pack_u(a, a)); } + + +template inline +v_uint16x16 v_rshr_pack(const v_uint32x8& a, const v_uint32x8& b) +{ + // we assume that n > 0, and so the shifted 32-bit values can be treated as signed numbers. + v_uint32x8 delta = v256_setall_u32(1 << (n-1)); + return v_pack_u(v_reinterpret_as_s32((a + delta) >> n), + v_reinterpret_as_s32((b + delta) >> n)); +} + +template inline +void v_rshr_pack_store(ushort* ptr, const v_uint32x8& a) +{ + v_uint32x8 delta = v256_setall_u32(1 << (n-1)); + v_pack_u_store(ptr, v_reinterpret_as_s32((a + delta) >> n)); +} + +template inline +v_uint16x16 v_rshr_pack_u(const v_int32x8& a, const v_int32x8& b) +{ + v_int32x8 delta = v256_setall_s32(1 << (n-1)); + return v_pack_u((a + delta) >> n, (b + delta) >> n); +} + +template inline +void v_rshr_pack_u_store(ushort* ptr, const v_int32x8& a) +{ + v_int32x8 delta = v256_setall_s32(1 << (n-1)); + v_pack_u_store(ptr, (a + delta) >> n); +} + +template inline +v_int16x16 v_rshr_pack(const v_int32x8& a, const v_int32x8& b) +{ + v_int32x8 delta = v256_setall_s32(1 << (n-1)); + return v_pack((a + delta) >> n, (b + delta) >> n); +} + +template inline +void v_rshr_pack_store(short* ptr, const v_int32x8& a) +{ + v_int32x8 delta = v256_setall_s32(1 << (n-1)); + v_pack_store(ptr, (a + delta) >> n); +} + +// 64 +// Non-saturating pack +inline v_uint32x8 v_pack(const v_uint64x4& a, const v_uint64x4& b) +{ + __m256i a0 = _mm256_shuffle_epi32(a.val, _MM_SHUFFLE(0, 0, 2, 0)); + __m256i b0 = _mm256_shuffle_epi32(b.val, _MM_SHUFFLE(0, 0, 2, 0)); + __m256i ab = _mm256_unpacklo_epi64(a0, b0); // a0, a1, b0, b1, a2, a3, b2, b3 + return v_uint32x8(_v256_shuffle_odd_64(ab)); +} + +inline v_int32x8 v_pack(const v_int64x4& a, const v_int64x4& b) +{ return v_reinterpret_as_s32(v_pack(v_reinterpret_as_u64(a), v_reinterpret_as_u64(b))); } + +inline void v_pack_store(unsigned* ptr, const v_uint64x4& a) +{ + __m256i a0 = _mm256_shuffle_epi32(a.val, _MM_SHUFFLE(0, 0, 2, 0)); + v_store_low(ptr, v_uint32x8(_v256_shuffle_odd_64(a0))); +} + +inline void v_pack_store(int* ptr, const v_int64x4& b) +{ v_pack_store((unsigned*)ptr, v_reinterpret_as_u64(b)); } + +template inline +v_uint32x8 v_rshr_pack(const v_uint64x4& a, const v_uint64x4& b) +{ + v_uint64x4 delta = v256_setall_u64((uint64)1 << (n-1)); + return v_pack((a + delta) >> n, (b + delta) >> n); +} + +template inline +void v_rshr_pack_store(unsigned* ptr, const v_uint64x4& a) +{ + v_uint64x4 delta = v256_setall_u64((uint64)1 << (n-1)); + v_pack_store(ptr, (a + delta) >> n); +} + +template inline +v_int32x8 v_rshr_pack(const v_int64x4& a, const v_int64x4& b) +{ + v_int64x4 delta = v256_setall_s64((int64)1 << (n-1)); + return v_pack((a + delta) >> n, (b + delta) >> n); +} + +template inline +void v_rshr_pack_store(int* ptr, const v_int64x4& a) +{ + v_int64x4 delta = v256_setall_s64((int64)1 << (n-1)); + v_pack_store(ptr, (a + delta) >> n); +} + +// pack boolean +inline v_uint8x32 v_pack_b(const v_uint16x16& a, const v_uint16x16& b) +{ + __m256i ab = _mm256_packs_epi16(a.val, b.val); + return v_uint8x32(_v256_shuffle_odd_64(ab)); +} + +inline v_uint8x32 v_pack_b(const v_uint32x8& a, const v_uint32x8& b, + const v_uint32x8& c, const v_uint32x8& d) +{ + __m256i ab = _mm256_packs_epi32(a.val, b.val); + __m256i cd = _mm256_packs_epi32(c.val, d.val); + + __m256i abcd = _v256_shuffle_odd_64(_mm256_packs_epi16(ab, cd)); + return v_uint8x32(_mm256_shuffle_epi32(abcd, _MM_SHUFFLE(3, 1, 2, 0))); +} + +inline v_uint8x32 v_pack_b(const v_uint64x4& a, const v_uint64x4& b, const v_uint64x4& c, + const v_uint64x4& d, const v_uint64x4& e, const v_uint64x4& f, + const v_uint64x4& g, const v_uint64x4& h) +{ + __m256i ab = _mm256_packs_epi32(a.val, b.val); + __m256i cd = _mm256_packs_epi32(c.val, d.val); + __m256i ef = _mm256_packs_epi32(e.val, f.val); + __m256i gh = _mm256_packs_epi32(g.val, h.val); + + __m256i abcd = _mm256_packs_epi32(ab, cd); + __m256i efgh = _mm256_packs_epi32(ef, gh); + __m256i pkall = _v256_shuffle_odd_64(_mm256_packs_epi16(abcd, efgh)); + + __m256i rev = _mm256_alignr_epi8(pkall, pkall, 8); + return v_uint8x32(_mm256_unpacklo_epi16(pkall, rev)); +} + +/* Recombine */ +// its up there with load and store operations + +/* Extract */ +#define OPENCV_HAL_IMPL_AVX_EXTRACT(_Tpvec) \ + template \ + inline _Tpvec v_extract(const _Tpvec& a, const _Tpvec& b) \ + { return v_rotate_right(a, b); } + +OPENCV_HAL_IMPL_AVX_EXTRACT(v_uint8x32) +OPENCV_HAL_IMPL_AVX_EXTRACT(v_int8x32) +OPENCV_HAL_IMPL_AVX_EXTRACT(v_uint16x16) +OPENCV_HAL_IMPL_AVX_EXTRACT(v_int16x16) +OPENCV_HAL_IMPL_AVX_EXTRACT(v_uint32x8) +OPENCV_HAL_IMPL_AVX_EXTRACT(v_int32x8) +OPENCV_HAL_IMPL_AVX_EXTRACT(v_uint64x4) +OPENCV_HAL_IMPL_AVX_EXTRACT(v_int64x4) +OPENCV_HAL_IMPL_AVX_EXTRACT(v_float32x8) +OPENCV_HAL_IMPL_AVX_EXTRACT(v_float64x4) + + +///////////////////// load deinterleave ///////////////////////////// + +inline void v_load_deinterleave( const uchar* ptr, v_uint8x32& a, v_uint8x32& b ) +{ + __m256i ab0 = _mm256_loadu_si256((const __m256i*)ptr); + __m256i ab1 = _mm256_loadu_si256((const __m256i*)(ptr + 32)); + + const __m256i sh = _mm256_setr_epi8(0, 2, 4, 6, 8, 10, 12, 14, 1, 3, 5, 7, 9, 11, 13, 15, + 0, 2, 4, 6, 8, 10, 12, 14, 1, 3, 5, 7, 9, 11, 13, 15); + __m256i p0 = _mm256_shuffle_epi8(ab0, sh); + __m256i p1 = _mm256_shuffle_epi8(ab1, sh); + __m256i pl = _mm256_permute2x128_si256(p0, p1, 0 + 2*16); + __m256i ph = _mm256_permute2x128_si256(p0, p1, 1 + 3*16); + __m256i a0 = _mm256_unpacklo_epi64(pl, ph); + __m256i b0 = _mm256_unpackhi_epi64(pl, ph); + a = v_uint8x32(a0); + b = v_uint8x32(b0); +} + +inline void v_load_deinterleave( const ushort* ptr, v_uint16x16& a, v_uint16x16& b ) +{ + __m256i ab0 = _mm256_loadu_si256((const __m256i*)ptr); + __m256i ab1 = _mm256_loadu_si256((const __m256i*)(ptr + 16)); + + const __m256i sh = _mm256_setr_epi8(0, 1, 4, 5, 8, 9, 12, 13, 2, 3, 6, 7, 10, 11, 14, 15, + 0, 1, 4, 5, 8, 9, 12, 13, 2, 3, 6, 7, 10, 11, 14, 15); + __m256i p0 = _mm256_shuffle_epi8(ab0, sh); + __m256i p1 = _mm256_shuffle_epi8(ab1, sh); + __m256i pl = _mm256_permute2x128_si256(p0, p1, 0 + 2*16); + __m256i ph = _mm256_permute2x128_si256(p0, p1, 1 + 3*16); + __m256i a0 = _mm256_unpacklo_epi64(pl, ph); + __m256i b0 = _mm256_unpackhi_epi64(pl, ph); + a = v_uint16x16(a0); + b = v_uint16x16(b0); +} + +inline void v_load_deinterleave( const unsigned* ptr, v_uint32x8& a, v_uint32x8& b ) +{ + __m256i ab0 = _mm256_loadu_si256((const __m256i*)ptr); + __m256i ab1 = _mm256_loadu_si256((const __m256i*)(ptr + 8)); + + const int sh = 0+2*4+1*16+3*64; + __m256i p0 = _mm256_shuffle_epi32(ab0, sh); + __m256i p1 = _mm256_shuffle_epi32(ab1, sh); + __m256i pl = _mm256_permute2x128_si256(p0, p1, 0 + 2*16); + __m256i ph = _mm256_permute2x128_si256(p0, p1, 1 + 3*16); + __m256i a0 = _mm256_unpacklo_epi64(pl, ph); + __m256i b0 = _mm256_unpackhi_epi64(pl, ph); + a = v_uint32x8(a0); + b = v_uint32x8(b0); +} + +inline void v_load_deinterleave( const uint64* ptr, v_uint64x4& a, v_uint64x4& b ) +{ + __m256i ab0 = _mm256_loadu_si256((const __m256i*)ptr); + __m256i ab1 = _mm256_loadu_si256((const __m256i*)(ptr + 4)); + + __m256i pl = _mm256_permute2x128_si256(ab0, ab1, 0 + 2*16); + __m256i ph = _mm256_permute2x128_si256(ab0, ab1, 1 + 3*16); + __m256i a0 = _mm256_unpacklo_epi64(pl, ph); + __m256i b0 = _mm256_unpackhi_epi64(pl, ph); + a = v_uint64x4(a0); + b = v_uint64x4(b0); +} + +inline void v_load_deinterleave( const uchar* ptr, v_uint8x32& b, v_uint8x32& g, v_uint8x32& r ) +{ + __m256i bgr0 = _mm256_loadu_si256((const __m256i*)ptr); + __m256i bgr1 = _mm256_loadu_si256((const __m256i*)(ptr + 32)); + __m256i bgr2 = _mm256_loadu_si256((const __m256i*)(ptr + 64)); + + __m256i s02_low = _mm256_permute2x128_si256(bgr0, bgr2, 0 + 2*16); + __m256i s02_high = _mm256_permute2x128_si256(bgr0, bgr2, 1 + 3*16); + + const __m256i m0 = _mm256_setr_epi8(0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, + 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0); + const __m256i m1 = _mm256_setr_epi8(0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, + -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1); + + __m256i b0 = _mm256_blendv_epi8(_mm256_blendv_epi8(s02_low, s02_high, m0), bgr1, m1); + __m256i g0 = _mm256_blendv_epi8(_mm256_blendv_epi8(s02_high, s02_low, m1), bgr1, m0); + __m256i r0 = _mm256_blendv_epi8(_mm256_blendv_epi8(bgr1, s02_low, m0), s02_high, m1); + + const __m256i + sh_b = _mm256_setr_epi8(0, 3, 6, 9, 12, 15, 2, 5, 8, 11, 14, 1, 4, 7, 10, 13, + 0, 3, 6, 9, 12, 15, 2, 5, 8, 11, 14, 1, 4, 7, 10, 13), + sh_g = _mm256_setr_epi8(1, 4, 7, 10, 13, 0, 3, 6, 9, 12, 15, 2, 5, 8, 11, 14, + 1, 4, 7, 10, 13, 0, 3, 6, 9, 12, 15, 2, 5, 8, 11, 14), + sh_r = _mm256_setr_epi8(2, 5, 8, 11, 14, 1, 4, 7, 10, 13, 0, 3, 6, 9, 12, 15, + 2, 5, 8, 11, 14, 1, 4, 7, 10, 13, 0, 3, 6, 9, 12, 15); + b0 = _mm256_shuffle_epi8(b0, sh_b); + g0 = _mm256_shuffle_epi8(g0, sh_g); + r0 = _mm256_shuffle_epi8(r0, sh_r); + + b = v_uint8x32(b0); + g = v_uint8x32(g0); + r = v_uint8x32(r0); +} + +inline void v_load_deinterleave( const ushort* ptr, v_uint16x16& b, v_uint16x16& g, v_uint16x16& r ) +{ + __m256i bgr0 = _mm256_loadu_si256((const __m256i*)ptr); + __m256i bgr1 = _mm256_loadu_si256((const __m256i*)(ptr + 16)); + __m256i bgr2 = _mm256_loadu_si256((const __m256i*)(ptr + 32)); + + __m256i s02_low = _mm256_permute2x128_si256(bgr0, bgr2, 0 + 2*16); + __m256i s02_high = _mm256_permute2x128_si256(bgr0, bgr2, 1 + 3*16); + + const __m256i m0 = _mm256_setr_epi8(0, 0, -1, -1, 0, 0, 0, 0, -1, -1, 0, 0, 0, 0, -1, -1, + 0, 0, 0, 0, -1, -1, 0, 0, 0, 0, -1, -1, 0, 0, 0, 0); + const __m256i m1 = _mm256_setr_epi8(0, 0, 0, 0, -1, -1, 0, 0, 0, 0, -1, -1, 0, 0, 0, 0, + -1, -1, 0, 0, 0, 0, -1, -1, 0, 0, 0, 0, -1, -1, 0, 0); + __m256i b0 = _mm256_blendv_epi8(_mm256_blendv_epi8(s02_low, s02_high, m0), bgr1, m1); + __m256i g0 = _mm256_blendv_epi8(_mm256_blendv_epi8(bgr1, s02_low, m0), s02_high, m1); + __m256i r0 = _mm256_blendv_epi8(_mm256_blendv_epi8(s02_high, s02_low, m1), bgr1, m0); + const __m256i sh_b = _mm256_setr_epi8(0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5, 10, 11, + 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5, 10, 11); + const __m256i sh_g = _mm256_setr_epi8(2, 3, 8, 9, 14, 15, 4, 5, 10, 11, 0, 1, 6, 7, 12, 13, + 2, 3, 8, 9, 14, 15, 4, 5, 10, 11, 0, 1, 6, 7, 12, 13); + const __m256i sh_r = _mm256_setr_epi8(4, 5, 10, 11, 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, + 4, 5, 10, 11, 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15); + b0 = _mm256_shuffle_epi8(b0, sh_b); + g0 = _mm256_shuffle_epi8(g0, sh_g); + r0 = _mm256_shuffle_epi8(r0, sh_r); + + b = v_uint16x16(b0); + g = v_uint16x16(g0); + r = v_uint16x16(r0); +} + +inline void v_load_deinterleave( const unsigned* ptr, v_uint32x8& b, v_uint32x8& g, v_uint32x8& r ) +{ + __m256i bgr0 = _mm256_loadu_si256((const __m256i*)ptr); + __m256i bgr1 = _mm256_loadu_si256((const __m256i*)(ptr + 8)); + __m256i bgr2 = _mm256_loadu_si256((const __m256i*)(ptr + 16)); + + __m256i s02_low = _mm256_permute2x128_si256(bgr0, bgr2, 0 + 2*16); + __m256i s02_high = _mm256_permute2x128_si256(bgr0, bgr2, 1 + 3*16); + + __m256i b0 = _mm256_blend_epi32(_mm256_blend_epi32(s02_low, s02_high, 0x24), bgr1, 0x92); + __m256i g0 = _mm256_blend_epi32(_mm256_blend_epi32(s02_high, s02_low, 0x92), bgr1, 0x24); + __m256i r0 = _mm256_blend_epi32(_mm256_blend_epi32(bgr1, s02_low, 0x24), s02_high, 0x92); + + b0 = _mm256_shuffle_epi32(b0, 0x6c); + g0 = _mm256_shuffle_epi32(g0, 0xb1); + r0 = _mm256_shuffle_epi32(r0, 0xc6); + + b = v_uint32x8(b0); + g = v_uint32x8(g0); + r = v_uint32x8(r0); +} + +inline void v_load_deinterleave( const uint64* ptr, v_uint64x4& b, v_uint64x4& g, v_uint64x4& r ) +{ + __m256i bgr0 = _mm256_loadu_si256((const __m256i*)ptr); + __m256i bgr1 = _mm256_loadu_si256((const __m256i*)(ptr + 4)); + __m256i bgr2 = _mm256_loadu_si256((const __m256i*)(ptr + 8)); + + __m256i s01 = _mm256_blend_epi32(bgr0, bgr1, 0xf0); + __m256i s12 = _mm256_blend_epi32(bgr1, bgr2, 0xf0); + __m256i s20r = _mm256_permute4x64_epi64(_mm256_blend_epi32(bgr2, bgr0, 0xf0), 0x1b); + __m256i b0 = _mm256_unpacklo_epi64(s01, s20r); + __m256i g0 = _mm256_alignr_epi8(s12, s01, 8); + __m256i r0 = _mm256_unpackhi_epi64(s20r, s12); + + b = v_uint64x4(b0); + g = v_uint64x4(g0); + r = v_uint64x4(r0); +} + +inline void v_load_deinterleave( const uchar* ptr, v_uint8x32& b, v_uint8x32& g, v_uint8x32& r, v_uint8x32& a ) +{ + __m256i bgr0 = _mm256_loadu_si256((const __m256i*)ptr); + __m256i bgr1 = _mm256_loadu_si256((const __m256i*)(ptr + 32)); + __m256i bgr2 = _mm256_loadu_si256((const __m256i*)(ptr + 64)); + __m256i bgr3 = _mm256_loadu_si256((const __m256i*)(ptr + 96)); + const __m256i sh = _mm256_setr_epi8(0, 4, 8, 12, 1, 5, 9, 13, 2, 6, 10, 14, 3, 7, 11, 15, + 0, 4, 8, 12, 1, 5, 9, 13, 2, 6, 10, 14, 3, 7, 11, 15); + + __m256i p0 = _mm256_shuffle_epi8(bgr0, sh); + __m256i p1 = _mm256_shuffle_epi8(bgr1, sh); + __m256i p2 = _mm256_shuffle_epi8(bgr2, sh); + __m256i p3 = _mm256_shuffle_epi8(bgr3, sh); + + __m256i p01l = _mm256_unpacklo_epi32(p0, p1); + __m256i p01h = _mm256_unpackhi_epi32(p0, p1); + __m256i p23l = _mm256_unpacklo_epi32(p2, p3); + __m256i p23h = _mm256_unpackhi_epi32(p2, p3); + + __m256i pll = _mm256_permute2x128_si256(p01l, p23l, 0 + 2*16); + __m256i plh = _mm256_permute2x128_si256(p01l, p23l, 1 + 3*16); + __m256i phl = _mm256_permute2x128_si256(p01h, p23h, 0 + 2*16); + __m256i phh = _mm256_permute2x128_si256(p01h, p23h, 1 + 3*16); + + __m256i b0 = _mm256_unpacklo_epi32(pll, plh); + __m256i g0 = _mm256_unpackhi_epi32(pll, plh); + __m256i r0 = _mm256_unpacklo_epi32(phl, phh); + __m256i a0 = _mm256_unpackhi_epi32(phl, phh); + + b = v_uint8x32(b0); + g = v_uint8x32(g0); + r = v_uint8x32(r0); + a = v_uint8x32(a0); +} + +inline void v_load_deinterleave( const ushort* ptr, v_uint16x16& b, v_uint16x16& g, v_uint16x16& r, v_uint16x16& a ) +{ + __m256i bgr0 = _mm256_loadu_si256((const __m256i*)ptr); + __m256i bgr1 = _mm256_loadu_si256((const __m256i*)(ptr + 16)); + __m256i bgr2 = _mm256_loadu_si256((const __m256i*)(ptr + 32)); + __m256i bgr3 = _mm256_loadu_si256((const __m256i*)(ptr + 48)); + const __m256i sh = _mm256_setr_epi8(0, 1, 8, 9, 2, 3, 10, 11, 4, 5, 12, 13, 6, 7, 14, 15, + 0, 1, 8, 9, 2, 3, 10, 11, 4, 5, 12, 13, 6, 7, 14, 15); + __m256i p0 = _mm256_shuffle_epi8(bgr0, sh); + __m256i p1 = _mm256_shuffle_epi8(bgr1, sh); + __m256i p2 = _mm256_shuffle_epi8(bgr2, sh); + __m256i p3 = _mm256_shuffle_epi8(bgr3, sh); + + __m256i p01l = _mm256_unpacklo_epi32(p0, p1); + __m256i p01h = _mm256_unpackhi_epi32(p0, p1); + __m256i p23l = _mm256_unpacklo_epi32(p2, p3); + __m256i p23h = _mm256_unpackhi_epi32(p2, p3); + + __m256i pll = _mm256_permute2x128_si256(p01l, p23l, 0 + 2*16); + __m256i plh = _mm256_permute2x128_si256(p01l, p23l, 1 + 3*16); + __m256i phl = _mm256_permute2x128_si256(p01h, p23h, 0 + 2*16); + __m256i phh = _mm256_permute2x128_si256(p01h, p23h, 1 + 3*16); + + __m256i b0 = _mm256_unpacklo_epi32(pll, plh); + __m256i g0 = _mm256_unpackhi_epi32(pll, plh); + __m256i r0 = _mm256_unpacklo_epi32(phl, phh); + __m256i a0 = _mm256_unpackhi_epi32(phl, phh); + + b = v_uint16x16(b0); + g = v_uint16x16(g0); + r = v_uint16x16(r0); + a = v_uint16x16(a0); +} + +inline void v_load_deinterleave( const unsigned* ptr, v_uint32x8& b, v_uint32x8& g, v_uint32x8& r, v_uint32x8& a ) +{ + __m256i p0 = _mm256_loadu_si256((const __m256i*)ptr); + __m256i p1 = _mm256_loadu_si256((const __m256i*)(ptr + 8)); + __m256i p2 = _mm256_loadu_si256((const __m256i*)(ptr + 16)); + __m256i p3 = _mm256_loadu_si256((const __m256i*)(ptr + 24)); + + __m256i p01l = _mm256_unpacklo_epi32(p0, p1); + __m256i p01h = _mm256_unpackhi_epi32(p0, p1); + __m256i p23l = _mm256_unpacklo_epi32(p2, p3); + __m256i p23h = _mm256_unpackhi_epi32(p2, p3); + + __m256i pll = _mm256_permute2x128_si256(p01l, p23l, 0 + 2*16); + __m256i plh = _mm256_permute2x128_si256(p01l, p23l, 1 + 3*16); + __m256i phl = _mm256_permute2x128_si256(p01h, p23h, 0 + 2*16); + __m256i phh = _mm256_permute2x128_si256(p01h, p23h, 1 + 3*16); + + __m256i b0 = _mm256_unpacklo_epi32(pll, plh); + __m256i g0 = _mm256_unpackhi_epi32(pll, plh); + __m256i r0 = _mm256_unpacklo_epi32(phl, phh); + __m256i a0 = _mm256_unpackhi_epi32(phl, phh); + + b = v_uint32x8(b0); + g = v_uint32x8(g0); + r = v_uint32x8(r0); + a = v_uint32x8(a0); +} + +inline void v_load_deinterleave( const uint64* ptr, v_uint64x4& b, v_uint64x4& g, v_uint64x4& r, v_uint64x4& a ) +{ + __m256i bgra0 = _mm256_loadu_si256((const __m256i*)ptr); + __m256i bgra1 = _mm256_loadu_si256((const __m256i*)(ptr + 4)); + __m256i bgra2 = _mm256_loadu_si256((const __m256i*)(ptr + 8)); + __m256i bgra3 = _mm256_loadu_si256((const __m256i*)(ptr + 12)); + + __m256i l02 = _mm256_permute2x128_si256(bgra0, bgra2, 0 + 2*16); + __m256i h02 = _mm256_permute2x128_si256(bgra0, bgra2, 1 + 3*16); + __m256i l13 = _mm256_permute2x128_si256(bgra1, bgra3, 0 + 2*16); + __m256i h13 = _mm256_permute2x128_si256(bgra1, bgra3, 1 + 3*16); + + __m256i b0 = _mm256_unpacklo_epi64(l02, l13); + __m256i g0 = _mm256_unpackhi_epi64(l02, l13); + __m256i r0 = _mm256_unpacklo_epi64(h02, h13); + __m256i a0 = _mm256_unpackhi_epi64(h02, h13); + + b = v_uint64x4(b0); + g = v_uint64x4(g0); + r = v_uint64x4(r0); + a = v_uint64x4(a0); +} + +///////////////////////////// store interleave ///////////////////////////////////// + +inline void v_store_interleave( uchar* ptr, const v_uint8x32& x, const v_uint8x32& y, + hal::StoreMode mode=hal::STORE_UNALIGNED ) +{ + __m256i xy_l = _mm256_unpacklo_epi8(x.val, y.val); + __m256i xy_h = _mm256_unpackhi_epi8(x.val, y.val); + + __m256i xy0 = _mm256_permute2x128_si256(xy_l, xy_h, 0 + 2*16); + __m256i xy1 = _mm256_permute2x128_si256(xy_l, xy_h, 1 + 3*16); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm256_stream_si256((__m256i*)ptr, xy0); + _mm256_stream_si256((__m256i*)(ptr + 32), xy1); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm256_store_si256((__m256i*)ptr, xy0); + _mm256_store_si256((__m256i*)(ptr + 32), xy1); + } + else + { + _mm256_storeu_si256((__m256i*)ptr, xy0); + _mm256_storeu_si256((__m256i*)(ptr + 32), xy1); + } +} + +inline void v_store_interleave( ushort* ptr, const v_uint16x16& x, const v_uint16x16& y, + hal::StoreMode mode=hal::STORE_UNALIGNED ) +{ + __m256i xy_l = _mm256_unpacklo_epi16(x.val, y.val); + __m256i xy_h = _mm256_unpackhi_epi16(x.val, y.val); + + __m256i xy0 = _mm256_permute2x128_si256(xy_l, xy_h, 0 + 2*16); + __m256i xy1 = _mm256_permute2x128_si256(xy_l, xy_h, 1 + 3*16); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm256_stream_si256((__m256i*)ptr, xy0); + _mm256_stream_si256((__m256i*)(ptr + 16), xy1); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm256_store_si256((__m256i*)ptr, xy0); + _mm256_store_si256((__m256i*)(ptr + 16), xy1); + } + else + { + _mm256_storeu_si256((__m256i*)ptr, xy0); + _mm256_storeu_si256((__m256i*)(ptr + 16), xy1); + } +} + +inline void v_store_interleave( unsigned* ptr, const v_uint32x8& x, const v_uint32x8& y, + hal::StoreMode mode=hal::STORE_UNALIGNED ) +{ + __m256i xy_l = _mm256_unpacklo_epi32(x.val, y.val); + __m256i xy_h = _mm256_unpackhi_epi32(x.val, y.val); + + __m256i xy0 = _mm256_permute2x128_si256(xy_l, xy_h, 0 + 2*16); + __m256i xy1 = _mm256_permute2x128_si256(xy_l, xy_h, 1 + 3*16); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm256_stream_si256((__m256i*)ptr, xy0); + _mm256_stream_si256((__m256i*)(ptr + 8), xy1); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm256_store_si256((__m256i*)ptr, xy0); + _mm256_store_si256((__m256i*)(ptr + 8), xy1); + } + else + { + _mm256_storeu_si256((__m256i*)ptr, xy0); + _mm256_storeu_si256((__m256i*)(ptr + 8), xy1); + } +} + +inline void v_store_interleave( uint64* ptr, const v_uint64x4& x, const v_uint64x4& y, + hal::StoreMode mode=hal::STORE_UNALIGNED ) +{ + __m256i xy_l = _mm256_unpacklo_epi64(x.val, y.val); + __m256i xy_h = _mm256_unpackhi_epi64(x.val, y.val); + + __m256i xy0 = _mm256_permute2x128_si256(xy_l, xy_h, 0 + 2*16); + __m256i xy1 = _mm256_permute2x128_si256(xy_l, xy_h, 1 + 3*16); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm256_stream_si256((__m256i*)ptr, xy0); + _mm256_stream_si256((__m256i*)(ptr + 4), xy1); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm256_store_si256((__m256i*)ptr, xy0); + _mm256_store_si256((__m256i*)(ptr + 4), xy1); + } + else + { + _mm256_storeu_si256((__m256i*)ptr, xy0); + _mm256_storeu_si256((__m256i*)(ptr + 4), xy1); + } +} + +inline void v_store_interleave( uchar* ptr, const v_uint8x32& b, const v_uint8x32& g, const v_uint8x32& r, + hal::StoreMode mode=hal::STORE_UNALIGNED ) +{ + const __m256i sh_b = _mm256_setr_epi8( + 0, 11, 6, 1, 12, 7, 2, 13, 8, 3, 14, 9, 4, 15, 10, 5, + 0, 11, 6, 1, 12, 7, 2, 13, 8, 3, 14, 9, 4, 15, 10, 5); + const __m256i sh_g = _mm256_setr_epi8( + 5, 0, 11, 6, 1, 12, 7, 2, 13, 8, 3, 14, 9, 4, 15, 10, + 5, 0, 11, 6, 1, 12, 7, 2, 13, 8, 3, 14, 9, 4, 15, 10); + const __m256i sh_r = _mm256_setr_epi8( + 10, 5, 0, 11, 6, 1, 12, 7, 2, 13, 8, 3, 14, 9, 4, 15, + 10, 5, 0, 11, 6, 1, 12, 7, 2, 13, 8, 3, 14, 9, 4, 15); + + __m256i b0 = _mm256_shuffle_epi8(b.val, sh_b); + __m256i g0 = _mm256_shuffle_epi8(g.val, sh_g); + __m256i r0 = _mm256_shuffle_epi8(r.val, sh_r); + + const __m256i m0 = _mm256_setr_epi8(0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, + 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0); + const __m256i m1 = _mm256_setr_epi8(0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, + 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0); + + __m256i p0 = _mm256_blendv_epi8(_mm256_blendv_epi8(b0, g0, m0), r0, m1); + __m256i p1 = _mm256_blendv_epi8(_mm256_blendv_epi8(g0, r0, m0), b0, m1); + __m256i p2 = _mm256_blendv_epi8(_mm256_blendv_epi8(r0, b0, m0), g0, m1); + + __m256i bgr0 = _mm256_permute2x128_si256(p0, p1, 0 + 2*16); + __m256i bgr1 = _mm256_permute2x128_si256(p2, p0, 0 + 3*16); + __m256i bgr2 = _mm256_permute2x128_si256(p1, p2, 1 + 3*16); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm256_stream_si256((__m256i*)ptr, bgr0); + _mm256_stream_si256((__m256i*)(ptr + 32), bgr1); + _mm256_stream_si256((__m256i*)(ptr + 64), bgr2); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm256_store_si256((__m256i*)ptr, bgr0); + _mm256_store_si256((__m256i*)(ptr + 32), bgr1); + _mm256_store_si256((__m256i*)(ptr + 64), bgr2); + } + else + { + _mm256_storeu_si256((__m256i*)ptr, bgr0); + _mm256_storeu_si256((__m256i*)(ptr + 32), bgr1); + _mm256_storeu_si256((__m256i*)(ptr + 64), bgr2); + } +} + +inline void v_store_interleave( ushort* ptr, const v_uint16x16& b, const v_uint16x16& g, const v_uint16x16& r, + hal::StoreMode mode=hal::STORE_UNALIGNED ) +{ + const __m256i sh_b = _mm256_setr_epi8( + 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5, 10, 11, + 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5, 10, 11); + const __m256i sh_g = _mm256_setr_epi8( + 10, 11, 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5, + 10, 11, 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5); + const __m256i sh_r = _mm256_setr_epi8( + 4, 5, 10, 11, 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, + 4, 5, 10, 11, 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15); + + __m256i b0 = _mm256_shuffle_epi8(b.val, sh_b); + __m256i g0 = _mm256_shuffle_epi8(g.val, sh_g); + __m256i r0 = _mm256_shuffle_epi8(r.val, sh_r); + + const __m256i m0 = _mm256_setr_epi8(0, 0, -1, -1, 0, 0, 0, 0, -1, -1, 0, 0, 0, 0, -1, -1, + 0, 0, 0, 0, -1, -1, 0, 0, 0, 0, -1, -1, 0, 0, 0, 0); + const __m256i m1 = _mm256_setr_epi8(0, 0, 0, 0, -1, -1, 0, 0, 0, 0, -1, -1, 0, 0, 0, 0, + -1, -1, 0, 0, 0, 0, -1, -1, 0, 0, 0, 0, -1, -1, 0, 0); + + __m256i p0 = _mm256_blendv_epi8(_mm256_blendv_epi8(b0, g0, m0), r0, m1); + __m256i p1 = _mm256_blendv_epi8(_mm256_blendv_epi8(g0, r0, m0), b0, m1); + __m256i p2 = _mm256_blendv_epi8(_mm256_blendv_epi8(r0, b0, m0), g0, m1); + + __m256i bgr0 = _mm256_permute2x128_si256(p0, p2, 0 + 2*16); + //__m256i bgr1 = p1; + __m256i bgr2 = _mm256_permute2x128_si256(p0, p2, 1 + 3*16); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm256_stream_si256((__m256i*)ptr, bgr0); + _mm256_stream_si256((__m256i*)(ptr + 16), p1); + _mm256_stream_si256((__m256i*)(ptr + 32), bgr2); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm256_store_si256((__m256i*)ptr, bgr0); + _mm256_store_si256((__m256i*)(ptr + 16), p1); + _mm256_store_si256((__m256i*)(ptr + 32), bgr2); + } + else + { + _mm256_storeu_si256((__m256i*)ptr, bgr0); + _mm256_storeu_si256((__m256i*)(ptr + 16), p1); + _mm256_storeu_si256((__m256i*)(ptr + 32), bgr2); + } +} + +inline void v_store_interleave( unsigned* ptr, const v_uint32x8& b, const v_uint32x8& g, const v_uint32x8& r, + hal::StoreMode mode=hal::STORE_UNALIGNED ) +{ + __m256i b0 = _mm256_shuffle_epi32(b.val, 0x6c); + __m256i g0 = _mm256_shuffle_epi32(g.val, 0xb1); + __m256i r0 = _mm256_shuffle_epi32(r.val, 0xc6); + + __m256i p0 = _mm256_blend_epi32(_mm256_blend_epi32(b0, g0, 0x92), r0, 0x24); + __m256i p1 = _mm256_blend_epi32(_mm256_blend_epi32(g0, r0, 0x92), b0, 0x24); + __m256i p2 = _mm256_blend_epi32(_mm256_blend_epi32(r0, b0, 0x92), g0, 0x24); + + __m256i bgr0 = _mm256_permute2x128_si256(p0, p1, 0 + 2*16); + //__m256i bgr1 = p2; + __m256i bgr2 = _mm256_permute2x128_si256(p0, p1, 1 + 3*16); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm256_stream_si256((__m256i*)ptr, bgr0); + _mm256_stream_si256((__m256i*)(ptr + 8), p2); + _mm256_stream_si256((__m256i*)(ptr + 16), bgr2); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm256_store_si256((__m256i*)ptr, bgr0); + _mm256_store_si256((__m256i*)(ptr + 8), p2); + _mm256_store_si256((__m256i*)(ptr + 16), bgr2); + } + else + { + _mm256_storeu_si256((__m256i*)ptr, bgr0); + _mm256_storeu_si256((__m256i*)(ptr + 8), p2); + _mm256_storeu_si256((__m256i*)(ptr + 16), bgr2); + } +} + +inline void v_store_interleave( uint64* ptr, const v_uint64x4& b, const v_uint64x4& g, const v_uint64x4& r, + hal::StoreMode mode=hal::STORE_UNALIGNED ) +{ + __m256i s01 = _mm256_unpacklo_epi64(b.val, g.val); + __m256i s12 = _mm256_unpackhi_epi64(g.val, r.val); + __m256i s20 = _mm256_blend_epi32(r.val, b.val, 0xcc); + + __m256i bgr0 = _mm256_permute2x128_si256(s01, s20, 0 + 2*16); + __m256i bgr1 = _mm256_blend_epi32(s01, s12, 0x0f); + __m256i bgr2 = _mm256_permute2x128_si256(s20, s12, 1 + 3*16); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm256_stream_si256((__m256i*)ptr, bgr0); + _mm256_stream_si256((__m256i*)(ptr + 4), bgr1); + _mm256_stream_si256((__m256i*)(ptr + 8), bgr2); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm256_store_si256((__m256i*)ptr, bgr0); + _mm256_store_si256((__m256i*)(ptr + 4), bgr1); + _mm256_store_si256((__m256i*)(ptr + 8), bgr2); + } + else + { + _mm256_storeu_si256((__m256i*)ptr, bgr0); + _mm256_storeu_si256((__m256i*)(ptr + 4), bgr1); + _mm256_storeu_si256((__m256i*)(ptr + 8), bgr2); + } +} + +inline void v_store_interleave( uchar* ptr, const v_uint8x32& b, const v_uint8x32& g, + const v_uint8x32& r, const v_uint8x32& a, + hal::StoreMode mode=hal::STORE_UNALIGNED ) +{ + __m256i bg0 = _mm256_unpacklo_epi8(b.val, g.val); + __m256i bg1 = _mm256_unpackhi_epi8(b.val, g.val); + __m256i ra0 = _mm256_unpacklo_epi8(r.val, a.val); + __m256i ra1 = _mm256_unpackhi_epi8(r.val, a.val); + + __m256i bgra0_ = _mm256_unpacklo_epi16(bg0, ra0); + __m256i bgra1_ = _mm256_unpackhi_epi16(bg0, ra0); + __m256i bgra2_ = _mm256_unpacklo_epi16(bg1, ra1); + __m256i bgra3_ = _mm256_unpackhi_epi16(bg1, ra1); + + __m256i bgra0 = _mm256_permute2x128_si256(bgra0_, bgra1_, 0 + 2*16); + __m256i bgra2 = _mm256_permute2x128_si256(bgra0_, bgra1_, 1 + 3*16); + __m256i bgra1 = _mm256_permute2x128_si256(bgra2_, bgra3_, 0 + 2*16); + __m256i bgra3 = _mm256_permute2x128_si256(bgra2_, bgra3_, 1 + 3*16); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm256_stream_si256((__m256i*)ptr, bgra0); + _mm256_stream_si256((__m256i*)(ptr + 32), bgra1); + _mm256_stream_si256((__m256i*)(ptr + 64), bgra2); + _mm256_stream_si256((__m256i*)(ptr + 96), bgra3); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm256_store_si256((__m256i*)ptr, bgra0); + _mm256_store_si256((__m256i*)(ptr + 32), bgra1); + _mm256_store_si256((__m256i*)(ptr + 64), bgra2); + _mm256_store_si256((__m256i*)(ptr + 96), bgra3); + } + else + { + _mm256_storeu_si256((__m256i*)ptr, bgra0); + _mm256_storeu_si256((__m256i*)(ptr + 32), bgra1); + _mm256_storeu_si256((__m256i*)(ptr + 64), bgra2); + _mm256_storeu_si256((__m256i*)(ptr + 96), bgra3); + } +} + +inline void v_store_interleave( ushort* ptr, const v_uint16x16& b, const v_uint16x16& g, + const v_uint16x16& r, const v_uint16x16& a, + hal::StoreMode mode=hal::STORE_UNALIGNED ) +{ + __m256i bg0 = _mm256_unpacklo_epi16(b.val, g.val); + __m256i bg1 = _mm256_unpackhi_epi16(b.val, g.val); + __m256i ra0 = _mm256_unpacklo_epi16(r.val, a.val); + __m256i ra1 = _mm256_unpackhi_epi16(r.val, a.val); + + __m256i bgra0_ = _mm256_unpacklo_epi32(bg0, ra0); + __m256i bgra1_ = _mm256_unpackhi_epi32(bg0, ra0); + __m256i bgra2_ = _mm256_unpacklo_epi32(bg1, ra1); + __m256i bgra3_ = _mm256_unpackhi_epi32(bg1, ra1); + + __m256i bgra0 = _mm256_permute2x128_si256(bgra0_, bgra1_, 0 + 2*16); + __m256i bgra2 = _mm256_permute2x128_si256(bgra0_, bgra1_, 1 + 3*16); + __m256i bgra1 = _mm256_permute2x128_si256(bgra2_, bgra3_, 0 + 2*16); + __m256i bgra3 = _mm256_permute2x128_si256(bgra2_, bgra3_, 1 + 3*16); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm256_stream_si256((__m256i*)ptr, bgra0); + _mm256_stream_si256((__m256i*)(ptr + 16), bgra1); + _mm256_stream_si256((__m256i*)(ptr + 32), bgra2); + _mm256_stream_si256((__m256i*)(ptr + 48), bgra3); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm256_store_si256((__m256i*)ptr, bgra0); + _mm256_store_si256((__m256i*)(ptr + 16), bgra1); + _mm256_store_si256((__m256i*)(ptr + 32), bgra2); + _mm256_store_si256((__m256i*)(ptr + 48), bgra3); + } + else + { + _mm256_storeu_si256((__m256i*)ptr, bgra0); + _mm256_storeu_si256((__m256i*)(ptr + 16), bgra1); + _mm256_storeu_si256((__m256i*)(ptr + 32), bgra2); + _mm256_storeu_si256((__m256i*)(ptr + 48), bgra3); + } +} + +inline void v_store_interleave( unsigned* ptr, const v_uint32x8& b, const v_uint32x8& g, + const v_uint32x8& r, const v_uint32x8& a, + hal::StoreMode mode=hal::STORE_UNALIGNED ) +{ + __m256i bg0 = _mm256_unpacklo_epi32(b.val, g.val); + __m256i bg1 = _mm256_unpackhi_epi32(b.val, g.val); + __m256i ra0 = _mm256_unpacklo_epi32(r.val, a.val); + __m256i ra1 = _mm256_unpackhi_epi32(r.val, a.val); + + __m256i bgra0_ = _mm256_unpacklo_epi64(bg0, ra0); + __m256i bgra1_ = _mm256_unpackhi_epi64(bg0, ra0); + __m256i bgra2_ = _mm256_unpacklo_epi64(bg1, ra1); + __m256i bgra3_ = _mm256_unpackhi_epi64(bg1, ra1); + + __m256i bgra0 = _mm256_permute2x128_si256(bgra0_, bgra1_, 0 + 2*16); + __m256i bgra2 = _mm256_permute2x128_si256(bgra0_, bgra1_, 1 + 3*16); + __m256i bgra1 = _mm256_permute2x128_si256(bgra2_, bgra3_, 0 + 2*16); + __m256i bgra3 = _mm256_permute2x128_si256(bgra2_, bgra3_, 1 + 3*16); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm256_stream_si256((__m256i*)ptr, bgra0); + _mm256_stream_si256((__m256i*)(ptr + 8), bgra1); + _mm256_stream_si256((__m256i*)(ptr + 16), bgra2); + _mm256_stream_si256((__m256i*)(ptr + 24), bgra3); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm256_store_si256((__m256i*)ptr, bgra0); + _mm256_store_si256((__m256i*)(ptr + 8), bgra1); + _mm256_store_si256((__m256i*)(ptr + 16), bgra2); + _mm256_store_si256((__m256i*)(ptr + 24), bgra3); + } + else + { + _mm256_storeu_si256((__m256i*)ptr, bgra0); + _mm256_storeu_si256((__m256i*)(ptr + 8), bgra1); + _mm256_storeu_si256((__m256i*)(ptr + 16), bgra2); + _mm256_storeu_si256((__m256i*)(ptr + 24), bgra3); + } +} + +inline void v_store_interleave( uint64* ptr, const v_uint64x4& b, const v_uint64x4& g, + const v_uint64x4& r, const v_uint64x4& a, + hal::StoreMode mode=hal::STORE_UNALIGNED ) +{ + __m256i bg0 = _mm256_unpacklo_epi64(b.val, g.val); + __m256i bg1 = _mm256_unpackhi_epi64(b.val, g.val); + __m256i ra0 = _mm256_unpacklo_epi64(r.val, a.val); + __m256i ra1 = _mm256_unpackhi_epi64(r.val, a.val); + + __m256i bgra0 = _mm256_permute2x128_si256(bg0, ra0, 0 + 2*16); + __m256i bgra1 = _mm256_permute2x128_si256(bg1, ra1, 0 + 2*16); + __m256i bgra2 = _mm256_permute2x128_si256(bg0, ra0, 1 + 3*16); + __m256i bgra3 = _mm256_permute2x128_si256(bg1, ra1, 1 + 3*16); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm256_stream_si256((__m256i*)ptr, bgra0); + _mm256_stream_si256((__m256i*)(ptr + 4), bgra1); + _mm256_stream_si256((__m256i*)(ptr + 8), bgra2); + _mm256_stream_si256((__m256i*)(ptr + 12), bgra3); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm256_store_si256((__m256i*)ptr, bgra0); + _mm256_store_si256((__m256i*)(ptr + 4), bgra1); + _mm256_store_si256((__m256i*)(ptr + 8), bgra2); + _mm256_store_si256((__m256i*)(ptr + 12), bgra3); + } + else + { + _mm256_storeu_si256((__m256i*)ptr, bgra0); + _mm256_storeu_si256((__m256i*)(ptr + 4), bgra1); + _mm256_storeu_si256((__m256i*)(ptr + 8), bgra2); + _mm256_storeu_si256((__m256i*)(ptr + 12), bgra3); + } +} + +#define OPENCV_HAL_IMPL_AVX_LOADSTORE_INTERLEAVE(_Tpvec0, _Tp0, suffix0, _Tpvec1, _Tp1, suffix1) \ +inline void v_load_deinterleave( const _Tp0* ptr, _Tpvec0& a0, _Tpvec0& b0 ) \ +{ \ + _Tpvec1 a1, b1; \ + v_load_deinterleave((const _Tp1*)ptr, a1, b1); \ + a0 = v_reinterpret_as_##suffix0(a1); \ + b0 = v_reinterpret_as_##suffix0(b1); \ +} \ +inline void v_load_deinterleave( const _Tp0* ptr, _Tpvec0& a0, _Tpvec0& b0, _Tpvec0& c0 ) \ +{ \ + _Tpvec1 a1, b1, c1; \ + v_load_deinterleave((const _Tp1*)ptr, a1, b1, c1); \ + a0 = v_reinterpret_as_##suffix0(a1); \ + b0 = v_reinterpret_as_##suffix0(b1); \ + c0 = v_reinterpret_as_##suffix0(c1); \ +} \ +inline void v_load_deinterleave( const _Tp0* ptr, _Tpvec0& a0, _Tpvec0& b0, _Tpvec0& c0, _Tpvec0& d0 ) \ +{ \ + _Tpvec1 a1, b1, c1, d1; \ + v_load_deinterleave((const _Tp1*)ptr, a1, b1, c1, d1); \ + a0 = v_reinterpret_as_##suffix0(a1); \ + b0 = v_reinterpret_as_##suffix0(b1); \ + c0 = v_reinterpret_as_##suffix0(c1); \ + d0 = v_reinterpret_as_##suffix0(d1); \ +} \ +inline void v_store_interleave( _Tp0* ptr, const _Tpvec0& a0, const _Tpvec0& b0, \ + hal::StoreMode mode=hal::STORE_UNALIGNED ) \ +{ \ + _Tpvec1 a1 = v_reinterpret_as_##suffix1(a0); \ + _Tpvec1 b1 = v_reinterpret_as_##suffix1(b0); \ + v_store_interleave((_Tp1*)ptr, a1, b1, mode); \ +} \ +inline void v_store_interleave( _Tp0* ptr, const _Tpvec0& a0, const _Tpvec0& b0, const _Tpvec0& c0, \ + hal::StoreMode mode=hal::STORE_UNALIGNED ) \ +{ \ + _Tpvec1 a1 = v_reinterpret_as_##suffix1(a0); \ + _Tpvec1 b1 = v_reinterpret_as_##suffix1(b0); \ + _Tpvec1 c1 = v_reinterpret_as_##suffix1(c0); \ + v_store_interleave((_Tp1*)ptr, a1, b1, c1, mode); \ +} \ +inline void v_store_interleave( _Tp0* ptr, const _Tpvec0& a0, const _Tpvec0& b0, \ + const _Tpvec0& c0, const _Tpvec0& d0, \ + hal::StoreMode mode=hal::STORE_UNALIGNED ) \ +{ \ + _Tpvec1 a1 = v_reinterpret_as_##suffix1(a0); \ + _Tpvec1 b1 = v_reinterpret_as_##suffix1(b0); \ + _Tpvec1 c1 = v_reinterpret_as_##suffix1(c0); \ + _Tpvec1 d1 = v_reinterpret_as_##suffix1(d0); \ + v_store_interleave((_Tp1*)ptr, a1, b1, c1, d1, mode); \ +} + +OPENCV_HAL_IMPL_AVX_LOADSTORE_INTERLEAVE(v_int8x32, schar, s8, v_uint8x32, uchar, u8) +OPENCV_HAL_IMPL_AVX_LOADSTORE_INTERLEAVE(v_int16x16, short, s16, v_uint16x16, ushort, u16) +OPENCV_HAL_IMPL_AVX_LOADSTORE_INTERLEAVE(v_int32x8, int, s32, v_uint32x8, unsigned, u32) +OPENCV_HAL_IMPL_AVX_LOADSTORE_INTERLEAVE(v_float32x8, float, f32, v_uint32x8, unsigned, u32) +OPENCV_HAL_IMPL_AVX_LOADSTORE_INTERLEAVE(v_int64x4, int64, s64, v_uint64x4, uint64, u64) +OPENCV_HAL_IMPL_AVX_LOADSTORE_INTERLEAVE(v_float64x4, double, f64, v_uint64x4, uint64, u64) + +// FP16 +inline v_float32x8 v256_load_expand(const float16_t* ptr) +{ + return v_float32x8(_mm256_cvtph_ps(_mm_loadu_si128((const __m128i*)ptr))); +} + +inline void v_pack_store(float16_t* ptr, const v_float32x8& a) +{ + __m128i ah = _mm256_cvtps_ph(a.val, 0); + _mm_storeu_si128((__m128i*)ptr, ah); +} + +inline void v256_cleanup() { _mm256_zeroall(); } + +//! @name Check SIMD256 support +//! @{ +//! @brief Check CPU capability of SIMD operation +static inline bool hasSIMD256() +{ + return (CV_CPU_HAS_SUPPORT_AVX2) ? true : false; +} +//! @} + +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END + +//! @endcond + +} // cv:: + +#endif // OPENCV_HAL_INTRIN_AVX_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/hal/intrin_cpp.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/hal/intrin_cpp.hpp new file mode 100644 index 0000000000000000000000000000000000000000..65a01f31d7183a25e92e8b6b4a78802e33501f22 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/hal/intrin_cpp.hpp @@ -0,0 +1,2310 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_HAL_INTRIN_CPP_HPP +#define OPENCV_HAL_INTRIN_CPP_HPP + +#include +#include +#include +#include "opencv2/core/saturate.hpp" + +namespace cv +{ + +#ifndef CV_DOXYGEN +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN +#endif + +/** @addtogroup core_hal_intrin + +"Universal intrinsics" is a types and functions set intended to simplify vectorization of code on +different platforms. Currently there are two supported SIMD extensions: __SSE/SSE2__ on x86 +architectures and __NEON__ on ARM architectures, both allow working with 128 bit registers +containing packed values of different types. In case when there is no SIMD extension available +during compilation, fallback C++ implementation of intrinsics will be chosen and code will work as +expected although it could be slower. + +### Types + +There are several types representing 128-bit register as a vector of packed values, each type is +implemented as a structure based on a one SIMD register. + +- cv::v_uint8x16 and cv::v_int8x16: sixteen 8-bit integer values (unsigned/signed) - char +- cv::v_uint16x8 and cv::v_int16x8: eight 16-bit integer values (unsigned/signed) - short +- cv::v_uint32x4 and cv::v_int32x4: four 32-bit integer values (unsgined/signed) - int +- cv::v_uint64x2 and cv::v_int64x2: two 64-bit integer values (unsigned/signed) - int64 +- cv::v_float32x4: four 32-bit floating point values (signed) - float +- cv::v_float64x2: two 64-bit floating point valies (signed) - double + +@note +cv::v_float64x2 is not implemented in NEON variant, if you want to use this type, don't forget to +check the CV_SIMD128_64F preprocessor definition: +@code +#if CV_SIMD128_64F +//... +#endif +@endcode + +### Load and store operations + +These operations allow to set contents of the register explicitly or by loading it from some memory +block and to save contents of the register to memory block. + +- Constructors: +@ref v_reg::v_reg(const _Tp *ptr) "from memory", +@ref v_reg::v_reg(_Tp s0, _Tp s1) "from two values", ... +- Other create methods: +@ref v_setall_s8, @ref v_setall_u8, ..., +@ref v_setzero_u8, @ref v_setzero_s8, ... +- Memory operations: +@ref v_load, @ref v_load_aligned, @ref v_load_low, @ref v_load_halves, +@ref v_store, @ref v_store_aligned, +@ref v_store_high, @ref v_store_low + +### Value reordering + +These operations allow to reorder or recombine elements in one or multiple vectors. + +- Interleave, deinterleave (2, 3 and 4 channels): @ref v_load_deinterleave, @ref v_store_interleave +- Expand: @ref v_load_expand, @ref v_load_expand_q, @ref v_expand, @ref v_expand_low, @ref v_expand_high +- Pack: @ref v_pack, @ref v_pack_u, @ref v_pack_b, @ref v_rshr_pack, @ref v_rshr_pack_u, +@ref v_pack_store, @ref v_pack_u_store, @ref v_rshr_pack_store, @ref v_rshr_pack_u_store +- Recombine: @ref v_zip, @ref v_recombine, @ref v_combine_low, @ref v_combine_high +- Extract: @ref v_extract + + +### Arithmetic, bitwise and comparison operations + +Element-wise binary and unary operations. + +- Arithmetics: +@ref operator +(const v_reg &a, const v_reg &b) "+", +@ref operator -(const v_reg &a, const v_reg &b) "-", +@ref operator *(const v_reg &a, const v_reg &b) "*", +@ref operator /(const v_reg &a, const v_reg &b) "/", +@ref v_mul_expand + +- Non-saturating arithmetics: @ref v_add_wrap, @ref v_sub_wrap + +- Bitwise shifts: +@ref operator <<(const v_reg &a, int s) "<<", +@ref operator >>(const v_reg &a, int s) ">>", +@ref v_shl, @ref v_shr + +- Bitwise logic: +@ref operator&(const v_reg &a, const v_reg &b) "&", +@ref operator |(const v_reg &a, const v_reg &b) "|", +@ref operator ^(const v_reg &a, const v_reg &b) "^", +@ref operator ~(const v_reg &a) "~" + +- Comparison: +@ref operator >(const v_reg &a, const v_reg &b) ">", +@ref operator >=(const v_reg &a, const v_reg &b) ">=", +@ref operator <(const v_reg &a, const v_reg &b) "<", +@ref operator <=(const v_reg &a, const v_reg &b) "<=", +@ref operator==(const v_reg &a, const v_reg &b) "==", +@ref operator !=(const v_reg &a, const v_reg &b) "!=" + +- min/max: @ref v_min, @ref v_max + +### Reduce and mask + +Most of these operations return only one value. + +- Reduce: @ref v_reduce_min, @ref v_reduce_max, @ref v_reduce_sum, @ref v_popcount +- Mask: @ref v_signmask, @ref v_check_all, @ref v_check_any, @ref v_select + +### Other math + +- Some frequent operations: @ref v_sqrt, @ref v_invsqrt, @ref v_magnitude, @ref v_sqr_magnitude +- Absolute values: @ref v_abs, @ref v_absdiff, @ref v_absdiffs + +### Conversions + +Different type conversions and casts: + +- Rounding: @ref v_round, @ref v_floor, @ref v_ceil, @ref v_trunc, +- To float: @ref v_cvt_f32, @ref v_cvt_f64 +- Reinterpret: @ref v_reinterpret_as_u8, @ref v_reinterpret_as_s8, ... + +### Matrix operations + +In these operations vectors represent matrix rows/columns: @ref v_dotprod, @ref v_matmul, @ref v_transpose4x4 + +### Usability + +Most operations are implemented only for some subset of the available types, following matrices +shows the applicability of different operations to the types. + +Regular integers: + +| Operations\\Types | uint 8x16 | int 8x16 | uint 16x8 | int 16x8 | uint 32x4 | int 32x4 | +|-------------------|:-:|:-:|:-:|:-:|:-:|:-:| +|load, store | x | x | x | x | x | x | +|interleave | x | x | x | x | x | x | +|expand | x | x | x | x | x | x | +|expand_low | x | x | x | x | x | x | +|expand_high | x | x | x | x | x | x | +|expand_q | x | x | | | | | +|add, sub | x | x | x | x | x | x | +|add_wrap, sub_wrap | x | x | x | x | | | +|mul_wrap | x | x | x | x | | | +|mul | x | x | x | x | x | x | +|mul_expand | x | x | x | x | x | | +|compare | x | x | x | x | x | x | +|shift | | | x | x | x | x | +|dotprod | | | | x | | | +|logical | x | x | x | x | x | x | +|min, max | x | x | x | x | x | x | +|absdiff | x | x | x | x | x | x | +|absdiffs | | x | | x | | | +|reduce | | | | | x | x | +|mask | x | x | x | x | x | x | +|pack | x | x | x | x | x | x | +|pack_u | x | | x | | | | +|pack_b | x | | | | | | +|unpack | x | x | x | x | x | x | +|extract | x | x | x | x | x | x | +|rotate (lanes) | x | x | x | x | x | x | +|cvt_flt32 | | | | | | x | +|cvt_flt64 | | | | | | x | +|transpose4x4 | | | | | x | x | + +Big integers: + +| Operations\\Types | uint 64x2 | int 64x2 | +|-------------------|:-:|:-:| +|load, store | x | x | +|add, sub | x | x | +|shift | x | x | +|logical | x | x | +|extract | x | x | +|rotate (lanes) | x | x | + +Floating point: + +| Operations\\Types | float 32x4 | float 64x2 | +|-------------------|:-:|:-:| +|load, store | x | x | +|interleave | x | | +|add, sub | x | x | +|mul | x | x | +|div | x | x | +|compare | x | x | +|min, max | x | x | +|absdiff | x | x | +|reduce | x | | +|mask | x | x | +|unpack | x | x | +|cvt_flt32 | | x | +|cvt_flt64 | x | | +|sqrt, abs | x | x | +|float math | x | x | +|transpose4x4 | x | | +|extract | x | x | +|rotate (lanes) | x | x | + + @{ */ + +template struct v_reg +{ +//! @cond IGNORED + typedef _Tp lane_type; + enum { nlanes = n }; +// !@endcond + + /** @brief Constructor + + Initializes register with data from memory + @param ptr pointer to memory block with data for register */ + explicit v_reg(const _Tp* ptr) { for( int i = 0; i < n; i++ ) s[i] = ptr[i]; } + + /** @brief Constructor + + Initializes register with two 64-bit values */ + v_reg(_Tp s0, _Tp s1) { s[0] = s0; s[1] = s1; } + + /** @brief Constructor + + Initializes register with four 32-bit values */ + v_reg(_Tp s0, _Tp s1, _Tp s2, _Tp s3) { s[0] = s0; s[1] = s1; s[2] = s2; s[3] = s3; } + + /** @brief Constructor + + Initializes register with eight 16-bit values */ + v_reg(_Tp s0, _Tp s1, _Tp s2, _Tp s3, + _Tp s4, _Tp s5, _Tp s6, _Tp s7) + { + s[0] = s0; s[1] = s1; s[2] = s2; s[3] = s3; + s[4] = s4; s[5] = s5; s[6] = s6; s[7] = s7; + } + + /** @brief Constructor + + Initializes register with sixteen 8-bit values */ + v_reg(_Tp s0, _Tp s1, _Tp s2, _Tp s3, + _Tp s4, _Tp s5, _Tp s6, _Tp s7, + _Tp s8, _Tp s9, _Tp s10, _Tp s11, + _Tp s12, _Tp s13, _Tp s14, _Tp s15) + { + s[0] = s0; s[1] = s1; s[2] = s2; s[3] = s3; + s[4] = s4; s[5] = s5; s[6] = s6; s[7] = s7; + s[8] = s8; s[9] = s9; s[10] = s10; s[11] = s11; + s[12] = s12; s[13] = s13; s[14] = s14; s[15] = s15; + } + + /** @brief Default constructor + + Does not initialize anything*/ + v_reg() {} + + /** @brief Copy constructor */ + v_reg(const v_reg<_Tp, n> & r) + { + for( int i = 0; i < n; i++ ) + s[i] = r.s[i]; + } + /** @brief Access first value + + Returns value of the first lane according to register type, for example: + @code{.cpp} + v_int32x4 r(1, 2, 3, 4); + int v = r.get0(); // returns 1 + v_uint64x2 r(1, 2); + uint64_t v = r.get0(); // returns 1 + @endcode + */ + _Tp get0() const { return s[0]; } + +//! @cond IGNORED + _Tp get(const int i) const { return s[i]; } + v_reg<_Tp, n> high() const + { + v_reg<_Tp, n> c; + int i; + for( i = 0; i < n/2; i++ ) + { + c.s[i] = s[i+(n/2)]; + c.s[i+(n/2)] = 0; + } + return c; + } + + static v_reg<_Tp, n> zero() + { + v_reg<_Tp, n> c; + for( int i = 0; i < n; i++ ) + c.s[i] = (_Tp)0; + return c; + } + + static v_reg<_Tp, n> all(_Tp s) + { + v_reg<_Tp, n> c; + for( int i = 0; i < n; i++ ) + c.s[i] = s; + return c; + } + + template v_reg<_Tp2, n2> reinterpret_as() const + { + size_t bytes = std::min(sizeof(_Tp2)*n2, sizeof(_Tp)*n); + v_reg<_Tp2, n2> c; + std::memcpy(&c.s[0], &s[0], bytes); + return c; + } + + _Tp s[n]; +//! @endcond +}; + +/** @brief Sixteen 8-bit unsigned integer values */ +typedef v_reg v_uint8x16; +/** @brief Sixteen 8-bit signed integer values */ +typedef v_reg v_int8x16; +/** @brief Eight 16-bit unsigned integer values */ +typedef v_reg v_uint16x8; +/** @brief Eight 16-bit signed integer values */ +typedef v_reg v_int16x8; +/** @brief Four 32-bit unsigned integer values */ +typedef v_reg v_uint32x4; +/** @brief Four 32-bit signed integer values */ +typedef v_reg v_int32x4; +/** @brief Four 32-bit floating point values (single precision) */ +typedef v_reg v_float32x4; +/** @brief Two 64-bit floating point values (double precision) */ +typedef v_reg v_float64x2; +/** @brief Two 64-bit unsigned integer values */ +typedef v_reg v_uint64x2; +/** @brief Two 64-bit signed integer values */ +typedef v_reg v_int64x2; + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_BIN_OP(bin_op) \ +template inline v_reg<_Tp, n> \ + operator bin_op (const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ +{ \ + v_reg<_Tp, n> c; \ + for( int i = 0; i < n; i++ ) \ + c.s[i] = saturate_cast<_Tp>(a.s[i] bin_op b.s[i]); \ + return c; \ +} \ +template inline v_reg<_Tp, n>& \ + operator bin_op##= (v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ +{ \ + for( int i = 0; i < n; i++ ) \ + a.s[i] = saturate_cast<_Tp>(a.s[i] bin_op b.s[i]); \ + return a; \ +} + +/** @brief Add values + +For all types. */ +OPENCV_HAL_IMPL_BIN_OP(+) + +/** @brief Subtract values + +For all types. */ +OPENCV_HAL_IMPL_BIN_OP(-) + +/** @brief Multiply values + +For 16- and 32-bit integer types and floating types. */ +OPENCV_HAL_IMPL_BIN_OP(*) + +/** @brief Divide values + +For floating types only. */ +OPENCV_HAL_IMPL_BIN_OP(/) + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_BIT_OP(bit_op) \ +template inline v_reg<_Tp, n> operator bit_op \ + (const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ +{ \ + v_reg<_Tp, n> c; \ + typedef typename V_TypeTraits<_Tp>::int_type itype; \ + for( int i = 0; i < n; i++ ) \ + c.s[i] = V_TypeTraits<_Tp>::reinterpret_from_int((itype)(V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) bit_op \ + V_TypeTraits<_Tp>::reinterpret_int(b.s[i]))); \ + return c; \ +} \ +template inline v_reg<_Tp, n>& operator \ + bit_op##= (v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ +{ \ + typedef typename V_TypeTraits<_Tp>::int_type itype; \ + for( int i = 0; i < n; i++ ) \ + a.s[i] = V_TypeTraits<_Tp>::reinterpret_from_int((itype)(V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) bit_op \ + V_TypeTraits<_Tp>::reinterpret_int(b.s[i]))); \ + return a; \ +} + +/** @brief Bitwise AND + +Only for integer types. */ +OPENCV_HAL_IMPL_BIT_OP(&) + +/** @brief Bitwise OR + +Only for integer types. */ +OPENCV_HAL_IMPL_BIT_OP(|) + +/** @brief Bitwise XOR + +Only for integer types.*/ +OPENCV_HAL_IMPL_BIT_OP(^) + +/** @brief Bitwise NOT + +Only for integer types.*/ +template inline v_reg<_Tp, n> operator ~ (const v_reg<_Tp, n>& a) +{ + v_reg<_Tp, n> c; + for( int i = 0; i < n; i++ ) + { + c.s[i] = V_TypeTraits<_Tp>::reinterpret_from_int(~V_TypeTraits<_Tp>::reinterpret_int(a.s[i])); + } + return c; +} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_MATH_FUNC(func, cfunc, _Tp2) \ +template inline v_reg<_Tp2, n> func(const v_reg<_Tp, n>& a) \ +{ \ + v_reg<_Tp2, n> c; \ + for( int i = 0; i < n; i++ ) \ + c.s[i] = cfunc(a.s[i]); \ + return c; \ +} + +/** @brief Square root of elements + +Only for floating point types.*/ +OPENCV_HAL_IMPL_MATH_FUNC(v_sqrt, std::sqrt, _Tp) + +//! @cond IGNORED +OPENCV_HAL_IMPL_MATH_FUNC(v_sin, std::sin, _Tp) +OPENCV_HAL_IMPL_MATH_FUNC(v_cos, std::cos, _Tp) +OPENCV_HAL_IMPL_MATH_FUNC(v_exp, std::exp, _Tp) +OPENCV_HAL_IMPL_MATH_FUNC(v_log, std::log, _Tp) +//! @endcond + +/** @brief Absolute value of elements + +Only for floating point types.*/ +OPENCV_HAL_IMPL_MATH_FUNC(v_abs, (typename V_TypeTraits<_Tp>::abs_type)std::abs, + typename V_TypeTraits<_Tp>::abs_type) + +/** @brief Round elements + +Only for floating point types.*/ +OPENCV_HAL_IMPL_MATH_FUNC(v_round, cvRound, int) + +/** @brief Floor elements + +Only for floating point types.*/ +OPENCV_HAL_IMPL_MATH_FUNC(v_floor, cvFloor, int) + +/** @brief Ceil elements + +Only for floating point types.*/ +OPENCV_HAL_IMPL_MATH_FUNC(v_ceil, cvCeil, int) + +/** @brief Truncate elements + +Only for floating point types.*/ +OPENCV_HAL_IMPL_MATH_FUNC(v_trunc, int, int) + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_MINMAX_FUNC(func, cfunc) \ +template inline v_reg<_Tp, n> func(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ +{ \ + v_reg<_Tp, n> c; \ + for( int i = 0; i < n; i++ ) \ + c.s[i] = cfunc(a.s[i], b.s[i]); \ + return c; \ +} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_REDUCE_MINMAX_FUNC(func, cfunc) \ +template inline _Tp func(const v_reg<_Tp, n>& a) \ +{ \ + _Tp c = a.s[0]; \ + for( int i = 1; i < n; i++ ) \ + c = cfunc(c, a.s[i]); \ + return c; \ +} + +/** @brief Choose min values for each pair + +Scheme: +@code +{A1 A2 ...} +{B1 B2 ...} +-------------- +{min(A1,B1) min(A2,B2) ...} +@endcode +For all types except 64-bit integer. */ +OPENCV_HAL_IMPL_MINMAX_FUNC(v_min, std::min) + +/** @brief Choose max values for each pair + +Scheme: +@code +{A1 A2 ...} +{B1 B2 ...} +-------------- +{max(A1,B1) max(A2,B2) ...} +@endcode +For all types except 64-bit integer. */ +OPENCV_HAL_IMPL_MINMAX_FUNC(v_max, std::max) + +/** @brief Find one min value + +Scheme: +@code +{A1 A2 A3 ...} => min(A1,A2,A3,...) +@endcode +For 32-bit integer and 32-bit floating point types. */ +OPENCV_HAL_IMPL_REDUCE_MINMAX_FUNC(v_reduce_min, std::min) + +/** @brief Find one max value + +Scheme: +@code +{A1 A2 A3 ...} => max(A1,A2,A3,...) +@endcode +For 32-bit integer and 32-bit floating point types. */ +OPENCV_HAL_IMPL_REDUCE_MINMAX_FUNC(v_reduce_max, std::max) + +static const unsigned char popCountTable[] = +{ + 0, 1, 1, 2, 1, 2, 2, 3, 1, 2, 2, 3, 2, 3, 3, 4, + 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, + 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, + 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, + 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, + 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, + 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, + 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, + 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, + 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, + 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, + 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, + 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, + 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, + 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, + 4, 5, 5, 6, 5, 6, 6, 7, 5, 6, 6, 7, 6, 7, 7, 8, +}; +/** @brief Count the 1 bits in the vector and return 4 values + +Scheme: +@code +{A1 A2 A3 ...} => popcount(A1) +@endcode +Any types but result will be in v_uint32x4*/ +template inline v_uint32x4 v_popcount(const v_reg<_Tp, n>& a) +{ + v_uint8x16 b; + b = v_reinterpret_as_u8(a); + for( int i = 0; i < v_uint8x16::nlanes; i++ ) + { + b.s[i] = popCountTable[b.s[i]]; + } + v_uint32x4 c; + for( int i = 0; i < v_uint32x4::nlanes; i++ ) + { + c.s[i] = b.s[i*4] + b.s[i*4+1] + b.s[i*4+2] + b.s[i*4+3]; + } + return c; +} + + +//! @cond IGNORED +template +inline void v_minmax( const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b, + v_reg<_Tp, n>& minval, v_reg<_Tp, n>& maxval ) +{ + for( int i = 0; i < n; i++ ) + { + minval.s[i] = std::min(a.s[i], b.s[i]); + maxval.s[i] = std::max(a.s[i], b.s[i]); + } +} +//! @endcond + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_CMP_OP(cmp_op) \ +template \ +inline v_reg<_Tp, n> operator cmp_op(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ +{ \ + typedef typename V_TypeTraits<_Tp>::int_type itype; \ + v_reg<_Tp, n> c; \ + for( int i = 0; i < n; i++ ) \ + c.s[i] = V_TypeTraits<_Tp>::reinterpret_from_int((itype)-(int)(a.s[i] cmp_op b.s[i])); \ + return c; \ +} + +/** @brief Less-than comparison + +For all types except 64-bit integer values. */ +OPENCV_HAL_IMPL_CMP_OP(<) + +/** @brief Greater-than comparison + +For all types except 64-bit integer values. */ +OPENCV_HAL_IMPL_CMP_OP(>) + +/** @brief Less-than or equal comparison + +For all types except 64-bit integer values. */ +OPENCV_HAL_IMPL_CMP_OP(<=) + +/** @brief Greater-than or equal comparison + +For all types except 64-bit integer values. */ +OPENCV_HAL_IMPL_CMP_OP(>=) + +/** @brief Equal comparison + +For all types except 64-bit integer values. */ +OPENCV_HAL_IMPL_CMP_OP(==) + +/** @brief Not equal comparison + +For all types except 64-bit integer values. */ +OPENCV_HAL_IMPL_CMP_OP(!=) + +template +inline v_reg v_not_nan(const v_reg& a) +{ + typedef typename V_TypeTraits::int_type itype; + v_reg c; + for (int i = 0; i < n; i++) + c.s[i] = V_TypeTraits::reinterpret_from_int((itype)-(int)(a.s[i] == a.s[i])); + return c; +} +template +inline v_reg v_not_nan(const v_reg& a) +{ + typedef typename V_TypeTraits::int_type itype; + v_reg c; + for (int i = 0; i < n; i++) + c.s[i] = V_TypeTraits::reinterpret_from_int((itype)-(int)(a.s[i] == a.s[i])); + return c; +} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_ARITHM_OP(func, bin_op, cast_op, _Tp2) \ +template \ +inline v_reg<_Tp2, n> func(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ +{ \ + typedef _Tp2 rtype; \ + v_reg c; \ + for( int i = 0; i < n; i++ ) \ + c.s[i] = cast_op(a.s[i] bin_op b.s[i]); \ + return c; \ +} + +/** @brief Add values without saturation + +For 8- and 16-bit integer values. */ +OPENCV_HAL_IMPL_ARITHM_OP(v_add_wrap, +, (_Tp), _Tp) + +/** @brief Subtract values without saturation + +For 8- and 16-bit integer values. */ +OPENCV_HAL_IMPL_ARITHM_OP(v_sub_wrap, -, (_Tp), _Tp) + +/** @brief Multiply values without saturation + +For 8- and 16-bit integer values. */ +OPENCV_HAL_IMPL_ARITHM_OP(v_mul_wrap, *, (_Tp), _Tp) + +//! @cond IGNORED +template inline T _absdiff(T a, T b) +{ + return a > b ? a - b : b - a; +} +//! @endcond + +/** @brief Absolute difference + +Returns \f$ |a - b| \f$ converted to corresponding unsigned type. +Example: +@code{.cpp} +v_int32x4 a, b; // {1, 2, 3, 4} and {4, 3, 2, 1} +v_uint32x4 c = v_absdiff(a, b); // result is {3, 1, 1, 3} +@endcode +For 8-, 16-, 32-bit integer source types. */ +template +inline v_reg::abs_type, n> v_absdiff(const v_reg<_Tp, n>& a, const v_reg<_Tp, n> & b) +{ + typedef typename V_TypeTraits<_Tp>::abs_type rtype; + v_reg c; + const rtype mask = (rtype)(std::numeric_limits<_Tp>::is_signed ? (1 << (sizeof(rtype)*8 - 1)) : 0); + for( int i = 0; i < n; i++ ) + { + rtype ua = a.s[i] ^ mask; + rtype ub = b.s[i] ^ mask; + c.s[i] = _absdiff(ua, ub); + } + return c; +} + +/** @overload + +For 32-bit floating point values */ +inline v_float32x4 v_absdiff(const v_float32x4& a, const v_float32x4& b) +{ + v_float32x4 c; + for( int i = 0; i < c.nlanes; i++ ) + c.s[i] = _absdiff(a.s[i], b.s[i]); + return c; +} + +/** @overload + +For 64-bit floating point values */ +inline v_float64x2 v_absdiff(const v_float64x2& a, const v_float64x2& b) +{ + v_float64x2 c; + for( int i = 0; i < c.nlanes; i++ ) + c.s[i] = _absdiff(a.s[i], b.s[i]); + return c; +} + +/** @brief Saturating absolute difference + +Returns \f$ saturate(|a - b|) \f$ . +For 8-, 16-bit signed integer source types. */ +template +inline v_reg<_Tp, n> v_absdiffs(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) +{ + v_reg<_Tp, n> c; + for( int i = 0; i < n; i++) + c.s[i] = saturate_cast<_Tp>(std::abs(a.s[i] - b.s[i])); + return c; +} + +/** @brief Inversed square root + +Returns \f$ 1/sqrt(a) \f$ +For floating point types only. */ +template +inline v_reg<_Tp, n> v_invsqrt(const v_reg<_Tp, n>& a) +{ + v_reg<_Tp, n> c; + for( int i = 0; i < n; i++ ) + c.s[i] = 1.f/std::sqrt(a.s[i]); + return c; +} + +/** @brief Magnitude + +Returns \f$ sqrt(a^2 + b^2) \f$ +For floating point types only. */ +template +inline v_reg<_Tp, n> v_magnitude(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) +{ + v_reg<_Tp, n> c; + for( int i = 0; i < n; i++ ) + c.s[i] = std::sqrt(a.s[i]*a.s[i] + b.s[i]*b.s[i]); + return c; +} + +/** @brief Square of the magnitude + +Returns \f$ a^2 + b^2 \f$ +For floating point types only. */ +template +inline v_reg<_Tp, n> v_sqr_magnitude(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) +{ + v_reg<_Tp, n> c; + for( int i = 0; i < n; i++ ) + c.s[i] = a.s[i]*a.s[i] + b.s[i]*b.s[i]; + return c; +} + +/** @brief Multiply and add + + Returns \f$ a*b + c \f$ + For floating point types and signed 32bit int only. */ +template +inline v_reg<_Tp, n> v_fma(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b, + const v_reg<_Tp, n>& c) +{ + v_reg<_Tp, n> d; + for( int i = 0; i < n; i++ ) + d.s[i] = a.s[i]*b.s[i] + c.s[i]; + return d; +} + +/** @brief A synonym for v_fma */ +template +inline v_reg<_Tp, n> v_muladd(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b, + const v_reg<_Tp, n>& c) +{ + return v_fma(a, b, c); +} + +/** @brief Dot product of elements + +Multiply values in two registers and sum adjacent result pairs. +Scheme: +@code + {A1 A2 ...} // 16-bit +x {B1 B2 ...} // 16-bit +------------- +{A1B1+A2B2 ...} // 32-bit +@endcode +Implemented only for 16-bit signed source type (v_int16x8). +*/ +template inline v_reg::w_type, n/2> + v_dotprod(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) +{ + typedef typename V_TypeTraits<_Tp>::w_type w_type; + v_reg c; + for( int i = 0; i < (n/2); i++ ) + c.s[i] = (w_type)a.s[i*2]*b.s[i*2] + (w_type)a.s[i*2+1]*b.s[i*2+1]; + return c; +} + +/** @brief Dot product of elements + +Same as cv::v_dotprod, but add a third element to the sum of adjacent pairs. +Scheme: +@code + {A1 A2 ...} // 16-bit +x {B1 B2 ...} // 16-bit +------------- + {A1B1+A2B2+C1 ...} // 32-bit + +@endcode +Implemented only for 16-bit signed source type (v_int16x8). +*/ +template inline v_reg::w_type, n/2> + v_dotprod(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b, const v_reg::w_type, n / 2>& c) +{ + typedef typename V_TypeTraits<_Tp>::w_type w_type; + v_reg s; + for( int i = 0; i < (n/2); i++ ) + s.s[i] = (w_type)a.s[i*2]*b.s[i*2] + (w_type)a.s[i*2+1]*b.s[i*2+1] + c.s[i]; + return s; +} + +/** @brief Multiply and expand + +Multiply values two registers and store results in two registers with wider pack type. +Scheme: +@code + {A B C D} // 32-bit +x {E F G H} // 32-bit +--------------- +{AE BF} // 64-bit + {CG DH} // 64-bit +@endcode +Example: +@code{.cpp} +v_uint32x4 a, b; // {1,2,3,4} and {2,2,2,2} +v_uint64x2 c, d; // results +v_mul_expand(a, b, c, d); // c, d = {2,4}, {6, 8} +@endcode +Implemented only for 16- and unsigned 32-bit source types (v_int16x8, v_uint16x8, v_uint32x4). +*/ +template inline void v_mul_expand(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b, + v_reg::w_type, n/2>& c, + v_reg::w_type, n/2>& d) +{ + typedef typename V_TypeTraits<_Tp>::w_type w_type; + for( int i = 0; i < (n/2); i++ ) + { + c.s[i] = (w_type)a.s[i]*b.s[i]; + d.s[i] = (w_type)a.s[i+(n/2)]*b.s[i+(n/2)]; + } +} + +/** @brief Multiply and extract high part + +Multiply values two registers and store high part of the results. +Implemented only for 16-bit source types (v_int16x8, v_uint16x8). Returns \f$ a*b >> 16 \f$ +*/ +template inline v_reg<_Tp, n> v_mul_hi(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) +{ + typedef typename V_TypeTraits<_Tp>::w_type w_type; + v_reg<_Tp, n> c; + for (int i = 0; i < n; i++) + c.s[i] = (_Tp)(((w_type)a.s[i] * b.s[i]) >> sizeof(_Tp)*8); + return c; +} + +//! @cond IGNORED +template inline void v_hsum(const v_reg<_Tp, n>& a, + v_reg::w_type, n/2>& c) +{ + typedef typename V_TypeTraits<_Tp>::w_type w_type; + for( int i = 0; i < (n/2); i++ ) + { + c.s[i] = (w_type)a.s[i*2] + a.s[i*2+1]; + } +} +//! @endcond + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_SHIFT_OP(shift_op) \ +template inline v_reg<_Tp, n> operator shift_op(const v_reg<_Tp, n>& a, int imm) \ +{ \ + v_reg<_Tp, n> c; \ + for( int i = 0; i < n; i++ ) \ + c.s[i] = (_Tp)(a.s[i] shift_op imm); \ + return c; \ +} + +/** @brief Bitwise shift left + +For 16-, 32- and 64-bit integer values. */ +OPENCV_HAL_IMPL_SHIFT_OP(<< ) + +/** @brief Bitwise shift right + +For 16-, 32- and 64-bit integer values. */ +OPENCV_HAL_IMPL_SHIFT_OP(>> ) + +/** @brief Element shift left among vector + +For all type */ +#define OPENCV_HAL_IMPL_ROTATE_SHIFT_OP(suffix,opA,opB) \ +template inline v_reg<_Tp, n> v_rotate_##suffix(const v_reg<_Tp, n>& a) \ +{ \ + v_reg<_Tp, n> b; \ + for (int i = 0; i < n; i++) \ + { \ + int sIndex = i opA imm; \ + if (0 <= sIndex && sIndex < n) \ + { \ + b.s[i] = a.s[sIndex]; \ + } \ + else \ + { \ + b.s[i] = 0; \ + } \ + } \ + return b; \ +} \ +template inline v_reg<_Tp, n> v_rotate_##suffix(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ +{ \ + v_reg<_Tp, n> c; \ + for (int i = 0; i < n; i++) \ + { \ + int aIndex = i opA imm; \ + int bIndex = i opA imm opB n; \ + if (0 <= bIndex && bIndex < n) \ + { \ + c.s[i] = b.s[bIndex]; \ + } \ + else if (0 <= aIndex && aIndex < n) \ + { \ + c.s[i] = a.s[aIndex]; \ + } \ + else \ + { \ + c.s[i] = 0; \ + } \ + } \ + return c; \ +} + +OPENCV_HAL_IMPL_ROTATE_SHIFT_OP(left, -, +) +OPENCV_HAL_IMPL_ROTATE_SHIFT_OP(right, +, -) + +/** @brief Sum packed values + +Scheme: +@code +{A1 A2 A3 ...} => sum{A1,A2,A3,...} +@endcode +For 32-bit integer and 32-bit floating point types.*/ +template inline typename V_TypeTraits<_Tp>::sum_type v_reduce_sum(const v_reg<_Tp, n>& a) +{ + typename V_TypeTraits<_Tp>::sum_type c = a.s[0]; + for( int i = 1; i < n; i++ ) + c += a.s[i]; + return c; +} + +/** @brief Sums all elements of each input vector, returns the vector of sums + + Scheme: + @code + result[0] = a[0] + a[1] + a[2] + a[3] + result[1] = b[0] + b[1] + b[2] + b[3] + result[2] = c[0] + c[1] + c[2] + c[3] + result[3] = d[0] + d[1] + d[2] + d[3] + @endcode +*/ +inline v_float32x4 v_reduce_sum4(const v_float32x4& a, const v_float32x4& b, + const v_float32x4& c, const v_float32x4& d) +{ + v_float32x4 r; + r.s[0] = a.s[0] + a.s[1] + a.s[2] + a.s[3]; + r.s[1] = b.s[0] + b.s[1] + b.s[2] + b.s[3]; + r.s[2] = c.s[0] + c.s[1] + c.s[2] + c.s[3]; + r.s[3] = d.s[0] + d.s[1] + d.s[2] + d.s[3]; + return r; +} + +/** @brief Sum absolute differences of values + +Scheme: +@code +{A1 A2 A3 ...} {B1 B2 B3 ...} => sum{ABS(A1-B1),abs(A2-B2),abs(A3-B3),...} +@endcode +For all types except 64-bit types.*/ +template inline typename V_TypeTraits< typename V_TypeTraits<_Tp>::abs_type >::sum_type v_reduce_sad(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) +{ + typename V_TypeTraits< typename V_TypeTraits<_Tp>::abs_type >::sum_type c = _absdiff(a.s[0], b.s[0]); + for (int i = 1; i < n; i++) + c += _absdiff(a.s[i], b.s[i]); + return c; +} + +/** @brief Get negative values mask + +Returned value is a bit mask with bits set to 1 on places corresponding to negative packed values indexes. +Example: +@code{.cpp} +v_int32x4 r; // set to {-1, -1, 1, 1} +int mask = v_signmask(r); // mask = 3 <== 00000000 00000000 00000000 00000011 +@endcode +For all types except 64-bit. */ +template inline int v_signmask(const v_reg<_Tp, n>& a) +{ + int mask = 0; + for( int i = 0; i < n; i++ ) + mask |= (V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) < 0) << i; + return mask; +} + +/** @brief Check if all packed values are less than zero + +Unsigned values will be casted to signed: `uchar 254 => char -2`. +For all types except 64-bit. */ +template inline bool v_check_all(const v_reg<_Tp, n>& a) +{ + for( int i = 0; i < n; i++ ) + if( V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) >= 0 ) + return false; + return true; +} + +/** @brief Check if any of packed values is less than zero + +Unsigned values will be casted to signed: `uchar 254 => char -2`. +For all types except 64-bit. */ +template inline bool v_check_any(const v_reg<_Tp, n>& a) +{ + for( int i = 0; i < n; i++ ) + if( V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) < 0 ) + return true; + return false; +} + +/** @brief Per-element select (blend operation) + +Return value will be built by combining values _a_ and _b_ using the following scheme: + result[i] = mask[i] ? a[i] : b[i]; + +@note: _mask_ element values are restricted to these values: +- 0: select element from _b_ +- 0xff/0xffff/etc: select element from _a_ +(fully compatible with bitwise-based operator) +*/ +template inline v_reg<_Tp, n> v_select(const v_reg<_Tp, n>& mask, + const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) +{ + typedef V_TypeTraits<_Tp> Traits; + typedef typename Traits::int_type int_type; + v_reg<_Tp, n> c; + for( int i = 0; i < n; i++ ) + { + int_type m = Traits::reinterpret_int(mask.s[i]); + CV_DbgAssert(m == 0 || m == (~(int_type)0)); // restrict mask values: 0 or 0xff/0xffff/etc + c.s[i] = m ? a.s[i] : b.s[i]; + } + return c; +} + +/** @brief Expand values to the wider pack type + +Copy contents of register to two registers with 2x wider pack type. +Scheme: +@code + int32x4 int64x2 int64x2 +{A B C D} ==> {A B} , {C D} +@endcode */ +template inline void v_expand(const v_reg<_Tp, n>& a, + v_reg::w_type, n/2>& b0, + v_reg::w_type, n/2>& b1) +{ + for( int i = 0; i < (n/2); i++ ) + { + b0.s[i] = a.s[i]; + b1.s[i] = a.s[i+(n/2)]; + } +} + +/** @brief Expand lower values to the wider pack type + +Same as cv::v_expand, but return lower half of the vector. + +Scheme: +@code + int32x4 int64x2 +{A B C D} ==> {A B} +@endcode */ +template +inline v_reg::w_type, n/2> +v_expand_low(const v_reg<_Tp, n>& a) +{ + v_reg::w_type, n/2> b; + for( int i = 0; i < (n/2); i++ ) + b.s[i] = a.s[i]; + return b; +} + +/** @brief Expand higher values to the wider pack type + +Same as cv::v_expand_low, but expand higher half of the vector instead. + +Scheme: +@code + int32x4 int64x2 +{A B C D} ==> {C D} +@endcode */ +template +inline v_reg::w_type, n/2> +v_expand_high(const v_reg<_Tp, n>& a) +{ + v_reg::w_type, n/2> b; + for( int i = 0; i < (n/2); i++ ) + b.s[i] = a.s[i+(n/2)]; + return b; +} + +//! @cond IGNORED +template inline v_reg::int_type, n> + v_reinterpret_as_int(const v_reg<_Tp, n>& a) +{ + v_reg::int_type, n> c; + for( int i = 0; i < n; i++ ) + c.s[i] = V_TypeTraits<_Tp>::reinterpret_int(a.s[i]); + return c; +} + +template inline v_reg::uint_type, n> + v_reinterpret_as_uint(const v_reg<_Tp, n>& a) +{ + v_reg::uint_type, n> c; + for( int i = 0; i < n; i++ ) + c.s[i] = V_TypeTraits<_Tp>::reinterpret_uint(a.s[i]); + return c; +} +//! @endcond + +/** @brief Interleave two vectors + +Scheme: +@code + {A1 A2 A3 A4} + {B1 B2 B3 B4} +--------------- + {A1 B1 A2 B2} and {A3 B3 A4 B4} +@endcode +For all types except 64-bit. +*/ +template inline void v_zip( const v_reg<_Tp, n>& a0, const v_reg<_Tp, n>& a1, + v_reg<_Tp, n>& b0, v_reg<_Tp, n>& b1 ) +{ + int i; + for( i = 0; i < n/2; i++ ) + { + b0.s[i*2] = a0.s[i]; + b0.s[i*2+1] = a1.s[i]; + } + for( ; i < n; i++ ) + { + b1.s[i*2-n] = a0.s[i]; + b1.s[i*2-n+1] = a1.s[i]; + } +} + +/** @brief Load register contents from memory + +@param ptr pointer to memory block with data +@return register object + +@note Returned type will be detected from passed pointer type, for example uchar ==> cv::v_uint8x16, int ==> cv::v_int32x4, etc. + */ +template +inline v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128> v_load(const _Tp* ptr) +{ + return v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128>(ptr); +} + +/** @brief Load register contents from memory (aligned) + +similar to cv::v_load, but source memory block should be aligned (to 16-byte boundary) + */ +template +inline v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128> v_load_aligned(const _Tp* ptr) +{ + return v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128>(ptr); +} + +/** @brief Load 64-bits of data to lower part (high part is undefined). + +@param ptr memory block containing data for first half (0..n/2) + +@code{.cpp} +int lo[2] = { 1, 2 }; +v_int32x4 r = v_load_low(lo); +@endcode + */ +template +inline v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128> v_load_low(const _Tp* ptr) +{ + v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128> c; + for( int i = 0; i < c.nlanes/2; i++ ) + { + c.s[i] = ptr[i]; + } + return c; +} + +/** @brief Load register contents from two memory blocks + +@param loptr memory block containing data for first half (0..n/2) +@param hiptr memory block containing data for second half (n/2..n) + +@code{.cpp} +int lo[2] = { 1, 2 }, hi[2] = { 3, 4 }; +v_int32x4 r = v_load_halves(lo, hi); +@endcode + */ +template +inline v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128> v_load_halves(const _Tp* loptr, const _Tp* hiptr) +{ + v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128> c; + for( int i = 0; i < c.nlanes/2; i++ ) + { + c.s[i] = loptr[i]; + c.s[i+c.nlanes/2] = hiptr[i]; + } + return c; +} + +/** @brief Load register contents from memory with double expand + +Same as cv::v_load, but result pack type will be 2x wider than memory type. + +@code{.cpp} +short buf[4] = {1, 2, 3, 4}; // type is int16 +v_int32x4 r = v_load_expand(buf); // r = {1, 2, 3, 4} - type is int32 +@endcode +For 8-, 16-, 32-bit integer source types. */ +template +inline v_reg::w_type, V_TypeTraits<_Tp>::nlanes128 / 2> +v_load_expand(const _Tp* ptr) +{ + typedef typename V_TypeTraits<_Tp>::w_type w_type; + v_reg::nlanes128> c; + for( int i = 0; i < c.nlanes; i++ ) + { + c.s[i] = ptr[i]; + } + return c; +} + +/** @brief Load register contents from memory with quad expand + +Same as cv::v_load_expand, but result type is 4 times wider than source. +@code{.cpp} +char buf[4] = {1, 2, 3, 4}; // type is int8 +v_int32x4 r = v_load_q(buf); // r = {1, 2, 3, 4} - type is int32 +@endcode +For 8-bit integer source types. */ +template +inline v_reg::q_type, V_TypeTraits<_Tp>::nlanes128 / 4> +v_load_expand_q(const _Tp* ptr) +{ + typedef typename V_TypeTraits<_Tp>::q_type q_type; + v_reg::nlanes128> c; + for( int i = 0; i < c.nlanes; i++ ) + { + c.s[i] = ptr[i]; + } + return c; +} + +/** @brief Load and deinterleave (2 channels) + +Load data from memory deinterleave and store to 2 registers. +Scheme: +@code +{A1 B1 A2 B2 ...} ==> {A1 A2 ...}, {B1 B2 ...} +@endcode +For all types except 64-bit. */ +template inline void v_load_deinterleave(const _Tp* ptr, v_reg<_Tp, n>& a, + v_reg<_Tp, n>& b) +{ + int i, i2; + for( i = i2 = 0; i < n; i++, i2 += 2 ) + { + a.s[i] = ptr[i2]; + b.s[i] = ptr[i2+1]; + } +} + +/** @brief Load and deinterleave (3 channels) + +Load data from memory deinterleave and store to 3 registers. +Scheme: +@code +{A1 B1 C1 A2 B2 C2 ...} ==> {A1 A2 ...}, {B1 B2 ...}, {C1 C2 ...} +@endcode +For all types except 64-bit. */ +template inline void v_load_deinterleave(const _Tp* ptr, v_reg<_Tp, n>& a, + v_reg<_Tp, n>& b, v_reg<_Tp, n>& c) +{ + int i, i3; + for( i = i3 = 0; i < n; i++, i3 += 3 ) + { + a.s[i] = ptr[i3]; + b.s[i] = ptr[i3+1]; + c.s[i] = ptr[i3+2]; + } +} + +/** @brief Load and deinterleave (4 channels) + +Load data from memory deinterleave and store to 4 registers. +Scheme: +@code +{A1 B1 C1 D1 A2 B2 C2 D2 ...} ==> {A1 A2 ...}, {B1 B2 ...}, {C1 C2 ...}, {D1 D2 ...} +@endcode +For all types except 64-bit. */ +template +inline void v_load_deinterleave(const _Tp* ptr, v_reg<_Tp, n>& a, + v_reg<_Tp, n>& b, v_reg<_Tp, n>& c, + v_reg<_Tp, n>& d) +{ + int i, i4; + for( i = i4 = 0; i < n; i++, i4 += 4 ) + { + a.s[i] = ptr[i4]; + b.s[i] = ptr[i4+1]; + c.s[i] = ptr[i4+2]; + d.s[i] = ptr[i4+3]; + } +} + +/** @brief Interleave and store (2 channels) + +Interleave and store data from 2 registers to memory. +Scheme: +@code +{A1 A2 ...}, {B1 B2 ...} ==> {A1 B1 A2 B2 ...} +@endcode +For all types except 64-bit. */ +template +inline void v_store_interleave( _Tp* ptr, const v_reg<_Tp, n>& a, + const v_reg<_Tp, n>& b, + hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) +{ + int i, i2; + for( i = i2 = 0; i < n; i++, i2 += 2 ) + { + ptr[i2] = a.s[i]; + ptr[i2+1] = b.s[i]; + } +} + +/** @brief Interleave and store (3 channels) + +Interleave and store data from 3 registers to memory. +Scheme: +@code +{A1 A2 ...}, {B1 B2 ...}, {C1 C2 ...} ==> {A1 B1 C1 A2 B2 C2 ...} +@endcode +For all types except 64-bit. */ +template +inline void v_store_interleave( _Tp* ptr, const v_reg<_Tp, n>& a, + const v_reg<_Tp, n>& b, const v_reg<_Tp, n>& c, + hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) +{ + int i, i3; + for( i = i3 = 0; i < n; i++, i3 += 3 ) + { + ptr[i3] = a.s[i]; + ptr[i3+1] = b.s[i]; + ptr[i3+2] = c.s[i]; + } +} + +/** @brief Interleave and store (4 channels) + +Interleave and store data from 4 registers to memory. +Scheme: +@code +{A1 A2 ...}, {B1 B2 ...}, {C1 C2 ...}, {D1 D2 ...} ==> {A1 B1 C1 D1 A2 B2 C2 D2 ...} +@endcode +For all types except 64-bit. */ +template inline void v_store_interleave( _Tp* ptr, const v_reg<_Tp, n>& a, + const v_reg<_Tp, n>& b, const v_reg<_Tp, n>& c, + const v_reg<_Tp, n>& d, + hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) +{ + int i, i4; + for( i = i4 = 0; i < n; i++, i4 += 4 ) + { + ptr[i4] = a.s[i]; + ptr[i4+1] = b.s[i]; + ptr[i4+2] = c.s[i]; + ptr[i4+3] = d.s[i]; + } +} + +/** @brief Store data to memory + +Store register contents to memory. +Scheme: +@code + REG {A B C D} ==> MEM {A B C D} +@endcode +Pointer can be unaligned. */ +template +inline void v_store(_Tp* ptr, const v_reg<_Tp, n>& a) +{ + for( int i = 0; i < n; i++ ) + ptr[i] = a.s[i]; +} + +/** @brief Store data to memory (lower half) + +Store lower half of register contents to memory. +Scheme: +@code + REG {A B C D} ==> MEM {A B} +@endcode */ +template +inline void v_store_low(_Tp* ptr, const v_reg<_Tp, n>& a) +{ + for( int i = 0; i < (n/2); i++ ) + ptr[i] = a.s[i]; +} + +/** @brief Store data to memory (higher half) + +Store higher half of register contents to memory. +Scheme: +@code + REG {A B C D} ==> MEM {C D} +@endcode */ +template +inline void v_store_high(_Tp* ptr, const v_reg<_Tp, n>& a) +{ + for( int i = 0; i < (n/2); i++ ) + ptr[i] = a.s[i+(n/2)]; +} + +/** @brief Store data to memory (aligned) + +Store register contents to memory. +Scheme: +@code + REG {A B C D} ==> MEM {A B C D} +@endcode +Pointer __should__ be aligned by 16-byte boundary. */ +template +inline void v_store_aligned(_Tp* ptr, const v_reg<_Tp, n>& a) +{ + for( int i = 0; i < n; i++ ) + ptr[i] = a.s[i]; +} + +template +inline void v_store_aligned_nocache(_Tp* ptr, const v_reg<_Tp, n>& a) +{ + for( int i = 0; i < n; i++ ) + ptr[i] = a.s[i]; +} + +template +inline void v_store_aligned(_Tp* ptr, const v_reg<_Tp, n>& a, hal::StoreMode /*mode*/) +{ + for( int i = 0; i < n; i++ ) + ptr[i] = a.s[i]; +} + +/** @brief Combine vector from first elements of two vectors + +Scheme: +@code + {A1 A2 A3 A4} + {B1 B2 B3 B4} +--------------- + {A1 A2 B1 B2} +@endcode +For all types except 64-bit. */ +template +inline v_reg<_Tp, n> v_combine_low(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) +{ + v_reg<_Tp, n> c; + for( int i = 0; i < (n/2); i++ ) + { + c.s[i] = a.s[i]; + c.s[i+(n/2)] = b.s[i]; + } + return c; +} + +/** @brief Combine vector from last elements of two vectors + +Scheme: +@code + {A1 A2 A3 A4} + {B1 B2 B3 B4} +--------------- + {A3 A4 B3 B4} +@endcode +For all types except 64-bit. */ +template +inline v_reg<_Tp, n> v_combine_high(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) +{ + v_reg<_Tp, n> c; + for( int i = 0; i < (n/2); i++ ) + { + c.s[i] = a.s[i+(n/2)]; + c.s[i+(n/2)] = b.s[i+(n/2)]; + } + return c; +} + +/** @brief Combine two vectors from lower and higher parts of two other vectors + +@code{.cpp} +low = cv::v_combine_low(a, b); +high = cv::v_combine_high(a, b); +@endcode */ +template +inline void v_recombine(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b, + v_reg<_Tp, n>& low, v_reg<_Tp, n>& high) +{ + for( int i = 0; i < (n/2); i++ ) + { + low.s[i] = a.s[i]; + low.s[i+(n/2)] = b.s[i]; + high.s[i] = a.s[i+(n/2)]; + high.s[i+(n/2)] = b.s[i+(n/2)]; + } +} + +/** @brief Vector extract + +Scheme: +@code + {A1 A2 A3 A4} + {B1 B2 B3 B4} +======================== +shift = 1 {A2 A3 A4 B1} +shift = 2 {A3 A4 B1 B2} +shift = 3 {A4 B1 B2 B3} +@endcode +Restriction: 0 <= shift < nlanes + +Usage: +@code +v_int32x4 a, b, c; +c = v_extract<2>(a, b); +@endcode +For all types. */ +template +inline v_reg<_Tp, n> v_extract(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) +{ + v_reg<_Tp, n> r; + const int shift = n - s; + int i = 0; + for (; i < shift; ++i) + r.s[i] = a.s[i+s]; + for (; i < n; ++i) + r.s[i] = b.s[i-shift]; + return r; +} + +/** @brief Round + +Rounds each value. Input type is float vector ==> output type is int vector.*/ +template inline v_reg v_round(const v_reg& a) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + c.s[i] = cvRound(a.s[i]); + return c; +} + +/** @overload */ +template inline v_reg v_round(const v_reg& a, const v_reg& b) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + { + c.s[i] = cvRound(a.s[i]); + c.s[i+n] = cvRound(b.s[i]); + } + return c; +} + +/** @brief Floor + +Floor each value. Input type is float vector ==> output type is int vector.*/ +template inline v_reg v_floor(const v_reg& a) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + c.s[i] = cvFloor(a.s[i]); + return c; +} + +/** @brief Ceil + +Ceil each value. Input type is float vector ==> output type is int vector.*/ +template inline v_reg v_ceil(const v_reg& a) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + c.s[i] = cvCeil(a.s[i]); + return c; +} + +/** @brief Trunc + +Truncate each value. Input type is float vector ==> output type is int vector.*/ +template inline v_reg v_trunc(const v_reg& a) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + c.s[i] = (int)(a.s[i]); + return c; +} + +/** @overload */ +template inline v_reg v_round(const v_reg& a) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + { + c.s[i] = cvRound(a.s[i]); + c.s[i+n] = 0; + } + return c; +} + +/** @overload */ +template inline v_reg v_floor(const v_reg& a) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + { + c.s[i] = cvFloor(a.s[i]); + c.s[i+n] = 0; + } + return c; +} + +/** @overload */ +template inline v_reg v_ceil(const v_reg& a) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + { + c.s[i] = cvCeil(a.s[i]); + c.s[i+n] = 0; + } + return c; +} + +/** @overload */ +template inline v_reg v_trunc(const v_reg& a) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + { + c.s[i] = cvCeil(a.s[i]); + c.s[i+n] = 0; + } + return c; +} + +/** @brief Convert to float + +Supported input type is cv::v_int32x4. */ +template inline v_reg v_cvt_f32(const v_reg& a) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + c.s[i] = (float)a.s[i]; + return c; +} + +template inline v_reg v_cvt_f32(const v_reg& a, const v_reg& b) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + { + c.s[i] = (float)a.s[i]; + c.s[i+n] = (float)b.s[i]; + } + return c; +} + +/** @brief Convert to double + +Supported input type is cv::v_int32x4. */ +template inline v_reg v_cvt_f64(const v_reg& a) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + c.s[i] = (double)a.s[i]; + return c; +} + +/** @brief Convert to double + +Supported input type is cv::v_float32x4. */ +template inline v_reg v_cvt_f64(const v_reg& a) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + c.s[i] = (double)a.s[i]; + return c; +} + +template inline v_reg v_lut(const int* tab, const v_reg& idx) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + c.s[i] = tab[idx.s[i]]; + return c; +} + +template inline v_reg v_lut(const float* tab, const v_reg& idx) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + c.s[i] = tab[idx.s[i]]; + return c; +} + +template inline v_reg v_lut(const double* tab, const v_reg& idx) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + c.s[i] = tab[idx.s[i]]; + return c; +} + +template inline void v_lut_deinterleave(const float* tab, const v_reg& idx, + v_reg& x, v_reg& y) +{ + for( int i = 0; i < n; i++ ) + { + int j = idx.s[i]; + x.s[i] = tab[j]; + y.s[i] = tab[j+1]; + } +} + +template inline void v_lut_deinterleave(const double* tab, const v_reg& idx, + v_reg& x, v_reg& y) +{ + for( int i = 0; i < n; i++ ) + { + int j = idx.s[i]; + x.s[i] = tab[j]; + y.s[i] = tab[j+1]; + } +} + +/** @brief Transpose 4x4 matrix + +Scheme: +@code +a0 {A1 A2 A3 A4} +a1 {B1 B2 B3 B4} +a2 {C1 C2 C3 C4} +a3 {D1 D2 D3 D4} +=============== +b0 {A1 B1 C1 D1} +b1 {A2 B2 C2 D2} +b2 {A3 B3 C3 D3} +b3 {A4 B4 C4 D4} +@endcode +*/ +template +inline void v_transpose4x4( v_reg<_Tp, 4>& a0, const v_reg<_Tp, 4>& a1, + const v_reg<_Tp, 4>& a2, const v_reg<_Tp, 4>& a3, + v_reg<_Tp, 4>& b0, v_reg<_Tp, 4>& b1, + v_reg<_Tp, 4>& b2, v_reg<_Tp, 4>& b3 ) +{ + b0 = v_reg<_Tp, 4>(a0.s[0], a1.s[0], a2.s[0], a3.s[0]); + b1 = v_reg<_Tp, 4>(a0.s[1], a1.s[1], a2.s[1], a3.s[1]); + b2 = v_reg<_Tp, 4>(a0.s[2], a1.s[2], a2.s[2], a3.s[2]); + b3 = v_reg<_Tp, 4>(a0.s[3], a1.s[3], a2.s[3], a3.s[3]); +} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_C_INIT_ZERO(_Tpvec, _Tp, suffix) \ +inline _Tpvec v_setzero_##suffix() { return _Tpvec::zero(); } + +//! @name Init with zero +//! @{ +//! @brief Create new vector with zero elements +OPENCV_HAL_IMPL_C_INIT_ZERO(v_uint8x16, uchar, u8) +OPENCV_HAL_IMPL_C_INIT_ZERO(v_int8x16, schar, s8) +OPENCV_HAL_IMPL_C_INIT_ZERO(v_uint16x8, ushort, u16) +OPENCV_HAL_IMPL_C_INIT_ZERO(v_int16x8, short, s16) +OPENCV_HAL_IMPL_C_INIT_ZERO(v_uint32x4, unsigned, u32) +OPENCV_HAL_IMPL_C_INIT_ZERO(v_int32x4, int, s32) +OPENCV_HAL_IMPL_C_INIT_ZERO(v_float32x4, float, f32) +OPENCV_HAL_IMPL_C_INIT_ZERO(v_float64x2, double, f64) +OPENCV_HAL_IMPL_C_INIT_ZERO(v_uint64x2, uint64, u64) +OPENCV_HAL_IMPL_C_INIT_ZERO(v_int64x2, int64, s64) +//! @} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_C_INIT_VAL(_Tpvec, _Tp, suffix) \ +inline _Tpvec v_setall_##suffix(_Tp val) { return _Tpvec::all(val); } + +//! @name Init with value +//! @{ +//! @brief Create new vector with elements set to a specific value +OPENCV_HAL_IMPL_C_INIT_VAL(v_uint8x16, uchar, u8) +OPENCV_HAL_IMPL_C_INIT_VAL(v_int8x16, schar, s8) +OPENCV_HAL_IMPL_C_INIT_VAL(v_uint16x8, ushort, u16) +OPENCV_HAL_IMPL_C_INIT_VAL(v_int16x8, short, s16) +OPENCV_HAL_IMPL_C_INIT_VAL(v_uint32x4, unsigned, u32) +OPENCV_HAL_IMPL_C_INIT_VAL(v_int32x4, int, s32) +OPENCV_HAL_IMPL_C_INIT_VAL(v_float32x4, float, f32) +OPENCV_HAL_IMPL_C_INIT_VAL(v_float64x2, double, f64) +OPENCV_HAL_IMPL_C_INIT_VAL(v_uint64x2, uint64, u64) +OPENCV_HAL_IMPL_C_INIT_VAL(v_int64x2, int64, s64) +//! @} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_C_REINTERPRET(_Tpvec, _Tp, suffix) \ +template inline _Tpvec \ + v_reinterpret_as_##suffix(const v_reg<_Tp0, n0>& a) \ +{ return a.template reinterpret_as<_Tp, _Tpvec::nlanes>(); } + +//! @name Reinterpret +//! @{ +//! @brief Convert vector to different type without modifying underlying data. +OPENCV_HAL_IMPL_C_REINTERPRET(v_uint8x16, uchar, u8) +OPENCV_HAL_IMPL_C_REINTERPRET(v_int8x16, schar, s8) +OPENCV_HAL_IMPL_C_REINTERPRET(v_uint16x8, ushort, u16) +OPENCV_HAL_IMPL_C_REINTERPRET(v_int16x8, short, s16) +OPENCV_HAL_IMPL_C_REINTERPRET(v_uint32x4, unsigned, u32) +OPENCV_HAL_IMPL_C_REINTERPRET(v_int32x4, int, s32) +OPENCV_HAL_IMPL_C_REINTERPRET(v_float32x4, float, f32) +OPENCV_HAL_IMPL_C_REINTERPRET(v_float64x2, double, f64) +OPENCV_HAL_IMPL_C_REINTERPRET(v_uint64x2, uint64, u64) +OPENCV_HAL_IMPL_C_REINTERPRET(v_int64x2, int64, s64) +//! @} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_C_SHIFTL(_Tpvec, _Tp) \ +template inline _Tpvec v_shl(const _Tpvec& a) \ +{ return a << n; } + +//! @name Left shift +//! @{ +//! @brief Shift left +OPENCV_HAL_IMPL_C_SHIFTL(v_uint16x8, ushort) +OPENCV_HAL_IMPL_C_SHIFTL(v_int16x8, short) +OPENCV_HAL_IMPL_C_SHIFTL(v_uint32x4, unsigned) +OPENCV_HAL_IMPL_C_SHIFTL(v_int32x4, int) +OPENCV_HAL_IMPL_C_SHIFTL(v_uint64x2, uint64) +OPENCV_HAL_IMPL_C_SHIFTL(v_int64x2, int64) +//! @} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_C_SHIFTR(_Tpvec, _Tp) \ +template inline _Tpvec v_shr(const _Tpvec& a) \ +{ return a >> n; } + +//! @name Right shift +//! @{ +//! @brief Shift right +OPENCV_HAL_IMPL_C_SHIFTR(v_uint16x8, ushort) +OPENCV_HAL_IMPL_C_SHIFTR(v_int16x8, short) +OPENCV_HAL_IMPL_C_SHIFTR(v_uint32x4, unsigned) +OPENCV_HAL_IMPL_C_SHIFTR(v_int32x4, int) +OPENCV_HAL_IMPL_C_SHIFTR(v_uint64x2, uint64) +OPENCV_HAL_IMPL_C_SHIFTR(v_int64x2, int64) +//! @} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_C_RSHIFTR(_Tpvec, _Tp) \ +template inline _Tpvec v_rshr(const _Tpvec& a) \ +{ \ + _Tpvec c; \ + for( int i = 0; i < _Tpvec::nlanes; i++ ) \ + c.s[i] = (_Tp)((a.s[i] + ((_Tp)1 << (n - 1))) >> n); \ + return c; \ +} + +//! @name Rounding shift +//! @{ +//! @brief Rounding shift right +OPENCV_HAL_IMPL_C_RSHIFTR(v_uint16x8, ushort) +OPENCV_HAL_IMPL_C_RSHIFTR(v_int16x8, short) +OPENCV_HAL_IMPL_C_RSHIFTR(v_uint32x4, unsigned) +OPENCV_HAL_IMPL_C_RSHIFTR(v_int32x4, int) +OPENCV_HAL_IMPL_C_RSHIFTR(v_uint64x2, uint64) +OPENCV_HAL_IMPL_C_RSHIFTR(v_int64x2, int64) +//! @} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_C_PACK(_Tpvec, _Tpnvec, _Tpn, pack_suffix, cast) \ +inline _Tpnvec v_##pack_suffix(const _Tpvec& a, const _Tpvec& b) \ +{ \ + _Tpnvec c; \ + for( int i = 0; i < _Tpvec::nlanes; i++ ) \ + { \ + c.s[i] = cast<_Tpn>(a.s[i]); \ + c.s[i+_Tpvec::nlanes] = cast<_Tpn>(b.s[i]); \ + } \ + return c; \ +} + +//! @name Pack +//! @{ +//! @brief Pack values from two vectors to one +//! +//! Return vector type have twice more elements than input vector types. Variant with _u_ suffix also +//! converts to corresponding unsigned type. +//! +//! - pack: for 16-, 32- and 64-bit integer input types +//! - pack_u: for 16- and 32-bit signed integer input types +//! +//! @note All variants except 64-bit use saturation. +OPENCV_HAL_IMPL_C_PACK(v_uint16x8, v_uint8x16, uchar, pack, saturate_cast) +OPENCV_HAL_IMPL_C_PACK(v_int16x8, v_int8x16, schar, pack, saturate_cast) +OPENCV_HAL_IMPL_C_PACK(v_uint32x4, v_uint16x8, ushort, pack, saturate_cast) +OPENCV_HAL_IMPL_C_PACK(v_int32x4, v_int16x8, short, pack, saturate_cast) +OPENCV_HAL_IMPL_C_PACK(v_uint64x2, v_uint32x4, unsigned, pack, static_cast) +OPENCV_HAL_IMPL_C_PACK(v_int64x2, v_int32x4, int, pack, static_cast) +OPENCV_HAL_IMPL_C_PACK(v_int16x8, v_uint8x16, uchar, pack_u, saturate_cast) +OPENCV_HAL_IMPL_C_PACK(v_int32x4, v_uint16x8, ushort, pack_u, saturate_cast) +//! @} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_C_RSHR_PACK(_Tpvec, _Tp, _Tpnvec, _Tpn, pack_suffix, cast) \ +template inline _Tpnvec v_rshr_##pack_suffix(const _Tpvec& a, const _Tpvec& b) \ +{ \ + _Tpnvec c; \ + for( int i = 0; i < _Tpvec::nlanes; i++ ) \ + { \ + c.s[i] = cast<_Tpn>((a.s[i] + ((_Tp)1 << (n - 1))) >> n); \ + c.s[i+_Tpvec::nlanes] = cast<_Tpn>((b.s[i] + ((_Tp)1 << (n - 1))) >> n); \ + } \ + return c; \ +} + +//! @name Pack with rounding shift +//! @{ +//! @brief Pack values from two vectors to one with rounding shift +//! +//! Values from the input vectors will be shifted right by _n_ bits with rounding, converted to narrower +//! type and returned in the result vector. Variant with _u_ suffix converts to unsigned type. +//! +//! - pack: for 16-, 32- and 64-bit integer input types +//! - pack_u: for 16- and 32-bit signed integer input types +//! +//! @note All variants except 64-bit use saturation. +OPENCV_HAL_IMPL_C_RSHR_PACK(v_uint16x8, ushort, v_uint8x16, uchar, pack, saturate_cast) +OPENCV_HAL_IMPL_C_RSHR_PACK(v_int16x8, short, v_int8x16, schar, pack, saturate_cast) +OPENCV_HAL_IMPL_C_RSHR_PACK(v_uint32x4, unsigned, v_uint16x8, ushort, pack, saturate_cast) +OPENCV_HAL_IMPL_C_RSHR_PACK(v_int32x4, int, v_int16x8, short, pack, saturate_cast) +OPENCV_HAL_IMPL_C_RSHR_PACK(v_uint64x2, uint64, v_uint32x4, unsigned, pack, static_cast) +OPENCV_HAL_IMPL_C_RSHR_PACK(v_int64x2, int64, v_int32x4, int, pack, static_cast) +OPENCV_HAL_IMPL_C_RSHR_PACK(v_int16x8, short, v_uint8x16, uchar, pack_u, saturate_cast) +OPENCV_HAL_IMPL_C_RSHR_PACK(v_int32x4, int, v_uint16x8, ushort, pack_u, saturate_cast) +//! @} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_C_PACK_STORE(_Tpvec, _Tp, _Tpnvec, _Tpn, pack_suffix, cast) \ +inline void v_##pack_suffix##_store(_Tpn* ptr, const _Tpvec& a) \ +{ \ + for( int i = 0; i < _Tpvec::nlanes; i++ ) \ + ptr[i] = cast<_Tpn>(a.s[i]); \ +} + +//! @name Pack and store +//! @{ +//! @brief Store values from the input vector into memory with pack +//! +//! Values will be stored into memory with conversion to narrower type. +//! Variant with _u_ suffix converts to corresponding unsigned type. +//! +//! - pack: for 16-, 32- and 64-bit integer input types +//! - pack_u: for 16- and 32-bit signed integer input types +//! +//! @note All variants except 64-bit use saturation. +OPENCV_HAL_IMPL_C_PACK_STORE(v_uint16x8, ushort, v_uint8x16, uchar, pack, saturate_cast) +OPENCV_HAL_IMPL_C_PACK_STORE(v_int16x8, short, v_int8x16, schar, pack, saturate_cast) +OPENCV_HAL_IMPL_C_PACK_STORE(v_uint32x4, unsigned, v_uint16x8, ushort, pack, saturate_cast) +OPENCV_HAL_IMPL_C_PACK_STORE(v_int32x4, int, v_int16x8, short, pack, saturate_cast) +OPENCV_HAL_IMPL_C_PACK_STORE(v_uint64x2, uint64, v_uint32x4, unsigned, pack, static_cast) +OPENCV_HAL_IMPL_C_PACK_STORE(v_int64x2, int64, v_int32x4, int, pack, static_cast) +OPENCV_HAL_IMPL_C_PACK_STORE(v_int16x8, short, v_uint8x16, uchar, pack_u, saturate_cast) +OPENCV_HAL_IMPL_C_PACK_STORE(v_int32x4, int, v_uint16x8, ushort, pack_u, saturate_cast) +//! @} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(_Tpvec, _Tp, _Tpnvec, _Tpn, pack_suffix, cast) \ +template inline void v_rshr_##pack_suffix##_store(_Tpn* ptr, const _Tpvec& a) \ +{ \ + for( int i = 0; i < _Tpvec::nlanes; i++ ) \ + ptr[i] = cast<_Tpn>((a.s[i] + ((_Tp)1 << (n - 1))) >> n); \ +} + +//! @name Pack and store with rounding shift +//! @{ +//! @brief Store values from the input vector into memory with pack +//! +//! Values will be shifted _n_ bits right with rounding, converted to narrower type and stored into +//! memory. Variant with _u_ suffix converts to unsigned type. +//! +//! - pack: for 16-, 32- and 64-bit integer input types +//! - pack_u: for 16- and 32-bit signed integer input types +//! +//! @note All variants except 64-bit use saturation. +OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_uint16x8, ushort, v_uint8x16, uchar, pack, saturate_cast) +OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_int16x8, short, v_int8x16, schar, pack, saturate_cast) +OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_uint32x4, unsigned, v_uint16x8, ushort, pack, saturate_cast) +OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_int32x4, int, v_int16x8, short, pack, saturate_cast) +OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_uint64x2, uint64, v_uint32x4, unsigned, pack, static_cast) +OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_int64x2, int64, v_int32x4, int, pack, static_cast) +OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_int16x8, short, v_uint8x16, uchar, pack_u, saturate_cast) +OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_int32x4, int, v_uint16x8, ushort, pack_u, saturate_cast) +//! @} + +//! @cond IGNORED +template +inline void _pack_b(_Tpm* mptr, const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) +{ + for (int i = 0; i < n; ++i) + { + mptr[i] = (_Tpm)a.s[i]; + mptr[i + n] = (_Tpm)b.s[i]; + } +} +//! @endcond + +//! @name Pack boolean values +//! @{ +//! @brief Pack boolean values from multiple vectors to one unsigned 8-bit integer vector +//! +//! @note Must provide valid boolean values to guarantee same result for all architectures. + +/** @brief +//! For 16-bit boolean values + +Scheme: +@code +a {0xFFFF 0 0 0xFFFF 0 0xFFFF 0xFFFF 0} +b {0xFFFF 0 0xFFFF 0 0 0xFFFF 0 0xFFFF} +=============== +{ + 0xFF 0 0 0xFF 0 0xFF 0xFF 0 + 0xFF 0 0xFF 0 0 0xFF 0 0xFF +} +@endcode */ + +inline v_uint8x16 v_pack_b(const v_uint16x8& a, const v_uint16x8& b) +{ + v_uint8x16 mask; + _pack_b(mask.s, a, b); + return mask; +} + +/** @overload +For 32-bit boolean values + +Scheme: +@code +a {0xFFFF.. 0 0 0xFFFF..} +b {0 0xFFFF.. 0xFFFF.. 0} +c {0xFFFF.. 0 0xFFFF.. 0} +d {0 0xFFFF.. 0 0xFFFF..} +=============== +{ + 0xFF 0 0 0xFF 0 0xFF 0xFF 0 + 0xFF 0 0xFF 0 0 0xFF 0 0xFF +} +@endcode */ + +inline v_uint8x16 v_pack_b(const v_uint32x4& a, const v_uint32x4& b, + const v_uint32x4& c, const v_uint32x4& d) +{ + v_uint8x16 mask; + _pack_b(mask.s, a, b); + _pack_b(mask.s + 8, c, d); + return mask; +} + +/** @overload +For 64-bit boolean values + +Scheme: +@code +a {0xFFFF.. 0} +b {0 0xFFFF..} +c {0xFFFF.. 0} +d {0 0xFFFF..} + +e {0xFFFF.. 0} +f {0xFFFF.. 0} +g {0 0xFFFF..} +h {0 0xFFFF..} +=============== +{ + 0xFF 0 0 0xFF 0xFF 0 0 0xFF + 0xFF 0 0xFF 0 0 0xFF 0 0xFF +} +@endcode */ +inline v_uint8x16 v_pack_b(const v_uint64x2& a, const v_uint64x2& b, const v_uint64x2& c, + const v_uint64x2& d, const v_uint64x2& e, const v_uint64x2& f, + const v_uint64x2& g, const v_uint64x2& h) +{ + v_uint8x16 mask; + _pack_b(mask.s, a, b); + _pack_b(mask.s + 4, c, d); + _pack_b(mask.s + 8, e, f); + _pack_b(mask.s + 12, g, h); + return mask; +} +//! @} + +/** @brief Matrix multiplication + +Scheme: +@code +{A0 A1 A2 A3} |V0| +{B0 B1 B2 B3} |V1| +{C0 C1 C2 C3} |V2| +{D0 D1 D2 D3} x |V3| +==================== +{R0 R1 R2 R3}, where: +R0 = A0V0 + A1V1 + A2V2 + A3V3, +R1 = B0V0 + B1V1 + B2V2 + B3V3 +... +@endcode +*/ +inline v_float32x4 v_matmul(const v_float32x4& v, const v_float32x4& m0, + const v_float32x4& m1, const v_float32x4& m2, + const v_float32x4& m3) +{ + return v_float32x4(v.s[0]*m0.s[0] + v.s[1]*m1.s[0] + v.s[2]*m2.s[0] + v.s[3]*m3.s[0], + v.s[0]*m0.s[1] + v.s[1]*m1.s[1] + v.s[2]*m2.s[1] + v.s[3]*m3.s[1], + v.s[0]*m0.s[2] + v.s[1]*m1.s[2] + v.s[2]*m2.s[2] + v.s[3]*m3.s[2], + v.s[0]*m0.s[3] + v.s[1]*m1.s[3] + v.s[2]*m2.s[3] + v.s[3]*m3.s[3]); +} + +/** @brief Matrix multiplication and add + +Scheme: +@code +{A0 A1 A2 } |V0| |D0| +{B0 B1 B2 } |V1| |D1| +{C0 C1 C2 } x |V2| + |D2| +==================== +{R0 R1 R2 R3}, where: +R0 = A0V0 + A1V1 + A2V2 + D0, +R1 = B0V0 + B1V1 + B2V2 + D1 +... +@endcode +*/ +inline v_float32x4 v_matmuladd(const v_float32x4& v, const v_float32x4& m0, + const v_float32x4& m1, const v_float32x4& m2, + const v_float32x4& m3) +{ + return v_float32x4(v.s[0]*m0.s[0] + v.s[1]*m1.s[0] + v.s[2]*m2.s[0] + m3.s[0], + v.s[0]*m0.s[1] + v.s[1]*m1.s[1] + v.s[2]*m2.s[1] + m3.s[1], + v.s[0]*m0.s[2] + v.s[1]*m1.s[2] + v.s[2]*m2.s[2] + m3.s[2], + v.s[0]*m0.s[3] + v.s[1]*m1.s[3] + v.s[2]*m2.s[3] + m3.s[3]); +} + +////// FP16 suport /////// + +inline v_reg::nlanes128> +v_load_expand(const float16_t* ptr) +{ + v_reg::nlanes128> v; + for( int i = 0; i < v.nlanes; i++ ) + { + v.s[i] = ptr[i]; + } + return v; +} + +inline void +v_pack_store(float16_t* ptr, v_reg::nlanes128>& v) +{ + for( int i = 0; i < v.nlanes; i++ ) + { + ptr[i] = float16_t(v.s[i]); + } +} + +inline void v_cleanup() {} + +//! @} + +//! @name Check SIMD support +//! @{ +//! @brief Check CPU capability of SIMD operation +static inline bool hasSIMD128() +{ + return false; +} + +//! @} + +#ifndef CV_DOXYGEN +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END +#endif +} + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/hal/intrin_forward.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/hal/intrin_forward.hpp new file mode 100644 index 0000000000000000000000000000000000000000..461855290743174351750ad30c0f3d3a3803129a --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/hal/intrin_forward.hpp @@ -0,0 +1,158 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html + +#ifndef CV__SIMD_FORWARD +#error "Need to pre-define forward width" +#endif + +namespace cv +{ + +//! @cond IGNORED + +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN + +/** Types **/ +#if CV__SIMD_FORWARD == 512 +// [todo] 512 +#error "AVX512 Not implemented yet" +#elif CV__SIMD_FORWARD == 256 +// 256 +#define __CV_VX(fun) v256_##fun +#define __CV_V_UINT8 v_uint8x32 +#define __CV_V_INT8 v_int8x32 +#define __CV_V_UINT16 v_uint16x16 +#define __CV_V_INT16 v_int16x16 +#define __CV_V_UINT32 v_uint32x8 +#define __CV_V_INT32 v_int32x8 +#define __CV_V_UINT64 v_uint64x4 +#define __CV_V_INT64 v_int64x4 +#define __CV_V_FLOAT32 v_float32x8 +#define __CV_V_FLOAT64 v_float64x4 +struct v_uint8x32; +struct v_int8x32; +struct v_uint16x16; +struct v_int16x16; +struct v_uint32x8; +struct v_int32x8; +struct v_uint64x4; +struct v_int64x4; +struct v_float32x8; +struct v_float64x4; +#else +// 128 +#define __CV_VX(fun) v_##fun +#define __CV_V_UINT8 v_uint8x16 +#define __CV_V_INT8 v_int8x16 +#define __CV_V_UINT16 v_uint16x8 +#define __CV_V_INT16 v_int16x8 +#define __CV_V_UINT32 v_uint32x4 +#define __CV_V_INT32 v_int32x4 +#define __CV_V_UINT64 v_uint64x2 +#define __CV_V_INT64 v_int64x2 +#define __CV_V_FLOAT32 v_float32x4 +#define __CV_V_FLOAT64 v_float64x2 +struct v_uint8x16; +struct v_int8x16; +struct v_uint16x8; +struct v_int16x8; +struct v_uint32x4; +struct v_int32x4; +struct v_uint64x2; +struct v_int64x2; +struct v_float32x4; +struct v_float64x2; +#endif + +/** Value reordering **/ + +// Expansion +void v_expand(const __CV_V_UINT8&, __CV_V_UINT16&, __CV_V_UINT16&); +void v_expand(const __CV_V_INT8&, __CV_V_INT16&, __CV_V_INT16&); +void v_expand(const __CV_V_UINT16&, __CV_V_UINT32&, __CV_V_UINT32&); +void v_expand(const __CV_V_INT16&, __CV_V_INT32&, __CV_V_INT32&); +void v_expand(const __CV_V_UINT32&, __CV_V_UINT64&, __CV_V_UINT64&); +void v_expand(const __CV_V_INT32&, __CV_V_INT64&, __CV_V_INT64&); +// Low Expansion +__CV_V_UINT16 v_expand_low(const __CV_V_UINT8&); +__CV_V_INT16 v_expand_low(const __CV_V_INT8&); +__CV_V_UINT32 v_expand_low(const __CV_V_UINT16&); +__CV_V_INT32 v_expand_low(const __CV_V_INT16&); +__CV_V_UINT64 v_expand_low(const __CV_V_UINT32&); +__CV_V_INT64 v_expand_low(const __CV_V_INT32&); +// High Expansion +__CV_V_UINT16 v_expand_high(const __CV_V_UINT8&); +__CV_V_INT16 v_expand_high(const __CV_V_INT8&); +__CV_V_UINT32 v_expand_high(const __CV_V_UINT16&); +__CV_V_INT32 v_expand_high(const __CV_V_INT16&); +__CV_V_UINT64 v_expand_high(const __CV_V_UINT32&); +__CV_V_INT64 v_expand_high(const __CV_V_INT32&); +// Load & Low Expansion +__CV_V_UINT16 __CV_VX(load_expand)(const uchar*); +__CV_V_INT16 __CV_VX(load_expand)(const schar*); +__CV_V_UINT32 __CV_VX(load_expand)(const ushort*); +__CV_V_INT32 __CV_VX(load_expand)(const short*); +__CV_V_UINT64 __CV_VX(load_expand)(const uint*); +__CV_V_INT64 __CV_VX(load_expand)(const int*); +// Load lower 8-bit and expand into 32-bit +__CV_V_UINT32 __CV_VX(load_expand_q)(const uchar*); +__CV_V_INT32 __CV_VX(load_expand_q)(const schar*); + +// Saturating Pack +__CV_V_UINT8 v_pack(const __CV_V_UINT16&, const __CV_V_UINT16&); +__CV_V_INT8 v_pack(const __CV_V_INT16&, const __CV_V_INT16&); +__CV_V_UINT16 v_pack(const __CV_V_UINT32&, const __CV_V_UINT32&); +__CV_V_INT16 v_pack(const __CV_V_INT32&, const __CV_V_INT32&); +// Non-saturating Pack +__CV_V_UINT32 v_pack(const __CV_V_UINT64&, const __CV_V_UINT64&); +__CV_V_INT32 v_pack(const __CV_V_INT64&, const __CV_V_INT64&); +// Pack signed integers with unsigned saturation +__CV_V_UINT8 v_pack_u(const __CV_V_INT16&, const __CV_V_INT16&); +__CV_V_UINT16 v_pack_u(const __CV_V_INT32&, const __CV_V_INT32&); + +/** Arithmetic, bitwise and comparison operations **/ + +// Non-saturating multiply +#if CV_VSX +template +Tvec v_mul_wrap(const Tvec& a, const Tvec& b); +#else +__CV_V_UINT8 v_mul_wrap(const __CV_V_UINT8&, const __CV_V_UINT8&); +__CV_V_INT8 v_mul_wrap(const __CV_V_INT8&, const __CV_V_INT8&); +__CV_V_UINT16 v_mul_wrap(const __CV_V_UINT16&, const __CV_V_UINT16&); +__CV_V_INT16 v_mul_wrap(const __CV_V_INT16&, const __CV_V_INT16&); +#endif + +// Multiply and expand +#if CV_VSX +template +void v_mul_expand(const Tvec& a, const Tvec& b, Twvec& c, Twvec& d); +#else +void v_mul_expand(const __CV_V_UINT8&, const __CV_V_UINT8&, __CV_V_UINT16&, __CV_V_UINT16&); +void v_mul_expand(const __CV_V_INT8&, const __CV_V_INT8&, __CV_V_INT16&, __CV_V_INT16&); +void v_mul_expand(const __CV_V_UINT16&, const __CV_V_UINT16&, __CV_V_UINT32&, __CV_V_UINT32&); +void v_mul_expand(const __CV_V_INT16&, const __CV_V_INT16&, __CV_V_INT32&, __CV_V_INT32&); +void v_mul_expand(const __CV_V_UINT32&, const __CV_V_UINT32&, __CV_V_UINT64&, __CV_V_UINT64&); +void v_mul_expand(const __CV_V_INT32&, const __CV_V_INT32&, __CV_V_INT64&, __CV_V_INT64&); +#endif + +/** Cleanup **/ +#undef CV__SIMD_FORWARD +#undef __CV_VX +#undef __CV_V_UINT8 +#undef __CV_V_INT8 +#undef __CV_V_UINT16 +#undef __CV_V_INT16 +#undef __CV_V_UINT32 +#undef __CV_V_INT32 +#undef __CV_V_UINT64 +#undef __CV_V_INT64 +#undef __CV_V_FLOAT32 +#undef __CV_V_FLOAT64 + +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END + +//! @endcond + +} // cv:: \ No newline at end of file diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/hal/intrin_neon.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/hal/intrin_neon.hpp new file mode 100644 index 0000000000000000000000000000000000000000..608dc97a87d806a133ad5fa851577b98b5071968 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/hal/intrin_neon.hpp @@ -0,0 +1,1697 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_HAL_INTRIN_NEON_HPP +#define OPENCV_HAL_INTRIN_NEON_HPP + +#include +#include "opencv2/core/utility.hpp" + +namespace cv +{ + +//! @cond IGNORED + +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN + +#define CV_SIMD128 1 +#if defined(__aarch64__) +#define CV_SIMD128_64F 1 +#else +#define CV_SIMD128_64F 0 +#endif + +#if CV_SIMD128_64F +#define OPENCV_HAL_IMPL_NEON_REINTERPRET(_Tpv, suffix) \ +template static inline \ +_Tpv vreinterpretq_##suffix##_f64(T a) { return (_Tpv) a; } \ +template static inline \ +float64x2_t vreinterpretq_f64_##suffix(T a) { return (float64x2_t) a; } +OPENCV_HAL_IMPL_NEON_REINTERPRET(uint8x16_t, u8) +OPENCV_HAL_IMPL_NEON_REINTERPRET(int8x16_t, s8) +OPENCV_HAL_IMPL_NEON_REINTERPRET(uint16x8_t, u16) +OPENCV_HAL_IMPL_NEON_REINTERPRET(int16x8_t, s16) +OPENCV_HAL_IMPL_NEON_REINTERPRET(uint32x4_t, u32) +OPENCV_HAL_IMPL_NEON_REINTERPRET(int32x4_t, s32) +OPENCV_HAL_IMPL_NEON_REINTERPRET(uint64x2_t, u64) +OPENCV_HAL_IMPL_NEON_REINTERPRET(int64x2_t, s64) +OPENCV_HAL_IMPL_NEON_REINTERPRET(float32x4_t, f32) +#endif + +struct v_uint8x16 +{ + typedef uchar lane_type; + enum { nlanes = 16 }; + + v_uint8x16() {} + explicit v_uint8x16(uint8x16_t v) : val(v) {} + v_uint8x16(uchar v0, uchar v1, uchar v2, uchar v3, uchar v4, uchar v5, uchar v6, uchar v7, + uchar v8, uchar v9, uchar v10, uchar v11, uchar v12, uchar v13, uchar v14, uchar v15) + { + uchar v[] = {v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15}; + val = vld1q_u8(v); + } + uchar get0() const + { + return vgetq_lane_u8(val, 0); + } + + uint8x16_t val; +}; + +struct v_int8x16 +{ + typedef schar lane_type; + enum { nlanes = 16 }; + + v_int8x16() {} + explicit v_int8x16(int8x16_t v) : val(v) {} + v_int8x16(schar v0, schar v1, schar v2, schar v3, schar v4, schar v5, schar v6, schar v7, + schar v8, schar v9, schar v10, schar v11, schar v12, schar v13, schar v14, schar v15) + { + schar v[] = {v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15}; + val = vld1q_s8(v); + } + schar get0() const + { + return vgetq_lane_s8(val, 0); + } + + int8x16_t val; +}; + +struct v_uint16x8 +{ + typedef ushort lane_type; + enum { nlanes = 8 }; + + v_uint16x8() {} + explicit v_uint16x8(uint16x8_t v) : val(v) {} + v_uint16x8(ushort v0, ushort v1, ushort v2, ushort v3, ushort v4, ushort v5, ushort v6, ushort v7) + { + ushort v[] = {v0, v1, v2, v3, v4, v5, v6, v7}; + val = vld1q_u16(v); + } + ushort get0() const + { + return vgetq_lane_u16(val, 0); + } + + uint16x8_t val; +}; + +struct v_int16x8 +{ + typedef short lane_type; + enum { nlanes = 8 }; + + v_int16x8() {} + explicit v_int16x8(int16x8_t v) : val(v) {} + v_int16x8(short v0, short v1, short v2, short v3, short v4, short v5, short v6, short v7) + { + short v[] = {v0, v1, v2, v3, v4, v5, v6, v7}; + val = vld1q_s16(v); + } + short get0() const + { + return vgetq_lane_s16(val, 0); + } + + int16x8_t val; +}; + +struct v_uint32x4 +{ + typedef unsigned lane_type; + enum { nlanes = 4 }; + + v_uint32x4() {} + explicit v_uint32x4(uint32x4_t v) : val(v) {} + v_uint32x4(unsigned v0, unsigned v1, unsigned v2, unsigned v3) + { + unsigned v[] = {v0, v1, v2, v3}; + val = vld1q_u32(v); + } + unsigned get0() const + { + return vgetq_lane_u32(val, 0); + } + + uint32x4_t val; +}; + +struct v_int32x4 +{ + typedef int lane_type; + enum { nlanes = 4 }; + + v_int32x4() {} + explicit v_int32x4(int32x4_t v) : val(v) {} + v_int32x4(int v0, int v1, int v2, int v3) + { + int v[] = {v0, v1, v2, v3}; + val = vld1q_s32(v); + } + int get0() const + { + return vgetq_lane_s32(val, 0); + } + int32x4_t val; +}; + +struct v_float32x4 +{ + typedef float lane_type; + enum { nlanes = 4 }; + + v_float32x4() {} + explicit v_float32x4(float32x4_t v) : val(v) {} + v_float32x4(float v0, float v1, float v2, float v3) + { + float v[] = {v0, v1, v2, v3}; + val = vld1q_f32(v); + } + float get0() const + { + return vgetq_lane_f32(val, 0); + } + float32x4_t val; +}; + +struct v_uint64x2 +{ + typedef uint64 lane_type; + enum { nlanes = 2 }; + + v_uint64x2() {} + explicit v_uint64x2(uint64x2_t v) : val(v) {} + v_uint64x2(uint64 v0, uint64 v1) + { + uint64 v[] = {v0, v1}; + val = vld1q_u64(v); + } + uint64 get0() const + { + return vgetq_lane_u64(val, 0); + } + uint64x2_t val; +}; + +struct v_int64x2 +{ + typedef int64 lane_type; + enum { nlanes = 2 }; + + v_int64x2() {} + explicit v_int64x2(int64x2_t v) : val(v) {} + v_int64x2(int64 v0, int64 v1) + { + int64 v[] = {v0, v1}; + val = vld1q_s64(v); + } + int64 get0() const + { + return vgetq_lane_s64(val, 0); + } + int64x2_t val; +}; + +#if CV_SIMD128_64F +struct v_float64x2 +{ + typedef double lane_type; + enum { nlanes = 2 }; + + v_float64x2() {} + explicit v_float64x2(float64x2_t v) : val(v) {} + v_float64x2(double v0, double v1) + { + double v[] = {v0, v1}; + val = vld1q_f64(v); + } + double get0() const + { + return vgetq_lane_f64(val, 0); + } + float64x2_t val; +}; +#endif + +#if CV_FP16 +// Workaround for old compilers +static inline int16x4_t vreinterpret_s16_f16(float16x4_t a) { return (int16x4_t)a; } +static inline float16x4_t vreinterpret_f16_s16(int16x4_t a) { return (float16x4_t)a; } + +static inline float16x4_t cv_vld1_f16(const void* ptr) +{ +#ifndef vld1_f16 // APPLE compiler defines vld1_f16 as macro + return vreinterpret_f16_s16(vld1_s16((const short*)ptr)); +#else + return vld1_f16((const __fp16*)ptr); +#endif +} +static inline void cv_vst1_f16(void* ptr, float16x4_t a) +{ +#ifndef vst1_f16 // APPLE compiler defines vst1_f16 as macro + vst1_s16((short*)ptr, vreinterpret_s16_f16(a)); +#else + vst1_f16((__fp16*)ptr, a); +#endif +} + +#ifndef vdup_n_f16 + #define vdup_n_f16(v) (float16x4_t){v, v, v, v} +#endif + +#endif // CV_FP16 + +#if CV_FP16 +inline v_float32x4 v128_load_fp16_f32(const short* ptr) +{ + float16x4_t a = cv_vld1_f16((const __fp16*)ptr); + return v_float32x4(vcvt_f32_f16(a)); +} + +inline void v_store_fp16(short* ptr, const v_float32x4& a) +{ + float16x4_t fp16 = vcvt_f16_f32(a.val); + cv_vst1_f16((short*)ptr, fp16); +} +#endif + +#define OPENCV_HAL_IMPL_NEON_INIT(_Tpv, _Tp, suffix) \ +inline v_##_Tpv v_setzero_##suffix() { return v_##_Tpv(vdupq_n_##suffix((_Tp)0)); } \ +inline v_##_Tpv v_setall_##suffix(_Tp v) { return v_##_Tpv(vdupq_n_##suffix(v)); } \ +inline _Tpv##_t vreinterpretq_##suffix##_##suffix(_Tpv##_t v) { return v; } \ +inline v_uint8x16 v_reinterpret_as_u8(const v_##_Tpv& v) { return v_uint8x16(vreinterpretq_u8_##suffix(v.val)); } \ +inline v_int8x16 v_reinterpret_as_s8(const v_##_Tpv& v) { return v_int8x16(vreinterpretq_s8_##suffix(v.val)); } \ +inline v_uint16x8 v_reinterpret_as_u16(const v_##_Tpv& v) { return v_uint16x8(vreinterpretq_u16_##suffix(v.val)); } \ +inline v_int16x8 v_reinterpret_as_s16(const v_##_Tpv& v) { return v_int16x8(vreinterpretq_s16_##suffix(v.val)); } \ +inline v_uint32x4 v_reinterpret_as_u32(const v_##_Tpv& v) { return v_uint32x4(vreinterpretq_u32_##suffix(v.val)); } \ +inline v_int32x4 v_reinterpret_as_s32(const v_##_Tpv& v) { return v_int32x4(vreinterpretq_s32_##suffix(v.val)); } \ +inline v_uint64x2 v_reinterpret_as_u64(const v_##_Tpv& v) { return v_uint64x2(vreinterpretq_u64_##suffix(v.val)); } \ +inline v_int64x2 v_reinterpret_as_s64(const v_##_Tpv& v) { return v_int64x2(vreinterpretq_s64_##suffix(v.val)); } \ +inline v_float32x4 v_reinterpret_as_f32(const v_##_Tpv& v) { return v_float32x4(vreinterpretq_f32_##suffix(v.val)); } + +OPENCV_HAL_IMPL_NEON_INIT(uint8x16, uchar, u8) +OPENCV_HAL_IMPL_NEON_INIT(int8x16, schar, s8) +OPENCV_HAL_IMPL_NEON_INIT(uint16x8, ushort, u16) +OPENCV_HAL_IMPL_NEON_INIT(int16x8, short, s16) +OPENCV_HAL_IMPL_NEON_INIT(uint32x4, unsigned, u32) +OPENCV_HAL_IMPL_NEON_INIT(int32x4, int, s32) +OPENCV_HAL_IMPL_NEON_INIT(uint64x2, uint64, u64) +OPENCV_HAL_IMPL_NEON_INIT(int64x2, int64, s64) +OPENCV_HAL_IMPL_NEON_INIT(float32x4, float, f32) +#if CV_SIMD128_64F +#define OPENCV_HAL_IMPL_NEON_INIT_64(_Tpv, suffix) \ +inline v_float64x2 v_reinterpret_as_f64(const v_##_Tpv& v) { return v_float64x2(vreinterpretq_f64_##suffix(v.val)); } +OPENCV_HAL_IMPL_NEON_INIT(float64x2, double, f64) +OPENCV_HAL_IMPL_NEON_INIT_64(uint8x16, u8) +OPENCV_HAL_IMPL_NEON_INIT_64(int8x16, s8) +OPENCV_HAL_IMPL_NEON_INIT_64(uint16x8, u16) +OPENCV_HAL_IMPL_NEON_INIT_64(int16x8, s16) +OPENCV_HAL_IMPL_NEON_INIT_64(uint32x4, u32) +OPENCV_HAL_IMPL_NEON_INIT_64(int32x4, s32) +OPENCV_HAL_IMPL_NEON_INIT_64(uint64x2, u64) +OPENCV_HAL_IMPL_NEON_INIT_64(int64x2, s64) +OPENCV_HAL_IMPL_NEON_INIT_64(float32x4, f32) +OPENCV_HAL_IMPL_NEON_INIT_64(float64x2, f64) +#endif + +#define OPENCV_HAL_IMPL_NEON_PACK(_Tpvec, _Tp, hreg, suffix, _Tpwvec, pack, mov, rshr) \ +inline _Tpvec v_##pack(const _Tpwvec& a, const _Tpwvec& b) \ +{ \ + hreg a1 = mov(a.val), b1 = mov(b.val); \ + return _Tpvec(vcombine_##suffix(a1, b1)); \ +} \ +inline void v_##pack##_store(_Tp* ptr, const _Tpwvec& a) \ +{ \ + hreg a1 = mov(a.val); \ + vst1_##suffix(ptr, a1); \ +} \ +template inline \ +_Tpvec v_rshr_##pack(const _Tpwvec& a, const _Tpwvec& b) \ +{ \ + hreg a1 = rshr(a.val, n); \ + hreg b1 = rshr(b.val, n); \ + return _Tpvec(vcombine_##suffix(a1, b1)); \ +} \ +template inline \ +void v_rshr_##pack##_store(_Tp* ptr, const _Tpwvec& a) \ +{ \ + hreg a1 = rshr(a.val, n); \ + vst1_##suffix(ptr, a1); \ +} + +OPENCV_HAL_IMPL_NEON_PACK(v_uint8x16, uchar, uint8x8_t, u8, v_uint16x8, pack, vqmovn_u16, vqrshrn_n_u16) +OPENCV_HAL_IMPL_NEON_PACK(v_int8x16, schar, int8x8_t, s8, v_int16x8, pack, vqmovn_s16, vqrshrn_n_s16) +OPENCV_HAL_IMPL_NEON_PACK(v_uint16x8, ushort, uint16x4_t, u16, v_uint32x4, pack, vqmovn_u32, vqrshrn_n_u32) +OPENCV_HAL_IMPL_NEON_PACK(v_int16x8, short, int16x4_t, s16, v_int32x4, pack, vqmovn_s32, vqrshrn_n_s32) +OPENCV_HAL_IMPL_NEON_PACK(v_uint32x4, unsigned, uint32x2_t, u32, v_uint64x2, pack, vmovn_u64, vrshrn_n_u64) +OPENCV_HAL_IMPL_NEON_PACK(v_int32x4, int, int32x2_t, s32, v_int64x2, pack, vmovn_s64, vrshrn_n_s64) + +OPENCV_HAL_IMPL_NEON_PACK(v_uint8x16, uchar, uint8x8_t, u8, v_int16x8, pack_u, vqmovun_s16, vqrshrun_n_s16) +OPENCV_HAL_IMPL_NEON_PACK(v_uint16x8, ushort, uint16x4_t, u16, v_int32x4, pack_u, vqmovun_s32, vqrshrun_n_s32) + +// pack boolean +inline v_uint8x16 v_pack_b(const v_uint16x8& a, const v_uint16x8& b) +{ + uint8x16_t ab = vcombine_u8(vmovn_u16(a.val), vmovn_u16(b.val)); + return v_uint8x16(ab); +} + +inline v_uint8x16 v_pack_b(const v_uint32x4& a, const v_uint32x4& b, + const v_uint32x4& c, const v_uint32x4& d) +{ + uint16x8_t nab = vcombine_u16(vmovn_u32(a.val), vmovn_u32(b.val)); + uint16x8_t ncd = vcombine_u16(vmovn_u32(c.val), vmovn_u32(d.val)); + return v_uint8x16(vcombine_u8(vmovn_u16(nab), vmovn_u16(ncd))); +} + +inline v_uint8x16 v_pack_b(const v_uint64x2& a, const v_uint64x2& b, const v_uint64x2& c, + const v_uint64x2& d, const v_uint64x2& e, const v_uint64x2& f, + const v_uint64x2& g, const v_uint64x2& h) +{ + uint32x4_t ab = vcombine_u32(vmovn_u64(a.val), vmovn_u64(b.val)); + uint32x4_t cd = vcombine_u32(vmovn_u64(c.val), vmovn_u64(d.val)); + uint32x4_t ef = vcombine_u32(vmovn_u64(e.val), vmovn_u64(f.val)); + uint32x4_t gh = vcombine_u32(vmovn_u64(g.val), vmovn_u64(h.val)); + + uint16x8_t abcd = vcombine_u16(vmovn_u32(ab), vmovn_u32(cd)); + uint16x8_t efgh = vcombine_u16(vmovn_u32(ef), vmovn_u32(gh)); + return v_uint8x16(vcombine_u8(vmovn_u16(abcd), vmovn_u16(efgh))); +} + +inline v_float32x4 v_matmul(const v_float32x4& v, const v_float32x4& m0, + const v_float32x4& m1, const v_float32x4& m2, + const v_float32x4& m3) +{ + float32x2_t vl = vget_low_f32(v.val), vh = vget_high_f32(v.val); + float32x4_t res = vmulq_lane_f32(m0.val, vl, 0); + res = vmlaq_lane_f32(res, m1.val, vl, 1); + res = vmlaq_lane_f32(res, m2.val, vh, 0); + res = vmlaq_lane_f32(res, m3.val, vh, 1); + return v_float32x4(res); +} + +inline v_float32x4 v_matmuladd(const v_float32x4& v, const v_float32x4& m0, + const v_float32x4& m1, const v_float32x4& m2, + const v_float32x4& a) +{ + float32x2_t vl = vget_low_f32(v.val), vh = vget_high_f32(v.val); + float32x4_t res = vmulq_lane_f32(m0.val, vl, 0); + res = vmlaq_lane_f32(res, m1.val, vl, 1); + res = vmlaq_lane_f32(res, m2.val, vh, 0); + res = vaddq_f32(res, a.val); + return v_float32x4(res); +} + +#define OPENCV_HAL_IMPL_NEON_BIN_OP(bin_op, _Tpvec, intrin) \ +inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b) \ +{ \ + return _Tpvec(intrin(a.val, b.val)); \ +} \ +inline _Tpvec& operator bin_op##= (_Tpvec& a, const _Tpvec& b) \ +{ \ + a.val = intrin(a.val, b.val); \ + return a; \ +} + +OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_uint8x16, vqaddq_u8) +OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_uint8x16, vqsubq_u8) +OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_int8x16, vqaddq_s8) +OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_int8x16, vqsubq_s8) +OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_uint16x8, vqaddq_u16) +OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_uint16x8, vqsubq_u16) +OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_int16x8, vqaddq_s16) +OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_int16x8, vqsubq_s16) +OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_int32x4, vaddq_s32) +OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_int32x4, vsubq_s32) +OPENCV_HAL_IMPL_NEON_BIN_OP(*, v_int32x4, vmulq_s32) +OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_uint32x4, vaddq_u32) +OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_uint32x4, vsubq_u32) +OPENCV_HAL_IMPL_NEON_BIN_OP(*, v_uint32x4, vmulq_u32) +OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_float32x4, vaddq_f32) +OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_float32x4, vsubq_f32) +OPENCV_HAL_IMPL_NEON_BIN_OP(*, v_float32x4, vmulq_f32) +OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_int64x2, vaddq_s64) +OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_int64x2, vsubq_s64) +OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_uint64x2, vaddq_u64) +OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_uint64x2, vsubq_u64) +#if CV_SIMD128_64F +OPENCV_HAL_IMPL_NEON_BIN_OP(/, v_float32x4, vdivq_f32) +OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_float64x2, vaddq_f64) +OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_float64x2, vsubq_f64) +OPENCV_HAL_IMPL_NEON_BIN_OP(*, v_float64x2, vmulq_f64) +OPENCV_HAL_IMPL_NEON_BIN_OP(/, v_float64x2, vdivq_f64) +#else +inline v_float32x4 operator / (const v_float32x4& a, const v_float32x4& b) +{ + float32x4_t reciprocal = vrecpeq_f32(b.val); + reciprocal = vmulq_f32(vrecpsq_f32(b.val, reciprocal), reciprocal); + reciprocal = vmulq_f32(vrecpsq_f32(b.val, reciprocal), reciprocal); + return v_float32x4(vmulq_f32(a.val, reciprocal)); +} +inline v_float32x4& operator /= (v_float32x4& a, const v_float32x4& b) +{ + float32x4_t reciprocal = vrecpeq_f32(b.val); + reciprocal = vmulq_f32(vrecpsq_f32(b.val, reciprocal), reciprocal); + reciprocal = vmulq_f32(vrecpsq_f32(b.val, reciprocal), reciprocal); + a.val = vmulq_f32(a.val, reciprocal); + return a; +} +#endif + +// saturating multiply 8-bit, 16-bit +#define OPENCV_HAL_IMPL_NEON_MUL_SAT(_Tpvec, _Tpwvec) \ + inline _Tpvec operator * (const _Tpvec& a, const _Tpvec& b) \ + { \ + _Tpwvec c, d; \ + v_mul_expand(a, b, c, d); \ + return v_pack(c, d); \ + } \ + inline _Tpvec& operator *= (_Tpvec& a, const _Tpvec& b) \ + { a = a * b; return a; } + +OPENCV_HAL_IMPL_NEON_MUL_SAT(v_int8x16, v_int16x8) +OPENCV_HAL_IMPL_NEON_MUL_SAT(v_uint8x16, v_uint16x8) +OPENCV_HAL_IMPL_NEON_MUL_SAT(v_int16x8, v_int32x4) +OPENCV_HAL_IMPL_NEON_MUL_SAT(v_uint16x8, v_uint32x4) + +// Multiply and expand +inline void v_mul_expand(const v_int8x16& a, const v_int8x16& b, + v_int16x8& c, v_int16x8& d) +{ + c.val = vmull_s8(vget_low_s8(a.val), vget_low_s8(b.val)); + d.val = vmull_s8(vget_high_s8(a.val), vget_high_s8(b.val)); +} + +inline void v_mul_expand(const v_uint8x16& a, const v_uint8x16& b, + v_uint16x8& c, v_uint16x8& d) +{ + c.val = vmull_u8(vget_low_u8(a.val), vget_low_u8(b.val)); + d.val = vmull_u8(vget_high_u8(a.val), vget_high_u8(b.val)); +} + +inline void v_mul_expand(const v_int16x8& a, const v_int16x8& b, + v_int32x4& c, v_int32x4& d) +{ + c.val = vmull_s16(vget_low_s16(a.val), vget_low_s16(b.val)); + d.val = vmull_s16(vget_high_s16(a.val), vget_high_s16(b.val)); +} + +inline void v_mul_expand(const v_uint16x8& a, const v_uint16x8& b, + v_uint32x4& c, v_uint32x4& d) +{ + c.val = vmull_u16(vget_low_u16(a.val), vget_low_u16(b.val)); + d.val = vmull_u16(vget_high_u16(a.val), vget_high_u16(b.val)); +} + +inline void v_mul_expand(const v_uint32x4& a, const v_uint32x4& b, + v_uint64x2& c, v_uint64x2& d) +{ + c.val = vmull_u32(vget_low_u32(a.val), vget_low_u32(b.val)); + d.val = vmull_u32(vget_high_u32(a.val), vget_high_u32(b.val)); +} + +inline v_int16x8 v_mul_hi(const v_int16x8& a, const v_int16x8& b) +{ + return v_int16x8(vcombine_s16( + vshrn_n_s32(vmull_s16( vget_low_s16(a.val), vget_low_s16(b.val)), 16), + vshrn_n_s32(vmull_s16(vget_high_s16(a.val), vget_high_s16(b.val)), 16) + )); +} +inline v_uint16x8 v_mul_hi(const v_uint16x8& a, const v_uint16x8& b) +{ + return v_uint16x8(vcombine_u16( + vshrn_n_u32(vmull_u16( vget_low_u16(a.val), vget_low_u16(b.val)), 16), + vshrn_n_u32(vmull_u16(vget_high_u16(a.val), vget_high_u16(b.val)), 16) + )); +} + +inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b) +{ + int32x4_t c = vmull_s16(vget_low_s16(a.val), vget_low_s16(b.val)); + int32x4_t d = vmull_s16(vget_high_s16(a.val), vget_high_s16(b.val)); + int32x4x2_t cd = vuzpq_s32(c, d); + return v_int32x4(vaddq_s32(cd.val[0], cd.val[1])); +} + +inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b, const v_int32x4& c) +{ + v_int32x4 s = v_dotprod(a, b); + return v_int32x4(vaddq_s32(s.val , c.val)); +} + +#define OPENCV_HAL_IMPL_NEON_LOGIC_OP(_Tpvec, suffix) \ + OPENCV_HAL_IMPL_NEON_BIN_OP(&, _Tpvec, vandq_##suffix) \ + OPENCV_HAL_IMPL_NEON_BIN_OP(|, _Tpvec, vorrq_##suffix) \ + OPENCV_HAL_IMPL_NEON_BIN_OP(^, _Tpvec, veorq_##suffix) \ + inline _Tpvec operator ~ (const _Tpvec& a) \ + { \ + return _Tpvec(vreinterpretq_##suffix##_u8(vmvnq_u8(vreinterpretq_u8_##suffix(a.val)))); \ + } + +OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_uint8x16, u8) +OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_int8x16, s8) +OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_uint16x8, u16) +OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_int16x8, s16) +OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_uint32x4, u32) +OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_int32x4, s32) +OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_uint64x2, u64) +OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_int64x2, s64) + +#define OPENCV_HAL_IMPL_NEON_FLT_BIT_OP(bin_op, intrin) \ +inline v_float32x4 operator bin_op (const v_float32x4& a, const v_float32x4& b) \ +{ \ + return v_float32x4(vreinterpretq_f32_s32(intrin(vreinterpretq_s32_f32(a.val), vreinterpretq_s32_f32(b.val)))); \ +} \ +inline v_float32x4& operator bin_op##= (v_float32x4& a, const v_float32x4& b) \ +{ \ + a.val = vreinterpretq_f32_s32(intrin(vreinterpretq_s32_f32(a.val), vreinterpretq_s32_f32(b.val))); \ + return a; \ +} + +OPENCV_HAL_IMPL_NEON_FLT_BIT_OP(&, vandq_s32) +OPENCV_HAL_IMPL_NEON_FLT_BIT_OP(|, vorrq_s32) +OPENCV_HAL_IMPL_NEON_FLT_BIT_OP(^, veorq_s32) + +inline v_float32x4 operator ~ (const v_float32x4& a) +{ + return v_float32x4(vreinterpretq_f32_s32(vmvnq_s32(vreinterpretq_s32_f32(a.val)))); +} + +#if CV_SIMD128_64F +inline v_float32x4 v_sqrt(const v_float32x4& x) +{ + return v_float32x4(vsqrtq_f32(x.val)); +} + +inline v_float32x4 v_invsqrt(const v_float32x4& x) +{ + v_float32x4 one = v_setall_f32(1.0f); + return one / v_sqrt(x); +} +#else +inline v_float32x4 v_sqrt(const v_float32x4& x) +{ + float32x4_t x1 = vmaxq_f32(x.val, vdupq_n_f32(FLT_MIN)); + float32x4_t e = vrsqrteq_f32(x1); + e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(x1, e), e), e); + e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(x1, e), e), e); + return v_float32x4(vmulq_f32(x.val, e)); +} + +inline v_float32x4 v_invsqrt(const v_float32x4& x) +{ + float32x4_t e = vrsqrteq_f32(x.val); + e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(x.val, e), e), e); + e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(x.val, e), e), e); + return v_float32x4(e); +} +#endif + +#define OPENCV_HAL_IMPL_NEON_ABS(_Tpuvec, _Tpsvec, usuffix, ssuffix) \ +inline _Tpuvec v_abs(const _Tpsvec& a) { return v_reinterpret_as_##usuffix(_Tpsvec(vabsq_##ssuffix(a.val))); } + +OPENCV_HAL_IMPL_NEON_ABS(v_uint8x16, v_int8x16, u8, s8) +OPENCV_HAL_IMPL_NEON_ABS(v_uint16x8, v_int16x8, u16, s16) +OPENCV_HAL_IMPL_NEON_ABS(v_uint32x4, v_int32x4, u32, s32) + +inline v_float32x4 v_abs(v_float32x4 x) +{ return v_float32x4(vabsq_f32(x.val)); } + +#if CV_SIMD128_64F +#define OPENCV_HAL_IMPL_NEON_DBL_BIT_OP(bin_op, intrin) \ +inline v_float64x2 operator bin_op (const v_float64x2& a, const v_float64x2& b) \ +{ \ + return v_float64x2(vreinterpretq_f64_s64(intrin(vreinterpretq_s64_f64(a.val), vreinterpretq_s64_f64(b.val)))); \ +} \ +inline v_float64x2& operator bin_op##= (v_float64x2& a, const v_float64x2& b) \ +{ \ + a.val = vreinterpretq_f64_s64(intrin(vreinterpretq_s64_f64(a.val), vreinterpretq_s64_f64(b.val))); \ + return a; \ +} + +OPENCV_HAL_IMPL_NEON_DBL_BIT_OP(&, vandq_s64) +OPENCV_HAL_IMPL_NEON_DBL_BIT_OP(|, vorrq_s64) +OPENCV_HAL_IMPL_NEON_DBL_BIT_OP(^, veorq_s64) + +inline v_float64x2 operator ~ (const v_float64x2& a) +{ + return v_float64x2(vreinterpretq_f64_s32(vmvnq_s32(vreinterpretq_s32_f64(a.val)))); +} + +inline v_float64x2 v_sqrt(const v_float64x2& x) +{ + return v_float64x2(vsqrtq_f64(x.val)); +} + +inline v_float64x2 v_invsqrt(const v_float64x2& x) +{ + v_float64x2 one = v_setall_f64(1.0f); + return one / v_sqrt(x); +} + +inline v_float64x2 v_abs(v_float64x2 x) +{ return v_float64x2(vabsq_f64(x.val)); } +#endif + +// TODO: exp, log, sin, cos + +#define OPENCV_HAL_IMPL_NEON_BIN_FUNC(_Tpvec, func, intrin) \ +inline _Tpvec func(const _Tpvec& a, const _Tpvec& b) \ +{ \ + return _Tpvec(intrin(a.val, b.val)); \ +} + +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_min, vminq_u8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_max, vmaxq_u8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int8x16, v_min, vminq_s8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int8x16, v_max, vmaxq_s8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_min, vminq_u16) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_max, vmaxq_u16) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int16x8, v_min, vminq_s16) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int16x8, v_max, vmaxq_s16) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint32x4, v_min, vminq_u32) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint32x4, v_max, vmaxq_u32) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int32x4, v_min, vminq_s32) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int32x4, v_max, vmaxq_s32) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_float32x4, v_min, vminq_f32) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_float32x4, v_max, vmaxq_f32) +#if CV_SIMD128_64F +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_float64x2, v_min, vminq_f64) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_float64x2, v_max, vmaxq_f64) +#endif + +#if CV_SIMD128_64F +inline int64x2_t vmvnq_s64(int64x2_t a) +{ + int64x2_t vx = vreinterpretq_s64_u32(vdupq_n_u32(0xFFFFFFFF)); + return veorq_s64(a, vx); +} +inline uint64x2_t vmvnq_u64(uint64x2_t a) +{ + uint64x2_t vx = vreinterpretq_u64_u32(vdupq_n_u32(0xFFFFFFFF)); + return veorq_u64(a, vx); +} +#endif +#define OPENCV_HAL_IMPL_NEON_INT_CMP_OP(_Tpvec, cast, suffix, not_suffix) \ +inline _Tpvec operator == (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(cast(vceqq_##suffix(a.val, b.val))); } \ +inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(cast(vmvnq_##not_suffix(vceqq_##suffix(a.val, b.val)))); } \ +inline _Tpvec operator < (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(cast(vcltq_##suffix(a.val, b.val))); } \ +inline _Tpvec operator > (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(cast(vcgtq_##suffix(a.val, b.val))); } \ +inline _Tpvec operator <= (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(cast(vcleq_##suffix(a.val, b.val))); } \ +inline _Tpvec operator >= (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(cast(vcgeq_##suffix(a.val, b.val))); } + +OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_uint8x16, OPENCV_HAL_NOP, u8, u8) +OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_int8x16, vreinterpretq_s8_u8, s8, u8) +OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_uint16x8, OPENCV_HAL_NOP, u16, u16) +OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_int16x8, vreinterpretq_s16_u16, s16, u16) +OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_uint32x4, OPENCV_HAL_NOP, u32, u32) +OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_int32x4, vreinterpretq_s32_u32, s32, u32) +OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_float32x4, vreinterpretq_f32_u32, f32, u32) +#if CV_SIMD128_64F +OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_uint64x2, OPENCV_HAL_NOP, u64, u64) +OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_int64x2, vreinterpretq_s64_u64, s64, u64) +OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_float64x2, vreinterpretq_f64_u64, f64, u64) +#endif + +inline v_float32x4 v_not_nan(const v_float32x4& a) +{ return v_float32x4(vreinterpretq_f32_u32(vceqq_f32(a.val, a.val))); } +#if CV_SIMD128_64F +inline v_float64x2 v_not_nan(const v_float64x2& a) +{ return v_float64x2(vreinterpretq_f64_u64(vceqq_f64(a.val, a.val))); } +#endif + +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_add_wrap, vaddq_u8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int8x16, v_add_wrap, vaddq_s8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_add_wrap, vaddq_u16) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int16x8, v_add_wrap, vaddq_s16) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_sub_wrap, vsubq_u8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int8x16, v_sub_wrap, vsubq_s8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_sub_wrap, vsubq_u16) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int16x8, v_sub_wrap, vsubq_s16) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_mul_wrap, vmulq_u8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int8x16, v_mul_wrap, vmulq_s8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_mul_wrap, vmulq_u16) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int16x8, v_mul_wrap, vmulq_s16) + +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_absdiff, vabdq_u8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_absdiff, vabdq_u16) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint32x4, v_absdiff, vabdq_u32) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_float32x4, v_absdiff, vabdq_f32) +#if CV_SIMD128_64F +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_float64x2, v_absdiff, vabdq_f64) +#endif + +/** Saturating absolute difference **/ +inline v_int8x16 v_absdiffs(const v_int8x16& a, const v_int8x16& b) +{ return v_int8x16(vqabsq_s8(vqsubq_s8(a.val, b.val))); } +inline v_int16x8 v_absdiffs(const v_int16x8& a, const v_int16x8& b) +{ return v_int16x8(vqabsq_s16(vqsubq_s16(a.val, b.val))); } + +#define OPENCV_HAL_IMPL_NEON_BIN_FUNC2(_Tpvec, _Tpvec2, cast, func, intrin) \ +inline _Tpvec2 func(const _Tpvec& a, const _Tpvec& b) \ +{ \ + return _Tpvec2(cast(intrin(a.val, b.val))); \ +} + +OPENCV_HAL_IMPL_NEON_BIN_FUNC2(v_int8x16, v_uint8x16, vreinterpretq_u8_s8, v_absdiff, vabdq_s8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC2(v_int16x8, v_uint16x8, vreinterpretq_u16_s16, v_absdiff, vabdq_s16) +OPENCV_HAL_IMPL_NEON_BIN_FUNC2(v_int32x4, v_uint32x4, vreinterpretq_u32_s32, v_absdiff, vabdq_s32) + +inline v_float32x4 v_magnitude(const v_float32x4& a, const v_float32x4& b) +{ + v_float32x4 x(vmlaq_f32(vmulq_f32(a.val, a.val), b.val, b.val)); + return v_sqrt(x); +} + +inline v_float32x4 v_sqr_magnitude(const v_float32x4& a, const v_float32x4& b) +{ + return v_float32x4(vmlaq_f32(vmulq_f32(a.val, a.val), b.val, b.val)); +} + +inline v_float32x4 v_fma(const v_float32x4& a, const v_float32x4& b, const v_float32x4& c) +{ +#if CV_SIMD128_64F + // ARMv8, which adds support for 64-bit floating-point (so CV_SIMD128_64F is defined), + // also adds FMA support both for single- and double-precision floating-point vectors + return v_float32x4(vfmaq_f32(c.val, a.val, b.val)); +#else + return v_float32x4(vmlaq_f32(c.val, a.val, b.val)); +#endif +} + +inline v_int32x4 v_fma(const v_int32x4& a, const v_int32x4& b, const v_int32x4& c) +{ + return v_int32x4(vmlaq_s32(c.val, a.val, b.val)); +} + +inline v_float32x4 v_muladd(const v_float32x4& a, const v_float32x4& b, const v_float32x4& c) +{ + return v_fma(a, b, c); +} + +inline v_int32x4 v_muladd(const v_int32x4& a, const v_int32x4& b, const v_int32x4& c) +{ + return v_fma(a, b, c); +} + +#if CV_SIMD128_64F +inline v_float64x2 v_magnitude(const v_float64x2& a, const v_float64x2& b) +{ + v_float64x2 x(vaddq_f64(vmulq_f64(a.val, a.val), vmulq_f64(b.val, b.val))); + return v_sqrt(x); +} + +inline v_float64x2 v_sqr_magnitude(const v_float64x2& a, const v_float64x2& b) +{ + return v_float64x2(vaddq_f64(vmulq_f64(a.val, a.val), vmulq_f64(b.val, b.val))); +} + +inline v_float64x2 v_fma(const v_float64x2& a, const v_float64x2& b, const v_float64x2& c) +{ + return v_float64x2(vfmaq_f64(c.val, a.val, b.val)); +} + +inline v_float64x2 v_muladd(const v_float64x2& a, const v_float64x2& b, const v_float64x2& c) +{ + return v_fma(a, b, c); +} +#endif + +// trade efficiency for convenience +#define OPENCV_HAL_IMPL_NEON_SHIFT_OP(_Tpvec, suffix, _Tps, ssuffix) \ +inline _Tpvec operator << (const _Tpvec& a, int n) \ +{ return _Tpvec(vshlq_##suffix(a.val, vdupq_n_##ssuffix((_Tps)n))); } \ +inline _Tpvec operator >> (const _Tpvec& a, int n) \ +{ return _Tpvec(vshlq_##suffix(a.val, vdupq_n_##ssuffix((_Tps)-n))); } \ +template inline _Tpvec v_shl(const _Tpvec& a) \ +{ return _Tpvec(vshlq_n_##suffix(a.val, n)); } \ +template inline _Tpvec v_shr(const _Tpvec& a) \ +{ return _Tpvec(vshrq_n_##suffix(a.val, n)); } \ +template inline _Tpvec v_rshr(const _Tpvec& a) \ +{ return _Tpvec(vrshrq_n_##suffix(a.val, n)); } + +OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_uint8x16, u8, schar, s8) +OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_int8x16, s8, schar, s8) +OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_uint16x8, u16, short, s16) +OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_int16x8, s16, short, s16) +OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_uint32x4, u32, int, s32) +OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_int32x4, s32, int, s32) +OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_uint64x2, u64, int64, s64) +OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_int64x2, s64, int64, s64) + +#define OPENCV_HAL_IMPL_NEON_ROTATE_OP(_Tpvec, suffix) \ +template inline _Tpvec v_rotate_right(const _Tpvec& a) \ +{ return _Tpvec(vextq_##suffix(a.val, vdupq_n_##suffix(0), n)); } \ +template inline _Tpvec v_rotate_left(const _Tpvec& a) \ +{ return _Tpvec(vextq_##suffix(vdupq_n_##suffix(0), a.val, _Tpvec::nlanes - n)); } \ +template<> inline _Tpvec v_rotate_left<0>(const _Tpvec& a) \ +{ return a; } \ +template inline _Tpvec v_rotate_right(const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(vextq_##suffix(a.val, b.val, n)); } \ +template inline _Tpvec v_rotate_left(const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(vextq_##suffix(b.val, a.val, _Tpvec::nlanes - n)); } \ +template<> inline _Tpvec v_rotate_left<0>(const _Tpvec& a, const _Tpvec& b) \ +{ CV_UNUSED(b); return a; } + +OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_uint8x16, u8) +OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_int8x16, s8) +OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_uint16x8, u16) +OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_int16x8, s16) +OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_uint32x4, u32) +OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_int32x4, s32) +OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_float32x4, f32) +OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_uint64x2, u64) +OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_int64x2, s64) +#if CV_SIMD128_64F +OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_float64x2, f64) +#endif + +#define OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(_Tpvec, _Tp, suffix) \ +inline _Tpvec v_load(const _Tp* ptr) \ +{ return _Tpvec(vld1q_##suffix(ptr)); } \ +inline _Tpvec v_load_aligned(const _Tp* ptr) \ +{ return _Tpvec(vld1q_##suffix(ptr)); } \ +inline _Tpvec v_load_low(const _Tp* ptr) \ +{ return _Tpvec(vcombine_##suffix(vld1_##suffix(ptr), vdup_n_##suffix((_Tp)0))); } \ +inline _Tpvec v_load_halves(const _Tp* ptr0, const _Tp* ptr1) \ +{ return _Tpvec(vcombine_##suffix(vld1_##suffix(ptr0), vld1_##suffix(ptr1))); } \ +inline void v_store(_Tp* ptr, const _Tpvec& a) \ +{ vst1q_##suffix(ptr, a.val); } \ +inline void v_store_aligned(_Tp* ptr, const _Tpvec& a) \ +{ vst1q_##suffix(ptr, a.val); } \ +inline void v_store_aligned_nocache(_Tp* ptr, const _Tpvec& a) \ +{ vst1q_##suffix(ptr, a.val); } \ +inline void v_store(_Tp* ptr, const _Tpvec& a, hal::StoreMode /*mode*/) \ +{ vst1q_##suffix(ptr, a.val); } \ +inline void v_store_low(_Tp* ptr, const _Tpvec& a) \ +{ vst1_##suffix(ptr, vget_low_##suffix(a.val)); } \ +inline void v_store_high(_Tp* ptr, const _Tpvec& a) \ +{ vst1_##suffix(ptr, vget_high_##suffix(a.val)); } + +OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_uint8x16, uchar, u8) +OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_int8x16, schar, s8) +OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_uint16x8, ushort, u16) +OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_int16x8, short, s16) +OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_uint32x4, unsigned, u32) +OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_int32x4, int, s32) +OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_uint64x2, uint64, u64) +OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_int64x2, int64, s64) +OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_float32x4, float, f32) +#if CV_SIMD128_64F +OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_float64x2, double, f64) +#endif + +#define OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(_Tpvec, _Tpnvec, scalartype, func, vectorfunc, suffix) \ +inline scalartype v_reduce_##func(const _Tpvec& a) \ +{ \ + _Tpnvec##_t a0 = vp##vectorfunc##_##suffix(vget_low_##suffix(a.val), vget_high_##suffix(a.val)); \ + a0 = vp##vectorfunc##_##suffix(a0, a0); \ + return (scalartype)vget_lane_##suffix(vp##vectorfunc##_##suffix(a0, a0),0); \ +} + +OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(v_uint16x8, uint16x4, unsigned short, sum, add, u16) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(v_uint16x8, uint16x4, unsigned short, max, max, u16) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(v_uint16x8, uint16x4, unsigned short, min, min, u16) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(v_int16x8, int16x4, short, sum, add, s16) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(v_int16x8, int16x4, short, max, max, s16) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(v_int16x8, int16x4, short, min, min, s16) + +#define OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(_Tpvec, _Tpnvec, scalartype, func, vectorfunc, suffix) \ +inline scalartype v_reduce_##func(const _Tpvec& a) \ +{ \ + _Tpnvec##_t a0 = vp##vectorfunc##_##suffix(vget_low_##suffix(a.val), vget_high_##suffix(a.val)); \ + return (scalartype)vget_lane_##suffix(vp##vectorfunc##_##suffix(a0, vget_high_##suffix(a.val)),0); \ +} + +OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_uint32x4, uint32x2, unsigned, sum, add, u32) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_uint32x4, uint32x2, unsigned, max, max, u32) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_uint32x4, uint32x2, unsigned, min, min, u32) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_int32x4, int32x2, int, sum, add, s32) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_int32x4, int32x2, int, max, max, s32) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_int32x4, int32x2, int, min, min, s32) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_float32x4, float32x2, float, sum, add, f32) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_float32x4, float32x2, float, max, max, f32) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_float32x4, float32x2, float, min, min, f32) + +#if CV_SIMD128_64F +inline double v_reduce_sum(const v_float64x2& a) +{ + return vgetq_lane_f64(a.val, 0) + vgetq_lane_f64(a.val, 1); +} +#endif + +inline v_float32x4 v_reduce_sum4(const v_float32x4& a, const v_float32x4& b, + const v_float32x4& c, const v_float32x4& d) +{ + float32x4x2_t ab = vtrnq_f32(a.val, b.val); + float32x4x2_t cd = vtrnq_f32(c.val, d.val); + + float32x4_t u0 = vaddq_f32(ab.val[0], ab.val[1]); // a0+a1 b0+b1 a2+a3 b2+b3 + float32x4_t u1 = vaddq_f32(cd.val[0], cd.val[1]); // c0+c1 d0+d1 c2+c3 d2+d3 + + float32x4_t v0 = vcombine_f32(vget_low_f32(u0), vget_low_f32(u1)); + float32x4_t v1 = vcombine_f32(vget_high_f32(u0), vget_high_f32(u1)); + + return v_float32x4(vaddq_f32(v0, v1)); +} + +inline unsigned v_reduce_sad(const v_uint8x16& a, const v_uint8x16& b) +{ + uint32x4_t t0 = vpaddlq_u16(vpaddlq_u8(vabdq_u8(a.val, b.val))); + uint32x2_t t1 = vpadd_u32(vget_low_u32(t0), vget_high_u32(t0)); + return vget_lane_u32(vpadd_u32(t1, t1), 0); +} +inline unsigned v_reduce_sad(const v_int8x16& a, const v_int8x16& b) +{ + uint32x4_t t0 = vpaddlq_u16(vpaddlq_u8(vreinterpretq_u8_s8(vabdq_s8(a.val, b.val)))); + uint32x2_t t1 = vpadd_u32(vget_low_u32(t0), vget_high_u32(t0)); + return vget_lane_u32(vpadd_u32(t1, t1), 0); +} +inline unsigned v_reduce_sad(const v_uint16x8& a, const v_uint16x8& b) +{ + uint32x4_t t0 = vpaddlq_u16(vabdq_u16(a.val, b.val)); + uint32x2_t t1 = vpadd_u32(vget_low_u32(t0), vget_high_u32(t0)); + return vget_lane_u32(vpadd_u32(t1, t1), 0); +} +inline unsigned v_reduce_sad(const v_int16x8& a, const v_int16x8& b) +{ + uint32x4_t t0 = vpaddlq_u16(vreinterpretq_u16_s16(vabdq_s16(a.val, b.val))); + uint32x2_t t1 = vpadd_u32(vget_low_u32(t0), vget_high_u32(t0)); + return vget_lane_u32(vpadd_u32(t1, t1), 0); +} +inline unsigned v_reduce_sad(const v_uint32x4& a, const v_uint32x4& b) +{ + uint32x4_t t0 = vabdq_u32(a.val, b.val); + uint32x2_t t1 = vpadd_u32(vget_low_u32(t0), vget_high_u32(t0)); + return vget_lane_u32(vpadd_u32(t1, t1), 0); +} +inline unsigned v_reduce_sad(const v_int32x4& a, const v_int32x4& b) +{ + uint32x4_t t0 = vreinterpretq_u32_s32(vabdq_s32(a.val, b.val)); + uint32x2_t t1 = vpadd_u32(vget_low_u32(t0), vget_high_u32(t0)); + return vget_lane_u32(vpadd_u32(t1, t1), 0); +} +inline float v_reduce_sad(const v_float32x4& a, const v_float32x4& b) +{ + float32x4_t t0 = vabdq_f32(a.val, b.val); + float32x2_t t1 = vpadd_f32(vget_low_f32(t0), vget_high_f32(t0)); + return vget_lane_f32(vpadd_f32(t1, t1), 0); +} + +#define OPENCV_HAL_IMPL_NEON_POPCOUNT(_Tpvec, cast) \ +inline v_uint32x4 v_popcount(const _Tpvec& a) \ +{ \ + uint8x16_t t = vcntq_u8(cast(a.val)); \ + uint16x8_t t0 = vpaddlq_u8(t); /* 16 -> 8 */ \ + uint32x4_t t1 = vpaddlq_u16(t0); /* 8 -> 4 */ \ + return v_uint32x4(t1); \ +} + +OPENCV_HAL_IMPL_NEON_POPCOUNT(v_uint8x16, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_NEON_POPCOUNT(v_uint16x8, vreinterpretq_u8_u16) +OPENCV_HAL_IMPL_NEON_POPCOUNT(v_uint32x4, vreinterpretq_u8_u32) +OPENCV_HAL_IMPL_NEON_POPCOUNT(v_int8x16, vreinterpretq_u8_s8) +OPENCV_HAL_IMPL_NEON_POPCOUNT(v_int16x8, vreinterpretq_u8_s16) +OPENCV_HAL_IMPL_NEON_POPCOUNT(v_int32x4, vreinterpretq_u8_s32) + +inline int v_signmask(const v_uint8x16& a) +{ + int8x8_t m0 = vcreate_s8(CV_BIG_UINT(0x0706050403020100)); + uint8x16_t v0 = vshlq_u8(vshrq_n_u8(a.val, 7), vcombine_s8(m0, m0)); + uint64x2_t v1 = vpaddlq_u32(vpaddlq_u16(vpaddlq_u8(v0))); + return (int)vgetq_lane_u64(v1, 0) + ((int)vgetq_lane_u64(v1, 1) << 8); +} +inline int v_signmask(const v_int8x16& a) +{ return v_signmask(v_reinterpret_as_u8(a)); } + +inline int v_signmask(const v_uint16x8& a) +{ + int16x4_t m0 = vcreate_s16(CV_BIG_UINT(0x0003000200010000)); + uint16x8_t v0 = vshlq_u16(vshrq_n_u16(a.val, 15), vcombine_s16(m0, m0)); + uint64x2_t v1 = vpaddlq_u32(vpaddlq_u16(v0)); + return (int)vgetq_lane_u64(v1, 0) + ((int)vgetq_lane_u64(v1, 1) << 4); +} +inline int v_signmask(const v_int16x8& a) +{ return v_signmask(v_reinterpret_as_u16(a)); } + +inline int v_signmask(const v_uint32x4& a) +{ + int32x2_t m0 = vcreate_s32(CV_BIG_UINT(0x0000000100000000)); + uint32x4_t v0 = vshlq_u32(vshrq_n_u32(a.val, 31), vcombine_s32(m0, m0)); + uint64x2_t v1 = vpaddlq_u32(v0); + return (int)vgetq_lane_u64(v1, 0) + ((int)vgetq_lane_u64(v1, 1) << 2); +} +inline int v_signmask(const v_int32x4& a) +{ return v_signmask(v_reinterpret_as_u32(a)); } +inline int v_signmask(const v_float32x4& a) +{ return v_signmask(v_reinterpret_as_u32(a)); } +#if CV_SIMD128_64F +inline int v_signmask(const v_uint64x2& a) +{ + int64x1_t m0 = vdup_n_s64(0); + uint64x2_t v0 = vshlq_u64(vshrq_n_u64(a.val, 63), vcombine_s64(m0, m0)); + return (int)vgetq_lane_u64(v0, 0) + ((int)vgetq_lane_u64(v0, 1) << 1); +} +inline int v_signmask(const v_float64x2& a) +{ return v_signmask(v_reinterpret_as_u64(a)); } +#endif + +#define OPENCV_HAL_IMPL_NEON_CHECK_ALLANY(_Tpvec, suffix, shift) \ +inline bool v_check_all(const v_##_Tpvec& a) \ +{ \ + _Tpvec##_t v0 = vshrq_n_##suffix(vmvnq_##suffix(a.val), shift); \ + uint64x2_t v1 = vreinterpretq_u64_##suffix(v0); \ + return (vgetq_lane_u64(v1, 0) | vgetq_lane_u64(v1, 1)) == 0; \ +} \ +inline bool v_check_any(const v_##_Tpvec& a) \ +{ \ + _Tpvec##_t v0 = vshrq_n_##suffix(a.val, shift); \ + uint64x2_t v1 = vreinterpretq_u64_##suffix(v0); \ + return (vgetq_lane_u64(v1, 0) | vgetq_lane_u64(v1, 1)) != 0; \ +} + +OPENCV_HAL_IMPL_NEON_CHECK_ALLANY(uint8x16, u8, 7) +OPENCV_HAL_IMPL_NEON_CHECK_ALLANY(uint16x8, u16, 15) +OPENCV_HAL_IMPL_NEON_CHECK_ALLANY(uint32x4, u32, 31) +#if CV_SIMD128_64F +OPENCV_HAL_IMPL_NEON_CHECK_ALLANY(uint64x2, u64, 63) +#endif + +inline bool v_check_all(const v_int8x16& a) +{ return v_check_all(v_reinterpret_as_u8(a)); } +inline bool v_check_all(const v_int16x8& a) +{ return v_check_all(v_reinterpret_as_u16(a)); } +inline bool v_check_all(const v_int32x4& a) +{ return v_check_all(v_reinterpret_as_u32(a)); } +inline bool v_check_all(const v_float32x4& a) +{ return v_check_all(v_reinterpret_as_u32(a)); } + +inline bool v_check_any(const v_int8x16& a) +{ return v_check_any(v_reinterpret_as_u8(a)); } +inline bool v_check_any(const v_int16x8& a) +{ return v_check_any(v_reinterpret_as_u16(a)); } +inline bool v_check_any(const v_int32x4& a) +{ return v_check_any(v_reinterpret_as_u32(a)); } +inline bool v_check_any(const v_float32x4& a) +{ return v_check_any(v_reinterpret_as_u32(a)); } + +#if CV_SIMD128_64F +inline bool v_check_all(const v_int64x2& a) +{ return v_check_all(v_reinterpret_as_u64(a)); } +inline bool v_check_all(const v_float64x2& a) +{ return v_check_all(v_reinterpret_as_u64(a)); } +inline bool v_check_any(const v_int64x2& a) +{ return v_check_any(v_reinterpret_as_u64(a)); } +inline bool v_check_any(const v_float64x2& a) +{ return v_check_any(v_reinterpret_as_u64(a)); } +#endif + +#define OPENCV_HAL_IMPL_NEON_SELECT(_Tpvec, suffix, usuffix) \ +inline _Tpvec v_select(const _Tpvec& mask, const _Tpvec& a, const _Tpvec& b) \ +{ \ + return _Tpvec(vbslq_##suffix(vreinterpretq_##usuffix##_##suffix(mask.val), a.val, b.val)); \ +} + +OPENCV_HAL_IMPL_NEON_SELECT(v_uint8x16, u8, u8) +OPENCV_HAL_IMPL_NEON_SELECT(v_int8x16, s8, u8) +OPENCV_HAL_IMPL_NEON_SELECT(v_uint16x8, u16, u16) +OPENCV_HAL_IMPL_NEON_SELECT(v_int16x8, s16, u16) +OPENCV_HAL_IMPL_NEON_SELECT(v_uint32x4, u32, u32) +OPENCV_HAL_IMPL_NEON_SELECT(v_int32x4, s32, u32) +OPENCV_HAL_IMPL_NEON_SELECT(v_float32x4, f32, u32) +#if CV_SIMD128_64F +OPENCV_HAL_IMPL_NEON_SELECT(v_float64x2, f64, u64) +#endif + +#define OPENCV_HAL_IMPL_NEON_EXPAND(_Tpvec, _Tpwvec, _Tp, suffix) \ +inline void v_expand(const _Tpvec& a, _Tpwvec& b0, _Tpwvec& b1) \ +{ \ + b0.val = vmovl_##suffix(vget_low_##suffix(a.val)); \ + b1.val = vmovl_##suffix(vget_high_##suffix(a.val)); \ +} \ +inline _Tpwvec v_expand_low(const _Tpvec& a) \ +{ \ + return _Tpwvec(vmovl_##suffix(vget_low_##suffix(a.val))); \ +} \ +inline _Tpwvec v_expand_high(const _Tpvec& a) \ +{ \ + return _Tpwvec(vmovl_##suffix(vget_high_##suffix(a.val))); \ +} \ +inline _Tpwvec v_load_expand(const _Tp* ptr) \ +{ \ + return _Tpwvec(vmovl_##suffix(vld1_##suffix(ptr))); \ +} + +OPENCV_HAL_IMPL_NEON_EXPAND(v_uint8x16, v_uint16x8, uchar, u8) +OPENCV_HAL_IMPL_NEON_EXPAND(v_int8x16, v_int16x8, schar, s8) +OPENCV_HAL_IMPL_NEON_EXPAND(v_uint16x8, v_uint32x4, ushort, u16) +OPENCV_HAL_IMPL_NEON_EXPAND(v_int16x8, v_int32x4, short, s16) +OPENCV_HAL_IMPL_NEON_EXPAND(v_uint32x4, v_uint64x2, uint, u32) +OPENCV_HAL_IMPL_NEON_EXPAND(v_int32x4, v_int64x2, int, s32) + +inline v_uint32x4 v_load_expand_q(const uchar* ptr) +{ + uint8x8_t v0 = vcreate_u8(*(unsigned*)ptr); + uint16x4_t v1 = vget_low_u16(vmovl_u8(v0)); + return v_uint32x4(vmovl_u16(v1)); +} + +inline v_int32x4 v_load_expand_q(const schar* ptr) +{ + int8x8_t v0 = vcreate_s8(*(unsigned*)ptr); + int16x4_t v1 = vget_low_s16(vmovl_s8(v0)); + return v_int32x4(vmovl_s16(v1)); +} + +#if defined(__aarch64__) +#define OPENCV_HAL_IMPL_NEON_UNPACKS(_Tpvec, suffix) \ +inline void v_zip(const v_##_Tpvec& a0, const v_##_Tpvec& a1, v_##_Tpvec& b0, v_##_Tpvec& b1) \ +{ \ + b0.val = vzip1q_##suffix(a0.val, a1.val); \ + b1.val = vzip2q_##suffix(a0.val, a1.val); \ +} \ +inline v_##_Tpvec v_combine_low(const v_##_Tpvec& a, const v_##_Tpvec& b) \ +{ \ + return v_##_Tpvec(vcombine_##suffix(vget_low_##suffix(a.val), vget_low_##suffix(b.val))); \ +} \ +inline v_##_Tpvec v_combine_high(const v_##_Tpvec& a, const v_##_Tpvec& b) \ +{ \ + return v_##_Tpvec(vcombine_##suffix(vget_high_##suffix(a.val), vget_high_##suffix(b.val))); \ +} \ +inline void v_recombine(const v_##_Tpvec& a, const v_##_Tpvec& b, v_##_Tpvec& c, v_##_Tpvec& d) \ +{ \ + c.val = vcombine_##suffix(vget_low_##suffix(a.val), vget_low_##suffix(b.val)); \ + d.val = vcombine_##suffix(vget_high_##suffix(a.val), vget_high_##suffix(b.val)); \ +} +#else +#define OPENCV_HAL_IMPL_NEON_UNPACKS(_Tpvec, suffix) \ +inline void v_zip(const v_##_Tpvec& a0, const v_##_Tpvec& a1, v_##_Tpvec& b0, v_##_Tpvec& b1) \ +{ \ + _Tpvec##x2_t p = vzipq_##suffix(a0.val, a1.val); \ + b0.val = p.val[0]; \ + b1.val = p.val[1]; \ +} \ +inline v_##_Tpvec v_combine_low(const v_##_Tpvec& a, const v_##_Tpvec& b) \ +{ \ + return v_##_Tpvec(vcombine_##suffix(vget_low_##suffix(a.val), vget_low_##suffix(b.val))); \ +} \ +inline v_##_Tpvec v_combine_high(const v_##_Tpvec& a, const v_##_Tpvec& b) \ +{ \ + return v_##_Tpvec(vcombine_##suffix(vget_high_##suffix(a.val), vget_high_##suffix(b.val))); \ +} \ +inline void v_recombine(const v_##_Tpvec& a, const v_##_Tpvec& b, v_##_Tpvec& c, v_##_Tpvec& d) \ +{ \ + c.val = vcombine_##suffix(vget_low_##suffix(a.val), vget_low_##suffix(b.val)); \ + d.val = vcombine_##suffix(vget_high_##suffix(a.val), vget_high_##suffix(b.val)); \ +} +#endif + +OPENCV_HAL_IMPL_NEON_UNPACKS(uint8x16, u8) +OPENCV_HAL_IMPL_NEON_UNPACKS(int8x16, s8) +OPENCV_HAL_IMPL_NEON_UNPACKS(uint16x8, u16) +OPENCV_HAL_IMPL_NEON_UNPACKS(int16x8, s16) +OPENCV_HAL_IMPL_NEON_UNPACKS(uint32x4, u32) +OPENCV_HAL_IMPL_NEON_UNPACKS(int32x4, s32) +OPENCV_HAL_IMPL_NEON_UNPACKS(float32x4, f32) +#if CV_SIMD128_64F +OPENCV_HAL_IMPL_NEON_UNPACKS(float64x2, f64) +#endif + +#define OPENCV_HAL_IMPL_NEON_EXTRACT(_Tpvec, suffix) \ +template \ +inline v_##_Tpvec v_extract(const v_##_Tpvec& a, const v_##_Tpvec& b) \ +{ \ + return v_##_Tpvec(vextq_##suffix(a.val, b.val, s)); \ +} + +OPENCV_HAL_IMPL_NEON_EXTRACT(uint8x16, u8) +OPENCV_HAL_IMPL_NEON_EXTRACT(int8x16, s8) +OPENCV_HAL_IMPL_NEON_EXTRACT(uint16x8, u16) +OPENCV_HAL_IMPL_NEON_EXTRACT(int16x8, s16) +OPENCV_HAL_IMPL_NEON_EXTRACT(uint32x4, u32) +OPENCV_HAL_IMPL_NEON_EXTRACT(int32x4, s32) +OPENCV_HAL_IMPL_NEON_EXTRACT(uint64x2, u64) +OPENCV_HAL_IMPL_NEON_EXTRACT(int64x2, s64) +OPENCV_HAL_IMPL_NEON_EXTRACT(float32x4, f32) +#if CV_SIMD128_64F +OPENCV_HAL_IMPL_NEON_EXTRACT(float64x2, f64) +#endif + +#if CV_SIMD128_64F +inline v_int32x4 v_round(const v_float32x4& a) +{ + float32x4_t a_ = a.val; + int32x4_t result; + __asm__ ("fcvtns %0.4s, %1.4s" + : "=w"(result) + : "w"(a_) + : /* No clobbers */); + return v_int32x4(result); +} +#else +inline v_int32x4 v_round(const v_float32x4& a) +{ + static const int32x4_t v_sign = vdupq_n_s32(1 << 31), + v_05 = vreinterpretq_s32_f32(vdupq_n_f32(0.5f)); + + int32x4_t v_addition = vorrq_s32(v_05, vandq_s32(v_sign, vreinterpretq_s32_f32(a.val))); + return v_int32x4(vcvtq_s32_f32(vaddq_f32(a.val, vreinterpretq_f32_s32(v_addition)))); +} +#endif +inline v_int32x4 v_floor(const v_float32x4& a) +{ + int32x4_t a1 = vcvtq_s32_f32(a.val); + uint32x4_t mask = vcgtq_f32(vcvtq_f32_s32(a1), a.val); + return v_int32x4(vaddq_s32(a1, vreinterpretq_s32_u32(mask))); +} + +inline v_int32x4 v_ceil(const v_float32x4& a) +{ + int32x4_t a1 = vcvtq_s32_f32(a.val); + uint32x4_t mask = vcgtq_f32(a.val, vcvtq_f32_s32(a1)); + return v_int32x4(vsubq_s32(a1, vreinterpretq_s32_u32(mask))); +} + +inline v_int32x4 v_trunc(const v_float32x4& a) +{ return v_int32x4(vcvtq_s32_f32(a.val)); } + +#if CV_SIMD128_64F +inline v_int32x4 v_round(const v_float64x2& a) +{ + static const int32x2_t zero = vdup_n_s32(0); + return v_int32x4(vcombine_s32(vmovn_s64(vcvtaq_s64_f64(a.val)), zero)); +} + +inline v_int32x4 v_round(const v_float64x2& a, const v_float64x2& b) +{ + return v_int32x4(vcombine_s32(vmovn_s64(vcvtaq_s64_f64(a.val)), vmovn_s64(vcvtaq_s64_f64(b.val)))); +} + +inline v_int32x4 v_floor(const v_float64x2& a) +{ + static const int32x2_t zero = vdup_n_s32(0); + int64x2_t a1 = vcvtq_s64_f64(a.val); + uint64x2_t mask = vcgtq_f64(vcvtq_f64_s64(a1), a.val); + a1 = vaddq_s64(a1, vreinterpretq_s64_u64(mask)); + return v_int32x4(vcombine_s32(vmovn_s64(a1), zero)); +} + +inline v_int32x4 v_ceil(const v_float64x2& a) +{ + static const int32x2_t zero = vdup_n_s32(0); + int64x2_t a1 = vcvtq_s64_f64(a.val); + uint64x2_t mask = vcgtq_f64(a.val, vcvtq_f64_s64(a1)); + a1 = vsubq_s64(a1, vreinterpretq_s64_u64(mask)); + return v_int32x4(vcombine_s32(vmovn_s64(a1), zero)); +} + +inline v_int32x4 v_trunc(const v_float64x2& a) +{ + static const int32x2_t zero = vdup_n_s32(0); + return v_int32x4(vcombine_s32(vmovn_s64(vcvtaq_s64_f64(a.val)), zero)); +} +#endif + +#define OPENCV_HAL_IMPL_NEON_TRANSPOSE4x4(_Tpvec, suffix) \ +inline void v_transpose4x4(const v_##_Tpvec& a0, const v_##_Tpvec& a1, \ + const v_##_Tpvec& a2, const v_##_Tpvec& a3, \ + v_##_Tpvec& b0, v_##_Tpvec& b1, \ + v_##_Tpvec& b2, v_##_Tpvec& b3) \ +{ \ + /* m00 m01 m02 m03 */ \ + /* m10 m11 m12 m13 */ \ + /* m20 m21 m22 m23 */ \ + /* m30 m31 m32 m33 */ \ + _Tpvec##x2_t t0 = vtrnq_##suffix(a0.val, a1.val); \ + _Tpvec##x2_t t1 = vtrnq_##suffix(a2.val, a3.val); \ + /* m00 m10 m02 m12 */ \ + /* m01 m11 m03 m13 */ \ + /* m20 m30 m22 m32 */ \ + /* m21 m31 m23 m33 */ \ + b0.val = vcombine_##suffix(vget_low_##suffix(t0.val[0]), vget_low_##suffix(t1.val[0])); \ + b1.val = vcombine_##suffix(vget_low_##suffix(t0.val[1]), vget_low_##suffix(t1.val[1])); \ + b2.val = vcombine_##suffix(vget_high_##suffix(t0.val[0]), vget_high_##suffix(t1.val[0])); \ + b3.val = vcombine_##suffix(vget_high_##suffix(t0.val[1]), vget_high_##suffix(t1.val[1])); \ +} + +OPENCV_HAL_IMPL_NEON_TRANSPOSE4x4(uint32x4, u32) +OPENCV_HAL_IMPL_NEON_TRANSPOSE4x4(int32x4, s32) +OPENCV_HAL_IMPL_NEON_TRANSPOSE4x4(float32x4, f32) + +#define OPENCV_HAL_IMPL_NEON_INTERLEAVED(_Tpvec, _Tp, suffix) \ +inline void v_load_deinterleave(const _Tp* ptr, v_##_Tpvec& a, v_##_Tpvec& b) \ +{ \ + _Tpvec##x2_t v = vld2q_##suffix(ptr); \ + a.val = v.val[0]; \ + b.val = v.val[1]; \ +} \ +inline void v_load_deinterleave(const _Tp* ptr, v_##_Tpvec& a, v_##_Tpvec& b, v_##_Tpvec& c) \ +{ \ + _Tpvec##x3_t v = vld3q_##suffix(ptr); \ + a.val = v.val[0]; \ + b.val = v.val[1]; \ + c.val = v.val[2]; \ +} \ +inline void v_load_deinterleave(const _Tp* ptr, v_##_Tpvec& a, v_##_Tpvec& b, \ + v_##_Tpvec& c, v_##_Tpvec& d) \ +{ \ + _Tpvec##x4_t v = vld4q_##suffix(ptr); \ + a.val = v.val[0]; \ + b.val = v.val[1]; \ + c.val = v.val[2]; \ + d.val = v.val[3]; \ +} \ +inline void v_store_interleave( _Tp* ptr, const v_##_Tpvec& a, const v_##_Tpvec& b, \ + hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \ +{ \ + _Tpvec##x2_t v; \ + v.val[0] = a.val; \ + v.val[1] = b.val; \ + vst2q_##suffix(ptr, v); \ +} \ +inline void v_store_interleave( _Tp* ptr, const v_##_Tpvec& a, const v_##_Tpvec& b, \ + const v_##_Tpvec& c, hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \ +{ \ + _Tpvec##x3_t v; \ + v.val[0] = a.val; \ + v.val[1] = b.val; \ + v.val[2] = c.val; \ + vst3q_##suffix(ptr, v); \ +} \ +inline void v_store_interleave( _Tp* ptr, const v_##_Tpvec& a, const v_##_Tpvec& b, \ + const v_##_Tpvec& c, const v_##_Tpvec& d, \ + hal::StoreMode /*mode*/=hal::STORE_UNALIGNED ) \ +{ \ + _Tpvec##x4_t v; \ + v.val[0] = a.val; \ + v.val[1] = b.val; \ + v.val[2] = c.val; \ + v.val[3] = d.val; \ + vst4q_##suffix(ptr, v); \ +} + +#define OPENCV_HAL_IMPL_NEON_INTERLEAVED_INT64(tp, suffix) \ +inline void v_load_deinterleave( const tp* ptr, v_##tp##x2& a, v_##tp##x2& b ) \ +{ \ + tp##x1_t a0 = vld1_##suffix(ptr); \ + tp##x1_t b0 = vld1_##suffix(ptr + 1); \ + tp##x1_t a1 = vld1_##suffix(ptr + 2); \ + tp##x1_t b1 = vld1_##suffix(ptr + 3); \ + a = v_##tp##x2(vcombine_##suffix(a0, a1)); \ + b = v_##tp##x2(vcombine_##suffix(b0, b1)); \ +} \ + \ +inline void v_load_deinterleave( const tp* ptr, v_##tp##x2& a, \ + v_##tp##x2& b, v_##tp##x2& c ) \ +{ \ + tp##x1_t a0 = vld1_##suffix(ptr); \ + tp##x1_t b0 = vld1_##suffix(ptr + 1); \ + tp##x1_t c0 = vld1_##suffix(ptr + 2); \ + tp##x1_t a1 = vld1_##suffix(ptr + 3); \ + tp##x1_t b1 = vld1_##suffix(ptr + 4); \ + tp##x1_t c1 = vld1_##suffix(ptr + 5); \ + a = v_##tp##x2(vcombine_##suffix(a0, a1)); \ + b = v_##tp##x2(vcombine_##suffix(b0, b1)); \ + c = v_##tp##x2(vcombine_##suffix(c0, c1)); \ +} \ + \ +inline void v_load_deinterleave( const tp* ptr, v_##tp##x2& a, v_##tp##x2& b, \ + v_##tp##x2& c, v_##tp##x2& d ) \ +{ \ + tp##x1_t a0 = vld1_##suffix(ptr); \ + tp##x1_t b0 = vld1_##suffix(ptr + 1); \ + tp##x1_t c0 = vld1_##suffix(ptr + 2); \ + tp##x1_t d0 = vld1_##suffix(ptr + 3); \ + tp##x1_t a1 = vld1_##suffix(ptr + 4); \ + tp##x1_t b1 = vld1_##suffix(ptr + 5); \ + tp##x1_t c1 = vld1_##suffix(ptr + 6); \ + tp##x1_t d1 = vld1_##suffix(ptr + 7); \ + a = v_##tp##x2(vcombine_##suffix(a0, a1)); \ + b = v_##tp##x2(vcombine_##suffix(b0, b1)); \ + c = v_##tp##x2(vcombine_##suffix(c0, c1)); \ + d = v_##tp##x2(vcombine_##suffix(d0, d1)); \ +} \ + \ +inline void v_store_interleave( tp* ptr, const v_##tp##x2& a, const v_##tp##x2& b, \ + hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \ +{ \ + vst1_##suffix(ptr, vget_low_##suffix(a.val)); \ + vst1_##suffix(ptr + 1, vget_low_##suffix(b.val)); \ + vst1_##suffix(ptr + 2, vget_high_##suffix(a.val)); \ + vst1_##suffix(ptr + 3, vget_high_##suffix(b.val)); \ +} \ + \ +inline void v_store_interleave( tp* ptr, const v_##tp##x2& a, \ + const v_##tp##x2& b, const v_##tp##x2& c, \ + hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \ +{ \ + vst1_##suffix(ptr, vget_low_##suffix(a.val)); \ + vst1_##suffix(ptr + 1, vget_low_##suffix(b.val)); \ + vst1_##suffix(ptr + 2, vget_low_##suffix(c.val)); \ + vst1_##suffix(ptr + 3, vget_high_##suffix(a.val)); \ + vst1_##suffix(ptr + 4, vget_high_##suffix(b.val)); \ + vst1_##suffix(ptr + 5, vget_high_##suffix(c.val)); \ +} \ + \ +inline void v_store_interleave( tp* ptr, const v_##tp##x2& a, const v_##tp##x2& b, \ + const v_##tp##x2& c, const v_##tp##x2& d, \ + hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \ +{ \ + vst1_##suffix(ptr, vget_low_##suffix(a.val)); \ + vst1_##suffix(ptr + 1, vget_low_##suffix(b.val)); \ + vst1_##suffix(ptr + 2, vget_low_##suffix(c.val)); \ + vst1_##suffix(ptr + 3, vget_low_##suffix(d.val)); \ + vst1_##suffix(ptr + 4, vget_high_##suffix(a.val)); \ + vst1_##suffix(ptr + 5, vget_high_##suffix(b.val)); \ + vst1_##suffix(ptr + 6, vget_high_##suffix(c.val)); \ + vst1_##suffix(ptr + 7, vget_high_##suffix(d.val)); \ +} + +OPENCV_HAL_IMPL_NEON_INTERLEAVED(uint8x16, uchar, u8) +OPENCV_HAL_IMPL_NEON_INTERLEAVED(int8x16, schar, s8) +OPENCV_HAL_IMPL_NEON_INTERLEAVED(uint16x8, ushort, u16) +OPENCV_HAL_IMPL_NEON_INTERLEAVED(int16x8, short, s16) +OPENCV_HAL_IMPL_NEON_INTERLEAVED(uint32x4, unsigned, u32) +OPENCV_HAL_IMPL_NEON_INTERLEAVED(int32x4, int, s32) +OPENCV_HAL_IMPL_NEON_INTERLEAVED(float32x4, float, f32) +#if CV_SIMD128_64F +OPENCV_HAL_IMPL_NEON_INTERLEAVED(float64x2, double, f64) +#endif + +OPENCV_HAL_IMPL_NEON_INTERLEAVED_INT64(int64, s64) +OPENCV_HAL_IMPL_NEON_INTERLEAVED_INT64(uint64, u64) + +inline v_float32x4 v_cvt_f32(const v_int32x4& a) +{ + return v_float32x4(vcvtq_f32_s32(a.val)); +} + +#if CV_SIMD128_64F +inline v_float32x4 v_cvt_f32(const v_float64x2& a) +{ + float32x2_t zero = vdup_n_f32(0.0f); + return v_float32x4(vcombine_f32(vcvt_f32_f64(a.val), zero)); +} + +inline v_float32x4 v_cvt_f32(const v_float64x2& a, const v_float64x2& b) +{ + return v_float32x4(vcombine_f32(vcvt_f32_f64(a.val), vcvt_f32_f64(b.val))); +} + +inline v_float64x2 v_cvt_f64(const v_int32x4& a) +{ + return v_float64x2(vcvt_f64_f32(vcvt_f32_s32(vget_low_s32(a.val)))); +} + +inline v_float64x2 v_cvt_f64_high(const v_int32x4& a) +{ + return v_float64x2(vcvt_f64_f32(vcvt_f32_s32(vget_high_s32(a.val)))); +} + +inline v_float64x2 v_cvt_f64(const v_float32x4& a) +{ + return v_float64x2(vcvt_f64_f32(vget_low_f32(a.val))); +} + +inline v_float64x2 v_cvt_f64_high(const v_float32x4& a) +{ + return v_float64x2(vcvt_f64_f32(vget_high_f32(a.val))); +} +#endif + +////////////// Lookup table access //////////////////// + +inline v_int32x4 v_lut(const int* tab, const v_int32x4& idxvec) +{ + int CV_DECL_ALIGNED(32) elems[4] = + { + tab[vgetq_lane_s32(idxvec.val, 0)], + tab[vgetq_lane_s32(idxvec.val, 1)], + tab[vgetq_lane_s32(idxvec.val, 2)], + tab[vgetq_lane_s32(idxvec.val, 3)] + }; + return v_int32x4(vld1q_s32(elems)); +} + +inline v_float32x4 v_lut(const float* tab, const v_int32x4& idxvec) +{ + float CV_DECL_ALIGNED(32) elems[4] = + { + tab[vgetq_lane_s32(idxvec.val, 0)], + tab[vgetq_lane_s32(idxvec.val, 1)], + tab[vgetq_lane_s32(idxvec.val, 2)], + tab[vgetq_lane_s32(idxvec.val, 3)] + }; + return v_float32x4(vld1q_f32(elems)); +} + +inline void v_lut_deinterleave(const float* tab, const v_int32x4& idxvec, v_float32x4& x, v_float32x4& y) +{ + /*int CV_DECL_ALIGNED(32) idx[4]; + v_store(idx, idxvec); + + float32x4_t xy02 = vcombine_f32(vld1_f32(tab + idx[0]), vld1_f32(tab + idx[2])); + float32x4_t xy13 = vcombine_f32(vld1_f32(tab + idx[1]), vld1_f32(tab + idx[3])); + + float32x4x2_t xxyy = vuzpq_f32(xy02, xy13); + x = v_float32x4(xxyy.val[0]); + y = v_float32x4(xxyy.val[1]);*/ + int CV_DECL_ALIGNED(32) idx[4]; + v_store_aligned(idx, idxvec); + + x = v_float32x4(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]]); + y = v_float32x4(tab[idx[0]+1], tab[idx[1]+1], tab[idx[2]+1], tab[idx[3]+1]); +} + +#if CV_SIMD128_64F +inline v_float64x2 v_lut(const double* tab, const v_int32x4& idxvec) +{ + double CV_DECL_ALIGNED(32) elems[2] = + { + tab[vgetq_lane_s32(idxvec.val, 0)], + tab[vgetq_lane_s32(idxvec.val, 1)], + }; + return v_float64x2(vld1q_f64(elems)); +} + +inline void v_lut_deinterleave(const double* tab, const v_int32x4& idxvec, v_float64x2& x, v_float64x2& y) +{ + int CV_DECL_ALIGNED(32) idx[4]; + v_store_aligned(idx, idxvec); + + x = v_float64x2(tab[idx[0]], tab[idx[1]]); + y = v_float64x2(tab[idx[0]+1], tab[idx[1]+1]); +} +#endif + +////// FP16 suport /////// +#if CV_FP16 +inline v_float32x4 v_load_expand(const float16_t* ptr) +{ + float16x4_t v = + #ifndef vld1_f16 // APPLE compiler defines vld1_f16 as macro + (float16x4_t)vld1_s16((const short*)ptr); + #else + vld1_f16((const __fp16*)ptr); + #endif + return v_float32x4(vcvt_f32_f16(v)); +} + +inline void v_pack_store(float16_t* ptr, const v_float32x4& v) +{ + float16x4_t hv = vcvt_f16_f32(v.val); + + #ifndef vst1_f16 // APPLE compiler defines vst1_f16 as macro + vst1_s16((short*)ptr, (int16x4_t)hv); + #else + vst1_f16((__fp16*)ptr, hv); + #endif +} +#else +inline v_float32x4 v_load_expand(const float16_t* ptr) +{ + const int N = 4; + float buf[N]; + for( int i = 0; i < N; i++ ) buf[i] = (float)ptr[i]; + return v_load(buf); +} + +inline void v_pack_store(float16_t* ptr, const v_float32x4& v) +{ + const int N = 4; + float buf[N]; + v_store(buf, v); + for( int i = 0; i < N; i++ ) ptr[i] = float16_t(buf[i]); +} +#endif + +inline void v_cleanup() {} + +//! @name Check SIMD support +//! @{ +//! @brief Check CPU capability of SIMD operation +static inline bool hasSIMD128() +{ + return (CV_CPU_HAS_SUPPORT_NEON) ? true : false; +} + +//! @} + +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END + +//! @endcond + +} + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/hal/intrin_sse.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/hal/intrin_sse.hpp new file mode 100644 index 0000000000000000000000000000000000000000..f7a67da1a5e614c7270ef860821ab1e22a7392ae --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/hal/intrin_sse.hpp @@ -0,0 +1,2816 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_HAL_SSE_HPP +#define OPENCV_HAL_SSE_HPP + +#include +#include "opencv2/core/utility.hpp" + +#define CV_SIMD128 1 +#define CV_SIMD128_64F 1 +#define CV_SIMD128_FP16 0 // no native operations with FP16 type. + +namespace cv +{ + +//! @cond IGNORED + +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN + +///////// Types //////////// + +struct v_uint8x16 +{ + typedef uchar lane_type; + typedef __m128i vector_type; + enum { nlanes = 16 }; + + v_uint8x16() : val(_mm_setzero_si128()) {} + explicit v_uint8x16(__m128i v) : val(v) {} + v_uint8x16(uchar v0, uchar v1, uchar v2, uchar v3, uchar v4, uchar v5, uchar v6, uchar v7, + uchar v8, uchar v9, uchar v10, uchar v11, uchar v12, uchar v13, uchar v14, uchar v15) + { + val = _mm_setr_epi8((char)v0, (char)v1, (char)v2, (char)v3, + (char)v4, (char)v5, (char)v6, (char)v7, + (char)v8, (char)v9, (char)v10, (char)v11, + (char)v12, (char)v13, (char)v14, (char)v15); + } + uchar get0() const + { + return (uchar)_mm_cvtsi128_si32(val); + } + + __m128i val; +}; + +struct v_int8x16 +{ + typedef schar lane_type; + typedef __m128i vector_type; + enum { nlanes = 16 }; + + v_int8x16() : val(_mm_setzero_si128()) {} + explicit v_int8x16(__m128i v) : val(v) {} + v_int8x16(schar v0, schar v1, schar v2, schar v3, schar v4, schar v5, schar v6, schar v7, + schar v8, schar v9, schar v10, schar v11, schar v12, schar v13, schar v14, schar v15) + { + val = _mm_setr_epi8((char)v0, (char)v1, (char)v2, (char)v3, + (char)v4, (char)v5, (char)v6, (char)v7, + (char)v8, (char)v9, (char)v10, (char)v11, + (char)v12, (char)v13, (char)v14, (char)v15); + } + schar get0() const + { + return (schar)_mm_cvtsi128_si32(val); + } + + __m128i val; +}; + +struct v_uint16x8 +{ + typedef ushort lane_type; + typedef __m128i vector_type; + enum { nlanes = 8 }; + + v_uint16x8() : val(_mm_setzero_si128()) {} + explicit v_uint16x8(__m128i v) : val(v) {} + v_uint16x8(ushort v0, ushort v1, ushort v2, ushort v3, ushort v4, ushort v5, ushort v6, ushort v7) + { + val = _mm_setr_epi16((short)v0, (short)v1, (short)v2, (short)v3, + (short)v4, (short)v5, (short)v6, (short)v7); + } + ushort get0() const + { + return (ushort)_mm_cvtsi128_si32(val); + } + + __m128i val; +}; + +struct v_int16x8 +{ + typedef short lane_type; + typedef __m128i vector_type; + enum { nlanes = 8 }; + + v_int16x8() : val(_mm_setzero_si128()) {} + explicit v_int16x8(__m128i v) : val(v) {} + v_int16x8(short v0, short v1, short v2, short v3, short v4, short v5, short v6, short v7) + { + val = _mm_setr_epi16((short)v0, (short)v1, (short)v2, (short)v3, + (short)v4, (short)v5, (short)v6, (short)v7); + } + short get0() const + { + return (short)_mm_cvtsi128_si32(val); + } + + __m128i val; +}; + +struct v_uint32x4 +{ + typedef unsigned lane_type; + typedef __m128i vector_type; + enum { nlanes = 4 }; + + v_uint32x4() : val(_mm_setzero_si128()) {} + explicit v_uint32x4(__m128i v) : val(v) {} + v_uint32x4(unsigned v0, unsigned v1, unsigned v2, unsigned v3) + { + val = _mm_setr_epi32((int)v0, (int)v1, (int)v2, (int)v3); + } + unsigned get0() const + { + return (unsigned)_mm_cvtsi128_si32(val); + } + + __m128i val; +}; + +struct v_int32x4 +{ + typedef int lane_type; + typedef __m128i vector_type; + enum { nlanes = 4 }; + + v_int32x4() : val(_mm_setzero_si128()) {} + explicit v_int32x4(__m128i v) : val(v) {} + v_int32x4(int v0, int v1, int v2, int v3) + { + val = _mm_setr_epi32(v0, v1, v2, v3); + } + int get0() const + { + return _mm_cvtsi128_si32(val); + } + + __m128i val; +}; + +struct v_float32x4 +{ + typedef float lane_type; + typedef __m128 vector_type; + enum { nlanes = 4 }; + + v_float32x4() : val(_mm_setzero_ps()) {} + explicit v_float32x4(__m128 v) : val(v) {} + v_float32x4(float v0, float v1, float v2, float v3) + { + val = _mm_setr_ps(v0, v1, v2, v3); + } + float get0() const + { + return _mm_cvtss_f32(val); + } + + __m128 val; +}; + +struct v_uint64x2 +{ + typedef uint64 lane_type; + typedef __m128i vector_type; + enum { nlanes = 2 }; + + v_uint64x2() : val(_mm_setzero_si128()) {} + explicit v_uint64x2(__m128i v) : val(v) {} + v_uint64x2(uint64 v0, uint64 v1) + { + val = _mm_setr_epi32((int)v0, (int)(v0 >> 32), (int)v1, (int)(v1 >> 32)); + } + uint64 get0() const + { + int a = _mm_cvtsi128_si32(val); + int b = _mm_cvtsi128_si32(_mm_srli_epi64(val, 32)); + return (unsigned)a | ((uint64)(unsigned)b << 32); + } + + __m128i val; +}; + +struct v_int64x2 +{ + typedef int64 lane_type; + typedef __m128i vector_type; + enum { nlanes = 2 }; + + v_int64x2() : val(_mm_setzero_si128()) {} + explicit v_int64x2(__m128i v) : val(v) {} + v_int64x2(int64 v0, int64 v1) + { + val = _mm_setr_epi32((int)v0, (int)(v0 >> 32), (int)v1, (int)(v1 >> 32)); + } + int64 get0() const + { + int a = _mm_cvtsi128_si32(val); + int b = _mm_cvtsi128_si32(_mm_srli_epi64(val, 32)); + return (int64)((unsigned)a | ((uint64)(unsigned)b << 32)); + } + + __m128i val; +}; + +struct v_float64x2 +{ + typedef double lane_type; + typedef __m128d vector_type; + enum { nlanes = 2 }; + + v_float64x2() : val(_mm_setzero_pd()) {} + explicit v_float64x2(__m128d v) : val(v) {} + v_float64x2(double v0, double v1) + { + val = _mm_setr_pd(v0, v1); + } + double get0() const + { + return _mm_cvtsd_f64(val); + } + + __m128d val; +}; + +namespace hal_sse_internal +{ + template + to_sse_type v_sse_reinterpret_as(const from_sse_type& val); + +#define OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(to_sse_type, from_sse_type, sse_cast_intrin) \ + template<> inline \ + to_sse_type v_sse_reinterpret_as(const from_sse_type& a) \ + { return sse_cast_intrin(a); } + + OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128i, __m128i, OPENCV_HAL_NOP) + OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128i, __m128, _mm_castps_si128) + OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128i, __m128d, _mm_castpd_si128) + OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128, __m128i, _mm_castsi128_ps) + OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128, __m128, OPENCV_HAL_NOP) + OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128, __m128d, _mm_castpd_ps) + OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128d, __m128i, _mm_castsi128_pd) + OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128d, __m128, _mm_castps_pd) + OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128d, __m128d, OPENCV_HAL_NOP) +} + +#define OPENCV_HAL_IMPL_SSE_INITVEC(_Tpvec, _Tp, suffix, zsuffix, ssuffix, _Tps, cast) \ +inline _Tpvec v_setzero_##suffix() { return _Tpvec(_mm_setzero_##zsuffix()); } \ +inline _Tpvec v_setall_##suffix(_Tp v) { return _Tpvec(_mm_set1_##ssuffix((_Tps)v)); } \ +template inline _Tpvec v_reinterpret_as_##suffix(const _Tpvec0& a) \ +{ return _Tpvec(cast(a.val)); } + +OPENCV_HAL_IMPL_SSE_INITVEC(v_uint8x16, uchar, u8, si128, epi8, char, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_INITVEC(v_int8x16, schar, s8, si128, epi8, char, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_INITVEC(v_uint16x8, ushort, u16, si128, epi16, short, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_INITVEC(v_int16x8, short, s16, si128, epi16, short, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_INITVEC(v_uint32x4, unsigned, u32, si128, epi32, int, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_INITVEC(v_int32x4, int, s32, si128, epi32, int, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_INITVEC(v_float32x4, float, f32, ps, ps, float, _mm_castsi128_ps) +OPENCV_HAL_IMPL_SSE_INITVEC(v_float64x2, double, f64, pd, pd, double, _mm_castsi128_pd) + +inline v_uint64x2 v_setzero_u64() { return v_uint64x2(_mm_setzero_si128()); } +inline v_int64x2 v_setzero_s64() { return v_int64x2(_mm_setzero_si128()); } +inline v_uint64x2 v_setall_u64(uint64 val) { return v_uint64x2(val, val); } +inline v_int64x2 v_setall_s64(int64 val) { return v_int64x2(val, val); } + +template inline +v_uint64x2 v_reinterpret_as_u64(const _Tpvec& a) { return v_uint64x2(a.val); } +template inline +v_int64x2 v_reinterpret_as_s64(const _Tpvec& a) { return v_int64x2(a.val); } +inline v_float32x4 v_reinterpret_as_f32(const v_uint64x2& a) +{ return v_float32x4(_mm_castsi128_ps(a.val)); } +inline v_float32x4 v_reinterpret_as_f32(const v_int64x2& a) +{ return v_float32x4(_mm_castsi128_ps(a.val)); } +inline v_float64x2 v_reinterpret_as_f64(const v_uint64x2& a) +{ return v_float64x2(_mm_castsi128_pd(a.val)); } +inline v_float64x2 v_reinterpret_as_f64(const v_int64x2& a) +{ return v_float64x2(_mm_castsi128_pd(a.val)); } + +#define OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(_Tpvec, suffix) \ +inline _Tpvec v_reinterpret_as_##suffix(const v_float32x4& a) \ +{ return _Tpvec(_mm_castps_si128(a.val)); } \ +inline _Tpvec v_reinterpret_as_##suffix(const v_float64x2& a) \ +{ return _Tpvec(_mm_castpd_si128(a.val)); } + +OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_uint8x16, u8) +OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_int8x16, s8) +OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_uint16x8, u16) +OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_int16x8, s16) +OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_uint32x4, u32) +OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_int32x4, s32) +OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_uint64x2, u64) +OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_int64x2, s64) + +inline v_float32x4 v_reinterpret_as_f32(const v_float32x4& a) {return a; } +inline v_float64x2 v_reinterpret_as_f64(const v_float64x2& a) {return a; } +inline v_float32x4 v_reinterpret_as_f32(const v_float64x2& a) {return v_float32x4(_mm_castpd_ps(a.val)); } +inline v_float64x2 v_reinterpret_as_f64(const v_float32x4& a) {return v_float64x2(_mm_castps_pd(a.val)); } + +//////////////// PACK /////////////// +inline v_uint8x16 v_pack(const v_uint16x8& a, const v_uint16x8& b) +{ + __m128i delta = _mm_set1_epi16(255); + return v_uint8x16(_mm_packus_epi16(_mm_subs_epu16(a.val, _mm_subs_epu16(a.val, delta)), + _mm_subs_epu16(b.val, _mm_subs_epu16(b.val, delta)))); +} + +inline void v_pack_store(uchar* ptr, const v_uint16x8& a) +{ + __m128i delta = _mm_set1_epi16(255); + __m128i a1 = _mm_subs_epu16(a.val, _mm_subs_epu16(a.val, delta)); + _mm_storel_epi64((__m128i*)ptr, _mm_packus_epi16(a1, a1)); +} + +inline v_uint8x16 v_pack_u(const v_int16x8& a, const v_int16x8& b) +{ return v_uint8x16(_mm_packus_epi16(a.val, b.val)); } + +inline void v_pack_u_store(uchar* ptr, const v_int16x8& a) +{ _mm_storel_epi64((__m128i*)ptr, _mm_packus_epi16(a.val, a.val)); } + +template inline +v_uint8x16 v_rshr_pack(const v_uint16x8& a, const v_uint16x8& b) +{ + // we assume that n > 0, and so the shifted 16-bit values can be treated as signed numbers. + __m128i delta = _mm_set1_epi16((short)(1 << (n-1))); + return v_uint8x16(_mm_packus_epi16(_mm_srli_epi16(_mm_adds_epu16(a.val, delta), n), + _mm_srli_epi16(_mm_adds_epu16(b.val, delta), n))); +} + +template inline +void v_rshr_pack_store(uchar* ptr, const v_uint16x8& a) +{ + __m128i delta = _mm_set1_epi16((short)(1 << (n-1))); + __m128i a1 = _mm_srli_epi16(_mm_adds_epu16(a.val, delta), n); + _mm_storel_epi64((__m128i*)ptr, _mm_packus_epi16(a1, a1)); +} + +template inline +v_uint8x16 v_rshr_pack_u(const v_int16x8& a, const v_int16x8& b) +{ + __m128i delta = _mm_set1_epi16((short)(1 << (n-1))); + return v_uint8x16(_mm_packus_epi16(_mm_srai_epi16(_mm_adds_epi16(a.val, delta), n), + _mm_srai_epi16(_mm_adds_epi16(b.val, delta), n))); +} + +template inline +void v_rshr_pack_u_store(uchar* ptr, const v_int16x8& a) +{ + __m128i delta = _mm_set1_epi16((short)(1 << (n-1))); + __m128i a1 = _mm_srai_epi16(_mm_adds_epi16(a.val, delta), n); + _mm_storel_epi64((__m128i*)ptr, _mm_packus_epi16(a1, a1)); +} + +inline v_int8x16 v_pack(const v_int16x8& a, const v_int16x8& b) +{ return v_int8x16(_mm_packs_epi16(a.val, b.val)); } + +inline void v_pack_store(schar* ptr, const v_int16x8& a) +{ _mm_storel_epi64((__m128i*)ptr, _mm_packs_epi16(a.val, a.val)); } + +template inline +v_int8x16 v_rshr_pack(const v_int16x8& a, const v_int16x8& b) +{ + // we assume that n > 0, and so the shifted 16-bit values can be treated as signed numbers. + __m128i delta = _mm_set1_epi16((short)(1 << (n-1))); + return v_int8x16(_mm_packs_epi16(_mm_srai_epi16(_mm_adds_epi16(a.val, delta), n), + _mm_srai_epi16(_mm_adds_epi16(b.val, delta), n))); +} +template inline +void v_rshr_pack_store(schar* ptr, const v_int16x8& a) +{ + // we assume that n > 0, and so the shifted 16-bit values can be treated as signed numbers. + __m128i delta = _mm_set1_epi16((short)(1 << (n-1))); + __m128i a1 = _mm_srai_epi16(_mm_adds_epi16(a.val, delta), n); + _mm_storel_epi64((__m128i*)ptr, _mm_packs_epi16(a1, a1)); +} + + +// byte-wise "mask ? a : b" +inline __m128i v_select_si128(__m128i mask, __m128i a, __m128i b) +{ +#if CV_SSE4_1 + return _mm_blendv_epi8(b, a, mask); +#else + return _mm_xor_si128(b, _mm_and_si128(_mm_xor_si128(a, b), mask)); +#endif +} + +inline v_uint16x8 v_pack(const v_uint32x4& a, const v_uint32x4& b) +{ return v_uint16x8(_v128_packs_epu32(a.val, b.val)); } + +inline void v_pack_store(ushort* ptr, const v_uint32x4& a) +{ + __m128i z = _mm_setzero_si128(), maxval32 = _mm_set1_epi32(65535), delta32 = _mm_set1_epi32(32768); + __m128i a1 = _mm_sub_epi32(v_select_si128(_mm_cmpgt_epi32(z, a.val), maxval32, a.val), delta32); + __m128i r = _mm_packs_epi32(a1, a1); + _mm_storel_epi64((__m128i*)ptr, _mm_sub_epi16(r, _mm_set1_epi16(-32768))); +} + +template inline +v_uint16x8 v_rshr_pack(const v_uint32x4& a, const v_uint32x4& b) +{ + __m128i delta = _mm_set1_epi32(1 << (n-1)), delta32 = _mm_set1_epi32(32768); + __m128i a1 = _mm_sub_epi32(_mm_srli_epi32(_mm_add_epi32(a.val, delta), n), delta32); + __m128i b1 = _mm_sub_epi32(_mm_srli_epi32(_mm_add_epi32(b.val, delta), n), delta32); + return v_uint16x8(_mm_sub_epi16(_mm_packs_epi32(a1, b1), _mm_set1_epi16(-32768))); +} + +template inline +void v_rshr_pack_store(ushort* ptr, const v_uint32x4& a) +{ + __m128i delta = _mm_set1_epi32(1 << (n-1)), delta32 = _mm_set1_epi32(32768); + __m128i a1 = _mm_sub_epi32(_mm_srli_epi32(_mm_add_epi32(a.val, delta), n), delta32); + __m128i a2 = _mm_sub_epi16(_mm_packs_epi32(a1, a1), _mm_set1_epi16(-32768)); + _mm_storel_epi64((__m128i*)ptr, a2); +} + +inline v_uint16x8 v_pack_u(const v_int32x4& a, const v_int32x4& b) +{ +#if CV_SSE4_1 + return v_uint16x8(_mm_packus_epi32(a.val, b.val)); +#else + __m128i delta32 = _mm_set1_epi32(32768); + + // preliminary saturate negative values to zero + __m128i a1 = _mm_and_si128(a.val, _mm_cmpgt_epi32(a.val, _mm_set1_epi32(0))); + __m128i b1 = _mm_and_si128(b.val, _mm_cmpgt_epi32(b.val, _mm_set1_epi32(0))); + + __m128i r = _mm_packs_epi32(_mm_sub_epi32(a1, delta32), _mm_sub_epi32(b1, delta32)); + return v_uint16x8(_mm_sub_epi16(r, _mm_set1_epi16(-32768))); +#endif +} + +inline void v_pack_u_store(ushort* ptr, const v_int32x4& a) +{ +#if CV_SSE4_1 + _mm_storel_epi64((__m128i*)ptr, _mm_packus_epi32(a.val, a.val)); +#else + __m128i delta32 = _mm_set1_epi32(32768); + __m128i a1 = _mm_sub_epi32(a.val, delta32); + __m128i r = _mm_sub_epi16(_mm_packs_epi32(a1, a1), _mm_set1_epi16(-32768)); + _mm_storel_epi64((__m128i*)ptr, r); +#endif +} + +template inline +v_uint16x8 v_rshr_pack_u(const v_int32x4& a, const v_int32x4& b) +{ +#if CV_SSE4_1 + __m128i delta = _mm_set1_epi32(1 << (n - 1)); + return v_uint16x8(_mm_packus_epi32(_mm_srai_epi32(_mm_add_epi32(a.val, delta), n), + _mm_srai_epi32(_mm_add_epi32(b.val, delta), n))); +#else + __m128i delta = _mm_set1_epi32(1 << (n-1)), delta32 = _mm_set1_epi32(32768); + __m128i a1 = _mm_sub_epi32(_mm_srai_epi32(_mm_add_epi32(a.val, delta), n), delta32); + __m128i a2 = _mm_sub_epi16(_mm_packs_epi32(a1, a1), _mm_set1_epi16(-32768)); + __m128i b1 = _mm_sub_epi32(_mm_srai_epi32(_mm_add_epi32(b.val, delta), n), delta32); + __m128i b2 = _mm_sub_epi16(_mm_packs_epi32(b1, b1), _mm_set1_epi16(-32768)); + return v_uint16x8(_mm_unpacklo_epi64(a2, b2)); +#endif +} + +template inline +void v_rshr_pack_u_store(ushort* ptr, const v_int32x4& a) +{ +#if CV_SSE4_1 + __m128i delta = _mm_set1_epi32(1 << (n - 1)); + __m128i a1 = _mm_srai_epi32(_mm_add_epi32(a.val, delta), n); + _mm_storel_epi64((__m128i*)ptr, _mm_packus_epi32(a1, a1)); +#else + __m128i delta = _mm_set1_epi32(1 << (n-1)), delta32 = _mm_set1_epi32(32768); + __m128i a1 = _mm_sub_epi32(_mm_srai_epi32(_mm_add_epi32(a.val, delta), n), delta32); + __m128i a2 = _mm_sub_epi16(_mm_packs_epi32(a1, a1), _mm_set1_epi16(-32768)); + _mm_storel_epi64((__m128i*)ptr, a2); +#endif +} + +inline v_int16x8 v_pack(const v_int32x4& a, const v_int32x4& b) +{ return v_int16x8(_mm_packs_epi32(a.val, b.val)); } + +inline void v_pack_store(short* ptr, const v_int32x4& a) +{ + _mm_storel_epi64((__m128i*)ptr, _mm_packs_epi32(a.val, a.val)); +} + +template inline +v_int16x8 v_rshr_pack(const v_int32x4& a, const v_int32x4& b) +{ + __m128i delta = _mm_set1_epi32(1 << (n-1)); + return v_int16x8(_mm_packs_epi32(_mm_srai_epi32(_mm_add_epi32(a.val, delta), n), + _mm_srai_epi32(_mm_add_epi32(b.val, delta), n))); +} + +template inline +void v_rshr_pack_store(short* ptr, const v_int32x4& a) +{ + __m128i delta = _mm_set1_epi32(1 << (n-1)); + __m128i a1 = _mm_srai_epi32(_mm_add_epi32(a.val, delta), n); + _mm_storel_epi64((__m128i*)ptr, _mm_packs_epi32(a1, a1)); +} + + +// [a0 0 | b0 0] [a1 0 | b1 0] +inline v_uint32x4 v_pack(const v_uint64x2& a, const v_uint64x2& b) +{ + __m128i v0 = _mm_unpacklo_epi32(a.val, b.val); // a0 a1 0 0 + __m128i v1 = _mm_unpackhi_epi32(a.val, b.val); // b0 b1 0 0 + return v_uint32x4(_mm_unpacklo_epi32(v0, v1)); +} + +inline void v_pack_store(unsigned* ptr, const v_uint64x2& a) +{ + __m128i a1 = _mm_shuffle_epi32(a.val, _MM_SHUFFLE(0, 2, 2, 0)); + _mm_storel_epi64((__m128i*)ptr, a1); +} + +// [a0 0 | b0 0] [a1 0 | b1 0] +inline v_int32x4 v_pack(const v_int64x2& a, const v_int64x2& b) +{ + __m128i v0 = _mm_unpacklo_epi32(a.val, b.val); // a0 a1 0 0 + __m128i v1 = _mm_unpackhi_epi32(a.val, b.val); // b0 b1 0 0 + return v_int32x4(_mm_unpacklo_epi32(v0, v1)); +} + +inline void v_pack_store(int* ptr, const v_int64x2& a) +{ + __m128i a1 = _mm_shuffle_epi32(a.val, _MM_SHUFFLE(0, 2, 2, 0)); + _mm_storel_epi64((__m128i*)ptr, a1); +} + +template inline +v_uint32x4 v_rshr_pack(const v_uint64x2& a, const v_uint64x2& b) +{ + uint64 delta = (uint64)1 << (n-1); + v_uint64x2 delta2(delta, delta); + __m128i a1 = _mm_srli_epi64(_mm_add_epi64(a.val, delta2.val), n); + __m128i b1 = _mm_srli_epi64(_mm_add_epi64(b.val, delta2.val), n); + __m128i v0 = _mm_unpacklo_epi32(a1, b1); // a0 a1 0 0 + __m128i v1 = _mm_unpackhi_epi32(a1, b1); // b0 b1 0 0 + return v_uint32x4(_mm_unpacklo_epi32(v0, v1)); +} + +template inline +void v_rshr_pack_store(unsigned* ptr, const v_uint64x2& a) +{ + uint64 delta = (uint64)1 << (n-1); + v_uint64x2 delta2(delta, delta); + __m128i a1 = _mm_srli_epi64(_mm_add_epi64(a.val, delta2.val), n); + __m128i a2 = _mm_shuffle_epi32(a1, _MM_SHUFFLE(0, 2, 2, 0)); + _mm_storel_epi64((__m128i*)ptr, a2); +} + +inline __m128i v_sign_epi64(__m128i a) +{ + return _mm_shuffle_epi32(_mm_srai_epi32(a, 31), _MM_SHUFFLE(3, 3, 1, 1)); // x m0 | x m1 +} + +inline __m128i v_srai_epi64(__m128i a, int imm) +{ + __m128i smask = v_sign_epi64(a); + return _mm_xor_si128(_mm_srli_epi64(_mm_xor_si128(a, smask), imm), smask); +} + +template inline +v_int32x4 v_rshr_pack(const v_int64x2& a, const v_int64x2& b) +{ + int64 delta = (int64)1 << (n-1); + v_int64x2 delta2(delta, delta); + __m128i a1 = v_srai_epi64(_mm_add_epi64(a.val, delta2.val), n); + __m128i b1 = v_srai_epi64(_mm_add_epi64(b.val, delta2.val), n); + __m128i v0 = _mm_unpacklo_epi32(a1, b1); // a0 a1 0 0 + __m128i v1 = _mm_unpackhi_epi32(a1, b1); // b0 b1 0 0 + return v_int32x4(_mm_unpacklo_epi32(v0, v1)); +} + +template inline +void v_rshr_pack_store(int* ptr, const v_int64x2& a) +{ + int64 delta = (int64)1 << (n-1); + v_int64x2 delta2(delta, delta); + __m128i a1 = v_srai_epi64(_mm_add_epi64(a.val, delta2.val), n); + __m128i a2 = _mm_shuffle_epi32(a1, _MM_SHUFFLE(0, 2, 2, 0)); + _mm_storel_epi64((__m128i*)ptr, a2); +} + +// pack boolean +inline v_uint8x16 v_pack_b(const v_uint16x8& a, const v_uint16x8& b) +{ + __m128i ab = _mm_packs_epi16(a.val, b.val); + return v_uint8x16(ab); +} + +inline v_uint8x16 v_pack_b(const v_uint32x4& a, const v_uint32x4& b, + const v_uint32x4& c, const v_uint32x4& d) +{ + __m128i ab = _mm_packs_epi32(a.val, b.val); + __m128i cd = _mm_packs_epi32(c.val, d.val); + return v_uint8x16(_mm_packs_epi16(ab, cd)); +} + +inline v_uint8x16 v_pack_b(const v_uint64x2& a, const v_uint64x2& b, const v_uint64x2& c, + const v_uint64x2& d, const v_uint64x2& e, const v_uint64x2& f, + const v_uint64x2& g, const v_uint64x2& h) +{ + __m128i ab = _mm_packs_epi32(a.val, b.val); + __m128i cd = _mm_packs_epi32(c.val, d.val); + __m128i ef = _mm_packs_epi32(e.val, f.val); + __m128i gh = _mm_packs_epi32(g.val, h.val); + + __m128i abcd = _mm_packs_epi32(ab, cd); + __m128i efgh = _mm_packs_epi32(ef, gh); + return v_uint8x16(_mm_packs_epi16(abcd, efgh)); +} + +inline v_float32x4 v_matmul(const v_float32x4& v, const v_float32x4& m0, + const v_float32x4& m1, const v_float32x4& m2, + const v_float32x4& m3) +{ + __m128 v0 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(0, 0, 0, 0)), m0.val); + __m128 v1 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(1, 1, 1, 1)), m1.val); + __m128 v2 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(2, 2, 2, 2)), m2.val); + __m128 v3 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(3, 3, 3, 3)), m3.val); + + return v_float32x4(_mm_add_ps(_mm_add_ps(v0, v1), _mm_add_ps(v2, v3))); +} + +inline v_float32x4 v_matmuladd(const v_float32x4& v, const v_float32x4& m0, + const v_float32x4& m1, const v_float32x4& m2, + const v_float32x4& a) +{ + __m128 v0 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(0, 0, 0, 0)), m0.val); + __m128 v1 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(1, 1, 1, 1)), m1.val); + __m128 v2 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(2, 2, 2, 2)), m2.val); + + return v_float32x4(_mm_add_ps(_mm_add_ps(v0, v1), _mm_add_ps(v2, a.val))); +} + +#define OPENCV_HAL_IMPL_SSE_BIN_OP(bin_op, _Tpvec, intrin) \ + inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b) \ + { \ + return _Tpvec(intrin(a.val, b.val)); \ + } \ + inline _Tpvec& operator bin_op##= (_Tpvec& a, const _Tpvec& b) \ + { \ + a.val = intrin(a.val, b.val); \ + return a; \ + } + +OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_uint8x16, _mm_adds_epu8) +OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_uint8x16, _mm_subs_epu8) +OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_int8x16, _mm_adds_epi8) +OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_int8x16, _mm_subs_epi8) +OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_uint16x8, _mm_adds_epu16) +OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_uint16x8, _mm_subs_epu16) +OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_int16x8, _mm_adds_epi16) +OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_int16x8, _mm_subs_epi16) +OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_uint32x4, _mm_add_epi32) +OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_uint32x4, _mm_sub_epi32) +OPENCV_HAL_IMPL_SSE_BIN_OP(*, v_uint32x4, _v128_mullo_epi32) +OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_int32x4, _mm_add_epi32) +OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_int32x4, _mm_sub_epi32) +OPENCV_HAL_IMPL_SSE_BIN_OP(*, v_int32x4, _v128_mullo_epi32) +OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_float32x4, _mm_add_ps) +OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_float32x4, _mm_sub_ps) +OPENCV_HAL_IMPL_SSE_BIN_OP(*, v_float32x4, _mm_mul_ps) +OPENCV_HAL_IMPL_SSE_BIN_OP(/, v_float32x4, _mm_div_ps) +OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_float64x2, _mm_add_pd) +OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_float64x2, _mm_sub_pd) +OPENCV_HAL_IMPL_SSE_BIN_OP(*, v_float64x2, _mm_mul_pd) +OPENCV_HAL_IMPL_SSE_BIN_OP(/, v_float64x2, _mm_div_pd) +OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_uint64x2, _mm_add_epi64) +OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_uint64x2, _mm_sub_epi64) +OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_int64x2, _mm_add_epi64) +OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_int64x2, _mm_sub_epi64) + +// saturating multiply 8-bit, 16-bit +#define OPENCV_HAL_IMPL_SSE_MUL_SAT(_Tpvec, _Tpwvec) \ + inline _Tpvec operator * (const _Tpvec& a, const _Tpvec& b) \ + { \ + _Tpwvec c, d; \ + v_mul_expand(a, b, c, d); \ + return v_pack(c, d); \ + } \ + inline _Tpvec& operator *= (_Tpvec& a, const _Tpvec& b) \ + { a = a * b; return a; } + +OPENCV_HAL_IMPL_SSE_MUL_SAT(v_uint8x16, v_uint16x8) +OPENCV_HAL_IMPL_SSE_MUL_SAT(v_int8x16, v_int16x8) +OPENCV_HAL_IMPL_SSE_MUL_SAT(v_uint16x8, v_uint32x4) +OPENCV_HAL_IMPL_SSE_MUL_SAT(v_int16x8, v_int32x4) + +// Multiply and expand +inline void v_mul_expand(const v_uint8x16& a, const v_uint8x16& b, + v_uint16x8& c, v_uint16x8& d) +{ + v_uint16x8 a0, a1, b0, b1; + v_expand(a, a0, a1); + v_expand(b, b0, b1); + c = v_mul_wrap(a0, b0); + d = v_mul_wrap(a1, b1); +} + +inline void v_mul_expand(const v_int8x16& a, const v_int8x16& b, + v_int16x8& c, v_int16x8& d) +{ + v_int16x8 a0, a1, b0, b1; + v_expand(a, a0, a1); + v_expand(b, b0, b1); + c = v_mul_wrap(a0, b0); + d = v_mul_wrap(a1, b1); +} + +inline void v_mul_expand(const v_int16x8& a, const v_int16x8& b, + v_int32x4& c, v_int32x4& d) +{ + __m128i v0 = _mm_mullo_epi16(a.val, b.val); + __m128i v1 = _mm_mulhi_epi16(a.val, b.val); + c.val = _mm_unpacklo_epi16(v0, v1); + d.val = _mm_unpackhi_epi16(v0, v1); +} + +inline void v_mul_expand(const v_uint16x8& a, const v_uint16x8& b, + v_uint32x4& c, v_uint32x4& d) +{ + __m128i v0 = _mm_mullo_epi16(a.val, b.val); + __m128i v1 = _mm_mulhi_epu16(a.val, b.val); + c.val = _mm_unpacklo_epi16(v0, v1); + d.val = _mm_unpackhi_epi16(v0, v1); +} + +inline void v_mul_expand(const v_uint32x4& a, const v_uint32x4& b, + v_uint64x2& c, v_uint64x2& d) +{ + __m128i c0 = _mm_mul_epu32(a.val, b.val); + __m128i c1 = _mm_mul_epu32(_mm_srli_epi64(a.val, 32), _mm_srli_epi64(b.val, 32)); + c.val = _mm_unpacklo_epi64(c0, c1); + d.val = _mm_unpackhi_epi64(c0, c1); +} + +inline v_int16x8 v_mul_hi(const v_int16x8& a, const v_int16x8& b) { return v_int16x8(_mm_mulhi_epi16(a.val, b.val)); } +inline v_uint16x8 v_mul_hi(const v_uint16x8& a, const v_uint16x8& b) { return v_uint16x8(_mm_mulhi_epu16(a.val, b.val)); } + +inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b) +{ + return v_int32x4(_mm_madd_epi16(a.val, b.val)); +} + +inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b, const v_int32x4& c) +{ + return v_int32x4(_mm_add_epi32(_mm_madd_epi16(a.val, b.val), c.val)); +} + +#define OPENCV_HAL_IMPL_SSE_LOGIC_OP(_Tpvec, suffix, not_const) \ + OPENCV_HAL_IMPL_SSE_BIN_OP(&, _Tpvec, _mm_and_##suffix) \ + OPENCV_HAL_IMPL_SSE_BIN_OP(|, _Tpvec, _mm_or_##suffix) \ + OPENCV_HAL_IMPL_SSE_BIN_OP(^, _Tpvec, _mm_xor_##suffix) \ + inline _Tpvec operator ~ (const _Tpvec& a) \ + { \ + return _Tpvec(_mm_xor_##suffix(a.val, not_const)); \ + } + +OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_uint8x16, si128, _mm_set1_epi32(-1)) +OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_int8x16, si128, _mm_set1_epi32(-1)) +OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_uint16x8, si128, _mm_set1_epi32(-1)) +OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_int16x8, si128, _mm_set1_epi32(-1)) +OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_uint32x4, si128, _mm_set1_epi32(-1)) +OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_int32x4, si128, _mm_set1_epi32(-1)) +OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_uint64x2, si128, _mm_set1_epi32(-1)) +OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_int64x2, si128, _mm_set1_epi32(-1)) +OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_float32x4, ps, _mm_castsi128_ps(_mm_set1_epi32(-1))) +OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_float64x2, pd, _mm_castsi128_pd(_mm_set1_epi32(-1))) + +inline v_float32x4 v_sqrt(const v_float32x4& x) +{ return v_float32x4(_mm_sqrt_ps(x.val)); } + +inline v_float32x4 v_invsqrt(const v_float32x4& x) +{ + const __m128 _0_5 = _mm_set1_ps(0.5f), _1_5 = _mm_set1_ps(1.5f); + __m128 t = x.val; + __m128 h = _mm_mul_ps(t, _0_5); + t = _mm_rsqrt_ps(t); + t = _mm_mul_ps(t, _mm_sub_ps(_1_5, _mm_mul_ps(_mm_mul_ps(t, t), h))); + return v_float32x4(t); +} + +inline v_float64x2 v_sqrt(const v_float64x2& x) +{ return v_float64x2(_mm_sqrt_pd(x.val)); } + +inline v_float64x2 v_invsqrt(const v_float64x2& x) +{ + const __m128d v_1 = _mm_set1_pd(1.); + return v_float64x2(_mm_div_pd(v_1, _mm_sqrt_pd(x.val))); +} + +#define OPENCV_HAL_IMPL_SSE_ABS_INT_FUNC(_Tpuvec, _Tpsvec, func, suffix, subWidth) \ +inline _Tpuvec v_abs(const _Tpsvec& x) \ +{ return _Tpuvec(_mm_##func##_ep##suffix(x.val, _mm_sub_ep##subWidth(_mm_setzero_si128(), x.val))); } + +OPENCV_HAL_IMPL_SSE_ABS_INT_FUNC(v_uint8x16, v_int8x16, min, u8, i8) +OPENCV_HAL_IMPL_SSE_ABS_INT_FUNC(v_uint16x8, v_int16x8, max, i16, i16) +inline v_uint32x4 v_abs(const v_int32x4& x) +{ + __m128i s = _mm_srli_epi32(x.val, 31); + __m128i f = _mm_srai_epi32(x.val, 31); + return v_uint32x4(_mm_add_epi32(_mm_xor_si128(x.val, f), s)); +} +inline v_float32x4 v_abs(const v_float32x4& x) +{ return v_float32x4(_mm_and_ps(x.val, _mm_castsi128_ps(_mm_set1_epi32(0x7fffffff)))); } +inline v_float64x2 v_abs(const v_float64x2& x) +{ + return v_float64x2(_mm_and_pd(x.val, + _mm_castsi128_pd(_mm_srli_epi64(_mm_set1_epi32(-1), 1)))); +} + +// TODO: exp, log, sin, cos + +#define OPENCV_HAL_IMPL_SSE_BIN_FUNC(_Tpvec, func, intrin) \ +inline _Tpvec func(const _Tpvec& a, const _Tpvec& b) \ +{ \ + return _Tpvec(intrin(a.val, b.val)); \ +} + +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint8x16, v_min, _mm_min_epu8) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint8x16, v_max, _mm_max_epu8) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int16x8, v_min, _mm_min_epi16) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int16x8, v_max, _mm_max_epi16) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_float32x4, v_min, _mm_min_ps) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_float32x4, v_max, _mm_max_ps) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_float64x2, v_min, _mm_min_pd) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_float64x2, v_max, _mm_max_pd) + +inline v_int8x16 v_min(const v_int8x16& a, const v_int8x16& b) +{ +#if CV_SSE4_1 + return v_int8x16(_mm_min_epi8(a.val, b.val)); +#else + __m128i delta = _mm_set1_epi8((char)-128); + return v_int8x16(_mm_xor_si128(delta, _mm_min_epu8(_mm_xor_si128(a.val, delta), + _mm_xor_si128(b.val, delta)))); +#endif +} +inline v_int8x16 v_max(const v_int8x16& a, const v_int8x16& b) +{ +#if CV_SSE4_1 + return v_int8x16(_mm_max_epi8(a.val, b.val)); +#else + __m128i delta = _mm_set1_epi8((char)-128); + return v_int8x16(_mm_xor_si128(delta, _mm_max_epu8(_mm_xor_si128(a.val, delta), + _mm_xor_si128(b.val, delta)))); +#endif +} +inline v_uint16x8 v_min(const v_uint16x8& a, const v_uint16x8& b) +{ +#if CV_SSE4_1 + return v_uint16x8(_mm_min_epu16(a.val, b.val)); +#else + return v_uint16x8(_mm_subs_epu16(a.val, _mm_subs_epu16(a.val, b.val))); +#endif +} +inline v_uint16x8 v_max(const v_uint16x8& a, const v_uint16x8& b) +{ +#if CV_SSE4_1 + return v_uint16x8(_mm_max_epu16(a.val, b.val)); +#else + return v_uint16x8(_mm_adds_epu16(_mm_subs_epu16(a.val, b.val), b.val)); +#endif +} +inline v_uint32x4 v_min(const v_uint32x4& a, const v_uint32x4& b) +{ +#if CV_SSE4_1 + return v_uint32x4(_mm_min_epu32(a.val, b.val)); +#else + __m128i delta = _mm_set1_epi32((int)0x80000000); + __m128i mask = _mm_cmpgt_epi32(_mm_xor_si128(a.val, delta), _mm_xor_si128(b.val, delta)); + return v_uint32x4(v_select_si128(mask, b.val, a.val)); +#endif +} +inline v_uint32x4 v_max(const v_uint32x4& a, const v_uint32x4& b) +{ +#if CV_SSE4_1 + return v_uint32x4(_mm_max_epu32(a.val, b.val)); +#else + __m128i delta = _mm_set1_epi32((int)0x80000000); + __m128i mask = _mm_cmpgt_epi32(_mm_xor_si128(a.val, delta), _mm_xor_si128(b.val, delta)); + return v_uint32x4(v_select_si128(mask, a.val, b.val)); +#endif +} +inline v_int32x4 v_min(const v_int32x4& a, const v_int32x4& b) +{ +#if CV_SSE4_1 + return v_int32x4(_mm_min_epi32(a.val, b.val)); +#else + return v_int32x4(v_select_si128(_mm_cmpgt_epi32(a.val, b.val), b.val, a.val)); +#endif +} +inline v_int32x4 v_max(const v_int32x4& a, const v_int32x4& b) +{ +#if CV_SSE4_1 + return v_int32x4(_mm_max_epi32(a.val, b.val)); +#else + return v_int32x4(v_select_si128(_mm_cmpgt_epi32(a.val, b.val), a.val, b.val)); +#endif +} + +#define OPENCV_HAL_IMPL_SSE_INT_CMP_OP(_Tpuvec, _Tpsvec, suffix, sbit) \ +inline _Tpuvec operator == (const _Tpuvec& a, const _Tpuvec& b) \ +{ return _Tpuvec(_mm_cmpeq_##suffix(a.val, b.val)); } \ +inline _Tpuvec operator != (const _Tpuvec& a, const _Tpuvec& b) \ +{ \ + __m128i not_mask = _mm_set1_epi32(-1); \ + return _Tpuvec(_mm_xor_si128(_mm_cmpeq_##suffix(a.val, b.val), not_mask)); \ +} \ +inline _Tpsvec operator == (const _Tpsvec& a, const _Tpsvec& b) \ +{ return _Tpsvec(_mm_cmpeq_##suffix(a.val, b.val)); } \ +inline _Tpsvec operator != (const _Tpsvec& a, const _Tpsvec& b) \ +{ \ + __m128i not_mask = _mm_set1_epi32(-1); \ + return _Tpsvec(_mm_xor_si128(_mm_cmpeq_##suffix(a.val, b.val), not_mask)); \ +} \ +inline _Tpuvec operator < (const _Tpuvec& a, const _Tpuvec& b) \ +{ \ + __m128i smask = _mm_set1_##suffix(sbit); \ + return _Tpuvec(_mm_cmpgt_##suffix(_mm_xor_si128(b.val, smask), _mm_xor_si128(a.val, smask))); \ +} \ +inline _Tpuvec operator > (const _Tpuvec& a, const _Tpuvec& b) \ +{ \ + __m128i smask = _mm_set1_##suffix(sbit); \ + return _Tpuvec(_mm_cmpgt_##suffix(_mm_xor_si128(a.val, smask), _mm_xor_si128(b.val, smask))); \ +} \ +inline _Tpuvec operator <= (const _Tpuvec& a, const _Tpuvec& b) \ +{ \ + __m128i smask = _mm_set1_##suffix(sbit); \ + __m128i not_mask = _mm_set1_epi32(-1); \ + __m128i res = _mm_cmpgt_##suffix(_mm_xor_si128(a.val, smask), _mm_xor_si128(b.val, smask)); \ + return _Tpuvec(_mm_xor_si128(res, not_mask)); \ +} \ +inline _Tpuvec operator >= (const _Tpuvec& a, const _Tpuvec& b) \ +{ \ + __m128i smask = _mm_set1_##suffix(sbit); \ + __m128i not_mask = _mm_set1_epi32(-1); \ + __m128i res = _mm_cmpgt_##suffix(_mm_xor_si128(b.val, smask), _mm_xor_si128(a.val, smask)); \ + return _Tpuvec(_mm_xor_si128(res, not_mask)); \ +} \ +inline _Tpsvec operator < (const _Tpsvec& a, const _Tpsvec& b) \ +{ \ + return _Tpsvec(_mm_cmpgt_##suffix(b.val, a.val)); \ +} \ +inline _Tpsvec operator > (const _Tpsvec& a, const _Tpsvec& b) \ +{ \ + return _Tpsvec(_mm_cmpgt_##suffix(a.val, b.val)); \ +} \ +inline _Tpsvec operator <= (const _Tpsvec& a, const _Tpsvec& b) \ +{ \ + __m128i not_mask = _mm_set1_epi32(-1); \ + return _Tpsvec(_mm_xor_si128(_mm_cmpgt_##suffix(a.val, b.val), not_mask)); \ +} \ +inline _Tpsvec operator >= (const _Tpsvec& a, const _Tpsvec& b) \ +{ \ + __m128i not_mask = _mm_set1_epi32(-1); \ + return _Tpsvec(_mm_xor_si128(_mm_cmpgt_##suffix(b.val, a.val), not_mask)); \ +} + +OPENCV_HAL_IMPL_SSE_INT_CMP_OP(v_uint8x16, v_int8x16, epi8, (char)-128) +OPENCV_HAL_IMPL_SSE_INT_CMP_OP(v_uint16x8, v_int16x8, epi16, (short)-32768) +OPENCV_HAL_IMPL_SSE_INT_CMP_OP(v_uint32x4, v_int32x4, epi32, (int)0x80000000) + +#define OPENCV_HAL_IMPL_SSE_FLT_CMP_OP(_Tpvec, suffix) \ +inline _Tpvec operator == (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(_mm_cmpeq_##suffix(a.val, b.val)); } \ +inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(_mm_cmpneq_##suffix(a.val, b.val)); } \ +inline _Tpvec operator < (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(_mm_cmplt_##suffix(a.val, b.val)); } \ +inline _Tpvec operator > (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(_mm_cmpgt_##suffix(a.val, b.val)); } \ +inline _Tpvec operator <= (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(_mm_cmple_##suffix(a.val, b.val)); } \ +inline _Tpvec operator >= (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(_mm_cmpge_##suffix(a.val, b.val)); } + +OPENCV_HAL_IMPL_SSE_FLT_CMP_OP(v_float32x4, ps) +OPENCV_HAL_IMPL_SSE_FLT_CMP_OP(v_float64x2, pd) + +#define OPENCV_HAL_IMPL_SSE_64BIT_CMP_OP(_Tpvec, cast) \ +inline _Tpvec operator == (const _Tpvec& a, const _Tpvec& b) \ +{ return cast(v_reinterpret_as_f64(a) == v_reinterpret_as_f64(b)); } \ +inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \ +{ return cast(v_reinterpret_as_f64(a) != v_reinterpret_as_f64(b)); } + +OPENCV_HAL_IMPL_SSE_64BIT_CMP_OP(v_uint64x2, v_reinterpret_as_u64) +OPENCV_HAL_IMPL_SSE_64BIT_CMP_OP(v_int64x2, v_reinterpret_as_s64) + +inline v_float32x4 v_not_nan(const v_float32x4& a) +{ return v_float32x4(_mm_cmpord_ps(a.val, a.val)); } +inline v_float64x2 v_not_nan(const v_float64x2& a) +{ return v_float64x2(_mm_cmpord_pd(a.val, a.val)); } + +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint8x16, v_add_wrap, _mm_add_epi8) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int8x16, v_add_wrap, _mm_add_epi8) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint16x8, v_add_wrap, _mm_add_epi16) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int16x8, v_add_wrap, _mm_add_epi16) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint8x16, v_sub_wrap, _mm_sub_epi8) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int8x16, v_sub_wrap, _mm_sub_epi8) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint16x8, v_sub_wrap, _mm_sub_epi16) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int16x8, v_sub_wrap, _mm_sub_epi16) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint16x8, v_mul_wrap, _mm_mullo_epi16) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int16x8, v_mul_wrap, _mm_mullo_epi16) + +inline v_uint8x16 v_mul_wrap(const v_uint8x16& a, const v_uint8x16& b) +{ + __m128i ad = _mm_srai_epi16(a.val, 8); + __m128i bd = _mm_srai_epi16(b.val, 8); + __m128i p0 = _mm_mullo_epi16(a.val, b.val); // even + __m128i p1 = _mm_slli_epi16(_mm_mullo_epi16(ad, bd), 8); // odd + const __m128i b01 = _mm_set1_epi32(0xFF00FF00); + return v_uint8x16(_v128_blendv_epi8(p0, p1, b01)); +} +inline v_int8x16 v_mul_wrap(const v_int8x16& a, const v_int8x16& b) +{ + return v_reinterpret_as_s8(v_mul_wrap(v_reinterpret_as_u8(a), v_reinterpret_as_u8(b))); +} + +/** Absolute difference **/ + +inline v_uint8x16 v_absdiff(const v_uint8x16& a, const v_uint8x16& b) +{ return v_add_wrap(a - b, b - a); } +inline v_uint16x8 v_absdiff(const v_uint16x8& a, const v_uint16x8& b) +{ return v_add_wrap(a - b, b - a); } +inline v_uint32x4 v_absdiff(const v_uint32x4& a, const v_uint32x4& b) +{ return v_max(a, b) - v_min(a, b); } + +inline v_uint8x16 v_absdiff(const v_int8x16& a, const v_int8x16& b) +{ + v_int8x16 d = v_sub_wrap(a, b); + v_int8x16 m = a < b; + return v_reinterpret_as_u8(v_sub_wrap(d ^ m, m)); +} +inline v_uint16x8 v_absdiff(const v_int16x8& a, const v_int16x8& b) +{ + return v_reinterpret_as_u16(v_sub_wrap(v_max(a, b), v_min(a, b))); +} +inline v_uint32x4 v_absdiff(const v_int32x4& a, const v_int32x4& b) +{ + v_int32x4 d = a - b; + v_int32x4 m = a < b; + return v_reinterpret_as_u32((d ^ m) - m); +} + +/** Saturating absolute difference **/ +inline v_int8x16 v_absdiffs(const v_int8x16& a, const v_int8x16& b) +{ + v_int8x16 d = a - b; + v_int8x16 m = a < b; + return (d ^ m) - m; + } +inline v_int16x8 v_absdiffs(const v_int16x8& a, const v_int16x8& b) +{ return v_max(a, b) - v_min(a, b); } + + +inline v_int32x4 v_fma(const v_int32x4& a, const v_int32x4& b, const v_int32x4& c) +{ + return a * b + c; +} + +inline v_int32x4 v_muladd(const v_int32x4& a, const v_int32x4& b, const v_int32x4& c) +{ + return v_fma(a, b, c); +} + +inline v_float32x4 v_fma(const v_float32x4& a, const v_float32x4& b, const v_float32x4& c) +{ +#if CV_FMA3 + return v_float32x4(_mm_fmadd_ps(a.val, b.val, c.val)); +#else + return v_float32x4(_mm_add_ps(_mm_mul_ps(a.val, b.val), c.val)); +#endif +} + +inline v_float64x2 v_fma(const v_float64x2& a, const v_float64x2& b, const v_float64x2& c) +{ +#if CV_FMA3 + return v_float64x2(_mm_fmadd_pd(a.val, b.val, c.val)); +#else + return v_float64x2(_mm_add_pd(_mm_mul_pd(a.val, b.val), c.val)); +#endif +} + +#define OPENCV_HAL_IMPL_SSE_MISC_FLT_OP(_Tpvec, _Tp, _Tpreg, suffix, absmask_vec) \ +inline _Tpvec v_absdiff(const _Tpvec& a, const _Tpvec& b) \ +{ \ + _Tpreg absmask = _mm_castsi128_##suffix(absmask_vec); \ + return _Tpvec(_mm_and_##suffix(_mm_sub_##suffix(a.val, b.val), absmask)); \ +} \ +inline _Tpvec v_magnitude(const _Tpvec& a, const _Tpvec& b) \ +{ \ + _Tpvec res = v_fma(a, a, b*b); \ + return _Tpvec(_mm_sqrt_##suffix(res.val)); \ +} \ +inline _Tpvec v_sqr_magnitude(const _Tpvec& a, const _Tpvec& b) \ +{ \ + return v_fma(a, a, b*b); \ +} \ +inline _Tpvec v_muladd(const _Tpvec& a, const _Tpvec& b, const _Tpvec& c) \ +{ \ + return v_fma(a, b, c); \ +} + +OPENCV_HAL_IMPL_SSE_MISC_FLT_OP(v_float32x4, float, __m128, ps, _mm_set1_epi32((int)0x7fffffff)) +OPENCV_HAL_IMPL_SSE_MISC_FLT_OP(v_float64x2, double, __m128d, pd, _mm_srli_epi64(_mm_set1_epi32(-1), 1)) + +#define OPENCV_HAL_IMPL_SSE_SHIFT_OP(_Tpuvec, _Tpsvec, suffix, srai) \ +inline _Tpuvec operator << (const _Tpuvec& a, int imm) \ +{ \ + return _Tpuvec(_mm_slli_##suffix(a.val, imm)); \ +} \ +inline _Tpsvec operator << (const _Tpsvec& a, int imm) \ +{ \ + return _Tpsvec(_mm_slli_##suffix(a.val, imm)); \ +} \ +inline _Tpuvec operator >> (const _Tpuvec& a, int imm) \ +{ \ + return _Tpuvec(_mm_srli_##suffix(a.val, imm)); \ +} \ +inline _Tpsvec operator >> (const _Tpsvec& a, int imm) \ +{ \ + return _Tpsvec(srai(a.val, imm)); \ +} \ +template \ +inline _Tpuvec v_shl(const _Tpuvec& a) \ +{ \ + return _Tpuvec(_mm_slli_##suffix(a.val, imm)); \ +} \ +template \ +inline _Tpsvec v_shl(const _Tpsvec& a) \ +{ \ + return _Tpsvec(_mm_slli_##suffix(a.val, imm)); \ +} \ +template \ +inline _Tpuvec v_shr(const _Tpuvec& a) \ +{ \ + return _Tpuvec(_mm_srli_##suffix(a.val, imm)); \ +} \ +template \ +inline _Tpsvec v_shr(const _Tpsvec& a) \ +{ \ + return _Tpsvec(srai(a.val, imm)); \ +} + +OPENCV_HAL_IMPL_SSE_SHIFT_OP(v_uint16x8, v_int16x8, epi16, _mm_srai_epi16) +OPENCV_HAL_IMPL_SSE_SHIFT_OP(v_uint32x4, v_int32x4, epi32, _mm_srai_epi32) +OPENCV_HAL_IMPL_SSE_SHIFT_OP(v_uint64x2, v_int64x2, epi64, v_srai_epi64) + +namespace hal_sse_internal +{ + template 16)), + bool is_first = (imm == 0), + bool is_half = (imm == 8), + bool is_second = (imm == 16), + bool is_other = (((imm > 0) && (imm < 8)) || ((imm > 8) && (imm < 16)))> + class v_sse_palignr_u8_class; + + template + class v_sse_palignr_u8_class; + + template + class v_sse_palignr_u8_class + { + public: + inline __m128i operator()(const __m128i& a, const __m128i&) const + { + return a; + } + }; + + template + class v_sse_palignr_u8_class + { + public: + inline __m128i operator()(const __m128i& a, const __m128i& b) const + { + return _mm_unpacklo_epi64(_mm_unpackhi_epi64(a, a), b); + } + }; + + template + class v_sse_palignr_u8_class + { + public: + inline __m128i operator()(const __m128i&, const __m128i& b) const + { + return b; + } + }; + + template + class v_sse_palignr_u8_class + { +#if CV_SSSE3 + public: + inline __m128i operator()(const __m128i& a, const __m128i& b) const + { + return _mm_alignr_epi8(b, a, imm); + } +#else + public: + inline __m128i operator()(const __m128i& a, const __m128i& b) const + { + enum { imm2 = (sizeof(__m128i) - imm) }; + return _mm_or_si128(_mm_srli_si128(a, imm), _mm_slli_si128(b, imm2)); + } +#endif + }; + + template + inline __m128i v_sse_palignr_u8(const __m128i& a, const __m128i& b) + { + CV_StaticAssert((imm >= 0) && (imm <= 16), "Invalid imm for v_sse_palignr_u8."); + return v_sse_palignr_u8_class()(a, b); + } +} + +template +inline _Tpvec v_rotate_right(const _Tpvec &a) +{ + using namespace hal_sse_internal; + enum { imm2 = (imm * sizeof(typename _Tpvec::lane_type)) }; + return _Tpvec(v_sse_reinterpret_as( + _mm_srli_si128( + v_sse_reinterpret_as<__m128i>(a.val), imm2))); +} + +template +inline _Tpvec v_rotate_left(const _Tpvec &a) +{ + using namespace hal_sse_internal; + enum { imm2 = (imm * sizeof(typename _Tpvec::lane_type)) }; + return _Tpvec(v_sse_reinterpret_as( + _mm_slli_si128( + v_sse_reinterpret_as<__m128i>(a.val), imm2))); +} + +template +inline _Tpvec v_rotate_right(const _Tpvec &a, const _Tpvec &b) +{ + using namespace hal_sse_internal; + enum { imm2 = (imm * sizeof(typename _Tpvec::lane_type)) }; + return _Tpvec(v_sse_reinterpret_as( + v_sse_palignr_u8( + v_sse_reinterpret_as<__m128i>(a.val), + v_sse_reinterpret_as<__m128i>(b.val)))); +} + +template +inline _Tpvec v_rotate_left(const _Tpvec &a, const _Tpvec &b) +{ + using namespace hal_sse_internal; + enum { imm2 = ((_Tpvec::nlanes - imm) * sizeof(typename _Tpvec::lane_type)) }; + return _Tpvec(v_sse_reinterpret_as( + v_sse_palignr_u8( + v_sse_reinterpret_as<__m128i>(b.val), + v_sse_reinterpret_as<__m128i>(a.val)))); +} + +#define OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(_Tpvec, _Tp) \ +inline _Tpvec v_load(const _Tp* ptr) \ +{ return _Tpvec(_mm_loadu_si128((const __m128i*)ptr)); } \ +inline _Tpvec v_load_aligned(const _Tp* ptr) \ +{ return _Tpvec(_mm_load_si128((const __m128i*)ptr)); } \ +inline _Tpvec v_load_low(const _Tp* ptr) \ +{ return _Tpvec(_mm_loadl_epi64((const __m128i*)ptr)); } \ +inline _Tpvec v_load_halves(const _Tp* ptr0, const _Tp* ptr1) \ +{ \ + return _Tpvec(_mm_unpacklo_epi64(_mm_loadl_epi64((const __m128i*)ptr0), \ + _mm_loadl_epi64((const __m128i*)ptr1))); \ +} \ +inline void v_store(_Tp* ptr, const _Tpvec& a) \ +{ _mm_storeu_si128((__m128i*)ptr, a.val); } \ +inline void v_store_aligned(_Tp* ptr, const _Tpvec& a) \ +{ _mm_store_si128((__m128i*)ptr, a.val); } \ +inline void v_store_aligned_nocache(_Tp* ptr, const _Tpvec& a) \ +{ _mm_stream_si128((__m128i*)ptr, a.val); } \ +inline void v_store(_Tp* ptr, const _Tpvec& a, hal::StoreMode mode) \ +{ \ + if( mode == hal::STORE_UNALIGNED ) \ + _mm_storeu_si128((__m128i*)ptr, a.val); \ + else if( mode == hal::STORE_ALIGNED_NOCACHE ) \ + _mm_stream_si128((__m128i*)ptr, a.val); \ + else \ + _mm_store_si128((__m128i*)ptr, a.val); \ +} \ +inline void v_store_low(_Tp* ptr, const _Tpvec& a) \ +{ _mm_storel_epi64((__m128i*)ptr, a.val); } \ +inline void v_store_high(_Tp* ptr, const _Tpvec& a) \ +{ _mm_storel_epi64((__m128i*)ptr, _mm_unpackhi_epi64(a.val, a.val)); } + +OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_uint8x16, uchar) +OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_int8x16, schar) +OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_uint16x8, ushort) +OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_int16x8, short) +OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_uint32x4, unsigned) +OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_int32x4, int) +OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_uint64x2, uint64) +OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_int64x2, int64) + +#define OPENCV_HAL_IMPL_SSE_LOADSTORE_FLT_OP(_Tpvec, _Tp, suffix) \ +inline _Tpvec v_load(const _Tp* ptr) \ +{ return _Tpvec(_mm_loadu_##suffix(ptr)); } \ +inline _Tpvec v_load_aligned(const _Tp* ptr) \ +{ return _Tpvec(_mm_load_##suffix(ptr)); } \ +inline _Tpvec v_load_low(const _Tp* ptr) \ +{ return _Tpvec(_mm_castsi128_##suffix(_mm_loadl_epi64((const __m128i*)ptr))); } \ +inline _Tpvec v_load_halves(const _Tp* ptr0, const _Tp* ptr1) \ +{ \ + return _Tpvec(_mm_castsi128_##suffix( \ + _mm_unpacklo_epi64(_mm_loadl_epi64((const __m128i*)ptr0), \ + _mm_loadl_epi64((const __m128i*)ptr1)))); \ +} \ +inline void v_store(_Tp* ptr, const _Tpvec& a) \ +{ _mm_storeu_##suffix(ptr, a.val); } \ +inline void v_store_aligned(_Tp* ptr, const _Tpvec& a) \ +{ _mm_store_##suffix(ptr, a.val); } \ +inline void v_store_aligned_nocache(_Tp* ptr, const _Tpvec& a) \ +{ _mm_stream_##suffix(ptr, a.val); } \ +inline void v_store(_Tp* ptr, const _Tpvec& a, hal::StoreMode mode) \ +{ \ + if( mode == hal::STORE_UNALIGNED ) \ + _mm_storeu_##suffix(ptr, a.val); \ + else if( mode == hal::STORE_ALIGNED_NOCACHE ) \ + _mm_stream_##suffix(ptr, a.val); \ + else \ + _mm_store_##suffix(ptr, a.val); \ +} \ +inline void v_store_low(_Tp* ptr, const _Tpvec& a) \ +{ _mm_storel_epi64((__m128i*)ptr, _mm_cast##suffix##_si128(a.val)); } \ +inline void v_store_high(_Tp* ptr, const _Tpvec& a) \ +{ \ + __m128i a1 = _mm_cast##suffix##_si128(a.val); \ + _mm_storel_epi64((__m128i*)ptr, _mm_unpackhi_epi64(a1, a1)); \ +} + +OPENCV_HAL_IMPL_SSE_LOADSTORE_FLT_OP(v_float32x4, float, ps) +OPENCV_HAL_IMPL_SSE_LOADSTORE_FLT_OP(v_float64x2, double, pd) + +#define OPENCV_HAL_IMPL_SSE_REDUCE_OP_8(_Tpvec, scalartype, func, suffix, sbit) \ +inline scalartype v_reduce_##func(const v_##_Tpvec& a) \ +{ \ + __m128i val = a.val; \ + val = _mm_##func##_##suffix(val, _mm_srli_si128(val,8)); \ + val = _mm_##func##_##suffix(val, _mm_srli_si128(val,4)); \ + val = _mm_##func##_##suffix(val, _mm_srli_si128(val,2)); \ + return (scalartype)_mm_cvtsi128_si32(val); \ +} \ +inline unsigned scalartype v_reduce_##func(const v_u##_Tpvec& a) \ +{ \ + __m128i val = a.val; \ + __m128i smask = _mm_set1_epi16(sbit); \ + val = _mm_xor_si128(val, smask); \ + val = _mm_##func##_##suffix(val, _mm_srli_si128(val,8)); \ + val = _mm_##func##_##suffix(val, _mm_srli_si128(val,4)); \ + val = _mm_##func##_##suffix(val, _mm_srli_si128(val,2)); \ + return (unsigned scalartype)(_mm_cvtsi128_si32(val) ^ sbit); \ +} +#define OPENCV_HAL_IMPL_SSE_REDUCE_OP_8_SUM(_Tpvec, scalartype, suffix) \ +inline scalartype v_reduce_sum(const v_##_Tpvec& a) \ +{ \ + __m128i val = a.val; \ + val = _mm_adds_epi##suffix(val, _mm_srli_si128(val, 8)); \ + val = _mm_adds_epi##suffix(val, _mm_srli_si128(val, 4)); \ + val = _mm_adds_epi##suffix(val, _mm_srli_si128(val, 2)); \ + return (scalartype)_mm_cvtsi128_si32(val); \ +} \ +inline unsigned scalartype v_reduce_sum(const v_u##_Tpvec& a) \ +{ \ + __m128i val = a.val; \ + val = _mm_adds_epu##suffix(val, _mm_srli_si128(val, 8)); \ + val = _mm_adds_epu##suffix(val, _mm_srli_si128(val, 4)); \ + val = _mm_adds_epu##suffix(val, _mm_srli_si128(val, 2)); \ + return (unsigned scalartype)_mm_cvtsi128_si32(val); \ +} +OPENCV_HAL_IMPL_SSE_REDUCE_OP_8(int16x8, short, max, epi16, (short)-32768) +OPENCV_HAL_IMPL_SSE_REDUCE_OP_8(int16x8, short, min, epi16, (short)-32768) +OPENCV_HAL_IMPL_SSE_REDUCE_OP_8_SUM(int16x8, short, 16) + +#define OPENCV_HAL_IMPL_SSE_REDUCE_OP_4_SUM(_Tpvec, scalartype, regtype, suffix, cast_from, cast_to, extract) \ +inline scalartype v_reduce_sum(const _Tpvec& a) \ +{ \ + regtype val = a.val; \ + val = _mm_add_##suffix(val, cast_to(_mm_srli_si128(cast_from(val), 8))); \ + val = _mm_add_##suffix(val, cast_to(_mm_srli_si128(cast_from(val), 4))); \ + return (scalartype)_mm_cvt##extract(val); \ +} + +#define OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(_Tpvec, scalartype, func, scalar_func) \ +inline scalartype v_reduce_##func(const _Tpvec& a) \ +{ \ + scalartype CV_DECL_ALIGNED(16) buf[4]; \ + v_store_aligned(buf, a); \ + scalartype s0 = scalar_func(buf[0], buf[1]); \ + scalartype s1 = scalar_func(buf[2], buf[3]); \ + return scalar_func(s0, s1); \ +} + +OPENCV_HAL_IMPL_SSE_REDUCE_OP_4_SUM(v_uint32x4, unsigned, __m128i, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP, si128_si32) +OPENCV_HAL_IMPL_SSE_REDUCE_OP_4_SUM(v_int32x4, int, __m128i, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP, si128_si32) +OPENCV_HAL_IMPL_SSE_REDUCE_OP_4_SUM(v_float32x4, float, __m128, ps, _mm_castps_si128, _mm_castsi128_ps, ss_f32) + +inline double v_reduce_sum(const v_float64x2& a) +{ + double CV_DECL_ALIGNED(32) idx[2]; + v_store_aligned(idx, a); + return idx[0] + idx[1]; +} + +inline v_float32x4 v_reduce_sum4(const v_float32x4& a, const v_float32x4& b, + const v_float32x4& c, const v_float32x4& d) +{ +#if CV_SSE3 + __m128 ab = _mm_hadd_ps(a.val, b.val); + __m128 cd = _mm_hadd_ps(c.val, d.val); + return v_float32x4(_mm_hadd_ps(ab, cd)); +#else + __m128 ac = _mm_add_ps(_mm_unpacklo_ps(a.val, c.val), _mm_unpackhi_ps(a.val, c.val)); + __m128 bd = _mm_add_ps(_mm_unpacklo_ps(b.val, d.val), _mm_unpackhi_ps(b.val, d.val)); + return v_float32x4(_mm_add_ps(_mm_unpacklo_ps(ac, bd), _mm_unpackhi_ps(ac, bd))); +#endif +} + +OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_uint32x4, unsigned, max, std::max) +OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_uint32x4, unsigned, min, std::min) +OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_int32x4, int, max, std::max) +OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_int32x4, int, min, std::min) +OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_float32x4, float, max, std::max) +OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_float32x4, float, min, std::min) + +inline unsigned v_reduce_sad(const v_uint8x16& a, const v_uint8x16& b) +{ + return (unsigned)_mm_cvtsi128_si32(_mm_sad_epu8(a.val, b.val)); +} +inline unsigned v_reduce_sad(const v_int8x16& a, const v_int8x16& b) +{ + __m128i half = _mm_set1_epi8(0x7f); + return (unsigned)_mm_cvtsi128_si32(_mm_sad_epu8(_mm_add_epi8(a.val, half), + _mm_add_epi8(b.val, half))); +} +inline unsigned v_reduce_sad(const v_uint16x8& a, const v_uint16x8& b) +{ + v_uint32x4 l, h; + v_expand(v_absdiff(a, b), l, h); + return v_reduce_sum(l + h); +} +inline unsigned v_reduce_sad(const v_int16x8& a, const v_int16x8& b) +{ + v_uint32x4 l, h; + v_expand(v_absdiff(a, b), l, h); + return v_reduce_sum(l + h); +} +inline unsigned v_reduce_sad(const v_uint32x4& a, const v_uint32x4& b) +{ + return v_reduce_sum(v_absdiff(a, b)); +} +inline unsigned v_reduce_sad(const v_int32x4& a, const v_int32x4& b) +{ + return v_reduce_sum(v_absdiff(a, b)); +} +inline float v_reduce_sad(const v_float32x4& a, const v_float32x4& b) +{ + return v_reduce_sum(v_absdiff(a, b)); +} + +#define OPENCV_HAL_IMPL_SSE_POPCOUNT(_Tpvec) \ +inline v_uint32x4 v_popcount(const _Tpvec& a) \ +{ \ + __m128i m1 = _mm_set1_epi32(0x55555555); \ + __m128i m2 = _mm_set1_epi32(0x33333333); \ + __m128i m4 = _mm_set1_epi32(0x0f0f0f0f); \ + __m128i p = a.val; \ + p = _mm_add_epi32(_mm_and_si128(_mm_srli_epi32(p, 1), m1), _mm_and_si128(p, m1)); \ + p = _mm_add_epi32(_mm_and_si128(_mm_srli_epi32(p, 2), m2), _mm_and_si128(p, m2)); \ + p = _mm_add_epi32(_mm_and_si128(_mm_srli_epi32(p, 4), m4), _mm_and_si128(p, m4)); \ + p = _mm_adds_epi8(p, _mm_srli_si128(p, 1)); \ + p = _mm_adds_epi8(p, _mm_srli_si128(p, 2)); \ + return v_uint32x4(_mm_and_si128(p, _mm_set1_epi32(0x000000ff))); \ +} + +OPENCV_HAL_IMPL_SSE_POPCOUNT(v_uint8x16) +OPENCV_HAL_IMPL_SSE_POPCOUNT(v_uint16x8) +OPENCV_HAL_IMPL_SSE_POPCOUNT(v_uint32x4) +OPENCV_HAL_IMPL_SSE_POPCOUNT(v_int8x16) +OPENCV_HAL_IMPL_SSE_POPCOUNT(v_int16x8) +OPENCV_HAL_IMPL_SSE_POPCOUNT(v_int32x4) + +#define OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(_Tpvec, suffix, pack_op, and_op, signmask, allmask) \ +inline int v_signmask(const _Tpvec& a) \ +{ \ + return and_op(_mm_movemask_##suffix(pack_op(a.val)), signmask); \ +} \ +inline bool v_check_all(const _Tpvec& a) \ +{ return and_op(_mm_movemask_##suffix(a.val), allmask) == allmask; } \ +inline bool v_check_any(const _Tpvec& a) \ +{ return and_op(_mm_movemask_##suffix(a.val), allmask) != 0; } + +#define OPENCV_HAL_PACKS(a) _mm_packs_epi16(a, a) +inline __m128i v_packq_epi32(__m128i a) +{ + __m128i b = _mm_packs_epi32(a, a); + return _mm_packs_epi16(b, b); +} + +OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_uint8x16, epi8, OPENCV_HAL_NOP, OPENCV_HAL_1ST, 65535, 65535) +OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_int8x16, epi8, OPENCV_HAL_NOP, OPENCV_HAL_1ST, 65535, 65535) +OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_uint16x8, epi8, OPENCV_HAL_PACKS, OPENCV_HAL_AND, 255, (int)0xaaaa) +OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_int16x8, epi8, OPENCV_HAL_PACKS, OPENCV_HAL_AND, 255, (int)0xaaaa) +OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_uint32x4, epi8, v_packq_epi32, OPENCV_HAL_AND, 15, (int)0x8888) +OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_int32x4, epi8, v_packq_epi32, OPENCV_HAL_AND, 15, (int)0x8888) +OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_float32x4, ps, OPENCV_HAL_NOP, OPENCV_HAL_1ST, 15, 15) +OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_float64x2, pd, OPENCV_HAL_NOP, OPENCV_HAL_1ST, 3, 3) + +#if CV_SSE4_1 +#define OPENCV_HAL_IMPL_SSE_SELECT(_Tpvec, cast_ret, cast, suffix) \ +inline _Tpvec v_select(const _Tpvec& mask, const _Tpvec& a, const _Tpvec& b) \ +{ \ + return _Tpvec(cast_ret(_mm_blendv_##suffix(cast(b.val), cast(a.val), cast(mask.val)))); \ +} + +OPENCV_HAL_IMPL_SSE_SELECT(v_uint8x16, OPENCV_HAL_NOP, OPENCV_HAL_NOP, epi8) +OPENCV_HAL_IMPL_SSE_SELECT(v_int8x16, OPENCV_HAL_NOP, OPENCV_HAL_NOP, epi8) +OPENCV_HAL_IMPL_SSE_SELECT(v_uint16x8, OPENCV_HAL_NOP, OPENCV_HAL_NOP, epi8) +OPENCV_HAL_IMPL_SSE_SELECT(v_int16x8, OPENCV_HAL_NOP, OPENCV_HAL_NOP, epi8) +OPENCV_HAL_IMPL_SSE_SELECT(v_uint32x4, _mm_castps_si128, _mm_castsi128_ps, ps) +OPENCV_HAL_IMPL_SSE_SELECT(v_int32x4, _mm_castps_si128, _mm_castsi128_ps, ps) +// OPENCV_HAL_IMPL_SSE_SELECT(v_uint64x2, TBD, TBD, pd) +// OPENCV_HAL_IMPL_SSE_SELECT(v_int64x2, TBD, TBD, ps) +OPENCV_HAL_IMPL_SSE_SELECT(v_float32x4, OPENCV_HAL_NOP, OPENCV_HAL_NOP, ps) +OPENCV_HAL_IMPL_SSE_SELECT(v_float64x2, OPENCV_HAL_NOP, OPENCV_HAL_NOP, pd) + +#else // CV_SSE4_1 + +#define OPENCV_HAL_IMPL_SSE_SELECT(_Tpvec, suffix) \ +inline _Tpvec v_select(const _Tpvec& mask, const _Tpvec& a, const _Tpvec& b) \ +{ \ + return _Tpvec(_mm_xor_##suffix(b.val, _mm_and_##suffix(_mm_xor_##suffix(b.val, a.val), mask.val))); \ +} + +OPENCV_HAL_IMPL_SSE_SELECT(v_uint8x16, si128) +OPENCV_HAL_IMPL_SSE_SELECT(v_int8x16, si128) +OPENCV_HAL_IMPL_SSE_SELECT(v_uint16x8, si128) +OPENCV_HAL_IMPL_SSE_SELECT(v_int16x8, si128) +OPENCV_HAL_IMPL_SSE_SELECT(v_uint32x4, si128) +OPENCV_HAL_IMPL_SSE_SELECT(v_int32x4, si128) +// OPENCV_HAL_IMPL_SSE_SELECT(v_uint64x2, si128) +// OPENCV_HAL_IMPL_SSE_SELECT(v_int64x2, si128) +OPENCV_HAL_IMPL_SSE_SELECT(v_float32x4, ps) +OPENCV_HAL_IMPL_SSE_SELECT(v_float64x2, pd) +#endif + +/* Expand */ +#define OPENCV_HAL_IMPL_SSE_EXPAND(_Tpvec, _Tpwvec, _Tp, intrin) \ + inline void v_expand(const _Tpvec& a, _Tpwvec& b0, _Tpwvec& b1) \ + { \ + b0.val = intrin(a.val); \ + b1.val = __CV_CAT(intrin, _high)(a.val); \ + } \ + inline _Tpwvec v_expand_low(const _Tpvec& a) \ + { return _Tpwvec(intrin(a.val)); } \ + inline _Tpwvec v_expand_high(const _Tpvec& a) \ + { return _Tpwvec(__CV_CAT(intrin, _high)(a.val)); } \ + inline _Tpwvec v_load_expand(const _Tp* ptr) \ + { \ + __m128i a = _mm_loadl_epi64((const __m128i*)ptr); \ + return _Tpwvec(intrin(a)); \ + } + +OPENCV_HAL_IMPL_SSE_EXPAND(v_uint8x16, v_uint16x8, uchar, _v128_cvtepu8_epi16) +OPENCV_HAL_IMPL_SSE_EXPAND(v_int8x16, v_int16x8, schar, _v128_cvtepi8_epi16) +OPENCV_HAL_IMPL_SSE_EXPAND(v_uint16x8, v_uint32x4, ushort, _v128_cvtepu16_epi32) +OPENCV_HAL_IMPL_SSE_EXPAND(v_int16x8, v_int32x4, short, _v128_cvtepi16_epi32) +OPENCV_HAL_IMPL_SSE_EXPAND(v_uint32x4, v_uint64x2, unsigned, _v128_cvtepu32_epi64) +OPENCV_HAL_IMPL_SSE_EXPAND(v_int32x4, v_int64x2, int, _v128_cvtepi32_epi64) + +#define OPENCV_HAL_IMPL_SSE_EXPAND_Q(_Tpvec, _Tp, intrin) \ + inline _Tpvec v_load_expand_q(const _Tp* ptr) \ + { \ + __m128i a = _mm_cvtsi32_si128(*(const int*)ptr); \ + return _Tpvec(intrin(a)); \ + } + +OPENCV_HAL_IMPL_SSE_EXPAND_Q(v_uint32x4, uchar, _v128_cvtepu8_epi32) +OPENCV_HAL_IMPL_SSE_EXPAND_Q(v_int32x4, schar, _v128_cvtepi8_epi32) + +#define OPENCV_HAL_IMPL_SSE_UNPACKS(_Tpvec, suffix, cast_from, cast_to) \ +inline void v_zip(const _Tpvec& a0, const _Tpvec& a1, _Tpvec& b0, _Tpvec& b1) \ +{ \ + b0.val = _mm_unpacklo_##suffix(a0.val, a1.val); \ + b1.val = _mm_unpackhi_##suffix(a0.val, a1.val); \ +} \ +inline _Tpvec v_combine_low(const _Tpvec& a, const _Tpvec& b) \ +{ \ + __m128i a1 = cast_from(a.val), b1 = cast_from(b.val); \ + return _Tpvec(cast_to(_mm_unpacklo_epi64(a1, b1))); \ +} \ +inline _Tpvec v_combine_high(const _Tpvec& a, const _Tpvec& b) \ +{ \ + __m128i a1 = cast_from(a.val), b1 = cast_from(b.val); \ + return _Tpvec(cast_to(_mm_unpackhi_epi64(a1, b1))); \ +} \ +inline void v_recombine(const _Tpvec& a, const _Tpvec& b, _Tpvec& c, _Tpvec& d) \ +{ \ + __m128i a1 = cast_from(a.val), b1 = cast_from(b.val); \ + c.val = cast_to(_mm_unpacklo_epi64(a1, b1)); \ + d.val = cast_to(_mm_unpackhi_epi64(a1, b1)); \ +} + +OPENCV_HAL_IMPL_SSE_UNPACKS(v_uint8x16, epi8, OPENCV_HAL_NOP, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_UNPACKS(v_int8x16, epi8, OPENCV_HAL_NOP, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_UNPACKS(v_uint16x8, epi16, OPENCV_HAL_NOP, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_UNPACKS(v_int16x8, epi16, OPENCV_HAL_NOP, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_UNPACKS(v_uint32x4, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_UNPACKS(v_int32x4, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_UNPACKS(v_float32x4, ps, _mm_castps_si128, _mm_castsi128_ps) +OPENCV_HAL_IMPL_SSE_UNPACKS(v_float64x2, pd, _mm_castpd_si128, _mm_castsi128_pd) + +template +inline _Tpvec v_extract(const _Tpvec& a, const _Tpvec& b) +{ + return v_rotate_right(a, b); +} + +inline v_int32x4 v_round(const v_float32x4& a) +{ return v_int32x4(_mm_cvtps_epi32(a.val)); } + +inline v_int32x4 v_floor(const v_float32x4& a) +{ + __m128i a1 = _mm_cvtps_epi32(a.val); + __m128i mask = _mm_castps_si128(_mm_cmpgt_ps(_mm_cvtepi32_ps(a1), a.val)); + return v_int32x4(_mm_add_epi32(a1, mask)); +} + +inline v_int32x4 v_ceil(const v_float32x4& a) +{ + __m128i a1 = _mm_cvtps_epi32(a.val); + __m128i mask = _mm_castps_si128(_mm_cmpgt_ps(a.val, _mm_cvtepi32_ps(a1))); + return v_int32x4(_mm_sub_epi32(a1, mask)); +} + +inline v_int32x4 v_trunc(const v_float32x4& a) +{ return v_int32x4(_mm_cvttps_epi32(a.val)); } + +inline v_int32x4 v_round(const v_float64x2& a) +{ return v_int32x4(_mm_cvtpd_epi32(a.val)); } + +inline v_int32x4 v_round(const v_float64x2& a, const v_float64x2& b) +{ + __m128i ai = _mm_cvtpd_epi32(a.val), bi = _mm_cvtpd_epi32(b.val); + return v_int32x4(_mm_unpacklo_epi64(ai, bi)); +} + +inline v_int32x4 v_floor(const v_float64x2& a) +{ + __m128i a1 = _mm_cvtpd_epi32(a.val); + __m128i mask = _mm_castpd_si128(_mm_cmpgt_pd(_mm_cvtepi32_pd(a1), a.val)); + mask = _mm_srli_si128(_mm_slli_si128(mask, 4), 8); // m0 m0 m1 m1 => m0 m1 0 0 + return v_int32x4(_mm_add_epi32(a1, mask)); +} + +inline v_int32x4 v_ceil(const v_float64x2& a) +{ + __m128i a1 = _mm_cvtpd_epi32(a.val); + __m128i mask = _mm_castpd_si128(_mm_cmpgt_pd(a.val, _mm_cvtepi32_pd(a1))); + mask = _mm_srli_si128(_mm_slli_si128(mask, 4), 8); // m0 m0 m1 m1 => m0 m1 0 0 + return v_int32x4(_mm_sub_epi32(a1, mask)); +} + +inline v_int32x4 v_trunc(const v_float64x2& a) +{ return v_int32x4(_mm_cvttpd_epi32(a.val)); } + +#define OPENCV_HAL_IMPL_SSE_TRANSPOSE4x4(_Tpvec, suffix, cast_from, cast_to) \ +inline void v_transpose4x4(const _Tpvec& a0, const _Tpvec& a1, \ + const _Tpvec& a2, const _Tpvec& a3, \ + _Tpvec& b0, _Tpvec& b1, \ + _Tpvec& b2, _Tpvec& b3) \ +{ \ + __m128i t0 = cast_from(_mm_unpacklo_##suffix(a0.val, a1.val)); \ + __m128i t1 = cast_from(_mm_unpacklo_##suffix(a2.val, a3.val)); \ + __m128i t2 = cast_from(_mm_unpackhi_##suffix(a0.val, a1.val)); \ + __m128i t3 = cast_from(_mm_unpackhi_##suffix(a2.val, a3.val)); \ +\ + b0.val = cast_to(_mm_unpacklo_epi64(t0, t1)); \ + b1.val = cast_to(_mm_unpackhi_epi64(t0, t1)); \ + b2.val = cast_to(_mm_unpacklo_epi64(t2, t3)); \ + b3.val = cast_to(_mm_unpackhi_epi64(t2, t3)); \ +} + +OPENCV_HAL_IMPL_SSE_TRANSPOSE4x4(v_uint32x4, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_TRANSPOSE4x4(v_int32x4, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_TRANSPOSE4x4(v_float32x4, ps, _mm_castps_si128, _mm_castsi128_ps) + +// load deinterleave +inline void v_load_deinterleave(const uchar* ptr, v_uint8x16& a, v_uint8x16& b) +{ + __m128i t00 = _mm_loadu_si128((const __m128i*)ptr); + __m128i t01 = _mm_loadu_si128((const __m128i*)(ptr + 16)); + + __m128i t10 = _mm_unpacklo_epi8(t00, t01); + __m128i t11 = _mm_unpackhi_epi8(t00, t01); + + __m128i t20 = _mm_unpacklo_epi8(t10, t11); + __m128i t21 = _mm_unpackhi_epi8(t10, t11); + + __m128i t30 = _mm_unpacklo_epi8(t20, t21); + __m128i t31 = _mm_unpackhi_epi8(t20, t21); + + a.val = _mm_unpacklo_epi8(t30, t31); + b.val = _mm_unpackhi_epi8(t30, t31); +} + +inline void v_load_deinterleave(const uchar* ptr, v_uint8x16& a, v_uint8x16& b, v_uint8x16& c) +{ +#if CV_SSE4_1 + const __m128i m0 = _mm_setr_epi8(0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0); + const __m128i m1 = _mm_setr_epi8(0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0); + __m128i s0 = _mm_loadu_si128((const __m128i*)ptr); + __m128i s1 = _mm_loadu_si128((const __m128i*)(ptr + 16)); + __m128i s2 = _mm_loadu_si128((const __m128i*)(ptr + 32)); + __m128i a0 = _mm_blendv_epi8(_mm_blendv_epi8(s0, s1, m0), s2, m1); + __m128i b0 = _mm_blendv_epi8(_mm_blendv_epi8(s1, s2, m0), s0, m1); + __m128i c0 = _mm_blendv_epi8(_mm_blendv_epi8(s2, s0, m0), s1, m1); + const __m128i sh_b = _mm_setr_epi8(0, 3, 6, 9, 12, 15, 2, 5, 8, 11, 14, 1, 4, 7, 10, 13); + const __m128i sh_g = _mm_setr_epi8(1, 4, 7, 10, 13, 0, 3, 6, 9, 12, 15, 2, 5, 8, 11, 14); + const __m128i sh_r = _mm_setr_epi8(2, 5, 8, 11, 14, 1, 4, 7, 10, 13, 0, 3, 6, 9, 12, 15); + a0 = _mm_shuffle_epi8(a0, sh_b); + b0 = _mm_shuffle_epi8(b0, sh_g); + c0 = _mm_shuffle_epi8(c0, sh_r); + a.val = a0; + b.val = b0; + c.val = c0; +#elif CV_SSSE3 + const __m128i m0 = _mm_setr_epi8(0, 3, 6, 9, 12, 15, 1, 4, 7, 10, 13, 2, 5, 8, 11, 14); + const __m128i m1 = _mm_alignr_epi8(m0, m0, 11); + const __m128i m2 = _mm_alignr_epi8(m0, m0, 6); + + __m128i t0 = _mm_loadu_si128((const __m128i*)ptr); + __m128i t1 = _mm_loadu_si128((const __m128i*)(ptr + 16)); + __m128i t2 = _mm_loadu_si128((const __m128i*)(ptr + 32)); + + __m128i s0 = _mm_shuffle_epi8(t0, m0); + __m128i s1 = _mm_shuffle_epi8(t1, m1); + __m128i s2 = _mm_shuffle_epi8(t2, m2); + + t0 = _mm_alignr_epi8(s1, _mm_slli_si128(s0, 10), 5); + a.val = _mm_alignr_epi8(s2, t0, 5); + + t1 = _mm_alignr_epi8(_mm_srli_si128(s1, 5), _mm_slli_si128(s0, 5), 6); + b.val = _mm_alignr_epi8(_mm_srli_si128(s2, 5), t1, 5); + + t2 = _mm_alignr_epi8(_mm_srli_si128(s2, 10), s1, 11); + c.val = _mm_alignr_epi8(t2, s0, 11); +#else + __m128i t00 = _mm_loadu_si128((const __m128i*)ptr); + __m128i t01 = _mm_loadu_si128((const __m128i*)(ptr + 16)); + __m128i t02 = _mm_loadu_si128((const __m128i*)(ptr + 32)); + + __m128i t10 = _mm_unpacklo_epi8(t00, _mm_unpackhi_epi64(t01, t01)); + __m128i t11 = _mm_unpacklo_epi8(_mm_unpackhi_epi64(t00, t00), t02); + __m128i t12 = _mm_unpacklo_epi8(t01, _mm_unpackhi_epi64(t02, t02)); + + __m128i t20 = _mm_unpacklo_epi8(t10, _mm_unpackhi_epi64(t11, t11)); + __m128i t21 = _mm_unpacklo_epi8(_mm_unpackhi_epi64(t10, t10), t12); + __m128i t22 = _mm_unpacklo_epi8(t11, _mm_unpackhi_epi64(t12, t12)); + + __m128i t30 = _mm_unpacklo_epi8(t20, _mm_unpackhi_epi64(t21, t21)); + __m128i t31 = _mm_unpacklo_epi8(_mm_unpackhi_epi64(t20, t20), t22); + __m128i t32 = _mm_unpacklo_epi8(t21, _mm_unpackhi_epi64(t22, t22)); + + a.val = _mm_unpacklo_epi8(t30, _mm_unpackhi_epi64(t31, t31)); + b.val = _mm_unpacklo_epi8(_mm_unpackhi_epi64(t30, t30), t32); + c.val = _mm_unpacklo_epi8(t31, _mm_unpackhi_epi64(t32, t32)); +#endif +} + +inline void v_load_deinterleave(const uchar* ptr, v_uint8x16& a, v_uint8x16& b, v_uint8x16& c, v_uint8x16& d) +{ + __m128i u0 = _mm_loadu_si128((const __m128i*)ptr); // a0 b0 c0 d0 a1 b1 c1 d1 ... + __m128i u1 = _mm_loadu_si128((const __m128i*)(ptr + 16)); // a4 b4 c4 d4 ... + __m128i u2 = _mm_loadu_si128((const __m128i*)(ptr + 32)); // a8 b8 c8 d8 ... + __m128i u3 = _mm_loadu_si128((const __m128i*)(ptr + 48)); // a12 b12 c12 d12 ... + + __m128i v0 = _mm_unpacklo_epi8(u0, u2); // a0 a8 b0 b8 ... + __m128i v1 = _mm_unpackhi_epi8(u0, u2); // a2 a10 b2 b10 ... + __m128i v2 = _mm_unpacklo_epi8(u1, u3); // a4 a12 b4 b12 ... + __m128i v3 = _mm_unpackhi_epi8(u1, u3); // a6 a14 b6 b14 ... + + u0 = _mm_unpacklo_epi8(v0, v2); // a0 a4 a8 a12 ... + u1 = _mm_unpacklo_epi8(v1, v3); // a2 a6 a10 a14 ... + u2 = _mm_unpackhi_epi8(v0, v2); // a1 a5 a9 a13 ... + u3 = _mm_unpackhi_epi8(v1, v3); // a3 a7 a11 a15 ... + + v0 = _mm_unpacklo_epi8(u0, u1); // a0 a2 a4 a6 ... + v1 = _mm_unpacklo_epi8(u2, u3); // a1 a3 a5 a7 ... + v2 = _mm_unpackhi_epi8(u0, u1); // c0 c2 c4 c6 ... + v3 = _mm_unpackhi_epi8(u2, u3); // c1 c3 c5 c7 ... + + a.val = _mm_unpacklo_epi8(v0, v1); + b.val = _mm_unpackhi_epi8(v0, v1); + c.val = _mm_unpacklo_epi8(v2, v3); + d.val = _mm_unpackhi_epi8(v2, v3); +} + +inline void v_load_deinterleave(const ushort* ptr, v_uint16x8& a, v_uint16x8& b) +{ + __m128i v0 = _mm_loadu_si128((__m128i*)(ptr)); // a0 b0 a1 b1 a2 b2 a3 b3 + __m128i v1 = _mm_loadu_si128((__m128i*)(ptr + 8)); // a4 b4 a5 b5 a6 b6 a7 b7 + + __m128i v2 = _mm_unpacklo_epi16(v0, v1); // a0 a4 b0 b4 a1 a5 b1 b5 + __m128i v3 = _mm_unpackhi_epi16(v0, v1); // a2 a6 b2 b6 a3 a7 b3 b7 + __m128i v4 = _mm_unpacklo_epi16(v2, v3); // a0 a2 a4 a6 b0 b2 b4 b6 + __m128i v5 = _mm_unpackhi_epi16(v2, v3); // a1 a3 a5 a7 b1 b3 b5 b7 + + a.val = _mm_unpacklo_epi16(v4, v5); // a0 a1 a2 a3 a4 a5 a6 a7 + b.val = _mm_unpackhi_epi16(v4, v5); // b0 b1 ab b3 b4 b5 b6 b7 +} + +inline void v_load_deinterleave(const ushort* ptr, v_uint16x8& a, v_uint16x8& b, v_uint16x8& c) +{ +#if CV_SSE4_1 + __m128i v0 = _mm_loadu_si128((__m128i*)(ptr)); + __m128i v1 = _mm_loadu_si128((__m128i*)(ptr + 8)); + __m128i v2 = _mm_loadu_si128((__m128i*)(ptr + 16)); + __m128i a0 = _mm_blend_epi16(_mm_blend_epi16(v0, v1, 0x92), v2, 0x24); + __m128i b0 = _mm_blend_epi16(_mm_blend_epi16(v2, v0, 0x92), v1, 0x24); + __m128i c0 = _mm_blend_epi16(_mm_blend_epi16(v1, v2, 0x92), v0, 0x24); + + const __m128i sh_a = _mm_setr_epi8(0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5, 10, 11); + const __m128i sh_b = _mm_setr_epi8(2, 3, 8, 9, 14, 15, 4, 5, 10, 11, 0, 1, 6, 7, 12, 13); + const __m128i sh_c = _mm_setr_epi8(4, 5, 10, 11, 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15); + a0 = _mm_shuffle_epi8(a0, sh_a); + b0 = _mm_shuffle_epi8(b0, sh_b); + c0 = _mm_shuffle_epi8(c0, sh_c); + + a.val = a0; + b.val = b0; + c.val = c0; +#else + __m128i t00 = _mm_loadu_si128((const __m128i*)ptr); + __m128i t01 = _mm_loadu_si128((const __m128i*)(ptr + 8)); + __m128i t02 = _mm_loadu_si128((const __m128i*)(ptr + 16)); + + __m128i t10 = _mm_unpacklo_epi16(t00, _mm_unpackhi_epi64(t01, t01)); + __m128i t11 = _mm_unpacklo_epi16(_mm_unpackhi_epi64(t00, t00), t02); + __m128i t12 = _mm_unpacklo_epi16(t01, _mm_unpackhi_epi64(t02, t02)); + + __m128i t20 = _mm_unpacklo_epi16(t10, _mm_unpackhi_epi64(t11, t11)); + __m128i t21 = _mm_unpacklo_epi16(_mm_unpackhi_epi64(t10, t10), t12); + __m128i t22 = _mm_unpacklo_epi16(t11, _mm_unpackhi_epi64(t12, t12)); + + a.val = _mm_unpacklo_epi16(t20, _mm_unpackhi_epi64(t21, t21)); + b.val = _mm_unpacklo_epi16(_mm_unpackhi_epi64(t20, t20), t22); + c.val = _mm_unpacklo_epi16(t21, _mm_unpackhi_epi64(t22, t22)); +#endif +} + +inline void v_load_deinterleave(const ushort* ptr, v_uint16x8& a, v_uint16x8& b, v_uint16x8& c, v_uint16x8& d) +{ + __m128i u0 = _mm_loadu_si128((const __m128i*)ptr); // a0 b0 c0 d0 a1 b1 c1 d1 + __m128i u1 = _mm_loadu_si128((const __m128i*)(ptr + 8)); // a2 b2 c2 d2 ... + __m128i u2 = _mm_loadu_si128((const __m128i*)(ptr + 16)); // a4 b4 c4 d4 ... + __m128i u3 = _mm_loadu_si128((const __m128i*)(ptr + 24)); // a6 b6 c6 d6 ... + + __m128i v0 = _mm_unpacklo_epi16(u0, u2); // a0 a4 b0 b4 ... + __m128i v1 = _mm_unpackhi_epi16(u0, u2); // a1 a5 b1 b5 ... + __m128i v2 = _mm_unpacklo_epi16(u1, u3); // a2 a6 b2 b6 ... + __m128i v3 = _mm_unpackhi_epi16(u1, u3); // a3 a7 b3 b7 ... + + u0 = _mm_unpacklo_epi16(v0, v2); // a0 a2 a4 a6 ... + u1 = _mm_unpacklo_epi16(v1, v3); // a1 a3 a5 a7 ... + u2 = _mm_unpackhi_epi16(v0, v2); // c0 c2 c4 c6 ... + u3 = _mm_unpackhi_epi16(v1, v3); // c1 c3 c5 c7 ... + + a.val = _mm_unpacklo_epi16(u0, u1); + b.val = _mm_unpackhi_epi16(u0, u1); + c.val = _mm_unpacklo_epi16(u2, u3); + d.val = _mm_unpackhi_epi16(u2, u3); +} + +inline void v_load_deinterleave(const unsigned* ptr, v_uint32x4& a, v_uint32x4& b) +{ + __m128i v0 = _mm_loadu_si128((__m128i*)(ptr)); // a0 b0 a1 b1 + __m128i v1 = _mm_loadu_si128((__m128i*)(ptr + 4)); // a2 b2 a3 b3 + + __m128i v2 = _mm_unpacklo_epi32(v0, v1); // a0 a2 b0 b2 + __m128i v3 = _mm_unpackhi_epi32(v0, v1); // a1 a3 b1 b3 + + a.val = _mm_unpacklo_epi32(v2, v3); // a0 a1 a2 a3 + b.val = _mm_unpackhi_epi32(v2, v3); // b0 b1 ab b3 +} + +inline void v_load_deinterleave(const unsigned* ptr, v_uint32x4& a, v_uint32x4& b, v_uint32x4& c) +{ + __m128i t00 = _mm_loadu_si128((const __m128i*)ptr); + __m128i t01 = _mm_loadu_si128((const __m128i*)(ptr + 4)); + __m128i t02 = _mm_loadu_si128((const __m128i*)(ptr + 8)); + + __m128i t10 = _mm_unpacklo_epi32(t00, _mm_unpackhi_epi64(t01, t01)); + __m128i t11 = _mm_unpacklo_epi32(_mm_unpackhi_epi64(t00, t00), t02); + __m128i t12 = _mm_unpacklo_epi32(t01, _mm_unpackhi_epi64(t02, t02)); + + a.val = _mm_unpacklo_epi32(t10, _mm_unpackhi_epi64(t11, t11)); + b.val = _mm_unpacklo_epi32(_mm_unpackhi_epi64(t10, t10), t12); + c.val = _mm_unpacklo_epi32(t11, _mm_unpackhi_epi64(t12, t12)); +} + +inline void v_load_deinterleave(const unsigned* ptr, v_uint32x4& a, v_uint32x4& b, v_uint32x4& c, v_uint32x4& d) +{ + v_uint32x4 s0(_mm_loadu_si128((const __m128i*)ptr)); // a0 b0 c0 d0 + v_uint32x4 s1(_mm_loadu_si128((const __m128i*)(ptr + 4))); // a1 b1 c1 d1 + v_uint32x4 s2(_mm_loadu_si128((const __m128i*)(ptr + 8))); // a2 b2 c2 d2 + v_uint32x4 s3(_mm_loadu_si128((const __m128i*)(ptr + 12))); // a3 b3 c3 d3 + + v_transpose4x4(s0, s1, s2, s3, a, b, c, d); +} + +inline void v_load_deinterleave(const float* ptr, v_float32x4& a, v_float32x4& b) +{ + __m128 u0 = _mm_loadu_ps(ptr); // a0 b0 a1 b1 + __m128 u1 = _mm_loadu_ps((ptr + 4)); // a2 b2 a3 b3 + + a.val = _mm_shuffle_ps(u0, u1, _MM_SHUFFLE(2, 0, 2, 0)); // a0 a1 a2 a3 + b.val = _mm_shuffle_ps(u0, u1, _MM_SHUFFLE(3, 1, 3, 1)); // b0 b1 ab b3 +} + +inline void v_load_deinterleave(const float* ptr, v_float32x4& a, v_float32x4& b, v_float32x4& c) +{ + __m128 t0 = _mm_loadu_ps(ptr + 0); + __m128 t1 = _mm_loadu_ps(ptr + 4); + __m128 t2 = _mm_loadu_ps(ptr + 8); + + __m128 at12 = _mm_shuffle_ps(t1, t2, _MM_SHUFFLE(0, 1, 0, 2)); + a.val = _mm_shuffle_ps(t0, at12, _MM_SHUFFLE(2, 0, 3, 0)); + + __m128 bt01 = _mm_shuffle_ps(t0, t1, _MM_SHUFFLE(0, 0, 0, 1)); + __m128 bt12 = _mm_shuffle_ps(t1, t2, _MM_SHUFFLE(0, 2, 0, 3)); + b.val = _mm_shuffle_ps(bt01, bt12, _MM_SHUFFLE(2, 0, 2, 0)); + + __m128 ct01 = _mm_shuffle_ps(t0, t1, _MM_SHUFFLE(0, 1, 0, 2)); + c.val = _mm_shuffle_ps(ct01, t2, _MM_SHUFFLE(3, 0, 2, 0)); +} + +inline void v_load_deinterleave(const float* ptr, v_float32x4& a, v_float32x4& b, v_float32x4& c, v_float32x4& d) +{ + __m128 t0 = _mm_loadu_ps(ptr + 0); + __m128 t1 = _mm_loadu_ps(ptr + 4); + __m128 t2 = _mm_loadu_ps(ptr + 8); + __m128 t3 = _mm_loadu_ps(ptr + 12); + __m128 t02lo = _mm_unpacklo_ps(t0, t2); + __m128 t13lo = _mm_unpacklo_ps(t1, t3); + __m128 t02hi = _mm_unpackhi_ps(t0, t2); + __m128 t13hi = _mm_unpackhi_ps(t1, t3); + a.val = _mm_unpacklo_ps(t02lo, t13lo); + b.val = _mm_unpackhi_ps(t02lo, t13lo); + c.val = _mm_unpacklo_ps(t02hi, t13hi); + d.val = _mm_unpackhi_ps(t02hi, t13hi); +} + +inline void v_load_deinterleave(const uint64 *ptr, v_uint64x2& a, v_uint64x2& b) +{ + __m128i t0 = _mm_loadu_si128((const __m128i*)ptr); + __m128i t1 = _mm_loadu_si128((const __m128i*)(ptr + 2)); + + a = v_uint64x2(_mm_unpacklo_epi64(t0, t1)); + b = v_uint64x2(_mm_unpackhi_epi64(t0, t1)); +} + +inline void v_load_deinterleave(const uint64 *ptr, v_uint64x2& a, v_uint64x2& b, v_uint64x2& c) +{ + __m128i t0 = _mm_loadu_si128((const __m128i*)ptr); // a0, b0 + __m128i t1 = _mm_loadu_si128((const __m128i*)(ptr + 2)); // c0, a1 + __m128i t2 = _mm_loadu_si128((const __m128i*)(ptr + 4)); // b1, c1 + + t1 = _mm_shuffle_epi32(t1, 0x4e); // a1, c0 + + a = v_uint64x2(_mm_unpacklo_epi64(t0, t1)); + b = v_uint64x2(_mm_unpacklo_epi64(_mm_unpackhi_epi64(t0, t0), t2)); + c = v_uint64x2(_mm_unpackhi_epi64(t1, t2)); +} + +inline void v_load_deinterleave(const uint64 *ptr, v_uint64x2& a, + v_uint64x2& b, v_uint64x2& c, v_uint64x2& d) +{ + __m128i t0 = _mm_loadu_si128((const __m128i*)ptr); // a0 b0 + __m128i t1 = _mm_loadu_si128((const __m128i*)(ptr + 2)); // c0 d0 + __m128i t2 = _mm_loadu_si128((const __m128i*)(ptr + 4)); // a1 b1 + __m128i t3 = _mm_loadu_si128((const __m128i*)(ptr + 6)); // c1 d1 + + a = v_uint64x2(_mm_unpacklo_epi64(t0, t2)); + b = v_uint64x2(_mm_unpackhi_epi64(t0, t2)); + c = v_uint64x2(_mm_unpacklo_epi64(t1, t3)); + d = v_uint64x2(_mm_unpackhi_epi64(t1, t3)); +} + +// store interleave + +inline void v_store_interleave( uchar* ptr, const v_uint8x16& a, const v_uint8x16& b, + hal::StoreMode mode = hal::STORE_UNALIGNED) +{ + __m128i v0 = _mm_unpacklo_epi8(a.val, b.val); + __m128i v1 = _mm_unpackhi_epi8(a.val, b.val); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm_stream_si128((__m128i*)(ptr), v0); + _mm_stream_si128((__m128i*)(ptr + 16), v1); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm_store_si128((__m128i*)(ptr), v0); + _mm_store_si128((__m128i*)(ptr + 16), v1); + } + else + { + _mm_storeu_si128((__m128i*)(ptr), v0); + _mm_storeu_si128((__m128i*)(ptr + 16), v1); + } +} + +inline void v_store_interleave( uchar* ptr, const v_uint8x16& a, const v_uint8x16& b, + const v_uint8x16& c, hal::StoreMode mode = hal::STORE_UNALIGNED) +{ +#if CV_SSE4_1 + const __m128i sh_a = _mm_setr_epi8(0, 11, 6, 1, 12, 7, 2, 13, 8, 3, 14, 9, 4, 15, 10, 5); + const __m128i sh_b = _mm_setr_epi8(5, 0, 11, 6, 1, 12, 7, 2, 13, 8, 3, 14, 9, 4, 15, 10); + const __m128i sh_c = _mm_setr_epi8(10, 5, 0, 11, 6, 1, 12, 7, 2, 13, 8, 3, 14, 9, 4, 15); + __m128i a0 = _mm_shuffle_epi8(a.val, sh_a); + __m128i b0 = _mm_shuffle_epi8(b.val, sh_b); + __m128i c0 = _mm_shuffle_epi8(c.val, sh_c); + + const __m128i m0 = _mm_setr_epi8(0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0); + const __m128i m1 = _mm_setr_epi8(0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0); + __m128i v0 = _mm_blendv_epi8(_mm_blendv_epi8(a0, b0, m1), c0, m0); + __m128i v1 = _mm_blendv_epi8(_mm_blendv_epi8(b0, c0, m1), a0, m0); + __m128i v2 = _mm_blendv_epi8(_mm_blendv_epi8(c0, a0, m1), b0, m0); +#elif CV_SSSE3 + const __m128i m0 = _mm_setr_epi8(0, 6, 11, 1, 7, 12, 2, 8, 13, 3, 9, 14, 4, 10, 15, 5); + const __m128i m1 = _mm_setr_epi8(5, 11, 0, 6, 12, 1, 7, 13, 2, 8, 14, 3, 9, 15, 4, 10); + const __m128i m2 = _mm_setr_epi8(10, 0, 5, 11, 1, 6, 12, 2, 7, 13, 3, 8, 14, 4, 9, 15); + + __m128i t0 = _mm_alignr_epi8(b.val, _mm_slli_si128(a.val, 10), 5); + t0 = _mm_alignr_epi8(c.val, t0, 5); + __m128i v0 = _mm_shuffle_epi8(t0, m0); + + __m128i t1 = _mm_alignr_epi8(_mm_srli_si128(b.val, 5), _mm_slli_si128(a.val, 5), 6); + t1 = _mm_alignr_epi8(_mm_srli_si128(c.val, 5), t1, 5); + __m128i v1 = _mm_shuffle_epi8(t1, m1); + + __m128i t2 = _mm_alignr_epi8(_mm_srli_si128(c.val, 10), b.val, 11); + t2 = _mm_alignr_epi8(t2, a.val, 11); + __m128i v2 = _mm_shuffle_epi8(t2, m2); +#else + __m128i z = _mm_setzero_si128(); + __m128i ab0 = _mm_unpacklo_epi8(a.val, b.val); + __m128i ab1 = _mm_unpackhi_epi8(a.val, b.val); + __m128i c0 = _mm_unpacklo_epi8(c.val, z); + __m128i c1 = _mm_unpackhi_epi8(c.val, z); + + __m128i p00 = _mm_unpacklo_epi16(ab0, c0); + __m128i p01 = _mm_unpackhi_epi16(ab0, c0); + __m128i p02 = _mm_unpacklo_epi16(ab1, c1); + __m128i p03 = _mm_unpackhi_epi16(ab1, c1); + + __m128i p10 = _mm_unpacklo_epi32(p00, p01); + __m128i p11 = _mm_unpackhi_epi32(p00, p01); + __m128i p12 = _mm_unpacklo_epi32(p02, p03); + __m128i p13 = _mm_unpackhi_epi32(p02, p03); + + __m128i p20 = _mm_unpacklo_epi64(p10, p11); + __m128i p21 = _mm_unpackhi_epi64(p10, p11); + __m128i p22 = _mm_unpacklo_epi64(p12, p13); + __m128i p23 = _mm_unpackhi_epi64(p12, p13); + + p20 = _mm_slli_si128(p20, 1); + p22 = _mm_slli_si128(p22, 1); + + __m128i p30 = _mm_slli_epi64(_mm_unpacklo_epi32(p20, p21), 8); + __m128i p31 = _mm_srli_epi64(_mm_unpackhi_epi32(p20, p21), 8); + __m128i p32 = _mm_slli_epi64(_mm_unpacklo_epi32(p22, p23), 8); + __m128i p33 = _mm_srli_epi64(_mm_unpackhi_epi32(p22, p23), 8); + + __m128i p40 = _mm_unpacklo_epi64(p30, p31); + __m128i p41 = _mm_unpackhi_epi64(p30, p31); + __m128i p42 = _mm_unpacklo_epi64(p32, p33); + __m128i p43 = _mm_unpackhi_epi64(p32, p33); + + __m128i v0 = _mm_or_si128(_mm_srli_si128(p40, 2), _mm_slli_si128(p41, 10)); + __m128i v1 = _mm_or_si128(_mm_srli_si128(p41, 6), _mm_slli_si128(p42, 6)); + __m128i v2 = _mm_or_si128(_mm_srli_si128(p42, 10), _mm_slli_si128(p43, 2)); +#endif + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm_stream_si128((__m128i*)(ptr), v0); + _mm_stream_si128((__m128i*)(ptr + 16), v1); + _mm_stream_si128((__m128i*)(ptr + 32), v2); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm_store_si128((__m128i*)(ptr), v0); + _mm_store_si128((__m128i*)(ptr + 16), v1); + _mm_store_si128((__m128i*)(ptr + 32), v2); + } + else + { + _mm_storeu_si128((__m128i*)(ptr), v0); + _mm_storeu_si128((__m128i*)(ptr + 16), v1); + _mm_storeu_si128((__m128i*)(ptr + 32), v2); + } +} + +inline void v_store_interleave( uchar* ptr, const v_uint8x16& a, const v_uint8x16& b, + const v_uint8x16& c, const v_uint8x16& d, + hal::StoreMode mode = hal::STORE_UNALIGNED) +{ + // a0 a1 a2 a3 .... + // b0 b1 b2 b3 .... + // c0 c1 c2 c3 .... + // d0 d1 d2 d3 .... + __m128i u0 = _mm_unpacklo_epi8(a.val, c.val); // a0 c0 a1 c1 ... + __m128i u1 = _mm_unpackhi_epi8(a.val, c.val); // a8 c8 a9 c9 ... + __m128i u2 = _mm_unpacklo_epi8(b.val, d.val); // b0 d0 b1 d1 ... + __m128i u3 = _mm_unpackhi_epi8(b.val, d.val); // b8 d8 b9 d9 ... + + __m128i v0 = _mm_unpacklo_epi8(u0, u2); // a0 b0 c0 d0 ... + __m128i v1 = _mm_unpackhi_epi8(u0, u2); // a4 b4 c4 d4 ... + __m128i v2 = _mm_unpacklo_epi8(u1, u3); // a8 b8 c8 d8 ... + __m128i v3 = _mm_unpackhi_epi8(u1, u3); // a12 b12 c12 d12 ... + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm_stream_si128((__m128i*)(ptr), v0); + _mm_stream_si128((__m128i*)(ptr + 16), v1); + _mm_stream_si128((__m128i*)(ptr + 32), v2); + _mm_stream_si128((__m128i*)(ptr + 48), v3); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm_store_si128((__m128i*)(ptr), v0); + _mm_store_si128((__m128i*)(ptr + 16), v1); + _mm_store_si128((__m128i*)(ptr + 32), v2); + _mm_store_si128((__m128i*)(ptr + 48), v3); + } + else + { + _mm_storeu_si128((__m128i*)(ptr), v0); + _mm_storeu_si128((__m128i*)(ptr + 16), v1); + _mm_storeu_si128((__m128i*)(ptr + 32), v2); + _mm_storeu_si128((__m128i*)(ptr + 48), v3); + } +} + +inline void v_store_interleave( ushort* ptr, const v_uint16x8& a, const v_uint16x8& b, + hal::StoreMode mode = hal::STORE_UNALIGNED) +{ + __m128i v0 = _mm_unpacklo_epi16(a.val, b.val); + __m128i v1 = _mm_unpackhi_epi16(a.val, b.val); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm_stream_si128((__m128i*)(ptr), v0); + _mm_stream_si128((__m128i*)(ptr + 8), v1); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm_store_si128((__m128i*)(ptr), v0); + _mm_store_si128((__m128i*)(ptr + 8), v1); + } + else + { + _mm_storeu_si128((__m128i*)(ptr), v0); + _mm_storeu_si128((__m128i*)(ptr + 8), v1); + } +} + +inline void v_store_interleave( ushort* ptr, const v_uint16x8& a, + const v_uint16x8& b, const v_uint16x8& c, + hal::StoreMode mode = hal::STORE_UNALIGNED) +{ +#if CV_SSE4_1 + const __m128i sh_a = _mm_setr_epi8(0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5, 10, 11); + const __m128i sh_b = _mm_setr_epi8(10, 11, 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5); + const __m128i sh_c = _mm_setr_epi8(4, 5, 10, 11, 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15); + __m128i a0 = _mm_shuffle_epi8(a.val, sh_a); + __m128i b0 = _mm_shuffle_epi8(b.val, sh_b); + __m128i c0 = _mm_shuffle_epi8(c.val, sh_c); + + __m128i v0 = _mm_blend_epi16(_mm_blend_epi16(a0, b0, 0x92), c0, 0x24); + __m128i v1 = _mm_blend_epi16(_mm_blend_epi16(c0, a0, 0x92), b0, 0x24); + __m128i v2 = _mm_blend_epi16(_mm_blend_epi16(b0, c0, 0x92), a0, 0x24); +#else + __m128i z = _mm_setzero_si128(); + __m128i ab0 = _mm_unpacklo_epi16(a.val, b.val); + __m128i ab1 = _mm_unpackhi_epi16(a.val, b.val); + __m128i c0 = _mm_unpacklo_epi16(c.val, z); + __m128i c1 = _mm_unpackhi_epi16(c.val, z); + + __m128i p10 = _mm_unpacklo_epi32(ab0, c0); + __m128i p11 = _mm_unpackhi_epi32(ab0, c0); + __m128i p12 = _mm_unpacklo_epi32(ab1, c1); + __m128i p13 = _mm_unpackhi_epi32(ab1, c1); + + __m128i p20 = _mm_unpacklo_epi64(p10, p11); + __m128i p21 = _mm_unpackhi_epi64(p10, p11); + __m128i p22 = _mm_unpacklo_epi64(p12, p13); + __m128i p23 = _mm_unpackhi_epi64(p12, p13); + + p20 = _mm_slli_si128(p20, 2); + p22 = _mm_slli_si128(p22, 2); + + __m128i p30 = _mm_unpacklo_epi64(p20, p21); + __m128i p31 = _mm_unpackhi_epi64(p20, p21); + __m128i p32 = _mm_unpacklo_epi64(p22, p23); + __m128i p33 = _mm_unpackhi_epi64(p22, p23); + + __m128i v0 = _mm_or_si128(_mm_srli_si128(p30, 2), _mm_slli_si128(p31, 10)); + __m128i v1 = _mm_or_si128(_mm_srli_si128(p31, 6), _mm_slli_si128(p32, 6)); + __m128i v2 = _mm_or_si128(_mm_srli_si128(p32, 10), _mm_slli_si128(p33, 2)); +#endif + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm_stream_si128((__m128i*)(ptr), v0); + _mm_stream_si128((__m128i*)(ptr + 8), v1); + _mm_stream_si128((__m128i*)(ptr + 16), v2); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm_store_si128((__m128i*)(ptr), v0); + _mm_store_si128((__m128i*)(ptr + 8), v1); + _mm_store_si128((__m128i*)(ptr + 16), v2); + } + else + { + _mm_storeu_si128((__m128i*)(ptr), v0); + _mm_storeu_si128((__m128i*)(ptr + 8), v1); + _mm_storeu_si128((__m128i*)(ptr + 16), v2); + } +} + +inline void v_store_interleave( ushort* ptr, const v_uint16x8& a, const v_uint16x8& b, + const v_uint16x8& c, const v_uint16x8& d, + hal::StoreMode mode = hal::STORE_UNALIGNED) +{ + // a0 a1 a2 a3 .... + // b0 b1 b2 b3 .... + // c0 c1 c2 c3 .... + // d0 d1 d2 d3 .... + __m128i u0 = _mm_unpacklo_epi16(a.val, c.val); // a0 c0 a1 c1 ... + __m128i u1 = _mm_unpackhi_epi16(a.val, c.val); // a4 c4 a5 c5 ... + __m128i u2 = _mm_unpacklo_epi16(b.val, d.val); // b0 d0 b1 d1 ... + __m128i u3 = _mm_unpackhi_epi16(b.val, d.val); // b4 d4 b5 d5 ... + + __m128i v0 = _mm_unpacklo_epi16(u0, u2); // a0 b0 c0 d0 ... + __m128i v1 = _mm_unpackhi_epi16(u0, u2); // a2 b2 c2 d2 ... + __m128i v2 = _mm_unpacklo_epi16(u1, u3); // a4 b4 c4 d4 ... + __m128i v3 = _mm_unpackhi_epi16(u1, u3); // a6 b6 c6 d6 ... + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm_stream_si128((__m128i*)(ptr), v0); + _mm_stream_si128((__m128i*)(ptr + 8), v1); + _mm_stream_si128((__m128i*)(ptr + 16), v2); + _mm_stream_si128((__m128i*)(ptr + 24), v3); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm_store_si128((__m128i*)(ptr), v0); + _mm_store_si128((__m128i*)(ptr + 8), v1); + _mm_store_si128((__m128i*)(ptr + 16), v2); + _mm_store_si128((__m128i*)(ptr + 24), v3); + } + else + { + _mm_storeu_si128((__m128i*)(ptr), v0); + _mm_storeu_si128((__m128i*)(ptr + 8), v1); + _mm_storeu_si128((__m128i*)(ptr + 16), v2); + _mm_storeu_si128((__m128i*)(ptr + 24), v3); + } +} + +inline void v_store_interleave( unsigned* ptr, const v_uint32x4& a, const v_uint32x4& b, + hal::StoreMode mode = hal::STORE_UNALIGNED) +{ + __m128i v0 = _mm_unpacklo_epi32(a.val, b.val); + __m128i v1 = _mm_unpackhi_epi32(a.val, b.val); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm_stream_si128((__m128i*)(ptr), v0); + _mm_stream_si128((__m128i*)(ptr + 4), v1); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm_store_si128((__m128i*)(ptr), v0); + _mm_store_si128((__m128i*)(ptr + 4), v1); + } + else + { + _mm_storeu_si128((__m128i*)(ptr), v0); + _mm_storeu_si128((__m128i*)(ptr + 4), v1); + } +} + +inline void v_store_interleave( unsigned* ptr, const v_uint32x4& a, const v_uint32x4& b, + const v_uint32x4& c, hal::StoreMode mode = hal::STORE_UNALIGNED) +{ + v_uint32x4 z = v_setzero_u32(), u0, u1, u2, u3; + v_transpose4x4(a, b, c, z, u0, u1, u2, u3); + + __m128i v0 = _mm_or_si128(u0.val, _mm_slli_si128(u1.val, 12)); + __m128i v1 = _mm_or_si128(_mm_srli_si128(u1.val, 4), _mm_slli_si128(u2.val, 8)); + __m128i v2 = _mm_or_si128(_mm_srli_si128(u2.val, 8), _mm_slli_si128(u3.val, 4)); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm_stream_si128((__m128i*)(ptr), v0); + _mm_stream_si128((__m128i*)(ptr + 4), v1); + _mm_stream_si128((__m128i*)(ptr + 8), v2); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm_store_si128((__m128i*)(ptr), v0); + _mm_store_si128((__m128i*)(ptr + 4), v1); + _mm_store_si128((__m128i*)(ptr + 8), v2); + } + else + { + _mm_storeu_si128((__m128i*)(ptr), v0); + _mm_storeu_si128((__m128i*)(ptr + 4), v1); + _mm_storeu_si128((__m128i*)(ptr + 8), v2); + } +} + +inline void v_store_interleave(unsigned* ptr, const v_uint32x4& a, const v_uint32x4& b, + const v_uint32x4& c, const v_uint32x4& d, + hal::StoreMode mode = hal::STORE_UNALIGNED) +{ + v_uint32x4 v0, v1, v2, v3; + v_transpose4x4(a, b, c, d, v0, v1, v2, v3); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm_stream_si128((__m128i*)(ptr), v0.val); + _mm_stream_si128((__m128i*)(ptr + 4), v1.val); + _mm_stream_si128((__m128i*)(ptr + 8), v2.val); + _mm_stream_si128((__m128i*)(ptr + 12), v3.val); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm_store_si128((__m128i*)(ptr), v0.val); + _mm_store_si128((__m128i*)(ptr + 4), v1.val); + _mm_store_si128((__m128i*)(ptr + 8), v2.val); + _mm_store_si128((__m128i*)(ptr + 12), v3.val); + } + else + { + _mm_storeu_si128((__m128i*)(ptr), v0.val); + _mm_storeu_si128((__m128i*)(ptr + 4), v1.val); + _mm_storeu_si128((__m128i*)(ptr + 8), v2.val); + _mm_storeu_si128((__m128i*)(ptr + 12), v3.val); + } +} + +// 2-channel, float only +inline void v_store_interleave(float* ptr, const v_float32x4& a, const v_float32x4& b, + hal::StoreMode mode = hal::STORE_UNALIGNED) +{ + __m128 v0 = _mm_unpacklo_ps(a.val, b.val); // a0 b0 a1 b1 + __m128 v1 = _mm_unpackhi_ps(a.val, b.val); // a2 b2 a3 b3 + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm_stream_ps(ptr, v0); + _mm_stream_ps(ptr + 4, v1); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm_store_ps(ptr, v0); + _mm_store_ps(ptr + 4, v1); + } + else + { + _mm_storeu_ps(ptr, v0); + _mm_storeu_ps(ptr + 4, v1); + } +} + +inline void v_store_interleave(float* ptr, const v_float32x4& a, const v_float32x4& b, + const v_float32x4& c, hal::StoreMode mode = hal::STORE_UNALIGNED) +{ + __m128 u0 = _mm_shuffle_ps(a.val, b.val, _MM_SHUFFLE(0, 0, 0, 0)); + __m128 u1 = _mm_shuffle_ps(c.val, a.val, _MM_SHUFFLE(1, 1, 0, 0)); + __m128 v0 = _mm_shuffle_ps(u0, u1, _MM_SHUFFLE(2, 0, 2, 0)); + __m128 u2 = _mm_shuffle_ps(b.val, c.val, _MM_SHUFFLE(1, 1, 1, 1)); + __m128 u3 = _mm_shuffle_ps(a.val, b.val, _MM_SHUFFLE(2, 2, 2, 2)); + __m128 v1 = _mm_shuffle_ps(u2, u3, _MM_SHUFFLE(2, 0, 2, 0)); + __m128 u4 = _mm_shuffle_ps(c.val, a.val, _MM_SHUFFLE(3, 3, 2, 2)); + __m128 u5 = _mm_shuffle_ps(b.val, c.val, _MM_SHUFFLE(3, 3, 3, 3)); + __m128 v2 = _mm_shuffle_ps(u4, u5, _MM_SHUFFLE(2, 0, 2, 0)); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm_stream_ps(ptr, v0); + _mm_stream_ps(ptr + 4, v1); + _mm_stream_ps(ptr + 8, v2); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm_store_ps(ptr, v0); + _mm_store_ps(ptr + 4, v1); + _mm_store_ps(ptr + 8, v2); + } + else + { + _mm_storeu_ps(ptr, v0); + _mm_storeu_ps(ptr + 4, v1); + _mm_storeu_ps(ptr + 8, v2); + } +} + +inline void v_store_interleave(float* ptr, const v_float32x4& a, const v_float32x4& b, + const v_float32x4& c, const v_float32x4& d, + hal::StoreMode mode = hal::STORE_UNALIGNED) +{ + __m128 u0 = _mm_unpacklo_ps(a.val, c.val); + __m128 u1 = _mm_unpacklo_ps(b.val, d.val); + __m128 u2 = _mm_unpackhi_ps(a.val, c.val); + __m128 u3 = _mm_unpackhi_ps(b.val, d.val); + __m128 v0 = _mm_unpacklo_ps(u0, u1); + __m128 v2 = _mm_unpacklo_ps(u2, u3); + __m128 v1 = _mm_unpackhi_ps(u0, u1); + __m128 v3 = _mm_unpackhi_ps(u2, u3); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm_stream_ps(ptr, v0); + _mm_stream_ps(ptr + 4, v1); + _mm_stream_ps(ptr + 8, v2); + _mm_stream_ps(ptr + 12, v3); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm_store_ps(ptr, v0); + _mm_store_ps(ptr + 4, v1); + _mm_store_ps(ptr + 8, v2); + _mm_store_ps(ptr + 12, v3); + } + else + { + _mm_storeu_ps(ptr, v0); + _mm_storeu_ps(ptr + 4, v1); + _mm_storeu_ps(ptr + 8, v2); + _mm_storeu_ps(ptr + 12, v3); + } +} + +inline void v_store_interleave(uint64 *ptr, const v_uint64x2& a, const v_uint64x2& b, + hal::StoreMode mode = hal::STORE_UNALIGNED) +{ + __m128i v0 = _mm_unpacklo_epi64(a.val, b.val); + __m128i v1 = _mm_unpackhi_epi64(a.val, b.val); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm_stream_si128((__m128i*)(ptr), v0); + _mm_stream_si128((__m128i*)(ptr + 2), v1); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm_store_si128((__m128i*)(ptr), v0); + _mm_store_si128((__m128i*)(ptr + 2), v1); + } + else + { + _mm_storeu_si128((__m128i*)(ptr), v0); + _mm_storeu_si128((__m128i*)(ptr + 2), v1); + } +} + +inline void v_store_interleave(uint64 *ptr, const v_uint64x2& a, const v_uint64x2& b, + const v_uint64x2& c, hal::StoreMode mode = hal::STORE_UNALIGNED) +{ + __m128i v0 = _mm_unpacklo_epi64(a.val, b.val); + __m128i v1 = _mm_unpacklo_epi64(c.val, _mm_unpackhi_epi64(a.val, a.val)); + __m128i v2 = _mm_unpackhi_epi64(b.val, c.val); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm_stream_si128((__m128i*)(ptr), v0); + _mm_stream_si128((__m128i*)(ptr + 2), v1); + _mm_stream_si128((__m128i*)(ptr + 4), v2); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm_store_si128((__m128i*)(ptr), v0); + _mm_store_si128((__m128i*)(ptr + 2), v1); + _mm_store_si128((__m128i*)(ptr + 4), v2); + } + else + { + _mm_storeu_si128((__m128i*)(ptr), v0); + _mm_storeu_si128((__m128i*)(ptr + 2), v1); + _mm_storeu_si128((__m128i*)(ptr + 4), v2); + } +} + +inline void v_store_interleave(uint64 *ptr, const v_uint64x2& a, const v_uint64x2& b, + const v_uint64x2& c, const v_uint64x2& d, + hal::StoreMode mode = hal::STORE_UNALIGNED) +{ + __m128i v0 = _mm_unpacklo_epi64(a.val, b.val); + __m128i v1 = _mm_unpacklo_epi64(c.val, d.val); + __m128i v2 = _mm_unpackhi_epi64(a.val, b.val); + __m128i v3 = _mm_unpackhi_epi64(c.val, d.val); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm_stream_si128((__m128i*)(ptr), v0); + _mm_stream_si128((__m128i*)(ptr + 2), v1); + _mm_stream_si128((__m128i*)(ptr + 4), v2); + _mm_stream_si128((__m128i*)(ptr + 6), v3); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm_store_si128((__m128i*)(ptr), v0); + _mm_store_si128((__m128i*)(ptr + 2), v1); + _mm_store_si128((__m128i*)(ptr + 4), v2); + _mm_store_si128((__m128i*)(ptr + 6), v3); + } + else + { + _mm_storeu_si128((__m128i*)(ptr), v0); + _mm_storeu_si128((__m128i*)(ptr + 2), v1); + _mm_storeu_si128((__m128i*)(ptr + 4), v2); + _mm_storeu_si128((__m128i*)(ptr + 6), v3); + } +} + +#define OPENCV_HAL_IMPL_SSE_LOADSTORE_INTERLEAVE(_Tpvec0, _Tp0, suffix0, _Tpvec1, _Tp1, suffix1) \ +inline void v_load_deinterleave( const _Tp0* ptr, _Tpvec0& a0, _Tpvec0& b0 ) \ +{ \ + _Tpvec1 a1, b1; \ + v_load_deinterleave((const _Tp1*)ptr, a1, b1); \ + a0 = v_reinterpret_as_##suffix0(a1); \ + b0 = v_reinterpret_as_##suffix0(b1); \ +} \ +inline void v_load_deinterleave( const _Tp0* ptr, _Tpvec0& a0, _Tpvec0& b0, _Tpvec0& c0 ) \ +{ \ + _Tpvec1 a1, b1, c1; \ + v_load_deinterleave((const _Tp1*)ptr, a1, b1, c1); \ + a0 = v_reinterpret_as_##suffix0(a1); \ + b0 = v_reinterpret_as_##suffix0(b1); \ + c0 = v_reinterpret_as_##suffix0(c1); \ +} \ +inline void v_load_deinterleave( const _Tp0* ptr, _Tpvec0& a0, _Tpvec0& b0, _Tpvec0& c0, _Tpvec0& d0 ) \ +{ \ + _Tpvec1 a1, b1, c1, d1; \ + v_load_deinterleave((const _Tp1*)ptr, a1, b1, c1, d1); \ + a0 = v_reinterpret_as_##suffix0(a1); \ + b0 = v_reinterpret_as_##suffix0(b1); \ + c0 = v_reinterpret_as_##suffix0(c1); \ + d0 = v_reinterpret_as_##suffix0(d1); \ +} \ +inline void v_store_interleave( _Tp0* ptr, const _Tpvec0& a0, const _Tpvec0& b0, \ + hal::StoreMode mode = hal::STORE_UNALIGNED ) \ +{ \ + _Tpvec1 a1 = v_reinterpret_as_##suffix1(a0); \ + _Tpvec1 b1 = v_reinterpret_as_##suffix1(b0); \ + v_store_interleave((_Tp1*)ptr, a1, b1, mode); \ +} \ +inline void v_store_interleave( _Tp0* ptr, const _Tpvec0& a0, const _Tpvec0& b0, \ + const _Tpvec0& c0, hal::StoreMode mode = hal::STORE_UNALIGNED ) \ +{ \ + _Tpvec1 a1 = v_reinterpret_as_##suffix1(a0); \ + _Tpvec1 b1 = v_reinterpret_as_##suffix1(b0); \ + _Tpvec1 c1 = v_reinterpret_as_##suffix1(c0); \ + v_store_interleave((_Tp1*)ptr, a1, b1, c1, mode); \ +} \ +inline void v_store_interleave( _Tp0* ptr, const _Tpvec0& a0, const _Tpvec0& b0, \ + const _Tpvec0& c0, const _Tpvec0& d0, \ + hal::StoreMode mode = hal::STORE_UNALIGNED ) \ +{ \ + _Tpvec1 a1 = v_reinterpret_as_##suffix1(a0); \ + _Tpvec1 b1 = v_reinterpret_as_##suffix1(b0); \ + _Tpvec1 c1 = v_reinterpret_as_##suffix1(c0); \ + _Tpvec1 d1 = v_reinterpret_as_##suffix1(d0); \ + v_store_interleave((_Tp1*)ptr, a1, b1, c1, d1, mode); \ +} + +OPENCV_HAL_IMPL_SSE_LOADSTORE_INTERLEAVE(v_int8x16, schar, s8, v_uint8x16, uchar, u8) +OPENCV_HAL_IMPL_SSE_LOADSTORE_INTERLEAVE(v_int16x8, short, s16, v_uint16x8, ushort, u16) +OPENCV_HAL_IMPL_SSE_LOADSTORE_INTERLEAVE(v_int32x4, int, s32, v_uint32x4, unsigned, u32) +OPENCV_HAL_IMPL_SSE_LOADSTORE_INTERLEAVE(v_int64x2, int64, s64, v_uint64x2, uint64, u64) +OPENCV_HAL_IMPL_SSE_LOADSTORE_INTERLEAVE(v_float64x2, double, f64, v_uint64x2, uint64, u64) + +inline v_float32x4 v_cvt_f32(const v_int32x4& a) +{ + return v_float32x4(_mm_cvtepi32_ps(a.val)); +} + +inline v_float32x4 v_cvt_f32(const v_float64x2& a) +{ + return v_float32x4(_mm_cvtpd_ps(a.val)); +} + +inline v_float32x4 v_cvt_f32(const v_float64x2& a, const v_float64x2& b) +{ + return v_float32x4(_mm_movelh_ps(_mm_cvtpd_ps(a.val), _mm_cvtpd_ps(b.val))); +} + +inline v_float64x2 v_cvt_f64(const v_int32x4& a) +{ + return v_float64x2(_mm_cvtepi32_pd(a.val)); +} + +inline v_float64x2 v_cvt_f64_high(const v_int32x4& a) +{ + return v_float64x2(_mm_cvtepi32_pd(_mm_srli_si128(a.val,8))); +} + +inline v_float64x2 v_cvt_f64(const v_float32x4& a) +{ + return v_float64x2(_mm_cvtps_pd(a.val)); +} + +inline v_float64x2 v_cvt_f64_high(const v_float32x4& a) +{ + return v_float64x2(_mm_cvtps_pd(_mm_movehl_ps(a.val, a.val))); +} + +#if CV_FP16 +inline v_float32x4 v128_load_fp16_f32(const short* ptr) +{ + return v_float32x4(_mm_cvtph_ps(_mm_loadu_si128((const __m128i*)ptr))); +} + +inline void v_store_fp16(short* ptr, const v_float32x4& a) +{ + __m128i fp16_value = _mm_cvtps_ph(a.val, 0); + _mm_storel_epi64((__m128i*)ptr, fp16_value); +} +#endif + +////////////// Lookup table access //////////////////// + +inline v_int32x4 v_lut(const int* tab, const v_int32x4& idxvec) +{ + int CV_DECL_ALIGNED(32) idx[4]; + v_store_aligned(idx, idxvec); + return v_int32x4(_mm_setr_epi32(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]])); +} + +inline v_float32x4 v_lut(const float* tab, const v_int32x4& idxvec) +{ + int CV_DECL_ALIGNED(32) idx[4]; + v_store_aligned(idx, idxvec); + return v_float32x4(_mm_setr_ps(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]])); +} + +inline v_float64x2 v_lut(const double* tab, const v_int32x4& idxvec) +{ + int idx[2]; + v_store_low(idx, idxvec); + return v_float64x2(_mm_setr_pd(tab[idx[0]], tab[idx[1]])); +} + +// loads pairs from the table and deinterleaves them, e.g. returns: +// x = (tab[idxvec[0], tab[idxvec[1]], tab[idxvec[2]], tab[idxvec[3]]), +// y = (tab[idxvec[0]+1], tab[idxvec[1]+1], tab[idxvec[2]+1], tab[idxvec[3]+1]) +// note that the indices are float's indices, not the float-pair indices. +// in theory, this function can be used to implement bilinear interpolation, +// when idxvec are the offsets within the image. +inline void v_lut_deinterleave(const float* tab, const v_int32x4& idxvec, v_float32x4& x, v_float32x4& y) +{ + int CV_DECL_ALIGNED(32) idx[4]; + v_store_aligned(idx, idxvec); + __m128 z = _mm_setzero_ps(); + __m128 xy01 = _mm_loadl_pi(z, (__m64*)(tab + idx[0])); + __m128 xy23 = _mm_loadl_pi(z, (__m64*)(tab + idx[2])); + xy01 = _mm_loadh_pi(xy01, (__m64*)(tab + idx[1])); + xy23 = _mm_loadh_pi(xy23, (__m64*)(tab + idx[3])); + __m128 xxyy02 = _mm_unpacklo_ps(xy01, xy23); + __m128 xxyy13 = _mm_unpackhi_ps(xy01, xy23); + x = v_float32x4(_mm_unpacklo_ps(xxyy02, xxyy13)); + y = v_float32x4(_mm_unpackhi_ps(xxyy02, xxyy13)); +} + +inline void v_lut_deinterleave(const double* tab, const v_int32x4& idxvec, v_float64x2& x, v_float64x2& y) +{ + int idx[2]; + v_store_low(idx, idxvec); + __m128d xy0 = _mm_loadu_pd(tab + idx[0]); + __m128d xy1 = _mm_loadu_pd(tab + idx[1]); + x = v_float64x2(_mm_unpacklo_pd(xy0, xy1)); + y = v_float64x2(_mm_unpackhi_pd(xy0, xy1)); +} + + +////////////// FP16 support /////////////////////////// + +inline v_float32x4 v_load_expand(const float16_t* ptr) +{ + const __m128i z = _mm_setzero_si128(), delta = _mm_set1_epi32(0x38000000); + const __m128i signmask = _mm_set1_epi32(0x80000000), maxexp = _mm_set1_epi32(0x7c000000); + const __m128 deltaf = _mm_castsi128_ps(_mm_set1_epi32(0x38800000)); + __m128i bits = _mm_unpacklo_epi16(z, _mm_loadl_epi64((const __m128i*)ptr)); // h << 16 + __m128i e = _mm_and_si128(bits, maxexp), sign = _mm_and_si128(bits, signmask); + __m128i t = _mm_add_epi32(_mm_srli_epi32(_mm_xor_si128(bits, sign), 3), delta); // ((h & 0x7fff) << 13) + delta + __m128i zt = _mm_castps_si128(_mm_sub_ps(_mm_castsi128_ps(_mm_add_epi32(t, _mm_set1_epi32(1 << 23))), deltaf)); + + t = _mm_add_epi32(t, _mm_and_si128(delta, _mm_cmpeq_epi32(maxexp, e))); + __m128i zmask = _mm_cmpeq_epi32(e, z); + __m128i ft = v_select_si128(zmask, zt, t); + return v_float32x4(_mm_castsi128_ps(_mm_or_si128(ft, sign))); +} + +inline void v_pack_store(float16_t* ptr, const v_float32x4& v) +{ + const __m128i signmask = _mm_set1_epi32(0x80000000); + const __m128i rval = _mm_set1_epi32(0x3f000000); + + __m128i t = _mm_castps_si128(v.val); + __m128i sign = _mm_srai_epi32(_mm_and_si128(t, signmask), 16); + t = _mm_andnot_si128(signmask, t); + + __m128i finitemask = _mm_cmpgt_epi32(_mm_set1_epi32(0x47800000), t); + __m128i isnan = _mm_cmpgt_epi32(t, _mm_set1_epi32(0x7f800000)); + __m128i naninf = v_select_si128(isnan, _mm_set1_epi32(0x7e00), _mm_set1_epi32(0x7c00)); + __m128i tinymask = _mm_cmpgt_epi32(_mm_set1_epi32(0x38800000), t); + __m128i tt = _mm_castps_si128(_mm_add_ps(_mm_castsi128_ps(t), _mm_castsi128_ps(rval))); + tt = _mm_sub_epi32(tt, rval); + __m128i odd = _mm_and_si128(_mm_srli_epi32(t, 13), _mm_set1_epi32(1)); + __m128i nt = _mm_add_epi32(t, _mm_set1_epi32(0xc8000fff)); + nt = _mm_srli_epi32(_mm_add_epi32(nt, odd), 13); + t = v_select_si128(tinymask, tt, nt); + t = v_select_si128(finitemask, t, naninf); + t = _mm_or_si128(t, sign); + t = _mm_packs_epi32(t, t); + _mm_storel_epi64((__m128i*)ptr, t); +} + +inline void v_cleanup() {} + +//! @name Check SIMD support +//! @{ +//! @brief Check CPU capability of SIMD operation +static inline bool hasSIMD128() +{ + return (CV_CPU_HAS_SUPPORT_SSE2) ? true : false; +} + +//! @} + +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END + +//! @endcond + +} + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/hal/intrin_sse_em.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/hal/intrin_sse_em.hpp new file mode 100644 index 0000000000000000000000000000000000000000..be2766847ca106619dc43229ba6ffdf2a8dc8f32 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/hal/intrin_sse_em.hpp @@ -0,0 +1,167 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html + +#ifndef OPENCV_HAL_INTRIN_SSE_EM_HPP +#define OPENCV_HAL_INTRIN_SSE_EM_HPP + +namespace cv +{ + +//! @cond IGNORED + +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN + +#define OPENCV_HAL_SSE_WRAP_1(fun, tp) \ + inline tp _v128_##fun(const tp& a) \ + { return _mm_##fun(a); } + +#define OPENCV_HAL_SSE_WRAP_2(fun, tp) \ + inline tp _v128_##fun(const tp& a, const tp& b) \ + { return _mm_##fun(a, b); } + +#define OPENCV_HAL_SSE_WRAP_3(fun, tp) \ + inline tp _v128_##fun(const tp& a, const tp& b, const tp& c) \ + { return _mm_##fun(a, b, c); } + +///////////////////////////// XOP ///////////////////////////// + +// [todo] define CV_XOP +#if 1 // CV_XOP +inline __m128i _v128_comgt_epu32(const __m128i& a, const __m128i& b) +{ + const __m128i delta = _mm_set1_epi32((int)0x80000000); + return _mm_cmpgt_epi32(_mm_xor_si128(a, delta), _mm_xor_si128(b, delta)); +} +// wrapping XOP +#else +OPENCV_HAL_SSE_WRAP_2(_v128_comgt_epu32, __m128i) +#endif // !CV_XOP + +///////////////////////////// SSE4.1 ///////////////////////////// + +#if !CV_SSE4_1 + +/** Swizzle **/ +inline __m128i _v128_blendv_epi8(const __m128i& a, const __m128i& b, const __m128i& mask) +{ return _mm_xor_si128(a, _mm_and_si128(_mm_xor_si128(b, a), mask)); } + +/** Convert **/ +// 8 >> 16 +inline __m128i _v128_cvtepu8_epi16(const __m128i& a) +{ + const __m128i z = _mm_setzero_si128(); + return _mm_unpacklo_epi8(a, z); +} +inline __m128i _v128_cvtepi8_epi16(const __m128i& a) +{ return _mm_srai_epi16(_mm_unpacklo_epi8(a, a), 8); } +// 8 >> 32 +inline __m128i _v128_cvtepu8_epi32(const __m128i& a) +{ + const __m128i z = _mm_setzero_si128(); + return _mm_unpacklo_epi16(_mm_unpacklo_epi8(a, z), z); +} +inline __m128i _v128_cvtepi8_epi32(const __m128i& a) +{ + __m128i r = _mm_unpacklo_epi8(a, a); + r = _mm_unpacklo_epi8(r, r); + return _mm_srai_epi32(r, 24); +} +// 16 >> 32 +inline __m128i _v128_cvtepu16_epi32(const __m128i& a) +{ + const __m128i z = _mm_setzero_si128(); + return _mm_unpacklo_epi16(a, z); +} +inline __m128i _v128_cvtepi16_epi32(const __m128i& a) +{ return _mm_srai_epi32(_mm_unpacklo_epi16(a, a), 16); } +// 32 >> 64 +inline __m128i _v128_cvtepu32_epi64(const __m128i& a) +{ + const __m128i z = _mm_setzero_si128(); + return _mm_unpacklo_epi32(a, z); +} +inline __m128i _v128_cvtepi32_epi64(const __m128i& a) +{ return _mm_unpacklo_epi32(a, _mm_srai_epi32(a, 31)); } + +/** Arithmetic **/ +inline __m128i _v128_mullo_epi32(const __m128i& a, const __m128i& b) +{ + __m128i c0 = _mm_mul_epu32(a, b); + __m128i c1 = _mm_mul_epu32(_mm_srli_epi64(a, 32), _mm_srli_epi64(b, 32)); + __m128i d0 = _mm_unpacklo_epi32(c0, c1); + __m128i d1 = _mm_unpackhi_epi32(c0, c1); + return _mm_unpacklo_epi64(d0, d1); +} + +/** Math **/ +inline __m128i _v128_min_epu32(const __m128i& a, const __m128i& b) +{ return _v128_blendv_epi8(a, b, _v128_comgt_epu32(a, b)); } + +// wrapping SSE4.1 +#else +OPENCV_HAL_SSE_WRAP_1(cvtepu8_epi16, __m128i) +OPENCV_HAL_SSE_WRAP_1(cvtepi8_epi16, __m128i) +OPENCV_HAL_SSE_WRAP_1(cvtepu8_epi32, __m128i) +OPENCV_HAL_SSE_WRAP_1(cvtepi8_epi32, __m128i) +OPENCV_HAL_SSE_WRAP_1(cvtepu16_epi32, __m128i) +OPENCV_HAL_SSE_WRAP_1(cvtepi16_epi32, __m128i) +OPENCV_HAL_SSE_WRAP_1(cvtepu32_epi64, __m128i) +OPENCV_HAL_SSE_WRAP_1(cvtepi32_epi64, __m128i) +OPENCV_HAL_SSE_WRAP_2(min_epu32, __m128i) +OPENCV_HAL_SSE_WRAP_2(mullo_epi32, __m128i) +OPENCV_HAL_SSE_WRAP_3(blendv_epi8, __m128i) +#endif // !CV_SSE4_1 + +///////////////////////////// Revolutionary ///////////////////////////// + +/** Convert **/ +// 16 << 8 +inline __m128i _v128_cvtepu8_epi16_high(const __m128i& a) +{ + const __m128i z = _mm_setzero_si128(); + return _mm_unpackhi_epi8(a, z); +} +inline __m128i _v128_cvtepi8_epi16_high(const __m128i& a) +{ return _mm_srai_epi16(_mm_unpackhi_epi8(a, a), 8); } +// 32 << 16 +inline __m128i _v128_cvtepu16_epi32_high(const __m128i& a) +{ + const __m128i z = _mm_setzero_si128(); + return _mm_unpackhi_epi16(a, z); +} +inline __m128i _v128_cvtepi16_epi32_high(const __m128i& a) +{ return _mm_srai_epi32(_mm_unpackhi_epi16(a, a), 16); } +// 64 << 32 +inline __m128i _v128_cvtepu32_epi64_high(const __m128i& a) +{ + const __m128i z = _mm_setzero_si128(); + return _mm_unpackhi_epi32(a, z); +} +inline __m128i _v128_cvtepi32_epi64_high(const __m128i& a) +{ return _mm_unpackhi_epi32(a, _mm_srai_epi32(a, 31)); } + +/** Miscellaneous **/ +inline __m128i _v128_packs_epu32(const __m128i& a, const __m128i& b) +{ + const __m128i m = _mm_set1_epi32(65535); + __m128i am = _v128_min_epu32(a, m); + __m128i bm = _v128_min_epu32(b, m); +#if CV_SSE4_1 + return _mm_packus_epi32(am, bm); +#else + const __m128i d = _mm_set1_epi32(32768), nd = _mm_set1_epi16(-32768); + am = _mm_sub_epi32(am, d); + bm = _mm_sub_epi32(bm, d); + am = _mm_packs_epi32(am, bm); + return _mm_sub_epi16(am, nd); +#endif +} + +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END + +//! @endcond + +} // cv:: + +#endif // OPENCV_HAL_INTRIN_SSE_EM_HPP \ No newline at end of file diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/hal/intrin_vsx.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/hal/intrin_vsx.hpp new file mode 100644 index 0000000000000000000000000000000000000000..fce5c444ed3fabd368b4e6b27ad0fdaba18b288d --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/hal/intrin_vsx.hpp @@ -0,0 +1,1120 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html + +#ifndef OPENCV_HAL_VSX_HPP +#define OPENCV_HAL_VSX_HPP + +#include +#include "opencv2/core/utility.hpp" + +#define CV_SIMD128 1 +#define CV_SIMD128_64F 1 + +/** + * todo: supporting half precision for power9 + * convert instractions xvcvhpsp, xvcvsphp +**/ + +namespace cv +{ + +//! @cond IGNORED + +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN + +///////// Types //////////// + +struct v_uint8x16 +{ + typedef uchar lane_type; + enum { nlanes = 16 }; + vec_uchar16 val; + + explicit v_uint8x16(const vec_uchar16& v) : val(v) + {} + v_uint8x16() : val(vec_uchar16_z) + {} + v_uint8x16(vec_bchar16 v) : val(vec_uchar16_c(v)) + {} + v_uint8x16(uchar v0, uchar v1, uchar v2, uchar v3, uchar v4, uchar v5, uchar v6, uchar v7, + uchar v8, uchar v9, uchar v10, uchar v11, uchar v12, uchar v13, uchar v14, uchar v15) + : val(vec_uchar16_set(v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15)) + {} + uchar get0() const + { return vec_extract(val, 0); } +}; + +struct v_int8x16 +{ + typedef schar lane_type; + enum { nlanes = 16 }; + vec_char16 val; + + explicit v_int8x16(const vec_char16& v) : val(v) + {} + v_int8x16() : val(vec_char16_z) + {} + v_int8x16(vec_bchar16 v) : val(vec_char16_c(v)) + {} + v_int8x16(schar v0, schar v1, schar v2, schar v3, schar v4, schar v5, schar v6, schar v7, + schar v8, schar v9, schar v10, schar v11, schar v12, schar v13, schar v14, schar v15) + : val(vec_char16_set(v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15)) + {} + schar get0() const + { return vec_extract(val, 0); } +}; + +struct v_uint16x8 +{ + typedef ushort lane_type; + enum { nlanes = 8 }; + vec_ushort8 val; + + explicit v_uint16x8(const vec_ushort8& v) : val(v) + {} + v_uint16x8() : val(vec_ushort8_z) + {} + v_uint16x8(vec_bshort8 v) : val(vec_ushort8_c(v)) + {} + v_uint16x8(ushort v0, ushort v1, ushort v2, ushort v3, ushort v4, ushort v5, ushort v6, ushort v7) + : val(vec_ushort8_set(v0, v1, v2, v3, v4, v5, v6, v7)) + {} + ushort get0() const + { return vec_extract(val, 0); } +}; + +struct v_int16x8 +{ + typedef short lane_type; + enum { nlanes = 8 }; + vec_short8 val; + + explicit v_int16x8(const vec_short8& v) : val(v) + {} + v_int16x8() : val(vec_short8_z) + {} + v_int16x8(vec_bshort8 v) : val(vec_short8_c(v)) + {} + v_int16x8(short v0, short v1, short v2, short v3, short v4, short v5, short v6, short v7) + : val(vec_short8_set(v0, v1, v2, v3, v4, v5, v6, v7)) + {} + short get0() const + { return vec_extract(val, 0); } +}; + +struct v_uint32x4 +{ + typedef unsigned lane_type; + enum { nlanes = 4 }; + vec_uint4 val; + + explicit v_uint32x4(const vec_uint4& v) : val(v) + {} + v_uint32x4() : val(vec_uint4_z) + {} + v_uint32x4(vec_bint4 v) : val(vec_uint4_c(v)) + {} + v_uint32x4(unsigned v0, unsigned v1, unsigned v2, unsigned v3) : val(vec_uint4_set(v0, v1, v2, v3)) + {} + uint get0() const + { return vec_extract(val, 0); } +}; + +struct v_int32x4 +{ + typedef int lane_type; + enum { nlanes = 4 }; + vec_int4 val; + + explicit v_int32x4(const vec_int4& v) : val(v) + {} + v_int32x4() : val(vec_int4_z) + {} + v_int32x4(vec_bint4 v) : val(vec_int4_c(v)) + {} + v_int32x4(int v0, int v1, int v2, int v3) : val(vec_int4_set(v0, v1, v2, v3)) + {} + int get0() const + { return vec_extract(val, 0); } +}; + +struct v_float32x4 +{ + typedef float lane_type; + enum { nlanes = 4 }; + vec_float4 val; + + explicit v_float32x4(const vec_float4& v) : val(v) + {} + v_float32x4() : val(vec_float4_z) + {} + v_float32x4(vec_bint4 v) : val(vec_float4_c(v)) + {} + v_float32x4(float v0, float v1, float v2, float v3) : val(vec_float4_set(v0, v1, v2, v3)) + {} + float get0() const + { return vec_extract(val, 0); } +}; + +struct v_uint64x2 +{ + typedef uint64 lane_type; + enum { nlanes = 2 }; + vec_udword2 val; + + explicit v_uint64x2(const vec_udword2& v) : val(v) + {} + v_uint64x2() : val(vec_udword2_z) + {} + v_uint64x2(vec_bdword2 v) : val(vec_udword2_c(v)) + {} + v_uint64x2(uint64 v0, uint64 v1) : val(vec_udword2_set(v0, v1)) + {} + uint64 get0() const + { return vec_extract(val, 0); } +}; + +struct v_int64x2 +{ + typedef int64 lane_type; + enum { nlanes = 2 }; + vec_dword2 val; + + explicit v_int64x2(const vec_dword2& v) : val(v) + {} + v_int64x2() : val(vec_dword2_z) + {} + v_int64x2(vec_bdword2 v) : val(vec_dword2_c(v)) + {} + v_int64x2(int64 v0, int64 v1) : val(vec_dword2_set(v0, v1)) + {} + int64 get0() const + { return vec_extract(val, 0); } +}; + +struct v_float64x2 +{ + typedef double lane_type; + enum { nlanes = 2 }; + vec_double2 val; + + explicit v_float64x2(const vec_double2& v) : val(v) + {} + v_float64x2() : val(vec_double2_z) + {} + v_float64x2(vec_bdword2 v) : val(vec_double2_c(v)) + {} + v_float64x2(double v0, double v1) : val(vec_double2_set(v0, v1)) + {} + double get0() const + { return vec_extract(val, 0); } +}; + +//////////////// Load and store operations /////////////// + +/* + * clang-5 aborted during parse "vec_xxx_c" only if it's + * inside a function template which is defined by preprocessor macro. + * + * if vec_xxx_c defined as C++ cast, clang-5 will pass it +*/ +#define OPENCV_HAL_IMPL_VSX_INITVEC(_Tpvec, _Tp, suffix, cast) \ +inline _Tpvec v_setzero_##suffix() { return _Tpvec(); } \ +inline _Tpvec v_setall_##suffix(_Tp v) { return _Tpvec(vec_splats((_Tp)v));} \ +template inline _Tpvec v_reinterpret_as_##suffix(const _Tpvec0 &a) \ +{ return _Tpvec((cast)a.val); } + +OPENCV_HAL_IMPL_VSX_INITVEC(v_uint8x16, uchar, u8, vec_uchar16) +OPENCV_HAL_IMPL_VSX_INITVEC(v_int8x16, schar, s8, vec_char16) +OPENCV_HAL_IMPL_VSX_INITVEC(v_uint16x8, ushort, u16, vec_ushort8) +OPENCV_HAL_IMPL_VSX_INITVEC(v_int16x8, short, s16, vec_short8) +OPENCV_HAL_IMPL_VSX_INITVEC(v_uint32x4, uint, u32, vec_uint4) +OPENCV_HAL_IMPL_VSX_INITVEC(v_int32x4, int, s32, vec_int4) +OPENCV_HAL_IMPL_VSX_INITVEC(v_uint64x2, uint64, u64, vec_udword2) +OPENCV_HAL_IMPL_VSX_INITVEC(v_int64x2, int64, s64, vec_dword2) +OPENCV_HAL_IMPL_VSX_INITVEC(v_float32x4, float, f32, vec_float4) +OPENCV_HAL_IMPL_VSX_INITVEC(v_float64x2, double, f64, vec_double2) + +#define OPENCV_HAL_IMPL_VSX_LOADSTORE_C(_Tpvec, _Tp, ld, ld_a, st, st_a) \ +inline _Tpvec v_load(const _Tp* ptr) \ +{ return _Tpvec(ld(0, ptr)); } \ +inline _Tpvec v_load_aligned(VSX_UNUSED(const _Tp* ptr)) \ +{ return _Tpvec(ld_a(0, ptr)); } \ +inline _Tpvec v_load_low(const _Tp* ptr) \ +{ return _Tpvec(vec_ld_l8(ptr)); } \ +inline _Tpvec v_load_halves(const _Tp* ptr0, const _Tp* ptr1) \ +{ return _Tpvec(vec_mergesqh(vec_ld_l8(ptr0), vec_ld_l8(ptr1))); } \ +inline void v_store(_Tp* ptr, const _Tpvec& a) \ +{ st(a.val, 0, ptr); } \ +inline void v_store_aligned(VSX_UNUSED(_Tp* ptr), const _Tpvec& a) \ +{ st_a(a.val, 0, ptr); } \ +inline void v_store_aligned_nocache(VSX_UNUSED(_Tp* ptr), const _Tpvec& a) \ +{ st_a(a.val, 0, ptr); } \ +inline void v_store(_Tp* ptr, const _Tpvec& a, hal::StoreMode mode) \ +{ if(mode == hal::STORE_UNALIGNED) st(a.val, 0, ptr); else st_a(a.val, 0, ptr); } \ +inline void v_store_low(_Tp* ptr, const _Tpvec& a) \ +{ vec_st_l8(a.val, ptr); } \ +inline void v_store_high(_Tp* ptr, const _Tpvec& a) \ +{ vec_st_h8(a.val, ptr); } + +#define OPENCV_HAL_IMPL_VSX_LOADSTORE(_Tpvec, _Tp) \ +OPENCV_HAL_IMPL_VSX_LOADSTORE_C(_Tpvec, _Tp, vsx_ld, vec_ld, vsx_st, vec_st) + +OPENCV_HAL_IMPL_VSX_LOADSTORE(v_uint8x16, uchar) +OPENCV_HAL_IMPL_VSX_LOADSTORE(v_int8x16, schar) +OPENCV_HAL_IMPL_VSX_LOADSTORE(v_uint16x8, ushort) +OPENCV_HAL_IMPL_VSX_LOADSTORE(v_int16x8, short) +OPENCV_HAL_IMPL_VSX_LOADSTORE(v_uint32x4, uint) +OPENCV_HAL_IMPL_VSX_LOADSTORE(v_int32x4, int) +OPENCV_HAL_IMPL_VSX_LOADSTORE(v_float32x4, float) + +OPENCV_HAL_IMPL_VSX_LOADSTORE_C(v_float64x2, double, vsx_ld, vsx_ld, vsx_st, vsx_st) +OPENCV_HAL_IMPL_VSX_LOADSTORE_C(v_uint64x2, uint64, vsx_ld2, vsx_ld2, vsx_st2, vsx_st2) +OPENCV_HAL_IMPL_VSX_LOADSTORE_C(v_int64x2, int64, vsx_ld2, vsx_ld2, vsx_st2, vsx_st2) + +//////////////// Value reordering /////////////// + +/* de&interleave */ +#define OPENCV_HAL_IMPL_VSX_INTERLEAVE(_Tp, _Tpvec) \ +inline void v_load_deinterleave(const _Tp* ptr, _Tpvec& a, _Tpvec& b) \ +{ vec_ld_deinterleave(ptr, a.val, b.val);} \ +inline void v_load_deinterleave(const _Tp* ptr, _Tpvec& a, \ + _Tpvec& b, _Tpvec& c) \ +{ vec_ld_deinterleave(ptr, a.val, b.val, c.val); } \ +inline void v_load_deinterleave(const _Tp* ptr, _Tpvec& a, _Tpvec& b, \ + _Tpvec& c, _Tpvec& d) \ +{ vec_ld_deinterleave(ptr, a.val, b.val, c.val, d.val); } \ +inline void v_store_interleave(_Tp* ptr, const _Tpvec& a, const _Tpvec& b, \ + hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \ +{ vec_st_interleave(a.val, b.val, ptr); } \ +inline void v_store_interleave(_Tp* ptr, const _Tpvec& a, \ + const _Tpvec& b, const _Tpvec& c, \ + hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \ +{ vec_st_interleave(a.val, b.val, c.val, ptr); } \ +inline void v_store_interleave(_Tp* ptr, const _Tpvec& a, const _Tpvec& b, \ + const _Tpvec& c, const _Tpvec& d, \ + hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \ +{ vec_st_interleave(a.val, b.val, c.val, d.val, ptr); } + +OPENCV_HAL_IMPL_VSX_INTERLEAVE(uchar, v_uint8x16) +OPENCV_HAL_IMPL_VSX_INTERLEAVE(schar, v_int8x16) +OPENCV_HAL_IMPL_VSX_INTERLEAVE(ushort, v_uint16x8) +OPENCV_HAL_IMPL_VSX_INTERLEAVE(short, v_int16x8) +OPENCV_HAL_IMPL_VSX_INTERLEAVE(uint, v_uint32x4) +OPENCV_HAL_IMPL_VSX_INTERLEAVE(int, v_int32x4) +OPENCV_HAL_IMPL_VSX_INTERLEAVE(float, v_float32x4) +OPENCV_HAL_IMPL_VSX_INTERLEAVE(double, v_float64x2) +OPENCV_HAL_IMPL_VSX_INTERLEAVE(int64, v_int64x2) +OPENCV_HAL_IMPL_VSX_INTERLEAVE(uint64, v_uint64x2) + +/* Expand */ +#define OPENCV_HAL_IMPL_VSX_EXPAND(_Tpvec, _Tpwvec, _Tp, fl, fh) \ +inline void v_expand(const _Tpvec& a, _Tpwvec& b0, _Tpwvec& b1) \ +{ \ + b0.val = fh(a.val); \ + b1.val = fl(a.val); \ +} \ +inline _Tpwvec v_expand_low(const _Tpvec& a) \ +{ return _Tpwvec(fh(a.val)); } \ +inline _Tpwvec v_expand_high(const _Tpvec& a) \ +{ return _Tpwvec(fl(a.val)); } \ +inline _Tpwvec v_load_expand(const _Tp* ptr) \ +{ return _Tpwvec(fh(vec_ld_l8(ptr))); } + +OPENCV_HAL_IMPL_VSX_EXPAND(v_uint8x16, v_uint16x8, uchar, vec_unpacklu, vec_unpackhu) +OPENCV_HAL_IMPL_VSX_EXPAND(v_int8x16, v_int16x8, schar, vec_unpackl, vec_unpackh) +OPENCV_HAL_IMPL_VSX_EXPAND(v_uint16x8, v_uint32x4, ushort, vec_unpacklu, vec_unpackhu) +OPENCV_HAL_IMPL_VSX_EXPAND(v_int16x8, v_int32x4, short, vec_unpackl, vec_unpackh) +OPENCV_HAL_IMPL_VSX_EXPAND(v_uint32x4, v_uint64x2, uint, vec_unpacklu, vec_unpackhu) +OPENCV_HAL_IMPL_VSX_EXPAND(v_int32x4, v_int64x2, int, vec_unpackl, vec_unpackh) + +inline v_uint32x4 v_load_expand_q(const uchar* ptr) +{ return v_uint32x4(vec_uint4_set(ptr[0], ptr[1], ptr[2], ptr[3])); } + +inline v_int32x4 v_load_expand_q(const schar* ptr) +{ return v_int32x4(vec_int4_set(ptr[0], ptr[1], ptr[2], ptr[3])); } + +/* pack */ +#define OPENCV_HAL_IMPL_VSX_PACK(_Tpvec, _Tp, _Tpwvec, _Tpvn, _Tpdel, sfnc, pkfnc, addfnc, pack) \ +inline _Tpvec v_##pack(const _Tpwvec& a, const _Tpwvec& b) \ +{ \ + return _Tpvec(pkfnc(a.val, b.val)); \ +} \ +inline void v_##pack##_store(_Tp* ptr, const _Tpwvec& a) \ +{ \ + vec_st_l8(pkfnc(a.val, a.val), ptr); \ +} \ +template \ +inline _Tpvec v_rshr_##pack(const _Tpwvec& a, const _Tpwvec& b) \ +{ \ + const __vector _Tpvn vn = vec_splats((_Tpvn)n); \ + const __vector _Tpdel delta = vec_splats((_Tpdel)((_Tpdel)1 << (n-1))); \ + return _Tpvec(pkfnc(sfnc(addfnc(a.val, delta), vn), sfnc(addfnc(b.val, delta), vn))); \ +} \ +template \ +inline void v_rshr_##pack##_store(_Tp* ptr, const _Tpwvec& a) \ +{ \ + const __vector _Tpvn vn = vec_splats((_Tpvn)n); \ + const __vector _Tpdel delta = vec_splats((_Tpdel)((_Tpdel)1 << (n-1))); \ + vec_st_l8(pkfnc(sfnc(addfnc(a.val, delta), vn), delta), ptr); \ +} + +OPENCV_HAL_IMPL_VSX_PACK(v_uint8x16, uchar, v_uint16x8, unsigned short, unsigned short, + vec_sr, vec_packs, vec_adds, pack) +OPENCV_HAL_IMPL_VSX_PACK(v_int8x16, schar, v_int16x8, unsigned short, short, + vec_sra, vec_packs, vec_adds, pack) + +OPENCV_HAL_IMPL_VSX_PACK(v_uint16x8, ushort, v_uint32x4, unsigned int, unsigned int, + vec_sr, vec_packs, vec_add, pack) +OPENCV_HAL_IMPL_VSX_PACK(v_int16x8, short, v_int32x4, unsigned int, int, + vec_sra, vec_packs, vec_add, pack) + +OPENCV_HAL_IMPL_VSX_PACK(v_uint32x4, uint, v_uint64x2, unsigned long long, unsigned long long, + vec_sr, vec_pack, vec_add, pack) +OPENCV_HAL_IMPL_VSX_PACK(v_int32x4, int, v_int64x2, unsigned long long, long long, + vec_sra, vec_pack, vec_add, pack) + +OPENCV_HAL_IMPL_VSX_PACK(v_uint8x16, uchar, v_int16x8, unsigned short, short, + vec_sra, vec_packsu, vec_adds, pack_u) +OPENCV_HAL_IMPL_VSX_PACK(v_uint16x8, ushort, v_int32x4, unsigned int, int, + vec_sra, vec_packsu, vec_add, pack_u) +// Following variant is not implemented on other platforms: +//OPENCV_HAL_IMPL_VSX_PACK(v_uint32x4, uint, v_int64x2, unsigned long long, long long, +// vec_sra, vec_packsu, vec_add, pack_u) + +// pack boolean +inline v_uint8x16 v_pack_b(const v_uint16x8& a, const v_uint16x8& b) +{ + vec_uchar16 ab = vec_pack(a.val, b.val); + return v_uint8x16(ab); +} + +inline v_uint8x16 v_pack_b(const v_uint32x4& a, const v_uint32x4& b, + const v_uint32x4& c, const v_uint32x4& d) +{ + vec_ushort8 ab = vec_pack(a.val, b.val); + vec_ushort8 cd = vec_pack(c.val, d.val); + return v_uint8x16(vec_pack(ab, cd)); +} + +inline v_uint8x16 v_pack_b(const v_uint64x2& a, const v_uint64x2& b, const v_uint64x2& c, + const v_uint64x2& d, const v_uint64x2& e, const v_uint64x2& f, + const v_uint64x2& g, const v_uint64x2& h) +{ + vec_uint4 ab = vec_pack(a.val, b.val); + vec_uint4 cd = vec_pack(c.val, d.val); + vec_uint4 ef = vec_pack(e.val, f.val); + vec_uint4 gh = vec_pack(g.val, h.val); + + vec_ushort8 abcd = vec_pack(ab, cd); + vec_ushort8 efgh = vec_pack(ef, gh); + return v_uint8x16(vec_pack(abcd, efgh)); +} + +/* Recombine */ +template +inline void v_zip(const _Tpvec& a0, const _Tpvec& a1, _Tpvec& b0, _Tpvec& b1) +{ + b0.val = vec_mergeh(a0.val, a1.val); + b1.val = vec_mergel(a0.val, a1.val); +} + +template +inline _Tpvec v_combine_high(const _Tpvec& a, const _Tpvec& b) +{ return _Tpvec(vec_mergesql(a.val, b.val)); } + +template +inline _Tpvec v_combine_low(const _Tpvec& a, const _Tpvec& b) +{ return _Tpvec(vec_mergesqh(a.val, b.val)); } + +template +inline void v_recombine(const _Tpvec& a, const _Tpvec& b, _Tpvec& c, _Tpvec& d) +{ + c.val = vec_mergesqh(a.val, b.val); + d.val = vec_mergesql(a.val, b.val); +} + +////////// Arithmetic, bitwise and comparison operations ///////// + +/* Element-wise binary and unary operations */ +/** Arithmetics **/ +#define OPENCV_HAL_IMPL_VSX_BIN_OP(bin_op, _Tpvec, intrin) \ +inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(intrin(a.val, b.val)); } \ +inline _Tpvec& operator bin_op##= (_Tpvec& a, const _Tpvec& b) \ +{ a.val = intrin(a.val, b.val); return a; } + +OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_uint8x16, vec_adds) +OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_uint8x16, vec_subs) +OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_int8x16, vec_adds) +OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_int8x16, vec_subs) +OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_uint16x8, vec_adds) +OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_uint16x8, vec_subs) +OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_int16x8, vec_adds) +OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_int16x8, vec_subs) +OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_uint32x4, vec_add) +OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_uint32x4, vec_sub) +OPENCV_HAL_IMPL_VSX_BIN_OP(*, v_uint32x4, vec_mul) +OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_int32x4, vec_add) +OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_int32x4, vec_sub) +OPENCV_HAL_IMPL_VSX_BIN_OP(*, v_int32x4, vec_mul) +OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_float32x4, vec_add) +OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_float32x4, vec_sub) +OPENCV_HAL_IMPL_VSX_BIN_OP(*, v_float32x4, vec_mul) +OPENCV_HAL_IMPL_VSX_BIN_OP(/, v_float32x4, vec_div) +OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_float64x2, vec_add) +OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_float64x2, vec_sub) +OPENCV_HAL_IMPL_VSX_BIN_OP(*, v_float64x2, vec_mul) +OPENCV_HAL_IMPL_VSX_BIN_OP(/, v_float64x2, vec_div) +OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_uint64x2, vec_add) +OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_uint64x2, vec_sub) +OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_int64x2, vec_add) +OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_int64x2, vec_sub) + +// saturating multiply +#define OPENCV_HAL_IMPL_VSX_MUL_SAT(_Tpvec, _Tpwvec) \ + inline _Tpvec operator * (const _Tpvec& a, const _Tpvec& b) \ + { \ + _Tpwvec c, d; \ + v_mul_expand(a, b, c, d); \ + return v_pack(c, d); \ + } \ + inline _Tpvec& operator *= (_Tpvec& a, const _Tpvec& b) \ + { a = a * b; return a; } + +OPENCV_HAL_IMPL_VSX_MUL_SAT(v_int8x16, v_int16x8) +OPENCV_HAL_IMPL_VSX_MUL_SAT(v_uint8x16, v_uint16x8) +OPENCV_HAL_IMPL_VSX_MUL_SAT(v_int16x8, v_int32x4) +OPENCV_HAL_IMPL_VSX_MUL_SAT(v_uint16x8, v_uint32x4) + +template +inline void v_mul_expand(const Tvec& a, const Tvec& b, Twvec& c, Twvec& d) +{ + Twvec p0 = Twvec(vec_mule(a.val, b.val)); + Twvec p1 = Twvec(vec_mulo(a.val, b.val)); + v_zip(p0, p1, c, d); +} + +inline void v_mul_expand(const v_uint32x4& a, const v_uint32x4& b, v_uint64x2& c, v_uint64x2& d) +{ + c.val = vec_mul(vec_unpackhu(a.val), vec_unpackhu(b.val)); + d.val = vec_mul(vec_unpacklu(a.val), vec_unpacklu(b.val)); +} + +inline v_int16x8 v_mul_hi(const v_int16x8& a, const v_int16x8& b) +{ + vec_int4 p0 = vec_mule(a.val, b.val); + vec_int4 p1 = vec_mulo(a.val, b.val); + static const vec_uchar16 perm = {2, 3, 18, 19, 6, 7, 22, 23, 10, 11, 26, 27, 14, 15, 30, 31}; + return v_int16x8(vec_perm(vec_short8_c(p0), vec_short8_c(p1), perm)); +} +inline v_uint16x8 v_mul_hi(const v_uint16x8& a, const v_uint16x8& b) +{ + vec_uint4 p0 = vec_mule(a.val, b.val); + vec_uint4 p1 = vec_mulo(a.val, b.val); + static const vec_uchar16 perm = {2, 3, 18, 19, 6, 7, 22, 23, 10, 11, 26, 27, 14, 15, 30, 31}; + return v_uint16x8(vec_perm(vec_ushort8_c(p0), vec_ushort8_c(p1), perm)); +} + +/** Non-saturating arithmetics **/ +#define OPENCV_HAL_IMPL_VSX_BIN_FUNC(func, intrin) \ +template \ +inline _Tpvec func(const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(intrin(a.val, b.val)); } + +OPENCV_HAL_IMPL_VSX_BIN_FUNC(v_add_wrap, vec_add) +OPENCV_HAL_IMPL_VSX_BIN_FUNC(v_sub_wrap, vec_sub) +OPENCV_HAL_IMPL_VSX_BIN_FUNC(v_mul_wrap, vec_mul) + +/** Bitwise shifts **/ +#define OPENCV_HAL_IMPL_VSX_SHIFT_OP(_Tpvec, shr, splfunc) \ +inline _Tpvec operator << (const _Tpvec& a, int imm) \ +{ return _Tpvec(vec_sl(a.val, splfunc(imm))); } \ +inline _Tpvec operator >> (const _Tpvec& a, int imm) \ +{ return _Tpvec(shr(a.val, splfunc(imm))); } \ +template inline _Tpvec v_shl(const _Tpvec& a) \ +{ return _Tpvec(vec_sl(a.val, splfunc(imm))); } \ +template inline _Tpvec v_shr(const _Tpvec& a) \ +{ return _Tpvec(shr(a.val, splfunc(imm))); } + +OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_uint8x16, vec_sr, vec_uchar16_sp) +OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_uint16x8, vec_sr, vec_ushort8_sp) +OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_uint32x4, vec_sr, vec_uint4_sp) +OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_uint64x2, vec_sr, vec_udword2_sp) +// algebraic right shift +OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_int8x16, vec_sra, vec_uchar16_sp) +OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_int16x8, vec_sra, vec_ushort8_sp) +OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_int32x4, vec_sra, vec_uint4_sp) +OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_int64x2, vec_sra, vec_udword2_sp) + +/** Bitwise logic **/ +#define OPENCV_HAL_IMPL_VSX_LOGIC_OP(_Tpvec) \ +OPENCV_HAL_IMPL_VSX_BIN_OP(&, _Tpvec, vec_and) \ +OPENCV_HAL_IMPL_VSX_BIN_OP(|, _Tpvec, vec_or) \ +OPENCV_HAL_IMPL_VSX_BIN_OP(^, _Tpvec, vec_xor) \ +inline _Tpvec operator ~ (const _Tpvec& a) \ +{ return _Tpvec(vec_not(a.val)); } + +OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_uint8x16) +OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_int8x16) +OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_uint16x8) +OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_int16x8) +OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_uint32x4) +OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_int32x4) +OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_uint64x2) +OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_int64x2) +OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_float32x4) +OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_float64x2) + +/** Bitwise select **/ +#define OPENCV_HAL_IMPL_VSX_SELECT(_Tpvec, cast) \ +inline _Tpvec v_select(const _Tpvec& mask, const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(vec_sel(b.val, a.val, cast(mask.val))); } + +OPENCV_HAL_IMPL_VSX_SELECT(v_uint8x16, vec_bchar16_c) +OPENCV_HAL_IMPL_VSX_SELECT(v_int8x16, vec_bchar16_c) +OPENCV_HAL_IMPL_VSX_SELECT(v_uint16x8, vec_bshort8_c) +OPENCV_HAL_IMPL_VSX_SELECT(v_int16x8, vec_bshort8_c) +OPENCV_HAL_IMPL_VSX_SELECT(v_uint32x4, vec_bint4_c) +OPENCV_HAL_IMPL_VSX_SELECT(v_int32x4, vec_bint4_c) +OPENCV_HAL_IMPL_VSX_SELECT(v_float32x4, vec_bint4_c) +OPENCV_HAL_IMPL_VSX_SELECT(v_float64x2, vec_bdword2_c) + +/** Comparison **/ +#define OPENCV_HAL_IMPL_VSX_INT_CMP_OP(_Tpvec) \ +inline _Tpvec operator == (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(vec_cmpeq(a.val, b.val)); } \ +inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(vec_cmpne(a.val, b.val)); } \ +inline _Tpvec operator < (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(vec_cmplt(a.val, b.val)); } \ +inline _Tpvec operator > (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(vec_cmpgt(a.val, b.val)); } \ +inline _Tpvec operator <= (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(vec_cmple(a.val, b.val)); } \ +inline _Tpvec operator >= (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(vec_cmpge(a.val, b.val)); } + +OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_uint8x16) +OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_int8x16) +OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_uint16x8) +OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_int16x8) +OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_uint32x4) +OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_int32x4) +OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_float32x4) +OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_float64x2) +OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_uint64x2) +OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_int64x2) + +inline v_float32x4 v_not_nan(const v_float32x4& a) +{ return v_float32x4(vec_cmpeq(a.val, a.val)); } +inline v_float64x2 v_not_nan(const v_float64x2& a) +{ return v_float64x2(vec_cmpeq(a.val, a.val)); } + +/** min/max **/ +OPENCV_HAL_IMPL_VSX_BIN_FUNC(v_min, vec_min) +OPENCV_HAL_IMPL_VSX_BIN_FUNC(v_max, vec_max) + +/** Rotate **/ +#define OPENCV_IMPL_VSX_ROTATE(_Tpvec, suffix, shf, cast) \ +template \ +inline _Tpvec v_rotate_##suffix(const _Tpvec& a) \ +{ \ + const int wd = imm * sizeof(typename _Tpvec::lane_type); \ + if (wd > 15) \ + return _Tpvec(); \ + return _Tpvec((cast)shf(vec_uchar16_c(a.val), vec_uchar16_sp(wd << 3))); \ +} + +#define OPENCV_IMPL_VSX_ROTATE_LR(_Tpvec, cast) \ +OPENCV_IMPL_VSX_ROTATE(_Tpvec, left, vec_slo, cast) \ +OPENCV_IMPL_VSX_ROTATE(_Tpvec, right, vec_sro, cast) + +OPENCV_IMPL_VSX_ROTATE_LR(v_uint8x16, vec_uchar16) +OPENCV_IMPL_VSX_ROTATE_LR(v_int8x16, vec_char16) +OPENCV_IMPL_VSX_ROTATE_LR(v_uint16x8, vec_ushort8) +OPENCV_IMPL_VSX_ROTATE_LR(v_int16x8, vec_short8) +OPENCV_IMPL_VSX_ROTATE_LR(v_uint32x4, vec_uint4) +OPENCV_IMPL_VSX_ROTATE_LR(v_int32x4, vec_int4) +OPENCV_IMPL_VSX_ROTATE_LR(v_float32x4, vec_float4) +OPENCV_IMPL_VSX_ROTATE_LR(v_uint64x2, vec_udword2) +OPENCV_IMPL_VSX_ROTATE_LR(v_int64x2, vec_dword2) +OPENCV_IMPL_VSX_ROTATE_LR(v_float64x2, vec_double2) + +template +inline _Tpvec v_rotate_right(const _Tpvec& a, const _Tpvec& b) +{ + enum { CV_SHIFT = 16 - imm * (sizeof(typename _Tpvec::lane_type)) }; + if (CV_SHIFT == 16) + return a; +#ifdef __IBMCPP__ + return _Tpvec(vec_sld(b.val, a.val, CV_SHIFT & 15)); +#else + return _Tpvec(vec_sld(b.val, a.val, CV_SHIFT)); +#endif +} + +template +inline _Tpvec v_rotate_left(const _Tpvec& a, const _Tpvec& b) +{ + enum { CV_SHIFT = imm * (sizeof(typename _Tpvec::lane_type)) }; + if (CV_SHIFT == 16) + return b; + return _Tpvec(vec_sld(a.val, b.val, CV_SHIFT)); +} + +#define OPENCV_IMPL_VSX_ROTATE_64_2RG(_Tpvec, suffix, rg1, rg2) \ +template \ +inline _Tpvec v_rotate_##suffix(const _Tpvec& a, const _Tpvec& b) \ +{ \ + if (imm == 1) \ + return _Tpvec(vec_permi(rg1.val, rg2.val, 2)); \ + return imm ? b : a; \ +} + +#define OPENCV_IMPL_VSX_ROTATE_64_2RG_LR(_Tpvec) \ +OPENCV_IMPL_VSX_ROTATE_64_2RG(_Tpvec, left, b, a) \ +OPENCV_IMPL_VSX_ROTATE_64_2RG(_Tpvec, right, a, b) + +OPENCV_IMPL_VSX_ROTATE_64_2RG_LR(v_float64x2) +OPENCV_IMPL_VSX_ROTATE_64_2RG_LR(v_uint64x2) +OPENCV_IMPL_VSX_ROTATE_64_2RG_LR(v_int64x2) + +/* Extract */ +template +inline _Tpvec v_extract(const _Tpvec& a, const _Tpvec& b) +{ return v_rotate_right(a, b); } + +////////// Reduce and mask ///////// + +/** Reduce **/ +inline short v_reduce_sum(const v_int16x8& a) +{ + const vec_int4 zero = vec_int4_z; + return saturate_cast(vec_extract(vec_sums(vec_sum4s(a.val, zero), zero), 3)); +} +inline ushort v_reduce_sum(const v_uint16x8& a) +{ + const vec_int4 v4 = vec_int4_c(vec_unpackhu(vec_adds(a.val, vec_sld(a.val, a.val, 8)))); + return saturate_cast(vec_extract(vec_sums(v4, vec_int4_z), 3)); +} + +#define OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(_Tpvec, _Tpvec2, scalartype, suffix, func) \ +inline scalartype v_reduce_##suffix(const _Tpvec& a) \ +{ \ + const _Tpvec2 rs = func(a.val, vec_sld(a.val, a.val, 8)); \ + return vec_extract(func(rs, vec_sld(rs, rs, 4)), 0); \ +} +OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_uint32x4, vec_uint4, uint, sum, vec_add) +OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_uint32x4, vec_uint4, uint, max, vec_max) +OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_uint32x4, vec_uint4, uint, min, vec_min) +OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_int32x4, vec_int4, int, sum, vec_add) +OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_int32x4, vec_int4, int, max, vec_max) +OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_int32x4, vec_int4, int, min, vec_min) +OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_float32x4, vec_float4, float, sum, vec_add) +OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_float32x4, vec_float4, float, max, vec_max) +OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_float32x4, vec_float4, float, min, vec_min) + +inline double v_reduce_sum(const v_float64x2& a) +{ + return vec_extract(vec_add(a.val, vec_permi(a.val, a.val, 3)), 0); +} + +#define OPENCV_HAL_IMPL_VSX_REDUCE_OP_8(_Tpvec, _Tpvec2, scalartype, suffix, func) \ +inline scalartype v_reduce_##suffix(const _Tpvec& a) \ +{ \ + _Tpvec2 rs = func(a.val, vec_sld(a.val, a.val, 8)); \ + rs = func(rs, vec_sld(rs, rs, 4)); \ + return vec_extract(func(rs, vec_sld(rs, rs, 2)), 0); \ +} +OPENCV_HAL_IMPL_VSX_REDUCE_OP_8(v_uint16x8, vec_ushort8, ushort, max, vec_max) +OPENCV_HAL_IMPL_VSX_REDUCE_OP_8(v_uint16x8, vec_ushort8, ushort, min, vec_min) +OPENCV_HAL_IMPL_VSX_REDUCE_OP_8(v_int16x8, vec_short8, short, max, vec_max) +OPENCV_HAL_IMPL_VSX_REDUCE_OP_8(v_int16x8, vec_short8, short, min, vec_min) + +inline v_float32x4 v_reduce_sum4(const v_float32x4& a, const v_float32x4& b, + const v_float32x4& c, const v_float32x4& d) +{ + vec_float4 ac = vec_add(vec_mergel(a.val, c.val), vec_mergeh(a.val, c.val)); + ac = vec_add(ac, vec_sld(ac, ac, 8)); + + vec_float4 bd = vec_add(vec_mergel(b.val, d.val), vec_mergeh(b.val, d.val)); + bd = vec_add(bd, vec_sld(bd, bd, 8)); + return v_float32x4(vec_mergeh(ac, bd)); +} + +inline unsigned v_reduce_sad(const v_uint8x16& a, const v_uint8x16& b) +{ + const vec_uint4 zero4 = vec_uint4_z; + vec_uint4 sum4 = vec_sum4s(vec_absd(a.val, b.val), zero4); + return (unsigned)vec_extract(vec_sums(vec_int4_c(sum4), vec_int4_c(zero4)), 3); +} +inline unsigned v_reduce_sad(const v_int8x16& a, const v_int8x16& b) +{ + const vec_int4 zero4 = vec_int4_z; + vec_char16 ad = vec_abss(vec_subs(a.val, b.val)); + vec_int4 sum4 = vec_sum4s(ad, zero4); + return (unsigned)vec_extract(vec_sums(sum4, zero4), 3); +} +inline unsigned v_reduce_sad(const v_uint16x8& a, const v_uint16x8& b) +{ + vec_ushort8 ad = vec_absd(a.val, b.val); + VSX_UNUSED(vec_int4) sum = vec_sums(vec_int4_c(vec_unpackhu(ad)), vec_int4_c(vec_unpacklu(ad))); + return (unsigned)vec_extract(sum, 3); +} +inline unsigned v_reduce_sad(const v_int16x8& a, const v_int16x8& b) +{ + const vec_int4 zero4 = vec_int4_z; + vec_short8 ad = vec_abss(vec_subs(a.val, b.val)); + vec_int4 sum4 = vec_sum4s(ad, zero4); + return (unsigned)vec_extract(vec_sums(sum4, zero4), 3); +} +inline unsigned v_reduce_sad(const v_uint32x4& a, const v_uint32x4& b) +{ + const vec_uint4 ad = vec_absd(a.val, b.val); + const vec_uint4 rd = vec_add(ad, vec_sld(ad, ad, 8)); + return vec_extract(vec_add(rd, vec_sld(rd, rd, 4)), 0); +} +inline unsigned v_reduce_sad(const v_int32x4& a, const v_int32x4& b) +{ + vec_int4 ad = vec_abss(vec_sub(a.val, b.val)); + return (unsigned)vec_extract(vec_sums(ad, vec_int4_z), 3); +} +inline float v_reduce_sad(const v_float32x4& a, const v_float32x4& b) +{ + const vec_float4 ad = vec_abs(vec_sub(a.val, b.val)); + const vec_float4 rd = vec_add(ad, vec_sld(ad, ad, 8)); + return vec_extract(vec_add(rd, vec_sld(rd, rd, 4)), 0); +} + +/** Popcount **/ +template +inline v_uint32x4 v_popcount(const _Tpvec& a) +{ return v_uint32x4(vec_popcntu(vec_uint4_c(a.val))); } + +/** Mask **/ +inline int v_signmask(const v_uint8x16& a) +{ + vec_uchar16 sv = vec_sr(a.val, vec_uchar16_sp(7)); + static const vec_uchar16 slm = {0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7}; + sv = vec_sl(sv, slm); + vec_uint4 sv4 = vec_sum4s(sv, vec_uint4_z); + static const vec_uint4 slm4 = {0, 0, 8, 8}; + sv4 = vec_sl(sv4, slm4); + return vec_extract(vec_sums((vec_int4) sv4, vec_int4_z), 3); +} +inline int v_signmask(const v_int8x16& a) +{ return v_signmask(v_reinterpret_as_u8(a)); } + +inline int v_signmask(const v_int16x8& a) +{ + static const vec_ushort8 slm = {0, 1, 2, 3, 4, 5, 6, 7}; + vec_short8 sv = vec_sr(a.val, vec_ushort8_sp(15)); + sv = vec_sl(sv, slm); + vec_int4 svi = vec_int4_z; + svi = vec_sums(vec_sum4s(sv, svi), svi); + return vec_extract(svi, 3); +} +inline int v_signmask(const v_uint16x8& a) +{ return v_signmask(v_reinterpret_as_s16(a)); } + +inline int v_signmask(const v_int32x4& a) +{ + static const vec_uint4 slm = {0, 1, 2, 3}; + vec_int4 sv = vec_sr(a.val, vec_uint4_sp(31)); + sv = vec_sl(sv, slm); + sv = vec_sums(sv, vec_int4_z); + return vec_extract(sv, 3); +} +inline int v_signmask(const v_uint32x4& a) +{ return v_signmask(v_reinterpret_as_s32(a)); } +inline int v_signmask(const v_float32x4& a) +{ return v_signmask(v_reinterpret_as_s32(a)); } + +inline int v_signmask(const v_int64x2& a) +{ + VSX_UNUSED(const vec_dword2) sv = vec_sr(a.val, vec_udword2_sp(63)); + return (int)vec_extract(sv, 0) | (int)vec_extract(sv, 1) << 1; +} +inline int v_signmask(const v_uint64x2& a) +{ return v_signmask(v_reinterpret_as_s64(a)); } +inline int v_signmask(const v_float64x2& a) +{ return v_signmask(v_reinterpret_as_s64(a)); } + +template +inline bool v_check_all(const _Tpvec& a) +{ return vec_all_lt(a.val, _Tpvec().val); } +inline bool v_check_all(const v_uint8x16& a) +{ return v_check_all(v_reinterpret_as_s8(a)); } +inline bool v_check_all(const v_uint16x8& a) +{ return v_check_all(v_reinterpret_as_s16(a)); } +inline bool v_check_all(const v_uint32x4& a) +{ return v_check_all(v_reinterpret_as_s32(a)); } +inline bool v_check_all(const v_float32x4& a) +{ return v_check_all(v_reinterpret_as_s32(a)); } +inline bool v_check_all(const v_float64x2& a) +{ return v_check_all(v_reinterpret_as_s64(a)); } + +template +inline bool v_check_any(const _Tpvec& a) +{ return vec_any_lt(a.val, _Tpvec().val); } +inline bool v_check_any(const v_uint8x16& a) +{ return v_check_any(v_reinterpret_as_s8(a)); } +inline bool v_check_any(const v_uint16x8& a) +{ return v_check_any(v_reinterpret_as_s16(a)); } +inline bool v_check_any(const v_uint32x4& a) +{ return v_check_any(v_reinterpret_as_s32(a)); } +inline bool v_check_any(const v_float32x4& a) +{ return v_check_any(v_reinterpret_as_s32(a)); } +inline bool v_check_any(const v_float64x2& a) +{ return v_check_any(v_reinterpret_as_s64(a)); } + +////////// Other math ///////// + +/** Some frequent operations **/ +inline v_float32x4 v_sqrt(const v_float32x4& x) +{ return v_float32x4(vec_sqrt(x.val)); } +inline v_float64x2 v_sqrt(const v_float64x2& x) +{ return v_float64x2(vec_sqrt(x.val)); } + +inline v_float32x4 v_invsqrt(const v_float32x4& x) +{ return v_float32x4(vec_rsqrt(x.val)); } +inline v_float64x2 v_invsqrt(const v_float64x2& x) +{ return v_float64x2(vec_rsqrt(x.val)); } + +#define OPENCV_HAL_IMPL_VSX_MULADD(_Tpvec) \ +inline _Tpvec v_magnitude(const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(vec_sqrt(vec_madd(a.val, a.val, vec_mul(b.val, b.val)))); } \ +inline _Tpvec v_sqr_magnitude(const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(vec_madd(a.val, a.val, vec_mul(b.val, b.val))); } \ +inline _Tpvec v_fma(const _Tpvec& a, const _Tpvec& b, const _Tpvec& c) \ +{ return _Tpvec(vec_madd(a.val, b.val, c.val)); } \ +inline _Tpvec v_muladd(const _Tpvec& a, const _Tpvec& b, const _Tpvec& c) \ +{ return _Tpvec(vec_madd(a.val, b.val, c.val)); } + +OPENCV_HAL_IMPL_VSX_MULADD(v_float32x4) +OPENCV_HAL_IMPL_VSX_MULADD(v_float64x2) + +inline v_int32x4 v_muladd(const v_int32x4& a, const v_int32x4& b, const v_int32x4& c) +{ return a * b + c; } + +// TODO: exp, log, sin, cos + +/** Absolute values **/ +inline v_uint8x16 v_abs(const v_int8x16& x) +{ return v_uint8x16(vec_uchar16_c(vec_abs(x.val))); } + +inline v_uint16x8 v_abs(const v_int16x8& x) +{ return v_uint16x8(vec_ushort8_c(vec_abs(x.val))); } + +inline v_uint32x4 v_abs(const v_int32x4& x) +{ return v_uint32x4(vec_uint4_c(vec_abs(x.val))); } + +inline v_float32x4 v_abs(const v_float32x4& x) +{ return v_float32x4(vec_abs(x.val)); } + +inline v_float64x2 v_abs(const v_float64x2& x) +{ return v_float64x2(vec_abs(x.val)); } + +/** Absolute difference **/ +// unsigned +OPENCV_HAL_IMPL_VSX_BIN_FUNC(v_absdiff, vec_absd) + +inline v_uint8x16 v_absdiff(const v_int8x16& a, const v_int8x16& b) +{ return v_reinterpret_as_u8(v_sub_wrap(v_max(a, b), v_min(a, b))); } +inline v_uint16x8 v_absdiff(const v_int16x8& a, const v_int16x8& b) +{ return v_reinterpret_as_u16(v_sub_wrap(v_max(a, b), v_min(a, b))); } +inline v_uint32x4 v_absdiff(const v_int32x4& a, const v_int32x4& b) +{ return v_reinterpret_as_u32(v_max(a, b) - v_min(a, b)); } + +inline v_float32x4 v_absdiff(const v_float32x4& a, const v_float32x4& b) +{ return v_abs(a - b); } +inline v_float64x2 v_absdiff(const v_float64x2& a, const v_float64x2& b) +{ return v_abs(a - b); } + +/** Absolute difference for signed integers **/ +inline v_int8x16 v_absdiffs(const v_int8x16& a, const v_int8x16& b) +{ return v_int8x16(vec_abss(vec_subs(a.val, b.val))); } +inline v_int16x8 v_absdiffs(const v_int16x8& a, const v_int16x8& b) +{ return v_int16x8(vec_abss(vec_subs(a.val, b.val))); } + +////////// Conversions ///////// + +/** Rounding **/ +inline v_int32x4 v_round(const v_float32x4& a) +{ return v_int32x4(vec_cts(vec_rint(a.val))); } + +inline v_int32x4 v_round(const v_float64x2& a) +{ return v_int32x4(vec_mergesqo(vec_ctso(vec_rint(a.val)), vec_int4_z)); } + +inline v_int32x4 v_round(const v_float64x2& a, const v_float64x2& b) +{ return v_int32x4(vec_mergesqo(vec_ctso(vec_rint(a.val)), vec_ctso(vec_rint(b.val)))); } + +inline v_int32x4 v_floor(const v_float32x4& a) +{ return v_int32x4(vec_cts(vec_floor(a.val))); } + +inline v_int32x4 v_floor(const v_float64x2& a) +{ return v_int32x4(vec_mergesqo(vec_ctso(vec_floor(a.val)), vec_int4_z)); } + +inline v_int32x4 v_ceil(const v_float32x4& a) +{ return v_int32x4(vec_cts(vec_ceil(a.val))); } + +inline v_int32x4 v_ceil(const v_float64x2& a) +{ return v_int32x4(vec_mergesqo(vec_ctso(vec_ceil(a.val)), vec_int4_z)); } + +inline v_int32x4 v_trunc(const v_float32x4& a) +{ return v_int32x4(vec_cts(a.val)); } + +inline v_int32x4 v_trunc(const v_float64x2& a) +{ return v_int32x4(vec_mergesqo(vec_ctso(a.val), vec_int4_z)); } + +/** To float **/ +inline v_float32x4 v_cvt_f32(const v_int32x4& a) +{ return v_float32x4(vec_ctf(a.val)); } + +inline v_float32x4 v_cvt_f32(const v_float64x2& a) +{ return v_float32x4(vec_mergesqo(vec_cvfo(a.val), vec_float4_z)); } + +inline v_float32x4 v_cvt_f32(const v_float64x2& a, const v_float64x2& b) +{ return v_float32x4(vec_mergesqo(vec_cvfo(a.val), vec_cvfo(b.val))); } + +inline v_float64x2 v_cvt_f64(const v_int32x4& a) +{ return v_float64x2(vec_ctdo(vec_mergeh(a.val, a.val))); } + +inline v_float64x2 v_cvt_f64_high(const v_int32x4& a) +{ return v_float64x2(vec_ctdo(vec_mergel(a.val, a.val))); } + +inline v_float64x2 v_cvt_f64(const v_float32x4& a) +{ return v_float64x2(vec_cvfo(vec_mergeh(a.val, a.val))); } + +inline v_float64x2 v_cvt_f64_high(const v_float32x4& a) +{ return v_float64x2(vec_cvfo(vec_mergel(a.val, a.val))); } + +////////////// Lookup table access //////////////////// + +inline v_int32x4 v_lut(const int* tab, const v_int32x4& idxvec) +{ + int CV_DECL_ALIGNED(32) idx[4]; + v_store_aligned(idx, idxvec); + return v_int32x4(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]]); +} + +inline v_float32x4 v_lut(const float* tab, const v_int32x4& idxvec) +{ + int CV_DECL_ALIGNED(32) idx[4]; + v_store_aligned(idx, idxvec); + return v_float32x4(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]]); +} + +inline v_float64x2 v_lut(const double* tab, const v_int32x4& idxvec) +{ + int CV_DECL_ALIGNED(32) idx[4]; + v_store_aligned(idx, idxvec); + return v_float64x2(tab[idx[0]], tab[idx[1]]); +} + +inline void v_lut_deinterleave(const float* tab, const v_int32x4& idxvec, v_float32x4& x, v_float32x4& y) +{ + int CV_DECL_ALIGNED(32) idx[4]; + v_store_aligned(idx, idxvec); + x = v_float32x4(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]]); + y = v_float32x4(tab[idx[0]+1], tab[idx[1]+1], tab[idx[2]+1], tab[idx[3]+1]); +} + +inline void v_lut_deinterleave(const double* tab, const v_int32x4& idxvec, v_float64x2& x, v_float64x2& y) +{ + int CV_DECL_ALIGNED(32) idx[4]; + v_store_aligned(idx, idxvec); + x = v_float64x2(tab[idx[0]], tab[idx[1]]); + y = v_float64x2(tab[idx[0]+1], tab[idx[1]+1]); +} + +/////// FP16 support //////// + +// [TODO] implement these 2 using VSX or universal intrinsics (copy from intrin_sse.cpp and adopt) +inline v_float32x4 v_load_expand(const float16_t* ptr) +{ + return v_float32x4((float)ptr[0], (float)ptr[1], (float)ptr[2], (float)ptr[3]); +} + +inline void v_pack_store(float16_t* ptr, const v_float32x4& v) +{ + float CV_DECL_ALIGNED(32) f[4]; + v_store_aligned(f, v); + ptr[0] = float16_t(f[0]); + ptr[1] = float16_t(f[1]); + ptr[2] = float16_t(f[2]); + ptr[3] = float16_t(f[3]); +} + +inline void v_cleanup() {} + + +/** Reinterpret **/ +/** its up there with load and store operations **/ + +////////// Matrix operations ///////// + +inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b) +{ return v_int32x4(vec_msum(a.val, b.val, vec_int4_z)); } + +inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b, const v_int32x4& c) +{ return v_int32x4(vec_msum(a.val, b.val, c.val)); } + +inline v_float32x4 v_matmul(const v_float32x4& v, const v_float32x4& m0, + const v_float32x4& m1, const v_float32x4& m2, + const v_float32x4& m3) +{ + const vec_float4 v0 = vec_splat(v.val, 0); + const vec_float4 v1 = vec_splat(v.val, 1); + const vec_float4 v2 = vec_splat(v.val, 2); + VSX_UNUSED(const vec_float4) v3 = vec_splat(v.val, 3); + return v_float32x4(vec_madd(v0, m0.val, vec_madd(v1, m1.val, vec_madd(v2, m2.val, vec_mul(v3, m3.val))))); +} + +inline v_float32x4 v_matmuladd(const v_float32x4& v, const v_float32x4& m0, + const v_float32x4& m1, const v_float32x4& m2, + const v_float32x4& a) +{ + const vec_float4 v0 = vec_splat(v.val, 0); + const vec_float4 v1 = vec_splat(v.val, 1); + const vec_float4 v2 = vec_splat(v.val, 2); + return v_float32x4(vec_madd(v0, m0.val, vec_madd(v1, m1.val, vec_madd(v2, m2.val, a.val)))); +} + +#define OPENCV_HAL_IMPL_VSX_TRANSPOSE4x4(_Tpvec, _Tpvec2) \ +inline void v_transpose4x4(const _Tpvec& a0, const _Tpvec& a1, \ + const _Tpvec& a2, const _Tpvec& a3, \ + _Tpvec& b0, _Tpvec& b1, _Tpvec& b2, _Tpvec& b3) \ +{ \ + _Tpvec2 a02 = vec_mergeh(a0.val, a2.val); \ + _Tpvec2 a13 = vec_mergeh(a1.val, a3.val); \ + b0.val = vec_mergeh(a02, a13); \ + b1.val = vec_mergel(a02, a13); \ + a02 = vec_mergel(a0.val, a2.val); \ + a13 = vec_mergel(a1.val, a3.val); \ + b2.val = vec_mergeh(a02, a13); \ + b3.val = vec_mergel(a02, a13); \ +} +OPENCV_HAL_IMPL_VSX_TRANSPOSE4x4(v_uint32x4, vec_uint4) +OPENCV_HAL_IMPL_VSX_TRANSPOSE4x4(v_int32x4, vec_int4) +OPENCV_HAL_IMPL_VSX_TRANSPOSE4x4(v_float32x4, vec_float4) + +//! @name Check SIMD support +//! @{ +//! @brief Check CPU capability of SIMD operation +static inline bool hasSIMD128() +{ + return (CV_CPU_HAS_SUPPORT_VSX) ? true : false; +} + +//! @} + +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END + +//! @endcond + +} + +#endif // OPENCV_HAL_VSX_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/ippasync.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/ippasync.hpp new file mode 100644 index 0000000000000000000000000000000000000000..c35d8d81a19c54b5628f547d7081c1c6c002ab77 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/ippasync.hpp @@ -0,0 +1,195 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2015, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_IPPASYNC_HPP +#define OPENCV_CORE_IPPASYNC_HPP + +#ifdef HAVE_IPP_A // this file will be removed in OpenCV 4.0 + +#include "opencv2/core.hpp" +#include +#include + +namespace cv +{ + +namespace hpp +{ + +/** @addtogroup core_ipp +This section describes conversion between OpenCV and [Intel® IPP Asynchronous +C/C++](http://software.intel.com/en-us/intel-ipp-preview) library. [Getting Started +Guide](http://registrationcenter.intel.com/irc_nas/3727/ipp_async_get_started.htm) help you to +install the library, configure header and library build paths. + */ +//! @{ + + //! convert OpenCV data type to hppDataType + inline int toHppType(const int cvType) + { + int depth = CV_MAT_DEPTH(cvType); + int hppType = depth == CV_8U ? HPP_DATA_TYPE_8U : + depth == CV_16U ? HPP_DATA_TYPE_16U : + depth == CV_16S ? HPP_DATA_TYPE_16S : + depth == CV_32S ? HPP_DATA_TYPE_32S : + depth == CV_32F ? HPP_DATA_TYPE_32F : + depth == CV_64F ? HPP_DATA_TYPE_64F : -1; + CV_Assert( hppType >= 0 ); + return hppType; + } + + //! convert hppDataType to OpenCV data type + inline int toCvType(const int hppType) + { + int cvType = hppType == HPP_DATA_TYPE_8U ? CV_8U : + hppType == HPP_DATA_TYPE_16U ? CV_16U : + hppType == HPP_DATA_TYPE_16S ? CV_16S : + hppType == HPP_DATA_TYPE_32S ? CV_32S : + hppType == HPP_DATA_TYPE_32F ? CV_32F : + hppType == HPP_DATA_TYPE_64F ? CV_64F : -1; + CV_Assert( cvType >= 0 ); + return cvType; + } + + /** @brief Convert hppiMatrix to Mat. + + This function allocates and initializes new matrix (if needed) that has the same size and type as + input matrix. Supports CV_8U, CV_16U, CV_16S, CV_32S, CV_32F, CV_64F. + @param src input hppiMatrix. + @param dst output matrix. + @param accel accelerator instance (see hpp::getHpp for the list of acceleration framework types). + @param cn number of channels. + */ + inline void copyHppToMat(hppiMatrix* src, Mat& dst, hppAccel accel, int cn) + { + hppDataType type; + hpp32u width, height; + hppStatus sts; + + if (src == NULL) + return dst.release(); + + sts = hppiInquireMatrix(src, &type, &width, &height); + + CV_Assert( sts == HPP_STATUS_NO_ERROR); + + int matType = CV_MAKETYPE(toCvType(type), cn); + + CV_Assert(width%cn == 0); + + width /= cn; + + dst.create((int)height, (int)width, (int)matType); + + size_t newSize = (size_t)(height*(hpp32u)(dst.step)); + + sts = hppiGetMatrixData(accel,src,(hpp32u)(dst.step),dst.data,&newSize); + + CV_Assert( sts == HPP_STATUS_NO_ERROR); + } + + /** @brief Create Mat from hppiMatrix. + + This function allocates and initializes the Mat that has the same size and type as input matrix. + Supports CV_8U, CV_16U, CV_16S, CV_32S, CV_32F, CV_64F. + @param src input hppiMatrix. + @param accel accelerator instance (see hpp::getHpp for the list of acceleration framework types). + @param cn number of channels. + @sa howToUseIPPAconversion, hpp::copyHppToMat, hpp::getHpp. + */ + inline Mat getMat(hppiMatrix* src, hppAccel accel, int cn) + { + Mat dst; + copyHppToMat(src, dst, accel, cn); + return dst; + } + + /** @brief Create hppiMatrix from Mat. + + This function allocates and initializes the hppiMatrix that has the same size and type as input + matrix, returns the hppiMatrix*. + + If you want to use zero-copy for GPU you should to have 4KB aligned matrix data. See details + [hppiCreateSharedMatrix](http://software.intel.com/ru-ru/node/501697). + + Supports CV_8U, CV_16U, CV_16S, CV_32S, CV_32F, CV_64F. + + @note The hppiMatrix pointer to the image buffer in system memory refers to the src.data. Control + the lifetime of the matrix and don't change its data, if there is no special need. + @param src input matrix. + @param accel accelerator instance. Supports type: + - **HPP_ACCEL_TYPE_CPU** - accelerated by optimized CPU instructions. + - **HPP_ACCEL_TYPE_GPU** - accelerated by GPU programmable units or fixed-function + accelerators. + - **HPP_ACCEL_TYPE_ANY** - any acceleration or no acceleration available. + @sa howToUseIPPAconversion, hpp::getMat + */ + inline hppiMatrix* getHpp(const Mat& src, hppAccel accel) + { + int htype = toHppType(src.type()); + int cn = src.channels(); + + CV_Assert(src.data); + hppAccelType accelType = hppQueryAccelType(accel); + + if (accelType!=HPP_ACCEL_TYPE_CPU) + { + hpp32u pitch, size; + hppQueryMatrixAllocParams(accel, src.cols*cn, src.rows, htype, &pitch, &size); + if (pitch!=0 && size!=0) + if ((int)(src.data)%4096==0 && pitch==(hpp32u)(src.step)) + { + return hppiCreateSharedMatrix(htype, src.cols*cn, src.rows, src.data, pitch, size); + } + } + + return hppiCreateMatrix(htype, src.cols*cn, src.rows, src.data, (hpp32s)(src.step));; + } + +//! @} +}} + +#endif + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/mat.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/mat.hpp new file mode 100644 index 0000000000000000000000000000000000000000..c0893b398d70e2356250af5e47b1e7ace15e5db4 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/mat.hpp @@ -0,0 +1,3701 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_MAT_HPP +#define OPENCV_CORE_MAT_HPP + +#ifndef __cplusplus +# error mat.hpp header must be compiled as C++ +#endif + +#include "opencv2/core/matx.hpp" +#include "opencv2/core/types.hpp" + +#include "opencv2/core/bufferpool.hpp" + +#ifdef CV_CXX11 +#include +#endif + +namespace cv +{ + +//! @addtogroup core_basic +//! @{ + +enum { ACCESS_READ=1<<24, ACCESS_WRITE=1<<25, + ACCESS_RW=3<<24, ACCESS_MASK=ACCESS_RW, ACCESS_FAST=1<<26 }; + +CV__DEBUG_NS_BEGIN + +class CV_EXPORTS _OutputArray; + +//////////////////////// Input/Output Array Arguments ///////////////////////////////// + +/** @brief This is the proxy class for passing read-only input arrays into OpenCV functions. + +It is defined as: +@code + typedef const _InputArray& InputArray; +@endcode +where _InputArray is a class that can be constructed from `Mat`, `Mat_`, `Matx`, +`std::vector`, `std::vector >`, `std::vector`, `std::vector >`, +`UMat`, `std::vector` or `double`. It can also be constructed from a matrix expression. + +Since this is mostly implementation-level class, and its interface may change in future versions, we +do not describe it in details. There are a few key things, though, that should be kept in mind: + +- When you see in the reference manual or in OpenCV source code a function that takes + InputArray, it means that you can actually pass `Mat`, `Matx`, `vector` etc. (see above the + complete list). +- Optional input arguments: If some of the input arrays may be empty, pass cv::noArray() (or + simply cv::Mat() as you probably did before). +- The class is designed solely for passing parameters. That is, normally you *should not* + declare class members, local and global variables of this type. +- If you want to design your own function or a class method that can operate of arrays of + multiple types, you can use InputArray (or OutputArray) for the respective parameters. Inside + a function you should use _InputArray::getMat() method to construct a matrix header for the + array (without copying data). _InputArray::kind() can be used to distinguish Mat from + `vector<>` etc., but normally it is not needed. + +Here is how you can use a function that takes InputArray : +@code + std::vector vec; + // points or a circle + for( int i = 0; i < 30; i++ ) + vec.push_back(Point2f((float)(100 + 30*cos(i*CV_PI*2/5)), + (float)(100 - 30*sin(i*CV_PI*2/5)))); + cv::transform(vec, vec, cv::Matx23f(0.707, -0.707, 10, 0.707, 0.707, 20)); +@endcode +That is, we form an STL vector containing points, and apply in-place affine transformation to the +vector using the 2x3 matrix created inline as `Matx` instance. + +Here is how such a function can be implemented (for simplicity, we implement a very specific case of +it, according to the assertion statement inside) : +@code + void myAffineTransform(InputArray _src, OutputArray _dst, InputArray _m) + { + // get Mat headers for input arrays. This is O(1) operation, + // unless _src and/or _m are matrix expressions. + Mat src = _src.getMat(), m = _m.getMat(); + CV_Assert( src.type() == CV_32FC2 && m.type() == CV_32F && m.size() == Size(3, 2) ); + + // [re]create the output array so that it has the proper size and type. + // In case of Mat it calls Mat::create, in case of STL vector it calls vector::resize. + _dst.create(src.size(), src.type()); + Mat dst = _dst.getMat(); + + for( int i = 0; i < src.rows; i++ ) + for( int j = 0; j < src.cols; j++ ) + { + Point2f pt = src.at(i, j); + dst.at(i, j) = Point2f(m.at(0, 0)*pt.x + + m.at(0, 1)*pt.y + + m.at(0, 2), + m.at(1, 0)*pt.x + + m.at(1, 1)*pt.y + + m.at(1, 2)); + } + } +@endcode +There is another related type, InputArrayOfArrays, which is currently defined as a synonym for +InputArray: +@code + typedef InputArray InputArrayOfArrays; +@endcode +It denotes function arguments that are either vectors of vectors or vectors of matrices. A separate +synonym is needed to generate Python/Java etc. wrappers properly. At the function implementation +level their use is similar, but _InputArray::getMat(idx) should be used to get header for the +idx-th component of the outer vector and _InputArray::size().area() should be used to find the +number of components (vectors/matrices) of the outer vector. + +In general, type support is limited to cv::Mat types. Other types are forbidden. +But in some cases we need to support passing of custom non-general Mat types, like arrays of cv::KeyPoint, cv::DMatch, etc. +This data is not intented to be interpreted as an image data, or processed somehow like regular cv::Mat. +To pass such custom type use rawIn() / rawOut() / rawInOut() wrappers. +Custom type is wrapped as Mat-compatible `CV_8UC` values (N = sizeof(T), N <= CV_CN_MAX). + */ +class CV_EXPORTS _InputArray +{ +public: + enum { + KIND_SHIFT = 16, + FIXED_TYPE = 0x8000 << KIND_SHIFT, + FIXED_SIZE = 0x4000 << KIND_SHIFT, + KIND_MASK = 31 << KIND_SHIFT, + + NONE = 0 << KIND_SHIFT, + MAT = 1 << KIND_SHIFT, + MATX = 2 << KIND_SHIFT, + STD_VECTOR = 3 << KIND_SHIFT, + STD_VECTOR_VECTOR = 4 << KIND_SHIFT, + STD_VECTOR_MAT = 5 << KIND_SHIFT, + EXPR = 6 << KIND_SHIFT, + OPENGL_BUFFER = 7 << KIND_SHIFT, + CUDA_HOST_MEM = 8 << KIND_SHIFT, + CUDA_GPU_MAT = 9 << KIND_SHIFT, + UMAT =10 << KIND_SHIFT, + STD_VECTOR_UMAT =11 << KIND_SHIFT, + STD_BOOL_VECTOR =12 << KIND_SHIFT, + STD_VECTOR_CUDA_GPU_MAT = 13 << KIND_SHIFT, + STD_ARRAY =14 << KIND_SHIFT, + STD_ARRAY_MAT =15 << KIND_SHIFT + }; + + _InputArray(); + _InputArray(int _flags, void* _obj); + _InputArray(const Mat& m); + _InputArray(const MatExpr& expr); + _InputArray(const std::vector& vec); + template _InputArray(const Mat_<_Tp>& m); + template _InputArray(const std::vector<_Tp>& vec); + _InputArray(const std::vector& vec); + template _InputArray(const std::vector >& vec); + _InputArray(const std::vector >&); + template _InputArray(const std::vector >& vec); + template _InputArray(const _Tp* vec, int n); + template _InputArray(const Matx<_Tp, m, n>& matx); + _InputArray(const double& val); + _InputArray(const cuda::GpuMat& d_mat); + _InputArray(const std::vector& d_mat_array); + _InputArray(const ogl::Buffer& buf); + _InputArray(const cuda::HostMem& cuda_mem); + template _InputArray(const cudev::GpuMat_<_Tp>& m); + _InputArray(const UMat& um); + _InputArray(const std::vector& umv); + +#ifdef CV_CXX_STD_ARRAY + template _InputArray(const std::array<_Tp, _Nm>& arr); + template _InputArray(const std::array& arr); +#endif + + template static _InputArray rawIn(const std::vector<_Tp>& vec); +#ifdef CV_CXX_STD_ARRAY + template static _InputArray rawIn(const std::array<_Tp, _Nm>& arr); +#endif + + Mat getMat(int idx=-1) const; + Mat getMat_(int idx=-1) const; + UMat getUMat(int idx=-1) const; + void getMatVector(std::vector& mv) const; + void getUMatVector(std::vector& umv) const; + void getGpuMatVector(std::vector& gpumv) const; + cuda::GpuMat getGpuMat() const; + ogl::Buffer getOGlBuffer() const; + + int getFlags() const; + void* getObj() const; + Size getSz() const; + + int kind() const; + int dims(int i=-1) const; + int cols(int i=-1) const; + int rows(int i=-1) const; + Size size(int i=-1) const; + int sizend(int* sz, int i=-1) const; + bool sameSize(const _InputArray& arr) const; + size_t total(int i=-1) const; + int type(int i=-1) const; + int depth(int i=-1) const; + int channels(int i=-1) const; + bool isContinuous(int i=-1) const; + bool isSubmatrix(int i=-1) const; + bool empty() const; + void copyTo(const _OutputArray& arr) const; + void copyTo(const _OutputArray& arr, const _InputArray & mask) const; + size_t offset(int i=-1) const; + size_t step(int i=-1) const; + bool isMat() const; + bool isUMat() const; + bool isMatVector() const; + bool isUMatVector() const; + bool isMatx() const; + bool isVector() const; + bool isGpuMat() const; + bool isGpuMatVector() const; + ~_InputArray(); + +protected: + int flags; + void* obj; + Size sz; + + void init(int _flags, const void* _obj); + void init(int _flags, const void* _obj, Size _sz); +}; + + +/** @brief This type is very similar to InputArray except that it is used for input/output and output function +parameters. + +Just like with InputArray, OpenCV users should not care about OutputArray, they just pass `Mat`, +`vector` etc. to the functions. The same limitation as for `InputArray`: *Do not explicitly +create OutputArray instances* applies here too. + +If you want to make your function polymorphic (i.e. accept different arrays as output parameters), +it is also not very difficult. Take the sample above as the reference. Note that +_OutputArray::create() needs to be called before _OutputArray::getMat(). This way you guarantee +that the output array is properly allocated. + +Optional output parameters. If you do not need certain output array to be computed and returned to +you, pass cv::noArray(), just like you would in the case of optional input array. At the +implementation level, use _OutputArray::needed() to check if certain output array needs to be +computed or not. + +There are several synonyms for OutputArray that are used to assist automatic Python/Java/... wrapper +generators: +@code + typedef OutputArray OutputArrayOfArrays; + typedef OutputArray InputOutputArray; + typedef OutputArray InputOutputArrayOfArrays; +@endcode + */ +class CV_EXPORTS _OutputArray : public _InputArray +{ +public: + enum + { + DEPTH_MASK_8U = 1 << CV_8U, + DEPTH_MASK_8S = 1 << CV_8S, + DEPTH_MASK_16U = 1 << CV_16U, + DEPTH_MASK_16S = 1 << CV_16S, + DEPTH_MASK_32S = 1 << CV_32S, + DEPTH_MASK_32F = 1 << CV_32F, + DEPTH_MASK_64F = 1 << CV_64F, + DEPTH_MASK_ALL = (DEPTH_MASK_64F<<1)-1, + DEPTH_MASK_ALL_BUT_8S = DEPTH_MASK_ALL & ~DEPTH_MASK_8S, + DEPTH_MASK_FLT = DEPTH_MASK_32F + DEPTH_MASK_64F + }; + + _OutputArray(); + _OutputArray(int _flags, void* _obj); + _OutputArray(Mat& m); + _OutputArray(std::vector& vec); + _OutputArray(cuda::GpuMat& d_mat); + _OutputArray(std::vector& d_mat); + _OutputArray(ogl::Buffer& buf); + _OutputArray(cuda::HostMem& cuda_mem); + template _OutputArray(cudev::GpuMat_<_Tp>& m); + template _OutputArray(std::vector<_Tp>& vec); + _OutputArray(std::vector& vec); + template _OutputArray(std::vector >& vec); + _OutputArray(std::vector >&); + template _OutputArray(std::vector >& vec); + template _OutputArray(Mat_<_Tp>& m); + template _OutputArray(_Tp* vec, int n); + template _OutputArray(Matx<_Tp, m, n>& matx); + _OutputArray(UMat& m); + _OutputArray(std::vector& vec); + + _OutputArray(const Mat& m); + _OutputArray(const std::vector& vec); + _OutputArray(const cuda::GpuMat& d_mat); + _OutputArray(const std::vector& d_mat); + _OutputArray(const ogl::Buffer& buf); + _OutputArray(const cuda::HostMem& cuda_mem); + template _OutputArray(const cudev::GpuMat_<_Tp>& m); + template _OutputArray(const std::vector<_Tp>& vec); + template _OutputArray(const std::vector >& vec); + template _OutputArray(const std::vector >& vec); + template _OutputArray(const Mat_<_Tp>& m); + template _OutputArray(const _Tp* vec, int n); + template _OutputArray(const Matx<_Tp, m, n>& matx); + _OutputArray(const UMat& m); + _OutputArray(const std::vector& vec); + +#ifdef CV_CXX_STD_ARRAY + template _OutputArray(std::array<_Tp, _Nm>& arr); + template _OutputArray(const std::array<_Tp, _Nm>& arr); + template _OutputArray(std::array& arr); + template _OutputArray(const std::array& arr); +#endif + + template static _OutputArray rawOut(std::vector<_Tp>& vec); +#ifdef CV_CXX_STD_ARRAY + template static _OutputArray rawOut(std::array<_Tp, _Nm>& arr); +#endif + + bool fixedSize() const; + bool fixedType() const; + bool needed() const; + Mat& getMatRef(int i=-1) const; + UMat& getUMatRef(int i=-1) const; + cuda::GpuMat& getGpuMatRef() const; + std::vector& getGpuMatVecRef() const; + ogl::Buffer& getOGlBufferRef() const; + cuda::HostMem& getHostMemRef() const; + void create(Size sz, int type, int i=-1, bool allowTransposed=false, int fixedDepthMask=0) const; + void create(int rows, int cols, int type, int i=-1, bool allowTransposed=false, int fixedDepthMask=0) const; + void create(int dims, const int* size, int type, int i=-1, bool allowTransposed=false, int fixedDepthMask=0) const; + void createSameSize(const _InputArray& arr, int mtype) const; + void release() const; + void clear() const; + void setTo(const _InputArray& value, const _InputArray & mask = _InputArray()) const; + + void assign(const UMat& u) const; + void assign(const Mat& m) const; + + void assign(const std::vector& v) const; + void assign(const std::vector& v) const; +}; + + +class CV_EXPORTS _InputOutputArray : public _OutputArray +{ +public: + _InputOutputArray(); + _InputOutputArray(int _flags, void* _obj); + _InputOutputArray(Mat& m); + _InputOutputArray(std::vector& vec); + _InputOutputArray(cuda::GpuMat& d_mat); + _InputOutputArray(ogl::Buffer& buf); + _InputOutputArray(cuda::HostMem& cuda_mem); + template _InputOutputArray(cudev::GpuMat_<_Tp>& m); + template _InputOutputArray(std::vector<_Tp>& vec); + _InputOutputArray(std::vector& vec); + template _InputOutputArray(std::vector >& vec); + template _InputOutputArray(std::vector >& vec); + template _InputOutputArray(Mat_<_Tp>& m); + template _InputOutputArray(_Tp* vec, int n); + template _InputOutputArray(Matx<_Tp, m, n>& matx); + _InputOutputArray(UMat& m); + _InputOutputArray(std::vector& vec); + + _InputOutputArray(const Mat& m); + _InputOutputArray(const std::vector& vec); + _InputOutputArray(const cuda::GpuMat& d_mat); + _InputOutputArray(const std::vector& d_mat); + _InputOutputArray(const ogl::Buffer& buf); + _InputOutputArray(const cuda::HostMem& cuda_mem); + template _InputOutputArray(const cudev::GpuMat_<_Tp>& m); + template _InputOutputArray(const std::vector<_Tp>& vec); + template _InputOutputArray(const std::vector >& vec); + template _InputOutputArray(const std::vector >& vec); + template _InputOutputArray(const Mat_<_Tp>& m); + template _InputOutputArray(const _Tp* vec, int n); + template _InputOutputArray(const Matx<_Tp, m, n>& matx); + _InputOutputArray(const UMat& m); + _InputOutputArray(const std::vector& vec); + +#ifdef CV_CXX_STD_ARRAY + template _InputOutputArray(std::array<_Tp, _Nm>& arr); + template _InputOutputArray(const std::array<_Tp, _Nm>& arr); + template _InputOutputArray(std::array& arr); + template _InputOutputArray(const std::array& arr); +#endif + + template static _InputOutputArray rawInOut(std::vector<_Tp>& vec); +#ifdef CV_CXX_STD_ARRAY + template _InputOutputArray rawInOut(std::array<_Tp, _Nm>& arr); +#endif + +}; + +/** Helper to wrap custom types. @see InputArray */ +template static inline _InputArray rawIn(_Tp& v); +/** Helper to wrap custom types. @see InputArray */ +template static inline _OutputArray rawOut(_Tp& v); +/** Helper to wrap custom types. @see InputArray */ +template static inline _InputOutputArray rawInOut(_Tp& v); + +CV__DEBUG_NS_END + +typedef const _InputArray& InputArray; +typedef InputArray InputArrayOfArrays; +typedef const _OutputArray& OutputArray; +typedef OutputArray OutputArrayOfArrays; +typedef const _InputOutputArray& InputOutputArray; +typedef InputOutputArray InputOutputArrayOfArrays; + +CV_EXPORTS InputOutputArray noArray(); + +/////////////////////////////////// MatAllocator ////////////////////////////////////// + +//! Usage flags for allocator +enum UMatUsageFlags +{ + USAGE_DEFAULT = 0, + + // buffer allocation policy is platform and usage specific + USAGE_ALLOCATE_HOST_MEMORY = 1 << 0, + USAGE_ALLOCATE_DEVICE_MEMORY = 1 << 1, + USAGE_ALLOCATE_SHARED_MEMORY = 1 << 2, // It is not equal to: USAGE_ALLOCATE_HOST_MEMORY | USAGE_ALLOCATE_DEVICE_MEMORY + + __UMAT_USAGE_FLAGS_32BIT = 0x7fffffff // Binary compatibility hint +}; + +struct CV_EXPORTS UMatData; + +/** @brief Custom array allocator +*/ +class CV_EXPORTS MatAllocator +{ +public: + MatAllocator() {} + virtual ~MatAllocator() {} + + // let's comment it off for now to detect and fix all the uses of allocator + //virtual void allocate(int dims, const int* sizes, int type, int*& refcount, + // uchar*& datastart, uchar*& data, size_t* step) = 0; + //virtual void deallocate(int* refcount, uchar* datastart, uchar* data) = 0; + virtual UMatData* allocate(int dims, const int* sizes, int type, + void* data, size_t* step, int flags, UMatUsageFlags usageFlags) const = 0; + virtual bool allocate(UMatData* data, int accessflags, UMatUsageFlags usageFlags) const = 0; + virtual void deallocate(UMatData* data) const = 0; + virtual void map(UMatData* data, int accessflags) const; + virtual void unmap(UMatData* data) const; + virtual void download(UMatData* data, void* dst, int dims, const size_t sz[], + const size_t srcofs[], const size_t srcstep[], + const size_t dststep[]) const; + virtual void upload(UMatData* data, const void* src, int dims, const size_t sz[], + const size_t dstofs[], const size_t dststep[], + const size_t srcstep[]) const; + virtual void copy(UMatData* srcdata, UMatData* dstdata, int dims, const size_t sz[], + const size_t srcofs[], const size_t srcstep[], + const size_t dstofs[], const size_t dststep[], bool sync) const; + + // default implementation returns DummyBufferPoolController + virtual BufferPoolController* getBufferPoolController(const char* id = NULL) const; +}; + + +//////////////////////////////// MatCommaInitializer ////////////////////////////////// + +/** @brief Comma-separated Matrix Initializer + + The class instances are usually not created explicitly. + Instead, they are created on "matrix << firstValue" operator. + + The sample below initializes 2x2 rotation matrix: + + \code + double angle = 30, a = cos(angle*CV_PI/180), b = sin(angle*CV_PI/180); + Mat R = (Mat_(2,2) << a, -b, b, a); + \endcode +*/ +template class MatCommaInitializer_ +{ +public: + //! the constructor, created by "matrix << firstValue" operator, where matrix is cv::Mat + MatCommaInitializer_(Mat_<_Tp>* _m); + //! the operator that takes the next value and put it to the matrix + template MatCommaInitializer_<_Tp>& operator , (T2 v); + //! another form of conversion operator + operator Mat_<_Tp>() const; +protected: + MatIterator_<_Tp> it; +}; + + +/////////////////////////////////////// Mat /////////////////////////////////////////// + +// note that umatdata might be allocated together +// with the matrix data, not as a separate object. +// therefore, it does not have constructor or destructor; +// it should be explicitly initialized using init(). +struct CV_EXPORTS UMatData +{ + enum { COPY_ON_MAP=1, HOST_COPY_OBSOLETE=2, + DEVICE_COPY_OBSOLETE=4, TEMP_UMAT=8, TEMP_COPIED_UMAT=24, + USER_ALLOCATED=32, DEVICE_MEM_MAPPED=64, + ASYNC_CLEANUP=128 + }; + UMatData(const MatAllocator* allocator); + ~UMatData(); + + // provide atomic access to the structure + void lock(); + void unlock(); + + bool hostCopyObsolete() const; + bool deviceCopyObsolete() const; + bool deviceMemMapped() const; + bool copyOnMap() const; + bool tempUMat() const; + bool tempCopiedUMat() const; + void markHostCopyObsolete(bool flag); + void markDeviceCopyObsolete(bool flag); + void markDeviceMemMapped(bool flag); + + const MatAllocator* prevAllocator; + const MatAllocator* currAllocator; + int urefcount; + int refcount; + uchar* data; + uchar* origdata; + size_t size; + + int flags; + void* handle; + void* userdata; + int allocatorFlags_; + int mapcount; + UMatData* originalUMatData; +}; + + +struct CV_EXPORTS MatSize +{ + explicit MatSize(int* _p); + int dims() const; + Size operator()() const; + const int& operator[](int i) const; + int& operator[](int i); + operator const int*() const; // TODO OpenCV 4.0: drop this + bool operator == (const MatSize& sz) const; + bool operator != (const MatSize& sz) const; + + int* p; +}; + +struct CV_EXPORTS MatStep +{ + MatStep(); + explicit MatStep(size_t s); + const size_t& operator[](int i) const; + size_t& operator[](int i); + operator size_t() const; + MatStep& operator = (size_t s); + + size_t* p; + size_t buf[2]; +protected: + MatStep& operator = (const MatStep&); +}; + +/** @example samples/cpp/cout_mat.cpp +An example demonstrating the serial out capabilities of cv::Mat +*/ + + /** @brief n-dimensional dense array class \anchor CVMat_Details + +The class Mat represents an n-dimensional dense numerical single-channel or multi-channel array. It +can be used to store real or complex-valued vectors and matrices, grayscale or color images, voxel +volumes, vector fields, point clouds, tensors, histograms (though, very high-dimensional histograms +may be better stored in a SparseMat ). The data layout of the array `M` is defined by the array +`M.step[]`, so that the address of element \f$(i_0,...,i_{M.dims-1})\f$, where \f$0\leq i_k= M.step[i+1]` (in fact, `M.step[i] >= M.step[i+1]*M.size[i+1]` ). This means +that 2-dimensional matrices are stored row-by-row, 3-dimensional matrices are stored plane-by-plane, +and so on. M.step[M.dims-1] is minimal and always equal to the element size M.elemSize() . + +So, the data layout in Mat is fully compatible with CvMat, IplImage, and CvMatND types from OpenCV +1.x. It is also compatible with the majority of dense array types from the standard toolkits and +SDKs, such as Numpy (ndarray), Win32 (independent device bitmaps), and others, that is, with any +array that uses *steps* (or *strides*) to compute the position of a pixel. Due to this +compatibility, it is possible to make a Mat header for user-allocated data and process it in-place +using OpenCV functions. + +There are many different ways to create a Mat object. The most popular options are listed below: + +- Use the create(nrows, ncols, type) method or the similar Mat(nrows, ncols, type[, fillValue]) +constructor. A new array of the specified size and type is allocated. type has the same meaning as +in the cvCreateMat method. For example, CV_8UC1 means a 8-bit single-channel array, CV_32FC2 +means a 2-channel (complex) floating-point array, and so on. +@code + // make a 7x7 complex matrix filled with 1+3j. + Mat M(7,7,CV_32FC2,Scalar(1,3)); + // and now turn M to a 100x60 15-channel 8-bit matrix. + // The old content will be deallocated + M.create(100,60,CV_8UC(15)); +@endcode +As noted in the introduction to this chapter, create() allocates only a new array when the shape +or type of the current array are different from the specified ones. + +- Create a multi-dimensional array: +@code + // create a 100x100x100 8-bit array + int sz[] = {100, 100, 100}; + Mat bigCube(3, sz, CV_8U, Scalar::all(0)); +@endcode +It passes the number of dimensions =1 to the Mat constructor but the created array will be +2-dimensional with the number of columns set to 1. So, Mat::dims is always \>= 2 (can also be 0 +when the array is empty). + +- Use a copy constructor or assignment operator where there can be an array or expression on the +right side (see below). As noted in the introduction, the array assignment is an O(1) operation +because it only copies the header and increases the reference counter. The Mat::clone() method can +be used to get a full (deep) copy of the array when you need it. + +- Construct a header for a part of another array. It can be a single row, single column, several +rows, several columns, rectangular region in the array (called a *minor* in algebra) or a +diagonal. Such operations are also O(1) because the new header references the same data. You can +actually modify a part of the array using this feature, for example: +@code + // add the 5-th row, multiplied by 3 to the 3rd row + M.row(3) = M.row(3) + M.row(5)*3; + // now copy the 7-th column to the 1-st column + // M.col(1) = M.col(7); // this will not work + Mat M1 = M.col(1); + M.col(7).copyTo(M1); + // create a new 320x240 image + Mat img(Size(320,240),CV_8UC3); + // select a ROI + Mat roi(img, Rect(10,10,100,100)); + // fill the ROI with (0,255,0) (which is green in RGB space); + // the original 320x240 image will be modified + roi = Scalar(0,255,0); +@endcode +Due to the additional datastart and dataend members, it is possible to compute a relative +sub-array position in the main *container* array using locateROI(): +@code + Mat A = Mat::eye(10, 10, CV_32S); + // extracts A columns, 1 (inclusive) to 3 (exclusive). + Mat B = A(Range::all(), Range(1, 3)); + // extracts B rows, 5 (inclusive) to 9 (exclusive). + // that is, C \~ A(Range(5, 9), Range(1, 3)) + Mat C = B(Range(5, 9), Range::all()); + Size size; Point ofs; + C.locateROI(size, ofs); + // size will be (width=10,height=10) and the ofs will be (x=1, y=5) +@endcode +As in case of whole matrices, if you need a deep copy, use the `clone()` method of the extracted +sub-matrices. + +- Make a header for user-allocated data. It can be useful to do the following: + -# Process "foreign" data using OpenCV (for example, when you implement a DirectShow\* filter or + a processing module for gstreamer, and so on). For example: + @code + void process_video_frame(const unsigned char* pixels, + int width, int height, int step) + { + Mat img(height, width, CV_8UC3, pixels, step); + GaussianBlur(img, img, Size(7,7), 1.5, 1.5); + } + @endcode + -# Quickly initialize small matrices and/or get a super-fast element access. + @code + double m[3][3] = {{a, b, c}, {d, e, f}, {g, h, i}}; + Mat M = Mat(3, 3, CV_64F, m).inv(); + @endcode + . + Partial yet very common cases of this *user-allocated data* case are conversions from CvMat and + IplImage to Mat. For this purpose, there is function cv::cvarrToMat taking pointers to CvMat or + IplImage and the optional flag indicating whether to copy the data or not. + @snippet samples/cpp/image.cpp iplimage + +- Use MATLAB-style array initializers, zeros(), ones(), eye(), for example: +@code + // create a double-precision identity matrix and add it to M. + M += Mat::eye(M.rows, M.cols, CV_64F); +@endcode + +- Use a comma-separated initializer: +@code + // create a 3x3 double-precision identity matrix + Mat M = (Mat_(3,3) << 1, 0, 0, 0, 1, 0, 0, 0, 1); +@endcode +With this approach, you first call a constructor of the Mat class with the proper parameters, and +then you just put `<< operator` followed by comma-separated values that can be constants, +variables, expressions, and so on. Also, note the extra parentheses required to avoid compilation +errors. + +Once the array is created, it is automatically managed via a reference-counting mechanism. If the +array header is built on top of user-allocated data, you should handle the data by yourself. The +array data is deallocated when no one points to it. If you want to release the data pointed by a +array header before the array destructor is called, use Mat::release(). + +The next important thing to learn about the array class is element access. This manual already +described how to compute an address of each array element. Normally, you are not required to use the +formula directly in the code. If you know the array element type (which can be retrieved using the +method Mat::type() ), you can access the element \f$M_{ij}\f$ of a 2-dimensional array as: +@code + M.at(i,j) += 1.f; +@endcode +assuming that `M` is a double-precision floating-point array. There are several variants of the method +at for a different number of dimensions. + +If you need to process a whole row of a 2D array, the most efficient way is to get the pointer to +the row first, and then just use the plain C operator [] : +@code + // compute sum of positive matrix elements + // (assuming that M is a double-precision matrix) + double sum=0; + for(int i = 0; i < M.rows; i++) + { + const double* Mi = M.ptr(i); + for(int j = 0; j < M.cols; j++) + sum += std::max(Mi[j], 0.); + } +@endcode +Some operations, like the one above, do not actually depend on the array shape. They just process +elements of an array one by one (or elements from multiple arrays that have the same coordinates, +for example, array addition). Such operations are called *element-wise*. It makes sense to check +whether all the input/output arrays are continuous, namely, have no gaps at the end of each row. If +yes, process them as a long single row: +@code + // compute the sum of positive matrix elements, optimized variant + double sum=0; + int cols = M.cols, rows = M.rows; + if(M.isContinuous()) + { + cols *= rows; + rows = 1; + } + for(int i = 0; i < rows; i++) + { + const double* Mi = M.ptr(i); + for(int j = 0; j < cols; j++) + sum += std::max(Mi[j], 0.); + } +@endcode +In case of the continuous matrix, the outer loop body is executed just once. So, the overhead is +smaller, which is especially noticeable in case of small matrices. + +Finally, there are STL-style iterators that are smart enough to skip gaps between successive rows: +@code + // compute sum of positive matrix elements, iterator-based variant + double sum=0; + MatConstIterator_ it = M.begin(), it_end = M.end(); + for(; it != it_end; ++it) + sum += std::max(*it, 0.); +@endcode +The matrix iterators are random-access iterators, so they can be passed to any STL algorithm, +including std::sort(). + +@note Matrix Expressions and arithmetic see MatExpr +*/ +class CV_EXPORTS Mat +{ +public: + /** + These are various constructors that form a matrix. As noted in the AutomaticAllocation, often + the default constructor is enough, and the proper matrix will be allocated by an OpenCV function. + The constructed matrix can further be assigned to another matrix or matrix expression or can be + allocated with Mat::create . In the former case, the old content is de-referenced. + */ + Mat(); + + /** @overload + @param rows Number of rows in a 2D array. + @param cols Number of columns in a 2D array. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + */ + Mat(int rows, int cols, int type); + + /** @overload + @param size 2D array size: Size(cols, rows) . In the Size() constructor, the number of rows and the + number of columns go in the reverse order. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + */ + Mat(Size size, int type); + + /** @overload + @param rows Number of rows in a 2D array. + @param cols Number of columns in a 2D array. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + @param s An optional value to initialize each matrix element with. To set all the matrix elements to + the particular value after the construction, use the assignment operator + Mat::operator=(const Scalar& value) . + */ + Mat(int rows, int cols, int type, const Scalar& s); + + /** @overload + @param size 2D array size: Size(cols, rows) . In the Size() constructor, the number of rows and the + number of columns go in the reverse order. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + @param s An optional value to initialize each matrix element with. To set all the matrix elements to + the particular value after the construction, use the assignment operator + Mat::operator=(const Scalar& value) . + */ + Mat(Size size, int type, const Scalar& s); + + /** @overload + @param ndims Array dimensionality. + @param sizes Array of integers specifying an n-dimensional array shape. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + */ + Mat(int ndims, const int* sizes, int type); + + /** @overload + @param sizes Array of integers specifying an n-dimensional array shape. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + */ + Mat(const std::vector& sizes, int type); + + /** @overload + @param ndims Array dimensionality. + @param sizes Array of integers specifying an n-dimensional array shape. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + @param s An optional value to initialize each matrix element with. To set all the matrix elements to + the particular value after the construction, use the assignment operator + Mat::operator=(const Scalar& value) . + */ + Mat(int ndims, const int* sizes, int type, const Scalar& s); + + /** @overload + @param sizes Array of integers specifying an n-dimensional array shape. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + @param s An optional value to initialize each matrix element with. To set all the matrix elements to + the particular value after the construction, use the assignment operator + Mat::operator=(const Scalar& value) . + */ + Mat(const std::vector& sizes, int type, const Scalar& s); + + + /** @overload + @param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied + by these constructors. Instead, the header pointing to m data or its sub-array is constructed and + associated with it. The reference counter, if any, is incremented. So, when you modify the matrix + formed using such a constructor, you also modify the corresponding elements of m . If you want to + have an independent copy of the sub-array, use Mat::clone() . + */ + Mat(const Mat& m); + + /** @overload + @param rows Number of rows in a 2D array. + @param cols Number of columns in a 2D array. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + @param data Pointer to the user data. Matrix constructors that take data and step parameters do not + allocate matrix data. Instead, they just initialize the matrix header that points to the specified + data, which means that no data is copied. This operation is very efficient and can be used to + process external data using OpenCV functions. The external data is not automatically deallocated, so + you should take care of it. + @param step Number of bytes each matrix row occupies. The value should include the padding bytes at + the end of each row, if any. If the parameter is missing (set to AUTO_STEP ), no padding is assumed + and the actual step is calculated as cols*elemSize(). See Mat::elemSize. + */ + Mat(int rows, int cols, int type, void* data, size_t step=AUTO_STEP); + + /** @overload + @param size 2D array size: Size(cols, rows) . In the Size() constructor, the number of rows and the + number of columns go in the reverse order. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + @param data Pointer to the user data. Matrix constructors that take data and step parameters do not + allocate matrix data. Instead, they just initialize the matrix header that points to the specified + data, which means that no data is copied. This operation is very efficient and can be used to + process external data using OpenCV functions. The external data is not automatically deallocated, so + you should take care of it. + @param step Number of bytes each matrix row occupies. The value should include the padding bytes at + the end of each row, if any. If the parameter is missing (set to AUTO_STEP ), no padding is assumed + and the actual step is calculated as cols*elemSize(). See Mat::elemSize. + */ + Mat(Size size, int type, void* data, size_t step=AUTO_STEP); + + /** @overload + @param ndims Array dimensionality. + @param sizes Array of integers specifying an n-dimensional array shape. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + @param data Pointer to the user data. Matrix constructors that take data and step parameters do not + allocate matrix data. Instead, they just initialize the matrix header that points to the specified + data, which means that no data is copied. This operation is very efficient and can be used to + process external data using OpenCV functions. The external data is not automatically deallocated, so + you should take care of it. + @param steps Array of ndims-1 steps in case of a multi-dimensional array (the last step is always + set to the element size). If not specified, the matrix is assumed to be continuous. + */ + Mat(int ndims, const int* sizes, int type, void* data, const size_t* steps=0); + + /** @overload + @param sizes Array of integers specifying an n-dimensional array shape. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + @param data Pointer to the user data. Matrix constructors that take data and step parameters do not + allocate matrix data. Instead, they just initialize the matrix header that points to the specified + data, which means that no data is copied. This operation is very efficient and can be used to + process external data using OpenCV functions. The external data is not automatically deallocated, so + you should take care of it. + @param steps Array of ndims-1 steps in case of a multi-dimensional array (the last step is always + set to the element size). If not specified, the matrix is assumed to be continuous. + */ + Mat(const std::vector& sizes, int type, void* data, const size_t* steps=0); + + /** @overload + @param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied + by these constructors. Instead, the header pointing to m data or its sub-array is constructed and + associated with it. The reference counter, if any, is incremented. So, when you modify the matrix + formed using such a constructor, you also modify the corresponding elements of m . If you want to + have an independent copy of the sub-array, use Mat::clone() . + @param rowRange Range of the m rows to take. As usual, the range start is inclusive and the range + end is exclusive. Use Range::all() to take all the rows. + @param colRange Range of the m columns to take. Use Range::all() to take all the columns. + */ + Mat(const Mat& m, const Range& rowRange, const Range& colRange=Range::all()); + + /** @overload + @param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied + by these constructors. Instead, the header pointing to m data or its sub-array is constructed and + associated with it. The reference counter, if any, is incremented. So, when you modify the matrix + formed using such a constructor, you also modify the corresponding elements of m . If you want to + have an independent copy of the sub-array, use Mat::clone() . + @param roi Region of interest. + */ + Mat(const Mat& m, const Rect& roi); + + /** @overload + @param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied + by these constructors. Instead, the header pointing to m data or its sub-array is constructed and + associated with it. The reference counter, if any, is incremented. So, when you modify the matrix + formed using such a constructor, you also modify the corresponding elements of m . If you want to + have an independent copy of the sub-array, use Mat::clone() . + @param ranges Array of selected ranges of m along each dimensionality. + */ + Mat(const Mat& m, const Range* ranges); + + /** @overload + @param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied + by these constructors. Instead, the header pointing to m data or its sub-array is constructed and + associated with it. The reference counter, if any, is incremented. So, when you modify the matrix + formed using such a constructor, you also modify the corresponding elements of m . If you want to + have an independent copy of the sub-array, use Mat::clone() . + @param ranges Array of selected ranges of m along each dimensionality. + */ + Mat(const Mat& m, const std::vector& ranges); + + /** @overload + @param vec STL vector whose elements form the matrix. The matrix has a single column and the number + of rows equal to the number of vector elements. Type of the matrix matches the type of vector + elements. The constructor can handle arbitrary types, for which there is a properly declared + DataType . This means that the vector elements must be primitive numbers or uni-type numerical + tuples of numbers. Mixed-type structures are not supported. The corresponding constructor is + explicit. Since STL vectors are not automatically converted to Mat instances, you should write + Mat(vec) explicitly. Unless you copy the data into the matrix ( copyData=true ), no new elements + will be added to the vector because it can potentially yield vector data reallocation, and, thus, + the matrix data pointer will be invalid. + @param copyData Flag to specify whether the underlying data of the STL vector should be copied + to (true) or shared with (false) the newly constructed matrix. When the data is copied, the + allocated buffer is managed using Mat reference counting mechanism. While the data is shared, + the reference counter is NULL, and you should not deallocate the data until the matrix is not + destructed. + */ + template explicit Mat(const std::vector<_Tp>& vec, bool copyData=false); + +#ifdef CV_CXX11 + /** @overload + */ + template::value>::type> + explicit Mat(const std::initializer_list<_Tp> list); + + /** @overload + */ + template explicit Mat(const std::initializer_list sizes, const std::initializer_list<_Tp> list); +#endif + +#ifdef CV_CXX_STD_ARRAY + /** @overload + */ + template explicit Mat(const std::array<_Tp, _Nm>& arr, bool copyData=false); +#endif + + /** @overload + */ + template explicit Mat(const Vec<_Tp, n>& vec, bool copyData=true); + + /** @overload + */ + template explicit Mat(const Matx<_Tp, m, n>& mtx, bool copyData=true); + + /** @overload + */ + template explicit Mat(const Point_<_Tp>& pt, bool copyData=true); + + /** @overload + */ + template explicit Mat(const Point3_<_Tp>& pt, bool copyData=true); + + /** @overload + */ + template explicit Mat(const MatCommaInitializer_<_Tp>& commaInitializer); + + //! download data from GpuMat + explicit Mat(const cuda::GpuMat& m); + + //! destructor - calls release() + ~Mat(); + + /** @brief assignment operators + + These are available assignment operators. Since they all are very different, make sure to read the + operator parameters description. + @param m Assigned, right-hand-side matrix. Matrix assignment is an O(1) operation. This means that + no data is copied but the data is shared and the reference counter, if any, is incremented. Before + assigning new data, the old data is de-referenced via Mat::release . + */ + Mat& operator = (const Mat& m); + + /** @overload + @param expr Assigned matrix expression object. As opposite to the first form of the assignment + operation, the second form can reuse already allocated matrix if it has the right size and type to + fit the matrix expression result. It is automatically handled by the real function that the matrix + expressions is expanded to. For example, C=A+B is expanded to add(A, B, C), and add takes care of + automatic C reallocation. + */ + Mat& operator = (const MatExpr& expr); + + //! retrieve UMat from Mat + UMat getUMat(int accessFlags, UMatUsageFlags usageFlags = USAGE_DEFAULT) const; + + /** @brief Creates a matrix header for the specified matrix row. + + The method makes a new header for the specified matrix row and returns it. This is an O(1) + operation, regardless of the matrix size. The underlying data of the new matrix is shared with the + original matrix. Here is the example of one of the classical basic matrix processing operations, + axpy, used by LU and many other algorithms: + @code + inline void matrix_axpy(Mat& A, int i, int j, double alpha) + { + A.row(i) += A.row(j)*alpha; + } + @endcode + @note In the current implementation, the following code does not work as expected: + @code + Mat A; + ... + A.row(i) = A.row(j); // will not work + @endcode + This happens because A.row(i) forms a temporary header that is further assigned to another header. + Remember that each of these operations is O(1), that is, no data is copied. Thus, the above + assignment is not true if you may have expected the j-th row to be copied to the i-th row. To + achieve that, you should either turn this simple assignment into an expression or use the + Mat::copyTo method: + @code + Mat A; + ... + // works, but looks a bit obscure. + A.row(i) = A.row(j) + 0; + // this is a bit longer, but the recommended method. + A.row(j).copyTo(A.row(i)); + @endcode + @param y A 0-based row index. + */ + Mat row(int y) const; + + /** @brief Creates a matrix header for the specified matrix column. + + The method makes a new header for the specified matrix column and returns it. This is an O(1) + operation, regardless of the matrix size. The underlying data of the new matrix is shared with the + original matrix. See also the Mat::row description. + @param x A 0-based column index. + */ + Mat col(int x) const; + + /** @brief Creates a matrix header for the specified row span. + + The method makes a new header for the specified row span of the matrix. Similarly to Mat::row and + Mat::col , this is an O(1) operation. + @param startrow An inclusive 0-based start index of the row span. + @param endrow An exclusive 0-based ending index of the row span. + */ + Mat rowRange(int startrow, int endrow) const; + + /** @overload + @param r Range structure containing both the start and the end indices. + */ + Mat rowRange(const Range& r) const; + + /** @brief Creates a matrix header for the specified column span. + + The method makes a new header for the specified column span of the matrix. Similarly to Mat::row and + Mat::col , this is an O(1) operation. + @param startcol An inclusive 0-based start index of the column span. + @param endcol An exclusive 0-based ending index of the column span. + */ + Mat colRange(int startcol, int endcol) const; + + /** @overload + @param r Range structure containing both the start and the end indices. + */ + Mat colRange(const Range& r) const; + + /** @brief Extracts a diagonal from a matrix + + The method makes a new header for the specified matrix diagonal. The new matrix is represented as a + single-column matrix. Similarly to Mat::row and Mat::col, this is an O(1) operation. + @param d index of the diagonal, with the following values: + - `d=0` is the main diagonal. + - `d<0` is a diagonal from the lower half. For example, d=-1 means the diagonal is set + immediately below the main one. + - `d>0` is a diagonal from the upper half. For example, d=1 means the diagonal is set + immediately above the main one. + For example: + @code + Mat m = (Mat_(3,3) << + 1,2,3, + 4,5,6, + 7,8,9); + Mat d0 = m.diag(0); + Mat d1 = m.diag(1); + Mat d_1 = m.diag(-1); + @endcode + The resulting matrices are + @code + d0 = + [1; + 5; + 9] + d1 = + [2; + 6] + d_1 = + [4; + 8] + @endcode + */ + Mat diag(int d=0) const; + + /** @brief creates a diagonal matrix + + The method creates a square diagonal matrix from specified main diagonal. + @param d One-dimensional matrix that represents the main diagonal. + */ + static Mat diag(const Mat& d); + + /** @brief Creates a full copy of the array and the underlying data. + + The method creates a full copy of the array. The original step[] is not taken into account. So, the + array copy is a continuous array occupying total()*elemSize() bytes. + */ + Mat clone() const CV_NODISCARD; + + /** @brief Copies the matrix to another one. + + The method copies the matrix data to another matrix. Before copying the data, the method invokes : + @code + m.create(this->size(), this->type()); + @endcode + so that the destination matrix is reallocated if needed. While m.copyTo(m); works flawlessly, the + function does not handle the case of a partial overlap between the source and the destination + matrices. + + When the operation mask is specified, if the Mat::create call shown above reallocates the matrix, + the newly allocated matrix is initialized with all zeros before copying the data. + @param m Destination matrix. If it does not have a proper size or type before the operation, it is + reallocated. + */ + void copyTo( OutputArray m ) const; + + /** @overload + @param m Destination matrix. If it does not have a proper size or type before the operation, it is + reallocated. + @param mask Operation mask of the same size as \*this. Its non-zero elements indicate which matrix + elements need to be copied. The mask has to be of type CV_8U and can have 1 or multiple channels. + */ + void copyTo( OutputArray m, InputArray mask ) const; + + /** @brief Converts an array to another data type with optional scaling. + + The method converts source pixel values to the target data type. saturate_cast\<\> is applied at + the end to avoid possible overflows: + + \f[m(x,y) = saturate \_ cast( \alpha (*this)(x,y) + \beta )\f] + @param m output matrix; if it does not have a proper size or type before the operation, it is + reallocated. + @param rtype desired output matrix type or, rather, the depth since the number of channels are the + same as the input has; if rtype is negative, the output matrix will have the same type as the input. + @param alpha optional scale factor. + @param beta optional delta added to the scaled values. + */ + void convertTo( OutputArray m, int rtype, double alpha=1, double beta=0 ) const; + + /** @brief Provides a functional form of convertTo. + + This is an internally used method called by the @ref MatrixExpressions engine. + @param m Destination array. + @param type Desired destination array depth (or -1 if it should be the same as the source type). + */ + void assignTo( Mat& m, int type=-1 ) const; + + /** @brief Sets all or some of the array elements to the specified value. + @param s Assigned scalar converted to the actual array type. + */ + Mat& operator = (const Scalar& s); + + /** @brief Sets all or some of the array elements to the specified value. + + This is an advanced variant of the Mat::operator=(const Scalar& s) operator. + @param value Assigned scalar converted to the actual array type. + @param mask Operation mask of the same size as \*this. Its non-zero elements indicate which matrix + elements need to be copied. The mask has to be of type CV_8U and can have 1 or multiple channels + */ + Mat& setTo(InputArray value, InputArray mask=noArray()); + + /** @brief Changes the shape and/or the number of channels of a 2D matrix without copying the data. + + The method makes a new matrix header for \*this elements. The new matrix may have a different size + and/or different number of channels. Any combination is possible if: + - No extra elements are included into the new matrix and no elements are excluded. Consequently, + the product rows\*cols\*channels() must stay the same after the transformation. + - No data is copied. That is, this is an O(1) operation. Consequently, if you change the number of + rows, or the operation changes the indices of elements row in some other way, the matrix must be + continuous. See Mat::isContinuous . + + For example, if there is a set of 3D points stored as an STL vector, and you want to represent the + points as a 3xN matrix, do the following: + @code + std::vector vec; + ... + Mat pointMat = Mat(vec). // convert vector to Mat, O(1) operation + reshape(1). // make Nx3 1-channel matrix out of Nx1 3-channel. + // Also, an O(1) operation + t(); // finally, transpose the Nx3 matrix. + // This involves copying all the elements + @endcode + @param cn New number of channels. If the parameter is 0, the number of channels remains the same. + @param rows New number of rows. If the parameter is 0, the number of rows remains the same. + */ + Mat reshape(int cn, int rows=0) const; + + /** @overload */ + Mat reshape(int cn, int newndims, const int* newsz) const; + + /** @overload */ + Mat reshape(int cn, const std::vector& newshape) const; + + /** @brief Transposes a matrix. + + The method performs matrix transposition by means of matrix expressions. It does not perform the + actual transposition but returns a temporary matrix transposition object that can be further used as + a part of more complex matrix expressions or can be assigned to a matrix: + @code + Mat A1 = A + Mat::eye(A.size(), A.type())*lambda; + Mat C = A1.t()*A1; // compute (A + lambda*I)^t * (A + lamda*I) + @endcode + */ + MatExpr t() const; + + /** @brief Inverses a matrix. + + The method performs a matrix inversion by means of matrix expressions. This means that a temporary + matrix inversion object is returned by the method and can be used further as a part of more complex + matrix expressions or can be assigned to a matrix. + @param method Matrix inversion method. One of cv::DecompTypes + */ + MatExpr inv(int method=DECOMP_LU) const; + + /** @brief Performs an element-wise multiplication or division of the two matrices. + + The method returns a temporary object encoding per-element array multiplication, with optional + scale. Note that this is not a matrix multiplication that corresponds to a simpler "\*" operator. + + Example: + @code + Mat C = A.mul(5/B); // equivalent to divide(A, B, C, 5) + @endcode + @param m Another array of the same type and the same size as \*this, or a matrix expression. + @param scale Optional scale factor. + */ + MatExpr mul(InputArray m, double scale=1) const; + + /** @brief Computes a cross-product of two 3-element vectors. + + The method computes a cross-product of two 3-element vectors. The vectors must be 3-element + floating-point vectors of the same shape and size. The result is another 3-element vector of the + same shape and type as operands. + @param m Another cross-product operand. + */ + Mat cross(InputArray m) const; + + /** @brief Computes a dot-product of two vectors. + + The method computes a dot-product of two matrices. If the matrices are not single-column or + single-row vectors, the top-to-bottom left-to-right scan ordering is used to treat them as 1D + vectors. The vectors must have the same size and type. If the matrices have more than one channel, + the dot products from all the channels are summed together. + @param m another dot-product operand. + */ + double dot(InputArray m) const; + + /** @brief Returns a zero array of the specified size and type. + + The method returns a Matlab-style zero array initializer. It can be used to quickly form a constant + array as a function parameter, part of a matrix expression, or as a matrix initializer: + @code + Mat A; + A = Mat::zeros(3, 3, CV_32F); + @endcode + In the example above, a new matrix is allocated only if A is not a 3x3 floating-point matrix. + Otherwise, the existing matrix A is filled with zeros. + @param rows Number of rows. + @param cols Number of columns. + @param type Created matrix type. + */ + static MatExpr zeros(int rows, int cols, int type); + + /** @overload + @param size Alternative to the matrix size specification Size(cols, rows) . + @param type Created matrix type. + */ + static MatExpr zeros(Size size, int type); + + /** @overload + @param ndims Array dimensionality. + @param sz Array of integers specifying the array shape. + @param type Created matrix type. + */ + static MatExpr zeros(int ndims, const int* sz, int type); + + /** @brief Returns an array of all 1's of the specified size and type. + + The method returns a Matlab-style 1's array initializer, similarly to Mat::zeros. Note that using + this method you can initialize an array with an arbitrary value, using the following Matlab idiom: + @code + Mat A = Mat::ones(100, 100, CV_8U)*3; // make 100x100 matrix filled with 3. + @endcode + The above operation does not form a 100x100 matrix of 1's and then multiply it by 3. Instead, it + just remembers the scale factor (3 in this case) and use it when actually invoking the matrix + initializer. + @note In case of multi-channels type, only the first channel will be initialized with 1's, the + others will be set to 0's. + @param rows Number of rows. + @param cols Number of columns. + @param type Created matrix type. + */ + static MatExpr ones(int rows, int cols, int type); + + /** @overload + @param size Alternative to the matrix size specification Size(cols, rows) . + @param type Created matrix type. + */ + static MatExpr ones(Size size, int type); + + /** @overload + @param ndims Array dimensionality. + @param sz Array of integers specifying the array shape. + @param type Created matrix type. + */ + static MatExpr ones(int ndims, const int* sz, int type); + + /** @brief Returns an identity matrix of the specified size and type. + + The method returns a Matlab-style identity matrix initializer, similarly to Mat::zeros. Similarly to + Mat::ones, you can use a scale operation to create a scaled identity matrix efficiently: + @code + // make a 4x4 diagonal matrix with 0.1's on the diagonal. + Mat A = Mat::eye(4, 4, CV_32F)*0.1; + @endcode + @note In case of multi-channels type, identity matrix will be initialized only for the first channel, + the others will be set to 0's + @param rows Number of rows. + @param cols Number of columns. + @param type Created matrix type. + */ + static MatExpr eye(int rows, int cols, int type); + + /** @overload + @param size Alternative matrix size specification as Size(cols, rows) . + @param type Created matrix type. + */ + static MatExpr eye(Size size, int type); + + /** @brief Allocates new array data if needed. + + This is one of the key Mat methods. Most new-style OpenCV functions and methods that produce arrays + call this method for each output array. The method uses the following algorithm: + + -# If the current array shape and the type match the new ones, return immediately. Otherwise, + de-reference the previous data by calling Mat::release. + -# Initialize the new header. + -# Allocate the new data of total()\*elemSize() bytes. + -# Allocate the new, associated with the data, reference counter and set it to 1. + + Such a scheme makes the memory management robust and efficient at the same time and helps avoid + extra typing for you. This means that usually there is no need to explicitly allocate output arrays. + That is, instead of writing: + @code + Mat color; + ... + Mat gray(color.rows, color.cols, color.depth()); + cvtColor(color, gray, COLOR_BGR2GRAY); + @endcode + you can simply write: + @code + Mat color; + ... + Mat gray; + cvtColor(color, gray, COLOR_BGR2GRAY); + @endcode + because cvtColor, as well as the most of OpenCV functions, calls Mat::create() for the output array + internally. + @param rows New number of rows. + @param cols New number of columns. + @param type New matrix type. + */ + void create(int rows, int cols, int type); + + /** @overload + @param size Alternative new matrix size specification: Size(cols, rows) + @param type New matrix type. + */ + void create(Size size, int type); + + /** @overload + @param ndims New array dimensionality. + @param sizes Array of integers specifying a new array shape. + @param type New matrix type. + */ + void create(int ndims, const int* sizes, int type); + + /** @overload + @param sizes Array of integers specifying a new array shape. + @param type New matrix type. + */ + void create(const std::vector& sizes, int type); + + /** @brief Increments the reference counter. + + The method increments the reference counter associated with the matrix data. If the matrix header + points to an external data set (see Mat::Mat ), the reference counter is NULL, and the method has no + effect in this case. Normally, to avoid memory leaks, the method should not be called explicitly. It + is called implicitly by the matrix assignment operator. The reference counter increment is an atomic + operation on the platforms that support it. Thus, it is safe to operate on the same matrices + asynchronously in different threads. + */ + void addref(); + + /** @brief Decrements the reference counter and deallocates the matrix if needed. + + The method decrements the reference counter associated with the matrix data. When the reference + counter reaches 0, the matrix data is deallocated and the data and the reference counter pointers + are set to NULL's. If the matrix header points to an external data set (see Mat::Mat ), the + reference counter is NULL, and the method has no effect in this case. + + This method can be called manually to force the matrix data deallocation. But since this method is + automatically called in the destructor, or by any other method that changes the data pointer, it is + usually not needed. The reference counter decrement and check for 0 is an atomic operation on the + platforms that support it. Thus, it is safe to operate on the same matrices asynchronously in + different threads. + */ + void release(); + + //! internal use function, consider to use 'release' method instead; deallocates the matrix data + void deallocate(); + //! internal use function; properly re-allocates _size, _step arrays + void copySize(const Mat& m); + + /** @brief Reserves space for the certain number of rows. + + The method reserves space for sz rows. If the matrix already has enough space to store sz rows, + nothing happens. If the matrix is reallocated, the first Mat::rows rows are preserved. The method + emulates the corresponding method of the STL vector class. + @param sz Number of rows. + */ + void reserve(size_t sz); + + /** @brief Reserves space for the certain number of bytes. + + The method reserves space for sz bytes. If the matrix already has enough space to store sz bytes, + nothing happens. If matrix has to be reallocated its previous content could be lost. + @param sz Number of bytes. + */ + void reserveBuffer(size_t sz); + + /** @brief Changes the number of matrix rows. + + The methods change the number of matrix rows. If the matrix is reallocated, the first + min(Mat::rows, sz) rows are preserved. The methods emulate the corresponding methods of the STL + vector class. + @param sz New number of rows. + */ + void resize(size_t sz); + + /** @overload + @param sz New number of rows. + @param s Value assigned to the newly added elements. + */ + void resize(size_t sz, const Scalar& s); + + //! internal function + void push_back_(const void* elem); + + /** @brief Adds elements to the bottom of the matrix. + + The methods add one or more elements to the bottom of the matrix. They emulate the corresponding + method of the STL vector class. When elem is Mat , its type and the number of columns must be the + same as in the container matrix. + @param elem Added element(s). + */ + template void push_back(const _Tp& elem); + + /** @overload + @param elem Added element(s). + */ + template void push_back(const Mat_<_Tp>& elem); + + /** @overload + @param elem Added element(s). + */ + template void push_back(const std::vector<_Tp>& elem); + + /** @overload + @param m Added line(s). + */ + void push_back(const Mat& m); + + /** @brief Removes elements from the bottom of the matrix. + + The method removes one or more rows from the bottom of the matrix. + @param nelems Number of removed rows. If it is greater than the total number of rows, an exception + is thrown. + */ + void pop_back(size_t nelems=1); + + /** @brief Locates the matrix header within a parent matrix. + + After you extracted a submatrix from a matrix using Mat::row, Mat::col, Mat::rowRange, + Mat::colRange, and others, the resultant submatrix points just to the part of the original big + matrix. However, each submatrix contains information (represented by datastart and dataend + fields) that helps reconstruct the original matrix size and the position of the extracted + submatrix within the original matrix. The method locateROI does exactly that. + @param wholeSize Output parameter that contains the size of the whole matrix containing *this* + as a part. + @param ofs Output parameter that contains an offset of *this* inside the whole matrix. + */ + void locateROI( Size& wholeSize, Point& ofs ) const; + + /** @brief Adjusts a submatrix size and position within the parent matrix. + + The method is complimentary to Mat::locateROI . The typical use of these functions is to determine + the submatrix position within the parent matrix and then shift the position somehow. Typically, it + can be required for filtering operations when pixels outside of the ROI should be taken into + account. When all the method parameters are positive, the ROI needs to grow in all directions by the + specified amount, for example: + @code + A.adjustROI(2, 2, 2, 2); + @endcode + In this example, the matrix size is increased by 4 elements in each direction. The matrix is shifted + by 2 elements to the left and 2 elements up, which brings in all the necessary pixels for the + filtering with the 5x5 kernel. + + adjustROI forces the adjusted ROI to be inside of the parent matrix that is boundaries of the + adjusted ROI are constrained by boundaries of the parent matrix. For example, if the submatrix A is + located in the first row of a parent matrix and you called A.adjustROI(2, 2, 2, 2) then A will not + be increased in the upward direction. + + The function is used internally by the OpenCV filtering functions, like filter2D , morphological + operations, and so on. + @param dtop Shift of the top submatrix boundary upwards. + @param dbottom Shift of the bottom submatrix boundary downwards. + @param dleft Shift of the left submatrix boundary to the left. + @param dright Shift of the right submatrix boundary to the right. + @sa copyMakeBorder + */ + Mat& adjustROI( int dtop, int dbottom, int dleft, int dright ); + + /** @brief Extracts a rectangular submatrix. + + The operators make a new header for the specified sub-array of \*this . They are the most + generalized forms of Mat::row, Mat::col, Mat::rowRange, and Mat::colRange . For example, + `A(Range(0, 10), Range::all())` is equivalent to `A.rowRange(0, 10)`. Similarly to all of the above, + the operators are O(1) operations, that is, no matrix data is copied. + @param rowRange Start and end row of the extracted submatrix. The upper boundary is not included. To + select all the rows, use Range::all(). + @param colRange Start and end column of the extracted submatrix. The upper boundary is not included. + To select all the columns, use Range::all(). + */ + Mat operator()( Range rowRange, Range colRange ) const; + + /** @overload + @param roi Extracted submatrix specified as a rectangle. + */ + Mat operator()( const Rect& roi ) const; + + /** @overload + @param ranges Array of selected ranges along each array dimension. + */ + Mat operator()( const Range* ranges ) const; + + /** @overload + @param ranges Array of selected ranges along each array dimension. + */ + Mat operator()(const std::vector& ranges) const; + + // //! converts header to CvMat; no data is copied + // operator CvMat() const; + // //! converts header to CvMatND; no data is copied + // operator CvMatND() const; + // //! converts header to IplImage; no data is copied + // operator IplImage() const; + + template operator std::vector<_Tp>() const; + template operator Vec<_Tp, n>() const; + template operator Matx<_Tp, m, n>() const; + +#ifdef CV_CXX_STD_ARRAY + template operator std::array<_Tp, _Nm>() const; +#endif + + /** @brief Reports whether the matrix is continuous or not. + + The method returns true if the matrix elements are stored continuously without gaps at the end of + each row. Otherwise, it returns false. Obviously, 1x1 or 1xN matrices are always continuous. + Matrices created with Mat::create are always continuous. But if you extract a part of the matrix + using Mat::col, Mat::diag, and so on, or constructed a matrix header for externally allocated data, + such matrices may no longer have this property. + + The continuity flag is stored as a bit in the Mat::flags field and is computed automatically when + you construct a matrix header. Thus, the continuity check is a very fast operation, though + theoretically it could be done as follows: + @code + // alternative implementation of Mat::isContinuous() + bool myCheckMatContinuity(const Mat& m) + { + //return (m.flags & Mat::CONTINUOUS_FLAG) != 0; + return m.rows == 1 || m.step == m.cols*m.elemSize(); + } + @endcode + The method is used in quite a few of OpenCV functions. The point is that element-wise operations + (such as arithmetic and logical operations, math functions, alpha blending, color space + transformations, and others) do not depend on the image geometry. Thus, if all the input and output + arrays are continuous, the functions can process them as very long single-row vectors. The example + below illustrates how an alpha-blending function can be implemented: + @code + template + void alphaBlendRGBA(const Mat& src1, const Mat& src2, Mat& dst) + { + const float alpha_scale = (float)std::numeric_limits::max(), + inv_scale = 1.f/alpha_scale; + + CV_Assert( src1.type() == src2.type() && + src1.type() == CV_MAKETYPE(traits::Depth::value, 4) && + src1.size() == src2.size()); + Size size = src1.size(); + dst.create(size, src1.type()); + + // here is the idiom: check the arrays for continuity and, + // if this is the case, + // treat the arrays as 1D vectors + if( src1.isContinuous() && src2.isContinuous() && dst.isContinuous() ) + { + size.width *= size.height; + size.height = 1; + } + size.width *= 4; + + for( int i = 0; i < size.height; i++ ) + { + // when the arrays are continuous, + // the outer loop is executed only once + const T* ptr1 = src1.ptr(i); + const T* ptr2 = src2.ptr(i); + T* dptr = dst.ptr(i); + + for( int j = 0; j < size.width; j += 4 ) + { + float alpha = ptr1[j+3]*inv_scale, beta = ptr2[j+3]*inv_scale; + dptr[j] = saturate_cast(ptr1[j]*alpha + ptr2[j]*beta); + dptr[j+1] = saturate_cast(ptr1[j+1]*alpha + ptr2[j+1]*beta); + dptr[j+2] = saturate_cast(ptr1[j+2]*alpha + ptr2[j+2]*beta); + dptr[j+3] = saturate_cast((1 - (1-alpha)*(1-beta))*alpha_scale); + } + } + } + @endcode + This approach, while being very simple, can boost the performance of a simple element-operation by + 10-20 percents, especially if the image is rather small and the operation is quite simple. + + Another OpenCV idiom in this function, a call of Mat::create for the destination array, that + allocates the destination array unless it already has the proper size and type. And while the newly + allocated arrays are always continuous, you still need to check the destination array because + Mat::create does not always allocate a new matrix. + */ + bool isContinuous() const; + + //! returns true if the matrix is a submatrix of another matrix + bool isSubmatrix() const; + + /** @brief Returns the matrix element size in bytes. + + The method returns the matrix element size in bytes. For example, if the matrix type is CV_16SC3 , + the method returns 3\*sizeof(short) or 6. + */ + size_t elemSize() const; + + /** @brief Returns the size of each matrix element channel in bytes. + + The method returns the matrix element channel size in bytes, that is, it ignores the number of + channels. For example, if the matrix type is CV_16SC3 , the method returns sizeof(short) or 2. + */ + size_t elemSize1() const; + + /** @brief Returns the type of a matrix element. + + The method returns a matrix element type. This is an identifier compatible with the CvMat type + system, like CV_16SC3 or 16-bit signed 3-channel array, and so on. + */ + int type() const; + + /** @brief Returns the depth of a matrix element. + + The method returns the identifier of the matrix element depth (the type of each individual channel). + For example, for a 16-bit signed element array, the method returns CV_16S . A complete list of + matrix types contains the following values: + - CV_8U - 8-bit unsigned integers ( 0..255 ) + - CV_8S - 8-bit signed integers ( -128..127 ) + - CV_16U - 16-bit unsigned integers ( 0..65535 ) + - CV_16S - 16-bit signed integers ( -32768..32767 ) + - CV_32S - 32-bit signed integers ( -2147483648..2147483647 ) + - CV_32F - 32-bit floating-point numbers ( -FLT_MAX..FLT_MAX, INF, NAN ) + - CV_64F - 64-bit floating-point numbers ( -DBL_MAX..DBL_MAX, INF, NAN ) + */ + int depth() const; + + /** @brief Returns the number of matrix channels. + + The method returns the number of matrix channels. + */ + int channels() const; + + /** @brief Returns a normalized step. + + The method returns a matrix step divided by Mat::elemSize1() . It can be useful to quickly access an + arbitrary matrix element. + */ + size_t step1(int i=0) const; + + /** @brief Returns true if the array has no elements. + + The method returns true if Mat::total() is 0 or if Mat::data is NULL. Because of pop_back() and + resize() methods `M.total() == 0` does not imply that `M.data == NULL`. + */ + bool empty() const; + + /** @brief Returns the total number of array elements. + + The method returns the number of array elements (a number of pixels if the array represents an + image). + */ + size_t total() const; + + /** @brief Returns the total number of array elements. + + The method returns the number of elements within a certain sub-array slice with startDim <= dim < endDim + */ + size_t total(int startDim, int endDim=INT_MAX) const; + + /** + * @param elemChannels Number of channels or number of columns the matrix should have. + * For a 2-D matrix, when the matrix has only 1 column, then it should have + * elemChannels channels; When the matrix has only 1 channel, + * then it should have elemChannels columns. + * For a 3-D matrix, it should have only one channel. Furthermore, + * if the number of planes is not one, then the number of rows + * within every plane has to be 1; if the number of rows within + * every plane is not 1, then the number of planes has to be 1. + * @param depth The depth the matrix should have. Set it to -1 when any depth is fine. + * @param requireContinuous Set it to true to require the matrix to be continuous + * @return -1 if the requirement is not satisfied. + * Otherwise, it returns the number of elements in the matrix. Note + * that an element may have multiple channels. + * + * The following code demonstrates its usage for a 2-d matrix: + * @snippet snippets/core_mat_checkVector.cpp example-2d + * + * The following code demonstrates its usage for a 3-d matrix: + * @snippet snippets/core_mat_checkVector.cpp example-3d + */ + int checkVector(int elemChannels, int depth=-1, bool requireContinuous=true) const; + + /** @brief Returns a pointer to the specified matrix row. + + The methods return `uchar*` or typed pointer to the specified matrix row. See the sample in + Mat::isContinuous to know how to use these methods. + @param i0 A 0-based row index. + */ + uchar* ptr(int i0=0); + /** @overload */ + const uchar* ptr(int i0=0) const; + + /** @overload + @param row Index along the dimension 0 + @param col Index along the dimension 1 + */ + uchar* ptr(int row, int col); + /** @overload + @param row Index along the dimension 0 + @param col Index along the dimension 1 + */ + const uchar* ptr(int row, int col) const; + + /** @overload */ + uchar* ptr(int i0, int i1, int i2); + /** @overload */ + const uchar* ptr(int i0, int i1, int i2) const; + + /** @overload */ + uchar* ptr(const int* idx); + /** @overload */ + const uchar* ptr(const int* idx) const; + /** @overload */ + template uchar* ptr(const Vec& idx); + /** @overload */ + template const uchar* ptr(const Vec& idx) const; + + /** @overload */ + template _Tp* ptr(int i0=0); + /** @overload */ + template const _Tp* ptr(int i0=0) const; + /** @overload + @param row Index along the dimension 0 + @param col Index along the dimension 1 + */ + template _Tp* ptr(int row, int col); + /** @overload + @param row Index along the dimension 0 + @param col Index along the dimension 1 + */ + template const _Tp* ptr(int row, int col) const; + /** @overload */ + template _Tp* ptr(int i0, int i1, int i2); + /** @overload */ + template const _Tp* ptr(int i0, int i1, int i2) const; + /** @overload */ + template _Tp* ptr(const int* idx); + /** @overload */ + template const _Tp* ptr(const int* idx) const; + /** @overload */ + template _Tp* ptr(const Vec& idx); + /** @overload */ + template const _Tp* ptr(const Vec& idx) const; + + /** @brief Returns a reference to the specified array element. + + The template methods return a reference to the specified array element. For the sake of higher + performance, the index range checks are only performed in the Debug configuration. + + Note that the variants with a single index (i) can be used to access elements of single-row or + single-column 2-dimensional arrays. That is, if, for example, A is a 1 x N floating-point matrix and + B is an M x 1 integer matrix, you can simply write `A.at(k+4)` and `B.at(2*i+1)` + instead of `A.at(0,k+4)` and `B.at(2*i+1,0)`, respectively. + + The example below initializes a Hilbert matrix: + @code + Mat H(100, 100, CV_64F); + for(int i = 0; i < H.rows; i++) + for(int j = 0; j < H.cols; j++) + H.at(i,j)=1./(i+j+1); + @endcode + + Keep in mind that the size identifier used in the at operator cannot be chosen at random. It depends + on the image from which you are trying to retrieve the data. The table below gives a better insight in this: + - If matrix is of type `CV_8U` then use `Mat.at(y,x)`. + - If matrix is of type `CV_8S` then use `Mat.at(y,x)`. + - If matrix is of type `CV_16U` then use `Mat.at(y,x)`. + - If matrix is of type `CV_16S` then use `Mat.at(y,x)`. + - If matrix is of type `CV_32S` then use `Mat.at(y,x)`. + - If matrix is of type `CV_32F` then use `Mat.at(y,x)`. + - If matrix is of type `CV_64F` then use `Mat.at(y,x)`. + + @param i0 Index along the dimension 0 + */ + template _Tp& at(int i0=0); + /** @overload + @param i0 Index along the dimension 0 + */ + template const _Tp& at(int i0=0) const; + /** @overload + @param row Index along the dimension 0 + @param col Index along the dimension 1 + */ + template _Tp& at(int row, int col); + /** @overload + @param row Index along the dimension 0 + @param col Index along the dimension 1 + */ + template const _Tp& at(int row, int col) const; + + /** @overload + @param i0 Index along the dimension 0 + @param i1 Index along the dimension 1 + @param i2 Index along the dimension 2 + */ + template _Tp& at(int i0, int i1, int i2); + /** @overload + @param i0 Index along the dimension 0 + @param i1 Index along the dimension 1 + @param i2 Index along the dimension 2 + */ + template const _Tp& at(int i0, int i1, int i2) const; + + /** @overload + @param idx Array of Mat::dims indices. + */ + template _Tp& at(const int* idx); + /** @overload + @param idx Array of Mat::dims indices. + */ + template const _Tp& at(const int* idx) const; + + /** @overload */ + template _Tp& at(const Vec& idx); + /** @overload */ + template const _Tp& at(const Vec& idx) const; + + /** @overload + special versions for 2D arrays (especially convenient for referencing image pixels) + @param pt Element position specified as Point(j,i) . + */ + template _Tp& at(Point pt); + /** @overload + special versions for 2D arrays (especially convenient for referencing image pixels) + @param pt Element position specified as Point(j,i) . + */ + template const _Tp& at(Point pt) const; + + /** @brief Returns the matrix iterator and sets it to the first matrix element. + + The methods return the matrix read-only or read-write iterators. The use of matrix iterators is very + similar to the use of bi-directional STL iterators. In the example below, the alpha blending + function is rewritten using the matrix iterators: + @code + template + void alphaBlendRGBA(const Mat& src1, const Mat& src2, Mat& dst) + { + typedef Vec VT; + + const float alpha_scale = (float)std::numeric_limits::max(), + inv_scale = 1.f/alpha_scale; + + CV_Assert( src1.type() == src2.type() && + src1.type() == traits::Type::value && + src1.size() == src2.size()); + Size size = src1.size(); + dst.create(size, src1.type()); + + MatConstIterator_ it1 = src1.begin(), it1_end = src1.end(); + MatConstIterator_ it2 = src2.begin(); + MatIterator_ dst_it = dst.begin(); + + for( ; it1 != it1_end; ++it1, ++it2, ++dst_it ) + { + VT pix1 = *it1, pix2 = *it2; + float alpha = pix1[3]*inv_scale, beta = pix2[3]*inv_scale; + *dst_it = VT(saturate_cast(pix1[0]*alpha + pix2[0]*beta), + saturate_cast(pix1[1]*alpha + pix2[1]*beta), + saturate_cast(pix1[2]*alpha + pix2[2]*beta), + saturate_cast((1 - (1-alpha)*(1-beta))*alpha_scale)); + } + } + @endcode + */ + template MatIterator_<_Tp> begin(); + template MatConstIterator_<_Tp> begin() const; + + /** @brief Returns the matrix iterator and sets it to the after-last matrix element. + + The methods return the matrix read-only or read-write iterators, set to the point following the last + matrix element. + */ + template MatIterator_<_Tp> end(); + template MatConstIterator_<_Tp> end() const; + + /** @brief Runs the given functor over all matrix elements in parallel. + + The operation passed as argument has to be a function pointer, a function object or a lambda(C++11). + + Example 1. All of the operations below put 0xFF the first channel of all matrix elements: + @code + Mat image(1920, 1080, CV_8UC3); + typedef cv::Point3_ Pixel; + + // first. raw pointer access. + for (int r = 0; r < image.rows; ++r) { + Pixel* ptr = image.ptr(r, 0); + const Pixel* ptr_end = ptr + image.cols; + for (; ptr != ptr_end; ++ptr) { + ptr->x = 255; + } + } + + // Using MatIterator. (Simple but there are a Iterator's overhead) + for (Pixel &p : cv::Mat_(image)) { + p.x = 255; + } + + // Parallel execution with function object. + struct Operator { + void operator ()(Pixel &pixel, const int * position) { + pixel.x = 255; + } + }; + image.forEach(Operator()); + + // Parallel execution using C++11 lambda. + image.forEach([](Pixel &p, const int * position) -> void { + p.x = 255; + }); + @endcode + Example 2. Using the pixel's position: + @code + // Creating 3D matrix (255 x 255 x 255) typed uint8_t + // and initialize all elements by the value which equals elements position. + // i.e. pixels (x,y,z) = (1,2,3) is (b,g,r) = (1,2,3). + + int sizes[] = { 255, 255, 255 }; + typedef cv::Point3_ Pixel; + + Mat_ image = Mat::zeros(3, sizes, CV_8UC3); + + image.forEach([&](Pixel& pixel, const int position[]) -> void { + pixel.x = position[0]; + pixel.y = position[1]; + pixel.z = position[2]; + }); + @endcode + */ + template void forEach(const Functor& operation); + /** @overload */ + template void forEach(const Functor& operation) const; + +#ifdef CV_CXX_MOVE_SEMANTICS + Mat(Mat&& m); + Mat& operator = (Mat&& m); +#endif + + enum { MAGIC_VAL = 0x42FF0000, AUTO_STEP = 0, CONTINUOUS_FLAG = CV_MAT_CONT_FLAG, SUBMATRIX_FLAG = CV_SUBMAT_FLAG }; + enum { MAGIC_MASK = 0xFFFF0000, TYPE_MASK = 0x00000FFF, DEPTH_MASK = 7 }; + + /*! includes several bit-fields: + - the magic signature + - continuity flag + - depth + - number of channels + */ + int flags; + //! the matrix dimensionality, >= 2 + int dims; + //! the number of rows and columns or (-1, -1) when the matrix has more than 2 dimensions + int rows, cols; + //! pointer to the data + uchar* data; + + //! helper fields used in locateROI and adjustROI + const uchar* datastart; + const uchar* dataend; + const uchar* datalimit; + + //! custom allocator + MatAllocator* allocator; + //! and the standard allocator + static MatAllocator* getStdAllocator(); + static MatAllocator* getDefaultAllocator(); + static void setDefaultAllocator(MatAllocator* allocator); + + //! internal use method: updates the continuity flag + void updateContinuityFlag(); + + //! interaction with UMat + UMatData* u; + + MatSize size; + MatStep step; + +protected: + template void forEach_impl(const Functor& operation); +}; + + +///////////////////////////////// Mat_<_Tp> //////////////////////////////////// + +/** @brief Template matrix class derived from Mat + +@code{.cpp} + template class Mat_ : public Mat + { + public: + // ... some specific methods + // and + // no new extra fields + }; +@endcode +The class `Mat_<_Tp>` is a *thin* template wrapper on top of the Mat class. It does not have any +extra data fields. Nor this class nor Mat has any virtual methods. Thus, references or pointers to +these two classes can be freely but carefully converted one to another. For example: +@code{.cpp} + // create a 100x100 8-bit matrix + Mat M(100,100,CV_8U); + // this will be compiled fine. no any data conversion will be done. + Mat_& M1 = (Mat_&)M; + // the program is likely to crash at the statement below + M1(99,99) = 1.f; +@endcode +While Mat is sufficient in most cases, Mat_ can be more convenient if you use a lot of element +access operations and if you know matrix type at the compilation time. Note that +`Mat::at(int y,int x)` and `Mat_::operator()(int y,int x)` do absolutely the same +and run at the same speed, but the latter is certainly shorter: +@code{.cpp} + Mat_ M(20,20); + for(int i = 0; i < M.rows; i++) + for(int j = 0; j < M.cols; j++) + M(i,j) = 1./(i+j+1); + Mat E, V; + eigen(M,E,V); + cout << E.at(0,0)/E.at(M.rows-1,0); +@endcode +To use Mat_ for multi-channel images/matrices, pass Vec as a Mat_ parameter: +@code{.cpp} + // allocate a 320x240 color image and fill it with green (in RGB space) + Mat_ img(240, 320, Vec3b(0,255,0)); + // now draw a diagonal white line + for(int i = 0; i < 100; i++) + img(i,i)=Vec3b(255,255,255); + // and now scramble the 2nd (red) channel of each pixel + for(int i = 0; i < img.rows; i++) + for(int j = 0; j < img.cols; j++) + img(i,j)[2] ^= (uchar)(i ^ j); +@endcode +Mat_ is fully compatible with C++11 range-based for loop. For example such loop +can be used to safely apply look-up table: +@code{.cpp} +void applyTable(Mat_& I, const uchar* const table) +{ + for(auto& pixel : I) + { + pixel = table[pixel]; + } +} +@endcode + */ +template class Mat_ : public Mat +{ +public: + typedef _Tp value_type; + typedef typename DataType<_Tp>::channel_type channel_type; + typedef MatIterator_<_Tp> iterator; + typedef MatConstIterator_<_Tp> const_iterator; + + //! default constructor + Mat_(); + //! equivalent to Mat(_rows, _cols, DataType<_Tp>::type) + Mat_(int _rows, int _cols); + //! constructor that sets each matrix element to specified value + Mat_(int _rows, int _cols, const _Tp& value); + //! equivalent to Mat(_size, DataType<_Tp>::type) + explicit Mat_(Size _size); + //! constructor that sets each matrix element to specified value + Mat_(Size _size, const _Tp& value); + //! n-dim array constructor + Mat_(int _ndims, const int* _sizes); + //! n-dim array constructor that sets each matrix element to specified value + Mat_(int _ndims, const int* _sizes, const _Tp& value); + //! copy/conversion constructor. If m is of different type, it's converted + Mat_(const Mat& m); + //! copy constructor + Mat_(const Mat_& m); + //! constructs a matrix on top of user-allocated data. step is in bytes(!!!), regardless of the type + Mat_(int _rows, int _cols, _Tp* _data, size_t _step=AUTO_STEP); + //! constructs n-dim matrix on top of user-allocated data. steps are in bytes(!!!), regardless of the type + Mat_(int _ndims, const int* _sizes, _Tp* _data, const size_t* _steps=0); + //! selects a submatrix + Mat_(const Mat_& m, const Range& rowRange, const Range& colRange=Range::all()); + //! selects a submatrix + Mat_(const Mat_& m, const Rect& roi); + //! selects a submatrix, n-dim version + Mat_(const Mat_& m, const Range* ranges); + //! selects a submatrix, n-dim version + Mat_(const Mat_& m, const std::vector& ranges); + //! from a matrix expression + explicit Mat_(const MatExpr& e); + //! makes a matrix out of Vec, std::vector, Point_ or Point3_. The matrix will have a single column + explicit Mat_(const std::vector<_Tp>& vec, bool copyData=false); + template explicit Mat_(const Vec::channel_type, n>& vec, bool copyData=true); + template explicit Mat_(const Matx::channel_type, m, n>& mtx, bool copyData=true); + explicit Mat_(const Point_::channel_type>& pt, bool copyData=true); + explicit Mat_(const Point3_::channel_type>& pt, bool copyData=true); + explicit Mat_(const MatCommaInitializer_<_Tp>& commaInitializer); + +#ifdef CV_CXX11 + Mat_(std::initializer_list<_Tp> values); + explicit Mat_(const std::initializer_list sizes, const std::initializer_list<_Tp> values); +#endif + +#ifdef CV_CXX_STD_ARRAY + template explicit Mat_(const std::array<_Tp, _Nm>& arr, bool copyData=false); +#endif + + Mat_& operator = (const Mat& m); + Mat_& operator = (const Mat_& m); + //! set all the elements to s. + Mat_& operator = (const _Tp& s); + //! assign a matrix expression + Mat_& operator = (const MatExpr& e); + + //! iterators; they are smart enough to skip gaps in the end of rows + iterator begin(); + iterator end(); + const_iterator begin() const; + const_iterator end() const; + + //! template methods for for operation over all matrix elements. + // the operations take care of skipping gaps in the end of rows (if any) + template void forEach(const Functor& operation); + template void forEach(const Functor& operation) const; + + //! equivalent to Mat::create(_rows, _cols, DataType<_Tp>::type) + void create(int _rows, int _cols); + //! equivalent to Mat::create(_size, DataType<_Tp>::type) + void create(Size _size); + //! equivalent to Mat::create(_ndims, _sizes, DatType<_Tp>::type) + void create(int _ndims, const int* _sizes); + //! equivalent to Mat::release() + void release(); + //! cross-product + Mat_ cross(const Mat_& m) const; + //! data type conversion + template operator Mat_() const; + //! overridden forms of Mat::row() etc. + Mat_ row(int y) const; + Mat_ col(int x) const; + Mat_ diag(int d=0) const; + Mat_ clone() const CV_NODISCARD; + + //! overridden forms of Mat::elemSize() etc. + size_t elemSize() const; + size_t elemSize1() const; + int type() const; + int depth() const; + int channels() const; + size_t step1(int i=0) const; + //! returns step()/sizeof(_Tp) + size_t stepT(int i=0) const; + + //! overridden forms of Mat::zeros() etc. Data type is omitted, of course + static MatExpr zeros(int rows, int cols); + static MatExpr zeros(Size size); + static MatExpr zeros(int _ndims, const int* _sizes); + static MatExpr ones(int rows, int cols); + static MatExpr ones(Size size); + static MatExpr ones(int _ndims, const int* _sizes); + static MatExpr eye(int rows, int cols); + static MatExpr eye(Size size); + + //! some more overridden methods + Mat_& adjustROI( int dtop, int dbottom, int dleft, int dright ); + Mat_ operator()( const Range& rowRange, const Range& colRange ) const; + Mat_ operator()( const Rect& roi ) const; + Mat_ operator()( const Range* ranges ) const; + Mat_ operator()(const std::vector& ranges) const; + + //! more convenient forms of row and element access operators + _Tp* operator [](int y); + const _Tp* operator [](int y) const; + + //! returns reference to the specified element + _Tp& operator ()(const int* idx); + //! returns read-only reference to the specified element + const _Tp& operator ()(const int* idx) const; + + //! returns reference to the specified element + template _Tp& operator ()(const Vec& idx); + //! returns read-only reference to the specified element + template const _Tp& operator ()(const Vec& idx) const; + + //! returns reference to the specified element (1D case) + _Tp& operator ()(int idx0); + //! returns read-only reference to the specified element (1D case) + const _Tp& operator ()(int idx0) const; + //! returns reference to the specified element (2D case) + _Tp& operator ()(int row, int col); + //! returns read-only reference to the specified element (2D case) + const _Tp& operator ()(int row, int col) const; + //! returns reference to the specified element (3D case) + _Tp& operator ()(int idx0, int idx1, int idx2); + //! returns read-only reference to the specified element (3D case) + const _Tp& operator ()(int idx0, int idx1, int idx2) const; + + _Tp& operator ()(Point pt); + const _Tp& operator ()(Point pt) const; + + //! conversion to vector. + operator std::vector<_Tp>() const; + +#ifdef CV_CXX_STD_ARRAY + //! conversion to array. + template operator std::array<_Tp, _Nm>() const; +#endif + + //! conversion to Vec + template operator Vec::channel_type, n>() const; + //! conversion to Matx + template operator Matx::channel_type, m, n>() const; + +#ifdef CV_CXX_MOVE_SEMANTICS + Mat_(Mat_&& m); + Mat_& operator = (Mat_&& m); + + Mat_(Mat&& m); + Mat_& operator = (Mat&& m); + + Mat_(MatExpr&& e); +#endif +}; + +typedef Mat_ Mat1b; +typedef Mat_ Mat2b; +typedef Mat_ Mat3b; +typedef Mat_ Mat4b; + +typedef Mat_ Mat1s; +typedef Mat_ Mat2s; +typedef Mat_ Mat3s; +typedef Mat_ Mat4s; + +typedef Mat_ Mat1w; +typedef Mat_ Mat2w; +typedef Mat_ Mat3w; +typedef Mat_ Mat4w; + +typedef Mat_ Mat1i; +typedef Mat_ Mat2i; +typedef Mat_ Mat3i; +typedef Mat_ Mat4i; + +typedef Mat_ Mat1f; +typedef Mat_ Mat2f; +typedef Mat_ Mat3f; +typedef Mat_ Mat4f; + +typedef Mat_ Mat1d; +typedef Mat_ Mat2d; +typedef Mat_ Mat3d; +typedef Mat_ Mat4d; + +/** @todo document */ +class CV_EXPORTS UMat +{ +public: + //! default constructor + UMat(UMatUsageFlags usageFlags = USAGE_DEFAULT); + //! constructs 2D matrix of the specified size and type + // (_type is CV_8UC1, CV_64FC3, CV_32SC(12) etc.) + UMat(int rows, int cols, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT); + UMat(Size size, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT); + //! constucts 2D matrix and fills it with the specified value _s. + UMat(int rows, int cols, int type, const Scalar& s, UMatUsageFlags usageFlags = USAGE_DEFAULT); + UMat(Size size, int type, const Scalar& s, UMatUsageFlags usageFlags = USAGE_DEFAULT); + + //! constructs n-dimensional matrix + UMat(int ndims, const int* sizes, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT); + UMat(int ndims, const int* sizes, int type, const Scalar& s, UMatUsageFlags usageFlags = USAGE_DEFAULT); + + //! copy constructor + UMat(const UMat& m); + + //! creates a matrix header for a part of the bigger matrix + UMat(const UMat& m, const Range& rowRange, const Range& colRange=Range::all()); + UMat(const UMat& m, const Rect& roi); + UMat(const UMat& m, const Range* ranges); + UMat(const UMat& m, const std::vector& ranges); + //! builds matrix from std::vector with or without copying the data + template explicit UMat(const std::vector<_Tp>& vec, bool copyData=false); + + //! builds matrix from cv::Vec; the data is copied by default + template explicit UMat(const Vec<_Tp, n>& vec, bool copyData=true); + //! builds matrix from cv::Matx; the data is copied by default + template explicit UMat(const Matx<_Tp, m, n>& mtx, bool copyData=true); + //! builds matrix from a 2D point + template explicit UMat(const Point_<_Tp>& pt, bool copyData=true); + //! builds matrix from a 3D point + template explicit UMat(const Point3_<_Tp>& pt, bool copyData=true); + //! builds matrix from comma initializer + template explicit UMat(const MatCommaInitializer_<_Tp>& commaInitializer); + + //! destructor - calls release() + ~UMat(); + //! assignment operators + UMat& operator = (const UMat& m); + + Mat getMat(int flags) const; + + //! returns a new matrix header for the specified row + UMat row(int y) const; + //! returns a new matrix header for the specified column + UMat col(int x) const; + //! ... for the specified row span + UMat rowRange(int startrow, int endrow) const; + UMat rowRange(const Range& r) const; + //! ... for the specified column span + UMat colRange(int startcol, int endcol) const; + UMat colRange(const Range& r) const; + //! ... for the specified diagonal + //! (d=0 - the main diagonal, + //! >0 - a diagonal from the upper half, + //! <0 - a diagonal from the lower half) + UMat diag(int d=0) const; + //! constructs a square diagonal matrix which main diagonal is vector "d" + static UMat diag(const UMat& d); + + //! returns deep copy of the matrix, i.e. the data is copied + UMat clone() const CV_NODISCARD; + //! copies the matrix content to "m". + // It calls m.create(this->size(), this->type()). + void copyTo( OutputArray m ) const; + //! copies those matrix elements to "m" that are marked with non-zero mask elements. + void copyTo( OutputArray m, InputArray mask ) const; + //! converts matrix to another datatype with optional scaling. See cvConvertScale. + void convertTo( OutputArray m, int rtype, double alpha=1, double beta=0 ) const; + + void assignTo( UMat& m, int type=-1 ) const; + + //! sets every matrix element to s + UMat& operator = (const Scalar& s); + //! sets some of the matrix elements to s, according to the mask + UMat& setTo(InputArray value, InputArray mask=noArray()); + //! creates alternative matrix header for the same data, with different + // number of channels and/or different number of rows. see cvReshape. + UMat reshape(int cn, int rows=0) const; + UMat reshape(int cn, int newndims, const int* newsz) const; + + //! matrix transposition by means of matrix expressions + UMat t() const; + //! matrix inversion by means of matrix expressions + UMat inv(int method=DECOMP_LU) const; + //! per-element matrix multiplication by means of matrix expressions + UMat mul(InputArray m, double scale=1) const; + + //! computes dot-product + double dot(InputArray m) const; + + //! Matlab-style matrix initialization + static UMat zeros(int rows, int cols, int type); + static UMat zeros(Size size, int type); + static UMat zeros(int ndims, const int* sz, int type); + static UMat ones(int rows, int cols, int type); + static UMat ones(Size size, int type); + static UMat ones(int ndims, const int* sz, int type); + static UMat eye(int rows, int cols, int type); + static UMat eye(Size size, int type); + + //! allocates new matrix data unless the matrix already has specified size and type. + // previous data is unreferenced if needed. + void create(int rows, int cols, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT); + void create(Size size, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT); + void create(int ndims, const int* sizes, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT); + void create(const std::vector& sizes, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT); + + //! increases the reference counter; use with care to avoid memleaks + void addref(); + //! decreases reference counter; + // deallocates the data when reference counter reaches 0. + void release(); + + //! deallocates the matrix data + void deallocate(); + //! internal use function; properly re-allocates _size, _step arrays + void copySize(const UMat& m); + + //! locates matrix header within a parent matrix. See below + void locateROI( Size& wholeSize, Point& ofs ) const; + //! moves/resizes the current matrix ROI inside the parent matrix. + UMat& adjustROI( int dtop, int dbottom, int dleft, int dright ); + //! extracts a rectangular sub-matrix + // (this is a generalized form of row, rowRange etc.) + UMat operator()( Range rowRange, Range colRange ) const; + UMat operator()( const Rect& roi ) const; + UMat operator()( const Range* ranges ) const; + UMat operator()(const std::vector& ranges) const; + + //! returns true iff the matrix data is continuous + // (i.e. when there are no gaps between successive rows). + // similar to CV_IS_MAT_CONT(cvmat->type) + bool isContinuous() const; + + //! returns true if the matrix is a submatrix of another matrix + bool isSubmatrix() const; + + //! returns element size in bytes, + // similar to CV_ELEM_SIZE(cvmat->type) + size_t elemSize() const; + //! returns the size of element channel in bytes. + size_t elemSize1() const; + //! returns element type, similar to CV_MAT_TYPE(cvmat->type) + int type() const; + //! returns element type, similar to CV_MAT_DEPTH(cvmat->type) + int depth() const; + //! returns element type, similar to CV_MAT_CN(cvmat->type) + int channels() const; + //! returns step/elemSize1() + size_t step1(int i=0) const; + //! returns true if matrix data is NULL + bool empty() const; + //! returns the total number of matrix elements + size_t total() const; + + //! returns N if the matrix is 1-channel (N x ptdim) or ptdim-channel (1 x N) or (N x 1); negative number otherwise + int checkVector(int elemChannels, int depth=-1, bool requireContinuous=true) const; + +#ifdef CV_CXX_MOVE_SEMANTICS + UMat(UMat&& m); + UMat& operator = (UMat&& m); +#endif + + /*! Returns the OpenCL buffer handle on which UMat operates on. + The UMat instance should be kept alive during the use of the handle to prevent the buffer to be + returned to the OpenCV buffer pool. + */ + void* handle(int accessFlags) const; + void ndoffset(size_t* ofs) const; + + enum { MAGIC_VAL = 0x42FF0000, AUTO_STEP = 0, CONTINUOUS_FLAG = CV_MAT_CONT_FLAG, SUBMATRIX_FLAG = CV_SUBMAT_FLAG }; + enum { MAGIC_MASK = 0xFFFF0000, TYPE_MASK = 0x00000FFF, DEPTH_MASK = 7 }; + + /*! includes several bit-fields: + - the magic signature + - continuity flag + - depth + - number of channels + */ + int flags; + //! the matrix dimensionality, >= 2 + int dims; + //! the number of rows and columns or (-1, -1) when the matrix has more than 2 dimensions + int rows, cols; + + //! custom allocator + MatAllocator* allocator; + UMatUsageFlags usageFlags; // usage flags for allocator + //! and the standard allocator + static MatAllocator* getStdAllocator(); + + //! internal use method: updates the continuity flag + void updateContinuityFlag(); + + // black-box container of UMat data + UMatData* u; + + // offset of the submatrix (or 0) + size_t offset; + + MatSize size; + MatStep step; + +protected: +}; + + +/////////////////////////// multi-dimensional sparse matrix ////////////////////////// + +/** @brief The class SparseMat represents multi-dimensional sparse numerical arrays. + +Such a sparse array can store elements of any type that Mat can store. *Sparse* means that only +non-zero elements are stored (though, as a result of operations on a sparse matrix, some of its +stored elements can actually become 0. It is up to you to detect such elements and delete them +using SparseMat::erase ). The non-zero elements are stored in a hash table that grows when it is +filled so that the search time is O(1) in average (regardless of whether element is there or not). +Elements can be accessed using the following methods: +- Query operations (SparseMat::ptr and the higher-level SparseMat::ref, SparseMat::value and + SparseMat::find), for example: + @code + const int dims = 5; + int size[5] = {10, 10, 10, 10, 10}; + SparseMat sparse_mat(dims, size, CV_32F); + for(int i = 0; i < 1000; i++) + { + int idx[dims]; + for(int k = 0; k < dims; k++) + idx[k] = rand() % size[k]; + sparse_mat.ref(idx) += 1.f; + } + cout << "nnz = " << sparse_mat.nzcount() << endl; + @endcode +- Sparse matrix iterators. They are similar to MatIterator but different from NAryMatIterator. + That is, the iteration loop is familiar to STL users: + @code + // prints elements of a sparse floating-point matrix + // and the sum of elements. + SparseMatConstIterator_ + it = sparse_mat.begin(), + it_end = sparse_mat.end(); + double s = 0; + int dims = sparse_mat.dims(); + for(; it != it_end; ++it) + { + // print element indices and the element value + const SparseMat::Node* n = it.node(); + printf("("); + for(int i = 0; i < dims; i++) + printf("%d%s", n->idx[i], i < dims-1 ? ", " : ")"); + printf(": %g\n", it.value()); + s += *it; + } + printf("Element sum is %g\n", s); + @endcode + If you run this loop, you will notice that elements are not enumerated in a logical order + (lexicographical, and so on). They come in the same order as they are stored in the hash table + (semi-randomly). You may collect pointers to the nodes and sort them to get the proper ordering. + Note, however, that pointers to the nodes may become invalid when you add more elements to the + matrix. This may happen due to possible buffer reallocation. +- Combination of the above 2 methods when you need to process 2 or more sparse matrices + simultaneously. For example, this is how you can compute unnormalized cross-correlation of the 2 + floating-point sparse matrices: + @code + double cross_corr(const SparseMat& a, const SparseMat& b) + { + const SparseMat *_a = &a, *_b = &b; + // if b contains less elements than a, + // it is faster to iterate through b + if(_a->nzcount() > _b->nzcount()) + std::swap(_a, _b); + SparseMatConstIterator_ it = _a->begin(), + it_end = _a->end(); + double ccorr = 0; + for(; it != it_end; ++it) + { + // take the next element from the first matrix + float avalue = *it; + const Node* anode = it.node(); + // and try to find an element with the same index in the second matrix. + // since the hash value depends only on the element index, + // reuse the hash value stored in the node + float bvalue = _b->value(anode->idx,&anode->hashval); + ccorr += avalue*bvalue; + } + return ccorr; + } + @endcode + */ +class CV_EXPORTS SparseMat +{ +public: + typedef SparseMatIterator iterator; + typedef SparseMatConstIterator const_iterator; + + enum { MAGIC_VAL=0x42FD0000, MAX_DIM=32, HASH_SCALE=0x5bd1e995, HASH_BIT=0x80000000 }; + + //! the sparse matrix header + struct CV_EXPORTS Hdr + { + Hdr(int _dims, const int* _sizes, int _type); + void clear(); + int refcount; + int dims; + int valueOffset; + size_t nodeSize; + size_t nodeCount; + size_t freeList; + std::vector pool; + std::vector hashtab; + int size[MAX_DIM]; + }; + + //! sparse matrix node - element of a hash table + struct CV_EXPORTS Node + { + //! hash value + size_t hashval; + //! index of the next node in the same hash table entry + size_t next; + //! index of the matrix element + int idx[MAX_DIM]; + }; + + /** @brief Various SparseMat constructors. + */ + SparseMat(); + + /** @overload + @param dims Array dimensionality. + @param _sizes Sparce matrix size on all dementions. + @param _type Sparse matrix data type. + */ + SparseMat(int dims, const int* _sizes, int _type); + + /** @overload + @param m Source matrix for copy constructor. If m is dense matrix (ocvMat) then it will be converted + to sparse representation. + */ + SparseMat(const SparseMat& m); + + /** @overload + @param m Source matrix for copy constructor. If m is dense matrix (ocvMat) then it will be converted + to sparse representation. + */ + explicit SparseMat(const Mat& m); + + //! the destructor + ~SparseMat(); + + //! assignment operator. This is O(1) operation, i.e. no data is copied + SparseMat& operator = (const SparseMat& m); + //! equivalent to the corresponding constructor + SparseMat& operator = (const Mat& m); + + //! creates full copy of the matrix + SparseMat clone() const CV_NODISCARD; + + //! copies all the data to the destination matrix. All the previous content of m is erased + void copyTo( SparseMat& m ) const; + //! converts sparse matrix to dense matrix. + void copyTo( Mat& m ) const; + //! multiplies all the matrix elements by the specified scale factor alpha and converts the results to the specified data type + void convertTo( SparseMat& m, int rtype, double alpha=1 ) const; + //! converts sparse matrix to dense n-dim matrix with optional type conversion and scaling. + /*! + @param [out] m - output matrix; if it does not have a proper size or type before the operation, + it is reallocated + @param [in] rtype - desired output matrix type or, rather, the depth since the number of channels + are the same as the input has; if rtype is negative, the output matrix will have the + same type as the input. + @param [in] alpha - optional scale factor + @param [in] beta - optional delta added to the scaled values + */ + void convertTo( Mat& m, int rtype, double alpha=1, double beta=0 ) const; + + // not used now + void assignTo( SparseMat& m, int type=-1 ) const; + + //! reallocates sparse matrix. + /*! + If the matrix already had the proper size and type, + it is simply cleared with clear(), otherwise, + the old matrix is released (using release()) and the new one is allocated. + */ + void create(int dims, const int* _sizes, int _type); + //! sets all the sparse matrix elements to 0, which means clearing the hash table. + void clear(); + //! manually increments the reference counter to the header. + void addref(); + // decrements the header reference counter. When the counter reaches 0, the header and all the underlying data are deallocated. + void release(); + + //! converts sparse matrix to the old-style representation; all the elements are copied. + //operator CvSparseMat*() const; + //! returns the size of each element in bytes (not including the overhead - the space occupied by SparseMat::Node elements) + size_t elemSize() const; + //! returns elemSize()/channels() + size_t elemSize1() const; + + //! returns type of sparse matrix elements + int type() const; + //! returns the depth of sparse matrix elements + int depth() const; + //! returns the number of channels + int channels() const; + + //! returns the array of sizes, or NULL if the matrix is not allocated + const int* size() const; + //! returns the size of i-th matrix dimension (or 0) + int size(int i) const; + //! returns the matrix dimensionality + int dims() const; + //! returns the number of non-zero elements (=the number of hash table nodes) + size_t nzcount() const; + + //! computes the element hash value (1D case) + size_t hash(int i0) const; + //! computes the element hash value (2D case) + size_t hash(int i0, int i1) const; + //! computes the element hash value (3D case) + size_t hash(int i0, int i1, int i2) const; + //! computes the element hash value (nD case) + size_t hash(const int* idx) const; + + //!@{ + /*! + specialized variants for 1D, 2D, 3D cases and the generic_type one for n-D case. + return pointer to the matrix element. + - if the element is there (it's non-zero), the pointer to it is returned + - if it's not there and createMissing=false, NULL pointer is returned + - if it's not there and createMissing=true, then the new element + is created and initialized with 0. Pointer to it is returned + - if the optional hashval pointer is not NULL, the element hash value is + not computed, but *hashval is taken instead. + */ + //! returns pointer to the specified element (1D case) + uchar* ptr(int i0, bool createMissing, size_t* hashval=0); + //! returns pointer to the specified element (2D case) + uchar* ptr(int i0, int i1, bool createMissing, size_t* hashval=0); + //! returns pointer to the specified element (3D case) + uchar* ptr(int i0, int i1, int i2, bool createMissing, size_t* hashval=0); + //! returns pointer to the specified element (nD case) + uchar* ptr(const int* idx, bool createMissing, size_t* hashval=0); + //!@} + + //!@{ + /*! + return read-write reference to the specified sparse matrix element. + + `ref<_Tp>(i0,...[,hashval])` is equivalent to `*(_Tp*)ptr(i0,...,true[,hashval])`. + The methods always return a valid reference. + If the element did not exist, it is created and initialiazed with 0. + */ + //! returns reference to the specified element (1D case) + template _Tp& ref(int i0, size_t* hashval=0); + //! returns reference to the specified element (2D case) + template _Tp& ref(int i0, int i1, size_t* hashval=0); + //! returns reference to the specified element (3D case) + template _Tp& ref(int i0, int i1, int i2, size_t* hashval=0); + //! returns reference to the specified element (nD case) + template _Tp& ref(const int* idx, size_t* hashval=0); + //!@} + + //!@{ + /*! + return value of the specified sparse matrix element. + + `value<_Tp>(i0,...[,hashval])` is equivalent to + @code + { const _Tp* p = find<_Tp>(i0,...[,hashval]); return p ? *p : _Tp(); } + @endcode + + That is, if the element did not exist, the methods return 0. + */ + //! returns value of the specified element (1D case) + template _Tp value(int i0, size_t* hashval=0) const; + //! returns value of the specified element (2D case) + template _Tp value(int i0, int i1, size_t* hashval=0) const; + //! returns value of the specified element (3D case) + template _Tp value(int i0, int i1, int i2, size_t* hashval=0) const; + //! returns value of the specified element (nD case) + template _Tp value(const int* idx, size_t* hashval=0) const; + //!@} + + //!@{ + /*! + Return pointer to the specified sparse matrix element if it exists + + `find<_Tp>(i0,...[,hashval])` is equivalent to `(_const Tp*)ptr(i0,...false[,hashval])`. + + If the specified element does not exist, the methods return NULL. + */ + //! returns pointer to the specified element (1D case) + template const _Tp* find(int i0, size_t* hashval=0) const; + //! returns pointer to the specified element (2D case) + template const _Tp* find(int i0, int i1, size_t* hashval=0) const; + //! returns pointer to the specified element (3D case) + template const _Tp* find(int i0, int i1, int i2, size_t* hashval=0) const; + //! returns pointer to the specified element (nD case) + template const _Tp* find(const int* idx, size_t* hashval=0) const; + //!@} + + //! erases the specified element (2D case) + void erase(int i0, int i1, size_t* hashval=0); + //! erases the specified element (3D case) + void erase(int i0, int i1, int i2, size_t* hashval=0); + //! erases the specified element (nD case) + void erase(const int* idx, size_t* hashval=0); + + //!@{ + /*! + return the sparse matrix iterator pointing to the first sparse matrix element + */ + //! returns the sparse matrix iterator at the matrix beginning + SparseMatIterator begin(); + //! returns the sparse matrix iterator at the matrix beginning + template SparseMatIterator_<_Tp> begin(); + //! returns the read-only sparse matrix iterator at the matrix beginning + SparseMatConstIterator begin() const; + //! returns the read-only sparse matrix iterator at the matrix beginning + template SparseMatConstIterator_<_Tp> begin() const; + //!@} + /*! + return the sparse matrix iterator pointing to the element following the last sparse matrix element + */ + //! returns the sparse matrix iterator at the matrix end + SparseMatIterator end(); + //! returns the read-only sparse matrix iterator at the matrix end + SparseMatConstIterator end() const; + //! returns the typed sparse matrix iterator at the matrix end + template SparseMatIterator_<_Tp> end(); + //! returns the typed read-only sparse matrix iterator at the matrix end + template SparseMatConstIterator_<_Tp> end() const; + + //! returns the value stored in the sparse martix node + template _Tp& value(Node* n); + //! returns the value stored in the sparse martix node + template const _Tp& value(const Node* n) const; + + ////////////// some internal-use methods /////////////// + Node* node(size_t nidx); + const Node* node(size_t nidx) const; + + uchar* newNode(const int* idx, size_t hashval); + void removeNode(size_t hidx, size_t nidx, size_t previdx); + void resizeHashTab(size_t newsize); + + int flags; + Hdr* hdr; +}; + + + +///////////////////////////////// SparseMat_<_Tp> //////////////////////////////////// + +/** @brief Template sparse n-dimensional array class derived from SparseMat + +SparseMat_ is a thin wrapper on top of SparseMat created in the same way as Mat_ . It simplifies +notation of some operations: +@code + int sz[] = {10, 20, 30}; + SparseMat_ M(3, sz); + ... + M.ref(1, 2, 3) = M(4, 5, 6) + M(7, 8, 9); +@endcode + */ +template class SparseMat_ : public SparseMat +{ +public: + typedef SparseMatIterator_<_Tp> iterator; + typedef SparseMatConstIterator_<_Tp> const_iterator; + + //! the default constructor + SparseMat_(); + //! the full constructor equivalent to SparseMat(dims, _sizes, DataType<_Tp>::type) + SparseMat_(int dims, const int* _sizes); + //! the copy constructor. If DataType<_Tp>.type != m.type(), the m elements are converted + SparseMat_(const SparseMat& m); + //! the copy constructor. This is O(1) operation - no data is copied + SparseMat_(const SparseMat_& m); + //! converts dense matrix to the sparse form + SparseMat_(const Mat& m); + //! converts the old-style sparse matrix to the C++ class. All the elements are copied + //SparseMat_(const CvSparseMat* m); + //! the assignment operator. If DataType<_Tp>.type != m.type(), the m elements are converted + SparseMat_& operator = (const SparseMat& m); + //! the assignment operator. This is O(1) operation - no data is copied + SparseMat_& operator = (const SparseMat_& m); + //! converts dense matrix to the sparse form + SparseMat_& operator = (const Mat& m); + + //! makes full copy of the matrix. All the elements are duplicated + SparseMat_ clone() const CV_NODISCARD; + //! equivalent to cv::SparseMat::create(dims, _sizes, DataType<_Tp>::type) + void create(int dims, const int* _sizes); + //! converts sparse matrix to the old-style CvSparseMat. All the elements are copied + //operator CvSparseMat*() const; + + //! returns type of the matrix elements + int type() const; + //! returns depth of the matrix elements + int depth() const; + //! returns the number of channels in each matrix element + int channels() const; + + //! equivalent to SparseMat::ref<_Tp>(i0, hashval) + _Tp& ref(int i0, size_t* hashval=0); + //! equivalent to SparseMat::ref<_Tp>(i0, i1, hashval) + _Tp& ref(int i0, int i1, size_t* hashval=0); + //! equivalent to SparseMat::ref<_Tp>(i0, i1, i2, hashval) + _Tp& ref(int i0, int i1, int i2, size_t* hashval=0); + //! equivalent to SparseMat::ref<_Tp>(idx, hashval) + _Tp& ref(const int* idx, size_t* hashval=0); + + //! equivalent to SparseMat::value<_Tp>(i0, hashval) + _Tp operator()(int i0, size_t* hashval=0) const; + //! equivalent to SparseMat::value<_Tp>(i0, i1, hashval) + _Tp operator()(int i0, int i1, size_t* hashval=0) const; + //! equivalent to SparseMat::value<_Tp>(i0, i1, i2, hashval) + _Tp operator()(int i0, int i1, int i2, size_t* hashval=0) const; + //! equivalent to SparseMat::value<_Tp>(idx, hashval) + _Tp operator()(const int* idx, size_t* hashval=0) const; + + //! returns sparse matrix iterator pointing to the first sparse matrix element + SparseMatIterator_<_Tp> begin(); + //! returns read-only sparse matrix iterator pointing to the first sparse matrix element + SparseMatConstIterator_<_Tp> begin() const; + //! returns sparse matrix iterator pointing to the element following the last sparse matrix element + SparseMatIterator_<_Tp> end(); + //! returns read-only sparse matrix iterator pointing to the element following the last sparse matrix element + SparseMatConstIterator_<_Tp> end() const; +}; + + + +////////////////////////////////// MatConstIterator ////////////////////////////////// + +class CV_EXPORTS MatConstIterator +{ +public: + typedef uchar* value_type; + typedef ptrdiff_t difference_type; + typedef const uchar** pointer; + typedef uchar* reference; + + typedef std::random_access_iterator_tag iterator_category; + + //! default constructor + MatConstIterator(); + //! constructor that sets the iterator to the beginning of the matrix + MatConstIterator(const Mat* _m); + //! constructor that sets the iterator to the specified element of the matrix + MatConstIterator(const Mat* _m, int _row, int _col=0); + //! constructor that sets the iterator to the specified element of the matrix + MatConstIterator(const Mat* _m, Point _pt); + //! constructor that sets the iterator to the specified element of the matrix + MatConstIterator(const Mat* _m, const int* _idx); + //! copy constructor + MatConstIterator(const MatConstIterator& it); + + //! copy operator + MatConstIterator& operator = (const MatConstIterator& it); + //! returns the current matrix element + const uchar* operator *() const; + //! returns the i-th matrix element, relative to the current + const uchar* operator [](ptrdiff_t i) const; + + //! shifts the iterator forward by the specified number of elements + MatConstIterator& operator += (ptrdiff_t ofs); + //! shifts the iterator backward by the specified number of elements + MatConstIterator& operator -= (ptrdiff_t ofs); + //! decrements the iterator + MatConstIterator& operator --(); + //! decrements the iterator + MatConstIterator operator --(int); + //! increments the iterator + MatConstIterator& operator ++(); + //! increments the iterator + MatConstIterator operator ++(int); + //! returns the current iterator position + Point pos() const; + //! returns the current iterator position + void pos(int* _idx) const; + + ptrdiff_t lpos() const; + void seek(ptrdiff_t ofs, bool relative = false); + void seek(const int* _idx, bool relative = false); + + const Mat* m; + size_t elemSize; + const uchar* ptr; + const uchar* sliceStart; + const uchar* sliceEnd; +}; + + + +////////////////////////////////// MatConstIterator_ ///////////////////////////////// + +/** @brief Matrix read-only iterator + */ +template +class MatConstIterator_ : public MatConstIterator +{ +public: + typedef _Tp value_type; + typedef ptrdiff_t difference_type; + typedef const _Tp* pointer; + typedef const _Tp& reference; + + typedef std::random_access_iterator_tag iterator_category; + + //! default constructor + MatConstIterator_(); + //! constructor that sets the iterator to the beginning of the matrix + MatConstIterator_(const Mat_<_Tp>* _m); + //! constructor that sets the iterator to the specified element of the matrix + MatConstIterator_(const Mat_<_Tp>* _m, int _row, int _col=0); + //! constructor that sets the iterator to the specified element of the matrix + MatConstIterator_(const Mat_<_Tp>* _m, Point _pt); + //! constructor that sets the iterator to the specified element of the matrix + MatConstIterator_(const Mat_<_Tp>* _m, const int* _idx); + //! copy constructor + MatConstIterator_(const MatConstIterator_& it); + + //! copy operator + MatConstIterator_& operator = (const MatConstIterator_& it); + //! returns the current matrix element + const _Tp& operator *() const; + //! returns the i-th matrix element, relative to the current + const _Tp& operator [](ptrdiff_t i) const; + + //! shifts the iterator forward by the specified number of elements + MatConstIterator_& operator += (ptrdiff_t ofs); + //! shifts the iterator backward by the specified number of elements + MatConstIterator_& operator -= (ptrdiff_t ofs); + //! decrements the iterator + MatConstIterator_& operator --(); + //! decrements the iterator + MatConstIterator_ operator --(int); + //! increments the iterator + MatConstIterator_& operator ++(); + //! increments the iterator + MatConstIterator_ operator ++(int); + //! returns the current iterator position + Point pos() const; +}; + + + +//////////////////////////////////// MatIterator_ //////////////////////////////////// + +/** @brief Matrix read-write iterator +*/ +template +class MatIterator_ : public MatConstIterator_<_Tp> +{ +public: + typedef _Tp* pointer; + typedef _Tp& reference; + + typedef std::random_access_iterator_tag iterator_category; + + //! the default constructor + MatIterator_(); + //! constructor that sets the iterator to the beginning of the matrix + MatIterator_(Mat_<_Tp>* _m); + //! constructor that sets the iterator to the specified element of the matrix + MatIterator_(Mat_<_Tp>* _m, int _row, int _col=0); + //! constructor that sets the iterator to the specified element of the matrix + MatIterator_(Mat_<_Tp>* _m, Point _pt); + //! constructor that sets the iterator to the specified element of the matrix + MatIterator_(Mat_<_Tp>* _m, const int* _idx); + //! copy constructor + MatIterator_(const MatIterator_& it); + //! copy operator + MatIterator_& operator = (const MatIterator_<_Tp>& it ); + + //! returns the current matrix element + _Tp& operator *() const; + //! returns the i-th matrix element, relative to the current + _Tp& operator [](ptrdiff_t i) const; + + //! shifts the iterator forward by the specified number of elements + MatIterator_& operator += (ptrdiff_t ofs); + //! shifts the iterator backward by the specified number of elements + MatIterator_& operator -= (ptrdiff_t ofs); + //! decrements the iterator + MatIterator_& operator --(); + //! decrements the iterator + MatIterator_ operator --(int); + //! increments the iterator + MatIterator_& operator ++(); + //! increments the iterator + MatIterator_ operator ++(int); +}; + + + +/////////////////////////////// SparseMatConstIterator /////////////////////////////// + +/** @brief Read-Only Sparse Matrix Iterator. + + Here is how to use the iterator to compute the sum of floating-point sparse matrix elements: + + \code + SparseMatConstIterator it = m.begin(), it_end = m.end(); + double s = 0; + CV_Assert( m.type() == CV_32F ); + for( ; it != it_end; ++it ) + s += it.value(); + \endcode +*/ +class CV_EXPORTS SparseMatConstIterator +{ +public: + //! the default constructor + SparseMatConstIterator(); + //! the full constructor setting the iterator to the first sparse matrix element + SparseMatConstIterator(const SparseMat* _m); + //! the copy constructor + SparseMatConstIterator(const SparseMatConstIterator& it); + + //! the assignment operator + SparseMatConstIterator& operator = (const SparseMatConstIterator& it); + + //! template method returning the current matrix element + template const _Tp& value() const; + //! returns the current node of the sparse matrix. it.node->idx is the current element index + const SparseMat::Node* node() const; + + //! moves iterator to the previous element + SparseMatConstIterator& operator --(); + //! moves iterator to the previous element + SparseMatConstIterator operator --(int); + //! moves iterator to the next element + SparseMatConstIterator& operator ++(); + //! moves iterator to the next element + SparseMatConstIterator operator ++(int); + + //! moves iterator to the element after the last element + void seekEnd(); + + const SparseMat* m; + size_t hashidx; + uchar* ptr; +}; + + + +////////////////////////////////// SparseMatIterator ///////////////////////////////// + +/** @brief Read-write Sparse Matrix Iterator + + The class is similar to cv::SparseMatConstIterator, + but can be used for in-place modification of the matrix elements. +*/ +class CV_EXPORTS SparseMatIterator : public SparseMatConstIterator +{ +public: + //! the default constructor + SparseMatIterator(); + //! the full constructor setting the iterator to the first sparse matrix element + SparseMatIterator(SparseMat* _m); + //! the full constructor setting the iterator to the specified sparse matrix element + SparseMatIterator(SparseMat* _m, const int* idx); + //! the copy constructor + SparseMatIterator(const SparseMatIterator& it); + + //! the assignment operator + SparseMatIterator& operator = (const SparseMatIterator& it); + //! returns read-write reference to the current sparse matrix element + template _Tp& value() const; + //! returns pointer to the current sparse matrix node. it.node->idx is the index of the current element (do not modify it!) + SparseMat::Node* node() const; + + //! moves iterator to the next element + SparseMatIterator& operator ++(); + //! moves iterator to the next element + SparseMatIterator operator ++(int); +}; + + + +/////////////////////////////// SparseMatConstIterator_ ////////////////////////////// + +/** @brief Template Read-Only Sparse Matrix Iterator Class. + + This is the derived from SparseMatConstIterator class that + introduces more convenient operator *() for accessing the current element. +*/ +template class SparseMatConstIterator_ : public SparseMatConstIterator +{ +public: + + typedef std::forward_iterator_tag iterator_category; + + //! the default constructor + SparseMatConstIterator_(); + //! the full constructor setting the iterator to the first sparse matrix element + SparseMatConstIterator_(const SparseMat_<_Tp>* _m); + SparseMatConstIterator_(const SparseMat* _m); + //! the copy constructor + SparseMatConstIterator_(const SparseMatConstIterator_& it); + + //! the assignment operator + SparseMatConstIterator_& operator = (const SparseMatConstIterator_& it); + //! the element access operator + const _Tp& operator *() const; + + //! moves iterator to the next element + SparseMatConstIterator_& operator ++(); + //! moves iterator to the next element + SparseMatConstIterator_ operator ++(int); +}; + + + +///////////////////////////////// SparseMatIterator_ ///////////////////////////////// + +/** @brief Template Read-Write Sparse Matrix Iterator Class. + + This is the derived from cv::SparseMatConstIterator_ class that + introduces more convenient operator *() for accessing the current element. +*/ +template class SparseMatIterator_ : public SparseMatConstIterator_<_Tp> +{ +public: + + typedef std::forward_iterator_tag iterator_category; + + //! the default constructor + SparseMatIterator_(); + //! the full constructor setting the iterator to the first sparse matrix element + SparseMatIterator_(SparseMat_<_Tp>* _m); + SparseMatIterator_(SparseMat* _m); + //! the copy constructor + SparseMatIterator_(const SparseMatIterator_& it); + + //! the assignment operator + SparseMatIterator_& operator = (const SparseMatIterator_& it); + //! returns the reference to the current element + _Tp& operator *() const; + + //! moves the iterator to the next element + SparseMatIterator_& operator ++(); + //! moves the iterator to the next element + SparseMatIterator_ operator ++(int); +}; + + + +/////////////////////////////////// NAryMatIterator ////////////////////////////////// + +/** @brief n-ary multi-dimensional array iterator. + +Use the class to implement unary, binary, and, generally, n-ary element-wise operations on +multi-dimensional arrays. Some of the arguments of an n-ary function may be continuous arrays, some +may be not. It is possible to use conventional MatIterator 's for each array but incrementing all of +the iterators after each small operations may be a big overhead. In this case consider using +NAryMatIterator to iterate through several matrices simultaneously as long as they have the same +geometry (dimensionality and all the dimension sizes are the same). On each iteration `it.planes[0]`, +`it.planes[1]`,... will be the slices of the corresponding matrices. + +The example below illustrates how you can compute a normalized and threshold 3D color histogram: +@code + void computeNormalizedColorHist(const Mat& image, Mat& hist, int N, double minProb) + { + const int histSize[] = {N, N, N}; + + // make sure that the histogram has a proper size and type + hist.create(3, histSize, CV_32F); + + // and clear it + hist = Scalar(0); + + // the loop below assumes that the image + // is a 8-bit 3-channel. check it. + CV_Assert(image.type() == CV_8UC3); + MatConstIterator_ it = image.begin(), + it_end = image.end(); + for( ; it != it_end; ++it ) + { + const Vec3b& pix = *it; + hist.at(pix[0]*N/256, pix[1]*N/256, pix[2]*N/256) += 1.f; + } + + minProb *= image.rows*image.cols; + + // initialize iterator (the style is different from STL). + // after initialization the iterator will contain + // the number of slices or planes the iterator will go through. + // it simultaneously increments iterators for several matrices + // supplied as a null terminated list of pointers + const Mat* arrays[] = {&hist, 0}; + Mat planes[1]; + NAryMatIterator itNAry(arrays, planes, 1); + double s = 0; + // iterate through the matrix. on each iteration + // itNAry.planes[i] (of type Mat) will be set to the current plane + // of the i-th n-dim matrix passed to the iterator constructor. + for(int p = 0; p < itNAry.nplanes; p++, ++itNAry) + { + threshold(itNAry.planes[0], itNAry.planes[0], minProb, 0, THRESH_TOZERO); + s += sum(itNAry.planes[0])[0]; + } + + s = 1./s; + itNAry = NAryMatIterator(arrays, planes, 1); + for(int p = 0; p < itNAry.nplanes; p++, ++itNAry) + itNAry.planes[0] *= s; + } +@endcode + */ +class CV_EXPORTS NAryMatIterator +{ +public: + //! the default constructor + NAryMatIterator(); + //! the full constructor taking arbitrary number of n-dim matrices + NAryMatIterator(const Mat** arrays, uchar** ptrs, int narrays=-1); + //! the full constructor taking arbitrary number of n-dim matrices + NAryMatIterator(const Mat** arrays, Mat* planes, int narrays=-1); + //! the separate iterator initialization method + void init(const Mat** arrays, Mat* planes, uchar** ptrs, int narrays=-1); + + //! proceeds to the next plane of every iterated matrix + NAryMatIterator& operator ++(); + //! proceeds to the next plane of every iterated matrix (postfix increment operator) + NAryMatIterator operator ++(int); + + //! the iterated arrays + const Mat** arrays; + //! the current planes + Mat* planes; + //! data pointers + uchar** ptrs; + //! the number of arrays + int narrays; + //! the number of hyper-planes that the iterator steps through + size_t nplanes; + //! the size of each segment (in elements) + size_t size; +protected: + int iterdepth; + size_t idx; +}; + + + +///////////////////////////////// Matrix Expressions ///////////////////////////////// + +class CV_EXPORTS MatOp +{ +public: + MatOp(); + virtual ~MatOp(); + + virtual bool elementWise(const MatExpr& expr) const; + virtual void assign(const MatExpr& expr, Mat& m, int type=-1) const = 0; + virtual void roi(const MatExpr& expr, const Range& rowRange, + const Range& colRange, MatExpr& res) const; + virtual void diag(const MatExpr& expr, int d, MatExpr& res) const; + virtual void augAssignAdd(const MatExpr& expr, Mat& m) const; + virtual void augAssignSubtract(const MatExpr& expr, Mat& m) const; + virtual void augAssignMultiply(const MatExpr& expr, Mat& m) const; + virtual void augAssignDivide(const MatExpr& expr, Mat& m) const; + virtual void augAssignAnd(const MatExpr& expr, Mat& m) const; + virtual void augAssignOr(const MatExpr& expr, Mat& m) const; + virtual void augAssignXor(const MatExpr& expr, Mat& m) const; + + virtual void add(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res) const; + virtual void add(const MatExpr& expr1, const Scalar& s, MatExpr& res) const; + + virtual void subtract(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res) const; + virtual void subtract(const Scalar& s, const MatExpr& expr, MatExpr& res) const; + + virtual void multiply(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res, double scale=1) const; + virtual void multiply(const MatExpr& expr1, double s, MatExpr& res) const; + + virtual void divide(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res, double scale=1) const; + virtual void divide(double s, const MatExpr& expr, MatExpr& res) const; + + virtual void abs(const MatExpr& expr, MatExpr& res) const; + + virtual void transpose(const MatExpr& expr, MatExpr& res) const; + virtual void matmul(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res) const; + virtual void invert(const MatExpr& expr, int method, MatExpr& res) const; + + virtual Size size(const MatExpr& expr) const; + virtual int type(const MatExpr& expr) const; +}; + +/** @brief Matrix expression representation +@anchor MatrixExpressions +This is a list of implemented matrix operations that can be combined in arbitrary complex +expressions (here A, B stand for matrices ( Mat ), s for a scalar ( Scalar ), alpha for a +real-valued scalar ( double )): +- Addition, subtraction, negation: `A+B`, `A-B`, `A+s`, `A-s`, `s+A`, `s-A`, `-A` +- Scaling: `A*alpha` +- Per-element multiplication and division: `A.mul(B)`, `A/B`, `alpha/A` +- Matrix multiplication: `A*B` +- Transposition: `A.t()` (means AT) +- Matrix inversion and pseudo-inversion, solving linear systems and least-squares problems: + `A.inv([method]) (~ A-1)`, `A.inv([method])*B (~ X: AX=B)` +- Comparison: `A cmpop B`, `A cmpop alpha`, `alpha cmpop A`, where *cmpop* is one of + `>`, `>=`, `==`, `!=`, `<=`, `<`. The result of comparison is an 8-bit single channel mask whose + elements are set to 255 (if the particular element or pair of elements satisfy the condition) or + 0. +- Bitwise logical operations: `A logicop B`, `A logicop s`, `s logicop A`, `~A`, where *logicop* is one of + `&`, `|`, `^`. +- Element-wise minimum and maximum: `min(A, B)`, `min(A, alpha)`, `max(A, B)`, `max(A, alpha)` +- Element-wise absolute value: `abs(A)` +- Cross-product, dot-product: `A.cross(B)`, `A.dot(B)` +- Any function of matrix or matrices and scalars that returns a matrix or a scalar, such as norm, + mean, sum, countNonZero, trace, determinant, repeat, and others. +- Matrix initializers ( Mat::eye(), Mat::zeros(), Mat::ones() ), matrix comma-separated + initializers, matrix constructors and operators that extract sub-matrices (see Mat description). +- Mat_() constructors to cast the result to the proper type. +@note Comma-separated initializers and probably some other operations may require additional +explicit Mat() or Mat_() constructor calls to resolve a possible ambiguity. + +Here are examples of matrix expressions: +@code + // compute pseudo-inverse of A, equivalent to A.inv(DECOMP_SVD) + SVD svd(A); + Mat pinvA = svd.vt.t()*Mat::diag(1./svd.w)*svd.u.t(); + + // compute the new vector of parameters in the Levenberg-Marquardt algorithm + x -= (A.t()*A + lambda*Mat::eye(A.cols,A.cols,A.type())).inv(DECOMP_CHOLESKY)*(A.t()*err); + + // sharpen image using "unsharp mask" algorithm + Mat blurred; double sigma = 1, threshold = 5, amount = 1; + GaussianBlur(img, blurred, Size(), sigma, sigma); + Mat lowContrastMask = abs(img - blurred) < threshold; + Mat sharpened = img*(1+amount) + blurred*(-amount); + img.copyTo(sharpened, lowContrastMask); +@endcode +*/ +class CV_EXPORTS MatExpr +{ +public: + MatExpr(); + explicit MatExpr(const Mat& m); + + MatExpr(const MatOp* _op, int _flags, const Mat& _a = Mat(), const Mat& _b = Mat(), + const Mat& _c = Mat(), double _alpha = 1, double _beta = 1, const Scalar& _s = Scalar()); + + operator Mat() const; + template operator Mat_<_Tp>() const; + + Size size() const; + int type() const; + + MatExpr row(int y) const; + MatExpr col(int x) const; + MatExpr diag(int d = 0) const; + MatExpr operator()( const Range& rowRange, const Range& colRange ) const; + MatExpr operator()( const Rect& roi ) const; + + MatExpr t() const; + MatExpr inv(int method = DECOMP_LU) const; + MatExpr mul(const MatExpr& e, double scale=1) const; + MatExpr mul(const Mat& m, double scale=1) const; + + Mat cross(const Mat& m) const; + double dot(const Mat& m) const; + + const MatOp* op; + int flags; + + Mat a, b, c; + double alpha, beta; + Scalar s; +}; + +//! @} core_basic + +//! @relates cv::MatExpr +//! @{ +CV_EXPORTS MatExpr operator + (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator + (const Mat& a, const Scalar& s); +CV_EXPORTS MatExpr operator + (const Scalar& s, const Mat& a); +CV_EXPORTS MatExpr operator + (const MatExpr& e, const Mat& m); +CV_EXPORTS MatExpr operator + (const Mat& m, const MatExpr& e); +CV_EXPORTS MatExpr operator + (const MatExpr& e, const Scalar& s); +CV_EXPORTS MatExpr operator + (const Scalar& s, const MatExpr& e); +CV_EXPORTS MatExpr operator + (const MatExpr& e1, const MatExpr& e2); + +CV_EXPORTS MatExpr operator - (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator - (const Mat& a, const Scalar& s); +CV_EXPORTS MatExpr operator - (const Scalar& s, const Mat& a); +CV_EXPORTS MatExpr operator - (const MatExpr& e, const Mat& m); +CV_EXPORTS MatExpr operator - (const Mat& m, const MatExpr& e); +CV_EXPORTS MatExpr operator - (const MatExpr& e, const Scalar& s); +CV_EXPORTS MatExpr operator - (const Scalar& s, const MatExpr& e); +CV_EXPORTS MatExpr operator - (const MatExpr& e1, const MatExpr& e2); + +CV_EXPORTS MatExpr operator - (const Mat& m); +CV_EXPORTS MatExpr operator - (const MatExpr& e); + +CV_EXPORTS MatExpr operator * (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator * (const Mat& a, double s); +CV_EXPORTS MatExpr operator * (double s, const Mat& a); +CV_EXPORTS MatExpr operator * (const MatExpr& e, const Mat& m); +CV_EXPORTS MatExpr operator * (const Mat& m, const MatExpr& e); +CV_EXPORTS MatExpr operator * (const MatExpr& e, double s); +CV_EXPORTS MatExpr operator * (double s, const MatExpr& e); +CV_EXPORTS MatExpr operator * (const MatExpr& e1, const MatExpr& e2); + +CV_EXPORTS MatExpr operator / (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator / (const Mat& a, double s); +CV_EXPORTS MatExpr operator / (double s, const Mat& a); +CV_EXPORTS MatExpr operator / (const MatExpr& e, const Mat& m); +CV_EXPORTS MatExpr operator / (const Mat& m, const MatExpr& e); +CV_EXPORTS MatExpr operator / (const MatExpr& e, double s); +CV_EXPORTS MatExpr operator / (double s, const MatExpr& e); +CV_EXPORTS MatExpr operator / (const MatExpr& e1, const MatExpr& e2); + +CV_EXPORTS MatExpr operator < (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator < (const Mat& a, double s); +CV_EXPORTS MatExpr operator < (double s, const Mat& a); + +CV_EXPORTS MatExpr operator <= (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator <= (const Mat& a, double s); +CV_EXPORTS MatExpr operator <= (double s, const Mat& a); + +CV_EXPORTS MatExpr operator == (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator == (const Mat& a, double s); +CV_EXPORTS MatExpr operator == (double s, const Mat& a); + +CV_EXPORTS MatExpr operator != (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator != (const Mat& a, double s); +CV_EXPORTS MatExpr operator != (double s, const Mat& a); + +CV_EXPORTS MatExpr operator >= (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator >= (const Mat& a, double s); +CV_EXPORTS MatExpr operator >= (double s, const Mat& a); + +CV_EXPORTS MatExpr operator > (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator > (const Mat& a, double s); +CV_EXPORTS MatExpr operator > (double s, const Mat& a); + +CV_EXPORTS MatExpr operator & (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator & (const Mat& a, const Scalar& s); +CV_EXPORTS MatExpr operator & (const Scalar& s, const Mat& a); + +CV_EXPORTS MatExpr operator | (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator | (const Mat& a, const Scalar& s); +CV_EXPORTS MatExpr operator | (const Scalar& s, const Mat& a); + +CV_EXPORTS MatExpr operator ^ (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator ^ (const Mat& a, const Scalar& s); +CV_EXPORTS MatExpr operator ^ (const Scalar& s, const Mat& a); + +CV_EXPORTS MatExpr operator ~(const Mat& m); + +CV_EXPORTS MatExpr min(const Mat& a, const Mat& b); +CV_EXPORTS MatExpr min(const Mat& a, double s); +CV_EXPORTS MatExpr min(double s, const Mat& a); + +CV_EXPORTS MatExpr max(const Mat& a, const Mat& b); +CV_EXPORTS MatExpr max(const Mat& a, double s); +CV_EXPORTS MatExpr max(double s, const Mat& a); + +/** @brief Calculates an absolute value of each matrix element. + +abs is a meta-function that is expanded to one of absdiff or convertScaleAbs forms: +- C = abs(A-B) is equivalent to `absdiff(A, B, C)` +- C = abs(A) is equivalent to `absdiff(A, Scalar::all(0), C)` +- C = `Mat_ >(abs(A*alpha + beta))` is equivalent to `convertScaleAbs(A, C, alpha, +beta)` + +The output matrix has the same size and the same type as the input one except for the last case, +where C is depth=CV_8U . +@param m matrix. +@sa @ref MatrixExpressions, absdiff, convertScaleAbs + */ +CV_EXPORTS MatExpr abs(const Mat& m); +/** @overload +@param e matrix expression. +*/ +CV_EXPORTS MatExpr abs(const MatExpr& e); +//! @} relates cv::MatExpr + +} // cv + +#include "opencv2/core/mat.inl.hpp" + +#endif // OPENCV_CORE_MAT_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/mat.inl.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/mat.inl.hpp new file mode 100644 index 0000000000000000000000000000000000000000..a2e7923b39340e9368dc0e02085410271532b43b --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/mat.inl.hpp @@ -0,0 +1,4027 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_MATRIX_OPERATIONS_HPP +#define OPENCV_CORE_MATRIX_OPERATIONS_HPP + +#ifndef __cplusplus +# error mat.inl.hpp header must be compiled as C++ +#endif + +#ifdef _MSC_VER +#pragma warning( push ) +#pragma warning( disable: 4127 ) +#endif + +namespace cv +{ +CV__DEBUG_NS_BEGIN + + +//! @cond IGNORED + +////////////////////////// Custom (raw) type wrapper ////////////////////////// + +template static inline +int rawType() +{ + CV_StaticAssert(sizeof(_Tp) <= CV_CN_MAX, "sizeof(_Tp) is too large"); + const int elemSize = sizeof(_Tp); + return (int)CV_MAKETYPE(CV_8U, elemSize); +} + +//////////////////////// Input/Output Arrays //////////////////////// + +inline void _InputArray::init(int _flags, const void* _obj) +{ flags = _flags; obj = (void*)_obj; } + +inline void _InputArray::init(int _flags, const void* _obj, Size _sz) +{ flags = _flags; obj = (void*)_obj; sz = _sz; } + +inline void* _InputArray::getObj() const { return obj; } +inline int _InputArray::getFlags() const { return flags; } +inline Size _InputArray::getSz() const { return sz; } + +inline _InputArray::_InputArray() { init(NONE, 0); } +inline _InputArray::_InputArray(int _flags, void* _obj) { init(_flags, _obj); } +inline _InputArray::_InputArray(const Mat& m) { init(MAT+ACCESS_READ, &m); } +inline _InputArray::_InputArray(const std::vector& vec) { init(STD_VECTOR_MAT+ACCESS_READ, &vec); } +inline _InputArray::_InputArray(const UMat& m) { init(UMAT+ACCESS_READ, &m); } +inline _InputArray::_InputArray(const std::vector& vec) { init(STD_VECTOR_UMAT+ACCESS_READ, &vec); } + +template inline +_InputArray::_InputArray(const std::vector<_Tp>& vec) +{ init(FIXED_TYPE + STD_VECTOR + traits::Type<_Tp>::value + ACCESS_READ, &vec); } + +#ifdef CV_CXX_STD_ARRAY +template inline +_InputArray::_InputArray(const std::array<_Tp, _Nm>& arr) +{ init(FIXED_TYPE + FIXED_SIZE + STD_ARRAY + traits::Type<_Tp>::value + ACCESS_READ, arr.data(), Size(1, _Nm)); } + +template inline +_InputArray::_InputArray(const std::array& arr) +{ init(STD_ARRAY_MAT + ACCESS_READ, arr.data(), Size(1, _Nm)); } +#endif + +inline +_InputArray::_InputArray(const std::vector& vec) +{ init(FIXED_TYPE + STD_BOOL_VECTOR + traits::Type::value + ACCESS_READ, &vec); } + +template inline +_InputArray::_InputArray(const std::vector >& vec) +{ init(FIXED_TYPE + STD_VECTOR_VECTOR + traits::Type<_Tp>::value + ACCESS_READ, &vec); } + +inline +_InputArray::_InputArray(const std::vector >&) +{ CV_Error(Error::StsUnsupportedFormat, "std::vector > is not supported!\n"); } + +template inline +_InputArray::_InputArray(const std::vector >& vec) +{ init(FIXED_TYPE + STD_VECTOR_MAT + traits::Type<_Tp>::value + ACCESS_READ, &vec); } + +template inline +_InputArray::_InputArray(const Matx<_Tp, m, n>& mtx) +{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_READ, &mtx, Size(n, m)); } + +template inline +_InputArray::_InputArray(const _Tp* vec, int n) +{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_READ, vec, Size(n, 1)); } + +template inline +_InputArray::_InputArray(const Mat_<_Tp>& m) +{ init(FIXED_TYPE + MAT + traits::Type<_Tp>::value + ACCESS_READ, &m); } + +inline _InputArray::_InputArray(const double& val) +{ init(FIXED_TYPE + FIXED_SIZE + MATX + CV_64F + ACCESS_READ, &val, Size(1,1)); } + +inline _InputArray::_InputArray(const MatExpr& expr) +{ init(FIXED_TYPE + FIXED_SIZE + EXPR + ACCESS_READ, &expr); } + +inline _InputArray::_InputArray(const cuda::GpuMat& d_mat) +{ init(CUDA_GPU_MAT + ACCESS_READ, &d_mat); } + +inline _InputArray::_InputArray(const std::vector& d_mat) +{ init(STD_VECTOR_CUDA_GPU_MAT + ACCESS_READ, &d_mat);} + +inline _InputArray::_InputArray(const ogl::Buffer& buf) +{ init(OPENGL_BUFFER + ACCESS_READ, &buf); } + +inline _InputArray::_InputArray(const cuda::HostMem& cuda_mem) +{ init(CUDA_HOST_MEM + ACCESS_READ, &cuda_mem); } + +template inline +_InputArray _InputArray::rawIn(const std::vector<_Tp>& vec) +{ + _InputArray v; + v.flags = _InputArray::FIXED_TYPE + _InputArray::STD_VECTOR + rawType<_Tp>() + ACCESS_READ; + v.obj = (void*)&vec; + return v; +} + +#ifdef CV_CXX_STD_ARRAY +template inline +_InputArray _InputArray::rawIn(const std::array<_Tp, _Nm>& arr) +{ + _InputArray v; + v.flags = FIXED_TYPE + FIXED_SIZE + STD_ARRAY + traits::Type<_Tp>::value + ACCESS_READ; + v.obj = (void*)arr.data(); + v.sz = Size(1, _Nm); + return v; +} +#endif + +inline _InputArray::~_InputArray() {} + +inline Mat _InputArray::getMat(int i) const +{ + if( kind() == MAT && i < 0 ) + return *(const Mat*)obj; + return getMat_(i); +} + +inline bool _InputArray::isMat() const { return kind() == _InputArray::MAT; } +inline bool _InputArray::isUMat() const { return kind() == _InputArray::UMAT; } +inline bool _InputArray::isMatVector() const { return kind() == _InputArray::STD_VECTOR_MAT; } +inline bool _InputArray::isUMatVector() const { return kind() == _InputArray::STD_VECTOR_UMAT; } +inline bool _InputArray::isMatx() const { return kind() == _InputArray::MATX; } +inline bool _InputArray::isVector() const { return kind() == _InputArray::STD_VECTOR || + kind() == _InputArray::STD_BOOL_VECTOR || + kind() == _InputArray::STD_ARRAY; } +inline bool _InputArray::isGpuMat() const { return kind() == _InputArray::CUDA_GPU_MAT; } +inline bool _InputArray::isGpuMatVector() const { return kind() == _InputArray::STD_VECTOR_CUDA_GPU_MAT; } + +//////////////////////////////////////////////////////////////////////////////////////// + +inline _OutputArray::_OutputArray() { init(ACCESS_WRITE, 0); } +inline _OutputArray::_OutputArray(int _flags, void* _obj) { init(_flags|ACCESS_WRITE, _obj); } +inline _OutputArray::_OutputArray(Mat& m) { init(MAT+ACCESS_WRITE, &m); } +inline _OutputArray::_OutputArray(std::vector& vec) { init(STD_VECTOR_MAT+ACCESS_WRITE, &vec); } +inline _OutputArray::_OutputArray(UMat& m) { init(UMAT+ACCESS_WRITE, &m); } +inline _OutputArray::_OutputArray(std::vector& vec) { init(STD_VECTOR_UMAT+ACCESS_WRITE, &vec); } + +template inline +_OutputArray::_OutputArray(std::vector<_Tp>& vec) +{ init(FIXED_TYPE + STD_VECTOR + traits::Type<_Tp>::value + ACCESS_WRITE, &vec); } + +#ifdef CV_CXX_STD_ARRAY +template inline +_OutputArray::_OutputArray(std::array<_Tp, _Nm>& arr) +{ init(FIXED_TYPE + FIXED_SIZE + STD_ARRAY + traits::Type<_Tp>::value + ACCESS_WRITE, arr.data(), Size(1, _Nm)); } + +template inline +_OutputArray::_OutputArray(std::array& arr) +{ init(STD_ARRAY_MAT + ACCESS_WRITE, arr.data(), Size(1, _Nm)); } +#endif + +inline +_OutputArray::_OutputArray(std::vector&) +{ CV_Error(Error::StsUnsupportedFormat, "std::vector cannot be an output array\n"); } + +template inline +_OutputArray::_OutputArray(std::vector >& vec) +{ init(FIXED_TYPE + STD_VECTOR_VECTOR + traits::Type<_Tp>::value + ACCESS_WRITE, &vec); } + +inline +_OutputArray::_OutputArray(std::vector >&) +{ CV_Error(Error::StsUnsupportedFormat, "std::vector > cannot be an output array\n"); } + +template inline +_OutputArray::_OutputArray(std::vector >& vec) +{ init(FIXED_TYPE + STD_VECTOR_MAT + traits::Type<_Tp>::value + ACCESS_WRITE, &vec); } + +template inline +_OutputArray::_OutputArray(Mat_<_Tp>& m) +{ init(FIXED_TYPE + MAT + traits::Type<_Tp>::value + ACCESS_WRITE, &m); } + +template inline +_OutputArray::_OutputArray(Matx<_Tp, m, n>& mtx) +{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_WRITE, &mtx, Size(n, m)); } + +template inline +_OutputArray::_OutputArray(_Tp* vec, int n) +{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_WRITE, vec, Size(n, 1)); } + +template inline +_OutputArray::_OutputArray(const std::vector<_Tp>& vec) +{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR + traits::Type<_Tp>::value + ACCESS_WRITE, &vec); } + +#ifdef CV_CXX_STD_ARRAY +template inline +_OutputArray::_OutputArray(const std::array<_Tp, _Nm>& arr) +{ init(FIXED_TYPE + FIXED_SIZE + STD_ARRAY + traits::Type<_Tp>::value + ACCESS_WRITE, arr.data(), Size(1, _Nm)); } + +template inline +_OutputArray::_OutputArray(const std::array& arr) +{ init(FIXED_SIZE + STD_ARRAY_MAT + ACCESS_WRITE, arr.data(), Size(1, _Nm)); } +#endif + +template inline +_OutputArray::_OutputArray(const std::vector >& vec) +{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_VECTOR + traits::Type<_Tp>::value + ACCESS_WRITE, &vec); } + +template inline +_OutputArray::_OutputArray(const std::vector >& vec) +{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_MAT + traits::Type<_Tp>::value + ACCESS_WRITE, &vec); } + +template inline +_OutputArray::_OutputArray(const Mat_<_Tp>& m) +{ init(FIXED_TYPE + FIXED_SIZE + MAT + traits::Type<_Tp>::value + ACCESS_WRITE, &m); } + +template inline +_OutputArray::_OutputArray(const Matx<_Tp, m, n>& mtx) +{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_WRITE, &mtx, Size(n, m)); } + +template inline +_OutputArray::_OutputArray(const _Tp* vec, int n) +{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_WRITE, vec, Size(n, 1)); } + +inline _OutputArray::_OutputArray(cuda::GpuMat& d_mat) +{ init(CUDA_GPU_MAT + ACCESS_WRITE, &d_mat); } + +inline _OutputArray::_OutputArray(std::vector& d_mat) +{ init(STD_VECTOR_CUDA_GPU_MAT + ACCESS_WRITE, &d_mat);} + +inline _OutputArray::_OutputArray(ogl::Buffer& buf) +{ init(OPENGL_BUFFER + ACCESS_WRITE, &buf); } + +inline _OutputArray::_OutputArray(cuda::HostMem& cuda_mem) +{ init(CUDA_HOST_MEM + ACCESS_WRITE, &cuda_mem); } + +inline _OutputArray::_OutputArray(const Mat& m) +{ init(FIXED_TYPE + FIXED_SIZE + MAT + ACCESS_WRITE, &m); } + +inline _OutputArray::_OutputArray(const std::vector& vec) +{ init(FIXED_SIZE + STD_VECTOR_MAT + ACCESS_WRITE, &vec); } + +inline _OutputArray::_OutputArray(const UMat& m) +{ init(FIXED_TYPE + FIXED_SIZE + UMAT + ACCESS_WRITE, &m); } + +inline _OutputArray::_OutputArray(const std::vector& vec) +{ init(FIXED_SIZE + STD_VECTOR_UMAT + ACCESS_WRITE, &vec); } + +inline _OutputArray::_OutputArray(const cuda::GpuMat& d_mat) +{ init(FIXED_TYPE + FIXED_SIZE + CUDA_GPU_MAT + ACCESS_WRITE, &d_mat); } + + +inline _OutputArray::_OutputArray(const ogl::Buffer& buf) +{ init(FIXED_TYPE + FIXED_SIZE + OPENGL_BUFFER + ACCESS_WRITE, &buf); } + +inline _OutputArray::_OutputArray(const cuda::HostMem& cuda_mem) +{ init(FIXED_TYPE + FIXED_SIZE + CUDA_HOST_MEM + ACCESS_WRITE, &cuda_mem); } + +template inline +_OutputArray _OutputArray::rawOut(std::vector<_Tp>& vec) +{ + _OutputArray v; + v.flags = _InputArray::FIXED_TYPE + _InputArray::STD_VECTOR + rawType<_Tp>() + ACCESS_WRITE; + v.obj = (void*)&vec; + return v; +} + +#ifdef CV_CXX_STD_ARRAY +template inline +_OutputArray _OutputArray::rawOut(std::array<_Tp, _Nm>& arr) +{ + _OutputArray v; + v.flags = FIXED_TYPE + FIXED_SIZE + STD_ARRAY + traits::Type<_Tp>::value + ACCESS_WRITE; + v.obj = (void*)arr.data(); + v.sz = Size(1, _Nm); + return v; +} +#endif + +/////////////////////////////////////////////////////////////////////////////////////////// + +inline _InputOutputArray::_InputOutputArray() { init(ACCESS_RW, 0); } +inline _InputOutputArray::_InputOutputArray(int _flags, void* _obj) { init(_flags|ACCESS_RW, _obj); } +inline _InputOutputArray::_InputOutputArray(Mat& m) { init(MAT+ACCESS_RW, &m); } +inline _InputOutputArray::_InputOutputArray(std::vector& vec) { init(STD_VECTOR_MAT+ACCESS_RW, &vec); } +inline _InputOutputArray::_InputOutputArray(UMat& m) { init(UMAT+ACCESS_RW, &m); } +inline _InputOutputArray::_InputOutputArray(std::vector& vec) { init(STD_VECTOR_UMAT+ACCESS_RW, &vec); } + +template inline +_InputOutputArray::_InputOutputArray(std::vector<_Tp>& vec) +{ init(FIXED_TYPE + STD_VECTOR + traits::Type<_Tp>::value + ACCESS_RW, &vec); } + +#ifdef CV_CXX_STD_ARRAY +template inline +_InputOutputArray::_InputOutputArray(std::array<_Tp, _Nm>& arr) +{ init(FIXED_TYPE + FIXED_SIZE + STD_ARRAY + traits::Type<_Tp>::value + ACCESS_RW, arr.data(), Size(1, _Nm)); } + +template inline +_InputOutputArray::_InputOutputArray(std::array& arr) +{ init(STD_ARRAY_MAT + ACCESS_RW, arr.data(), Size(1, _Nm)); } +#endif + +inline _InputOutputArray::_InputOutputArray(std::vector&) +{ CV_Error(Error::StsUnsupportedFormat, "std::vector cannot be an input/output array\n"); } + +template inline +_InputOutputArray::_InputOutputArray(std::vector >& vec) +{ init(FIXED_TYPE + STD_VECTOR_VECTOR + traits::Type<_Tp>::value + ACCESS_RW, &vec); } + +template inline +_InputOutputArray::_InputOutputArray(std::vector >& vec) +{ init(FIXED_TYPE + STD_VECTOR_MAT + traits::Type<_Tp>::value + ACCESS_RW, &vec); } + +template inline +_InputOutputArray::_InputOutputArray(Mat_<_Tp>& m) +{ init(FIXED_TYPE + MAT + traits::Type<_Tp>::value + ACCESS_RW, &m); } + +template inline +_InputOutputArray::_InputOutputArray(Matx<_Tp, m, n>& mtx) +{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_RW, &mtx, Size(n, m)); } + +template inline +_InputOutputArray::_InputOutputArray(_Tp* vec, int n) +{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_RW, vec, Size(n, 1)); } + +template inline +_InputOutputArray::_InputOutputArray(const std::vector<_Tp>& vec) +{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR + traits::Type<_Tp>::value + ACCESS_RW, &vec); } + +#ifdef CV_CXX_STD_ARRAY +template inline +_InputOutputArray::_InputOutputArray(const std::array<_Tp, _Nm>& arr) +{ init(FIXED_TYPE + FIXED_SIZE + STD_ARRAY + traits::Type<_Tp>::value + ACCESS_RW, arr.data(), Size(1, _Nm)); } + +template inline +_InputOutputArray::_InputOutputArray(const std::array& arr) +{ init(FIXED_SIZE + STD_ARRAY_MAT + ACCESS_RW, arr.data(), Size(1, _Nm)); } +#endif + +template inline +_InputOutputArray::_InputOutputArray(const std::vector >& vec) +{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_VECTOR + traits::Type<_Tp>::value + ACCESS_RW, &vec); } + +template inline +_InputOutputArray::_InputOutputArray(const std::vector >& vec) +{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_MAT + traits::Type<_Tp>::value + ACCESS_RW, &vec); } + +template inline +_InputOutputArray::_InputOutputArray(const Mat_<_Tp>& m) +{ init(FIXED_TYPE + FIXED_SIZE + MAT + traits::Type<_Tp>::value + ACCESS_RW, &m); } + +template inline +_InputOutputArray::_InputOutputArray(const Matx<_Tp, m, n>& mtx) +{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_RW, &mtx, Size(n, m)); } + +template inline +_InputOutputArray::_InputOutputArray(const _Tp* vec, int n) +{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_RW, vec, Size(n, 1)); } + +inline _InputOutputArray::_InputOutputArray(cuda::GpuMat& d_mat) +{ init(CUDA_GPU_MAT + ACCESS_RW, &d_mat); } + +inline _InputOutputArray::_InputOutputArray(ogl::Buffer& buf) +{ init(OPENGL_BUFFER + ACCESS_RW, &buf); } + +inline _InputOutputArray::_InputOutputArray(cuda::HostMem& cuda_mem) +{ init(CUDA_HOST_MEM + ACCESS_RW, &cuda_mem); } + +inline _InputOutputArray::_InputOutputArray(const Mat& m) +{ init(FIXED_TYPE + FIXED_SIZE + MAT + ACCESS_RW, &m); } + +inline _InputOutputArray::_InputOutputArray(const std::vector& vec) +{ init(FIXED_SIZE + STD_VECTOR_MAT + ACCESS_RW, &vec); } + +inline _InputOutputArray::_InputOutputArray(const UMat& m) +{ init(FIXED_TYPE + FIXED_SIZE + UMAT + ACCESS_RW, &m); } + +inline _InputOutputArray::_InputOutputArray(const std::vector& vec) +{ init(FIXED_SIZE + STD_VECTOR_UMAT + ACCESS_RW, &vec); } + +inline _InputOutputArray::_InputOutputArray(const cuda::GpuMat& d_mat) +{ init(FIXED_TYPE + FIXED_SIZE + CUDA_GPU_MAT + ACCESS_RW, &d_mat); } + +inline _InputOutputArray::_InputOutputArray(const std::vector& d_mat) +{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_CUDA_GPU_MAT + ACCESS_RW, &d_mat);} + +template<> inline _InputOutputArray::_InputOutputArray(std::vector& d_mat) +{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_CUDA_GPU_MAT + ACCESS_RW, &d_mat);} + +inline _InputOutputArray::_InputOutputArray(const ogl::Buffer& buf) +{ init(FIXED_TYPE + FIXED_SIZE + OPENGL_BUFFER + ACCESS_RW, &buf); } + +inline _InputOutputArray::_InputOutputArray(const cuda::HostMem& cuda_mem) +{ init(FIXED_TYPE + FIXED_SIZE + CUDA_HOST_MEM + ACCESS_RW, &cuda_mem); } + +template inline +_InputOutputArray _InputOutputArray::rawInOut(std::vector<_Tp>& vec) +{ + _InputOutputArray v; + v.flags = _InputArray::FIXED_TYPE + _InputArray::STD_VECTOR + rawType<_Tp>() + ACCESS_RW; + v.obj = (void*)&vec; + return v; +} + +#ifdef CV_CXX_STD_ARRAY +template inline +_InputOutputArray _InputOutputArray::rawInOut(std::array<_Tp, _Nm>& arr) +{ + _InputOutputArray v; + v.flags = FIXED_TYPE + FIXED_SIZE + STD_ARRAY + traits::Type<_Tp>::value + ACCESS_RW; + v.obj = (void*)arr.data(); + v.sz = Size(1, _Nm); + return v; +} +#endif + + +template static inline _InputArray rawIn(_Tp& v) { return _InputArray::rawIn(v); } +template static inline _OutputArray rawOut(_Tp& v) { return _OutputArray::rawOut(v); } +template static inline _InputOutputArray rawInOut(_Tp& v) { return _InputOutputArray::rawInOut(v); } + +CV__DEBUG_NS_END + +//////////////////////////////////////////// Mat ////////////////////////////////////////// + +inline +Mat::Mat() + : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), + datalimit(0), allocator(0), u(0), size(&rows), step(0) +{} + +inline +Mat::Mat(int _rows, int _cols, int _type) + : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), + datalimit(0), allocator(0), u(0), size(&rows), step(0) +{ + create(_rows, _cols, _type); +} + +inline +Mat::Mat(int _rows, int _cols, int _type, const Scalar& _s) + : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), + datalimit(0), allocator(0), u(0), size(&rows), step(0) +{ + create(_rows, _cols, _type); + *this = _s; +} + +inline +Mat::Mat(Size _sz, int _type) + : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), + datalimit(0), allocator(0), u(0), size(&rows), step(0) +{ + create( _sz.height, _sz.width, _type ); +} + +inline +Mat::Mat(Size _sz, int _type, const Scalar& _s) + : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), + datalimit(0), allocator(0), u(0), size(&rows), step(0) +{ + create(_sz.height, _sz.width, _type); + *this = _s; +} + +inline +Mat::Mat(int _dims, const int* _sz, int _type) + : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), + datalimit(0), allocator(0), u(0), size(&rows), step(0) +{ + create(_dims, _sz, _type); +} + +inline +Mat::Mat(int _dims, const int* _sz, int _type, const Scalar& _s) + : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), + datalimit(0), allocator(0), u(0), size(&rows), step(0) +{ + create(_dims, _sz, _type); + *this = _s; +} + +inline +Mat::Mat(const std::vector& _sz, int _type) + : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), + datalimit(0), allocator(0), u(0), size(&rows), step(0) +{ + create(_sz, _type); +} + +inline +Mat::Mat(const std::vector& _sz, int _type, const Scalar& _s) + : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), + datalimit(0), allocator(0), u(0), size(&rows), step(0) +{ + create(_sz, _type); + *this = _s; +} + +inline +Mat::Mat(const Mat& m) + : flags(m.flags), dims(m.dims), rows(m.rows), cols(m.cols), data(m.data), + datastart(m.datastart), dataend(m.dataend), datalimit(m.datalimit), allocator(m.allocator), + u(m.u), size(&rows), step(0) +{ + if( u ) + CV_XADD(&u->refcount, 1); + if( m.dims <= 2 ) + { + step[0] = m.step[0]; step[1] = m.step[1]; + } + else + { + dims = 0; + copySize(m); + } +} + +inline +Mat::Mat(int _rows, int _cols, int _type, void* _data, size_t _step) + : flags(MAGIC_VAL + (_type & TYPE_MASK)), dims(2), rows(_rows), cols(_cols), + data((uchar*)_data), datastart((uchar*)_data), dataend(0), datalimit(0), + allocator(0), u(0), size(&rows) +{ + CV_Assert(total() == 0 || data != NULL); + + size_t esz = CV_ELEM_SIZE(_type), esz1 = CV_ELEM_SIZE1(_type); + size_t minstep = cols * esz; + if( _step == AUTO_STEP ) + { + _step = minstep; + } + else + { + CV_DbgAssert( _step >= minstep ); + if (_step % esz1 != 0) + { + CV_Error(Error::BadStep, "Step must be a multiple of esz1"); + } + } + step[0] = _step; + step[1] = esz; + datalimit = datastart + _step * rows; + dataend = datalimit - _step + minstep; + updateContinuityFlag(); +} + +inline +Mat::Mat(Size _sz, int _type, void* _data, size_t _step) + : flags(MAGIC_VAL + (_type & TYPE_MASK)), dims(2), rows(_sz.height), cols(_sz.width), + data((uchar*)_data), datastart((uchar*)_data), dataend(0), datalimit(0), + allocator(0), u(0), size(&rows) +{ + CV_Assert(total() == 0 || data != NULL); + + size_t esz = CV_ELEM_SIZE(_type), esz1 = CV_ELEM_SIZE1(_type); + size_t minstep = cols*esz; + if( _step == AUTO_STEP ) + { + _step = minstep; + } + else + { + CV_DbgAssert( _step >= minstep ); + + if (_step % esz1 != 0) + { + CV_Error(Error::BadStep, "Step must be a multiple of esz1"); + } + } + step[0] = _step; + step[1] = esz; + datalimit = datastart + _step*rows; + dataend = datalimit - _step + minstep; + updateContinuityFlag(); +} + +template inline +Mat::Mat(const std::vector<_Tp>& vec, bool copyData) + : flags(MAGIC_VAL | traits::Type<_Tp>::value | CV_MAT_CONT_FLAG), dims(2), rows((int)vec.size()), + cols(1), data(0), datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows), step(0) +{ + if(vec.empty()) + return; + if( !copyData ) + { + step[0] = step[1] = sizeof(_Tp); + datastart = data = (uchar*)&vec[0]; + datalimit = dataend = datastart + rows * step[0]; + } + else + Mat((int)vec.size(), 1, traits::Type<_Tp>::value, (uchar*)&vec[0]).copyTo(*this); +} + +#ifdef CV_CXX11 +template inline +Mat::Mat(const std::initializer_list<_Tp> list) + : Mat() +{ + CV_Assert(list.size() != 0); + Mat((int)list.size(), 1, traits::Type<_Tp>::value, (uchar*)list.begin()).copyTo(*this); +} + +template inline +Mat::Mat(const std::initializer_list sizes, const std::initializer_list<_Tp> list) + : Mat() +{ + size_t size_total = 1; + for(auto s : sizes) + size_total *= s; + CV_Assert(list.size() != 0); + CV_Assert(size_total == list.size()); + Mat((int)sizes.size(), (int*)sizes.begin(), traits::Type<_Tp>::value, (uchar*)list.begin()).copyTo(*this); +} +#endif + +#ifdef CV_CXX_STD_ARRAY +template inline +Mat::Mat(const std::array<_Tp, _Nm>& arr, bool copyData) + : flags(MAGIC_VAL | traits::Type<_Tp>::value | CV_MAT_CONT_FLAG), dims(2), rows((int)arr.size()), + cols(1), data(0), datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows), step(0) +{ + if(arr.empty()) + return; + if( !copyData ) + { + step[0] = step[1] = sizeof(_Tp); + datastart = data = (uchar*)arr.data(); + datalimit = dataend = datastart + rows * step[0]; + } + else + Mat((int)arr.size(), 1, traits::Type<_Tp>::value, (uchar*)arr.data()).copyTo(*this); +} +#endif + +template inline +Mat::Mat(const Vec<_Tp, n>& vec, bool copyData) + : flags(MAGIC_VAL | traits::Type<_Tp>::value | CV_MAT_CONT_FLAG), dims(2), rows(n), cols(1), data(0), + datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows), step(0) +{ + if( !copyData ) + { + step[0] = step[1] = sizeof(_Tp); + datastart = data = (uchar*)vec.val; + datalimit = dataend = datastart + rows * step[0]; + } + else + Mat(n, 1, traits::Type<_Tp>::value, (void*)vec.val).copyTo(*this); +} + + +template inline +Mat::Mat(const Matx<_Tp,m,n>& M, bool copyData) + : flags(MAGIC_VAL | traits::Type<_Tp>::value | CV_MAT_CONT_FLAG), dims(2), rows(m), cols(n), data(0), + datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows), step(0) +{ + if( !copyData ) + { + step[0] = cols * sizeof(_Tp); + step[1] = sizeof(_Tp); + datastart = data = (uchar*)M.val; + datalimit = dataend = datastart + rows * step[0]; + } + else + Mat(m, n, traits::Type<_Tp>::value, (uchar*)M.val).copyTo(*this); +} + +template inline +Mat::Mat(const Point_<_Tp>& pt, bool copyData) + : flags(MAGIC_VAL | traits::Type<_Tp>::value | CV_MAT_CONT_FLAG), dims(2), rows(2), cols(1), data(0), + datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows), step(0) +{ + if( !copyData ) + { + step[0] = step[1] = sizeof(_Tp); + datastart = data = (uchar*)&pt.x; + datalimit = dataend = datastart + rows * step[0]; + } + else + { + create(2, 1, traits::Type<_Tp>::value); + ((_Tp*)data)[0] = pt.x; + ((_Tp*)data)[1] = pt.y; + } +} + +template inline +Mat::Mat(const Point3_<_Tp>& pt, bool copyData) + : flags(MAGIC_VAL | traits::Type<_Tp>::value | CV_MAT_CONT_FLAG), dims(2), rows(3), cols(1), data(0), + datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows), step(0) +{ + if( !copyData ) + { + step[0] = step[1] = sizeof(_Tp); + datastart = data = (uchar*)&pt.x; + datalimit = dataend = datastart + rows * step[0]; + } + else + { + create(3, 1, traits::Type<_Tp>::value); + ((_Tp*)data)[0] = pt.x; + ((_Tp*)data)[1] = pt.y; + ((_Tp*)data)[2] = pt.z; + } +} + +template inline +Mat::Mat(const MatCommaInitializer_<_Tp>& commaInitializer) + : flags(MAGIC_VAL | traits::Type<_Tp>::value | CV_MAT_CONT_FLAG), dims(0), rows(0), cols(0), data(0), + datastart(0), dataend(0), allocator(0), u(0), size(&rows) +{ + *this = commaInitializer.operator Mat_<_Tp>(); +} + +inline +Mat::~Mat() +{ + release(); + if( step.p != step.buf ) + fastFree(step.p); +} + +inline +Mat& Mat::operator = (const Mat& m) +{ + if( this != &m ) + { + if( m.u ) + CV_XADD(&m.u->refcount, 1); + release(); + flags = m.flags; + if( dims <= 2 && m.dims <= 2 ) + { + dims = m.dims; + rows = m.rows; + cols = m.cols; + step[0] = m.step[0]; + step[1] = m.step[1]; + } + else + copySize(m); + data = m.data; + datastart = m.datastart; + dataend = m.dataend; + datalimit = m.datalimit; + allocator = m.allocator; + u = m.u; + } + return *this; +} + +inline +Mat Mat::row(int y) const +{ + return Mat(*this, Range(y, y + 1), Range::all()); +} + +inline +Mat Mat::col(int x) const +{ + return Mat(*this, Range::all(), Range(x, x + 1)); +} + +inline +Mat Mat::rowRange(int startrow, int endrow) const +{ + return Mat(*this, Range(startrow, endrow), Range::all()); +} + +inline +Mat Mat::rowRange(const Range& r) const +{ + return Mat(*this, r, Range::all()); +} + +inline +Mat Mat::colRange(int startcol, int endcol) const +{ + return Mat(*this, Range::all(), Range(startcol, endcol)); +} + +inline +Mat Mat::colRange(const Range& r) const +{ + return Mat(*this, Range::all(), r); +} + +inline +Mat Mat::clone() const +{ + Mat m; + copyTo(m); + return m; +} + +inline +void Mat::assignTo( Mat& m, int _type ) const +{ + if( _type < 0 ) + m = *this; + else + convertTo(m, _type); +} + +inline +void Mat::create(int _rows, int _cols, int _type) +{ + _type &= TYPE_MASK; + if( dims <= 2 && rows == _rows && cols == _cols && type() == _type && data ) + return; + int sz[] = {_rows, _cols}; + create(2, sz, _type); +} + +inline +void Mat::create(Size _sz, int _type) +{ + create(_sz.height, _sz.width, _type); +} + +inline +void Mat::addref() +{ + if( u ) + CV_XADD(&u->refcount, 1); +} + +inline +void Mat::release() +{ + if( u && CV_XADD(&u->refcount, -1) == 1 ) + deallocate(); + u = NULL; + datastart = dataend = datalimit = data = 0; + for(int i = 0; i < dims; i++) + size.p[i] = 0; +#ifdef _DEBUG + flags = MAGIC_VAL; + dims = rows = cols = 0; + if(step.p != step.buf) + { + fastFree(step.p); + step.p = step.buf; + size.p = &rows; + } +#endif +} + +inline +Mat Mat::operator()( Range _rowRange, Range _colRange ) const +{ + return Mat(*this, _rowRange, _colRange); +} + +inline +Mat Mat::operator()( const Rect& roi ) const +{ + return Mat(*this, roi); +} + +inline +Mat Mat::operator()(const Range* ranges) const +{ + return Mat(*this, ranges); +} + +inline +Mat Mat::operator()(const std::vector& ranges) const +{ + return Mat(*this, ranges); +} + +inline +bool Mat::isContinuous() const +{ + return (flags & CONTINUOUS_FLAG) != 0; +} + +inline +bool Mat::isSubmatrix() const +{ + return (flags & SUBMATRIX_FLAG) != 0; +} + +inline +size_t Mat::elemSize() const +{ + size_t res = dims > 0 ? step.p[dims - 1] : 0; + CV_DbgAssert(res != 0); + return res; +} + +inline +size_t Mat::elemSize1() const +{ + return CV_ELEM_SIZE1(flags); +} + +inline +int Mat::type() const +{ + return CV_MAT_TYPE(flags); +} + +inline +int Mat::depth() const +{ + return CV_MAT_DEPTH(flags); +} + +inline +int Mat::channels() const +{ + return CV_MAT_CN(flags); +} + +inline +size_t Mat::step1(int i) const +{ + return step.p[i] / elemSize1(); +} + +inline +bool Mat::empty() const +{ + return data == 0 || total() == 0 || dims == 0; +} + +inline +size_t Mat::total() const +{ + if( dims <= 2 ) + return (size_t)rows * cols; + size_t p = 1; + for( int i = 0; i < dims; i++ ) + p *= size[i]; + return p; +} + +inline +size_t Mat::total(int startDim, int endDim) const +{ + CV_Assert( 0 <= startDim && startDim <= endDim); + size_t p = 1; + int endDim_ = endDim <= dims ? endDim : dims; + for( int i = startDim; i < endDim_; i++ ) + p *= size[i]; + return p; +} + +inline +uchar* Mat::ptr(int y) +{ + CV_DbgAssert( y == 0 || (data && dims >= 1 && (unsigned)y < (unsigned)size.p[0]) ); + return data + step.p[0] * y; +} + +inline +const uchar* Mat::ptr(int y) const +{ + CV_DbgAssert( y == 0 || (data && dims >= 1 && (unsigned)y < (unsigned)size.p[0]) ); + return data + step.p[0] * y; +} + +template inline +_Tp* Mat::ptr(int y) +{ + CV_DbgAssert( y == 0 || (data && dims >= 1 && (unsigned)y < (unsigned)size.p[0]) ); + return (_Tp*)(data + step.p[0] * y); +} + +template inline +const _Tp* Mat::ptr(int y) const +{ + CV_DbgAssert( y == 0 || (data && dims >= 1 && (unsigned)y < (unsigned)size.p[0]) ); + return (const _Tp*)(data + step.p[0] * y); +} + +inline +uchar* Mat::ptr(int i0, int i1) +{ + CV_DbgAssert(dims >= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); + return data + i0 * step.p[0] + i1 * step.p[1]; +} + +inline +const uchar* Mat::ptr(int i0, int i1) const +{ + CV_DbgAssert(dims >= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); + return data + i0 * step.p[0] + i1 * step.p[1]; +} + +template inline +_Tp* Mat::ptr(int i0, int i1) +{ + CV_DbgAssert(dims >= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); + return (_Tp*)(data + i0 * step.p[0] + i1 * step.p[1]); +} + +template inline +const _Tp* Mat::ptr(int i0, int i1) const +{ + CV_DbgAssert(dims >= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); + return (const _Tp*)(data + i0 * step.p[0] + i1 * step.p[1]); +} + +inline +uchar* Mat::ptr(int i0, int i1, int i2) +{ + CV_DbgAssert(dims >= 3); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); + CV_DbgAssert((unsigned)i2 < (unsigned)size.p[2]); + return data + i0 * step.p[0] + i1 * step.p[1] + i2 * step.p[2]; +} + +inline +const uchar* Mat::ptr(int i0, int i1, int i2) const +{ + CV_DbgAssert(dims >= 3); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); + CV_DbgAssert((unsigned)i2 < (unsigned)size.p[2]); + return data + i0 * step.p[0] + i1 * step.p[1] + i2 * step.p[2]; +} + +template inline +_Tp* Mat::ptr(int i0, int i1, int i2) +{ + CV_DbgAssert(dims >= 3); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); + CV_DbgAssert((unsigned)i2 < (unsigned)size.p[2]); + return (_Tp*)(data + i0 * step.p[0] + i1 * step.p[1] + i2 * step.p[2]); +} + +template inline +const _Tp* Mat::ptr(int i0, int i1, int i2) const +{ + CV_DbgAssert(dims >= 3); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); + CV_DbgAssert((unsigned)i2 < (unsigned)size.p[2]); + return (const _Tp*)(data + i0 * step.p[0] + i1 * step.p[1] + i2 * step.p[2]); +} + +inline +uchar* Mat::ptr(const int* idx) +{ + int i, d = dims; + uchar* p = data; + CV_DbgAssert( d >= 1 && p ); + for( i = 0; i < d; i++ ) + { + CV_DbgAssert( (unsigned)idx[i] < (unsigned)size.p[i] ); + p += idx[i] * step.p[i]; + } + return p; +} + +inline +const uchar* Mat::ptr(const int* idx) const +{ + int i, d = dims; + uchar* p = data; + CV_DbgAssert( d >= 1 && p ); + for( i = 0; i < d; i++ ) + { + CV_DbgAssert( (unsigned)idx[i] < (unsigned)size.p[i] ); + p += idx[i] * step.p[i]; + } + return p; +} + +template inline +_Tp* Mat::ptr(const int* idx) +{ + int i, d = dims; + uchar* p = data; + CV_DbgAssert( d >= 1 && p ); + for( i = 0; i < d; i++ ) + { + CV_DbgAssert( (unsigned)idx[i] < (unsigned)size.p[i] ); + p += idx[i] * step.p[i]; + } + return (_Tp*)p; +} + +template inline +const _Tp* Mat::ptr(const int* idx) const +{ + int i, d = dims; + uchar* p = data; + CV_DbgAssert( d >= 1 && p ); + for( i = 0; i < d; i++ ) + { + CV_DbgAssert( (unsigned)idx[i] < (unsigned)size.p[i] ); + p += idx[i] * step.p[i]; + } + return (const _Tp*)p; +} + +template inline +_Tp& Mat::at(int i0, int i1) +{ + CV_DbgAssert(dims <= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)(i1 * DataType<_Tp>::channels) < (unsigned)(size.p[1] * channels())); + CV_DbgAssert(CV_ELEM_SIZE1(traits::Depth<_Tp>::value) == elemSize1()); + return ((_Tp*)(data + step.p[0] * i0))[i1]; +} + +template inline +const _Tp& Mat::at(int i0, int i1) const +{ + CV_DbgAssert(dims <= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)(i1 * DataType<_Tp>::channels) < (unsigned)(size.p[1] * channels())); + CV_DbgAssert(CV_ELEM_SIZE1(traits::Depth<_Tp>::value) == elemSize1()); + return ((const _Tp*)(data + step.p[0] * i0))[i1]; +} + +template inline +_Tp& Mat::at(Point pt) +{ + CV_DbgAssert(dims <= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)pt.y < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)(pt.x * DataType<_Tp>::channels) < (unsigned)(size.p[1] * channels())); + CV_DbgAssert(CV_ELEM_SIZE1(traits::Depth<_Tp>::value) == elemSize1()); + return ((_Tp*)(data + step.p[0] * pt.y))[pt.x]; +} + +template inline +const _Tp& Mat::at(Point pt) const +{ + CV_DbgAssert(dims <= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)pt.y < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)(pt.x * DataType<_Tp>::channels) < (unsigned)(size.p[1] * channels())); + CV_DbgAssert(CV_ELEM_SIZE1(traits::Depth<_Tp>::value) == elemSize1()); + return ((const _Tp*)(data + step.p[0] * pt.y))[pt.x]; +} + +template inline +_Tp& Mat::at(int i0) +{ + CV_DbgAssert(dims <= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)(size.p[0] * size.p[1])); + CV_DbgAssert(elemSize() == sizeof(_Tp)); + if( isContinuous() || size.p[0] == 1 ) + return ((_Tp*)data)[i0]; + if( size.p[1] == 1 ) + return *(_Tp*)(data + step.p[0] * i0); + int i = i0 / cols, j = i0 - i * cols; + return ((_Tp*)(data + step.p[0] * i))[j]; +} + +template inline +const _Tp& Mat::at(int i0) const +{ + CV_DbgAssert(dims <= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)(size.p[0] * size.p[1])); + CV_DbgAssert(elemSize() == sizeof(_Tp)); + if( isContinuous() || size.p[0] == 1 ) + return ((const _Tp*)data)[i0]; + if( size.p[1] == 1 ) + return *(const _Tp*)(data + step.p[0] * i0); + int i = i0 / cols, j = i0 - i * cols; + return ((const _Tp*)(data + step.p[0] * i))[j]; +} + +template inline +_Tp& Mat::at(int i0, int i1, int i2) +{ + CV_DbgAssert( elemSize() == sizeof(_Tp) ); + return *(_Tp*)ptr(i0, i1, i2); +} + +template inline +const _Tp& Mat::at(int i0, int i1, int i2) const +{ + CV_DbgAssert( elemSize() == sizeof(_Tp) ); + return *(const _Tp*)ptr(i0, i1, i2); +} + +template inline +_Tp& Mat::at(const int* idx) +{ + CV_DbgAssert( elemSize() == sizeof(_Tp) ); + return *(_Tp*)ptr(idx); +} + +template inline +const _Tp& Mat::at(const int* idx) const +{ + CV_DbgAssert( elemSize() == sizeof(_Tp) ); + return *(const _Tp*)ptr(idx); +} + +template inline +_Tp& Mat::at(const Vec& idx) +{ + CV_DbgAssert( elemSize() == sizeof(_Tp) ); + return *(_Tp*)ptr(idx.val); +} + +template inline +const _Tp& Mat::at(const Vec& idx) const +{ + CV_DbgAssert( elemSize() == sizeof(_Tp) ); + return *(const _Tp*)ptr(idx.val); +} + +template inline +MatConstIterator_<_Tp> Mat::begin() const +{ + CV_DbgAssert( elemSize() == sizeof(_Tp) ); + return MatConstIterator_<_Tp>((const Mat_<_Tp>*)this); +} + +template inline +MatConstIterator_<_Tp> Mat::end() const +{ + CV_DbgAssert( elemSize() == sizeof(_Tp) ); + MatConstIterator_<_Tp> it((const Mat_<_Tp>*)this); + it += total(); + return it; +} + +template inline +MatIterator_<_Tp> Mat::begin() +{ + CV_DbgAssert( elemSize() == sizeof(_Tp) ); + return MatIterator_<_Tp>((Mat_<_Tp>*)this); +} + +template inline +MatIterator_<_Tp> Mat::end() +{ + CV_DbgAssert( elemSize() == sizeof(_Tp) ); + MatIterator_<_Tp> it((Mat_<_Tp>*)this); + it += total(); + return it; +} + +template inline +void Mat::forEach(const Functor& operation) { + this->forEach_impl<_Tp>(operation); +} + +template inline +void Mat::forEach(const Functor& operation) const { + // call as not const + (const_cast(this))->forEach<_Tp>(operation); +} + +template inline +Mat::operator std::vector<_Tp>() const +{ + std::vector<_Tp> v; + copyTo(v); + return v; +} + +#ifdef CV_CXX_STD_ARRAY +template inline +Mat::operator std::array<_Tp, _Nm>() const +{ + std::array<_Tp, _Nm> v; + copyTo(v); + return v; +} +#endif + +template inline +Mat::operator Vec<_Tp, n>() const +{ + CV_Assert( data && dims <= 2 && (rows == 1 || cols == 1) && + rows + cols - 1 == n && channels() == 1 ); + + if( isContinuous() && type() == traits::Type<_Tp>::value ) + return Vec<_Tp, n>((_Tp*)data); + Vec<_Tp, n> v; + Mat tmp(rows, cols, traits::Type<_Tp>::value, v.val); + convertTo(tmp, tmp.type()); + return v; +} + +template inline +Mat::operator Matx<_Tp, m, n>() const +{ + CV_Assert( data && dims <= 2 && rows == m && cols == n && channels() == 1 ); + + if( isContinuous() && type() == traits::Type<_Tp>::value ) + return Matx<_Tp, m, n>((_Tp*)data); + Matx<_Tp, m, n> mtx; + Mat tmp(rows, cols, traits::Type<_Tp>::value, mtx.val); + convertTo(tmp, tmp.type()); + return mtx; +} + +template inline +void Mat::push_back(const _Tp& elem) +{ + if( !data ) + { + *this = Mat(1, 1, traits::Type<_Tp>::value, (void*)&elem).clone(); + return; + } + CV_Assert(traits::Type<_Tp>::value == type() && cols == 1 + /* && dims == 2 (cols == 1 implies dims == 2) */); + const uchar* tmp = dataend + step[0]; + if( !isSubmatrix() && isContinuous() && tmp <= datalimit ) + { + *(_Tp*)(data + (size.p[0]++) * step.p[0]) = elem; + dataend = tmp; + } + else + push_back_(&elem); +} + +template inline +void Mat::push_back(const Mat_<_Tp>& m) +{ + push_back((const Mat&)m); +} + +template<> inline +void Mat::push_back(const MatExpr& expr) +{ + push_back(static_cast(expr)); +} + + +template inline +void Mat::push_back(const std::vector<_Tp>& v) +{ + push_back(Mat(v)); +} + +#ifdef CV_CXX_MOVE_SEMANTICS + +inline +Mat::Mat(Mat&& m) + : flags(m.flags), dims(m.dims), rows(m.rows), cols(m.cols), data(m.data), + datastart(m.datastart), dataend(m.dataend), datalimit(m.datalimit), allocator(m.allocator), + u(m.u), size(&rows) +{ + if (m.dims <= 2) // move new step/size info + { + step[0] = m.step[0]; + step[1] = m.step[1]; + } + else + { + CV_DbgAssert(m.step.p != m.step.buf); + step.p = m.step.p; + size.p = m.size.p; + m.step.p = m.step.buf; + m.size.p = &m.rows; + } + m.flags = MAGIC_VAL; m.dims = m.rows = m.cols = 0; + m.data = NULL; m.datastart = NULL; m.dataend = NULL; m.datalimit = NULL; + m.allocator = NULL; + m.u = NULL; +} + +inline +Mat& Mat::operator = (Mat&& m) +{ + if (this == &m) + return *this; + + release(); + flags = m.flags; dims = m.dims; rows = m.rows; cols = m.cols; data = m.data; + datastart = m.datastart; dataend = m.dataend; datalimit = m.datalimit; allocator = m.allocator; + u = m.u; + if (step.p != step.buf) // release self step/size + { + fastFree(step.p); + step.p = step.buf; + size.p = &rows; + } + if (m.dims <= 2) // move new step/size info + { + step[0] = m.step[0]; + step[1] = m.step[1]; + } + else + { + CV_DbgAssert(m.step.p != m.step.buf); + step.p = m.step.p; + size.p = m.size.p; + m.step.p = m.step.buf; + m.size.p = &m.rows; + } + m.flags = MAGIC_VAL; m.dims = m.rows = m.cols = 0; + m.data = NULL; m.datastart = NULL; m.dataend = NULL; m.datalimit = NULL; + m.allocator = NULL; + m.u = NULL; + return *this; +} + +#endif + + +///////////////////////////// MatSize //////////////////////////// + +inline +MatSize::MatSize(int* _p) + : p(_p) {} + +inline +int MatSize::dims() const +{ + return (p - 1)[0]; +} + +inline +Size MatSize::operator()() const +{ + CV_DbgAssert(dims() <= 2); + return Size(p[1], p[0]); +} + +inline +const int& MatSize::operator[](int i) const +{ + CV_DbgAssert(i < dims()); +#ifdef __OPENCV_BUILD + CV_DbgAssert(i >= 0); +#endif + return p[i]; +} + +inline +int& MatSize::operator[](int i) +{ + CV_DbgAssert(i < dims()); +#ifdef __OPENCV_BUILD + CV_DbgAssert(i >= 0); +#endif + return p[i]; +} + +inline +MatSize::operator const int*() const +{ + return p; +} + +inline +bool MatSize::operator == (const MatSize& sz) const +{ + int d = dims(); + int dsz = sz.dims(); + if( d != dsz ) + return false; + if( d == 2 ) + return p[0] == sz.p[0] && p[1] == sz.p[1]; + + for( int i = 0; i < d; i++ ) + if( p[i] != sz.p[i] ) + return false; + return true; +} + +inline +bool MatSize::operator != (const MatSize& sz) const +{ + return !(*this == sz); +} + + + +///////////////////////////// MatStep //////////////////////////// + +inline +MatStep::MatStep() +{ + p = buf; p[0] = p[1] = 0; +} + +inline +MatStep::MatStep(size_t s) +{ + p = buf; p[0] = s; p[1] = 0; +} + +inline +const size_t& MatStep::operator[](int i) const +{ + return p[i]; +} + +inline +size_t& MatStep::operator[](int i) +{ + return p[i]; +} + +inline MatStep::operator size_t() const +{ + CV_DbgAssert( p == buf ); + return buf[0]; +} + +inline MatStep& MatStep::operator = (size_t s) +{ + CV_DbgAssert( p == buf ); + buf[0] = s; + return *this; +} + + + +////////////////////////////// Mat_<_Tp> //////////////////////////// + +template inline +Mat_<_Tp>::Mat_() + : Mat() +{ + flags = (flags & ~CV_MAT_TYPE_MASK) | traits::Type<_Tp>::value; +} + +template inline +Mat_<_Tp>::Mat_(int _rows, int _cols) + : Mat(_rows, _cols, traits::Type<_Tp>::value) +{ +} + +template inline +Mat_<_Tp>::Mat_(int _rows, int _cols, const _Tp& value) + : Mat(_rows, _cols, traits::Type<_Tp>::value) +{ + *this = value; +} + +template inline +Mat_<_Tp>::Mat_(Size _sz) + : Mat(_sz.height, _sz.width, traits::Type<_Tp>::value) +{} + +template inline +Mat_<_Tp>::Mat_(Size _sz, const _Tp& value) + : Mat(_sz.height, _sz.width, traits::Type<_Tp>::value) +{ + *this = value; +} + +template inline +Mat_<_Tp>::Mat_(int _dims, const int* _sz) + : Mat(_dims, _sz, traits::Type<_Tp>::value) +{} + +template inline +Mat_<_Tp>::Mat_(int _dims, const int* _sz, const _Tp& _s) + : Mat(_dims, _sz, traits::Type<_Tp>::value, Scalar(_s)) +{} + +template inline +Mat_<_Tp>::Mat_(int _dims, const int* _sz, _Tp* _data, const size_t* _steps) + : Mat(_dims, _sz, traits::Type<_Tp>::value, _data, _steps) +{} + +template inline +Mat_<_Tp>::Mat_(const Mat_<_Tp>& m, const Range* ranges) + : Mat(m, ranges) +{} + +template inline +Mat_<_Tp>::Mat_(const Mat_<_Tp>& m, const std::vector& ranges) + : Mat(m, ranges) +{} + +template inline +Mat_<_Tp>::Mat_(const Mat& m) + : Mat() +{ + flags = (flags & ~CV_MAT_TYPE_MASK) | traits::Type<_Tp>::value; + *this = m; +} + +template inline +Mat_<_Tp>::Mat_(const Mat_& m) + : Mat(m) +{} + +template inline +Mat_<_Tp>::Mat_(int _rows, int _cols, _Tp* _data, size_t steps) + : Mat(_rows, _cols, traits::Type<_Tp>::value, _data, steps) +{} + +template inline +Mat_<_Tp>::Mat_(const Mat_& m, const Range& _rowRange, const Range& _colRange) + : Mat(m, _rowRange, _colRange) +{} + +template inline +Mat_<_Tp>::Mat_(const Mat_& m, const Rect& roi) + : Mat(m, roi) +{} + +template template inline +Mat_<_Tp>::Mat_(const Vec::channel_type, n>& vec, bool copyData) + : Mat(n / DataType<_Tp>::channels, 1, traits::Type<_Tp>::value, (void*)&vec) +{ + CV_Assert(n%DataType<_Tp>::channels == 0); + if( copyData ) + *this = clone(); +} + +template template inline +Mat_<_Tp>::Mat_(const Matx::channel_type, m, n>& M, bool copyData) + : Mat(m, n / DataType<_Tp>::channels, traits::Type<_Tp>::value, (void*)&M) +{ + CV_Assert(n % DataType<_Tp>::channels == 0); + if( copyData ) + *this = clone(); +} + +template inline +Mat_<_Tp>::Mat_(const Point_::channel_type>& pt, bool copyData) + : Mat(2 / DataType<_Tp>::channels, 1, traits::Type<_Tp>::value, (void*)&pt) +{ + CV_Assert(2 % DataType<_Tp>::channels == 0); + if( copyData ) + *this = clone(); +} + +template inline +Mat_<_Tp>::Mat_(const Point3_::channel_type>& pt, bool copyData) + : Mat(3 / DataType<_Tp>::channels, 1, traits::Type<_Tp>::value, (void*)&pt) +{ + CV_Assert(3 % DataType<_Tp>::channels == 0); + if( copyData ) + *this = clone(); +} + +template inline +Mat_<_Tp>::Mat_(const MatCommaInitializer_<_Tp>& commaInitializer) + : Mat(commaInitializer) +{} + +template inline +Mat_<_Tp>::Mat_(const std::vector<_Tp>& vec, bool copyData) + : Mat(vec, copyData) +{} + +#ifdef CV_CXX11 +template inline +Mat_<_Tp>::Mat_(std::initializer_list<_Tp> list) + : Mat(list) +{} + +template inline +Mat_<_Tp>::Mat_(const std::initializer_list sizes, std::initializer_list<_Tp> list) + : Mat(sizes, list) +{} +#endif + +#ifdef CV_CXX_STD_ARRAY +template template inline +Mat_<_Tp>::Mat_(const std::array<_Tp, _Nm>& arr, bool copyData) + : Mat(arr, copyData) +{} +#endif + +template inline +Mat_<_Tp>& Mat_<_Tp>::operator = (const Mat& m) +{ + if( traits::Type<_Tp>::value == m.type() ) + { + Mat::operator = (m); + return *this; + } + if( traits::Depth<_Tp>::value == m.depth() ) + { + return (*this = m.reshape(DataType<_Tp>::channels, m.dims, 0)); + } + CV_Assert(DataType<_Tp>::channels == m.channels() || m.empty()); + m.convertTo(*this, type()); + return *this; +} + +template inline +Mat_<_Tp>& Mat_<_Tp>::operator = (const Mat_& m) +{ + Mat::operator=(m); + return *this; +} + +template inline +Mat_<_Tp>& Mat_<_Tp>::operator = (const _Tp& s) +{ + typedef typename DataType<_Tp>::vec_type VT; + Mat::operator=(Scalar((const VT&)s)); + return *this; +} + +template inline +void Mat_<_Tp>::create(int _rows, int _cols) +{ + Mat::create(_rows, _cols, traits::Type<_Tp>::value); +} + +template inline +void Mat_<_Tp>::create(Size _sz) +{ + Mat::create(_sz, traits::Type<_Tp>::value); +} + +template inline +void Mat_<_Tp>::create(int _dims, const int* _sz) +{ + Mat::create(_dims, _sz, traits::Type<_Tp>::value); +} + +template inline +void Mat_<_Tp>::release() +{ + Mat::release(); +#ifdef _DEBUG + flags = (flags & ~CV_MAT_TYPE_MASK) | traits::Type<_Tp>::value; +#endif +} + +template inline +Mat_<_Tp> Mat_<_Tp>::cross(const Mat_& m) const +{ + return Mat_<_Tp>(Mat::cross(m)); +} + +template template inline +Mat_<_Tp>::operator Mat_() const +{ + return Mat_(*this); +} + +template inline +Mat_<_Tp> Mat_<_Tp>::row(int y) const +{ + return Mat_(*this, Range(y, y+1), Range::all()); +} + +template inline +Mat_<_Tp> Mat_<_Tp>::col(int x) const +{ + return Mat_(*this, Range::all(), Range(x, x+1)); +} + +template inline +Mat_<_Tp> Mat_<_Tp>::diag(int d) const +{ + return Mat_(Mat::diag(d)); +} + +template inline +Mat_<_Tp> Mat_<_Tp>::clone() const +{ + return Mat_(Mat::clone()); +} + +template inline +size_t Mat_<_Tp>::elemSize() const +{ + CV_DbgAssert( Mat::elemSize() == sizeof(_Tp) ); + return sizeof(_Tp); +} + +template inline +size_t Mat_<_Tp>::elemSize1() const +{ + CV_DbgAssert( Mat::elemSize1() == sizeof(_Tp) / DataType<_Tp>::channels ); + return sizeof(_Tp) / DataType<_Tp>::channels; +} + +template inline +int Mat_<_Tp>::type() const +{ + CV_DbgAssert( Mat::type() == traits::Type<_Tp>::value ); + return traits::Type<_Tp>::value; +} + +template inline +int Mat_<_Tp>::depth() const +{ + CV_DbgAssert( Mat::depth() == traits::Depth<_Tp>::value ); + return traits::Depth<_Tp>::value; +} + +template inline +int Mat_<_Tp>::channels() const +{ + CV_DbgAssert( Mat::channels() == DataType<_Tp>::channels ); + return DataType<_Tp>::channels; +} + +template inline +size_t Mat_<_Tp>::stepT(int i) const +{ + return step.p[i] / elemSize(); +} + +template inline +size_t Mat_<_Tp>::step1(int i) const +{ + return step.p[i] / elemSize1(); +} + +template inline +Mat_<_Tp>& Mat_<_Tp>::adjustROI( int dtop, int dbottom, int dleft, int dright ) +{ + return (Mat_<_Tp>&)(Mat::adjustROI(dtop, dbottom, dleft, dright)); +} + +template inline +Mat_<_Tp> Mat_<_Tp>::operator()( const Range& _rowRange, const Range& _colRange ) const +{ + return Mat_<_Tp>(*this, _rowRange, _colRange); +} + +template inline +Mat_<_Tp> Mat_<_Tp>::operator()( const Rect& roi ) const +{ + return Mat_<_Tp>(*this, roi); +} + +template inline +Mat_<_Tp> Mat_<_Tp>::operator()( const Range* ranges ) const +{ + return Mat_<_Tp>(*this, ranges); +} + +template inline +Mat_<_Tp> Mat_<_Tp>::operator()(const std::vector& ranges) const +{ + return Mat_<_Tp>(*this, ranges); +} + +template inline +_Tp* Mat_<_Tp>::operator [](int y) +{ + CV_DbgAssert( 0 <= y && y < size.p[0] ); + return (_Tp*)(data + y*step.p[0]); +} + +template inline +const _Tp* Mat_<_Tp>::operator [](int y) const +{ + CV_DbgAssert( 0 <= y && y < size.p[0] ); + return (const _Tp*)(data + y*step.p[0]); +} + +template inline +_Tp& Mat_<_Tp>::operator ()(int i0, int i1) +{ + CV_DbgAssert(dims <= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); + CV_DbgAssert(type() == traits::Type<_Tp>::value); + return ((_Tp*)(data + step.p[0] * i0))[i1]; +} + +template inline +const _Tp& Mat_<_Tp>::operator ()(int i0, int i1) const +{ + CV_DbgAssert(dims <= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); + CV_DbgAssert(type() == traits::Type<_Tp>::value); + return ((const _Tp*)(data + step.p[0] * i0))[i1]; +} + +template inline +_Tp& Mat_<_Tp>::operator ()(Point pt) +{ + CV_DbgAssert(dims <= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)pt.y < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)pt.x < (unsigned)size.p[1]); + CV_DbgAssert(type() == traits::Type<_Tp>::value); + return ((_Tp*)(data + step.p[0] * pt.y))[pt.x]; +} + +template inline +const _Tp& Mat_<_Tp>::operator ()(Point pt) const +{ + CV_DbgAssert(dims <= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)pt.y < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)pt.x < (unsigned)size.p[1]); + CV_DbgAssert(type() == traits::Type<_Tp>::value); + return ((const _Tp*)(data + step.p[0] * pt.y))[pt.x]; +} + +template inline +_Tp& Mat_<_Tp>::operator ()(const int* idx) +{ + return Mat::at<_Tp>(idx); +} + +template inline +const _Tp& Mat_<_Tp>::operator ()(const int* idx) const +{ + return Mat::at<_Tp>(idx); +} + +template template inline +_Tp& Mat_<_Tp>::operator ()(const Vec& idx) +{ + return Mat::at<_Tp>(idx); +} + +template template inline +const _Tp& Mat_<_Tp>::operator ()(const Vec& idx) const +{ + return Mat::at<_Tp>(idx); +} + +template inline +_Tp& Mat_<_Tp>::operator ()(int i0) +{ + return this->at<_Tp>(i0); +} + +template inline +const _Tp& Mat_<_Tp>::operator ()(int i0) const +{ + return this->at<_Tp>(i0); +} + +template inline +_Tp& Mat_<_Tp>::operator ()(int i0, int i1, int i2) +{ + return this->at<_Tp>(i0, i1, i2); +} + +template inline +const _Tp& Mat_<_Tp>::operator ()(int i0, int i1, int i2) const +{ + return this->at<_Tp>(i0, i1, i2); +} + +template inline +Mat_<_Tp>::operator std::vector<_Tp>() const +{ + std::vector<_Tp> v; + copyTo(v); + return v; +} + +#ifdef CV_CXX_STD_ARRAY +template template inline +Mat_<_Tp>::operator std::array<_Tp, _Nm>() const +{ + std::array<_Tp, _Nm> a; + copyTo(a); + return a; +} +#endif + +template template inline +Mat_<_Tp>::operator Vec::channel_type, n>() const +{ + CV_Assert(n % DataType<_Tp>::channels == 0); + +#if defined _MSC_VER + const Mat* pMat = (const Mat*)this; // workaround for MSVS <= 2012 compiler bugs (but GCC 4.6 dislikes this workaround) + return pMat->operator Vec::channel_type, n>(); +#else + return this->Mat::operator Vec::channel_type, n>(); +#endif +} + +template template inline +Mat_<_Tp>::operator Matx::channel_type, m, n>() const +{ + CV_Assert(n % DataType<_Tp>::channels == 0); + +#if defined _MSC_VER + const Mat* pMat = (const Mat*)this; // workaround for MSVS <= 2012 compiler bugs (but GCC 4.6 dislikes this workaround) + Matx::channel_type, m, n> res = pMat->operator Matx::channel_type, m, n>(); + return res; +#else + Matx::channel_type, m, n> res = this->Mat::operator Matx::channel_type, m, n>(); + return res; +#endif +} + +template inline +MatConstIterator_<_Tp> Mat_<_Tp>::begin() const +{ + return Mat::begin<_Tp>(); +} + +template inline +MatConstIterator_<_Tp> Mat_<_Tp>::end() const +{ + return Mat::end<_Tp>(); +} + +template inline +MatIterator_<_Tp> Mat_<_Tp>::begin() +{ + return Mat::begin<_Tp>(); +} + +template inline +MatIterator_<_Tp> Mat_<_Tp>::end() +{ + return Mat::end<_Tp>(); +} + +template template inline +void Mat_<_Tp>::forEach(const Functor& operation) { + Mat::forEach<_Tp, Functor>(operation); +} + +template template inline +void Mat_<_Tp>::forEach(const Functor& operation) const { + Mat::forEach<_Tp, Functor>(operation); +} + +#ifdef CV_CXX_MOVE_SEMANTICS + +template inline +Mat_<_Tp>::Mat_(Mat_&& m) + : Mat(m) +{ +} + +template inline +Mat_<_Tp>& Mat_<_Tp>::operator = (Mat_&& m) +{ + Mat::operator = (std::move(m)); + return *this; +} + +template inline +Mat_<_Tp>::Mat_(Mat&& m) + : Mat() +{ + flags = (flags & ~CV_MAT_TYPE_MASK) | traits::Type<_Tp>::value; + *this = m; +} + +template inline +Mat_<_Tp>& Mat_<_Tp>::operator = (Mat&& m) +{ + if( traits::Type<_Tp>::value == m.type() ) + { + Mat::operator = ((Mat&&)m); + return *this; + } + if( traits::Depth<_Tp>::value == m.depth() ) + { + Mat::operator = ((Mat&&)m.reshape(DataType<_Tp>::channels, m.dims, 0)); + return *this; + } + CV_DbgAssert(DataType<_Tp>::channels == m.channels()); + m.convertTo(*this, type()); + return *this; +} + +template inline +Mat_<_Tp>::Mat_(MatExpr&& e) + : Mat() +{ + flags = (flags & ~CV_MAT_TYPE_MASK) | traits::Type<_Tp>::value; + *this = Mat(e); +} + +#endif + +///////////////////////////// SparseMat ///////////////////////////// + +inline +SparseMat::SparseMat() + : flags(MAGIC_VAL), hdr(0) +{} + +inline +SparseMat::SparseMat(int _dims, const int* _sizes, int _type) + : flags(MAGIC_VAL), hdr(0) +{ + create(_dims, _sizes, _type); +} + +inline +SparseMat::SparseMat(const SparseMat& m) + : flags(m.flags), hdr(m.hdr) +{ + addref(); +} + +inline +SparseMat::~SparseMat() +{ + release(); +} + +inline +SparseMat& SparseMat::operator = (const SparseMat& m) +{ + if( this != &m ) + { + if( m.hdr ) + CV_XADD(&m.hdr->refcount, 1); + release(); + flags = m.flags; + hdr = m.hdr; + } + return *this; +} + +inline +SparseMat& SparseMat::operator = (const Mat& m) +{ + return (*this = SparseMat(m)); +} + +inline +SparseMat SparseMat::clone() const +{ + SparseMat temp; + this->copyTo(temp); + return temp; +} + +inline +void SparseMat::assignTo( SparseMat& m, int _type ) const +{ + if( _type < 0 ) + m = *this; + else + convertTo(m, _type); +} + +inline +void SparseMat::addref() +{ + if( hdr ) + CV_XADD(&hdr->refcount, 1); +} + +inline +void SparseMat::release() +{ + if( hdr && CV_XADD(&hdr->refcount, -1) == 1 ) + delete hdr; + hdr = 0; +} + +inline +size_t SparseMat::elemSize() const +{ + return CV_ELEM_SIZE(flags); +} + +inline +size_t SparseMat::elemSize1() const +{ + return CV_ELEM_SIZE1(flags); +} + +inline +int SparseMat::type() const +{ + return CV_MAT_TYPE(flags); +} + +inline +int SparseMat::depth() const +{ + return CV_MAT_DEPTH(flags); +} + +inline +int SparseMat::channels() const +{ + return CV_MAT_CN(flags); +} + +inline +const int* SparseMat::size() const +{ + return hdr ? hdr->size : 0; +} + +inline +int SparseMat::size(int i) const +{ + if( hdr ) + { + CV_DbgAssert((unsigned)i < (unsigned)hdr->dims); + return hdr->size[i]; + } + return 0; +} + +inline +int SparseMat::dims() const +{ + return hdr ? hdr->dims : 0; +} + +inline +size_t SparseMat::nzcount() const +{ + return hdr ? hdr->nodeCount : 0; +} + +inline +size_t SparseMat::hash(int i0) const +{ + return (size_t)i0; +} + +inline +size_t SparseMat::hash(int i0, int i1) const +{ + return (size_t)(unsigned)i0 * HASH_SCALE + (unsigned)i1; +} + +inline +size_t SparseMat::hash(int i0, int i1, int i2) const +{ + return ((size_t)(unsigned)i0 * HASH_SCALE + (unsigned)i1) * HASH_SCALE + (unsigned)i2; +} + +inline +size_t SparseMat::hash(const int* idx) const +{ + size_t h = (unsigned)idx[0]; + if( !hdr ) + return 0; + int d = hdr->dims; + for(int i = 1; i < d; i++ ) + h = h * HASH_SCALE + (unsigned)idx[i]; + return h; +} + +template inline +_Tp& SparseMat::ref(int i0, size_t* hashval) +{ + return *(_Tp*)((SparseMat*)this)->ptr(i0, true, hashval); +} + +template inline +_Tp& SparseMat::ref(int i0, int i1, size_t* hashval) +{ + return *(_Tp*)((SparseMat*)this)->ptr(i0, i1, true, hashval); +} + +template inline +_Tp& SparseMat::ref(int i0, int i1, int i2, size_t* hashval) +{ + return *(_Tp*)((SparseMat*)this)->ptr(i0, i1, i2, true, hashval); +} + +template inline +_Tp& SparseMat::ref(const int* idx, size_t* hashval) +{ + return *(_Tp*)((SparseMat*)this)->ptr(idx, true, hashval); +} + +template inline +_Tp SparseMat::value(int i0, size_t* hashval) const +{ + const _Tp* p = (const _Tp*)((SparseMat*)this)->ptr(i0, false, hashval); + return p ? *p : _Tp(); +} + +template inline +_Tp SparseMat::value(int i0, int i1, size_t* hashval) const +{ + const _Tp* p = (const _Tp*)((SparseMat*)this)->ptr(i0, i1, false, hashval); + return p ? *p : _Tp(); +} + +template inline +_Tp SparseMat::value(int i0, int i1, int i2, size_t* hashval) const +{ + const _Tp* p = (const _Tp*)((SparseMat*)this)->ptr(i0, i1, i2, false, hashval); + return p ? *p : _Tp(); +} + +template inline +_Tp SparseMat::value(const int* idx, size_t* hashval) const +{ + const _Tp* p = (const _Tp*)((SparseMat*)this)->ptr(idx, false, hashval); + return p ? *p : _Tp(); +} + +template inline +const _Tp* SparseMat::find(int i0, size_t* hashval) const +{ + return (const _Tp*)((SparseMat*)this)->ptr(i0, false, hashval); +} + +template inline +const _Tp* SparseMat::find(int i0, int i1, size_t* hashval) const +{ + return (const _Tp*)((SparseMat*)this)->ptr(i0, i1, false, hashval); +} + +template inline +const _Tp* SparseMat::find(int i0, int i1, int i2, size_t* hashval) const +{ + return (const _Tp*)((SparseMat*)this)->ptr(i0, i1, i2, false, hashval); +} + +template inline +const _Tp* SparseMat::find(const int* idx, size_t* hashval) const +{ + return (const _Tp*)((SparseMat*)this)->ptr(idx, false, hashval); +} + +template inline +_Tp& SparseMat::value(Node* n) +{ + return *(_Tp*)((uchar*)n + hdr->valueOffset); +} + +template inline +const _Tp& SparseMat::value(const Node* n) const +{ + return *(const _Tp*)((const uchar*)n + hdr->valueOffset); +} + +inline +SparseMat::Node* SparseMat::node(size_t nidx) +{ + return (Node*)(void*)&hdr->pool[nidx]; +} + +inline +const SparseMat::Node* SparseMat::node(size_t nidx) const +{ + return (const Node*)(const void*)&hdr->pool[nidx]; +} + +inline +SparseMatIterator SparseMat::begin() +{ + return SparseMatIterator(this); +} + +inline +SparseMatConstIterator SparseMat::begin() const +{ + return SparseMatConstIterator(this); +} + +inline +SparseMatIterator SparseMat::end() +{ + SparseMatIterator it(this); + it.seekEnd(); + return it; +} + +inline +SparseMatConstIterator SparseMat::end() const +{ + SparseMatConstIterator it(this); + it.seekEnd(); + return it; +} + +template inline +SparseMatIterator_<_Tp> SparseMat::begin() +{ + return SparseMatIterator_<_Tp>(this); +} + +template inline +SparseMatConstIterator_<_Tp> SparseMat::begin() const +{ + return SparseMatConstIterator_<_Tp>(this); +} + +template inline +SparseMatIterator_<_Tp> SparseMat::end() +{ + SparseMatIterator_<_Tp> it(this); + it.seekEnd(); + return it; +} + +template inline +SparseMatConstIterator_<_Tp> SparseMat::end() const +{ + SparseMatConstIterator_<_Tp> it(this); + it.seekEnd(); + return it; +} + + + +///////////////////////////// SparseMat_ //////////////////////////// + +template inline +SparseMat_<_Tp>::SparseMat_() +{ + flags = MAGIC_VAL | traits::Type<_Tp>::value; +} + +template inline +SparseMat_<_Tp>::SparseMat_(int _dims, const int* _sizes) + : SparseMat(_dims, _sizes, traits::Type<_Tp>::value) +{} + +template inline +SparseMat_<_Tp>::SparseMat_(const SparseMat& m) +{ + if( m.type() == traits::Type<_Tp>::value ) + *this = (const SparseMat_<_Tp>&)m; + else + m.convertTo(*this, traits::Type<_Tp>::value); +} + +template inline +SparseMat_<_Tp>::SparseMat_(const SparseMat_<_Tp>& m) +{ + this->flags = m.flags; + this->hdr = m.hdr; + if( this->hdr ) + CV_XADD(&this->hdr->refcount, 1); +} + +template inline +SparseMat_<_Tp>::SparseMat_(const Mat& m) +{ + SparseMat sm(m); + *this = sm; +} + +template inline +SparseMat_<_Tp>& SparseMat_<_Tp>::operator = (const SparseMat_<_Tp>& m) +{ + if( this != &m ) + { + if( m.hdr ) CV_XADD(&m.hdr->refcount, 1); + release(); + flags = m.flags; + hdr = m.hdr; + } + return *this; +} + +template inline +SparseMat_<_Tp>& SparseMat_<_Tp>::operator = (const SparseMat& m) +{ + if( m.type() == traits::Type<_Tp>::value ) + return (*this = (const SparseMat_<_Tp>&)m); + m.convertTo(*this, traits::Type<_Tp>::value); + return *this; +} + +template inline +SparseMat_<_Tp>& SparseMat_<_Tp>::operator = (const Mat& m) +{ + return (*this = SparseMat(m)); +} + +template inline +SparseMat_<_Tp> SparseMat_<_Tp>::clone() const +{ + SparseMat_<_Tp> m; + this->copyTo(m); + return m; +} + +template inline +void SparseMat_<_Tp>::create(int _dims, const int* _sizes) +{ + SparseMat::create(_dims, _sizes, traits::Type<_Tp>::value); +} + +template inline +int SparseMat_<_Tp>::type() const +{ + return traits::Type<_Tp>::value; +} + +template inline +int SparseMat_<_Tp>::depth() const +{ + return traits::Depth<_Tp>::value; +} + +template inline +int SparseMat_<_Tp>::channels() const +{ + return DataType<_Tp>::channels; +} + +template inline +_Tp& SparseMat_<_Tp>::ref(int i0, size_t* hashval) +{ + return SparseMat::ref<_Tp>(i0, hashval); +} + +template inline +_Tp SparseMat_<_Tp>::operator()(int i0, size_t* hashval) const +{ + return SparseMat::value<_Tp>(i0, hashval); +} + +template inline +_Tp& SparseMat_<_Tp>::ref(int i0, int i1, size_t* hashval) +{ + return SparseMat::ref<_Tp>(i0, i1, hashval); +} + +template inline +_Tp SparseMat_<_Tp>::operator()(int i0, int i1, size_t* hashval) const +{ + return SparseMat::value<_Tp>(i0, i1, hashval); +} + +template inline +_Tp& SparseMat_<_Tp>::ref(int i0, int i1, int i2, size_t* hashval) +{ + return SparseMat::ref<_Tp>(i0, i1, i2, hashval); +} + +template inline +_Tp SparseMat_<_Tp>::operator()(int i0, int i1, int i2, size_t* hashval) const +{ + return SparseMat::value<_Tp>(i0, i1, i2, hashval); +} + +template inline +_Tp& SparseMat_<_Tp>::ref(const int* idx, size_t* hashval) +{ + return SparseMat::ref<_Tp>(idx, hashval); +} + +template inline +_Tp SparseMat_<_Tp>::operator()(const int* idx, size_t* hashval) const +{ + return SparseMat::value<_Tp>(idx, hashval); +} + +template inline +SparseMatIterator_<_Tp> SparseMat_<_Tp>::begin() +{ + return SparseMatIterator_<_Tp>(this); +} + +template inline +SparseMatConstIterator_<_Tp> SparseMat_<_Tp>::begin() const +{ + return SparseMatConstIterator_<_Tp>(this); +} + +template inline +SparseMatIterator_<_Tp> SparseMat_<_Tp>::end() +{ + SparseMatIterator_<_Tp> it(this); + it.seekEnd(); + return it; +} + +template inline +SparseMatConstIterator_<_Tp> SparseMat_<_Tp>::end() const +{ + SparseMatConstIterator_<_Tp> it(this); + it.seekEnd(); + return it; +} + + + +////////////////////////// MatConstIterator ///////////////////////// + +inline +MatConstIterator::MatConstIterator() + : m(0), elemSize(0), ptr(0), sliceStart(0), sliceEnd(0) +{} + +inline +MatConstIterator::MatConstIterator(const Mat* _m) + : m(_m), elemSize(_m->elemSize()), ptr(0), sliceStart(0), sliceEnd(0) +{ + if( m && m->isContinuous() ) + { + sliceStart = m->ptr(); + sliceEnd = sliceStart + m->total()*elemSize; + } + seek((const int*)0); +} + +inline +MatConstIterator::MatConstIterator(const Mat* _m, int _row, int _col) + : m(_m), elemSize(_m->elemSize()), ptr(0), sliceStart(0), sliceEnd(0) +{ + CV_Assert(m && m->dims <= 2); + if( m->isContinuous() ) + { + sliceStart = m->ptr(); + sliceEnd = sliceStart + m->total()*elemSize; + } + int idx[] = {_row, _col}; + seek(idx); +} + +inline +MatConstIterator::MatConstIterator(const Mat* _m, Point _pt) + : m(_m), elemSize(_m->elemSize()), ptr(0), sliceStart(0), sliceEnd(0) +{ + CV_Assert(m && m->dims <= 2); + if( m->isContinuous() ) + { + sliceStart = m->ptr(); + sliceEnd = sliceStart + m->total()*elemSize; + } + int idx[] = {_pt.y, _pt.x}; + seek(idx); +} + +inline +MatConstIterator::MatConstIterator(const MatConstIterator& it) + : m(it.m), elemSize(it.elemSize), ptr(it.ptr), sliceStart(it.sliceStart), sliceEnd(it.sliceEnd) +{} + +inline +MatConstIterator& MatConstIterator::operator = (const MatConstIterator& it ) +{ + m = it.m; elemSize = it.elemSize; ptr = it.ptr; + sliceStart = it.sliceStart; sliceEnd = it.sliceEnd; + return *this; +} + +inline +const uchar* MatConstIterator::operator *() const +{ + return ptr; +} + +inline MatConstIterator& MatConstIterator::operator += (ptrdiff_t ofs) +{ + if( !m || ofs == 0 ) + return *this; + ptrdiff_t ofsb = ofs*elemSize; + ptr += ofsb; + if( ptr < sliceStart || sliceEnd <= ptr ) + { + ptr -= ofsb; + seek(ofs, true); + } + return *this; +} + +inline +MatConstIterator& MatConstIterator::operator -= (ptrdiff_t ofs) +{ + return (*this += -ofs); +} + +inline +MatConstIterator& MatConstIterator::operator --() +{ + if( m && (ptr -= elemSize) < sliceStart ) + { + ptr += elemSize; + seek(-1, true); + } + return *this; +} + +inline +MatConstIterator MatConstIterator::operator --(int) +{ + MatConstIterator b = *this; + *this += -1; + return b; +} + +inline +MatConstIterator& MatConstIterator::operator ++() +{ + if( m && (ptr += elemSize) >= sliceEnd ) + { + ptr -= elemSize; + seek(1, true); + } + return *this; +} + +inline MatConstIterator MatConstIterator::operator ++(int) +{ + MatConstIterator b = *this; + *this += 1; + return b; +} + + +static inline +bool operator == (const MatConstIterator& a, const MatConstIterator& b) +{ + return a.m == b.m && a.ptr == b.ptr; +} + +static inline +bool operator != (const MatConstIterator& a, const MatConstIterator& b) +{ + return !(a == b); +} + +static inline +bool operator < (const MatConstIterator& a, const MatConstIterator& b) +{ + return a.ptr < b.ptr; +} + +static inline +bool operator > (const MatConstIterator& a, const MatConstIterator& b) +{ + return a.ptr > b.ptr; +} + +static inline +bool operator <= (const MatConstIterator& a, const MatConstIterator& b) +{ + return a.ptr <= b.ptr; +} + +static inline +bool operator >= (const MatConstIterator& a, const MatConstIterator& b) +{ + return a.ptr >= b.ptr; +} + +static inline +ptrdiff_t operator - (const MatConstIterator& b, const MatConstIterator& a) +{ + if( a.m != b.m ) + return ((size_t)(-1) >> 1); + if( a.sliceEnd == b.sliceEnd ) + return (b.ptr - a.ptr)/static_cast(b.elemSize); + + return b.lpos() - a.lpos(); +} + +static inline +MatConstIterator operator + (const MatConstIterator& a, ptrdiff_t ofs) +{ + MatConstIterator b = a; + return b += ofs; +} + +static inline +MatConstIterator operator + (ptrdiff_t ofs, const MatConstIterator& a) +{ + MatConstIterator b = a; + return b += ofs; +} + +static inline +MatConstIterator operator - (const MatConstIterator& a, ptrdiff_t ofs) +{ + MatConstIterator b = a; + return b += -ofs; +} + + +inline +const uchar* MatConstIterator::operator [](ptrdiff_t i) const +{ + return *(*this + i); +} + + + +///////////////////////// MatConstIterator_ ///////////////////////// + +template inline +MatConstIterator_<_Tp>::MatConstIterator_() +{} + +template inline +MatConstIterator_<_Tp>::MatConstIterator_(const Mat_<_Tp>* _m) + : MatConstIterator(_m) +{} + +template inline +MatConstIterator_<_Tp>::MatConstIterator_(const Mat_<_Tp>* _m, int _row, int _col) + : MatConstIterator(_m, _row, _col) +{} + +template inline +MatConstIterator_<_Tp>::MatConstIterator_(const Mat_<_Tp>* _m, Point _pt) + : MatConstIterator(_m, _pt) +{} + +template inline +MatConstIterator_<_Tp>::MatConstIterator_(const MatConstIterator_& it) + : MatConstIterator(it) +{} + +template inline +MatConstIterator_<_Tp>& MatConstIterator_<_Tp>::operator = (const MatConstIterator_& it ) +{ + MatConstIterator::operator = (it); + return *this; +} + +template inline +const _Tp& MatConstIterator_<_Tp>::operator *() const +{ + return *(_Tp*)(this->ptr); +} + +template inline +MatConstIterator_<_Tp>& MatConstIterator_<_Tp>::operator += (ptrdiff_t ofs) +{ + MatConstIterator::operator += (ofs); + return *this; +} + +template inline +MatConstIterator_<_Tp>& MatConstIterator_<_Tp>::operator -= (ptrdiff_t ofs) +{ + return (*this += -ofs); +} + +template inline +MatConstIterator_<_Tp>& MatConstIterator_<_Tp>::operator --() +{ + MatConstIterator::operator --(); + return *this; +} + +template inline +MatConstIterator_<_Tp> MatConstIterator_<_Tp>::operator --(int) +{ + MatConstIterator_ b = *this; + MatConstIterator::operator --(); + return b; +} + +template inline +MatConstIterator_<_Tp>& MatConstIterator_<_Tp>::operator ++() +{ + MatConstIterator::operator ++(); + return *this; +} + +template inline +MatConstIterator_<_Tp> MatConstIterator_<_Tp>::operator ++(int) +{ + MatConstIterator_ b = *this; + MatConstIterator::operator ++(); + return b; +} + + +template inline +Point MatConstIterator_<_Tp>::pos() const +{ + if( !m ) + return Point(); + CV_DbgAssert( m->dims <= 2 ); + if( m->isContinuous() ) + { + ptrdiff_t ofs = (const _Tp*)ptr - (const _Tp*)m->data; + int y = (int)(ofs / m->cols); + int x = (int)(ofs - (ptrdiff_t)y * m->cols); + return Point(x, y); + } + else + { + ptrdiff_t ofs = (uchar*)ptr - m->data; + int y = (int)(ofs / m->step); + int x = (int)((ofs - y * m->step)/sizeof(_Tp)); + return Point(x, y); + } +} + + +template static inline +bool operator == (const MatConstIterator_<_Tp>& a, const MatConstIterator_<_Tp>& b) +{ + return a.m == b.m && a.ptr == b.ptr; +} + +template static inline +bool operator != (const MatConstIterator_<_Tp>& a, const MatConstIterator_<_Tp>& b) +{ + return a.m != b.m || a.ptr != b.ptr; +} + +template static inline +MatConstIterator_<_Tp> operator + (const MatConstIterator_<_Tp>& a, ptrdiff_t ofs) +{ + MatConstIterator t = (const MatConstIterator&)a + ofs; + return (MatConstIterator_<_Tp>&)t; +} + +template static inline +MatConstIterator_<_Tp> operator + (ptrdiff_t ofs, const MatConstIterator_<_Tp>& a) +{ + MatConstIterator t = (const MatConstIterator&)a + ofs; + return (MatConstIterator_<_Tp>&)t; +} + +template static inline +MatConstIterator_<_Tp> operator - (const MatConstIterator_<_Tp>& a, ptrdiff_t ofs) +{ + MatConstIterator t = (const MatConstIterator&)a - ofs; + return (MatConstIterator_<_Tp>&)t; +} + +template inline +const _Tp& MatConstIterator_<_Tp>::operator [](ptrdiff_t i) const +{ + return *(_Tp*)MatConstIterator::operator [](i); +} + + + +//////////////////////////// MatIterator_ /////////////////////////// + +template inline +MatIterator_<_Tp>::MatIterator_() + : MatConstIterator_<_Tp>() +{} + +template inline +MatIterator_<_Tp>::MatIterator_(Mat_<_Tp>* _m) + : MatConstIterator_<_Tp>(_m) +{} + +template inline +MatIterator_<_Tp>::MatIterator_(Mat_<_Tp>* _m, int _row, int _col) + : MatConstIterator_<_Tp>(_m, _row, _col) +{} + +template inline +MatIterator_<_Tp>::MatIterator_(Mat_<_Tp>* _m, Point _pt) + : MatConstIterator_<_Tp>(_m, _pt) +{} + +template inline +MatIterator_<_Tp>::MatIterator_(Mat_<_Tp>* _m, const int* _idx) + : MatConstIterator_<_Tp>(_m, _idx) +{} + +template inline +MatIterator_<_Tp>::MatIterator_(const MatIterator_& it) + : MatConstIterator_<_Tp>(it) +{} + +template inline +MatIterator_<_Tp>& MatIterator_<_Tp>::operator = (const MatIterator_<_Tp>& it ) +{ + MatConstIterator::operator = (it); + return *this; +} + +template inline +_Tp& MatIterator_<_Tp>::operator *() const +{ + return *(_Tp*)(this->ptr); +} + +template inline +MatIterator_<_Tp>& MatIterator_<_Tp>::operator += (ptrdiff_t ofs) +{ + MatConstIterator::operator += (ofs); + return *this; +} + +template inline +MatIterator_<_Tp>& MatIterator_<_Tp>::operator -= (ptrdiff_t ofs) +{ + MatConstIterator::operator += (-ofs); + return *this; +} + +template inline +MatIterator_<_Tp>& MatIterator_<_Tp>::operator --() +{ + MatConstIterator::operator --(); + return *this; +} + +template inline +MatIterator_<_Tp> MatIterator_<_Tp>::operator --(int) +{ + MatIterator_ b = *this; + MatConstIterator::operator --(); + return b; +} + +template inline +MatIterator_<_Tp>& MatIterator_<_Tp>::operator ++() +{ + MatConstIterator::operator ++(); + return *this; +} + +template inline +MatIterator_<_Tp> MatIterator_<_Tp>::operator ++(int) +{ + MatIterator_ b = *this; + MatConstIterator::operator ++(); + return b; +} + +template inline +_Tp& MatIterator_<_Tp>::operator [](ptrdiff_t i) const +{ + return *(*this + i); +} + + +template static inline +bool operator == (const MatIterator_<_Tp>& a, const MatIterator_<_Tp>& b) +{ + return a.m == b.m && a.ptr == b.ptr; +} + +template static inline +bool operator != (const MatIterator_<_Tp>& a, const MatIterator_<_Tp>& b) +{ + return a.m != b.m || a.ptr != b.ptr; +} + +template static inline +MatIterator_<_Tp> operator + (const MatIterator_<_Tp>& a, ptrdiff_t ofs) +{ + MatConstIterator t = (const MatConstIterator&)a + ofs; + return (MatIterator_<_Tp>&)t; +} + +template static inline +MatIterator_<_Tp> operator + (ptrdiff_t ofs, const MatIterator_<_Tp>& a) +{ + MatConstIterator t = (const MatConstIterator&)a + ofs; + return (MatIterator_<_Tp>&)t; +} + +template static inline +MatIterator_<_Tp> operator - (const MatIterator_<_Tp>& a, ptrdiff_t ofs) +{ + MatConstIterator t = (const MatConstIterator&)a - ofs; + return (MatIterator_<_Tp>&)t; +} + + + +/////////////////////// SparseMatConstIterator ////////////////////// + +inline +SparseMatConstIterator::SparseMatConstIterator() + : m(0), hashidx(0), ptr(0) +{} + +inline +SparseMatConstIterator::SparseMatConstIterator(const SparseMatConstIterator& it) + : m(it.m), hashidx(it.hashidx), ptr(it.ptr) +{} + +inline SparseMatConstIterator& SparseMatConstIterator::operator = (const SparseMatConstIterator& it) +{ + if( this != &it ) + { + m = it.m; + hashidx = it.hashidx; + ptr = it.ptr; + } + return *this; +} + +template inline +const _Tp& SparseMatConstIterator::value() const +{ + return *(const _Tp*)ptr; +} + +inline +const SparseMat::Node* SparseMatConstIterator::node() const +{ + return (ptr && m && m->hdr) ? (const SparseMat::Node*)(const void*)(ptr - m->hdr->valueOffset) : 0; +} + +inline +SparseMatConstIterator SparseMatConstIterator::operator ++(int) +{ + SparseMatConstIterator it = *this; + ++*this; + return it; +} + +inline +void SparseMatConstIterator::seekEnd() +{ + if( m && m->hdr ) + { + hashidx = m->hdr->hashtab.size(); + ptr = 0; + } +} + + +static inline +bool operator == (const SparseMatConstIterator& it1, const SparseMatConstIterator& it2) +{ + return it1.m == it2.m && it1.ptr == it2.ptr; +} + +static inline +bool operator != (const SparseMatConstIterator& it1, const SparseMatConstIterator& it2) +{ + return !(it1 == it2); +} + + + +///////////////////////// SparseMatIterator ///////////////////////// + +inline +SparseMatIterator::SparseMatIterator() +{} + +inline +SparseMatIterator::SparseMatIterator(SparseMat* _m) + : SparseMatConstIterator(_m) +{} + +inline +SparseMatIterator::SparseMatIterator(const SparseMatIterator& it) + : SparseMatConstIterator(it) +{} + +inline +SparseMatIterator& SparseMatIterator::operator = (const SparseMatIterator& it) +{ + (SparseMatConstIterator&)*this = it; + return *this; +} + +template inline +_Tp& SparseMatIterator::value() const +{ + return *(_Tp*)ptr; +} + +inline +SparseMat::Node* SparseMatIterator::node() const +{ + return (SparseMat::Node*)SparseMatConstIterator::node(); +} + +inline +SparseMatIterator& SparseMatIterator::operator ++() +{ + SparseMatConstIterator::operator ++(); + return *this; +} + +inline +SparseMatIterator SparseMatIterator::operator ++(int) +{ + SparseMatIterator it = *this; + ++*this; + return it; +} + + + +////////////////////// SparseMatConstIterator_ ////////////////////// + +template inline +SparseMatConstIterator_<_Tp>::SparseMatConstIterator_() +{} + +template inline +SparseMatConstIterator_<_Tp>::SparseMatConstIterator_(const SparseMat_<_Tp>* _m) + : SparseMatConstIterator(_m) +{} + +template inline +SparseMatConstIterator_<_Tp>::SparseMatConstIterator_(const SparseMat* _m) + : SparseMatConstIterator(_m) +{ + CV_Assert( _m->type() == traits::Type<_Tp>::value ); +} + +template inline +SparseMatConstIterator_<_Tp>::SparseMatConstIterator_(const SparseMatConstIterator_<_Tp>& it) + : SparseMatConstIterator(it) +{} + +template inline +SparseMatConstIterator_<_Tp>& SparseMatConstIterator_<_Tp>::operator = (const SparseMatConstIterator_<_Tp>& it) +{ + return reinterpret_cast&> + (*reinterpret_cast(this) = + reinterpret_cast(it)); +} + +template inline +const _Tp& SparseMatConstIterator_<_Tp>::operator *() const +{ + return *(const _Tp*)this->ptr; +} + +template inline +SparseMatConstIterator_<_Tp>& SparseMatConstIterator_<_Tp>::operator ++() +{ + SparseMatConstIterator::operator ++(); + return *this; +} + +template inline +SparseMatConstIterator_<_Tp> SparseMatConstIterator_<_Tp>::operator ++(int) +{ + SparseMatConstIterator_<_Tp> it = *this; + SparseMatConstIterator::operator ++(); + return it; +} + + + +///////////////////////// SparseMatIterator_ //////////////////////// + +template inline +SparseMatIterator_<_Tp>::SparseMatIterator_() +{} + +template inline +SparseMatIterator_<_Tp>::SparseMatIterator_(SparseMat_<_Tp>* _m) + : SparseMatConstIterator_<_Tp>(_m) +{} + +template inline +SparseMatIterator_<_Tp>::SparseMatIterator_(SparseMat* _m) + : SparseMatConstIterator_<_Tp>(_m) +{} + +template inline +SparseMatIterator_<_Tp>::SparseMatIterator_(const SparseMatIterator_<_Tp>& it) + : SparseMatConstIterator_<_Tp>(it) +{} + +template inline +SparseMatIterator_<_Tp>& SparseMatIterator_<_Tp>::operator = (const SparseMatIterator_<_Tp>& it) +{ + return reinterpret_cast&> + (*reinterpret_cast(this) = + reinterpret_cast(it)); +} + +template inline +_Tp& SparseMatIterator_<_Tp>::operator *() const +{ + return *(_Tp*)this->ptr; +} + +template inline +SparseMatIterator_<_Tp>& SparseMatIterator_<_Tp>::operator ++() +{ + SparseMatConstIterator::operator ++(); + return *this; +} + +template inline +SparseMatIterator_<_Tp> SparseMatIterator_<_Tp>::operator ++(int) +{ + SparseMatIterator_<_Tp> it = *this; + SparseMatConstIterator::operator ++(); + return it; +} + + + +//////////////////////// MatCommaInitializer_ /////////////////////// + +template inline +MatCommaInitializer_<_Tp>::MatCommaInitializer_(Mat_<_Tp>* _m) + : it(_m) +{} + +template template inline +MatCommaInitializer_<_Tp>& MatCommaInitializer_<_Tp>::operator , (T2 v) +{ + CV_DbgAssert( this->it < ((const Mat_<_Tp>*)this->it.m)->end() ); + *this->it = _Tp(v); + ++this->it; + return *this; +} + +template inline +MatCommaInitializer_<_Tp>::operator Mat_<_Tp>() const +{ + CV_DbgAssert( this->it == ((const Mat_<_Tp>*)this->it.m)->end() ); + return Mat_<_Tp>(*this->it.m); +} + + +template static inline +MatCommaInitializer_<_Tp> operator << (const Mat_<_Tp>& m, T2 val) +{ + MatCommaInitializer_<_Tp> commaInitializer((Mat_<_Tp>*)&m); + return (commaInitializer, val); +} + + + +///////////////////////// Matrix Expressions //////////////////////// + +inline +Mat& Mat::operator = (const MatExpr& e) +{ + e.op->assign(e, *this); + return *this; +} + +template inline +Mat_<_Tp>::Mat_(const MatExpr& e) +{ + e.op->assign(e, *this, traits::Type<_Tp>::value); +} + +template inline +Mat_<_Tp>& Mat_<_Tp>::operator = (const MatExpr& e) +{ + e.op->assign(e, *this, traits::Type<_Tp>::value); + return *this; +} + +template inline +MatExpr Mat_<_Tp>::zeros(int rows, int cols) +{ + return Mat::zeros(rows, cols, traits::Type<_Tp>::value); +} + +template inline +MatExpr Mat_<_Tp>::zeros(Size sz) +{ + return Mat::zeros(sz, traits::Type<_Tp>::value); +} + +template inline +MatExpr Mat_<_Tp>::ones(int rows, int cols) +{ + return Mat::ones(rows, cols, traits::Type<_Tp>::value); +} + +template inline +MatExpr Mat_<_Tp>::ones(Size sz) +{ + return Mat::ones(sz, traits::Type<_Tp>::value); +} + +template inline +MatExpr Mat_<_Tp>::eye(int rows, int cols) +{ + return Mat::eye(rows, cols, traits::Type<_Tp>::value); +} + +template inline +MatExpr Mat_<_Tp>::eye(Size sz) +{ + return Mat::eye(sz, traits::Type<_Tp>::value); +} + +inline +MatExpr::MatExpr() + : op(0), flags(0), a(Mat()), b(Mat()), c(Mat()), alpha(0), beta(0), s() +{} + +inline +MatExpr::MatExpr(const MatOp* _op, int _flags, const Mat& _a, const Mat& _b, + const Mat& _c, double _alpha, double _beta, const Scalar& _s) + : op(_op), flags(_flags), a(_a), b(_b), c(_c), alpha(_alpha), beta(_beta), s(_s) +{} + +inline +MatExpr::operator Mat() const +{ + Mat m; + op->assign(*this, m); + return m; +} + +template inline +MatExpr::operator Mat_<_Tp>() const +{ + Mat_<_Tp> m; + op->assign(*this, m, traits::Type<_Tp>::value); + return m; +} + + +template static inline +MatExpr min(const Mat_<_Tp>& a, const Mat_<_Tp>& b) +{ + return cv::min((const Mat&)a, (const Mat&)b); +} + +template static inline +MatExpr min(const Mat_<_Tp>& a, double s) +{ + return cv::min((const Mat&)a, s); +} + +template static inline +MatExpr min(double s, const Mat_<_Tp>& a) +{ + return cv::min((const Mat&)a, s); +} + +template static inline +MatExpr max(const Mat_<_Tp>& a, const Mat_<_Tp>& b) +{ + return cv::max((const Mat&)a, (const Mat&)b); +} + +template static inline +MatExpr max(const Mat_<_Tp>& a, double s) +{ + return cv::max((const Mat&)a, s); +} + +template static inline +MatExpr max(double s, const Mat_<_Tp>& a) +{ + return cv::max((const Mat&)a, s); +} + +template static inline +MatExpr abs(const Mat_<_Tp>& m) +{ + return cv::abs((const Mat&)m); +} + + +static inline +Mat& operator += (Mat& a, const MatExpr& b) +{ + b.op->augAssignAdd(b, a); + return a; +} + +static inline +const Mat& operator += (const Mat& a, const MatExpr& b) +{ + b.op->augAssignAdd(b, (Mat&)a); + return a; +} + +template static inline +Mat_<_Tp>& operator += (Mat_<_Tp>& a, const MatExpr& b) +{ + b.op->augAssignAdd(b, a); + return a; +} + +template static inline +const Mat_<_Tp>& operator += (const Mat_<_Tp>& a, const MatExpr& b) +{ + b.op->augAssignAdd(b, (Mat&)a); + return a; +} + +static inline +Mat& operator -= (Mat& a, const MatExpr& b) +{ + b.op->augAssignSubtract(b, a); + return a; +} + +static inline +const Mat& operator -= (const Mat& a, const MatExpr& b) +{ + b.op->augAssignSubtract(b, (Mat&)a); + return a; +} + +template static inline +Mat_<_Tp>& operator -= (Mat_<_Tp>& a, const MatExpr& b) +{ + b.op->augAssignSubtract(b, a); + return a; +} + +template static inline +const Mat_<_Tp>& operator -= (const Mat_<_Tp>& a, const MatExpr& b) +{ + b.op->augAssignSubtract(b, (Mat&)a); + return a; +} + +static inline +Mat& operator *= (Mat& a, const MatExpr& b) +{ + b.op->augAssignMultiply(b, a); + return a; +} + +static inline +const Mat& operator *= (const Mat& a, const MatExpr& b) +{ + b.op->augAssignMultiply(b, (Mat&)a); + return a; +} + +template static inline +Mat_<_Tp>& operator *= (Mat_<_Tp>& a, const MatExpr& b) +{ + b.op->augAssignMultiply(b, a); + return a; +} + +template static inline +const Mat_<_Tp>& operator *= (const Mat_<_Tp>& a, const MatExpr& b) +{ + b.op->augAssignMultiply(b, (Mat&)a); + return a; +} + +static inline +Mat& operator /= (Mat& a, const MatExpr& b) +{ + b.op->augAssignDivide(b, a); + return a; +} + +static inline +const Mat& operator /= (const Mat& a, const MatExpr& b) +{ + b.op->augAssignDivide(b, (Mat&)a); + return a; +} + +template static inline +Mat_<_Tp>& operator /= (Mat_<_Tp>& a, const MatExpr& b) +{ + b.op->augAssignDivide(b, a); + return a; +} + +template static inline +const Mat_<_Tp>& operator /= (const Mat_<_Tp>& a, const MatExpr& b) +{ + b.op->augAssignDivide(b, (Mat&)a); + return a; +} + + +//////////////////////////////// UMat //////////////////////////////// + +inline +UMat::UMat(UMatUsageFlags _usageFlags) +: flags(MAGIC_VAL), dims(0), rows(0), cols(0), allocator(0), usageFlags(_usageFlags), u(0), offset(0), size(&rows) +{} + +inline +UMat::UMat(int _rows, int _cols, int _type, UMatUsageFlags _usageFlags) +: flags(MAGIC_VAL), dims(0), rows(0), cols(0), allocator(0), usageFlags(_usageFlags), u(0), offset(0), size(&rows) +{ + create(_rows, _cols, _type); +} + +inline +UMat::UMat(int _rows, int _cols, int _type, const Scalar& _s, UMatUsageFlags _usageFlags) +: flags(MAGIC_VAL), dims(0), rows(0), cols(0), allocator(0), usageFlags(_usageFlags), u(0), offset(0), size(&rows) +{ + create(_rows, _cols, _type); + *this = _s; +} + +inline +UMat::UMat(Size _sz, int _type, UMatUsageFlags _usageFlags) +: flags(MAGIC_VAL), dims(0), rows(0), cols(0), allocator(0), usageFlags(_usageFlags), u(0), offset(0), size(&rows) +{ + create( _sz.height, _sz.width, _type ); +} + +inline +UMat::UMat(Size _sz, int _type, const Scalar& _s, UMatUsageFlags _usageFlags) +: flags(MAGIC_VAL), dims(0), rows(0), cols(0), allocator(0), usageFlags(_usageFlags), u(0), offset(0), size(&rows) +{ + create(_sz.height, _sz.width, _type); + *this = _s; +} + +inline +UMat::UMat(int _dims, const int* _sz, int _type, UMatUsageFlags _usageFlags) +: flags(MAGIC_VAL), dims(0), rows(0), cols(0), allocator(0), usageFlags(_usageFlags), u(0), offset(0), size(&rows) +{ + create(_dims, _sz, _type); +} + +inline +UMat::UMat(int _dims, const int* _sz, int _type, const Scalar& _s, UMatUsageFlags _usageFlags) +: flags(MAGIC_VAL), dims(0), rows(0), cols(0), allocator(0), usageFlags(_usageFlags), u(0), offset(0), size(&rows) +{ + create(_dims, _sz, _type); + *this = _s; +} + +inline +UMat::UMat(const UMat& m) +: flags(m.flags), dims(m.dims), rows(m.rows), cols(m.cols), allocator(m.allocator), + usageFlags(m.usageFlags), u(m.u), offset(m.offset), size(&rows) +{ + addref(); + if( m.dims <= 2 ) + { + step[0] = m.step[0]; step[1] = m.step[1]; + } + else + { + dims = 0; + copySize(m); + } +} + + +template inline +UMat::UMat(const std::vector<_Tp>& vec, bool copyData) +: flags(MAGIC_VAL | traits::Type<_Tp>::value | CV_MAT_CONT_FLAG), dims(2), rows((int)vec.size()), +cols(1), allocator(0), usageFlags(USAGE_DEFAULT), u(0), offset(0), size(&rows) +{ + if(vec.empty()) + return; + if( !copyData ) + { + // !!!TODO!!! + CV_Error(Error::StsNotImplemented, ""); + } + else + Mat((int)vec.size(), 1, traits::Type<_Tp>::value, (uchar*)&vec[0]).copyTo(*this); +} + +inline +UMat& UMat::operator = (const UMat& m) +{ + if( this != &m ) + { + const_cast(m).addref(); + release(); + flags = m.flags; + if( dims <= 2 && m.dims <= 2 ) + { + dims = m.dims; + rows = m.rows; + cols = m.cols; + step[0] = m.step[0]; + step[1] = m.step[1]; + } + else + copySize(m); + allocator = m.allocator; + if (usageFlags == USAGE_DEFAULT) + usageFlags = m.usageFlags; + u = m.u; + offset = m.offset; + } + return *this; +} + +inline +UMat UMat::row(int y) const +{ + return UMat(*this, Range(y, y + 1), Range::all()); +} + +inline +UMat UMat::col(int x) const +{ + return UMat(*this, Range::all(), Range(x, x + 1)); +} + +inline +UMat UMat::rowRange(int startrow, int endrow) const +{ + return UMat(*this, Range(startrow, endrow), Range::all()); +} + +inline +UMat UMat::rowRange(const Range& r) const +{ + return UMat(*this, r, Range::all()); +} + +inline +UMat UMat::colRange(int startcol, int endcol) const +{ + return UMat(*this, Range::all(), Range(startcol, endcol)); +} + +inline +UMat UMat::colRange(const Range& r) const +{ + return UMat(*this, Range::all(), r); +} + +inline +UMat UMat::clone() const +{ + UMat m; + copyTo(m); + return m; +} + +inline +void UMat::assignTo( UMat& m, int _type ) const +{ + if( _type < 0 ) + m = *this; + else + convertTo(m, _type); +} + +inline +void UMat::create(int _rows, int _cols, int _type, UMatUsageFlags _usageFlags) +{ + _type &= TYPE_MASK; + if( dims <= 2 && rows == _rows && cols == _cols && type() == _type && u ) + return; + int sz[] = {_rows, _cols}; + create(2, sz, _type, _usageFlags); +} + +inline +void UMat::create(Size _sz, int _type, UMatUsageFlags _usageFlags) +{ + create(_sz.height, _sz.width, _type, _usageFlags); +} + +inline +void UMat::addref() +{ + if( u ) + CV_XADD(&(u->urefcount), 1); +} + +inline void UMat::release() +{ + if( u && CV_XADD(&(u->urefcount), -1) == 1 ) + deallocate(); + for(int i = 0; i < dims; i++) + size.p[i] = 0; + u = 0; +} + +inline +UMat UMat::operator()( Range _rowRange, Range _colRange ) const +{ + return UMat(*this, _rowRange, _colRange); +} + +inline +UMat UMat::operator()( const Rect& roi ) const +{ + return UMat(*this, roi); +} + +inline +UMat UMat::operator()(const Range* ranges) const +{ + return UMat(*this, ranges); +} + +inline +UMat UMat::operator()(const std::vector& ranges) const +{ + return UMat(*this, ranges); +} + +inline +bool UMat::isContinuous() const +{ + return (flags & CONTINUOUS_FLAG) != 0; +} + +inline +bool UMat::isSubmatrix() const +{ + return (flags & SUBMATRIX_FLAG) != 0; +} + +inline +size_t UMat::elemSize() const +{ + size_t res = dims > 0 ? step.p[dims - 1] : 0; + CV_DbgAssert(res != 0); + return res; +} + +inline +size_t UMat::elemSize1() const +{ + return CV_ELEM_SIZE1(flags); +} + +inline +int UMat::type() const +{ + return CV_MAT_TYPE(flags); +} + +inline +int UMat::depth() const +{ + return CV_MAT_DEPTH(flags); +} + +inline +int UMat::channels() const +{ + return CV_MAT_CN(flags); +} + +inline +size_t UMat::step1(int i) const +{ + return step.p[i] / elemSize1(); +} + +inline +bool UMat::empty() const +{ + return u == 0 || total() == 0 || dims == 0; +} + +inline +size_t UMat::total() const +{ + if( dims <= 2 ) + return (size_t)rows * cols; + size_t p = 1; + for( int i = 0; i < dims; i++ ) + p *= size[i]; + return p; +} + +#ifdef CV_CXX_MOVE_SEMANTICS + +inline +UMat::UMat(UMat&& m) +: flags(m.flags), dims(m.dims), rows(m.rows), cols(m.cols), allocator(m.allocator), + usageFlags(m.usageFlags), u(m.u), offset(m.offset), size(&rows) +{ + if (m.dims <= 2) // move new step/size info + { + step[0] = m.step[0]; + step[1] = m.step[1]; + } + else + { + CV_DbgAssert(m.step.p != m.step.buf); + step.p = m.step.p; + size.p = m.size.p; + m.step.p = m.step.buf; + m.size.p = &m.rows; + } + m.flags = MAGIC_VAL; m.dims = m.rows = m.cols = 0; + m.allocator = NULL; + m.u = NULL; + m.offset = 0; +} + +inline +UMat& UMat::operator = (UMat&& m) +{ + if (this == &m) + return *this; + release(); + flags = m.flags; dims = m.dims; rows = m.rows; cols = m.cols; + allocator = m.allocator; usageFlags = m.usageFlags; + u = m.u; + offset = m.offset; + if (step.p != step.buf) // release self step/size + { + fastFree(step.p); + step.p = step.buf; + size.p = &rows; + } + if (m.dims <= 2) // move new step/size info + { + step[0] = m.step[0]; + step[1] = m.step[1]; + } + else + { + CV_DbgAssert(m.step.p != m.step.buf); + step.p = m.step.p; + size.p = m.size.p; + m.step.p = m.step.buf; + m.size.p = &m.rows; + } + m.flags = MAGIC_VAL; m.dims = m.rows = m.cols = 0; + m.allocator = NULL; + m.u = NULL; + m.offset = 0; + return *this; +} + +#endif + + +inline bool UMatData::hostCopyObsolete() const { return (flags & HOST_COPY_OBSOLETE) != 0; } +inline bool UMatData::deviceCopyObsolete() const { return (flags & DEVICE_COPY_OBSOLETE) != 0; } +inline bool UMatData::deviceMemMapped() const { return (flags & DEVICE_MEM_MAPPED) != 0; } +inline bool UMatData::copyOnMap() const { return (flags & COPY_ON_MAP) != 0; } +inline bool UMatData::tempUMat() const { return (flags & TEMP_UMAT) != 0; } +inline bool UMatData::tempCopiedUMat() const { return (flags & TEMP_COPIED_UMAT) == TEMP_COPIED_UMAT; } + +inline void UMatData::markDeviceMemMapped(bool flag) +{ + if(flag) + flags |= DEVICE_MEM_MAPPED; + else + flags &= ~DEVICE_MEM_MAPPED; +} + +inline void UMatData::markHostCopyObsolete(bool flag) +{ + if(flag) + flags |= HOST_COPY_OBSOLETE; + else + flags &= ~HOST_COPY_OBSOLETE; +} +inline void UMatData::markDeviceCopyObsolete(bool flag) +{ + if(flag) + flags |= DEVICE_COPY_OBSOLETE; + else + flags &= ~DEVICE_COPY_OBSOLETE; +} + +//! @endcond + +} //cv + +#ifdef _MSC_VER +#pragma warning( pop ) +#endif + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/matx.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/matx.hpp new file mode 100644 index 0000000000000000000000000000000000000000..bf3f046276a6e893dcb5138e5bef1be6417701f0 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/matx.hpp @@ -0,0 +1,1477 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_MATX_HPP +#define OPENCV_CORE_MATX_HPP + +#ifndef __cplusplus +# error matx.hpp header must be compiled as C++ +#endif + +#include "opencv2/core/cvdef.h" +#include "opencv2/core/base.hpp" +#include "opencv2/core/traits.hpp" +#include "opencv2/core/saturate.hpp" + +#ifdef CV_CXX11 +#include +#endif + +namespace cv +{ + +//! @addtogroup core_basic +//! @{ + +////////////////////////////// Small Matrix /////////////////////////// + +//! @cond IGNORED +// FIXIT Remove this (especially CV_EXPORTS modifier) +struct CV_EXPORTS Matx_AddOp { Matx_AddOp() {} Matx_AddOp(const Matx_AddOp&) {} }; +struct CV_EXPORTS Matx_SubOp { Matx_SubOp() {} Matx_SubOp(const Matx_SubOp&) {} }; +struct CV_EXPORTS Matx_ScaleOp { Matx_ScaleOp() {} Matx_ScaleOp(const Matx_ScaleOp&) {} }; +struct CV_EXPORTS Matx_MulOp { Matx_MulOp() {} Matx_MulOp(const Matx_MulOp&) {} }; +struct CV_EXPORTS Matx_DivOp { Matx_DivOp() {} Matx_DivOp(const Matx_DivOp&) {} }; +struct CV_EXPORTS Matx_MatMulOp { Matx_MatMulOp() {} Matx_MatMulOp(const Matx_MatMulOp&) {} }; +struct CV_EXPORTS Matx_TOp { Matx_TOp() {} Matx_TOp(const Matx_TOp&) {} }; +//! @endcond + +/** @brief Template class for small matrices whose type and size are known at compilation time + +If you need a more flexible type, use Mat . The elements of the matrix M are accessible using the +M(i,j) notation. Most of the common matrix operations (see also @ref MatrixExpressions ) are +available. To do an operation on Matx that is not implemented, you can easily convert the matrix to +Mat and backwards: +@code{.cpp} + Matx33f m(1, 2, 3, + 4, 5, 6, + 7, 8, 9); + cout << sum(Mat(m*m.t())) << endl; +@endcode +Except of the plain constructor which takes a list of elements, Matx can be initialized from a C-array: +@code{.cpp} + float values[] = { 1, 2, 3}; + Matx31f m(values); +@endcode +In case if C++11 features are available, std::initializer_list can be also used to initialize Matx: +@code{.cpp} + Matx31f m = { 1, 2, 3}; +@endcode + */ +template class Matx +{ +public: + enum { + rows = m, + cols = n, + channels = rows*cols, +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + depth = traits::Type<_Tp>::value, + type = CV_MAKETYPE(depth, channels), +#endif + shortdim = (m < n ? m : n) + }; + + typedef _Tp value_type; + typedef Matx<_Tp, m, n> mat_type; + typedef Matx<_Tp, shortdim, 1> diag_type; + + //! default constructor + Matx(); + + explicit Matx(_Tp v0); //!< 1x1 matrix + Matx(_Tp v0, _Tp v1); //!< 1x2 or 2x1 matrix + Matx(_Tp v0, _Tp v1, _Tp v2); //!< 1x3 or 3x1 matrix + Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3); //!< 1x4, 2x2 or 4x1 matrix + Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4); //!< 1x5 or 5x1 matrix + Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5); //!< 1x6, 2x3, 3x2 or 6x1 matrix + Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6); //!< 1x7 or 7x1 matrix + Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7); //!< 1x8, 2x4, 4x2 or 8x1 matrix + Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8); //!< 1x9, 3x3 or 9x1 matrix + Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9); //!< 1x10, 2x5 or 5x2 or 10x1 matrix + Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, + _Tp v4, _Tp v5, _Tp v6, _Tp v7, + _Tp v8, _Tp v9, _Tp v10, _Tp v11); //!< 1x12, 2x6, 3x4, 4x3, 6x2 or 12x1 matrix + Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, + _Tp v4, _Tp v5, _Tp v6, _Tp v7, + _Tp v8, _Tp v9, _Tp v10, _Tp v11, + _Tp v12, _Tp v13); //!< 1x14, 2x7, 7x2 or 14x1 matrix + Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, + _Tp v4, _Tp v5, _Tp v6, _Tp v7, + _Tp v8, _Tp v9, _Tp v10, _Tp v11, + _Tp v12, _Tp v13, _Tp v14, _Tp v15); //!< 1x16, 4x4 or 16x1 matrix + explicit Matx(const _Tp* vals); //!< initialize from a plain array + +#ifdef CV_CXX11 + Matx(std::initializer_list<_Tp>); //!< initialize from an initializer list +#endif + + static Matx all(_Tp alpha); + static Matx zeros(); + static Matx ones(); + static Matx eye(); + static Matx diag(const diag_type& d); + static Matx randu(_Tp a, _Tp b); + static Matx randn(_Tp a, _Tp b); + + //! dot product computed with the default precision + _Tp dot(const Matx<_Tp, m, n>& v) const; + + //! dot product computed in double-precision arithmetics + double ddot(const Matx<_Tp, m, n>& v) const; + + //! conversion to another data type + template operator Matx() const; + + //! change the matrix shape + template Matx<_Tp, m1, n1> reshape() const; + + //! extract part of the matrix + template Matx<_Tp, m1, n1> get_minor(int i, int j) const; + + //! extract the matrix row + Matx<_Tp, 1, n> row(int i) const; + + //! extract the matrix column + Matx<_Tp, m, 1> col(int i) const; + + //! extract the matrix diagonal + diag_type diag() const; + + //! transpose the matrix + Matx<_Tp, n, m> t() const; + + //! invert the matrix + Matx<_Tp, n, m> inv(int method=DECOMP_LU, bool *p_is_ok = NULL) const; + + //! solve linear system + template Matx<_Tp, n, l> solve(const Matx<_Tp, m, l>& rhs, int flags=DECOMP_LU) const; + Vec<_Tp, n> solve(const Vec<_Tp, m>& rhs, int method) const; + + //! multiply two matrices element-wise + Matx<_Tp, m, n> mul(const Matx<_Tp, m, n>& a) const; + + //! divide two matrices element-wise + Matx<_Tp, m, n> div(const Matx<_Tp, m, n>& a) const; + + //! element access + const _Tp& operator ()(int i, int j) const; + _Tp& operator ()(int i, int j); + + //! 1D element access + const _Tp& operator ()(int i) const; + _Tp& operator ()(int i); + + Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_AddOp); + Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_SubOp); + template Matx(const Matx<_Tp, m, n>& a, _T2 alpha, Matx_ScaleOp); + Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_MulOp); + Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_DivOp); + template Matx(const Matx<_Tp, m, l>& a, const Matx<_Tp, l, n>& b, Matx_MatMulOp); + Matx(const Matx<_Tp, n, m>& a, Matx_TOp); + + _Tp val[m*n]; //< matrix elements +}; + +typedef Matx Matx12f; +typedef Matx Matx12d; +typedef Matx Matx13f; +typedef Matx Matx13d; +typedef Matx Matx14f; +typedef Matx Matx14d; +typedef Matx Matx16f; +typedef Matx Matx16d; + +typedef Matx Matx21f; +typedef Matx Matx21d; +typedef Matx Matx31f; +typedef Matx Matx31d; +typedef Matx Matx41f; +typedef Matx Matx41d; +typedef Matx Matx61f; +typedef Matx Matx61d; + +typedef Matx Matx22f; +typedef Matx Matx22d; +typedef Matx Matx23f; +typedef Matx Matx23d; +typedef Matx Matx32f; +typedef Matx Matx32d; + +typedef Matx Matx33f; +typedef Matx Matx33d; + +typedef Matx Matx34f; +typedef Matx Matx34d; +typedef Matx Matx43f; +typedef Matx Matx43d; + +typedef Matx Matx44f; +typedef Matx Matx44d; +typedef Matx Matx66f; +typedef Matx Matx66d; + +/*! + traits +*/ +template class DataType< Matx<_Tp, m, n> > +{ +public: + typedef Matx<_Tp, m, n> value_type; + typedef Matx::work_type, m, n> work_type; + typedef _Tp channel_type; + typedef value_type vec_type; + + enum { generic_type = 0, + channels = m * n, + fmt = traits::SafeFmt::fmt + ((channels - 1) << 8) +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + ,depth = DataType::depth + ,type = CV_MAKETYPE(depth, channels) +#endif + }; +}; + +namespace traits { +template +struct Depth< Matx<_Tp, m, n> > { enum { value = Depth<_Tp>::value }; }; +template +struct Type< Matx<_Tp, m, n> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, n*m) }; }; +} // namespace + + +/** @brief Comma-separated Matrix Initializer +*/ +template class MatxCommaInitializer +{ +public: + MatxCommaInitializer(Matx<_Tp, m, n>* _mtx); + template MatxCommaInitializer<_Tp, m, n>& operator , (T2 val); + Matx<_Tp, m, n> operator *() const; + + Matx<_Tp, m, n>* dst; + int idx; +}; + +/* + Utility methods +*/ +template static double determinant(const Matx<_Tp, m, m>& a); +template static double trace(const Matx<_Tp, m, n>& a); +template static double norm(const Matx<_Tp, m, n>& M); +template static double norm(const Matx<_Tp, m, n>& M, int normType); + + + +/////////////////////// Vec (used as element of multi-channel images ///////////////////// + +/** @brief Template class for short numerical vectors, a partial case of Matx + +This template class represents short numerical vectors (of 1, 2, 3, 4 ... elements) on which you +can perform basic arithmetical operations, access individual elements using [] operator etc. The +vectors are allocated on stack, as opposite to std::valarray, std::vector, cv::Mat etc., which +elements are dynamically allocated in the heap. + +The template takes 2 parameters: +@tparam _Tp element type +@tparam cn the number of elements + +In addition to the universal notation like Vec, you can use shorter aliases +for the most popular specialized variants of Vec, e.g. Vec3f ~ Vec. + +It is possible to convert Vec\ to/from Point_, Vec\ to/from Point3_ , and Vec\ +to CvScalar or Scalar_. Use operator[] to access the elements of Vec. + +All the expected vector operations are also implemented: +- v1 = v2 + v3 +- v1 = v2 - v3 +- v1 = v2 \* scale +- v1 = scale \* v2 +- v1 = -v2 +- v1 += v2 and other augmenting operations +- v1 == v2, v1 != v2 +- norm(v1) (euclidean norm) +The Vec class is commonly used to describe pixel types of multi-channel arrays. See Mat for details. +*/ +template class Vec : public Matx<_Tp, cn, 1> +{ +public: + typedef _Tp value_type; + enum { + channels = cn, +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + depth = Matx<_Tp, cn, 1>::depth, + type = CV_MAKETYPE(depth, channels), +#endif + _dummy_enum_finalizer = 0 + }; + + //! default constructor + Vec(); + + Vec(_Tp v0); //!< 1-element vector constructor + Vec(_Tp v0, _Tp v1); //!< 2-element vector constructor + Vec(_Tp v0, _Tp v1, _Tp v2); //!< 3-element vector constructor + Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3); //!< 4-element vector constructor + Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4); //!< 5-element vector constructor + Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5); //!< 6-element vector constructor + Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6); //!< 7-element vector constructor + Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7); //!< 8-element vector constructor + Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8); //!< 9-element vector constructor + Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9); //!< 10-element vector constructor + Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9, _Tp v10, _Tp v11, _Tp v12, _Tp v13); //!< 14-element vector constructor + explicit Vec(const _Tp* values); + +#ifdef CV_CXX11 + Vec(std::initializer_list<_Tp>); +#endif + + Vec(const Vec<_Tp, cn>& v); + + static Vec all(_Tp alpha); + + //! per-element multiplication + Vec mul(const Vec<_Tp, cn>& v) const; + + //! conjugation (makes sense for complex numbers and quaternions) + Vec conj() const; + + /*! + cross product of the two 3D vectors. + + For other dimensionalities the exception is raised + */ + Vec cross(const Vec& v) const; + //! conversion to another data type + template operator Vec() const; + + /*! element access */ + const _Tp& operator [](int i) const; + _Tp& operator[](int i); + const _Tp& operator ()(int i) const; + _Tp& operator ()(int i); + + Vec(const Matx<_Tp, cn, 1>& a, const Matx<_Tp, cn, 1>& b, Matx_AddOp); + Vec(const Matx<_Tp, cn, 1>& a, const Matx<_Tp, cn, 1>& b, Matx_SubOp); + template Vec(const Matx<_Tp, cn, 1>& a, _T2 alpha, Matx_ScaleOp); +}; + +/** @name Shorter aliases for the most popular specializations of Vec + @{ +*/ +typedef Vec Vec2b; +typedef Vec Vec3b; +typedef Vec Vec4b; + +typedef Vec Vec2s; +typedef Vec Vec3s; +typedef Vec Vec4s; + +typedef Vec Vec2w; +typedef Vec Vec3w; +typedef Vec Vec4w; + +typedef Vec Vec2i; +typedef Vec Vec3i; +typedef Vec Vec4i; +typedef Vec Vec6i; +typedef Vec Vec8i; + +typedef Vec Vec2f; +typedef Vec Vec3f; +typedef Vec Vec4f; +typedef Vec Vec6f; + +typedef Vec Vec2d; +typedef Vec Vec3d; +typedef Vec Vec4d; +typedef Vec Vec6d; +/** @} */ + +/*! + traits +*/ +template class DataType< Vec<_Tp, cn> > +{ +public: + typedef Vec<_Tp, cn> value_type; + typedef Vec::work_type, cn> work_type; + typedef _Tp channel_type; + typedef value_type vec_type; + + enum { generic_type = 0, + channels = cn, + fmt = DataType::fmt + ((channels - 1) << 8), +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + depth = DataType::depth, + type = CV_MAKETYPE(depth, channels), +#endif + _dummy_enum_finalizer = 0 + }; +}; + +namespace traits { +template +struct Depth< Vec<_Tp, cn> > { enum { value = Depth<_Tp>::value }; }; +template +struct Type< Vec<_Tp, cn> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, cn) }; }; +} // namespace + + +/** @brief Comma-separated Vec Initializer +*/ +template class VecCommaInitializer : public MatxCommaInitializer<_Tp, m, 1> +{ +public: + VecCommaInitializer(Vec<_Tp, m>* _vec); + template VecCommaInitializer<_Tp, m>& operator , (T2 val); + Vec<_Tp, m> operator *() const; +}; + +template static Vec<_Tp, cn> normalize(const Vec<_Tp, cn>& v); + +//! @} core_basic + +//! @cond IGNORED + +///////////////////////////////////// helper classes ///////////////////////////////////// +namespace internal +{ + +template struct Matx_DetOp +{ + double operator ()(const Matx<_Tp, m, m>& a) const + { + Matx<_Tp, m, m> temp = a; + double p = LU(temp.val, m*sizeof(_Tp), m, 0, 0, 0); + if( p == 0 ) + return p; + for( int i = 0; i < m; i++ ) + p *= temp(i, i); + return p; + } +}; + +template struct Matx_DetOp<_Tp, 1> +{ + double operator ()(const Matx<_Tp, 1, 1>& a) const + { + return a(0,0); + } +}; + +template struct Matx_DetOp<_Tp, 2> +{ + double operator ()(const Matx<_Tp, 2, 2>& a) const + { + return a(0,0)*a(1,1) - a(0,1)*a(1,0); + } +}; + +template struct Matx_DetOp<_Tp, 3> +{ + double operator ()(const Matx<_Tp, 3, 3>& a) const + { + return a(0,0)*(a(1,1)*a(2,2) - a(2,1)*a(1,2)) - + a(0,1)*(a(1,0)*a(2,2) - a(2,0)*a(1,2)) + + a(0,2)*(a(1,0)*a(2,1) - a(2,0)*a(1,1)); + } +}; + +template Vec<_Tp, 2> inline conjugate(const Vec<_Tp, 2>& v) +{ + return Vec<_Tp, 2>(v[0], -v[1]); +} + +template Vec<_Tp, 4> inline conjugate(const Vec<_Tp, 4>& v) +{ + return Vec<_Tp, 4>(v[0], -v[1], -v[2], -v[3]); +} + +} // internal + + + +////////////////////////////////// Matx Implementation /////////////////////////////////// + +template inline +Matx<_Tp, m, n>::Matx() +{ + for(int i = 0; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp, m, n>::Matx(_Tp v0) +{ + val[0] = v0; + for(int i = 1; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1) +{ + CV_StaticAssert(channels >= 2, "Matx should have at least 2 elements."); + val[0] = v0; val[1] = v1; + for(int i = 2; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2) +{ + CV_StaticAssert(channels >= 3, "Matx should have at least 3 elements."); + val[0] = v0; val[1] = v1; val[2] = v2; + for(int i = 3; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3) +{ + CV_StaticAssert(channels >= 4, "Matx should have at least 4 elements."); + val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; + for(int i = 4; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4) +{ + CV_StaticAssert(channels >= 5, "Matx should have at least 5 elements."); + val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; val[4] = v4; + for(int i = 5; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5) +{ + CV_StaticAssert(channels >= 6, "Matx should have at least 6 elements."); + val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; + val[4] = v4; val[5] = v5; + for(int i = 6; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6) +{ + CV_StaticAssert(channels >= 7, "Matx should have at least 7 elements."); + val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; + val[4] = v4; val[5] = v5; val[6] = v6; + for(int i = 7; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7) +{ + CV_StaticAssert(channels >= 8, "Matx should have at least 8 elements."); + val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; + val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7; + for(int i = 8; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8) +{ + CV_StaticAssert(channels >= 9, "Matx should have at least 9 elements."); + val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; + val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7; + val[8] = v8; + for(int i = 9; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9) +{ + CV_StaticAssert(channels >= 10, "Matx should have at least 10 elements."); + val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; + val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7; + val[8] = v8; val[9] = v9; + for(int i = 10; i < channels; i++) val[i] = _Tp(0); +} + + +template inline +Matx<_Tp,m,n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9, _Tp v10, _Tp v11) +{ + CV_StaticAssert(channels >= 12, "Matx should have at least 12 elements."); + val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; + val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7; + val[8] = v8; val[9] = v9; val[10] = v10; val[11] = v11; + for(int i = 12; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp,m,n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9, _Tp v10, _Tp v11, _Tp v12, _Tp v13) +{ + CV_StaticAssert(channels >= 14, "Matx should have at least 14 elements."); + val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; + val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7; + val[8] = v8; val[9] = v9; val[10] = v10; val[11] = v11; + val[12] = v12; val[13] = v13; + for (int i = 14; i < channels; i++) val[i] = _Tp(0); +} + + +template inline +Matx<_Tp,m,n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9, _Tp v10, _Tp v11, _Tp v12, _Tp v13, _Tp v14, _Tp v15) +{ + CV_StaticAssert(channels >= 16, "Matx should have at least 16 elements."); + val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; + val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7; + val[8] = v8; val[9] = v9; val[10] = v10; val[11] = v11; + val[12] = v12; val[13] = v13; val[14] = v14; val[15] = v15; + for(int i = 16; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp, m, n>::Matx(const _Tp* values) +{ + for( int i = 0; i < channels; i++ ) val[i] = values[i]; +} + +#ifdef CV_CXX11 +template inline +Matx<_Tp, m, n>::Matx(std::initializer_list<_Tp> list) +{ + CV_DbgAssert(list.size() == channels); + int i = 0; + for(const auto& elem : list) + { + val[i++] = elem; + } +} +#endif + +template inline +Matx<_Tp, m, n> Matx<_Tp, m, n>::all(_Tp alpha) +{ + Matx<_Tp, m, n> M; + for( int i = 0; i < m*n; i++ ) M.val[i] = alpha; + return M; +} + +template inline +Matx<_Tp,m,n> Matx<_Tp,m,n>::zeros() +{ + return all(0); +} + +template inline +Matx<_Tp,m,n> Matx<_Tp,m,n>::ones() +{ + return all(1); +} + +template inline +Matx<_Tp,m,n> Matx<_Tp,m,n>::eye() +{ + Matx<_Tp,m,n> M; + for(int i = 0; i < shortdim; i++) + M(i,i) = 1; + return M; +} + +template inline +_Tp Matx<_Tp, m, n>::dot(const Matx<_Tp, m, n>& M) const +{ + _Tp s = 0; + for( int i = 0; i < channels; i++ ) s += val[i]*M.val[i]; + return s; +} + +template inline +double Matx<_Tp, m, n>::ddot(const Matx<_Tp, m, n>& M) const +{ + double s = 0; + for( int i = 0; i < channels; i++ ) s += (double)val[i]*M.val[i]; + return s; +} + +template inline +Matx<_Tp,m,n> Matx<_Tp,m,n>::diag(const typename Matx<_Tp,m,n>::diag_type& d) +{ + Matx<_Tp,m,n> M; + for(int i = 0; i < shortdim; i++) + M(i,i) = d(i, 0); + return M; +} + +template template +inline Matx<_Tp, m, n>::operator Matx() const +{ + Matx M; + for( int i = 0; i < m*n; i++ ) M.val[i] = saturate_cast(val[i]); + return M; +} + +template template inline +Matx<_Tp, m1, n1> Matx<_Tp, m, n>::reshape() const +{ + CV_StaticAssert(m1*n1 == m*n, "Input and destnarion matrices must have the same number of elements"); + return (const Matx<_Tp, m1, n1>&)*this; +} + +template +template inline +Matx<_Tp, m1, n1> Matx<_Tp, m, n>::get_minor(int i, int j) const +{ + CV_DbgAssert(0 <= i && i+m1 <= m && 0 <= j && j+n1 <= n); + Matx<_Tp, m1, n1> s; + for( int di = 0; di < m1; di++ ) + for( int dj = 0; dj < n1; dj++ ) + s(di, dj) = (*this)(i+di, j+dj); + return s; +} + +template inline +Matx<_Tp, 1, n> Matx<_Tp, m, n>::row(int i) const +{ + CV_DbgAssert((unsigned)i < (unsigned)m); + return Matx<_Tp, 1, n>(&val[i*n]); +} + +template inline +Matx<_Tp, m, 1> Matx<_Tp, m, n>::col(int j) const +{ + CV_DbgAssert((unsigned)j < (unsigned)n); + Matx<_Tp, m, 1> v; + for( int i = 0; i < m; i++ ) + v.val[i] = val[i*n + j]; + return v; +} + +template inline +typename Matx<_Tp, m, n>::diag_type Matx<_Tp, m, n>::diag() const +{ + diag_type d; + for( int i = 0; i < shortdim; i++ ) + d.val[i] = val[i*n + i]; + return d; +} + +template inline +const _Tp& Matx<_Tp, m, n>::operator()(int i, int j) const +{ + CV_DbgAssert( (unsigned)i < (unsigned)m && (unsigned)j < (unsigned)n ); + return this->val[i*n + j]; +} + +template inline +_Tp& Matx<_Tp, m, n>::operator ()(int i, int j) +{ + CV_DbgAssert( (unsigned)i < (unsigned)m && (unsigned)j < (unsigned)n ); + return val[i*n + j]; +} + +template inline +const _Tp& Matx<_Tp, m, n>::operator ()(int i) const +{ + CV_StaticAssert(m == 1 || n == 1, "Single index indexation requires matrix to be a column or a row"); + CV_DbgAssert( (unsigned)i < (unsigned)(m+n-1) ); + return val[i]; +} + +template inline +_Tp& Matx<_Tp, m, n>::operator ()(int i) +{ + CV_StaticAssert(m == 1 || n == 1, "Single index indexation requires matrix to be a column or a row"); + CV_DbgAssert( (unsigned)i < (unsigned)(m+n-1) ); + return val[i]; +} + +template inline +Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_AddOp) +{ + for( int i = 0; i < channels; i++ ) + val[i] = saturate_cast<_Tp>(a.val[i] + b.val[i]); +} + +template inline +Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_SubOp) +{ + for( int i = 0; i < channels; i++ ) + val[i] = saturate_cast<_Tp>(a.val[i] - b.val[i]); +} + +template template inline +Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, _T2 alpha, Matx_ScaleOp) +{ + for( int i = 0; i < channels; i++ ) + val[i] = saturate_cast<_Tp>(a.val[i] * alpha); +} + +template inline +Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_MulOp) +{ + for( int i = 0; i < channels; i++ ) + val[i] = saturate_cast<_Tp>(a.val[i] * b.val[i]); +} + +template inline +Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_DivOp) +{ + for( int i = 0; i < channels; i++ ) + val[i] = saturate_cast<_Tp>(a.val[i] / b.val[i]); +} + +template template inline +Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, l>& a, const Matx<_Tp, l, n>& b, Matx_MatMulOp) +{ + for( int i = 0; i < m; i++ ) + for( int j = 0; j < n; j++ ) + { + _Tp s = 0; + for( int k = 0; k < l; k++ ) + s += a(i, k) * b(k, j); + val[i*n + j] = s; + } +} + +template inline +Matx<_Tp,m,n>::Matx(const Matx<_Tp, n, m>& a, Matx_TOp) +{ + for( int i = 0; i < m; i++ ) + for( int j = 0; j < n; j++ ) + val[i*n + j] = a(j, i); +} + +template inline +Matx<_Tp, m, n> Matx<_Tp, m, n>::mul(const Matx<_Tp, m, n>& a) const +{ + return Matx<_Tp, m, n>(*this, a, Matx_MulOp()); +} + +template inline +Matx<_Tp, m, n> Matx<_Tp, m, n>::div(const Matx<_Tp, m, n>& a) const +{ + return Matx<_Tp, m, n>(*this, a, Matx_DivOp()); +} + +template inline +Matx<_Tp, n, m> Matx<_Tp, m, n>::t() const +{ + return Matx<_Tp, n, m>(*this, Matx_TOp()); +} + +template inline +Vec<_Tp, n> Matx<_Tp, m, n>::solve(const Vec<_Tp, m>& rhs, int method) const +{ + Matx<_Tp, n, 1> x = solve((const Matx<_Tp, m, 1>&)(rhs), method); + return (Vec<_Tp, n>&)(x); +} + +template static inline +double determinant(const Matx<_Tp, m, m>& a) +{ + return cv::internal::Matx_DetOp<_Tp, m>()(a); +} + +template static inline +double trace(const Matx<_Tp, m, n>& a) +{ + _Tp s = 0; + for( int i = 0; i < std::min(m, n); i++ ) + s += a(i,i); + return s; +} + +template static inline +double norm(const Matx<_Tp, m, n>& M) +{ + return std::sqrt(normL2Sqr<_Tp, double>(M.val, m*n)); +} + +template static inline +double norm(const Matx<_Tp, m, n>& M, int normType) +{ + switch(normType) { + case NORM_INF: + return (double)normInf<_Tp, typename DataType<_Tp>::work_type>(M.val, m*n); + case NORM_L1: + return (double)normL1<_Tp, typename DataType<_Tp>::work_type>(M.val, m*n); + case NORM_L2SQR: + return (double)normL2Sqr<_Tp, typename DataType<_Tp>::work_type>(M.val, m*n); + default: + case NORM_L2: + return std::sqrt((double)normL2Sqr<_Tp, typename DataType<_Tp>::work_type>(M.val, m*n)); + } +} + + + +//////////////////////////////// matx comma initializer ////////////////////////////////// + +template static inline +MatxCommaInitializer<_Tp, m, n> operator << (const Matx<_Tp, m, n>& mtx, _T2 val) +{ + MatxCommaInitializer<_Tp, m, n> commaInitializer((Matx<_Tp, m, n>*)&mtx); + return (commaInitializer, val); +} + +template inline +MatxCommaInitializer<_Tp, m, n>::MatxCommaInitializer(Matx<_Tp, m, n>* _mtx) + : dst(_mtx), idx(0) +{} + +template template inline +MatxCommaInitializer<_Tp, m, n>& MatxCommaInitializer<_Tp, m, n>::operator , (_T2 value) +{ + CV_DbgAssert( idx < m*n ); + dst->val[idx++] = saturate_cast<_Tp>(value); + return *this; +} + +template inline +Matx<_Tp, m, n> MatxCommaInitializer<_Tp, m, n>::operator *() const +{ + CV_DbgAssert( idx == n*m ); + return *dst; +} + + + +/////////////////////////////////// Vec Implementation /////////////////////////////////// + +template inline +Vec<_Tp, cn>::Vec() {} + +template inline +Vec<_Tp, cn>::Vec(_Tp v0) + : Matx<_Tp, cn, 1>(v0) {} + +template inline +Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1) + : Matx<_Tp, cn, 1>(v0, v1) {} + +template inline +Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2) + : Matx<_Tp, cn, 1>(v0, v1, v2) {} + +template inline +Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3) + : Matx<_Tp, cn, 1>(v0, v1, v2, v3) {} + +template inline +Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4) + : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4) {} + +template inline +Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5) + : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5) {} + +template inline +Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6) + : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6) {} + +template inline +Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7) + : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6, v7) {} + +template inline +Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8) + : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6, v7, v8) {} + +template inline +Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9) + : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6, v7, v8, v9) {} + +template inline +Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9, _Tp v10, _Tp v11, _Tp v12, _Tp v13) + : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13) {} + +template inline +Vec<_Tp, cn>::Vec(const _Tp* values) + : Matx<_Tp, cn, 1>(values) {} + +#ifdef CV_CXX11 +template inline +Vec<_Tp, cn>::Vec(std::initializer_list<_Tp> list) + : Matx<_Tp, cn, 1>(list) {} +#endif + +template inline +Vec<_Tp, cn>::Vec(const Vec<_Tp, cn>& m) + : Matx<_Tp, cn, 1>(m.val) {} + +template inline +Vec<_Tp, cn>::Vec(const Matx<_Tp, cn, 1>& a, const Matx<_Tp, cn, 1>& b, Matx_AddOp op) + : Matx<_Tp, cn, 1>(a, b, op) {} + +template inline +Vec<_Tp, cn>::Vec(const Matx<_Tp, cn, 1>& a, const Matx<_Tp, cn, 1>& b, Matx_SubOp op) + : Matx<_Tp, cn, 1>(a, b, op) {} + +template template inline +Vec<_Tp, cn>::Vec(const Matx<_Tp, cn, 1>& a, _T2 alpha, Matx_ScaleOp op) + : Matx<_Tp, cn, 1>(a, alpha, op) {} + +template inline +Vec<_Tp, cn> Vec<_Tp, cn>::all(_Tp alpha) +{ + Vec v; + for( int i = 0; i < cn; i++ ) v.val[i] = alpha; + return v; +} + +template inline +Vec<_Tp, cn> Vec<_Tp, cn>::mul(const Vec<_Tp, cn>& v) const +{ + Vec<_Tp, cn> w; + for( int i = 0; i < cn; i++ ) w.val[i] = saturate_cast<_Tp>(this->val[i]*v.val[i]); + return w; +} + +template<> inline +Vec Vec::conj() const +{ + return cv::internal::conjugate(*this); +} + +template<> inline +Vec Vec::conj() const +{ + return cv::internal::conjugate(*this); +} + +template<> inline +Vec Vec::conj() const +{ + return cv::internal::conjugate(*this); +} + +template<> inline +Vec Vec::conj() const +{ + return cv::internal::conjugate(*this); +} + +template inline +Vec<_Tp, cn> Vec<_Tp, cn>::cross(const Vec<_Tp, cn>&) const +{ + CV_StaticAssert(cn == 3, "for arbitrary-size vector there is no cross-product defined"); + return Vec<_Tp, cn>(); +} + +template<> inline +Vec Vec::cross(const Vec& v) const +{ + return Vec(this->val[1]*v.val[2] - this->val[2]*v.val[1], + this->val[2]*v.val[0] - this->val[0]*v.val[2], + this->val[0]*v.val[1] - this->val[1]*v.val[0]); +} + +template<> inline +Vec Vec::cross(const Vec& v) const +{ + return Vec(this->val[1]*v.val[2] - this->val[2]*v.val[1], + this->val[2]*v.val[0] - this->val[0]*v.val[2], + this->val[0]*v.val[1] - this->val[1]*v.val[0]); +} + +template template inline +Vec<_Tp, cn>::operator Vec() const +{ + Vec v; + for( int i = 0; i < cn; i++ ) v.val[i] = saturate_cast(this->val[i]); + return v; +} + +template inline +const _Tp& Vec<_Tp, cn>::operator [](int i) const +{ + CV_DbgAssert( (unsigned)i < (unsigned)cn ); + return this->val[i]; +} + +template inline +_Tp& Vec<_Tp, cn>::operator [](int i) +{ + CV_DbgAssert( (unsigned)i < (unsigned)cn ); + return this->val[i]; +} + +template inline +const _Tp& Vec<_Tp, cn>::operator ()(int i) const +{ + CV_DbgAssert( (unsigned)i < (unsigned)cn ); + return this->val[i]; +} + +template inline +_Tp& Vec<_Tp, cn>::operator ()(int i) +{ + CV_DbgAssert( (unsigned)i < (unsigned)cn ); + return this->val[i]; +} + +template inline +Vec<_Tp, cn> normalize(const Vec<_Tp, cn>& v) +{ + double nv = norm(v); + return v * (nv ? 1./nv : 0.); +} + + + +//////////////////////////////// vec comma initializer ////////////////////////////////// + + +template static inline +VecCommaInitializer<_Tp, cn> operator << (const Vec<_Tp, cn>& vec, _T2 val) +{ + VecCommaInitializer<_Tp, cn> commaInitializer((Vec<_Tp, cn>*)&vec); + return (commaInitializer, val); +} + +template inline +VecCommaInitializer<_Tp, cn>::VecCommaInitializer(Vec<_Tp, cn>* _vec) + : MatxCommaInitializer<_Tp, cn, 1>(_vec) +{} + +template template inline +VecCommaInitializer<_Tp, cn>& VecCommaInitializer<_Tp, cn>::operator , (_T2 value) +{ + CV_DbgAssert( this->idx < cn ); + this->dst->val[this->idx++] = saturate_cast<_Tp>(value); + return *this; +} + +template inline +Vec<_Tp, cn> VecCommaInitializer<_Tp, cn>::operator *() const +{ + CV_DbgAssert( this->idx == cn ); + return *this->dst; +} + +//! @endcond + +///////////////////////////// Matx out-of-class operators //////////////////////////////// + +//! @relates cv::Matx +//! @{ + +template static inline +Matx<_Tp1, m, n>& operator += (Matx<_Tp1, m, n>& a, const Matx<_Tp2, m, n>& b) +{ + for( int i = 0; i < m*n; i++ ) + a.val[i] = saturate_cast<_Tp1>(a.val[i] + b.val[i]); + return a; +} + +template static inline +Matx<_Tp1, m, n>& operator -= (Matx<_Tp1, m, n>& a, const Matx<_Tp2, m, n>& b) +{ + for( int i = 0; i < m*n; i++ ) + a.val[i] = saturate_cast<_Tp1>(a.val[i] - b.val[i]); + return a; +} + +template static inline +Matx<_Tp, m, n> operator + (const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b) +{ + return Matx<_Tp, m, n>(a, b, Matx_AddOp()); +} + +template static inline +Matx<_Tp, m, n> operator - (const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b) +{ + return Matx<_Tp, m, n>(a, b, Matx_SubOp()); +} + +template static inline +Matx<_Tp, m, n>& operator *= (Matx<_Tp, m, n>& a, int alpha) +{ + for( int i = 0; i < m*n; i++ ) + a.val[i] = saturate_cast<_Tp>(a.val[i] * alpha); + return a; +} + +template static inline +Matx<_Tp, m, n>& operator *= (Matx<_Tp, m, n>& a, float alpha) +{ + for( int i = 0; i < m*n; i++ ) + a.val[i] = saturate_cast<_Tp>(a.val[i] * alpha); + return a; +} + +template static inline +Matx<_Tp, m, n>& operator *= (Matx<_Tp, m, n>& a, double alpha) +{ + for( int i = 0; i < m*n; i++ ) + a.val[i] = saturate_cast<_Tp>(a.val[i] * alpha); + return a; +} + +template static inline +Matx<_Tp, m, n> operator * (const Matx<_Tp, m, n>& a, int alpha) +{ + return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Matx<_Tp, m, n> operator * (const Matx<_Tp, m, n>& a, float alpha) +{ + return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Matx<_Tp, m, n> operator * (const Matx<_Tp, m, n>& a, double alpha) +{ + return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Matx<_Tp, m, n> operator * (int alpha, const Matx<_Tp, m, n>& a) +{ + return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Matx<_Tp, m, n> operator * (float alpha, const Matx<_Tp, m, n>& a) +{ + return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Matx<_Tp, m, n> operator * (double alpha, const Matx<_Tp, m, n>& a) +{ + return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Matx<_Tp, m, n> operator - (const Matx<_Tp, m, n>& a) +{ + return Matx<_Tp, m, n>(a, -1, Matx_ScaleOp()); +} + +template static inline +Matx<_Tp, m, n> operator * (const Matx<_Tp, m, l>& a, const Matx<_Tp, l, n>& b) +{ + return Matx<_Tp, m, n>(a, b, Matx_MatMulOp()); +} + +template static inline +Vec<_Tp, m> operator * (const Matx<_Tp, m, n>& a, const Vec<_Tp, n>& b) +{ + Matx<_Tp, m, 1> c(a, b, Matx_MatMulOp()); + return (const Vec<_Tp, m>&)(c); +} + +template static inline +bool operator == (const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b) +{ + for( int i = 0; i < m*n; i++ ) + if( a.val[i] != b.val[i] ) return false; + return true; +} + +template static inline +bool operator != (const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b) +{ + return !(a == b); +} + +//! @} + +////////////////////////////// Vec out-of-class operators //////////////////////////////// + +//! @relates cv::Vec +//! @{ + +template static inline +Vec<_Tp1, cn>& operator += (Vec<_Tp1, cn>& a, const Vec<_Tp2, cn>& b) +{ + for( int i = 0; i < cn; i++ ) + a.val[i] = saturate_cast<_Tp1>(a.val[i] + b.val[i]); + return a; +} + +template static inline +Vec<_Tp1, cn>& operator -= (Vec<_Tp1, cn>& a, const Vec<_Tp2, cn>& b) +{ + for( int i = 0; i < cn; i++ ) + a.val[i] = saturate_cast<_Tp1>(a.val[i] - b.val[i]); + return a; +} + +template static inline +Vec<_Tp, cn> operator + (const Vec<_Tp, cn>& a, const Vec<_Tp, cn>& b) +{ + return Vec<_Tp, cn>(a, b, Matx_AddOp()); +} + +template static inline +Vec<_Tp, cn> operator - (const Vec<_Tp, cn>& a, const Vec<_Tp, cn>& b) +{ + return Vec<_Tp, cn>(a, b, Matx_SubOp()); +} + +template static inline +Vec<_Tp, cn>& operator *= (Vec<_Tp, cn>& a, int alpha) +{ + for( int i = 0; i < cn; i++ ) + a[i] = saturate_cast<_Tp>(a[i]*alpha); + return a; +} + +template static inline +Vec<_Tp, cn>& operator *= (Vec<_Tp, cn>& a, float alpha) +{ + for( int i = 0; i < cn; i++ ) + a[i] = saturate_cast<_Tp>(a[i]*alpha); + return a; +} + +template static inline +Vec<_Tp, cn>& operator *= (Vec<_Tp, cn>& a, double alpha) +{ + for( int i = 0; i < cn; i++ ) + a[i] = saturate_cast<_Tp>(a[i]*alpha); + return a; +} + +template static inline +Vec<_Tp, cn>& operator /= (Vec<_Tp, cn>& a, int alpha) +{ + double ialpha = 1./alpha; + for( int i = 0; i < cn; i++ ) + a[i] = saturate_cast<_Tp>(a[i]*ialpha); + return a; +} + +template static inline +Vec<_Tp, cn>& operator /= (Vec<_Tp, cn>& a, float alpha) +{ + float ialpha = 1.f/alpha; + for( int i = 0; i < cn; i++ ) + a[i] = saturate_cast<_Tp>(a[i]*ialpha); + return a; +} + +template static inline +Vec<_Tp, cn>& operator /= (Vec<_Tp, cn>& a, double alpha) +{ + double ialpha = 1./alpha; + for( int i = 0; i < cn; i++ ) + a[i] = saturate_cast<_Tp>(a[i]*ialpha); + return a; +} + +template static inline +Vec<_Tp, cn> operator * (const Vec<_Tp, cn>& a, int alpha) +{ + return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Vec<_Tp, cn> operator * (int alpha, const Vec<_Tp, cn>& a) +{ + return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Vec<_Tp, cn> operator * (const Vec<_Tp, cn>& a, float alpha) +{ + return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Vec<_Tp, cn> operator * (float alpha, const Vec<_Tp, cn>& a) +{ + return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Vec<_Tp, cn> operator * (const Vec<_Tp, cn>& a, double alpha) +{ + return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Vec<_Tp, cn> operator * (double alpha, const Vec<_Tp, cn>& a) +{ + return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Vec<_Tp, cn> operator / (const Vec<_Tp, cn>& a, int alpha) +{ + return Vec<_Tp, cn>(a, 1./alpha, Matx_ScaleOp()); +} + +template static inline +Vec<_Tp, cn> operator / (const Vec<_Tp, cn>& a, float alpha) +{ + return Vec<_Tp, cn>(a, 1.f/alpha, Matx_ScaleOp()); +} + +template static inline +Vec<_Tp, cn> operator / (const Vec<_Tp, cn>& a, double alpha) +{ + return Vec<_Tp, cn>(a, 1./alpha, Matx_ScaleOp()); +} + +template static inline +Vec<_Tp, cn> operator - (const Vec<_Tp, cn>& a) +{ + Vec<_Tp,cn> t; + for( int i = 0; i < cn; i++ ) t.val[i] = saturate_cast<_Tp>(-a.val[i]); + return t; +} + +template inline Vec<_Tp, 4> operator * (const Vec<_Tp, 4>& v1, const Vec<_Tp, 4>& v2) +{ + return Vec<_Tp, 4>(saturate_cast<_Tp>(v1[0]*v2[0] - v1[1]*v2[1] - v1[2]*v2[2] - v1[3]*v2[3]), + saturate_cast<_Tp>(v1[0]*v2[1] + v1[1]*v2[0] + v1[2]*v2[3] - v1[3]*v2[2]), + saturate_cast<_Tp>(v1[0]*v2[2] - v1[1]*v2[3] + v1[2]*v2[0] + v1[3]*v2[1]), + saturate_cast<_Tp>(v1[0]*v2[3] + v1[1]*v2[2] - v1[2]*v2[1] + v1[3]*v2[0])); +} + +template inline Vec<_Tp, 4>& operator *= (Vec<_Tp, 4>& v1, const Vec<_Tp, 4>& v2) +{ + v1 = v1 * v2; + return v1; +} + +//! @} + +} // cv + +#endif // OPENCV_CORE_MATX_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/neon_utils.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/neon_utils.hpp new file mode 100644 index 0000000000000000000000000000000000000000..573ba99ec3569e5dc13b637daf1493b7c1ad6afb --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/neon_utils.hpp @@ -0,0 +1,128 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_HAL_NEON_UTILS_HPP +#define OPENCV_HAL_NEON_UTILS_HPP + +#include "opencv2/core/cvdef.h" + +//! @addtogroup core_utils_neon +//! @{ + +#if CV_NEON + +inline int32x2_t cv_vrnd_s32_f32(float32x2_t v) +{ + static int32x2_t v_sign = vdup_n_s32(1 << 31), + v_05 = vreinterpret_s32_f32(vdup_n_f32(0.5f)); + + int32x2_t v_addition = vorr_s32(v_05, vand_s32(v_sign, vreinterpret_s32_f32(v))); + return vcvt_s32_f32(vadd_f32(v, vreinterpret_f32_s32(v_addition))); +} + +inline int32x4_t cv_vrndq_s32_f32(float32x4_t v) +{ + static int32x4_t v_sign = vdupq_n_s32(1 << 31), + v_05 = vreinterpretq_s32_f32(vdupq_n_f32(0.5f)); + + int32x4_t v_addition = vorrq_s32(v_05, vandq_s32(v_sign, vreinterpretq_s32_f32(v))); + return vcvtq_s32_f32(vaddq_f32(v, vreinterpretq_f32_s32(v_addition))); +} + +inline uint32x2_t cv_vrnd_u32_f32(float32x2_t v) +{ + static float32x2_t v_05 = vdup_n_f32(0.5f); + return vcvt_u32_f32(vadd_f32(v, v_05)); +} + +inline uint32x4_t cv_vrndq_u32_f32(float32x4_t v) +{ + static float32x4_t v_05 = vdupq_n_f32(0.5f); + return vcvtq_u32_f32(vaddq_f32(v, v_05)); +} + +inline float32x4_t cv_vrecpq_f32(float32x4_t val) +{ + float32x4_t reciprocal = vrecpeq_f32(val); + reciprocal = vmulq_f32(vrecpsq_f32(val, reciprocal), reciprocal); + reciprocal = vmulq_f32(vrecpsq_f32(val, reciprocal), reciprocal); + return reciprocal; +} + +inline float32x2_t cv_vrecp_f32(float32x2_t val) +{ + float32x2_t reciprocal = vrecpe_f32(val); + reciprocal = vmul_f32(vrecps_f32(val, reciprocal), reciprocal); + reciprocal = vmul_f32(vrecps_f32(val, reciprocal), reciprocal); + return reciprocal; +} + +inline float32x4_t cv_vrsqrtq_f32(float32x4_t val) +{ + float32x4_t e = vrsqrteq_f32(val); + e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(e, e), val), e); + e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(e, e), val), e); + return e; +} + +inline float32x2_t cv_vrsqrt_f32(float32x2_t val) +{ + float32x2_t e = vrsqrte_f32(val); + e = vmul_f32(vrsqrts_f32(vmul_f32(e, e), val), e); + e = vmul_f32(vrsqrts_f32(vmul_f32(e, e), val), e); + return e; +} + +inline float32x4_t cv_vsqrtq_f32(float32x4_t val) +{ + return cv_vrecpq_f32(cv_vrsqrtq_f32(val)); +} + +inline float32x2_t cv_vsqrt_f32(float32x2_t val) +{ + return cv_vrecp_f32(cv_vrsqrt_f32(val)); +} + +#endif + +//! @} + +#endif // OPENCV_HAL_NEON_UTILS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/ocl.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/ocl.hpp new file mode 100644 index 0000000000000000000000000000000000000000..95f0fcdd66987fb97b75905c8a4f0716e338ac2b --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/ocl.hpp @@ -0,0 +1,843 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the OpenCV Foundation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OPENCL_HPP +#define OPENCV_OPENCL_HPP + +#include "opencv2/core.hpp" + +namespace cv { namespace ocl { + +//! @addtogroup core_opencl +//! @{ + +CV_EXPORTS_W bool haveOpenCL(); +CV_EXPORTS_W bool useOpenCL(); +CV_EXPORTS_W bool haveAmdBlas(); +CV_EXPORTS_W bool haveAmdFft(); +CV_EXPORTS_W void setUseOpenCL(bool flag); +CV_EXPORTS_W void finish(); + +CV_EXPORTS bool haveSVM(); + +class CV_EXPORTS Context; +class CV_EXPORTS_W_SIMPLE Device; +class CV_EXPORTS Kernel; +class CV_EXPORTS Program; +class CV_EXPORTS ProgramSource; +class CV_EXPORTS Queue; +class CV_EXPORTS PlatformInfo; +class CV_EXPORTS Image2D; + +class CV_EXPORTS_W_SIMPLE Device +{ +public: + CV_WRAP Device(); + explicit Device(void* d); + Device(const Device& d); + Device& operator = (const Device& d); + CV_WRAP ~Device(); + + void set(void* d); + + enum + { + TYPE_DEFAULT = (1 << 0), + TYPE_CPU = (1 << 1), + TYPE_GPU = (1 << 2), + TYPE_ACCELERATOR = (1 << 3), + TYPE_DGPU = TYPE_GPU + (1 << 16), + TYPE_IGPU = TYPE_GPU + (1 << 17), + TYPE_ALL = 0xFFFFFFFF + }; + + CV_WRAP String name() const; + CV_WRAP String extensions() const; + CV_WRAP bool isExtensionSupported(const String& extensionName) const; + CV_WRAP String version() const; + CV_WRAP String vendorName() const; + CV_WRAP String OpenCL_C_Version() const; + CV_WRAP String OpenCLVersion() const; + CV_WRAP int deviceVersionMajor() const; + CV_WRAP int deviceVersionMinor() const; + CV_WRAP String driverVersion() const; + void* ptr() const; + + CV_WRAP int type() const; + + CV_WRAP int addressBits() const; + CV_WRAP bool available() const; + CV_WRAP bool compilerAvailable() const; + CV_WRAP bool linkerAvailable() const; + + enum + { + FP_DENORM=(1 << 0), + FP_INF_NAN=(1 << 1), + FP_ROUND_TO_NEAREST=(1 << 2), + FP_ROUND_TO_ZERO=(1 << 3), + FP_ROUND_TO_INF=(1 << 4), + FP_FMA=(1 << 5), + FP_SOFT_FLOAT=(1 << 6), + FP_CORRECTLY_ROUNDED_DIVIDE_SQRT=(1 << 7) + }; + CV_WRAP int doubleFPConfig() const; + CV_WRAP int singleFPConfig() const; + CV_WRAP int halfFPConfig() const; + + CV_WRAP bool endianLittle() const; + CV_WRAP bool errorCorrectionSupport() const; + + enum + { + EXEC_KERNEL=(1 << 0), + EXEC_NATIVE_KERNEL=(1 << 1) + }; + CV_WRAP int executionCapabilities() const; + + CV_WRAP size_t globalMemCacheSize() const; + + enum + { + NO_CACHE=0, + READ_ONLY_CACHE=1, + READ_WRITE_CACHE=2 + }; + CV_WRAP int globalMemCacheType() const; + CV_WRAP int globalMemCacheLineSize() const; + CV_WRAP size_t globalMemSize() const; + + CV_WRAP size_t localMemSize() const; + enum + { + NO_LOCAL_MEM=0, + LOCAL_IS_LOCAL=1, + LOCAL_IS_GLOBAL=2 + }; + CV_WRAP int localMemType() const; + CV_WRAP bool hostUnifiedMemory() const; + + CV_WRAP bool imageSupport() const; + + CV_WRAP bool imageFromBufferSupport() const; + uint imagePitchAlignment() const; + uint imageBaseAddressAlignment() const; + + /// deprecated, use isExtensionSupported() method (probably with "cl_khr_subgroups" value) + CV_WRAP bool intelSubgroupsSupport() const; + + CV_WRAP size_t image2DMaxWidth() const; + CV_WRAP size_t image2DMaxHeight() const; + + CV_WRAP size_t image3DMaxWidth() const; + CV_WRAP size_t image3DMaxHeight() const; + CV_WRAP size_t image3DMaxDepth() const; + + CV_WRAP size_t imageMaxBufferSize() const; + CV_WRAP size_t imageMaxArraySize() const; + + enum + { + UNKNOWN_VENDOR=0, + VENDOR_AMD=1, + VENDOR_INTEL=2, + VENDOR_NVIDIA=3 + }; + CV_WRAP int vendorID() const; + // FIXIT + // dev.isAMD() doesn't work for OpenCL CPU devices from AMD OpenCL platform. + // This method should use platform name instead of vendor name. + // After fix restore code in arithm.cpp: ocl_compare() + CV_WRAP inline bool isAMD() const { return vendorID() == VENDOR_AMD; } + CV_WRAP inline bool isIntel() const { return vendorID() == VENDOR_INTEL; } + CV_WRAP inline bool isNVidia() const { return vendorID() == VENDOR_NVIDIA; } + + CV_WRAP int maxClockFrequency() const; + CV_WRAP int maxComputeUnits() const; + CV_WRAP int maxConstantArgs() const; + CV_WRAP size_t maxConstantBufferSize() const; + + CV_WRAP size_t maxMemAllocSize() const; + CV_WRAP size_t maxParameterSize() const; + + CV_WRAP int maxReadImageArgs() const; + CV_WRAP int maxWriteImageArgs() const; + CV_WRAP int maxSamplers() const; + + CV_WRAP size_t maxWorkGroupSize() const; + CV_WRAP int maxWorkItemDims() const; + void maxWorkItemSizes(size_t*) const; + + CV_WRAP int memBaseAddrAlign() const; + + CV_WRAP int nativeVectorWidthChar() const; + CV_WRAP int nativeVectorWidthShort() const; + CV_WRAP int nativeVectorWidthInt() const; + CV_WRAP int nativeVectorWidthLong() const; + CV_WRAP int nativeVectorWidthFloat() const; + CV_WRAP int nativeVectorWidthDouble() const; + CV_WRAP int nativeVectorWidthHalf() const; + + CV_WRAP int preferredVectorWidthChar() const; + CV_WRAP int preferredVectorWidthShort() const; + CV_WRAP int preferredVectorWidthInt() const; + CV_WRAP int preferredVectorWidthLong() const; + CV_WRAP int preferredVectorWidthFloat() const; + CV_WRAP int preferredVectorWidthDouble() const; + CV_WRAP int preferredVectorWidthHalf() const; + + CV_WRAP size_t printfBufferSize() const; + CV_WRAP size_t profilingTimerResolution() const; + + CV_WRAP static const Device& getDefault(); + +protected: + struct Impl; + Impl* p; +}; + + +class CV_EXPORTS Context +{ +public: + Context(); + explicit Context(int dtype); + ~Context(); + Context(const Context& c); + Context& operator = (const Context& c); + + bool create(); + bool create(int dtype); + size_t ndevices() const; + const Device& device(size_t idx) const; + Program getProg(const ProgramSource& prog, + const String& buildopt, String& errmsg); + void unloadProg(Program& prog); + + static Context& getDefault(bool initialize = true); + void* ptr() const; + + friend void initializeContextFromHandle(Context& ctx, void* platform, void* context, void* device); + + bool useSVM() const; + void setUseSVM(bool enabled); + + struct Impl; + inline Impl* getImpl() const { return (Impl*)p; } +//protected: + Impl* p; +}; + +class CV_EXPORTS Platform +{ +public: + Platform(); + ~Platform(); + Platform(const Platform& p); + Platform& operator = (const Platform& p); + + void* ptr() const; + static Platform& getDefault(); + + friend void initializeContextFromHandle(Context& ctx, void* platform, void* context, void* device); +protected: + struct Impl; + Impl* p; +}; + +/** @brief Attaches OpenCL context to OpenCV +@note + OpenCV will check if available OpenCL platform has platformName name, then assign context to + OpenCV and call `clRetainContext` function. The deviceID device will be used as target device and + new command queue will be created. +@param platformName name of OpenCL platform to attach, this string is used to check if platform is available to OpenCV at runtime +@param platformID ID of platform attached context was created for +@param context OpenCL context to be attached to OpenCV +@param deviceID ID of device, must be created from attached context +*/ +CV_EXPORTS void attachContext(const String& platformName, void* platformID, void* context, void* deviceID); + +/** @brief Convert OpenCL buffer to UMat +@note + OpenCL buffer (cl_mem_buffer) should contain 2D image data, compatible with OpenCV. Memory + content is not copied from `clBuffer` to UMat. Instead, buffer handle assigned to UMat and + `clRetainMemObject` is called. +@param cl_mem_buffer source clBuffer handle +@param step num of bytes in single row +@param rows number of rows +@param cols number of cols +@param type OpenCV type of image +@param dst destination UMat +*/ +CV_EXPORTS void convertFromBuffer(void* cl_mem_buffer, size_t step, int rows, int cols, int type, UMat& dst); + +/** @brief Convert OpenCL image2d_t to UMat +@note + OpenCL `image2d_t` (cl_mem_image), should be compatible with OpenCV UMat formats. Memory content + is copied from image to UMat with `clEnqueueCopyImageToBuffer` function. +@param cl_mem_image source image2d_t handle +@param dst destination UMat +*/ +CV_EXPORTS void convertFromImage(void* cl_mem_image, UMat& dst); + +// TODO Move to internal header +void initializeContextFromHandle(Context& ctx, void* platform, void* context, void* device); + +class CV_EXPORTS Queue +{ +public: + Queue(); + explicit Queue(const Context& c, const Device& d=Device()); + ~Queue(); + Queue(const Queue& q); + Queue& operator = (const Queue& q); + + bool create(const Context& c=Context(), const Device& d=Device()); + void finish(); + void* ptr() const; + static Queue& getDefault(); + + /// @brief Returns OpenCL command queue with enable profiling mode support + const Queue& getProfilingQueue() const; + + struct Impl; friend struct Impl; + inline Impl* getImpl() const { return p; } +protected: + Impl* p; +}; + + +class CV_EXPORTS KernelArg +{ +public: + enum { LOCAL=1, READ_ONLY=2, WRITE_ONLY=4, READ_WRITE=6, CONSTANT=8, PTR_ONLY = 16, NO_SIZE=256 }; + KernelArg(int _flags, UMat* _m, int wscale=1, int iwscale=1, const void* _obj=0, size_t _sz=0); + KernelArg(); + + static KernelArg Local(size_t localMemSize) + { return KernelArg(LOCAL, 0, 1, 1, 0, localMemSize); } + static KernelArg PtrWriteOnly(const UMat& m) + { return KernelArg(PTR_ONLY+WRITE_ONLY, (UMat*)&m); } + static KernelArg PtrReadOnly(const UMat& m) + { return KernelArg(PTR_ONLY+READ_ONLY, (UMat*)&m); } + static KernelArg PtrReadWrite(const UMat& m) + { return KernelArg(PTR_ONLY+READ_WRITE, (UMat*)&m); } + static KernelArg ReadWrite(const UMat& m, int wscale=1, int iwscale=1) + { return KernelArg(READ_WRITE, (UMat*)&m, wscale, iwscale); } + static KernelArg ReadWriteNoSize(const UMat& m, int wscale=1, int iwscale=1) + { return KernelArg(READ_WRITE+NO_SIZE, (UMat*)&m, wscale, iwscale); } + static KernelArg ReadOnly(const UMat& m, int wscale=1, int iwscale=1) + { return KernelArg(READ_ONLY, (UMat*)&m, wscale, iwscale); } + static KernelArg WriteOnly(const UMat& m, int wscale=1, int iwscale=1) + { return KernelArg(WRITE_ONLY, (UMat*)&m, wscale, iwscale); } + static KernelArg ReadOnlyNoSize(const UMat& m, int wscale=1, int iwscale=1) + { return KernelArg(READ_ONLY+NO_SIZE, (UMat*)&m, wscale, iwscale); } + static KernelArg WriteOnlyNoSize(const UMat& m, int wscale=1, int iwscale=1) + { return KernelArg(WRITE_ONLY+NO_SIZE, (UMat*)&m, wscale, iwscale); } + static KernelArg Constant(const Mat& m); + template static KernelArg Constant(const _Tp* arr, size_t n) + { return KernelArg(CONSTANT, 0, 1, 1, (void*)arr, n); } + + int flags; + UMat* m; + const void* obj; + size_t sz; + int wscale, iwscale; +}; + + +class CV_EXPORTS Kernel +{ +public: + Kernel(); + Kernel(const char* kname, const Program& prog); + Kernel(const char* kname, const ProgramSource& prog, + const String& buildopts = String(), String* errmsg=0); + ~Kernel(); + Kernel(const Kernel& k); + Kernel& operator = (const Kernel& k); + + bool empty() const; + bool create(const char* kname, const Program& prog); + bool create(const char* kname, const ProgramSource& prog, + const String& buildopts, String* errmsg=0); + + int set(int i, const void* value, size_t sz); + int set(int i, const Image2D& image2D); + int set(int i, const UMat& m); + int set(int i, const KernelArg& arg); + template int set(int i, const _Tp& value) + { return set(i, &value, sizeof(value)); } + + template + Kernel& args(const _Tp0& a0) + { + set(0, a0); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1) + { + int i = set(0, a0); set(i, a1); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2) + { + int i = set(0, a0); i = set(i, a1); set(i, a2); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, + const _Tp3& a3, const _Tp4& a4) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); + i = set(i, a3); set(i, a4); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, + const _Tp3& a3, const _Tp4& a4, const _Tp5& a5) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); + i = set(i, a3); i = set(i, a4); set(i, a5); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, + const _Tp4& a4, const _Tp5& a5, const _Tp6& a6) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); + i = set(i, a4); i = set(i, a5); set(i, a6); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, + const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); + i = set(i, a4); i = set(i, a5); i = set(i, a6); set(i, a7); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, + const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, + const _Tp8& a8) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); + i = set(i, a5); i = set(i, a6); i = set(i, a7); set(i, a8); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, + const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, + const _Tp8& a8, const _Tp9& a9) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5); + i = set(i, a6); i = set(i, a7); i = set(i, a8); set(i, a9); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, + const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, + const _Tp8& a8, const _Tp9& a9, const _Tp10& a10) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5); + i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); set(i, a10); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, + const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, + const _Tp8& a8, const _Tp9& a9, const _Tp10& a10, const _Tp11& a11) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5); + i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); i = set(i, a10); set(i, a11); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, + const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, + const _Tp8& a8, const _Tp9& a9, const _Tp10& a10, const _Tp11& a11, + const _Tp12& a12) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5); + i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); i = set(i, a10); i = set(i, a11); + set(i, a12); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, + const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, + const _Tp8& a8, const _Tp9& a9, const _Tp10& a10, const _Tp11& a11, + const _Tp12& a12, const _Tp13& a13) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5); + i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); i = set(i, a10); i = set(i, a11); + i = set(i, a12); set(i, a13); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, + const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, + const _Tp8& a8, const _Tp9& a9, const _Tp10& a10, const _Tp11& a11, + const _Tp12& a12, const _Tp13& a13, const _Tp14& a14) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5); + i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); i = set(i, a10); i = set(i, a11); + i = set(i, a12); i = set(i, a13); set(i, a14); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, + const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, + const _Tp8& a8, const _Tp9& a9, const _Tp10& a10, const _Tp11& a11, + const _Tp12& a12, const _Tp13& a13, const _Tp14& a14, const _Tp15& a15) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5); + i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); i = set(i, a10); i = set(i, a11); + i = set(i, a12); i = set(i, a13); i = set(i, a14); set(i, a15); return *this; + } + /** @brief Run the OpenCL kernel. + @param dims the work problem dimensions. It is the length of globalsize and localsize. It can be either 1, 2 or 3. + @param globalsize work items for each dimension. It is not the final globalsize passed to + OpenCL. Each dimension will be adjusted to the nearest integer divisible by the corresponding + value in localsize. If localsize is NULL, it will still be adjusted depending on dims. The + adjusted values are greater than or equal to the original values. + @param localsize work-group size for each dimension. + @param sync specify whether to wait for OpenCL computation to finish before return. + @param q command queue + */ + bool run(int dims, size_t globalsize[], + size_t localsize[], bool sync, const Queue& q=Queue()); + bool runTask(bool sync, const Queue& q=Queue()); + + /** @brief Similar to synchronized run() call with returning of kernel execution time + * Separate OpenCL command queue may be used (with CL_QUEUE_PROFILING_ENABLE) + * @return Execution time in nanoseconds or negative number on error + */ + int64 runProfiling(int dims, size_t globalsize[], size_t localsize[], const Queue& q=Queue()); + + size_t workGroupSize() const; + size_t preferedWorkGroupSizeMultiple() const; + bool compileWorkGroupSize(size_t wsz[]) const; + size_t localMemSize() const; + + void* ptr() const; + struct Impl; + +protected: + Impl* p; +}; + +class CV_EXPORTS Program +{ +public: + Program(); + Program(const ProgramSource& src, + const String& buildflags, String& errmsg); + Program(const Program& prog); + + Program& operator = (const Program& prog); + ~Program(); + + bool create(const ProgramSource& src, + const String& buildflags, String& errmsg); + + void* ptr() const; + + /** + * @brief Query device-specific program binary. + * + * Returns RAW OpenCL executable binary without additional attachments. + * + * @sa ProgramSource::fromBinary + * + * @param[out] binary output buffer + */ + void getBinary(std::vector& binary) const; + + struct Impl; friend struct Impl; + inline Impl* getImpl() const { return (Impl*)p; } +protected: + Impl* p; +public: +#ifndef OPENCV_REMOVE_DEPRECATED_API + // TODO Remove this + CV_DEPRECATED bool read(const String& buf, const String& buildflags); // removed, use ProgramSource instead + CV_DEPRECATED bool write(String& buf) const; // removed, use getBinary() method instead (RAW OpenCL binary) + CV_DEPRECATED const ProgramSource& source() const; // implementation removed + CV_DEPRECATED String getPrefix() const; // deprecated, implementation replaced + CV_DEPRECATED static String getPrefix(const String& buildflags); // deprecated, implementation replaced +#endif +}; + + +class CV_EXPORTS ProgramSource +{ +public: + typedef uint64 hash_t; // deprecated + + ProgramSource(); + explicit ProgramSource(const String& module, const String& name, const String& codeStr, const String& codeHash); + explicit ProgramSource(const String& prog); // deprecated + explicit ProgramSource(const char* prog); // deprecated + ~ProgramSource(); + ProgramSource(const ProgramSource& prog); + ProgramSource& operator = (const ProgramSource& prog); + + const String& source() const; // deprecated + hash_t hash() const; // deprecated + + + /** @brief Describe OpenCL program binary. + * Do not call clCreateProgramWithBinary() and/or clBuildProgram(). + * + * Caller should guarantee binary buffer lifetime greater than ProgramSource object (and any of its copies). + * + * This kind of binary is not portable between platforms in general - it is specific to OpenCL vendor / device / driver version. + * + * @param module name of program owner module + * @param name unique name of program (module+name is used as key for OpenCL program caching) + * @param binary buffer address. See buffer lifetime requirement in description. + * @param size buffer size + * @param buildOptions additional program-related build options passed to clBuildProgram() + * @return created ProgramSource object + */ + static ProgramSource fromBinary(const String& module, const String& name, + const unsigned char* binary, const size_t size, + const cv::String& buildOptions = cv::String()); + + /** @brief Describe OpenCL program in SPIR format. + * Do not call clCreateProgramWithBinary() and/or clBuildProgram(). + * + * Supports SPIR 1.2 by default (pass '-spir-std=X.Y' in buildOptions to override this behavior) + * + * Caller should guarantee binary buffer lifetime greater than ProgramSource object (and any of its copies). + * + * Programs in this format are portable between OpenCL implementations with 'khr_spir' extension: + * https://www.khronos.org/registry/OpenCL/sdk/2.0/docs/man/xhtml/cl_khr_spir.html + * (but they are not portable between different platforms: 32-bit / 64-bit) + * + * Note: these programs can't support vendor specific extensions, like 'cl_intel_subgroups'. + * + * @param module name of program owner module + * @param name unique name of program (module+name is used as key for OpenCL program caching) + * @param binary buffer address. See buffer lifetime requirement in description. + * @param size buffer size + * @param buildOptions additional program-related build options passed to clBuildProgram() + * (these options are added automatically: '-x spir' and '-spir-std=1.2') + * @return created ProgramSource object. + */ + static ProgramSource fromSPIR(const String& module, const String& name, + const unsigned char* binary, const size_t size, + const cv::String& buildOptions = cv::String()); + + //OpenCL 2.1+ only + //static Program fromSPIRV(const String& module, const String& name, + // const unsigned char* binary, const size_t size, + // const cv::String& buildOptions = cv::String()); + + struct Impl; friend struct Impl; + inline Impl* getImpl() const { return (Impl*)p; } +protected: + Impl* p; +}; + +class CV_EXPORTS PlatformInfo +{ +public: + PlatformInfo(); + explicit PlatformInfo(void* id); + ~PlatformInfo(); + + PlatformInfo(const PlatformInfo& i); + PlatformInfo& operator =(const PlatformInfo& i); + + String name() const; + String vendor() const; + String version() const; + int deviceNumber() const; + void getDevice(Device& device, int d) const; + +protected: + struct Impl; + Impl* p; +}; + +CV_EXPORTS const char* convertTypeStr(int sdepth, int ddepth, int cn, char* buf); +CV_EXPORTS const char* typeToStr(int t); +CV_EXPORTS const char* memopTypeToStr(int t); +CV_EXPORTS const char* vecopTypeToStr(int t); +CV_EXPORTS const char* getOpenCLErrorString(int errorCode); +CV_EXPORTS String kernelToStr(InputArray _kernel, int ddepth = -1, const char * name = NULL); +CV_EXPORTS void getPlatfomsInfo(std::vector& platform_info); + + +enum OclVectorStrategy +{ + // all matrices have its own vector width + OCL_VECTOR_OWN = 0, + // all matrices have maximal vector width among all matrices + // (useful for cases when matrices have different data types) + OCL_VECTOR_MAX = 1, + + // default strategy + OCL_VECTOR_DEFAULT = OCL_VECTOR_OWN +}; + +CV_EXPORTS int predictOptimalVectorWidth(InputArray src1, InputArray src2 = noArray(), InputArray src3 = noArray(), + InputArray src4 = noArray(), InputArray src5 = noArray(), InputArray src6 = noArray(), + InputArray src7 = noArray(), InputArray src8 = noArray(), InputArray src9 = noArray(), + OclVectorStrategy strat = OCL_VECTOR_DEFAULT); + +CV_EXPORTS int checkOptimalVectorWidth(const int *vectorWidths, + InputArray src1, InputArray src2 = noArray(), InputArray src3 = noArray(), + InputArray src4 = noArray(), InputArray src5 = noArray(), InputArray src6 = noArray(), + InputArray src7 = noArray(), InputArray src8 = noArray(), InputArray src9 = noArray(), + OclVectorStrategy strat = OCL_VECTOR_DEFAULT); + +// with OCL_VECTOR_MAX strategy +CV_EXPORTS int predictOptimalVectorWidthMax(InputArray src1, InputArray src2 = noArray(), InputArray src3 = noArray(), + InputArray src4 = noArray(), InputArray src5 = noArray(), InputArray src6 = noArray(), + InputArray src7 = noArray(), InputArray src8 = noArray(), InputArray src9 = noArray()); + +CV_EXPORTS void buildOptionsAddMatrixDescription(String& buildOptions, const String& name, InputArray _m); + +class CV_EXPORTS Image2D +{ +public: + Image2D(); + + /** + @param src UMat object from which to get image properties and data + @param norm flag to enable the use of normalized channel data types + @param alias flag indicating that the image should alias the src UMat. If true, changes to the + image or src will be reflected in both objects. + */ + explicit Image2D(const UMat &src, bool norm = false, bool alias = false); + Image2D(const Image2D & i); + ~Image2D(); + + Image2D & operator = (const Image2D & i); + + /** Indicates if creating an aliased image should succeed. + Depends on the underlying platform and the dimensions of the UMat. + */ + static bool canCreateAlias(const UMat &u); + + /** Indicates if the image format is supported. + */ + static bool isFormatSupported(int depth, int cn, bool norm); + + void* ptr() const; +protected: + struct Impl; + Impl* p; +}; + +class CV_EXPORTS Timer +{ +public: + Timer(const Queue& q); + ~Timer(); + void start(); + void stop(); + + uint64 durationNS() const; //< duration in nanoseconds + +protected: + struct Impl; + Impl* const p; + +private: + Timer(const Timer&); // disabled + Timer& operator=(const Timer&); // disabled +}; + +CV_EXPORTS MatAllocator* getOpenCLAllocator(); + + +#ifdef __OPENCV_BUILD +namespace internal { + +CV_EXPORTS bool isOpenCLForced(); +#define OCL_FORCE_CHECK(condition) (cv::ocl::internal::isOpenCLForced() || (condition)) + +CV_EXPORTS bool isPerformanceCheckBypassed(); +#define OCL_PERFORMANCE_CHECK(condition) (cv::ocl::internal::isPerformanceCheckBypassed() || (condition)) + +CV_EXPORTS bool isCLBuffer(UMat& u); + +} // namespace internal +#endif + +//! @} + +}} + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/ocl_genbase.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/ocl_genbase.hpp new file mode 100644 index 0000000000000000000000000000000000000000..5334cf1f4f2db81ade82e43035396fb8ea9dc1e0 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/ocl_genbase.hpp @@ -0,0 +1,69 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the OpenCV Foundation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OPENCL_GENBASE_HPP +#define OPENCV_OPENCL_GENBASE_HPP + +//! @cond IGNORED + +namespace cv { +namespace ocl { + +class ProgramSource; + +namespace internal { + +struct CV_EXPORTS ProgramEntry +{ + const char* module; + const char* name; + const char* programCode; + const char* programHash; + ProgramSource* pProgramSource; + + operator ProgramSource& () const; +}; + +} } } // namespace + +//! @endcond + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/ocl_defs.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/ocl_defs.hpp new file mode 100644 index 0000000000000000000000000000000000000000..605a65f8d8fc42885eabc2e9e30cbb47c26f7bad --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/ocl_defs.hpp @@ -0,0 +1,75 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +// Copyright (C) 2014, Advanced Micro Devices, Inc., all rights reserved. +// Third party copyrights are property of their respective owners. + +#ifndef OPENCV_CORE_OPENCL_DEFS_HPP +#define OPENCV_CORE_OPENCL_DEFS_HPP + +#include "opencv2/core/utility.hpp" +#include "cvconfig.h" + +namespace cv { namespace ocl { +#ifdef HAVE_OPENCL +/// Call is similar to useOpenCL() but doesn't try to load OpenCL runtime or create OpenCL context +CV_EXPORTS bool isOpenCLActivated(); +#else +static inline bool isOpenCLActivated() { return false; } +#endif +}} // namespace + + +//#define CV_OPENCL_RUN_ASSERT + +#ifdef HAVE_OPENCL + +#ifdef CV_OPENCL_RUN_VERBOSE +#define CV_OCL_RUN_(condition, func, ...) \ + { \ + if (cv::ocl::isOpenCLActivated() && (condition) && func) \ + { \ + printf("%s: OpenCL implementation is running\n", CV_Func); \ + fflush(stdout); \ + CV_IMPL_ADD(CV_IMPL_OCL); \ + return __VA_ARGS__; \ + } \ + else \ + { \ + printf("%s: Plain implementation is running\n", CV_Func); \ + fflush(stdout); \ + } \ + } +#elif defined CV_OPENCL_RUN_ASSERT +#define CV_OCL_RUN_(condition, func, ...) \ + { \ + if (cv::ocl::isOpenCLActivated() && (condition)) \ + { \ + if(func) \ + { \ + CV_IMPL_ADD(CV_IMPL_OCL); \ + } \ + else \ + { \ + CV_Error(cv::Error::StsAssert, #func); \ + } \ + return __VA_ARGS__; \ + } \ + } +#else +#define CV_OCL_RUN_(condition, func, ...) \ + if (cv::ocl::isOpenCLActivated() && (condition) && func) \ + { \ + CV_IMPL_ADD(CV_IMPL_OCL); \ + return __VA_ARGS__; \ + } +#endif + +#else +#define CV_OCL_RUN_(condition, func, ...) +#endif + +#define CV_OCL_RUN(condition, func) CV_OCL_RUN_(condition, func) + +#endif // OPENCV_CORE_OPENCL_DEFS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/opencl_info.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/opencl_info.hpp new file mode 100644 index 0000000000000000000000000000000000000000..b5d37394a73670bc04d2edde1725072b9c51c523 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/opencl_info.hpp @@ -0,0 +1,198 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#include + +#include +#include + +#ifndef DUMP_CONFIG_PROPERTY +#define DUMP_CONFIG_PROPERTY(...) +#endif + +#ifndef DUMP_MESSAGE_STDOUT +#define DUMP_MESSAGE_STDOUT(...) do { std::cout << __VA_ARGS__ << std::endl; } while (false) +#endif + +namespace cv { + +namespace { +static std::string bytesToStringRepr(size_t value) +{ + size_t b = value % 1024; + value /= 1024; + + size_t kb = value % 1024; + value /= 1024; + + size_t mb = value % 1024; + value /= 1024; + + size_t gb = value; + + std::ostringstream stream; + + if (gb > 0) + stream << gb << " GB "; + if (mb > 0) + stream << mb << " MB "; + if (kb > 0) + stream << kb << " KB "; + if (b > 0) + stream << b << " B"; + + std::string s = stream.str(); + if (s[s.size() - 1] == ' ') + s = s.substr(0, s.size() - 1); + return s; +} +} // namespace + +static void dumpOpenCLInformation() +{ + using namespace cv::ocl; + + try + { + if (!haveOpenCL() || !useOpenCL()) + { + DUMP_MESSAGE_STDOUT("OpenCL is disabled"); + DUMP_CONFIG_PROPERTY("cv_ocl", "disabled"); + return; + } + + std::vector platforms; + cv::ocl::getPlatfomsInfo(platforms); + if (platforms.size() > 0) + { + DUMP_MESSAGE_STDOUT("OpenCL Platforms: "); + for (size_t i = 0; i < platforms.size(); i++) + { + const PlatformInfo* platform = &platforms[i]; + DUMP_MESSAGE_STDOUT(" " << platform->name().c_str()); + Device current_device; + for (int j = 0; j < platform->deviceNumber(); j++) + { + platform->getDevice(current_device, j); + const char* deviceTypeStr = current_device.type() == Device::TYPE_CPU + ? ("CPU") : (current_device.type() == Device::TYPE_GPU ? current_device.hostUnifiedMemory() ? "iGPU" : "dGPU" : "unknown"); + DUMP_MESSAGE_STDOUT( " " << deviceTypeStr << ": " << current_device.name().c_str() << " (" << current_device.version().c_str() << ")"); + DUMP_CONFIG_PROPERTY( cv::format("cv_ocl_platform_%d_device_%d", (int)i, (int)j ), + cv::format("(Platform=%s)(Type=%s)(Name=%s)(Version=%s)", + platform->name().c_str(), deviceTypeStr, current_device.name().c_str(), current_device.version().c_str()) ); + } + } + } + else + { + DUMP_MESSAGE_STDOUT("OpenCL is not available"); + DUMP_CONFIG_PROPERTY("cv_ocl", "not available"); + return; + } + + const Device& device = Device::getDefault(); + if (!device.available()) + CV_Error(Error::OpenCLInitError, "OpenCL device is not available"); + + DUMP_MESSAGE_STDOUT("Current OpenCL device: "); + +#if 0 + DUMP_MESSAGE_STDOUT(" Platform = " << device.getPlatform().name()); + DUMP_CONFIG_PROPERTY("cv_ocl_current_platformName", device.getPlatform().name()); +#endif + + const char* deviceTypeStr = device.type() == Device::TYPE_CPU + ? ("CPU") : (device.type() == Device::TYPE_GPU ? device.hostUnifiedMemory() ? "iGPU" : "dGPU" : "unknown"); + DUMP_MESSAGE_STDOUT(" Type = " << deviceTypeStr); + DUMP_CONFIG_PROPERTY("cv_ocl_current_deviceType", deviceTypeStr); + + DUMP_MESSAGE_STDOUT(" Name = " << device.name()); + DUMP_CONFIG_PROPERTY("cv_ocl_current_deviceName", device.name()); + + DUMP_MESSAGE_STDOUT(" Version = " << device.version()); + DUMP_CONFIG_PROPERTY("cv_ocl_current_deviceVersion", device.version()); + + DUMP_MESSAGE_STDOUT(" Driver version = " << device.driverVersion()); + DUMP_CONFIG_PROPERTY("cv_ocl_current_driverVersion", device.driverVersion()); + + DUMP_MESSAGE_STDOUT(" Address bits = " << device.addressBits()); + DUMP_CONFIG_PROPERTY("cv_ocl_current_addressBits", device.addressBits()); + + DUMP_MESSAGE_STDOUT(" Compute units = " << device.maxComputeUnits()); + DUMP_CONFIG_PROPERTY("cv_ocl_current_maxComputeUnits", device.maxComputeUnits()); + + DUMP_MESSAGE_STDOUT(" Max work group size = " << device.maxWorkGroupSize()); + DUMP_CONFIG_PROPERTY("cv_ocl_current_maxWorkGroupSize", device.maxWorkGroupSize()); + + std::string localMemorySizeStr = bytesToStringRepr(device.localMemSize()); + DUMP_MESSAGE_STDOUT(" Local memory size = " << localMemorySizeStr); + DUMP_CONFIG_PROPERTY("cv_ocl_current_localMemSize", device.localMemSize()); + + std::string maxMemAllocSizeStr = bytesToStringRepr(device.maxMemAllocSize()); + DUMP_MESSAGE_STDOUT(" Max memory allocation size = " << maxMemAllocSizeStr); + DUMP_CONFIG_PROPERTY("cv_ocl_current_maxMemAllocSize", device.maxMemAllocSize()); + + const char* doubleSupportStr = device.doubleFPConfig() > 0 ? "Yes" : "No"; + DUMP_MESSAGE_STDOUT(" Double support = " << doubleSupportStr); + DUMP_CONFIG_PROPERTY("cv_ocl_current_haveDoubleSupport", device.doubleFPConfig() > 0); + + const char* isUnifiedMemoryStr = device.hostUnifiedMemory() ? "Yes" : "No"; + DUMP_MESSAGE_STDOUT(" Host unified memory = " << isUnifiedMemoryStr); + DUMP_CONFIG_PROPERTY("cv_ocl_current_hostUnifiedMemory", device.hostUnifiedMemory()); + + DUMP_MESSAGE_STDOUT(" Device extensions:"); + String extensionsStr = device.extensions(); + size_t pos = 0; + while (pos < extensionsStr.size()) + { + size_t pos2 = extensionsStr.find(' ', pos); + if (pos2 == String::npos) + pos2 = extensionsStr.size(); + if (pos2 > pos) + { + String extensionName = extensionsStr.substr(pos, pos2 - pos); + DUMP_MESSAGE_STDOUT(" " << extensionName); + } + pos = pos2 + 1; + } + DUMP_CONFIG_PROPERTY("cv_ocl_current_extensions", extensionsStr.c_str()); + + const char* haveAmdBlasStr = haveAmdBlas() ? "Yes" : "No"; + DUMP_MESSAGE_STDOUT(" Has AMD Blas = " << haveAmdBlasStr); + DUMP_CONFIG_PROPERTY("cv_ocl_current_AmdBlas", haveAmdBlas()); + + const char* haveAmdFftStr = haveAmdFft() ? "Yes" : "No"; + DUMP_MESSAGE_STDOUT(" Has AMD Fft = " << haveAmdFftStr); + DUMP_CONFIG_PROPERTY("cv_ocl_current_AmdFft", haveAmdFft()); + + + DUMP_MESSAGE_STDOUT(" Preferred vector width char = " << device.preferredVectorWidthChar()); + DUMP_CONFIG_PROPERTY("cv_ocl_current_preferredVectorWidthChar", device.preferredVectorWidthChar()); + + DUMP_MESSAGE_STDOUT(" Preferred vector width short = " << device.preferredVectorWidthShort()); + DUMP_CONFIG_PROPERTY("cv_ocl_current_preferredVectorWidthShort", device.preferredVectorWidthShort()); + + DUMP_MESSAGE_STDOUT(" Preferred vector width int = " << device.preferredVectorWidthInt()); + DUMP_CONFIG_PROPERTY("cv_ocl_current_preferredVectorWidthInt", device.preferredVectorWidthInt()); + + DUMP_MESSAGE_STDOUT(" Preferred vector width long = " << device.preferredVectorWidthLong()); + DUMP_CONFIG_PROPERTY("cv_ocl_current_preferredVectorWidthLong", device.preferredVectorWidthLong()); + + DUMP_MESSAGE_STDOUT(" Preferred vector width float = " << device.preferredVectorWidthFloat()); + DUMP_CONFIG_PROPERTY("cv_ocl_current_preferredVectorWidthFloat", device.preferredVectorWidthFloat()); + + DUMP_MESSAGE_STDOUT(" Preferred vector width double = " << device.preferredVectorWidthDouble()); + DUMP_CONFIG_PROPERTY("cv_ocl_current_preferredVectorWidthDouble", device.preferredVectorWidthDouble()); + } + catch (...) + { + DUMP_MESSAGE_STDOUT("Exception. Can't dump OpenCL info"); + DUMP_MESSAGE_STDOUT("OpenCL device not available"); + DUMP_CONFIG_PROPERTY("cv_ocl", "not available"); + } +} +#undef DUMP_MESSAGE_STDOUT +#undef DUMP_CONFIG_PROPERTY + +} // namespace diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/opencl_svm.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/opencl_svm.hpp new file mode 100644 index 0000000000000000000000000000000000000000..7453082a67b374c01591e7480bbf62f595683fa5 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/opencl_svm.hpp @@ -0,0 +1,81 @@ +/* See LICENSE file in the root OpenCV directory */ + +#ifndef OPENCV_CORE_OPENCL_SVM_HPP +#define OPENCV_CORE_OPENCL_SVM_HPP + +// +// Internal usage only (binary compatibility is not guaranteed) +// +#ifndef __OPENCV_BUILD +#error Internal header file +#endif + +#if defined(HAVE_OPENCL) && defined(HAVE_OPENCL_SVM) +#include "runtime/opencl_core.hpp" +#include "runtime/opencl_svm_20.hpp" +#include "runtime/opencl_svm_hsa_extension.hpp" + +namespace cv { namespace ocl { namespace svm { + +struct SVMCapabilities +{ + enum Value + { + SVM_COARSE_GRAIN_BUFFER = (1 << 0), + SVM_FINE_GRAIN_BUFFER = (1 << 1), + SVM_FINE_GRAIN_SYSTEM = (1 << 2), + SVM_ATOMICS = (1 << 3), + }; + int value_; + + SVMCapabilities(int capabilities = 0) : value_(capabilities) { } + operator int() const { return value_; } + + inline bool isNoSVMSupport() const { return value_ == 0; } + inline bool isSupportCoarseGrainBuffer() const { return (value_ & SVM_COARSE_GRAIN_BUFFER) != 0; } + inline bool isSupportFineGrainBuffer() const { return (value_ & SVM_FINE_GRAIN_BUFFER) != 0; } + inline bool isSupportFineGrainSystem() const { return (value_ & SVM_FINE_GRAIN_SYSTEM) != 0; } + inline bool isSupportAtomics() const { return (value_ & SVM_ATOMICS) != 0; } +}; + +CV_EXPORTS const SVMCapabilities getSVMCapabilitites(const ocl::Context& context); + +struct SVMFunctions +{ + clSVMAllocAMD_fn fn_clSVMAlloc; + clSVMFreeAMD_fn fn_clSVMFree; + clSetKernelArgSVMPointerAMD_fn fn_clSetKernelArgSVMPointer; + //clSetKernelExecInfoAMD_fn fn_clSetKernelExecInfo; + //clEnqueueSVMFreeAMD_fn fn_clEnqueueSVMFree; + clEnqueueSVMMemcpyAMD_fn fn_clEnqueueSVMMemcpy; + clEnqueueSVMMemFillAMD_fn fn_clEnqueueSVMMemFill; + clEnqueueSVMMapAMD_fn fn_clEnqueueSVMMap; + clEnqueueSVMUnmapAMD_fn fn_clEnqueueSVMUnmap; + + inline SVMFunctions() + : fn_clSVMAlloc(NULL), fn_clSVMFree(NULL), + fn_clSetKernelArgSVMPointer(NULL), /*fn_clSetKernelExecInfo(NULL),*/ + /*fn_clEnqueueSVMFree(NULL),*/ fn_clEnqueueSVMMemcpy(NULL), fn_clEnqueueSVMMemFill(NULL), + fn_clEnqueueSVMMap(NULL), fn_clEnqueueSVMUnmap(NULL) + { + // nothing + } + + inline bool isValid() const + { + return fn_clSVMAlloc != NULL && fn_clSVMFree && fn_clSetKernelArgSVMPointer && + /*fn_clSetKernelExecInfo && fn_clEnqueueSVMFree &&*/ fn_clEnqueueSVMMemcpy && + fn_clEnqueueSVMMemFill && fn_clEnqueueSVMMap && fn_clEnqueueSVMUnmap; + } +}; + +// We should guarantee that SVMFunctions lifetime is not less than context's lifetime +CV_EXPORTS const SVMFunctions* getSVMFunctions(const ocl::Context& context); + +CV_EXPORTS bool useSVM(UMatUsageFlags usageFlags); + +}}} //namespace cv::ocl::svm +#endif + +#endif // OPENCV_CORE_OPENCL_SVM_HPP +/* End of file. */ diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/runtime/autogenerated/opencl_clamdblas.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/runtime/autogenerated/opencl_clamdblas.hpp new file mode 100644 index 0000000000000000000000000000000000000000..65c84935240d3618e0e984eff5102154e3b97a6c --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/runtime/autogenerated/opencl_clamdblas.hpp @@ -0,0 +1,714 @@ +// +// AUTOGENERATED, DO NOT EDIT +// +#ifndef OPENCV_CORE_OCL_RUNTIME_CLAMDBLAS_HPP +#error "Invalid usage" +#endif + +// generated by parser_clamdblas.py +#define clAmdBlasAddScratchImage clAmdBlasAddScratchImage_ +#define clAmdBlasCaxpy clAmdBlasCaxpy_ +#define clAmdBlasCcopy clAmdBlasCcopy_ +#define clAmdBlasCdotc clAmdBlasCdotc_ +#define clAmdBlasCdotu clAmdBlasCdotu_ +#define clAmdBlasCgbmv clAmdBlasCgbmv_ +#define clAmdBlasCgemm clAmdBlasCgemm_ +#define clAmdBlasCgemmEx clAmdBlasCgemmEx_ +#define clAmdBlasCgemv clAmdBlasCgemv_ +#define clAmdBlasCgemvEx clAmdBlasCgemvEx_ +#define clAmdBlasCgerc clAmdBlasCgerc_ +#define clAmdBlasCgeru clAmdBlasCgeru_ +#define clAmdBlasChbmv clAmdBlasChbmv_ +#define clAmdBlasChemm clAmdBlasChemm_ +#define clAmdBlasChemv clAmdBlasChemv_ +#define clAmdBlasCher clAmdBlasCher_ +#define clAmdBlasCher2 clAmdBlasCher2_ +#define clAmdBlasCher2k clAmdBlasCher2k_ +#define clAmdBlasCherk clAmdBlasCherk_ +#define clAmdBlasChpmv clAmdBlasChpmv_ +#define clAmdBlasChpr clAmdBlasChpr_ +#define clAmdBlasChpr2 clAmdBlasChpr2_ +#define clAmdBlasCrotg clAmdBlasCrotg_ +#define clAmdBlasCscal clAmdBlasCscal_ +#define clAmdBlasCsrot clAmdBlasCsrot_ +#define clAmdBlasCsscal clAmdBlasCsscal_ +#define clAmdBlasCswap clAmdBlasCswap_ +#define clAmdBlasCsymm clAmdBlasCsymm_ +#define clAmdBlasCsyr2k clAmdBlasCsyr2k_ +#define clAmdBlasCsyr2kEx clAmdBlasCsyr2kEx_ +#define clAmdBlasCsyrk clAmdBlasCsyrk_ +#define clAmdBlasCsyrkEx clAmdBlasCsyrkEx_ +#define clAmdBlasCtbmv clAmdBlasCtbmv_ +#define clAmdBlasCtbsv clAmdBlasCtbsv_ +#define clAmdBlasCtpmv clAmdBlasCtpmv_ +#define clAmdBlasCtpsv clAmdBlasCtpsv_ +#define clAmdBlasCtrmm clAmdBlasCtrmm_ +#define clAmdBlasCtrmmEx clAmdBlasCtrmmEx_ +#define clAmdBlasCtrmv clAmdBlasCtrmv_ +#define clAmdBlasCtrsm clAmdBlasCtrsm_ +#define clAmdBlasCtrsmEx clAmdBlasCtrsmEx_ +#define clAmdBlasCtrsv clAmdBlasCtrsv_ +#define clAmdBlasDasum clAmdBlasDasum_ +#define clAmdBlasDaxpy clAmdBlasDaxpy_ +#define clAmdBlasDcopy clAmdBlasDcopy_ +#define clAmdBlasDdot clAmdBlasDdot_ +#define clAmdBlasDgbmv clAmdBlasDgbmv_ +#define clAmdBlasDgemm clAmdBlasDgemm_ +#define clAmdBlasDgemmEx clAmdBlasDgemmEx_ +#define clAmdBlasDgemv clAmdBlasDgemv_ +#define clAmdBlasDgemvEx clAmdBlasDgemvEx_ +#define clAmdBlasDger clAmdBlasDger_ +#define clAmdBlasDnrm2 clAmdBlasDnrm2_ +#define clAmdBlasDrot clAmdBlasDrot_ +#define clAmdBlasDrotg clAmdBlasDrotg_ +#define clAmdBlasDrotm clAmdBlasDrotm_ +#define clAmdBlasDrotmg clAmdBlasDrotmg_ +#define clAmdBlasDsbmv clAmdBlasDsbmv_ +#define clAmdBlasDscal clAmdBlasDscal_ +#define clAmdBlasDspmv clAmdBlasDspmv_ +#define clAmdBlasDspr clAmdBlasDspr_ +#define clAmdBlasDspr2 clAmdBlasDspr2_ +#define clAmdBlasDswap clAmdBlasDswap_ +#define clAmdBlasDsymm clAmdBlasDsymm_ +#define clAmdBlasDsymv clAmdBlasDsymv_ +#define clAmdBlasDsymvEx clAmdBlasDsymvEx_ +#define clAmdBlasDsyr clAmdBlasDsyr_ +#define clAmdBlasDsyr2 clAmdBlasDsyr2_ +#define clAmdBlasDsyr2k clAmdBlasDsyr2k_ +#define clAmdBlasDsyr2kEx clAmdBlasDsyr2kEx_ +#define clAmdBlasDsyrk clAmdBlasDsyrk_ +#define clAmdBlasDsyrkEx clAmdBlasDsyrkEx_ +#define clAmdBlasDtbmv clAmdBlasDtbmv_ +#define clAmdBlasDtbsv clAmdBlasDtbsv_ +#define clAmdBlasDtpmv clAmdBlasDtpmv_ +#define clAmdBlasDtpsv clAmdBlasDtpsv_ +#define clAmdBlasDtrmm clAmdBlasDtrmm_ +#define clAmdBlasDtrmmEx clAmdBlasDtrmmEx_ +#define clAmdBlasDtrmv clAmdBlasDtrmv_ +#define clAmdBlasDtrsm clAmdBlasDtrsm_ +#define clAmdBlasDtrsmEx clAmdBlasDtrsmEx_ +#define clAmdBlasDtrsv clAmdBlasDtrsv_ +#define clAmdBlasDzasum clAmdBlasDzasum_ +#define clAmdBlasDznrm2 clAmdBlasDznrm2_ +#define clAmdBlasGetVersion clAmdBlasGetVersion_ +#define clAmdBlasRemoveScratchImage clAmdBlasRemoveScratchImage_ +#define clAmdBlasSasum clAmdBlasSasum_ +#define clAmdBlasSaxpy clAmdBlasSaxpy_ +#define clAmdBlasScasum clAmdBlasScasum_ +#define clAmdBlasScnrm2 clAmdBlasScnrm2_ +#define clAmdBlasScopy clAmdBlasScopy_ +#define clAmdBlasSdot clAmdBlasSdot_ +#define clAmdBlasSetup clAmdBlasSetup_ +#define clAmdBlasSgbmv clAmdBlasSgbmv_ +#define clAmdBlasSgemm clAmdBlasSgemm_ +#define clAmdBlasSgemmEx clAmdBlasSgemmEx_ +#define clAmdBlasSgemv clAmdBlasSgemv_ +#define clAmdBlasSgemvEx clAmdBlasSgemvEx_ +#define clAmdBlasSger clAmdBlasSger_ +#define clAmdBlasSnrm2 clAmdBlasSnrm2_ +#define clAmdBlasSrot clAmdBlasSrot_ +#define clAmdBlasSrotg clAmdBlasSrotg_ +#define clAmdBlasSrotm clAmdBlasSrotm_ +#define clAmdBlasSrotmg clAmdBlasSrotmg_ +#define clAmdBlasSsbmv clAmdBlasSsbmv_ +#define clAmdBlasSscal clAmdBlasSscal_ +#define clAmdBlasSspmv clAmdBlasSspmv_ +#define clAmdBlasSspr clAmdBlasSspr_ +#define clAmdBlasSspr2 clAmdBlasSspr2_ +#define clAmdBlasSswap clAmdBlasSswap_ +#define clAmdBlasSsymm clAmdBlasSsymm_ +#define clAmdBlasSsymv clAmdBlasSsymv_ +#define clAmdBlasSsymvEx clAmdBlasSsymvEx_ +#define clAmdBlasSsyr clAmdBlasSsyr_ +#define clAmdBlasSsyr2 clAmdBlasSsyr2_ +#define clAmdBlasSsyr2k clAmdBlasSsyr2k_ +#define clAmdBlasSsyr2kEx clAmdBlasSsyr2kEx_ +#define clAmdBlasSsyrk clAmdBlasSsyrk_ +#define clAmdBlasSsyrkEx clAmdBlasSsyrkEx_ +#define clAmdBlasStbmv clAmdBlasStbmv_ +#define clAmdBlasStbsv clAmdBlasStbsv_ +#define clAmdBlasStpmv clAmdBlasStpmv_ +#define clAmdBlasStpsv clAmdBlasStpsv_ +#define clAmdBlasStrmm clAmdBlasStrmm_ +#define clAmdBlasStrmmEx clAmdBlasStrmmEx_ +#define clAmdBlasStrmv clAmdBlasStrmv_ +#define clAmdBlasStrsm clAmdBlasStrsm_ +#define clAmdBlasStrsmEx clAmdBlasStrsmEx_ +#define clAmdBlasStrsv clAmdBlasStrsv_ +#define clAmdBlasTeardown clAmdBlasTeardown_ +#define clAmdBlasZaxpy clAmdBlasZaxpy_ +#define clAmdBlasZcopy clAmdBlasZcopy_ +#define clAmdBlasZdotc clAmdBlasZdotc_ +#define clAmdBlasZdotu clAmdBlasZdotu_ +#define clAmdBlasZdrot clAmdBlasZdrot_ +#define clAmdBlasZdscal clAmdBlasZdscal_ +#define clAmdBlasZgbmv clAmdBlasZgbmv_ +#define clAmdBlasZgemm clAmdBlasZgemm_ +#define clAmdBlasZgemmEx clAmdBlasZgemmEx_ +#define clAmdBlasZgemv clAmdBlasZgemv_ +#define clAmdBlasZgemvEx clAmdBlasZgemvEx_ +#define clAmdBlasZgerc clAmdBlasZgerc_ +#define clAmdBlasZgeru clAmdBlasZgeru_ +#define clAmdBlasZhbmv clAmdBlasZhbmv_ +#define clAmdBlasZhemm clAmdBlasZhemm_ +#define clAmdBlasZhemv clAmdBlasZhemv_ +#define clAmdBlasZher clAmdBlasZher_ +#define clAmdBlasZher2 clAmdBlasZher2_ +#define clAmdBlasZher2k clAmdBlasZher2k_ +#define clAmdBlasZherk clAmdBlasZherk_ +#define clAmdBlasZhpmv clAmdBlasZhpmv_ +#define clAmdBlasZhpr clAmdBlasZhpr_ +#define clAmdBlasZhpr2 clAmdBlasZhpr2_ +#define clAmdBlasZrotg clAmdBlasZrotg_ +#define clAmdBlasZscal clAmdBlasZscal_ +#define clAmdBlasZswap clAmdBlasZswap_ +#define clAmdBlasZsymm clAmdBlasZsymm_ +#define clAmdBlasZsyr2k clAmdBlasZsyr2k_ +#define clAmdBlasZsyr2kEx clAmdBlasZsyr2kEx_ +#define clAmdBlasZsyrk clAmdBlasZsyrk_ +#define clAmdBlasZsyrkEx clAmdBlasZsyrkEx_ +#define clAmdBlasZtbmv clAmdBlasZtbmv_ +#define clAmdBlasZtbsv clAmdBlasZtbsv_ +#define clAmdBlasZtpmv clAmdBlasZtpmv_ +#define clAmdBlasZtpsv clAmdBlasZtpsv_ +#define clAmdBlasZtrmm clAmdBlasZtrmm_ +#define clAmdBlasZtrmmEx clAmdBlasZtrmmEx_ +#define clAmdBlasZtrmv clAmdBlasZtrmv_ +#define clAmdBlasZtrsm clAmdBlasZtrsm_ +#define clAmdBlasZtrsmEx clAmdBlasZtrsmEx_ +#define clAmdBlasZtrsv clAmdBlasZtrsv_ +#define clAmdBlasiCamax clAmdBlasiCamax_ +#define clAmdBlasiDamax clAmdBlasiDamax_ +#define clAmdBlasiSamax clAmdBlasiSamax_ +#define clAmdBlasiZamax clAmdBlasiZamax_ + +#include + +// generated by parser_clamdblas.py +#undef clAmdBlasAddScratchImage +//#define clAmdBlasAddScratchImage clAmdBlasAddScratchImage_pfn +#undef clAmdBlasCaxpy +//#define clAmdBlasCaxpy clAmdBlasCaxpy_pfn +#undef clAmdBlasCcopy +//#define clAmdBlasCcopy clAmdBlasCcopy_pfn +#undef clAmdBlasCdotc +//#define clAmdBlasCdotc clAmdBlasCdotc_pfn +#undef clAmdBlasCdotu +//#define clAmdBlasCdotu clAmdBlasCdotu_pfn +#undef clAmdBlasCgbmv +//#define clAmdBlasCgbmv clAmdBlasCgbmv_pfn +#undef clAmdBlasCgemm +//#define clAmdBlasCgemm clAmdBlasCgemm_pfn +#undef clAmdBlasCgemmEx +#define clAmdBlasCgemmEx clAmdBlasCgemmEx_pfn +#undef clAmdBlasCgemv +//#define clAmdBlasCgemv clAmdBlasCgemv_pfn +#undef clAmdBlasCgemvEx +//#define clAmdBlasCgemvEx clAmdBlasCgemvEx_pfn +#undef clAmdBlasCgerc +//#define clAmdBlasCgerc clAmdBlasCgerc_pfn +#undef clAmdBlasCgeru +//#define clAmdBlasCgeru clAmdBlasCgeru_pfn +#undef clAmdBlasChbmv +//#define clAmdBlasChbmv clAmdBlasChbmv_pfn +#undef clAmdBlasChemm +//#define clAmdBlasChemm clAmdBlasChemm_pfn +#undef clAmdBlasChemv +//#define clAmdBlasChemv clAmdBlasChemv_pfn +#undef clAmdBlasCher +//#define clAmdBlasCher clAmdBlasCher_pfn +#undef clAmdBlasCher2 +//#define clAmdBlasCher2 clAmdBlasCher2_pfn +#undef clAmdBlasCher2k +//#define clAmdBlasCher2k clAmdBlasCher2k_pfn +#undef clAmdBlasCherk +//#define clAmdBlasCherk clAmdBlasCherk_pfn +#undef clAmdBlasChpmv +//#define clAmdBlasChpmv clAmdBlasChpmv_pfn +#undef clAmdBlasChpr +//#define clAmdBlasChpr clAmdBlasChpr_pfn +#undef clAmdBlasChpr2 +//#define clAmdBlasChpr2 clAmdBlasChpr2_pfn +#undef clAmdBlasCrotg +//#define clAmdBlasCrotg clAmdBlasCrotg_pfn +#undef clAmdBlasCscal +//#define clAmdBlasCscal clAmdBlasCscal_pfn +#undef clAmdBlasCsrot +//#define clAmdBlasCsrot clAmdBlasCsrot_pfn +#undef clAmdBlasCsscal +//#define clAmdBlasCsscal clAmdBlasCsscal_pfn +#undef clAmdBlasCswap +//#define clAmdBlasCswap clAmdBlasCswap_pfn +#undef clAmdBlasCsymm +//#define clAmdBlasCsymm clAmdBlasCsymm_pfn +#undef clAmdBlasCsyr2k +//#define clAmdBlasCsyr2k clAmdBlasCsyr2k_pfn +#undef clAmdBlasCsyr2kEx +//#define clAmdBlasCsyr2kEx clAmdBlasCsyr2kEx_pfn +#undef clAmdBlasCsyrk +//#define clAmdBlasCsyrk clAmdBlasCsyrk_pfn +#undef clAmdBlasCsyrkEx +//#define clAmdBlasCsyrkEx clAmdBlasCsyrkEx_pfn +#undef clAmdBlasCtbmv +//#define clAmdBlasCtbmv clAmdBlasCtbmv_pfn +#undef clAmdBlasCtbsv +//#define clAmdBlasCtbsv clAmdBlasCtbsv_pfn +#undef clAmdBlasCtpmv +//#define clAmdBlasCtpmv clAmdBlasCtpmv_pfn +#undef clAmdBlasCtpsv +//#define clAmdBlasCtpsv clAmdBlasCtpsv_pfn +#undef clAmdBlasCtrmm +//#define clAmdBlasCtrmm clAmdBlasCtrmm_pfn +#undef clAmdBlasCtrmmEx +//#define clAmdBlasCtrmmEx clAmdBlasCtrmmEx_pfn +#undef clAmdBlasCtrmv +//#define clAmdBlasCtrmv clAmdBlasCtrmv_pfn +#undef clAmdBlasCtrsm +//#define clAmdBlasCtrsm clAmdBlasCtrsm_pfn +#undef clAmdBlasCtrsmEx +//#define clAmdBlasCtrsmEx clAmdBlasCtrsmEx_pfn +#undef clAmdBlasCtrsv +//#define clAmdBlasCtrsv clAmdBlasCtrsv_pfn +#undef clAmdBlasDasum +//#define clAmdBlasDasum clAmdBlasDasum_pfn +#undef clAmdBlasDaxpy +//#define clAmdBlasDaxpy clAmdBlasDaxpy_pfn +#undef clAmdBlasDcopy +//#define clAmdBlasDcopy clAmdBlasDcopy_pfn +#undef clAmdBlasDdot +//#define clAmdBlasDdot clAmdBlasDdot_pfn +#undef clAmdBlasDgbmv +//#define clAmdBlasDgbmv clAmdBlasDgbmv_pfn +#undef clAmdBlasDgemm +//#define clAmdBlasDgemm clAmdBlasDgemm_pfn +#undef clAmdBlasDgemmEx +#define clAmdBlasDgemmEx clAmdBlasDgemmEx_pfn +#undef clAmdBlasDgemv +//#define clAmdBlasDgemv clAmdBlasDgemv_pfn +#undef clAmdBlasDgemvEx +//#define clAmdBlasDgemvEx clAmdBlasDgemvEx_pfn +#undef clAmdBlasDger +//#define clAmdBlasDger clAmdBlasDger_pfn +#undef clAmdBlasDnrm2 +//#define clAmdBlasDnrm2 clAmdBlasDnrm2_pfn +#undef clAmdBlasDrot +//#define clAmdBlasDrot clAmdBlasDrot_pfn +#undef clAmdBlasDrotg +//#define clAmdBlasDrotg clAmdBlasDrotg_pfn +#undef clAmdBlasDrotm +//#define clAmdBlasDrotm clAmdBlasDrotm_pfn +#undef clAmdBlasDrotmg +//#define clAmdBlasDrotmg clAmdBlasDrotmg_pfn +#undef clAmdBlasDsbmv +//#define clAmdBlasDsbmv clAmdBlasDsbmv_pfn +#undef clAmdBlasDscal +//#define clAmdBlasDscal clAmdBlasDscal_pfn +#undef clAmdBlasDspmv +//#define clAmdBlasDspmv clAmdBlasDspmv_pfn +#undef clAmdBlasDspr +//#define clAmdBlasDspr clAmdBlasDspr_pfn +#undef clAmdBlasDspr2 +//#define clAmdBlasDspr2 clAmdBlasDspr2_pfn +#undef clAmdBlasDswap +//#define clAmdBlasDswap clAmdBlasDswap_pfn +#undef clAmdBlasDsymm +//#define clAmdBlasDsymm clAmdBlasDsymm_pfn +#undef clAmdBlasDsymv +//#define clAmdBlasDsymv clAmdBlasDsymv_pfn +#undef clAmdBlasDsymvEx +//#define clAmdBlasDsymvEx clAmdBlasDsymvEx_pfn +#undef clAmdBlasDsyr +//#define clAmdBlasDsyr clAmdBlasDsyr_pfn +#undef clAmdBlasDsyr2 +//#define clAmdBlasDsyr2 clAmdBlasDsyr2_pfn +#undef clAmdBlasDsyr2k +//#define clAmdBlasDsyr2k clAmdBlasDsyr2k_pfn +#undef clAmdBlasDsyr2kEx +//#define clAmdBlasDsyr2kEx clAmdBlasDsyr2kEx_pfn +#undef clAmdBlasDsyrk +//#define clAmdBlasDsyrk clAmdBlasDsyrk_pfn +#undef clAmdBlasDsyrkEx +//#define clAmdBlasDsyrkEx clAmdBlasDsyrkEx_pfn +#undef clAmdBlasDtbmv +//#define clAmdBlasDtbmv clAmdBlasDtbmv_pfn +#undef clAmdBlasDtbsv +//#define clAmdBlasDtbsv clAmdBlasDtbsv_pfn +#undef clAmdBlasDtpmv +//#define clAmdBlasDtpmv clAmdBlasDtpmv_pfn +#undef clAmdBlasDtpsv +//#define clAmdBlasDtpsv clAmdBlasDtpsv_pfn +#undef clAmdBlasDtrmm +//#define clAmdBlasDtrmm clAmdBlasDtrmm_pfn +#undef clAmdBlasDtrmmEx +//#define clAmdBlasDtrmmEx clAmdBlasDtrmmEx_pfn +#undef clAmdBlasDtrmv +//#define clAmdBlasDtrmv clAmdBlasDtrmv_pfn +#undef clAmdBlasDtrsm +//#define clAmdBlasDtrsm clAmdBlasDtrsm_pfn +#undef clAmdBlasDtrsmEx +//#define clAmdBlasDtrsmEx clAmdBlasDtrsmEx_pfn +#undef clAmdBlasDtrsv +//#define clAmdBlasDtrsv clAmdBlasDtrsv_pfn +#undef clAmdBlasDzasum +//#define clAmdBlasDzasum clAmdBlasDzasum_pfn +#undef clAmdBlasDznrm2 +//#define clAmdBlasDznrm2 clAmdBlasDznrm2_pfn +#undef clAmdBlasGetVersion +//#define clAmdBlasGetVersion clAmdBlasGetVersion_pfn +#undef clAmdBlasRemoveScratchImage +//#define clAmdBlasRemoveScratchImage clAmdBlasRemoveScratchImage_pfn +#undef clAmdBlasSasum +//#define clAmdBlasSasum clAmdBlasSasum_pfn +#undef clAmdBlasSaxpy +//#define clAmdBlasSaxpy clAmdBlasSaxpy_pfn +#undef clAmdBlasScasum +//#define clAmdBlasScasum clAmdBlasScasum_pfn +#undef clAmdBlasScnrm2 +//#define clAmdBlasScnrm2 clAmdBlasScnrm2_pfn +#undef clAmdBlasScopy +//#define clAmdBlasScopy clAmdBlasScopy_pfn +#undef clAmdBlasSdot +//#define clAmdBlasSdot clAmdBlasSdot_pfn +#undef clAmdBlasSetup +#define clAmdBlasSetup clAmdBlasSetup_pfn +#undef clAmdBlasSgbmv +//#define clAmdBlasSgbmv clAmdBlasSgbmv_pfn +#undef clAmdBlasSgemm +//#define clAmdBlasSgemm clAmdBlasSgemm_pfn +#undef clAmdBlasSgemmEx +#define clAmdBlasSgemmEx clAmdBlasSgemmEx_pfn +#undef clAmdBlasSgemv +//#define clAmdBlasSgemv clAmdBlasSgemv_pfn +#undef clAmdBlasSgemvEx +//#define clAmdBlasSgemvEx clAmdBlasSgemvEx_pfn +#undef clAmdBlasSger +//#define clAmdBlasSger clAmdBlasSger_pfn +#undef clAmdBlasSnrm2 +//#define clAmdBlasSnrm2 clAmdBlasSnrm2_pfn +#undef clAmdBlasSrot +//#define clAmdBlasSrot clAmdBlasSrot_pfn +#undef clAmdBlasSrotg +//#define clAmdBlasSrotg clAmdBlasSrotg_pfn +#undef clAmdBlasSrotm +//#define clAmdBlasSrotm clAmdBlasSrotm_pfn +#undef clAmdBlasSrotmg +//#define clAmdBlasSrotmg clAmdBlasSrotmg_pfn +#undef clAmdBlasSsbmv +//#define clAmdBlasSsbmv clAmdBlasSsbmv_pfn +#undef clAmdBlasSscal +//#define clAmdBlasSscal clAmdBlasSscal_pfn +#undef clAmdBlasSspmv +//#define clAmdBlasSspmv clAmdBlasSspmv_pfn +#undef clAmdBlasSspr +//#define clAmdBlasSspr clAmdBlasSspr_pfn +#undef clAmdBlasSspr2 +//#define clAmdBlasSspr2 clAmdBlasSspr2_pfn +#undef clAmdBlasSswap +//#define clAmdBlasSswap clAmdBlasSswap_pfn +#undef clAmdBlasSsymm +//#define clAmdBlasSsymm clAmdBlasSsymm_pfn +#undef clAmdBlasSsymv +//#define clAmdBlasSsymv clAmdBlasSsymv_pfn +#undef clAmdBlasSsymvEx +//#define clAmdBlasSsymvEx clAmdBlasSsymvEx_pfn +#undef clAmdBlasSsyr +//#define clAmdBlasSsyr clAmdBlasSsyr_pfn +#undef clAmdBlasSsyr2 +//#define clAmdBlasSsyr2 clAmdBlasSsyr2_pfn +#undef clAmdBlasSsyr2k +//#define clAmdBlasSsyr2k clAmdBlasSsyr2k_pfn +#undef clAmdBlasSsyr2kEx +//#define clAmdBlasSsyr2kEx clAmdBlasSsyr2kEx_pfn +#undef clAmdBlasSsyrk +//#define clAmdBlasSsyrk clAmdBlasSsyrk_pfn +#undef clAmdBlasSsyrkEx +//#define clAmdBlasSsyrkEx clAmdBlasSsyrkEx_pfn +#undef clAmdBlasStbmv +//#define clAmdBlasStbmv clAmdBlasStbmv_pfn +#undef clAmdBlasStbsv +//#define clAmdBlasStbsv clAmdBlasStbsv_pfn +#undef clAmdBlasStpmv +//#define clAmdBlasStpmv clAmdBlasStpmv_pfn +#undef clAmdBlasStpsv +//#define clAmdBlasStpsv clAmdBlasStpsv_pfn +#undef clAmdBlasStrmm +//#define clAmdBlasStrmm clAmdBlasStrmm_pfn +#undef clAmdBlasStrmmEx +//#define clAmdBlasStrmmEx clAmdBlasStrmmEx_pfn +#undef clAmdBlasStrmv +//#define clAmdBlasStrmv clAmdBlasStrmv_pfn +#undef clAmdBlasStrsm +//#define clAmdBlasStrsm clAmdBlasStrsm_pfn +#undef clAmdBlasStrsmEx +//#define clAmdBlasStrsmEx clAmdBlasStrsmEx_pfn +#undef clAmdBlasStrsv +//#define clAmdBlasStrsv clAmdBlasStrsv_pfn +#undef clAmdBlasTeardown +#define clAmdBlasTeardown clAmdBlasTeardown_pfn +#undef clAmdBlasZaxpy +//#define clAmdBlasZaxpy clAmdBlasZaxpy_pfn +#undef clAmdBlasZcopy +//#define clAmdBlasZcopy clAmdBlasZcopy_pfn +#undef clAmdBlasZdotc +//#define clAmdBlasZdotc clAmdBlasZdotc_pfn +#undef clAmdBlasZdotu +//#define clAmdBlasZdotu clAmdBlasZdotu_pfn +#undef clAmdBlasZdrot +//#define clAmdBlasZdrot clAmdBlasZdrot_pfn +#undef clAmdBlasZdscal +//#define clAmdBlasZdscal clAmdBlasZdscal_pfn +#undef clAmdBlasZgbmv +//#define clAmdBlasZgbmv clAmdBlasZgbmv_pfn +#undef clAmdBlasZgemm +//#define clAmdBlasZgemm clAmdBlasZgemm_pfn +#undef clAmdBlasZgemmEx +#define clAmdBlasZgemmEx clAmdBlasZgemmEx_pfn +#undef clAmdBlasZgemv +//#define clAmdBlasZgemv clAmdBlasZgemv_pfn +#undef clAmdBlasZgemvEx +//#define clAmdBlasZgemvEx clAmdBlasZgemvEx_pfn +#undef clAmdBlasZgerc +//#define clAmdBlasZgerc clAmdBlasZgerc_pfn +#undef clAmdBlasZgeru +//#define clAmdBlasZgeru clAmdBlasZgeru_pfn +#undef clAmdBlasZhbmv +//#define clAmdBlasZhbmv clAmdBlasZhbmv_pfn +#undef clAmdBlasZhemm +//#define clAmdBlasZhemm clAmdBlasZhemm_pfn +#undef clAmdBlasZhemv +//#define clAmdBlasZhemv clAmdBlasZhemv_pfn +#undef clAmdBlasZher +//#define clAmdBlasZher clAmdBlasZher_pfn +#undef clAmdBlasZher2 +//#define clAmdBlasZher2 clAmdBlasZher2_pfn +#undef clAmdBlasZher2k +//#define clAmdBlasZher2k clAmdBlasZher2k_pfn +#undef clAmdBlasZherk +//#define clAmdBlasZherk clAmdBlasZherk_pfn +#undef clAmdBlasZhpmv +//#define clAmdBlasZhpmv clAmdBlasZhpmv_pfn +#undef clAmdBlasZhpr +//#define clAmdBlasZhpr clAmdBlasZhpr_pfn +#undef clAmdBlasZhpr2 +//#define clAmdBlasZhpr2 clAmdBlasZhpr2_pfn +#undef clAmdBlasZrotg +//#define clAmdBlasZrotg clAmdBlasZrotg_pfn +#undef clAmdBlasZscal +//#define clAmdBlasZscal clAmdBlasZscal_pfn +#undef clAmdBlasZswap +//#define clAmdBlasZswap clAmdBlasZswap_pfn +#undef clAmdBlasZsymm +//#define clAmdBlasZsymm clAmdBlasZsymm_pfn +#undef clAmdBlasZsyr2k +//#define clAmdBlasZsyr2k clAmdBlasZsyr2k_pfn +#undef clAmdBlasZsyr2kEx +//#define clAmdBlasZsyr2kEx clAmdBlasZsyr2kEx_pfn +#undef clAmdBlasZsyrk +//#define clAmdBlasZsyrk clAmdBlasZsyrk_pfn +#undef clAmdBlasZsyrkEx +//#define clAmdBlasZsyrkEx clAmdBlasZsyrkEx_pfn +#undef clAmdBlasZtbmv +//#define clAmdBlasZtbmv clAmdBlasZtbmv_pfn +#undef clAmdBlasZtbsv +//#define clAmdBlasZtbsv clAmdBlasZtbsv_pfn +#undef clAmdBlasZtpmv +//#define clAmdBlasZtpmv clAmdBlasZtpmv_pfn +#undef clAmdBlasZtpsv +//#define clAmdBlasZtpsv clAmdBlasZtpsv_pfn +#undef clAmdBlasZtrmm +//#define clAmdBlasZtrmm clAmdBlasZtrmm_pfn +#undef clAmdBlasZtrmmEx +//#define clAmdBlasZtrmmEx clAmdBlasZtrmmEx_pfn +#undef clAmdBlasZtrmv +//#define clAmdBlasZtrmv clAmdBlasZtrmv_pfn +#undef clAmdBlasZtrsm +//#define clAmdBlasZtrsm clAmdBlasZtrsm_pfn +#undef clAmdBlasZtrsmEx +//#define clAmdBlasZtrsmEx clAmdBlasZtrsmEx_pfn +#undef clAmdBlasZtrsv +//#define clAmdBlasZtrsv clAmdBlasZtrsv_pfn +#undef clAmdBlasiCamax +//#define clAmdBlasiCamax clAmdBlasiCamax_pfn +#undef clAmdBlasiDamax +//#define clAmdBlasiDamax clAmdBlasiDamax_pfn +#undef clAmdBlasiSamax +//#define clAmdBlasiSamax clAmdBlasiSamax_pfn +#undef clAmdBlasiZamax +//#define clAmdBlasiZamax clAmdBlasiZamax_pfn + +// generated by parser_clamdblas.py +//extern CL_RUNTIME_EXPORT cl_ulong (*clAmdBlasAddScratchImage)(cl_context context, size_t width, size_t height, clAmdBlasStatus* status); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCaxpy)(size_t N, cl_float2 alpha, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCcopy)(size_t N, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCdotc)(size_t N, cl_mem dotProduct, size_t offDP, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCdotu)(size_t N, cl_mem dotProduct, size_t offDP, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCgbmv)(clAmdBlasOrder order, clAmdBlasTranspose trans, size_t M, size_t N, size_t KL, size_t KU, cl_float2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_float2 beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCgemm)(clAmdBlasOrder order, clAmdBlasTranspose transA, clAmdBlasTranspose transB, size_t M, size_t N, size_t K, FloatComplex alpha, const cl_mem A, size_t lda, const cl_mem B, size_t ldb, FloatComplex beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCgemmEx)(clAmdBlasOrder order, clAmdBlasTranspose transA, clAmdBlasTranspose transB, size_t M, size_t N, size_t K, FloatComplex alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, FloatComplex beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCgemv)(clAmdBlasOrder order, clAmdBlasTranspose transA, size_t M, size_t N, FloatComplex alpha, const cl_mem A, size_t lda, const cl_mem x, size_t offx, int incx, FloatComplex beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCgemvEx)(clAmdBlasOrder order, clAmdBlasTranspose transA, size_t M, size_t N, FloatComplex alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem x, size_t offx, int incx, FloatComplex beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCgerc)(clAmdBlasOrder order, size_t M, size_t N, cl_float2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCgeru)(clAmdBlasOrder order, size_t M, size_t N, cl_float2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasChbmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, size_t K, cl_float2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_float2 beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasChemm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, size_t M, size_t N, cl_float2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_float2 beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasChemv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, FloatComplex alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, FloatComplex beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCher)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCher2)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCher2k)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, size_t N, size_t K, FloatComplex alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_float beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCherk)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, float alpha, const cl_mem A, size_t offa, size_t lda, float beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasChpmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float2 alpha, const cl_mem AP, size_t offa, const cl_mem X, size_t offx, int incx, cl_float2 beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasChpr)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasChpr2)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCrotg)(cl_mem CA, size_t offCA, cl_mem CB, size_t offCB, cl_mem C, size_t offC, cl_mem S, size_t offS, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCscal)(size_t N, cl_float2 alpha, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCsrot)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_float C, cl_float S, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCsscal)(size_t N, cl_float alpha, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCswap)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCsymm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, size_t M, size_t N, cl_float2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_float2 beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCsyr2k)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transAB, size_t N, size_t K, FloatComplex alpha, const cl_mem A, size_t lda, const cl_mem B, size_t ldb, FloatComplex beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCsyr2kEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transAB, size_t N, size_t K, FloatComplex alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, FloatComplex beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCsyrk)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, FloatComplex alpha, const cl_mem A, size_t lda, FloatComplex beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCsyrkEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, FloatComplex alpha, const cl_mem A, size_t offA, size_t lda, FloatComplex beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtbmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtbsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtpmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem AP, size_t offa, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtpsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtrmm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, FloatComplex alpha, const cl_mem A, size_t lda, cl_mem B, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtrmmEx)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, FloatComplex alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtrmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtrsm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, FloatComplex alpha, const cl_mem A, size_t lda, cl_mem B, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtrsmEx)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, FloatComplex alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtrsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDasum)(size_t N, cl_mem asum, size_t offAsum, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDaxpy)(size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDcopy)(size_t N, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDdot)(size_t N, cl_mem dotProduct, size_t offDP, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDgbmv)(clAmdBlasOrder order, clAmdBlasTranspose trans, size_t M, size_t N, size_t KL, size_t KU, cl_double alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_double beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDgemm)(clAmdBlasOrder order, clAmdBlasTranspose transA, clAmdBlasTranspose transB, size_t M, size_t N, size_t K, cl_double alpha, const cl_mem A, size_t lda, const cl_mem B, size_t ldb, cl_double beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDgemmEx)(clAmdBlasOrder order, clAmdBlasTranspose transA, clAmdBlasTranspose transB, size_t M, size_t N, size_t K, cl_double alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, cl_double beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDgemv)(clAmdBlasOrder order, clAmdBlasTranspose transA, size_t M, size_t N, cl_double alpha, const cl_mem A, size_t lda, const cl_mem x, size_t offx, int incx, cl_double beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDgemvEx)(clAmdBlasOrder order, clAmdBlasTranspose transA, size_t M, size_t N, cl_double alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem x, size_t offx, int incx, cl_double beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDger)(clAmdBlasOrder order, size_t M, size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDnrm2)(size_t N, cl_mem NRM2, size_t offNRM2, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDrot)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_double C, cl_double S, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDrotg)(cl_mem DA, size_t offDA, cl_mem DB, size_t offDB, cl_mem C, size_t offC, cl_mem S, size_t offS, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDrotm)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, const cl_mem DPARAM, size_t offDparam, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDrotmg)(cl_mem DD1, size_t offDD1, cl_mem DD2, size_t offDD2, cl_mem DX1, size_t offDX1, const cl_mem DY1, size_t offDY1, cl_mem DPARAM, size_t offDparam, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsbmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, size_t K, cl_double alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_double beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDscal)(size_t N, cl_double alpha, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDspmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double alpha, const cl_mem AP, size_t offa, const cl_mem X, size_t offx, int incx, cl_double beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDspr)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDspr2)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDswap)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsymm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, size_t M, size_t N, cl_double alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_double beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsymv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double alpha, const cl_mem A, size_t lda, const cl_mem x, size_t offx, int incx, cl_double beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsymvEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem x, size_t offx, int incx, cl_double beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsyr)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsyr2)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsyr2k)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transAB, size_t N, size_t K, cl_double alpha, const cl_mem A, size_t lda, const cl_mem B, size_t ldb, cl_double beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsyr2kEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transAB, size_t N, size_t K, cl_double alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, cl_double beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsyrk)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, cl_double alpha, const cl_mem A, size_t lda, cl_double beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsyrkEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, cl_double alpha, const cl_mem A, size_t offA, size_t lda, cl_double beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtbmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtbsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtpmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem AP, size_t offa, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtpsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtrmm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, cl_double alpha, const cl_mem A, size_t lda, cl_mem B, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtrmmEx)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, cl_double alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtrmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtrsm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, cl_double alpha, const cl_mem A, size_t lda, cl_mem B, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtrsmEx)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, cl_double alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtrsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDzasum)(size_t N, cl_mem asum, size_t offAsum, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDznrm2)(size_t N, cl_mem NRM2, size_t offNRM2, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasGetVersion)(cl_uint* major, cl_uint* minor, cl_uint* patch); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasRemoveScratchImage)(cl_ulong imageID); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSasum)(size_t N, cl_mem asum, size_t offAsum, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSaxpy)(size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasScasum)(size_t N, cl_mem asum, size_t offAsum, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasScnrm2)(size_t N, cl_mem NRM2, size_t offNRM2, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasScopy)(size_t N, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSdot)(size_t N, cl_mem dotProduct, size_t offDP, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSetup)(); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSgbmv)(clAmdBlasOrder order, clAmdBlasTranspose trans, size_t M, size_t N, size_t KL, size_t KU, cl_float alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_float beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSgemm)(clAmdBlasOrder order, clAmdBlasTranspose transA, clAmdBlasTranspose transB, size_t M, size_t N, size_t K, cl_float alpha, const cl_mem A, size_t lda, const cl_mem B, size_t ldb, cl_float beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSgemmEx)(clAmdBlasOrder order, clAmdBlasTranspose transA, clAmdBlasTranspose transB, size_t M, size_t N, size_t K, cl_float alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, cl_float beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSgemv)(clAmdBlasOrder order, clAmdBlasTranspose transA, size_t M, size_t N, cl_float alpha, const cl_mem A, size_t lda, const cl_mem x, size_t offx, int incx, cl_float beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSgemvEx)(clAmdBlasOrder order, clAmdBlasTranspose transA, size_t M, size_t N, cl_float alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem x, size_t offx, int incx, cl_float beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSger)(clAmdBlasOrder order, size_t M, size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSnrm2)(size_t N, cl_mem NRM2, size_t offNRM2, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSrot)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_float C, cl_float S, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSrotg)(cl_mem SA, size_t offSA, cl_mem SB, size_t offSB, cl_mem C, size_t offC, cl_mem S, size_t offS, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSrotm)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, const cl_mem SPARAM, size_t offSparam, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSrotmg)(cl_mem SD1, size_t offSD1, cl_mem SD2, size_t offSD2, cl_mem SX1, size_t offSX1, const cl_mem SY1, size_t offSY1, cl_mem SPARAM, size_t offSparam, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsbmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, size_t K, cl_float alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_float beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSscal)(size_t N, cl_float alpha, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSspmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float alpha, const cl_mem AP, size_t offa, const cl_mem X, size_t offx, int incx, cl_float beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSspr)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSspr2)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSswap)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsymm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, size_t M, size_t N, cl_float alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_float beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsymv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float alpha, const cl_mem A, size_t lda, const cl_mem x, size_t offx, int incx, cl_float beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsymvEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem x, size_t offx, int incx, cl_float beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsyr)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsyr2)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsyr2k)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transAB, size_t N, size_t K, cl_float alpha, const cl_mem A, size_t lda, const cl_mem B, size_t ldb, cl_float beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsyr2kEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transAB, size_t N, size_t K, cl_float alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, cl_float beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsyrk)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, cl_float alpha, const cl_mem A, size_t lda, cl_float beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsyrkEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, cl_float alpha, const cl_mem A, size_t offA, size_t lda, cl_float beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStbmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStbsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStpmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem AP, size_t offa, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStpsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStrmm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, cl_float alpha, const cl_mem A, size_t lda, cl_mem B, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStrmmEx)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, cl_float alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStrmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStrsm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, cl_float alpha, const cl_mem A, size_t lda, cl_mem B, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStrsmEx)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, cl_float alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStrsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +extern CL_RUNTIME_EXPORT void (*clAmdBlasTeardown)(); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZaxpy)(size_t N, cl_double2 alpha, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZcopy)(size_t N, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZdotc)(size_t N, cl_mem dotProduct, size_t offDP, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZdotu)(size_t N, cl_mem dotProduct, size_t offDP, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZdrot)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_double C, cl_double S, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZdscal)(size_t N, cl_double alpha, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZgbmv)(clAmdBlasOrder order, clAmdBlasTranspose trans, size_t M, size_t N, size_t KL, size_t KU, cl_double2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_double2 beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZgemm)(clAmdBlasOrder order, clAmdBlasTranspose transA, clAmdBlasTranspose transB, size_t M, size_t N, size_t K, DoubleComplex alpha, const cl_mem A, size_t lda, const cl_mem B, size_t ldb, DoubleComplex beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZgemmEx)(clAmdBlasOrder order, clAmdBlasTranspose transA, clAmdBlasTranspose transB, size_t M, size_t N, size_t K, DoubleComplex alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, DoubleComplex beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZgemv)(clAmdBlasOrder order, clAmdBlasTranspose transA, size_t M, size_t N, DoubleComplex alpha, const cl_mem A, size_t lda, const cl_mem x, size_t offx, int incx, DoubleComplex beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZgemvEx)(clAmdBlasOrder order, clAmdBlasTranspose transA, size_t M, size_t N, DoubleComplex alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem x, size_t offx, int incx, DoubleComplex beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZgerc)(clAmdBlasOrder order, size_t M, size_t N, cl_double2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZgeru)(clAmdBlasOrder order, size_t M, size_t N, cl_double2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZhbmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, size_t K, cl_double2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_double2 beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZhemm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, size_t M, size_t N, cl_double2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_double2 beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZhemv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, DoubleComplex alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, DoubleComplex beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZher)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZher2)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZher2k)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, size_t N, size_t K, DoubleComplex alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_double beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZherk)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, double alpha, const cl_mem A, size_t offa, size_t lda, double beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZhpmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double2 alpha, const cl_mem AP, size_t offa, const cl_mem X, size_t offx, int incx, cl_double2 beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZhpr)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZhpr2)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZrotg)(cl_mem CA, size_t offCA, cl_mem CB, size_t offCB, cl_mem C, size_t offC, cl_mem S, size_t offS, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZscal)(size_t N, cl_double2 alpha, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZswap)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZsymm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, size_t M, size_t N, cl_double2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_double2 beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZsyr2k)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transAB, size_t N, size_t K, DoubleComplex alpha, const cl_mem A, size_t lda, const cl_mem B, size_t ldb, DoubleComplex beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZsyr2kEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transAB, size_t N, size_t K, DoubleComplex alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, DoubleComplex beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZsyrk)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, DoubleComplex alpha, const cl_mem A, size_t lda, DoubleComplex beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZsyrkEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, DoubleComplex alpha, const cl_mem A, size_t offA, size_t lda, DoubleComplex beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtbmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtbsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtpmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem AP, size_t offa, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtpsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtrmm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, DoubleComplex alpha, const cl_mem A, size_t lda, cl_mem B, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtrmmEx)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, DoubleComplex alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtrmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtrsm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, DoubleComplex alpha, const cl_mem A, size_t lda, cl_mem B, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtrsmEx)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, DoubleComplex alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtrsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasiCamax)(size_t N, cl_mem iMax, size_t offiMax, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasiDamax)(size_t N, cl_mem iMax, size_t offiMax, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasiSamax)(size_t N, cl_mem iMax, size_t offiMax, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasiZamax)(size_t N, cl_mem iMax, size_t offiMax, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/runtime/autogenerated/opencl_clamdfft.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/runtime/autogenerated/opencl_clamdfft.hpp new file mode 100644 index 0000000000000000000000000000000000000000..1457d7eb8d69a56197b8e80430728d571282088f --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/runtime/autogenerated/opencl_clamdfft.hpp @@ -0,0 +1,142 @@ +// +// AUTOGENERATED, DO NOT EDIT +// +#ifndef OPENCV_CORE_OCL_RUNTIME_CLAMDFFT_HPP +#error "Invalid usage" +#endif + +// generated by parser_clamdfft.py +#define clAmdFftBakePlan clAmdFftBakePlan_ +#define clAmdFftCopyPlan clAmdFftCopyPlan_ +#define clAmdFftCreateDefaultPlan clAmdFftCreateDefaultPlan_ +#define clAmdFftDestroyPlan clAmdFftDestroyPlan_ +#define clAmdFftEnqueueTransform clAmdFftEnqueueTransform_ +#define clAmdFftGetLayout clAmdFftGetLayout_ +#define clAmdFftGetPlanBatchSize clAmdFftGetPlanBatchSize_ +#define clAmdFftGetPlanContext clAmdFftGetPlanContext_ +#define clAmdFftGetPlanDim clAmdFftGetPlanDim_ +#define clAmdFftGetPlanDistance clAmdFftGetPlanDistance_ +#define clAmdFftGetPlanInStride clAmdFftGetPlanInStride_ +#define clAmdFftGetPlanLength clAmdFftGetPlanLength_ +#define clAmdFftGetPlanOutStride clAmdFftGetPlanOutStride_ +#define clAmdFftGetPlanPrecision clAmdFftGetPlanPrecision_ +#define clAmdFftGetPlanScale clAmdFftGetPlanScale_ +#define clAmdFftGetPlanTransposeResult clAmdFftGetPlanTransposeResult_ +#define clAmdFftGetResultLocation clAmdFftGetResultLocation_ +#define clAmdFftGetTmpBufSize clAmdFftGetTmpBufSize_ +#define clAmdFftGetVersion clAmdFftGetVersion_ +#define clAmdFftSetLayout clAmdFftSetLayout_ +#define clAmdFftSetPlanBatchSize clAmdFftSetPlanBatchSize_ +#define clAmdFftSetPlanDim clAmdFftSetPlanDim_ +#define clAmdFftSetPlanDistance clAmdFftSetPlanDistance_ +#define clAmdFftSetPlanInStride clAmdFftSetPlanInStride_ +#define clAmdFftSetPlanLength clAmdFftSetPlanLength_ +#define clAmdFftSetPlanOutStride clAmdFftSetPlanOutStride_ +#define clAmdFftSetPlanPrecision clAmdFftSetPlanPrecision_ +#define clAmdFftSetPlanScale clAmdFftSetPlanScale_ +#define clAmdFftSetPlanTransposeResult clAmdFftSetPlanTransposeResult_ +#define clAmdFftSetResultLocation clAmdFftSetResultLocation_ +#define clAmdFftSetup clAmdFftSetup_ +#define clAmdFftTeardown clAmdFftTeardown_ + +#include + +// generated by parser_clamdfft.py +#undef clAmdFftBakePlan +#define clAmdFftBakePlan clAmdFftBakePlan_pfn +#undef clAmdFftCopyPlan +//#define clAmdFftCopyPlan clAmdFftCopyPlan_pfn +#undef clAmdFftCreateDefaultPlan +#define clAmdFftCreateDefaultPlan clAmdFftCreateDefaultPlan_pfn +#undef clAmdFftDestroyPlan +#define clAmdFftDestroyPlan clAmdFftDestroyPlan_pfn +#undef clAmdFftEnqueueTransform +#define clAmdFftEnqueueTransform clAmdFftEnqueueTransform_pfn +#undef clAmdFftGetLayout +//#define clAmdFftGetLayout clAmdFftGetLayout_pfn +#undef clAmdFftGetPlanBatchSize +//#define clAmdFftGetPlanBatchSize clAmdFftGetPlanBatchSize_pfn +#undef clAmdFftGetPlanContext +//#define clAmdFftGetPlanContext clAmdFftGetPlanContext_pfn +#undef clAmdFftGetPlanDim +//#define clAmdFftGetPlanDim clAmdFftGetPlanDim_pfn +#undef clAmdFftGetPlanDistance +//#define clAmdFftGetPlanDistance clAmdFftGetPlanDistance_pfn +#undef clAmdFftGetPlanInStride +//#define clAmdFftGetPlanInStride clAmdFftGetPlanInStride_pfn +#undef clAmdFftGetPlanLength +//#define clAmdFftGetPlanLength clAmdFftGetPlanLength_pfn +#undef clAmdFftGetPlanOutStride +//#define clAmdFftGetPlanOutStride clAmdFftGetPlanOutStride_pfn +#undef clAmdFftGetPlanPrecision +//#define clAmdFftGetPlanPrecision clAmdFftGetPlanPrecision_pfn +#undef clAmdFftGetPlanScale +//#define clAmdFftGetPlanScale clAmdFftGetPlanScale_pfn +#undef clAmdFftGetPlanTransposeResult +//#define clAmdFftGetPlanTransposeResult clAmdFftGetPlanTransposeResult_pfn +#undef clAmdFftGetResultLocation +//#define clAmdFftGetResultLocation clAmdFftGetResultLocation_pfn +#undef clAmdFftGetTmpBufSize +#define clAmdFftGetTmpBufSize clAmdFftGetTmpBufSize_pfn +#undef clAmdFftGetVersion +#define clAmdFftGetVersion clAmdFftGetVersion_pfn +#undef clAmdFftSetLayout +#define clAmdFftSetLayout clAmdFftSetLayout_pfn +#undef clAmdFftSetPlanBatchSize +#define clAmdFftSetPlanBatchSize clAmdFftSetPlanBatchSize_pfn +#undef clAmdFftSetPlanDim +//#define clAmdFftSetPlanDim clAmdFftSetPlanDim_pfn +#undef clAmdFftSetPlanDistance +#define clAmdFftSetPlanDistance clAmdFftSetPlanDistance_pfn +#undef clAmdFftSetPlanInStride +#define clAmdFftSetPlanInStride clAmdFftSetPlanInStride_pfn +#undef clAmdFftSetPlanLength +//#define clAmdFftSetPlanLength clAmdFftSetPlanLength_pfn +#undef clAmdFftSetPlanOutStride +#define clAmdFftSetPlanOutStride clAmdFftSetPlanOutStride_pfn +#undef clAmdFftSetPlanPrecision +#define clAmdFftSetPlanPrecision clAmdFftSetPlanPrecision_pfn +#undef clAmdFftSetPlanScale +#define clAmdFftSetPlanScale clAmdFftSetPlanScale_pfn +#undef clAmdFftSetPlanTransposeResult +//#define clAmdFftSetPlanTransposeResult clAmdFftSetPlanTransposeResult_pfn +#undef clAmdFftSetResultLocation +#define clAmdFftSetResultLocation clAmdFftSetResultLocation_pfn +#undef clAmdFftSetup +#define clAmdFftSetup clAmdFftSetup_pfn +#undef clAmdFftTeardown +#define clAmdFftTeardown clAmdFftTeardown_pfn + +// generated by parser_clamdfft.py +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftBakePlan)(clAmdFftPlanHandle plHandle, cl_uint numQueues, cl_command_queue* commQueueFFT, void (CL_CALLBACK* pfn_notify) (clAmdFftPlanHandle plHandle, void* user_data), void* user_data); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftCopyPlan)(clAmdFftPlanHandle* out_plHandle, cl_context new_context, clAmdFftPlanHandle in_plHandle); +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftCreateDefaultPlan)(clAmdFftPlanHandle* plHandle, cl_context context, const clAmdFftDim dim, const size_t* clLengths); +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftDestroyPlan)(clAmdFftPlanHandle* plHandle); +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftEnqueueTransform)(clAmdFftPlanHandle plHandle, clAmdFftDirection dir, cl_uint numQueuesAndEvents, cl_command_queue* commQueues, cl_uint numWaitEvents, const cl_event* waitEvents, cl_event* outEvents, cl_mem* inputBuffers, cl_mem* outputBuffers, cl_mem tmpBuffer); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetLayout)(const clAmdFftPlanHandle plHandle, clAmdFftLayout* iLayout, clAmdFftLayout* oLayout); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanBatchSize)(const clAmdFftPlanHandle plHandle, size_t* batchSize); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanContext)(const clAmdFftPlanHandle plHandle, cl_context* context); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanDim)(const clAmdFftPlanHandle plHandle, clAmdFftDim* dim, cl_uint* size); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanDistance)(const clAmdFftPlanHandle plHandle, size_t* iDist, size_t* oDist); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanInStride)(const clAmdFftPlanHandle plHandle, const clAmdFftDim dim, size_t* clStrides); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanLength)(const clAmdFftPlanHandle plHandle, const clAmdFftDim dim, size_t* clLengths); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanOutStride)(const clAmdFftPlanHandle plHandle, const clAmdFftDim dim, size_t* clStrides); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanPrecision)(const clAmdFftPlanHandle plHandle, clAmdFftPrecision* precision); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanScale)(const clAmdFftPlanHandle plHandle, clAmdFftDirection dir, cl_float* scale); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanTransposeResult)(const clAmdFftPlanHandle plHandle, clAmdFftResultTransposed* transposed); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetResultLocation)(const clAmdFftPlanHandle plHandle, clAmdFftResultLocation* placeness); +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetTmpBufSize)(const clAmdFftPlanHandle plHandle, size_t* buffersize); +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetVersion)(cl_uint* major, cl_uint* minor, cl_uint* patch); +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetLayout)(clAmdFftPlanHandle plHandle, clAmdFftLayout iLayout, clAmdFftLayout oLayout); +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetPlanBatchSize)(clAmdFftPlanHandle plHandle, size_t batchSize); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetPlanDim)(clAmdFftPlanHandle plHandle, const clAmdFftDim dim); +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetPlanDistance)(clAmdFftPlanHandle plHandle, size_t iDist, size_t oDist); +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetPlanInStride)(clAmdFftPlanHandle plHandle, const clAmdFftDim dim, size_t* clStrides); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetPlanLength)(clAmdFftPlanHandle plHandle, const clAmdFftDim dim, const size_t* clLengths); +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetPlanOutStride)(clAmdFftPlanHandle plHandle, const clAmdFftDim dim, size_t* clStrides); +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetPlanPrecision)(clAmdFftPlanHandle plHandle, clAmdFftPrecision precision); +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetPlanScale)(clAmdFftPlanHandle plHandle, clAmdFftDirection dir, cl_float scale); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetPlanTransposeResult)(clAmdFftPlanHandle plHandle, clAmdFftResultTransposed transposed); +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetResultLocation)(clAmdFftPlanHandle plHandle, clAmdFftResultLocation placeness); +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetup)(const clAmdFftSetupData* setupData); +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftTeardown)(); diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/runtime/autogenerated/opencl_core.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/runtime/autogenerated/opencl_core.hpp new file mode 100644 index 0000000000000000000000000000000000000000..fdaf469ff1cee7dada0e45311533f14a4db89d71 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/runtime/autogenerated/opencl_core.hpp @@ -0,0 +1,370 @@ +// +// AUTOGENERATED, DO NOT EDIT +// +#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_CORE_HPP +#error "Invalid usage" +#endif + +// generated by parser_cl.py +#define clBuildProgram clBuildProgram_ +#define clCompileProgram clCompileProgram_ +#define clCreateBuffer clCreateBuffer_ +#define clCreateCommandQueue clCreateCommandQueue_ +#define clCreateContext clCreateContext_ +#define clCreateContextFromType clCreateContextFromType_ +#define clCreateImage clCreateImage_ +#define clCreateImage2D clCreateImage2D_ +#define clCreateImage3D clCreateImage3D_ +#define clCreateKernel clCreateKernel_ +#define clCreateKernelsInProgram clCreateKernelsInProgram_ +#define clCreateProgramWithBinary clCreateProgramWithBinary_ +#define clCreateProgramWithBuiltInKernels clCreateProgramWithBuiltInKernels_ +#define clCreateProgramWithSource clCreateProgramWithSource_ +#define clCreateSampler clCreateSampler_ +#define clCreateSubBuffer clCreateSubBuffer_ +#define clCreateSubDevices clCreateSubDevices_ +#define clCreateUserEvent clCreateUserEvent_ +#define clEnqueueBarrier clEnqueueBarrier_ +#define clEnqueueBarrierWithWaitList clEnqueueBarrierWithWaitList_ +#define clEnqueueCopyBuffer clEnqueueCopyBuffer_ +#define clEnqueueCopyBufferRect clEnqueueCopyBufferRect_ +#define clEnqueueCopyBufferToImage clEnqueueCopyBufferToImage_ +#define clEnqueueCopyImage clEnqueueCopyImage_ +#define clEnqueueCopyImageToBuffer clEnqueueCopyImageToBuffer_ +#define clEnqueueFillBuffer clEnqueueFillBuffer_ +#define clEnqueueFillImage clEnqueueFillImage_ +#define clEnqueueMapBuffer clEnqueueMapBuffer_ +#define clEnqueueMapImage clEnqueueMapImage_ +#define clEnqueueMarker clEnqueueMarker_ +#define clEnqueueMarkerWithWaitList clEnqueueMarkerWithWaitList_ +#define clEnqueueMigrateMemObjects clEnqueueMigrateMemObjects_ +#define clEnqueueNDRangeKernel clEnqueueNDRangeKernel_ +#define clEnqueueNativeKernel clEnqueueNativeKernel_ +#define clEnqueueReadBuffer clEnqueueReadBuffer_ +#define clEnqueueReadBufferRect clEnqueueReadBufferRect_ +#define clEnqueueReadImage clEnqueueReadImage_ +#define clEnqueueTask clEnqueueTask_ +#define clEnqueueUnmapMemObject clEnqueueUnmapMemObject_ +#define clEnqueueWaitForEvents clEnqueueWaitForEvents_ +#define clEnqueueWriteBuffer clEnqueueWriteBuffer_ +#define clEnqueueWriteBufferRect clEnqueueWriteBufferRect_ +#define clEnqueueWriteImage clEnqueueWriteImage_ +#define clFinish clFinish_ +#define clFlush clFlush_ +#define clGetCommandQueueInfo clGetCommandQueueInfo_ +#define clGetContextInfo clGetContextInfo_ +#define clGetDeviceIDs clGetDeviceIDs_ +#define clGetDeviceInfo clGetDeviceInfo_ +#define clGetEventInfo clGetEventInfo_ +#define clGetEventProfilingInfo clGetEventProfilingInfo_ +#define clGetExtensionFunctionAddress clGetExtensionFunctionAddress_ +#define clGetExtensionFunctionAddressForPlatform clGetExtensionFunctionAddressForPlatform_ +#define clGetImageInfo clGetImageInfo_ +#define clGetKernelArgInfo clGetKernelArgInfo_ +#define clGetKernelInfo clGetKernelInfo_ +#define clGetKernelWorkGroupInfo clGetKernelWorkGroupInfo_ +#define clGetMemObjectInfo clGetMemObjectInfo_ +#define clGetPlatformIDs clGetPlatformIDs_ +#define clGetPlatformInfo clGetPlatformInfo_ +#define clGetProgramBuildInfo clGetProgramBuildInfo_ +#define clGetProgramInfo clGetProgramInfo_ +#define clGetSamplerInfo clGetSamplerInfo_ +#define clGetSupportedImageFormats clGetSupportedImageFormats_ +#define clLinkProgram clLinkProgram_ +#define clReleaseCommandQueue clReleaseCommandQueue_ +#define clReleaseContext clReleaseContext_ +#define clReleaseDevice clReleaseDevice_ +#define clReleaseEvent clReleaseEvent_ +#define clReleaseKernel clReleaseKernel_ +#define clReleaseMemObject clReleaseMemObject_ +#define clReleaseProgram clReleaseProgram_ +#define clReleaseSampler clReleaseSampler_ +#define clRetainCommandQueue clRetainCommandQueue_ +#define clRetainContext clRetainContext_ +#define clRetainDevice clRetainDevice_ +#define clRetainEvent clRetainEvent_ +#define clRetainKernel clRetainKernel_ +#define clRetainMemObject clRetainMemObject_ +#define clRetainProgram clRetainProgram_ +#define clRetainSampler clRetainSampler_ +#define clSetEventCallback clSetEventCallback_ +#define clSetKernelArg clSetKernelArg_ +#define clSetMemObjectDestructorCallback clSetMemObjectDestructorCallback_ +#define clSetUserEventStatus clSetUserEventStatus_ +#define clUnloadCompiler clUnloadCompiler_ +#define clUnloadPlatformCompiler clUnloadPlatformCompiler_ +#define clWaitForEvents clWaitForEvents_ + +#if defined __APPLE__ +#include +#else +#include +#endif + +// generated by parser_cl.py +#undef clBuildProgram +#define clBuildProgram clBuildProgram_pfn +#undef clCompileProgram +#define clCompileProgram clCompileProgram_pfn +#undef clCreateBuffer +#define clCreateBuffer clCreateBuffer_pfn +#undef clCreateCommandQueue +#define clCreateCommandQueue clCreateCommandQueue_pfn +#undef clCreateContext +#define clCreateContext clCreateContext_pfn +#undef clCreateContextFromType +#define clCreateContextFromType clCreateContextFromType_pfn +#undef clCreateImage +#define clCreateImage clCreateImage_pfn +#undef clCreateImage2D +#define clCreateImage2D clCreateImage2D_pfn +#undef clCreateImage3D +#define clCreateImage3D clCreateImage3D_pfn +#undef clCreateKernel +#define clCreateKernel clCreateKernel_pfn +#undef clCreateKernelsInProgram +#define clCreateKernelsInProgram clCreateKernelsInProgram_pfn +#undef clCreateProgramWithBinary +#define clCreateProgramWithBinary clCreateProgramWithBinary_pfn +#undef clCreateProgramWithBuiltInKernels +#define clCreateProgramWithBuiltInKernels clCreateProgramWithBuiltInKernels_pfn +#undef clCreateProgramWithSource +#define clCreateProgramWithSource clCreateProgramWithSource_pfn +#undef clCreateSampler +#define clCreateSampler clCreateSampler_pfn +#undef clCreateSubBuffer +#define clCreateSubBuffer clCreateSubBuffer_pfn +#undef clCreateSubDevices +#define clCreateSubDevices clCreateSubDevices_pfn +#undef clCreateUserEvent +#define clCreateUserEvent clCreateUserEvent_pfn +#undef clEnqueueBarrier +#define clEnqueueBarrier clEnqueueBarrier_pfn +#undef clEnqueueBarrierWithWaitList +#define clEnqueueBarrierWithWaitList clEnqueueBarrierWithWaitList_pfn +#undef clEnqueueCopyBuffer +#define clEnqueueCopyBuffer clEnqueueCopyBuffer_pfn +#undef clEnqueueCopyBufferRect +#define clEnqueueCopyBufferRect clEnqueueCopyBufferRect_pfn +#undef clEnqueueCopyBufferToImage +#define clEnqueueCopyBufferToImage clEnqueueCopyBufferToImage_pfn +#undef clEnqueueCopyImage +#define clEnqueueCopyImage clEnqueueCopyImage_pfn +#undef clEnqueueCopyImageToBuffer +#define clEnqueueCopyImageToBuffer clEnqueueCopyImageToBuffer_pfn +#undef clEnqueueFillBuffer +#define clEnqueueFillBuffer clEnqueueFillBuffer_pfn +#undef clEnqueueFillImage +#define clEnqueueFillImage clEnqueueFillImage_pfn +#undef clEnqueueMapBuffer +#define clEnqueueMapBuffer clEnqueueMapBuffer_pfn +#undef clEnqueueMapImage +#define clEnqueueMapImage clEnqueueMapImage_pfn +#undef clEnqueueMarker +#define clEnqueueMarker clEnqueueMarker_pfn +#undef clEnqueueMarkerWithWaitList +#define clEnqueueMarkerWithWaitList clEnqueueMarkerWithWaitList_pfn +#undef clEnqueueMigrateMemObjects +#define clEnqueueMigrateMemObjects clEnqueueMigrateMemObjects_pfn +#undef clEnqueueNDRangeKernel +#define clEnqueueNDRangeKernel clEnqueueNDRangeKernel_pfn +#undef clEnqueueNativeKernel +#define clEnqueueNativeKernel clEnqueueNativeKernel_pfn +#undef clEnqueueReadBuffer +#define clEnqueueReadBuffer clEnqueueReadBuffer_pfn +#undef clEnqueueReadBufferRect +#define clEnqueueReadBufferRect clEnqueueReadBufferRect_pfn +#undef clEnqueueReadImage +#define clEnqueueReadImage clEnqueueReadImage_pfn +#undef clEnqueueTask +#define clEnqueueTask clEnqueueTask_pfn +#undef clEnqueueUnmapMemObject +#define clEnqueueUnmapMemObject clEnqueueUnmapMemObject_pfn +#undef clEnqueueWaitForEvents +#define clEnqueueWaitForEvents clEnqueueWaitForEvents_pfn +#undef clEnqueueWriteBuffer +#define clEnqueueWriteBuffer clEnqueueWriteBuffer_pfn +#undef clEnqueueWriteBufferRect +#define clEnqueueWriteBufferRect clEnqueueWriteBufferRect_pfn +#undef clEnqueueWriteImage +#define clEnqueueWriteImage clEnqueueWriteImage_pfn +#undef clFinish +#define clFinish clFinish_pfn +#undef clFlush +#define clFlush clFlush_pfn +#undef clGetCommandQueueInfo +#define clGetCommandQueueInfo clGetCommandQueueInfo_pfn +#undef clGetContextInfo +#define clGetContextInfo clGetContextInfo_pfn +#undef clGetDeviceIDs +#define clGetDeviceIDs clGetDeviceIDs_pfn +#undef clGetDeviceInfo +#define clGetDeviceInfo clGetDeviceInfo_pfn +#undef clGetEventInfo +#define clGetEventInfo clGetEventInfo_pfn +#undef clGetEventProfilingInfo +#define clGetEventProfilingInfo clGetEventProfilingInfo_pfn +#undef clGetExtensionFunctionAddress +#define clGetExtensionFunctionAddress clGetExtensionFunctionAddress_pfn +#undef clGetExtensionFunctionAddressForPlatform +#define clGetExtensionFunctionAddressForPlatform clGetExtensionFunctionAddressForPlatform_pfn +#undef clGetImageInfo +#define clGetImageInfo clGetImageInfo_pfn +#undef clGetKernelArgInfo +#define clGetKernelArgInfo clGetKernelArgInfo_pfn +#undef clGetKernelInfo +#define clGetKernelInfo clGetKernelInfo_pfn +#undef clGetKernelWorkGroupInfo +#define clGetKernelWorkGroupInfo clGetKernelWorkGroupInfo_pfn +#undef clGetMemObjectInfo +#define clGetMemObjectInfo clGetMemObjectInfo_pfn +#undef clGetPlatformIDs +#define clGetPlatformIDs clGetPlatformIDs_pfn +#undef clGetPlatformInfo +#define clGetPlatformInfo clGetPlatformInfo_pfn +#undef clGetProgramBuildInfo +#define clGetProgramBuildInfo clGetProgramBuildInfo_pfn +#undef clGetProgramInfo +#define clGetProgramInfo clGetProgramInfo_pfn +#undef clGetSamplerInfo +#define clGetSamplerInfo clGetSamplerInfo_pfn +#undef clGetSupportedImageFormats +#define clGetSupportedImageFormats clGetSupportedImageFormats_pfn +#undef clLinkProgram +#define clLinkProgram clLinkProgram_pfn +#undef clReleaseCommandQueue +#define clReleaseCommandQueue clReleaseCommandQueue_pfn +#undef clReleaseContext +#define clReleaseContext clReleaseContext_pfn +#undef clReleaseDevice +#define clReleaseDevice clReleaseDevice_pfn +#undef clReleaseEvent +#define clReleaseEvent clReleaseEvent_pfn +#undef clReleaseKernel +#define clReleaseKernel clReleaseKernel_pfn +#undef clReleaseMemObject +#define clReleaseMemObject clReleaseMemObject_pfn +#undef clReleaseProgram +#define clReleaseProgram clReleaseProgram_pfn +#undef clReleaseSampler +#define clReleaseSampler clReleaseSampler_pfn +#undef clRetainCommandQueue +#define clRetainCommandQueue clRetainCommandQueue_pfn +#undef clRetainContext +#define clRetainContext clRetainContext_pfn +#undef clRetainDevice +#define clRetainDevice clRetainDevice_pfn +#undef clRetainEvent +#define clRetainEvent clRetainEvent_pfn +#undef clRetainKernel +#define clRetainKernel clRetainKernel_pfn +#undef clRetainMemObject +#define clRetainMemObject clRetainMemObject_pfn +#undef clRetainProgram +#define clRetainProgram clRetainProgram_pfn +#undef clRetainSampler +#define clRetainSampler clRetainSampler_pfn +#undef clSetEventCallback +#define clSetEventCallback clSetEventCallback_pfn +#undef clSetKernelArg +#define clSetKernelArg clSetKernelArg_pfn +#undef clSetMemObjectDestructorCallback +#define clSetMemObjectDestructorCallback clSetMemObjectDestructorCallback_pfn +#undef clSetUserEventStatus +#define clSetUserEventStatus clSetUserEventStatus_pfn +#undef clUnloadCompiler +#define clUnloadCompiler clUnloadCompiler_pfn +#undef clUnloadPlatformCompiler +#define clUnloadPlatformCompiler clUnloadPlatformCompiler_pfn +#undef clWaitForEvents +#define clWaitForEvents clWaitForEvents_pfn + +// generated by parser_cl.py +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clBuildProgram)(cl_program, cl_uint, const cl_device_id*, const char*, void (CL_CALLBACK*) (cl_program, void*), void*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clCompileProgram)(cl_program, cl_uint, const cl_device_id*, const char*, cl_uint, const cl_program*, const char**, void (CL_CALLBACK*) (cl_program, void*), void*); +extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateBuffer)(cl_context, cl_mem_flags, size_t, void*, cl_int*); +extern CL_RUNTIME_EXPORT cl_command_queue (CL_API_CALL*clCreateCommandQueue)(cl_context, cl_device_id, cl_command_queue_properties, cl_int*); +extern CL_RUNTIME_EXPORT cl_context (CL_API_CALL*clCreateContext)(const cl_context_properties*, cl_uint, const cl_device_id*, void (CL_CALLBACK*) (const char*, const void*, size_t, void*), void*, cl_int*); +extern CL_RUNTIME_EXPORT cl_context (CL_API_CALL*clCreateContextFromType)(const cl_context_properties*, cl_device_type, void (CL_CALLBACK*) (const char*, const void*, size_t, void*), void*, cl_int*); +extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateImage)(cl_context, cl_mem_flags, const cl_image_format*, const cl_image_desc*, void*, cl_int*); +extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateImage2D)(cl_context, cl_mem_flags, const cl_image_format*, size_t, size_t, size_t, void*, cl_int*); +extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateImage3D)(cl_context, cl_mem_flags, const cl_image_format*, size_t, size_t, size_t, size_t, size_t, void*, cl_int*); +extern CL_RUNTIME_EXPORT cl_kernel (CL_API_CALL*clCreateKernel)(cl_program, const char*, cl_int*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clCreateKernelsInProgram)(cl_program, cl_uint, cl_kernel*, cl_uint*); +extern CL_RUNTIME_EXPORT cl_program (CL_API_CALL*clCreateProgramWithBinary)(cl_context, cl_uint, const cl_device_id*, const size_t*, const unsigned char**, cl_int*, cl_int*); +extern CL_RUNTIME_EXPORT cl_program (CL_API_CALL*clCreateProgramWithBuiltInKernels)(cl_context, cl_uint, const cl_device_id*, const char*, cl_int*); +extern CL_RUNTIME_EXPORT cl_program (CL_API_CALL*clCreateProgramWithSource)(cl_context, cl_uint, const char**, const size_t*, cl_int*); +extern CL_RUNTIME_EXPORT cl_sampler (CL_API_CALL*clCreateSampler)(cl_context, cl_bool, cl_addressing_mode, cl_filter_mode, cl_int*); +extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateSubBuffer)(cl_mem, cl_mem_flags, cl_buffer_create_type, const void*, cl_int*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clCreateSubDevices)(cl_device_id, const cl_device_partition_property*, cl_uint, cl_device_id*, cl_uint*); +extern CL_RUNTIME_EXPORT cl_event (CL_API_CALL*clCreateUserEvent)(cl_context, cl_int*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueBarrier)(cl_command_queue); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueBarrierWithWaitList)(cl_command_queue, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueCopyBuffer)(cl_command_queue, cl_mem, cl_mem, size_t, size_t, size_t, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueCopyBufferRect)(cl_command_queue, cl_mem, cl_mem, const size_t*, const size_t*, const size_t*, size_t, size_t, size_t, size_t, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueCopyBufferToImage)(cl_command_queue, cl_mem, cl_mem, size_t, const size_t*, const size_t*, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueCopyImage)(cl_command_queue, cl_mem, cl_mem, const size_t*, const size_t*, const size_t*, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueCopyImageToBuffer)(cl_command_queue, cl_mem, cl_mem, const size_t*, const size_t*, size_t, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueFillBuffer)(cl_command_queue, cl_mem, const void*, size_t, size_t, size_t, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueFillImage)(cl_command_queue, cl_mem, const void*, const size_t*, const size_t*, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT void* (CL_API_CALL*clEnqueueMapBuffer)(cl_command_queue, cl_mem, cl_bool, cl_map_flags, size_t, size_t, cl_uint, const cl_event*, cl_event*, cl_int*); +extern CL_RUNTIME_EXPORT void* (CL_API_CALL*clEnqueueMapImage)(cl_command_queue, cl_mem, cl_bool, cl_map_flags, const size_t*, const size_t*, size_t*, size_t*, cl_uint, const cl_event*, cl_event*, cl_int*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueMarker)(cl_command_queue, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueMarkerWithWaitList)(cl_command_queue, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueMigrateMemObjects)(cl_command_queue, cl_uint, const cl_mem*, cl_mem_migration_flags, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueNDRangeKernel)(cl_command_queue, cl_kernel, cl_uint, const size_t*, const size_t*, const size_t*, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueNativeKernel)(cl_command_queue, void (CL_CALLBACK*) (void*), void*, size_t, cl_uint, const cl_mem*, const void**, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueReadBuffer)(cl_command_queue, cl_mem, cl_bool, size_t, size_t, void*, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueReadBufferRect)(cl_command_queue, cl_mem, cl_bool, const size_t*, const size_t*, const size_t*, size_t, size_t, size_t, size_t, void*, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueReadImage)(cl_command_queue, cl_mem, cl_bool, const size_t*, const size_t*, size_t, size_t, void*, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueTask)(cl_command_queue, cl_kernel, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueUnmapMemObject)(cl_command_queue, cl_mem, void*, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueWaitForEvents)(cl_command_queue, cl_uint, const cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueWriteBuffer)(cl_command_queue, cl_mem, cl_bool, size_t, size_t, const void*, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueWriteBufferRect)(cl_command_queue, cl_mem, cl_bool, const size_t*, const size_t*, const size_t*, size_t, size_t, size_t, size_t, const void*, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueWriteImage)(cl_command_queue, cl_mem, cl_bool, const size_t*, const size_t*, size_t, size_t, const void*, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clFinish)(cl_command_queue); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clFlush)(cl_command_queue); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetCommandQueueInfo)(cl_command_queue, cl_command_queue_info, size_t, void*, size_t*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetContextInfo)(cl_context, cl_context_info, size_t, void*, size_t*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetDeviceIDs)(cl_platform_id, cl_device_type, cl_uint, cl_device_id*, cl_uint*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetDeviceInfo)(cl_device_id, cl_device_info, size_t, void*, size_t*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetEventInfo)(cl_event, cl_event_info, size_t, void*, size_t*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetEventProfilingInfo)(cl_event, cl_profiling_info, size_t, void*, size_t*); +extern CL_RUNTIME_EXPORT void* (CL_API_CALL*clGetExtensionFunctionAddress)(const char*); +extern CL_RUNTIME_EXPORT void* (CL_API_CALL*clGetExtensionFunctionAddressForPlatform)(cl_platform_id, const char*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetImageInfo)(cl_mem, cl_image_info, size_t, void*, size_t*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetKernelArgInfo)(cl_kernel, cl_uint, cl_kernel_arg_info, size_t, void*, size_t*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetKernelInfo)(cl_kernel, cl_kernel_info, size_t, void*, size_t*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetKernelWorkGroupInfo)(cl_kernel, cl_device_id, cl_kernel_work_group_info, size_t, void*, size_t*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetMemObjectInfo)(cl_mem, cl_mem_info, size_t, void*, size_t*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetPlatformIDs)(cl_uint, cl_platform_id*, cl_uint*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetPlatformInfo)(cl_platform_id, cl_platform_info, size_t, void*, size_t*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetProgramBuildInfo)(cl_program, cl_device_id, cl_program_build_info, size_t, void*, size_t*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetProgramInfo)(cl_program, cl_program_info, size_t, void*, size_t*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetSamplerInfo)(cl_sampler, cl_sampler_info, size_t, void*, size_t*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetSupportedImageFormats)(cl_context, cl_mem_flags, cl_mem_object_type, cl_uint, cl_image_format*, cl_uint*); +extern CL_RUNTIME_EXPORT cl_program (CL_API_CALL*clLinkProgram)(cl_context, cl_uint, const cl_device_id*, const char*, cl_uint, const cl_program*, void (CL_CALLBACK*) (cl_program, void*), void*, cl_int*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseCommandQueue)(cl_command_queue); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseContext)(cl_context); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseDevice)(cl_device_id); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseEvent)(cl_event); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseKernel)(cl_kernel); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseMemObject)(cl_mem); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseProgram)(cl_program); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseSampler)(cl_sampler); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainCommandQueue)(cl_command_queue); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainContext)(cl_context); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainDevice)(cl_device_id); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainEvent)(cl_event); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainKernel)(cl_kernel); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainMemObject)(cl_mem); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainProgram)(cl_program); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainSampler)(cl_sampler); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clSetEventCallback)(cl_event, cl_int, void (CL_CALLBACK*) (cl_event, cl_int, void*), void*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clSetKernelArg)(cl_kernel, cl_uint, size_t, const void*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clSetMemObjectDestructorCallback)(cl_mem, void (CL_CALLBACK*) (cl_mem, void*), void*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clSetUserEventStatus)(cl_event, cl_int); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clUnloadCompiler)(); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clUnloadPlatformCompiler)(cl_platform_id); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clWaitForEvents)(cl_uint, const cl_event*); diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/runtime/autogenerated/opencl_core_wrappers.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/runtime/autogenerated/opencl_core_wrappers.hpp new file mode 100644 index 0000000000000000000000000000000000000000..216b22b8a85c825db0645536bbfcfe392f6b9e3f --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/runtime/autogenerated/opencl_core_wrappers.hpp @@ -0,0 +1,272 @@ +// +// AUTOGENERATED, DO NOT EDIT +// +#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_WRAPPERS_HPP +#error "Invalid usage" +#endif + +// generated by parser_cl.py +#undef clBuildProgram +#define clBuildProgram clBuildProgram_fn +inline cl_int clBuildProgram(cl_program p0, cl_uint p1, const cl_device_id* p2, const char* p3, void (CL_CALLBACK*p4) (cl_program, void*), void* p5) { return clBuildProgram_pfn(p0, p1, p2, p3, p4, p5); } +#undef clCompileProgram +#define clCompileProgram clCompileProgram_fn +inline cl_int clCompileProgram(cl_program p0, cl_uint p1, const cl_device_id* p2, const char* p3, cl_uint p4, const cl_program* p5, const char** p6, void (CL_CALLBACK*p7) (cl_program, void*), void* p8) { return clCompileProgram_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); } +#undef clCreateBuffer +#define clCreateBuffer clCreateBuffer_fn +inline cl_mem clCreateBuffer(cl_context p0, cl_mem_flags p1, size_t p2, void* p3, cl_int* p4) { return clCreateBuffer_pfn(p0, p1, p2, p3, p4); } +#undef clCreateCommandQueue +#define clCreateCommandQueue clCreateCommandQueue_fn +inline cl_command_queue clCreateCommandQueue(cl_context p0, cl_device_id p1, cl_command_queue_properties p2, cl_int* p3) { return clCreateCommandQueue_pfn(p0, p1, p2, p3); } +#undef clCreateContext +#define clCreateContext clCreateContext_fn +inline cl_context clCreateContext(const cl_context_properties* p0, cl_uint p1, const cl_device_id* p2, void (CL_CALLBACK*p3) (const char*, const void*, size_t, void*), void* p4, cl_int* p5) { return clCreateContext_pfn(p0, p1, p2, p3, p4, p5); } +#undef clCreateContextFromType +#define clCreateContextFromType clCreateContextFromType_fn +inline cl_context clCreateContextFromType(const cl_context_properties* p0, cl_device_type p1, void (CL_CALLBACK*p2) (const char*, const void*, size_t, void*), void* p3, cl_int* p4) { return clCreateContextFromType_pfn(p0, p1, p2, p3, p4); } +#undef clCreateImage +#define clCreateImage clCreateImage_fn +inline cl_mem clCreateImage(cl_context p0, cl_mem_flags p1, const cl_image_format* p2, const cl_image_desc* p3, void* p4, cl_int* p5) { return clCreateImage_pfn(p0, p1, p2, p3, p4, p5); } +#undef clCreateImage2D +#define clCreateImage2D clCreateImage2D_fn +inline cl_mem clCreateImage2D(cl_context p0, cl_mem_flags p1, const cl_image_format* p2, size_t p3, size_t p4, size_t p5, void* p6, cl_int* p7) { return clCreateImage2D_pfn(p0, p1, p2, p3, p4, p5, p6, p7); } +#undef clCreateImage3D +#define clCreateImage3D clCreateImage3D_fn +inline cl_mem clCreateImage3D(cl_context p0, cl_mem_flags p1, const cl_image_format* p2, size_t p3, size_t p4, size_t p5, size_t p6, size_t p7, void* p8, cl_int* p9) { return clCreateImage3D_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9); } +#undef clCreateKernel +#define clCreateKernel clCreateKernel_fn +inline cl_kernel clCreateKernel(cl_program p0, const char* p1, cl_int* p2) { return clCreateKernel_pfn(p0, p1, p2); } +#undef clCreateKernelsInProgram +#define clCreateKernelsInProgram clCreateKernelsInProgram_fn +inline cl_int clCreateKernelsInProgram(cl_program p0, cl_uint p1, cl_kernel* p2, cl_uint* p3) { return clCreateKernelsInProgram_pfn(p0, p1, p2, p3); } +#undef clCreateProgramWithBinary +#define clCreateProgramWithBinary clCreateProgramWithBinary_fn +inline cl_program clCreateProgramWithBinary(cl_context p0, cl_uint p1, const cl_device_id* p2, const size_t* p3, const unsigned char** p4, cl_int* p5, cl_int* p6) { return clCreateProgramWithBinary_pfn(p0, p1, p2, p3, p4, p5, p6); } +#undef clCreateProgramWithBuiltInKernels +#define clCreateProgramWithBuiltInKernels clCreateProgramWithBuiltInKernels_fn +inline cl_program clCreateProgramWithBuiltInKernels(cl_context p0, cl_uint p1, const cl_device_id* p2, const char* p3, cl_int* p4) { return clCreateProgramWithBuiltInKernels_pfn(p0, p1, p2, p3, p4); } +#undef clCreateProgramWithSource +#define clCreateProgramWithSource clCreateProgramWithSource_fn +inline cl_program clCreateProgramWithSource(cl_context p0, cl_uint p1, const char** p2, const size_t* p3, cl_int* p4) { return clCreateProgramWithSource_pfn(p0, p1, p2, p3, p4); } +#undef clCreateSampler +#define clCreateSampler clCreateSampler_fn +inline cl_sampler clCreateSampler(cl_context p0, cl_bool p1, cl_addressing_mode p2, cl_filter_mode p3, cl_int* p4) { return clCreateSampler_pfn(p0, p1, p2, p3, p4); } +#undef clCreateSubBuffer +#define clCreateSubBuffer clCreateSubBuffer_fn +inline cl_mem clCreateSubBuffer(cl_mem p0, cl_mem_flags p1, cl_buffer_create_type p2, const void* p3, cl_int* p4) { return clCreateSubBuffer_pfn(p0, p1, p2, p3, p4); } +#undef clCreateSubDevices +#define clCreateSubDevices clCreateSubDevices_fn +inline cl_int clCreateSubDevices(cl_device_id p0, const cl_device_partition_property* p1, cl_uint p2, cl_device_id* p3, cl_uint* p4) { return clCreateSubDevices_pfn(p0, p1, p2, p3, p4); } +#undef clCreateUserEvent +#define clCreateUserEvent clCreateUserEvent_fn +inline cl_event clCreateUserEvent(cl_context p0, cl_int* p1) { return clCreateUserEvent_pfn(p0, p1); } +#undef clEnqueueBarrier +#define clEnqueueBarrier clEnqueueBarrier_fn +inline cl_int clEnqueueBarrier(cl_command_queue p0) { return clEnqueueBarrier_pfn(p0); } +#undef clEnqueueBarrierWithWaitList +#define clEnqueueBarrierWithWaitList clEnqueueBarrierWithWaitList_fn +inline cl_int clEnqueueBarrierWithWaitList(cl_command_queue p0, cl_uint p1, const cl_event* p2, cl_event* p3) { return clEnqueueBarrierWithWaitList_pfn(p0, p1, p2, p3); } +#undef clEnqueueCopyBuffer +#define clEnqueueCopyBuffer clEnqueueCopyBuffer_fn +inline cl_int clEnqueueCopyBuffer(cl_command_queue p0, cl_mem p1, cl_mem p2, size_t p3, size_t p4, size_t p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueCopyBuffer_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); } +#undef clEnqueueCopyBufferRect +#define clEnqueueCopyBufferRect clEnqueueCopyBufferRect_fn +inline cl_int clEnqueueCopyBufferRect(cl_command_queue p0, cl_mem p1, cl_mem p2, const size_t* p3, const size_t* p4, const size_t* p5, size_t p6, size_t p7, size_t p8, size_t p9, cl_uint p10, const cl_event* p11, cl_event* p12) { return clEnqueueCopyBufferRect_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12); } +#undef clEnqueueCopyBufferToImage +#define clEnqueueCopyBufferToImage clEnqueueCopyBufferToImage_fn +inline cl_int clEnqueueCopyBufferToImage(cl_command_queue p0, cl_mem p1, cl_mem p2, size_t p3, const size_t* p4, const size_t* p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueCopyBufferToImage_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); } +#undef clEnqueueCopyImage +#define clEnqueueCopyImage clEnqueueCopyImage_fn +inline cl_int clEnqueueCopyImage(cl_command_queue p0, cl_mem p1, cl_mem p2, const size_t* p3, const size_t* p4, const size_t* p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueCopyImage_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); } +#undef clEnqueueCopyImageToBuffer +#define clEnqueueCopyImageToBuffer clEnqueueCopyImageToBuffer_fn +inline cl_int clEnqueueCopyImageToBuffer(cl_command_queue p0, cl_mem p1, cl_mem p2, const size_t* p3, const size_t* p4, size_t p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueCopyImageToBuffer_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); } +#undef clEnqueueFillBuffer +#define clEnqueueFillBuffer clEnqueueFillBuffer_fn +inline cl_int clEnqueueFillBuffer(cl_command_queue p0, cl_mem p1, const void* p2, size_t p3, size_t p4, size_t p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueFillBuffer_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); } +#undef clEnqueueFillImage +#define clEnqueueFillImage clEnqueueFillImage_fn +inline cl_int clEnqueueFillImage(cl_command_queue p0, cl_mem p1, const void* p2, const size_t* p3, const size_t* p4, cl_uint p5, const cl_event* p6, cl_event* p7) { return clEnqueueFillImage_pfn(p0, p1, p2, p3, p4, p5, p6, p7); } +#undef clEnqueueMapBuffer +#define clEnqueueMapBuffer clEnqueueMapBuffer_fn +inline void* clEnqueueMapBuffer(cl_command_queue p0, cl_mem p1, cl_bool p2, cl_map_flags p3, size_t p4, size_t p5, cl_uint p6, const cl_event* p7, cl_event* p8, cl_int* p9) { return clEnqueueMapBuffer_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9); } +#undef clEnqueueMapImage +#define clEnqueueMapImage clEnqueueMapImage_fn +inline void* clEnqueueMapImage(cl_command_queue p0, cl_mem p1, cl_bool p2, cl_map_flags p3, const size_t* p4, const size_t* p5, size_t* p6, size_t* p7, cl_uint p8, const cl_event* p9, cl_event* p10, cl_int* p11) { return clEnqueueMapImage_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11); } +#undef clEnqueueMarker +#define clEnqueueMarker clEnqueueMarker_fn +inline cl_int clEnqueueMarker(cl_command_queue p0, cl_event* p1) { return clEnqueueMarker_pfn(p0, p1); } +#undef clEnqueueMarkerWithWaitList +#define clEnqueueMarkerWithWaitList clEnqueueMarkerWithWaitList_fn +inline cl_int clEnqueueMarkerWithWaitList(cl_command_queue p0, cl_uint p1, const cl_event* p2, cl_event* p3) { return clEnqueueMarkerWithWaitList_pfn(p0, p1, p2, p3); } +#undef clEnqueueMigrateMemObjects +#define clEnqueueMigrateMemObjects clEnqueueMigrateMemObjects_fn +inline cl_int clEnqueueMigrateMemObjects(cl_command_queue p0, cl_uint p1, const cl_mem* p2, cl_mem_migration_flags p3, cl_uint p4, const cl_event* p5, cl_event* p6) { return clEnqueueMigrateMemObjects_pfn(p0, p1, p2, p3, p4, p5, p6); } +#undef clEnqueueNDRangeKernel +#define clEnqueueNDRangeKernel clEnqueueNDRangeKernel_fn +inline cl_int clEnqueueNDRangeKernel(cl_command_queue p0, cl_kernel p1, cl_uint p2, const size_t* p3, const size_t* p4, const size_t* p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueNDRangeKernel_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); } +#undef clEnqueueNativeKernel +#define clEnqueueNativeKernel clEnqueueNativeKernel_fn +inline cl_int clEnqueueNativeKernel(cl_command_queue p0, void (CL_CALLBACK*p1) (void*), void* p2, size_t p3, cl_uint p4, const cl_mem* p5, const void** p6, cl_uint p7, const cl_event* p8, cl_event* p9) { return clEnqueueNativeKernel_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9); } +#undef clEnqueueReadBuffer +#define clEnqueueReadBuffer clEnqueueReadBuffer_fn +inline cl_int clEnqueueReadBuffer(cl_command_queue p0, cl_mem p1, cl_bool p2, size_t p3, size_t p4, void* p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueReadBuffer_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); } +#undef clEnqueueReadBufferRect +#define clEnqueueReadBufferRect clEnqueueReadBufferRect_fn +inline cl_int clEnqueueReadBufferRect(cl_command_queue p0, cl_mem p1, cl_bool p2, const size_t* p3, const size_t* p4, const size_t* p5, size_t p6, size_t p7, size_t p8, size_t p9, void* p10, cl_uint p11, const cl_event* p12, cl_event* p13) { return clEnqueueReadBufferRect_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, p13); } +#undef clEnqueueReadImage +#define clEnqueueReadImage clEnqueueReadImage_fn +inline cl_int clEnqueueReadImage(cl_command_queue p0, cl_mem p1, cl_bool p2, const size_t* p3, const size_t* p4, size_t p5, size_t p6, void* p7, cl_uint p8, const cl_event* p9, cl_event* p10) { return clEnqueueReadImage_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, p10); } +#undef clEnqueueTask +#define clEnqueueTask clEnqueueTask_fn +inline cl_int clEnqueueTask(cl_command_queue p0, cl_kernel p1, cl_uint p2, const cl_event* p3, cl_event* p4) { return clEnqueueTask_pfn(p0, p1, p2, p3, p4); } +#undef clEnqueueUnmapMemObject +#define clEnqueueUnmapMemObject clEnqueueUnmapMemObject_fn +inline cl_int clEnqueueUnmapMemObject(cl_command_queue p0, cl_mem p1, void* p2, cl_uint p3, const cl_event* p4, cl_event* p5) { return clEnqueueUnmapMemObject_pfn(p0, p1, p2, p3, p4, p5); } +#undef clEnqueueWaitForEvents +#define clEnqueueWaitForEvents clEnqueueWaitForEvents_fn +inline cl_int clEnqueueWaitForEvents(cl_command_queue p0, cl_uint p1, const cl_event* p2) { return clEnqueueWaitForEvents_pfn(p0, p1, p2); } +#undef clEnqueueWriteBuffer +#define clEnqueueWriteBuffer clEnqueueWriteBuffer_fn +inline cl_int clEnqueueWriteBuffer(cl_command_queue p0, cl_mem p1, cl_bool p2, size_t p3, size_t p4, const void* p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueWriteBuffer_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); } +#undef clEnqueueWriteBufferRect +#define clEnqueueWriteBufferRect clEnqueueWriteBufferRect_fn +inline cl_int clEnqueueWriteBufferRect(cl_command_queue p0, cl_mem p1, cl_bool p2, const size_t* p3, const size_t* p4, const size_t* p5, size_t p6, size_t p7, size_t p8, size_t p9, const void* p10, cl_uint p11, const cl_event* p12, cl_event* p13) { return clEnqueueWriteBufferRect_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, p13); } +#undef clEnqueueWriteImage +#define clEnqueueWriteImage clEnqueueWriteImage_fn +inline cl_int clEnqueueWriteImage(cl_command_queue p0, cl_mem p1, cl_bool p2, const size_t* p3, const size_t* p4, size_t p5, size_t p6, const void* p7, cl_uint p8, const cl_event* p9, cl_event* p10) { return clEnqueueWriteImage_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, p10); } +#undef clFinish +#define clFinish clFinish_fn +inline cl_int clFinish(cl_command_queue p0) { return clFinish_pfn(p0); } +#undef clFlush +#define clFlush clFlush_fn +inline cl_int clFlush(cl_command_queue p0) { return clFlush_pfn(p0); } +#undef clGetCommandQueueInfo +#define clGetCommandQueueInfo clGetCommandQueueInfo_fn +inline cl_int clGetCommandQueueInfo(cl_command_queue p0, cl_command_queue_info p1, size_t p2, void* p3, size_t* p4) { return clGetCommandQueueInfo_pfn(p0, p1, p2, p3, p4); } +#undef clGetContextInfo +#define clGetContextInfo clGetContextInfo_fn +inline cl_int clGetContextInfo(cl_context p0, cl_context_info p1, size_t p2, void* p3, size_t* p4) { return clGetContextInfo_pfn(p0, p1, p2, p3, p4); } +#undef clGetDeviceIDs +#define clGetDeviceIDs clGetDeviceIDs_fn +inline cl_int clGetDeviceIDs(cl_platform_id p0, cl_device_type p1, cl_uint p2, cl_device_id* p3, cl_uint* p4) { return clGetDeviceIDs_pfn(p0, p1, p2, p3, p4); } +#undef clGetDeviceInfo +#define clGetDeviceInfo clGetDeviceInfo_fn +inline cl_int clGetDeviceInfo(cl_device_id p0, cl_device_info p1, size_t p2, void* p3, size_t* p4) { return clGetDeviceInfo_pfn(p0, p1, p2, p3, p4); } +#undef clGetEventInfo +#define clGetEventInfo clGetEventInfo_fn +inline cl_int clGetEventInfo(cl_event p0, cl_event_info p1, size_t p2, void* p3, size_t* p4) { return clGetEventInfo_pfn(p0, p1, p2, p3, p4); } +#undef clGetEventProfilingInfo +#define clGetEventProfilingInfo clGetEventProfilingInfo_fn +inline cl_int clGetEventProfilingInfo(cl_event p0, cl_profiling_info p1, size_t p2, void* p3, size_t* p4) { return clGetEventProfilingInfo_pfn(p0, p1, p2, p3, p4); } +#undef clGetExtensionFunctionAddress +#define clGetExtensionFunctionAddress clGetExtensionFunctionAddress_fn +inline void* clGetExtensionFunctionAddress(const char* p0) { return clGetExtensionFunctionAddress_pfn(p0); } +#undef clGetExtensionFunctionAddressForPlatform +#define clGetExtensionFunctionAddressForPlatform clGetExtensionFunctionAddressForPlatform_fn +inline void* clGetExtensionFunctionAddressForPlatform(cl_platform_id p0, const char* p1) { return clGetExtensionFunctionAddressForPlatform_pfn(p0, p1); } +#undef clGetImageInfo +#define clGetImageInfo clGetImageInfo_fn +inline cl_int clGetImageInfo(cl_mem p0, cl_image_info p1, size_t p2, void* p3, size_t* p4) { return clGetImageInfo_pfn(p0, p1, p2, p3, p4); } +#undef clGetKernelArgInfo +#define clGetKernelArgInfo clGetKernelArgInfo_fn +inline cl_int clGetKernelArgInfo(cl_kernel p0, cl_uint p1, cl_kernel_arg_info p2, size_t p3, void* p4, size_t* p5) { return clGetKernelArgInfo_pfn(p0, p1, p2, p3, p4, p5); } +#undef clGetKernelInfo +#define clGetKernelInfo clGetKernelInfo_fn +inline cl_int clGetKernelInfo(cl_kernel p0, cl_kernel_info p1, size_t p2, void* p3, size_t* p4) { return clGetKernelInfo_pfn(p0, p1, p2, p3, p4); } +#undef clGetKernelWorkGroupInfo +#define clGetKernelWorkGroupInfo clGetKernelWorkGroupInfo_fn +inline cl_int clGetKernelWorkGroupInfo(cl_kernel p0, cl_device_id p1, cl_kernel_work_group_info p2, size_t p3, void* p4, size_t* p5) { return clGetKernelWorkGroupInfo_pfn(p0, p1, p2, p3, p4, p5); } +#undef clGetMemObjectInfo +#define clGetMemObjectInfo clGetMemObjectInfo_fn +inline cl_int clGetMemObjectInfo(cl_mem p0, cl_mem_info p1, size_t p2, void* p3, size_t* p4) { return clGetMemObjectInfo_pfn(p0, p1, p2, p3, p4); } +#undef clGetPlatformIDs +#define clGetPlatformIDs clGetPlatformIDs_fn +inline cl_int clGetPlatformIDs(cl_uint p0, cl_platform_id* p1, cl_uint* p2) { return clGetPlatformIDs_pfn(p0, p1, p2); } +#undef clGetPlatformInfo +#define clGetPlatformInfo clGetPlatformInfo_fn +inline cl_int clGetPlatformInfo(cl_platform_id p0, cl_platform_info p1, size_t p2, void* p3, size_t* p4) { return clGetPlatformInfo_pfn(p0, p1, p2, p3, p4); } +#undef clGetProgramBuildInfo +#define clGetProgramBuildInfo clGetProgramBuildInfo_fn +inline cl_int clGetProgramBuildInfo(cl_program p0, cl_device_id p1, cl_program_build_info p2, size_t p3, void* p4, size_t* p5) { return clGetProgramBuildInfo_pfn(p0, p1, p2, p3, p4, p5); } +#undef clGetProgramInfo +#define clGetProgramInfo clGetProgramInfo_fn +inline cl_int clGetProgramInfo(cl_program p0, cl_program_info p1, size_t p2, void* p3, size_t* p4) { return clGetProgramInfo_pfn(p0, p1, p2, p3, p4); } +#undef clGetSamplerInfo +#define clGetSamplerInfo clGetSamplerInfo_fn +inline cl_int clGetSamplerInfo(cl_sampler p0, cl_sampler_info p1, size_t p2, void* p3, size_t* p4) { return clGetSamplerInfo_pfn(p0, p1, p2, p3, p4); } +#undef clGetSupportedImageFormats +#define clGetSupportedImageFormats clGetSupportedImageFormats_fn +inline cl_int clGetSupportedImageFormats(cl_context p0, cl_mem_flags p1, cl_mem_object_type p2, cl_uint p3, cl_image_format* p4, cl_uint* p5) { return clGetSupportedImageFormats_pfn(p0, p1, p2, p3, p4, p5); } +#undef clLinkProgram +#define clLinkProgram clLinkProgram_fn +inline cl_program clLinkProgram(cl_context p0, cl_uint p1, const cl_device_id* p2, const char* p3, cl_uint p4, const cl_program* p5, void (CL_CALLBACK*p6) (cl_program, void*), void* p7, cl_int* p8) { return clLinkProgram_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); } +#undef clReleaseCommandQueue +#define clReleaseCommandQueue clReleaseCommandQueue_fn +inline cl_int clReleaseCommandQueue(cl_command_queue p0) { return clReleaseCommandQueue_pfn(p0); } +#undef clReleaseContext +#define clReleaseContext clReleaseContext_fn +inline cl_int clReleaseContext(cl_context p0) { return clReleaseContext_pfn(p0); } +#undef clReleaseDevice +#define clReleaseDevice clReleaseDevice_fn +inline cl_int clReleaseDevice(cl_device_id p0) { return clReleaseDevice_pfn(p0); } +#undef clReleaseEvent +#define clReleaseEvent clReleaseEvent_fn +inline cl_int clReleaseEvent(cl_event p0) { return clReleaseEvent_pfn(p0); } +#undef clReleaseKernel +#define clReleaseKernel clReleaseKernel_fn +inline cl_int clReleaseKernel(cl_kernel p0) { return clReleaseKernel_pfn(p0); } +#undef clReleaseMemObject +#define clReleaseMemObject clReleaseMemObject_fn +inline cl_int clReleaseMemObject(cl_mem p0) { return clReleaseMemObject_pfn(p0); } +#undef clReleaseProgram +#define clReleaseProgram clReleaseProgram_fn +inline cl_int clReleaseProgram(cl_program p0) { return clReleaseProgram_pfn(p0); } +#undef clReleaseSampler +#define clReleaseSampler clReleaseSampler_fn +inline cl_int clReleaseSampler(cl_sampler p0) { return clReleaseSampler_pfn(p0); } +#undef clRetainCommandQueue +#define clRetainCommandQueue clRetainCommandQueue_fn +inline cl_int clRetainCommandQueue(cl_command_queue p0) { return clRetainCommandQueue_pfn(p0); } +#undef clRetainContext +#define clRetainContext clRetainContext_fn +inline cl_int clRetainContext(cl_context p0) { return clRetainContext_pfn(p0); } +#undef clRetainDevice +#define clRetainDevice clRetainDevice_fn +inline cl_int clRetainDevice(cl_device_id p0) { return clRetainDevice_pfn(p0); } +#undef clRetainEvent +#define clRetainEvent clRetainEvent_fn +inline cl_int clRetainEvent(cl_event p0) { return clRetainEvent_pfn(p0); } +#undef clRetainKernel +#define clRetainKernel clRetainKernel_fn +inline cl_int clRetainKernel(cl_kernel p0) { return clRetainKernel_pfn(p0); } +#undef clRetainMemObject +#define clRetainMemObject clRetainMemObject_fn +inline cl_int clRetainMemObject(cl_mem p0) { return clRetainMemObject_pfn(p0); } +#undef clRetainProgram +#define clRetainProgram clRetainProgram_fn +inline cl_int clRetainProgram(cl_program p0) { return clRetainProgram_pfn(p0); } +#undef clRetainSampler +#define clRetainSampler clRetainSampler_fn +inline cl_int clRetainSampler(cl_sampler p0) { return clRetainSampler_pfn(p0); } +#undef clSetEventCallback +#define clSetEventCallback clSetEventCallback_fn +inline cl_int clSetEventCallback(cl_event p0, cl_int p1, void (CL_CALLBACK*p2) (cl_event, cl_int, void*), void* p3) { return clSetEventCallback_pfn(p0, p1, p2, p3); } +#undef clSetKernelArg +#define clSetKernelArg clSetKernelArg_fn +inline cl_int clSetKernelArg(cl_kernel p0, cl_uint p1, size_t p2, const void* p3) { return clSetKernelArg_pfn(p0, p1, p2, p3); } +#undef clSetMemObjectDestructorCallback +#define clSetMemObjectDestructorCallback clSetMemObjectDestructorCallback_fn +inline cl_int clSetMemObjectDestructorCallback(cl_mem p0, void (CL_CALLBACK*p1) (cl_mem, void*), void* p2) { return clSetMemObjectDestructorCallback_pfn(p0, p1, p2); } +#undef clSetUserEventStatus +#define clSetUserEventStatus clSetUserEventStatus_fn +inline cl_int clSetUserEventStatus(cl_event p0, cl_int p1) { return clSetUserEventStatus_pfn(p0, p1); } +#undef clUnloadCompiler +#define clUnloadCompiler clUnloadCompiler_fn +inline cl_int clUnloadCompiler() { return clUnloadCompiler_pfn(); } +#undef clUnloadPlatformCompiler +#define clUnloadPlatformCompiler clUnloadPlatformCompiler_fn +inline cl_int clUnloadPlatformCompiler(cl_platform_id p0) { return clUnloadPlatformCompiler_pfn(p0); } +#undef clWaitForEvents +#define clWaitForEvents clWaitForEvents_fn +inline cl_int clWaitForEvents(cl_uint p0, const cl_event* p1) { return clWaitForEvents_pfn(p0, p1); } diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/runtime/autogenerated/opencl_gl.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/runtime/autogenerated/opencl_gl.hpp new file mode 100644 index 0000000000000000000000000000000000000000..0b12aed6c6ce9def83b9fecc15f8be14b4790363 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/runtime/autogenerated/opencl_gl.hpp @@ -0,0 +1,62 @@ +// +// AUTOGENERATED, DO NOT EDIT +// +#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_HPP +#error "Invalid usage" +#endif + +// generated by parser_cl.py +#define clCreateFromGLBuffer clCreateFromGLBuffer_ +#define clCreateFromGLRenderbuffer clCreateFromGLRenderbuffer_ +#define clCreateFromGLTexture clCreateFromGLTexture_ +#define clCreateFromGLTexture2D clCreateFromGLTexture2D_ +#define clCreateFromGLTexture3D clCreateFromGLTexture3D_ +#define clEnqueueAcquireGLObjects clEnqueueAcquireGLObjects_ +#define clEnqueueReleaseGLObjects clEnqueueReleaseGLObjects_ +#define clGetGLContextInfoKHR clGetGLContextInfoKHR_ +#define clGetGLObjectInfo clGetGLObjectInfo_ +#define clGetGLTextureInfo clGetGLTextureInfo_ + +#if defined __APPLE__ +#include +#else +#include +#endif + +// generated by parser_cl.py +#undef clCreateFromGLBuffer +#define clCreateFromGLBuffer clCreateFromGLBuffer_pfn +#undef clCreateFromGLRenderbuffer +#define clCreateFromGLRenderbuffer clCreateFromGLRenderbuffer_pfn +#undef clCreateFromGLTexture +#define clCreateFromGLTexture clCreateFromGLTexture_pfn +#undef clCreateFromGLTexture2D +#define clCreateFromGLTexture2D clCreateFromGLTexture2D_pfn +#undef clCreateFromGLTexture3D +#define clCreateFromGLTexture3D clCreateFromGLTexture3D_pfn +#undef clEnqueueAcquireGLObjects +#define clEnqueueAcquireGLObjects clEnqueueAcquireGLObjects_pfn +#undef clEnqueueReleaseGLObjects +#define clEnqueueReleaseGLObjects clEnqueueReleaseGLObjects_pfn +#undef clGetGLContextInfoKHR +#define clGetGLContextInfoKHR clGetGLContextInfoKHR_pfn +#undef clGetGLObjectInfo +#define clGetGLObjectInfo clGetGLObjectInfo_pfn +#undef clGetGLTextureInfo +#define clGetGLTextureInfo clGetGLTextureInfo_pfn + +#ifdef cl_khr_gl_sharing + +// generated by parser_cl.py +extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateFromGLBuffer)(cl_context, cl_mem_flags, cl_GLuint, int*); +extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateFromGLRenderbuffer)(cl_context, cl_mem_flags, cl_GLuint, cl_int*); +extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateFromGLTexture)(cl_context, cl_mem_flags, cl_GLenum, cl_GLint, cl_GLuint, cl_int*); +extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateFromGLTexture2D)(cl_context, cl_mem_flags, cl_GLenum, cl_GLint, cl_GLuint, cl_int*); +extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateFromGLTexture3D)(cl_context, cl_mem_flags, cl_GLenum, cl_GLint, cl_GLuint, cl_int*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueAcquireGLObjects)(cl_command_queue, cl_uint, const cl_mem*, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueReleaseGLObjects)(cl_command_queue, cl_uint, const cl_mem*, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetGLContextInfoKHR)(const cl_context_properties*, cl_gl_context_info, size_t, void*, size_t*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetGLObjectInfo)(cl_mem, cl_gl_object_type*, cl_GLuint*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetGLTextureInfo)(cl_mem, cl_gl_texture_info, size_t, void*, size_t*); + +#endif // cl_khr_gl_sharing diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/runtime/autogenerated/opencl_gl_wrappers.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/runtime/autogenerated/opencl_gl_wrappers.hpp new file mode 100644 index 0000000000000000000000000000000000000000..12f342b2e4be18b0bfb3a9fb661d47fdf8bac44c --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/runtime/autogenerated/opencl_gl_wrappers.hpp @@ -0,0 +1,42 @@ +// +// AUTOGENERATED, DO NOT EDIT +// +#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_WRAPPERS_HPP +#error "Invalid usage" +#endif + +#ifdef cl_khr_gl_sharing + +// generated by parser_cl.py +#undef clCreateFromGLBuffer +#define clCreateFromGLBuffer clCreateFromGLBuffer_fn +inline cl_mem clCreateFromGLBuffer(cl_context p0, cl_mem_flags p1, cl_GLuint p2, int* p3) { return clCreateFromGLBuffer_pfn(p0, p1, p2, p3); } +#undef clCreateFromGLRenderbuffer +#define clCreateFromGLRenderbuffer clCreateFromGLRenderbuffer_fn +inline cl_mem clCreateFromGLRenderbuffer(cl_context p0, cl_mem_flags p1, cl_GLuint p2, cl_int* p3) { return clCreateFromGLRenderbuffer_pfn(p0, p1, p2, p3); } +#undef clCreateFromGLTexture +#define clCreateFromGLTexture clCreateFromGLTexture_fn +inline cl_mem clCreateFromGLTexture(cl_context p0, cl_mem_flags p1, cl_GLenum p2, cl_GLint p3, cl_GLuint p4, cl_int* p5) { return clCreateFromGLTexture_pfn(p0, p1, p2, p3, p4, p5); } +#undef clCreateFromGLTexture2D +#define clCreateFromGLTexture2D clCreateFromGLTexture2D_fn +inline cl_mem clCreateFromGLTexture2D(cl_context p0, cl_mem_flags p1, cl_GLenum p2, cl_GLint p3, cl_GLuint p4, cl_int* p5) { return clCreateFromGLTexture2D_pfn(p0, p1, p2, p3, p4, p5); } +#undef clCreateFromGLTexture3D +#define clCreateFromGLTexture3D clCreateFromGLTexture3D_fn +inline cl_mem clCreateFromGLTexture3D(cl_context p0, cl_mem_flags p1, cl_GLenum p2, cl_GLint p3, cl_GLuint p4, cl_int* p5) { return clCreateFromGLTexture3D_pfn(p0, p1, p2, p3, p4, p5); } +#undef clEnqueueAcquireGLObjects +#define clEnqueueAcquireGLObjects clEnqueueAcquireGLObjects_fn +inline cl_int clEnqueueAcquireGLObjects(cl_command_queue p0, cl_uint p1, const cl_mem* p2, cl_uint p3, const cl_event* p4, cl_event* p5) { return clEnqueueAcquireGLObjects_pfn(p0, p1, p2, p3, p4, p5); } +#undef clEnqueueReleaseGLObjects +#define clEnqueueReleaseGLObjects clEnqueueReleaseGLObjects_fn +inline cl_int clEnqueueReleaseGLObjects(cl_command_queue p0, cl_uint p1, const cl_mem* p2, cl_uint p3, const cl_event* p4, cl_event* p5) { return clEnqueueReleaseGLObjects_pfn(p0, p1, p2, p3, p4, p5); } +#undef clGetGLContextInfoKHR +#define clGetGLContextInfoKHR clGetGLContextInfoKHR_fn +inline cl_int clGetGLContextInfoKHR(const cl_context_properties* p0, cl_gl_context_info p1, size_t p2, void* p3, size_t* p4) { return clGetGLContextInfoKHR_pfn(p0, p1, p2, p3, p4); } +#undef clGetGLObjectInfo +#define clGetGLObjectInfo clGetGLObjectInfo_fn +inline cl_int clGetGLObjectInfo(cl_mem p0, cl_gl_object_type* p1, cl_GLuint* p2) { return clGetGLObjectInfo_pfn(p0, p1, p2); } +#undef clGetGLTextureInfo +#define clGetGLTextureInfo clGetGLTextureInfo_fn +inline cl_int clGetGLTextureInfo(cl_mem p0, cl_gl_texture_info p1, size_t p2, void* p3, size_t* p4) { return clGetGLTextureInfo_pfn(p0, p1, p2, p3, p4); } + +#endif // cl_khr_gl_sharing diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_clamdblas.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_clamdblas.hpp new file mode 100644 index 0000000000000000000000000000000000000000..2ad8ac0b5db8bdd0587f60dd3ed3d2ecd28e7048 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_clamdblas.hpp @@ -0,0 +1,53 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the OpenCV Foundation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_OCL_RUNTIME_CLAMDBLAS_HPP +#define OPENCV_CORE_OCL_RUNTIME_CLAMDBLAS_HPP + +#ifdef HAVE_CLAMDBLAS + +#include "opencl_core.hpp" + +#include "autogenerated/opencl_clamdblas.hpp" + +#endif // HAVE_CLAMDBLAS + +#endif // OPENCV_CORE_OCL_RUNTIME_CLAMDBLAS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_clamdfft.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_clamdfft.hpp new file mode 100644 index 0000000000000000000000000000000000000000..a328f722fccacd8e7491abfd464b8b7c43b51074 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_clamdfft.hpp @@ -0,0 +1,53 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the OpenCV Foundation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_OCL_RUNTIME_CLAMDFFT_HPP +#define OPENCV_CORE_OCL_RUNTIME_CLAMDFFT_HPP + +#ifdef HAVE_CLAMDFFT + +#include "opencl_core.hpp" + +#include "autogenerated/opencl_clamdfft.hpp" + +#endif // HAVE_CLAMDFFT + +#endif // OPENCV_CORE_OCL_RUNTIME_CLAMDFFT_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_core.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_core.hpp new file mode 100644 index 0000000000000000000000000000000000000000..0404b3177a58f51a4137d92803198a17f8ca3ab4 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_core.hpp @@ -0,0 +1,84 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the OpenCV Foundation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_CORE_HPP +#define OPENCV_CORE_OCL_RUNTIME_OPENCL_CORE_HPP + +#ifdef HAVE_OPENCL + +#ifndef CL_RUNTIME_EXPORT +#if (defined(BUILD_SHARED_LIBS) || defined(OPENCV_CORE_SHARED)) && (defined _WIN32 || defined WINCE) && \ + !(defined(__OPENCV_BUILD) && defined(OPENCV_MODULE_IS_PART_OF_WORLD)) +#define CL_RUNTIME_EXPORT __declspec(dllimport) +#else +#define CL_RUNTIME_EXPORT +#endif +#endif + +#ifdef HAVE_OPENCL_SVM +#define clSVMAlloc clSVMAlloc_ +#define clSVMFree clSVMFree_ +#define clSetKernelArgSVMPointer clSetKernelArgSVMPointer_ +#define clSetKernelExecInfo clSetKernelExecInfo_ +#define clEnqueueSVMFree clEnqueueSVMFree_ +#define clEnqueueSVMMemcpy clEnqueueSVMMemcpy_ +#define clEnqueueSVMMemFill clEnqueueSVMMemFill_ +#define clEnqueueSVMMap clEnqueueSVMMap_ +#define clEnqueueSVMUnmap clEnqueueSVMUnmap_ +#endif + +#include "autogenerated/opencl_core.hpp" + +#ifndef CL_DEVICE_DOUBLE_FP_CONFIG +#define CL_DEVICE_DOUBLE_FP_CONFIG 0x1032 +#endif + +#ifndef CL_DEVICE_HALF_FP_CONFIG +#define CL_DEVICE_HALF_FP_CONFIG 0x1033 +#endif + +#ifndef CL_VERSION_1_2 +#define CV_REQUIRE_OPENCL_1_2_ERROR CV_Error(cv::Error::OpenCLApiCallError, "OpenCV compiled without OpenCL v1.2 support, so we can't use functionality from OpenCL v1.2") +#endif + +#endif // HAVE_OPENCL + +#endif // OPENCV_CORE_OCL_RUNTIME_OPENCL_CORE_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_core_wrappers.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_core_wrappers.hpp new file mode 100644 index 0000000000000000000000000000000000000000..38fcae9952f2838d15f7dbf79549aa9cd30bbe26 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_core_wrappers.hpp @@ -0,0 +1,47 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the OpenCV Foundation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_WRAPPERS_HPP +#define OPENCV_CORE_OCL_RUNTIME_OPENCL_WRAPPERS_HPP + +#include "autogenerated/opencl_core_wrappers.hpp" + +#endif // OPENCV_CORE_OCL_RUNTIME_OPENCL_WRAPPERS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_gl.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_gl.hpp new file mode 100644 index 0000000000000000000000000000000000000000..659c7d80581489d95c91c5ce844bc574b8c81598 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_gl.hpp @@ -0,0 +1,53 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the OpenCV Foundation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_HPP +#define OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_HPP + +#if defined HAVE_OPENCL && defined HAVE_OPENGL + +#include "opencl_core.hpp" + +#include "autogenerated/opencl_gl.hpp" + +#endif // defined HAVE_OPENCL && defined HAVE_OPENGL + +#endif // OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_gl_wrappers.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_gl_wrappers.hpp new file mode 100644 index 0000000000000000000000000000000000000000..9700004cae5ac6e11b76652ce8c78d4430e05688 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_gl_wrappers.hpp @@ -0,0 +1,47 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the OpenCV Foundation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_WRAPPERS_HPP +#define OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_WRAPPERS_HPP + +#include "autogenerated/opencl_gl_wrappers.hpp" + +#endif // OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_WRAPPERS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_svm_20.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_svm_20.hpp new file mode 100644 index 0000000000000000000000000000000000000000..9636b19b027fcc5ec2ff78c94f28b46919425fda --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_svm_20.hpp @@ -0,0 +1,48 @@ +/* See LICENSE file in the root OpenCV directory */ + +#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_2_0_HPP +#define OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_2_0_HPP + +#if defined(HAVE_OPENCL_SVM) +#include "opencl_core.hpp" + +#include "opencl_svm_definitions.hpp" + +#undef clSVMAlloc +#define clSVMAlloc clSVMAlloc_pfn +#undef clSVMFree +#define clSVMFree clSVMFree_pfn +#undef clSetKernelArgSVMPointer +#define clSetKernelArgSVMPointer clSetKernelArgSVMPointer_pfn +#undef clSetKernelExecInfo +//#define clSetKernelExecInfo clSetKernelExecInfo_pfn +#undef clEnqueueSVMFree +//#define clEnqueueSVMFree clEnqueueSVMFree_pfn +#undef clEnqueueSVMMemcpy +#define clEnqueueSVMMemcpy clEnqueueSVMMemcpy_pfn +#undef clEnqueueSVMMemFill +#define clEnqueueSVMMemFill clEnqueueSVMMemFill_pfn +#undef clEnqueueSVMMap +#define clEnqueueSVMMap clEnqueueSVMMap_pfn +#undef clEnqueueSVMUnmap +#define clEnqueueSVMUnmap clEnqueueSVMUnmap_pfn + +extern CL_RUNTIME_EXPORT void* (CL_API_CALL *clSVMAlloc)(cl_context context, cl_svm_mem_flags flags, size_t size, unsigned int alignment); +extern CL_RUNTIME_EXPORT void (CL_API_CALL *clSVMFree)(cl_context context, void* svm_pointer); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL *clSetKernelArgSVMPointer)(cl_kernel kernel, cl_uint arg_index, const void* arg_value); +//extern CL_RUNTIME_EXPORT void* (CL_API_CALL *clSetKernelExecInfo)(cl_kernel kernel, cl_kernel_exec_info param_name, size_t param_value_size, const void* param_value); +//extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL *clEnqueueSVMFree)(cl_command_queue command_queue, cl_uint num_svm_pointers, void* svm_pointers[], +// void (CL_CALLBACK *pfn_free_func)(cl_command_queue queue, cl_uint num_svm_pointers, void* svm_pointers[], void* user_data), void* user_data, +// cl_uint num_events_in_wait_list, const cl_event* event_wait_list, cl_event* event); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL *clEnqueueSVMMemcpy)(cl_command_queue command_queue, cl_bool blocking_copy, void* dst_ptr, const void* src_ptr, size_t size, + cl_uint num_events_in_wait_list, const cl_event* event_wait_list, cl_event* event); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL *clEnqueueSVMMemFill)(cl_command_queue command_queue, void* svm_ptr, const void* pattern, size_t pattern_size, size_t size, + cl_uint num_events_in_wait_list, const cl_event* event_wait_list, cl_event* event); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL *clEnqueueSVMMap)(cl_command_queue command_queue, cl_bool blocking_map, cl_map_flags map_flags, void* svm_ptr, size_t size, + cl_uint num_events_in_wait_list, const cl_event* event_wait_list, cl_event* event); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL *clEnqueueSVMUnmap)(cl_command_queue command_queue, void* svm_ptr, + cl_uint num_events_in_wait_list, const cl_event* event_wait_list, cl_event* event); + +#endif // HAVE_OPENCL_SVM + +#endif // OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_2_0_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_svm_definitions.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_svm_definitions.hpp new file mode 100644 index 0000000000000000000000000000000000000000..97c927b44d8cc6310623ad4156ff2aae7b7c193c --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_svm_definitions.hpp @@ -0,0 +1,42 @@ +/* See LICENSE file in the root OpenCV directory */ + +#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_DEFINITIONS_HPP +#define OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_DEFINITIONS_HPP + +#if defined(HAVE_OPENCL_SVM) +#if defined(CL_VERSION_2_0) + +// OpenCL 2.0 contains SVM definitions + +#else + +typedef cl_bitfield cl_device_svm_capabilities; +typedef cl_bitfield cl_svm_mem_flags; +typedef cl_uint cl_kernel_exec_info; + +// +// TODO Add real values after OpenCL 2.0 release +// + +#ifndef CL_DEVICE_SVM_CAPABILITIES +#define CL_DEVICE_SVM_CAPABILITIES 0x1053 + +#define CL_DEVICE_SVM_COARSE_GRAIN_BUFFER (1 << 0) +#define CL_DEVICE_SVM_FINE_GRAIN_BUFFER (1 << 1) +#define CL_DEVICE_SVM_FINE_GRAIN_SYSTEM (1 << 2) +#define CL_DEVICE_SVM_ATOMICS (1 << 3) +#endif + +#ifndef CL_MEM_SVM_FINE_GRAIN_BUFFER +#define CL_MEM_SVM_FINE_GRAIN_BUFFER (1 << 10) +#endif + +#ifndef CL_MEM_SVM_ATOMICS +#define CL_MEM_SVM_ATOMICS (1 << 11) +#endif + + +#endif // CL_VERSION_2_0 +#endif // HAVE_OPENCL_SVM + +#endif // OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_DEFINITIONS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_svm_hsa_extension.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_svm_hsa_extension.hpp new file mode 100644 index 0000000000000000000000000000000000000000..497bc3de7205d6bad28328772c3b20f48611d63c --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_svm_hsa_extension.hpp @@ -0,0 +1,166 @@ +/* See LICENSE file in the root OpenCV directory */ + +#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_HSA_EXTENSION_HPP +#define OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_HSA_EXTENSION_HPP + +#if defined(HAVE_OPENCL_SVM) +#include "opencl_core.hpp" + +#ifndef CL_DEVICE_SVM_CAPABILITIES_AMD +// +// Part of the file is an extract from the cl_ext.h file from AMD APP SDK package. +// Below is the original copyright. +// +/******************************************************************************* + * Copyright (c) 2008-2013 The Khronos Group Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a + * copy of this software and/or associated documentation files (the + * "Materials"), to deal in the Materials without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, + * distribute, sublicense, and/or sell copies of the Materials, and to + * permit persons to whom the Materials are furnished to do so, subject to + * the following conditions: + * + * The above copyright notice and this permission notice shall be included + * in all copies or substantial portions of the Materials. + * + * THE MATERIALS ARE PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF + * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. + * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY + * CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, + * TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE + * MATERIALS OR THE USE OR OTHER DEALINGS IN THE MATERIALS. + ******************************************************************************/ + +/******************************************* + * Shared Virtual Memory (SVM) extension + *******************************************/ +typedef cl_bitfield cl_device_svm_capabilities_amd; +typedef cl_bitfield cl_svm_mem_flags_amd; +typedef cl_uint cl_kernel_exec_info_amd; + +/* cl_device_info */ +#define CL_DEVICE_SVM_CAPABILITIES_AMD 0x1053 +#define CL_DEVICE_PREFERRED_PLATFORM_ATOMIC_ALIGNMENT_AMD 0x1054 + +/* cl_device_svm_capabilities_amd */ +#define CL_DEVICE_SVM_COARSE_GRAIN_BUFFER_AMD (1 << 0) +#define CL_DEVICE_SVM_FINE_GRAIN_BUFFER_AMD (1 << 1) +#define CL_DEVICE_SVM_FINE_GRAIN_SYSTEM_AMD (1 << 2) +#define CL_DEVICE_SVM_ATOMICS_AMD (1 << 3) + +/* cl_svm_mem_flags_amd */ +#define CL_MEM_SVM_FINE_GRAIN_BUFFER_AMD (1 << 10) +#define CL_MEM_SVM_ATOMICS_AMD (1 << 11) + +/* cl_mem_info */ +#define CL_MEM_USES_SVM_POINTER_AMD 0x1109 + +/* cl_kernel_exec_info_amd */ +#define CL_KERNEL_EXEC_INFO_SVM_PTRS_AMD 0x11B6 +#define CL_KERNEL_EXEC_INFO_SVM_FINE_GRAIN_SYSTEM_AMD 0x11B7 + +/* cl_command_type */ +#define CL_COMMAND_SVM_FREE_AMD 0x1209 +#define CL_COMMAND_SVM_MEMCPY_AMD 0x120A +#define CL_COMMAND_SVM_MEMFILL_AMD 0x120B +#define CL_COMMAND_SVM_MAP_AMD 0x120C +#define CL_COMMAND_SVM_UNMAP_AMD 0x120D + +typedef CL_API_ENTRY void* +(CL_API_CALL * clSVMAllocAMD_fn)( + cl_context /* context */, + cl_svm_mem_flags_amd /* flags */, + size_t /* size */, + unsigned int /* alignment */ +) CL_EXT_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY void +(CL_API_CALL * clSVMFreeAMD_fn)( + cl_context /* context */, + void* /* svm_pointer */ +) CL_EXT_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_int +(CL_API_CALL * clEnqueueSVMFreeAMD_fn)( + cl_command_queue /* command_queue */, + cl_uint /* num_svm_pointers */, + void** /* svm_pointers */, + void (CL_CALLBACK *)( /*pfn_free_func*/ + cl_command_queue /* queue */, + cl_uint /* num_svm_pointers */, + void** /* svm_pointers */, + void* /* user_data */), + void* /* user_data */, + cl_uint /* num_events_in_wait_list */, + const cl_event* /* event_wait_list */, + cl_event* /* event */ +) CL_EXT_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_int +(CL_API_CALL * clEnqueueSVMMemcpyAMD_fn)( + cl_command_queue /* command_queue */, + cl_bool /* blocking_copy */, + void* /* dst_ptr */, + const void* /* src_ptr */, + size_t /* size */, + cl_uint /* num_events_in_wait_list */, + const cl_event* /* event_wait_list */, + cl_event* /* event */ +) CL_EXT_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_int +(CL_API_CALL * clEnqueueSVMMemFillAMD_fn)( + cl_command_queue /* command_queue */, + void* /* svm_ptr */, + const void* /* pattern */, + size_t /* pattern_size */, + size_t /* size */, + cl_uint /* num_events_in_wait_list */, + const cl_event* /* event_wait_list */, + cl_event* /* event */ +) CL_EXT_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_int +(CL_API_CALL * clEnqueueSVMMapAMD_fn)( + cl_command_queue /* command_queue */, + cl_bool /* blocking_map */, + cl_map_flags /* map_flags */, + void* /* svm_ptr */, + size_t /* size */, + cl_uint /* num_events_in_wait_list */, + const cl_event* /* event_wait_list */, + cl_event* /* event */ +) CL_EXT_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_int +(CL_API_CALL * clEnqueueSVMUnmapAMD_fn)( + cl_command_queue /* command_queue */, + void* /* svm_ptr */, + cl_uint /* num_events_in_wait_list */, + const cl_event* /* event_wait_list */, + cl_event* /* event */ +) CL_EXT_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_int +(CL_API_CALL * clSetKernelArgSVMPointerAMD_fn)( + cl_kernel /* kernel */, + cl_uint /* arg_index */, + const void * /* arg_value */ +) CL_EXT_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_int +(CL_API_CALL * clSetKernelExecInfoAMD_fn)( + cl_kernel /* kernel */, + cl_kernel_exec_info_amd /* param_name */, + size_t /* param_value_size */, + const void * /* param_value */ +) CL_EXT_SUFFIX__VERSION_1_2; + +#endif + +#endif // HAVE_OPENCL_SVM + +#endif // OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_HSA_EXTENSION_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opengl.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opengl.hpp new file mode 100644 index 0000000000000000000000000000000000000000..a6288bebe8f51cd64f2a089bc8830320845b22c5 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/opengl.hpp @@ -0,0 +1,725 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_OPENGL_HPP +#define OPENCV_CORE_OPENGL_HPP + +#ifndef __cplusplus +# error opengl.hpp header must be compiled as C++ +#endif + +#include "opencv2/core.hpp" +#include "ocl.hpp" + +namespace cv { namespace ogl { + +/** @addtogroup core_opengl +This section describes OpenGL interoperability. + +To enable OpenGL support, configure OpenCV using CMake with WITH_OPENGL=ON . Currently OpenGL is +supported only with WIN32, GTK and Qt backends on Windows and Linux (MacOS and Android are not +supported). For GTK backend gtkglext-1.0 library is required. + +To use OpenGL functionality you should first create OpenGL context (window or frame buffer). You can +do this with namedWindow function or with other OpenGL toolkit (GLUT, for example). +*/ +//! @{ + +/////////////////// OpenGL Objects /////////////////// + +/** @brief Smart pointer for OpenGL buffer object with reference counting. + +Buffer Objects are OpenGL objects that store an array of unformatted memory allocated by the OpenGL +context. These can be used to store vertex data, pixel data retrieved from images or the +framebuffer, and a variety of other things. + +ogl::Buffer has interface similar with Mat interface and represents 2D array memory. + +ogl::Buffer supports memory transfers between host and device and also can be mapped to CUDA memory. + */ +class CV_EXPORTS Buffer +{ +public: + /** @brief The target defines how you intend to use the buffer object. + */ + enum Target + { + ARRAY_BUFFER = 0x8892, //!< The buffer will be used as a source for vertex data + ELEMENT_ARRAY_BUFFER = 0x8893, //!< The buffer will be used for indices (in glDrawElements, for example) + PIXEL_PACK_BUFFER = 0x88EB, //!< The buffer will be used for reading from OpenGL textures + PIXEL_UNPACK_BUFFER = 0x88EC //!< The buffer will be used for writing to OpenGL textures + }; + + enum Access + { + READ_ONLY = 0x88B8, + WRITE_ONLY = 0x88B9, + READ_WRITE = 0x88BA + }; + + /** @brief The constructors. + + Creates empty ogl::Buffer object, creates ogl::Buffer object from existed buffer ( abufId + parameter), allocates memory for ogl::Buffer object or copies from host/device memory. + */ + Buffer(); + + /** @overload + @param arows Number of rows in a 2D array. + @param acols Number of columns in a 2D array. + @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details. + @param abufId Buffer object name. + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + Buffer(int arows, int acols, int atype, unsigned int abufId, bool autoRelease = false); + + /** @overload + @param asize 2D array size. + @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details. + @param abufId Buffer object name. + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + Buffer(Size asize, int atype, unsigned int abufId, bool autoRelease = false); + + /** @overload + @param arows Number of rows in a 2D array. + @param acols Number of columns in a 2D array. + @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details. + @param target Buffer usage. See cv::ogl::Buffer::Target . + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + Buffer(int arows, int acols, int atype, Target target = ARRAY_BUFFER, bool autoRelease = false); + + /** @overload + @param asize 2D array size. + @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details. + @param target Buffer usage. See cv::ogl::Buffer::Target . + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + Buffer(Size asize, int atype, Target target = ARRAY_BUFFER, bool autoRelease = false); + + /** @overload + @param arr Input array (host or device memory, it can be Mat , cuda::GpuMat or std::vector ). + @param target Buffer usage. See cv::ogl::Buffer::Target . + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + explicit Buffer(InputArray arr, Target target = ARRAY_BUFFER, bool autoRelease = false); + + /** @brief Allocates memory for ogl::Buffer object. + + @param arows Number of rows in a 2D array. + @param acols Number of columns in a 2D array. + @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details. + @param target Buffer usage. See cv::ogl::Buffer::Target . + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + void create(int arows, int acols, int atype, Target target = ARRAY_BUFFER, bool autoRelease = false); + + /** @overload + @param asize 2D array size. + @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details. + @param target Buffer usage. See cv::ogl::Buffer::Target . + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + void create(Size asize, int atype, Target target = ARRAY_BUFFER, bool autoRelease = false); + + /** @brief Decrements the reference counter and destroys the buffer object if needed. + + The function will call setAutoRelease(true) . + */ + void release(); + + /** @brief Sets auto release mode. + + The lifetime of the OpenGL object is tied to the lifetime of the context. If OpenGL context was + bound to a window it could be released at any time (user can close a window). If object's destructor + is called after destruction of the context it will cause an error. Thus ogl::Buffer doesn't destroy + OpenGL object in destructor by default (all OpenGL resources will be released with OpenGL context). + This function can force ogl::Buffer destructor to destroy OpenGL object. + @param flag Auto release mode (if true, release will be called in object's destructor). + */ + void setAutoRelease(bool flag); + + /** @brief Copies from host/device memory to OpenGL buffer. + @param arr Input array (host or device memory, it can be Mat , cuda::GpuMat or std::vector ). + @param target Buffer usage. See cv::ogl::Buffer::Target . + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + void copyFrom(InputArray arr, Target target = ARRAY_BUFFER, bool autoRelease = false); + + /** @overload */ + void copyFrom(InputArray arr, cuda::Stream& stream, Target target = ARRAY_BUFFER, bool autoRelease = false); + + /** @brief Copies from OpenGL buffer to host/device memory or another OpenGL buffer object. + + @param arr Destination array (host or device memory, can be Mat , cuda::GpuMat , std::vector or + ogl::Buffer ). + */ + void copyTo(OutputArray arr) const; + + /** @overload */ + void copyTo(OutputArray arr, cuda::Stream& stream) const; + + /** @brief Creates a full copy of the buffer object and the underlying data. + + @param target Buffer usage for destination buffer. + @param autoRelease Auto release mode for destination buffer. + */ + Buffer clone(Target target = ARRAY_BUFFER, bool autoRelease = false) const; + + /** @brief Binds OpenGL buffer to the specified buffer binding point. + + @param target Binding point. See cv::ogl::Buffer::Target . + */ + void bind(Target target) const; + + /** @brief Unbind any buffers from the specified binding point. + + @param target Binding point. See cv::ogl::Buffer::Target . + */ + static void unbind(Target target); + + /** @brief Maps OpenGL buffer to host memory. + + mapHost maps to the client's address space the entire data store of the buffer object. The data can + then be directly read and/or written relative to the returned pointer, depending on the specified + access policy. + + A mapped data store must be unmapped with ogl::Buffer::unmapHost before its buffer object is used. + + This operation can lead to memory transfers between host and device. + + Only one buffer object can be mapped at a time. + @param access Access policy, indicating whether it will be possible to read from, write to, or both + read from and write to the buffer object's mapped data store. The symbolic constant must be + ogl::Buffer::READ_ONLY , ogl::Buffer::WRITE_ONLY or ogl::Buffer::READ_WRITE . + */ + Mat mapHost(Access access); + + /** @brief Unmaps OpenGL buffer. + */ + void unmapHost(); + + //! map to device memory (blocking) + cuda::GpuMat mapDevice(); + void unmapDevice(); + + /** @brief Maps OpenGL buffer to CUDA device memory. + + This operation doesn't copy data. Several buffer objects can be mapped to CUDA memory at a time. + + A mapped data store must be unmapped with ogl::Buffer::unmapDevice before its buffer object is used. + */ + cuda::GpuMat mapDevice(cuda::Stream& stream); + + /** @brief Unmaps OpenGL buffer. + */ + void unmapDevice(cuda::Stream& stream); + + int rows() const; + int cols() const; + Size size() const; + bool empty() const; + + int type() const; + int depth() const; + int channels() const; + int elemSize() const; + int elemSize1() const; + + //! get OpenGL opject id + unsigned int bufId() const; + + class Impl; + +private: + Ptr impl_; + int rows_; + int cols_; + int type_; +}; + +/** @brief Smart pointer for OpenGL 2D texture memory with reference counting. + */ +class CV_EXPORTS Texture2D +{ +public: + /** @brief An Image Format describes the way that the images in Textures store their data. + */ + enum Format + { + NONE = 0, + DEPTH_COMPONENT = 0x1902, //!< Depth + RGB = 0x1907, //!< Red, Green, Blue + RGBA = 0x1908 //!< Red, Green, Blue, Alpha + }; + + /** @brief The constructors. + + Creates empty ogl::Texture2D object, allocates memory for ogl::Texture2D object or copies from + host/device memory. + */ + Texture2D(); + + /** @overload */ + Texture2D(int arows, int acols, Format aformat, unsigned int atexId, bool autoRelease = false); + + /** @overload */ + Texture2D(Size asize, Format aformat, unsigned int atexId, bool autoRelease = false); + + /** @overload + @param arows Number of rows. + @param acols Number of columns. + @param aformat Image format. See cv::ogl::Texture2D::Format . + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + Texture2D(int arows, int acols, Format aformat, bool autoRelease = false); + + /** @overload + @param asize 2D array size. + @param aformat Image format. See cv::ogl::Texture2D::Format . + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + Texture2D(Size asize, Format aformat, bool autoRelease = false); + + /** @overload + @param arr Input array (host or device memory, it can be Mat , cuda::GpuMat or ogl::Buffer ). + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + explicit Texture2D(InputArray arr, bool autoRelease = false); + + /** @brief Allocates memory for ogl::Texture2D object. + + @param arows Number of rows. + @param acols Number of columns. + @param aformat Image format. See cv::ogl::Texture2D::Format . + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + void create(int arows, int acols, Format aformat, bool autoRelease = false); + /** @overload + @param asize 2D array size. + @param aformat Image format. See cv::ogl::Texture2D::Format . + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + void create(Size asize, Format aformat, bool autoRelease = false); + + /** @brief Decrements the reference counter and destroys the texture object if needed. + + The function will call setAutoRelease(true) . + */ + void release(); + + /** @brief Sets auto release mode. + + @param flag Auto release mode (if true, release will be called in object's destructor). + + The lifetime of the OpenGL object is tied to the lifetime of the context. If OpenGL context was + bound to a window it could be released at any time (user can close a window). If object's destructor + is called after destruction of the context it will cause an error. Thus ogl::Texture2D doesn't + destroy OpenGL object in destructor by default (all OpenGL resources will be released with OpenGL + context). This function can force ogl::Texture2D destructor to destroy OpenGL object. + */ + void setAutoRelease(bool flag); + + /** @brief Copies from host/device memory to OpenGL texture. + + @param arr Input array (host or device memory, it can be Mat , cuda::GpuMat or ogl::Buffer ). + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + void copyFrom(InputArray arr, bool autoRelease = false); + + /** @brief Copies from OpenGL texture to host/device memory or another OpenGL texture object. + + @param arr Destination array (host or device memory, can be Mat , cuda::GpuMat , ogl::Buffer or + ogl::Texture2D ). + @param ddepth Destination depth. + @param autoRelease Auto release mode for destination buffer (if arr is OpenGL buffer or texture). + */ + void copyTo(OutputArray arr, int ddepth = CV_32F, bool autoRelease = false) const; + + /** @brief Binds texture to current active texture unit for GL_TEXTURE_2D target. + */ + void bind() const; + + int rows() const; + int cols() const; + Size size() const; + bool empty() const; + + Format format() const; + + //! get OpenGL opject id + unsigned int texId() const; + + class Impl; + +private: + Ptr impl_; + int rows_; + int cols_; + Format format_; +}; + +/** @brief Wrapper for OpenGL Client-Side Vertex arrays. + +ogl::Arrays stores vertex data in ogl::Buffer objects. + */ +class CV_EXPORTS Arrays +{ +public: + /** @brief Default constructor + */ + Arrays(); + + /** @brief Sets an array of vertex coordinates. + @param vertex array with vertex coordinates, can be both host and device memory. + */ + void setVertexArray(InputArray vertex); + + /** @brief Resets vertex coordinates. + */ + void resetVertexArray(); + + /** @brief Sets an array of vertex colors. + @param color array with vertex colors, can be both host and device memory. + */ + void setColorArray(InputArray color); + + /** @brief Resets vertex colors. + */ + void resetColorArray(); + + /** @brief Sets an array of vertex normals. + @param normal array with vertex normals, can be both host and device memory. + */ + void setNormalArray(InputArray normal); + + /** @brief Resets vertex normals. + */ + void resetNormalArray(); + + /** @brief Sets an array of vertex texture coordinates. + @param texCoord array with vertex texture coordinates, can be both host and device memory. + */ + void setTexCoordArray(InputArray texCoord); + + /** @brief Resets vertex texture coordinates. + */ + void resetTexCoordArray(); + + /** @brief Releases all inner buffers. + */ + void release(); + + /** @brief Sets auto release mode all inner buffers. + @param flag Auto release mode. + */ + void setAutoRelease(bool flag); + + /** @brief Binds all vertex arrays. + */ + void bind() const; + + /** @brief Returns the vertex count. + */ + int size() const; + bool empty() const; + +private: + int size_; + Buffer vertex_; + Buffer color_; + Buffer normal_; + Buffer texCoord_; +}; + +/////////////////// Render Functions /////////////////// + +//! render mode +enum RenderModes { + POINTS = 0x0000, + LINES = 0x0001, + LINE_LOOP = 0x0002, + LINE_STRIP = 0x0003, + TRIANGLES = 0x0004, + TRIANGLE_STRIP = 0x0005, + TRIANGLE_FAN = 0x0006, + QUADS = 0x0007, + QUAD_STRIP = 0x0008, + POLYGON = 0x0009 +}; + +/** @brief Render OpenGL texture or primitives. +@param tex Texture to draw. +@param wndRect Region of window, where to draw a texture (normalized coordinates). +@param texRect Region of texture to draw (normalized coordinates). + */ +CV_EXPORTS void render(const Texture2D& tex, + Rect_ wndRect = Rect_(0.0, 0.0, 1.0, 1.0), + Rect_ texRect = Rect_(0.0, 0.0, 1.0, 1.0)); + +/** @overload +@param arr Array of privitives vertices. +@param mode Render mode. One of cv::ogl::RenderModes +@param color Color for all vertices. Will be used if arr doesn't contain color array. +*/ +CV_EXPORTS void render(const Arrays& arr, int mode = POINTS, Scalar color = Scalar::all(255)); + +/** @overload +@param arr Array of privitives vertices. +@param indices Array of vertices indices (host or device memory). +@param mode Render mode. One of cv::ogl::RenderModes +@param color Color for all vertices. Will be used if arr doesn't contain color array. +*/ +CV_EXPORTS void render(const Arrays& arr, InputArray indices, int mode = POINTS, Scalar color = Scalar::all(255)); + +/////////////////// CL-GL Interoperability Functions /////////////////// + +namespace ocl { +using namespace cv::ocl; + +// TODO static functions in the Context class +/** @brief Creates OpenCL context from GL. +@return Returns reference to OpenCL Context + */ +CV_EXPORTS Context& initializeContextFromGL(); + +} // namespace cv::ogl::ocl + +/** @brief Converts InputArray to Texture2D object. +@param src - source InputArray. +@param texture - destination Texture2D object. + */ +CV_EXPORTS void convertToGLTexture2D(InputArray src, Texture2D& texture); + +/** @brief Converts Texture2D object to OutputArray. +@param texture - source Texture2D object. +@param dst - destination OutputArray. + */ +CV_EXPORTS void convertFromGLTexture2D(const Texture2D& texture, OutputArray dst); + +/** @brief Maps Buffer object to process on CL side (convert to UMat). + +Function creates CL buffer from GL one, and then constructs UMat that can be used +to process buffer data with OpenCV functions. Note that in current implementation +UMat constructed this way doesn't own corresponding GL buffer object, so it is +the user responsibility to close down CL/GL buffers relationships by explicitly +calling unmapGLBuffer() function. +@param buffer - source Buffer object. +@param accessFlags - data access flags (ACCESS_READ|ACCESS_WRITE). +@return Returns UMat object + */ +CV_EXPORTS UMat mapGLBuffer(const Buffer& buffer, int accessFlags = ACCESS_READ|ACCESS_WRITE); + +/** @brief Unmaps Buffer object (releases UMat, previously mapped from Buffer). + +Function must be called explicitly by the user for each UMat previously constructed +by the call to mapGLBuffer() function. +@param u - source UMat, created by mapGLBuffer(). + */ +CV_EXPORTS void unmapGLBuffer(UMat& u); + +//! @} +}} // namespace cv::ogl + +namespace cv { namespace cuda { + +/** @brief Sets a CUDA device and initializes it for the current thread with OpenGL interoperability. + +This function should be explicitly called after OpenGL context creation and before any CUDA calls. +@param device System index of a CUDA device starting with 0. +@ingroup core_opengl + */ +CV_EXPORTS void setGlDevice(int device = 0); + +}} + +//! @cond IGNORED + +//////////////////////////////////////////////////////////////////////// +//////////////////////////////////////////////////////////////////////// +//////////////////////////////////////////////////////////////////////// + +inline +cv::ogl::Buffer::Buffer(int arows, int acols, int atype, Target target, bool autoRelease) : rows_(0), cols_(0), type_(0) +{ + create(arows, acols, atype, target, autoRelease); +} + +inline +cv::ogl::Buffer::Buffer(Size asize, int atype, Target target, bool autoRelease) : rows_(0), cols_(0), type_(0) +{ + create(asize, atype, target, autoRelease); +} + +inline +void cv::ogl::Buffer::create(Size asize, int atype, Target target, bool autoRelease) +{ + create(asize.height, asize.width, atype, target, autoRelease); +} + +inline +int cv::ogl::Buffer::rows() const +{ + return rows_; +} + +inline +int cv::ogl::Buffer::cols() const +{ + return cols_; +} + +inline +cv::Size cv::ogl::Buffer::size() const +{ + return Size(cols_, rows_); +} + +inline +bool cv::ogl::Buffer::empty() const +{ + return rows_ == 0 || cols_ == 0; +} + +inline +int cv::ogl::Buffer::type() const +{ + return type_; +} + +inline +int cv::ogl::Buffer::depth() const +{ + return CV_MAT_DEPTH(type_); +} + +inline +int cv::ogl::Buffer::channels() const +{ + return CV_MAT_CN(type_); +} + +inline +int cv::ogl::Buffer::elemSize() const +{ + return CV_ELEM_SIZE(type_); +} + +inline +int cv::ogl::Buffer::elemSize1() const +{ + return CV_ELEM_SIZE1(type_); +} + +/////// + +inline +cv::ogl::Texture2D::Texture2D(int arows, int acols, Format aformat, bool autoRelease) : rows_(0), cols_(0), format_(NONE) +{ + create(arows, acols, aformat, autoRelease); +} + +inline +cv::ogl::Texture2D::Texture2D(Size asize, Format aformat, bool autoRelease) : rows_(0), cols_(0), format_(NONE) +{ + create(asize, aformat, autoRelease); +} + +inline +void cv::ogl::Texture2D::create(Size asize, Format aformat, bool autoRelease) +{ + create(asize.height, asize.width, aformat, autoRelease); +} + +inline +int cv::ogl::Texture2D::rows() const +{ + return rows_; +} + +inline +int cv::ogl::Texture2D::cols() const +{ + return cols_; +} + +inline +cv::Size cv::ogl::Texture2D::size() const +{ + return Size(cols_, rows_); +} + +inline +bool cv::ogl::Texture2D::empty() const +{ + return rows_ == 0 || cols_ == 0; +} + +inline +cv::ogl::Texture2D::Format cv::ogl::Texture2D::format() const +{ + return format_; +} + +/////// + +inline +cv::ogl::Arrays::Arrays() : size_(0) +{ +} + +inline +int cv::ogl::Arrays::size() const +{ + return size_; +} + +inline +bool cv::ogl::Arrays::empty() const +{ + return size_ == 0; +} + +//! @endcond + +#endif /* OPENCV_CORE_OPENGL_HPP */ diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/operations.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/operations.hpp new file mode 100644 index 0000000000000000000000000000000000000000..d706d9664e0b1882a6b2c77bb55823f9fc2f2568 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/operations.hpp @@ -0,0 +1,554 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_OPERATIONS_HPP +#define OPENCV_CORE_OPERATIONS_HPP + +#ifndef __cplusplus +# error operations.hpp header must be compiled as C++ +#endif + +#include + +//! @cond IGNORED + +namespace cv +{ + +////////////////////////////// Matx methods depending on core API ///////////////////////////// + +namespace internal +{ + +template struct Matx_FastInvOp +{ + bool operator()(const Matx<_Tp, m, n>& a, Matx<_Tp, n, m>& b, int method) const + { + return invert(a, b, method) != 0; + } +}; + +template struct Matx_FastInvOp<_Tp, m, m> +{ + bool operator()(const Matx<_Tp, m, m>& a, Matx<_Tp, m, m>& b, int method) const + { + if (method == DECOMP_LU || method == DECOMP_CHOLESKY) + { + Matx<_Tp, m, m> temp = a; + + // assume that b is all 0's on input => make it a unity matrix + for (int i = 0; i < m; i++) + b(i, i) = (_Tp)1; + + if (method == DECOMP_CHOLESKY) + return Cholesky(temp.val, m*sizeof(_Tp), m, b.val, m*sizeof(_Tp), m); + + return LU(temp.val, m*sizeof(_Tp), m, b.val, m*sizeof(_Tp), m) != 0; + } + else + { + return invert(a, b, method) != 0; + } + } +}; + +template struct Matx_FastInvOp<_Tp, 2, 2> +{ + bool operator()(const Matx<_Tp, 2, 2>& a, Matx<_Tp, 2, 2>& b, int /*method*/) const + { + _Tp d = (_Tp)determinant(a); + if (d == 0) + return false; + d = 1/d; + b(1,1) = a(0,0)*d; + b(0,0) = a(1,1)*d; + b(0,1) = -a(0,1)*d; + b(1,0) = -a(1,0)*d; + return true; + } +}; + +template struct Matx_FastInvOp<_Tp, 3, 3> +{ + bool operator()(const Matx<_Tp, 3, 3>& a, Matx<_Tp, 3, 3>& b, int /*method*/) const + { + _Tp d = (_Tp)determinant(a); + if (d == 0) + return false; + d = 1/d; + b(0,0) = (a(1,1) * a(2,2) - a(1,2) * a(2,1)) * d; + b(0,1) = (a(0,2) * a(2,1) - a(0,1) * a(2,2)) * d; + b(0,2) = (a(0,1) * a(1,2) - a(0,2) * a(1,1)) * d; + + b(1,0) = (a(1,2) * a(2,0) - a(1,0) * a(2,2)) * d; + b(1,1) = (a(0,0) * a(2,2) - a(0,2) * a(2,0)) * d; + b(1,2) = (a(0,2) * a(1,0) - a(0,0) * a(1,2)) * d; + + b(2,0) = (a(1,0) * a(2,1) - a(1,1) * a(2,0)) * d; + b(2,1) = (a(0,1) * a(2,0) - a(0,0) * a(2,1)) * d; + b(2,2) = (a(0,0) * a(1,1) - a(0,1) * a(1,0)) * d; + return true; + } +}; + + +template struct Matx_FastSolveOp +{ + bool operator()(const Matx<_Tp, m, l>& a, const Matx<_Tp, m, n>& b, + Matx<_Tp, l, n>& x, int method) const + { + return cv::solve(a, b, x, method); + } +}; + +template struct Matx_FastSolveOp<_Tp, m, m, n> +{ + bool operator()(const Matx<_Tp, m, m>& a, const Matx<_Tp, m, n>& b, + Matx<_Tp, m, n>& x, int method) const + { + if (method == DECOMP_LU || method == DECOMP_CHOLESKY) + { + Matx<_Tp, m, m> temp = a; + x = b; + if( method == DECOMP_CHOLESKY ) + return Cholesky(temp.val, m*sizeof(_Tp), m, x.val, n*sizeof(_Tp), n); + + return LU(temp.val, m*sizeof(_Tp), m, x.val, n*sizeof(_Tp), n) != 0; + } + else + { + return cv::solve(a, b, x, method); + } + } +}; + +template struct Matx_FastSolveOp<_Tp, 2, 2, 1> +{ + bool operator()(const Matx<_Tp, 2, 2>& a, const Matx<_Tp, 2, 1>& b, + Matx<_Tp, 2, 1>& x, int) const + { + _Tp d = (_Tp)determinant(a); + if (d == 0) + return false; + d = 1/d; + x(0) = (b(0)*a(1,1) - b(1)*a(0,1))*d; + x(1) = (b(1)*a(0,0) - b(0)*a(1,0))*d; + return true; + } +}; + +template struct Matx_FastSolveOp<_Tp, 3, 3, 1> +{ + bool operator()(const Matx<_Tp, 3, 3>& a, const Matx<_Tp, 3, 1>& b, + Matx<_Tp, 3, 1>& x, int) const + { + _Tp d = (_Tp)determinant(a); + if (d == 0) + return false; + d = 1/d; + x(0) = d*(b(0)*(a(1,1)*a(2,2) - a(1,2)*a(2,1)) - + a(0,1)*(b(1)*a(2,2) - a(1,2)*b(2)) + + a(0,2)*(b(1)*a(2,1) - a(1,1)*b(2))); + + x(1) = d*(a(0,0)*(b(1)*a(2,2) - a(1,2)*b(2)) - + b(0)*(a(1,0)*a(2,2) - a(1,2)*a(2,0)) + + a(0,2)*(a(1,0)*b(2) - b(1)*a(2,0))); + + x(2) = d*(a(0,0)*(a(1,1)*b(2) - b(1)*a(2,1)) - + a(0,1)*(a(1,0)*b(2) - b(1)*a(2,0)) + + b(0)*(a(1,0)*a(2,1) - a(1,1)*a(2,0))); + return true; + } +}; + +} // internal + +template inline +Matx<_Tp,m,n> Matx<_Tp,m,n>::randu(_Tp a, _Tp b) +{ + Matx<_Tp,m,n> M; + cv::randu(M, Scalar(a), Scalar(b)); + return M; +} + +template inline +Matx<_Tp,m,n> Matx<_Tp,m,n>::randn(_Tp a, _Tp b) +{ + Matx<_Tp,m,n> M; + cv::randn(M, Scalar(a), Scalar(b)); + return M; +} + +template inline +Matx<_Tp, n, m> Matx<_Tp, m, n>::inv(int method, bool *p_is_ok /*= NULL*/) const +{ + Matx<_Tp, n, m> b; + bool ok = cv::internal::Matx_FastInvOp<_Tp, m, n>()(*this, b, method); + if (p_is_ok) *p_is_ok = ok; + return ok ? b : Matx<_Tp, n, m>::zeros(); +} + +template template inline +Matx<_Tp, n, l> Matx<_Tp, m, n>::solve(const Matx<_Tp, m, l>& rhs, int method) const +{ + Matx<_Tp, n, l> x; + bool ok = cv::internal::Matx_FastSolveOp<_Tp, m, n, l>()(*this, rhs, x, method); + return ok ? x : Matx<_Tp, n, l>::zeros(); +} + + + +////////////////////////// Augmenting algebraic & logical operations ////////////////////////// + +#define CV_MAT_AUG_OPERATOR1(op, cvop, A, B) \ + static inline A& operator op (A& a, const B& b) { cvop; return a; } + +#define CV_MAT_AUG_OPERATOR(op, cvop, A, B) \ + CV_MAT_AUG_OPERATOR1(op, cvop, A, B) \ + CV_MAT_AUG_OPERATOR1(op, cvop, const A, B) + +#define CV_MAT_AUG_OPERATOR_T(op, cvop, A, B) \ + template CV_MAT_AUG_OPERATOR1(op, cvop, A, B) \ + template CV_MAT_AUG_OPERATOR1(op, cvop, const A, B) + +CV_MAT_AUG_OPERATOR (+=, cv::add(a,b,a), Mat, Mat) +CV_MAT_AUG_OPERATOR (+=, cv::add(a,b,a), Mat, Scalar) +CV_MAT_AUG_OPERATOR_T(+=, cv::add(a,b,a), Mat_<_Tp>, Mat) +CV_MAT_AUG_OPERATOR_T(+=, cv::add(a,b,a), Mat_<_Tp>, Scalar) +CV_MAT_AUG_OPERATOR_T(+=, cv::add(a,b,a), Mat_<_Tp>, Mat_<_Tp>) + +CV_MAT_AUG_OPERATOR (-=, cv::subtract(a,b,a), Mat, Mat) +CV_MAT_AUG_OPERATOR (-=, cv::subtract(a,b,a), Mat, Scalar) +CV_MAT_AUG_OPERATOR_T(-=, cv::subtract(a,b,a), Mat_<_Tp>, Mat) +CV_MAT_AUG_OPERATOR_T(-=, cv::subtract(a,b,a), Mat_<_Tp>, Scalar) +CV_MAT_AUG_OPERATOR_T(-=, cv::subtract(a,b,a), Mat_<_Tp>, Mat_<_Tp>) + +CV_MAT_AUG_OPERATOR (*=, cv::gemm(a, b, 1, Mat(), 0, a, 0), Mat, Mat) +CV_MAT_AUG_OPERATOR_T(*=, cv::gemm(a, b, 1, Mat(), 0, a, 0), Mat_<_Tp>, Mat) +CV_MAT_AUG_OPERATOR_T(*=, cv::gemm(a, b, 1, Mat(), 0, a, 0), Mat_<_Tp>, Mat_<_Tp>) +CV_MAT_AUG_OPERATOR (*=, a.convertTo(a, -1, b), Mat, double) +CV_MAT_AUG_OPERATOR_T(*=, a.convertTo(a, -1, b), Mat_<_Tp>, double) + +CV_MAT_AUG_OPERATOR (/=, cv::divide(a,b,a), Mat, Mat) +CV_MAT_AUG_OPERATOR_T(/=, cv::divide(a,b,a), Mat_<_Tp>, Mat) +CV_MAT_AUG_OPERATOR_T(/=, cv::divide(a,b,a), Mat_<_Tp>, Mat_<_Tp>) +CV_MAT_AUG_OPERATOR (/=, a.convertTo((Mat&)a, -1, 1./b), Mat, double) +CV_MAT_AUG_OPERATOR_T(/=, a.convertTo((Mat&)a, -1, 1./b), Mat_<_Tp>, double) + +CV_MAT_AUG_OPERATOR (&=, cv::bitwise_and(a,b,a), Mat, Mat) +CV_MAT_AUG_OPERATOR (&=, cv::bitwise_and(a,b,a), Mat, Scalar) +CV_MAT_AUG_OPERATOR_T(&=, cv::bitwise_and(a,b,a), Mat_<_Tp>, Mat) +CV_MAT_AUG_OPERATOR_T(&=, cv::bitwise_and(a,b,a), Mat_<_Tp>, Scalar) +CV_MAT_AUG_OPERATOR_T(&=, cv::bitwise_and(a,b,a), Mat_<_Tp>, Mat_<_Tp>) + +CV_MAT_AUG_OPERATOR (|=, cv::bitwise_or(a,b,a), Mat, Mat) +CV_MAT_AUG_OPERATOR (|=, cv::bitwise_or(a,b,a), Mat, Scalar) +CV_MAT_AUG_OPERATOR_T(|=, cv::bitwise_or(a,b,a), Mat_<_Tp>, Mat) +CV_MAT_AUG_OPERATOR_T(|=, cv::bitwise_or(a,b,a), Mat_<_Tp>, Scalar) +CV_MAT_AUG_OPERATOR_T(|=, cv::bitwise_or(a,b,a), Mat_<_Tp>, Mat_<_Tp>) + +CV_MAT_AUG_OPERATOR (^=, cv::bitwise_xor(a,b,a), Mat, Mat) +CV_MAT_AUG_OPERATOR (^=, cv::bitwise_xor(a,b,a), Mat, Scalar) +CV_MAT_AUG_OPERATOR_T(^=, cv::bitwise_xor(a,b,a), Mat_<_Tp>, Mat) +CV_MAT_AUG_OPERATOR_T(^=, cv::bitwise_xor(a,b,a), Mat_<_Tp>, Scalar) +CV_MAT_AUG_OPERATOR_T(^=, cv::bitwise_xor(a,b,a), Mat_<_Tp>, Mat_<_Tp>) + +#undef CV_MAT_AUG_OPERATOR_T +#undef CV_MAT_AUG_OPERATOR +#undef CV_MAT_AUG_OPERATOR1 + + + +///////////////////////////////////////////// SVD ///////////////////////////////////////////// + +inline SVD::SVD() {} +inline SVD::SVD( InputArray m, int flags ) { operator ()(m, flags); } +inline void SVD::solveZ( InputArray m, OutputArray _dst ) +{ + Mat mtx = m.getMat(); + SVD svd(mtx, (mtx.rows >= mtx.cols ? 0 : SVD::FULL_UV)); + _dst.create(svd.vt.cols, 1, svd.vt.type()); + Mat dst = _dst.getMat(); + svd.vt.row(svd.vt.rows-1).reshape(1,svd.vt.cols).copyTo(dst); +} + +template inline void + SVD::compute( const Matx<_Tp, m, n>& a, Matx<_Tp, nm, 1>& w, Matx<_Tp, m, nm>& u, Matx<_Tp, n, nm>& vt ) +{ + CV_StaticAssert( nm == MIN(m, n), "Invalid size of output vector."); + Mat _a(a, false), _u(u, false), _w(w, false), _vt(vt, false); + SVD::compute(_a, _w, _u, _vt); + CV_Assert(_w.data == (uchar*)&w.val[0] && _u.data == (uchar*)&u.val[0] && _vt.data == (uchar*)&vt.val[0]); +} + +template inline void +SVD::compute( const Matx<_Tp, m, n>& a, Matx<_Tp, nm, 1>& w ) +{ + CV_StaticAssert( nm == MIN(m, n), "Invalid size of output vector."); + Mat _a(a, false), _w(w, false); + SVD::compute(_a, _w); + CV_Assert(_w.data == (uchar*)&w.val[0]); +} + +template inline void +SVD::backSubst( const Matx<_Tp, nm, 1>& w, const Matx<_Tp, m, nm>& u, + const Matx<_Tp, n, nm>& vt, const Matx<_Tp, m, nb>& rhs, + Matx<_Tp, n, nb>& dst ) +{ + CV_StaticAssert( nm == MIN(m, n), "Invalid size of output vector."); + Mat _u(u, false), _w(w, false), _vt(vt, false), _rhs(rhs, false), _dst(dst, false); + SVD::backSubst(_w, _u, _vt, _rhs, _dst); + CV_Assert(_dst.data == (uchar*)&dst.val[0]); +} + + + +/////////////////////////////////// Multiply-with-Carry RNG /////////////////////////////////// + +inline RNG::RNG() { state = 0xffffffff; } +inline RNG::RNG(uint64 _state) { state = _state ? _state : 0xffffffff; } + +inline RNG::operator uchar() { return (uchar)next(); } +inline RNG::operator schar() { return (schar)next(); } +inline RNG::operator ushort() { return (ushort)next(); } +inline RNG::operator short() { return (short)next(); } +inline RNG::operator int() { return (int)next(); } +inline RNG::operator unsigned() { return next(); } +inline RNG::operator float() { return next()*2.3283064365386962890625e-10f; } +inline RNG::operator double() { unsigned t = next(); return (((uint64)t << 32) | next()) * 5.4210108624275221700372640043497e-20; } + +inline unsigned RNG::operator ()(unsigned N) { return (unsigned)uniform(0,N); } +inline unsigned RNG::operator ()() { return next(); } + +inline int RNG::uniform(int a, int b) { return a == b ? a : (int)(next() % (b - a) + a); } +inline float RNG::uniform(float a, float b) { return ((float)*this)*(b - a) + a; } +inline double RNG::uniform(double a, double b) { return ((double)*this)*(b - a) + a; } + +inline bool RNG::operator ==(const RNG& other) const { return state == other.state; } + +inline unsigned RNG::next() +{ + state = (uint64)(unsigned)state* /*CV_RNG_COEFF*/ 4164903690U + (unsigned)(state >> 32); + return (unsigned)state; +} + +//! returns the next unifomly-distributed random number of the specified type +template static inline _Tp randu() +{ + return (_Tp)theRNG(); +} + +///////////////////////////////// Formatted string generation ///////////////////////////////// + +/** @brief Returns a text string formatted using the printf-like expression. + +The function acts like sprintf but forms and returns an STL string. It can be used to form an error +message in the Exception constructor. +@param fmt printf-compatible formatting specifiers. + */ +CV_EXPORTS String format( const char* fmt, ... ); + +///////////////////////////////// Formatted output of cv::Mat ///////////////////////////////// + +static inline +Ptr format(InputArray mtx, int fmt) +{ + return Formatter::get(fmt)->format(mtx.getMat()); +} + +static inline +int print(Ptr fmtd, FILE* stream = stdout) +{ + int written = 0; + fmtd->reset(); + for(const char* str = fmtd->next(); str; str = fmtd->next()) + written += fputs(str, stream); + + return written; +} + +static inline +int print(const Mat& mtx, FILE* stream = stdout) +{ + return print(Formatter::get()->format(mtx), stream); +} + +static inline +int print(const UMat& mtx, FILE* stream = stdout) +{ + return print(Formatter::get()->format(mtx.getMat(ACCESS_READ)), stream); +} + +template static inline +int print(const std::vector >& vec, FILE* stream = stdout) +{ + return print(Formatter::get()->format(Mat(vec)), stream); +} + +template static inline +int print(const std::vector >& vec, FILE* stream = stdout) +{ + return print(Formatter::get()->format(Mat(vec)), stream); +} + +template static inline +int print(const Matx<_Tp, m, n>& matx, FILE* stream = stdout) +{ + return print(Formatter::get()->format(cv::Mat(matx)), stream); +} + +//! @endcond + +/****************************************************************************************\ +* Auxiliary algorithms * +\****************************************************************************************/ + +/** @brief Splits an element set into equivalency classes. + +The generic function partition implements an \f$O(N^2)\f$ algorithm for splitting a set of \f$N\f$ elements +into one or more equivalency classes, as described in + . The function returns the number of +equivalency classes. +@param _vec Set of elements stored as a vector. +@param labels Output vector of labels. It contains as many elements as vec. Each label labels[i] is +a 0-based cluster index of `vec[i]`. +@param predicate Equivalence predicate (pointer to a boolean function of two arguments or an +instance of the class that has the method bool operator()(const _Tp& a, const _Tp& b) ). The +predicate returns true when the elements are certainly in the same class, and returns false if they +may or may not be in the same class. +@ingroup core_cluster +*/ +template int +partition( const std::vector<_Tp>& _vec, std::vector& labels, + _EqPredicate predicate=_EqPredicate()) +{ + int i, j, N = (int)_vec.size(); + const _Tp* vec = &_vec[0]; + + const int PARENT=0; + const int RANK=1; + + std::vector _nodes(N*2); + int (*nodes)[2] = (int(*)[2])&_nodes[0]; + + // The first O(N) pass: create N single-vertex trees + for(i = 0; i < N; i++) + { + nodes[i][PARENT]=-1; + nodes[i][RANK] = 0; + } + + // The main O(N^2) pass: merge connected components + for( i = 0; i < N; i++ ) + { + int root = i; + + // find root + while( nodes[root][PARENT] >= 0 ) + root = nodes[root][PARENT]; + + for( j = 0; j < N; j++ ) + { + if( i == j || !predicate(vec[i], vec[j])) + continue; + int root2 = j; + + while( nodes[root2][PARENT] >= 0 ) + root2 = nodes[root2][PARENT]; + + if( root2 != root ) + { + // unite both trees + int rank = nodes[root][RANK], rank2 = nodes[root2][RANK]; + if( rank > rank2 ) + nodes[root2][PARENT] = root; + else + { + nodes[root][PARENT] = root2; + nodes[root2][RANK] += rank == rank2; + root = root2; + } + CV_Assert( nodes[root][PARENT] < 0 ); + + int k = j, parent; + + // compress the path from node2 to root + while( (parent = nodes[k][PARENT]) >= 0 ) + { + nodes[k][PARENT] = root; + k = parent; + } + + // compress the path from node to root + k = i; + while( (parent = nodes[k][PARENT]) >= 0 ) + { + nodes[k][PARENT] = root; + k = parent; + } + } + } + } + + // Final O(N) pass: enumerate classes + labels.resize(N); + int nclasses = 0; + + for( i = 0; i < N; i++ ) + { + int root = i; + while( nodes[root][PARENT] >= 0 ) + root = nodes[root][PARENT]; + // re-use the rank as the class label + if( nodes[root][RANK] >= 0 ) + nodes[root][RANK] = ~nclasses++; + labels[i] = ~nodes[root][RANK]; + } + + return nclasses; +} + +} // cv + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/optim.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/optim.hpp new file mode 100644 index 0000000000000000000000000000000000000000..c4729a9c105d61f3a43c278fd0f41660b12f4d59 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/optim.hpp @@ -0,0 +1,302 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the OpenCV Foundation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OPTIM_HPP +#define OPENCV_OPTIM_HPP + +#include "opencv2/core.hpp" + +namespace cv +{ + +/** @addtogroup core_optim +The algorithms in this section minimize or maximize function value within specified constraints or +without any constraints. +@{ +*/ + +/** @brief Basic interface for all solvers + */ +class CV_EXPORTS MinProblemSolver : public Algorithm +{ +public: + /** @brief Represents function being optimized + */ + class CV_EXPORTS Function + { + public: + virtual ~Function() {} + virtual int getDims() const = 0; + virtual double getGradientEps() const; + virtual double calc(const double* x) const = 0; + virtual void getGradient(const double* x,double* grad); + }; + + /** @brief Getter for the optimized function. + + The optimized function is represented by Function interface, which requires derivatives to + implement the calc(double*) and getDim() methods to evaluate the function. + + @return Smart-pointer to an object that implements Function interface - it represents the + function that is being optimized. It can be empty, if no function was given so far. + */ + virtual Ptr getFunction() const = 0; + + /** @brief Setter for the optimized function. + + *It should be called at least once before the call to* minimize(), as default value is not usable. + + @param f The new function to optimize. + */ + virtual void setFunction(const Ptr& f) = 0; + + /** @brief Getter for the previously set terminal criteria for this algorithm. + + @return Deep copy of the terminal criteria used at the moment. + */ + virtual TermCriteria getTermCriteria() const = 0; + + /** @brief Set terminal criteria for solver. + + This method *is not necessary* to be called before the first call to minimize(), as the default + value is sensible. + + Algorithm stops when the number of function evaluations done exceeds termcrit.maxCount, when + the function values at the vertices of simplex are within termcrit.epsilon range or simplex + becomes so small that it can enclosed in a box with termcrit.epsilon sides, whatever comes + first. + @param termcrit Terminal criteria to be used, represented as cv::TermCriteria structure. + */ + virtual void setTermCriteria(const TermCriteria& termcrit) = 0; + + /** @brief actually runs the algorithm and performs the minimization. + + The sole input parameter determines the centroid of the starting simplex (roughly, it tells + where to start), all the others (terminal criteria, initial step, function to be minimized) are + supposed to be set via the setters before the call to this method or the default values (not + always sensible) will be used. + + @param x The initial point, that will become a centroid of an initial simplex. After the algorithm + will terminate, it will be set to the point where the algorithm stops, the point of possible + minimum. + @return The value of a function at the point found. + */ + virtual double minimize(InputOutputArray x) = 0; +}; + +/** @brief This class is used to perform the non-linear non-constrained minimization of a function, + +defined on an `n`-dimensional Euclidean space, using the **Nelder-Mead method**, also known as +**downhill simplex method**. The basic idea about the method can be obtained from +. + +It should be noted, that this method, although deterministic, is rather a heuristic and therefore +may converge to a local minima, not necessary a global one. It is iterative optimization technique, +which at each step uses an information about the values of a function evaluated only at `n+1` +points, arranged as a *simplex* in `n`-dimensional space (hence the second name of the method). At +each step new point is chosen to evaluate function at, obtained value is compared with previous +ones and based on this information simplex changes it's shape , slowly moving to the local minimum. +Thus this method is using *only* function values to make decision, on contrary to, say, Nonlinear +Conjugate Gradient method (which is also implemented in optim). + +Algorithm stops when the number of function evaluations done exceeds termcrit.maxCount, when the +function values at the vertices of simplex are within termcrit.epsilon range or simplex becomes so +small that it can enclosed in a box with termcrit.epsilon sides, whatever comes first, for some +defined by user positive integer termcrit.maxCount and positive non-integer termcrit.epsilon. + +@note DownhillSolver is a derivative of the abstract interface +cv::MinProblemSolver, which in turn is derived from the Algorithm interface and is used to +encapsulate the functionality, common to all non-linear optimization algorithms in the optim +module. + +@note term criteria should meet following condition: +@code + termcrit.type == (TermCriteria::MAX_ITER + TermCriteria::EPS) && termcrit.epsilon > 0 && termcrit.maxCount > 0 +@endcode + */ +class CV_EXPORTS DownhillSolver : public MinProblemSolver +{ +public: + /** @brief Returns the initial step that will be used in downhill simplex algorithm. + + @param step Initial step that will be used in algorithm. Note, that although corresponding setter + accepts column-vectors as well as row-vectors, this method will return a row-vector. + @see DownhillSolver::setInitStep + */ + virtual void getInitStep(OutputArray step) const=0; + + /** @brief Sets the initial step that will be used in downhill simplex algorithm. + + Step, together with initial point (givin in DownhillSolver::minimize) are two `n`-dimensional + vectors that are used to determine the shape of initial simplex. Roughly said, initial point + determines the position of a simplex (it will become simplex's centroid), while step determines the + spread (size in each dimension) of a simplex. To be more precise, if \f$s,x_0\in\mathbb{R}^n\f$ are + the initial step and initial point respectively, the vertices of a simplex will be: + \f$v_0:=x_0-\frac{1}{2} s\f$ and \f$v_i:=x_0+s_i\f$ for \f$i=1,2,\dots,n\f$ where \f$s_i\f$ denotes + projections of the initial step of *n*-th coordinate (the result of projection is treated to be + vector given by \f$s_i:=e_i\cdot\left\f$, where \f$e_i\f$ form canonical basis) + + @param step Initial step that will be used in algorithm. Roughly said, it determines the spread + (size in each dimension) of an initial simplex. + */ + virtual void setInitStep(InputArray step)=0; + + /** @brief This function returns the reference to the ready-to-use DownhillSolver object. + + All the parameters are optional, so this procedure can be called even without parameters at + all. In this case, the default values will be used. As default value for terminal criteria are + the only sensible ones, MinProblemSolver::setFunction() and DownhillSolver::setInitStep() + should be called upon the obtained object, if the respective parameters were not given to + create(). Otherwise, the two ways (give parameters to createDownhillSolver() or miss them out + and call the MinProblemSolver::setFunction() and DownhillSolver::setInitStep()) are absolutely + equivalent (and will drop the same errors in the same way, should invalid input be detected). + @param f Pointer to the function that will be minimized, similarly to the one you submit via + MinProblemSolver::setFunction. + @param initStep Initial step, that will be used to construct the initial simplex, similarly to the one + you submit via MinProblemSolver::setInitStep. + @param termcrit Terminal criteria to the algorithm, similarly to the one you submit via + MinProblemSolver::setTermCriteria. + */ + static Ptr create(const Ptr& f=Ptr(), + InputArray initStep=Mat_(1,1,0.0), + TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5000,0.000001)); +}; + +/** @brief This class is used to perform the non-linear non-constrained minimization of a function +with known gradient, + +defined on an *n*-dimensional Euclidean space, using the **Nonlinear Conjugate Gradient method**. +The implementation was done based on the beautifully clear explanatory article [An Introduction to +the Conjugate Gradient Method Without the Agonizing +Pain](http://www.cs.cmu.edu/~quake-papers/painless-conjugate-gradient.pdf) by Jonathan Richard +Shewchuk. The method can be seen as an adaptation of a standard Conjugate Gradient method (see, for +example ) for numerically solving the +systems of linear equations. + +It should be noted, that this method, although deterministic, is rather a heuristic method and +therefore may converge to a local minima, not necessary a global one. What is even more disastrous, +most of its behaviour is ruled by gradient, therefore it essentially cannot distinguish between +local minima and maxima. Therefore, if it starts sufficiently near to the local maximum, it may +converge to it. Another obvious restriction is that it should be possible to compute the gradient of +a function at any point, thus it is preferable to have analytic expression for gradient and +computational burden should be born by the user. + +The latter responsibility is accompilished via the getGradient method of a +MinProblemSolver::Function interface (which represents function being optimized). This method takes +point a point in *n*-dimensional space (first argument represents the array of coordinates of that +point) and comput its gradient (it should be stored in the second argument as an array). + +@note class ConjGradSolver thus does not add any new methods to the basic MinProblemSolver interface. + +@note term criteria should meet following condition: +@code + termcrit.type == (TermCriteria::MAX_ITER + TermCriteria::EPS) && termcrit.epsilon > 0 && termcrit.maxCount > 0 + // or + termcrit.type == TermCriteria::MAX_ITER) && termcrit.maxCount > 0 +@endcode + */ +class CV_EXPORTS ConjGradSolver : public MinProblemSolver +{ +public: + /** @brief This function returns the reference to the ready-to-use ConjGradSolver object. + + All the parameters are optional, so this procedure can be called even without parameters at + all. In this case, the default values will be used. As default value for terminal criteria are + the only sensible ones, MinProblemSolver::setFunction() should be called upon the obtained + object, if the function was not given to create(). Otherwise, the two ways (submit it to + create() or miss it out and call the MinProblemSolver::setFunction()) are absolutely equivalent + (and will drop the same errors in the same way, should invalid input be detected). + @param f Pointer to the function that will be minimized, similarly to the one you submit via + MinProblemSolver::setFunction. + @param termcrit Terminal criteria to the algorithm, similarly to the one you submit via + MinProblemSolver::setTermCriteria. + */ + static Ptr create(const Ptr& f=Ptr(), + TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5000,0.000001)); +}; + +//! return codes for cv::solveLP() function +enum SolveLPResult +{ + SOLVELP_UNBOUNDED = -2, //!< problem is unbounded (target function can achieve arbitrary high values) + SOLVELP_UNFEASIBLE = -1, //!< problem is unfeasible (there are no points that satisfy all the constraints imposed) + SOLVELP_SINGLE = 0, //!< there is only one maximum for target function + SOLVELP_MULTI = 1 //!< there are multiple maxima for target function - the arbitrary one is returned +}; + +/** @brief Solve given (non-integer) linear programming problem using the Simplex Algorithm (Simplex Method). + +What we mean here by "linear programming problem" (or LP problem, for short) can be formulated as: + +\f[\mbox{Maximize } c\cdot x\\ + \mbox{Subject to:}\\ + Ax\leq b\\ + x\geq 0\f] + +Where \f$c\f$ is fixed `1`-by-`n` row-vector, \f$A\f$ is fixed `m`-by-`n` matrix, \f$b\f$ is fixed `m`-by-`1` +column vector and \f$x\f$ is an arbitrary `n`-by-`1` column vector, which satisfies the constraints. + +Simplex algorithm is one of many algorithms that are designed to handle this sort of problems +efficiently. Although it is not optimal in theoretical sense (there exist algorithms that can solve +any problem written as above in polynomial time, while simplex method degenerates to exponential +time for some special cases), it is well-studied, easy to implement and is shown to work well for +real-life purposes. + +The particular implementation is taken almost verbatim from **Introduction to Algorithms, third +edition** by T. H. Cormen, C. E. Leiserson, R. L. Rivest and Clifford Stein. In particular, the +Bland's rule is used to prevent cycling. + +@param Func This row-vector corresponds to \f$c\f$ in the LP problem formulation (see above). It should +contain 32- or 64-bit floating point numbers. As a convenience, column-vector may be also submitted, +in the latter case it is understood to correspond to \f$c^T\f$. +@param Constr `m`-by-`n+1` matrix, whose rightmost column corresponds to \f$b\f$ in formulation above +and the remaining to \f$A\f$. It should contain 32- or 64-bit floating point numbers. +@param z The solution will be returned here as a column-vector - it corresponds to \f$c\f$ in the +formulation above. It will contain 64-bit floating point numbers. +@return One of cv::SolveLPResult + */ +CV_EXPORTS_W int solveLP(const Mat& Func, const Mat& Constr, Mat& z); + +//! @} + +}// cv + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/ovx.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/ovx.hpp new file mode 100644 index 0000000000000000000000000000000000000000..8bb7d54911722ba38212bc1e082aa9281d879af4 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/ovx.hpp @@ -0,0 +1,28 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +// Copyright (C) 2016, Intel Corporation, all rights reserved. +// Third party copyrights are property of their respective owners. + +// OpenVX related definitions and declarations + +#pragma once +#ifndef OPENCV_OVX_HPP +#define OPENCV_OVX_HPP + +#include "cvdef.h" + +namespace cv +{ +/// Check if use of OpenVX is possible +CV_EXPORTS_W bool haveOpenVX(); + +/// Check if use of OpenVX is enabled +CV_EXPORTS_W bool useOpenVX(); + +/// Enable/disable use of OpenVX +CV_EXPORTS_W void setUseOpenVX(bool flag); +} // namespace cv + +#endif // OPENCV_OVX_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/persistence.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/persistence.hpp new file mode 100644 index 0000000000000000000000000000000000000000..126393fae13272d17e2114c7574a38b9a7cca6a2 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/persistence.hpp @@ -0,0 +1,1366 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_PERSISTENCE_HPP +#define OPENCV_CORE_PERSISTENCE_HPP + +#ifndef CV_DOXYGEN +/// Define to support persistence legacy formats +#define CV__LEGACY_PERSISTENCE +#endif + +#ifndef __cplusplus +# error persistence.hpp header must be compiled as C++ +#endif + +//! @addtogroup core_c +//! @{ + +/** @brief "black box" representation of the file storage associated with a file on disk. + +Several functions that are described below take CvFileStorage\* as inputs and allow the user to +save or to load hierarchical collections that consist of scalar values, standard CXCore objects +(such as matrices, sequences, graphs), and user-defined objects. + +OpenCV can read and write data in XML (), YAML () or +JSON () formats. Below is an example of 3x3 floating-point identity matrix A, +stored in XML and YAML files +using CXCore functions: +XML: +@code{.xml} + + + + 3 + 3 +
f
+ 1. 0. 0. 0. 1. 0. 0. 0. 1. + + +@endcode +YAML: +@code{.yaml} + %YAML:1.0 + A: !!opencv-matrix + rows: 3 + cols: 3 + dt: f + data: [ 1., 0., 0., 0., 1., 0., 0., 0., 1.] +@endcode +As it can be seen from the examples, XML uses nested tags to represent hierarchy, while YAML uses +indentation for that purpose (similar to the Python programming language). + +The same functions can read and write data in both formats; the particular format is determined by +the extension of the opened file, ".xml" for XML files, ".yml" or ".yaml" for YAML and ".json" for +JSON. + */ +typedef struct CvFileStorage CvFileStorage; +typedef struct CvFileNode CvFileNode; +typedef struct CvMat CvMat; +typedef struct CvMatND CvMatND; + +//! @} core_c + +#include "opencv2/core/types.hpp" +#include "opencv2/core/mat.hpp" + +namespace cv { + +/** @addtogroup core_xml + +XML/YAML/JSON file storages. {#xml_storage} +======================= +Writing to a file storage. +-------------------------- +You can store and then restore various OpenCV data structures to/from XML (), +YAML () or JSON () formats. Also, it is possible to store +and load arbitrarily complex data structures, which include OpenCV data structures, as well as +primitive data types (integer and floating-point numbers and text strings) as their elements. + +Use the following procedure to write something to XML, YAML or JSON: +-# Create new FileStorage and open it for writing. It can be done with a single call to +FileStorage::FileStorage constructor that takes a filename, or you can use the default constructor +and then call FileStorage::open. Format of the file (XML, YAML or JSON) is determined from the filename +extension (".xml", ".yml"/".yaml" and ".json", respectively) +-# Write all the data you want using the streaming operator `<<`, just like in the case of STL +streams. +-# Close the file using FileStorage::release. FileStorage destructor also closes the file. + +Here is an example: +@code + #include "opencv2/opencv.hpp" + #include + + using namespace cv; + + int main(int, char** argv) + { + FileStorage fs("test.yml", FileStorage::WRITE); + + fs << "frameCount" << 5; + time_t rawtime; time(&rawtime); + fs << "calibrationDate" << asctime(localtime(&rawtime)); + Mat cameraMatrix = (Mat_(3,3) << 1000, 0, 320, 0, 1000, 240, 0, 0, 1); + Mat distCoeffs = (Mat_(5,1) << 0.1, 0.01, -0.001, 0, 0); + fs << "cameraMatrix" << cameraMatrix << "distCoeffs" << distCoeffs; + fs << "features" << "["; + for( int i = 0; i < 3; i++ ) + { + int x = rand() % 640; + int y = rand() % 480; + uchar lbp = rand() % 256; + + fs << "{:" << "x" << x << "y" << y << "lbp" << "[:"; + for( int j = 0; j < 8; j++ ) + fs << ((lbp >> j) & 1); + fs << "]" << "}"; + } + fs << "]"; + fs.release(); + return 0; + } +@endcode +The sample above stores to YML an integer, a text string (calibration date), 2 matrices, and a custom +structure "feature", which includes feature coordinates and LBP (local binary pattern) value. Here +is output of the sample: +@code{.yaml} +%YAML:1.0 +frameCount: 5 +calibrationDate: "Fri Jun 17 14:09:29 2011\n" +cameraMatrix: !!opencv-matrix + rows: 3 + cols: 3 + dt: d + data: [ 1000., 0., 320., 0., 1000., 240., 0., 0., 1. ] +distCoeffs: !!opencv-matrix + rows: 5 + cols: 1 + dt: d + data: [ 1.0000000000000001e-01, 1.0000000000000000e-02, + -1.0000000000000000e-03, 0., 0. ] +features: + - { x:167, y:49, lbp:[ 1, 0, 0, 1, 1, 0, 1, 1 ] } + - { x:298, y:130, lbp:[ 0, 0, 0, 1, 0, 0, 1, 1 ] } + - { x:344, y:158, lbp:[ 1, 1, 0, 0, 0, 0, 1, 0 ] } +@endcode + +As an exercise, you can replace ".yml" with ".xml" or ".json" in the sample above and see, how the +corresponding XML file will look like. + +Several things can be noted by looking at the sample code and the output: + +- The produced YAML (and XML/JSON) consists of heterogeneous collections that can be nested. There are + 2 types of collections: named collections (mappings) and unnamed collections (sequences). In mappings + each element has a name and is accessed by name. This is similar to structures and std::map in + C/C++ and dictionaries in Python. In sequences elements do not have names, they are accessed by + indices. This is similar to arrays and std::vector in C/C++ and lists, tuples in Python. + "Heterogeneous" means that elements of each single collection can have different types. + + Top-level collection in YAML/XML/JSON is a mapping. Each matrix is stored as a mapping, and the matrix + elements are stored as a sequence. Then, there is a sequence of features, where each feature is + represented a mapping, and lbp value in a nested sequence. + +- When you write to a mapping (a structure), you write element name followed by its value. When you + write to a sequence, you simply write the elements one by one. OpenCV data structures (such as + cv::Mat) are written in absolutely the same way as simple C data structures - using `<<` + operator. + +- To write a mapping, you first write the special string `{` to the storage, then write the + elements as pairs (`fs << << `) and then write the closing + `}`. + +- To write a sequence, you first write the special string `[`, then write the elements, then + write the closing `]`. + +- In YAML/JSON (but not XML), mappings and sequences can be written in a compact Python-like inline + form. In the sample above matrix elements, as well as each feature, including its lbp value, is + stored in such inline form. To store a mapping/sequence in a compact form, put `:` after the + opening character, e.g. use `{:` instead of `{` and `[:` instead of `[`. When the + data is written to XML, those extra `:` are ignored. + +Reading data from a file storage. +--------------------------------- +To read the previously written XML, YAML or JSON file, do the following: +-# Open the file storage using FileStorage::FileStorage constructor or FileStorage::open method. + In the current implementation the whole file is parsed and the whole representation of file + storage is built in memory as a hierarchy of file nodes (see FileNode) + +-# Read the data you are interested in. Use FileStorage::operator [], FileNode::operator [] + and/or FileNodeIterator. + +-# Close the storage using FileStorage::release. + +Here is how to read the file created by the code sample above: +@code + FileStorage fs2("test.yml", FileStorage::READ); + + // first method: use (type) operator on FileNode. + int frameCount = (int)fs2["frameCount"]; + + String date; + // second method: use FileNode::operator >> + fs2["calibrationDate"] >> date; + + Mat cameraMatrix2, distCoeffs2; + fs2["cameraMatrix"] >> cameraMatrix2; + fs2["distCoeffs"] >> distCoeffs2; + + cout << "frameCount: " << frameCount << endl + << "calibration date: " << date << endl + << "camera matrix: " << cameraMatrix2 << endl + << "distortion coeffs: " << distCoeffs2 << endl; + + FileNode features = fs2["features"]; + FileNodeIterator it = features.begin(), it_end = features.end(); + int idx = 0; + std::vector lbpval; + + // iterate through a sequence using FileNodeIterator + for( ; it != it_end; ++it, idx++ ) + { + cout << "feature #" << idx << ": "; + cout << "x=" << (int)(*it)["x"] << ", y=" << (int)(*it)["y"] << ", lbp: ("; + // you can also easily read numerical arrays using FileNode >> std::vector operator. + (*it)["lbp"] >> lbpval; + for( int i = 0; i < (int)lbpval.size(); i++ ) + cout << " " << (int)lbpval[i]; + cout << ")" << endl; + } + fs2.release(); +@endcode + +Format specification {#format_spec} +-------------------- +`([count]{u|c|w|s|i|f|d})`... where the characters correspond to fundamental C++ types: +- `u` 8-bit unsigned number +- `c` 8-bit signed number +- `w` 16-bit unsigned number +- `s` 16-bit signed number +- `i` 32-bit signed number +- `f` single precision floating-point number +- `d` double precision floating-point number +- `r` pointer, 32 lower bits of which are written as a signed integer. The type can be used to + store structures with links between the elements. + +`count` is the optional counter of values of a given type. For example, `2if` means that each array +element is a structure of 2 integers, followed by a single-precision floating-point number. The +equivalent notations of the above specification are `iif`, `2i1f` and so forth. Other examples: `u` +means that the array consists of bytes, and `2d` means the array consists of pairs of doubles. + +@see @ref samples/cpp/filestorage.cpp +*/ + +//! @{ + +/** @example samples/cpp/filestorage.cpp +A complete example using the FileStorage interface +*/ + +////////////////////////// XML & YAML I/O ////////////////////////// + +class CV_EXPORTS FileNode; +class CV_EXPORTS FileNodeIterator; + +/** @brief XML/YAML/JSON file storage class that encapsulates all the information necessary for writing or +reading data to/from a file. + */ +class CV_EXPORTS_W FileStorage +{ +public: + //! file storage mode + enum Mode + { + READ = 0, //!< value, open the file for reading + WRITE = 1, //!< value, open the file for writing + APPEND = 2, //!< value, open the file for appending + MEMORY = 4, //!< flag, read data from source or write data to the internal buffer (which is + //!< returned by FileStorage::release) + FORMAT_MASK = (7<<3), //!< mask for format flags + FORMAT_AUTO = 0, //!< flag, auto format + FORMAT_XML = (1<<3), //!< flag, XML format + FORMAT_YAML = (2<<3), //!< flag, YAML format + FORMAT_JSON = (3<<3), //!< flag, JSON format + + BASE64 = 64, //!< flag, write rawdata in Base64 by default. (consider using WRITE_BASE64) + WRITE_BASE64 = BASE64 | WRITE, //!< flag, enable both WRITE and BASE64 + }; + enum + { + UNDEFINED = 0, + VALUE_EXPECTED = 1, + NAME_EXPECTED = 2, + INSIDE_MAP = 4 + }; + + /** @brief The constructors. + + The full constructor opens the file. Alternatively you can use the default constructor and then + call FileStorage::open. + */ + CV_WRAP FileStorage(); + + /** @overload + @copydoc open() + */ + CV_WRAP FileStorage(const String& filename, int flags, const String& encoding=String()); + + /** @overload */ + FileStorage(CvFileStorage* fs, bool owning=true); + + //! the destructor. calls release() + virtual ~FileStorage(); + + /** @brief Opens a file. + + See description of parameters in FileStorage::FileStorage. The method calls FileStorage::release + before opening the file. + @param filename Name of the file to open or the text string to read the data from. + Extension of the file (.xml, .yml/.yaml or .json) determines its format (XML, YAML or JSON + respectively). Also you can append .gz to work with compressed files, for example myHugeMatrix.xml.gz. If both + FileStorage::WRITE and FileStorage::MEMORY flags are specified, source is used just to specify + the output file format (e.g. mydata.xml, .yml etc.). A file name can also contain parameters. + You can use this format, "*?base64" (e.g. "file.json?base64" (case sensitive)), as an alternative to + FileStorage::BASE64 flag. + @param flags Mode of operation. One of FileStorage::Mode + @param encoding Encoding of the file. Note that UTF-16 XML encoding is not supported currently and + you should use 8-bit encoding instead of it. + */ + CV_WRAP virtual bool open(const String& filename, int flags, const String& encoding=String()); + + /** @brief Checks whether the file is opened. + + @returns true if the object is associated with the current file and false otherwise. It is a + good practice to call this method after you tried to open a file. + */ + CV_WRAP virtual bool isOpened() const; + + /** @brief Closes the file and releases all the memory buffers. + + Call this method after all I/O operations with the storage are finished. + */ + CV_WRAP virtual void release(); + + /** @brief Closes the file and releases all the memory buffers. + + Call this method after all I/O operations with the storage are finished. If the storage was + opened for writing data and FileStorage::WRITE was specified + */ + CV_WRAP virtual String releaseAndGetString(); + + /** @brief Returns the first element of the top-level mapping. + @returns The first element of the top-level mapping. + */ + CV_WRAP FileNode getFirstTopLevelNode() const; + + /** @brief Returns the top-level mapping + @param streamidx Zero-based index of the stream. In most cases there is only one stream in the file. + However, YAML supports multiple streams and so there can be several. + @returns The top-level mapping. + */ + CV_WRAP FileNode root(int streamidx=0) const; + + /** @brief Returns the specified element of the top-level mapping. + @param nodename Name of the file node. + @returns Node with the given name. + */ + FileNode operator[](const String& nodename) const; + + /** @overload */ + CV_WRAP_AS(getNode) FileNode operator[](const char* nodename) const; + + /** @brief Returns the obsolete C FileStorage structure. + @returns Pointer to the underlying C FileStorage structure + */ + CvFileStorage* operator *() { return fs.get(); } + + /** @overload */ + const CvFileStorage* operator *() const { return fs.get(); } + + /** @brief Writes multiple numbers. + + Writes one or more numbers of the specified format to the currently written structure. Usually it is + more convenient to use operator `<<` instead of this method. + @param fmt Specification of each array element, see @ref format_spec "format specification" + @param vec Pointer to the written array. + @param len Number of the uchar elements to write. + */ + void writeRaw( const String& fmt, const uchar* vec, size_t len ); + + /** @brief Writes the registered C structure (CvMat, CvMatND, CvSeq). + @param name Name of the written object. + @param obj Pointer to the object. + @see ocvWrite for details. + */ + void writeObj( const String& name, const void* obj ); + + /** + * @brief Simplified writing API to use with bindings. + * @param name Name of the written object + * @param val Value of the written object + */ + CV_WRAP void write(const String& name, int val); + /// @overload + CV_WRAP void write(const String& name, double val); + /// @overload + CV_WRAP void write(const String& name, const String& val); + /// @overload + CV_WRAP void write(const String& name, InputArray val); + + /** @brief Writes a comment. + + The function writes a comment into file storage. The comments are skipped when the storage is read. + @param comment The written comment, single-line or multi-line + @param append If true, the function tries to put the comment at the end of current line. + Else if the comment is multi-line, or if it does not fit at the end of the current + line, the comment starts a new line. + */ + CV_WRAP void writeComment(const String& comment, bool append = false); + + /** @brief Returns the normalized object name for the specified name of a file. + @param filename Name of a file + @returns The normalized object name. + */ + static String getDefaultObjectName(const String& filename); + + /** @brief Returns the current format. + * @returns The current format, see FileStorage::Mode + */ + CV_WRAP int getFormat() const; + + Ptr fs; //!< the underlying C FileStorage structure + String elname; //!< the currently written element + std::vector structs; //!< the stack of written structures + int state; //!< the writer state +}; + +template<> CV_EXPORTS void DefaultDeleter::operator ()(CvFileStorage* obj) const; + +/** @brief File Storage Node class. + +The node is used to store each and every element of the file storage opened for reading. When +XML/YAML file is read, it is first parsed and stored in the memory as a hierarchical collection of +nodes. Each node can be a "leaf" that is contain a single number or a string, or be a collection of +other nodes. There can be named collections (mappings) where each element has a name and it is +accessed by a name, and ordered collections (sequences) where elements do not have names but rather +accessed by index. Type of the file node can be determined using FileNode::type method. + +Note that file nodes are only used for navigating file storages opened for reading. When a file +storage is opened for writing, no data is stored in memory after it is written. + */ +class CV_EXPORTS_W_SIMPLE FileNode +{ +public: + //! type of the file storage node + enum Type + { + NONE = 0, //!< empty node + INT = 1, //!< an integer + REAL = 2, //!< floating-point number + FLOAT = REAL, //!< synonym or REAL + STR = 3, //!< text string in UTF-8 encoding + STRING = STR, //!< synonym for STR + REF = 4, //!< integer of size size_t. Typically used for storing complex dynamic structures where some elements reference the others + SEQ = 5, //!< sequence + MAP = 6, //!< mapping + TYPE_MASK = 7, + FLOW = 8, //!< compact representation of a sequence or mapping. Used only by YAML writer + USER = 16, //!< a registered object (e.g. a matrix) + EMPTY = 32, //!< empty structure (sequence or mapping) + NAMED = 64 //!< the node has a name (i.e. it is element of a mapping) + }; + /** @brief The constructors. + + These constructors are used to create a default file node, construct it from obsolete structures or + from the another file node. + */ + CV_WRAP FileNode(); + + /** @overload + @param fs Pointer to the obsolete file storage structure. + @param node File node to be used as initialization for the created file node. + */ + FileNode(const CvFileStorage* fs, const CvFileNode* node); + + /** @overload + @param node File node to be used as initialization for the created file node. + */ + FileNode(const FileNode& node); + + /** @brief Returns element of a mapping node or a sequence node. + @param nodename Name of an element in the mapping node. + @returns Returns the element with the given identifier. + */ + FileNode operator[](const String& nodename) const; + + /** @overload + @param nodename Name of an element in the mapping node. + */ + CV_WRAP_AS(getNode) FileNode operator[](const char* nodename) const; + + /** @overload + @param i Index of an element in the sequence node. + */ + CV_WRAP_AS(at) FileNode operator[](int i) const; + + /** @brief Returns keys of a mapping node. + @returns Keys of a mapping node. + */ + CV_WRAP std::vector keys() const; + + /** @brief Returns type of the node. + @returns Type of the node. See FileNode::Type + */ + CV_WRAP int type() const; + + //! returns true if the node is empty + CV_WRAP bool empty() const; + //! returns true if the node is a "none" object + CV_WRAP bool isNone() const; + //! returns true if the node is a sequence + CV_WRAP bool isSeq() const; + //! returns true if the node is a mapping + CV_WRAP bool isMap() const; + //! returns true if the node is an integer + CV_WRAP bool isInt() const; + //! returns true if the node is a floating-point number + CV_WRAP bool isReal() const; + //! returns true if the node is a text string + CV_WRAP bool isString() const; + //! returns true if the node has a name + CV_WRAP bool isNamed() const; + //! returns the node name or an empty string if the node is nameless + CV_WRAP String name() const; + //! returns the number of elements in the node, if it is a sequence or mapping, or 1 otherwise. + CV_WRAP size_t size() const; + //! returns the node content as an integer. If the node stores floating-point number, it is rounded. + operator int() const; + //! returns the node content as float + operator float() const; + //! returns the node content as double + operator double() const; + //! returns the node content as text string + operator String() const; + operator std::string() const; + + //! returns pointer to the underlying file node + CvFileNode* operator *(); + //! returns pointer to the underlying file node + const CvFileNode* operator* () const; + + //! returns iterator pointing to the first node element + FileNodeIterator begin() const; + //! returns iterator pointing to the element following the last node element + FileNodeIterator end() const; + + /** @brief Reads node elements to the buffer with the specified format. + + Usually it is more convenient to use operator `>>` instead of this method. + @param fmt Specification of each array element. See @ref format_spec "format specification" + @param vec Pointer to the destination array. + @param len Number of elements to read. If it is greater than number of remaining elements then all + of them will be read. + */ + void readRaw( const String& fmt, uchar* vec, size_t len ) const; + + //! reads the registered object and returns pointer to it + void* readObj() const; + + //! Simplified reading API to use with bindings. + CV_WRAP double real() const; + //! Simplified reading API to use with bindings. + CV_WRAP String string() const; + //! Simplified reading API to use with bindings. + CV_WRAP Mat mat() const; + + // do not use wrapper pointer classes for better efficiency + const CvFileStorage* fs; + const CvFileNode* node; +}; + + +/** @brief used to iterate through sequences and mappings. + +A standard STL notation, with node.begin(), node.end() denoting the beginning and the end of a +sequence, stored in node. See the data reading sample in the beginning of the section. + */ +class CV_EXPORTS FileNodeIterator +{ +public: + /** @brief The constructors. + + These constructors are used to create a default iterator, set it to specific element in a file node + or construct it from another iterator. + */ + FileNodeIterator(); + + /** @overload + @param fs File storage for the iterator. + @param node File node for the iterator. + @param ofs Index of the element in the node. The created iterator will point to this element. + */ + FileNodeIterator(const CvFileStorage* fs, const CvFileNode* node, size_t ofs=0); + + /** @overload + @param it Iterator to be used as initialization for the created iterator. + */ + FileNodeIterator(const FileNodeIterator& it); + + //! returns the currently observed element + FileNode operator *() const; + //! accesses the currently observed element methods + FileNode operator ->() const; + + //! moves iterator to the next node + FileNodeIterator& operator ++ (); + //! moves iterator to the next node + FileNodeIterator operator ++ (int); + //! moves iterator to the previous node + FileNodeIterator& operator -- (); + //! moves iterator to the previous node + FileNodeIterator operator -- (int); + //! moves iterator forward by the specified offset (possibly negative) + FileNodeIterator& operator += (int ofs); + //! moves iterator backward by the specified offset (possibly negative) + FileNodeIterator& operator -= (int ofs); + + /** @brief Reads node elements to the buffer with the specified format. + + Usually it is more convenient to use operator `>>` instead of this method. + @param fmt Specification of each array element. See @ref format_spec "format specification" + @param vec Pointer to the destination array. + @param maxCount Number of elements to read. If it is greater than number of remaining elements then + all of them will be read. + */ + FileNodeIterator& readRaw( const String& fmt, uchar* vec, + size_t maxCount=(size_t)INT_MAX ); + + struct SeqReader + { + int header_size; + void* seq; /* sequence, beign read; CvSeq */ + void* block; /* current block; CvSeqBlock */ + schar* ptr; /* pointer to element be read next */ + schar* block_min; /* pointer to the beginning of block */ + schar* block_max; /* pointer to the end of block */ + int delta_index;/* = seq->first->start_index */ + schar* prev_elem; /* pointer to previous element */ + }; + + const CvFileStorage* fs; + const CvFileNode* container; + SeqReader reader; + size_t remaining; +}; + +//! @} core_xml + +/////////////////// XML & YAML I/O implementation ////////////////// + +//! @relates cv::FileStorage +//! @{ + +CV_EXPORTS void write( FileStorage& fs, const String& name, int value ); +CV_EXPORTS void write( FileStorage& fs, const String& name, float value ); +CV_EXPORTS void write( FileStorage& fs, const String& name, double value ); +CV_EXPORTS void write( FileStorage& fs, const String& name, const String& value ); +CV_EXPORTS void write( FileStorage& fs, const String& name, const Mat& value ); +CV_EXPORTS void write( FileStorage& fs, const String& name, const SparseMat& value ); +#ifdef CV__LEGACY_PERSISTENCE +CV_EXPORTS void write( FileStorage& fs, const String& name, const std::vector& value); +CV_EXPORTS void write( FileStorage& fs, const String& name, const std::vector& value); +#endif + +CV_EXPORTS void writeScalar( FileStorage& fs, int value ); +CV_EXPORTS void writeScalar( FileStorage& fs, float value ); +CV_EXPORTS void writeScalar( FileStorage& fs, double value ); +CV_EXPORTS void writeScalar( FileStorage& fs, const String& value ); + +//! @} + +//! @relates cv::FileNode +//! @{ + +CV_EXPORTS void read(const FileNode& node, int& value, int default_value); +CV_EXPORTS void read(const FileNode& node, float& value, float default_value); +CV_EXPORTS void read(const FileNode& node, double& value, double default_value); +CV_EXPORTS void read(const FileNode& node, String& value, const String& default_value); +CV_EXPORTS void read(const FileNode& node, std::string& value, const std::string& default_value); +CV_EXPORTS void read(const FileNode& node, Mat& mat, const Mat& default_mat = Mat() ); +CV_EXPORTS void read(const FileNode& node, SparseMat& mat, const SparseMat& default_mat = SparseMat() ); +#ifdef CV__LEGACY_PERSISTENCE +CV_EXPORTS void read(const FileNode& node, std::vector& keypoints); +CV_EXPORTS void read(const FileNode& node, std::vector& matches); +#endif +CV_EXPORTS void read(const FileNode& node, KeyPoint& value, const KeyPoint& default_value); +CV_EXPORTS void read(const FileNode& node, DMatch& value, const DMatch& default_value); + +template static inline void read(const FileNode& node, Point_<_Tp>& value, const Point_<_Tp>& default_value) +{ + std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp; + value = temp.size() != 2 ? default_value : Point_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1])); +} + +template static inline void read(const FileNode& node, Point3_<_Tp>& value, const Point3_<_Tp>& default_value) +{ + std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp; + value = temp.size() != 3 ? default_value : Point3_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1]), + saturate_cast<_Tp>(temp[2])); +} + +template static inline void read(const FileNode& node, Size_<_Tp>& value, const Size_<_Tp>& default_value) +{ + std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp; + value = temp.size() != 2 ? default_value : Size_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1])); +} + +template static inline void read(const FileNode& node, Complex<_Tp>& value, const Complex<_Tp>& default_value) +{ + std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp; + value = temp.size() != 2 ? default_value : Complex<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1])); +} + +template static inline void read(const FileNode& node, Rect_<_Tp>& value, const Rect_<_Tp>& default_value) +{ + std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp; + value = temp.size() != 4 ? default_value : Rect_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1]), + saturate_cast<_Tp>(temp[2]), saturate_cast<_Tp>(temp[3])); +} + +template static inline void read(const FileNode& node, Vec<_Tp, cn>& value, const Vec<_Tp, cn>& default_value) +{ + std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp; + value = temp.size() != cn ? default_value : Vec<_Tp, cn>(&temp[0]); +} + +template static inline void read(const FileNode& node, Scalar_<_Tp>& value, const Scalar_<_Tp>& default_value) +{ + std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp; + value = temp.size() != 4 ? default_value : Scalar_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1]), + saturate_cast<_Tp>(temp[2]), saturate_cast<_Tp>(temp[3])); +} + +static inline void read(const FileNode& node, Range& value, const Range& default_value) +{ + Point2i temp(value.start, value.end); const Point2i default_temp = Point2i(default_value.start, default_value.end); + read(node, temp, default_temp); + value.start = temp.x; value.end = temp.y; +} + +//! @} + +/** @brief Writes string to a file storage. +@relates cv::FileStorage + */ +CV_EXPORTS FileStorage& operator << (FileStorage& fs, const String& str); + +//! @cond IGNORED + +namespace internal +{ + class CV_EXPORTS WriteStructContext + { + public: + WriteStructContext(FileStorage& _fs, const String& name, int flags, const String& typeName = String()); + ~WriteStructContext(); + private: + FileStorage* fs; + }; + + template class VecWriterProxy + { + public: + VecWriterProxy( FileStorage* _fs ) : fs(_fs) {} + void operator()(const std::vector<_Tp>& vec) const + { + size_t count = vec.size(); + for (size_t i = 0; i < count; i++) + write(*fs, vec[i]); + } + private: + FileStorage* fs; + }; + + template class VecWriterProxy<_Tp, 1> + { + public: + VecWriterProxy( FileStorage* _fs ) : fs(_fs) {} + void operator()(const std::vector<_Tp>& vec) const + { + int _fmt = traits::SafeFmt<_Tp>::fmt; + char fmt[] = { (char)((_fmt >> 8) + '1'), (char)_fmt, '\0' }; + fs->writeRaw(fmt, !vec.empty() ? (uchar*)&vec[0] : 0, vec.size() * sizeof(_Tp)); + } + private: + FileStorage* fs; + }; + + template class VecReaderProxy + { + public: + VecReaderProxy( FileNodeIterator* _it ) : it(_it) {} + void operator()(std::vector<_Tp>& vec, size_t count) const + { + count = std::min(count, it->remaining); + vec.resize(count); + for (size_t i = 0; i < count; i++, ++(*it)) + read(**it, vec[i], _Tp()); + } + private: + FileNodeIterator* it; + }; + + template class VecReaderProxy<_Tp, 1> + { + public: + VecReaderProxy( FileNodeIterator* _it ) : it(_it) {} + void operator()(std::vector<_Tp>& vec, size_t count) const + { + size_t remaining = it->remaining; + size_t cn = DataType<_Tp>::channels; + int _fmt = traits::SafeFmt<_Tp>::fmt; + CV_Assert((_fmt >> 8) < 9); + char fmt[] = { (char)((_fmt >> 8)+'1'), (char)_fmt, '\0' }; + CV_Assert((remaining % cn) == 0); + size_t remaining1 = remaining / cn; + count = count < remaining1 ? count : remaining1; + vec.resize(count); + it->readRaw(fmt, !vec.empty() ? (uchar*)&vec[0] : 0, count*sizeof(_Tp)); + } + private: + FileNodeIterator* it; + }; + +} // internal + +//! @endcond + +//! @relates cv::FileStorage +//! @{ + +template static inline +void write(FileStorage& fs, const _Tp& value) +{ + write(fs, String(), value); +} + +template<> inline +void write( FileStorage& fs, const int& value ) +{ + writeScalar(fs, value); +} + +template<> inline +void write( FileStorage& fs, const float& value ) +{ + writeScalar(fs, value); +} + +template<> inline +void write( FileStorage& fs, const double& value ) +{ + writeScalar(fs, value); +} + +template<> inline +void write( FileStorage& fs, const String& value ) +{ + writeScalar(fs, value); +} + +template static inline +void write(FileStorage& fs, const Point_<_Tp>& pt ) +{ + write(fs, pt.x); + write(fs, pt.y); +} + +template static inline +void write(FileStorage& fs, const Point3_<_Tp>& pt ) +{ + write(fs, pt.x); + write(fs, pt.y); + write(fs, pt.z); +} + +template static inline +void write(FileStorage& fs, const Size_<_Tp>& sz ) +{ + write(fs, sz.width); + write(fs, sz.height); +} + +template static inline +void write(FileStorage& fs, const Complex<_Tp>& c ) +{ + write(fs, c.re); + write(fs, c.im); +} + +template static inline +void write(FileStorage& fs, const Rect_<_Tp>& r ) +{ + write(fs, r.x); + write(fs, r.y); + write(fs, r.width); + write(fs, r.height); +} + +template static inline +void write(FileStorage& fs, const Vec<_Tp, cn>& v ) +{ + for(int i = 0; i < cn; i++) + write(fs, v.val[i]); +} + +template static inline +void write(FileStorage& fs, const Scalar_<_Tp>& s ) +{ + write(fs, s.val[0]); + write(fs, s.val[1]); + write(fs, s.val[2]); + write(fs, s.val[3]); +} + +static inline +void write(FileStorage& fs, const Range& r ) +{ + write(fs, r.start); + write(fs, r.end); +} + +template static inline +void write( FileStorage& fs, const std::vector<_Tp>& vec ) +{ + cv::internal::VecWriterProxy<_Tp, traits::SafeFmt<_Tp>::fmt != 0> w(&fs); + w(vec); +} + +template static inline +void write(FileStorage& fs, const String& name, const Point_<_Tp>& pt ) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); + write(fs, pt); +} + +template static inline +void write(FileStorage& fs, const String& name, const Point3_<_Tp>& pt ) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); + write(fs, pt); +} + +template static inline +void write(FileStorage& fs, const String& name, const Size_<_Tp>& sz ) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); + write(fs, sz); +} + +template static inline +void write(FileStorage& fs, const String& name, const Complex<_Tp>& c ) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); + write(fs, c); +} + +template static inline +void write(FileStorage& fs, const String& name, const Rect_<_Tp>& r ) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); + write(fs, r); +} + +template static inline +void write(FileStorage& fs, const String& name, const Vec<_Tp, cn>& v ) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); + write(fs, v); +} + +template static inline +void write(FileStorage& fs, const String& name, const Scalar_<_Tp>& s ) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); + write(fs, s); +} + +static inline +void write(FileStorage& fs, const String& name, const Range& r ) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); + write(fs, r); +} + +static inline +void write(FileStorage& fs, const String& name, const KeyPoint& kpt) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); + write(fs, kpt.pt.x); + write(fs, kpt.pt.y); + write(fs, kpt.size); + write(fs, kpt.angle); + write(fs, kpt.response); + write(fs, kpt.octave); + write(fs, kpt.class_id); +} + +static inline +void write(FileStorage& fs, const String& name, const DMatch& m) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); + write(fs, m.queryIdx); + write(fs, m.trainIdx); + write(fs, m.imgIdx); + write(fs, m.distance); +} + +template static inline +void write( FileStorage& fs, const String& name, const std::vector<_Tp>& vec ) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+(traits::SafeFmt<_Tp>::fmt != 0 ? FileNode::FLOW : 0)); + write(fs, vec); +} + +template static inline +void write( FileStorage& fs, const String& name, const std::vector< std::vector<_Tp> >& vec ) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ); + for(size_t i = 0; i < vec.size(); i++) + { + cv::internal::WriteStructContext ws_(fs, name, FileNode::SEQ+(traits::SafeFmt<_Tp>::fmt != 0 ? FileNode::FLOW : 0)); + write(fs, vec[i]); + } +} + +#ifdef CV__LEGACY_PERSISTENCE +// This code is not needed anymore, but it is preserved here to keep source compatibility +// Implementation is similar to templates instantiations +static inline void write(FileStorage& fs, const KeyPoint& kpt) { write(fs, String(), kpt); } +static inline void write(FileStorage& fs, const DMatch& m) { write(fs, String(), m); } +static inline void write(FileStorage& fs, const std::vector& vec) +{ + cv::internal::VecWriterProxy w(&fs); + w(vec); +} +static inline void write(FileStorage& fs, const std::vector& vec) +{ + cv::internal::VecWriterProxy w(&fs); + w(vec); + +} +#endif + +//! @} FileStorage + +//! @relates cv::FileNode +//! @{ + +static inline +void read(const FileNode& node, bool& value, bool default_value) +{ + int temp; + read(node, temp, (int)default_value); + value = temp != 0; +} + +static inline +void read(const FileNode& node, uchar& value, uchar default_value) +{ + int temp; + read(node, temp, (int)default_value); + value = saturate_cast(temp); +} + +static inline +void read(const FileNode& node, schar& value, schar default_value) +{ + int temp; + read(node, temp, (int)default_value); + value = saturate_cast(temp); +} + +static inline +void read(const FileNode& node, ushort& value, ushort default_value) +{ + int temp; + read(node, temp, (int)default_value); + value = saturate_cast(temp); +} + +static inline +void read(const FileNode& node, short& value, short default_value) +{ + int temp; + read(node, temp, (int)default_value); + value = saturate_cast(temp); +} + +template static inline +void read( FileNodeIterator& it, std::vector<_Tp>& vec, size_t maxCount = (size_t)INT_MAX ) +{ + cv::internal::VecReaderProxy<_Tp, traits::SafeFmt<_Tp>::fmt != 0> r(&it); + r(vec, maxCount); +} + +template static inline +void read( const FileNode& node, std::vector<_Tp>& vec, const std::vector<_Tp>& default_value = std::vector<_Tp>() ) +{ + if(!node.node) + vec = default_value; + else + { + FileNodeIterator it = node.begin(); + read( it, vec ); + } +} + +static inline +void read( const FileNode& node, std::vector& vec, const std::vector& default_value ) +{ + if(!node.node) + vec = default_value; + else + read(node, vec); +} + +static inline +void read( const FileNode& node, std::vector& vec, const std::vector& default_value ) +{ + if(!node.node) + vec = default_value; + else + read(node, vec); +} + +//! @} FileNode + +//! @relates cv::FileStorage +//! @{ + +/** @brief Writes data to a file storage. + */ +template static inline +FileStorage& operator << (FileStorage& fs, const _Tp& value) +{ + if( !fs.isOpened() ) + return fs; + if( fs.state == FileStorage::NAME_EXPECTED + FileStorage::INSIDE_MAP ) + CV_Error( Error::StsError, "No element name has been given" ); + write( fs, fs.elname, value ); + if( fs.state & FileStorage::INSIDE_MAP ) + fs.state = FileStorage::NAME_EXPECTED + FileStorage::INSIDE_MAP; + return fs; +} + +/** @brief Writes data to a file storage. + */ +static inline +FileStorage& operator << (FileStorage& fs, const char* str) +{ + return (fs << String(str)); +} + +/** @brief Writes data to a file storage. + */ +static inline +FileStorage& operator << (FileStorage& fs, char* value) +{ + return (fs << String(value)); +} + +//! @} FileStorage + +//! @relates cv::FileNodeIterator +//! @{ + +/** @brief Reads data from a file storage. + */ +template static inline +FileNodeIterator& operator >> (FileNodeIterator& it, _Tp& value) +{ + read( *it, value, _Tp()); + return ++it; +} + +/** @brief Reads data from a file storage. + */ +template static inline +FileNodeIterator& operator >> (FileNodeIterator& it, std::vector<_Tp>& vec) +{ + cv::internal::VecReaderProxy<_Tp, traits::SafeFmt<_Tp>::fmt != 0> r(&it); + r(vec, (size_t)INT_MAX); + return it; +} + +//! @} FileNodeIterator + +//! @relates cv::FileNode +//! @{ + +/** @brief Reads data from a file storage. + */ +template static inline +void operator >> (const FileNode& n, _Tp& value) +{ + read( n, value, _Tp()); +} + +/** @brief Reads data from a file storage. + */ +template static inline +void operator >> (const FileNode& n, std::vector<_Tp>& vec) +{ + FileNodeIterator it = n.begin(); + it >> vec; +} + +/** @brief Reads KeyPoint from a file storage. +*/ +//It needs special handling because it contains two types of fields, int & float. +static inline +void operator >> (const FileNode& n, KeyPoint& kpt) +{ + FileNodeIterator it = n.begin(); + it >> kpt.pt.x >> kpt.pt.y >> kpt.size >> kpt.angle >> kpt.response >> kpt.octave >> kpt.class_id; +} + +#ifdef CV__LEGACY_PERSISTENCE +static inline +void operator >> (const FileNode& n, std::vector& vec) +{ + read(n, vec); +} +static inline +void operator >> (const FileNode& n, std::vector& vec) +{ + read(n, vec); +} +#endif + +/** @brief Reads DMatch from a file storage. +*/ +//It needs special handling because it contains two types of fields, int & float. +static inline +void operator >> (const FileNode& n, DMatch& m) +{ + FileNodeIterator it = n.begin(); + it >> m.queryIdx >> m.trainIdx >> m.imgIdx >> m.distance; +} + +//! @} FileNode + +//! @relates cv::FileNodeIterator +//! @{ + +static inline +bool operator == (const FileNodeIterator& it1, const FileNodeIterator& it2) +{ + return it1.fs == it2.fs && it1.container == it2.container && + it1.reader.ptr == it2.reader.ptr && it1.remaining == it2.remaining; +} + +static inline +bool operator != (const FileNodeIterator& it1, const FileNodeIterator& it2) +{ + return !(it1 == it2); +} + +static inline +ptrdiff_t operator - (const FileNodeIterator& it1, const FileNodeIterator& it2) +{ + return it2.remaining - it1.remaining; +} + +static inline +bool operator < (const FileNodeIterator& it1, const FileNodeIterator& it2) +{ + return it1.remaining > it2.remaining; +} + +//! @} FileNodeIterator + +//! @cond IGNORED + +inline FileNode FileStorage::getFirstTopLevelNode() const { FileNode r = root(); FileNodeIterator it = r.begin(); return it != r.end() ? *it : FileNode(); } +inline FileNode::FileNode() : fs(0), node(0) {} +inline FileNode::FileNode(const CvFileStorage* _fs, const CvFileNode* _node) : fs(_fs), node(_node) {} +inline FileNode::FileNode(const FileNode& _node) : fs(_node.fs), node(_node.node) {} +inline bool FileNode::empty() const { return node == 0; } +inline bool FileNode::isNone() const { return type() == NONE; } +inline bool FileNode::isSeq() const { return type() == SEQ; } +inline bool FileNode::isMap() const { return type() == MAP; } +inline bool FileNode::isInt() const { return type() == INT; } +inline bool FileNode::isReal() const { return type() == REAL; } +inline bool FileNode::isString() const { return type() == STR; } +inline CvFileNode* FileNode::operator *() { return (CvFileNode*)node; } +inline const CvFileNode* FileNode::operator* () const { return node; } +inline FileNode::operator int() const { int value; read(*this, value, 0); return value; } +inline FileNode::operator float() const { float value; read(*this, value, 0.f); return value; } +inline FileNode::operator double() const { double value; read(*this, value, 0.); return value; } +inline FileNode::operator String() const { String value; read(*this, value, value); return value; } +inline double FileNode::real() const { return double(*this); } +inline String FileNode::string() const { return String(*this); } +inline Mat FileNode::mat() const { Mat value; read(*this, value, value); return value; } +inline FileNodeIterator FileNode::begin() const { return FileNodeIterator(fs, node); } +inline FileNodeIterator FileNode::end() const { return FileNodeIterator(fs, node, size()); } +inline void FileNode::readRaw( const String& fmt, uchar* vec, size_t len ) const { begin().readRaw( fmt, vec, len ); } +inline FileNode FileNodeIterator::operator *() const { return FileNode(fs, (const CvFileNode*)(const void*)reader.ptr); } +inline FileNode FileNodeIterator::operator ->() const { return FileNode(fs, (const CvFileNode*)(const void*)reader.ptr); } +inline String::String(const FileNode& fn): cstr_(0), len_(0) { read(fn, *this, *this); } + +//! @endcond + + +CV_EXPORTS void cvStartWriteRawData_Base64(::CvFileStorage * fs, const char* name, int len, const char* dt); + +CV_EXPORTS void cvWriteRawData_Base64(::CvFileStorage * fs, const void* _data, int len); + +CV_EXPORTS void cvEndWriteRawData_Base64(::CvFileStorage * fs); + +CV_EXPORTS void cvWriteMat_Base64(::CvFileStorage* fs, const char* name, const ::CvMat* mat); + +CV_EXPORTS void cvWriteMatND_Base64(::CvFileStorage* fs, const char* name, const ::CvMatND* mat); + +} // cv + +#endif // OPENCV_CORE_PERSISTENCE_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/ptr.inl.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/ptr.inl.hpp new file mode 100644 index 0000000000000000000000000000000000000000..466f6342ec523717bff6dbbba2a6f0754a335567 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/ptr.inl.hpp @@ -0,0 +1,379 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2013, NVIDIA Corporation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the copyright holders or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_PTR_INL_HPP +#define OPENCV_CORE_PTR_INL_HPP + +#include + +//! @cond IGNORED + +namespace cv { + +template +void DefaultDeleter::operator () (Y* p) const +{ + delete p; +} + +namespace detail +{ + +struct PtrOwner +{ + PtrOwner() : refCount(1) + {} + + void incRef() + { + CV_XADD(&refCount, 1); + } + + void decRef() + { + if (CV_XADD(&refCount, -1) == 1) deleteSelf(); + } + +protected: + /* This doesn't really need to be virtual, since PtrOwner is never deleted + directly, but it doesn't hurt and it helps avoid warnings. */ + virtual ~PtrOwner() + {} + + virtual void deleteSelf() = 0; + +private: + unsigned int refCount; + + // noncopyable + PtrOwner(const PtrOwner&); + PtrOwner& operator = (const PtrOwner&); +}; + +template +struct PtrOwnerImpl CV_FINAL : PtrOwner +{ + PtrOwnerImpl(Y* p, D d) : owned(p), deleter(d) + {} + + void deleteSelf() CV_OVERRIDE + { + deleter(owned); + delete this; + } + +private: + Y* owned; + D deleter; +}; + + +} + +template +Ptr::Ptr() : owner(NULL), stored(NULL) +{} + +template +template +Ptr::Ptr(Y* p) + : owner(p + ? new detail::PtrOwnerImpl >(p, DefaultDeleter()) + : NULL), + stored(p) +{} + +template +template +Ptr::Ptr(Y* p, D d) + : owner(p + ? new detail::PtrOwnerImpl(p, d) + : NULL), + stored(p) +{} + +template +Ptr::Ptr(const Ptr& o) : owner(o.owner), stored(o.stored) +{ + if (owner) owner->incRef(); +} + +template +template +Ptr::Ptr(const Ptr& o) : owner(o.owner), stored(o.stored) +{ + if (owner) owner->incRef(); +} + +template +template +Ptr::Ptr(const Ptr& o, T* p) : owner(o.owner), stored(p) +{ + if (owner) owner->incRef(); +} + +template +Ptr::~Ptr() +{ + release(); +} + +template +Ptr& Ptr::operator = (const Ptr& o) +{ + Ptr(o).swap(*this); + return *this; +} + +template +template +Ptr& Ptr::operator = (const Ptr& o) +{ + Ptr(o).swap(*this); + return *this; +} + +template +void Ptr::release() +{ + if (owner) owner->decRef(); + owner = NULL; + stored = NULL; +} + +template +template +void Ptr::reset(Y* p) +{ + Ptr(p).swap(*this); +} + +template +template +void Ptr::reset(Y* p, D d) +{ + Ptr(p, d).swap(*this); +} + +template +void Ptr::swap(Ptr& o) +{ + std::swap(owner, o.owner); + std::swap(stored, o.stored); +} + +template +T* Ptr::get() const +{ + return stored; +} + +template +typename detail::RefOrVoid::type Ptr::operator * () const +{ + return *stored; +} + +template +T* Ptr::operator -> () const +{ + return stored; +} + +template +Ptr::operator T* () const +{ + return stored; +} + + +template +bool Ptr::empty() const +{ + return !stored; +} + +template +template +Ptr Ptr::staticCast() const +{ + return Ptr(*this, static_cast(stored)); +} + +template +template +Ptr Ptr::constCast() const +{ + return Ptr(*this, const_cast(stored)); +} + +template +template +Ptr Ptr::dynamicCast() const +{ + return Ptr(*this, dynamic_cast(stored)); +} + +#ifdef CV_CXX_MOVE_SEMANTICS + +template +Ptr::Ptr(Ptr&& o) : owner(o.owner), stored(o.stored) +{ + o.owner = NULL; + o.stored = NULL; +} + +template +Ptr& Ptr::operator = (Ptr&& o) +{ + if (this == &o) + return *this; + + release(); + owner = o.owner; + stored = o.stored; + o.owner = NULL; + o.stored = NULL; + return *this; +} + +#endif + + +template +void swap(Ptr& ptr1, Ptr& ptr2){ + ptr1.swap(ptr2); +} + +template +bool operator == (const Ptr& ptr1, const Ptr& ptr2) +{ + return ptr1.get() == ptr2.get(); +} + +template +bool operator != (const Ptr& ptr1, const Ptr& ptr2) +{ + return ptr1.get() != ptr2.get(); +} + +template +Ptr makePtr() +{ + return Ptr(new T()); +} + +template +Ptr makePtr(const A1& a1) +{ + return Ptr(new T(a1)); +} + +template +Ptr makePtr(const A1& a1, const A2& a2) +{ + return Ptr(new T(a1, a2)); +} + +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3) +{ + return Ptr(new T(a1, a2, a3)); +} + +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4) +{ + return Ptr(new T(a1, a2, a3, a4)); +} + +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5) +{ + return Ptr(new T(a1, a2, a3, a4, a5)); +} + +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6) +{ + return Ptr(new T(a1, a2, a3, a4, a5, a6)); +} + +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7) +{ + return Ptr(new T(a1, a2, a3, a4, a5, a6, a7)); +} + +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8) +{ + return Ptr(new T(a1, a2, a3, a4, a5, a6, a7, a8)); +} + +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8, const A9& a9) +{ + return Ptr(new T(a1, a2, a3, a4, a5, a6, a7, a8, a9)); +} + +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8, const A9& a9, const A10& a10) +{ + return Ptr(new T(a1, a2, a3, a4, a5, a6, a7, a8, a9, a10)); +} + +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8, const A9& a9, const A10& a10, const A11& a11) +{ + return Ptr(new T(a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11)); +} + +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8, const A9& a9, const A10& a10, const A11& a11, const A12& a12) +{ + return Ptr(new T(a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12)); +} +} // namespace cv + +//! @endcond + +#endif // OPENCV_CORE_PTR_INL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/saturate.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/saturate.hpp new file mode 100644 index 0000000000000000000000000000000000000000..118599f8f97e1e3e5049857f23e3593beaf3d4b0 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/saturate.hpp @@ -0,0 +1,165 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2014, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_SATURATE_HPP +#define OPENCV_CORE_SATURATE_HPP + +#include "opencv2/core/cvdef.h" +#include "opencv2/core/fast_math.hpp" + +namespace cv +{ + +//! @addtogroup core_utils +//! @{ + +/////////////// saturate_cast (used in image & signal processing) /////////////////// + +/** @brief Template function for accurate conversion from one primitive type to another. + + The function saturate_cast resembles the standard C++ cast operations, such as static_cast\() + and others. It perform an efficient and accurate conversion from one primitive type to another + (see the introduction chapter). saturate in the name means that when the input value v is out of the + range of the target type, the result is not formed just by taking low bits of the input, but instead + the value is clipped. For example: + @code + uchar a = saturate_cast(-100); // a = 0 (UCHAR_MIN) + short b = saturate_cast(33333.33333); // b = 32767 (SHRT_MAX) + @endcode + Such clipping is done when the target type is unsigned char , signed char , unsigned short or + signed short . For 32-bit integers, no clipping is done. + + When the parameter is a floating-point value and the target type is an integer (8-, 16- or 32-bit), + the floating-point value is first rounded to the nearest integer and then clipped if needed (when + the target type is 8- or 16-bit). + + This operation is used in the simplest or most complex image processing functions in OpenCV. + + @param v Function parameter. + @sa add, subtract, multiply, divide, Mat::convertTo + */ +template static inline _Tp saturate_cast(uchar v) { return _Tp(v); } +/** @overload */ +template static inline _Tp saturate_cast(schar v) { return _Tp(v); } +/** @overload */ +template static inline _Tp saturate_cast(ushort v) { return _Tp(v); } +/** @overload */ +template static inline _Tp saturate_cast(short v) { return _Tp(v); } +/** @overload */ +template static inline _Tp saturate_cast(unsigned v) { return _Tp(v); } +/** @overload */ +template static inline _Tp saturate_cast(int v) { return _Tp(v); } +/** @overload */ +template static inline _Tp saturate_cast(float v) { return _Tp(v); } +/** @overload */ +template static inline _Tp saturate_cast(double v) { return _Tp(v); } +/** @overload */ +template static inline _Tp saturate_cast(int64 v) { return _Tp(v); } +/** @overload */ +template static inline _Tp saturate_cast(uint64 v) { return _Tp(v); } + +template<> inline uchar saturate_cast(schar v) { return (uchar)std::max((int)v, 0); } +template<> inline uchar saturate_cast(ushort v) { return (uchar)std::min((unsigned)v, (unsigned)UCHAR_MAX); } +template<> inline uchar saturate_cast(int v) { return (uchar)((unsigned)v <= UCHAR_MAX ? v : v > 0 ? UCHAR_MAX : 0); } +template<> inline uchar saturate_cast(short v) { return saturate_cast((int)v); } +template<> inline uchar saturate_cast(unsigned v) { return (uchar)std::min(v, (unsigned)UCHAR_MAX); } +template<> inline uchar saturate_cast(float v) { int iv = cvRound(v); return saturate_cast(iv); } +template<> inline uchar saturate_cast(double v) { int iv = cvRound(v); return saturate_cast(iv); } +template<> inline uchar saturate_cast(int64 v) { return (uchar)((uint64)v <= (uint64)UCHAR_MAX ? v : v > 0 ? UCHAR_MAX : 0); } +template<> inline uchar saturate_cast(uint64 v) { return (uchar)std::min(v, (uint64)UCHAR_MAX); } + +template<> inline schar saturate_cast(uchar v) { return (schar)std::min((int)v, SCHAR_MAX); } +template<> inline schar saturate_cast(ushort v) { return (schar)std::min((unsigned)v, (unsigned)SCHAR_MAX); } +template<> inline schar saturate_cast(int v) { return (schar)((unsigned)(v-SCHAR_MIN) <= (unsigned)UCHAR_MAX ? v : v > 0 ? SCHAR_MAX : SCHAR_MIN); } +template<> inline schar saturate_cast(short v) { return saturate_cast((int)v); } +template<> inline schar saturate_cast(unsigned v) { return (schar)std::min(v, (unsigned)SCHAR_MAX); } +template<> inline schar saturate_cast(float v) { int iv = cvRound(v); return saturate_cast(iv); } +template<> inline schar saturate_cast(double v) { int iv = cvRound(v); return saturate_cast(iv); } +template<> inline schar saturate_cast(int64 v) { return (schar)((uint64)((int64)v-SCHAR_MIN) <= (uint64)UCHAR_MAX ? v : v > 0 ? SCHAR_MAX : SCHAR_MIN); } +template<> inline schar saturate_cast(uint64 v) { return (schar)std::min(v, (uint64)SCHAR_MAX); } + +template<> inline ushort saturate_cast(schar v) { return (ushort)std::max((int)v, 0); } +template<> inline ushort saturate_cast(short v) { return (ushort)std::max((int)v, 0); } +template<> inline ushort saturate_cast(int v) { return (ushort)((unsigned)v <= (unsigned)USHRT_MAX ? v : v > 0 ? USHRT_MAX : 0); } +template<> inline ushort saturate_cast(unsigned v) { return (ushort)std::min(v, (unsigned)USHRT_MAX); } +template<> inline ushort saturate_cast(float v) { int iv = cvRound(v); return saturate_cast(iv); } +template<> inline ushort saturate_cast(double v) { int iv = cvRound(v); return saturate_cast(iv); } +template<> inline ushort saturate_cast(int64 v) { return (ushort)((uint64)v <= (uint64)USHRT_MAX ? v : v > 0 ? USHRT_MAX : 0); } +template<> inline ushort saturate_cast(uint64 v) { return (ushort)std::min(v, (uint64)USHRT_MAX); } + +template<> inline short saturate_cast(ushort v) { return (short)std::min((int)v, SHRT_MAX); } +template<> inline short saturate_cast(int v) { return (short)((unsigned)(v - SHRT_MIN) <= (unsigned)USHRT_MAX ? v : v > 0 ? SHRT_MAX : SHRT_MIN); } +template<> inline short saturate_cast(unsigned v) { return (short)std::min(v, (unsigned)SHRT_MAX); } +template<> inline short saturate_cast(float v) { int iv = cvRound(v); return saturate_cast(iv); } +template<> inline short saturate_cast(double v) { int iv = cvRound(v); return saturate_cast(iv); } +template<> inline short saturate_cast(int64 v) { return (short)((uint64)((int64)v - SHRT_MIN) <= (uint64)USHRT_MAX ? v : v > 0 ? SHRT_MAX : SHRT_MIN); } +template<> inline short saturate_cast(uint64 v) { return (short)std::min(v, (uint64)SHRT_MAX); } + +template<> inline int saturate_cast(unsigned v) { return (int)std::min(v, (unsigned)INT_MAX); } +template<> inline int saturate_cast(int64 v) { return (int)((uint64)(v - INT_MIN) <= (uint64)UINT_MAX ? v : v > 0 ? INT_MAX : INT_MIN); } +template<> inline int saturate_cast(uint64 v) { return (int)std::min(v, (uint64)INT_MAX); } +template<> inline int saturate_cast(float v) { return cvRound(v); } +template<> inline int saturate_cast(double v) { return cvRound(v); } + +template<> inline unsigned saturate_cast(schar v) { return (unsigned)std::max(v, (schar)0); } +template<> inline unsigned saturate_cast(short v) { return (unsigned)std::max(v, (short)0); } +template<> inline unsigned saturate_cast(int v) { return (unsigned)std::max(v, (int)0); } +template<> inline unsigned saturate_cast(int64 v) { return (unsigned)((uint64)v <= (uint64)UINT_MAX ? v : v > 0 ? UINT_MAX : 0); } +template<> inline unsigned saturate_cast(uint64 v) { return (unsigned)std::min(v, (uint64)UINT_MAX); } +// we intentionally do not clip negative numbers, to make -1 become 0xffffffff etc. +template<> inline unsigned saturate_cast(float v) { return static_cast(cvRound(v)); } +template<> inline unsigned saturate_cast(double v) { return static_cast(cvRound(v)); } + +template<> inline uint64 saturate_cast(schar v) { return (uint64)std::max(v, (schar)0); } +template<> inline uint64 saturate_cast(short v) { return (uint64)std::max(v, (short)0); } +template<> inline uint64 saturate_cast(int v) { return (uint64)std::max(v, (int)0); } +template<> inline uint64 saturate_cast(int64 v) { return (uint64)std::max(v, (int64)0); } + +template<> inline int64 saturate_cast(uint64 v) { return (int64)std::min(v, (uint64)LLONG_MAX); } + +//! @} + +} // cv + +#endif // OPENCV_CORE_SATURATE_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/softfloat.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/softfloat.hpp new file mode 100644 index 0000000000000000000000000000000000000000..547098038d8349ce3a23f72829875030b44a02e6 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/softfloat.hpp @@ -0,0 +1,514 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html + +// This file is based on files from package issued with the following license: + +/*============================================================================ + +This C header file is part of the SoftFloat IEEE Floating-Point Arithmetic +Package, Release 3c, by John R. Hauser. + +Copyright 2011, 2012, 2013, 2014, 2015, 2016, 2017 The Regents of the +University of California. All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + + 1. Redistributions of source code must retain the above copyright notice, + this list of conditions, and the following disclaimer. + + 2. Redistributions in binary form must reproduce the above copyright notice, + this list of conditions, and the following disclaimer in the documentation + and/or other materials provided with the distribution. + + 3. Neither the name of the University nor the names of its contributors may + be used to endorse or promote products derived from this software without + specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE REGENTS AND CONTRIBUTORS "AS IS", AND ANY +EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE, ARE +DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR ANY +DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND +ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +=============================================================================*/ + +#pragma once +#ifndef softfloat_h +#define softfloat_h 1 + +#include "cvdef.h" + +namespace cv +{ + +/** @addtogroup core_utils_softfloat + + [SoftFloat](http://www.jhauser.us/arithmetic/SoftFloat.html) is a software implementation + of floating-point calculations according to IEEE 754 standard. + All calculations are done in integers, that's why they are machine-independent and bit-exact. + This library can be useful in accuracy-critical parts like look-up tables generation, tests, etc. + OpenCV contains a subset of SoftFloat partially rewritten to C++. + + ### Types + + There are two basic types: @ref softfloat and @ref softdouble. + These types are binary compatible with float and double types respectively + and support conversions to/from them. + Other types from original SoftFloat library like fp16 or fp128 were thrown away + as well as quiet/signaling NaN support, on-the-fly rounding mode switch + and exception flags (though exceptions can be implemented in the future). + + ### Operations + + Both types support the following: + - Construction from signed and unsigned 32-bit and 64 integers, + float/double or raw binary representation + - Conversions between each other, to float or double and to int + using @ref cvRound, @ref cvTrunc, @ref cvFloor, @ref cvCeil or a bunch of + saturate_cast functions + - Add, subtract, multiply, divide, remainder, square root, FMA with absolute precision + - Comparison operations + - Explicit sign, exponent and significand manipulation through get/set methods, + number state indicators (isInf, isNan, isSubnormal) + - Type-specific constants like eps, minimum/maximum value, best pi approximation, etc. + - min(), max(), abs(), exp(), log() and pow() functions + +*/ +//! @{ + +struct softfloat; +struct softdouble; + +struct CV_EXPORTS softfloat +{ +public: + /** @brief Default constructor */ + softfloat() { v = 0; } + /** @brief Copy constructor */ + softfloat( const softfloat& c) { v = c.v; } + /** @brief Assign constructor */ + softfloat& operator=( const softfloat& c ) + { + if(&c != this) v = c.v; + return *this; + } + /** @brief Construct from raw + + Builds new value from raw binary representation + */ + static const softfloat fromRaw( const uint32_t a ) { softfloat x; x.v = a; return x; } + + /** @brief Construct from integer */ + explicit softfloat( const uint32_t ); + explicit softfloat( const uint64_t ); + explicit softfloat( const int32_t ); + explicit softfloat( const int64_t ); + +#ifdef CV_INT32_T_IS_LONG_INT + // for platforms with int32_t = long int + explicit softfloat( const int a ) { *this = softfloat(static_cast(a)); } +#endif + + /** @brief Construct from float */ + explicit softfloat( const float a ) { Cv32suf s; s.f = a; v = s.u; } + + /** @brief Type casts */ + operator softdouble() const; + operator float() const { Cv32suf s; s.u = v; return s.f; } + + /** @brief Basic arithmetics */ + softfloat operator + (const softfloat&) const; + softfloat operator - (const softfloat&) const; + softfloat operator * (const softfloat&) const; + softfloat operator / (const softfloat&) const; + softfloat operator - () const { softfloat x; x.v = v ^ (1U << 31); return x; } + + /** @brief Remainder operator + + A quote from original SoftFloat manual: + + > The IEEE Standard remainder operation computes the value + > a - n * b, where n is the integer closest to a / b. + > If a / b is exactly halfway between two integers, n is the even integer + > closest to a / b. The IEEE Standard’s remainder operation is always exact and so requires no rounding. + > Depending on the relative magnitudes of the operands, the remainder functions + > can take considerably longer to execute than the other SoftFloat functions. + > This is an inherent characteristic of the remainder operation itself and is not a flaw + > in the SoftFloat implementation. + */ + softfloat operator % (const softfloat&) const; + + softfloat& operator += (const softfloat& a) { *this = *this + a; return *this; } + softfloat& operator -= (const softfloat& a) { *this = *this - a; return *this; } + softfloat& operator *= (const softfloat& a) { *this = *this * a; return *this; } + softfloat& operator /= (const softfloat& a) { *this = *this / a; return *this; } + softfloat& operator %= (const softfloat& a) { *this = *this % a; return *this; } + + /** @brief Comparison operations + + - Any operation with NaN produces false + + The only exception is when x is NaN: x != y for any y. + - Positive and negative zeros are equal + */ + bool operator == ( const softfloat& ) const; + bool operator != ( const softfloat& ) const; + bool operator > ( const softfloat& ) const; + bool operator >= ( const softfloat& ) const; + bool operator < ( const softfloat& ) const; + bool operator <= ( const softfloat& ) const; + + /** @brief NaN state indicator */ + inline bool isNaN() const { return (v & 0x7fffffff) > 0x7f800000; } + /** @brief Inf state indicator */ + inline bool isInf() const { return (v & 0x7fffffff) == 0x7f800000; } + /** @brief Subnormal number indicator */ + inline bool isSubnormal() const { return ((v >> 23) & 0xFF) == 0; } + + /** @brief Get sign bit */ + inline bool getSign() const { return (v >> 31) != 0; } + /** @brief Construct a copy with new sign bit */ + inline softfloat setSign(bool sign) const { softfloat x; x.v = (v & ((1U << 31) - 1)) | ((uint32_t)sign << 31); return x; } + /** @brief Get 0-based exponent */ + inline int getExp() const { return ((v >> 23) & 0xFF) - 127; } + /** @brief Construct a copy with new 0-based exponent */ + inline softfloat setExp(int e) const { softfloat x; x.v = (v & 0x807fffff) | (((e + 127) & 0xFF) << 23 ); return x; } + + /** @brief Get a fraction part + + Returns a number 1 <= x < 2 with the same significand + */ + inline softfloat getFrac() const + { + uint_fast32_t vv = (v & 0x007fffff) | (127 << 23); + return softfloat::fromRaw(vv); + } + /** @brief Construct a copy with provided significand + + Constructs a copy of a number with significand taken from parameter + */ + inline softfloat setFrac(const softfloat& s) const + { + softfloat x; + x.v = (v & 0xff800000) | (s.v & 0x007fffff); + return x; + } + + /** @brief Zero constant */ + static softfloat zero() { return softfloat::fromRaw( 0 ); } + /** @brief Positive infinity constant */ + static softfloat inf() { return softfloat::fromRaw( 0xFF << 23 ); } + /** @brief Default NaN constant */ + static softfloat nan() { return softfloat::fromRaw( 0x7fffffff ); } + /** @brief One constant */ + static softfloat one() { return softfloat::fromRaw( 127 << 23 ); } + /** @brief Smallest normalized value */ + static softfloat min() { return softfloat::fromRaw( 0x01 << 23 ); } + /** @brief Difference between 1 and next representable value */ + static softfloat eps() { return softfloat::fromRaw( (127 - 23) << 23 ); } + /** @brief Biggest finite value */ + static softfloat max() { return softfloat::fromRaw( (0xFF << 23) - 1 ); } + /** @brief Correct pi approximation */ + static softfloat pi() { return softfloat::fromRaw( 0x40490fdb ); } + + uint32_t v; +}; + +/*---------------------------------------------------------------------------- +*----------------------------------------------------------------------------*/ + +struct CV_EXPORTS softdouble +{ +public: + /** @brief Default constructor */ + softdouble() : v(0) { } + /** @brief Copy constructor */ + softdouble( const softdouble& c) { v = c.v; } + /** @brief Assign constructor */ + softdouble& operator=( const softdouble& c ) + { + if(&c != this) v = c.v; + return *this; + } + /** @brief Construct from raw + + Builds new value from raw binary representation + */ + static softdouble fromRaw( const uint64_t a ) { softdouble x; x.v = a; return x; } + + /** @brief Construct from integer */ + explicit softdouble( const uint32_t ); + explicit softdouble( const uint64_t ); + explicit softdouble( const int32_t ); + explicit softdouble( const int64_t ); + +#ifdef CV_INT32_T_IS_LONG_INT + // for platforms with int32_t = long int + explicit softdouble( const int a ) { *this = softdouble(static_cast(a)); } +#endif + + /** @brief Construct from double */ + explicit softdouble( const double a ) { Cv64suf s; s.f = a; v = s.u; } + + /** @brief Type casts */ + operator softfloat() const; + operator double() const { Cv64suf s; s.u = v; return s.f; } + + /** @brief Basic arithmetics */ + softdouble operator + (const softdouble&) const; + softdouble operator - (const softdouble&) const; + softdouble operator * (const softdouble&) const; + softdouble operator / (const softdouble&) const; + softdouble operator - () const { softdouble x; x.v = v ^ (1ULL << 63); return x; } + + /** @brief Remainder operator + + A quote from original SoftFloat manual: + + > The IEEE Standard remainder operation computes the value + > a - n * b, where n is the integer closest to a / b. + > If a / b is exactly halfway between two integers, n is the even integer + > closest to a / b. The IEEE Standard’s remainder operation is always exact and so requires no rounding. + > Depending on the relative magnitudes of the operands, the remainder functions + > can take considerably longer to execute than the other SoftFloat functions. + > This is an inherent characteristic of the remainder operation itself and is not a flaw + > in the SoftFloat implementation. + */ + softdouble operator % (const softdouble&) const; + + softdouble& operator += (const softdouble& a) { *this = *this + a; return *this; } + softdouble& operator -= (const softdouble& a) { *this = *this - a; return *this; } + softdouble& operator *= (const softdouble& a) { *this = *this * a; return *this; } + softdouble& operator /= (const softdouble& a) { *this = *this / a; return *this; } + softdouble& operator %= (const softdouble& a) { *this = *this % a; return *this; } + + /** @brief Comparison operations + + - Any operation with NaN produces false + + The only exception is when x is NaN: x != y for any y. + - Positive and negative zeros are equal + */ + bool operator == ( const softdouble& ) const; + bool operator != ( const softdouble& ) const; + bool operator > ( const softdouble& ) const; + bool operator >= ( const softdouble& ) const; + bool operator < ( const softdouble& ) const; + bool operator <= ( const softdouble& ) const; + + /** @brief NaN state indicator */ + inline bool isNaN() const { return (v & 0x7fffffffffffffff) > 0x7ff0000000000000; } + /** @brief Inf state indicator */ + inline bool isInf() const { return (v & 0x7fffffffffffffff) == 0x7ff0000000000000; } + /** @brief Subnormal number indicator */ + inline bool isSubnormal() const { return ((v >> 52) & 0x7FF) == 0; } + + /** @brief Get sign bit */ + inline bool getSign() const { return (v >> 63) != 0; } + /** @brief Construct a copy with new sign bit */ + softdouble setSign(bool sign) const { softdouble x; x.v = (v & ((1ULL << 63) - 1)) | ((uint_fast64_t)(sign) << 63); return x; } + /** @brief Get 0-based exponent */ + inline int getExp() const { return ((v >> 52) & 0x7FF) - 1023; } + /** @brief Construct a copy with new 0-based exponent */ + inline softdouble setExp(int e) const + { + softdouble x; + x.v = (v & 0x800FFFFFFFFFFFFF) | ((uint_fast64_t)((e + 1023) & 0x7FF) << 52); + return x; + } + + /** @brief Get a fraction part + + Returns a number 1 <= x < 2 with the same significand + */ + inline softdouble getFrac() const + { + uint_fast64_t vv = (v & 0x000FFFFFFFFFFFFF) | ((uint_fast64_t)(1023) << 52); + return softdouble::fromRaw(vv); + } + /** @brief Construct a copy with provided significand + + Constructs a copy of a number with significand taken from parameter + */ + inline softdouble setFrac(const softdouble& s) const + { + softdouble x; + x.v = (v & 0xFFF0000000000000) | (s.v & 0x000FFFFFFFFFFFFF); + return x; + } + + /** @brief Zero constant */ + static softdouble zero() { return softdouble::fromRaw( 0 ); } + /** @brief Positive infinity constant */ + static softdouble inf() { return softdouble::fromRaw( (uint_fast64_t)(0x7FF) << 52 ); } + /** @brief Default NaN constant */ + static softdouble nan() { return softdouble::fromRaw( CV_BIG_INT(0x7FFFFFFFFFFFFFFF) ); } + /** @brief One constant */ + static softdouble one() { return softdouble::fromRaw( (uint_fast64_t)( 1023) << 52 ); } + /** @brief Smallest normalized value */ + static softdouble min() { return softdouble::fromRaw( (uint_fast64_t)( 0x01) << 52 ); } + /** @brief Difference between 1 and next representable value */ + static softdouble eps() { return softdouble::fromRaw( (uint_fast64_t)( 1023 - 52 ) << 52 ); } + /** @brief Biggest finite value */ + static softdouble max() { return softdouble::fromRaw( ((uint_fast64_t)(0x7FF) << 52) - 1 ); } + /** @brief Correct pi approximation */ + static softdouble pi() { return softdouble::fromRaw( CV_BIG_INT(0x400921FB54442D18) ); } + + uint64_t v; +}; + +/*---------------------------------------------------------------------------- +*----------------------------------------------------------------------------*/ + +/** @brief Fused Multiplication and Addition + +Computes (a*b)+c with single rounding +*/ +CV_EXPORTS softfloat mulAdd( const softfloat& a, const softfloat& b, const softfloat & c); +CV_EXPORTS softdouble mulAdd( const softdouble& a, const softdouble& b, const softdouble& c); + +/** @brief Square root */ +CV_EXPORTS softfloat sqrt( const softfloat& a ); +CV_EXPORTS softdouble sqrt( const softdouble& a ); +} + +/*---------------------------------------------------------------------------- +| Ported from OpenCV and added for usability +*----------------------------------------------------------------------------*/ + +/** @brief Truncates number to integer with minimum magnitude */ +CV_EXPORTS int cvTrunc(const cv::softfloat& a); +CV_EXPORTS int cvTrunc(const cv::softdouble& a); + +/** @brief Rounds a number to nearest even integer */ +CV_EXPORTS int cvRound(const cv::softfloat& a); +CV_EXPORTS int cvRound(const cv::softdouble& a); + +/** @brief Rounds a number to nearest even long long integer */ +CV_EXPORTS int64_t cvRound64(const cv::softdouble& a); + +/** @brief Rounds a number down to integer */ +CV_EXPORTS int cvFloor(const cv::softfloat& a); +CV_EXPORTS int cvFloor(const cv::softdouble& a); + +/** @brief Rounds number up to integer */ +CV_EXPORTS int cvCeil(const cv::softfloat& a); +CV_EXPORTS int cvCeil(const cv::softdouble& a); + +namespace cv +{ +/** @brief Saturate casts */ +template static inline _Tp saturate_cast(softfloat a) { return _Tp(a); } +template static inline _Tp saturate_cast(softdouble a) { return _Tp(a); } + +template<> inline uchar saturate_cast(softfloat a) { return (uchar)std::max(std::min(cvRound(a), (int)UCHAR_MAX), 0); } +template<> inline uchar saturate_cast(softdouble a) { return (uchar)std::max(std::min(cvRound(a), (int)UCHAR_MAX), 0); } + +template<> inline schar saturate_cast(softfloat a) { return (schar)std::min(std::max(cvRound(a), (int)SCHAR_MIN), (int)SCHAR_MAX); } +template<> inline schar saturate_cast(softdouble a) { return (schar)std::min(std::max(cvRound(a), (int)SCHAR_MIN), (int)SCHAR_MAX); } + +template<> inline ushort saturate_cast(softfloat a) { return (ushort)std::max(std::min(cvRound(a), (int)USHRT_MAX), 0); } +template<> inline ushort saturate_cast(softdouble a) { return (ushort)std::max(std::min(cvRound(a), (int)USHRT_MAX), 0); } + +template<> inline short saturate_cast(softfloat a) { return (short)std::min(std::max(cvRound(a), (int)SHRT_MIN), (int)SHRT_MAX); } +template<> inline short saturate_cast(softdouble a) { return (short)std::min(std::max(cvRound(a), (int)SHRT_MIN), (int)SHRT_MAX); } + +template<> inline int saturate_cast(softfloat a) { return cvRound(a); } +template<> inline int saturate_cast(softdouble a) { return cvRound(a); } + +template<> inline int64_t saturate_cast(softfloat a) { return cvRound(a); } +template<> inline int64_t saturate_cast(softdouble a) { return cvRound64(a); } + +/** @brief Saturate cast to unsigned integer and unsigned long long integer +We intentionally do not clip negative numbers, to make -1 become 0xffffffff etc. +*/ +template<> inline unsigned saturate_cast(softfloat a) { return cvRound(a); } +template<> inline unsigned saturate_cast(softdouble a) { return cvRound(a); } + +template<> inline uint64_t saturate_cast(softfloat a) { return cvRound(a); } +template<> inline uint64_t saturate_cast(softdouble a) { return cvRound64(a); } + +/** @brief Min and Max functions */ +inline softfloat min(const softfloat& a, const softfloat& b) { return (a > b) ? b : a; } +inline softdouble min(const softdouble& a, const softdouble& b) { return (a > b) ? b : a; } + +inline softfloat max(const softfloat& a, const softfloat& b) { return (a > b) ? a : b; } +inline softdouble max(const softdouble& a, const softdouble& b) { return (a > b) ? a : b; } + +/** @brief Absolute value */ +inline softfloat abs( softfloat a) { softfloat x; x.v = a.v & ((1U << 31) - 1); return x; } +inline softdouble abs( softdouble a) { softdouble x; x.v = a.v & ((1ULL << 63) - 1); return x; } + +/** @brief Exponent + +Special cases: +- exp(NaN) is NaN +- exp(-Inf) == 0 +- exp(+Inf) == +Inf +*/ +CV_EXPORTS softfloat exp( const softfloat& a); +CV_EXPORTS softdouble exp( const softdouble& a); + +/** @brief Natural logarithm + +Special cases: +- log(NaN), log(x < 0) are NaN +- log(0) == -Inf +*/ +CV_EXPORTS softfloat log( const softfloat& a ); +CV_EXPORTS softdouble log( const softdouble& a ); + +/** @brief Raising to the power + +Special cases: +- x**NaN is NaN for any x +- ( |x| == 1 )**Inf is NaN +- ( |x| > 1 )**+Inf or ( |x| < 1 )**-Inf is +Inf +- ( |x| > 1 )**-Inf or ( |x| < 1 )**+Inf is 0 +- x ** 0 == 1 for any x +- x ** 1 == 1 for any x +- NaN ** y is NaN for any other y +- Inf**(y < 0) == 0 +- Inf ** y is +Inf for any other y +- (x < 0)**y is NaN for any other y if x can't be correctly rounded to integer +- 0 ** 0 == 1 +- 0 ** (y < 0) is +Inf +- 0 ** (y > 0) is 0 +*/ +CV_EXPORTS softfloat pow( const softfloat& a, const softfloat& b); +CV_EXPORTS softdouble pow( const softdouble& a, const softdouble& b); + +/** @brief Cube root + +Special cases: +- cbrt(NaN) is NaN +- cbrt(+/-Inf) is +/-Inf +*/ +CV_EXPORTS softfloat cbrt( const softfloat& a ); + +/** @brief Sine + +Special cases: +- sin(Inf) or sin(NaN) is NaN +- sin(x) == x when sin(x) is close to zero +*/ +CV_EXPORTS softdouble sin( const softdouble& a ); + +/** @brief Cosine + * +Special cases: +- cos(Inf) or cos(NaN) is NaN +- cos(x) == +/- 1 when cos(x) is close to +/- 1 +*/ +CV_EXPORTS softdouble cos( const softdouble& a ); + +} + +//! @} + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/sse_utils.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/sse_utils.hpp new file mode 100644 index 0000000000000000000000000000000000000000..0906583ea433e289e883270d81f8641985cc3ebb --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/sse_utils.hpp @@ -0,0 +1,652 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_SSE_UTILS_HPP +#define OPENCV_CORE_SSE_UTILS_HPP + +#ifndef __cplusplus +# error sse_utils.hpp header must be compiled as C++ +#endif + +#include "opencv2/core/cvdef.h" + +//! @addtogroup core_utils_sse +//! @{ + +#if CV_SSE2 + +inline void _mm_deinterleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1) +{ + __m128i layer1_chunk0 = _mm_unpacklo_epi8(v_r0, v_g0); + __m128i layer1_chunk1 = _mm_unpackhi_epi8(v_r0, v_g0); + __m128i layer1_chunk2 = _mm_unpacklo_epi8(v_r1, v_g1); + __m128i layer1_chunk3 = _mm_unpackhi_epi8(v_r1, v_g1); + + __m128i layer2_chunk0 = _mm_unpacklo_epi8(layer1_chunk0, layer1_chunk2); + __m128i layer2_chunk1 = _mm_unpackhi_epi8(layer1_chunk0, layer1_chunk2); + __m128i layer2_chunk2 = _mm_unpacklo_epi8(layer1_chunk1, layer1_chunk3); + __m128i layer2_chunk3 = _mm_unpackhi_epi8(layer1_chunk1, layer1_chunk3); + + __m128i layer3_chunk0 = _mm_unpacklo_epi8(layer2_chunk0, layer2_chunk2); + __m128i layer3_chunk1 = _mm_unpackhi_epi8(layer2_chunk0, layer2_chunk2); + __m128i layer3_chunk2 = _mm_unpacklo_epi8(layer2_chunk1, layer2_chunk3); + __m128i layer3_chunk3 = _mm_unpackhi_epi8(layer2_chunk1, layer2_chunk3); + + __m128i layer4_chunk0 = _mm_unpacklo_epi8(layer3_chunk0, layer3_chunk2); + __m128i layer4_chunk1 = _mm_unpackhi_epi8(layer3_chunk0, layer3_chunk2); + __m128i layer4_chunk2 = _mm_unpacklo_epi8(layer3_chunk1, layer3_chunk3); + __m128i layer4_chunk3 = _mm_unpackhi_epi8(layer3_chunk1, layer3_chunk3); + + v_r0 = _mm_unpacklo_epi8(layer4_chunk0, layer4_chunk2); + v_r1 = _mm_unpackhi_epi8(layer4_chunk0, layer4_chunk2); + v_g0 = _mm_unpacklo_epi8(layer4_chunk1, layer4_chunk3); + v_g1 = _mm_unpackhi_epi8(layer4_chunk1, layer4_chunk3); +} + +inline void _mm_deinterleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, + __m128i & v_g1, __m128i & v_b0, __m128i & v_b1) +{ + __m128i layer1_chunk0 = _mm_unpacklo_epi8(v_r0, v_g1); + __m128i layer1_chunk1 = _mm_unpackhi_epi8(v_r0, v_g1); + __m128i layer1_chunk2 = _mm_unpacklo_epi8(v_r1, v_b0); + __m128i layer1_chunk3 = _mm_unpackhi_epi8(v_r1, v_b0); + __m128i layer1_chunk4 = _mm_unpacklo_epi8(v_g0, v_b1); + __m128i layer1_chunk5 = _mm_unpackhi_epi8(v_g0, v_b1); + + __m128i layer2_chunk0 = _mm_unpacklo_epi8(layer1_chunk0, layer1_chunk3); + __m128i layer2_chunk1 = _mm_unpackhi_epi8(layer1_chunk0, layer1_chunk3); + __m128i layer2_chunk2 = _mm_unpacklo_epi8(layer1_chunk1, layer1_chunk4); + __m128i layer2_chunk3 = _mm_unpackhi_epi8(layer1_chunk1, layer1_chunk4); + __m128i layer2_chunk4 = _mm_unpacklo_epi8(layer1_chunk2, layer1_chunk5); + __m128i layer2_chunk5 = _mm_unpackhi_epi8(layer1_chunk2, layer1_chunk5); + + __m128i layer3_chunk0 = _mm_unpacklo_epi8(layer2_chunk0, layer2_chunk3); + __m128i layer3_chunk1 = _mm_unpackhi_epi8(layer2_chunk0, layer2_chunk3); + __m128i layer3_chunk2 = _mm_unpacklo_epi8(layer2_chunk1, layer2_chunk4); + __m128i layer3_chunk3 = _mm_unpackhi_epi8(layer2_chunk1, layer2_chunk4); + __m128i layer3_chunk4 = _mm_unpacklo_epi8(layer2_chunk2, layer2_chunk5); + __m128i layer3_chunk5 = _mm_unpackhi_epi8(layer2_chunk2, layer2_chunk5); + + __m128i layer4_chunk0 = _mm_unpacklo_epi8(layer3_chunk0, layer3_chunk3); + __m128i layer4_chunk1 = _mm_unpackhi_epi8(layer3_chunk0, layer3_chunk3); + __m128i layer4_chunk2 = _mm_unpacklo_epi8(layer3_chunk1, layer3_chunk4); + __m128i layer4_chunk3 = _mm_unpackhi_epi8(layer3_chunk1, layer3_chunk4); + __m128i layer4_chunk4 = _mm_unpacklo_epi8(layer3_chunk2, layer3_chunk5); + __m128i layer4_chunk5 = _mm_unpackhi_epi8(layer3_chunk2, layer3_chunk5); + + v_r0 = _mm_unpacklo_epi8(layer4_chunk0, layer4_chunk3); + v_r1 = _mm_unpackhi_epi8(layer4_chunk0, layer4_chunk3); + v_g0 = _mm_unpacklo_epi8(layer4_chunk1, layer4_chunk4); + v_g1 = _mm_unpackhi_epi8(layer4_chunk1, layer4_chunk4); + v_b0 = _mm_unpacklo_epi8(layer4_chunk2, layer4_chunk5); + v_b1 = _mm_unpackhi_epi8(layer4_chunk2, layer4_chunk5); +} + +inline void _mm_deinterleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1, + __m128i & v_b0, __m128i & v_b1, __m128i & v_a0, __m128i & v_a1) +{ + __m128i layer1_chunk0 = _mm_unpacklo_epi8(v_r0, v_b0); + __m128i layer1_chunk1 = _mm_unpackhi_epi8(v_r0, v_b0); + __m128i layer1_chunk2 = _mm_unpacklo_epi8(v_r1, v_b1); + __m128i layer1_chunk3 = _mm_unpackhi_epi8(v_r1, v_b1); + __m128i layer1_chunk4 = _mm_unpacklo_epi8(v_g0, v_a0); + __m128i layer1_chunk5 = _mm_unpackhi_epi8(v_g0, v_a0); + __m128i layer1_chunk6 = _mm_unpacklo_epi8(v_g1, v_a1); + __m128i layer1_chunk7 = _mm_unpackhi_epi8(v_g1, v_a1); + + __m128i layer2_chunk0 = _mm_unpacklo_epi8(layer1_chunk0, layer1_chunk4); + __m128i layer2_chunk1 = _mm_unpackhi_epi8(layer1_chunk0, layer1_chunk4); + __m128i layer2_chunk2 = _mm_unpacklo_epi8(layer1_chunk1, layer1_chunk5); + __m128i layer2_chunk3 = _mm_unpackhi_epi8(layer1_chunk1, layer1_chunk5); + __m128i layer2_chunk4 = _mm_unpacklo_epi8(layer1_chunk2, layer1_chunk6); + __m128i layer2_chunk5 = _mm_unpackhi_epi8(layer1_chunk2, layer1_chunk6); + __m128i layer2_chunk6 = _mm_unpacklo_epi8(layer1_chunk3, layer1_chunk7); + __m128i layer2_chunk7 = _mm_unpackhi_epi8(layer1_chunk3, layer1_chunk7); + + __m128i layer3_chunk0 = _mm_unpacklo_epi8(layer2_chunk0, layer2_chunk4); + __m128i layer3_chunk1 = _mm_unpackhi_epi8(layer2_chunk0, layer2_chunk4); + __m128i layer3_chunk2 = _mm_unpacklo_epi8(layer2_chunk1, layer2_chunk5); + __m128i layer3_chunk3 = _mm_unpackhi_epi8(layer2_chunk1, layer2_chunk5); + __m128i layer3_chunk4 = _mm_unpacklo_epi8(layer2_chunk2, layer2_chunk6); + __m128i layer3_chunk5 = _mm_unpackhi_epi8(layer2_chunk2, layer2_chunk6); + __m128i layer3_chunk6 = _mm_unpacklo_epi8(layer2_chunk3, layer2_chunk7); + __m128i layer3_chunk7 = _mm_unpackhi_epi8(layer2_chunk3, layer2_chunk7); + + __m128i layer4_chunk0 = _mm_unpacklo_epi8(layer3_chunk0, layer3_chunk4); + __m128i layer4_chunk1 = _mm_unpackhi_epi8(layer3_chunk0, layer3_chunk4); + __m128i layer4_chunk2 = _mm_unpacklo_epi8(layer3_chunk1, layer3_chunk5); + __m128i layer4_chunk3 = _mm_unpackhi_epi8(layer3_chunk1, layer3_chunk5); + __m128i layer4_chunk4 = _mm_unpacklo_epi8(layer3_chunk2, layer3_chunk6); + __m128i layer4_chunk5 = _mm_unpackhi_epi8(layer3_chunk2, layer3_chunk6); + __m128i layer4_chunk6 = _mm_unpacklo_epi8(layer3_chunk3, layer3_chunk7); + __m128i layer4_chunk7 = _mm_unpackhi_epi8(layer3_chunk3, layer3_chunk7); + + v_r0 = _mm_unpacklo_epi8(layer4_chunk0, layer4_chunk4); + v_r1 = _mm_unpackhi_epi8(layer4_chunk0, layer4_chunk4); + v_g0 = _mm_unpacklo_epi8(layer4_chunk1, layer4_chunk5); + v_g1 = _mm_unpackhi_epi8(layer4_chunk1, layer4_chunk5); + v_b0 = _mm_unpacklo_epi8(layer4_chunk2, layer4_chunk6); + v_b1 = _mm_unpackhi_epi8(layer4_chunk2, layer4_chunk6); + v_a0 = _mm_unpacklo_epi8(layer4_chunk3, layer4_chunk7); + v_a1 = _mm_unpackhi_epi8(layer4_chunk3, layer4_chunk7); +} + +inline void _mm_interleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1) +{ + __m128i v_mask = _mm_set1_epi16(0x00ff); + + __m128i layer4_chunk0 = _mm_packus_epi16(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask)); + __m128i layer4_chunk2 = _mm_packus_epi16(_mm_srli_epi16(v_r0, 8), _mm_srli_epi16(v_r1, 8)); + __m128i layer4_chunk1 = _mm_packus_epi16(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask)); + __m128i layer4_chunk3 = _mm_packus_epi16(_mm_srli_epi16(v_g0, 8), _mm_srli_epi16(v_g1, 8)); + + __m128i layer3_chunk0 = _mm_packus_epi16(_mm_and_si128(layer4_chunk0, v_mask), _mm_and_si128(layer4_chunk1, v_mask)); + __m128i layer3_chunk2 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk0, 8), _mm_srli_epi16(layer4_chunk1, 8)); + __m128i layer3_chunk1 = _mm_packus_epi16(_mm_and_si128(layer4_chunk2, v_mask), _mm_and_si128(layer4_chunk3, v_mask)); + __m128i layer3_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk2, 8), _mm_srli_epi16(layer4_chunk3, 8)); + + __m128i layer2_chunk0 = _mm_packus_epi16(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask)); + __m128i layer2_chunk2 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk0, 8), _mm_srli_epi16(layer3_chunk1, 8)); + __m128i layer2_chunk1 = _mm_packus_epi16(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask)); + __m128i layer2_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk2, 8), _mm_srli_epi16(layer3_chunk3, 8)); + + __m128i layer1_chunk0 = _mm_packus_epi16(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask)); + __m128i layer1_chunk2 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk0, 8), _mm_srli_epi16(layer2_chunk1, 8)); + __m128i layer1_chunk1 = _mm_packus_epi16(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask)); + __m128i layer1_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk2, 8), _mm_srli_epi16(layer2_chunk3, 8)); + + v_r0 = _mm_packus_epi16(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask)); + v_g0 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk0, 8), _mm_srli_epi16(layer1_chunk1, 8)); + v_r1 = _mm_packus_epi16(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask)); + v_g1 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk2, 8), _mm_srli_epi16(layer1_chunk3, 8)); +} + +inline void _mm_interleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, + __m128i & v_g1, __m128i & v_b0, __m128i & v_b1) +{ + __m128i v_mask = _mm_set1_epi16(0x00ff); + + __m128i layer4_chunk0 = _mm_packus_epi16(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask)); + __m128i layer4_chunk3 = _mm_packus_epi16(_mm_srli_epi16(v_r0, 8), _mm_srli_epi16(v_r1, 8)); + __m128i layer4_chunk1 = _mm_packus_epi16(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask)); + __m128i layer4_chunk4 = _mm_packus_epi16(_mm_srli_epi16(v_g0, 8), _mm_srli_epi16(v_g1, 8)); + __m128i layer4_chunk2 = _mm_packus_epi16(_mm_and_si128(v_b0, v_mask), _mm_and_si128(v_b1, v_mask)); + __m128i layer4_chunk5 = _mm_packus_epi16(_mm_srli_epi16(v_b0, 8), _mm_srli_epi16(v_b1, 8)); + + __m128i layer3_chunk0 = _mm_packus_epi16(_mm_and_si128(layer4_chunk0, v_mask), _mm_and_si128(layer4_chunk1, v_mask)); + __m128i layer3_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk0, 8), _mm_srli_epi16(layer4_chunk1, 8)); + __m128i layer3_chunk1 = _mm_packus_epi16(_mm_and_si128(layer4_chunk2, v_mask), _mm_and_si128(layer4_chunk3, v_mask)); + __m128i layer3_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk2, 8), _mm_srli_epi16(layer4_chunk3, 8)); + __m128i layer3_chunk2 = _mm_packus_epi16(_mm_and_si128(layer4_chunk4, v_mask), _mm_and_si128(layer4_chunk5, v_mask)); + __m128i layer3_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk4, 8), _mm_srli_epi16(layer4_chunk5, 8)); + + __m128i layer2_chunk0 = _mm_packus_epi16(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask)); + __m128i layer2_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk0, 8), _mm_srli_epi16(layer3_chunk1, 8)); + __m128i layer2_chunk1 = _mm_packus_epi16(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask)); + __m128i layer2_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk2, 8), _mm_srli_epi16(layer3_chunk3, 8)); + __m128i layer2_chunk2 = _mm_packus_epi16(_mm_and_si128(layer3_chunk4, v_mask), _mm_and_si128(layer3_chunk5, v_mask)); + __m128i layer2_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk4, 8), _mm_srli_epi16(layer3_chunk5, 8)); + + __m128i layer1_chunk0 = _mm_packus_epi16(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask)); + __m128i layer1_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk0, 8), _mm_srli_epi16(layer2_chunk1, 8)); + __m128i layer1_chunk1 = _mm_packus_epi16(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask)); + __m128i layer1_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk2, 8), _mm_srli_epi16(layer2_chunk3, 8)); + __m128i layer1_chunk2 = _mm_packus_epi16(_mm_and_si128(layer2_chunk4, v_mask), _mm_and_si128(layer2_chunk5, v_mask)); + __m128i layer1_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk4, 8), _mm_srli_epi16(layer2_chunk5, 8)); + + v_r0 = _mm_packus_epi16(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask)); + v_g1 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk0, 8), _mm_srli_epi16(layer1_chunk1, 8)); + v_r1 = _mm_packus_epi16(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask)); + v_b0 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk2, 8), _mm_srli_epi16(layer1_chunk3, 8)); + v_g0 = _mm_packus_epi16(_mm_and_si128(layer1_chunk4, v_mask), _mm_and_si128(layer1_chunk5, v_mask)); + v_b1 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk4, 8), _mm_srli_epi16(layer1_chunk5, 8)); +} + +inline void _mm_interleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1, + __m128i & v_b0, __m128i & v_b1, __m128i & v_a0, __m128i & v_a1) +{ + __m128i v_mask = _mm_set1_epi16(0x00ff); + + __m128i layer4_chunk0 = _mm_packus_epi16(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask)); + __m128i layer4_chunk4 = _mm_packus_epi16(_mm_srli_epi16(v_r0, 8), _mm_srli_epi16(v_r1, 8)); + __m128i layer4_chunk1 = _mm_packus_epi16(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask)); + __m128i layer4_chunk5 = _mm_packus_epi16(_mm_srli_epi16(v_g0, 8), _mm_srli_epi16(v_g1, 8)); + __m128i layer4_chunk2 = _mm_packus_epi16(_mm_and_si128(v_b0, v_mask), _mm_and_si128(v_b1, v_mask)); + __m128i layer4_chunk6 = _mm_packus_epi16(_mm_srli_epi16(v_b0, 8), _mm_srli_epi16(v_b1, 8)); + __m128i layer4_chunk3 = _mm_packus_epi16(_mm_and_si128(v_a0, v_mask), _mm_and_si128(v_a1, v_mask)); + __m128i layer4_chunk7 = _mm_packus_epi16(_mm_srli_epi16(v_a0, 8), _mm_srli_epi16(v_a1, 8)); + + __m128i layer3_chunk0 = _mm_packus_epi16(_mm_and_si128(layer4_chunk0, v_mask), _mm_and_si128(layer4_chunk1, v_mask)); + __m128i layer3_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk0, 8), _mm_srli_epi16(layer4_chunk1, 8)); + __m128i layer3_chunk1 = _mm_packus_epi16(_mm_and_si128(layer4_chunk2, v_mask), _mm_and_si128(layer4_chunk3, v_mask)); + __m128i layer3_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk2, 8), _mm_srli_epi16(layer4_chunk3, 8)); + __m128i layer3_chunk2 = _mm_packus_epi16(_mm_and_si128(layer4_chunk4, v_mask), _mm_and_si128(layer4_chunk5, v_mask)); + __m128i layer3_chunk6 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk4, 8), _mm_srli_epi16(layer4_chunk5, 8)); + __m128i layer3_chunk3 = _mm_packus_epi16(_mm_and_si128(layer4_chunk6, v_mask), _mm_and_si128(layer4_chunk7, v_mask)); + __m128i layer3_chunk7 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk6, 8), _mm_srli_epi16(layer4_chunk7, 8)); + + __m128i layer2_chunk0 = _mm_packus_epi16(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask)); + __m128i layer2_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk0, 8), _mm_srli_epi16(layer3_chunk1, 8)); + __m128i layer2_chunk1 = _mm_packus_epi16(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask)); + __m128i layer2_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk2, 8), _mm_srli_epi16(layer3_chunk3, 8)); + __m128i layer2_chunk2 = _mm_packus_epi16(_mm_and_si128(layer3_chunk4, v_mask), _mm_and_si128(layer3_chunk5, v_mask)); + __m128i layer2_chunk6 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk4, 8), _mm_srli_epi16(layer3_chunk5, 8)); + __m128i layer2_chunk3 = _mm_packus_epi16(_mm_and_si128(layer3_chunk6, v_mask), _mm_and_si128(layer3_chunk7, v_mask)); + __m128i layer2_chunk7 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk6, 8), _mm_srli_epi16(layer3_chunk7, 8)); + + __m128i layer1_chunk0 = _mm_packus_epi16(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask)); + __m128i layer1_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk0, 8), _mm_srli_epi16(layer2_chunk1, 8)); + __m128i layer1_chunk1 = _mm_packus_epi16(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask)); + __m128i layer1_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk2, 8), _mm_srli_epi16(layer2_chunk3, 8)); + __m128i layer1_chunk2 = _mm_packus_epi16(_mm_and_si128(layer2_chunk4, v_mask), _mm_and_si128(layer2_chunk5, v_mask)); + __m128i layer1_chunk6 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk4, 8), _mm_srli_epi16(layer2_chunk5, 8)); + __m128i layer1_chunk3 = _mm_packus_epi16(_mm_and_si128(layer2_chunk6, v_mask), _mm_and_si128(layer2_chunk7, v_mask)); + __m128i layer1_chunk7 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk6, 8), _mm_srli_epi16(layer2_chunk7, 8)); + + v_r0 = _mm_packus_epi16(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask)); + v_b0 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk0, 8), _mm_srli_epi16(layer1_chunk1, 8)); + v_r1 = _mm_packus_epi16(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask)); + v_b1 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk2, 8), _mm_srli_epi16(layer1_chunk3, 8)); + v_g0 = _mm_packus_epi16(_mm_and_si128(layer1_chunk4, v_mask), _mm_and_si128(layer1_chunk5, v_mask)); + v_a0 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk4, 8), _mm_srli_epi16(layer1_chunk5, 8)); + v_g1 = _mm_packus_epi16(_mm_and_si128(layer1_chunk6, v_mask), _mm_and_si128(layer1_chunk7, v_mask)); + v_a1 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk6, 8), _mm_srli_epi16(layer1_chunk7, 8)); +} + +inline void _mm_deinterleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1) +{ + __m128i layer1_chunk0 = _mm_unpacklo_epi16(v_r0, v_g0); + __m128i layer1_chunk1 = _mm_unpackhi_epi16(v_r0, v_g0); + __m128i layer1_chunk2 = _mm_unpacklo_epi16(v_r1, v_g1); + __m128i layer1_chunk3 = _mm_unpackhi_epi16(v_r1, v_g1); + + __m128i layer2_chunk0 = _mm_unpacklo_epi16(layer1_chunk0, layer1_chunk2); + __m128i layer2_chunk1 = _mm_unpackhi_epi16(layer1_chunk0, layer1_chunk2); + __m128i layer2_chunk2 = _mm_unpacklo_epi16(layer1_chunk1, layer1_chunk3); + __m128i layer2_chunk3 = _mm_unpackhi_epi16(layer1_chunk1, layer1_chunk3); + + __m128i layer3_chunk0 = _mm_unpacklo_epi16(layer2_chunk0, layer2_chunk2); + __m128i layer3_chunk1 = _mm_unpackhi_epi16(layer2_chunk0, layer2_chunk2); + __m128i layer3_chunk2 = _mm_unpacklo_epi16(layer2_chunk1, layer2_chunk3); + __m128i layer3_chunk3 = _mm_unpackhi_epi16(layer2_chunk1, layer2_chunk3); + + v_r0 = _mm_unpacklo_epi16(layer3_chunk0, layer3_chunk2); + v_r1 = _mm_unpackhi_epi16(layer3_chunk0, layer3_chunk2); + v_g0 = _mm_unpacklo_epi16(layer3_chunk1, layer3_chunk3); + v_g1 = _mm_unpackhi_epi16(layer3_chunk1, layer3_chunk3); +} + +inline void _mm_deinterleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, + __m128i & v_g1, __m128i & v_b0, __m128i & v_b1) +{ + __m128i layer1_chunk0 = _mm_unpacklo_epi16(v_r0, v_g1); + __m128i layer1_chunk1 = _mm_unpackhi_epi16(v_r0, v_g1); + __m128i layer1_chunk2 = _mm_unpacklo_epi16(v_r1, v_b0); + __m128i layer1_chunk3 = _mm_unpackhi_epi16(v_r1, v_b0); + __m128i layer1_chunk4 = _mm_unpacklo_epi16(v_g0, v_b1); + __m128i layer1_chunk5 = _mm_unpackhi_epi16(v_g0, v_b1); + + __m128i layer2_chunk0 = _mm_unpacklo_epi16(layer1_chunk0, layer1_chunk3); + __m128i layer2_chunk1 = _mm_unpackhi_epi16(layer1_chunk0, layer1_chunk3); + __m128i layer2_chunk2 = _mm_unpacklo_epi16(layer1_chunk1, layer1_chunk4); + __m128i layer2_chunk3 = _mm_unpackhi_epi16(layer1_chunk1, layer1_chunk4); + __m128i layer2_chunk4 = _mm_unpacklo_epi16(layer1_chunk2, layer1_chunk5); + __m128i layer2_chunk5 = _mm_unpackhi_epi16(layer1_chunk2, layer1_chunk5); + + __m128i layer3_chunk0 = _mm_unpacklo_epi16(layer2_chunk0, layer2_chunk3); + __m128i layer3_chunk1 = _mm_unpackhi_epi16(layer2_chunk0, layer2_chunk3); + __m128i layer3_chunk2 = _mm_unpacklo_epi16(layer2_chunk1, layer2_chunk4); + __m128i layer3_chunk3 = _mm_unpackhi_epi16(layer2_chunk1, layer2_chunk4); + __m128i layer3_chunk4 = _mm_unpacklo_epi16(layer2_chunk2, layer2_chunk5); + __m128i layer3_chunk5 = _mm_unpackhi_epi16(layer2_chunk2, layer2_chunk5); + + v_r0 = _mm_unpacklo_epi16(layer3_chunk0, layer3_chunk3); + v_r1 = _mm_unpackhi_epi16(layer3_chunk0, layer3_chunk3); + v_g0 = _mm_unpacklo_epi16(layer3_chunk1, layer3_chunk4); + v_g1 = _mm_unpackhi_epi16(layer3_chunk1, layer3_chunk4); + v_b0 = _mm_unpacklo_epi16(layer3_chunk2, layer3_chunk5); + v_b1 = _mm_unpackhi_epi16(layer3_chunk2, layer3_chunk5); +} + +inline void _mm_deinterleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1, + __m128i & v_b0, __m128i & v_b1, __m128i & v_a0, __m128i & v_a1) +{ + __m128i layer1_chunk0 = _mm_unpacklo_epi16(v_r0, v_b0); + __m128i layer1_chunk1 = _mm_unpackhi_epi16(v_r0, v_b0); + __m128i layer1_chunk2 = _mm_unpacklo_epi16(v_r1, v_b1); + __m128i layer1_chunk3 = _mm_unpackhi_epi16(v_r1, v_b1); + __m128i layer1_chunk4 = _mm_unpacklo_epi16(v_g0, v_a0); + __m128i layer1_chunk5 = _mm_unpackhi_epi16(v_g0, v_a0); + __m128i layer1_chunk6 = _mm_unpacklo_epi16(v_g1, v_a1); + __m128i layer1_chunk7 = _mm_unpackhi_epi16(v_g1, v_a1); + + __m128i layer2_chunk0 = _mm_unpacklo_epi16(layer1_chunk0, layer1_chunk4); + __m128i layer2_chunk1 = _mm_unpackhi_epi16(layer1_chunk0, layer1_chunk4); + __m128i layer2_chunk2 = _mm_unpacklo_epi16(layer1_chunk1, layer1_chunk5); + __m128i layer2_chunk3 = _mm_unpackhi_epi16(layer1_chunk1, layer1_chunk5); + __m128i layer2_chunk4 = _mm_unpacklo_epi16(layer1_chunk2, layer1_chunk6); + __m128i layer2_chunk5 = _mm_unpackhi_epi16(layer1_chunk2, layer1_chunk6); + __m128i layer2_chunk6 = _mm_unpacklo_epi16(layer1_chunk3, layer1_chunk7); + __m128i layer2_chunk7 = _mm_unpackhi_epi16(layer1_chunk3, layer1_chunk7); + + __m128i layer3_chunk0 = _mm_unpacklo_epi16(layer2_chunk0, layer2_chunk4); + __m128i layer3_chunk1 = _mm_unpackhi_epi16(layer2_chunk0, layer2_chunk4); + __m128i layer3_chunk2 = _mm_unpacklo_epi16(layer2_chunk1, layer2_chunk5); + __m128i layer3_chunk3 = _mm_unpackhi_epi16(layer2_chunk1, layer2_chunk5); + __m128i layer3_chunk4 = _mm_unpacklo_epi16(layer2_chunk2, layer2_chunk6); + __m128i layer3_chunk5 = _mm_unpackhi_epi16(layer2_chunk2, layer2_chunk6); + __m128i layer3_chunk6 = _mm_unpacklo_epi16(layer2_chunk3, layer2_chunk7); + __m128i layer3_chunk7 = _mm_unpackhi_epi16(layer2_chunk3, layer2_chunk7); + + v_r0 = _mm_unpacklo_epi16(layer3_chunk0, layer3_chunk4); + v_r1 = _mm_unpackhi_epi16(layer3_chunk0, layer3_chunk4); + v_g0 = _mm_unpacklo_epi16(layer3_chunk1, layer3_chunk5); + v_g1 = _mm_unpackhi_epi16(layer3_chunk1, layer3_chunk5); + v_b0 = _mm_unpacklo_epi16(layer3_chunk2, layer3_chunk6); + v_b1 = _mm_unpackhi_epi16(layer3_chunk2, layer3_chunk6); + v_a0 = _mm_unpacklo_epi16(layer3_chunk3, layer3_chunk7); + v_a1 = _mm_unpackhi_epi16(layer3_chunk3, layer3_chunk7); +} + +#if CV_SSE4_1 + +inline void _mm_interleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1) +{ + __m128i v_mask = _mm_set1_epi32(0x0000ffff); + + __m128i layer3_chunk0 = _mm_packus_epi32(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask)); + __m128i layer3_chunk2 = _mm_packus_epi32(_mm_srli_epi32(v_r0, 16), _mm_srli_epi32(v_r1, 16)); + __m128i layer3_chunk1 = _mm_packus_epi32(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask)); + __m128i layer3_chunk3 = _mm_packus_epi32(_mm_srli_epi32(v_g0, 16), _mm_srli_epi32(v_g1, 16)); + + __m128i layer2_chunk0 = _mm_packus_epi32(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask)); + __m128i layer2_chunk2 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk0, 16), _mm_srli_epi32(layer3_chunk1, 16)); + __m128i layer2_chunk1 = _mm_packus_epi32(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask)); + __m128i layer2_chunk3 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk2, 16), _mm_srli_epi32(layer3_chunk3, 16)); + + __m128i layer1_chunk0 = _mm_packus_epi32(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask)); + __m128i layer1_chunk2 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk0, 16), _mm_srli_epi32(layer2_chunk1, 16)); + __m128i layer1_chunk1 = _mm_packus_epi32(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask)); + __m128i layer1_chunk3 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk2, 16), _mm_srli_epi32(layer2_chunk3, 16)); + + v_r0 = _mm_packus_epi32(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask)); + v_g0 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk0, 16), _mm_srli_epi32(layer1_chunk1, 16)); + v_r1 = _mm_packus_epi32(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask)); + v_g1 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk2, 16), _mm_srli_epi32(layer1_chunk3, 16)); +} + +inline void _mm_interleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, + __m128i & v_g1, __m128i & v_b0, __m128i & v_b1) +{ + __m128i v_mask = _mm_set1_epi32(0x0000ffff); + + __m128i layer3_chunk0 = _mm_packus_epi32(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask)); + __m128i layer3_chunk3 = _mm_packus_epi32(_mm_srli_epi32(v_r0, 16), _mm_srli_epi32(v_r1, 16)); + __m128i layer3_chunk1 = _mm_packus_epi32(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask)); + __m128i layer3_chunk4 = _mm_packus_epi32(_mm_srli_epi32(v_g0, 16), _mm_srli_epi32(v_g1, 16)); + __m128i layer3_chunk2 = _mm_packus_epi32(_mm_and_si128(v_b0, v_mask), _mm_and_si128(v_b1, v_mask)); + __m128i layer3_chunk5 = _mm_packus_epi32(_mm_srli_epi32(v_b0, 16), _mm_srli_epi32(v_b1, 16)); + + __m128i layer2_chunk0 = _mm_packus_epi32(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask)); + __m128i layer2_chunk3 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk0, 16), _mm_srli_epi32(layer3_chunk1, 16)); + __m128i layer2_chunk1 = _mm_packus_epi32(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask)); + __m128i layer2_chunk4 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk2, 16), _mm_srli_epi32(layer3_chunk3, 16)); + __m128i layer2_chunk2 = _mm_packus_epi32(_mm_and_si128(layer3_chunk4, v_mask), _mm_and_si128(layer3_chunk5, v_mask)); + __m128i layer2_chunk5 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk4, 16), _mm_srli_epi32(layer3_chunk5, 16)); + + __m128i layer1_chunk0 = _mm_packus_epi32(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask)); + __m128i layer1_chunk3 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk0, 16), _mm_srli_epi32(layer2_chunk1, 16)); + __m128i layer1_chunk1 = _mm_packus_epi32(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask)); + __m128i layer1_chunk4 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk2, 16), _mm_srli_epi32(layer2_chunk3, 16)); + __m128i layer1_chunk2 = _mm_packus_epi32(_mm_and_si128(layer2_chunk4, v_mask), _mm_and_si128(layer2_chunk5, v_mask)); + __m128i layer1_chunk5 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk4, 16), _mm_srli_epi32(layer2_chunk5, 16)); + + v_r0 = _mm_packus_epi32(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask)); + v_g1 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk0, 16), _mm_srli_epi32(layer1_chunk1, 16)); + v_r1 = _mm_packus_epi32(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask)); + v_b0 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk2, 16), _mm_srli_epi32(layer1_chunk3, 16)); + v_g0 = _mm_packus_epi32(_mm_and_si128(layer1_chunk4, v_mask), _mm_and_si128(layer1_chunk5, v_mask)); + v_b1 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk4, 16), _mm_srli_epi32(layer1_chunk5, 16)); +} + +inline void _mm_interleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1, + __m128i & v_b0, __m128i & v_b1, __m128i & v_a0, __m128i & v_a1) +{ + __m128i v_mask = _mm_set1_epi32(0x0000ffff); + + __m128i layer3_chunk0 = _mm_packus_epi32(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask)); + __m128i layer3_chunk4 = _mm_packus_epi32(_mm_srli_epi32(v_r0, 16), _mm_srli_epi32(v_r1, 16)); + __m128i layer3_chunk1 = _mm_packus_epi32(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask)); + __m128i layer3_chunk5 = _mm_packus_epi32(_mm_srli_epi32(v_g0, 16), _mm_srli_epi32(v_g1, 16)); + __m128i layer3_chunk2 = _mm_packus_epi32(_mm_and_si128(v_b0, v_mask), _mm_and_si128(v_b1, v_mask)); + __m128i layer3_chunk6 = _mm_packus_epi32(_mm_srli_epi32(v_b0, 16), _mm_srli_epi32(v_b1, 16)); + __m128i layer3_chunk3 = _mm_packus_epi32(_mm_and_si128(v_a0, v_mask), _mm_and_si128(v_a1, v_mask)); + __m128i layer3_chunk7 = _mm_packus_epi32(_mm_srli_epi32(v_a0, 16), _mm_srli_epi32(v_a1, 16)); + + __m128i layer2_chunk0 = _mm_packus_epi32(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask)); + __m128i layer2_chunk4 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk0, 16), _mm_srli_epi32(layer3_chunk1, 16)); + __m128i layer2_chunk1 = _mm_packus_epi32(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask)); + __m128i layer2_chunk5 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk2, 16), _mm_srli_epi32(layer3_chunk3, 16)); + __m128i layer2_chunk2 = _mm_packus_epi32(_mm_and_si128(layer3_chunk4, v_mask), _mm_and_si128(layer3_chunk5, v_mask)); + __m128i layer2_chunk6 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk4, 16), _mm_srli_epi32(layer3_chunk5, 16)); + __m128i layer2_chunk3 = _mm_packus_epi32(_mm_and_si128(layer3_chunk6, v_mask), _mm_and_si128(layer3_chunk7, v_mask)); + __m128i layer2_chunk7 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk6, 16), _mm_srli_epi32(layer3_chunk7, 16)); + + __m128i layer1_chunk0 = _mm_packus_epi32(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask)); + __m128i layer1_chunk4 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk0, 16), _mm_srli_epi32(layer2_chunk1, 16)); + __m128i layer1_chunk1 = _mm_packus_epi32(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask)); + __m128i layer1_chunk5 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk2, 16), _mm_srli_epi32(layer2_chunk3, 16)); + __m128i layer1_chunk2 = _mm_packus_epi32(_mm_and_si128(layer2_chunk4, v_mask), _mm_and_si128(layer2_chunk5, v_mask)); + __m128i layer1_chunk6 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk4, 16), _mm_srli_epi32(layer2_chunk5, 16)); + __m128i layer1_chunk3 = _mm_packus_epi32(_mm_and_si128(layer2_chunk6, v_mask), _mm_and_si128(layer2_chunk7, v_mask)); + __m128i layer1_chunk7 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk6, 16), _mm_srli_epi32(layer2_chunk7, 16)); + + v_r0 = _mm_packus_epi32(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask)); + v_b0 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk0, 16), _mm_srli_epi32(layer1_chunk1, 16)); + v_r1 = _mm_packus_epi32(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask)); + v_b1 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk2, 16), _mm_srli_epi32(layer1_chunk3, 16)); + v_g0 = _mm_packus_epi32(_mm_and_si128(layer1_chunk4, v_mask), _mm_and_si128(layer1_chunk5, v_mask)); + v_a0 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk4, 16), _mm_srli_epi32(layer1_chunk5, 16)); + v_g1 = _mm_packus_epi32(_mm_and_si128(layer1_chunk6, v_mask), _mm_and_si128(layer1_chunk7, v_mask)); + v_a1 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk6, 16), _mm_srli_epi32(layer1_chunk7, 16)); +} + +#endif // CV_SSE4_1 + +inline void _mm_deinterleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0, __m128 & v_g1) +{ + __m128 layer1_chunk0 = _mm_unpacklo_ps(v_r0, v_g0); + __m128 layer1_chunk1 = _mm_unpackhi_ps(v_r0, v_g0); + __m128 layer1_chunk2 = _mm_unpacklo_ps(v_r1, v_g1); + __m128 layer1_chunk3 = _mm_unpackhi_ps(v_r1, v_g1); + + __m128 layer2_chunk0 = _mm_unpacklo_ps(layer1_chunk0, layer1_chunk2); + __m128 layer2_chunk1 = _mm_unpackhi_ps(layer1_chunk0, layer1_chunk2); + __m128 layer2_chunk2 = _mm_unpacklo_ps(layer1_chunk1, layer1_chunk3); + __m128 layer2_chunk3 = _mm_unpackhi_ps(layer1_chunk1, layer1_chunk3); + + v_r0 = _mm_unpacklo_ps(layer2_chunk0, layer2_chunk2); + v_r1 = _mm_unpackhi_ps(layer2_chunk0, layer2_chunk2); + v_g0 = _mm_unpacklo_ps(layer2_chunk1, layer2_chunk3); + v_g1 = _mm_unpackhi_ps(layer2_chunk1, layer2_chunk3); +} + +inline void _mm_deinterleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0, + __m128 & v_g1, __m128 & v_b0, __m128 & v_b1) +{ + __m128 layer1_chunk0 = _mm_unpacklo_ps(v_r0, v_g1); + __m128 layer1_chunk1 = _mm_unpackhi_ps(v_r0, v_g1); + __m128 layer1_chunk2 = _mm_unpacklo_ps(v_r1, v_b0); + __m128 layer1_chunk3 = _mm_unpackhi_ps(v_r1, v_b0); + __m128 layer1_chunk4 = _mm_unpacklo_ps(v_g0, v_b1); + __m128 layer1_chunk5 = _mm_unpackhi_ps(v_g0, v_b1); + + __m128 layer2_chunk0 = _mm_unpacklo_ps(layer1_chunk0, layer1_chunk3); + __m128 layer2_chunk1 = _mm_unpackhi_ps(layer1_chunk0, layer1_chunk3); + __m128 layer2_chunk2 = _mm_unpacklo_ps(layer1_chunk1, layer1_chunk4); + __m128 layer2_chunk3 = _mm_unpackhi_ps(layer1_chunk1, layer1_chunk4); + __m128 layer2_chunk4 = _mm_unpacklo_ps(layer1_chunk2, layer1_chunk5); + __m128 layer2_chunk5 = _mm_unpackhi_ps(layer1_chunk2, layer1_chunk5); + + v_r0 = _mm_unpacklo_ps(layer2_chunk0, layer2_chunk3); + v_r1 = _mm_unpackhi_ps(layer2_chunk0, layer2_chunk3); + v_g0 = _mm_unpacklo_ps(layer2_chunk1, layer2_chunk4); + v_g1 = _mm_unpackhi_ps(layer2_chunk1, layer2_chunk4); + v_b0 = _mm_unpacklo_ps(layer2_chunk2, layer2_chunk5); + v_b1 = _mm_unpackhi_ps(layer2_chunk2, layer2_chunk5); +} + +inline void _mm_deinterleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0, __m128 & v_g1, + __m128 & v_b0, __m128 & v_b1, __m128 & v_a0, __m128 & v_a1) +{ + __m128 layer1_chunk0 = _mm_unpacklo_ps(v_r0, v_b0); + __m128 layer1_chunk1 = _mm_unpackhi_ps(v_r0, v_b0); + __m128 layer1_chunk2 = _mm_unpacklo_ps(v_r1, v_b1); + __m128 layer1_chunk3 = _mm_unpackhi_ps(v_r1, v_b1); + __m128 layer1_chunk4 = _mm_unpacklo_ps(v_g0, v_a0); + __m128 layer1_chunk5 = _mm_unpackhi_ps(v_g0, v_a0); + __m128 layer1_chunk6 = _mm_unpacklo_ps(v_g1, v_a1); + __m128 layer1_chunk7 = _mm_unpackhi_ps(v_g1, v_a1); + + __m128 layer2_chunk0 = _mm_unpacklo_ps(layer1_chunk0, layer1_chunk4); + __m128 layer2_chunk1 = _mm_unpackhi_ps(layer1_chunk0, layer1_chunk4); + __m128 layer2_chunk2 = _mm_unpacklo_ps(layer1_chunk1, layer1_chunk5); + __m128 layer2_chunk3 = _mm_unpackhi_ps(layer1_chunk1, layer1_chunk5); + __m128 layer2_chunk4 = _mm_unpacklo_ps(layer1_chunk2, layer1_chunk6); + __m128 layer2_chunk5 = _mm_unpackhi_ps(layer1_chunk2, layer1_chunk6); + __m128 layer2_chunk6 = _mm_unpacklo_ps(layer1_chunk3, layer1_chunk7); + __m128 layer2_chunk7 = _mm_unpackhi_ps(layer1_chunk3, layer1_chunk7); + + v_r0 = _mm_unpacklo_ps(layer2_chunk0, layer2_chunk4); + v_r1 = _mm_unpackhi_ps(layer2_chunk0, layer2_chunk4); + v_g0 = _mm_unpacklo_ps(layer2_chunk1, layer2_chunk5); + v_g1 = _mm_unpackhi_ps(layer2_chunk1, layer2_chunk5); + v_b0 = _mm_unpacklo_ps(layer2_chunk2, layer2_chunk6); + v_b1 = _mm_unpackhi_ps(layer2_chunk2, layer2_chunk6); + v_a0 = _mm_unpacklo_ps(layer2_chunk3, layer2_chunk7); + v_a1 = _mm_unpackhi_ps(layer2_chunk3, layer2_chunk7); +} + +inline void _mm_interleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0, __m128 & v_g1) +{ + enum { mask_lo = _MM_SHUFFLE(2, 0, 2, 0), mask_hi = _MM_SHUFFLE(3, 1, 3, 1) }; + + __m128 layer2_chunk0 = _mm_shuffle_ps(v_r0, v_r1, mask_lo); + __m128 layer2_chunk2 = _mm_shuffle_ps(v_r0, v_r1, mask_hi); + __m128 layer2_chunk1 = _mm_shuffle_ps(v_g0, v_g1, mask_lo); + __m128 layer2_chunk3 = _mm_shuffle_ps(v_g0, v_g1, mask_hi); + + __m128 layer1_chunk0 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_lo); + __m128 layer1_chunk2 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_hi); + __m128 layer1_chunk1 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_lo); + __m128 layer1_chunk3 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_hi); + + v_r0 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_lo); + v_g0 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_hi); + v_r1 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_lo); + v_g1 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_hi); +} + +inline void _mm_interleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0, + __m128 & v_g1, __m128 & v_b0, __m128 & v_b1) +{ + enum { mask_lo = _MM_SHUFFLE(2, 0, 2, 0), mask_hi = _MM_SHUFFLE(3, 1, 3, 1) }; + + __m128 layer2_chunk0 = _mm_shuffle_ps(v_r0, v_r1, mask_lo); + __m128 layer2_chunk3 = _mm_shuffle_ps(v_r0, v_r1, mask_hi); + __m128 layer2_chunk1 = _mm_shuffle_ps(v_g0, v_g1, mask_lo); + __m128 layer2_chunk4 = _mm_shuffle_ps(v_g0, v_g1, mask_hi); + __m128 layer2_chunk2 = _mm_shuffle_ps(v_b0, v_b1, mask_lo); + __m128 layer2_chunk5 = _mm_shuffle_ps(v_b0, v_b1, mask_hi); + + __m128 layer1_chunk0 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_lo); + __m128 layer1_chunk3 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_hi); + __m128 layer1_chunk1 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_lo); + __m128 layer1_chunk4 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_hi); + __m128 layer1_chunk2 = _mm_shuffle_ps(layer2_chunk4, layer2_chunk5, mask_lo); + __m128 layer1_chunk5 = _mm_shuffle_ps(layer2_chunk4, layer2_chunk5, mask_hi); + + v_r0 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_lo); + v_g1 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_hi); + v_r1 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_lo); + v_b0 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_hi); + v_g0 = _mm_shuffle_ps(layer1_chunk4, layer1_chunk5, mask_lo); + v_b1 = _mm_shuffle_ps(layer1_chunk4, layer1_chunk5, mask_hi); +} + +inline void _mm_interleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0, __m128 & v_g1, + __m128 & v_b0, __m128 & v_b1, __m128 & v_a0, __m128 & v_a1) +{ + enum { mask_lo = _MM_SHUFFLE(2, 0, 2, 0), mask_hi = _MM_SHUFFLE(3, 1, 3, 1) }; + + __m128 layer2_chunk0 = _mm_shuffle_ps(v_r0, v_r1, mask_lo); + __m128 layer2_chunk4 = _mm_shuffle_ps(v_r0, v_r1, mask_hi); + __m128 layer2_chunk1 = _mm_shuffle_ps(v_g0, v_g1, mask_lo); + __m128 layer2_chunk5 = _mm_shuffle_ps(v_g0, v_g1, mask_hi); + __m128 layer2_chunk2 = _mm_shuffle_ps(v_b0, v_b1, mask_lo); + __m128 layer2_chunk6 = _mm_shuffle_ps(v_b0, v_b1, mask_hi); + __m128 layer2_chunk3 = _mm_shuffle_ps(v_a0, v_a1, mask_lo); + __m128 layer2_chunk7 = _mm_shuffle_ps(v_a0, v_a1, mask_hi); + + __m128 layer1_chunk0 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_lo); + __m128 layer1_chunk4 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_hi); + __m128 layer1_chunk1 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_lo); + __m128 layer1_chunk5 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_hi); + __m128 layer1_chunk2 = _mm_shuffle_ps(layer2_chunk4, layer2_chunk5, mask_lo); + __m128 layer1_chunk6 = _mm_shuffle_ps(layer2_chunk4, layer2_chunk5, mask_hi); + __m128 layer1_chunk3 = _mm_shuffle_ps(layer2_chunk6, layer2_chunk7, mask_lo); + __m128 layer1_chunk7 = _mm_shuffle_ps(layer2_chunk6, layer2_chunk7, mask_hi); + + v_r0 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_lo); + v_b0 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_hi); + v_r1 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_lo); + v_b1 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_hi); + v_g0 = _mm_shuffle_ps(layer1_chunk4, layer1_chunk5, mask_lo); + v_a0 = _mm_shuffle_ps(layer1_chunk4, layer1_chunk5, mask_hi); + v_g1 = _mm_shuffle_ps(layer1_chunk6, layer1_chunk7, mask_lo); + v_a1 = _mm_shuffle_ps(layer1_chunk6, layer1_chunk7, mask_hi); +} + +#endif // CV_SSE2 + +//! @} + +#endif //OPENCV_CORE_SSE_UTILS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/traits.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/traits.hpp new file mode 100644 index 0000000000000000000000000000000000000000..6cb10f44cf59fde0dccf6768281bb4bd86d28b3d --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/traits.hpp @@ -0,0 +1,397 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_TRAITS_HPP +#define OPENCV_CORE_TRAITS_HPP + +#include "opencv2/core/cvdef.h" + +namespace cv +{ + +//#define OPENCV_TRAITS_ENABLE_DEPRECATED + +//! @addtogroup core_basic +//! @{ + +/** @brief Template "trait" class for OpenCV primitive data types. + +@note Deprecated. This is replaced by "single purpose" traits: traits::Type and traits::Depth + +A primitive OpenCV data type is one of unsigned char, bool, signed char, unsigned short, signed +short, int, float, double, or a tuple of values of one of these types, where all the values in the +tuple have the same type. Any primitive type from the list can be defined by an identifier in the +form CV_\{U|S|F}C(\), for example: uchar \~ CV_8UC1, 3-element +floating-point tuple \~ CV_32FC3, and so on. A universal OpenCV structure that is able to store a +single instance of such a primitive data type is Vec. Multiple instances of such a type can be +stored in a std::vector, Mat, Mat_, SparseMat, SparseMat_, or any other container that is able to +store Vec instances. + +The DataType class is basically used to provide a description of such primitive data types without +adding any fields or methods to the corresponding classes (and it is actually impossible to add +anything to primitive C/C++ data types). This technique is known in C++ as class traits. It is not +DataType itself that is used but its specialized versions, such as: +@code + template<> class DataType + { + typedef uchar value_type; + typedef int work_type; + typedef uchar channel_type; + enum { channel_type = CV_8U, channels = 1, fmt='u', type = CV_8U }; + }; + ... + template DataType > + { + typedef std::complex<_Tp> value_type; + typedef std::complex<_Tp> work_type; + typedef _Tp channel_type; + // DataDepth is another helper trait class + enum { depth = DataDepth<_Tp>::value, channels=2, + fmt=(channels-1)*256+DataDepth<_Tp>::fmt, + type=CV_MAKETYPE(depth, channels) }; + }; + ... +@endcode +The main purpose of this class is to convert compilation-time type information to an +OpenCV-compatible data type identifier, for example: +@code + // allocates a 30x40 floating-point matrix + Mat A(30, 40, DataType::type); + + Mat B = Mat_ >(3, 3); + // the statement below will print 6, 2 , that is depth == CV_64F, channels == 2 + cout << B.depth() << ", " << B.channels() << endl; +@endcode +So, such traits are used to tell OpenCV which data type you are working with, even if such a type is +not native to OpenCV. For example, the matrix B initialization above is compiled because OpenCV +defines the proper specialized template class DataType\ \> . This mechanism is also +useful (and used in OpenCV this way) for generic algorithms implementations. + +@note Default values were dropped to stop confusing developers about using of unsupported types (see #7599) +*/ +template class DataType +{ +public: +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + typedef _Tp value_type; + typedef value_type work_type; + typedef value_type channel_type; + typedef value_type vec_type; + enum { generic_type = 1, + depth = -1, + channels = 1, + fmt = 0, + type = CV_MAKETYPE(depth, channels) + }; +#endif +}; + +template<> class DataType +{ +public: + typedef bool value_type; + typedef int work_type; + typedef value_type channel_type; + typedef value_type vec_type; + enum { generic_type = 0, + depth = CV_8U, + channels = 1, + fmt = (int)'u', + type = CV_MAKETYPE(depth, channels) + }; +}; + +template<> class DataType +{ +public: + typedef uchar value_type; + typedef int work_type; + typedef value_type channel_type; + typedef value_type vec_type; + enum { generic_type = 0, + depth = CV_8U, + channels = 1, + fmt = (int)'u', + type = CV_MAKETYPE(depth, channels) + }; +}; + +template<> class DataType +{ +public: + typedef schar value_type; + typedef int work_type; + typedef value_type channel_type; + typedef value_type vec_type; + enum { generic_type = 0, + depth = CV_8S, + channels = 1, + fmt = (int)'c', + type = CV_MAKETYPE(depth, channels) + }; +}; + +template<> class DataType +{ +public: + typedef schar value_type; + typedef int work_type; + typedef value_type channel_type; + typedef value_type vec_type; + enum { generic_type = 0, + depth = CV_8S, + channels = 1, + fmt = (int)'c', + type = CV_MAKETYPE(depth, channels) + }; +}; + +template<> class DataType +{ +public: + typedef ushort value_type; + typedef int work_type; + typedef value_type channel_type; + typedef value_type vec_type; + enum { generic_type = 0, + depth = CV_16U, + channels = 1, + fmt = (int)'w', + type = CV_MAKETYPE(depth, channels) + }; +}; + +template<> class DataType +{ +public: + typedef short value_type; + typedef int work_type; + typedef value_type channel_type; + typedef value_type vec_type; + enum { generic_type = 0, + depth = CV_16S, + channels = 1, + fmt = (int)'s', + type = CV_MAKETYPE(depth, channels) + }; +}; + +template<> class DataType +{ +public: + typedef int value_type; + typedef value_type work_type; + typedef value_type channel_type; + typedef value_type vec_type; + enum { generic_type = 0, + depth = CV_32S, + channels = 1, + fmt = (int)'i', + type = CV_MAKETYPE(depth, channels) + }; +}; + +template<> class DataType +{ +public: + typedef float value_type; + typedef value_type work_type; + typedef value_type channel_type; + typedef value_type vec_type; + enum { generic_type = 0, + depth = CV_32F, + channels = 1, + fmt = (int)'f', + type = CV_MAKETYPE(depth, channels) + }; +}; + +template<> class DataType +{ +public: + typedef double value_type; + typedef value_type work_type; + typedef value_type channel_type; + typedef value_type vec_type; + enum { generic_type = 0, + depth = CV_64F, + channels = 1, + fmt = (int)'d', + type = CV_MAKETYPE(depth, channels) + }; +}; + + +/** @brief A helper class for cv::DataType + +The class is specialized for each fundamental numerical data type supported by OpenCV. It provides +DataDepth::value constant. +*/ +template class DataDepth +{ +public: + enum + { + value = DataType<_Tp>::depth, + fmt = DataType<_Tp>::fmt + }; +}; + + +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + +template class TypeDepth +{ +#ifdef OPENCV_TRAITS_ENABLE_LEGACY_DEFAULTS + enum { depth = CV_USRTYPE1 }; + typedef void value_type; +#endif +}; + +template<> class TypeDepth +{ + enum { depth = CV_8U }; + typedef uchar value_type; +}; + +template<> class TypeDepth +{ + enum { depth = CV_8S }; + typedef schar value_type; +}; + +template<> class TypeDepth +{ + enum { depth = CV_16U }; + typedef ushort value_type; +}; + +template<> class TypeDepth +{ + enum { depth = CV_16S }; + typedef short value_type; +}; + +template<> class TypeDepth +{ + enum { depth = CV_32S }; + typedef int value_type; +}; + +template<> class TypeDepth +{ + enum { depth = CV_32F }; + typedef float value_type; +}; + +template<> class TypeDepth +{ + enum { depth = CV_64F }; + typedef double value_type; +}; + +#endif + +//! @} + +namespace traits { + +namespace internal { +#define CV_CREATE_MEMBER_CHECK(X) \ +template class CheckMember_##X { \ + struct Fallback { int X; }; \ + struct Derived : T, Fallback { }; \ + template struct Check; \ + typedef char CV_NO[1]; \ + typedef char CV_YES[2]; \ + template static CV_NO & func(Check *); \ + template static CV_YES & func(...); \ +public: \ + typedef CheckMember_##X type; \ + enum { value = sizeof(func(0)) == sizeof(CV_YES) }; \ +}; + +CV_CREATE_MEMBER_CHECK(fmt) +CV_CREATE_MEMBER_CHECK(type) + +} // namespace internal + + +template +struct Depth +{ enum { value = DataType::depth }; }; + +template +struct Type +{ enum { value = DataType::type }; }; + +/** Similar to traits::Type but has value = -1 in case of unknown type (instead of compiler error) */ +template >::value > +struct SafeType {}; + +template +struct SafeType +{ enum { value = -1 }; }; + +template +struct SafeType +{ enum { value = Type::value }; }; + + +template >::value > +struct SafeFmt {}; + +template +struct SafeFmt +{ enum { fmt = 0 }; }; + +template +struct SafeFmt +{ enum { fmt = DataType::fmt }; }; + + +} // namespace + +} // cv + +#endif // OPENCV_CORE_TRAITS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/types.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/types.hpp new file mode 100644 index 0000000000000000000000000000000000000000..ef9ab593830ace4e94f60f5c1d52419d48e43435 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/types.hpp @@ -0,0 +1,2391 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_TYPES_HPP +#define OPENCV_CORE_TYPES_HPP + +#ifndef __cplusplus +# error types.hpp header must be compiled as C++ +#endif + +#include +#include +#include +#include + +#include "opencv2/core/cvdef.h" +#include "opencv2/core/cvstd.hpp" +#include "opencv2/core/matx.hpp" + +namespace cv +{ + +//! @addtogroup core_basic +//! @{ + +//////////////////////////////// Complex ////////////////////////////// + +/** @brief A complex number class. + + The template class is similar and compatible with std::complex, however it provides slightly + more convenient access to the real and imaginary parts using through the simple field access, as opposite + to std::complex::real() and std::complex::imag(). +*/ +template class Complex +{ +public: + + //! default constructor + Complex(); + Complex( _Tp _re, _Tp _im = 0 ); + + //! conversion to another data type + template operator Complex() const; + //! conjugation + Complex conj() const; + + _Tp re, im; //< the real and the imaginary parts +}; + +typedef Complex Complexf; +typedef Complex Complexd; + +template class DataType< Complex<_Tp> > +{ +public: + typedef Complex<_Tp> value_type; + typedef value_type work_type; + typedef _Tp channel_type; + + enum { generic_type = 0, + channels = 2, + fmt = DataType::fmt + ((channels - 1) << 8) +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + ,depth = DataType::depth + ,type = CV_MAKETYPE(depth, channels) +#endif + }; + + typedef Vec vec_type; +}; + +namespace traits { +template +struct Depth< Complex<_Tp> > { enum { value = Depth<_Tp>::value }; }; +template +struct Type< Complex<_Tp> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, 2) }; }; +} // namespace + + +//////////////////////////////// Point_ //////////////////////////////// + +/** @brief Template class for 2D points specified by its coordinates `x` and `y`. + +An instance of the class is interchangeable with C structures, CvPoint and CvPoint2D32f . There is +also a cast operator to convert point coordinates to the specified type. The conversion from +floating-point coordinates to integer coordinates is done by rounding. Commonly, the conversion +uses this operation for each of the coordinates. Besides the class members listed in the +declaration above, the following operations on points are implemented: +@code + pt1 = pt2 + pt3; + pt1 = pt2 - pt3; + pt1 = pt2 * a; + pt1 = a * pt2; + pt1 = pt2 / a; + pt1 += pt2; + pt1 -= pt2; + pt1 *= a; + pt1 /= a; + double value = norm(pt); // L2 norm + pt1 == pt2; + pt1 != pt2; +@endcode +For your convenience, the following type aliases are defined: +@code + typedef Point_ Point2i; + typedef Point2i Point; + typedef Point_ Point2f; + typedef Point_ Point2d; +@endcode +Example: +@code + Point2f a(0.3f, 0.f), b(0.f, 0.4f); + Point pt = (a + b)*10.f; + cout << pt.x << ", " << pt.y << endl; +@endcode +*/ +template class Point_ +{ +public: + typedef _Tp value_type; + + //! default constructor + Point_(); + Point_(_Tp _x, _Tp _y); + Point_(const Point_& pt); + Point_(const Size_<_Tp>& sz); + Point_(const Vec<_Tp, 2>& v); + + Point_& operator = (const Point_& pt); + //! conversion to another data type + template operator Point_<_Tp2>() const; + + //! conversion to the old-style C structures + operator Vec<_Tp, 2>() const; + + //! dot product + _Tp dot(const Point_& pt) const; + //! dot product computed in double-precision arithmetics + double ddot(const Point_& pt) const; + //! cross-product + double cross(const Point_& pt) const; + //! checks whether the point is inside the specified rectangle + bool inside(const Rect_<_Tp>& r) const; + _Tp x; //!< x coordinate of the point + _Tp y; //!< y coordinate of the point +}; + +typedef Point_ Point2i; +typedef Point_ Point2l; +typedef Point_ Point2f; +typedef Point_ Point2d; +typedef Point2i Point; + +template class DataType< Point_<_Tp> > +{ +public: + typedef Point_<_Tp> value_type; + typedef Point_::work_type> work_type; + typedef _Tp channel_type; + + enum { generic_type = 0, + channels = 2, + fmt = traits::SafeFmt::fmt + ((channels - 1) << 8) +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + ,depth = DataType::depth + ,type = CV_MAKETYPE(depth, channels) +#endif + }; + + typedef Vec vec_type; +}; + +namespace traits { +template +struct Depth< Point_<_Tp> > { enum { value = Depth<_Tp>::value }; }; +template +struct Type< Point_<_Tp> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, 2) }; }; +} // namespace + + +//////////////////////////////// Point3_ //////////////////////////////// + +/** @brief Template class for 3D points specified by its coordinates `x`, `y` and `z`. + +An instance of the class is interchangeable with the C structure CvPoint2D32f . Similarly to +Point_ , the coordinates of 3D points can be converted to another type. The vector arithmetic and +comparison operations are also supported. + +The following Point3_\<\> aliases are available: +@code + typedef Point3_ Point3i; + typedef Point3_ Point3f; + typedef Point3_ Point3d; +@endcode +@see cv::Point3i, cv::Point3f and cv::Point3d +*/ +template class Point3_ +{ +public: + typedef _Tp value_type; + + //! default constructor + Point3_(); + Point3_(_Tp _x, _Tp _y, _Tp _z); + Point3_(const Point3_& pt); + explicit Point3_(const Point_<_Tp>& pt); + Point3_(const Vec<_Tp, 3>& v); + + Point3_& operator = (const Point3_& pt); + //! conversion to another data type + template operator Point3_<_Tp2>() const; + //! conversion to cv::Vec<> +#if OPENCV_ABI_COMPATIBILITY > 300 + template operator Vec<_Tp2, 3>() const; +#else + operator Vec<_Tp, 3>() const; +#endif + + //! dot product + _Tp dot(const Point3_& pt) const; + //! dot product computed in double-precision arithmetics + double ddot(const Point3_& pt) const; + //! cross product of the 2 3D points + Point3_ cross(const Point3_& pt) const; + _Tp x; //!< x coordinate of the 3D point + _Tp y; //!< y coordinate of the 3D point + _Tp z; //!< z coordinate of the 3D point +}; + +typedef Point3_ Point3i; +typedef Point3_ Point3f; +typedef Point3_ Point3d; + +template class DataType< Point3_<_Tp> > +{ +public: + typedef Point3_<_Tp> value_type; + typedef Point3_::work_type> work_type; + typedef _Tp channel_type; + + enum { generic_type = 0, + channels = 3, + fmt = traits::SafeFmt::fmt + ((channels - 1) << 8) +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + ,depth = DataType::depth + ,type = CV_MAKETYPE(depth, channels) +#endif + }; + + typedef Vec vec_type; +}; + +namespace traits { +template +struct Depth< Point3_<_Tp> > { enum { value = Depth<_Tp>::value }; }; +template +struct Type< Point3_<_Tp> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, 3) }; }; +} // namespace + +//////////////////////////////// Size_ //////////////////////////////// + +/** @brief Template class for specifying the size of an image or rectangle. + +The class includes two members called width and height. The structure can be converted to and from +the old OpenCV structures CvSize and CvSize2D32f . The same set of arithmetic and comparison +operations as for Point_ is available. + +OpenCV defines the following Size_\<\> aliases: +@code + typedef Size_ Size2i; + typedef Size2i Size; + typedef Size_ Size2f; +@endcode +*/ +template class Size_ +{ +public: + typedef _Tp value_type; + + //! default constructor + Size_(); + Size_(_Tp _width, _Tp _height); + Size_(const Size_& sz); + Size_(const Point_<_Tp>& pt); + + Size_& operator = (const Size_& sz); + //! the area (width*height) + _Tp area() const; + //! true if empty + bool empty() const; + + //! conversion of another data type. + template operator Size_<_Tp2>() const; + + _Tp width; //!< the width + _Tp height; //!< the height +}; + +typedef Size_ Size2i; +typedef Size_ Size2l; +typedef Size_ Size2f; +typedef Size_ Size2d; +typedef Size2i Size; + +template class DataType< Size_<_Tp> > +{ +public: + typedef Size_<_Tp> value_type; + typedef Size_::work_type> work_type; + typedef _Tp channel_type; + + enum { generic_type = 0, + channels = 2, + fmt = DataType::fmt + ((channels - 1) << 8) +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + ,depth = DataType::depth + ,type = CV_MAKETYPE(depth, channels) +#endif + }; + + typedef Vec vec_type; +}; + +namespace traits { +template +struct Depth< Size_<_Tp> > { enum { value = Depth<_Tp>::value }; }; +template +struct Type< Size_<_Tp> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, 2) }; }; +} // namespace + +//////////////////////////////// Rect_ //////////////////////////////// + +/** @brief Template class for 2D rectangles + +described by the following parameters: +- Coordinates of the top-left corner. This is a default interpretation of Rect_::x and Rect_::y + in OpenCV. Though, in your algorithms you may count x and y from the bottom-left corner. +- Rectangle width and height. + +OpenCV typically assumes that the top and left boundary of the rectangle are inclusive, while the +right and bottom boundaries are not. For example, the method Rect_::contains returns true if + +\f[x \leq pt.x < x+width, + y \leq pt.y < y+height\f] + +Virtually every loop over an image ROI in OpenCV (where ROI is specified by Rect_\ ) is +implemented as: +@code + for(int y = roi.y; y < roi.y + roi.height; y++) + for(int x = roi.x; x < roi.x + roi.width; x++) + { + // ... + } +@endcode +In addition to the class members, the following operations on rectangles are implemented: +- \f$\texttt{rect} = \texttt{rect} \pm \texttt{point}\f$ (shifting a rectangle by a certain offset) +- \f$\texttt{rect} = \texttt{rect} \pm \texttt{size}\f$ (expanding or shrinking a rectangle by a + certain amount) +- rect += point, rect -= point, rect += size, rect -= size (augmenting operations) +- rect = rect1 & rect2 (rectangle intersection) +- rect = rect1 | rect2 (minimum area rectangle containing rect1 and rect2 ) +- rect &= rect1, rect |= rect1 (and the corresponding augmenting operations) +- rect == rect1, rect != rect1 (rectangle comparison) + +This is an example how the partial ordering on rectangles can be established (rect1 \f$\subseteq\f$ +rect2): +@code + template inline bool + operator <= (const Rect_<_Tp>& r1, const Rect_<_Tp>& r2) + { + return (r1 & r2) == r1; + } +@endcode +For your convenience, the Rect_\<\> alias is available: cv::Rect +*/ +template class Rect_ +{ +public: + typedef _Tp value_type; + + //! default constructor + Rect_(); + Rect_(_Tp _x, _Tp _y, _Tp _width, _Tp _height); + Rect_(const Rect_& r); + Rect_(const Point_<_Tp>& org, const Size_<_Tp>& sz); + Rect_(const Point_<_Tp>& pt1, const Point_<_Tp>& pt2); + + Rect_& operator = ( const Rect_& r ); + //! the top-left corner + Point_<_Tp> tl() const; + //! the bottom-right corner + Point_<_Tp> br() const; + + //! size (width, height) of the rectangle + Size_<_Tp> size() const; + //! area (width*height) of the rectangle + _Tp area() const; + //! true if empty + bool empty() const; + + //! conversion to another data type + template operator Rect_<_Tp2>() const; + + //! checks whether the rectangle contains the point + bool contains(const Point_<_Tp>& pt) const; + + _Tp x; //!< x coordinate of the top-left corner + _Tp y; //!< y coordinate of the top-left corner + _Tp width; //!< width of the rectangle + _Tp height; //!< height of the rectangle +}; + +typedef Rect_ Rect2i; +typedef Rect_ Rect2f; +typedef Rect_ Rect2d; +typedef Rect2i Rect; + +template class DataType< Rect_<_Tp> > +{ +public: + typedef Rect_<_Tp> value_type; + typedef Rect_::work_type> work_type; + typedef _Tp channel_type; + + enum { generic_type = 0, + channels = 4, + fmt = traits::SafeFmt::fmt + ((channels - 1) << 8) +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + ,depth = DataType::depth + ,type = CV_MAKETYPE(depth, channels) +#endif + }; + + typedef Vec vec_type; +}; + +namespace traits { +template +struct Depth< Rect_<_Tp> > { enum { value = Depth<_Tp>::value }; }; +template +struct Type< Rect_<_Tp> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, 4) }; }; +} // namespace + +///////////////////////////// RotatedRect ///////////////////////////// + +/** @brief The class represents rotated (i.e. not up-right) rectangles on a plane. + +Each rectangle is specified by the center point (mass center), length of each side (represented by +#Size2f structure) and the rotation angle in degrees. + +The sample below demonstrates how to use RotatedRect: +@snippet snippets/core_various.cpp RotatedRect_demo +![image](pics/rotatedrect.png) + +@sa CamShift, fitEllipse, minAreaRect, CvBox2D +*/ +class CV_EXPORTS RotatedRect +{ +public: + //! default constructor + RotatedRect(); + /** full constructor + @param center The rectangle mass center. + @param size Width and height of the rectangle. + @param angle The rotation angle in a clockwise direction. When the angle is 0, 90, 180, 270 etc., + the rectangle becomes an up-right rectangle. + */ + RotatedRect(const Point2f& center, const Size2f& size, float angle); + /** + Any 3 end points of the RotatedRect. They must be given in order (either clockwise or + anticlockwise). + */ + RotatedRect(const Point2f& point1, const Point2f& point2, const Point2f& point3); + + /** returns 4 vertices of the rectangle + @param pts The points array for storing rectangle vertices. The order is bottomLeft, topLeft, topRight, bottomRight. + */ + void points(Point2f pts[]) const; + //! returns the minimal up-right integer rectangle containing the rotated rectangle + Rect boundingRect() const; + //! returns the minimal (exact) floating point rectangle containing the rotated rectangle, not intended for use with images + Rect_ boundingRect2f() const; + //! returns the rectangle mass center + Point2f center; + //! returns width and height of the rectangle + Size2f size; + //! returns the rotation angle. When the angle is 0, 90, 180, 270 etc., the rectangle becomes an up-right rectangle. + float angle; +}; + +template<> class DataType< RotatedRect > +{ +public: + typedef RotatedRect value_type; + typedef value_type work_type; + typedef float channel_type; + + enum { generic_type = 0, + channels = (int)sizeof(value_type)/sizeof(channel_type), // 5 + fmt = traits::SafeFmt::fmt + ((channels - 1) << 8) +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + ,depth = DataType::depth + ,type = CV_MAKETYPE(depth, channels) +#endif + }; + + typedef Vec vec_type; +}; + +namespace traits { +template<> +struct Depth< RotatedRect > { enum { value = Depth::value }; }; +template<> +struct Type< RotatedRect > { enum { value = CV_MAKETYPE(Depth::value, (int)sizeof(RotatedRect)/sizeof(float)) }; }; +} // namespace + + +//////////////////////////////// Range ///////////////////////////////// + +/** @brief Template class specifying a continuous subsequence (slice) of a sequence. + +The class is used to specify a row or a column span in a matrix ( Mat ) and for many other purposes. +Range(a,b) is basically the same as a:b in Matlab or a..b in Python. As in Python, start is an +inclusive left boundary of the range and end is an exclusive right boundary of the range. Such a +half-opened interval is usually denoted as \f$[start,end)\f$ . + +The static method Range::all() returns a special variable that means "the whole sequence" or "the +whole range", just like " : " in Matlab or " ... " in Python. All the methods and functions in +OpenCV that take Range support this special Range::all() value. But, of course, in case of your own +custom processing, you will probably have to check and handle it explicitly: +@code + void my_function(..., const Range& r, ....) + { + if(r == Range::all()) { + // process all the data + } + else { + // process [r.start, r.end) + } + } +@endcode +*/ +class CV_EXPORTS Range +{ +public: + Range(); + Range(int _start, int _end); + int size() const; + bool empty() const; + static Range all(); + + int start, end; +}; + +template<> class DataType +{ +public: + typedef Range value_type; + typedef value_type work_type; + typedef int channel_type; + + enum { generic_type = 0, + channels = 2, + fmt = traits::SafeFmt::fmt + ((channels - 1) << 8) +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + ,depth = DataType::depth + ,type = CV_MAKETYPE(depth, channels) +#endif + }; + + typedef Vec vec_type; +}; + +namespace traits { +template<> +struct Depth< Range > { enum { value = Depth::value }; }; +template<> +struct Type< Range > { enum { value = CV_MAKETYPE(Depth::value, 2) }; }; +} // namespace + + +//////////////////////////////// Scalar_ /////////////////////////////// + +/** @brief Template class for a 4-element vector derived from Vec. + +Being derived from Vec\<_Tp, 4\> , Scalar\_ and Scalar can be used just as typical 4-element +vectors. In addition, they can be converted to/from CvScalar . The type Scalar is widely used in +OpenCV to pass pixel values. +*/ +template class Scalar_ : public Vec<_Tp, 4> +{ +public: + //! default constructor + Scalar_(); + Scalar_(_Tp v0, _Tp v1, _Tp v2=0, _Tp v3=0); + Scalar_(_Tp v0); + + template + Scalar_(const Vec<_Tp2, cn>& v); + + //! returns a scalar with all elements set to v0 + static Scalar_<_Tp> all(_Tp v0); + + //! conversion to another data type + template operator Scalar_() const; + + //! per-element product + Scalar_<_Tp> mul(const Scalar_<_Tp>& a, double scale=1 ) const; + + //! returns (v0, -v1, -v2, -v3) + Scalar_<_Tp> conj() const; + + //! returns true iff v1 == v2 == v3 == 0 + bool isReal() const; +}; + +typedef Scalar_ Scalar; + +template class DataType< Scalar_<_Tp> > +{ +public: + typedef Scalar_<_Tp> value_type; + typedef Scalar_::work_type> work_type; + typedef _Tp channel_type; + + enum { generic_type = 0, + channels = 4, + fmt = traits::SafeFmt::fmt + ((channels - 1) << 8) +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + ,depth = DataType::depth + ,type = CV_MAKETYPE(depth, channels) +#endif + }; + + typedef Vec vec_type; +}; + +namespace traits { +template +struct Depth< Scalar_<_Tp> > { enum { value = Depth<_Tp>::value }; }; +template +struct Type< Scalar_<_Tp> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, 4) }; }; +} // namespace + + +/////////////////////////////// KeyPoint //////////////////////////////// + +/** @brief Data structure for salient point detectors. + +The class instance stores a keypoint, i.e. a point feature found by one of many available keypoint +detectors, such as Harris corner detector, #FAST, %StarDetector, %SURF, %SIFT etc. + +The keypoint is characterized by the 2D position, scale (proportional to the diameter of the +neighborhood that needs to be taken into account), orientation and some other parameters. The +keypoint neighborhood is then analyzed by another algorithm that builds a descriptor (usually +represented as a feature vector). The keypoints representing the same object in different images +can then be matched using %KDTree or another method. +*/ +class CV_EXPORTS_W_SIMPLE KeyPoint +{ +public: + //! the default constructor + CV_WRAP KeyPoint(); + /** + @param _pt x & y coordinates of the keypoint + @param _size keypoint diameter + @param _angle keypoint orientation + @param _response keypoint detector response on the keypoint (that is, strength of the keypoint) + @param _octave pyramid octave in which the keypoint has been detected + @param _class_id object id + */ + KeyPoint(Point2f _pt, float _size, float _angle=-1, float _response=0, int _octave=0, int _class_id=-1); + /** + @param x x-coordinate of the keypoint + @param y y-coordinate of the keypoint + @param _size keypoint diameter + @param _angle keypoint orientation + @param _response keypoint detector response on the keypoint (that is, strength of the keypoint) + @param _octave pyramid octave in which the keypoint has been detected + @param _class_id object id + */ + CV_WRAP KeyPoint(float x, float y, float _size, float _angle=-1, float _response=0, int _octave=0, int _class_id=-1); + + size_t hash() const; + + /** + This method converts vector of keypoints to vector of points or the reverse, where each keypoint is + assigned the same size and the same orientation. + + @param keypoints Keypoints obtained from any feature detection algorithm like SIFT/SURF/ORB + @param points2f Array of (x,y) coordinates of each keypoint + @param keypointIndexes Array of indexes of keypoints to be converted to points. (Acts like a mask to + convert only specified keypoints) + */ + CV_WRAP static void convert(const std::vector& keypoints, + CV_OUT std::vector& points2f, + const std::vector& keypointIndexes=std::vector()); + /** @overload + @param points2f Array of (x,y) coordinates of each keypoint + @param keypoints Keypoints obtained from any feature detection algorithm like SIFT/SURF/ORB + @param size keypoint diameter + @param response keypoint detector response on the keypoint (that is, strength of the keypoint) + @param octave pyramid octave in which the keypoint has been detected + @param class_id object id + */ + CV_WRAP static void convert(const std::vector& points2f, + CV_OUT std::vector& keypoints, + float size=1, float response=1, int octave=0, int class_id=-1); + + /** + This method computes overlap for pair of keypoints. Overlap is the ratio between area of keypoint + regions' intersection and area of keypoint regions' union (considering keypoint region as circle). + If they don't overlap, we get zero. If they coincide at same location with same size, we get 1. + @param kp1 First keypoint + @param kp2 Second keypoint + */ + CV_WRAP static float overlap(const KeyPoint& kp1, const KeyPoint& kp2); + + CV_PROP_RW Point2f pt; //!< coordinates of the keypoints + CV_PROP_RW float size; //!< diameter of the meaningful keypoint neighborhood + CV_PROP_RW float angle; //!< computed orientation of the keypoint (-1 if not applicable); + //!< it's in [0,360) degrees and measured relative to + //!< image coordinate system, ie in clockwise. + CV_PROP_RW float response; //!< the response by which the most strong keypoints have been selected. Can be used for the further sorting or subsampling + CV_PROP_RW int octave; //!< octave (pyramid layer) from which the keypoint has been extracted + CV_PROP_RW int class_id; //!< object class (if the keypoints need to be clustered by an object they belong to) +}; + +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED +template<> class DataType +{ +public: + typedef KeyPoint value_type; + typedef float work_type; + typedef float channel_type; + + enum { generic_type = 0, + depth = DataType::depth, + channels = (int)(sizeof(value_type)/sizeof(channel_type)), // 7 + fmt = DataType::fmt + ((channels - 1) << 8), + type = CV_MAKETYPE(depth, channels) + }; + + typedef Vec vec_type; +}; +#endif + + +//////////////////////////////// DMatch ///////////////////////////////// + +/** @brief Class for matching keypoint descriptors + +query descriptor index, train descriptor index, train image index, and distance between +descriptors. +*/ +class CV_EXPORTS_W_SIMPLE DMatch +{ +public: + CV_WRAP DMatch(); + CV_WRAP DMatch(int _queryIdx, int _trainIdx, float _distance); + CV_WRAP DMatch(int _queryIdx, int _trainIdx, int _imgIdx, float _distance); + + CV_PROP_RW int queryIdx; //!< query descriptor index + CV_PROP_RW int trainIdx; //!< train descriptor index + CV_PROP_RW int imgIdx; //!< train image index + + CV_PROP_RW float distance; + + // less is better + bool operator<(const DMatch &m) const; +}; + +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED +template<> class DataType +{ +public: + typedef DMatch value_type; + typedef int work_type; + typedef int channel_type; + + enum { generic_type = 0, + depth = DataType::depth, + channels = (int)(sizeof(value_type)/sizeof(channel_type)), // 4 + fmt = DataType::fmt + ((channels - 1) << 8), + type = CV_MAKETYPE(depth, channels) + }; + + typedef Vec vec_type; +}; +#endif + + +///////////////////////////// TermCriteria ////////////////////////////// + +/** @brief The class defining termination criteria for iterative algorithms. + +You can initialize it by default constructor and then override any parameters, or the structure may +be fully initialized using the advanced variant of the constructor. +*/ +class CV_EXPORTS TermCriteria +{ +public: + /** + Criteria type, can be one of: COUNT, EPS or COUNT + EPS + */ + enum Type + { + COUNT=1, //!< the maximum number of iterations or elements to compute + MAX_ITER=COUNT, //!< ditto + EPS=2 //!< the desired accuracy or change in parameters at which the iterative algorithm stops + }; + + //! default constructor + TermCriteria(); + /** + @param type The type of termination criteria, one of TermCriteria::Type + @param maxCount The maximum number of iterations or elements to compute. + @param epsilon The desired accuracy or change in parameters at which the iterative algorithm stops. + */ + TermCriteria(int type, int maxCount, double epsilon); + + inline bool isValid() const + { + const bool isCount = (type & COUNT) && maxCount > 0; + const bool isEps = (type & EPS) && !cvIsNaN(epsilon); + return isCount || isEps; + } + + int type; //!< the type of termination criteria: COUNT, EPS or COUNT + EPS + int maxCount; //!< the maximum number of iterations/elements + double epsilon; //!< the desired accuracy +}; + + +//! @} core_basic + +///////////////////////// raster image moments ////////////////////////// + +//! @addtogroup imgproc_shape +//! @{ + +/** @brief struct returned by cv::moments + +The spatial moments \f$\texttt{Moments::m}_{ji}\f$ are computed as: + +\f[\texttt{m} _{ji}= \sum _{x,y} \left ( \texttt{array} (x,y) \cdot x^j \cdot y^i \right )\f] + +The central moments \f$\texttt{Moments::mu}_{ji}\f$ are computed as: + +\f[\texttt{mu} _{ji}= \sum _{x,y} \left ( \texttt{array} (x,y) \cdot (x - \bar{x} )^j \cdot (y - \bar{y} )^i \right )\f] + +where \f$(\bar{x}, \bar{y})\f$ is the mass center: + +\f[\bar{x} = \frac{\texttt{m}_{10}}{\texttt{m}_{00}} , \; \bar{y} = \frac{\texttt{m}_{01}}{\texttt{m}_{00}}\f] + +The normalized central moments \f$\texttt{Moments::nu}_{ij}\f$ are computed as: + +\f[\texttt{nu} _{ji}= \frac{\texttt{mu}_{ji}}{\texttt{m}_{00}^{(i+j)/2+1}} .\f] + +@note +\f$\texttt{mu}_{00}=\texttt{m}_{00}\f$, \f$\texttt{nu}_{00}=1\f$ +\f$\texttt{nu}_{10}=\texttt{mu}_{10}=\texttt{mu}_{01}=\texttt{mu}_{10}=0\f$ , hence the values are not +stored. + +The moments of a contour are defined in the same way but computed using the Green's formula (see +). So, due to a limited raster resolution, the moments +computed for a contour are slightly different from the moments computed for the same rasterized +contour. + +@note +Since the contour moments are computed using Green formula, you may get seemingly odd results for +contours with self-intersections, e.g. a zero area (m00) for butterfly-shaped contours. + */ +class CV_EXPORTS_W_MAP Moments +{ +public: + //! the default constructor + Moments(); + //! the full constructor + Moments(double m00, double m10, double m01, double m20, double m11, + double m02, double m30, double m21, double m12, double m03 ); + ////! the conversion from CvMoments + //Moments( const CvMoments& moments ); + ////! the conversion to CvMoments + //operator CvMoments() const; + + //! @name spatial moments + //! @{ + CV_PROP_RW double m00, m10, m01, m20, m11, m02, m30, m21, m12, m03; + //! @} + + //! @name central moments + //! @{ + CV_PROP_RW double mu20, mu11, mu02, mu30, mu21, mu12, mu03; + //! @} + + //! @name central normalized moments + //! @{ + CV_PROP_RW double nu20, nu11, nu02, nu30, nu21, nu12, nu03; + //! @} +}; + +template<> class DataType +{ +public: + typedef Moments value_type; + typedef double work_type; + typedef double channel_type; + + enum { generic_type = 0, + channels = (int)(sizeof(value_type)/sizeof(channel_type)), // 24 + fmt = DataType::fmt + ((channels - 1) << 8) +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + ,depth = DataType::depth + ,type = CV_MAKETYPE(depth, channels) +#endif + }; + + typedef Vec vec_type; +}; + +namespace traits { +template<> +struct Depth< Moments > { enum { value = Depth::value }; }; +template<> +struct Type< Moments > { enum { value = CV_MAKETYPE(Depth::value, (int)(sizeof(Moments)/sizeof(double))) }; }; +} // namespace + +//! @} imgproc_shape + +//! @cond IGNORED + +///////////////////////////////////////////////////////////////////////// +///////////////////////////// Implementation //////////////////////////// +///////////////////////////////////////////////////////////////////////// + +//////////////////////////////// Complex //////////////////////////////// + +template inline +Complex<_Tp>::Complex() + : re(0), im(0) {} + +template inline +Complex<_Tp>::Complex( _Tp _re, _Tp _im ) + : re(_re), im(_im) {} + +template template inline +Complex<_Tp>::operator Complex() const +{ + return Complex(saturate_cast(re), saturate_cast(im)); +} + +template inline +Complex<_Tp> Complex<_Tp>::conj() const +{ + return Complex<_Tp>(re, -im); +} + + +template static inline +bool operator == (const Complex<_Tp>& a, const Complex<_Tp>& b) +{ + return a.re == b.re && a.im == b.im; +} + +template static inline +bool operator != (const Complex<_Tp>& a, const Complex<_Tp>& b) +{ + return a.re != b.re || a.im != b.im; +} + +template static inline +Complex<_Tp> operator + (const Complex<_Tp>& a, const Complex<_Tp>& b) +{ + return Complex<_Tp>( a.re + b.re, a.im + b.im ); +} + +template static inline +Complex<_Tp>& operator += (Complex<_Tp>& a, const Complex<_Tp>& b) +{ + a.re += b.re; a.im += b.im; + return a; +} + +template static inline +Complex<_Tp> operator - (const Complex<_Tp>& a, const Complex<_Tp>& b) +{ + return Complex<_Tp>( a.re - b.re, a.im - b.im ); +} + +template static inline +Complex<_Tp>& operator -= (Complex<_Tp>& a, const Complex<_Tp>& b) +{ + a.re -= b.re; a.im -= b.im; + return a; +} + +template static inline +Complex<_Tp> operator - (const Complex<_Tp>& a) +{ + return Complex<_Tp>(-a.re, -a.im); +} + +template static inline +Complex<_Tp> operator * (const Complex<_Tp>& a, const Complex<_Tp>& b) +{ + return Complex<_Tp>( a.re*b.re - a.im*b.im, a.re*b.im + a.im*b.re ); +} + +template static inline +Complex<_Tp> operator * (const Complex<_Tp>& a, _Tp b) +{ + return Complex<_Tp>( a.re*b, a.im*b ); +} + +template static inline +Complex<_Tp> operator * (_Tp b, const Complex<_Tp>& a) +{ + return Complex<_Tp>( a.re*b, a.im*b ); +} + +template static inline +Complex<_Tp> operator + (const Complex<_Tp>& a, _Tp b) +{ + return Complex<_Tp>( a.re + b, a.im ); +} + +template static inline +Complex<_Tp> operator - (const Complex<_Tp>& a, _Tp b) +{ return Complex<_Tp>( a.re - b, a.im ); } + +template static inline +Complex<_Tp> operator + (_Tp b, const Complex<_Tp>& a) +{ + return Complex<_Tp>( a.re + b, a.im ); +} + +template static inline +Complex<_Tp> operator - (_Tp b, const Complex<_Tp>& a) +{ + return Complex<_Tp>( b - a.re, -a.im ); +} + +template static inline +Complex<_Tp>& operator += (Complex<_Tp>& a, _Tp b) +{ + a.re += b; return a; +} + +template static inline +Complex<_Tp>& operator -= (Complex<_Tp>& a, _Tp b) +{ + a.re -= b; return a; +} + +template static inline +Complex<_Tp>& operator *= (Complex<_Tp>& a, _Tp b) +{ + a.re *= b; a.im *= b; return a; +} + +template static inline +double abs(const Complex<_Tp>& a) +{ + return std::sqrt( (double)a.re*a.re + (double)a.im*a.im); +} + +template static inline +Complex<_Tp> operator / (const Complex<_Tp>& a, const Complex<_Tp>& b) +{ + double t = 1./((double)b.re*b.re + (double)b.im*b.im); + return Complex<_Tp>( (_Tp)((a.re*b.re + a.im*b.im)*t), + (_Tp)((-a.re*b.im + a.im*b.re)*t) ); +} + +template static inline +Complex<_Tp>& operator /= (Complex<_Tp>& a, const Complex<_Tp>& b) +{ + a = a / b; + return a; +} + +template static inline +Complex<_Tp> operator / (const Complex<_Tp>& a, _Tp b) +{ + _Tp t = (_Tp)1/b; + return Complex<_Tp>( a.re*t, a.im*t ); +} + +template static inline +Complex<_Tp> operator / (_Tp b, const Complex<_Tp>& a) +{ + return Complex<_Tp>(b)/a; +} + +template static inline +Complex<_Tp> operator /= (const Complex<_Tp>& a, _Tp b) +{ + _Tp t = (_Tp)1/b; + a.re *= t; a.im *= t; return a; +} + + + +//////////////////////////////// 2D Point /////////////////////////////// + +template inline +Point_<_Tp>::Point_() + : x(0), y(0) {} + +template inline +Point_<_Tp>::Point_(_Tp _x, _Tp _y) + : x(_x), y(_y) {} + +template inline +Point_<_Tp>::Point_(const Point_& pt) + : x(pt.x), y(pt.y) {} + +template inline +Point_<_Tp>::Point_(const Size_<_Tp>& sz) + : x(sz.width), y(sz.height) {} + +template inline +Point_<_Tp>::Point_(const Vec<_Tp,2>& v) + : x(v[0]), y(v[1]) {} + +template inline +Point_<_Tp>& Point_<_Tp>::operator = (const Point_& pt) +{ + x = pt.x; y = pt.y; + return *this; +} + +template template inline +Point_<_Tp>::operator Point_<_Tp2>() const +{ + return Point_<_Tp2>(saturate_cast<_Tp2>(x), saturate_cast<_Tp2>(y)); +} + +template inline +Point_<_Tp>::operator Vec<_Tp, 2>() const +{ + return Vec<_Tp, 2>(x, y); +} + +template inline +_Tp Point_<_Tp>::dot(const Point_& pt) const +{ + return saturate_cast<_Tp>(x*pt.x + y*pt.y); +} + +template inline +double Point_<_Tp>::ddot(const Point_& pt) const +{ + return (double)x*pt.x + (double)y*pt.y; +} + +template inline +double Point_<_Tp>::cross(const Point_& pt) const +{ + return (double)x*pt.y - (double)y*pt.x; +} + +template inline bool +Point_<_Tp>::inside( const Rect_<_Tp>& r ) const +{ + return r.contains(*this); +} + + +template static inline +Point_<_Tp>& operator += (Point_<_Tp>& a, const Point_<_Tp>& b) +{ + a.x += b.x; + a.y += b.y; + return a; +} + +template static inline +Point_<_Tp>& operator -= (Point_<_Tp>& a, const Point_<_Tp>& b) +{ + a.x -= b.x; + a.y -= b.y; + return a; +} + +template static inline +Point_<_Tp>& operator *= (Point_<_Tp>& a, int b) +{ + a.x = saturate_cast<_Tp>(a.x * b); + a.y = saturate_cast<_Tp>(a.y * b); + return a; +} + +template static inline +Point_<_Tp>& operator *= (Point_<_Tp>& a, float b) +{ + a.x = saturate_cast<_Tp>(a.x * b); + a.y = saturate_cast<_Tp>(a.y * b); + return a; +} + +template static inline +Point_<_Tp>& operator *= (Point_<_Tp>& a, double b) +{ + a.x = saturate_cast<_Tp>(a.x * b); + a.y = saturate_cast<_Tp>(a.y * b); + return a; +} + +template static inline +Point_<_Tp>& operator /= (Point_<_Tp>& a, int b) +{ + a.x = saturate_cast<_Tp>(a.x / b); + a.y = saturate_cast<_Tp>(a.y / b); + return a; +} + +template static inline +Point_<_Tp>& operator /= (Point_<_Tp>& a, float b) +{ + a.x = saturate_cast<_Tp>(a.x / b); + a.y = saturate_cast<_Tp>(a.y / b); + return a; +} + +template static inline +Point_<_Tp>& operator /= (Point_<_Tp>& a, double b) +{ + a.x = saturate_cast<_Tp>(a.x / b); + a.y = saturate_cast<_Tp>(a.y / b); + return a; +} + +template static inline +double norm(const Point_<_Tp>& pt) +{ + return std::sqrt((double)pt.x*pt.x + (double)pt.y*pt.y); +} + +template static inline +bool operator == (const Point_<_Tp>& a, const Point_<_Tp>& b) +{ + return a.x == b.x && a.y == b.y; +} + +template static inline +bool operator != (const Point_<_Tp>& a, const Point_<_Tp>& b) +{ + return a.x != b.x || a.y != b.y; +} + +template static inline +Point_<_Tp> operator + (const Point_<_Tp>& a, const Point_<_Tp>& b) +{ + return Point_<_Tp>( saturate_cast<_Tp>(a.x + b.x), saturate_cast<_Tp>(a.y + b.y) ); +} + +template static inline +Point_<_Tp> operator - (const Point_<_Tp>& a, const Point_<_Tp>& b) +{ + return Point_<_Tp>( saturate_cast<_Tp>(a.x - b.x), saturate_cast<_Tp>(a.y - b.y) ); +} + +template static inline +Point_<_Tp> operator - (const Point_<_Tp>& a) +{ + return Point_<_Tp>( saturate_cast<_Tp>(-a.x), saturate_cast<_Tp>(-a.y) ); +} + +template static inline +Point_<_Tp> operator * (const Point_<_Tp>& a, int b) +{ + return Point_<_Tp>( saturate_cast<_Tp>(a.x*b), saturate_cast<_Tp>(a.y*b) ); +} + +template static inline +Point_<_Tp> operator * (int a, const Point_<_Tp>& b) +{ + return Point_<_Tp>( saturate_cast<_Tp>(b.x*a), saturate_cast<_Tp>(b.y*a) ); +} + +template static inline +Point_<_Tp> operator * (const Point_<_Tp>& a, float b) +{ + return Point_<_Tp>( saturate_cast<_Tp>(a.x*b), saturate_cast<_Tp>(a.y*b) ); +} + +template static inline +Point_<_Tp> operator * (float a, const Point_<_Tp>& b) +{ + return Point_<_Tp>( saturate_cast<_Tp>(b.x*a), saturate_cast<_Tp>(b.y*a) ); +} + +template static inline +Point_<_Tp> operator * (const Point_<_Tp>& a, double b) +{ + return Point_<_Tp>( saturate_cast<_Tp>(a.x*b), saturate_cast<_Tp>(a.y*b) ); +} + +template static inline +Point_<_Tp> operator * (double a, const Point_<_Tp>& b) +{ + return Point_<_Tp>( saturate_cast<_Tp>(b.x*a), saturate_cast<_Tp>(b.y*a) ); +} + +template static inline +Point_<_Tp> operator * (const Matx<_Tp, 2, 2>& a, const Point_<_Tp>& b) +{ + Matx<_Tp, 2, 1> tmp = a * Vec<_Tp,2>(b.x, b.y); + return Point_<_Tp>(tmp.val[0], tmp.val[1]); +} + +template static inline +Point3_<_Tp> operator * (const Matx<_Tp, 3, 3>& a, const Point_<_Tp>& b) +{ + Matx<_Tp, 3, 1> tmp = a * Vec<_Tp,3>(b.x, b.y, 1); + return Point3_<_Tp>(tmp.val[0], tmp.val[1], tmp.val[2]); +} + +template static inline +Point_<_Tp> operator / (const Point_<_Tp>& a, int b) +{ + Point_<_Tp> tmp(a); + tmp /= b; + return tmp; +} + +template static inline +Point_<_Tp> operator / (const Point_<_Tp>& a, float b) +{ + Point_<_Tp> tmp(a); + tmp /= b; + return tmp; +} + +template static inline +Point_<_Tp> operator / (const Point_<_Tp>& a, double b) +{ + Point_<_Tp> tmp(a); + tmp /= b; + return tmp; +} + + +template static inline _AccTp normL2Sqr(const Point_& pt); +template static inline _AccTp normL2Sqr(const Point_& pt); +template static inline _AccTp normL2Sqr(const Point_& pt); +template static inline _AccTp normL2Sqr(const Point_& pt); + +template<> inline int normL2Sqr(const Point_& pt) { return pt.dot(pt); } +template<> inline int64 normL2Sqr(const Point_& pt) { return pt.dot(pt); } +template<> inline float normL2Sqr(const Point_& pt) { return pt.dot(pt); } +template<> inline double normL2Sqr(const Point_& pt) { return pt.dot(pt); } + +template<> inline double normL2Sqr(const Point_& pt) { return pt.ddot(pt); } +template<> inline double normL2Sqr(const Point_& pt) { return pt.ddot(pt); } + + + +//////////////////////////////// 3D Point /////////////////////////////// + +template inline +Point3_<_Tp>::Point3_() + : x(0), y(0), z(0) {} + +template inline +Point3_<_Tp>::Point3_(_Tp _x, _Tp _y, _Tp _z) + : x(_x), y(_y), z(_z) {} + +template inline +Point3_<_Tp>::Point3_(const Point3_& pt) + : x(pt.x), y(pt.y), z(pt.z) {} + +template inline +Point3_<_Tp>::Point3_(const Point_<_Tp>& pt) + : x(pt.x), y(pt.y), z(_Tp()) {} + +template inline +Point3_<_Tp>::Point3_(const Vec<_Tp, 3>& v) + : x(v[0]), y(v[1]), z(v[2]) {} + +template template inline +Point3_<_Tp>::operator Point3_<_Tp2>() const +{ + return Point3_<_Tp2>(saturate_cast<_Tp2>(x), saturate_cast<_Tp2>(y), saturate_cast<_Tp2>(z)); +} + +#if OPENCV_ABI_COMPATIBILITY > 300 +template template inline +Point3_<_Tp>::operator Vec<_Tp2, 3>() const +{ + return Vec<_Tp2, 3>(x, y, z); +} +#else +template inline +Point3_<_Tp>::operator Vec<_Tp, 3>() const +{ + return Vec<_Tp, 3>(x, y, z); +} +#endif + +template inline +Point3_<_Tp>& Point3_<_Tp>::operator = (const Point3_& pt) +{ + x = pt.x; y = pt.y; z = pt.z; + return *this; +} + +template inline +_Tp Point3_<_Tp>::dot(const Point3_& pt) const +{ + return saturate_cast<_Tp>(x*pt.x + y*pt.y + z*pt.z); +} + +template inline +double Point3_<_Tp>::ddot(const Point3_& pt) const +{ + return (double)x*pt.x + (double)y*pt.y + (double)z*pt.z; +} + +template inline +Point3_<_Tp> Point3_<_Tp>::cross(const Point3_<_Tp>& pt) const +{ + return Point3_<_Tp>(y*pt.z - z*pt.y, z*pt.x - x*pt.z, x*pt.y - y*pt.x); +} + + +template static inline +Point3_<_Tp>& operator += (Point3_<_Tp>& a, const Point3_<_Tp>& b) +{ + a.x += b.x; + a.y += b.y; + a.z += b.z; + return a; +} + +template static inline +Point3_<_Tp>& operator -= (Point3_<_Tp>& a, const Point3_<_Tp>& b) +{ + a.x -= b.x; + a.y -= b.y; + a.z -= b.z; + return a; +} + +template static inline +Point3_<_Tp>& operator *= (Point3_<_Tp>& a, int b) +{ + a.x = saturate_cast<_Tp>(a.x * b); + a.y = saturate_cast<_Tp>(a.y * b); + a.z = saturate_cast<_Tp>(a.z * b); + return a; +} + +template static inline +Point3_<_Tp>& operator *= (Point3_<_Tp>& a, float b) +{ + a.x = saturate_cast<_Tp>(a.x * b); + a.y = saturate_cast<_Tp>(a.y * b); + a.z = saturate_cast<_Tp>(a.z * b); + return a; +} + +template static inline +Point3_<_Tp>& operator *= (Point3_<_Tp>& a, double b) +{ + a.x = saturate_cast<_Tp>(a.x * b); + a.y = saturate_cast<_Tp>(a.y * b); + a.z = saturate_cast<_Tp>(a.z * b); + return a; +} + +template static inline +Point3_<_Tp>& operator /= (Point3_<_Tp>& a, int b) +{ + a.x = saturate_cast<_Tp>(a.x / b); + a.y = saturate_cast<_Tp>(a.y / b); + a.z = saturate_cast<_Tp>(a.z / b); + return a; +} + +template static inline +Point3_<_Tp>& operator /= (Point3_<_Tp>& a, float b) +{ + a.x = saturate_cast<_Tp>(a.x / b); + a.y = saturate_cast<_Tp>(a.y / b); + a.z = saturate_cast<_Tp>(a.z / b); + return a; +} + +template static inline +Point3_<_Tp>& operator /= (Point3_<_Tp>& a, double b) +{ + a.x = saturate_cast<_Tp>(a.x / b); + a.y = saturate_cast<_Tp>(a.y / b); + a.z = saturate_cast<_Tp>(a.z / b); + return a; +} + +template static inline +double norm(const Point3_<_Tp>& pt) +{ + return std::sqrt((double)pt.x*pt.x + (double)pt.y*pt.y + (double)pt.z*pt.z); +} + +template static inline +bool operator == (const Point3_<_Tp>& a, const Point3_<_Tp>& b) +{ + return a.x == b.x && a.y == b.y && a.z == b.z; +} + +template static inline +bool operator != (const Point3_<_Tp>& a, const Point3_<_Tp>& b) +{ + return a.x != b.x || a.y != b.y || a.z != b.z; +} + +template static inline +Point3_<_Tp> operator + (const Point3_<_Tp>& a, const Point3_<_Tp>& b) +{ + return Point3_<_Tp>( saturate_cast<_Tp>(a.x + b.x), saturate_cast<_Tp>(a.y + b.y), saturate_cast<_Tp>(a.z + b.z)); +} + +template static inline +Point3_<_Tp> operator - (const Point3_<_Tp>& a, const Point3_<_Tp>& b) +{ + return Point3_<_Tp>( saturate_cast<_Tp>(a.x - b.x), saturate_cast<_Tp>(a.y - b.y), saturate_cast<_Tp>(a.z - b.z)); +} + +template static inline +Point3_<_Tp> operator - (const Point3_<_Tp>& a) +{ + return Point3_<_Tp>( saturate_cast<_Tp>(-a.x), saturate_cast<_Tp>(-a.y), saturate_cast<_Tp>(-a.z) ); +} + +template static inline +Point3_<_Tp> operator * (const Point3_<_Tp>& a, int b) +{ + return Point3_<_Tp>( saturate_cast<_Tp>(a.x*b), saturate_cast<_Tp>(a.y*b), saturate_cast<_Tp>(a.z*b) ); +} + +template static inline +Point3_<_Tp> operator * (int a, const Point3_<_Tp>& b) +{ + return Point3_<_Tp>( saturate_cast<_Tp>(b.x * a), saturate_cast<_Tp>(b.y * a), saturate_cast<_Tp>(b.z * a) ); +} + +template static inline +Point3_<_Tp> operator * (const Point3_<_Tp>& a, float b) +{ + return Point3_<_Tp>( saturate_cast<_Tp>(a.x * b), saturate_cast<_Tp>(a.y * b), saturate_cast<_Tp>(a.z * b) ); +} + +template static inline +Point3_<_Tp> operator * (float a, const Point3_<_Tp>& b) +{ + return Point3_<_Tp>( saturate_cast<_Tp>(b.x * a), saturate_cast<_Tp>(b.y * a), saturate_cast<_Tp>(b.z * a) ); +} + +template static inline +Point3_<_Tp> operator * (const Point3_<_Tp>& a, double b) +{ + return Point3_<_Tp>( saturate_cast<_Tp>(a.x * b), saturate_cast<_Tp>(a.y * b), saturate_cast<_Tp>(a.z * b) ); +} + +template static inline +Point3_<_Tp> operator * (double a, const Point3_<_Tp>& b) +{ + return Point3_<_Tp>( saturate_cast<_Tp>(b.x * a), saturate_cast<_Tp>(b.y * a), saturate_cast<_Tp>(b.z * a) ); +} + +template static inline +Point3_<_Tp> operator * (const Matx<_Tp, 3, 3>& a, const Point3_<_Tp>& b) +{ + Matx<_Tp, 3, 1> tmp = a * Vec<_Tp,3>(b.x, b.y, b.z); + return Point3_<_Tp>(tmp.val[0], tmp.val[1], tmp.val[2]); +} + +template static inline +Matx<_Tp, 4, 1> operator * (const Matx<_Tp, 4, 4>& a, const Point3_<_Tp>& b) +{ + return a * Matx<_Tp, 4, 1>(b.x, b.y, b.z, 1); +} + +template static inline +Point3_<_Tp> operator / (const Point3_<_Tp>& a, int b) +{ + Point3_<_Tp> tmp(a); + tmp /= b; + return tmp; +} + +template static inline +Point3_<_Tp> operator / (const Point3_<_Tp>& a, float b) +{ + Point3_<_Tp> tmp(a); + tmp /= b; + return tmp; +} + +template static inline +Point3_<_Tp> operator / (const Point3_<_Tp>& a, double b) +{ + Point3_<_Tp> tmp(a); + tmp /= b; + return tmp; +} + + + +////////////////////////////////// Size ///////////////////////////////// + +template inline +Size_<_Tp>::Size_() + : width(0), height(0) {} + +template inline +Size_<_Tp>::Size_(_Tp _width, _Tp _height) + : width(_width), height(_height) {} + +template inline +Size_<_Tp>::Size_(const Size_& sz) + : width(sz.width), height(sz.height) {} + +template inline +Size_<_Tp>::Size_(const Point_<_Tp>& pt) + : width(pt.x), height(pt.y) {} + +template template inline +Size_<_Tp>::operator Size_<_Tp2>() const +{ + return Size_<_Tp2>(saturate_cast<_Tp2>(width), saturate_cast<_Tp2>(height)); +} + +template inline +Size_<_Tp>& Size_<_Tp>::operator = (const Size_<_Tp>& sz) +{ + width = sz.width; height = sz.height; + return *this; +} + +template inline +_Tp Size_<_Tp>::area() const +{ + const _Tp result = width * height; + CV_DbgAssert(!std::numeric_limits<_Tp>::is_integer + || width == 0 || result / width == height); // make sure the result fits in the return value + return result; +} + +template inline +bool Size_<_Tp>::empty() const +{ + return width <= 0 || height <= 0; +} + + +template static inline +Size_<_Tp>& operator *= (Size_<_Tp>& a, _Tp b) +{ + a.width *= b; + a.height *= b; + return a; +} + +template static inline +Size_<_Tp> operator * (const Size_<_Tp>& a, _Tp b) +{ + Size_<_Tp> tmp(a); + tmp *= b; + return tmp; +} + +template static inline +Size_<_Tp>& operator /= (Size_<_Tp>& a, _Tp b) +{ + a.width /= b; + a.height /= b; + return a; +} + +template static inline +Size_<_Tp> operator / (const Size_<_Tp>& a, _Tp b) +{ + Size_<_Tp> tmp(a); + tmp /= b; + return tmp; +} + +template static inline +Size_<_Tp>& operator += (Size_<_Tp>& a, const Size_<_Tp>& b) +{ + a.width += b.width; + a.height += b.height; + return a; +} + +template static inline +Size_<_Tp> operator + (const Size_<_Tp>& a, const Size_<_Tp>& b) +{ + Size_<_Tp> tmp(a); + tmp += b; + return tmp; +} + +template static inline +Size_<_Tp>& operator -= (Size_<_Tp>& a, const Size_<_Tp>& b) +{ + a.width -= b.width; + a.height -= b.height; + return a; +} + +template static inline +Size_<_Tp> operator - (const Size_<_Tp>& a, const Size_<_Tp>& b) +{ + Size_<_Tp> tmp(a); + tmp -= b; + return tmp; +} + +template static inline +bool operator == (const Size_<_Tp>& a, const Size_<_Tp>& b) +{ + return a.width == b.width && a.height == b.height; +} + +template static inline +bool operator != (const Size_<_Tp>& a, const Size_<_Tp>& b) +{ + return !(a == b); +} + + + +////////////////////////////////// Rect ///////////////////////////////// + +template inline +Rect_<_Tp>::Rect_() + : x(0), y(0), width(0), height(0) {} + +template inline +Rect_<_Tp>::Rect_(_Tp _x, _Tp _y, _Tp _width, _Tp _height) + : x(_x), y(_y), width(_width), height(_height) {} + +template inline +Rect_<_Tp>::Rect_(const Rect_<_Tp>& r) + : x(r.x), y(r.y), width(r.width), height(r.height) {} + +template inline +Rect_<_Tp>::Rect_(const Point_<_Tp>& org, const Size_<_Tp>& sz) + : x(org.x), y(org.y), width(sz.width), height(sz.height) {} + +template inline +Rect_<_Tp>::Rect_(const Point_<_Tp>& pt1, const Point_<_Tp>& pt2) +{ + x = std::min(pt1.x, pt2.x); + y = std::min(pt1.y, pt2.y); + width = std::max(pt1.x, pt2.x) - x; + height = std::max(pt1.y, pt2.y) - y; +} + +template inline +Rect_<_Tp>& Rect_<_Tp>::operator = ( const Rect_<_Tp>& r ) +{ + x = r.x; + y = r.y; + width = r.width; + height = r.height; + return *this; +} + +template inline +Point_<_Tp> Rect_<_Tp>::tl() const +{ + return Point_<_Tp>(x,y); +} + +template inline +Point_<_Tp> Rect_<_Tp>::br() const +{ + return Point_<_Tp>(x + width, y + height); +} + +template inline +Size_<_Tp> Rect_<_Tp>::size() const +{ + return Size_<_Tp>(width, height); +} + +template inline +_Tp Rect_<_Tp>::area() const +{ + const _Tp result = width * height; + CV_DbgAssert(!std::numeric_limits<_Tp>::is_integer + || width == 0 || result / width == height); // make sure the result fits in the return value + return result; +} + +template inline +bool Rect_<_Tp>::empty() const +{ + return width <= 0 || height <= 0; +} + +template template inline +Rect_<_Tp>::operator Rect_<_Tp2>() const +{ + return Rect_<_Tp2>(saturate_cast<_Tp2>(x), saturate_cast<_Tp2>(y), saturate_cast<_Tp2>(width), saturate_cast<_Tp2>(height)); +} + +template inline +bool Rect_<_Tp>::contains(const Point_<_Tp>& pt) const +{ + return x <= pt.x && pt.x < x + width && y <= pt.y && pt.y < y + height; +} + + +template static inline +Rect_<_Tp>& operator += ( Rect_<_Tp>& a, const Point_<_Tp>& b ) +{ + a.x += b.x; + a.y += b.y; + return a; +} + +template static inline +Rect_<_Tp>& operator -= ( Rect_<_Tp>& a, const Point_<_Tp>& b ) +{ + a.x -= b.x; + a.y -= b.y; + return a; +} + +template static inline +Rect_<_Tp>& operator += ( Rect_<_Tp>& a, const Size_<_Tp>& b ) +{ + a.width += b.width; + a.height += b.height; + return a; +} + +template static inline +Rect_<_Tp>& operator -= ( Rect_<_Tp>& a, const Size_<_Tp>& b ) +{ + const _Tp width = a.width - b.width; + const _Tp height = a.height - b.height; + CV_DbgAssert(width >= 0 && height >= 0); + a.width = width; + a.height = height; + return a; +} + +template static inline +Rect_<_Tp>& operator &= ( Rect_<_Tp>& a, const Rect_<_Tp>& b ) +{ + _Tp x1 = std::max(a.x, b.x); + _Tp y1 = std::max(a.y, b.y); + a.width = std::min(a.x + a.width, b.x + b.width) - x1; + a.height = std::min(a.y + a.height, b.y + b.height) - y1; + a.x = x1; + a.y = y1; + if( a.width <= 0 || a.height <= 0 ) + a = Rect(); + return a; +} + +template static inline +Rect_<_Tp>& operator |= ( Rect_<_Tp>& a, const Rect_<_Tp>& b ) +{ + if (a.empty()) { + a = b; + } + else if (!b.empty()) { + _Tp x1 = std::min(a.x, b.x); + _Tp y1 = std::min(a.y, b.y); + a.width = std::max(a.x + a.width, b.x + b.width) - x1; + a.height = std::max(a.y + a.height, b.y + b.height) - y1; + a.x = x1; + a.y = y1; + } + return a; +} + +template static inline +bool operator == (const Rect_<_Tp>& a, const Rect_<_Tp>& b) +{ + return a.x == b.x && a.y == b.y && a.width == b.width && a.height == b.height; +} + +template static inline +bool operator != (const Rect_<_Tp>& a, const Rect_<_Tp>& b) +{ + return a.x != b.x || a.y != b.y || a.width != b.width || a.height != b.height; +} + +template static inline +Rect_<_Tp> operator + (const Rect_<_Tp>& a, const Point_<_Tp>& b) +{ + return Rect_<_Tp>( a.x + b.x, a.y + b.y, a.width, a.height ); +} + +template static inline +Rect_<_Tp> operator - (const Rect_<_Tp>& a, const Point_<_Tp>& b) +{ + return Rect_<_Tp>( a.x - b.x, a.y - b.y, a.width, a.height ); +} + +template static inline +Rect_<_Tp> operator + (const Rect_<_Tp>& a, const Size_<_Tp>& b) +{ + return Rect_<_Tp>( a.x, a.y, a.width + b.width, a.height + b.height ); +} + +template static inline +Rect_<_Tp> operator - (const Rect_<_Tp>& a, const Size_<_Tp>& b) +{ + const _Tp width = a.width - b.width; + const _Tp height = a.height - b.height; + CV_DbgAssert(width >= 0 && height >= 0); + return Rect_<_Tp>( a.x, a.y, width, height ); +} + +template static inline +Rect_<_Tp> operator & (const Rect_<_Tp>& a, const Rect_<_Tp>& b) +{ + Rect_<_Tp> c = a; + return c &= b; +} + +template static inline +Rect_<_Tp> operator | (const Rect_<_Tp>& a, const Rect_<_Tp>& b) +{ + Rect_<_Tp> c = a; + return c |= b; +} + +/** + * @brief measure dissimilarity between two sample sets + * + * computes the complement of the Jaccard Index as described in . + * For rectangles this reduces to computing the intersection over the union. + */ +template static inline +double jaccardDistance(const Rect_<_Tp>& a, const Rect_<_Tp>& b) { + _Tp Aa = a.area(); + _Tp Ab = b.area(); + + if ((Aa + Ab) <= std::numeric_limits<_Tp>::epsilon()) { + // jaccard_index = 1 -> distance = 0 + return 0.0; + } + + double Aab = (a & b).area(); + // distance = 1 - jaccard_index + return 1.0 - Aab / (Aa + Ab - Aab); +} + +////////////////////////////// RotatedRect ////////////////////////////// + +inline +RotatedRect::RotatedRect() + : center(), size(), angle(0) {} + +inline +RotatedRect::RotatedRect(const Point2f& _center, const Size2f& _size, float _angle) + : center(_center), size(_size), angle(_angle) {} + + + +///////////////////////////////// Range ///////////////////////////////// + +inline +Range::Range() + : start(0), end(0) {} + +inline +Range::Range(int _start, int _end) + : start(_start), end(_end) {} + +inline +int Range::size() const +{ + return end - start; +} + +inline +bool Range::empty() const +{ + return start == end; +} + +inline +Range Range::all() +{ + return Range(INT_MIN, INT_MAX); +} + + +static inline +bool operator == (const Range& r1, const Range& r2) +{ + return r1.start == r2.start && r1.end == r2.end; +} + +static inline +bool operator != (const Range& r1, const Range& r2) +{ + return !(r1 == r2); +} + +static inline +bool operator !(const Range& r) +{ + return r.start == r.end; +} + +static inline +Range operator & (const Range& r1, const Range& r2) +{ + Range r(std::max(r1.start, r2.start), std::min(r1.end, r2.end)); + r.end = std::max(r.end, r.start); + return r; +} + +static inline +Range& operator &= (Range& r1, const Range& r2) +{ + r1 = r1 & r2; + return r1; +} + +static inline +Range operator + (const Range& r1, int delta) +{ + return Range(r1.start + delta, r1.end + delta); +} + +static inline +Range operator + (int delta, const Range& r1) +{ + return Range(r1.start + delta, r1.end + delta); +} + +static inline +Range operator - (const Range& r1, int delta) +{ + return r1 + (-delta); +} + + + +///////////////////////////////// Scalar //////////////////////////////// + +template inline +Scalar_<_Tp>::Scalar_() +{ + this->val[0] = this->val[1] = this->val[2] = this->val[3] = 0; +} + +template inline +Scalar_<_Tp>::Scalar_(_Tp v0, _Tp v1, _Tp v2, _Tp v3) +{ + this->val[0] = v0; + this->val[1] = v1; + this->val[2] = v2; + this->val[3] = v3; +} + +template template inline +Scalar_<_Tp>::Scalar_(const Vec<_Tp2, cn>& v) +{ + int i; + for( i = 0; i < (cn < 4 ? cn : 4); i++ ) + this->val[i] = cv::saturate_cast<_Tp>(v.val[i]); + for( ; i < 4; i++ ) + this->val[i] = 0; +} + +template inline +Scalar_<_Tp>::Scalar_(_Tp v0) +{ + this->val[0] = v0; + this->val[1] = this->val[2] = this->val[3] = 0; +} + +template inline +Scalar_<_Tp> Scalar_<_Tp>::all(_Tp v0) +{ + return Scalar_<_Tp>(v0, v0, v0, v0); +} + + +template inline +Scalar_<_Tp> Scalar_<_Tp>::mul(const Scalar_<_Tp>& a, double scale ) const +{ + return Scalar_<_Tp>(saturate_cast<_Tp>(this->val[0] * a.val[0] * scale), + saturate_cast<_Tp>(this->val[1] * a.val[1] * scale), + saturate_cast<_Tp>(this->val[2] * a.val[2] * scale), + saturate_cast<_Tp>(this->val[3] * a.val[3] * scale)); +} + +template inline +Scalar_<_Tp> Scalar_<_Tp>::conj() const +{ + return Scalar_<_Tp>(saturate_cast<_Tp>( this->val[0]), + saturate_cast<_Tp>(-this->val[1]), + saturate_cast<_Tp>(-this->val[2]), + saturate_cast<_Tp>(-this->val[3])); +} + +template inline +bool Scalar_<_Tp>::isReal() const +{ + return this->val[1] == 0 && this->val[2] == 0 && this->val[3] == 0; +} + + +template template inline +Scalar_<_Tp>::operator Scalar_() const +{ + return Scalar_(saturate_cast(this->val[0]), + saturate_cast(this->val[1]), + saturate_cast(this->val[2]), + saturate_cast(this->val[3])); +} + + +template static inline +Scalar_<_Tp>& operator += (Scalar_<_Tp>& a, const Scalar_<_Tp>& b) +{ + a.val[0] += b.val[0]; + a.val[1] += b.val[1]; + a.val[2] += b.val[2]; + a.val[3] += b.val[3]; + return a; +} + +template static inline +Scalar_<_Tp>& operator -= (Scalar_<_Tp>& a, const Scalar_<_Tp>& b) +{ + a.val[0] -= b.val[0]; + a.val[1] -= b.val[1]; + a.val[2] -= b.val[2]; + a.val[3] -= b.val[3]; + return a; +} + +template static inline +Scalar_<_Tp>& operator *= ( Scalar_<_Tp>& a, _Tp v ) +{ + a.val[0] *= v; + a.val[1] *= v; + a.val[2] *= v; + a.val[3] *= v; + return a; +} + +template static inline +bool operator == ( const Scalar_<_Tp>& a, const Scalar_<_Tp>& b ) +{ + return a.val[0] == b.val[0] && a.val[1] == b.val[1] && + a.val[2] == b.val[2] && a.val[3] == b.val[3]; +} + +template static inline +bool operator != ( const Scalar_<_Tp>& a, const Scalar_<_Tp>& b ) +{ + return a.val[0] != b.val[0] || a.val[1] != b.val[1] || + a.val[2] != b.val[2] || a.val[3] != b.val[3]; +} + +template static inline +Scalar_<_Tp> operator + (const Scalar_<_Tp>& a, const Scalar_<_Tp>& b) +{ + return Scalar_<_Tp>(a.val[0] + b.val[0], + a.val[1] + b.val[1], + a.val[2] + b.val[2], + a.val[3] + b.val[3]); +} + +template static inline +Scalar_<_Tp> operator - (const Scalar_<_Tp>& a, const Scalar_<_Tp>& b) +{ + return Scalar_<_Tp>(saturate_cast<_Tp>(a.val[0] - b.val[0]), + saturate_cast<_Tp>(a.val[1] - b.val[1]), + saturate_cast<_Tp>(a.val[2] - b.val[2]), + saturate_cast<_Tp>(a.val[3] - b.val[3])); +} + +template static inline +Scalar_<_Tp> operator * (const Scalar_<_Tp>& a, _Tp alpha) +{ + return Scalar_<_Tp>(a.val[0] * alpha, + a.val[1] * alpha, + a.val[2] * alpha, + a.val[3] * alpha); +} + +template static inline +Scalar_<_Tp> operator * (_Tp alpha, const Scalar_<_Tp>& a) +{ + return a*alpha; +} + +template static inline +Scalar_<_Tp> operator - (const Scalar_<_Tp>& a) +{ + return Scalar_<_Tp>(saturate_cast<_Tp>(-a.val[0]), + saturate_cast<_Tp>(-a.val[1]), + saturate_cast<_Tp>(-a.val[2]), + saturate_cast<_Tp>(-a.val[3])); +} + + +template static inline +Scalar_<_Tp> operator * (const Scalar_<_Tp>& a, const Scalar_<_Tp>& b) +{ + return Scalar_<_Tp>(saturate_cast<_Tp>(a[0]*b[0] - a[1]*b[1] - a[2]*b[2] - a[3]*b[3]), + saturate_cast<_Tp>(a[0]*b[1] + a[1]*b[0] + a[2]*b[3] - a[3]*b[2]), + saturate_cast<_Tp>(a[0]*b[2] - a[1]*b[3] + a[2]*b[0] + a[3]*b[1]), + saturate_cast<_Tp>(a[0]*b[3] + a[1]*b[2] - a[2]*b[1] + a[3]*b[0])); +} + +template static inline +Scalar_<_Tp>& operator *= (Scalar_<_Tp>& a, const Scalar_<_Tp>& b) +{ + a = a * b; + return a; +} + +template static inline +Scalar_<_Tp> operator / (const Scalar_<_Tp>& a, _Tp alpha) +{ + return Scalar_<_Tp>(a.val[0] / alpha, + a.val[1] / alpha, + a.val[2] / alpha, + a.val[3] / alpha); +} + +template static inline +Scalar_ operator / (const Scalar_& a, float alpha) +{ + float s = 1 / alpha; + return Scalar_(a.val[0] * s, a.val[1] * s, a.val[2] * s, a.val[3] * s); +} + +template static inline +Scalar_ operator / (const Scalar_& a, double alpha) +{ + double s = 1 / alpha; + return Scalar_(a.val[0] * s, a.val[1] * s, a.val[2] * s, a.val[3] * s); +} + +template static inline +Scalar_<_Tp>& operator /= (Scalar_<_Tp>& a, _Tp alpha) +{ + a = a / alpha; + return a; +} + +template static inline +Scalar_<_Tp> operator / (_Tp a, const Scalar_<_Tp>& b) +{ + _Tp s = a / (b[0]*b[0] + b[1]*b[1] + b[2]*b[2] + b[3]*b[3]); + return b.conj() * s; +} + +template static inline +Scalar_<_Tp> operator / (const Scalar_<_Tp>& a, const Scalar_<_Tp>& b) +{ + return a * ((_Tp)1 / b); +} + +template static inline +Scalar_<_Tp>& operator /= (Scalar_<_Tp>& a, const Scalar_<_Tp>& b) +{ + a = a / b; + return a; +} + +template static inline +Scalar operator * (const Matx<_Tp, 4, 4>& a, const Scalar& b) +{ + Matx c((Matx)a, b, Matx_MatMulOp()); + return reinterpret_cast(c); +} + +template<> inline +Scalar operator * (const Matx& a, const Scalar& b) +{ + Matx c(a, b, Matx_MatMulOp()); + return reinterpret_cast(c); +} + + + +//////////////////////////////// KeyPoint /////////////////////////////// + +inline +KeyPoint::KeyPoint() + : pt(0,0), size(0), angle(-1), response(0), octave(0), class_id(-1) {} + +inline +KeyPoint::KeyPoint(Point2f _pt, float _size, float _angle, float _response, int _octave, int _class_id) + : pt(_pt), size(_size), angle(_angle), response(_response), octave(_octave), class_id(_class_id) {} + +inline +KeyPoint::KeyPoint(float x, float y, float _size, float _angle, float _response, int _octave, int _class_id) + : pt(x, y), size(_size), angle(_angle), response(_response), octave(_octave), class_id(_class_id) {} + + + +///////////////////////////////// DMatch //////////////////////////////// + +inline +DMatch::DMatch() + : queryIdx(-1), trainIdx(-1), imgIdx(-1), distance(FLT_MAX) {} + +inline +DMatch::DMatch(int _queryIdx, int _trainIdx, float _distance) + : queryIdx(_queryIdx), trainIdx(_trainIdx), imgIdx(-1), distance(_distance) {} + +inline +DMatch::DMatch(int _queryIdx, int _trainIdx, int _imgIdx, float _distance) + : queryIdx(_queryIdx), trainIdx(_trainIdx), imgIdx(_imgIdx), distance(_distance) {} + +inline +bool DMatch::operator < (const DMatch &m) const +{ + return distance < m.distance; +} + + + +////////////////////////////// TermCriteria ///////////////////////////// + +inline +TermCriteria::TermCriteria() + : type(0), maxCount(0), epsilon(0) {} + +inline +TermCriteria::TermCriteria(int _type, int _maxCount, double _epsilon) + : type(_type), maxCount(_maxCount), epsilon(_epsilon) {} + +//! @endcond + +} // cv + +#endif //OPENCV_CORE_TYPES_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/types_c.h b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/types_c.h new file mode 100644 index 0000000000000000000000000000000000000000..5f63eb8fb43aaed60b38b7ee3e7386c2b29bbd96 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/types_c.h @@ -0,0 +1,2139 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_TYPES_H +#define OPENCV_CORE_TYPES_H + +#if !defined(__OPENCV_BUILD) && !defined(CV__DISABLE_C_API_CTORS) +#define CV__ENABLE_C_API_CTORS // enable C API ctors (must be removed) +#endif + +//#define CV__VALIDATE_UNUNITIALIZED_VARS 1 // C++11 & GCC only + +#ifdef __cplusplus + +#ifdef CV__VALIDATE_UNUNITIALIZED_VARS +#pragma GCC diagnostic ignored "-Wmissing-field-initializers" +#define CV_STRUCT_INITIALIZER {0,} +#else +#if defined(__GNUC__) && __GNUC__ == 4 // GCC 4.x warns on "= {}" initialization, fixed in GCC 5.0 +#pragma GCC diagnostic ignored "-Wmissing-field-initializers" +#endif +#define CV_STRUCT_INITIALIZER {} +#endif + +#else +#define CV_STRUCT_INITIALIZER {0} +#endif + + +#ifdef HAVE_IPL +# ifndef __IPL_H__ +# if defined _WIN32 +# include +# else +# include +# endif +# endif +#elif defined __IPL_H__ +# define HAVE_IPL +#endif + +#include "opencv2/core/cvdef.h" + +#ifndef SKIP_INCLUDES +#include +#include +#include +#include +#endif // SKIP_INCLUDES + +#if defined _WIN32 +# define CV_CDECL __cdecl +# define CV_STDCALL __stdcall +#else +# define CV_CDECL +# define CV_STDCALL +#endif + +#ifndef CV_DEFAULT +# ifdef __cplusplus +# define CV_DEFAULT(val) = val +# else +# define CV_DEFAULT(val) +# endif +#endif + +#ifndef CV_EXTERN_C_FUNCPTR +# ifdef __cplusplus +# define CV_EXTERN_C_FUNCPTR(x) extern "C" { typedef x; } +# else +# define CV_EXTERN_C_FUNCPTR(x) typedef x +# endif +#endif + +#ifndef CVAPI +# define CVAPI(rettype) CV_EXTERN_C CV_EXPORTS rettype CV_CDECL +#endif + +#ifndef CV_IMPL +# define CV_IMPL CV_EXTERN_C +#endif + +#ifdef __cplusplus +# include "opencv2/core.hpp" +#endif + +/** @addtogroup core_c + @{ +*/ + +/** @brief This is the "metatype" used *only* as a function parameter. + +It denotes that the function accepts arrays of multiple types, such as IplImage*, CvMat* or even +CvSeq* sometimes. The particular array type is determined at runtime by analyzing the first 4 +bytes of the header. In C++ interface the role of CvArr is played by InputArray and OutputArray. + */ +typedef void CvArr; + +typedef int CVStatus; + +/** @see cv::Error::Code */ +enum { + CV_StsOk= 0, /**< everything is ok */ + CV_StsBackTrace= -1, /**< pseudo error for back trace */ + CV_StsError= -2, /**< unknown /unspecified error */ + CV_StsInternal= -3, /**< internal error (bad state) */ + CV_StsNoMem= -4, /**< insufficient memory */ + CV_StsBadArg= -5, /**< function arg/param is bad */ + CV_StsBadFunc= -6, /**< unsupported function */ + CV_StsNoConv= -7, /**< iter. didn't converge */ + CV_StsAutoTrace= -8, /**< tracing */ + CV_HeaderIsNull= -9, /**< image header is NULL */ + CV_BadImageSize= -10, /**< image size is invalid */ + CV_BadOffset= -11, /**< offset is invalid */ + CV_BadDataPtr= -12, /**/ + CV_BadStep= -13, /**< image step is wrong, this may happen for a non-continuous matrix */ + CV_BadModelOrChSeq= -14, /**/ + CV_BadNumChannels= -15, /**< bad number of channels, for example, some functions accept only single channel matrices */ + CV_BadNumChannel1U= -16, /**/ + CV_BadDepth= -17, /**< input image depth is not supported by the function */ + CV_BadAlphaChannel= -18, /**/ + CV_BadOrder= -19, /**< number of dimensions is out of range */ + CV_BadOrigin= -20, /**< incorrect input origin */ + CV_BadAlign= -21, /**< incorrect input align */ + CV_BadCallBack= -22, /**/ + CV_BadTileSize= -23, /**/ + CV_BadCOI= -24, /**< input COI is not supported */ + CV_BadROISize= -25, /**< incorrect input roi */ + CV_MaskIsTiled= -26, /**/ + CV_StsNullPtr= -27, /**< null pointer */ + CV_StsVecLengthErr= -28, /**< incorrect vector length */ + CV_StsFilterStructContentErr= -29, /**< incorrect filter structure content */ + CV_StsKernelStructContentErr= -30, /**< incorrect transform kernel content */ + CV_StsFilterOffsetErr= -31, /**< incorrect filter offset value */ + CV_StsBadSize= -201, /**< the input/output structure size is incorrect */ + CV_StsDivByZero= -202, /**< division by zero */ + CV_StsInplaceNotSupported= -203, /**< in-place operation is not supported */ + CV_StsObjectNotFound= -204, /**< request can't be completed */ + CV_StsUnmatchedFormats= -205, /**< formats of input/output arrays differ */ + CV_StsBadFlag= -206, /**< flag is wrong or not supported */ + CV_StsBadPoint= -207, /**< bad CvPoint */ + CV_StsBadMask= -208, /**< bad format of mask (neither 8uC1 nor 8sC1)*/ + CV_StsUnmatchedSizes= -209, /**< sizes of input/output structures do not match */ + CV_StsUnsupportedFormat= -210, /**< the data format/type is not supported by the function*/ + CV_StsOutOfRange= -211, /**< some of parameters are out of range */ + CV_StsParseError= -212, /**< invalid syntax/structure of the parsed file */ + CV_StsNotImplemented= -213, /**< the requested function/feature is not implemented */ + CV_StsBadMemBlock= -214, /**< an allocated block has been corrupted */ + CV_StsAssert= -215, /**< assertion failed */ + CV_GpuNotSupported= -216, /**< no CUDA support */ + CV_GpuApiCallError= -217, /**< GPU API call error */ + CV_OpenGlNotSupported= -218, /**< no OpenGL support */ + CV_OpenGlApiCallError= -219, /**< OpenGL API call error */ + CV_OpenCLApiCallError= -220, /**< OpenCL API call error */ + CV_OpenCLDoubleNotSupported= -221, + CV_OpenCLInitError= -222, /**< OpenCL initialization error */ + CV_OpenCLNoAMDBlasFft= -223 +}; + +/****************************************************************************************\ +* Common macros and inline functions * +\****************************************************************************************/ + +#define CV_SWAP(a,b,t) ((t) = (a), (a) = (b), (b) = (t)) + +/** min & max without jumps */ +#define CV_IMIN(a, b) ((a) ^ (((a)^(b)) & (((a) < (b)) - 1))) + +#define CV_IMAX(a, b) ((a) ^ (((a)^(b)) & (((a) > (b)) - 1))) + +/** absolute value without jumps */ +#ifndef __cplusplus +# define CV_IABS(a) (((a) ^ ((a) < 0 ? -1 : 0)) - ((a) < 0 ? -1 : 0)) +#else +# define CV_IABS(a) abs(a) +#endif +#define CV_CMP(a,b) (((a) > (b)) - ((a) < (b))) +#define CV_SIGN(a) CV_CMP((a),0) + +#define cvInvSqrt(value) ((float)(1./sqrt(value))) +#define cvSqrt(value) ((float)sqrt(value)) + + +/*************** Random number generation *******************/ + +typedef uint64 CvRNG; + +#define CV_RNG_COEFF 4164903690U + +/** @brief Initializes a random number generator state. + +The function initializes a random number generator and returns the state. The pointer to the state +can be then passed to the cvRandInt, cvRandReal and cvRandArr functions. In the current +implementation a multiply-with-carry generator is used. +@param seed 64-bit value used to initiate a random sequence +@sa the C++ class RNG replaced CvRNG. + */ +CV_INLINE CvRNG cvRNG( int64 seed CV_DEFAULT(-1)) +{ + CvRNG rng = seed ? (uint64)seed : (uint64)(int64)-1; + return rng; +} + +/** @brief Returns a 32-bit unsigned integer and updates RNG. + +The function returns a uniformly-distributed random 32-bit unsigned integer and updates the RNG +state. It is similar to the rand() function from the C runtime library, except that OpenCV functions +always generates a 32-bit random number, regardless of the platform. +@param rng CvRNG state initialized by cvRNG. + */ +CV_INLINE unsigned cvRandInt( CvRNG* rng ) +{ + uint64 temp = *rng; + temp = (uint64)(unsigned)temp*CV_RNG_COEFF + (temp >> 32); + *rng = temp; + return (unsigned)temp; +} + +/** @brief Returns a floating-point random number and updates RNG. + +The function returns a uniformly-distributed random floating-point number between 0 and 1 (1 is not +included). +@param rng RNG state initialized by cvRNG + */ +CV_INLINE double cvRandReal( CvRNG* rng ) +{ + return cvRandInt(rng)*2.3283064365386962890625e-10 /* 2^-32 */; +} + +/****************************************************************************************\ +* Image type (IplImage) * +\****************************************************************************************/ + +#ifndef HAVE_IPL + +/* + * The following definitions (until #endif) + * is an extract from IPL headers. + * Copyright (c) 1995 Intel Corporation. + */ +#define IPL_DEPTH_SIGN 0x80000000 + +#define IPL_DEPTH_1U 1 +#define IPL_DEPTH_8U 8 +#define IPL_DEPTH_16U 16 +#define IPL_DEPTH_32F 32 + +#define IPL_DEPTH_8S (IPL_DEPTH_SIGN| 8) +#define IPL_DEPTH_16S (IPL_DEPTH_SIGN|16) +#define IPL_DEPTH_32S (IPL_DEPTH_SIGN|32) + +#define IPL_DATA_ORDER_PIXEL 0 +#define IPL_DATA_ORDER_PLANE 1 + +#define IPL_ORIGIN_TL 0 +#define IPL_ORIGIN_BL 1 + +#define IPL_ALIGN_4BYTES 4 +#define IPL_ALIGN_8BYTES 8 +#define IPL_ALIGN_16BYTES 16 +#define IPL_ALIGN_32BYTES 32 + +#define IPL_ALIGN_DWORD IPL_ALIGN_4BYTES +#define IPL_ALIGN_QWORD IPL_ALIGN_8BYTES + +#define IPL_BORDER_CONSTANT 0 +#define IPL_BORDER_REPLICATE 1 +#define IPL_BORDER_REFLECT 2 +#define IPL_BORDER_WRAP 3 + +#ifdef __cplusplus +typedef struct _IplImage IplImage; +CV_EXPORTS _IplImage cvIplImage(const cv::Mat& m); +#endif + +/** The IplImage is taken from the Intel Image Processing Library, in which the format is native. OpenCV +only supports a subset of possible IplImage formats, as outlined in the parameter list above. + +In addition to the above restrictions, OpenCV handles ROIs differently. OpenCV functions require +that the image size or ROI size of all source and destination images match exactly. On the other +hand, the Intel Image Processing Library processes the area of intersection between the source and +destination images (or ROIs), allowing them to vary independently. +*/ +typedef struct +_IplImage +{ + int nSize; /**< sizeof(IplImage) */ + int ID; /**< version (=0)*/ + int nChannels; /**< Most of OpenCV functions support 1,2,3 or 4 channels */ + int alphaChannel; /**< Ignored by OpenCV */ + int depth; /**< Pixel depth in bits: IPL_DEPTH_8U, IPL_DEPTH_8S, IPL_DEPTH_16S, + IPL_DEPTH_32S, IPL_DEPTH_32F and IPL_DEPTH_64F are supported. */ + char colorModel[4]; /**< Ignored by OpenCV */ + char channelSeq[4]; /**< ditto */ + int dataOrder; /**< 0 - interleaved color channels, 1 - separate color channels. + cvCreateImage can only create interleaved images */ + int origin; /**< 0 - top-left origin, + 1 - bottom-left origin (Windows bitmaps style). */ + int align; /**< Alignment of image rows (4 or 8). + OpenCV ignores it and uses widthStep instead. */ + int width; /**< Image width in pixels. */ + int height; /**< Image height in pixels. */ + struct _IplROI *roi; /**< Image ROI. If NULL, the whole image is selected. */ + struct _IplImage *maskROI; /**< Must be NULL. */ + void *imageId; /**< " " */ + struct _IplTileInfo *tileInfo; /**< " " */ + int imageSize; /**< Image data size in bytes + (==image->height*image->widthStep + in case of interleaved data)*/ + char *imageData; /**< Pointer to aligned image data. */ + int widthStep; /**< Size of aligned image row in bytes. */ + int BorderMode[4]; /**< Ignored by OpenCV. */ + int BorderConst[4]; /**< Ditto. */ + char *imageDataOrigin; /**< Pointer to very origin of image data + (not necessarily aligned) - + needed for correct deallocation */ + +#if defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) + _IplImage() {} + _IplImage(const cv::Mat& m) { *this = cvIplImage(m); } +#endif +} +IplImage; + +CV_INLINE IplImage cvIplImage() +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + IplImage self = CV_STRUCT_INITIALIZER; self.nSize = sizeof(IplImage); return self; +#else + return _IplImage(); +#endif +} + +typedef struct _IplTileInfo IplTileInfo; + +typedef struct _IplROI +{ + int coi; /**< 0 - no COI (all channels are selected), 1 - 0th channel is selected ...*/ + int xOffset; + int yOffset; + int width; + int height; +} +IplROI; + +typedef struct _IplConvKernel +{ + int nCols; + int nRows; + int anchorX; + int anchorY; + int *values; + int nShiftR; +} +IplConvKernel; + +typedef struct _IplConvKernelFP +{ + int nCols; + int nRows; + int anchorX; + int anchorY; + float *values; +} +IplConvKernelFP; + +#define IPL_IMAGE_HEADER 1 +#define IPL_IMAGE_DATA 2 +#define IPL_IMAGE_ROI 4 + +#endif/*HAVE_IPL*/ + +/** extra border mode */ +#define IPL_BORDER_REFLECT_101 4 +#define IPL_BORDER_TRANSPARENT 5 + +#define IPL_IMAGE_MAGIC_VAL ((int)sizeof(IplImage)) +#define CV_TYPE_NAME_IMAGE "opencv-image" + +#define CV_IS_IMAGE_HDR(img) \ + ((img) != NULL && ((const IplImage*)(img))->nSize == sizeof(IplImage)) + +#define CV_IS_IMAGE(img) \ + (CV_IS_IMAGE_HDR(img) && ((IplImage*)img)->imageData != NULL) + +/** for storing double-precision + floating point data in IplImage's */ +#define IPL_DEPTH_64F 64 + +/** get reference to pixel at (col,row), + for multi-channel images (col) should be multiplied by number of channels */ +#define CV_IMAGE_ELEM( image, elemtype, row, col ) \ + (((elemtype*)((image)->imageData + (image)->widthStep*(row)))[(col)]) + +/****************************************************************************************\ +* Matrix type (CvMat) * +\****************************************************************************************/ + +#define CV_AUTO_STEP 0x7fffffff +#define CV_WHOLE_ARR cvSlice( 0, 0x3fffffff ) + +#define CV_MAGIC_MASK 0xFFFF0000 +#define CV_MAT_MAGIC_VAL 0x42420000 +#define CV_TYPE_NAME_MAT "opencv-matrix" + +#ifdef __cplusplus +typedef struct CvMat CvMat; +CV_INLINE CvMat cvMat(const cv::Mat& m); +#endif + +/** Matrix elements are stored row by row. Element (i, j) (i - 0-based row index, j - 0-based column +index) of a matrix can be retrieved or modified using CV_MAT_ELEM macro: + + uchar pixval = CV_MAT_ELEM(grayimg, uchar, i, j) + CV_MAT_ELEM(cameraMatrix, float, 0, 2) = image.width*0.5f; + +To access multiple-channel matrices, you can use +CV_MAT_ELEM(matrix, type, i, j\*nchannels + channel_idx). + +@deprecated CvMat is now obsolete; consider using Mat instead. + */ +typedef struct CvMat +{ + int type; + int step; + + /* for internal use only */ + int* refcount; + int hdr_refcount; + + union + { + uchar* ptr; + short* s; + int* i; + float* fl; + double* db; + } data; + +#ifdef __cplusplus + union + { + int rows; + int height; + }; + + union + { + int cols; + int width; + }; +#else + int rows; + int cols; +#endif + +#if defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) + CvMat() {} + CvMat(const cv::Mat& m) { *this = cvMat(m); } +#endif +} +CvMat; + + +#define CV_IS_MAT_HDR(mat) \ + ((mat) != NULL && \ + (((const CvMat*)(mat))->type & CV_MAGIC_MASK) == CV_MAT_MAGIC_VAL && \ + ((const CvMat*)(mat))->cols > 0 && ((const CvMat*)(mat))->rows > 0) + +#define CV_IS_MAT_HDR_Z(mat) \ + ((mat) != NULL && \ + (((const CvMat*)(mat))->type & CV_MAGIC_MASK) == CV_MAT_MAGIC_VAL && \ + ((const CvMat*)(mat))->cols >= 0 && ((const CvMat*)(mat))->rows >= 0) + +#define CV_IS_MAT(mat) \ + (CV_IS_MAT_HDR(mat) && ((const CvMat*)(mat))->data.ptr != NULL) + +#define CV_IS_MASK_ARR(mat) \ + (((mat)->type & (CV_MAT_TYPE_MASK & ~CV_8SC1)) == 0) + +#define CV_ARE_TYPES_EQ(mat1, mat2) \ + ((((mat1)->type ^ (mat2)->type) & CV_MAT_TYPE_MASK) == 0) + +#define CV_ARE_CNS_EQ(mat1, mat2) \ + ((((mat1)->type ^ (mat2)->type) & CV_MAT_CN_MASK) == 0) + +#define CV_ARE_DEPTHS_EQ(mat1, mat2) \ + ((((mat1)->type ^ (mat2)->type) & CV_MAT_DEPTH_MASK) == 0) + +#define CV_ARE_SIZES_EQ(mat1, mat2) \ + ((mat1)->rows == (mat2)->rows && (mat1)->cols == (mat2)->cols) + +#define CV_IS_MAT_CONST(mat) \ + (((mat)->rows|(mat)->cols) == 1) + +#define IPL2CV_DEPTH(depth) \ + ((((CV_8U)+(CV_16U<<4)+(CV_32F<<8)+(CV_64F<<16)+(CV_8S<<20)+ \ + (CV_16S<<24)+(CV_32S<<28)) >> ((((depth) & 0xF0) >> 2) + \ + (((depth) & IPL_DEPTH_SIGN) ? 20 : 0))) & 15) + +/** Inline constructor. No data is allocated internally!!! + * (Use together with cvCreateData, or use cvCreateMat instead to + * get a matrix with allocated data): + */ +CV_INLINE CvMat cvMat( int rows, int cols, int type, void* data CV_DEFAULT(NULL)) +{ + CvMat m; + + assert( (unsigned)CV_MAT_DEPTH(type) <= CV_64F ); + type = CV_MAT_TYPE(type); + m.type = CV_MAT_MAGIC_VAL | CV_MAT_CONT_FLAG | type; + m.cols = cols; + m.rows = rows; + m.step = m.cols*CV_ELEM_SIZE(type); + m.data.ptr = (uchar*)data; + m.refcount = NULL; + m.hdr_refcount = 0; + + return m; +} + +#ifdef __cplusplus + +CV_INLINE CvMat cvMat(const cv::Mat& m) +{ + CvMat self; + CV_DbgAssert(m.dims <= 2); + self = cvMat(m.rows, m.dims == 1 ? 1 : m.cols, m.type(), m.data); + self.step = (int)m.step[0]; + self.type = (self.type & ~cv::Mat::CONTINUOUS_FLAG) | (m.flags & cv::Mat::CONTINUOUS_FLAG); + return self; +} +CV_INLINE CvMat cvMat() +{ +#if !defined(CV__ENABLE_C_API_CTORS) + CvMat self = CV_STRUCT_INITIALIZER; return self; +#else + return CvMat(); +#endif +} +CV_INLINE CvMat cvMat(const CvMat& m) +{ +#if !defined(CV__ENABLE_C_API_CTORS) + CvMat self = CV_STRUCT_INITIALIZER; memcpy(&self, &m, sizeof(self)); return self; +#else + return CvMat(m); +#endif +} + +#endif // __cplusplus + + +#define CV_MAT_ELEM_PTR_FAST( mat, row, col, pix_size ) \ + (assert( (unsigned)(row) < (unsigned)(mat).rows && \ + (unsigned)(col) < (unsigned)(mat).cols ), \ + (mat).data.ptr + (size_t)(mat).step*(row) + (pix_size)*(col)) + +#define CV_MAT_ELEM_PTR( mat, row, col ) \ + CV_MAT_ELEM_PTR_FAST( mat, row, col, CV_ELEM_SIZE((mat).type) ) + +#define CV_MAT_ELEM( mat, elemtype, row, col ) \ + (*(elemtype*)CV_MAT_ELEM_PTR_FAST( mat, row, col, sizeof(elemtype))) + +/** @brief Returns the particular element of single-channel floating-point matrix. + +The function is a fast replacement for cvGetReal2D in the case of single-channel floating-point +matrices. It is faster because it is inline, it does fewer checks for array type and array element +type, and it checks for the row and column ranges only in debug mode. +@param mat Input matrix +@param row The zero-based index of row +@param col The zero-based index of column + */ +CV_INLINE double cvmGet( const CvMat* mat, int row, int col ) +{ + int type; + + type = CV_MAT_TYPE(mat->type); + assert( (unsigned)row < (unsigned)mat->rows && + (unsigned)col < (unsigned)mat->cols ); + + if( type == CV_32FC1 ) + return ((float*)(void*)(mat->data.ptr + (size_t)mat->step*row))[col]; + else + { + assert( type == CV_64FC1 ); + return ((double*)(void*)(mat->data.ptr + (size_t)mat->step*row))[col]; + } +} + +/** @brief Sets a specific element of a single-channel floating-point matrix. + +The function is a fast replacement for cvSetReal2D in the case of single-channel floating-point +matrices. It is faster because it is inline, it does fewer checks for array type and array element +type, and it checks for the row and column ranges only in debug mode. +@param mat The matrix +@param row The zero-based index of row +@param col The zero-based index of column +@param value The new value of the matrix element + */ +CV_INLINE void cvmSet( CvMat* mat, int row, int col, double value ) +{ + int type; + type = CV_MAT_TYPE(mat->type); + assert( (unsigned)row < (unsigned)mat->rows && + (unsigned)col < (unsigned)mat->cols ); + + if( type == CV_32FC1 ) + ((float*)(void*)(mat->data.ptr + (size_t)mat->step*row))[col] = (float)value; + else + { + assert( type == CV_64FC1 ); + ((double*)(void*)(mat->data.ptr + (size_t)mat->step*row))[col] = value; + } +} + + +CV_INLINE int cvIplDepth( int type ) +{ + int depth = CV_MAT_DEPTH(type); + return CV_ELEM_SIZE1(depth)*8 | (depth == CV_8S || depth == CV_16S || + depth == CV_32S ? IPL_DEPTH_SIGN : 0); +} + + +/****************************************************************************************\ +* Multi-dimensional dense array (CvMatND) * +\****************************************************************************************/ + +#define CV_MATND_MAGIC_VAL 0x42430000 +#define CV_TYPE_NAME_MATND "opencv-nd-matrix" + +#define CV_MAX_DIM 32 + +#ifdef __cplusplus +typedef struct CvMatND CvMatND; +CV_EXPORTS CvMatND cvMatND(const cv::Mat& m); +#endif + +/** + @deprecated consider using cv::Mat instead + */ +typedef struct +CvMatND +{ + int type; + int dims; + + int* refcount; + int hdr_refcount; + + union + { + uchar* ptr; + float* fl; + double* db; + int* i; + short* s; + } data; + + struct + { + int size; + int step; + } + dim[CV_MAX_DIM]; + +#if defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) + CvMatND() {} + CvMatND(const cv::Mat& m) { *this = cvMatND(m); } +#endif +} +CvMatND; + + +CV_INLINE CvMatND cvMatND() +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvMatND self = CV_STRUCT_INITIALIZER; return self; +#else + return CvMatND(); +#endif +} + +#define CV_IS_MATND_HDR(mat) \ + ((mat) != NULL && (((const CvMatND*)(mat))->type & CV_MAGIC_MASK) == CV_MATND_MAGIC_VAL) + +#define CV_IS_MATND(mat) \ + (CV_IS_MATND_HDR(mat) && ((const CvMatND*)(mat))->data.ptr != NULL) + + +/****************************************************************************************\ +* Multi-dimensional sparse array (CvSparseMat) * +\****************************************************************************************/ + +#define CV_SPARSE_MAT_MAGIC_VAL 0x42440000 +#define CV_TYPE_NAME_SPARSE_MAT "opencv-sparse-matrix" + +struct CvSet; + +typedef struct CvSparseMat +{ + int type; + int dims; + int* refcount; + int hdr_refcount; + + struct CvSet* heap; + void** hashtable; + int hashsize; + int valoffset; + int idxoffset; + int size[CV_MAX_DIM]; + +#ifdef __cplusplus + CV_EXPORTS void copyToSparseMat(cv::SparseMat& m) const; +#endif +} +CvSparseMat; + +#ifdef __cplusplus +CV_EXPORTS CvSparseMat* cvCreateSparseMat(const cv::SparseMat& m); +#endif + +#define CV_IS_SPARSE_MAT_HDR(mat) \ + ((mat) != NULL && \ + (((const CvSparseMat*)(mat))->type & CV_MAGIC_MASK) == CV_SPARSE_MAT_MAGIC_VAL) + +#define CV_IS_SPARSE_MAT(mat) \ + CV_IS_SPARSE_MAT_HDR(mat) + +/**************** iteration through a sparse array *****************/ + +typedef struct CvSparseNode +{ + unsigned hashval; + struct CvSparseNode* next; +} +CvSparseNode; + +typedef struct CvSparseMatIterator +{ + CvSparseMat* mat; + CvSparseNode* node; + int curidx; +} +CvSparseMatIterator; + +#define CV_NODE_VAL(mat,node) ((void*)((uchar*)(node) + (mat)->valoffset)) +#define CV_NODE_IDX(mat,node) ((int*)((uchar*)(node) + (mat)->idxoffset)) + +/****************************************************************************************\ +* Histogram * +\****************************************************************************************/ + +typedef int CvHistType; + +#define CV_HIST_MAGIC_VAL 0x42450000 +#define CV_HIST_UNIFORM_FLAG (1 << 10) + +/** indicates whether bin ranges are set already or not */ +#define CV_HIST_RANGES_FLAG (1 << 11) + +#define CV_HIST_ARRAY 0 +#define CV_HIST_SPARSE 1 +#define CV_HIST_TREE CV_HIST_SPARSE + +/** should be used as a parameter only, + it turns to CV_HIST_UNIFORM_FLAG of hist->type */ +#define CV_HIST_UNIFORM 1 + +typedef struct CvHistogram +{ + int type; + CvArr* bins; + float thresh[CV_MAX_DIM][2]; /**< For uniform histograms. */ + float** thresh2; /**< For non-uniform histograms. */ + CvMatND mat; /**< Embedded matrix header for array histograms. */ +} +CvHistogram; + +#define CV_IS_HIST( hist ) \ + ((hist) != NULL && \ + (((CvHistogram*)(hist))->type & CV_MAGIC_MASK) == CV_HIST_MAGIC_VAL && \ + (hist)->bins != NULL) + +#define CV_IS_UNIFORM_HIST( hist ) \ + (((hist)->type & CV_HIST_UNIFORM_FLAG) != 0) + +#define CV_IS_SPARSE_HIST( hist ) \ + CV_IS_SPARSE_MAT((hist)->bins) + +#define CV_HIST_HAS_RANGES( hist ) \ + (((hist)->type & CV_HIST_RANGES_FLAG) != 0) + +/****************************************************************************************\ +* Other supplementary data type definitions * +\****************************************************************************************/ + +/*************************************** CvRect *****************************************/ +/** @sa Rect_ */ +typedef struct CvRect +{ + int x; + int y; + int width; + int height; + +#ifdef CV__VALIDATE_UNUNITIALIZED_VARS + CvRect() __attribute__(( warning("Non-initialized variable") )) {}; + template CvRect(const std::initializer_list<_Tp> list) + { + CV_Assert(list.size() == 0 || list.size() == 4); + x = y = width = height = 0; + if (list.size() == 4) + { + x = list.begin()[0]; y = list.begin()[1]; width = list.begin()[2]; height = list.begin()[3]; + } + }; +#elif defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) + CvRect(int _x = 0, int _y = 0, int w = 0, int h = 0): x(_x), y(_y), width(w), height(h) {} + template + CvRect(const cv::Rect_<_Tp>& r): x(cv::saturate_cast(r.x)), y(cv::saturate_cast(r.y)), width(cv::saturate_cast(r.width)), height(cv::saturate_cast(r.height)) {} +#endif +#ifdef __cplusplus + template + operator cv::Rect_<_Tp>() const { return cv::Rect_<_Tp>((_Tp)x, (_Tp)y, (_Tp)width, (_Tp)height); } +#endif +} +CvRect; + +/** constructs CvRect structure. */ +CV_INLINE CvRect cvRect( int x, int y, int width, int height ) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvRect r = {x, y, width, height}; +#else + CvRect r(x, y , width, height); +#endif + return r; +} +#ifdef __cplusplus +CV_INLINE CvRect cvRect(const cv::Rect& rc) { return cvRect(rc.x, rc.y, rc.width, rc.height); } +#endif + +CV_INLINE IplROI cvRectToROI( CvRect rect, int coi ) +{ + IplROI roi; + roi.xOffset = rect.x; + roi.yOffset = rect.y; + roi.width = rect.width; + roi.height = rect.height; + roi.coi = coi; + + return roi; +} + + +CV_INLINE CvRect cvROIToRect( IplROI roi ) +{ + return cvRect( roi.xOffset, roi.yOffset, roi.width, roi.height ); +} + +/*********************************** CvTermCriteria *************************************/ + +#define CV_TERMCRIT_ITER 1 +#define CV_TERMCRIT_NUMBER CV_TERMCRIT_ITER +#define CV_TERMCRIT_EPS 2 + +/** @sa TermCriteria + */ +typedef struct CvTermCriteria +{ + int type; /**< may be combination of + CV_TERMCRIT_ITER + CV_TERMCRIT_EPS */ + int max_iter; + double epsilon; +#if defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) + CvTermCriteria(int _type = 0, int _iter = 0, double _eps = 0) : type(_type), max_iter(_iter), epsilon(_eps) {} + CvTermCriteria(const cv::TermCriteria& t) : type(t.type), max_iter(t.maxCount), epsilon(t.epsilon) {} +#endif +#ifdef __cplusplus + operator cv::TermCriteria() const { return cv::TermCriteria(type, max_iter, epsilon); } +#endif +} +CvTermCriteria; + +CV_INLINE CvTermCriteria cvTermCriteria( int type, int max_iter, double epsilon ) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvTermCriteria t = { type, max_iter, (float)epsilon}; +#else + CvTermCriteria t(type, max_iter, epsilon); +#endif + return t; +} +#ifdef __cplusplus +CV_INLINE CvTermCriteria cvTermCriteria(const cv::TermCriteria& t) { return cvTermCriteria(t.type, t.maxCount, t.epsilon); } +#endif + + +/******************************* CvPoint and variants ***********************************/ + +typedef struct CvPoint +{ + int x; + int y; + +#ifdef CV__VALIDATE_UNUNITIALIZED_VARS + CvPoint() __attribute__(( warning("Non-initialized variable") )) {} + template CvPoint(const std::initializer_list<_Tp> list) + { + CV_Assert(list.size() == 0 || list.size() == 2); + x = y = 0; + if (list.size() == 2) + { + x = list.begin()[0]; y = list.begin()[1]; + } + }; +#elif defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) + CvPoint(int _x = 0, int _y = 0): x(_x), y(_y) {} + template + CvPoint(const cv::Point_<_Tp>& pt): x((int)pt.x), y((int)pt.y) {} +#endif +#ifdef __cplusplus + template + operator cv::Point_<_Tp>() const { return cv::Point_<_Tp>(cv::saturate_cast<_Tp>(x), cv::saturate_cast<_Tp>(y)); } +#endif +} +CvPoint; + +/** constructs CvPoint structure. */ +CV_INLINE CvPoint cvPoint( int x, int y ) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvPoint p = {x, y}; +#else + CvPoint p(x, y); +#endif + return p; +} +#ifdef __cplusplus +CV_INLINE CvPoint cvPoint(const cv::Point& pt) { return cvPoint(pt.x, pt.y); } +#endif + +typedef struct CvPoint2D32f +{ + float x; + float y; + +#ifdef CV__VALIDATE_UNUNITIALIZED_VARS + CvPoint2D32f() __attribute__(( warning("Non-initialized variable") )) {} + template CvPoint2D32f(const std::initializer_list<_Tp> list) + { + CV_Assert(list.size() == 0 || list.size() == 2); + x = y = 0; + if (list.size() == 2) + { + x = list.begin()[0]; y = list.begin()[1]; + } + }; +#elif defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) + CvPoint2D32f(float _x = 0, float _y = 0): x(_x), y(_y) {} + template + CvPoint2D32f(const cv::Point_<_Tp>& pt): x((float)pt.x), y((float)pt.y) {} +#endif +#ifdef __cplusplus + template + operator cv::Point_<_Tp>() const { return cv::Point_<_Tp>(cv::saturate_cast<_Tp>(x), cv::saturate_cast<_Tp>(y)); } +#endif +} +CvPoint2D32f; + +/** constructs CvPoint2D32f structure. */ +CV_INLINE CvPoint2D32f cvPoint2D32f( double x, double y ) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvPoint2D32f p = { (float)x, (float)y }; +#else + CvPoint2D32f p((float)x, (float)y); +#endif + return p; +} + +#ifdef __cplusplus +template +CvPoint2D32f cvPoint2D32f(const cv::Point_<_Tp>& pt) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvPoint2D32f p = { (float)pt.x, (float)pt.y }; +#else + CvPoint2D32f p((float)pt.x, (float)pt.y); +#endif + return p; +} +#endif + +/** converts CvPoint to CvPoint2D32f. */ +CV_INLINE CvPoint2D32f cvPointTo32f( CvPoint point ) +{ + return cvPoint2D32f( (float)point.x, (float)point.y ); +} + +/** converts CvPoint2D32f to CvPoint. */ +CV_INLINE CvPoint cvPointFrom32f( CvPoint2D32f point ) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvPoint ipt = { cvRound(point.x), cvRound(point.y) }; +#else + CvPoint ipt(cvRound(point.x), cvRound(point.y)); +#endif + return ipt; +} + + +typedef struct CvPoint3D32f +{ + float x; + float y; + float z; + +#ifdef CV__VALIDATE_UNUNITIALIZED_VARS + CvPoint3D32f() __attribute__(( warning("Non-initialized variable") )) {} + template CvPoint3D32f(const std::initializer_list<_Tp> list) + { + CV_Assert(list.size() == 0 || list.size() == 3); + x = y = z = 0; + if (list.size() == 3) + { + x = list.begin()[0]; y = list.begin()[1]; z = list.begin()[2]; + } + }; +#elif defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) + CvPoint3D32f(float _x = 0, float _y = 0, float _z = 0): x(_x), y(_y), z(_z) {} + template + CvPoint3D32f(const cv::Point3_<_Tp>& pt): x((float)pt.x), y((float)pt.y), z((float)pt.z) {} +#endif +#ifdef __cplusplus + template + operator cv::Point3_<_Tp>() const { return cv::Point3_<_Tp>(cv::saturate_cast<_Tp>(x), cv::saturate_cast<_Tp>(y), cv::saturate_cast<_Tp>(z)); } +#endif +} +CvPoint3D32f; + +/** constructs CvPoint3D32f structure. */ +CV_INLINE CvPoint3D32f cvPoint3D32f( double x, double y, double z ) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvPoint3D32f p = { (float)x, (float)y, (float)z }; +#else + CvPoint3D32f p((float)x, (float)y, (float)z); +#endif + return p; +} + +#ifdef __cplusplus +template +CvPoint3D32f cvPoint3D32f(const cv::Point3_<_Tp>& pt) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvPoint3D32f p = { (float)pt.x, (float)pt.y, (float)pt.z }; +#else + CvPoint3D32f p((float)pt.x, (float)pt.y, (float)pt.z); +#endif + return p; +} +#endif + + +typedef struct CvPoint2D64f +{ + double x; + double y; +#ifdef CV__VALIDATE_UNUNITIALIZED_VARS + CvPoint2D64f() __attribute__(( warning("Non-initialized variable") )) {} + template CvPoint2D64f(const std::initializer_list<_Tp> list) + { + CV_Assert(list.size() == 0 || list.size() == 2); + x = y = 0; + if (list.size() == 2) + { + x = list.begin()[0]; y = list.begin()[1]; + } + }; +#endif +} +CvPoint2D64f; + +/** constructs CvPoint2D64f structure.*/ +CV_INLINE CvPoint2D64f cvPoint2D64f( double x, double y ) +{ + CvPoint2D64f p = { x, y }; + return p; +} + + +typedef struct CvPoint3D64f +{ + double x; + double y; + double z; +#ifdef CV__VALIDATE_UNUNITIALIZED_VARS + CvPoint3D64f() __attribute__(( warning("Non-initialized variable") )) {} + template CvPoint3D64f(const std::initializer_list<_Tp> list) + { + CV_Assert(list.size() == 0 || list.size() == 3); + x = y = z = 0; + if (list.size() == 3) + { + x = list.begin()[0]; y = list.begin()[1]; z = list.begin()[2]; + } + }; +#endif +} +CvPoint3D64f; + +/** constructs CvPoint3D64f structure. */ +CV_INLINE CvPoint3D64f cvPoint3D64f( double x, double y, double z ) +{ + CvPoint3D64f p = { x, y, z }; + return p; +} + + +/******************************** CvSize's & CvBox **************************************/ + +typedef struct CvSize +{ + int width; + int height; + +#ifdef CV__VALIDATE_UNUNITIALIZED_VARS + CvSize() __attribute__(( warning("Non-initialized variable") )) {} + template CvSize(const std::initializer_list<_Tp> list) + { + CV_Assert(list.size() == 0 || list.size() == 2); + width = 0; height = 0; + if (list.size() == 2) + { + width = list.begin()[0]; height = list.begin()[1]; + } + }; +#elif defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) + CvSize(int w = 0, int h = 0): width(w), height(h) {} + template + CvSize(const cv::Size_<_Tp>& sz): width(cv::saturate_cast(sz.width)), height(cv::saturate_cast(sz.height)) {} +#endif +#ifdef __cplusplus + template + operator cv::Size_<_Tp>() const { return cv::Size_<_Tp>(cv::saturate_cast<_Tp>(width), cv::saturate_cast<_Tp>(height)); } +#endif +} +CvSize; + +/** constructs CvSize structure. */ +CV_INLINE CvSize cvSize( int width, int height ) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvSize s = { width, height }; +#else + CvSize s(width, height); +#endif + return s; +} + +#ifdef __cplusplus +CV_INLINE CvSize cvSize(const cv::Size& sz) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvSize s = { sz.width, sz.height }; +#else + CvSize s(sz.width, sz.height); +#endif + return s; +} +#endif + +typedef struct CvSize2D32f +{ + float width; + float height; + +#ifdef CV__VALIDATE_UNUNITIALIZED_VARS + CvSize2D32f() __attribute__(( warning("Non-initialized variable") )) {} + template CvSize2D32f(const std::initializer_list<_Tp> list) + { + CV_Assert(list.size() == 0 || list.size() == 2); + width = 0; height = 0; + if (list.size() == 2) + { + width = list.begin()[0]; height = list.begin()[1]; + } + }; +#elif defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) + CvSize2D32f(float w = 0, float h = 0): width(w), height(h) {} + template + CvSize2D32f(const cv::Size_<_Tp>& sz): width(cv::saturate_cast(sz.width)), height(cv::saturate_cast(sz.height)) {} +#endif +#ifdef __cplusplus + template + operator cv::Size_<_Tp>() const { return cv::Size_<_Tp>(cv::saturate_cast<_Tp>(width), cv::saturate_cast<_Tp>(height)); } +#endif +} +CvSize2D32f; + +/** constructs CvSize2D32f structure. */ +CV_INLINE CvSize2D32f cvSize2D32f( double width, double height ) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvSize2D32f s = { (float)width, (float)height }; +#else + CvSize2D32f s((float)width, (float)height); +#endif + return s; +} +#ifdef __cplusplus +template +CvSize2D32f cvSize2D32f(const cv::Size_<_Tp>& sz) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvSize2D32f s = { (float)sz.width, (float)sz.height }; +#else + CvSize2D32f s((float)sz.width, (float)sz.height); +#endif + return s; +} +#endif + +/** @sa RotatedRect + */ +typedef struct CvBox2D +{ + CvPoint2D32f center; /**< Center of the box. */ + CvSize2D32f size; /**< Box width and length. */ + float angle; /**< Angle between the horizontal axis */ + /**< and the first side (i.e. length) in degrees */ + +#if defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) + CvBox2D(CvPoint2D32f c = CvPoint2D32f(), CvSize2D32f s = CvSize2D32f(), float a = 0) : center(c), size(s), angle(a) {} + CvBox2D(const cv::RotatedRect& rr) : center(rr.center), size(rr.size), angle(rr.angle) {} +#endif +#ifdef __cplusplus + operator cv::RotatedRect() const { return cv::RotatedRect(center, size, angle); } +#endif +} +CvBox2D; + + +#ifdef __cplusplus +CV_INLINE CvBox2D cvBox2D(CvPoint2D32f c = CvPoint2D32f(), CvSize2D32f s = CvSize2D32f(), float a = 0) +{ + CvBox2D self; + self.center = c; + self.size = s; + self.angle = a; + return self; +} +CV_INLINE CvBox2D cvBox2D(const cv::RotatedRect& rr) +{ + CvBox2D self; + self.center = cvPoint2D32f(rr.center); + self.size = cvSize2D32f(rr.size); + self.angle = rr.angle; + return self; +} +#endif + + +/** Line iterator state: */ +typedef struct CvLineIterator +{ + /** Pointer to the current point: */ + uchar* ptr; + + /* Bresenham algorithm state: */ + int err; + int plus_delta; + int minus_delta; + int plus_step; + int minus_step; +} +CvLineIterator; + + + +/************************************* CvSlice ******************************************/ +#define CV_WHOLE_SEQ_END_INDEX 0x3fffffff +#define CV_WHOLE_SEQ cvSlice(0, CV_WHOLE_SEQ_END_INDEX) + +typedef struct CvSlice +{ + int start_index, end_index; + +#ifdef CV__VALIDATE_UNUNITIALIZED_VARS + CvSlice() __attribute__(( warning("Non-initialized variable") )) {} + template CvSlice(const std::initializer_list<_Tp> list) + { + CV_Assert(list.size() == 0 || list.size() == 2); + start_index = end_index = 0; + if (list.size() == 2) + { + start_index = list.begin()[0]; end_index = list.begin()[1]; + } + }; +#endif +#if defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) && !defined(__CUDACC__) + CvSlice(int start = 0, int end = 0) : start_index(start), end_index(end) {} + CvSlice(const cv::Range& r) { *this = (r.start != INT_MIN && r.end != INT_MAX) ? CvSlice(r.start, r.end) : CvSlice(0, CV_WHOLE_SEQ_END_INDEX); } + operator cv::Range() const { return (start_index == 0 && end_index == CV_WHOLE_SEQ_END_INDEX ) ? cv::Range::all() : cv::Range(start_index, end_index); } +#endif +} +CvSlice; + +CV_INLINE CvSlice cvSlice( int start, int end ) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) && !defined(__CUDACC__)) + CvSlice slice = { start, end }; +#else + CvSlice slice(start, end); +#endif + return slice; +} + +#if defined(__cplusplus) +CV_INLINE CvSlice cvSlice(const cv::Range& r) +{ + CvSlice slice = (r.start != INT_MIN && r.end != INT_MAX) ? cvSlice(r.start, r.end) : cvSlice(0, CV_WHOLE_SEQ_END_INDEX); + return slice; +} +#endif + + +/************************************* CvScalar *****************************************/ +/** @sa Scalar_ + */ +typedef struct CvScalar +{ + double val[4]; + +#ifdef CV__VALIDATE_UNUNITIALIZED_VARS + CvScalar() __attribute__(( warning("Non-initialized variable") )) {} + CvScalar(const std::initializer_list list) + { + CV_Assert(list.size() == 0 || list.size() == 4); + val[0] = val[1] = val[2] = val[3] = 0; + if (list.size() == 4) + { + val[0] = list.begin()[0]; val[1] = list.begin()[1]; val[2] = list.begin()[2]; val[3] = list.begin()[3]; + } + }; +#elif defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) + CvScalar() {} + CvScalar(double d0, double d1 = 0, double d2 = 0, double d3 = 0) { val[0] = d0; val[1] = d1; val[2] = d2; val[3] = d3; } + template + CvScalar(const cv::Scalar_<_Tp>& s) { val[0] = s.val[0]; val[1] = s.val[1]; val[2] = s.val[2]; val[3] = s.val[3]; } + template + CvScalar(const cv::Vec<_Tp, cn>& v) + { + int i; + for( i = 0; i < (cn < 4 ? cn : 4); i++ ) val[i] = v.val[i]; + for( ; i < 4; i++ ) val[i] = 0; + } +#endif +#ifdef __cplusplus + template + operator cv::Scalar_<_Tp>() const { return cv::Scalar_<_Tp>(cv::saturate_cast<_Tp>(val[0]), cv::saturate_cast<_Tp>(val[1]), cv::saturate_cast<_Tp>(val[2]), cv::saturate_cast<_Tp>(val[3])); } +#endif +} +CvScalar; + +CV_INLINE CvScalar cvScalar( double val0, double val1 CV_DEFAULT(0), + double val2 CV_DEFAULT(0), double val3 CV_DEFAULT(0)) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvScalar scalar = CV_STRUCT_INITIALIZER; +#else + CvScalar scalar; +#endif + scalar.val[0] = val0; scalar.val[1] = val1; + scalar.val[2] = val2; scalar.val[3] = val3; + return scalar; +} + +#ifdef __cplusplus +CV_INLINE CvScalar cvScalar() +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvScalar scalar = CV_STRUCT_INITIALIZER; +#else + CvScalar scalar; +#endif + scalar.val[0] = scalar.val[1] = scalar.val[2] = scalar.val[3] = 0; + return scalar; +} +CV_INLINE CvScalar cvScalar(const cv::Scalar& s) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvScalar scalar = CV_STRUCT_INITIALIZER; +#else + CvScalar scalar; +#endif + scalar.val[0] = s.val[0]; + scalar.val[1] = s.val[1]; + scalar.val[2] = s.val[2]; + scalar.val[3] = s.val[3]; + return scalar; +} +#endif + +CV_INLINE CvScalar cvRealScalar( double val0 ) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvScalar scalar = CV_STRUCT_INITIALIZER; +#else + CvScalar scalar; +#endif + scalar.val[0] = val0; + scalar.val[1] = scalar.val[2] = scalar.val[3] = 0; + return scalar; +} + +CV_INLINE CvScalar cvScalarAll( double val0123 ) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvScalar scalar = CV_STRUCT_INITIALIZER; +#else + CvScalar scalar; +#endif + scalar.val[0] = val0123; + scalar.val[1] = val0123; + scalar.val[2] = val0123; + scalar.val[3] = val0123; + return scalar; +} + +/****************************************************************************************\ +* Dynamic Data structures * +\****************************************************************************************/ + +/******************************** Memory storage ****************************************/ + +typedef struct CvMemBlock +{ + struct CvMemBlock* prev; + struct CvMemBlock* next; +} +CvMemBlock; + +#define CV_STORAGE_MAGIC_VAL 0x42890000 + +typedef struct CvMemStorage +{ + int signature; + CvMemBlock* bottom; /**< First allocated block. */ + CvMemBlock* top; /**< Current memory block - top of the stack. */ + struct CvMemStorage* parent; /**< We get new blocks from parent as needed. */ + int block_size; /**< Block size. */ + int free_space; /**< Remaining free space in current block. */ +} +CvMemStorage; + +#define CV_IS_STORAGE(storage) \ + ((storage) != NULL && \ + (((CvMemStorage*)(storage))->signature & CV_MAGIC_MASK) == CV_STORAGE_MAGIC_VAL) + + +typedef struct CvMemStoragePos +{ + CvMemBlock* top; + int free_space; +} +CvMemStoragePos; + + +/*********************************** Sequence *******************************************/ + +typedef struct CvSeqBlock +{ + struct CvSeqBlock* prev; /**< Previous sequence block. */ + struct CvSeqBlock* next; /**< Next sequence block. */ + int start_index; /**< Index of the first element in the block + */ + /**< sequence->first->start_index. */ + int count; /**< Number of elements in the block. */ + schar* data; /**< Pointer to the first element of the block. */ +} +CvSeqBlock; + + +#define CV_TREE_NODE_FIELDS(node_type) \ + int flags; /**< Miscellaneous flags. */ \ + int header_size; /**< Size of sequence header. */ \ + struct node_type* h_prev; /**< Previous sequence. */ \ + struct node_type* h_next; /**< Next sequence. */ \ + struct node_type* v_prev; /**< 2nd previous sequence. */ \ + struct node_type* v_next /**< 2nd next sequence. */ + +/** + Read/Write sequence. + Elements can be dynamically inserted to or deleted from the sequence. +*/ +#define CV_SEQUENCE_FIELDS() \ + CV_TREE_NODE_FIELDS(CvSeq); \ + int total; /**< Total number of elements. */ \ + int elem_size; /**< Size of sequence element in bytes. */ \ + schar* block_max; /**< Maximal bound of the last block. */ \ + schar* ptr; /**< Current write pointer. */ \ + int delta_elems; /**< Grow seq this many at a time. */ \ + CvMemStorage* storage; /**< Where the seq is stored. */ \ + CvSeqBlock* free_blocks; /**< Free blocks list. */ \ + CvSeqBlock* first; /**< Pointer to the first sequence block. */ + +typedef struct CvSeq +{ + CV_SEQUENCE_FIELDS() +} +CvSeq; + +#define CV_TYPE_NAME_SEQ "opencv-sequence" +#define CV_TYPE_NAME_SEQ_TREE "opencv-sequence-tree" + +/*************************************** Set ********************************************/ +/** @brief Set + Order is not preserved. There can be gaps between sequence elements. + After the element has been inserted it stays in the same place all the time. + The MSB(most-significant or sign bit) of the first field (flags) is 0 iff the element exists. +*/ +#define CV_SET_ELEM_FIELDS(elem_type) \ + int flags; \ + struct elem_type* next_free; + +typedef struct CvSetElem +{ + CV_SET_ELEM_FIELDS(CvSetElem) +} +CvSetElem; + +#define CV_SET_FIELDS() \ + CV_SEQUENCE_FIELDS() \ + CvSetElem* free_elems; \ + int active_count; + +typedef struct CvSet +{ + CV_SET_FIELDS() +} +CvSet; + + +#define CV_SET_ELEM_IDX_MASK ((1 << 26) - 1) +#define CV_SET_ELEM_FREE_FLAG (1 << (sizeof(int)*8-1)) + +/** Checks whether the element pointed by ptr belongs to a set or not */ +#define CV_IS_SET_ELEM( ptr ) (((CvSetElem*)(ptr))->flags >= 0) + +/************************************* Graph ********************************************/ + +/** @name Graph + +We represent a graph as a set of vertices. Vertices contain their adjacency lists (more exactly, +pointers to first incoming or outcoming edge (or 0 if isolated vertex)). Edges are stored in +another set. There is a singly-linked list of incoming/outcoming edges for each vertex. + +Each edge consists of: + +- Two pointers to the starting and ending vertices (vtx[0] and vtx[1] respectively). + + A graph may be oriented or not. In the latter case, edges between vertex i to vertex j are not +distinguished during search operations. + +- Two pointers to next edges for the starting and ending vertices, where next[0] points to the +next edge in the vtx[0] adjacency list and next[1] points to the next edge in the vtx[1] +adjacency list. + +@see CvGraphEdge, CvGraphVtx, CvGraphVtx2D, CvGraph +@{ +*/ +#define CV_GRAPH_EDGE_FIELDS() \ + int flags; \ + float weight; \ + struct CvGraphEdge* next[2]; \ + struct CvGraphVtx* vtx[2]; + + +#define CV_GRAPH_VERTEX_FIELDS() \ + int flags; \ + struct CvGraphEdge* first; + + +typedef struct CvGraphEdge +{ + CV_GRAPH_EDGE_FIELDS() +} +CvGraphEdge; + +typedef struct CvGraphVtx +{ + CV_GRAPH_VERTEX_FIELDS() +} +CvGraphVtx; + +typedef struct CvGraphVtx2D +{ + CV_GRAPH_VERTEX_FIELDS() + CvPoint2D32f* ptr; +} +CvGraphVtx2D; + +/** + Graph is "derived" from the set (this is set a of vertices) + and includes another set (edges) +*/ +#define CV_GRAPH_FIELDS() \ + CV_SET_FIELDS() \ + CvSet* edges; + +typedef struct CvGraph +{ + CV_GRAPH_FIELDS() +} +CvGraph; + +#define CV_TYPE_NAME_GRAPH "opencv-graph" + +/** @} */ + +/*********************************** Chain/Contour *************************************/ + +typedef struct CvChain +{ + CV_SEQUENCE_FIELDS() + CvPoint origin; +} +CvChain; + +#define CV_CONTOUR_FIELDS() \ + CV_SEQUENCE_FIELDS() \ + CvRect rect; \ + int color; \ + int reserved[3]; + +typedef struct CvContour +{ + CV_CONTOUR_FIELDS() +} +CvContour; + +typedef CvContour CvPoint2DSeq; + +/****************************************************************************************\ +* Sequence types * +\****************************************************************************************/ + +#define CV_SEQ_MAGIC_VAL 0x42990000 + +#define CV_IS_SEQ(seq) \ + ((seq) != NULL && (((CvSeq*)(seq))->flags & CV_MAGIC_MASK) == CV_SEQ_MAGIC_VAL) + +#define CV_SET_MAGIC_VAL 0x42980000 +#define CV_IS_SET(set) \ + ((set) != NULL && (((CvSeq*)(set))->flags & CV_MAGIC_MASK) == CV_SET_MAGIC_VAL) + +#define CV_SEQ_ELTYPE_BITS 12 +#define CV_SEQ_ELTYPE_MASK ((1 << CV_SEQ_ELTYPE_BITS) - 1) + +#define CV_SEQ_ELTYPE_POINT CV_32SC2 /**< (x,y) */ +#define CV_SEQ_ELTYPE_CODE CV_8UC1 /**< freeman code: 0..7 */ +#define CV_SEQ_ELTYPE_GENERIC 0 +#define CV_SEQ_ELTYPE_PTR CV_USRTYPE1 +#define CV_SEQ_ELTYPE_PPOINT CV_SEQ_ELTYPE_PTR /**< &(x,y) */ +#define CV_SEQ_ELTYPE_INDEX CV_32SC1 /**< #(x,y) */ +#define CV_SEQ_ELTYPE_GRAPH_EDGE 0 /**< &next_o, &next_d, &vtx_o, &vtx_d */ +#define CV_SEQ_ELTYPE_GRAPH_VERTEX 0 /**< first_edge, &(x,y) */ +#define CV_SEQ_ELTYPE_TRIAN_ATR 0 /**< vertex of the binary tree */ +#define CV_SEQ_ELTYPE_CONNECTED_COMP 0 /**< connected component */ +#define CV_SEQ_ELTYPE_POINT3D CV_32FC3 /**< (x,y,z) */ + +#define CV_SEQ_KIND_BITS 2 +#define CV_SEQ_KIND_MASK (((1 << CV_SEQ_KIND_BITS) - 1)<flags & CV_SEQ_ELTYPE_MASK) +#define CV_SEQ_KIND( seq ) ((seq)->flags & CV_SEQ_KIND_MASK ) + +/** flag checking */ +#define CV_IS_SEQ_INDEX( seq ) ((CV_SEQ_ELTYPE(seq) == CV_SEQ_ELTYPE_INDEX) && \ + (CV_SEQ_KIND(seq) == CV_SEQ_KIND_GENERIC)) + +#define CV_IS_SEQ_CURVE( seq ) (CV_SEQ_KIND(seq) == CV_SEQ_KIND_CURVE) +#define CV_IS_SEQ_CLOSED( seq ) (((seq)->flags & CV_SEQ_FLAG_CLOSED) != 0) +#define CV_IS_SEQ_CONVEX( seq ) 0 +#define CV_IS_SEQ_HOLE( seq ) (((seq)->flags & CV_SEQ_FLAG_HOLE) != 0) +#define CV_IS_SEQ_SIMPLE( seq ) 1 + +/** type checking macros */ +#define CV_IS_SEQ_POINT_SET( seq ) \ + ((CV_SEQ_ELTYPE(seq) == CV_32SC2 || CV_SEQ_ELTYPE(seq) == CV_32FC2)) + +#define CV_IS_SEQ_POINT_SUBSET( seq ) \ + (CV_IS_SEQ_INDEX( seq ) || CV_SEQ_ELTYPE(seq) == CV_SEQ_ELTYPE_PPOINT) + +#define CV_IS_SEQ_POLYLINE( seq ) \ + (CV_SEQ_KIND(seq) == CV_SEQ_KIND_CURVE && CV_IS_SEQ_POINT_SET(seq)) + +#define CV_IS_SEQ_POLYGON( seq ) \ + (CV_IS_SEQ_POLYLINE(seq) && CV_IS_SEQ_CLOSED(seq)) + +#define CV_IS_SEQ_CHAIN( seq ) \ + (CV_SEQ_KIND(seq) == CV_SEQ_KIND_CURVE && (seq)->elem_size == 1) + +#define CV_IS_SEQ_CONTOUR( seq ) \ + (CV_IS_SEQ_CLOSED(seq) && (CV_IS_SEQ_POLYLINE(seq) || CV_IS_SEQ_CHAIN(seq))) + +#define CV_IS_SEQ_CHAIN_CONTOUR( seq ) \ + (CV_IS_SEQ_CHAIN( seq ) && CV_IS_SEQ_CLOSED( seq )) + +#define CV_IS_SEQ_POLYGON_TREE( seq ) \ + (CV_SEQ_ELTYPE (seq) == CV_SEQ_ELTYPE_TRIAN_ATR && \ + CV_SEQ_KIND( seq ) == CV_SEQ_KIND_BIN_TREE ) + +#define CV_IS_GRAPH( seq ) \ + (CV_IS_SET(seq) && CV_SEQ_KIND((CvSet*)(seq)) == CV_SEQ_KIND_GRAPH) + +#define CV_IS_GRAPH_ORIENTED( seq ) \ + (((seq)->flags & CV_GRAPH_FLAG_ORIENTED) != 0) + +#define CV_IS_SUBDIV2D( seq ) \ + (CV_IS_SET(seq) && CV_SEQ_KIND((CvSet*)(seq)) == CV_SEQ_KIND_SUBDIV2D) + +/****************************************************************************************/ +/* Sequence writer & reader */ +/****************************************************************************************/ + +#define CV_SEQ_WRITER_FIELDS() \ + int header_size; \ + CvSeq* seq; /**< the sequence written */ \ + CvSeqBlock* block; /**< current block */ \ + schar* ptr; /**< pointer to free space */ \ + schar* block_min; /**< pointer to the beginning of block*/\ + schar* block_max; /**< pointer to the end of block */ + +typedef struct CvSeqWriter +{ + CV_SEQ_WRITER_FIELDS() +} +CvSeqWriter; + + +#define CV_SEQ_READER_FIELDS() \ + int header_size; \ + CvSeq* seq; /**< sequence, beign read */ \ + CvSeqBlock* block; /**< current block */ \ + schar* ptr; /**< pointer to element be read next */ \ + schar* block_min; /**< pointer to the beginning of block */\ + schar* block_max; /**< pointer to the end of block */ \ + int delta_index;/**< = seq->first->start_index */ \ + schar* prev_elem; /**< pointer to previous element */ + +typedef struct CvSeqReader +{ + CV_SEQ_READER_FIELDS() +} +CvSeqReader; + +/****************************************************************************************/ +/* Operations on sequences */ +/****************************************************************************************/ + +#define CV_SEQ_ELEM( seq, elem_type, index ) \ +/** assert gives some guarantee that parameter is valid */ \ +( assert(sizeof((seq)->first[0]) == sizeof(CvSeqBlock) && \ + (seq)->elem_size == sizeof(elem_type)), \ + (elem_type*)((seq)->first && (unsigned)index < \ + (unsigned)((seq)->first->count) ? \ + (seq)->first->data + (index) * sizeof(elem_type) : \ + cvGetSeqElem( (CvSeq*)(seq), (index) ))) +#define CV_GET_SEQ_ELEM( elem_type, seq, index ) CV_SEQ_ELEM( (seq), elem_type, (index) ) + +/** Add element to sequence: */ +#define CV_WRITE_SEQ_ELEM_VAR( elem_ptr, writer ) \ +{ \ + if( (writer).ptr >= (writer).block_max ) \ + { \ + cvCreateSeqBlock( &writer); \ + } \ + memcpy((writer).ptr, elem_ptr, (writer).seq->elem_size);\ + (writer).ptr += (writer).seq->elem_size; \ +} + +#define CV_WRITE_SEQ_ELEM( elem, writer ) \ +{ \ + assert( (writer).seq->elem_size == sizeof(elem)); \ + if( (writer).ptr >= (writer).block_max ) \ + { \ + cvCreateSeqBlock( &writer); \ + } \ + assert( (writer).ptr <= (writer).block_max - sizeof(elem));\ + memcpy((writer).ptr, &(elem), sizeof(elem)); \ + (writer).ptr += sizeof(elem); \ +} + + +/** Move reader position forward: */ +#define CV_NEXT_SEQ_ELEM( elem_size, reader ) \ +{ \ + if( ((reader).ptr += (elem_size)) >= (reader).block_max ) \ + { \ + cvChangeSeqBlock( &(reader), 1 ); \ + } \ +} + + +/** Move reader position backward: */ +#define CV_PREV_SEQ_ELEM( elem_size, reader ) \ +{ \ + if( ((reader).ptr -= (elem_size)) < (reader).block_min ) \ + { \ + cvChangeSeqBlock( &(reader), -1 ); \ + } \ +} + +/** Read element and move read position forward: */ +#define CV_READ_SEQ_ELEM( elem, reader ) \ +{ \ + assert( (reader).seq->elem_size == sizeof(elem)); \ + memcpy( &(elem), (reader).ptr, sizeof((elem))); \ + CV_NEXT_SEQ_ELEM( sizeof(elem), reader ) \ +} + +/** Read element and move read position backward: */ +#define CV_REV_READ_SEQ_ELEM( elem, reader ) \ +{ \ + assert( (reader).seq->elem_size == sizeof(elem)); \ + memcpy(&(elem), (reader).ptr, sizeof((elem))); \ + CV_PREV_SEQ_ELEM( sizeof(elem), reader ) \ +} + + +#define CV_READ_CHAIN_POINT( _pt, reader ) \ +{ \ + (_pt) = (reader).pt; \ + if( (reader).ptr ) \ + { \ + CV_READ_SEQ_ELEM( (reader).code, (reader)); \ + assert( ((reader).code & ~7) == 0 ); \ + (reader).pt.x += (reader).deltas[(int)(reader).code][0]; \ + (reader).pt.y += (reader).deltas[(int)(reader).code][1]; \ + } \ +} + +#define CV_CURRENT_POINT( reader ) (*((CvPoint*)((reader).ptr))) +#define CV_PREV_POINT( reader ) (*((CvPoint*)((reader).prev_elem))) + +#define CV_READ_EDGE( pt1, pt2, reader ) \ +{ \ + assert( sizeof(pt1) == sizeof(CvPoint) && \ + sizeof(pt2) == sizeof(CvPoint) && \ + reader.seq->elem_size == sizeof(CvPoint)); \ + (pt1) = CV_PREV_POINT( reader ); \ + (pt2) = CV_CURRENT_POINT( reader ); \ + (reader).prev_elem = (reader).ptr; \ + CV_NEXT_SEQ_ELEM( sizeof(CvPoint), (reader)); \ +} + +/************ Graph macros ************/ + +/** Return next graph edge for given vertex: */ +#define CV_NEXT_GRAPH_EDGE( edge, vertex ) \ + (assert((edge)->vtx[0] == (vertex) || (edge)->vtx[1] == (vertex)), \ + (edge)->next[(edge)->vtx[1] == (vertex)]) + + + +/****************************************************************************************\ +* Data structures for persistence (a.k.a serialization) functionality * +\****************************************************************************************/ + +/** "black box" file storage */ +typedef struct CvFileStorage CvFileStorage; + +/** Storage flags: */ +#define CV_STORAGE_READ 0 +#define CV_STORAGE_WRITE 1 +#define CV_STORAGE_WRITE_TEXT CV_STORAGE_WRITE +#define CV_STORAGE_WRITE_BINARY CV_STORAGE_WRITE +#define CV_STORAGE_APPEND 2 +#define CV_STORAGE_MEMORY 4 +#define CV_STORAGE_FORMAT_MASK (7<<3) +#define CV_STORAGE_FORMAT_AUTO 0 +#define CV_STORAGE_FORMAT_XML 8 +#define CV_STORAGE_FORMAT_YAML 16 +#define CV_STORAGE_FORMAT_JSON 24 +#define CV_STORAGE_BASE64 64 +#define CV_STORAGE_WRITE_BASE64 (CV_STORAGE_BASE64 | CV_STORAGE_WRITE) + +/** @brief List of attributes. : + +In the current implementation, attributes are used to pass extra parameters when writing user +objects (see cvWrite). XML attributes inside tags are not supported, aside from the object type +specification (type_id attribute). +@see cvAttrList, cvAttrValue + */ +typedef struct CvAttrList +{ + const char** attr; /**< NULL-terminated array of (attribute_name,attribute_value) pairs. */ + struct CvAttrList* next; /**< Pointer to next chunk of the attributes list. */ +} +CvAttrList; + +/** initializes CvAttrList structure */ +CV_INLINE CvAttrList cvAttrList( const char** attr CV_DEFAULT(NULL), + CvAttrList* next CV_DEFAULT(NULL) ) +{ + CvAttrList l; + l.attr = attr; + l.next = next; + + return l; +} + +struct CvTypeInfo; + +#define CV_NODE_NONE 0 +#define CV_NODE_INT 1 +#define CV_NODE_INTEGER CV_NODE_INT +#define CV_NODE_REAL 2 +#define CV_NODE_FLOAT CV_NODE_REAL +#define CV_NODE_STR 3 +#define CV_NODE_STRING CV_NODE_STR +#define CV_NODE_REF 4 /**< not used */ +#define CV_NODE_SEQ 5 +#define CV_NODE_MAP 6 +#define CV_NODE_TYPE_MASK 7 + +#define CV_NODE_TYPE(flags) ((flags) & CV_NODE_TYPE_MASK) + +/** file node flags */ +#define CV_NODE_FLOW 8 /**= CV_NODE_SEQ) +#define CV_NODE_IS_FLOW(flags) (((flags) & CV_NODE_FLOW) != 0) +#define CV_NODE_IS_EMPTY(flags) (((flags) & CV_NODE_EMPTY) != 0) +#define CV_NODE_IS_USER(flags) (((flags) & CV_NODE_USER) != 0) +#define CV_NODE_HAS_NAME(flags) (((flags) & CV_NODE_NAMED) != 0) + +#define CV_NODE_SEQ_SIMPLE 256 +#define CV_NODE_SEQ_IS_SIMPLE(seq) (((seq)->flags & CV_NODE_SEQ_SIMPLE) != 0) + +typedef struct CvString +{ + int len; + char* ptr; +} +CvString; + +/** All the keys (names) of elements in the read file storage + are stored in the hash to speed up the lookup operations: */ +typedef struct CvStringHashNode +{ + unsigned hashval; + CvString str; + struct CvStringHashNode* next; +} +CvStringHashNode; + +typedef struct CvGenericHash CvFileNodeHash; + +/** Basic element of the file storage - scalar or collection: */ +typedef struct CvFileNode +{ + int tag; + struct CvTypeInfo* info; /**< type information + (only for user-defined object, for others it is 0) */ + union + { + double f; /**< scalar floating-point number */ + int i; /**< scalar integer number */ + CvString str; /**< text string */ + CvSeq* seq; /**< sequence (ordered collection of file nodes) */ + CvFileNodeHash* map; /**< map (collection of named file nodes) */ + } data; +} +CvFileNode; + +#ifdef __cplusplus +extern "C" { +#endif +typedef int (CV_CDECL *CvIsInstanceFunc)( const void* struct_ptr ); +typedef void (CV_CDECL *CvReleaseFunc)( void** struct_dblptr ); +typedef void* (CV_CDECL *CvReadFunc)( CvFileStorage* storage, CvFileNode* node ); +typedef void (CV_CDECL *CvWriteFunc)( CvFileStorage* storage, const char* name, + const void* struct_ptr, CvAttrList attributes ); +typedef void* (CV_CDECL *CvCloneFunc)( const void* struct_ptr ); +#ifdef __cplusplus +} +#endif + +/** @brief Type information + +The structure contains information about one of the standard or user-defined types. Instances of the +type may or may not contain a pointer to the corresponding CvTypeInfo structure. In any case, there +is a way to find the type info structure for a given object using the cvTypeOf function. +Alternatively, type info can be found by type name using cvFindType, which is used when an object +is read from file storage. The user can register a new type with cvRegisterType that adds the type +information structure into the beginning of the type list. Thus, it is possible to create +specialized types from generic standard types and override the basic methods. + */ +typedef struct CvTypeInfo +{ + int flags; /**< not used */ + int header_size; /**< sizeof(CvTypeInfo) */ + struct CvTypeInfo* prev; /**< previous registered type in the list */ + struct CvTypeInfo* next; /**< next registered type in the list */ + const char* type_name; /**< type name, written to file storage */ + CvIsInstanceFunc is_instance; /**< checks if the passed object belongs to the type */ + CvReleaseFunc release; /**< releases object (memory etc.) */ + CvReadFunc read; /**< reads object from file storage */ + CvWriteFunc write; /**< writes object to file storage */ + CvCloneFunc clone; /**< creates a copy of the object */ +} +CvTypeInfo; + + +/**** System data types ******/ + +typedef struct CvPluginFuncInfo +{ + void** func_addr; + void* default_func_addr; + const char* func_names; + int search_modules; + int loaded_from; +} +CvPluginFuncInfo; + +typedef struct CvModuleInfo +{ + struct CvModuleInfo* next; + const char* name; + const char* version; + CvPluginFuncInfo* func_tab; +} +CvModuleInfo; + +/** @} */ + +#endif /*OPENCV_CORE_TYPES_H*/ + +/* End of file. */ diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/utility.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/utility.hpp new file mode 100644 index 0000000000000000000000000000000000000000..7a7158f5f9d5173c740c46f623b9cb723eabdcfd --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/utility.hpp @@ -0,0 +1,1358 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_UTILITY_H +#define OPENCV_CORE_UTILITY_H + +#ifndef __cplusplus +# error utility.hpp header must be compiled as C++ +#endif + +#if defined(check) +# warning Detected Apple 'check' macro definition, it can cause build conflicts. Please, include this header before any Apple headers. +#endif + +#include "opencv2/core.hpp" +#include + +#ifdef CV_CXX11 +#include +#endif + +namespace cv +{ + +#ifdef CV_COLLECT_IMPL_DATA +CV_EXPORTS void setImpl(int flags); // set implementation flags and reset storage arrays +CV_EXPORTS void addImpl(int flag, const char* func = 0); // add implementation and function name to storage arrays +// Get stored implementation flags and functions names arrays +// Each implementation entry correspond to function name entry, so you can find which implementation was executed in which function +CV_EXPORTS int getImpl(std::vector &impl, std::vector &funName); + +CV_EXPORTS bool useCollection(); // return implementation collection state +CV_EXPORTS void setUseCollection(bool flag); // set implementation collection state + +#define CV_IMPL_PLAIN 0x01 // native CPU OpenCV implementation +#define CV_IMPL_OCL 0x02 // OpenCL implementation +#define CV_IMPL_IPP 0x04 // IPP implementation +#define CV_IMPL_MT 0x10 // multithreaded implementation + +#define CV_IMPL_ADD(impl) \ + if(cv::useCollection()) \ + { \ + cv::addImpl(impl, CV_Func); \ + } +#else +#define CV_IMPL_ADD(impl) +#endif + +//! @addtogroup core_utils +//! @{ + +/** @brief Automatically Allocated Buffer Class + + The class is used for temporary buffers in functions and methods. + If a temporary buffer is usually small (a few K's of memory), + but its size depends on the parameters, it makes sense to create a small + fixed-size array on stack and use it if it's large enough. If the required buffer size + is larger than the fixed size, another buffer of sufficient size is allocated dynamically + and released after the processing. Therefore, in typical cases, when the buffer size is small, + there is no overhead associated with malloc()/free(). + At the same time, there is no limit on the size of processed data. + + This is what AutoBuffer does. The template takes 2 parameters - type of the buffer elements and + the number of stack-allocated elements. Here is how the class is used: + + \code + void my_func(const cv::Mat& m) + { + cv::AutoBuffer buf(1000); // create automatic buffer containing 1000 floats + + buf.allocate(m.rows); // if m.rows <= 1000, the pre-allocated buffer is used, + // otherwise the buffer of "m.rows" floats will be allocated + // dynamically and deallocated in cv::AutoBuffer destructor + ... + } + \endcode +*/ +template class AutoBuffer +{ +public: + typedef _Tp value_type; + + //! the default constructor + AutoBuffer(); + //! constructor taking the real buffer size + explicit AutoBuffer(size_t _size); + + //! the copy constructor + AutoBuffer(const AutoBuffer<_Tp, fixed_size>& buf); + //! the assignment operator + AutoBuffer<_Tp, fixed_size>& operator = (const AutoBuffer<_Tp, fixed_size>& buf); + + //! destructor. calls deallocate() + ~AutoBuffer(); + + //! allocates the new buffer of size _size. if the _size is small enough, stack-allocated buffer is used + void allocate(size_t _size); + //! deallocates the buffer if it was dynamically allocated + void deallocate(); + //! resizes the buffer and preserves the content + void resize(size_t _size); + //! returns the current buffer size + size_t size() const; + //! returns pointer to the real buffer, stack-allocated or heap-allocated + inline _Tp* data() { return ptr; } + //! returns read-only pointer to the real buffer, stack-allocated or heap-allocated + inline const _Tp* data() const { return ptr; } + +#if !defined(OPENCV_DISABLE_DEPRECATED_COMPATIBILITY) // use to .data() calls instead + //! returns pointer to the real buffer, stack-allocated or heap-allocated + operator _Tp* () { return ptr; } + //! returns read-only pointer to the real buffer, stack-allocated or heap-allocated + operator const _Tp* () const { return ptr; } +#else + //! returns a reference to the element at specified location. No bounds checking is performed in Release builds. + inline _Tp& operator[] (size_t i) { CV_DbgCheckLT(i, sz, "out of range"); return ptr[i]; } + //! returns a reference to the element at specified location. No bounds checking is performed in Release builds. + inline const _Tp& operator[] (size_t i) const { CV_DbgCheckLT(i, sz, "out of range"); return ptr[i]; } +#endif + +protected: + //! pointer to the real buffer, can point to buf if the buffer is small enough + _Tp* ptr; + //! size of the real buffer + size_t sz; + //! pre-allocated buffer. At least 1 element to confirm C++ standard requirements + _Tp buf[(fixed_size > 0) ? fixed_size : 1]; +}; + +/** @brief Sets/resets the break-on-error mode. + +When the break-on-error mode is set, the default error handler issues a hardware exception, which +can make debugging more convenient. + +\return the previous state + */ +CV_EXPORTS bool setBreakOnError(bool flag); + +extern "C" typedef int (*ErrorCallback)( int status, const char* func_name, + const char* err_msg, const char* file_name, + int line, void* userdata ); + + +/** @brief Sets the new error handler and the optional user data. + + The function sets the new error handler, called from cv::error(). + + \param errCallback the new error handler. If NULL, the default error handler is used. + \param userdata the optional user data pointer, passed to the callback. + \param prevUserdata the optional output parameter where the previous user data pointer is stored + + \return the previous error handler +*/ +CV_EXPORTS ErrorCallback redirectError( ErrorCallback errCallback, void* userdata=0, void** prevUserdata=0); + +CV_EXPORTS String tempfile( const char* suffix = 0); +CV_EXPORTS void glob(String pattern, std::vector& result, bool recursive = false); + +/** @brief OpenCV will try to set the number of threads for the next parallel region. + +If threads == 0, OpenCV will disable threading optimizations and run all it's functions +sequentially. Passing threads \< 0 will reset threads number to system default. This function must +be called outside of parallel region. + +OpenCV will try to run its functions with specified threads number, but some behaviour differs from +framework: +- `TBB` - User-defined parallel constructions will run with the same threads number, if + another is not specified. If later on user creates his own scheduler, OpenCV will use it. +- `OpenMP` - No special defined behaviour. +- `Concurrency` - If threads == 1, OpenCV will disable threading optimizations and run its + functions sequentially. +- `GCD` - Supports only values \<= 0. +- `C=` - No special defined behaviour. +@param nthreads Number of threads used by OpenCV. +@sa getNumThreads, getThreadNum + */ +CV_EXPORTS_W void setNumThreads(int nthreads); + +/** @brief Returns the number of threads used by OpenCV for parallel regions. + +Always returns 1 if OpenCV is built without threading support. + +The exact meaning of return value depends on the threading framework used by OpenCV library: +- `TBB` - The number of threads, that OpenCV will try to use for parallel regions. If there is + any tbb::thread_scheduler_init in user code conflicting with OpenCV, then function returns + default number of threads used by TBB library. +- `OpenMP` - An upper bound on the number of threads that could be used to form a new team. +- `Concurrency` - The number of threads, that OpenCV will try to use for parallel regions. +- `GCD` - Unsupported; returns the GCD thread pool limit (512) for compatibility. +- `C=` - The number of threads, that OpenCV will try to use for parallel regions, if before + called setNumThreads with threads \> 0, otherwise returns the number of logical CPUs, + available for the process. +@sa setNumThreads, getThreadNum + */ +CV_EXPORTS_W int getNumThreads(); + +/** @brief Returns the index of the currently executed thread within the current parallel region. Always +returns 0 if called outside of parallel region. + +@deprecated Current implementation doesn't corresponding to this documentation. + +The exact meaning of the return value depends on the threading framework used by OpenCV library: +- `TBB` - Unsupported with current 4.1 TBB release. Maybe will be supported in future. +- `OpenMP` - The thread number, within the current team, of the calling thread. +- `Concurrency` - An ID for the virtual processor that the current context is executing on (0 + for master thread and unique number for others, but not necessary 1,2,3,...). +- `GCD` - System calling thread's ID. Never returns 0 inside parallel region. +- `C=` - The index of the current parallel task. +@sa setNumThreads, getNumThreads + */ +CV_EXPORTS_W int getThreadNum(); + +/** @brief Returns full configuration time cmake output. + +Returned value is raw cmake output including version control system revision, compiler version, +compiler flags, enabled modules and third party libraries, etc. Output format depends on target +architecture. + */ +CV_EXPORTS_W const String& getBuildInformation(); + +/** @brief Returns library version string + +For example "3.4.1-dev". + +@sa getMajorVersion, getMinorVersion, getRevisionVersion +*/ +CV_EXPORTS_W String getVersionString(); + +/** @brief Returns major library version */ +CV_EXPORTS_W int getVersionMajor(); + +/** @brief Returns minor library version */ +CV_EXPORTS_W int getVersionMinor(); + +/** @brief Returns revision field of the library version */ +CV_EXPORTS_W int getVersionRevision(); + +/** @brief Returns the number of ticks. + +The function returns the number of ticks after the certain event (for example, when the machine was +turned on). It can be used to initialize RNG or to measure a function execution time by reading the +tick count before and after the function call. +@sa getTickFrequency, TickMeter + */ +CV_EXPORTS_W int64 getTickCount(); + +/** @brief Returns the number of ticks per second. + +The function returns the number of ticks per second. That is, the following code computes the +execution time in seconds: +@code + double t = (double)getTickCount(); + // do something ... + t = ((double)getTickCount() - t)/getTickFrequency(); +@endcode +@sa getTickCount, TickMeter + */ +CV_EXPORTS_W double getTickFrequency(); + +/** @brief a Class to measure passing time. + +The class computes passing time by counting the number of ticks per second. That is, the following code computes the +execution time in seconds: +@code +TickMeter tm; +tm.start(); +// do something ... +tm.stop(); +std::cout << tm.getTimeSec(); +@endcode + +It is also possible to compute the average time over multiple runs: +@code +TickMeter tm; +for (int i = 0; i < 100; i++) +{ + tm.start(); + // do something ... + tm.stop(); +} +double average_time = tm.getTimeSec() / tm.getCounter(); +std::cout << "Average time in second per iteration is: " << average_time << std::endl; +@endcode +@sa getTickCount, getTickFrequency +*/ + +class CV_EXPORTS_W TickMeter +{ +public: + //! the default constructor + CV_WRAP TickMeter() + { + reset(); + } + + /** + starts counting ticks. + */ + CV_WRAP void start() + { + startTime = cv::getTickCount(); + } + + /** + stops counting ticks. + */ + CV_WRAP void stop() + { + int64 time = cv::getTickCount(); + if (startTime == 0) + return; + ++counter; + sumTime += (time - startTime); + startTime = 0; + } + + /** + returns counted ticks. + */ + CV_WRAP int64 getTimeTicks() const + { + return sumTime; + } + + /** + returns passed time in microseconds. + */ + CV_WRAP double getTimeMicro() const + { + return getTimeMilli()*1e3; + } + + /** + returns passed time in milliseconds. + */ + CV_WRAP double getTimeMilli() const + { + return getTimeSec()*1e3; + } + + /** + returns passed time in seconds. + */ + CV_WRAP double getTimeSec() const + { + return (double)getTimeTicks() / getTickFrequency(); + } + + /** + returns internal counter value. + */ + CV_WRAP int64 getCounter() const + { + return counter; + } + + /** + resets internal values. + */ + CV_WRAP void reset() + { + startTime = 0; + sumTime = 0; + counter = 0; + } + +private: + int64 counter; + int64 sumTime; + int64 startTime; +}; + +/** @brief output operator +@code +TickMeter tm; +tm.start(); +// do something ... +tm.stop(); +std::cout << tm; +@endcode +*/ + +static inline +std::ostream& operator << (std::ostream& out, const TickMeter& tm) +{ + return out << tm.getTimeSec() << "sec"; +} + +/** @brief Returns the number of CPU ticks. + +The function returns the current number of CPU ticks on some architectures (such as x86, x64, +PowerPC). On other platforms the function is equivalent to getTickCount. It can also be used for +very accurate time measurements, as well as for RNG initialization. Note that in case of multi-CPU +systems a thread, from which getCPUTickCount is called, can be suspended and resumed at another CPU +with its own counter. So, theoretically (and practically) the subsequent calls to the function do +not necessary return the monotonously increasing values. Also, since a modern CPU varies the CPU +frequency depending on the load, the number of CPU clocks spent in some code cannot be directly +converted to time units. Therefore, getTickCount is generally a preferable solution for measuring +execution time. + */ +CV_EXPORTS_W int64 getCPUTickCount(); + +/** @brief Returns true if the specified feature is supported by the host hardware. + +The function returns true if the host hardware supports the specified feature. When user calls +setUseOptimized(false), the subsequent calls to checkHardwareSupport() will return false until +setUseOptimized(true) is called. This way user can dynamically switch on and off the optimized code +in OpenCV. +@param feature The feature of interest, one of cv::CpuFeatures + */ +CV_EXPORTS_W bool checkHardwareSupport(int feature); + +/** @brief Returns feature name by ID + +Returns empty string if feature is not defined +*/ +CV_EXPORTS_W String getHardwareFeatureName(int feature); + +/** @brief Returns list of CPU features enabled during compilation. + +Returned value is a string containing space separated list of CPU features with following markers: + +- no markers - baseline features +- prefix `*` - features enabled in dispatcher +- suffix `?` - features enabled but not available in HW + +Example: `SSE SSE2 SSE3 *SSE4.1 *SSE4.2 *FP16 *AVX *AVX2 *AVX512-SKX?` +*/ +CV_EXPORTS std::string getCPUFeaturesLine(); + +/** @brief Returns the number of logical CPUs available for the process. + */ +CV_EXPORTS_W int getNumberOfCPUs(); + + +/** @brief Aligns a pointer to the specified number of bytes. + +The function returns the aligned pointer of the same type as the input pointer: +\f[\texttt{(_Tp*)(((size_t)ptr + n-1) & -n)}\f] +@param ptr Aligned pointer. +@param n Alignment size that must be a power of two. + */ +template static inline _Tp* alignPtr(_Tp* ptr, int n=(int)sizeof(_Tp)) +{ + CV_DbgAssert((n & (n - 1)) == 0); // n is a power of 2 + return (_Tp*)(((size_t)ptr + n-1) & -n); +} + +/** @brief Aligns a buffer size to the specified number of bytes. + +The function returns the minimum number that is greater than or equal to sz and is divisible by n : +\f[\texttt{(sz + n-1) & -n}\f] +@param sz Buffer size to align. +@param n Alignment size that must be a power of two. + */ +static inline size_t alignSize(size_t sz, int n) +{ + CV_DbgAssert((n & (n - 1)) == 0); // n is a power of 2 + return (sz + n-1) & -n; +} + +/** @brief Integer division with result round up. + +Use this function instead of `ceil((float)a / b)` expressions. + +@sa alignSize +*/ +static inline int divUp(int a, unsigned int b) +{ + CV_DbgAssert(a >= 0); + return (a + b - 1) / b; +} +/** @overload */ +static inline size_t divUp(size_t a, unsigned int b) +{ + return (a + b - 1) / b; +} + +/** @brief Round first value up to the nearest multiple of second value. + +Use this function instead of `ceil((float)a / b) * b` expressions. + +@sa divUp +*/ +static inline int roundUp(int a, unsigned int b) +{ + CV_DbgAssert(a >= 0); + return a + b - 1 - (a + b -1) % b; +} +/** @overload */ +static inline size_t roundUp(size_t a, unsigned int b) +{ + return a + b - 1 - (a + b - 1) % b; +} + +/** @brief Enables or disables the optimized code. + +The function can be used to dynamically turn on and off optimized dispatched code (code that uses SSE4.2, AVX/AVX2, +and other instructions on the platforms that support it). It sets a global flag that is further +checked by OpenCV functions. Since the flag is not checked in the inner OpenCV loops, it is only +safe to call the function on the very top level in your application where you can be sure that no +other OpenCV function is currently executed. + +By default, the optimized code is enabled unless you disable it in CMake. The current status can be +retrieved using useOptimized. +@param onoff The boolean flag specifying whether the optimized code should be used (onoff=true) +or not (onoff=false). + */ +CV_EXPORTS_W void setUseOptimized(bool onoff); + +/** @brief Returns the status of optimized code usage. + +The function returns true if the optimized code is enabled. Otherwise, it returns false. + */ +CV_EXPORTS_W bool useOptimized(); + +static inline size_t getElemSize(int type) { return (size_t)CV_ELEM_SIZE(type); } + +/////////////////////////////// Parallel Primitives ////////////////////////////////// + +/** @brief Base class for parallel data processors +*/ +class CV_EXPORTS ParallelLoopBody +{ +public: + virtual ~ParallelLoopBody(); + virtual void operator() (const Range& range) const = 0; +}; + +/** @brief Parallel data processor +*/ +CV_EXPORTS void parallel_for_(const Range& range, const ParallelLoopBody& body, double nstripes=-1.); + +#ifdef CV_CXX11 +class ParallelLoopBodyLambdaWrapper : public ParallelLoopBody +{ +private: + std::function m_functor; +public: + ParallelLoopBodyLambdaWrapper(std::function functor) : + m_functor(functor) + { } + + virtual void operator() (const cv::Range& range) const CV_OVERRIDE + { + m_functor(range); + } +}; + +inline void parallel_for_(const Range& range, std::function functor, double nstripes=-1.) +{ + parallel_for_(range, ParallelLoopBodyLambdaWrapper(functor), nstripes); +} +#endif + +/////////////////////////////// forEach method of cv::Mat //////////////////////////// +template inline +void Mat::forEach_impl(const Functor& operation) { + if (false) { + operation(*reinterpret_cast<_Tp*>(0), reinterpret_cast(0)); + // If your compiler fails in this line. + // Please check that your functor signature is + // (_Tp&, const int*) <- multi-dimensional + // or (_Tp&, void*) <- in case you don't need current idx. + } + + CV_Assert(this->total() / this->size[this->dims - 1] <= INT_MAX); + const int LINES = static_cast(this->total() / this->size[this->dims - 1]); + + class PixelOperationWrapper :public ParallelLoopBody + { + public: + PixelOperationWrapper(Mat_<_Tp>* const frame, const Functor& _operation) + : mat(frame), op(_operation) {} + virtual ~PixelOperationWrapper(){} + // ! Overloaded virtual operator + // convert range call to row call. + virtual void operator()(const Range &range) const CV_OVERRIDE + { + const int DIMS = mat->dims; + const int COLS = mat->size[DIMS - 1]; + if (DIMS <= 2) { + for (int row = range.start; row < range.end; ++row) { + this->rowCall2(row, COLS); + } + } else { + std::vector idx(DIMS); /// idx is modified in this->rowCall + idx[DIMS - 2] = range.start - 1; + + for (int line_num = range.start; line_num < range.end; ++line_num) { + idx[DIMS - 2]++; + for (int i = DIMS - 2; i >= 0; --i) { + if (idx[i] >= mat->size[i]) { + idx[i - 1] += idx[i] / mat->size[i]; + idx[i] %= mat->size[i]; + continue; // carry-over; + } + else { + break; + } + } + this->rowCall(&idx[0], COLS, DIMS); + } + } + } + private: + Mat_<_Tp>* const mat; + const Functor op; + // ! Call operator for each elements in this row. + inline void rowCall(int* const idx, const int COLS, const int DIMS) const { + int &col = idx[DIMS - 1]; + col = 0; + _Tp* pixel = &(mat->template at<_Tp>(idx)); + + while (col < COLS) { + op(*pixel, const_cast(idx)); + pixel++; col++; + } + col = 0; + } + // ! Call operator for each elements in this row. 2d mat special version. + inline void rowCall2(const int row, const int COLS) const { + union Index{ + int body[2]; + operator const int*() const { + return reinterpret_cast(this); + } + int& operator[](const int i) { + return body[i]; + } + } idx = {{row, 0}}; + // Special union is needed to avoid + // "error: array subscript is above array bounds [-Werror=array-bounds]" + // when call the functor `op` such that access idx[3]. + + _Tp* pixel = &(mat->template at<_Tp>(idx)); + const _Tp* const pixel_end = pixel + COLS; + while(pixel < pixel_end) { + op(*pixel++, static_cast(idx)); + idx[1]++; + } + } + PixelOperationWrapper& operator=(const PixelOperationWrapper &) { + CV_Assert(false); + // We can not remove this implementation because Visual Studio warning C4822. + return *this; + } + }; + + parallel_for_(cv::Range(0, LINES), PixelOperationWrapper(reinterpret_cast*>(this), operation)); +} + +/////////////////////////// Synchronization Primitives /////////////////////////////// + +class CV_EXPORTS Mutex +{ +public: + Mutex(); + ~Mutex(); + Mutex(const Mutex& m); + Mutex& operator = (const Mutex& m); + + void lock(); + bool trylock(); + void unlock(); + + struct Impl; +protected: + Impl* impl; +}; + +class CV_EXPORTS AutoLock +{ +public: + AutoLock(Mutex& m) : mutex(&m) { mutex->lock(); } + ~AutoLock() { mutex->unlock(); } +protected: + Mutex* mutex; +private: + AutoLock(const AutoLock&); + AutoLock& operator = (const AutoLock&); +}; + +// TLS interface +class CV_EXPORTS TLSDataContainer +{ +protected: + TLSDataContainer(); + virtual ~TLSDataContainer(); + + void gatherData(std::vector &data) const; +#if OPENCV_ABI_COMPATIBILITY > 300 + void* getData() const; + void release(); + +private: +#else + void release(); + +public: + void* getData() const; +#endif + virtual void* createDataInstance() const = 0; + virtual void deleteDataInstance(void* pData) const = 0; + + int key_; + +public: + void cleanup(); //! Release created TLS data container objects. It is similar to release() call, but it keeps TLS container valid. +}; + +// Main TLS data class +template +class TLSData : protected TLSDataContainer +{ +public: + inline TLSData() {} + inline ~TLSData() { release(); } // Release key and delete associated data + inline T* get() const { return (T*)getData(); } // Get data associated with key + inline T& getRef() const { T* ptr = (T*)getData(); CV_Assert(ptr); return *ptr; } // Get data associated with key + + // Get data from all threads + inline void gather(std::vector &data) const + { + std::vector &dataVoid = reinterpret_cast&>(data); + gatherData(dataVoid); + } + + inline void cleanup() { TLSDataContainer::cleanup(); } + +private: + virtual void* createDataInstance() const CV_OVERRIDE {return new T;} // Wrapper to allocate data by template + virtual void deleteDataInstance(void* pData) const CV_OVERRIDE {delete (T*)pData;} // Wrapper to release data by template + + // Disable TLS copy operations + TLSData(TLSData &) {} + TLSData& operator =(const TLSData &) {return *this;} +}; + +/** @brief Designed for command line parsing + +The sample below demonstrates how to use CommandLineParser: +@code + CommandLineParser parser(argc, argv, keys); + parser.about("Application name v1.0.0"); + + if (parser.has("help")) + { + parser.printMessage(); + return 0; + } + + int N = parser.get("N"); + double fps = parser.get("fps"); + String path = parser.get("path"); + + use_time_stamp = parser.has("timestamp"); + + String img1 = parser.get(0); + String img2 = parser.get(1); + + int repeat = parser.get(2); + + if (!parser.check()) + { + parser.printErrors(); + return 0; + } +@endcode + +### Keys syntax + +The keys parameter is a string containing several blocks, each one is enclosed in curly braces and +describes one argument. Each argument contains three parts separated by the `|` symbol: + +-# argument names is a space-separated list of option synonyms (to mark argument as positional, prefix it with the `@` symbol) +-# default value will be used if the argument was not provided (can be empty) +-# help message (can be empty) + +For example: + +@code{.cpp} + const String keys = + "{help h usage ? | | print this message }" + "{@image1 | | image1 for compare }" + "{@image2 || image2 for compare }" + "{@repeat |1 | number }" + "{path |. | path to file }" + "{fps | -1.0 | fps for output video }" + "{N count |100 | count of objects }" + "{ts timestamp | | use time stamp }" + ; +} +@endcode + +Note that there are no default values for `help` and `timestamp` so we can check their presence using the `has()` method. +Arguments with default values are considered to be always present. Use the `get()` method in these cases to check their +actual value instead. + +String keys like `get("@image1")` return the empty string `""` by default - even with an empty default value. +Use the special `` default value to enforce that the returned string must not be empty. (like in `get("@image2")`) + +### Usage + +For the described keys: + +@code{.sh} + # Good call (3 positional parameters: image1, image2 and repeat; N is 200, ts is true) + $ ./app -N=200 1.png 2.jpg 19 -ts + + # Bad call + $ ./app -fps=aaa + ERRORS: + Parameter 'fps': can not convert: [aaa] to [double] +@endcode + */ +class CV_EXPORTS CommandLineParser +{ +public: + + /** @brief Constructor + + Initializes command line parser object + + @param argc number of command line arguments (from main()) + @param argv array of command line arguments (from main()) + @param keys string describing acceptable command line parameters (see class description for syntax) + */ + CommandLineParser(int argc, const char* const argv[], const String& keys); + + /** @brief Copy constructor */ + CommandLineParser(const CommandLineParser& parser); + + /** @brief Assignment operator */ + CommandLineParser& operator = (const CommandLineParser& parser); + + /** @brief Destructor */ + ~CommandLineParser(); + + /** @brief Returns application path + + This method returns the path to the executable from the command line (`argv[0]`). + + For example, if the application has been started with such a command: + @code{.sh} + $ ./bin/my-executable + @endcode + this method will return `./bin`. + */ + String getPathToApplication() const; + + /** @brief Access arguments by name + + Returns argument converted to selected type. If the argument is not known or can not be + converted to selected type, the error flag is set (can be checked with @ref check). + + For example, define: + @code{.cpp} + String keys = "{N count||}"; + @endcode + + Call: + @code{.sh} + $ ./my-app -N=20 + # or + $ ./my-app --count=20 + @endcode + + Access: + @code{.cpp} + int N = parser.get("N"); + @endcode + + @param name name of the argument + @param space_delete remove spaces from the left and right of the string + @tparam T the argument will be converted to this type if possible + + @note You can access positional arguments by their `@`-prefixed name: + @code{.cpp} + parser.get("@image"); + @endcode + */ + template + T get(const String& name, bool space_delete = true) const + { + T val = T(); + getByName(name, space_delete, ParamType::type, (void*)&val); + return val; + } + + /** @brief Access positional arguments by index + + Returns argument converted to selected type. Indexes are counted from zero. + + For example, define: + @code{.cpp} + String keys = "{@arg1||}{@arg2||}" + @endcode + + Call: + @code{.sh} + ./my-app abc qwe + @endcode + + Access arguments: + @code{.cpp} + String val_1 = parser.get(0); // returns "abc", arg1 + String val_2 = parser.get(1); // returns "qwe", arg2 + @endcode + + @param index index of the argument + @param space_delete remove spaces from the left and right of the string + @tparam T the argument will be converted to this type if possible + */ + template + T get(int index, bool space_delete = true) const + { + T val = T(); + getByIndex(index, space_delete, ParamType::type, (void*)&val); + return val; + } + + /** @brief Check if field was provided in the command line + + @param name argument name to check + */ + bool has(const String& name) const; + + /** @brief Check for parsing errors + + Returns false if error occurred while accessing the parameters (bad conversion, missing arguments, + etc.). Call @ref printErrors to print error messages list. + */ + bool check() const; + + /** @brief Set the about message + + The about message will be shown when @ref printMessage is called, right before arguments table. + */ + void about(const String& message); + + /** @brief Print help message + + This method will print standard help message containing the about message and arguments description. + + @sa about + */ + void printMessage() const; + + /** @brief Print list of errors occurred + + @sa check + */ + void printErrors() const; + +protected: + void getByName(const String& name, bool space_delete, int type, void* dst) const; + void getByIndex(int index, bool space_delete, int type, void* dst) const; + + struct Impl; + Impl* impl; +}; + +//! @} core_utils + +//! @cond IGNORED + +/////////////////////////////// AutoBuffer implementation //////////////////////////////////////// + +template inline +AutoBuffer<_Tp, fixed_size>::AutoBuffer() +{ + ptr = buf; + sz = fixed_size; +} + +template inline +AutoBuffer<_Tp, fixed_size>::AutoBuffer(size_t _size) +{ + ptr = buf; + sz = fixed_size; + allocate(_size); +} + +template inline +AutoBuffer<_Tp, fixed_size>::AutoBuffer(const AutoBuffer<_Tp, fixed_size>& abuf ) +{ + ptr = buf; + sz = fixed_size; + allocate(abuf.size()); + for( size_t i = 0; i < sz; i++ ) + ptr[i] = abuf.ptr[i]; +} + +template inline AutoBuffer<_Tp, fixed_size>& +AutoBuffer<_Tp, fixed_size>::operator = (const AutoBuffer<_Tp, fixed_size>& abuf) +{ + if( this != &abuf ) + { + deallocate(); + allocate(abuf.size()); + for( size_t i = 0; i < sz; i++ ) + ptr[i] = abuf.ptr[i]; + } + return *this; +} + +template inline +AutoBuffer<_Tp, fixed_size>::~AutoBuffer() +{ deallocate(); } + +template inline void +AutoBuffer<_Tp, fixed_size>::allocate(size_t _size) +{ + if(_size <= sz) + { + sz = _size; + return; + } + deallocate(); + sz = _size; + if(_size > fixed_size) + { + ptr = new _Tp[_size]; + } +} + +template inline void +AutoBuffer<_Tp, fixed_size>::deallocate() +{ + if( ptr != buf ) + { + delete[] ptr; + ptr = buf; + sz = fixed_size; + } +} + +template inline void +AutoBuffer<_Tp, fixed_size>::resize(size_t _size) +{ + if(_size <= sz) + { + sz = _size; + return; + } + size_t i, prevsize = sz, minsize = MIN(prevsize, _size); + _Tp* prevptr = ptr; + + ptr = _size > fixed_size ? new _Tp[_size] : buf; + sz = _size; + + if( ptr != prevptr ) + for( i = 0; i < minsize; i++ ) + ptr[i] = prevptr[i]; + for( i = prevsize; i < _size; i++ ) + ptr[i] = _Tp(); + + if( prevptr != buf ) + delete[] prevptr; +} + +template inline size_t +AutoBuffer<_Tp, fixed_size>::size() const +{ return sz; } + +template<> inline std::string CommandLineParser::get(int index, bool space_delete) const +{ + return get(index, space_delete); +} +template<> inline std::string CommandLineParser::get(const String& name, bool space_delete) const +{ + return get(name, space_delete); +} + +//! @endcond + + +// Basic Node class for tree building +template +class CV_EXPORTS Node +{ +public: + Node() + { + m_pParent = 0; + } + Node(OBJECT& payload) : m_payload(payload) + { + m_pParent = 0; + } + ~Node() + { + removeChilds(); + if (m_pParent) + { + int idx = m_pParent->findChild(this); + if (idx >= 0) + m_pParent->m_childs.erase(m_pParent->m_childs.begin() + idx); + } + } + + Node* findChild(OBJECT& payload) const + { + for(size_t i = 0; i < this->m_childs.size(); i++) + { + if(this->m_childs[i]->m_payload == payload) + return this->m_childs[i]; + } + return NULL; + } + + int findChild(Node *pNode) const + { + for (size_t i = 0; i < this->m_childs.size(); i++) + { + if(this->m_childs[i] == pNode) + return (int)i; + } + return -1; + } + + void addChild(Node *pNode) + { + if(!pNode) + return; + + CV_Assert(pNode->m_pParent == 0); + pNode->m_pParent = this; + this->m_childs.push_back(pNode); + } + + void removeChilds() + { + for(size_t i = 0; i < m_childs.size(); i++) + { + m_childs[i]->m_pParent = 0; // avoid excessive parent vector trimming + delete m_childs[i]; + } + m_childs.clear(); + } + + int getDepth() + { + int count = 0; + Node *pParent = m_pParent; + while(pParent) count++, pParent = pParent->m_pParent; + return count; + } + +public: + OBJECT m_payload; + Node* m_pParent; + std::vector*> m_childs; +}; + +// Instrumentation external interface +namespace instr +{ + +#if !defined OPENCV_ABI_CHECK + +enum TYPE +{ + TYPE_GENERAL = 0, // OpenCV API function, e.g. exported function + TYPE_MARKER, // Information marker + TYPE_WRAPPER, // Wrapper function for implementation + TYPE_FUN, // Simple function call +}; + +enum IMPL +{ + IMPL_PLAIN = 0, + IMPL_IPP, + IMPL_OPENCL, +}; + +struct NodeDataTls +{ + NodeDataTls() + { + m_ticksTotal = 0; + } + uint64 m_ticksTotal; +}; + +class CV_EXPORTS NodeData +{ +public: + NodeData(const char* funName = 0, const char* fileName = NULL, int lineNum = 0, void* retAddress = NULL, bool alwaysExpand = false, cv::instr::TYPE instrType = TYPE_GENERAL, cv::instr::IMPL implType = IMPL_PLAIN); + NodeData(NodeData &ref); + ~NodeData(); + NodeData& operator=(const NodeData&); + + cv::String m_funName; + cv::instr::TYPE m_instrType; + cv::instr::IMPL m_implType; + const char* m_fileName; + int m_lineNum; + void* m_retAddress; + bool m_alwaysExpand; + bool m_funError; + + volatile int m_counter; + volatile uint64 m_ticksTotal; + TLSData m_tls; + int m_threads; + + // No synchronization + double getTotalMs() const { return ((double)m_ticksTotal / cv::getTickFrequency()) * 1000; } + double getMeanMs() const { return (((double)m_ticksTotal/m_counter) / cv::getTickFrequency()) * 1000; } +}; +bool operator==(const NodeData& lhs, const NodeData& rhs); + +typedef Node InstrNode; + +CV_EXPORTS InstrNode* getTrace(); + +#endif // !defined OPENCV_ABI_CHECK + + +CV_EXPORTS bool useInstrumentation(); +CV_EXPORTS void setUseInstrumentation(bool flag); +CV_EXPORTS void resetTrace(); + +enum FLAGS +{ + FLAGS_NONE = 0, + FLAGS_MAPPING = 0x01, + FLAGS_EXPAND_SAME_NAMES = 0x02, +}; + +CV_EXPORTS void setFlags(FLAGS modeFlags); +static inline void setFlags(int modeFlags) { setFlags((FLAGS)modeFlags); } +CV_EXPORTS FLAGS getFlags(); + +} // namespace instr + + +namespace samples { + +//! @addtogroup core_utils_samples +// This section describes utility functions for OpenCV samples. +// +// @note Implementation of these utilities is not thread-safe. +// +//! @{ + +/** @brief Try to find requested data file + +Search directories: + +1. Directories passed via `addSamplesDataSearchPath()` +2. OPENCV_SAMPLES_DATA_PATH_HINT environment variable +3. OPENCV_SAMPLES_DATA_PATH environment variable + If parameter value is not empty and nothing is found then stop searching. +4. Detects build/install path based on: + a. current working directory (CWD) + b. and/or binary module location (opencv_core/opencv_world, doesn't work with static linkage) +5. Scan `/{,data,samples/data}` directories if build directory is detected or the current directory is in source tree. +6. Scan `/share/OpenCV` directory if install directory is detected. + +@see cv::utils::findDataFile + +@param relative_path Relative path to data file +@param required Specify "file not found" handling. + If true, function prints information message and raises cv::Exception. + If false, function returns empty result +@param silentMode Disables messages +@return Returns path (absolute or relative to the current directory) or empty string if file is not found +*/ +CV_EXPORTS_W cv::String findFile(const cv::String& relative_path, bool required = true, bool silentMode = false); + +CV_EXPORTS_W cv::String findFileOrKeep(const cv::String& relative_path, bool silentMode = false); + +inline cv::String findFileOrKeep(const cv::String& relative_path, bool silentMode) +{ + cv::String res = findFile(relative_path, false, silentMode); + if (res.empty()) + return relative_path; + return res; +} + +/** @brief Override search data path by adding new search location + +Use this only to override default behavior +Passed paths are used in LIFO order. + +@param path Path to used samples data +*/ +CV_EXPORTS_W void addSamplesDataSearchPath(const cv::String& path); + +/** @brief Append samples search data sub directory + +General usage is to add OpenCV modules name (`/modules//samples/data` -> `/samples/data` + `modules//samples/data`). +Passed subdirectories are used in LIFO order. + +@param subdir samples data sub directory +*/ +CV_EXPORTS_W void addSamplesDataSearchSubDirectory(const cv::String& subdir); + +//! @} +} // namespace samples + +namespace utils { + +CV_EXPORTS int getThreadID(); + +} // namespace + +} //namespace cv + +#ifndef DISABLE_OPENCV_24_COMPATIBILITY +#include "opencv2/core/core_c.h" +#endif + +#endif //OPENCV_CORE_UTILITY_H diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/utils/filesystem.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/utils/filesystem.hpp new file mode 100644 index 0000000000000000000000000000000000000000..00b0dd1c123e2e3bfa80f9372ecd47502eb19875 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/utils/filesystem.hpp @@ -0,0 +1,78 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#ifndef OPENCV_UTILS_FILESYSTEM_HPP +#define OPENCV_UTILS_FILESYSTEM_HPP + +namespace cv { namespace utils { namespace fs { + + +CV_EXPORTS bool exists(const cv::String& path); +CV_EXPORTS bool isDirectory(const cv::String& path); + +CV_EXPORTS void remove_all(const cv::String& path); + + +CV_EXPORTS cv::String getcwd(); + +/** @brief Converts path p to a canonical absolute path + * Symlinks are processed if there is support for them on running platform. + * + * @param path input path. Target file/directory should exist. + */ +CV_EXPORTS cv::String canonical(const cv::String& path); + +/** Join path components */ +CV_EXPORTS cv::String join(const cv::String& base, const cv::String& path); + +/** + * Generate a list of all files that match the globbing pattern. + * + * Result entries are prefixed by base directory path. + * + * @param directory base directory + * @param pattern filter pattern (based on '*'/'?' symbols). Use empty string to disable filtering and return all results + * @param[out] result result of globing. + * @param recursive scan nested directories too + * @param includeDirectories include directories into results list + */ +CV_EXPORTS void glob(const cv::String& directory, const cv::String& pattern, + CV_OUT std::vector& result, + bool recursive = false, bool includeDirectories = false); + +/** + * Generate a list of all files that match the globbing pattern. + * + * @param directory base directory + * @param pattern filter pattern (based on '*'/'?' symbols). Use empty string to disable filtering and return all results + * @param[out] result globbing result with relative paths from base directory + * @param recursive scan nested directories too + * @param includeDirectories include directories into results list + */ +CV_EXPORTS void glob_relative(const cv::String& directory, const cv::String& pattern, + CV_OUT std::vector& result, + bool recursive = false, bool includeDirectories = false); + + +CV_EXPORTS bool createDirectory(const cv::String& path); +CV_EXPORTS bool createDirectories(const cv::String& path); + +#ifdef __OPENCV_BUILD +// TODO +//CV_EXPORTS cv::String getTempDirectory(); + +/** + * @brief Returns directory to store OpenCV cache files + * Create sub-directory in common OpenCV cache directory if it doesn't exist. + * @param sub_directory_name name of sub-directory. NULL or "" value asks to return root cache directory. + * @param configuration_name optional name of configuration parameter name which overrides default behavior. + * @return Path to cache directory. Returns empty string if cache directories support is not available. Returns "disabled" if cache disabled by user. + */ +CV_EXPORTS cv::String getCacheDirectory(const char* sub_directory_name, const char* configuration_name = NULL); + +#endif + +}}} // namespace + +#endif // OPENCV_UTILS_FILESYSTEM_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/utils/logger.defines.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/utils/logger.defines.hpp new file mode 100644 index 0000000000000000000000000000000000000000..b2dfc417045f69e7586b212d3280655d11b29f57 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/utils/logger.defines.hpp @@ -0,0 +1,22 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#ifndef OPENCV_LOGGER_DEFINES_HPP +#define OPENCV_LOGGER_DEFINES_HPP + +//! @addtogroup core_logging +//! @{ + +// Supported logging levels and their semantic +#define CV_LOG_LEVEL_SILENT 0 //!< for using in setLogLevel() call +#define CV_LOG_LEVEL_FATAL 1 //!< Fatal (critical) error (unrecoverable internal error) +#define CV_LOG_LEVEL_ERROR 2 //!< Error message +#define CV_LOG_LEVEL_WARN 3 //!< Warning message +#define CV_LOG_LEVEL_INFO 4 //!< Info message +#define CV_LOG_LEVEL_DEBUG 5 //!< Debug message. Disabled in the "Release" build. +#define CV_LOG_LEVEL_VERBOSE 6 //!< Verbose (trace) messages. Requires verbosity level. Disabled in the "Release" build. + +//! @} + +#endif // OPENCV_LOGGER_DEFINES_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/utils/logger.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/utils/logger.hpp new file mode 100644 index 0000000000000000000000000000000000000000..47094f994d09e21c50611f6629f45a7ced042082 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/utils/logger.hpp @@ -0,0 +1,87 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#ifndef OPENCV_LOGGER_HPP +#define OPENCV_LOGGER_HPP + +#include +#include +#include // INT_MAX + +#include "logger.defines.hpp" + +//! @addtogroup core_logging +// This section describes OpenCV logging utilities. +// +//! @{ + +namespace cv { +namespace utils { +namespace logging { + +//! Supported logging levels and their semantic +enum LogLevel { + LOG_LEVEL_SILENT = 0, //!< for using in setLogVevel() call + LOG_LEVEL_FATAL = 1, //!< Fatal (critical) error (unrecoverable internal error) + LOG_LEVEL_ERROR = 2, //!< Error message + LOG_LEVEL_WARNING = 3, //!< Warning message + LOG_LEVEL_INFO = 4, //!< Info message + LOG_LEVEL_DEBUG = 5, //!< Debug message. Disabled in the "Release" build. + LOG_LEVEL_VERBOSE = 6, //!< Verbose (trace) messages. Requires verbosity level. Disabled in the "Release" build. +#ifndef CV_DOXYGEN + ENUM_LOG_LEVEL_FORCE_INT = INT_MAX +#endif +}; + +/** Set global logging level +@return previous logging level +*/ +CV_EXPORTS LogLevel setLogLevel(LogLevel logLevel); +/** Get global logging level */ +CV_EXPORTS LogLevel getLogLevel(); + +namespace internal { +/** Write log message */ +CV_EXPORTS void writeLogMessage(LogLevel logLevel, const char* message); +} // namespace + +/** + * \def CV_LOG_STRIP_LEVEL + * + * Define CV_LOG_STRIP_LEVEL=CV_LOG_LEVEL_[DEBUG|INFO|WARN|ERROR|FATAL|DISABLED] to compile out anything at that and before that logging level + */ +#ifndef CV_LOG_STRIP_LEVEL +# if defined NDEBUG +# define CV_LOG_STRIP_LEVEL CV_LOG_LEVEL_DEBUG +# else +# define CV_LOG_STRIP_LEVEL CV_LOG_LEVEL_VERBOSE +# endif +#endif + + +#define CV_LOG_FATAL(tag, ...) for(;;) { if (cv::utils::logging::getLogLevel() < cv::utils::logging::LOG_LEVEL_FATAL) break; std::stringstream ss; ss << __VA_ARGS__; cv::utils::logging::internal::writeLogMessage(cv::utils::logging::LOG_LEVEL_FATAL, ss.str().c_str()); break; } +#define CV_LOG_ERROR(tag, ...) for(;;) { if (cv::utils::logging::getLogLevel() < cv::utils::logging::LOG_LEVEL_ERROR) break; std::stringstream ss; ss << __VA_ARGS__; cv::utils::logging::internal::writeLogMessage(cv::utils::logging::LOG_LEVEL_ERROR, ss.str().c_str()); break; } +#define CV_LOG_WARNING(tag, ...) for(;;) { if (cv::utils::logging::getLogLevel() < cv::utils::logging::LOG_LEVEL_WARNING) break; std::stringstream ss; ss << __VA_ARGS__; cv::utils::logging::internal::writeLogMessage(cv::utils::logging::LOG_LEVEL_WARNING, ss.str().c_str()); break; } +#if CV_LOG_STRIP_LEVEL <= CV_LOG_LEVEL_INFO +#define CV_LOG_INFO(tag, ...) +#else +#define CV_LOG_INFO(tag, ...) for(;;) { if (cv::utils::logging::getLogLevel() < cv::utils::logging::LOG_LEVEL_INFO) break; std::stringstream ss; ss << __VA_ARGS__; cv::utils::logging::internal::writeLogMessage(cv::utils::logging::LOG_LEVEL_INFO, ss.str().c_str()); break; } +#endif +#if CV_LOG_STRIP_LEVEL <= CV_LOG_LEVEL_DEBUG +#define CV_LOG_DEBUG(tag, ...) +#else +#define CV_LOG_DEBUG(tag, ...) for(;;) { if (cv::utils::logging::getLogLevel() < cv::utils::logging::LOG_LEVEL_DEBUG) break; std::stringstream ss; ss << __VA_ARGS__; cv::utils::logging::internal::writeLogMessage(cv::utils::logging::LOG_LEVEL_DEBUG, ss.str().c_str()); break; } +#endif +#if CV_LOG_STRIP_LEVEL <= CV_LOG_LEVEL_VERBOSE +#define CV_LOG_VERBOSE(tag, v, ...) +#else +#define CV_LOG_VERBOSE(tag, v, ...) for(;;) { if (cv::utils::logging::getLogLevel() < cv::utils::logging::LOG_LEVEL_VERBOSE) break; std::stringstream ss; ss << "[VERB" << v << ":" << cv::utils::getThreadID() << "] " << __VA_ARGS__; cv::utils::logging::internal::writeLogMessage(cv::utils::logging::LOG_LEVEL_VERBOSE, ss.str().c_str()); break; } +#endif + + +}}} // namespace + +//! @} + +#endif // OPENCV_LOGGER_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/utils/trace.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/utils/trace.hpp new file mode 100644 index 0000000000000000000000000000000000000000..858e973a831370c17b87e3b9550b3b9a6c4873c3 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/utils/trace.hpp @@ -0,0 +1,254 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#ifndef OPENCV_TRACE_HPP +#define OPENCV_TRACE_HPP + +#include + +//! @addtogroup core_logging +// This section describes OpenCV tracing utilities. +// +//! @{ + +namespace cv { +namespace utils { +namespace trace { + +//! Macro to trace function +#define CV_TRACE_FUNCTION() + +#define CV_TRACE_FUNCTION_SKIP_NESTED() + +//! Trace code scope. +//! @note Dynamic names are not supported in this macro (on stack or heap). Use string literals here only, like "initialize". +#define CV_TRACE_REGION(name_as_static_string_literal) +//! mark completed of the current opened region and create new one +//! @note Dynamic names are not supported in this macro (on stack or heap). Use string literals here only, like "step1". +#define CV_TRACE_REGION_NEXT(name_as_static_string_literal) + +//! Macro to trace argument value +#define CV_TRACE_ARG(arg_id) + +//! Macro to trace argument value (expanded version) +#define CV_TRACE_ARG_VALUE(arg_id, arg_name, value) + +//! @cond IGNORED +#define CV_TRACE_NS cv::utils::trace + +#if !defined(OPENCV_DISABLE_TRACE) && defined(__EMSCRIPTEN__) +#define OPENCV_DISABLE_TRACE 1 +#endif + +namespace details { + +#ifndef __OPENCV_TRACE +# if defined __OPENCV_BUILD && !defined __OPENCV_TESTS && !defined __OPENCV_APPS +# define __OPENCV_TRACE 1 +# else +# define __OPENCV_TRACE 0 +# endif +#endif + +#ifndef CV_TRACE_FILENAME +# define CV_TRACE_FILENAME __FILE__ +#endif + +#ifndef CV__TRACE_FUNCTION +# if defined _MSC_VER +# define CV__TRACE_FUNCTION __FUNCSIG__ +# elif defined __GNUC__ +# define CV__TRACE_FUNCTION __PRETTY_FUNCTION__ +# else +# define CV__TRACE_FUNCTION "" +# endif +#endif + +//! Thread-local instance (usually allocated on stack) +class CV_EXPORTS Region +{ +public: + struct LocationExtraData; + struct LocationStaticStorage + { + LocationExtraData** ppExtra; //< implementation specific data + const char* name; //< region name (function name or other custom name) + const char* filename; //< source code filename + int line; //< source code line + int flags; //< flags (implementation code path: Plain, IPP, OpenCL) + }; + + Region(const LocationStaticStorage& location); + inline ~Region() + { + if (implFlags != 0) + destroy(); + CV_DbgAssert(implFlags == 0); + CV_DbgAssert(pImpl == NULL); + } + + class Impl; + Impl* pImpl; // NULL if current region is not active + int implFlags; // see RegionFlag, 0 if region is ignored + + bool isActive() const { return pImpl != NULL; } + + void destroy(); +private: + Region(const Region&); // disabled + Region& operator= (const Region&); // disabled +}; + +//! Specify region flags +enum RegionLocationFlag { + REGION_FLAG_FUNCTION = (1 << 0), //< region is function (=1) / nested named region (=0) + REGION_FLAG_APP_CODE = (1 << 1), //< region is Application code (=1) / OpenCV library code (=0) + REGION_FLAG_SKIP_NESTED = (1 << 2), //< avoid processing of nested regions + + REGION_FLAG_IMPL_IPP = (1 << 16), //< region is part of IPP code path + REGION_FLAG_IMPL_OPENCL = (2 << 16), //< region is part of OpenCL code path + REGION_FLAG_IMPL_OPENVX = (3 << 16), //< region is part of OpenVX code path + + REGION_FLAG_IMPL_MASK = (15 << 16), + + REGION_FLAG_REGION_FORCE = (1 << 30), + REGION_FLAG_REGION_NEXT = (1 << 31), //< close previous region (see #CV_TRACE_REGION_NEXT macro) + + ENUM_REGION_FLAG_FORCE_INT = INT_MAX +}; + +struct CV_EXPORTS TraceArg { +public: + struct ExtraData; + ExtraData** ppExtra; + const char* name; + int flags; +}; +/** @brief Add meta information to current region (function) + * See CV_TRACE_ARG macro + * @param arg argument information structure (global static cache) + * @param value argument value (can by dynamic string literal in case of string, static allocation is not required) + */ +CV_EXPORTS void traceArg(const TraceArg& arg, const char* value); +//! @overload +CV_EXPORTS void traceArg(const TraceArg& arg, int value); +//! @overload +CV_EXPORTS void traceArg(const TraceArg& arg, int64 value); +//! @overload +CV_EXPORTS void traceArg(const TraceArg& arg, double value); + +#define CV__TRACE_LOCATION_VARNAME(loc_id) CVAUX_CONCAT(CVAUX_CONCAT(__cv_trace_location_, loc_id), __LINE__) +#define CV__TRACE_LOCATION_EXTRA_VARNAME(loc_id) CVAUX_CONCAT(CVAUX_CONCAT(__cv_trace_location_extra_, loc_id) , __LINE__) + +#define CV__TRACE_DEFINE_LOCATION_(loc_id, name, flags) \ + static CV_TRACE_NS::details::Region::LocationExtraData* CV__TRACE_LOCATION_EXTRA_VARNAME(loc_id) = 0; \ + static const CV_TRACE_NS::details::Region::LocationStaticStorage \ + CV__TRACE_LOCATION_VARNAME(loc_id) = { &(CV__TRACE_LOCATION_EXTRA_VARNAME(loc_id)), name, CV_TRACE_FILENAME, __LINE__, flags}; + +#define CV__TRACE_DEFINE_LOCATION_FN(name, flags) CV__TRACE_DEFINE_LOCATION_(fn, name, ((flags) | CV_TRACE_NS::details::REGION_FLAG_FUNCTION)) + + +#define CV__TRACE_OPENCV_FUNCTION() \ + CV__TRACE_DEFINE_LOCATION_FN(CV__TRACE_FUNCTION, 0); \ + const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn)); + +#define CV__TRACE_OPENCV_FUNCTION_NAME(name) \ + CV__TRACE_DEFINE_LOCATION_FN(name, 0); \ + const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn)); + +#define CV__TRACE_APP_FUNCTION() \ + CV__TRACE_DEFINE_LOCATION_FN(CV__TRACE_FUNCTION, CV_TRACE_NS::details::REGION_FLAG_APP_CODE); \ + const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn)); + +#define CV__TRACE_APP_FUNCTION_NAME(name) \ + CV__TRACE_DEFINE_LOCATION_FN(name, CV_TRACE_NS::details::REGION_FLAG_APP_CODE); \ + const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn)); + + +#define CV__TRACE_OPENCV_FUNCTION_SKIP_NESTED() \ + CV__TRACE_DEFINE_LOCATION_FN(CV__TRACE_FUNCTION, CV_TRACE_NS::details::REGION_FLAG_SKIP_NESTED); \ + const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn)); + +#define CV__TRACE_OPENCV_FUNCTION_NAME_SKIP_NESTED(name) \ + CV__TRACE_DEFINE_LOCATION_FN(name, CV_TRACE_NS::details::REGION_FLAG_SKIP_NESTED); \ + const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn)); + +#define CV__TRACE_APP_FUNCTION_SKIP_NESTED() \ + CV__TRACE_DEFINE_LOCATION_FN(CV__TRACE_FUNCTION, CV_TRACE_NS::details::REGION_FLAG_SKIP_NESTED | CV_TRACE_NS::details::REGION_FLAG_APP_CODE); \ + const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn)); + + +#define CV__TRACE_REGION_(name_as_static_string_literal, flags) \ + CV__TRACE_DEFINE_LOCATION_(region, name_as_static_string_literal, flags); \ + CV_TRACE_NS::details::Region CVAUX_CONCAT(__region_, __LINE__)(CV__TRACE_LOCATION_VARNAME(region)); + +#define CV__TRACE_REGION(name_as_static_string_literal) CV__TRACE_REGION_(name_as_static_string_literal, 0) +#define CV__TRACE_REGION_NEXT(name_as_static_string_literal) CV__TRACE_REGION_(name_as_static_string_literal, CV_TRACE_NS::details::REGION_FLAG_REGION_NEXT) + +#define CV__TRACE_ARG_VARNAME(arg_id) CVAUX_CONCAT(__cv_trace_arg_ ## arg_id, __LINE__) +#define CV__TRACE_ARG_EXTRA_VARNAME(arg_id) CVAUX_CONCAT(__cv_trace_arg_extra_ ## arg_id, __LINE__) + +#define CV__TRACE_DEFINE_ARG_(arg_id, name, flags) \ + static CV_TRACE_NS::details::TraceArg::ExtraData* CV__TRACE_ARG_EXTRA_VARNAME(arg_id) = 0; \ + static const CV_TRACE_NS::details::TraceArg \ + CV__TRACE_ARG_VARNAME(arg_id) = { &(CV__TRACE_ARG_EXTRA_VARNAME(arg_id)), name, flags }; + +#define CV__TRACE_ARG_VALUE(arg_id, arg_name, value) \ + CV__TRACE_DEFINE_ARG_(arg_id, arg_name, 0); \ + CV_TRACE_NS::details::traceArg((CV__TRACE_ARG_VARNAME(arg_id)), value); + +#define CV__TRACE_ARG(arg_id) CV_TRACE_ARG_VALUE(arg_id, #arg_id, (arg_id)) + +} // namespace + +#ifndef OPENCV_DISABLE_TRACE +#undef CV_TRACE_FUNCTION +#undef CV_TRACE_FUNCTION_SKIP_NESTED +#if __OPENCV_TRACE +#define CV_TRACE_FUNCTION CV__TRACE_OPENCV_FUNCTION +#define CV_TRACE_FUNCTION_SKIP_NESTED CV__TRACE_OPENCV_FUNCTION_SKIP_NESTED +#else +#define CV_TRACE_FUNCTION CV__TRACE_APP_FUNCTION +#define CV_TRACE_FUNCTION_SKIP_NESTED CV__TRACE_APP_FUNCTION_SKIP_NESTED +#endif + +#undef CV_TRACE_REGION +#define CV_TRACE_REGION CV__TRACE_REGION + +#undef CV_TRACE_REGION_NEXT +#define CV_TRACE_REGION_NEXT CV__TRACE_REGION_NEXT + +#undef CV_TRACE_ARG_VALUE +#define CV_TRACE_ARG_VALUE(arg_id, arg_name, value) \ + if (__region_fn.isActive()) \ + { \ + CV__TRACE_ARG_VALUE(arg_id, arg_name, value); \ + } + +#undef CV_TRACE_ARG +#define CV_TRACE_ARG CV__TRACE_ARG + +#endif // OPENCV_DISABLE_TRACE + +#ifdef OPENCV_TRACE_VERBOSE +#define CV_TRACE_FUNCTION_VERBOSE CV_TRACE_FUNCTION +#define CV_TRACE_REGION_VERBOSE CV_TRACE_REGION +#define CV_TRACE_REGION_NEXT_VERBOSE CV_TRACE_REGION_NEXT +#define CV_TRACE_ARG_VALUE_VERBOSE CV_TRACE_ARG_VALUE +#define CV_TRACE_ARG_VERBOSE CV_TRACE_ARG +#else +#define CV_TRACE_FUNCTION_VERBOSE(...) +#define CV_TRACE_REGION_VERBOSE(...) +#define CV_TRACE_REGION_NEXT_VERBOSE(...) +#define CV_TRACE_ARG_VALUE_VERBOSE(...) +#define CV_TRACE_ARG_VERBOSE(...) +#endif + +//! @endcond + +}}} // namespace + +//! @} + +#endif // OPENCV_TRACE_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/va_intel.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/va_intel.hpp new file mode 100644 index 0000000000000000000000000000000000000000..f66547093e0ad69533af4cceb02bc796448f8db9 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/va_intel.hpp @@ -0,0 +1,78 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +// Copyright (C) 2015, Itseez, Inc., all rights reserved. +// Third party copyrights are property of their respective owners. + +#ifndef OPENCV_CORE_VA_INTEL_HPP +#define OPENCV_CORE_VA_INTEL_HPP + +#ifndef __cplusplus +# error va_intel.hpp header must be compiled as C++ +#endif + +#include "opencv2/core.hpp" +#include "ocl.hpp" + +#if defined(HAVE_VA) +# include "va/va.h" +#else // HAVE_VA +# if !defined(_VA_H_) + typedef void* VADisplay; + typedef unsigned int VASurfaceID; +# endif // !_VA_H_ +#endif // HAVE_VA + +namespace cv { namespace va_intel { + +/** @addtogroup core_va_intel +This section describes Intel VA-API/OpenCL (CL-VA) interoperability. + +To enable CL-VA interoperability support, configure OpenCV using CMake with WITH_VA_INTEL=ON . Currently VA-API is +supported on Linux only. You should also install Intel Media Server Studio (MSS) to use this feature. You may +have to specify the path(s) to MSS components for cmake in environment variables: + +- VA_INTEL_IOCL_ROOT for Intel OpenCL (default is "/opt/intel/opencl"). + +To use CL-VA interoperability you should first create VADisplay (libva), and then call initializeContextFromVA() +function to create OpenCL context and set up interoperability. +*/ +//! @{ + +/////////////////// CL-VA Interoperability Functions /////////////////// + +namespace ocl { +using namespace cv::ocl; + +// TODO static functions in the Context class +/** @brief Creates OpenCL context from VA. +@param display - VADisplay for which CL interop should be established. +@param tryInterop - try to set up for interoperability, if true; set up for use slow copy if false. +@return Returns reference to OpenCL Context + */ +CV_EXPORTS Context& initializeContextFromVA(VADisplay display, bool tryInterop = true); + +} // namespace cv::va_intel::ocl + +/** @brief Converts InputArray to VASurfaceID object. +@param display - VADisplay object. +@param src - source InputArray. +@param surface - destination VASurfaceID object. +@param size - size of image represented by VASurfaceID object. + */ +CV_EXPORTS void convertToVASurface(VADisplay display, InputArray src, VASurfaceID surface, Size size); + +/** @brief Converts VASurfaceID object to OutputArray. +@param display - VADisplay object. +@param surface - source VASurfaceID object. +@param size - size of image represented by VASurfaceID object. +@param dst - destination OutputArray. + */ +CV_EXPORTS void convertFromVASurface(VADisplay display, VASurfaceID surface, Size size, OutputArray dst); + +//! @} + +}} // namespace cv::va_intel + +#endif /* OPENCV_CORE_VA_INTEL_HPP */ diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/version.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/version.hpp new file mode 100644 index 0000000000000000000000000000000000000000..91f95eeec4439ae3941fe4dc0c4e13b55cf3f974 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/version.hpp @@ -0,0 +1,26 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#ifndef OPENCV_VERSION_HPP +#define OPENCV_VERSION_HPP + +#define CV_VERSION_MAJOR 3 +#define CV_VERSION_MINOR 4 +#define CV_VERSION_REVISION 5 +#define CV_VERSION_STATUS "" + +#define CVAUX_STR_EXP(__A) #__A +#define CVAUX_STR(__A) CVAUX_STR_EXP(__A) + +#define CVAUX_STRW_EXP(__A) L ## #__A +#define CVAUX_STRW(__A) CVAUX_STRW_EXP(__A) + +#define CV_VERSION CVAUX_STR(CV_VERSION_MAJOR) "." CVAUX_STR(CV_VERSION_MINOR) "." CVAUX_STR(CV_VERSION_REVISION) CV_VERSION_STATUS + +/* old style version constants*/ +#define CV_MAJOR_VERSION CV_VERSION_MAJOR +#define CV_MINOR_VERSION CV_VERSION_MINOR +#define CV_SUBMINOR_VERSION CV_VERSION_REVISION + +#endif // OPENCV_VERSION_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/vsx_utils.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/vsx_utils.hpp new file mode 100644 index 0000000000000000000000000000000000000000..b4e3f30562b7c4e8367d32c5eea8b8fb9e1685c6 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/vsx_utils.hpp @@ -0,0 +1,1011 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html + +#ifndef OPENCV_HAL_VSX_UTILS_HPP +#define OPENCV_HAL_VSX_UTILS_HPP + +#include "opencv2/core/cvdef.h" + +#ifndef SKIP_INCLUDES +# include +#endif + +//! @addtogroup core_utils_vsx +//! @{ +#if CV_VSX + +#define __VSX_S16__(c, v) (c){v, v, v, v, v, v, v, v, v, v, v, v, v, v, v, v} +#define __VSX_S8__(c, v) (c){v, v, v, v, v, v, v, v} +#define __VSX_S4__(c, v) (c){v, v, v, v} +#define __VSX_S2__(c, v) (c){v, v} + +typedef __vector unsigned char vec_uchar16; +#define vec_uchar16_set(...) (vec_uchar16){__VA_ARGS__} +#define vec_uchar16_sp(c) (__VSX_S16__(vec_uchar16, c)) +#define vec_uchar16_c(v) ((vec_uchar16)(v)) +#define vec_uchar16_z vec_uchar16_sp(0) + +typedef __vector signed char vec_char16; +#define vec_char16_set(...) (vec_char16){__VA_ARGS__} +#define vec_char16_sp(c) (__VSX_S16__(vec_char16, c)) +#define vec_char16_c(v) ((vec_char16)(v)) +#define vec_char16_z vec_char16_sp(0) + +typedef __vector unsigned short vec_ushort8; +#define vec_ushort8_set(...) (vec_ushort8){__VA_ARGS__} +#define vec_ushort8_sp(c) (__VSX_S8__(vec_ushort8, c)) +#define vec_ushort8_c(v) ((vec_ushort8)(v)) +#define vec_ushort8_z vec_ushort8_sp(0) + +typedef __vector signed short vec_short8; +#define vec_short8_set(...) (vec_short8){__VA_ARGS__} +#define vec_short8_sp(c) (__VSX_S8__(vec_short8, c)) +#define vec_short8_c(v) ((vec_short8)(v)) +#define vec_short8_z vec_short8_sp(0) + +typedef __vector unsigned int vec_uint4; +#define vec_uint4_set(...) (vec_uint4){__VA_ARGS__} +#define vec_uint4_sp(c) (__VSX_S4__(vec_uint4, c)) +#define vec_uint4_c(v) ((vec_uint4)(v)) +#define vec_uint4_z vec_uint4_sp(0) + +typedef __vector signed int vec_int4; +#define vec_int4_set(...) (vec_int4){__VA_ARGS__} +#define vec_int4_sp(c) (__VSX_S4__(vec_int4, c)) +#define vec_int4_c(v) ((vec_int4)(v)) +#define vec_int4_z vec_int4_sp(0) + +typedef __vector float vec_float4; +#define vec_float4_set(...) (vec_float4){__VA_ARGS__} +#define vec_float4_sp(c) (__VSX_S4__(vec_float4, c)) +#define vec_float4_c(v) ((vec_float4)(v)) +#define vec_float4_z vec_float4_sp(0) + +typedef __vector unsigned long long vec_udword2; +#define vec_udword2_set(...) (vec_udword2){__VA_ARGS__} +#define vec_udword2_sp(c) (__VSX_S2__(vec_udword2, c)) +#define vec_udword2_c(v) ((vec_udword2)(v)) +#define vec_udword2_z vec_udword2_sp(0) + +typedef __vector signed long long vec_dword2; +#define vec_dword2_set(...) (vec_dword2){__VA_ARGS__} +#define vec_dword2_sp(c) (__VSX_S2__(vec_dword2, c)) +#define vec_dword2_c(v) ((vec_dword2)(v)) +#define vec_dword2_z vec_dword2_sp(0) + +typedef __vector double vec_double2; +#define vec_double2_set(...) (vec_double2){__VA_ARGS__} +#define vec_double2_c(v) ((vec_double2)(v)) +#define vec_double2_sp(c) (__VSX_S2__(vec_double2, c)) +#define vec_double2_z vec_double2_sp(0) + +#define vec_bchar16 __vector __bool char +#define vec_bchar16_set(...) (vec_bchar16){__VA_ARGS__} +#define vec_bchar16_c(v) ((vec_bchar16)(v)) + +#define vec_bshort8 __vector __bool short +#define vec_bshort8_set(...) (vec_bshort8){__VA_ARGS__} +#define vec_bshort8_c(v) ((vec_bshort8)(v)) + +#define vec_bint4 __vector __bool int +#define vec_bint4_set(...) (vec_bint4){__VA_ARGS__} +#define vec_bint4_c(v) ((vec_bint4)(v)) + +#define vec_bdword2 __vector __bool long long +#define vec_bdword2_set(...) (vec_bdword2){__VA_ARGS__} +#define vec_bdword2_c(v) ((vec_bdword2)(v)) + +#define VSX_FINLINE(tp) extern inline tp __attribute__((always_inline)) + +#define VSX_REDIRECT_1RG(rt, rg, fnm, fn2) \ +VSX_FINLINE(rt) fnm(const rg& a) { return fn2(a); } + +#define VSX_REDIRECT_2RG(rt, rg, fnm, fn2) \ +VSX_FINLINE(rt) fnm(const rg& a, const rg& b) { return fn2(a, b); } + +/* + * GCC VSX compatibility +**/ +#if defined(__GNUG__) && !defined(__clang__) + +// inline asm helper +#define VSX_IMPL_1RG(rt, rto, rg, rgo, opc, fnm) \ +VSX_FINLINE(rt) fnm(const rg& a) \ +{ rt rs; __asm__ __volatile__(#opc" %x0,%x1" : "="#rto (rs) : #rgo (a)); return rs; } + +#define VSX_IMPL_1VRG(rt, rg, opc, fnm) \ +VSX_FINLINE(rt) fnm(const rg& a) \ +{ rt rs; __asm__ __volatile__(#opc" %0,%1" : "=v" (rs) : "v" (a)); return rs; } + +#define VSX_IMPL_2VRG_F(rt, rg, fopc, fnm) \ +VSX_FINLINE(rt) fnm(const rg& a, const rg& b) \ +{ rt rs; __asm__ __volatile__(fopc : "=v" (rs) : "v" (a), "v" (b)); return rs; } + +#define VSX_IMPL_2VRG(rt, rg, opc, fnm) VSX_IMPL_2VRG_F(rt, rg, #opc" %0,%1,%2", fnm) + +#if __GNUG__ < 7 +// up to GCC 6 vec_mul only supports precisions and llong +# ifdef vec_mul +# undef vec_mul +# endif +/* + * there's no a direct instruction for supporting 8-bit, 16-bit multiplication in ISA 2.07, + * XLC Implement it by using instruction "multiply even", "multiply odd" and "permute" +**/ +# define VSX_IMPL_MULH(Tvec, cperm) \ + VSX_FINLINE(Tvec) vec_mul(const Tvec& a, const Tvec& b) \ + { \ + static const vec_uchar16 ev_od = {cperm}; \ + return vec_perm((Tvec)vec_mule(a, b), (Tvec)vec_mulo(a, b), ev_od); \ + } + #define VSX_IMPL_MULH_P16 0, 16, 2, 18, 4, 20, 6, 22, 8, 24, 10, 26, 12, 28, 14, 30 + VSX_IMPL_MULH(vec_char16, VSX_IMPL_MULH_P16) + VSX_IMPL_MULH(vec_uchar16, VSX_IMPL_MULH_P16) + #define VSX_IMPL_MULH_P8 0, 1, 16, 17, 4, 5, 20, 21, 8, 9, 24, 25, 12, 13, 28, 29 + VSX_IMPL_MULH(vec_short8, VSX_IMPL_MULH_P8) + VSX_IMPL_MULH(vec_ushort8, VSX_IMPL_MULH_P8) + // vmuluwm can be used for unsigned or signed integers, that's what they said + VSX_IMPL_2VRG(vec_int4, vec_int4, vmuluwm, vec_mul) + VSX_IMPL_2VRG(vec_uint4, vec_uint4, vmuluwm, vec_mul) + // redirect to GCC builtin vec_mul, since it already supports precisions and llong + VSX_REDIRECT_2RG(vec_float4, vec_float4, vec_mul, __builtin_vec_mul) + VSX_REDIRECT_2RG(vec_double2, vec_double2, vec_mul, __builtin_vec_mul) + VSX_REDIRECT_2RG(vec_dword2, vec_dword2, vec_mul, __builtin_vec_mul) + VSX_REDIRECT_2RG(vec_udword2, vec_udword2, vec_mul, __builtin_vec_mul) +#endif // __GNUG__ < 7 + +#if __GNUG__ < 6 +/* + * Instruction "compare greater than or equal" in ISA 2.07 only supports single + * and double precision. + * In XLC and new versions of GCC implement integers by using instruction "greater than" and NOR. +**/ +# ifdef vec_cmpge +# undef vec_cmpge +# endif +# ifdef vec_cmple +# undef vec_cmple +# endif +# define vec_cmple(a, b) vec_cmpge(b, a) +# define VSX_IMPL_CMPGE(rt, rg, opc, fnm) \ + VSX_IMPL_2VRG_F(rt, rg, #opc" %0,%2,%1\n\t xxlnor %x0,%x0,%x0", fnm) + + VSX_IMPL_CMPGE(vec_bchar16, vec_char16, vcmpgtsb, vec_cmpge) + VSX_IMPL_CMPGE(vec_bchar16, vec_uchar16, vcmpgtub, vec_cmpge) + VSX_IMPL_CMPGE(vec_bshort8, vec_short8, vcmpgtsh, vec_cmpge) + VSX_IMPL_CMPGE(vec_bshort8, vec_ushort8, vcmpgtuh, vec_cmpge) + VSX_IMPL_CMPGE(vec_bint4, vec_int4, vcmpgtsw, vec_cmpge) + VSX_IMPL_CMPGE(vec_bint4, vec_uint4, vcmpgtuw, vec_cmpge) + VSX_IMPL_CMPGE(vec_bdword2, vec_dword2, vcmpgtsd, vec_cmpge) + VSX_IMPL_CMPGE(vec_bdword2, vec_udword2, vcmpgtud, vec_cmpge) + +// redirect to GCC builtin cmpge, since it already supports precisions + VSX_REDIRECT_2RG(vec_bint4, vec_float4, vec_cmpge, __builtin_vec_cmpge) + VSX_REDIRECT_2RG(vec_bdword2, vec_double2, vec_cmpge, __builtin_vec_cmpge) + +// up to gcc5 vec_nor doesn't support bool long long +# undef vec_nor + template + VSX_REDIRECT_2RG(T, T, vec_nor, __builtin_vec_nor) + + VSX_FINLINE(vec_bdword2) vec_nor(const vec_bdword2& a, const vec_bdword2& b) + { return vec_bdword2_c(__builtin_vec_nor(vec_dword2_c(a), vec_dword2_c(b))); } + +// vec_packs doesn't support double words in gcc4 and old versions of gcc5 +# undef vec_packs + VSX_REDIRECT_2RG(vec_char16, vec_short8, vec_packs, __builtin_vec_packs) + VSX_REDIRECT_2RG(vec_uchar16, vec_ushort8, vec_packs, __builtin_vec_packs) + VSX_REDIRECT_2RG(vec_short8, vec_int4, vec_packs, __builtin_vec_packs) + VSX_REDIRECT_2RG(vec_ushort8, vec_uint4, vec_packs, __builtin_vec_packs) + + VSX_IMPL_2VRG_F(vec_int4, vec_dword2, "vpksdss %0,%2,%1", vec_packs) + VSX_IMPL_2VRG_F(vec_uint4, vec_udword2, "vpkudus %0,%2,%1", vec_packs) +#endif // __GNUG__ < 6 + +#if __GNUG__ < 5 +// vec_xxpermdi in gcc4 missing little-endian supports just like clang +# define vec_permi(a, b, c) vec_xxpermdi(b, a, (3 ^ (((c) & 1) << 1 | (c) >> 1))) +#else +# define vec_permi vec_xxpermdi +#endif // __GNUG__ < 5 + +// shift left double by word immediate +#ifndef vec_sldw +# define vec_sldw __builtin_vsx_xxsldwi +#endif + +// vector population count +VSX_IMPL_1VRG(vec_uchar16, vec_uchar16, vpopcntb, vec_popcntu) +VSX_IMPL_1VRG(vec_uchar16, vec_char16, vpopcntb, vec_popcntu) +VSX_IMPL_1VRG(vec_ushort8, vec_ushort8, vpopcnth, vec_popcntu) +VSX_IMPL_1VRG(vec_ushort8, vec_short8, vpopcnth, vec_popcntu) +VSX_IMPL_1VRG(vec_uint4, vec_uint4, vpopcntw, vec_popcntu) +VSX_IMPL_1VRG(vec_uint4, vec_int4, vpopcntw, vec_popcntu) +VSX_IMPL_1VRG(vec_udword2, vec_udword2, vpopcntd, vec_popcntu) +VSX_IMPL_1VRG(vec_udword2, vec_dword2, vpopcntd, vec_popcntu) + +// converts between single and double-precision +VSX_REDIRECT_1RG(vec_float4, vec_double2, vec_cvfo, __builtin_vsx_xvcvdpsp) +VSX_REDIRECT_1RG(vec_double2, vec_float4, vec_cvfo, __builtin_vsx_xvcvspdp) + +// converts word and doubleword to double-precision +#ifdef vec_ctd +# undef vec_ctd +#endif +VSX_IMPL_1RG(vec_double2, wd, vec_int4, wa, xvcvsxwdp, vec_ctdo) +VSX_IMPL_1RG(vec_double2, wd, vec_uint4, wa, xvcvuxwdp, vec_ctdo) +VSX_IMPL_1RG(vec_double2, wd, vec_dword2, wi, xvcvsxddp, vec_ctd) +VSX_IMPL_1RG(vec_double2, wd, vec_udword2, wi, xvcvuxddp, vec_ctd) + +// converts word and doubleword to single-precision +#undef vec_ctf +VSX_IMPL_1RG(vec_float4, wf, vec_int4, wa, xvcvsxwsp, vec_ctf) +VSX_IMPL_1RG(vec_float4, wf, vec_uint4, wa, xvcvuxwsp, vec_ctf) +VSX_IMPL_1RG(vec_float4, wf, vec_dword2, wi, xvcvsxdsp, vec_ctfo) +VSX_IMPL_1RG(vec_float4, wf, vec_udword2, wi, xvcvuxdsp, vec_ctfo) + +// converts single and double precision to signed word +#undef vec_cts +VSX_IMPL_1RG(vec_int4, wa, vec_double2, wd, xvcvdpsxws, vec_ctso) +VSX_IMPL_1RG(vec_int4, wa, vec_float4, wf, xvcvspsxws, vec_cts) + +// converts single and double precision to unsigned word +#undef vec_ctu +VSX_IMPL_1RG(vec_uint4, wa, vec_double2, wd, xvcvdpuxws, vec_ctuo) +VSX_IMPL_1RG(vec_uint4, wa, vec_float4, wf, xvcvspuxws, vec_ctu) + +// converts single and double precision to signed doubleword +#ifdef vec_ctsl +# undef vec_ctsl +#endif +VSX_IMPL_1RG(vec_dword2, wi, vec_double2, wd, xvcvdpsxds, vec_ctsl) +VSX_IMPL_1RG(vec_dword2, wi, vec_float4, wf, xvcvspsxds, vec_ctslo) + +// converts single and double precision to unsigned doubleword +#ifdef vec_ctul +# undef vec_ctul +#endif +VSX_IMPL_1RG(vec_udword2, wi, vec_double2, wd, xvcvdpuxds, vec_ctul) +VSX_IMPL_1RG(vec_udword2, wi, vec_float4, wf, xvcvspuxds, vec_ctulo) + +// just in case if GCC doesn't define it +#ifndef vec_xl +# define vec_xl vec_vsx_ld +# define vec_xst vec_vsx_st +#endif + +#endif // GCC VSX compatibility + +/* + * CLANG VSX compatibility +**/ +#if defined(__clang__) && !defined(__IBMCPP__) + +/* + * CLANG doesn't support %x in the inline asm template which fixes register number + * when using any of the register constraints wa, wd, wf + * + * For more explanation checkout PowerPC and IBM RS6000 in https://gcc.gnu.org/onlinedocs/gcc/Machine-Constraints.html + * Also there's already an open bug https://bugs.llvm.org/show_bug.cgi?id=31837 + * + * So we're not able to use inline asm and only use built-in functions that CLANG supports + * and use __builtin_convertvector if clang missng any of vector conversions built-in functions +*/ + +// convert vector helper +#define VSX_IMPL_CONVERT(rt, rg, fnm) \ +VSX_FINLINE(rt) fnm(const rg& a) { return __builtin_convertvector(a, rt); } + +#if __clang_major__ < 5 +// implement vec_permi in a dirty way +# define VSX_IMPL_CLANG_4_PERMI(Tvec) \ + VSX_FINLINE(Tvec) vec_permi(const Tvec& a, const Tvec& b, unsigned const char c) \ + { \ + switch (c) \ + { \ + case 0: \ + return vec_mergeh(a, b); \ + case 1: \ + return vec_mergel(vec_mergeh(a, a), b); \ + case 2: \ + return vec_mergeh(vec_mergel(a, a), b); \ + default: \ + return vec_mergel(a, b); \ + } \ + } + VSX_IMPL_CLANG_4_PERMI(vec_udword2) + VSX_IMPL_CLANG_4_PERMI(vec_dword2) + VSX_IMPL_CLANG_4_PERMI(vec_double2) + +// vec_xxsldwi is missing in clang 4 +# define vec_xxsldwi(a, b, c) vec_sld(a, b, (c) * 4) +#else +// vec_xxpermdi is missing little-endian supports in clang 4 just like gcc4 +# define vec_permi(a, b, c) vec_xxpermdi(b, a, (3 ^ (((c) & 1) << 1 | (c) >> 1))) +#endif // __clang_major__ < 5 + +// shift left double by word immediate +#ifndef vec_sldw +# define vec_sldw vec_xxsldwi +#endif + +// Implement vec_rsqrt since clang only supports vec_rsqrte +#ifndef vec_rsqrt + VSX_FINLINE(vec_float4) vec_rsqrt(const vec_float4& a) + { return vec_div(vec_float4_sp(1), vec_sqrt(a)); } + + VSX_FINLINE(vec_double2) vec_rsqrt(const vec_double2& a) + { return vec_div(vec_double2_sp(1), vec_sqrt(a)); } +#endif + +// vec_promote missing support for doubleword +VSX_FINLINE(vec_dword2) vec_promote(long long a, int b) +{ + vec_dword2 ret = vec_dword2_z; + ret[b & 1] = a; + return ret; +} + +VSX_FINLINE(vec_udword2) vec_promote(unsigned long long a, int b) +{ + vec_udword2 ret = vec_udword2_z; + ret[b & 1] = a; + return ret; +} + +// vec_popcnt should return unsigned but clang has different thought just like gcc in vec_vpopcnt +#define VSX_IMPL_POPCNTU(Tvec, Tvec2, ucast) \ +VSX_FINLINE(Tvec) vec_popcntu(const Tvec2& a) \ +{ return ucast(vec_popcnt(a)); } +VSX_IMPL_POPCNTU(vec_uchar16, vec_char16, vec_uchar16_c); +VSX_IMPL_POPCNTU(vec_ushort8, vec_short8, vec_ushort8_c); +VSX_IMPL_POPCNTU(vec_uint4, vec_int4, vec_uint4_c); +// redirect unsigned types +VSX_REDIRECT_1RG(vec_uchar16, vec_uchar16, vec_popcntu, vec_popcnt) +VSX_REDIRECT_1RG(vec_ushort8, vec_ushort8, vec_popcntu, vec_popcnt) +VSX_REDIRECT_1RG(vec_uint4, vec_uint4, vec_popcntu, vec_popcnt) + +// converts between single and double precision +VSX_REDIRECT_1RG(vec_float4, vec_double2, vec_cvfo, __builtin_vsx_xvcvdpsp) +VSX_REDIRECT_1RG(vec_double2, vec_float4, vec_cvfo, __builtin_vsx_xvcvspdp) + +// converts word and doubleword to double-precision +#ifdef vec_ctd +# undef vec_ctd +#endif +VSX_REDIRECT_1RG(vec_double2, vec_int4, vec_ctdo, __builtin_vsx_xvcvsxwdp) +VSX_REDIRECT_1RG(vec_double2, vec_uint4, vec_ctdo, __builtin_vsx_xvcvuxwdp) + +VSX_IMPL_CONVERT(vec_double2, vec_dword2, vec_ctd) +VSX_IMPL_CONVERT(vec_double2, vec_udword2, vec_ctd) + +// converts word and doubleword to single-precision +#if __clang_major__ > 4 +# undef vec_ctf +#endif +VSX_IMPL_CONVERT(vec_float4, vec_int4, vec_ctf) +VSX_IMPL_CONVERT(vec_float4, vec_uint4, vec_ctf) +VSX_REDIRECT_1RG(vec_float4, vec_dword2, vec_ctfo, __builtin_vsx_xvcvsxdsp) +VSX_REDIRECT_1RG(vec_float4, vec_udword2, vec_ctfo, __builtin_vsx_xvcvuxdsp) + +// converts single and double precision to signed word +#if __clang_major__ > 4 +# undef vec_cts +#endif +VSX_REDIRECT_1RG(vec_int4, vec_double2, vec_ctso, __builtin_vsx_xvcvdpsxws) +VSX_IMPL_CONVERT(vec_int4, vec_float4, vec_cts) + +// converts single and double precision to unsigned word +#if __clang_major__ > 4 +# undef vec_ctu +#endif +VSX_REDIRECT_1RG(vec_uint4, vec_double2, vec_ctuo, __builtin_vsx_xvcvdpuxws) +VSX_IMPL_CONVERT(vec_uint4, vec_float4, vec_ctu) + +// converts single and double precision to signed doubleword +#ifdef vec_ctsl +# undef vec_ctsl +#endif +VSX_IMPL_CONVERT(vec_dword2, vec_double2, vec_ctsl) +// __builtin_convertvector unable to convert, xvcvspsxds is missing on it +VSX_FINLINE(vec_dword2) vec_ctslo(const vec_float4& a) +{ return vec_ctsl(vec_cvfo(a)); } + +// converts single and double precision to unsigned doubleword +#ifdef vec_ctul +# undef vec_ctul +#endif +VSX_IMPL_CONVERT(vec_udword2, vec_double2, vec_ctul) +// __builtin_convertvector unable to convert, xvcvspuxds is missing on it +VSX_FINLINE(vec_udword2) vec_ctulo(const vec_float4& a) +{ return vec_ctul(vec_cvfo(a)); } + +#endif // CLANG VSX compatibility + +/* + * Common GCC, CLANG compatibility +**/ +#if defined(__GNUG__) && !defined(__IBMCPP__) + +#ifdef vec_cvf +# undef vec_cvf +#endif + +#define VSX_IMPL_CONV_EVEN_4_2(rt, rg, fnm, fn2) \ +VSX_FINLINE(rt) fnm(const rg& a) \ +{ return fn2(vec_sldw(a, a, 1)); } + +VSX_IMPL_CONV_EVEN_4_2(vec_double2, vec_float4, vec_cvf, vec_cvfo) +VSX_IMPL_CONV_EVEN_4_2(vec_double2, vec_int4, vec_ctd, vec_ctdo) +VSX_IMPL_CONV_EVEN_4_2(vec_double2, vec_uint4, vec_ctd, vec_ctdo) + +VSX_IMPL_CONV_EVEN_4_2(vec_dword2, vec_float4, vec_ctsl, vec_ctslo) +VSX_IMPL_CONV_EVEN_4_2(vec_udword2, vec_float4, vec_ctul, vec_ctulo) + +#define VSX_IMPL_CONV_EVEN_2_4(rt, rg, fnm, fn2) \ +VSX_FINLINE(rt) fnm(const rg& a) \ +{ \ + rt v4 = fn2(a); \ + return vec_sldw(v4, v4, 3); \ +} + +VSX_IMPL_CONV_EVEN_2_4(vec_float4, vec_double2, vec_cvf, vec_cvfo) +VSX_IMPL_CONV_EVEN_2_4(vec_float4, vec_dword2, vec_ctf, vec_ctfo) +VSX_IMPL_CONV_EVEN_2_4(vec_float4, vec_udword2, vec_ctf, vec_ctfo) + +VSX_IMPL_CONV_EVEN_2_4(vec_int4, vec_double2, vec_cts, vec_ctso) +VSX_IMPL_CONV_EVEN_2_4(vec_uint4, vec_double2, vec_ctu, vec_ctuo) + +// Only for Eigen! +/* + * changing behavior of conversion intrinsics for gcc has effect on Eigen + * so we redfine old behavior again only on gcc, clang +*/ +#if !defined(__clang__) || __clang_major__ > 4 + // ignoring second arg since Eigen only truncates toward zero +# define VSX_IMPL_CONV_2VARIANT(rt, rg, fnm, fn2) \ + VSX_FINLINE(rt) fnm(const rg& a, int only_truncate) \ + { \ + assert(only_truncate == 0); \ + CV_UNUSED(only_truncate); \ + return fn2(a); \ + } + VSX_IMPL_CONV_2VARIANT(vec_int4, vec_float4, vec_cts, vec_cts) + VSX_IMPL_CONV_2VARIANT(vec_float4, vec_int4, vec_ctf, vec_ctf) + // define vec_cts for converting double precision to signed doubleword + // which isn't combitable with xlc but its okay since Eigen only use it for gcc + VSX_IMPL_CONV_2VARIANT(vec_dword2, vec_double2, vec_cts, vec_ctsl) +#endif // Eigen + +#endif // Common GCC, CLANG compatibility + +/* + * XLC VSX compatibility +**/ +#if defined(__IBMCPP__) + +// vector population count +#define vec_popcntu vec_popcnt + +// overload and redirect with setting second arg to zero +// since we only support conversions without the second arg +#define VSX_IMPL_OVERLOAD_Z2(rt, rg, fnm) \ +VSX_FINLINE(rt) fnm(const rg& a) { return fnm(a, 0); } + +VSX_IMPL_OVERLOAD_Z2(vec_double2, vec_int4, vec_ctd) +VSX_IMPL_OVERLOAD_Z2(vec_double2, vec_uint4, vec_ctd) +VSX_IMPL_OVERLOAD_Z2(vec_double2, vec_dword2, vec_ctd) +VSX_IMPL_OVERLOAD_Z2(vec_double2, vec_udword2, vec_ctd) + +VSX_IMPL_OVERLOAD_Z2(vec_float4, vec_int4, vec_ctf) +VSX_IMPL_OVERLOAD_Z2(vec_float4, vec_uint4, vec_ctf) +VSX_IMPL_OVERLOAD_Z2(vec_float4, vec_dword2, vec_ctf) +VSX_IMPL_OVERLOAD_Z2(vec_float4, vec_udword2, vec_ctf) + +VSX_IMPL_OVERLOAD_Z2(vec_int4, vec_double2, vec_cts) +VSX_IMPL_OVERLOAD_Z2(vec_int4, vec_float4, vec_cts) + +VSX_IMPL_OVERLOAD_Z2(vec_uint4, vec_double2, vec_ctu) +VSX_IMPL_OVERLOAD_Z2(vec_uint4, vec_float4, vec_ctu) + +VSX_IMPL_OVERLOAD_Z2(vec_dword2, vec_double2, vec_ctsl) +VSX_IMPL_OVERLOAD_Z2(vec_dword2, vec_float4, vec_ctsl) + +VSX_IMPL_OVERLOAD_Z2(vec_udword2, vec_double2, vec_ctul) +VSX_IMPL_OVERLOAD_Z2(vec_udword2, vec_float4, vec_ctul) + +// fixme: implement conversions of odd-numbered elements in a dirty way +// since xlc doesn't support VSX registers operand in inline asm. +#define VSX_IMPL_CONV_ODD_4_2(rt, rg, fnm, fn2) \ +VSX_FINLINE(rt) fnm(const rg& a) { return fn2(vec_sldw(a, a, 3)); } + +VSX_IMPL_CONV_ODD_4_2(vec_double2, vec_float4, vec_cvfo, vec_cvf) +VSX_IMPL_CONV_ODD_4_2(vec_double2, vec_int4, vec_ctdo, vec_ctd) +VSX_IMPL_CONV_ODD_4_2(vec_double2, vec_uint4, vec_ctdo, vec_ctd) + +VSX_IMPL_CONV_ODD_4_2(vec_dword2, vec_float4, vec_ctslo, vec_ctsl) +VSX_IMPL_CONV_ODD_4_2(vec_udword2, vec_float4, vec_ctulo, vec_ctul) + +#define VSX_IMPL_CONV_ODD_2_4(rt, rg, fnm, fn2) \ +VSX_FINLINE(rt) fnm(const rg& a) \ +{ \ + rt v4 = fn2(a); \ + return vec_sldw(v4, v4, 1); \ +} + +VSX_IMPL_CONV_ODD_2_4(vec_float4, vec_double2, vec_cvfo, vec_cvf) +VSX_IMPL_CONV_ODD_2_4(vec_float4, vec_dword2, vec_ctfo, vec_ctf) +VSX_IMPL_CONV_ODD_2_4(vec_float4, vec_udword2, vec_ctfo, vec_ctf) + +VSX_IMPL_CONV_ODD_2_4(vec_int4, vec_double2, vec_ctso, vec_cts) +VSX_IMPL_CONV_ODD_2_4(vec_uint4, vec_double2, vec_ctuo, vec_ctu) + +#endif // XLC VSX compatibility + +// ignore GCC warning that caused by -Wunused-but-set-variable in rare cases +#if defined(__GNUG__) && !defined(__clang__) +# define VSX_UNUSED(Tvec) Tvec __attribute__((__unused__)) +#else // CLANG, XLC +# define VSX_UNUSED(Tvec) Tvec +#endif + +// gcc can find his way in casting log int and XLC, CLANG ambiguous +#if defined(__clang__) || defined(__IBMCPP__) + VSX_FINLINE(vec_udword2) vec_splats(uint64 v) + { return vec_splats((unsigned long long) v); } + + VSX_FINLINE(vec_dword2) vec_splats(int64 v) + { return vec_splats((long long) v); } + + VSX_FINLINE(vec_udword2) vec_promote(uint64 a, int b) + { return vec_promote((unsigned long long) a, b); } + + VSX_FINLINE(vec_dword2) vec_promote(int64 a, int b) + { return vec_promote((long long) a, b); } +#endif + +/* + * implement vsx_ld(offset, pointer), vsx_st(vector, offset, pointer) + * load and set using offset depend on the pointer type + * + * implement vsx_ldf(offset, pointer), vsx_stf(vector, offset, pointer) + * load and set using offset depend on fixed bytes size + * + * Note: In clang vec_xl and vec_xst fails to load unaligned addresses + * so we are using vec_vsx_ld, vec_vsx_st instead +*/ + +#if defined(__clang__) && !defined(__IBMCPP__) +# define vsx_ldf vec_vsx_ld +# define vsx_stf vec_vsx_st +#else // GCC , XLC +# define vsx_ldf vec_xl +# define vsx_stf vec_xst +#endif + +#define VSX_OFFSET(o, p) ((o) * sizeof(*(p))) +#define vsx_ld(o, p) vsx_ldf(VSX_OFFSET(o, p), p) +#define vsx_st(v, o, p) vsx_stf(v, VSX_OFFSET(o, p), p) + +/* + * implement vsx_ld2(offset, pointer), vsx_st2(vector, offset, pointer) to load and store double words + * In GCC vec_xl and vec_xst it maps to vec_vsx_ld, vec_vsx_st which doesn't support long long + * and in CLANG we are using vec_vsx_ld, vec_vsx_st because vec_xl, vec_xst fails to load unaligned addresses + * + * In XLC vec_xl and vec_xst fail to cast int64(long int) to long long +*/ +#if (defined(__GNUG__) || defined(__clang__)) && !defined(__IBMCPP__) + VSX_FINLINE(vec_udword2) vsx_ld2(long o, const uint64* p) + { return vec_udword2_c(vsx_ldf(VSX_OFFSET(o, p), (unsigned int*)p)); } + + VSX_FINLINE(vec_dword2) vsx_ld2(long o, const int64* p) + { return vec_dword2_c(vsx_ldf(VSX_OFFSET(o, p), (int*)p)); } + + VSX_FINLINE(void) vsx_st2(const vec_udword2& vec, long o, uint64* p) + { vsx_stf(vec_uint4_c(vec), VSX_OFFSET(o, p), (unsigned int*)p); } + + VSX_FINLINE(void) vsx_st2(const vec_dword2& vec, long o, int64* p) + { vsx_stf(vec_int4_c(vec), VSX_OFFSET(o, p), (int*)p); } +#else // XLC + VSX_FINLINE(vec_udword2) vsx_ld2(long o, const uint64* p) + { return vsx_ldf(VSX_OFFSET(o, p), (unsigned long long*)p); } + + VSX_FINLINE(vec_dword2) vsx_ld2(long o, const int64* p) + { return vsx_ldf(VSX_OFFSET(o, p), (long long*)p); } + + VSX_FINLINE(void) vsx_st2(const vec_udword2& vec, long o, uint64* p) + { vsx_stf(vec, VSX_OFFSET(o, p), (unsigned long long*)p); } + + VSX_FINLINE(void) vsx_st2(const vec_dword2& vec, long o, int64* p) + { vsx_stf(vec, VSX_OFFSET(o, p), (long long*)p); } +#endif + +// Store lower 8 byte +#define vec_st_l8(v, p) *((uint64*)(p)) = vec_extract(vec_udword2_c(v), 0) + +// Store higher 8 byte +#define vec_st_h8(v, p) *((uint64*)(p)) = vec_extract(vec_udword2_c(v), 1) + +// Load 64-bits of integer data to lower part +#define VSX_IMPL_LOAD_L8(Tvec, Tp) \ +VSX_FINLINE(Tvec) vec_ld_l8(const Tp *p) \ +{ return ((Tvec)vec_promote(*((uint64*)p), 0)); } + +VSX_IMPL_LOAD_L8(vec_uchar16, uchar) +VSX_IMPL_LOAD_L8(vec_char16, schar) +VSX_IMPL_LOAD_L8(vec_ushort8, ushort) +VSX_IMPL_LOAD_L8(vec_short8, short) +VSX_IMPL_LOAD_L8(vec_uint4, uint) +VSX_IMPL_LOAD_L8(vec_int4, int) +VSX_IMPL_LOAD_L8(vec_float4, float) +VSX_IMPL_LOAD_L8(vec_udword2, uint64) +VSX_IMPL_LOAD_L8(vec_dword2, int64) +VSX_IMPL_LOAD_L8(vec_double2, double) + +// logical not +#define vec_not(a) vec_nor(a, a) + +// power9 yaya +// not equal +#ifndef vec_cmpne +# define vec_cmpne(a, b) vec_not(vec_cmpeq(a, b)) +#endif + +// absolute difference +#ifndef vec_absd +# define vec_absd(a, b) vec_sub(vec_max(a, b), vec_min(a, b)) +#endif + +/* + * Implement vec_unpacklu and vec_unpackhu + * since vec_unpackl, vec_unpackh only support signed integers +**/ +#define VSX_IMPL_UNPACKU(rt, rg, zero) \ +VSX_FINLINE(rt) vec_unpacklu(const rg& a) \ +{ return (rt)(vec_mergel(a, zero)); } \ +VSX_FINLINE(rt) vec_unpackhu(const rg& a) \ +{ return (rt)(vec_mergeh(a, zero)); } + +VSX_IMPL_UNPACKU(vec_ushort8, vec_uchar16, vec_uchar16_z) +VSX_IMPL_UNPACKU(vec_uint4, vec_ushort8, vec_ushort8_z) +VSX_IMPL_UNPACKU(vec_udword2, vec_uint4, vec_uint4_z) + +/* + * Implement vec_mergesqe and vec_mergesqo + * Merges the sequence values of even and odd elements of two vectors +*/ +#define VSX_IMPL_PERM(rt, fnm, ...) \ +VSX_FINLINE(rt) fnm(const rt& a, const rt& b) \ +{ static const vec_uchar16 perm = {__VA_ARGS__}; return vec_perm(a, b, perm); } + +// 16 +#define perm16_mergesqe 0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30 +#define perm16_mergesqo 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31 +VSX_IMPL_PERM(vec_uchar16, vec_mergesqe, perm16_mergesqe) +VSX_IMPL_PERM(vec_uchar16, vec_mergesqo, perm16_mergesqo) +VSX_IMPL_PERM(vec_char16, vec_mergesqe, perm16_mergesqe) +VSX_IMPL_PERM(vec_char16, vec_mergesqo, perm16_mergesqo) +// 8 +#define perm8_mergesqe 0, 1, 4, 5, 8, 9, 12, 13, 16, 17, 20, 21, 24, 25, 28, 29 +#define perm8_mergesqo 2, 3, 6, 7, 10, 11, 14, 15, 18, 19, 22, 23, 26, 27, 30, 31 +VSX_IMPL_PERM(vec_ushort8, vec_mergesqe, perm8_mergesqe) +VSX_IMPL_PERM(vec_ushort8, vec_mergesqo, perm8_mergesqo) +VSX_IMPL_PERM(vec_short8, vec_mergesqe, perm8_mergesqe) +VSX_IMPL_PERM(vec_short8, vec_mergesqo, perm8_mergesqo) +// 4 +#define perm4_mergesqe 0, 1, 2, 3, 8, 9, 10, 11, 16, 17, 18, 19, 24, 25, 26, 27 +#define perm4_mergesqo 4, 5, 6, 7, 12, 13, 14, 15, 20, 21, 22, 23, 28, 29, 30, 31 +VSX_IMPL_PERM(vec_uint4, vec_mergesqe, perm4_mergesqe) +VSX_IMPL_PERM(vec_uint4, vec_mergesqo, perm4_mergesqo) +VSX_IMPL_PERM(vec_int4, vec_mergesqe, perm4_mergesqe) +VSX_IMPL_PERM(vec_int4, vec_mergesqo, perm4_mergesqo) +VSX_IMPL_PERM(vec_float4, vec_mergesqe, perm4_mergesqe) +VSX_IMPL_PERM(vec_float4, vec_mergesqo, perm4_mergesqo) +// 2 +VSX_REDIRECT_2RG(vec_double2, vec_double2, vec_mergesqe, vec_mergeh) +VSX_REDIRECT_2RG(vec_double2, vec_double2, vec_mergesqo, vec_mergel) +VSX_REDIRECT_2RG(vec_dword2, vec_dword2, vec_mergesqe, vec_mergeh) +VSX_REDIRECT_2RG(vec_dword2, vec_dword2, vec_mergesqo, vec_mergel) +VSX_REDIRECT_2RG(vec_udword2, vec_udword2, vec_mergesqe, vec_mergeh) +VSX_REDIRECT_2RG(vec_udword2, vec_udword2, vec_mergesqo, vec_mergel) + +/* + * Implement vec_mergesqh and vec_mergesql + * Merges the sequence most and least significant halves of two vectors +*/ +#define VSX_IMPL_MERGESQHL(Tvec) \ +VSX_FINLINE(Tvec) vec_mergesqh(const Tvec& a, const Tvec& b) \ +{ return (Tvec)vec_mergeh(vec_udword2_c(a), vec_udword2_c(b)); } \ +VSX_FINLINE(Tvec) vec_mergesql(const Tvec& a, const Tvec& b) \ +{ return (Tvec)vec_mergel(vec_udword2_c(a), vec_udword2_c(b)); } +VSX_IMPL_MERGESQHL(vec_uchar16) +VSX_IMPL_MERGESQHL(vec_char16) +VSX_IMPL_MERGESQHL(vec_ushort8) +VSX_IMPL_MERGESQHL(vec_short8) +VSX_IMPL_MERGESQHL(vec_uint4) +VSX_IMPL_MERGESQHL(vec_int4) +VSX_IMPL_MERGESQHL(vec_float4) +VSX_REDIRECT_2RG(vec_udword2, vec_udword2, vec_mergesqh, vec_mergeh) +VSX_REDIRECT_2RG(vec_udword2, vec_udword2, vec_mergesql, vec_mergel) +VSX_REDIRECT_2RG(vec_dword2, vec_dword2, vec_mergesqh, vec_mergeh) +VSX_REDIRECT_2RG(vec_dword2, vec_dword2, vec_mergesql, vec_mergel) +VSX_REDIRECT_2RG(vec_double2, vec_double2, vec_mergesqh, vec_mergeh) +VSX_REDIRECT_2RG(vec_double2, vec_double2, vec_mergesql, vec_mergel) + + +// 2 and 4 channels interleave for all types except 2 lanes +#define VSX_IMPL_ST_INTERLEAVE(Tp, Tvec) \ +VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b, Tp* ptr) \ +{ \ + vsx_stf(vec_mergeh(a, b), 0, ptr); \ + vsx_stf(vec_mergel(a, b), 16, ptr); \ +} \ +VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b, \ + const Tvec& c, const Tvec& d, Tp* ptr) \ +{ \ + Tvec ac = vec_mergeh(a, c); \ + Tvec bd = vec_mergeh(b, d); \ + vsx_stf(vec_mergeh(ac, bd), 0, ptr); \ + vsx_stf(vec_mergel(ac, bd), 16, ptr); \ + ac = vec_mergel(a, c); \ + bd = vec_mergel(b, d); \ + vsx_stf(vec_mergeh(ac, bd), 32, ptr); \ + vsx_stf(vec_mergel(ac, bd), 48, ptr); \ +} +VSX_IMPL_ST_INTERLEAVE(uchar, vec_uchar16) +VSX_IMPL_ST_INTERLEAVE(schar, vec_char16) +VSX_IMPL_ST_INTERLEAVE(ushort, vec_ushort8) +VSX_IMPL_ST_INTERLEAVE(short, vec_short8) +VSX_IMPL_ST_INTERLEAVE(uint, vec_uint4) +VSX_IMPL_ST_INTERLEAVE(int, vec_int4) +VSX_IMPL_ST_INTERLEAVE(float, vec_float4) + +// 2 and 4 channels deinterleave for 16 lanes +#define VSX_IMPL_ST_DINTERLEAVE_8(Tp, Tvec) \ +VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b) \ +{ \ + Tvec v0 = vsx_ld(0, ptr); \ + Tvec v1 = vsx_ld(16, ptr); \ + a = vec_mergesqe(v0, v1); \ + b = vec_mergesqo(v0, v1); \ +} \ +VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b, \ + Tvec& c, Tvec& d) \ +{ \ + Tvec v0 = vsx_ld(0, ptr); \ + Tvec v1 = vsx_ld(16, ptr); \ + Tvec v2 = vsx_ld(32, ptr); \ + Tvec v3 = vsx_ld(48, ptr); \ + Tvec m0 = vec_mergesqe(v0, v1); \ + Tvec m1 = vec_mergesqe(v2, v3); \ + a = vec_mergesqe(m0, m1); \ + c = vec_mergesqo(m0, m1); \ + m0 = vec_mergesqo(v0, v1); \ + m1 = vec_mergesqo(v2, v3); \ + b = vec_mergesqe(m0, m1); \ + d = vec_mergesqo(m0, m1); \ +} +VSX_IMPL_ST_DINTERLEAVE_8(uchar, vec_uchar16) +VSX_IMPL_ST_DINTERLEAVE_8(schar, vec_char16) + +// 2 and 4 channels deinterleave for 8 lanes +#define VSX_IMPL_ST_DINTERLEAVE_16(Tp, Tvec) \ +VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b) \ +{ \ + Tvec v0 = vsx_ld(0, ptr); \ + Tvec v1 = vsx_ld(8, ptr); \ + a = vec_mergesqe(v0, v1); \ + b = vec_mergesqo(v0, v1); \ +} \ +VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b, \ + Tvec& c, Tvec& d) \ +{ \ + Tvec v0 = vsx_ld(0, ptr); \ + Tvec v1 = vsx_ld(8, ptr); \ + Tvec m0 = vec_mergeh(v0, v1); \ + Tvec m1 = vec_mergel(v0, v1); \ + Tvec ab0 = vec_mergeh(m0, m1); \ + Tvec cd0 = vec_mergel(m0, m1); \ + v0 = vsx_ld(16, ptr); \ + v1 = vsx_ld(24, ptr); \ + m0 = vec_mergeh(v0, v1); \ + m1 = vec_mergel(v0, v1); \ + Tvec ab1 = vec_mergeh(m0, m1); \ + Tvec cd1 = vec_mergel(m0, m1); \ + a = vec_mergesqh(ab0, ab1); \ + b = vec_mergesql(ab0, ab1); \ + c = vec_mergesqh(cd0, cd1); \ + d = vec_mergesql(cd0, cd1); \ +} +VSX_IMPL_ST_DINTERLEAVE_16(ushort, vec_ushort8) +VSX_IMPL_ST_DINTERLEAVE_16(short, vec_short8) + +// 2 and 4 channels deinterleave for 4 lanes +#define VSX_IMPL_ST_DINTERLEAVE_32(Tp, Tvec) \ +VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b) \ +{ \ + a = vsx_ld(0, ptr); \ + b = vsx_ld(4, ptr); \ + Tvec m0 = vec_mergeh(a, b); \ + Tvec m1 = vec_mergel(a, b); \ + a = vec_mergeh(m0, m1); \ + b = vec_mergel(m0, m1); \ +} \ +VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b, \ + Tvec& c, Tvec& d) \ +{ \ + Tvec v0 = vsx_ld(0, ptr); \ + Tvec v1 = vsx_ld(4, ptr); \ + Tvec v2 = vsx_ld(8, ptr); \ + Tvec v3 = vsx_ld(12, ptr); \ + Tvec m0 = vec_mergeh(v0, v2); \ + Tvec m1 = vec_mergeh(v1, v3); \ + a = vec_mergeh(m0, m1); \ + b = vec_mergel(m0, m1); \ + m0 = vec_mergel(v0, v2); \ + m1 = vec_mergel(v1, v3); \ + c = vec_mergeh(m0, m1); \ + d = vec_mergel(m0, m1); \ +} +VSX_IMPL_ST_DINTERLEAVE_32(uint, vec_uint4) +VSX_IMPL_ST_DINTERLEAVE_32(int, vec_int4) +VSX_IMPL_ST_DINTERLEAVE_32(float, vec_float4) + +// 2 and 4 channels interleave and deinterleave for 2 lanes +#define VSX_IMPL_ST_D_INTERLEAVE_64(Tp, Tvec, ld_func, st_func) \ +VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b, Tp* ptr) \ +{ \ + st_func(vec_mergeh(a, b), 0, ptr); \ + st_func(vec_mergel(a, b), 2, ptr); \ +} \ +VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b, \ + const Tvec& c, const Tvec& d, Tp* ptr) \ +{ \ + st_func(vec_mergeh(a, b), 0, ptr); \ + st_func(vec_mergeh(c, d), 2, ptr); \ + st_func(vec_mergel(a, b), 4, ptr); \ + st_func(vec_mergel(c, d), 6, ptr); \ +} \ +VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b) \ +{ \ + Tvec m0 = ld_func(0, ptr); \ + Tvec m1 = ld_func(2, ptr); \ + a = vec_mergeh(m0, m1); \ + b = vec_mergel(m0, m1); \ +} \ +VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b, \ + Tvec& c, Tvec& d) \ +{ \ + Tvec v0 = ld_func(0, ptr); \ + Tvec v1 = ld_func(2, ptr); \ + Tvec v2 = ld_func(4, ptr); \ + Tvec v3 = ld_func(6, ptr); \ + a = vec_mergeh(v0, v2); \ + b = vec_mergel(v0, v2); \ + c = vec_mergeh(v1, v3); \ + d = vec_mergel(v1, v3); \ +} +VSX_IMPL_ST_D_INTERLEAVE_64(int64, vec_dword2, vsx_ld2, vsx_st2) +VSX_IMPL_ST_D_INTERLEAVE_64(uint64, vec_udword2, vsx_ld2, vsx_st2) +VSX_IMPL_ST_D_INTERLEAVE_64(double, vec_double2, vsx_ld, vsx_st) + +/* 3 channels */ +#define VSX_IMPL_ST_INTERLEAVE_3CH_16(Tp, Tvec) \ +VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b, \ + const Tvec& c, Tp* ptr) \ +{ \ + static const vec_uchar16 a12 = {0, 16, 0, 1, 17, 0, 2, 18, 0, 3, 19, 0, 4, 20, 0, 5}; \ + static const vec_uchar16 a123 = {0, 1, 16, 3, 4, 17, 6, 7, 18, 9, 10, 19, 12, 13, 20, 15}; \ + vsx_st(vec_perm(vec_perm(a, b, a12), c, a123), 0, ptr); \ + static const vec_uchar16 b12 = {21, 0, 6, 22, 0, 7, 23, 0, 8, 24, 0, 9, 25, 0, 10, 26}; \ + static const vec_uchar16 b123 = {0, 21, 2, 3, 22, 5, 6, 23, 8, 9, 24, 11, 12, 25, 14, 15}; \ + vsx_st(vec_perm(vec_perm(a, b, b12), c, b123), 16, ptr); \ + static const vec_uchar16 c12 = {0, 11, 27, 0, 12, 28, 0, 13, 29, 0, 14, 30, 0, 15, 31, 0}; \ + static const vec_uchar16 c123 = {26, 1, 2, 27, 4, 5, 28, 7, 8, 29, 10, 11, 30, 13, 14, 31}; \ + vsx_st(vec_perm(vec_perm(a, b, c12), c, c123), 32, ptr); \ +} \ +VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b, Tvec& c) \ +{ \ + Tvec v1 = vsx_ld(0, ptr); \ + Tvec v2 = vsx_ld(16, ptr); \ + Tvec v3 = vsx_ld(32, ptr); \ + static const vec_uchar16 a12_perm = {0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 0, 0, 0, 0, 0}; \ + static const vec_uchar16 a123_perm = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 17, 20, 23, 26, 29}; \ + a = vec_perm(vec_perm(v1, v2, a12_perm), v3, a123_perm); \ + static const vec_uchar16 b12_perm = {1, 4, 7, 10, 13, 16, 19, 22, 25, 28, 31, 0, 0, 0, 0, 0}; \ + static const vec_uchar16 b123_perm = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 18, 21, 24, 27, 30}; \ + b = vec_perm(vec_perm(v1, v2, b12_perm), v3, b123_perm); \ + static const vec_uchar16 c12_perm = {2, 5, 8, 11, 14, 17, 20, 23, 26, 29, 0, 0, 0, 0, 0, 0}; \ + static const vec_uchar16 c123_perm = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 16, 19, 22, 25, 28, 31}; \ + c = vec_perm(vec_perm(v1, v2, c12_perm), v3, c123_perm); \ +} +VSX_IMPL_ST_INTERLEAVE_3CH_16(uchar, vec_uchar16) +VSX_IMPL_ST_INTERLEAVE_3CH_16(schar, vec_char16) + +#define VSX_IMPL_ST_INTERLEAVE_3CH_8(Tp, Tvec) \ +VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b, \ + const Tvec& c, Tp* ptr) \ +{ \ + static const vec_uchar16 a12 = {0, 1, 16, 17, 0, 0, 2, 3, 18, 19, 0, 0, 4, 5, 20, 21}; \ + static const vec_uchar16 a123 = {0, 1, 2, 3, 16, 17, 6, 7, 8, 9, 18, 19, 12, 13, 14, 15}; \ + vsx_st(vec_perm(vec_perm(a, b, a12), c, a123), 0, ptr); \ + static const vec_uchar16 b12 = {0, 0, 6, 7, 22, 23, 0, 0, 8, 9, 24, 25, 0, 0, 10, 11}; \ + static const vec_uchar16 b123 = {20, 21, 2, 3, 4, 5, 22, 23, 8, 9, 10, 11, 24, 25, 14, 15}; \ + vsx_st(vec_perm(vec_perm(a, b, b12), c, b123), 8, ptr); \ + static const vec_uchar16 c12 = {26, 27, 0, 0, 12, 13, 28, 29, 0, 0, 14, 15, 30, 31, 0, 0}; \ + static const vec_uchar16 c123 = {0, 1, 26, 27, 4, 5, 6, 7, 28, 29, 10, 11, 12, 13, 30, 31}; \ + vsx_st(vec_perm(vec_perm(a, b, c12), c, c123), 16, ptr); \ +} \ +VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b, Tvec& c) \ +{ \ + Tvec v1 = vsx_ld(0, ptr); \ + Tvec v2 = vsx_ld(8, ptr); \ + Tvec v3 = vsx_ld(16, ptr); \ + static const vec_uchar16 a12_perm = {0, 1, 6, 7, 12, 13, 18, 19, 24, 25, 30, 31, 0, 0, 0, 0}; \ + static const vec_uchar16 a123_perm = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 20, 21, 26, 27}; \ + a = vec_perm(vec_perm(v1, v2, a12_perm), v3, a123_perm); \ + static const vec_uchar16 b12_perm = {2, 3, 8, 9, 14, 15, 20, 21, 26, 27, 0, 0, 0, 0, 0, 0}; \ + static const vec_uchar16 b123_perm = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 16, 17, 22, 23, 28, 29}; \ + b = vec_perm(vec_perm(v1, v2, b12_perm), v3, b123_perm); \ + static const vec_uchar16 c12_perm = {4, 5, 10, 11, 16, 17, 22, 23, 28, 29, 0, 0, 0, 0, 0, 0}; \ + static const vec_uchar16 c123_perm = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 18, 19, 24, 25, 30, 31}; \ + c = vec_perm(vec_perm(v1, v2, c12_perm), v3, c123_perm); \ +} +VSX_IMPL_ST_INTERLEAVE_3CH_8(ushort, vec_ushort8) +VSX_IMPL_ST_INTERLEAVE_3CH_8(short, vec_short8) + +#define VSX_IMPL_ST_INTERLEAVE_3CH_4(Tp, Tvec) \ +VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b, \ + const Tvec& c, Tp* ptr) \ +{ \ + Tvec hbc = vec_mergeh(b, c); \ + static const vec_uchar16 ahbc = {0, 1, 2, 3, 16, 17, 18, 19, 20, 21, 22, 23, 4, 5, 6, 7}; \ + vsx_st(vec_perm(a, hbc, ahbc), 0, ptr); \ + Tvec lab = vec_mergel(a, b); \ + vsx_st(vec_sld(lab, hbc, 8), 4, ptr); \ + static const vec_uchar16 clab = {8, 9, 10, 11, 24, 25, 26, 27, 28, 29, 30, 31, 12, 13, 14, 15};\ + vsx_st(vec_perm(c, lab, clab), 8, ptr); \ +} \ +VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b, Tvec& c) \ +{ \ + Tvec v1 = vsx_ld(0, ptr); \ + Tvec v2 = vsx_ld(4, ptr); \ + Tvec v3 = vsx_ld(8, ptr); \ + static const vec_uchar16 flp = {0, 1, 2, 3, 12, 13, 14, 15, 16, 17, 18, 19, 28, 29, 30, 31}; \ + a = vec_perm(v1, vec_sld(v3, v2, 8), flp); \ + static const vec_uchar16 flp2 = {28, 29, 30, 31, 0, 1, 2, 3, 12, 13, 14, 15, 16, 17, 18, 19}; \ + b = vec_perm(v2, vec_sld(v1, v3, 8), flp2); \ + c = vec_perm(vec_sld(v2, v1, 8), v3, flp); \ +} +VSX_IMPL_ST_INTERLEAVE_3CH_4(uint, vec_uint4) +VSX_IMPL_ST_INTERLEAVE_3CH_4(int, vec_int4) +VSX_IMPL_ST_INTERLEAVE_3CH_4(float, vec_float4) + +#define VSX_IMPL_ST_INTERLEAVE_3CH_2(Tp, Tvec, ld_func, st_func) \ +VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b, \ + const Tvec& c, Tp* ptr) \ +{ \ + st_func(vec_mergeh(a, b), 0, ptr); \ + st_func(vec_permi(c, a, 1), 2, ptr); \ + st_func(vec_mergel(b, c), 4, ptr); \ +} \ +VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, \ + Tvec& b, Tvec& c) \ +{ \ + Tvec v1 = ld_func(0, ptr); \ + Tvec v2 = ld_func(2, ptr); \ + Tvec v3 = ld_func(4, ptr); \ + a = vec_permi(v1, v2, 1); \ + b = vec_permi(v1, v3, 2); \ + c = vec_permi(v2, v3, 1); \ +} +VSX_IMPL_ST_INTERLEAVE_3CH_2(int64, vec_dword2, vsx_ld2, vsx_st2) +VSX_IMPL_ST_INTERLEAVE_3CH_2(uint64, vec_udword2, vsx_ld2, vsx_st2) +VSX_IMPL_ST_INTERLEAVE_3CH_2(double, vec_double2, vsx_ld, vsx_st) + +#endif // CV_VSX + +//! @} + +#endif // OPENCV_HAL_VSX_UTILS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/wimage.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/wimage.hpp new file mode 100644 index 0000000000000000000000000000000000000000..c7b6efa06a70e984035cb2bc7ad050ded3c3e985 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/core/wimage.hpp @@ -0,0 +1,603 @@ +/*M////////////////////////////////////////////////////////////////////////////// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to +// this license. If you do not agree to this license, do not download, +// install, copy or use the software. +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2008, Google, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of Intel Corporation or contributors may not be used to endorse +// or promote products derived from this software without specific +// prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" +// and any express or implied warranties, including, but not limited to, the +// implied warranties of merchantability and fitness for a particular purpose +// are disclaimed. In no event shall the Intel Corporation or contributors be +// liable for any direct, indirect, incidental, special, exemplary, or +// consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +///////////////////////////////////////////////////////////////////////////////// +//M*/ + +#ifndef OPENCV_CORE_WIMAGE_HPP +#define OPENCV_CORE_WIMAGE_HPP + +#include "opencv2/core/core_c.h" + +#ifdef __cplusplus + +namespace cv { + +//! @addtogroup core +//! @{ + +template class WImage; +template class WImageBuffer; +template class WImageView; + +template class WImageC; +template class WImageBufferC; +template class WImageViewC; + +// Commonly used typedefs. +typedef WImage WImage_b; +typedef WImageView WImageView_b; +typedef WImageBuffer WImageBuffer_b; + +typedef WImageC WImage1_b; +typedef WImageViewC WImageView1_b; +typedef WImageBufferC WImageBuffer1_b; + +typedef WImageC WImage3_b; +typedef WImageViewC WImageView3_b; +typedef WImageBufferC WImageBuffer3_b; + +typedef WImage WImage_f; +typedef WImageView WImageView_f; +typedef WImageBuffer WImageBuffer_f; + +typedef WImageC WImage1_f; +typedef WImageViewC WImageView1_f; +typedef WImageBufferC WImageBuffer1_f; + +typedef WImageC WImage3_f; +typedef WImageViewC WImageView3_f; +typedef WImageBufferC WImageBuffer3_f; + +// There isn't a standard for signed and unsigned short so be more +// explicit in the typename for these cases. +typedef WImage WImage_16s; +typedef WImageView WImageView_16s; +typedef WImageBuffer WImageBuffer_16s; + +typedef WImageC WImage1_16s; +typedef WImageViewC WImageView1_16s; +typedef WImageBufferC WImageBuffer1_16s; + +typedef WImageC WImage3_16s; +typedef WImageViewC WImageView3_16s; +typedef WImageBufferC WImageBuffer3_16s; + +typedef WImage WImage_16u; +typedef WImageView WImageView_16u; +typedef WImageBuffer WImageBuffer_16u; + +typedef WImageC WImage1_16u; +typedef WImageViewC WImageView1_16u; +typedef WImageBufferC WImageBuffer1_16u; + +typedef WImageC WImage3_16u; +typedef WImageViewC WImageView3_16u; +typedef WImageBufferC WImageBuffer3_16u; + +/** @brief Image class which provides a thin layer around an IplImage. + +The goals of the class design are: + + -# All the data has explicit ownership to avoid memory leaks + -# No hidden allocations or copies for performance. + -# Easy access to OpenCV methods (which will access IPP if available) + -# Can easily treat external data as an image + -# Easy to create images which are subsets of other images + -# Fast pixel access which can take advantage of number of channels if known at compile time. + +The WImage class is the image class which provides the data accessors. The 'W' comes from the fact +that it is also a wrapper around the popular but inconvenient IplImage class. A WImage can be +constructed either using a WImageBuffer class which allocates and frees the data, or using a +WImageView class which constructs a subimage or a view into external data. The view class does no +memory management. Each class actually has two versions, one when the number of channels is known +at compile time and one when it isn't. Using the one with the number of channels specified can +provide some compile time optimizations by using the fact that the number of channels is a +constant. + +We use the convention (c,r) to refer to column c and row r with (0,0) being the upper left corner. +This is similar to standard Euclidean coordinates with the first coordinate varying in the +horizontal direction and the second coordinate varying in the vertical direction. Thus (c,r) is +usually in the domain [0, width) X [0, height) + +Example usage: +@code +WImageBuffer3_b im(5,7); // Make a 5X7 3 channel image of type uchar +WImageView3_b sub_im(im, 2,2, 3,3); // 3X3 submatrix +vector vec(10, 3.0f); +WImageView1_f user_im(&vec[0], 2, 5); // 2X5 image w/ supplied data + +im.SetZero(); // same as cvSetZero(im.Ipl()) +*im(2, 3) = 15; // Modify the element at column 2, row 3 +MySetRand(&sub_im); + +// Copy the second row into the first. This can be done with no memory +// allocation and will use SSE if IPP is available. +int w = im.Width(); +im.View(0,0, w,1).CopyFrom(im.View(0,1, w,1)); + +// Doesn't care about source of data since using WImage +void MySetRand(WImage_b* im) { // Works with any number of channels +for (int r = 0; r < im->Height(); ++r) { + float* row = im->Row(r); + for (int c = 0; c < im->Width(); ++c) { + for (int ch = 0; ch < im->Channels(); ++ch, ++row) { + *row = uchar(rand() & 255); + } + } +} +} +@endcode + +Functions that are not part of the basic image allocation, viewing, and access should come from +OpenCV, except some useful functions that are not part of OpenCV can be found in wimage_util.h +*/ +template +class WImage +{ +public: + typedef T BaseType; + + // WImage is an abstract class with no other virtual methods so make the + // destructor virtual. + virtual ~WImage() = 0; + + // Accessors + IplImage* Ipl() {return image_; } + const IplImage* Ipl() const {return image_; } + T* ImageData() { return reinterpret_cast(image_->imageData); } + const T* ImageData() const { + return reinterpret_cast(image_->imageData); + } + + int Width() const {return image_->width; } + int Height() const {return image_->height; } + + // WidthStep is the number of bytes to go to the pixel with the next y coord + int WidthStep() const {return image_->widthStep; } + + int Channels() const {return image_->nChannels; } + int ChannelSize() const {return sizeof(T); } // number of bytes per channel + + // Number of bytes per pixel + int PixelSize() const {return Channels() * ChannelSize(); } + + // Return depth type (e.g. IPL_DEPTH_8U, IPL_DEPTH_32F) which is the number + // of bits per channel and with the signed bit set. + // This is known at compile time using specializations. + int Depth() const; + + inline const T* Row(int r) const { + return reinterpret_cast(image_->imageData + r*image_->widthStep); + } + + inline T* Row(int r) { + return reinterpret_cast(image_->imageData + r*image_->widthStep); + } + + // Pixel accessors which returns a pointer to the start of the channel + inline T* operator() (int c, int r) { + return reinterpret_cast(image_->imageData + r*image_->widthStep) + + c*Channels(); + } + + inline const T* operator() (int c, int r) const { + return reinterpret_cast(image_->imageData + r*image_->widthStep) + + c*Channels(); + } + + // Copy the contents from another image which is just a convenience to cvCopy + void CopyFrom(const WImage& src) { cvCopy(src.Ipl(), image_); } + + // Set contents to zero which is just a convenient to cvSetZero + void SetZero() { cvSetZero(image_); } + + // Construct a view into a region of this image + WImageView View(int c, int r, int width, int height); + +protected: + // Disallow copy and assignment + WImage(const WImage&); + void operator=(const WImage&); + + explicit WImage(IplImage* img) : image_(img) { + assert(!img || img->depth == Depth()); + } + + void SetIpl(IplImage* image) { + assert(!image || image->depth == Depth()); + image_ = image; + } + + IplImage* image_; +}; + + +/** Image class when both the pixel type and number of channels +are known at compile time. This wrapper will speed up some of the operations +like accessing individual pixels using the () operator. +*/ +template +class WImageC : public WImage +{ +public: + typedef typename WImage::BaseType BaseType; + enum { kChannels = C }; + + explicit WImageC(IplImage* img) : WImage(img) { + assert(!img || img->nChannels == Channels()); + } + + // Construct a view into a region of this image + WImageViewC View(int c, int r, int width, int height); + + // Copy the contents from another image which is just a convenience to cvCopy + void CopyFrom(const WImageC& src) { + cvCopy(src.Ipl(), WImage::image_); + } + + // WImageC is an abstract class with no other virtual methods so make the + // destructor virtual. + virtual ~WImageC() = 0; + + int Channels() const {return C; } + +protected: + // Disallow copy and assignment + WImageC(const WImageC&); + void operator=(const WImageC&); + + void SetIpl(IplImage* image) { + assert(!image || image->depth == WImage::Depth()); + WImage::SetIpl(image); + } +}; + +/** Image class which owns the data, so it can be allocated and is always +freed. It cannot be copied but can be explicitly cloned. +*/ +template +class WImageBuffer : public WImage +{ +public: + typedef typename WImage::BaseType BaseType; + + // Default constructor which creates an object that can be + WImageBuffer() : WImage(0) {} + + WImageBuffer(int width, int height, int nchannels) : WImage(0) { + Allocate(width, height, nchannels); + } + + // Constructor which takes ownership of a given IplImage so releases + // the image on destruction. + explicit WImageBuffer(IplImage* img) : WImage(img) {} + + // Allocate an image. Does nothing if current size is the same as + // the new size. + void Allocate(int width, int height, int nchannels); + + // Set the data to point to an image, releasing the old data + void SetIpl(IplImage* img) { + ReleaseImage(); + WImage::SetIpl(img); + } + + // Clone an image which reallocates the image if of a different dimension. + void CloneFrom(const WImage& src) { + Allocate(src.Width(), src.Height(), src.Channels()); + CopyFrom(src); + } + + ~WImageBuffer() { + ReleaseImage(); + } + + // Release the image if it isn't null. + void ReleaseImage() { + if (WImage::image_) { + IplImage* image = WImage::image_; + cvReleaseImage(&image); + WImage::SetIpl(0); + } + } + + bool IsNull() const {return WImage::image_ == NULL; } + +private: + // Disallow copy and assignment + WImageBuffer(const WImageBuffer&); + void operator=(const WImageBuffer&); +}; + +/** Like a WImageBuffer class but when the number of channels is known at compile time. +*/ +template +class WImageBufferC : public WImageC +{ +public: + typedef typename WImage::BaseType BaseType; + enum { kChannels = C }; + + // Default constructor which creates an object that can be + WImageBufferC() : WImageC(0) {} + + WImageBufferC(int width, int height) : WImageC(0) { + Allocate(width, height); + } + + // Constructor which takes ownership of a given IplImage so releases + // the image on destruction. + explicit WImageBufferC(IplImage* img) : WImageC(img) {} + + // Allocate an image. Does nothing if current size is the same as + // the new size. + void Allocate(int width, int height); + + // Set the data to point to an image, releasing the old data + void SetIpl(IplImage* img) { + ReleaseImage(); + WImageC::SetIpl(img); + } + + // Clone an image which reallocates the image if of a different dimension. + void CloneFrom(const WImageC& src) { + Allocate(src.Width(), src.Height()); + CopyFrom(src); + } + + ~WImageBufferC() { + ReleaseImage(); + } + + // Release the image if it isn't null. + void ReleaseImage() { + if (WImage::image_) { + IplImage* image = WImage::image_; + cvReleaseImage(&image); + WImageC::SetIpl(0); + } + } + + bool IsNull() const {return WImage::image_ == NULL; } + +private: + // Disallow copy and assignment + WImageBufferC(const WImageBufferC&); + void operator=(const WImageBufferC&); +}; + +/** View into an image class which allows treating a subimage as an image or treating external data +as an image +*/ +template class WImageView : public WImage +{ +public: + typedef typename WImage::BaseType BaseType; + + // Construct a subimage. No checks are done that the subimage lies + // completely inside the original image. + WImageView(WImage* img, int c, int r, int width, int height); + + // Refer to external data. + // If not given width_step assumed to be same as width. + WImageView(T* data, int width, int height, int channels, int width_step = -1); + + // Refer to external data. This does NOT take ownership + // of the supplied IplImage. + WImageView(IplImage* img) : WImage(img) {} + + // Copy constructor + WImageView(const WImage& img) : WImage(0) { + header_ = *(img.Ipl()); + WImage::SetIpl(&header_); + } + + WImageView& operator=(const WImage& img) { + header_ = *(img.Ipl()); + WImage::SetIpl(&header_); + return *this; + } + +protected: + IplImage header_; +}; + + +template +class WImageViewC : public WImageC +{ +public: + typedef typename WImage::BaseType BaseType; + enum { kChannels = C }; + + // Default constructor needed for vectors of views. + WImageViewC(); + + virtual ~WImageViewC() {} + + // Construct a subimage. No checks are done that the subimage lies + // completely inside the original image. + WImageViewC(WImageC* img, + int c, int r, int width, int height); + + // Refer to external data + WImageViewC(T* data, int width, int height, int width_step = -1); + + // Refer to external data. This does NOT take ownership + // of the supplied IplImage. + WImageViewC(IplImage* img) : WImageC(img) {} + + // Copy constructor which does a shallow copy to allow multiple views + // of same data. gcc-4.1.1 gets confused if both versions of + // the constructor and assignment operator are not provided. + WImageViewC(const WImageC& img) : WImageC(0) { + header_ = *(img.Ipl()); + WImageC::SetIpl(&header_); + } + WImageViewC(const WImageViewC& img) : WImageC(0) { + header_ = *(img.Ipl()); + WImageC::SetIpl(&header_); + } + + WImageViewC& operator=(const WImageC& img) { + header_ = *(img.Ipl()); + WImageC::SetIpl(&header_); + return *this; + } + WImageViewC& operator=(const WImageViewC& img) { + header_ = *(img.Ipl()); + WImageC::SetIpl(&header_); + return *this; + } + +protected: + IplImage header_; +}; + + +// Specializations for depth +template<> +inline int WImage::Depth() const {return IPL_DEPTH_8U; } +template<> +inline int WImage::Depth() const {return IPL_DEPTH_8S; } +template<> +inline int WImage::Depth() const {return IPL_DEPTH_16S; } +template<> +inline int WImage::Depth() const {return IPL_DEPTH_16U; } +template<> +inline int WImage::Depth() const {return IPL_DEPTH_32S; } +template<> +inline int WImage::Depth() const {return IPL_DEPTH_32F; } +template<> +inline int WImage::Depth() const {return IPL_DEPTH_64F; } + +template inline WImage::~WImage() {} +template inline WImageC::~WImageC() {} + +template +inline void WImageBuffer::Allocate(int width, int height, int nchannels) +{ + if (IsNull() || WImage::Width() != width || + WImage::Height() != height || WImage::Channels() != nchannels) { + ReleaseImage(); + WImage::image_ = cvCreateImage(cvSize(width, height), + WImage::Depth(), nchannels); + } +} + +template +inline void WImageBufferC::Allocate(int width, int height) +{ + if (IsNull() || WImage::Width() != width || WImage::Height() != height) { + ReleaseImage(); + WImageC::SetIpl(cvCreateImage(cvSize(width, height),WImage::Depth(), C)); + } +} + +template +WImageView::WImageView(WImage* img, int c, int r, int width, int height) + : WImage(0) +{ + header_ = *(img->Ipl()); + header_.imageData = reinterpret_cast((*img)(c, r)); + header_.width = width; + header_.height = height; + WImage::SetIpl(&header_); +} + +template +WImageView::WImageView(T* data, int width, int height, int nchannels, int width_step) + : WImage(0) +{ + cvInitImageHeader(&header_, cvSize(width, height), WImage::Depth(), nchannels); + header_.imageData = reinterpret_cast(data); + if (width_step > 0) { + header_.widthStep = width_step; + } + WImage::SetIpl(&header_); +} + +template +WImageViewC::WImageViewC(WImageC* img, int c, int r, int width, int height) + : WImageC(0) +{ + header_ = *(img->Ipl()); + header_.imageData = reinterpret_cast((*img)(c, r)); + header_.width = width; + header_.height = height; + WImageC::SetIpl(&header_); +} + +template +WImageViewC::WImageViewC() : WImageC(0) { + cvInitImageHeader(&header_, cvSize(0, 0), WImage::Depth(), C); + header_.imageData = reinterpret_cast(0); + WImageC::SetIpl(&header_); +} + +template +WImageViewC::WImageViewC(T* data, int width, int height, int width_step) + : WImageC(0) +{ + cvInitImageHeader(&header_, cvSize(width, height), WImage::Depth(), C); + header_.imageData = reinterpret_cast(data); + if (width_step > 0) { + header_.widthStep = width_step; + } + WImageC::SetIpl(&header_); +} + +// Construct a view into a region of an image +template +WImageView WImage::View(int c, int r, int width, int height) { + return WImageView(this, c, r, width, height); +} + +template +WImageViewC WImageC::View(int c, int r, int width, int height) { + return WImageViewC(this, c, r, width, height); +} + +//! @} core + +} // end of namespace + +#endif // __cplusplus + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/cvconfig.h b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/cvconfig.h new file mode 100644 index 0000000000000000000000000000000000000000..d20ee87d01d242a5fcf3a6a7b980e69969584122 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/cvconfig.h @@ -0,0 +1,248 @@ +#ifndef OPENCV_CVCONFIG_H_INCLUDED +#define OPENCV_CVCONFIG_H_INCLUDED + +/* OpenCV compiled as static or dynamic libs */ +/* #undef BUILD_SHARED_LIBS */ + +/* OpenCV intrinsics optimized code */ +#define CV_ENABLE_INTRINSICS + +/* OpenCV additional optimized code */ +/* #undef CV_DISABLE_OPTIMIZATION */ + +/* Compile for 'real' NVIDIA GPU architectures */ +#define CUDA_ARCH_BIN "" + +/* Create PTX or BIN for 1.0 compute capability */ +/* #undef CUDA_ARCH_BIN_OR_PTX_10 */ + +/* NVIDIA GPU features are used */ +#define CUDA_ARCH_FEATURES "" + +/* Compile for 'virtual' NVIDIA PTX architectures */ +#define CUDA_ARCH_PTX "" + +/* AVFoundation video libraries */ +/* #undef HAVE_AVFOUNDATION */ + +/* V4L capturing support */ +/* #undef HAVE_CAMV4L */ + +/* V4L2 capturing support */ +/* #undef HAVE_CAMV4L2 */ + +/* Carbon windowing environment */ +/* #undef HAVE_CARBON */ + +/* AMD's Basic Linear Algebra Subprograms Library*/ +/* #undef HAVE_CLAMDBLAS */ + +/* AMD's OpenCL Fast Fourier Transform Library*/ +/* #undef HAVE_CLAMDFFT */ + +/* Clp support */ +/* #undef HAVE_CLP */ + +/* Cocoa API */ +/* #undef HAVE_COCOA */ + +/* C= */ +/* #undef HAVE_CSTRIPES */ + +/* NVIDIA CUDA Basic Linear Algebra Subprograms (BLAS) API*/ +/* #undef HAVE_CUBLAS */ + +/* NVIDIA CUDA Runtime API*/ +/* #undef HAVE_CUDA */ + +/* NVIDIA CUDA Fast Fourier Transform (FFT) API*/ +/* #undef HAVE_CUFFT */ + +/* IEEE1394 capturing support */ +/* #undef HAVE_DC1394 */ + +/* IEEE1394 capturing support - libdc1394 v2.x */ +/* #undef HAVE_DC1394_2 */ + +/* DirectX */ +/* #undef HAVE_DIRECTX */ +/* #undef HAVE_DIRECTX_NV12 */ +/* #undef HAVE_D3D11 */ +/* #undef HAVE_D3D10 */ +/* #undef HAVE_D3D9 */ + +/* DirectShow Video Capture library */ +/* #undef HAVE_DSHOW */ + +/* Eigen Matrix & Linear Algebra Library */ +/* #undef HAVE_EIGEN */ + +/* FFMpeg video library */ +/* #undef HAVE_FFMPEG */ + +/* Geospatial Data Abstraction Library */ +/* #undef HAVE_GDAL */ + +/* GStreamer multimedia framework */ +/* #undef HAVE_GSTREAMER */ + +/* GTK+ 2.0 Thread support */ +/* #undef HAVE_GTHREAD */ + +/* GTK+ 2.x toolkit */ +/* #undef HAVE_GTK */ + +/* Halide support */ +/* #undef HAVE_HALIDE */ + +/* Define to 1 if you have the header file. */ +#define HAVE_INTTYPES_H 1 + +/* Intel Perceptual Computing SDK library */ +/* #undef HAVE_INTELPERC */ + +/* Intel Integrated Performance Primitives */ +/* #undef HAVE_IPP */ +/* #undef HAVE_IPP_ICV */ +/* #undef HAVE_IPP_IW */ +/* #undef HAVE_IPP_IW_LL */ + +/* JPEG-2000 codec */ +#define HAVE_JASPER + +/* IJG JPEG codec */ +#define HAVE_JPEG + +/* libpng/png.h needs to be included */ +/* #undef HAVE_LIBPNG_PNG_H */ + +/* GDCM DICOM codec */ +/* #undef HAVE_GDCM */ + +/* V4L/V4L2 capturing support via libv4l */ +/* #undef HAVE_LIBV4L */ + +/* Microsoft Media Foundation Capture library */ +/* #undef HAVE_MSMF */ + +/* NVIDIA Video Decoding API*/ +/* #undef HAVE_NVCUVID */ + +/* NVIDIA Video Encoding API*/ +/* #undef HAVE_NVCUVENC */ + +/* OpenCL Support */ +/* #undef HAVE_OPENCL */ +/* #undef HAVE_OPENCL_STATIC */ +/* #undef HAVE_OPENCL_SVM */ + +/* OpenEXR codec */ +#define HAVE_OPENEXR + +/* OpenGL support*/ +/* #undef HAVE_OPENGL */ + +/* OpenNI library */ +/* #undef HAVE_OPENNI */ + +/* OpenNI library */ +/* #undef HAVE_OPENNI2 */ + +/* PNG codec */ +#define HAVE_PNG + +/* Posix threads (pthreads) */ +#define HAVE_PTHREAD + +/* parallel_for with pthreads */ +#define HAVE_PTHREADS_PF + +/* Qt support */ +/* #undef HAVE_QT */ + +/* Qt OpenGL support */ +/* #undef HAVE_QT_OPENGL */ + +/* QuickTime video libraries */ +/* #undef HAVE_QUICKTIME */ + +/* QTKit video libraries */ +/* #undef HAVE_QTKIT */ + +/* Intel Threading Building Blocks */ +/* #undef HAVE_TBB */ + +/* TIFF codec */ +#define HAVE_TIFF + +/* Unicap video capture library */ +/* #undef HAVE_UNICAP */ + +/* Video for Windows support */ +/* #undef HAVE_VFW */ + +/* V4L2 capturing support in videoio.h */ +/* #undef HAVE_VIDEOIO */ + +/* Win32 UI */ +/* #undef HAVE_WIN32UI */ + +/* XIMEA camera support */ +/* #undef HAVE_XIMEA */ + +/* Xine video library */ +/* #undef HAVE_XINE */ + +/* Define if your processor stores words with the most significant byte + first (like Motorola and SPARC, unlike Intel and VAX). */ +/* #undef WORDS_BIGENDIAN */ + +/* gPhoto2 library */ +/* #undef HAVE_GPHOTO2 */ + +/* VA library (libva) */ +/* #undef HAVE_VA */ + +/* Intel VA-API/OpenCL */ +/* #undef HAVE_VA_INTEL */ + +/* Intel Media SDK */ +/* #undef HAVE_MFX */ + +/* Lapack */ +/* #undef HAVE_LAPACK */ + +/* Library was compiled with functions instrumentation */ +/* #undef ENABLE_INSTRUMENTATION */ + +/* OpenVX */ +/* #undef HAVE_OPENVX */ + +#if defined(HAVE_XINE) || \ + defined(HAVE_GSTREAMER) || \ + defined(HAVE_QUICKTIME) || \ + defined(HAVE_QTKIT) || \ + defined(HAVE_AVFOUNDATION) || \ + /*defined(HAVE_OPENNI) || too specialized */ \ + defined(HAVE_FFMPEG) || \ + defined(HAVE_MSMF) +#define HAVE_VIDEO_INPUT +#endif + +#if /*defined(HAVE_XINE) || */\ + defined(HAVE_GSTREAMER) || \ + defined(HAVE_QUICKTIME) || \ + defined(HAVE_QTKIT) || \ + defined(HAVE_AVFOUNDATION) || \ + defined(HAVE_FFMPEG) || \ + defined(HAVE_MSMF) +#define HAVE_VIDEO_OUTPUT +#endif + +/* OpenCV trace utilities */ +#define OPENCV_TRACE + +/* Library QR-code decoding */ +#define HAVE_QUIRC + +#endif // OPENCV_CVCONFIG_H_INCLUDED diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/features2d.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/features2d.hpp new file mode 100644 index 0000000000000000000000000000000000000000..ee81ebe38562da7be600a5101e0d8aa1f9b60318 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/features2d.hpp @@ -0,0 +1,1428 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_FEATURES_2D_HPP +#define OPENCV_FEATURES_2D_HPP + +#include "opencv2/opencv_modules.hpp" +#include "opencv2/core.hpp" + +#ifdef HAVE_OPENCV_FLANN +#include "opencv2/flann/miniflann.hpp" +#endif + +/** + @defgroup features2d 2D Features Framework + @{ + @defgroup features2d_main Feature Detection and Description + @defgroup features2d_match Descriptor Matchers + +Matchers of keypoint descriptors in OpenCV have wrappers with a common interface that enables you to +easily switch between different algorithms solving the same problem. This section is devoted to +matching descriptors that are represented as vectors in a multidimensional space. All objects that +implement vector descriptor matchers inherit the DescriptorMatcher interface. + +@note + - An example explaining keypoint matching can be found at + opencv_source_code/samples/cpp/descriptor_extractor_matcher.cpp + - An example on descriptor matching evaluation can be found at + opencv_source_code/samples/cpp/detector_descriptor_matcher_evaluation.cpp + - An example on one to many image matching can be found at + opencv_source_code/samples/cpp/matching_to_many_images.cpp + + @defgroup features2d_draw Drawing Function of Keypoints and Matches + @defgroup features2d_category Object Categorization + +This section describes approaches based on local 2D features and used to categorize objects. + +@note + - A complete Bag-Of-Words sample can be found at + opencv_source_code/samples/cpp/bagofwords_classification.cpp + - (Python) An example using the features2D framework to perform object categorization can be + found at opencv_source_code/samples/python/find_obj.py + + @} + */ + +namespace cv +{ + +//! @addtogroup features2d +//! @{ + +// //! writes vector of keypoints to the file storage +// CV_EXPORTS void write(FileStorage& fs, const String& name, const std::vector& keypoints); +// //! reads vector of keypoints from the specified file storage node +// CV_EXPORTS void read(const FileNode& node, CV_OUT std::vector& keypoints); + +/** @brief A class filters a vector of keypoints. + + Because now it is difficult to provide a convenient interface for all usage scenarios of the + keypoints filter class, it has only several needed by now static methods. + */ +class CV_EXPORTS KeyPointsFilter +{ +public: + KeyPointsFilter(){} + + /* + * Remove keypoints within borderPixels of an image edge. + */ + static void runByImageBorder( std::vector& keypoints, Size imageSize, int borderSize ); + /* + * Remove keypoints of sizes out of range. + */ + static void runByKeypointSize( std::vector& keypoints, float minSize, + float maxSize=FLT_MAX ); + /* + * Remove keypoints from some image by mask for pixels of this image. + */ + static void runByPixelsMask( std::vector& keypoints, const Mat& mask ); + /* + * Remove duplicated keypoints. + */ + static void removeDuplicated( std::vector& keypoints ); + /* + * Remove duplicated keypoints and sort the remaining keypoints + */ + static void removeDuplicatedSorted( std::vector& keypoints ); + + /* + * Retain the specified number of the best keypoints (according to the response) + */ + static void retainBest( std::vector& keypoints, int npoints ); +}; + + +/************************************ Base Classes ************************************/ + +/** @brief Abstract base class for 2D image feature detectors and descriptor extractors +*/ +#ifdef __EMSCRIPTEN__ +class CV_EXPORTS_W Feature2D : public Algorithm +#else +class CV_EXPORTS_W Feature2D : public virtual Algorithm +#endif +{ +public: + virtual ~Feature2D(); + + /** @brief Detects keypoints in an image (first variant) or image set (second variant). + + @param image Image. + @param keypoints The detected keypoints. In the second variant of the method keypoints[i] is a set + of keypoints detected in images[i] . + @param mask Mask specifying where to look for keypoints (optional). It must be a 8-bit integer + matrix with non-zero values in the region of interest. + */ + CV_WRAP virtual void detect( InputArray image, + CV_OUT std::vector& keypoints, + InputArray mask=noArray() ); + + /** @overload + @param images Image set. + @param keypoints The detected keypoints. In the second variant of the method keypoints[i] is a set + of keypoints detected in images[i] . + @param masks Masks for each input image specifying where to look for keypoints (optional). + masks[i] is a mask for images[i]. + */ + CV_WRAP virtual void detect( InputArrayOfArrays images, + CV_OUT std::vector >& keypoints, + InputArrayOfArrays masks=noArray() ); + + /** @brief Computes the descriptors for a set of keypoints detected in an image (first variant) or image set + (second variant). + + @param image Image. + @param keypoints Input collection of keypoints. Keypoints for which a descriptor cannot be + computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint + with several dominant orientations (for each orientation). + @param descriptors Computed descriptors. In the second variant of the method descriptors[i] are + descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the + descriptor for keypoint j-th keypoint. + */ + CV_WRAP virtual void compute( InputArray image, + CV_OUT CV_IN_OUT std::vector& keypoints, + OutputArray descriptors ); + + /** @overload + + @param images Image set. + @param keypoints Input collection of keypoints. Keypoints for which a descriptor cannot be + computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint + with several dominant orientations (for each orientation). + @param descriptors Computed descriptors. In the second variant of the method descriptors[i] are + descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the + descriptor for keypoint j-th keypoint. + */ + CV_WRAP virtual void compute( InputArrayOfArrays images, + CV_OUT CV_IN_OUT std::vector >& keypoints, + OutputArrayOfArrays descriptors ); + + /** Detects keypoints and computes the descriptors */ + CV_WRAP virtual void detectAndCompute( InputArray image, InputArray mask, + CV_OUT std::vector& keypoints, + OutputArray descriptors, + bool useProvidedKeypoints=false ); + + CV_WRAP virtual int descriptorSize() const; + CV_WRAP virtual int descriptorType() const; + CV_WRAP virtual int defaultNorm() const; + + CV_WRAP void write( const String& fileName ) const; + + CV_WRAP void read( const String& fileName ); + + virtual void write( FileStorage&) const CV_OVERRIDE; + + // see corresponding cv::Algorithm method + CV_WRAP virtual void read( const FileNode&) CV_OVERRIDE; + + //! Return true if detector object is empty + CV_WRAP virtual bool empty() const CV_OVERRIDE; + CV_WRAP virtual String getDefaultName() const CV_OVERRIDE; + + // see corresponding cv::Algorithm method + CV_WRAP inline void write(const Ptr& fs, const String& name = String()) const { Algorithm::write(fs, name); } +}; + +/** Feature detectors in OpenCV have wrappers with a common interface that enables you to easily switch +between different algorithms solving the same problem. All objects that implement keypoint detectors +inherit the FeatureDetector interface. */ +typedef Feature2D FeatureDetector; + +/** Extractors of keypoint descriptors in OpenCV have wrappers with a common interface that enables you +to easily switch between different algorithms solving the same problem. This section is devoted to +computing descriptors represented as vectors in a multidimensional space. All objects that implement +the vector descriptor extractors inherit the DescriptorExtractor interface. + */ +typedef Feature2D DescriptorExtractor; + +//! @addtogroup features2d_main +//! @{ + +/** @brief Class implementing the BRISK keypoint detector and descriptor extractor, described in @cite LCS11 . + */ +class CV_EXPORTS_W BRISK : public Feature2D +{ +public: + /** @brief The BRISK constructor + + @param thresh AGAST detection threshold score. + @param octaves detection octaves. Use 0 to do single scale. + @param patternScale apply this scale to the pattern used for sampling the neighbourhood of a + keypoint. + */ + CV_WRAP static Ptr create(int thresh=30, int octaves=3, float patternScale=1.0f); + + /** @brief The BRISK constructor for a custom pattern + + @param radiusList defines the radii (in pixels) where the samples around a keypoint are taken (for + keypoint scale 1). + @param numberList defines the number of sampling points on the sampling circle. Must be the same + size as radiusList.. + @param dMax threshold for the short pairings used for descriptor formation (in pixels for keypoint + scale 1). + @param dMin threshold for the long pairings used for orientation determination (in pixels for + keypoint scale 1). + @param indexChange index remapping of the bits. */ + CV_WRAP static Ptr create(const std::vector &radiusList, const std::vector &numberList, + float dMax=5.85f, float dMin=8.2f, const std::vector& indexChange=std::vector()); + + /** @brief The BRISK constructor for a custom pattern, detection threshold and octaves + + @param thresh AGAST detection threshold score. + @param octaves detection octaves. Use 0 to do single scale. + @param radiusList defines the radii (in pixels) where the samples around a keypoint are taken (for + keypoint scale 1). + @param numberList defines the number of sampling points on the sampling circle. Must be the same + size as radiusList.. + @param dMax threshold for the short pairings used for descriptor formation (in pixels for keypoint + scale 1). + @param dMin threshold for the long pairings used for orientation determination (in pixels for + keypoint scale 1). + @param indexChange index remapping of the bits. */ + CV_WRAP static Ptr create(int thresh, int octaves, const std::vector &radiusList, + const std::vector &numberList, float dMax=5.85f, float dMin=8.2f, + const std::vector& indexChange=std::vector()); + CV_WRAP virtual String getDefaultName() const CV_OVERRIDE; +}; + +/** @brief Class implementing the ORB (*oriented BRIEF*) keypoint detector and descriptor extractor + +described in @cite RRKB11 . The algorithm uses FAST in pyramids to detect stable keypoints, selects +the strongest features using FAST or Harris response, finds their orientation using first-order +moments and computes the descriptors using BRIEF (where the coordinates of random point pairs (or +k-tuples) are rotated according to the measured orientation). + */ +class CV_EXPORTS_W ORB : public Feature2D +{ +public: + enum { kBytes = 32, HARRIS_SCORE=0, FAST_SCORE=1 }; + + /** @brief The ORB constructor + + @param nfeatures The maximum number of features to retain. + @param scaleFactor Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical + pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor + will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor + will mean that to cover certain scale range you will need more pyramid levels and so the speed + will suffer. + @param nlevels The number of pyramid levels. The smallest level will have linear size equal to + input_image_linear_size/pow(scaleFactor, nlevels - firstLevel). + @param edgeThreshold This is size of the border where the features are not detected. It should + roughly match the patchSize parameter. + @param firstLevel The level of pyramid to put source image to. Previous layers are filled + with upscaled source image. + @param WTA_K The number of points that produce each element of the oriented BRIEF descriptor. The + default value 2 means the BRIEF where we take a random point pair and compare their brightnesses, + so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3 + random points (of course, those point coordinates are random, but they are generated from the + pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel + rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such + output will occupy 2 bits, and therefore it will need a special variant of Hamming distance, + denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each + bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3). + @param scoreType The default HARRIS_SCORE means that Harris algorithm is used to rank features + (the score is written to KeyPoint::score and is used to retain best nfeatures features); + FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints, + but it is a little faster to compute. + @param patchSize size of the patch used by the oriented BRIEF descriptor. Of course, on smaller + pyramid layers the perceived image area covered by a feature will be larger. + @param fastThreshold + */ + CV_WRAP static Ptr create(int nfeatures=500, float scaleFactor=1.2f, int nlevels=8, int edgeThreshold=31, + int firstLevel=0, int WTA_K=2, int scoreType=ORB::HARRIS_SCORE, int patchSize=31, int fastThreshold=20); + + CV_WRAP virtual void setMaxFeatures(int maxFeatures) = 0; + CV_WRAP virtual int getMaxFeatures() const = 0; + + CV_WRAP virtual void setScaleFactor(double scaleFactor) = 0; + CV_WRAP virtual double getScaleFactor() const = 0; + + CV_WRAP virtual void setNLevels(int nlevels) = 0; + CV_WRAP virtual int getNLevels() const = 0; + + CV_WRAP virtual void setEdgeThreshold(int edgeThreshold) = 0; + CV_WRAP virtual int getEdgeThreshold() const = 0; + + CV_WRAP virtual void setFirstLevel(int firstLevel) = 0; + CV_WRAP virtual int getFirstLevel() const = 0; + + CV_WRAP virtual void setWTA_K(int wta_k) = 0; + CV_WRAP virtual int getWTA_K() const = 0; + + CV_WRAP virtual void setScoreType(int scoreType) = 0; + CV_WRAP virtual int getScoreType() const = 0; + + CV_WRAP virtual void setPatchSize(int patchSize) = 0; + CV_WRAP virtual int getPatchSize() const = 0; + + CV_WRAP virtual void setFastThreshold(int fastThreshold) = 0; + CV_WRAP virtual int getFastThreshold() const = 0; + CV_WRAP virtual String getDefaultName() const CV_OVERRIDE; +}; + +/** @brief Maximally stable extremal region extractor + +The class encapsulates all the parameters of the %MSER extraction algorithm (see [wiki +article](http://en.wikipedia.org/wiki/Maximally_stable_extremal_regions)). + +- there are two different implementation of %MSER: one for grey image, one for color image + +- the grey image algorithm is taken from: @cite nister2008linear ; the paper claims to be faster +than union-find method; it actually get 1.5~2m/s on my centrino L7200 1.2GHz laptop. + +- the color image algorithm is taken from: @cite forssen2007maximally ; it should be much slower +than grey image method ( 3~4 times ); the chi_table.h file is taken directly from paper's source +code which is distributed under GPL. + +- (Python) A complete example showing the use of the %MSER detector can be found at samples/python/mser.py +*/ +class CV_EXPORTS_W MSER : public Feature2D +{ +public: + /** @brief Full consturctor for %MSER detector + + @param _delta it compares \f$(size_{i}-size_{i-delta})/size_{i-delta}\f$ + @param _min_area prune the area which smaller than minArea + @param _max_area prune the area which bigger than maxArea + @param _max_variation prune the area have similar size to its children + @param _min_diversity for color image, trace back to cut off mser with diversity less than min_diversity + @param _max_evolution for color image, the evolution steps + @param _area_threshold for color image, the area threshold to cause re-initialize + @param _min_margin for color image, ignore too small margin + @param _edge_blur_size for color image, the aperture size for edge blur + */ + CV_WRAP static Ptr create( int _delta=5, int _min_area=60, int _max_area=14400, + double _max_variation=0.25, double _min_diversity=.2, + int _max_evolution=200, double _area_threshold=1.01, + double _min_margin=0.003, int _edge_blur_size=5 ); + + /** @brief Detect %MSER regions + + @param image input image (8UC1, 8UC3 or 8UC4, must be greater or equal than 3x3) + @param msers resulting list of point sets + @param bboxes resulting bounding boxes + */ + CV_WRAP virtual void detectRegions( InputArray image, + CV_OUT std::vector >& msers, + CV_OUT std::vector& bboxes ) = 0; + + CV_WRAP virtual void setDelta(int delta) = 0; + CV_WRAP virtual int getDelta() const = 0; + + CV_WRAP virtual void setMinArea(int minArea) = 0; + CV_WRAP virtual int getMinArea() const = 0; + + CV_WRAP virtual void setMaxArea(int maxArea) = 0; + CV_WRAP virtual int getMaxArea() const = 0; + + CV_WRAP virtual void setPass2Only(bool f) = 0; + CV_WRAP virtual bool getPass2Only() const = 0; + CV_WRAP virtual String getDefaultName() const CV_OVERRIDE; +}; + +/** @overload */ +CV_EXPORTS void FAST( InputArray image, CV_OUT std::vector& keypoints, + int threshold, bool nonmaxSuppression=true ); + +/** @brief Detects corners using the FAST algorithm + +@param image grayscale image where keypoints (corners) are detected. +@param keypoints keypoints detected on the image. +@param threshold threshold on difference between intensity of the central pixel and pixels of a +circle around this pixel. +@param nonmaxSuppression if true, non-maximum suppression is applied to detected corners +(keypoints). +@param type one of the three neighborhoods as defined in the paper: +FastFeatureDetector::TYPE_9_16, FastFeatureDetector::TYPE_7_12, +FastFeatureDetector::TYPE_5_8 + +Detects corners using the FAST algorithm by @cite Rosten06 . + +@note In Python API, types are given as cv2.FAST_FEATURE_DETECTOR_TYPE_5_8, +cv2.FAST_FEATURE_DETECTOR_TYPE_7_12 and cv2.FAST_FEATURE_DETECTOR_TYPE_9_16. For corner +detection, use cv2.FAST.detect() method. + */ +CV_EXPORTS void FAST( InputArray image, CV_OUT std::vector& keypoints, + int threshold, bool nonmaxSuppression, int type ); + +//! @} features2d_main + +//! @addtogroup features2d_main +//! @{ + +/** @brief Wrapping class for feature detection using the FAST method. : + */ +class CV_EXPORTS_W FastFeatureDetector : public Feature2D +{ +public: + enum + { + TYPE_5_8 = 0, TYPE_7_12 = 1, TYPE_9_16 = 2, + THRESHOLD = 10000, NONMAX_SUPPRESSION=10001, FAST_N=10002, + }; + + CV_WRAP static Ptr create( int threshold=10, + bool nonmaxSuppression=true, + int type=FastFeatureDetector::TYPE_9_16 ); + + CV_WRAP virtual void setThreshold(int threshold) = 0; + CV_WRAP virtual int getThreshold() const = 0; + + CV_WRAP virtual void setNonmaxSuppression(bool f) = 0; + CV_WRAP virtual bool getNonmaxSuppression() const = 0; + + CV_WRAP virtual void setType(int type) = 0; + CV_WRAP virtual int getType() const = 0; + CV_WRAP virtual String getDefaultName() const CV_OVERRIDE; +}; + +/** @overload */ +CV_EXPORTS void AGAST( InputArray image, CV_OUT std::vector& keypoints, + int threshold, bool nonmaxSuppression=true ); + +/** @brief Detects corners using the AGAST algorithm + +@param image grayscale image where keypoints (corners) are detected. +@param keypoints keypoints detected on the image. +@param threshold threshold on difference between intensity of the central pixel and pixels of a +circle around this pixel. +@param nonmaxSuppression if true, non-maximum suppression is applied to detected corners +(keypoints). +@param type one of the four neighborhoods as defined in the paper: +AgastFeatureDetector::AGAST_5_8, AgastFeatureDetector::AGAST_7_12d, +AgastFeatureDetector::AGAST_7_12s, AgastFeatureDetector::OAST_9_16 + +For non-Intel platforms, there is a tree optimised variant of AGAST with same numerical results. +The 32-bit binary tree tables were generated automatically from original code using perl script. +The perl script and examples of tree generation are placed in features2d/doc folder. +Detects corners using the AGAST algorithm by @cite mair2010_agast . + + */ +CV_EXPORTS void AGAST( InputArray image, CV_OUT std::vector& keypoints, + int threshold, bool nonmaxSuppression, int type ); +//! @} features2d_main + +//! @addtogroup features2d_main +//! @{ + +/** @brief Wrapping class for feature detection using the AGAST method. : + */ +class CV_EXPORTS_W AgastFeatureDetector : public Feature2D +{ +public: + enum + { + AGAST_5_8 = 0, AGAST_7_12d = 1, AGAST_7_12s = 2, OAST_9_16 = 3, + THRESHOLD = 10000, NONMAX_SUPPRESSION = 10001, + }; + + CV_WRAP static Ptr create( int threshold=10, + bool nonmaxSuppression=true, + int type=AgastFeatureDetector::OAST_9_16 ); + + CV_WRAP virtual void setThreshold(int threshold) = 0; + CV_WRAP virtual int getThreshold() const = 0; + + CV_WRAP virtual void setNonmaxSuppression(bool f) = 0; + CV_WRAP virtual bool getNonmaxSuppression() const = 0; + + CV_WRAP virtual void setType(int type) = 0; + CV_WRAP virtual int getType() const = 0; + CV_WRAP virtual String getDefaultName() const CV_OVERRIDE; +}; + +/** @brief Wrapping class for feature detection using the goodFeaturesToTrack function. : + */ +class CV_EXPORTS_W GFTTDetector : public Feature2D +{ +public: + CV_WRAP static Ptr create( int maxCorners=1000, double qualityLevel=0.01, double minDistance=1, + int blockSize=3, bool useHarrisDetector=false, double k=0.04 ); + CV_WRAP static Ptr create( int maxCorners, double qualityLevel, double minDistance, + int blockSize, int gradiantSize, bool useHarrisDetector=false, double k=0.04 ); + CV_WRAP virtual void setMaxFeatures(int maxFeatures) = 0; + CV_WRAP virtual int getMaxFeatures() const = 0; + + CV_WRAP virtual void setQualityLevel(double qlevel) = 0; + CV_WRAP virtual double getQualityLevel() const = 0; + + CV_WRAP virtual void setMinDistance(double minDistance) = 0; + CV_WRAP virtual double getMinDistance() const = 0; + + CV_WRAP virtual void setBlockSize(int blockSize) = 0; + CV_WRAP virtual int getBlockSize() const = 0; + + CV_WRAP virtual void setHarrisDetector(bool val) = 0; + CV_WRAP virtual bool getHarrisDetector() const = 0; + + CV_WRAP virtual void setK(double k) = 0; + CV_WRAP virtual double getK() const = 0; + CV_WRAP virtual String getDefaultName() const CV_OVERRIDE; +}; + +/** @brief Class for extracting blobs from an image. : + +The class implements a simple algorithm for extracting blobs from an image: + +1. Convert the source image to binary images by applying thresholding with several thresholds from + minThreshold (inclusive) to maxThreshold (exclusive) with distance thresholdStep between + neighboring thresholds. +2. Extract connected components from every binary image by findContours and calculate their + centers. +3. Group centers from several binary images by their coordinates. Close centers form one group that + corresponds to one blob, which is controlled by the minDistBetweenBlobs parameter. +4. From the groups, estimate final centers of blobs and their radiuses and return as locations and + sizes of keypoints. + +This class performs several filtrations of returned blobs. You should set filterBy\* to true/false +to turn on/off corresponding filtration. Available filtrations: + +- **By color**. This filter compares the intensity of a binary image at the center of a blob to +blobColor. If they differ, the blob is filtered out. Use blobColor = 0 to extract dark blobs +and blobColor = 255 to extract light blobs. +- **By area**. Extracted blobs have an area between minArea (inclusive) and maxArea (exclusive). +- **By circularity**. Extracted blobs have circularity +(\f$\frac{4*\pi*Area}{perimeter * perimeter}\f$) between minCircularity (inclusive) and +maxCircularity (exclusive). +- **By ratio of the minimum inertia to maximum inertia**. Extracted blobs have this ratio +between minInertiaRatio (inclusive) and maxInertiaRatio (exclusive). +- **By convexity**. Extracted blobs have convexity (area / area of blob convex hull) between +minConvexity (inclusive) and maxConvexity (exclusive). + +Default values of parameters are tuned to extract dark circular blobs. + */ +class CV_EXPORTS_W SimpleBlobDetector : public Feature2D +{ +public: + struct CV_EXPORTS_W_SIMPLE Params + { + CV_WRAP Params(); + CV_PROP_RW float thresholdStep; + CV_PROP_RW float minThreshold; + CV_PROP_RW float maxThreshold; + CV_PROP_RW size_t minRepeatability; + CV_PROP_RW float minDistBetweenBlobs; + + CV_PROP_RW bool filterByColor; + CV_PROP_RW uchar blobColor; + + CV_PROP_RW bool filterByArea; + CV_PROP_RW float minArea, maxArea; + + CV_PROP_RW bool filterByCircularity; + CV_PROP_RW float minCircularity, maxCircularity; + + CV_PROP_RW bool filterByInertia; + CV_PROP_RW float minInertiaRatio, maxInertiaRatio; + + CV_PROP_RW bool filterByConvexity; + CV_PROP_RW float minConvexity, maxConvexity; + + void read( const FileNode& fn ); + void write( FileStorage& fs ) const; + }; + + CV_WRAP static Ptr + create(const SimpleBlobDetector::Params ¶meters = SimpleBlobDetector::Params()); + CV_WRAP virtual String getDefaultName() const CV_OVERRIDE; +}; + +//! @} features2d_main + +//! @addtogroup features2d_main +//! @{ + +/** @brief Class implementing the KAZE keypoint detector and descriptor extractor, described in @cite ABD12 . + +@note AKAZE descriptor can only be used with KAZE or AKAZE keypoints .. [ABD12] KAZE Features. Pablo +F. Alcantarilla, Adrien Bartoli and Andrew J. Davison. In European Conference on Computer Vision +(ECCV), Fiorenze, Italy, October 2012. +*/ +class CV_EXPORTS_W KAZE : public Feature2D +{ +public: + enum + { + DIFF_PM_G1 = 0, + DIFF_PM_G2 = 1, + DIFF_WEICKERT = 2, + DIFF_CHARBONNIER = 3 + }; + + /** @brief The KAZE constructor + + @param extended Set to enable extraction of extended (128-byte) descriptor. + @param upright Set to enable use of upright descriptors (non rotation-invariant). + @param threshold Detector response threshold to accept point + @param nOctaves Maximum octave evolution of the image + @param nOctaveLayers Default number of sublevels per scale level + @param diffusivity Diffusivity type. DIFF_PM_G1, DIFF_PM_G2, DIFF_WEICKERT or + DIFF_CHARBONNIER + */ + CV_WRAP static Ptr create(bool extended=false, bool upright=false, + float threshold = 0.001f, + int nOctaves = 4, int nOctaveLayers = 4, + int diffusivity = KAZE::DIFF_PM_G2); + + CV_WRAP virtual void setExtended(bool extended) = 0; + CV_WRAP virtual bool getExtended() const = 0; + + CV_WRAP virtual void setUpright(bool upright) = 0; + CV_WRAP virtual bool getUpright() const = 0; + + CV_WRAP virtual void setThreshold(double threshold) = 0; + CV_WRAP virtual double getThreshold() const = 0; + + CV_WRAP virtual void setNOctaves(int octaves) = 0; + CV_WRAP virtual int getNOctaves() const = 0; + + CV_WRAP virtual void setNOctaveLayers(int octaveLayers) = 0; + CV_WRAP virtual int getNOctaveLayers() const = 0; + + CV_WRAP virtual void setDiffusivity(int diff) = 0; + CV_WRAP virtual int getDiffusivity() const = 0; + CV_WRAP virtual String getDefaultName() const CV_OVERRIDE; +}; + +/** @brief Class implementing the AKAZE keypoint detector and descriptor extractor, described in @cite ANB13. + +@details AKAZE descriptors can only be used with KAZE or AKAZE keypoints. This class is thread-safe. + +@note When you need descriptors use Feature2D::detectAndCompute, which +provides better performance. When using Feature2D::detect followed by +Feature2D::compute scale space pyramid is computed twice. + +@note AKAZE implements T-API. When image is passed as UMat some parts of the algorithm +will use OpenCL. + +@note [ANB13] Fast Explicit Diffusion for Accelerated Features in Nonlinear +Scale Spaces. Pablo F. Alcantarilla, Jesús Nuevo and Adrien Bartoli. In +British Machine Vision Conference (BMVC), Bristol, UK, September 2013. + +*/ +class CV_EXPORTS_W AKAZE : public Feature2D +{ +public: + // AKAZE descriptor type + enum + { + DESCRIPTOR_KAZE_UPRIGHT = 2, ///< Upright descriptors, not invariant to rotation + DESCRIPTOR_KAZE = 3, + DESCRIPTOR_MLDB_UPRIGHT = 4, ///< Upright descriptors, not invariant to rotation + DESCRIPTOR_MLDB = 5 + }; + + /** @brief The AKAZE constructor + + @param descriptor_type Type of the extracted descriptor: DESCRIPTOR_KAZE, + DESCRIPTOR_KAZE_UPRIGHT, DESCRIPTOR_MLDB or DESCRIPTOR_MLDB_UPRIGHT. + @param descriptor_size Size of the descriptor in bits. 0 -\> Full size + @param descriptor_channels Number of channels in the descriptor (1, 2, 3) + @param threshold Detector response threshold to accept point + @param nOctaves Maximum octave evolution of the image + @param nOctaveLayers Default number of sublevels per scale level + @param diffusivity Diffusivity type. DIFF_PM_G1, DIFF_PM_G2, DIFF_WEICKERT or + DIFF_CHARBONNIER + */ + CV_WRAP static Ptr create(int descriptor_type=AKAZE::DESCRIPTOR_MLDB, + int descriptor_size = 0, int descriptor_channels = 3, + float threshold = 0.001f, int nOctaves = 4, + int nOctaveLayers = 4, int diffusivity = KAZE::DIFF_PM_G2); + + CV_WRAP virtual void setDescriptorType(int dtype) = 0; + CV_WRAP virtual int getDescriptorType() const = 0; + + CV_WRAP virtual void setDescriptorSize(int dsize) = 0; + CV_WRAP virtual int getDescriptorSize() const = 0; + + CV_WRAP virtual void setDescriptorChannels(int dch) = 0; + CV_WRAP virtual int getDescriptorChannels() const = 0; + + CV_WRAP virtual void setThreshold(double threshold) = 0; + CV_WRAP virtual double getThreshold() const = 0; + + CV_WRAP virtual void setNOctaves(int octaves) = 0; + CV_WRAP virtual int getNOctaves() const = 0; + + CV_WRAP virtual void setNOctaveLayers(int octaveLayers) = 0; + CV_WRAP virtual int getNOctaveLayers() const = 0; + + CV_WRAP virtual void setDiffusivity(int diff) = 0; + CV_WRAP virtual int getDiffusivity() const = 0; + CV_WRAP virtual String getDefaultName() const CV_OVERRIDE; +}; + +//! @} features2d_main + +/****************************************************************************************\ +* Distance * +\****************************************************************************************/ + +template +struct CV_EXPORTS Accumulator +{ + typedef T Type; +}; + +template<> struct Accumulator { typedef float Type; }; +template<> struct Accumulator { typedef float Type; }; +template<> struct Accumulator { typedef float Type; }; +template<> struct Accumulator { typedef float Type; }; + +/* + * Squared Euclidean distance functor + */ +template +struct CV_EXPORTS SL2 +{ + enum { normType = NORM_L2SQR }; + typedef T ValueType; + typedef typename Accumulator::Type ResultType; + + ResultType operator()( const T* a, const T* b, int size ) const + { + return normL2Sqr(a, b, size); + } +}; + +/* + * Euclidean distance functor + */ +template +struct L2 +{ + enum { normType = NORM_L2 }; + typedef T ValueType; + typedef typename Accumulator::Type ResultType; + + ResultType operator()( const T* a, const T* b, int size ) const + { + return (ResultType)std::sqrt((double)normL2Sqr(a, b, size)); + } +}; + +/* + * Manhattan distance (city block distance) functor + */ +template +struct L1 +{ + enum { normType = NORM_L1 }; + typedef T ValueType; + typedef typename Accumulator::Type ResultType; + + ResultType operator()( const T* a, const T* b, int size ) const + { + return normL1(a, b, size); + } +}; + +/****************************************************************************************\ +* DescriptorMatcher * +\****************************************************************************************/ + +//! @addtogroup features2d_match +//! @{ + +/** @brief Abstract base class for matching keypoint descriptors. + +It has two groups of match methods: for matching descriptors of an image with another image or with +an image set. + */ +class CV_EXPORTS_W DescriptorMatcher : public Algorithm +{ +public: + enum + { + FLANNBASED = 1, + BRUTEFORCE = 2, + BRUTEFORCE_L1 = 3, + BRUTEFORCE_HAMMING = 4, + BRUTEFORCE_HAMMINGLUT = 5, + BRUTEFORCE_SL2 = 6 + }; + virtual ~DescriptorMatcher(); + + /** @brief Adds descriptors to train a CPU(trainDescCollectionis) or GPU(utrainDescCollectionis) descriptor + collection. + + If the collection is not empty, the new descriptors are added to existing train descriptors. + + @param descriptors Descriptors to add. Each descriptors[i] is a set of descriptors from the same + train image. + */ + CV_WRAP virtual void add( InputArrayOfArrays descriptors ); + + /** @brief Returns a constant link to the train descriptor collection trainDescCollection . + */ + CV_WRAP const std::vector& getTrainDescriptors() const; + + /** @brief Clears the train descriptor collections. + */ + CV_WRAP virtual void clear() CV_OVERRIDE; + + /** @brief Returns true if there are no train descriptors in the both collections. + */ + CV_WRAP virtual bool empty() const CV_OVERRIDE; + + /** @brief Returns true if the descriptor matcher supports masking permissible matches. + */ + CV_WRAP virtual bool isMaskSupported() const = 0; + + /** @brief Trains a descriptor matcher + + Trains a descriptor matcher (for example, the flann index). In all methods to match, the method + train() is run every time before matching. Some descriptor matchers (for example, BruteForceMatcher) + have an empty implementation of this method. Other matchers really train their inner structures (for + example, FlannBasedMatcher trains flann::Index ). + */ + CV_WRAP virtual void train(); + + /** @brief Finds the best match for each descriptor from a query set. + + @param queryDescriptors Query set of descriptors. + @param trainDescriptors Train set of descriptors. This set is not added to the train descriptors + collection stored in the class object. + @param matches Matches. If a query descriptor is masked out in mask , no match is added for this + descriptor. So, matches size may be smaller than the query descriptors count. + @param mask Mask specifying permissible matches between an input query and train matrices of + descriptors. + + In the first variant of this method, the train descriptors are passed as an input argument. In the + second variant of the method, train descriptors collection that was set by DescriptorMatcher::add is + used. Optional mask (or masks) can be passed to specify which query and training descriptors can be + matched. Namely, queryDescriptors[i] can be matched with trainDescriptors[j] only if + mask.at\(i,j) is non-zero. + */ + CV_WRAP void match( InputArray queryDescriptors, InputArray trainDescriptors, + CV_OUT std::vector& matches, InputArray mask=noArray() ) const; + + /** @brief Finds the k best matches for each descriptor from a query set. + + @param queryDescriptors Query set of descriptors. + @param trainDescriptors Train set of descriptors. This set is not added to the train descriptors + collection stored in the class object. + @param mask Mask specifying permissible matches between an input query and train matrices of + descriptors. + @param matches Matches. Each matches[i] is k or less matches for the same query descriptor. + @param k Count of best matches found per each query descriptor or less if a query descriptor has + less than k possible matches in total. + @param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is + false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, + the matches vector does not contain matches for fully masked-out query descriptors. + + These extended variants of DescriptorMatcher::match methods find several best matches for each query + descriptor. The matches are returned in the distance increasing order. See DescriptorMatcher::match + for the details about query and train descriptors. + */ + CV_WRAP void knnMatch( InputArray queryDescriptors, InputArray trainDescriptors, + CV_OUT std::vector >& matches, int k, + InputArray mask=noArray(), bool compactResult=false ) const; + + /** @brief For each query descriptor, finds the training descriptors not farther than the specified distance. + + @param queryDescriptors Query set of descriptors. + @param trainDescriptors Train set of descriptors. This set is not added to the train descriptors + collection stored in the class object. + @param matches Found matches. + @param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is + false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, + the matches vector does not contain matches for fully masked-out query descriptors. + @param maxDistance Threshold for the distance between matched descriptors. Distance means here + metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured + in Pixels)! + @param mask Mask specifying permissible matches between an input query and train matrices of + descriptors. + + For each query descriptor, the methods find such training descriptors that the distance between the + query descriptor and the training descriptor is equal or smaller than maxDistance. Found matches are + returned in the distance increasing order. + */ + CV_WRAP void radiusMatch( InputArray queryDescriptors, InputArray trainDescriptors, + CV_OUT std::vector >& matches, float maxDistance, + InputArray mask=noArray(), bool compactResult=false ) const; + + /** @overload + @param queryDescriptors Query set of descriptors. + @param matches Matches. If a query descriptor is masked out in mask , no match is added for this + descriptor. So, matches size may be smaller than the query descriptors count. + @param masks Set of masks. Each masks[i] specifies permissible matches between the input query + descriptors and stored train descriptors from the i-th image trainDescCollection[i]. + */ + CV_WRAP void match( InputArray queryDescriptors, CV_OUT std::vector& matches, + InputArrayOfArrays masks=noArray() ); + /** @overload + @param queryDescriptors Query set of descriptors. + @param matches Matches. Each matches[i] is k or less matches for the same query descriptor. + @param k Count of best matches found per each query descriptor or less if a query descriptor has + less than k possible matches in total. + @param masks Set of masks. Each masks[i] specifies permissible matches between the input query + descriptors and stored train descriptors from the i-th image trainDescCollection[i]. + @param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is + false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, + the matches vector does not contain matches for fully masked-out query descriptors. + */ + CV_WRAP void knnMatch( InputArray queryDescriptors, CV_OUT std::vector >& matches, int k, + InputArrayOfArrays masks=noArray(), bool compactResult=false ); + /** @overload + @param queryDescriptors Query set of descriptors. + @param matches Found matches. + @param maxDistance Threshold for the distance between matched descriptors. Distance means here + metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured + in Pixels)! + @param masks Set of masks. Each masks[i] specifies permissible matches between the input query + descriptors and stored train descriptors from the i-th image trainDescCollection[i]. + @param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is + false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, + the matches vector does not contain matches for fully masked-out query descriptors. + */ + CV_WRAP void radiusMatch( InputArray queryDescriptors, CV_OUT std::vector >& matches, float maxDistance, + InputArrayOfArrays masks=noArray(), bool compactResult=false ); + + + CV_WRAP void write( const String& fileName ) const + { + FileStorage fs(fileName, FileStorage::WRITE); + write(fs); + } + + CV_WRAP void read( const String& fileName ) + { + FileStorage fs(fileName, FileStorage::READ); + read(fs.root()); + } + // Reads matcher object from a file node + // see corresponding cv::Algorithm method + CV_WRAP virtual void read( const FileNode& ) CV_OVERRIDE; + // Writes matcher object to a file storage + virtual void write( FileStorage& ) const CV_OVERRIDE; + + /** @brief Clones the matcher. + + @param emptyTrainData If emptyTrainData is false, the method creates a deep copy of the object, + that is, copies both parameters and train data. If emptyTrainData is true, the method creates an + object copy with the current parameters but with empty train data. + */ + CV_WRAP virtual Ptr clone( bool emptyTrainData=false ) const = 0; + + /** @brief Creates a descriptor matcher of a given type with the default parameters (using default + constructor). + + @param descriptorMatcherType Descriptor matcher type. Now the following matcher types are + supported: + - `BruteForce` (it uses L2 ) + - `BruteForce-L1` + - `BruteForce-Hamming` + - `BruteForce-Hamming(2)` + - `FlannBased` + */ + CV_WRAP static Ptr create( const String& descriptorMatcherType ); + + CV_WRAP static Ptr create( int matcherType ); + + + // see corresponding cv::Algorithm method + CV_WRAP inline void write(const Ptr& fs, const String& name = String()) const { Algorithm::write(fs, name); } + +protected: + /** + * Class to work with descriptors from several images as with one merged matrix. + * It is used e.g. in FlannBasedMatcher. + */ + class CV_EXPORTS DescriptorCollection + { + public: + DescriptorCollection(); + DescriptorCollection( const DescriptorCollection& collection ); + virtual ~DescriptorCollection(); + + // Vector of matrices "descriptors" will be merged to one matrix "mergedDescriptors" here. + void set( const std::vector& descriptors ); + virtual void clear(); + + const Mat& getDescriptors() const; + const Mat getDescriptor( int imgIdx, int localDescIdx ) const; + const Mat getDescriptor( int globalDescIdx ) const; + void getLocalIdx( int globalDescIdx, int& imgIdx, int& localDescIdx ) const; + + int size() const; + + protected: + Mat mergedDescriptors; + std::vector startIdxs; + }; + + //! In fact the matching is implemented only by the following two methods. These methods suppose + //! that the class object has been trained already. Public match methods call these methods + //! after calling train(). + virtual void knnMatchImpl( InputArray queryDescriptors, std::vector >& matches, int k, + InputArrayOfArrays masks=noArray(), bool compactResult=false ) = 0; + virtual void radiusMatchImpl( InputArray queryDescriptors, std::vector >& matches, float maxDistance, + InputArrayOfArrays masks=noArray(), bool compactResult=false ) = 0; + + static bool isPossibleMatch( InputArray mask, int queryIdx, int trainIdx ); + static bool isMaskedOut( InputArrayOfArrays masks, int queryIdx ); + + static Mat clone_op( Mat m ) { return m.clone(); } + void checkMasks( InputArrayOfArrays masks, int queryDescriptorsCount ) const; + + //! Collection of descriptors from train images. + std::vector trainDescCollection; + std::vector utrainDescCollection; +}; + +/** @brief Brute-force descriptor matcher. + +For each descriptor in the first set, this matcher finds the closest descriptor in the second set +by trying each one. This descriptor matcher supports masking permissible matches of descriptor +sets. + */ +class CV_EXPORTS_W BFMatcher : public DescriptorMatcher +{ +public: + /** @brief Brute-force matcher constructor (obsolete). Please use BFMatcher.create() + * + * + */ + CV_WRAP BFMatcher( int normType=NORM_L2, bool crossCheck=false ); + + virtual ~BFMatcher() {} + + virtual bool isMaskSupported() const CV_OVERRIDE { return true; } + + /** @brief Brute-force matcher create method. + @param normType One of NORM_L1, NORM_L2, NORM_HAMMING, NORM_HAMMING2. L1 and L2 norms are + preferable choices for SIFT and SURF descriptors, NORM_HAMMING should be used with ORB, BRISK and + BRIEF, NORM_HAMMING2 should be used with ORB when WTA_K==3 or 4 (see ORB::ORB constructor + description). + @param crossCheck If it is false, this is will be default BFMatcher behaviour when it finds the k + nearest neighbors for each query descriptor. If crossCheck==true, then the knnMatch() method with + k=1 will only return pairs (i,j) such that for i-th query descriptor the j-th descriptor in the + matcher's collection is the nearest and vice versa, i.e. the BFMatcher will only return consistent + pairs. Such technique usually produces best results with minimal number of outliers when there are + enough matches. This is alternative to the ratio test, used by D. Lowe in SIFT paper. + */ + CV_WRAP static Ptr create( int normType=NORM_L2, bool crossCheck=false ) ; + + virtual Ptr clone( bool emptyTrainData=false ) const CV_OVERRIDE; +protected: + virtual void knnMatchImpl( InputArray queryDescriptors, std::vector >& matches, int k, + InputArrayOfArrays masks=noArray(), bool compactResult=false ) CV_OVERRIDE; + virtual void radiusMatchImpl( InputArray queryDescriptors, std::vector >& matches, float maxDistance, + InputArrayOfArrays masks=noArray(), bool compactResult=false ) CV_OVERRIDE; + + int normType; + bool crossCheck; +}; + +#if defined(HAVE_OPENCV_FLANN) || defined(CV_DOXYGEN) + +/** @brief Flann-based descriptor matcher. + +This matcher trains cv::flann::Index on a train descriptor collection and calls its nearest search +methods to find the best matches. So, this matcher may be faster when matching a large train +collection than the brute force matcher. FlannBasedMatcher does not support masking permissible +matches of descriptor sets because flann::Index does not support this. : + */ +class CV_EXPORTS_W FlannBasedMatcher : public DescriptorMatcher +{ +public: + CV_WRAP FlannBasedMatcher( const Ptr& indexParams=makePtr(), + const Ptr& searchParams=makePtr() ); + + virtual void add( InputArrayOfArrays descriptors ) CV_OVERRIDE; + virtual void clear() CV_OVERRIDE; + + // Reads matcher object from a file node + virtual void read( const FileNode& ) CV_OVERRIDE; + // Writes matcher object to a file storage + virtual void write( FileStorage& ) const CV_OVERRIDE; + + virtual void train() CV_OVERRIDE; + virtual bool isMaskSupported() const CV_OVERRIDE; + + CV_WRAP static Ptr create(); + + virtual Ptr clone( bool emptyTrainData=false ) const CV_OVERRIDE; +protected: + static void convertToDMatches( const DescriptorCollection& descriptors, + const Mat& indices, const Mat& distances, + std::vector >& matches ); + + virtual void knnMatchImpl( InputArray queryDescriptors, std::vector >& matches, int k, + InputArrayOfArrays masks=noArray(), bool compactResult=false ) CV_OVERRIDE; + virtual void radiusMatchImpl( InputArray queryDescriptors, std::vector >& matches, float maxDistance, + InputArrayOfArrays masks=noArray(), bool compactResult=false ) CV_OVERRIDE; + + Ptr indexParams; + Ptr searchParams; + Ptr flannIndex; + + DescriptorCollection mergedDescriptors; + int addedDescCount; +}; + +#endif + +//! @} features2d_match + +/****************************************************************************************\ +* Drawing functions * +\****************************************************************************************/ + +//! @addtogroup features2d_draw +//! @{ + +struct CV_EXPORTS DrawMatchesFlags +{ + enum{ DEFAULT = 0, //!< Output image matrix will be created (Mat::create), + //!< i.e. existing memory of output image may be reused. + //!< Two source image, matches and single keypoints will be drawn. + //!< For each keypoint only the center point will be drawn (without + //!< the circle around keypoint with keypoint size and orientation). + DRAW_OVER_OUTIMG = 1, //!< Output image matrix will not be created (Mat::create). + //!< Matches will be drawn on existing content of output image. + NOT_DRAW_SINGLE_POINTS = 2, //!< Single keypoints will not be drawn. + DRAW_RICH_KEYPOINTS = 4 //!< For each keypoint the circle around keypoint with keypoint size and + //!< orientation will be drawn. + }; +}; + +/** @brief Draws keypoints. + +@param image Source image. +@param keypoints Keypoints from the source image. +@param outImage Output image. Its content depends on the flags value defining what is drawn in the +output image. See possible flags bit values below. +@param color Color of keypoints. +@param flags Flags setting drawing features. Possible flags bit values are defined by +DrawMatchesFlags. See details above in drawMatches . + +@note +For Python API, flags are modified as cv2.DRAW_MATCHES_FLAGS_DEFAULT, +cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS, cv2.DRAW_MATCHES_FLAGS_DRAW_OVER_OUTIMG, +cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS + */ +CV_EXPORTS_W void drawKeypoints( InputArray image, const std::vector& keypoints, InputOutputArray outImage, + const Scalar& color=Scalar::all(-1), int flags=DrawMatchesFlags::DEFAULT ); + +/** @brief Draws the found matches of keypoints from two images. + +@param img1 First source image. +@param keypoints1 Keypoints from the first source image. +@param img2 Second source image. +@param keypoints2 Keypoints from the second source image. +@param matches1to2 Matches from the first image to the second one, which means that keypoints1[i] +has a corresponding point in keypoints2[matches[i]] . +@param outImg Output image. Its content depends on the flags value defining what is drawn in the +output image. See possible flags bit values below. +@param matchColor Color of matches (lines and connected keypoints). If matchColor==Scalar::all(-1) +, the color is generated randomly. +@param singlePointColor Color of single keypoints (circles), which means that keypoints do not +have the matches. If singlePointColor==Scalar::all(-1) , the color is generated randomly. +@param matchesMask Mask determining which matches are drawn. If the mask is empty, all matches are +drawn. +@param flags Flags setting drawing features. Possible flags bit values are defined by +DrawMatchesFlags. + +This function draws matches of keypoints from two images in the output image. Match is a line +connecting two keypoints (circles). See cv::DrawMatchesFlags. + */ +CV_EXPORTS_W void drawMatches( InputArray img1, const std::vector& keypoints1, + InputArray img2, const std::vector& keypoints2, + const std::vector& matches1to2, InputOutputArray outImg, + const Scalar& matchColor=Scalar::all(-1), const Scalar& singlePointColor=Scalar::all(-1), + const std::vector& matchesMask=std::vector(), int flags=DrawMatchesFlags::DEFAULT ); + +/** @overload */ +CV_EXPORTS_AS(drawMatchesKnn) void drawMatches( InputArray img1, const std::vector& keypoints1, + InputArray img2, const std::vector& keypoints2, + const std::vector >& matches1to2, InputOutputArray outImg, + const Scalar& matchColor=Scalar::all(-1), const Scalar& singlePointColor=Scalar::all(-1), + const std::vector >& matchesMask=std::vector >(), int flags=DrawMatchesFlags::DEFAULT ); + +//! @} features2d_draw + +/****************************************************************************************\ +* Functions to evaluate the feature detectors and [generic] descriptor extractors * +\****************************************************************************************/ + +CV_EXPORTS void evaluateFeatureDetector( const Mat& img1, const Mat& img2, const Mat& H1to2, + std::vector* keypoints1, std::vector* keypoints2, + float& repeatability, int& correspCount, + const Ptr& fdetector=Ptr() ); + +CV_EXPORTS void computeRecallPrecisionCurve( const std::vector >& matches1to2, + const std::vector >& correctMatches1to2Mask, + std::vector& recallPrecisionCurve ); + +CV_EXPORTS float getRecall( const std::vector& recallPrecisionCurve, float l_precision ); +CV_EXPORTS int getNearestPoint( const std::vector& recallPrecisionCurve, float l_precision ); + +/****************************************************************************************\ +* Bag of visual words * +\****************************************************************************************/ + +//! @addtogroup features2d_category +//! @{ + +/** @brief Abstract base class for training the *bag of visual words* vocabulary from a set of descriptors. + +For details, see, for example, *Visual Categorization with Bags of Keypoints* by Gabriella Csurka, +Christopher R. Dance, Lixin Fan, Jutta Willamowski, Cedric Bray, 2004. : + */ +class CV_EXPORTS_W BOWTrainer +{ +public: + BOWTrainer(); + virtual ~BOWTrainer(); + + /** @brief Adds descriptors to a training set. + + @param descriptors Descriptors to add to a training set. Each row of the descriptors matrix is a + descriptor. + + The training set is clustered using clustermethod to construct the vocabulary. + */ + CV_WRAP void add( const Mat& descriptors ); + + /** @brief Returns a training set of descriptors. + */ + CV_WRAP const std::vector& getDescriptors() const; + + /** @brief Returns the count of all descriptors stored in the training set. + */ + CV_WRAP int descriptorsCount() const; + + CV_WRAP virtual void clear(); + + /** @overload */ + CV_WRAP virtual Mat cluster() const = 0; + + /** @brief Clusters train descriptors. + + @param descriptors Descriptors to cluster. Each row of the descriptors matrix is a descriptor. + Descriptors are not added to the inner train descriptor set. + + The vocabulary consists of cluster centers. So, this method returns the vocabulary. In the first + variant of the method, train descriptors stored in the object are clustered. In the second variant, + input descriptors are clustered. + */ + CV_WRAP virtual Mat cluster( const Mat& descriptors ) const = 0; + +protected: + std::vector descriptors; + int size; +}; + +/** @brief kmeans -based class to train visual vocabulary using the *bag of visual words* approach. : + */ +class CV_EXPORTS_W BOWKMeansTrainer : public BOWTrainer +{ +public: + /** @brief The constructor. + + @see cv::kmeans + */ + CV_WRAP BOWKMeansTrainer( int clusterCount, const TermCriteria& termcrit=TermCriteria(), + int attempts=3, int flags=KMEANS_PP_CENTERS ); + virtual ~BOWKMeansTrainer(); + + // Returns trained vocabulary (i.e. cluster centers). + CV_WRAP virtual Mat cluster() const CV_OVERRIDE; + CV_WRAP virtual Mat cluster( const Mat& descriptors ) const CV_OVERRIDE; + +protected: + + int clusterCount; + TermCriteria termcrit; + int attempts; + int flags; +}; + +/** @brief Class to compute an image descriptor using the *bag of visual words*. + +Such a computation consists of the following steps: + +1. Compute descriptors for a given image and its keypoints set. +2. Find the nearest visual words from the vocabulary for each keypoint descriptor. +3. Compute the bag-of-words image descriptor as is a normalized histogram of vocabulary words +encountered in the image. The i-th bin of the histogram is a frequency of i-th word of the +vocabulary in the given image. + */ +class CV_EXPORTS_W BOWImgDescriptorExtractor +{ +public: + /** @brief The constructor. + + @param dextractor Descriptor extractor that is used to compute descriptors for an input image and + its keypoints. + @param dmatcher Descriptor matcher that is used to find the nearest word of the trained vocabulary + for each keypoint descriptor of the image. + */ + CV_WRAP BOWImgDescriptorExtractor( const Ptr& dextractor, + const Ptr& dmatcher ); + /** @overload */ + BOWImgDescriptorExtractor( const Ptr& dmatcher ); + virtual ~BOWImgDescriptorExtractor(); + + /** @brief Sets a visual vocabulary. + + @param vocabulary Vocabulary (can be trained using the inheritor of BOWTrainer ). Each row of the + vocabulary is a visual word (cluster center). + */ + CV_WRAP void setVocabulary( const Mat& vocabulary ); + + /** @brief Returns the set vocabulary. + */ + CV_WRAP const Mat& getVocabulary() const; + + /** @brief Computes an image descriptor using the set visual vocabulary. + + @param image Image, for which the descriptor is computed. + @param keypoints Keypoints detected in the input image. + @param imgDescriptor Computed output image descriptor. + @param pointIdxsOfClusters Indices of keypoints that belong to the cluster. This means that + pointIdxsOfClusters[i] are keypoint indices that belong to the i -th cluster (word of vocabulary) + returned if it is non-zero. + @param descriptors Descriptors of the image keypoints that are returned if they are non-zero. + */ + void compute( InputArray image, std::vector& keypoints, OutputArray imgDescriptor, + std::vector >* pointIdxsOfClusters=0, Mat* descriptors=0 ); + /** @overload + @param keypointDescriptors Computed descriptors to match with vocabulary. + @param imgDescriptor Computed output image descriptor. + @param pointIdxsOfClusters Indices of keypoints that belong to the cluster. This means that + pointIdxsOfClusters[i] are keypoint indices that belong to the i -th cluster (word of vocabulary) + returned if it is non-zero. + */ + void compute( InputArray keypointDescriptors, OutputArray imgDescriptor, + std::vector >* pointIdxsOfClusters=0 ); + // compute() is not constant because DescriptorMatcher::match is not constant + + CV_WRAP_AS(compute) void compute2( const Mat& image, std::vector& keypoints, CV_OUT Mat& imgDescriptor ) + { compute(image,keypoints,imgDescriptor); } + + /** @brief Returns an image descriptor size if the vocabulary is set. Otherwise, it returns 0. + */ + CV_WRAP int descriptorSize() const; + + /** @brief Returns an image descriptor type. + */ + CV_WRAP int descriptorType() const; + +protected: + Mat vocabulary; + Ptr dextractor; + Ptr dmatcher; +}; + +//! @} features2d_category + +//! @} features2d + +} /* namespace cv */ + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/features2d/features2d.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/features2d/features2d.hpp new file mode 100644 index 0000000000000000000000000000000000000000..e81df0ad08c1757800a26376e047b0746b67a2ae --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/features2d/features2d.hpp @@ -0,0 +1,48 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifdef __OPENCV_BUILD +#error this is a compatibility header which should not be used inside the OpenCV library +#endif + +#include "opencv2/features2d.hpp" diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/features2d/hal/interface.h b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/features2d/hal/interface.h new file mode 100644 index 0000000000000000000000000000000000000000..bcc6577c19f2241af17994f149c29a3fba5c5951 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/features2d/hal/interface.h @@ -0,0 +1,33 @@ +#ifndef OPENCV_FEATURE2D_HAL_INTERFACE_H +#define OPENCV_FEATURE2D_HAL_INTERFACE_H + +#include "opencv2/core/cvdef.h" +//! @addtogroup featrure2d_hal_interface +//! @{ + +//! @name Fast feature detector types +//! @sa cv::FastFeatureDetector +//! @{ +#define CV_HAL_TYPE_5_8 0 +#define CV_HAL_TYPE_7_12 1 +#define CV_HAL_TYPE_9_16 2 +//! @} + +//! @name Key point +//! @sa cv::KeyPoint +//! @{ +struct CV_EXPORTS cvhalKeyPoint +{ + float x; + float y; + float size; + float angle; + float response; + int octave; + int class_id; +}; +//! @} + +//! @} + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/imgcodecs.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/imgcodecs.hpp new file mode 100644 index 0000000000000000000000000000000000000000..4e79518ae16fc03fd1db0f9c280e9290ab500bbb --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/imgcodecs.hpp @@ -0,0 +1,259 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_IMGCODECS_HPP +#define OPENCV_IMGCODECS_HPP + +#include "opencv2/core.hpp" + +/** + @defgroup imgcodecs Image file reading and writing + @{ + @defgroup imgcodecs_c C API + @defgroup imgcodecs_ios iOS glue + @} +*/ + +//////////////////////////////// image codec //////////////////////////////// +namespace cv +{ + +//! @addtogroup imgcodecs +//! @{ + +//! Imread flags +enum ImreadModes { + IMREAD_UNCHANGED = -1, //!< If set, return the loaded image as is (with alpha channel, otherwise it gets cropped). + IMREAD_GRAYSCALE = 0, //!< If set, always convert image to the single channel grayscale image (codec internal conversion). + IMREAD_COLOR = 1, //!< If set, always convert image to the 3 channel BGR color image. + IMREAD_ANYDEPTH = 2, //!< If set, return 16-bit/32-bit image when the input has the corresponding depth, otherwise convert it to 8-bit. + IMREAD_ANYCOLOR = 4, //!< If set, the image is read in any possible color format. + IMREAD_LOAD_GDAL = 8, //!< If set, use the gdal driver for loading the image. + IMREAD_REDUCED_GRAYSCALE_2 = 16, //!< If set, always convert image to the single channel grayscale image and the image size reduced 1/2. + IMREAD_REDUCED_COLOR_2 = 17, //!< If set, always convert image to the 3 channel BGR color image and the image size reduced 1/2. + IMREAD_REDUCED_GRAYSCALE_4 = 32, //!< If set, always convert image to the single channel grayscale image and the image size reduced 1/4. + IMREAD_REDUCED_COLOR_4 = 33, //!< If set, always convert image to the 3 channel BGR color image and the image size reduced 1/4. + IMREAD_REDUCED_GRAYSCALE_8 = 64, //!< If set, always convert image to the single channel grayscale image and the image size reduced 1/8. + IMREAD_REDUCED_COLOR_8 = 65, //!< If set, always convert image to the 3 channel BGR color image and the image size reduced 1/8. + IMREAD_IGNORE_ORIENTATION = 128 //!< If set, do not rotate the image according to EXIF's orientation flag. + }; + +//! Imwrite flags +enum ImwriteFlags { + IMWRITE_JPEG_QUALITY = 1, //!< For JPEG, it can be a quality from 0 to 100 (the higher is the better). Default value is 95. + IMWRITE_JPEG_PROGRESSIVE = 2, //!< Enable JPEG features, 0 or 1, default is False. + IMWRITE_JPEG_OPTIMIZE = 3, //!< Enable JPEG features, 0 or 1, default is False. + IMWRITE_JPEG_RST_INTERVAL = 4, //!< JPEG restart interval, 0 - 65535, default is 0 - no restart. + IMWRITE_JPEG_LUMA_QUALITY = 5, //!< Separate luma quality level, 0 - 100, default is 0 - don't use. + IMWRITE_JPEG_CHROMA_QUALITY = 6, //!< Separate chroma quality level, 0 - 100, default is 0 - don't use. + IMWRITE_PNG_COMPRESSION = 16, //!< For PNG, it can be the compression level from 0 to 9. A higher value means a smaller size and longer compression time. If specified, strategy is changed to IMWRITE_PNG_STRATEGY_DEFAULT (Z_DEFAULT_STRATEGY). Default value is 1 (best speed setting). + IMWRITE_PNG_STRATEGY = 17, //!< One of cv::ImwritePNGFlags, default is IMWRITE_PNG_STRATEGY_RLE. + IMWRITE_PNG_BILEVEL = 18, //!< Binary level PNG, 0 or 1, default is 0. + IMWRITE_PXM_BINARY = 32, //!< For PPM, PGM, or PBM, it can be a binary format flag, 0 or 1. Default value is 1. + IMWRITE_EXR_TYPE = (3 << 4) + 0, /* 48 */ //!< override EXR storage type (FLOAT (FP32) is default) + IMWRITE_WEBP_QUALITY = 64, //!< For WEBP, it can be a quality from 1 to 100 (the higher is the better). By default (without any parameter) and for quality above 100 the lossless compression is used. + IMWRITE_PAM_TUPLETYPE = 128,//!< For PAM, sets the TUPLETYPE field to the corresponding string value that is defined for the format + IMWRITE_TIFF_RESUNIT = 256,//!< For TIFF, use to specify which DPI resolution unit to set; see libtiff documentation for valid values + IMWRITE_TIFF_XDPI = 257,//!< For TIFF, use to specify the X direction DPI + IMWRITE_TIFF_YDPI = 258 //!< For TIFF, use to specify the Y direction DPI + }; + +enum ImwriteEXRTypeFlags { + /*IMWRITE_EXR_TYPE_UNIT = 0, //!< not supported */ + IMWRITE_EXR_TYPE_HALF = 1, //!< store as HALF (FP16) + IMWRITE_EXR_TYPE_FLOAT = 2 //!< store as FP32 (default) + }; + +//! Imwrite PNG specific flags used to tune the compression algorithm. +/** These flags will be modify the way of PNG image compression and will be passed to the underlying zlib processing stage. + +- The effect of IMWRITE_PNG_STRATEGY_FILTERED is to force more Huffman coding and less string matching; it is somewhat intermediate between IMWRITE_PNG_STRATEGY_DEFAULT and IMWRITE_PNG_STRATEGY_HUFFMAN_ONLY. +- IMWRITE_PNG_STRATEGY_RLE is designed to be almost as fast as IMWRITE_PNG_STRATEGY_HUFFMAN_ONLY, but give better compression for PNG image data. +- The strategy parameter only affects the compression ratio but not the correctness of the compressed output even if it is not set appropriately. +- IMWRITE_PNG_STRATEGY_FIXED prevents the use of dynamic Huffman codes, allowing for a simpler decoder for special applications. +*/ +enum ImwritePNGFlags { + IMWRITE_PNG_STRATEGY_DEFAULT = 0, //!< Use this value for normal data. + IMWRITE_PNG_STRATEGY_FILTERED = 1, //!< Use this value for data produced by a filter (or predictor).Filtered data consists mostly of small values with a somewhat random distribution. In this case, the compression algorithm is tuned to compress them better. + IMWRITE_PNG_STRATEGY_HUFFMAN_ONLY = 2, //!< Use this value to force Huffman encoding only (no string match). + IMWRITE_PNG_STRATEGY_RLE = 3, //!< Use this value to limit match distances to one (run-length encoding). + IMWRITE_PNG_STRATEGY_FIXED = 4 //!< Using this value prevents the use of dynamic Huffman codes, allowing for a simpler decoder for special applications. + }; + +//! Imwrite PAM specific tupletype flags used to define the 'TUPETYPE' field of a PAM file. +enum ImwritePAMFlags { + IMWRITE_PAM_FORMAT_NULL = 0, + IMWRITE_PAM_FORMAT_BLACKANDWHITE = 1, + IMWRITE_PAM_FORMAT_GRAYSCALE = 2, + IMWRITE_PAM_FORMAT_GRAYSCALE_ALPHA = 3, + IMWRITE_PAM_FORMAT_RGB = 4, + IMWRITE_PAM_FORMAT_RGB_ALPHA = 5, + }; + +/** @brief Loads an image from a file. + +@anchor imread + +The function imread loads an image from the specified file and returns it. If the image cannot be +read (because of missing file, improper permissions, unsupported or invalid format), the function +returns an empty matrix ( Mat::data==NULL ). + +Currently, the following file formats are supported: + +- Windows bitmaps - \*.bmp, \*.dib (always supported) +- JPEG files - \*.jpeg, \*.jpg, \*.jpe (see the *Note* section) +- JPEG 2000 files - \*.jp2 (see the *Note* section) +- Portable Network Graphics - \*.png (see the *Note* section) +- WebP - \*.webp (see the *Note* section) +- Portable image format - \*.pbm, \*.pgm, \*.ppm \*.pxm, \*.pnm (always supported) +- Sun rasters - \*.sr, \*.ras (always supported) +- TIFF files - \*.tiff, \*.tif (see the *Note* section) +- OpenEXR Image files - \*.exr (see the *Note* section) +- Radiance HDR - \*.hdr, \*.pic (always supported) +- Raster and Vector geospatial data supported by GDAL (see the *Note* section) + +@note +- The function determines the type of an image by the content, not by the file extension. +- In the case of color images, the decoded images will have the channels stored in **B G R** order. +- When using IMREAD_GRAYSCALE, the codec's internal grayscale conversion will be used, if available. + Results may differ to the output of cvtColor() +- On Microsoft Windows\* OS and MacOSX\*, the codecs shipped with an OpenCV image (libjpeg, + libpng, libtiff, and libjasper) are used by default. So, OpenCV can always read JPEGs, PNGs, + and TIFFs. On MacOSX, there is also an option to use native MacOSX image readers. But beware + that currently these native image loaders give images with different pixel values because of + the color management embedded into MacOSX. +- On Linux\*, BSD flavors and other Unix-like open-source operating systems, OpenCV looks for + codecs supplied with an OS image. Install the relevant packages (do not forget the development + files, for example, "libjpeg-dev", in Debian\* and Ubuntu\*) to get the codec support or turn + on the OPENCV_BUILD_3RDPARTY_LIBS flag in CMake. +- In the case you set *WITH_GDAL* flag to true in CMake and @ref IMREAD_LOAD_GDAL to load the image, + then the [GDAL](http://www.gdal.org) driver will be used in order to decode the image, supporting + the following formats: [Raster](http://www.gdal.org/formats_list.html), + [Vector](http://www.gdal.org/ogr_formats.html). +- If EXIF information are embedded in the image file, the EXIF orientation will be taken into account + and thus the image will be rotated accordingly except if the flag @ref IMREAD_IGNORE_ORIENTATION is passed. +- By default number of pixels must be less than 2^30. Limit can be set using system + variable OPENCV_IO_MAX_IMAGE_PIXELS + +@param filename Name of file to be loaded. +@param flags Flag that can take values of cv::ImreadModes +*/ +CV_EXPORTS_W Mat imread( const String& filename, int flags = IMREAD_COLOR ); + +/** @brief Loads a multi-page image from a file. + +The function imreadmulti loads a multi-page image from the specified file into a vector of Mat objects. +@param filename Name of file to be loaded. +@param flags Flag that can take values of cv::ImreadModes, default with cv::IMREAD_ANYCOLOR. +@param mats A vector of Mat objects holding each page, if more than one. +@sa cv::imread +*/ +CV_EXPORTS_W bool imreadmulti(const String& filename, CV_OUT std::vector& mats, int flags = IMREAD_ANYCOLOR); + +/** @brief Saves an image to a specified file. + +The function imwrite saves the image to the specified file. The image format is chosen based on the +filename extension (see cv::imread for the list of extensions). In general, only 8-bit +single-channel or 3-channel (with 'BGR' channel order) images +can be saved using this function, with these exceptions: + +- 16-bit unsigned (CV_16U) images can be saved in the case of PNG, JPEG 2000, and TIFF formats +- 32-bit float (CV_32F) images can be saved in TIFF, OpenEXR, and Radiance HDR formats; 3-channel +(CV_32FC3) TIFF images will be saved using the LogLuv high dynamic range encoding (4 bytes per pixel) +- PNG images with an alpha channel can be saved using this function. To do this, create +8-bit (or 16-bit) 4-channel image BGRA, where the alpha channel goes last. Fully transparent pixels +should have alpha set to 0, fully opaque pixels should have alpha set to 255/65535 (see the code sample below). + +If the format, depth or channel order is different, use +Mat::convertTo and cv::cvtColor to convert it before saving. Or, use the universal FileStorage I/O +functions to save the image to XML or YAML format. + +The sample below shows how to create a BGRA image and save it to a PNG file. It also demonstrates how to set custom +compression parameters: +@include snippets/imgcodecs_imwrite.cpp +@param filename Name of the file. +@param img Image to be saved. +@param params Format-specific parameters encoded as pairs (paramId_1, paramValue_1, paramId_2, paramValue_2, ... .) see cv::ImwriteFlags +*/ +CV_EXPORTS_W bool imwrite( const String& filename, InputArray img, + const std::vector& params = std::vector()); + +/** @brief Reads an image from a buffer in memory. + +The function imdecode reads an image from the specified buffer in the memory. If the buffer is too short or +contains invalid data, the function returns an empty matrix ( Mat::data==NULL ). + +See cv::imread for the list of supported formats and flags description. + +@note In the case of color images, the decoded images will have the channels stored in **B G R** order. +@param buf Input array or vector of bytes. +@param flags The same flags as in cv::imread, see cv::ImreadModes. +*/ +CV_EXPORTS_W Mat imdecode( InputArray buf, int flags ); + +/** @overload +@param buf +@param flags +@param dst The optional output placeholder for the decoded matrix. It can save the image +reallocations when the function is called repeatedly for images of the same size. +*/ +CV_EXPORTS Mat imdecode( InputArray buf, int flags, Mat* dst); + +/** @brief Encodes an image into a memory buffer. + +The function imencode compresses the image and stores it in the memory buffer that is resized to fit the +result. See cv::imwrite for the list of supported formats and flags description. + +@param ext File extension that defines the output format. +@param img Image to be written. +@param buf Output buffer resized to fit the compressed image. +@param params Format-specific parameters. See cv::imwrite and cv::ImwriteFlags. +*/ +CV_EXPORTS_W bool imencode( const String& ext, InputArray img, + CV_OUT std::vector& buf, + const std::vector& params = std::vector()); + +//! @} imgcodecs + +} // cv + +#endif //OPENCV_IMGCODECS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/imgcodecs/imgcodecs.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/imgcodecs/imgcodecs.hpp new file mode 100644 index 0000000000000000000000000000000000000000..a3cd232645465f4c8148d1fcd3d9fde6eddddffb --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/imgcodecs/imgcodecs.hpp @@ -0,0 +1,48 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifdef __OPENCV_BUILD +#error this is a compatibility header which should not be used inside the OpenCV library +#endif + +#include "opencv2/imgcodecs.hpp" diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/imgcodecs/imgcodecs_c.h b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/imgcodecs/imgcodecs_c.h new file mode 100644 index 0000000000000000000000000000000000000000..c36dac33435605d126b3dd683b7edb72110e3ae8 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/imgcodecs/imgcodecs_c.h @@ -0,0 +1,149 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// Intel License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000, Intel Corporation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of Intel Corporation may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_IMGCODECS_H +#define OPENCV_IMGCODECS_H + +#include "opencv2/core/core_c.h" + +#ifdef __cplusplus +extern "C" { +#endif /* __cplusplus */ + +/** @addtogroup imgcodecs_c + @{ + */ + +enum +{ +/* 8bit, color or not */ + CV_LOAD_IMAGE_UNCHANGED =-1, +/* 8bit, gray */ + CV_LOAD_IMAGE_GRAYSCALE =0, +/* ?, color */ + CV_LOAD_IMAGE_COLOR =1, +/* any depth, ? */ + CV_LOAD_IMAGE_ANYDEPTH =2, +/* ?, any color */ + CV_LOAD_IMAGE_ANYCOLOR =4, +/* ?, no rotate */ + CV_LOAD_IMAGE_IGNORE_ORIENTATION =128 +}; + +/* load image from file + iscolor can be a combination of above flags where CV_LOAD_IMAGE_UNCHANGED + overrides the other flags + using CV_LOAD_IMAGE_ANYCOLOR alone is equivalent to CV_LOAD_IMAGE_UNCHANGED + unless CV_LOAD_IMAGE_ANYDEPTH is specified images are converted to 8bit +*/ +CVAPI(IplImage*) cvLoadImage( const char* filename, int iscolor CV_DEFAULT(CV_LOAD_IMAGE_COLOR)); +CVAPI(CvMat*) cvLoadImageM( const char* filename, int iscolor CV_DEFAULT(CV_LOAD_IMAGE_COLOR)); + +enum +{ + CV_IMWRITE_JPEG_QUALITY =1, + CV_IMWRITE_JPEG_PROGRESSIVE =2, + CV_IMWRITE_JPEG_OPTIMIZE =3, + CV_IMWRITE_JPEG_RST_INTERVAL =4, + CV_IMWRITE_JPEG_LUMA_QUALITY =5, + CV_IMWRITE_JPEG_CHROMA_QUALITY =6, + CV_IMWRITE_PNG_COMPRESSION =16, + CV_IMWRITE_PNG_STRATEGY =17, + CV_IMWRITE_PNG_BILEVEL =18, + CV_IMWRITE_PNG_STRATEGY_DEFAULT =0, + CV_IMWRITE_PNG_STRATEGY_FILTERED =1, + CV_IMWRITE_PNG_STRATEGY_HUFFMAN_ONLY =2, + CV_IMWRITE_PNG_STRATEGY_RLE =3, + CV_IMWRITE_PNG_STRATEGY_FIXED =4, + CV_IMWRITE_PXM_BINARY =32, + CV_IMWRITE_EXR_TYPE = 48, + CV_IMWRITE_WEBP_QUALITY =64, + CV_IMWRITE_PAM_TUPLETYPE = 128, + CV_IMWRITE_PAM_FORMAT_NULL = 0, + CV_IMWRITE_PAM_FORMAT_BLACKANDWHITE = 1, + CV_IMWRITE_PAM_FORMAT_GRAYSCALE = 2, + CV_IMWRITE_PAM_FORMAT_GRAYSCALE_ALPHA = 3, + CV_IMWRITE_PAM_FORMAT_RGB = 4, + CV_IMWRITE_PAM_FORMAT_RGB_ALPHA = 5, +}; + + + +/* save image to file */ +CVAPI(int) cvSaveImage( const char* filename, const CvArr* image, + const int* params CV_DEFAULT(0) ); + +/* decode image stored in the buffer */ +CVAPI(IplImage*) cvDecodeImage( const CvMat* buf, int iscolor CV_DEFAULT(CV_LOAD_IMAGE_COLOR)); +CVAPI(CvMat*) cvDecodeImageM( const CvMat* buf, int iscolor CV_DEFAULT(CV_LOAD_IMAGE_COLOR)); + +/* encode image and store the result as a byte vector (single-row 8uC1 matrix) */ +CVAPI(CvMat*) cvEncodeImage( const char* ext, const CvArr* image, + const int* params CV_DEFAULT(0) ); + +enum +{ + CV_CVTIMG_FLIP =1, + CV_CVTIMG_SWAP_RB =2 +}; + +/* utility function: convert one image to another with optional vertical flip */ +CVAPI(void) cvConvertImage( const CvArr* src, CvArr* dst, int flags CV_DEFAULT(0)); + +CVAPI(int) cvHaveImageReader(const char* filename); +CVAPI(int) cvHaveImageWriter(const char* filename); + + +/****************************************************************************************\ +* Obsolete functions/synonyms * +\****************************************************************************************/ + +#define cvvLoadImage(name) cvLoadImage((name),1) +#define cvvSaveImage cvSaveImage +#define cvvConvertImage cvConvertImage + +/** @} imgcodecs_c */ + +#ifdef __cplusplus +} +#endif + +#endif // OPENCV_IMGCODECS_H diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/imgcodecs/ios.h b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/imgcodecs/ios.h new file mode 100644 index 0000000000000000000000000000000000000000..a90c6d37a86c8bd6fd3c93d7b89f3c52b5ffbd96 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/imgcodecs/ios.h @@ -0,0 +1,57 @@ + +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#import +#import +#import +#import +#include "opencv2/core/core.hpp" + +//! @addtogroup imgcodecs_ios +//! @{ + +CV_EXPORTS UIImage* MatToUIImage(const cv::Mat& image); +CV_EXPORTS void UIImageToMat(const UIImage* image, + cv::Mat& m, bool alphaExist = false); + +//! @} diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/imgproc.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/imgproc.hpp new file mode 100644 index 0000000000000000000000000000000000000000..14a63f3671cc8c91516ffa36219dcc26f559c02c --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/imgproc.hpp @@ -0,0 +1,4915 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_IMGPROC_HPP +#define OPENCV_IMGPROC_HPP + +#include "opencv2/core.hpp" + +/** + @defgroup imgproc Image Processing + +This module includes image-processing functions. + + @{ + @defgroup imgproc_filter Image Filtering + +Functions and classes described in this section are used to perform various linear or non-linear +filtering operations on 2D images (represented as Mat's). It means that for each pixel location +\f$(x,y)\f$ in the source image (normally, rectangular), its neighborhood is considered and used to +compute the response. In case of a linear filter, it is a weighted sum of pixel values. In case of +morphological operations, it is the minimum or maximum values, and so on. The computed response is +stored in the destination image at the same location \f$(x,y)\f$. It means that the output image +will be of the same size as the input image. Normally, the functions support multi-channel arrays, +in which case every channel is processed independently. Therefore, the output image will also have +the same number of channels as the input one. + +Another common feature of the functions and classes described in this section is that, unlike +simple arithmetic functions, they need to extrapolate values of some non-existing pixels. For +example, if you want to smooth an image using a Gaussian \f$3 \times 3\f$ filter, then, when +processing the left-most pixels in each row, you need pixels to the left of them, that is, outside +of the image. You can let these pixels be the same as the left-most image pixels ("replicated +border" extrapolation method), or assume that all the non-existing pixels are zeros ("constant +border" extrapolation method), and so on. OpenCV enables you to specify the extrapolation method. +For details, see #BorderTypes + +@anchor filter_depths +### Depth combinations +Input depth (src.depth()) | Output depth (ddepth) +--------------------------|---------------------- +CV_8U | -1/CV_16S/CV_32F/CV_64F +CV_16U/CV_16S | -1/CV_32F/CV_64F +CV_32F | -1/CV_32F/CV_64F +CV_64F | -1/CV_64F + +@note when ddepth=-1, the output image will have the same depth as the source. + + @defgroup imgproc_transform Geometric Image Transformations + +The functions in this section perform various geometrical transformations of 2D images. They do not +change the image content but deform the pixel grid and map this deformed grid to the destination +image. In fact, to avoid sampling artifacts, the mapping is done in the reverse order, from +destination to the source. That is, for each pixel \f$(x, y)\f$ of the destination image, the +functions compute coordinates of the corresponding "donor" pixel in the source image and copy the +pixel value: + +\f[\texttt{dst} (x,y)= \texttt{src} (f_x(x,y), f_y(x,y))\f] + +In case when you specify the forward mapping \f$\left: \texttt{src} \rightarrow +\texttt{dst}\f$, the OpenCV functions first compute the corresponding inverse mapping +\f$\left: \texttt{dst} \rightarrow \texttt{src}\f$ and then use the above formula. + +The actual implementations of the geometrical transformations, from the most generic remap and to +the simplest and the fastest resize, need to solve two main problems with the above formula: + +- Extrapolation of non-existing pixels. Similarly to the filtering functions described in the +previous section, for some \f$(x,y)\f$, either one of \f$f_x(x,y)\f$, or \f$f_y(x,y)\f$, or both +of them may fall outside of the image. In this case, an extrapolation method needs to be used. +OpenCV provides the same selection of extrapolation methods as in the filtering functions. In +addition, it provides the method #BORDER_TRANSPARENT. This means that the corresponding pixels in +the destination image will not be modified at all. + +- Interpolation of pixel values. Usually \f$f_x(x,y)\f$ and \f$f_y(x,y)\f$ are floating-point +numbers. This means that \f$\left\f$ can be either an affine or perspective +transformation, or radial lens distortion correction, and so on. So, a pixel value at fractional +coordinates needs to be retrieved. In the simplest case, the coordinates can be just rounded to the +nearest integer coordinates and the corresponding pixel can be used. This is called a +nearest-neighbor interpolation. However, a better result can be achieved by using more +sophisticated [interpolation methods](http://en.wikipedia.org/wiki/Multivariate_interpolation) , +where a polynomial function is fit into some neighborhood of the computed pixel \f$(f_x(x,y), +f_y(x,y))\f$, and then the value of the polynomial at \f$(f_x(x,y), f_y(x,y))\f$ is taken as the +interpolated pixel value. In OpenCV, you can choose between several interpolation methods. See +resize for details. + +@note The geometrical transformations do not work with `CV_8S` or `CV_32S` images. + + @defgroup imgproc_misc Miscellaneous Image Transformations + @defgroup imgproc_draw Drawing Functions + +Drawing functions work with matrices/images of arbitrary depth. The boundaries of the shapes can be +rendered with antialiasing (implemented only for 8-bit images for now). All the functions include +the parameter color that uses an RGB value (that may be constructed with the Scalar constructor ) +for color images and brightness for grayscale images. For color images, the channel ordering is +normally *Blue, Green, Red*. This is what imshow, imread, and imwrite expect. So, if you form a +color using the Scalar constructor, it should look like: + +\f[\texttt{Scalar} (blue \_ component, green \_ component, red \_ component[, alpha \_ component])\f] + +If you are using your own image rendering and I/O functions, you can use any channel ordering. The +drawing functions process each channel independently and do not depend on the channel order or even +on the used color space. The whole image can be converted from BGR to RGB or to a different color +space using cvtColor . + +If a drawn figure is partially or completely outside the image, the drawing functions clip it. Also, +many drawing functions can handle pixel coordinates specified with sub-pixel accuracy. This means +that the coordinates can be passed as fixed-point numbers encoded as integers. The number of +fractional bits is specified by the shift parameter and the real point coordinates are calculated as +\f$\texttt{Point}(x,y)\rightarrow\texttt{Point2f}(x*2^{-shift},y*2^{-shift})\f$ . This feature is +especially effective when rendering antialiased shapes. + +@note The functions do not support alpha-transparency when the target image is 4-channel. In this +case, the color[3] is simply copied to the repainted pixels. Thus, if you want to paint +semi-transparent shapes, you can paint them in a separate buffer and then blend it with the main +image. + + @defgroup imgproc_color_conversions Color Space Conversions + @defgroup imgproc_colormap ColorMaps in OpenCV + +The human perception isn't built for observing fine changes in grayscale images. Human eyes are more +sensitive to observing changes between colors, so you often need to recolor your grayscale images to +get a clue about them. OpenCV now comes with various colormaps to enhance the visualization in your +computer vision application. + +In OpenCV you only need applyColorMap to apply a colormap on a given image. The following sample +code reads the path to an image from command line, applies a Jet colormap on it and shows the +result: + +@include snippets/imgproc_applyColorMap.cpp + +@see #ColormapTypes + + @defgroup imgproc_subdiv2d Planar Subdivision + +The Subdiv2D class described in this section is used to perform various planar subdivision on +a set of 2D points (represented as vector of Point2f). OpenCV subdivides a plane into triangles +using the Delaunay's algorithm, which corresponds to the dual graph of the Voronoi diagram. +In the figure below, the Delaunay's triangulation is marked with black lines and the Voronoi +diagram with red lines. + +![Delaunay triangulation (black) and Voronoi (red)](pics/delaunay_voronoi.png) + +The subdivisions can be used for the 3D piece-wise transformation of a plane, morphing, fast +location of points on the plane, building special graphs (such as NNG,RNG), and so forth. + + @defgroup imgproc_hist Histograms + @defgroup imgproc_shape Structural Analysis and Shape Descriptors + @defgroup imgproc_motion Motion Analysis and Object Tracking + @defgroup imgproc_feature Feature Detection + @defgroup imgproc_object Object Detection + @defgroup imgproc_c C API + @defgroup imgproc_hal Hardware Acceleration Layer + @{ + @defgroup imgproc_hal_functions Functions + @defgroup imgproc_hal_interface Interface + @} + @} +*/ + +namespace cv +{ + +/** @addtogroup imgproc +@{ +*/ + +//! @addtogroup imgproc_filter +//! @{ + +//! type of morphological operation +enum MorphTypes{ + MORPH_ERODE = 0, //!< see #erode + MORPH_DILATE = 1, //!< see #dilate + MORPH_OPEN = 2, //!< an opening operation + //!< \f[\texttt{dst} = \mathrm{open} ( \texttt{src} , \texttt{element} )= \mathrm{dilate} ( \mathrm{erode} ( \texttt{src} , \texttt{element} ))\f] + MORPH_CLOSE = 3, //!< a closing operation + //!< \f[\texttt{dst} = \mathrm{close} ( \texttt{src} , \texttt{element} )= \mathrm{erode} ( \mathrm{dilate} ( \texttt{src} , \texttt{element} ))\f] + MORPH_GRADIENT = 4, //!< a morphological gradient + //!< \f[\texttt{dst} = \mathrm{morph\_grad} ( \texttt{src} , \texttt{element} )= \mathrm{dilate} ( \texttt{src} , \texttt{element} )- \mathrm{erode} ( \texttt{src} , \texttt{element} )\f] + MORPH_TOPHAT = 5, //!< "top hat" + //!< \f[\texttt{dst} = \mathrm{tophat} ( \texttt{src} , \texttt{element} )= \texttt{src} - \mathrm{open} ( \texttt{src} , \texttt{element} )\f] + MORPH_BLACKHAT = 6, //!< "black hat" + //!< \f[\texttt{dst} = \mathrm{blackhat} ( \texttt{src} , \texttt{element} )= \mathrm{close} ( \texttt{src} , \texttt{element} )- \texttt{src}\f] + MORPH_HITMISS = 7 //!< "hit or miss" + //!< .- Only supported for CV_8UC1 binary images. A tutorial can be found in the documentation +}; + +//! shape of the structuring element +enum MorphShapes { + MORPH_RECT = 0, //!< a rectangular structuring element: \f[E_{ij}=1\f] + MORPH_CROSS = 1, //!< a cross-shaped structuring element: + //!< \f[E_{ij} = \fork{1}{if i=\texttt{anchor.y} or j=\texttt{anchor.x}}{0}{otherwise}\f] + MORPH_ELLIPSE = 2 //!< an elliptic structuring element, that is, a filled ellipse inscribed + //!< into the rectangle Rect(0, 0, esize.width, 0.esize.height) +}; + +//! @} imgproc_filter + +//! @addtogroup imgproc_transform +//! @{ + +//! interpolation algorithm +enum InterpolationFlags{ + /** nearest neighbor interpolation */ + INTER_NEAREST = 0, + /** bilinear interpolation */ + INTER_LINEAR = 1, + /** bicubic interpolation */ + INTER_CUBIC = 2, + /** resampling using pixel area relation. It may be a preferred method for image decimation, as + it gives moire'-free results. But when the image is zoomed, it is similar to the INTER_NEAREST + method. */ + INTER_AREA = 3, + /** Lanczos interpolation over 8x8 neighborhood */ + INTER_LANCZOS4 = 4, + /** Bit exact bilinear interpolation */ + INTER_LINEAR_EXACT = 5, + /** mask for interpolation codes */ + INTER_MAX = 7, + /** flag, fills all of the destination image pixels. If some of them correspond to outliers in the + source image, they are set to zero */ + WARP_FILL_OUTLIERS = 8, + /** flag, inverse transformation + + For example, #linearPolar or #logPolar transforms: + - flag is __not__ set: \f$dst( \rho , \phi ) = src(x,y)\f$ + - flag is set: \f$dst(x,y) = src( \rho , \phi )\f$ + */ + WARP_INVERSE_MAP = 16 +}; + +/** \brief Specify the polar mapping mode +@sa warpPolar +*/ +enum WarpPolarMode +{ + WARP_POLAR_LINEAR = 0, ///< Remaps an image to/from polar space. + WARP_POLAR_LOG = 256 ///< Remaps an image to/from semilog-polar space. +}; + +enum InterpolationMasks { + INTER_BITS = 5, + INTER_BITS2 = INTER_BITS * 2, + INTER_TAB_SIZE = 1 << INTER_BITS, + INTER_TAB_SIZE2 = INTER_TAB_SIZE * INTER_TAB_SIZE + }; + +//! @} imgproc_transform + +//! @addtogroup imgproc_misc +//! @{ + +//! Distance types for Distance Transform and M-estimators +//! @see distanceTransform, fitLine +enum DistanceTypes { + DIST_USER = -1, //!< User defined distance + DIST_L1 = 1, //!< distance = |x1-x2| + |y1-y2| + DIST_L2 = 2, //!< the simple euclidean distance + DIST_C = 3, //!< distance = max(|x1-x2|,|y1-y2|) + DIST_L12 = 4, //!< L1-L2 metric: distance = 2(sqrt(1+x*x/2) - 1)) + DIST_FAIR = 5, //!< distance = c^2(|x|/c-log(1+|x|/c)), c = 1.3998 + DIST_WELSCH = 6, //!< distance = c^2/2(1-exp(-(x/c)^2)), c = 2.9846 + DIST_HUBER = 7 //!< distance = |x| \texttt{thresh}\)}{0}{otherwise}\f] + THRESH_BINARY_INV = 1, //!< \f[\texttt{dst} (x,y) = \fork{0}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{\texttt{maxval}}{otherwise}\f] + THRESH_TRUNC = 2, //!< \f[\texttt{dst} (x,y) = \fork{\texttt{threshold}}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{\texttt{src}(x,y)}{otherwise}\f] + THRESH_TOZERO = 3, //!< \f[\texttt{dst} (x,y) = \fork{\texttt{src}(x,y)}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{0}{otherwise}\f] + THRESH_TOZERO_INV = 4, //!< \f[\texttt{dst} (x,y) = \fork{0}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{\texttt{src}(x,y)}{otherwise}\f] + THRESH_MASK = 7, + THRESH_OTSU = 8, //!< flag, use Otsu algorithm to choose the optimal threshold value + THRESH_TRIANGLE = 16 //!< flag, use Triangle algorithm to choose the optimal threshold value +}; + +//! adaptive threshold algorithm +//! @see adaptiveThreshold +enum AdaptiveThresholdTypes { + /** the threshold value \f$T(x,y)\f$ is a mean of the \f$\texttt{blockSize} \times + \texttt{blockSize}\f$ neighborhood of \f$(x, y)\f$ minus C */ + ADAPTIVE_THRESH_MEAN_C = 0, + /** the threshold value \f$T(x, y)\f$ is a weighted sum (cross-correlation with a Gaussian + window) of the \f$\texttt{blockSize} \times \texttt{blockSize}\f$ neighborhood of \f$(x, y)\f$ + minus C . The default sigma (standard deviation) is used for the specified blockSize . See + #getGaussianKernel*/ + ADAPTIVE_THRESH_GAUSSIAN_C = 1 +}; + +//! cv::undistort mode +enum UndistortTypes { + PROJ_SPHERICAL_ORTHO = 0, + PROJ_SPHERICAL_EQRECT = 1 + }; + +//! class of the pixel in GrabCut algorithm +enum GrabCutClasses { + GC_BGD = 0, //!< an obvious background pixels + GC_FGD = 1, //!< an obvious foreground (object) pixel + GC_PR_BGD = 2, //!< a possible background pixel + GC_PR_FGD = 3 //!< a possible foreground pixel +}; + +//! GrabCut algorithm flags +enum GrabCutModes { + /** The function initializes the state and the mask using the provided rectangle. After that it + runs iterCount iterations of the algorithm. */ + GC_INIT_WITH_RECT = 0, + /** The function initializes the state using the provided mask. Note that GC_INIT_WITH_RECT + and GC_INIT_WITH_MASK can be combined. Then, all the pixels outside of the ROI are + automatically initialized with GC_BGD .*/ + GC_INIT_WITH_MASK = 1, + /** The value means that the algorithm should just resume. */ + GC_EVAL = 2, + /** The value means that the algorithm should just run the grabCut algorithm (a single iteration) with the fixed model */ + GC_EVAL_FREEZE_MODEL = 3 +}; + +//! distanceTransform algorithm flags +enum DistanceTransformLabelTypes { + /** each connected component of zeros in src (as well as all the non-zero pixels closest to the + connected component) will be assigned the same label */ + DIST_LABEL_CCOMP = 0, + /** each zero pixel (and all the non-zero pixels closest to it) gets its own label. */ + DIST_LABEL_PIXEL = 1 +}; + +//! floodfill algorithm flags +enum FloodFillFlags { + /** If set, the difference between the current pixel and seed pixel is considered. Otherwise, + the difference between neighbor pixels is considered (that is, the range is floating). */ + FLOODFILL_FIXED_RANGE = 1 << 16, + /** If set, the function does not change the image ( newVal is ignored), and only fills the + mask with the value specified in bits 8-16 of flags as described above. This option only make + sense in function variants that have the mask parameter. */ + FLOODFILL_MASK_ONLY = 1 << 17 +}; + +//! @} imgproc_misc + +//! @addtogroup imgproc_shape +//! @{ + +//! connected components algorithm output formats +enum ConnectedComponentsTypes { + CC_STAT_LEFT = 0, //!< The leftmost (x) coordinate which is the inclusive start of the bounding + //!< box in the horizontal direction. + CC_STAT_TOP = 1, //!< The topmost (y) coordinate which is the inclusive start of the bounding + //!< box in the vertical direction. + CC_STAT_WIDTH = 2, //!< The horizontal size of the bounding box + CC_STAT_HEIGHT = 3, //!< The vertical size of the bounding box + CC_STAT_AREA = 4, //!< The total area (in pixels) of the connected component + CC_STAT_MAX = 5 +}; + +//! connected components algorithm +enum ConnectedComponentsAlgorithmsTypes { + CCL_WU = 0, //!< SAUF algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity + CCL_DEFAULT = -1, //!< BBDT algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity + CCL_GRANA = 1 //!< BBDT algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity +}; + +//! mode of the contour retrieval algorithm +enum RetrievalModes { + /** retrieves only the extreme outer contours. It sets `hierarchy[i][2]=hierarchy[i][3]=-1` for + all the contours. */ + RETR_EXTERNAL = 0, + /** retrieves all of the contours without establishing any hierarchical relationships. */ + RETR_LIST = 1, + /** retrieves all of the contours and organizes them into a two-level hierarchy. At the top + level, there are external boundaries of the components. At the second level, there are + boundaries of the holes. If there is another contour inside a hole of a connected component, it + is still put at the top level. */ + RETR_CCOMP = 2, + /** retrieves all of the contours and reconstructs a full hierarchy of nested contours.*/ + RETR_TREE = 3, + RETR_FLOODFILL = 4 //!< +}; + +//! the contour approximation algorithm +enum ContourApproximationModes { + /** stores absolutely all the contour points. That is, any 2 subsequent points (x1,y1) and + (x2,y2) of the contour will be either horizontal, vertical or diagonal neighbors, that is, + max(abs(x1-x2),abs(y2-y1))==1. */ + CHAIN_APPROX_NONE = 1, + /** compresses horizontal, vertical, and diagonal segments and leaves only their end points. + For example, an up-right rectangular contour is encoded with 4 points. */ + CHAIN_APPROX_SIMPLE = 2, + /** applies one of the flavors of the Teh-Chin chain approximation algorithm @cite TehChin89 */ + CHAIN_APPROX_TC89_L1 = 3, + /** applies one of the flavors of the Teh-Chin chain approximation algorithm @cite TehChin89 */ + CHAIN_APPROX_TC89_KCOS = 4 +}; + +/** @brief Shape matching methods + +\f$A\f$ denotes object1,\f$B\f$ denotes object2 + +\f$\begin{array}{l} m^A_i = \mathrm{sign} (h^A_i) \cdot \log{h^A_i} \\ m^B_i = \mathrm{sign} (h^B_i) \cdot \log{h^B_i} \end{array}\f$ + +and \f$h^A_i, h^B_i\f$ are the Hu moments of \f$A\f$ and \f$B\f$ , respectively. +*/ +enum ShapeMatchModes { + CONTOURS_MATCH_I1 =1, //!< \f[I_1(A,B) = \sum _{i=1...7} \left | \frac{1}{m^A_i} - \frac{1}{m^B_i} \right |\f] + CONTOURS_MATCH_I2 =2, //!< \f[I_2(A,B) = \sum _{i=1...7} \left | m^A_i - m^B_i \right |\f] + CONTOURS_MATCH_I3 =3 //!< \f[I_3(A,B) = \max _{i=1...7} \frac{ \left| m^A_i - m^B_i \right| }{ \left| m^A_i \right| }\f] +}; + +//! @} imgproc_shape + +//! @addtogroup imgproc_feature +//! @{ + +//! Variants of a Hough transform +enum HoughModes { + + /** classical or standard Hough transform. Every line is represented by two floating-point + numbers \f$(\rho, \theta)\f$ , where \f$\rho\f$ is a distance between (0,0) point and the line, + and \f$\theta\f$ is the angle between x-axis and the normal to the line. Thus, the matrix must + be (the created sequence will be) of CV_32FC2 type */ + HOUGH_STANDARD = 0, + /** probabilistic Hough transform (more efficient in case if the picture contains a few long + linear segments). It returns line segments rather than the whole line. Each segment is + represented by starting and ending points, and the matrix must be (the created sequence will + be) of the CV_32SC4 type. */ + HOUGH_PROBABILISTIC = 1, + /** multi-scale variant of the classical Hough transform. The lines are encoded the same way as + HOUGH_STANDARD. */ + HOUGH_MULTI_SCALE = 2, + HOUGH_GRADIENT = 3 //!< basically *21HT*, described in @cite Yuen90 +}; + +//! Variants of Line Segment %Detector +enum LineSegmentDetectorModes { + LSD_REFINE_NONE = 0, //!< No refinement applied + LSD_REFINE_STD = 1, //!< Standard refinement is applied. E.g. breaking arches into smaller straighter line approximations. + LSD_REFINE_ADV = 2 //!< Advanced refinement. Number of false alarms is calculated, lines are + //!< refined through increase of precision, decrement in size, etc. +}; + +//! @} imgproc_feature + +/** Histogram comparison methods + @ingroup imgproc_hist +*/ +enum HistCompMethods { + /** Correlation + \f[d(H_1,H_2) = \frac{\sum_I (H_1(I) - \bar{H_1}) (H_2(I) - \bar{H_2})}{\sqrt{\sum_I(H_1(I) - \bar{H_1})^2 \sum_I(H_2(I) - \bar{H_2})^2}}\f] + where + \f[\bar{H_k} = \frac{1}{N} \sum _J H_k(J)\f] + and \f$N\f$ is a total number of histogram bins. */ + HISTCMP_CORREL = 0, + /** Chi-Square + \f[d(H_1,H_2) = \sum _I \frac{\left(H_1(I)-H_2(I)\right)^2}{H_1(I)}\f] */ + HISTCMP_CHISQR = 1, + /** Intersection + \f[d(H_1,H_2) = \sum _I \min (H_1(I), H_2(I))\f] */ + HISTCMP_INTERSECT = 2, + /** Bhattacharyya distance + (In fact, OpenCV computes Hellinger distance, which is related to Bhattacharyya coefficient.) + \f[d(H_1,H_2) = \sqrt{1 - \frac{1}{\sqrt{\bar{H_1} \bar{H_2} N^2}} \sum_I \sqrt{H_1(I) \cdot H_2(I)}}\f] */ + HISTCMP_BHATTACHARYYA = 3, + HISTCMP_HELLINGER = HISTCMP_BHATTACHARYYA, //!< Synonym for HISTCMP_BHATTACHARYYA + /** Alternative Chi-Square + \f[d(H_1,H_2) = 2 * \sum _I \frac{\left(H_1(I)-H_2(I)\right)^2}{H_1(I)+H_2(I)}\f] + This alternative formula is regularly used for texture comparison. See e.g. @cite Puzicha1997 */ + HISTCMP_CHISQR_ALT = 4, + /** Kullback-Leibler divergence + \f[d(H_1,H_2) = \sum _I H_1(I) \log \left(\frac{H_1(I)}{H_2(I)}\right)\f] */ + HISTCMP_KL_DIV = 5 +}; + +/** the color conversion codes +@see @ref imgproc_color_conversions +@ingroup imgproc_color_conversions + */ +enum ColorConversionCodes { + COLOR_BGR2BGRA = 0, //!< add alpha channel to RGB or BGR image + COLOR_RGB2RGBA = COLOR_BGR2BGRA, + + COLOR_BGRA2BGR = 1, //!< remove alpha channel from RGB or BGR image + COLOR_RGBA2RGB = COLOR_BGRA2BGR, + + COLOR_BGR2RGBA = 2, //!< convert between RGB and BGR color spaces (with or without alpha channel) + COLOR_RGB2BGRA = COLOR_BGR2RGBA, + + COLOR_RGBA2BGR = 3, + COLOR_BGRA2RGB = COLOR_RGBA2BGR, + + COLOR_BGR2RGB = 4, + COLOR_RGB2BGR = COLOR_BGR2RGB, + + COLOR_BGRA2RGBA = 5, + COLOR_RGBA2BGRA = COLOR_BGRA2RGBA, + + COLOR_BGR2GRAY = 6, //!< convert between RGB/BGR and grayscale, @ref color_convert_rgb_gray "color conversions" + COLOR_RGB2GRAY = 7, + COLOR_GRAY2BGR = 8, + COLOR_GRAY2RGB = COLOR_GRAY2BGR, + COLOR_GRAY2BGRA = 9, + COLOR_GRAY2RGBA = COLOR_GRAY2BGRA, + COLOR_BGRA2GRAY = 10, + COLOR_RGBA2GRAY = 11, + + COLOR_BGR2BGR565 = 12, //!< convert between RGB/BGR and BGR565 (16-bit images) + COLOR_RGB2BGR565 = 13, + COLOR_BGR5652BGR = 14, + COLOR_BGR5652RGB = 15, + COLOR_BGRA2BGR565 = 16, + COLOR_RGBA2BGR565 = 17, + COLOR_BGR5652BGRA = 18, + COLOR_BGR5652RGBA = 19, + + COLOR_GRAY2BGR565 = 20, //!< convert between grayscale to BGR565 (16-bit images) + COLOR_BGR5652GRAY = 21, + + COLOR_BGR2BGR555 = 22, //!< convert between RGB/BGR and BGR555 (16-bit images) + COLOR_RGB2BGR555 = 23, + COLOR_BGR5552BGR = 24, + COLOR_BGR5552RGB = 25, + COLOR_BGRA2BGR555 = 26, + COLOR_RGBA2BGR555 = 27, + COLOR_BGR5552BGRA = 28, + COLOR_BGR5552RGBA = 29, + + COLOR_GRAY2BGR555 = 30, //!< convert between grayscale and BGR555 (16-bit images) + COLOR_BGR5552GRAY = 31, + + COLOR_BGR2XYZ = 32, //!< convert RGB/BGR to CIE XYZ, @ref color_convert_rgb_xyz "color conversions" + COLOR_RGB2XYZ = 33, + COLOR_XYZ2BGR = 34, + COLOR_XYZ2RGB = 35, + + COLOR_BGR2YCrCb = 36, //!< convert RGB/BGR to luma-chroma (aka YCC), @ref color_convert_rgb_ycrcb "color conversions" + COLOR_RGB2YCrCb = 37, + COLOR_YCrCb2BGR = 38, + COLOR_YCrCb2RGB = 39, + + COLOR_BGR2HSV = 40, //!< convert RGB/BGR to HSV (hue saturation value), @ref color_convert_rgb_hsv "color conversions" + COLOR_RGB2HSV = 41, + + COLOR_BGR2Lab = 44, //!< convert RGB/BGR to CIE Lab, @ref color_convert_rgb_lab "color conversions" + COLOR_RGB2Lab = 45, + + COLOR_BGR2Luv = 50, //!< convert RGB/BGR to CIE Luv, @ref color_convert_rgb_luv "color conversions" + COLOR_RGB2Luv = 51, + COLOR_BGR2HLS = 52, //!< convert RGB/BGR to HLS (hue lightness saturation), @ref color_convert_rgb_hls "color conversions" + COLOR_RGB2HLS = 53, + + COLOR_HSV2BGR = 54, //!< backward conversions to RGB/BGR + COLOR_HSV2RGB = 55, + + COLOR_Lab2BGR = 56, + COLOR_Lab2RGB = 57, + COLOR_Luv2BGR = 58, + COLOR_Luv2RGB = 59, + COLOR_HLS2BGR = 60, + COLOR_HLS2RGB = 61, + + COLOR_BGR2HSV_FULL = 66, + COLOR_RGB2HSV_FULL = 67, + COLOR_BGR2HLS_FULL = 68, + COLOR_RGB2HLS_FULL = 69, + + COLOR_HSV2BGR_FULL = 70, + COLOR_HSV2RGB_FULL = 71, + COLOR_HLS2BGR_FULL = 72, + COLOR_HLS2RGB_FULL = 73, + + COLOR_LBGR2Lab = 74, + COLOR_LRGB2Lab = 75, + COLOR_LBGR2Luv = 76, + COLOR_LRGB2Luv = 77, + + COLOR_Lab2LBGR = 78, + COLOR_Lab2LRGB = 79, + COLOR_Luv2LBGR = 80, + COLOR_Luv2LRGB = 81, + + COLOR_BGR2YUV = 82, //!< convert between RGB/BGR and YUV + COLOR_RGB2YUV = 83, + COLOR_YUV2BGR = 84, + COLOR_YUV2RGB = 85, + + //! YUV 4:2:0 family to RGB + COLOR_YUV2RGB_NV12 = 90, + COLOR_YUV2BGR_NV12 = 91, + COLOR_YUV2RGB_NV21 = 92, + COLOR_YUV2BGR_NV21 = 93, + COLOR_YUV420sp2RGB = COLOR_YUV2RGB_NV21, + COLOR_YUV420sp2BGR = COLOR_YUV2BGR_NV21, + + COLOR_YUV2RGBA_NV12 = 94, + COLOR_YUV2BGRA_NV12 = 95, + COLOR_YUV2RGBA_NV21 = 96, + COLOR_YUV2BGRA_NV21 = 97, + COLOR_YUV420sp2RGBA = COLOR_YUV2RGBA_NV21, + COLOR_YUV420sp2BGRA = COLOR_YUV2BGRA_NV21, + + COLOR_YUV2RGB_YV12 = 98, + COLOR_YUV2BGR_YV12 = 99, + COLOR_YUV2RGB_IYUV = 100, + COLOR_YUV2BGR_IYUV = 101, + COLOR_YUV2RGB_I420 = COLOR_YUV2RGB_IYUV, + COLOR_YUV2BGR_I420 = COLOR_YUV2BGR_IYUV, + COLOR_YUV420p2RGB = COLOR_YUV2RGB_YV12, + COLOR_YUV420p2BGR = COLOR_YUV2BGR_YV12, + + COLOR_YUV2RGBA_YV12 = 102, + COLOR_YUV2BGRA_YV12 = 103, + COLOR_YUV2RGBA_IYUV = 104, + COLOR_YUV2BGRA_IYUV = 105, + COLOR_YUV2RGBA_I420 = COLOR_YUV2RGBA_IYUV, + COLOR_YUV2BGRA_I420 = COLOR_YUV2BGRA_IYUV, + COLOR_YUV420p2RGBA = COLOR_YUV2RGBA_YV12, + COLOR_YUV420p2BGRA = COLOR_YUV2BGRA_YV12, + + COLOR_YUV2GRAY_420 = 106, + COLOR_YUV2GRAY_NV21 = COLOR_YUV2GRAY_420, + COLOR_YUV2GRAY_NV12 = COLOR_YUV2GRAY_420, + COLOR_YUV2GRAY_YV12 = COLOR_YUV2GRAY_420, + COLOR_YUV2GRAY_IYUV = COLOR_YUV2GRAY_420, + COLOR_YUV2GRAY_I420 = COLOR_YUV2GRAY_420, + COLOR_YUV420sp2GRAY = COLOR_YUV2GRAY_420, + COLOR_YUV420p2GRAY = COLOR_YUV2GRAY_420, + + //! YUV 4:2:2 family to RGB + COLOR_YUV2RGB_UYVY = 107, + COLOR_YUV2BGR_UYVY = 108, + //COLOR_YUV2RGB_VYUY = 109, + //COLOR_YUV2BGR_VYUY = 110, + COLOR_YUV2RGB_Y422 = COLOR_YUV2RGB_UYVY, + COLOR_YUV2BGR_Y422 = COLOR_YUV2BGR_UYVY, + COLOR_YUV2RGB_UYNV = COLOR_YUV2RGB_UYVY, + COLOR_YUV2BGR_UYNV = COLOR_YUV2BGR_UYVY, + + COLOR_YUV2RGBA_UYVY = 111, + COLOR_YUV2BGRA_UYVY = 112, + //COLOR_YUV2RGBA_VYUY = 113, + //COLOR_YUV2BGRA_VYUY = 114, + COLOR_YUV2RGBA_Y422 = COLOR_YUV2RGBA_UYVY, + COLOR_YUV2BGRA_Y422 = COLOR_YUV2BGRA_UYVY, + COLOR_YUV2RGBA_UYNV = COLOR_YUV2RGBA_UYVY, + COLOR_YUV2BGRA_UYNV = COLOR_YUV2BGRA_UYVY, + + COLOR_YUV2RGB_YUY2 = 115, + COLOR_YUV2BGR_YUY2 = 116, + COLOR_YUV2RGB_YVYU = 117, + COLOR_YUV2BGR_YVYU = 118, + COLOR_YUV2RGB_YUYV = COLOR_YUV2RGB_YUY2, + COLOR_YUV2BGR_YUYV = COLOR_YUV2BGR_YUY2, + COLOR_YUV2RGB_YUNV = COLOR_YUV2RGB_YUY2, + COLOR_YUV2BGR_YUNV = COLOR_YUV2BGR_YUY2, + + COLOR_YUV2RGBA_YUY2 = 119, + COLOR_YUV2BGRA_YUY2 = 120, + COLOR_YUV2RGBA_YVYU = 121, + COLOR_YUV2BGRA_YVYU = 122, + COLOR_YUV2RGBA_YUYV = COLOR_YUV2RGBA_YUY2, + COLOR_YUV2BGRA_YUYV = COLOR_YUV2BGRA_YUY2, + COLOR_YUV2RGBA_YUNV = COLOR_YUV2RGBA_YUY2, + COLOR_YUV2BGRA_YUNV = COLOR_YUV2BGRA_YUY2, + + COLOR_YUV2GRAY_UYVY = 123, + COLOR_YUV2GRAY_YUY2 = 124, + //CV_YUV2GRAY_VYUY = CV_YUV2GRAY_UYVY, + COLOR_YUV2GRAY_Y422 = COLOR_YUV2GRAY_UYVY, + COLOR_YUV2GRAY_UYNV = COLOR_YUV2GRAY_UYVY, + COLOR_YUV2GRAY_YVYU = COLOR_YUV2GRAY_YUY2, + COLOR_YUV2GRAY_YUYV = COLOR_YUV2GRAY_YUY2, + COLOR_YUV2GRAY_YUNV = COLOR_YUV2GRAY_YUY2, + + //! alpha premultiplication + COLOR_RGBA2mRGBA = 125, + COLOR_mRGBA2RGBA = 126, + + //! RGB to YUV 4:2:0 family + COLOR_RGB2YUV_I420 = 127, + COLOR_BGR2YUV_I420 = 128, + COLOR_RGB2YUV_IYUV = COLOR_RGB2YUV_I420, + COLOR_BGR2YUV_IYUV = COLOR_BGR2YUV_I420, + + COLOR_RGBA2YUV_I420 = 129, + COLOR_BGRA2YUV_I420 = 130, + COLOR_RGBA2YUV_IYUV = COLOR_RGBA2YUV_I420, + COLOR_BGRA2YUV_IYUV = COLOR_BGRA2YUV_I420, + COLOR_RGB2YUV_YV12 = 131, + COLOR_BGR2YUV_YV12 = 132, + COLOR_RGBA2YUV_YV12 = 133, + COLOR_BGRA2YUV_YV12 = 134, + + //! Demosaicing + COLOR_BayerBG2BGR = 46, + COLOR_BayerGB2BGR = 47, + COLOR_BayerRG2BGR = 48, + COLOR_BayerGR2BGR = 49, + + COLOR_BayerBG2RGB = COLOR_BayerRG2BGR, + COLOR_BayerGB2RGB = COLOR_BayerGR2BGR, + COLOR_BayerRG2RGB = COLOR_BayerBG2BGR, + COLOR_BayerGR2RGB = COLOR_BayerGB2BGR, + + COLOR_BayerBG2GRAY = 86, + COLOR_BayerGB2GRAY = 87, + COLOR_BayerRG2GRAY = 88, + COLOR_BayerGR2GRAY = 89, + + //! Demosaicing using Variable Number of Gradients + COLOR_BayerBG2BGR_VNG = 62, + COLOR_BayerGB2BGR_VNG = 63, + COLOR_BayerRG2BGR_VNG = 64, + COLOR_BayerGR2BGR_VNG = 65, + + COLOR_BayerBG2RGB_VNG = COLOR_BayerRG2BGR_VNG, + COLOR_BayerGB2RGB_VNG = COLOR_BayerGR2BGR_VNG, + COLOR_BayerRG2RGB_VNG = COLOR_BayerBG2BGR_VNG, + COLOR_BayerGR2RGB_VNG = COLOR_BayerGB2BGR_VNG, + + //! Edge-Aware Demosaicing + COLOR_BayerBG2BGR_EA = 135, + COLOR_BayerGB2BGR_EA = 136, + COLOR_BayerRG2BGR_EA = 137, + COLOR_BayerGR2BGR_EA = 138, + + COLOR_BayerBG2RGB_EA = COLOR_BayerRG2BGR_EA, + COLOR_BayerGB2RGB_EA = COLOR_BayerGR2BGR_EA, + COLOR_BayerRG2RGB_EA = COLOR_BayerBG2BGR_EA, + COLOR_BayerGR2RGB_EA = COLOR_BayerGB2BGR_EA, + + //! Demosaicing with alpha channel + COLOR_BayerBG2BGRA = 139, + COLOR_BayerGB2BGRA = 140, + COLOR_BayerRG2BGRA = 141, + COLOR_BayerGR2BGRA = 142, + + COLOR_BayerBG2RGBA = COLOR_BayerRG2BGRA, + COLOR_BayerGB2RGBA = COLOR_BayerGR2BGRA, + COLOR_BayerRG2RGBA = COLOR_BayerBG2BGRA, + COLOR_BayerGR2RGBA = COLOR_BayerGB2BGRA, + + COLOR_COLORCVT_MAX = 143 +}; + +//! @addtogroup imgproc_shape +//! @{ + +//! types of intersection between rectangles +enum RectanglesIntersectTypes { + INTERSECT_NONE = 0, //!< No intersection + INTERSECT_PARTIAL = 1, //!< There is a partial intersection + INTERSECT_FULL = 2 //!< One of the rectangle is fully enclosed in the other +}; + +/** @brief finds arbitrary template in the grayscale image using Generalized Hough Transform +*/ +class CV_EXPORTS GeneralizedHough : public Algorithm +{ +public: + //! set template to search + virtual void setTemplate(InputArray templ, Point templCenter = Point(-1, -1)) = 0; + virtual void setTemplate(InputArray edges, InputArray dx, InputArray dy, Point templCenter = Point(-1, -1)) = 0; + + //! find template on image + virtual void detect(InputArray image, OutputArray positions, OutputArray votes = noArray()) = 0; + virtual void detect(InputArray edges, InputArray dx, InputArray dy, OutputArray positions, OutputArray votes = noArray()) = 0; + + //! Canny low threshold. + virtual void setCannyLowThresh(int cannyLowThresh) = 0; + virtual int getCannyLowThresh() const = 0; + + //! Canny high threshold. + virtual void setCannyHighThresh(int cannyHighThresh) = 0; + virtual int getCannyHighThresh() const = 0; + + //! Minimum distance between the centers of the detected objects. + virtual void setMinDist(double minDist) = 0; + virtual double getMinDist() const = 0; + + //! Inverse ratio of the accumulator resolution to the image resolution. + virtual void setDp(double dp) = 0; + virtual double getDp() const = 0; + + //! Maximal size of inner buffers. + virtual void setMaxBufferSize(int maxBufferSize) = 0; + virtual int getMaxBufferSize() const = 0; +}; + +/** @brief finds arbitrary template in the grayscale image using Generalized Hough Transform + +Detects position only without translation and rotation @cite Ballard1981 . +*/ +class CV_EXPORTS GeneralizedHoughBallard : public GeneralizedHough +{ +public: + //! R-Table levels. + virtual void setLevels(int levels) = 0; + virtual int getLevels() const = 0; + + //! The accumulator threshold for the template centers at the detection stage. The smaller it is, the more false positions may be detected. + virtual void setVotesThreshold(int votesThreshold) = 0; + virtual int getVotesThreshold() const = 0; +}; + +/** @brief finds arbitrary template in the grayscale image using Generalized Hough Transform + +Detects position, translation and rotation @cite Guil1999 . +*/ +class CV_EXPORTS GeneralizedHoughGuil : public GeneralizedHough +{ +public: + //! Angle difference in degrees between two points in feature. + virtual void setXi(double xi) = 0; + virtual double getXi() const = 0; + + //! Feature table levels. + virtual void setLevels(int levels) = 0; + virtual int getLevels() const = 0; + + //! Maximal difference between angles that treated as equal. + virtual void setAngleEpsilon(double angleEpsilon) = 0; + virtual double getAngleEpsilon() const = 0; + + //! Minimal rotation angle to detect in degrees. + virtual void setMinAngle(double minAngle) = 0; + virtual double getMinAngle() const = 0; + + //! Maximal rotation angle to detect in degrees. + virtual void setMaxAngle(double maxAngle) = 0; + virtual double getMaxAngle() const = 0; + + //! Angle step in degrees. + virtual void setAngleStep(double angleStep) = 0; + virtual double getAngleStep() const = 0; + + //! Angle votes threshold. + virtual void setAngleThresh(int angleThresh) = 0; + virtual int getAngleThresh() const = 0; + + //! Minimal scale to detect. + virtual void setMinScale(double minScale) = 0; + virtual double getMinScale() const = 0; + + //! Maximal scale to detect. + virtual void setMaxScale(double maxScale) = 0; + virtual double getMaxScale() const = 0; + + //! Scale step. + virtual void setScaleStep(double scaleStep) = 0; + virtual double getScaleStep() const = 0; + + //! Scale votes threshold. + virtual void setScaleThresh(int scaleThresh) = 0; + virtual int getScaleThresh() const = 0; + + //! Position votes threshold. + virtual void setPosThresh(int posThresh) = 0; + virtual int getPosThresh() const = 0; +}; + +//! @} imgproc_shape + +//! @addtogroup imgproc_hist +//! @{ + +/** @brief Base class for Contrast Limited Adaptive Histogram Equalization. +*/ +class CV_EXPORTS_W CLAHE : public Algorithm +{ +public: + /** @brief Equalizes the histogram of a grayscale image using Contrast Limited Adaptive Histogram Equalization. + + @param src Source image of type CV_8UC1 or CV_16UC1. + @param dst Destination image. + */ + CV_WRAP virtual void apply(InputArray src, OutputArray dst) = 0; + + /** @brief Sets threshold for contrast limiting. + + @param clipLimit threshold value. + */ + CV_WRAP virtual void setClipLimit(double clipLimit) = 0; + + //! Returns threshold value for contrast limiting. + CV_WRAP virtual double getClipLimit() const = 0; + + /** @brief Sets size of grid for histogram equalization. Input image will be divided into + equally sized rectangular tiles. + + @param tileGridSize defines the number of tiles in row and column. + */ + CV_WRAP virtual void setTilesGridSize(Size tileGridSize) = 0; + + //!@brief Returns Size defines the number of tiles in row and column. + CV_WRAP virtual Size getTilesGridSize() const = 0; + + CV_WRAP virtual void collectGarbage() = 0; +}; + +//! @} imgproc_hist + +//! @addtogroup imgproc_subdiv2d +//! @{ + +class CV_EXPORTS_W Subdiv2D +{ +public: + /** Subdiv2D point location cases */ + enum { PTLOC_ERROR = -2, //!< Point location error + PTLOC_OUTSIDE_RECT = -1, //!< Point outside the subdivision bounding rect + PTLOC_INSIDE = 0, //!< Point inside some facet + PTLOC_VERTEX = 1, //!< Point coincides with one of the subdivision vertices + PTLOC_ON_EDGE = 2 //!< Point on some edge + }; + + /** Subdiv2D edge type navigation (see: getEdge()) */ + enum { NEXT_AROUND_ORG = 0x00, + NEXT_AROUND_DST = 0x22, + PREV_AROUND_ORG = 0x11, + PREV_AROUND_DST = 0x33, + NEXT_AROUND_LEFT = 0x13, + NEXT_AROUND_RIGHT = 0x31, + PREV_AROUND_LEFT = 0x20, + PREV_AROUND_RIGHT = 0x02 + }; + + /** creates an empty Subdiv2D object. + To create a new empty Delaunay subdivision you need to use the #initDelaunay function. + */ + CV_WRAP Subdiv2D(); + + /** @overload + + @param rect Rectangle that includes all of the 2D points that are to be added to the subdivision. + + The function creates an empty Delaunay subdivision where 2D points can be added using the function + insert() . All of the points to be added must be within the specified rectangle, otherwise a runtime + error is raised. + */ + CV_WRAP Subdiv2D(Rect rect); + + /** @brief Creates a new empty Delaunay subdivision + + @param rect Rectangle that includes all of the 2D points that are to be added to the subdivision. + + */ + CV_WRAP void initDelaunay(Rect rect); + + /** @brief Insert a single point into a Delaunay triangulation. + + @param pt Point to insert. + + The function inserts a single point into a subdivision and modifies the subdivision topology + appropriately. If a point with the same coordinates exists already, no new point is added. + @returns the ID of the point. + + @note If the point is outside of the triangulation specified rect a runtime error is raised. + */ + CV_WRAP int insert(Point2f pt); + + /** @brief Insert multiple points into a Delaunay triangulation. + + @param ptvec Points to insert. + + The function inserts a vector of points into a subdivision and modifies the subdivision topology + appropriately. + */ + CV_WRAP void insert(const std::vector& ptvec); + + /** @brief Returns the location of a point within a Delaunay triangulation. + + @param pt Point to locate. + @param edge Output edge that the point belongs to or is located to the right of it. + @param vertex Optional output vertex the input point coincides with. + + The function locates the input point within the subdivision and gives one of the triangle edges + or vertices. + + @returns an integer which specify one of the following five cases for point location: + - The point falls into some facet. The function returns #PTLOC_INSIDE and edge will contain one of + edges of the facet. + - The point falls onto the edge. The function returns #PTLOC_ON_EDGE and edge will contain this edge. + - The point coincides with one of the subdivision vertices. The function returns #PTLOC_VERTEX and + vertex will contain a pointer to the vertex. + - The point is outside the subdivision reference rectangle. The function returns #PTLOC_OUTSIDE_RECT + and no pointers are filled. + - One of input arguments is invalid. A runtime error is raised or, if silent or "parent" error + processing mode is selected, #PTLOC_ERROR is returned. + */ + CV_WRAP int locate(Point2f pt, CV_OUT int& edge, CV_OUT int& vertex); + + /** @brief Finds the subdivision vertex closest to the given point. + + @param pt Input point. + @param nearestPt Output subdivision vertex point. + + The function is another function that locates the input point within the subdivision. It finds the + subdivision vertex that is the closest to the input point. It is not necessarily one of vertices + of the facet containing the input point, though the facet (located using locate() ) is used as a + starting point. + + @returns vertex ID. + */ + CV_WRAP int findNearest(Point2f pt, CV_OUT Point2f* nearestPt = 0); + + /** @brief Returns a list of all edges. + + @param edgeList Output vector. + + The function gives each edge as a 4 numbers vector, where each two are one of the edge + vertices. i.e. org_x = v[0], org_y = v[1], dst_x = v[2], dst_y = v[3]. + */ + CV_WRAP void getEdgeList(CV_OUT std::vector& edgeList) const; + + /** @brief Returns a list of the leading edge ID connected to each triangle. + + @param leadingEdgeList Output vector. + + The function gives one edge ID for each triangle. + */ + CV_WRAP void getLeadingEdgeList(CV_OUT std::vector& leadingEdgeList) const; + + /** @brief Returns a list of all triangles. + + @param triangleList Output vector. + + The function gives each triangle as a 6 numbers vector, where each two are one of the triangle + vertices. i.e. p1_x = v[0], p1_y = v[1], p2_x = v[2], p2_y = v[3], p3_x = v[4], p3_y = v[5]. + */ + CV_WRAP void getTriangleList(CV_OUT std::vector& triangleList) const; + + /** @brief Returns a list of all Voroni facets. + + @param idx Vector of vertices IDs to consider. For all vertices you can pass empty vector. + @param facetList Output vector of the Voroni facets. + @param facetCenters Output vector of the Voroni facets center points. + + */ + CV_WRAP void getVoronoiFacetList(const std::vector& idx, CV_OUT std::vector >& facetList, + CV_OUT std::vector& facetCenters); + + /** @brief Returns vertex location from vertex ID. + + @param vertex vertex ID. + @param firstEdge Optional. The first edge ID which is connected to the vertex. + @returns vertex (x,y) + + */ + CV_WRAP Point2f getVertex(int vertex, CV_OUT int* firstEdge = 0) const; + + /** @brief Returns one of the edges related to the given edge. + + @param edge Subdivision edge ID. + @param nextEdgeType Parameter specifying which of the related edges to return. + The following values are possible: + - NEXT_AROUND_ORG next around the edge origin ( eOnext on the picture below if e is the input edge) + - NEXT_AROUND_DST next around the edge vertex ( eDnext ) + - PREV_AROUND_ORG previous around the edge origin (reversed eRnext ) + - PREV_AROUND_DST previous around the edge destination (reversed eLnext ) + - NEXT_AROUND_LEFT next around the left facet ( eLnext ) + - NEXT_AROUND_RIGHT next around the right facet ( eRnext ) + - PREV_AROUND_LEFT previous around the left facet (reversed eOnext ) + - PREV_AROUND_RIGHT previous around the right facet (reversed eDnext ) + + ![sample output](pics/quadedge.png) + + @returns edge ID related to the input edge. + */ + CV_WRAP int getEdge( int edge, int nextEdgeType ) const; + + /** @brief Returns next edge around the edge origin. + + @param edge Subdivision edge ID. + + @returns an integer which is next edge ID around the edge origin: eOnext on the + picture above if e is the input edge). + */ + CV_WRAP int nextEdge(int edge) const; + + /** @brief Returns another edge of the same quad-edge. + + @param edge Subdivision edge ID. + @param rotate Parameter specifying which of the edges of the same quad-edge as the input + one to return. The following values are possible: + - 0 - the input edge ( e on the picture below if e is the input edge) + - 1 - the rotated edge ( eRot ) + - 2 - the reversed edge (reversed e (in green)) + - 3 - the reversed rotated edge (reversed eRot (in green)) + + @returns one of the edges ID of the same quad-edge as the input edge. + */ + CV_WRAP int rotateEdge(int edge, int rotate) const; + CV_WRAP int symEdge(int edge) const; + + /** @brief Returns the edge origin. + + @param edge Subdivision edge ID. + @param orgpt Output vertex location. + + @returns vertex ID. + */ + CV_WRAP int edgeOrg(int edge, CV_OUT Point2f* orgpt = 0) const; + + /** @brief Returns the edge destination. + + @param edge Subdivision edge ID. + @param dstpt Output vertex location. + + @returns vertex ID. + */ + CV_WRAP int edgeDst(int edge, CV_OUT Point2f* dstpt = 0) const; + +protected: + int newEdge(); + void deleteEdge(int edge); + int newPoint(Point2f pt, bool isvirtual, int firstEdge = 0); + void deletePoint(int vtx); + void setEdgePoints( int edge, int orgPt, int dstPt ); + void splice( int edgeA, int edgeB ); + int connectEdges( int edgeA, int edgeB ); + void swapEdges( int edge ); + int isRightOf(Point2f pt, int edge) const; + void calcVoronoi(); + void clearVoronoi(); + void checkSubdiv() const; + + struct CV_EXPORTS Vertex + { + Vertex(); + Vertex(Point2f pt, bool _isvirtual, int _firstEdge=0); + bool isvirtual() const; + bool isfree() const; + + int firstEdge; + int type; + Point2f pt; + }; + + struct CV_EXPORTS QuadEdge + { + QuadEdge(); + QuadEdge(int edgeidx); + bool isfree() const; + + int next[4]; + int pt[4]; + }; + + //! All of the vertices + std::vector vtx; + //! All of the edges + std::vector qedges; + int freeQEdge; + int freePoint; + bool validGeometry; + + int recentEdge; + //! Top left corner of the bounding rect + Point2f topLeft; + //! Bottom right corner of the bounding rect + Point2f bottomRight; +}; + +//! @} imgproc_subdiv2d + +//! @addtogroup imgproc_feature +//! @{ + +/** @example samples/cpp/lsd_lines.cpp +An example using the LineSegmentDetector +\image html building_lsd.png "Sample output image" width=434 height=300 +*/ + +/** @brief Line segment detector class + +following the algorithm described at @cite Rafael12 . +*/ +class CV_EXPORTS_W LineSegmentDetector : public Algorithm +{ +public: + + /** @brief Finds lines in the input image. + + This is the output of the default parameters of the algorithm on the above shown image. + + ![image](pics/building_lsd.png) + + @param _image A grayscale (CV_8UC1) input image. If only a roi needs to be selected, use: + `lsd_ptr-\>detect(image(roi), lines, ...); lines += Scalar(roi.x, roi.y, roi.x, roi.y);` + @param _lines A vector of Vec4i or Vec4f elements specifying the beginning and ending point of a line. Where + Vec4i/Vec4f is (x1, y1, x2, y2), point 1 is the start, point 2 - end. Returned lines are strictly + oriented depending on the gradient. + @param width Vector of widths of the regions, where the lines are found. E.g. Width of line. + @param prec Vector of precisions with which the lines are found. + @param nfa Vector containing number of false alarms in the line region, with precision of 10%. The + bigger the value, logarithmically better the detection. + - -1 corresponds to 10 mean false alarms + - 0 corresponds to 1 mean false alarm + - 1 corresponds to 0.1 mean false alarms + This vector will be calculated only when the objects type is #LSD_REFINE_ADV. + */ + CV_WRAP virtual void detect(InputArray _image, OutputArray _lines, + OutputArray width = noArray(), OutputArray prec = noArray(), + OutputArray nfa = noArray()) = 0; + + /** @brief Draws the line segments on a given image. + @param _image The image, where the lines will be drawn. Should be bigger or equal to the image, + where the lines were found. + @param lines A vector of the lines that needed to be drawn. + */ + CV_WRAP virtual void drawSegments(InputOutputArray _image, InputArray lines) = 0; + + /** @brief Draws two groups of lines in blue and red, counting the non overlapping (mismatching) pixels. + + @param size The size of the image, where lines1 and lines2 were found. + @param lines1 The first group of lines that needs to be drawn. It is visualized in blue color. + @param lines2 The second group of lines. They visualized in red color. + @param _image Optional image, where the lines will be drawn. The image should be color(3-channel) + in order for lines1 and lines2 to be drawn in the above mentioned colors. + */ + CV_WRAP virtual int compareSegments(const Size& size, InputArray lines1, InputArray lines2, InputOutputArray _image = noArray()) = 0; + + virtual ~LineSegmentDetector() { } +}; + +/** @brief Creates a smart pointer to a LineSegmentDetector object and initializes it. + +The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want +to edit those, as to tailor it for their own application. + +@param _refine The way found lines will be refined, see #LineSegmentDetectorModes +@param _scale The scale of the image that will be used to find the lines. Range (0..1]. +@param _sigma_scale Sigma for Gaussian filter. It is computed as sigma = _sigma_scale/_scale. +@param _quant Bound to the quantization error on the gradient norm. +@param _ang_th Gradient angle tolerance in degrees. +@param _log_eps Detection threshold: -log10(NFA) \> log_eps. Used only when advance refinement +is chosen. +@param _density_th Minimal density of aligned region points in the enclosing rectangle. +@param _n_bins Number of bins in pseudo-ordering of gradient modulus. + */ +CV_EXPORTS_W Ptr createLineSegmentDetector( + int _refine = LSD_REFINE_STD, double _scale = 0.8, + double _sigma_scale = 0.6, double _quant = 2.0, double _ang_th = 22.5, + double _log_eps = 0, double _density_th = 0.7, int _n_bins = 1024); + +//! @} imgproc_feature + +//! @addtogroup imgproc_filter +//! @{ + +/** @brief Returns Gaussian filter coefficients. + +The function computes and returns the \f$\texttt{ksize} \times 1\f$ matrix of Gaussian filter +coefficients: + +\f[G_i= \alpha *e^{-(i-( \texttt{ksize} -1)/2)^2/(2* \texttt{sigma}^2)},\f] + +where \f$i=0..\texttt{ksize}-1\f$ and \f$\alpha\f$ is the scale factor chosen so that \f$\sum_i G_i=1\f$. + +Two of such generated kernels can be passed to sepFilter2D. Those functions automatically recognize +smoothing kernels (a symmetrical kernel with sum of weights equal to 1) and handle them accordingly. +You may also use the higher-level GaussianBlur. +@param ksize Aperture size. It should be odd ( \f$\texttt{ksize} \mod 2 = 1\f$ ) and positive. +@param sigma Gaussian standard deviation. If it is non-positive, it is computed from ksize as +`sigma = 0.3*((ksize-1)*0.5 - 1) + 0.8`. +@param ktype Type of filter coefficients. It can be CV_32F or CV_64F . +@sa sepFilter2D, getDerivKernels, getStructuringElement, GaussianBlur + */ +CV_EXPORTS_W Mat getGaussianKernel( int ksize, double sigma, int ktype = CV_64F ); + +/** @brief Returns filter coefficients for computing spatial image derivatives. + +The function computes and returns the filter coefficients for spatial image derivatives. When +`ksize=CV_SCHARR`, the Scharr \f$3 \times 3\f$ kernels are generated (see #Scharr). Otherwise, Sobel +kernels are generated (see #Sobel). The filters are normally passed to #sepFilter2D or to + +@param kx Output matrix of row filter coefficients. It has the type ktype . +@param ky Output matrix of column filter coefficients. It has the type ktype . +@param dx Derivative order in respect of x. +@param dy Derivative order in respect of y. +@param ksize Aperture size. It can be CV_SCHARR, 1, 3, 5, or 7. +@param normalize Flag indicating whether to normalize (scale down) the filter coefficients or not. +Theoretically, the coefficients should have the denominator \f$=2^{ksize*2-dx-dy-2}\f$. If you are +going to filter floating-point images, you are likely to use the normalized kernels. But if you +compute derivatives of an 8-bit image, store the results in a 16-bit image, and wish to preserve +all the fractional bits, you may want to set normalize=false . +@param ktype Type of filter coefficients. It can be CV_32f or CV_64F . + */ +CV_EXPORTS_W void getDerivKernels( OutputArray kx, OutputArray ky, + int dx, int dy, int ksize, + bool normalize = false, int ktype = CV_32F ); + +/** @brief Returns Gabor filter coefficients. + +For more details about gabor filter equations and parameters, see: [Gabor +Filter](http://en.wikipedia.org/wiki/Gabor_filter). + +@param ksize Size of the filter returned. +@param sigma Standard deviation of the gaussian envelope. +@param theta Orientation of the normal to the parallel stripes of a Gabor function. +@param lambd Wavelength of the sinusoidal factor. +@param gamma Spatial aspect ratio. +@param psi Phase offset. +@param ktype Type of filter coefficients. It can be CV_32F or CV_64F . + */ +CV_EXPORTS_W Mat getGaborKernel( Size ksize, double sigma, double theta, double lambd, + double gamma, double psi = CV_PI*0.5, int ktype = CV_64F ); + +//! returns "magic" border value for erosion and dilation. It is automatically transformed to Scalar::all(-DBL_MAX) for dilation. +static inline Scalar morphologyDefaultBorderValue() { return Scalar::all(DBL_MAX); } + +/** @brief Returns a structuring element of the specified size and shape for morphological operations. + +The function constructs and returns the structuring element that can be further passed to #erode, +#dilate or #morphologyEx. But you can also construct an arbitrary binary mask yourself and use it as +the structuring element. + +@param shape Element shape that could be one of #MorphShapes +@param ksize Size of the structuring element. +@param anchor Anchor position within the element. The default value \f$(-1, -1)\f$ means that the +anchor is at the center. Note that only the shape of a cross-shaped element depends on the anchor +position. In other cases the anchor just regulates how much the result of the morphological +operation is shifted. + */ +CV_EXPORTS_W Mat getStructuringElement(int shape, Size ksize, Point anchor = Point(-1,-1)); + +/** @example samples/cpp/tutorial_code/ImgProc/Smoothing/Smoothing.cpp +Sample code for simple filters +![Sample screenshot](Smoothing_Tutorial_Result_Median_Filter.jpg) +Check @ref tutorial_gausian_median_blur_bilateral_filter "the corresponding tutorial" for more details + */ + +/** @brief Blurs an image using the median filter. + +The function smoothes an image using the median filter with the \f$\texttt{ksize} \times +\texttt{ksize}\f$ aperture. Each channel of a multi-channel image is processed independently. +In-place operation is supported. + +@note The median filter uses #BORDER_REPLICATE internally to cope with border pixels, see #BorderTypes + +@param src input 1-, 3-, or 4-channel image; when ksize is 3 or 5, the image depth should be +CV_8U, CV_16U, or CV_32F, for larger aperture sizes, it can only be CV_8U. +@param dst destination array of the same size and type as src. +@param ksize aperture linear size; it must be odd and greater than 1, for example: 3, 5, 7 ... +@sa bilateralFilter, blur, boxFilter, GaussianBlur + */ +CV_EXPORTS_W void medianBlur( InputArray src, OutputArray dst, int ksize ); + +/** @brief Blurs an image using a Gaussian filter. + +The function convolves the source image with the specified Gaussian kernel. In-place filtering is +supported. + +@param src input image; the image can have any number of channels, which are processed +independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. +@param dst output image of the same size and type as src. +@param ksize Gaussian kernel size. ksize.width and ksize.height can differ but they both must be +positive and odd. Or, they can be zero's and then they are computed from sigma. +@param sigmaX Gaussian kernel standard deviation in X direction. +@param sigmaY Gaussian kernel standard deviation in Y direction; if sigmaY is zero, it is set to be +equal to sigmaX, if both sigmas are zeros, they are computed from ksize.width and ksize.height, +respectively (see #getGaussianKernel for details); to fully control the result regardless of +possible future modifications of all this semantics, it is recommended to specify all of ksize, +sigmaX, and sigmaY. +@param borderType pixel extrapolation method, see #BorderTypes + +@sa sepFilter2D, filter2D, blur, boxFilter, bilateralFilter, medianBlur + */ +CV_EXPORTS_W void GaussianBlur( InputArray src, OutputArray dst, Size ksize, + double sigmaX, double sigmaY = 0, + int borderType = BORDER_DEFAULT ); + +/** @brief Applies the bilateral filter to an image. + +The function applies bilateral filtering to the input image, as described in +http://www.dai.ed.ac.uk/CVonline/LOCAL_COPIES/MANDUCHI1/Bilateral_Filtering.html +bilateralFilter can reduce unwanted noise very well while keeping edges fairly sharp. However, it is +very slow compared to most filters. + +_Sigma values_: For simplicity, you can set the 2 sigma values to be the same. If they are small (\< +10), the filter will not have much effect, whereas if they are large (\> 150), they will have a very +strong effect, making the image look "cartoonish". + +_Filter size_: Large filters (d \> 5) are very slow, so it is recommended to use d=5 for real-time +applications, and perhaps d=9 for offline applications that need heavy noise filtering. + +This filter does not work inplace. +@param src Source 8-bit or floating-point, 1-channel or 3-channel image. +@param dst Destination image of the same size and type as src . +@param d Diameter of each pixel neighborhood that is used during filtering. If it is non-positive, +it is computed from sigmaSpace. +@param sigmaColor Filter sigma in the color space. A larger value of the parameter means that +farther colors within the pixel neighborhood (see sigmaSpace) will be mixed together, resulting +in larger areas of semi-equal color. +@param sigmaSpace Filter sigma in the coordinate space. A larger value of the parameter means that +farther pixels will influence each other as long as their colors are close enough (see sigmaColor +). When d\>0, it specifies the neighborhood size regardless of sigmaSpace. Otherwise, d is +proportional to sigmaSpace. +@param borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes + */ +CV_EXPORTS_W void bilateralFilter( InputArray src, OutputArray dst, int d, + double sigmaColor, double sigmaSpace, + int borderType = BORDER_DEFAULT ); + +/** @brief Blurs an image using the box filter. + +The function smooths an image using the kernel: + +\f[\texttt{K} = \alpha \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \end{bmatrix}\f] + +where + +\f[\alpha = \fork{\frac{1}{\texttt{ksize.width*ksize.height}}}{when \texttt{normalize=true}}{1}{otherwise}\f] + +Unnormalized box filter is useful for computing various integral characteristics over each pixel +neighborhood, such as covariance matrices of image derivatives (used in dense optical flow +algorithms, and so on). If you need to compute pixel sums over variable-size windows, use #integral. + +@param src input image. +@param dst output image of the same size and type as src. +@param ddepth the output image depth (-1 to use src.depth()). +@param ksize blurring kernel size. +@param anchor anchor point; default value Point(-1,-1) means that the anchor is at the kernel +center. +@param normalize flag, specifying whether the kernel is normalized by its area or not. +@param borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes +@sa blur, bilateralFilter, GaussianBlur, medianBlur, integral + */ +CV_EXPORTS_W void boxFilter( InputArray src, OutputArray dst, int ddepth, + Size ksize, Point anchor = Point(-1,-1), + bool normalize = true, + int borderType = BORDER_DEFAULT ); + +/** @brief Calculates the normalized sum of squares of the pixel values overlapping the filter. + +For every pixel \f$ (x, y) \f$ in the source image, the function calculates the sum of squares of those neighboring +pixel values which overlap the filter placed over the pixel \f$ (x, y) \f$. + +The unnormalized square box filter can be useful in computing local image statistics such as the the local +variance and standard deviation around the neighborhood of a pixel. + +@param src input image +@param dst output image of the same size and type as _src +@param ddepth the output image depth (-1 to use src.depth()) +@param ksize kernel size +@param anchor kernel anchor point. The default value of Point(-1, -1) denotes that the anchor is at the kernel +center. +@param normalize flag, specifying whether the kernel is to be normalized by it's area or not. +@param borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes +@sa boxFilter +*/ +CV_EXPORTS_W void sqrBoxFilter( InputArray src, OutputArray dst, int ddepth, + Size ksize, Point anchor = Point(-1, -1), + bool normalize = true, + int borderType = BORDER_DEFAULT ); + +/** @brief Blurs an image using the normalized box filter. + +The function smooths an image using the kernel: + +\f[\texttt{K} = \frac{1}{\texttt{ksize.width*ksize.height}} \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \end{bmatrix}\f] + +The call `blur(src, dst, ksize, anchor, borderType)` is equivalent to `boxFilter(src, dst, src.type(), +anchor, true, borderType)`. + +@param src input image; it can have any number of channels, which are processed independently, but +the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. +@param dst output image of the same size and type as src. +@param ksize blurring kernel size. +@param anchor anchor point; default value Point(-1,-1) means that the anchor is at the kernel +center. +@param borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes +@sa boxFilter, bilateralFilter, GaussianBlur, medianBlur + */ +CV_EXPORTS_W void blur( InputArray src, OutputArray dst, + Size ksize, Point anchor = Point(-1,-1), + int borderType = BORDER_DEFAULT ); + +/** @brief Convolves an image with the kernel. + +The function applies an arbitrary linear filter to an image. In-place operation is supported. When +the aperture is partially outside the image, the function interpolates outlier pixel values +according to the specified border mode. + +The function does actually compute correlation, not the convolution: + +\f[\texttt{dst} (x,y) = \sum _{ \stackrel{0\leq x' < \texttt{kernel.cols},}{0\leq y' < \texttt{kernel.rows}} } \texttt{kernel} (x',y')* \texttt{src} (x+x'- \texttt{anchor.x} ,y+y'- \texttt{anchor.y} )\f] + +That is, the kernel is not mirrored around the anchor point. If you need a real convolution, flip +the kernel using #flip and set the new anchor to `(kernel.cols - anchor.x - 1, kernel.rows - +anchor.y - 1)`. + +The function uses the DFT-based algorithm in case of sufficiently large kernels (~`11 x 11` or +larger) and the direct algorithm for small kernels. + +@param src input image. +@param dst output image of the same size and the same number of channels as src. +@param ddepth desired depth of the destination image, see @ref filter_depths "combinations" +@param kernel convolution kernel (or rather a correlation kernel), a single-channel floating point +matrix; if you want to apply different kernels to different channels, split the image into +separate color planes using split and process them individually. +@param anchor anchor of the kernel that indicates the relative position of a filtered point within +the kernel; the anchor should lie within the kernel; default value (-1,-1) means that the anchor +is at the kernel center. +@param delta optional value added to the filtered pixels before storing them in dst. +@param borderType pixel extrapolation method, see #BorderTypes +@sa sepFilter2D, dft, matchTemplate + */ +CV_EXPORTS_W void filter2D( InputArray src, OutputArray dst, int ddepth, + InputArray kernel, Point anchor = Point(-1,-1), + double delta = 0, int borderType = BORDER_DEFAULT ); + +/** @brief Applies a separable linear filter to an image. + +The function applies a separable linear filter to the image. That is, first, every row of src is +filtered with the 1D kernel kernelX. Then, every column of the result is filtered with the 1D +kernel kernelY. The final result shifted by delta is stored in dst . + +@param src Source image. +@param dst Destination image of the same size and the same number of channels as src . +@param ddepth Destination image depth, see @ref filter_depths "combinations" +@param kernelX Coefficients for filtering each row. +@param kernelY Coefficients for filtering each column. +@param anchor Anchor position within the kernel. The default value \f$(-1,-1)\f$ means that the anchor +is at the kernel center. +@param delta Value added to the filtered results before storing them. +@param borderType Pixel extrapolation method, see #BorderTypes +@sa filter2D, Sobel, GaussianBlur, boxFilter, blur + */ +CV_EXPORTS_W void sepFilter2D( InputArray src, OutputArray dst, int ddepth, + InputArray kernelX, InputArray kernelY, + Point anchor = Point(-1,-1), + double delta = 0, int borderType = BORDER_DEFAULT ); + +/** @example samples/cpp/tutorial_code/ImgTrans/Sobel_Demo.cpp +Sample code using Sobel and/or Scharr OpenCV functions to make a simple Edge Detector +![Sample screenshot](Sobel_Derivatives_Tutorial_Result.jpg) +Check @ref tutorial_sobel_derivatives "the corresponding tutorial" for more details +*/ + +/** @brief Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator. + +In all cases except one, the \f$\texttt{ksize} \times \texttt{ksize}\f$ separable kernel is used to +calculate the derivative. When \f$\texttt{ksize = 1}\f$, the \f$3 \times 1\f$ or \f$1 \times 3\f$ +kernel is used (that is, no Gaussian smoothing is done). `ksize = 1` can only be used for the first +or the second x- or y- derivatives. + +There is also the special value `ksize = #CV_SCHARR (-1)` that corresponds to the \f$3\times3\f$ Scharr +filter that may give more accurate results than the \f$3\times3\f$ Sobel. The Scharr aperture is + +\f[\vecthreethree{-3}{0}{3}{-10}{0}{10}{-3}{0}{3}\f] + +for the x-derivative, or transposed for the y-derivative. + +The function calculates an image derivative by convolving the image with the appropriate kernel: + +\f[\texttt{dst} = \frac{\partial^{xorder+yorder} \texttt{src}}{\partial x^{xorder} \partial y^{yorder}}\f] + +The Sobel operators combine Gaussian smoothing and differentiation, so the result is more or less +resistant to the noise. Most often, the function is called with ( xorder = 1, yorder = 0, ksize = 3) +or ( xorder = 0, yorder = 1, ksize = 3) to calculate the first x- or y- image derivative. The first +case corresponds to a kernel of: + +\f[\vecthreethree{-1}{0}{1}{-2}{0}{2}{-1}{0}{1}\f] + +The second case corresponds to a kernel of: + +\f[\vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}\f] + +@param src input image. +@param dst output image of the same size and the same number of channels as src . +@param ddepth output image depth, see @ref filter_depths "combinations"; in the case of + 8-bit input images it will result in truncated derivatives. +@param dx order of the derivative x. +@param dy order of the derivative y. +@param ksize size of the extended Sobel kernel; it must be 1, 3, 5, or 7. +@param scale optional scale factor for the computed derivative values; by default, no scaling is +applied (see #getDerivKernels for details). +@param delta optional delta value that is added to the results prior to storing them in dst. +@param borderType pixel extrapolation method, see #BorderTypes +@sa Scharr, Laplacian, sepFilter2D, filter2D, GaussianBlur, cartToPolar + */ +CV_EXPORTS_W void Sobel( InputArray src, OutputArray dst, int ddepth, + int dx, int dy, int ksize = 3, + double scale = 1, double delta = 0, + int borderType = BORDER_DEFAULT ); + +/** @brief Calculates the first order image derivative in both x and y using a Sobel operator + +Equivalent to calling: + +@code +Sobel( src, dx, CV_16SC1, 1, 0, 3 ); +Sobel( src, dy, CV_16SC1, 0, 1, 3 ); +@endcode + +@param src input image. +@param dx output image with first-order derivative in x. +@param dy output image with first-order derivative in y. +@param ksize size of Sobel kernel. It must be 3. +@param borderType pixel extrapolation method, see #BorderTypes + +@sa Sobel + */ + +CV_EXPORTS_W void spatialGradient( InputArray src, OutputArray dx, + OutputArray dy, int ksize = 3, + int borderType = BORDER_DEFAULT ); + +/** @brief Calculates the first x- or y- image derivative using Scharr operator. + +The function computes the first x- or y- spatial image derivative using the Scharr operator. The +call + +\f[\texttt{Scharr(src, dst, ddepth, dx, dy, scale, delta, borderType)}\f] + +is equivalent to + +\f[\texttt{Sobel(src, dst, ddepth, dx, dy, CV_SCHARR, scale, delta, borderType)} .\f] + +@param src input image. +@param dst output image of the same size and the same number of channels as src. +@param ddepth output image depth, see @ref filter_depths "combinations" +@param dx order of the derivative x. +@param dy order of the derivative y. +@param scale optional scale factor for the computed derivative values; by default, no scaling is +applied (see #getDerivKernels for details). +@param delta optional delta value that is added to the results prior to storing them in dst. +@param borderType pixel extrapolation method, see #BorderTypes +@sa cartToPolar + */ +CV_EXPORTS_W void Scharr( InputArray src, OutputArray dst, int ddepth, + int dx, int dy, double scale = 1, double delta = 0, + int borderType = BORDER_DEFAULT ); + +/** @example samples/cpp/laplace.cpp +An example using Laplace transformations for edge detection +*/ + +/** @brief Calculates the Laplacian of an image. + +The function calculates the Laplacian of the source image by adding up the second x and y +derivatives calculated using the Sobel operator: + +\f[\texttt{dst} = \Delta \texttt{src} = \frac{\partial^2 \texttt{src}}{\partial x^2} + \frac{\partial^2 \texttt{src}}{\partial y^2}\f] + +This is done when `ksize > 1`. When `ksize == 1`, the Laplacian is computed by filtering the image +with the following \f$3 \times 3\f$ aperture: + +\f[\vecthreethree {0}{1}{0}{1}{-4}{1}{0}{1}{0}\f] + +@param src Source image. +@param dst Destination image of the same size and the same number of channels as src . +@param ddepth Desired depth of the destination image. +@param ksize Aperture size used to compute the second-derivative filters. See #getDerivKernels for +details. The size must be positive and odd. +@param scale Optional scale factor for the computed Laplacian values. By default, no scaling is +applied. See #getDerivKernels for details. +@param delta Optional delta value that is added to the results prior to storing them in dst . +@param borderType Pixel extrapolation method, see #BorderTypes +@sa Sobel, Scharr + */ +CV_EXPORTS_W void Laplacian( InputArray src, OutputArray dst, int ddepth, + int ksize = 1, double scale = 1, double delta = 0, + int borderType = BORDER_DEFAULT ); + +//! @} imgproc_filter + +//! @addtogroup imgproc_feature +//! @{ + +/** @example samples/cpp/edge.cpp +This program demonstrates usage of the Canny edge detector + +Check @ref tutorial_canny_detector "the corresponding tutorial" for more details +*/ + +/** @brief Finds edges in an image using the Canny algorithm @cite Canny86 . + +The function finds edges in the input image and marks them in the output map edges using the +Canny algorithm. The smallest value between threshold1 and threshold2 is used for edge linking. The +largest value is used to find initial segments of strong edges. See + + +@param image 8-bit input image. +@param edges output edge map; single channels 8-bit image, which has the same size as image . +@param threshold1 first threshold for the hysteresis procedure. +@param threshold2 second threshold for the hysteresis procedure. +@param apertureSize aperture size for the Sobel operator. +@param L2gradient a flag, indicating whether a more accurate \f$L_2\f$ norm +\f$=\sqrt{(dI/dx)^2 + (dI/dy)^2}\f$ should be used to calculate the image gradient magnitude ( +L2gradient=true ), or whether the default \f$L_1\f$ norm \f$=|dI/dx|+|dI/dy|\f$ is enough ( +L2gradient=false ). + */ +CV_EXPORTS_W void Canny( InputArray image, OutputArray edges, + double threshold1, double threshold2, + int apertureSize = 3, bool L2gradient = false ); + +/** \overload + +Finds edges in an image using the Canny algorithm with custom image gradient. + +@param dx 16-bit x derivative of input image (CV_16SC1 or CV_16SC3). +@param dy 16-bit y derivative of input image (same type as dx). +@param edges output edge map; single channels 8-bit image, which has the same size as image . +@param threshold1 first threshold for the hysteresis procedure. +@param threshold2 second threshold for the hysteresis procedure. +@param L2gradient a flag, indicating whether a more accurate \f$L_2\f$ norm +\f$=\sqrt{(dI/dx)^2 + (dI/dy)^2}\f$ should be used to calculate the image gradient magnitude ( +L2gradient=true ), or whether the default \f$L_1\f$ norm \f$=|dI/dx|+|dI/dy|\f$ is enough ( +L2gradient=false ). + */ +CV_EXPORTS_W void Canny( InputArray dx, InputArray dy, + OutputArray edges, + double threshold1, double threshold2, + bool L2gradient = false ); + +/** @brief Calculates the minimal eigenvalue of gradient matrices for corner detection. + +The function is similar to cornerEigenValsAndVecs but it calculates and stores only the minimal +eigenvalue of the covariance matrix of derivatives, that is, \f$\min(\lambda_1, \lambda_2)\f$ in terms +of the formulae in the cornerEigenValsAndVecs description. + +@param src Input single-channel 8-bit or floating-point image. +@param dst Image to store the minimal eigenvalues. It has the type CV_32FC1 and the same size as +src . +@param blockSize Neighborhood size (see the details on #cornerEigenValsAndVecs ). +@param ksize Aperture parameter for the Sobel operator. +@param borderType Pixel extrapolation method. See #BorderTypes. + */ +CV_EXPORTS_W void cornerMinEigenVal( InputArray src, OutputArray dst, + int blockSize, int ksize = 3, + int borderType = BORDER_DEFAULT ); + +/** @brief Harris corner detector. + +The function runs the Harris corner detector on the image. Similarly to cornerMinEigenVal and +cornerEigenValsAndVecs , for each pixel \f$(x, y)\f$ it calculates a \f$2\times2\f$ gradient covariance +matrix \f$M^{(x,y)}\f$ over a \f$\texttt{blockSize} \times \texttt{blockSize}\f$ neighborhood. Then, it +computes the following characteristic: + +\f[\texttt{dst} (x,y) = \mathrm{det} M^{(x,y)} - k \cdot \left ( \mathrm{tr} M^{(x,y)} \right )^2\f] + +Corners in the image can be found as the local maxima of this response map. + +@param src Input single-channel 8-bit or floating-point image. +@param dst Image to store the Harris detector responses. It has the type CV_32FC1 and the same +size as src . +@param blockSize Neighborhood size (see the details on #cornerEigenValsAndVecs ). +@param ksize Aperture parameter for the Sobel operator. +@param k Harris detector free parameter. See the formula above. +@param borderType Pixel extrapolation method. See #BorderTypes. + */ +CV_EXPORTS_W void cornerHarris( InputArray src, OutputArray dst, int blockSize, + int ksize, double k, + int borderType = BORDER_DEFAULT ); + +/** @brief Calculates eigenvalues and eigenvectors of image blocks for corner detection. + +For every pixel \f$p\f$ , the function cornerEigenValsAndVecs considers a blockSize \f$\times\f$ blockSize +neighborhood \f$S(p)\f$ . It calculates the covariation matrix of derivatives over the neighborhood as: + +\f[M = \begin{bmatrix} \sum _{S(p)}(dI/dx)^2 & \sum _{S(p)}dI/dx dI/dy \\ \sum _{S(p)}dI/dx dI/dy & \sum _{S(p)}(dI/dy)^2 \end{bmatrix}\f] + +where the derivatives are computed using the Sobel operator. + +After that, it finds eigenvectors and eigenvalues of \f$M\f$ and stores them in the destination image as +\f$(\lambda_1, \lambda_2, x_1, y_1, x_2, y_2)\f$ where + +- \f$\lambda_1, \lambda_2\f$ are the non-sorted eigenvalues of \f$M\f$ +- \f$x_1, y_1\f$ are the eigenvectors corresponding to \f$\lambda_1\f$ +- \f$x_2, y_2\f$ are the eigenvectors corresponding to \f$\lambda_2\f$ + +The output of the function can be used for robust edge or corner detection. + +@param src Input single-channel 8-bit or floating-point image. +@param dst Image to store the results. It has the same size as src and the type CV_32FC(6) . +@param blockSize Neighborhood size (see details below). +@param ksize Aperture parameter for the Sobel operator. +@param borderType Pixel extrapolation method. See #BorderTypes. + +@sa cornerMinEigenVal, cornerHarris, preCornerDetect + */ +CV_EXPORTS_W void cornerEigenValsAndVecs( InputArray src, OutputArray dst, + int blockSize, int ksize, + int borderType = BORDER_DEFAULT ); + +/** @brief Calculates a feature map for corner detection. + +The function calculates the complex spatial derivative-based function of the source image + +\f[\texttt{dst} = (D_x \texttt{src} )^2 \cdot D_{yy} \texttt{src} + (D_y \texttt{src} )^2 \cdot D_{xx} \texttt{src} - 2 D_x \texttt{src} \cdot D_y \texttt{src} \cdot D_{xy} \texttt{src}\f] + +where \f$D_x\f$,\f$D_y\f$ are the first image derivatives, \f$D_{xx}\f$,\f$D_{yy}\f$ are the second image +derivatives, and \f$D_{xy}\f$ is the mixed derivative. + +The corners can be found as local maximums of the functions, as shown below: +@code + Mat corners, dilated_corners; + preCornerDetect(image, corners, 3); + // dilation with 3x3 rectangular structuring element + dilate(corners, dilated_corners, Mat(), 1); + Mat corner_mask = corners == dilated_corners; +@endcode + +@param src Source single-channel 8-bit of floating-point image. +@param dst Output image that has the type CV_32F and the same size as src . +@param ksize %Aperture size of the Sobel . +@param borderType Pixel extrapolation method. See #BorderTypes. + */ +CV_EXPORTS_W void preCornerDetect( InputArray src, OutputArray dst, int ksize, + int borderType = BORDER_DEFAULT ); + +/** @brief Refines the corner locations. + +The function iterates to find the sub-pixel accurate location of corners or radial saddle points, as +shown on the figure below. + +![image](pics/cornersubpix.png) + +Sub-pixel accurate corner locator is based on the observation that every vector from the center \f$q\f$ +to a point \f$p\f$ located within a neighborhood of \f$q\f$ is orthogonal to the image gradient at \f$p\f$ +subject to image and measurement noise. Consider the expression: + +\f[\epsilon _i = {DI_{p_i}}^T \cdot (q - p_i)\f] + +where \f${DI_{p_i}}\f$ is an image gradient at one of the points \f$p_i\f$ in a neighborhood of \f$q\f$ . The +value of \f$q\f$ is to be found so that \f$\epsilon_i\f$ is minimized. A system of equations may be set up +with \f$\epsilon_i\f$ set to zero: + +\f[\sum _i(DI_{p_i} \cdot {DI_{p_i}}^T) \cdot q - \sum _i(DI_{p_i} \cdot {DI_{p_i}}^T \cdot p_i)\f] + +where the gradients are summed within a neighborhood ("search window") of \f$q\f$ . Calling the first +gradient term \f$G\f$ and the second gradient term \f$b\f$ gives: + +\f[q = G^{-1} \cdot b\f] + +The algorithm sets the center of the neighborhood window at this new center \f$q\f$ and then iterates +until the center stays within a set threshold. + +@param image Input single-channel, 8-bit or float image. +@param corners Initial coordinates of the input corners and refined coordinates provided for +output. +@param winSize Half of the side length of the search window. For example, if winSize=Size(5,5) , +then a \f$(5*2+1) \times (5*2+1) = 11 \times 11\f$ search window is used. +@param zeroZone Half of the size of the dead region in the middle of the search zone over which +the summation in the formula below is not done. It is used sometimes to avoid possible +singularities of the autocorrelation matrix. The value of (-1,-1) indicates that there is no such +a size. +@param criteria Criteria for termination of the iterative process of corner refinement. That is, +the process of corner position refinement stops either after criteria.maxCount iterations or when +the corner position moves by less than criteria.epsilon on some iteration. + */ +CV_EXPORTS_W void cornerSubPix( InputArray image, InputOutputArray corners, + Size winSize, Size zeroZone, + TermCriteria criteria ); + +/** @brief Determines strong corners on an image. + +The function finds the most prominent corners in the image or in the specified image region, as +described in @cite Shi94 + +- Function calculates the corner quality measure at every source image pixel using the + #cornerMinEigenVal or #cornerHarris . +- Function performs a non-maximum suppression (the local maximums in *3 x 3* neighborhood are + retained). +- The corners with the minimal eigenvalue less than + \f$\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)\f$ are rejected. +- The remaining corners are sorted by the quality measure in the descending order. +- Function throws away each corner for which there is a stronger corner at a distance less than + maxDistance. + +The function can be used to initialize a point-based tracker of an object. + +@note If the function is called with different values A and B of the parameter qualityLevel , and +A \> B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector +with qualityLevel=B . + +@param image Input 8-bit or floating-point 32-bit, single-channel image. +@param corners Output vector of detected corners. +@param maxCorners Maximum number of corners to return. If there are more corners than are found, +the strongest of them is returned. `maxCorners <= 0` implies that no limit on the maximum is set +and all detected corners are returned. +@param qualityLevel Parameter characterizing the minimal accepted quality of image corners. The +parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue +(see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the +quality measure less than the product are rejected. For example, if the best corner has the +quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure +less than 15 are rejected. +@param minDistance Minimum possible Euclidean distance between the returned corners. +@param mask Optional region of interest. If the image is not empty (it needs to have the type +CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected. +@param blockSize Size of an average block for computing a derivative covariation matrix over each +pixel neighborhood. See cornerEigenValsAndVecs . +@param useHarrisDetector Parameter indicating whether to use a Harris detector (see #cornerHarris) +or #cornerMinEigenVal. +@param k Free parameter of the Harris detector. + +@sa cornerMinEigenVal, cornerHarris, calcOpticalFlowPyrLK, estimateRigidTransform, + */ + +CV_EXPORTS_W void goodFeaturesToTrack( InputArray image, OutputArray corners, + int maxCorners, double qualityLevel, double minDistance, + InputArray mask = noArray(), int blockSize = 3, + bool useHarrisDetector = false, double k = 0.04 ); + +CV_EXPORTS_W void goodFeaturesToTrack( InputArray image, OutputArray corners, + int maxCorners, double qualityLevel, double minDistance, + InputArray mask, int blockSize, + int gradientSize, bool useHarrisDetector = false, + double k = 0.04 ); +/** @example samples/cpp/tutorial_code/ImgTrans/houghlines.cpp +An example using the Hough line detector +![Sample input image](Hough_Lines_Tutorial_Original_Image.jpg) ![Output image](Hough_Lines_Tutorial_Result.jpg) +*/ + +/** @brief Finds lines in a binary image using the standard Hough transform. + +The function implements the standard or standard multi-scale Hough transform algorithm for line +detection. See for a good explanation of Hough +transform. + +@param image 8-bit, single-channel binary source image. The image may be modified by the function. +@param lines Output vector of lines. Each line is represented by a 2 or 3 element vector +\f$(\rho, \theta)\f$ or \f$(\rho, \theta, \textrm{votes})\f$ . \f$\rho\f$ is the distance from the coordinate origin \f$(0,0)\f$ (top-left corner of +the image). \f$\theta\f$ is the line rotation angle in radians ( +\f$0 \sim \textrm{vertical line}, \pi/2 \sim \textrm{horizontal line}\f$ ). +\f$\textrm{votes}\f$ is the value of accumulator. +@param rho Distance resolution of the accumulator in pixels. +@param theta Angle resolution of the accumulator in radians. +@param threshold Accumulator threshold parameter. Only those lines are returned that get enough +votes ( \f$>\texttt{threshold}\f$ ). +@param srn For the multi-scale Hough transform, it is a divisor for the distance resolution rho . +The coarse accumulator distance resolution is rho and the accurate accumulator resolution is +rho/srn . If both srn=0 and stn=0 , the classical Hough transform is used. Otherwise, both these +parameters should be positive. +@param stn For the multi-scale Hough transform, it is a divisor for the distance resolution theta. +@param min_theta For standard and multi-scale Hough transform, minimum angle to check for lines. +Must fall between 0 and max_theta. +@param max_theta For standard and multi-scale Hough transform, maximum angle to check for lines. +Must fall between min_theta and CV_PI. + */ +CV_EXPORTS_W void HoughLines( InputArray image, OutputArray lines, + double rho, double theta, int threshold, + double srn = 0, double stn = 0, + double min_theta = 0, double max_theta = CV_PI ); + +/** @brief Finds line segments in a binary image using the probabilistic Hough transform. + +The function implements the probabilistic Hough transform algorithm for line detection, described +in @cite Matas00 + +See the line detection example below: +@include snippets/imgproc_HoughLinesP.cpp +This is a sample picture the function parameters have been tuned for: + +![image](pics/building.jpg) + +And this is the output of the above program in case of the probabilistic Hough transform: + +![image](pics/houghp.png) + +@param image 8-bit, single-channel binary source image. The image may be modified by the function. +@param lines Output vector of lines. Each line is represented by a 4-element vector +\f$(x_1, y_1, x_2, y_2)\f$ , where \f$(x_1,y_1)\f$ and \f$(x_2, y_2)\f$ are the ending points of each detected +line segment. +@param rho Distance resolution of the accumulator in pixels. +@param theta Angle resolution of the accumulator in radians. +@param threshold Accumulator threshold parameter. Only those lines are returned that get enough +votes ( \f$>\texttt{threshold}\f$ ). +@param minLineLength Minimum line length. Line segments shorter than that are rejected. +@param maxLineGap Maximum allowed gap between points on the same line to link them. + +@sa LineSegmentDetector + */ +CV_EXPORTS_W void HoughLinesP( InputArray image, OutputArray lines, + double rho, double theta, int threshold, + double minLineLength = 0, double maxLineGap = 0 ); + +/** @brief Finds lines in a set of points using the standard Hough transform. + +The function finds lines in a set of points using a modification of the Hough transform. +@include snippets/imgproc_HoughLinesPointSet.cpp +@param _point Input vector of points. Each vector must be encoded as a Point vector \f$(x,y)\f$. Type must be CV_32FC2 or CV_32SC2. +@param _lines Output vector of found lines. Each vector is encoded as a vector \f$(votes, rho, theta)\f$. +The larger the value of 'votes', the higher the reliability of the Hough line. +@param lines_max Max count of hough lines. +@param threshold Accumulator threshold parameter. Only those lines are returned that get enough +votes ( \f$>\texttt{threshold}\f$ ) +@param min_rho Minimum Distance value of the accumulator in pixels. +@param max_rho Maximum Distance value of the accumulator in pixels. +@param rho_step Distance resolution of the accumulator in pixels. +@param min_theta Minimum angle value of the accumulator in radians. +@param max_theta Maximum angle value of the accumulator in radians. +@param theta_step Angle resolution of the accumulator in radians. + */ +CV_EXPORTS_W void HoughLinesPointSet( InputArray _point, OutputArray _lines, int lines_max, int threshold, + double min_rho, double max_rho, double rho_step, + double min_theta, double max_theta, double theta_step ); + +/** @example samples/cpp/tutorial_code/ImgTrans/houghcircles.cpp +An example using the Hough circle detector +*/ + +/** @brief Finds circles in a grayscale image using the Hough transform. + +The function finds circles in a grayscale image using a modification of the Hough transform. + +Example: : +@include snippets/imgproc_HoughLinesCircles.cpp + +@note Usually the function detects the centers of circles well. However, it may fail to find correct +radii. You can assist to the function by specifying the radius range ( minRadius and maxRadius ) if +you know it. Or, you may set maxRadius to a negative number to return centers only without radius +search, and find the correct radius using an additional procedure. + +@param image 8-bit, single-channel, grayscale input image. +@param circles Output vector of found circles. Each vector is encoded as 3 or 4 element +floating-point vector \f$(x, y, radius)\f$ or \f$(x, y, radius, votes)\f$ . +@param method Detection method, see #HoughModes. Currently, the only implemented method is #HOUGH_GRADIENT +@param dp Inverse ratio of the accumulator resolution to the image resolution. For example, if +dp=1 , the accumulator has the same resolution as the input image. If dp=2 , the accumulator has +half as big width and height. +@param minDist Minimum distance between the centers of the detected circles. If the parameter is +too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is +too large, some circles may be missed. +@param param1 First method-specific parameter. In case of #HOUGH_GRADIENT , it is the higher +threshold of the two passed to the Canny edge detector (the lower one is twice smaller). +@param param2 Second method-specific parameter. In case of #HOUGH_GRADIENT , it is the +accumulator threshold for the circle centers at the detection stage. The smaller it is, the more +false circles may be detected. Circles, corresponding to the larger accumulator values, will be +returned first. +@param minRadius Minimum circle radius. +@param maxRadius Maximum circle radius. If <= 0, uses the maximum image dimension. If < 0, returns +centers without finding the radius. + +@sa fitEllipse, minEnclosingCircle + */ +CV_EXPORTS_W void HoughCircles( InputArray image, OutputArray circles, + int method, double dp, double minDist, + double param1 = 100, double param2 = 100, + int minRadius = 0, int maxRadius = 0 ); + +//! @} imgproc_feature + +//! @addtogroup imgproc_filter +//! @{ + +/** @example samples/cpp/tutorial_code/ImgProc/Morphology_2.cpp +Advanced morphology Transformations sample code +![Sample screenshot](Morphology_2_Tutorial_Result.jpg) +Check @ref tutorial_opening_closing_hats "the corresponding tutorial" for more details +*/ + +/** @brief Erodes an image by using a specific structuring element. + +The function erodes the source image using the specified structuring element that determines the +shape of a pixel neighborhood over which the minimum is taken: + +\f[\texttt{dst} (x,y) = \min _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\f] + +The function supports the in-place mode. Erosion can be applied several ( iterations ) times. In +case of multi-channel images, each channel is processed independently. + +@param src input image; the number of channels can be arbitrary, but the depth should be one of +CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. +@param dst output image of the same size and type as src. +@param kernel structuring element used for erosion; if `element=Mat()`, a `3 x 3` rectangular +structuring element is used. Kernel can be created using #getStructuringElement. +@param anchor position of the anchor within the element; default value (-1, -1) means that the +anchor is at the element center. +@param iterations number of times erosion is applied. +@param borderType pixel extrapolation method, see #BorderTypes +@param borderValue border value in case of a constant border +@sa dilate, morphologyEx, getStructuringElement + */ +CV_EXPORTS_W void erode( InputArray src, OutputArray dst, InputArray kernel, + Point anchor = Point(-1,-1), int iterations = 1, + int borderType = BORDER_CONSTANT, + const Scalar& borderValue = morphologyDefaultBorderValue() ); + +/** @example samples/cpp/tutorial_code/ImgProc/Morphology_1.cpp +Erosion and Dilation sample code +![Sample Screenshot-Erosion](Morphology_1_Tutorial_Erosion_Result.jpg)![Sample Screenshot-Dilation](Morphology_1_Tutorial_Dilation_Result.jpg) +Check @ref tutorial_erosion_dilatation "the corresponding tutorial" for more details +*/ + +/** @brief Dilates an image by using a specific structuring element. + +The function dilates the source image using the specified structuring element that determines the +shape of a pixel neighborhood over which the maximum is taken: +\f[\texttt{dst} (x,y) = \max _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\f] + +The function supports the in-place mode. Dilation can be applied several ( iterations ) times. In +case of multi-channel images, each channel is processed independently. + +@param src input image; the number of channels can be arbitrary, but the depth should be one of +CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. +@param dst output image of the same size and type as src. +@param kernel structuring element used for dilation; if elemenat=Mat(), a 3 x 3 rectangular +structuring element is used. Kernel can be created using #getStructuringElement +@param anchor position of the anchor within the element; default value (-1, -1) means that the +anchor is at the element center. +@param iterations number of times dilation is applied. +@param borderType pixel extrapolation method, see #BorderTypes +@param borderValue border value in case of a constant border +@sa erode, morphologyEx, getStructuringElement + */ +CV_EXPORTS_W void dilate( InputArray src, OutputArray dst, InputArray kernel, + Point anchor = Point(-1,-1), int iterations = 1, + int borderType = BORDER_CONSTANT, + const Scalar& borderValue = morphologyDefaultBorderValue() ); + +/** @brief Performs advanced morphological transformations. + +The function cv::morphologyEx can perform advanced morphological transformations using an erosion and dilation as +basic operations. + +Any of the operations can be done in-place. In case of multi-channel images, each channel is +processed independently. + +@param src Source image. The number of channels can be arbitrary. The depth should be one of +CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. +@param dst Destination image of the same size and type as source image. +@param op Type of a morphological operation, see #MorphTypes +@param kernel Structuring element. It can be created using #getStructuringElement. +@param anchor Anchor position with the kernel. Negative values mean that the anchor is at the +kernel center. +@param iterations Number of times erosion and dilation are applied. +@param borderType Pixel extrapolation method, see #BorderTypes +@param borderValue Border value in case of a constant border. The default value has a special +meaning. +@sa dilate, erode, getStructuringElement +@note The number of iterations is the number of times erosion or dilatation operation will be applied. +For instance, an opening operation (#MORPH_OPEN) with two iterations is equivalent to apply +successively: erode -> erode -> dilate -> dilate (and not erode -> dilate -> erode -> dilate). + */ +CV_EXPORTS_W void morphologyEx( InputArray src, OutputArray dst, + int op, InputArray kernel, + Point anchor = Point(-1,-1), int iterations = 1, + int borderType = BORDER_CONSTANT, + const Scalar& borderValue = morphologyDefaultBorderValue() ); + +//! @} imgproc_filter + +//! @addtogroup imgproc_transform +//! @{ + +/** @brief Resizes an image. + +The function resize resizes the image src down to or up to the specified size. Note that the +initial dst type or size are not taken into account. Instead, the size and type are derived from +the `src`,`dsize`,`fx`, and `fy`. If you want to resize src so that it fits the pre-created dst, +you may call the function as follows: +@code + // explicitly specify dsize=dst.size(); fx and fy will be computed from that. + resize(src, dst, dst.size(), 0, 0, interpolation); +@endcode +If you want to decimate the image by factor of 2 in each direction, you can call the function this +way: +@code + // specify fx and fy and let the function compute the destination image size. + resize(src, dst, Size(), 0.5, 0.5, interpolation); +@endcode +To shrink an image, it will generally look best with #INTER_AREA interpolation, whereas to +enlarge an image, it will generally look best with c#INTER_CUBIC (slow) or #INTER_LINEAR +(faster but still looks OK). + +@param src input image. +@param dst output image; it has the size dsize (when it is non-zero) or the size computed from +src.size(), fx, and fy; the type of dst is the same as of src. +@param dsize output image size; if it equals zero, it is computed as: + \f[\texttt{dsize = Size(round(fx*src.cols), round(fy*src.rows))}\f] + Either dsize or both fx and fy must be non-zero. +@param fx scale factor along the horizontal axis; when it equals 0, it is computed as +\f[\texttt{(double)dsize.width/src.cols}\f] +@param fy scale factor along the vertical axis; when it equals 0, it is computed as +\f[\texttt{(double)dsize.height/src.rows}\f] +@param interpolation interpolation method, see #InterpolationFlags + +@sa warpAffine, warpPerspective, remap + */ +CV_EXPORTS_W void resize( InputArray src, OutputArray dst, + Size dsize, double fx = 0, double fy = 0, + int interpolation = INTER_LINEAR ); + +/** @brief Applies an affine transformation to an image. + +The function warpAffine transforms the source image using the specified matrix: + +\f[\texttt{dst} (x,y) = \texttt{src} ( \texttt{M} _{11} x + \texttt{M} _{12} y + \texttt{M} _{13}, \texttt{M} _{21} x + \texttt{M} _{22} y + \texttt{M} _{23})\f] + +when the flag #WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted +with #invertAffineTransform and then put in the formula above instead of M. The function cannot +operate in-place. + +@param src input image. +@param dst output image that has the size dsize and the same type as src . +@param M \f$2\times 3\f$ transformation matrix. +@param dsize size of the output image. +@param flags combination of interpolation methods (see #InterpolationFlags) and the optional +flag #WARP_INVERSE_MAP that means that M is the inverse transformation ( +\f$\texttt{dst}\rightarrow\texttt{src}\f$ ). +@param borderMode pixel extrapolation method (see #BorderTypes); when +borderMode=#BORDER_TRANSPARENT, it means that the pixels in the destination image corresponding to +the "outliers" in the source image are not modified by the function. +@param borderValue value used in case of a constant border; by default, it is 0. + +@sa warpPerspective, resize, remap, getRectSubPix, transform + */ +CV_EXPORTS_W void warpAffine( InputArray src, OutputArray dst, + InputArray M, Size dsize, + int flags = INTER_LINEAR, + int borderMode = BORDER_CONSTANT, + const Scalar& borderValue = Scalar()); + +/** @example samples/cpp/warpPerspective_demo.cpp +An example program shows using cv::findHomography and cv::warpPerspective for image warping +*/ + +/** @brief Applies a perspective transformation to an image. + +The function warpPerspective transforms the source image using the specified matrix: + +\f[\texttt{dst} (x,y) = \texttt{src} \left ( \frac{M_{11} x + M_{12} y + M_{13}}{M_{31} x + M_{32} y + M_{33}} , + \frac{M_{21} x + M_{22} y + M_{23}}{M_{31} x + M_{32} y + M_{33}} \right )\f] + +when the flag #WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted with invert +and then put in the formula above instead of M. The function cannot operate in-place. + +@param src input image. +@param dst output image that has the size dsize and the same type as src . +@param M \f$3\times 3\f$ transformation matrix. +@param dsize size of the output image. +@param flags combination of interpolation methods (#INTER_LINEAR or #INTER_NEAREST) and the +optional flag #WARP_INVERSE_MAP, that sets M as the inverse transformation ( +\f$\texttt{dst}\rightarrow\texttt{src}\f$ ). +@param borderMode pixel extrapolation method (#BORDER_CONSTANT or #BORDER_REPLICATE). +@param borderValue value used in case of a constant border; by default, it equals 0. + +@sa warpAffine, resize, remap, getRectSubPix, perspectiveTransform + */ +CV_EXPORTS_W void warpPerspective( InputArray src, OutputArray dst, + InputArray M, Size dsize, + int flags = INTER_LINEAR, + int borderMode = BORDER_CONSTANT, + const Scalar& borderValue = Scalar()); + +/** @brief Applies a generic geometrical transformation to an image. + +The function remap transforms the source image using the specified map: + +\f[\texttt{dst} (x,y) = \texttt{src} (map_x(x,y),map_y(x,y))\f] + +where values of pixels with non-integer coordinates are computed using one of available +interpolation methods. \f$map_x\f$ and \f$map_y\f$ can be encoded as separate floating-point maps +in \f$map_1\f$ and \f$map_2\f$ respectively, or interleaved floating-point maps of \f$(x,y)\f$ in +\f$map_1\f$, or fixed-point maps created by using convertMaps. The reason you might want to +convert from floating to fixed-point representations of a map is that they can yield much faster +(\~2x) remapping operations. In the converted case, \f$map_1\f$ contains pairs (cvFloor(x), +cvFloor(y)) and \f$map_2\f$ contains indices in a table of interpolation coefficients. + +This function cannot operate in-place. + +@param src Source image. +@param dst Destination image. It has the same size as map1 and the same type as src . +@param map1 The first map of either (x,y) points or just x values having the type CV_16SC2 , +CV_32FC1, or CV_32FC2. See convertMaps for details on converting a floating point +representation to fixed-point for speed. +@param map2 The second map of y values having the type CV_16UC1, CV_32FC1, or none (empty map +if map1 is (x,y) points), respectively. +@param interpolation Interpolation method (see #InterpolationFlags). The method #INTER_AREA is +not supported by this function. +@param borderMode Pixel extrapolation method (see #BorderTypes). When +borderMode=#BORDER_TRANSPARENT, it means that the pixels in the destination image that +corresponds to the "outliers" in the source image are not modified by the function. +@param borderValue Value used in case of a constant border. By default, it is 0. +@note +Due to current implementation limitations the size of an input and output images should be less than 32767x32767. + */ +CV_EXPORTS_W void remap( InputArray src, OutputArray dst, + InputArray map1, InputArray map2, + int interpolation, int borderMode = BORDER_CONSTANT, + const Scalar& borderValue = Scalar()); + +/** @brief Converts image transformation maps from one representation to another. + +The function converts a pair of maps for remap from one representation to another. The following +options ( (map1.type(), map2.type()) \f$\rightarrow\f$ (dstmap1.type(), dstmap2.type()) ) are +supported: + +- \f$\texttt{(CV_32FC1, CV_32FC1)} \rightarrow \texttt{(CV_16SC2, CV_16UC1)}\f$. This is the +most frequently used conversion operation, in which the original floating-point maps (see remap ) +are converted to a more compact and much faster fixed-point representation. The first output array +contains the rounded coordinates and the second array (created only when nninterpolation=false ) +contains indices in the interpolation tables. + +- \f$\texttt{(CV_32FC2)} \rightarrow \texttt{(CV_16SC2, CV_16UC1)}\f$. The same as above but +the original maps are stored in one 2-channel matrix. + +- Reverse conversion. Obviously, the reconstructed floating-point maps will not be exactly the same +as the originals. + +@param map1 The first input map of type CV_16SC2, CV_32FC1, or CV_32FC2 . +@param map2 The second input map of type CV_16UC1, CV_32FC1, or none (empty matrix), +respectively. +@param dstmap1 The first output map that has the type dstmap1type and the same size as src . +@param dstmap2 The second output map. +@param dstmap1type Type of the first output map that should be CV_16SC2, CV_32FC1, or +CV_32FC2 . +@param nninterpolation Flag indicating whether the fixed-point maps are used for the +nearest-neighbor or for a more complex interpolation. + +@sa remap, undistort, initUndistortRectifyMap + */ +CV_EXPORTS_W void convertMaps( InputArray map1, InputArray map2, + OutputArray dstmap1, OutputArray dstmap2, + int dstmap1type, bool nninterpolation = false ); + +/** @brief Calculates an affine matrix of 2D rotation. + +The function calculates the following matrix: + +\f[\begin{bmatrix} \alpha & \beta & (1- \alpha ) \cdot \texttt{center.x} - \beta \cdot \texttt{center.y} \\ - \beta & \alpha & \beta \cdot \texttt{center.x} + (1- \alpha ) \cdot \texttt{center.y} \end{bmatrix}\f] + +where + +\f[\begin{array}{l} \alpha = \texttt{scale} \cdot \cos \texttt{angle} , \\ \beta = \texttt{scale} \cdot \sin \texttt{angle} \end{array}\f] + +The transformation maps the rotation center to itself. If this is not the target, adjust the shift. + +@param center Center of the rotation in the source image. +@param angle Rotation angle in degrees. Positive values mean counter-clockwise rotation (the +coordinate origin is assumed to be the top-left corner). +@param scale Isotropic scale factor. + +@sa getAffineTransform, warpAffine, transform + */ +CV_EXPORTS_W Mat getRotationMatrix2D( Point2f center, double angle, double scale ); + +//! returns 3x3 perspective transformation for the corresponding 4 point pairs. +CV_EXPORTS Mat getPerspectiveTransform( const Point2f src[], const Point2f dst[] ); + +/** @brief Calculates an affine transform from three pairs of the corresponding points. + +The function calculates the \f$2 \times 3\f$ matrix of an affine transform so that: + +\f[\begin{bmatrix} x'_i \\ y'_i \end{bmatrix} = \texttt{map_matrix} \cdot \begin{bmatrix} x_i \\ y_i \\ 1 \end{bmatrix}\f] + +where + +\f[dst(i)=(x'_i,y'_i), src(i)=(x_i, y_i), i=0,1,2\f] + +@param src Coordinates of triangle vertices in the source image. +@param dst Coordinates of the corresponding triangle vertices in the destination image. + +@sa warpAffine, transform + */ +CV_EXPORTS Mat getAffineTransform( const Point2f src[], const Point2f dst[] ); + +/** @brief Inverts an affine transformation. + +The function computes an inverse affine transformation represented by \f$2 \times 3\f$ matrix M: + +\f[\begin{bmatrix} a_{11} & a_{12} & b_1 \\ a_{21} & a_{22} & b_2 \end{bmatrix}\f] + +The result is also a \f$2 \times 3\f$ matrix of the same type as M. + +@param M Original affine transformation. +@param iM Output reverse affine transformation. + */ +CV_EXPORTS_W void invertAffineTransform( InputArray M, OutputArray iM ); + +/** @brief Calculates a perspective transform from four pairs of the corresponding points. + +The function calculates the \f$3 \times 3\f$ matrix of a perspective transform so that: + +\f[\begin{bmatrix} t_i x'_i \\ t_i y'_i \\ t_i \end{bmatrix} = \texttt{map_matrix} \cdot \begin{bmatrix} x_i \\ y_i \\ 1 \end{bmatrix}\f] + +where + +\f[dst(i)=(x'_i,y'_i), src(i)=(x_i, y_i), i=0,1,2,3\f] + +@param src Coordinates of quadrangle vertices in the source image. +@param dst Coordinates of the corresponding quadrangle vertices in the destination image. + +@sa findHomography, warpPerspective, perspectiveTransform + */ +CV_EXPORTS_W Mat getPerspectiveTransform( InputArray src, InputArray dst ); + +CV_EXPORTS_W Mat getAffineTransform( InputArray src, InputArray dst ); + +/** @brief Retrieves a pixel rectangle from an image with sub-pixel accuracy. + +The function getRectSubPix extracts pixels from src: + +\f[patch(x, y) = src(x + \texttt{center.x} - ( \texttt{dst.cols} -1)*0.5, y + \texttt{center.y} - ( \texttt{dst.rows} -1)*0.5)\f] + +where the values of the pixels at non-integer coordinates are retrieved using bilinear +interpolation. Every channel of multi-channel images is processed independently. Also +the image should be a single channel or three channel image. While the center of the +rectangle must be inside the image, parts of the rectangle may be outside. + +@param image Source image. +@param patchSize Size of the extracted patch. +@param center Floating point coordinates of the center of the extracted rectangle within the +source image. The center must be inside the image. +@param patch Extracted patch that has the size patchSize and the same number of channels as src . +@param patchType Depth of the extracted pixels. By default, they have the same depth as src . + +@sa warpAffine, warpPerspective + */ +CV_EXPORTS_W void getRectSubPix( InputArray image, Size patchSize, + Point2f center, OutputArray patch, int patchType = -1 ); + +/** @example samples/cpp/polar_transforms.cpp +An example using the cv::linearPolar and cv::logPolar operations +*/ + +/** @brief Remaps an image to semilog-polar coordinates space. + +@deprecated This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags+WARP_POLAR_LOG); + +@internal +Transform the source image using the following transformation (See @ref polar_remaps_reference_image "Polar remaps reference image d)"): +\f[\begin{array}{l} + dst( \rho , \phi ) = src(x,y) \\ + dst.size() \leftarrow src.size() +\end{array}\f] + +where +\f[\begin{array}{l} + I = (dx,dy) = (x - center.x,y - center.y) \\ + \rho = M \cdot log_e(\texttt{magnitude} (I)) ,\\ + \phi = Kangle \cdot \texttt{angle} (I) \\ +\end{array}\f] + +and +\f[\begin{array}{l} + M = src.cols / log_e(maxRadius) \\ + Kangle = src.rows / 2\Pi \\ +\end{array}\f] + +The function emulates the human "foveal" vision and can be used for fast scale and +rotation-invariant template matching, for object tracking and so forth. +@param src Source image +@param dst Destination image. It will have same size and type as src. +@param center The transformation center; where the output precision is maximal +@param M Magnitude scale parameter. It determines the radius of the bounding circle to transform too. +@param flags A combination of interpolation methods, see #InterpolationFlags + +@note +- The function can not operate in-place. +- To calculate magnitude and angle in degrees #cartToPolar is used internally thus angles are measured from 0 to 360 with accuracy about 0.3 degrees. + +@sa cv::linearPolar +@endinternal +*/ +CV_EXPORTS_W void logPolar( InputArray src, OutputArray dst, + Point2f center, double M, int flags ); + +/** @brief Remaps an image to polar coordinates space. + +@deprecated This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags) + +@internal +Transform the source image using the following transformation (See @ref polar_remaps_reference_image "Polar remaps reference image c)"): +\f[\begin{array}{l} + dst( \rho , \phi ) = src(x,y) \\ + dst.size() \leftarrow src.size() +\end{array}\f] + +where +\f[\begin{array}{l} + I = (dx,dy) = (x - center.x,y - center.y) \\ + \rho = Kmag \cdot \texttt{magnitude} (I) ,\\ + \phi = angle \cdot \texttt{angle} (I) +\end{array}\f] + +and +\f[\begin{array}{l} + Kx = src.cols / maxRadius \\ + Ky = src.rows / 2\Pi +\end{array}\f] + + +@param src Source image +@param dst Destination image. It will have same size and type as src. +@param center The transformation center; +@param maxRadius The radius of the bounding circle to transform. It determines the inverse magnitude scale parameter too. +@param flags A combination of interpolation methods, see #InterpolationFlags + +@note +- The function can not operate in-place. +- To calculate magnitude and angle in degrees #cartToPolar is used internally thus angles are measured from 0 to 360 with accuracy about 0.3 degrees. + +@sa cv::logPolar +@endinternal +*/ +CV_EXPORTS_W void linearPolar( InputArray src, OutputArray dst, + Point2f center, double maxRadius, int flags ); + + +/** \brief Remaps an image to polar or semilog-polar coordinates space + +@anchor polar_remaps_reference_image +![Polar remaps reference](pics/polar_remap_doc.png) + +Transform the source image using the following transformation: +\f[ +dst(\rho , \phi ) = src(x,y) +\f] + +where +\f[ +\begin{array}{l} +\vec{I} = (x - center.x, \;y - center.y) \\ +\phi = Kangle \cdot \texttt{angle} (\vec{I}) \\ +\rho = \left\{\begin{matrix} +Klin \cdot \texttt{magnitude} (\vec{I}) & default \\ +Klog \cdot log_e(\texttt{magnitude} (\vec{I})) & if \; semilog \\ +\end{matrix}\right. +\end{array} +\f] + +and +\f[ +\begin{array}{l} +Kangle = dsize.height / 2\Pi \\ +Klin = dsize.width / maxRadius \\ +Klog = dsize.width / log_e(maxRadius) \\ +\end{array} +\f] + + +\par Linear vs semilog mapping + +Polar mapping can be linear or semi-log. Add one of #WarpPolarMode to `flags` to specify the polar mapping mode. + +Linear is the default mode. + +The semilog mapping emulates the human "foveal" vision that permit very high acuity on the line of sight (central vision) +in contrast to peripheral vision where acuity is minor. + +\par Option on `dsize`: + +- if both values in `dsize <=0 ` (default), +the destination image will have (almost) same area of source bounding circle: +\f[\begin{array}{l} +dsize.area \leftarrow (maxRadius^2 \cdot \Pi) \\ +dsize.width = \texttt{cvRound}(maxRadius) \\ +dsize.height = \texttt{cvRound}(maxRadius \cdot \Pi) \\ +\end{array}\f] + + +- if only `dsize.height <= 0`, +the destination image area will be proportional to the bounding circle area but scaled by `Kx * Kx`: +\f[\begin{array}{l} +dsize.height = \texttt{cvRound}(dsize.width \cdot \Pi) \\ +\end{array} +\f] + +- if both values in `dsize > 0 `, +the destination image will have the given size therefore the area of the bounding circle will be scaled to `dsize`. + + +\par Reverse mapping + +You can get reverse mapping adding #WARP_INVERSE_MAP to `flags` +\snippet polar_transforms.cpp InverseMap + +In addiction, to calculate the original coordinate from a polar mapped coordinate \f$(rho, phi)->(x, y)\f$: +\snippet polar_transforms.cpp InverseCoordinate + +@param src Source image. +@param dst Destination image. It will have same type as src. +@param dsize The destination image size (see description for valid options). +@param center The transformation center. +@param maxRadius The radius of the bounding circle to transform. It determines the inverse magnitude scale parameter too. +@param flags A combination of interpolation methods, #InterpolationFlags + #WarpPolarMode. + - Add #WARP_POLAR_LINEAR to select linear polar mapping (default) + - Add #WARP_POLAR_LOG to select semilog polar mapping + - Add #WARP_INVERSE_MAP for reverse mapping. +@note +- The function can not operate in-place. +- To calculate magnitude and angle in degrees #cartToPolar is used internally thus angles are measured from 0 to 360 with accuracy about 0.3 degrees. +- This function uses #remap. Due to current implementation limitations the size of an input and output images should be less than 32767x32767. + +@sa cv::remap +*/ +CV_EXPORTS_W void warpPolar(InputArray src, OutputArray dst, Size dsize, + Point2f center, double maxRadius, int flags); + + +//! @} imgproc_transform + +//! @addtogroup imgproc_misc +//! @{ + +/** @overload */ +CV_EXPORTS_W void integral( InputArray src, OutputArray sum, int sdepth = -1 ); + +/** @overload */ +CV_EXPORTS_AS(integral2) void integral( InputArray src, OutputArray sum, + OutputArray sqsum, int sdepth = -1, int sqdepth = -1 ); + +/** @brief Calculates the integral of an image. + +The function calculates one or more integral images for the source image as follows: + +\f[\texttt{sum} (X,Y) = \sum _{x + +Calculates the cross-power spectrum of two supplied source arrays. The arrays are padded if needed +with getOptimalDFTSize. + +The function performs the following equations: +- First it applies a Hanning window (see ) to each +image to remove possible edge effects. This window is cached until the array size changes to speed +up processing time. +- Next it computes the forward DFTs of each source array: +\f[\mathbf{G}_a = \mathcal{F}\{src_1\}, \; \mathbf{G}_b = \mathcal{F}\{src_2\}\f] +where \f$\mathcal{F}\f$ is the forward DFT. +- It then computes the cross-power spectrum of each frequency domain array: +\f[R = \frac{ \mathbf{G}_a \mathbf{G}_b^*}{|\mathbf{G}_a \mathbf{G}_b^*|}\f] +- Next the cross-correlation is converted back into the time domain via the inverse DFT: +\f[r = \mathcal{F}^{-1}\{R\}\f] +- Finally, it computes the peak location and computes a 5x5 weighted centroid around the peak to +achieve sub-pixel accuracy. +\f[(\Delta x, \Delta y) = \texttt{weightedCentroid} \{\arg \max_{(x, y)}\{r\}\}\f] +- If non-zero, the response parameter is computed as the sum of the elements of r within the 5x5 +centroid around the peak location. It is normalized to a maximum of 1 (meaning there is a single +peak) and will be smaller when there are multiple peaks. + +@param src1 Source floating point array (CV_32FC1 or CV_64FC1) +@param src2 Source floating point array (CV_32FC1 or CV_64FC1) +@param window Floating point array with windowing coefficients to reduce edge effects (optional). +@param response Signal power within the 5x5 centroid around the peak, between 0 and 1 (optional). +@returns detected phase shift (sub-pixel) between the two arrays. + +@sa dft, getOptimalDFTSize, idft, mulSpectrums createHanningWindow + */ +CV_EXPORTS_W Point2d phaseCorrelate(InputArray src1, InputArray src2, + InputArray window = noArray(), CV_OUT double* response = 0); + +/** @brief This function computes a Hanning window coefficients in two dimensions. + +See (http://en.wikipedia.org/wiki/Hann_function) and (http://en.wikipedia.org/wiki/Window_function) +for more information. + +An example is shown below: +@code + // create hanning window of size 100x100 and type CV_32F + Mat hann; + createHanningWindow(hann, Size(100, 100), CV_32F); +@endcode +@param dst Destination array to place Hann coefficients in +@param winSize The window size specifications (both width and height must be > 1) +@param type Created array type + */ +CV_EXPORTS_W void createHanningWindow(OutputArray dst, Size winSize, int type); + +//! @} imgproc_motion + +//! @addtogroup imgproc_misc +//! @{ + +/** @brief Applies a fixed-level threshold to each array element. + +The function applies fixed-level thresholding to a multiple-channel array. The function is typically +used to get a bi-level (binary) image out of a grayscale image ( #compare could be also used for +this purpose) or for removing a noise, that is, filtering out pixels with too small or too large +values. There are several types of thresholding supported by the function. They are determined by +type parameter. + +Also, the special values #THRESH_OTSU or #THRESH_TRIANGLE may be combined with one of the +above values. In these cases, the function determines the optimal threshold value using the Otsu's +or Triangle algorithm and uses it instead of the specified thresh. + +@note Currently, the Otsu's and Triangle methods are implemented only for 8-bit single-channel images. + +@param src input array (multiple-channel, 8-bit or 32-bit floating point). +@param dst output array of the same size and type and the same number of channels as src. +@param thresh threshold value. +@param maxval maximum value to use with the #THRESH_BINARY and #THRESH_BINARY_INV thresholding +types. +@param type thresholding type (see #ThresholdTypes). +@return the computed threshold value if Otsu's or Triangle methods used. + +@sa adaptiveThreshold, findContours, compare, min, max + */ +CV_EXPORTS_W double threshold( InputArray src, OutputArray dst, + double thresh, double maxval, int type ); + + +/** @brief Applies an adaptive threshold to an array. + +The function transforms a grayscale image to a binary image according to the formulae: +- **THRESH_BINARY** + \f[dst(x,y) = \fork{\texttt{maxValue}}{if \(src(x,y) > T(x,y)\)}{0}{otherwise}\f] +- **THRESH_BINARY_INV** + \f[dst(x,y) = \fork{0}{if \(src(x,y) > T(x,y)\)}{\texttt{maxValue}}{otherwise}\f] +where \f$T(x,y)\f$ is a threshold calculated individually for each pixel (see adaptiveMethod parameter). + +The function can process the image in-place. + +@param src Source 8-bit single-channel image. +@param dst Destination image of the same size and the same type as src. +@param maxValue Non-zero value assigned to the pixels for which the condition is satisfied +@param adaptiveMethod Adaptive thresholding algorithm to use, see #AdaptiveThresholdTypes. +The #BORDER_REPLICATE | #BORDER_ISOLATED is used to process boundaries. +@param thresholdType Thresholding type that must be either #THRESH_BINARY or #THRESH_BINARY_INV, +see #ThresholdTypes. +@param blockSize Size of a pixel neighborhood that is used to calculate a threshold value for the +pixel: 3, 5, 7, and so on. +@param C Constant subtracted from the mean or weighted mean (see the details below). Normally, it +is positive but may be zero or negative as well. + +@sa threshold, blur, GaussianBlur + */ +CV_EXPORTS_W void adaptiveThreshold( InputArray src, OutputArray dst, + double maxValue, int adaptiveMethod, + int thresholdType, int blockSize, double C ); + +//! @} imgproc_misc + +//! @addtogroup imgproc_filter +//! @{ + +/** @example samples/cpp/tutorial_code/ImgProc/Pyramids/Pyramids.cpp +An example using pyrDown and pyrUp functions +*/ + +/** @brief Blurs an image and downsamples it. + +By default, size of the output image is computed as `Size((src.cols+1)/2, (src.rows+1)/2)`, but in +any case, the following conditions should be satisfied: + +\f[\begin{array}{l} | \texttt{dstsize.width} *2-src.cols| \leq 2 \\ | \texttt{dstsize.height} *2-src.rows| \leq 2 \end{array}\f] + +The function performs the downsampling step of the Gaussian pyramid construction. First, it +convolves the source image with the kernel: + +\f[\frac{1}{256} \begin{bmatrix} 1 & 4 & 6 & 4 & 1 \\ 4 & 16 & 24 & 16 & 4 \\ 6 & 24 & 36 & 24 & 6 \\ 4 & 16 & 24 & 16 & 4 \\ 1 & 4 & 6 & 4 & 1 \end{bmatrix}\f] + +Then, it downsamples the image by rejecting even rows and columns. + +@param src input image. +@param dst output image; it has the specified size and the same type as src. +@param dstsize size of the output image. +@param borderType Pixel extrapolation method, see #BorderTypes (#BORDER_CONSTANT isn't supported) + */ +CV_EXPORTS_W void pyrDown( InputArray src, OutputArray dst, + const Size& dstsize = Size(), int borderType = BORDER_DEFAULT ); + +/** @brief Upsamples an image and then blurs it. + +By default, size of the output image is computed as `Size(src.cols\*2, (src.rows\*2)`, but in any +case, the following conditions should be satisfied: + +\f[\begin{array}{l} | \texttt{dstsize.width} -src.cols*2| \leq ( \texttt{dstsize.width} \mod 2) \\ | \texttt{dstsize.height} -src.rows*2| \leq ( \texttt{dstsize.height} \mod 2) \end{array}\f] + +The function performs the upsampling step of the Gaussian pyramid construction, though it can +actually be used to construct the Laplacian pyramid. First, it upsamples the source image by +injecting even zero rows and columns and then convolves the result with the same kernel as in +pyrDown multiplied by 4. + +@param src input image. +@param dst output image. It has the specified size and the same type as src . +@param dstsize size of the output image. +@param borderType Pixel extrapolation method, see #BorderTypes (only #BORDER_DEFAULT is supported) + */ +CV_EXPORTS_W void pyrUp( InputArray src, OutputArray dst, + const Size& dstsize = Size(), int borderType = BORDER_DEFAULT ); + +/** @brief Constructs the Gaussian pyramid for an image. + +The function constructs a vector of images and builds the Gaussian pyramid by recursively applying +pyrDown to the previously built pyramid layers, starting from `dst[0]==src`. + +@param src Source image. Check pyrDown for the list of supported types. +@param dst Destination vector of maxlevel+1 images of the same type as src. dst[0] will be the +same as src. dst[1] is the next pyramid layer, a smoothed and down-sized src, and so on. +@param maxlevel 0-based index of the last (the smallest) pyramid layer. It must be non-negative. +@param borderType Pixel extrapolation method, see #BorderTypes (#BORDER_CONSTANT isn't supported) + */ +CV_EXPORTS void buildPyramid( InputArray src, OutputArrayOfArrays dst, + int maxlevel, int borderType = BORDER_DEFAULT ); + +//! @} imgproc_filter + +//! @addtogroup imgproc_transform +//! @{ + +/** @brief Transforms an image to compensate for lens distortion. + +The function transforms an image to compensate radial and tangential lens distortion. + +The function is simply a combination of #initUndistortRectifyMap (with unity R ) and #remap +(with bilinear interpolation). See the former function for details of the transformation being +performed. + +Those pixels in the destination image, for which there is no correspondent pixels in the source +image, are filled with zeros (black color). + +A particular subset of the source image that will be visible in the corrected image can be regulated +by newCameraMatrix. You can use #getOptimalNewCameraMatrix to compute the appropriate +newCameraMatrix depending on your requirements. + +The camera matrix and the distortion parameters can be determined using #calibrateCamera. If +the resolution of images is different from the resolution used at the calibration stage, \f$f_x, +f_y, c_x\f$ and \f$c_y\f$ need to be scaled accordingly, while the distortion coefficients remain +the same. + +@param src Input (distorted) image. +@param dst Output (corrected) image that has the same size and type as src . +@param cameraMatrix Input camera matrix \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . +@param distCoeffs Input vector of distortion coefficients +\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ +of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed. +@param newCameraMatrix Camera matrix of the distorted image. By default, it is the same as +cameraMatrix but you may additionally scale and shift the result by using a different matrix. + */ +CV_EXPORTS_W void undistort( InputArray src, OutputArray dst, + InputArray cameraMatrix, + InputArray distCoeffs, + InputArray newCameraMatrix = noArray() ); + +/** @brief Computes the undistortion and rectification transformation map. + +The function computes the joint undistortion and rectification transformation and represents the +result in the form of maps for remap. The undistorted image looks like original, as if it is +captured with a camera using the camera matrix =newCameraMatrix and zero distortion. In case of a +monocular camera, newCameraMatrix is usually equal to cameraMatrix, or it can be computed by +#getOptimalNewCameraMatrix for a better control over scaling. In case of a stereo camera, +newCameraMatrix is normally set to P1 or P2 computed by #stereoRectify . + +Also, this new camera is oriented differently in the coordinate space, according to R. That, for +example, helps to align two heads of a stereo camera so that the epipolar lines on both images +become horizontal and have the same y- coordinate (in case of a horizontally aligned stereo camera). + +The function actually builds the maps for the inverse mapping algorithm that is used by remap. That +is, for each pixel \f$(u, v)\f$ in the destination (corrected and rectified) image, the function +computes the corresponding coordinates in the source image (that is, in the original image from +camera). The following process is applied: +\f[ +\begin{array}{l} +x \leftarrow (u - {c'}_x)/{f'}_x \\ +y \leftarrow (v - {c'}_y)/{f'}_y \\ +{[X\,Y\,W]} ^T \leftarrow R^{-1}*[x \, y \, 1]^T \\ +x' \leftarrow X/W \\ +y' \leftarrow Y/W \\ +r^2 \leftarrow x'^2 + y'^2 \\ +x'' \leftarrow x' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} ++ 2p_1 x' y' + p_2(r^2 + 2 x'^2) + s_1 r^2 + s_2 r^4\\ +y'' \leftarrow y' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} ++ p_1 (r^2 + 2 y'^2) + 2 p_2 x' y' + s_3 r^2 + s_4 r^4 \\ +s\vecthree{x'''}{y'''}{1} = +\vecthreethree{R_{33}(\tau_x, \tau_y)}{0}{-R_{13}((\tau_x, \tau_y)} +{0}{R_{33}(\tau_x, \tau_y)}{-R_{23}(\tau_x, \tau_y)} +{0}{0}{1} R(\tau_x, \tau_y) \vecthree{x''}{y''}{1}\\ +map_x(u,v) \leftarrow x''' f_x + c_x \\ +map_y(u,v) \leftarrow y''' f_y + c_y +\end{array} +\f] +where \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ +are the distortion coefficients. + +In case of a stereo camera, this function is called twice: once for each camera head, after +stereoRectify, which in its turn is called after #stereoCalibrate. But if the stereo camera +was not calibrated, it is still possible to compute the rectification transformations directly from +the fundamental matrix using #stereoRectifyUncalibrated. For each camera, the function computes +homography H as the rectification transformation in a pixel domain, not a rotation matrix R in 3D +space. R can be computed from H as +\f[\texttt{R} = \texttt{cameraMatrix} ^{-1} \cdot \texttt{H} \cdot \texttt{cameraMatrix}\f] +where cameraMatrix can be chosen arbitrarily. + +@param cameraMatrix Input camera matrix \f$A=\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . +@param distCoeffs Input vector of distortion coefficients +\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ +of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed. +@param R Optional rectification transformation in the object space (3x3 matrix). R1 or R2 , +computed by #stereoRectify can be passed here. If the matrix is empty, the identity transformation +is assumed. In cvInitUndistortMap R assumed to be an identity matrix. +@param newCameraMatrix New camera matrix \f$A'=\vecthreethree{f_x'}{0}{c_x'}{0}{f_y'}{c_y'}{0}{0}{1}\f$. +@param size Undistorted image size. +@param m1type Type of the first output map that can be CV_32FC1, CV_32FC2 or CV_16SC2, see #convertMaps +@param map1 The first output map. +@param map2 The second output map. + */ +CV_EXPORTS_W void initUndistortRectifyMap( InputArray cameraMatrix, InputArray distCoeffs, + InputArray R, InputArray newCameraMatrix, + Size size, int m1type, OutputArray map1, OutputArray map2 ); + +//! initializes maps for #remap for wide-angle +CV_EXPORTS_W float initWideAngleProjMap( InputArray cameraMatrix, InputArray distCoeffs, + Size imageSize, int destImageWidth, + int m1type, OutputArray map1, OutputArray map2, + int projType = PROJ_SPHERICAL_EQRECT, double alpha = 0); + +/** @brief Returns the default new camera matrix. + +The function returns the camera matrix that is either an exact copy of the input cameraMatrix (when +centerPrinicipalPoint=false ), or the modified one (when centerPrincipalPoint=true). + +In the latter case, the new camera matrix will be: + +\f[\begin{bmatrix} f_x && 0 && ( \texttt{imgSize.width} -1)*0.5 \\ 0 && f_y && ( \texttt{imgSize.height} -1)*0.5 \\ 0 && 0 && 1 \end{bmatrix} ,\f] + +where \f$f_x\f$ and \f$f_y\f$ are \f$(0,0)\f$ and \f$(1,1)\f$ elements of cameraMatrix, respectively. + +By default, the undistortion functions in OpenCV (see #initUndistortRectifyMap, #undistort) do not +move the principal point. However, when you work with stereo, it is important to move the principal +points in both views to the same y-coordinate (which is required by most of stereo correspondence +algorithms), and may be to the same x-coordinate too. So, you can form the new camera matrix for +each view where the principal points are located at the center. + +@param cameraMatrix Input camera matrix. +@param imgsize Camera view image size in pixels. +@param centerPrincipalPoint Location of the principal point in the new camera matrix. The +parameter indicates whether this location should be at the image center or not. + */ +CV_EXPORTS_W Mat getDefaultNewCameraMatrix( InputArray cameraMatrix, Size imgsize = Size(), + bool centerPrincipalPoint = false ); + +/** @brief Computes the ideal point coordinates from the observed point coordinates. + +The function is similar to #undistort and #initUndistortRectifyMap but it operates on a +sparse set of points instead of a raster image. Also the function performs a reverse transformation +to projectPoints. In case of a 3D object, it does not reconstruct its 3D coordinates, but for a +planar object, it does, up to a translation vector, if the proper R is specified. + +For each observed point coordinate \f$(u, v)\f$ the function computes: +\f[ +\begin{array}{l} +x^{"} \leftarrow (u - c_x)/f_x \\ +y^{"} \leftarrow (v - c_y)/f_y \\ +(x',y') = undistort(x^{"},y^{"}, \texttt{distCoeffs}) \\ +{[X\,Y\,W]} ^T \leftarrow R*[x' \, y' \, 1]^T \\ +x \leftarrow X/W \\ +y \leftarrow Y/W \\ +\text{only performed if P is specified:} \\ +u' \leftarrow x {f'}_x + {c'}_x \\ +v' \leftarrow y {f'}_y + {c'}_y +\end{array} +\f] + +where *undistort* is an approximate iterative algorithm that estimates the normalized original +point coordinates out of the normalized distorted point coordinates ("normalized" means that the +coordinates do not depend on the camera matrix). + +The function can be used for both a stereo camera head or a monocular camera (when R is empty). + +@param src Observed point coordinates, 1xN or Nx1 2-channel (CV_32FC2 or CV_64FC2). +@param dst Output ideal point coordinates after undistortion and reverse perspective +transformation. If matrix P is identity or omitted, dst will contain normalized point coordinates. +@param cameraMatrix Camera matrix \f$\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . +@param distCoeffs Input vector of distortion coefficients +\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ +of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed. +@param R Rectification transformation in the object space (3x3 matrix). R1 or R2 computed by +#stereoRectify can be passed here. If the matrix is empty, the identity transformation is used. +@param P New camera matrix (3x3) or new projection matrix (3x4) \f$\begin{bmatrix} {f'}_x & 0 & {c'}_x & t_x \\ 0 & {f'}_y & {c'}_y & t_y \\ 0 & 0 & 1 & t_z \end{bmatrix}\f$. P1 or P2 computed by +#stereoRectify can be passed here. If the matrix is empty, the identity new camera matrix is used. + */ +CV_EXPORTS_W void undistortPoints( InputArray src, OutputArray dst, + InputArray cameraMatrix, InputArray distCoeffs, + InputArray R = noArray(), InputArray P = noArray()); +/** @overload + @note Default version of #undistortPoints does 5 iterations to compute undistorted points. + + */ +CV_EXPORTS_AS(undistortPointsIter) void undistortPoints( InputArray src, OutputArray dst, + InputArray cameraMatrix, InputArray distCoeffs, + InputArray R, InputArray P, TermCriteria criteria); + +//! @} imgproc_transform + +//! @addtogroup imgproc_hist +//! @{ + +/** @example samples/cpp/demhist.cpp +An example for creating histograms of an image +*/ + +/** @brief Calculates a histogram of a set of arrays. + +The function cv::calcHist calculates the histogram of one or more arrays. The elements of a tuple used +to increment a histogram bin are taken from the corresponding input arrays at the same location. The +sample below shows how to compute a 2D Hue-Saturation histogram for a color image. : +@include snippets/imgproc_calcHist.cpp + +@param images Source arrays. They all should have the same depth, CV_8U, CV_16U or CV_32F , and the same +size. Each of them can have an arbitrary number of channels. +@param nimages Number of source images. +@param channels List of the dims channels used to compute the histogram. The first array channels +are numerated from 0 to images[0].channels()-1 , the second array channels are counted from +images[0].channels() to images[0].channels() + images[1].channels()-1, and so on. +@param mask Optional mask. If the matrix is not empty, it must be an 8-bit array of the same size +as images[i] . The non-zero mask elements mark the array elements counted in the histogram. +@param hist Output histogram, which is a dense or sparse dims -dimensional array. +@param dims Histogram dimensionality that must be positive and not greater than CV_MAX_DIMS +(equal to 32 in the current OpenCV version). +@param histSize Array of histogram sizes in each dimension. +@param ranges Array of the dims arrays of the histogram bin boundaries in each dimension. When the +histogram is uniform ( uniform =true), then for each dimension i it is enough to specify the lower +(inclusive) boundary \f$L_0\f$ of the 0-th histogram bin and the upper (exclusive) boundary +\f$U_{\texttt{histSize}[i]-1}\f$ for the last histogram bin histSize[i]-1 . That is, in case of a +uniform histogram each of ranges[i] is an array of 2 elements. When the histogram is not uniform ( +uniform=false ), then each of ranges[i] contains histSize[i]+1 elements: +\f$L_0, U_0=L_1, U_1=L_2, ..., U_{\texttt{histSize[i]}-2}=L_{\texttt{histSize[i]}-1}, U_{\texttt{histSize[i]}-1}\f$ +. The array elements, that are not between \f$L_0\f$ and \f$U_{\texttt{histSize[i]}-1}\f$ , are not +counted in the histogram. +@param uniform Flag indicating whether the histogram is uniform or not (see above). +@param accumulate Accumulation flag. If it is set, the histogram is not cleared in the beginning +when it is allocated. This feature enables you to compute a single histogram from several sets of +arrays, or to update the histogram in time. +*/ +CV_EXPORTS void calcHist( const Mat* images, int nimages, + const int* channels, InputArray mask, + OutputArray hist, int dims, const int* histSize, + const float** ranges, bool uniform = true, bool accumulate = false ); + +/** @overload + +this variant uses %SparseMat for output +*/ +CV_EXPORTS void calcHist( const Mat* images, int nimages, + const int* channels, InputArray mask, + SparseMat& hist, int dims, + const int* histSize, const float** ranges, + bool uniform = true, bool accumulate = false ); + +/** @overload */ +CV_EXPORTS_W void calcHist( InputArrayOfArrays images, + const std::vector& channels, + InputArray mask, OutputArray hist, + const std::vector& histSize, + const std::vector& ranges, + bool accumulate = false ); + +/** @brief Calculates the back projection of a histogram. + +The function cv::calcBackProject calculates the back project of the histogram. That is, similarly to +#calcHist , at each location (x, y) the function collects the values from the selected channels +in the input images and finds the corresponding histogram bin. But instead of incrementing it, the +function reads the bin value, scales it by scale , and stores in backProject(x,y) . In terms of +statistics, the function computes probability of each element value in respect with the empirical +probability distribution represented by the histogram. See how, for example, you can find and track +a bright-colored object in a scene: + +- Before tracking, show the object to the camera so that it covers almost the whole frame. +Calculate a hue histogram. The histogram may have strong maximums, corresponding to the dominant +colors in the object. + +- When tracking, calculate a back projection of a hue plane of each input video frame using that +pre-computed histogram. Threshold the back projection to suppress weak colors. It may also make +sense to suppress pixels with non-sufficient color saturation and too dark or too bright pixels. + +- Find connected components in the resulting picture and choose, for example, the largest +component. + +This is an approximate algorithm of the CamShift color object tracker. + +@param images Source arrays. They all should have the same depth, CV_8U, CV_16U or CV_32F , and the same +size. Each of them can have an arbitrary number of channels. +@param nimages Number of source images. +@param channels The list of channels used to compute the back projection. The number of channels +must match the histogram dimensionality. The first array channels are numerated from 0 to +images[0].channels()-1 , the second array channels are counted from images[0].channels() to +images[0].channels() + images[1].channels()-1, and so on. +@param hist Input histogram that can be dense or sparse. +@param backProject Destination back projection array that is a single-channel array of the same +size and depth as images[0] . +@param ranges Array of arrays of the histogram bin boundaries in each dimension. See #calcHist . +@param scale Optional scale factor for the output back projection. +@param uniform Flag indicating whether the histogram is uniform or not (see above). + +@sa calcHist, compareHist + */ +CV_EXPORTS void calcBackProject( const Mat* images, int nimages, + const int* channels, InputArray hist, + OutputArray backProject, const float** ranges, + double scale = 1, bool uniform = true ); + +/** @overload */ +CV_EXPORTS void calcBackProject( const Mat* images, int nimages, + const int* channels, const SparseMat& hist, + OutputArray backProject, const float** ranges, + double scale = 1, bool uniform = true ); + +/** @overload */ +CV_EXPORTS_W void calcBackProject( InputArrayOfArrays images, const std::vector& channels, + InputArray hist, OutputArray dst, + const std::vector& ranges, + double scale ); + +/** @brief Compares two histograms. + +The function cv::compareHist compares two dense or two sparse histograms using the specified method. + +The function returns \f$d(H_1, H_2)\f$ . + +While the function works well with 1-, 2-, 3-dimensional dense histograms, it may not be suitable +for high-dimensional sparse histograms. In such histograms, because of aliasing and sampling +problems, the coordinates of non-zero histogram bins can slightly shift. To compare such histograms +or more general sparse configurations of weighted points, consider using the #EMD function. + +@param H1 First compared histogram. +@param H2 Second compared histogram of the same size as H1 . +@param method Comparison method, see #HistCompMethods + */ +CV_EXPORTS_W double compareHist( InputArray H1, InputArray H2, int method ); + +/** @overload */ +CV_EXPORTS double compareHist( const SparseMat& H1, const SparseMat& H2, int method ); + +/** @brief Equalizes the histogram of a grayscale image. + +The function equalizes the histogram of the input image using the following algorithm: + +- Calculate the histogram \f$H\f$ for src . +- Normalize the histogram so that the sum of histogram bins is 255. +- Compute the integral of the histogram: +\f[H'_i = \sum _{0 \le j < i} H(j)\f] +- Transform the image using \f$H'\f$ as a look-up table: \f$\texttt{dst}(x,y) = H'(\texttt{src}(x,y))\f$ + +The algorithm normalizes the brightness and increases the contrast of the image. + +@param src Source 8-bit single channel image. +@param dst Destination image of the same size and type as src . + */ +CV_EXPORTS_W void equalizeHist( InputArray src, OutputArray dst ); + +/** @brief Creates a smart pointer to a cv::CLAHE class and initializes it. + +@param clipLimit Threshold for contrast limiting. +@param tileGridSize Size of grid for histogram equalization. Input image will be divided into +equally sized rectangular tiles. tileGridSize defines the number of tiles in row and column. + */ +CV_EXPORTS_W Ptr createCLAHE(double clipLimit = 40.0, Size tileGridSize = Size(8, 8)); + +/** @brief Computes the "minimal work" distance between two weighted point configurations. + +The function computes the earth mover distance and/or a lower boundary of the distance between the +two weighted point configurations. One of the applications described in @cite RubnerSept98, +@cite Rubner2000 is multi-dimensional histogram comparison for image retrieval. EMD is a transportation +problem that is solved using some modification of a simplex algorithm, thus the complexity is +exponential in the worst case, though, on average it is much faster. In the case of a real metric +the lower boundary can be calculated even faster (using linear-time algorithm) and it can be used +to determine roughly whether the two signatures are far enough so that they cannot relate to the +same object. + +@param signature1 First signature, a \f$\texttt{size1}\times \texttt{dims}+1\f$ floating-point matrix. +Each row stores the point weight followed by the point coordinates. The matrix is allowed to have +a single column (weights only) if the user-defined cost matrix is used. The weights must be +non-negative and have at least one non-zero value. +@param signature2 Second signature of the same format as signature1 , though the number of rows +may be different. The total weights may be different. In this case an extra "dummy" point is added +to either signature1 or signature2. The weights must be non-negative and have at least one non-zero +value. +@param distType Used metric. See #DistanceTypes. +@param cost User-defined \f$\texttt{size1}\times \texttt{size2}\f$ cost matrix. Also, if a cost matrix +is used, lower boundary lowerBound cannot be calculated because it needs a metric function. +@param lowerBound Optional input/output parameter: lower boundary of a distance between the two +signatures that is a distance between mass centers. The lower boundary may not be calculated if +the user-defined cost matrix is used, the total weights of point configurations are not equal, or +if the signatures consist of weights only (the signature matrices have a single column). You +**must** initialize \*lowerBound . If the calculated distance between mass centers is greater or +equal to \*lowerBound (it means that the signatures are far enough), the function does not +calculate EMD. In any case \*lowerBound is set to the calculated distance between mass centers on +return. Thus, if you want to calculate both distance between mass centers and EMD, \*lowerBound +should be set to 0. +@param flow Resultant \f$\texttt{size1} \times \texttt{size2}\f$ flow matrix: \f$\texttt{flow}_{i,j}\f$ is +a flow from \f$i\f$ -th point of signature1 to \f$j\f$ -th point of signature2 . + */ +CV_EXPORTS float EMD( InputArray signature1, InputArray signature2, + int distType, InputArray cost=noArray(), + float* lowerBound = 0, OutputArray flow = noArray() ); + +CV_EXPORTS_AS(EMD) float wrapperEMD( InputArray signature1, InputArray signature2, + int distType, InputArray cost=noArray(), + CV_IN_OUT Ptr lowerBound = Ptr(), OutputArray flow = noArray() ); + +//! @} imgproc_hist + +/** @example samples/cpp/watershed.cpp +An example using the watershed algorithm +*/ + +/** @brief Performs a marker-based image segmentation using the watershed algorithm. + +The function implements one of the variants of watershed, non-parametric marker-based segmentation +algorithm, described in @cite Meyer92 . + +Before passing the image to the function, you have to roughly outline the desired regions in the +image markers with positive (\>0) indices. So, every region is represented as one or more connected +components with the pixel values 1, 2, 3, and so on. Such markers can be retrieved from a binary +mask using #findContours and #drawContours (see the watershed.cpp demo). The markers are "seeds" of +the future image regions. All the other pixels in markers , whose relation to the outlined regions +is not known and should be defined by the algorithm, should be set to 0's. In the function output, +each pixel in markers is set to a value of the "seed" components or to -1 at boundaries between the +regions. + +@note Any two neighbor connected components are not necessarily separated by a watershed boundary +(-1's pixels); for example, they can touch each other in the initial marker image passed to the +function. + +@param image Input 8-bit 3-channel image. +@param markers Input/output 32-bit single-channel image (map) of markers. It should have the same +size as image . + +@sa findContours + +@ingroup imgproc_misc + */ +CV_EXPORTS_W void watershed( InputArray image, InputOutputArray markers ); + +//! @addtogroup imgproc_filter +//! @{ + +/** @brief Performs initial step of meanshift segmentation of an image. + +The function implements the filtering stage of meanshift segmentation, that is, the output of the +function is the filtered "posterized" image with color gradients and fine-grain texture flattened. +At every pixel (X,Y) of the input image (or down-sized input image, see below) the function executes +meanshift iterations, that is, the pixel (X,Y) neighborhood in the joint space-color hyperspace is +considered: + +\f[(x,y): X- \texttt{sp} \le x \le X+ \texttt{sp} , Y- \texttt{sp} \le y \le Y+ \texttt{sp} , ||(R,G,B)-(r,g,b)|| \le \texttt{sr}\f] + +where (R,G,B) and (r,g,b) are the vectors of color components at (X,Y) and (x,y), respectively +(though, the algorithm does not depend on the color space used, so any 3-component color space can +be used instead). Over the neighborhood the average spatial value (X',Y') and average color vector +(R',G',B') are found and they act as the neighborhood center on the next iteration: + +\f[(X,Y)~(X',Y'), (R,G,B)~(R',G',B').\f] + +After the iterations over, the color components of the initial pixel (that is, the pixel from where +the iterations started) are set to the final value (average color at the last iteration): + +\f[I(X,Y) <- (R*,G*,B*)\f] + +When maxLevel \> 0, the gaussian pyramid of maxLevel+1 levels is built, and the above procedure is +run on the smallest layer first. After that, the results are propagated to the larger layer and the +iterations are run again only on those pixels where the layer colors differ by more than sr from the +lower-resolution layer of the pyramid. That makes boundaries of color regions sharper. Note that the +results will be actually different from the ones obtained by running the meanshift procedure on the +whole original image (i.e. when maxLevel==0). + +@param src The source 8-bit, 3-channel image. +@param dst The destination image of the same format and the same size as the source. +@param sp The spatial window radius. +@param sr The color window radius. +@param maxLevel Maximum level of the pyramid for the segmentation. +@param termcrit Termination criteria: when to stop meanshift iterations. + */ +CV_EXPORTS_W void pyrMeanShiftFiltering( InputArray src, OutputArray dst, + double sp, double sr, int maxLevel = 1, + TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5,1) ); + +//! @} + +//! @addtogroup imgproc_misc +//! @{ + +/** @example samples/cpp/grabcut.cpp +An example using the GrabCut algorithm +![Sample Screenshot](grabcut_output1.jpg) +*/ + +/** @brief Runs the GrabCut algorithm. + +The function implements the [GrabCut image segmentation algorithm](http://en.wikipedia.org/wiki/GrabCut). + +@param img Input 8-bit 3-channel image. +@param mask Input/output 8-bit single-channel mask. The mask is initialized by the function when +mode is set to #GC_INIT_WITH_RECT. Its elements may have one of the #GrabCutClasses. +@param rect ROI containing a segmented object. The pixels outside of the ROI are marked as +"obvious background". The parameter is only used when mode==#GC_INIT_WITH_RECT . +@param bgdModel Temporary array for the background model. Do not modify it while you are +processing the same image. +@param fgdModel Temporary arrays for the foreground model. Do not modify it while you are +processing the same image. +@param iterCount Number of iterations the algorithm should make before returning the result. Note +that the result can be refined with further calls with mode==#GC_INIT_WITH_MASK or +mode==GC_EVAL . +@param mode Operation mode that could be one of the #GrabCutModes + */ +CV_EXPORTS_W void grabCut( InputArray img, InputOutputArray mask, Rect rect, + InputOutputArray bgdModel, InputOutputArray fgdModel, + int iterCount, int mode = GC_EVAL ); + +/** @example samples/cpp/distrans.cpp +An example on using the distance transform +*/ + +/** @brief Calculates the distance to the closest zero pixel for each pixel of the source image. + +The function cv::distanceTransform calculates the approximate or precise distance from every binary +image pixel to the nearest zero pixel. For zero image pixels, the distance will obviously be zero. + +When maskSize == #DIST_MASK_PRECISE and distanceType == #DIST_L2 , the function runs the +algorithm described in @cite Felzenszwalb04 . This algorithm is parallelized with the TBB library. + +In other cases, the algorithm @cite Borgefors86 is used. This means that for a pixel the function +finds the shortest path to the nearest zero pixel consisting of basic shifts: horizontal, vertical, +diagonal, or knight's move (the latest is available for a \f$5\times 5\f$ mask). The overall +distance is calculated as a sum of these basic distances. Since the distance function should be +symmetric, all of the horizontal and vertical shifts must have the same cost (denoted as a ), all +the diagonal shifts must have the same cost (denoted as `b`), and all knight's moves must have the +same cost (denoted as `c`). For the #DIST_C and #DIST_L1 types, the distance is calculated +precisely, whereas for #DIST_L2 (Euclidean distance) the distance can be calculated only with a +relative error (a \f$5\times 5\f$ mask gives more accurate results). For `a`,`b`, and `c`, OpenCV +uses the values suggested in the original paper: +- DIST_L1: `a = 1, b = 2` +- DIST_L2: + - `3 x 3`: `a=0.955, b=1.3693` + - `5 x 5`: `a=1, b=1.4, c=2.1969` +- DIST_C: `a = 1, b = 1` + +Typically, for a fast, coarse distance estimation #DIST_L2, a \f$3\times 3\f$ mask is used. For a +more accurate distance estimation #DIST_L2, a \f$5\times 5\f$ mask or the precise algorithm is used. +Note that both the precise and the approximate algorithms are linear on the number of pixels. + +This variant of the function does not only compute the minimum distance for each pixel \f$(x, y)\f$ +but also identifies the nearest connected component consisting of zero pixels +(labelType==#DIST_LABEL_CCOMP) or the nearest zero pixel (labelType==#DIST_LABEL_PIXEL). Index of the +component/pixel is stored in `labels(x, y)`. When labelType==#DIST_LABEL_CCOMP, the function +automatically finds connected components of zero pixels in the input image and marks them with +distinct labels. When labelType==#DIST_LABEL_CCOMP, the function scans through the input image and +marks all the zero pixels with distinct labels. + +In this mode, the complexity is still linear. That is, the function provides a very fast way to +compute the Voronoi diagram for a binary image. Currently, the second variant can use only the +approximate distance transform algorithm, i.e. maskSize=#DIST_MASK_PRECISE is not supported +yet. + +@param src 8-bit, single-channel (binary) source image. +@param dst Output image with calculated distances. It is a 8-bit or 32-bit floating-point, +single-channel image of the same size as src. +@param labels Output 2D array of labels (the discrete Voronoi diagram). It has the type +CV_32SC1 and the same size as src. +@param distanceType Type of distance, see #DistanceTypes +@param maskSize Size of the distance transform mask, see #DistanceTransformMasks. +#DIST_MASK_PRECISE is not supported by this variant. In case of the #DIST_L1 or #DIST_C distance type, +the parameter is forced to 3 because a \f$3\times 3\f$ mask gives the same result as \f$5\times +5\f$ or any larger aperture. +@param labelType Type of the label array to build, see #DistanceTransformLabelTypes. + */ +CV_EXPORTS_AS(distanceTransformWithLabels) void distanceTransform( InputArray src, OutputArray dst, + OutputArray labels, int distanceType, int maskSize, + int labelType = DIST_LABEL_CCOMP ); + +/** @overload +@param src 8-bit, single-channel (binary) source image. +@param dst Output image with calculated distances. It is a 8-bit or 32-bit floating-point, +single-channel image of the same size as src . +@param distanceType Type of distance, see #DistanceTypes +@param maskSize Size of the distance transform mask, see #DistanceTransformMasks. In case of the +#DIST_L1 or #DIST_C distance type, the parameter is forced to 3 because a \f$3\times 3\f$ mask gives +the same result as \f$5\times 5\f$ or any larger aperture. +@param dstType Type of output image. It can be CV_8U or CV_32F. Type CV_8U can be used only for +the first variant of the function and distanceType == #DIST_L1. +*/ +CV_EXPORTS_W void distanceTransform( InputArray src, OutputArray dst, + int distanceType, int maskSize, int dstType=CV_32F); + +/** @example samples/cpp/ffilldemo.cpp +An example using the FloodFill technique +*/ + +/** @overload + +variant without `mask` parameter +*/ +CV_EXPORTS int floodFill( InputOutputArray image, + Point seedPoint, Scalar newVal, CV_OUT Rect* rect = 0, + Scalar loDiff = Scalar(), Scalar upDiff = Scalar(), + int flags = 4 ); + +/** @brief Fills a connected component with the given color. + +The function cv::floodFill fills a connected component starting from the seed point with the specified +color. The connectivity is determined by the color/brightness closeness of the neighbor pixels. The +pixel at \f$(x,y)\f$ is considered to belong to the repainted domain if: + +- in case of a grayscale image and floating range +\f[\texttt{src} (x',y')- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} (x',y')+ \texttt{upDiff}\f] + + +- in case of a grayscale image and fixed range +\f[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)+ \texttt{upDiff}\f] + + +- in case of a color image and floating range +\f[\texttt{src} (x',y')_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} (x',y')_r+ \texttt{upDiff} _r,\f] +\f[\texttt{src} (x',y')_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} (x',y')_g+ \texttt{upDiff} _g\f] +and +\f[\texttt{src} (x',y')_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} (x',y')_b+ \texttt{upDiff} _b\f] + + +- in case of a color image and fixed range +\f[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r+ \texttt{upDiff} _r,\f] +\f[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g+ \texttt{upDiff} _g\f] +and +\f[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b\f] + + +where \f$src(x',y')\f$ is the value of one of pixel neighbors that is already known to belong to the +component. That is, to be added to the connected component, a color/brightness of the pixel should +be close enough to: +- Color/brightness of one of its neighbors that already belong to the connected component in case +of a floating range. +- Color/brightness of the seed point in case of a fixed range. + +Use these functions to either mark a connected component with the specified color in-place, or build +a mask and then extract the contour, or copy the region to another image, and so on. + +@param image Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the +function unless the #FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See +the details below. +@param mask Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixels +taller than image. Since this is both an input and output parameter, you must take responsibility +of initializing it. Flood-filling cannot go across non-zero pixels in the input mask. For example, +an edge detector output can be used as a mask to stop filling at edges. On output, pixels in the +mask corresponding to filled pixels in the image are set to 1 or to the a value specified in flags +as described below. Additionally, the function fills the border of the mask with ones to simplify +internal processing. It is therefore possible to use the same mask in multiple calls to the function +to make sure the filled areas do not overlap. +@param seedPoint Starting point. +@param newVal New value of the repainted domain pixels. +@param loDiff Maximal lower brightness/color difference between the currently observed pixel and +one of its neighbors belonging to the component, or a seed pixel being added to the component. +@param upDiff Maximal upper brightness/color difference between the currently observed pixel and +one of its neighbors belonging to the component, or a seed pixel being added to the component. +@param rect Optional output parameter set by the function to the minimum bounding rectangle of the +repainted domain. +@param flags Operation flags. The first 8 bits contain a connectivity value. The default value of +4 means that only the four nearest neighbor pixels (those that share an edge) are considered. A +connectivity value of 8 means that the eight nearest neighbor pixels (those that share a corner) +will be considered. The next 8 bits (8-16) contain a value between 1 and 255 with which to fill +the mask (the default value is 1). For example, 4 | ( 255 \<\< 8 ) will consider 4 nearest +neighbours and fill the mask with a value of 255. The following additional options occupy higher +bits and therefore may be further combined with the connectivity and mask fill values using +bit-wise or (|), see #FloodFillFlags. + +@note Since the mask is larger than the filled image, a pixel \f$(x, y)\f$ in image corresponds to the +pixel \f$(x+1, y+1)\f$ in the mask . + +@sa findContours + */ +CV_EXPORTS_W int floodFill( InputOutputArray image, InputOutputArray mask, + Point seedPoint, Scalar newVal, CV_OUT Rect* rect=0, + Scalar loDiff = Scalar(), Scalar upDiff = Scalar(), + int flags = 4 ); + +//! Performs linear blending of two images: +//! \f[ \texttt{dst}(i,j) = \texttt{weights1}(i,j)*\texttt{src1}(i,j) + \texttt{weights2}(i,j)*\texttt{src2}(i,j) \f] +//! @param src1 It has a type of CV_8UC(n) or CV_32FC(n), where n is a positive integer. +//! @param src2 It has the same type and size as src1. +//! @param weights1 It has a type of CV_32FC1 and the same size with src1. +//! @param weights2 It has a type of CV_32FC1 and the same size with src1. +//! @param dst It is created if it does not have the same size and type with src1. +CV_EXPORTS void blendLinear(InputArray src1, InputArray src2, InputArray weights1, InputArray weights2, OutputArray dst); + +//! @} imgproc_misc + +//! @addtogroup imgproc_color_conversions +//! @{ + +/** @brief Converts an image from one color space to another. + +The function converts an input image from one color space to another. In case of a transformation +to-from RGB color space, the order of the channels should be specified explicitly (RGB or BGR). Note +that the default color format in OpenCV is often referred to as RGB but it is actually BGR (the +bytes are reversed). So the first byte in a standard (24-bit) color image will be an 8-bit Blue +component, the second byte will be Green, and the third byte will be Red. The fourth, fifth, and +sixth bytes would then be the second pixel (Blue, then Green, then Red), and so on. + +The conventional ranges for R, G, and B channel values are: +- 0 to 255 for CV_8U images +- 0 to 65535 for CV_16U images +- 0 to 1 for CV_32F images + +In case of linear transformations, the range does not matter. But in case of a non-linear +transformation, an input RGB image should be normalized to the proper value range to get the correct +results, for example, for RGB \f$\rightarrow\f$ L\*u\*v\* transformation. For example, if you have a +32-bit floating-point image directly converted from an 8-bit image without any scaling, then it will +have the 0..255 value range instead of 0..1 assumed by the function. So, before calling #cvtColor , +you need first to scale the image down: +@code + img *= 1./255; + cvtColor(img, img, COLOR_BGR2Luv); +@endcode +If you use #cvtColor with 8-bit images, the conversion will have some information lost. For many +applications, this will not be noticeable but it is recommended to use 32-bit images in applications +that need the full range of colors or that convert an image before an operation and then convert +back. + +If conversion adds the alpha channel, its value will set to the maximum of corresponding channel +range: 255 for CV_8U, 65535 for CV_16U, 1 for CV_32F. + +@param src input image: 8-bit unsigned, 16-bit unsigned ( CV_16UC... ), or single-precision +floating-point. +@param dst output image of the same size and depth as src. +@param code color space conversion code (see #ColorConversionCodes). +@param dstCn number of channels in the destination image; if the parameter is 0, the number of the +channels is derived automatically from src and code. + +@see @ref imgproc_color_conversions + */ +CV_EXPORTS_W void cvtColor( InputArray src, OutputArray dst, int code, int dstCn = 0 ); + +/** @brief Converts an image from one color space to another where the source image is +stored in two planes. + +This function only supports YUV420 to RGB conversion as of now. + +@param src1: 8-bit image (#CV_8U) of the Y plane. +@param src2: image containing interleaved U/V plane. +@param dst: output image. +@param code: Specifies the type of conversion. It can take any of the following values: +- #COLOR_YUV2BGR_NV12 +- #COLOR_YUV2RGB_NV12 +- #COLOR_YUV2BGRA_NV12 +- #COLOR_YUV2RGBA_NV12 +- #COLOR_YUV2BGR_NV21 +- #COLOR_YUV2RGB_NV21 +- #COLOR_YUV2BGRA_NV21 +- #COLOR_YUV2RGBA_NV21 +*/ +CV_EXPORTS_W void cvtColorTwoPlane( InputArray src1, InputArray src2, OutputArray dst, int code ); + +/** @brief main function for all demosaicing processes + +@param src input image: 8-bit unsigned or 16-bit unsigned. +@param dst output image of the same size and depth as src. +@param code Color space conversion code (see the description below). +@param dstCn number of channels in the destination image; if the parameter is 0, the number of the +channels is derived automatically from src and code. + +The function can do the following transformations: + +- Demosaicing using bilinear interpolation + + #COLOR_BayerBG2BGR , #COLOR_BayerGB2BGR , #COLOR_BayerRG2BGR , #COLOR_BayerGR2BGR + + #COLOR_BayerBG2GRAY , #COLOR_BayerGB2GRAY , #COLOR_BayerRG2GRAY , #COLOR_BayerGR2GRAY + +- Demosaicing using Variable Number of Gradients. + + #COLOR_BayerBG2BGR_VNG , #COLOR_BayerGB2BGR_VNG , #COLOR_BayerRG2BGR_VNG , #COLOR_BayerGR2BGR_VNG + +- Edge-Aware Demosaicing. + + #COLOR_BayerBG2BGR_EA , #COLOR_BayerGB2BGR_EA , #COLOR_BayerRG2BGR_EA , #COLOR_BayerGR2BGR_EA + +- Demosaicing with alpha channel + + #COLOR_BayerBG2BGRA , #COLOR_BayerGB2BGRA , #COLOR_BayerRG2BGRA , #COLOR_BayerGR2BGRA + +@sa cvtColor +*/ +CV_EXPORTS_W void demosaicing(InputArray src, OutputArray dst, int code, int dstCn = 0); + +//! @} imgproc_color_conversions + +//! @addtogroup imgproc_shape +//! @{ + +/** @brief Calculates all of the moments up to the third order of a polygon or rasterized shape. + +The function computes moments, up to the 3rd order, of a vector shape or a rasterized shape. The +results are returned in the structure cv::Moments. + +@param array Raster image (single-channel, 8-bit or floating-point 2D array) or an array ( +\f$1 \times N\f$ or \f$N \times 1\f$ ) of 2D points (Point or Point2f ). +@param binaryImage If it is true, all non-zero image pixels are treated as 1's. The parameter is +used for images only. +@returns moments. + +@note Only applicable to contour moments calculations from Python bindings: Note that the numpy +type for the input array should be either np.int32 or np.float32. + +@sa contourArea, arcLength + */ +CV_EXPORTS_W Moments moments( InputArray array, bool binaryImage = false ); + +/** @brief Calculates seven Hu invariants. + +The function calculates seven Hu invariants (introduced in @cite Hu62; see also +) defined as: + +\f[\begin{array}{l} hu[0]= \eta _{20}+ \eta _{02} \\ hu[1]=( \eta _{20}- \eta _{02})^{2}+4 \eta _{11}^{2} \\ hu[2]=( \eta _{30}-3 \eta _{12})^{2}+ (3 \eta _{21}- \eta _{03})^{2} \\ hu[3]=( \eta _{30}+ \eta _{12})^{2}+ ( \eta _{21}+ \eta _{03})^{2} \\ hu[4]=( \eta _{30}-3 \eta _{12})( \eta _{30}+ \eta _{12})[( \eta _{30}+ \eta _{12})^{2}-3( \eta _{21}+ \eta _{03})^{2}]+(3 \eta _{21}- \eta _{03})( \eta _{21}+ \eta _{03})[3( \eta _{30}+ \eta _{12})^{2}-( \eta _{21}+ \eta _{03})^{2}] \\ hu[5]=( \eta _{20}- \eta _{02})[( \eta _{30}+ \eta _{12})^{2}- ( \eta _{21}+ \eta _{03})^{2}]+4 \eta _{11}( \eta _{30}+ \eta _{12})( \eta _{21}+ \eta _{03}) \\ hu[6]=(3 \eta _{21}- \eta _{03})( \eta _{21}+ \eta _{03})[3( \eta _{30}+ \eta _{12})^{2}-( \eta _{21}+ \eta _{03})^{2}]-( \eta _{30}-3 \eta _{12})( \eta _{21}+ \eta _{03})[3( \eta _{30}+ \eta _{12})^{2}-( \eta _{21}+ \eta _{03})^{2}] \\ \end{array}\f] + +where \f$\eta_{ji}\f$ stands for \f$\texttt{Moments::nu}_{ji}\f$ . + +These values are proved to be invariants to the image scale, rotation, and reflection except the +seventh one, whose sign is changed by reflection. This invariance is proved with the assumption of +infinite image resolution. In case of raster images, the computed Hu invariants for the original and +transformed images are a bit different. + +@param moments Input moments computed with moments . +@param hu Output Hu invariants. + +@sa matchShapes + */ +CV_EXPORTS void HuMoments( const Moments& moments, double hu[7] ); + +/** @overload */ +CV_EXPORTS_W void HuMoments( const Moments& m, OutputArray hu ); + +//! @} imgproc_shape + +//! @addtogroup imgproc_object +//! @{ + +//! type of the template matching operation +enum TemplateMatchModes { + TM_SQDIFF = 0, //!< \f[R(x,y)= \sum _{x',y'} (T(x',y')-I(x+x',y+y'))^2\f] + TM_SQDIFF_NORMED = 1, //!< \f[R(x,y)= \frac{\sum_{x',y'} (T(x',y')-I(x+x',y+y'))^2}{\sqrt{\sum_{x',y'}T(x',y')^2 \cdot \sum_{x',y'} I(x+x',y+y')^2}}\f] + TM_CCORR = 2, //!< \f[R(x,y)= \sum _{x',y'} (T(x',y') \cdot I(x+x',y+y'))\f] + TM_CCORR_NORMED = 3, //!< \f[R(x,y)= \frac{\sum_{x',y'} (T(x',y') \cdot I(x+x',y+y'))}{\sqrt{\sum_{x',y'}T(x',y')^2 \cdot \sum_{x',y'} I(x+x',y+y')^2}}\f] + TM_CCOEFF = 4, //!< \f[R(x,y)= \sum _{x',y'} (T'(x',y') \cdot I'(x+x',y+y'))\f] + //!< where + //!< \f[\begin{array}{l} T'(x',y')=T(x',y') - 1/(w \cdot h) \cdot \sum _{x'',y''} T(x'',y'') \\ I'(x+x',y+y')=I(x+x',y+y') - 1/(w \cdot h) \cdot \sum _{x'',y''} I(x+x'',y+y'') \end{array}\f] + TM_CCOEFF_NORMED = 5 //!< \f[R(x,y)= \frac{ \sum_{x',y'} (T'(x',y') \cdot I'(x+x',y+y')) }{ \sqrt{\sum_{x',y'}T'(x',y')^2 \cdot \sum_{x',y'} I'(x+x',y+y')^2} }\f] +}; + +/** @example samples/cpp/tutorial_code/Histograms_Matching/MatchTemplate_Demo.cpp +An example using Template Matching algorithm +*/ + +/** @brief Compares a template against overlapped image regions. + +The function slides through image , compares the overlapped patches of size \f$w \times h\f$ against +templ using the specified method and stores the comparison results in result . Here are the formulae +for the available comparison methods ( \f$I\f$ denotes image, \f$T\f$ template, \f$R\f$ result ). The summation +is done over template and/or the image patch: \f$x' = 0...w-1, y' = 0...h-1\f$ + +After the function finishes the comparison, the best matches can be found as global minimums (when +#TM_SQDIFF was used) or maximums (when #TM_CCORR or #TM_CCOEFF was used) using the +#minMaxLoc function. In case of a color image, template summation in the numerator and each sum in +the denominator is done over all of the channels and separate mean values are used for each channel. +That is, the function can take a color template and a color image. The result will still be a +single-channel image, which is easier to analyze. + +@param image Image where the search is running. It must be 8-bit or 32-bit floating-point. +@param templ Searched template. It must be not greater than the source image and have the same +data type. +@param result Map of comparison results. It must be single-channel 32-bit floating-point. If image +is \f$W \times H\f$ and templ is \f$w \times h\f$ , then result is \f$(W-w+1) \times (H-h+1)\f$ . +@param method Parameter specifying the comparison method, see #TemplateMatchModes +@param mask Mask of searched template. It must have the same datatype and size with templ. It is +not set by default. Currently, only the #TM_SQDIFF and #TM_CCORR_NORMED methods are supported. + */ +CV_EXPORTS_W void matchTemplate( InputArray image, InputArray templ, + OutputArray result, int method, InputArray mask = noArray() ); + +//! @} + +//! @addtogroup imgproc_shape +//! @{ + +/** @example samples/cpp/connected_components.cpp +This program demonstrates connected components and use of the trackbar +*/ + +/** @brief computes the connected components labeled image of boolean image + +image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0 +represents the background label. ltype specifies the output label image type, an important +consideration based on the total number of labels or alternatively the total number of pixels in +the source image. ccltype specifies the connected components labeling algorithm to use, currently +Grana (BBDT) and Wu's (SAUF) algorithms are supported, see the #ConnectedComponentsAlgorithmsTypes +for details. Note that SAUF algorithm forces a row major ordering of labels while BBDT does not. +This function uses parallel version of both Grana and Wu's algorithms if at least one allowed +parallel framework is enabled and if the rows of the image are at least twice the number returned by #getNumberOfCPUs. + +@param image the 8-bit single-channel image to be labeled +@param labels destination labeled image +@param connectivity 8 or 4 for 8-way or 4-way connectivity respectively +@param ltype output image label type. Currently CV_32S and CV_16U are supported. +@param ccltype connected components algorithm type (see the #ConnectedComponentsAlgorithmsTypes). +*/ +CV_EXPORTS_AS(connectedComponentsWithAlgorithm) int connectedComponents(InputArray image, OutputArray labels, + int connectivity, int ltype, int ccltype); + + +/** @overload + +@param image the 8-bit single-channel image to be labeled +@param labels destination labeled image +@param connectivity 8 or 4 for 8-way or 4-way connectivity respectively +@param ltype output image label type. Currently CV_32S and CV_16U are supported. +*/ +CV_EXPORTS_W int connectedComponents(InputArray image, OutputArray labels, + int connectivity = 8, int ltype = CV_32S); + + +/** @brief computes the connected components labeled image of boolean image and also produces a statistics output for each label + +image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0 +represents the background label. ltype specifies the output label image type, an important +consideration based on the total number of labels or alternatively the total number of pixels in +the source image. ccltype specifies the connected components labeling algorithm to use, currently +Grana's (BBDT) and Wu's (SAUF) algorithms are supported, see the #ConnectedComponentsAlgorithmsTypes +for details. Note that SAUF algorithm forces a row major ordering of labels while BBDT does not. +This function uses parallel version of both Grana and Wu's algorithms (statistics included) if at least one allowed +parallel framework is enabled and if the rows of the image are at least twice the number returned by #getNumberOfCPUs. + +@param image the 8-bit single-channel image to be labeled +@param labels destination labeled image +@param stats statistics output for each label, including the background label, see below for +available statistics. Statistics are accessed via stats(label, COLUMN) where COLUMN is one of +#ConnectedComponentsTypes. The data type is CV_32S. +@param centroids centroid output for each label, including the background label. Centroids are +accessed via centroids(label, 0) for x and centroids(label, 1) for y. The data type CV_64F. +@param connectivity 8 or 4 for 8-way or 4-way connectivity respectively +@param ltype output image label type. Currently CV_32S and CV_16U are supported. +@param ccltype connected components algorithm type (see #ConnectedComponentsAlgorithmsTypes). +*/ +CV_EXPORTS_AS(connectedComponentsWithStatsWithAlgorithm) int connectedComponentsWithStats(InputArray image, OutputArray labels, + OutputArray stats, OutputArray centroids, + int connectivity, int ltype, int ccltype); + +/** @overload +@param image the 8-bit single-channel image to be labeled +@param labels destination labeled image +@param stats statistics output for each label, including the background label, see below for +available statistics. Statistics are accessed via stats(label, COLUMN) where COLUMN is one of +#ConnectedComponentsTypes. The data type is CV_32S. +@param centroids centroid output for each label, including the background label. Centroids are +accessed via centroids(label, 0) for x and centroids(label, 1) for y. The data type CV_64F. +@param connectivity 8 or 4 for 8-way or 4-way connectivity respectively +@param ltype output image label type. Currently CV_32S and CV_16U are supported. +*/ +CV_EXPORTS_W int connectedComponentsWithStats(InputArray image, OutputArray labels, + OutputArray stats, OutputArray centroids, + int connectivity = 8, int ltype = CV_32S); + + +/** @brief Finds contours in a binary image. + +The function retrieves contours from the binary image using the algorithm @cite Suzuki85 . The contours +are a useful tool for shape analysis and object detection and recognition. See squares.cpp in the +OpenCV sample directory. +@note Since opencv 3.2 source image is not modified by this function. + +@param image Source, an 8-bit single-channel image. Non-zero pixels are treated as 1's. Zero +pixels remain 0's, so the image is treated as binary . You can use #compare, #inRange, #threshold , +#adaptiveThreshold, #Canny, and others to create a binary image out of a grayscale or color one. +If mode equals to #RETR_CCOMP or #RETR_FLOODFILL, the input can also be a 32-bit integer image of labels (CV_32SC1). +@param contours Detected contours. Each contour is stored as a vector of points (e.g. +std::vector >). +@param hierarchy Optional output vector (e.g. std::vector), containing information about the image topology. It has +as many elements as the number of contours. For each i-th contour contours[i], the elements +hierarchy[i][0] , hierarchy[i][1] , hierarchy[i][2] , and hierarchy[i][3] are set to 0-based indices +in contours of the next and previous contours at the same hierarchical level, the first child +contour and the parent contour, respectively. If for the contour i there are no next, previous, +parent, or nested contours, the corresponding elements of hierarchy[i] will be negative. +@param mode Contour retrieval mode, see #RetrievalModes +@param method Contour approximation method, see #ContourApproximationModes +@param offset Optional offset by which every contour point is shifted. This is useful if the +contours are extracted from the image ROI and then they should be analyzed in the whole image +context. + */ +CV_EXPORTS_W void findContours( InputOutputArray image, OutputArrayOfArrays contours, + OutputArray hierarchy, int mode, + int method, Point offset = Point()); + +/** @overload */ +CV_EXPORTS void findContours( InputOutputArray image, OutputArrayOfArrays contours, + int mode, int method, Point offset = Point()); + +/** @example samples/cpp/squares.cpp +A program using pyramid scaling, Canny, contours and contour simplification to find +squares in a list of images (pic1-6.png). Returns sequence of squares detected on the image. +*/ + +/** @example samples/tapi/squares.cpp +A program using pyramid scaling, Canny, contours and contour simplification to find +squares in the input image. +*/ + +/** @brief Approximates a polygonal curve(s) with the specified precision. + +The function cv::approxPolyDP approximates a curve or a polygon with another curve/polygon with less +vertices so that the distance between them is less or equal to the specified precision. It uses the +Douglas-Peucker algorithm + +@param curve Input vector of a 2D point stored in std::vector or Mat +@param approxCurve Result of the approximation. The type should match the type of the input curve. +@param epsilon Parameter specifying the approximation accuracy. This is the maximum distance +between the original curve and its approximation. +@param closed If true, the approximated curve is closed (its first and last vertices are +connected). Otherwise, it is not closed. + */ +CV_EXPORTS_W void approxPolyDP( InputArray curve, + OutputArray approxCurve, + double epsilon, bool closed ); + +/** @brief Calculates a contour perimeter or a curve length. + +The function computes a curve length or a closed contour perimeter. + +@param curve Input vector of 2D points, stored in std::vector or Mat. +@param closed Flag indicating whether the curve is closed or not. + */ +CV_EXPORTS_W double arcLength( InputArray curve, bool closed ); + +/** @brief Calculates the up-right bounding rectangle of a point set or non-zero pixels of gray-scale image. + +The function calculates and returns the minimal up-right bounding rectangle for the specified point set or +non-zero pixels of gray-scale image. + +@param array Input gray-scale image or 2D point set, stored in std::vector or Mat. + */ +CV_EXPORTS_W Rect boundingRect( InputArray array ); + +/** @brief Calculates a contour area. + +The function computes a contour area. Similarly to moments , the area is computed using the Green +formula. Thus, the returned area and the number of non-zero pixels, if you draw the contour using +#drawContours or #fillPoly , can be different. Also, the function will most certainly give a wrong +results for contours with self-intersections. + +Example: +@code + vector contour; + contour.push_back(Point2f(0, 0)); + contour.push_back(Point2f(10, 0)); + contour.push_back(Point2f(10, 10)); + contour.push_back(Point2f(5, 4)); + + double area0 = contourArea(contour); + vector approx; + approxPolyDP(contour, approx, 5, true); + double area1 = contourArea(approx); + + cout << "area0 =" << area0 << endl << + "area1 =" << area1 << endl << + "approx poly vertices" << approx.size() << endl; +@endcode +@param contour Input vector of 2D points (contour vertices), stored in std::vector or Mat. +@param oriented Oriented area flag. If it is true, the function returns a signed area value, +depending on the contour orientation (clockwise or counter-clockwise). Using this feature you can +determine orientation of a contour by taking the sign of an area. By default, the parameter is +false, which means that the absolute value is returned. + */ +CV_EXPORTS_W double contourArea( InputArray contour, bool oriented = false ); + +/** @brief Finds a rotated rectangle of the minimum area enclosing the input 2D point set. + +The function calculates and returns the minimum-area bounding rectangle (possibly rotated) for a +specified point set. Developer should keep in mind that the returned RotatedRect can contain negative +indices when data is close to the containing Mat element boundary. + +@param points Input vector of 2D points, stored in std::vector\<\> or Mat + */ +CV_EXPORTS_W RotatedRect minAreaRect( InputArray points ); + +/** @brief Finds the four vertices of a rotated rect. Useful to draw the rotated rectangle. + +The function finds the four vertices of a rotated rectangle. This function is useful to draw the +rectangle. In C++, instead of using this function, you can directly use RotatedRect::points method. Please +visit the @ref tutorial_bounding_rotated_ellipses "tutorial on Creating Bounding rotated boxes and ellipses for contours" for more information. + +@param box The input rotated rectangle. It may be the output of +@param points The output array of four vertices of rectangles. + */ +CV_EXPORTS_W void boxPoints(RotatedRect box, OutputArray points); + +/** @brief Finds a circle of the minimum area enclosing a 2D point set. + +The function finds the minimal enclosing circle of a 2D point set using an iterative algorithm. + +@param points Input vector of 2D points, stored in std::vector\<\> or Mat +@param center Output center of the circle. +@param radius Output radius of the circle. + */ +CV_EXPORTS_W void minEnclosingCircle( InputArray points, + CV_OUT Point2f& center, CV_OUT float& radius ); + +/** @example samples/cpp/minarea.cpp +*/ + +/** @brief Finds a triangle of minimum area enclosing a 2D point set and returns its area. + +The function finds a triangle of minimum area enclosing the given set of 2D points and returns its +area. The output for a given 2D point set is shown in the image below. 2D points are depicted in +*red* and the enclosing triangle in *yellow*. + +![Sample output of the minimum enclosing triangle function](pics/minenclosingtriangle.png) + +The implementation of the algorithm is based on O'Rourke's @cite ORourke86 and Klee and Laskowski's +@cite KleeLaskowski85 papers. O'Rourke provides a \f$\theta(n)\f$ algorithm for finding the minimal +enclosing triangle of a 2D convex polygon with n vertices. Since the #minEnclosingTriangle function +takes a 2D point set as input an additional preprocessing step of computing the convex hull of the +2D point set is required. The complexity of the #convexHull function is \f$O(n log(n))\f$ which is higher +than \f$\theta(n)\f$. Thus the overall complexity of the function is \f$O(n log(n))\f$. + +@param points Input vector of 2D points with depth CV_32S or CV_32F, stored in std::vector\<\> or Mat +@param triangle Output vector of three 2D points defining the vertices of the triangle. The depth +of the OutputArray must be CV_32F. + */ +CV_EXPORTS_W double minEnclosingTriangle( InputArray points, CV_OUT OutputArray triangle ); + +/** @brief Compares two shapes. + +The function compares two shapes. All three implemented methods use the Hu invariants (see #HuMoments) + +@param contour1 First contour or grayscale image. +@param contour2 Second contour or grayscale image. +@param method Comparison method, see #ShapeMatchModes +@param parameter Method-specific parameter (not supported now). + */ +CV_EXPORTS_W double matchShapes( InputArray contour1, InputArray contour2, + int method, double parameter ); + +/** @example samples/cpp/convexhull.cpp +An example using the convexHull functionality +*/ + +/** @brief Finds the convex hull of a point set. + +The function cv::convexHull finds the convex hull of a 2D point set using the Sklansky's algorithm @cite Sklansky82 +that has *O(N logN)* complexity in the current implementation. + +@param points Input 2D point set, stored in std::vector or Mat. +@param hull Output convex hull. It is either an integer vector of indices or vector of points. In +the first case, the hull elements are 0-based indices of the convex hull points in the original +array (since the set of convex hull points is a subset of the original point set). In the second +case, hull elements are the convex hull points themselves. +@param clockwise Orientation flag. If it is true, the output convex hull is oriented clockwise. +Otherwise, it is oriented counter-clockwise. The assumed coordinate system has its X axis pointing +to the right, and its Y axis pointing upwards. +@param returnPoints Operation flag. In case of a matrix, when the flag is true, the function +returns convex hull points. Otherwise, it returns indices of the convex hull points. When the +output array is std::vector, the flag is ignored, and the output depends on the type of the +vector: std::vector\ implies returnPoints=false, std::vector\ implies +returnPoints=true. + +@note `points` and `hull` should be different arrays, inplace processing isn't supported. + +Check @ref tutorial_hull "the corresponding tutorial" for more details. + +useful links: + +https://www.learnopencv.com/convex-hull-using-opencv-in-python-and-c/ + */ +CV_EXPORTS_W void convexHull( InputArray points, OutputArray hull, + bool clockwise = false, bool returnPoints = true ); + +/** @brief Finds the convexity defects of a contour. + +The figure below displays convexity defects of a hand contour: + +![image](pics/defects.png) + +@param contour Input contour. +@param convexhull Convex hull obtained using convexHull that should contain indices of the contour +points that make the hull. +@param convexityDefects The output vector of convexity defects. In C++ and the new Python/Java +interface each convexity defect is represented as 4-element integer vector (a.k.a. #Vec4i): +(start_index, end_index, farthest_pt_index, fixpt_depth), where indices are 0-based indices +in the original contour of the convexity defect beginning, end and the farthest point, and +fixpt_depth is fixed-point approximation (with 8 fractional bits) of the distance between the +farthest contour point and the hull. That is, to get the floating-point value of the depth will be +fixpt_depth/256.0. + */ +CV_EXPORTS_W void convexityDefects( InputArray contour, InputArray convexhull, OutputArray convexityDefects ); + +/** @brief Tests a contour convexity. + +The function tests whether the input contour is convex or not. The contour must be simple, that is, +without self-intersections. Otherwise, the function output is undefined. + +@param contour Input vector of 2D points, stored in std::vector\<\> or Mat + */ +CV_EXPORTS_W bool isContourConvex( InputArray contour ); + +//! finds intersection of two convex polygons +CV_EXPORTS_W float intersectConvexConvex( InputArray _p1, InputArray _p2, + OutputArray _p12, bool handleNested = true ); + +/** @example samples/cpp/fitellipse.cpp +An example using the fitEllipse technique +*/ + +/** @brief Fits an ellipse around a set of 2D points. + +The function calculates the ellipse that fits (in a least-squares sense) a set of 2D points best of +all. It returns the rotated rectangle in which the ellipse is inscribed. The first algorithm described by @cite Fitzgibbon95 +is used. Developer should keep in mind that it is possible that the returned +ellipse/rotatedRect data contains negative indices, due to the data points being close to the +border of the containing Mat element. + +@param points Input 2D point set, stored in std::vector\<\> or Mat + */ +CV_EXPORTS_W RotatedRect fitEllipse( InputArray points ); + +/** @brief Fits an ellipse around a set of 2D points. + + The function calculates the ellipse that fits a set of 2D points. + It returns the rotated rectangle in which the ellipse is inscribed. + The Approximate Mean Square (AMS) proposed by @cite Taubin1991 is used. + + For an ellipse, this basis set is \f$ \chi= \left(x^2, x y, y^2, x, y, 1\right) \f$, + which is a set of six free coefficients \f$ A^T=\left\{A_{\text{xx}},A_{\text{xy}},A_{\text{yy}},A_x,A_y,A_0\right\} \f$. + However, to specify an ellipse, all that is needed is five numbers; the major and minor axes lengths \f$ (a,b) \f$, + the position \f$ (x_0,y_0) \f$, and the orientation \f$ \theta \f$. This is because the basis set includes lines, + quadratics, parabolic and hyperbolic functions as well as elliptical functions as possible fits. + If the fit is found to be a parabolic or hyperbolic function then the standard #fitEllipse method is used. + The AMS method restricts the fit to parabolic, hyperbolic and elliptical curves + by imposing the condition that \f$ A^T ( D_x^T D_x + D_y^T D_y) A = 1 \f$ where + the matrices \f$ Dx \f$ and \f$ Dy \f$ are the partial derivatives of the design matrix \f$ D \f$ with + respect to x and y. The matrices are formed row by row applying the following to + each of the points in the set: + \f{align*}{ + D(i,:)&=\left\{x_i^2, x_i y_i, y_i^2, x_i, y_i, 1\right\} & + D_x(i,:)&=\left\{2 x_i,y_i,0,1,0,0\right\} & + D_y(i,:)&=\left\{0,x_i,2 y_i,0,1,0\right\} + \f} + The AMS method minimizes the cost function + \f{equation*}{ + \epsilon ^2=\frac{ A^T D^T D A }{ A^T (D_x^T D_x + D_y^T D_y) A^T } + \f} + + The minimum cost is found by solving the generalized eigenvalue problem. + + \f{equation*}{ + D^T D A = \lambda \left( D_x^T D_x + D_y^T D_y\right) A + \f} + + @param points Input 2D point set, stored in std::vector\<\> or Mat + */ +CV_EXPORTS_W RotatedRect fitEllipseAMS( InputArray points ); + + +/** @brief Fits an ellipse around a set of 2D points. + + The function calculates the ellipse that fits a set of 2D points. + It returns the rotated rectangle in which the ellipse is inscribed. + The Direct least square (Direct) method by @cite Fitzgibbon1999 is used. + + For an ellipse, this basis set is \f$ \chi= \left(x^2, x y, y^2, x, y, 1\right) \f$, + which is a set of six free coefficients \f$ A^T=\left\{A_{\text{xx}},A_{\text{xy}},A_{\text{yy}},A_x,A_y,A_0\right\} \f$. + However, to specify an ellipse, all that is needed is five numbers; the major and minor axes lengths \f$ (a,b) \f$, + the position \f$ (x_0,y_0) \f$, and the orientation \f$ \theta \f$. This is because the basis set includes lines, + quadratics, parabolic and hyperbolic functions as well as elliptical functions as possible fits. + The Direct method confines the fit to ellipses by ensuring that \f$ 4 A_{xx} A_{yy}- A_{xy}^2 > 0 \f$. + The condition imposed is that \f$ 4 A_{xx} A_{yy}- A_{xy}^2=1 \f$ which satisfies the inequality + and as the coefficients can be arbitrarily scaled is not overly restrictive. + + \f{equation*}{ + \epsilon ^2= A^T D^T D A \quad \text{with} \quad A^T C A =1 \quad \text{and} \quad C=\left(\begin{matrix} + 0 & 0 & 2 & 0 & 0 & 0 \\ + 0 & -1 & 0 & 0 & 0 & 0 \\ + 2 & 0 & 0 & 0 & 0 & 0 \\ + 0 & 0 & 0 & 0 & 0 & 0 \\ + 0 & 0 & 0 & 0 & 0 & 0 \\ + 0 & 0 & 0 & 0 & 0 & 0 + \end{matrix} \right) + \f} + + The minimum cost is found by solving the generalized eigenvalue problem. + + \f{equation*}{ + D^T D A = \lambda \left( C\right) A + \f} + + The system produces only one positive eigenvalue \f$ \lambda\f$ which is chosen as the solution + with its eigenvector \f$\mathbf{u}\f$. These are used to find the coefficients + + \f{equation*}{ + A = \sqrt{\frac{1}{\mathbf{u}^T C \mathbf{u}}} \mathbf{u} + \f} + The scaling factor guarantees that \f$A^T C A =1\f$. + + @param points Input 2D point set, stored in std::vector\<\> or Mat + */ +CV_EXPORTS_W RotatedRect fitEllipseDirect( InputArray points ); + +/** @brief Fits a line to a 2D or 3D point set. + +The function fitLine fits a line to a 2D or 3D point set by minimizing \f$\sum_i \rho(r_i)\f$ where +\f$r_i\f$ is a distance between the \f$i^{th}\f$ point, the line and \f$\rho(r)\f$ is a distance function, one +of the following: +- DIST_L2 +\f[\rho (r) = r^2/2 \quad \text{(the simplest and the fastest least-squares method)}\f] +- DIST_L1 +\f[\rho (r) = r\f] +- DIST_L12 +\f[\rho (r) = 2 \cdot ( \sqrt{1 + \frac{r^2}{2}} - 1)\f] +- DIST_FAIR +\f[\rho \left (r \right ) = C^2 \cdot \left ( \frac{r}{C} - \log{\left(1 + \frac{r}{C}\right)} \right ) \quad \text{where} \quad C=1.3998\f] +- DIST_WELSCH +\f[\rho \left (r \right ) = \frac{C^2}{2} \cdot \left ( 1 - \exp{\left(-\left(\frac{r}{C}\right)^2\right)} \right ) \quad \text{where} \quad C=2.9846\f] +- DIST_HUBER +\f[\rho (r) = \fork{r^2/2}{if \(r < C\)}{C \cdot (r-C/2)}{otherwise} \quad \text{where} \quad C=1.345\f] + +The algorithm is based on the M-estimator ( ) technique +that iteratively fits the line using the weighted least-squares algorithm. After each iteration the +weights \f$w_i\f$ are adjusted to be inversely proportional to \f$\rho(r_i)\f$ . + +@param points Input vector of 2D or 3D points, stored in std::vector\<\> or Mat. +@param line Output line parameters. In case of 2D fitting, it should be a vector of 4 elements +(like Vec4f) - (vx, vy, x0, y0), where (vx, vy) is a normalized vector collinear to the line and +(x0, y0) is a point on the line. In case of 3D fitting, it should be a vector of 6 elements (like +Vec6f) - (vx, vy, vz, x0, y0, z0), where (vx, vy, vz) is a normalized vector collinear to the line +and (x0, y0, z0) is a point on the line. +@param distType Distance used by the M-estimator, see #DistanceTypes +@param param Numerical parameter ( C ) for some types of distances. If it is 0, an optimal value +is chosen. +@param reps Sufficient accuracy for the radius (distance between the coordinate origin and the line). +@param aeps Sufficient accuracy for the angle. 0.01 would be a good default value for reps and aeps. + */ +CV_EXPORTS_W void fitLine( InputArray points, OutputArray line, int distType, + double param, double reps, double aeps ); + +/** @brief Performs a point-in-contour test. + +The function determines whether the point is inside a contour, outside, or lies on an edge (or +coincides with a vertex). It returns positive (inside), negative (outside), or zero (on an edge) +value, correspondingly. When measureDist=false , the return value is +1, -1, and 0, respectively. +Otherwise, the return value is a signed distance between the point and the nearest contour edge. + +See below a sample output of the function where each image pixel is tested against the contour: + +![sample output](pics/pointpolygon.png) + +@param contour Input contour. +@param pt Point tested against the contour. +@param measureDist If true, the function estimates the signed distance from the point to the +nearest contour edge. Otherwise, the function only checks if the point is inside a contour or not. + */ +CV_EXPORTS_W double pointPolygonTest( InputArray contour, Point2f pt, bool measureDist ); + +/** @brief Finds out if there is any intersection between two rotated rectangles. + +If there is then the vertices of the intersecting region are returned as well. + +Below are some examples of intersection configurations. The hatched pattern indicates the +intersecting region and the red vertices are returned by the function. + +![intersection examples](pics/intersection.png) + +@param rect1 First rectangle +@param rect2 Second rectangle +@param intersectingRegion The output array of the vertices of the intersecting region. It returns +at most 8 vertices. Stored as std::vector\ or cv::Mat as Mx1 of type CV_32FC2. +@returns One of #RectanglesIntersectTypes + */ +CV_EXPORTS_W int rotatedRectangleIntersection( const RotatedRect& rect1, const RotatedRect& rect2, OutputArray intersectingRegion ); + +/** @brief Creates a smart pointer to a cv::GeneralizedHoughBallard class and initializes it. +*/ +CV_EXPORTS Ptr createGeneralizedHoughBallard(); + +/** @brief Creates a smart pointer to a cv::GeneralizedHoughGuil class and initializes it. +*/ +CV_EXPORTS Ptr createGeneralizedHoughGuil(); + +//! @} imgproc_shape + +//! @addtogroup imgproc_colormap +//! @{ + +//! GNU Octave/MATLAB equivalent colormaps +enum ColormapTypes +{ + COLORMAP_AUTUMN = 0, //!< ![autumn](pics/colormaps/colorscale_autumn.jpg) + COLORMAP_BONE = 1, //!< ![bone](pics/colormaps/colorscale_bone.jpg) + COLORMAP_JET = 2, //!< ![jet](pics/colormaps/colorscale_jet.jpg) + COLORMAP_WINTER = 3, //!< ![winter](pics/colormaps/colorscale_winter.jpg) + COLORMAP_RAINBOW = 4, //!< ![rainbow](pics/colormaps/colorscale_rainbow.jpg) + COLORMAP_OCEAN = 5, //!< ![ocean](pics/colormaps/colorscale_ocean.jpg) + COLORMAP_SUMMER = 6, //!< ![summer](pics/colormaps/colorscale_summer.jpg) + COLORMAP_SPRING = 7, //!< ![spring](pics/colormaps/colorscale_spring.jpg) + COLORMAP_COOL = 8, //!< ![cool](pics/colormaps/colorscale_cool.jpg) + COLORMAP_HSV = 9, //!< ![HSV](pics/colormaps/colorscale_hsv.jpg) + COLORMAP_PINK = 10, //!< ![pink](pics/colormaps/colorscale_pink.jpg) + COLORMAP_HOT = 11, //!< ![hot](pics/colormaps/colorscale_hot.jpg) + COLORMAP_PARULA = 12 //!< ![parula](pics/colormaps/colorscale_parula.jpg) +}; + +/** @example samples/cpp/falsecolor.cpp +An example using applyColorMap function +*/ + +/** @brief Applies a GNU Octave/MATLAB equivalent colormap on a given image. + +@param src The source image, grayscale or colored of type CV_8UC1 or CV_8UC3. +@param dst The result is the colormapped source image. Note: Mat::create is called on dst. +@param colormap The colormap to apply, see #ColormapTypes +*/ +CV_EXPORTS_W void applyColorMap(InputArray src, OutputArray dst, int colormap); + +/** @brief Applies a user colormap on a given image. + +@param src The source image, grayscale or colored of type CV_8UC1 or CV_8UC3. +@param dst The result is the colormapped source image. Note: Mat::create is called on dst. +@param userColor The colormap to apply of type CV_8UC1 or CV_8UC3 and size 256 +*/ +CV_EXPORTS_W void applyColorMap(InputArray src, OutputArray dst, InputArray userColor); + +//! @} imgproc_colormap + +//! @addtogroup imgproc_draw +//! @{ + + +/** OpenCV color channel order is BGR[A] */ +#define CV_RGB(r, g, b) cv::Scalar((b), (g), (r), 0) + +/** @brief Draws a line segment connecting two points. + +The function line draws the line segment between pt1 and pt2 points in the image. The line is +clipped by the image boundaries. For non-antialiased lines with integer coordinates, the 8-connected +or 4-connected Bresenham algorithm is used. Thick lines are drawn with rounding endings. Antialiased +lines are drawn using Gaussian filtering. + +@param img Image. +@param pt1 First point of the line segment. +@param pt2 Second point of the line segment. +@param color Line color. +@param thickness Line thickness. +@param lineType Type of the line. See #LineTypes. +@param shift Number of fractional bits in the point coordinates. + */ +CV_EXPORTS_W void line(InputOutputArray img, Point pt1, Point pt2, const Scalar& color, + int thickness = 1, int lineType = LINE_8, int shift = 0); + +/** @brief Draws a arrow segment pointing from the first point to the second one. + +The function cv::arrowedLine draws an arrow between pt1 and pt2 points in the image. See also #line. + +@param img Image. +@param pt1 The point the arrow starts from. +@param pt2 The point the arrow points to. +@param color Line color. +@param thickness Line thickness. +@param line_type Type of the line. See #LineTypes +@param shift Number of fractional bits in the point coordinates. +@param tipLength The length of the arrow tip in relation to the arrow length + */ +CV_EXPORTS_W void arrowedLine(InputOutputArray img, Point pt1, Point pt2, const Scalar& color, + int thickness=1, int line_type=8, int shift=0, double tipLength=0.1); + +/** @brief Draws a simple, thick, or filled up-right rectangle. + +The function cv::rectangle draws a rectangle outline or a filled rectangle whose two opposite corners +are pt1 and pt2. + +@param img Image. +@param pt1 Vertex of the rectangle. +@param pt2 Vertex of the rectangle opposite to pt1 . +@param color Rectangle color or brightness (grayscale image). +@param thickness Thickness of lines that make up the rectangle. Negative values, like #FILLED, +mean that the function has to draw a filled rectangle. +@param lineType Type of the line. See #LineTypes +@param shift Number of fractional bits in the point coordinates. + */ +CV_EXPORTS_W void rectangle(InputOutputArray img, Point pt1, Point pt2, + const Scalar& color, int thickness = 1, + int lineType = LINE_8, int shift = 0); + +/** @overload + +use `rec` parameter as alternative specification of the drawn rectangle: `r.tl() and +r.br()-Point(1,1)` are opposite corners +*/ +CV_EXPORTS void rectangle(CV_IN_OUT Mat& img, Rect rec, + const Scalar& color, int thickness = 1, + int lineType = LINE_8, int shift = 0); + +/** @example samples/cpp/tutorial_code/ImgProc/basic_drawing/Drawing_2.cpp +An example using drawing functions +*/ + +/** @brief Draws a circle. + +The function cv::circle draws a simple or filled circle with a given center and radius. +@param img Image where the circle is drawn. +@param center Center of the circle. +@param radius Radius of the circle. +@param color Circle color. +@param thickness Thickness of the circle outline, if positive. Negative values, like #FILLED, +mean that a filled circle is to be drawn. +@param lineType Type of the circle boundary. See #LineTypes +@param shift Number of fractional bits in the coordinates of the center and in the radius value. + */ +CV_EXPORTS_W void circle(InputOutputArray img, Point center, int radius, + const Scalar& color, int thickness = 1, + int lineType = LINE_8, int shift = 0); + +/** @brief Draws a simple or thick elliptic arc or fills an ellipse sector. + +The function cv::ellipse with more parameters draws an ellipse outline, a filled ellipse, an elliptic +arc, or a filled ellipse sector. The drawing code uses general parametric form. +A piecewise-linear curve is used to approximate the elliptic arc +boundary. If you need more control of the ellipse rendering, you can retrieve the curve using +#ellipse2Poly and then render it with #polylines or fill it with #fillPoly. If you use the first +variant of the function and want to draw the whole ellipse, not an arc, pass `startAngle=0` and +`endAngle=360`. If `startAngle` is greater than `endAngle`, they are swapped. The figure below explains +the meaning of the parameters to draw the blue arc. + +![Parameters of Elliptic Arc](pics/ellipse.svg) + +@param img Image. +@param center Center of the ellipse. +@param axes Half of the size of the ellipse main axes. +@param angle Ellipse rotation angle in degrees. +@param startAngle Starting angle of the elliptic arc in degrees. +@param endAngle Ending angle of the elliptic arc in degrees. +@param color Ellipse color. +@param thickness Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that +a filled ellipse sector is to be drawn. +@param lineType Type of the ellipse boundary. See #LineTypes +@param shift Number of fractional bits in the coordinates of the center and values of axes. + */ +CV_EXPORTS_W void ellipse(InputOutputArray img, Point center, Size axes, + double angle, double startAngle, double endAngle, + const Scalar& color, int thickness = 1, + int lineType = LINE_8, int shift = 0); + +/** @overload +@param img Image. +@param box Alternative ellipse representation via RotatedRect. This means that the function draws +an ellipse inscribed in the rotated rectangle. +@param color Ellipse color. +@param thickness Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that +a filled ellipse sector is to be drawn. +@param lineType Type of the ellipse boundary. See #LineTypes +*/ +CV_EXPORTS_W void ellipse(InputOutputArray img, const RotatedRect& box, const Scalar& color, + int thickness = 1, int lineType = LINE_8); + +/* ----------------------------------------------------------------------------------------- */ +/* ADDING A SET OF PREDEFINED MARKERS WHICH COULD BE USED TO HIGHLIGHT POSITIONS IN AN IMAGE */ +/* ----------------------------------------------------------------------------------------- */ + +//! Possible set of marker types used for the cv::drawMarker function +enum MarkerTypes +{ + MARKER_CROSS = 0, //!< A crosshair marker shape + MARKER_TILTED_CROSS = 1, //!< A 45 degree tilted crosshair marker shape + MARKER_STAR = 2, //!< A star marker shape, combination of cross and tilted cross + MARKER_DIAMOND = 3, //!< A diamond marker shape + MARKER_SQUARE = 4, //!< A square marker shape + MARKER_TRIANGLE_UP = 5, //!< An upwards pointing triangle marker shape + MARKER_TRIANGLE_DOWN = 6 //!< A downwards pointing triangle marker shape +}; + +/** @brief Draws a marker on a predefined position in an image. + +The function cv::drawMarker draws a marker on a given position in the image. For the moment several +marker types are supported, see #MarkerTypes for more information. + +@param img Image. +@param position The point where the crosshair is positioned. +@param color Line color. +@param markerType The specific type of marker you want to use, see #MarkerTypes +@param thickness Line thickness. +@param line_type Type of the line, See #LineTypes +@param markerSize The length of the marker axis [default = 20 pixels] + */ +CV_EXPORTS_W void drawMarker(CV_IN_OUT Mat& img, Point position, const Scalar& color, + int markerType = MARKER_CROSS, int markerSize=20, int thickness=1, + int line_type=8); + +/* ----------------------------------------------------------------------------------------- */ +/* END OF MARKER SECTION */ +/* ----------------------------------------------------------------------------------------- */ + +/** @overload */ +CV_EXPORTS void fillConvexPoly(Mat& img, const Point* pts, int npts, + const Scalar& color, int lineType = LINE_8, + int shift = 0); + +/** @brief Fills a convex polygon. + +The function cv::fillConvexPoly draws a filled convex polygon. This function is much faster than the +function #fillPoly . It can fill not only convex polygons but any monotonic polygon without +self-intersections, that is, a polygon whose contour intersects every horizontal line (scan line) +twice at the most (though, its top-most and/or the bottom edge could be horizontal). + +@param img Image. +@param points Polygon vertices. +@param color Polygon color. +@param lineType Type of the polygon boundaries. See #LineTypes +@param shift Number of fractional bits in the vertex coordinates. + */ +CV_EXPORTS_W void fillConvexPoly(InputOutputArray img, InputArray points, + const Scalar& color, int lineType = LINE_8, + int shift = 0); + +/** @overload */ +CV_EXPORTS void fillPoly(Mat& img, const Point** pts, + const int* npts, int ncontours, + const Scalar& color, int lineType = LINE_8, int shift = 0, + Point offset = Point() ); + +/** @example samples/cpp/tutorial_code/ImgProc/basic_drawing/Drawing_1.cpp +An example using drawing functions +Check @ref tutorial_random_generator_and_text "the corresponding tutorial" for more details +*/ + +/** @brief Fills the area bounded by one or more polygons. + +The function cv::fillPoly fills an area bounded by several polygonal contours. The function can fill +complex areas, for example, areas with holes, contours with self-intersections (some of their +parts), and so forth. + +@param img Image. +@param pts Array of polygons where each polygon is represented as an array of points. +@param color Polygon color. +@param lineType Type of the polygon boundaries. See #LineTypes +@param shift Number of fractional bits in the vertex coordinates. +@param offset Optional offset of all points of the contours. + */ +CV_EXPORTS_W void fillPoly(InputOutputArray img, InputArrayOfArrays pts, + const Scalar& color, int lineType = LINE_8, int shift = 0, + Point offset = Point() ); + +/** @overload */ +CV_EXPORTS void polylines(Mat& img, const Point* const* pts, const int* npts, + int ncontours, bool isClosed, const Scalar& color, + int thickness = 1, int lineType = LINE_8, int shift = 0 ); + +/** @brief Draws several polygonal curves. + +@param img Image. +@param pts Array of polygonal curves. +@param isClosed Flag indicating whether the drawn polylines are closed or not. If they are closed, +the function draws a line from the last vertex of each curve to its first vertex. +@param color Polyline color. +@param thickness Thickness of the polyline edges. +@param lineType Type of the line segments. See #LineTypes +@param shift Number of fractional bits in the vertex coordinates. + +The function cv::polylines draws one or more polygonal curves. + */ +CV_EXPORTS_W void polylines(InputOutputArray img, InputArrayOfArrays pts, + bool isClosed, const Scalar& color, + int thickness = 1, int lineType = LINE_8, int shift = 0 ); + +/** @example samples/cpp/contours2.cpp +An example program illustrates the use of cv::findContours and cv::drawContours +\image html WindowsQtContoursOutput.png "Screenshot of the program" +*/ + +/** @example samples/cpp/segment_objects.cpp +An example using drawContours to clean up a background segmentation result +*/ + +/** @brief Draws contours outlines or filled contours. + +The function draws contour outlines in the image if \f$\texttt{thickness} \ge 0\f$ or fills the area +bounded by the contours if \f$\texttt{thickness}<0\f$ . The example below shows how to retrieve +connected components from the binary image and label them: : +@include snippets/imgproc_drawContours.cpp + +@param image Destination image. +@param contours All the input contours. Each contour is stored as a point vector. +@param contourIdx Parameter indicating a contour to draw. If it is negative, all the contours are drawn. +@param color Color of the contours. +@param thickness Thickness of lines the contours are drawn with. If it is negative (for example, +thickness=#FILLED ), the contour interiors are drawn. +@param lineType Line connectivity. See #LineTypes +@param hierarchy Optional information about hierarchy. It is only needed if you want to draw only +some of the contours (see maxLevel ). +@param maxLevel Maximal level for drawn contours. If it is 0, only the specified contour is drawn. +If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function +draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This +parameter is only taken into account when there is hierarchy available. +@param offset Optional contour shift parameter. Shift all the drawn contours by the specified +\f$\texttt{offset}=(dx,dy)\f$ . +@note When thickness=#FILLED, the function is designed to handle connected components with holes correctly +even when no hierarchy date is provided. This is done by analyzing all the outlines together +using even-odd rule. This may give incorrect results if you have a joint collection of separately retrieved +contours. In order to solve this problem, you need to call #drawContours separately for each sub-group +of contours, or iterate over the collection using contourIdx parameter. + */ +CV_EXPORTS_W void drawContours( InputOutputArray image, InputArrayOfArrays contours, + int contourIdx, const Scalar& color, + int thickness = 1, int lineType = LINE_8, + InputArray hierarchy = noArray(), + int maxLevel = INT_MAX, Point offset = Point() ); + +/** @brief Clips the line against the image rectangle. + +The function cv::clipLine calculates a part of the line segment that is entirely within the specified +rectangle. it returns false if the line segment is completely outside the rectangle. Otherwise, +it returns true . +@param imgSize Image size. The image rectangle is Rect(0, 0, imgSize.width, imgSize.height) . +@param pt1 First line point. +@param pt2 Second line point. + */ +CV_EXPORTS bool clipLine(Size imgSize, CV_IN_OUT Point& pt1, CV_IN_OUT Point& pt2); + +/** @overload +@param imgSize Image size. The image rectangle is Rect(0, 0, imgSize.width, imgSize.height) . +@param pt1 First line point. +@param pt2 Second line point. +*/ +CV_EXPORTS bool clipLine(Size2l imgSize, CV_IN_OUT Point2l& pt1, CV_IN_OUT Point2l& pt2); + +/** @overload +@param imgRect Image rectangle. +@param pt1 First line point. +@param pt2 Second line point. +*/ +CV_EXPORTS_W bool clipLine(Rect imgRect, CV_OUT CV_IN_OUT Point& pt1, CV_OUT CV_IN_OUT Point& pt2); + +/** @brief Approximates an elliptic arc with a polyline. + +The function ellipse2Poly computes the vertices of a polyline that approximates the specified +elliptic arc. It is used by #ellipse. If `arcStart` is greater than `arcEnd`, they are swapped. + +@param center Center of the arc. +@param axes Half of the size of the ellipse main axes. See #ellipse for details. +@param angle Rotation angle of the ellipse in degrees. See #ellipse for details. +@param arcStart Starting angle of the elliptic arc in degrees. +@param arcEnd Ending angle of the elliptic arc in degrees. +@param delta Angle between the subsequent polyline vertices. It defines the approximation +accuracy. +@param pts Output vector of polyline vertices. + */ +CV_EXPORTS_W void ellipse2Poly( Point center, Size axes, int angle, + int arcStart, int arcEnd, int delta, + CV_OUT std::vector& pts ); + +/** @overload +@param center Center of the arc. +@param axes Half of the size of the ellipse main axes. See #ellipse for details. +@param angle Rotation angle of the ellipse in degrees. See #ellipse for details. +@param arcStart Starting angle of the elliptic arc in degrees. +@param arcEnd Ending angle of the elliptic arc in degrees. +@param delta Angle between the subsequent polyline vertices. It defines the approximation accuracy. +@param pts Output vector of polyline vertices. +*/ +CV_EXPORTS void ellipse2Poly(Point2d center, Size2d axes, int angle, + int arcStart, int arcEnd, int delta, + CV_OUT std::vector& pts); + +/** @brief Draws a text string. + +The function cv::putText renders the specified text string in the image. Symbols that cannot be rendered +using the specified font are replaced by question marks. See #getTextSize for a text rendering code +example. + +@param img Image. +@param text Text string to be drawn. +@param org Bottom-left corner of the text string in the image. +@param fontFace Font type, see #HersheyFonts. +@param fontScale Font scale factor that is multiplied by the font-specific base size. +@param color Text color. +@param thickness Thickness of the lines used to draw a text. +@param lineType Line type. See #LineTypes +@param bottomLeftOrigin When true, the image data origin is at the bottom-left corner. Otherwise, +it is at the top-left corner. + */ +CV_EXPORTS_W void putText( InputOutputArray img, const String& text, Point org, + int fontFace, double fontScale, Scalar color, + int thickness = 1, int lineType = LINE_8, + bool bottomLeftOrigin = false ); + +/** @brief Calculates the width and height of a text string. + +The function cv::getTextSize calculates and returns the size of a box that contains the specified text. +That is, the following code renders some text, the tight box surrounding it, and the baseline: : +@code + String text = "Funny text inside the box"; + int fontFace = FONT_HERSHEY_SCRIPT_SIMPLEX; + double fontScale = 2; + int thickness = 3; + + Mat img(600, 800, CV_8UC3, Scalar::all(0)); + + int baseline=0; + Size textSize = getTextSize(text, fontFace, + fontScale, thickness, &baseline); + baseline += thickness; + + // center the text + Point textOrg((img.cols - textSize.width)/2, + (img.rows + textSize.height)/2); + + // draw the box + rectangle(img, textOrg + Point(0, baseline), + textOrg + Point(textSize.width, -textSize.height), + Scalar(0,0,255)); + // ... and the baseline first + line(img, textOrg + Point(0, thickness), + textOrg + Point(textSize.width, thickness), + Scalar(0, 0, 255)); + + // then put the text itself + putText(img, text, textOrg, fontFace, fontScale, + Scalar::all(255), thickness, 8); +@endcode + +@param text Input text string. +@param fontFace Font to use, see #HersheyFonts. +@param fontScale Font scale factor that is multiplied by the font-specific base size. +@param thickness Thickness of lines used to render the text. See #putText for details. +@param[out] baseLine y-coordinate of the baseline relative to the bottom-most text +point. +@return The size of a box that contains the specified text. + +@see putText + */ +CV_EXPORTS_W Size getTextSize(const String& text, int fontFace, + double fontScale, int thickness, + CV_OUT int* baseLine); + + +/** @brief Calculates the font-specific size to use to achieve a given height in pixels. + +@param fontFace Font to use, see cv::HersheyFonts. +@param pixelHeight Pixel height to compute the fontScale for +@param thickness Thickness of lines used to render the text.See putText for details. +@return The fontSize to use for cv::putText + +@see cv::putText +*/ +CV_EXPORTS_W double getFontScaleFromHeight(const int fontFace, + const int pixelHeight, + const int thickness = 1); + +/** @brief Line iterator + +The class is used to iterate over all the pixels on the raster line +segment connecting two specified points. + +The class LineIterator is used to get each pixel of a raster line. It +can be treated as versatile implementation of the Bresenham algorithm +where you can stop at each pixel and do some extra processing, for +example, grab pixel values along the line or draw a line with an effect +(for example, with XOR operation). + +The number of pixels along the line is stored in LineIterator::count. +The method LineIterator::pos returns the current position in the image: + +@code{.cpp} +// grabs pixels along the line (pt1, pt2) +// from 8-bit 3-channel image to the buffer +LineIterator it(img, pt1, pt2, 8); +LineIterator it2 = it; +vector buf(it.count); + +for(int i = 0; i < it.count; i++, ++it) + buf[i] = *(const Vec3b*)*it; + +// alternative way of iterating through the line +for(int i = 0; i < it2.count; i++, ++it2) +{ + Vec3b val = img.at(it2.pos()); + CV_Assert(buf[i] == val); +} +@endcode +*/ +class CV_EXPORTS LineIterator +{ +public: + /** @brief initializes the iterator + + creates iterators for the line connecting pt1 and pt2 + the line will be clipped on the image boundaries + the line is 8-connected or 4-connected + If leftToRight=true, then the iteration is always done + from the left-most point to the right most, + not to depend on the ordering of pt1 and pt2 parameters + */ + LineIterator( const Mat& img, Point pt1, Point pt2, + int connectivity = 8, bool leftToRight = false ); + /** @brief returns pointer to the current pixel + */ + uchar* operator *(); + /** @brief prefix increment operator (++it). shifts iterator to the next pixel + */ + LineIterator& operator ++(); + /** @brief postfix increment operator (it++). shifts iterator to the next pixel + */ + LineIterator operator ++(int); + /** @brief returns coordinates of the current pixel + */ + Point pos() const; + + uchar* ptr; + const uchar* ptr0; + int step, elemSize; + int err, count; + int minusDelta, plusDelta; + int minusStep, plusStep; +}; + +//! @cond IGNORED + +// === LineIterator implementation === + +inline +uchar* LineIterator::operator *() +{ + return ptr; +} + +inline +LineIterator& LineIterator::operator ++() +{ + int mask = err < 0 ? -1 : 0; + err += minusDelta + (plusDelta & mask); + ptr += minusStep + (plusStep & mask); + return *this; +} + +inline +LineIterator LineIterator::operator ++(int) +{ + LineIterator it = *this; + ++(*this); + return it; +} + +inline +Point LineIterator::pos() const +{ + Point p; + p.y = (int)((ptr - ptr0)/step); + p.x = (int)(((ptr - ptr0) - p.y*step)/elemSize); + return p; +} + +//! @endcond + +//! @} imgproc_draw + +//! @} imgproc + +} // cv + +#ifndef DISABLE_OPENCV_24_COMPATIBILITY +#include "opencv2/imgproc/imgproc_c.h" +#endif + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/imgproc/detail/distortion_model.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/imgproc/detail/distortion_model.hpp new file mode 100644 index 0000000000000000000000000000000000000000..a9c3ddec8a7febbff1adbf78a9e2d4d403819bfb --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/imgproc/detail/distortion_model.hpp @@ -0,0 +1,123 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_IMGPROC_DETAIL_DISTORTION_MODEL_HPP +#define OPENCV_IMGPROC_DETAIL_DISTORTION_MODEL_HPP + +//! @cond IGNORED + +namespace cv { namespace detail { +/** +Computes the matrix for the projection onto a tilted image sensor +\param tauX angular parameter rotation around x-axis +\param tauY angular parameter rotation around y-axis +\param matTilt if not NULL returns the matrix +\f[ +\vecthreethree{R_{33}(\tau_x, \tau_y)}{0}{-R_{13}((\tau_x, \tau_y)} +{0}{R_{33}(\tau_x, \tau_y)}{-R_{23}(\tau_x, \tau_y)} +{0}{0}{1} R(\tau_x, \tau_y) +\f] +where +\f[ +R(\tau_x, \tau_y) = +\vecthreethree{\cos(\tau_y)}{0}{-\sin(\tau_y)}{0}{1}{0}{\sin(\tau_y)}{0}{\cos(\tau_y)} +\vecthreethree{1}{0}{0}{0}{\cos(\tau_x)}{\sin(\tau_x)}{0}{-\sin(\tau_x)}{\cos(\tau_x)} = +\vecthreethree{\cos(\tau_y)}{\sin(\tau_y)\sin(\tau_x)}{-\sin(\tau_y)\cos(\tau_x)} +{0}{\cos(\tau_x)}{\sin(\tau_x)} +{\sin(\tau_y)}{-\cos(\tau_y)\sin(\tau_x)}{\cos(\tau_y)\cos(\tau_x)}. +\f] +\param dMatTiltdTauX if not NULL it returns the derivative of matTilt with +respect to \f$\tau_x\f$. +\param dMatTiltdTauY if not NULL it returns the derivative of matTilt with +respect to \f$\tau_y\f$. +\param invMatTilt if not NULL it returns the inverse of matTilt +**/ +template +void computeTiltProjectionMatrix(FLOAT tauX, + FLOAT tauY, + Matx* matTilt = 0, + Matx* dMatTiltdTauX = 0, + Matx* dMatTiltdTauY = 0, + Matx* invMatTilt = 0) +{ + FLOAT cTauX = cos(tauX); + FLOAT sTauX = sin(tauX); + FLOAT cTauY = cos(tauY); + FLOAT sTauY = sin(tauY); + Matx matRotX = Matx(1,0,0,0,cTauX,sTauX,0,-sTauX,cTauX); + Matx matRotY = Matx(cTauY,0,-sTauY,0,1,0,sTauY,0,cTauY); + Matx matRotXY = matRotY * matRotX; + Matx matProjZ = Matx(matRotXY(2,2),0,-matRotXY(0,2),0,matRotXY(2,2),-matRotXY(1,2),0,0,1); + if (matTilt) + { + // Matrix for trapezoidal distortion of tilted image sensor + *matTilt = matProjZ * matRotXY; + } + if (dMatTiltdTauX) + { + // Derivative with respect to tauX + Matx dMatRotXYdTauX = matRotY * Matx(0,0,0,0,-sTauX,cTauX,0,-cTauX,-sTauX); + Matx dMatProjZdTauX = Matx(dMatRotXYdTauX(2,2),0,-dMatRotXYdTauX(0,2), + 0,dMatRotXYdTauX(2,2),-dMatRotXYdTauX(1,2),0,0,0); + *dMatTiltdTauX = (matProjZ * dMatRotXYdTauX) + (dMatProjZdTauX * matRotXY); + } + if (dMatTiltdTauY) + { + // Derivative with respect to tauY + Matx dMatRotXYdTauY = Matx(-sTauY,0,-cTauY,0,0,0,cTauY,0,-sTauY) * matRotX; + Matx dMatProjZdTauY = Matx(dMatRotXYdTauY(2,2),0,-dMatRotXYdTauY(0,2), + 0,dMatRotXYdTauY(2,2),-dMatRotXYdTauY(1,2),0,0,0); + *dMatTiltdTauY = (matProjZ * dMatRotXYdTauY) + (dMatProjZdTauY * matRotXY); + } + if (invMatTilt) + { + FLOAT inv = 1./matRotXY(2,2); + Matx invMatProjZ = Matx(inv,0,inv*matRotXY(0,2),0,inv,inv*matRotXY(1,2),0,0,1); + *invMatTilt = matRotXY.t()*invMatProjZ; + } +} +}} // namespace detail, cv + + +//! @endcond + +#endif // OPENCV_IMGPROC_DETAIL_DISTORTION_MODEL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/imgproc/hal/hal.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/imgproc/hal/hal.hpp new file mode 100644 index 0000000000000000000000000000000000000000..a435fd6b8558ec7460cada0656c894ea26429989 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/imgproc/hal/hal.hpp @@ -0,0 +1,241 @@ +#ifndef CV_IMGPROC_HAL_HPP +#define CV_IMGPROC_HAL_HPP + +#include "opencv2/core/cvdef.h" +#include "opencv2/core/cvstd.hpp" +#include "opencv2/core/hal/interface.h" + +namespace cv { namespace hal { + +//! @addtogroup imgproc_hal_functions +//! @{ + +//--------------------------- +//! @cond IGNORED + +struct CV_EXPORTS Filter2D +{ + CV_DEPRECATED static Ptr create(uchar * , size_t , int , + int , int , + int , int , + int , int , + int , double , + int , int , + bool , bool ); + virtual void apply(uchar * , size_t , + uchar * , size_t , + int , int , + int , int , + int , int ) = 0; + virtual ~Filter2D() {} +}; + +struct CV_EXPORTS SepFilter2D +{ + CV_DEPRECATED static Ptr create(int , int , int , + uchar * , int , + uchar * , int , + int , int , + double , int ); + virtual void apply(uchar * , size_t , + uchar * , size_t , + int , int , + int , int , + int , int ) = 0; + virtual ~SepFilter2D() {} +}; + + +struct CV_EXPORTS Morph +{ + CV_DEPRECATED static Ptr create(int , int , int , int , int , + int , uchar * , size_t , + int , int , + int , int , + int , const double *, + int , bool , bool ); + virtual void apply(uchar * , size_t , uchar * , size_t , int , int , + int , int , int , int , + int , int , int , int ) = 0; + virtual ~Morph() {} +}; + +//! @endcond +//--------------------------- + +CV_EXPORTS void filter2D(int stype, int dtype, int kernel_type, + uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int full_width, int full_height, + int offset_x, int offset_y, + uchar * kernel_data, size_t kernel_step, + int kernel_width, int kernel_height, + int anchor_x, int anchor_y, + double delta, int borderType, + bool isSubmatrix); + +CV_EXPORTS void sepFilter2D(int stype, int dtype, int ktype, + uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int full_width, int full_height, + int offset_x, int offset_y, + uchar * kernelx_data, int kernelx_len, + uchar * kernely_data, int kernely_len, + int anchor_x, int anchor_y, + double delta, int borderType); + +CV_EXPORTS void morph(int op, int src_type, int dst_type, + uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int roi_width, int roi_height, int roi_x, int roi_y, + int roi_width2, int roi_height2, int roi_x2, int roi_y2, + int kernel_type, uchar * kernel_data, size_t kernel_step, + int kernel_width, int kernel_height, int anchor_x, int anchor_y, + int borderType, const double borderValue[4], + int iterations, bool isSubmatrix); + + +CV_EXPORTS void resize(int src_type, + const uchar * src_data, size_t src_step, int src_width, int src_height, + uchar * dst_data, size_t dst_step, int dst_width, int dst_height, + double inv_scale_x, double inv_scale_y, int interpolation); + +CV_EXPORTS void warpAffine(int src_type, + const uchar * src_data, size_t src_step, int src_width, int src_height, + uchar * dst_data, size_t dst_step, int dst_width, int dst_height, + const double M[6], int interpolation, int borderType, const double borderValue[4]); + +CV_EXPORTS void warpPerspectve(int src_type, + const uchar * src_data, size_t src_step, int src_width, int src_height, + uchar * dst_data, size_t dst_step, int dst_width, int dst_height, + const double M[9], int interpolation, int borderType, const double borderValue[4]); + +CV_EXPORTS void cvtBGRtoBGR(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int depth, int scn, int dcn, bool swapBlue); + +CV_EXPORTS void cvtBGRtoBGR5x5(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int scn, bool swapBlue, int greenBits); + +CV_EXPORTS void cvtBGR5x5toBGR(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int dcn, bool swapBlue, int greenBits); + +CV_EXPORTS void cvtBGRtoGray(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int depth, int scn, bool swapBlue); + +CV_EXPORTS void cvtGraytoBGR(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int depth, int dcn); + +CV_EXPORTS void cvtBGR5x5toGray(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int greenBits); + +CV_EXPORTS void cvtGraytoBGR5x5(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int greenBits); +CV_EXPORTS void cvtBGRtoYUV(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int depth, int scn, bool swapBlue, bool isCbCr); + +CV_EXPORTS void cvtYUVtoBGR(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int depth, int dcn, bool swapBlue, bool isCbCr); + +CV_EXPORTS void cvtBGRtoXYZ(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int depth, int scn, bool swapBlue); + +CV_EXPORTS void cvtXYZtoBGR(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int depth, int dcn, bool swapBlue); + +CV_EXPORTS void cvtBGRtoHSV(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int depth, int scn, bool swapBlue, bool isFullRange, bool isHSV); + +CV_EXPORTS void cvtHSVtoBGR(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int depth, int dcn, bool swapBlue, bool isFullRange, bool isHSV); + +CV_EXPORTS void cvtBGRtoLab(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int depth, int scn, bool swapBlue, bool isLab, bool srgb); + +CV_EXPORTS void cvtLabtoBGR(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int depth, int dcn, bool swapBlue, bool isLab, bool srgb); + +CV_EXPORTS void cvtTwoPlaneYUVtoBGR(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int dst_width, int dst_height, + int dcn, bool swapBlue, int uIdx); + +//! Separate Y and UV planes +CV_EXPORTS void cvtTwoPlaneYUVtoBGR(const uchar * y_data, const uchar * uv_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int dst_width, int dst_height, + int dcn, bool swapBlue, int uIdx); + +CV_EXPORTS void cvtThreePlaneYUVtoBGR(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int dst_width, int dst_height, + int dcn, bool swapBlue, int uIdx); + +CV_EXPORTS void cvtBGRtoThreePlaneYUV(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int scn, bool swapBlue, int uIdx); + +//! Separate Y and UV planes +CV_EXPORTS void cvtBGRtoTwoPlaneYUV(const uchar * src_data, size_t src_step, + uchar * y_data, uchar * uv_data, size_t dst_step, + int width, int height, + int scn, bool swapBlue, int uIdx); + +CV_EXPORTS void cvtOnePlaneYUVtoBGR(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int dcn, bool swapBlue, int uIdx, int ycn); + +CV_EXPORTS void cvtRGBAtoMultipliedRGBA(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height); + +CV_EXPORTS void cvtMultipliedRGBAtoRGBA(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height); + +CV_EXPORTS void integral(int depth, int sdepth, int sqdepth, + const uchar* src, size_t srcstep, + uchar* sum, size_t sumstep, + uchar* sqsum, size_t sqsumstep, + uchar* tilted, size_t tstep, + int width, int height, int cn); + +//! @} + +}} + +#endif // CV_IMGPROC_HAL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/imgproc/hal/interface.h b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/imgproc/hal/interface.h new file mode 100644 index 0000000000000000000000000000000000000000..f8dbcfe791d90fd957cace7b7700d737864990e9 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/imgproc/hal/interface.h @@ -0,0 +1,46 @@ +#ifndef OPENCV_IMGPROC_HAL_INTERFACE_H +#define OPENCV_IMGPROC_HAL_INTERFACE_H + +//! @addtogroup imgproc_hal_interface +//! @{ + +//! @name Interpolation modes +//! @sa cv::InterpolationFlags +//! @{ +#define CV_HAL_INTER_NEAREST 0 +#define CV_HAL_INTER_LINEAR 1 +#define CV_HAL_INTER_CUBIC 2 +#define CV_HAL_INTER_AREA 3 +#define CV_HAL_INTER_LANCZOS4 4 +//! @} + +//! @name Morphology operations +//! @sa cv::MorphTypes +//! @{ +#define CV_HAL_MORPH_ERODE 0 +#define CV_HAL_MORPH_DILATE 1 +//! @} + +//! @name Threshold types +//! @sa cv::ThresholdTypes +//! @{ +#define CV_HAL_THRESH_BINARY 0 +#define CV_HAL_THRESH_BINARY_INV 1 +#define CV_HAL_THRESH_TRUNC 2 +#define CV_HAL_THRESH_TOZERO 3 +#define CV_HAL_THRESH_TOZERO_INV 4 +#define CV_HAL_THRESH_MASK 7 +#define CV_HAL_THRESH_OTSU 8 +#define CV_HAL_THRESH_TRIANGLE 16 +//! @} + +//! @name Adaptive threshold algorithm +//! @sa cv::AdaptiveThresholdTypes +//! @{ +#define CV_HAL_ADAPTIVE_THRESH_MEAN_C 0 +#define CV_HAL_ADAPTIVE_THRESH_GAUSSIAN_C 1 +//! @} + +//! @} + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/imgproc/imgproc.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/imgproc/imgproc.hpp new file mode 100644 index 0000000000000000000000000000000000000000..4175bd0bc01e0189abc4c698053c00e28277ef33 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/imgproc/imgproc.hpp @@ -0,0 +1,48 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifdef __OPENCV_BUILD +#error this is a compatibility header which should not be used inside the OpenCV library +#endif + +#include "opencv2/imgproc.hpp" diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/imgproc/imgproc_c.h b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/imgproc/imgproc_c.h new file mode 100644 index 0000000000000000000000000000000000000000..cec0f3653ab425b86cc6840edee9d6ced8b31854 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/imgproc/imgproc_c.h @@ -0,0 +1,1210 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_IMGPROC_IMGPROC_C_H +#define OPENCV_IMGPROC_IMGPROC_C_H + +#include "opencv2/imgproc/types_c.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/** @addtogroup imgproc_c +@{ +*/ + +/*********************** Background statistics accumulation *****************************/ + +/** @brief Adds image to accumulator +@see cv::accumulate +*/ +CVAPI(void) cvAcc( const CvArr* image, CvArr* sum, + const CvArr* mask CV_DEFAULT(NULL) ); + +/** @brief Adds squared image to accumulator +@see cv::accumulateSquare +*/ +CVAPI(void) cvSquareAcc( const CvArr* image, CvArr* sqsum, + const CvArr* mask CV_DEFAULT(NULL) ); + +/** @brief Adds a product of two images to accumulator +@see cv::accumulateProduct +*/ +CVAPI(void) cvMultiplyAcc( const CvArr* image1, const CvArr* image2, CvArr* acc, + const CvArr* mask CV_DEFAULT(NULL) ); + +/** @brief Adds image to accumulator with weights: acc = acc*(1-alpha) + image*alpha +@see cv::accumulateWeighted +*/ +CVAPI(void) cvRunningAvg( const CvArr* image, CvArr* acc, double alpha, + const CvArr* mask CV_DEFAULT(NULL) ); + +/****************************************************************************************\ +* Image Processing * +\****************************************************************************************/ + +/** Copies source 2D array inside of the larger destination array and + makes a border of the specified type (IPL_BORDER_*) around the copied area. */ +CVAPI(void) cvCopyMakeBorder( const CvArr* src, CvArr* dst, CvPoint offset, + int bordertype, CvScalar value CV_DEFAULT(cvScalarAll(0))); + +/** @brief Smooths the image in one of several ways. + +@param src The source image +@param dst The destination image +@param smoothtype Type of the smoothing, see SmoothMethod_c +@param size1 The first parameter of the smoothing operation, the aperture width. Must be a +positive odd number (1, 3, 5, ...) +@param size2 The second parameter of the smoothing operation, the aperture height. Ignored by +CV_MEDIAN and CV_BILATERAL methods. In the case of simple scaled/non-scaled and Gaussian blur if +size2 is zero, it is set to size1. Otherwise it must be a positive odd number. +@param sigma1 In the case of a Gaussian parameter this parameter may specify Gaussian \f$\sigma\f$ +(standard deviation). If it is zero, it is calculated from the kernel size: +\f[\sigma = 0.3 (n/2 - 1) + 0.8 \quad \text{where} \quad n= \begin{array}{l l} \mbox{\texttt{size1} for horizontal kernel} \\ \mbox{\texttt{size2} for vertical kernel} \end{array}\f] +Using standard sigma for small kernels ( \f$3\times 3\f$ to \f$7\times 7\f$ ) gives better speed. If +sigma1 is not zero, while size1 and size2 are zeros, the kernel size is calculated from the +sigma (to provide accurate enough operation). +@param sigma2 additional parameter for bilateral filtering + +@see cv::GaussianBlur, cv::blur, cv::medianBlur, cv::bilateralFilter. + */ +CVAPI(void) cvSmooth( const CvArr* src, CvArr* dst, + int smoothtype CV_DEFAULT(CV_GAUSSIAN), + int size1 CV_DEFAULT(3), + int size2 CV_DEFAULT(0), + double sigma1 CV_DEFAULT(0), + double sigma2 CV_DEFAULT(0)); + +/** @brief Convolves an image with the kernel. + +@param src input image. +@param dst output image of the same size and the same number of channels as src. +@param kernel convolution kernel (or rather a correlation kernel), a single-channel floating point +matrix; if you want to apply different kernels to different channels, split the image into +separate color planes using split and process them individually. +@param anchor anchor of the kernel that indicates the relative position of a filtered point within +the kernel; the anchor should lie within the kernel; default value (-1,-1) means that the anchor +is at the kernel center. + +@see cv::filter2D + */ +CVAPI(void) cvFilter2D( const CvArr* src, CvArr* dst, const CvMat* kernel, + CvPoint anchor CV_DEFAULT(cvPoint(-1,-1))); + +/** @brief Finds integral image: SUM(X,Y) = sum(x \texttt{hist1}(I)\)}{\frac{\texttt{hist2}(I) \cdot \texttt{scale}}{\texttt{hist1}(I)}}{if \(\texttt{hist1}(I) \ne 0\) and \(\texttt{hist2}(I) \le \texttt{hist1}(I)\)}\f] + +@param hist1 First histogram (the divisor). +@param hist2 Second histogram. +@param dst_hist Destination histogram. +@param scale Scale factor for the destination histogram. + */ +CVAPI(void) cvCalcProbDensity( const CvHistogram* hist1, const CvHistogram* hist2, + CvHistogram* dst_hist, double scale CV_DEFAULT(255) ); + +/** @brief equalizes histogram of 8-bit single-channel image +@see cv::equalizeHist +*/ +CVAPI(void) cvEqualizeHist( const CvArr* src, CvArr* dst ); + + +/** @brief Applies distance transform to binary image +@see cv::distanceTransform +*/ +CVAPI(void) cvDistTransform( const CvArr* src, CvArr* dst, + int distance_type CV_DEFAULT(CV_DIST_L2), + int mask_size CV_DEFAULT(3), + const float* mask CV_DEFAULT(NULL), + CvArr* labels CV_DEFAULT(NULL), + int labelType CV_DEFAULT(CV_DIST_LABEL_CCOMP)); + + +/** @brief Applies fixed-level threshold to grayscale image. + + This is a basic operation applied before retrieving contours +@see cv::threshold +*/ +CVAPI(double) cvThreshold( const CvArr* src, CvArr* dst, + double threshold, double max_value, + int threshold_type ); + +/** @brief Applies adaptive threshold to grayscale image. + + The two parameters for methods CV_ADAPTIVE_THRESH_MEAN_C and + CV_ADAPTIVE_THRESH_GAUSSIAN_C are: + neighborhood size (3, 5, 7 etc.), + and a constant subtracted from mean (...,-3,-2,-1,0,1,2,3,...) +@see cv::adaptiveThreshold +*/ +CVAPI(void) cvAdaptiveThreshold( const CvArr* src, CvArr* dst, double max_value, + int adaptive_method CV_DEFAULT(CV_ADAPTIVE_THRESH_MEAN_C), + int threshold_type CV_DEFAULT(CV_THRESH_BINARY), + int block_size CV_DEFAULT(3), + double param1 CV_DEFAULT(5)); + +/** @brief Fills the connected component until the color difference gets large enough +@see cv::floodFill +*/ +CVAPI(void) cvFloodFill( CvArr* image, CvPoint seed_point, + CvScalar new_val, CvScalar lo_diff CV_DEFAULT(cvScalarAll(0)), + CvScalar up_diff CV_DEFAULT(cvScalarAll(0)), + CvConnectedComp* comp CV_DEFAULT(NULL), + int flags CV_DEFAULT(4), + CvArr* mask CV_DEFAULT(NULL)); + +/****************************************************************************************\ +* Feature detection * +\****************************************************************************************/ + +/** @brief Runs canny edge detector +@see cv::Canny +*/ +CVAPI(void) cvCanny( const CvArr* image, CvArr* edges, double threshold1, + double threshold2, int aperture_size CV_DEFAULT(3) ); + +/** @brief Calculates constraint image for corner detection + + Dx^2 * Dyy + Dxx * Dy^2 - 2 * Dx * Dy * Dxy. + Applying threshold to the result gives coordinates of corners +@see cv::preCornerDetect +*/ +CVAPI(void) cvPreCornerDetect( const CvArr* image, CvArr* corners, + int aperture_size CV_DEFAULT(3) ); + +/** @brief Calculates eigen values and vectors of 2x2 + gradient covariation matrix at every image pixel +@see cv::cornerEigenValsAndVecs +*/ +CVAPI(void) cvCornerEigenValsAndVecs( const CvArr* image, CvArr* eigenvv, + int block_size, int aperture_size CV_DEFAULT(3) ); + +/** @brief Calculates minimal eigenvalue for 2x2 gradient covariation matrix at + every image pixel +@see cv::cornerMinEigenVal +*/ +CVAPI(void) cvCornerMinEigenVal( const CvArr* image, CvArr* eigenval, + int block_size, int aperture_size CV_DEFAULT(3) ); + +/** @brief Harris corner detector: + + Calculates det(M) - k*(trace(M)^2), where M is 2x2 gradient covariation matrix for each pixel +@see cv::cornerHarris +*/ +CVAPI(void) cvCornerHarris( const CvArr* image, CvArr* harris_response, + int block_size, int aperture_size CV_DEFAULT(3), + double k CV_DEFAULT(0.04) ); + +/** @brief Adjust corner position using some sort of gradient search +@see cv::cornerSubPix +*/ +CVAPI(void) cvFindCornerSubPix( const CvArr* image, CvPoint2D32f* corners, + int count, CvSize win, CvSize zero_zone, + CvTermCriteria criteria ); + +/** @brief Finds a sparse set of points within the selected region + that seem to be easy to track +@see cv::goodFeaturesToTrack +*/ +CVAPI(void) cvGoodFeaturesToTrack( const CvArr* image, CvArr* eig_image, + CvArr* temp_image, CvPoint2D32f* corners, + int* corner_count, double quality_level, + double min_distance, + const CvArr* mask CV_DEFAULT(NULL), + int block_size CV_DEFAULT(3), + int use_harris CV_DEFAULT(0), + double k CV_DEFAULT(0.04) ); + +/** @brief Finds lines on binary image using one of several methods. + + line_storage is either memory storage or 1 x _max number of lines_ CvMat, its + number of columns is changed by the function. + method is one of CV_HOUGH_*; + rho, theta and threshold are used for each of those methods; + param1 ~ line length, param2 ~ line gap - for probabilistic, + param1 ~ srn, param2 ~ stn - for multi-scale +@see cv::HoughLines +*/ +CVAPI(CvSeq*) cvHoughLines2( CvArr* image, void* line_storage, int method, + double rho, double theta, int threshold, + double param1 CV_DEFAULT(0), double param2 CV_DEFAULT(0), + double min_theta CV_DEFAULT(0), double max_theta CV_DEFAULT(CV_PI)); + +/** @brief Finds circles in the image +@see cv::HoughCircles +*/ +CVAPI(CvSeq*) cvHoughCircles( CvArr* image, void* circle_storage, + int method, double dp, double min_dist, + double param1 CV_DEFAULT(100), + double param2 CV_DEFAULT(100), + int min_radius CV_DEFAULT(0), + int max_radius CV_DEFAULT(0)); + +/** @brief Fits a line into set of 2d or 3d points in a robust way (M-estimator technique) +@see cv::fitLine +*/ +CVAPI(void) cvFitLine( const CvArr* points, int dist_type, double param, + double reps, double aeps, float* line ); + +/****************************************************************************************\ +* Drawing * +\****************************************************************************************/ + +/****************************************************************************************\ +* Drawing functions work with images/matrices of arbitrary type. * +* For color images the channel order is BGR[A] * +* Antialiasing is supported only for 8-bit image now. * +* All the functions include parameter color that means rgb value (that may be * +* constructed with CV_RGB macro) for color images and brightness * +* for grayscale images. * +* If a drawn figure is partially or completely outside of the image, it is clipped.* +\****************************************************************************************/ + +#define CV_FILLED -1 + +#define CV_AA 16 + +/** @brief Draws 4-connected, 8-connected or antialiased line segment connecting two points +@see cv::line +*/ +CVAPI(void) cvLine( CvArr* img, CvPoint pt1, CvPoint pt2, + CvScalar color, int thickness CV_DEFAULT(1), + int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0) ); + +/** @brief Draws a rectangle given two opposite corners of the rectangle (pt1 & pt2) + + if thickness<0 (e.g. thickness == CV_FILLED), the filled box is drawn +@see cv::rectangle +*/ +CVAPI(void) cvRectangle( CvArr* img, CvPoint pt1, CvPoint pt2, + CvScalar color, int thickness CV_DEFAULT(1), + int line_type CV_DEFAULT(8), + int shift CV_DEFAULT(0)); + +/** @brief Draws a rectangle specified by a CvRect structure +@see cv::rectangle +*/ +CVAPI(void) cvRectangleR( CvArr* img, CvRect r, + CvScalar color, int thickness CV_DEFAULT(1), + int line_type CV_DEFAULT(8), + int shift CV_DEFAULT(0)); + + +/** @brief Draws a circle with specified center and radius. + + Thickness works in the same way as with cvRectangle +@see cv::circle +*/ +CVAPI(void) cvCircle( CvArr* img, CvPoint center, int radius, + CvScalar color, int thickness CV_DEFAULT(1), + int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0)); + +/** @brief Draws ellipse outline, filled ellipse, elliptic arc or filled elliptic sector + + depending on _thickness_, _start_angle_ and _end_angle_ parameters. The resultant figure + is rotated by _angle_. All the angles are in degrees +@see cv::ellipse +*/ +CVAPI(void) cvEllipse( CvArr* img, CvPoint center, CvSize axes, + double angle, double start_angle, double end_angle, + CvScalar color, int thickness CV_DEFAULT(1), + int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0)); + +CV_INLINE void cvEllipseBox( CvArr* img, CvBox2D box, CvScalar color, + int thickness CV_DEFAULT(1), + int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0) ) +{ + CvSize axes = cvSize( + cvRound(box.size.width*0.5), + cvRound(box.size.height*0.5) + ); + + cvEllipse( img, cvPointFrom32f( box.center ), axes, box.angle, + 0, 360, color, thickness, line_type, shift ); +} + +/** @brief Fills convex or monotonous polygon. +@see cv::fillConvexPoly +*/ +CVAPI(void) cvFillConvexPoly( CvArr* img, const CvPoint* pts, int npts, CvScalar color, + int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0)); + +/** @brief Fills an area bounded by one or more arbitrary polygons +@see cv::fillPoly +*/ +CVAPI(void) cvFillPoly( CvArr* img, CvPoint** pts, const int* npts, + int contours, CvScalar color, + int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0) ); + +/** @brief Draws one or more polygonal curves +@see cv::polylines +*/ +CVAPI(void) cvPolyLine( CvArr* img, CvPoint** pts, const int* npts, int contours, + int is_closed, CvScalar color, int thickness CV_DEFAULT(1), + int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0) ); + +#define cvDrawRect cvRectangle +#define cvDrawLine cvLine +#define cvDrawCircle cvCircle +#define cvDrawEllipse cvEllipse +#define cvDrawPolyLine cvPolyLine + +/** @brief Clips the line segment connecting *pt1 and *pt2 + by the rectangular window + + (0<=xptr will point to pt1 (or pt2, see left_to_right description) location in +the image. Returns the number of pixels on the line between the ending points. +@see cv::LineIterator +*/ +CVAPI(int) cvInitLineIterator( const CvArr* image, CvPoint pt1, CvPoint pt2, + CvLineIterator* line_iterator, + int connectivity CV_DEFAULT(8), + int left_to_right CV_DEFAULT(0)); + +#define CV_NEXT_LINE_POINT( line_iterator ) \ +{ \ + int _line_iterator_mask = (line_iterator).err < 0 ? -1 : 0; \ + (line_iterator).err += (line_iterator).minus_delta + \ + ((line_iterator).plus_delta & _line_iterator_mask); \ + (line_iterator).ptr += (line_iterator).minus_step + \ + ((line_iterator).plus_step & _line_iterator_mask); \ +} + + +#define CV_FONT_HERSHEY_SIMPLEX 0 +#define CV_FONT_HERSHEY_PLAIN 1 +#define CV_FONT_HERSHEY_DUPLEX 2 +#define CV_FONT_HERSHEY_COMPLEX 3 +#define CV_FONT_HERSHEY_TRIPLEX 4 +#define CV_FONT_HERSHEY_COMPLEX_SMALL 5 +#define CV_FONT_HERSHEY_SCRIPT_SIMPLEX 6 +#define CV_FONT_HERSHEY_SCRIPT_COMPLEX 7 + +#define CV_FONT_ITALIC 16 + +#define CV_FONT_VECTOR0 CV_FONT_HERSHEY_SIMPLEX + + +/** Font structure */ +typedef struct CvFont +{ + const char* nameFont; //Qt:nameFont + CvScalar color; //Qt:ColorFont -> cvScalar(blue_component, green_component, red_component[, alpha_component]) + int font_face; //Qt: bool italic /** =CV_FONT_* */ + const int* ascii; //!< font data and metrics + const int* greek; + const int* cyrillic; + float hscale, vscale; + float shear; //!< slope coefficient: 0 - normal, >0 - italic + int thickness; //!< Qt: weight /** letters thickness */ + float dx; //!< horizontal interval between letters + int line_type; //!< Qt: PointSize +} +CvFont; + +/** @brief Initializes font structure (OpenCV 1.x API). + +The function initializes the font structure that can be passed to text rendering functions. + +@param font Pointer to the font structure initialized by the function +@param font_face Font name identifier. See cv::HersheyFonts and corresponding old CV_* identifiers. +@param hscale Horizontal scale. If equal to 1.0f , the characters have the original width +depending on the font type. If equal to 0.5f , the characters are of half the original width. +@param vscale Vertical scale. If equal to 1.0f , the characters have the original height depending +on the font type. If equal to 0.5f , the characters are of half the original height. +@param shear Approximate tangent of the character slope relative to the vertical line. A zero +value means a non-italic font, 1.0f means about a 45 degree slope, etc. +@param thickness Thickness of the text strokes +@param line_type Type of the strokes, see line description + +@sa cvPutText + */ +CVAPI(void) cvInitFont( CvFont* font, int font_face, + double hscale, double vscale, + double shear CV_DEFAULT(0), + int thickness CV_DEFAULT(1), + int line_type CV_DEFAULT(8)); + +CV_INLINE CvFont cvFont( double scale, int thickness CV_DEFAULT(1) ) +{ + CvFont font; + cvInitFont( &font, CV_FONT_HERSHEY_PLAIN, scale, scale, 0, thickness, CV_AA ); + return font; +} + +/** @brief Renders text stroke with specified font and color at specified location. + CvFont should be initialized with cvInitFont +@see cvInitFont, cvGetTextSize, cvFont, cv::putText +*/ +CVAPI(void) cvPutText( CvArr* img, const char* text, CvPoint org, + const CvFont* font, CvScalar color ); + +/** @brief Calculates bounding box of text stroke (useful for alignment) +@see cv::getTextSize +*/ +CVAPI(void) cvGetTextSize( const char* text_string, const CvFont* font, + CvSize* text_size, int* baseline ); + +/** @brief Unpacks color value + +if arrtype is CV_8UC?, _color_ is treated as packed color value, otherwise the first channels +(depending on arrtype) of destination scalar are set to the same value = _color_ +*/ +CVAPI(CvScalar) cvColorToScalar( double packed_color, int arrtype ); + +/** @brief Returns the polygon points which make up the given ellipse. + +The ellipse is define by the box of size 'axes' rotated 'angle' around the 'center'. A partial +sweep of the ellipse arc can be done by spcifying arc_start and arc_end to be something other than +0 and 360, respectively. The input array 'pts' must be large enough to hold the result. The total +number of points stored into 'pts' is returned by this function. +@see cv::ellipse2Poly +*/ +CVAPI(int) cvEllipse2Poly( CvPoint center, CvSize axes, + int angle, int arc_start, int arc_end, CvPoint * pts, int delta ); + +/** @brief Draws contour outlines or filled interiors on the image +@see cv::drawContours +*/ +CVAPI(void) cvDrawContours( CvArr *img, CvSeq* contour, + CvScalar external_color, CvScalar hole_color, + int max_level, int thickness CV_DEFAULT(1), + int line_type CV_DEFAULT(8), + CvPoint offset CV_DEFAULT(cvPoint(0,0))); + +/** @} */ + +#ifdef __cplusplus +} +#endif + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/imgproc/types_c.h b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/imgproc/types_c.h new file mode 100644 index 0000000000000000000000000000000000000000..d3e55f576f6e93136d030285f7e753865a34ed41 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/imgproc/types_c.h @@ -0,0 +1,659 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_IMGPROC_TYPES_C_H +#define OPENCV_IMGPROC_TYPES_C_H + +#include "opencv2/core/core_c.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/** @addtogroup imgproc_c + @{ +*/ + +/** Connected component structure */ +typedef struct CvConnectedComp +{ + double area; /** DBL_EPSILON ? 1./std::sqrt(am00) : 0; + } + operator cv::Moments() const + { + return cv::Moments(m00, m10, m01, m20, m11, m02, m30, m21, m12, m03); + } +#endif +} +CvMoments; + +#ifdef __cplusplus +} // extern "C" + +CV_INLINE CvMoments cvMoments() +{ +#if !defined(CV__ENABLE_C_API_CTORS) + CvMoments self = CV_STRUCT_INITIALIZER; return self; +#else + return CvMoments(); +#endif +} + +CV_INLINE CvMoments cvMoments(const cv::Moments& m) +{ +#if !defined(CV__ENABLE_C_API_CTORS) + double am00 = std::abs(m.m00); + CvMoments self = { + m.m00, m.m10, m.m01, m.m20, m.m11, m.m02, m.m30, m.m21, m.m12, m.m03, + m.mu20, m.mu11, m.mu02, m.mu30, m.mu21, m.mu12, m.mu03, + am00 > DBL_EPSILON ? 1./std::sqrt(am00) : 0 + }; + return self; +#else + return CvMoments(m); +#endif +} + +extern "C" { +#endif // __cplusplus + +/** Hu invariants */ +typedef struct CvHuMoments +{ + double hu1, hu2, hu3, hu4, hu5, hu6, hu7; /**< Hu invariants */ +} +CvHuMoments; + +/** Template matching methods */ +enum +{ + CV_TM_SQDIFF =0, + CV_TM_SQDIFF_NORMED =1, + CV_TM_CCORR =2, + CV_TM_CCORR_NORMED =3, + CV_TM_CCOEFF =4, + CV_TM_CCOEFF_NORMED =5 +}; + +typedef float (CV_CDECL * CvDistanceFunction)( const float* a, const float* b, void* user_param ); + +/** Contour retrieval modes */ +enum +{ + CV_RETR_EXTERNAL=0, + CV_RETR_LIST=1, + CV_RETR_CCOMP=2, + CV_RETR_TREE=3, + CV_RETR_FLOODFILL=4 +}; + +/** Contour approximation methods */ +enum +{ + CV_CHAIN_CODE=0, + CV_CHAIN_APPROX_NONE=1, + CV_CHAIN_APPROX_SIMPLE=2, + CV_CHAIN_APPROX_TC89_L1=3, + CV_CHAIN_APPROX_TC89_KCOS=4, + CV_LINK_RUNS=5 +}; + +/* +Internal structure that is used for sequential retrieving contours from the image. +It supports both hierarchical and plane variants of Suzuki algorithm. +*/ +typedef struct _CvContourScanner* CvContourScanner; + +/** Freeman chain reader state */ +typedef struct CvChainPtReader +{ + CV_SEQ_READER_FIELDS() + char code; + CvPoint pt; + schar deltas[8][2]; +} +CvChainPtReader; + +/** initializes 8-element array for fast access to 3x3 neighborhood of a pixel */ +#define CV_INIT_3X3_DELTAS( deltas, step, nch ) \ + ((deltas)[0] = (nch), (deltas)[1] = -(step) + (nch), \ + (deltas)[2] = -(step), (deltas)[3] = -(step) - (nch), \ + (deltas)[4] = -(nch), (deltas)[5] = (step) - (nch), \ + (deltas)[6] = (step), (deltas)[7] = (step) + (nch)) + + +/** Contour approximation algorithms */ +enum +{ + CV_POLY_APPROX_DP = 0 +}; + +/** Shape matching methods */ +enum +{ + CV_CONTOURS_MATCH_I1 =1, //!< \f[I_1(A,B) = \sum _{i=1...7} \left | \frac{1}{m^A_i} - \frac{1}{m^B_i} \right |\f] + CV_CONTOURS_MATCH_I2 =2, //!< \f[I_2(A,B) = \sum _{i=1...7} \left | m^A_i - m^B_i \right |\f] + CV_CONTOURS_MATCH_I3 =3 //!< \f[I_3(A,B) = \max _{i=1...7} \frac{ \left| m^A_i - m^B_i \right| }{ \left| m^A_i \right| }\f] +}; + +/** Shape orientation */ +enum +{ + CV_CLOCKWISE =1, + CV_COUNTER_CLOCKWISE =2 +}; + + +/** Convexity defect */ +typedef struct CvConvexityDefect +{ + CvPoint* start; /**< point of the contour where the defect begins */ + CvPoint* end; /**< point of the contour where the defect ends */ + CvPoint* depth_point; /**< the farthest from the convex hull point within the defect */ + float depth; /**< distance between the farthest point and the convex hull */ +} CvConvexityDefect; + + +/** Histogram comparison methods */ +enum +{ + CV_COMP_CORREL =0, + CV_COMP_CHISQR =1, + CV_COMP_INTERSECT =2, + CV_COMP_BHATTACHARYYA =3, + CV_COMP_HELLINGER =CV_COMP_BHATTACHARYYA, + CV_COMP_CHISQR_ALT =4, + CV_COMP_KL_DIV =5 +}; + +/** Mask size for distance transform */ +enum +{ + CV_DIST_MASK_3 =3, + CV_DIST_MASK_5 =5, + CV_DIST_MASK_PRECISE =0 +}; + +/** Content of output label array: connected components or pixels */ +enum +{ + CV_DIST_LABEL_CCOMP = 0, + CV_DIST_LABEL_PIXEL = 1 +}; + +/** Distance types for Distance Transform and M-estimators */ +enum +{ + CV_DIST_USER =-1, /**< User defined distance */ + CV_DIST_L1 =1, /**< distance = |x1-x2| + |y1-y2| */ + CV_DIST_L2 =2, /**< the simple euclidean distance */ + CV_DIST_C =3, /**< distance = max(|x1-x2|,|y1-y2|) */ + CV_DIST_L12 =4, /**< L1-L2 metric: distance = 2(sqrt(1+x*x/2) - 1)) */ + CV_DIST_FAIR =5, /**< distance = c^2(|x|/c-log(1+|x|/c)), c = 1.3998 */ + CV_DIST_WELSCH =6, /**< distance = c^2/2(1-exp(-(x/c)^2)), c = 2.9846 */ + CV_DIST_HUBER =7 /**< distance = |x| threshold ? max_value : 0 */ + CV_THRESH_BINARY_INV =1, /**< value = value > threshold ? 0 : max_value */ + CV_THRESH_TRUNC =2, /**< value = value > threshold ? threshold : value */ + CV_THRESH_TOZERO =3, /**< value = value > threshold ? value : 0 */ + CV_THRESH_TOZERO_INV =4, /**< value = value > threshold ? 0 : value */ + CV_THRESH_MASK =7, + CV_THRESH_OTSU =8, /**< use Otsu algorithm to choose the optimal threshold value; + combine the flag with one of the above CV_THRESH_* values */ + CV_THRESH_TRIANGLE =16 /**< use Triangle algorithm to choose the optimal threshold value; + combine the flag with one of the above CV_THRESH_* values, but not + with CV_THRESH_OTSU */ +}; + +/** Adaptive threshold methods */ +enum +{ + CV_ADAPTIVE_THRESH_MEAN_C =0, + CV_ADAPTIVE_THRESH_GAUSSIAN_C =1 +}; + +/** FloodFill flags */ +enum +{ + CV_FLOODFILL_FIXED_RANGE =(1 << 16), + CV_FLOODFILL_MASK_ONLY =(1 << 17) +}; + + +/** Canny edge detector flags */ +enum +{ + CV_CANNY_L2_GRADIENT =(1 << 31) +}; + +/** Variants of a Hough transform */ +enum +{ + CV_HOUGH_STANDARD =0, + CV_HOUGH_PROBABILISTIC =1, + CV_HOUGH_MULTI_SCALE =2, + CV_HOUGH_GRADIENT =3 +}; + + +/* Fast search data structures */ +struct CvFeatureTree; +struct CvLSH; +struct CvLSHOperations; + +/** @} */ + +#ifdef __cplusplus +} +#endif + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/opencv.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/opencv.hpp new file mode 100644 index 0000000000000000000000000000000000000000..40481587f05782ae97989657dec822276096a48c --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/opencv.hpp @@ -0,0 +1,139 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009-2010, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_ALL_HPP +#define OPENCV_ALL_HPP + +// File that defines what modules where included during the build of OpenCV +// These are purely the defines of the correct HAVE_OPENCV_modulename values +#include "opencv2/opencv_modules.hpp" + +// Then the list of defines is checked to include the correct headers +// Core library is always included --> without no OpenCV functionality available +#include "opencv2/core.hpp" + +// Then the optional modules are checked +#ifdef HAVE_OPENCV_CALIB3D +#include "opencv2/calib3d.hpp" +#endif +#ifdef HAVE_OPENCV_FEATURES2D +#include "opencv2/features2d.hpp" +#endif +#ifdef HAVE_OPENCV_DNN +#include "opencv2/dnn.hpp" +#endif +#ifdef HAVE_OPENCV_FLANN +#include "opencv2/flann.hpp" +#endif +#ifdef HAVE_OPENCV_HIGHGUI +#include "opencv2/highgui.hpp" +#endif +#ifdef HAVE_OPENCV_IMGCODECS +#include "opencv2/imgcodecs.hpp" +#endif +#ifdef HAVE_OPENCV_IMGPROC +#include "opencv2/imgproc.hpp" +#endif +#ifdef HAVE_OPENCV_ML +#include "opencv2/ml.hpp" +#endif +#ifdef HAVE_OPENCV_OBJDETECT +#include "opencv2/objdetect.hpp" +#endif +#ifdef HAVE_OPENCV_PHOTO +#include "opencv2/photo.hpp" +#endif +#ifdef HAVE_OPENCV_SHAPE +#include "opencv2/shape.hpp" +#endif +#ifdef HAVE_OPENCV_STITCHING +#include "opencv2/stitching.hpp" +#endif +#ifdef HAVE_OPENCV_SUPERRES +#include "opencv2/superres.hpp" +#endif +#ifdef HAVE_OPENCV_VIDEO +#include "opencv2/video.hpp" +#endif +#ifdef HAVE_OPENCV_VIDEOIO +#include "opencv2/videoio.hpp" +#endif +#ifdef HAVE_OPENCV_VIDEOSTAB +#include "opencv2/videostab.hpp" +#endif +#ifdef HAVE_OPENCV_VIZ +#include "opencv2/viz.hpp" +#endif + +// Finally CUDA specific entries are checked and added +#ifdef HAVE_OPENCV_CUDAARITHM +#include "opencv2/cudaarithm.hpp" +#endif +#ifdef HAVE_OPENCV_CUDABGSEGM +#include "opencv2/cudabgsegm.hpp" +#endif +#ifdef HAVE_OPENCV_CUDACODEC +#include "opencv2/cudacodec.hpp" +#endif +#ifdef HAVE_OPENCV_CUDAFEATURES2D +#include "opencv2/cudafeatures2d.hpp" +#endif +#ifdef HAVE_OPENCV_CUDAFILTERS +#include "opencv2/cudafilters.hpp" +#endif +#ifdef HAVE_OPENCV_CUDAIMGPROC +#include "opencv2/cudaimgproc.hpp" +#endif +#ifdef HAVE_OPENCV_CUDAOBJDETECT +#include "opencv2/cudaobjdetect.hpp" +#endif +#ifdef HAVE_OPENCV_CUDAOPTFLOW +#include "opencv2/cudaoptflow.hpp" +#endif +#ifdef HAVE_OPENCV_CUDASTEREO +#include "opencv2/cudastereo.hpp" +#endif +#ifdef HAVE_OPENCV_CUDAWARPING +#include "opencv2/cudawarping.hpp" +#endif + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/opencv_modules.hpp b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/opencv_modules.hpp new file mode 100644 index 0000000000000000000000000000000000000000..e864591929483571189b505d9968569bf15e8bb2 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/include/opencv2/opencv_modules.hpp @@ -0,0 +1,19 @@ +/* + * ** File generated automatically, do not modify ** + * + * This file defines the list of modules available in current build configuration + * + * +*/ + +// This definition means that OpenCV is built with enabled non-free code. +// For example, patented algorithms for non-profit/non-commercial use only. +/* #undef OPENCV_ENABLE_NONFREE */ + +#define HAVE_OPENCV_CALIB3D +#define HAVE_OPENCV_CORE +#define HAVE_OPENCV_FEATURES2D +#define HAVE_OPENCV_IMGCODECS +#define HAVE_OPENCV_IMGPROC + + diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/staticlibs/arm64-v8a/libopencv_calib3d.a b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/staticlibs/arm64-v8a/libopencv_calib3d.a new file mode 100644 index 0000000000000000000000000000000000000000..a6b38f086eb80ba6889a90c02d5efe60ab6bac6d --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/staticlibs/arm64-v8a/libopencv_calib3d.a @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:eb07e32e1174fe9406e4aa084551098cf0e9f30b7e707d3cae59423aa1890a58 +size 23567986 diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/staticlibs/arm64-v8a/libopencv_core.a b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/staticlibs/arm64-v8a/libopencv_core.a new file mode 100644 index 0000000000000000000000000000000000000000..022efafe2ca52c5e5103a49c554844a084ffe8c1 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/staticlibs/arm64-v8a/libopencv_core.a @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6d064dd9b15b7047d64ece95b2a12860878ac22fe6bdfaff752c743463614c76 +size 50520160 diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/staticlibs/arm64-v8a/libopencv_features2d.a b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/staticlibs/arm64-v8a/libopencv_features2d.a new file mode 100644 index 0000000000000000000000000000000000000000..ff7e223783c681f7db89915ae340b812e77d9c22 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/staticlibs/arm64-v8a/libopencv_features2d.a @@ -0,0 +1,3 @@ +version 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a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/staticlibs/armeabi-v7a/libopencv_core.a b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/staticlibs/armeabi-v7a/libopencv_core.a new file mode 100644 index 0000000000000000000000000000000000000000..013d62f5a87c7a8d162396f74374df09789a598e --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/staticlibs/armeabi-v7a/libopencv_core.a @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1fc70d2c09b09ae9db59c702f030f4d81cde6628601d6d54cfddad24135b60a3 +size 34061762 diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/staticlibs/armeabi-v7a/libopencv_features2d.a b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/staticlibs/armeabi-v7a/libopencv_features2d.a new file mode 100644 index 0000000000000000000000000000000000000000..1ce0ea1ef434838df16d315676727b3210bd0473 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/staticlibs/armeabi-v7a/libopencv_features2d.a @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:82595b0b0a04ddfbb2900c8a367f22806bb7467e15d59a946beee05613b83175 +size 10534122 diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/staticlibs/armeabi-v7a/libopencv_imgcodecs.a b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/staticlibs/armeabi-v7a/libopencv_imgcodecs.a new file mode 100644 index 0000000000000000000000000000000000000000..2e258ba6bbf8a31d8c0fabda9f8703a89b92cd3a --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/staticlibs/armeabi-v7a/libopencv_imgcodecs.a @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b3db5c9ce915f2c61ce4e299a20c564aa87a45da4d8c828c7e3d75c14d603487 +size 6170820 diff --git a/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/staticlibs/armeabi-v7a/libopencv_imgproc.a b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/staticlibs/armeabi-v7a/libopencv_imgproc.a new file mode 100644 index 0000000000000000000000000000000000000000..4c02336e51c10a51dfb3777bbd53083056e8c6d6 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/native/staticlibs/armeabi-v7a/libopencv_imgproc.a @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:788ebd035e0bf5fcab5d75de4020a94b42e4b7eb9f5eecc496ba28ba412422aa +size 28365484 diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/bin/opencv_version b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/bin/opencv_version new file mode 100755 index 0000000000000000000000000000000000000000..01c8fde8c9e993f575a4508a8e34efab11559810 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/bin/opencv_version @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b2dce0ffd64fc4c5d6ced958252ff3cb78eab7fb4d41ad053d9853a3e6885764 +size 1395496 diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/bin/setup_vars_opencv3.sh b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/bin/setup_vars_opencv3.sh new file mode 100755 index 0000000000000000000000000000000000000000..384f8cad431a5610d843f00ce4a6ff0ed26715ce --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/bin/setup_vars_opencv3.sh @@ -0,0 +1,20 @@ +#!/bin/bash + +SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null && pwd )" + +[[ ! "${OPENCV_QUIET}" ]] && ( echo "Setting vars for OpenCV 3.4.5" ) +export LD_LIBRARY_PATH="$SCRIPT_DIR/../lib:$LD_LIBRARY_PATH" + +if [[ ! "$OPENCV_SKIP_PYTHON" ]]; then + PYTHONPATH_OPENCV="$SCRIPT_DIR/python_loader_is_not_installed" + [[ ! "${OPENCV_QUIET}" ]] && ( echo "Append PYTHONPATH: ${PYTHONPATH_OPENCV}" ) + export PYTHONPATH="${PYTHONPATH_OPENCV}:$PYTHONPATH" +fi + +# Don't exec in "sourced" mode +if [[ "${BASH_SOURCE[0]}" == "${0}" ]]; then + if [[ $# -ne 0 ]]; then + [[ ! "${OPENCV_QUIET}" && "${OPENCV_VERBOSE}" ]] && ( echo "Executing: $*" ) + exec "$@" + fi +fi diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv/cv.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv/cv.h new file mode 100644 index 0000000000000000000000000000000000000000..19a74e297cfb58d8bfd0d2d5bbec9de46e55f975 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv/cv.h @@ -0,0 +1,73 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OLD_CV_H +#define OPENCV_OLD_CV_H + +#if defined(_MSC_VER) + #define CV_DO_PRAGMA(x) __pragma(x) + #define __CVSTR2__(x) #x + #define __CVSTR1__(x) __CVSTR2__(x) + #define __CVMSVCLOC__ __FILE__ "("__CVSTR1__(__LINE__)") : " + #define CV_MSG_PRAGMA(_msg) CV_DO_PRAGMA(message (__CVMSVCLOC__ _msg)) +#elif defined(__GNUC__) + #define CV_DO_PRAGMA(x) _Pragma (#x) + #define CV_MSG_PRAGMA(_msg) CV_DO_PRAGMA(message (_msg)) +#else + #define CV_DO_PRAGMA(x) + #define CV_MSG_PRAGMA(_msg) +#endif +#define CV_WARNING(x) CV_MSG_PRAGMA("Warning: " #x) + +//CV_WARNING("This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module") + +#include "opencv2/core/core_c.h" +#include "opencv2/imgproc/imgproc_c.h" +#include "opencv2/photo/photo_c.h" +#include "opencv2/video/tracking_c.h" +#include "opencv2/objdetect/objdetect_c.h" + +#if !defined(CV_IMPL) +#define CV_IMPL extern "C" +#endif //CV_IMPL + +#endif // __OPENCV_OLD_CV_H_ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv/cv.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv/cv.hpp new file mode 100644 index 0000000000000000000000000000000000000000..867395644b4f44d0c1290de73c5285e71d3a1e70 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv/cv.hpp @@ -0,0 +1,60 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OLD_CV_HPP +#define OPENCV_OLD_CV_HPP + +//#if defined(__GNUC__) +//#warning "This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module" +//#endif + +#include "cv.h" +#include "opencv2/core.hpp" +#include "opencv2/imgproc.hpp" +#include "opencv2/photo.hpp" +#include "opencv2/video.hpp" +#include "opencv2/highgui.hpp" +#include "opencv2/features2d.hpp" +#include "opencv2/calib3d.hpp" +#include "opencv2/objdetect.hpp" + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv/cvaux.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv/cvaux.h new file mode 100644 index 0000000000000000000000000000000000000000..c0367cc25b765d545ab12e463295d34793c2f7e9 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv/cvaux.h @@ -0,0 +1,57 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// Intel License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000, Intel Corporation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of Intel Corporation may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OLD_AUX_H +#define OPENCV_OLD_AUX_H + +//#if defined(__GNUC__) +//#warning "This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module" +//#endif + +#include "opencv2/core/core_c.h" +#include "opencv2/imgproc/imgproc_c.h" +#include "opencv2/photo/photo_c.h" +#include "opencv2/video/tracking_c.h" +#include "opencv2/objdetect/objdetect_c.h" + +#endif + +/* End of file. */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv/cvaux.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv/cvaux.hpp new file mode 100644 index 0000000000000000000000000000000000000000..4888eef282ecb2ef5e0e5ebf88b745aec3eb70c7 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv/cvaux.hpp @@ -0,0 +1,52 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// Intel License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000, Intel Corporation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of Intel Corporation may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OLD_AUX_HPP +#define OPENCV_OLD_AUX_HPP + +//#if defined(__GNUC__) +//#warning "This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module" +//#endif + +#include "cvaux.h" +#include "opencv2/core/utility.hpp" + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv/cvwimage.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv/cvwimage.h new file mode 100644 index 0000000000000000000000000000000000000000..ec0ab141d9974866f29ff1a84961a50b67176b1c --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv/cvwimage.h @@ -0,0 +1,46 @@ +/////////////////////////////////////////////////////////////////////////////// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to +// this license. If you do not agree to this license, do not download, +// install, copy or use the software. +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2008, Google, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of Intel Corporation or contributors may not be used to endorse +// or promote products derived from this software without specific +// prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" +// and any express or implied warranties, including, but not limited to, the +// implied warranties of merchantability and fitness for a particular purpose +// are disclaimed. In no event shall the Intel Corporation or contributors be +// liable for any direct, indirect, incidental, special, exemplary, or +// consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. + + +#ifndef OPENCV_OLD_WIMAGE_HPP +#define OPENCV_OLD_WIMAGE_HPP + +#include "opencv2/core/wimage.hpp" + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv/cxcore.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv/cxcore.h new file mode 100644 index 0000000000000000000000000000000000000000..dc070c77d2890a4443ebcb776ac978712c92e0ec --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv/cxcore.h @@ -0,0 +1,52 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OLD_CXCORE_H +#define OPENCV_OLD_CXCORE_H + +//#if defined(__GNUC__) +//#warning "This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module" +//#endif + +#include "opencv2/core/core_c.h" + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv/cxcore.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv/cxcore.hpp new file mode 100644 index 0000000000000000000000000000000000000000..c371677c933fdd520af741bc95825c71d14a5508 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv/cxcore.hpp @@ -0,0 +1,53 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OLD_CXCORE_HPP +#define OPENCV_OLD_CXCORE_HPP + +//#if defined(__GNUC__) +//#warning "This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module" +//#endif + +#include "cxcore.h" +#include "opencv2/core.hpp" + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv/cxeigen.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv/cxeigen.hpp new file mode 100644 index 0000000000000000000000000000000000000000..1d3df914ac95f387775587a692b1fc06e01da953 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv/cxeigen.hpp @@ -0,0 +1,48 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OLD_EIGEN_HPP +#define OPENCV_OLD_EIGEN_HPP + +#include "opencv2/core/eigen.hpp" + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv/cxmisc.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv/cxmisc.h new file mode 100644 index 0000000000000000000000000000000000000000..9b9bc8204bd810f5c57bc6c93552c4684af63b93 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv/cxmisc.h @@ -0,0 +1,8 @@ +#ifndef OPENCV_OLD_CXMISC_H +#define OPENCV_OLD_CXMISC_H + +#ifdef __cplusplus +# include "opencv2/core/utility.hpp" +#endif + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv/highgui.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv/highgui.h new file mode 100644 index 0000000000000000000000000000000000000000..69b394e08ace38c92a2ea35a5e9f7c5eca22ae80 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv/highgui.h @@ -0,0 +1,48 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// Intel License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000, Intel Corporation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of Intel Corporation may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OLD_HIGHGUI_H +#define OPENCV_OLD_HIGHGUI_H + +#include "opencv2/core/core_c.h" +#include "opencv2/highgui/highgui_c.h" + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv/ml.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv/ml.h new file mode 100644 index 0000000000000000000000000000000000000000..0c376bacb9ab5ef9167f54b688ce474f2a92ac2e --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv/ml.h @@ -0,0 +1,47 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// Intel License Agreement +// +// Copyright (C) 2000, Intel Corporation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of Intel Corporation may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OLD_ML_H +#define OPENCV_OLD_ML_H + +#include "opencv2/core/core_c.h" +#include "opencv2/ml.hpp" + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/calib3d.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/calib3d.hpp new file mode 100644 index 0000000000000000000000000000000000000000..c5b3ffd4d3ca5aa5a3dbedd1472a771e1dc9662f --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/calib3d.hpp @@ -0,0 +1,2479 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CALIB3D_HPP +#define OPENCV_CALIB3D_HPP + +#include "opencv2/core.hpp" +#include "opencv2/features2d.hpp" +#include "opencv2/core/affine.hpp" + +/** + @defgroup calib3d Camera Calibration and 3D Reconstruction + +The functions in this section use a so-called pinhole camera model. In this model, a scene view is +formed by projecting 3D points into the image plane using a perspective transformation. + +\f[s \; m' = A [R|t] M'\f] + +or + +\f[s \vecthree{u}{v}{1} = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1} +\begin{bmatrix} +r_{11} & r_{12} & r_{13} & t_1 \\ +r_{21} & r_{22} & r_{23} & t_2 \\ +r_{31} & r_{32} & r_{33} & t_3 +\end{bmatrix} +\begin{bmatrix} +X \\ +Y \\ +Z \\ +1 +\end{bmatrix}\f] + +where: + +- \f$(X, Y, Z)\f$ are the coordinates of a 3D point in the world coordinate space +- \f$(u, v)\f$ are the coordinates of the projection point in pixels +- \f$A\f$ is a camera matrix, or a matrix of intrinsic parameters +- \f$(cx, cy)\f$ is a principal point that is usually at the image center +- \f$fx, fy\f$ are the focal lengths expressed in pixel units. + +Thus, if an image from the camera is scaled by a factor, all of these parameters should be scaled +(multiplied/divided, respectively) by the same factor. The matrix of intrinsic parameters does not +depend on the scene viewed. So, once estimated, it can be re-used as long as the focal length is +fixed (in case of zoom lens). The joint rotation-translation matrix \f$[R|t]\f$ is called a matrix of +extrinsic parameters. It is used to describe the camera motion around a static scene, or vice versa, +rigid motion of an object in front of a still camera. That is, \f$[R|t]\f$ translates coordinates of a +point \f$(X, Y, Z)\f$ to a coordinate system, fixed with respect to the camera. The transformation above +is equivalent to the following (when \f$z \ne 0\f$ ): + +\f[\begin{array}{l} +\vecthree{x}{y}{z} = R \vecthree{X}{Y}{Z} + t \\ +x' = x/z \\ +y' = y/z \\ +u = f_x*x' + c_x \\ +v = f_y*y' + c_y +\end{array}\f] + +The following figure illustrates the pinhole camera model. + +![Pinhole camera model](pics/pinhole_camera_model.png) + +Real lenses usually have some distortion, mostly radial distortion and slight tangential distortion. +So, the above model is extended as: + +\f[\begin{array}{l} +\vecthree{x}{y}{z} = R \vecthree{X}{Y}{Z} + t \\ +x' = x/z \\ +y' = y/z \\ +x'' = x' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} + 2 p_1 x' y' + p_2(r^2 + 2 x'^2) + s_1 r^2 + s_2 r^4 \\ +y'' = y' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} + p_1 (r^2 + 2 y'^2) + 2 p_2 x' y' + s_3 r^2 + s_4 r^4 \\ +\text{where} \quad r^2 = x'^2 + y'^2 \\ +u = f_x*x'' + c_x \\ +v = f_y*y'' + c_y +\end{array}\f] + +\f$k_1\f$, \f$k_2\f$, \f$k_3\f$, \f$k_4\f$, \f$k_5\f$, and \f$k_6\f$ are radial distortion coefficients. \f$p_1\f$ and \f$p_2\f$ are +tangential distortion coefficients. \f$s_1\f$, \f$s_2\f$, \f$s_3\f$, and \f$s_4\f$, are the thin prism distortion +coefficients. Higher-order coefficients are not considered in OpenCV. + +The next figures show two common types of radial distortion: barrel distortion (typically \f$ k_1 < 0 \f$) and pincushion distortion (typically \f$ k_1 > 0 \f$). + +![](pics/distortion_examples.png) +![](pics/distortion_examples2.png) + +In some cases the image sensor may be tilted in order to focus an oblique plane in front of the +camera (Scheimpfug condition). This can be useful for particle image velocimetry (PIV) or +triangulation with a laser fan. The tilt causes a perspective distortion of \f$x''\f$ and +\f$y''\f$. This distortion can be modelled in the following way, see e.g. @cite Louhichi07. + +\f[\begin{array}{l} +s\vecthree{x'''}{y'''}{1} = +\vecthreethree{R_{33}(\tau_x, \tau_y)}{0}{-R_{13}(\tau_x, \tau_y)} +{0}{R_{33}(\tau_x, \tau_y)}{-R_{23}(\tau_x, \tau_y)} +{0}{0}{1} R(\tau_x, \tau_y) \vecthree{x''}{y''}{1}\\ +u = f_x*x''' + c_x \\ +v = f_y*y''' + c_y +\end{array}\f] + +where the matrix \f$R(\tau_x, \tau_y)\f$ is defined by two rotations with angular parameter \f$\tau_x\f$ +and \f$\tau_y\f$, respectively, + +\f[ +R(\tau_x, \tau_y) = +\vecthreethree{\cos(\tau_y)}{0}{-\sin(\tau_y)}{0}{1}{0}{\sin(\tau_y)}{0}{\cos(\tau_y)} +\vecthreethree{1}{0}{0}{0}{\cos(\tau_x)}{\sin(\tau_x)}{0}{-\sin(\tau_x)}{\cos(\tau_x)} = +\vecthreethree{\cos(\tau_y)}{\sin(\tau_y)\sin(\tau_x)}{-\sin(\tau_y)\cos(\tau_x)} +{0}{\cos(\tau_x)}{\sin(\tau_x)} +{\sin(\tau_y)}{-\cos(\tau_y)\sin(\tau_x)}{\cos(\tau_y)\cos(\tau_x)}. +\f] + +In the functions below the coefficients are passed or returned as + +\f[(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f] + +vector. That is, if the vector contains four elements, it means that \f$k_3=0\f$ . The distortion +coefficients do not depend on the scene viewed. Thus, they also belong to the intrinsic camera +parameters. And they remain the same regardless of the captured image resolution. If, for example, a +camera has been calibrated on images of 320 x 240 resolution, absolutely the same distortion +coefficients can be used for 640 x 480 images from the same camera while \f$f_x\f$, \f$f_y\f$, \f$c_x\f$, and +\f$c_y\f$ need to be scaled appropriately. + +The functions below use the above model to do the following: + +- Project 3D points to the image plane given intrinsic and extrinsic parameters. +- Compute extrinsic parameters given intrinsic parameters, a few 3D points, and their +projections. +- Estimate intrinsic and extrinsic camera parameters from several views of a known calibration +pattern (every view is described by several 3D-2D point correspondences). +- Estimate the relative position and orientation of the stereo camera "heads" and compute the +*rectification* transformation that makes the camera optical axes parallel. + +@note + - A calibration sample for 3 cameras in horizontal position can be found at + opencv_source_code/samples/cpp/3calibration.cpp + - A calibration sample based on a sequence of images can be found at + opencv_source_code/samples/cpp/calibration.cpp + - A calibration sample in order to do 3D reconstruction can be found at + opencv_source_code/samples/cpp/build3dmodel.cpp + - A calibration sample of an artificially generated camera and chessboard patterns can be + found at opencv_source_code/samples/cpp/calibration_artificial.cpp + - A calibration example on stereo calibration can be found at + opencv_source_code/samples/cpp/stereo_calib.cpp + - A calibration example on stereo matching can be found at + opencv_source_code/samples/cpp/stereo_match.cpp + - (Python) A camera calibration sample can be found at + opencv_source_code/samples/python/calibrate.py + + @{ + @defgroup calib3d_fisheye Fisheye camera model + + Definitions: Let P be a point in 3D of coordinates X in the world reference frame (stored in the + matrix X) The coordinate vector of P in the camera reference frame is: + + \f[Xc = R X + T\f] + + where R is the rotation matrix corresponding to the rotation vector om: R = rodrigues(om); call x, y + and z the 3 coordinates of Xc: + + \f[x = Xc_1 \\ y = Xc_2 \\ z = Xc_3\f] + + The pinhole projection coordinates of P is [a; b] where + + \f[a = x / z \ and \ b = y / z \\ r^2 = a^2 + b^2 \\ \theta = atan(r)\f] + + Fisheye distortion: + + \f[\theta_d = \theta (1 + k_1 \theta^2 + k_2 \theta^4 + k_3 \theta^6 + k_4 \theta^8)\f] + + The distorted point coordinates are [x'; y'] where + + \f[x' = (\theta_d / r) a \\ y' = (\theta_d / r) b \f] + + Finally, conversion into pixel coordinates: The final pixel coordinates vector [u; v] where: + + \f[u = f_x (x' + \alpha y') + c_x \\ + v = f_y y' + c_y\f] + + @defgroup calib3d_c C API + + @} + */ + +namespace cv +{ + +//! @addtogroup calib3d +//! @{ + +//! type of the robust estimation algorithm +enum { LMEDS = 4, //!< least-median of squares algorithm + RANSAC = 8, //!< RANSAC algorithm + RHO = 16 //!< RHO algorithm + }; + +enum { SOLVEPNP_ITERATIVE = 0, + SOLVEPNP_EPNP = 1, //!< EPnP: Efficient Perspective-n-Point Camera Pose Estimation @cite lepetit2009epnp + SOLVEPNP_P3P = 2, //!< Complete Solution Classification for the Perspective-Three-Point Problem @cite gao2003complete + SOLVEPNP_DLS = 3, //!< A Direct Least-Squares (DLS) Method for PnP @cite hesch2011direct + SOLVEPNP_UPNP = 4, //!< Exhaustive Linearization for Robust Camera Pose and Focal Length Estimation @cite penate2013exhaustive + SOLVEPNP_AP3P = 5, //!< An Efficient Algebraic Solution to the Perspective-Three-Point Problem @cite Ke17 + SOLVEPNP_MAX_COUNT //!< Used for count +}; + +enum { CALIB_CB_ADAPTIVE_THRESH = 1, + CALIB_CB_NORMALIZE_IMAGE = 2, + CALIB_CB_FILTER_QUADS = 4, + CALIB_CB_FAST_CHECK = 8 + }; + +enum { CALIB_CB_SYMMETRIC_GRID = 1, + CALIB_CB_ASYMMETRIC_GRID = 2, + CALIB_CB_CLUSTERING = 4 + }; + +enum { CALIB_USE_INTRINSIC_GUESS = 0x00001, + CALIB_FIX_ASPECT_RATIO = 0x00002, + CALIB_FIX_PRINCIPAL_POINT = 0x00004, + CALIB_ZERO_TANGENT_DIST = 0x00008, + CALIB_FIX_FOCAL_LENGTH = 0x00010, + CALIB_FIX_K1 = 0x00020, + CALIB_FIX_K2 = 0x00040, + CALIB_FIX_K3 = 0x00080, + CALIB_FIX_K4 = 0x00800, + CALIB_FIX_K5 = 0x01000, + CALIB_FIX_K6 = 0x02000, + CALIB_RATIONAL_MODEL = 0x04000, + CALIB_THIN_PRISM_MODEL = 0x08000, + CALIB_FIX_S1_S2_S3_S4 = 0x10000, + CALIB_TILTED_MODEL = 0x40000, + CALIB_FIX_TAUX_TAUY = 0x80000, + CALIB_USE_QR = 0x100000, //!< use QR instead of SVD decomposition for solving. Faster but potentially less precise + CALIB_FIX_TANGENT_DIST = 0x200000, + // only for stereo + CALIB_FIX_INTRINSIC = 0x00100, + CALIB_SAME_FOCAL_LENGTH = 0x00200, + // for stereo rectification + CALIB_ZERO_DISPARITY = 0x00400, + CALIB_USE_LU = (1 << 17), //!< use LU instead of SVD decomposition for solving. much faster but potentially less precise + CALIB_USE_EXTRINSIC_GUESS = (1 << 22), //!< for stereoCalibrate + }; + +//! the algorithm for finding fundamental matrix +enum { FM_7POINT = 1, //!< 7-point algorithm + FM_8POINT = 2, //!< 8-point algorithm + FM_LMEDS = 4, //!< least-median algorithm. 7-point algorithm is used. + FM_RANSAC = 8 //!< RANSAC algorithm. It needs at least 15 points. 7-point algorithm is used. + }; + + + +/** @brief Converts a rotation matrix to a rotation vector or vice versa. + +@param src Input rotation vector (3x1 or 1x3) or rotation matrix (3x3). +@param dst Output rotation matrix (3x3) or rotation vector (3x1 or 1x3), respectively. +@param jacobian Optional output Jacobian matrix, 3x9 or 9x3, which is a matrix of partial +derivatives of the output array components with respect to the input array components. + +\f[\begin{array}{l} \theta \leftarrow norm(r) \\ r \leftarrow r/ \theta \\ R = \cos{\theta} I + (1- \cos{\theta} ) r r^T + \sin{\theta} \vecthreethree{0}{-r_z}{r_y}{r_z}{0}{-r_x}{-r_y}{r_x}{0} \end{array}\f] + +Inverse transformation can be also done easily, since + +\f[\sin ( \theta ) \vecthreethree{0}{-r_z}{r_y}{r_z}{0}{-r_x}{-r_y}{r_x}{0} = \frac{R - R^T}{2}\f] + +A rotation vector is a convenient and most compact representation of a rotation matrix (since any +rotation matrix has just 3 degrees of freedom). The representation is used in the global 3D geometry +optimization procedures like calibrateCamera, stereoCalibrate, or solvePnP . + */ +CV_EXPORTS_W void Rodrigues( InputArray src, OutputArray dst, OutputArray jacobian = noArray() ); + +/** @example samples/cpp/tutorial_code/features2D/Homography/pose_from_homography.cpp +An example program about pose estimation from coplanar points + +Check @ref tutorial_homography "the corresponding tutorial" for more details +*/ + +/** @brief Finds a perspective transformation between two planes. + +@param srcPoints Coordinates of the points in the original plane, a matrix of the type CV_32FC2 +or vector\ . +@param dstPoints Coordinates of the points in the target plane, a matrix of the type CV_32FC2 or +a vector\ . +@param method Method used to compute a homography matrix. The following methods are possible: +- **0** - a regular method using all the points, i.e., the least squares method +- **RANSAC** - RANSAC-based robust method +- **LMEDS** - Least-Median robust method +- **RHO** - PROSAC-based robust method +@param ransacReprojThreshold Maximum allowed reprojection error to treat a point pair as an inlier +(used in the RANSAC and RHO methods only). That is, if +\f[\| \texttt{dstPoints} _i - \texttt{convertPointsHomogeneous} ( \texttt{H} * \texttt{srcPoints} _i) \|_2 > \texttt{ransacReprojThreshold}\f] +then the point \f$i\f$ is considered as an outlier. If srcPoints and dstPoints are measured in pixels, +it usually makes sense to set this parameter somewhere in the range of 1 to 10. +@param mask Optional output mask set by a robust method ( RANSAC or LMEDS ). Note that the input +mask values are ignored. +@param maxIters The maximum number of RANSAC iterations. +@param confidence Confidence level, between 0 and 1. + +The function finds and returns the perspective transformation \f$H\f$ between the source and the +destination planes: + +\f[s_i \vecthree{x'_i}{y'_i}{1} \sim H \vecthree{x_i}{y_i}{1}\f] + +so that the back-projection error + +\f[\sum _i \left ( x'_i- \frac{h_{11} x_i + h_{12} y_i + h_{13}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2+ \left ( y'_i- \frac{h_{21} x_i + h_{22} y_i + h_{23}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2\f] + +is minimized. If the parameter method is set to the default value 0, the function uses all the point +pairs to compute an initial homography estimate with a simple least-squares scheme. + +However, if not all of the point pairs ( \f$srcPoints_i\f$, \f$dstPoints_i\f$ ) fit the rigid perspective +transformation (that is, there are some outliers), this initial estimate will be poor. In this case, +you can use one of the three robust methods. The methods RANSAC, LMeDS and RHO try many different +random subsets of the corresponding point pairs (of four pairs each, collinear pairs are discarded), estimate the homography matrix +using this subset and a simple least-squares algorithm, and then compute the quality/goodness of the +computed homography (which is the number of inliers for RANSAC or the least median re-projection error for +LMeDS). The best subset is then used to produce the initial estimate of the homography matrix and +the mask of inliers/outliers. + +Regardless of the method, robust or not, the computed homography matrix is refined further (using +inliers only in case of a robust method) with the Levenberg-Marquardt method to reduce the +re-projection error even more. + +The methods RANSAC and RHO can handle practically any ratio of outliers but need a threshold to +distinguish inliers from outliers. The method LMeDS does not need any threshold but it works +correctly only when there are more than 50% of inliers. Finally, if there are no outliers and the +noise is rather small, use the default method (method=0). + +The function is used to find initial intrinsic and extrinsic matrices. Homography matrix is +determined up to a scale. Thus, it is normalized so that \f$h_{33}=1\f$. Note that whenever an \f$H\f$ matrix +cannot be estimated, an empty one will be returned. + +@sa +getAffineTransform, estimateAffine2D, estimateAffinePartial2D, getPerspectiveTransform, warpPerspective, +perspectiveTransform + */ +CV_EXPORTS_W Mat findHomography( InputArray srcPoints, InputArray dstPoints, + int method = 0, double ransacReprojThreshold = 3, + OutputArray mask=noArray(), const int maxIters = 2000, + const double confidence = 0.995); + +/** @overload */ +CV_EXPORTS Mat findHomography( InputArray srcPoints, InputArray dstPoints, + OutputArray mask, int method = 0, double ransacReprojThreshold = 3 ); + +/** @brief Computes an RQ decomposition of 3x3 matrices. + +@param src 3x3 input matrix. +@param mtxR Output 3x3 upper-triangular matrix. +@param mtxQ Output 3x3 orthogonal matrix. +@param Qx Optional output 3x3 rotation matrix around x-axis. +@param Qy Optional output 3x3 rotation matrix around y-axis. +@param Qz Optional output 3x3 rotation matrix around z-axis. + +The function computes a RQ decomposition using the given rotations. This function is used in +decomposeProjectionMatrix to decompose the left 3x3 submatrix of a projection matrix into a camera +and a rotation matrix. + +It optionally returns three rotation matrices, one for each axis, and the three Euler angles in +degrees (as the return value) that could be used in OpenGL. Note, there is always more than one +sequence of rotations about the three principal axes that results in the same orientation of an +object, e.g. see @cite Slabaugh . Returned tree rotation matrices and corresponding three Euler angles +are only one of the possible solutions. + */ +CV_EXPORTS_W Vec3d RQDecomp3x3( InputArray src, OutputArray mtxR, OutputArray mtxQ, + OutputArray Qx = noArray(), + OutputArray Qy = noArray(), + OutputArray Qz = noArray()); + +/** @brief Decomposes a projection matrix into a rotation matrix and a camera matrix. + +@param projMatrix 3x4 input projection matrix P. +@param cameraMatrix Output 3x3 camera matrix K. +@param rotMatrix Output 3x3 external rotation matrix R. +@param transVect Output 4x1 translation vector T. +@param rotMatrixX Optional 3x3 rotation matrix around x-axis. +@param rotMatrixY Optional 3x3 rotation matrix around y-axis. +@param rotMatrixZ Optional 3x3 rotation matrix around z-axis. +@param eulerAngles Optional three-element vector containing three Euler angles of rotation in +degrees. + +The function computes a decomposition of a projection matrix into a calibration and a rotation +matrix and the position of a camera. + +It optionally returns three rotation matrices, one for each axis, and three Euler angles that could +be used in OpenGL. Note, there is always more than one sequence of rotations about the three +principal axes that results in the same orientation of an object, e.g. see @cite Slabaugh . Returned +tree rotation matrices and corresponding three Euler angles are only one of the possible solutions. + +The function is based on RQDecomp3x3 . + */ +CV_EXPORTS_W void decomposeProjectionMatrix( InputArray projMatrix, OutputArray cameraMatrix, + OutputArray rotMatrix, OutputArray transVect, + OutputArray rotMatrixX = noArray(), + OutputArray rotMatrixY = noArray(), + OutputArray rotMatrixZ = noArray(), + OutputArray eulerAngles =noArray() ); + +/** @brief Computes partial derivatives of the matrix product for each multiplied matrix. + +@param A First multiplied matrix. +@param B Second multiplied matrix. +@param dABdA First output derivative matrix d(A\*B)/dA of size +\f$\texttt{A.rows*B.cols} \times {A.rows*A.cols}\f$ . +@param dABdB Second output derivative matrix d(A\*B)/dB of size +\f$\texttt{A.rows*B.cols} \times {B.rows*B.cols}\f$ . + +The function computes partial derivatives of the elements of the matrix product \f$A*B\f$ with regard to +the elements of each of the two input matrices. The function is used to compute the Jacobian +matrices in stereoCalibrate but can also be used in any other similar optimization function. + */ +CV_EXPORTS_W void matMulDeriv( InputArray A, InputArray B, OutputArray dABdA, OutputArray dABdB ); + +/** @brief Combines two rotation-and-shift transformations. + +@param rvec1 First rotation vector. +@param tvec1 First translation vector. +@param rvec2 Second rotation vector. +@param tvec2 Second translation vector. +@param rvec3 Output rotation vector of the superposition. +@param tvec3 Output translation vector of the superposition. +@param dr3dr1 +@param dr3dt1 +@param dr3dr2 +@param dr3dt2 +@param dt3dr1 +@param dt3dt1 +@param dt3dr2 +@param dt3dt2 Optional output derivatives of rvec3 or tvec3 with regard to rvec1, rvec2, tvec1 and +tvec2, respectively. + +The functions compute: + +\f[\begin{array}{l} \texttt{rvec3} = \mathrm{rodrigues} ^{-1} \left ( \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \mathrm{rodrigues} ( \texttt{rvec1} ) \right ) \\ \texttt{tvec3} = \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \texttt{tvec1} + \texttt{tvec2} \end{array} ,\f] + +where \f$\mathrm{rodrigues}\f$ denotes a rotation vector to a rotation matrix transformation, and +\f$\mathrm{rodrigues}^{-1}\f$ denotes the inverse transformation. See Rodrigues for details. + +Also, the functions can compute the derivatives of the output vectors with regards to the input +vectors (see matMulDeriv ). The functions are used inside stereoCalibrate but can also be used in +your own code where Levenberg-Marquardt or another gradient-based solver is used to optimize a +function that contains a matrix multiplication. + */ +CV_EXPORTS_W void composeRT( InputArray rvec1, InputArray tvec1, + InputArray rvec2, InputArray tvec2, + OutputArray rvec3, OutputArray tvec3, + OutputArray dr3dr1 = noArray(), OutputArray dr3dt1 = noArray(), + OutputArray dr3dr2 = noArray(), OutputArray dr3dt2 = noArray(), + OutputArray dt3dr1 = noArray(), OutputArray dt3dt1 = noArray(), + OutputArray dt3dr2 = noArray(), OutputArray dt3dt2 = noArray() ); + +/** @brief Projects 3D points to an image plane. + +@param objectPoints Array of object points, 3xN/Nx3 1-channel or 1xN/Nx1 3-channel (or +vector\ ), where N is the number of points in the view. +@param rvec Rotation vector. See Rodrigues for details. +@param tvec Translation vector. +@param cameraMatrix Camera matrix \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$ . +@param distCoeffs Input vector of distortion coefficients +\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of +4, 5, 8, 12 or 14 elements. If the vector is empty, the zero distortion coefficients are assumed. +@param imagePoints Output array of image points, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel, or +vector\ . +@param jacobian Optional output 2Nx(10+\) jacobian matrix of derivatives of image +points with respect to components of the rotation vector, translation vector, focal lengths, +coordinates of the principal point and the distortion coefficients. In the old interface different +components of the jacobian are returned via different output parameters. +@param aspectRatio Optional "fixed aspect ratio" parameter. If the parameter is not 0, the +function assumes that the aspect ratio (*fx/fy*) is fixed and correspondingly adjusts the jacobian +matrix. + +The function computes projections of 3D points to the image plane given intrinsic and extrinsic +camera parameters. Optionally, the function computes Jacobians - matrices of partial derivatives of +image points coordinates (as functions of all the input parameters) with respect to the particular +parameters, intrinsic and/or extrinsic. The Jacobians are used during the global optimization in +calibrateCamera, solvePnP, and stereoCalibrate . The function itself can also be used to compute a +re-projection error given the current intrinsic and extrinsic parameters. + +@note By setting rvec=tvec=(0,0,0) or by setting cameraMatrix to a 3x3 identity matrix, or by +passing zero distortion coefficients, you can get various useful partial cases of the function. This +means that you can compute the distorted coordinates for a sparse set of points or apply a +perspective transformation (and also compute the derivatives) in the ideal zero-distortion setup. + */ +CV_EXPORTS_W void projectPoints( InputArray objectPoints, + InputArray rvec, InputArray tvec, + InputArray cameraMatrix, InputArray distCoeffs, + OutputArray imagePoints, + OutputArray jacobian = noArray(), + double aspectRatio = 0 ); + +/** @example samples/cpp/tutorial_code/features2D/Homography/homography_from_camera_displacement.cpp +An example program about homography from the camera displacement + +Check @ref tutorial_homography "the corresponding tutorial" for more details +*/ + +/** @brief Finds an object pose from 3D-2D point correspondences. + +@param objectPoints Array of object points in the object coordinate space, Nx3 1-channel or +1xN/Nx1 3-channel, where N is the number of points. vector\ can be also passed here. +@param imagePoints Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, +where N is the number of points. vector\ can be also passed here. +@param cameraMatrix Input camera matrix \f$A = \vecthreethree{fx}{0}{cx}{0}{fy}{cy}{0}{0}{1}\f$ . +@param distCoeffs Input vector of distortion coefficients +\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of +4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are +assumed. +@param rvec Output rotation vector (see @ref Rodrigues ) that, together with tvec , brings points from +the model coordinate system to the camera coordinate system. +@param tvec Output translation vector. +@param useExtrinsicGuess Parameter used for #SOLVEPNP_ITERATIVE. If true (1), the function uses +the provided rvec and tvec values as initial approximations of the rotation and translation +vectors, respectively, and further optimizes them. +@param flags Method for solving a PnP problem: +- **SOLVEPNP_ITERATIVE** Iterative method is based on Levenberg-Marquardt optimization. In +this case the function finds such a pose that minimizes reprojection error, that is the sum +of squared distances between the observed projections imagePoints and the projected (using +projectPoints ) objectPoints . +- **SOLVEPNP_P3P** Method is based on the paper of X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang +"Complete Solution Classification for the Perspective-Three-Point Problem" (@cite gao2003complete). +In this case the function requires exactly four object and image points. +- **SOLVEPNP_AP3P** Method is based on the paper of T. Ke, S. Roumeliotis +"An Efficient Algebraic Solution to the Perspective-Three-Point Problem" (@cite Ke17). +In this case the function requires exactly four object and image points. +- **SOLVEPNP_EPNP** Method has been introduced by F.Moreno-Noguer, V.Lepetit and P.Fua in the +paper "EPnP: Efficient Perspective-n-Point Camera Pose Estimation" (@cite lepetit2009epnp). +- **SOLVEPNP_DLS** Method is based on the paper of Joel A. Hesch and Stergios I. Roumeliotis. +"A Direct Least-Squares (DLS) Method for PnP" (@cite hesch2011direct). +- **SOLVEPNP_UPNP** Method is based on the paper of A.Penate-Sanchez, J.Andrade-Cetto, +F.Moreno-Noguer. "Exhaustive Linearization for Robust Camera Pose and Focal Length +Estimation" (@cite penate2013exhaustive). In this case the function also estimates the parameters \f$f_x\f$ and \f$f_y\f$ +assuming that both have the same value. Then the cameraMatrix is updated with the estimated +focal length. +- **SOLVEPNP_AP3P** Method is based on the paper of Tong Ke and Stergios I. Roumeliotis. +"An Efficient Algebraic Solution to the Perspective-Three-Point Problem" (@cite Ke17). In this case the +function requires exactly four object and image points. + +The function estimates the object pose given a set of object points, their corresponding image +projections, as well as the camera matrix and the distortion coefficients, see the figure below +(more precisely, the X-axis of the camera frame is pointing to the right, the Y-axis downward +and the Z-axis forward). + +![](pnp.jpg) + +Points expressed in the world frame \f$ \bf{X}_w \f$ are projected into the image plane \f$ \left[ u, v \right] \f$ +using the perspective projection model \f$ \Pi \f$ and the camera intrinsic parameters matrix \f$ \bf{A} \f$: + +\f[ + \begin{align*} + \begin{bmatrix} + u \\ + v \\ + 1 + \end{bmatrix} &= + \bf{A} \hspace{0.1em} \Pi \hspace{0.2em} ^{c}\bf{M}_w + \begin{bmatrix} + X_{w} \\ + Y_{w} \\ + Z_{w} \\ + 1 + \end{bmatrix} \\ + \begin{bmatrix} + u \\ + v \\ + 1 + \end{bmatrix} &= + \begin{bmatrix} + f_x & 0 & c_x \\ + 0 & f_y & c_y \\ + 0 & 0 & 1 + \end{bmatrix} + \begin{bmatrix} + 1 & 0 & 0 & 0 \\ + 0 & 1 & 0 & 0 \\ + 0 & 0 & 1 & 0 + \end{bmatrix} + \begin{bmatrix} + r_{11} & r_{12} & r_{13} & t_x \\ + r_{21} & r_{22} & r_{23} & t_y \\ + r_{31} & r_{32} & r_{33} & t_z \\ + 0 & 0 & 0 & 1 + \end{bmatrix} + \begin{bmatrix} + X_{w} \\ + Y_{w} \\ + Z_{w} \\ + 1 + \end{bmatrix} + \end{align*} +\f] + +The estimated pose is thus the rotation (`rvec`) and the translation (`tvec`) vectors that allow to transform +a 3D point expressed in the world frame into the camera frame: + +\f[ + \begin{align*} + \begin{bmatrix} + X_c \\ + Y_c \\ + Z_c \\ + 1 + \end{bmatrix} &= + \hspace{0.2em} ^{c}\bf{M}_w + \begin{bmatrix} + X_{w} \\ + Y_{w} \\ + Z_{w} \\ + 1 + \end{bmatrix} \\ + \begin{bmatrix} + X_c \\ + Y_c \\ + Z_c \\ + 1 + \end{bmatrix} &= + \begin{bmatrix} + r_{11} & r_{12} & r_{13} & t_x \\ + r_{21} & r_{22} & r_{23} & t_y \\ + r_{31} & r_{32} & r_{33} & t_z \\ + 0 & 0 & 0 & 1 + \end{bmatrix} + \begin{bmatrix} + X_{w} \\ + Y_{w} \\ + Z_{w} \\ + 1 + \end{bmatrix} + \end{align*} +\f] + +@note + - An example of how to use solvePnP for planar augmented reality can be found at + opencv_source_code/samples/python/plane_ar.py + - If you are using Python: + - Numpy array slices won't work as input because solvePnP requires contiguous + arrays (enforced by the assertion using cv::Mat::checkVector() around line 55 of + modules/calib3d/src/solvepnp.cpp version 2.4.9) + - The P3P algorithm requires image points to be in an array of shape (N,1,2) due + to its calling of cv::undistortPoints (around line 75 of modules/calib3d/src/solvepnp.cpp version 2.4.9) + which requires 2-channel information. + - Thus, given some data D = np.array(...) where D.shape = (N,M), in order to use a subset of + it as, e.g., imagePoints, one must effectively copy it into a new array: imagePoints = + np.ascontiguousarray(D[:,:2]).reshape((N,1,2)) + - The methods **SOLVEPNP_DLS** and **SOLVEPNP_UPNP** cannot be used as the current implementations are + unstable and sometimes give completely wrong results. If you pass one of these two + flags, **SOLVEPNP_EPNP** method will be used instead. + - The minimum number of points is 4 in the general case. In the case of **SOLVEPNP_P3P** and **SOLVEPNP_AP3P** + methods, it is required to use exactly 4 points (the first 3 points are used to estimate all the solutions + of the P3P problem, the last one is used to retain the best solution that minimizes the reprojection error). + - With **SOLVEPNP_ITERATIVE** method and `useExtrinsicGuess=true`, the minimum number of points is 3 (3 points + are sufficient to compute a pose but there are up to 4 solutions). The initial solution should be close to the + global solution to converge. + */ +CV_EXPORTS_W bool solvePnP( InputArray objectPoints, InputArray imagePoints, + InputArray cameraMatrix, InputArray distCoeffs, + OutputArray rvec, OutputArray tvec, + bool useExtrinsicGuess = false, int flags = SOLVEPNP_ITERATIVE ); + +/** @brief Finds an object pose from 3D-2D point correspondences using the RANSAC scheme. + +@param objectPoints Array of object points in the object coordinate space, Nx3 1-channel or +1xN/Nx1 3-channel, where N is the number of points. vector\ can be also passed here. +@param imagePoints Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, +where N is the number of points. vector\ can be also passed here. +@param cameraMatrix Input camera matrix \f$A = \vecthreethree{fx}{0}{cx}{0}{fy}{cy}{0}{0}{1}\f$ . +@param distCoeffs Input vector of distortion coefficients +\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of +4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are +assumed. +@param rvec Output rotation vector (see Rodrigues ) that, together with tvec , brings points from +the model coordinate system to the camera coordinate system. +@param tvec Output translation vector. +@param useExtrinsicGuess Parameter used for SOLVEPNP_ITERATIVE. If true (1), the function uses +the provided rvec and tvec values as initial approximations of the rotation and translation +vectors, respectively, and further optimizes them. +@param iterationsCount Number of iterations. +@param reprojectionError Inlier threshold value used by the RANSAC procedure. The parameter value +is the maximum allowed distance between the observed and computed point projections to consider it +an inlier. +@param confidence The probability that the algorithm produces a useful result. +@param inliers Output vector that contains indices of inliers in objectPoints and imagePoints . +@param flags Method for solving a PnP problem (see solvePnP ). + +The function estimates an object pose given a set of object points, their corresponding image +projections, as well as the camera matrix and the distortion coefficients. This function finds such +a pose that minimizes reprojection error, that is, the sum of squared distances between the observed +projections imagePoints and the projected (using projectPoints ) objectPoints. The use of RANSAC +makes the function resistant to outliers. + +@note + - An example of how to use solvePNPRansac for object detection can be found at + opencv_source_code/samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/ + - The default method used to estimate the camera pose for the Minimal Sample Sets step + is #SOLVEPNP_EPNP. Exceptions are: + - if you choose #SOLVEPNP_P3P or #SOLVEPNP_AP3P, these methods will be used. + - if the number of input points is equal to 4, #SOLVEPNP_P3P is used. + - The method used to estimate the camera pose using all the inliers is defined by the + flags parameters unless it is equal to #SOLVEPNP_P3P or #SOLVEPNP_AP3P. In this case, + the method #SOLVEPNP_EPNP will be used instead. + */ +CV_EXPORTS_W bool solvePnPRansac( InputArray objectPoints, InputArray imagePoints, + InputArray cameraMatrix, InputArray distCoeffs, + OutputArray rvec, OutputArray tvec, + bool useExtrinsicGuess = false, int iterationsCount = 100, + float reprojectionError = 8.0, double confidence = 0.99, + OutputArray inliers = noArray(), int flags = SOLVEPNP_ITERATIVE ); +/** @brief Finds an object pose from 3 3D-2D point correspondences. + +@param objectPoints Array of object points in the object coordinate space, 3x3 1-channel or +1x3/3x1 3-channel. vector\ can be also passed here. +@param imagePoints Array of corresponding image points, 3x2 1-channel or 1x3/3x1 2-channel. + vector\ can be also passed here. +@param cameraMatrix Input camera matrix \f$A = \vecthreethree{fx}{0}{cx}{0}{fy}{cy}{0}{0}{1}\f$ . +@param distCoeffs Input vector of distortion coefficients +\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of +4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are +assumed. +@param rvecs Output rotation vectors (see Rodrigues ) that, together with tvecs , brings points from +the model coordinate system to the camera coordinate system. A P3P problem has up to 4 solutions. +@param tvecs Output translation vectors. +@param flags Method for solving a P3P problem: +- **SOLVEPNP_P3P** Method is based on the paper of X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang +"Complete Solution Classification for the Perspective-Three-Point Problem" (@cite gao2003complete). +- **SOLVEPNP_AP3P** Method is based on the paper of Tong Ke and Stergios I. Roumeliotis. +"An Efficient Algebraic Solution to the Perspective-Three-Point Problem" (@cite Ke17). + +The function estimates the object pose given 3 object points, their corresponding image +projections, as well as the camera matrix and the distortion coefficients. + */ +CV_EXPORTS_W int solveP3P( InputArray objectPoints, InputArray imagePoints, + InputArray cameraMatrix, InputArray distCoeffs, + OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, + int flags ); + +/** @brief Finds an initial camera matrix from 3D-2D point correspondences. + +@param objectPoints Vector of vectors of the calibration pattern points in the calibration pattern +coordinate space. In the old interface all the per-view vectors are concatenated. See +calibrateCamera for details. +@param imagePoints Vector of vectors of the projections of the calibration pattern points. In the +old interface all the per-view vectors are concatenated. +@param imageSize Image size in pixels used to initialize the principal point. +@param aspectRatio If it is zero or negative, both \f$f_x\f$ and \f$f_y\f$ are estimated independently. +Otherwise, \f$f_x = f_y * \texttt{aspectRatio}\f$ . + +The function estimates and returns an initial camera matrix for the camera calibration process. +Currently, the function only supports planar calibration patterns, which are patterns where each +object point has z-coordinate =0. + */ +CV_EXPORTS_W Mat initCameraMatrix2D( InputArrayOfArrays objectPoints, + InputArrayOfArrays imagePoints, + Size imageSize, double aspectRatio = 1.0 ); + +/** @brief Finds the positions of internal corners of the chessboard. + +@param image Source chessboard view. It must be an 8-bit grayscale or color image. +@param patternSize Number of inner corners per a chessboard row and column +( patternSize = cvSize(points_per_row,points_per_colum) = cvSize(columns,rows) ). +@param corners Output array of detected corners. +@param flags Various operation flags that can be zero or a combination of the following values: +- **CALIB_CB_ADAPTIVE_THRESH** Use adaptive thresholding to convert the image to black +and white, rather than a fixed threshold level (computed from the average image brightness). +- **CALIB_CB_NORMALIZE_IMAGE** Normalize the image gamma with equalizeHist before +applying fixed or adaptive thresholding. +- **CALIB_CB_FILTER_QUADS** Use additional criteria (like contour area, perimeter, +square-like shape) to filter out false quads extracted at the contour retrieval stage. +- **CALIB_CB_FAST_CHECK** Run a fast check on the image that looks for chessboard corners, +and shortcut the call if none is found. This can drastically speed up the call in the +degenerate condition when no chessboard is observed. + +The function attempts to determine whether the input image is a view of the chessboard pattern and +locate the internal chessboard corners. The function returns a non-zero value if all of the corners +are found and they are placed in a certain order (row by row, left to right in every row). +Otherwise, if the function fails to find all the corners or reorder them, it returns 0. For example, +a regular chessboard has 8 x 8 squares and 7 x 7 internal corners, that is, points where the black +squares touch each other. The detected coordinates are approximate, and to determine their positions +more accurately, the function calls cornerSubPix. You also may use the function cornerSubPix with +different parameters if returned coordinates are not accurate enough. + +Sample usage of detecting and drawing chessboard corners: : +@code + Size patternsize(8,6); //interior number of corners + Mat gray = ....; //source image + vector corners; //this will be filled by the detected corners + + //CALIB_CB_FAST_CHECK saves a lot of time on images + //that do not contain any chessboard corners + bool patternfound = findChessboardCorners(gray, patternsize, corners, + CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE + + CALIB_CB_FAST_CHECK); + + if(patternfound) + cornerSubPix(gray, corners, Size(11, 11), Size(-1, -1), + TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 30, 0.1)); + + drawChessboardCorners(img, patternsize, Mat(corners), patternfound); +@endcode +@note The function requires white space (like a square-thick border, the wider the better) around +the board to make the detection more robust in various environments. Otherwise, if there is no +border and the background is dark, the outer black squares cannot be segmented properly and so the +square grouping and ordering algorithm fails. + */ +CV_EXPORTS_W bool findChessboardCorners( InputArray image, Size patternSize, OutputArray corners, + int flags = CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE ); + +//! finds subpixel-accurate positions of the chessboard corners +CV_EXPORTS bool find4QuadCornerSubpix( InputArray img, InputOutputArray corners, Size region_size ); + +/** @brief Renders the detected chessboard corners. + +@param image Destination image. It must be an 8-bit color image. +@param patternSize Number of inner corners per a chessboard row and column +(patternSize = cv::Size(points_per_row,points_per_column)). +@param corners Array of detected corners, the output of findChessboardCorners. +@param patternWasFound Parameter indicating whether the complete board was found or not. The +return value of findChessboardCorners should be passed here. + +The function draws individual chessboard corners detected either as red circles if the board was not +found, or as colored corners connected with lines if the board was found. + */ +CV_EXPORTS_W void drawChessboardCorners( InputOutputArray image, Size patternSize, + InputArray corners, bool patternWasFound ); + +/** @brief Draw axes of the world/object coordinate system from pose estimation. @sa solvePnP + +@param image Input/output image. It must have 1 or 3 channels. The number of channels is not altered. +@param cameraMatrix Input 3x3 floating-point matrix of camera intrinsic parameters. +\f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ +@param distCoeffs Input vector of distortion coefficients +\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of +4, 5, 8, 12 or 14 elements. If the vector is empty, the zero distortion coefficients are assumed. +@param rvec Rotation vector (see @ref Rodrigues ) that, together with tvec , brings points from +the model coordinate system to the camera coordinate system. +@param tvec Translation vector. +@param length Length of the painted axes in the same unit than tvec (usually in meters). +@param thickness Line thickness of the painted axes. + +This function draws the axes of the world/object coordinate system w.r.t. to the camera frame. +OX is drawn in red, OY in green and OZ in blue. + */ +CV_EXPORTS_W void drawFrameAxes(InputOutputArray image, InputArray cameraMatrix, InputArray distCoeffs, + InputArray rvec, InputArray tvec, float length, int thickness=3); + +struct CV_EXPORTS_W_SIMPLE CirclesGridFinderParameters +{ + CV_WRAP CirclesGridFinderParameters(); + CV_PROP_RW cv::Size2f densityNeighborhoodSize; + CV_PROP_RW float minDensity; + CV_PROP_RW int kmeansAttempts; + CV_PROP_RW int minDistanceToAddKeypoint; + CV_PROP_RW int keypointScale; + CV_PROP_RW float minGraphConfidence; + CV_PROP_RW float vertexGain; + CV_PROP_RW float vertexPenalty; + CV_PROP_RW float existingVertexGain; + CV_PROP_RW float edgeGain; + CV_PROP_RW float edgePenalty; + CV_PROP_RW float convexHullFactor; + CV_PROP_RW float minRNGEdgeSwitchDist; + + enum GridType + { + SYMMETRIC_GRID, ASYMMETRIC_GRID + }; + GridType gridType; +}; + +struct CV_EXPORTS_W_SIMPLE CirclesGridFinderParameters2 : public CirclesGridFinderParameters +{ + CV_WRAP CirclesGridFinderParameters2(); + + CV_PROP_RW float squareSize; //!< Distance between two adjacent points. Used by CALIB_CB_CLUSTERING. + CV_PROP_RW float maxRectifiedDistance; //!< Max deviation from predicion. Used by CALIB_CB_CLUSTERING. +}; + +/** @brief Finds centers in the grid of circles. + +@param image grid view of input circles; it must be an 8-bit grayscale or color image. +@param patternSize number of circles per row and column +( patternSize = Size(points_per_row, points_per_colum) ). +@param centers output array of detected centers. +@param flags various operation flags that can be one of the following values: +- **CALIB_CB_SYMMETRIC_GRID** uses symmetric pattern of circles. +- **CALIB_CB_ASYMMETRIC_GRID** uses asymmetric pattern of circles. +- **CALIB_CB_CLUSTERING** uses a special algorithm for grid detection. It is more robust to +perspective distortions but much more sensitive to background clutter. +@param blobDetector feature detector that finds blobs like dark circles on light background. +@param parameters struct for finding circles in a grid pattern. + +The function attempts to determine whether the input image contains a grid of circles. If it is, the +function locates centers of the circles. The function returns a non-zero value if all of the centers +have been found and they have been placed in a certain order (row by row, left to right in every +row). Otherwise, if the function fails to find all the corners or reorder them, it returns 0. + +Sample usage of detecting and drawing the centers of circles: : +@code + Size patternsize(7,7); //number of centers + Mat gray = ....; //source image + vector centers; //this will be filled by the detected centers + + bool patternfound = findCirclesGrid(gray, patternsize, centers); + + drawChessboardCorners(img, patternsize, Mat(centers), patternfound); +@endcode +@note The function requires white space (like a square-thick border, the wider the better) around +the board to make the detection more robust in various environments. + */ +CV_EXPORTS_W bool findCirclesGrid( InputArray image, Size patternSize, + OutputArray centers, int flags, + const Ptr &blobDetector, + CirclesGridFinderParameters parameters); + +/** @overload */ +CV_EXPORTS_W bool findCirclesGrid2( InputArray image, Size patternSize, + OutputArray centers, int flags, + const Ptr &blobDetector, + CirclesGridFinderParameters2 parameters); + +/** @overload */ +CV_EXPORTS_W bool findCirclesGrid( InputArray image, Size patternSize, + OutputArray centers, int flags = CALIB_CB_SYMMETRIC_GRID, + const Ptr &blobDetector = SimpleBlobDetector::create()); + +/** @brief Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern. + +@param objectPoints In the new interface it is a vector of vectors of calibration pattern points in +the calibration pattern coordinate space (e.g. std::vector>). The outer +vector contains as many elements as the number of the pattern views. If the same calibration pattern +is shown in each view and it is fully visible, all the vectors will be the same. Although, it is +possible to use partially occluded patterns, or even different patterns in different views. Then, +the vectors will be different. The points are 3D, but since they are in a pattern coordinate system, +then, if the rig is planar, it may make sense to put the model to a XY coordinate plane so that +Z-coordinate of each input object point is 0. +In the old interface all the vectors of object points from different views are concatenated +together. +@param imagePoints In the new interface it is a vector of vectors of the projections of calibration +pattern points (e.g. std::vector>). imagePoints.size() and +objectPoints.size() and imagePoints[i].size() must be equal to objectPoints[i].size() for each i. +In the old interface all the vectors of object points from different views are concatenated +together. +@param imageSize Size of the image used only to initialize the intrinsic camera matrix. +@param cameraMatrix Output 3x3 floating-point camera matrix +\f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . If CV\_CALIB\_USE\_INTRINSIC\_GUESS +and/or CALIB_FIX_ASPECT_RATIO are specified, some or all of fx, fy, cx, cy must be +initialized before calling the function. +@param distCoeffs Output vector of distortion coefficients +\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of +4, 5, 8, 12 or 14 elements. +@param rvecs Output vector of rotation vectors (see Rodrigues ) estimated for each pattern view +(e.g. std::vector>). That is, each k-th rotation vector together with the corresponding +k-th translation vector (see the next output parameter description) brings the calibration pattern +from the model coordinate space (in which object points are specified) to the world coordinate +space, that is, a real position of the calibration pattern in the k-th pattern view (k=0.. *M* -1). +@param tvecs Output vector of translation vectors estimated for each pattern view. +@param stdDeviationsIntrinsics Output vector of standard deviations estimated for intrinsic parameters. + Order of deviations values: +\f$(f_x, f_y, c_x, c_y, k_1, k_2, p_1, p_2, k_3, k_4, k_5, k_6 , s_1, s_2, s_3, + s_4, \tau_x, \tau_y)\f$ If one of parameters is not estimated, it's deviation is equals to zero. +@param stdDeviationsExtrinsics Output vector of standard deviations estimated for extrinsic parameters. + Order of deviations values: \f$(R_1, T_1, \dotsc , R_M, T_M)\f$ where M is number of pattern views, + \f$R_i, T_i\f$ are concatenated 1x3 vectors. + @param perViewErrors Output vector of the RMS re-projection error estimated for each pattern view. +@param flags Different flags that may be zero or a combination of the following values: +- **CALIB_USE_INTRINSIC_GUESS** cameraMatrix contains valid initial values of +fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image +center ( imageSize is used), and focal distances are computed in a least-squares fashion. +Note, that if intrinsic parameters are known, there is no need to use this function just to +estimate extrinsic parameters. Use solvePnP instead. +- **CALIB_FIX_PRINCIPAL_POINT** The principal point is not changed during the global +optimization. It stays at the center or at a different location specified when +CALIB_USE_INTRINSIC_GUESS is set too. +- **CALIB_FIX_ASPECT_RATIO** The functions considers only fy as a free parameter. The +ratio fx/fy stays the same as in the input cameraMatrix . When +CALIB_USE_INTRINSIC_GUESS is not set, the actual input values of fx and fy are +ignored, only their ratio is computed and used further. +- **CALIB_ZERO_TANGENT_DIST** Tangential distortion coefficients \f$(p_1, p_2)\f$ are set +to zeros and stay zero. +- **CALIB_FIX_K1,...,CALIB_FIX_K6** The corresponding radial distortion +coefficient is not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is +set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. +- **CALIB_RATIONAL_MODEL** Coefficients k4, k5, and k6 are enabled. To provide the +backward compatibility, this extra flag should be explicitly specified to make the +calibration function use the rational model and return 8 coefficients. If the flag is not +set, the function computes and returns only 5 distortion coefficients. +- **CALIB_THIN_PRISM_MODEL** Coefficients s1, s2, s3 and s4 are enabled. To provide the +backward compatibility, this extra flag should be explicitly specified to make the +calibration function use the thin prism model and return 12 coefficients. If the flag is not +set, the function computes and returns only 5 distortion coefficients. +- **CALIB_FIX_S1_S2_S3_S4** The thin prism distortion coefficients are not changed during +the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the +supplied distCoeffs matrix is used. Otherwise, it is set to 0. +- **CALIB_TILTED_MODEL** Coefficients tauX and tauY are enabled. To provide the +backward compatibility, this extra flag should be explicitly specified to make the +calibration function use the tilted sensor model and return 14 coefficients. If the flag is not +set, the function computes and returns only 5 distortion coefficients. +- **CALIB_FIX_TAUX_TAUY** The coefficients of the tilted sensor model are not changed during +the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the +supplied distCoeffs matrix is used. Otherwise, it is set to 0. +@param criteria Termination criteria for the iterative optimization algorithm. + +@return the overall RMS re-projection error. + +The function estimates the intrinsic camera parameters and extrinsic parameters for each of the +views. The algorithm is based on @cite Zhang2000 and @cite BouguetMCT . The coordinates of 3D object +points and their corresponding 2D projections in each view must be specified. That may be achieved +by using an object with a known geometry and easily detectable feature points. Such an object is +called a calibration rig or calibration pattern, and OpenCV has built-in support for a chessboard as +a calibration rig (see findChessboardCorners ). Currently, initialization of intrinsic parameters +(when CALIB_USE_INTRINSIC_GUESS is not set) is only implemented for planar calibration +patterns (where Z-coordinates of the object points must be all zeros). 3D calibration rigs can also +be used as long as initial cameraMatrix is provided. + +The algorithm performs the following steps: + +- Compute the initial intrinsic parameters (the option only available for planar calibration + patterns) or read them from the input parameters. The distortion coefficients are all set to + zeros initially unless some of CALIB_FIX_K? are specified. + +- Estimate the initial camera pose as if the intrinsic parameters have been already known. This is + done using solvePnP . + +- Run the global Levenberg-Marquardt optimization algorithm to minimize the reprojection error, + that is, the total sum of squared distances between the observed feature points imagePoints and + the projected (using the current estimates for camera parameters and the poses) object points + objectPoints. See projectPoints for details. + +@note + If you use a non-square (=non-NxN) grid and findChessboardCorners for calibration, and + calibrateCamera returns bad values (zero distortion coefficients, an image center very far from + (w/2-0.5,h/2-0.5), and/or large differences between \f$f_x\f$ and \f$f_y\f$ (ratios of 10:1 or more)), + then you have probably used patternSize=cvSize(rows,cols) instead of using + patternSize=cvSize(cols,rows) in findChessboardCorners . + +@sa + findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate, undistort + */ +CV_EXPORTS_AS(calibrateCameraExtended) double calibrateCamera( InputArrayOfArrays objectPoints, + InputArrayOfArrays imagePoints, Size imageSize, + InputOutputArray cameraMatrix, InputOutputArray distCoeffs, + OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, + OutputArray stdDeviationsIntrinsics, + OutputArray stdDeviationsExtrinsics, + OutputArray perViewErrors, + int flags = 0, TermCriteria criteria = TermCriteria( + TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON) ); + +/** @overload double calibrateCamera( InputArrayOfArrays objectPoints, + InputArrayOfArrays imagePoints, Size imageSize, + InputOutputArray cameraMatrix, InputOutputArray distCoeffs, + OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, + OutputArray stdDeviations, OutputArray perViewErrors, + int flags = 0, TermCriteria criteria = TermCriteria( + TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON) ) + */ +CV_EXPORTS_W double calibrateCamera( InputArrayOfArrays objectPoints, + InputArrayOfArrays imagePoints, Size imageSize, + InputOutputArray cameraMatrix, InputOutputArray distCoeffs, + OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, + int flags = 0, TermCriteria criteria = TermCriteria( + TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON) ); + +/** @brief Computes useful camera characteristics from the camera matrix. + +@param cameraMatrix Input camera matrix that can be estimated by calibrateCamera or +stereoCalibrate . +@param imageSize Input image size in pixels. +@param apertureWidth Physical width in mm of the sensor. +@param apertureHeight Physical height in mm of the sensor. +@param fovx Output field of view in degrees along the horizontal sensor axis. +@param fovy Output field of view in degrees along the vertical sensor axis. +@param focalLength Focal length of the lens in mm. +@param principalPoint Principal point in mm. +@param aspectRatio \f$f_y/f_x\f$ + +The function computes various useful camera characteristics from the previously estimated camera +matrix. + +@note + Do keep in mind that the unity measure 'mm' stands for whatever unit of measure one chooses for + the chessboard pitch (it can thus be any value). + */ +CV_EXPORTS_W void calibrationMatrixValues( InputArray cameraMatrix, Size imageSize, + double apertureWidth, double apertureHeight, + CV_OUT double& fovx, CV_OUT double& fovy, + CV_OUT double& focalLength, CV_OUT Point2d& principalPoint, + CV_OUT double& aspectRatio ); + +/** @brief Calibrates the stereo camera. + +@param objectPoints Vector of vectors of the calibration pattern points. +@param imagePoints1 Vector of vectors of the projections of the calibration pattern points, +observed by the first camera. +@param imagePoints2 Vector of vectors of the projections of the calibration pattern points, +observed by the second camera. +@param cameraMatrix1 Input/output first camera matrix: +\f$\vecthreethree{f_x^{(j)}}{0}{c_x^{(j)}}{0}{f_y^{(j)}}{c_y^{(j)}}{0}{0}{1}\f$ , \f$j = 0,\, 1\f$ . If +any of CALIB_USE_INTRINSIC_GUESS , CALIB_FIX_ASPECT_RATIO , +CALIB_FIX_INTRINSIC , or CALIB_FIX_FOCAL_LENGTH are specified, some or all of the +matrix components must be initialized. See the flags description for details. +@param distCoeffs1 Input/output vector of distortion coefficients +\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of +4, 5, 8, 12 or 14 elements. The output vector length depends on the flags. +@param cameraMatrix2 Input/output second camera matrix. The parameter is similar to cameraMatrix1 +@param distCoeffs2 Input/output lens distortion coefficients for the second camera. The parameter +is similar to distCoeffs1 . +@param imageSize Size of the image used only to initialize intrinsic camera matrix. +@param R Output rotation matrix between the 1st and the 2nd camera coordinate systems. +@param T Output translation vector between the coordinate systems of the cameras. +@param E Output essential matrix. +@param F Output fundamental matrix. +@param perViewErrors Output vector of the RMS re-projection error estimated for each pattern view. +@param flags Different flags that may be zero or a combination of the following values: +- **CALIB_FIX_INTRINSIC** Fix cameraMatrix? and distCoeffs? so that only R, T, E , and F +matrices are estimated. +- **CALIB_USE_INTRINSIC_GUESS** Optimize some or all of the intrinsic parameters +according to the specified flags. Initial values are provided by the user. +- **CALIB_USE_EXTRINSIC_GUESS** R, T contain valid initial values that are optimized further. +Otherwise R, T are initialized to the median value of the pattern views (each dimension separately). +- **CALIB_FIX_PRINCIPAL_POINT** Fix the principal points during the optimization. +- **CALIB_FIX_FOCAL_LENGTH** Fix \f$f^{(j)}_x\f$ and \f$f^{(j)}_y\f$ . +- **CALIB_FIX_ASPECT_RATIO** Optimize \f$f^{(j)}_y\f$ . Fix the ratio \f$f^{(j)}_x/f^{(j)}_y\f$ +. +- **CALIB_SAME_FOCAL_LENGTH** Enforce \f$f^{(0)}_x=f^{(1)}_x\f$ and \f$f^{(0)}_y=f^{(1)}_y\f$ . +- **CALIB_ZERO_TANGENT_DIST** Set tangential distortion coefficients for each camera to +zeros and fix there. +- **CALIB_FIX_K1,...,CALIB_FIX_K6** Do not change the corresponding radial +distortion coefficient during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, +the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. +- **CALIB_RATIONAL_MODEL** Enable coefficients k4, k5, and k6. To provide the backward +compatibility, this extra flag should be explicitly specified to make the calibration +function use the rational model and return 8 coefficients. If the flag is not set, the +function computes and returns only 5 distortion coefficients. +- **CALIB_THIN_PRISM_MODEL** Coefficients s1, s2, s3 and s4 are enabled. To provide the +backward compatibility, this extra flag should be explicitly specified to make the +calibration function use the thin prism model and return 12 coefficients. If the flag is not +set, the function computes and returns only 5 distortion coefficients. +- **CALIB_FIX_S1_S2_S3_S4** The thin prism distortion coefficients are not changed during +the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the +supplied distCoeffs matrix is used. Otherwise, it is set to 0. +- **CALIB_TILTED_MODEL** Coefficients tauX and tauY are enabled. To provide the +backward compatibility, this extra flag should be explicitly specified to make the +calibration function use the tilted sensor model and return 14 coefficients. If the flag is not +set, the function computes and returns only 5 distortion coefficients. +- **CALIB_FIX_TAUX_TAUY** The coefficients of the tilted sensor model are not changed during +the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the +supplied distCoeffs matrix is used. Otherwise, it is set to 0. +@param criteria Termination criteria for the iterative optimization algorithm. + +The function estimates transformation between two cameras making a stereo pair. If you have a stereo +camera where the relative position and orientation of two cameras is fixed, and if you computed +poses of an object relative to the first camera and to the second camera, (R1, T1) and (R2, T2), +respectively (this can be done with solvePnP ), then those poses definitely relate to each other. +This means that, given ( \f$R_1\f$,\f$T_1\f$ ), it should be possible to compute ( \f$R_2\f$,\f$T_2\f$ ). You only +need to know the position and orientation of the second camera relative to the first camera. This is +what the described function does. It computes ( \f$R\f$,\f$T\f$ ) so that: + +\f[R_2=R*R_1\f] +\f[T_2=R*T_1 + T,\f] + +Optionally, it computes the essential matrix E: + +\f[E= \vecthreethree{0}{-T_2}{T_1}{T_2}{0}{-T_0}{-T_1}{T_0}{0} *R\f] + +where \f$T_i\f$ are components of the translation vector \f$T\f$ : \f$T=[T_0, T_1, T_2]^T\f$ . And the function +can also compute the fundamental matrix F: + +\f[F = cameraMatrix2^{-T} E cameraMatrix1^{-1}\f] + +Besides the stereo-related information, the function can also perform a full calibration of each of +two cameras. However, due to the high dimensionality of the parameter space and noise in the input +data, the function can diverge from the correct solution. If the intrinsic parameters can be +estimated with high accuracy for each of the cameras individually (for example, using +calibrateCamera ), you are recommended to do so and then pass CALIB_FIX_INTRINSIC flag to the +function along with the computed intrinsic parameters. Otherwise, if all the parameters are +estimated at once, it makes sense to restrict some parameters, for example, pass +CALIB_SAME_FOCAL_LENGTH and CALIB_ZERO_TANGENT_DIST flags, which is usually a +reasonable assumption. + +Similarly to calibrateCamera , the function minimizes the total re-projection error for all the +points in all the available views from both cameras. The function returns the final value of the +re-projection error. + */ +CV_EXPORTS_AS(stereoCalibrateExtended) double stereoCalibrate( InputArrayOfArrays objectPoints, + InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2, + InputOutputArray cameraMatrix1, InputOutputArray distCoeffs1, + InputOutputArray cameraMatrix2, InputOutputArray distCoeffs2, + Size imageSize, InputOutputArray R,InputOutputArray T, OutputArray E, OutputArray F, + OutputArray perViewErrors, int flags = CALIB_FIX_INTRINSIC, + TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 1e-6) ); + +/// @overload +CV_EXPORTS_W double stereoCalibrate( InputArrayOfArrays objectPoints, + InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2, + InputOutputArray cameraMatrix1, InputOutputArray distCoeffs1, + InputOutputArray cameraMatrix2, InputOutputArray distCoeffs2, + Size imageSize, OutputArray R,OutputArray T, OutputArray E, OutputArray F, + int flags = CALIB_FIX_INTRINSIC, + TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 1e-6) ); + +/** @brief Computes rectification transforms for each head of a calibrated stereo camera. + +@param cameraMatrix1 First camera matrix. +@param distCoeffs1 First camera distortion parameters. +@param cameraMatrix2 Second camera matrix. +@param distCoeffs2 Second camera distortion parameters. +@param imageSize Size of the image used for stereo calibration. +@param R Rotation matrix between the coordinate systems of the first and the second cameras. +@param T Translation vector between coordinate systems of the cameras. +@param R1 Output 3x3 rectification transform (rotation matrix) for the first camera. +@param R2 Output 3x3 rectification transform (rotation matrix) for the second camera. +@param P1 Output 3x4 projection matrix in the new (rectified) coordinate systems for the first +camera. +@param P2 Output 3x4 projection matrix in the new (rectified) coordinate systems for the second +camera. +@param Q Output \f$4 \times 4\f$ disparity-to-depth mapping matrix (see reprojectImageTo3D ). +@param flags Operation flags that may be zero or CALIB_ZERO_DISPARITY . If the flag is set, +the function makes the principal points of each camera have the same pixel coordinates in the +rectified views. And if the flag is not set, the function may still shift the images in the +horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the +useful image area. +@param alpha Free scaling parameter. If it is -1 or absent, the function performs the default +scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified +images are zoomed and shifted so that only valid pixels are visible (no black areas after +rectification). alpha=1 means that the rectified image is decimated and shifted so that all the +pixels from the original images from the cameras are retained in the rectified images (no source +image pixels are lost). Obviously, any intermediate value yields an intermediate result between +those two extreme cases. +@param newImageSize New image resolution after rectification. The same size should be passed to +initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) +is passed (default), it is set to the original imageSize . Setting it to larger value can help you +preserve details in the original image, especially when there is a big radial distortion. +@param validPixROI1 Optional output rectangles inside the rectified images where all the pixels +are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller +(see the picture below). +@param validPixROI2 Optional output rectangles inside the rectified images where all the pixels +are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller +(see the picture below). + +The function computes the rotation matrices for each camera that (virtually) make both camera image +planes the same plane. Consequently, this makes all the epipolar lines parallel and thus simplifies +the dense stereo correspondence problem. The function takes the matrices computed by stereoCalibrate +as input. As output, it provides two rotation matrices and also two projection matrices in the new +coordinates. The function distinguishes the following two cases: + +- **Horizontal stereo**: the first and the second camera views are shifted relative to each other + mainly along the x axis (with possible small vertical shift). In the rectified images, the + corresponding epipolar lines in the left and right cameras are horizontal and have the same + y-coordinate. P1 and P2 look like: + + \f[\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\f] + + \f[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x*f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\f] + + where \f$T_x\f$ is a horizontal shift between the cameras and \f$cx_1=cx_2\f$ if + CALIB_ZERO_DISPARITY is set. + +- **Vertical stereo**: the first and the second camera views are shifted relative to each other + mainly in vertical direction (and probably a bit in the horizontal direction too). The epipolar + lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like: + + \f[\texttt{P1} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\f] + + \f[\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y*f \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\f] + + where \f$T_y\f$ is a vertical shift between the cameras and \f$cy_1=cy_2\f$ if CALIB_ZERO_DISPARITY is + set. + +As you can see, the first three columns of P1 and P2 will effectively be the new "rectified" camera +matrices. The matrices, together with R1 and R2 , can then be passed to initUndistortRectifyMap to +initialize the rectification map for each camera. + +See below the screenshot from the stereo_calib.cpp sample. Some red horizontal lines pass through +the corresponding image regions. This means that the images are well rectified, which is what most +stereo correspondence algorithms rely on. The green rectangles are roi1 and roi2 . You see that +their interiors are all valid pixels. + +![image](pics/stereo_undistort.jpg) + */ +CV_EXPORTS_W void stereoRectify( InputArray cameraMatrix1, InputArray distCoeffs1, + InputArray cameraMatrix2, InputArray distCoeffs2, + Size imageSize, InputArray R, InputArray T, + OutputArray R1, OutputArray R2, + OutputArray P1, OutputArray P2, + OutputArray Q, int flags = CALIB_ZERO_DISPARITY, + double alpha = -1, Size newImageSize = Size(), + CV_OUT Rect* validPixROI1 = 0, CV_OUT Rect* validPixROI2 = 0 ); + +/** @brief Computes a rectification transform for an uncalibrated stereo camera. + +@param points1 Array of feature points in the first image. +@param points2 The corresponding points in the second image. The same formats as in +findFundamentalMat are supported. +@param F Input fundamental matrix. It can be computed from the same set of point pairs using +findFundamentalMat . +@param imgSize Size of the image. +@param H1 Output rectification homography matrix for the first image. +@param H2 Output rectification homography matrix for the second image. +@param threshold Optional threshold used to filter out the outliers. If the parameter is greater +than zero, all the point pairs that do not comply with the epipolar geometry (that is, the points +for which \f$|\texttt{points2[i]}^T*\texttt{F}*\texttt{points1[i]}|>\texttt{threshold}\f$ ) are +rejected prior to computing the homographies. Otherwise, all the points are considered inliers. + +The function computes the rectification transformations without knowing intrinsic parameters of the +cameras and their relative position in the space, which explains the suffix "uncalibrated". Another +related difference from stereoRectify is that the function outputs not the rectification +transformations in the object (3D) space, but the planar perspective transformations encoded by the +homography matrices H1 and H2 . The function implements the algorithm @cite Hartley99 . + +@note + While the algorithm does not need to know the intrinsic parameters of the cameras, it heavily + depends on the epipolar geometry. Therefore, if the camera lenses have a significant distortion, + it would be better to correct it before computing the fundamental matrix and calling this + function. For example, distortion coefficients can be estimated for each head of stereo camera + separately by using calibrateCamera . Then, the images can be corrected using undistort , or + just the point coordinates can be corrected with undistortPoints . + */ +CV_EXPORTS_W bool stereoRectifyUncalibrated( InputArray points1, InputArray points2, + InputArray F, Size imgSize, + OutputArray H1, OutputArray H2, + double threshold = 5 ); + +//! computes the rectification transformations for 3-head camera, where all the heads are on the same line. +CV_EXPORTS_W float rectify3Collinear( InputArray cameraMatrix1, InputArray distCoeffs1, + InputArray cameraMatrix2, InputArray distCoeffs2, + InputArray cameraMatrix3, InputArray distCoeffs3, + InputArrayOfArrays imgpt1, InputArrayOfArrays imgpt3, + Size imageSize, InputArray R12, InputArray T12, + InputArray R13, InputArray T13, + OutputArray R1, OutputArray R2, OutputArray R3, + OutputArray P1, OutputArray P2, OutputArray P3, + OutputArray Q, double alpha, Size newImgSize, + CV_OUT Rect* roi1, CV_OUT Rect* roi2, int flags ); + +/** @brief Returns the new camera matrix based on the free scaling parameter. + +@param cameraMatrix Input camera matrix. +@param distCoeffs Input vector of distortion coefficients +\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of +4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are +assumed. +@param imageSize Original image size. +@param alpha Free scaling parameter between 0 (when all the pixels in the undistorted image are +valid) and 1 (when all the source image pixels are retained in the undistorted image). See +stereoRectify for details. +@param newImgSize Image size after rectification. By default, it is set to imageSize . +@param validPixROI Optional output rectangle that outlines all-good-pixels region in the +undistorted image. See roi1, roi2 description in stereoRectify . +@param centerPrincipalPoint Optional flag that indicates whether in the new camera matrix the +principal point should be at the image center or not. By default, the principal point is chosen to +best fit a subset of the source image (determined by alpha) to the corrected image. +@return new_camera_matrix Output new camera matrix. + +The function computes and returns the optimal new camera matrix based on the free scaling parameter. +By varying this parameter, you may retrieve only sensible pixels alpha=0 , keep all the original +image pixels if there is valuable information in the corners alpha=1 , or get something in between. +When alpha\>0 , the undistorted result is likely to have some black pixels corresponding to +"virtual" pixels outside of the captured distorted image. The original camera matrix, distortion +coefficients, the computed new camera matrix, and newImageSize should be passed to +initUndistortRectifyMap to produce the maps for remap . + */ +CV_EXPORTS_W Mat getOptimalNewCameraMatrix( InputArray cameraMatrix, InputArray distCoeffs, + Size imageSize, double alpha, Size newImgSize = Size(), + CV_OUT Rect* validPixROI = 0, + bool centerPrincipalPoint = false); + +/** @brief Converts points from Euclidean to homogeneous space. + +@param src Input vector of N-dimensional points. +@param dst Output vector of N+1-dimensional points. + +The function converts points from Euclidean to homogeneous space by appending 1's to the tuple of +point coordinates. That is, each point (x1, x2, ..., xn) is converted to (x1, x2, ..., xn, 1). + */ +CV_EXPORTS_W void convertPointsToHomogeneous( InputArray src, OutputArray dst ); + +/** @brief Converts points from homogeneous to Euclidean space. + +@param src Input vector of N-dimensional points. +@param dst Output vector of N-1-dimensional points. + +The function converts points homogeneous to Euclidean space using perspective projection. That is, +each point (x1, x2, ... x(n-1), xn) is converted to (x1/xn, x2/xn, ..., x(n-1)/xn). When xn=0, the +output point coordinates will be (0,0,0,...). + */ +CV_EXPORTS_W void convertPointsFromHomogeneous( InputArray src, OutputArray dst ); + +/** @brief Converts points to/from homogeneous coordinates. + +@param src Input array or vector of 2D, 3D, or 4D points. +@param dst Output vector of 2D, 3D, or 4D points. + +The function converts 2D or 3D points from/to homogeneous coordinates by calling either +convertPointsToHomogeneous or convertPointsFromHomogeneous. + +@note The function is obsolete. Use one of the previous two functions instead. + */ +CV_EXPORTS void convertPointsHomogeneous( InputArray src, OutputArray dst ); + +/** @brief Calculates a fundamental matrix from the corresponding points in two images. + +@param points1 Array of N points from the first image. The point coordinates should be +floating-point (single or double precision). +@param points2 Array of the second image points of the same size and format as points1 . +@param method Method for computing a fundamental matrix. +- **CV_FM_7POINT** for a 7-point algorithm. \f$N = 7\f$ +- **CV_FM_8POINT** for an 8-point algorithm. \f$N \ge 8\f$ +- **CV_FM_RANSAC** for the RANSAC algorithm. \f$N \ge 8\f$ +- **CV_FM_LMEDS** for the LMedS algorithm. \f$N \ge 8\f$ +@param ransacReprojThreshold Parameter used only for RANSAC. It is the maximum distance from a point to an epipolar +line in pixels, beyond which the point is considered an outlier and is not used for computing the +final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the +point localization, image resolution, and the image noise. +@param confidence Parameter used for the RANSAC and LMedS methods only. It specifies a desirable level +of confidence (probability) that the estimated matrix is correct. +@param mask + +The epipolar geometry is described by the following equation: + +\f[[p_2; 1]^T F [p_1; 1] = 0\f] + +where \f$F\f$ is a fundamental matrix, \f$p_1\f$ and \f$p_2\f$ are corresponding points in the first and the +second images, respectively. + +The function calculates the fundamental matrix using one of four methods listed above and returns +the found fundamental matrix. Normally just one matrix is found. But in case of the 7-point +algorithm, the function may return up to 3 solutions ( \f$9 \times 3\f$ matrix that stores all 3 +matrices sequentially). + +The calculated fundamental matrix may be passed further to computeCorrespondEpilines that finds the +epipolar lines corresponding to the specified points. It can also be passed to +stereoRectifyUncalibrated to compute the rectification transformation. : +@code + // Example. Estimation of fundamental matrix using the RANSAC algorithm + int point_count = 100; + vector points1(point_count); + vector points2(point_count); + + // initialize the points here ... + for( int i = 0; i < point_count; i++ ) + { + points1[i] = ...; + points2[i] = ...; + } + + Mat fundamental_matrix = + findFundamentalMat(points1, points2, FM_RANSAC, 3, 0.99); +@endcode + */ +CV_EXPORTS_W Mat findFundamentalMat( InputArray points1, InputArray points2, + int method = FM_RANSAC, + double ransacReprojThreshold = 3., double confidence = 0.99, + OutputArray mask = noArray() ); + +/** @overload */ +CV_EXPORTS Mat findFundamentalMat( InputArray points1, InputArray points2, + OutputArray mask, int method = FM_RANSAC, + double ransacReprojThreshold = 3., double confidence = 0.99 ); + +/** @brief Calculates an essential matrix from the corresponding points in two images. + +@param points1 Array of N (N \>= 5) 2D points from the first image. The point coordinates should +be floating-point (single or double precision). +@param points2 Array of the second image points of the same size and format as points1 . +@param cameraMatrix Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . +Note that this function assumes that points1 and points2 are feature points from cameras with the +same camera matrix. +@param method Method for computing an essential matrix. +- **RANSAC** for the RANSAC algorithm. +- **LMEDS** for the LMedS algorithm. +@param prob Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of +confidence (probability) that the estimated matrix is correct. +@param threshold Parameter used for RANSAC. It is the maximum distance from a point to an epipolar +line in pixels, beyond which the point is considered an outlier and is not used for computing the +final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the +point localization, image resolution, and the image noise. +@param mask Output array of N elements, every element of which is set to 0 for outliers and to 1 +for the other points. The array is computed only in the RANSAC and LMedS methods. + +This function estimates essential matrix based on the five-point algorithm solver in @cite Nister03 . +@cite SteweniusCFS is also a related. The epipolar geometry is described by the following equation: + +\f[[p_2; 1]^T K^{-T} E K^{-1} [p_1; 1] = 0\f] + +where \f$E\f$ is an essential matrix, \f$p_1\f$ and \f$p_2\f$ are corresponding points in the first and the +second images, respectively. The result of this function may be passed further to +decomposeEssentialMat or recoverPose to recover the relative pose between cameras. + */ +CV_EXPORTS_W Mat findEssentialMat( InputArray points1, InputArray points2, + InputArray cameraMatrix, int method = RANSAC, + double prob = 0.999, double threshold = 1.0, + OutputArray mask = noArray() ); + +/** @overload +@param points1 Array of N (N \>= 5) 2D points from the first image. The point coordinates should +be floating-point (single or double precision). +@param points2 Array of the second image points of the same size and format as points1 . +@param focal focal length of the camera. Note that this function assumes that points1 and points2 +are feature points from cameras with same focal length and principal point. +@param pp principal point of the camera. +@param method Method for computing a fundamental matrix. +- **RANSAC** for the RANSAC algorithm. +- **LMEDS** for the LMedS algorithm. +@param threshold Parameter used for RANSAC. It is the maximum distance from a point to an epipolar +line in pixels, beyond which the point is considered an outlier and is not used for computing the +final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the +point localization, image resolution, and the image noise. +@param prob Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of +confidence (probability) that the estimated matrix is correct. +@param mask Output array of N elements, every element of which is set to 0 for outliers and to 1 +for the other points. The array is computed only in the RANSAC and LMedS methods. + +This function differs from the one above that it computes camera matrix from focal length and +principal point: + +\f[K = +\begin{bmatrix} +f & 0 & x_{pp} \\ +0 & f & y_{pp} \\ +0 & 0 & 1 +\end{bmatrix}\f] + */ +CV_EXPORTS_W Mat findEssentialMat( InputArray points1, InputArray points2, + double focal = 1.0, Point2d pp = Point2d(0, 0), + int method = RANSAC, double prob = 0.999, + double threshold = 1.0, OutputArray mask = noArray() ); + +/** @brief Decompose an essential matrix to possible rotations and translation. + +@param E The input essential matrix. +@param R1 One possible rotation matrix. +@param R2 Another possible rotation matrix. +@param t One possible translation. + +This function decompose an essential matrix E using svd decomposition @cite HartleyZ00 . Generally 4 +possible poses exists for a given E. They are \f$[R_1, t]\f$, \f$[R_1, -t]\f$, \f$[R_2, t]\f$, \f$[R_2, -t]\f$. By +decomposing E, you can only get the direction of the translation, so the function returns unit t. + */ +CV_EXPORTS_W void decomposeEssentialMat( InputArray E, OutputArray R1, OutputArray R2, OutputArray t ); + +/** @brief Recover relative camera rotation and translation from an estimated essential matrix and the +corresponding points in two images, using cheirality check. Returns the number of inliers which pass +the check. + +@param E The input essential matrix. +@param points1 Array of N 2D points from the first image. The point coordinates should be +floating-point (single or double precision). +@param points2 Array of the second image points of the same size and format as points1 . +@param cameraMatrix Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . +Note that this function assumes that points1 and points2 are feature points from cameras with the +same camera matrix. +@param R Recovered relative rotation. +@param t Recovered relative translation. +@param mask Input/output mask for inliers in points1 and points2. +: If it is not empty, then it marks inliers in points1 and points2 for then given essential +matrix E. Only these inliers will be used to recover pose. In the output mask only inliers +which pass the cheirality check. +This function decomposes an essential matrix using decomposeEssentialMat and then verifies possible +pose hypotheses by doing cheirality check. The cheirality check basically means that the +triangulated 3D points should have positive depth. Some details can be found in @cite Nister03 . + +This function can be used to process output E and mask from findEssentialMat. In this scenario, +points1 and points2 are the same input for findEssentialMat. : +@code + // Example. Estimation of fundamental matrix using the RANSAC algorithm + int point_count = 100; + vector points1(point_count); + vector points2(point_count); + + // initialize the points here ... + for( int i = 0; i < point_count; i++ ) + { + points1[i] = ...; + points2[i] = ...; + } + + // cametra matrix with both focal lengths = 1, and principal point = (0, 0) + Mat cameraMatrix = Mat::eye(3, 3, CV_64F); + + Mat E, R, t, mask; + + E = findEssentialMat(points1, points2, cameraMatrix, RANSAC, 0.999, 1.0, mask); + recoverPose(E, points1, points2, cameraMatrix, R, t, mask); +@endcode + */ +CV_EXPORTS_W int recoverPose( InputArray E, InputArray points1, InputArray points2, + InputArray cameraMatrix, OutputArray R, OutputArray t, + InputOutputArray mask = noArray() ); + +/** @overload +@param E The input essential matrix. +@param points1 Array of N 2D points from the first image. The point coordinates should be +floating-point (single or double precision). +@param points2 Array of the second image points of the same size and format as points1 . +@param R Recovered relative rotation. +@param t Recovered relative translation. +@param focal Focal length of the camera. Note that this function assumes that points1 and points2 +are feature points from cameras with same focal length and principal point. +@param pp principal point of the camera. +@param mask Input/output mask for inliers in points1 and points2. +: If it is not empty, then it marks inliers in points1 and points2 for then given essential +matrix E. Only these inliers will be used to recover pose. In the output mask only inliers +which pass the cheirality check. + +This function differs from the one above that it computes camera matrix from focal length and +principal point: + +\f[K = +\begin{bmatrix} +f & 0 & x_{pp} \\ +0 & f & y_{pp} \\ +0 & 0 & 1 +\end{bmatrix}\f] + */ +CV_EXPORTS_W int recoverPose( InputArray E, InputArray points1, InputArray points2, + OutputArray R, OutputArray t, + double focal = 1.0, Point2d pp = Point2d(0, 0), + InputOutputArray mask = noArray() ); + +/** @overload +@param E The input essential matrix. +@param points1 Array of N 2D points from the first image. The point coordinates should be +floating-point (single or double precision). +@param points2 Array of the second image points of the same size and format as points1. +@param cameraMatrix Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . +Note that this function assumes that points1 and points2 are feature points from cameras with the +same camera matrix. +@param R Recovered relative rotation. +@param t Recovered relative translation. +@param distanceThresh threshold distance which is used to filter out far away points (i.e. infinite points). +@param mask Input/output mask for inliers in points1 and points2. +: If it is not empty, then it marks inliers in points1 and points2 for then given essential +matrix E. Only these inliers will be used to recover pose. In the output mask only inliers +which pass the cheirality check. +@param triangulatedPoints 3d points which were reconstructed by triangulation. + */ + +CV_EXPORTS_W int recoverPose( InputArray E, InputArray points1, InputArray points2, + InputArray cameraMatrix, OutputArray R, OutputArray t, double distanceThresh, InputOutputArray mask = noArray(), + OutputArray triangulatedPoints = noArray()); + +/** @brief For points in an image of a stereo pair, computes the corresponding epilines in the other image. + +@param points Input points. \f$N \times 1\f$ or \f$1 \times N\f$ matrix of type CV_32FC2 or +vector\ . +@param whichImage Index of the image (1 or 2) that contains the points . +@param F Fundamental matrix that can be estimated using findFundamentalMat or stereoRectify . +@param lines Output vector of the epipolar lines corresponding to the points in the other image. +Each line \f$ax + by + c=0\f$ is encoded by 3 numbers \f$(a, b, c)\f$ . + +For every point in one of the two images of a stereo pair, the function finds the equation of the +corresponding epipolar line in the other image. + +From the fundamental matrix definition (see findFundamentalMat ), line \f$l^{(2)}_i\f$ in the second +image for the point \f$p^{(1)}_i\f$ in the first image (when whichImage=1 ) is computed as: + +\f[l^{(2)}_i = F p^{(1)}_i\f] + +And vice versa, when whichImage=2, \f$l^{(1)}_i\f$ is computed from \f$p^{(2)}_i\f$ as: + +\f[l^{(1)}_i = F^T p^{(2)}_i\f] + +Line coefficients are defined up to a scale. They are normalized so that \f$a_i^2+b_i^2=1\f$ . + */ +CV_EXPORTS_W void computeCorrespondEpilines( InputArray points, int whichImage, + InputArray F, OutputArray lines ); + +/** @brief Reconstructs points by triangulation. + +@param projMatr1 3x4 projection matrix of the first camera. +@param projMatr2 3x4 projection matrix of the second camera. +@param projPoints1 2xN array of feature points in the first image. In case of c++ version it can +be also a vector of feature points or two-channel matrix of size 1xN or Nx1. +@param projPoints2 2xN array of corresponding points in the second image. In case of c++ version +it can be also a vector of feature points or two-channel matrix of size 1xN or Nx1. +@param points4D 4xN array of reconstructed points in homogeneous coordinates. + +The function reconstructs 3-dimensional points (in homogeneous coordinates) by using their +observations with a stereo camera. Projections matrices can be obtained from stereoRectify. + +@note + Keep in mind that all input data should be of float type in order for this function to work. + +@sa + reprojectImageTo3D + */ +CV_EXPORTS_W void triangulatePoints( InputArray projMatr1, InputArray projMatr2, + InputArray projPoints1, InputArray projPoints2, + OutputArray points4D ); + +/** @brief Refines coordinates of corresponding points. + +@param F 3x3 fundamental matrix. +@param points1 1xN array containing the first set of points. +@param points2 1xN array containing the second set of points. +@param newPoints1 The optimized points1. +@param newPoints2 The optimized points2. + +The function implements the Optimal Triangulation Method (see Multiple View Geometry for details). +For each given point correspondence points1[i] \<-\> points2[i], and a fundamental matrix F, it +computes the corrected correspondences newPoints1[i] \<-\> newPoints2[i] that minimize the geometric +error \f$d(points1[i], newPoints1[i])^2 + d(points2[i],newPoints2[i])^2\f$ (where \f$d(a,b)\f$ is the +geometric distance between points \f$a\f$ and \f$b\f$ ) subject to the epipolar constraint +\f$newPoints2^T * F * newPoints1 = 0\f$ . + */ +CV_EXPORTS_W void correctMatches( InputArray F, InputArray points1, InputArray points2, + OutputArray newPoints1, OutputArray newPoints2 ); + +/** @brief Filters off small noise blobs (speckles) in the disparity map + +@param img The input 16-bit signed disparity image +@param newVal The disparity value used to paint-off the speckles +@param maxSpeckleSize The maximum speckle size to consider it a speckle. Larger blobs are not +affected by the algorithm +@param maxDiff Maximum difference between neighbor disparity pixels to put them into the same +blob. Note that since StereoBM, StereoSGBM and may be other algorithms return a fixed-point +disparity map, where disparity values are multiplied by 16, this scale factor should be taken into +account when specifying this parameter value. +@param buf The optional temporary buffer to avoid memory allocation within the function. + */ +CV_EXPORTS_W void filterSpeckles( InputOutputArray img, double newVal, + int maxSpeckleSize, double maxDiff, + InputOutputArray buf = noArray() ); + +//! computes valid disparity ROI from the valid ROIs of the rectified images (that are returned by cv::stereoRectify()) +CV_EXPORTS_W Rect getValidDisparityROI( Rect roi1, Rect roi2, + int minDisparity, int numberOfDisparities, + int SADWindowSize ); + +//! validates disparity using the left-right check. The matrix "cost" should be computed by the stereo correspondence algorithm +CV_EXPORTS_W void validateDisparity( InputOutputArray disparity, InputArray cost, + int minDisparity, int numberOfDisparities, + int disp12MaxDisp = 1 ); + +/** @brief Reprojects a disparity image to 3D space. + +@param disparity Input single-channel 8-bit unsigned, 16-bit signed, 32-bit signed or 32-bit +floating-point disparity image. If 16-bit signed format is used, the values are assumed to have no +fractional bits. +@param _3dImage Output 3-channel floating-point image of the same size as disparity . Each +element of _3dImage(x,y) contains 3D coordinates of the point (x,y) computed from the disparity +map. +@param Q \f$4 \times 4\f$ perspective transformation matrix that can be obtained with stereoRectify. +@param handleMissingValues Indicates, whether the function should handle missing values (i.e. +points where the disparity was not computed). If handleMissingValues=true, then pixels with the +minimal disparity that corresponds to the outliers (see StereoMatcher::compute ) are transformed +to 3D points with a very large Z value (currently set to 10000). +@param ddepth The optional output array depth. If it is -1, the output image will have CV_32F +depth. ddepth can also be set to CV_16S, CV_32S or CV_32F. + +The function transforms a single-channel disparity map to a 3-channel image representing a 3D +surface. That is, for each pixel (x,y) and the corresponding disparity d=disparity(x,y) , it +computes: + +\f[\begin{array}{l} [X \; Y \; Z \; W]^T = \texttt{Q} *[x \; y \; \texttt{disparity} (x,y) \; 1]^T \\ \texttt{\_3dImage} (x,y) = (X/W, \; Y/W, \; Z/W) \end{array}\f] + +The matrix Q can be an arbitrary \f$4 \times 4\f$ matrix (for example, the one computed by +stereoRectify). To reproject a sparse set of points {(x,y,d),...} to 3D space, use +perspectiveTransform . + */ +CV_EXPORTS_W void reprojectImageTo3D( InputArray disparity, + OutputArray _3dImage, InputArray Q, + bool handleMissingValues = false, + int ddepth = -1 ); + +/** @brief Calculates the Sampson Distance between two points. + +The function cv::sampsonDistance calculates and returns the first order approximation of the geometric error as: +\f[ +sd( \texttt{pt1} , \texttt{pt2} )= +\frac{(\texttt{pt2}^t \cdot \texttt{F} \cdot \texttt{pt1})^2} +{((\texttt{F} \cdot \texttt{pt1})(0))^2 + +((\texttt{F} \cdot \texttt{pt1})(1))^2 + +((\texttt{F}^t \cdot \texttt{pt2})(0))^2 + +((\texttt{F}^t \cdot \texttt{pt2})(1))^2} +\f] +The fundamental matrix may be calculated using the cv::findFundamentalMat function. See @cite HartleyZ00 11.4.3 for details. +@param pt1 first homogeneous 2d point +@param pt2 second homogeneous 2d point +@param F fundamental matrix +@return The computed Sampson distance. +*/ +CV_EXPORTS_W double sampsonDistance(InputArray pt1, InputArray pt2, InputArray F); + +/** @brief Computes an optimal affine transformation between two 3D point sets. + +It computes +\f[ +\begin{bmatrix} +x\\ +y\\ +z\\ +\end{bmatrix} += +\begin{bmatrix} +a_{11} & a_{12} & a_{13}\\ +a_{21} & a_{22} & a_{23}\\ +a_{31} & a_{32} & a_{33}\\ +\end{bmatrix} +\begin{bmatrix} +X\\ +Y\\ +Z\\ +\end{bmatrix} ++ +\begin{bmatrix} +b_1\\ +b_2\\ +b_3\\ +\end{bmatrix} +\f] + +@param src First input 3D point set containing \f$(X,Y,Z)\f$. +@param dst Second input 3D point set containing \f$(x,y,z)\f$. +@param out Output 3D affine transformation matrix \f$3 \times 4\f$ of the form +\f[ +\begin{bmatrix} +a_{11} & a_{12} & a_{13} & b_1\\ +a_{21} & a_{22} & a_{23} & b_2\\ +a_{31} & a_{32} & a_{33} & b_3\\ +\end{bmatrix} +\f] +@param inliers Output vector indicating which points are inliers (1-inlier, 0-outlier). +@param ransacThreshold Maximum reprojection error in the RANSAC algorithm to consider a point as +an inlier. +@param confidence Confidence level, between 0 and 1, for the estimated transformation. Anything +between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation +significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation. + +The function estimates an optimal 3D affine transformation between two 3D point sets using the +RANSAC algorithm. + */ +CV_EXPORTS_W int estimateAffine3D(InputArray src, InputArray dst, + OutputArray out, OutputArray inliers, + double ransacThreshold = 3, double confidence = 0.99); + +/** @brief Computes an optimal affine transformation between two 2D point sets. + +It computes +\f[ +\begin{bmatrix} +x\\ +y\\ +\end{bmatrix} += +\begin{bmatrix} +a_{11} & a_{12}\\ +a_{21} & a_{22}\\ +\end{bmatrix} +\begin{bmatrix} +X\\ +Y\\ +\end{bmatrix} ++ +\begin{bmatrix} +b_1\\ +b_2\\ +\end{bmatrix} +\f] + +@param from First input 2D point set containing \f$(X,Y)\f$. +@param to Second input 2D point set containing \f$(x,y)\f$. +@param inliers Output vector indicating which points are inliers (1-inlier, 0-outlier). +@param method Robust method used to compute transformation. The following methods are possible: +- cv::RANSAC - RANSAC-based robust method +- cv::LMEDS - Least-Median robust method +RANSAC is the default method. +@param ransacReprojThreshold Maximum reprojection error in the RANSAC algorithm to consider +a point as an inlier. Applies only to RANSAC. +@param maxIters The maximum number of robust method iterations. +@param confidence Confidence level, between 0 and 1, for the estimated transformation. Anything +between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation +significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation. +@param refineIters Maximum number of iterations of refining algorithm (Levenberg-Marquardt). +Passing 0 will disable refining, so the output matrix will be output of robust method. + +@return Output 2D affine transformation matrix \f$2 \times 3\f$ or empty matrix if transformation +could not be estimated. The returned matrix has the following form: +\f[ +\begin{bmatrix} +a_{11} & a_{12} & b_1\\ +a_{21} & a_{22} & b_2\\ +\end{bmatrix} +\f] + +The function estimates an optimal 2D affine transformation between two 2D point sets using the +selected robust algorithm. + +The computed transformation is then refined further (using only inliers) with the +Levenberg-Marquardt method to reduce the re-projection error even more. + +@note +The RANSAC method can handle practically any ratio of outliers but needs a threshold to +distinguish inliers from outliers. The method LMeDS does not need any threshold but it works +correctly only when there are more than 50% of inliers. + +@sa estimateAffinePartial2D, getAffineTransform +*/ +CV_EXPORTS_W cv::Mat estimateAffine2D(InputArray from, InputArray to, OutputArray inliers = noArray(), + int method = RANSAC, double ransacReprojThreshold = 3, + size_t maxIters = 2000, double confidence = 0.99, + size_t refineIters = 10); + +/** @brief Computes an optimal limited affine transformation with 4 degrees of freedom between +two 2D point sets. + +@param from First input 2D point set. +@param to Second input 2D point set. +@param inliers Output vector indicating which points are inliers. +@param method Robust method used to compute transformation. The following methods are possible: +- cv::RANSAC - RANSAC-based robust method +- cv::LMEDS - Least-Median robust method +RANSAC is the default method. +@param ransacReprojThreshold Maximum reprojection error in the RANSAC algorithm to consider +a point as an inlier. Applies only to RANSAC. +@param maxIters The maximum number of robust method iterations. +@param confidence Confidence level, between 0 and 1, for the estimated transformation. Anything +between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation +significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation. +@param refineIters Maximum number of iterations of refining algorithm (Levenberg-Marquardt). +Passing 0 will disable refining, so the output matrix will be output of robust method. + +@return Output 2D affine transformation (4 degrees of freedom) matrix \f$2 \times 3\f$ or +empty matrix if transformation could not be estimated. + +The function estimates an optimal 2D affine transformation with 4 degrees of freedom limited to +combinations of translation, rotation, and uniform scaling. Uses the selected algorithm for robust +estimation. + +The computed transformation is then refined further (using only inliers) with the +Levenberg-Marquardt method to reduce the re-projection error even more. + +Estimated transformation matrix is: +\f[ \begin{bmatrix} \cos(\theta) \cdot s & -\sin(\theta) \cdot s & t_x \\ + \sin(\theta) \cdot s & \cos(\theta) \cdot s & t_y +\end{bmatrix} \f] +Where \f$ \theta \f$ is the rotation angle, \f$ s \f$ the scaling factor and \f$ t_x, t_y \f$ are +translations in \f$ x, y \f$ axes respectively. + +@note +The RANSAC method can handle practically any ratio of outliers but need a threshold to +distinguish inliers from outliers. The method LMeDS does not need any threshold but it works +correctly only when there are more than 50% of inliers. + +@sa estimateAffine2D, getAffineTransform +*/ +CV_EXPORTS_W cv::Mat estimateAffinePartial2D(InputArray from, InputArray to, OutputArray inliers = noArray(), + int method = RANSAC, double ransacReprojThreshold = 3, + size_t maxIters = 2000, double confidence = 0.99, + size_t refineIters = 10); + +/** @example samples/cpp/tutorial_code/features2D/Homography/decompose_homography.cpp +An example program with homography decomposition. + +Check @ref tutorial_homography "the corresponding tutorial" for more details. +*/ + +/** @brief Decompose a homography matrix to rotation(s), translation(s) and plane normal(s). + +@param H The input homography matrix between two images. +@param K The input intrinsic camera calibration matrix. +@param rotations Array of rotation matrices. +@param translations Array of translation matrices. +@param normals Array of plane normal matrices. + +This function extracts relative camera motion between two views observing a planar object from the +homography H induced by the plane. The intrinsic camera matrix K must also be provided. The function +may return up to four mathematical solution sets. At least two of the solutions may further be +invalidated if point correspondences are available by applying positive depth constraint (all points +must be in front of the camera). The decomposition method is described in detail in @cite Malis . + */ +CV_EXPORTS_W int decomposeHomographyMat(InputArray H, + InputArray K, + OutputArrayOfArrays rotations, + OutputArrayOfArrays translations, + OutputArrayOfArrays normals); + +/** @brief Filters homography decompositions based on additional information. + +@param rotations Vector of rotation matrices. +@param normals Vector of plane normal matrices. +@param beforePoints Vector of (rectified) visible reference points before the homography is applied +@param afterPoints Vector of (rectified) visible reference points after the homography is applied +@param possibleSolutions Vector of int indices representing the viable solution set after filtering +@param pointsMask optional Mat/Vector of 8u type representing the mask for the inliers as given by the findHomography function + +This function is intended to filter the output of the decomposeHomographyMat based on additional +information as described in @cite Malis . The summary of the method: the decomposeHomographyMat function +returns 2 unique solutions and their "opposites" for a total of 4 solutions. If we have access to the +sets of points visible in the camera frame before and after the homography transformation is applied, +we can determine which are the true potential solutions and which are the opposites by verifying which +homographies are consistent with all visible reference points being in front of the camera. The inputs +are left unchanged; the filtered solution set is returned as indices into the existing one. + +*/ +CV_EXPORTS_W void filterHomographyDecompByVisibleRefpoints(InputArrayOfArrays rotations, + InputArrayOfArrays normals, + InputArray beforePoints, + InputArray afterPoints, + OutputArray possibleSolutions, + InputArray pointsMask = noArray()); + +/** @brief The base class for stereo correspondence algorithms. + */ +class CV_EXPORTS_W StereoMatcher : public Algorithm +{ +public: + enum { DISP_SHIFT = 4, + DISP_SCALE = (1 << DISP_SHIFT) + }; + + /** @brief Computes disparity map for the specified stereo pair + + @param left Left 8-bit single-channel image. + @param right Right image of the same size and the same type as the left one. + @param disparity Output disparity map. It has the same size as the input images. Some algorithms, + like StereoBM or StereoSGBM compute 16-bit fixed-point disparity map (where each disparity value + has 4 fractional bits), whereas other algorithms output 32-bit floating-point disparity map. + */ + CV_WRAP virtual void compute( InputArray left, InputArray right, + OutputArray disparity ) = 0; + + CV_WRAP virtual int getMinDisparity() const = 0; + CV_WRAP virtual void setMinDisparity(int minDisparity) = 0; + + CV_WRAP virtual int getNumDisparities() const = 0; + CV_WRAP virtual void setNumDisparities(int numDisparities) = 0; + + CV_WRAP virtual int getBlockSize() const = 0; + CV_WRAP virtual void setBlockSize(int blockSize) = 0; + + CV_WRAP virtual int getSpeckleWindowSize() const = 0; + CV_WRAP virtual void setSpeckleWindowSize(int speckleWindowSize) = 0; + + CV_WRAP virtual int getSpeckleRange() const = 0; + CV_WRAP virtual void setSpeckleRange(int speckleRange) = 0; + + CV_WRAP virtual int getDisp12MaxDiff() const = 0; + CV_WRAP virtual void setDisp12MaxDiff(int disp12MaxDiff) = 0; +}; + + +/** @brief Class for computing stereo correspondence using the block matching algorithm, introduced and +contributed to OpenCV by K. Konolige. + */ +class CV_EXPORTS_W StereoBM : public StereoMatcher +{ +public: + enum { PREFILTER_NORMALIZED_RESPONSE = 0, + PREFILTER_XSOBEL = 1 + }; + + CV_WRAP virtual int getPreFilterType() const = 0; + CV_WRAP virtual void setPreFilterType(int preFilterType) = 0; + + CV_WRAP virtual int getPreFilterSize() const = 0; + CV_WRAP virtual void setPreFilterSize(int preFilterSize) = 0; + + CV_WRAP virtual int getPreFilterCap() const = 0; + CV_WRAP virtual void setPreFilterCap(int preFilterCap) = 0; + + CV_WRAP virtual int getTextureThreshold() const = 0; + CV_WRAP virtual void setTextureThreshold(int textureThreshold) = 0; + + CV_WRAP virtual int getUniquenessRatio() const = 0; + CV_WRAP virtual void setUniquenessRatio(int uniquenessRatio) = 0; + + CV_WRAP virtual int getSmallerBlockSize() const = 0; + CV_WRAP virtual void setSmallerBlockSize(int blockSize) = 0; + + CV_WRAP virtual Rect getROI1() const = 0; + CV_WRAP virtual void setROI1(Rect roi1) = 0; + + CV_WRAP virtual Rect getROI2() const = 0; + CV_WRAP virtual void setROI2(Rect roi2) = 0; + + /** @brief Creates StereoBM object + + @param numDisparities the disparity search range. For each pixel algorithm will find the best + disparity from 0 (default minimum disparity) to numDisparities. The search range can then be + shifted by changing the minimum disparity. + @param blockSize the linear size of the blocks compared by the algorithm. The size should be odd + (as the block is centered at the current pixel). Larger block size implies smoother, though less + accurate disparity map. Smaller block size gives more detailed disparity map, but there is higher + chance for algorithm to find a wrong correspondence. + + The function create StereoBM object. You can then call StereoBM::compute() to compute disparity for + a specific stereo pair. + */ + CV_WRAP static Ptr create(int numDisparities = 0, int blockSize = 21); +}; + +/** @brief The class implements the modified H. Hirschmuller algorithm @cite HH08 that differs from the original +one as follows: + +- By default, the algorithm is single-pass, which means that you consider only 5 directions +instead of 8. Set mode=StereoSGBM::MODE_HH in createStereoSGBM to run the full variant of the +algorithm but beware that it may consume a lot of memory. +- The algorithm matches blocks, not individual pixels. Though, setting blockSize=1 reduces the +blocks to single pixels. +- Mutual information cost function is not implemented. Instead, a simpler Birchfield-Tomasi +sub-pixel metric from @cite BT98 is used. Though, the color images are supported as well. +- Some pre- and post- processing steps from K. Konolige algorithm StereoBM are included, for +example: pre-filtering (StereoBM::PREFILTER_XSOBEL type) and post-filtering (uniqueness +check, quadratic interpolation and speckle filtering). + +@note + - (Python) An example illustrating the use of the StereoSGBM matching algorithm can be found + at opencv_source_code/samples/python/stereo_match.py + */ +class CV_EXPORTS_W StereoSGBM : public StereoMatcher +{ +public: + enum + { + MODE_SGBM = 0, + MODE_HH = 1, + MODE_SGBM_3WAY = 2, + MODE_HH4 = 3 + }; + + CV_WRAP virtual int getPreFilterCap() const = 0; + CV_WRAP virtual void setPreFilterCap(int preFilterCap) = 0; + + CV_WRAP virtual int getUniquenessRatio() const = 0; + CV_WRAP virtual void setUniquenessRatio(int uniquenessRatio) = 0; + + CV_WRAP virtual int getP1() const = 0; + CV_WRAP virtual void setP1(int P1) = 0; + + CV_WRAP virtual int getP2() const = 0; + CV_WRAP virtual void setP2(int P2) = 0; + + CV_WRAP virtual int getMode() const = 0; + CV_WRAP virtual void setMode(int mode) = 0; + + /** @brief Creates StereoSGBM object + + @param minDisparity Minimum possible disparity value. Normally, it is zero but sometimes + rectification algorithms can shift images, so this parameter needs to be adjusted accordingly. + @param numDisparities Maximum disparity minus minimum disparity. The value is always greater than + zero. In the current implementation, this parameter must be divisible by 16. + @param blockSize Matched block size. It must be an odd number \>=1 . Normally, it should be + somewhere in the 3..11 range. + @param P1 The first parameter controlling the disparity smoothness. See below. + @param P2 The second parameter controlling the disparity smoothness. The larger the values are, + the smoother the disparity is. P1 is the penalty on the disparity change by plus or minus 1 + between neighbor pixels. P2 is the penalty on the disparity change by more than 1 between neighbor + pixels. The algorithm requires P2 \> P1 . See stereo_match.cpp sample where some reasonably good + P1 and P2 values are shown (like 8\*number_of_image_channels\*SADWindowSize\*SADWindowSize and + 32\*number_of_image_channels\*SADWindowSize\*SADWindowSize , respectively). + @param disp12MaxDiff Maximum allowed difference (in integer pixel units) in the left-right + disparity check. Set it to a non-positive value to disable the check. + @param preFilterCap Truncation value for the prefiltered image pixels. The algorithm first + computes x-derivative at each pixel and clips its value by [-preFilterCap, preFilterCap] interval. + The result values are passed to the Birchfield-Tomasi pixel cost function. + @param uniquenessRatio Margin in percentage by which the best (minimum) computed cost function + value should "win" the second best value to consider the found match correct. Normally, a value + within the 5-15 range is good enough. + @param speckleWindowSize Maximum size of smooth disparity regions to consider their noise speckles + and invalidate. Set it to 0 to disable speckle filtering. Otherwise, set it somewhere in the + 50-200 range. + @param speckleRange Maximum disparity variation within each connected component. If you do speckle + filtering, set the parameter to a positive value, it will be implicitly multiplied by 16. + Normally, 1 or 2 is good enough. + @param mode Set it to StereoSGBM::MODE_HH to run the full-scale two-pass dynamic programming + algorithm. It will consume O(W\*H\*numDisparities) bytes, which is large for 640x480 stereo and + huge for HD-size pictures. By default, it is set to false . + + The first constructor initializes StereoSGBM with all the default parameters. So, you only have to + set StereoSGBM::numDisparities at minimum. The second constructor enables you to set each parameter + to a custom value. + */ + CV_WRAP static Ptr create(int minDisparity = 0, int numDisparities = 16, int blockSize = 3, + int P1 = 0, int P2 = 0, int disp12MaxDiff = 0, + int preFilterCap = 0, int uniquenessRatio = 0, + int speckleWindowSize = 0, int speckleRange = 0, + int mode = StereoSGBM::MODE_SGBM); +}; + +//! @} calib3d + +/** @brief The methods in this namespace use a so-called fisheye camera model. + @ingroup calib3d_fisheye +*/ +namespace fisheye +{ +//! @addtogroup calib3d_fisheye +//! @{ + + enum{ + CALIB_USE_INTRINSIC_GUESS = 1 << 0, + CALIB_RECOMPUTE_EXTRINSIC = 1 << 1, + CALIB_CHECK_COND = 1 << 2, + CALIB_FIX_SKEW = 1 << 3, + CALIB_FIX_K1 = 1 << 4, + CALIB_FIX_K2 = 1 << 5, + CALIB_FIX_K3 = 1 << 6, + CALIB_FIX_K4 = 1 << 7, + CALIB_FIX_INTRINSIC = 1 << 8, + CALIB_FIX_PRINCIPAL_POINT = 1 << 9 + }; + + /** @brief Projects points using fisheye model + + @param objectPoints Array of object points, 1xN/Nx1 3-channel (or vector\ ), where N is + the number of points in the view. + @param imagePoints Output array of image points, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel, or + vector\. + @param affine + @param K Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$. + @param D Input vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$. + @param alpha The skew coefficient. + @param jacobian Optional output 2Nx15 jacobian matrix of derivatives of image points with respect + to components of the focal lengths, coordinates of the principal point, distortion coefficients, + rotation vector, translation vector, and the skew. In the old interface different components of + the jacobian are returned via different output parameters. + + The function computes projections of 3D points to the image plane given intrinsic and extrinsic + camera parameters. Optionally, the function computes Jacobians - matrices of partial derivatives of + image points coordinates (as functions of all the input parameters) with respect to the particular + parameters, intrinsic and/or extrinsic. + */ + CV_EXPORTS void projectPoints(InputArray objectPoints, OutputArray imagePoints, const Affine3d& affine, + InputArray K, InputArray D, double alpha = 0, OutputArray jacobian = noArray()); + + /** @overload */ + CV_EXPORTS_W void projectPoints(InputArray objectPoints, OutputArray imagePoints, InputArray rvec, InputArray tvec, + InputArray K, InputArray D, double alpha = 0, OutputArray jacobian = noArray()); + + /** @brief Distorts 2D points using fisheye model. + + @param undistorted Array of object points, 1xN/Nx1 2-channel (or vector\ ), where N is + the number of points in the view. + @param K Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$. + @param D Input vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$. + @param alpha The skew coefficient. + @param distorted Output array of image points, 1xN/Nx1 2-channel, or vector\ . + + Note that the function assumes the camera matrix of the undistorted points to be identity. + This means if you want to transform back points undistorted with undistortPoints() you have to + multiply them with \f$P^{-1}\f$. + */ + CV_EXPORTS_W void distortPoints(InputArray undistorted, OutputArray distorted, InputArray K, InputArray D, double alpha = 0); + + /** @brief Undistorts 2D points using fisheye model + + @param distorted Array of object points, 1xN/Nx1 2-channel (or vector\ ), where N is the + number of points in the view. + @param K Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$. + @param D Input vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$. + @param R Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 + 1-channel or 1x1 3-channel + @param P New camera matrix (3x3) or new projection matrix (3x4) + @param undistorted Output array of image points, 1xN/Nx1 2-channel, or vector\ . + */ + CV_EXPORTS_W void undistortPoints(InputArray distorted, OutputArray undistorted, + InputArray K, InputArray D, InputArray R = noArray(), InputArray P = noArray()); + + /** @brief Computes undistortion and rectification maps for image transform by cv::remap(). If D is empty zero + distortion is used, if R or P is empty identity matrixes are used. + + @param K Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$. + @param D Input vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$. + @param R Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 + 1-channel or 1x1 3-channel + @param P New camera matrix (3x3) or new projection matrix (3x4) + @param size Undistorted image size. + @param m1type Type of the first output map that can be CV_32FC1 or CV_16SC2 . See convertMaps() + for details. + @param map1 The first output map. + @param map2 The second output map. + */ + CV_EXPORTS_W void initUndistortRectifyMap(InputArray K, InputArray D, InputArray R, InputArray P, + const cv::Size& size, int m1type, OutputArray map1, OutputArray map2); + + /** @brief Transforms an image to compensate for fisheye lens distortion. + + @param distorted image with fisheye lens distortion. + @param undistorted Output image with compensated fisheye lens distortion. + @param K Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$. + @param D Input vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$. + @param Knew Camera matrix of the distorted image. By default, it is the identity matrix but you + may additionally scale and shift the result by using a different matrix. + @param new_size + + The function transforms an image to compensate radial and tangential lens distortion. + + The function is simply a combination of fisheye::initUndistortRectifyMap (with unity R ) and remap + (with bilinear interpolation). See the former function for details of the transformation being + performed. + + See below the results of undistortImage. + - a\) result of undistort of perspective camera model (all possible coefficients (k_1, k_2, k_3, + k_4, k_5, k_6) of distortion were optimized under calibration) + - b\) result of fisheye::undistortImage of fisheye camera model (all possible coefficients (k_1, k_2, + k_3, k_4) of fisheye distortion were optimized under calibration) + - c\) original image was captured with fisheye lens + + Pictures a) and b) almost the same. But if we consider points of image located far from the center + of image, we can notice that on image a) these points are distorted. + + ![image](pics/fisheye_undistorted.jpg) + */ + CV_EXPORTS_W void undistortImage(InputArray distorted, OutputArray undistorted, + InputArray K, InputArray D, InputArray Knew = cv::noArray(), const Size& new_size = Size()); + + /** @brief Estimates new camera matrix for undistortion or rectification. + + @param K Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$. + @param image_size + @param D Input vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$. + @param R Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 + 1-channel or 1x1 3-channel + @param P New camera matrix (3x3) or new projection matrix (3x4) + @param balance Sets the new focal length in range between the min focal length and the max focal + length. Balance is in range of [0, 1]. + @param new_size + @param fov_scale Divisor for new focal length. + */ + CV_EXPORTS_W void estimateNewCameraMatrixForUndistortRectify(InputArray K, InputArray D, const Size &image_size, InputArray R, + OutputArray P, double balance = 0.0, const Size& new_size = Size(), double fov_scale = 1.0); + + /** @brief Performs camera calibaration + + @param objectPoints vector of vectors of calibration pattern points in the calibration pattern + coordinate space. + @param imagePoints vector of vectors of the projections of calibration pattern points. + imagePoints.size() and objectPoints.size() and imagePoints[i].size() must be equal to + objectPoints[i].size() for each i. + @param image_size Size of the image used only to initialize the intrinsic camera matrix. + @param K Output 3x3 floating-point camera matrix + \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . If + fisheye::CALIB_USE_INTRINSIC_GUESS/ is specified, some or all of fx, fy, cx, cy must be + initialized before calling the function. + @param D Output vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$. + @param rvecs Output vector of rotation vectors (see Rodrigues ) estimated for each pattern view. + That is, each k-th rotation vector together with the corresponding k-th translation vector (see + the next output parameter description) brings the calibration pattern from the model coordinate + space (in which object points are specified) to the world coordinate space, that is, a real + position of the calibration pattern in the k-th pattern view (k=0.. *M* -1). + @param tvecs Output vector of translation vectors estimated for each pattern view. + @param flags Different flags that may be zero or a combination of the following values: + - **fisheye::CALIB_USE_INTRINSIC_GUESS** cameraMatrix contains valid initial values of + fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image + center ( imageSize is used), and focal distances are computed in a least-squares fashion. + - **fisheye::CALIB_RECOMPUTE_EXTRINSIC** Extrinsic will be recomputed after each iteration + of intrinsic optimization. + - **fisheye::CALIB_CHECK_COND** The functions will check validity of condition number. + - **fisheye::CALIB_FIX_SKEW** Skew coefficient (alpha) is set to zero and stay zero. + - **fisheye::CALIB_FIX_K1..fisheye::CALIB_FIX_K4** Selected distortion coefficients + are set to zeros and stay zero. + - **fisheye::CALIB_FIX_PRINCIPAL_POINT** The principal point is not changed during the global +optimization. It stays at the center or at a different location specified when CALIB_USE_INTRINSIC_GUESS is set too. + @param criteria Termination criteria for the iterative optimization algorithm. + */ + CV_EXPORTS_W double calibrate(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, const Size& image_size, + InputOutputArray K, InputOutputArray D, OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, int flags = 0, + TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 100, DBL_EPSILON)); + + /** @brief Stereo rectification for fisheye camera model + + @param K1 First camera matrix. + @param D1 First camera distortion parameters. + @param K2 Second camera matrix. + @param D2 Second camera distortion parameters. + @param imageSize Size of the image used for stereo calibration. + @param R Rotation matrix between the coordinate systems of the first and the second + cameras. + @param tvec Translation vector between coordinate systems of the cameras. + @param R1 Output 3x3 rectification transform (rotation matrix) for the first camera. + @param R2 Output 3x3 rectification transform (rotation matrix) for the second camera. + @param P1 Output 3x4 projection matrix in the new (rectified) coordinate systems for the first + camera. + @param P2 Output 3x4 projection matrix in the new (rectified) coordinate systems for the second + camera. + @param Q Output \f$4 \times 4\f$ disparity-to-depth mapping matrix (see reprojectImageTo3D ). + @param flags Operation flags that may be zero or CALIB_ZERO_DISPARITY . If the flag is set, + the function makes the principal points of each camera have the same pixel coordinates in the + rectified views. And if the flag is not set, the function may still shift the images in the + horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the + useful image area. + @param newImageSize New image resolution after rectification. The same size should be passed to + initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) + is passed (default), it is set to the original imageSize . Setting it to larger value can help you + preserve details in the original image, especially when there is a big radial distortion. + @param balance Sets the new focal length in range between the min focal length and the max focal + length. Balance is in range of [0, 1]. + @param fov_scale Divisor for new focal length. + */ + CV_EXPORTS_W void stereoRectify(InputArray K1, InputArray D1, InputArray K2, InputArray D2, const Size &imageSize, InputArray R, InputArray tvec, + OutputArray R1, OutputArray R2, OutputArray P1, OutputArray P2, OutputArray Q, int flags, const Size &newImageSize = Size(), + double balance = 0.0, double fov_scale = 1.0); + + /** @brief Performs stereo calibration + + @param objectPoints Vector of vectors of the calibration pattern points. + @param imagePoints1 Vector of vectors of the projections of the calibration pattern points, + observed by the first camera. + @param imagePoints2 Vector of vectors of the projections of the calibration pattern points, + observed by the second camera. + @param K1 Input/output first camera matrix: + \f$\vecthreethree{f_x^{(j)}}{0}{c_x^{(j)}}{0}{f_y^{(j)}}{c_y^{(j)}}{0}{0}{1}\f$ , \f$j = 0,\, 1\f$ . If + any of fisheye::CALIB_USE_INTRINSIC_GUESS , fisheye::CALIB_FIX_INTRINSIC are specified, + some or all of the matrix components must be initialized. + @param D1 Input/output vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$ of 4 elements. + @param K2 Input/output second camera matrix. The parameter is similar to K1 . + @param D2 Input/output lens distortion coefficients for the second camera. The parameter is + similar to D1 . + @param imageSize Size of the image used only to initialize intrinsic camera matrix. + @param R Output rotation matrix between the 1st and the 2nd camera coordinate systems. + @param T Output translation vector between the coordinate systems of the cameras. + @param flags Different flags that may be zero or a combination of the following values: + - **fisheye::CALIB_FIX_INTRINSIC** Fix K1, K2? and D1, D2? so that only R, T matrices + are estimated. + - **fisheye::CALIB_USE_INTRINSIC_GUESS** K1, K2 contains valid initial values of + fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image + center (imageSize is used), and focal distances are computed in a least-squares fashion. + - **fisheye::CALIB_RECOMPUTE_EXTRINSIC** Extrinsic will be recomputed after each iteration + of intrinsic optimization. + - **fisheye::CALIB_CHECK_COND** The functions will check validity of condition number. + - **fisheye::CALIB_FIX_SKEW** Skew coefficient (alpha) is set to zero and stay zero. + - **fisheye::CALIB_FIX_K1..4** Selected distortion coefficients are set to zeros and stay + zero. + @param criteria Termination criteria for the iterative optimization algorithm. + */ + CV_EXPORTS_W double stereoCalibrate(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2, + InputOutputArray K1, InputOutputArray D1, InputOutputArray K2, InputOutputArray D2, Size imageSize, + OutputArray R, OutputArray T, int flags = fisheye::CALIB_FIX_INTRINSIC, + TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 100, DBL_EPSILON)); + +//! @} calib3d_fisheye +} // end namespace fisheye + +} //end namespace cv + +#ifndef DISABLE_OPENCV_24_COMPATIBILITY +#include "opencv2/calib3d/calib3d_c.h" +#endif + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/calib3d/calib3d.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/calib3d/calib3d.hpp new file mode 100644 index 0000000000000000000000000000000000000000..b3da45edd59c0a0807abb834e0e4216522316443 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/calib3d/calib3d.hpp @@ -0,0 +1,48 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifdef __OPENCV_BUILD +#error this is a compatibility header which should not be used inside the OpenCV library +#endif + +#include "opencv2/calib3d.hpp" diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/calib3d/calib3d_c.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/calib3d/calib3d_c.h new file mode 100644 index 0000000000000000000000000000000000000000..8ec6390d70e3064debb62ad7301ba31707f6339c --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/calib3d/calib3d_c.h @@ -0,0 +1,427 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CALIB3D_C_H +#define OPENCV_CALIB3D_C_H + +#include "opencv2/core/core_c.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/** @addtogroup calib3d_c + @{ + */ + +/****************************************************************************************\ +* Camera Calibration, Pose Estimation and Stereo * +\****************************************************************************************/ + +typedef struct CvPOSITObject CvPOSITObject; + +/* Allocates and initializes CvPOSITObject structure before doing cvPOSIT */ +CVAPI(CvPOSITObject*) cvCreatePOSITObject( CvPoint3D32f* points, int point_count ); + + +/* Runs POSIT (POSe from ITeration) algorithm for determining 3d position of + an object given its model and projection in a weak-perspective case */ +CVAPI(void) cvPOSIT( CvPOSITObject* posit_object, CvPoint2D32f* image_points, + double focal_length, CvTermCriteria criteria, + float* rotation_matrix, float* translation_vector); + +/* Releases CvPOSITObject structure */ +CVAPI(void) cvReleasePOSITObject( CvPOSITObject** posit_object ); + +/* updates the number of RANSAC iterations */ +CVAPI(int) cvRANSACUpdateNumIters( double p, double err_prob, + int model_points, int max_iters ); + +CVAPI(void) cvConvertPointsHomogeneous( const CvMat* src, CvMat* dst ); + +/* Calculates fundamental matrix given a set of corresponding points */ +#define CV_FM_7POINT 1 +#define CV_FM_8POINT 2 + +#define CV_LMEDS 4 +#define CV_RANSAC 8 + +#define CV_FM_LMEDS_ONLY CV_LMEDS +#define CV_FM_RANSAC_ONLY CV_RANSAC +#define CV_FM_LMEDS CV_LMEDS +#define CV_FM_RANSAC CV_RANSAC + +enum +{ + CV_ITERATIVE = 0, + CV_EPNP = 1, // F.Moreno-Noguer, V.Lepetit and P.Fua "EPnP: Efficient Perspective-n-Point Camera Pose Estimation" + CV_P3P = 2, // X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang; "Complete Solution Classification for the Perspective-Three-Point Problem" + CV_DLS = 3 // Joel A. Hesch and Stergios I. Roumeliotis. "A Direct Least-Squares (DLS) Method for PnP" +}; + +CVAPI(int) cvFindFundamentalMat( const CvMat* points1, const CvMat* points2, + CvMat* fundamental_matrix, + int method CV_DEFAULT(CV_FM_RANSAC), + double param1 CV_DEFAULT(3.), double param2 CV_DEFAULT(0.99), + CvMat* status CV_DEFAULT(NULL) ); + +/* For each input point on one of images + computes parameters of the corresponding + epipolar line on the other image */ +CVAPI(void) cvComputeCorrespondEpilines( const CvMat* points, + int which_image, + const CvMat* fundamental_matrix, + CvMat* correspondent_lines ); + +/* Triangulation functions */ + +CVAPI(void) cvTriangulatePoints(CvMat* projMatr1, CvMat* projMatr2, + CvMat* projPoints1, CvMat* projPoints2, + CvMat* points4D); + +CVAPI(void) cvCorrectMatches(CvMat* F, CvMat* points1, CvMat* points2, + CvMat* new_points1, CvMat* new_points2); + + +/* Computes the optimal new camera matrix according to the free scaling parameter alpha: + alpha=0 - only valid pixels will be retained in the undistorted image + alpha=1 - all the source image pixels will be retained in the undistorted image +*/ +CVAPI(void) cvGetOptimalNewCameraMatrix( const CvMat* camera_matrix, + const CvMat* dist_coeffs, + CvSize image_size, double alpha, + CvMat* new_camera_matrix, + CvSize new_imag_size CV_DEFAULT(cvSize(0,0)), + CvRect* valid_pixel_ROI CV_DEFAULT(0), + int center_principal_point CV_DEFAULT(0)); + +/* Converts rotation vector to rotation matrix or vice versa */ +CVAPI(int) cvRodrigues2( const CvMat* src, CvMat* dst, + CvMat* jacobian CV_DEFAULT(0) ); + +/* Finds perspective transformation between the object plane and image (view) plane */ +CVAPI(int) cvFindHomography( const CvMat* src_points, + const CvMat* dst_points, + CvMat* homography, + int method CV_DEFAULT(0), + double ransacReprojThreshold CV_DEFAULT(3), + CvMat* mask CV_DEFAULT(0), + int maxIters CV_DEFAULT(2000), + double confidence CV_DEFAULT(0.995)); + +/* Computes RQ decomposition for 3x3 matrices */ +CVAPI(void) cvRQDecomp3x3( const CvMat *matrixM, CvMat *matrixR, CvMat *matrixQ, + CvMat *matrixQx CV_DEFAULT(NULL), + CvMat *matrixQy CV_DEFAULT(NULL), + CvMat *matrixQz CV_DEFAULT(NULL), + CvPoint3D64f *eulerAngles CV_DEFAULT(NULL)); + +/* Computes projection matrix decomposition */ +CVAPI(void) cvDecomposeProjectionMatrix( const CvMat *projMatr, CvMat *calibMatr, + CvMat *rotMatr, CvMat *posVect, + CvMat *rotMatrX CV_DEFAULT(NULL), + CvMat *rotMatrY CV_DEFAULT(NULL), + CvMat *rotMatrZ CV_DEFAULT(NULL), + CvPoint3D64f *eulerAngles CV_DEFAULT(NULL)); + +/* Computes d(AB)/dA and d(AB)/dB */ +CVAPI(void) cvCalcMatMulDeriv( const CvMat* A, const CvMat* B, CvMat* dABdA, CvMat* dABdB ); + +/* Computes r3 = rodrigues(rodrigues(r2)*rodrigues(r1)), + t3 = rodrigues(r2)*t1 + t2 and the respective derivatives */ +CVAPI(void) cvComposeRT( const CvMat* _rvec1, const CvMat* _tvec1, + const CvMat* _rvec2, const CvMat* _tvec2, + CvMat* _rvec3, CvMat* _tvec3, + CvMat* dr3dr1 CV_DEFAULT(0), CvMat* dr3dt1 CV_DEFAULT(0), + CvMat* dr3dr2 CV_DEFAULT(0), CvMat* dr3dt2 CV_DEFAULT(0), + CvMat* dt3dr1 CV_DEFAULT(0), CvMat* dt3dt1 CV_DEFAULT(0), + CvMat* dt3dr2 CV_DEFAULT(0), CvMat* dt3dt2 CV_DEFAULT(0) ); + +/* Projects object points to the view plane using + the specified extrinsic and intrinsic camera parameters */ +CVAPI(void) cvProjectPoints2( const CvMat* object_points, const CvMat* rotation_vector, + const CvMat* translation_vector, const CvMat* camera_matrix, + const CvMat* distortion_coeffs, CvMat* image_points, + CvMat* dpdrot CV_DEFAULT(NULL), CvMat* dpdt CV_DEFAULT(NULL), + CvMat* dpdf CV_DEFAULT(NULL), CvMat* dpdc CV_DEFAULT(NULL), + CvMat* dpddist CV_DEFAULT(NULL), + double aspect_ratio CV_DEFAULT(0)); + +/* Finds extrinsic camera parameters from + a few known corresponding point pairs and intrinsic parameters */ +CVAPI(void) cvFindExtrinsicCameraParams2( const CvMat* object_points, + const CvMat* image_points, + const CvMat* camera_matrix, + const CvMat* distortion_coeffs, + CvMat* rotation_vector, + CvMat* translation_vector, + int use_extrinsic_guess CV_DEFAULT(0) ); + +/* Computes initial estimate of the intrinsic camera parameters + in case of planar calibration target (e.g. chessboard) */ +CVAPI(void) cvInitIntrinsicParams2D( const CvMat* object_points, + const CvMat* image_points, + const CvMat* npoints, CvSize image_size, + CvMat* camera_matrix, + double aspect_ratio CV_DEFAULT(1.) ); + +#define CV_CALIB_CB_ADAPTIVE_THRESH 1 +#define CV_CALIB_CB_NORMALIZE_IMAGE 2 +#define CV_CALIB_CB_FILTER_QUADS 4 +#define CV_CALIB_CB_FAST_CHECK 8 + +// Performs a fast check if a chessboard is in the input image. This is a workaround to +// a problem of cvFindChessboardCorners being slow on images with no chessboard +// - src: input image +// - size: chessboard size +// Returns 1 if a chessboard can be in this image and findChessboardCorners should be called, +// 0 if there is no chessboard, -1 in case of error +CVAPI(int) cvCheckChessboard(IplImage* src, CvSize size); + + /* Detects corners on a chessboard calibration pattern */ +CVAPI(int) cvFindChessboardCorners( const void* image, CvSize pattern_size, + CvPoint2D32f* corners, + int* corner_count CV_DEFAULT(NULL), + int flags CV_DEFAULT(CV_CALIB_CB_ADAPTIVE_THRESH+CV_CALIB_CB_NORMALIZE_IMAGE) ); + +/* Draws individual chessboard corners or the whole chessboard detected */ +CVAPI(void) cvDrawChessboardCorners( CvArr* image, CvSize pattern_size, + CvPoint2D32f* corners, + int count, int pattern_was_found ); + +#define CV_CALIB_USE_INTRINSIC_GUESS 1 +#define CV_CALIB_FIX_ASPECT_RATIO 2 +#define CV_CALIB_FIX_PRINCIPAL_POINT 4 +#define CV_CALIB_ZERO_TANGENT_DIST 8 +#define CV_CALIB_FIX_FOCAL_LENGTH 16 +#define CV_CALIB_FIX_K1 32 +#define CV_CALIB_FIX_K2 64 +#define CV_CALIB_FIX_K3 128 +#define CV_CALIB_FIX_K4 2048 +#define CV_CALIB_FIX_K5 4096 +#define CV_CALIB_FIX_K6 8192 +#define CV_CALIB_RATIONAL_MODEL 16384 +#define CV_CALIB_THIN_PRISM_MODEL 32768 +#define CV_CALIB_FIX_S1_S2_S3_S4 65536 +#define CV_CALIB_TILTED_MODEL 262144 +#define CV_CALIB_FIX_TAUX_TAUY 524288 +#define CV_CALIB_FIX_TANGENT_DIST 2097152 + +#define CV_CALIB_NINTRINSIC 18 + +/* Finds intrinsic and extrinsic camera parameters + from a few views of known calibration pattern */ +CVAPI(double) cvCalibrateCamera2( const CvMat* object_points, + const CvMat* image_points, + const CvMat* point_counts, + CvSize image_size, + CvMat* camera_matrix, + CvMat* distortion_coeffs, + CvMat* rotation_vectors CV_DEFAULT(NULL), + CvMat* translation_vectors CV_DEFAULT(NULL), + int flags CV_DEFAULT(0), + CvTermCriteria term_crit CV_DEFAULT(cvTermCriteria( + CV_TERMCRIT_ITER+CV_TERMCRIT_EPS,30,DBL_EPSILON)) ); + +/* Computes various useful characteristics of the camera from the data computed by + cvCalibrateCamera2 */ +CVAPI(void) cvCalibrationMatrixValues( const CvMat *camera_matrix, + CvSize image_size, + double aperture_width CV_DEFAULT(0), + double aperture_height CV_DEFAULT(0), + double *fovx CV_DEFAULT(NULL), + double *fovy CV_DEFAULT(NULL), + double *focal_length CV_DEFAULT(NULL), + CvPoint2D64f *principal_point CV_DEFAULT(NULL), + double *pixel_aspect_ratio CV_DEFAULT(NULL)); + +#define CV_CALIB_FIX_INTRINSIC 256 +#define CV_CALIB_SAME_FOCAL_LENGTH 512 + +/* Computes the transformation from one camera coordinate system to another one + from a few correspondent views of the same calibration target. Optionally, calibrates + both cameras */ +CVAPI(double) cvStereoCalibrate( const CvMat* object_points, const CvMat* image_points1, + const CvMat* image_points2, const CvMat* npoints, + CvMat* camera_matrix1, CvMat* dist_coeffs1, + CvMat* camera_matrix2, CvMat* dist_coeffs2, + CvSize image_size, CvMat* R, CvMat* T, + CvMat* E CV_DEFAULT(0), CvMat* F CV_DEFAULT(0), + int flags CV_DEFAULT(CV_CALIB_FIX_INTRINSIC), + CvTermCriteria term_crit CV_DEFAULT(cvTermCriteria( + CV_TERMCRIT_ITER+CV_TERMCRIT_EPS,30,1e-6)) ); + +#define CV_CALIB_ZERO_DISPARITY 1024 + +/* Computes 3D rotations (+ optional shift) for each camera coordinate system to make both + views parallel (=> to make all the epipolar lines horizontal or vertical) */ +CVAPI(void) cvStereoRectify( const CvMat* camera_matrix1, const CvMat* camera_matrix2, + const CvMat* dist_coeffs1, const CvMat* dist_coeffs2, + CvSize image_size, const CvMat* R, const CvMat* T, + CvMat* R1, CvMat* R2, CvMat* P1, CvMat* P2, + CvMat* Q CV_DEFAULT(0), + int flags CV_DEFAULT(CV_CALIB_ZERO_DISPARITY), + double alpha CV_DEFAULT(-1), + CvSize new_image_size CV_DEFAULT(cvSize(0,0)), + CvRect* valid_pix_ROI1 CV_DEFAULT(0), + CvRect* valid_pix_ROI2 CV_DEFAULT(0)); + +/* Computes rectification transformations for uncalibrated pair of images using a set + of point correspondences */ +CVAPI(int) cvStereoRectifyUncalibrated( const CvMat* points1, const CvMat* points2, + const CvMat* F, CvSize img_size, + CvMat* H1, CvMat* H2, + double threshold CV_DEFAULT(5)); + + + +/* stereo correspondence parameters and functions */ + +#define CV_STEREO_BM_NORMALIZED_RESPONSE 0 +#define CV_STEREO_BM_XSOBEL 1 + +/* Block matching algorithm structure */ +typedef struct CvStereoBMState +{ + // pre-filtering (normalization of input images) + int preFilterType; // =CV_STEREO_BM_NORMALIZED_RESPONSE now + int preFilterSize; // averaging window size: ~5x5..21x21 + int preFilterCap; // the output of pre-filtering is clipped by [-preFilterCap,preFilterCap] + + // correspondence using Sum of Absolute Difference (SAD) + int SADWindowSize; // ~5x5..21x21 + int minDisparity; // minimum disparity (can be negative) + int numberOfDisparities; // maximum disparity - minimum disparity (> 0) + + // post-filtering + int textureThreshold; // the disparity is only computed for pixels + // with textured enough neighborhood + int uniquenessRatio; // accept the computed disparity d* only if + // SAD(d) >= SAD(d*)*(1 + uniquenessRatio/100.) + // for any d != d*+/-1 within the search range. + int speckleWindowSize; // disparity variation window + int speckleRange; // acceptable range of variation in window + + int trySmallerWindows; // if 1, the results may be more accurate, + // at the expense of slower processing + CvRect roi1, roi2; + int disp12MaxDiff; + + // temporary buffers + CvMat* preFilteredImg0; + CvMat* preFilteredImg1; + CvMat* slidingSumBuf; + CvMat* cost; + CvMat* disp; +} CvStereoBMState; + +#define CV_STEREO_BM_BASIC 0 +#define CV_STEREO_BM_FISH_EYE 1 +#define CV_STEREO_BM_NARROW 2 + +CVAPI(CvStereoBMState*) cvCreateStereoBMState(int preset CV_DEFAULT(CV_STEREO_BM_BASIC), + int numberOfDisparities CV_DEFAULT(0)); + +CVAPI(void) cvReleaseStereoBMState( CvStereoBMState** state ); + +CVAPI(void) cvFindStereoCorrespondenceBM( const CvArr* left, const CvArr* right, + CvArr* disparity, CvStereoBMState* state ); + +CVAPI(CvRect) cvGetValidDisparityROI( CvRect roi1, CvRect roi2, int minDisparity, + int numberOfDisparities, int SADWindowSize ); + +CVAPI(void) cvValidateDisparity( CvArr* disparity, const CvArr* cost, + int minDisparity, int numberOfDisparities, + int disp12MaxDiff CV_DEFAULT(1) ); + +/* Reprojects the computed disparity image to the 3D space using the specified 4x4 matrix */ +CVAPI(void) cvReprojectImageTo3D( const CvArr* disparityImage, + CvArr* _3dImage, const CvMat* Q, + int handleMissingValues CV_DEFAULT(0) ); + +/** @} calib3d_c */ + +#ifdef __cplusplus +} // extern "C" + +////////////////////////////////////////////////////////////////////////////////////////// +class CV_EXPORTS CvLevMarq +{ +public: + CvLevMarq(); + CvLevMarq( int nparams, int nerrs, CvTermCriteria criteria= + cvTermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER,30,DBL_EPSILON), + bool completeSymmFlag=false ); + ~CvLevMarq(); + void init( int nparams, int nerrs, CvTermCriteria criteria= + cvTermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER,30,DBL_EPSILON), + bool completeSymmFlag=false ); + bool update( const CvMat*& param, CvMat*& J, CvMat*& err ); + bool updateAlt( const CvMat*& param, CvMat*& JtJ, CvMat*& JtErr, double*& errNorm ); + + void clear(); + void step(); + enum { DONE=0, STARTED=1, CALC_J=2, CHECK_ERR=3 }; + + cv::Ptr mask; + cv::Ptr prevParam; + cv::Ptr param; + cv::Ptr J; + cv::Ptr err; + cv::Ptr JtJ; + cv::Ptr JtJN; + cv::Ptr JtErr; + cv::Ptr JtJV; + cv::Ptr JtJW; + double prevErrNorm, errNorm; + int lambdaLg10; + CvTermCriteria criteria; + int state; + int iters; + bool completeSymmFlag; + int solveMethod; +}; + +#endif + +#endif /* OPENCV_CALIB3D_C_H */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core.hpp new file mode 100644 index 0000000000000000000000000000000000000000..089c0db3342048c008487c00b8125e070d75f6a0 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core.hpp @@ -0,0 +1,3285 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2015, Intel Corporation, all rights reserved. +// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved. +// Copyright (C) 2015, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_HPP +#define OPENCV_CORE_HPP + +#ifndef __cplusplus +# error core.hpp header must be compiled as C++ +#endif + +#include "opencv2/core/cvdef.h" +#include "opencv2/core/version.hpp" +#include "opencv2/core/base.hpp" +#include "opencv2/core/cvstd.hpp" +#include "opencv2/core/traits.hpp" +#include "opencv2/core/matx.hpp" +#include "opencv2/core/types.hpp" +#include "opencv2/core/mat.hpp" +#include "opencv2/core/persistence.hpp" + +/** +@defgroup core Core functionality +@{ + @defgroup core_basic Basic structures + @defgroup core_c C structures and operations + @{ + @defgroup core_c_glue Connections with C++ + @} + @defgroup core_array Operations on arrays + @defgroup core_xml XML/YAML Persistence + @defgroup core_cluster Clustering + @defgroup core_utils Utility and system functions and macros + @{ + @defgroup core_utils_sse SSE utilities + @defgroup core_utils_neon NEON utilities + @defgroup core_utils_softfloat Softfloat support + @defgroup core_utils_samples Utility functions for OpenCV samples + @} + @defgroup core_opengl OpenGL interoperability + @defgroup core_ipp Intel IPP Asynchronous C/C++ Converters + @defgroup core_optim Optimization Algorithms + @defgroup core_directx DirectX interoperability + @defgroup core_eigen Eigen support + @defgroup core_opencl OpenCL support + @defgroup core_va_intel Intel VA-API/OpenCL (CL-VA) interoperability + @defgroup core_hal Hardware Acceleration Layer + @{ + @defgroup core_hal_functions Functions + @defgroup core_hal_interface Interface + @defgroup core_hal_intrin Universal intrinsics + @{ + @defgroup core_hal_intrin_impl Private implementation helpers + @} + @} +@} + */ + +namespace cv { + +//! @addtogroup core_utils +//! @{ + +/*! @brief Class passed to an error. + +This class encapsulates all or almost all necessary +information about the error happened in the program. The exception is +usually constructed and thrown implicitly via CV_Error and CV_Error_ macros. +@see error + */ +class CV_EXPORTS Exception : public std::exception +{ +public: + /*! + Default constructor + */ + Exception(); + /*! + Full constructor. Normally the constructor is not called explicitly. + Instead, the macros CV_Error(), CV_Error_() and CV_Assert() are used. + */ + Exception(int _code, const String& _err, const String& _func, const String& _file, int _line); + virtual ~Exception() throw(); + + /*! + \return the error description and the context as a text string. + */ + virtual const char *what() const throw() CV_OVERRIDE; + void formatMessage(); + + String msg; ///< the formatted error message + + int code; ///< error code @see CVStatus + String err; ///< error description + String func; ///< function name. Available only when the compiler supports getting it + String file; ///< source file name where the error has occurred + int line; ///< line number in the source file where the error has occurred +}; + +/*! @brief Signals an error and raises the exception. + +By default the function prints information about the error to stderr, +then it either stops if cv::setBreakOnError() had been called before or raises the exception. +It is possible to alternate error processing by using #redirectError(). +@param exc the exception raisen. +@deprecated drop this version + */ +CV_EXPORTS void error( const Exception& exc ); + +enum SortFlags { SORT_EVERY_ROW = 0, //!< each matrix row is sorted independently + SORT_EVERY_COLUMN = 1, //!< each matrix column is sorted + //!< independently; this flag and the previous one are + //!< mutually exclusive. + SORT_ASCENDING = 0, //!< each matrix row is sorted in the ascending + //!< order. + SORT_DESCENDING = 16 //!< each matrix row is sorted in the + //!< descending order; this flag and the previous one are also + //!< mutually exclusive. + }; + +//! @} core_utils + +//! @addtogroup core +//! @{ + +//! Covariation flags +enum CovarFlags { + /** The output covariance matrix is calculated as: + \f[\texttt{scale} \cdot [ \texttt{vects} [0]- \texttt{mean} , \texttt{vects} [1]- \texttt{mean} ,...]^T \cdot [ \texttt{vects} [0]- \texttt{mean} , \texttt{vects} [1]- \texttt{mean} ,...],\f] + The covariance matrix will be nsamples x nsamples. Such an unusual covariance matrix is used + for fast PCA of a set of very large vectors (see, for example, the EigenFaces technique for + face recognition). Eigenvalues of this "scrambled" matrix match the eigenvalues of the true + covariance matrix. The "true" eigenvectors can be easily calculated from the eigenvectors of + the "scrambled" covariance matrix. */ + COVAR_SCRAMBLED = 0, + /**The output covariance matrix is calculated as: + \f[\texttt{scale} \cdot [ \texttt{vects} [0]- \texttt{mean} , \texttt{vects} [1]- \texttt{mean} ,...] \cdot [ \texttt{vects} [0]- \texttt{mean} , \texttt{vects} [1]- \texttt{mean} ,...]^T,\f] + covar will be a square matrix of the same size as the total number of elements in each input + vector. One and only one of #COVAR_SCRAMBLED and #COVAR_NORMAL must be specified.*/ + COVAR_NORMAL = 1, + /** If the flag is specified, the function does not calculate mean from + the input vectors but, instead, uses the passed mean vector. This is useful if mean has been + pre-calculated or known in advance, or if the covariance matrix is calculated by parts. In + this case, mean is not a mean vector of the input sub-set of vectors but rather the mean + vector of the whole set.*/ + COVAR_USE_AVG = 2, + /** If the flag is specified, the covariance matrix is scaled. In the + "normal" mode, scale is 1./nsamples . In the "scrambled" mode, scale is the reciprocal of the + total number of elements in each input vector. By default (if the flag is not specified), the + covariance matrix is not scaled ( scale=1 ).*/ + COVAR_SCALE = 4, + /** If the flag is + specified, all the input vectors are stored as rows of the samples matrix. mean should be a + single-row vector in this case.*/ + COVAR_ROWS = 8, + /** If the flag is + specified, all the input vectors are stored as columns of the samples matrix. mean should be a + single-column vector in this case.*/ + COVAR_COLS = 16 +}; + +//! k-Means flags +enum KmeansFlags { + /** Select random initial centers in each attempt.*/ + KMEANS_RANDOM_CENTERS = 0, + /** Use kmeans++ center initialization by Arthur and Vassilvitskii [Arthur2007].*/ + KMEANS_PP_CENTERS = 2, + /** During the first (and possibly the only) attempt, use the + user-supplied labels instead of computing them from the initial centers. For the second and + further attempts, use the random or semi-random centers. Use one of KMEANS_\*_CENTERS flag + to specify the exact method.*/ + KMEANS_USE_INITIAL_LABELS = 1 +}; + +//! type of line +enum LineTypes { + FILLED = -1, + LINE_4 = 4, //!< 4-connected line + LINE_8 = 8, //!< 8-connected line + LINE_AA = 16 //!< antialiased line +}; + +//! Only a subset of Hershey fonts are supported +enum HersheyFonts { + FONT_HERSHEY_SIMPLEX = 0, //!< normal size sans-serif font + FONT_HERSHEY_PLAIN = 1, //!< small size sans-serif font + FONT_HERSHEY_DUPLEX = 2, //!< normal size sans-serif font (more complex than FONT_HERSHEY_SIMPLEX) + FONT_HERSHEY_COMPLEX = 3, //!< normal size serif font + FONT_HERSHEY_TRIPLEX = 4, //!< normal size serif font (more complex than FONT_HERSHEY_COMPLEX) + FONT_HERSHEY_COMPLEX_SMALL = 5, //!< smaller version of FONT_HERSHEY_COMPLEX + FONT_HERSHEY_SCRIPT_SIMPLEX = 6, //!< hand-writing style font + FONT_HERSHEY_SCRIPT_COMPLEX = 7, //!< more complex variant of FONT_HERSHEY_SCRIPT_SIMPLEX + FONT_ITALIC = 16 //!< flag for italic font +}; + +enum ReduceTypes { REDUCE_SUM = 0, //!< the output is the sum of all rows/columns of the matrix. + REDUCE_AVG = 1, //!< the output is the mean vector of all rows/columns of the matrix. + REDUCE_MAX = 2, //!< the output is the maximum (column/row-wise) of all rows/columns of the matrix. + REDUCE_MIN = 3 //!< the output is the minimum (column/row-wise) of all rows/columns of the matrix. + }; + + +/** @brief Swaps two matrices +*/ +CV_EXPORTS void swap(Mat& a, Mat& b); +/** @overload */ +CV_EXPORTS void swap( UMat& a, UMat& b ); + +//! @} core + +//! @addtogroup core_array +//! @{ + +/** @brief Computes the source location of an extrapolated pixel. + +The function computes and returns the coordinate of a donor pixel corresponding to the specified +extrapolated pixel when using the specified extrapolation border mode. For example, if you use +cv::BORDER_WRAP mode in the horizontal direction, cv::BORDER_REFLECT_101 in the vertical direction and +want to compute value of the "virtual" pixel Point(-5, 100) in a floating-point image img , it +looks like: +@code{.cpp} + float val = img.at(borderInterpolate(100, img.rows, cv::BORDER_REFLECT_101), + borderInterpolate(-5, img.cols, cv::BORDER_WRAP)); +@endcode +Normally, the function is not called directly. It is used inside filtering functions and also in +copyMakeBorder. +@param p 0-based coordinate of the extrapolated pixel along one of the axes, likely \<0 or \>= len +@param len Length of the array along the corresponding axis. +@param borderType Border type, one of the #BorderTypes, except for #BORDER_TRANSPARENT and +#BORDER_ISOLATED . When borderType==#BORDER_CONSTANT , the function always returns -1, regardless +of p and len. + +@sa copyMakeBorder +*/ +CV_EXPORTS_W int borderInterpolate(int p, int len, int borderType); + +/** @example samples/cpp/tutorial_code/ImgTrans/copyMakeBorder_demo.cpp +An example using copyMakeBorder function. +Check @ref tutorial_copyMakeBorder "the corresponding tutorial" for more details +*/ + +/** @brief Forms a border around an image. + +The function copies the source image into the middle of the destination image. The areas to the +left, to the right, above and below the copied source image will be filled with extrapolated +pixels. This is not what filtering functions based on it do (they extrapolate pixels on-fly), but +what other more complex functions, including your own, may do to simplify image boundary handling. + +The function supports the mode when src is already in the middle of dst . In this case, the +function does not copy src itself but simply constructs the border, for example: + +@code{.cpp} + // let border be the same in all directions + int border=2; + // constructs a larger image to fit both the image and the border + Mat gray_buf(rgb.rows + border*2, rgb.cols + border*2, rgb.depth()); + // select the middle part of it w/o copying data + Mat gray(gray_canvas, Rect(border, border, rgb.cols, rgb.rows)); + // convert image from RGB to grayscale + cvtColor(rgb, gray, COLOR_RGB2GRAY); + // form a border in-place + copyMakeBorder(gray, gray_buf, border, border, + border, border, BORDER_REPLICATE); + // now do some custom filtering ... + ... +@endcode +@note When the source image is a part (ROI) of a bigger image, the function will try to use the +pixels outside of the ROI to form a border. To disable this feature and always do extrapolation, as +if src was not a ROI, use borderType | #BORDER_ISOLATED. + +@param src Source image. +@param dst Destination image of the same type as src and the size Size(src.cols+left+right, +src.rows+top+bottom) . +@param top +@param bottom +@param left +@param right Parameter specifying how many pixels in each direction from the source image rectangle +to extrapolate. For example, top=1, bottom=1, left=1, right=1 mean that 1 pixel-wide border needs +to be built. +@param borderType Border type. See borderInterpolate for details. +@param value Border value if borderType==BORDER_CONSTANT . + +@sa borderInterpolate +*/ +CV_EXPORTS_W void copyMakeBorder(InputArray src, OutputArray dst, + int top, int bottom, int left, int right, + int borderType, const Scalar& value = Scalar() ); + +/** @brief Calculates the per-element sum of two arrays or an array and a scalar. + +The function add calculates: +- Sum of two arrays when both input arrays have the same size and the same number of channels: +\f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) + \texttt{src2}(I)) \quad \texttt{if mask}(I) \ne0\f] +- Sum of an array and a scalar when src2 is constructed from Scalar or has the same number of +elements as `src1.channels()`: +\f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) + \texttt{src2} ) \quad \texttt{if mask}(I) \ne0\f] +- Sum of a scalar and an array when src1 is constructed from Scalar or has the same number of +elements as `src2.channels()`: +\f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1} + \texttt{src2}(I) ) \quad \texttt{if mask}(I) \ne0\f] +where `I` is a multi-dimensional index of array elements. In case of multi-channel arrays, each +channel is processed independently. + +The first function in the list above can be replaced with matrix expressions: +@code{.cpp} + dst = src1 + src2; + dst += src1; // equivalent to add(dst, src1, dst); +@endcode +The input arrays and the output array can all have the same or different depths. For example, you +can add a 16-bit unsigned array to a 8-bit signed array and store the sum as a 32-bit +floating-point array. Depth of the output array is determined by the dtype parameter. In the second +and third cases above, as well as in the first case, when src1.depth() == src2.depth(), dtype can +be set to the default -1. In this case, the output array will have the same depth as the input +array, be it src1, src2 or both. +@note Saturation is not applied when the output array has the depth CV_32S. You may even get +result of an incorrect sign in the case of overflow. +@param src1 first input array or a scalar. +@param src2 second input array or a scalar. +@param dst output array that has the same size and number of channels as the input array(s); the +depth is defined by dtype or src1/src2. +@param mask optional operation mask - 8-bit single channel array, that specifies elements of the +output array to be changed. +@param dtype optional depth of the output array (see the discussion below). +@sa subtract, addWeighted, scaleAdd, Mat::convertTo +*/ +CV_EXPORTS_W void add(InputArray src1, InputArray src2, OutputArray dst, + InputArray mask = noArray(), int dtype = -1); + +/** @brief Calculates the per-element difference between two arrays or array and a scalar. + +The function subtract calculates: +- Difference between two arrays, when both input arrays have the same size and the same number of +channels: + \f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) - \texttt{src2}(I)) \quad \texttt{if mask}(I) \ne0\f] +- Difference between an array and a scalar, when src2 is constructed from Scalar or has the same +number of elements as `src1.channels()`: + \f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) - \texttt{src2} ) \quad \texttt{if mask}(I) \ne0\f] +- Difference between a scalar and an array, when src1 is constructed from Scalar or has the same +number of elements as `src2.channels()`: + \f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1} - \texttt{src2}(I) ) \quad \texttt{if mask}(I) \ne0\f] +- The reverse difference between a scalar and an array in the case of `SubRS`: + \f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src2} - \texttt{src1}(I) ) \quad \texttt{if mask}(I) \ne0\f] +where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each +channel is processed independently. + +The first function in the list above can be replaced with matrix expressions: +@code{.cpp} + dst = src1 - src2; + dst -= src1; // equivalent to subtract(dst, src1, dst); +@endcode +The input arrays and the output array can all have the same or different depths. For example, you +can subtract to 8-bit unsigned arrays and store the difference in a 16-bit signed array. Depth of +the output array is determined by dtype parameter. In the second and third cases above, as well as +in the first case, when src1.depth() == src2.depth(), dtype can be set to the default -1. In this +case the output array will have the same depth as the input array, be it src1, src2 or both. +@note Saturation is not applied when the output array has the depth CV_32S. You may even get +result of an incorrect sign in the case of overflow. +@param src1 first input array or a scalar. +@param src2 second input array or a scalar. +@param dst output array of the same size and the same number of channels as the input array. +@param mask optional operation mask; this is an 8-bit single channel array that specifies elements +of the output array to be changed. +@param dtype optional depth of the output array +@sa add, addWeighted, scaleAdd, Mat::convertTo + */ +CV_EXPORTS_W void subtract(InputArray src1, InputArray src2, OutputArray dst, + InputArray mask = noArray(), int dtype = -1); + + +/** @brief Calculates the per-element scaled product of two arrays. + +The function multiply calculates the per-element product of two arrays: + +\f[\texttt{dst} (I)= \texttt{saturate} ( \texttt{scale} \cdot \texttt{src1} (I) \cdot \texttt{src2} (I))\f] + +There is also a @ref MatrixExpressions -friendly variant of the first function. See Mat::mul . + +For a not-per-element matrix product, see gemm . + +@note Saturation is not applied when the output array has the depth +CV_32S. You may even get result of an incorrect sign in the case of +overflow. +@param src1 first input array. +@param src2 second input array of the same size and the same type as src1. +@param dst output array of the same size and type as src1. +@param scale optional scale factor. +@param dtype optional depth of the output array +@sa add, subtract, divide, scaleAdd, addWeighted, accumulate, accumulateProduct, accumulateSquare, +Mat::convertTo +*/ +CV_EXPORTS_W void multiply(InputArray src1, InputArray src2, + OutputArray dst, double scale = 1, int dtype = -1); + +/** @brief Performs per-element division of two arrays or a scalar by an array. + +The function cv::divide divides one array by another: +\f[\texttt{dst(I) = saturate(src1(I)*scale/src2(I))}\f] +or a scalar by an array when there is no src1 : +\f[\texttt{dst(I) = saturate(scale/src2(I))}\f] + +When src2(I) is zero, dst(I) will also be zero. Different channels of +multi-channel arrays are processed independently. + +@note Saturation is not applied when the output array has the depth CV_32S. You may even get +result of an incorrect sign in the case of overflow. +@param src1 first input array. +@param src2 second input array of the same size and type as src1. +@param scale scalar factor. +@param dst output array of the same size and type as src2. +@param dtype optional depth of the output array; if -1, dst will have depth src2.depth(), but in +case of an array-by-array division, you can only pass -1 when src1.depth()==src2.depth(). +@sa multiply, add, subtract +*/ +CV_EXPORTS_W void divide(InputArray src1, InputArray src2, OutputArray dst, + double scale = 1, int dtype = -1); + +/** @overload */ +CV_EXPORTS_W void divide(double scale, InputArray src2, + OutputArray dst, int dtype = -1); + +/** @brief Calculates the sum of a scaled array and another array. + +The function scaleAdd is one of the classical primitive linear algebra operations, known as DAXPY +or SAXPY in [BLAS](http://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms). It calculates +the sum of a scaled array and another array: +\f[\texttt{dst} (I)= \texttt{scale} \cdot \texttt{src1} (I) + \texttt{src2} (I)\f] +The function can also be emulated with a matrix expression, for example: +@code{.cpp} + Mat A(3, 3, CV_64F); + ... + A.row(0) = A.row(1)*2 + A.row(2); +@endcode +@param src1 first input array. +@param alpha scale factor for the first array. +@param src2 second input array of the same size and type as src1. +@param dst output array of the same size and type as src1. +@sa add, addWeighted, subtract, Mat::dot, Mat::convertTo +*/ +CV_EXPORTS_W void scaleAdd(InputArray src1, double alpha, InputArray src2, OutputArray dst); + +/** @example samples/cpp/tutorial_code/HighGUI/AddingImagesTrackbar.cpp +Check @ref tutorial_trackbar "the corresponding tutorial" for more details +*/ + +/** @brief Calculates the weighted sum of two arrays. + +The function addWeighted calculates the weighted sum of two arrays as follows: +\f[\texttt{dst} (I)= \texttt{saturate} ( \texttt{src1} (I)* \texttt{alpha} + \texttt{src2} (I)* \texttt{beta} + \texttt{gamma} )\f] +where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each +channel is processed independently. +The function can be replaced with a matrix expression: +@code{.cpp} + dst = src1*alpha + src2*beta + gamma; +@endcode +@note Saturation is not applied when the output array has the depth CV_32S. You may even get +result of an incorrect sign in the case of overflow. +@param src1 first input array. +@param alpha weight of the first array elements. +@param src2 second input array of the same size and channel number as src1. +@param beta weight of the second array elements. +@param gamma scalar added to each sum. +@param dst output array that has the same size and number of channels as the input arrays. +@param dtype optional depth of the output array; when both input arrays have the same depth, dtype +can be set to -1, which will be equivalent to src1.depth(). +@sa add, subtract, scaleAdd, Mat::convertTo +*/ +CV_EXPORTS_W void addWeighted(InputArray src1, double alpha, InputArray src2, + double beta, double gamma, OutputArray dst, int dtype = -1); + +/** @brief Scales, calculates absolute values, and converts the result to 8-bit. + +On each element of the input array, the function convertScaleAbs +performs three operations sequentially: scaling, taking an absolute +value, conversion to an unsigned 8-bit type: +\f[\texttt{dst} (I)= \texttt{saturate\_cast} (| \texttt{src} (I)* \texttt{alpha} + \texttt{beta} |)\f] +In case of multi-channel arrays, the function processes each channel +independently. When the output is not 8-bit, the operation can be +emulated by calling the Mat::convertTo method (or by using matrix +expressions) and then by calculating an absolute value of the result. +For example: +@code{.cpp} + Mat_ A(30,30); + randu(A, Scalar(-100), Scalar(100)); + Mat_ B = A*5 + 3; + B = abs(B); + // Mat_ B = abs(A*5+3) will also do the job, + // but it will allocate a temporary matrix +@endcode +@param src input array. +@param dst output array. +@param alpha optional scale factor. +@param beta optional delta added to the scaled values. +@sa Mat::convertTo, cv::abs(const Mat&) +*/ +CV_EXPORTS_W void convertScaleAbs(InputArray src, OutputArray dst, + double alpha = 1, double beta = 0); + +/** @brief Converts an array to half precision floating number. + +This function converts FP32 (single precision floating point) from/to FP16 (half precision floating point). CV_16S format is used to represent FP16 data. +There are two use modes (src -> dst): CV_32F -> CV_16S and CV_16S -> CV_32F. The input array has to have type of CV_32F or +CV_16S to represent the bit depth. If the input array is neither of them, the function will raise an error. +The format of half precision floating point is defined in IEEE 754-2008. + +@param src input array. +@param dst output array. +*/ +CV_EXPORTS_W void convertFp16(InputArray src, OutputArray dst); + +/** @brief Performs a look-up table transform of an array. + +The function LUT fills the output array with values from the look-up table. Indices of the entries +are taken from the input array. That is, the function processes each element of src as follows: +\f[\texttt{dst} (I) \leftarrow \texttt{lut(src(I) + d)}\f] +where +\f[d = \fork{0}{if \(\texttt{src}\) has depth \(\texttt{CV_8U}\)}{128}{if \(\texttt{src}\) has depth \(\texttt{CV_8S}\)}\f] +@param src input array of 8-bit elements. +@param lut look-up table of 256 elements; in case of multi-channel input array, the table should +either have a single channel (in this case the same table is used for all channels) or the same +number of channels as in the input array. +@param dst output array of the same size and number of channels as src, and the same depth as lut. +@sa convertScaleAbs, Mat::convertTo +*/ +CV_EXPORTS_W void LUT(InputArray src, InputArray lut, OutputArray dst); + +/** @brief Calculates the sum of array elements. + +The function cv::sum calculates and returns the sum of array elements, +independently for each channel. +@param src input array that must have from 1 to 4 channels. +@sa countNonZero, mean, meanStdDev, norm, minMaxLoc, reduce +*/ +CV_EXPORTS_AS(sumElems) Scalar sum(InputArray src); + +/** @brief Counts non-zero array elements. + +The function returns the number of non-zero elements in src : +\f[\sum _{I: \; \texttt{src} (I) \ne0 } 1\f] +@param src single-channel array. +@sa mean, meanStdDev, norm, minMaxLoc, calcCovarMatrix +*/ +CV_EXPORTS_W int countNonZero( InputArray src ); + +/** @brief Returns the list of locations of non-zero pixels + +Given a binary matrix (likely returned from an operation such +as threshold(), compare(), >, ==, etc, return all of +the non-zero indices as a cv::Mat or std::vector (x,y) +For example: +@code{.cpp} + cv::Mat binaryImage; // input, binary image + cv::Mat locations; // output, locations of non-zero pixels + cv::findNonZero(binaryImage, locations); + + // access pixel coordinates + Point pnt = locations.at(i); +@endcode +or +@code{.cpp} + cv::Mat binaryImage; // input, binary image + vector locations; // output, locations of non-zero pixels + cv::findNonZero(binaryImage, locations); + + // access pixel coordinates + Point pnt = locations[i]; +@endcode +@param src single-channel array (type CV_8UC1) +@param idx the output array, type of cv::Mat or std::vector, corresponding to non-zero indices in the input +*/ +CV_EXPORTS_W void findNonZero( InputArray src, OutputArray idx ); + +/** @brief Calculates an average (mean) of array elements. + +The function cv::mean calculates the mean value M of array elements, +independently for each channel, and return it: +\f[\begin{array}{l} N = \sum _{I: \; \texttt{mask} (I) \ne 0} 1 \\ M_c = \left ( \sum _{I: \; \texttt{mask} (I) \ne 0}{ \texttt{mtx} (I)_c} \right )/N \end{array}\f] +When all the mask elements are 0's, the function returns Scalar::all(0) +@param src input array that should have from 1 to 4 channels so that the result can be stored in +Scalar_ . +@param mask optional operation mask. +@sa countNonZero, meanStdDev, norm, minMaxLoc +*/ +CV_EXPORTS_W Scalar mean(InputArray src, InputArray mask = noArray()); + +/** Calculates a mean and standard deviation of array elements. + +The function cv::meanStdDev calculates the mean and the standard deviation M +of array elements independently for each channel and returns it via the +output parameters: +\f[\begin{array}{l} N = \sum _{I, \texttt{mask} (I) \ne 0} 1 \\ \texttt{mean} _c = \frac{\sum_{ I: \; \texttt{mask}(I) \ne 0} \texttt{src} (I)_c}{N} \\ \texttt{stddev} _c = \sqrt{\frac{\sum_{ I: \; \texttt{mask}(I) \ne 0} \left ( \texttt{src} (I)_c - \texttt{mean} _c \right )^2}{N}} \end{array}\f] +When all the mask elements are 0's, the function returns +mean=stddev=Scalar::all(0). +@note The calculated standard deviation is only the diagonal of the +complete normalized covariance matrix. If the full matrix is needed, you +can reshape the multi-channel array M x N to the single-channel array +M\*N x mtx.channels() (only possible when the matrix is continuous) and +then pass the matrix to calcCovarMatrix . +@param src input array that should have from 1 to 4 channels so that the results can be stored in +Scalar_ 's. +@param mean output parameter: calculated mean value. +@param stddev output parameter: calculated standard deviation. +@param mask optional operation mask. +@sa countNonZero, mean, norm, minMaxLoc, calcCovarMatrix +*/ +CV_EXPORTS_W void meanStdDev(InputArray src, OutputArray mean, OutputArray stddev, + InputArray mask=noArray()); + +/** @brief Calculates the absolute norm of an array. + +This version of #norm calculates the absolute norm of src1. The type of norm to calculate is specified using #NormTypes. + +As example for one array consider the function \f$r(x)= \begin{pmatrix} x \\ 1-x \end{pmatrix}, x \in [-1;1]\f$. +The \f$ L_{1}, L_{2} \f$ and \f$ L_{\infty} \f$ norm for the sample value \f$r(-1) = \begin{pmatrix} -1 \\ 2 \end{pmatrix}\f$ +is calculated as follows +\f{align*} + \| r(-1) \|_{L_1} &= |-1| + |2| = 3 \\ + \| r(-1) \|_{L_2} &= \sqrt{(-1)^{2} + (2)^{2}} = \sqrt{5} \\ + \| r(-1) \|_{L_\infty} &= \max(|-1|,|2|) = 2 +\f} +and for \f$r(0.5) = \begin{pmatrix} 0.5 \\ 0.5 \end{pmatrix}\f$ the calculation is +\f{align*} + \| r(0.5) \|_{L_1} &= |0.5| + |0.5| = 1 \\ + \| r(0.5) \|_{L_2} &= \sqrt{(0.5)^{2} + (0.5)^{2}} = \sqrt{0.5} \\ + \| r(0.5) \|_{L_\infty} &= \max(|0.5|,|0.5|) = 0.5. +\f} +The following graphic shows all values for the three norm functions \f$\| r(x) \|_{L_1}, \| r(x) \|_{L_2}\f$ and \f$\| r(x) \|_{L_\infty}\f$. +It is notable that the \f$ L_{1} \f$ norm forms the upper and the \f$ L_{\infty} \f$ norm forms the lower border for the example function \f$ r(x) \f$. +![Graphs for the different norm functions from the above example](pics/NormTypes_OneArray_1-2-INF.png) + +When the mask parameter is specified and it is not empty, the norm is + +If normType is not specified, #NORM_L2 is used. +calculated only over the region specified by the mask. + +Multi-channel input arrays are treated as single-channel arrays, that is, +the results for all channels are combined. + +Hamming norms can only be calculated with CV_8U depth arrays. + +@param src1 first input array. +@param normType type of the norm (see #NormTypes). +@param mask optional operation mask; it must have the same size as src1 and CV_8UC1 type. +*/ +CV_EXPORTS_W double norm(InputArray src1, int normType = NORM_L2, InputArray mask = noArray()); + +/** @brief Calculates an absolute difference norm or a relative difference norm. + +This version of cv::norm calculates the absolute difference norm +or the relative difference norm of arrays src1 and src2. +The type of norm to calculate is specified using #NormTypes. + +@param src1 first input array. +@param src2 second input array of the same size and the same type as src1. +@param normType type of the norm (see #NormTypes). +@param mask optional operation mask; it must have the same size as src1 and CV_8UC1 type. +*/ +CV_EXPORTS_W double norm(InputArray src1, InputArray src2, + int normType = NORM_L2, InputArray mask = noArray()); +/** @overload +@param src first input array. +@param normType type of the norm (see #NormTypes). +*/ +CV_EXPORTS double norm( const SparseMat& src, int normType ); + +/** @brief Computes the Peak Signal-to-Noise Ratio (PSNR) image quality metric. + +This function calculates the Peak Signal-to-Noise Ratio (PSNR) image quality metric in decibels (dB), between two input arrays src1 and src2. Arrays must have depth CV_8U. + +The PSNR is calculated as follows: + +\f[ +\texttt{PSNR} = 10 \cdot \log_{10}{\left( \frac{R^2}{MSE} \right) } +\f] + +where R is the maximum integer value of depth CV_8U (255) and MSE is the mean squared error between the two arrays. + +@param src1 first input array. +@param src2 second input array of the same size as src1. + + */ +CV_EXPORTS_W double PSNR(InputArray src1, InputArray src2); + +/** @brief naive nearest neighbor finder + +see http://en.wikipedia.org/wiki/Nearest_neighbor_search +@todo document + */ +CV_EXPORTS_W void batchDistance(InputArray src1, InputArray src2, + OutputArray dist, int dtype, OutputArray nidx, + int normType = NORM_L2, int K = 0, + InputArray mask = noArray(), int update = 0, + bool crosscheck = false); + +/** @brief Normalizes the norm or value range of an array. + +The function cv::normalize normalizes scale and shift the input array elements so that +\f[\| \texttt{dst} \| _{L_p}= \texttt{alpha}\f] +(where p=Inf, 1 or 2) when normType=NORM_INF, NORM_L1, or NORM_L2, respectively; or so that +\f[\min _I \texttt{dst} (I)= \texttt{alpha} , \, \, \max _I \texttt{dst} (I)= \texttt{beta}\f] + +when normType=NORM_MINMAX (for dense arrays only). The optional mask specifies a sub-array to be +normalized. This means that the norm or min-n-max are calculated over the sub-array, and then this +sub-array is modified to be normalized. If you want to only use the mask to calculate the norm or +min-max but modify the whole array, you can use norm and Mat::convertTo. + +In case of sparse matrices, only the non-zero values are analyzed and transformed. Because of this, +the range transformation for sparse matrices is not allowed since it can shift the zero level. + +Possible usage with some positive example data: +@code{.cpp} + vector positiveData = { 2.0, 8.0, 10.0 }; + vector normalizedData_l1, normalizedData_l2, normalizedData_inf, normalizedData_minmax; + + // Norm to probability (total count) + // sum(numbers) = 20.0 + // 2.0 0.1 (2.0/20.0) + // 8.0 0.4 (8.0/20.0) + // 10.0 0.5 (10.0/20.0) + normalize(positiveData, normalizedData_l1, 1.0, 0.0, NORM_L1); + + // Norm to unit vector: ||positiveData|| = 1.0 + // 2.0 0.15 + // 8.0 0.62 + // 10.0 0.77 + normalize(positiveData, normalizedData_l2, 1.0, 0.0, NORM_L2); + + // Norm to max element + // 2.0 0.2 (2.0/10.0) + // 8.0 0.8 (8.0/10.0) + // 10.0 1.0 (10.0/10.0) + normalize(positiveData, normalizedData_inf, 1.0, 0.0, NORM_INF); + + // Norm to range [0.0;1.0] + // 2.0 0.0 (shift to left border) + // 8.0 0.75 (6.0/8.0) + // 10.0 1.0 (shift to right border) + normalize(positiveData, normalizedData_minmax, 1.0, 0.0, NORM_MINMAX); +@endcode + +@param src input array. +@param dst output array of the same size as src . +@param alpha norm value to normalize to or the lower range boundary in case of the range +normalization. +@param beta upper range boundary in case of the range normalization; it is not used for the norm +normalization. +@param norm_type normalization type (see cv::NormTypes). +@param dtype when negative, the output array has the same type as src; otherwise, it has the same +number of channels as src and the depth =CV_MAT_DEPTH(dtype). +@param mask optional operation mask. +@sa norm, Mat::convertTo, SparseMat::convertTo +*/ +CV_EXPORTS_W void normalize( InputArray src, InputOutputArray dst, double alpha = 1, double beta = 0, + int norm_type = NORM_L2, int dtype = -1, InputArray mask = noArray()); + +/** @overload +@param src input array. +@param dst output array of the same size as src . +@param alpha norm value to normalize to or the lower range boundary in case of the range +normalization. +@param normType normalization type (see cv::NormTypes). +*/ +CV_EXPORTS void normalize( const SparseMat& src, SparseMat& dst, double alpha, int normType ); + +/** @brief Finds the global minimum and maximum in an array. + +The function cv::minMaxLoc finds the minimum and maximum element values and their positions. The +extremums are searched across the whole array or, if mask is not an empty array, in the specified +array region. + +The function do not work with multi-channel arrays. If you need to find minimum or maximum +elements across all the channels, use Mat::reshape first to reinterpret the array as +single-channel. Or you may extract the particular channel using either extractImageCOI , or +mixChannels , or split . +@param src input single-channel array. +@param minVal pointer to the returned minimum value; NULL is used if not required. +@param maxVal pointer to the returned maximum value; NULL is used if not required. +@param minLoc pointer to the returned minimum location (in 2D case); NULL is used if not required. +@param maxLoc pointer to the returned maximum location (in 2D case); NULL is used if not required. +@param mask optional mask used to select a sub-array. +@sa max, min, compare, inRange, extractImageCOI, mixChannels, split, Mat::reshape +*/ +CV_EXPORTS_W void minMaxLoc(InputArray src, CV_OUT double* minVal, + CV_OUT double* maxVal = 0, CV_OUT Point* minLoc = 0, + CV_OUT Point* maxLoc = 0, InputArray mask = noArray()); + + +/** @brief Finds the global minimum and maximum in an array + +The function cv::minMaxIdx finds the minimum and maximum element values and their positions. The +extremums are searched across the whole array or, if mask is not an empty array, in the specified +array region. The function does not work with multi-channel arrays. If you need to find minimum or +maximum elements across all the channels, use Mat::reshape first to reinterpret the array as +single-channel. Or you may extract the particular channel using either extractImageCOI , or +mixChannels , or split . In case of a sparse matrix, the minimum is found among non-zero elements +only. +@note When minIdx is not NULL, it must have at least 2 elements (as well as maxIdx), even if src is +a single-row or single-column matrix. In OpenCV (following MATLAB) each array has at least 2 +dimensions, i.e. single-column matrix is Mx1 matrix (and therefore minIdx/maxIdx will be +(i1,0)/(i2,0)) and single-row matrix is 1xN matrix (and therefore minIdx/maxIdx will be +(0,j1)/(0,j2)). +@param src input single-channel array. +@param minVal pointer to the returned minimum value; NULL is used if not required. +@param maxVal pointer to the returned maximum value; NULL is used if not required. +@param minIdx pointer to the returned minimum location (in nD case); NULL is used if not required; +Otherwise, it must point to an array of src.dims elements, the coordinates of the minimum element +in each dimension are stored there sequentially. +@param maxIdx pointer to the returned maximum location (in nD case). NULL is used if not required. +@param mask specified array region +*/ +CV_EXPORTS void minMaxIdx(InputArray src, double* minVal, double* maxVal = 0, + int* minIdx = 0, int* maxIdx = 0, InputArray mask = noArray()); + +/** @overload +@param a input single-channel array. +@param minVal pointer to the returned minimum value; NULL is used if not required. +@param maxVal pointer to the returned maximum value; NULL is used if not required. +@param minIdx pointer to the returned minimum location (in nD case); NULL is used if not required; +Otherwise, it must point to an array of src.dims elements, the coordinates of the minimum element +in each dimension are stored there sequentially. +@param maxIdx pointer to the returned maximum location (in nD case). NULL is used if not required. +*/ +CV_EXPORTS void minMaxLoc(const SparseMat& a, double* minVal, + double* maxVal, int* minIdx = 0, int* maxIdx = 0); + +/** @brief Reduces a matrix to a vector. + +The function #reduce reduces the matrix to a vector by treating the matrix rows/columns as a set of +1D vectors and performing the specified operation on the vectors until a single row/column is +obtained. For example, the function can be used to compute horizontal and vertical projections of a +raster image. In case of #REDUCE_MAX and #REDUCE_MIN , the output image should have the same type as the source one. +In case of #REDUCE_SUM and #REDUCE_AVG , the output may have a larger element bit-depth to preserve accuracy. +And multi-channel arrays are also supported in these two reduction modes. + +The following code demonstrates its usage for a single channel matrix. +@snippet snippets/core_reduce.cpp example + +And the following code demonstrates its usage for a two-channel matrix. +@snippet snippets/core_reduce.cpp example2 + +@param src input 2D matrix. +@param dst output vector. Its size and type is defined by dim and dtype parameters. +@param dim dimension index along which the matrix is reduced. 0 means that the matrix is reduced to +a single row. 1 means that the matrix is reduced to a single column. +@param rtype reduction operation that could be one of #ReduceTypes +@param dtype when negative, the output vector will have the same type as the input matrix, +otherwise, its type will be CV_MAKE_TYPE(CV_MAT_DEPTH(dtype), src.channels()). +@sa repeat +*/ +CV_EXPORTS_W void reduce(InputArray src, OutputArray dst, int dim, int rtype, int dtype = -1); + +/** @brief Creates one multi-channel array out of several single-channel ones. + +The function cv::merge merges several arrays to make a single multi-channel array. That is, each +element of the output array will be a concatenation of the elements of the input arrays, where +elements of i-th input array are treated as mv[i].channels()-element vectors. + +The function cv::split does the reverse operation. If you need to shuffle channels in some other +advanced way, use cv::mixChannels. + +The following example shows how to merge 3 single channel matrices into a single 3-channel matrix. +@snippet snippets/core_merge.cpp example + +@param mv input array of matrices to be merged; all the matrices in mv must have the same +size and the same depth. +@param count number of input matrices when mv is a plain C array; it must be greater than zero. +@param dst output array of the same size and the same depth as mv[0]; The number of channels will +be equal to the parameter count. +@sa mixChannels, split, Mat::reshape +*/ +CV_EXPORTS void merge(const Mat* mv, size_t count, OutputArray dst); + +/** @overload +@param mv input vector of matrices to be merged; all the matrices in mv must have the same +size and the same depth. +@param dst output array of the same size and the same depth as mv[0]; The number of channels will +be the total number of channels in the matrix array. + */ +CV_EXPORTS_W void merge(InputArrayOfArrays mv, OutputArray dst); + +/** @brief Divides a multi-channel array into several single-channel arrays. + +The function cv::split splits a multi-channel array into separate single-channel arrays: +\f[\texttt{mv} [c](I) = \texttt{src} (I)_c\f] +If you need to extract a single channel or do some other sophisticated channel permutation, use +mixChannels . + +The following example demonstrates how to split a 3-channel matrix into 3 single channel matrices. +@snippet snippets/core_split.cpp example + +@param src input multi-channel array. +@param mvbegin output array; the number of arrays must match src.channels(); the arrays themselves are +reallocated, if needed. +@sa merge, mixChannels, cvtColor +*/ +CV_EXPORTS void split(const Mat& src, Mat* mvbegin); + +/** @overload +@param m input multi-channel array. +@param mv output vector of arrays; the arrays themselves are reallocated, if needed. +*/ +CV_EXPORTS_W void split(InputArray m, OutputArrayOfArrays mv); + +/** @brief Copies specified channels from input arrays to the specified channels of +output arrays. + +The function cv::mixChannels provides an advanced mechanism for shuffling image channels. + +cv::split,cv::merge,cv::extractChannel,cv::insertChannel and some forms of cv::cvtColor are partial cases of cv::mixChannels. + +In the example below, the code splits a 4-channel BGRA image into a 3-channel BGR (with B and R +channels swapped) and a separate alpha-channel image: +@code{.cpp} + Mat bgra( 100, 100, CV_8UC4, Scalar(255,0,0,255) ); + Mat bgr( bgra.rows, bgra.cols, CV_8UC3 ); + Mat alpha( bgra.rows, bgra.cols, CV_8UC1 ); + + // forming an array of matrices is a quite efficient operation, + // because the matrix data is not copied, only the headers + Mat out[] = { bgr, alpha }; + // bgra[0] -> bgr[2], bgra[1] -> bgr[1], + // bgra[2] -> bgr[0], bgra[3] -> alpha[0] + int from_to[] = { 0,2, 1,1, 2,0, 3,3 }; + mixChannels( &bgra, 1, out, 2, from_to, 4 ); +@endcode +@note Unlike many other new-style C++ functions in OpenCV (see the introduction section and +Mat::create ), cv::mixChannels requires the output arrays to be pre-allocated before calling the +function. +@param src input array or vector of matrices; all of the matrices must have the same size and the +same depth. +@param nsrcs number of matrices in `src`. +@param dst output array or vector of matrices; all the matrices **must be allocated**; their size and +depth must be the same as in `src[0]`. +@param ndsts number of matrices in `dst`. +@param fromTo array of index pairs specifying which channels are copied and where; fromTo[k\*2] is +a 0-based index of the input channel in src, fromTo[k\*2+1] is an index of the output channel in +dst; the continuous channel numbering is used: the first input image channels are indexed from 0 to +src[0].channels()-1, the second input image channels are indexed from src[0].channels() to +src[0].channels() + src[1].channels()-1, and so on, the same scheme is used for the output image +channels; as a special case, when fromTo[k\*2] is negative, the corresponding output channel is +filled with zero . +@param npairs number of index pairs in `fromTo`. +@sa split, merge, extractChannel, insertChannel, cvtColor +*/ +CV_EXPORTS void mixChannels(const Mat* src, size_t nsrcs, Mat* dst, size_t ndsts, + const int* fromTo, size_t npairs); + +/** @overload +@param src input array or vector of matrices; all of the matrices must have the same size and the +same depth. +@param dst output array or vector of matrices; all the matrices **must be allocated**; their size and +depth must be the same as in src[0]. +@param fromTo array of index pairs specifying which channels are copied and where; fromTo[k\*2] is +a 0-based index of the input channel in src, fromTo[k\*2+1] is an index of the output channel in +dst; the continuous channel numbering is used: the first input image channels are indexed from 0 to +src[0].channels()-1, the second input image channels are indexed from src[0].channels() to +src[0].channels() + src[1].channels()-1, and so on, the same scheme is used for the output image +channels; as a special case, when fromTo[k\*2] is negative, the corresponding output channel is +filled with zero . +@param npairs number of index pairs in fromTo. +*/ +CV_EXPORTS void mixChannels(InputArrayOfArrays src, InputOutputArrayOfArrays dst, + const int* fromTo, size_t npairs); + +/** @overload +@param src input array or vector of matrices; all of the matrices must have the same size and the +same depth. +@param dst output array or vector of matrices; all the matrices **must be allocated**; their size and +depth must be the same as in src[0]. +@param fromTo array of index pairs specifying which channels are copied and where; fromTo[k\*2] is +a 0-based index of the input channel in src, fromTo[k\*2+1] is an index of the output channel in +dst; the continuous channel numbering is used: the first input image channels are indexed from 0 to +src[0].channels()-1, the second input image channels are indexed from src[0].channels() to +src[0].channels() + src[1].channels()-1, and so on, the same scheme is used for the output image +channels; as a special case, when fromTo[k\*2] is negative, the corresponding output channel is +filled with zero . +*/ +CV_EXPORTS_W void mixChannels(InputArrayOfArrays src, InputOutputArrayOfArrays dst, + const std::vector& fromTo); + +/** @brief Extracts a single channel from src (coi is 0-based index) +@param src input array +@param dst output array +@param coi index of channel to extract +@sa mixChannels, split +*/ +CV_EXPORTS_W void extractChannel(InputArray src, OutputArray dst, int coi); + +/** @brief Inserts a single channel to dst (coi is 0-based index) +@param src input array +@param dst output array +@param coi index of channel for insertion +@sa mixChannels, merge +*/ +CV_EXPORTS_W void insertChannel(InputArray src, InputOutputArray dst, int coi); + +/** @brief Flips a 2D array around vertical, horizontal, or both axes. + +The function cv::flip flips the array in one of three different ways (row +and column indices are 0-based): +\f[\texttt{dst} _{ij} = +\left\{ +\begin{array}{l l} +\texttt{src} _{\texttt{src.rows}-i-1,j} & if\; \texttt{flipCode} = 0 \\ +\texttt{src} _{i, \texttt{src.cols} -j-1} & if\; \texttt{flipCode} > 0 \\ +\texttt{src} _{ \texttt{src.rows} -i-1, \texttt{src.cols} -j-1} & if\; \texttt{flipCode} < 0 \\ +\end{array} +\right.\f] +The example scenarios of using the function are the following: +* Vertical flipping of the image (flipCode == 0) to switch between + top-left and bottom-left image origin. This is a typical operation + in video processing on Microsoft Windows\* OS. +* Horizontal flipping of the image with the subsequent horizontal + shift and absolute difference calculation to check for a + vertical-axis symmetry (flipCode \> 0). +* Simultaneous horizontal and vertical flipping of the image with + the subsequent shift and absolute difference calculation to check + for a central symmetry (flipCode \< 0). +* Reversing the order of point arrays (flipCode \> 0 or + flipCode == 0). +@param src input array. +@param dst output array of the same size and type as src. +@param flipCode a flag to specify how to flip the array; 0 means +flipping around the x-axis and positive value (for example, 1) means +flipping around y-axis. Negative value (for example, -1) means flipping +around both axes. +@sa transpose , repeat , completeSymm +*/ +CV_EXPORTS_W void flip(InputArray src, OutputArray dst, int flipCode); + +enum RotateFlags { + ROTATE_90_CLOCKWISE = 0, //! A = (cv::Mat_(3, 2) << 1, 4, + 2, 5, + 3, 6); + cv::Mat_ B = (cv::Mat_(3, 2) << 7, 10, + 8, 11, + 9, 12); + + cv::Mat C; + cv::hconcat(A, B, C); + //C: + //[1, 4, 7, 10; + // 2, 5, 8, 11; + // 3, 6, 9, 12] + @endcode + @param src1 first input array to be considered for horizontal concatenation. + @param src2 second input array to be considered for horizontal concatenation. + @param dst output array. It has the same number of rows and depth as the src1 and src2, and the sum of cols of the src1 and src2. + */ +CV_EXPORTS void hconcat(InputArray src1, InputArray src2, OutputArray dst); +/** @overload + @code{.cpp} + std::vector matrices = { cv::Mat(4, 1, CV_8UC1, cv::Scalar(1)), + cv::Mat(4, 1, CV_8UC1, cv::Scalar(2)), + cv::Mat(4, 1, CV_8UC1, cv::Scalar(3)),}; + + cv::Mat out; + cv::hconcat( matrices, out ); + //out: + //[1, 2, 3; + // 1, 2, 3; + // 1, 2, 3; + // 1, 2, 3] + @endcode + @param src input array or vector of matrices. all of the matrices must have the same number of rows and the same depth. + @param dst output array. It has the same number of rows and depth as the src, and the sum of cols of the src. +same depth. + */ +CV_EXPORTS_W void hconcat(InputArrayOfArrays src, OutputArray dst); + +/** @brief Applies vertical concatenation to given matrices. + +The function vertically concatenates two or more cv::Mat matrices (with the same number of cols). +@code{.cpp} + cv::Mat matArray[] = { cv::Mat(1, 4, CV_8UC1, cv::Scalar(1)), + cv::Mat(1, 4, CV_8UC1, cv::Scalar(2)), + cv::Mat(1, 4, CV_8UC1, cv::Scalar(3)),}; + + cv::Mat out; + cv::vconcat( matArray, 3, out ); + //out: + //[1, 1, 1, 1; + // 2, 2, 2, 2; + // 3, 3, 3, 3] +@endcode +@param src input array or vector of matrices. all of the matrices must have the same number of cols and the same depth. +@param nsrc number of matrices in src. +@param dst output array. It has the same number of cols and depth as the src, and the sum of rows of the src. +@sa cv::hconcat(const Mat*, size_t, OutputArray), @sa cv::hconcat(InputArrayOfArrays, OutputArray) and @sa cv::hconcat(InputArray, InputArray, OutputArray) +*/ +CV_EXPORTS void vconcat(const Mat* src, size_t nsrc, OutputArray dst); +/** @overload + @code{.cpp} + cv::Mat_ A = (cv::Mat_(3, 2) << 1, 7, + 2, 8, + 3, 9); + cv::Mat_ B = (cv::Mat_(3, 2) << 4, 10, + 5, 11, + 6, 12); + + cv::Mat C; + cv::vconcat(A, B, C); + //C: + //[1, 7; + // 2, 8; + // 3, 9; + // 4, 10; + // 5, 11; + // 6, 12] + @endcode + @param src1 first input array to be considered for vertical concatenation. + @param src2 second input array to be considered for vertical concatenation. + @param dst output array. It has the same number of cols and depth as the src1 and src2, and the sum of rows of the src1 and src2. + */ +CV_EXPORTS void vconcat(InputArray src1, InputArray src2, OutputArray dst); +/** @overload + @code{.cpp} + std::vector matrices = { cv::Mat(1, 4, CV_8UC1, cv::Scalar(1)), + cv::Mat(1, 4, CV_8UC1, cv::Scalar(2)), + cv::Mat(1, 4, CV_8UC1, cv::Scalar(3)),}; + + cv::Mat out; + cv::vconcat( matrices, out ); + //out: + //[1, 1, 1, 1; + // 2, 2, 2, 2; + // 3, 3, 3, 3] + @endcode + @param src input array or vector of matrices. all of the matrices must have the same number of cols and the same depth + @param dst output array. It has the same number of cols and depth as the src, and the sum of rows of the src. +same depth. + */ +CV_EXPORTS_W void vconcat(InputArrayOfArrays src, OutputArray dst); + +/** @brief computes bitwise conjunction of the two arrays (dst = src1 & src2) +Calculates the per-element bit-wise conjunction of two arrays or an +array and a scalar. + +The function cv::bitwise_and calculates the per-element bit-wise logical conjunction for: +* Two arrays when src1 and src2 have the same size: + \f[\texttt{dst} (I) = \texttt{src1} (I) \wedge \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f] +* An array and a scalar when src2 is constructed from Scalar or has + the same number of elements as `src1.channels()`: + \f[\texttt{dst} (I) = \texttt{src1} (I) \wedge \texttt{src2} \quad \texttt{if mask} (I) \ne0\f] +* A scalar and an array when src1 is constructed from Scalar or has + the same number of elements as `src2.channels()`: + \f[\texttt{dst} (I) = \texttt{src1} \wedge \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f] +In case of floating-point arrays, their machine-specific bit +representations (usually IEEE754-compliant) are used for the operation. +In case of multi-channel arrays, each channel is processed +independently. In the second and third cases above, the scalar is first +converted to the array type. +@param src1 first input array or a scalar. +@param src2 second input array or a scalar. +@param dst output array that has the same size and type as the input +arrays. +@param mask optional operation mask, 8-bit single channel array, that +specifies elements of the output array to be changed. +*/ +CV_EXPORTS_W void bitwise_and(InputArray src1, InputArray src2, + OutputArray dst, InputArray mask = noArray()); + +/** @brief Calculates the per-element bit-wise disjunction of two arrays or an +array and a scalar. + +The function cv::bitwise_or calculates the per-element bit-wise logical disjunction for: +* Two arrays when src1 and src2 have the same size: + \f[\texttt{dst} (I) = \texttt{src1} (I) \vee \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f] +* An array and a scalar when src2 is constructed from Scalar or has + the same number of elements as `src1.channels()`: + \f[\texttt{dst} (I) = \texttt{src1} (I) \vee \texttt{src2} \quad \texttt{if mask} (I) \ne0\f] +* A scalar and an array when src1 is constructed from Scalar or has + the same number of elements as `src2.channels()`: + \f[\texttt{dst} (I) = \texttt{src1} \vee \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f] +In case of floating-point arrays, their machine-specific bit +representations (usually IEEE754-compliant) are used for the operation. +In case of multi-channel arrays, each channel is processed +independently. In the second and third cases above, the scalar is first +converted to the array type. +@param src1 first input array or a scalar. +@param src2 second input array or a scalar. +@param dst output array that has the same size and type as the input +arrays. +@param mask optional operation mask, 8-bit single channel array, that +specifies elements of the output array to be changed. +*/ +CV_EXPORTS_W void bitwise_or(InputArray src1, InputArray src2, + OutputArray dst, InputArray mask = noArray()); + +/** @brief Calculates the per-element bit-wise "exclusive or" operation on two +arrays or an array and a scalar. + +The function cv::bitwise_xor calculates the per-element bit-wise logical "exclusive-or" +operation for: +* Two arrays when src1 and src2 have the same size: + \f[\texttt{dst} (I) = \texttt{src1} (I) \oplus \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f] +* An array and a scalar when src2 is constructed from Scalar or has + the same number of elements as `src1.channels()`: + \f[\texttt{dst} (I) = \texttt{src1} (I) \oplus \texttt{src2} \quad \texttt{if mask} (I) \ne0\f] +* A scalar and an array when src1 is constructed from Scalar or has + the same number of elements as `src2.channels()`: + \f[\texttt{dst} (I) = \texttt{src1} \oplus \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f] +In case of floating-point arrays, their machine-specific bit +representations (usually IEEE754-compliant) are used for the operation. +In case of multi-channel arrays, each channel is processed +independently. In the 2nd and 3rd cases above, the scalar is first +converted to the array type. +@param src1 first input array or a scalar. +@param src2 second input array or a scalar. +@param dst output array that has the same size and type as the input +arrays. +@param mask optional operation mask, 8-bit single channel array, that +specifies elements of the output array to be changed. +*/ +CV_EXPORTS_W void bitwise_xor(InputArray src1, InputArray src2, + OutputArray dst, InputArray mask = noArray()); + +/** @brief Inverts every bit of an array. + +The function cv::bitwise_not calculates per-element bit-wise inversion of the input +array: +\f[\texttt{dst} (I) = \neg \texttt{src} (I)\f] +In case of a floating-point input array, its machine-specific bit +representation (usually IEEE754-compliant) is used for the operation. In +case of multi-channel arrays, each channel is processed independently. +@param src input array. +@param dst output array that has the same size and type as the input +array. +@param mask optional operation mask, 8-bit single channel array, that +specifies elements of the output array to be changed. +*/ +CV_EXPORTS_W void bitwise_not(InputArray src, OutputArray dst, + InputArray mask = noArray()); + +/** @brief Calculates the per-element absolute difference between two arrays or between an array and a scalar. + +The function cv::absdiff calculates: +* Absolute difference between two arrays when they have the same + size and type: + \f[\texttt{dst}(I) = \texttt{saturate} (| \texttt{src1}(I) - \texttt{src2}(I)|)\f] +* Absolute difference between an array and a scalar when the second + array is constructed from Scalar or has as many elements as the + number of channels in `src1`: + \f[\texttt{dst}(I) = \texttt{saturate} (| \texttt{src1}(I) - \texttt{src2} |)\f] +* Absolute difference between a scalar and an array when the first + array is constructed from Scalar or has as many elements as the + number of channels in `src2`: + \f[\texttt{dst}(I) = \texttt{saturate} (| \texttt{src1} - \texttt{src2}(I) |)\f] + where I is a multi-dimensional index of array elements. In case of + multi-channel arrays, each channel is processed independently. +@note Saturation is not applied when the arrays have the depth CV_32S. +You may even get a negative value in the case of overflow. +@param src1 first input array or a scalar. +@param src2 second input array or a scalar. +@param dst output array that has the same size and type as input arrays. +@sa cv::abs(const Mat&) +*/ +CV_EXPORTS_W void absdiff(InputArray src1, InputArray src2, OutputArray dst); + +/** @brief Checks if array elements lie between the elements of two other arrays. + +The function checks the range as follows: +- For every element of a single-channel input array: + \f[\texttt{dst} (I)= \texttt{lowerb} (I)_0 \leq \texttt{src} (I)_0 \leq \texttt{upperb} (I)_0\f] +- For two-channel arrays: + \f[\texttt{dst} (I)= \texttt{lowerb} (I)_0 \leq \texttt{src} (I)_0 \leq \texttt{upperb} (I)_0 \land \texttt{lowerb} (I)_1 \leq \texttt{src} (I)_1 \leq \texttt{upperb} (I)_1\f] +- and so forth. + +That is, dst (I) is set to 255 (all 1 -bits) if src (I) is within the +specified 1D, 2D, 3D, ... box and 0 otherwise. + +When the lower and/or upper boundary parameters are scalars, the indexes +(I) at lowerb and upperb in the above formulas should be omitted. +@param src first input array. +@param lowerb inclusive lower boundary array or a scalar. +@param upperb inclusive upper boundary array or a scalar. +@param dst output array of the same size as src and CV_8U type. +*/ +CV_EXPORTS_W void inRange(InputArray src, InputArray lowerb, + InputArray upperb, OutputArray dst); + +/** @brief Performs the per-element comparison of two arrays or an array and scalar value. + +The function compares: +* Elements of two arrays when src1 and src2 have the same size: + \f[\texttt{dst} (I) = \texttt{src1} (I) \,\texttt{cmpop}\, \texttt{src2} (I)\f] +* Elements of src1 with a scalar src2 when src2 is constructed from + Scalar or has a single element: + \f[\texttt{dst} (I) = \texttt{src1}(I) \,\texttt{cmpop}\, \texttt{src2}\f] +* src1 with elements of src2 when src1 is constructed from Scalar or + has a single element: + \f[\texttt{dst} (I) = \texttt{src1} \,\texttt{cmpop}\, \texttt{src2} (I)\f] +When the comparison result is true, the corresponding element of output +array is set to 255. The comparison operations can be replaced with the +equivalent matrix expressions: +@code{.cpp} + Mat dst1 = src1 >= src2; + Mat dst2 = src1 < 8; + ... +@endcode +@param src1 first input array or a scalar; when it is an array, it must have a single channel. +@param src2 second input array or a scalar; when it is an array, it must have a single channel. +@param dst output array of type ref CV_8U that has the same size and the same number of channels as + the input arrays. +@param cmpop a flag, that specifies correspondence between the arrays (cv::CmpTypes) +@sa checkRange, min, max, threshold +*/ +CV_EXPORTS_W void compare(InputArray src1, InputArray src2, OutputArray dst, int cmpop); + +/** @brief Calculates per-element minimum of two arrays or an array and a scalar. + +The function cv::min calculates the per-element minimum of two arrays: +\f[\texttt{dst} (I)= \min ( \texttt{src1} (I), \texttt{src2} (I))\f] +or array and a scalar: +\f[\texttt{dst} (I)= \min ( \texttt{src1} (I), \texttt{value} )\f] +@param src1 first input array. +@param src2 second input array of the same size and type as src1. +@param dst output array of the same size and type as src1. +@sa max, compare, inRange, minMaxLoc +*/ +CV_EXPORTS_W void min(InputArray src1, InputArray src2, OutputArray dst); +/** @overload +needed to avoid conflicts with const _Tp& std::min(const _Tp&, const _Tp&, _Compare) +*/ +CV_EXPORTS void min(const Mat& src1, const Mat& src2, Mat& dst); +/** @overload +needed to avoid conflicts with const _Tp& std::min(const _Tp&, const _Tp&, _Compare) +*/ +CV_EXPORTS void min(const UMat& src1, const UMat& src2, UMat& dst); + +/** @brief Calculates per-element maximum of two arrays or an array and a scalar. + +The function cv::max calculates the per-element maximum of two arrays: +\f[\texttt{dst} (I)= \max ( \texttt{src1} (I), \texttt{src2} (I))\f] +or array and a scalar: +\f[\texttt{dst} (I)= \max ( \texttt{src1} (I), \texttt{value} )\f] +@param src1 first input array. +@param src2 second input array of the same size and type as src1 . +@param dst output array of the same size and type as src1. +@sa min, compare, inRange, minMaxLoc, @ref MatrixExpressions +*/ +CV_EXPORTS_W void max(InputArray src1, InputArray src2, OutputArray dst); +/** @overload +needed to avoid conflicts with const _Tp& std::min(const _Tp&, const _Tp&, _Compare) +*/ +CV_EXPORTS void max(const Mat& src1, const Mat& src2, Mat& dst); +/** @overload +needed to avoid conflicts with const _Tp& std::min(const _Tp&, const _Tp&, _Compare) +*/ +CV_EXPORTS void max(const UMat& src1, const UMat& src2, UMat& dst); + +/** @brief Calculates a square root of array elements. + +The function cv::sqrt calculates a square root of each input array element. +In case of multi-channel arrays, each channel is processed +independently. The accuracy is approximately the same as of the built-in +std::sqrt . +@param src input floating-point array. +@param dst output array of the same size and type as src. +*/ +CV_EXPORTS_W void sqrt(InputArray src, OutputArray dst); + +/** @brief Raises every array element to a power. + +The function cv::pow raises every element of the input array to power : +\f[\texttt{dst} (I) = \fork{\texttt{src}(I)^{power}}{if \(\texttt{power}\) is integer}{|\texttt{src}(I)|^{power}}{otherwise}\f] + +So, for a non-integer power exponent, the absolute values of input array +elements are used. However, it is possible to get true values for +negative values using some extra operations. In the example below, +computing the 5th root of array src shows: +@code{.cpp} + Mat mask = src < 0; + pow(src, 1./5, dst); + subtract(Scalar::all(0), dst, dst, mask); +@endcode +For some values of power, such as integer values, 0.5 and -0.5, +specialized faster algorithms are used. + +Special values (NaN, Inf) are not handled. +@param src input array. +@param power exponent of power. +@param dst output array of the same size and type as src. +@sa sqrt, exp, log, cartToPolar, polarToCart +*/ +CV_EXPORTS_W void pow(InputArray src, double power, OutputArray dst); + +/** @brief Calculates the exponent of every array element. + +The function cv::exp calculates the exponent of every element of the input +array: +\f[\texttt{dst} [I] = e^{ src(I) }\f] + +The maximum relative error is about 7e-6 for single-precision input and +less than 1e-10 for double-precision input. Currently, the function +converts denormalized values to zeros on output. Special values (NaN, +Inf) are not handled. +@param src input array. +@param dst output array of the same size and type as src. +@sa log , cartToPolar , polarToCart , phase , pow , sqrt , magnitude +*/ +CV_EXPORTS_W void exp(InputArray src, OutputArray dst); + +/** @brief Calculates the natural logarithm of every array element. + +The function cv::log calculates the natural logarithm of every element of the input array: +\f[\texttt{dst} (I) = \log (\texttt{src}(I)) \f] + +Output on zero, negative and special (NaN, Inf) values is undefined. + +@param src input array. +@param dst output array of the same size and type as src . +@sa exp, cartToPolar, polarToCart, phase, pow, sqrt, magnitude +*/ +CV_EXPORTS_W void log(InputArray src, OutputArray dst); + +/** @brief Calculates x and y coordinates of 2D vectors from their magnitude and angle. + +The function cv::polarToCart calculates the Cartesian coordinates of each 2D +vector represented by the corresponding elements of magnitude and angle: +\f[\begin{array}{l} \texttt{x} (I) = \texttt{magnitude} (I) \cos ( \texttt{angle} (I)) \\ \texttt{y} (I) = \texttt{magnitude} (I) \sin ( \texttt{angle} (I)) \\ \end{array}\f] + +The relative accuracy of the estimated coordinates is about 1e-6. +@param magnitude input floating-point array of magnitudes of 2D vectors; +it can be an empty matrix (=Mat()), in this case, the function assumes +that all the magnitudes are =1; if it is not empty, it must have the +same size and type as angle. +@param angle input floating-point array of angles of 2D vectors. +@param x output array of x-coordinates of 2D vectors; it has the same +size and type as angle. +@param y output array of y-coordinates of 2D vectors; it has the same +size and type as angle. +@param angleInDegrees when true, the input angles are measured in +degrees, otherwise, they are measured in radians. +@sa cartToPolar, magnitude, phase, exp, log, pow, sqrt +*/ +CV_EXPORTS_W void polarToCart(InputArray magnitude, InputArray angle, + OutputArray x, OutputArray y, bool angleInDegrees = false); + +/** @brief Calculates the magnitude and angle of 2D vectors. + +The function cv::cartToPolar calculates either the magnitude, angle, or both +for every 2D vector (x(I),y(I)): +\f[\begin{array}{l} \texttt{magnitude} (I)= \sqrt{\texttt{x}(I)^2+\texttt{y}(I)^2} , \\ \texttt{angle} (I)= \texttt{atan2} ( \texttt{y} (I), \texttt{x} (I))[ \cdot180 / \pi ] \end{array}\f] + +The angles are calculated with accuracy about 0.3 degrees. For the point +(0,0), the angle is set to 0. +@param x array of x-coordinates; this must be a single-precision or +double-precision floating-point array. +@param y array of y-coordinates, that must have the same size and same type as x. +@param magnitude output array of magnitudes of the same size and type as x. +@param angle output array of angles that has the same size and type as +x; the angles are measured in radians (from 0 to 2\*Pi) or in degrees (0 to 360 degrees). +@param angleInDegrees a flag, indicating whether the angles are measured +in radians (which is by default), or in degrees. +@sa Sobel, Scharr +*/ +CV_EXPORTS_W void cartToPolar(InputArray x, InputArray y, + OutputArray magnitude, OutputArray angle, + bool angleInDegrees = false); + +/** @brief Calculates the rotation angle of 2D vectors. + +The function cv::phase calculates the rotation angle of each 2D vector that +is formed from the corresponding elements of x and y : +\f[\texttt{angle} (I) = \texttt{atan2} ( \texttt{y} (I), \texttt{x} (I))\f] + +The angle estimation accuracy is about 0.3 degrees. When x(I)=y(I)=0 , +the corresponding angle(I) is set to 0. +@param x input floating-point array of x-coordinates of 2D vectors. +@param y input array of y-coordinates of 2D vectors; it must have the +same size and the same type as x. +@param angle output array of vector angles; it has the same size and +same type as x . +@param angleInDegrees when true, the function calculates the angle in +degrees, otherwise, they are measured in radians. +*/ +CV_EXPORTS_W void phase(InputArray x, InputArray y, OutputArray angle, + bool angleInDegrees = false); + +/** @brief Calculates the magnitude of 2D vectors. + +The function cv::magnitude calculates the magnitude of 2D vectors formed +from the corresponding elements of x and y arrays: +\f[\texttt{dst} (I) = \sqrt{\texttt{x}(I)^2 + \texttt{y}(I)^2}\f] +@param x floating-point array of x-coordinates of the vectors. +@param y floating-point array of y-coordinates of the vectors; it must +have the same size as x. +@param magnitude output array of the same size and type as x. +@sa cartToPolar, polarToCart, phase, sqrt +*/ +CV_EXPORTS_W void magnitude(InputArray x, InputArray y, OutputArray magnitude); + +/** @brief Checks every element of an input array for invalid values. + +The function cv::checkRange checks that every array element is neither NaN nor infinite. When minVal \> +-DBL_MAX and maxVal \< DBL_MAX, the function also checks that each value is between minVal and +maxVal. In case of multi-channel arrays, each channel is processed independently. If some values +are out of range, position of the first outlier is stored in pos (when pos != NULL). Then, the +function either returns false (when quiet=true) or throws an exception. +@param a input array. +@param quiet a flag, indicating whether the functions quietly return false when the array elements +are out of range or they throw an exception. +@param pos optional output parameter, when not NULL, must be a pointer to array of src.dims +elements. +@param minVal inclusive lower boundary of valid values range. +@param maxVal exclusive upper boundary of valid values range. +*/ +CV_EXPORTS_W bool checkRange(InputArray a, bool quiet = true, CV_OUT Point* pos = 0, + double minVal = -DBL_MAX, double maxVal = DBL_MAX); + +/** @brief converts NaN's to the given number +*/ +CV_EXPORTS_W void patchNaNs(InputOutputArray a, double val = 0); + +/** @brief Performs generalized matrix multiplication. + +The function cv::gemm performs generalized matrix multiplication similar to the +gemm functions in BLAS level 3. For example, +`gemm(src1, src2, alpha, src3, beta, dst, GEMM_1_T + GEMM_3_T)` +corresponds to +\f[\texttt{dst} = \texttt{alpha} \cdot \texttt{src1} ^T \cdot \texttt{src2} + \texttt{beta} \cdot \texttt{src3} ^T\f] + +In case of complex (two-channel) data, performed a complex matrix +multiplication. + +The function can be replaced with a matrix expression. For example, the +above call can be replaced with: +@code{.cpp} + dst = alpha*src1.t()*src2 + beta*src3.t(); +@endcode +@param src1 first multiplied input matrix that could be real(CV_32FC1, +CV_64FC1) or complex(CV_32FC2, CV_64FC2). +@param src2 second multiplied input matrix of the same type as src1. +@param alpha weight of the matrix product. +@param src3 third optional delta matrix added to the matrix product; it +should have the same type as src1 and src2. +@param beta weight of src3. +@param dst output matrix; it has the proper size and the same type as +input matrices. +@param flags operation flags (cv::GemmFlags) +@sa mulTransposed , transform +*/ +CV_EXPORTS_W void gemm(InputArray src1, InputArray src2, double alpha, + InputArray src3, double beta, OutputArray dst, int flags = 0); + +/** @brief Calculates the product of a matrix and its transposition. + +The function cv::mulTransposed calculates the product of src and its +transposition: +\f[\texttt{dst} = \texttt{scale} ( \texttt{src} - \texttt{delta} )^T ( \texttt{src} - \texttt{delta} )\f] +if aTa=true , and +\f[\texttt{dst} = \texttt{scale} ( \texttt{src} - \texttt{delta} ) ( \texttt{src} - \texttt{delta} )^T\f] +otherwise. The function is used to calculate the covariance matrix. With +zero delta, it can be used as a faster substitute for general matrix +product A\*B when B=A' +@param src input single-channel matrix. Note that unlike gemm, the +function can multiply not only floating-point matrices. +@param dst output square matrix. +@param aTa Flag specifying the multiplication ordering. See the +description below. +@param delta Optional delta matrix subtracted from src before the +multiplication. When the matrix is empty ( delta=noArray() ), it is +assumed to be zero, that is, nothing is subtracted. If it has the same +size as src , it is simply subtracted. Otherwise, it is "repeated" (see +repeat ) to cover the full src and then subtracted. Type of the delta +matrix, when it is not empty, must be the same as the type of created +output matrix. See the dtype parameter description below. +@param scale Optional scale factor for the matrix product. +@param dtype Optional type of the output matrix. When it is negative, +the output matrix will have the same type as src . Otherwise, it will be +type=CV_MAT_DEPTH(dtype) that should be either CV_32F or CV_64F . +@sa calcCovarMatrix, gemm, repeat, reduce +*/ +CV_EXPORTS_W void mulTransposed( InputArray src, OutputArray dst, bool aTa, + InputArray delta = noArray(), + double scale = 1, int dtype = -1 ); + +/** @brief Transposes a matrix. + +The function cv::transpose transposes the matrix src : +\f[\texttt{dst} (i,j) = \texttt{src} (j,i)\f] +@note No complex conjugation is done in case of a complex matrix. It +should be done separately if needed. +@param src input array. +@param dst output array of the same type as src. +*/ +CV_EXPORTS_W void transpose(InputArray src, OutputArray dst); + +/** @brief Performs the matrix transformation of every array element. + +The function cv::transform performs the matrix transformation of every +element of the array src and stores the results in dst : +\f[\texttt{dst} (I) = \texttt{m} \cdot \texttt{src} (I)\f] +(when m.cols=src.channels() ), or +\f[\texttt{dst} (I) = \texttt{m} \cdot [ \texttt{src} (I); 1]\f] +(when m.cols=src.channels()+1 ) + +Every element of the N -channel array src is interpreted as N -element +vector that is transformed using the M x N or M x (N+1) matrix m to +M-element vector - the corresponding element of the output array dst . + +The function may be used for geometrical transformation of +N -dimensional points, arbitrary linear color space transformation (such +as various kinds of RGB to YUV transforms), shuffling the image +channels, and so forth. +@param src input array that must have as many channels (1 to 4) as +m.cols or m.cols-1. +@param dst output array of the same size and depth as src; it has as +many channels as m.rows. +@param m transformation 2x2 or 2x3 floating-point matrix. +@sa perspectiveTransform, getAffineTransform, estimateAffine2D, warpAffine, warpPerspective +*/ +CV_EXPORTS_W void transform(InputArray src, OutputArray dst, InputArray m ); + +/** @brief Performs the perspective matrix transformation of vectors. + +The function cv::perspectiveTransform transforms every element of src by +treating it as a 2D or 3D vector, in the following way: +\f[(x, y, z) \rightarrow (x'/w, y'/w, z'/w)\f] +where +\f[(x', y', z', w') = \texttt{mat} \cdot \begin{bmatrix} x & y & z & 1 \end{bmatrix}\f] +and +\f[w = \fork{w'}{if \(w' \ne 0\)}{\infty}{otherwise}\f] + +Here a 3D vector transformation is shown. In case of a 2D vector +transformation, the z component is omitted. + +@note The function transforms a sparse set of 2D or 3D vectors. If you +want to transform an image using perspective transformation, use +warpPerspective . If you have an inverse problem, that is, you want to +compute the most probable perspective transformation out of several +pairs of corresponding points, you can use getPerspectiveTransform or +findHomography . +@param src input two-channel or three-channel floating-point array; each +element is a 2D/3D vector to be transformed. +@param dst output array of the same size and type as src. +@param m 3x3 or 4x4 floating-point transformation matrix. +@sa transform, warpPerspective, getPerspectiveTransform, findHomography +*/ +CV_EXPORTS_W void perspectiveTransform(InputArray src, OutputArray dst, InputArray m ); + +/** @brief Copies the lower or the upper half of a square matrix to its another half. + +The function cv::completeSymm copies the lower or the upper half of a square matrix to +its another half. The matrix diagonal remains unchanged: + - \f$\texttt{m}_{ij}=\texttt{m}_{ji}\f$ for \f$i > j\f$ if + lowerToUpper=false + - \f$\texttt{m}_{ij}=\texttt{m}_{ji}\f$ for \f$i < j\f$ if + lowerToUpper=true + +@param m input-output floating-point square matrix. +@param lowerToUpper operation flag; if true, the lower half is copied to +the upper half. Otherwise, the upper half is copied to the lower half. +@sa flip, transpose +*/ +CV_EXPORTS_W void completeSymm(InputOutputArray m, bool lowerToUpper = false); + +/** @brief Initializes a scaled identity matrix. + +The function cv::setIdentity initializes a scaled identity matrix: +\f[\texttt{mtx} (i,j)= \fork{\texttt{value}}{ if \(i=j\)}{0}{otherwise}\f] + +The function can also be emulated using the matrix initializers and the +matrix expressions: +@code + Mat A = Mat::eye(4, 3, CV_32F)*5; + // A will be set to [[5, 0, 0], [0, 5, 0], [0, 0, 5], [0, 0, 0]] +@endcode +@param mtx matrix to initialize (not necessarily square). +@param s value to assign to diagonal elements. +@sa Mat::zeros, Mat::ones, Mat::setTo, Mat::operator= +*/ +CV_EXPORTS_W void setIdentity(InputOutputArray mtx, const Scalar& s = Scalar(1)); + +/** @brief Returns the determinant of a square floating-point matrix. + +The function cv::determinant calculates and returns the determinant of the +specified matrix. For small matrices ( mtx.cols=mtx.rows\<=3 ), the +direct method is used. For larger matrices, the function uses LU +factorization with partial pivoting. + +For symmetric positively-determined matrices, it is also possible to use +eigen decomposition to calculate the determinant. +@param mtx input matrix that must have CV_32FC1 or CV_64FC1 type and +square size. +@sa trace, invert, solve, eigen, @ref MatrixExpressions +*/ +CV_EXPORTS_W double determinant(InputArray mtx); + +/** @brief Returns the trace of a matrix. + +The function cv::trace returns the sum of the diagonal elements of the +matrix mtx . +\f[\mathrm{tr} ( \texttt{mtx} ) = \sum _i \texttt{mtx} (i,i)\f] +@param mtx input matrix. +*/ +CV_EXPORTS_W Scalar trace(InputArray mtx); + +/** @brief Finds the inverse or pseudo-inverse of a matrix. + +The function cv::invert inverts the matrix src and stores the result in dst +. When the matrix src is singular or non-square, the function calculates +the pseudo-inverse matrix (the dst matrix) so that norm(src\*dst - I) is +minimal, where I is an identity matrix. + +In case of the #DECOMP_LU method, the function returns non-zero value if +the inverse has been successfully calculated and 0 if src is singular. + +In case of the #DECOMP_SVD method, the function returns the inverse +condition number of src (the ratio of the smallest singular value to the +largest singular value) and 0 if src is singular. The SVD method +calculates a pseudo-inverse matrix if src is singular. + +Similarly to #DECOMP_LU, the method #DECOMP_CHOLESKY works only with +non-singular square matrices that should also be symmetrical and +positively defined. In this case, the function stores the inverted +matrix in dst and returns non-zero. Otherwise, it returns 0. + +@param src input floating-point M x N matrix. +@param dst output matrix of N x M size and the same type as src. +@param flags inversion method (cv::DecompTypes) +@sa solve, SVD +*/ +CV_EXPORTS_W double invert(InputArray src, OutputArray dst, int flags = DECOMP_LU); + +/** @brief Solves one or more linear systems or least-squares problems. + +The function cv::solve solves a linear system or least-squares problem (the +latter is possible with SVD or QR methods, or by specifying the flag +#DECOMP_NORMAL ): +\f[\texttt{dst} = \arg \min _X \| \texttt{src1} \cdot \texttt{X} - \texttt{src2} \|\f] + +If #DECOMP_LU or #DECOMP_CHOLESKY method is used, the function returns 1 +if src1 (or \f$\texttt{src1}^T\texttt{src1}\f$ ) is non-singular. Otherwise, +it returns 0. In the latter case, dst is not valid. Other methods find a +pseudo-solution in case of a singular left-hand side part. + +@note If you want to find a unity-norm solution of an under-defined +singular system \f$\texttt{src1}\cdot\texttt{dst}=0\f$ , the function solve +will not do the work. Use SVD::solveZ instead. + +@param src1 input matrix on the left-hand side of the system. +@param src2 input matrix on the right-hand side of the system. +@param dst output solution. +@param flags solution (matrix inversion) method (#DecompTypes) +@sa invert, SVD, eigen +*/ +CV_EXPORTS_W bool solve(InputArray src1, InputArray src2, + OutputArray dst, int flags = DECOMP_LU); + +/** @brief Sorts each row or each column of a matrix. + +The function cv::sort sorts each matrix row or each matrix column in +ascending or descending order. So you should pass two operation flags to +get desired behaviour. If you want to sort matrix rows or columns +lexicographically, you can use STL std::sort generic function with the +proper comparison predicate. + +@param src input single-channel array. +@param dst output array of the same size and type as src. +@param flags operation flags, a combination of #SortFlags +@sa sortIdx, randShuffle +*/ +CV_EXPORTS_W void sort(InputArray src, OutputArray dst, int flags); + +/** @brief Sorts each row or each column of a matrix. + +The function cv::sortIdx sorts each matrix row or each matrix column in the +ascending or descending order. So you should pass two operation flags to +get desired behaviour. Instead of reordering the elements themselves, it +stores the indices of sorted elements in the output array. For example: +@code + Mat A = Mat::eye(3,3,CV_32F), B; + sortIdx(A, B, SORT_EVERY_ROW + SORT_ASCENDING); + // B will probably contain + // (because of equal elements in A some permutations are possible): + // [[1, 2, 0], [0, 2, 1], [0, 1, 2]] +@endcode +@param src input single-channel array. +@param dst output integer array of the same size as src. +@param flags operation flags that could be a combination of cv::SortFlags +@sa sort, randShuffle +*/ +CV_EXPORTS_W void sortIdx(InputArray src, OutputArray dst, int flags); + +/** @brief Finds the real roots of a cubic equation. + +The function solveCubic finds the real roots of a cubic equation: +- if coeffs is a 4-element vector: +\f[\texttt{coeffs} [0] x^3 + \texttt{coeffs} [1] x^2 + \texttt{coeffs} [2] x + \texttt{coeffs} [3] = 0\f] +- if coeffs is a 3-element vector: +\f[x^3 + \texttt{coeffs} [0] x^2 + \texttt{coeffs} [1] x + \texttt{coeffs} [2] = 0\f] + +The roots are stored in the roots array. +@param coeffs equation coefficients, an array of 3 or 4 elements. +@param roots output array of real roots that has 1 or 3 elements. +@return number of real roots. It can be 0, 1 or 2. +*/ +CV_EXPORTS_W int solveCubic(InputArray coeffs, OutputArray roots); + +/** @brief Finds the real or complex roots of a polynomial equation. + +The function cv::solvePoly finds real and complex roots of a polynomial equation: +\f[\texttt{coeffs} [n] x^{n} + \texttt{coeffs} [n-1] x^{n-1} + ... + \texttt{coeffs} [1] x + \texttt{coeffs} [0] = 0\f] +@param coeffs array of polynomial coefficients. +@param roots output (complex) array of roots. +@param maxIters maximum number of iterations the algorithm does. +*/ +CV_EXPORTS_W double solvePoly(InputArray coeffs, OutputArray roots, int maxIters = 300); + +/** @brief Calculates eigenvalues and eigenvectors of a symmetric matrix. + +The function cv::eigen calculates just eigenvalues, or eigenvalues and eigenvectors of the symmetric +matrix src: +@code + src*eigenvectors.row(i).t() = eigenvalues.at(i)*eigenvectors.row(i).t() +@endcode + +@note Use cv::eigenNonSymmetric for calculation of real eigenvalues and eigenvectors of non-symmetric matrix. + +@param src input matrix that must have CV_32FC1 or CV_64FC1 type, square size and be symmetrical +(src ^T^ == src). +@param eigenvalues output vector of eigenvalues of the same type as src; the eigenvalues are stored +in the descending order. +@param eigenvectors output matrix of eigenvectors; it has the same size and type as src; the +eigenvectors are stored as subsequent matrix rows, in the same order as the corresponding +eigenvalues. +@sa eigenNonSymmetric, completeSymm , PCA +*/ +CV_EXPORTS_W bool eigen(InputArray src, OutputArray eigenvalues, + OutputArray eigenvectors = noArray()); + +/** @brief Calculates eigenvalues and eigenvectors of a non-symmetric matrix (real eigenvalues only). + +@note Assumes real eigenvalues. + +The function calculates eigenvalues and eigenvectors (optional) of the square matrix src: +@code + src*eigenvectors.row(i).t() = eigenvalues.at(i)*eigenvectors.row(i).t() +@endcode + +@param src input matrix (CV_32FC1 or CV_64FC1 type). +@param eigenvalues output vector of eigenvalues (type is the same type as src). +@param eigenvectors output matrix of eigenvectors (type is the same type as src). The eigenvectors are stored as subsequent matrix rows, in the same order as the corresponding eigenvalues. +@sa eigen +*/ +CV_EXPORTS_W void eigenNonSymmetric(InputArray src, OutputArray eigenvalues, + OutputArray eigenvectors); + +/** @brief Calculates the covariance matrix of a set of vectors. + +The function cv::calcCovarMatrix calculates the covariance matrix and, optionally, the mean vector of +the set of input vectors. +@param samples samples stored as separate matrices +@param nsamples number of samples +@param covar output covariance matrix of the type ctype and square size. +@param mean input or output (depending on the flags) array as the average value of the input vectors. +@param flags operation flags as a combination of #CovarFlags +@param ctype type of the matrixl; it equals 'CV_64F' by default. +@sa PCA, mulTransposed, Mahalanobis +@todo InputArrayOfArrays +*/ +CV_EXPORTS void calcCovarMatrix( const Mat* samples, int nsamples, Mat& covar, Mat& mean, + int flags, int ctype = CV_64F); + +/** @overload +@note use #COVAR_ROWS or #COVAR_COLS flag +@param samples samples stored as rows/columns of a single matrix. +@param covar output covariance matrix of the type ctype and square size. +@param mean input or output (depending on the flags) array as the average value of the input vectors. +@param flags operation flags as a combination of #CovarFlags +@param ctype type of the matrixl; it equals 'CV_64F' by default. +*/ +CV_EXPORTS_W void calcCovarMatrix( InputArray samples, OutputArray covar, + InputOutputArray mean, int flags, int ctype = CV_64F); + +/** wrap PCA::operator() */ +CV_EXPORTS_W void PCACompute(InputArray data, InputOutputArray mean, + OutputArray eigenvectors, int maxComponents = 0); + +/** wrap PCA::operator() and add eigenvalues output parameter */ +CV_EXPORTS_AS(PCACompute2) void PCACompute(InputArray data, InputOutputArray mean, + OutputArray eigenvectors, OutputArray eigenvalues, + int maxComponents = 0); + +/** wrap PCA::operator() */ +CV_EXPORTS_W void PCACompute(InputArray data, InputOutputArray mean, + OutputArray eigenvectors, double retainedVariance); + +/** wrap PCA::operator() and add eigenvalues output parameter */ +CV_EXPORTS_AS(PCACompute2) void PCACompute(InputArray data, InputOutputArray mean, + OutputArray eigenvectors, OutputArray eigenvalues, + double retainedVariance); + +/** wrap PCA::project */ +CV_EXPORTS_W void PCAProject(InputArray data, InputArray mean, + InputArray eigenvectors, OutputArray result); + +/** wrap PCA::backProject */ +CV_EXPORTS_W void PCABackProject(InputArray data, InputArray mean, + InputArray eigenvectors, OutputArray result); + +/** wrap SVD::compute */ +CV_EXPORTS_W void SVDecomp( InputArray src, OutputArray w, OutputArray u, OutputArray vt, int flags = 0 ); + +/** wrap SVD::backSubst */ +CV_EXPORTS_W void SVBackSubst( InputArray w, InputArray u, InputArray vt, + InputArray rhs, OutputArray dst ); + +/** @brief Calculates the Mahalanobis distance between two vectors. + +The function cv::Mahalanobis calculates and returns the weighted distance between two vectors: +\f[d( \texttt{vec1} , \texttt{vec2} )= \sqrt{\sum_{i,j}{\texttt{icovar(i,j)}\cdot(\texttt{vec1}(I)-\texttt{vec2}(I))\cdot(\texttt{vec1(j)}-\texttt{vec2(j)})} }\f] +The covariance matrix may be calculated using the #calcCovarMatrix function and then inverted using +the invert function (preferably using the #DECOMP_SVD method, as the most accurate). +@param v1 first 1D input vector. +@param v2 second 1D input vector. +@param icovar inverse covariance matrix. +*/ +CV_EXPORTS_W double Mahalanobis(InputArray v1, InputArray v2, InputArray icovar); + +/** @brief Performs a forward or inverse Discrete Fourier transform of a 1D or 2D floating-point array. + +The function cv::dft performs one of the following: +- Forward the Fourier transform of a 1D vector of N elements: + \f[Y = F^{(N)} \cdot X,\f] + where \f$F^{(N)}_{jk}=\exp(-2\pi i j k/N)\f$ and \f$i=\sqrt{-1}\f$ +- Inverse the Fourier transform of a 1D vector of N elements: + \f[\begin{array}{l} X'= \left (F^{(N)} \right )^{-1} \cdot Y = \left (F^{(N)} \right )^* \cdot y \\ X = (1/N) \cdot X, \end{array}\f] + where \f$F^*=\left(\textrm{Re}(F^{(N)})-\textrm{Im}(F^{(N)})\right)^T\f$ +- Forward the 2D Fourier transform of a M x N matrix: + \f[Y = F^{(M)} \cdot X \cdot F^{(N)}\f] +- Inverse the 2D Fourier transform of a M x N matrix: + \f[\begin{array}{l} X'= \left (F^{(M)} \right )^* \cdot Y \cdot \left (F^{(N)} \right )^* \\ X = \frac{1}{M \cdot N} \cdot X' \end{array}\f] + +In case of real (single-channel) data, the output spectrum of the forward Fourier transform or input +spectrum of the inverse Fourier transform can be represented in a packed format called *CCS* +(complex-conjugate-symmetrical). It was borrowed from IPL (Intel\* Image Processing Library). Here +is how 2D *CCS* spectrum looks: +\f[\begin{bmatrix} Re Y_{0,0} & Re Y_{0,1} & Im Y_{0,1} & Re Y_{0,2} & Im Y_{0,2} & \cdots & Re Y_{0,N/2-1} & Im Y_{0,N/2-1} & Re Y_{0,N/2} \\ Re Y_{1,0} & Re Y_{1,1} & Im Y_{1,1} & Re Y_{1,2} & Im Y_{1,2} & \cdots & Re Y_{1,N/2-1} & Im Y_{1,N/2-1} & Re Y_{1,N/2} \\ Im Y_{1,0} & Re Y_{2,1} & Im Y_{2,1} & Re Y_{2,2} & Im Y_{2,2} & \cdots & Re Y_{2,N/2-1} & Im Y_{2,N/2-1} & Im Y_{1,N/2} \\ \hdotsfor{9} \\ Re Y_{M/2-1,0} & Re Y_{M-3,1} & Im Y_{M-3,1} & \hdotsfor{3} & Re Y_{M-3,N/2-1} & Im Y_{M-3,N/2-1}& Re Y_{M/2-1,N/2} \\ Im Y_{M/2-1,0} & Re Y_{M-2,1} & Im Y_{M-2,1} & \hdotsfor{3} & Re Y_{M-2,N/2-1} & Im Y_{M-2,N/2-1}& Im Y_{M/2-1,N/2} \\ Re Y_{M/2,0} & Re Y_{M-1,1} & Im Y_{M-1,1} & \hdotsfor{3} & Re Y_{M-1,N/2-1} & Im Y_{M-1,N/2-1}& Re Y_{M/2,N/2} \end{bmatrix}\f] + +In case of 1D transform of a real vector, the output looks like the first row of the matrix above. + +So, the function chooses an operation mode depending on the flags and size of the input array: +- If #DFT_ROWS is set or the input array has a single row or single column, the function + performs a 1D forward or inverse transform of each row of a matrix when #DFT_ROWS is set. + Otherwise, it performs a 2D transform. +- If the input array is real and #DFT_INVERSE is not set, the function performs a forward 1D or + 2D transform: + - When #DFT_COMPLEX_OUTPUT is set, the output is a complex matrix of the same size as + input. + - When #DFT_COMPLEX_OUTPUT is not set, the output is a real matrix of the same size as + input. In case of 2D transform, it uses the packed format as shown above. In case of a + single 1D transform, it looks like the first row of the matrix above. In case of + multiple 1D transforms (when using the #DFT_ROWS flag), each row of the output matrix + looks like the first row of the matrix above. +- If the input array is complex and either #DFT_INVERSE or #DFT_REAL_OUTPUT are not set, the + output is a complex array of the same size as input. The function performs a forward or + inverse 1D or 2D transform of the whole input array or each row of the input array + independently, depending on the flags DFT_INVERSE and DFT_ROWS. +- When #DFT_INVERSE is set and the input array is real, or it is complex but #DFT_REAL_OUTPUT + is set, the output is a real array of the same size as input. The function performs a 1D or 2D + inverse transformation of the whole input array or each individual row, depending on the flags + #DFT_INVERSE and #DFT_ROWS. + +If #DFT_SCALE is set, the scaling is done after the transformation. + +Unlike dct , the function supports arrays of arbitrary size. But only those arrays are processed +efficiently, whose sizes can be factorized in a product of small prime numbers (2, 3, and 5 in the +current implementation). Such an efficient DFT size can be calculated using the getOptimalDFTSize +method. + +The sample below illustrates how to calculate a DFT-based convolution of two 2D real arrays: +@code + void convolveDFT(InputArray A, InputArray B, OutputArray C) + { + // reallocate the output array if needed + C.create(abs(A.rows - B.rows)+1, abs(A.cols - B.cols)+1, A.type()); + Size dftSize; + // calculate the size of DFT transform + dftSize.width = getOptimalDFTSize(A.cols + B.cols - 1); + dftSize.height = getOptimalDFTSize(A.rows + B.rows - 1); + + // allocate temporary buffers and initialize them with 0's + Mat tempA(dftSize, A.type(), Scalar::all(0)); + Mat tempB(dftSize, B.type(), Scalar::all(0)); + + // copy A and B to the top-left corners of tempA and tempB, respectively + Mat roiA(tempA, Rect(0,0,A.cols,A.rows)); + A.copyTo(roiA); + Mat roiB(tempB, Rect(0,0,B.cols,B.rows)); + B.copyTo(roiB); + + // now transform the padded A & B in-place; + // use "nonzeroRows" hint for faster processing + dft(tempA, tempA, 0, A.rows); + dft(tempB, tempB, 0, B.rows); + + // multiply the spectrums; + // the function handles packed spectrum representations well + mulSpectrums(tempA, tempB, tempA); + + // transform the product back from the frequency domain. + // Even though all the result rows will be non-zero, + // you need only the first C.rows of them, and thus you + // pass nonzeroRows == C.rows + dft(tempA, tempA, DFT_INVERSE + DFT_SCALE, C.rows); + + // now copy the result back to C. + tempA(Rect(0, 0, C.cols, C.rows)).copyTo(C); + + // all the temporary buffers will be deallocated automatically + } +@endcode +To optimize this sample, consider the following approaches: +- Since nonzeroRows != 0 is passed to the forward transform calls and since A and B are copied to + the top-left corners of tempA and tempB, respectively, it is not necessary to clear the whole + tempA and tempB. It is only necessary to clear the tempA.cols - A.cols ( tempB.cols - B.cols) + rightmost columns of the matrices. +- This DFT-based convolution does not have to be applied to the whole big arrays, especially if B + is significantly smaller than A or vice versa. Instead, you can calculate convolution by parts. + To do this, you need to split the output array C into multiple tiles. For each tile, estimate + which parts of A and B are required to calculate convolution in this tile. If the tiles in C are + too small, the speed will decrease a lot because of repeated work. In the ultimate case, when + each tile in C is a single pixel, the algorithm becomes equivalent to the naive convolution + algorithm. If the tiles are too big, the temporary arrays tempA and tempB become too big and + there is also a slowdown because of bad cache locality. So, there is an optimal tile size + somewhere in the middle. +- If different tiles in C can be calculated in parallel and, thus, the convolution is done by + parts, the loop can be threaded. + +All of the above improvements have been implemented in #matchTemplate and #filter2D . Therefore, by +using them, you can get the performance even better than with the above theoretically optimal +implementation. Though, those two functions actually calculate cross-correlation, not convolution, +so you need to "flip" the second convolution operand B vertically and horizontally using flip . +@note +- An example using the discrete fourier transform can be found at + opencv_source_code/samples/cpp/dft.cpp +- (Python) An example using the dft functionality to perform Wiener deconvolution can be found + at opencv_source/samples/python/deconvolution.py +- (Python) An example rearranging the quadrants of a Fourier image can be found at + opencv_source/samples/python/dft.py +@param src input array that could be real or complex. +@param dst output array whose size and type depends on the flags . +@param flags transformation flags, representing a combination of the #DftFlags +@param nonzeroRows when the parameter is not zero, the function assumes that only the first +nonzeroRows rows of the input array (#DFT_INVERSE is not set) or only the first nonzeroRows of the +output array (#DFT_INVERSE is set) contain non-zeros, thus, the function can handle the rest of the +rows more efficiently and save some time; this technique is very useful for calculating array +cross-correlation or convolution using DFT. +@sa dct , getOptimalDFTSize , mulSpectrums, filter2D , matchTemplate , flip , cartToPolar , +magnitude , phase +*/ +CV_EXPORTS_W void dft(InputArray src, OutputArray dst, int flags = 0, int nonzeroRows = 0); + +/** @brief Calculates the inverse Discrete Fourier Transform of a 1D or 2D array. + +idft(src, dst, flags) is equivalent to dft(src, dst, flags | #DFT_INVERSE) . +@note None of dft and idft scales the result by default. So, you should pass #DFT_SCALE to one of +dft or idft explicitly to make these transforms mutually inverse. +@sa dft, dct, idct, mulSpectrums, getOptimalDFTSize +@param src input floating-point real or complex array. +@param dst output array whose size and type depend on the flags. +@param flags operation flags (see dft and #DftFlags). +@param nonzeroRows number of dst rows to process; the rest of the rows have undefined content (see +the convolution sample in dft description. +*/ +CV_EXPORTS_W void idft(InputArray src, OutputArray dst, int flags = 0, int nonzeroRows = 0); + +/** @brief Performs a forward or inverse discrete Cosine transform of 1D or 2D array. + +The function cv::dct performs a forward or inverse discrete Cosine transform (DCT) of a 1D or 2D +floating-point array: +- Forward Cosine transform of a 1D vector of N elements: + \f[Y = C^{(N)} \cdot X\f] + where + \f[C^{(N)}_{jk}= \sqrt{\alpha_j/N} \cos \left ( \frac{\pi(2k+1)j}{2N} \right )\f] + and + \f$\alpha_0=1\f$, \f$\alpha_j=2\f$ for *j \> 0*. +- Inverse Cosine transform of a 1D vector of N elements: + \f[X = \left (C^{(N)} \right )^{-1} \cdot Y = \left (C^{(N)} \right )^T \cdot Y\f] + (since \f$C^{(N)}\f$ is an orthogonal matrix, \f$C^{(N)} \cdot \left(C^{(N)}\right)^T = I\f$ ) +- Forward 2D Cosine transform of M x N matrix: + \f[Y = C^{(N)} \cdot X \cdot \left (C^{(N)} \right )^T\f] +- Inverse 2D Cosine transform of M x N matrix: + \f[X = \left (C^{(N)} \right )^T \cdot X \cdot C^{(N)}\f] + +The function chooses the mode of operation by looking at the flags and size of the input array: +- If (flags & #DCT_INVERSE) == 0 , the function does a forward 1D or 2D transform. Otherwise, it + is an inverse 1D or 2D transform. +- If (flags & #DCT_ROWS) != 0 , the function performs a 1D transform of each row. +- If the array is a single column or a single row, the function performs a 1D transform. +- If none of the above is true, the function performs a 2D transform. + +@note Currently dct supports even-size arrays (2, 4, 6 ...). For data analysis and approximation, you +can pad the array when necessary. +Also, the function performance depends very much, and not monotonically, on the array size (see +getOptimalDFTSize ). In the current implementation DCT of a vector of size N is calculated via DFT +of a vector of size N/2 . Thus, the optimal DCT size N1 \>= N can be calculated as: +@code + size_t getOptimalDCTSize(size_t N) { return 2*getOptimalDFTSize((N+1)/2); } + N1 = getOptimalDCTSize(N); +@endcode +@param src input floating-point array. +@param dst output array of the same size and type as src . +@param flags transformation flags as a combination of cv::DftFlags (DCT_*) +@sa dft , getOptimalDFTSize , idct +*/ +CV_EXPORTS_W void dct(InputArray src, OutputArray dst, int flags = 0); + +/** @brief Calculates the inverse Discrete Cosine Transform of a 1D or 2D array. + +idct(src, dst, flags) is equivalent to dct(src, dst, flags | DCT_INVERSE). +@param src input floating-point single-channel array. +@param dst output array of the same size and type as src. +@param flags operation flags. +@sa dct, dft, idft, getOptimalDFTSize +*/ +CV_EXPORTS_W void idct(InputArray src, OutputArray dst, int flags = 0); + +/** @brief Performs the per-element multiplication of two Fourier spectrums. + +The function cv::mulSpectrums performs the per-element multiplication of the two CCS-packed or complex +matrices that are results of a real or complex Fourier transform. + +The function, together with dft and idft , may be used to calculate convolution (pass conjB=false ) +or correlation (pass conjB=true ) of two arrays rapidly. When the arrays are complex, they are +simply multiplied (per element) with an optional conjugation of the second-array elements. When the +arrays are real, they are assumed to be CCS-packed (see dft for details). +@param a first input array. +@param b second input array of the same size and type as src1 . +@param c output array of the same size and type as src1 . +@param flags operation flags; currently, the only supported flag is cv::DFT_ROWS, which indicates that +each row of src1 and src2 is an independent 1D Fourier spectrum. If you do not want to use this flag, then simply add a `0` as value. +@param conjB optional flag that conjugates the second input array before the multiplication (true) +or not (false). +*/ +CV_EXPORTS_W void mulSpectrums(InputArray a, InputArray b, OutputArray c, + int flags, bool conjB = false); + +/** @brief Returns the optimal DFT size for a given vector size. + +DFT performance is not a monotonic function of a vector size. Therefore, when you calculate +convolution of two arrays or perform the spectral analysis of an array, it usually makes sense to +pad the input data with zeros to get a bit larger array that can be transformed much faster than the +original one. Arrays whose size is a power-of-two (2, 4, 8, 16, 32, ...) are the fastest to process. +Though, the arrays whose size is a product of 2's, 3's, and 5's (for example, 300 = 5\*5\*3\*2\*2) +are also processed quite efficiently. + +The function cv::getOptimalDFTSize returns the minimum number N that is greater than or equal to vecsize +so that the DFT of a vector of size N can be processed efficiently. In the current implementation N += 2 ^p^ \* 3 ^q^ \* 5 ^r^ for some integer p, q, r. + +The function returns a negative number if vecsize is too large (very close to INT_MAX ). + +While the function cannot be used directly to estimate the optimal vector size for DCT transform +(since the current DCT implementation supports only even-size vectors), it can be easily processed +as getOptimalDFTSize((vecsize+1)/2)\*2. +@param vecsize vector size. +@sa dft , dct , idft , idct , mulSpectrums +*/ +CV_EXPORTS_W int getOptimalDFTSize(int vecsize); + +/** @brief Returns the default random number generator. + +The function cv::theRNG returns the default random number generator. For each thread, there is a +separate random number generator, so you can use the function safely in multi-thread environments. +If you just need to get a single random number using this generator or initialize an array, you can +use randu or randn instead. But if you are going to generate many random numbers inside a loop, it +is much faster to use this function to retrieve the generator and then use RNG::operator _Tp() . +@sa RNG, randu, randn +*/ +CV_EXPORTS RNG& theRNG(); + +/** @brief Sets state of default random number generator. + +The function cv::setRNGSeed sets state of default random number generator to custom value. +@param seed new state for default random number generator +@sa RNG, randu, randn +*/ +CV_EXPORTS_W void setRNGSeed(int seed); + +/** @brief Generates a single uniformly-distributed random number or an array of random numbers. + +Non-template variant of the function fills the matrix dst with uniformly-distributed +random numbers from the specified range: +\f[\texttt{low} _c \leq \texttt{dst} (I)_c < \texttt{high} _c\f] +@param dst output array of random numbers; the array must be pre-allocated. +@param low inclusive lower boundary of the generated random numbers. +@param high exclusive upper boundary of the generated random numbers. +@sa RNG, randn, theRNG +*/ +CV_EXPORTS_W void randu(InputOutputArray dst, InputArray low, InputArray high); + +/** @brief Fills the array with normally distributed random numbers. + +The function cv::randn fills the matrix dst with normally distributed random numbers with the specified +mean vector and the standard deviation matrix. The generated random numbers are clipped to fit the +value range of the output array data type. +@param dst output array of random numbers; the array must be pre-allocated and have 1 to 4 channels. +@param mean mean value (expectation) of the generated random numbers. +@param stddev standard deviation of the generated random numbers; it can be either a vector (in +which case a diagonal standard deviation matrix is assumed) or a square matrix. +@sa RNG, randu +*/ +CV_EXPORTS_W void randn(InputOutputArray dst, InputArray mean, InputArray stddev); + +/** @brief Shuffles the array elements randomly. + +The function cv::randShuffle shuffles the specified 1D array by randomly choosing pairs of elements and +swapping them. The number of such swap operations will be dst.rows\*dst.cols\*iterFactor . +@param dst input/output numerical 1D array. +@param iterFactor scale factor that determines the number of random swap operations (see the details +below). +@param rng optional random number generator used for shuffling; if it is zero, theRNG () is used +instead. +@sa RNG, sort +*/ +CV_EXPORTS_W void randShuffle(InputOutputArray dst, double iterFactor = 1., RNG* rng = 0); + +/** @brief Principal Component Analysis + +The class is used to calculate a special basis for a set of vectors. The +basis will consist of eigenvectors of the covariance matrix calculated +from the input set of vectors. The class %PCA can also transform +vectors to/from the new coordinate space defined by the basis. Usually, +in this new coordinate system, each vector from the original set (and +any linear combination of such vectors) can be quite accurately +approximated by taking its first few components, corresponding to the +eigenvectors of the largest eigenvalues of the covariance matrix. +Geometrically it means that you calculate a projection of the vector to +a subspace formed by a few eigenvectors corresponding to the dominant +eigenvalues of the covariance matrix. And usually such a projection is +very close to the original vector. So, you can represent the original +vector from a high-dimensional space with a much shorter vector +consisting of the projected vector's coordinates in the subspace. Such a +transformation is also known as Karhunen-Loeve Transform, or KLT. +See http://en.wikipedia.org/wiki/Principal_component_analysis + +The sample below is the function that takes two matrices. The first +function stores a set of vectors (a row per vector) that is used to +calculate PCA. The second function stores another "test" set of vectors +(a row per vector). First, these vectors are compressed with PCA, then +reconstructed back, and then the reconstruction error norm is computed +and printed for each vector. : + +@code{.cpp} +using namespace cv; + +PCA compressPCA(const Mat& pcaset, int maxComponents, + const Mat& testset, Mat& compressed) +{ + PCA pca(pcaset, // pass the data + Mat(), // we do not have a pre-computed mean vector, + // so let the PCA engine to compute it + PCA::DATA_AS_ROW, // indicate that the vectors + // are stored as matrix rows + // (use PCA::DATA_AS_COL if the vectors are + // the matrix columns) + maxComponents // specify, how many principal components to retain + ); + // if there is no test data, just return the computed basis, ready-to-use + if( !testset.data ) + return pca; + CV_Assert( testset.cols == pcaset.cols ); + + compressed.create(testset.rows, maxComponents, testset.type()); + + Mat reconstructed; + for( int i = 0; i < testset.rows; i++ ) + { + Mat vec = testset.row(i), coeffs = compressed.row(i), reconstructed; + // compress the vector, the result will be stored + // in the i-th row of the output matrix + pca.project(vec, coeffs); + // and then reconstruct it + pca.backProject(coeffs, reconstructed); + // and measure the error + printf("%d. diff = %g\n", i, norm(vec, reconstructed, NORM_L2)); + } + return pca; +} +@endcode +@sa calcCovarMatrix, mulTransposed, SVD, dft, dct +*/ +class CV_EXPORTS PCA +{ +public: + enum Flags { DATA_AS_ROW = 0, //!< indicates that the input samples are stored as matrix rows + DATA_AS_COL = 1, //!< indicates that the input samples are stored as matrix columns + USE_AVG = 2 //! + }; + + /** @brief default constructor + + The default constructor initializes an empty %PCA structure. The other + constructors initialize the structure and call PCA::operator()(). + */ + PCA(); + + /** @overload + @param data input samples stored as matrix rows or matrix columns. + @param mean optional mean value; if the matrix is empty (@c noArray()), + the mean is computed from the data. + @param flags operation flags; currently the parameter is only used to + specify the data layout (PCA::Flags) + @param maxComponents maximum number of components that %PCA should + retain; by default, all the components are retained. + */ + PCA(InputArray data, InputArray mean, int flags, int maxComponents = 0); + + /** @overload + @param data input samples stored as matrix rows or matrix columns. + @param mean optional mean value; if the matrix is empty (noArray()), + the mean is computed from the data. + @param flags operation flags; currently the parameter is only used to + specify the data layout (PCA::Flags) + @param retainedVariance Percentage of variance that PCA should retain. + Using this parameter will let the PCA decided how many components to + retain but it will always keep at least 2. + */ + PCA(InputArray data, InputArray mean, int flags, double retainedVariance); + + /** @brief performs %PCA + + The operator performs %PCA of the supplied dataset. It is safe to reuse + the same PCA structure for multiple datasets. That is, if the structure + has been previously used with another dataset, the existing internal + data is reclaimed and the new @ref eigenvalues, @ref eigenvectors and @ref + mean are allocated and computed. + + The computed @ref eigenvalues are sorted from the largest to the smallest and + the corresponding @ref eigenvectors are stored as eigenvectors rows. + + @param data input samples stored as the matrix rows or as the matrix + columns. + @param mean optional mean value; if the matrix is empty (noArray()), + the mean is computed from the data. + @param flags operation flags; currently the parameter is only used to + specify the data layout. (Flags) + @param maxComponents maximum number of components that PCA should + retain; by default, all the components are retained. + */ + PCA& operator()(InputArray data, InputArray mean, int flags, int maxComponents = 0); + + /** @overload + @param data input samples stored as the matrix rows or as the matrix + columns. + @param mean optional mean value; if the matrix is empty (noArray()), + the mean is computed from the data. + @param flags operation flags; currently the parameter is only used to + specify the data layout. (PCA::Flags) + @param retainedVariance Percentage of variance that %PCA should retain. + Using this parameter will let the %PCA decided how many components to + retain but it will always keep at least 2. + */ + PCA& operator()(InputArray data, InputArray mean, int flags, double retainedVariance); + + /** @brief Projects vector(s) to the principal component subspace. + + The methods project one or more vectors to the principal component + subspace, where each vector projection is represented by coefficients in + the principal component basis. The first form of the method returns the + matrix that the second form writes to the result. So the first form can + be used as a part of expression while the second form can be more + efficient in a processing loop. + @param vec input vector(s); must have the same dimensionality and the + same layout as the input data used at %PCA phase, that is, if + DATA_AS_ROW are specified, then `vec.cols==data.cols` + (vector dimensionality) and `vec.rows` is the number of vectors to + project, and the same is true for the PCA::DATA_AS_COL case. + */ + Mat project(InputArray vec) const; + + /** @overload + @param vec input vector(s); must have the same dimensionality and the + same layout as the input data used at PCA phase, that is, if + DATA_AS_ROW are specified, then `vec.cols==data.cols` + (vector dimensionality) and `vec.rows` is the number of vectors to + project, and the same is true for the PCA::DATA_AS_COL case. + @param result output vectors; in case of PCA::DATA_AS_COL, the + output matrix has as many columns as the number of input vectors, this + means that `result.cols==vec.cols` and the number of rows match the + number of principal components (for example, `maxComponents` parameter + passed to the constructor). + */ + void project(InputArray vec, OutputArray result) const; + + /** @brief Reconstructs vectors from their PC projections. + + The methods are inverse operations to PCA::project. They take PC + coordinates of projected vectors and reconstruct the original vectors. + Unless all the principal components have been retained, the + reconstructed vectors are different from the originals. But typically, + the difference is small if the number of components is large enough (but + still much smaller than the original vector dimensionality). As a + result, PCA is used. + @param vec coordinates of the vectors in the principal component + subspace, the layout and size are the same as of PCA::project output + vectors. + */ + Mat backProject(InputArray vec) const; + + /** @overload + @param vec coordinates of the vectors in the principal component + subspace, the layout and size are the same as of PCA::project output + vectors. + @param result reconstructed vectors; the layout and size are the same as + of PCA::project input vectors. + */ + void backProject(InputArray vec, OutputArray result) const; + + /** @brief write PCA objects + + Writes @ref eigenvalues @ref eigenvectors and @ref mean to specified FileStorage + */ + void write(FileStorage& fs) const; + + /** @brief load PCA objects + + Loads @ref eigenvalues @ref eigenvectors and @ref mean from specified FileNode + */ + void read(const FileNode& fn); + + Mat eigenvectors; //!< eigenvectors of the covariation matrix + Mat eigenvalues; //!< eigenvalues of the covariation matrix + Mat mean; //!< mean value subtracted before the projection and added after the back projection +}; + +/** @example samples/cpp/pca.cpp +An example using %PCA for dimensionality reduction while maintaining an amount of variance +*/ + +/** @example samples/cpp/tutorial_code/ml/introduction_to_pca/introduction_to_pca.cpp +Check @ref tutorial_introduction_to_pca "the corresponding tutorial" for more details +*/ + +/** +@brief Linear Discriminant Analysis +@todo document this class +*/ +class CV_EXPORTS LDA +{ +public: + /** @brief constructor + Initializes a LDA with num_components (default 0). + */ + explicit LDA(int num_components = 0); + + /** Initializes and performs a Discriminant Analysis with Fisher's + Optimization Criterion on given data in src and corresponding labels + in labels. If 0 (or less) number of components are given, they are + automatically determined for given data in computation. + */ + LDA(InputArrayOfArrays src, InputArray labels, int num_components = 0); + + /** Serializes this object to a given filename. + */ + void save(const String& filename) const; + + /** Deserializes this object from a given filename. + */ + void load(const String& filename); + + /** Serializes this object to a given cv::FileStorage. + */ + void save(FileStorage& fs) const; + + /** Deserializes this object from a given cv::FileStorage. + */ + void load(const FileStorage& node); + + /** destructor + */ + ~LDA(); + + /** Compute the discriminants for data in src (row aligned) and labels. + */ + void compute(InputArrayOfArrays src, InputArray labels); + + /** Projects samples into the LDA subspace. + src may be one or more row aligned samples. + */ + Mat project(InputArray src); + + /** Reconstructs projections from the LDA subspace. + src may be one or more row aligned projections. + */ + Mat reconstruct(InputArray src); + + /** Returns the eigenvectors of this LDA. + */ + Mat eigenvectors() const { return _eigenvectors; } + + /** Returns the eigenvalues of this LDA. + */ + Mat eigenvalues() const { return _eigenvalues; } + + static Mat subspaceProject(InputArray W, InputArray mean, InputArray src); + static Mat subspaceReconstruct(InputArray W, InputArray mean, InputArray src); + +protected: + bool _dataAsRow; // unused, but needed for 3.0 ABI compatibility. + int _num_components; + Mat _eigenvectors; + Mat _eigenvalues; + void lda(InputArrayOfArrays src, InputArray labels); +}; + +/** @brief Singular Value Decomposition + +Class for computing Singular Value Decomposition of a floating-point +matrix. The Singular Value Decomposition is used to solve least-square +problems, under-determined linear systems, invert matrices, compute +condition numbers, and so on. + +If you want to compute a condition number of a matrix or an absolute value of +its determinant, you do not need `u` and `vt`. You can pass +flags=SVD::NO_UV|... . Another flag SVD::FULL_UV indicates that full-size u +and vt must be computed, which is not necessary most of the time. + +@sa invert, solve, eigen, determinant +*/ +class CV_EXPORTS SVD +{ +public: + enum Flags { + /** allow the algorithm to modify the decomposed matrix; it can save space and speed up + processing. currently ignored. */ + MODIFY_A = 1, + /** indicates that only a vector of singular values `w` is to be processed, while u and vt + will be set to empty matrices */ + NO_UV = 2, + /** when the matrix is not square, by default the algorithm produces u and vt matrices of + sufficiently large size for the further A reconstruction; if, however, FULL_UV flag is + specified, u and vt will be full-size square orthogonal matrices.*/ + FULL_UV = 4 + }; + + /** @brief the default constructor + + initializes an empty SVD structure + */ + SVD(); + + /** @overload + initializes an empty SVD structure and then calls SVD::operator() + @param src decomposed matrix. The depth has to be CV_32F or CV_64F. + @param flags operation flags (SVD::Flags) + */ + SVD( InputArray src, int flags = 0 ); + + /** @brief the operator that performs SVD. The previously allocated u, w and vt are released. + + The operator performs the singular value decomposition of the supplied + matrix. The u,`vt` , and the vector of singular values w are stored in + the structure. The same SVD structure can be reused many times with + different matrices. Each time, if needed, the previous u,`vt` , and w + are reclaimed and the new matrices are created, which is all handled by + Mat::create. + @param src decomposed matrix. The depth has to be CV_32F or CV_64F. + @param flags operation flags (SVD::Flags) + */ + SVD& operator ()( InputArray src, int flags = 0 ); + + /** @brief decomposes matrix and stores the results to user-provided matrices + + The methods/functions perform SVD of matrix. Unlike SVD::SVD constructor + and SVD::operator(), they store the results to the user-provided + matrices: + + @code{.cpp} + Mat A, w, u, vt; + SVD::compute(A, w, u, vt); + @endcode + + @param src decomposed matrix. The depth has to be CV_32F or CV_64F. + @param w calculated singular values + @param u calculated left singular vectors + @param vt transposed matrix of right singular vectors + @param flags operation flags - see SVD::Flags. + */ + static void compute( InputArray src, OutputArray w, + OutputArray u, OutputArray vt, int flags = 0 ); + + /** @overload + computes singular values of a matrix + @param src decomposed matrix. The depth has to be CV_32F or CV_64F. + @param w calculated singular values + @param flags operation flags - see SVD::Flags. + */ + static void compute( InputArray src, OutputArray w, int flags = 0 ); + + /** @brief performs back substitution + */ + static void backSubst( InputArray w, InputArray u, + InputArray vt, InputArray rhs, + OutputArray dst ); + + /** @brief solves an under-determined singular linear system + + The method finds a unit-length solution x of a singular linear system + A\*x = 0. Depending on the rank of A, there can be no solutions, a + single solution or an infinite number of solutions. In general, the + algorithm solves the following problem: + \f[dst = \arg \min _{x: \| x \| =1} \| src \cdot x \|\f] + @param src left-hand-side matrix. + @param dst found solution. + */ + static void solveZ( InputArray src, OutputArray dst ); + + /** @brief performs a singular value back substitution. + + The method calculates a back substitution for the specified right-hand + side: + + \f[\texttt{x} = \texttt{vt} ^T \cdot diag( \texttt{w} )^{-1} \cdot \texttt{u} ^T \cdot \texttt{rhs} \sim \texttt{A} ^{-1} \cdot \texttt{rhs}\f] + + Using this technique you can either get a very accurate solution of the + convenient linear system, or the best (in the least-squares terms) + pseudo-solution of an overdetermined linear system. + + @param rhs right-hand side of a linear system (u\*w\*v')\*dst = rhs to + be solved, where A has been previously decomposed. + + @param dst found solution of the system. + + @note Explicit SVD with the further back substitution only makes sense + if you need to solve many linear systems with the same left-hand side + (for example, src ). If all you need is to solve a single system + (possibly with multiple rhs immediately available), simply call solve + add pass #DECOMP_SVD there. It does absolutely the same thing. + */ + void backSubst( InputArray rhs, OutputArray dst ) const; + + /** @todo document */ + template static + void compute( const Matx<_Tp, m, n>& a, Matx<_Tp, nm, 1>& w, Matx<_Tp, m, nm>& u, Matx<_Tp, n, nm>& vt ); + + /** @todo document */ + template static + void compute( const Matx<_Tp, m, n>& a, Matx<_Tp, nm, 1>& w ); + + /** @todo document */ + template static + void backSubst( const Matx<_Tp, nm, 1>& w, const Matx<_Tp, m, nm>& u, const Matx<_Tp, n, nm>& vt, const Matx<_Tp, m, nb>& rhs, Matx<_Tp, n, nb>& dst ); + + Mat u, w, vt; +}; + +/** @brief Random Number Generator + +Random number generator. It encapsulates the state (currently, a 64-bit +integer) and has methods to return scalar random values and to fill +arrays with random values. Currently it supports uniform and Gaussian +(normal) distributions. The generator uses Multiply-With-Carry +algorithm, introduced by G. Marsaglia ( + ). +Gaussian-distribution random numbers are generated using the Ziggurat +algorithm ( ), +introduced by G. Marsaglia and W. W. Tsang. +*/ +class CV_EXPORTS RNG +{ +public: + enum { UNIFORM = 0, + NORMAL = 1 + }; + + /** @brief constructor + + These are the RNG constructors. The first form sets the state to some + pre-defined value, equal to 2\*\*32-1 in the current implementation. The + second form sets the state to the specified value. If you passed state=0 + , the constructor uses the above default value instead to avoid the + singular random number sequence, consisting of all zeros. + */ + RNG(); + /** @overload + @param state 64-bit value used to initialize the RNG. + */ + RNG(uint64 state); + /**The method updates the state using the MWC algorithm and returns the + next 32-bit random number.*/ + unsigned next(); + + /**Each of the methods updates the state using the MWC algorithm and + returns the next random number of the specified type. In case of integer + types, the returned number is from the available value range for the + specified type. In case of floating-point types, the returned value is + from [0,1) range. + */ + operator uchar(); + /** @overload */ + operator schar(); + /** @overload */ + operator ushort(); + /** @overload */ + operator short(); + /** @overload */ + operator unsigned(); + /** @overload */ + operator int(); + /** @overload */ + operator float(); + /** @overload */ + operator double(); + + /** @brief returns a random integer sampled uniformly from [0, N). + + The methods transform the state using the MWC algorithm and return the + next random number. The first form is equivalent to RNG::next . The + second form returns the random number modulo N , which means that the + result is in the range [0, N) . + */ + unsigned operator ()(); + /** @overload + @param N upper non-inclusive boundary of the returned random number. + */ + unsigned operator ()(unsigned N); + + /** @brief returns uniformly distributed integer random number from [a,b) range + + The methods transform the state using the MWC algorithm and return the + next uniformly-distributed random number of the specified type, deduced + from the input parameter type, from the range [a, b) . There is a nuance + illustrated by the following sample: + + @code{.cpp} + RNG rng; + + // always produces 0 + double a = rng.uniform(0, 1); + + // produces double from [0, 1) + double a1 = rng.uniform((double)0, (double)1); + + // produces float from [0, 1) + float b = rng.uniform(0.f, 1.f); + + // produces double from [0, 1) + double c = rng.uniform(0., 1.); + + // may cause compiler error because of ambiguity: + // RNG::uniform(0, (int)0.999999)? or RNG::uniform((double)0, 0.99999)? + double d = rng.uniform(0, 0.999999); + @endcode + + The compiler does not take into account the type of the variable to + which you assign the result of RNG::uniform . The only thing that + matters to the compiler is the type of a and b parameters. So, if you + want a floating-point random number, but the range boundaries are + integer numbers, either put dots in the end, if they are constants, or + use explicit type cast operators, as in the a1 initialization above. + @param a lower inclusive boundary of the returned random number. + @param b upper non-inclusive boundary of the returned random number. + */ + int uniform(int a, int b); + /** @overload */ + float uniform(float a, float b); + /** @overload */ + double uniform(double a, double b); + + /** @brief Fills arrays with random numbers. + + @param mat 2D or N-dimensional matrix; currently matrices with more than + 4 channels are not supported by the methods, use Mat::reshape as a + possible workaround. + @param distType distribution type, RNG::UNIFORM or RNG::NORMAL. + @param a first distribution parameter; in case of the uniform + distribution, this is an inclusive lower boundary, in case of the normal + distribution, this is a mean value. + @param b second distribution parameter; in case of the uniform + distribution, this is a non-inclusive upper boundary, in case of the + normal distribution, this is a standard deviation (diagonal of the + standard deviation matrix or the full standard deviation matrix). + @param saturateRange pre-saturation flag; for uniform distribution only; + if true, the method will first convert a and b to the acceptable value + range (according to the mat datatype) and then will generate uniformly + distributed random numbers within the range [saturate(a), saturate(b)), + if saturateRange=false, the method will generate uniformly distributed + random numbers in the original range [a, b) and then will saturate them, + it means, for example, that + theRNG().fill(mat_8u, RNG::UNIFORM, -DBL_MAX, DBL_MAX) will likely + produce array mostly filled with 0's and 255's, since the range (0, 255) + is significantly smaller than [-DBL_MAX, DBL_MAX). + + Each of the methods fills the matrix with the random values from the + specified distribution. As the new numbers are generated, the RNG state + is updated accordingly. In case of multiple-channel images, every + channel is filled independently, which means that RNG cannot generate + samples from the multi-dimensional Gaussian distribution with + non-diagonal covariance matrix directly. To do that, the method + generates samples from multi-dimensional standard Gaussian distribution + with zero mean and identity covariation matrix, and then transforms them + using transform to get samples from the specified Gaussian distribution. + */ + void fill( InputOutputArray mat, int distType, InputArray a, InputArray b, bool saturateRange = false ); + + /** @brief Returns the next random number sampled from the Gaussian distribution + @param sigma standard deviation of the distribution. + + The method transforms the state using the MWC algorithm and returns the + next random number from the Gaussian distribution N(0,sigma) . That is, + the mean value of the returned random numbers is zero and the standard + deviation is the specified sigma . + */ + double gaussian(double sigma); + + uint64 state; + + bool operator ==(const RNG& other) const; +}; + +/** @brief Mersenne Twister random number generator + +Inspired by http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/MT2002/CODES/mt19937ar.c +@todo document +*/ +class CV_EXPORTS RNG_MT19937 +{ +public: + RNG_MT19937(); + RNG_MT19937(unsigned s); + void seed(unsigned s); + + unsigned next(); + + operator int(); + operator unsigned(); + operator float(); + operator double(); + + unsigned operator ()(unsigned N); + unsigned operator ()(); + + /** @brief returns uniformly distributed integer random number from [a,b) range*/ + int uniform(int a, int b); + /** @brief returns uniformly distributed floating-point random number from [a,b) range*/ + float uniform(float a, float b); + /** @brief returns uniformly distributed double-precision floating-point random number from [a,b) range*/ + double uniform(double a, double b); + +private: + enum PeriodParameters {N = 624, M = 397}; + unsigned state[N]; + int mti; +}; + +//! @} core_array + +//! @addtogroup core_cluster +//! @{ + +/** @example samples/cpp/kmeans.cpp +An example on K-means clustering +*/ + +/** @brief Finds centers of clusters and groups input samples around the clusters. + +The function kmeans implements a k-means algorithm that finds the centers of cluster_count clusters +and groups the input samples around the clusters. As an output, \f$\texttt{bestLabels}_i\f$ contains a +0-based cluster index for the sample stored in the \f$i^{th}\f$ row of the samples matrix. + +@note +- (Python) An example on K-means clustering can be found at + opencv_source_code/samples/python/kmeans.py +@param data Data for clustering. An array of N-Dimensional points with float coordinates is needed. +Examples of this array can be: +- Mat points(count, 2, CV_32F); +- Mat points(count, 1, CV_32FC2); +- Mat points(1, count, CV_32FC2); +- std::vector\ points(sampleCount); +@param K Number of clusters to split the set by. +@param bestLabels Input/output integer array that stores the cluster indices for every sample. +@param criteria The algorithm termination criteria, that is, the maximum number of iterations and/or +the desired accuracy. The accuracy is specified as criteria.epsilon. As soon as each of the cluster +centers moves by less than criteria.epsilon on some iteration, the algorithm stops. +@param attempts Flag to specify the number of times the algorithm is executed using different +initial labellings. The algorithm returns the labels that yield the best compactness (see the last +function parameter). +@param flags Flag that can take values of cv::KmeansFlags +@param centers Output matrix of the cluster centers, one row per each cluster center. +@return The function returns the compactness measure that is computed as +\f[\sum _i \| \texttt{samples} _i - \texttt{centers} _{ \texttt{labels} _i} \| ^2\f] +after every attempt. The best (minimum) value is chosen and the corresponding labels and the +compactness value are returned by the function. Basically, you can use only the core of the +function, set the number of attempts to 1, initialize labels each time using a custom algorithm, +pass them with the ( flags = #KMEANS_USE_INITIAL_LABELS ) flag, and then choose the best +(most-compact) clustering. +*/ +CV_EXPORTS_W double kmeans( InputArray data, int K, InputOutputArray bestLabels, + TermCriteria criteria, int attempts, + int flags, OutputArray centers = noArray() ); + +//! @} core_cluster + +//! @addtogroup core_basic +//! @{ + +/////////////////////////////// Formatted output of cv::Mat /////////////////////////// + +/** @todo document */ +class CV_EXPORTS Formatted +{ +public: + virtual const char* next() = 0; + virtual void reset() = 0; + virtual ~Formatted(); +}; + +/** @todo document */ +class CV_EXPORTS Formatter +{ +public: + enum { FMT_DEFAULT = 0, + FMT_MATLAB = 1, + FMT_CSV = 2, + FMT_PYTHON = 3, + FMT_NUMPY = 4, + FMT_C = 5 + }; + + virtual ~Formatter(); + + virtual Ptr format(const Mat& mtx) const = 0; + + virtual void set32fPrecision(int p = 8) = 0; + virtual void set64fPrecision(int p = 16) = 0; + virtual void setMultiline(bool ml = true) = 0; + + static Ptr get(int fmt = FMT_DEFAULT); + +}; + +static inline +String& operator << (String& out, Ptr fmtd) +{ + fmtd->reset(); + for(const char* str = fmtd->next(); str; str = fmtd->next()) + out += cv::String(str); + return out; +} + +static inline +String& operator << (String& out, const Mat& mtx) +{ + return out << Formatter::get()->format(mtx); +} + +//////////////////////////////////////// Algorithm //////////////////////////////////// + +class CV_EXPORTS Algorithm; + +template struct ParamType {}; + + +/** @brief This is a base class for all more or less complex algorithms in OpenCV + +especially for classes of algorithms, for which there can be multiple implementations. The examples +are stereo correspondence (for which there are algorithms like block matching, semi-global block +matching, graph-cut etc.), background subtraction (which can be done using mixture-of-gaussians +models, codebook-based algorithm etc.), optical flow (block matching, Lucas-Kanade, Horn-Schunck +etc.). + +Here is example of SimpleBlobDetector use in your application via Algorithm interface: +@snippet snippets/core_various.cpp Algorithm +*/ +class CV_EXPORTS_W Algorithm +{ +public: + Algorithm(); + virtual ~Algorithm(); + + /** @brief Clears the algorithm state + */ + CV_WRAP virtual void clear() {} + + /** @brief Stores algorithm parameters in a file storage + */ + virtual void write(FileStorage& fs) const { CV_UNUSED(fs); } + + /** @brief simplified API for language bindings + * @overload + */ + CV_WRAP void write(const Ptr& fs, const String& name = String()) const; + + /** @brief Reads algorithm parameters from a file storage + */ + CV_WRAP virtual void read(const FileNode& fn) { CV_UNUSED(fn); } + + /** @brief Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read + */ + CV_WRAP virtual bool empty() const { return false; } + + /** @brief Reads algorithm from the file node + + This is static template method of Algorithm. It's usage is following (in the case of SVM): + @code + cv::FileStorage fsRead("example.xml", FileStorage::READ); + Ptr svm = Algorithm::read(fsRead.root()); + @endcode + In order to make this method work, the derived class must overwrite Algorithm::read(const + FileNode& fn) and also have static create() method without parameters + (or with all the optional parameters) + */ + template static Ptr<_Tp> read(const FileNode& fn) + { + Ptr<_Tp> obj = _Tp::create(); + obj->read(fn); + return !obj->empty() ? obj : Ptr<_Tp>(); + } + + /** @brief Loads algorithm from the file + + @param filename Name of the file to read. + @param objname The optional name of the node to read (if empty, the first top-level node will be used) + + This is static template method of Algorithm. It's usage is following (in the case of SVM): + @code + Ptr svm = Algorithm::load("my_svm_model.xml"); + @endcode + In order to make this method work, the derived class must overwrite Algorithm::read(const + FileNode& fn). + */ + template static Ptr<_Tp> load(const String& filename, const String& objname=String()) + { + FileStorage fs(filename, FileStorage::READ); + CV_Assert(fs.isOpened()); + FileNode fn = objname.empty() ? fs.getFirstTopLevelNode() : fs[objname]; + if (fn.empty()) return Ptr<_Tp>(); + Ptr<_Tp> obj = _Tp::create(); + obj->read(fn); + return !obj->empty() ? obj : Ptr<_Tp>(); + } + + /** @brief Loads algorithm from a String + + @param strModel The string variable containing the model you want to load. + @param objname The optional name of the node to read (if empty, the first top-level node will be used) + + This is static template method of Algorithm. It's usage is following (in the case of SVM): + @code + Ptr svm = Algorithm::loadFromString(myStringModel); + @endcode + */ + template static Ptr<_Tp> loadFromString(const String& strModel, const String& objname=String()) + { + FileStorage fs(strModel, FileStorage::READ + FileStorage::MEMORY); + FileNode fn = objname.empty() ? fs.getFirstTopLevelNode() : fs[objname]; + Ptr<_Tp> obj = _Tp::create(); + obj->read(fn); + return !obj->empty() ? obj : Ptr<_Tp>(); + } + + /** Saves the algorithm to a file. + In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs). */ + CV_WRAP virtual void save(const String& filename) const; + + /** Returns the algorithm string identifier. + This string is used as top level xml/yml node tag when the object is saved to a file or string. */ + CV_WRAP virtual String getDefaultName() const; + +protected: + void writeFormat(FileStorage& fs) const; +}; + +struct Param { + enum { INT=0, BOOLEAN=1, REAL=2, STRING=3, MAT=4, MAT_VECTOR=5, ALGORITHM=6, FLOAT=7, + UNSIGNED_INT=8, UINT64=9, UCHAR=11, SCALAR=12 }; +}; + + + +template<> struct ParamType +{ + typedef bool const_param_type; + typedef bool member_type; + + enum { type = Param::BOOLEAN }; +}; + +template<> struct ParamType +{ + typedef int const_param_type; + typedef int member_type; + + enum { type = Param::INT }; +}; + +template<> struct ParamType +{ + typedef double const_param_type; + typedef double member_type; + + enum { type = Param::REAL }; +}; + +template<> struct ParamType +{ + typedef const String& const_param_type; + typedef String member_type; + + enum { type = Param::STRING }; +}; + +template<> struct ParamType +{ + typedef const Mat& const_param_type; + typedef Mat member_type; + + enum { type = Param::MAT }; +}; + +template<> struct ParamType > +{ + typedef const std::vector& const_param_type; + typedef std::vector member_type; + + enum { type = Param::MAT_VECTOR }; +}; + +template<> struct ParamType +{ + typedef const Ptr& const_param_type; + typedef Ptr member_type; + + enum { type = Param::ALGORITHM }; +}; + +template<> struct ParamType +{ + typedef float const_param_type; + typedef float member_type; + + enum { type = Param::FLOAT }; +}; + +template<> struct ParamType +{ + typedef unsigned const_param_type; + typedef unsigned member_type; + + enum { type = Param::UNSIGNED_INT }; +}; + +template<> struct ParamType +{ + typedef uint64 const_param_type; + typedef uint64 member_type; + + enum { type = Param::UINT64 }; +}; + +template<> struct ParamType +{ + typedef uchar const_param_type; + typedef uchar member_type; + + enum { type = Param::UCHAR }; +}; + +template<> struct ParamType +{ + typedef const Scalar& const_param_type; + typedef Scalar member_type; + + enum { type = Param::SCALAR }; +}; + +//! @} core_basic + +} //namespace cv + +#include "opencv2/core/operations.hpp" +#include "opencv2/core/cvstd.inl.hpp" +#include "opencv2/core/utility.hpp" +#include "opencv2/core/optim.hpp" +#include "opencv2/core/ovx.hpp" + +#endif /*OPENCV_CORE_HPP*/ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/affine.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/affine.hpp new file mode 100644 index 0000000000000000000000000000000000000000..7e2ed307858323ffbb4fa95c7c1275b9932c0574 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/affine.hpp @@ -0,0 +1,678 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_AFFINE3_HPP +#define OPENCV_CORE_AFFINE3_HPP + +#ifdef __cplusplus + +#include + +namespace cv +{ + +//! @addtogroup core +//! @{ + + /** @brief Affine transform + * + * It represents a 4x4 homogeneous transformation matrix \f$T\f$ + * + * \f[T = + * \begin{bmatrix} + * R & t\\ + * 0 & 1\\ + * \end{bmatrix} + * \f] + * + * where \f$R\f$ is a 3x3 rotation matrix and \f$t\f$ is a 3x1 translation vector. + * + * You can specify \f$R\f$ either by a 3x3 rotation matrix or by a 3x1 rotation vector, + * which is converted to a 3x3 rotation matrix by the Rodrigues formula. + * + * To construct a matrix \f$T\f$ representing first rotation around the axis \f$r\f$ with rotation + * angle \f$|r|\f$ in radian (right hand rule) and then translation by the vector \f$t\f$, you can use + * + * @code + * cv::Vec3f r, t; + * cv::Affine3f T(r, t); + * @endcode + * + * If you already have the rotation matrix \f$R\f$, then you can use + * + * @code + * cv::Matx33f R; + * cv::Affine3f T(R, t); + * @endcode + * + * To extract the rotation matrix \f$R\f$ from \f$T\f$, use + * + * @code + * cv::Matx33f R = T.rotation(); + * @endcode + * + * To extract the translation vector \f$t\f$ from \f$T\f$, use + * + * @code + * cv::Vec3f t = T.translation(); + * @endcode + * + * To extract the rotation vector \f$r\f$ from \f$T\f$, use + * + * @code + * cv::Vec3f r = T.rvec(); + * @endcode + * + * Note that since the mapping from rotation vectors to rotation matrices + * is many to one. The returned rotation vector is not necessarily the one + * you used before to set the matrix. + * + * If you have two transformations \f$T = T_1 * T_2\f$, use + * + * @code + * cv::Affine3f T, T1, T2; + * T = T2.concatenate(T1); + * @endcode + * + * To get the inverse transform of \f$T\f$, use + * + * @code + * cv::Affine3f T, T_inv; + * T_inv = T.inv(); + * @endcode + * + */ + template + class Affine3 + { + public: + typedef T float_type; + typedef Matx Mat3; + typedef Matx Mat4; + typedef Vec Vec3; + + //! Default constructor. It represents a 4x4 identity matrix. + Affine3(); + + //! Augmented affine matrix + Affine3(const Mat4& affine); + + /** + * The resulting 4x4 matrix is + * + * \f[ + * \begin{bmatrix} + * R & t\\ + * 0 & 1\\ + * \end{bmatrix} + * \f] + * + * @param R 3x3 rotation matrix. + * @param t 3x1 translation vector. + */ + Affine3(const Mat3& R, const Vec3& t = Vec3::all(0)); + + /** + * Rodrigues vector. + * + * The last row of the current matrix is set to [0,0,0,1]. + * + * @param rvec 3x1 rotation vector. Its direction indicates the rotation axis and its length + * indicates the rotation angle in radian (using right hand rule). + * @param t 3x1 translation vector. + */ + Affine3(const Vec3& rvec, const Vec3& t = Vec3::all(0)); + + /** + * Combines all constructors above. Supports 4x4, 3x4, 3x3, 1x3, 3x1 sizes of data matrix. + * + * The last row of the current matrix is set to [0,0,0,1] when data is not 4x4. + * + * @param data 1-channel matrix. + * when it is 4x4, it is copied to the current matrix and t is not used. + * When it is 3x4, it is copied to the upper part 3x4 of the current matrix and t is not used. + * When it is 3x3, it is copied to the upper left 3x3 part of the current matrix. + * When it is 3x1 or 1x3, it is treated as a rotation vector and the Rodrigues formula is used + * to compute a 3x3 rotation matrix. + * @param t 3x1 translation vector. It is used only when data is neither 4x4 nor 3x4. + */ + explicit Affine3(const Mat& data, const Vec3& t = Vec3::all(0)); + + //! From 16-element array + explicit Affine3(const float_type* vals); + + //! Create an 4x4 identity transform + static Affine3 Identity(); + + /** + * Rotation matrix. + * + * Copy the rotation matrix to the upper left 3x3 part of the current matrix. + * The remaining elements of the current matrix are not changed. + * + * @param R 3x3 rotation matrix. + * + */ + void rotation(const Mat3& R); + + /** + * Rodrigues vector. + * + * It sets the upper left 3x3 part of the matrix. The remaining part is unaffected. + * + * @param rvec 3x1 rotation vector. The direction indicates the rotation axis and + * its length indicates the rotation angle in radian (using the right thumb convention). + */ + void rotation(const Vec3& rvec); + + /** + * Combines rotation methods above. Supports 3x3, 1x3, 3x1 sizes of data matrix. + * + * It sets the upper left 3x3 part of the matrix. The remaining part is unaffected. + * + * @param data 1-channel matrix. + * When it is a 3x3 matrix, it sets the upper left 3x3 part of the current matrix. + * When it is a 1x3 or 3x1 matrix, it is used as a rotation vector. The Rodrigues formula + * is used to compute the rotation matrix and sets the upper left 3x3 part of the current matrix. + */ + void rotation(const Mat& data); + + /** + * Copy the 3x3 matrix L to the upper left part of the current matrix + * + * It sets the upper left 3x3 part of the matrix. The remaining part is unaffected. + * + * @param L 3x3 matrix. + */ + void linear(const Mat3& L); + + /** + * Copy t to the first three elements of the last column of the current matrix + * + * It sets the upper right 3x1 part of the matrix. The remaining part is unaffected. + * + * @param t 3x1 translation vector. + */ + void translation(const Vec3& t); + + //! @return the upper left 3x3 part + Mat3 rotation() const; + + //! @return the upper left 3x3 part + Mat3 linear() const; + + //! @return the upper right 3x1 part + Vec3 translation() const; + + //! Rodrigues vector. + //! @return a vector representing the upper left 3x3 rotation matrix of the current matrix. + //! @warning Since the mapping between rotation vectors and rotation matrices is many to one, + //! this function returns only one rotation vector that represents the current rotation matrix, + //! which is not necessarily the same one set by `rotation(const Vec3& rvec)`. + Vec3 rvec() const; + + //! @return the inverse of the current matrix. + Affine3 inv(int method = cv::DECOMP_SVD) const; + + //! a.rotate(R) is equivalent to Affine(R, 0) * a; + Affine3 rotate(const Mat3& R) const; + + //! a.rotate(rvec) is equivalent to Affine(rvec, 0) * a; + Affine3 rotate(const Vec3& rvec) const; + + //! a.translate(t) is equivalent to Affine(E, t) * a, where E is an identity matrix + Affine3 translate(const Vec3& t) const; + + //! a.concatenate(affine) is equivalent to affine * a; + Affine3 concatenate(const Affine3& affine) const; + + template operator Affine3() const; + + template Affine3 cast() const; + + Mat4 matrix; + +#if defined EIGEN_WORLD_VERSION && defined EIGEN_GEOMETRY_MODULE_H + Affine3(const Eigen::Transform& affine); + Affine3(const Eigen::Transform& affine); + operator Eigen::Transform() const; + operator Eigen::Transform() const; +#endif + }; + + template static + Affine3 operator*(const Affine3& affine1, const Affine3& affine2); + + //! V is a 3-element vector with member fields x, y and z + template static + V operator*(const Affine3& affine, const V& vector); + + typedef Affine3 Affine3f; + typedef Affine3 Affine3d; + + static Vec3f operator*(const Affine3f& affine, const Vec3f& vector); + static Vec3d operator*(const Affine3d& affine, const Vec3d& vector); + + template class DataType< Affine3<_Tp> > + { + public: + typedef Affine3<_Tp> value_type; + typedef Affine3::work_type> work_type; + typedef _Tp channel_type; + + enum { generic_type = 0, + channels = 16, + fmt = traits::SafeFmt::fmt + ((channels - 1) << 8) +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + ,depth = DataType::depth + ,type = CV_MAKETYPE(depth, channels) +#endif + }; + + typedef Vec vec_type; + }; + + namespace traits { + template + struct Depth< Affine3<_Tp> > { enum { value = Depth<_Tp>::value }; }; + template + struct Type< Affine3<_Tp> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, 16) }; }; + } // namespace + +//! @} core + +} + +//! @cond IGNORED + +/////////////////////////////////////////////////////////////////////////////////// +// Implementation + +template inline +cv::Affine3::Affine3() + : matrix(Mat4::eye()) +{} + +template inline +cv::Affine3::Affine3(const Mat4& affine) + : matrix(affine) +{} + +template inline +cv::Affine3::Affine3(const Mat3& R, const Vec3& t) +{ + rotation(R); + translation(t); + matrix.val[12] = matrix.val[13] = matrix.val[14] = 0; + matrix.val[15] = 1; +} + +template inline +cv::Affine3::Affine3(const Vec3& _rvec, const Vec3& t) +{ + rotation(_rvec); + translation(t); + matrix.val[12] = matrix.val[13] = matrix.val[14] = 0; + matrix.val[15] = 1; +} + +template inline +cv::Affine3::Affine3(const cv::Mat& data, const Vec3& t) +{ + CV_Assert(data.type() == cv::traits::Type::value); + CV_Assert(data.channels() == 1); + + if (data.cols == 4 && data.rows == 4) + { + data.copyTo(matrix); + return; + } + else if (data.cols == 4 && data.rows == 3) + { + rotation(data(Rect(0, 0, 3, 3))); + translation(data(Rect(3, 0, 1, 3))); + } + else + { + rotation(data); + translation(t); + } + + matrix.val[12] = matrix.val[13] = matrix.val[14] = 0; + matrix.val[15] = 1; +} + +template inline +cv::Affine3::Affine3(const float_type* vals) : matrix(vals) +{} + +template inline +cv::Affine3 cv::Affine3::Identity() +{ + return Affine3(cv::Affine3::Mat4::eye()); +} + +template inline +void cv::Affine3::rotation(const Mat3& R) +{ + linear(R); +} + +template inline +void cv::Affine3::rotation(const Vec3& _rvec) +{ + double theta = norm(_rvec); + + if (theta < DBL_EPSILON) + rotation(Mat3::eye()); + else + { + double c = std::cos(theta); + double s = std::sin(theta); + double c1 = 1. - c; + double itheta = (theta != 0) ? 1./theta : 0.; + + Point3_ r = _rvec*itheta; + + Mat3 rrt( r.x*r.x, r.x*r.y, r.x*r.z, r.x*r.y, r.y*r.y, r.y*r.z, r.x*r.z, r.y*r.z, r.z*r.z ); + Mat3 r_x( 0, -r.z, r.y, r.z, 0, -r.x, -r.y, r.x, 0 ); + + // R = cos(theta)*I + (1 - cos(theta))*r*rT + sin(theta)*[r_x] + // where [r_x] is [0 -rz ry; rz 0 -rx; -ry rx 0] + Mat3 R = c*Mat3::eye() + c1*rrt + s*r_x; + + rotation(R); + } +} + +//Combines rotation methods above. Supports 3x3, 1x3, 3x1 sizes of data matrix; +template inline +void cv::Affine3::rotation(const cv::Mat& data) +{ + CV_Assert(data.type() == cv::traits::Type::value); + CV_Assert(data.channels() == 1); + + if (data.cols == 3 && data.rows == 3) + { + Mat3 R; + data.copyTo(R); + rotation(R); + } + else if ((data.cols == 3 && data.rows == 1) || (data.cols == 1 && data.rows == 3)) + { + Vec3 _rvec; + data.reshape(1, 3).copyTo(_rvec); + rotation(_rvec); + } + else + CV_Error(Error::StsError, "Input matrix can only be 3x3, 1x3 or 3x1"); +} + +template inline +void cv::Affine3::linear(const Mat3& L) +{ + matrix.val[0] = L.val[0]; matrix.val[1] = L.val[1]; matrix.val[ 2] = L.val[2]; + matrix.val[4] = L.val[3]; matrix.val[5] = L.val[4]; matrix.val[ 6] = L.val[5]; + matrix.val[8] = L.val[6]; matrix.val[9] = L.val[7]; matrix.val[10] = L.val[8]; +} + +template inline +void cv::Affine3::translation(const Vec3& t) +{ + matrix.val[3] = t[0]; matrix.val[7] = t[1]; matrix.val[11] = t[2]; +} + +template inline +typename cv::Affine3::Mat3 cv::Affine3::rotation() const +{ + return linear(); +} + +template inline +typename cv::Affine3::Mat3 cv::Affine3::linear() const +{ + typename cv::Affine3::Mat3 R; + R.val[0] = matrix.val[0]; R.val[1] = matrix.val[1]; R.val[2] = matrix.val[ 2]; + R.val[3] = matrix.val[4]; R.val[4] = matrix.val[5]; R.val[5] = matrix.val[ 6]; + R.val[6] = matrix.val[8]; R.val[7] = matrix.val[9]; R.val[8] = matrix.val[10]; + return R; +} + +template inline +typename cv::Affine3::Vec3 cv::Affine3::translation() const +{ + return Vec3(matrix.val[3], matrix.val[7], matrix.val[11]); +} + +template inline +typename cv::Affine3::Vec3 cv::Affine3::rvec() const +{ + cv::Vec3d w; + cv::Matx33d u, vt, R = rotation(); + cv::SVD::compute(R, w, u, vt, cv::SVD::FULL_UV + cv::SVD::MODIFY_A); + R = u * vt; + + double rx = R.val[7] - R.val[5]; + double ry = R.val[2] - R.val[6]; + double rz = R.val[3] - R.val[1]; + + double s = std::sqrt((rx*rx + ry*ry + rz*rz)*0.25); + double c = (R.val[0] + R.val[4] + R.val[8] - 1) * 0.5; + c = c > 1.0 ? 1.0 : c < -1.0 ? -1.0 : c; + double theta = acos(c); + + if( s < 1e-5 ) + { + if( c > 0 ) + rx = ry = rz = 0; + else + { + double t; + t = (R.val[0] + 1) * 0.5; + rx = std::sqrt(std::max(t, 0.0)); + t = (R.val[4] + 1) * 0.5; + ry = std::sqrt(std::max(t, 0.0)) * (R.val[1] < 0 ? -1.0 : 1.0); + t = (R.val[8] + 1) * 0.5; + rz = std::sqrt(std::max(t, 0.0)) * (R.val[2] < 0 ? -1.0 : 1.0); + + if( fabs(rx) < fabs(ry) && fabs(rx) < fabs(rz) && (R.val[5] > 0) != (ry*rz > 0) ) + rz = -rz; + theta /= std::sqrt(rx*rx + ry*ry + rz*rz); + rx *= theta; + ry *= theta; + rz *= theta; + } + } + else + { + double vth = 1/(2*s); + vth *= theta; + rx *= vth; ry *= vth; rz *= vth; + } + + return cv::Vec3d(rx, ry, rz); +} + +template inline +cv::Affine3 cv::Affine3::inv(int method) const +{ + return matrix.inv(method); +} + +template inline +cv::Affine3 cv::Affine3::rotate(const Mat3& R) const +{ + Mat3 Lc = linear(); + Vec3 tc = translation(); + Mat4 result; + result.val[12] = result.val[13] = result.val[14] = 0; + result.val[15] = 1; + + for(int j = 0; j < 3; ++j) + { + for(int i = 0; i < 3; ++i) + { + float_type value = 0; + for(int k = 0; k < 3; ++k) + value += R(j, k) * Lc(k, i); + result(j, i) = value; + } + + result(j, 3) = R.row(j).dot(tc.t()); + } + return result; +} + +template inline +cv::Affine3 cv::Affine3::rotate(const Vec3& _rvec) const +{ + return rotate(Affine3f(_rvec).rotation()); +} + +template inline +cv::Affine3 cv::Affine3::translate(const Vec3& t) const +{ + Mat4 m = matrix; + m.val[ 3] += t[0]; + m.val[ 7] += t[1]; + m.val[11] += t[2]; + return m; +} + +template inline +cv::Affine3 cv::Affine3::concatenate(const Affine3& affine) const +{ + return (*this).rotate(affine.rotation()).translate(affine.translation()); +} + +template template inline +cv::Affine3::operator Affine3() const +{ + return Affine3(matrix); +} + +template template inline +cv::Affine3 cv::Affine3::cast() const +{ + return Affine3(matrix); +} + +template inline +cv::Affine3 cv::operator*(const cv::Affine3& affine1, const cv::Affine3& affine2) +{ + return affine2.concatenate(affine1); +} + +template inline +V cv::operator*(const cv::Affine3& affine, const V& v) +{ + const typename Affine3::Mat4& m = affine.matrix; + + V r; + r.x = m.val[0] * v.x + m.val[1] * v.y + m.val[ 2] * v.z + m.val[ 3]; + r.y = m.val[4] * v.x + m.val[5] * v.y + m.val[ 6] * v.z + m.val[ 7]; + r.z = m.val[8] * v.x + m.val[9] * v.y + m.val[10] * v.z + m.val[11]; + return r; +} + +static inline +cv::Vec3f cv::operator*(const cv::Affine3f& affine, const cv::Vec3f& v) +{ + const cv::Matx44f& m = affine.matrix; + cv::Vec3f r; + r.val[0] = m.val[0] * v[0] + m.val[1] * v[1] + m.val[ 2] * v[2] + m.val[ 3]; + r.val[1] = m.val[4] * v[0] + m.val[5] * v[1] + m.val[ 6] * v[2] + m.val[ 7]; + r.val[2] = m.val[8] * v[0] + m.val[9] * v[1] + m.val[10] * v[2] + m.val[11]; + return r; +} + +static inline +cv::Vec3d cv::operator*(const cv::Affine3d& affine, const cv::Vec3d& v) +{ + const cv::Matx44d& m = affine.matrix; + cv::Vec3d r; + r.val[0] = m.val[0] * v[0] + m.val[1] * v[1] + m.val[ 2] * v[2] + m.val[ 3]; + r.val[1] = m.val[4] * v[0] + m.val[5] * v[1] + m.val[ 6] * v[2] + m.val[ 7]; + r.val[2] = m.val[8] * v[0] + m.val[9] * v[1] + m.val[10] * v[2] + m.val[11]; + return r; +} + + + +#if defined EIGEN_WORLD_VERSION && defined EIGEN_GEOMETRY_MODULE_H + +template inline +cv::Affine3::Affine3(const Eigen::Transform& affine) +{ + cv::Mat(4, 4, cv::traits::Type::value, affine.matrix().data()).copyTo(matrix); +} + +template inline +cv::Affine3::Affine3(const Eigen::Transform& affine) +{ + Eigen::Transform a = affine; + cv::Mat(4, 4, cv::traits::Type::value, a.matrix().data()).copyTo(matrix); +} + +template inline +cv::Affine3::operator Eigen::Transform() const +{ + Eigen::Transform r; + cv::Mat hdr(4, 4, cv::traits::Type::value, r.matrix().data()); + cv::Mat(matrix, false).copyTo(hdr); + return r; +} + +template inline +cv::Affine3::operator Eigen::Transform() const +{ + return this->operator Eigen::Transform(); +} + +#endif /* defined EIGEN_WORLD_VERSION && defined EIGEN_GEOMETRY_MODULE_H */ + +//! @endcond + +#endif /* __cplusplus */ + +#endif /* OPENCV_CORE_AFFINE3_HPP */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/base.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/base.hpp new file mode 100644 index 0000000000000000000000000000000000000000..31cd7a8202acfb0e001ee0346caff7233f459e23 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/base.hpp @@ -0,0 +1,707 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2014, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_BASE_HPP +#define OPENCV_CORE_BASE_HPP + +#ifndef __cplusplus +# error base.hpp header must be compiled as C++ +#endif + +#include "opencv2/opencv_modules.hpp" + +#include +#include + +#include "opencv2/core/cvdef.h" +#include "opencv2/core/cvstd.hpp" + +namespace cv +{ + +//! @addtogroup core_utils +//! @{ + +namespace Error { +//! error codes +enum Code { + StsOk= 0, //!< everything is ok + StsBackTrace= -1, //!< pseudo error for back trace + StsError= -2, //!< unknown /unspecified error + StsInternal= -3, //!< internal error (bad state) + StsNoMem= -4, //!< insufficient memory + StsBadArg= -5, //!< function arg/param is bad + StsBadFunc= -6, //!< unsupported function + StsNoConv= -7, //!< iteration didn't converge + StsAutoTrace= -8, //!< tracing + HeaderIsNull= -9, //!< image header is NULL + BadImageSize= -10, //!< image size is invalid + BadOffset= -11, //!< offset is invalid + BadDataPtr= -12, //!< + BadStep= -13, //!< image step is wrong, this may happen for a non-continuous matrix. + BadModelOrChSeq= -14, //!< + BadNumChannels= -15, //!< bad number of channels, for example, some functions accept only single channel matrices. + BadNumChannel1U= -16, //!< + BadDepth= -17, //!< input image depth is not supported by the function + BadAlphaChannel= -18, //!< + BadOrder= -19, //!< number of dimensions is out of range + BadOrigin= -20, //!< incorrect input origin + BadAlign= -21, //!< incorrect input align + BadCallBack= -22, //!< + BadTileSize= -23, //!< + BadCOI= -24, //!< input COI is not supported + BadROISize= -25, //!< incorrect input roi + MaskIsTiled= -26, //!< + StsNullPtr= -27, //!< null pointer + StsVecLengthErr= -28, //!< incorrect vector length + StsFilterStructContentErr= -29, //!< incorrect filter structure content + StsKernelStructContentErr= -30, //!< incorrect transform kernel content + StsFilterOffsetErr= -31, //!< incorrect filter offset value + StsBadSize= -201, //!< the input/output structure size is incorrect + StsDivByZero= -202, //!< division by zero + StsInplaceNotSupported= -203, //!< in-place operation is not supported + StsObjectNotFound= -204, //!< request can't be completed + StsUnmatchedFormats= -205, //!< formats of input/output arrays differ + StsBadFlag= -206, //!< flag is wrong or not supported + StsBadPoint= -207, //!< bad CvPoint + StsBadMask= -208, //!< bad format of mask (neither 8uC1 nor 8sC1) + StsUnmatchedSizes= -209, //!< sizes of input/output structures do not match + StsUnsupportedFormat= -210, //!< the data format/type is not supported by the function + StsOutOfRange= -211, //!< some of parameters are out of range + StsParseError= -212, //!< invalid syntax/structure of the parsed file + StsNotImplemented= -213, //!< the requested function/feature is not implemented + StsBadMemBlock= -214, //!< an allocated block has been corrupted + StsAssert= -215, //!< assertion failed + GpuNotSupported= -216, //!< no CUDA support + GpuApiCallError= -217, //!< GPU API call error + OpenGlNotSupported= -218, //!< no OpenGL support + OpenGlApiCallError= -219, //!< OpenGL API call error + OpenCLApiCallError= -220, //!< OpenCL API call error + OpenCLDoubleNotSupported= -221, + OpenCLInitError= -222, //!< OpenCL initialization error + OpenCLNoAMDBlasFft= -223 +}; +} //Error + +//! @} core_utils + +//! @addtogroup core_array +//! @{ + +//! matrix decomposition types +enum DecompTypes { + /** Gaussian elimination with the optimal pivot element chosen. */ + DECOMP_LU = 0, + /** singular value decomposition (SVD) method; the system can be over-defined and/or the matrix + src1 can be singular */ + DECOMP_SVD = 1, + /** eigenvalue decomposition; the matrix src1 must be symmetrical */ + DECOMP_EIG = 2, + /** Cholesky \f$LL^T\f$ factorization; the matrix src1 must be symmetrical and positively + defined */ + DECOMP_CHOLESKY = 3, + /** QR factorization; the system can be over-defined and/or the matrix src1 can be singular */ + DECOMP_QR = 4, + /** while all the previous flags are mutually exclusive, this flag can be used together with + any of the previous; it means that the normal equations + \f$\texttt{src1}^T\cdot\texttt{src1}\cdot\texttt{dst}=\texttt{src1}^T\texttt{src2}\f$ are + solved instead of the original system + \f$\texttt{src1}\cdot\texttt{dst}=\texttt{src2}\f$ */ + DECOMP_NORMAL = 16 +}; + +/** norm types + +src1 and src2 denote input arrays. +*/ + +enum NormTypes { + /** + \f[ + norm = \forkthree + {\|\texttt{src1}\|_{L_{\infty}} = \max _I | \texttt{src1} (I)|}{if \(\texttt{normType} = \texttt{NORM_INF}\) } + {\|\texttt{src1}-\texttt{src2}\|_{L_{\infty}} = \max _I | \texttt{src1} (I) - \texttt{src2} (I)|}{if \(\texttt{normType} = \texttt{NORM_INF}\) } + {\frac{\|\texttt{src1}-\texttt{src2}\|_{L_{\infty}} }{\|\texttt{src2}\|_{L_{\infty}} }}{if \(\texttt{normType} = \texttt{NORM_RELATIVE | NORM_INF}\) } + \f] + */ + NORM_INF = 1, + /** + \f[ + norm = \forkthree + {\| \texttt{src1} \| _{L_1} = \sum _I | \texttt{src1} (I)|}{if \(\texttt{normType} = \texttt{NORM_L1}\)} + { \| \texttt{src1} - \texttt{src2} \| _{L_1} = \sum _I | \texttt{src1} (I) - \texttt{src2} (I)|}{if \(\texttt{normType} = \texttt{NORM_L1}\) } + { \frac{\|\texttt{src1}-\texttt{src2}\|_{L_1} }{\|\texttt{src2}\|_{L_1}} }{if \(\texttt{normType} = \texttt{NORM_RELATIVE | NORM_L1}\) } + \f]*/ + NORM_L1 = 2, + /** + \f[ + norm = \forkthree + { \| \texttt{src1} \| _{L_2} = \sqrt{\sum_I \texttt{src1}(I)^2} }{if \(\texttt{normType} = \texttt{NORM_L2}\) } + { \| \texttt{src1} - \texttt{src2} \| _{L_2} = \sqrt{\sum_I (\texttt{src1}(I) - \texttt{src2}(I))^2} }{if \(\texttt{normType} = \texttt{NORM_L2}\) } + { \frac{\|\texttt{src1}-\texttt{src2}\|_{L_2} }{\|\texttt{src2}\|_{L_2}} }{if \(\texttt{normType} = \texttt{NORM_RELATIVE | NORM_L2}\) } + \f] + */ + NORM_L2 = 4, + /** + \f[ + norm = \forkthree + { \| \texttt{src1} \| _{L_2} ^{2} = \sum_I \texttt{src1}(I)^2} {if \(\texttt{normType} = \texttt{NORM_L2SQR}\)} + { \| \texttt{src1} - \texttt{src2} \| _{L_2} ^{2} = \sum_I (\texttt{src1}(I) - \texttt{src2}(I))^2 }{if \(\texttt{normType} = \texttt{NORM_L2SQR}\) } + { \left(\frac{\|\texttt{src1}-\texttt{src2}\|_{L_2} }{\|\texttt{src2}\|_{L_2}}\right)^2 }{if \(\texttt{normType} = \texttt{NORM_RELATIVE | NORM_L2}\) } + \f] + */ + NORM_L2SQR = 5, + /** + In the case of one input array, calculates the Hamming distance of the array from zero, + In the case of two input arrays, calculates the Hamming distance between the arrays. + */ + NORM_HAMMING = 6, + /** + Similar to NORM_HAMMING, but in the calculation, each two bits of the input sequence will + be added and treated as a single bit to be used in the same calculation as NORM_HAMMING. + */ + NORM_HAMMING2 = 7, + NORM_TYPE_MASK = 7, //!< bit-mask which can be used to separate norm type from norm flags + NORM_RELATIVE = 8, //!< flag + NORM_MINMAX = 32 //!< flag + }; + +//! comparison types +enum CmpTypes { CMP_EQ = 0, //!< src1 is equal to src2. + CMP_GT = 1, //!< src1 is greater than src2. + CMP_GE = 2, //!< src1 is greater than or equal to src2. + CMP_LT = 3, //!< src1 is less than src2. + CMP_LE = 4, //!< src1 is less than or equal to src2. + CMP_NE = 5 //!< src1 is unequal to src2. + }; + +//! generalized matrix multiplication flags +enum GemmFlags { GEMM_1_T = 1, //!< transposes src1 + GEMM_2_T = 2, //!< transposes src2 + GEMM_3_T = 4 //!< transposes src3 + }; + +enum DftFlags { + /** performs an inverse 1D or 2D transform instead of the default forward + transform. */ + DFT_INVERSE = 1, + /** scales the result: divide it by the number of array elements. Normally, it is + combined with DFT_INVERSE. */ + DFT_SCALE = 2, + /** performs a forward or inverse transform of every individual row of the input + matrix; this flag enables you to transform multiple vectors simultaneously and can be used to + decrease the overhead (which is sometimes several times larger than the processing itself) to + perform 3D and higher-dimensional transformations and so forth.*/ + DFT_ROWS = 4, + /** performs a forward transformation of 1D or 2D real array; the result, + though being a complex array, has complex-conjugate symmetry (*CCS*, see the function + description below for details), and such an array can be packed into a real array of the same + size as input, which is the fastest option and which is what the function does by default; + however, you may wish to get a full complex array (for simpler spectrum analysis, and so on) - + pass the flag to enable the function to produce a full-size complex output array. */ + DFT_COMPLEX_OUTPUT = 16, + /** performs an inverse transformation of a 1D or 2D complex array; the + result is normally a complex array of the same size, however, if the input array has + conjugate-complex symmetry (for example, it is a result of forward transformation with + DFT_COMPLEX_OUTPUT flag), the output is a real array; while the function itself does not + check whether the input is symmetrical or not, you can pass the flag and then the function + will assume the symmetry and produce the real output array (note that when the input is packed + into a real array and inverse transformation is executed, the function treats the input as a + packed complex-conjugate symmetrical array, and the output will also be a real array). */ + DFT_REAL_OUTPUT = 32, + /** specifies that input is complex input. If this flag is set, the input must have 2 channels. + On the other hand, for backwards compatibility reason, if input has 2 channels, input is + already considered complex. */ + DFT_COMPLEX_INPUT = 64, + /** performs an inverse 1D or 2D transform instead of the default forward transform. */ + DCT_INVERSE = DFT_INVERSE, + /** performs a forward or inverse transform of every individual row of the input + matrix. This flag enables you to transform multiple vectors simultaneously and can be used to + decrease the overhead (which is sometimes several times larger than the processing itself) to + perform 3D and higher-dimensional transforms and so forth.*/ + DCT_ROWS = DFT_ROWS +}; + +//! Various border types, image boundaries are denoted with `|` +//! @see borderInterpolate, copyMakeBorder +enum BorderTypes { + BORDER_CONSTANT = 0, //!< `iiiiii|abcdefgh|iiiiiii` with some specified `i` + BORDER_REPLICATE = 1, //!< `aaaaaa|abcdefgh|hhhhhhh` + BORDER_REFLECT = 2, //!< `fedcba|abcdefgh|hgfedcb` + BORDER_WRAP = 3, //!< `cdefgh|abcdefgh|abcdefg` + BORDER_REFLECT_101 = 4, //!< `gfedcb|abcdefgh|gfedcba` + BORDER_TRANSPARENT = 5, //!< `uvwxyz|abcdefgh|ijklmno` + + BORDER_REFLECT101 = BORDER_REFLECT_101, //!< same as BORDER_REFLECT_101 + BORDER_DEFAULT = BORDER_REFLECT_101, //!< same as BORDER_REFLECT_101 + BORDER_ISOLATED = 16 //!< do not look outside of ROI +}; + +//! @} core_array + +//! @addtogroup core_utils +//! @{ + +/*! @brief Signals an error and raises the exception. + +By default the function prints information about the error to stderr, +then it either stops if setBreakOnError() had been called before or raises the exception. +It is possible to alternate error processing by using redirectError(). +@param _code - error code (Error::Code) +@param _err - error description +@param _func - function name. Available only when the compiler supports getting it +@param _file - source file name where the error has occurred +@param _line - line number in the source file where the error has occurred +@see CV_Error, CV_Error_, CV_Assert, CV_DbgAssert + */ +CV_EXPORTS void error(int _code, const String& _err, const char* _func, const char* _file, int _line); + +#ifdef __GNUC__ +# if defined __clang__ || defined __APPLE__ +# pragma GCC diagnostic push +# pragma GCC diagnostic ignored "-Winvalid-noreturn" +# endif +#endif + +/** same as cv::error, but does not return */ +CV_INLINE CV_NORETURN void errorNoReturn(int _code, const String& _err, const char* _func, const char* _file, int _line) +{ + error(_code, _err, _func, _file, _line); +#ifdef __GNUC__ +# if !defined __clang__ && !defined __APPLE__ + // this suppresses this warning: "noreturn" function does return [enabled by default] + __builtin_trap(); + // or use infinite loop: for (;;) {} +# endif +#endif +} +#ifdef __GNUC__ +# if defined __clang__ || defined __APPLE__ +# pragma GCC diagnostic pop +# endif +#endif + +#ifdef CV_STATIC_ANALYSIS + +// In practice, some macro are not processed correctly (noreturn is not detected). +// We need to use simplified definition for them. +#define CV_Error(...) do { abort(); } while (0) +#define CV_Error_( code, args ) do { cv::format args; abort(); } while (0) +#define CV_Assert( expr ) do { if (!(expr)) abort(); } while (0) +#define CV_ErrorNoReturn CV_Error +#define CV_ErrorNoReturn_ CV_Error_ + +#else // CV_STATIC_ANALYSIS + +/** @brief Call the error handler. + +Currently, the error handler prints the error code and the error message to the standard +error stream `stderr`. In the Debug configuration, it then provokes memory access violation, so that +the execution stack and all the parameters can be analyzed by the debugger. In the Release +configuration, the exception is thrown. + +@param code one of Error::Code +@param msg error message +*/ +#define CV_Error( code, msg ) cv::error( code, msg, CV_Func, __FILE__, __LINE__ ) + +/** @brief Call the error handler. + +This macro can be used to construct an error message on-fly to include some dynamic information, +for example: +@code + // note the extra parentheses around the formatted text message + CV_Error_(Error::StsOutOfRange, + ("the value at (%d, %d)=%g is out of range", badPt.x, badPt.y, badValue)); +@endcode +@param code one of Error::Code +@param args printf-like formatted error message in parentheses +*/ +#define CV_Error_( code, args ) cv::error( code, cv::format args, CV_Func, __FILE__, __LINE__ ) + +/** @brief Checks a condition at runtime and throws exception if it fails + +The macros CV_Assert (and CV_DbgAssert(expr)) evaluate the specified expression. If it is 0, the macros +raise an error (see cv::error). The macro CV_Assert checks the condition in both Debug and Release +configurations while CV_DbgAssert is only retained in the Debug configuration. +*/ +#define CV_Assert( expr ) do { if(!!(expr)) ; else cv::error( cv::Error::StsAssert, #expr, CV_Func, __FILE__, __LINE__ ); } while(0) + +//! @cond IGNORED +#define CV__ErrorNoReturn( code, msg ) cv::errorNoReturn( code, msg, CV_Func, __FILE__, __LINE__ ) +#define CV__ErrorNoReturn_( code, args ) cv::errorNoReturn( code, cv::format args, CV_Func, __FILE__, __LINE__ ) +#ifdef __OPENCV_BUILD +#undef CV_Error +#define CV_Error CV__ErrorNoReturn +#undef CV_Error_ +#define CV_Error_ CV__ErrorNoReturn_ +#undef CV_Assert +#define CV_Assert( expr ) do { if(!!(expr)) ; else cv::errorNoReturn( cv::Error::StsAssert, #expr, CV_Func, __FILE__, __LINE__ ); } while(0) +#else +// backward compatibility +#define CV_ErrorNoReturn CV__ErrorNoReturn +#define CV_ErrorNoReturn_ CV__ErrorNoReturn_ +#endif +//! @endcond + +#endif // CV_STATIC_ANALYSIS + +//! @cond IGNORED + +#if defined OPENCV_FORCE_MULTIARG_ASSERT_CHECK && defined CV_STATIC_ANALYSIS +#warning "OPENCV_FORCE_MULTIARG_ASSERT_CHECK can't be used with CV_STATIC_ANALYSIS" +#undef OPENCV_FORCE_MULTIARG_ASSERT_CHECK +#endif + +#ifdef OPENCV_FORCE_MULTIARG_ASSERT_CHECK +#define CV_Assert_1( expr ) do { if(!!(expr)) ; else cv::error( cv::Error::StsAssert, #expr, CV_Func, __FILE__, __LINE__ ); } while(0) +#else +#define CV_Assert_1 CV_Assert +#endif +#define CV_Assert_2( expr1, expr2 ) CV_Assert_1(expr1); CV_Assert_1(expr2) +#define CV_Assert_3( expr1, expr2, expr3 ) CV_Assert_2(expr1, expr2); CV_Assert_1(expr3) +#define CV_Assert_4( expr1, expr2, expr3, expr4 ) CV_Assert_3(expr1, expr2, expr3); CV_Assert_1(expr4) +#define CV_Assert_5( expr1, expr2, expr3, expr4, expr5 ) CV_Assert_4(expr1, expr2, expr3, expr4); CV_Assert_1(expr5) +#define CV_Assert_6( expr1, expr2, expr3, expr4, expr5, expr6 ) CV_Assert_5(expr1, expr2, expr3, expr4, expr5); CV_Assert_1(expr6) +#define CV_Assert_7( expr1, expr2, expr3, expr4, expr5, expr6, expr7 ) CV_Assert_6(expr1, expr2, expr3, expr4, expr5, expr6 ); CV_Assert_1(expr7) +#define CV_Assert_8( expr1, expr2, expr3, expr4, expr5, expr6, expr7, expr8 ) CV_Assert_7(expr1, expr2, expr3, expr4, expr5, expr6, expr7 ); CV_Assert_1(expr8) +#define CV_Assert_9( expr1, expr2, expr3, expr4, expr5, expr6, expr7, expr8, expr9 ) CV_Assert_8(expr1, expr2, expr3, expr4, expr5, expr6, expr7, expr8 ); CV_Assert_1(expr9) +#define CV_Assert_10( expr1, expr2, expr3, expr4, expr5, expr6, expr7, expr8, expr9, expr10 ) CV_Assert_9(expr1, expr2, expr3, expr4, expr5, expr6, expr7, expr8, expr9 ); CV_Assert_1(expr10) + +#define CV_Assert_N(...) do { __CV_CAT(CV_Assert_, __CV_VA_NUM_ARGS(__VA_ARGS__)) (__VA_ARGS__); } while(0) + +#ifdef OPENCV_FORCE_MULTIARG_ASSERT_CHECK +#undef CV_Assert +#define CV_Assert CV_Assert_N +#endif +//! @endcond + +#if defined _DEBUG || defined CV_STATIC_ANALYSIS +# define CV_DbgAssert(expr) CV_Assert(expr) +#else +/** replaced with CV_Assert(expr) in Debug configuration */ +# define CV_DbgAssert(expr) +#endif + +/* + * Hamming distance functor - counts the bit differences between two strings - useful for the Brief descriptor + * bit count of A exclusive XOR'ed with B + */ +struct CV_EXPORTS Hamming +{ + enum { normType = NORM_HAMMING }; + typedef unsigned char ValueType; + typedef int ResultType; + + /** this will count the bits in a ^ b + */ + ResultType operator()( const unsigned char* a, const unsigned char* b, int size ) const; +}; + +typedef Hamming HammingLUT; + +/////////////////////////////////// inline norms //////////////////////////////////// + +template inline _Tp cv_abs(_Tp x) { return std::abs(x); } +inline int cv_abs(uchar x) { return x; } +inline int cv_abs(schar x) { return std::abs(x); } +inline int cv_abs(ushort x) { return x; } +inline int cv_abs(short x) { return std::abs(x); } + +template static inline +_AccTp normL2Sqr(const _Tp* a, int n) +{ + _AccTp s = 0; + int i=0; +#if CV_ENABLE_UNROLLED + for( ; i <= n - 4; i += 4 ) + { + _AccTp v0 = a[i], v1 = a[i+1], v2 = a[i+2], v3 = a[i+3]; + s += v0*v0 + v1*v1 + v2*v2 + v3*v3; + } +#endif + for( ; i < n; i++ ) + { + _AccTp v = a[i]; + s += v*v; + } + return s; +} + +template static inline +_AccTp normL1(const _Tp* a, int n) +{ + _AccTp s = 0; + int i = 0; +#if CV_ENABLE_UNROLLED + for(; i <= n - 4; i += 4 ) + { + s += (_AccTp)cv_abs(a[i]) + (_AccTp)cv_abs(a[i+1]) + + (_AccTp)cv_abs(a[i+2]) + (_AccTp)cv_abs(a[i+3]); + } +#endif + for( ; i < n; i++ ) + s += cv_abs(a[i]); + return s; +} + +template static inline +_AccTp normInf(const _Tp* a, int n) +{ + _AccTp s = 0; + for( int i = 0; i < n; i++ ) + s = std::max(s, (_AccTp)cv_abs(a[i])); + return s; +} + +template static inline +_AccTp normL2Sqr(const _Tp* a, const _Tp* b, int n) +{ + _AccTp s = 0; + int i= 0; +#if CV_ENABLE_UNROLLED + for(; i <= n - 4; i += 4 ) + { + _AccTp v0 = _AccTp(a[i] - b[i]), v1 = _AccTp(a[i+1] - b[i+1]), v2 = _AccTp(a[i+2] - b[i+2]), v3 = _AccTp(a[i+3] - b[i+3]); + s += v0*v0 + v1*v1 + v2*v2 + v3*v3; + } +#endif + for( ; i < n; i++ ) + { + _AccTp v = _AccTp(a[i] - b[i]); + s += v*v; + } + return s; +} + +static inline float normL2Sqr(const float* a, const float* b, int n) +{ + float s = 0.f; + for( int i = 0; i < n; i++ ) + { + float v = a[i] - b[i]; + s += v*v; + } + return s; +} + +template static inline +_AccTp normL1(const _Tp* a, const _Tp* b, int n) +{ + _AccTp s = 0; + int i= 0; +#if CV_ENABLE_UNROLLED + for(; i <= n - 4; i += 4 ) + { + _AccTp v0 = _AccTp(a[i] - b[i]), v1 = _AccTp(a[i+1] - b[i+1]), v2 = _AccTp(a[i+2] - b[i+2]), v3 = _AccTp(a[i+3] - b[i+3]); + s += std::abs(v0) + std::abs(v1) + std::abs(v2) + std::abs(v3); + } +#endif + for( ; i < n; i++ ) + { + _AccTp v = _AccTp(a[i] - b[i]); + s += std::abs(v); + } + return s; +} + +inline float normL1(const float* a, const float* b, int n) +{ + float s = 0.f; + for( int i = 0; i < n; i++ ) + { + s += std::abs(a[i] - b[i]); + } + return s; +} + +inline int normL1(const uchar* a, const uchar* b, int n) +{ + int s = 0; + for( int i = 0; i < n; i++ ) + { + s += std::abs(a[i] - b[i]); + } + return s; +} + +template static inline +_AccTp normInf(const _Tp* a, const _Tp* b, int n) +{ + _AccTp s = 0; + for( int i = 0; i < n; i++ ) + { + _AccTp v0 = a[i] - b[i]; + s = std::max(s, std::abs(v0)); + } + return s; +} + +/** @brief Computes the cube root of an argument. + + The function cubeRoot computes \f$\sqrt[3]{\texttt{val}}\f$. Negative arguments are handled correctly. + NaN and Inf are not handled. The accuracy approaches the maximum possible accuracy for + single-precision data. + @param val A function argument. + */ +CV_EXPORTS_W float cubeRoot(float val); + +/** @brief Calculates the angle of a 2D vector in degrees. + + The function fastAtan2 calculates the full-range angle of an input 2D vector. The angle is measured + in degrees and varies from 0 to 360 degrees. The accuracy is about 0.3 degrees. + @param x x-coordinate of the vector. + @param y y-coordinate of the vector. + */ +CV_EXPORTS_W float fastAtan2(float y, float x); + +/** proxy for hal::LU */ +CV_EXPORTS int LU(float* A, size_t astep, int m, float* b, size_t bstep, int n); +/** proxy for hal::LU */ +CV_EXPORTS int LU(double* A, size_t astep, int m, double* b, size_t bstep, int n); +/** proxy for hal::Cholesky */ +CV_EXPORTS bool Cholesky(float* A, size_t astep, int m, float* b, size_t bstep, int n); +/** proxy for hal::Cholesky */ +CV_EXPORTS bool Cholesky(double* A, size_t astep, int m, double* b, size_t bstep, int n); + +////////////////// forward declarations for important OpenCV types ////////////////// + +//! @cond IGNORED + +template class Vec; +template class Matx; + +template class Complex; +template class Point_; +template class Point3_; +template class Size_; +template class Rect_; +template class Scalar_; + +class CV_EXPORTS RotatedRect; +class CV_EXPORTS Range; +class CV_EXPORTS TermCriteria; +class CV_EXPORTS KeyPoint; +class CV_EXPORTS DMatch; +class CV_EXPORTS RNG; + +class CV_EXPORTS Mat; +class CV_EXPORTS MatExpr; + +class CV_EXPORTS UMat; + +class CV_EXPORTS SparseMat; +typedef Mat MatND; + +template class Mat_; +template class SparseMat_; + +class CV_EXPORTS MatConstIterator; +class CV_EXPORTS SparseMatIterator; +class CV_EXPORTS SparseMatConstIterator; +template class MatIterator_; +template class MatConstIterator_; +template class SparseMatIterator_; +template class SparseMatConstIterator_; + +namespace ogl +{ + class CV_EXPORTS Buffer; + class CV_EXPORTS Texture2D; + class CV_EXPORTS Arrays; +} + +namespace cuda +{ + class CV_EXPORTS GpuMat; + class CV_EXPORTS HostMem; + class CV_EXPORTS Stream; + class CV_EXPORTS Event; +} + +namespace cudev +{ + template class GpuMat_; +} + +namespace ipp +{ +#if OPENCV_ABI_COMPATIBILITY > 300 +CV_EXPORTS unsigned long long getIppFeatures(); +#else +CV_EXPORTS int getIppFeatures(); +#endif +CV_EXPORTS void setIppStatus(int status, const char * const funcname = NULL, const char * const filename = NULL, + int line = 0); +CV_EXPORTS int getIppStatus(); +CV_EXPORTS String getIppErrorLocation(); +CV_EXPORTS_W bool useIPP(); +CV_EXPORTS_W void setUseIPP(bool flag); +CV_EXPORTS_W String getIppVersion(); + +// IPP Not-Exact mode. This function may force use of IPP then both IPP and OpenCV provide proper results +// but have internal accuracy differences which have too much direct or indirect impact on accuracy tests. +CV_EXPORTS_W bool useIPP_NotExact(); +CV_EXPORTS_W void setUseIPP_NotExact(bool flag); +#if OPENCV_ABI_COMPATIBILITY < 400 +CV_EXPORTS_W bool useIPP_NE(); +CV_EXPORTS_W void setUseIPP_NE(bool flag); +#endif + +} // ipp + +//! @endcond + +//! @} core_utils + + + + +} // cv + +#include "opencv2/core/neon_utils.hpp" +#include "opencv2/core/vsx_utils.hpp" +#include "opencv2/core/check.hpp" + +#endif //OPENCV_CORE_BASE_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/bindings_utils.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/bindings_utils.hpp new file mode 100644 index 0000000000000000000000000000000000000000..c1123f2b0e3fa8b712961e1bd818f8c37b9beaa6 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/bindings_utils.hpp @@ -0,0 +1,23 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#ifndef OPENCV_CORE_BINDINGS_UTILS_HPP +#define OPENCV_CORE_BINDINGS_UTILS_HPP + +namespace cv { namespace utils { +//! @addtogroup core_utils +//! @{ + +CV_EXPORTS_W String dumpInputArray(InputArray argument); + +CV_EXPORTS_W String dumpInputArrayOfArrays(InputArrayOfArrays argument); + +CV_EXPORTS_W String dumpInputOutputArray(InputOutputArray argument); + +CV_EXPORTS_W String dumpInputOutputArrayOfArrays(InputOutputArrayOfArrays argument); + +//! @} +}} // namespace + +#endif // OPENCV_CORE_BINDINGS_UTILS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/bufferpool.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/bufferpool.hpp new file mode 100644 index 0000000000000000000000000000000000000000..4698e5da167d6a3b6a9ef7672faa3542b23fa662 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/bufferpool.hpp @@ -0,0 +1,40 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. +// +// Copyright (C) 2014, Advanced Micro Devices, Inc., all rights reserved. + +#ifndef OPENCV_CORE_BUFFER_POOL_HPP +#define OPENCV_CORE_BUFFER_POOL_HPP + +#ifdef _MSC_VER +#pragma warning(push) +#pragma warning(disable: 4265) +#endif + +namespace cv +{ + +//! @addtogroup core +//! @{ + +class BufferPoolController +{ +protected: + ~BufferPoolController() { } +public: + virtual size_t getReservedSize() const = 0; + virtual size_t getMaxReservedSize() const = 0; + virtual void setMaxReservedSize(size_t size) = 0; + virtual void freeAllReservedBuffers() = 0; +}; + +//! @} + +} + +#ifdef _MSC_VER +#pragma warning(pop) +#endif + +#endif // OPENCV_CORE_BUFFER_POOL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/check.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/check.hpp new file mode 100644 index 0000000000000000000000000000000000000000..bf441383e84059ae41263e9208018ea976df0774 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/check.hpp @@ -0,0 +1,157 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#ifndef OPENCV_CORE_CHECK_HPP +#define OPENCV_CORE_CHECK_HPP + +#include + +namespace cv { + +/** Returns string of cv::Mat depth value: CV_8U -> "CV_8U" or "" */ +CV_EXPORTS const char* depthToString(int depth); + +/** Returns string of cv::Mat depth value: CV_8UC3 -> "CV_8UC3" or "" */ +CV_EXPORTS const String typeToString(int type); + + +//! @cond IGNORED +namespace detail { + +/** Returns string of cv::Mat depth value: CV_8U -> "CV_8U" or NULL */ +CV_EXPORTS const char* depthToString_(int depth); + +/** Returns string of cv::Mat depth value: CV_8UC3 -> "CV_8UC3" or cv::String() */ +CV_EXPORTS const cv::String typeToString_(int type); + +enum TestOp { + TEST_CUSTOM = 0, + TEST_EQ = 1, + TEST_NE = 2, + TEST_LE = 3, + TEST_LT = 4, + TEST_GE = 5, + TEST_GT = 6, + CV__LAST_TEST_OP +}; + +struct CheckContext { + const char* func; + const char* file; + int line; + enum TestOp testOp; + const char* message; + const char* p1_str; + const char* p2_str; +}; + +#ifndef CV__CHECK_FILENAME +# define CV__CHECK_FILENAME __FILE__ +#endif + +#ifndef CV__CHECK_FUNCTION +# if defined _MSC_VER +# define CV__CHECK_FUNCTION __FUNCSIG__ +# elif defined __GNUC__ +# define CV__CHECK_FUNCTION __PRETTY_FUNCTION__ +# else +# define CV__CHECK_FUNCTION "" +# endif +#endif + +#define CV__CHECK_LOCATION_VARNAME(id) CVAUX_CONCAT(CVAUX_CONCAT(__cv_check_, id), __LINE__) +#define CV__DEFINE_CHECK_CONTEXT(id, message, testOp, p1_str, p2_str) \ + static const cv::detail::CheckContext CV__CHECK_LOCATION_VARNAME(id) = \ + { CV__CHECK_FUNCTION, CV__CHECK_FILENAME, __LINE__, testOp, message, p1_str, p2_str } + +CV_EXPORTS void CV_NORETURN check_failed_auto(const int v1, const int v2, const CheckContext& ctx); +CV_EXPORTS void CV_NORETURN check_failed_auto(const size_t v1, const size_t v2, const CheckContext& ctx); +CV_EXPORTS void CV_NORETURN check_failed_auto(const float v1, const float v2, const CheckContext& ctx); +CV_EXPORTS void CV_NORETURN check_failed_auto(const double v1, const double v2, const CheckContext& ctx); +CV_EXPORTS void CV_NORETURN check_failed_MatDepth(const int v1, const int v2, const CheckContext& ctx); +CV_EXPORTS void CV_NORETURN check_failed_MatType(const int v1, const int v2, const CheckContext& ctx); +CV_EXPORTS void CV_NORETURN check_failed_MatChannels(const int v1, const int v2, const CheckContext& ctx); + +CV_EXPORTS void CV_NORETURN check_failed_auto(const int v, const CheckContext& ctx); +CV_EXPORTS void CV_NORETURN check_failed_auto(const size_t v, const CheckContext& ctx); +CV_EXPORTS void CV_NORETURN check_failed_auto(const float v, const CheckContext& ctx); +CV_EXPORTS void CV_NORETURN check_failed_auto(const double v, const CheckContext& ctx); +CV_EXPORTS void CV_NORETURN check_failed_MatDepth(const int v, const CheckContext& ctx); +CV_EXPORTS void CV_NORETURN check_failed_MatType(const int v, const CheckContext& ctx); +CV_EXPORTS void CV_NORETURN check_failed_MatChannels(const int v, const CheckContext& ctx); + + +#define CV__TEST_EQ(v1, v2) ((v1) == (v2)) +#define CV__TEST_NE(v1, v2) ((v1) != (v2)) +#define CV__TEST_LE(v1, v2) ((v1) <= (v2)) +#define CV__TEST_LT(v1, v2) ((v1) < (v2)) +#define CV__TEST_GE(v1, v2) ((v1) >= (v2)) +#define CV__TEST_GT(v1, v2) ((v1) > (v2)) + +#define CV__CHECK(id, op, type, v1, v2, v1_str, v2_str, msg_str) do { \ + if(CV__TEST_##op((v1), (v2))) ; else { \ + CV__DEFINE_CHECK_CONTEXT(id, msg_str, cv::detail::TEST_ ## op, v1_str, v2_str); \ + cv::detail::check_failed_ ## type((v1), (v2), CV__CHECK_LOCATION_VARNAME(id)); \ + } \ +} while (0) + +#define CV__CHECK_CUSTOM_TEST(id, type, v, test_expr, v_str, test_expr_str, msg_str) do { \ + if(!!(test_expr)) ; else { \ + CV__DEFINE_CHECK_CONTEXT(id, msg_str, cv::detail::TEST_CUSTOM, v_str, test_expr_str); \ + cv::detail::check_failed_ ## type((v), CV__CHECK_LOCATION_VARNAME(id)); \ + } \ +} while (0) + +} // namespace +//! @endcond + + +/// Supported values of these types: int, float, double +#define CV_CheckEQ(v1, v2, msg) CV__CHECK(_, EQ, auto, v1, v2, #v1, #v2, msg) +#define CV_CheckNE(v1, v2, msg) CV__CHECK(_, NE, auto, v1, v2, #v1, #v2, msg) +#define CV_CheckLE(v1, v2, msg) CV__CHECK(_, LE, auto, v1, v2, #v1, #v2, msg) +#define CV_CheckLT(v1, v2, msg) CV__CHECK(_, LT, auto, v1, v2, #v1, #v2, msg) +#define CV_CheckGE(v1, v2, msg) CV__CHECK(_, GE, auto, v1, v2, #v1, #v2, msg) +#define CV_CheckGT(v1, v2, msg) CV__CHECK(_, GT, auto, v1, v2, #v1, #v2, msg) + +/// Check with additional "decoding" of type values in error message +#define CV_CheckTypeEQ(t1, t2, msg) CV__CHECK(_, EQ, MatType, t1, t2, #t1, #t2, msg) +/// Check with additional "decoding" of depth values in error message +#define CV_CheckDepthEQ(d1, d2, msg) CV__CHECK(_, EQ, MatDepth, d1, d2, #d1, #d2, msg) + +#define CV_CheckChannelsEQ(c1, c2, msg) CV__CHECK(_, EQ, MatChannels, c1, c2, #c1, #c2, msg) + +/// Example: type == CV_8UC1 || type == CV_8UC3 +#define CV_CheckType(t, test_expr, msg) CV__CHECK_CUSTOM_TEST(_, MatType, t, (test_expr), #t, #test_expr, msg) + +/// Example: depth == CV_32F || depth == CV_64F +#define CV_CheckDepth(t, test_expr, msg) CV__CHECK_CUSTOM_TEST(_, MatDepth, t, (test_expr), #t, #test_expr, msg) + +/// Example: v == A || v == B +#define CV_Check(v, test_expr, msg) CV__CHECK_CUSTOM_TEST(_, auto, v, (test_expr), #v, #test_expr, msg) + +/// Some complex conditions: CV_Check(src2, src2.empty() || (src2.type() == src1.type() && src2.size() == src1.size()), "src2 should have same size/type as src1") +// TODO define pretty-printers + +#ifndef NDEBUG +#define CV_DbgCheck(v, test_expr, msg) CV__CHECK_CUSTOM_TEST(_, auto, v, (test_expr), #v, #test_expr, msg) +#define CV_DbgCheckEQ(v1, v2, msg) CV__CHECK(_, EQ, auto, v1, v2, #v1, #v2, msg) +#define CV_DbgCheckNE(v1, v2, msg) CV__CHECK(_, NE, auto, v1, v2, #v1, #v2, msg) +#define CV_DbgCheckLE(v1, v2, msg) CV__CHECK(_, LE, auto, v1, v2, #v1, #v2, msg) +#define CV_DbgCheckLT(v1, v2, msg) CV__CHECK(_, LT, auto, v1, v2, #v1, #v2, msg) +#define CV_DbgCheckGE(v1, v2, msg) CV__CHECK(_, GE, auto, v1, v2, #v1, #v2, msg) +#define CV_DbgCheckGT(v1, v2, msg) CV__CHECK(_, GT, auto, v1, v2, #v1, #v2, msg) +#else +#define CV_DbgCheck(v, test_expr, msg) do { } while (0) +#define CV_DbgCheckEQ(v1, v2, msg) do { } while (0) +#define CV_DbgCheckNE(v1, v2, msg) do { } while (0) +#define CV_DbgCheckLE(v1, v2, msg) do { } while (0) +#define CV_DbgCheckLT(v1, v2, msg) do { } while (0) +#define CV_DbgCheckGE(v1, v2, msg) do { } while (0) +#define CV_DbgCheckGT(v1, v2, msg) do { } while (0) +#endif + +} // namespace + +#endif // OPENCV_CORE_CHECK_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/core.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/core.hpp new file mode 100644 index 0000000000000000000000000000000000000000..438918359be44fcf2489dc4f10617385fe3770e9 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/core.hpp @@ -0,0 +1,48 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifdef __OPENCV_BUILD +#error this is a compatibility header which should not be used inside the OpenCV library +#endif + +#include "opencv2/core.hpp" diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/core_c.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/core_c.h new file mode 100644 index 0000000000000000000000000000000000000000..e5fe516d264e30060360087b472e1eaec6cd0e44 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/core_c.h @@ -0,0 +1,3175 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + + +#ifndef OPENCV_CORE_C_H +#define OPENCV_CORE_C_H + +#include "opencv2/core/types_c.h" + +#ifdef __cplusplus +# ifdef _MSC_VER +/* disable warning C4190: 'function' has C-linkage specified, but returns UDT 'typename' + which is incompatible with C + + It is OK to disable it because we only extend few plain structures with + C++ construrtors for simpler interoperability with C++ API of the library +*/ +# pragma warning(disable:4190) +# elif defined __clang__ && __clang_major__ >= 3 +# pragma GCC diagnostic ignored "-Wreturn-type-c-linkage" +# endif +#endif + +#ifdef __cplusplus +extern "C" { +#endif + +/** @addtogroup core_c + @{ +*/ + +/****************************************************************************************\ +* Array allocation, deallocation, initialization and access to elements * +\****************************************************************************************/ + +/** `malloc` wrapper. + If there is no enough memory, the function + (as well as other OpenCV functions that call cvAlloc) + raises an error. */ +CVAPI(void*) cvAlloc( size_t size ); + +/** `free` wrapper. + Here and further all the memory releasing functions + (that all call cvFree) take double pointer in order to + to clear pointer to the data after releasing it. + Passing pointer to NULL pointer is Ok: nothing happens in this case +*/ +CVAPI(void) cvFree_( void* ptr ); +#define cvFree(ptr) (cvFree_(*(ptr)), *(ptr)=0) + +/** @brief Creates an image header but does not allocate the image data. + +@param size Image width and height +@param depth Image depth (see cvCreateImage ) +@param channels Number of channels (see cvCreateImage ) + */ +CVAPI(IplImage*) cvCreateImageHeader( CvSize size, int depth, int channels ); + +/** @brief Initializes an image header that was previously allocated. + +The returned IplImage\* points to the initialized header. +@param image Image header to initialize +@param size Image width and height +@param depth Image depth (see cvCreateImage ) +@param channels Number of channels (see cvCreateImage ) +@param origin Top-left IPL_ORIGIN_TL or bottom-left IPL_ORIGIN_BL +@param align Alignment for image rows, typically 4 or 8 bytes + */ +CVAPI(IplImage*) cvInitImageHeader( IplImage* image, CvSize size, int depth, + int channels, int origin CV_DEFAULT(0), + int align CV_DEFAULT(4)); + +/** @brief Creates an image header and allocates the image data. + +This function call is equivalent to the following code: +@code + header = cvCreateImageHeader(size, depth, channels); + cvCreateData(header); +@endcode +@param size Image width and height +@param depth Bit depth of image elements. See IplImage for valid depths. +@param channels Number of channels per pixel. See IplImage for details. This function only creates +images with interleaved channels. + */ +CVAPI(IplImage*) cvCreateImage( CvSize size, int depth, int channels ); + +/** @brief Deallocates an image header. + +This call is an analogue of : +@code + if(image ) + { + iplDeallocate(*image, IPL_IMAGE_HEADER | IPL_IMAGE_ROI); + *image = 0; + } +@endcode +but it does not use IPL functions by default (see the CV_TURN_ON_IPL_COMPATIBILITY macro). +@param image Double pointer to the image header + */ +CVAPI(void) cvReleaseImageHeader( IplImage** image ); + +/** @brief Deallocates the image header and the image data. + +This call is a shortened form of : +@code + if(*image ) + { + cvReleaseData(*image); + cvReleaseImageHeader(image); + } +@endcode +@param image Double pointer to the image header +*/ +CVAPI(void) cvReleaseImage( IplImage** image ); + +/** Creates a copy of IPL image (widthStep may differ) */ +CVAPI(IplImage*) cvCloneImage( const IplImage* image ); + +/** @brief Sets the channel of interest in an IplImage. + +If the ROI is set to NULL and the coi is *not* 0, the ROI is allocated. Most OpenCV functions do +*not* support the COI setting, so to process an individual image/matrix channel one may copy (via +cvCopy or cvSplit) the channel to a separate image/matrix, process it and then copy the result +back (via cvCopy or cvMerge) if needed. +@param image A pointer to the image header +@param coi The channel of interest. 0 - all channels are selected, 1 - first channel is selected, +etc. Note that the channel indices become 1-based. + */ +CVAPI(void) cvSetImageCOI( IplImage* image, int coi ); + +/** @brief Returns the index of the channel of interest. + +Returns the channel of interest of in an IplImage. Returned values correspond to the coi in +cvSetImageCOI. +@param image A pointer to the image header + */ +CVAPI(int) cvGetImageCOI( const IplImage* image ); + +/** @brief Sets an image Region Of Interest (ROI) for a given rectangle. + +If the original image ROI was NULL and the rect is not the whole image, the ROI structure is +allocated. + +Most OpenCV functions support the use of ROI and treat the image rectangle as a separate image. For +example, all of the pixel coordinates are counted from the top-left (or bottom-left) corner of the +ROI, not the original image. +@param image A pointer to the image header +@param rect The ROI rectangle + */ +CVAPI(void) cvSetImageROI( IplImage* image, CvRect rect ); + +/** @brief Resets the image ROI to include the entire image and releases the ROI structure. + +This produces a similar result to the following, but in addition it releases the ROI structure. : +@code + cvSetImageROI(image, cvRect(0, 0, image->width, image->height )); + cvSetImageCOI(image, 0); +@endcode +@param image A pointer to the image header + */ +CVAPI(void) cvResetImageROI( IplImage* image ); + +/** @brief Returns the image ROI. + +If there is no ROI set, cvRect(0,0,image-\>width,image-\>height) is returned. +@param image A pointer to the image header + */ +CVAPI(CvRect) cvGetImageROI( const IplImage* image ); + +/** @brief Creates a matrix header but does not allocate the matrix data. + +The function allocates a new matrix header and returns a pointer to it. The matrix data can then be +allocated using cvCreateData or set explicitly to user-allocated data via cvSetData. +@param rows Number of rows in the matrix +@param cols Number of columns in the matrix +@param type Type of the matrix elements, see cvCreateMat + */ +CVAPI(CvMat*) cvCreateMatHeader( int rows, int cols, int type ); + +#define CV_AUTOSTEP 0x7fffffff + +/** @brief Initializes a pre-allocated matrix header. + +This function is often used to process raw data with OpenCV matrix functions. For example, the +following code computes the matrix product of two matrices, stored as ordinary arrays: +@code + double a[] = { 1, 2, 3, 4, + 5, 6, 7, 8, + 9, 10, 11, 12 }; + + double b[] = { 1, 5, 9, + 2, 6, 10, + 3, 7, 11, + 4, 8, 12 }; + + double c[9]; + CvMat Ma, Mb, Mc ; + + cvInitMatHeader(&Ma, 3, 4, CV_64FC1, a); + cvInitMatHeader(&Mb, 4, 3, CV_64FC1, b); + cvInitMatHeader(&Mc, 3, 3, CV_64FC1, c); + + cvMatMulAdd(&Ma, &Mb, 0, &Mc); + // the c array now contains the product of a (3x4) and b (4x3) +@endcode +@param mat A pointer to the matrix header to be initialized +@param rows Number of rows in the matrix +@param cols Number of columns in the matrix +@param type Type of the matrix elements, see cvCreateMat . +@param data Optional: data pointer assigned to the matrix header +@param step Optional: full row width in bytes of the assigned data. By default, the minimal +possible step is used which assumes there are no gaps between subsequent rows of the matrix. + */ +CVAPI(CvMat*) cvInitMatHeader( CvMat* mat, int rows, int cols, + int type, void* data CV_DEFAULT(NULL), + int step CV_DEFAULT(CV_AUTOSTEP) ); + +/** @brief Creates a matrix header and allocates the matrix data. + +The function call is equivalent to the following code: +@code + CvMat* mat = cvCreateMatHeader(rows, cols, type); + cvCreateData(mat); +@endcode +@param rows Number of rows in the matrix +@param cols Number of columns in the matrix +@param type The type of the matrix elements in the form +CV_\\C\ , where S=signed, U=unsigned, F=float. For +example, CV _ 8UC1 means the elements are 8-bit unsigned and the there is 1 channel, and CV _ +32SC2 means the elements are 32-bit signed and there are 2 channels. + */ +CVAPI(CvMat*) cvCreateMat( int rows, int cols, int type ); + +/** @brief Deallocates a matrix. + +The function decrements the matrix data reference counter and deallocates matrix header. If the data +reference counter is 0, it also deallocates the data. : +@code + if(*mat ) + cvDecRefData(*mat); + cvFree((void**)mat); +@endcode +@param mat Double pointer to the matrix + */ +CVAPI(void) cvReleaseMat( CvMat** mat ); + +/** @brief Decrements an array data reference counter. + +The function decrements the data reference counter in a CvMat or CvMatND if the reference counter + +pointer is not NULL. If the counter reaches zero, the data is deallocated. In the current +implementation the reference counter is not NULL only if the data was allocated using the +cvCreateData function. The counter will be NULL in other cases such as: external data was assigned +to the header using cvSetData, header is part of a larger matrix or image, or the header was +converted from an image or n-dimensional matrix header. +@param arr Pointer to an array header + */ +CV_INLINE void cvDecRefData( CvArr* arr ) +{ + if( CV_IS_MAT( arr )) + { + CvMat* mat = (CvMat*)arr; + mat->data.ptr = NULL; + if( mat->refcount != NULL && --*mat->refcount == 0 ) + cvFree( &mat->refcount ); + mat->refcount = NULL; + } + else if( CV_IS_MATND( arr )) + { + CvMatND* mat = (CvMatND*)arr; + mat->data.ptr = NULL; + if( mat->refcount != NULL && --*mat->refcount == 0 ) + cvFree( &mat->refcount ); + mat->refcount = NULL; + } +} + +/** @brief Increments array data reference counter. + +The function increments CvMat or CvMatND data reference counter and returns the new counter value if +the reference counter pointer is not NULL, otherwise it returns zero. +@param arr Array header + */ +CV_INLINE int cvIncRefData( CvArr* arr ) +{ + int refcount = 0; + if( CV_IS_MAT( arr )) + { + CvMat* mat = (CvMat*)arr; + if( mat->refcount != NULL ) + refcount = ++*mat->refcount; + } + else if( CV_IS_MATND( arr )) + { + CvMatND* mat = (CvMatND*)arr; + if( mat->refcount != NULL ) + refcount = ++*mat->refcount; + } + return refcount; +} + + +/** Creates an exact copy of the input matrix (except, may be, step value) */ +CVAPI(CvMat*) cvCloneMat( const CvMat* mat ); + + +/** @brief Returns matrix header corresponding to the rectangular sub-array of input image or matrix. + +The function returns header, corresponding to a specified rectangle of the input array. In other + +words, it allows the user to treat a rectangular part of input array as a stand-alone array. ROI is +taken into account by the function so the sub-array of ROI is actually extracted. +@param arr Input array +@param submat Pointer to the resultant sub-array header +@param rect Zero-based coordinates of the rectangle of interest + */ +CVAPI(CvMat*) cvGetSubRect( const CvArr* arr, CvMat* submat, CvRect rect ); +#define cvGetSubArr cvGetSubRect + +/** @brief Returns array row or row span. + +The function returns the header, corresponding to a specified row/row span of the input array. +cvGetRow(arr, submat, row) is a shortcut for cvGetRows(arr, submat, row, row+1). +@param arr Input array +@param submat Pointer to the resulting sub-array header +@param start_row Zero-based index of the starting row (inclusive) of the span +@param end_row Zero-based index of the ending row (exclusive) of the span +@param delta_row Index step in the row span. That is, the function extracts every delta_row -th +row from start_row and up to (but not including) end_row . + */ +CVAPI(CvMat*) cvGetRows( const CvArr* arr, CvMat* submat, + int start_row, int end_row, + int delta_row CV_DEFAULT(1)); + +/** @overload +@param arr Input array +@param submat Pointer to the resulting sub-array header +@param row Zero-based index of the selected row +*/ +CV_INLINE CvMat* cvGetRow( const CvArr* arr, CvMat* submat, int row ) +{ + return cvGetRows( arr, submat, row, row + 1, 1 ); +} + + +/** @brief Returns one of more array columns. + +The function returns the header, corresponding to a specified column span of the input array. That + +is, no data is copied. Therefore, any modifications of the submatrix will affect the original array. +If you need to copy the columns, use cvCloneMat. cvGetCol(arr, submat, col) is a shortcut for +cvGetCols(arr, submat, col, col+1). +@param arr Input array +@param submat Pointer to the resulting sub-array header +@param start_col Zero-based index of the starting column (inclusive) of the span +@param end_col Zero-based index of the ending column (exclusive) of the span + */ +CVAPI(CvMat*) cvGetCols( const CvArr* arr, CvMat* submat, + int start_col, int end_col ); + +/** @overload +@param arr Input array +@param submat Pointer to the resulting sub-array header +@param col Zero-based index of the selected column +*/ +CV_INLINE CvMat* cvGetCol( const CvArr* arr, CvMat* submat, int col ) +{ + return cvGetCols( arr, submat, col, col + 1 ); +} + +/** @brief Returns one of array diagonals. + +The function returns the header, corresponding to a specified diagonal of the input array. +@param arr Input array +@param submat Pointer to the resulting sub-array header +@param diag Index of the array diagonal. Zero value corresponds to the main diagonal, -1 +corresponds to the diagonal above the main, 1 corresponds to the diagonal below the main, and so +forth. + */ +CVAPI(CvMat*) cvGetDiag( const CvArr* arr, CvMat* submat, + int diag CV_DEFAULT(0)); + +/** low-level scalar <-> raw data conversion functions */ +CVAPI(void) cvScalarToRawData( const CvScalar* scalar, void* data, int type, + int extend_to_12 CV_DEFAULT(0) ); + +CVAPI(void) cvRawDataToScalar( const void* data, int type, CvScalar* scalar ); + +/** @brief Creates a new matrix header but does not allocate the matrix data. + +The function allocates a header for a multi-dimensional dense array. The array data can further be +allocated using cvCreateData or set explicitly to user-allocated data via cvSetData. +@param dims Number of array dimensions +@param sizes Array of dimension sizes +@param type Type of array elements, see cvCreateMat + */ +CVAPI(CvMatND*) cvCreateMatNDHeader( int dims, const int* sizes, int type ); + +/** @brief Creates the header and allocates the data for a multi-dimensional dense array. + +This function call is equivalent to the following code: +@code + CvMatND* mat = cvCreateMatNDHeader(dims, sizes, type); + cvCreateData(mat); +@endcode +@param dims Number of array dimensions. This must not exceed CV_MAX_DIM (32 by default, but can be +changed at build time). +@param sizes Array of dimension sizes. +@param type Type of array elements, see cvCreateMat . + */ +CVAPI(CvMatND*) cvCreateMatND( int dims, const int* sizes, int type ); + +/** @brief Initializes a pre-allocated multi-dimensional array header. + +@param mat A pointer to the array header to be initialized +@param dims The number of array dimensions +@param sizes An array of dimension sizes +@param type Type of array elements, see cvCreateMat +@param data Optional data pointer assigned to the matrix header + */ +CVAPI(CvMatND*) cvInitMatNDHeader( CvMatND* mat, int dims, const int* sizes, + int type, void* data CV_DEFAULT(NULL) ); + +/** @brief Deallocates a multi-dimensional array. + +The function decrements the array data reference counter and releases the array header. If the +reference counter reaches 0, it also deallocates the data. : +@code + if(*mat ) + cvDecRefData(*mat); + cvFree((void**)mat); +@endcode +@param mat Double pointer to the array + */ +CV_INLINE void cvReleaseMatND( CvMatND** mat ) +{ + cvReleaseMat( (CvMat**)mat ); +} + +/** Creates a copy of CvMatND (except, may be, steps) */ +CVAPI(CvMatND*) cvCloneMatND( const CvMatND* mat ); + +/** @brief Creates sparse array. + +The function allocates a multi-dimensional sparse array. Initially the array contain no elements, +that is PtrND and other related functions will return 0 for every index. +@param dims Number of array dimensions. In contrast to the dense matrix, the number of dimensions is +practically unlimited (up to \f$2^{16}\f$ ). +@param sizes Array of dimension sizes +@param type Type of array elements. The same as for CvMat + */ +CVAPI(CvSparseMat*) cvCreateSparseMat( int dims, const int* sizes, int type ); + +/** @brief Deallocates sparse array. + +The function releases the sparse array and clears the array pointer upon exit. +@param mat Double pointer to the array + */ +CVAPI(void) cvReleaseSparseMat( CvSparseMat** mat ); + +/** Creates a copy of CvSparseMat (except, may be, zero items) */ +CVAPI(CvSparseMat*) cvCloneSparseMat( const CvSparseMat* mat ); + +/** @brief Initializes sparse array elements iterator. + +The function initializes iterator of sparse array elements and returns pointer to the first element, +or NULL if the array is empty. +@param mat Input array +@param mat_iterator Initialized iterator + */ +CVAPI(CvSparseNode*) cvInitSparseMatIterator( const CvSparseMat* mat, + CvSparseMatIterator* mat_iterator ); + +/** @brief Returns the next sparse matrix element + +The function moves iterator to the next sparse matrix element and returns pointer to it. In the +current version there is no any particular order of the elements, because they are stored in the +hash table. The sample below demonstrates how to iterate through the sparse matrix: +@code + // print all the non-zero sparse matrix elements and compute their sum + double sum = 0; + int i, dims = cvGetDims(sparsemat); + CvSparseMatIterator it; + CvSparseNode* node = cvInitSparseMatIterator(sparsemat, &it); + + for(; node != 0; node = cvGetNextSparseNode(&it)) + { + int* idx = CV_NODE_IDX(array, node); + float val = *(float*)CV_NODE_VAL(array, node); + printf("M"); + for(i = 0; i < dims; i++ ) + printf("[%d]", idx[i]); + printf("=%g\n", val); + + sum += val; + } + + printf("nTotal sum = %g\n", sum); +@endcode +@param mat_iterator Sparse array iterator + */ +CV_INLINE CvSparseNode* cvGetNextSparseNode( CvSparseMatIterator* mat_iterator ) +{ + if( mat_iterator->node->next ) + return mat_iterator->node = mat_iterator->node->next; + else + { + int idx; + for( idx = ++mat_iterator->curidx; idx < mat_iterator->mat->hashsize; idx++ ) + { + CvSparseNode* node = (CvSparseNode*)mat_iterator->mat->hashtable[idx]; + if( node ) + { + mat_iterator->curidx = idx; + return mat_iterator->node = node; + } + } + return NULL; + } +} + + +#define CV_MAX_ARR 10 + +/** matrix iterator: used for n-ary operations on dense arrays */ +typedef struct CvNArrayIterator +{ + int count; /**< number of arrays */ + int dims; /**< number of dimensions to iterate */ + CvSize size; /**< maximal common linear size: { width = size, height = 1 } */ + uchar* ptr[CV_MAX_ARR]; /**< pointers to the array slices */ + int stack[CV_MAX_DIM]; /**< for internal use */ + CvMatND* hdr[CV_MAX_ARR]; /**< pointers to the headers of the + matrices that are processed */ +} +CvNArrayIterator; + +#define CV_NO_DEPTH_CHECK 1 +#define CV_NO_CN_CHECK 2 +#define CV_NO_SIZE_CHECK 4 + +/** initializes iterator that traverses through several arrays simulteneously + (the function together with cvNextArraySlice is used for + N-ari element-wise operations) */ +CVAPI(int) cvInitNArrayIterator( int count, CvArr** arrs, + const CvArr* mask, CvMatND* stubs, + CvNArrayIterator* array_iterator, + int flags CV_DEFAULT(0) ); + +/** returns zero value if iteration is finished, non-zero (slice length) otherwise */ +CVAPI(int) cvNextNArraySlice( CvNArrayIterator* array_iterator ); + + +/** @brief Returns type of array elements. + +The function returns type of the array elements. In the case of IplImage the type is converted to +CvMat-like representation. For example, if the image has been created as: +@code + IplImage* img = cvCreateImage(cvSize(640, 480), IPL_DEPTH_8U, 3); +@endcode +The code cvGetElemType(img) will return CV_8UC3. +@param arr Input array + */ +CVAPI(int) cvGetElemType( const CvArr* arr ); + +/** @brief Return number of array dimensions + +The function returns the array dimensionality and the array of dimension sizes. In the case of +IplImage or CvMat it always returns 2 regardless of number of image/matrix rows. For example, the +following code calculates total number of array elements: +@code + int sizes[CV_MAX_DIM]; + int i, total = 1; + int dims = cvGetDims(arr, size); + for(i = 0; i < dims; i++ ) + total *= sizes[i]; +@endcode +@param arr Input array +@param sizes Optional output vector of the array dimension sizes. For 2d arrays the number of rows +(height) goes first, number of columns (width) next. + */ +CVAPI(int) cvGetDims( const CvArr* arr, int* sizes CV_DEFAULT(NULL) ); + + +/** @brief Returns array size along the specified dimension. + +@param arr Input array +@param index Zero-based dimension index (for matrices 0 means number of rows, 1 means number of +columns; for images 0 means height, 1 means width) + */ +CVAPI(int) cvGetDimSize( const CvArr* arr, int index ); + + +/** @brief Return pointer to a particular array element. + +The functions return a pointer to a specific array element. Number of array dimension should match +to the number of indices passed to the function except for cvPtr1D function that can be used for +sequential access to 1D, 2D or nD dense arrays. + +The functions can be used for sparse arrays as well - if the requested node does not exist they +create it and set it to zero. + +All these as well as other functions accessing array elements ( cvGetND , cvGetRealND , cvSet +, cvSetND , cvSetRealND ) raise an error in case if the element index is out of range. +@param arr Input array +@param idx0 The first zero-based component of the element index +@param type Optional output parameter: type of matrix elements + */ +CVAPI(uchar*) cvPtr1D( const CvArr* arr, int idx0, int* type CV_DEFAULT(NULL)); +/** @overload */ +CVAPI(uchar*) cvPtr2D( const CvArr* arr, int idx0, int idx1, int* type CV_DEFAULT(NULL) ); +/** @overload */ +CVAPI(uchar*) cvPtr3D( const CvArr* arr, int idx0, int idx1, int idx2, + int* type CV_DEFAULT(NULL)); +/** @overload +@param arr Input array +@param idx Array of the element indices +@param type Optional output parameter: type of matrix elements +@param create_node Optional input parameter for sparse matrices. Non-zero value of the parameter +means that the requested element is created if it does not exist already. +@param precalc_hashval Optional input parameter for sparse matrices. If the pointer is not NULL, +the function does not recalculate the node hash value, but takes it from the specified location. +It is useful for speeding up pair-wise operations (TODO: provide an example) +*/ +CVAPI(uchar*) cvPtrND( const CvArr* arr, const int* idx, int* type CV_DEFAULT(NULL), + int create_node CV_DEFAULT(1), + unsigned* precalc_hashval CV_DEFAULT(NULL)); + +/** @brief Return a specific array element. + +The functions return a specific array element. In the case of a sparse array the functions return 0 +if the requested node does not exist (no new node is created by the functions). +@param arr Input array +@param idx0 The first zero-based component of the element index + */ +CVAPI(CvScalar) cvGet1D( const CvArr* arr, int idx0 ); +/** @overload */ +CVAPI(CvScalar) cvGet2D( const CvArr* arr, int idx0, int idx1 ); +/** @overload */ +CVAPI(CvScalar) cvGet3D( const CvArr* arr, int idx0, int idx1, int idx2 ); +/** @overload +@param arr Input array +@param idx Array of the element indices +*/ +CVAPI(CvScalar) cvGetND( const CvArr* arr, const int* idx ); + +/** @brief Return a specific element of single-channel 1D, 2D, 3D or nD array. + +Returns a specific element of a single-channel array. If the array has multiple channels, a runtime +error is raised. Note that Get?D functions can be used safely for both single-channel and +multiple-channel arrays though they are a bit slower. + +In the case of a sparse array the functions return 0 if the requested node does not exist (no new +node is created by the functions). +@param arr Input array. Must have a single channel. +@param idx0 The first zero-based component of the element index + */ +CVAPI(double) cvGetReal1D( const CvArr* arr, int idx0 ); +/** @overload */ +CVAPI(double) cvGetReal2D( const CvArr* arr, int idx0, int idx1 ); +/** @overload */ +CVAPI(double) cvGetReal3D( const CvArr* arr, int idx0, int idx1, int idx2 ); +/** @overload +@param arr Input array. Must have a single channel. +@param idx Array of the element indices +*/ +CVAPI(double) cvGetRealND( const CvArr* arr, const int* idx ); + +/** @brief Change the particular array element. + +The functions assign the new value to a particular array element. In the case of a sparse array the +functions create the node if it does not exist yet. +@param arr Input array +@param idx0 The first zero-based component of the element index +@param value The assigned value + */ +CVAPI(void) cvSet1D( CvArr* arr, int idx0, CvScalar value ); +/** @overload */ +CVAPI(void) cvSet2D( CvArr* arr, int idx0, int idx1, CvScalar value ); +/** @overload */ +CVAPI(void) cvSet3D( CvArr* arr, int idx0, int idx1, int idx2, CvScalar value ); +/** @overload +@param arr Input array +@param idx Array of the element indices +@param value The assigned value +*/ +CVAPI(void) cvSetND( CvArr* arr, const int* idx, CvScalar value ); + +/** @brief Change a specific array element. + +The functions assign a new value to a specific element of a single-channel array. If the array has +multiple channels, a runtime error is raised. Note that the Set\*D function can be used safely for +both single-channel and multiple-channel arrays, though they are a bit slower. + +In the case of a sparse array the functions create the node if it does not yet exist. +@param arr Input array +@param idx0 The first zero-based component of the element index +@param value The assigned value + */ +CVAPI(void) cvSetReal1D( CvArr* arr, int idx0, double value ); +/** @overload */ +CVAPI(void) cvSetReal2D( CvArr* arr, int idx0, int idx1, double value ); +/** @overload */ +CVAPI(void) cvSetReal3D( CvArr* arr, int idx0, + int idx1, int idx2, double value ); +/** @overload +@param arr Input array +@param idx Array of the element indices +@param value The assigned value +*/ +CVAPI(void) cvSetRealND( CvArr* arr, const int* idx, double value ); + +/** clears element of ND dense array, + in case of sparse arrays it deletes the specified node */ +CVAPI(void) cvClearND( CvArr* arr, const int* idx ); + +/** @brief Returns matrix header for arbitrary array. + +The function returns a matrix header for the input array that can be a matrix - CvMat, an image - +IplImage, or a multi-dimensional dense array - CvMatND (the third option is allowed only if +allowND != 0) . In the case of matrix the function simply returns the input pointer. In the case of +IplImage\* or CvMatND it initializes the header structure with parameters of the current image ROI +and returns &header. Because COI is not supported by CvMat, it is returned separately. + +The function provides an easy way to handle both types of arrays - IplImage and CvMat using the same +code. Input array must have non-zero data pointer, otherwise the function will report an error. + +@note If the input array is IplImage with planar data layout and COI set, the function returns the +pointer to the selected plane and COI == 0. This feature allows user to process IplImage structures +with planar data layout, even though OpenCV does not support such images. +@param arr Input array +@param header Pointer to CvMat structure used as a temporary buffer +@param coi Optional output parameter for storing COI +@param allowND If non-zero, the function accepts multi-dimensional dense arrays (CvMatND\*) and +returns 2D matrix (if CvMatND has two dimensions) or 1D matrix (when CvMatND has 1 dimension or +more than 2 dimensions). The CvMatND array must be continuous. +@sa cvGetImage, cvarrToMat. + */ +CVAPI(CvMat*) cvGetMat( const CvArr* arr, CvMat* header, + int* coi CV_DEFAULT(NULL), + int allowND CV_DEFAULT(0)); + +/** @brief Returns image header for arbitrary array. + +The function returns the image header for the input array that can be a matrix (CvMat) or image +(IplImage). In the case of an image the function simply returns the input pointer. In the case of +CvMat it initializes an image_header structure with the parameters of the input matrix. Note that +if we transform IplImage to CvMat using cvGetMat and then transform CvMat back to IplImage using +this function, we will get different headers if the ROI is set in the original image. +@param arr Input array +@param image_header Pointer to IplImage structure used as a temporary buffer + */ +CVAPI(IplImage*) cvGetImage( const CvArr* arr, IplImage* image_header ); + + +/** @brief Changes the shape of a multi-dimensional array without copying the data. + +The function is an advanced version of cvReshape that can work with multi-dimensional arrays as +well (though it can work with ordinary images and matrices) and change the number of dimensions. + +Below are the two samples from the cvReshape description rewritten using cvReshapeMatND: +@code + IplImage* color_img = cvCreateImage(cvSize(320,240), IPL_DEPTH_8U, 3); + IplImage gray_img_hdr, *gray_img; + gray_img = (IplImage*)cvReshapeMatND(color_img, sizeof(gray_img_hdr), &gray_img_hdr, 1, 0, 0); + ... + int size[] = { 2, 2, 2 }; + CvMatND* mat = cvCreateMatND(3, size, CV_32F); + CvMat row_header, *row; + row = (CvMat*)cvReshapeMatND(mat, sizeof(row_header), &row_header, 0, 1, 0); +@endcode +In C, the header file for this function includes a convenient macro cvReshapeND that does away with +the sizeof_header parameter. So, the lines containing the call to cvReshapeMatND in the examples +may be replaced as follow: +@code + gray_img = (IplImage*)cvReshapeND(color_img, &gray_img_hdr, 1, 0, 0); + ... + row = (CvMat*)cvReshapeND(mat, &row_header, 0, 1, 0); +@endcode +@param arr Input array +@param sizeof_header Size of output header to distinguish between IplImage, CvMat and CvMatND +output headers +@param header Output header to be filled +@param new_cn New number of channels. new_cn = 0 means that the number of channels remains +unchanged. +@param new_dims New number of dimensions. new_dims = 0 means that the number of dimensions +remains the same. +@param new_sizes Array of new dimension sizes. Only new_dims-1 values are used, because the +total number of elements must remain the same. Thus, if new_dims = 1, new_sizes array is not +used. + */ +CVAPI(CvArr*) cvReshapeMatND( const CvArr* arr, + int sizeof_header, CvArr* header, + int new_cn, int new_dims, int* new_sizes ); + +#define cvReshapeND( arr, header, new_cn, new_dims, new_sizes ) \ + cvReshapeMatND( (arr), sizeof(*(header)), (header), \ + (new_cn), (new_dims), (new_sizes)) + +/** @brief Changes shape of matrix/image without copying data. + +The function initializes the CvMat header so that it points to the same data as the original array +but has a different shape - different number of channels, different number of rows, or both. + +The following example code creates one image buffer and two image headers, the first is for a +320x240x3 image and the second is for a 960x240x1 image: +@code + IplImage* color_img = cvCreateImage(cvSize(320,240), IPL_DEPTH_8U, 3); + CvMat gray_mat_hdr; + IplImage gray_img_hdr, *gray_img; + cvReshape(color_img, &gray_mat_hdr, 1); + gray_img = cvGetImage(&gray_mat_hdr, &gray_img_hdr); +@endcode +And the next example converts a 3x3 matrix to a single 1x9 vector: +@code + CvMat* mat = cvCreateMat(3, 3, CV_32F); + CvMat row_header, *row; + row = cvReshape(mat, &row_header, 0, 1); +@endcode +@param arr Input array +@param header Output header to be filled +@param new_cn New number of channels. 'new_cn = 0' means that the number of channels remains +unchanged. +@param new_rows New number of rows. 'new_rows = 0' means that the number of rows remains +unchanged unless it needs to be changed according to new_cn value. +*/ +CVAPI(CvMat*) cvReshape( const CvArr* arr, CvMat* header, + int new_cn, int new_rows CV_DEFAULT(0) ); + +/** Repeats source 2d array several times in both horizontal and + vertical direction to fill destination array */ +CVAPI(void) cvRepeat( const CvArr* src, CvArr* dst ); + +/** @brief Allocates array data + +The function allocates image, matrix or multi-dimensional dense array data. Note that in the case of +matrix types OpenCV allocation functions are used. In the case of IplImage they are used unless +CV_TURN_ON_IPL_COMPATIBILITY() has been called before. In the latter case IPL functions are used +to allocate the data. +@param arr Array header + */ +CVAPI(void) cvCreateData( CvArr* arr ); + +/** @brief Releases array data. + +The function releases the array data. In the case of CvMat or CvMatND it simply calls +cvDecRefData(), that is the function can not deallocate external data. See also the note to +cvCreateData . +@param arr Array header + */ +CVAPI(void) cvReleaseData( CvArr* arr ); + +/** @brief Assigns user data to the array header. + +The function assigns user data to the array header. Header should be initialized before using +cvCreateMatHeader, cvCreateImageHeader, cvCreateMatNDHeader, cvInitMatHeader, +cvInitImageHeader or cvInitMatNDHeader. +@param arr Array header +@param data User data +@param step Full row length in bytes + */ +CVAPI(void) cvSetData( CvArr* arr, void* data, int step ); + +/** @brief Retrieves low-level information about the array. + +The function fills output variables with low-level information about the array data. All output + +parameters are optional, so some of the pointers may be set to NULL. If the array is IplImage with +ROI set, the parameters of ROI are returned. + +The following example shows how to get access to array elements. It computes absolute values of the +array elements : +@code + float* data; + int step; + CvSize size; + + cvGetRawData(array, (uchar**)&data, &step, &size); + step /= sizeof(data[0]); + + for(int y = 0; y < size.height; y++, data += step ) + for(int x = 0; x < size.width; x++ ) + data[x] = (float)fabs(data[x]); +@endcode +@param arr Array header +@param data Output pointer to the whole image origin or ROI origin if ROI is set +@param step Output full row length in bytes +@param roi_size Output ROI size + */ +CVAPI(void) cvGetRawData( const CvArr* arr, uchar** data, + int* step CV_DEFAULT(NULL), + CvSize* roi_size CV_DEFAULT(NULL)); + +/** @brief Returns size of matrix or image ROI. + +The function returns number of rows (CvSize::height) and number of columns (CvSize::width) of the +input matrix or image. In the case of image the size of ROI is returned. +@param arr array header + */ +CVAPI(CvSize) cvGetSize( const CvArr* arr ); + +/** @brief Copies one array to another. + +The function copies selected elements from an input array to an output array: + +\f[\texttt{dst} (I)= \texttt{src} (I) \quad \text{if} \quad \texttt{mask} (I) \ne 0.\f] + +If any of the passed arrays is of IplImage type, then its ROI and COI fields are used. Both arrays +must have the same type, the same number of dimensions, and the same size. The function can also +copy sparse arrays (mask is not supported in this case). +@param src The source array +@param dst The destination array +@param mask Operation mask, 8-bit single channel array; specifies elements of the destination array +to be changed + */ +CVAPI(void) cvCopy( const CvArr* src, CvArr* dst, + const CvArr* mask CV_DEFAULT(NULL) ); + +/** @brief Sets every element of an array to a given value. + +The function copies the scalar value to every selected element of the destination array: +\f[\texttt{arr} (I)= \texttt{value} \quad \text{if} \quad \texttt{mask} (I) \ne 0\f] +If array arr is of IplImage type, then is ROI used, but COI must not be set. +@param arr The destination array +@param value Fill value +@param mask Operation mask, 8-bit single channel array; specifies elements of the destination +array to be changed + */ +CVAPI(void) cvSet( CvArr* arr, CvScalar value, + const CvArr* mask CV_DEFAULT(NULL) ); + +/** @brief Clears the array. + +The function clears the array. In the case of dense arrays (CvMat, CvMatND or IplImage), +cvZero(array) is equivalent to cvSet(array,cvScalarAll(0),0). In the case of sparse arrays all the +elements are removed. +@param arr Array to be cleared + */ +CVAPI(void) cvSetZero( CvArr* arr ); +#define cvZero cvSetZero + + +/** Splits a multi-channel array into the set of single-channel arrays or + extracts particular [color] plane */ +CVAPI(void) cvSplit( const CvArr* src, CvArr* dst0, CvArr* dst1, + CvArr* dst2, CvArr* dst3 ); + +/** Merges a set of single-channel arrays into the single multi-channel array + or inserts one particular [color] plane to the array */ +CVAPI(void) cvMerge( const CvArr* src0, const CvArr* src1, + const CvArr* src2, const CvArr* src3, + CvArr* dst ); + +/** Copies several channels from input arrays to + certain channels of output arrays */ +CVAPI(void) cvMixChannels( const CvArr** src, int src_count, + CvArr** dst, int dst_count, + const int* from_to, int pair_count ); + +/** @brief Converts one array to another with optional linear transformation. + +The function has several different purposes, and thus has several different names. It copies one +array to another with optional scaling, which is performed first, and/or optional type conversion, +performed after: + +\f[\texttt{dst} (I) = \texttt{scale} \texttt{src} (I) + ( \texttt{shift} _0, \texttt{shift} _1,...)\f] + +All the channels of multi-channel arrays are processed independently. + +The type of conversion is done with rounding and saturation, that is if the result of scaling + +conversion can not be represented exactly by a value of the destination array element type, it is +set to the nearest representable value on the real axis. +@param src Source array +@param dst Destination array +@param scale Scale factor +@param shift Value added to the scaled source array elements + */ +CVAPI(void) cvConvertScale( const CvArr* src, CvArr* dst, + double scale CV_DEFAULT(1), + double shift CV_DEFAULT(0) ); +#define cvCvtScale cvConvertScale +#define cvScale cvConvertScale +#define cvConvert( src, dst ) cvConvertScale( (src), (dst), 1, 0 ) + + +/** Performs linear transformation on every source array element, + stores absolute value of the result: + dst(x,y,c) = abs(scale*src(x,y,c)+shift). + destination array must have 8u type. + In other cases one may use cvConvertScale + cvAbsDiffS */ +CVAPI(void) cvConvertScaleAbs( const CvArr* src, CvArr* dst, + double scale CV_DEFAULT(1), + double shift CV_DEFAULT(0) ); +#define cvCvtScaleAbs cvConvertScaleAbs + + +/** checks termination criteria validity and + sets eps to default_eps (if it is not set), + max_iter to default_max_iters (if it is not set) +*/ +CVAPI(CvTermCriteria) cvCheckTermCriteria( CvTermCriteria criteria, + double default_eps, + int default_max_iters ); + +/****************************************************************************************\ +* Arithmetic, logic and comparison operations * +\****************************************************************************************/ + +/** dst(mask) = src1(mask) + src2(mask) */ +CVAPI(void) cvAdd( const CvArr* src1, const CvArr* src2, CvArr* dst, + const CvArr* mask CV_DEFAULT(NULL)); + +/** dst(mask) = src(mask) + value */ +CVAPI(void) cvAddS( const CvArr* src, CvScalar value, CvArr* dst, + const CvArr* mask CV_DEFAULT(NULL)); + +/** dst(mask) = src1(mask) - src2(mask) */ +CVAPI(void) cvSub( const CvArr* src1, const CvArr* src2, CvArr* dst, + const CvArr* mask CV_DEFAULT(NULL)); + +/** dst(mask) = src(mask) - value = src(mask) + (-value) */ +CV_INLINE void cvSubS( const CvArr* src, CvScalar value, CvArr* dst, + const CvArr* mask CV_DEFAULT(NULL)) +{ + cvAddS( src, cvScalar( -value.val[0], -value.val[1], -value.val[2], -value.val[3]), + dst, mask ); +} + +/** dst(mask) = value - src(mask) */ +CVAPI(void) cvSubRS( const CvArr* src, CvScalar value, CvArr* dst, + const CvArr* mask CV_DEFAULT(NULL)); + +/** dst(idx) = src1(idx) * src2(idx) * scale + (scaled element-wise multiplication of 2 arrays) */ +CVAPI(void) cvMul( const CvArr* src1, const CvArr* src2, + CvArr* dst, double scale CV_DEFAULT(1) ); + +/** element-wise division/inversion with scaling: + dst(idx) = src1(idx) * scale / src2(idx) + or dst(idx) = scale / src2(idx) if src1 == 0 */ +CVAPI(void) cvDiv( const CvArr* src1, const CvArr* src2, + CvArr* dst, double scale CV_DEFAULT(1)); + +/** dst = src1 * scale + src2 */ +CVAPI(void) cvScaleAdd( const CvArr* src1, CvScalar scale, + const CvArr* src2, CvArr* dst ); +#define cvAXPY( A, real_scalar, B, C ) cvScaleAdd(A, cvRealScalar(real_scalar), B, C) + +/** dst = src1 * alpha + src2 * beta + gamma */ +CVAPI(void) cvAddWeighted( const CvArr* src1, double alpha, + const CvArr* src2, double beta, + double gamma, CvArr* dst ); + +/** @brief Calculates the dot product of two arrays in Euclidean metrics. + +The function calculates and returns the Euclidean dot product of two arrays. + +\f[src1 \bullet src2 = \sum _I ( \texttt{src1} (I) \texttt{src2} (I))\f] + +In the case of multiple channel arrays, the results for all channels are accumulated. In particular, +cvDotProduct(a,a) where a is a complex vector, will return \f$||\texttt{a}||^2\f$. The function can +process multi-dimensional arrays, row by row, layer by layer, and so on. +@param src1 The first source array +@param src2 The second source array + */ +CVAPI(double) cvDotProduct( const CvArr* src1, const CvArr* src2 ); + +/** dst(idx) = src1(idx) & src2(idx) */ +CVAPI(void) cvAnd( const CvArr* src1, const CvArr* src2, + CvArr* dst, const CvArr* mask CV_DEFAULT(NULL)); + +/** dst(idx) = src(idx) & value */ +CVAPI(void) cvAndS( const CvArr* src, CvScalar value, + CvArr* dst, const CvArr* mask CV_DEFAULT(NULL)); + +/** dst(idx) = src1(idx) | src2(idx) */ +CVAPI(void) cvOr( const CvArr* src1, const CvArr* src2, + CvArr* dst, const CvArr* mask CV_DEFAULT(NULL)); + +/** dst(idx) = src(idx) | value */ +CVAPI(void) cvOrS( const CvArr* src, CvScalar value, + CvArr* dst, const CvArr* mask CV_DEFAULT(NULL)); + +/** dst(idx) = src1(idx) ^ src2(idx) */ +CVAPI(void) cvXor( const CvArr* src1, const CvArr* src2, + CvArr* dst, const CvArr* mask CV_DEFAULT(NULL)); + +/** dst(idx) = src(idx) ^ value */ +CVAPI(void) cvXorS( const CvArr* src, CvScalar value, + CvArr* dst, const CvArr* mask CV_DEFAULT(NULL)); + +/** dst(idx) = ~src(idx) */ +CVAPI(void) cvNot( const CvArr* src, CvArr* dst ); + +/** dst(idx) = lower(idx) <= src(idx) < upper(idx) */ +CVAPI(void) cvInRange( const CvArr* src, const CvArr* lower, + const CvArr* upper, CvArr* dst ); + +/** dst(idx) = lower <= src(idx) < upper */ +CVAPI(void) cvInRangeS( const CvArr* src, CvScalar lower, + CvScalar upper, CvArr* dst ); + +#define CV_CMP_EQ 0 +#define CV_CMP_GT 1 +#define CV_CMP_GE 2 +#define CV_CMP_LT 3 +#define CV_CMP_LE 4 +#define CV_CMP_NE 5 + +/** The comparison operation support single-channel arrays only. + Destination image should be 8uC1 or 8sC1 */ + +/** dst(idx) = src1(idx) _cmp_op_ src2(idx) */ +CVAPI(void) cvCmp( const CvArr* src1, const CvArr* src2, CvArr* dst, int cmp_op ); + +/** dst(idx) = src1(idx) _cmp_op_ value */ +CVAPI(void) cvCmpS( const CvArr* src, double value, CvArr* dst, int cmp_op ); + +/** dst(idx) = min(src1(idx),src2(idx)) */ +CVAPI(void) cvMin( const CvArr* src1, const CvArr* src2, CvArr* dst ); + +/** dst(idx) = max(src1(idx),src2(idx)) */ +CVAPI(void) cvMax( const CvArr* src1, const CvArr* src2, CvArr* dst ); + +/** dst(idx) = min(src(idx),value) */ +CVAPI(void) cvMinS( const CvArr* src, double value, CvArr* dst ); + +/** dst(idx) = max(src(idx),value) */ +CVAPI(void) cvMaxS( const CvArr* src, double value, CvArr* dst ); + +/** dst(x,y,c) = abs(src1(x,y,c) - src2(x,y,c)) */ +CVAPI(void) cvAbsDiff( const CvArr* src1, const CvArr* src2, CvArr* dst ); + +/** dst(x,y,c) = abs(src(x,y,c) - value(c)) */ +CVAPI(void) cvAbsDiffS( const CvArr* src, CvArr* dst, CvScalar value ); +#define cvAbs( src, dst ) cvAbsDiffS( (src), (dst), cvScalarAll(0)) + +/****************************************************************************************\ +* Math operations * +\****************************************************************************************/ + +/** Does cartesian->polar coordinates conversion. + Either of output components (magnitude or angle) is optional */ +CVAPI(void) cvCartToPolar( const CvArr* x, const CvArr* y, + CvArr* magnitude, CvArr* angle CV_DEFAULT(NULL), + int angle_in_degrees CV_DEFAULT(0)); + +/** Does polar->cartesian coordinates conversion. + Either of output components (magnitude or angle) is optional. + If magnitude is missing it is assumed to be all 1's */ +CVAPI(void) cvPolarToCart( const CvArr* magnitude, const CvArr* angle, + CvArr* x, CvArr* y, + int angle_in_degrees CV_DEFAULT(0)); + +/** Does powering: dst(idx) = src(idx)^power */ +CVAPI(void) cvPow( const CvArr* src, CvArr* dst, double power ); + +/** Does exponention: dst(idx) = exp(src(idx)). + Overflow is not handled yet. Underflow is handled. + Maximal relative error is ~7e-6 for single-precision input */ +CVAPI(void) cvExp( const CvArr* src, CvArr* dst ); + +/** Calculates natural logarithms: dst(idx) = log(abs(src(idx))). + Logarithm of 0 gives large negative number(~-700) + Maximal relative error is ~3e-7 for single-precision output +*/ +CVAPI(void) cvLog( const CvArr* src, CvArr* dst ); + +/** Fast arctangent calculation */ +CVAPI(float) cvFastArctan( float y, float x ); + +/** Fast cubic root calculation */ +CVAPI(float) cvCbrt( float value ); + +#define CV_CHECK_RANGE 1 +#define CV_CHECK_QUIET 2 +/** Checks array values for NaNs, Infs or simply for too large numbers + (if CV_CHECK_RANGE is set). If CV_CHECK_QUIET is set, + no runtime errors is raised (function returns zero value in case of "bad" values). + Otherwise cvError is called */ +CVAPI(int) cvCheckArr( const CvArr* arr, int flags CV_DEFAULT(0), + double min_val CV_DEFAULT(0), double max_val CV_DEFAULT(0)); +#define cvCheckArray cvCheckArr + +#define CV_RAND_UNI 0 +#define CV_RAND_NORMAL 1 + +/** @brief Fills an array with random numbers and updates the RNG state. + +The function fills the destination array with uniformly or normally distributed random numbers. +@param rng CvRNG state initialized by cvRNG +@param arr The destination array +@param dist_type Distribution type +> - **CV_RAND_UNI** uniform distribution +> - **CV_RAND_NORMAL** normal or Gaussian distribution +@param param1 The first parameter of the distribution. In the case of a uniform distribution it is +the inclusive lower boundary of the random numbers range. In the case of a normal distribution it +is the mean value of the random numbers. +@param param2 The second parameter of the distribution. In the case of a uniform distribution it +is the exclusive upper boundary of the random numbers range. In the case of a normal distribution +it is the standard deviation of the random numbers. +@sa randu, randn, RNG::fill. + */ +CVAPI(void) cvRandArr( CvRNG* rng, CvArr* arr, int dist_type, + CvScalar param1, CvScalar param2 ); + +CVAPI(void) cvRandShuffle( CvArr* mat, CvRNG* rng, + double iter_factor CV_DEFAULT(1.)); + +#define CV_SORT_EVERY_ROW 0 +#define CV_SORT_EVERY_COLUMN 1 +#define CV_SORT_ASCENDING 0 +#define CV_SORT_DESCENDING 16 + +CVAPI(void) cvSort( const CvArr* src, CvArr* dst CV_DEFAULT(NULL), + CvArr* idxmat CV_DEFAULT(NULL), + int flags CV_DEFAULT(0)); + +/** Finds real roots of a cubic equation */ +CVAPI(int) cvSolveCubic( const CvMat* coeffs, CvMat* roots ); + +/** Finds all real and complex roots of a polynomial equation */ +CVAPI(void) cvSolvePoly(const CvMat* coeffs, CvMat *roots2, + int maxiter CV_DEFAULT(20), int fig CV_DEFAULT(100)); + +/****************************************************************************************\ +* Matrix operations * +\****************************************************************************************/ + +/** @brief Calculates the cross product of two 3D vectors. + +The function calculates the cross product of two 3D vectors: +\f[\texttt{dst} = \texttt{src1} \times \texttt{src2}\f] +or: +\f[\begin{array}{l} \texttt{dst} _1 = \texttt{src1} _2 \texttt{src2} _3 - \texttt{src1} _3 \texttt{src2} _2 \\ \texttt{dst} _2 = \texttt{src1} _3 \texttt{src2} _1 - \texttt{src1} _1 \texttt{src2} _3 \\ \texttt{dst} _3 = \texttt{src1} _1 \texttt{src2} _2 - \texttt{src1} _2 \texttt{src2} _1 \end{array}\f] +@param src1 The first source vector +@param src2 The second source vector +@param dst The destination vector + */ +CVAPI(void) cvCrossProduct( const CvArr* src1, const CvArr* src2, CvArr* dst ); + +/** Matrix transform: dst = A*B + C, C is optional */ +#define cvMatMulAdd( src1, src2, src3, dst ) cvGEMM( (src1), (src2), 1., (src3), 1., (dst), 0 ) +#define cvMatMul( src1, src2, dst ) cvMatMulAdd( (src1), (src2), NULL, (dst)) + +#define CV_GEMM_A_T 1 +#define CV_GEMM_B_T 2 +#define CV_GEMM_C_T 4 +/** Extended matrix transform: + dst = alpha*op(A)*op(B) + beta*op(C), where op(X) is X or X^T */ +CVAPI(void) cvGEMM( const CvArr* src1, const CvArr* src2, double alpha, + const CvArr* src3, double beta, CvArr* dst, + int tABC CV_DEFAULT(0)); +#define cvMatMulAddEx cvGEMM + +/** Transforms each element of source array and stores + resultant vectors in destination array */ +CVAPI(void) cvTransform( const CvArr* src, CvArr* dst, + const CvMat* transmat, + const CvMat* shiftvec CV_DEFAULT(NULL)); +#define cvMatMulAddS cvTransform + +/** Does perspective transform on every element of input array */ +CVAPI(void) cvPerspectiveTransform( const CvArr* src, CvArr* dst, + const CvMat* mat ); + +/** Calculates (A-delta)*(A-delta)^T (order=0) or (A-delta)^T*(A-delta) (order=1) */ +CVAPI(void) cvMulTransposed( const CvArr* src, CvArr* dst, int order, + const CvArr* delta CV_DEFAULT(NULL), + double scale CV_DEFAULT(1.) ); + +/** Tranposes matrix. Square matrices can be transposed in-place */ +CVAPI(void) cvTranspose( const CvArr* src, CvArr* dst ); +#define cvT cvTranspose + +/** Completes the symmetric matrix from the lower (LtoR=0) or from the upper (LtoR!=0) part */ +CVAPI(void) cvCompleteSymm( CvMat* matrix, int LtoR CV_DEFAULT(0) ); + +/** Mirror array data around horizontal (flip=0), + vertical (flip=1) or both(flip=-1) axises: + cvFlip(src) flips images vertically and sequences horizontally (inplace) */ +CVAPI(void) cvFlip( const CvArr* src, CvArr* dst CV_DEFAULT(NULL), + int flip_mode CV_DEFAULT(0)); +#define cvMirror cvFlip + + +#define CV_SVD_MODIFY_A 1 +#define CV_SVD_U_T 2 +#define CV_SVD_V_T 4 + +/** Performs Singular Value Decomposition of a matrix */ +CVAPI(void) cvSVD( CvArr* A, CvArr* W, CvArr* U CV_DEFAULT(NULL), + CvArr* V CV_DEFAULT(NULL), int flags CV_DEFAULT(0)); + +/** Performs Singular Value Back Substitution (solves A*X = B): + flags must be the same as in cvSVD */ +CVAPI(void) cvSVBkSb( const CvArr* W, const CvArr* U, + const CvArr* V, const CvArr* B, + CvArr* X, int flags ); + +#define CV_LU 0 +#define CV_SVD 1 +#define CV_SVD_SYM 2 +#define CV_CHOLESKY 3 +#define CV_QR 4 +#define CV_NORMAL 16 + +/** Inverts matrix */ +CVAPI(double) cvInvert( const CvArr* src, CvArr* dst, + int method CV_DEFAULT(CV_LU)); +#define cvInv cvInvert + +/** Solves linear system (src1)*(dst) = (src2) + (returns 0 if src1 is a singular and CV_LU method is used) */ +CVAPI(int) cvSolve( const CvArr* src1, const CvArr* src2, CvArr* dst, + int method CV_DEFAULT(CV_LU)); + +/** Calculates determinant of input matrix */ +CVAPI(double) cvDet( const CvArr* mat ); + +/** Calculates trace of the matrix (sum of elements on the main diagonal) */ +CVAPI(CvScalar) cvTrace( const CvArr* mat ); + +/** Finds eigen values and vectors of a symmetric matrix */ +CVAPI(void) cvEigenVV( CvArr* mat, CvArr* evects, CvArr* evals, + double eps CV_DEFAULT(0), + int lowindex CV_DEFAULT(-1), + int highindex CV_DEFAULT(-1)); + +///* Finds selected eigen values and vectors of a symmetric matrix */ +//CVAPI(void) cvSelectedEigenVV( CvArr* mat, CvArr* evects, CvArr* evals, +// int lowindex, int highindex ); + +/** Makes an identity matrix (mat_ij = i == j) */ +CVAPI(void) cvSetIdentity( CvArr* mat, CvScalar value CV_DEFAULT(cvRealScalar(1)) ); + +/** Fills matrix with given range of numbers */ +CVAPI(CvArr*) cvRange( CvArr* mat, double start, double end ); + +/** @anchor core_c_CovarFlags +@name Flags for cvCalcCovarMatrix +@see cvCalcCovarMatrix + @{ +*/ + +/** flag for cvCalcCovarMatrix, transpose([v1-avg, v2-avg,...]) * [v1-avg,v2-avg,...] */ +#define CV_COVAR_SCRAMBLED 0 + +/** flag for cvCalcCovarMatrix, [v1-avg, v2-avg,...] * transpose([v1-avg,v2-avg,...]) */ +#define CV_COVAR_NORMAL 1 + +/** flag for cvCalcCovarMatrix, do not calc average (i.e. mean vector) - use the input vector instead + (useful for calculating covariance matrix by parts) */ +#define CV_COVAR_USE_AVG 2 + +/** flag for cvCalcCovarMatrix, scale the covariance matrix coefficients by number of the vectors */ +#define CV_COVAR_SCALE 4 + +/** flag for cvCalcCovarMatrix, all the input vectors are stored in a single matrix, as its rows */ +#define CV_COVAR_ROWS 8 + +/** flag for cvCalcCovarMatrix, all the input vectors are stored in a single matrix, as its columns */ +#define CV_COVAR_COLS 16 + +/** @} */ + +/** Calculates covariation matrix for a set of vectors +@see @ref core_c_CovarFlags "flags" +*/ +CVAPI(void) cvCalcCovarMatrix( const CvArr** vects, int count, + CvArr* cov_mat, CvArr* avg, int flags ); + +#define CV_PCA_DATA_AS_ROW 0 +#define CV_PCA_DATA_AS_COL 1 +#define CV_PCA_USE_AVG 2 +CVAPI(void) cvCalcPCA( const CvArr* data, CvArr* mean, + CvArr* eigenvals, CvArr* eigenvects, int flags ); + +CVAPI(void) cvProjectPCA( const CvArr* data, const CvArr* mean, + const CvArr* eigenvects, CvArr* result ); + +CVAPI(void) cvBackProjectPCA( const CvArr* proj, const CvArr* mean, + const CvArr* eigenvects, CvArr* result ); + +/** Calculates Mahalanobis(weighted) distance */ +CVAPI(double) cvMahalanobis( const CvArr* vec1, const CvArr* vec2, const CvArr* mat ); +#define cvMahalonobis cvMahalanobis + +/****************************************************************************************\ +* Array Statistics * +\****************************************************************************************/ + +/** Finds sum of array elements */ +CVAPI(CvScalar) cvSum( const CvArr* arr ); + +/** Calculates number of non-zero pixels */ +CVAPI(int) cvCountNonZero( const CvArr* arr ); + +/** Calculates mean value of array elements */ +CVAPI(CvScalar) cvAvg( const CvArr* arr, const CvArr* mask CV_DEFAULT(NULL) ); + +/** Calculates mean and standard deviation of pixel values */ +CVAPI(void) cvAvgSdv( const CvArr* arr, CvScalar* mean, CvScalar* std_dev, + const CvArr* mask CV_DEFAULT(NULL) ); + +/** Finds global minimum, maximum and their positions */ +CVAPI(void) cvMinMaxLoc( const CvArr* arr, double* min_val, double* max_val, + CvPoint* min_loc CV_DEFAULT(NULL), + CvPoint* max_loc CV_DEFAULT(NULL), + const CvArr* mask CV_DEFAULT(NULL) ); + +/** @anchor core_c_NormFlags + @name Flags for cvNorm and cvNormalize + @{ +*/ +#define CV_C 1 +#define CV_L1 2 +#define CV_L2 4 +#define CV_NORM_MASK 7 +#define CV_RELATIVE 8 +#define CV_DIFF 16 +#define CV_MINMAX 32 + +#define CV_DIFF_C (CV_DIFF | CV_C) +#define CV_DIFF_L1 (CV_DIFF | CV_L1) +#define CV_DIFF_L2 (CV_DIFF | CV_L2) +#define CV_RELATIVE_C (CV_RELATIVE | CV_C) +#define CV_RELATIVE_L1 (CV_RELATIVE | CV_L1) +#define CV_RELATIVE_L2 (CV_RELATIVE | CV_L2) +/** @} */ + +/** Finds norm, difference norm or relative difference norm for an array (or two arrays) +@see ref core_c_NormFlags "flags" +*/ +CVAPI(double) cvNorm( const CvArr* arr1, const CvArr* arr2 CV_DEFAULT(NULL), + int norm_type CV_DEFAULT(CV_L2), + const CvArr* mask CV_DEFAULT(NULL) ); + +/** @see ref core_c_NormFlags "flags" */ +CVAPI(void) cvNormalize( const CvArr* src, CvArr* dst, + double a CV_DEFAULT(1.), double b CV_DEFAULT(0.), + int norm_type CV_DEFAULT(CV_L2), + const CvArr* mask CV_DEFAULT(NULL) ); + +/** @anchor core_c_ReduceFlags + @name Flags for cvReduce + @{ +*/ +#define CV_REDUCE_SUM 0 +#define CV_REDUCE_AVG 1 +#define CV_REDUCE_MAX 2 +#define CV_REDUCE_MIN 3 +/** @} */ + +/** @see @ref core_c_ReduceFlags "flags" */ +CVAPI(void) cvReduce( const CvArr* src, CvArr* dst, int dim CV_DEFAULT(-1), + int op CV_DEFAULT(CV_REDUCE_SUM) ); + +/****************************************************************************************\ +* Discrete Linear Transforms and Related Functions * +\****************************************************************************************/ + +/** @anchor core_c_DftFlags + @name Flags for cvDFT, cvDCT and cvMulSpectrums + @{ + */ +#define CV_DXT_FORWARD 0 +#define CV_DXT_INVERSE 1 +#define CV_DXT_SCALE 2 /**< divide result by size of array */ +#define CV_DXT_INV_SCALE (CV_DXT_INVERSE + CV_DXT_SCALE) +#define CV_DXT_INVERSE_SCALE CV_DXT_INV_SCALE +#define CV_DXT_ROWS 4 /**< transform each row individually */ +#define CV_DXT_MUL_CONJ 8 /**< conjugate the second argument of cvMulSpectrums */ +/** @} */ + +/** Discrete Fourier Transform: + complex->complex, + real->ccs (forward), + ccs->real (inverse) +@see core_c_DftFlags "flags" +*/ +CVAPI(void) cvDFT( const CvArr* src, CvArr* dst, int flags, + int nonzero_rows CV_DEFAULT(0) ); +#define cvFFT cvDFT + +/** Multiply results of DFTs: DFT(X)*DFT(Y) or DFT(X)*conj(DFT(Y)) +@see core_c_DftFlags "flags" +*/ +CVAPI(void) cvMulSpectrums( const CvArr* src1, const CvArr* src2, + CvArr* dst, int flags ); + +/** Finds optimal DFT vector size >= size0 */ +CVAPI(int) cvGetOptimalDFTSize( int size0 ); + +/** Discrete Cosine Transform +@see core_c_DftFlags "flags" +*/ +CVAPI(void) cvDCT( const CvArr* src, CvArr* dst, int flags ); + +/****************************************************************************************\ +* Dynamic data structures * +\****************************************************************************************/ + +/** Calculates length of sequence slice (with support of negative indices). */ +CVAPI(int) cvSliceLength( CvSlice slice, const CvSeq* seq ); + + +/** Creates new memory storage. + block_size == 0 means that default, + somewhat optimal size, is used (currently, it is 64K) */ +CVAPI(CvMemStorage*) cvCreateMemStorage( int block_size CV_DEFAULT(0)); + + +/** Creates a memory storage that will borrow memory blocks from parent storage */ +CVAPI(CvMemStorage*) cvCreateChildMemStorage( CvMemStorage* parent ); + + +/** Releases memory storage. All the children of a parent must be released before + the parent. A child storage returns all the blocks to parent when it is released */ +CVAPI(void) cvReleaseMemStorage( CvMemStorage** storage ); + + +/** Clears memory storage. This is the only way(!!!) (besides cvRestoreMemStoragePos) + to reuse memory allocated for the storage - cvClearSeq,cvClearSet ... + do not free any memory. + A child storage returns all the blocks to the parent when it is cleared */ +CVAPI(void) cvClearMemStorage( CvMemStorage* storage ); + +/** Remember a storage "free memory" position */ +CVAPI(void) cvSaveMemStoragePos( const CvMemStorage* storage, CvMemStoragePos* pos ); + +/** Restore a storage "free memory" position */ +CVAPI(void) cvRestoreMemStoragePos( CvMemStorage* storage, CvMemStoragePos* pos ); + +/** Allocates continuous buffer of the specified size in the storage */ +CVAPI(void*) cvMemStorageAlloc( CvMemStorage* storage, size_t size ); + +/** Allocates string in memory storage */ +CVAPI(CvString) cvMemStorageAllocString( CvMemStorage* storage, const char* ptr, + int len CV_DEFAULT(-1) ); + +/** Creates new empty sequence that will reside in the specified storage */ +CVAPI(CvSeq*) cvCreateSeq( int seq_flags, size_t header_size, + size_t elem_size, CvMemStorage* storage ); + +/** Changes default size (granularity) of sequence blocks. + The default size is ~1Kbyte */ +CVAPI(void) cvSetSeqBlockSize( CvSeq* seq, int delta_elems ); + + +/** Adds new element to the end of sequence. Returns pointer to the element */ +CVAPI(schar*) cvSeqPush( CvSeq* seq, const void* element CV_DEFAULT(NULL)); + + +/** Adds new element to the beginning of sequence. Returns pointer to it */ +CVAPI(schar*) cvSeqPushFront( CvSeq* seq, const void* element CV_DEFAULT(NULL)); + + +/** Removes the last element from sequence and optionally saves it */ +CVAPI(void) cvSeqPop( CvSeq* seq, void* element CV_DEFAULT(NULL)); + + +/** Removes the first element from sequence and optioanally saves it */ +CVAPI(void) cvSeqPopFront( CvSeq* seq, void* element CV_DEFAULT(NULL)); + + +#define CV_FRONT 1 +#define CV_BACK 0 +/** Adds several new elements to the end of sequence */ +CVAPI(void) cvSeqPushMulti( CvSeq* seq, const void* elements, + int count, int in_front CV_DEFAULT(0) ); + +/** Removes several elements from the end of sequence and optionally saves them */ +CVAPI(void) cvSeqPopMulti( CvSeq* seq, void* elements, + int count, int in_front CV_DEFAULT(0) ); + +/** Inserts a new element in the middle of sequence. + cvSeqInsert(seq,0,elem) == cvSeqPushFront(seq,elem) */ +CVAPI(schar*) cvSeqInsert( CvSeq* seq, int before_index, + const void* element CV_DEFAULT(NULL)); + +/** Removes specified sequence element */ +CVAPI(void) cvSeqRemove( CvSeq* seq, int index ); + + +/** Removes all the elements from the sequence. The freed memory + can be reused later only by the same sequence unless cvClearMemStorage + or cvRestoreMemStoragePos is called */ +CVAPI(void) cvClearSeq( CvSeq* seq ); + + +/** Retrieves pointer to specified sequence element. + Negative indices are supported and mean counting from the end + (e.g -1 means the last sequence element) */ +CVAPI(schar*) cvGetSeqElem( const CvSeq* seq, int index ); + +/** Calculates index of the specified sequence element. + Returns -1 if element does not belong to the sequence */ +CVAPI(int) cvSeqElemIdx( const CvSeq* seq, const void* element, + CvSeqBlock** block CV_DEFAULT(NULL) ); + +/** Initializes sequence writer. The new elements will be added to the end of sequence */ +CVAPI(void) cvStartAppendToSeq( CvSeq* seq, CvSeqWriter* writer ); + + +/** Combination of cvCreateSeq and cvStartAppendToSeq */ +CVAPI(void) cvStartWriteSeq( int seq_flags, int header_size, + int elem_size, CvMemStorage* storage, + CvSeqWriter* writer ); + +/** Closes sequence writer, updates sequence header and returns pointer + to the resultant sequence + (which may be useful if the sequence was created using cvStartWriteSeq)) +*/ +CVAPI(CvSeq*) cvEndWriteSeq( CvSeqWriter* writer ); + + +/** Updates sequence header. May be useful to get access to some of previously + written elements via cvGetSeqElem or sequence reader */ +CVAPI(void) cvFlushSeqWriter( CvSeqWriter* writer ); + + +/** Initializes sequence reader. + The sequence can be read in forward or backward direction */ +CVAPI(void) cvStartReadSeq( const CvSeq* seq, CvSeqReader* reader, + int reverse CV_DEFAULT(0) ); + + +/** Returns current sequence reader position (currently observed sequence element) */ +CVAPI(int) cvGetSeqReaderPos( CvSeqReader* reader ); + + +/** Changes sequence reader position. It may seek to an absolute or + to relative to the current position */ +CVAPI(void) cvSetSeqReaderPos( CvSeqReader* reader, int index, + int is_relative CV_DEFAULT(0)); + +/** Copies sequence content to a continuous piece of memory */ +CVAPI(void*) cvCvtSeqToArray( const CvSeq* seq, void* elements, + CvSlice slice CV_DEFAULT(CV_WHOLE_SEQ) ); + +/** Creates sequence header for array. + After that all the operations on sequences that do not alter the content + can be applied to the resultant sequence */ +CVAPI(CvSeq*) cvMakeSeqHeaderForArray( int seq_type, int header_size, + int elem_size, void* elements, int total, + CvSeq* seq, CvSeqBlock* block ); + +/** Extracts sequence slice (with or without copying sequence elements) */ +CVAPI(CvSeq*) cvSeqSlice( const CvSeq* seq, CvSlice slice, + CvMemStorage* storage CV_DEFAULT(NULL), + int copy_data CV_DEFAULT(0)); + +CV_INLINE CvSeq* cvCloneSeq( const CvSeq* seq, CvMemStorage* storage CV_DEFAULT(NULL)) +{ + return cvSeqSlice( seq, CV_WHOLE_SEQ, storage, 1 ); +} + +/** Removes sequence slice */ +CVAPI(void) cvSeqRemoveSlice( CvSeq* seq, CvSlice slice ); + +/** Inserts a sequence or array into another sequence */ +CVAPI(void) cvSeqInsertSlice( CvSeq* seq, int before_index, const CvArr* from_arr ); + +/** a < b ? -1 : a > b ? 1 : 0 */ +typedef int (CV_CDECL* CvCmpFunc)(const void* a, const void* b, void* userdata ); + +/** Sorts sequence in-place given element comparison function */ +CVAPI(void) cvSeqSort( CvSeq* seq, CvCmpFunc func, void* userdata CV_DEFAULT(NULL) ); + +/** Finds element in a [sorted] sequence */ +CVAPI(schar*) cvSeqSearch( CvSeq* seq, const void* elem, CvCmpFunc func, + int is_sorted, int* elem_idx, + void* userdata CV_DEFAULT(NULL) ); + +/** Reverses order of sequence elements in-place */ +CVAPI(void) cvSeqInvert( CvSeq* seq ); + +/** Splits sequence into one or more equivalence classes using the specified criteria */ +CVAPI(int) cvSeqPartition( const CvSeq* seq, CvMemStorage* storage, + CvSeq** labels, CvCmpFunc is_equal, void* userdata ); + +/************ Internal sequence functions ************/ +CVAPI(void) cvChangeSeqBlock( void* reader, int direction ); +CVAPI(void) cvCreateSeqBlock( CvSeqWriter* writer ); + + +/** Creates a new set */ +CVAPI(CvSet*) cvCreateSet( int set_flags, int header_size, + int elem_size, CvMemStorage* storage ); + +/** Adds new element to the set and returns pointer to it */ +CVAPI(int) cvSetAdd( CvSet* set_header, CvSetElem* elem CV_DEFAULT(NULL), + CvSetElem** inserted_elem CV_DEFAULT(NULL) ); + +/** Fast variant of cvSetAdd */ +CV_INLINE CvSetElem* cvSetNew( CvSet* set_header ) +{ + CvSetElem* elem = set_header->free_elems; + if( elem ) + { + set_header->free_elems = elem->next_free; + elem->flags = elem->flags & CV_SET_ELEM_IDX_MASK; + set_header->active_count++; + } + else + cvSetAdd( set_header, NULL, &elem ); + return elem; +} + +/** Removes set element given its pointer */ +CV_INLINE void cvSetRemoveByPtr( CvSet* set_header, void* elem ) +{ + CvSetElem* _elem = (CvSetElem*)elem; + assert( _elem->flags >= 0 /*&& (elem->flags & CV_SET_ELEM_IDX_MASK) < set_header->total*/ ); + _elem->next_free = set_header->free_elems; + _elem->flags = (_elem->flags & CV_SET_ELEM_IDX_MASK) | CV_SET_ELEM_FREE_FLAG; + set_header->free_elems = _elem; + set_header->active_count--; +} + +/** Removes element from the set by its index */ +CVAPI(void) cvSetRemove( CvSet* set_header, int index ); + +/** Returns a set element by index. If the element doesn't belong to the set, + NULL is returned */ +CV_INLINE CvSetElem* cvGetSetElem( const CvSet* set_header, int idx ) +{ + CvSetElem* elem = (CvSetElem*)(void *)cvGetSeqElem( (CvSeq*)set_header, idx ); + return elem && CV_IS_SET_ELEM( elem ) ? elem : 0; +} + +/** Removes all the elements from the set */ +CVAPI(void) cvClearSet( CvSet* set_header ); + +/** Creates new graph */ +CVAPI(CvGraph*) cvCreateGraph( int graph_flags, int header_size, + int vtx_size, int edge_size, + CvMemStorage* storage ); + +/** Adds new vertex to the graph */ +CVAPI(int) cvGraphAddVtx( CvGraph* graph, const CvGraphVtx* vtx CV_DEFAULT(NULL), + CvGraphVtx** inserted_vtx CV_DEFAULT(NULL) ); + + +/** Removes vertex from the graph together with all incident edges */ +CVAPI(int) cvGraphRemoveVtx( CvGraph* graph, int index ); +CVAPI(int) cvGraphRemoveVtxByPtr( CvGraph* graph, CvGraphVtx* vtx ); + + +/** Link two vertices specified by indices or pointers if they + are not connected or return pointer to already existing edge + connecting the vertices. + Functions return 1 if a new edge was created, 0 otherwise */ +CVAPI(int) cvGraphAddEdge( CvGraph* graph, + int start_idx, int end_idx, + const CvGraphEdge* edge CV_DEFAULT(NULL), + CvGraphEdge** inserted_edge CV_DEFAULT(NULL) ); + +CVAPI(int) cvGraphAddEdgeByPtr( CvGraph* graph, + CvGraphVtx* start_vtx, CvGraphVtx* end_vtx, + const CvGraphEdge* edge CV_DEFAULT(NULL), + CvGraphEdge** inserted_edge CV_DEFAULT(NULL) ); + +/** Remove edge connecting two vertices */ +CVAPI(void) cvGraphRemoveEdge( CvGraph* graph, int start_idx, int end_idx ); +CVAPI(void) cvGraphRemoveEdgeByPtr( CvGraph* graph, CvGraphVtx* start_vtx, + CvGraphVtx* end_vtx ); + +/** Find edge connecting two vertices */ +CVAPI(CvGraphEdge*) cvFindGraphEdge( const CvGraph* graph, int start_idx, int end_idx ); +CVAPI(CvGraphEdge*) cvFindGraphEdgeByPtr( const CvGraph* graph, + const CvGraphVtx* start_vtx, + const CvGraphVtx* end_vtx ); +#define cvGraphFindEdge cvFindGraphEdge +#define cvGraphFindEdgeByPtr cvFindGraphEdgeByPtr + +/** Remove all vertices and edges from the graph */ +CVAPI(void) cvClearGraph( CvGraph* graph ); + + +/** Count number of edges incident to the vertex */ +CVAPI(int) cvGraphVtxDegree( const CvGraph* graph, int vtx_idx ); +CVAPI(int) cvGraphVtxDegreeByPtr( const CvGraph* graph, const CvGraphVtx* vtx ); + + +/** Retrieves graph vertex by given index */ +#define cvGetGraphVtx( graph, idx ) (CvGraphVtx*)cvGetSetElem((CvSet*)(graph), (idx)) + +/** Retrieves index of a graph vertex given its pointer */ +#define cvGraphVtxIdx( graph, vtx ) ((vtx)->flags & CV_SET_ELEM_IDX_MASK) + +/** Retrieves index of a graph edge given its pointer */ +#define cvGraphEdgeIdx( graph, edge ) ((edge)->flags & CV_SET_ELEM_IDX_MASK) + +#define cvGraphGetVtxCount( graph ) ((graph)->active_count) +#define cvGraphGetEdgeCount( graph ) ((graph)->edges->active_count) + +#define CV_GRAPH_VERTEX 1 +#define CV_GRAPH_TREE_EDGE 2 +#define CV_GRAPH_BACK_EDGE 4 +#define CV_GRAPH_FORWARD_EDGE 8 +#define CV_GRAPH_CROSS_EDGE 16 +#define CV_GRAPH_ANY_EDGE 30 +#define CV_GRAPH_NEW_TREE 32 +#define CV_GRAPH_BACKTRACKING 64 +#define CV_GRAPH_OVER -1 + +#define CV_GRAPH_ALL_ITEMS -1 + +/** flags for graph vertices and edges */ +#define CV_GRAPH_ITEM_VISITED_FLAG (1 << 30) +#define CV_IS_GRAPH_VERTEX_VISITED(vtx) \ + (((CvGraphVtx*)(vtx))->flags & CV_GRAPH_ITEM_VISITED_FLAG) +#define CV_IS_GRAPH_EDGE_VISITED(edge) \ + (((CvGraphEdge*)(edge))->flags & CV_GRAPH_ITEM_VISITED_FLAG) +#define CV_GRAPH_SEARCH_TREE_NODE_FLAG (1 << 29) +#define CV_GRAPH_FORWARD_EDGE_FLAG (1 << 28) + +typedef struct CvGraphScanner +{ + CvGraphVtx* vtx; /* current graph vertex (or current edge origin) */ + CvGraphVtx* dst; /* current graph edge destination vertex */ + CvGraphEdge* edge; /* current edge */ + + CvGraph* graph; /* the graph */ + CvSeq* stack; /* the graph vertex stack */ + int index; /* the lower bound of certainly visited vertices */ + int mask; /* event mask */ +} +CvGraphScanner; + +/** Creates new graph scanner. */ +CVAPI(CvGraphScanner*) cvCreateGraphScanner( CvGraph* graph, + CvGraphVtx* vtx CV_DEFAULT(NULL), + int mask CV_DEFAULT(CV_GRAPH_ALL_ITEMS)); + +/** Releases graph scanner. */ +CVAPI(void) cvReleaseGraphScanner( CvGraphScanner** scanner ); + +/** Get next graph element */ +CVAPI(int) cvNextGraphItem( CvGraphScanner* scanner ); + +/** Creates a copy of graph */ +CVAPI(CvGraph*) cvCloneGraph( const CvGraph* graph, CvMemStorage* storage ); + + +/** Does look-up transformation. Elements of the source array + (that should be 8uC1 or 8sC1) are used as indexes in lutarr 256-element table */ +CVAPI(void) cvLUT( const CvArr* src, CvArr* dst, const CvArr* lut ); + + +/******************* Iteration through the sequence tree *****************/ +typedef struct CvTreeNodeIterator +{ + const void* node; + int level; + int max_level; +} +CvTreeNodeIterator; + +CVAPI(void) cvInitTreeNodeIterator( CvTreeNodeIterator* tree_iterator, + const void* first, int max_level ); +CVAPI(void*) cvNextTreeNode( CvTreeNodeIterator* tree_iterator ); +CVAPI(void*) cvPrevTreeNode( CvTreeNodeIterator* tree_iterator ); + +/** Inserts sequence into tree with specified "parent" sequence. + If parent is equal to frame (e.g. the most external contour), + then added contour will have null pointer to parent. */ +CVAPI(void) cvInsertNodeIntoTree( void* node, void* parent, void* frame ); + +/** Removes contour from tree (together with the contour children). */ +CVAPI(void) cvRemoveNodeFromTree( void* node, void* frame ); + +/** Gathers pointers to all the sequences, + accessible from the `first`, to the single sequence */ +CVAPI(CvSeq*) cvTreeToNodeSeq( const void* first, int header_size, + CvMemStorage* storage ); + +/** The function implements the K-means algorithm for clustering an array of sample + vectors in a specified number of classes */ +#define CV_KMEANS_USE_INITIAL_LABELS 1 +CVAPI(int) cvKMeans2( const CvArr* samples, int cluster_count, CvArr* labels, + CvTermCriteria termcrit, int attempts CV_DEFAULT(1), + CvRNG* rng CV_DEFAULT(0), int flags CV_DEFAULT(0), + CvArr* _centers CV_DEFAULT(0), double* compactness CV_DEFAULT(0) ); + +/****************************************************************************************\ +* System functions * +\****************************************************************************************/ + +/** Loads optimized functions from IPP, MKL etc. or switches back to pure C code */ +CVAPI(int) cvUseOptimized( int on_off ); + +typedef IplImage* (CV_STDCALL* Cv_iplCreateImageHeader) + (int,int,int,char*,char*,int,int,int,int,int, + IplROI*,IplImage*,void*,IplTileInfo*); +typedef void (CV_STDCALL* Cv_iplAllocateImageData)(IplImage*,int,int); +typedef void (CV_STDCALL* Cv_iplDeallocate)(IplImage*,int); +typedef IplROI* (CV_STDCALL* Cv_iplCreateROI)(int,int,int,int,int); +typedef IplImage* (CV_STDCALL* Cv_iplCloneImage)(const IplImage*); + +/** @brief Makes OpenCV use IPL functions for allocating IplImage and IplROI structures. + +Normally, the function is not called directly. Instead, a simple macro +CV_TURN_ON_IPL_COMPATIBILITY() is used that calls cvSetIPLAllocators and passes there pointers +to IPL allocation functions. : +@code + ... + CV_TURN_ON_IPL_COMPATIBILITY() + ... +@endcode +@param create_header pointer to a function, creating IPL image header. +@param allocate_data pointer to a function, allocating IPL image data. +@param deallocate pointer to a function, deallocating IPL image. +@param create_roi pointer to a function, creating IPL image ROI (i.e. Region of Interest). +@param clone_image pointer to a function, cloning an IPL image. + */ +CVAPI(void) cvSetIPLAllocators( Cv_iplCreateImageHeader create_header, + Cv_iplAllocateImageData allocate_data, + Cv_iplDeallocate deallocate, + Cv_iplCreateROI create_roi, + Cv_iplCloneImage clone_image ); + +#define CV_TURN_ON_IPL_COMPATIBILITY() \ + cvSetIPLAllocators( iplCreateImageHeader, iplAllocateImage, \ + iplDeallocate, iplCreateROI, iplCloneImage ) + +/****************************************************************************************\ +* Data Persistence * +\****************************************************************************************/ + +/********************************** High-level functions ********************************/ + +/** @brief Opens file storage for reading or writing data. + +The function opens file storage for reading or writing data. In the latter case, a new file is +created or an existing file is rewritten. The type of the read or written file is determined by the +filename extension: .xml for XML, .yml or .yaml for YAML and .json for JSON. + +At the same time, it also supports adding parameters like "example.xml?base64". + +The function returns a pointer to the CvFileStorage structure. +If the file cannot be opened then the function returns NULL. +@param filename Name of the file associated with the storage +@param memstorage Memory storage used for temporary data and for +: storing dynamic structures, such as CvSeq or CvGraph . If it is NULL, a temporary memory + storage is created and used. +@param flags Can be one of the following: +> - **CV_STORAGE_READ** the storage is open for reading +> - **CV_STORAGE_WRITE** the storage is open for writing + (use **CV_STORAGE_WRITE | CV_STORAGE_WRITE_BASE64** to write rawdata in Base64) +@param encoding + */ +CVAPI(CvFileStorage*) cvOpenFileStorage( const char* filename, CvMemStorage* memstorage, + int flags, const char* encoding CV_DEFAULT(NULL) ); + +/** @brief Releases file storage. + +The function closes the file associated with the storage and releases all the temporary structures. +It must be called after all I/O operations with the storage are finished. +@param fs Double pointer to the released file storage + */ +CVAPI(void) cvReleaseFileStorage( CvFileStorage** fs ); + +/** returns attribute value or 0 (NULL) if there is no such attribute */ +CVAPI(const char*) cvAttrValue( const CvAttrList* attr, const char* attr_name ); + +/** @brief Starts writing a new structure. + +The function starts writing a compound structure (collection) that can be a sequence or a map. After +all the structure fields, which can be scalars or structures, are written, cvEndWriteStruct should +be called. The function can be used to group some objects or to implement the write function for a +some user object (see CvTypeInfo). +@param fs File storage +@param name Name of the written structure. The structure can be accessed by this name when the +storage is read. +@param struct_flags A combination one of the following values: +- **CV_NODE_SEQ** the written structure is a sequence (see discussion of CvFileStorage ), + that is, its elements do not have a name. +- **CV_NODE_MAP** the written structure is a map (see discussion of CvFileStorage ), that + is, all its elements have names. +One and only one of the two above flags must be specified +- **CV_NODE_FLOW** the optional flag that makes sense only for YAML streams. It means that + the structure is written as a flow (not as a block), which is more compact. It is + recommended to use this flag for structures or arrays whose elements are all scalars. +@param type_name Optional parameter - the object type name. In + case of XML it is written as a type_id attribute of the structure opening tag. In the case of + YAML it is written after a colon following the structure name (see the example in + CvFileStorage description). In case of JSON it is written as a name/value pair. + Mainly it is used with user objects. When the storage is read, the + encoded type name is used to determine the object type (see CvTypeInfo and cvFindType ). +@param attributes This parameter is not used in the current implementation + */ +CVAPI(void) cvStartWriteStruct( CvFileStorage* fs, const char* name, + int struct_flags, const char* type_name CV_DEFAULT(NULL), + CvAttrList attributes CV_DEFAULT(cvAttrList())); + +/** @brief Finishes writing to a file node collection. +@param fs File storage +@sa cvStartWriteStruct. + */ +CVAPI(void) cvEndWriteStruct( CvFileStorage* fs ); + +/** @brief Writes an integer value. + +The function writes a single integer value (with or without a name) to the file storage. +@param fs File storage +@param name Name of the written value. Should be NULL if and only if the parent structure is a +sequence. +@param value The written value + */ +CVAPI(void) cvWriteInt( CvFileStorage* fs, const char* name, int value ); + +/** @brief Writes a floating-point value. + +The function writes a single floating-point value (with or without a name) to file storage. Special +values are encoded as follows: NaN (Not A Number) as .NaN, infinity as +.Inf or -.Inf. + +The following example shows how to use the low-level writing functions to store custom structures, +such as termination criteria, without registering a new type. : +@code + void write_termcriteria( CvFileStorage* fs, const char* struct_name, + CvTermCriteria* termcrit ) + { + cvStartWriteStruct( fs, struct_name, CV_NODE_MAP, NULL, cvAttrList(0,0)); + cvWriteComment( fs, "termination criteria", 1 ); // just a description + if( termcrit->type & CV_TERMCRIT_ITER ) + cvWriteInteger( fs, "max_iterations", termcrit->max_iter ); + if( termcrit->type & CV_TERMCRIT_EPS ) + cvWriteReal( fs, "accuracy", termcrit->epsilon ); + cvEndWriteStruct( fs ); + } +@endcode +@param fs File storage +@param name Name of the written value. Should be NULL if and only if the parent structure is a +sequence. +@param value The written value +*/ +CVAPI(void) cvWriteReal( CvFileStorage* fs, const char* name, double value ); + +/** @brief Writes a text string. + +The function writes a text string to file storage. +@param fs File storage +@param name Name of the written string . Should be NULL if and only if the parent structure is a +sequence. +@param str The written text string +@param quote If non-zero, the written string is put in quotes, regardless of whether they are +required. Otherwise, if the flag is zero, quotes are used only when they are required (e.g. when +the string starts with a digit or contains spaces). + */ +CVAPI(void) cvWriteString( CvFileStorage* fs, const char* name, + const char* str, int quote CV_DEFAULT(0) ); + +/** @brief Writes a comment. + +The function writes a comment into file storage. The comments are skipped when the storage is read. +@param fs File storage +@param comment The written comment, single-line or multi-line +@param eol_comment If non-zero, the function tries to put the comment at the end of current line. +If the flag is zero, if the comment is multi-line, or if it does not fit at the end of the current +line, the comment starts a new line. + */ +CVAPI(void) cvWriteComment( CvFileStorage* fs, const char* comment, + int eol_comment ); + +/** @brief Writes an object to file storage. + +The function writes an object to file storage. First, the appropriate type info is found using +cvTypeOf. Then, the write method associated with the type info is called. + +Attributes are used to customize the writing procedure. The standard types support the following +attributes (all the dt attributes have the same format as in cvWriteRawData): + +-# CvSeq + - **header_dt** description of user fields of the sequence header that follow CvSeq, or + CvChain (if the sequence is a Freeman chain) or CvContour (if the sequence is a contour or + point sequence) + - **dt** description of the sequence elements. + - **recursive** if the attribute is present and is not equal to "0" or "false", the whole + tree of sequences (contours) is stored. +-# CvGraph + - **header_dt** description of user fields of the graph header that follows CvGraph; + - **vertex_dt** description of user fields of graph vertices + - **edge_dt** description of user fields of graph edges (note that the edge weight is + always written, so there is no need to specify it explicitly) + +Below is the code that creates the YAML file shown in the CvFileStorage description: +@code + #include "cxcore.h" + + int main( int argc, char** argv ) + { + CvMat* mat = cvCreateMat( 3, 3, CV_32F ); + CvFileStorage* fs = cvOpenFileStorage( "example.yml", 0, CV_STORAGE_WRITE ); + + cvSetIdentity( mat ); + cvWrite( fs, "A", mat, cvAttrList(0,0) ); + + cvReleaseFileStorage( &fs ); + cvReleaseMat( &mat ); + return 0; + } +@endcode +@param fs File storage +@param name Name of the written object. Should be NULL if and only if the parent structure is a +sequence. +@param ptr Pointer to the object +@param attributes The attributes of the object. They are specific for each particular type (see +the discussion below). + */ +CVAPI(void) cvWrite( CvFileStorage* fs, const char* name, const void* ptr, + CvAttrList attributes CV_DEFAULT(cvAttrList())); + +/** @brief Starts the next stream. + +The function finishes the currently written stream and starts the next stream. In the case of XML +the file with multiple streams looks like this: +@code{.xml} + + + + + + + ... +@endcode +The YAML file will look like this: +@code{.yaml} + %YAML 1.0 + # stream #1 data + ... + --- + # stream #2 data +@endcode +This is useful for concatenating files or for resuming the writing process. +@param fs File storage + */ +CVAPI(void) cvStartNextStream( CvFileStorage* fs ); + +/** @brief Writes multiple numbers. + +The function writes an array, whose elements consist of single or multiple numbers. The function +call can be replaced with a loop containing a few cvWriteInt and cvWriteReal calls, but a single +call is more efficient. Note that because none of the elements have a name, they should be written +to a sequence rather than a map. +@param fs File storage +@param src Pointer to the written array +@param len Number of the array elements to write +@param dt Specification of each array element, see @ref format_spec "format specification" + */ +CVAPI(void) cvWriteRawData( CvFileStorage* fs, const void* src, + int len, const char* dt ); + +/** @brief Writes multiple numbers in Base64. + +If either CV_STORAGE_WRITE_BASE64 or cv::FileStorage::WRITE_BASE64 is used, +this function will be the same as cvWriteRawData. If neither, the main +difference is that it outputs a sequence in Base64 encoding rather than +in plain text. + +This function can only be used to write a sequence with a type "binary". + +@param fs File storage +@param src Pointer to the written array +@param len Number of the array elements to write +@param dt Specification of each array element, see @ref format_spec "format specification" +*/ +CVAPI(void) cvWriteRawDataBase64( CvFileStorage* fs, const void* src, + int len, const char* dt ); + +/** @brief Returns a unique pointer for a given name. + +The function returns a unique pointer for each particular file node name. This pointer can be then +passed to the cvGetFileNode function that is faster than cvGetFileNodeByName because it compares +text strings by comparing pointers rather than the strings' content. + +Consider the following example where an array of points is encoded as a sequence of 2-entry maps: +@code + points: + - { x: 10, y: 10 } + - { x: 20, y: 20 } + - { x: 30, y: 30 } + # ... +@endcode +Then, it is possible to get hashed "x" and "y" pointers to speed up decoding of the points. : +@code + #include "cxcore.h" + + int main( int argc, char** argv ) + { + CvFileStorage* fs = cvOpenFileStorage( "points.yml", 0, CV_STORAGE_READ ); + CvStringHashNode* x_key = cvGetHashedNode( fs, "x", -1, 1 ); + CvStringHashNode* y_key = cvGetHashedNode( fs, "y", -1, 1 ); + CvFileNode* points = cvGetFileNodeByName( fs, 0, "points" ); + + if( CV_NODE_IS_SEQ(points->tag) ) + { + CvSeq* seq = points->data.seq; + int i, total = seq->total; + CvSeqReader reader; + cvStartReadSeq( seq, &reader, 0 ); + for( i = 0; i < total; i++ ) + { + CvFileNode* pt = (CvFileNode*)reader.ptr; + #if 1 // faster variant + CvFileNode* xnode = cvGetFileNode( fs, pt, x_key, 0 ); + CvFileNode* ynode = cvGetFileNode( fs, pt, y_key, 0 ); + assert( xnode && CV_NODE_IS_INT(xnode->tag) && + ynode && CV_NODE_IS_INT(ynode->tag)); + int x = xnode->data.i; // or x = cvReadInt( xnode, 0 ); + int y = ynode->data.i; // or y = cvReadInt( ynode, 0 ); + #elif 1 // slower variant; does not use x_key & y_key + CvFileNode* xnode = cvGetFileNodeByName( fs, pt, "x" ); + CvFileNode* ynode = cvGetFileNodeByName( fs, pt, "y" ); + assert( xnode && CV_NODE_IS_INT(xnode->tag) && + ynode && CV_NODE_IS_INT(ynode->tag)); + int x = xnode->data.i; // or x = cvReadInt( xnode, 0 ); + int y = ynode->data.i; // or y = cvReadInt( ynode, 0 ); + #else // the slowest yet the easiest to use variant + int x = cvReadIntByName( fs, pt, "x", 0 ); + int y = cvReadIntByName( fs, pt, "y", 0 ); + #endif + CV_NEXT_SEQ_ELEM( seq->elem_size, reader ); + printf(" + } + } + cvReleaseFileStorage( &fs ); + return 0; + } +@endcode +Please note that whatever method of accessing a map you are using, it is still much slower than +using plain sequences; for example, in the above example, it is more efficient to encode the points +as pairs of integers in a single numeric sequence. +@param fs File storage +@param name Literal node name +@param len Length of the name (if it is known apriori), or -1 if it needs to be calculated +@param create_missing Flag that specifies, whether an absent key should be added into the hash table +*/ +CVAPI(CvStringHashNode*) cvGetHashedKey( CvFileStorage* fs, const char* name, + int len CV_DEFAULT(-1), + int create_missing CV_DEFAULT(0)); + +/** @brief Retrieves one of the top-level nodes of the file storage. + +The function returns one of the top-level file nodes. The top-level nodes do not have a name, they +correspond to the streams that are stored one after another in the file storage. If the index is out +of range, the function returns a NULL pointer, so all the top-level nodes can be iterated by +subsequent calls to the function with stream_index=0,1,..., until the NULL pointer is returned. +This function can be used as a base for recursive traversal of the file storage. +@param fs File storage +@param stream_index Zero-based index of the stream. See cvStartNextStream . In most cases, +there is only one stream in the file; however, there can be several. + */ +CVAPI(CvFileNode*) cvGetRootFileNode( const CvFileStorage* fs, + int stream_index CV_DEFAULT(0) ); + +/** @brief Finds a node in a map or file storage. + +The function finds a file node. It is a faster version of cvGetFileNodeByName (see +cvGetHashedKey discussion). Also, the function can insert a new node, if it is not in the map yet. +@param fs File storage +@param map The parent map. If it is NULL, the function searches a top-level node. If both map and +key are NULLs, the function returns the root file node - a map that contains top-level nodes. +@param key Unique pointer to the node name, retrieved with cvGetHashedKey +@param create_missing Flag that specifies whether an absent node should be added to the map + */ +CVAPI(CvFileNode*) cvGetFileNode( CvFileStorage* fs, CvFileNode* map, + const CvStringHashNode* key, + int create_missing CV_DEFAULT(0) ); + +/** @brief Finds a node in a map or file storage. + +The function finds a file node by name. The node is searched either in map or, if the pointer is +NULL, among the top-level file storage nodes. Using this function for maps and cvGetSeqElem (or +sequence reader) for sequences, it is possible to navigate through the file storage. To speed up +multiple queries for a certain key (e.g., in the case of an array of structures) one may use a +combination of cvGetHashedKey and cvGetFileNode. +@param fs File storage +@param map The parent map. If it is NULL, the function searches in all the top-level nodes +(streams), starting with the first one. +@param name The file node name + */ +CVAPI(CvFileNode*) cvGetFileNodeByName( const CvFileStorage* fs, + const CvFileNode* map, + const char* name ); + +/** @brief Retrieves an integer value from a file node. + +The function returns an integer that is represented by the file node. If the file node is NULL, the +default_value is returned (thus, it is convenient to call the function right after cvGetFileNode +without checking for a NULL pointer). If the file node has type CV_NODE_INT, then node-\>data.i is +returned. If the file node has type CV_NODE_REAL, then node-\>data.f is converted to an integer +and returned. Otherwise the error is reported. +@param node File node +@param default_value The value that is returned if node is NULL + */ +CV_INLINE int cvReadInt( const CvFileNode* node, int default_value CV_DEFAULT(0) ) +{ + return !node ? default_value : + CV_NODE_IS_INT(node->tag) ? node->data.i : + CV_NODE_IS_REAL(node->tag) ? cvRound(node->data.f) : 0x7fffffff; +} + +/** @brief Finds a file node and returns its value. + +The function is a simple superposition of cvGetFileNodeByName and cvReadInt. +@param fs File storage +@param map The parent map. If it is NULL, the function searches a top-level node. +@param name The node name +@param default_value The value that is returned if the file node is not found + */ +CV_INLINE int cvReadIntByName( const CvFileStorage* fs, const CvFileNode* map, + const char* name, int default_value CV_DEFAULT(0) ) +{ + return cvReadInt( cvGetFileNodeByName( fs, map, name ), default_value ); +} + +/** @brief Retrieves a floating-point value from a file node. + +The function returns a floating-point value that is represented by the file node. If the file node +is NULL, the default_value is returned (thus, it is convenient to call the function right after +cvGetFileNode without checking for a NULL pointer). If the file node has type CV_NODE_REAL , +then node-\>data.f is returned. If the file node has type CV_NODE_INT , then node-:math:\>data.f +is converted to floating-point and returned. Otherwise the result is not determined. +@param node File node +@param default_value The value that is returned if node is NULL + */ +CV_INLINE double cvReadReal( const CvFileNode* node, double default_value CV_DEFAULT(0.) ) +{ + return !node ? default_value : + CV_NODE_IS_INT(node->tag) ? (double)node->data.i : + CV_NODE_IS_REAL(node->tag) ? node->data.f : 1e300; +} + +/** @brief Finds a file node and returns its value. + +The function is a simple superposition of cvGetFileNodeByName and cvReadReal . +@param fs File storage +@param map The parent map. If it is NULL, the function searches a top-level node. +@param name The node name +@param default_value The value that is returned if the file node is not found + */ +CV_INLINE double cvReadRealByName( const CvFileStorage* fs, const CvFileNode* map, + const char* name, double default_value CV_DEFAULT(0.) ) +{ + return cvReadReal( cvGetFileNodeByName( fs, map, name ), default_value ); +} + +/** @brief Retrieves a text string from a file node. + +The function returns a text string that is represented by the file node. If the file node is NULL, +the default_value is returned (thus, it is convenient to call the function right after +cvGetFileNode without checking for a NULL pointer). If the file node has type CV_NODE_STR , then +node-:math:\>data.str.ptr is returned. Otherwise the result is not determined. +@param node File node +@param default_value The value that is returned if node is NULL + */ +CV_INLINE const char* cvReadString( const CvFileNode* node, + const char* default_value CV_DEFAULT(NULL) ) +{ + return !node ? default_value : CV_NODE_IS_STRING(node->tag) ? node->data.str.ptr : 0; +} + +/** @brief Finds a file node by its name and returns its value. + +The function is a simple superposition of cvGetFileNodeByName and cvReadString . +@param fs File storage +@param map The parent map. If it is NULL, the function searches a top-level node. +@param name The node name +@param default_value The value that is returned if the file node is not found + */ +CV_INLINE const char* cvReadStringByName( const CvFileStorage* fs, const CvFileNode* map, + const char* name, const char* default_value CV_DEFAULT(NULL) ) +{ + return cvReadString( cvGetFileNodeByName( fs, map, name ), default_value ); +} + + +/** @brief Decodes an object and returns a pointer to it. + +The function decodes a user object (creates an object in a native representation from the file +storage subtree) and returns it. The object to be decoded must be an instance of a registered type +that supports the read method (see CvTypeInfo). The type of the object is determined by the type +name that is encoded in the file. If the object is a dynamic structure, it is created either in +memory storage and passed to cvOpenFileStorage or, if a NULL pointer was passed, in temporary +memory storage, which is released when cvReleaseFileStorage is called. Otherwise, if the object is +not a dynamic structure, it is created in a heap and should be released with a specialized function +or by using the generic cvRelease. +@param fs File storage +@param node The root object node +@param attributes Unused parameter + */ +CVAPI(void*) cvRead( CvFileStorage* fs, CvFileNode* node, + CvAttrList* attributes CV_DEFAULT(NULL)); + +/** @brief Finds an object by name and decodes it. + +The function is a simple superposition of cvGetFileNodeByName and cvRead. +@param fs File storage +@param map The parent map. If it is NULL, the function searches a top-level node. +@param name The node name +@param attributes Unused parameter + */ +CV_INLINE void* cvReadByName( CvFileStorage* fs, const CvFileNode* map, + const char* name, CvAttrList* attributes CV_DEFAULT(NULL) ) +{ + return cvRead( fs, cvGetFileNodeByName( fs, map, name ), attributes ); +} + + +/** @brief Initializes the file node sequence reader. + +The function initializes the sequence reader to read data from a file node. The initialized reader +can be then passed to cvReadRawDataSlice. +@param fs File storage +@param src The file node (a sequence) to read numbers from +@param reader Pointer to the sequence reader + */ +CVAPI(void) cvStartReadRawData( const CvFileStorage* fs, const CvFileNode* src, + CvSeqReader* reader ); + +/** @brief Initializes file node sequence reader. + +The function reads one or more elements from the file node, representing a sequence, to a +user-specified array. The total number of read sequence elements is a product of total and the +number of components in each array element. For example, if dt=2if, the function will read total\*3 +sequence elements. As with any sequence, some parts of the file node sequence can be skipped or read +repeatedly by repositioning the reader using cvSetSeqReaderPos. +@param fs File storage +@param reader The sequence reader. Initialize it with cvStartReadRawData . +@param count The number of elements to read +@param dst Pointer to the destination array +@param dt Specification of each array element. It has the same format as in cvWriteRawData . + */ +CVAPI(void) cvReadRawDataSlice( const CvFileStorage* fs, CvSeqReader* reader, + int count, void* dst, const char* dt ); + +/** @brief Reads multiple numbers. + +The function reads elements from a file node that represents a sequence of scalars. +@param fs File storage +@param src The file node (a sequence) to read numbers from +@param dst Pointer to the destination array +@param dt Specification of each array element. It has the same format as in cvWriteRawData . + */ +CVAPI(void) cvReadRawData( const CvFileStorage* fs, const CvFileNode* src, + void* dst, const char* dt ); + +/** @brief Writes a file node to another file storage. + +The function writes a copy of a file node to file storage. Possible applications of the function are +merging several file storages into one and conversion between XML, YAML and JSON formats. +@param fs Destination file storage +@param new_node_name New name of the file node in the destination file storage. To keep the +existing name, use cvcvGetFileNodeName +@param node The written node +@param embed If the written node is a collection and this parameter is not zero, no extra level of +hierarchy is created. Instead, all the elements of node are written into the currently written +structure. Of course, map elements can only be embedded into another map, and sequence elements +can only be embedded into another sequence. + */ +CVAPI(void) cvWriteFileNode( CvFileStorage* fs, const char* new_node_name, + const CvFileNode* node, int embed ); + +/** @brief Returns the name of a file node. + +The function returns the name of a file node or NULL, if the file node does not have a name or if +node is NULL. +@param node File node + */ +CVAPI(const char*) cvGetFileNodeName( const CvFileNode* node ); + +/*********************************** Adding own types ***********************************/ + +/** @brief Registers a new type. + +The function registers a new type, which is described by info . The function creates a copy of the +structure, so the user should delete it after calling the function. +@param info Type info structure + */ +CVAPI(void) cvRegisterType( const CvTypeInfo* info ); + +/** @brief Unregisters the type. + +The function unregisters a type with a specified name. If the name is unknown, it is possible to +locate the type info by an instance of the type using cvTypeOf or by iterating the type list, +starting from cvFirstType, and then calling cvUnregisterType(info-\>typeName). +@param type_name Name of an unregistered type + */ +CVAPI(void) cvUnregisterType( const char* type_name ); + +/** @brief Returns the beginning of a type list. + +The function returns the first type in the list of registered types. Navigation through the list can +be done via the prev and next fields of the CvTypeInfo structure. + */ +CVAPI(CvTypeInfo*) cvFirstType(void); + +/** @brief Finds a type by its name. + +The function finds a registered type by its name. It returns NULL if there is no type with the +specified name. +@param type_name Type name + */ +CVAPI(CvTypeInfo*) cvFindType( const char* type_name ); + +/** @brief Returns the type of an object. + +The function finds the type of a given object. It iterates through the list of registered types and +calls the is_instance function/method for every type info structure with that object until one of +them returns non-zero or until the whole list has been traversed. In the latter case, the function +returns NULL. +@param struct_ptr The object pointer + */ +CVAPI(CvTypeInfo*) cvTypeOf( const void* struct_ptr ); + +/** @brief Releases an object. + +The function finds the type of a given object and calls release with the double pointer. +@param struct_ptr Double pointer to the object + */ +CVAPI(void) cvRelease( void** struct_ptr ); + +/** @brief Makes a clone of an object. + +The function finds the type of a given object and calls clone with the passed object. Of course, if +you know the object type, for example, struct_ptr is CvMat\*, it is faster to call the specific +function, like cvCloneMat. +@param struct_ptr The object to clone + */ +CVAPI(void*) cvClone( const void* struct_ptr ); + +/** @brief Saves an object to a file. + +The function saves an object to a file. It provides a simple interface to cvWrite . +@param filename File name +@param struct_ptr Object to save +@param name Optional object name. If it is NULL, the name will be formed from filename . +@param comment Optional comment to put in the beginning of the file +@param attributes Optional attributes passed to cvWrite + */ +CVAPI(void) cvSave( const char* filename, const void* struct_ptr, + const char* name CV_DEFAULT(NULL), + const char* comment CV_DEFAULT(NULL), + CvAttrList attributes CV_DEFAULT(cvAttrList())); + +/** @brief Loads an object from a file. + +The function loads an object from a file. It basically reads the specified file, find the first +top-level node and calls cvRead for that node. If the file node does not have type information or +the type information can not be found by the type name, the function returns NULL. After the object +is loaded, the file storage is closed and all the temporary buffers are deleted. Thus, to load a +dynamic structure, such as a sequence, contour, or graph, one should pass a valid memory storage +destination to the function. +@param filename File name +@param memstorage Memory storage for dynamic structures, such as CvSeq or CvGraph . It is not used +for matrices or images. +@param name Optional object name. If it is NULL, the first top-level object in the storage will be +loaded. +@param real_name Optional output parameter that will contain the name of the loaded object +(useful if name=NULL ) + */ +CVAPI(void*) cvLoad( const char* filename, + CvMemStorage* memstorage CV_DEFAULT(NULL), + const char* name CV_DEFAULT(NULL), + const char** real_name CV_DEFAULT(NULL) ); + +/*********************************** Measuring Execution Time ***************************/ + +/** helper functions for RNG initialization and accurate time measurement: + uses internal clock counter on x86 */ +CVAPI(int64) cvGetTickCount( void ); +CVAPI(double) cvGetTickFrequency( void ); + +/*********************************** CPU capabilities ***********************************/ + +CVAPI(int) cvCheckHardwareSupport(int feature); + +/*********************************** Multi-Threading ************************************/ + +/** retrieve/set the number of threads used in OpenMP implementations */ +CVAPI(int) cvGetNumThreads( void ); +CVAPI(void) cvSetNumThreads( int threads CV_DEFAULT(0) ); +/** get index of the thread being executed */ +CVAPI(int) cvGetThreadNum( void ); + + +/********************************** Error Handling **************************************/ + +/** Get current OpenCV error status */ +CVAPI(int) cvGetErrStatus( void ); + +/** Sets error status silently */ +CVAPI(void) cvSetErrStatus( int status ); + +#define CV_ErrModeLeaf 0 /* Print error and exit program */ +#define CV_ErrModeParent 1 /* Print error and continue */ +#define CV_ErrModeSilent 2 /* Don't print and continue */ + +/** Retrieves current error processing mode */ +CVAPI(int) cvGetErrMode( void ); + +/** Sets error processing mode, returns previously used mode */ +CVAPI(int) cvSetErrMode( int mode ); + +/** Sets error status and performs some additional actions (displaying message box, + writing message to stderr, terminating application etc.) + depending on the current error mode */ +CVAPI(void) cvError( int status, const char* func_name, + const char* err_msg, const char* file_name, int line ); + +/** Retrieves textual description of the error given its code */ +CVAPI(const char*) cvErrorStr( int status ); + +/** Retrieves detailed information about the last error occurred */ +CVAPI(int) cvGetErrInfo( const char** errcode_desc, const char** description, + const char** filename, int* line ); + +/** Maps IPP error codes to the counterparts from OpenCV */ +CVAPI(int) cvErrorFromIppStatus( int ipp_status ); + +typedef int (CV_CDECL *CvErrorCallback)( int status, const char* func_name, + const char* err_msg, const char* file_name, int line, void* userdata ); + +/** Assigns a new error-handling function */ +CVAPI(CvErrorCallback) cvRedirectError( CvErrorCallback error_handler, + void* userdata CV_DEFAULT(NULL), + void** prev_userdata CV_DEFAULT(NULL) ); + +/** Output nothing */ +CVAPI(int) cvNulDevReport( int status, const char* func_name, const char* err_msg, + const char* file_name, int line, void* userdata ); + +/** Output to console(fprintf(stderr,...)) */ +CVAPI(int) cvStdErrReport( int status, const char* func_name, const char* err_msg, + const char* file_name, int line, void* userdata ); + +/** Output to MessageBox(WIN32) */ +CVAPI(int) cvGuiBoxReport( int status, const char* func_name, const char* err_msg, + const char* file_name, int line, void* userdata ); + +#define OPENCV_ERROR(status,func,context) \ +cvError((status),(func),(context),__FILE__,__LINE__) + +#define OPENCV_ASSERT(expr,func,context) \ +{if (! (expr)) \ +{OPENCV_ERROR(CV_StsInternal,(func),(context));}} + +#define OPENCV_CALL( Func ) \ +{ \ +Func; \ +} + + +/** CV_FUNCNAME macro defines icvFuncName constant which is used by CV_ERROR macro */ +#ifdef CV_NO_FUNC_NAMES +#define CV_FUNCNAME( Name ) +#define cvFuncName "" +#else +#define CV_FUNCNAME( Name ) \ +static char cvFuncName[] = Name +#endif + + +/** + CV_ERROR macro unconditionally raises error with passed code and message. + After raising error, control will be transferred to the exit label. + */ +#define CV_ERROR( Code, Msg ) \ +{ \ + cvError( (Code), cvFuncName, Msg, __FILE__, __LINE__ ); \ + __CV_EXIT__; \ +} + +/** + CV_CHECK macro checks error status after CV (or IPL) + function call. If error detected, control will be transferred to the exit + label. + */ +#define CV_CHECK() \ +{ \ + if( cvGetErrStatus() < 0 ) \ + CV_ERROR( CV_StsBackTrace, "Inner function failed." ); \ +} + + +/** + CV_CALL macro calls CV (or IPL) function, checks error status and + signals a error if the function failed. Useful in "parent node" + error processing mode + */ +#define CV_CALL( Func ) \ +{ \ + Func; \ + CV_CHECK(); \ +} + + +/** Runtime assertion macro */ +#define CV_ASSERT( Condition ) \ +{ \ + if( !(Condition) ) \ + CV_ERROR( CV_StsInternal, "Assertion: " #Condition " failed" ); \ +} + +#define __CV_BEGIN__ { +#define __CV_END__ goto exit; exit: ; } +#define __CV_EXIT__ goto exit + +/** @} core_c */ + +#ifdef __cplusplus +} // extern "C" +#endif + +#ifdef __cplusplus + +//! @addtogroup core_c_glue +//! @{ + +//! class for automatic module/RTTI data registration/unregistration +struct CV_EXPORTS CvType +{ + CvType( const char* type_name, + CvIsInstanceFunc is_instance, CvReleaseFunc release=0, + CvReadFunc read=0, CvWriteFunc write=0, CvCloneFunc clone=0 ); + ~CvType(); + CvTypeInfo* info; + + static CvTypeInfo* first; + static CvTypeInfo* last; +}; + +//! @} + +#include "opencv2/core/utility.hpp" + +namespace cv +{ + +//! @addtogroup core_c_glue +//! @{ + +/////////////////////////////////////////// glue /////////////////////////////////////////// + +//! converts array (CvMat or IplImage) to cv::Mat +CV_EXPORTS Mat cvarrToMat(const CvArr* arr, bool copyData=false, + bool allowND=true, int coiMode=0, + AutoBuffer* buf=0); + +static inline Mat cvarrToMatND(const CvArr* arr, bool copyData=false, int coiMode=0) +{ + return cvarrToMat(arr, copyData, true, coiMode); +} + + +//! extracts Channel of Interest from CvMat or IplImage and makes cv::Mat out of it. +CV_EXPORTS void extractImageCOI(const CvArr* arr, OutputArray coiimg, int coi=-1); +//! inserts single-channel cv::Mat into a multi-channel CvMat or IplImage +CV_EXPORTS void insertImageCOI(InputArray coiimg, CvArr* arr, int coi=-1); + + + +////// specialized implementations of DefaultDeleter::operator() for classic OpenCV types ////// + +template<> CV_EXPORTS void DefaultDeleter::operator ()(CvMat* obj) const; +template<> CV_EXPORTS void DefaultDeleter::operator ()(IplImage* obj) const; +template<> CV_EXPORTS void DefaultDeleter::operator ()(CvMatND* obj) const; +template<> CV_EXPORTS void DefaultDeleter::operator ()(CvSparseMat* obj) const; +template<> CV_EXPORTS void DefaultDeleter::operator ()(CvMemStorage* obj) const; + +////////////// convenient wrappers for operating old-style dynamic structures ////////////// + +template class SeqIterator; + +typedef Ptr MemStorage; + +/*! + Template Sequence Class derived from CvSeq + + The class provides more convenient access to sequence elements, + STL-style operations and iterators. + + \note The class is targeted for simple data types, + i.e. no constructors or destructors + are called for the sequence elements. +*/ +template class Seq +{ +public: + typedef SeqIterator<_Tp> iterator; + typedef SeqIterator<_Tp> const_iterator; + + //! the default constructor + Seq(); + //! the constructor for wrapping CvSeq structure. The real element type in CvSeq should match _Tp. + Seq(const CvSeq* seq); + //! creates the empty sequence that resides in the specified storage + Seq(MemStorage& storage, int headerSize = sizeof(CvSeq)); + //! returns read-write reference to the specified element + _Tp& operator [](int idx); + //! returns read-only reference to the specified element + const _Tp& operator[](int idx) const; + //! returns iterator pointing to the beginning of the sequence + SeqIterator<_Tp> begin() const; + //! returns iterator pointing to the element following the last sequence element + SeqIterator<_Tp> end() const; + //! returns the number of elements in the sequence + size_t size() const; + //! returns the type of sequence elements (CV_8UC1 ... CV_64FC(CV_CN_MAX) ...) + int type() const; + //! returns the depth of sequence elements (CV_8U ... CV_64F) + int depth() const; + //! returns the number of channels in each sequence element + int channels() const; + //! returns the size of each sequence element + size_t elemSize() const; + //! returns index of the specified sequence element + size_t index(const _Tp& elem) const; + //! appends the specified element to the end of the sequence + void push_back(const _Tp& elem); + //! appends the specified element to the front of the sequence + void push_front(const _Tp& elem); + //! appends zero or more elements to the end of the sequence + void push_back(const _Tp* elems, size_t count); + //! appends zero or more elements to the front of the sequence + void push_front(const _Tp* elems, size_t count); + //! inserts the specified element to the specified position + void insert(int idx, const _Tp& elem); + //! inserts zero or more elements to the specified position + void insert(int idx, const _Tp* elems, size_t count); + //! removes element at the specified position + void remove(int idx); + //! removes the specified subsequence + void remove(const Range& r); + + //! returns reference to the first sequence element + _Tp& front(); + //! returns read-only reference to the first sequence element + const _Tp& front() const; + //! returns reference to the last sequence element + _Tp& back(); + //! returns read-only reference to the last sequence element + const _Tp& back() const; + //! returns true iff the sequence contains no elements + bool empty() const; + + //! removes all the elements from the sequence + void clear(); + //! removes the first element from the sequence + void pop_front(); + //! removes the last element from the sequence + void pop_back(); + //! removes zero or more elements from the beginning of the sequence + void pop_front(_Tp* elems, size_t count); + //! removes zero or more elements from the end of the sequence + void pop_back(_Tp* elems, size_t count); + + //! copies the whole sequence or the sequence slice to the specified vector + void copyTo(std::vector<_Tp>& vec, const Range& range=Range::all()) const; + //! returns the vector containing all the sequence elements + operator std::vector<_Tp>() const; + + CvSeq* seq; +}; + + +/*! + STL-style Sequence Iterator inherited from the CvSeqReader structure +*/ +template class SeqIterator : public CvSeqReader +{ +public: + //! the default constructor + SeqIterator(); + //! the constructor setting the iterator to the beginning or to the end of the sequence + SeqIterator(const Seq<_Tp>& seq, bool seekEnd=false); + //! positions the iterator within the sequence + void seek(size_t pos); + //! reports the current iterator position + size_t tell() const; + //! returns reference to the current sequence element + _Tp& operator *(); + //! returns read-only reference to the current sequence element + const _Tp& operator *() const; + //! moves iterator to the next sequence element + SeqIterator& operator ++(); + //! moves iterator to the next sequence element + SeqIterator operator ++(int) const; + //! moves iterator to the previous sequence element + SeqIterator& operator --(); + //! moves iterator to the previous sequence element + SeqIterator operator --(int) const; + + //! moves iterator forward by the specified offset (possibly negative) + SeqIterator& operator +=(int); + //! moves iterator backward by the specified offset (possibly negative) + SeqIterator& operator -=(int); + + // this is index of the current element module seq->total*2 + // (to distinguish between 0 and seq->total) + int index; +}; + + + +// bridge C++ => C Seq API +CV_EXPORTS schar* seqPush( CvSeq* seq, const void* element=0); +CV_EXPORTS schar* seqPushFront( CvSeq* seq, const void* element=0); +CV_EXPORTS void seqPop( CvSeq* seq, void* element=0); +CV_EXPORTS void seqPopFront( CvSeq* seq, void* element=0); +CV_EXPORTS void seqPopMulti( CvSeq* seq, void* elements, + int count, int in_front=0 ); +CV_EXPORTS void seqRemove( CvSeq* seq, int index ); +CV_EXPORTS void clearSeq( CvSeq* seq ); +CV_EXPORTS schar* getSeqElem( const CvSeq* seq, int index ); +CV_EXPORTS void seqRemoveSlice( CvSeq* seq, CvSlice slice ); +CV_EXPORTS void seqInsertSlice( CvSeq* seq, int before_index, const CvArr* from_arr ); + +template inline Seq<_Tp>::Seq() : seq(0) {} +template inline Seq<_Tp>::Seq( const CvSeq* _seq ) : seq((CvSeq*)_seq) +{ + CV_Assert(!_seq || _seq->elem_size == sizeof(_Tp)); +} + +template inline Seq<_Tp>::Seq( MemStorage& storage, + int headerSize ) +{ + CV_Assert(headerSize >= (int)sizeof(CvSeq)); + seq = cvCreateSeq(DataType<_Tp>::type, headerSize, sizeof(_Tp), storage); +} + +template inline _Tp& Seq<_Tp>::operator [](int idx) +{ return *(_Tp*)getSeqElem(seq, idx); } + +template inline const _Tp& Seq<_Tp>::operator [](int idx) const +{ return *(_Tp*)getSeqElem(seq, idx); } + +template inline SeqIterator<_Tp> Seq<_Tp>::begin() const +{ return SeqIterator<_Tp>(*this); } + +template inline SeqIterator<_Tp> Seq<_Tp>::end() const +{ return SeqIterator<_Tp>(*this, true); } + +template inline size_t Seq<_Tp>::size() const +{ return seq ? seq->total : 0; } + +template inline int Seq<_Tp>::type() const +{ return seq ? CV_MAT_TYPE(seq->flags) : 0; } + +template inline int Seq<_Tp>::depth() const +{ return seq ? CV_MAT_DEPTH(seq->flags) : 0; } + +template inline int Seq<_Tp>::channels() const +{ return seq ? CV_MAT_CN(seq->flags) : 0; } + +template inline size_t Seq<_Tp>::elemSize() const +{ return seq ? seq->elem_size : 0; } + +template inline size_t Seq<_Tp>::index(const _Tp& elem) const +{ return cvSeqElemIdx(seq, &elem); } + +template inline void Seq<_Tp>::push_back(const _Tp& elem) +{ cvSeqPush(seq, &elem); } + +template inline void Seq<_Tp>::push_front(const _Tp& elem) +{ cvSeqPushFront(seq, &elem); } + +template inline void Seq<_Tp>::push_back(const _Tp* elem, size_t count) +{ cvSeqPushMulti(seq, elem, (int)count, 0); } + +template inline void Seq<_Tp>::push_front(const _Tp* elem, size_t count) +{ cvSeqPushMulti(seq, elem, (int)count, 1); } + +template inline _Tp& Seq<_Tp>::back() +{ return *(_Tp*)getSeqElem(seq, -1); } + +template inline const _Tp& Seq<_Tp>::back() const +{ return *(const _Tp*)getSeqElem(seq, -1); } + +template inline _Tp& Seq<_Tp>::front() +{ return *(_Tp*)getSeqElem(seq, 0); } + +template inline const _Tp& Seq<_Tp>::front() const +{ return *(const _Tp*)getSeqElem(seq, 0); } + +template inline bool Seq<_Tp>::empty() const +{ return !seq || seq->total == 0; } + +template inline void Seq<_Tp>::clear() +{ if(seq) clearSeq(seq); } + +template inline void Seq<_Tp>::pop_back() +{ seqPop(seq); } + +template inline void Seq<_Tp>::pop_front() +{ seqPopFront(seq); } + +template inline void Seq<_Tp>::pop_back(_Tp* elem, size_t count) +{ seqPopMulti(seq, elem, (int)count, 0); } + +template inline void Seq<_Tp>::pop_front(_Tp* elem, size_t count) +{ seqPopMulti(seq, elem, (int)count, 1); } + +template inline void Seq<_Tp>::insert(int idx, const _Tp& elem) +{ seqInsert(seq, idx, &elem); } + +template inline void Seq<_Tp>::insert(int idx, const _Tp* elems, size_t count) +{ + CvMat m = cvMat(1, count, DataType<_Tp>::type, elems); + seqInsertSlice(seq, idx, &m); +} + +template inline void Seq<_Tp>::remove(int idx) +{ seqRemove(seq, idx); } + +template inline void Seq<_Tp>::remove(const Range& r) +{ seqRemoveSlice(seq, cvSlice(r.start, r.end)); } + +template inline void Seq<_Tp>::copyTo(std::vector<_Tp>& vec, const Range& range) const +{ + size_t len = !seq ? 0 : range == Range::all() ? seq->total : range.end - range.start; + vec.resize(len); + if( seq && len ) + cvCvtSeqToArray(seq, &vec[0], cvSlice(range)); +} + +template inline Seq<_Tp>::operator std::vector<_Tp>() const +{ + std::vector<_Tp> vec; + copyTo(vec); + return vec; +} + +template inline SeqIterator<_Tp>::SeqIterator() +{ memset(this, 0, sizeof(*this)); } + +template inline SeqIterator<_Tp>::SeqIterator(const Seq<_Tp>& _seq, bool seekEnd) +{ + cvStartReadSeq(_seq.seq, this); + index = seekEnd ? _seq.seq->total : 0; +} + +template inline void SeqIterator<_Tp>::seek(size_t pos) +{ + cvSetSeqReaderPos(this, (int)pos, false); + index = pos; +} + +template inline size_t SeqIterator<_Tp>::tell() const +{ return index; } + +template inline _Tp& SeqIterator<_Tp>::operator *() +{ return *(_Tp*)ptr; } + +template inline const _Tp& SeqIterator<_Tp>::operator *() const +{ return *(const _Tp*)ptr; } + +template inline SeqIterator<_Tp>& SeqIterator<_Tp>::operator ++() +{ + CV_NEXT_SEQ_ELEM(sizeof(_Tp), *this); + if( ++index >= seq->total*2 ) + index = 0; + return *this; +} + +template inline SeqIterator<_Tp> SeqIterator<_Tp>::operator ++(int) const +{ + SeqIterator<_Tp> it = *this; + ++*this; + return it; +} + +template inline SeqIterator<_Tp>& SeqIterator<_Tp>::operator --() +{ + CV_PREV_SEQ_ELEM(sizeof(_Tp), *this); + if( --index < 0 ) + index = seq->total*2-1; + return *this; +} + +template inline SeqIterator<_Tp> SeqIterator<_Tp>::operator --(int) const +{ + SeqIterator<_Tp> it = *this; + --*this; + return it; +} + +template inline SeqIterator<_Tp>& SeqIterator<_Tp>::operator +=(int delta) +{ + cvSetSeqReaderPos(this, delta, 1); + index += delta; + int n = seq->total*2; + if( index < 0 ) + index += n; + if( index >= n ) + index -= n; + return *this; +} + +template inline SeqIterator<_Tp>& SeqIterator<_Tp>::operator -=(int delta) +{ + return (*this += -delta); +} + +template inline ptrdiff_t operator - (const SeqIterator<_Tp>& a, + const SeqIterator<_Tp>& b) +{ + ptrdiff_t delta = a.index - b.index, n = a.seq->total; + if( delta > n || delta < -n ) + delta += delta < 0 ? n : -n; + return delta; +} + +template inline bool operator == (const SeqIterator<_Tp>& a, + const SeqIterator<_Tp>& b) +{ + return a.seq == b.seq && a.index == b.index; +} + +template inline bool operator != (const SeqIterator<_Tp>& a, + const SeqIterator<_Tp>& b) +{ + return !(a == b); +} + +//! @} + +} // cv + +#endif + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda.hpp new file mode 100644 index 0000000000000000000000000000000000000000..820aba71ecb7276fc6a08aff3d50598b85daa11e --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda.hpp @@ -0,0 +1,1049 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_CUDA_HPP +#define OPENCV_CORE_CUDA_HPP + +#ifndef __cplusplus +# error cuda.hpp header must be compiled as C++ +#endif + +#include "opencv2/core.hpp" +#include "opencv2/core/cuda_types.hpp" + +/** + @defgroup cuda CUDA-accelerated Computer Vision + @{ + @defgroup cudacore Core part + @{ + @defgroup cudacore_init Initialization and Information + @defgroup cudacore_struct Data Structures + @} + @} + */ + +namespace cv { namespace cuda { + +//! @addtogroup cudacore_struct +//! @{ + +//=================================================================================== +// GpuMat +//=================================================================================== + +/** @brief Base storage class for GPU memory with reference counting. + +Its interface matches the Mat interface with the following limitations: + +- no arbitrary dimensions support (only 2D) +- no functions that return references to their data (because references on GPU are not valid for + CPU) +- no expression templates technique support + +Beware that the latter limitation may lead to overloaded matrix operators that cause memory +allocations. The GpuMat class is convertible to cuda::PtrStepSz and cuda::PtrStep so it can be +passed directly to the kernel. + +@note In contrast with Mat, in most cases GpuMat::isContinuous() == false . This means that rows are +aligned to a size depending on the hardware. Single-row GpuMat is always a continuous matrix. + +@note You are not recommended to leave static or global GpuMat variables allocated, that is, to rely +on its destructor. The destruction order of such variables and CUDA context is undefined. GPU memory +release function returns error if the CUDA context has been destroyed before. + +Some member functions are described as a "Blocking Call" while some are described as a +"Non-Blocking Call". Blocking functions are synchronous to host. It is guaranteed that the GPU +operation is finished when the function returns. However, non-blocking functions are asynchronous to +host. Those functions may return even if the GPU operation is not finished. + +Compared to their blocking counterpart, non-blocking functions accept Stream as an additional +argument. If a non-default stream is passed, the GPU operation may overlap with operations in other +streams. + +@sa Mat + */ +class CV_EXPORTS GpuMat +{ +public: + class CV_EXPORTS Allocator + { + public: + virtual ~Allocator() {} + + // allocator must fill data, step and refcount fields + virtual bool allocate(GpuMat* mat, int rows, int cols, size_t elemSize) = 0; + virtual void free(GpuMat* mat) = 0; + }; + + //! default allocator + static Allocator* defaultAllocator(); + static void setDefaultAllocator(Allocator* allocator); + + //! default constructor + explicit GpuMat(Allocator* allocator = defaultAllocator()); + + //! constructs GpuMat of the specified size and type + GpuMat(int rows, int cols, int type, Allocator* allocator = defaultAllocator()); + GpuMat(Size size, int type, Allocator* allocator = defaultAllocator()); + + //! constucts GpuMat and fills it with the specified value _s + GpuMat(int rows, int cols, int type, Scalar s, Allocator* allocator = defaultAllocator()); + GpuMat(Size size, int type, Scalar s, Allocator* allocator = defaultAllocator()); + + //! copy constructor + GpuMat(const GpuMat& m); + + //! constructor for GpuMat headers pointing to user-allocated data + GpuMat(int rows, int cols, int type, void* data, size_t step = Mat::AUTO_STEP); + GpuMat(Size size, int type, void* data, size_t step = Mat::AUTO_STEP); + + //! creates a GpuMat header for a part of the bigger matrix + GpuMat(const GpuMat& m, Range rowRange, Range colRange); + GpuMat(const GpuMat& m, Rect roi); + + //! builds GpuMat from host memory (Blocking call) + explicit GpuMat(InputArray arr, Allocator* allocator = defaultAllocator()); + + //! destructor - calls release() + ~GpuMat(); + + //! assignment operators + GpuMat& operator =(const GpuMat& m); + + //! allocates new GpuMat data unless the GpuMat already has specified size and type + void create(int rows, int cols, int type); + void create(Size size, int type); + + //! decreases reference counter, deallocate the data when reference counter reaches 0 + void release(); + + //! swaps with other smart pointer + void swap(GpuMat& mat); + + /** @brief Performs data upload to GpuMat (Blocking call) + + This function copies data from host memory to device memory. As being a blocking call, it is + guaranteed that the copy operation is finished when this function returns. + */ + void upload(InputArray arr); + + /** @brief Performs data upload to GpuMat (Non-Blocking call) + + This function copies data from host memory to device memory. As being a non-blocking call, this + function may return even if the copy operation is not finished. + + The copy operation may be overlapped with operations in other non-default streams if \p stream is + not the default stream and \p dst is HostMem allocated with HostMem::PAGE_LOCKED option. + */ + void upload(InputArray arr, Stream& stream); + + /** @brief Performs data download from GpuMat (Blocking call) + + This function copies data from device memory to host memory. As being a blocking call, it is + guaranteed that the copy operation is finished when this function returns. + */ + void download(OutputArray dst) const; + + /** @brief Performs data download from GpuMat (Non-Blocking call) + + This function copies data from device memory to host memory. As being a non-blocking call, this + function may return even if the copy operation is not finished. + + The copy operation may be overlapped with operations in other non-default streams if \p stream is + not the default stream and \p dst is HostMem allocated with HostMem::PAGE_LOCKED option. + */ + void download(OutputArray dst, Stream& stream) const; + + //! returns deep copy of the GpuMat, i.e. the data is copied + GpuMat clone() const; + + //! copies the GpuMat content to device memory (Blocking call) + void copyTo(OutputArray dst) const; + + //! copies the GpuMat content to device memory (Non-Blocking call) + void copyTo(OutputArray dst, Stream& stream) const; + + //! copies those GpuMat elements to "m" that are marked with non-zero mask elements (Blocking call) + void copyTo(OutputArray dst, InputArray mask) const; + + //! copies those GpuMat elements to "m" that are marked with non-zero mask elements (Non-Blocking call) + void copyTo(OutputArray dst, InputArray mask, Stream& stream) const; + + //! sets some of the GpuMat elements to s (Blocking call) + GpuMat& setTo(Scalar s); + + //! sets some of the GpuMat elements to s (Non-Blocking call) + GpuMat& setTo(Scalar s, Stream& stream); + + //! sets some of the GpuMat elements to s, according to the mask (Blocking call) + GpuMat& setTo(Scalar s, InputArray mask); + + //! sets some of the GpuMat elements to s, according to the mask (Non-Blocking call) + GpuMat& setTo(Scalar s, InputArray mask, Stream& stream); + + //! converts GpuMat to another datatype (Blocking call) + void convertTo(OutputArray dst, int rtype) const; + + //! converts GpuMat to another datatype (Non-Blocking call) + void convertTo(OutputArray dst, int rtype, Stream& stream) const; + + //! converts GpuMat to another datatype with scaling (Blocking call) + void convertTo(OutputArray dst, int rtype, double alpha, double beta = 0.0) const; + + //! converts GpuMat to another datatype with scaling (Non-Blocking call) + void convertTo(OutputArray dst, int rtype, double alpha, Stream& stream) const; + + //! converts GpuMat to another datatype with scaling (Non-Blocking call) + void convertTo(OutputArray dst, int rtype, double alpha, double beta, Stream& stream) const; + + void assignTo(GpuMat& m, int type=-1) const; + + //! returns pointer to y-th row + uchar* ptr(int y = 0); + const uchar* ptr(int y = 0) const; + + //! template version of the above method + template _Tp* ptr(int y = 0); + template const _Tp* ptr(int y = 0) const; + + template operator PtrStepSz<_Tp>() const; + template operator PtrStep<_Tp>() const; + + //! returns a new GpuMat header for the specified row + GpuMat row(int y) const; + + //! returns a new GpuMat header for the specified column + GpuMat col(int x) const; + + //! ... for the specified row span + GpuMat rowRange(int startrow, int endrow) const; + GpuMat rowRange(Range r) const; + + //! ... for the specified column span + GpuMat colRange(int startcol, int endcol) const; + GpuMat colRange(Range r) const; + + //! extracts a rectangular sub-GpuMat (this is a generalized form of row, rowRange etc.) + GpuMat operator ()(Range rowRange, Range colRange) const; + GpuMat operator ()(Rect roi) const; + + //! creates alternative GpuMat header for the same data, with different + //! number of channels and/or different number of rows + GpuMat reshape(int cn, int rows = 0) const; + + //! locates GpuMat header within a parent GpuMat + void locateROI(Size& wholeSize, Point& ofs) const; + + //! moves/resizes the current GpuMat ROI inside the parent GpuMat + GpuMat& adjustROI(int dtop, int dbottom, int dleft, int dright); + + //! returns true iff the GpuMat data is continuous + //! (i.e. when there are no gaps between successive rows) + bool isContinuous() const; + + //! returns element size in bytes + size_t elemSize() const; + + //! returns the size of element channel in bytes + size_t elemSize1() const; + + //! returns element type + int type() const; + + //! returns element type + int depth() const; + + //! returns number of channels + int channels() const; + + //! returns step/elemSize1() + size_t step1() const; + + //! returns GpuMat size : width == number of columns, height == number of rows + Size size() const; + + //! returns true if GpuMat data is NULL + bool empty() const; + + //! internal use method: updates the continuity flag + void updateContinuityFlag(); + + /*! includes several bit-fields: + - the magic signature + - continuity flag + - depth + - number of channels + */ + int flags; + + //! the number of rows and columns + int rows, cols; + + //! a distance between successive rows in bytes; includes the gap if any + size_t step; + + //! pointer to the data + uchar* data; + + //! pointer to the reference counter; + //! when GpuMat points to user-allocated data, the pointer is NULL + int* refcount; + + //! helper fields used in locateROI and adjustROI + uchar* datastart; + const uchar* dataend; + + //! allocator + Allocator* allocator; +}; + +/** @brief Creates a continuous matrix. + +@param rows Row count. +@param cols Column count. +@param type Type of the matrix. +@param arr Destination matrix. This parameter changes only if it has a proper type and area ( +\f$\texttt{rows} \times \texttt{cols}\f$ ). + +Matrix is called continuous if its elements are stored continuously, that is, without gaps at the +end of each row. + */ +CV_EXPORTS void createContinuous(int rows, int cols, int type, OutputArray arr); + +/** @brief Ensures that the size of a matrix is big enough and the matrix has a proper type. + +@param rows Minimum desired number of rows. +@param cols Minimum desired number of columns. +@param type Desired matrix type. +@param arr Destination matrix. + +The function does not reallocate memory if the matrix has proper attributes already. + */ +CV_EXPORTS void ensureSizeIsEnough(int rows, int cols, int type, OutputArray arr); + +/** @brief BufferPool for use with CUDA streams + +BufferPool utilizes Stream's allocator to create new buffers for GpuMat's. It is +only useful when enabled with #setBufferPoolUsage. + +@code + setBufferPoolUsage(true); +@endcode + +@note #setBufferPoolUsage must be called \em before any Stream declaration. + +Users may specify custom allocator for Stream and may implement their own stream based +functions utilizing the same underlying GPU memory management. + +If custom allocator is not specified, BufferPool utilizes StackAllocator by +default. StackAllocator allocates a chunk of GPU device memory beforehand, +and when GpuMat is declared later on, it is given the pre-allocated memory. +This kind of strategy reduces the number of calls for memory allocating APIs +such as cudaMalloc or cudaMallocPitch. + +Below is an example that utilizes BufferPool with StackAllocator: + +@code + #include + + using namespace cv; + using namespace cv::cuda + + int main() + { + setBufferPoolUsage(true); // Tell OpenCV that we are going to utilize BufferPool + setBufferPoolConfig(getDevice(), 1024 * 1024 * 64, 2); // Allocate 64 MB, 2 stacks (default is 10 MB, 5 stacks) + + Stream stream1, stream2; // Each stream uses 1 stack + BufferPool pool1(stream1), pool2(stream2); + + GpuMat d_src1 = pool1.getBuffer(4096, 4096, CV_8UC1); // 16MB + GpuMat d_dst1 = pool1.getBuffer(4096, 4096, CV_8UC3); // 48MB, pool1 is now full + + GpuMat d_src2 = pool2.getBuffer(1024, 1024, CV_8UC1); // 1MB + GpuMat d_dst2 = pool2.getBuffer(1024, 1024, CV_8UC3); // 3MB + + cvtColor(d_src1, d_dst1, CV_GRAY2BGR, 0, stream1); + cvtColor(d_src2, d_dst2, CV_GRAY2BGR, 0, stream2); + } +@endcode + +If we allocate another GpuMat on pool1 in the above example, it will be carried out by +the DefaultAllocator since the stack for pool1 is full. + +@code + GpuMat d_add1 = pool1.getBuffer(1024, 1024, CV_8UC1); // Stack for pool1 is full, memory is allocated with DefaultAllocator +@endcode + +If a third stream is declared in the above example, allocating with #getBuffer +within that stream will also be carried out by the DefaultAllocator because we've run out of +stacks. + +@code + Stream stream3; // Only 2 stacks were allocated, we've run out of stacks + BufferPool pool3(stream3); + GpuMat d_src3 = pool3.getBuffer(1024, 1024, CV_8UC1); // Memory is allocated with DefaultAllocator +@endcode + +@warning When utilizing StackAllocator, deallocation order is important. + +Just like a stack, deallocation must be done in LIFO order. Below is an example of +erroneous usage that violates LIFO rule. If OpenCV is compiled in Debug mode, this +sample code will emit CV_Assert error. + +@code + int main() + { + setBufferPoolUsage(true); // Tell OpenCV that we are going to utilize BufferPool + Stream stream; // A default size (10 MB) stack is allocated to this stream + BufferPool pool(stream); + + GpuMat mat1 = pool.getBuffer(1024, 1024, CV_8UC1); // Allocate mat1 (1MB) + GpuMat mat2 = pool.getBuffer(1024, 1024, CV_8UC1); // Allocate mat2 (1MB) + + mat1.release(); // erroneous usage : mat2 must be deallocated before mat1 + } +@endcode + +Since C++ local variables are destroyed in the reverse order of construction, +the code sample below satisfies the LIFO rule. Local GpuMat's are deallocated +and the corresponding memory is automatically returned to the pool for later usage. + +@code + int main() + { + setBufferPoolUsage(true); // Tell OpenCV that we are going to utilize BufferPool + setBufferPoolConfig(getDevice(), 1024 * 1024 * 64, 2); // Allocate 64 MB, 2 stacks (default is 10 MB, 5 stacks) + + Stream stream1, stream2; // Each stream uses 1 stack + BufferPool pool1(stream1), pool2(stream2); + + for (int i = 0; i < 10; i++) + { + GpuMat d_src1 = pool1.getBuffer(4096, 4096, CV_8UC1); // 16MB + GpuMat d_dst1 = pool1.getBuffer(4096, 4096, CV_8UC3); // 48MB, pool1 is now full + + GpuMat d_src2 = pool2.getBuffer(1024, 1024, CV_8UC1); // 1MB + GpuMat d_dst2 = pool2.getBuffer(1024, 1024, CV_8UC3); // 3MB + + d_src1.setTo(Scalar(i), stream1); + d_src2.setTo(Scalar(i), stream2); + + cvtColor(d_src1, d_dst1, CV_GRAY2BGR, 0, stream1); + cvtColor(d_src2, d_dst2, CV_GRAY2BGR, 0, stream2); + // The order of destruction of the local variables is: + // d_dst2 => d_src2 => d_dst1 => d_src1 + // LIFO rule is satisfied, this code runs without error + } + } +@endcode + */ +class CV_EXPORTS BufferPool +{ +public: + + //! Gets the BufferPool for the given stream. + explicit BufferPool(Stream& stream); + + //! Allocates a new GpuMat of given size and type. + GpuMat getBuffer(int rows, int cols, int type); + + //! Allocates a new GpuMat of given size and type. + GpuMat getBuffer(Size size, int type) { return getBuffer(size.height, size.width, type); } + + //! Returns the allocator associated with the stream. + Ptr getAllocator() const { return allocator_; } + +private: + Ptr allocator_; +}; + +//! BufferPool management (must be called before Stream creation) +CV_EXPORTS void setBufferPoolUsage(bool on); +CV_EXPORTS void setBufferPoolConfig(int deviceId, size_t stackSize, int stackCount); + +//=================================================================================== +// HostMem +//=================================================================================== + +/** @brief Class with reference counting wrapping special memory type allocation functions from CUDA. + +Its interface is also Mat-like but with additional memory type parameters. + +- **PAGE_LOCKED** sets a page locked memory type used commonly for fast and asynchronous + uploading/downloading data from/to GPU. +- **SHARED** specifies a zero copy memory allocation that enables mapping the host memory to GPU + address space, if supported. +- **WRITE_COMBINED** sets the write combined buffer that is not cached by CPU. Such buffers are + used to supply GPU with data when GPU only reads it. The advantage is a better CPU cache + utilization. + +@note Allocation size of such memory types is usually limited. For more details, see *CUDA 2.2 +Pinned Memory APIs* document or *CUDA C Programming Guide*. + */ +class CV_EXPORTS HostMem +{ +public: + enum AllocType { PAGE_LOCKED = 1, SHARED = 2, WRITE_COMBINED = 4 }; + + static MatAllocator* getAllocator(AllocType alloc_type = PAGE_LOCKED); + + explicit HostMem(AllocType alloc_type = PAGE_LOCKED); + + HostMem(const HostMem& m); + + HostMem(int rows, int cols, int type, AllocType alloc_type = PAGE_LOCKED); + HostMem(Size size, int type, AllocType alloc_type = PAGE_LOCKED); + + //! creates from host memory with coping data + explicit HostMem(InputArray arr, AllocType alloc_type = PAGE_LOCKED); + + ~HostMem(); + + HostMem& operator =(const HostMem& m); + + //! swaps with other smart pointer + void swap(HostMem& b); + + //! returns deep copy of the matrix, i.e. the data is copied + HostMem clone() const; + + //! allocates new matrix data unless the matrix already has specified size and type. + void create(int rows, int cols, int type); + void create(Size size, int type); + + //! creates alternative HostMem header for the same data, with different + //! number of channels and/or different number of rows + HostMem reshape(int cn, int rows = 0) const; + + //! decrements reference counter and released memory if needed. + void release(); + + //! returns matrix header with disabled reference counting for HostMem data. + Mat createMatHeader() const; + + /** @brief Maps CPU memory to GPU address space and creates the cuda::GpuMat header without reference counting + for it. + + This can be done only if memory was allocated with the SHARED flag and if it is supported by the + hardware. Laptops often share video and CPU memory, so address spaces can be mapped, which + eliminates an extra copy. + */ + GpuMat createGpuMatHeader() const; + + // Please see cv::Mat for descriptions + bool isContinuous() const; + size_t elemSize() const; + size_t elemSize1() const; + int type() const; + int depth() const; + int channels() const; + size_t step1() const; + Size size() const; + bool empty() const; + + // Please see cv::Mat for descriptions + int flags; + int rows, cols; + size_t step; + + uchar* data; + int* refcount; + + uchar* datastart; + const uchar* dataend; + + AllocType alloc_type; +}; + +/** @brief Page-locks the memory of matrix and maps it for the device(s). + +@param m Input matrix. + */ +CV_EXPORTS void registerPageLocked(Mat& m); + +/** @brief Unmaps the memory of matrix and makes it pageable again. + +@param m Input matrix. + */ +CV_EXPORTS void unregisterPageLocked(Mat& m); + +//=================================================================================== +// Stream +//=================================================================================== + +/** @brief This class encapsulates a queue of asynchronous calls. + +@note Currently, you may face problems if an operation is enqueued twice with different data. Some +functions use the constant GPU memory, and next call may update the memory before the previous one +has been finished. But calling different operations asynchronously is safe because each operation +has its own constant buffer. Memory copy/upload/download/set operations to the buffers you hold are +also safe. + +@note The Stream class is not thread-safe. Please use different Stream objects for different CPU threads. + +@code +void thread1() +{ + cv::cuda::Stream stream1; + cv::cuda::func1(..., stream1); +} + +void thread2() +{ + cv::cuda::Stream stream2; + cv::cuda::func2(..., stream2); +} +@endcode + +@note By default all CUDA routines are launched in Stream::Null() object, if the stream is not specified by user. +In multi-threading environment the stream objects must be passed explicitly (see previous note). + */ +class CV_EXPORTS Stream +{ + typedef void (Stream::*bool_type)() const; + void this_type_does_not_support_comparisons() const {} + +public: + typedef void (*StreamCallback)(int status, void* userData); + + //! creates a new asynchronous stream + Stream(); + + //! creates a new asynchronous stream with custom allocator + Stream(const Ptr& allocator); + + /** @brief Returns true if the current stream queue is finished. Otherwise, it returns false. + */ + bool queryIfComplete() const; + + /** @brief Blocks the current CPU thread until all operations in the stream are complete. + */ + void waitForCompletion(); + + /** @brief Makes a compute stream wait on an event. + */ + void waitEvent(const Event& event); + + /** @brief Adds a callback to be called on the host after all currently enqueued items in the stream have + completed. + + @note Callbacks must not make any CUDA API calls. Callbacks must not perform any synchronization + that may depend on outstanding device work or other callbacks that are not mandated to run earlier. + Callbacks without a mandated order (in independent streams) execute in undefined order and may be + serialized. + */ + void enqueueHostCallback(StreamCallback callback, void* userData); + + //! return Stream object for default CUDA stream + static Stream& Null(); + + //! returns true if stream object is not default (!= 0) + operator bool_type() const; + + class Impl; + +private: + Ptr impl_; + Stream(const Ptr& impl); + + friend struct StreamAccessor; + friend class BufferPool; + friend class DefaultDeviceInitializer; +}; + +class CV_EXPORTS Event +{ +public: + enum CreateFlags + { + DEFAULT = 0x00, /**< Default event flag */ + BLOCKING_SYNC = 0x01, /**< Event uses blocking synchronization */ + DISABLE_TIMING = 0x02, /**< Event will not record timing data */ + INTERPROCESS = 0x04 /**< Event is suitable for interprocess use. DisableTiming must be set */ + }; + + explicit Event(CreateFlags flags = DEFAULT); + + //! records an event + void record(Stream& stream = Stream::Null()); + + //! queries an event's status + bool queryIfComplete() const; + + //! waits for an event to complete + void waitForCompletion(); + + //! computes the elapsed time between events + static float elapsedTime(const Event& start, const Event& end); + + class Impl; + +private: + Ptr impl_; + Event(const Ptr& impl); + + friend struct EventAccessor; +}; + +//! @} cudacore_struct + +//=================================================================================== +// Initialization & Info +//=================================================================================== + +//! @addtogroup cudacore_init +//! @{ + +/** @brief Returns the number of installed CUDA-enabled devices. + +Use this function before any other CUDA functions calls. If OpenCV is compiled without CUDA support, +this function returns 0. If the CUDA driver is not installed, or is incompatible, this function +returns -1. + */ +CV_EXPORTS int getCudaEnabledDeviceCount(); + +/** @brief Sets a device and initializes it for the current thread. + +@param device System index of a CUDA device starting with 0. + +If the call of this function is omitted, a default device is initialized at the fist CUDA usage. + */ +CV_EXPORTS void setDevice(int device); + +/** @brief Returns the current device index set by cuda::setDevice or initialized by default. + */ +CV_EXPORTS int getDevice(); + +/** @brief Explicitly destroys and cleans up all resources associated with the current device in the current +process. + +Any subsequent API call to this device will reinitialize the device. + */ +CV_EXPORTS void resetDevice(); + +/** @brief Enumeration providing CUDA computing features. + */ +enum FeatureSet +{ + FEATURE_SET_COMPUTE_10 = 10, + FEATURE_SET_COMPUTE_11 = 11, + FEATURE_SET_COMPUTE_12 = 12, + FEATURE_SET_COMPUTE_13 = 13, + FEATURE_SET_COMPUTE_20 = 20, + FEATURE_SET_COMPUTE_21 = 21, + FEATURE_SET_COMPUTE_30 = 30, + FEATURE_SET_COMPUTE_32 = 32, + FEATURE_SET_COMPUTE_35 = 35, + FEATURE_SET_COMPUTE_50 = 50, + + GLOBAL_ATOMICS = FEATURE_SET_COMPUTE_11, + SHARED_ATOMICS = FEATURE_SET_COMPUTE_12, + NATIVE_DOUBLE = FEATURE_SET_COMPUTE_13, + WARP_SHUFFLE_FUNCTIONS = FEATURE_SET_COMPUTE_30, + DYNAMIC_PARALLELISM = FEATURE_SET_COMPUTE_35 +}; + +//! checks whether current device supports the given feature +CV_EXPORTS bool deviceSupports(FeatureSet feature_set); + +/** @brief Class providing a set of static methods to check what NVIDIA\* card architecture the CUDA module was +built for. + +According to the CUDA C Programming Guide Version 3.2: "PTX code produced for some specific compute +capability can always be compiled to binary code of greater or equal compute capability". + */ +class CV_EXPORTS TargetArchs +{ +public: + /** @brief The following method checks whether the module was built with the support of the given feature: + + @param feature_set Features to be checked. See :ocvcuda::FeatureSet. + */ + static bool builtWith(FeatureSet feature_set); + + /** @brief There is a set of methods to check whether the module contains intermediate (PTX) or binary CUDA + code for the given architecture(s): + + @param major Major compute capability version. + @param minor Minor compute capability version. + */ + static bool has(int major, int minor); + static bool hasPtx(int major, int minor); + static bool hasBin(int major, int minor); + + static bool hasEqualOrLessPtx(int major, int minor); + static bool hasEqualOrGreater(int major, int minor); + static bool hasEqualOrGreaterPtx(int major, int minor); + static bool hasEqualOrGreaterBin(int major, int minor); +}; + +/** @brief Class providing functionality for querying the specified GPU properties. + */ +class CV_EXPORTS DeviceInfo +{ +public: + //! creates DeviceInfo object for the current GPU + DeviceInfo(); + + /** @brief The constructors. + + @param device_id System index of the CUDA device starting with 0. + + Constructs the DeviceInfo object for the specified device. If device_id parameter is missed, it + constructs an object for the current device. + */ + DeviceInfo(int device_id); + + /** @brief Returns system index of the CUDA device starting with 0. + */ + int deviceID() const; + + //! ASCII string identifying device + const char* name() const; + + //! global memory available on device in bytes + size_t totalGlobalMem() const; + + //! shared memory available per block in bytes + size_t sharedMemPerBlock() const; + + //! 32-bit registers available per block + int regsPerBlock() const; + + //! warp size in threads + int warpSize() const; + + //! maximum pitch in bytes allowed by memory copies + size_t memPitch() const; + + //! maximum number of threads per block + int maxThreadsPerBlock() const; + + //! maximum size of each dimension of a block + Vec3i maxThreadsDim() const; + + //! maximum size of each dimension of a grid + Vec3i maxGridSize() const; + + //! clock frequency in kilohertz + int clockRate() const; + + //! constant memory available on device in bytes + size_t totalConstMem() const; + + //! major compute capability + int majorVersion() const; + + //! minor compute capability + int minorVersion() const; + + //! alignment requirement for textures + size_t textureAlignment() const; + + //! pitch alignment requirement for texture references bound to pitched memory + size_t texturePitchAlignment() const; + + //! number of multiprocessors on device + int multiProcessorCount() const; + + //! specified whether there is a run time limit on kernels + bool kernelExecTimeoutEnabled() const; + + //! device is integrated as opposed to discrete + bool integrated() const; + + //! device can map host memory with cudaHostAlloc/cudaHostGetDevicePointer + bool canMapHostMemory() const; + + enum ComputeMode + { + ComputeModeDefault, /**< default compute mode (Multiple threads can use cudaSetDevice with this device) */ + ComputeModeExclusive, /**< compute-exclusive-thread mode (Only one thread in one process will be able to use cudaSetDevice with this device) */ + ComputeModeProhibited, /**< compute-prohibited mode (No threads can use cudaSetDevice with this device) */ + ComputeModeExclusiveProcess /**< compute-exclusive-process mode (Many threads in one process will be able to use cudaSetDevice with this device) */ + }; + + //! compute mode + ComputeMode computeMode() const; + + //! maximum 1D texture size + int maxTexture1D() const; + + //! maximum 1D mipmapped texture size + int maxTexture1DMipmap() const; + + //! maximum size for 1D textures bound to linear memory + int maxTexture1DLinear() const; + + //! maximum 2D texture dimensions + Vec2i maxTexture2D() const; + + //! maximum 2D mipmapped texture dimensions + Vec2i maxTexture2DMipmap() const; + + //! maximum dimensions (width, height, pitch) for 2D textures bound to pitched memory + Vec3i maxTexture2DLinear() const; + + //! maximum 2D texture dimensions if texture gather operations have to be performed + Vec2i maxTexture2DGather() const; + + //! maximum 3D texture dimensions + Vec3i maxTexture3D() const; + + //! maximum Cubemap texture dimensions + int maxTextureCubemap() const; + + //! maximum 1D layered texture dimensions + Vec2i maxTexture1DLayered() const; + + //! maximum 2D layered texture dimensions + Vec3i maxTexture2DLayered() const; + + //! maximum Cubemap layered texture dimensions + Vec2i maxTextureCubemapLayered() const; + + //! maximum 1D surface size + int maxSurface1D() const; + + //! maximum 2D surface dimensions + Vec2i maxSurface2D() const; + + //! maximum 3D surface dimensions + Vec3i maxSurface3D() const; + + //! maximum 1D layered surface dimensions + Vec2i maxSurface1DLayered() const; + + //! maximum 2D layered surface dimensions + Vec3i maxSurface2DLayered() const; + + //! maximum Cubemap surface dimensions + int maxSurfaceCubemap() const; + + //! maximum Cubemap layered surface dimensions + Vec2i maxSurfaceCubemapLayered() const; + + //! alignment requirements for surfaces + size_t surfaceAlignment() const; + + //! device can possibly execute multiple kernels concurrently + bool concurrentKernels() const; + + //! device has ECC support enabled + bool ECCEnabled() const; + + //! PCI bus ID of the device + int pciBusID() const; + + //! PCI device ID of the device + int pciDeviceID() const; + + //! PCI domain ID of the device + int pciDomainID() const; + + //! true if device is a Tesla device using TCC driver, false otherwise + bool tccDriver() const; + + //! number of asynchronous engines + int asyncEngineCount() const; + + //! device shares a unified address space with the host + bool unifiedAddressing() const; + + //! peak memory clock frequency in kilohertz + int memoryClockRate() const; + + //! global memory bus width in bits + int memoryBusWidth() const; + + //! size of L2 cache in bytes + int l2CacheSize() const; + + //! maximum resident threads per multiprocessor + int maxThreadsPerMultiProcessor() const; + + //! gets free and total device memory + void queryMemory(size_t& totalMemory, size_t& freeMemory) const; + size_t freeMemory() const; + size_t totalMemory() const; + + /** @brief Provides information on CUDA feature support. + + @param feature_set Features to be checked. See cuda::FeatureSet. + + This function returns true if the device has the specified CUDA feature. Otherwise, it returns false + */ + bool supports(FeatureSet feature_set) const; + + /** @brief Checks the CUDA module and device compatibility. + + This function returns true if the CUDA module can be run on the specified device. Otherwise, it + returns false . + */ + bool isCompatible() const; + +private: + int device_id_; +}; + +CV_EXPORTS void printCudaDeviceInfo(int device); +CV_EXPORTS void printShortCudaDeviceInfo(int device); + +/** @brief Converts an array to half precision floating number. + +@param _src input array. +@param _dst output array. +@param stream Stream for the asynchronous version. +@sa convertFp16 +*/ +CV_EXPORTS void convertFp16(InputArray _src, OutputArray _dst, Stream& stream = Stream::Null()); + +//! @} cudacore_init + +}} // namespace cv { namespace cuda { + + +#include "opencv2/core/cuda.inl.hpp" + +#endif /* OPENCV_CORE_CUDA_HPP */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda.inl.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda.inl.hpp new file mode 100644 index 0000000000000000000000000000000000000000..35ae2e49d77f331cca5d6cb13a48f3a6885c2ab7 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda.inl.hpp @@ -0,0 +1,631 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_CUDAINL_HPP +#define OPENCV_CORE_CUDAINL_HPP + +#include "opencv2/core/cuda.hpp" + +//! @cond IGNORED + +namespace cv { namespace cuda { + +//=================================================================================== +// GpuMat +//=================================================================================== + +inline +GpuMat::GpuMat(Allocator* allocator_) + : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_) +{} + +inline +GpuMat::GpuMat(int rows_, int cols_, int type_, Allocator* allocator_) + : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_) +{ + if (rows_ > 0 && cols_ > 0) + create(rows_, cols_, type_); +} + +inline +GpuMat::GpuMat(Size size_, int type_, Allocator* allocator_) + : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_) +{ + if (size_.height > 0 && size_.width > 0) + create(size_.height, size_.width, type_); +} + +inline +GpuMat::GpuMat(int rows_, int cols_, int type_, Scalar s_, Allocator* allocator_) + : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_) +{ + if (rows_ > 0 && cols_ > 0) + { + create(rows_, cols_, type_); + setTo(s_); + } +} + +inline +GpuMat::GpuMat(Size size_, int type_, Scalar s_, Allocator* allocator_) + : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_) +{ + if (size_.height > 0 && size_.width > 0) + { + create(size_.height, size_.width, type_); + setTo(s_); + } +} + +inline +GpuMat::GpuMat(const GpuMat& m) + : flags(m.flags), rows(m.rows), cols(m.cols), step(m.step), data(m.data), refcount(m.refcount), datastart(m.datastart), dataend(m.dataend), allocator(m.allocator) +{ + if (refcount) + CV_XADD(refcount, 1); +} + +inline +GpuMat::GpuMat(InputArray arr, Allocator* allocator_) : + flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_) +{ + upload(arr); +} + +inline +GpuMat::~GpuMat() +{ + release(); +} + +inline +GpuMat& GpuMat::operator =(const GpuMat& m) +{ + if (this != &m) + { + GpuMat temp(m); + swap(temp); + } + + return *this; +} + +inline +void GpuMat::create(Size size_, int type_) +{ + create(size_.height, size_.width, type_); +} + +inline +void GpuMat::swap(GpuMat& b) +{ + std::swap(flags, b.flags); + std::swap(rows, b.rows); + std::swap(cols, b.cols); + std::swap(step, b.step); + std::swap(data, b.data); + std::swap(datastart, b.datastart); + std::swap(dataend, b.dataend); + std::swap(refcount, b.refcount); + std::swap(allocator, b.allocator); +} + +inline +GpuMat GpuMat::clone() const +{ + GpuMat m; + copyTo(m); + return m; +} + +inline +void GpuMat::copyTo(OutputArray dst, InputArray mask) const +{ + copyTo(dst, mask, Stream::Null()); +} + +inline +GpuMat& GpuMat::setTo(Scalar s) +{ + return setTo(s, Stream::Null()); +} + +inline +GpuMat& GpuMat::setTo(Scalar s, InputArray mask) +{ + return setTo(s, mask, Stream::Null()); +} + +inline +void GpuMat::convertTo(OutputArray dst, int rtype) const +{ + convertTo(dst, rtype, Stream::Null()); +} + +inline +void GpuMat::convertTo(OutputArray dst, int rtype, double alpha, double beta) const +{ + convertTo(dst, rtype, alpha, beta, Stream::Null()); +} + +inline +void GpuMat::convertTo(OutputArray dst, int rtype, double alpha, Stream& stream) const +{ + convertTo(dst, rtype, alpha, 0.0, stream); +} + +inline +void GpuMat::assignTo(GpuMat& m, int _type) const +{ + if (_type < 0) + m = *this; + else + convertTo(m, _type); +} + +inline +uchar* GpuMat::ptr(int y) +{ + CV_DbgAssert( (unsigned)y < (unsigned)rows ); + return data + step * y; +} + +inline +const uchar* GpuMat::ptr(int y) const +{ + CV_DbgAssert( (unsigned)y < (unsigned)rows ); + return data + step * y; +} + +template inline +_Tp* GpuMat::ptr(int y) +{ + return (_Tp*)ptr(y); +} + +template inline +const _Tp* GpuMat::ptr(int y) const +{ + return (const _Tp*)ptr(y); +} + +template inline +GpuMat::operator PtrStepSz() const +{ + return PtrStepSz(rows, cols, (T*)data, step); +} + +template inline +GpuMat::operator PtrStep() const +{ + return PtrStep((T*)data, step); +} + +inline +GpuMat GpuMat::row(int y) const +{ + return GpuMat(*this, Range(y, y+1), Range::all()); +} + +inline +GpuMat GpuMat::col(int x) const +{ + return GpuMat(*this, Range::all(), Range(x, x+1)); +} + +inline +GpuMat GpuMat::rowRange(int startrow, int endrow) const +{ + return GpuMat(*this, Range(startrow, endrow), Range::all()); +} + +inline +GpuMat GpuMat::rowRange(Range r) const +{ + return GpuMat(*this, r, Range::all()); +} + +inline +GpuMat GpuMat::colRange(int startcol, int endcol) const +{ + return GpuMat(*this, Range::all(), Range(startcol, endcol)); +} + +inline +GpuMat GpuMat::colRange(Range r) const +{ + return GpuMat(*this, Range::all(), r); +} + +inline +GpuMat GpuMat::operator ()(Range rowRange_, Range colRange_) const +{ + return GpuMat(*this, rowRange_, colRange_); +} + +inline +GpuMat GpuMat::operator ()(Rect roi) const +{ + return GpuMat(*this, roi); +} + +inline +bool GpuMat::isContinuous() const +{ + return (flags & Mat::CONTINUOUS_FLAG) != 0; +} + +inline +size_t GpuMat::elemSize() const +{ + return CV_ELEM_SIZE(flags); +} + +inline +size_t GpuMat::elemSize1() const +{ + return CV_ELEM_SIZE1(flags); +} + +inline +int GpuMat::type() const +{ + return CV_MAT_TYPE(flags); +} + +inline +int GpuMat::depth() const +{ + return CV_MAT_DEPTH(flags); +} + +inline +int GpuMat::channels() const +{ + return CV_MAT_CN(flags); +} + +inline +size_t GpuMat::step1() const +{ + return step / elemSize1(); +} + +inline +Size GpuMat::size() const +{ + return Size(cols, rows); +} + +inline +bool GpuMat::empty() const +{ + return data == 0; +} + +static inline +GpuMat createContinuous(int rows, int cols, int type) +{ + GpuMat m; + createContinuous(rows, cols, type, m); + return m; +} + +static inline +void createContinuous(Size size, int type, OutputArray arr) +{ + createContinuous(size.height, size.width, type, arr); +} + +static inline +GpuMat createContinuous(Size size, int type) +{ + GpuMat m; + createContinuous(size, type, m); + return m; +} + +static inline +void ensureSizeIsEnough(Size size, int type, OutputArray arr) +{ + ensureSizeIsEnough(size.height, size.width, type, arr); +} + +static inline +void swap(GpuMat& a, GpuMat& b) +{ + a.swap(b); +} + +//=================================================================================== +// HostMem +//=================================================================================== + +inline +HostMem::HostMem(AllocType alloc_type_) + : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), alloc_type(alloc_type_) +{ +} + +inline +HostMem::HostMem(const HostMem& m) + : flags(m.flags), rows(m.rows), cols(m.cols), step(m.step), data(m.data), refcount(m.refcount), datastart(m.datastart), dataend(m.dataend), alloc_type(m.alloc_type) +{ + if( refcount ) + CV_XADD(refcount, 1); +} + +inline +HostMem::HostMem(int rows_, int cols_, int type_, AllocType alloc_type_) + : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), alloc_type(alloc_type_) +{ + if (rows_ > 0 && cols_ > 0) + create(rows_, cols_, type_); +} + +inline +HostMem::HostMem(Size size_, int type_, AllocType alloc_type_) + : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), alloc_type(alloc_type_) +{ + if (size_.height > 0 && size_.width > 0) + create(size_.height, size_.width, type_); +} + +inline +HostMem::HostMem(InputArray arr, AllocType alloc_type_) + : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), alloc_type(alloc_type_) +{ + arr.getMat().copyTo(*this); +} + +inline +HostMem::~HostMem() +{ + release(); +} + +inline +HostMem& HostMem::operator =(const HostMem& m) +{ + if (this != &m) + { + HostMem temp(m); + swap(temp); + } + + return *this; +} + +inline +void HostMem::swap(HostMem& b) +{ + std::swap(flags, b.flags); + std::swap(rows, b.rows); + std::swap(cols, b.cols); + std::swap(step, b.step); + std::swap(data, b.data); + std::swap(datastart, b.datastart); + std::swap(dataend, b.dataend); + std::swap(refcount, b.refcount); + std::swap(alloc_type, b.alloc_type); +} + +inline +HostMem HostMem::clone() const +{ + HostMem m(size(), type(), alloc_type); + createMatHeader().copyTo(m); + return m; +} + +inline +void HostMem::create(Size size_, int type_) +{ + create(size_.height, size_.width, type_); +} + +inline +Mat HostMem::createMatHeader() const +{ + return Mat(size(), type(), data, step); +} + +inline +bool HostMem::isContinuous() const +{ + return (flags & Mat::CONTINUOUS_FLAG) != 0; +} + +inline +size_t HostMem::elemSize() const +{ + return CV_ELEM_SIZE(flags); +} + +inline +size_t HostMem::elemSize1() const +{ + return CV_ELEM_SIZE1(flags); +} + +inline +int HostMem::type() const +{ + return CV_MAT_TYPE(flags); +} + +inline +int HostMem::depth() const +{ + return CV_MAT_DEPTH(flags); +} + +inline +int HostMem::channels() const +{ + return CV_MAT_CN(flags); +} + +inline +size_t HostMem::step1() const +{ + return step / elemSize1(); +} + +inline +Size HostMem::size() const +{ + return Size(cols, rows); +} + +inline +bool HostMem::empty() const +{ + return data == 0; +} + +static inline +void swap(HostMem& a, HostMem& b) +{ + a.swap(b); +} + +//=================================================================================== +// Stream +//=================================================================================== + +inline +Stream::Stream(const Ptr& impl) + : impl_(impl) +{ +} + +//=================================================================================== +// Event +//=================================================================================== + +inline +Event::Event(const Ptr& impl) + : impl_(impl) +{ +} + +//=================================================================================== +// Initialization & Info +//=================================================================================== + +inline +bool TargetArchs::has(int major, int minor) +{ + return hasPtx(major, minor) || hasBin(major, minor); +} + +inline +bool TargetArchs::hasEqualOrGreater(int major, int minor) +{ + return hasEqualOrGreaterPtx(major, minor) || hasEqualOrGreaterBin(major, minor); +} + +inline +DeviceInfo::DeviceInfo() +{ + device_id_ = getDevice(); +} + +inline +DeviceInfo::DeviceInfo(int device_id) +{ + CV_Assert( device_id >= 0 && device_id < getCudaEnabledDeviceCount() ); + device_id_ = device_id; +} + +inline +int DeviceInfo::deviceID() const +{ + return device_id_; +} + +inline +size_t DeviceInfo::freeMemory() const +{ + size_t _totalMemory = 0, _freeMemory = 0; + queryMemory(_totalMemory, _freeMemory); + return _freeMemory; +} + +inline +size_t DeviceInfo::totalMemory() const +{ + size_t _totalMemory = 0, _freeMemory = 0; + queryMemory(_totalMemory, _freeMemory); + return _totalMemory; +} + +inline +bool DeviceInfo::supports(FeatureSet feature_set) const +{ + int version = majorVersion() * 10 + minorVersion(); + return version >= feature_set; +} + + +}} // namespace cv { namespace cuda { + +//=================================================================================== +// Mat +//=================================================================================== + +namespace cv { + +inline +Mat::Mat(const cuda::GpuMat& m) + : flags(0), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows) +{ + m.download(*this); +} + +} + +//! @endcond + +#endif // OPENCV_CORE_CUDAINL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/block.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/block.hpp new file mode 100644 index 0000000000000000000000000000000000000000..c277f0ea9c411b6763ba04d6dec56644eb25479a --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/block.hpp @@ -0,0 +1,211 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_DEVICE_BLOCK_HPP +#define OPENCV_CUDA_DEVICE_BLOCK_HPP + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + struct Block + { + static __device__ __forceinline__ unsigned int id() + { + return blockIdx.x; + } + + static __device__ __forceinline__ unsigned int stride() + { + return blockDim.x * blockDim.y * blockDim.z; + } + + static __device__ __forceinline__ void sync() + { + __syncthreads(); + } + + static __device__ __forceinline__ int flattenedThreadId() + { + return threadIdx.z * blockDim.x * blockDim.y + threadIdx.y * blockDim.x + threadIdx.x; + } + + template + static __device__ __forceinline__ void fill(It beg, It end, const T& value) + { + int STRIDE = stride(); + It t = beg + flattenedThreadId(); + + for(; t < end; t += STRIDE) + *t = value; + } + + template + static __device__ __forceinline__ void yota(OutIt beg, OutIt end, T value) + { + int STRIDE = stride(); + int tid = flattenedThreadId(); + value += tid; + + for(OutIt t = beg + tid; t < end; t += STRIDE, value += STRIDE) + *t = value; + } + + template + static __device__ __forceinline__ void copy(InIt beg, InIt end, OutIt out) + { + int STRIDE = stride(); + InIt t = beg + flattenedThreadId(); + OutIt o = out + (t - beg); + + for(; t < end; t += STRIDE, o += STRIDE) + *o = *t; + } + + template + static __device__ __forceinline__ void transform(InIt beg, InIt end, OutIt out, UnOp op) + { + int STRIDE = stride(); + InIt t = beg + flattenedThreadId(); + OutIt o = out + (t - beg); + + for(; t < end; t += STRIDE, o += STRIDE) + *o = op(*t); + } + + template + static __device__ __forceinline__ void transform(InIt1 beg1, InIt1 end1, InIt2 beg2, OutIt out, BinOp op) + { + int STRIDE = stride(); + InIt1 t1 = beg1 + flattenedThreadId(); + InIt2 t2 = beg2 + flattenedThreadId(); + OutIt o = out + (t1 - beg1); + + for(; t1 < end1; t1 += STRIDE, t2 += STRIDE, o += STRIDE) + *o = op(*t1, *t2); + } + + template + static __device__ __forceinline__ void reduce(volatile T* buffer, BinOp op) + { + int tid = flattenedThreadId(); + T val = buffer[tid]; + + if (CTA_SIZE >= 1024) { if (tid < 512) buffer[tid] = val = op(val, buffer[tid + 512]); __syncthreads(); } + if (CTA_SIZE >= 512) { if (tid < 256) buffer[tid] = val = op(val, buffer[tid + 256]); __syncthreads(); } + if (CTA_SIZE >= 256) { if (tid < 128) buffer[tid] = val = op(val, buffer[tid + 128]); __syncthreads(); } + if (CTA_SIZE >= 128) { if (tid < 64) buffer[tid] = val = op(val, buffer[tid + 64]); __syncthreads(); } + + if (tid < 32) + { + if (CTA_SIZE >= 64) { buffer[tid] = val = op(val, buffer[tid + 32]); } + if (CTA_SIZE >= 32) { buffer[tid] = val = op(val, buffer[tid + 16]); } + if (CTA_SIZE >= 16) { buffer[tid] = val = op(val, buffer[tid + 8]); } + if (CTA_SIZE >= 8) { buffer[tid] = val = op(val, buffer[tid + 4]); } + if (CTA_SIZE >= 4) { buffer[tid] = val = op(val, buffer[tid + 2]); } + if (CTA_SIZE >= 2) { buffer[tid] = val = op(val, buffer[tid + 1]); } + } + } + + template + static __device__ __forceinline__ T reduce(volatile T* buffer, T init, BinOp op) + { + int tid = flattenedThreadId(); + T val = buffer[tid] = init; + __syncthreads(); + + if (CTA_SIZE >= 1024) { if (tid < 512) buffer[tid] = val = op(val, buffer[tid + 512]); __syncthreads(); } + if (CTA_SIZE >= 512) { if (tid < 256) buffer[tid] = val = op(val, buffer[tid + 256]); __syncthreads(); } + if (CTA_SIZE >= 256) { if (tid < 128) buffer[tid] = val = op(val, buffer[tid + 128]); __syncthreads(); } + if (CTA_SIZE >= 128) { if (tid < 64) buffer[tid] = val = op(val, buffer[tid + 64]); __syncthreads(); } + + if (tid < 32) + { + if (CTA_SIZE >= 64) { buffer[tid] = val = op(val, buffer[tid + 32]); } + if (CTA_SIZE >= 32) { buffer[tid] = val = op(val, buffer[tid + 16]); } + if (CTA_SIZE >= 16) { buffer[tid] = val = op(val, buffer[tid + 8]); } + if (CTA_SIZE >= 8) { buffer[tid] = val = op(val, buffer[tid + 4]); } + if (CTA_SIZE >= 4) { buffer[tid] = val = op(val, buffer[tid + 2]); } + if (CTA_SIZE >= 2) { buffer[tid] = val = op(val, buffer[tid + 1]); } + } + __syncthreads(); + return buffer[0]; + } + + template + static __device__ __forceinline__ void reduce_n(T* data, unsigned int n, BinOp op) + { + int ftid = flattenedThreadId(); + int sft = stride(); + + if (sft < n) + { + for (unsigned int i = sft + ftid; i < n; i += sft) + data[ftid] = op(data[ftid], data[i]); + + __syncthreads(); + + n = sft; + } + + while (n > 1) + { + unsigned int half = n/2; + + if (ftid < half) + data[ftid] = op(data[ftid], data[n - ftid - 1]); + + __syncthreads(); + + n = n - half; + } + } + }; +}}} + +//! @endcond + +#endif /* OPENCV_CUDA_DEVICE_BLOCK_HPP */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/border_interpolate.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/border_interpolate.hpp new file mode 100644 index 0000000000000000000000000000000000000000..874f705baf0249644f0b92ea31cfa2cb31b4ae19 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/border_interpolate.hpp @@ -0,0 +1,722 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_BORDER_INTERPOLATE_HPP +#define OPENCV_CUDA_BORDER_INTERPOLATE_HPP + +#include "saturate_cast.hpp" +#include "vec_traits.hpp" +#include "vec_math.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + ////////////////////////////////////////////////////////////// + // BrdConstant + + template struct BrdRowConstant + { + typedef D result_type; + + explicit __host__ __device__ __forceinline__ BrdRowConstant(int width_, const D& val_ = VecTraits::all(0)) : width(width_), val(val_) {} + + template __device__ __forceinline__ D at_low(int x, const T* data) const + { + return x >= 0 ? saturate_cast(data[x]) : val; + } + + template __device__ __forceinline__ D at_high(int x, const T* data) const + { + return x < width ? saturate_cast(data[x]) : val; + } + + template __device__ __forceinline__ D at(int x, const T* data) const + { + return (x >= 0 && x < width) ? saturate_cast(data[x]) : val; + } + + int width; + D val; + }; + + template struct BrdColConstant + { + typedef D result_type; + + explicit __host__ __device__ __forceinline__ BrdColConstant(int height_, const D& val_ = VecTraits::all(0)) : height(height_), val(val_) {} + + template __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const + { + return y >= 0 ? saturate_cast(*(const T*)((const char*)data + y * step)) : val; + } + + template __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const + { + return y < height ? saturate_cast(*(const T*)((const char*)data + y * step)) : val; + } + + template __device__ __forceinline__ D at(int y, const T* data, size_t step) const + { + return (y >= 0 && y < height) ? saturate_cast(*(const T*)((const char*)data + y * step)) : val; + } + + int height; + D val; + }; + + template struct BrdConstant + { + typedef D result_type; + + __host__ __device__ __forceinline__ BrdConstant(int height_, int width_, const D& val_ = VecTraits::all(0)) : height(height_), width(width_), val(val_) + { + } + + template __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const + { + return (x >= 0 && x < width && y >= 0 && y < height) ? saturate_cast(((const T*)((const uchar*)data + y * step))[x]) : val; + } + + template __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const + { + return (x >= 0 && x < width && y >= 0 && y < height) ? saturate_cast(src(y, x)) : val; + } + + int height; + int width; + D val; + }; + + ////////////////////////////////////////////////////////////// + // BrdReplicate + + template struct BrdRowReplicate + { + typedef D result_type; + + explicit __host__ __device__ __forceinline__ BrdRowReplicate(int width) : last_col(width - 1) {} + template __host__ __device__ __forceinline__ BrdRowReplicate(int width, U) : last_col(width - 1) {} + + __device__ __forceinline__ int idx_col_low(int x) const + { + return ::max(x, 0); + } + + __device__ __forceinline__ int idx_col_high(int x) const + { + return ::min(x, last_col); + } + + __device__ __forceinline__ int idx_col(int x) const + { + return idx_col_low(idx_col_high(x)); + } + + template __device__ __forceinline__ D at_low(int x, const T* data) const + { + return saturate_cast(data[idx_col_low(x)]); + } + + template __device__ __forceinline__ D at_high(int x, const T* data) const + { + return saturate_cast(data[idx_col_high(x)]); + } + + template __device__ __forceinline__ D at(int x, const T* data) const + { + return saturate_cast(data[idx_col(x)]); + } + + int last_col; + }; + + template struct BrdColReplicate + { + typedef D result_type; + + explicit __host__ __device__ __forceinline__ BrdColReplicate(int height) : last_row(height - 1) {} + template __host__ __device__ __forceinline__ BrdColReplicate(int height, U) : last_row(height - 1) {} + + __device__ __forceinline__ int idx_row_low(int y) const + { + return ::max(y, 0); + } + + __device__ __forceinline__ int idx_row_high(int y) const + { + return ::min(y, last_row); + } + + __device__ __forceinline__ int idx_row(int y) const + { + return idx_row_low(idx_row_high(y)); + } + + template __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const + { + return saturate_cast(*(const T*)((const char*)data + idx_row_low(y) * step)); + } + + template __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const + { + return saturate_cast(*(const T*)((const char*)data + idx_row_high(y) * step)); + } + + template __device__ __forceinline__ D at(int y, const T* data, size_t step) const + { + return saturate_cast(*(const T*)((const char*)data + idx_row(y) * step)); + } + + int last_row; + }; + + template struct BrdReplicate + { + typedef D result_type; + + __host__ __device__ __forceinline__ BrdReplicate(int height, int width) : last_row(height - 1), last_col(width - 1) {} + template __host__ __device__ __forceinline__ BrdReplicate(int height, int width, U) : last_row(height - 1), last_col(width - 1) {} + + __device__ __forceinline__ int idx_row_low(int y) const + { + return ::max(y, 0); + } + + __device__ __forceinline__ int idx_row_high(int y) const + { + return ::min(y, last_row); + } + + __device__ __forceinline__ int idx_row(int y) const + { + return idx_row_low(idx_row_high(y)); + } + + __device__ __forceinline__ int idx_col_low(int x) const + { + return ::max(x, 0); + } + + __device__ __forceinline__ int idx_col_high(int x) const + { + return ::min(x, last_col); + } + + __device__ __forceinline__ int idx_col(int x) const + { + return idx_col_low(idx_col_high(x)); + } + + template __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const + { + return saturate_cast(((const T*)((const char*)data + idx_row(y) * step))[idx_col(x)]); + } + + template __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const + { + return saturate_cast(src(idx_row(y), idx_col(x))); + } + + int last_row; + int last_col; + }; + + ////////////////////////////////////////////////////////////// + // BrdReflect101 + + template struct BrdRowReflect101 + { + typedef D result_type; + + explicit __host__ __device__ __forceinline__ BrdRowReflect101(int width) : last_col(width - 1) {} + template __host__ __device__ __forceinline__ BrdRowReflect101(int width, U) : last_col(width - 1) {} + + __device__ __forceinline__ int idx_col_low(int x) const + { + return ::abs(x) % (last_col + 1); + } + + __device__ __forceinline__ int idx_col_high(int x) const + { + return ::abs(last_col - ::abs(last_col - x)) % (last_col + 1); + } + + __device__ __forceinline__ int idx_col(int x) const + { + return idx_col_low(idx_col_high(x)); + } + + template __device__ __forceinline__ D at_low(int x, const T* data) const + { + return saturate_cast(data[idx_col_low(x)]); + } + + template __device__ __forceinline__ D at_high(int x, const T* data) const + { + return saturate_cast(data[idx_col_high(x)]); + } + + template __device__ __forceinline__ D at(int x, const T* data) const + { + return saturate_cast(data[idx_col(x)]); + } + + int last_col; + }; + + template struct BrdColReflect101 + { + typedef D result_type; + + explicit __host__ __device__ __forceinline__ BrdColReflect101(int height) : last_row(height - 1) {} + template __host__ __device__ __forceinline__ BrdColReflect101(int height, U) : last_row(height - 1) {} + + __device__ __forceinline__ int idx_row_low(int y) const + { + return ::abs(y) % (last_row + 1); + } + + __device__ __forceinline__ int idx_row_high(int y) const + { + return ::abs(last_row - ::abs(last_row - y)) % (last_row + 1); + } + + __device__ __forceinline__ int idx_row(int y) const + { + return idx_row_low(idx_row_high(y)); + } + + template __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const + { + return saturate_cast(*(const D*)((const char*)data + idx_row_low(y) * step)); + } + + template __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const + { + return saturate_cast(*(const D*)((const char*)data + idx_row_high(y) * step)); + } + + template __device__ __forceinline__ D at(int y, const T* data, size_t step) const + { + return saturate_cast(*(const D*)((const char*)data + idx_row(y) * step)); + } + + int last_row; + }; + + template struct BrdReflect101 + { + typedef D result_type; + + __host__ __device__ __forceinline__ BrdReflect101(int height, int width) : last_row(height - 1), last_col(width - 1) {} + template __host__ __device__ __forceinline__ BrdReflect101(int height, int width, U) : last_row(height - 1), last_col(width - 1) {} + + __device__ __forceinline__ int idx_row_low(int y) const + { + return ::abs(y) % (last_row + 1); + } + + __device__ __forceinline__ int idx_row_high(int y) const + { + return ::abs(last_row - ::abs(last_row - y)) % (last_row + 1); + } + + __device__ __forceinline__ int idx_row(int y) const + { + return idx_row_low(idx_row_high(y)); + } + + __device__ __forceinline__ int idx_col_low(int x) const + { + return ::abs(x) % (last_col + 1); + } + + __device__ __forceinline__ int idx_col_high(int x) const + { + return ::abs(last_col - ::abs(last_col - x)) % (last_col + 1); + } + + __device__ __forceinline__ int idx_col(int x) const + { + return idx_col_low(idx_col_high(x)); + } + + template __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const + { + return saturate_cast(((const T*)((const char*)data + idx_row(y) * step))[idx_col(x)]); + } + + template __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const + { + return saturate_cast(src(idx_row(y), idx_col(x))); + } + + int last_row; + int last_col; + }; + + ////////////////////////////////////////////////////////////// + // BrdReflect + + template struct BrdRowReflect + { + typedef D result_type; + + explicit __host__ __device__ __forceinline__ BrdRowReflect(int width) : last_col(width - 1) {} + template __host__ __device__ __forceinline__ BrdRowReflect(int width, U) : last_col(width - 1) {} + + __device__ __forceinline__ int idx_col_low(int x) const + { + return (::abs(x) - (x < 0)) % (last_col + 1); + } + + __device__ __forceinline__ int idx_col_high(int x) const + { + return ::abs(last_col - ::abs(last_col - x) + (x > last_col)) % (last_col + 1); + } + + __device__ __forceinline__ int idx_col(int x) const + { + return idx_col_high(::abs(x) - (x < 0)); + } + + template __device__ __forceinline__ D at_low(int x, const T* data) const + { + return saturate_cast(data[idx_col_low(x)]); + } + + template __device__ __forceinline__ D at_high(int x, const T* data) const + { + return saturate_cast(data[idx_col_high(x)]); + } + + template __device__ __forceinline__ D at(int x, const T* data) const + { + return saturate_cast(data[idx_col(x)]); + } + + int last_col; + }; + + template struct BrdColReflect + { + typedef D result_type; + + explicit __host__ __device__ __forceinline__ BrdColReflect(int height) : last_row(height - 1) {} + template __host__ __device__ __forceinline__ BrdColReflect(int height, U) : last_row(height - 1) {} + + __device__ __forceinline__ int idx_row_low(int y) const + { + return (::abs(y) - (y < 0)) % (last_row + 1); + } + + __device__ __forceinline__ int idx_row_high(int y) const + { + return ::abs(last_row - ::abs(last_row - y) + (y > last_row)) % (last_row + 1); + } + + __device__ __forceinline__ int idx_row(int y) const + { + return idx_row_high(::abs(y) - (y < 0)); + } + + template __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const + { + return saturate_cast(*(const D*)((const char*)data + idx_row_low(y) * step)); + } + + template __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const + { + return saturate_cast(*(const D*)((const char*)data + idx_row_high(y) * step)); + } + + template __device__ __forceinline__ D at(int y, const T* data, size_t step) const + { + return saturate_cast(*(const D*)((const char*)data + idx_row(y) * step)); + } + + int last_row; + }; + + template struct BrdReflect + { + typedef D result_type; + + __host__ __device__ __forceinline__ BrdReflect(int height, int width) : last_row(height - 1), last_col(width - 1) {} + template __host__ __device__ __forceinline__ BrdReflect(int height, int width, U) : last_row(height - 1), last_col(width - 1) {} + + __device__ __forceinline__ int idx_row_low(int y) const + { + return (::abs(y) - (y < 0)) % (last_row + 1); + } + + __device__ __forceinline__ int idx_row_high(int y) const + { + return /*::abs*/(last_row - ::abs(last_row - y) + (y > last_row)) /*% (last_row + 1)*/; + } + + __device__ __forceinline__ int idx_row(int y) const + { + return idx_row_low(idx_row_high(y)); + } + + __device__ __forceinline__ int idx_col_low(int x) const + { + return (::abs(x) - (x < 0)) % (last_col + 1); + } + + __device__ __forceinline__ int idx_col_high(int x) const + { + return (last_col - ::abs(last_col - x) + (x > last_col)); + } + + __device__ __forceinline__ int idx_col(int x) const + { + return idx_col_low(idx_col_high(x)); + } + + template __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const + { + return saturate_cast(((const T*)((const char*)data + idx_row(y) * step))[idx_col(x)]); + } + + template __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const + { + return saturate_cast(src(idx_row(y), idx_col(x))); + } + + int last_row; + int last_col; + }; + + ////////////////////////////////////////////////////////////// + // BrdWrap + + template struct BrdRowWrap + { + typedef D result_type; + + explicit __host__ __device__ __forceinline__ BrdRowWrap(int width_) : width(width_) {} + template __host__ __device__ __forceinline__ BrdRowWrap(int width_, U) : width(width_) {} + + __device__ __forceinline__ int idx_col_low(int x) const + { + return (x >= 0) * x + (x < 0) * (x - ((x - width + 1) / width) * width); + } + + __device__ __forceinline__ int idx_col_high(int x) const + { + return (x < width) * x + (x >= width) * (x % width); + } + + __device__ __forceinline__ int idx_col(int x) const + { + return idx_col_high(idx_col_low(x)); + } + + template __device__ __forceinline__ D at_low(int x, const T* data) const + { + return saturate_cast(data[idx_col_low(x)]); + } + + template __device__ __forceinline__ D at_high(int x, const T* data) const + { + return saturate_cast(data[idx_col_high(x)]); + } + + template __device__ __forceinline__ D at(int x, const T* data) const + { + return saturate_cast(data[idx_col(x)]); + } + + int width; + }; + + template struct BrdColWrap + { + typedef D result_type; + + explicit __host__ __device__ __forceinline__ BrdColWrap(int height_) : height(height_) {} + template __host__ __device__ __forceinline__ BrdColWrap(int height_, U) : height(height_) {} + + __device__ __forceinline__ int idx_row_low(int y) const + { + return (y >= 0) * y + (y < 0) * (y - ((y - height + 1) / height) * height); + } + + __device__ __forceinline__ int idx_row_high(int y) const + { + return (y < height) * y + (y >= height) * (y % height); + } + + __device__ __forceinline__ int idx_row(int y) const + { + return idx_row_high(idx_row_low(y)); + } + + template __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const + { + return saturate_cast(*(const D*)((const char*)data + idx_row_low(y) * step)); + } + + template __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const + { + return saturate_cast(*(const D*)((const char*)data + idx_row_high(y) * step)); + } + + template __device__ __forceinline__ D at(int y, const T* data, size_t step) const + { + return saturate_cast(*(const D*)((const char*)data + idx_row(y) * step)); + } + + int height; + }; + + template struct BrdWrap + { + typedef D result_type; + + __host__ __device__ __forceinline__ BrdWrap(int height_, int width_) : + height(height_), width(width_) + { + } + template + __host__ __device__ __forceinline__ BrdWrap(int height_, int width_, U) : + height(height_), width(width_) + { + } + + __device__ __forceinline__ int idx_row_low(int y) const + { + return (y >= 0) ? y : (y - ((y - height + 1) / height) * height); + } + + __device__ __forceinline__ int idx_row_high(int y) const + { + return (y < height) ? y : (y % height); + } + + __device__ __forceinline__ int idx_row(int y) const + { + return idx_row_high(idx_row_low(y)); + } + + __device__ __forceinline__ int idx_col_low(int x) const + { + return (x >= 0) ? x : (x - ((x - width + 1) / width) * width); + } + + __device__ __forceinline__ int idx_col_high(int x) const + { + return (x < width) ? x : (x % width); + } + + __device__ __forceinline__ int idx_col(int x) const + { + return idx_col_high(idx_col_low(x)); + } + + template __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const + { + return saturate_cast(((const T*)((const char*)data + idx_row(y) * step))[idx_col(x)]); + } + + template __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const + { + return saturate_cast(src(idx_row(y), idx_col(x))); + } + + int height; + int width; + }; + + ////////////////////////////////////////////////////////////// + // BorderReader + + template struct BorderReader + { + typedef typename B::result_type elem_type; + typedef typename Ptr2D::index_type index_type; + + __host__ __device__ __forceinline__ BorderReader(const Ptr2D& ptr_, const B& b_) : ptr(ptr_), b(b_) {} + + __device__ __forceinline__ elem_type operator ()(index_type y, index_type x) const + { + return b.at(y, x, ptr); + } + + Ptr2D ptr; + B b; + }; + + // under win32 there is some bug with templated types that passed as kernel parameters + // with this specialization all works fine + template struct BorderReader< Ptr2D, BrdConstant > + { + typedef typename BrdConstant::result_type elem_type; + typedef typename Ptr2D::index_type index_type; + + __host__ __device__ __forceinline__ BorderReader(const Ptr2D& src_, const BrdConstant& b) : + src(src_), height(b.height), width(b.width), val(b.val) + { + } + + __device__ __forceinline__ D operator ()(index_type y, index_type x) const + { + return (x >= 0 && x < width && y >= 0 && y < height) ? saturate_cast(src(y, x)) : val; + } + + Ptr2D src; + int height; + int width; + D val; + }; +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_BORDER_INTERPOLATE_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/color.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/color.hpp new file mode 100644 index 0000000000000000000000000000000000000000..dcce280214826d3495bb89699be3197822085090 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/color.hpp @@ -0,0 +1,309 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_COLOR_HPP +#define OPENCV_CUDA_COLOR_HPP + +#include "detail/color_detail.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + // All OPENCV_CUDA_IMPLEMENT_*_TRAITS(ColorSpace1_to_ColorSpace2, ...) macros implements + // template class ColorSpace1_to_ColorSpace2_traits + // { + // typedef ... functor_type; + // static __host__ __device__ functor_type create_functor(); + // }; + + OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgr_to_rgb, 3, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgr_to_bgra, 3, 4, 0) + OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgr_to_rgba, 3, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgra_to_bgr, 4, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgra_to_rgb, 4, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgra_to_rgba, 4, 4, 2) + + #undef OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS + + OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(bgr_to_bgr555, 3, 0, 5) + OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(bgr_to_bgr565, 3, 0, 6) + OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(rgb_to_bgr555, 3, 2, 5) + OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(rgb_to_bgr565, 3, 2, 6) + OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(bgra_to_bgr555, 4, 0, 5) + OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(bgra_to_bgr565, 4, 0, 6) + OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(rgba_to_bgr555, 4, 2, 5) + OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(rgba_to_bgr565, 4, 2, 6) + + #undef OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS + + OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr555_to_rgb, 3, 2, 5) + OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr565_to_rgb, 3, 2, 6) + OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr555_to_bgr, 3, 0, 5) + OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr565_to_bgr, 3, 0, 6) + OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr555_to_rgba, 4, 2, 5) + OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr565_to_rgba, 4, 2, 6) + OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr555_to_bgra, 4, 0, 5) + OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr565_to_bgra, 4, 0, 6) + + #undef OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS + + OPENCV_CUDA_IMPLEMENT_GRAY2RGB_TRAITS(gray_to_bgr, 3) + OPENCV_CUDA_IMPLEMENT_GRAY2RGB_TRAITS(gray_to_bgra, 4) + + #undef OPENCV_CUDA_IMPLEMENT_GRAY2RGB_TRAITS + + OPENCV_CUDA_IMPLEMENT_GRAY2RGB5x5_TRAITS(gray_to_bgr555, 5) + OPENCV_CUDA_IMPLEMENT_GRAY2RGB5x5_TRAITS(gray_to_bgr565, 6) + + #undef OPENCV_CUDA_IMPLEMENT_GRAY2RGB5x5_TRAITS + + OPENCV_CUDA_IMPLEMENT_RGB5x52GRAY_TRAITS(bgr555_to_gray, 5) + OPENCV_CUDA_IMPLEMENT_RGB5x52GRAY_TRAITS(bgr565_to_gray, 6) + + #undef OPENCV_CUDA_IMPLEMENT_RGB5x52GRAY_TRAITS + + OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS(rgb_to_gray, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS(bgr_to_gray, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS(rgba_to_gray, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS(bgra_to_gray, 4, 0) + + #undef OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS + + OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(rgb_to_yuv, 3, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(rgba_to_yuv, 4, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(rgb_to_yuv4, 3, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(rgba_to_yuv4, 4, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(bgr_to_yuv, 3, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(bgra_to_yuv, 4, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(bgr_to_yuv4, 3, 4, 0) + OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(bgra_to_yuv4, 4, 4, 0) + + #undef OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS + + OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv_to_rgb, 3, 3, 2) + OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv_to_rgba, 3, 4, 2) + OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv4_to_rgb, 4, 3, 2) + OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv4_to_rgba, 4, 4, 2) + OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv_to_bgr, 3, 3, 0) + OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv_to_bgra, 3, 4, 0) + OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv4_to_bgr, 4, 3, 0) + OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv4_to_bgra, 4, 4, 0) + + #undef OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS + + OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(rgb_to_YCrCb, 3, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(rgba_to_YCrCb, 4, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(rgb_to_YCrCb4, 3, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(rgba_to_YCrCb4, 4, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(bgr_to_YCrCb, 3, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(bgra_to_YCrCb, 4, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(bgr_to_YCrCb4, 3, 4, 0) + OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(bgra_to_YCrCb4, 4, 4, 0) + + #undef OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS + + OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb_to_rgb, 3, 3, 2) + OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb_to_rgba, 3, 4, 2) + OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb4_to_rgb, 4, 3, 2) + OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb4_to_rgba, 4, 4, 2) + OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb_to_bgr, 3, 3, 0) + OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb_to_bgra, 3, 4, 0) + OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb4_to_bgr, 4, 3, 0) + OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb4_to_bgra, 4, 4, 0) + + #undef OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS + + OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(rgb_to_xyz, 3, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(rgba_to_xyz, 4, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(rgb_to_xyz4, 3, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(rgba_to_xyz4, 4, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(bgr_to_xyz, 3, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(bgra_to_xyz, 4, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(bgr_to_xyz4, 3, 4, 0) + OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(bgra_to_xyz4, 4, 4, 0) + + #undef OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS + + OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz_to_rgb, 3, 3, 2) + OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz4_to_rgb, 4, 3, 2) + OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz_to_rgba, 3, 4, 2) + OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz4_to_rgba, 4, 4, 2) + OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz_to_bgr, 3, 3, 0) + OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz4_to_bgr, 4, 3, 0) + OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz_to_bgra, 3, 4, 0) + OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz4_to_bgra, 4, 4, 0) + + #undef OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS + + OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(rgb_to_hsv, 3, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(rgba_to_hsv, 4, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(rgb_to_hsv4, 3, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(rgba_to_hsv4, 4, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(bgr_to_hsv, 3, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(bgra_to_hsv, 4, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(bgr_to_hsv4, 3, 4, 0) + OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(bgra_to_hsv4, 4, 4, 0) + + #undef OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS + + OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv_to_rgb, 3, 3, 2) + OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv_to_rgba, 3, 4, 2) + OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv4_to_rgb, 4, 3, 2) + OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv4_to_rgba, 4, 4, 2) + OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv_to_bgr, 3, 3, 0) + OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv_to_bgra, 3, 4, 0) + OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv4_to_bgr, 4, 3, 0) + OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv4_to_bgra, 4, 4, 0) + + #undef OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS + + OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(rgb_to_hls, 3, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(rgba_to_hls, 4, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(rgb_to_hls4, 3, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(rgba_to_hls4, 4, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(bgr_to_hls, 3, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(bgra_to_hls, 4, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(bgr_to_hls4, 3, 4, 0) + OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(bgra_to_hls4, 4, 4, 0) + + #undef OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS + + OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls_to_rgb, 3, 3, 2) + OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls_to_rgba, 3, 4, 2) + OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls4_to_rgb, 4, 3, 2) + OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls4_to_rgba, 4, 4, 2) + OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls_to_bgr, 3, 3, 0) + OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls_to_bgra, 3, 4, 0) + OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls4_to_bgr, 4, 3, 0) + OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls4_to_bgra, 4, 4, 0) + + #undef OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS + + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(rgb_to_lab, 3, 3, true, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(rgba_to_lab, 4, 3, true, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(rgb_to_lab4, 3, 4, true, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(rgba_to_lab4, 4, 4, true, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(bgr_to_lab, 3, 3, true, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(bgra_to_lab, 4, 3, true, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(bgr_to_lab4, 3, 4, true, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(bgra_to_lab4, 4, 4, true, 0) + + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lrgb_to_lab, 3, 3, false, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lrgba_to_lab, 4, 3, false, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lrgb_to_lab4, 3, 4, false, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lrgba_to_lab4, 4, 4, false, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lbgr_to_lab, 3, 3, false, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lbgra_to_lab, 4, 3, false, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lbgr_to_lab4, 3, 4, false, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lbgra_to_lab4, 4, 4, false, 0) + + #undef OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS + + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_rgb, 3, 3, true, 2) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_rgb, 4, 3, true, 2) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_rgba, 3, 4, true, 2) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_rgba, 4, 4, true, 2) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_bgr, 3, 3, true, 0) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_bgr, 4, 3, true, 0) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_bgra, 3, 4, true, 0) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_bgra, 4, 4, true, 0) + + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_lrgb, 3, 3, false, 2) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_lrgb, 4, 3, false, 2) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_lrgba, 3, 4, false, 2) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_lrgba, 4, 4, false, 2) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_lbgr, 3, 3, false, 0) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_lbgr, 4, 3, false, 0) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_lbgra, 3, 4, false, 0) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_lbgra, 4, 4, false, 0) + + #undef OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS + + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(rgb_to_luv, 3, 3, true, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(rgba_to_luv, 4, 3, true, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(rgb_to_luv4, 3, 4, true, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(rgba_to_luv4, 4, 4, true, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(bgr_to_luv, 3, 3, true, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(bgra_to_luv, 4, 3, true, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(bgr_to_luv4, 3, 4, true, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(bgra_to_luv4, 4, 4, true, 0) + + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lrgb_to_luv, 3, 3, false, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lrgba_to_luv, 4, 3, false, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lrgb_to_luv4, 3, 4, false, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lrgba_to_luv4, 4, 4, false, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lbgr_to_luv, 3, 3, false, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lbgra_to_luv, 4, 3, false, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lbgr_to_luv4, 3, 4, false, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lbgra_to_luv4, 4, 4, false, 0) + + #undef OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS + + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_rgb, 3, 3, true, 2) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_rgb, 4, 3, true, 2) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_rgba, 3, 4, true, 2) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_rgba, 4, 4, true, 2) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_bgr, 3, 3, true, 0) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_bgr, 4, 3, true, 0) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_bgra, 3, 4, true, 0) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_bgra, 4, 4, true, 0) + + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_lrgb, 3, 3, false, 2) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_lrgb, 4, 3, false, 2) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_lrgba, 3, 4, false, 2) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_lrgba, 4, 4, false, 2) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_lbgr, 3, 3, false, 0) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_lbgr, 4, 3, false, 0) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_lbgra, 3, 4, false, 0) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_lbgra, 4, 4, false, 0) + + #undef OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_COLOR_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/common.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/common.hpp new file mode 100644 index 0000000000000000000000000000000000000000..14b1f3f5aecd40562c3b8b9ad06d05098fa3da30 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/common.hpp @@ -0,0 +1,109 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_COMMON_HPP +#define OPENCV_CUDA_COMMON_HPP + +#include +#include "opencv2/core/cuda_types.hpp" +#include "opencv2/core/cvdef.h" +#include "opencv2/core/base.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +#ifndef CV_PI_F + #ifndef CV_PI + #define CV_PI_F 3.14159265f + #else + #define CV_PI_F ((float)CV_PI) + #endif +#endif + +namespace cv { namespace cuda { + static inline void checkCudaError(cudaError_t err, const char* file, const int line, const char* func) + { + if (cudaSuccess != err) + cv::error(cv::Error::GpuApiCallError, cudaGetErrorString(err), func, file, line); + } +}} + +#ifndef cudaSafeCall + #define cudaSafeCall(expr) cv::cuda::checkCudaError(expr, __FILE__, __LINE__, CV_Func) +#endif + +namespace cv { namespace cuda +{ + template static inline bool isAligned(const T* ptr, size_t size) + { + return reinterpret_cast(ptr) % size == 0; + } + + static inline bool isAligned(size_t step, size_t size) + { + return step % size == 0; + } +}} + +namespace cv { namespace cuda +{ + namespace device + { + __host__ __device__ __forceinline__ int divUp(int total, int grain) + { + return (total + grain - 1) / grain; + } + + template inline void bindTexture(const textureReference* tex, const PtrStepSz& img) + { + cudaChannelFormatDesc desc = cudaCreateChannelDesc(); + cudaSafeCall( cudaBindTexture2D(0, tex, img.ptr(), &desc, img.cols, img.rows, img.step) ); + } + } +}} + +//! @endcond + +#endif // OPENCV_CUDA_COMMON_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/datamov_utils.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/datamov_utils.hpp new file mode 100644 index 0000000000000000000000000000000000000000..6820d0fd64de9090dd9f02a296ea3de0a3884ef1 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/datamov_utils.hpp @@ -0,0 +1,113 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_DATAMOV_UTILS_HPP +#define OPENCV_CUDA_DATAMOV_UTILS_HPP + +#include "common.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 200 + + // for Fermi memory space is detected automatically + template struct ForceGlob + { + __device__ __forceinline__ static void Load(const T* ptr, int offset, T& val) { val = ptr[offset]; } + }; + + #else // __CUDA_ARCH__ >= 200 + + #if defined(_WIN64) || defined(__LP64__) + // 64-bit register modifier for inlined asm + #define OPENCV_CUDA_ASM_PTR "l" + #else + // 32-bit register modifier for inlined asm + #define OPENCV_CUDA_ASM_PTR "r" + #endif + + template struct ForceGlob; + + #define OPENCV_CUDA_DEFINE_FORCE_GLOB(base_type, ptx_type, reg_mod) \ + template <> struct ForceGlob \ + { \ + __device__ __forceinline__ static void Load(const base_type* ptr, int offset, base_type& val) \ + { \ + asm("ld.global."#ptx_type" %0, [%1];" : "="#reg_mod(val) : OPENCV_CUDA_ASM_PTR(ptr + offset)); \ + } \ + }; + + #define OPENCV_CUDA_DEFINE_FORCE_GLOB_B(base_type, ptx_type) \ + template <> struct ForceGlob \ + { \ + __device__ __forceinline__ static void Load(const base_type* ptr, int offset, base_type& val) \ + { \ + asm("ld.global."#ptx_type" %0, [%1];" : "=r"(*reinterpret_cast(&val)) : OPENCV_CUDA_ASM_PTR(ptr + offset)); \ + } \ + }; + + OPENCV_CUDA_DEFINE_FORCE_GLOB_B(uchar, u8) + OPENCV_CUDA_DEFINE_FORCE_GLOB_B(schar, s8) + OPENCV_CUDA_DEFINE_FORCE_GLOB_B(char, b8) + OPENCV_CUDA_DEFINE_FORCE_GLOB (ushort, u16, h) + OPENCV_CUDA_DEFINE_FORCE_GLOB (short, s16, h) + OPENCV_CUDA_DEFINE_FORCE_GLOB (uint, u32, r) + OPENCV_CUDA_DEFINE_FORCE_GLOB (int, s32, r) + OPENCV_CUDA_DEFINE_FORCE_GLOB (float, f32, f) + OPENCV_CUDA_DEFINE_FORCE_GLOB (double, f64, d) + + #undef OPENCV_CUDA_DEFINE_FORCE_GLOB + #undef OPENCV_CUDA_DEFINE_FORCE_GLOB_B + #undef OPENCV_CUDA_ASM_PTR + + #endif // __CUDA_ARCH__ >= 200 +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_DATAMOV_UTILS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/detail/color_detail.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/detail/color_detail.hpp new file mode 100644 index 0000000000000000000000000000000000000000..bfb40550dcda7b48b2ebdf7342447468dfa350ce --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/detail/color_detail.hpp @@ -0,0 +1,1980 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_COLOR_DETAIL_HPP +#define OPENCV_CUDA_COLOR_DETAIL_HPP + +#include "../common.hpp" +#include "../vec_traits.hpp" +#include "../saturate_cast.hpp" +#include "../limits.hpp" +#include "../functional.hpp" + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + #ifndef CV_DESCALE + #define CV_DESCALE(x, n) (((x) + (1 << ((n)-1))) >> (n)) + #endif + + namespace color_detail + { + template struct ColorChannel + { + typedef float worktype_f; + static __device__ __forceinline__ T max() { return numeric_limits::max(); } + static __device__ __forceinline__ T half() { return (T)(max()/2 + 1); } + }; + + template<> struct ColorChannel + { + typedef float worktype_f; + static __device__ __forceinline__ float max() { return 1.f; } + static __device__ __forceinline__ float half() { return 0.5f; } + }; + + template static __device__ __forceinline__ void setAlpha(typename TypeVec::vec_type& vec, T val) + { + } + + template static __device__ __forceinline__ void setAlpha(typename TypeVec::vec_type& vec, T val) + { + vec.w = val; + } + + template static __device__ __forceinline__ T getAlpha(const typename TypeVec::vec_type& vec) + { + return ColorChannel::max(); + } + + template static __device__ __forceinline__ T getAlpha(const typename TypeVec::vec_type& vec) + { + return vec.w; + } + + enum + { + yuv_shift = 14, + xyz_shift = 12, + R2Y = 4899, + G2Y = 9617, + B2Y = 1868, + BLOCK_SIZE = 256 + }; + } + +////////////////// Various 3/4-channel to 3/4-channel RGB transformations ///////////////// + + namespace color_detail + { + template struct RGB2RGB + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + dst.x = (&src.x)[bidx]; + dst.y = src.y; + dst.z = (&src.x)[bidx^2]; + setAlpha(dst, getAlpha(src)); + + return dst; + } + + __host__ __device__ __forceinline__ RGB2RGB() {} + __host__ __device__ __forceinline__ RGB2RGB(const RGB2RGB&) {} + }; + + template <> struct RGB2RGB : unary_function + { + __device__ uint operator()(uint src) const + { + uint dst = 0; + + dst |= (0xffu & (src >> 16)); + dst |= (0xffu & (src >> 8)) << 8; + dst |= (0xffu & (src)) << 16; + dst |= (0xffu & (src >> 24)) << 24; + + return dst; + } + + __host__ __device__ __forceinline__ RGB2RGB() {} + __host__ __device__ __forceinline__ RGB2RGB(const RGB2RGB&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(name, scn, dcn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + +/////////// Transforming 16-bit (565 or 555) RGB to/from 24/32-bit (888[8]) RGB ////////// + + namespace color_detail + { + template struct RGB2RGB5x5Converter; + template struct RGB2RGB5x5Converter<6, bidx> + { + static __device__ __forceinline__ ushort cvt(const uchar3& src) + { + return (ushort)(((&src.x)[bidx] >> 3) | ((src.y & ~3) << 3) | (((&src.x)[bidx^2] & ~7) << 8)); + } + + static __device__ __forceinline__ ushort cvt(uint src) + { + uint b = 0xffu & (src >> (bidx * 8)); + uint g = 0xffu & (src >> 8); + uint r = 0xffu & (src >> ((bidx ^ 2) * 8)); + return (ushort)((b >> 3) | ((g & ~3) << 3) | ((r & ~7) << 8)); + } + }; + + template struct RGB2RGB5x5Converter<5, bidx> + { + static __device__ __forceinline__ ushort cvt(const uchar3& src) + { + return (ushort)(((&src.x)[bidx] >> 3) | ((src.y & ~7) << 2) | (((&src.x)[bidx^2] & ~7) << 7)); + } + + static __device__ __forceinline__ ushort cvt(uint src) + { + uint b = 0xffu & (src >> (bidx * 8)); + uint g = 0xffu & (src >> 8); + uint r = 0xffu & (src >> ((bidx ^ 2) * 8)); + uint a = 0xffu & (src >> 24); + return (ushort)((b >> 3) | ((g & ~7) << 2) | ((r & ~7) << 7) | (a * 0x8000)); + } + }; + + template struct RGB2RGB5x5; + + template struct RGB2RGB5x5<3, bidx,green_bits> : unary_function + { + __device__ __forceinline__ ushort operator()(const uchar3& src) const + { + return RGB2RGB5x5Converter::cvt(src); + } + + __host__ __device__ __forceinline__ RGB2RGB5x5() {} + __host__ __device__ __forceinline__ RGB2RGB5x5(const RGB2RGB5x5&) {} + }; + + template struct RGB2RGB5x5<4, bidx,green_bits> : unary_function + { + __device__ __forceinline__ ushort operator()(uint src) const + { + return RGB2RGB5x5Converter::cvt(src); + } + + __host__ __device__ __forceinline__ RGB2RGB5x5() {} + __host__ __device__ __forceinline__ RGB2RGB5x5(const RGB2RGB5x5&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(name, scn, bidx, green_bits) \ + struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2RGB5x5 functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + + namespace color_detail + { + template struct RGB5x52RGBConverter; + + template struct RGB5x52RGBConverter<5, bidx> + { + static __device__ __forceinline__ void cvt(uint src, uchar3& dst) + { + (&dst.x)[bidx] = src << 3; + dst.y = (src >> 2) & ~7; + (&dst.x)[bidx ^ 2] = (src >> 7) & ~7; + } + + static __device__ __forceinline__ void cvt(uint src, uint& dst) + { + dst = 0; + + dst |= (0xffu & (src << 3)) << (bidx * 8); + dst |= (0xffu & ((src >> 2) & ~7)) << 8; + dst |= (0xffu & ((src >> 7) & ~7)) << ((bidx ^ 2) * 8); + dst |= ((src & 0x8000) * 0xffu) << 24; + } + }; + + template struct RGB5x52RGBConverter<6, bidx> + { + static __device__ __forceinline__ void cvt(uint src, uchar3& dst) + { + (&dst.x)[bidx] = src << 3; + dst.y = (src >> 3) & ~3; + (&dst.x)[bidx ^ 2] = (src >> 8) & ~7; + } + + static __device__ __forceinline__ void cvt(uint src, uint& dst) + { + dst = 0xffu << 24; + + dst |= (0xffu & (src << 3)) << (bidx * 8); + dst |= (0xffu &((src >> 3) & ~3)) << 8; + dst |= (0xffu & ((src >> 8) & ~7)) << ((bidx ^ 2) * 8); + } + }; + + template struct RGB5x52RGB; + + template struct RGB5x52RGB<3, bidx, green_bits> : unary_function + { + __device__ __forceinline__ uchar3 operator()(ushort src) const + { + uchar3 dst; + RGB5x52RGBConverter::cvt(src, dst); + return dst; + } + __host__ __device__ __forceinline__ RGB5x52RGB() {} + __host__ __device__ __forceinline__ RGB5x52RGB(const RGB5x52RGB&) {} + + }; + + template struct RGB5x52RGB<4, bidx, green_bits> : unary_function + { + __device__ __forceinline__ uint operator()(ushort src) const + { + uint dst; + RGB5x52RGBConverter::cvt(src, dst); + return dst; + } + __host__ __device__ __forceinline__ RGB5x52RGB() {} + __host__ __device__ __forceinline__ RGB5x52RGB(const RGB5x52RGB&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(name, dcn, bidx, green_bits) \ + struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB5x52RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + +///////////////////////////////// Grayscale to Color //////////////////////////////// + + namespace color_detail + { + template struct Gray2RGB : unary_function::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator()(T src) const + { + typename TypeVec::vec_type dst; + + dst.z = dst.y = dst.x = src; + setAlpha(dst, ColorChannel::max()); + + return dst; + } + __host__ __device__ __forceinline__ Gray2RGB() {} + __host__ __device__ __forceinline__ Gray2RGB(const Gray2RGB&) {} + }; + + template <> struct Gray2RGB : unary_function + { + __device__ __forceinline__ uint operator()(uint src) const + { + uint dst = 0xffu << 24; + + dst |= src; + dst |= src << 8; + dst |= src << 16; + + return dst; + } + __host__ __device__ __forceinline__ Gray2RGB() {} + __host__ __device__ __forceinline__ Gray2RGB(const Gray2RGB&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_GRAY2RGB_TRAITS(name, dcn) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::Gray2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + + namespace color_detail + { + template struct Gray2RGB5x5Converter; + template<> struct Gray2RGB5x5Converter<6> + { + static __device__ __forceinline__ ushort cvt(uint t) + { + return (ushort)((t >> 3) | ((t & ~3) << 3) | ((t & ~7) << 8)); + } + }; + + template<> struct Gray2RGB5x5Converter<5> + { + static __device__ __forceinline__ ushort cvt(uint t) + { + t >>= 3; + return (ushort)(t | (t << 5) | (t << 10)); + } + }; + + template struct Gray2RGB5x5 : unary_function + { + __device__ __forceinline__ ushort operator()(uint src) const + { + return Gray2RGB5x5Converter::cvt(src); + } + + __host__ __device__ __forceinline__ Gray2RGB5x5() {} + __host__ __device__ __forceinline__ Gray2RGB5x5(const Gray2RGB5x5&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_GRAY2RGB5x5_TRAITS(name, green_bits) \ + struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::Gray2RGB5x5 functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + +///////////////////////////////// Color to Grayscale //////////////////////////////// + + namespace color_detail + { + template struct RGB5x52GrayConverter; + template <> struct RGB5x52GrayConverter<6> + { + static __device__ __forceinline__ uchar cvt(uint t) + { + return (uchar)CV_DESCALE(((t << 3) & 0xf8) * B2Y + ((t >> 3) & 0xfc) * G2Y + ((t >> 8) & 0xf8) * R2Y, yuv_shift); + } + }; + + template <> struct RGB5x52GrayConverter<5> + { + static __device__ __forceinline__ uchar cvt(uint t) + { + return (uchar)CV_DESCALE(((t << 3) & 0xf8) * B2Y + ((t >> 2) & 0xf8) * G2Y + ((t >> 7) & 0xf8) * R2Y, yuv_shift); + } + }; + + template struct RGB5x52Gray : unary_function + { + __device__ __forceinline__ uchar operator()(uint src) const + { + return RGB5x52GrayConverter::cvt(src); + } + __host__ __device__ __forceinline__ RGB5x52Gray() {} + __host__ __device__ __forceinline__ RGB5x52Gray(const RGB5x52Gray&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB5x52GRAY_TRAITS(name, green_bits) \ + struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB5x52Gray functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + + namespace color_detail + { + template static __device__ __forceinline__ T RGB2GrayConvert(const T* src) + { + return (T)CV_DESCALE((unsigned)(src[bidx] * B2Y + src[1] * G2Y + src[bidx^2] * R2Y), yuv_shift); + } + + template static __device__ __forceinline__ uchar RGB2GrayConvert(uint src) + { + uint b = 0xffu & (src >> (bidx * 8)); + uint g = 0xffu & (src >> 8); + uint r = 0xffu & (src >> ((bidx ^ 2) * 8)); + return CV_DESCALE((uint)(b * B2Y + g * G2Y + r * R2Y), yuv_shift); + } + + template static __device__ __forceinline__ float RGB2GrayConvert(const float* src) + { + return src[bidx] * 0.114f + src[1] * 0.587f + src[bidx^2] * 0.299f; + } + + template struct RGB2Gray : unary_function::vec_type, T> + { + __device__ __forceinline__ T operator()(const typename TypeVec::vec_type& src) const + { + return RGB2GrayConvert(&src.x); + } + __host__ __device__ __forceinline__ RGB2Gray() {} + __host__ __device__ __forceinline__ RGB2Gray(const RGB2Gray&) {} + }; + + template struct RGB2Gray : unary_function + { + __device__ __forceinline__ uchar operator()(uint src) const + { + return RGB2GrayConvert(src); + } + __host__ __device__ __forceinline__ RGB2Gray() {} + __host__ __device__ __forceinline__ RGB2Gray(const RGB2Gray&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS(name, scn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2Gray functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + +///////////////////////////////////// RGB <-> YUV ////////////////////////////////////// + + namespace color_detail + { + __constant__ float c_RGB2YUVCoeffs_f[5] = { 0.114f, 0.587f, 0.299f, 0.492f, 0.877f }; + __constant__ int c_RGB2YUVCoeffs_i[5] = { B2Y, G2Y, R2Y, 8061, 14369 }; + + template static __device__ void RGB2YUVConvert(const T* src, D& dst) + { + const int delta = ColorChannel::half() * (1 << yuv_shift); + + const int Y = CV_DESCALE(src[0] * c_RGB2YUVCoeffs_i[bidx^2] + src[1] * c_RGB2YUVCoeffs_i[1] + src[2] * c_RGB2YUVCoeffs_i[bidx], yuv_shift); + const int Cr = CV_DESCALE((src[bidx^2] - Y) * c_RGB2YUVCoeffs_i[3] + delta, yuv_shift); + const int Cb = CV_DESCALE((src[bidx] - Y) * c_RGB2YUVCoeffs_i[4] + delta, yuv_shift); + + dst.x = saturate_cast(Y); + dst.y = saturate_cast(Cr); + dst.z = saturate_cast(Cb); + } + + template static __device__ __forceinline__ void RGB2YUVConvert(const float* src, D& dst) + { + dst.x = src[0] * c_RGB2YUVCoeffs_f[bidx^2] + src[1] * c_RGB2YUVCoeffs_f[1] + src[2] * c_RGB2YUVCoeffs_f[bidx]; + dst.y = (src[bidx^2] - dst.x) * c_RGB2YUVCoeffs_f[3] + ColorChannel::half(); + dst.z = (src[bidx] - dst.x) * c_RGB2YUVCoeffs_f[4] + ColorChannel::half(); + } + + template struct RGB2YUV + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + RGB2YUVConvert(&src.x, dst); + return dst; + } + __host__ __device__ __forceinline__ RGB2YUV() {} + __host__ __device__ __forceinline__ RGB2YUV(const RGB2YUV&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(name, scn, dcn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2YUV functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + + namespace color_detail + { + __constant__ float c_YUV2RGBCoeffs_f[5] = { 2.032f, -0.395f, -0.581f, 1.140f }; + __constant__ int c_YUV2RGBCoeffs_i[5] = { 33292, -6472, -9519, 18678 }; + + template static __device__ void YUV2RGBConvert(const T& src, D* dst) + { + const int b = src.x + CV_DESCALE((src.z - ColorChannel::half()) * c_YUV2RGBCoeffs_i[3], yuv_shift); + + const int g = src.x + CV_DESCALE((src.z - ColorChannel::half()) * c_YUV2RGBCoeffs_i[2] + + (src.y - ColorChannel::half()) * c_YUV2RGBCoeffs_i[1], yuv_shift); + + const int r = src.x + CV_DESCALE((src.y - ColorChannel::half()) * c_YUV2RGBCoeffs_i[0], yuv_shift); + + dst[bidx] = saturate_cast(b); + dst[1] = saturate_cast(g); + dst[bidx^2] = saturate_cast(r); + } + + template static __device__ uint YUV2RGBConvert(uint src) + { + const int x = 0xff & (src); + const int y = 0xff & (src >> 8); + const int z = 0xff & (src >> 16); + + const int b = x + CV_DESCALE((z - ColorChannel::half()) * c_YUV2RGBCoeffs_i[3], yuv_shift); + + const int g = x + CV_DESCALE((z - ColorChannel::half()) * c_YUV2RGBCoeffs_i[2] + + (y - ColorChannel::half()) * c_YUV2RGBCoeffs_i[1], yuv_shift); + + const int r = x + CV_DESCALE((y - ColorChannel::half()) * c_YUV2RGBCoeffs_i[0], yuv_shift); + + uint dst = 0xffu << 24; + + dst |= saturate_cast(b) << (bidx * 8); + dst |= saturate_cast(g) << 8; + dst |= saturate_cast(r) << ((bidx ^ 2) * 8); + + return dst; + } + + template static __device__ __forceinline__ void YUV2RGBConvert(const T& src, float* dst) + { + dst[bidx] = src.x + (src.z - ColorChannel::half()) * c_YUV2RGBCoeffs_f[3]; + + dst[1] = src.x + (src.z - ColorChannel::half()) * c_YUV2RGBCoeffs_f[2] + + (src.y - ColorChannel::half()) * c_YUV2RGBCoeffs_f[1]; + + dst[bidx^2] = src.x + (src.y - ColorChannel::half()) * c_YUV2RGBCoeffs_f[0]; + } + + template struct YUV2RGB + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + YUV2RGBConvert(src, &dst.x); + setAlpha(dst, ColorChannel::max()); + + return dst; + } + __host__ __device__ __forceinline__ YUV2RGB() {} + __host__ __device__ __forceinline__ YUV2RGB(const YUV2RGB&) {} + }; + + template struct YUV2RGB : unary_function + { + __device__ __forceinline__ uint operator ()(uint src) const + { + return YUV2RGBConvert(src); + } + __host__ __device__ __forceinline__ YUV2RGB() {} + __host__ __device__ __forceinline__ YUV2RGB(const YUV2RGB&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(name, scn, dcn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::YUV2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + +///////////////////////////////////// RGB <-> YCrCb ////////////////////////////////////// + + namespace color_detail + { + __constant__ float c_RGB2YCrCbCoeffs_f[5] = {0.299f, 0.587f, 0.114f, 0.713f, 0.564f}; + __constant__ int c_RGB2YCrCbCoeffs_i[5] = {R2Y, G2Y, B2Y, 11682, 9241}; + + template static __device__ void RGB2YCrCbConvert(const T* src, D& dst) + { + const int delta = ColorChannel::half() * (1 << yuv_shift); + + const int Y = CV_DESCALE(src[0] * c_RGB2YCrCbCoeffs_i[bidx^2] + src[1] * c_RGB2YCrCbCoeffs_i[1] + src[2] * c_RGB2YCrCbCoeffs_i[bidx], yuv_shift); + const int Cr = CV_DESCALE((src[bidx^2] - Y) * c_RGB2YCrCbCoeffs_i[3] + delta, yuv_shift); + const int Cb = CV_DESCALE((src[bidx] - Y) * c_RGB2YCrCbCoeffs_i[4] + delta, yuv_shift); + + dst.x = saturate_cast(Y); + dst.y = saturate_cast(Cr); + dst.z = saturate_cast(Cb); + } + + template static __device__ uint RGB2YCrCbConvert(uint src) + { + const int delta = ColorChannel::half() * (1 << yuv_shift); + + const int Y = CV_DESCALE((0xffu & src) * c_RGB2YCrCbCoeffs_i[bidx^2] + (0xffu & (src >> 8)) * c_RGB2YCrCbCoeffs_i[1] + (0xffu & (src >> 16)) * c_RGB2YCrCbCoeffs_i[bidx], yuv_shift); + const int Cr = CV_DESCALE(((0xffu & (src >> ((bidx ^ 2) * 8))) - Y) * c_RGB2YCrCbCoeffs_i[3] + delta, yuv_shift); + const int Cb = CV_DESCALE(((0xffu & (src >> (bidx * 8))) - Y) * c_RGB2YCrCbCoeffs_i[4] + delta, yuv_shift); + + uint dst = 0; + + dst |= saturate_cast(Y); + dst |= saturate_cast(Cr) << 8; + dst |= saturate_cast(Cb) << 16; + + return dst; + } + + template static __device__ __forceinline__ void RGB2YCrCbConvert(const float* src, D& dst) + { + dst.x = src[0] * c_RGB2YCrCbCoeffs_f[bidx^2] + src[1] * c_RGB2YCrCbCoeffs_f[1] + src[2] * c_RGB2YCrCbCoeffs_f[bidx]; + dst.y = (src[bidx^2] - dst.x) * c_RGB2YCrCbCoeffs_f[3] + ColorChannel::half(); + dst.z = (src[bidx] - dst.x) * c_RGB2YCrCbCoeffs_f[4] + ColorChannel::half(); + } + + template struct RGB2YCrCb + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + RGB2YCrCbConvert(&src.x, dst); + return dst; + } + __host__ __device__ __forceinline__ RGB2YCrCb() {} + __host__ __device__ __forceinline__ RGB2YCrCb(const RGB2YCrCb&) {} + }; + + template struct RGB2YCrCb : unary_function + { + __device__ __forceinline__ uint operator ()(uint src) const + { + return RGB2YCrCbConvert(src); + } + + __host__ __device__ __forceinline__ RGB2YCrCb() {} + __host__ __device__ __forceinline__ RGB2YCrCb(const RGB2YCrCb&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(name, scn, dcn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2YCrCb functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + + namespace color_detail + { + __constant__ float c_YCrCb2RGBCoeffs_f[5] = {1.403f, -0.714f, -0.344f, 1.773f}; + __constant__ int c_YCrCb2RGBCoeffs_i[5] = {22987, -11698, -5636, 29049}; + + template static __device__ void YCrCb2RGBConvert(const T& src, D* dst) + { + const int b = src.x + CV_DESCALE((src.z - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[3], yuv_shift); + const int g = src.x + CV_DESCALE((src.z - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[2] + (src.y - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[1], yuv_shift); + const int r = src.x + CV_DESCALE((src.y - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[0], yuv_shift); + + dst[bidx] = saturate_cast(b); + dst[1] = saturate_cast(g); + dst[bidx^2] = saturate_cast(r); + } + + template static __device__ uint YCrCb2RGBConvert(uint src) + { + const int x = 0xff & (src); + const int y = 0xff & (src >> 8); + const int z = 0xff & (src >> 16); + + const int b = x + CV_DESCALE((z - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[3], yuv_shift); + const int g = x + CV_DESCALE((z - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[2] + (y - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[1], yuv_shift); + const int r = x + CV_DESCALE((y - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[0], yuv_shift); + + uint dst = 0xffu << 24; + + dst |= saturate_cast(b) << (bidx * 8); + dst |= saturate_cast(g) << 8; + dst |= saturate_cast(r) << ((bidx ^ 2) * 8); + + return dst; + } + + template __device__ __forceinline__ void YCrCb2RGBConvert(const T& src, float* dst) + { + dst[bidx] = src.x + (src.z - ColorChannel::half()) * c_YCrCb2RGBCoeffs_f[3]; + dst[1] = src.x + (src.z - ColorChannel::half()) * c_YCrCb2RGBCoeffs_f[2] + (src.y - ColorChannel::half()) * c_YCrCb2RGBCoeffs_f[1]; + dst[bidx^2] = src.x + (src.y - ColorChannel::half()) * c_YCrCb2RGBCoeffs_f[0]; + } + + template struct YCrCb2RGB + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + YCrCb2RGBConvert(src, &dst.x); + setAlpha(dst, ColorChannel::max()); + + return dst; + } + __host__ __device__ __forceinline__ YCrCb2RGB() {} + __host__ __device__ __forceinline__ YCrCb2RGB(const YCrCb2RGB&) {} + }; + + template struct YCrCb2RGB : unary_function + { + __device__ __forceinline__ uint operator ()(uint src) const + { + return YCrCb2RGBConvert(src); + } + __host__ __device__ __forceinline__ YCrCb2RGB() {} + __host__ __device__ __forceinline__ YCrCb2RGB(const YCrCb2RGB&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(name, scn, dcn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::YCrCb2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + +////////////////////////////////////// RGB <-> XYZ /////////////////////////////////////// + + namespace color_detail + { + __constant__ float c_RGB2XYZ_D65f[9] = { 0.412453f, 0.357580f, 0.180423f, 0.212671f, 0.715160f, 0.072169f, 0.019334f, 0.119193f, 0.950227f }; + __constant__ int c_RGB2XYZ_D65i[9] = { 1689, 1465, 739, 871, 2929, 296, 79, 488, 3892 }; + + template static __device__ __forceinline__ void RGB2XYZConvert(const T* src, D& dst) + { + dst.z = saturate_cast(CV_DESCALE(src[bidx^2] * c_RGB2XYZ_D65i[6] + src[1] * c_RGB2XYZ_D65i[7] + src[bidx] * c_RGB2XYZ_D65i[8], xyz_shift)); + dst.x = saturate_cast(CV_DESCALE(src[bidx^2] * c_RGB2XYZ_D65i[0] + src[1] * c_RGB2XYZ_D65i[1] + src[bidx] * c_RGB2XYZ_D65i[2], xyz_shift)); + dst.y = saturate_cast(CV_DESCALE(src[bidx^2] * c_RGB2XYZ_D65i[3] + src[1] * c_RGB2XYZ_D65i[4] + src[bidx] * c_RGB2XYZ_D65i[5], xyz_shift)); + } + + template static __device__ __forceinline__ uint RGB2XYZConvert(uint src) + { + const uint b = 0xffu & (src >> (bidx * 8)); + const uint g = 0xffu & (src >> 8); + const uint r = 0xffu & (src >> ((bidx ^ 2) * 8)); + + const uint x = saturate_cast(CV_DESCALE(r * c_RGB2XYZ_D65i[0] + g * c_RGB2XYZ_D65i[1] + b * c_RGB2XYZ_D65i[2], xyz_shift)); + const uint y = saturate_cast(CV_DESCALE(r * c_RGB2XYZ_D65i[3] + g * c_RGB2XYZ_D65i[4] + b * c_RGB2XYZ_D65i[5], xyz_shift)); + const uint z = saturate_cast(CV_DESCALE(r * c_RGB2XYZ_D65i[6] + g * c_RGB2XYZ_D65i[7] + b * c_RGB2XYZ_D65i[8], xyz_shift)); + + uint dst = 0; + + dst |= x; + dst |= y << 8; + dst |= z << 16; + + return dst; + } + + template static __device__ __forceinline__ void RGB2XYZConvert(const float* src, D& dst) + { + dst.x = src[bidx^2] * c_RGB2XYZ_D65f[0] + src[1] * c_RGB2XYZ_D65f[1] + src[bidx] * c_RGB2XYZ_D65f[2]; + dst.y = src[bidx^2] * c_RGB2XYZ_D65f[3] + src[1] * c_RGB2XYZ_D65f[4] + src[bidx] * c_RGB2XYZ_D65f[5]; + dst.z = src[bidx^2] * c_RGB2XYZ_D65f[6] + src[1] * c_RGB2XYZ_D65f[7] + src[bidx] * c_RGB2XYZ_D65f[8]; + } + + template struct RGB2XYZ + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + RGB2XYZConvert(&src.x, dst); + + return dst; + } + __host__ __device__ __forceinline__ RGB2XYZ() {} + __host__ __device__ __forceinline__ RGB2XYZ(const RGB2XYZ&) {} + }; + + template struct RGB2XYZ : unary_function + { + __device__ __forceinline__ uint operator()(uint src) const + { + return RGB2XYZConvert(src); + } + __host__ __device__ __forceinline__ RGB2XYZ() {} + __host__ __device__ __forceinline__ RGB2XYZ(const RGB2XYZ&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(name, scn, dcn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2XYZ functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + + namespace color_detail + { + __constant__ float c_XYZ2sRGB_D65f[9] = { 3.240479f, -1.53715f, -0.498535f, -0.969256f, 1.875991f, 0.041556f, 0.055648f, -0.204043f, 1.057311f }; + __constant__ int c_XYZ2sRGB_D65i[9] = { 13273, -6296, -2042, -3970, 7684, 170, 228, -836, 4331 }; + + template static __device__ __forceinline__ void XYZ2RGBConvert(const T& src, D* dst) + { + dst[bidx^2] = saturate_cast(CV_DESCALE(src.x * c_XYZ2sRGB_D65i[0] + src.y * c_XYZ2sRGB_D65i[1] + src.z * c_XYZ2sRGB_D65i[2], xyz_shift)); + dst[1] = saturate_cast(CV_DESCALE(src.x * c_XYZ2sRGB_D65i[3] + src.y * c_XYZ2sRGB_D65i[4] + src.z * c_XYZ2sRGB_D65i[5], xyz_shift)); + dst[bidx] = saturate_cast(CV_DESCALE(src.x * c_XYZ2sRGB_D65i[6] + src.y * c_XYZ2sRGB_D65i[7] + src.z * c_XYZ2sRGB_D65i[8], xyz_shift)); + } + + template static __device__ __forceinline__ uint XYZ2RGBConvert(uint src) + { + const int x = 0xff & src; + const int y = 0xff & (src >> 8); + const int z = 0xff & (src >> 16); + + const uint r = saturate_cast(CV_DESCALE(x * c_XYZ2sRGB_D65i[0] + y * c_XYZ2sRGB_D65i[1] + z * c_XYZ2sRGB_D65i[2], xyz_shift)); + const uint g = saturate_cast(CV_DESCALE(x * c_XYZ2sRGB_D65i[3] + y * c_XYZ2sRGB_D65i[4] + z * c_XYZ2sRGB_D65i[5], xyz_shift)); + const uint b = saturate_cast(CV_DESCALE(x * c_XYZ2sRGB_D65i[6] + y * c_XYZ2sRGB_D65i[7] + z * c_XYZ2sRGB_D65i[8], xyz_shift)); + + uint dst = 0xffu << 24; + + dst |= b << (bidx * 8); + dst |= g << 8; + dst |= r << ((bidx ^ 2) * 8); + + return dst; + } + + template static __device__ __forceinline__ void XYZ2RGBConvert(const T& src, float* dst) + { + dst[bidx^2] = src.x * c_XYZ2sRGB_D65f[0] + src.y * c_XYZ2sRGB_D65f[1] + src.z * c_XYZ2sRGB_D65f[2]; + dst[1] = src.x * c_XYZ2sRGB_D65f[3] + src.y * c_XYZ2sRGB_D65f[4] + src.z * c_XYZ2sRGB_D65f[5]; + dst[bidx] = src.x * c_XYZ2sRGB_D65f[6] + src.y * c_XYZ2sRGB_D65f[7] + src.z * c_XYZ2sRGB_D65f[8]; + } + + template struct XYZ2RGB + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + XYZ2RGBConvert(src, &dst.x); + setAlpha(dst, ColorChannel::max()); + + return dst; + } + __host__ __device__ __forceinline__ XYZ2RGB() {} + __host__ __device__ __forceinline__ XYZ2RGB(const XYZ2RGB&) {} + }; + + template struct XYZ2RGB : unary_function + { + __device__ __forceinline__ uint operator()(uint src) const + { + return XYZ2RGBConvert(src); + } + __host__ __device__ __forceinline__ XYZ2RGB() {} + __host__ __device__ __forceinline__ XYZ2RGB(const XYZ2RGB&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(name, scn, dcn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::XYZ2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + +////////////////////////////////////// RGB <-> HSV /////////////////////////////////////// + + namespace color_detail + { + __constant__ int c_HsvDivTable [256] = {0, 1044480, 522240, 348160, 261120, 208896, 174080, 149211, 130560, 116053, 104448, 94953, 87040, 80345, 74606, 69632, 65280, 61440, 58027, 54973, 52224, 49737, 47476, 45412, 43520, 41779, 40172, 38684, 37303, 36017, 34816, 33693, 32640, 31651, 30720, 29842, 29013, 28229, 27486, 26782, 26112, 25475, 24869, 24290, 23738, 23211, 22706, 22223, 21760, 21316, 20890, 20480, 20086, 19707, 19342, 18991, 18651, 18324, 18008, 17703, 17408, 17123, 16846, 16579, 16320, 16069, 15825, 15589, 15360, 15137, 14921, 14711, 14507, 14308, 14115, 13926, 13743, 13565, 13391, 13221, 13056, 12895, 12738, 12584, 12434, 12288, 12145, 12006, 11869, 11736, 11605, 11478, 11353, 11231, 11111, 10995, 10880, 10768, 10658, 10550, 10445, 10341, 10240, 10141, 10043, 9947, 9854, 9761, 9671, 9582, 9495, 9410, 9326, 9243, 9162, 9082, 9004, 8927, 8852, 8777, 8704, 8632, 8561, 8492, 8423, 8356, 8290, 8224, 8160, 8097, 8034, 7973, 7913, 7853, 7795, 7737, 7680, 7624, 7569, 7514, 7461, 7408, 7355, 7304, 7253, 7203, 7154, 7105, 7057, 7010, 6963, 6917, 6872, 6827, 6782, 6739, 6695, 6653, 6611, 6569, 6528, 6487, 6447, 6408, 6369, 6330, 6292, 6254, 6217, 6180, 6144, 6108, 6073, 6037, 6003, 5968, 5935, 5901, 5868, 5835, 5803, 5771, 5739, 5708, 5677, 5646, 5615, 5585, 5556, 5526, 5497, 5468, 5440, 5412, 5384, 5356, 5329, 5302, 5275, 5249, 5222, 5196, 5171, 5145, 5120, 5095, 5070, 5046, 5022, 4998, 4974, 4950, 4927, 4904, 4881, 4858, 4836, 4813, 4791, 4769, 4748, 4726, 4705, 4684, 4663, 4642, 4622, 4601, 4581, 4561, 4541, 4522, 4502, 4483, 4464, 4445, 4426, 4407, 4389, 4370, 4352, 4334, 4316, 4298, 4281, 4263, 4246, 4229, 4212, 4195, 4178, 4161, 4145, 4128, 4112, 4096}; + __constant__ int c_HsvDivTable180[256] = {0, 122880, 61440, 40960, 30720, 24576, 20480, 17554, 15360, 13653, 12288, 11171, 10240, 9452, 8777, 8192, 7680, 7228, 6827, 6467, 6144, 5851, 5585, 5343, 5120, 4915, 4726, 4551, 4389, 4237, 4096, 3964, 3840, 3724, 3614, 3511, 3413, 3321, 3234, 3151, 3072, 2997, 2926, 2858, 2793, 2731, 2671, 2614, 2560, 2508, 2458, 2409, 2363, 2318, 2276, 2234, 2194, 2156, 2119, 2083, 2048, 2014, 1982, 1950, 1920, 1890, 1862, 1834, 1807, 1781, 1755, 1731, 1707, 1683, 1661, 1638, 1617, 1596, 1575, 1555, 1536, 1517, 1499, 1480, 1463, 1446, 1429, 1412, 1396, 1381, 1365, 1350, 1336, 1321, 1307, 1293, 1280, 1267, 1254, 1241, 1229, 1217, 1205, 1193, 1182, 1170, 1159, 1148, 1138, 1127, 1117, 1107, 1097, 1087, 1078, 1069, 1059, 1050, 1041, 1033, 1024, 1016, 1007, 999, 991, 983, 975, 968, 960, 953, 945, 938, 931, 924, 917, 910, 904, 897, 890, 884, 878, 871, 865, 859, 853, 847, 842, 836, 830, 825, 819, 814, 808, 803, 798, 793, 788, 783, 778, 773, 768, 763, 759, 754, 749, 745, 740, 736, 731, 727, 723, 719, 714, 710, 706, 702, 698, 694, 690, 686, 683, 679, 675, 671, 668, 664, 661, 657, 654, 650, 647, 643, 640, 637, 633, 630, 627, 624, 621, 617, 614, 611, 608, 605, 602, 599, 597, 594, 591, 588, 585, 582, 580, 577, 574, 572, 569, 566, 564, 561, 559, 556, 554, 551, 549, 546, 544, 541, 539, 537, 534, 532, 530, 527, 525, 523, 521, 518, 516, 514, 512, 510, 508, 506, 504, 502, 500, 497, 495, 493, 492, 490, 488, 486, 484, 482}; + __constant__ int c_HsvDivTable256[256] = {0, 174763, 87381, 58254, 43691, 34953, 29127, 24966, 21845, 19418, 17476, 15888, 14564, 13443, 12483, 11651, 10923, 10280, 9709, 9198, 8738, 8322, 7944, 7598, 7282, 6991, 6722, 6473, 6242, 6026, 5825, 5638, 5461, 5296, 5140, 4993, 4855, 4723, 4599, 4481, 4369, 4263, 4161, 4064, 3972, 3884, 3799, 3718, 3641, 3567, 3495, 3427, 3361, 3297, 3236, 3178, 3121, 3066, 3013, 2962, 2913, 2865, 2819, 2774, 2731, 2689, 2648, 2608, 2570, 2533, 2497, 2461, 2427, 2394, 2362, 2330, 2300, 2270, 2241, 2212, 2185, 2158, 2131, 2106, 2081, 2056, 2032, 2009, 1986, 1964, 1942, 1920, 1900, 1879, 1859, 1840, 1820, 1802, 1783, 1765, 1748, 1730, 1713, 1697, 1680, 1664, 1649, 1633, 1618, 1603, 1589, 1574, 1560, 1547, 1533, 1520, 1507, 1494, 1481, 1469, 1456, 1444, 1432, 1421, 1409, 1398, 1387, 1376, 1365, 1355, 1344, 1334, 1324, 1314, 1304, 1295, 1285, 1276, 1266, 1257, 1248, 1239, 1231, 1222, 1214, 1205, 1197, 1189, 1181, 1173, 1165, 1157, 1150, 1142, 1135, 1128, 1120, 1113, 1106, 1099, 1092, 1085, 1079, 1072, 1066, 1059, 1053, 1046, 1040, 1034, 1028, 1022, 1016, 1010, 1004, 999, 993, 987, 982, 976, 971, 966, 960, 955, 950, 945, 940, 935, 930, 925, 920, 915, 910, 906, 901, 896, 892, 887, 883, 878, 874, 869, 865, 861, 857, 853, 848, 844, 840, 836, 832, 828, 824, 820, 817, 813, 809, 805, 802, 798, 794, 791, 787, 784, 780, 777, 773, 770, 767, 763, 760, 757, 753, 750, 747, 744, 741, 737, 734, 731, 728, 725, 722, 719, 716, 713, 710, 708, 705, 702, 699, 696, 694, 691, 688, 685}; + + template static __device__ void RGB2HSVConvert(const uchar* src, D& dst) + { + const int hsv_shift = 12; + const int* hdiv_table = hr == 180 ? c_HsvDivTable180 : c_HsvDivTable256; + + int b = src[bidx], g = src[1], r = src[bidx^2]; + int h, s, v = b; + int vmin = b, diff; + int vr, vg; + + v = ::max(v, g); + v = ::max(v, r); + vmin = ::min(vmin, g); + vmin = ::min(vmin, r); + + diff = v - vmin; + vr = (v == r) * -1; + vg = (v == g) * -1; + + s = (diff * c_HsvDivTable[v] + (1 << (hsv_shift-1))) >> hsv_shift; + h = (vr & (g - b)) + (~vr & ((vg & (b - r + 2 * diff)) + ((~vg) & (r - g + 4 * diff)))); + h = (h * hdiv_table[diff] + (1 << (hsv_shift-1))) >> hsv_shift; + h += (h < 0) * hr; + + dst.x = saturate_cast(h); + dst.y = (uchar)s; + dst.z = (uchar)v; + } + + template static __device__ uint RGB2HSVConvert(uint src) + { + const int hsv_shift = 12; + const int* hdiv_table = hr == 180 ? c_HsvDivTable180 : c_HsvDivTable256; + + const int b = 0xff & (src >> (bidx * 8)); + const int g = 0xff & (src >> 8); + const int r = 0xff & (src >> ((bidx ^ 2) * 8)); + + int h, s, v = b; + int vmin = b, diff; + int vr, vg; + + v = ::max(v, g); + v = ::max(v, r); + vmin = ::min(vmin, g); + vmin = ::min(vmin, r); + + diff = v - vmin; + vr = (v == r) * -1; + vg = (v == g) * -1; + + s = (diff * c_HsvDivTable[v] + (1 << (hsv_shift-1))) >> hsv_shift; + h = (vr & (g - b)) + (~vr & ((vg & (b - r + 2 * diff)) + ((~vg) & (r - g + 4 * diff)))); + h = (h * hdiv_table[diff] + (1 << (hsv_shift-1))) >> hsv_shift; + h += (h < 0) * hr; + + uint dst = 0; + + dst |= saturate_cast(h); + dst |= (0xffu & s) << 8; + dst |= (0xffu & v) << 16; + + return dst; + } + + template static __device__ void RGB2HSVConvert(const float* src, D& dst) + { + const float hscale = hr * (1.f / 360.f); + + float b = src[bidx], g = src[1], r = src[bidx^2]; + float h, s, v; + + float vmin, diff; + + v = vmin = r; + v = fmax(v, g); + v = fmax(v, b); + vmin = fmin(vmin, g); + vmin = fmin(vmin, b); + + diff = v - vmin; + s = diff / (float)(::fabs(v) + numeric_limits::epsilon()); + diff = (float)(60. / (diff + numeric_limits::epsilon())); + + h = (v == r) * (g - b) * diff; + h += (v != r && v == g) * ((b - r) * diff + 120.f); + h += (v != r && v != g) * ((r - g) * diff + 240.f); + h += (h < 0) * 360.f; + + dst.x = h * hscale; + dst.y = s; + dst.z = v; + } + + template struct RGB2HSV + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + RGB2HSVConvert(&src.x, dst); + + return dst; + } + __host__ __device__ __forceinline__ RGB2HSV() {} + __host__ __device__ __forceinline__ RGB2HSV(const RGB2HSV&) {} + }; + + template struct RGB2HSV : unary_function + { + __device__ __forceinline__ uint operator()(uint src) const + { + return RGB2HSVConvert(src); + } + __host__ __device__ __forceinline__ RGB2HSV() {} + __host__ __device__ __forceinline__ RGB2HSV(const RGB2HSV&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(name, scn, dcn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2HSV functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template struct name ## _full_traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2HSV functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template <> struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2HSV functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template <> struct name ## _full_traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2HSV functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + + namespace color_detail + { + __constant__ int c_HsvSectorData[6][3] = { {1,3,0}, {1,0,2}, {3,0,1}, {0,2,1}, {0,1,3}, {2,1,0} }; + + template static __device__ void HSV2RGBConvert(const T& src, float* dst) + { + const float hscale = 6.f / hr; + + float h = src.x, s = src.y, v = src.z; + float b = v, g = v, r = v; + + if (s != 0) + { + h *= hscale; + + if( h < 0 ) + do h += 6; while( h < 0 ); + else if( h >= 6 ) + do h -= 6; while( h >= 6 ); + + int sector = __float2int_rd(h); + h -= sector; + + if ( (unsigned)sector >= 6u ) + { + sector = 0; + h = 0.f; + } + + float tab[4]; + tab[0] = v; + tab[1] = v * (1.f - s); + tab[2] = v * (1.f - s * h); + tab[3] = v * (1.f - s * (1.f - h)); + + b = tab[c_HsvSectorData[sector][0]]; + g = tab[c_HsvSectorData[sector][1]]; + r = tab[c_HsvSectorData[sector][2]]; + } + + dst[bidx] = b; + dst[1] = g; + dst[bidx^2] = r; + } + + template static __device__ void HSV2RGBConvert(const T& src, uchar* dst) + { + float3 buf; + + buf.x = src.x; + buf.y = src.y * (1.f / 255.f); + buf.z = src.z * (1.f / 255.f); + + HSV2RGBConvert(buf, &buf.x); + + dst[0] = saturate_cast(buf.x * 255.f); + dst[1] = saturate_cast(buf.y * 255.f); + dst[2] = saturate_cast(buf.z * 255.f); + } + + template static __device__ uint HSV2RGBConvert(uint src) + { + float3 buf; + + buf.x = src & 0xff; + buf.y = ((src >> 8) & 0xff) * (1.f/255.f); + buf.z = ((src >> 16) & 0xff) * (1.f/255.f); + + HSV2RGBConvert(buf, &buf.x); + + uint dst = 0xffu << 24; + + dst |= saturate_cast(buf.x * 255.f); + dst |= saturate_cast(buf.y * 255.f) << 8; + dst |= saturate_cast(buf.z * 255.f) << 16; + + return dst; + } + + template struct HSV2RGB + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + HSV2RGBConvert(src, &dst.x); + setAlpha(dst, ColorChannel::max()); + + return dst; + } + __host__ __device__ __forceinline__ HSV2RGB() {} + __host__ __device__ __forceinline__ HSV2RGB(const HSV2RGB&) {} + }; + + template struct HSV2RGB : unary_function + { + __device__ __forceinline__ uint operator()(uint src) const + { + return HSV2RGBConvert(src); + } + __host__ __device__ __forceinline__ HSV2RGB() {} + __host__ __device__ __forceinline__ HSV2RGB(const HSV2RGB&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(name, scn, dcn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::HSV2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template struct name ## _full_traits \ + { \ + typedef ::cv::cuda::device::color_detail::HSV2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template <> struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::HSV2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template <> struct name ## _full_traits \ + { \ + typedef ::cv::cuda::device::color_detail::HSV2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + +/////////////////////////////////////// RGB <-> HLS //////////////////////////////////////// + + namespace color_detail + { + template static __device__ void RGB2HLSConvert(const float* src, D& dst) + { + const float hscale = hr * (1.f / 360.f); + + float b = src[bidx], g = src[1], r = src[bidx^2]; + float h = 0.f, s = 0.f, l; + float vmin, vmax, diff; + + vmax = vmin = r; + vmax = fmax(vmax, g); + vmax = fmax(vmax, b); + vmin = fmin(vmin, g); + vmin = fmin(vmin, b); + + diff = vmax - vmin; + l = (vmax + vmin) * 0.5f; + + if (diff > numeric_limits::epsilon()) + { + s = (l < 0.5f) * diff / (vmax + vmin); + s += (l >= 0.5f) * diff / (2.0f - vmax - vmin); + + diff = 60.f / diff; + + h = (vmax == r) * (g - b) * diff; + h += (vmax != r && vmax == g) * ((b - r) * diff + 120.f); + h += (vmax != r && vmax != g) * ((r - g) * diff + 240.f); + h += (h < 0.f) * 360.f; + } + + dst.x = h * hscale; + dst.y = l; + dst.z = s; + } + + template static __device__ void RGB2HLSConvert(const uchar* src, D& dst) + { + float3 buf; + + buf.x = src[0] * (1.f / 255.f); + buf.y = src[1] * (1.f / 255.f); + buf.z = src[2] * (1.f / 255.f); + + RGB2HLSConvert(&buf.x, buf); + + dst.x = saturate_cast(buf.x); + dst.y = saturate_cast(buf.y*255.f); + dst.z = saturate_cast(buf.z*255.f); + } + + template static __device__ uint RGB2HLSConvert(uint src) + { + float3 buf; + + buf.x = (0xff & src) * (1.f / 255.f); + buf.y = (0xff & (src >> 8)) * (1.f / 255.f); + buf.z = (0xff & (src >> 16)) * (1.f / 255.f); + + RGB2HLSConvert(&buf.x, buf); + + uint dst = 0xffu << 24; + + dst |= saturate_cast(buf.x); + dst |= saturate_cast(buf.y * 255.f) << 8; + dst |= saturate_cast(buf.z * 255.f) << 16; + + return dst; + } + + template struct RGB2HLS + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + RGB2HLSConvert(&src.x, dst); + + return dst; + } + __host__ __device__ __forceinline__ RGB2HLS() {} + __host__ __device__ __forceinline__ RGB2HLS(const RGB2HLS&) {} + }; + + template struct RGB2HLS : unary_function + { + __device__ __forceinline__ uint operator()(uint src) const + { + return RGB2HLSConvert(src); + } + __host__ __device__ __forceinline__ RGB2HLS() {} + __host__ __device__ __forceinline__ RGB2HLS(const RGB2HLS&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(name, scn, dcn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2HLS functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template struct name ## _full_traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2HLS functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template <> struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2HLS functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template <> struct name ## _full_traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2HLS functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + + namespace color_detail + { + __constant__ int c_HlsSectorData[6][3] = { {1,3,0}, {1,0,2}, {3,0,1}, {0,2,1}, {0,1,3}, {2,1,0} }; + + template static __device__ void HLS2RGBConvert(const T& src, float* dst) + { + const float hscale = 6.0f / hr; + + float h = src.x, l = src.y, s = src.z; + float b = l, g = l, r = l; + + if (s != 0) + { + float p2 = (l <= 0.5f) * l * (1 + s); + p2 += (l > 0.5f) * (l + s - l * s); + float p1 = 2 * l - p2; + + h *= hscale; + + if( h < 0 ) + do h += 6; while( h < 0 ); + else if( h >= 6 ) + do h -= 6; while( h >= 6 ); + + int sector; + sector = __float2int_rd(h); + + h -= sector; + + float tab[4]; + tab[0] = p2; + tab[1] = p1; + tab[2] = p1 + (p2 - p1) * (1 - h); + tab[3] = p1 + (p2 - p1) * h; + + b = tab[c_HlsSectorData[sector][0]]; + g = tab[c_HlsSectorData[sector][1]]; + r = tab[c_HlsSectorData[sector][2]]; + } + + dst[bidx] = b; + dst[1] = g; + dst[bidx^2] = r; + } + + template static __device__ void HLS2RGBConvert(const T& src, uchar* dst) + { + float3 buf; + + buf.x = src.x; + buf.y = src.y * (1.f / 255.f); + buf.z = src.z * (1.f / 255.f); + + HLS2RGBConvert(buf, &buf.x); + + dst[0] = saturate_cast(buf.x * 255.f); + dst[1] = saturate_cast(buf.y * 255.f); + dst[2] = saturate_cast(buf.z * 255.f); + } + + template static __device__ uint HLS2RGBConvert(uint src) + { + float3 buf; + + buf.x = 0xff & src; + buf.y = (0xff & (src >> 8)) * (1.f / 255.f); + buf.z = (0xff & (src >> 16)) * (1.f / 255.f); + + HLS2RGBConvert(buf, &buf.x); + + uint dst = 0xffu << 24; + + dst |= saturate_cast(buf.x * 255.f); + dst |= saturate_cast(buf.y * 255.f) << 8; + dst |= saturate_cast(buf.z * 255.f) << 16; + + return dst; + } + + template struct HLS2RGB + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + HLS2RGBConvert(src, &dst.x); + setAlpha(dst, ColorChannel::max()); + + return dst; + } + __host__ __device__ __forceinline__ HLS2RGB() {} + __host__ __device__ __forceinline__ HLS2RGB(const HLS2RGB&) {} + }; + + template struct HLS2RGB : unary_function + { + __device__ __forceinline__ uint operator()(uint src) const + { + return HLS2RGBConvert(src); + } + __host__ __device__ __forceinline__ HLS2RGB() {} + __host__ __device__ __forceinline__ HLS2RGB(const HLS2RGB&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(name, scn, dcn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::HLS2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template struct name ## _full_traits \ + { \ + typedef ::cv::cuda::device::color_detail::HLS2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template <> struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::HLS2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template <> struct name ## _full_traits \ + { \ + typedef ::cv::cuda::device::color_detail::HLS2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + +///////////////////////////////////// RGB <-> Lab ///////////////////////////////////// + + namespace color_detail + { + enum + { + LAB_CBRT_TAB_SIZE = 1024, + GAMMA_TAB_SIZE = 1024, + lab_shift = xyz_shift, + gamma_shift = 3, + lab_shift2 = (lab_shift + gamma_shift), + LAB_CBRT_TAB_SIZE_B = (256 * 3 / 2 * (1 << gamma_shift)) + }; + + __constant__ ushort c_sRGBGammaTab_b[] = {0,1,1,2,2,3,4,4,5,6,6,7,8,8,9,10,11,11,12,13,14,15,16,17,19,20,21,22,24,25,26,28,29,31,33,34,36,38,40,41,43,45,47,49,51,54,56,58,60,63,65,68,70,73,75,78,81,83,86,89,92,95,98,101,105,108,111,115,118,121,125,129,132,136,140,144,147,151,155,160,164,168,172,176,181,185,190,194,199,204,209,213,218,223,228,233,239,244,249,255,260,265,271,277,282,288,294,300,306,312,318,324,331,337,343,350,356,363,370,376,383,390,397,404,411,418,426,433,440,448,455,463,471,478,486,494,502,510,518,527,535,543,552,560,569,578,586,595,604,613,622,631,641,650,659,669,678,688,698,707,717,727,737,747,757,768,778,788,799,809,820,831,842,852,863,875,886,897,908,920,931,943,954,966,978,990,1002,1014,1026,1038,1050,1063,1075,1088,1101,1113,1126,1139,1152,1165,1178,1192,1205,1218,1232,1245,1259,1273,1287,1301,1315,1329,1343,1357,1372,1386,1401,1415,1430,1445,1460,1475,1490,1505,1521,1536,1551,1567,1583,1598,1614,1630,1646,1662,1678,1695,1711,1728,1744,1761,1778,1794,1811,1828,1846,1863,1880,1897,1915,1933,1950,1968,1986,2004,2022,2040}; + + __device__ __forceinline__ int LabCbrt_b(int i) + { + float x = i * (1.f / (255.f * (1 << gamma_shift))); + return (1 << lab_shift2) * (x < 0.008856f ? x * 7.787f + 0.13793103448275862f : ::cbrtf(x)); + } + + template + __device__ __forceinline__ void RGB2LabConvert_b(const T& src, D& dst) + { + const int Lscale = (116 * 255 + 50) / 100; + const int Lshift = -((16 * 255 * (1 << lab_shift2) + 50) / 100); + + int B = blueIdx == 0 ? src.x : src.z; + int G = src.y; + int R = blueIdx == 0 ? src.z : src.x; + + if (srgb) + { + B = c_sRGBGammaTab_b[B]; + G = c_sRGBGammaTab_b[G]; + R = c_sRGBGammaTab_b[R]; + } + else + { + B <<= 3; + G <<= 3; + R <<= 3; + } + + int fX = LabCbrt_b(CV_DESCALE(B * 778 + G * 1541 + R * 1777, lab_shift)); + int fY = LabCbrt_b(CV_DESCALE(B * 296 + G * 2929 + R * 871, lab_shift)); + int fZ = LabCbrt_b(CV_DESCALE(B * 3575 + G * 448 + R * 73, lab_shift)); + + int L = CV_DESCALE(Lscale * fY + Lshift, lab_shift2); + int a = CV_DESCALE(500 * (fX - fY) + 128 * (1 << lab_shift2), lab_shift2); + int b = CV_DESCALE(200 * (fY - fZ) + 128 * (1 << lab_shift2), lab_shift2); + + dst.x = saturate_cast(L); + dst.y = saturate_cast(a); + dst.z = saturate_cast(b); + } + + __device__ __forceinline__ float splineInterpolate(float x, const float* tab, int n) + { + int ix = ::min(::max(int(x), 0), n-1); + x -= ix; + tab += ix * 4; + return ((tab[3] * x + tab[2]) * x + tab[1]) * x + tab[0]; + } + + __constant__ float c_sRGBGammaTab[] = 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+ + template + __device__ __forceinline__ void RGB2LabConvert_f(const T& src, D& dst) + { + const float _1_3 = 1.0f / 3.0f; + const float _a = 16.0f / 116.0f; + + float B = blueIdx == 0 ? src.x : src.z; + float G = src.y; + float R = blueIdx == 0 ? src.z : src.x; + + if (srgb) + { + B = splineInterpolate(B * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE); + G = splineInterpolate(G * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE); + R = splineInterpolate(R * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE); + } + + float X = B * 0.189828f + G * 0.376219f + R * 0.433953f; + float Y = B * 0.072169f + G * 0.715160f + R * 0.212671f; + float Z = B * 0.872766f + G * 0.109477f + R * 0.017758f; + + float FX = X > 0.008856f ? ::powf(X, _1_3) : (7.787f * X + _a); + float FY = Y > 0.008856f ? ::powf(Y, _1_3) : (7.787f * Y + _a); + float FZ = Z > 0.008856f ? ::powf(Z, _1_3) : (7.787f * Z + _a); + + float L = Y > 0.008856f ? (116.f * FY - 16.f) : (903.3f * Y); + float a = 500.f * (FX - FY); + float b = 200.f * (FY - FZ); + + dst.x = L; + dst.y = a; + dst.z = b; + } + + template struct RGB2Lab; + template + struct RGB2Lab + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + RGB2LabConvert_b(src, dst); + + return dst; + } + __host__ __device__ __forceinline__ RGB2Lab() {} + __host__ __device__ __forceinline__ RGB2Lab(const RGB2Lab&) {} + }; + template + struct RGB2Lab + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + RGB2LabConvert_f(src, dst); + + return dst; + } + __host__ __device__ __forceinline__ RGB2Lab() {} + __host__ __device__ __forceinline__ RGB2Lab(const RGB2Lab&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(name, scn, dcn, srgb, blueIdx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2Lab functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + + namespace color_detail + { + __constant__ float c_sRGBInvGammaTab[] = 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+ + template + __device__ __forceinline__ void Lab2RGBConvert_f(const T& src, D& dst) + { + const float lThresh = 0.008856f * 903.3f; + const float fThresh = 7.787f * 0.008856f + 16.0f / 116.0f; + + float Y, fy; + + if (src.x <= lThresh) + { + Y = src.x / 903.3f; + fy = 7.787f * Y + 16.0f / 116.0f; + } + else + { + fy = (src.x + 16.0f) / 116.0f; + Y = fy * fy * fy; + } + + float X = src.y / 500.0f + fy; + float Z = fy - src.z / 200.0f; + + if (X <= fThresh) + X = (X - 16.0f / 116.0f) / 7.787f; + else + X = X * X * X; + + if (Z <= fThresh) + Z = (Z - 16.0f / 116.0f) / 7.787f; + else + Z = Z * Z * Z; + + float B = 0.052891f * X - 0.204043f * Y + 1.151152f * Z; + float G = -0.921235f * X + 1.875991f * Y + 0.045244f * Z; + float R = 3.079933f * X - 1.537150f * Y - 0.542782f * Z; + + if (srgb) + { + B = splineInterpolate(B * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE); + G = splineInterpolate(G * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE); + R = splineInterpolate(R * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE); + } + + dst.x = blueIdx == 0 ? B : R; + dst.y = G; + dst.z = blueIdx == 0 ? R : B; + setAlpha(dst, ColorChannel::max()); + } + + template + __device__ __forceinline__ void Lab2RGBConvert_b(const T& src, D& dst) + { + float3 srcf, dstf; + + srcf.x = src.x * (100.f / 255.f); + srcf.y = src.y - 128; + srcf.z = src.z - 128; + + Lab2RGBConvert_f(srcf, dstf); + + dst.x = saturate_cast(dstf.x * 255.f); + dst.y = saturate_cast(dstf.y * 255.f); + dst.z = saturate_cast(dstf.z * 255.f); + setAlpha(dst, ColorChannel::max()); + } + + template struct Lab2RGB; + template + struct Lab2RGB + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + Lab2RGBConvert_b(src, dst); + + return dst; + } + __host__ __device__ __forceinline__ Lab2RGB() {} + __host__ __device__ __forceinline__ Lab2RGB(const Lab2RGB&) {} + }; + template + struct Lab2RGB + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + Lab2RGBConvert_f(src, dst); + + return dst; + } + __host__ __device__ __forceinline__ Lab2RGB() {} + __host__ __device__ __forceinline__ Lab2RGB(const Lab2RGB&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(name, scn, dcn, srgb, blueIdx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::Lab2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + +///////////////////////////////////// RGB <-> Luv ///////////////////////////////////// + + namespace color_detail + { + __constant__ float c_LabCbrtTab[] = 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+ + template + __device__ __forceinline__ void RGB2LuvConvert_f(const T& src, D& dst) + { + const float _d = 1.f / (0.950456f + 15 + 1.088754f * 3); + const float _un = 13 * (4 * 0.950456f * _d); + const float _vn = 13 * (9 * _d); + + float B = blueIdx == 0 ? src.x : src.z; + float G = src.y; + float R = blueIdx == 0 ? src.z : src.x; + + if (srgb) + { + B = splineInterpolate(B * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE); + G = splineInterpolate(G * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE); + R = splineInterpolate(R * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE); + } + + float X = R * 0.412453f + G * 0.357580f + B * 0.180423f; + float Y = R * 0.212671f + G * 0.715160f + B * 0.072169f; + float Z = R * 0.019334f + G * 0.119193f + B * 0.950227f; + + float L = splineInterpolate(Y * (LAB_CBRT_TAB_SIZE / 1.5f), c_LabCbrtTab, LAB_CBRT_TAB_SIZE); + L = 116.f * L - 16.f; + + const float d = (4 * 13) / ::fmaxf(X + 15 * Y + 3 * Z, numeric_limits::epsilon()); + float u = L * (X * d - _un); + float v = L * ((9 * 0.25f) * Y * d - _vn); + + dst.x = L; + dst.y = u; + dst.z = v; + } + + template + __device__ __forceinline__ void RGB2LuvConvert_b(const T& src, D& dst) + { + float3 srcf, dstf; + + srcf.x = src.x * (1.f / 255.f); + srcf.y = src.y * (1.f / 255.f); + srcf.z = src.z * (1.f / 255.f); + + RGB2LuvConvert_f(srcf, dstf); + + dst.x = saturate_cast(dstf.x * 2.55f); + dst.y = saturate_cast(dstf.y * 0.72033898305084743f + 96.525423728813564f); + dst.z = saturate_cast(dstf.z * 0.9732824427480916f + 136.259541984732824f); + } + + template struct RGB2Luv; + template + struct RGB2Luv + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + RGB2LuvConvert_b(src, dst); + + return dst; + } + __host__ __device__ __forceinline__ RGB2Luv() {} + __host__ __device__ __forceinline__ RGB2Luv(const RGB2Luv&) {} + }; + template + struct RGB2Luv + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + RGB2LuvConvert_f(src, dst); + + return dst; + } + __host__ __device__ __forceinline__ RGB2Luv() {} + __host__ __device__ __forceinline__ RGB2Luv(const RGB2Luv&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(name, scn, dcn, srgb, blueIdx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2Luv functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + + namespace color_detail + { + template + __device__ __forceinline__ void Luv2RGBConvert_f(const T& src, D& dst) + { + const float _d = 1.f / (0.950456f + 15 + 1.088754f * 3); + const float _un = 4 * 0.950456f * _d; + const float _vn = 9 * _d; + + float L = src.x; + float u = src.y; + float v = src.z; + + float Y = (L + 16.f) * (1.f / 116.f); + Y = Y * Y * Y; + + float d = (1.f / 13.f) / L; + u = u * d + _un; + v = v * d + _vn; + + float iv = 1.f / v; + float X = 2.25f * u * Y * iv; + float Z = (12 - 3 * u - 20 * v) * Y * 0.25f * iv; + + float B = 0.055648f * X - 0.204043f * Y + 1.057311f * Z; + float G = -0.969256f * X + 1.875991f * Y + 0.041556f * Z; + float R = 3.240479f * X - 1.537150f * Y - 0.498535f * Z; + + if (srgb) + { + B = splineInterpolate(B * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE); + G = splineInterpolate(G * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE); + R = splineInterpolate(R * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE); + } + + dst.x = blueIdx == 0 ? B : R; + dst.y = G; + dst.z = blueIdx == 0 ? R : B; + setAlpha(dst, ColorChannel::max()); + } + + template + __device__ __forceinline__ void Luv2RGBConvert_b(const T& src, D& dst) + { + float3 srcf, dstf; + + srcf.x = src.x * (100.f / 255.f); + srcf.y = src.y * 1.388235294117647f - 134.f; + srcf.z = src.z * 1.027450980392157f - 140.f; + + Luv2RGBConvert_f(srcf, dstf); + + dst.x = saturate_cast(dstf.x * 255.f); + dst.y = saturate_cast(dstf.y * 255.f); + dst.z = saturate_cast(dstf.z * 255.f); + setAlpha(dst, ColorChannel::max()); + } + + template struct Luv2RGB; + template + struct Luv2RGB + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + Luv2RGBConvert_b(src, dst); + + return dst; + } + __host__ __device__ __forceinline__ Luv2RGB() {} + __host__ __device__ __forceinline__ Luv2RGB(const Luv2RGB&) {} + }; + template + struct Luv2RGB + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + Luv2RGBConvert_f(src, dst); + + return dst; + } + __host__ __device__ __forceinline__ Luv2RGB() {} + __host__ __device__ __forceinline__ Luv2RGB(const Luv2RGB&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(name, scn, dcn, srgb, blueIdx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::Luv2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + + #undef CV_DESCALE + +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_COLOR_DETAIL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/detail/reduce.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/detail/reduce.hpp new file mode 100644 index 0000000000000000000000000000000000000000..8af20b0dc86d76232220c5111bbdf5a2bc31748c --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/detail/reduce.hpp @@ -0,0 +1,365 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_REDUCE_DETAIL_HPP +#define OPENCV_CUDA_REDUCE_DETAIL_HPP + +#include +#include "../warp.hpp" +#include "../warp_shuffle.hpp" + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + namespace reduce_detail + { + template struct GetType; + template struct GetType + { + typedef T type; + }; + template struct GetType + { + typedef T type; + }; + template struct GetType + { + typedef T type; + }; + + template + struct For + { + template + static __device__ void loadToSmem(const PointerTuple& smem, const ValTuple& val, unsigned int tid) + { + thrust::get(smem)[tid] = thrust::get(val); + + For::loadToSmem(smem, val, tid); + } + template + static __device__ void loadFromSmem(const PointerTuple& smem, const ValTuple& val, unsigned int tid) + { + thrust::get(val) = thrust::get(smem)[tid]; + + For::loadFromSmem(smem, val, tid); + } + + template + static __device__ void merge(const PointerTuple& smem, const ValTuple& val, unsigned int tid, unsigned int delta, const OpTuple& op) + { + typename GetType::type>::type reg = thrust::get(smem)[tid + delta]; + thrust::get(smem)[tid] = thrust::get(val) = thrust::get(op)(thrust::get(val), reg); + + For::merge(smem, val, tid, delta, op); + } + template + static __device__ void mergeShfl(const ValTuple& val, unsigned int delta, unsigned int width, const OpTuple& op) + { + typename GetType::type>::type reg = shfl_down(thrust::get(val), delta, width); + thrust::get(val) = thrust::get(op)(thrust::get(val), reg); + + For::mergeShfl(val, delta, width, op); + } + }; + template + struct For + { + template + static __device__ void loadToSmem(const PointerTuple&, const ValTuple&, unsigned int) + { + } + template + static __device__ void loadFromSmem(const PointerTuple&, const ValTuple&, unsigned int) + { + } + + template + static __device__ void merge(const PointerTuple&, const ValTuple&, unsigned int, unsigned int, const OpTuple&) + { + } + template + static __device__ void mergeShfl(const ValTuple&, unsigned int, unsigned int, const OpTuple&) + { + } + }; + + template + __device__ __forceinline__ void loadToSmem(volatile T* smem, T& val, unsigned int tid) + { + smem[tid] = val; + } + template + __device__ __forceinline__ void loadFromSmem(volatile T* smem, T& val, unsigned int tid) + { + val = smem[tid]; + } + template + __device__ __forceinline__ void loadToSmem(const thrust::tuple& smem, + const thrust::tuple& val, + unsigned int tid) + { + For<0, thrust::tuple_size >::value>::loadToSmem(smem, val, tid); + } + template + __device__ __forceinline__ void loadFromSmem(const thrust::tuple& smem, + const thrust::tuple& val, + unsigned int tid) + { + For<0, thrust::tuple_size >::value>::loadFromSmem(smem, val, tid); + } + + template + __device__ __forceinline__ void merge(volatile T* smem, T& val, unsigned int tid, unsigned int delta, const Op& op) + { + T reg = smem[tid + delta]; + smem[tid] = val = op(val, reg); + } + template + __device__ __forceinline__ void mergeShfl(T& val, unsigned int delta, unsigned int width, const Op& op) + { + T reg = shfl_down(val, delta, width); + val = op(val, reg); + } + template + __device__ __forceinline__ void merge(const thrust::tuple& smem, + const thrust::tuple& val, + unsigned int tid, + unsigned int delta, + const thrust::tuple& op) + { + For<0, thrust::tuple_size >::value>::merge(smem, val, tid, delta, op); + } + template + __device__ __forceinline__ void mergeShfl(const thrust::tuple& val, + unsigned int delta, + unsigned int width, + const thrust::tuple& op) + { + For<0, thrust::tuple_size >::value>::mergeShfl(val, delta, width, op); + } + + template struct Generic + { + template + static __device__ void reduce(Pointer smem, Reference val, unsigned int tid, Op op) + { + loadToSmem(smem, val, tid); + if (N >= 32) + __syncthreads(); + + if (N >= 2048) + { + if (tid < 1024) + merge(smem, val, tid, 1024, op); + + __syncthreads(); + } + if (N >= 1024) + { + if (tid < 512) + merge(smem, val, tid, 512, op); + + __syncthreads(); + } + if (N >= 512) + { + if (tid < 256) + merge(smem, val, tid, 256, op); + + __syncthreads(); + } + if (N >= 256) + { + if (tid < 128) + merge(smem, val, tid, 128, op); + + __syncthreads(); + } + if (N >= 128) + { + if (tid < 64) + merge(smem, val, tid, 64, op); + + __syncthreads(); + } + if (N >= 64) + { + if (tid < 32) + merge(smem, val, tid, 32, op); + } + + if (tid < 16) + { + merge(smem, val, tid, 16, op); + merge(smem, val, tid, 8, op); + merge(smem, val, tid, 4, op); + merge(smem, val, tid, 2, op); + merge(smem, val, tid, 1, op); + } + } + }; + + template + struct Unroll + { + static __device__ void loopShfl(Reference val, Op op, unsigned int N) + { + mergeShfl(val, I, N, op); + Unroll::loopShfl(val, op, N); + } + static __device__ void loop(Pointer smem, Reference val, unsigned int tid, Op op) + { + merge(smem, val, tid, I, op); + Unroll::loop(smem, val, tid, op); + } + }; + template + struct Unroll<0, Pointer, Reference, Op> + { + static __device__ void loopShfl(Reference, Op, unsigned int) + { + } + static __device__ void loop(Pointer, Reference, unsigned int, Op) + { + } + }; + + template struct WarpOptimized + { + template + static __device__ void reduce(Pointer smem, Reference val, unsigned int tid, Op op) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + CV_UNUSED(smem); + CV_UNUSED(tid); + + Unroll::loopShfl(val, op, N); + #else + loadToSmem(smem, val, tid); + + if (tid < N / 2) + Unroll::loop(smem, val, tid, op); + #endif + } + }; + + template struct GenericOptimized32 + { + enum { M = N / 32 }; + + template + static __device__ void reduce(Pointer smem, Reference val, unsigned int tid, Op op) + { + const unsigned int laneId = Warp::laneId(); + + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + Unroll<16, Pointer, Reference, Op>::loopShfl(val, op, warpSize); + + if (laneId == 0) + loadToSmem(smem, val, tid / 32); + #else + loadToSmem(smem, val, tid); + + if (laneId < 16) + Unroll<16, Pointer, Reference, Op>::loop(smem, val, tid, op); + + __syncthreads(); + + if (laneId == 0) + loadToSmem(smem, val, tid / 32); + #endif + + __syncthreads(); + + loadFromSmem(smem, val, tid); + + if (tid < 32) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + Unroll::loopShfl(val, op, M); + #else + Unroll::loop(smem, val, tid, op); + #endif + } + } + }; + + template struct StaticIf; + template struct StaticIf + { + typedef T1 type; + }; + template struct StaticIf + { + typedef T2 type; + }; + + template struct IsPowerOf2 + { + enum { value = ((N != 0) && !(N & (N - 1))) }; + }; + + template struct Dispatcher + { + typedef typename StaticIf< + (N <= 32) && IsPowerOf2::value, + WarpOptimized, + typename StaticIf< + (N <= 1024) && IsPowerOf2::value, + GenericOptimized32, + Generic + >::type + >::type reductor; + }; + } +}}} + +//! @endcond + +#endif // OPENCV_CUDA_REDUCE_DETAIL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/detail/reduce_key_val.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/detail/reduce_key_val.hpp new file mode 100644 index 0000000000000000000000000000000000000000..df37c173be64bed1331f49260388acceea6de56b --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/detail/reduce_key_val.hpp @@ -0,0 +1,502 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_PRED_VAL_REDUCE_DETAIL_HPP +#define OPENCV_CUDA_PRED_VAL_REDUCE_DETAIL_HPP + +#include +#include "../warp.hpp" +#include "../warp_shuffle.hpp" + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + namespace reduce_key_val_detail + { + template struct GetType; + template struct GetType + { + typedef T type; + }; + template struct GetType + { + typedef T type; + }; + template struct GetType + { + typedef T type; + }; + + template + struct For + { + template + static __device__ void loadToSmem(const PointerTuple& smem, const ReferenceTuple& data, unsigned int tid) + { + thrust::get(smem)[tid] = thrust::get(data); + + For::loadToSmem(smem, data, tid); + } + template + static __device__ void loadFromSmem(const PointerTuple& smem, const ReferenceTuple& data, unsigned int tid) + { + thrust::get(data) = thrust::get(smem)[tid]; + + For::loadFromSmem(smem, data, tid); + } + + template + static __device__ void copyShfl(const ReferenceTuple& val, unsigned int delta, int width) + { + thrust::get(val) = shfl_down(thrust::get(val), delta, width); + + For::copyShfl(val, delta, width); + } + template + static __device__ void copy(const PointerTuple& svals, const ReferenceTuple& val, unsigned int tid, unsigned int delta) + { + thrust::get(svals)[tid] = thrust::get(val) = thrust::get(svals)[tid + delta]; + + For::copy(svals, val, tid, delta); + } + + template + static __device__ void mergeShfl(const KeyReferenceTuple& key, const ValReferenceTuple& val, const CmpTuple& cmp, unsigned int delta, int width) + { + typename GetType::type>::type reg = shfl_down(thrust::get(key), delta, width); + + if (thrust::get(cmp)(reg, thrust::get(key))) + { + thrust::get(key) = reg; + thrust::get(val) = shfl_down(thrust::get(val), delta, width); + } + + For::mergeShfl(key, val, cmp, delta, width); + } + template + static __device__ void merge(const KeyPointerTuple& skeys, const KeyReferenceTuple& key, + const ValPointerTuple& svals, const ValReferenceTuple& val, + const CmpTuple& cmp, + unsigned int tid, unsigned int delta) + { + typename GetType::type>::type reg = thrust::get(skeys)[tid + delta]; + + if (thrust::get(cmp)(reg, thrust::get(key))) + { + thrust::get(skeys)[tid] = thrust::get(key) = reg; + thrust::get(svals)[tid] = thrust::get(val) = thrust::get(svals)[tid + delta]; + } + + For::merge(skeys, key, svals, val, cmp, tid, delta); + } + }; + template + struct For + { + template + static __device__ void loadToSmem(const PointerTuple&, const ReferenceTuple&, unsigned int) + { + } + template + static __device__ void loadFromSmem(const PointerTuple&, const ReferenceTuple&, unsigned int) + { + } + + template + static __device__ void copyShfl(const ReferenceTuple&, unsigned int, int) + { + } + template + static __device__ void copy(const PointerTuple&, const ReferenceTuple&, unsigned int, unsigned int) + { + } + + template + static __device__ void mergeShfl(const KeyReferenceTuple&, const ValReferenceTuple&, const CmpTuple&, unsigned int, int) + { + } + template + static __device__ void merge(const KeyPointerTuple&, const KeyReferenceTuple&, + const ValPointerTuple&, const ValReferenceTuple&, + const CmpTuple&, + unsigned int, unsigned int) + { + } + }; + + ////////////////////////////////////////////////////// + // loadToSmem + + template + __device__ __forceinline__ void loadToSmem(volatile T* smem, T& data, unsigned int tid) + { + smem[tid] = data; + } + template + __device__ __forceinline__ void loadFromSmem(volatile T* smem, T& data, unsigned int tid) + { + data = smem[tid]; + } + template + __device__ __forceinline__ void loadToSmem(const thrust::tuple& smem, + const thrust::tuple& data, + unsigned int tid) + { + For<0, thrust::tuple_size >::value>::loadToSmem(smem, data, tid); + } + template + __device__ __forceinline__ void loadFromSmem(const thrust::tuple& smem, + const thrust::tuple& data, + unsigned int tid) + { + For<0, thrust::tuple_size >::value>::loadFromSmem(smem, data, tid); + } + + ////////////////////////////////////////////////////// + // copyVals + + template + __device__ __forceinline__ void copyValsShfl(V& val, unsigned int delta, int width) + { + val = shfl_down(val, delta, width); + } + template + __device__ __forceinline__ void copyVals(volatile V* svals, V& val, unsigned int tid, unsigned int delta) + { + svals[tid] = val = svals[tid + delta]; + } + template + __device__ __forceinline__ void copyValsShfl(const thrust::tuple& val, + unsigned int delta, + int width) + { + For<0, thrust::tuple_size >::value>::copyShfl(val, delta, width); + } + template + __device__ __forceinline__ void copyVals(const thrust::tuple& svals, + const thrust::tuple& val, + unsigned int tid, unsigned int delta) + { + For<0, thrust::tuple_size >::value>::copy(svals, val, tid, delta); + } + + ////////////////////////////////////////////////////// + // merge + + template + __device__ __forceinline__ void mergeShfl(K& key, V& val, const Cmp& cmp, unsigned int delta, int width) + { + K reg = shfl_down(key, delta, width); + + if (cmp(reg, key)) + { + key = reg; + copyValsShfl(val, delta, width); + } + } + template + __device__ __forceinline__ void merge(volatile K* skeys, K& key, volatile V* svals, V& val, const Cmp& cmp, unsigned int tid, unsigned int delta) + { + K reg = skeys[tid + delta]; + + if (cmp(reg, key)) + { + skeys[tid] = key = reg; + copyVals(svals, val, tid, delta); + } + } + template + __device__ __forceinline__ void mergeShfl(K& key, + const thrust::tuple& val, + const Cmp& cmp, + unsigned int delta, int width) + { + K reg = shfl_down(key, delta, width); + + if (cmp(reg, key)) + { + key = reg; + copyValsShfl(val, delta, width); + } + } + template + __device__ __forceinline__ void merge(volatile K* skeys, K& key, + const thrust::tuple& svals, + const thrust::tuple& val, + const Cmp& cmp, unsigned int tid, unsigned int delta) + { + K reg = skeys[tid + delta]; + + if (cmp(reg, key)) + { + skeys[tid] = key = reg; + copyVals(svals, val, tid, delta); + } + } + template + __device__ __forceinline__ void mergeShfl(const thrust::tuple& key, + const thrust::tuple& val, + const thrust::tuple& cmp, + unsigned int delta, int width) + { + For<0, thrust::tuple_size >::value>::mergeShfl(key, val, cmp, delta, width); + } + template + __device__ __forceinline__ void merge(const thrust::tuple& skeys, + const thrust::tuple& key, + const thrust::tuple& svals, + const thrust::tuple& val, + const thrust::tuple& cmp, + unsigned int tid, unsigned int delta) + { + For<0, thrust::tuple_size >::value>::merge(skeys, key, svals, val, cmp, tid, delta); + } + + ////////////////////////////////////////////////////// + // Generic + + template struct Generic + { + template + static __device__ void reduce(KP skeys, KR key, VP svals, VR val, unsigned int tid, Cmp cmp) + { + loadToSmem(skeys, key, tid); + loadValsToSmem(svals, val, tid); + if (N >= 32) + __syncthreads(); + + if (N >= 2048) + { + if (tid < 1024) + merge(skeys, key, svals, val, cmp, tid, 1024); + + __syncthreads(); + } + if (N >= 1024) + { + if (tid < 512) + merge(skeys, key, svals, val, cmp, tid, 512); + + __syncthreads(); + } + if (N >= 512) + { + if (tid < 256) + merge(skeys, key, svals, val, cmp, tid, 256); + + __syncthreads(); + } + if (N >= 256) + { + if (tid < 128) + merge(skeys, key, svals, val, cmp, tid, 128); + + __syncthreads(); + } + if (N >= 128) + { + if (tid < 64) + merge(skeys, key, svals, val, cmp, tid, 64); + + __syncthreads(); + } + if (N >= 64) + { + if (tid < 32) + merge(skeys, key, svals, val, cmp, tid, 32); + } + + if (tid < 16) + { + merge(skeys, key, svals, val, cmp, tid, 16); + merge(skeys, key, svals, val, cmp, tid, 8); + merge(skeys, key, svals, val, cmp, tid, 4); + merge(skeys, key, svals, val, cmp, tid, 2); + merge(skeys, key, svals, val, cmp, tid, 1); + } + } + }; + + template + struct Unroll + { + static __device__ void loopShfl(KR key, VR val, Cmp cmp, unsigned int N) + { + mergeShfl(key, val, cmp, I, N); + Unroll::loopShfl(key, val, cmp, N); + } + static __device__ void loop(KP skeys, KR key, VP svals, VR val, unsigned int tid, Cmp cmp) + { + merge(skeys, key, svals, val, cmp, tid, I); + Unroll::loop(skeys, key, svals, val, tid, cmp); + } + }; + template + struct Unroll<0, KP, KR, VP, VR, Cmp> + { + static __device__ void loopShfl(KR, VR, Cmp, unsigned int) + { + } + static __device__ void loop(KP, KR, VP, VR, unsigned int, Cmp) + { + } + }; + + template struct WarpOptimized + { + template + static __device__ void reduce(KP skeys, KR key, VP svals, VR val, unsigned int tid, Cmp cmp) + { + #if 0 // __CUDA_ARCH__ >= 300 + CV_UNUSED(skeys); + CV_UNUSED(svals); + CV_UNUSED(tid); + + Unroll::loopShfl(key, val, cmp, N); + #else + loadToSmem(skeys, key, tid); + loadToSmem(svals, val, tid); + + if (tid < N / 2) + Unroll::loop(skeys, key, svals, val, tid, cmp); + #endif + } + }; + + template struct GenericOptimized32 + { + enum { M = N / 32 }; + + template + static __device__ void reduce(KP skeys, KR key, VP svals, VR val, unsigned int tid, Cmp cmp) + { + const unsigned int laneId = Warp::laneId(); + + #if 0 // __CUDA_ARCH__ >= 300 + Unroll<16, KP, KR, VP, VR, Cmp>::loopShfl(key, val, cmp, warpSize); + + if (laneId == 0) + { + loadToSmem(skeys, key, tid / 32); + loadToSmem(svals, val, tid / 32); + } + #else + loadToSmem(skeys, key, tid); + loadToSmem(svals, val, tid); + + if (laneId < 16) + Unroll<16, KP, KR, VP, VR, Cmp>::loop(skeys, key, svals, val, tid, cmp); + + __syncthreads(); + + if (laneId == 0) + { + loadToSmem(skeys, key, tid / 32); + loadToSmem(svals, val, tid / 32); + } + #endif + + __syncthreads(); + + loadFromSmem(skeys, key, tid); + + if (tid < 32) + { + #if 0 // __CUDA_ARCH__ >= 300 + loadFromSmem(svals, val, tid); + + Unroll::loopShfl(key, val, cmp, M); + #else + Unroll::loop(skeys, key, svals, val, tid, cmp); + #endif + } + } + }; + + template struct StaticIf; + template struct StaticIf + { + typedef T1 type; + }; + template struct StaticIf + { + typedef T2 type; + }; + + template struct IsPowerOf2 + { + enum { value = ((N != 0) && !(N & (N - 1))) }; + }; + + template struct Dispatcher + { + typedef typename StaticIf< + (N <= 32) && IsPowerOf2::value, + WarpOptimized, + typename StaticIf< + (N <= 1024) && IsPowerOf2::value, + GenericOptimized32, + Generic + >::type + >::type reductor; + }; + } +}}} + +//! @endcond + +#endif // OPENCV_CUDA_PRED_VAL_REDUCE_DETAIL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/detail/transform_detail.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/detail/transform_detail.hpp new file mode 100644 index 0000000000000000000000000000000000000000..1919848827543461c62aeacc90b41c8c105fa5ea --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/detail/transform_detail.hpp @@ -0,0 +1,392 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_TRANSFORM_DETAIL_HPP +#define OPENCV_CUDA_TRANSFORM_DETAIL_HPP + +#include "../common.hpp" +#include "../vec_traits.hpp" +#include "../functional.hpp" + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + namespace transform_detail + { + //! Read Write Traits + + template struct UnaryReadWriteTraits + { + typedef typename TypeVec::vec_type read_type; + typedef typename TypeVec::vec_type write_type; + }; + + template struct BinaryReadWriteTraits + { + typedef typename TypeVec::vec_type read_type1; + typedef typename TypeVec::vec_type read_type2; + typedef typename TypeVec::vec_type write_type; + }; + + //! Transform kernels + + template struct OpUnroller; + template <> struct OpUnroller<1> + { + template + static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, UnOp& op, int x_shifted, int y) + { + if (mask(y, x_shifted)) + dst.x = op(src.x); + } + + template + static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, BinOp& op, int x_shifted, int y) + { + if (mask(y, x_shifted)) + dst.x = op(src1.x, src2.x); + } + }; + template <> struct OpUnroller<2> + { + template + static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, UnOp& op, int x_shifted, int y) + { + if (mask(y, x_shifted)) + dst.x = op(src.x); + if (mask(y, x_shifted + 1)) + dst.y = op(src.y); + } + + template + static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, BinOp& op, int x_shifted, int y) + { + if (mask(y, x_shifted)) + dst.x = op(src1.x, src2.x); + if (mask(y, x_shifted + 1)) + dst.y = op(src1.y, src2.y); + } + }; + template <> struct OpUnroller<3> + { + template + static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, const UnOp& op, int x_shifted, int y) + { + if (mask(y, x_shifted)) + dst.x = op(src.x); + if (mask(y, x_shifted + 1)) + dst.y = op(src.y); + if (mask(y, x_shifted + 2)) + dst.z = op(src.z); + } + + template + static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, const BinOp& op, int x_shifted, int y) + { + if (mask(y, x_shifted)) + dst.x = op(src1.x, src2.x); + if (mask(y, x_shifted + 1)) + dst.y = op(src1.y, src2.y); + if (mask(y, x_shifted + 2)) + dst.z = op(src1.z, src2.z); + } + }; + template <> struct OpUnroller<4> + { + template + static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, const UnOp& op, int x_shifted, int y) + { + if (mask(y, x_shifted)) + dst.x = op(src.x); + if (mask(y, x_shifted + 1)) + dst.y = op(src.y); + if (mask(y, x_shifted + 2)) + dst.z = op(src.z); + if (mask(y, x_shifted + 3)) + dst.w = op(src.w); + } + + template + static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, const BinOp& op, int x_shifted, int y) + { + if (mask(y, x_shifted)) + dst.x = op(src1.x, src2.x); + if (mask(y, x_shifted + 1)) + dst.y = op(src1.y, src2.y); + if (mask(y, x_shifted + 2)) + dst.z = op(src1.z, src2.z); + if (mask(y, x_shifted + 3)) + dst.w = op(src1.w, src2.w); + } + }; + template <> struct OpUnroller<8> + { + template + static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, const UnOp& op, int x_shifted, int y) + { + if (mask(y, x_shifted)) + dst.a0 = op(src.a0); + if (mask(y, x_shifted + 1)) + dst.a1 = op(src.a1); + if (mask(y, x_shifted + 2)) + dst.a2 = op(src.a2); + if (mask(y, x_shifted + 3)) + dst.a3 = op(src.a3); + if (mask(y, x_shifted + 4)) + dst.a4 = op(src.a4); + if (mask(y, x_shifted + 5)) + dst.a5 = op(src.a5); + if (mask(y, x_shifted + 6)) + dst.a6 = op(src.a6); + if (mask(y, x_shifted + 7)) + dst.a7 = op(src.a7); + } + + template + static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, const BinOp& op, int x_shifted, int y) + { + if (mask(y, x_shifted)) + dst.a0 = op(src1.a0, src2.a0); + if (mask(y, x_shifted + 1)) + dst.a1 = op(src1.a1, src2.a1); + if (mask(y, x_shifted + 2)) + dst.a2 = op(src1.a2, src2.a2); + if (mask(y, x_shifted + 3)) + dst.a3 = op(src1.a3, src2.a3); + if (mask(y, x_shifted + 4)) + dst.a4 = op(src1.a4, src2.a4); + if (mask(y, x_shifted + 5)) + dst.a5 = op(src1.a5, src2.a5); + if (mask(y, x_shifted + 6)) + dst.a6 = op(src1.a6, src2.a6); + if (mask(y, x_shifted + 7)) + dst.a7 = op(src1.a7, src2.a7); + } + }; + + template + static __global__ void transformSmart(const PtrStepSz src_, PtrStep dst_, const Mask mask, const UnOp op) + { + typedef TransformFunctorTraits ft; + typedef typename UnaryReadWriteTraits::read_type read_type; + typedef typename UnaryReadWriteTraits::write_type write_type; + + const int x = threadIdx.x + blockIdx.x * blockDim.x; + const int y = threadIdx.y + blockIdx.y * blockDim.y; + const int x_shifted = x * ft::smart_shift; + + if (y < src_.rows) + { + const T* src = src_.ptr(y); + D* dst = dst_.ptr(y); + + if (x_shifted + ft::smart_shift - 1 < src_.cols) + { + const read_type src_n_el = ((const read_type*)src)[x]; + OpUnroller::unroll(src_n_el, ((write_type*)dst)[x], mask, op, x_shifted, y); + } + else + { + for (int real_x = x_shifted; real_x < src_.cols; ++real_x) + { + if (mask(y, real_x)) + dst[real_x] = op(src[real_x]); + } + } + } + } + + template + __global__ static void transformSimple(const PtrStepSz src, PtrStep dst, const Mask mask, const UnOp op) + { + const int x = blockDim.x * blockIdx.x + threadIdx.x; + const int y = blockDim.y * blockIdx.y + threadIdx.y; + + if (x < src.cols && y < src.rows && mask(y, x)) + { + dst.ptr(y)[x] = op(src.ptr(y)[x]); + } + } + + template + static __global__ void transformSmart(const PtrStepSz src1_, const PtrStep src2_, PtrStep dst_, + const Mask mask, const BinOp op) + { + typedef TransformFunctorTraits ft; + typedef typename BinaryReadWriteTraits::read_type1 read_type1; + typedef typename BinaryReadWriteTraits::read_type2 read_type2; + typedef typename BinaryReadWriteTraits::write_type write_type; + + const int x = threadIdx.x + blockIdx.x * blockDim.x; + const int y = threadIdx.y + blockIdx.y * blockDim.y; + const int x_shifted = x * ft::smart_shift; + + if (y < src1_.rows) + { + const T1* src1 = src1_.ptr(y); + const T2* src2 = src2_.ptr(y); + D* dst = dst_.ptr(y); + + if (x_shifted + ft::smart_shift - 1 < src1_.cols) + { + const read_type1 src1_n_el = ((const read_type1*)src1)[x]; + const read_type2 src2_n_el = ((const read_type2*)src2)[x]; + + OpUnroller::unroll(src1_n_el, src2_n_el, ((write_type*)dst)[x], mask, op, x_shifted, y); + } + else + { + for (int real_x = x_shifted; real_x < src1_.cols; ++real_x) + { + if (mask(y, real_x)) + dst[real_x] = op(src1[real_x], src2[real_x]); + } + } + } + } + + template + static __global__ void transformSimple(const PtrStepSz src1, const PtrStep src2, PtrStep dst, + const Mask mask, const BinOp op) + { + const int x = blockDim.x * blockIdx.x + threadIdx.x; + const int y = blockDim.y * blockIdx.y + threadIdx.y; + + if (x < src1.cols && y < src1.rows && mask(y, x)) + { + const T1 src1_data = src1.ptr(y)[x]; + const T2 src2_data = src2.ptr(y)[x]; + dst.ptr(y)[x] = op(src1_data, src2_data); + } + } + + template struct TransformDispatcher; + template<> struct TransformDispatcher + { + template + static void call(PtrStepSz src, PtrStepSz dst, UnOp op, Mask mask, cudaStream_t stream) + { + typedef TransformFunctorTraits ft; + + const dim3 threads(ft::simple_block_dim_x, ft::simple_block_dim_y, 1); + const dim3 grid(divUp(src.cols, threads.x), divUp(src.rows, threads.y), 1); + + transformSimple<<>>(src, dst, mask, op); + cudaSafeCall( cudaGetLastError() ); + + if (stream == 0) + cudaSafeCall( cudaDeviceSynchronize() ); + } + + template + static void call(PtrStepSz src1, PtrStepSz src2, PtrStepSz dst, BinOp op, Mask mask, cudaStream_t stream) + { + typedef TransformFunctorTraits ft; + + const dim3 threads(ft::simple_block_dim_x, ft::simple_block_dim_y, 1); + const dim3 grid(divUp(src1.cols, threads.x), divUp(src1.rows, threads.y), 1); + + transformSimple<<>>(src1, src2, dst, mask, op); + cudaSafeCall( cudaGetLastError() ); + + if (stream == 0) + cudaSafeCall( cudaDeviceSynchronize() ); + } + }; + template<> struct TransformDispatcher + { + template + static void call(PtrStepSz src, PtrStepSz dst, UnOp op, Mask mask, cudaStream_t stream) + { + typedef TransformFunctorTraits ft; + + CV_StaticAssert(ft::smart_shift != 1, ""); + + if (!isAligned(src.data, ft::smart_shift * sizeof(T)) || !isAligned(src.step, ft::smart_shift * sizeof(T)) || + !isAligned(dst.data, ft::smart_shift * sizeof(D)) || !isAligned(dst.step, ft::smart_shift * sizeof(D))) + { + TransformDispatcher::call(src, dst, op, mask, stream); + return; + } + + const dim3 threads(ft::smart_block_dim_x, ft::smart_block_dim_y, 1); + const dim3 grid(divUp(src.cols, threads.x * ft::smart_shift), divUp(src.rows, threads.y), 1); + + transformSmart<<>>(src, dst, mask, op); + cudaSafeCall( cudaGetLastError() ); + + if (stream == 0) + cudaSafeCall( cudaDeviceSynchronize() ); + } + + template + static void call(PtrStepSz src1, PtrStepSz src2, PtrStepSz dst, BinOp op, Mask mask, cudaStream_t stream) + { + typedef TransformFunctorTraits ft; + + CV_StaticAssert(ft::smart_shift != 1, ""); + + if (!isAligned(src1.data, ft::smart_shift * sizeof(T1)) || !isAligned(src1.step, ft::smart_shift * sizeof(T1)) || + !isAligned(src2.data, ft::smart_shift * sizeof(T2)) || !isAligned(src2.step, ft::smart_shift * sizeof(T2)) || + !isAligned(dst.data, ft::smart_shift * sizeof(D)) || !isAligned(dst.step, ft::smart_shift * sizeof(D))) + { + TransformDispatcher::call(src1, src2, dst, op, mask, stream); + return; + } + + const dim3 threads(ft::smart_block_dim_x, ft::smart_block_dim_y, 1); + const dim3 grid(divUp(src1.cols, threads.x * ft::smart_shift), divUp(src1.rows, threads.y), 1); + + transformSmart<<>>(src1, src2, dst, mask, op); + cudaSafeCall( cudaGetLastError() ); + + if (stream == 0) + cudaSafeCall( cudaDeviceSynchronize() ); + } + }; + } // namespace transform_detail +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_TRANSFORM_DETAIL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/detail/type_traits_detail.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/detail/type_traits_detail.hpp new file mode 100644 index 0000000000000000000000000000000000000000..a78bd2c0d858f10d785cabef7258ac4ed656586f --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/detail/type_traits_detail.hpp @@ -0,0 +1,191 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_TYPE_TRAITS_DETAIL_HPP +#define OPENCV_CUDA_TYPE_TRAITS_DETAIL_HPP + +#include "../common.hpp" +#include "../vec_traits.hpp" + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + namespace type_traits_detail + { + template struct Select { typedef T1 type; }; + template struct Select { typedef T2 type; }; + + template struct IsSignedIntergral { enum {value = 0}; }; + template <> struct IsSignedIntergral { enum {value = 1}; }; + template <> struct IsSignedIntergral { enum {value = 1}; }; + template <> struct IsSignedIntergral { enum {value = 1}; }; + template <> struct IsSignedIntergral { enum {value = 1}; }; + template <> struct IsSignedIntergral { enum {value = 1}; }; + template <> struct IsSignedIntergral { enum {value = 1}; }; + + template struct IsUnsignedIntegral { enum {value = 0}; }; + template <> struct IsUnsignedIntegral { enum {value = 1}; }; + template <> struct IsUnsignedIntegral { enum {value = 1}; }; + template <> struct IsUnsignedIntegral { enum {value = 1}; }; + template <> struct IsUnsignedIntegral { enum {value = 1}; }; + template <> struct IsUnsignedIntegral { enum {value = 1}; }; + template <> struct IsUnsignedIntegral { enum {value = 1}; }; + + template struct IsIntegral { enum {value = IsSignedIntergral::value || IsUnsignedIntegral::value}; }; + template <> struct IsIntegral { enum {value = 1}; }; + template <> struct IsIntegral { enum {value = 1}; }; + + template struct IsFloat { enum {value = 0}; }; + template <> struct IsFloat { enum {value = 1}; }; + template <> struct IsFloat { enum {value = 1}; }; + + template struct IsVec { enum {value = 0}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + + template struct AddParameterType { typedef const U& type; }; + template struct AddParameterType { typedef U& type; }; + template <> struct AddParameterType { typedef void type; }; + + template struct ReferenceTraits + { + enum { value = false }; + typedef U type; + }; + template struct ReferenceTraits + { + enum { value = true }; + typedef U type; + }; + + template struct PointerTraits + { + enum { value = false }; + typedef void type; + }; + template struct PointerTraits + { + enum { value = true }; + typedef U type; + }; + template struct PointerTraits + { + enum { value = true }; + typedef U type; + }; + + template struct UnConst + { + typedef U type; + enum { value = 0 }; + }; + template struct UnConst + { + typedef U type; + enum { value = 1 }; + }; + template struct UnConst + { + typedef U& type; + enum { value = 1 }; + }; + + template struct UnVolatile + { + typedef U type; + enum { value = 0 }; + }; + template struct UnVolatile + { + typedef U type; + enum { value = 1 }; + }; + template struct UnVolatile + { + typedef U& type; + enum { value = 1 }; + }; + } // namespace type_traits_detail +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_TYPE_TRAITS_DETAIL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/detail/vec_distance_detail.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/detail/vec_distance_detail.hpp new file mode 100644 index 0000000000000000000000000000000000000000..8283a99560d995f34a768fb40b0c4a33d5bcfee2 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/detail/vec_distance_detail.hpp @@ -0,0 +1,121 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_VEC_DISTANCE_DETAIL_HPP +#define OPENCV_CUDA_VEC_DISTANCE_DETAIL_HPP + +#include "../datamov_utils.hpp" + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + namespace vec_distance_detail + { + template struct UnrollVecDiffCached + { + template + static __device__ void calcCheck(const T1* vecCached, const T2* vecGlob, int len, Dist& dist, int ind) + { + if (ind < len) + { + T1 val1 = *vecCached++; + + T2 val2; + ForceGlob::Load(vecGlob, ind, val2); + + dist.reduceIter(val1, val2); + + UnrollVecDiffCached::calcCheck(vecCached, vecGlob, len, dist, ind + THREAD_DIM); + } + } + + template + static __device__ void calcWithoutCheck(const T1* vecCached, const T2* vecGlob, Dist& dist) + { + T1 val1 = *vecCached++; + + T2 val2; + ForceGlob::Load(vecGlob, 0, val2); + vecGlob += THREAD_DIM; + + dist.reduceIter(val1, val2); + + UnrollVecDiffCached::calcWithoutCheck(vecCached, vecGlob, dist); + } + }; + template struct UnrollVecDiffCached + { + template + static __device__ __forceinline__ void calcCheck(const T1*, const T2*, int, Dist&, int) + { + } + + template + static __device__ __forceinline__ void calcWithoutCheck(const T1*, const T2*, Dist&) + { + } + }; + + template struct VecDiffCachedCalculator; + template struct VecDiffCachedCalculator + { + template + static __device__ __forceinline__ void calc(const T1* vecCached, const T2* vecGlob, int len, Dist& dist, int tid) + { + UnrollVecDiffCached::calcCheck(vecCached, vecGlob, len, dist, tid); + } + }; + template struct VecDiffCachedCalculator + { + template + static __device__ __forceinline__ void calc(const T1* vecCached, const T2* vecGlob, int len, Dist& dist, int tid) + { + UnrollVecDiffCached::calcWithoutCheck(vecCached, vecGlob + tid, dist); + } + }; + } // namespace vec_distance_detail +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_VEC_DISTANCE_DETAIL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/dynamic_smem.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/dynamic_smem.hpp new file mode 100644 index 0000000000000000000000000000000000000000..42570c6830c41bb53e7f9802f2a430f5181218c0 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/dynamic_smem.hpp @@ -0,0 +1,88 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_DYNAMIC_SMEM_HPP +#define OPENCV_CUDA_DYNAMIC_SMEM_HPP + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + template struct DynamicSharedMem + { + __device__ __forceinline__ operator T*() + { + extern __shared__ int __smem[]; + return (T*)__smem; + } + + __device__ __forceinline__ operator const T*() const + { + extern __shared__ int __smem[]; + return (T*)__smem; + } + }; + + // specialize for double to avoid unaligned memory access compile errors + template<> struct DynamicSharedMem + { + __device__ __forceinline__ operator double*() + { + extern __shared__ double __smem_d[]; + return (double*)__smem_d; + } + + __device__ __forceinline__ operator const double*() const + { + extern __shared__ double __smem_d[]; + return (double*)__smem_d; + } + }; +}}} + +//! @endcond + +#endif // OPENCV_CUDA_DYNAMIC_SMEM_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/emulation.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/emulation.hpp new file mode 100644 index 0000000000000000000000000000000000000000..17dc1171a237e2f1979ad2f782e79c3a9b7f9d53 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/emulation.hpp @@ -0,0 +1,269 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_EMULATION_HPP_ +#define OPENCV_CUDA_EMULATION_HPP_ + +#include "common.hpp" +#include "warp_reduce.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + struct Emulation + { + + static __device__ __forceinline__ int syncthreadsOr(int pred) + { +#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ < 200) + // just campilation stab + return 0; +#else + return __syncthreads_or(pred); +#endif + } + + template + static __forceinline__ __device__ int Ballot(int predicate) + { +#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ >= 200) + return __ballot(predicate); +#else + __shared__ volatile int cta_buffer[CTA_SIZE]; + + int tid = threadIdx.x; + cta_buffer[tid] = predicate ? (1 << (tid & 31)) : 0; + return warp_reduce(cta_buffer); +#endif + } + + struct smem + { + enum { TAG_MASK = (1U << ( (sizeof(unsigned int) << 3) - 5U)) - 1U }; + + template + static __device__ __forceinline__ T atomicInc(T* address, T val) + { +#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ < 120) + T count; + unsigned int tag = threadIdx.x << ( (sizeof(unsigned int) << 3) - 5U); + do + { + count = *address & TAG_MASK; + count = tag | (count + 1); + *address = count; + } while (*address != count); + + return (count & TAG_MASK) - 1; +#else + return ::atomicInc(address, val); +#endif + } + + template + static __device__ __forceinline__ T atomicAdd(T* address, T val) + { +#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ < 120) + T count; + unsigned int tag = threadIdx.x << ( (sizeof(unsigned int) << 3) - 5U); + do + { + count = *address & TAG_MASK; + count = tag | (count + val); + *address = count; + } while (*address != count); + + return (count & TAG_MASK) - val; +#else + return ::atomicAdd(address, val); +#endif + } + + template + static __device__ __forceinline__ T atomicMin(T* address, T val) + { +#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ < 120) + T count = ::min(*address, val); + do + { + *address = count; + } while (*address > count); + + return count; +#else + return ::atomicMin(address, val); +#endif + } + }; // struct cmem + + struct glob + { + static __device__ __forceinline__ int atomicAdd(int* address, int val) + { + return ::atomicAdd(address, val); + } + static __device__ __forceinline__ unsigned int atomicAdd(unsigned int* address, unsigned int val) + { + return ::atomicAdd(address, val); + } + static __device__ __forceinline__ float atomicAdd(float* address, float val) + { + #if __CUDA_ARCH__ >= 200 + return ::atomicAdd(address, val); + #else + int* address_as_i = (int*) address; + int old = *address_as_i, assumed; + do { + assumed = old; + old = ::atomicCAS(address_as_i, assumed, + __float_as_int(val + __int_as_float(assumed))); + } while (assumed != old); + return __int_as_float(old); + #endif + } + static __device__ __forceinline__ double atomicAdd(double* address, double val) + { + #if __CUDA_ARCH__ >= 130 + unsigned long long int* address_as_ull = (unsigned long long int*) address; + unsigned long long int old = *address_as_ull, assumed; + do { + assumed = old; + old = ::atomicCAS(address_as_ull, assumed, + __double_as_longlong(val + __longlong_as_double(assumed))); + } while (assumed != old); + return __longlong_as_double(old); + #else + CV_UNUSED(address); + CV_UNUSED(val); + return 0.0; + #endif + } + + static __device__ __forceinline__ int atomicMin(int* address, int val) + { + return ::atomicMin(address, val); + } + static __device__ __forceinline__ float atomicMin(float* address, float val) + { + #if __CUDA_ARCH__ >= 120 + int* address_as_i = (int*) address; + int old = *address_as_i, assumed; + do { + assumed = old; + old = ::atomicCAS(address_as_i, assumed, + __float_as_int(::fminf(val, __int_as_float(assumed)))); + } while (assumed != old); + return __int_as_float(old); + #else + CV_UNUSED(address); + CV_UNUSED(val); + return 0.0f; + #endif + } + static __device__ __forceinline__ double atomicMin(double* address, double val) + { + #if __CUDA_ARCH__ >= 130 + unsigned long long int* address_as_ull = (unsigned long long int*) address; + unsigned long long int old = *address_as_ull, assumed; + do { + assumed = old; + old = ::atomicCAS(address_as_ull, assumed, + __double_as_longlong(::fmin(val, __longlong_as_double(assumed)))); + } while (assumed != old); + return __longlong_as_double(old); + #else + CV_UNUSED(address); + CV_UNUSED(val); + return 0.0; + #endif + } + + static __device__ __forceinline__ int atomicMax(int* address, int val) + { + return ::atomicMax(address, val); + } + static __device__ __forceinline__ float atomicMax(float* address, float val) + { + #if __CUDA_ARCH__ >= 120 + int* address_as_i = (int*) address; + int old = *address_as_i, assumed; + do { + assumed = old; + old = ::atomicCAS(address_as_i, assumed, + __float_as_int(::fmaxf(val, __int_as_float(assumed)))); + } while (assumed != old); + return __int_as_float(old); + #else + CV_UNUSED(address); + CV_UNUSED(val); + return 0.0f; + #endif + } + static __device__ __forceinline__ double atomicMax(double* address, double val) + { + #if __CUDA_ARCH__ >= 130 + unsigned long long int* address_as_ull = (unsigned long long int*) address; + unsigned long long int old = *address_as_ull, assumed; + do { + assumed = old; + old = ::atomicCAS(address_as_ull, assumed, + __double_as_longlong(::fmax(val, __longlong_as_double(assumed)))); + } while (assumed != old); + return __longlong_as_double(old); + #else + CV_UNUSED(address); + CV_UNUSED(val); + return 0.0; + #endif + } + }; + }; //struct Emulation +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif /* OPENCV_CUDA_EMULATION_HPP_ */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/filters.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/filters.hpp new file mode 100644 index 0000000000000000000000000000000000000000..bb94212299a7f2ae5a2fa9fe7d38323adddfb3c3 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/filters.hpp @@ -0,0 +1,286 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_FILTERS_HPP +#define OPENCV_CUDA_FILTERS_HPP + +#include "saturate_cast.hpp" +#include "vec_traits.hpp" +#include "vec_math.hpp" +#include "type_traits.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + template struct PointFilter + { + typedef typename Ptr2D::elem_type elem_type; + typedef float index_type; + + explicit __host__ __device__ __forceinline__ PointFilter(const Ptr2D& src_, float fx = 0.f, float fy = 0.f) + : src(src_) + { + CV_UNUSED(fx); + CV_UNUSED(fy); + } + + __device__ __forceinline__ elem_type operator ()(float y, float x) const + { + return src(__float2int_rz(y), __float2int_rz(x)); + } + + Ptr2D src; + }; + + template struct LinearFilter + { + typedef typename Ptr2D::elem_type elem_type; + typedef float index_type; + + explicit __host__ __device__ __forceinline__ LinearFilter(const Ptr2D& src_, float fx = 0.f, float fy = 0.f) + : src(src_) + { + CV_UNUSED(fx); + CV_UNUSED(fy); + } + __device__ __forceinline__ elem_type operator ()(float y, float x) const + { + typedef typename TypeVec::cn>::vec_type work_type; + + work_type out = VecTraits::all(0); + + const int x1 = __float2int_rd(x); + const int y1 = __float2int_rd(y); + const int x2 = x1 + 1; + const int y2 = y1 + 1; + + elem_type src_reg = src(y1, x1); + out = out + src_reg * ((x2 - x) * (y2 - y)); + + src_reg = src(y1, x2); + out = out + src_reg * ((x - x1) * (y2 - y)); + + src_reg = src(y2, x1); + out = out + src_reg * ((x2 - x) * (y - y1)); + + src_reg = src(y2, x2); + out = out + src_reg * ((x - x1) * (y - y1)); + + return saturate_cast(out); + } + + Ptr2D src; + }; + + template struct CubicFilter + { + typedef typename Ptr2D::elem_type elem_type; + typedef float index_type; + typedef typename TypeVec::cn>::vec_type work_type; + + explicit __host__ __device__ __forceinline__ CubicFilter(const Ptr2D& src_, float fx = 0.f, float fy = 0.f) + : src(src_) + { + CV_UNUSED(fx); + CV_UNUSED(fy); + } + + static __device__ __forceinline__ float bicubicCoeff(float x_) + { + float x = fabsf(x_); + if (x <= 1.0f) + { + return x * x * (1.5f * x - 2.5f) + 1.0f; + } + else if (x < 2.0f) + { + return x * (x * (-0.5f * x + 2.5f) - 4.0f) + 2.0f; + } + else + { + return 0.0f; + } + } + + __device__ elem_type operator ()(float y, float x) const + { + const float xmin = ::ceilf(x - 2.0f); + const float xmax = ::floorf(x + 2.0f); + + const float ymin = ::ceilf(y - 2.0f); + const float ymax = ::floorf(y + 2.0f); + + work_type sum = VecTraits::all(0); + float wsum = 0.0f; + + for (float cy = ymin; cy <= ymax; cy += 1.0f) + { + for (float cx = xmin; cx <= xmax; cx += 1.0f) + { + const float w = bicubicCoeff(x - cx) * bicubicCoeff(y - cy); + sum = sum + w * src(__float2int_rd(cy), __float2int_rd(cx)); + wsum += w; + } + } + + work_type res = (!wsum)? VecTraits::all(0) : sum / wsum; + + return saturate_cast(res); + } + + Ptr2D src; + }; + // for integer scaling + template struct IntegerAreaFilter + { + typedef typename Ptr2D::elem_type elem_type; + typedef float index_type; + + explicit __host__ __device__ __forceinline__ IntegerAreaFilter(const Ptr2D& src_, float scale_x_, float scale_y_) + : src(src_), scale_x(scale_x_), scale_y(scale_y_), scale(1.f / (scale_x * scale_y)) {} + + __device__ __forceinline__ elem_type operator ()(float y, float x) const + { + float fsx1 = x * scale_x; + float fsx2 = fsx1 + scale_x; + + int sx1 = __float2int_ru(fsx1); + int sx2 = __float2int_rd(fsx2); + + float fsy1 = y * scale_y; + float fsy2 = fsy1 + scale_y; + + int sy1 = __float2int_ru(fsy1); + int sy2 = __float2int_rd(fsy2); + + typedef typename TypeVec::cn>::vec_type work_type; + work_type out = VecTraits::all(0.f); + + for(int dy = sy1; dy < sy2; ++dy) + for(int dx = sx1; dx < sx2; ++dx) + { + out = out + src(dy, dx) * scale; + } + + return saturate_cast(out); + } + + Ptr2D src; + float scale_x, scale_y ,scale; + }; + + template struct AreaFilter + { + typedef typename Ptr2D::elem_type elem_type; + typedef float index_type; + + explicit __host__ __device__ __forceinline__ AreaFilter(const Ptr2D& src_, float scale_x_, float scale_y_) + : src(src_), scale_x(scale_x_), scale_y(scale_y_){} + + __device__ __forceinline__ elem_type operator ()(float y, float x) const + { + float fsx1 = x * scale_x; + float fsx2 = fsx1 + scale_x; + + int sx1 = __float2int_ru(fsx1); + int sx2 = __float2int_rd(fsx2); + + float fsy1 = y * scale_y; + float fsy2 = fsy1 + scale_y; + + int sy1 = __float2int_ru(fsy1); + int sy2 = __float2int_rd(fsy2); + + float scale = 1.f / (fminf(scale_x, src.width - fsx1) * fminf(scale_y, src.height - fsy1)); + + typedef typename TypeVec::cn>::vec_type work_type; + work_type out = VecTraits::all(0.f); + + for (int dy = sy1; dy < sy2; ++dy) + { + for (int dx = sx1; dx < sx2; ++dx) + out = out + src(dy, dx) * scale; + + if (sx1 > fsx1) + out = out + src(dy, (sx1 -1) ) * ((sx1 - fsx1) * scale); + + if (sx2 < fsx2) + out = out + src(dy, sx2) * ((fsx2 -sx2) * scale); + } + + if (sy1 > fsy1) + for (int dx = sx1; dx < sx2; ++dx) + out = out + src( (sy1 - 1) , dx) * ((sy1 -fsy1) * scale); + + if (sy2 < fsy2) + for (int dx = sx1; dx < sx2; ++dx) + out = out + src(sy2, dx) * ((fsy2 -sy2) * scale); + + if ((sy1 > fsy1) && (sx1 > fsx1)) + out = out + src( (sy1 - 1) , (sx1 - 1)) * ((sy1 -fsy1) * (sx1 -fsx1) * scale); + + if ((sy1 > fsy1) && (sx2 < fsx2)) + out = out + src( (sy1 - 1) , sx2) * ((sy1 -fsy1) * (fsx2 -sx2) * scale); + + if ((sy2 < fsy2) && (sx2 < fsx2)) + out = out + src(sy2, sx2) * ((fsy2 -sy2) * (fsx2 -sx2) * scale); + + if ((sy2 < fsy2) && (sx1 > fsx1)) + out = out + src(sy2, (sx1 - 1)) * ((fsy2 -sy2) * (sx1 -fsx1) * scale); + + return saturate_cast(out); + } + + Ptr2D src; + float scale_x, scale_y; + int width, haight; + }; +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_FILTERS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/funcattrib.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/funcattrib.hpp new file mode 100644 index 0000000000000000000000000000000000000000..f58208048882076cb4748706491c46462833ad44 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/funcattrib.hpp @@ -0,0 +1,79 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_DEVICE_FUNCATTRIB_HPP +#define OPENCV_CUDA_DEVICE_FUNCATTRIB_HPP + +#include + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + template + void printFuncAttrib(Func& func) + { + + cudaFuncAttributes attrs; + cudaFuncGetAttributes(&attrs, func); + + printf("=== Function stats ===\n"); + printf("Name: \n"); + printf("sharedSizeBytes = %d\n", attrs.sharedSizeBytes); + printf("constSizeBytes = %d\n", attrs.constSizeBytes); + printf("localSizeBytes = %d\n", attrs.localSizeBytes); + printf("maxThreadsPerBlock = %d\n", attrs.maxThreadsPerBlock); + printf("numRegs = %d\n", attrs.numRegs); + printf("ptxVersion = %d\n", attrs.ptxVersion); + printf("binaryVersion = %d\n", attrs.binaryVersion); + printf("\n"); + fflush(stdout); + } +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif /* OPENCV_CUDA_DEVICE_FUNCATTRIB_HPP */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/functional.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/functional.hpp new file mode 100644 index 0000000000000000000000000000000000000000..4944381ddaa82591f158f87cd41b3784c2cf1787 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/functional.hpp @@ -0,0 +1,811 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_FUNCTIONAL_HPP +#define OPENCV_CUDA_FUNCTIONAL_HPP + +#include +#include "saturate_cast.hpp" +#include "vec_traits.hpp" +#include "type_traits.hpp" +#include "device_functions.h" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + // Function Objects +#ifdef CV_CXX11 + template struct unary_function + { + typedef Argument argument_type; + typedef Result result_type; + }; + template struct binary_function + { + typedef Argument1 first_argument_type; + typedef Argument2 second_argument_type; + typedef Result result_type; + }; +#else + template struct unary_function : public std::unary_function {}; + template struct binary_function : public std::binary_function {}; +#endif + + // Arithmetic Operations + template struct plus : binary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a + b; + } + __host__ __device__ __forceinline__ plus() {} + __host__ __device__ __forceinline__ plus(const plus&) {} + }; + + template struct minus : binary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a - b; + } + __host__ __device__ __forceinline__ minus() {} + __host__ __device__ __forceinline__ minus(const minus&) {} + }; + + template struct multiplies : binary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a * b; + } + __host__ __device__ __forceinline__ multiplies() {} + __host__ __device__ __forceinline__ multiplies(const multiplies&) {} + }; + + template struct divides : binary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a / b; + } + __host__ __device__ __forceinline__ divides() {} + __host__ __device__ __forceinline__ divides(const divides&) {} + }; + + template struct modulus : binary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a % b; + } + __host__ __device__ __forceinline__ modulus() {} + __host__ __device__ __forceinline__ modulus(const modulus&) {} + }; + + template struct negate : unary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a) const + { + return -a; + } + __host__ __device__ __forceinline__ negate() {} + __host__ __device__ __forceinline__ negate(const negate&) {} + }; + + // Comparison Operations + template struct equal_to : binary_function + { + __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a == b; + } + __host__ __device__ __forceinline__ equal_to() {} + __host__ __device__ __forceinline__ equal_to(const equal_to&) {} + }; + + template struct not_equal_to : binary_function + { + __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a != b; + } + __host__ __device__ __forceinline__ not_equal_to() {} + __host__ __device__ __forceinline__ not_equal_to(const not_equal_to&) {} + }; + + template struct greater : binary_function + { + __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a > b; + } + __host__ __device__ __forceinline__ greater() {} + __host__ __device__ __forceinline__ greater(const greater&) {} + }; + + template struct less : binary_function + { + __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a < b; + } + __host__ __device__ __forceinline__ less() {} + __host__ __device__ __forceinline__ less(const less&) {} + }; + + template struct greater_equal : binary_function + { + __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a >= b; + } + __host__ __device__ __forceinline__ greater_equal() {} + __host__ __device__ __forceinline__ greater_equal(const greater_equal&) {} + }; + + template struct less_equal : binary_function + { + __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a <= b; + } + __host__ __device__ __forceinline__ less_equal() {} + __host__ __device__ __forceinline__ less_equal(const less_equal&) {} + }; + + // Logical Operations + template struct logical_and : binary_function + { + __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a && b; + } + __host__ __device__ __forceinline__ logical_and() {} + __host__ __device__ __forceinline__ logical_and(const logical_and&) {} + }; + + template struct logical_or : binary_function + { + __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a || b; + } + __host__ __device__ __forceinline__ logical_or() {} + __host__ __device__ __forceinline__ logical_or(const logical_or&) {} + }; + + template struct logical_not : unary_function + { + __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a) const + { + return !a; + } + __host__ __device__ __forceinline__ logical_not() {} + __host__ __device__ __forceinline__ logical_not(const logical_not&) {} + }; + + // Bitwise Operations + template struct bit_and : binary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a & b; + } + __host__ __device__ __forceinline__ bit_and() {} + __host__ __device__ __forceinline__ bit_and(const bit_and&) {} + }; + + template struct bit_or : binary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a | b; + } + __host__ __device__ __forceinline__ bit_or() {} + __host__ __device__ __forceinline__ bit_or(const bit_or&) {} + }; + + template struct bit_xor : binary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a ^ b; + } + __host__ __device__ __forceinline__ bit_xor() {} + __host__ __device__ __forceinline__ bit_xor(const bit_xor&) {} + }; + + template struct bit_not : unary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType v) const + { + return ~v; + } + __host__ __device__ __forceinline__ bit_not() {} + __host__ __device__ __forceinline__ bit_not(const bit_not&) {} + }; + + // Generalized Identity Operations + template struct identity : unary_function + { + __device__ __forceinline__ typename TypeTraits::ParameterType operator()(typename TypeTraits::ParameterType x) const + { + return x; + } + __host__ __device__ __forceinline__ identity() {} + __host__ __device__ __forceinline__ identity(const identity&) {} + }; + + template struct project1st : binary_function + { + __device__ __forceinline__ typename TypeTraits::ParameterType operator()(typename TypeTraits::ParameterType lhs, typename TypeTraits::ParameterType rhs) const + { + return lhs; + } + __host__ __device__ __forceinline__ project1st() {} + __host__ __device__ __forceinline__ project1st(const project1st&) {} + }; + + template struct project2nd : binary_function + { + __device__ __forceinline__ typename TypeTraits::ParameterType operator()(typename TypeTraits::ParameterType lhs, typename TypeTraits::ParameterType rhs) const + { + return rhs; + } + __host__ __device__ __forceinline__ project2nd() {} + __host__ __device__ __forceinline__ project2nd(const project2nd&) {} + }; + + // Min/Max Operations + +#define OPENCV_CUDA_IMPLEMENT_MINMAX(name, type, op) \ + template <> struct name : binary_function \ + { \ + __device__ __forceinline__ type operator()(type lhs, type rhs) const {return op(lhs, rhs);} \ + __host__ __device__ __forceinline__ name() {}\ + __host__ __device__ __forceinline__ name(const name&) {}\ + }; + + template struct maximum : binary_function + { + __device__ __forceinline__ T operator()(typename TypeTraits::ParameterType lhs, typename TypeTraits::ParameterType rhs) const + { + return max(lhs, rhs); + } + __host__ __device__ __forceinline__ maximum() {} + __host__ __device__ __forceinline__ maximum(const maximum&) {} + }; + + OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, uchar, ::max) + OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, schar, ::max) + OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, char, ::max) + OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, ushort, ::max) + OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, short, ::max) + OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, int, ::max) + OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, uint, ::max) + OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, float, ::fmax) + OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, double, ::fmax) + + template struct minimum : binary_function + { + __device__ __forceinline__ T operator()(typename TypeTraits::ParameterType lhs, typename TypeTraits::ParameterType rhs) const + { + return min(lhs, rhs); + } + __host__ __device__ __forceinline__ minimum() {} + __host__ __device__ __forceinline__ minimum(const minimum&) {} + }; + + OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, uchar, ::min) + OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, schar, ::min) + OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, char, ::min) + OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, ushort, ::min) + OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, short, ::min) + OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, int, ::min) + OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, uint, ::min) + OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, float, ::fmin) + OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, double, ::fmin) + +#undef OPENCV_CUDA_IMPLEMENT_MINMAX + + // Math functions + + template struct abs_func : unary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType x) const + { + return abs(x); + } + + __host__ __device__ __forceinline__ abs_func() {} + __host__ __device__ __forceinline__ abs_func(const abs_func&) {} + }; + template <> struct abs_func : unary_function + { + __device__ __forceinline__ unsigned char operator ()(unsigned char x) const + { + return x; + } + + __host__ __device__ __forceinline__ abs_func() {} + __host__ __device__ __forceinline__ abs_func(const abs_func&) {} + }; + template <> struct abs_func : unary_function + { + __device__ __forceinline__ signed char operator ()(signed char x) const + { + return ::abs((int)x); + } + + __host__ __device__ __forceinline__ abs_func() {} + __host__ __device__ __forceinline__ abs_func(const abs_func&) {} + }; + template <> struct abs_func : unary_function + { + __device__ __forceinline__ char operator ()(char x) const + { + return ::abs((int)x); + } + + __host__ __device__ __forceinline__ abs_func() {} + __host__ __device__ __forceinline__ abs_func(const abs_func&) {} + }; + template <> struct abs_func : unary_function + { + __device__ __forceinline__ unsigned short operator ()(unsigned short x) const + { + return x; + } + + __host__ __device__ __forceinline__ abs_func() {} + __host__ __device__ __forceinline__ abs_func(const abs_func&) {} + }; + template <> struct abs_func : unary_function + { + __device__ __forceinline__ short operator ()(short x) const + { + return ::abs((int)x); + } + + __host__ __device__ __forceinline__ abs_func() {} + __host__ __device__ __forceinline__ abs_func(const abs_func&) {} + }; + template <> struct abs_func : unary_function + { + __device__ __forceinline__ unsigned int operator ()(unsigned int x) const + { + return x; + } + + __host__ __device__ __forceinline__ abs_func() {} + __host__ __device__ __forceinline__ abs_func(const abs_func&) {} + }; + template <> struct abs_func : unary_function + { + __device__ __forceinline__ int operator ()(int x) const + { + return ::abs(x); + } + + __host__ __device__ __forceinline__ abs_func() {} + __host__ __device__ __forceinline__ abs_func(const abs_func&) {} + }; + template <> struct abs_func : unary_function + { + __device__ __forceinline__ float operator ()(float x) const + { + return ::fabsf(x); + } + + __host__ __device__ __forceinline__ abs_func() {} + __host__ __device__ __forceinline__ abs_func(const abs_func&) {} + }; + template <> struct abs_func : unary_function + { + __device__ __forceinline__ double operator ()(double x) const + { + return ::fabs(x); + } + + __host__ __device__ __forceinline__ abs_func() {} + __host__ __device__ __forceinline__ abs_func(const abs_func&) {} + }; + +#define OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(name, func) \ + template struct name ## _func : unary_function \ + { \ + __device__ __forceinline__ float operator ()(typename TypeTraits::ParameterType v) const \ + { \ + return func ## f(v); \ + } \ + __host__ __device__ __forceinline__ name ## _func() {} \ + __host__ __device__ __forceinline__ name ## _func(const name ## _func&) {} \ + }; \ + template <> struct name ## _func : unary_function \ + { \ + __device__ __forceinline__ double operator ()(double v) const \ + { \ + return func(v); \ + } \ + __host__ __device__ __forceinline__ name ## _func() {} \ + __host__ __device__ __forceinline__ name ## _func(const name ## _func&) {} \ + }; + +#define OPENCV_CUDA_IMPLEMENT_BIN_FUNCTOR(name, func) \ + template struct name ## _func : binary_function \ + { \ + __device__ __forceinline__ float operator ()(typename TypeTraits::ParameterType v1, typename TypeTraits::ParameterType v2) const \ + { \ + return func ## f(v1, v2); \ + } \ + __host__ __device__ __forceinline__ name ## _func() {} \ + __host__ __device__ __forceinline__ name ## _func(const name ## _func&) {} \ + }; \ + template <> struct name ## _func : binary_function \ + { \ + __device__ __forceinline__ double operator ()(double v1, double v2) const \ + { \ + return func(v1, v2); \ + } \ + __host__ __device__ __forceinline__ name ## _func() {} \ + __host__ __device__ __forceinline__ name ## _func(const name ## _func&) {} \ + }; + + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(sqrt, ::sqrt) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(exp, ::exp) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(exp2, ::exp2) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(exp10, ::exp10) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(log, ::log) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(log2, ::log2) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(log10, ::log10) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(sin, ::sin) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(cos, ::cos) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(tan, ::tan) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(asin, ::asin) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(acos, ::acos) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(atan, ::atan) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(sinh, ::sinh) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(cosh, ::cosh) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(tanh, ::tanh) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(asinh, ::asinh) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(acosh, ::acosh) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(atanh, ::atanh) + + OPENCV_CUDA_IMPLEMENT_BIN_FUNCTOR(hypot, ::hypot) + OPENCV_CUDA_IMPLEMENT_BIN_FUNCTOR(atan2, ::atan2) + OPENCV_CUDA_IMPLEMENT_BIN_FUNCTOR(pow, ::pow) + + #undef OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR + #undef OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR_NO_DOUBLE + #undef OPENCV_CUDA_IMPLEMENT_BIN_FUNCTOR + + template struct hypot_sqr_func : binary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType src1, typename TypeTraits::ParameterType src2) const + { + return src1 * src1 + src2 * src2; + } + __host__ __device__ __forceinline__ hypot_sqr_func() {} + __host__ __device__ __forceinline__ hypot_sqr_func(const hypot_sqr_func&) {} + }; + + // Saturate Cast Functor + template struct saturate_cast_func : unary_function + { + __device__ __forceinline__ D operator ()(typename TypeTraits::ParameterType v) const + { + return saturate_cast(v); + } + __host__ __device__ __forceinline__ saturate_cast_func() {} + __host__ __device__ __forceinline__ saturate_cast_func(const saturate_cast_func&) {} + }; + + // Threshold Functors + template struct thresh_binary_func : unary_function + { + __host__ __device__ __forceinline__ thresh_binary_func(T thresh_, T maxVal_) : thresh(thresh_), maxVal(maxVal_) {} + + __device__ __forceinline__ T operator()(typename TypeTraits::ParameterType src) const + { + return (src > thresh) * maxVal; + } + + __host__ __device__ __forceinline__ thresh_binary_func() {} + __host__ __device__ __forceinline__ thresh_binary_func(const thresh_binary_func& other) + : thresh(other.thresh), maxVal(other.maxVal) {} + + T thresh; + T maxVal; + }; + + template struct thresh_binary_inv_func : unary_function + { + __host__ __device__ __forceinline__ thresh_binary_inv_func(T thresh_, T maxVal_) : thresh(thresh_), maxVal(maxVal_) {} + + __device__ __forceinline__ T operator()(typename TypeTraits::ParameterType src) const + { + return (src <= thresh) * maxVal; + } + + __host__ __device__ __forceinline__ thresh_binary_inv_func() {} + __host__ __device__ __forceinline__ thresh_binary_inv_func(const thresh_binary_inv_func& other) + : thresh(other.thresh), maxVal(other.maxVal) {} + + T thresh; + T maxVal; + }; + + template struct thresh_trunc_func : unary_function + { + explicit __host__ __device__ __forceinline__ thresh_trunc_func(T thresh_, T maxVal_ = 0) : thresh(thresh_) {CV_UNUSED(maxVal_);} + + __device__ __forceinline__ T operator()(typename TypeTraits::ParameterType src) const + { + return minimum()(src, thresh); + } + + __host__ __device__ __forceinline__ thresh_trunc_func() {} + __host__ __device__ __forceinline__ thresh_trunc_func(const thresh_trunc_func& other) + : thresh(other.thresh) {} + + T thresh; + }; + + template struct thresh_to_zero_func : unary_function + { + explicit __host__ __device__ __forceinline__ thresh_to_zero_func(T thresh_, T maxVal_ = 0) : thresh(thresh_) {CV_UNUSED(maxVal_);} + + __device__ __forceinline__ T operator()(typename TypeTraits::ParameterType src) const + { + return (src > thresh) * src; + } + + __host__ __device__ __forceinline__ thresh_to_zero_func() {} + __host__ __device__ __forceinline__ thresh_to_zero_func(const thresh_to_zero_func& other) + : thresh(other.thresh) {} + + T thresh; + }; + + template struct thresh_to_zero_inv_func : unary_function + { + explicit __host__ __device__ __forceinline__ thresh_to_zero_inv_func(T thresh_, T maxVal_ = 0) : thresh(thresh_) {CV_UNUSED(maxVal_);} + + __device__ __forceinline__ T operator()(typename TypeTraits::ParameterType src) const + { + return (src <= thresh) * src; + } + + __host__ __device__ __forceinline__ thresh_to_zero_inv_func() {} + __host__ __device__ __forceinline__ thresh_to_zero_inv_func(const thresh_to_zero_inv_func& other) + : thresh(other.thresh) {} + + T thresh; + }; + + // Function Object Adaptors + template struct unary_negate : unary_function + { + explicit __host__ __device__ __forceinline__ unary_negate(const Predicate& p) : pred(p) {} + + __device__ __forceinline__ bool operator()(typename TypeTraits::ParameterType x) const + { + return !pred(x); + } + + __host__ __device__ __forceinline__ unary_negate() {} + __host__ __device__ __forceinline__ unary_negate(const unary_negate& other) : pred(other.pred) {} + + Predicate pred; + }; + + template __host__ __device__ __forceinline__ unary_negate not1(const Predicate& pred) + { + return unary_negate(pred); + } + + template struct binary_negate : binary_function + { + explicit __host__ __device__ __forceinline__ binary_negate(const Predicate& p) : pred(p) {} + + __device__ __forceinline__ bool operator()(typename TypeTraits::ParameterType x, + typename TypeTraits::ParameterType y) const + { + return !pred(x,y); + } + + __host__ __device__ __forceinline__ binary_negate() {} + __host__ __device__ __forceinline__ binary_negate(const binary_negate& other) : pred(other.pred) {} + + Predicate pred; + }; + + template __host__ __device__ __forceinline__ binary_negate not2(const BinaryPredicate& pred) + { + return binary_negate(pred); + } + + template struct binder1st : unary_function + { + __host__ __device__ __forceinline__ binder1st(const Op& op_, const typename Op::first_argument_type& arg1_) : op(op_), arg1(arg1_) {} + + __device__ __forceinline__ typename Op::result_type operator ()(typename TypeTraits::ParameterType a) const + { + return op(arg1, a); + } + + __host__ __device__ __forceinline__ binder1st() {} + __host__ __device__ __forceinline__ binder1st(const binder1st& other) : op(other.op), arg1(other.arg1) {} + + Op op; + typename Op::first_argument_type arg1; + }; + + template __host__ __device__ __forceinline__ binder1st bind1st(const Op& op, const T& x) + { + return binder1st(op, typename Op::first_argument_type(x)); + } + + template struct binder2nd : unary_function + { + __host__ __device__ __forceinline__ binder2nd(const Op& op_, const typename Op::second_argument_type& arg2_) : op(op_), arg2(arg2_) {} + + __forceinline__ __device__ typename Op::result_type operator ()(typename TypeTraits::ParameterType a) const + { + return op(a, arg2); + } + + __host__ __device__ __forceinline__ binder2nd() {} + __host__ __device__ __forceinline__ binder2nd(const binder2nd& other) : op(other.op), arg2(other.arg2) {} + + Op op; + typename Op::second_argument_type arg2; + }; + + template __host__ __device__ __forceinline__ binder2nd bind2nd(const Op& op, const T& x) + { + return binder2nd(op, typename Op::second_argument_type(x)); + } + + // Functor Traits + template struct IsUnaryFunction + { + typedef char Yes; + struct No {Yes a[2];}; + + template static Yes check(unary_function); + static No check(...); + + static F makeF(); + + enum { value = (sizeof(check(makeF())) == sizeof(Yes)) }; + }; + + template struct IsBinaryFunction + { + typedef char Yes; + struct No {Yes a[2];}; + + template static Yes check(binary_function); + static No check(...); + + static F makeF(); + + enum { value = (sizeof(check(makeF())) == sizeof(Yes)) }; + }; + + namespace functional_detail + { + template struct UnOpShift { enum { shift = 1 }; }; + template struct UnOpShift { enum { shift = 4 }; }; + template struct UnOpShift { enum { shift = 2 }; }; + + template struct DefaultUnaryShift + { + enum { shift = UnOpShift::shift }; + }; + + template struct BinOpShift { enum { shift = 1 }; }; + template struct BinOpShift { enum { shift = 4 }; }; + template struct BinOpShift { enum { shift = 2 }; }; + + template struct DefaultBinaryShift + { + enum { shift = BinOpShift::shift }; + }; + + template ::value> struct ShiftDispatcher; + template struct ShiftDispatcher + { + enum { shift = DefaultUnaryShift::shift }; + }; + template struct ShiftDispatcher + { + enum { shift = DefaultBinaryShift::shift }; + }; + } + + template struct DefaultTransformShift + { + enum { shift = functional_detail::ShiftDispatcher::shift }; + }; + + template struct DefaultTransformFunctorTraits + { + enum { simple_block_dim_x = 16 }; + enum { simple_block_dim_y = 16 }; + + enum { smart_block_dim_x = 16 }; + enum { smart_block_dim_y = 16 }; + enum { smart_shift = DefaultTransformShift::shift }; + }; + + template struct TransformFunctorTraits : DefaultTransformFunctorTraits {}; + +#define OPENCV_CUDA_TRANSFORM_FUNCTOR_TRAITS(type) \ + template <> struct TransformFunctorTraits< type > : DefaultTransformFunctorTraits< type > +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_FUNCTIONAL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/limits.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/limits.hpp new file mode 100644 index 0000000000000000000000000000000000000000..7e15ed629a395ae7a79e46883bd8f0d67eebb8ee --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/limits.hpp @@ -0,0 +1,128 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_LIMITS_HPP +#define OPENCV_CUDA_LIMITS_HPP + +#include +#include +#include "common.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ +template struct numeric_limits; + +template <> struct numeric_limits +{ + __device__ __forceinline__ static bool min() { return false; } + __device__ __forceinline__ static bool max() { return true; } + static const bool is_signed = false; +}; + +template <> struct numeric_limits +{ + __device__ __forceinline__ static signed char min() { return SCHAR_MIN; } + __device__ __forceinline__ static signed char max() { return SCHAR_MAX; } + static const bool is_signed = true; +}; + +template <> struct numeric_limits +{ + __device__ __forceinline__ static unsigned char min() { return 0; } + __device__ __forceinline__ static unsigned char max() { return UCHAR_MAX; } + static const bool is_signed = false; +}; + +template <> struct numeric_limits +{ + __device__ __forceinline__ static short min() { return SHRT_MIN; } + __device__ __forceinline__ static short max() { return SHRT_MAX; } + static const bool is_signed = true; +}; + +template <> struct numeric_limits +{ + __device__ __forceinline__ static unsigned short min() { return 0; } + __device__ __forceinline__ static unsigned short max() { return USHRT_MAX; } + static const bool is_signed = false; +}; + +template <> struct numeric_limits +{ + __device__ __forceinline__ static int min() { return INT_MIN; } + __device__ __forceinline__ static int max() { return INT_MAX; } + static const bool is_signed = true; +}; + +template <> struct numeric_limits +{ + __device__ __forceinline__ static unsigned int min() { return 0; } + __device__ __forceinline__ static unsigned int max() { return UINT_MAX; } + static const bool is_signed = false; +}; + +template <> struct numeric_limits +{ + __device__ __forceinline__ static float min() { return FLT_MIN; } + __device__ __forceinline__ static float max() { return FLT_MAX; } + __device__ __forceinline__ static float epsilon() { return FLT_EPSILON; } + static const bool is_signed = true; +}; + +template <> struct numeric_limits +{ + __device__ __forceinline__ static double min() { return DBL_MIN; } + __device__ __forceinline__ static double max() { return DBL_MAX; } + __device__ __forceinline__ static double epsilon() { return DBL_EPSILON; } + static const bool is_signed = true; +}; +}}} // namespace cv { namespace cuda { namespace cudev { + +//! @endcond + +#endif // OPENCV_CUDA_LIMITS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/reduce.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/reduce.hpp new file mode 100644 index 0000000000000000000000000000000000000000..5de365081789d6be1b6e5b35f3648fa07e091650 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/reduce.hpp @@ -0,0 +1,209 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_REDUCE_HPP +#define OPENCV_CUDA_REDUCE_HPP + +#ifndef THRUST_DEBUG // eliminate -Wundef warning +#define THRUST_DEBUG 0 +#endif + +#include +#include "detail/reduce.hpp" +#include "detail/reduce_key_val.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + template + __device__ __forceinline__ void reduce(volatile T* smem, T& val, unsigned int tid, const Op& op) + { + reduce_detail::Dispatcher::reductor::template reduce(smem, val, tid, op); + } + template + __device__ __forceinline__ void reduce(const thrust::tuple& smem, + const thrust::tuple& val, + unsigned int tid, + const thrust::tuple& op) + { + reduce_detail::Dispatcher::reductor::template reduce< + const thrust::tuple&, + const thrust::tuple&, + const thrust::tuple&>(smem, val, tid, op); + } + + template + __device__ __forceinline__ void reduceKeyVal(volatile K* skeys, K& key, volatile V* svals, V& val, unsigned int tid, const Cmp& cmp) + { + reduce_key_val_detail::Dispatcher::reductor::template reduce(skeys, key, svals, val, tid, cmp); + } + template + __device__ __forceinline__ void reduceKeyVal(volatile K* skeys, K& key, + const thrust::tuple& svals, + const thrust::tuple& val, + unsigned int tid, const Cmp& cmp) + { + reduce_key_val_detail::Dispatcher::reductor::template reduce&, + const thrust::tuple&, + const Cmp&>(skeys, key, svals, val, tid, cmp); + } + template + __device__ __forceinline__ void reduceKeyVal(const thrust::tuple& skeys, + const thrust::tuple& key, + const thrust::tuple& svals, + const thrust::tuple& val, + unsigned int tid, + const thrust::tuple& cmp) + { + reduce_key_val_detail::Dispatcher::reductor::template reduce< + const thrust::tuple&, + const thrust::tuple&, + const thrust::tuple&, + const thrust::tuple&, + const thrust::tuple& + >(skeys, key, svals, val, tid, cmp); + } + + // smem_tuple + + template + __device__ __forceinline__ + thrust::tuple + smem_tuple(T0* t0) + { + return thrust::make_tuple((volatile T0*) t0); + } + + template + __device__ __forceinline__ + thrust::tuple + smem_tuple(T0* t0, T1* t1) + { + return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1); + } + + template + __device__ __forceinline__ + thrust::tuple + smem_tuple(T0* t0, T1* t1, T2* t2) + { + return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2); + } + + template + __device__ __forceinline__ + thrust::tuple + smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3) + { + return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3); + } + + template + __device__ __forceinline__ + thrust::tuple + smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4) + { + return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4); + } + + template + __device__ __forceinline__ + thrust::tuple + smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5) + { + return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5); + } + + template + __device__ __forceinline__ + thrust::tuple + smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5, T6* t6) + { + return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5, (volatile T6*) t6); + } + + template + __device__ __forceinline__ + thrust::tuple + smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5, T6* t6, T7* t7) + { + return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5, (volatile T6*) t6, (volatile T7*) t7); + } + + template + __device__ __forceinline__ + thrust::tuple + smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5, T6* t6, T7* t7, T8* t8) + { + return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5, (volatile T6*) t6, (volatile T7*) t7, (volatile T8*) t8); + } + + template + __device__ __forceinline__ + thrust::tuple + smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5, T6* t6, T7* t7, T8* t8, T9* t9) + { + return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5, (volatile T6*) t6, (volatile T7*) t7, (volatile T8*) t8, (volatile T9*) t9); + } +}}} + +//! @endcond + +#endif // OPENCV_CUDA_REDUCE_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/saturate_cast.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/saturate_cast.hpp new file mode 100644 index 0000000000000000000000000000000000000000..c3a3d1cb8338375118930fc7abf918e57a041ceb --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/saturate_cast.hpp @@ -0,0 +1,292 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_SATURATE_CAST_HPP +#define OPENCV_CUDA_SATURATE_CAST_HPP + +#include "common.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + template __device__ __forceinline__ _Tp saturate_cast(uchar v) { return _Tp(v); } + template __device__ __forceinline__ _Tp saturate_cast(schar v) { return _Tp(v); } + template __device__ __forceinline__ _Tp saturate_cast(ushort v) { return _Tp(v); } + template __device__ __forceinline__ _Tp saturate_cast(short v) { return _Tp(v); } + template __device__ __forceinline__ _Tp saturate_cast(uint v) { return _Tp(v); } + template __device__ __forceinline__ _Tp saturate_cast(int v) { return _Tp(v); } + template __device__ __forceinline__ _Tp saturate_cast(float v) { return _Tp(v); } + template __device__ __forceinline__ _Tp saturate_cast(double v) { return _Tp(v); } + + template<> __device__ __forceinline__ uchar saturate_cast(schar v) + { + uint res = 0; + int vi = v; + asm("cvt.sat.u8.s8 %0, %1;" : "=r"(res) : "r"(vi)); + return res; + } + template<> __device__ __forceinline__ uchar saturate_cast(short v) + { + uint res = 0; + asm("cvt.sat.u8.s16 %0, %1;" : "=r"(res) : "h"(v)); + return res; + } + template<> __device__ __forceinline__ uchar saturate_cast(ushort v) + { + uint res = 0; + asm("cvt.sat.u8.u16 %0, %1;" : "=r"(res) : "h"(v)); + return res; + } + template<> __device__ __forceinline__ uchar saturate_cast(int v) + { + uint res = 0; + asm("cvt.sat.u8.s32 %0, %1;" : "=r"(res) : "r"(v)); + return res; + } + template<> __device__ __forceinline__ uchar saturate_cast(uint v) + { + uint res = 0; + asm("cvt.sat.u8.u32 %0, %1;" : "=r"(res) : "r"(v)); + return res; + } + template<> __device__ __forceinline__ uchar saturate_cast(float v) + { + uint res = 0; + asm("cvt.rni.sat.u8.f32 %0, %1;" : "=r"(res) : "f"(v)); + return res; + } + template<> __device__ __forceinline__ uchar saturate_cast(double v) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130 + uint res = 0; + asm("cvt.rni.sat.u8.f64 %0, %1;" : "=r"(res) : "d"(v)); + return res; + #else + return saturate_cast((float)v); + #endif + } + + template<> __device__ __forceinline__ schar saturate_cast(uchar v) + { + uint res = 0; + uint vi = v; + asm("cvt.sat.s8.u8 %0, %1;" : "=r"(res) : "r"(vi)); + return res; + } + template<> __device__ __forceinline__ schar saturate_cast(short v) + { + uint res = 0; + asm("cvt.sat.s8.s16 %0, %1;" : "=r"(res) : "h"(v)); + return res; + } + template<> __device__ __forceinline__ schar saturate_cast(ushort v) + { + uint res = 0; + asm("cvt.sat.s8.u16 %0, %1;" : "=r"(res) : "h"(v)); + return res; + } + template<> __device__ __forceinline__ schar saturate_cast(int v) + { + uint res = 0; + asm("cvt.sat.s8.s32 %0, %1;" : "=r"(res) : "r"(v)); + return res; + } + template<> __device__ __forceinline__ schar saturate_cast(uint v) + { + uint res = 0; + asm("cvt.sat.s8.u32 %0, %1;" : "=r"(res) : "r"(v)); + return res; + } + template<> __device__ __forceinline__ schar saturate_cast(float v) + { + uint res = 0; + asm("cvt.rni.sat.s8.f32 %0, %1;" : "=r"(res) : "f"(v)); + return res; + } + template<> __device__ __forceinline__ schar saturate_cast(double v) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130 + uint res = 0; + asm("cvt.rni.sat.s8.f64 %0, %1;" : "=r"(res) : "d"(v)); + return res; + #else + return saturate_cast((float)v); + #endif + } + + template<> __device__ __forceinline__ ushort saturate_cast(schar v) + { + ushort res = 0; + int vi = v; + asm("cvt.sat.u16.s8 %0, %1;" : "=h"(res) : "r"(vi)); + return res; + } + template<> __device__ __forceinline__ ushort saturate_cast(short v) + { + ushort res = 0; + asm("cvt.sat.u16.s16 %0, %1;" : "=h"(res) : "h"(v)); + return res; + } + template<> __device__ __forceinline__ ushort saturate_cast(int v) + { + ushort res = 0; + asm("cvt.sat.u16.s32 %0, %1;" : "=h"(res) : "r"(v)); + return res; + } + template<> __device__ __forceinline__ ushort saturate_cast(uint v) + { + ushort res = 0; + asm("cvt.sat.u16.u32 %0, %1;" : "=h"(res) : "r"(v)); + return res; + } + template<> __device__ __forceinline__ ushort saturate_cast(float v) + { + ushort res = 0; + asm("cvt.rni.sat.u16.f32 %0, %1;" : "=h"(res) : "f"(v)); + return res; + } + template<> __device__ __forceinline__ ushort saturate_cast(double v) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130 + ushort res = 0; + asm("cvt.rni.sat.u16.f64 %0, %1;" : "=h"(res) : "d"(v)); + return res; + #else + return saturate_cast((float)v); + #endif + } + + template<> __device__ __forceinline__ short saturate_cast(ushort v) + { + short res = 0; + asm("cvt.sat.s16.u16 %0, %1;" : "=h"(res) : "h"(v)); + return res; + } + template<> __device__ __forceinline__ short saturate_cast(int v) + { + short res = 0; + asm("cvt.sat.s16.s32 %0, %1;" : "=h"(res) : "r"(v)); + return res; + } + template<> __device__ __forceinline__ short saturate_cast(uint v) + { + short res = 0; + asm("cvt.sat.s16.u32 %0, %1;" : "=h"(res) : "r"(v)); + return res; + } + template<> __device__ __forceinline__ short saturate_cast(float v) + { + short res = 0; + asm("cvt.rni.sat.s16.f32 %0, %1;" : "=h"(res) : "f"(v)); + return res; + } + template<> __device__ __forceinline__ short saturate_cast(double v) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130 + short res = 0; + asm("cvt.rni.sat.s16.f64 %0, %1;" : "=h"(res) : "d"(v)); + return res; + #else + return saturate_cast((float)v); + #endif + } + + template<> __device__ __forceinline__ int saturate_cast(uint v) + { + int res = 0; + asm("cvt.sat.s32.u32 %0, %1;" : "=r"(res) : "r"(v)); + return res; + } + template<> __device__ __forceinline__ int saturate_cast(float v) + { + return __float2int_rn(v); + } + template<> __device__ __forceinline__ int saturate_cast(double v) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130 + return __double2int_rn(v); + #else + return saturate_cast((float)v); + #endif + } + + template<> __device__ __forceinline__ uint saturate_cast(schar v) + { + uint res = 0; + int vi = v; + asm("cvt.sat.u32.s8 %0, %1;" : "=r"(res) : "r"(vi)); + return res; + } + template<> __device__ __forceinline__ uint saturate_cast(short v) + { + uint res = 0; + asm("cvt.sat.u32.s16 %0, %1;" : "=r"(res) : "h"(v)); + return res; + } + template<> __device__ __forceinline__ uint saturate_cast(int v) + { + uint res = 0; + asm("cvt.sat.u32.s32 %0, %1;" : "=r"(res) : "r"(v)); + return res; + } + template<> __device__ __forceinline__ uint saturate_cast(float v) + { + return __float2uint_rn(v); + } + template<> __device__ __forceinline__ uint saturate_cast(double v) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130 + return __double2uint_rn(v); + #else + return saturate_cast((float)v); + #endif + } +}}} + +//! @endcond + +#endif /* OPENCV_CUDA_SATURATE_CAST_HPP */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/scan.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/scan.hpp new file mode 100644 index 0000000000000000000000000000000000000000..e128fb0962efb20ecaa6b16c545b16d5917549f4 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/scan.hpp @@ -0,0 +1,258 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_SCAN_HPP +#define OPENCV_CUDA_SCAN_HPP + +#include "opencv2/core/cuda/common.hpp" +#include "opencv2/core/cuda/utility.hpp" +#include "opencv2/core/cuda/warp.hpp" +#include "opencv2/core/cuda/warp_shuffle.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + enum ScanKind { EXCLUSIVE = 0, INCLUSIVE = 1 }; + + template struct WarpScan + { + __device__ __forceinline__ WarpScan() {} + __device__ __forceinline__ WarpScan(const WarpScan& other) { CV_UNUSED(other); } + + __device__ __forceinline__ T operator()( volatile T *ptr , const unsigned int idx) + { + const unsigned int lane = idx & 31; + F op; + + if ( lane >= 1) ptr [idx ] = op(ptr [idx - 1], ptr [idx]); + if ( lane >= 2) ptr [idx ] = op(ptr [idx - 2], ptr [idx]); + if ( lane >= 4) ptr [idx ] = op(ptr [idx - 4], ptr [idx]); + if ( lane >= 8) ptr [idx ] = op(ptr [idx - 8], ptr [idx]); + if ( lane >= 16) ptr [idx ] = op(ptr [idx - 16], ptr [idx]); + + if( Kind == INCLUSIVE ) + return ptr [idx]; + else + return (lane > 0) ? ptr [idx - 1] : 0; + } + + __device__ __forceinline__ unsigned int index(const unsigned int tid) + { + return tid; + } + + __device__ __forceinline__ void init(volatile T *ptr){} + + static const int warp_offset = 0; + + typedef WarpScan merge; + }; + + template struct WarpScanNoComp + { + __device__ __forceinline__ WarpScanNoComp() {} + __device__ __forceinline__ WarpScanNoComp(const WarpScanNoComp& other) { CV_UNUSED(other); } + + __device__ __forceinline__ T operator()( volatile T *ptr , const unsigned int idx) + { + const unsigned int lane = threadIdx.x & 31; + F op; + + ptr [idx ] = op(ptr [idx - 1], ptr [idx]); + ptr [idx ] = op(ptr [idx - 2], ptr [idx]); + ptr [idx ] = op(ptr [idx - 4], ptr [idx]); + ptr [idx ] = op(ptr [idx - 8], ptr [idx]); + ptr [idx ] = op(ptr [idx - 16], ptr [idx]); + + if( Kind == INCLUSIVE ) + return ptr [idx]; + else + return (lane > 0) ? ptr [idx - 1] : 0; + } + + __device__ __forceinline__ unsigned int index(const unsigned int tid) + { + return (tid >> warp_log) * warp_smem_stride + 16 + (tid & warp_mask); + } + + __device__ __forceinline__ void init(volatile T *ptr) + { + ptr[threadIdx.x] = 0; + } + + static const int warp_smem_stride = 32 + 16 + 1; + static const int warp_offset = 16; + static const int warp_log = 5; + static const int warp_mask = 31; + + typedef WarpScanNoComp merge; + }; + + template struct BlockScan + { + __device__ __forceinline__ BlockScan() {} + __device__ __forceinline__ BlockScan(const BlockScan& other) { CV_UNUSED(other); } + + __device__ __forceinline__ T operator()(volatile T *ptr) + { + const unsigned int tid = threadIdx.x; + const unsigned int lane = tid & warp_mask; + const unsigned int warp = tid >> warp_log; + + Sc scan; + typename Sc::merge merge_scan; + const unsigned int idx = scan.index(tid); + + T val = scan(ptr, idx); + __syncthreads (); + + if( warp == 0) + scan.init(ptr); + __syncthreads (); + + if( lane == 31 ) + ptr [scan.warp_offset + warp ] = (Kind == INCLUSIVE) ? val : ptr [idx]; + __syncthreads (); + + if( warp == 0 ) + merge_scan(ptr, idx); + __syncthreads(); + + if ( warp > 0) + val = ptr [scan.warp_offset + warp - 1] + val; + __syncthreads (); + + ptr[idx] = val; + __syncthreads (); + + return val ; + } + + static const int warp_log = 5; + static const int warp_mask = 31; + }; + + template + __device__ T warpScanInclusive(T idata, volatile T* s_Data, unsigned int tid) + { + #if __CUDA_ARCH__ >= 300 + const unsigned int laneId = cv::cuda::device::Warp::laneId(); + + // scan on shuffl functions + #pragma unroll + for (int i = 1; i <= (OPENCV_CUDA_WARP_SIZE / 2); i *= 2) + { + const T n = cv::cuda::device::shfl_up(idata, i); + if (laneId >= i) + idata += n; + } + + return idata; + #else + unsigned int pos = 2 * tid - (tid & (OPENCV_CUDA_WARP_SIZE - 1)); + s_Data[pos] = 0; + pos += OPENCV_CUDA_WARP_SIZE; + s_Data[pos] = idata; + + s_Data[pos] += s_Data[pos - 1]; + s_Data[pos] += s_Data[pos - 2]; + s_Data[pos] += s_Data[pos - 4]; + s_Data[pos] += s_Data[pos - 8]; + s_Data[pos] += s_Data[pos - 16]; + + return s_Data[pos]; + #endif + } + + template + __device__ __forceinline__ T warpScanExclusive(T idata, volatile T* s_Data, unsigned int tid) + { + return warpScanInclusive(idata, s_Data, tid) - idata; + } + + template + __device__ T blockScanInclusive(T idata, volatile T* s_Data, unsigned int tid) + { + if (tiNumScanThreads > OPENCV_CUDA_WARP_SIZE) + { + //Bottom-level inclusive warp scan + T warpResult = warpScanInclusive(idata, s_Data, tid); + + //Save top elements of each warp for exclusive warp scan + //sync to wait for warp scans to complete (because s_Data is being overwritten) + __syncthreads(); + if ((tid & (OPENCV_CUDA_WARP_SIZE - 1)) == (OPENCV_CUDA_WARP_SIZE - 1)) + { + s_Data[tid >> OPENCV_CUDA_LOG_WARP_SIZE] = warpResult; + } + + //wait for warp scans to complete + __syncthreads(); + + if (tid < (tiNumScanThreads / OPENCV_CUDA_WARP_SIZE) ) + { + //grab top warp elements + T val = s_Data[tid]; + //calculate exclusive scan and write back to shared memory + s_Data[tid] = warpScanExclusive(val, s_Data, tid); + } + + //return updated warp scans with exclusive scan results + __syncthreads(); + + return warpResult + s_Data[tid >> OPENCV_CUDA_LOG_WARP_SIZE]; + } + else + { + return warpScanInclusive(idata, s_Data, tid); + } + } +}}} + +//! @endcond + +#endif // OPENCV_CUDA_SCAN_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/simd_functions.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/simd_functions.hpp new file mode 100644 index 0000000000000000000000000000000000000000..3d8c2e0d8e56ae109e1f7ab839aec46b492c556c --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/simd_functions.hpp @@ -0,0 +1,869 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +/* + * Copyright (c) 2013 NVIDIA Corporation. All rights reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * Redistributions of source code must retain the above copyright notice, + * this list of conditions and the following disclaimer. + * + * Redistributions in binary form must reproduce the above copyright notice, + * this list of conditions and the following disclaimer in the documentation + * and/or other materials provided with the distribution. + * + * Neither the name of NVIDIA Corporation nor the names of its contributors + * may be used to endorse or promote products derived from this software + * without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + * ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE + * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + * POSSIBILITY OF SUCH DAMAGE. + */ + +#ifndef OPENCV_CUDA_SIMD_FUNCTIONS_HPP +#define OPENCV_CUDA_SIMD_FUNCTIONS_HPP + +#include "common.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + // 2 + + static __device__ __forceinline__ unsigned int vadd2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vadd2.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #elif __CUDA_ARCH__ >= 200 + asm("vadd.u32.u32.u32.sat %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vadd.u32.u32.u32.sat %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int s; + s = a ^ b; // sum bits + r = a + b; // actual sum + s = s ^ r; // determine carry-ins for each bit position + s = s & 0x00010000; // carry-in to high word (= carry-out from low word) + r = r - s; // subtract out carry-out from low word + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vsub2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vsub2.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #elif __CUDA_ARCH__ >= 200 + asm("vsub.u32.u32.u32.sat %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vsub.u32.u32.u32.sat %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int s; + s = a ^ b; // sum bits + r = a - b; // actual sum + s = s ^ r; // determine carry-ins for each bit position + s = s & 0x00010000; // borrow to high word + r = r + s; // compensate for borrow from low word + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vabsdiff2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vabsdiff2.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #elif __CUDA_ARCH__ >= 200 + asm("vabsdiff.u32.u32.u32.sat %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vabsdiff.u32.u32.u32.sat %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int s, t, u, v; + s = a & 0x0000ffff; // extract low halfword + r = b & 0x0000ffff; // extract low halfword + u = ::max(r, s); // maximum of low halfwords + v = ::min(r, s); // minimum of low halfwords + s = a & 0xffff0000; // extract high halfword + r = b & 0xffff0000; // extract high halfword + t = ::max(r, s); // maximum of high halfwords + s = ::min(r, s); // minimum of high halfwords + r = u | t; // maximum of both halfwords + s = v | s; // minimum of both halfwords + r = r - s; // |a - b| = max(a,b) - min(a,b); + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vavg2(unsigned int a, unsigned int b) + { + unsigned int r, s; + + // HAKMEM #23: a + b = 2 * (a & b) + (a ^ b) ==> + // (a + b) / 2 = (a & b) + ((a ^ b) >> 1) + s = a ^ b; + r = a & b; + s = s & 0xfffefffe; // ensure shift doesn't cross halfword boundaries + s = s >> 1; + s = r + s; + + return s; + } + + static __device__ __forceinline__ unsigned int vavrg2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vavrg2.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + // HAKMEM #23: a + b = 2 * (a | b) - (a ^ b) ==> + // (a + b + 1) / 2 = (a | b) - ((a ^ b) >> 1) + unsigned int s; + s = a ^ b; + r = a | b; + s = s & 0xfffefffe; // ensure shift doesn't cross half-word boundaries + s = s >> 1; + r = r - s; + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vseteq2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vset2.u32.u32.eq %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + // inspired by Alan Mycroft's null-byte detection algorithm: + // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) + unsigned int c; + r = a ^ b; // 0x0000 if a == b + c = r | 0x80008000; // set msbs, to catch carry out + r = r ^ c; // extract msbs, msb = 1 if r < 0x8000 + c = c - 0x00010001; // msb = 0, if r was 0x0000 or 0x8000 + c = r & ~c; // msb = 1, if r was 0x0000 + r = c >> 15; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmpeq2(unsigned int a, unsigned int b) + { + unsigned int r, c; + + #if __CUDA_ARCH__ >= 300 + r = vseteq2(a, b); + c = r << 16; // convert bool + r = c - r; // into mask + #else + // inspired by Alan Mycroft's null-byte detection algorithm: + // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) + r = a ^ b; // 0x0000 if a == b + c = r | 0x80008000; // set msbs, to catch carry out + r = r ^ c; // extract msbs, msb = 1 if r < 0x8000 + c = c - 0x00010001; // msb = 0, if r was 0x0000 or 0x8000 + c = r & ~c; // msb = 1, if r was 0x0000 + r = c >> 15; // convert + r = c - r; // msbs to + r = c | r; // mask + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vsetge2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vset2.u32.u32.ge %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int c; + asm("not.b32 %0, %0;" : "+r"(b)); + c = vavrg2(a, b); // (a + ~b + 1) / 2 = (a - b) / 2 + c = c & 0x80008000; // msb = carry-outs + r = c >> 15; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmpge2(unsigned int a, unsigned int b) + { + unsigned int r, c; + + #if __CUDA_ARCH__ >= 300 + r = vsetge2(a, b); + c = r << 16; // convert bool + r = c - r; // into mask + #else + asm("not.b32 %0, %0;" : "+r"(b)); + c = vavrg2(a, b); // (a + ~b + 1) / 2 = (a - b) / 2 + c = c & 0x80008000; // msb = carry-outs + r = c >> 15; // convert + r = c - r; // msbs to + r = c | r; // mask + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vsetgt2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vset2.u32.u32.gt %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int c; + asm("not.b32 %0, %0;" : "+r"(b)); + c = vavg2(a, b); // (a + ~b) / 2 = (a - b) / 2 [rounded down] + c = c & 0x80008000; // msbs = carry-outs + r = c >> 15; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmpgt2(unsigned int a, unsigned int b) + { + unsigned int r, c; + + #if __CUDA_ARCH__ >= 300 + r = vsetgt2(a, b); + c = r << 16; // convert bool + r = c - r; // into mask + #else + asm("not.b32 %0, %0;" : "+r"(b)); + c = vavg2(a, b); // (a + ~b) / 2 = (a - b) / 2 [rounded down] + c = c & 0x80008000; // msbs = carry-outs + r = c >> 15; // convert + r = c - r; // msbs to + r = c | r; // mask + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vsetle2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vset2.u32.u32.le %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int c; + asm("not.b32 %0, %0;" : "+r"(a)); + c = vavrg2(a, b); // (b + ~a + 1) / 2 = (b - a) / 2 + c = c & 0x80008000; // msb = carry-outs + r = c >> 15; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmple2(unsigned int a, unsigned int b) + { + unsigned int r, c; + + #if __CUDA_ARCH__ >= 300 + r = vsetle2(a, b); + c = r << 16; // convert bool + r = c - r; // into mask + #else + asm("not.b32 %0, %0;" : "+r"(a)); + c = vavrg2(a, b); // (b + ~a + 1) / 2 = (b - a) / 2 + c = c & 0x80008000; // msb = carry-outs + r = c >> 15; // convert + r = c - r; // msbs to + r = c | r; // mask + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vsetlt2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vset2.u32.u32.lt %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int c; + asm("not.b32 %0, %0;" : "+r"(a)); + c = vavg2(a, b); // (b + ~a) / 2 = (b - a) / 2 [rounded down] + c = c & 0x80008000; // msb = carry-outs + r = c >> 15; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmplt2(unsigned int a, unsigned int b) + { + unsigned int r, c; + + #if __CUDA_ARCH__ >= 300 + r = vsetlt2(a, b); + c = r << 16; // convert bool + r = c - r; // into mask + #else + asm("not.b32 %0, %0;" : "+r"(a)); + c = vavg2(a, b); // (b + ~a) / 2 = (b - a) / 2 [rounded down] + c = c & 0x80008000; // msb = carry-outs + r = c >> 15; // convert + r = c - r; // msbs to + r = c | r; // mask + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vsetne2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm ("vset2.u32.u32.ne %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + // inspired by Alan Mycroft's null-byte detection algorithm: + // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) + unsigned int c; + r = a ^ b; // 0x0000 if a == b + c = r | 0x80008000; // set msbs, to catch carry out + c = c - 0x00010001; // msb = 0, if r was 0x0000 or 0x8000 + c = r | c; // msb = 1, if r was not 0x0000 + c = c & 0x80008000; // extract msbs + r = c >> 15; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmpne2(unsigned int a, unsigned int b) + { + unsigned int r, c; + + #if __CUDA_ARCH__ >= 300 + r = vsetne2(a, b); + c = r << 16; // convert bool + r = c - r; // into mask + #else + // inspired by Alan Mycroft's null-byte detection algorithm: + // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) + r = a ^ b; // 0x0000 if a == b + c = r | 0x80008000; // set msbs, to catch carry out + c = c - 0x00010001; // msb = 0, if r was 0x0000 or 0x8000 + c = r | c; // msb = 1, if r was not 0x0000 + c = c & 0x80008000; // extract msbs + r = c >> 15; // convert + r = c - r; // msbs to + r = c | r; // mask + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vmax2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vmax2.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #elif __CUDA_ARCH__ >= 200 + asm("vmax.u32.u32.u32 %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vmax.u32.u32.u32 %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int s, t, u; + r = a & 0x0000ffff; // extract low halfword + s = b & 0x0000ffff; // extract low halfword + t = ::max(r, s); // maximum of low halfwords + r = a & 0xffff0000; // extract high halfword + s = b & 0xffff0000; // extract high halfword + u = ::max(r, s); // maximum of high halfwords + r = t | u; // combine halfword maximums + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vmin2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vmin2.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #elif __CUDA_ARCH__ >= 200 + asm("vmin.u32.u32.u32 %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vmin.u32.u32.u32 %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int s, t, u; + r = a & 0x0000ffff; // extract low halfword + s = b & 0x0000ffff; // extract low halfword + t = ::min(r, s); // minimum of low halfwords + r = a & 0xffff0000; // extract high halfword + s = b & 0xffff0000; // extract high halfword + u = ::min(r, s); // minimum of high halfwords + r = t | u; // combine halfword minimums + #endif + + return r; + } + + // 4 + + static __device__ __forceinline__ unsigned int vadd4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vadd4.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #elif __CUDA_ARCH__ >= 200 + asm("vadd.u32.u32.u32.sat %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vadd.u32.u32.u32.sat %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vadd.u32.u32.u32.sat %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vadd.u32.u32.u32.sat %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int s, t; + s = a ^ b; // sum bits + r = a & 0x7f7f7f7f; // clear msbs + t = b & 0x7f7f7f7f; // clear msbs + s = s & 0x80808080; // msb sum bits + r = r + t; // add without msbs, record carry-out in msbs + r = r ^ s; // sum of msb sum and carry-in bits, w/o carry-out + #endif /* __CUDA_ARCH__ >= 300 */ + + return r; + } + + static __device__ __forceinline__ unsigned int vsub4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vsub4.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #elif __CUDA_ARCH__ >= 200 + asm("vsub.u32.u32.u32.sat %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vsub.u32.u32.u32.sat %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vsub.u32.u32.u32.sat %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vsub.u32.u32.u32.sat %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int s, t; + s = a ^ ~b; // inverted sum bits + r = a | 0x80808080; // set msbs + t = b & 0x7f7f7f7f; // clear msbs + s = s & 0x80808080; // inverted msb sum bits + r = r - t; // subtract w/o msbs, record inverted borrows in msb + r = r ^ s; // combine inverted msb sum bits and borrows + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vavg4(unsigned int a, unsigned int b) + { + unsigned int r, s; + + // HAKMEM #23: a + b = 2 * (a & b) + (a ^ b) ==> + // (a + b) / 2 = (a & b) + ((a ^ b) >> 1) + s = a ^ b; + r = a & b; + s = s & 0xfefefefe; // ensure following shift doesn't cross byte boundaries + s = s >> 1; + s = r + s; + + return s; + } + + static __device__ __forceinline__ unsigned int vavrg4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vavrg4.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + // HAKMEM #23: a + b = 2 * (a | b) - (a ^ b) ==> + // (a + b + 1) / 2 = (a | b) - ((a ^ b) >> 1) + unsigned int c; + c = a ^ b; + r = a | b; + c = c & 0xfefefefe; // ensure following shift doesn't cross byte boundaries + c = c >> 1; + r = r - c; + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vseteq4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vset4.u32.u32.eq %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + // inspired by Alan Mycroft's null-byte detection algorithm: + // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) + unsigned int c; + r = a ^ b; // 0x00 if a == b + c = r | 0x80808080; // set msbs, to catch carry out + r = r ^ c; // extract msbs, msb = 1 if r < 0x80 + c = c - 0x01010101; // msb = 0, if r was 0x00 or 0x80 + c = r & ~c; // msb = 1, if r was 0x00 + r = c >> 7; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmpeq4(unsigned int a, unsigned int b) + { + unsigned int r, t; + + #if __CUDA_ARCH__ >= 300 + r = vseteq4(a, b); + t = r << 8; // convert bool + r = t - r; // to mask + #else + // inspired by Alan Mycroft's null-byte detection algorithm: + // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) + t = a ^ b; // 0x00 if a == b + r = t | 0x80808080; // set msbs, to catch carry out + t = t ^ r; // extract msbs, msb = 1 if t < 0x80 + r = r - 0x01010101; // msb = 0, if t was 0x00 or 0x80 + r = t & ~r; // msb = 1, if t was 0x00 + t = r >> 7; // build mask + t = r - t; // from + r = t | r; // msbs + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vsetle4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vset4.u32.u32.le %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int c; + asm("not.b32 %0, %0;" : "+r"(a)); + c = vavrg4(a, b); // (b + ~a + 1) / 2 = (b - a) / 2 + c = c & 0x80808080; // msb = carry-outs + r = c >> 7; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmple4(unsigned int a, unsigned int b) + { + unsigned int r, c; + + #if __CUDA_ARCH__ >= 300 + r = vsetle4(a, b); + c = r << 8; // convert bool + r = c - r; // to mask + #else + asm("not.b32 %0, %0;" : "+r"(a)); + c = vavrg4(a, b); // (b + ~a + 1) / 2 = (b - a) / 2 + c = c & 0x80808080; // msbs = carry-outs + r = c >> 7; // convert + r = c - r; // msbs to + r = c | r; // mask + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vsetlt4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vset4.u32.u32.lt %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int c; + asm("not.b32 %0, %0;" : "+r"(a)); + c = vavg4(a, b); // (b + ~a) / 2 = (b - a) / 2 [rounded down] + c = c & 0x80808080; // msb = carry-outs + r = c >> 7; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmplt4(unsigned int a, unsigned int b) + { + unsigned int r, c; + + #if __CUDA_ARCH__ >= 300 + r = vsetlt4(a, b); + c = r << 8; // convert bool + r = c - r; // to mask + #else + asm("not.b32 %0, %0;" : "+r"(a)); + c = vavg4(a, b); // (b + ~a) / 2 = (b - a) / 2 [rounded down] + c = c & 0x80808080; // msbs = carry-outs + r = c >> 7; // convert + r = c - r; // msbs to + r = c | r; // mask + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vsetge4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vset4.u32.u32.ge %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int c; + asm("not.b32 %0, %0;" : "+r"(b)); + c = vavrg4(a, b); // (a + ~b + 1) / 2 = (a - b) / 2 + c = c & 0x80808080; // msb = carry-outs + r = c >> 7; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmpge4(unsigned int a, unsigned int b) + { + unsigned int r, s; + + #if __CUDA_ARCH__ >= 300 + r = vsetge4(a, b); + s = r << 8; // convert bool + r = s - r; // to mask + #else + asm ("not.b32 %0,%0;" : "+r"(b)); + r = vavrg4 (a, b); // (a + ~b + 1) / 2 = (a - b) / 2 + r = r & 0x80808080; // msb = carry-outs + s = r >> 7; // build mask + s = r - s; // from + r = s | r; // msbs + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vsetgt4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vset4.u32.u32.gt %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int c; + asm("not.b32 %0, %0;" : "+r"(b)); + c = vavg4(a, b); // (a + ~b) / 2 = (a - b) / 2 [rounded down] + c = c & 0x80808080; // msb = carry-outs + r = c >> 7; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmpgt4(unsigned int a, unsigned int b) + { + unsigned int r, c; + + #if __CUDA_ARCH__ >= 300 + r = vsetgt4(a, b); + c = r << 8; // convert bool + r = c - r; // to mask + #else + asm("not.b32 %0, %0;" : "+r"(b)); + c = vavg4(a, b); // (a + ~b) / 2 = (a - b) / 2 [rounded down] + c = c & 0x80808080; // msb = carry-outs + r = c >> 7; // convert + r = c - r; // msbs to + r = c | r; // mask + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vsetne4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vset4.u32.u32.ne %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + // inspired by Alan Mycroft's null-byte detection algorithm: + // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) + unsigned int c; + r = a ^ b; // 0x00 if a == b + c = r | 0x80808080; // set msbs, to catch carry out + c = c - 0x01010101; // msb = 0, if r was 0x00 or 0x80 + c = r | c; // msb = 1, if r was not 0x00 + c = c & 0x80808080; // extract msbs + r = c >> 7; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmpne4(unsigned int a, unsigned int b) + { + unsigned int r, c; + + #if __CUDA_ARCH__ >= 300 + r = vsetne4(a, b); + c = r << 8; // convert bool + r = c - r; // to mask + #else + // inspired by Alan Mycroft's null-byte detection algorithm: + // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) + r = a ^ b; // 0x00 if a == b + c = r | 0x80808080; // set msbs, to catch carry out + c = c - 0x01010101; // msb = 0, if r was 0x00 or 0x80 + c = r | c; // msb = 1, if r was not 0x00 + c = c & 0x80808080; // extract msbs + r = c >> 7; // convert + r = c - r; // msbs to + r = c | r; // mask + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vabsdiff4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vabsdiff4.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #elif __CUDA_ARCH__ >= 200 + asm("vabsdiff.u32.u32.u32.sat %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vabsdiff.u32.u32.u32.sat %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vabsdiff.u32.u32.u32.sat %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vabsdiff.u32.u32.u32.sat %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int s; + s = vcmpge4(a, b); // mask = 0xff if a >= b + r = a ^ b; // + s = (r & s) ^ b; // select a when a >= b, else select b => max(a,b) + r = s ^ r; // select a when b >= a, else select b => min(a,b) + r = s - r; // |a - b| = max(a,b) - min(a,b); + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vmax4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vmax4.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #elif __CUDA_ARCH__ >= 200 + asm("vmax.u32.u32.u32 %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vmax.u32.u32.u32 %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vmax.u32.u32.u32 %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vmax.u32.u32.u32 %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int s; + s = vcmpge4(a, b); // mask = 0xff if a >= b + r = a & s; // select a when b >= a + s = b & ~s; // select b when b < a + r = r | s; // combine byte selections + #endif + + return r; // byte-wise unsigned maximum + } + + static __device__ __forceinline__ unsigned int vmin4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vmin4.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #elif __CUDA_ARCH__ >= 200 + asm("vmin.u32.u32.u32 %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vmin.u32.u32.u32 %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vmin.u32.u32.u32 %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vmin.u32.u32.u32 %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int s; + s = vcmpge4(b, a); // mask = 0xff if a >= b + r = a & s; // select a when b >= a + s = b & ~s; // select b when b < a + r = r | s; // combine byte selections + #endif + + return r; + } +}}} + +//! @endcond + +#endif // OPENCV_CUDA_SIMD_FUNCTIONS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/transform.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/transform.hpp new file mode 100644 index 0000000000000000000000000000000000000000..42aa6ea170f21f95fc1e3702c2c410dbd3cc45e7 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/transform.hpp @@ -0,0 +1,75 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_TRANSFORM_HPP +#define OPENCV_CUDA_TRANSFORM_HPP + +#include "common.hpp" +#include "utility.hpp" +#include "detail/transform_detail.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + template + static inline void transform(PtrStepSz src, PtrStepSz dst, UnOp op, const Mask& mask, cudaStream_t stream) + { + typedef TransformFunctorTraits ft; + transform_detail::TransformDispatcher::cn == 1 && VecTraits::cn == 1 && ft::smart_shift != 1>::call(src, dst, op, mask, stream); + } + + template + static inline void transform(PtrStepSz src1, PtrStepSz src2, PtrStepSz dst, BinOp op, const Mask& mask, cudaStream_t stream) + { + typedef TransformFunctorTraits ft; + transform_detail::TransformDispatcher::cn == 1 && VecTraits::cn == 1 && VecTraits::cn == 1 && ft::smart_shift != 1>::call(src1, src2, dst, op, mask, stream); + } +}}} + +//! @endcond + +#endif // OPENCV_CUDA_TRANSFORM_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/type_traits.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/type_traits.hpp new file mode 100644 index 0000000000000000000000000000000000000000..8b7a3fd16832df41f6bdb47a06569f6265ccad9d --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/type_traits.hpp @@ -0,0 +1,90 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_TYPE_TRAITS_HPP +#define OPENCV_CUDA_TYPE_TRAITS_HPP + +#include "detail/type_traits_detail.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + template struct IsSimpleParameter + { + enum {value = type_traits_detail::IsIntegral::value || type_traits_detail::IsFloat::value || + type_traits_detail::PointerTraits::type>::value}; + }; + + template struct TypeTraits + { + typedef typename type_traits_detail::UnConst::type NonConstType; + typedef typename type_traits_detail::UnVolatile::type NonVolatileType; + typedef typename type_traits_detail::UnVolatile::type>::type UnqualifiedType; + typedef typename type_traits_detail::PointerTraits::type PointeeType; + typedef typename type_traits_detail::ReferenceTraits::type ReferredType; + + enum { isConst = type_traits_detail::UnConst::value }; + enum { isVolatile = type_traits_detail::UnVolatile::value }; + + enum { isReference = type_traits_detail::ReferenceTraits::value }; + enum { isPointer = type_traits_detail::PointerTraits::type>::value }; + + enum { isUnsignedInt = type_traits_detail::IsUnsignedIntegral::value }; + enum { isSignedInt = type_traits_detail::IsSignedIntergral::value }; + enum { isIntegral = type_traits_detail::IsIntegral::value }; + enum { isFloat = type_traits_detail::IsFloat::value }; + enum { isArith = isIntegral || isFloat }; + enum { isVec = type_traits_detail::IsVec::value }; + + typedef typename type_traits_detail::Select::value, + T, typename type_traits_detail::AddParameterType::type>::type ParameterType; + }; +}}} + +//! @endcond + +#endif // OPENCV_CUDA_TYPE_TRAITS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/utility.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/utility.hpp new file mode 100644 index 0000000000000000000000000000000000000000..7f5db48a500f6e8b0399c04ad05d79a0a0137741 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/utility.hpp @@ -0,0 +1,230 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_UTILITY_HPP +#define OPENCV_CUDA_UTILITY_HPP + +#include "saturate_cast.hpp" +#include "datamov_utils.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + struct CV_EXPORTS ThrustAllocator + { + typedef uchar value_type; + virtual ~ThrustAllocator(); + virtual __device__ __host__ uchar* allocate(size_t numBytes) = 0; + virtual __device__ __host__ void deallocate(uchar* ptr, size_t numBytes) = 0; + static ThrustAllocator& getAllocator(); + static void setAllocator(ThrustAllocator* allocator); + }; + #define OPENCV_CUDA_LOG_WARP_SIZE (5) + #define OPENCV_CUDA_WARP_SIZE (1 << OPENCV_CUDA_LOG_WARP_SIZE) + #define OPENCV_CUDA_LOG_MEM_BANKS ((__CUDA_ARCH__ >= 200) ? 5 : 4) // 32 banks on fermi, 16 on tesla + #define OPENCV_CUDA_MEM_BANKS (1 << OPENCV_CUDA_LOG_MEM_BANKS) + + /////////////////////////////////////////////////////////////////////////////// + // swap + + template void __device__ __host__ __forceinline__ swap(T& a, T& b) + { + const T temp = a; + a = b; + b = temp; + } + + /////////////////////////////////////////////////////////////////////////////// + // Mask Reader + + struct SingleMask + { + explicit __host__ __device__ __forceinline__ SingleMask(PtrStepb mask_) : mask(mask_) {} + __host__ __device__ __forceinline__ SingleMask(const SingleMask& mask_): mask(mask_.mask){} + + __device__ __forceinline__ bool operator()(int y, int x) const + { + return mask.ptr(y)[x] != 0; + } + + PtrStepb mask; + }; + + struct SingleMaskChannels + { + __host__ __device__ __forceinline__ SingleMaskChannels(PtrStepb mask_, int channels_) + : mask(mask_), channels(channels_) {} + __host__ __device__ __forceinline__ SingleMaskChannels(const SingleMaskChannels& mask_) + :mask(mask_.mask), channels(mask_.channels){} + + __device__ __forceinline__ bool operator()(int y, int x) const + { + return mask.ptr(y)[x / channels] != 0; + } + + PtrStepb mask; + int channels; + }; + + struct MaskCollection + { + explicit __host__ __device__ __forceinline__ MaskCollection(PtrStepb* maskCollection_) + : maskCollection(maskCollection_) {} + + __device__ __forceinline__ MaskCollection(const MaskCollection& masks_) + : maskCollection(masks_.maskCollection), curMask(masks_.curMask){} + + __device__ __forceinline__ void next() + { + curMask = *maskCollection++; + } + __device__ __forceinline__ void setMask(int z) + { + curMask = maskCollection[z]; + } + + __device__ __forceinline__ bool operator()(int y, int x) const + { + uchar val; + return curMask.data == 0 || (ForceGlob::Load(curMask.ptr(y), x, val), (val != 0)); + } + + const PtrStepb* maskCollection; + PtrStepb curMask; + }; + + struct WithOutMask + { + __host__ __device__ __forceinline__ WithOutMask(){} + __host__ __device__ __forceinline__ WithOutMask(const WithOutMask&){} + + __device__ __forceinline__ void next() const + { + } + __device__ __forceinline__ void setMask(int) const + { + } + + __device__ __forceinline__ bool operator()(int, int) const + { + return true; + } + + __device__ __forceinline__ bool operator()(int, int, int) const + { + return true; + } + + static __device__ __forceinline__ bool check(int, int) + { + return true; + } + + static __device__ __forceinline__ bool check(int, int, int) + { + return true; + } + }; + + /////////////////////////////////////////////////////////////////////////////// + // Solve linear system + + // solve 2x2 linear system Ax=b + template __device__ __forceinline__ bool solve2x2(const T A[2][2], const T b[2], T x[2]) + { + T det = A[0][0] * A[1][1] - A[1][0] * A[0][1]; + + if (det != 0) + { + double invdet = 1.0 / det; + + x[0] = saturate_cast(invdet * (b[0] * A[1][1] - b[1] * A[0][1])); + + x[1] = saturate_cast(invdet * (A[0][0] * b[1] - A[1][0] * b[0])); + + return true; + } + + return false; + } + + // solve 3x3 linear system Ax=b + template __device__ __forceinline__ bool solve3x3(const T A[3][3], const T b[3], T x[3]) + { + T det = A[0][0] * (A[1][1] * A[2][2] - A[1][2] * A[2][1]) + - A[0][1] * (A[1][0] * A[2][2] - A[1][2] * A[2][0]) + + A[0][2] * (A[1][0] * A[2][1] - A[1][1] * A[2][0]); + + if (det != 0) + { + double invdet = 1.0 / det; + + x[0] = saturate_cast(invdet * + (b[0] * (A[1][1] * A[2][2] - A[1][2] * A[2][1]) - + A[0][1] * (b[1] * A[2][2] - A[1][2] * b[2] ) + + A[0][2] * (b[1] * A[2][1] - A[1][1] * b[2] ))); + + x[1] = saturate_cast(invdet * + (A[0][0] * (b[1] * A[2][2] - A[1][2] * b[2] ) - + b[0] * (A[1][0] * A[2][2] - A[1][2] * A[2][0]) + + A[0][2] * (A[1][0] * b[2] - b[1] * A[2][0]))); + + x[2] = saturate_cast(invdet * + (A[0][0] * (A[1][1] * b[2] - b[1] * A[2][1]) - + A[0][1] * (A[1][0] * b[2] - b[1] * A[2][0]) + + b[0] * (A[1][0] * A[2][1] - A[1][1] * A[2][0]))); + + return true; + } + + return false; + } +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_UTILITY_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/vec_distance.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/vec_distance.hpp new file mode 100644 index 0000000000000000000000000000000000000000..ef6e51087d33e8fd859c63be04515a7e99fd6afb --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/vec_distance.hpp @@ -0,0 +1,232 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_VEC_DISTANCE_HPP +#define OPENCV_CUDA_VEC_DISTANCE_HPP + +#include "reduce.hpp" +#include "functional.hpp" +#include "detail/vec_distance_detail.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + template struct L1Dist + { + typedef int value_type; + typedef int result_type; + + __device__ __forceinline__ L1Dist() : mySum(0) {} + + __device__ __forceinline__ void reduceIter(int val1, int val2) + { + mySum = __sad(val1, val2, mySum); + } + + template __device__ __forceinline__ void reduceAll(int* smem, int tid) + { + reduce(smem, mySum, tid, plus()); + } + + __device__ __forceinline__ operator int() const + { + return mySum; + } + + int mySum; + }; + template <> struct L1Dist + { + typedef float value_type; + typedef float result_type; + + __device__ __forceinline__ L1Dist() : mySum(0.0f) {} + + __device__ __forceinline__ void reduceIter(float val1, float val2) + { + mySum += ::fabs(val1 - val2); + } + + template __device__ __forceinline__ void reduceAll(float* smem, int tid) + { + reduce(smem, mySum, tid, plus()); + } + + __device__ __forceinline__ operator float() const + { + return mySum; + } + + float mySum; + }; + + struct L2Dist + { + typedef float value_type; + typedef float result_type; + + __device__ __forceinline__ L2Dist() : mySum(0.0f) {} + + __device__ __forceinline__ void reduceIter(float val1, float val2) + { + float reg = val1 - val2; + mySum += reg * reg; + } + + template __device__ __forceinline__ void reduceAll(float* smem, int tid) + { + reduce(smem, mySum, tid, plus()); + } + + __device__ __forceinline__ operator float() const + { + return sqrtf(mySum); + } + + float mySum; + }; + + struct HammingDist + { + typedef int value_type; + typedef int result_type; + + __device__ __forceinline__ HammingDist() : mySum(0) {} + + __device__ __forceinline__ void reduceIter(int val1, int val2) + { + mySum += __popc(val1 ^ val2); + } + + template __device__ __forceinline__ void reduceAll(int* smem, int tid) + { + reduce(smem, mySum, tid, plus()); + } + + __device__ __forceinline__ operator int() const + { + return mySum; + } + + int mySum; + }; + + // calc distance between two vectors in global memory + template + __device__ void calcVecDiffGlobal(const T1* vec1, const T2* vec2, int len, Dist& dist, typename Dist::result_type* smem, int tid) + { + for (int i = tid; i < len; i += THREAD_DIM) + { + T1 val1; + ForceGlob::Load(vec1, i, val1); + + T2 val2; + ForceGlob::Load(vec2, i, val2); + + dist.reduceIter(val1, val2); + } + + dist.reduceAll(smem, tid); + } + + // calc distance between two vectors, first vector is cached in register or shared memory, second vector is in global memory + template + __device__ __forceinline__ void calcVecDiffCached(const T1* vecCached, const T2* vecGlob, int len, Dist& dist, typename Dist::result_type* smem, int tid) + { + vec_distance_detail::VecDiffCachedCalculator::calc(vecCached, vecGlob, len, dist, tid); + + dist.reduceAll(smem, tid); + } + + // calc distance between two vectors in global memory + template struct VecDiffGlobal + { + explicit __device__ __forceinline__ VecDiffGlobal(const T1* vec1_, int = 0, void* = 0, int = 0, int = 0) + { + vec1 = vec1_; + } + + template + __device__ __forceinline__ void calc(const T2* vec2, int len, Dist& dist, typename Dist::result_type* smem, int tid) const + { + calcVecDiffGlobal(vec1, vec2, len, dist, smem, tid); + } + + const T1* vec1; + }; + + // calc distance between two vectors, first vector is cached in register memory, second vector is in global memory + template struct VecDiffCachedRegister + { + template __device__ __forceinline__ VecDiffCachedRegister(const T1* vec1, int len, U* smem, int glob_tid, int tid) + { + if (glob_tid < len) + smem[glob_tid] = vec1[glob_tid]; + __syncthreads(); + + U* vec1ValsPtr = vec1Vals; + + #pragma unroll + for (int i = tid; i < MAX_LEN; i += THREAD_DIM) + *vec1ValsPtr++ = smem[i]; + + __syncthreads(); + } + + template + __device__ __forceinline__ void calc(const T2* vec2, int len, Dist& dist, typename Dist::result_type* smem, int tid) const + { + calcVecDiffCached(vec1Vals, vec2, len, dist, smem, tid); + } + + U vec1Vals[MAX_LEN / THREAD_DIM]; + }; +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_VEC_DISTANCE_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/vec_math.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/vec_math.hpp new file mode 100644 index 0000000000000000000000000000000000000000..9085b92397d8db583a3890d688a1fe8b7bf72152 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/vec_math.hpp @@ -0,0 +1,930 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_VECMATH_HPP +#define OPENCV_CUDA_VECMATH_HPP + +#include "vec_traits.hpp" +#include "saturate_cast.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + +// saturate_cast + +namespace vec_math_detail +{ + template struct SatCastHelper; + template struct SatCastHelper<1, VecD> + { + template static __device__ __forceinline__ VecD cast(const VecS& v) + { + typedef typename VecTraits::elem_type D; + return VecTraits::make(saturate_cast(v.x)); + } + }; + template struct SatCastHelper<2, VecD> + { + template static __device__ __forceinline__ VecD cast(const VecS& v) + { + typedef typename VecTraits::elem_type D; + return VecTraits::make(saturate_cast(v.x), saturate_cast(v.y)); + } + }; + template struct SatCastHelper<3, VecD> + { + template static __device__ __forceinline__ VecD cast(const VecS& v) + { + typedef typename VecTraits::elem_type D; + return VecTraits::make(saturate_cast(v.x), saturate_cast(v.y), saturate_cast(v.z)); + } + }; + template struct SatCastHelper<4, VecD> + { + template static __device__ __forceinline__ VecD cast(const VecS& v) + { + typedef typename VecTraits::elem_type D; + return VecTraits::make(saturate_cast(v.x), saturate_cast(v.y), saturate_cast(v.z), saturate_cast(v.w)); + } + }; + + template static __device__ __forceinline__ VecD saturate_cast_helper(const VecS& v) + { + return SatCastHelper::cn, VecD>::cast(v); + } +} + +template static __device__ __forceinline__ T saturate_cast(const uchar1& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const char1& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const ushort1& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const short1& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const uint1& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const int1& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const float1& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const double1& v) {return vec_math_detail::saturate_cast_helper(v);} + +template static __device__ __forceinline__ T saturate_cast(const uchar2& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const char2& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const ushort2& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const short2& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const uint2& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const int2& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const float2& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const double2& v) {return vec_math_detail::saturate_cast_helper(v);} + +template static __device__ __forceinline__ T saturate_cast(const uchar3& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const char3& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const ushort3& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const short3& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const uint3& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const int3& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const float3& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const double3& v) {return vec_math_detail::saturate_cast_helper(v);} + +template static __device__ __forceinline__ T saturate_cast(const uchar4& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const char4& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const ushort4& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const short4& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const uint4& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const int4& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const float4& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const double4& v) {return vec_math_detail::saturate_cast_helper(v);} + +// unary operators + +#define CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(op, input_type, output_type) \ + __device__ __forceinline__ output_type ## 1 operator op(const input_type ## 1 & a) \ + { \ + return VecTraits::make(op (a.x)); \ + } \ + __device__ __forceinline__ output_type ## 2 operator op(const input_type ## 2 & a) \ + { \ + return VecTraits::make(op (a.x), op (a.y)); \ + } \ + __device__ __forceinline__ output_type ## 3 operator op(const input_type ## 3 & a) \ + { \ + return VecTraits::make(op (a.x), op (a.y), op (a.z)); \ + } \ + __device__ __forceinline__ output_type ## 4 operator op(const input_type ## 4 & a) \ + { \ + return VecTraits::make(op (a.x), op (a.y), op (a.z), op (a.w)); \ + } + +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, char, char) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, short, short) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, int, int) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, char, uchar) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, ushort, uchar) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, short, uchar) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, int, uchar) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, uint, uchar) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, float, uchar) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, double, uchar) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, char, char) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, ushort, ushort) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, short, short) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, int, int) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, uint, uint) + +#undef CV_CUDEV_IMPLEMENT_VEC_UNARY_OP + +// unary functions + +#define CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(func_name, func, input_type, output_type) \ + __device__ __forceinline__ output_type ## 1 func_name(const input_type ## 1 & a) \ + { \ + return VecTraits::make(func (a.x)); \ + } \ + __device__ __forceinline__ output_type ## 2 func_name(const input_type ## 2 & a) \ + { \ + return VecTraits::make(func (a.x), func (a.y)); \ + } \ + __device__ __forceinline__ output_type ## 3 func_name(const input_type ## 3 & a) \ + { \ + return VecTraits::make(func (a.x), func (a.y), func (a.z)); \ + } \ + __device__ __forceinline__ output_type ## 4 func_name(const input_type ## 4 & a) \ + { \ + return VecTraits::make(func (a.x), func (a.y), func (a.z), func (a.w)); \ + } + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, /*::abs*/, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::abs, char, char) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, /*::abs*/, ushort, ushort) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::abs, short, short) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::abs, int, int) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, /*::abs*/, uint, uint) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::fabsf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::fabs, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrt, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::exp, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::log, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sin, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cos, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tan, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asin, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acos, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atan, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinh, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::cosh, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanh, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinh, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acosh, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanh, double, double) + +#undef CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC + +// binary operators (vec & vec) + +#define CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(op, input_type, output_type) \ + __device__ __forceinline__ output_type ## 1 operator op(const input_type ## 1 & a, const input_type ## 1 & b) \ + { \ + return VecTraits::make(a.x op b.x); \ + } \ + __device__ __forceinline__ output_type ## 2 operator op(const input_type ## 2 & a, const input_type ## 2 & b) \ + { \ + return VecTraits::make(a.x op b.x, a.y op b.y); \ + } \ + __device__ __forceinline__ output_type ## 3 operator op(const input_type ## 3 & a, const input_type ## 3 & b) \ + { \ + return VecTraits::make(a.x op b.x, a.y op b.y, a.z op b.z); \ + } \ + __device__ __forceinline__ output_type ## 4 operator op(const input_type ## 4 & a, const input_type ## 4 & b) \ + { \ + return VecTraits::make(a.x op b.x, a.y op b.y, a.z op b.z, a.w op b.w); \ + } + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, uchar, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, char, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, ushort, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, short, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, int, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, uint, uint) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, float, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, double, double) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, uchar, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, char, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, ushort, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, short, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, int, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, uint, uint) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, float, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, double, double) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, uchar, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, char, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, ushort, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, short, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, int, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, uint, uint) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, float, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, double, double) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, uchar, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, char, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, ushort, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, short, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, int, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, uint, uint) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, float, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, double, double) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, char, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, ushort, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, short, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, int, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, uint, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, float, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, double, uchar) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, char, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, ushort, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, short, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, int, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, uint, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, float, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, double, uchar) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, char, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, ushort, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, short, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, int, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, uint, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, float, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, double, uchar) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, char, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, ushort, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, short, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, int, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, uint, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, float, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, double, uchar) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, char, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, ushort, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, short, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, int, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, uint, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, float, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, double, uchar) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, char, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, ushort, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, short, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, int, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, uint, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, float, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, double, uchar) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, char, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, ushort, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, short, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, int, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, uint, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, float, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, double, uchar) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, char, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, ushort, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, short, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, int, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, uint, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, float, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, double, uchar) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, char, char) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, ushort, ushort) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, short, short) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, int, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, uint, uint) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, char, char) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, ushort, ushort) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, short, short) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, int, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, uint, uint) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, char, char) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, ushort, ushort) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, short, short) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, int, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, uint, uint) + +#undef CV_CUDEV_IMPLEMENT_VEC_BINARY_OP + +// binary operators (vec & scalar) + +#define CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(op, input_type, scalar_type, output_type) \ + __device__ __forceinline__ output_type ## 1 operator op(const input_type ## 1 & a, scalar_type s) \ + { \ + return VecTraits::make(a.x op s); \ + } \ + __device__ __forceinline__ output_type ## 1 operator op(scalar_type s, const input_type ## 1 & b) \ + { \ + return VecTraits::make(s op b.x); \ + } \ + __device__ __forceinline__ output_type ## 2 operator op(const input_type ## 2 & a, scalar_type s) \ + { \ + return VecTraits::make(a.x op s, a.y op s); \ + } \ + __device__ __forceinline__ output_type ## 2 operator op(scalar_type s, const input_type ## 2 & b) \ + { \ + return VecTraits::make(s op b.x, s op b.y); \ + } \ + __device__ __forceinline__ output_type ## 3 operator op(const input_type ## 3 & a, scalar_type s) \ + { \ + return VecTraits::make(a.x op s, a.y op s, a.z op s); \ + } \ + __device__ __forceinline__ output_type ## 3 operator op(scalar_type s, const input_type ## 3 & b) \ + { \ + return VecTraits::make(s op b.x, s op b.y, s op b.z); \ + } \ + __device__ __forceinline__ output_type ## 4 operator op(const input_type ## 4 & a, scalar_type s) \ + { \ + return VecTraits::make(a.x op s, a.y op s, a.z op s, a.w op s); \ + } \ + __device__ __forceinline__ output_type ## 4 operator op(scalar_type s, const input_type ## 4 & b) \ + { \ + return VecTraits::make(s op b.x, s op b.y, s op b.z, s op b.w); \ + } + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uchar, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uchar, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uchar, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, char, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, char, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, char, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, ushort, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, ushort, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, ushort, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, short, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, short, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, short, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, int, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, int, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, int, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uint, uint, uint) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uint, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uint, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, float, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, float, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, double, double, double) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uchar, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uchar, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uchar, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, char, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, char, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, char, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, ushort, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, ushort, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, ushort, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, short, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, short, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, short, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, int, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, int, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, int, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uint, uint, uint) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uint, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uint, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, float, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, float, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, double, double, double) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uchar, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uchar, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uchar, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, char, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, char, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, char, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, ushort, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, ushort, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, ushort, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, short, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, short, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, short, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, int, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, int, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, int, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uint, uint, uint) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uint, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uint, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, float, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, float, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, double, double, double) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uchar, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uchar, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uchar, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, char, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, char, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, char, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, ushort, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, ushort, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, ushort, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, short, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, short, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, short, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, int, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, int, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, int, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uint, uint, uint) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uint, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uint, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, float, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, float, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, double, double, double) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, char, char, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, ushort, ushort, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, short, short, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, int, int, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, uint, uint, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, float, float, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, double, double, uchar) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, char, char, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, ushort, ushort, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, short, short, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, int, int, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, uint, uint, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, float, float, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, double, double, uchar) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, char, char, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, ushort, ushort, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, short, short, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, int, int, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, uint, uint, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, float, float, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, double, double, uchar) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, char, char, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, ushort, ushort, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, short, short, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, int, int, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, uint, uint, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, float, float, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, double, double, uchar) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, char, char, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, ushort, ushort, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, short, short, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, int, int, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, uint, uint, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, float, float, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, double, double, uchar) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, char, char, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, ushort, ushort, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, short, short, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, int, int, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, uint, uint, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, float, float, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, double, double, uchar) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, char, char, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, ushort, ushort, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, short, short, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, int, int, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, uint, uint, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, float, float, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, double, double, uchar) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, char, char, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, ushort, ushort, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, short, short, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, int, int, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, uint, uint, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, float, float, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, double, double, uchar) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, char, char, char) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, ushort, ushort, ushort) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, short, short, short) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, int, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, uint, uint, uint) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, char, char, char) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, ushort, ushort, ushort) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, short, short, short) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, int, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, uint, uint, uint) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, char, char, char) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, ushort, ushort, ushort) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, short, short, short) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, int, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, uint, uint, uint) + +#undef CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP + +// binary function (vec & vec) + +#define CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(func_name, func, input_type, output_type) \ + __device__ __forceinline__ output_type ## 1 func_name(const input_type ## 1 & a, const input_type ## 1 & b) \ + { \ + return VecTraits::make(func (a.x, b.x)); \ + } \ + __device__ __forceinline__ output_type ## 2 func_name(const input_type ## 2 & a, const input_type ## 2 & b) \ + { \ + return VecTraits::make(func (a.x, b.x), func (a.y, b.y)); \ + } \ + __device__ __forceinline__ output_type ## 3 func_name(const input_type ## 3 & a, const input_type ## 3 & b) \ + { \ + return VecTraits::make(func (a.x, b.x), func (a.y, b.y), func (a.z, b.z)); \ + } \ + __device__ __forceinline__ output_type ## 4 func_name(const input_type ## 4 & a, const input_type ## 4 & b) \ + { \ + return VecTraits::make(func (a.x, b.x), func (a.y, b.y), func (a.z, b.z), func (a.w, b.w)); \ + } + +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, char, char) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, ushort, ushort) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, short, short) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, uint, uint) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, int, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::fmaxf, float, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::fmax, double, double) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, char, char) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, ushort, ushort) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, short, short) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, uint, uint) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, int, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::fminf, float, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::fmin, double, double) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, char, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, short, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, int, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, float, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypot, double, double) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, char, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, short, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, uint, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, int, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, float, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2, double, double) + +#undef CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC + +// binary function (vec & scalar) + +#define CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(func_name, func, input_type, scalar_type, output_type) \ + __device__ __forceinline__ output_type ## 1 func_name(const input_type ## 1 & a, scalar_type s) \ + { \ + return VecTraits::make(func ((output_type) a.x, (output_type) s)); \ + } \ + __device__ __forceinline__ output_type ## 1 func_name(scalar_type s, const input_type ## 1 & b) \ + { \ + return VecTraits::make(func ((output_type) s, (output_type) b.x)); \ + } \ + __device__ __forceinline__ output_type ## 2 func_name(const input_type ## 2 & a, scalar_type s) \ + { \ + return VecTraits::make(func ((output_type) a.x, (output_type) s), func ((output_type) a.y, (output_type) s)); \ + } \ + __device__ __forceinline__ output_type ## 2 func_name(scalar_type s, const input_type ## 2 & b) \ + { \ + return VecTraits::make(func ((output_type) s, (output_type) b.x), func ((output_type) s, (output_type) b.y)); \ + } \ + __device__ __forceinline__ output_type ## 3 func_name(const input_type ## 3 & a, scalar_type s) \ + { \ + return VecTraits::make(func ((output_type) a.x, (output_type) s), func ((output_type) a.y, (output_type) s), func ((output_type) a.z, (output_type) s)); \ + } \ + __device__ __forceinline__ output_type ## 3 func_name(scalar_type s, const input_type ## 3 & b) \ + { \ + return VecTraits::make(func ((output_type) s, (output_type) b.x), func ((output_type) s, (output_type) b.y), func ((output_type) s, (output_type) b.z)); \ + } \ + __device__ __forceinline__ output_type ## 4 func_name(const input_type ## 4 & a, scalar_type s) \ + { \ + return VecTraits::make(func ((output_type) a.x, (output_type) s), func ((output_type) a.y, (output_type) s), func ((output_type) a.z, (output_type) s), func ((output_type) a.w, (output_type) s)); \ + } \ + __device__ __forceinline__ output_type ## 4 func_name(scalar_type s, const input_type ## 4 & b) \ + { \ + return VecTraits::make(func ((output_type) s, (output_type) b.x), func ((output_type) s, (output_type) b.y), func ((output_type) s, (output_type) b.z), func ((output_type) s, (output_type) b.w)); \ + } + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, uchar, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, uchar, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, char, char, char) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, char, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, char, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, ushort, ushort, ushort) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, ushort, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, ushort, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, short, short, short) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, short, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, short, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, uint, uint, uint) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, uint, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, uint, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, int, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, int, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, int, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, float, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, float, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, double, double, double) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, uchar, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, uchar, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, char, char, char) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, char, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, char, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, ushort, ushort, ushort) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, ushort, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, ushort, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, short, short, short) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, short, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, short, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, uint, uint, uint) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, uint, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, uint, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, int, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, int, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, int, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, float, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, float, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, double, double, double) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, uchar, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, uchar, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, char, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, char, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, ushort, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, ushort, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, short, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, short, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, uint, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, uint, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, int, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, int, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, float, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, float, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, double, double, double) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, uchar, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, uchar, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, char, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, char, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, ushort, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, ushort, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, short, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, short, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, uint, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, uint, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, int, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, int, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, float, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, float, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, double, double, double) + +#undef CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC + +}}} // namespace cv { namespace cuda { namespace device + +//! @endcond + +#endif // OPENCV_CUDA_VECMATH_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/vec_traits.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/vec_traits.hpp new file mode 100644 index 0000000000000000000000000000000000000000..b5ff281a0b8a3b7c5a6a481fa7880b7f24e4786f --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/vec_traits.hpp @@ -0,0 +1,288 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_VEC_TRAITS_HPP +#define OPENCV_CUDA_VEC_TRAITS_HPP + +#include "common.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + template struct TypeVec; + + struct __align__(8) uchar8 + { + uchar a0, a1, a2, a3, a4, a5, a6, a7; + }; + static __host__ __device__ __forceinline__ uchar8 make_uchar8(uchar a0, uchar a1, uchar a2, uchar a3, uchar a4, uchar a5, uchar a6, uchar a7) + { + uchar8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; + return val; + } + struct __align__(8) char8 + { + schar a0, a1, a2, a3, a4, a5, a6, a7; + }; + static __host__ __device__ __forceinline__ char8 make_char8(schar a0, schar a1, schar a2, schar a3, schar a4, schar a5, schar a6, schar a7) + { + char8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; + return val; + } + struct __align__(16) ushort8 + { + ushort a0, a1, a2, a3, a4, a5, a6, a7; + }; + static __host__ __device__ __forceinline__ ushort8 make_ushort8(ushort a0, ushort a1, ushort a2, ushort a3, ushort a4, ushort a5, ushort a6, ushort a7) + { + ushort8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; + return val; + } + struct __align__(16) short8 + { + short a0, a1, a2, a3, a4, a5, a6, a7; + }; + static __host__ __device__ __forceinline__ short8 make_short8(short a0, short a1, short a2, short a3, short a4, short a5, short a6, short a7) + { + short8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; + return val; + } + struct __align__(32) uint8 + { + uint a0, a1, a2, a3, a4, a5, a6, a7; + }; + static __host__ __device__ __forceinline__ uint8 make_uint8(uint a0, uint a1, uint a2, uint a3, uint a4, uint a5, uint a6, uint a7) + { + uint8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; + return val; + } + struct __align__(32) int8 + { + int a0, a1, a2, a3, a4, a5, a6, a7; + }; + static __host__ __device__ __forceinline__ int8 make_int8(int a0, int a1, int a2, int a3, int a4, int a5, int a6, int a7) + { + int8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; + return val; + } + struct __align__(32) float8 + { + float a0, a1, a2, a3, a4, a5, a6, a7; + }; + static __host__ __device__ __forceinline__ float8 make_float8(float a0, float a1, float a2, float a3, float a4, float a5, float a6, float a7) + { + float8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; + return val; + } + struct double8 + { + double a0, a1, a2, a3, a4, a5, a6, a7; + }; + static __host__ __device__ __forceinline__ double8 make_double8(double a0, double a1, double a2, double a3, double a4, double a5, double a6, double a7) + { + double8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; + return val; + } + +#define OPENCV_CUDA_IMPLEMENT_TYPE_VEC(type) \ + template<> struct TypeVec { typedef type vec_type; }; \ + template<> struct TypeVec { typedef type ## 1 vec_type; }; \ + template<> struct TypeVec { typedef type ## 2 vec_type; }; \ + template<> struct TypeVec { typedef type ## 2 vec_type; }; \ + template<> struct TypeVec { typedef type ## 3 vec_type; }; \ + template<> struct TypeVec { typedef type ## 3 vec_type; }; \ + template<> struct TypeVec { typedef type ## 4 vec_type; }; \ + template<> struct TypeVec { typedef type ## 4 vec_type; }; \ + template<> struct TypeVec { typedef type ## 8 vec_type; }; \ + template<> struct TypeVec { typedef type ## 8 vec_type; }; + + OPENCV_CUDA_IMPLEMENT_TYPE_VEC(uchar) + OPENCV_CUDA_IMPLEMENT_TYPE_VEC(char) + OPENCV_CUDA_IMPLEMENT_TYPE_VEC(ushort) + OPENCV_CUDA_IMPLEMENT_TYPE_VEC(short) + OPENCV_CUDA_IMPLEMENT_TYPE_VEC(int) + OPENCV_CUDA_IMPLEMENT_TYPE_VEC(uint) + OPENCV_CUDA_IMPLEMENT_TYPE_VEC(float) + OPENCV_CUDA_IMPLEMENT_TYPE_VEC(double) + + #undef OPENCV_CUDA_IMPLEMENT_TYPE_VEC + + template<> struct TypeVec { typedef schar vec_type; }; + template<> struct TypeVec { typedef char2 vec_type; }; + template<> struct TypeVec { typedef char3 vec_type; }; + template<> struct TypeVec { typedef char4 vec_type; }; + template<> struct TypeVec { typedef char8 vec_type; }; + + template<> struct TypeVec { typedef uchar vec_type; }; + template<> struct TypeVec { typedef uchar2 vec_type; }; + template<> struct TypeVec { typedef uchar3 vec_type; }; + template<> struct TypeVec { typedef uchar4 vec_type; }; + template<> struct TypeVec { typedef uchar8 vec_type; }; + + template struct VecTraits; + +#define OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(type) \ + template<> struct VecTraits \ + { \ + typedef type elem_type; \ + enum {cn=1}; \ + static __device__ __host__ __forceinline__ type all(type v) {return v;} \ + static __device__ __host__ __forceinline__ type make(type x) {return x;} \ + static __device__ __host__ __forceinline__ type make(const type* v) {return *v;} \ + }; \ + template<> struct VecTraits \ + { \ + typedef type elem_type; \ + enum {cn=1}; \ + static __device__ __host__ __forceinline__ type ## 1 all(type v) {return make_ ## type ## 1(v);} \ + static __device__ __host__ __forceinline__ type ## 1 make(type x) {return make_ ## type ## 1(x);} \ + static __device__ __host__ __forceinline__ type ## 1 make(const type* v) {return make_ ## type ## 1(*v);} \ + }; \ + template<> struct VecTraits \ + { \ + typedef type elem_type; \ + enum {cn=2}; \ + static __device__ __host__ __forceinline__ type ## 2 all(type v) {return make_ ## type ## 2(v, v);} \ + static __device__ __host__ __forceinline__ type ## 2 make(type x, type y) {return make_ ## type ## 2(x, y);} \ + static __device__ __host__ __forceinline__ type ## 2 make(const type* v) {return make_ ## type ## 2(v[0], v[1]);} \ + }; \ + template<> struct VecTraits \ + { \ + typedef type elem_type; \ + enum {cn=3}; \ + static __device__ __host__ __forceinline__ type ## 3 all(type v) {return make_ ## type ## 3(v, v, v);} \ + static __device__ __host__ __forceinline__ type ## 3 make(type x, type y, type z) {return make_ ## type ## 3(x, y, z);} \ + static __device__ __host__ __forceinline__ type ## 3 make(const type* v) {return make_ ## type ## 3(v[0], v[1], v[2]);} \ + }; \ + template<> struct VecTraits \ + { \ + typedef type elem_type; \ + enum {cn=4}; \ + static __device__ __host__ __forceinline__ type ## 4 all(type v) {return make_ ## type ## 4(v, v, v, v);} \ + static __device__ __host__ __forceinline__ type ## 4 make(type x, type y, type z, type w) {return make_ ## type ## 4(x, y, z, w);} \ + static __device__ __host__ __forceinline__ type ## 4 make(const type* v) {return make_ ## type ## 4(v[0], v[1], v[2], v[3]);} \ + }; \ + template<> struct VecTraits \ + { \ + typedef type elem_type; \ + enum {cn=8}; \ + static __device__ __host__ __forceinline__ type ## 8 all(type v) {return make_ ## type ## 8(v, v, v, v, v, v, v, v);} \ + static __device__ __host__ __forceinline__ type ## 8 make(type a0, type a1, type a2, type a3, type a4, type a5, type a6, type a7) {return make_ ## type ## 8(a0, a1, a2, a3, a4, a5, a6, a7);} \ + static __device__ __host__ __forceinline__ type ## 8 make(const type* v) {return make_ ## type ## 8(v[0], v[1], v[2], v[3], v[4], v[5], v[6], v[7]);} \ + }; + + OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(uchar) + OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(ushort) + OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(short) + OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(int) + OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(uint) + OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(float) + OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(double) + + #undef OPENCV_CUDA_IMPLEMENT_VEC_TRAITS + + template<> struct VecTraits + { + typedef char elem_type; + enum {cn=1}; + static __device__ __host__ __forceinline__ char all(char v) {return v;} + static __device__ __host__ __forceinline__ char make(char x) {return x;} + static __device__ __host__ __forceinline__ char make(const char* x) {return *x;} + }; + template<> struct VecTraits + { + typedef schar elem_type; + enum {cn=1}; + static __device__ __host__ __forceinline__ schar all(schar v) {return v;} + static __device__ __host__ __forceinline__ schar make(schar x) {return x;} + static __device__ __host__ __forceinline__ schar make(const schar* x) {return *x;} + }; + template<> struct VecTraits + { + typedef schar elem_type; + enum {cn=1}; + static __device__ __host__ __forceinline__ char1 all(schar v) {return make_char1(v);} + static __device__ __host__ __forceinline__ char1 make(schar x) {return make_char1(x);} + static __device__ __host__ __forceinline__ char1 make(const schar* v) {return make_char1(v[0]);} + }; + template<> struct VecTraits + { + typedef schar elem_type; + enum {cn=2}; + static __device__ __host__ __forceinline__ char2 all(schar v) {return make_char2(v, v);} + static __device__ __host__ __forceinline__ char2 make(schar x, schar y) {return make_char2(x, y);} + static __device__ __host__ __forceinline__ char2 make(const schar* v) {return make_char2(v[0], v[1]);} + }; + template<> struct VecTraits + { + typedef schar elem_type; + enum {cn=3}; + static __device__ __host__ __forceinline__ char3 all(schar v) {return make_char3(v, v, v);} + static __device__ __host__ __forceinline__ char3 make(schar x, schar y, schar z) {return make_char3(x, y, z);} + static __device__ __host__ __forceinline__ char3 make(const schar* v) {return make_char3(v[0], v[1], v[2]);} + }; + template<> struct VecTraits + { + typedef schar elem_type; + enum {cn=4}; + static __device__ __host__ __forceinline__ char4 all(schar v) {return make_char4(v, v, v, v);} + static __device__ __host__ __forceinline__ char4 make(schar x, schar y, schar z, schar w) {return make_char4(x, y, z, w);} + static __device__ __host__ __forceinline__ char4 make(const schar* v) {return make_char4(v[0], v[1], v[2], v[3]);} + }; + template<> struct VecTraits + { + typedef schar elem_type; + enum {cn=8}; + static __device__ __host__ __forceinline__ char8 all(schar v) {return make_char8(v, v, v, v, v, v, v, v);} + static __device__ __host__ __forceinline__ char8 make(schar a0, schar a1, schar a2, schar a3, schar a4, schar a5, schar a6, schar a7) {return make_char8(a0, a1, a2, a3, a4, a5, a6, a7);} + static __device__ __host__ __forceinline__ char8 make(const schar* v) {return make_char8(v[0], v[1], v[2], v[3], v[4], v[5], v[6], v[7]);} + }; +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_VEC_TRAITS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/warp.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/warp.hpp new file mode 100644 index 0000000000000000000000000000000000000000..8af7e6a212afd45cc9ddd00c26010875f55d80bf --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/warp.hpp @@ -0,0 +1,139 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_DEVICE_WARP_HPP +#define OPENCV_CUDA_DEVICE_WARP_HPP + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + struct Warp + { + enum + { + LOG_WARP_SIZE = 5, + WARP_SIZE = 1 << LOG_WARP_SIZE, + STRIDE = WARP_SIZE + }; + + /** \brief Returns the warp lane ID of the calling thread. */ + static __device__ __forceinline__ unsigned int laneId() + { + unsigned int ret; + asm("mov.u32 %0, %%laneid;" : "=r"(ret) ); + return ret; + } + + template + static __device__ __forceinline__ void fill(It beg, It end, const T& value) + { + for(It t = beg + laneId(); t < end; t += STRIDE) + *t = value; + } + + template + static __device__ __forceinline__ OutIt copy(InIt beg, InIt end, OutIt out) + { + for(InIt t = beg + laneId(); t < end; t += STRIDE, out += STRIDE) + *out = *t; + return out; + } + + template + static __device__ __forceinline__ OutIt transform(InIt beg, InIt end, OutIt out, UnOp op) + { + for(InIt t = beg + laneId(); t < end; t += STRIDE, out += STRIDE) + *out = op(*t); + return out; + } + + template + static __device__ __forceinline__ OutIt transform(InIt1 beg1, InIt1 end1, InIt2 beg2, OutIt out, BinOp op) + { + unsigned int lane = laneId(); + + InIt1 t1 = beg1 + lane; + InIt2 t2 = beg2 + lane; + for(; t1 < end1; t1 += STRIDE, t2 += STRIDE, out += STRIDE) + *out = op(*t1, *t2); + return out; + } + + template + static __device__ __forceinline__ T reduce(volatile T *ptr, BinOp op) + { + const unsigned int lane = laneId(); + + if (lane < 16) + { + T partial = ptr[lane]; + + ptr[lane] = partial = op(partial, ptr[lane + 16]); + ptr[lane] = partial = op(partial, ptr[lane + 8]); + ptr[lane] = partial = op(partial, ptr[lane + 4]); + ptr[lane] = partial = op(partial, ptr[lane + 2]); + ptr[lane] = partial = op(partial, ptr[lane + 1]); + } + + return *ptr; + } + + template + static __device__ __forceinline__ void yota(OutIt beg, OutIt end, T value) + { + unsigned int lane = laneId(); + value += lane; + + for(OutIt t = beg + lane; t < end; t += STRIDE, value += STRIDE) + *t = value; + } + }; +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif /* OPENCV_CUDA_DEVICE_WARP_HPP */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/warp_reduce.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/warp_reduce.hpp new file mode 100644 index 0000000000000000000000000000000000000000..530303d2495fe878340b80bf1a4b330ed7aadc5f --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/warp_reduce.hpp @@ -0,0 +1,76 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_WARP_REDUCE_HPP__ +#define OPENCV_CUDA_WARP_REDUCE_HPP__ + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + template + __device__ __forceinline__ T warp_reduce(volatile T *ptr , const unsigned int tid = threadIdx.x) + { + const unsigned int lane = tid & 31; // index of thread in warp (0..31) + + if (lane < 16) + { + T partial = ptr[tid]; + + ptr[tid] = partial = partial + ptr[tid + 16]; + ptr[tid] = partial = partial + ptr[tid + 8]; + ptr[tid] = partial = partial + ptr[tid + 4]; + ptr[tid] = partial = partial + ptr[tid + 2]; + ptr[tid] = partial = partial + ptr[tid + 1]; + } + + return ptr[tid - lane]; + } +}}} // namespace cv { namespace cuda { namespace cudev { + +//! @endcond + +#endif /* OPENCV_CUDA_WARP_REDUCE_HPP__ */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/warp_shuffle.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/warp_shuffle.hpp new file mode 100644 index 0000000000000000000000000000000000000000..0da54aee99bf82f0946b64ed39d3bb9bceaf5d7b --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda/warp_shuffle.hpp @@ -0,0 +1,162 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_WARP_SHUFFLE_HPP +#define OPENCV_CUDA_WARP_SHUFFLE_HPP + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ +#if __CUDACC_VER_MAJOR__ >= 9 +# define __shfl(x, y, z) __shfl_sync(0xFFFFFFFFU, x, y, z) +# define __shfl_up(x, y, z) __shfl_up_sync(0xFFFFFFFFU, x, y, z) +# define __shfl_down(x, y, z) __shfl_down_sync(0xFFFFFFFFU, x, y, z) +#endif + template + __device__ __forceinline__ T shfl(T val, int srcLane, int width = warpSize) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + return __shfl(val, srcLane, width); + #else + return T(); + #endif + } + __device__ __forceinline__ unsigned int shfl(unsigned int val, int srcLane, int width = warpSize) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + return (unsigned int) __shfl((int) val, srcLane, width); + #else + return 0; + #endif + } + __device__ __forceinline__ double shfl(double val, int srcLane, int width = warpSize) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + int lo = __double2loint(val); + int hi = __double2hiint(val); + + lo = __shfl(lo, srcLane, width); + hi = __shfl(hi, srcLane, width); + + return __hiloint2double(hi, lo); + #else + return 0.0; + #endif + } + + template + __device__ __forceinline__ T shfl_down(T val, unsigned int delta, int width = warpSize) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + return __shfl_down(val, delta, width); + #else + return T(); + #endif + } + __device__ __forceinline__ unsigned int shfl_down(unsigned int val, unsigned int delta, int width = warpSize) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + return (unsigned int) __shfl_down((int) val, delta, width); + #else + return 0; + #endif + } + __device__ __forceinline__ double shfl_down(double val, unsigned int delta, int width = warpSize) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + int lo = __double2loint(val); + int hi = __double2hiint(val); + + lo = __shfl_down(lo, delta, width); + hi = __shfl_down(hi, delta, width); + + return __hiloint2double(hi, lo); + #else + return 0.0; + #endif + } + + template + __device__ __forceinline__ T shfl_up(T val, unsigned int delta, int width = warpSize) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + return __shfl_up(val, delta, width); + #else + return T(); + #endif + } + __device__ __forceinline__ unsigned int shfl_up(unsigned int val, unsigned int delta, int width = warpSize) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + return (unsigned int) __shfl_up((int) val, delta, width); + #else + return 0; + #endif + } + __device__ __forceinline__ double shfl_up(double val, unsigned int delta, int width = warpSize) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + int lo = __double2loint(val); + int hi = __double2hiint(val); + + lo = __shfl_up(lo, delta, width); + hi = __shfl_up(hi, delta, width); + + return __hiloint2double(hi, lo); + #else + return 0.0; + #endif + } +}}} + +# undef __shfl +# undef __shfl_up +# undef __shfl_down + +//! @endcond + +#endif // OPENCV_CUDA_WARP_SHUFFLE_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda_stream_accessor.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda_stream_accessor.hpp new file mode 100644 index 0000000000000000000000000000000000000000..deaf356fff4311ffddcc388ee28106d0b5459f3f --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda_stream_accessor.hpp @@ -0,0 +1,86 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_CUDA_STREAM_ACCESSOR_HPP +#define OPENCV_CORE_CUDA_STREAM_ACCESSOR_HPP + +#ifndef __cplusplus +# error cuda_stream_accessor.hpp header must be compiled as C++ +#endif + +/** @file cuda_stream_accessor.hpp + * This is only header file that depends on CUDA Runtime API. All other headers are independent. + */ + +#include +#include "opencv2/core/cuda.hpp" + +namespace cv +{ + namespace cuda + { + +//! @addtogroup cudacore_struct +//! @{ + + /** @brief Class that enables getting cudaStream_t from cuda::Stream + */ + struct StreamAccessor + { + CV_EXPORTS static cudaStream_t getStream(const Stream& stream); + CV_EXPORTS static Stream wrapStream(cudaStream_t stream); + }; + + /** @brief Class that enables getting cudaEvent_t from cuda::Event + */ + struct EventAccessor + { + CV_EXPORTS static cudaEvent_t getEvent(const Event& event); + CV_EXPORTS static Event wrapEvent(cudaEvent_t event); + }; + +//! @} + + } +} + +#endif /* OPENCV_CORE_CUDA_STREAM_ACCESSOR_HPP */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda_types.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda_types.hpp new file mode 100644 index 0000000000000000000000000000000000000000..e2647c0455b1e501cf96b7badee7cfc6c5e18eff --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cuda_types.hpp @@ -0,0 +1,142 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_CUDA_TYPES_HPP +#define OPENCV_CORE_CUDA_TYPES_HPP + +#ifndef __cplusplus +# error cuda_types.hpp header must be compiled as C++ +#endif + +#if defined(__OPENCV_BUILD) && defined(__clang__) +#pragma clang diagnostic ignored "-Winconsistent-missing-override" +#endif +#if defined(__OPENCV_BUILD) && defined(__GNUC__) && __GNUC__ >= 5 +#pragma GCC diagnostic ignored "-Wsuggest-override" +#endif + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +#ifdef __CUDACC__ + #define __CV_CUDA_HOST_DEVICE__ __host__ __device__ __forceinline__ +#else + #define __CV_CUDA_HOST_DEVICE__ +#endif + +namespace cv +{ + namespace cuda + { + + // Simple lightweight structures that encapsulates information about an image on device. + // It is intended to pass to nvcc-compiled code. GpuMat depends on headers that nvcc can't compile + + template struct DevPtr + { + typedef T elem_type; + typedef int index_type; + + enum { elem_size = sizeof(elem_type) }; + + T* data; + + __CV_CUDA_HOST_DEVICE__ DevPtr() : data(0) {} + __CV_CUDA_HOST_DEVICE__ DevPtr(T* data_) : data(data_) {} + + __CV_CUDA_HOST_DEVICE__ size_t elemSize() const { return elem_size; } + __CV_CUDA_HOST_DEVICE__ operator T*() { return data; } + __CV_CUDA_HOST_DEVICE__ operator const T*() const { return data; } + }; + + template struct PtrSz : public DevPtr + { + __CV_CUDA_HOST_DEVICE__ PtrSz() : size(0) {} + __CV_CUDA_HOST_DEVICE__ PtrSz(T* data_, size_t size_) : DevPtr(data_), size(size_) {} + + size_t size; + }; + + template struct PtrStep : public DevPtr + { + __CV_CUDA_HOST_DEVICE__ PtrStep() : step(0) {} + __CV_CUDA_HOST_DEVICE__ PtrStep(T* data_, size_t step_) : DevPtr(data_), step(step_) {} + + size_t step; + + __CV_CUDA_HOST_DEVICE__ T* ptr(int y = 0) { return ( T*)( ( char*)DevPtr::data + y * step); } + __CV_CUDA_HOST_DEVICE__ const T* ptr(int y = 0) const { return (const T*)( (const char*)DevPtr::data + y * step); } + + __CV_CUDA_HOST_DEVICE__ T& operator ()(int y, int x) { return ptr(y)[x]; } + __CV_CUDA_HOST_DEVICE__ const T& operator ()(int y, int x) const { return ptr(y)[x]; } + }; + + template struct PtrStepSz : public PtrStep + { + __CV_CUDA_HOST_DEVICE__ PtrStepSz() : cols(0), rows(0) {} + __CV_CUDA_HOST_DEVICE__ PtrStepSz(int rows_, int cols_, T* data_, size_t step_) + : PtrStep(data_, step_), cols(cols_), rows(rows_) {} + + template + explicit PtrStepSz(const PtrStepSz& d) : PtrStep((T*)d.data, d.step), cols(d.cols), rows(d.rows){} + + int cols; + int rows; + }; + + typedef PtrStepSz PtrStepSzb; + typedef PtrStepSz PtrStepSzf; + typedef PtrStepSz PtrStepSzi; + + typedef PtrStep PtrStepb; + typedef PtrStep PtrStepf; + typedef PtrStep PtrStepi; + + } +} + +//! @endcond + +#endif /* OPENCV_CORE_CUDA_TYPES_HPP */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cv_cpu_dispatch.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cv_cpu_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..57aa0ce2fb2b4978b59dbab2b6332f11b37128f6 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cv_cpu_dispatch.h @@ -0,0 +1,247 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#if defined __OPENCV_BUILD \ + +#include "cv_cpu_config.h" +#include "cv_cpu_helper.h" + +#ifdef CV_CPU_DISPATCH_MODE +#define CV_CPU_OPTIMIZATION_NAMESPACE __CV_CAT(opt_, CV_CPU_DISPATCH_MODE) +#define CV_CPU_OPTIMIZATION_NAMESPACE_BEGIN namespace __CV_CAT(opt_, CV_CPU_DISPATCH_MODE) { +#define CV_CPU_OPTIMIZATION_NAMESPACE_END } +#else +#define CV_CPU_OPTIMIZATION_NAMESPACE cpu_baseline +#define CV_CPU_OPTIMIZATION_NAMESPACE_BEGIN namespace cpu_baseline { +#define CV_CPU_OPTIMIZATION_NAMESPACE_END } +#endif + + +#define __CV_CPU_DISPATCH_CHAIN_END(fn, args, mode, ...) /* done */ +#define __CV_CPU_DISPATCH(fn, args, mode, ...) __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) +#define __CV_CPU_DISPATCH_EXPAND(fn, args, ...) __CV_EXPAND(__CV_CPU_DISPATCH(fn, args, __VA_ARGS__)) +#define CV_CPU_DISPATCH(fn, args, ...) __CV_CPU_DISPATCH_EXPAND(fn, args, __VA_ARGS__, END) // expand macros + + +#if defined CV_ENABLE_INTRINSICS \ + && !defined CV_DISABLE_OPTIMIZATION \ + && !defined __CUDACC__ /* do not include SSE/AVX/NEON headers for NVCC compiler */ \ + +#ifdef CV_CPU_COMPILE_SSE2 +# include +# define CV_MMX 1 +# define CV_SSE 1 +# define CV_SSE2 1 +#endif +#ifdef CV_CPU_COMPILE_SSE3 +# include +# define CV_SSE3 1 +#endif +#ifdef CV_CPU_COMPILE_SSSE3 +# include +# define CV_SSSE3 1 +#endif +#ifdef CV_CPU_COMPILE_SSE4_1 +# include +# define CV_SSE4_1 1 +#endif +#ifdef CV_CPU_COMPILE_SSE4_2 +# include +# define CV_SSE4_2 1 +#endif +#ifdef CV_CPU_COMPILE_POPCNT +# ifdef _MSC_VER +# include +# if defined(_M_X64) +# define CV_POPCNT_U64 _mm_popcnt_u64 +# endif +# define CV_POPCNT_U32 _mm_popcnt_u32 +# else +# include +# if defined(__x86_64__) +# define CV_POPCNT_U64 __builtin_popcountll +# endif +# define CV_POPCNT_U32 __builtin_popcount +# endif +# define CV_POPCNT 1 +#endif +#ifdef CV_CPU_COMPILE_AVX +# include +# define CV_AVX 1 +#endif +#ifdef CV_CPU_COMPILE_FP16 +# if defined(__arm__) || defined(__aarch64__) || defined(_M_ARM) +# include +# else +# include +# endif +# define CV_FP16 1 +#endif +#ifdef CV_CPU_COMPILE_AVX2 +# include +# define CV_AVX2 1 +#endif +#ifdef CV_CPU_COMPILE_AVX_512F +# include +# define CV_AVX_512F 1 +#endif +#ifdef CV_CPU_COMPILE_AVX512_SKX +# include +# define CV_AVX512_SKX 1 +#endif +#ifdef CV_CPU_COMPILE_FMA3 +# define CV_FMA3 1 +#endif + +#if defined _WIN32 && defined(_M_ARM) +# include +# include +# define CV_NEON 1 +#elif defined(__ARM_NEON__) || (defined (__ARM_NEON) && defined(__aarch64__)) +# include +# define CV_NEON 1 +#endif + +#if defined(__ARM_NEON__) || defined(__aarch64__) +# include +#endif + +#ifdef CV_CPU_COMPILE_VSX +# include +# undef vector +# undef pixel +# undef bool +# define CV_VSX 1 +#endif + +#ifdef CV_CPU_COMPILE_VSX3 +# define CV_VSX3 1 +#endif + +#endif // CV_ENABLE_INTRINSICS && !CV_DISABLE_OPTIMIZATION && !__CUDACC__ + +#if defined CV_CPU_COMPILE_AVX && !defined CV_CPU_BASELINE_COMPILE_AVX +struct VZeroUpperGuard { +#ifdef __GNUC__ + __attribute__((always_inline)) +#endif + inline ~VZeroUpperGuard() { _mm256_zeroupper(); } +}; +#define __CV_AVX_GUARD VZeroUpperGuard __vzeroupper_guard; CV_UNUSED(__vzeroupper_guard); +#endif + +#ifdef __CV_AVX_GUARD +#define CV_AVX_GUARD __CV_AVX_GUARD +#else +#define CV_AVX_GUARD +#endif + +#endif // __OPENCV_BUILD + + + +#if !defined __OPENCV_BUILD /* Compatibility code */ \ + && !defined __CUDACC__ /* do not include SSE/AVX/NEON headers for NVCC compiler */ +#if defined __SSE2__ || defined _M_X64 || (defined _M_IX86_FP && _M_IX86_FP >= 2) +# include +# define CV_MMX 1 +# define CV_SSE 1 +# define CV_SSE2 1 +#elif defined _WIN32 && defined(_M_ARM) +# include +# include +# define CV_NEON 1 +#elif defined(__ARM_NEON__) || (defined (__ARM_NEON) && defined(__aarch64__)) +# include +# define CV_NEON 1 +#elif defined(__VSX__) && defined(__PPC64__) && defined(__LITTLE_ENDIAN__) +# include +# undef vector +# undef pixel +# undef bool +# define CV_VSX 1 +#endif + +#endif // !__OPENCV_BUILD && !__CUDACC (Compatibility code) + + + +#ifndef CV_MMX +# define CV_MMX 0 +#endif +#ifndef CV_SSE +# define CV_SSE 0 +#endif +#ifndef CV_SSE2 +# define CV_SSE2 0 +#endif +#ifndef CV_SSE3 +# define CV_SSE3 0 +#endif +#ifndef CV_SSSE3 +# define CV_SSSE3 0 +#endif +#ifndef CV_SSE4_1 +# define CV_SSE4_1 0 +#endif +#ifndef CV_SSE4_2 +# define CV_SSE4_2 0 +#endif +#ifndef CV_POPCNT +# define CV_POPCNT 0 +#endif +#ifndef CV_AVX +# define CV_AVX 0 +#endif +#ifndef CV_FP16 +# define CV_FP16 0 +#endif +#ifndef CV_AVX2 +# define CV_AVX2 0 +#endif +#ifndef CV_FMA3 +# define CV_FMA3 0 +#endif +#ifndef CV_AVX_512F +# define CV_AVX_512F 0 +#endif +#ifndef CV_AVX_512BW +# define CV_AVX_512BW 0 +#endif +#ifndef CV_AVX_512CD +# define CV_AVX_512CD 0 +#endif +#ifndef CV_AVX_512DQ +# define CV_AVX_512DQ 0 +#endif +#ifndef CV_AVX_512ER +# define CV_AVX_512ER 0 +#endif +#ifndef CV_AVX_512IFMA512 +# define CV_AVX_512IFMA512 0 +#endif +#ifndef CV_AVX_512PF +# define CV_AVX_512PF 0 +#endif +#ifndef CV_AVX_512VBMI +# define CV_AVX_512VBMI 0 +#endif +#ifndef CV_AVX_512VL +# define CV_AVX_512VL 0 +#endif +#ifndef CV_AVX512_SKX +# define CV_AVX512_SKX 0 +#endif + +#ifndef CV_NEON +# define CV_NEON 0 +#endif + +#ifndef CV_VSX +# define CV_VSX 0 +#endif + +#ifndef CV_VSX3 +# define CV_VSX3 0 +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cv_cpu_helper.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cv_cpu_helper.h new file mode 100644 index 0000000000000000000000000000000000000000..ad1339796d8b4cccd8cd794ed4eb1d2ea59a9f4f --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cv_cpu_helper.h @@ -0,0 +1,340 @@ +// AUTOGENERATED, DO NOT EDIT + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSE +# define CV_TRY_SSE 1 +# define CV_CPU_FORCE_SSE 1 +# define CV_CPU_HAS_SUPPORT_SSE 1 +# define CV_CPU_CALL_SSE(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_SSE_(fn, args) return (opt_SSE::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSE +# define CV_TRY_SSE 1 +# define CV_CPU_FORCE_SSE 0 +# define CV_CPU_HAS_SUPPORT_SSE (cv::checkHardwareSupport(CV_CPU_SSE)) +# define CV_CPU_CALL_SSE(fn, args) if (CV_CPU_HAS_SUPPORT_SSE) return (opt_SSE::fn args) +# define CV_CPU_CALL_SSE_(fn, args) if (CV_CPU_HAS_SUPPORT_SSE) return (opt_SSE::fn args) +#else +# define CV_TRY_SSE 0 +# define CV_CPU_FORCE_SSE 0 +# define CV_CPU_HAS_SUPPORT_SSE 0 +# define CV_CPU_CALL_SSE(fn, args) +# define CV_CPU_CALL_SSE_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_SSE(fn, args, mode, ...) CV_CPU_CALL_SSE(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSE2 +# define CV_TRY_SSE2 1 +# define CV_CPU_FORCE_SSE2 1 +# define CV_CPU_HAS_SUPPORT_SSE2 1 +# define CV_CPU_CALL_SSE2(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_SSE2_(fn, args) return (opt_SSE2::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSE2 +# define CV_TRY_SSE2 1 +# define CV_CPU_FORCE_SSE2 0 +# define CV_CPU_HAS_SUPPORT_SSE2 (cv::checkHardwareSupport(CV_CPU_SSE2)) +# define CV_CPU_CALL_SSE2(fn, args) if (CV_CPU_HAS_SUPPORT_SSE2) return (opt_SSE2::fn args) +# define CV_CPU_CALL_SSE2_(fn, args) if (CV_CPU_HAS_SUPPORT_SSE2) return (opt_SSE2::fn args) +#else +# define CV_TRY_SSE2 0 +# define CV_CPU_FORCE_SSE2 0 +# define CV_CPU_HAS_SUPPORT_SSE2 0 +# define CV_CPU_CALL_SSE2(fn, args) +# define CV_CPU_CALL_SSE2_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_SSE2(fn, args, mode, ...) CV_CPU_CALL_SSE2(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSE3 +# define CV_TRY_SSE3 1 +# define CV_CPU_FORCE_SSE3 1 +# define CV_CPU_HAS_SUPPORT_SSE3 1 +# define CV_CPU_CALL_SSE3(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_SSE3_(fn, args) return (opt_SSE3::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSE3 +# define CV_TRY_SSE3 1 +# define CV_CPU_FORCE_SSE3 0 +# define CV_CPU_HAS_SUPPORT_SSE3 (cv::checkHardwareSupport(CV_CPU_SSE3)) +# define CV_CPU_CALL_SSE3(fn, args) if (CV_CPU_HAS_SUPPORT_SSE3) return (opt_SSE3::fn args) +# define CV_CPU_CALL_SSE3_(fn, args) if (CV_CPU_HAS_SUPPORT_SSE3) return (opt_SSE3::fn args) +#else +# define CV_TRY_SSE3 0 +# define CV_CPU_FORCE_SSE3 0 +# define CV_CPU_HAS_SUPPORT_SSE3 0 +# define CV_CPU_CALL_SSE3(fn, args) +# define CV_CPU_CALL_SSE3_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_SSE3(fn, args, mode, ...) CV_CPU_CALL_SSE3(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSSE3 +# define CV_TRY_SSSE3 1 +# define CV_CPU_FORCE_SSSE3 1 +# define CV_CPU_HAS_SUPPORT_SSSE3 1 +# define CV_CPU_CALL_SSSE3(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_SSSE3_(fn, args) return (opt_SSSE3::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSSE3 +# define CV_TRY_SSSE3 1 +# define CV_CPU_FORCE_SSSE3 0 +# define CV_CPU_HAS_SUPPORT_SSSE3 (cv::checkHardwareSupport(CV_CPU_SSSE3)) +# define CV_CPU_CALL_SSSE3(fn, args) if (CV_CPU_HAS_SUPPORT_SSSE3) return (opt_SSSE3::fn args) +# define CV_CPU_CALL_SSSE3_(fn, args) if (CV_CPU_HAS_SUPPORT_SSSE3) return (opt_SSSE3::fn args) +#else +# define CV_TRY_SSSE3 0 +# define CV_CPU_FORCE_SSSE3 0 +# define CV_CPU_HAS_SUPPORT_SSSE3 0 +# define CV_CPU_CALL_SSSE3(fn, args) +# define CV_CPU_CALL_SSSE3_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_SSSE3(fn, args, mode, ...) CV_CPU_CALL_SSSE3(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSE4_1 +# define CV_TRY_SSE4_1 1 +# define CV_CPU_FORCE_SSE4_1 1 +# define CV_CPU_HAS_SUPPORT_SSE4_1 1 +# define CV_CPU_CALL_SSE4_1(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_SSE4_1_(fn, args) return (opt_SSE4_1::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSE4_1 +# define CV_TRY_SSE4_1 1 +# define CV_CPU_FORCE_SSE4_1 0 +# define CV_CPU_HAS_SUPPORT_SSE4_1 (cv::checkHardwareSupport(CV_CPU_SSE4_1)) +# define CV_CPU_CALL_SSE4_1(fn, args) if (CV_CPU_HAS_SUPPORT_SSE4_1) return (opt_SSE4_1::fn args) +# define CV_CPU_CALL_SSE4_1_(fn, args) if (CV_CPU_HAS_SUPPORT_SSE4_1) return (opt_SSE4_1::fn args) +#else +# define CV_TRY_SSE4_1 0 +# define CV_CPU_FORCE_SSE4_1 0 +# define CV_CPU_HAS_SUPPORT_SSE4_1 0 +# define CV_CPU_CALL_SSE4_1(fn, args) +# define CV_CPU_CALL_SSE4_1_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_SSE4_1(fn, args, mode, ...) CV_CPU_CALL_SSE4_1(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSE4_2 +# define CV_TRY_SSE4_2 1 +# define CV_CPU_FORCE_SSE4_2 1 +# define CV_CPU_HAS_SUPPORT_SSE4_2 1 +# define CV_CPU_CALL_SSE4_2(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_SSE4_2_(fn, args) return (opt_SSE4_2::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSE4_2 +# define CV_TRY_SSE4_2 1 +# define CV_CPU_FORCE_SSE4_2 0 +# define CV_CPU_HAS_SUPPORT_SSE4_2 (cv::checkHardwareSupport(CV_CPU_SSE4_2)) +# define CV_CPU_CALL_SSE4_2(fn, args) if (CV_CPU_HAS_SUPPORT_SSE4_2) return (opt_SSE4_2::fn args) +# define CV_CPU_CALL_SSE4_2_(fn, args) if (CV_CPU_HAS_SUPPORT_SSE4_2) return (opt_SSE4_2::fn args) +#else +# define CV_TRY_SSE4_2 0 +# define CV_CPU_FORCE_SSE4_2 0 +# define CV_CPU_HAS_SUPPORT_SSE4_2 0 +# define CV_CPU_CALL_SSE4_2(fn, args) +# define CV_CPU_CALL_SSE4_2_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_SSE4_2(fn, args, mode, ...) CV_CPU_CALL_SSE4_2(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_POPCNT +# define CV_TRY_POPCNT 1 +# define CV_CPU_FORCE_POPCNT 1 +# define CV_CPU_HAS_SUPPORT_POPCNT 1 +# define CV_CPU_CALL_POPCNT(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_POPCNT_(fn, args) return (opt_POPCNT::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_POPCNT +# define CV_TRY_POPCNT 1 +# define CV_CPU_FORCE_POPCNT 0 +# define CV_CPU_HAS_SUPPORT_POPCNT (cv::checkHardwareSupport(CV_CPU_POPCNT)) +# define CV_CPU_CALL_POPCNT(fn, args) if (CV_CPU_HAS_SUPPORT_POPCNT) return (opt_POPCNT::fn args) +# define CV_CPU_CALL_POPCNT_(fn, args) if (CV_CPU_HAS_SUPPORT_POPCNT) return (opt_POPCNT::fn args) +#else +# define CV_TRY_POPCNT 0 +# define CV_CPU_FORCE_POPCNT 0 +# define CV_CPU_HAS_SUPPORT_POPCNT 0 +# define CV_CPU_CALL_POPCNT(fn, args) +# define CV_CPU_CALL_POPCNT_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_POPCNT(fn, args, mode, ...) CV_CPU_CALL_POPCNT(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX +# define CV_TRY_AVX 1 +# define CV_CPU_FORCE_AVX 1 +# define CV_CPU_HAS_SUPPORT_AVX 1 +# define CV_CPU_CALL_AVX(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_AVX_(fn, args) return (opt_AVX::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX +# define CV_TRY_AVX 1 +# define CV_CPU_FORCE_AVX 0 +# define CV_CPU_HAS_SUPPORT_AVX (cv::checkHardwareSupport(CV_CPU_AVX)) +# define CV_CPU_CALL_AVX(fn, args) if (CV_CPU_HAS_SUPPORT_AVX) return (opt_AVX::fn args) +# define CV_CPU_CALL_AVX_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX) return (opt_AVX::fn args) +#else +# define CV_TRY_AVX 0 +# define CV_CPU_FORCE_AVX 0 +# define CV_CPU_HAS_SUPPORT_AVX 0 +# define CV_CPU_CALL_AVX(fn, args) +# define CV_CPU_CALL_AVX_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_AVX(fn, args, mode, ...) CV_CPU_CALL_AVX(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_FP16 +# define CV_TRY_FP16 1 +# define CV_CPU_FORCE_FP16 1 +# define CV_CPU_HAS_SUPPORT_FP16 1 +# define CV_CPU_CALL_FP16(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_FP16_(fn, args) return (opt_FP16::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_FP16 +# define CV_TRY_FP16 1 +# define CV_CPU_FORCE_FP16 0 +# define CV_CPU_HAS_SUPPORT_FP16 (cv::checkHardwareSupport(CV_CPU_FP16)) +# define CV_CPU_CALL_FP16(fn, args) if (CV_CPU_HAS_SUPPORT_FP16) return (opt_FP16::fn args) +# define CV_CPU_CALL_FP16_(fn, args) if (CV_CPU_HAS_SUPPORT_FP16) return (opt_FP16::fn args) +#else +# define CV_TRY_FP16 0 +# define CV_CPU_FORCE_FP16 0 +# define CV_CPU_HAS_SUPPORT_FP16 0 +# define CV_CPU_CALL_FP16(fn, args) +# define CV_CPU_CALL_FP16_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_FP16(fn, args, mode, ...) CV_CPU_CALL_FP16(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX2 +# define CV_TRY_AVX2 1 +# define CV_CPU_FORCE_AVX2 1 +# define CV_CPU_HAS_SUPPORT_AVX2 1 +# define CV_CPU_CALL_AVX2(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_AVX2_(fn, args) return (opt_AVX2::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX2 +# define CV_TRY_AVX2 1 +# define CV_CPU_FORCE_AVX2 0 +# define CV_CPU_HAS_SUPPORT_AVX2 (cv::checkHardwareSupport(CV_CPU_AVX2)) +# define CV_CPU_CALL_AVX2(fn, args) if (CV_CPU_HAS_SUPPORT_AVX2) return (opt_AVX2::fn args) +# define CV_CPU_CALL_AVX2_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX2) return (opt_AVX2::fn args) +#else +# define CV_TRY_AVX2 0 +# define CV_CPU_FORCE_AVX2 0 +# define CV_CPU_HAS_SUPPORT_AVX2 0 +# define CV_CPU_CALL_AVX2(fn, args) +# define CV_CPU_CALL_AVX2_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_AVX2(fn, args, mode, ...) CV_CPU_CALL_AVX2(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_FMA3 +# define CV_TRY_FMA3 1 +# define CV_CPU_FORCE_FMA3 1 +# define CV_CPU_HAS_SUPPORT_FMA3 1 +# define CV_CPU_CALL_FMA3(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_FMA3_(fn, args) return (opt_FMA3::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_FMA3 +# define CV_TRY_FMA3 1 +# define CV_CPU_FORCE_FMA3 0 +# define CV_CPU_HAS_SUPPORT_FMA3 (cv::checkHardwareSupport(CV_CPU_FMA3)) +# define CV_CPU_CALL_FMA3(fn, args) if (CV_CPU_HAS_SUPPORT_FMA3) return (opt_FMA3::fn args) +# define CV_CPU_CALL_FMA3_(fn, args) if (CV_CPU_HAS_SUPPORT_FMA3) return (opt_FMA3::fn args) +#else +# define CV_TRY_FMA3 0 +# define CV_CPU_FORCE_FMA3 0 +# define CV_CPU_HAS_SUPPORT_FMA3 0 +# define CV_CPU_CALL_FMA3(fn, args) +# define CV_CPU_CALL_FMA3_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_FMA3(fn, args, mode, ...) CV_CPU_CALL_FMA3(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX_512F +# define CV_TRY_AVX_512F 1 +# define CV_CPU_FORCE_AVX_512F 1 +# define CV_CPU_HAS_SUPPORT_AVX_512F 1 +# define CV_CPU_CALL_AVX_512F(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_AVX_512F_(fn, args) return (opt_AVX_512F::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX_512F +# define CV_TRY_AVX_512F 1 +# define CV_CPU_FORCE_AVX_512F 0 +# define CV_CPU_HAS_SUPPORT_AVX_512F (cv::checkHardwareSupport(CV_CPU_AVX_512F)) +# define CV_CPU_CALL_AVX_512F(fn, args) if (CV_CPU_HAS_SUPPORT_AVX_512F) return (opt_AVX_512F::fn args) +# define CV_CPU_CALL_AVX_512F_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX_512F) return (opt_AVX_512F::fn args) +#else +# define CV_TRY_AVX_512F 0 +# define CV_CPU_FORCE_AVX_512F 0 +# define CV_CPU_HAS_SUPPORT_AVX_512F 0 +# define CV_CPU_CALL_AVX_512F(fn, args) +# define CV_CPU_CALL_AVX_512F_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_AVX_512F(fn, args, mode, ...) CV_CPU_CALL_AVX_512F(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX512_SKX +# define CV_TRY_AVX512_SKX 1 +# define CV_CPU_FORCE_AVX512_SKX 1 +# define CV_CPU_HAS_SUPPORT_AVX512_SKX 1 +# define CV_CPU_CALL_AVX512_SKX(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_AVX512_SKX_(fn, args) return (opt_AVX512_SKX::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX512_SKX +# define CV_TRY_AVX512_SKX 1 +# define CV_CPU_FORCE_AVX512_SKX 0 +# define CV_CPU_HAS_SUPPORT_AVX512_SKX (cv::checkHardwareSupport(CV_CPU_AVX512_SKX)) +# define CV_CPU_CALL_AVX512_SKX(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_SKX) return (opt_AVX512_SKX::fn args) +# define CV_CPU_CALL_AVX512_SKX_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_SKX) return (opt_AVX512_SKX::fn args) +#else +# define CV_TRY_AVX512_SKX 0 +# define CV_CPU_FORCE_AVX512_SKX 0 +# define CV_CPU_HAS_SUPPORT_AVX512_SKX 0 +# define CV_CPU_CALL_AVX512_SKX(fn, args) +# define CV_CPU_CALL_AVX512_SKX_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_AVX512_SKX(fn, args, mode, ...) CV_CPU_CALL_AVX512_SKX(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_NEON +# define CV_TRY_NEON 1 +# define CV_CPU_FORCE_NEON 1 +# define CV_CPU_HAS_SUPPORT_NEON 1 +# define CV_CPU_CALL_NEON(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_NEON_(fn, args) return (opt_NEON::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_NEON +# define CV_TRY_NEON 1 +# define CV_CPU_FORCE_NEON 0 +# define CV_CPU_HAS_SUPPORT_NEON (cv::checkHardwareSupport(CV_CPU_NEON)) +# define CV_CPU_CALL_NEON(fn, args) if (CV_CPU_HAS_SUPPORT_NEON) return (opt_NEON::fn args) +# define CV_CPU_CALL_NEON_(fn, args) if (CV_CPU_HAS_SUPPORT_NEON) return (opt_NEON::fn args) +#else +# define CV_TRY_NEON 0 +# define CV_CPU_FORCE_NEON 0 +# define CV_CPU_HAS_SUPPORT_NEON 0 +# define CV_CPU_CALL_NEON(fn, args) +# define CV_CPU_CALL_NEON_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_NEON(fn, args, mode, ...) CV_CPU_CALL_NEON(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_VSX +# define CV_TRY_VSX 1 +# define CV_CPU_FORCE_VSX 1 +# define CV_CPU_HAS_SUPPORT_VSX 1 +# define CV_CPU_CALL_VSX(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_VSX_(fn, args) return (opt_VSX::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_VSX +# define CV_TRY_VSX 1 +# define CV_CPU_FORCE_VSX 0 +# define CV_CPU_HAS_SUPPORT_VSX (cv::checkHardwareSupport(CV_CPU_VSX)) +# define CV_CPU_CALL_VSX(fn, args) if (CV_CPU_HAS_SUPPORT_VSX) return (opt_VSX::fn args) +# define CV_CPU_CALL_VSX_(fn, args) if (CV_CPU_HAS_SUPPORT_VSX) return (opt_VSX::fn args) +#else +# define CV_TRY_VSX 0 +# define CV_CPU_FORCE_VSX 0 +# define CV_CPU_HAS_SUPPORT_VSX 0 +# define CV_CPU_CALL_VSX(fn, args) +# define CV_CPU_CALL_VSX_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_VSX(fn, args, mode, ...) CV_CPU_CALL_VSX(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_VSX3 +# define CV_TRY_VSX3 1 +# define CV_CPU_FORCE_VSX3 1 +# define CV_CPU_HAS_SUPPORT_VSX3 1 +# define CV_CPU_CALL_VSX3(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_VSX3_(fn, args) return (opt_VSX3::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_VSX3 +# define CV_TRY_VSX3 1 +# define CV_CPU_FORCE_VSX3 0 +# define CV_CPU_HAS_SUPPORT_VSX3 (cv::checkHardwareSupport(CV_CPU_VSX3)) +# define CV_CPU_CALL_VSX3(fn, args) if (CV_CPU_HAS_SUPPORT_VSX3) return (opt_VSX3::fn args) +# define CV_CPU_CALL_VSX3_(fn, args) if (CV_CPU_HAS_SUPPORT_VSX3) return (opt_VSX3::fn args) +#else +# define CV_TRY_VSX3 0 +# define CV_CPU_FORCE_VSX3 0 +# define CV_CPU_HAS_SUPPORT_VSX3 0 +# define CV_CPU_CALL_VSX3(fn, args) +# define CV_CPU_CALL_VSX3_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_VSX3(fn, args, mode, ...) CV_CPU_CALL_VSX3(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#define CV_CPU_CALL_BASELINE(fn, args) return (cpu_baseline::fn args) +#define __CV_CPU_DISPATCH_CHAIN_BASELINE(fn, args, mode, ...) CV_CPU_CALL_BASELINE(fn, args) /* last in sequence */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cvdef.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cvdef.h new file mode 100644 index 0000000000000000000000000000000000000000..1e8a697e90ae28175928b3ebe6a4793457c144bf --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cvdef.h @@ -0,0 +1,753 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_CVDEF_H +#define OPENCV_CORE_CVDEF_H + +//! @addtogroup core_utils +//! @{ + +#if !defined CV_DOXYGEN && !defined CV_IGNORE_DEBUG_BUILD_GUARD +#if (defined(_MSC_VER) && (defined(DEBUG) || defined(_DEBUG))) || \ + (defined(_GLIBCXX_DEBUG) || defined(_GLIBCXX_DEBUG_PEDANTIC)) +// Guard to prevent using of binary incompatible binaries / runtimes +// https://github.com/opencv/opencv/pull/9161 +#define CV__DEBUG_NS_BEGIN namespace debug_build_guard { +#define CV__DEBUG_NS_END } +namespace cv { namespace debug_build_guard { } using namespace debug_build_guard; } +#endif +#endif + +#ifndef CV__DEBUG_NS_BEGIN +#define CV__DEBUG_NS_BEGIN +#define CV__DEBUG_NS_END +#endif + + +#ifdef __OPENCV_BUILD +#include "cvconfig.h" +#endif + +#ifndef __CV_EXPAND +#define __CV_EXPAND(x) x +#endif + +#ifndef __CV_CAT +#define __CV_CAT__(x, y) x ## y +#define __CV_CAT_(x, y) __CV_CAT__(x, y) +#define __CV_CAT(x, y) __CV_CAT_(x, y) +#endif + +#define __CV_VA_NUM_ARGS_HELPER(_1, _2, _3, _4, _5, _6, _7, _8, _9, _10, N, ...) N +#define __CV_VA_NUM_ARGS(...) __CV_VA_NUM_ARGS_HELPER(__VA_ARGS__, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0) + +#if defined __GNUC__ +#define CV_Func __func__ +#elif defined _MSC_VER +#define CV_Func __FUNCTION__ +#else +#define CV_Func "" +#endif + +//! @cond IGNORED + +//////////////// static assert ///////////////// +#define CVAUX_CONCAT_EXP(a, b) a##b +#define CVAUX_CONCAT(a, b) CVAUX_CONCAT_EXP(a,b) + +#if defined(__clang__) +# ifndef __has_extension +# define __has_extension __has_feature /* compatibility, for older versions of clang */ +# endif +# if __has_extension(cxx_static_assert) +# define CV_StaticAssert(condition, reason) static_assert((condition), reason " " #condition) +# elif __has_extension(c_static_assert) +# define CV_StaticAssert(condition, reason) _Static_assert((condition), reason " " #condition) +# endif +#elif defined(__GNUC__) +# if (defined(__GXX_EXPERIMENTAL_CXX0X__) || __cplusplus >= 201103L) +# define CV_StaticAssert(condition, reason) static_assert((condition), reason " " #condition) +# endif +#elif defined(_MSC_VER) +# if _MSC_VER >= 1600 /* MSVC 10 */ +# define CV_StaticAssert(condition, reason) static_assert((condition), reason " " #condition) +# endif +#endif +#ifndef CV_StaticAssert +# if !defined(__clang__) && defined(__GNUC__) && (__GNUC__*100 + __GNUC_MINOR__ > 302) +# define CV_StaticAssert(condition, reason) ({ extern int __attribute__((error("CV_StaticAssert: " reason " " #condition))) CV_StaticAssert(); ((condition) ? 0 : CV_StaticAssert()); }) +# else + template struct CV_StaticAssert_failed; + template <> struct CV_StaticAssert_failed { enum { val = 1 }; }; + template struct CV_StaticAssert_test {}; +# define CV_StaticAssert(condition, reason)\ + typedef cv::CV_StaticAssert_test< sizeof(cv::CV_StaticAssert_failed< static_cast(condition) >) > CVAUX_CONCAT(CV_StaticAssert_failed_at_, __LINE__) +# endif +#endif + +// Suppress warning "-Wdeprecated-declarations" / C4996 +#if defined(_MSC_VER) + #define CV_DO_PRAGMA(x) __pragma(x) +#elif defined(__GNUC__) + #define CV_DO_PRAGMA(x) _Pragma (#x) +#else + #define CV_DO_PRAGMA(x) +#endif + +#ifdef _MSC_VER +#define CV_SUPPRESS_DEPRECATED_START \ + CV_DO_PRAGMA(warning(push)) \ + CV_DO_PRAGMA(warning(disable: 4996)) +#define CV_SUPPRESS_DEPRECATED_END CV_DO_PRAGMA(warning(pop)) +#elif defined (__clang__) || ((__GNUC__) && (__GNUC__*100 + __GNUC_MINOR__ > 405)) +#define CV_SUPPRESS_DEPRECATED_START \ + CV_DO_PRAGMA(GCC diagnostic push) \ + CV_DO_PRAGMA(GCC diagnostic ignored "-Wdeprecated-declarations") +#define CV_SUPPRESS_DEPRECATED_END CV_DO_PRAGMA(GCC diagnostic pop) +#else +#define CV_SUPPRESS_DEPRECATED_START +#define CV_SUPPRESS_DEPRECATED_END +#endif + +#define CV_UNUSED(name) (void)name + +#if defined __GNUC__ && !defined __EXCEPTIONS +#define CV_TRY +#define CV_CATCH(A, B) for (A B; false; ) +#define CV_CATCH_ALL if (false) +#define CV_THROW(A) abort() +#define CV_RETHROW() abort() +#else +#define CV_TRY try +#define CV_CATCH(A, B) catch(const A & B) +#define CV_CATCH_ALL catch(...) +#define CV_THROW(A) throw A +#define CV_RETHROW() throw +#endif + +//! @endcond + +// undef problematic defines sometimes defined by system headers (windows.h in particular) +#undef small +#undef min +#undef max +#undef abs +#undef Complex + +#include +#include "opencv2/core/hal/interface.h" + +#if defined __ICL +# define CV_ICC __ICL +#elif defined __ICC +# define CV_ICC __ICC +#elif defined __ECL +# define CV_ICC __ECL +#elif defined __ECC +# define CV_ICC __ECC +#elif defined __INTEL_COMPILER +# define CV_ICC __INTEL_COMPILER +#endif + +#ifndef CV_INLINE +# if defined __cplusplus +# define CV_INLINE static inline +# elif defined _MSC_VER +# define CV_INLINE __inline +# else +# define CV_INLINE static +# endif +#endif + +#if defined CV_DISABLE_OPTIMIZATION || (defined CV_ICC && !defined CV_ENABLE_UNROLLED) +# define CV_ENABLE_UNROLLED 0 +#else +# define CV_ENABLE_UNROLLED 1 +#endif + +#ifdef __GNUC__ +# define CV_DECL_ALIGNED(x) __attribute__ ((aligned (x))) +#elif defined _MSC_VER +# define CV_DECL_ALIGNED(x) __declspec(align(x)) +#else +# define CV_DECL_ALIGNED(x) +#endif + +/* CPU features and intrinsics support */ +#define CV_CPU_NONE 0 +#define CV_CPU_MMX 1 +#define CV_CPU_SSE 2 +#define CV_CPU_SSE2 3 +#define CV_CPU_SSE3 4 +#define CV_CPU_SSSE3 5 +#define CV_CPU_SSE4_1 6 +#define CV_CPU_SSE4_2 7 +#define CV_CPU_POPCNT 8 +#define CV_CPU_FP16 9 +#define CV_CPU_AVX 10 +#define CV_CPU_AVX2 11 +#define CV_CPU_FMA3 12 + +#define CV_CPU_AVX_512F 13 +#define CV_CPU_AVX_512BW 14 +#define CV_CPU_AVX_512CD 15 +#define CV_CPU_AVX_512DQ 16 +#define CV_CPU_AVX_512ER 17 +#define CV_CPU_AVX_512IFMA512 18 // deprecated +#define CV_CPU_AVX_512IFMA 18 +#define CV_CPU_AVX_512PF 19 +#define CV_CPU_AVX_512VBMI 20 +#define CV_CPU_AVX_512VL 21 + +#define CV_CPU_NEON 100 + +#define CV_CPU_VSX 200 +#define CV_CPU_VSX3 201 + +// CPU features groups +#define CV_CPU_AVX512_SKX 256 + +// when adding to this list remember to update the following enum +#define CV_HARDWARE_MAX_FEATURE 512 + +/** @brief Available CPU features. +*/ +enum CpuFeatures { + CPU_MMX = 1, + CPU_SSE = 2, + CPU_SSE2 = 3, + CPU_SSE3 = 4, + CPU_SSSE3 = 5, + CPU_SSE4_1 = 6, + CPU_SSE4_2 = 7, + CPU_POPCNT = 8, + CPU_FP16 = 9, + CPU_AVX = 10, + CPU_AVX2 = 11, + CPU_FMA3 = 12, + + CPU_AVX_512F = 13, + CPU_AVX_512BW = 14, + CPU_AVX_512CD = 15, + CPU_AVX_512DQ = 16, + CPU_AVX_512ER = 17, + CPU_AVX_512IFMA512 = 18, // deprecated + CPU_AVX_512IFMA = 18, + CPU_AVX_512PF = 19, + CPU_AVX_512VBMI = 20, + CPU_AVX_512VL = 21, + + CPU_NEON = 100, + + CPU_VSX = 200, + CPU_VSX3 = 201, + + CPU_AVX512_SKX = 256, //!< Skylake-X with AVX-512F/CD/BW/DQ/VL + + CPU_MAX_FEATURE = 512 // see CV_HARDWARE_MAX_FEATURE +}; + + +#include "cv_cpu_dispatch.h" + + +/* fundamental constants */ +#define CV_PI 3.1415926535897932384626433832795 +#define CV_2PI 6.283185307179586476925286766559 +#define CV_LOG2 0.69314718055994530941723212145818 + +#if defined __ARM_FP16_FORMAT_IEEE \ + && !defined __CUDACC__ +# define CV_FP16_TYPE 1 +#else +# define CV_FP16_TYPE 0 +#endif + +typedef union Cv16suf +{ + short i; + ushort u; +#if CV_FP16_TYPE + __fp16 h; +#endif +} +Cv16suf; + +typedef union Cv32suf +{ + int i; + unsigned u; + float f; +} +Cv32suf; + +typedef union Cv64suf +{ + int64 i; + uint64 u; + double f; +} +Cv64suf; + +#define OPENCV_ABI_COMPATIBILITY 300 + +#ifdef __OPENCV_BUILD +# define DISABLE_OPENCV_24_COMPATIBILITY +# define OPENCV_DISABLE_DEPRECATED_COMPATIBILITY +#endif + +#ifdef CVAPI_EXPORTS +# if (defined _WIN32 || defined WINCE || defined __CYGWIN__) +# define CV_EXPORTS __declspec(dllexport) +# elif defined __GNUC__ && __GNUC__ >= 4 +# define CV_EXPORTS __attribute__ ((visibility ("default"))) +# endif +#endif + +#ifndef CV_EXPORTS +# define CV_EXPORTS +#endif + +#ifdef _MSC_VER +# define CV_EXPORTS_TEMPLATE +#else +# define CV_EXPORTS_TEMPLATE CV_EXPORTS +#endif + +#ifndef CV_DEPRECATED +# if defined(__GNUC__) +# define CV_DEPRECATED __attribute__ ((deprecated)) +# elif defined(_MSC_VER) +# define CV_DEPRECATED __declspec(deprecated) +# else +# define CV_DEPRECATED +# endif +#endif + +#ifndef CV_DEPRECATED_EXTERNAL +# if defined(__OPENCV_BUILD) +# define CV_DEPRECATED_EXTERNAL /* nothing */ +# else +# define CV_DEPRECATED_EXTERNAL CV_DEPRECATED +# endif +#endif + + +#ifndef CV_EXTERN_C +# ifdef __cplusplus +# define CV_EXTERN_C extern "C" +# else +# define CV_EXTERN_C +# endif +#endif + +/* special informative macros for wrapper generators */ +#define CV_EXPORTS_W CV_EXPORTS +#define CV_EXPORTS_W_SIMPLE CV_EXPORTS +#define CV_EXPORTS_AS(synonym) CV_EXPORTS +#define CV_EXPORTS_W_MAP CV_EXPORTS +#define CV_IN_OUT +#define CV_OUT +#define CV_PROP +#define CV_PROP_RW +#define CV_WRAP +#define CV_WRAP_AS(synonym) + +/****************************************************************************************\ +* Matrix type (Mat) * +\****************************************************************************************/ + +#define CV_MAT_CN_MASK ((CV_CN_MAX - 1) << CV_CN_SHIFT) +#define CV_MAT_CN(flags) ((((flags) & CV_MAT_CN_MASK) >> CV_CN_SHIFT) + 1) +#define CV_MAT_TYPE_MASK (CV_DEPTH_MAX*CV_CN_MAX - 1) +#define CV_MAT_TYPE(flags) ((flags) & CV_MAT_TYPE_MASK) +#define CV_MAT_CONT_FLAG_SHIFT 14 +#define CV_MAT_CONT_FLAG (1 << CV_MAT_CONT_FLAG_SHIFT) +#define CV_IS_MAT_CONT(flags) ((flags) & CV_MAT_CONT_FLAG) +#define CV_IS_CONT_MAT CV_IS_MAT_CONT +#define CV_SUBMAT_FLAG_SHIFT 15 +#define CV_SUBMAT_FLAG (1 << CV_SUBMAT_FLAG_SHIFT) +#define CV_IS_SUBMAT(flags) ((flags) & CV_MAT_SUBMAT_FLAG) + +/** Size of each channel item, + 0x8442211 = 1000 0100 0100 0010 0010 0001 0001 ~ array of sizeof(arr_type_elem) */ +#define CV_ELEM_SIZE1(type) \ + ((((sizeof(size_t)<<28)|0x8442211) >> CV_MAT_DEPTH(type)*4) & 15) + +/** 0x3a50 = 11 10 10 01 01 00 00 ~ array of log2(sizeof(arr_type_elem)) */ +#define CV_ELEM_SIZE(type) \ + (CV_MAT_CN(type) << ((((sizeof(size_t)/4+1)*16384|0x3a50) >> CV_MAT_DEPTH(type)*2) & 3)) + +#ifndef MIN +# define MIN(a,b) ((a) > (b) ? (b) : (a)) +#endif + +#ifndef MAX +# define MAX(a,b) ((a) < (b) ? (b) : (a)) +#endif + +/****************************************************************************************\ +* static analysys * +\****************************************************************************************/ + +// In practice, some macro are not processed correctly (noreturn is not detected). +// We need to use simplified definition for them. +#ifndef CV_STATIC_ANALYSIS +# if defined(__KLOCWORK__) || defined(__clang_analyzer__) || defined(__COVERITY__) +# define CV_STATIC_ANALYSIS 1 +# endif +#else +# if defined(CV_STATIC_ANALYSIS) && !(__CV_CAT(1, CV_STATIC_ANALYSIS) == 1) // defined and not empty +# if 0 == CV_STATIC_ANALYSIS +# undef CV_STATIC_ANALYSIS +# endif +# endif +#endif + +/****************************************************************************************\ +* Thread sanitizer * +\****************************************************************************************/ +#ifndef CV_THREAD_SANITIZER +# if defined(__has_feature) +# if __has_feature(thread_sanitizer) +# define CV_THREAD_SANITIZER +# endif +# endif +#endif + +/****************************************************************************************\ +* exchange-add operation for atomic operations on reference counters * +\****************************************************************************************/ + +#ifdef CV_XADD + // allow to use user-defined macro +#elif defined __GNUC__ || defined __clang__ +# if defined __clang__ && __clang_major__ >= 3 && !defined __ANDROID__ && !defined __EMSCRIPTEN__ && !defined(__CUDACC__) +# ifdef __ATOMIC_ACQ_REL +# define CV_XADD(addr, delta) __c11_atomic_fetch_add((_Atomic(int)*)(addr), delta, __ATOMIC_ACQ_REL) +# else +# define CV_XADD(addr, delta) __atomic_fetch_add((_Atomic(int)*)(addr), delta, 4) +# endif +# else +# if defined __ATOMIC_ACQ_REL && !defined __clang__ + // version for gcc >= 4.7 +# define CV_XADD(addr, delta) (int)__atomic_fetch_add((unsigned*)(addr), (unsigned)(delta), __ATOMIC_ACQ_REL) +# else +# define CV_XADD(addr, delta) (int)__sync_fetch_and_add((unsigned*)(addr), (unsigned)(delta)) +# endif +# endif +#elif defined _MSC_VER && !defined RC_INVOKED +# include +# define CV_XADD(addr, delta) (int)_InterlockedExchangeAdd((long volatile*)addr, delta) +#else + CV_INLINE CV_XADD(int* addr, int delta) { int tmp = *addr; *addr += delta; return tmp; } +#endif + + +/****************************************************************************************\ +* CV_NORETURN attribute * +\****************************************************************************************/ + +#ifndef CV_NORETURN +# if defined(__GNUC__) +# define CV_NORETURN __attribute__((__noreturn__)) +# elif defined(_MSC_VER) && (_MSC_VER >= 1300) +# define CV_NORETURN __declspec(noreturn) +# else +# define CV_NORETURN /* nothing by default */ +# endif +#endif + + +/****************************************************************************************\ +* CV_NODISCARD attribute * +* encourages the compiler to issue a warning if the return value is discarded (C++17) * +\****************************************************************************************/ +#ifndef CV_NODISCARD +# if defined(__GNUC__) +# define CV_NODISCARD __attribute__((__warn_unused_result__)) // at least available with GCC 3.4 +# elif defined(__clang__) && defined(__has_attribute) +# if __has_attribute(__warn_unused_result__) +# define CV_NODISCARD __attribute__((__warn_unused_result__)) +# endif +# endif +#endif +#ifndef CV_NODISCARD +# define CV_NODISCARD /* nothing by default */ +#endif + + +/****************************************************************************************\ +* C++ 11 * +\****************************************************************************************/ +#ifndef CV_CXX11 +# if __cplusplus >= 201103L || (defined(_MSC_VER) && _MSC_VER >= 1800) +# define CV_CXX11 1 +# endif +#else +# if CV_CXX11 == 0 +# undef CV_CXX11 +# endif +#endif + + +/****************************************************************************************\ +* C++ Move semantics * +\****************************************************************************************/ + +#ifndef CV_CXX_MOVE_SEMANTICS +# if __cplusplus >= 201103L || defined(__GXX_EXPERIMENTAL_CXX0X__) || (defined(_MSC_VER) && _MSC_VER >= 1600) +# define CV_CXX_MOVE_SEMANTICS 1 +# elif defined(__clang) +# if __has_feature(cxx_rvalue_references) +# define CV_CXX_MOVE_SEMANTICS 1 +# endif +# endif +#else +# if CV_CXX_MOVE_SEMANTICS == 0 +# undef CV_CXX_MOVE_SEMANTICS +# endif +#endif + +/****************************************************************************************\ +* C++11 std::array * +\****************************************************************************************/ + +#ifndef CV_CXX_STD_ARRAY +# if __cplusplus >= 201103L || (defined(_MSC_VER) && _MSC_VER >= 1900/*MSVS 2015*/) +# define CV_CXX_STD_ARRAY 1 +# include +# endif +#else +# if CV_CXX_STD_ARRAY == 0 +# undef CV_CXX_STD_ARRAY +# endif +#endif + + +/****************************************************************************************\ +* C++11 override / final * +\****************************************************************************************/ + +#ifndef CV_OVERRIDE +# ifdef CV_CXX11 +# define CV_OVERRIDE override +# endif +#endif +#ifndef CV_OVERRIDE +# define CV_OVERRIDE +#endif + +#ifndef CV_FINAL +# ifdef CV_CXX11 +# define CV_FINAL final +# endif +#endif +#ifndef CV_FINAL +# define CV_FINAL +#endif + + + +// Integer types portatibility +#ifdef OPENCV_STDINT_HEADER +#include OPENCV_STDINT_HEADER +#elif defined(__cplusplus) +#if defined(_MSC_VER) && _MSC_VER < 1600 /* MSVS 2010 */ +namespace cv { +typedef signed char int8_t; +typedef unsigned char uint8_t; +typedef signed short int16_t; +typedef unsigned short uint16_t; +typedef signed int int32_t; +typedef unsigned int uint32_t; +typedef signed __int64 int64_t; +typedef unsigned __int64 uint64_t; +} +#elif defined(_MSC_VER) || __cplusplus >= 201103L +#include +namespace cv { +using std::int8_t; +using std::uint8_t; +using std::int16_t; +using std::uint16_t; +using std::int32_t; +using std::uint32_t; +using std::int64_t; +using std::uint64_t; +} +#else +#include +namespace cv { +typedef ::int8_t int8_t; +typedef ::uint8_t uint8_t; +typedef ::int16_t int16_t; +typedef ::uint16_t uint16_t; +typedef ::int32_t int32_t; +typedef ::uint32_t uint32_t; +typedef ::int64_t int64_t; +typedef ::uint64_t uint64_t; +} +#endif +#else // pure C +#include +#endif + +#ifdef __cplusplus +namespace cv +{ + +class float16_t +{ +public: +#if CV_FP16_TYPE + + float16_t() {} + explicit float16_t(float x) { h = (__fp16)x; } + operator float() const { return (float)h; } + static float16_t fromBits(ushort w) + { + Cv16suf u; + u.u = w; + float16_t result; + result.h = u.h; + return result; + } + static float16_t zero() + { + float16_t result; + result.h = (__fp16)0; + return result; + } + ushort bits() const + { + Cv16suf u; + u.h = h; + return u.u; + } +protected: + __fp16 h; + +#else + float16_t() {} + explicit float16_t(float x) + { + #if CV_AVX2 + __m128 v = _mm_load_ss(&x); + w = (ushort)_mm_cvtsi128_si32(_mm_cvtps_ph(v, 0)); + #else + Cv32suf in; + in.f = x; + unsigned sign = in.u & 0x80000000; + in.u ^= sign; + + if( in.u >= 0x47800000 ) + w = (ushort)(in.u > 0x7f800000 ? 0x7e00 : 0x7c00); + else + { + if (in.u < 0x38800000) + { + in.f += 0.5f; + w = (ushort)(in.u - 0x3f000000); + } + else + { + unsigned t = in.u + 0xc8000fff; + w = (ushort)((t + ((in.u >> 13) & 1)) >> 13); + } + } + + w = (ushort)(w | (sign >> 16)); + #endif + } + + operator float() const + { + #if CV_AVX2 + float f; + _mm_store_ss(&f, _mm_cvtph_ps(_mm_cvtsi32_si128(w))); + return f; + #else + Cv32suf out; + + unsigned t = ((w & 0x7fff) << 13) + 0x38000000; + unsigned sign = (w & 0x8000) << 16; + unsigned e = w & 0x7c00; + + out.u = t + (1 << 23); + out.u = (e >= 0x7c00 ? t + 0x38000000 : + e == 0 ? (out.f -= 6.103515625e-05f, out.u) : t) | sign; + return out.f; + #endif + } + + static float16_t fromBits(ushort b) + { + float16_t result; + result.w = b; + return result; + } + static float16_t zero() + { + float16_t result; + result.w = (ushort)0; + return result; + } + ushort bits() const { return w; } +protected: + ushort w; + +#endif +}; + +} +#endif + +//! @} + +#ifndef __cplusplus +#include "opencv2/core/fast_math.hpp" // define cvRound(double) +#endif + +#endif // OPENCV_CORE_CVDEF_H diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cvstd.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cvstd.hpp new file mode 100644 index 0000000000000000000000000000000000000000..0a3f553ab89f986dac83bbdc46dbdb36e6c0a7c7 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cvstd.hpp @@ -0,0 +1,1040 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_CVSTD_HPP +#define OPENCV_CORE_CVSTD_HPP + +#ifndef __cplusplus +# error cvstd.hpp header must be compiled as C++ +#endif + +#include "opencv2/core/cvdef.h" +#include +#include +#include + +#include + +// import useful primitives from stl +# include +# include +# include //for abs(int) +# include + +namespace cv +{ + static inline uchar abs(uchar a) { return a; } + static inline ushort abs(ushort a) { return a; } + static inline unsigned abs(unsigned a) { return a; } + static inline uint64 abs(uint64 a) { return a; } + + using std::min; + using std::max; + using std::abs; + using std::swap; + using std::sqrt; + using std::exp; + using std::pow; + using std::log; +} + +namespace cv { + +//! @addtogroup core_utils +//! @{ + +//////////////////////////// memory management functions //////////////////////////// + +/** @brief Allocates an aligned memory buffer. + +The function allocates the buffer of the specified size and returns it. When the buffer size is 16 +bytes or more, the returned buffer is aligned to 16 bytes. +@param bufSize Allocated buffer size. + */ +CV_EXPORTS void* fastMalloc(size_t bufSize); + +/** @brief Deallocates a memory buffer. + +The function deallocates the buffer allocated with fastMalloc . If NULL pointer is passed, the +function does nothing. C version of the function clears the pointer *pptr* to avoid problems with +double memory deallocation. +@param ptr Pointer to the allocated buffer. + */ +CV_EXPORTS void fastFree(void* ptr); + +/*! + The STL-compilant memory Allocator based on cv::fastMalloc() and cv::fastFree() +*/ +template class Allocator +{ +public: + typedef _Tp value_type; + typedef value_type* pointer; + typedef const value_type* const_pointer; + typedef value_type& reference; + typedef const value_type& const_reference; + typedef size_t size_type; + typedef ptrdiff_t difference_type; + template class rebind { typedef Allocator other; }; + + explicit Allocator() {} + ~Allocator() {} + explicit Allocator(Allocator const&) {} + template + explicit Allocator(Allocator const&) {} + + // address + pointer address(reference r) { return &r; } + const_pointer address(const_reference r) { return &r; } + + pointer allocate(size_type count, const void* =0) { return reinterpret_cast(fastMalloc(count * sizeof (_Tp))); } + void deallocate(pointer p, size_type) { fastFree(p); } + + void construct(pointer p, const _Tp& v) { new(static_cast(p)) _Tp(v); } + void destroy(pointer p) { p->~_Tp(); } + + size_type max_size() const { return cv::max(static_cast<_Tp>(-1)/sizeof(_Tp), 1); } +}; + +//! @} core_utils + +//! @cond IGNORED + +namespace detail +{ + +// Metafunction to avoid taking a reference to void. +template +struct RefOrVoid { typedef T& type; }; + +template<> +struct RefOrVoid{ typedef void type; }; + +template<> +struct RefOrVoid{ typedef const void type; }; + +template<> +struct RefOrVoid{ typedef volatile void type; }; + +template<> +struct RefOrVoid{ typedef const volatile void type; }; + +// This class would be private to Ptr, if it didn't have to be a non-template. +struct PtrOwner; + +} + +template +struct DefaultDeleter +{ + void operator () (Y* p) const; +}; + +//! @endcond + +//! @addtogroup core_basic +//! @{ + +/** @brief Template class for smart pointers with shared ownership + +A Ptr\ pretends to be a pointer to an object of type T. Unlike an ordinary pointer, however, the +object will be automatically cleaned up once all Ptr instances pointing to it are destroyed. + +Ptr is similar to boost::shared_ptr that is part of the Boost library +() and std::shared_ptr from +the [C++11](http://en.wikipedia.org/wiki/C++11) standard. + +This class provides the following advantages: +- Default constructor, copy constructor, and assignment operator for an arbitrary C++ class or C + structure. For some objects, like files, windows, mutexes, sockets, and others, a copy + constructor or an assignment operator are difficult to define. For some other objects, like + complex classifiers in OpenCV, copy constructors are absent and not easy to implement. Finally, + some of complex OpenCV and your own data structures may be written in C. However, copy + constructors and default constructors can simplify programming a lot. Besides, they are often + required (for example, by STL containers). By using a Ptr to such an object instead of the + object itself, you automatically get all of the necessary constructors and the assignment + operator. +- *O(1)* complexity of the above-mentioned operations. While some structures, like std::vector, + provide a copy constructor and an assignment operator, the operations may take a considerable + amount of time if the data structures are large. But if the structures are put into a Ptr, the + overhead is small and independent of the data size. +- Automatic and customizable cleanup, even for C structures. See the example below with FILE\*. +- Heterogeneous collections of objects. The standard STL and most other C++ and OpenCV containers + can store only objects of the same type and the same size. The classical solution to store + objects of different types in the same container is to store pointers to the base class (Base\*) + instead but then you lose the automatic memory management. Again, by using Ptr\ instead + of raw pointers, you can solve the problem. + +A Ptr is said to *own* a pointer - that is, for each Ptr there is a pointer that will be deleted +once all Ptr instances that own it are destroyed. The owned pointer may be null, in which case +nothing is deleted. Each Ptr also *stores* a pointer. The stored pointer is the pointer the Ptr +pretends to be; that is, the one you get when you use Ptr::get or the conversion to T\*. It's +usually the same as the owned pointer, but if you use casts or the general shared-ownership +constructor, the two may diverge: the Ptr will still own the original pointer, but will itself point +to something else. + +The owned pointer is treated as a black box. The only thing Ptr needs to know about it is how to +delete it. This knowledge is encapsulated in the *deleter* - an auxiliary object that is associated +with the owned pointer and shared between all Ptr instances that own it. The default deleter is an +instance of DefaultDeleter, which uses the standard C++ delete operator; as such it will work with +any pointer allocated with the standard new operator. + +However, if the pointer must be deleted in a different way, you must specify a custom deleter upon +Ptr construction. A deleter is simply a callable object that accepts the pointer as its sole +argument. For example, if you want to wrap FILE, you may do so as follows: +@code + Ptr f(fopen("myfile.txt", "w"), fclose); + if(!f) throw ...; + fprintf(f, ....); + ... + // the file will be closed automatically by f's destructor. +@endcode +Alternatively, if you want all pointers of a particular type to be deleted the same way, you can +specialize DefaultDeleter::operator() for that type, like this: +@code + namespace cv { + template<> void DefaultDeleter::operator ()(FILE * obj) const + { + fclose(obj); + } + } +@endcode +For convenience, the following types from the OpenCV C API already have such a specialization that +calls the appropriate release function: +- CvCapture +- CvFileStorage +- CvHaarClassifierCascade +- CvMat +- CvMatND +- CvMemStorage +- CvSparseMat +- CvVideoWriter +- IplImage +@note The shared ownership mechanism is implemented with reference counting. As such, cyclic +ownership (e.g. when object a contains a Ptr to object b, which contains a Ptr to object a) will +lead to all involved objects never being cleaned up. Avoid such situations. +@note It is safe to concurrently read (but not write) a Ptr instance from multiple threads and +therefore it is normally safe to use it in multi-threaded applications. The same is true for Mat and +other C++ OpenCV classes that use internal reference counts. +*/ +template +struct Ptr +{ + /** Generic programming support. */ + typedef T element_type; + + /** The default constructor creates a null Ptr - one that owns and stores a null pointer. + */ + Ptr(); + + /** + If p is null, these are equivalent to the default constructor. + Otherwise, these constructors assume ownership of p - that is, the created Ptr owns and stores p + and assumes it is the sole owner of it. Don't use them if p is already owned by another Ptr, or + else p will get deleted twice. + With the first constructor, DefaultDeleter\() becomes the associated deleter (so p will + eventually be deleted with the standard delete operator). Y must be a complete type at the point + of invocation. + With the second constructor, d becomes the associated deleter. + Y\* must be convertible to T\*. + @param p Pointer to own. + @note It is often easier to use makePtr instead. + */ + template +#ifdef DISABLE_OPENCV_24_COMPATIBILITY + explicit +#endif + Ptr(Y* p); + + /** @overload + @param d Deleter to use for the owned pointer. + @param p Pointer to own. + */ + template + Ptr(Y* p, D d); + + /** + These constructors create a Ptr that shares ownership with another Ptr - that is, own the same + pointer as o. + With the first two, the same pointer is stored, as well; for the second, Y\* must be convertible + to T\*. + With the third, p is stored, and Y may be any type. This constructor allows to have completely + unrelated owned and stored pointers, and should be used with care to avoid confusion. A relatively + benign use is to create a non-owning Ptr, like this: + @code + ptr = Ptr(Ptr(), dont_delete_me); // owns nothing; will not delete the pointer. + @endcode + @param o Ptr to share ownership with. + */ + Ptr(const Ptr& o); + + /** @overload + @param o Ptr to share ownership with. + */ + template + Ptr(const Ptr& o); + + /** @overload + @param o Ptr to share ownership with. + @param p Pointer to store. + */ + template + Ptr(const Ptr& o, T* p); + + /** The destructor is equivalent to calling Ptr::release. */ + ~Ptr(); + + /** + Assignment replaces the current Ptr instance with one that owns and stores same pointers as o and + then destroys the old instance. + @param o Ptr to share ownership with. + */ + Ptr& operator = (const Ptr& o); + + /** @overload */ + template + Ptr& operator = (const Ptr& o); + + /** If no other Ptr instance owns the owned pointer, deletes it with the associated deleter. Then sets + both the owned and the stored pointers to NULL. + */ + void release(); + + /** + `ptr.reset(...)` is equivalent to `ptr = Ptr(...)`. + @param p Pointer to own. + */ + template + void reset(Y* p); + + /** @overload + @param d Deleter to use for the owned pointer. + @param p Pointer to own. + */ + template + void reset(Y* p, D d); + + /** + Swaps the owned and stored pointers (and deleters, if any) of this and o. + @param o Ptr to swap with. + */ + void swap(Ptr& o); + + /** Returns the stored pointer. */ + T* get() const; + + /** Ordinary pointer emulation. */ + typename detail::RefOrVoid::type operator * () const; + + /** Ordinary pointer emulation. */ + T* operator -> () const; + + /** Equivalent to get(). */ + operator T* () const; + + /** ptr.empty() is equivalent to `!ptr.get()`. */ + bool empty() const; + + /** Returns a Ptr that owns the same pointer as this, and stores the same + pointer as this, except converted via static_cast to Y*. + */ + template + Ptr staticCast() const; + + /** Ditto for const_cast. */ + template + Ptr constCast() const; + + /** Ditto for dynamic_cast. */ + template + Ptr dynamicCast() const; + +#ifdef CV_CXX_MOVE_SEMANTICS + Ptr(Ptr&& o); + Ptr& operator = (Ptr&& o); +#endif + +private: + detail::PtrOwner* owner; + T* stored; + + template + friend struct Ptr; // have to do this for the cross-type copy constructor +}; + +/** Equivalent to ptr1.swap(ptr2). Provided to help write generic algorithms. */ +template +void swap(Ptr& ptr1, Ptr& ptr2); + +/** Return whether ptr1.get() and ptr2.get() are equal and not equal, respectively. */ +template +bool operator == (const Ptr& ptr1, const Ptr& ptr2); +template +bool operator != (const Ptr& ptr1, const Ptr& ptr2); + +/** `makePtr(...)` is equivalent to `Ptr(new T(...))`. It is shorter than the latter, and it's +marginally safer than using a constructor or Ptr::reset, since it ensures that the owned pointer +is new and thus not owned by any other Ptr instance. +Unfortunately, perfect forwarding is impossible to implement in C++03, and so makePtr is limited +to constructors of T that have up to 10 arguments, none of which are non-const references. + */ +template +Ptr makePtr(); +/** @overload */ +template +Ptr makePtr(const A1& a1); +/** @overload */ +template +Ptr makePtr(const A1& a1, const A2& a2); +/** @overload */ +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3); +/** @overload */ +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4); +/** @overload */ +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5); +/** @overload */ +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6); +/** @overload */ +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7); +/** @overload */ +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8); +/** @overload */ +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8, const A9& a9); +/** @overload */ +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8, const A9& a9, const A10& a10); + +//////////////////////////////// string class //////////////////////////////// + +class CV_EXPORTS FileNode; //for string constructor from FileNode + +class CV_EXPORTS String +{ +public: + typedef char value_type; + typedef char& reference; + typedef const char& const_reference; + typedef char* pointer; + typedef const char* const_pointer; + typedef ptrdiff_t difference_type; + typedef size_t size_type; + typedef char* iterator; + typedef const char* const_iterator; + + static const size_t npos = size_t(-1); + + String(); + String(const String& str); + String(const String& str, size_t pos, size_t len = npos); + String(const char* s); + String(const char* s, size_t n); + String(size_t n, char c); + String(const char* first, const char* last); + template String(Iterator first, Iterator last); + explicit String(const FileNode& fn); + ~String(); + + String& operator=(const String& str); + String& operator=(const char* s); + String& operator=(char c); + + String& operator+=(const String& str); + String& operator+=(const char* s); + String& operator+=(char c); + + size_t size() const; + size_t length() const; + + char operator[](size_t idx) const; + char operator[](int idx) const; + + const char* begin() const; + const char* end() const; + + const char* c_str() const; + + bool empty() const; + void clear(); + + int compare(const char* s) const; + int compare(const String& str) const; + + void swap(String& str); + String substr(size_t pos = 0, size_t len = npos) const; + + size_t find(const char* s, size_t pos, size_t n) const; + size_t find(char c, size_t pos = 0) const; + size_t find(const String& str, size_t pos = 0) const; + size_t find(const char* s, size_t pos = 0) const; + + size_t rfind(const char* s, size_t pos, size_t n) const; + size_t rfind(char c, size_t pos = npos) const; + size_t rfind(const String& str, size_t pos = npos) const; + size_t rfind(const char* s, size_t pos = npos) const; + + size_t find_first_of(const char* s, size_t pos, size_t n) const; + size_t find_first_of(char c, size_t pos = 0) const; + size_t find_first_of(const String& str, size_t pos = 0) const; + size_t find_first_of(const char* s, size_t pos = 0) const; + + size_t find_last_of(const char* s, size_t pos, size_t n) const; + size_t find_last_of(char c, size_t pos = npos) const; + size_t find_last_of(const String& str, size_t pos = npos) const; + size_t find_last_of(const char* s, size_t pos = npos) const; + + friend String operator+ (const String& lhs, const String& rhs); + friend String operator+ (const String& lhs, const char* rhs); + friend String operator+ (const char* lhs, const String& rhs); + friend String operator+ (const String& lhs, char rhs); + friend String operator+ (char lhs, const String& rhs); + + String toLowerCase() const; + + String(const std::string& str); + String(const std::string& str, size_t pos, size_t len = npos); + String& operator=(const std::string& str); + String& operator+=(const std::string& str); + operator std::string() const; + + friend String operator+ (const String& lhs, const std::string& rhs); + friend String operator+ (const std::string& lhs, const String& rhs); + +private: + char* cstr_; + size_t len_; + + char* allocate(size_t len); // len without trailing 0 + void deallocate(); + + String(int); // disabled and invalid. Catch invalid usages like, commandLineParser.has(0) problem +}; + +//! @} core_basic + +////////////////////////// cv::String implementation ///////////////////////// + +//! @cond IGNORED + +inline +String::String() + : cstr_(0), len_(0) +{} + +inline +String::String(const String& str) + : cstr_(str.cstr_), len_(str.len_) +{ + if (cstr_) + CV_XADD(((int*)cstr_)-1, 1); +} + +inline +String::String(const String& str, size_t pos, size_t len) + : cstr_(0), len_(0) +{ + pos = min(pos, str.len_); + len = min(str.len_ - pos, len); + if (!len) return; + if (len == str.len_) + { + CV_XADD(((int*)str.cstr_)-1, 1); + cstr_ = str.cstr_; + len_ = str.len_; + return; + } + memcpy(allocate(len), str.cstr_ + pos, len); +} + +inline +String::String(const char* s) + : cstr_(0), len_(0) +{ + if (!s) return; + size_t len = strlen(s); + if (!len) return; + memcpy(allocate(len), s, len); +} + +inline +String::String(const char* s, size_t n) + : cstr_(0), len_(0) +{ + if (!n) return; + if (!s) return; + memcpy(allocate(n), s, n); +} + +inline +String::String(size_t n, char c) + : cstr_(0), len_(0) +{ + if (!n) return; + memset(allocate(n), c, n); +} + +inline +String::String(const char* first, const char* last) + : cstr_(0), len_(0) +{ + size_t len = (size_t)(last - first); + if (!len) return; + memcpy(allocate(len), first, len); +} + +template inline +String::String(Iterator first, Iterator last) + : cstr_(0), len_(0) +{ + size_t len = (size_t)(last - first); + if (!len) return; + char* str = allocate(len); + while (first != last) + { + *str++ = *first; + ++first; + } +} + +inline +String::~String() +{ + deallocate(); +} + +inline +String& String::operator=(const String& str) +{ + if (&str == this) return *this; + + deallocate(); + if (str.cstr_) CV_XADD(((int*)str.cstr_)-1, 1); + cstr_ = str.cstr_; + len_ = str.len_; + return *this; +} + +inline +String& String::operator=(const char* s) +{ + deallocate(); + if (!s) return *this; + size_t len = strlen(s); + if (len) memcpy(allocate(len), s, len); + return *this; +} + +inline +String& String::operator=(char c) +{ + deallocate(); + allocate(1)[0] = c; + return *this; +} + +inline +String& String::operator+=(const String& str) +{ + *this = *this + str; + return *this; +} + +inline +String& String::operator+=(const char* s) +{ + *this = *this + s; + return *this; +} + +inline +String& String::operator+=(char c) +{ + *this = *this + c; + return *this; +} + +inline +size_t String::size() const +{ + return len_; +} + +inline +size_t String::length() const +{ + return len_; +} + +inline +char String::operator[](size_t idx) const +{ + return cstr_[idx]; +} + +inline +char String::operator[](int idx) const +{ + return cstr_[idx]; +} + +inline +const char* String::begin() const +{ + return cstr_; +} + +inline +const char* String::end() const +{ + return len_ ? cstr_ + len_ : NULL; +} + +inline +bool String::empty() const +{ + return len_ == 0; +} + +inline +const char* String::c_str() const +{ + return cstr_ ? cstr_ : ""; +} + +inline +void String::swap(String& str) +{ + cv::swap(cstr_, str.cstr_); + cv::swap(len_, str.len_); +} + +inline +void String::clear() +{ + deallocate(); +} + +inline +int String::compare(const char* s) const +{ + if (cstr_ == s) return 0; + return strcmp(c_str(), s); +} + +inline +int String::compare(const String& str) const +{ + if (cstr_ == str.cstr_) return 0; + return strcmp(c_str(), str.c_str()); +} + +inline +String String::substr(size_t pos, size_t len) const +{ + return String(*this, pos, len); +} + +inline +size_t String::find(const char* s, size_t pos, size_t n) const +{ + if (n == 0 || pos + n > len_) return npos; + const char* lmax = cstr_ + len_ - n; + for (const char* i = cstr_ + pos; i <= lmax; ++i) + { + size_t j = 0; + while (j < n && s[j] == i[j]) ++j; + if (j == n) return (size_t)(i - cstr_); + } + return npos; +} + +inline +size_t String::find(char c, size_t pos) const +{ + return find(&c, pos, 1); +} + +inline +size_t String::find(const String& str, size_t pos) const +{ + return find(str.c_str(), pos, str.len_); +} + +inline +size_t String::find(const char* s, size_t pos) const +{ + if (pos >= len_ || !s[0]) return npos; + const char* lmax = cstr_ + len_; + for (const char* i = cstr_ + pos; i < lmax; ++i) + { + size_t j = 0; + while (s[j] && s[j] == i[j]) + { if(i + j >= lmax) return npos; + ++j; + } + if (!s[j]) return (size_t)(i - cstr_); + } + return npos; +} + +inline +size_t String::rfind(const char* s, size_t pos, size_t n) const +{ + if (n > len_) return npos; + if (pos > len_ - n) pos = len_ - n; + for (const char* i = cstr_ + pos; i >= cstr_; --i) + { + size_t j = 0; + while (j < n && s[j] == i[j]) ++j; + if (j == n) return (size_t)(i - cstr_); + } + return npos; +} + +inline +size_t String::rfind(char c, size_t pos) const +{ + return rfind(&c, pos, 1); +} + +inline +size_t String::rfind(const String& str, size_t pos) const +{ + return rfind(str.c_str(), pos, str.len_); +} + +inline +size_t String::rfind(const char* s, size_t pos) const +{ + return rfind(s, pos, strlen(s)); +} + +inline +size_t String::find_first_of(const char* s, size_t pos, size_t n) const +{ + if (n == 0 || pos + n > len_) return npos; + const char* lmax = cstr_ + len_; + for (const char* i = cstr_ + pos; i < lmax; ++i) + { + for (size_t j = 0; j < n; ++j) + if (s[j] == *i) + return (size_t)(i - cstr_); + } + return npos; +} + +inline +size_t String::find_first_of(char c, size_t pos) const +{ + return find_first_of(&c, pos, 1); +} + +inline +size_t String::find_first_of(const String& str, size_t pos) const +{ + return find_first_of(str.c_str(), pos, str.len_); +} + +inline +size_t String::find_first_of(const char* s, size_t pos) const +{ + if (len_ == 0) return npos; + if (pos >= len_ || !s[0]) return npos; + const char* lmax = cstr_ + len_; + for (const char* i = cstr_ + pos; i < lmax; ++i) + { + for (size_t j = 0; s[j]; ++j) + if (s[j] == *i) + return (size_t)(i - cstr_); + } + return npos; +} + +inline +size_t String::find_last_of(const char* s, size_t pos, size_t n) const +{ + if (len_ == 0) return npos; + if (pos >= len_) pos = len_ - 1; + for (const char* i = cstr_ + pos; i >= cstr_; --i) + { + for (size_t j = 0; j < n; ++j) + if (s[j] == *i) + return (size_t)(i - cstr_); + } + return npos; +} + +inline +size_t String::find_last_of(char c, size_t pos) const +{ + return find_last_of(&c, pos, 1); +} + +inline +size_t String::find_last_of(const String& str, size_t pos) const +{ + return find_last_of(str.c_str(), pos, str.len_); +} + +inline +size_t String::find_last_of(const char* s, size_t pos) const +{ + if (len_ == 0) return npos; + if (pos >= len_) pos = len_ - 1; + for (const char* i = cstr_ + pos; i >= cstr_; --i) + { + for (size_t j = 0; s[j]; ++j) + if (s[j] == *i) + return (size_t)(i - cstr_); + } + return npos; +} + +inline +String String::toLowerCase() const +{ + if (!cstr_) + return String(); + String res(cstr_, len_); + for (size_t i = 0; i < len_; ++i) + res.cstr_[i] = (char) ::tolower(cstr_[i]); + + return res; +} + +//! @endcond + +// ************************* cv::String non-member functions ************************* + +//! @relates cv::String +//! @{ + +inline +String operator + (const String& lhs, const String& rhs) +{ + String s; + s.allocate(lhs.len_ + rhs.len_); + if (lhs.len_) memcpy(s.cstr_, lhs.cstr_, lhs.len_); + if (rhs.len_) memcpy(s.cstr_ + lhs.len_, rhs.cstr_, rhs.len_); + return s; +} + +inline +String operator + (const String& lhs, const char* rhs) +{ + String s; + size_t rhslen = strlen(rhs); + s.allocate(lhs.len_ + rhslen); + if (lhs.len_) memcpy(s.cstr_, lhs.cstr_, lhs.len_); + if (rhslen) memcpy(s.cstr_ + lhs.len_, rhs, rhslen); + return s; +} + +inline +String operator + (const char* lhs, const String& rhs) +{ + String s; + size_t lhslen = strlen(lhs); + s.allocate(lhslen + rhs.len_); + if (lhslen) memcpy(s.cstr_, lhs, lhslen); + if (rhs.len_) memcpy(s.cstr_ + lhslen, rhs.cstr_, rhs.len_); + return s; +} + +inline +String operator + (const String& lhs, char rhs) +{ + String s; + s.allocate(lhs.len_ + 1); + if (lhs.len_) memcpy(s.cstr_, lhs.cstr_, lhs.len_); + s.cstr_[lhs.len_] = rhs; + return s; +} + +inline +String operator + (char lhs, const String& rhs) +{ + String s; + s.allocate(rhs.len_ + 1); + s.cstr_[0] = lhs; + if (rhs.len_) memcpy(s.cstr_ + 1, rhs.cstr_, rhs.len_); + return s; +} + +static inline bool operator== (const String& lhs, const String& rhs) { return 0 == lhs.compare(rhs); } +static inline bool operator== (const char* lhs, const String& rhs) { return 0 == rhs.compare(lhs); } +static inline bool operator== (const String& lhs, const char* rhs) { return 0 == lhs.compare(rhs); } +static inline bool operator!= (const String& lhs, const String& rhs) { return 0 != lhs.compare(rhs); } +static inline bool operator!= (const char* lhs, const String& rhs) { return 0 != rhs.compare(lhs); } +static inline bool operator!= (const String& lhs, const char* rhs) { return 0 != lhs.compare(rhs); } +static inline bool operator< (const String& lhs, const String& rhs) { return lhs.compare(rhs) < 0; } +static inline bool operator< (const char* lhs, const String& rhs) { return rhs.compare(lhs) > 0; } +static inline bool operator< (const String& lhs, const char* rhs) { return lhs.compare(rhs) < 0; } +static inline bool operator<= (const String& lhs, const String& rhs) { return lhs.compare(rhs) <= 0; } +static inline bool operator<= (const char* lhs, const String& rhs) { return rhs.compare(lhs) >= 0; } +static inline bool operator<= (const String& lhs, const char* rhs) { return lhs.compare(rhs) <= 0; } +static inline bool operator> (const String& lhs, const String& rhs) { return lhs.compare(rhs) > 0; } +static inline bool operator> (const char* lhs, const String& rhs) { return rhs.compare(lhs) < 0; } +static inline bool operator> (const String& lhs, const char* rhs) { return lhs.compare(rhs) > 0; } +static inline bool operator>= (const String& lhs, const String& rhs) { return lhs.compare(rhs) >= 0; } +static inline bool operator>= (const char* lhs, const String& rhs) { return rhs.compare(lhs) <= 0; } +static inline bool operator>= (const String& lhs, const char* rhs) { return lhs.compare(rhs) >= 0; } + +//! @} relates cv::String + +} // cv + +namespace std +{ + static inline void swap(cv::String& a, cv::String& b) { a.swap(b); } +} + +#include "opencv2/core/ptr.inl.hpp" + +#endif //OPENCV_CORE_CVSTD_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cvstd.inl.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cvstd.inl.hpp new file mode 100644 index 0000000000000000000000000000000000000000..ed37cacb300e13d17aed7d1441e7bb77eed365b5 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/cvstd.inl.hpp @@ -0,0 +1,285 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_CVSTDINL_HPP +#define OPENCV_CORE_CVSTDINL_HPP + +#include +#include + +//! @cond IGNORED + +#ifdef _MSC_VER +#pragma warning( push ) +#pragma warning( disable: 4127 ) +#endif + +namespace cv +{ + +template class DataType< std::complex<_Tp> > +{ +public: + typedef std::complex<_Tp> value_type; + typedef value_type work_type; + typedef _Tp channel_type; + + enum { generic_type = 0, + depth = DataType::depth, + channels = 2, + fmt = DataType::fmt + ((channels - 1) << 8), + type = CV_MAKETYPE(depth, channels) }; + + typedef Vec vec_type; +}; + +inline +String::String(const std::string& str) + : cstr_(0), len_(0) +{ + size_t len = str.size(); + if (len) memcpy(allocate(len), str.c_str(), len); +} + +inline +String::String(const std::string& str, size_t pos, size_t len) + : cstr_(0), len_(0) +{ + size_t strlen = str.size(); + pos = min(pos, strlen); + len = min(strlen - pos, len); + if (!len) return; + memcpy(allocate(len), str.c_str() + pos, len); +} + +inline +String& String::operator = (const std::string& str) +{ + deallocate(); + size_t len = str.size(); + if (len) memcpy(allocate(len), str.c_str(), len); + return *this; +} + +inline +String& String::operator += (const std::string& str) +{ + *this = *this + str; + return *this; +} + +inline +String::operator std::string() const +{ + return std::string(cstr_, len_); +} + +inline +String operator + (const String& lhs, const std::string& rhs) +{ + String s; + size_t rhslen = rhs.size(); + s.allocate(lhs.len_ + rhslen); + if (lhs.len_) memcpy(s.cstr_, lhs.cstr_, lhs.len_); + if (rhslen) memcpy(s.cstr_ + lhs.len_, rhs.c_str(), rhslen); + return s; +} + +inline +String operator + (const std::string& lhs, const String& rhs) +{ + String s; + size_t lhslen = lhs.size(); + s.allocate(lhslen + rhs.len_); + if (lhslen) memcpy(s.cstr_, lhs.c_str(), lhslen); + if (rhs.len_) memcpy(s.cstr_ + lhslen, rhs.cstr_, rhs.len_); + return s; +} + +inline +FileNode::operator std::string() const +{ + String value; + read(*this, value, value); + return value; +} + +template<> inline +void operator >> (const FileNode& n, std::string& value) +{ + read(n, value, std::string()); +} + +template<> inline +FileStorage& operator << (FileStorage& fs, const std::string& value) +{ + return fs << cv::String(value); +} + +static inline +std::ostream& operator << (std::ostream& os, const String& str) +{ + return os << str.c_str(); +} + +static inline +std::ostream& operator << (std::ostream& out, Ptr fmtd) +{ + fmtd->reset(); + for(const char* str = fmtd->next(); str; str = fmtd->next()) + out << str; + return out; +} + +static inline +std::ostream& operator << (std::ostream& out, const Mat& mtx) +{ + return out << Formatter::get()->format(mtx); +} + +static inline +std::ostream& operator << (std::ostream& out, const UMat& m) +{ + return out << m.getMat(ACCESS_READ); +} + +template static inline +std::ostream& operator << (std::ostream& out, const Complex<_Tp>& c) +{ + return out << "(" << c.re << "," << c.im << ")"; +} + +template static inline +std::ostream& operator << (std::ostream& out, const std::vector >& vec) +{ + return out << Formatter::get()->format(Mat(vec)); +} + + +template static inline +std::ostream& operator << (std::ostream& out, const std::vector >& vec) +{ + return out << Formatter::get()->format(Mat(vec)); +} + + +template static inline +std::ostream& operator << (std::ostream& out, const Matx<_Tp, m, n>& matx) +{ + return out << Formatter::get()->format(Mat(matx)); +} + +template static inline +std::ostream& operator << (std::ostream& out, const Point_<_Tp>& p) +{ + out << "[" << p.x << ", " << p.y << "]"; + return out; +} + +template static inline +std::ostream& operator << (std::ostream& out, const Point3_<_Tp>& p) +{ + out << "[" << p.x << ", " << p.y << ", " << p.z << "]"; + return out; +} + +template static inline +std::ostream& operator << (std::ostream& out, const Vec<_Tp, n>& vec) +{ + out << "["; + if (cv::traits::Depth<_Tp>::value <= CV_32S) + { + for (int i = 0; i < n - 1; ++i) { + out << (int)vec[i] << ", "; + } + out << (int)vec[n-1] << "]"; + } + else + { + for (int i = 0; i < n - 1; ++i) { + out << vec[i] << ", "; + } + out << vec[n-1] << "]"; + } + + return out; +} + +template static inline +std::ostream& operator << (std::ostream& out, const Size_<_Tp>& size) +{ + return out << "[" << size.width << " x " << size.height << "]"; +} + +template static inline +std::ostream& operator << (std::ostream& out, const Rect_<_Tp>& rect) +{ + return out << "[" << rect.width << " x " << rect.height << " from (" << rect.x << ", " << rect.y << ")]"; +} + +static inline std::ostream& operator << (std::ostream& out, const MatSize& msize) +{ + int i, dims = msize.dims(); + for( i = 0; i < dims; i++ ) + { + out << msize[i]; + if( i < dims-1 ) + out << " x "; + } + return out; +} + +static inline std::ostream &operator<< (std::ostream &s, cv::Range &r) +{ + return s << "[" << r.start << " : " << r.end << ")"; +} + +} // cv + +#ifdef _MSC_VER +#pragma warning( pop ) +#endif + +//! @endcond + +#endif // OPENCV_CORE_CVSTDINL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/directx.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/directx.hpp new file mode 100644 index 0000000000000000000000000000000000000000..056a85a1bc996f1483b681273887fc25840f7251 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/directx.hpp @@ -0,0 +1,184 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors as is and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the copyright holders or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_DIRECTX_HPP +#define OPENCV_CORE_DIRECTX_HPP + +#include "mat.hpp" +#include "ocl.hpp" + +#if !defined(__d3d11_h__) +struct ID3D11Device; +struct ID3D11Texture2D; +#endif + +#if !defined(__d3d10_h__) +struct ID3D10Device; +struct ID3D10Texture2D; +#endif + +#if !defined(_D3D9_H_) +struct IDirect3DDevice9; +struct IDirect3DDevice9Ex; +struct IDirect3DSurface9; +#endif + + +namespace cv { namespace directx { + +namespace ocl { +using namespace cv::ocl; + +//! @addtogroup core_directx +// This section describes OpenCL and DirectX interoperability. +// +// To enable DirectX support, configure OpenCV using CMake with WITH_DIRECTX=ON . Note, DirectX is +// supported only on Windows. +// +// To use OpenCL functionality you should first initialize OpenCL context from DirectX resource. +// +//! @{ + +// TODO static functions in the Context class +//! @brief Creates OpenCL context from D3D11 device +// +//! @param pD3D11Device - pointer to D3D11 device +//! @return Returns reference to OpenCL Context +CV_EXPORTS Context& initializeContextFromD3D11Device(ID3D11Device* pD3D11Device); + +//! @brief Creates OpenCL context from D3D10 device +// +//! @param pD3D10Device - pointer to D3D10 device +//! @return Returns reference to OpenCL Context +CV_EXPORTS Context& initializeContextFromD3D10Device(ID3D10Device* pD3D10Device); + +//! @brief Creates OpenCL context from Direct3DDevice9Ex device +// +//! @param pDirect3DDevice9Ex - pointer to Direct3DDevice9Ex device +//! @return Returns reference to OpenCL Context +CV_EXPORTS Context& initializeContextFromDirect3DDevice9Ex(IDirect3DDevice9Ex* pDirect3DDevice9Ex); + +//! @brief Creates OpenCL context from Direct3DDevice9 device +// +//! @param pDirect3DDevice9 - pointer to Direct3Device9 device +//! @return Returns reference to OpenCL Context +CV_EXPORTS Context& initializeContextFromDirect3DDevice9(IDirect3DDevice9* pDirect3DDevice9); + +//! @} + +} // namespace cv::directx::ocl + +//! @addtogroup core_directx +//! @{ + +//! @brief Converts InputArray to ID3D11Texture2D. If destination texture format is DXGI_FORMAT_NV12 then +//! input UMat expected to be in BGR format and data will be downsampled and color-converted to NV12. +// +//! @note Note: Destination texture must be allocated by application. Function does memory copy from src to +//! pD3D11Texture2D +// +//! @param src - source InputArray +//! @param pD3D11Texture2D - destination D3D11 texture +CV_EXPORTS void convertToD3D11Texture2D(InputArray src, ID3D11Texture2D* pD3D11Texture2D); + +//! @brief Converts ID3D11Texture2D to OutputArray. If input texture format is DXGI_FORMAT_NV12 then +//! data will be upsampled and color-converted to BGR format. +// +//! @note Note: Destination matrix will be re-allocated if it has not enough memory to match texture size. +//! function does memory copy from pD3D11Texture2D to dst +// +//! @param pD3D11Texture2D - source D3D11 texture +//! @param dst - destination OutputArray +CV_EXPORTS void convertFromD3D11Texture2D(ID3D11Texture2D* pD3D11Texture2D, OutputArray dst); + +//! @brief Converts InputArray to ID3D10Texture2D +// +//! @note Note: function does memory copy from src to +//! pD3D10Texture2D +// +//! @param src - source InputArray +//! @param pD3D10Texture2D - destination D3D10 texture +CV_EXPORTS void convertToD3D10Texture2D(InputArray src, ID3D10Texture2D* pD3D10Texture2D); + +//! @brief Converts ID3D10Texture2D to OutputArray +// +//! @note Note: function does memory copy from pD3D10Texture2D +//! to dst +// +//! @param pD3D10Texture2D - source D3D10 texture +//! @param dst - destination OutputArray +CV_EXPORTS void convertFromD3D10Texture2D(ID3D10Texture2D* pD3D10Texture2D, OutputArray dst); + +//! @brief Converts InputArray to IDirect3DSurface9 +// +//! @note Note: function does memory copy from src to +//! pDirect3DSurface9 +// +//! @param src - source InputArray +//! @param pDirect3DSurface9 - destination D3D10 texture +//! @param surfaceSharedHandle - shared handle +CV_EXPORTS void convertToDirect3DSurface9(InputArray src, IDirect3DSurface9* pDirect3DSurface9, void* surfaceSharedHandle = NULL); + +//! @brief Converts IDirect3DSurface9 to OutputArray +// +//! @note Note: function does memory copy from pDirect3DSurface9 +//! to dst +// +//! @param pDirect3DSurface9 - source D3D10 texture +//! @param dst - destination OutputArray +//! @param surfaceSharedHandle - shared handle +CV_EXPORTS void convertFromDirect3DSurface9(IDirect3DSurface9* pDirect3DSurface9, OutputArray dst, void* surfaceSharedHandle = NULL); + +//! @brief Get OpenCV type from DirectX type +//! @param iDXGI_FORMAT - enum DXGI_FORMAT for D3D10/D3D11 +//! @return OpenCV type or -1 if there is no equivalent +CV_EXPORTS int getTypeFromDXGI_FORMAT(const int iDXGI_FORMAT); // enum DXGI_FORMAT for D3D10/D3D11 + +//! @brief Get OpenCV type from DirectX type +//! @param iD3DFORMAT - enum D3DTYPE for D3D9 +//! @return OpenCV type or -1 if there is no equivalent +CV_EXPORTS int getTypeFromD3DFORMAT(const int iD3DFORMAT); // enum D3DTYPE for D3D9 + +//! @} + +} } // namespace cv::directx + +#endif // OPENCV_CORE_DIRECTX_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/eigen.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/eigen.hpp new file mode 100644 index 0000000000000000000000000000000000000000..741648edb8fb12e6597d75691fd0019f07ded093 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/eigen.hpp @@ -0,0 +1,280 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + + +#ifndef OPENCV_CORE_EIGEN_HPP +#define OPENCV_CORE_EIGEN_HPP + +#include "opencv2/core.hpp" + +#if defined _MSC_VER && _MSC_VER >= 1200 +#pragma warning( disable: 4714 ) //__forceinline is not inlined +#pragma warning( disable: 4127 ) //conditional expression is constant +#pragma warning( disable: 4244 ) //conversion from '__int64' to 'int', possible loss of data +#endif + +namespace cv +{ + +//! @addtogroup core_eigen +//! @{ + +template static inline +void eigen2cv( const Eigen::Matrix<_Tp, _rows, _cols, _options, _maxRows, _maxCols>& src, OutputArray dst ) +{ + if( !(src.Flags & Eigen::RowMajorBit) ) + { + Mat _src(src.cols(), src.rows(), traits::Type<_Tp>::value, + (void*)src.data(), src.outerStride()*sizeof(_Tp)); + transpose(_src, dst); + } + else + { + Mat _src(src.rows(), src.cols(), traits::Type<_Tp>::value, + (void*)src.data(), src.outerStride()*sizeof(_Tp)); + _src.copyTo(dst); + } +} + +// Matx case +template static inline +void eigen2cv( const Eigen::Matrix<_Tp, _rows, _cols, _options, _maxRows, _maxCols>& src, + Matx<_Tp, _rows, _cols>& dst ) +{ + if( !(src.Flags & Eigen::RowMajorBit) ) + { + dst = Matx<_Tp, _cols, _rows>(static_cast(src.data())).t(); + } + else + { + dst = Matx<_Tp, _rows, _cols>(static_cast(src.data())); + } +} + +template static inline +void cv2eigen( const Mat& src, + Eigen::Matrix<_Tp, _rows, _cols, _options, _maxRows, _maxCols>& dst ) +{ + CV_DbgAssert(src.rows == _rows && src.cols == _cols); + if( !(dst.Flags & Eigen::RowMajorBit) ) + { + const Mat _dst(src.cols, src.rows, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + if( src.type() == _dst.type() ) + transpose(src, _dst); + else if( src.cols == src.rows ) + { + src.convertTo(_dst, _dst.type()); + transpose(_dst, _dst); + } + else + Mat(src.t()).convertTo(_dst, _dst.type()); + } + else + { + const Mat _dst(src.rows, src.cols, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + src.convertTo(_dst, _dst.type()); + } +} + +// Matx case +template static inline +void cv2eigen( const Matx<_Tp, _rows, _cols>& src, + Eigen::Matrix<_Tp, _rows, _cols, _options, _maxRows, _maxCols>& dst ) +{ + if( !(dst.Flags & Eigen::RowMajorBit) ) + { + const Mat _dst(_cols, _rows, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + transpose(src, _dst); + } + else + { + const Mat _dst(_rows, _cols, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + Mat(src).copyTo(_dst); + } +} + +template static inline +void cv2eigen( const Mat& src, + Eigen::Matrix<_Tp, Eigen::Dynamic, Eigen::Dynamic>& dst ) +{ + dst.resize(src.rows, src.cols); + if( !(dst.Flags & Eigen::RowMajorBit) ) + { + const Mat _dst(src.cols, src.rows, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + if( src.type() == _dst.type() ) + transpose(src, _dst); + else if( src.cols == src.rows ) + { + src.convertTo(_dst, _dst.type()); + transpose(_dst, _dst); + } + else + Mat(src.t()).convertTo(_dst, _dst.type()); + } + else + { + const Mat _dst(src.rows, src.cols, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + src.convertTo(_dst, _dst.type()); + } +} + +// Matx case +template static inline +void cv2eigen( const Matx<_Tp, _rows, _cols>& src, + Eigen::Matrix<_Tp, Eigen::Dynamic, Eigen::Dynamic>& dst ) +{ + dst.resize(_rows, _cols); + if( !(dst.Flags & Eigen::RowMajorBit) ) + { + const Mat _dst(_cols, _rows, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + transpose(src, _dst); + } + else + { + const Mat _dst(_rows, _cols, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + Mat(src).copyTo(_dst); + } +} + +template static inline +void cv2eigen( const Mat& src, + Eigen::Matrix<_Tp, Eigen::Dynamic, 1>& dst ) +{ + CV_Assert(src.cols == 1); + dst.resize(src.rows); + + if( !(dst.Flags & Eigen::RowMajorBit) ) + { + const Mat _dst(src.cols, src.rows, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + if( src.type() == _dst.type() ) + transpose(src, _dst); + else + Mat(src.t()).convertTo(_dst, _dst.type()); + } + else + { + const Mat _dst(src.rows, src.cols, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + src.convertTo(_dst, _dst.type()); + } +} + +// Matx case +template static inline +void cv2eigen( const Matx<_Tp, _rows, 1>& src, + Eigen::Matrix<_Tp, Eigen::Dynamic, 1>& dst ) +{ + dst.resize(_rows); + + if( !(dst.Flags & Eigen::RowMajorBit) ) + { + const Mat _dst(1, _rows, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + transpose(src, _dst); + } + else + { + const Mat _dst(_rows, 1, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + src.copyTo(_dst); + } +} + + +template static inline +void cv2eigen( const Mat& src, + Eigen::Matrix<_Tp, 1, Eigen::Dynamic>& dst ) +{ + CV_Assert(src.rows == 1); + dst.resize(src.cols); + if( !(dst.Flags & Eigen::RowMajorBit) ) + { + const Mat _dst(src.cols, src.rows, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + if( src.type() == _dst.type() ) + transpose(src, _dst); + else + Mat(src.t()).convertTo(_dst, _dst.type()); + } + else + { + const Mat _dst(src.rows, src.cols, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + src.convertTo(_dst, _dst.type()); + } +} + +//Matx +template static inline +void cv2eigen( const Matx<_Tp, 1, _cols>& src, + Eigen::Matrix<_Tp, 1, Eigen::Dynamic>& dst ) +{ + dst.resize(_cols); + if( !(dst.Flags & Eigen::RowMajorBit) ) + { + const Mat _dst(_cols, 1, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + transpose(src, _dst); + } + else + { + const Mat _dst(1, _cols, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + Mat(src).copyTo(_dst); + } +} + +//! @} + +} // cv + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/fast_math.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/fast_math.hpp new file mode 100644 index 0000000000000000000000000000000000000000..d9ea28e6325a8e76df473d22371617b3ce324f64 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/fast_math.hpp @@ -0,0 +1,271 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_FAST_MATH_HPP +#define OPENCV_CORE_FAST_MATH_HPP + +#include "opencv2/core/cvdef.h" + +#if ((defined _MSC_VER && defined _M_X64) || (defined __GNUC__ && defined __x86_64__ \ + && defined __SSE2__ && !defined __APPLE__)) && !defined(__CUDACC__) +#include +#endif + + +//! @addtogroup core_utils +//! @{ + +/****************************************************************************************\ +* fast math * +\****************************************************************************************/ + +#ifdef __cplusplus +# include +#else +# ifdef __BORLANDC__ +# include +# else +# include +# endif +#endif + +#ifdef HAVE_TEGRA_OPTIMIZATION +# include "tegra_round.hpp" +#endif + +#if defined __GNUC__ && defined __arm__ && (defined __ARM_PCS_VFP || defined __ARM_VFPV3__ || defined __ARM_NEON__) && !defined __SOFTFP__ && !defined(__CUDACC__) + // 1. general scheme + #define ARM_ROUND(_value, _asm_string) \ + int res; \ + float temp; \ + CV_UNUSED(temp); \ + __asm__(_asm_string : [res] "=r" (res), [temp] "=w" (temp) : [value] "w" (_value)); \ + return res + // 2. version for double + #ifdef __clang__ + #define ARM_ROUND_DBL(value) ARM_ROUND(value, "vcvtr.s32.f64 %[temp], %[value] \n vmov %[res], %[temp]") + #else + #define ARM_ROUND_DBL(value) ARM_ROUND(value, "vcvtr.s32.f64 %[temp], %P[value] \n vmov %[res], %[temp]") + #endif + // 3. version for float + #define ARM_ROUND_FLT(value) ARM_ROUND(value, "vcvtr.s32.f32 %[temp], %[value]\n vmov %[res], %[temp]") +#endif + +/** @brief Rounds floating-point number to the nearest integer + + @param value floating-point number. If the value is outside of INT_MIN ... INT_MAX range, the + result is not defined. + */ +CV_INLINE int +cvRound( double value ) +{ +#if ((defined _MSC_VER && defined _M_X64) || (defined __GNUC__ && defined __x86_64__ \ + && defined __SSE2__ && !defined __APPLE__) || CV_SSE2) && !defined(__CUDACC__) + __m128d t = _mm_set_sd( value ); + return _mm_cvtsd_si32(t); +#elif defined _MSC_VER && defined _M_IX86 + int t; + __asm + { + fld value; + fistp t; + } + return t; +#elif ((defined _MSC_VER && defined _M_ARM) || defined CV_ICC || \ + defined __GNUC__) && defined HAVE_TEGRA_OPTIMIZATION + TEGRA_ROUND_DBL(value); +#elif defined CV_ICC || defined __GNUC__ +# if defined ARM_ROUND_DBL + ARM_ROUND_DBL(value); +# else + return (int)lrint(value); +# endif +#else + /* it's ok if round does not comply with IEEE754 standard; + the tests should allow +/-1 difference when the tested functions use round */ + return (int)(value + (value >= 0 ? 0.5 : -0.5)); +#endif +} + + +/** @brief Rounds floating-point number to the nearest integer not larger than the original. + + The function computes an integer i such that: + \f[i \le \texttt{value} < i+1\f] + @param value floating-point number. If the value is outside of INT_MIN ... INT_MAX range, the + result is not defined. + */ +CV_INLINE int cvFloor( double value ) +{ + int i = (int)value; + return i - (i > value); +} + +/** @brief Rounds floating-point number to the nearest integer not smaller than the original. + + The function computes an integer i such that: + \f[i \le \texttt{value} < i+1\f] + @param value floating-point number. If the value is outside of INT_MIN ... INT_MAX range, the + result is not defined. + */ +CV_INLINE int cvCeil( double value ) +{ + int i = (int)value; + return i + (i < value); +} + +/** @brief Determines if the argument is Not A Number. + + @param value The input floating-point value + + The function returns 1 if the argument is Not A Number (as defined by IEEE754 standard), 0 + otherwise. */ +CV_INLINE int cvIsNaN( double value ) +{ + Cv64suf ieee754; + ieee754.f = value; + return ((unsigned)(ieee754.u >> 32) & 0x7fffffff) + + ((unsigned)ieee754.u != 0) > 0x7ff00000; +} + +/** @brief Determines if the argument is Infinity. + + @param value The input floating-point value + + The function returns 1 if the argument is a plus or minus infinity (as defined by IEEE754 standard) + and 0 otherwise. */ +CV_INLINE int cvIsInf( double value ) +{ + Cv64suf ieee754; + ieee754.f = value; + return ((unsigned)(ieee754.u >> 32) & 0x7fffffff) == 0x7ff00000 && + (unsigned)ieee754.u == 0; +} + +#ifdef __cplusplus + +/** @overload */ +CV_INLINE int cvRound(float value) +{ +#if ((defined _MSC_VER && defined _M_X64) || (defined __GNUC__ && defined __x86_64__ \ + && defined __SSE2__ && !defined __APPLE__) || CV_SSE2) && !defined(__CUDACC__) + __m128 t = _mm_set_ss( value ); + return _mm_cvtss_si32(t); +#elif defined _MSC_VER && defined _M_IX86 + int t; + __asm + { + fld value; + fistp t; + } + return t; +#elif ((defined _MSC_VER && defined _M_ARM) || defined CV_ICC || \ + defined __GNUC__) && defined HAVE_TEGRA_OPTIMIZATION + TEGRA_ROUND_FLT(value); +#elif defined CV_ICC || defined __GNUC__ +# if defined ARM_ROUND_FLT + ARM_ROUND_FLT(value); +# else + return (int)lrintf(value); +# endif +#else + /* it's ok if round does not comply with IEEE754 standard; + the tests should allow +/-1 difference when the tested functions use round */ + return (int)(value + (value >= 0 ? 0.5f : -0.5f)); +#endif +} + +/** @overload */ +CV_INLINE int cvRound( int value ) +{ + return value; +} + +/** @overload */ +CV_INLINE int cvFloor( float value ) +{ + int i = (int)value; + return i - (i > value); +} + +/** @overload */ +CV_INLINE int cvFloor( int value ) +{ + return value; +} + +/** @overload */ +CV_INLINE int cvCeil( float value ) +{ + int i = (int)value; + return i + (i < value); +} + +/** @overload */ +CV_INLINE int cvCeil( int value ) +{ + return value; +} + +/** @overload */ +CV_INLINE int cvIsNaN( float value ) +{ + Cv32suf ieee754; + ieee754.f = value; + return (ieee754.u & 0x7fffffff) > 0x7f800000; +} + +/** @overload */ +CV_INLINE int cvIsInf( float value ) +{ + Cv32suf ieee754; + ieee754.f = value; + return (ieee754.u & 0x7fffffff) == 0x7f800000; +} + +#endif // __cplusplus + +//! @} core_utils + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/hal/hal.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/hal/hal.hpp new file mode 100644 index 0000000000000000000000000000000000000000..68900ec4281028f445b44b603790181d9731f7b3 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/hal/hal.hpp @@ -0,0 +1,250 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_HAL_HPP +#define OPENCV_HAL_HPP + +#include "opencv2/core/cvdef.h" +#include "opencv2/core/cvstd.hpp" +#include "opencv2/core/hal/interface.h" + +namespace cv { namespace hal { + +//! @addtogroup core_hal_functions +//! @{ + +CV_EXPORTS int normHamming(const uchar* a, int n); +CV_EXPORTS int normHamming(const uchar* a, const uchar* b, int n); + +CV_EXPORTS int normHamming(const uchar* a, int n, int cellSize); +CV_EXPORTS int normHamming(const uchar* a, const uchar* b, int n, int cellSize); + +CV_EXPORTS int LU32f(float* A, size_t astep, int m, float* b, size_t bstep, int n); +CV_EXPORTS int LU64f(double* A, size_t astep, int m, double* b, size_t bstep, int n); +CV_EXPORTS bool Cholesky32f(float* A, size_t astep, int m, float* b, size_t bstep, int n); +CV_EXPORTS bool Cholesky64f(double* A, size_t astep, int m, double* b, size_t bstep, int n); +CV_EXPORTS void SVD32f(float* At, size_t astep, float* W, float* U, size_t ustep, float* Vt, size_t vstep, int m, int n, int flags); +CV_EXPORTS void SVD64f(double* At, size_t astep, double* W, double* U, size_t ustep, double* Vt, size_t vstep, int m, int n, int flags); +CV_EXPORTS int QR32f(float* A, size_t astep, int m, int n, int k, float* b, size_t bstep, float* hFactors); +CV_EXPORTS int QR64f(double* A, size_t astep, int m, int n, int k, double* b, size_t bstep, double* hFactors); + +CV_EXPORTS void gemm32f(const float* src1, size_t src1_step, const float* src2, size_t src2_step, + float alpha, const float* src3, size_t src3_step, float beta, float* dst, size_t dst_step, + int m_a, int n_a, int n_d, int flags); +CV_EXPORTS void gemm64f(const double* src1, size_t src1_step, const double* src2, size_t src2_step, + double alpha, const double* src3, size_t src3_step, double beta, double* dst, size_t dst_step, + int m_a, int n_a, int n_d, int flags); +CV_EXPORTS void gemm32fc(const float* src1, size_t src1_step, const float* src2, size_t src2_step, + float alpha, const float* src3, size_t src3_step, float beta, float* dst, size_t dst_step, + int m_a, int n_a, int n_d, int flags); +CV_EXPORTS void gemm64fc(const double* src1, size_t src1_step, const double* src2, size_t src2_step, + double alpha, const double* src3, size_t src3_step, double beta, double* dst, size_t dst_step, + int m_a, int n_a, int n_d, int flags); + +CV_EXPORTS int normL1_(const uchar* a, const uchar* b, int n); +CV_EXPORTS float normL1_(const float* a, const float* b, int n); +CV_EXPORTS float normL2Sqr_(const float* a, const float* b, int n); + +CV_EXPORTS void exp32f(const float* src, float* dst, int n); +CV_EXPORTS void exp64f(const double* src, double* dst, int n); +CV_EXPORTS void log32f(const float* src, float* dst, int n); +CV_EXPORTS void log64f(const double* src, double* dst, int n); + +CV_EXPORTS void fastAtan32f(const float* y, const float* x, float* dst, int n, bool angleInDegrees); +CV_EXPORTS void fastAtan64f(const double* y, const double* x, double* dst, int n, bool angleInDegrees); +CV_EXPORTS void magnitude32f(const float* x, const float* y, float* dst, int n); +CV_EXPORTS void magnitude64f(const double* x, const double* y, double* dst, int n); +CV_EXPORTS void sqrt32f(const float* src, float* dst, int len); +CV_EXPORTS void sqrt64f(const double* src, double* dst, int len); +CV_EXPORTS void invSqrt32f(const float* src, float* dst, int len); +CV_EXPORTS void invSqrt64f(const double* src, double* dst, int len); + +CV_EXPORTS void split8u(const uchar* src, uchar** dst, int len, int cn ); +CV_EXPORTS void split16u(const ushort* src, ushort** dst, int len, int cn ); +CV_EXPORTS void split32s(const int* src, int** dst, int len, int cn ); +CV_EXPORTS void split64s(const int64* src, int64** dst, int len, int cn ); + +CV_EXPORTS void merge8u(const uchar** src, uchar* dst, int len, int cn ); +CV_EXPORTS void merge16u(const ushort** src, ushort* dst, int len, int cn ); +CV_EXPORTS void merge32s(const int** src, int* dst, int len, int cn ); +CV_EXPORTS void merge64s(const int64** src, int64* dst, int len, int cn ); + +CV_EXPORTS void add8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void add8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void add16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void add16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void add32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void add32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void add64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* ); + +CV_EXPORTS void sub8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void sub8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void sub16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void sub16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void sub32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void sub32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void sub64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* ); + +CV_EXPORTS void max8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void max8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void max16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void max16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void max32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void max32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void max64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* ); + +CV_EXPORTS void min8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void min8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void min16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void min16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void min32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void min32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void min64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* ); + +CV_EXPORTS void absdiff8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void absdiff8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void absdiff16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void absdiff16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void absdiff32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void absdiff32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void absdiff64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* ); + +CV_EXPORTS void and8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void or8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void xor8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void not8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); + +CV_EXPORTS void cmp8u(const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop); +CV_EXPORTS void cmp8s(const schar* src1, size_t step1, const schar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop); +CV_EXPORTS void cmp16u(const ushort* src1, size_t step1, const ushort* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop); +CV_EXPORTS void cmp16s(const short* src1, size_t step1, const short* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop); +CV_EXPORTS void cmp32s(const int* src1, size_t step1, const int* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop); +CV_EXPORTS void cmp32f(const float* src1, size_t step1, const float* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop); +CV_EXPORTS void cmp64f(const double* src1, size_t step1, const double* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop); + +CV_EXPORTS void mul8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void mul8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void mul16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void mul16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void mul32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void mul32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void mul64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* scale); + +CV_EXPORTS void div8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void div8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void div16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void div16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void div32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void div32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void div64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* scale); + +CV_EXPORTS void recip8u( const uchar *, size_t, const uchar * src2, size_t step2, uchar* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void recip8s( const schar *, size_t, const schar * src2, size_t step2, schar* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void recip16u( const ushort *, size_t, const ushort * src2, size_t step2, ushort* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void recip16s( const short *, size_t, const short * src2, size_t step2, short* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void recip32s( const int *, size_t, const int * src2, size_t step2, int* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void recip32f( const float *, size_t, const float * src2, size_t step2, float* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void recip64f( const double *, size_t, const double * src2, size_t step2, double* dst, size_t step, int width, int height, void* scale); + +CV_EXPORTS void addWeighted8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _scalars ); +CV_EXPORTS void addWeighted8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* scalars ); +CV_EXPORTS void addWeighted16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* scalars ); +CV_EXPORTS void addWeighted16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* scalars ); +CV_EXPORTS void addWeighted32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* scalars ); +CV_EXPORTS void addWeighted32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* scalars ); +CV_EXPORTS void addWeighted64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* scalars ); + +struct CV_EXPORTS DFT1D +{ + static Ptr create(int len, int count, int depth, int flags, bool * useBuffer = 0); + virtual void apply(const uchar *src, uchar *dst) = 0; + virtual ~DFT1D() {} +}; + +struct CV_EXPORTS DFT2D +{ + static Ptr create(int width, int height, int depth, + int src_channels, int dst_channels, + int flags, int nonzero_rows = 0); + virtual void apply(const uchar *src_data, size_t src_step, uchar *dst_data, size_t dst_step) = 0; + virtual ~DFT2D() {} +}; + +struct CV_EXPORTS DCT2D +{ + static Ptr create(int width, int height, int depth, int flags); + virtual void apply(const uchar *src_data, size_t src_step, uchar *dst_data, size_t dst_step) = 0; + virtual ~DCT2D() {} +}; + +//! @} core_hal + +//============================================================================= +// for binary compatibility with 3.0 + +//! @cond IGNORED + +CV_EXPORTS int LU(float* A, size_t astep, int m, float* b, size_t bstep, int n); +CV_EXPORTS int LU(double* A, size_t astep, int m, double* b, size_t bstep, int n); +CV_EXPORTS bool Cholesky(float* A, size_t astep, int m, float* b, size_t bstep, int n); +CV_EXPORTS bool Cholesky(double* A, size_t astep, int m, double* b, size_t bstep, int n); + +CV_EXPORTS void exp(const float* src, float* dst, int n); +CV_EXPORTS void exp(const double* src, double* dst, int n); +CV_EXPORTS void log(const float* src, float* dst, int n); +CV_EXPORTS void log(const double* src, double* dst, int n); + +CV_EXPORTS void fastAtan2(const float* y, const float* x, float* dst, int n, bool angleInDegrees); +CV_EXPORTS void magnitude(const float* x, const float* y, float* dst, int n); +CV_EXPORTS void magnitude(const double* x, const double* y, double* dst, int n); +CV_EXPORTS void sqrt(const float* src, float* dst, int len); +CV_EXPORTS void sqrt(const double* src, double* dst, int len); +CV_EXPORTS void invSqrt(const float* src, float* dst, int len); +CV_EXPORTS void invSqrt(const double* src, double* dst, int len); + +//! @endcond + +}} //cv::hal + +#endif //OPENCV_HAL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/hal/interface.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/hal/interface.h new file mode 100644 index 0000000000000000000000000000000000000000..8f640254c373728d46d3e4c32ebb15518c6f9b9a --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/hal/interface.h @@ -0,0 +1,182 @@ +#ifndef OPENCV_CORE_HAL_INTERFACE_H +#define OPENCV_CORE_HAL_INTERFACE_H + +//! @addtogroup core_hal_interface +//! @{ + +//! @name Return codes +//! @{ +#define CV_HAL_ERROR_OK 0 +#define CV_HAL_ERROR_NOT_IMPLEMENTED 1 +#define CV_HAL_ERROR_UNKNOWN -1 +//! @} + +#ifdef __cplusplus +#include +#else +#include +#include +#endif + +//! @name Data types +//! primitive types +//! - schar - signed 1 byte integer +//! - uchar - unsigned 1 byte integer +//! - short - signed 2 byte integer +//! - ushort - unsigned 2 byte integer +//! - int - signed 4 byte integer +//! - uint - unsigned 4 byte integer +//! - int64 - signed 8 byte integer +//! - uint64 - unsigned 8 byte integer +//! @{ +#if !defined _MSC_VER && !defined __BORLANDC__ +# if defined __cplusplus && __cplusplus >= 201103L && !defined __APPLE__ +# include +# ifdef __NEWLIB__ + typedef unsigned int uint; +# else + typedef std::uint32_t uint; +# endif +# else +# include + typedef uint32_t uint; +# endif +#else + typedef unsigned uint; +#endif + +typedef signed char schar; + +#ifndef __IPL_H__ + typedef unsigned char uchar; + typedef unsigned short ushort; +#endif + +#if defined _MSC_VER || defined __BORLANDC__ + typedef __int64 int64; + typedef unsigned __int64 uint64; +# define CV_BIG_INT(n) n##I64 +# define CV_BIG_UINT(n) n##UI64 +#else + typedef int64_t int64; + typedef uint64_t uint64; +# define CV_BIG_INT(n) n##LL +# define CV_BIG_UINT(n) n##ULL +#endif + +#define CV_CN_MAX 512 +#define CV_CN_SHIFT 3 +#define CV_DEPTH_MAX (1 << CV_CN_SHIFT) + +#define CV_8U 0 +#define CV_8S 1 +#define CV_16U 2 +#define CV_16S 3 +#define CV_32S 4 +#define CV_32F 5 +#define CV_64F 6 +#define CV_USRTYPE1 7 + +#define CV_MAT_DEPTH_MASK (CV_DEPTH_MAX - 1) +#define CV_MAT_DEPTH(flags) ((flags) & CV_MAT_DEPTH_MASK) + +#define CV_MAKETYPE(depth,cn) (CV_MAT_DEPTH(depth) + (((cn)-1) << CV_CN_SHIFT)) +#define CV_MAKE_TYPE CV_MAKETYPE + +#define CV_8UC1 CV_MAKETYPE(CV_8U,1) +#define CV_8UC2 CV_MAKETYPE(CV_8U,2) +#define CV_8UC3 CV_MAKETYPE(CV_8U,3) +#define CV_8UC4 CV_MAKETYPE(CV_8U,4) +#define CV_8UC(n) CV_MAKETYPE(CV_8U,(n)) + +#define CV_8SC1 CV_MAKETYPE(CV_8S,1) +#define CV_8SC2 CV_MAKETYPE(CV_8S,2) +#define CV_8SC3 CV_MAKETYPE(CV_8S,3) +#define CV_8SC4 CV_MAKETYPE(CV_8S,4) +#define CV_8SC(n) CV_MAKETYPE(CV_8S,(n)) + +#define CV_16UC1 CV_MAKETYPE(CV_16U,1) +#define CV_16UC2 CV_MAKETYPE(CV_16U,2) +#define CV_16UC3 CV_MAKETYPE(CV_16U,3) +#define CV_16UC4 CV_MAKETYPE(CV_16U,4) +#define CV_16UC(n) CV_MAKETYPE(CV_16U,(n)) + +#define CV_16SC1 CV_MAKETYPE(CV_16S,1) +#define CV_16SC2 CV_MAKETYPE(CV_16S,2) +#define CV_16SC3 CV_MAKETYPE(CV_16S,3) +#define CV_16SC4 CV_MAKETYPE(CV_16S,4) +#define CV_16SC(n) CV_MAKETYPE(CV_16S,(n)) + +#define CV_32SC1 CV_MAKETYPE(CV_32S,1) +#define CV_32SC2 CV_MAKETYPE(CV_32S,2) +#define CV_32SC3 CV_MAKETYPE(CV_32S,3) +#define CV_32SC4 CV_MAKETYPE(CV_32S,4) +#define CV_32SC(n) CV_MAKETYPE(CV_32S,(n)) + +#define CV_32FC1 CV_MAKETYPE(CV_32F,1) +#define CV_32FC2 CV_MAKETYPE(CV_32F,2) +#define CV_32FC3 CV_MAKETYPE(CV_32F,3) +#define CV_32FC4 CV_MAKETYPE(CV_32F,4) +#define CV_32FC(n) CV_MAKETYPE(CV_32F,(n)) + +#define CV_64FC1 CV_MAKETYPE(CV_64F,1) +#define CV_64FC2 CV_MAKETYPE(CV_64F,2) +#define CV_64FC3 CV_MAKETYPE(CV_64F,3) +#define CV_64FC4 CV_MAKETYPE(CV_64F,4) +#define CV_64FC(n) CV_MAKETYPE(CV_64F,(n)) +//! @} + +//! @name Comparison operation +//! @sa cv::CmpTypes +//! @{ +#define CV_HAL_CMP_EQ 0 +#define CV_HAL_CMP_GT 1 +#define CV_HAL_CMP_GE 2 +#define CV_HAL_CMP_LT 3 +#define CV_HAL_CMP_LE 4 +#define CV_HAL_CMP_NE 5 +//! @} + +//! @name Border processing modes +//! @sa cv::BorderTypes +//! @{ +#define CV_HAL_BORDER_CONSTANT 0 +#define CV_HAL_BORDER_REPLICATE 1 +#define CV_HAL_BORDER_REFLECT 2 +#define CV_HAL_BORDER_WRAP 3 +#define CV_HAL_BORDER_REFLECT_101 4 +#define CV_HAL_BORDER_TRANSPARENT 5 +#define CV_HAL_BORDER_ISOLATED 16 +//! @} + +//! @name DFT flags +//! @{ +#define CV_HAL_DFT_INVERSE 1 +#define CV_HAL_DFT_SCALE 2 +#define CV_HAL_DFT_ROWS 4 +#define CV_HAL_DFT_COMPLEX_OUTPUT 16 +#define CV_HAL_DFT_REAL_OUTPUT 32 +#define CV_HAL_DFT_TWO_STAGE 64 +#define CV_HAL_DFT_STAGE_COLS 128 +#define CV_HAL_DFT_IS_CONTINUOUS 512 +#define CV_HAL_DFT_IS_INPLACE 1024 +//! @} + +//! @name SVD flags +//! @{ +#define CV_HAL_SVD_NO_UV 1 +#define CV_HAL_SVD_SHORT_UV 2 +#define CV_HAL_SVD_MODIFY_A 4 +#define CV_HAL_SVD_FULL_UV 8 +//! @} + +//! @name Gemm flags +//! @{ +#define CV_HAL_GEMM_1_T 1 +#define CV_HAL_GEMM_2_T 2 +#define CV_HAL_GEMM_3_T 4 +//! @} + +//! @} + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/hal/intrin.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/hal/intrin.hpp new file mode 100644 index 0000000000000000000000000000000000000000..ef74176f3389a47644c92ea34fc65a1c8634e681 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/hal/intrin.hpp @@ -0,0 +1,420 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_HAL_INTRIN_HPP +#define OPENCV_HAL_INTRIN_HPP + +#include +#include +#include +#include "opencv2/core/cvdef.h" + +#define OPENCV_HAL_ADD(a, b) ((a) + (b)) +#define OPENCV_HAL_AND(a, b) ((a) & (b)) +#define OPENCV_HAL_NOP(a) (a) +#define OPENCV_HAL_1ST(a, b) (a) + +// unlike HAL API, which is in cv::hal, +// we put intrinsics into cv namespace to make its +// access from within opencv code more accessible +namespace cv { + +namespace hal { + +enum StoreMode +{ + STORE_UNALIGNED = 0, + STORE_ALIGNED = 1, + STORE_ALIGNED_NOCACHE = 2 +}; + +} + +template struct V_TypeTraits +{ +}; + +#define CV_INTRIN_DEF_TYPE_TRAITS(type, int_type_, uint_type_, abs_type_, w_type_, q_type_, sum_type_, nlanes128_) \ + template<> struct V_TypeTraits \ + { \ + typedef type value_type; \ + typedef int_type_ int_type; \ + typedef abs_type_ abs_type; \ + typedef uint_type_ uint_type; \ + typedef w_type_ w_type; \ + typedef q_type_ q_type; \ + typedef sum_type_ sum_type; \ + enum { nlanes128 = nlanes128_ }; \ + \ + static inline int_type reinterpret_int(type x) \ + { \ + union { type l; int_type i; } v; \ + v.l = x; \ + return v.i; \ + } \ + \ + static inline type reinterpret_from_int(int_type x) \ + { \ + union { type l; int_type i; } v; \ + v.i = x; \ + return v.l; \ + } \ + } + +CV_INTRIN_DEF_TYPE_TRAITS(uchar, schar, uchar, uchar, ushort, unsigned, unsigned, 16); +CV_INTRIN_DEF_TYPE_TRAITS(schar, schar, uchar, uchar, short, int, int, 16); +CV_INTRIN_DEF_TYPE_TRAITS(ushort, short, ushort, ushort, unsigned, uint64, unsigned, 8); +CV_INTRIN_DEF_TYPE_TRAITS(short, short, ushort, ushort, int, int64, int, 8); +CV_INTRIN_DEF_TYPE_TRAITS(unsigned, int, unsigned, unsigned, uint64, void, unsigned, 4); +CV_INTRIN_DEF_TYPE_TRAITS(int, int, unsigned, unsigned, int64, void, int, 4); +CV_INTRIN_DEF_TYPE_TRAITS(float, int, unsigned, float, double, void, float, 4); +CV_INTRIN_DEF_TYPE_TRAITS(uint64, int64, uint64, uint64, void, void, uint64, 2); +CV_INTRIN_DEF_TYPE_TRAITS(int64, int64, uint64, uint64, void, void, int64, 2); +CV_INTRIN_DEF_TYPE_TRAITS(double, int64, uint64, double, void, void, double, 2); + +#ifndef CV_DOXYGEN + +#ifdef CV_CPU_DISPATCH_MODE + #define CV_CPU_OPTIMIZATION_HAL_NAMESPACE __CV_CAT(hal_, CV_CPU_DISPATCH_MODE) + #define CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN namespace __CV_CAT(hal_, CV_CPU_DISPATCH_MODE) { + #define CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END } +#else + #define CV_CPU_OPTIMIZATION_HAL_NAMESPACE hal_baseline + #define CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN namespace hal_baseline { + #define CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END } +#endif + +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END +using namespace CV_CPU_OPTIMIZATION_HAL_NAMESPACE; +#endif +} + +#ifdef CV_DOXYGEN +# undef CV_AVX2 +# undef CV_SSE2 +# undef CV_NEON +# undef CV_VSX +# undef CV_FP16 +#endif + +#if CV_SSE2 || CV_NEON || CV_VSX +#define CV__SIMD_FORWARD 128 +#include "opencv2/core/hal/intrin_forward.hpp" +#endif + +#if CV_SSE2 + +#include "opencv2/core/hal/intrin_sse_em.hpp" +#include "opencv2/core/hal/intrin_sse.hpp" + +#elif CV_NEON + +#include "opencv2/core/hal/intrin_neon.hpp" + +#elif CV_VSX + +#include "opencv2/core/hal/intrin_vsx.hpp" + +#else + +#define CV_SIMD128_CPP 1 +#include "opencv2/core/hal/intrin_cpp.hpp" + +#endif + +// AVX2 can be used together with SSE2, so +// we define those two sets of intrinsics at once. +// Most of the intrinsics do not conflict (the proper overloaded variant is +// resolved by the argument types, e.g. v_float32x4 ~ SSE2, v_float32x8 ~ AVX2), +// but some of AVX2 intrinsics get v256_ prefix instead of v_, e.g. v256_load() vs v_load(). +// Correspondingly, the wide intrinsics (which are mapped to the "widest" +// available instruction set) will get vx_ prefix +// (and will be mapped to v256_ counterparts) (e.g. vx_load() => v256_load()) +#if CV_AVX2 + +#define CV__SIMD_FORWARD 256 +#include "opencv2/core/hal/intrin_forward.hpp" +#include "opencv2/core/hal/intrin_avx.hpp" + +#endif + +//! @cond IGNORED + +namespace cv { + +#ifndef CV_DOXYGEN +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN +#endif + +#ifndef CV_SIMD128 +#define CV_SIMD128 0 +#endif + +#ifndef CV_SIMD128_64F +#define CV_SIMD128_64F 0 +#endif + +#ifndef CV_SIMD256 +#define CV_SIMD256 0 +#endif + +#ifndef CV_SIMD256_64F +#define CV_SIMD256_64F 0 +#endif + +#ifndef CV_SIMD512 +#define CV_SIMD512 0 +#endif + +#ifndef CV_SIMD512_64F +#define CV_SIMD512_64F 0 +#endif + +#ifndef CV_SIMD128_FP16 +#define CV_SIMD128_FP16 0 +#endif + +#ifndef CV_SIMD256_FP16 +#define CV_SIMD256_FP16 0 +#endif + +#ifndef CV_SIMD512_FP16 +#define CV_SIMD512_FP16 0 +#endif + +//================================================================================================== + +#define CV_INTRIN_DEFINE_WIDE_INTRIN(typ, vtyp, short_typ, prefix, loadsfx) \ + inline vtyp vx_setall_##short_typ(typ v) { return prefix##_setall_##short_typ(v); } \ + inline vtyp vx_setzero_##short_typ() { return prefix##_setzero_##short_typ(); } \ + inline vtyp vx_##loadsfx(const typ* ptr) { return prefix##_##loadsfx(ptr); } \ + inline vtyp vx_##loadsfx##_aligned(const typ* ptr) { return prefix##_##loadsfx##_aligned(ptr); } \ + inline vtyp vx_##loadsfx##_low(const typ* ptr) { return prefix##_##loadsfx##_low(ptr); } \ + inline vtyp vx_##loadsfx##_halves(const typ* ptr0, const typ* ptr1) { return prefix##_##loadsfx##_halves(ptr0, ptr1); } \ + inline void vx_store(typ* ptr, const vtyp& v) { return v_store(ptr, v); } \ + inline void vx_store_aligned(typ* ptr, const vtyp& v) { return v_store_aligned(ptr, v); } + +#define CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND(typ, wtyp, prefix) \ + inline wtyp vx_load_expand(const typ* ptr) { return prefix##_load_expand(ptr); } + +#define CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND_Q(typ, qtyp, prefix) \ + inline qtyp vx_load_expand_q(const typ* ptr) { return prefix##_load_expand_q(ptr); } + +#define CV_INTRIN_DEFINE_WIDE_INTRIN_WITH_EXPAND(typ, vtyp, short_typ, wtyp, qtyp, prefix, loadsfx) \ + CV_INTRIN_DEFINE_WIDE_INTRIN(typ, vtyp, short_typ, prefix, loadsfx) \ + CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND(typ, wtyp, prefix) \ + CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND_Q(typ, qtyp, prefix) + +#define CV_INTRIN_DEFINE_WIDE_INTRIN_ALL_TYPES(prefix) \ + CV_INTRIN_DEFINE_WIDE_INTRIN_WITH_EXPAND(uchar, v_uint8, u8, v_uint16, v_uint32, prefix, load) \ + CV_INTRIN_DEFINE_WIDE_INTRIN_WITH_EXPAND(schar, v_int8, s8, v_int16, v_int32, prefix, load) \ + CV_INTRIN_DEFINE_WIDE_INTRIN(ushort, v_uint16, u16, prefix, load) \ + CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND(ushort, v_uint32, prefix) \ + CV_INTRIN_DEFINE_WIDE_INTRIN(short, v_int16, s16, prefix, load) \ + CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND(short, v_int32, prefix) \ + CV_INTRIN_DEFINE_WIDE_INTRIN(int, v_int32, s32, prefix, load) \ + CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND(int, v_int64, prefix) \ + CV_INTRIN_DEFINE_WIDE_INTRIN(unsigned, v_uint32, u32, prefix, load) \ + CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND(unsigned, v_uint64, prefix) \ + CV_INTRIN_DEFINE_WIDE_INTRIN(float, v_float32, f32, prefix, load) \ + CV_INTRIN_DEFINE_WIDE_INTRIN(int64, v_int64, s64, prefix, load) \ + CV_INTRIN_DEFINE_WIDE_INTRIN(uint64, v_uint64, u64, prefix, load) \ + CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND(float16_t, v_float32, prefix) + +template struct V_RegTraits +{ +}; + +#define CV_DEF_REG_TRAITS(prefix, _reg, lane_type, suffix, _u_reg, _w_reg, _q_reg, _int_reg, _round_reg) \ + template<> struct V_RegTraits<_reg> \ + { \ + typedef _reg reg; \ + typedef _u_reg u_reg; \ + typedef _w_reg w_reg; \ + typedef _q_reg q_reg; \ + typedef _int_reg int_reg; \ + typedef _round_reg round_reg; \ + } + +#if CV_SIMD128 || CV_SIMD128_CPP + CV_DEF_REG_TRAITS(v, v_uint8x16, uchar, u8, v_uint8x16, v_uint16x8, v_uint32x4, v_int8x16, void); + CV_DEF_REG_TRAITS(v, v_int8x16, schar, s8, v_uint8x16, v_int16x8, v_int32x4, v_int8x16, void); + CV_DEF_REG_TRAITS(v, v_uint16x8, ushort, u16, v_uint16x8, v_uint32x4, v_uint64x2, v_int16x8, void); + CV_DEF_REG_TRAITS(v, v_int16x8, short, s16, v_uint16x8, v_int32x4, v_int64x2, v_int16x8, void); + CV_DEF_REG_TRAITS(v, v_uint32x4, unsigned, u32, v_uint32x4, v_uint64x2, void, v_int32x4, void); + CV_DEF_REG_TRAITS(v, v_int32x4, int, s32, v_uint32x4, v_int64x2, void, v_int32x4, void); +#if CV_SIMD128_64F + CV_DEF_REG_TRAITS(v, v_float32x4, float, f32, v_float32x4, v_float64x2, void, v_int32x4, v_int32x4); +#else + CV_DEF_REG_TRAITS(v, v_float32x4, float, f32, v_float32x4, void, void, v_int32x4, v_int32x4); +#endif + CV_DEF_REG_TRAITS(v, v_uint64x2, uint64, u64, v_uint64x2, void, void, v_int64x2, void); + CV_DEF_REG_TRAITS(v, v_int64x2, int64, s64, v_uint64x2, void, void, v_int64x2, void); +#if CV_SIMD128_64F + CV_DEF_REG_TRAITS(v, v_float64x2, double, f64, v_float64x2, void, void, v_int64x2, v_int32x4); +#endif +#endif + +#if CV_SIMD256 + CV_DEF_REG_TRAITS(v256, v_uint8x32, uchar, u8, v_uint8x32, v_uint16x16, v_uint32x8, v_int8x32, void); + CV_DEF_REG_TRAITS(v256, v_int8x32, schar, s8, v_uint8x32, v_int16x16, v_int32x8, v_int8x32, void); + CV_DEF_REG_TRAITS(v256, v_uint16x16, ushort, u16, v_uint16x16, v_uint32x8, v_uint64x4, v_int16x16, void); + CV_DEF_REG_TRAITS(v256, v_int16x16, short, s16, v_uint16x16, v_int32x8, v_int64x4, v_int16x16, void); + CV_DEF_REG_TRAITS(v256, v_uint32x8, unsigned, u32, v_uint32x8, v_uint64x4, void, v_int32x8, void); + CV_DEF_REG_TRAITS(v256, v_int32x8, int, s32, v_uint32x8, v_int64x4, void, v_int32x8, void); + CV_DEF_REG_TRAITS(v256, v_float32x8, float, f32, v_float32x8, v_float64x4, void, v_int32x8, v_int32x8); + CV_DEF_REG_TRAITS(v256, v_uint64x4, uint64, u64, v_uint64x4, void, void, v_int64x4, void); + CV_DEF_REG_TRAITS(v256, v_int64x4, int64, s64, v_uint64x4, void, void, v_int64x4, void); + CV_DEF_REG_TRAITS(v256, v_float64x4, double, f64, v_float64x4, void, void, v_int64x4, v_int32x8); +#endif + +#if CV_SIMD512 && (!defined(CV__SIMD_FORCE_WIDTH) || CV__SIMD_FORCE_WIDTH == 512) +#define CV__SIMD_NAMESPACE simd512 +namespace CV__SIMD_NAMESPACE { + #define CV_SIMD 1 + #define CV_SIMD_64F CV_SIMD512_64F + #define CV_SIMD_WIDTH 64 + // TODO typedef v_uint8 / v_int32 / etc types here +} // namespace +using namespace CV__SIMD_NAMESPACE; +#elif CV_SIMD256 && (!defined(CV__SIMD_FORCE_WIDTH) || CV__SIMD_FORCE_WIDTH == 256) +#define CV__SIMD_NAMESPACE simd256 +namespace CV__SIMD_NAMESPACE { + #define CV_SIMD 1 + #define CV_SIMD_64F CV_SIMD256_64F + #define CV_SIMD_FP16 CV_SIMD256_FP16 + #define CV_SIMD_WIDTH 32 + typedef v_uint8x32 v_uint8; + typedef v_int8x32 v_int8; + typedef v_uint16x16 v_uint16; + typedef v_int16x16 v_int16; + typedef v_uint32x8 v_uint32; + typedef v_int32x8 v_int32; + typedef v_uint64x4 v_uint64; + typedef v_int64x4 v_int64; + typedef v_float32x8 v_float32; + #if CV_SIMD256_64F + typedef v_float64x4 v_float64; + #endif + CV_INTRIN_DEFINE_WIDE_INTRIN_ALL_TYPES(v256) + CV_INTRIN_DEFINE_WIDE_INTRIN(double, v_float64, f64, v256, load) + inline void vx_cleanup() { v256_cleanup(); } +} // namespace +using namespace CV__SIMD_NAMESPACE; +#elif (CV_SIMD128 || CV_SIMD128_CPP) && (!defined(CV__SIMD_FORCE_WIDTH) || CV__SIMD_FORCE_WIDTH == 128) +#define CV__SIMD_NAMESPACE simd128 +namespace CV__SIMD_NAMESPACE { + #define CV_SIMD CV_SIMD128 + #define CV_SIMD_64F CV_SIMD128_64F + #define CV_SIMD_WIDTH 16 + typedef v_uint8x16 v_uint8; + typedef v_int8x16 v_int8; + typedef v_uint16x8 v_uint16; + typedef v_int16x8 v_int16; + typedef v_uint32x4 v_uint32; + typedef v_int32x4 v_int32; + typedef v_uint64x2 v_uint64; + typedef v_int64x2 v_int64; + typedef v_float32x4 v_float32; + #if CV_SIMD128_64F + typedef v_float64x2 v_float64; + #endif + CV_INTRIN_DEFINE_WIDE_INTRIN_ALL_TYPES(v) + #if CV_SIMD128_64F + CV_INTRIN_DEFINE_WIDE_INTRIN(double, v_float64, f64, v, load) + #endif + inline void vx_cleanup() { v_cleanup(); } +} // namespace +using namespace CV__SIMD_NAMESPACE; +#endif + +inline unsigned int trailingZeros32(unsigned int value) { +#if defined(_MSC_VER) +#if (_MSC_VER < 1700) || defined(_M_ARM) + unsigned long index = 0; + _BitScanForward(&index, value); + return (unsigned int)index; +#elif defined(__clang__) + // clang-cl doesn't export _tzcnt_u32 for non BMI systems + return value ? __builtin_ctz(value) : 32; +#else + return _tzcnt_u32(value); +#endif +#elif defined(__GNUC__) || defined(__GNUG__) + return __builtin_ctz(value); +#elif defined(__ICC) || defined(__INTEL_COMPILER) + return _bit_scan_forward(value); +#elif defined(__clang__) + return llvm.cttz.i32(value, true); +#else + static const int MultiplyDeBruijnBitPosition[32] = { + 0, 1, 28, 2, 29, 14, 24, 3, 30, 22, 20, 15, 25, 17, 4, 8, + 31, 27, 13, 23, 21, 19, 16, 7, 26, 12, 18, 6, 11, 5, 10, 9 }; + return MultiplyDeBruijnBitPosition[((uint32_t)((value & -value) * 0x077CB531U)) >> 27]; +#endif +} + +#ifndef CV_DOXYGEN +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END +#endif + +#ifndef CV_SIMD_64F +#define CV_SIMD_64F 0 +#endif + +#ifndef CV_SIMD_FP16 +#define CV_SIMD_FP16 0 //!< Defined to 1 on native support of operations with float16x8_t / float16x16_t (SIMD256) types +#endif + + +#ifndef CV_SIMD +#define CV_SIMD 0 +#endif + +} // cv:: + +//! @endcond + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/hal/intrin_avx.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/hal/intrin_avx.hpp new file mode 100644 index 0000000000000000000000000000000000000000..c3797d67c1031bcc5d27f546b49734766a1e6e4d --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/hal/intrin_avx.hpp @@ -0,0 +1,2628 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html + +#ifndef OPENCV_HAL_INTRIN_AVX_HPP +#define OPENCV_HAL_INTRIN_AVX_HPP + +#define CV_SIMD256 1 +#define CV_SIMD256_64F 1 +#define CV_SIMD256_FP16 0 // no native operations with FP16 type. Only load/store from float32x8 are available (if CV_FP16 == 1) + +namespace cv +{ + +//! @cond IGNORED + +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN + +///////// Utils //////////// + +inline __m256i _v256_combine(const __m128i& lo, const __m128i& hi) +{ return _mm256_inserti128_si256(_mm256_castsi128_si256(lo), hi, 1); } + +inline __m256 _v256_combine(const __m128& lo, const __m128& hi) +{ return _mm256_insertf128_ps(_mm256_castps128_ps256(lo), hi, 1); } + +inline __m256d _v256_combine(const __m128d& lo, const __m128d& hi) +{ return _mm256_insertf128_pd(_mm256_castpd128_pd256(lo), hi, 1); } + +inline int _v_cvtsi256_si32(const __m256i& a) +{ return _mm_cvtsi128_si32(_mm256_castsi256_si128(a)); } + +inline __m256i _v256_shuffle_odd_64(const __m256i& v) +{ return _mm256_permute4x64_epi64(v, _MM_SHUFFLE(3, 1, 2, 0)); } + +inline __m256d _v256_shuffle_odd_64(const __m256d& v) +{ return _mm256_permute4x64_pd(v, _MM_SHUFFLE(3, 1, 2, 0)); } + +template +inline __m256i _v256_permute2x128(const __m256i& a, const __m256i& b) +{ return _mm256_permute2x128_si256(a, b, imm); } + +template +inline __m256 _v256_permute2x128(const __m256& a, const __m256& b) +{ return _mm256_permute2f128_ps(a, b, imm); } + +template +inline __m256d _v256_permute2x128(const __m256d& a, const __m256d& b) +{ return _mm256_permute2f128_pd(a, b, imm); } + +template +inline _Tpvec v256_permute2x128(const _Tpvec& a, const _Tpvec& b) +{ return _Tpvec(_v256_permute2x128(a.val, b.val)); } + +template +inline __m256i _v256_permute4x64(const __m256i& a) +{ return _mm256_permute4x64_epi64(a, imm); } + +template +inline __m256d _v256_permute4x64(const __m256d& a) +{ return _mm256_permute4x64_pd(a, imm); } + +template +inline _Tpvec v256_permute4x64(const _Tpvec& a) +{ return _Tpvec(_v256_permute4x64(a.val)); } + +inline __m128i _v256_extract_high(const __m256i& v) +{ return _mm256_extracti128_si256(v, 1); } + +inline __m128 _v256_extract_high(const __m256& v) +{ return _mm256_extractf128_ps(v, 1); } + +inline __m128d _v256_extract_high(const __m256d& v) +{ return _mm256_extractf128_pd(v, 1); } + +inline __m128i _v256_extract_low(const __m256i& v) +{ return _mm256_castsi256_si128(v); } + +inline __m128 _v256_extract_low(const __m256& v) +{ return _mm256_castps256_ps128(v); } + +inline __m128d _v256_extract_low(const __m256d& v) +{ return _mm256_castpd256_pd128(v); } + +inline __m256i _v256_packs_epu32(const __m256i& a, const __m256i& b) +{ + const __m256i m = _mm256_set1_epi32(65535); + __m256i am = _mm256_min_epu32(a, m); + __m256i bm = _mm256_min_epu32(b, m); + return _mm256_packus_epi32(am, bm); +} + +///////// Types //////////// + +struct v_uint8x32 +{ + typedef uchar lane_type; + enum { nlanes = 32 }; + __m256i val; + + explicit v_uint8x32(__m256i v) : val(v) {} + v_uint8x32(uchar v0, uchar v1, uchar v2, uchar v3, + uchar v4, uchar v5, uchar v6, uchar v7, + uchar v8, uchar v9, uchar v10, uchar v11, + uchar v12, uchar v13, uchar v14, uchar v15, + uchar v16, uchar v17, uchar v18, uchar v19, + uchar v20, uchar v21, uchar v22, uchar v23, + uchar v24, uchar v25, uchar v26, uchar v27, + uchar v28, uchar v29, uchar v30, uchar v31) + { + val = _mm256_setr_epi8((char)v0, (char)v1, (char)v2, (char)v3, + (char)v4, (char)v5, (char)v6 , (char)v7, (char)v8, (char)v9, + (char)v10, (char)v11, (char)v12, (char)v13, (char)v14, (char)v15, + (char)v16, (char)v17, (char)v18, (char)v19, (char)v20, (char)v21, + (char)v22, (char)v23, (char)v24, (char)v25, (char)v26, (char)v27, + (char)v28, (char)v29, (char)v30, (char)v31); + } + v_uint8x32() : val(_mm256_setzero_si256()) {} + uchar get0() const { return (uchar)_v_cvtsi256_si32(val); } +}; + +struct v_int8x32 +{ + typedef schar lane_type; + enum { nlanes = 32 }; + __m256i val; + + explicit v_int8x32(__m256i v) : val(v) {} + v_int8x32(schar v0, schar v1, schar v2, schar v3, + schar v4, schar v5, schar v6, schar v7, + schar v8, schar v9, schar v10, schar v11, + schar v12, schar v13, schar v14, schar v15, + schar v16, schar v17, schar v18, schar v19, + schar v20, schar v21, schar v22, schar v23, + schar v24, schar v25, schar v26, schar v27, + schar v28, schar v29, schar v30, schar v31) + { + val = _mm256_setr_epi8(v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, + v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, + v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31); + } + v_int8x32() : val(_mm256_setzero_si256()) {} + schar get0() const { return (schar)_v_cvtsi256_si32(val); } +}; + +struct v_uint16x16 +{ + typedef ushort lane_type; + enum { nlanes = 16 }; + __m256i val; + + explicit v_uint16x16(__m256i v) : val(v) {} + v_uint16x16(ushort v0, ushort v1, ushort v2, ushort v3, + ushort v4, ushort v5, ushort v6, ushort v7, + ushort v8, ushort v9, ushort v10, ushort v11, + ushort v12, ushort v13, ushort v14, ushort v15) + { + val = _mm256_setr_epi16((short)v0, (short)v1, (short)v2, (short)v3, + (short)v4, (short)v5, (short)v6, (short)v7, (short)v8, (short)v9, + (short)v10, (short)v11, (short)v12, (short)v13, (short)v14, (short)v15); + } + v_uint16x16() : val(_mm256_setzero_si256()) {} + ushort get0() const { return (ushort)_v_cvtsi256_si32(val); } +}; + +struct v_int16x16 +{ + typedef short lane_type; + enum { nlanes = 16 }; + __m256i val; + + explicit v_int16x16(__m256i v) : val(v) {} + v_int16x16(short v0, short v1, short v2, short v3, + short v4, short v5, short v6, short v7, + short v8, short v9, short v10, short v11, + short v12, short v13, short v14, short v15) + { + val = _mm256_setr_epi16(v0, v1, v2, v3, v4, v5, v6, v7, + v8, v9, v10, v11, v12, v13, v14, v15); + } + v_int16x16() : val(_mm256_setzero_si256()) {} + short get0() const { return (short)_v_cvtsi256_si32(val); } +}; + +struct v_uint32x8 +{ + typedef unsigned lane_type; + enum { nlanes = 8 }; + __m256i val; + + explicit v_uint32x8(__m256i v) : val(v) {} + v_uint32x8(unsigned v0, unsigned v1, unsigned v2, unsigned v3, + unsigned v4, unsigned v5, unsigned v6, unsigned v7) + { + val = _mm256_setr_epi32((unsigned)v0, (unsigned)v1, (unsigned)v2, + (unsigned)v3, (unsigned)v4, (unsigned)v5, (unsigned)v6, (unsigned)v7); + } + v_uint32x8() : val(_mm256_setzero_si256()) {} + unsigned get0() const { return (unsigned)_v_cvtsi256_si32(val); } +}; + +struct v_int32x8 +{ + typedef int lane_type; + enum { nlanes = 8 }; + __m256i val; + + explicit v_int32x8(__m256i v) : val(v) {} + v_int32x8(int v0, int v1, int v2, int v3, + int v4, int v5, int v6, int v7) + { + val = _mm256_setr_epi32(v0, v1, v2, v3, v4, v5, v6, v7); + } + v_int32x8() : val(_mm256_setzero_si256()) {} + int get0() const { return _v_cvtsi256_si32(val); } +}; + +struct v_float32x8 +{ + typedef float lane_type; + enum { nlanes = 8 }; + __m256 val; + + explicit v_float32x8(__m256 v) : val(v) {} + v_float32x8(float v0, float v1, float v2, float v3, + float v4, float v5, float v6, float v7) + { + val = _mm256_setr_ps(v0, v1, v2, v3, v4, v5, v6, v7); + } + v_float32x8() : val(_mm256_setzero_ps()) {} + float get0() const { return _mm_cvtss_f32(_mm256_castps256_ps128(val)); } +}; + +struct v_uint64x4 +{ + typedef uint64 lane_type; + enum { nlanes = 4 }; + __m256i val; + + explicit v_uint64x4(__m256i v) : val(v) {} + v_uint64x4(uint64 v0, uint64 v1, uint64 v2, uint64 v3) + { val = _mm256_setr_epi64x((int64)v0, (int64)v1, (int64)v2, (int64)v3); } + v_uint64x4() : val(_mm256_setzero_si256()) {} + uint64 get0() const + { + #if defined __x86_64__ || defined _M_X64 + return (uint64)_mm_cvtsi128_si64(_mm256_castsi256_si128(val)); + #else + int a = _mm_cvtsi128_si32(_mm256_castsi256_si128(val)); + int b = _mm_cvtsi128_si32(_mm256_castsi256_si128(_mm256_srli_epi64(val, 32))); + return (unsigned)a | ((uint64)(unsigned)b << 32); + #endif + } +}; + +struct v_int64x4 +{ + typedef int64 lane_type; + enum { nlanes = 4 }; + __m256i val; + + explicit v_int64x4(__m256i v) : val(v) {} + v_int64x4(int64 v0, int64 v1, int64 v2, int64 v3) + { val = _mm256_setr_epi64x(v0, v1, v2, v3); } + v_int64x4() : val(_mm256_setzero_si256()) {} + + int64 get0() const + { + #if defined __x86_64__ || defined _M_X64 + return (int64)_mm_cvtsi128_si64(_mm256_castsi256_si128(val)); + #else + int a = _mm_cvtsi128_si32(_mm256_castsi256_si128(val)); + int b = _mm_cvtsi128_si32(_mm256_castsi256_si128(_mm256_srli_epi64(val, 32))); + return (int64)((unsigned)a | ((uint64)(unsigned)b << 32)); + #endif + } +}; + +struct v_float64x4 +{ + typedef double lane_type; + enum { nlanes = 4 }; + __m256d val; + + explicit v_float64x4(__m256d v) : val(v) {} + v_float64x4(double v0, double v1, double v2, double v3) + { val = _mm256_setr_pd(v0, v1, v2, v3); } + v_float64x4() : val(_mm256_setzero_pd()) {} + double get0() const { return _mm_cvtsd_f64(_mm256_castpd256_pd128(val)); } +}; + +//////////////// Load and store operations /////////////// + +#define OPENCV_HAL_IMPL_AVX_LOADSTORE(_Tpvec, _Tp) \ + inline _Tpvec v256_load(const _Tp* ptr) \ + { return _Tpvec(_mm256_loadu_si256((const __m256i*)ptr)); } \ + inline _Tpvec v256_load_aligned(const _Tp* ptr) \ + { return _Tpvec(_mm256_load_si256((const __m256i*)ptr)); } \ + inline _Tpvec v256_load_low(const _Tp* ptr) \ + { \ + __m128i v128 = _mm_loadu_si128((const __m128i*)ptr); \ + return _Tpvec(_mm256_castsi128_si256(v128)); \ + } \ + inline _Tpvec v256_load_halves(const _Tp* ptr0, const _Tp* ptr1) \ + { \ + __m128i vlo = _mm_loadu_si128((const __m128i*)ptr0); \ + __m128i vhi = _mm_loadu_si128((const __m128i*)ptr1); \ + return _Tpvec(_v256_combine(vlo, vhi)); \ + } \ + inline void v_store(_Tp* ptr, const _Tpvec& a) \ + { _mm256_storeu_si256((__m256i*)ptr, a.val); } \ + inline void v_store_aligned(_Tp* ptr, const _Tpvec& a) \ + { _mm256_store_si256((__m256i*)ptr, a.val); } \ + inline void v_store_aligned_nocache(_Tp* ptr, const _Tpvec& a) \ + { _mm256_stream_si256((__m256i*)ptr, a.val); } \ + inline void v_store(_Tp* ptr, const _Tpvec& a, hal::StoreMode mode) \ + { \ + if( mode == hal::STORE_UNALIGNED ) \ + _mm256_storeu_si256((__m256i*)ptr, a.val); \ + else if( mode == hal::STORE_ALIGNED_NOCACHE ) \ + _mm256_stream_si256((__m256i*)ptr, a.val); \ + else \ + _mm256_store_si256((__m256i*)ptr, a.val); \ + } \ + inline void v_store_low(_Tp* ptr, const _Tpvec& a) \ + { _mm_storeu_si128((__m128i*)ptr, _v256_extract_low(a.val)); } \ + inline void v_store_high(_Tp* ptr, const _Tpvec& a) \ + { _mm_storeu_si128((__m128i*)ptr, _v256_extract_high(a.val)); } + +OPENCV_HAL_IMPL_AVX_LOADSTORE(v_uint8x32, uchar) +OPENCV_HAL_IMPL_AVX_LOADSTORE(v_int8x32, schar) +OPENCV_HAL_IMPL_AVX_LOADSTORE(v_uint16x16, ushort) +OPENCV_HAL_IMPL_AVX_LOADSTORE(v_int16x16, short) +OPENCV_HAL_IMPL_AVX_LOADSTORE(v_uint32x8, unsigned) +OPENCV_HAL_IMPL_AVX_LOADSTORE(v_int32x8, int) +OPENCV_HAL_IMPL_AVX_LOADSTORE(v_uint64x4, uint64) +OPENCV_HAL_IMPL_AVX_LOADSTORE(v_int64x4, int64) + +#define OPENCV_HAL_IMPL_AVX_LOADSTORE_FLT(_Tpvec, _Tp, suffix, halfreg) \ + inline _Tpvec v256_load(const _Tp* ptr) \ + { return _Tpvec(_mm256_loadu_##suffix(ptr)); } \ + inline _Tpvec v256_load_aligned(const _Tp* ptr) \ + { return _Tpvec(_mm256_load_##suffix(ptr)); } \ + inline _Tpvec v256_load_low(const _Tp* ptr) \ + { \ + return _Tpvec(_mm256_cast##suffix##128_##suffix##256 \ + (_mm_loadu_##suffix(ptr))); \ + } \ + inline _Tpvec v256_load_halves(const _Tp* ptr0, const _Tp* ptr1) \ + { \ + halfreg vlo = _mm_loadu_##suffix(ptr0); \ + halfreg vhi = _mm_loadu_##suffix(ptr1); \ + return _Tpvec(_v256_combine(vlo, vhi)); \ + } \ + inline void v_store(_Tp* ptr, const _Tpvec& a) \ + { _mm256_storeu_##suffix(ptr, a.val); } \ + inline void v_store_aligned(_Tp* ptr, const _Tpvec& a) \ + { _mm256_store_##suffix(ptr, a.val); } \ + inline void v_store_aligned_nocache(_Tp* ptr, const _Tpvec& a) \ + { _mm256_stream_##suffix(ptr, a.val); } \ + inline void v_store(_Tp* ptr, const _Tpvec& a, hal::StoreMode mode) \ + { \ + if( mode == hal::STORE_UNALIGNED ) \ + _mm256_storeu_##suffix(ptr, a.val); \ + else if( mode == hal::STORE_ALIGNED_NOCACHE ) \ + _mm256_stream_##suffix(ptr, a.val); \ + else \ + _mm256_store_##suffix(ptr, a.val); \ + } \ + inline void v_store_low(_Tp* ptr, const _Tpvec& a) \ + { _mm_storeu_##suffix(ptr, _v256_extract_low(a.val)); } \ + inline void v_store_high(_Tp* ptr, const _Tpvec& a) \ + { _mm_storeu_##suffix(ptr, _v256_extract_high(a.val)); } + +OPENCV_HAL_IMPL_AVX_LOADSTORE_FLT(v_float32x8, float, ps, __m128) +OPENCV_HAL_IMPL_AVX_LOADSTORE_FLT(v_float64x4, double, pd, __m128d) + +#define OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, _Tpvecf, suffix, cast) \ + inline _Tpvec v_reinterpret_as_##suffix(const _Tpvecf& a) \ + { return _Tpvec(cast(a.val)); } + +#define OPENCV_HAL_IMPL_AVX_INIT(_Tpvec, _Tp, suffix, ssuffix, ctype_s) \ + inline _Tpvec v256_setzero_##suffix() \ + { return _Tpvec(_mm256_setzero_si256()); } \ + inline _Tpvec v256_setall_##suffix(_Tp v) \ + { return _Tpvec(_mm256_set1_##ssuffix((ctype_s)v)); } \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_uint8x32, suffix, OPENCV_HAL_NOP) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_int8x32, suffix, OPENCV_HAL_NOP) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_uint16x16, suffix, OPENCV_HAL_NOP) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_int16x16, suffix, OPENCV_HAL_NOP) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_uint32x8, suffix, OPENCV_HAL_NOP) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_int32x8, suffix, OPENCV_HAL_NOP) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_uint64x4, suffix, OPENCV_HAL_NOP) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_int64x4, suffix, OPENCV_HAL_NOP) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_float32x8, suffix, _mm256_castps_si256) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_float64x4, suffix, _mm256_castpd_si256) + +OPENCV_HAL_IMPL_AVX_INIT(v_uint8x32, uchar, u8, epi8, char) +OPENCV_HAL_IMPL_AVX_INIT(v_int8x32, schar, s8, epi8, char) +OPENCV_HAL_IMPL_AVX_INIT(v_uint16x16, ushort, u16, epi16, short) +OPENCV_HAL_IMPL_AVX_INIT(v_int16x16, short, s16, epi16, short) +OPENCV_HAL_IMPL_AVX_INIT(v_uint32x8, unsigned, u32, epi32, int) +OPENCV_HAL_IMPL_AVX_INIT(v_int32x8, int, s32, epi32, int) +OPENCV_HAL_IMPL_AVX_INIT(v_uint64x4, uint64, u64, epi64x, int64) +OPENCV_HAL_IMPL_AVX_INIT(v_int64x4, int64, s64, epi64x, int64) + +#define OPENCV_HAL_IMPL_AVX_INIT_FLT(_Tpvec, _Tp, suffix, zsuffix, cast) \ + inline _Tpvec v256_setzero_##suffix() \ + { return _Tpvec(_mm256_setzero_##zsuffix()); } \ + inline _Tpvec v256_setall_##suffix(_Tp v) \ + { return _Tpvec(_mm256_set1_##zsuffix(v)); } \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_uint8x32, suffix, cast) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_int8x32, suffix, cast) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_uint16x16, suffix, cast) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_int16x16, suffix, cast) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_uint32x8, suffix, cast) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_int32x8, suffix, cast) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_uint64x4, suffix, cast) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_int64x4, suffix, cast) + +OPENCV_HAL_IMPL_AVX_INIT_FLT(v_float32x8, float, f32, ps, _mm256_castsi256_ps) +OPENCV_HAL_IMPL_AVX_INIT_FLT(v_float64x4, double, f64, pd, _mm256_castsi256_pd) + +inline v_float32x8 v_reinterpret_as_f32(const v_float32x8& a) +{ return a; } +inline v_float32x8 v_reinterpret_as_f32(const v_float64x4& a) +{ return v_float32x8(_mm256_castpd_ps(a.val)); } + +inline v_float64x4 v_reinterpret_as_f64(const v_float64x4& a) +{ return a; } +inline v_float64x4 v_reinterpret_as_f64(const v_float32x8& a) +{ return v_float64x4(_mm256_castps_pd(a.val)); } + +#if CV_FP16 +inline v_float32x8 v256_load_fp16_f32(const short* ptr) +{ + return v_float32x8(_mm256_cvtph_ps(_mm_loadu_si128((const __m128i*)ptr))); +} + +inline void v_store_fp16(short* ptr, const v_float32x8& a) +{ + __m128i fp16_value = _mm256_cvtps_ph(a.val, 0); + _mm_store_si128((__m128i*)ptr, fp16_value); +} +#endif + +/* Recombine */ +/*#define OPENCV_HAL_IMPL_AVX_COMBINE(_Tpvec, perm) \ + inline _Tpvec v_combine_low(const _Tpvec& a, const _Tpvec& b) \ + { return _Tpvec(perm(a.val, b.val, 0x20)); } \ + inline _Tpvec v_combine_high(const _Tpvec& a, const _Tpvec& b) \ + { return _Tpvec(perm(a.val, b.val, 0x31)); } \ + inline void v_recombine(const _Tpvec& a, const _Tpvec& b, \ + _Tpvec& c, _Tpvec& d) \ + { c = v_combine_low(a, b); d = v_combine_high(a, b); } + +#define OPENCV_HAL_IMPL_AVX_UNPACKS(_Tpvec, suffix) \ + OPENCV_HAL_IMPL_AVX_COMBINE(_Tpvec, _mm256_permute2x128_si256) \ + inline void v_zip(const _Tpvec& a0, const _Tpvec& a1, \ + _Tpvec& b0, _Tpvec& b1) \ + { \ + __m256i v0 = _v256_shuffle_odd_64(a0.val); \ + __m256i v1 = _v256_shuffle_odd_64(a1.val); \ + b0.val = _mm256_unpacklo_##suffix(v0, v1); \ + b1.val = _mm256_unpackhi_##suffix(v0, v1); \ + } + +OPENCV_HAL_IMPL_AVX_UNPACKS(v_uint8x32, epi8) +OPENCV_HAL_IMPL_AVX_UNPACKS(v_int8x32, epi8) +OPENCV_HAL_IMPL_AVX_UNPACKS(v_uint16x16, epi16) +OPENCV_HAL_IMPL_AVX_UNPACKS(v_int16x16, epi16) +OPENCV_HAL_IMPL_AVX_UNPACKS(v_uint32x8, epi32) +OPENCV_HAL_IMPL_AVX_UNPACKS(v_int32x8, epi32) +OPENCV_HAL_IMPL_AVX_UNPACKS(v_uint64x4, epi64) +OPENCV_HAL_IMPL_AVX_UNPACKS(v_int64x4, epi64) +OPENCV_HAL_IMPL_AVX_COMBINE(v_float32x8, _mm256_permute2f128_ps) +OPENCV_HAL_IMPL_AVX_COMBINE(v_float64x4, _mm256_permute2f128_pd) + +inline void v_zip(const v_float32x8& a0, const v_float32x8& a1, v_float32x8& b0, v_float32x8& b1) +{ + __m256 v0 = _mm256_unpacklo_ps(a0.val, a1.val); + __m256 v1 = _mm256_unpackhi_ps(a0.val, a1.val); + v_recombine(v_float32x8(v0), v_float32x8(v1), b0, b1); +} + +inline void v_zip(const v_float64x4& a0, const v_float64x4& a1, v_float64x4& b0, v_float64x4& b1) +{ + __m256d v0 = _v_shuffle_odd_64(a0.val); + __m256d v1 = _v_shuffle_odd_64(a1.val); + b0.val = _mm256_unpacklo_pd(v0, v1); + b1.val = _mm256_unpackhi_pd(v0, v1); +}*/ + +//////////////// Variant Value reordering /////////////// + +// unpacks +#define OPENCV_HAL_IMPL_AVX_UNPACK(_Tpvec, suffix) \ + inline _Tpvec v256_unpacklo(const _Tpvec& a, const _Tpvec& b) \ + { return _Tpvec(_mm256_unpacklo_##suffix(a.val, b.val)); } \ + inline _Tpvec v256_unpackhi(const _Tpvec& a, const _Tpvec& b) \ + { return _Tpvec(_mm256_unpackhi_##suffix(a.val, b.val)); } + +OPENCV_HAL_IMPL_AVX_UNPACK(v_uint8x32, epi8) +OPENCV_HAL_IMPL_AVX_UNPACK(v_int8x32, epi8) +OPENCV_HAL_IMPL_AVX_UNPACK(v_uint16x16, epi16) +OPENCV_HAL_IMPL_AVX_UNPACK(v_int16x16, epi16) +OPENCV_HAL_IMPL_AVX_UNPACK(v_uint32x8, epi32) +OPENCV_HAL_IMPL_AVX_UNPACK(v_int32x8, epi32) +OPENCV_HAL_IMPL_AVX_UNPACK(v_uint64x4, epi64) +OPENCV_HAL_IMPL_AVX_UNPACK(v_int64x4, epi64) +OPENCV_HAL_IMPL_AVX_UNPACK(v_float32x8, ps) +OPENCV_HAL_IMPL_AVX_UNPACK(v_float64x4, pd) + +// blend +#define OPENCV_HAL_IMPL_AVX_BLEND(_Tpvec, suffix) \ + template \ + inline _Tpvec v256_blend(const _Tpvec& a, const _Tpvec& b) \ + { return _Tpvec(_mm256_blend_##suffix(a.val, b.val, m)); } + +OPENCV_HAL_IMPL_AVX_BLEND(v_uint16x16, epi16) +OPENCV_HAL_IMPL_AVX_BLEND(v_int16x16, epi16) +OPENCV_HAL_IMPL_AVX_BLEND(v_uint32x8, epi32) +OPENCV_HAL_IMPL_AVX_BLEND(v_int32x8, epi32) +OPENCV_HAL_IMPL_AVX_BLEND(v_float32x8, ps) +OPENCV_HAL_IMPL_AVX_BLEND(v_float64x4, pd) + +template +inline v_uint64x4 v256_blend(const v_uint64x4& a, const v_uint64x4& b) +{ + enum {M0 = m}; + enum {M1 = (M0 | (M0 << 2)) & 0x33}; + enum {M2 = (M1 | (M1 << 1)) & 0x55}; + enum {MM = M2 | (M2 << 1)}; + return v_uint64x4(_mm256_blend_epi32(a.val, b.val, MM)); +} +template +inline v_int64x4 v256_blend(const v_int64x4& a, const v_int64x4& b) +{ return v_int64x4(v256_blend(v_uint64x4(a.val), v_uint64x4(b.val)).val); } + +// shuffle +// todo: emluate 64bit +#define OPENCV_HAL_IMPL_AVX_SHUFFLE(_Tpvec, intrin) \ + template \ + inline _Tpvec v256_shuffle(const _Tpvec& a) \ + { return _Tpvec(_mm256_##intrin(a.val, m)); } + +OPENCV_HAL_IMPL_AVX_SHUFFLE(v_uint32x8, shuffle_epi32) +OPENCV_HAL_IMPL_AVX_SHUFFLE(v_int32x8, shuffle_epi32) +OPENCV_HAL_IMPL_AVX_SHUFFLE(v_float32x8, permute_ps) +OPENCV_HAL_IMPL_AVX_SHUFFLE(v_float64x4, permute_pd) + +template +inline void v256_zip(const _Tpvec& a, const _Tpvec& b, _Tpvec& ab0, _Tpvec& ab1) +{ + ab0 = v256_unpacklo(a, b); + ab1 = v256_unpackhi(a, b); +} + +template +inline _Tpvec v256_combine_diagonal(const _Tpvec& a, const _Tpvec& b) +{ return _Tpvec(_mm256_blend_epi32(a.val, b.val, 0xf0)); } + +inline v_float32x8 v256_combine_diagonal(const v_float32x8& a, const v_float32x8& b) +{ return v256_blend<0xf0>(a, b); } + +inline v_float64x4 v256_combine_diagonal(const v_float64x4& a, const v_float64x4& b) +{ return v256_blend<0xc>(a, b); } + +template +inline _Tpvec v256_alignr_128(const _Tpvec& a, const _Tpvec& b) +{ return v256_permute2x128<0x21>(a, b); } + +template +inline _Tpvec v256_alignr_64(const _Tpvec& a, const _Tpvec& b) +{ return _Tpvec(_mm256_alignr_epi8(a.val, b.val, 8)); } +inline v_float64x4 v256_alignr_64(const v_float64x4& a, const v_float64x4& b) +{ return v_float64x4(_mm256_shuffle_pd(b.val, a.val, _MM_SHUFFLE(0, 0, 1, 1))); } +// todo: emulate float32 + +template +inline _Tpvec v256_swap_halves(const _Tpvec& a) +{ return v256_permute2x128<1>(a, a); } + +template +inline _Tpvec v256_reverse_64(const _Tpvec& a) +{ return v256_permute4x64<_MM_SHUFFLE(0, 1, 2, 3)>(a); } + +// ZIP +#define OPENCV_HAL_IMPL_AVX_ZIP(_Tpvec) \ + inline _Tpvec v_combine_low(const _Tpvec& a, const _Tpvec& b) \ + { return v256_permute2x128<0x20>(a, b); } \ + inline _Tpvec v_combine_high(const _Tpvec& a, const _Tpvec& b) \ + { return v256_permute2x128<0x31>(a, b); } \ + inline void v_recombine(const _Tpvec& a, const _Tpvec& b, \ + _Tpvec& c, _Tpvec& d) \ + { \ + _Tpvec a1b0 = v256_alignr_128(a, b); \ + c = v256_combine_diagonal(a, a1b0); \ + d = v256_combine_diagonal(a1b0, b); \ + } \ + inline void v_zip(const _Tpvec& a, const _Tpvec& b, \ + _Tpvec& ab0, _Tpvec& ab1) \ + { \ + _Tpvec ab0ab2, ab1ab3; \ + v256_zip(a, b, ab0ab2, ab1ab3); \ + v_recombine(ab0ab2, ab1ab3, ab0, ab1); \ + } + +OPENCV_HAL_IMPL_AVX_ZIP(v_uint8x32) +OPENCV_HAL_IMPL_AVX_ZIP(v_int8x32) +OPENCV_HAL_IMPL_AVX_ZIP(v_uint16x16) +OPENCV_HAL_IMPL_AVX_ZIP(v_int16x16) +OPENCV_HAL_IMPL_AVX_ZIP(v_uint32x8) +OPENCV_HAL_IMPL_AVX_ZIP(v_int32x8) +OPENCV_HAL_IMPL_AVX_ZIP(v_uint64x4) +OPENCV_HAL_IMPL_AVX_ZIP(v_int64x4) +OPENCV_HAL_IMPL_AVX_ZIP(v_float32x8) +OPENCV_HAL_IMPL_AVX_ZIP(v_float64x4) + +////////// Arithmetic, bitwise and comparison operations ///////// + +/* Element-wise binary and unary operations */ + +/** Arithmetics **/ +#define OPENCV_HAL_IMPL_AVX_BIN_OP(bin_op, _Tpvec, intrin) \ + inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b) \ + { return _Tpvec(intrin(a.val, b.val)); } \ + inline _Tpvec& operator bin_op##= (_Tpvec& a, const _Tpvec& b) \ + { a.val = intrin(a.val, b.val); return a; } + +OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_uint8x32, _mm256_adds_epu8) +OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_uint8x32, _mm256_subs_epu8) +OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_int8x32, _mm256_adds_epi8) +OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_int8x32, _mm256_subs_epi8) +OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_uint16x16, _mm256_adds_epu16) +OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_uint16x16, _mm256_subs_epu16) +OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_int16x16, _mm256_adds_epi16) +OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_int16x16, _mm256_subs_epi16) +OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_uint32x8, _mm256_add_epi32) +OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_uint32x8, _mm256_sub_epi32) +OPENCV_HAL_IMPL_AVX_BIN_OP(*, v_uint32x8, _mm256_mullo_epi32) +OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_int32x8, _mm256_add_epi32) +OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_int32x8, _mm256_sub_epi32) +OPENCV_HAL_IMPL_AVX_BIN_OP(*, v_int32x8, _mm256_mullo_epi32) +OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_uint64x4, _mm256_add_epi64) +OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_uint64x4, _mm256_sub_epi64) +OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_int64x4, _mm256_add_epi64) +OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_int64x4, _mm256_sub_epi64) + +OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_float32x8, _mm256_add_ps) +OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_float32x8, _mm256_sub_ps) +OPENCV_HAL_IMPL_AVX_BIN_OP(*, v_float32x8, _mm256_mul_ps) +OPENCV_HAL_IMPL_AVX_BIN_OP(/, v_float32x8, _mm256_div_ps) +OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_float64x4, _mm256_add_pd) +OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_float64x4, _mm256_sub_pd) +OPENCV_HAL_IMPL_AVX_BIN_OP(*, v_float64x4, _mm256_mul_pd) +OPENCV_HAL_IMPL_AVX_BIN_OP(/, v_float64x4, _mm256_div_pd) + +// saturating multiply 8-bit, 16-bit +inline v_uint8x32 operator * (const v_uint8x32& a, const v_uint8x32& b) +{ + v_uint16x16 c, d; + v_mul_expand(a, b, c, d); + return v_pack(c, d); +} +inline v_int8x32 operator * (const v_int8x32& a, const v_int8x32& b) +{ + v_int16x16 c, d; + v_mul_expand(a, b, c, d); + return v_pack(c, d); +} +inline v_uint16x16 operator * (const v_uint16x16& a, const v_uint16x16& b) +{ + __m256i pl = _mm256_mullo_epi16(a.val, b.val); + __m256i ph = _mm256_mulhi_epu16(a.val, b.val); + __m256i p0 = _mm256_unpacklo_epi16(pl, ph); + __m256i p1 = _mm256_unpackhi_epi16(pl, ph); + return v_uint16x16(_v256_packs_epu32(p0, p1)); +} +inline v_int16x16 operator * (const v_int16x16& a, const v_int16x16& b) +{ + __m256i pl = _mm256_mullo_epi16(a.val, b.val); + __m256i ph = _mm256_mulhi_epi16(a.val, b.val); + __m256i p0 = _mm256_unpacklo_epi16(pl, ph); + __m256i p1 = _mm256_unpackhi_epi16(pl, ph); + return v_int16x16(_mm256_packs_epi32(p0, p1)); +} +inline v_uint8x32& operator *= (v_uint8x32& a, const v_uint8x32& b) +{ a = a * b; return a; } +inline v_int8x32& operator *= (v_int8x32& a, const v_int8x32& b) +{ a = a * b; return a; } +inline v_uint16x16& operator *= (v_uint16x16& a, const v_uint16x16& b) +{ a = a * b; return a; } +inline v_int16x16& operator *= (v_int16x16& a, const v_int16x16& b) +{ a = a * b; return a; } + +/** Non-saturating arithmetics **/ +#define OPENCV_HAL_IMPL_AVX_BIN_FUNC(func, _Tpvec, intrin) \ + inline _Tpvec func(const _Tpvec& a, const _Tpvec& b) \ + { return _Tpvec(intrin(a.val, b.val)); } + +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_add_wrap, v_uint8x32, _mm256_add_epi8) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_add_wrap, v_int8x32, _mm256_add_epi8) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_add_wrap, v_uint16x16, _mm256_add_epi16) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_add_wrap, v_int16x16, _mm256_add_epi16) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_sub_wrap, v_uint8x32, _mm256_sub_epi8) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_sub_wrap, v_int8x32, _mm256_sub_epi8) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_sub_wrap, v_uint16x16, _mm256_sub_epi16) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_sub_wrap, v_int16x16, _mm256_sub_epi16) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_mul_wrap, v_uint16x16, _mm256_mullo_epi16) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_mul_wrap, v_int16x16, _mm256_mullo_epi16) + +inline v_uint8x32 v_mul_wrap(const v_uint8x32& a, const v_uint8x32& b) +{ + __m256i ad = _mm256_srai_epi16(a.val, 8); + __m256i bd = _mm256_srai_epi16(b.val, 8); + __m256i p0 = _mm256_mullo_epi16(a.val, b.val); // even + __m256i p1 = _mm256_slli_epi16(_mm256_mullo_epi16(ad, bd), 8); // odd + + const __m256i b01 = _mm256_set1_epi32(0xFF00FF00); + return v_uint8x32(_mm256_blendv_epi8(p0, p1, b01)); +} +inline v_int8x32 v_mul_wrap(const v_int8x32& a, const v_int8x32& b) +{ + return v_reinterpret_as_s8(v_mul_wrap(v_reinterpret_as_u8(a), v_reinterpret_as_u8(b))); +} + +// Multiply and expand +inline void v_mul_expand(const v_uint8x32& a, const v_uint8x32& b, + v_uint16x16& c, v_uint16x16& d) +{ + v_uint16x16 a0, a1, b0, b1; + v_expand(a, a0, a1); + v_expand(b, b0, b1); + c = v_mul_wrap(a0, b0); + d = v_mul_wrap(a1, b1); +} + +inline void v_mul_expand(const v_int8x32& a, const v_int8x32& b, + v_int16x16& c, v_int16x16& d) +{ + v_int16x16 a0, a1, b0, b1; + v_expand(a, a0, a1); + v_expand(b, b0, b1); + c = v_mul_wrap(a0, b0); + d = v_mul_wrap(a1, b1); +} + +inline void v_mul_expand(const v_int16x16& a, const v_int16x16& b, + v_int32x8& c, v_int32x8& d) +{ + v_int16x16 vhi = v_int16x16(_mm256_mulhi_epi16(a.val, b.val)); + + v_int16x16 v0, v1; + v_zip(v_mul_wrap(a, b), vhi, v0, v1); + + c = v_reinterpret_as_s32(v0); + d = v_reinterpret_as_s32(v1); +} + +inline void v_mul_expand(const v_uint16x16& a, const v_uint16x16& b, + v_uint32x8& c, v_uint32x8& d) +{ + v_uint16x16 vhi = v_uint16x16(_mm256_mulhi_epu16(a.val, b.val)); + + v_uint16x16 v0, v1; + v_zip(v_mul_wrap(a, b), vhi, v0, v1); + + c = v_reinterpret_as_u32(v0); + d = v_reinterpret_as_u32(v1); +} + +inline void v_mul_expand(const v_uint32x8& a, const v_uint32x8& b, + v_uint64x4& c, v_uint64x4& d) +{ + __m256i v0 = _mm256_mul_epu32(a.val, b.val); + __m256i v1 = _mm256_mul_epu32(_mm256_srli_epi64(a.val, 32), _mm256_srli_epi64(b.val, 32)); + v_zip(v_uint64x4(v0), v_uint64x4(v1), c, d); +} + +inline v_int16x16 v_mul_hi(const v_int16x16& a, const v_int16x16& b) { return v_int16x16(_mm256_mulhi_epi16(a.val, b.val)); } +inline v_uint16x16 v_mul_hi(const v_uint16x16& a, const v_uint16x16& b) { return v_uint16x16(_mm256_mulhi_epu16(a.val, b.val)); } + +/** Bitwise shifts **/ +#define OPENCV_HAL_IMPL_AVX_SHIFT_OP(_Tpuvec, _Tpsvec, suffix, srai) \ + inline _Tpuvec operator << (const _Tpuvec& a, int imm) \ + { return _Tpuvec(_mm256_slli_##suffix(a.val, imm)); } \ + inline _Tpsvec operator << (const _Tpsvec& a, int imm) \ + { return _Tpsvec(_mm256_slli_##suffix(a.val, imm)); } \ + inline _Tpuvec operator >> (const _Tpuvec& a, int imm) \ + { return _Tpuvec(_mm256_srli_##suffix(a.val, imm)); } \ + inline _Tpsvec operator >> (const _Tpsvec& a, int imm) \ + { return _Tpsvec(srai(a.val, imm)); } \ + template \ + inline _Tpuvec v_shl(const _Tpuvec& a) \ + { return _Tpuvec(_mm256_slli_##suffix(a.val, imm)); } \ + template \ + inline _Tpsvec v_shl(const _Tpsvec& a) \ + { return _Tpsvec(_mm256_slli_##suffix(a.val, imm)); } \ + template \ + inline _Tpuvec v_shr(const _Tpuvec& a) \ + { return _Tpuvec(_mm256_srli_##suffix(a.val, imm)); } \ + template \ + inline _Tpsvec v_shr(const _Tpsvec& a) \ + { return _Tpsvec(srai(a.val, imm)); } + +OPENCV_HAL_IMPL_AVX_SHIFT_OP(v_uint16x16, v_int16x16, epi16, _mm256_srai_epi16) +OPENCV_HAL_IMPL_AVX_SHIFT_OP(v_uint32x8, v_int32x8, epi32, _mm256_srai_epi32) + +inline __m256i _mm256_srai_epi64xx(const __m256i a, int imm) +{ + __m256i d = _mm256_set1_epi64x((int64)1 << 63); + __m256i r = _mm256_srli_epi64(_mm256_add_epi64(a, d), imm); + return _mm256_sub_epi64(r, _mm256_srli_epi64(d, imm)); +} +OPENCV_HAL_IMPL_AVX_SHIFT_OP(v_uint64x4, v_int64x4, epi64, _mm256_srai_epi64xx) + + +/** Bitwise logic **/ +#define OPENCV_HAL_IMPL_AVX_LOGIC_OP(_Tpvec, suffix, not_const) \ + OPENCV_HAL_IMPL_AVX_BIN_OP(&, _Tpvec, _mm256_and_##suffix) \ + OPENCV_HAL_IMPL_AVX_BIN_OP(|, _Tpvec, _mm256_or_##suffix) \ + OPENCV_HAL_IMPL_AVX_BIN_OP(^, _Tpvec, _mm256_xor_##suffix) \ + inline _Tpvec operator ~ (const _Tpvec& a) \ + { return _Tpvec(_mm256_xor_##suffix(a.val, not_const)); } + +OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_uint8x32, si256, _mm256_set1_epi32(-1)) +OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_int8x32, si256, _mm256_set1_epi32(-1)) +OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_uint16x16, si256, _mm256_set1_epi32(-1)) +OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_int16x16, si256, _mm256_set1_epi32(-1)) +OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_uint32x8, si256, _mm256_set1_epi32(-1)) +OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_int32x8, si256, _mm256_set1_epi32(-1)) +OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_uint64x4, si256, _mm256_set1_epi64x(-1)) +OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_int64x4, si256, _mm256_set1_epi64x(-1)) +OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_float32x8, ps, _mm256_castsi256_ps(_mm256_set1_epi32(-1))) +OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_float64x4, pd, _mm256_castsi256_pd(_mm256_set1_epi32(-1))) + +/** Select **/ +#define OPENCV_HAL_IMPL_AVX_SELECT(_Tpvec, suffix) \ + inline _Tpvec v_select(const _Tpvec& mask, const _Tpvec& a, const _Tpvec& b) \ + { return _Tpvec(_mm256_blendv_##suffix(b.val, a.val, mask.val)); } + +OPENCV_HAL_IMPL_AVX_SELECT(v_uint8x32, epi8) +OPENCV_HAL_IMPL_AVX_SELECT(v_int8x32, epi8) +OPENCV_HAL_IMPL_AVX_SELECT(v_uint16x16, epi8) +OPENCV_HAL_IMPL_AVX_SELECT(v_int16x16, epi8) +OPENCV_HAL_IMPL_AVX_SELECT(v_uint32x8, epi8) +OPENCV_HAL_IMPL_AVX_SELECT(v_int32x8, epi8) +OPENCV_HAL_IMPL_AVX_SELECT(v_float32x8, ps) +OPENCV_HAL_IMPL_AVX_SELECT(v_float64x4, pd) + +/** Comparison **/ +#define OPENCV_HAL_IMPL_AVX_CMP_OP_OV(_Tpvec) \ + inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \ + { return ~(a == b); } \ + inline _Tpvec operator < (const _Tpvec& a, const _Tpvec& b) \ + { return b > a; } \ + inline _Tpvec operator >= (const _Tpvec& a, const _Tpvec& b) \ + { return ~(a < b); } \ + inline _Tpvec operator <= (const _Tpvec& a, const _Tpvec& b) \ + { return b >= a; } + +#define OPENCV_HAL_IMPL_AVX_CMP_OP_INT(_Tpuvec, _Tpsvec, suffix, sbit) \ + inline _Tpuvec operator == (const _Tpuvec& a, const _Tpuvec& b) \ + { return _Tpuvec(_mm256_cmpeq_##suffix(a.val, b.val)); } \ + inline _Tpuvec operator > (const _Tpuvec& a, const _Tpuvec& b) \ + { \ + __m256i smask = _mm256_set1_##suffix(sbit); \ + return _Tpuvec(_mm256_cmpgt_##suffix( \ + _mm256_xor_si256(a.val, smask), \ + _mm256_xor_si256(b.val, smask))); \ + } \ + inline _Tpsvec operator == (const _Tpsvec& a, const _Tpsvec& b) \ + { return _Tpsvec(_mm256_cmpeq_##suffix(a.val, b.val)); } \ + inline _Tpsvec operator > (const _Tpsvec& a, const _Tpsvec& b) \ + { return _Tpsvec(_mm256_cmpgt_##suffix(a.val, b.val)); } \ + OPENCV_HAL_IMPL_AVX_CMP_OP_OV(_Tpuvec) \ + OPENCV_HAL_IMPL_AVX_CMP_OP_OV(_Tpsvec) + +OPENCV_HAL_IMPL_AVX_CMP_OP_INT(v_uint8x32, v_int8x32, epi8, (char)-128) +OPENCV_HAL_IMPL_AVX_CMP_OP_INT(v_uint16x16, v_int16x16, epi16, (short)-32768) +OPENCV_HAL_IMPL_AVX_CMP_OP_INT(v_uint32x8, v_int32x8, epi32, (int)0x80000000) + +#define OPENCV_HAL_IMPL_AVX_CMP_OP_64BIT(_Tpvec) \ + inline _Tpvec operator == (const _Tpvec& a, const _Tpvec& b) \ + { return _Tpvec(_mm256_cmpeq_epi64(a.val, b.val)); } \ + inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \ + { return ~(a == b); } + +OPENCV_HAL_IMPL_AVX_CMP_OP_64BIT(v_uint64x4) +OPENCV_HAL_IMPL_AVX_CMP_OP_64BIT(v_int64x4) + +#define OPENCV_HAL_IMPL_AVX_CMP_FLT(bin_op, imm8, _Tpvec, suffix) \ + inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b) \ + { return _Tpvec(_mm256_cmp_##suffix(a.val, b.val, imm8)); } + +#define OPENCV_HAL_IMPL_AVX_CMP_OP_FLT(_Tpvec, suffix) \ + OPENCV_HAL_IMPL_AVX_CMP_FLT(==, _CMP_EQ_OQ, _Tpvec, suffix) \ + OPENCV_HAL_IMPL_AVX_CMP_FLT(!=, _CMP_NEQ_OQ, _Tpvec, suffix) \ + OPENCV_HAL_IMPL_AVX_CMP_FLT(<, _CMP_LT_OQ, _Tpvec, suffix) \ + OPENCV_HAL_IMPL_AVX_CMP_FLT(>, _CMP_GT_OQ, _Tpvec, suffix) \ + OPENCV_HAL_IMPL_AVX_CMP_FLT(<=, _CMP_LE_OQ, _Tpvec, suffix) \ + OPENCV_HAL_IMPL_AVX_CMP_FLT(>=, _CMP_GE_OQ, _Tpvec, suffix) + +OPENCV_HAL_IMPL_AVX_CMP_OP_FLT(v_float32x8, ps) +OPENCV_HAL_IMPL_AVX_CMP_OP_FLT(v_float64x4, pd) + +inline v_float32x8 v_not_nan(const v_float32x8& a) +{ return v_float32x8(_mm256_cmp_ps(a.val, a.val, _CMP_ORD_Q)); } +inline v_float64x4 v_not_nan(const v_float64x4& a) +{ return v_float64x4(_mm256_cmp_pd(a.val, a.val, _CMP_ORD_Q)); } + +/** min/max **/ +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_min, v_uint8x32, _mm256_min_epu8) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_max, v_uint8x32, _mm256_max_epu8) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_min, v_int8x32, _mm256_min_epi8) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_max, v_int8x32, _mm256_max_epi8) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_min, v_uint16x16, _mm256_min_epu16) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_max, v_uint16x16, _mm256_max_epu16) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_min, v_int16x16, _mm256_min_epi16) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_max, v_int16x16, _mm256_max_epi16) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_min, v_uint32x8, _mm256_min_epu32) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_max, v_uint32x8, _mm256_max_epu32) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_min, v_int32x8, _mm256_min_epi32) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_max, v_int32x8, _mm256_max_epi32) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_min, v_float32x8, _mm256_min_ps) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_max, v_float32x8, _mm256_max_ps) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_min, v_float64x4, _mm256_min_pd) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_max, v_float64x4, _mm256_max_pd) + +/** Rotate **/ +template +inline v_uint8x32 v_rotate_left(const v_uint8x32& a, const v_uint8x32& b) +{ + enum {IMM_R = (16 - imm) & 0xFF}; + enum {IMM_R2 = (32 - imm) & 0xFF}; + + if (imm == 0) return a; + if (imm == 32) return b; + if (imm > 32) return v_uint8x32(); + + __m256i swap = _mm256_permute2x128_si256(a.val, b.val, 0x03); + if (imm == 16) return v_uint8x32(swap); + if (imm < 16) return v_uint8x32(_mm256_alignr_epi8(a.val, swap, IMM_R)); + return v_uint8x32(_mm256_alignr_epi8(swap, b.val, IMM_R2)); // imm < 32 +} + +template +inline v_uint8x32 v_rotate_right(const v_uint8x32& a, const v_uint8x32& b) +{ + enum {IMM_L = (imm - 16) & 0xFF}; + + if (imm == 0) return a; + if (imm == 32) return b; + if (imm > 32) return v_uint8x32(); + + __m256i swap = _mm256_permute2x128_si256(a.val, b.val, 0x21); + if (imm == 16) return v_uint8x32(swap); + if (imm < 16) return v_uint8x32(_mm256_alignr_epi8(swap, a.val, imm)); + return v_uint8x32(_mm256_alignr_epi8(b.val, swap, IMM_L)); +} + +template +inline v_uint8x32 v_rotate_left(const v_uint8x32& a) +{ + enum {IMM_L = (imm - 16) & 0xFF}; + enum {IMM_R = (16 - imm) & 0xFF}; + + if (imm == 0) return a; + if (imm > 32) return v_uint8x32(); + + // ESAC control[3] ? [127:0] = 0 + __m256i swapz = _mm256_permute2x128_si256(a.val, a.val, _MM_SHUFFLE(0, 0, 2, 0)); + if (imm == 16) return v_uint8x32(swapz); + if (imm < 16) return v_uint8x32(_mm256_alignr_epi8(a.val, swapz, IMM_R)); + return v_uint8x32(_mm256_slli_si256(swapz, IMM_L)); +} + +template +inline v_uint8x32 v_rotate_right(const v_uint8x32& a) +{ + enum {IMM_L = (imm - 16) & 0xFF}; + + if (imm == 0) return a; + if (imm > 32) return v_uint8x32(); + + // ESAC control[3] ? [127:0] = 0 + __m256i swapz = _mm256_permute2x128_si256(a.val, a.val, _MM_SHUFFLE(2, 0, 0, 1)); + if (imm == 16) return v_uint8x32(swapz); + if (imm < 16) return v_uint8x32(_mm256_alignr_epi8(swapz, a.val, imm)); + return v_uint8x32(_mm256_srli_si256(swapz, IMM_L)); +} + +#define OPENCV_HAL_IMPL_AVX_ROTATE_CAST(intrin, _Tpvec, cast) \ + template \ + inline _Tpvec intrin(const _Tpvec& a, const _Tpvec& b) \ + { \ + enum {IMMxW = imm * sizeof(typename _Tpvec::lane_type)}; \ + v_uint8x32 ret = intrin(v_reinterpret_as_u8(a), \ + v_reinterpret_as_u8(b)); \ + return _Tpvec(cast(ret.val)); \ + } \ + template \ + inline _Tpvec intrin(const _Tpvec& a) \ + { \ + enum {IMMxW = imm * sizeof(typename _Tpvec::lane_type)}; \ + v_uint8x32 ret = intrin(v_reinterpret_as_u8(a)); \ + return _Tpvec(cast(ret.val)); \ + } + +#define OPENCV_HAL_IMPL_AVX_ROTATE(_Tpvec) \ + OPENCV_HAL_IMPL_AVX_ROTATE_CAST(v_rotate_left, _Tpvec, OPENCV_HAL_NOP) \ + OPENCV_HAL_IMPL_AVX_ROTATE_CAST(v_rotate_right, _Tpvec, OPENCV_HAL_NOP) + +OPENCV_HAL_IMPL_AVX_ROTATE(v_int8x32) +OPENCV_HAL_IMPL_AVX_ROTATE(v_uint16x16) +OPENCV_HAL_IMPL_AVX_ROTATE(v_int16x16) +OPENCV_HAL_IMPL_AVX_ROTATE(v_uint32x8) +OPENCV_HAL_IMPL_AVX_ROTATE(v_int32x8) +OPENCV_HAL_IMPL_AVX_ROTATE(v_uint64x4) +OPENCV_HAL_IMPL_AVX_ROTATE(v_int64x4) + +OPENCV_HAL_IMPL_AVX_ROTATE_CAST(v_rotate_left, v_float32x8, _mm256_castsi256_ps) +OPENCV_HAL_IMPL_AVX_ROTATE_CAST(v_rotate_right, v_float32x8, _mm256_castsi256_ps) +OPENCV_HAL_IMPL_AVX_ROTATE_CAST(v_rotate_left, v_float64x4, _mm256_castsi256_pd) +OPENCV_HAL_IMPL_AVX_ROTATE_CAST(v_rotate_right, v_float64x4, _mm256_castsi256_pd) + +////////// Reduce and mask ///////// + +/** Reduce **/ +#define OPENCV_HAL_IMPL_AVX_REDUCE_16(_Tpvec, sctype, func, intrin) \ + inline sctype v_reduce_##func(const _Tpvec& a) \ + { \ + __m128i v0 = _v256_extract_low(a.val); \ + __m128i v1 = _v256_extract_high(a.val); \ + v0 = intrin(v0, v1); \ + v0 = intrin(v0, _mm_srli_si128(v0, 8)); \ + v0 = intrin(v0, _mm_srli_si128(v0, 4)); \ + v0 = intrin(v0, _mm_srli_si128(v0, 2)); \ + return (sctype) _mm_cvtsi128_si32(v0); \ + } + +OPENCV_HAL_IMPL_AVX_REDUCE_16(v_uint16x16, ushort, min, _mm_min_epu16) +OPENCV_HAL_IMPL_AVX_REDUCE_16(v_int16x16, short, min, _mm_min_epi16) +OPENCV_HAL_IMPL_AVX_REDUCE_16(v_uint16x16, ushort, max, _mm_max_epu16) +OPENCV_HAL_IMPL_AVX_REDUCE_16(v_int16x16, short, max, _mm_max_epi16) + +#define OPENCV_HAL_IMPL_AVX_REDUCE_8(_Tpvec, sctype, func, intrin) \ + inline sctype v_reduce_##func(const _Tpvec& a) \ + { \ + __m128i v0 = _v256_extract_low(a.val); \ + __m128i v1 = _v256_extract_high(a.val); \ + v0 = intrin(v0, v1); \ + v0 = intrin(v0, _mm_srli_si128(v0, 8)); \ + v0 = intrin(v0, _mm_srli_si128(v0, 4)); \ + return (sctype) _mm_cvtsi128_si32(v0); \ + } + +OPENCV_HAL_IMPL_AVX_REDUCE_8(v_uint32x8, unsigned, min, _mm_min_epu32) +OPENCV_HAL_IMPL_AVX_REDUCE_8(v_int32x8, int, min, _mm_min_epi32) +OPENCV_HAL_IMPL_AVX_REDUCE_8(v_uint32x8, unsigned, max, _mm_max_epu32) +OPENCV_HAL_IMPL_AVX_REDUCE_8(v_int32x8, int, max, _mm_max_epi32) + +#define OPENCV_HAL_IMPL_AVX_REDUCE_FLT(func, intrin) \ + inline float v_reduce_##func(const v_float32x8& a) \ + { \ + __m128 v0 = _v256_extract_low(a.val); \ + __m128 v1 = _v256_extract_high(a.val); \ + v0 = intrin(v0, v1); \ + v0 = intrin(v0, _mm_permute_ps(v0, _MM_SHUFFLE(0, 0, 3, 2))); \ + v0 = intrin(v0, _mm_permute_ps(v0, _MM_SHUFFLE(0, 0, 0, 3))); \ + return _mm_cvtss_f32(v0); \ + } + +OPENCV_HAL_IMPL_AVX_REDUCE_FLT(min, _mm_min_ps) +OPENCV_HAL_IMPL_AVX_REDUCE_FLT(max, _mm_max_ps) + +inline ushort v_reduce_sum(const v_uint16x16& a) +{ + __m128i a0 = _v256_extract_low(a.val); + __m128i a1 = _v256_extract_high(a.val); + + __m128i s0 = _mm_adds_epu16(a0, a1); + s0 = _mm_adds_epu16(s0, _mm_srli_si128(s0, 8)); + s0 = _mm_adds_epu16(s0, _mm_srli_si128(s0, 4)); + s0 = _mm_adds_epu16(s0, _mm_srli_si128(s0, 2)); + + return (ushort)_mm_cvtsi128_si32(s0); +} + +inline short v_reduce_sum(const v_int16x16& a) +{ + __m256i s0 = _mm256_hadds_epi16(a.val, a.val); + s0 = _mm256_hadds_epi16(s0, s0); + s0 = _mm256_hadds_epi16(s0, s0); + + __m128i s1 = _v256_extract_high(s0); + s1 = _mm_adds_epi16(_v256_extract_low(s0), s1); + + return (short)_mm_cvtsi128_si32(s1); +} + +inline int v_reduce_sum(const v_int32x8& a) +{ + __m256i s0 = _mm256_hadd_epi32(a.val, a.val); + s0 = _mm256_hadd_epi32(s0, s0); + + __m128i s1 = _v256_extract_high(s0); + s1 = _mm_add_epi32(_v256_extract_low(s0), s1); + + return _mm_cvtsi128_si32(s1); +} + +inline unsigned v_reduce_sum(const v_uint32x8& a) +{ return v_reduce_sum(v_reinterpret_as_s32(a)); } + +inline float v_reduce_sum(const v_float32x8& a) +{ + __m256 s0 = _mm256_hadd_ps(a.val, a.val); + s0 = _mm256_hadd_ps(s0, s0); + + __m128 s1 = _v256_extract_high(s0); + s1 = _mm_add_ps(_v256_extract_low(s0), s1); + + return _mm_cvtss_f32(s1); +} + +inline double v_reduce_sum(const v_float64x4& a) +{ + __m256d s0 = _mm256_hadd_pd(a.val, a.val); + return _mm_cvtsd_f64(_mm_add_pd(_v256_extract_low(s0), _v256_extract_high(s0))); +} + +inline v_float32x8 v_reduce_sum4(const v_float32x8& a, const v_float32x8& b, + const v_float32x8& c, const v_float32x8& d) +{ + __m256 ab = _mm256_hadd_ps(a.val, b.val); + __m256 cd = _mm256_hadd_ps(c.val, d.val); + return v_float32x8(_mm256_hadd_ps(ab, cd)); +} + +inline unsigned v_reduce_sad(const v_uint8x32& a, const v_uint8x32& b) +{ + return (unsigned)_v_cvtsi256_si32(_mm256_sad_epu8(a.val, b.val)); +} +inline unsigned v_reduce_sad(const v_int8x32& a, const v_int8x32& b) +{ + __m256i half = _mm256_set1_epi8(0x7f); + return (unsigned)_v_cvtsi256_si32(_mm256_sad_epu8(_mm256_add_epi8(a.val, half), _mm256_add_epi8(b.val, half))); +} +inline unsigned v_reduce_sad(const v_uint16x16& a, const v_uint16x16& b) +{ + v_uint32x8 l, h; + v_expand(v_add_wrap(a - b, b - a), l, h); + return v_reduce_sum(l + h); +} +inline unsigned v_reduce_sad(const v_int16x16& a, const v_int16x16& b) +{ + v_uint32x8 l, h; + v_expand(v_reinterpret_as_u16(v_sub_wrap(v_max(a, b), v_min(a, b))), l, h); + return v_reduce_sum(l + h); +} +inline unsigned v_reduce_sad(const v_uint32x8& a, const v_uint32x8& b) +{ + return v_reduce_sum(v_max(a, b) - v_min(a, b)); +} +inline unsigned v_reduce_sad(const v_int32x8& a, const v_int32x8& b) +{ + v_int32x8 m = a < b; + return v_reduce_sum(v_reinterpret_as_u32(((a - b) ^ m) - m)); +} +inline float v_reduce_sad(const v_float32x8& a, const v_float32x8& b) +{ + return v_reduce_sum((a - b) & v_float32x8(_mm256_castsi256_ps(_mm256_set1_epi32(0x7fffffff)))); +} + +/** Popcount **/ +#define OPENCV_HAL_IMPL_AVX_POPCOUNT(_Tpvec) \ + inline v_uint32x8 v_popcount(const _Tpvec& a) \ + { \ + const v_uint32x8 m1 = v256_setall_u32(0x55555555); \ + const v_uint32x8 m2 = v256_setall_u32(0x33333333); \ + const v_uint32x8 m4 = v256_setall_u32(0x0f0f0f0f); \ + v_uint32x8 p = v_reinterpret_as_u32(a); \ + p = ((p >> 1) & m1) + (p & m1); \ + p = ((p >> 2) & m2) + (p & m2); \ + p = ((p >> 4) & m4) + (p & m4); \ + p.val = _mm256_sad_epu8(p.val, _mm256_setzero_si256()); \ + return p; \ + } + +OPENCV_HAL_IMPL_AVX_POPCOUNT(v_uint8x32) +OPENCV_HAL_IMPL_AVX_POPCOUNT(v_int8x32) +OPENCV_HAL_IMPL_AVX_POPCOUNT(v_uint16x16) +OPENCV_HAL_IMPL_AVX_POPCOUNT(v_int16x16) +OPENCV_HAL_IMPL_AVX_POPCOUNT(v_uint32x8) +OPENCV_HAL_IMPL_AVX_POPCOUNT(v_int32x8) + +/** Mask **/ +inline int v_signmask(const v_int8x32& a) +{ return _mm256_movemask_epi8(a.val); } +inline int v_signmask(const v_uint8x32& a) +{ return v_signmask(v_reinterpret_as_s8(a)); } + +inline int v_signmask(const v_int16x16& a) +{ + v_int8x32 v = v_int8x32(_mm256_packs_epi16(a.val, a.val)); + return v_signmask(v) & 255; +} +inline int v_signmask(const v_uint16x16& a) +{ return v_signmask(v_reinterpret_as_s16(a)); } + +inline int v_signmask(const v_int32x8& a) +{ + __m256i a16 = _mm256_packs_epi32(a.val, a.val); + v_int8x32 v = v_int8x32(_mm256_packs_epi16(a16, a16)); + return v_signmask(v) & 15; +} +inline int v_signmask(const v_uint32x8& a) +{ return v_signmask(v_reinterpret_as_s32(a)); } + +inline int v_signmask(const v_float32x8& a) +{ return _mm256_movemask_ps(a.val); } +inline int v_signmask(const v_float64x4& a) +{ return _mm256_movemask_pd(a.val); } + +/** Checks **/ +#define OPENCV_HAL_IMPL_AVX_CHECK(_Tpvec, and_op, allmask) \ + inline bool v_check_all(const _Tpvec& a) \ + { \ + int mask = v_signmask(v_reinterpret_as_s8(a)); \ + return and_op(mask, allmask) == allmask; \ + } \ + inline bool v_check_any(const _Tpvec& a) \ + { \ + int mask = v_signmask(v_reinterpret_as_s8(a)); \ + return and_op(mask, allmask) != 0; \ + } + +OPENCV_HAL_IMPL_AVX_CHECK(v_uint8x32, OPENCV_HAL_1ST, -1) +OPENCV_HAL_IMPL_AVX_CHECK(v_int8x32, OPENCV_HAL_1ST, -1) +OPENCV_HAL_IMPL_AVX_CHECK(v_uint16x16, OPENCV_HAL_AND, (int)0xaaaa) +OPENCV_HAL_IMPL_AVX_CHECK(v_int16x16, OPENCV_HAL_AND, (int)0xaaaa) +OPENCV_HAL_IMPL_AVX_CHECK(v_uint32x8, OPENCV_HAL_AND, (int)0x8888) +OPENCV_HAL_IMPL_AVX_CHECK(v_int32x8, OPENCV_HAL_AND, (int)0x8888) + +#define OPENCV_HAL_IMPL_AVX_CHECK_FLT(_Tpvec, allmask) \ + inline bool v_check_all(const _Tpvec& a) \ + { \ + int mask = v_signmask(a); \ + return mask == allmask; \ + } \ + inline bool v_check_any(const _Tpvec& a) \ + { \ + int mask = v_signmask(a); \ + return mask != 0; \ + } + +OPENCV_HAL_IMPL_AVX_CHECK_FLT(v_float32x8, 255) +OPENCV_HAL_IMPL_AVX_CHECK_FLT(v_float64x4, 15) + + +////////// Other math ///////// + +/** Some frequent operations **/ +#define OPENCV_HAL_IMPL_AVX_MULADD(_Tpvec, suffix) \ + inline _Tpvec v_fma(const _Tpvec& a, const _Tpvec& b, const _Tpvec& c) \ + { return _Tpvec(_mm256_fmadd_##suffix(a.val, b.val, c.val)); } \ + inline _Tpvec v_muladd(const _Tpvec& a, const _Tpvec& b, const _Tpvec& c) \ + { return _Tpvec(_mm256_fmadd_##suffix(a.val, b.val, c.val)); } \ + inline _Tpvec v_sqrt(const _Tpvec& x) \ + { return _Tpvec(_mm256_sqrt_##suffix(x.val)); } \ + inline _Tpvec v_sqr_magnitude(const _Tpvec& a, const _Tpvec& b) \ + { return v_fma(a, a, b * b); } \ + inline _Tpvec v_magnitude(const _Tpvec& a, const _Tpvec& b) \ + { return v_sqrt(v_fma(a, a, b*b)); } + +OPENCV_HAL_IMPL_AVX_MULADD(v_float32x8, ps) +OPENCV_HAL_IMPL_AVX_MULADD(v_float64x4, pd) + +inline v_int32x8 v_fma(const v_int32x8& a, const v_int32x8& b, const v_int32x8& c) +{ + return a * b + c; +} + +inline v_int32x8 v_muladd(const v_int32x8& a, const v_int32x8& b, const v_int32x8& c) +{ + return v_fma(a, b, c); +} + +inline v_float32x8 v_invsqrt(const v_float32x8& x) +{ + v_float32x8 half = x * v256_setall_f32(0.5); + v_float32x8 t = v_float32x8(_mm256_rsqrt_ps(x.val)); + // todo: _mm256_fnmsub_ps + t *= v256_setall_f32(1.5) - ((t * t) * half); + return t; +} + +inline v_float64x4 v_invsqrt(const v_float64x4& x) +{ + return v256_setall_f64(1.) / v_sqrt(x); +} + +/** Absolute values **/ +#define OPENCV_HAL_IMPL_AVX_ABS(_Tpvec, suffix) \ + inline v_u##_Tpvec v_abs(const v_##_Tpvec& x) \ + { return v_u##_Tpvec(_mm256_abs_##suffix(x.val)); } + +OPENCV_HAL_IMPL_AVX_ABS(int8x32, epi8) +OPENCV_HAL_IMPL_AVX_ABS(int16x16, epi16) +OPENCV_HAL_IMPL_AVX_ABS(int32x8, epi32) + +inline v_float32x8 v_abs(const v_float32x8& x) +{ return x & v_float32x8(_mm256_castsi256_ps(_mm256_set1_epi32(0x7fffffff))); } +inline v_float64x4 v_abs(const v_float64x4& x) +{ return x & v_float64x4(_mm256_castsi256_pd(_mm256_srli_epi64(_mm256_set1_epi64x(-1), 1))); } + +/** Absolute difference **/ +inline v_uint8x32 v_absdiff(const v_uint8x32& a, const v_uint8x32& b) +{ return v_add_wrap(a - b, b - a); } +inline v_uint16x16 v_absdiff(const v_uint16x16& a, const v_uint16x16& b) +{ return v_add_wrap(a - b, b - a); } +inline v_uint32x8 v_absdiff(const v_uint32x8& a, const v_uint32x8& b) +{ return v_max(a, b) - v_min(a, b); } + +inline v_uint8x32 v_absdiff(const v_int8x32& a, const v_int8x32& b) +{ + v_int8x32 d = v_sub_wrap(a, b); + v_int8x32 m = a < b; + return v_reinterpret_as_u8(v_sub_wrap(d ^ m, m)); +} + +inline v_uint16x16 v_absdiff(const v_int16x16& a, const v_int16x16& b) +{ return v_reinterpret_as_u16(v_sub_wrap(v_max(a, b), v_min(a, b))); } + +inline v_uint32x8 v_absdiff(const v_int32x8& a, const v_int32x8& b) +{ + v_int32x8 d = a - b; + v_int32x8 m = a < b; + return v_reinterpret_as_u32((d ^ m) - m); +} + +inline v_float32x8 v_absdiff(const v_float32x8& a, const v_float32x8& b) +{ return v_abs(a - b); } + +inline v_float64x4 v_absdiff(const v_float64x4& a, const v_float64x4& b) +{ return v_abs(a - b); } + +/** Saturating absolute difference **/ +inline v_int8x32 v_absdiffs(const v_int8x32& a, const v_int8x32& b) +{ + v_int8x32 d = a - b; + v_int8x32 m = a < b; + return (d ^ m) - m; +} +inline v_int16x16 v_absdiffs(const v_int16x16& a, const v_int16x16& b) +{ return v_max(a, b) - v_min(a, b); } + +////////// Conversions ///////// + +/** Rounding **/ +inline v_int32x8 v_round(const v_float32x8& a) +{ return v_int32x8(_mm256_cvtps_epi32(a.val)); } + +inline v_int32x8 v_round(const v_float64x4& a) +{ return v_int32x8(_mm256_castsi128_si256(_mm256_cvtpd_epi32(a.val))); } + +inline v_int32x8 v_round(const v_float64x4& a, const v_float64x4& b) +{ + __m128i ai = _mm256_cvtpd_epi32(a.val), bi = _mm256_cvtpd_epi32(b.val); + return v_int32x8(_v256_combine(ai, bi)); +} + +inline v_int32x8 v_trunc(const v_float32x8& a) +{ return v_int32x8(_mm256_cvttps_epi32(a.val)); } + +inline v_int32x8 v_trunc(const v_float64x4& a) +{ return v_int32x8(_mm256_castsi128_si256(_mm256_cvttpd_epi32(a.val))); } + +inline v_int32x8 v_floor(const v_float32x8& a) +{ return v_int32x8(_mm256_cvttps_epi32(_mm256_floor_ps(a.val))); } + +inline v_int32x8 v_floor(const v_float64x4& a) +{ return v_trunc(v_float64x4(_mm256_floor_pd(a.val))); } + +inline v_int32x8 v_ceil(const v_float32x8& a) +{ return v_int32x8(_mm256_cvttps_epi32(_mm256_ceil_ps(a.val))); } + +inline v_int32x8 v_ceil(const v_float64x4& a) +{ return v_trunc(v_float64x4(_mm256_ceil_pd(a.val))); } + +/** To float **/ +inline v_float32x8 v_cvt_f32(const v_int32x8& a) +{ return v_float32x8(_mm256_cvtepi32_ps(a.val)); } + +inline v_float32x8 v_cvt_f32(const v_float64x4& a) +{ return v_float32x8(_mm256_castps128_ps256(_mm256_cvtpd_ps(a.val))); } + +inline v_float32x8 v_cvt_f32(const v_float64x4& a, const v_float64x4& b) +{ + __m128 af = _mm256_cvtpd_ps(a.val), bf = _mm256_cvtpd_ps(b.val); + return v_float32x8(_mm256_insertf128_ps(_mm256_castps128_ps256(af), bf, 1)); +} + +inline v_float64x4 v_cvt_f64(const v_int32x8& a) +{ return v_float64x4(_mm256_cvtepi32_pd(_v256_extract_low(a.val))); } + +inline v_float64x4 v_cvt_f64_high(const v_int32x8& a) +{ return v_float64x4(_mm256_cvtepi32_pd(_v256_extract_high(a.val))); } + +inline v_float64x4 v_cvt_f64(const v_float32x8& a) +{ return v_float64x4(_mm256_cvtps_pd(_v256_extract_low(a.val))); } + +inline v_float64x4 v_cvt_f64_high(const v_float32x8& a) +{ return v_float64x4(_mm256_cvtps_pd(_v256_extract_high(a.val))); } + +////////////// Lookup table access //////////////////// + +inline v_int32x8 v_lut(const int* tab, const v_int32x8& idxvec) +{ + return v_int32x8(_mm256_i32gather_epi32(tab, idxvec.val, 4)); +} + +inline v_uint32x8 v_lut(const unsigned* tab, const v_int32x8& idxvec) +{ + return v_reinterpret_as_u32(v_lut((const int *)tab, idxvec)); +} + +inline v_float32x8 v_lut(const float* tab, const v_int32x8& idxvec) +{ + return v_float32x8(_mm256_i32gather_ps(tab, idxvec.val, 4)); +} + +inline v_float64x4 v_lut(const double* tab, const v_int32x8& idxvec) +{ + return v_float64x4(_mm256_i32gather_pd(tab, _mm256_castsi256_si128(idxvec.val), 8)); +} + +inline void v_lut_deinterleave(const float* tab, const v_int32x8& idxvec, v_float32x8& x, v_float32x8& y) +{ + int CV_DECL_ALIGNED(32) idx[8]; + v_store_aligned(idx, idxvec); + __m128 z = _mm_setzero_ps(); + __m128 xy01, xy45, xy23, xy67; + xy01 = _mm_loadl_pi(z, (const __m64*)(tab + idx[0])); + xy01 = _mm_loadh_pi(xy01, (const __m64*)(tab + idx[1])); + xy45 = _mm_loadl_pi(z, (const __m64*)(tab + idx[4])); + xy45 = _mm_loadh_pi(xy45, (const __m64*)(tab + idx[5])); + __m256 xy0145 = _v256_combine(xy01, xy45); + xy23 = _mm_loadl_pi(z, (const __m64*)(tab + idx[2])); + xy23 = _mm_loadh_pi(xy23, (const __m64*)(tab + idx[3])); + xy67 = _mm_loadl_pi(z, (const __m64*)(tab + idx[6])); + xy67 = _mm_loadh_pi(xy67, (const __m64*)(tab + idx[7])); + __m256 xy2367 = _v256_combine(xy23, xy67); + + __m256 xxyy0145 = _mm256_unpacklo_ps(xy0145, xy2367); + __m256 xxyy2367 = _mm256_unpackhi_ps(xy0145, xy2367); + + x = v_float32x8(_mm256_unpacklo_ps(xxyy0145, xxyy2367)); + y = v_float32x8(_mm256_unpackhi_ps(xxyy0145, xxyy2367)); +} + +inline void v_lut_deinterleave(const double* tab, const v_int32x8& idxvec, v_float64x4& x, v_float64x4& y) +{ + int CV_DECL_ALIGNED(32) idx[4]; + v_store_low(idx, idxvec); + __m128d xy0 = _mm_loadu_pd(tab + idx[0]); + __m128d xy2 = _mm_loadu_pd(tab + idx[2]); + __m128d xy1 = _mm_loadu_pd(tab + idx[1]); + __m128d xy3 = _mm_loadu_pd(tab + idx[3]); + __m256d xy02 = _v256_combine(xy0, xy2); + __m256d xy13 = _v256_combine(xy1, xy3); + + x = v_float64x4(_mm256_unpacklo_pd(xy02, xy13)); + y = v_float64x4(_mm256_unpackhi_pd(xy02, xy13)); +} + +////////// Matrix operations ///////// + +inline v_int32x8 v_dotprod(const v_int16x16& a, const v_int16x16& b) +{ return v_int32x8(_mm256_madd_epi16(a.val, b.val)); } + +inline v_int32x8 v_dotprod(const v_int16x16& a, const v_int16x16& b, const v_int32x8& c) +{ return v_dotprod(a, b) + c; } + +#define OPENCV_HAL_AVX_SPLAT2_PS(a, im) \ + v_float32x8(_mm256_permute_ps(a.val, _MM_SHUFFLE(im, im, im, im))) + +inline v_float32x8 v_matmul(const v_float32x8& v, const v_float32x8& m0, + const v_float32x8& m1, const v_float32x8& m2, + const v_float32x8& m3) +{ + v_float32x8 v04 = OPENCV_HAL_AVX_SPLAT2_PS(v, 0); + v_float32x8 v15 = OPENCV_HAL_AVX_SPLAT2_PS(v, 1); + v_float32x8 v26 = OPENCV_HAL_AVX_SPLAT2_PS(v, 2); + v_float32x8 v37 = OPENCV_HAL_AVX_SPLAT2_PS(v, 3); + return v_fma(v04, m0, v_fma(v15, m1, v_fma(v26, m2, v37 * m3))); +} + +inline v_float32x8 v_matmuladd(const v_float32x8& v, const v_float32x8& m0, + const v_float32x8& m1, const v_float32x8& m2, + const v_float32x8& a) +{ + v_float32x8 v04 = OPENCV_HAL_AVX_SPLAT2_PS(v, 0); + v_float32x8 v15 = OPENCV_HAL_AVX_SPLAT2_PS(v, 1); + v_float32x8 v26 = OPENCV_HAL_AVX_SPLAT2_PS(v, 2); + return v_fma(v04, m0, v_fma(v15, m1, v_fma(v26, m2, a))); +} + +#define OPENCV_HAL_IMPL_AVX_TRANSPOSE4x4(_Tpvec, suffix, cast_from, cast_to) \ + inline void v_transpose4x4(const _Tpvec& a0, const _Tpvec& a1, \ + const _Tpvec& a2, const _Tpvec& a3, \ + _Tpvec& b0, _Tpvec& b1, _Tpvec& b2, _Tpvec& b3) \ + { \ + __m256i t0 = cast_from(_mm256_unpacklo_##suffix(a0.val, a1.val)); \ + __m256i t1 = cast_from(_mm256_unpacklo_##suffix(a2.val, a3.val)); \ + __m256i t2 = cast_from(_mm256_unpackhi_##suffix(a0.val, a1.val)); \ + __m256i t3 = cast_from(_mm256_unpackhi_##suffix(a2.val, a3.val)); \ + b0.val = cast_to(_mm256_unpacklo_epi64(t0, t1)); \ + b1.val = cast_to(_mm256_unpackhi_epi64(t0, t1)); \ + b2.val = cast_to(_mm256_unpacklo_epi64(t2, t3)); \ + b3.val = cast_to(_mm256_unpackhi_epi64(t2, t3)); \ + } + +OPENCV_HAL_IMPL_AVX_TRANSPOSE4x4(v_uint32x8, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_AVX_TRANSPOSE4x4(v_int32x8, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_AVX_TRANSPOSE4x4(v_float32x8, ps, _mm256_castps_si256, _mm256_castsi256_ps) + +//////////////// Value reordering /////////////// + +/* Expand */ +#define OPENCV_HAL_IMPL_AVX_EXPAND(_Tpvec, _Tpwvec, _Tp, intrin) \ + inline void v_expand(const _Tpvec& a, _Tpwvec& b0, _Tpwvec& b1) \ + { \ + b0.val = intrin(_v256_extract_low(a.val)); \ + b1.val = intrin(_v256_extract_high(a.val)); \ + } \ + inline _Tpwvec v_expand_low(const _Tpvec& a) \ + { return _Tpwvec(intrin(_v256_extract_low(a.val))); } \ + inline _Tpwvec v_expand_high(const _Tpvec& a) \ + { return _Tpwvec(intrin(_v256_extract_high(a.val))); } \ + inline _Tpwvec v256_load_expand(const _Tp* ptr) \ + { \ + __m128i a = _mm_loadu_si128((const __m128i*)ptr); \ + return _Tpwvec(intrin(a)); \ + } + +OPENCV_HAL_IMPL_AVX_EXPAND(v_uint8x32, v_uint16x16, uchar, _mm256_cvtepu8_epi16) +OPENCV_HAL_IMPL_AVX_EXPAND(v_int8x32, v_int16x16, schar, _mm256_cvtepi8_epi16) +OPENCV_HAL_IMPL_AVX_EXPAND(v_uint16x16, v_uint32x8, ushort, _mm256_cvtepu16_epi32) +OPENCV_HAL_IMPL_AVX_EXPAND(v_int16x16, v_int32x8, short, _mm256_cvtepi16_epi32) +OPENCV_HAL_IMPL_AVX_EXPAND(v_uint32x8, v_uint64x4, unsigned, _mm256_cvtepu32_epi64) +OPENCV_HAL_IMPL_AVX_EXPAND(v_int32x8, v_int64x4, int, _mm256_cvtepi32_epi64) + +#define OPENCV_HAL_IMPL_AVX_EXPAND_Q(_Tpvec, _Tp, intrin) \ + inline _Tpvec v256_load_expand_q(const _Tp* ptr) \ + { \ + __m128i a = _mm_loadl_epi64((const __m128i*)ptr); \ + return _Tpvec(intrin(a)); \ + } + +OPENCV_HAL_IMPL_AVX_EXPAND_Q(v_uint32x8, uchar, _mm256_cvtepu8_epi32) +OPENCV_HAL_IMPL_AVX_EXPAND_Q(v_int32x8, schar, _mm256_cvtepi8_epi32) + +/* pack */ +// 16 +inline v_int8x32 v_pack(const v_int16x16& a, const v_int16x16& b) +{ return v_int8x32(_v256_shuffle_odd_64(_mm256_packs_epi16(a.val, b.val))); } + +inline v_uint8x32 v_pack(const v_uint16x16& a, const v_uint16x16& b) +{ + __m256i t = _mm256_set1_epi16(255); + __m256i a1 = _mm256_min_epu16(a.val, t); + __m256i b1 = _mm256_min_epu16(b.val, t); + return v_uint8x32(_v256_shuffle_odd_64(_mm256_packus_epi16(a1, b1))); +} + +inline v_uint8x32 v_pack_u(const v_int16x16& a, const v_int16x16& b) +{ + return v_uint8x32(_v256_shuffle_odd_64(_mm256_packus_epi16(a.val, b.val))); +} + +inline void v_pack_store(schar* ptr, const v_int16x16& a) +{ v_store_low(ptr, v_pack(a, a)); } + +inline void v_pack_store(uchar* ptr, const v_uint16x16& a) +{ + const __m256i m = _mm256_set1_epi16(255); + __m256i am = _mm256_min_epu16(a.val, m); + am = _v256_shuffle_odd_64(_mm256_packus_epi16(am, am)); + v_store_low(ptr, v_uint8x32(am)); +} + +inline void v_pack_u_store(uchar* ptr, const v_int16x16& a) +{ v_store_low(ptr, v_pack_u(a, a)); } + +template inline +v_uint8x32 v_rshr_pack(const v_uint16x16& a, const v_uint16x16& b) +{ + // we assume that n > 0, and so the shifted 16-bit values can be treated as signed numbers. + v_uint16x16 delta = v256_setall_u16((short)(1 << (n-1))); + return v_pack_u(v_reinterpret_as_s16((a + delta) >> n), + v_reinterpret_as_s16((b + delta) >> n)); +} + +template inline +void v_rshr_pack_store(uchar* ptr, const v_uint16x16& a) +{ + v_uint16x16 delta = v256_setall_u16((short)(1 << (n-1))); + v_pack_u_store(ptr, v_reinterpret_as_s16((a + delta) >> n)); +} + +template inline +v_uint8x32 v_rshr_pack_u(const v_int16x16& a, const v_int16x16& b) +{ + v_int16x16 delta = v256_setall_s16((short)(1 << (n-1))); + return v_pack_u((a + delta) >> n, (b + delta) >> n); +} + +template inline +void v_rshr_pack_u_store(uchar* ptr, const v_int16x16& a) +{ + v_int16x16 delta = v256_setall_s16((short)(1 << (n-1))); + v_pack_u_store(ptr, (a + delta) >> n); +} + +template inline +v_int8x32 v_rshr_pack(const v_int16x16& a, const v_int16x16& b) +{ + v_int16x16 delta = v256_setall_s16((short)(1 << (n-1))); + return v_pack((a + delta) >> n, (b + delta) >> n); +} + +template inline +void v_rshr_pack_store(schar* ptr, const v_int16x16& a) +{ + v_int16x16 delta = v256_setall_s16((short)(1 << (n-1))); + v_pack_store(ptr, (a + delta) >> n); +} + +// 32 +inline v_int16x16 v_pack(const v_int32x8& a, const v_int32x8& b) +{ return v_int16x16(_v256_shuffle_odd_64(_mm256_packs_epi32(a.val, b.val))); } + +inline v_uint16x16 v_pack(const v_uint32x8& a, const v_uint32x8& b) +{ return v_uint16x16(_v256_shuffle_odd_64(_v256_packs_epu32(a.val, b.val))); } + +inline v_uint16x16 v_pack_u(const v_int32x8& a, const v_int32x8& b) +{ return v_uint16x16(_v256_shuffle_odd_64(_mm256_packus_epi32(a.val, b.val))); } + +inline void v_pack_store(short* ptr, const v_int32x8& a) +{ v_store_low(ptr, v_pack(a, a)); } + +inline void v_pack_store(ushort* ptr, const v_uint32x8& a) +{ + const __m256i m = _mm256_set1_epi32(65535); + __m256i am = _mm256_min_epu32(a.val, m); + am = _v256_shuffle_odd_64(_mm256_packus_epi32(am, am)); + v_store_low(ptr, v_uint16x16(am)); +} + +inline void v_pack_u_store(ushort* ptr, const v_int32x8& a) +{ v_store_low(ptr, v_pack_u(a, a)); } + + +template inline +v_uint16x16 v_rshr_pack(const v_uint32x8& a, const v_uint32x8& b) +{ + // we assume that n > 0, and so the shifted 32-bit values can be treated as signed numbers. + v_uint32x8 delta = v256_setall_u32(1 << (n-1)); + return v_pack_u(v_reinterpret_as_s32((a + delta) >> n), + v_reinterpret_as_s32((b + delta) >> n)); +} + +template inline +void v_rshr_pack_store(ushort* ptr, const v_uint32x8& a) +{ + v_uint32x8 delta = v256_setall_u32(1 << (n-1)); + v_pack_u_store(ptr, v_reinterpret_as_s32((a + delta) >> n)); +} + +template inline +v_uint16x16 v_rshr_pack_u(const v_int32x8& a, const v_int32x8& b) +{ + v_int32x8 delta = v256_setall_s32(1 << (n-1)); + return v_pack_u((a + delta) >> n, (b + delta) >> n); +} + +template inline +void v_rshr_pack_u_store(ushort* ptr, const v_int32x8& a) +{ + v_int32x8 delta = v256_setall_s32(1 << (n-1)); + v_pack_u_store(ptr, (a + delta) >> n); +} + +template inline +v_int16x16 v_rshr_pack(const v_int32x8& a, const v_int32x8& b) +{ + v_int32x8 delta = v256_setall_s32(1 << (n-1)); + return v_pack((a + delta) >> n, (b + delta) >> n); +} + +template inline +void v_rshr_pack_store(short* ptr, const v_int32x8& a) +{ + v_int32x8 delta = v256_setall_s32(1 << (n-1)); + v_pack_store(ptr, (a + delta) >> n); +} + +// 64 +// Non-saturating pack +inline v_uint32x8 v_pack(const v_uint64x4& a, const v_uint64x4& b) +{ + __m256i a0 = _mm256_shuffle_epi32(a.val, _MM_SHUFFLE(0, 0, 2, 0)); + __m256i b0 = _mm256_shuffle_epi32(b.val, _MM_SHUFFLE(0, 0, 2, 0)); + __m256i ab = _mm256_unpacklo_epi64(a0, b0); // a0, a1, b0, b1, a2, a3, b2, b3 + return v_uint32x8(_v256_shuffle_odd_64(ab)); +} + +inline v_int32x8 v_pack(const v_int64x4& a, const v_int64x4& b) +{ return v_reinterpret_as_s32(v_pack(v_reinterpret_as_u64(a), v_reinterpret_as_u64(b))); } + +inline void v_pack_store(unsigned* ptr, const v_uint64x4& a) +{ + __m256i a0 = _mm256_shuffle_epi32(a.val, _MM_SHUFFLE(0, 0, 2, 0)); + v_store_low(ptr, v_uint32x8(_v256_shuffle_odd_64(a0))); +} + +inline void v_pack_store(int* ptr, const v_int64x4& b) +{ v_pack_store((unsigned*)ptr, v_reinterpret_as_u64(b)); } + +template inline +v_uint32x8 v_rshr_pack(const v_uint64x4& a, const v_uint64x4& b) +{ + v_uint64x4 delta = v256_setall_u64((uint64)1 << (n-1)); + return v_pack((a + delta) >> n, (b + delta) >> n); +} + +template inline +void v_rshr_pack_store(unsigned* ptr, const v_uint64x4& a) +{ + v_uint64x4 delta = v256_setall_u64((uint64)1 << (n-1)); + v_pack_store(ptr, (a + delta) >> n); +} + +template inline +v_int32x8 v_rshr_pack(const v_int64x4& a, const v_int64x4& b) +{ + v_int64x4 delta = v256_setall_s64((int64)1 << (n-1)); + return v_pack((a + delta) >> n, (b + delta) >> n); +} + +template inline +void v_rshr_pack_store(int* ptr, const v_int64x4& a) +{ + v_int64x4 delta = v256_setall_s64((int64)1 << (n-1)); + v_pack_store(ptr, (a + delta) >> n); +} + +// pack boolean +inline v_uint8x32 v_pack_b(const v_uint16x16& a, const v_uint16x16& b) +{ + __m256i ab = _mm256_packs_epi16(a.val, b.val); + return v_uint8x32(_v256_shuffle_odd_64(ab)); +} + +inline v_uint8x32 v_pack_b(const v_uint32x8& a, const v_uint32x8& b, + const v_uint32x8& c, const v_uint32x8& d) +{ + __m256i ab = _mm256_packs_epi32(a.val, b.val); + __m256i cd = _mm256_packs_epi32(c.val, d.val); + + __m256i abcd = _v256_shuffle_odd_64(_mm256_packs_epi16(ab, cd)); + return v_uint8x32(_mm256_shuffle_epi32(abcd, _MM_SHUFFLE(3, 1, 2, 0))); +} + +inline v_uint8x32 v_pack_b(const v_uint64x4& a, const v_uint64x4& b, const v_uint64x4& c, + const v_uint64x4& d, const v_uint64x4& e, const v_uint64x4& f, + const v_uint64x4& g, const v_uint64x4& h) +{ + __m256i ab = _mm256_packs_epi32(a.val, b.val); + __m256i cd = _mm256_packs_epi32(c.val, d.val); + __m256i ef = _mm256_packs_epi32(e.val, f.val); + __m256i gh = _mm256_packs_epi32(g.val, h.val); + + __m256i abcd = _mm256_packs_epi32(ab, cd); + __m256i efgh = _mm256_packs_epi32(ef, gh); + __m256i pkall = _v256_shuffle_odd_64(_mm256_packs_epi16(abcd, efgh)); + + __m256i rev = _mm256_alignr_epi8(pkall, pkall, 8); + return v_uint8x32(_mm256_unpacklo_epi16(pkall, rev)); +} + +/* Recombine */ +// its up there with load and store operations + +/* Extract */ +#define OPENCV_HAL_IMPL_AVX_EXTRACT(_Tpvec) \ + template \ + inline _Tpvec v_extract(const _Tpvec& a, const _Tpvec& b) \ + { return v_rotate_right(a, b); } + +OPENCV_HAL_IMPL_AVX_EXTRACT(v_uint8x32) +OPENCV_HAL_IMPL_AVX_EXTRACT(v_int8x32) +OPENCV_HAL_IMPL_AVX_EXTRACT(v_uint16x16) +OPENCV_HAL_IMPL_AVX_EXTRACT(v_int16x16) +OPENCV_HAL_IMPL_AVX_EXTRACT(v_uint32x8) +OPENCV_HAL_IMPL_AVX_EXTRACT(v_int32x8) +OPENCV_HAL_IMPL_AVX_EXTRACT(v_uint64x4) +OPENCV_HAL_IMPL_AVX_EXTRACT(v_int64x4) +OPENCV_HAL_IMPL_AVX_EXTRACT(v_float32x8) +OPENCV_HAL_IMPL_AVX_EXTRACT(v_float64x4) + + +///////////////////// load deinterleave ///////////////////////////// + +inline void v_load_deinterleave( const uchar* ptr, v_uint8x32& a, v_uint8x32& b ) +{ + __m256i ab0 = _mm256_loadu_si256((const __m256i*)ptr); + __m256i ab1 = _mm256_loadu_si256((const __m256i*)(ptr + 32)); + + const __m256i sh = _mm256_setr_epi8(0, 2, 4, 6, 8, 10, 12, 14, 1, 3, 5, 7, 9, 11, 13, 15, + 0, 2, 4, 6, 8, 10, 12, 14, 1, 3, 5, 7, 9, 11, 13, 15); + __m256i p0 = _mm256_shuffle_epi8(ab0, sh); + __m256i p1 = _mm256_shuffle_epi8(ab1, sh); + __m256i pl = _mm256_permute2x128_si256(p0, p1, 0 + 2*16); + __m256i ph = _mm256_permute2x128_si256(p0, p1, 1 + 3*16); + __m256i a0 = _mm256_unpacklo_epi64(pl, ph); + __m256i b0 = _mm256_unpackhi_epi64(pl, ph); + a = v_uint8x32(a0); + b = v_uint8x32(b0); +} + +inline void v_load_deinterleave( const ushort* ptr, v_uint16x16& a, v_uint16x16& b ) +{ + __m256i ab0 = _mm256_loadu_si256((const __m256i*)ptr); + __m256i ab1 = _mm256_loadu_si256((const __m256i*)(ptr + 16)); + + const __m256i sh = _mm256_setr_epi8(0, 1, 4, 5, 8, 9, 12, 13, 2, 3, 6, 7, 10, 11, 14, 15, + 0, 1, 4, 5, 8, 9, 12, 13, 2, 3, 6, 7, 10, 11, 14, 15); + __m256i p0 = _mm256_shuffle_epi8(ab0, sh); + __m256i p1 = _mm256_shuffle_epi8(ab1, sh); + __m256i pl = _mm256_permute2x128_si256(p0, p1, 0 + 2*16); + __m256i ph = _mm256_permute2x128_si256(p0, p1, 1 + 3*16); + __m256i a0 = _mm256_unpacklo_epi64(pl, ph); + __m256i b0 = _mm256_unpackhi_epi64(pl, ph); + a = v_uint16x16(a0); + b = v_uint16x16(b0); +} + +inline void v_load_deinterleave( const unsigned* ptr, v_uint32x8& a, v_uint32x8& b ) +{ + __m256i ab0 = _mm256_loadu_si256((const __m256i*)ptr); + __m256i ab1 = _mm256_loadu_si256((const __m256i*)(ptr + 8)); + + const int sh = 0+2*4+1*16+3*64; + __m256i p0 = _mm256_shuffle_epi32(ab0, sh); + __m256i p1 = _mm256_shuffle_epi32(ab1, sh); + __m256i pl = _mm256_permute2x128_si256(p0, p1, 0 + 2*16); + __m256i ph = _mm256_permute2x128_si256(p0, p1, 1 + 3*16); + __m256i a0 = _mm256_unpacklo_epi64(pl, ph); + __m256i b0 = _mm256_unpackhi_epi64(pl, ph); + a = v_uint32x8(a0); + b = v_uint32x8(b0); +} + +inline void v_load_deinterleave( const uint64* ptr, v_uint64x4& a, v_uint64x4& b ) +{ + __m256i ab0 = _mm256_loadu_si256((const __m256i*)ptr); + __m256i ab1 = _mm256_loadu_si256((const __m256i*)(ptr + 4)); + + __m256i pl = _mm256_permute2x128_si256(ab0, ab1, 0 + 2*16); + __m256i ph = _mm256_permute2x128_si256(ab0, ab1, 1 + 3*16); + __m256i a0 = _mm256_unpacklo_epi64(pl, ph); + __m256i b0 = _mm256_unpackhi_epi64(pl, ph); + a = v_uint64x4(a0); + b = v_uint64x4(b0); +} + +inline void v_load_deinterleave( const uchar* ptr, v_uint8x32& b, v_uint8x32& g, v_uint8x32& r ) +{ + __m256i bgr0 = _mm256_loadu_si256((const __m256i*)ptr); + __m256i bgr1 = _mm256_loadu_si256((const __m256i*)(ptr + 32)); + __m256i bgr2 = _mm256_loadu_si256((const __m256i*)(ptr + 64)); + + __m256i s02_low = _mm256_permute2x128_si256(bgr0, bgr2, 0 + 2*16); + __m256i s02_high = _mm256_permute2x128_si256(bgr0, bgr2, 1 + 3*16); + + const __m256i m0 = _mm256_setr_epi8(0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, + 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0); + const __m256i m1 = _mm256_setr_epi8(0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, + -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1); + + __m256i b0 = _mm256_blendv_epi8(_mm256_blendv_epi8(s02_low, s02_high, m0), bgr1, m1); + __m256i g0 = _mm256_blendv_epi8(_mm256_blendv_epi8(s02_high, s02_low, m1), bgr1, m0); + __m256i r0 = _mm256_blendv_epi8(_mm256_blendv_epi8(bgr1, s02_low, m0), s02_high, m1); + + const __m256i + sh_b = _mm256_setr_epi8(0, 3, 6, 9, 12, 15, 2, 5, 8, 11, 14, 1, 4, 7, 10, 13, + 0, 3, 6, 9, 12, 15, 2, 5, 8, 11, 14, 1, 4, 7, 10, 13), + sh_g = _mm256_setr_epi8(1, 4, 7, 10, 13, 0, 3, 6, 9, 12, 15, 2, 5, 8, 11, 14, + 1, 4, 7, 10, 13, 0, 3, 6, 9, 12, 15, 2, 5, 8, 11, 14), + sh_r = _mm256_setr_epi8(2, 5, 8, 11, 14, 1, 4, 7, 10, 13, 0, 3, 6, 9, 12, 15, + 2, 5, 8, 11, 14, 1, 4, 7, 10, 13, 0, 3, 6, 9, 12, 15); + b0 = _mm256_shuffle_epi8(b0, sh_b); + g0 = _mm256_shuffle_epi8(g0, sh_g); + r0 = _mm256_shuffle_epi8(r0, sh_r); + + b = v_uint8x32(b0); + g = v_uint8x32(g0); + r = v_uint8x32(r0); +} + +inline void v_load_deinterleave( const ushort* ptr, v_uint16x16& b, v_uint16x16& g, v_uint16x16& r ) +{ + __m256i bgr0 = _mm256_loadu_si256((const __m256i*)ptr); + __m256i bgr1 = _mm256_loadu_si256((const __m256i*)(ptr + 16)); + __m256i bgr2 = _mm256_loadu_si256((const __m256i*)(ptr + 32)); + + __m256i s02_low = _mm256_permute2x128_si256(bgr0, bgr2, 0 + 2*16); + __m256i s02_high = _mm256_permute2x128_si256(bgr0, bgr2, 1 + 3*16); + + const __m256i m0 = _mm256_setr_epi8(0, 0, -1, -1, 0, 0, 0, 0, -1, -1, 0, 0, 0, 0, -1, -1, + 0, 0, 0, 0, -1, -1, 0, 0, 0, 0, -1, -1, 0, 0, 0, 0); + const __m256i m1 = _mm256_setr_epi8(0, 0, 0, 0, -1, -1, 0, 0, 0, 0, -1, -1, 0, 0, 0, 0, + -1, -1, 0, 0, 0, 0, -1, -1, 0, 0, 0, 0, -1, -1, 0, 0); + __m256i b0 = _mm256_blendv_epi8(_mm256_blendv_epi8(s02_low, s02_high, m0), bgr1, m1); + __m256i g0 = _mm256_blendv_epi8(_mm256_blendv_epi8(bgr1, s02_low, m0), s02_high, m1); + __m256i r0 = _mm256_blendv_epi8(_mm256_blendv_epi8(s02_high, s02_low, m1), bgr1, m0); + const __m256i sh_b = _mm256_setr_epi8(0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5, 10, 11, + 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5, 10, 11); + const __m256i sh_g = _mm256_setr_epi8(2, 3, 8, 9, 14, 15, 4, 5, 10, 11, 0, 1, 6, 7, 12, 13, + 2, 3, 8, 9, 14, 15, 4, 5, 10, 11, 0, 1, 6, 7, 12, 13); + const __m256i sh_r = _mm256_setr_epi8(4, 5, 10, 11, 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, + 4, 5, 10, 11, 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15); + b0 = _mm256_shuffle_epi8(b0, sh_b); + g0 = _mm256_shuffle_epi8(g0, sh_g); + r0 = _mm256_shuffle_epi8(r0, sh_r); + + b = v_uint16x16(b0); + g = v_uint16x16(g0); + r = v_uint16x16(r0); +} + +inline void v_load_deinterleave( const unsigned* ptr, v_uint32x8& b, v_uint32x8& g, v_uint32x8& r ) +{ + __m256i bgr0 = _mm256_loadu_si256((const __m256i*)ptr); + __m256i bgr1 = _mm256_loadu_si256((const __m256i*)(ptr + 8)); + __m256i bgr2 = _mm256_loadu_si256((const __m256i*)(ptr + 16)); + + __m256i s02_low = _mm256_permute2x128_si256(bgr0, bgr2, 0 + 2*16); + __m256i s02_high = _mm256_permute2x128_si256(bgr0, bgr2, 1 + 3*16); + + __m256i b0 = _mm256_blend_epi32(_mm256_blend_epi32(s02_low, s02_high, 0x24), bgr1, 0x92); + __m256i g0 = _mm256_blend_epi32(_mm256_blend_epi32(s02_high, s02_low, 0x92), bgr1, 0x24); + __m256i r0 = _mm256_blend_epi32(_mm256_blend_epi32(bgr1, s02_low, 0x24), s02_high, 0x92); + + b0 = _mm256_shuffle_epi32(b0, 0x6c); + g0 = _mm256_shuffle_epi32(g0, 0xb1); + r0 = _mm256_shuffle_epi32(r0, 0xc6); + + b = v_uint32x8(b0); + g = v_uint32x8(g0); + r = v_uint32x8(r0); +} + +inline void v_load_deinterleave( const uint64* ptr, v_uint64x4& b, v_uint64x4& g, v_uint64x4& r ) +{ + __m256i bgr0 = _mm256_loadu_si256((const __m256i*)ptr); + __m256i bgr1 = _mm256_loadu_si256((const __m256i*)(ptr + 4)); + __m256i bgr2 = _mm256_loadu_si256((const __m256i*)(ptr + 8)); + + __m256i s01 = _mm256_blend_epi32(bgr0, bgr1, 0xf0); + __m256i s12 = _mm256_blend_epi32(bgr1, bgr2, 0xf0); + __m256i s20r = _mm256_permute4x64_epi64(_mm256_blend_epi32(bgr2, bgr0, 0xf0), 0x1b); + __m256i b0 = _mm256_unpacklo_epi64(s01, s20r); + __m256i g0 = _mm256_alignr_epi8(s12, s01, 8); + __m256i r0 = _mm256_unpackhi_epi64(s20r, s12); + + b = v_uint64x4(b0); + g = v_uint64x4(g0); + r = v_uint64x4(r0); +} + +inline void v_load_deinterleave( const uchar* ptr, v_uint8x32& b, v_uint8x32& g, v_uint8x32& r, v_uint8x32& a ) +{ + __m256i bgr0 = _mm256_loadu_si256((const __m256i*)ptr); + __m256i bgr1 = _mm256_loadu_si256((const __m256i*)(ptr + 32)); + __m256i bgr2 = _mm256_loadu_si256((const __m256i*)(ptr + 64)); + __m256i bgr3 = _mm256_loadu_si256((const __m256i*)(ptr + 96)); + const __m256i sh = _mm256_setr_epi8(0, 4, 8, 12, 1, 5, 9, 13, 2, 6, 10, 14, 3, 7, 11, 15, + 0, 4, 8, 12, 1, 5, 9, 13, 2, 6, 10, 14, 3, 7, 11, 15); + + __m256i p0 = _mm256_shuffle_epi8(bgr0, sh); + __m256i p1 = _mm256_shuffle_epi8(bgr1, sh); + __m256i p2 = _mm256_shuffle_epi8(bgr2, sh); + __m256i p3 = _mm256_shuffle_epi8(bgr3, sh); + + __m256i p01l = _mm256_unpacklo_epi32(p0, p1); + __m256i p01h = _mm256_unpackhi_epi32(p0, p1); + __m256i p23l = _mm256_unpacklo_epi32(p2, p3); + __m256i p23h = _mm256_unpackhi_epi32(p2, p3); + + __m256i pll = _mm256_permute2x128_si256(p01l, p23l, 0 + 2*16); + __m256i plh = _mm256_permute2x128_si256(p01l, p23l, 1 + 3*16); + __m256i phl = _mm256_permute2x128_si256(p01h, p23h, 0 + 2*16); + __m256i phh = _mm256_permute2x128_si256(p01h, p23h, 1 + 3*16); + + __m256i b0 = _mm256_unpacklo_epi32(pll, plh); + __m256i g0 = _mm256_unpackhi_epi32(pll, plh); + __m256i r0 = _mm256_unpacklo_epi32(phl, phh); + __m256i a0 = _mm256_unpackhi_epi32(phl, phh); + + b = v_uint8x32(b0); + g = v_uint8x32(g0); + r = v_uint8x32(r0); + a = v_uint8x32(a0); +} + +inline void v_load_deinterleave( const ushort* ptr, v_uint16x16& b, v_uint16x16& g, v_uint16x16& r, v_uint16x16& a ) +{ + __m256i bgr0 = _mm256_loadu_si256((const __m256i*)ptr); + __m256i bgr1 = _mm256_loadu_si256((const __m256i*)(ptr + 16)); + __m256i bgr2 = _mm256_loadu_si256((const __m256i*)(ptr + 32)); + __m256i bgr3 = _mm256_loadu_si256((const __m256i*)(ptr + 48)); + const __m256i sh = _mm256_setr_epi8(0, 1, 8, 9, 2, 3, 10, 11, 4, 5, 12, 13, 6, 7, 14, 15, + 0, 1, 8, 9, 2, 3, 10, 11, 4, 5, 12, 13, 6, 7, 14, 15); + __m256i p0 = _mm256_shuffle_epi8(bgr0, sh); + __m256i p1 = _mm256_shuffle_epi8(bgr1, sh); + __m256i p2 = _mm256_shuffle_epi8(bgr2, sh); + __m256i p3 = _mm256_shuffle_epi8(bgr3, sh); + + __m256i p01l = _mm256_unpacklo_epi32(p0, p1); + __m256i p01h = _mm256_unpackhi_epi32(p0, p1); + __m256i p23l = _mm256_unpacklo_epi32(p2, p3); + __m256i p23h = _mm256_unpackhi_epi32(p2, p3); + + __m256i pll = _mm256_permute2x128_si256(p01l, p23l, 0 + 2*16); + __m256i plh = _mm256_permute2x128_si256(p01l, p23l, 1 + 3*16); + __m256i phl = _mm256_permute2x128_si256(p01h, p23h, 0 + 2*16); + __m256i phh = _mm256_permute2x128_si256(p01h, p23h, 1 + 3*16); + + __m256i b0 = _mm256_unpacklo_epi32(pll, plh); + __m256i g0 = _mm256_unpackhi_epi32(pll, plh); + __m256i r0 = _mm256_unpacklo_epi32(phl, phh); + __m256i a0 = _mm256_unpackhi_epi32(phl, phh); + + b = v_uint16x16(b0); + g = v_uint16x16(g0); + r = v_uint16x16(r0); + a = v_uint16x16(a0); +} + +inline void v_load_deinterleave( const unsigned* ptr, v_uint32x8& b, v_uint32x8& g, v_uint32x8& r, v_uint32x8& a ) +{ + __m256i p0 = _mm256_loadu_si256((const __m256i*)ptr); + __m256i p1 = _mm256_loadu_si256((const __m256i*)(ptr + 8)); + __m256i p2 = _mm256_loadu_si256((const __m256i*)(ptr + 16)); + __m256i p3 = _mm256_loadu_si256((const __m256i*)(ptr + 24)); + + __m256i p01l = _mm256_unpacklo_epi32(p0, p1); + __m256i p01h = _mm256_unpackhi_epi32(p0, p1); + __m256i p23l = _mm256_unpacklo_epi32(p2, p3); + __m256i p23h = _mm256_unpackhi_epi32(p2, p3); + + __m256i pll = _mm256_permute2x128_si256(p01l, p23l, 0 + 2*16); + __m256i plh = _mm256_permute2x128_si256(p01l, p23l, 1 + 3*16); + __m256i phl = _mm256_permute2x128_si256(p01h, p23h, 0 + 2*16); + __m256i phh = _mm256_permute2x128_si256(p01h, p23h, 1 + 3*16); + + __m256i b0 = _mm256_unpacklo_epi32(pll, plh); + __m256i g0 = _mm256_unpackhi_epi32(pll, plh); + __m256i r0 = _mm256_unpacklo_epi32(phl, phh); + __m256i a0 = _mm256_unpackhi_epi32(phl, phh); + + b = v_uint32x8(b0); + g = v_uint32x8(g0); + r = v_uint32x8(r0); + a = v_uint32x8(a0); +} + +inline void v_load_deinterleave( const uint64* ptr, v_uint64x4& b, v_uint64x4& g, v_uint64x4& r, v_uint64x4& a ) +{ + __m256i bgra0 = _mm256_loadu_si256((const __m256i*)ptr); + __m256i bgra1 = _mm256_loadu_si256((const __m256i*)(ptr + 4)); + __m256i bgra2 = _mm256_loadu_si256((const __m256i*)(ptr + 8)); + __m256i bgra3 = _mm256_loadu_si256((const __m256i*)(ptr + 12)); + + __m256i l02 = _mm256_permute2x128_si256(bgra0, bgra2, 0 + 2*16); + __m256i h02 = _mm256_permute2x128_si256(bgra0, bgra2, 1 + 3*16); + __m256i l13 = _mm256_permute2x128_si256(bgra1, bgra3, 0 + 2*16); + __m256i h13 = _mm256_permute2x128_si256(bgra1, bgra3, 1 + 3*16); + + __m256i b0 = _mm256_unpacklo_epi64(l02, l13); + __m256i g0 = _mm256_unpackhi_epi64(l02, l13); + __m256i r0 = _mm256_unpacklo_epi64(h02, h13); + __m256i a0 = _mm256_unpackhi_epi64(h02, h13); + + b = v_uint64x4(b0); + g = v_uint64x4(g0); + r = v_uint64x4(r0); + a = v_uint64x4(a0); +} + +///////////////////////////// store interleave ///////////////////////////////////// + +inline void v_store_interleave( uchar* ptr, const v_uint8x32& x, const v_uint8x32& y, + hal::StoreMode mode=hal::STORE_UNALIGNED ) +{ + __m256i xy_l = _mm256_unpacklo_epi8(x.val, y.val); + __m256i xy_h = _mm256_unpackhi_epi8(x.val, y.val); + + __m256i xy0 = _mm256_permute2x128_si256(xy_l, xy_h, 0 + 2*16); + __m256i xy1 = _mm256_permute2x128_si256(xy_l, xy_h, 1 + 3*16); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm256_stream_si256((__m256i*)ptr, xy0); + _mm256_stream_si256((__m256i*)(ptr + 32), xy1); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm256_store_si256((__m256i*)ptr, xy0); + _mm256_store_si256((__m256i*)(ptr + 32), xy1); + } + else + { + _mm256_storeu_si256((__m256i*)ptr, xy0); + _mm256_storeu_si256((__m256i*)(ptr + 32), xy1); + } +} + +inline void v_store_interleave( ushort* ptr, const v_uint16x16& x, const v_uint16x16& y, + hal::StoreMode mode=hal::STORE_UNALIGNED ) +{ + __m256i xy_l = _mm256_unpacklo_epi16(x.val, y.val); + __m256i xy_h = _mm256_unpackhi_epi16(x.val, y.val); + + __m256i xy0 = _mm256_permute2x128_si256(xy_l, xy_h, 0 + 2*16); + __m256i xy1 = _mm256_permute2x128_si256(xy_l, xy_h, 1 + 3*16); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm256_stream_si256((__m256i*)ptr, xy0); + _mm256_stream_si256((__m256i*)(ptr + 16), xy1); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm256_store_si256((__m256i*)ptr, xy0); + _mm256_store_si256((__m256i*)(ptr + 16), xy1); + } + else + { + _mm256_storeu_si256((__m256i*)ptr, xy0); + _mm256_storeu_si256((__m256i*)(ptr + 16), xy1); + } +} + +inline void v_store_interleave( unsigned* ptr, const v_uint32x8& x, const v_uint32x8& y, + hal::StoreMode mode=hal::STORE_UNALIGNED ) +{ + __m256i xy_l = _mm256_unpacklo_epi32(x.val, y.val); + __m256i xy_h = _mm256_unpackhi_epi32(x.val, y.val); + + __m256i xy0 = _mm256_permute2x128_si256(xy_l, xy_h, 0 + 2*16); + __m256i xy1 = _mm256_permute2x128_si256(xy_l, xy_h, 1 + 3*16); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm256_stream_si256((__m256i*)ptr, xy0); + _mm256_stream_si256((__m256i*)(ptr + 8), xy1); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm256_store_si256((__m256i*)ptr, xy0); + _mm256_store_si256((__m256i*)(ptr + 8), xy1); + } + else + { + _mm256_storeu_si256((__m256i*)ptr, xy0); + _mm256_storeu_si256((__m256i*)(ptr + 8), xy1); + } +} + +inline void v_store_interleave( uint64* ptr, const v_uint64x4& x, const v_uint64x4& y, + hal::StoreMode mode=hal::STORE_UNALIGNED ) +{ + __m256i xy_l = _mm256_unpacklo_epi64(x.val, y.val); + __m256i xy_h = _mm256_unpackhi_epi64(x.val, y.val); + + __m256i xy0 = _mm256_permute2x128_si256(xy_l, xy_h, 0 + 2*16); + __m256i xy1 = _mm256_permute2x128_si256(xy_l, xy_h, 1 + 3*16); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm256_stream_si256((__m256i*)ptr, xy0); + _mm256_stream_si256((__m256i*)(ptr + 4), xy1); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm256_store_si256((__m256i*)ptr, xy0); + _mm256_store_si256((__m256i*)(ptr + 4), xy1); + } + else + { + _mm256_storeu_si256((__m256i*)ptr, xy0); + _mm256_storeu_si256((__m256i*)(ptr + 4), xy1); + } +} + +inline void v_store_interleave( uchar* ptr, const v_uint8x32& b, const v_uint8x32& g, const v_uint8x32& r, + hal::StoreMode mode=hal::STORE_UNALIGNED ) +{ + const __m256i sh_b = _mm256_setr_epi8( + 0, 11, 6, 1, 12, 7, 2, 13, 8, 3, 14, 9, 4, 15, 10, 5, + 0, 11, 6, 1, 12, 7, 2, 13, 8, 3, 14, 9, 4, 15, 10, 5); + const __m256i sh_g = _mm256_setr_epi8( + 5, 0, 11, 6, 1, 12, 7, 2, 13, 8, 3, 14, 9, 4, 15, 10, + 5, 0, 11, 6, 1, 12, 7, 2, 13, 8, 3, 14, 9, 4, 15, 10); + const __m256i sh_r = _mm256_setr_epi8( + 10, 5, 0, 11, 6, 1, 12, 7, 2, 13, 8, 3, 14, 9, 4, 15, + 10, 5, 0, 11, 6, 1, 12, 7, 2, 13, 8, 3, 14, 9, 4, 15); + + __m256i b0 = _mm256_shuffle_epi8(b.val, sh_b); + __m256i g0 = _mm256_shuffle_epi8(g.val, sh_g); + __m256i r0 = _mm256_shuffle_epi8(r.val, sh_r); + + const __m256i m0 = _mm256_setr_epi8(0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, + 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0); + const __m256i m1 = _mm256_setr_epi8(0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, + 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0); + + __m256i p0 = _mm256_blendv_epi8(_mm256_blendv_epi8(b0, g0, m0), r0, m1); + __m256i p1 = _mm256_blendv_epi8(_mm256_blendv_epi8(g0, r0, m0), b0, m1); + __m256i p2 = _mm256_blendv_epi8(_mm256_blendv_epi8(r0, b0, m0), g0, m1); + + __m256i bgr0 = _mm256_permute2x128_si256(p0, p1, 0 + 2*16); + __m256i bgr1 = _mm256_permute2x128_si256(p2, p0, 0 + 3*16); + __m256i bgr2 = _mm256_permute2x128_si256(p1, p2, 1 + 3*16); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm256_stream_si256((__m256i*)ptr, bgr0); + _mm256_stream_si256((__m256i*)(ptr + 32), bgr1); + _mm256_stream_si256((__m256i*)(ptr + 64), bgr2); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm256_store_si256((__m256i*)ptr, bgr0); + _mm256_store_si256((__m256i*)(ptr + 32), bgr1); + _mm256_store_si256((__m256i*)(ptr + 64), bgr2); + } + else + { + _mm256_storeu_si256((__m256i*)ptr, bgr0); + _mm256_storeu_si256((__m256i*)(ptr + 32), bgr1); + _mm256_storeu_si256((__m256i*)(ptr + 64), bgr2); + } +} + +inline void v_store_interleave( ushort* ptr, const v_uint16x16& b, const v_uint16x16& g, const v_uint16x16& r, + hal::StoreMode mode=hal::STORE_UNALIGNED ) +{ + const __m256i sh_b = _mm256_setr_epi8( + 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5, 10, 11, + 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5, 10, 11); + const __m256i sh_g = _mm256_setr_epi8( + 10, 11, 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5, + 10, 11, 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5); + const __m256i sh_r = _mm256_setr_epi8( + 4, 5, 10, 11, 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, + 4, 5, 10, 11, 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15); + + __m256i b0 = _mm256_shuffle_epi8(b.val, sh_b); + __m256i g0 = _mm256_shuffle_epi8(g.val, sh_g); + __m256i r0 = _mm256_shuffle_epi8(r.val, sh_r); + + const __m256i m0 = _mm256_setr_epi8(0, 0, -1, -1, 0, 0, 0, 0, -1, -1, 0, 0, 0, 0, -1, -1, + 0, 0, 0, 0, -1, -1, 0, 0, 0, 0, -1, -1, 0, 0, 0, 0); + const __m256i m1 = _mm256_setr_epi8(0, 0, 0, 0, -1, -1, 0, 0, 0, 0, -1, -1, 0, 0, 0, 0, + -1, -1, 0, 0, 0, 0, -1, -1, 0, 0, 0, 0, -1, -1, 0, 0); + + __m256i p0 = _mm256_blendv_epi8(_mm256_blendv_epi8(b0, g0, m0), r0, m1); + __m256i p1 = _mm256_blendv_epi8(_mm256_blendv_epi8(g0, r0, m0), b0, m1); + __m256i p2 = _mm256_blendv_epi8(_mm256_blendv_epi8(r0, b0, m0), g0, m1); + + __m256i bgr0 = _mm256_permute2x128_si256(p0, p2, 0 + 2*16); + //__m256i bgr1 = p1; + __m256i bgr2 = _mm256_permute2x128_si256(p0, p2, 1 + 3*16); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm256_stream_si256((__m256i*)ptr, bgr0); + _mm256_stream_si256((__m256i*)(ptr + 16), p1); + _mm256_stream_si256((__m256i*)(ptr + 32), bgr2); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm256_store_si256((__m256i*)ptr, bgr0); + _mm256_store_si256((__m256i*)(ptr + 16), p1); + _mm256_store_si256((__m256i*)(ptr + 32), bgr2); + } + else + { + _mm256_storeu_si256((__m256i*)ptr, bgr0); + _mm256_storeu_si256((__m256i*)(ptr + 16), p1); + _mm256_storeu_si256((__m256i*)(ptr + 32), bgr2); + } +} + +inline void v_store_interleave( unsigned* ptr, const v_uint32x8& b, const v_uint32x8& g, const v_uint32x8& r, + hal::StoreMode mode=hal::STORE_UNALIGNED ) +{ + __m256i b0 = _mm256_shuffle_epi32(b.val, 0x6c); + __m256i g0 = _mm256_shuffle_epi32(g.val, 0xb1); + __m256i r0 = _mm256_shuffle_epi32(r.val, 0xc6); + + __m256i p0 = _mm256_blend_epi32(_mm256_blend_epi32(b0, g0, 0x92), r0, 0x24); + __m256i p1 = _mm256_blend_epi32(_mm256_blend_epi32(g0, r0, 0x92), b0, 0x24); + __m256i p2 = _mm256_blend_epi32(_mm256_blend_epi32(r0, b0, 0x92), g0, 0x24); + + __m256i bgr0 = _mm256_permute2x128_si256(p0, p1, 0 + 2*16); + //__m256i bgr1 = p2; + __m256i bgr2 = _mm256_permute2x128_si256(p0, p1, 1 + 3*16); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm256_stream_si256((__m256i*)ptr, bgr0); + _mm256_stream_si256((__m256i*)(ptr + 8), p2); + _mm256_stream_si256((__m256i*)(ptr + 16), bgr2); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm256_store_si256((__m256i*)ptr, bgr0); + _mm256_store_si256((__m256i*)(ptr + 8), p2); + _mm256_store_si256((__m256i*)(ptr + 16), bgr2); + } + else + { + _mm256_storeu_si256((__m256i*)ptr, bgr0); + _mm256_storeu_si256((__m256i*)(ptr + 8), p2); + _mm256_storeu_si256((__m256i*)(ptr + 16), bgr2); + } +} + +inline void v_store_interleave( uint64* ptr, const v_uint64x4& b, const v_uint64x4& g, const v_uint64x4& r, + hal::StoreMode mode=hal::STORE_UNALIGNED ) +{ + __m256i s01 = _mm256_unpacklo_epi64(b.val, g.val); + __m256i s12 = _mm256_unpackhi_epi64(g.val, r.val); + __m256i s20 = _mm256_blend_epi32(r.val, b.val, 0xcc); + + __m256i bgr0 = _mm256_permute2x128_si256(s01, s20, 0 + 2*16); + __m256i bgr1 = _mm256_blend_epi32(s01, s12, 0x0f); + __m256i bgr2 = _mm256_permute2x128_si256(s20, s12, 1 + 3*16); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm256_stream_si256((__m256i*)ptr, bgr0); + _mm256_stream_si256((__m256i*)(ptr + 4), bgr1); + _mm256_stream_si256((__m256i*)(ptr + 8), bgr2); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm256_store_si256((__m256i*)ptr, bgr0); + _mm256_store_si256((__m256i*)(ptr + 4), bgr1); + _mm256_store_si256((__m256i*)(ptr + 8), bgr2); + } + else + { + _mm256_storeu_si256((__m256i*)ptr, bgr0); + _mm256_storeu_si256((__m256i*)(ptr + 4), bgr1); + _mm256_storeu_si256((__m256i*)(ptr + 8), bgr2); + } +} + +inline void v_store_interleave( uchar* ptr, const v_uint8x32& b, const v_uint8x32& g, + const v_uint8x32& r, const v_uint8x32& a, + hal::StoreMode mode=hal::STORE_UNALIGNED ) +{ + __m256i bg0 = _mm256_unpacklo_epi8(b.val, g.val); + __m256i bg1 = _mm256_unpackhi_epi8(b.val, g.val); + __m256i ra0 = _mm256_unpacklo_epi8(r.val, a.val); + __m256i ra1 = _mm256_unpackhi_epi8(r.val, a.val); + + __m256i bgra0_ = _mm256_unpacklo_epi16(bg0, ra0); + __m256i bgra1_ = _mm256_unpackhi_epi16(bg0, ra0); + __m256i bgra2_ = _mm256_unpacklo_epi16(bg1, ra1); + __m256i bgra3_ = _mm256_unpackhi_epi16(bg1, ra1); + + __m256i bgra0 = _mm256_permute2x128_si256(bgra0_, bgra1_, 0 + 2*16); + __m256i bgra2 = _mm256_permute2x128_si256(bgra0_, bgra1_, 1 + 3*16); + __m256i bgra1 = _mm256_permute2x128_si256(bgra2_, bgra3_, 0 + 2*16); + __m256i bgra3 = _mm256_permute2x128_si256(bgra2_, bgra3_, 1 + 3*16); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm256_stream_si256((__m256i*)ptr, bgra0); + _mm256_stream_si256((__m256i*)(ptr + 32), bgra1); + _mm256_stream_si256((__m256i*)(ptr + 64), bgra2); + _mm256_stream_si256((__m256i*)(ptr + 96), bgra3); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm256_store_si256((__m256i*)ptr, bgra0); + _mm256_store_si256((__m256i*)(ptr + 32), bgra1); + _mm256_store_si256((__m256i*)(ptr + 64), bgra2); + _mm256_store_si256((__m256i*)(ptr + 96), bgra3); + } + else + { + _mm256_storeu_si256((__m256i*)ptr, bgra0); + _mm256_storeu_si256((__m256i*)(ptr + 32), bgra1); + _mm256_storeu_si256((__m256i*)(ptr + 64), bgra2); + _mm256_storeu_si256((__m256i*)(ptr + 96), bgra3); + } +} + +inline void v_store_interleave( ushort* ptr, const v_uint16x16& b, const v_uint16x16& g, + const v_uint16x16& r, const v_uint16x16& a, + hal::StoreMode mode=hal::STORE_UNALIGNED ) +{ + __m256i bg0 = _mm256_unpacklo_epi16(b.val, g.val); + __m256i bg1 = _mm256_unpackhi_epi16(b.val, g.val); + __m256i ra0 = _mm256_unpacklo_epi16(r.val, a.val); + __m256i ra1 = _mm256_unpackhi_epi16(r.val, a.val); + + __m256i bgra0_ = _mm256_unpacklo_epi32(bg0, ra0); + __m256i bgra1_ = _mm256_unpackhi_epi32(bg0, ra0); + __m256i bgra2_ = _mm256_unpacklo_epi32(bg1, ra1); + __m256i bgra3_ = _mm256_unpackhi_epi32(bg1, ra1); + + __m256i bgra0 = _mm256_permute2x128_si256(bgra0_, bgra1_, 0 + 2*16); + __m256i bgra2 = _mm256_permute2x128_si256(bgra0_, bgra1_, 1 + 3*16); + __m256i bgra1 = _mm256_permute2x128_si256(bgra2_, bgra3_, 0 + 2*16); + __m256i bgra3 = _mm256_permute2x128_si256(bgra2_, bgra3_, 1 + 3*16); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm256_stream_si256((__m256i*)ptr, bgra0); + _mm256_stream_si256((__m256i*)(ptr + 16), bgra1); + _mm256_stream_si256((__m256i*)(ptr + 32), bgra2); + _mm256_stream_si256((__m256i*)(ptr + 48), bgra3); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm256_store_si256((__m256i*)ptr, bgra0); + _mm256_store_si256((__m256i*)(ptr + 16), bgra1); + _mm256_store_si256((__m256i*)(ptr + 32), bgra2); + _mm256_store_si256((__m256i*)(ptr + 48), bgra3); + } + else + { + _mm256_storeu_si256((__m256i*)ptr, bgra0); + _mm256_storeu_si256((__m256i*)(ptr + 16), bgra1); + _mm256_storeu_si256((__m256i*)(ptr + 32), bgra2); + _mm256_storeu_si256((__m256i*)(ptr + 48), bgra3); + } +} + +inline void v_store_interleave( unsigned* ptr, const v_uint32x8& b, const v_uint32x8& g, + const v_uint32x8& r, const v_uint32x8& a, + hal::StoreMode mode=hal::STORE_UNALIGNED ) +{ + __m256i bg0 = _mm256_unpacklo_epi32(b.val, g.val); + __m256i bg1 = _mm256_unpackhi_epi32(b.val, g.val); + __m256i ra0 = _mm256_unpacklo_epi32(r.val, a.val); + __m256i ra1 = _mm256_unpackhi_epi32(r.val, a.val); + + __m256i bgra0_ = _mm256_unpacklo_epi64(bg0, ra0); + __m256i bgra1_ = _mm256_unpackhi_epi64(bg0, ra0); + __m256i bgra2_ = _mm256_unpacklo_epi64(bg1, ra1); + __m256i bgra3_ = _mm256_unpackhi_epi64(bg1, ra1); + + __m256i bgra0 = _mm256_permute2x128_si256(bgra0_, bgra1_, 0 + 2*16); + __m256i bgra2 = _mm256_permute2x128_si256(bgra0_, bgra1_, 1 + 3*16); + __m256i bgra1 = _mm256_permute2x128_si256(bgra2_, bgra3_, 0 + 2*16); + __m256i bgra3 = _mm256_permute2x128_si256(bgra2_, bgra3_, 1 + 3*16); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm256_stream_si256((__m256i*)ptr, bgra0); + _mm256_stream_si256((__m256i*)(ptr + 8), bgra1); + _mm256_stream_si256((__m256i*)(ptr + 16), bgra2); + _mm256_stream_si256((__m256i*)(ptr + 24), bgra3); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm256_store_si256((__m256i*)ptr, bgra0); + _mm256_store_si256((__m256i*)(ptr + 8), bgra1); + _mm256_store_si256((__m256i*)(ptr + 16), bgra2); + _mm256_store_si256((__m256i*)(ptr + 24), bgra3); + } + else + { + _mm256_storeu_si256((__m256i*)ptr, bgra0); + _mm256_storeu_si256((__m256i*)(ptr + 8), bgra1); + _mm256_storeu_si256((__m256i*)(ptr + 16), bgra2); + _mm256_storeu_si256((__m256i*)(ptr + 24), bgra3); + } +} + +inline void v_store_interleave( uint64* ptr, const v_uint64x4& b, const v_uint64x4& g, + const v_uint64x4& r, const v_uint64x4& a, + hal::StoreMode mode=hal::STORE_UNALIGNED ) +{ + __m256i bg0 = _mm256_unpacklo_epi64(b.val, g.val); + __m256i bg1 = _mm256_unpackhi_epi64(b.val, g.val); + __m256i ra0 = _mm256_unpacklo_epi64(r.val, a.val); + __m256i ra1 = _mm256_unpackhi_epi64(r.val, a.val); + + __m256i bgra0 = _mm256_permute2x128_si256(bg0, ra0, 0 + 2*16); + __m256i bgra1 = _mm256_permute2x128_si256(bg1, ra1, 0 + 2*16); + __m256i bgra2 = _mm256_permute2x128_si256(bg0, ra0, 1 + 3*16); + __m256i bgra3 = _mm256_permute2x128_si256(bg1, ra1, 1 + 3*16); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm256_stream_si256((__m256i*)ptr, bgra0); + _mm256_stream_si256((__m256i*)(ptr + 4), bgra1); + _mm256_stream_si256((__m256i*)(ptr + 8), bgra2); + _mm256_stream_si256((__m256i*)(ptr + 12), bgra3); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm256_store_si256((__m256i*)ptr, bgra0); + _mm256_store_si256((__m256i*)(ptr + 4), bgra1); + _mm256_store_si256((__m256i*)(ptr + 8), bgra2); + _mm256_store_si256((__m256i*)(ptr + 12), bgra3); + } + else + { + _mm256_storeu_si256((__m256i*)ptr, bgra0); + _mm256_storeu_si256((__m256i*)(ptr + 4), bgra1); + _mm256_storeu_si256((__m256i*)(ptr + 8), bgra2); + _mm256_storeu_si256((__m256i*)(ptr + 12), bgra3); + } +} + +#define OPENCV_HAL_IMPL_AVX_LOADSTORE_INTERLEAVE(_Tpvec0, _Tp0, suffix0, _Tpvec1, _Tp1, suffix1) \ +inline void v_load_deinterleave( const _Tp0* ptr, _Tpvec0& a0, _Tpvec0& b0 ) \ +{ \ + _Tpvec1 a1, b1; \ + v_load_deinterleave((const _Tp1*)ptr, a1, b1); \ + a0 = v_reinterpret_as_##suffix0(a1); \ + b0 = v_reinterpret_as_##suffix0(b1); \ +} \ +inline void v_load_deinterleave( const _Tp0* ptr, _Tpvec0& a0, _Tpvec0& b0, _Tpvec0& c0 ) \ +{ \ + _Tpvec1 a1, b1, c1; \ + v_load_deinterleave((const _Tp1*)ptr, a1, b1, c1); \ + a0 = v_reinterpret_as_##suffix0(a1); \ + b0 = v_reinterpret_as_##suffix0(b1); \ + c0 = v_reinterpret_as_##suffix0(c1); \ +} \ +inline void v_load_deinterleave( const _Tp0* ptr, _Tpvec0& a0, _Tpvec0& b0, _Tpvec0& c0, _Tpvec0& d0 ) \ +{ \ + _Tpvec1 a1, b1, c1, d1; \ + v_load_deinterleave((const _Tp1*)ptr, a1, b1, c1, d1); \ + a0 = v_reinterpret_as_##suffix0(a1); \ + b0 = v_reinterpret_as_##suffix0(b1); \ + c0 = v_reinterpret_as_##suffix0(c1); \ + d0 = v_reinterpret_as_##suffix0(d1); \ +} \ +inline void v_store_interleave( _Tp0* ptr, const _Tpvec0& a0, const _Tpvec0& b0, \ + hal::StoreMode mode=hal::STORE_UNALIGNED ) \ +{ \ + _Tpvec1 a1 = v_reinterpret_as_##suffix1(a0); \ + _Tpvec1 b1 = v_reinterpret_as_##suffix1(b0); \ + v_store_interleave((_Tp1*)ptr, a1, b1, mode); \ +} \ +inline void v_store_interleave( _Tp0* ptr, const _Tpvec0& a0, const _Tpvec0& b0, const _Tpvec0& c0, \ + hal::StoreMode mode=hal::STORE_UNALIGNED ) \ +{ \ + _Tpvec1 a1 = v_reinterpret_as_##suffix1(a0); \ + _Tpvec1 b1 = v_reinterpret_as_##suffix1(b0); \ + _Tpvec1 c1 = v_reinterpret_as_##suffix1(c0); \ + v_store_interleave((_Tp1*)ptr, a1, b1, c1, mode); \ +} \ +inline void v_store_interleave( _Tp0* ptr, const _Tpvec0& a0, const _Tpvec0& b0, \ + const _Tpvec0& c0, const _Tpvec0& d0, \ + hal::StoreMode mode=hal::STORE_UNALIGNED ) \ +{ \ + _Tpvec1 a1 = v_reinterpret_as_##suffix1(a0); \ + _Tpvec1 b1 = v_reinterpret_as_##suffix1(b0); \ + _Tpvec1 c1 = v_reinterpret_as_##suffix1(c0); \ + _Tpvec1 d1 = v_reinterpret_as_##suffix1(d0); \ + v_store_interleave((_Tp1*)ptr, a1, b1, c1, d1, mode); \ +} + +OPENCV_HAL_IMPL_AVX_LOADSTORE_INTERLEAVE(v_int8x32, schar, s8, v_uint8x32, uchar, u8) +OPENCV_HAL_IMPL_AVX_LOADSTORE_INTERLEAVE(v_int16x16, short, s16, v_uint16x16, ushort, u16) +OPENCV_HAL_IMPL_AVX_LOADSTORE_INTERLEAVE(v_int32x8, int, s32, v_uint32x8, unsigned, u32) +OPENCV_HAL_IMPL_AVX_LOADSTORE_INTERLEAVE(v_float32x8, float, f32, v_uint32x8, unsigned, u32) +OPENCV_HAL_IMPL_AVX_LOADSTORE_INTERLEAVE(v_int64x4, int64, s64, v_uint64x4, uint64, u64) +OPENCV_HAL_IMPL_AVX_LOADSTORE_INTERLEAVE(v_float64x4, double, f64, v_uint64x4, uint64, u64) + +// FP16 +inline v_float32x8 v256_load_expand(const float16_t* ptr) +{ + return v_float32x8(_mm256_cvtph_ps(_mm_loadu_si128((const __m128i*)ptr))); +} + +inline void v_pack_store(float16_t* ptr, const v_float32x8& a) +{ + __m128i ah = _mm256_cvtps_ph(a.val, 0); + _mm_storeu_si128((__m128i*)ptr, ah); +} + +inline void v256_cleanup() { _mm256_zeroall(); } + +//! @name Check SIMD256 support +//! @{ +//! @brief Check CPU capability of SIMD operation +static inline bool hasSIMD256() +{ + return (CV_CPU_HAS_SUPPORT_AVX2) ? true : false; +} +//! @} + +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END + +//! @endcond + +} // cv:: + +#endif // OPENCV_HAL_INTRIN_AVX_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/hal/intrin_cpp.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/hal/intrin_cpp.hpp new file mode 100644 index 0000000000000000000000000000000000000000..65a01f31d7183a25e92e8b6b4a78802e33501f22 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/hal/intrin_cpp.hpp @@ -0,0 +1,2310 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_HAL_INTRIN_CPP_HPP +#define OPENCV_HAL_INTRIN_CPP_HPP + +#include +#include +#include +#include "opencv2/core/saturate.hpp" + +namespace cv +{ + +#ifndef CV_DOXYGEN +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN +#endif + +/** @addtogroup core_hal_intrin + +"Universal intrinsics" is a types and functions set intended to simplify vectorization of code on +different platforms. Currently there are two supported SIMD extensions: __SSE/SSE2__ on x86 +architectures and __NEON__ on ARM architectures, both allow working with 128 bit registers +containing packed values of different types. In case when there is no SIMD extension available +during compilation, fallback C++ implementation of intrinsics will be chosen and code will work as +expected although it could be slower. + +### Types + +There are several types representing 128-bit register as a vector of packed values, each type is +implemented as a structure based on a one SIMD register. + +- cv::v_uint8x16 and cv::v_int8x16: sixteen 8-bit integer values (unsigned/signed) - char +- cv::v_uint16x8 and cv::v_int16x8: eight 16-bit integer values (unsigned/signed) - short +- cv::v_uint32x4 and cv::v_int32x4: four 32-bit integer values (unsgined/signed) - int +- cv::v_uint64x2 and cv::v_int64x2: two 64-bit integer values (unsigned/signed) - int64 +- cv::v_float32x4: four 32-bit floating point values (signed) - float +- cv::v_float64x2: two 64-bit floating point valies (signed) - double + +@note +cv::v_float64x2 is not implemented in NEON variant, if you want to use this type, don't forget to +check the CV_SIMD128_64F preprocessor definition: +@code +#if CV_SIMD128_64F +//... +#endif +@endcode + +### Load and store operations + +These operations allow to set contents of the register explicitly or by loading it from some memory +block and to save contents of the register to memory block. + +- Constructors: +@ref v_reg::v_reg(const _Tp *ptr) "from memory", +@ref v_reg::v_reg(_Tp s0, _Tp s1) "from two values", ... +- Other create methods: +@ref v_setall_s8, @ref v_setall_u8, ..., +@ref v_setzero_u8, @ref v_setzero_s8, ... +- Memory operations: +@ref v_load, @ref v_load_aligned, @ref v_load_low, @ref v_load_halves, +@ref v_store, @ref v_store_aligned, +@ref v_store_high, @ref v_store_low + +### Value reordering + +These operations allow to reorder or recombine elements in one or multiple vectors. + +- Interleave, deinterleave (2, 3 and 4 channels): @ref v_load_deinterleave, @ref v_store_interleave +- Expand: @ref v_load_expand, @ref v_load_expand_q, @ref v_expand, @ref v_expand_low, @ref v_expand_high +- Pack: @ref v_pack, @ref v_pack_u, @ref v_pack_b, @ref v_rshr_pack, @ref v_rshr_pack_u, +@ref v_pack_store, @ref v_pack_u_store, @ref v_rshr_pack_store, @ref v_rshr_pack_u_store +- Recombine: @ref v_zip, @ref v_recombine, @ref v_combine_low, @ref v_combine_high +- Extract: @ref v_extract + + +### Arithmetic, bitwise and comparison operations + +Element-wise binary and unary operations. + +- Arithmetics: +@ref operator +(const v_reg &a, const v_reg &b) "+", +@ref operator -(const v_reg &a, const v_reg &b) "-", +@ref operator *(const v_reg &a, const v_reg &b) "*", +@ref operator /(const v_reg &a, const v_reg &b) "/", +@ref v_mul_expand + +- Non-saturating arithmetics: @ref v_add_wrap, @ref v_sub_wrap + +- Bitwise shifts: +@ref operator <<(const v_reg &a, int s) "<<", +@ref operator >>(const v_reg &a, int s) ">>", +@ref v_shl, @ref v_shr + +- Bitwise logic: +@ref operator&(const v_reg &a, const v_reg &b) "&", +@ref operator |(const v_reg &a, const v_reg &b) "|", +@ref operator ^(const v_reg &a, const v_reg &b) "^", +@ref operator ~(const v_reg &a) "~" + +- Comparison: +@ref operator >(const v_reg &a, const v_reg &b) ">", +@ref operator >=(const v_reg &a, const v_reg &b) ">=", +@ref operator <(const v_reg &a, const v_reg &b) "<", +@ref operator <=(const v_reg &a, const v_reg &b) "<=", +@ref operator==(const v_reg &a, const v_reg &b) "==", +@ref operator !=(const v_reg &a, const v_reg &b) "!=" + +- min/max: @ref v_min, @ref v_max + +### Reduce and mask + +Most of these operations return only one value. + +- Reduce: @ref v_reduce_min, @ref v_reduce_max, @ref v_reduce_sum, @ref v_popcount +- Mask: @ref v_signmask, @ref v_check_all, @ref v_check_any, @ref v_select + +### Other math + +- Some frequent operations: @ref v_sqrt, @ref v_invsqrt, @ref v_magnitude, @ref v_sqr_magnitude +- Absolute values: @ref v_abs, @ref v_absdiff, @ref v_absdiffs + +### Conversions + +Different type conversions and casts: + +- Rounding: @ref v_round, @ref v_floor, @ref v_ceil, @ref v_trunc, +- To float: @ref v_cvt_f32, @ref v_cvt_f64 +- Reinterpret: @ref v_reinterpret_as_u8, @ref v_reinterpret_as_s8, ... + +### Matrix operations + +In these operations vectors represent matrix rows/columns: @ref v_dotprod, @ref v_matmul, @ref v_transpose4x4 + +### Usability + +Most operations are implemented only for some subset of the available types, following matrices +shows the applicability of different operations to the types. + +Regular integers: + +| Operations\\Types | uint 8x16 | int 8x16 | uint 16x8 | int 16x8 | uint 32x4 | int 32x4 | +|-------------------|:-:|:-:|:-:|:-:|:-:|:-:| +|load, store | x | x | x | x | x | x | +|interleave | x | x | x | x | x | x | +|expand | x | x | x | x | x | x | +|expand_low | x | x | x | x | x | x | +|expand_high | x | x | x | x | x | x | +|expand_q | x | x | | | | | +|add, sub | x | x | x | x | x | x | +|add_wrap, sub_wrap | x | x | x | x | | | +|mul_wrap | x | x | x | x | | | +|mul | x | x | x | x | x | x | +|mul_expand | x | x | x | x | x | | +|compare | x | x | x | x | x | x | +|shift | | | x | x | x | x | +|dotprod | | | | x | | | +|logical | x | x | x | x | x | x | +|min, max | x | x | x | x | x | x | +|absdiff | x | x | x | x | x | x | +|absdiffs | | x | | x | | | +|reduce | | | | | x | x | +|mask | x | x | x | x | x | x | +|pack | x | x | x | x | x | x | +|pack_u | x | | x | | | | +|pack_b | x | | | | | | +|unpack | x | x | x | x | x | x | +|extract | x | x | x | x | x | x | +|rotate (lanes) | x | x | x | x | x | x | +|cvt_flt32 | | | | | | x | +|cvt_flt64 | | | | | | x | +|transpose4x4 | | | | | x | x | + +Big integers: + +| Operations\\Types | uint 64x2 | int 64x2 | +|-------------------|:-:|:-:| +|load, store | x | x | +|add, sub | x | x | +|shift | x | x | +|logical | x | x | +|extract | x | x | +|rotate (lanes) | x | x | + +Floating point: + +| Operations\\Types | float 32x4 | float 64x2 | +|-------------------|:-:|:-:| +|load, store | x | x | +|interleave | x | | +|add, sub | x | x | +|mul | x | x | +|div | x | x | +|compare | x | x | +|min, max | x | x | +|absdiff | x | x | +|reduce | x | | +|mask | x | x | +|unpack | x | x | +|cvt_flt32 | | x | +|cvt_flt64 | x | | +|sqrt, abs | x | x | +|float math | x | x | +|transpose4x4 | x | | +|extract | x | x | +|rotate (lanes) | x | x | + + @{ */ + +template struct v_reg +{ +//! @cond IGNORED + typedef _Tp lane_type; + enum { nlanes = n }; +// !@endcond + + /** @brief Constructor + + Initializes register with data from memory + @param ptr pointer to memory block with data for register */ + explicit v_reg(const _Tp* ptr) { for( int i = 0; i < n; i++ ) s[i] = ptr[i]; } + + /** @brief Constructor + + Initializes register with two 64-bit values */ + v_reg(_Tp s0, _Tp s1) { s[0] = s0; s[1] = s1; } + + /** @brief Constructor + + Initializes register with four 32-bit values */ + v_reg(_Tp s0, _Tp s1, _Tp s2, _Tp s3) { s[0] = s0; s[1] = s1; s[2] = s2; s[3] = s3; } + + /** @brief Constructor + + Initializes register with eight 16-bit values */ + v_reg(_Tp s0, _Tp s1, _Tp s2, _Tp s3, + _Tp s4, _Tp s5, _Tp s6, _Tp s7) + { + s[0] = s0; s[1] = s1; s[2] = s2; s[3] = s3; + s[4] = s4; s[5] = s5; s[6] = s6; s[7] = s7; + } + + /** @brief Constructor + + Initializes register with sixteen 8-bit values */ + v_reg(_Tp s0, _Tp s1, _Tp s2, _Tp s3, + _Tp s4, _Tp s5, _Tp s6, _Tp s7, + _Tp s8, _Tp s9, _Tp s10, _Tp s11, + _Tp s12, _Tp s13, _Tp s14, _Tp s15) + { + s[0] = s0; s[1] = s1; s[2] = s2; s[3] = s3; + s[4] = s4; s[5] = s5; s[6] = s6; s[7] = s7; + s[8] = s8; s[9] = s9; s[10] = s10; s[11] = s11; + s[12] = s12; s[13] = s13; s[14] = s14; s[15] = s15; + } + + /** @brief Default constructor + + Does not initialize anything*/ + v_reg() {} + + /** @brief Copy constructor */ + v_reg(const v_reg<_Tp, n> & r) + { + for( int i = 0; i < n; i++ ) + s[i] = r.s[i]; + } + /** @brief Access first value + + Returns value of the first lane according to register type, for example: + @code{.cpp} + v_int32x4 r(1, 2, 3, 4); + int v = r.get0(); // returns 1 + v_uint64x2 r(1, 2); + uint64_t v = r.get0(); // returns 1 + @endcode + */ + _Tp get0() const { return s[0]; } + +//! @cond IGNORED + _Tp get(const int i) const { return s[i]; } + v_reg<_Tp, n> high() const + { + v_reg<_Tp, n> c; + int i; + for( i = 0; i < n/2; i++ ) + { + c.s[i] = s[i+(n/2)]; + c.s[i+(n/2)] = 0; + } + return c; + } + + static v_reg<_Tp, n> zero() + { + v_reg<_Tp, n> c; + for( int i = 0; i < n; i++ ) + c.s[i] = (_Tp)0; + return c; + } + + static v_reg<_Tp, n> all(_Tp s) + { + v_reg<_Tp, n> c; + for( int i = 0; i < n; i++ ) + c.s[i] = s; + return c; + } + + template v_reg<_Tp2, n2> reinterpret_as() const + { + size_t bytes = std::min(sizeof(_Tp2)*n2, sizeof(_Tp)*n); + v_reg<_Tp2, n2> c; + std::memcpy(&c.s[0], &s[0], bytes); + return c; + } + + _Tp s[n]; +//! @endcond +}; + +/** @brief Sixteen 8-bit unsigned integer values */ +typedef v_reg v_uint8x16; +/** @brief Sixteen 8-bit signed integer values */ +typedef v_reg v_int8x16; +/** @brief Eight 16-bit unsigned integer values */ +typedef v_reg v_uint16x8; +/** @brief Eight 16-bit signed integer values */ +typedef v_reg v_int16x8; +/** @brief Four 32-bit unsigned integer values */ +typedef v_reg v_uint32x4; +/** @brief Four 32-bit signed integer values */ +typedef v_reg v_int32x4; +/** @brief Four 32-bit floating point values (single precision) */ +typedef v_reg v_float32x4; +/** @brief Two 64-bit floating point values (double precision) */ +typedef v_reg v_float64x2; +/** @brief Two 64-bit unsigned integer values */ +typedef v_reg v_uint64x2; +/** @brief Two 64-bit signed integer values */ +typedef v_reg v_int64x2; + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_BIN_OP(bin_op) \ +template inline v_reg<_Tp, n> \ + operator bin_op (const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ +{ \ + v_reg<_Tp, n> c; \ + for( int i = 0; i < n; i++ ) \ + c.s[i] = saturate_cast<_Tp>(a.s[i] bin_op b.s[i]); \ + return c; \ +} \ +template inline v_reg<_Tp, n>& \ + operator bin_op##= (v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ +{ \ + for( int i = 0; i < n; i++ ) \ + a.s[i] = saturate_cast<_Tp>(a.s[i] bin_op b.s[i]); \ + return a; \ +} + +/** @brief Add values + +For all types. */ +OPENCV_HAL_IMPL_BIN_OP(+) + +/** @brief Subtract values + +For all types. */ +OPENCV_HAL_IMPL_BIN_OP(-) + +/** @brief Multiply values + +For 16- and 32-bit integer types and floating types. */ +OPENCV_HAL_IMPL_BIN_OP(*) + +/** @brief Divide values + +For floating types only. */ +OPENCV_HAL_IMPL_BIN_OP(/) + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_BIT_OP(bit_op) \ +template inline v_reg<_Tp, n> operator bit_op \ + (const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ +{ \ + v_reg<_Tp, n> c; \ + typedef typename V_TypeTraits<_Tp>::int_type itype; \ + for( int i = 0; i < n; i++ ) \ + c.s[i] = V_TypeTraits<_Tp>::reinterpret_from_int((itype)(V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) bit_op \ + V_TypeTraits<_Tp>::reinterpret_int(b.s[i]))); \ + return c; \ +} \ +template inline v_reg<_Tp, n>& operator \ + bit_op##= (v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ +{ \ + typedef typename V_TypeTraits<_Tp>::int_type itype; \ + for( int i = 0; i < n; i++ ) \ + a.s[i] = V_TypeTraits<_Tp>::reinterpret_from_int((itype)(V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) bit_op \ + V_TypeTraits<_Tp>::reinterpret_int(b.s[i]))); \ + return a; \ +} + +/** @brief Bitwise AND + +Only for integer types. */ +OPENCV_HAL_IMPL_BIT_OP(&) + +/** @brief Bitwise OR + +Only for integer types. */ +OPENCV_HAL_IMPL_BIT_OP(|) + +/** @brief Bitwise XOR + +Only for integer types.*/ +OPENCV_HAL_IMPL_BIT_OP(^) + +/** @brief Bitwise NOT + +Only for integer types.*/ +template inline v_reg<_Tp, n> operator ~ (const v_reg<_Tp, n>& a) +{ + v_reg<_Tp, n> c; + for( int i = 0; i < n; i++ ) + { + c.s[i] = V_TypeTraits<_Tp>::reinterpret_from_int(~V_TypeTraits<_Tp>::reinterpret_int(a.s[i])); + } + return c; +} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_MATH_FUNC(func, cfunc, _Tp2) \ +template inline v_reg<_Tp2, n> func(const v_reg<_Tp, n>& a) \ +{ \ + v_reg<_Tp2, n> c; \ + for( int i = 0; i < n; i++ ) \ + c.s[i] = cfunc(a.s[i]); \ + return c; \ +} + +/** @brief Square root of elements + +Only for floating point types.*/ +OPENCV_HAL_IMPL_MATH_FUNC(v_sqrt, std::sqrt, _Tp) + +//! @cond IGNORED +OPENCV_HAL_IMPL_MATH_FUNC(v_sin, std::sin, _Tp) +OPENCV_HAL_IMPL_MATH_FUNC(v_cos, std::cos, _Tp) +OPENCV_HAL_IMPL_MATH_FUNC(v_exp, std::exp, _Tp) +OPENCV_HAL_IMPL_MATH_FUNC(v_log, std::log, _Tp) +//! @endcond + +/** @brief Absolute value of elements + +Only for floating point types.*/ +OPENCV_HAL_IMPL_MATH_FUNC(v_abs, (typename V_TypeTraits<_Tp>::abs_type)std::abs, + typename V_TypeTraits<_Tp>::abs_type) + +/** @brief Round elements + +Only for floating point types.*/ +OPENCV_HAL_IMPL_MATH_FUNC(v_round, cvRound, int) + +/** @brief Floor elements + +Only for floating point types.*/ +OPENCV_HAL_IMPL_MATH_FUNC(v_floor, cvFloor, int) + +/** @brief Ceil elements + +Only for floating point types.*/ +OPENCV_HAL_IMPL_MATH_FUNC(v_ceil, cvCeil, int) + +/** @brief Truncate elements + +Only for floating point types.*/ +OPENCV_HAL_IMPL_MATH_FUNC(v_trunc, int, int) + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_MINMAX_FUNC(func, cfunc) \ +template inline v_reg<_Tp, n> func(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ +{ \ + v_reg<_Tp, n> c; \ + for( int i = 0; i < n; i++ ) \ + c.s[i] = cfunc(a.s[i], b.s[i]); \ + return c; \ +} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_REDUCE_MINMAX_FUNC(func, cfunc) \ +template inline _Tp func(const v_reg<_Tp, n>& a) \ +{ \ + _Tp c = a.s[0]; \ + for( int i = 1; i < n; i++ ) \ + c = cfunc(c, a.s[i]); \ + return c; \ +} + +/** @brief Choose min values for each pair + +Scheme: +@code +{A1 A2 ...} +{B1 B2 ...} +-------------- +{min(A1,B1) min(A2,B2) ...} +@endcode +For all types except 64-bit integer. */ +OPENCV_HAL_IMPL_MINMAX_FUNC(v_min, std::min) + +/** @brief Choose max values for each pair + +Scheme: +@code +{A1 A2 ...} +{B1 B2 ...} +-------------- +{max(A1,B1) max(A2,B2) ...} +@endcode +For all types except 64-bit integer. */ +OPENCV_HAL_IMPL_MINMAX_FUNC(v_max, std::max) + +/** @brief Find one min value + +Scheme: +@code +{A1 A2 A3 ...} => min(A1,A2,A3,...) +@endcode +For 32-bit integer and 32-bit floating point types. */ +OPENCV_HAL_IMPL_REDUCE_MINMAX_FUNC(v_reduce_min, std::min) + +/** @brief Find one max value + +Scheme: +@code +{A1 A2 A3 ...} => max(A1,A2,A3,...) +@endcode +For 32-bit integer and 32-bit floating point types. */ +OPENCV_HAL_IMPL_REDUCE_MINMAX_FUNC(v_reduce_max, std::max) + +static const unsigned char popCountTable[] = +{ + 0, 1, 1, 2, 1, 2, 2, 3, 1, 2, 2, 3, 2, 3, 3, 4, + 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, + 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, + 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, + 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, + 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, + 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, + 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, + 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, + 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, + 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, + 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, + 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, + 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, + 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, + 4, 5, 5, 6, 5, 6, 6, 7, 5, 6, 6, 7, 6, 7, 7, 8, +}; +/** @brief Count the 1 bits in the vector and return 4 values + +Scheme: +@code +{A1 A2 A3 ...} => popcount(A1) +@endcode +Any types but result will be in v_uint32x4*/ +template inline v_uint32x4 v_popcount(const v_reg<_Tp, n>& a) +{ + v_uint8x16 b; + b = v_reinterpret_as_u8(a); + for( int i = 0; i < v_uint8x16::nlanes; i++ ) + { + b.s[i] = popCountTable[b.s[i]]; + } + v_uint32x4 c; + for( int i = 0; i < v_uint32x4::nlanes; i++ ) + { + c.s[i] = b.s[i*4] + b.s[i*4+1] + b.s[i*4+2] + b.s[i*4+3]; + } + return c; +} + + +//! @cond IGNORED +template +inline void v_minmax( const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b, + v_reg<_Tp, n>& minval, v_reg<_Tp, n>& maxval ) +{ + for( int i = 0; i < n; i++ ) + { + minval.s[i] = std::min(a.s[i], b.s[i]); + maxval.s[i] = std::max(a.s[i], b.s[i]); + } +} +//! @endcond + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_CMP_OP(cmp_op) \ +template \ +inline v_reg<_Tp, n> operator cmp_op(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ +{ \ + typedef typename V_TypeTraits<_Tp>::int_type itype; \ + v_reg<_Tp, n> c; \ + for( int i = 0; i < n; i++ ) \ + c.s[i] = V_TypeTraits<_Tp>::reinterpret_from_int((itype)-(int)(a.s[i] cmp_op b.s[i])); \ + return c; \ +} + +/** @brief Less-than comparison + +For all types except 64-bit integer values. */ +OPENCV_HAL_IMPL_CMP_OP(<) + +/** @brief Greater-than comparison + +For all types except 64-bit integer values. */ +OPENCV_HAL_IMPL_CMP_OP(>) + +/** @brief Less-than or equal comparison + +For all types except 64-bit integer values. */ +OPENCV_HAL_IMPL_CMP_OP(<=) + +/** @brief Greater-than or equal comparison + +For all types except 64-bit integer values. */ +OPENCV_HAL_IMPL_CMP_OP(>=) + +/** @brief Equal comparison + +For all types except 64-bit integer values. */ +OPENCV_HAL_IMPL_CMP_OP(==) + +/** @brief Not equal comparison + +For all types except 64-bit integer values. */ +OPENCV_HAL_IMPL_CMP_OP(!=) + +template +inline v_reg v_not_nan(const v_reg& a) +{ + typedef typename V_TypeTraits::int_type itype; + v_reg c; + for (int i = 0; i < n; i++) + c.s[i] = V_TypeTraits::reinterpret_from_int((itype)-(int)(a.s[i] == a.s[i])); + return c; +} +template +inline v_reg v_not_nan(const v_reg& a) +{ + typedef typename V_TypeTraits::int_type itype; + v_reg c; + for (int i = 0; i < n; i++) + c.s[i] = V_TypeTraits::reinterpret_from_int((itype)-(int)(a.s[i] == a.s[i])); + return c; +} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_ARITHM_OP(func, bin_op, cast_op, _Tp2) \ +template \ +inline v_reg<_Tp2, n> func(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ +{ \ + typedef _Tp2 rtype; \ + v_reg c; \ + for( int i = 0; i < n; i++ ) \ + c.s[i] = cast_op(a.s[i] bin_op b.s[i]); \ + return c; \ +} + +/** @brief Add values without saturation + +For 8- and 16-bit integer values. */ +OPENCV_HAL_IMPL_ARITHM_OP(v_add_wrap, +, (_Tp), _Tp) + +/** @brief Subtract values without saturation + +For 8- and 16-bit integer values. */ +OPENCV_HAL_IMPL_ARITHM_OP(v_sub_wrap, -, (_Tp), _Tp) + +/** @brief Multiply values without saturation + +For 8- and 16-bit integer values. */ +OPENCV_HAL_IMPL_ARITHM_OP(v_mul_wrap, *, (_Tp), _Tp) + +//! @cond IGNORED +template inline T _absdiff(T a, T b) +{ + return a > b ? a - b : b - a; +} +//! @endcond + +/** @brief Absolute difference + +Returns \f$ |a - b| \f$ converted to corresponding unsigned type. +Example: +@code{.cpp} +v_int32x4 a, b; // {1, 2, 3, 4} and {4, 3, 2, 1} +v_uint32x4 c = v_absdiff(a, b); // result is {3, 1, 1, 3} +@endcode +For 8-, 16-, 32-bit integer source types. */ +template +inline v_reg::abs_type, n> v_absdiff(const v_reg<_Tp, n>& a, const v_reg<_Tp, n> & b) +{ + typedef typename V_TypeTraits<_Tp>::abs_type rtype; + v_reg c; + const rtype mask = (rtype)(std::numeric_limits<_Tp>::is_signed ? (1 << (sizeof(rtype)*8 - 1)) : 0); + for( int i = 0; i < n; i++ ) + { + rtype ua = a.s[i] ^ mask; + rtype ub = b.s[i] ^ mask; + c.s[i] = _absdiff(ua, ub); + } + return c; +} + +/** @overload + +For 32-bit floating point values */ +inline v_float32x4 v_absdiff(const v_float32x4& a, const v_float32x4& b) +{ + v_float32x4 c; + for( int i = 0; i < c.nlanes; i++ ) + c.s[i] = _absdiff(a.s[i], b.s[i]); + return c; +} + +/** @overload + +For 64-bit floating point values */ +inline v_float64x2 v_absdiff(const v_float64x2& a, const v_float64x2& b) +{ + v_float64x2 c; + for( int i = 0; i < c.nlanes; i++ ) + c.s[i] = _absdiff(a.s[i], b.s[i]); + return c; +} + +/** @brief Saturating absolute difference + +Returns \f$ saturate(|a - b|) \f$ . +For 8-, 16-bit signed integer source types. */ +template +inline v_reg<_Tp, n> v_absdiffs(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) +{ + v_reg<_Tp, n> c; + for( int i = 0; i < n; i++) + c.s[i] = saturate_cast<_Tp>(std::abs(a.s[i] - b.s[i])); + return c; +} + +/** @brief Inversed square root + +Returns \f$ 1/sqrt(a) \f$ +For floating point types only. */ +template +inline v_reg<_Tp, n> v_invsqrt(const v_reg<_Tp, n>& a) +{ + v_reg<_Tp, n> c; + for( int i = 0; i < n; i++ ) + c.s[i] = 1.f/std::sqrt(a.s[i]); + return c; +} + +/** @brief Magnitude + +Returns \f$ sqrt(a^2 + b^2) \f$ +For floating point types only. */ +template +inline v_reg<_Tp, n> v_magnitude(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) +{ + v_reg<_Tp, n> c; + for( int i = 0; i < n; i++ ) + c.s[i] = std::sqrt(a.s[i]*a.s[i] + b.s[i]*b.s[i]); + return c; +} + +/** @brief Square of the magnitude + +Returns \f$ a^2 + b^2 \f$ +For floating point types only. */ +template +inline v_reg<_Tp, n> v_sqr_magnitude(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) +{ + v_reg<_Tp, n> c; + for( int i = 0; i < n; i++ ) + c.s[i] = a.s[i]*a.s[i] + b.s[i]*b.s[i]; + return c; +} + +/** @brief Multiply and add + + Returns \f$ a*b + c \f$ + For floating point types and signed 32bit int only. */ +template +inline v_reg<_Tp, n> v_fma(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b, + const v_reg<_Tp, n>& c) +{ + v_reg<_Tp, n> d; + for( int i = 0; i < n; i++ ) + d.s[i] = a.s[i]*b.s[i] + c.s[i]; + return d; +} + +/** @brief A synonym for v_fma */ +template +inline v_reg<_Tp, n> v_muladd(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b, + const v_reg<_Tp, n>& c) +{ + return v_fma(a, b, c); +} + +/** @brief Dot product of elements + +Multiply values in two registers and sum adjacent result pairs. +Scheme: +@code + {A1 A2 ...} // 16-bit +x {B1 B2 ...} // 16-bit +------------- +{A1B1+A2B2 ...} // 32-bit +@endcode +Implemented only for 16-bit signed source type (v_int16x8). +*/ +template inline v_reg::w_type, n/2> + v_dotprod(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) +{ + typedef typename V_TypeTraits<_Tp>::w_type w_type; + v_reg c; + for( int i = 0; i < (n/2); i++ ) + c.s[i] = (w_type)a.s[i*2]*b.s[i*2] + (w_type)a.s[i*2+1]*b.s[i*2+1]; + return c; +} + +/** @brief Dot product of elements + +Same as cv::v_dotprod, but add a third element to the sum of adjacent pairs. +Scheme: +@code + {A1 A2 ...} // 16-bit +x {B1 B2 ...} // 16-bit +------------- + {A1B1+A2B2+C1 ...} // 32-bit + +@endcode +Implemented only for 16-bit signed source type (v_int16x8). +*/ +template inline v_reg::w_type, n/2> + v_dotprod(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b, const v_reg::w_type, n / 2>& c) +{ + typedef typename V_TypeTraits<_Tp>::w_type w_type; + v_reg s; + for( int i = 0; i < (n/2); i++ ) + s.s[i] = (w_type)a.s[i*2]*b.s[i*2] + (w_type)a.s[i*2+1]*b.s[i*2+1] + c.s[i]; + return s; +} + +/** @brief Multiply and expand + +Multiply values two registers and store results in two registers with wider pack type. +Scheme: +@code + {A B C D} // 32-bit +x {E F G H} // 32-bit +--------------- +{AE BF} // 64-bit + {CG DH} // 64-bit +@endcode +Example: +@code{.cpp} +v_uint32x4 a, b; // {1,2,3,4} and {2,2,2,2} +v_uint64x2 c, d; // results +v_mul_expand(a, b, c, d); // c, d = {2,4}, {6, 8} +@endcode +Implemented only for 16- and unsigned 32-bit source types (v_int16x8, v_uint16x8, v_uint32x4). +*/ +template inline void v_mul_expand(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b, + v_reg::w_type, n/2>& c, + v_reg::w_type, n/2>& d) +{ + typedef typename V_TypeTraits<_Tp>::w_type w_type; + for( int i = 0; i < (n/2); i++ ) + { + c.s[i] = (w_type)a.s[i]*b.s[i]; + d.s[i] = (w_type)a.s[i+(n/2)]*b.s[i+(n/2)]; + } +} + +/** @brief Multiply and extract high part + +Multiply values two registers and store high part of the results. +Implemented only for 16-bit source types (v_int16x8, v_uint16x8). Returns \f$ a*b >> 16 \f$ +*/ +template inline v_reg<_Tp, n> v_mul_hi(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) +{ + typedef typename V_TypeTraits<_Tp>::w_type w_type; + v_reg<_Tp, n> c; + for (int i = 0; i < n; i++) + c.s[i] = (_Tp)(((w_type)a.s[i] * b.s[i]) >> sizeof(_Tp)*8); + return c; +} + +//! @cond IGNORED +template inline void v_hsum(const v_reg<_Tp, n>& a, + v_reg::w_type, n/2>& c) +{ + typedef typename V_TypeTraits<_Tp>::w_type w_type; + for( int i = 0; i < (n/2); i++ ) + { + c.s[i] = (w_type)a.s[i*2] + a.s[i*2+1]; + } +} +//! @endcond + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_SHIFT_OP(shift_op) \ +template inline v_reg<_Tp, n> operator shift_op(const v_reg<_Tp, n>& a, int imm) \ +{ \ + v_reg<_Tp, n> c; \ + for( int i = 0; i < n; i++ ) \ + c.s[i] = (_Tp)(a.s[i] shift_op imm); \ + return c; \ +} + +/** @brief Bitwise shift left + +For 16-, 32- and 64-bit integer values. */ +OPENCV_HAL_IMPL_SHIFT_OP(<< ) + +/** @brief Bitwise shift right + +For 16-, 32- and 64-bit integer values. */ +OPENCV_HAL_IMPL_SHIFT_OP(>> ) + +/** @brief Element shift left among vector + +For all type */ +#define OPENCV_HAL_IMPL_ROTATE_SHIFT_OP(suffix,opA,opB) \ +template inline v_reg<_Tp, n> v_rotate_##suffix(const v_reg<_Tp, n>& a) \ +{ \ + v_reg<_Tp, n> b; \ + for (int i = 0; i < n; i++) \ + { \ + int sIndex = i opA imm; \ + if (0 <= sIndex && sIndex < n) \ + { \ + b.s[i] = a.s[sIndex]; \ + } \ + else \ + { \ + b.s[i] = 0; \ + } \ + } \ + return b; \ +} \ +template inline v_reg<_Tp, n> v_rotate_##suffix(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ +{ \ + v_reg<_Tp, n> c; \ + for (int i = 0; i < n; i++) \ + { \ + int aIndex = i opA imm; \ + int bIndex = i opA imm opB n; \ + if (0 <= bIndex && bIndex < n) \ + { \ + c.s[i] = b.s[bIndex]; \ + } \ + else if (0 <= aIndex && aIndex < n) \ + { \ + c.s[i] = a.s[aIndex]; \ + } \ + else \ + { \ + c.s[i] = 0; \ + } \ + } \ + return c; \ +} + +OPENCV_HAL_IMPL_ROTATE_SHIFT_OP(left, -, +) +OPENCV_HAL_IMPL_ROTATE_SHIFT_OP(right, +, -) + +/** @brief Sum packed values + +Scheme: +@code +{A1 A2 A3 ...} => sum{A1,A2,A3,...} +@endcode +For 32-bit integer and 32-bit floating point types.*/ +template inline typename V_TypeTraits<_Tp>::sum_type v_reduce_sum(const v_reg<_Tp, n>& a) +{ + typename V_TypeTraits<_Tp>::sum_type c = a.s[0]; + for( int i = 1; i < n; i++ ) + c += a.s[i]; + return c; +} + +/** @brief Sums all elements of each input vector, returns the vector of sums + + Scheme: + @code + result[0] = a[0] + a[1] + a[2] + a[3] + result[1] = b[0] + b[1] + b[2] + b[3] + result[2] = c[0] + c[1] + c[2] + c[3] + result[3] = d[0] + d[1] + d[2] + d[3] + @endcode +*/ +inline v_float32x4 v_reduce_sum4(const v_float32x4& a, const v_float32x4& b, + const v_float32x4& c, const v_float32x4& d) +{ + v_float32x4 r; + r.s[0] = a.s[0] + a.s[1] + a.s[2] + a.s[3]; + r.s[1] = b.s[0] + b.s[1] + b.s[2] + b.s[3]; + r.s[2] = c.s[0] + c.s[1] + c.s[2] + c.s[3]; + r.s[3] = d.s[0] + d.s[1] + d.s[2] + d.s[3]; + return r; +} + +/** @brief Sum absolute differences of values + +Scheme: +@code +{A1 A2 A3 ...} {B1 B2 B3 ...} => sum{ABS(A1-B1),abs(A2-B2),abs(A3-B3),...} +@endcode +For all types except 64-bit types.*/ +template inline typename V_TypeTraits< typename V_TypeTraits<_Tp>::abs_type >::sum_type v_reduce_sad(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) +{ + typename V_TypeTraits< typename V_TypeTraits<_Tp>::abs_type >::sum_type c = _absdiff(a.s[0], b.s[0]); + for (int i = 1; i < n; i++) + c += _absdiff(a.s[i], b.s[i]); + return c; +} + +/** @brief Get negative values mask + +Returned value is a bit mask with bits set to 1 on places corresponding to negative packed values indexes. +Example: +@code{.cpp} +v_int32x4 r; // set to {-1, -1, 1, 1} +int mask = v_signmask(r); // mask = 3 <== 00000000 00000000 00000000 00000011 +@endcode +For all types except 64-bit. */ +template inline int v_signmask(const v_reg<_Tp, n>& a) +{ + int mask = 0; + for( int i = 0; i < n; i++ ) + mask |= (V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) < 0) << i; + return mask; +} + +/** @brief Check if all packed values are less than zero + +Unsigned values will be casted to signed: `uchar 254 => char -2`. +For all types except 64-bit. */ +template inline bool v_check_all(const v_reg<_Tp, n>& a) +{ + for( int i = 0; i < n; i++ ) + if( V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) >= 0 ) + return false; + return true; +} + +/** @brief Check if any of packed values is less than zero + +Unsigned values will be casted to signed: `uchar 254 => char -2`. +For all types except 64-bit. */ +template inline bool v_check_any(const v_reg<_Tp, n>& a) +{ + for( int i = 0; i < n; i++ ) + if( V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) < 0 ) + return true; + return false; +} + +/** @brief Per-element select (blend operation) + +Return value will be built by combining values _a_ and _b_ using the following scheme: + result[i] = mask[i] ? a[i] : b[i]; + +@note: _mask_ element values are restricted to these values: +- 0: select element from _b_ +- 0xff/0xffff/etc: select element from _a_ +(fully compatible with bitwise-based operator) +*/ +template inline v_reg<_Tp, n> v_select(const v_reg<_Tp, n>& mask, + const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) +{ + typedef V_TypeTraits<_Tp> Traits; + typedef typename Traits::int_type int_type; + v_reg<_Tp, n> c; + for( int i = 0; i < n; i++ ) + { + int_type m = Traits::reinterpret_int(mask.s[i]); + CV_DbgAssert(m == 0 || m == (~(int_type)0)); // restrict mask values: 0 or 0xff/0xffff/etc + c.s[i] = m ? a.s[i] : b.s[i]; + } + return c; +} + +/** @brief Expand values to the wider pack type + +Copy contents of register to two registers with 2x wider pack type. +Scheme: +@code + int32x4 int64x2 int64x2 +{A B C D} ==> {A B} , {C D} +@endcode */ +template inline void v_expand(const v_reg<_Tp, n>& a, + v_reg::w_type, n/2>& b0, + v_reg::w_type, n/2>& b1) +{ + for( int i = 0; i < (n/2); i++ ) + { + b0.s[i] = a.s[i]; + b1.s[i] = a.s[i+(n/2)]; + } +} + +/** @brief Expand lower values to the wider pack type + +Same as cv::v_expand, but return lower half of the vector. + +Scheme: +@code + int32x4 int64x2 +{A B C D} ==> {A B} +@endcode */ +template +inline v_reg::w_type, n/2> +v_expand_low(const v_reg<_Tp, n>& a) +{ + v_reg::w_type, n/2> b; + for( int i = 0; i < (n/2); i++ ) + b.s[i] = a.s[i]; + return b; +} + +/** @brief Expand higher values to the wider pack type + +Same as cv::v_expand_low, but expand higher half of the vector instead. + +Scheme: +@code + int32x4 int64x2 +{A B C D} ==> {C D} +@endcode */ +template +inline v_reg::w_type, n/2> +v_expand_high(const v_reg<_Tp, n>& a) +{ + v_reg::w_type, n/2> b; + for( int i = 0; i < (n/2); i++ ) + b.s[i] = a.s[i+(n/2)]; + return b; +} + +//! @cond IGNORED +template inline v_reg::int_type, n> + v_reinterpret_as_int(const v_reg<_Tp, n>& a) +{ + v_reg::int_type, n> c; + for( int i = 0; i < n; i++ ) + c.s[i] = V_TypeTraits<_Tp>::reinterpret_int(a.s[i]); + return c; +} + +template inline v_reg::uint_type, n> + v_reinterpret_as_uint(const v_reg<_Tp, n>& a) +{ + v_reg::uint_type, n> c; + for( int i = 0; i < n; i++ ) + c.s[i] = V_TypeTraits<_Tp>::reinterpret_uint(a.s[i]); + return c; +} +//! @endcond + +/** @brief Interleave two vectors + +Scheme: +@code + {A1 A2 A3 A4} + {B1 B2 B3 B4} +--------------- + {A1 B1 A2 B2} and {A3 B3 A4 B4} +@endcode +For all types except 64-bit. +*/ +template inline void v_zip( const v_reg<_Tp, n>& a0, const v_reg<_Tp, n>& a1, + v_reg<_Tp, n>& b0, v_reg<_Tp, n>& b1 ) +{ + int i; + for( i = 0; i < n/2; i++ ) + { + b0.s[i*2] = a0.s[i]; + b0.s[i*2+1] = a1.s[i]; + } + for( ; i < n; i++ ) + { + b1.s[i*2-n] = a0.s[i]; + b1.s[i*2-n+1] = a1.s[i]; + } +} + +/** @brief Load register contents from memory + +@param ptr pointer to memory block with data +@return register object + +@note Returned type will be detected from passed pointer type, for example uchar ==> cv::v_uint8x16, int ==> cv::v_int32x4, etc. + */ +template +inline v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128> v_load(const _Tp* ptr) +{ + return v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128>(ptr); +} + +/** @brief Load register contents from memory (aligned) + +similar to cv::v_load, but source memory block should be aligned (to 16-byte boundary) + */ +template +inline v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128> v_load_aligned(const _Tp* ptr) +{ + return v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128>(ptr); +} + +/** @brief Load 64-bits of data to lower part (high part is undefined). + +@param ptr memory block containing data for first half (0..n/2) + +@code{.cpp} +int lo[2] = { 1, 2 }; +v_int32x4 r = v_load_low(lo); +@endcode + */ +template +inline v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128> v_load_low(const _Tp* ptr) +{ + v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128> c; + for( int i = 0; i < c.nlanes/2; i++ ) + { + c.s[i] = ptr[i]; + } + return c; +} + +/** @brief Load register contents from two memory blocks + +@param loptr memory block containing data for first half (0..n/2) +@param hiptr memory block containing data for second half (n/2..n) + +@code{.cpp} +int lo[2] = { 1, 2 }, hi[2] = { 3, 4 }; +v_int32x4 r = v_load_halves(lo, hi); +@endcode + */ +template +inline v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128> v_load_halves(const _Tp* loptr, const _Tp* hiptr) +{ + v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128> c; + for( int i = 0; i < c.nlanes/2; i++ ) + { + c.s[i] = loptr[i]; + c.s[i+c.nlanes/2] = hiptr[i]; + } + return c; +} + +/** @brief Load register contents from memory with double expand + +Same as cv::v_load, but result pack type will be 2x wider than memory type. + +@code{.cpp} +short buf[4] = {1, 2, 3, 4}; // type is int16 +v_int32x4 r = v_load_expand(buf); // r = {1, 2, 3, 4} - type is int32 +@endcode +For 8-, 16-, 32-bit integer source types. */ +template +inline v_reg::w_type, V_TypeTraits<_Tp>::nlanes128 / 2> +v_load_expand(const _Tp* ptr) +{ + typedef typename V_TypeTraits<_Tp>::w_type w_type; + v_reg::nlanes128> c; + for( int i = 0; i < c.nlanes; i++ ) + { + c.s[i] = ptr[i]; + } + return c; +} + +/** @brief Load register contents from memory with quad expand + +Same as cv::v_load_expand, but result type is 4 times wider than source. +@code{.cpp} +char buf[4] = {1, 2, 3, 4}; // type is int8 +v_int32x4 r = v_load_q(buf); // r = {1, 2, 3, 4} - type is int32 +@endcode +For 8-bit integer source types. */ +template +inline v_reg::q_type, V_TypeTraits<_Tp>::nlanes128 / 4> +v_load_expand_q(const _Tp* ptr) +{ + typedef typename V_TypeTraits<_Tp>::q_type q_type; + v_reg::nlanes128> c; + for( int i = 0; i < c.nlanes; i++ ) + { + c.s[i] = ptr[i]; + } + return c; +} + +/** @brief Load and deinterleave (2 channels) + +Load data from memory deinterleave and store to 2 registers. +Scheme: +@code +{A1 B1 A2 B2 ...} ==> {A1 A2 ...}, {B1 B2 ...} +@endcode +For all types except 64-bit. */ +template inline void v_load_deinterleave(const _Tp* ptr, v_reg<_Tp, n>& a, + v_reg<_Tp, n>& b) +{ + int i, i2; + for( i = i2 = 0; i < n; i++, i2 += 2 ) + { + a.s[i] = ptr[i2]; + b.s[i] = ptr[i2+1]; + } +} + +/** @brief Load and deinterleave (3 channels) + +Load data from memory deinterleave and store to 3 registers. +Scheme: +@code +{A1 B1 C1 A2 B2 C2 ...} ==> {A1 A2 ...}, {B1 B2 ...}, {C1 C2 ...} +@endcode +For all types except 64-bit. */ +template inline void v_load_deinterleave(const _Tp* ptr, v_reg<_Tp, n>& a, + v_reg<_Tp, n>& b, v_reg<_Tp, n>& c) +{ + int i, i3; + for( i = i3 = 0; i < n; i++, i3 += 3 ) + { + a.s[i] = ptr[i3]; + b.s[i] = ptr[i3+1]; + c.s[i] = ptr[i3+2]; + } +} + +/** @brief Load and deinterleave (4 channels) + +Load data from memory deinterleave and store to 4 registers. +Scheme: +@code +{A1 B1 C1 D1 A2 B2 C2 D2 ...} ==> {A1 A2 ...}, {B1 B2 ...}, {C1 C2 ...}, {D1 D2 ...} +@endcode +For all types except 64-bit. */ +template +inline void v_load_deinterleave(const _Tp* ptr, v_reg<_Tp, n>& a, + v_reg<_Tp, n>& b, v_reg<_Tp, n>& c, + v_reg<_Tp, n>& d) +{ + int i, i4; + for( i = i4 = 0; i < n; i++, i4 += 4 ) + { + a.s[i] = ptr[i4]; + b.s[i] = ptr[i4+1]; + c.s[i] = ptr[i4+2]; + d.s[i] = ptr[i4+3]; + } +} + +/** @brief Interleave and store (2 channels) + +Interleave and store data from 2 registers to memory. +Scheme: +@code +{A1 A2 ...}, {B1 B2 ...} ==> {A1 B1 A2 B2 ...} +@endcode +For all types except 64-bit. */ +template +inline void v_store_interleave( _Tp* ptr, const v_reg<_Tp, n>& a, + const v_reg<_Tp, n>& b, + hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) +{ + int i, i2; + for( i = i2 = 0; i < n; i++, i2 += 2 ) + { + ptr[i2] = a.s[i]; + ptr[i2+1] = b.s[i]; + } +} + +/** @brief Interleave and store (3 channels) + +Interleave and store data from 3 registers to memory. +Scheme: +@code +{A1 A2 ...}, {B1 B2 ...}, {C1 C2 ...} ==> {A1 B1 C1 A2 B2 C2 ...} +@endcode +For all types except 64-bit. */ +template +inline void v_store_interleave( _Tp* ptr, const v_reg<_Tp, n>& a, + const v_reg<_Tp, n>& b, const v_reg<_Tp, n>& c, + hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) +{ + int i, i3; + for( i = i3 = 0; i < n; i++, i3 += 3 ) + { + ptr[i3] = a.s[i]; + ptr[i3+1] = b.s[i]; + ptr[i3+2] = c.s[i]; + } +} + +/** @brief Interleave and store (4 channels) + +Interleave and store data from 4 registers to memory. +Scheme: +@code +{A1 A2 ...}, {B1 B2 ...}, {C1 C2 ...}, {D1 D2 ...} ==> {A1 B1 C1 D1 A2 B2 C2 D2 ...} +@endcode +For all types except 64-bit. */ +template inline void v_store_interleave( _Tp* ptr, const v_reg<_Tp, n>& a, + const v_reg<_Tp, n>& b, const v_reg<_Tp, n>& c, + const v_reg<_Tp, n>& d, + hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) +{ + int i, i4; + for( i = i4 = 0; i < n; i++, i4 += 4 ) + { + ptr[i4] = a.s[i]; + ptr[i4+1] = b.s[i]; + ptr[i4+2] = c.s[i]; + ptr[i4+3] = d.s[i]; + } +} + +/** @brief Store data to memory + +Store register contents to memory. +Scheme: +@code + REG {A B C D} ==> MEM {A B C D} +@endcode +Pointer can be unaligned. */ +template +inline void v_store(_Tp* ptr, const v_reg<_Tp, n>& a) +{ + for( int i = 0; i < n; i++ ) + ptr[i] = a.s[i]; +} + +/** @brief Store data to memory (lower half) + +Store lower half of register contents to memory. +Scheme: +@code + REG {A B C D} ==> MEM {A B} +@endcode */ +template +inline void v_store_low(_Tp* ptr, const v_reg<_Tp, n>& a) +{ + for( int i = 0; i < (n/2); i++ ) + ptr[i] = a.s[i]; +} + +/** @brief Store data to memory (higher half) + +Store higher half of register contents to memory. +Scheme: +@code + REG {A B C D} ==> MEM {C D} +@endcode */ +template +inline void v_store_high(_Tp* ptr, const v_reg<_Tp, n>& a) +{ + for( int i = 0; i < (n/2); i++ ) + ptr[i] = a.s[i+(n/2)]; +} + +/** @brief Store data to memory (aligned) + +Store register contents to memory. +Scheme: +@code + REG {A B C D} ==> MEM {A B C D} +@endcode +Pointer __should__ be aligned by 16-byte boundary. */ +template +inline void v_store_aligned(_Tp* ptr, const v_reg<_Tp, n>& a) +{ + for( int i = 0; i < n; i++ ) + ptr[i] = a.s[i]; +} + +template +inline void v_store_aligned_nocache(_Tp* ptr, const v_reg<_Tp, n>& a) +{ + for( int i = 0; i < n; i++ ) + ptr[i] = a.s[i]; +} + +template +inline void v_store_aligned(_Tp* ptr, const v_reg<_Tp, n>& a, hal::StoreMode /*mode*/) +{ + for( int i = 0; i < n; i++ ) + ptr[i] = a.s[i]; +} + +/** @brief Combine vector from first elements of two vectors + +Scheme: +@code + {A1 A2 A3 A4} + {B1 B2 B3 B4} +--------------- + {A1 A2 B1 B2} +@endcode +For all types except 64-bit. */ +template +inline v_reg<_Tp, n> v_combine_low(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) +{ + v_reg<_Tp, n> c; + for( int i = 0; i < (n/2); i++ ) + { + c.s[i] = a.s[i]; + c.s[i+(n/2)] = b.s[i]; + } + return c; +} + +/** @brief Combine vector from last elements of two vectors + +Scheme: +@code + {A1 A2 A3 A4} + {B1 B2 B3 B4} +--------------- + {A3 A4 B3 B4} +@endcode +For all types except 64-bit. */ +template +inline v_reg<_Tp, n> v_combine_high(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) +{ + v_reg<_Tp, n> c; + for( int i = 0; i < (n/2); i++ ) + { + c.s[i] = a.s[i+(n/2)]; + c.s[i+(n/2)] = b.s[i+(n/2)]; + } + return c; +} + +/** @brief Combine two vectors from lower and higher parts of two other vectors + +@code{.cpp} +low = cv::v_combine_low(a, b); +high = cv::v_combine_high(a, b); +@endcode */ +template +inline void v_recombine(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b, + v_reg<_Tp, n>& low, v_reg<_Tp, n>& high) +{ + for( int i = 0; i < (n/2); i++ ) + { + low.s[i] = a.s[i]; + low.s[i+(n/2)] = b.s[i]; + high.s[i] = a.s[i+(n/2)]; + high.s[i+(n/2)] = b.s[i+(n/2)]; + } +} + +/** @brief Vector extract + +Scheme: +@code + {A1 A2 A3 A4} + {B1 B2 B3 B4} +======================== +shift = 1 {A2 A3 A4 B1} +shift = 2 {A3 A4 B1 B2} +shift = 3 {A4 B1 B2 B3} +@endcode +Restriction: 0 <= shift < nlanes + +Usage: +@code +v_int32x4 a, b, c; +c = v_extract<2>(a, b); +@endcode +For all types. */ +template +inline v_reg<_Tp, n> v_extract(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) +{ + v_reg<_Tp, n> r; + const int shift = n - s; + int i = 0; + for (; i < shift; ++i) + r.s[i] = a.s[i+s]; + for (; i < n; ++i) + r.s[i] = b.s[i-shift]; + return r; +} + +/** @brief Round + +Rounds each value. Input type is float vector ==> output type is int vector.*/ +template inline v_reg v_round(const v_reg& a) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + c.s[i] = cvRound(a.s[i]); + return c; +} + +/** @overload */ +template inline v_reg v_round(const v_reg& a, const v_reg& b) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + { + c.s[i] = cvRound(a.s[i]); + c.s[i+n] = cvRound(b.s[i]); + } + return c; +} + +/** @brief Floor + +Floor each value. Input type is float vector ==> output type is int vector.*/ +template inline v_reg v_floor(const v_reg& a) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + c.s[i] = cvFloor(a.s[i]); + return c; +} + +/** @brief Ceil + +Ceil each value. Input type is float vector ==> output type is int vector.*/ +template inline v_reg v_ceil(const v_reg& a) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + c.s[i] = cvCeil(a.s[i]); + return c; +} + +/** @brief Trunc + +Truncate each value. Input type is float vector ==> output type is int vector.*/ +template inline v_reg v_trunc(const v_reg& a) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + c.s[i] = (int)(a.s[i]); + return c; +} + +/** @overload */ +template inline v_reg v_round(const v_reg& a) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + { + c.s[i] = cvRound(a.s[i]); + c.s[i+n] = 0; + } + return c; +} + +/** @overload */ +template inline v_reg v_floor(const v_reg& a) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + { + c.s[i] = cvFloor(a.s[i]); + c.s[i+n] = 0; + } + return c; +} + +/** @overload */ +template inline v_reg v_ceil(const v_reg& a) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + { + c.s[i] = cvCeil(a.s[i]); + c.s[i+n] = 0; + } + return c; +} + +/** @overload */ +template inline v_reg v_trunc(const v_reg& a) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + { + c.s[i] = cvCeil(a.s[i]); + c.s[i+n] = 0; + } + return c; +} + +/** @brief Convert to float + +Supported input type is cv::v_int32x4. */ +template inline v_reg v_cvt_f32(const v_reg& a) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + c.s[i] = (float)a.s[i]; + return c; +} + +template inline v_reg v_cvt_f32(const v_reg& a, const v_reg& b) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + { + c.s[i] = (float)a.s[i]; + c.s[i+n] = (float)b.s[i]; + } + return c; +} + +/** @brief Convert to double + +Supported input type is cv::v_int32x4. */ +template inline v_reg v_cvt_f64(const v_reg& a) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + c.s[i] = (double)a.s[i]; + return c; +} + +/** @brief Convert to double + +Supported input type is cv::v_float32x4. */ +template inline v_reg v_cvt_f64(const v_reg& a) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + c.s[i] = (double)a.s[i]; + return c; +} + +template inline v_reg v_lut(const int* tab, const v_reg& idx) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + c.s[i] = tab[idx.s[i]]; + return c; +} + +template inline v_reg v_lut(const float* tab, const v_reg& idx) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + c.s[i] = tab[idx.s[i]]; + return c; +} + +template inline v_reg v_lut(const double* tab, const v_reg& idx) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + c.s[i] = tab[idx.s[i]]; + return c; +} + +template inline void v_lut_deinterleave(const float* tab, const v_reg& idx, + v_reg& x, v_reg& y) +{ + for( int i = 0; i < n; i++ ) + { + int j = idx.s[i]; + x.s[i] = tab[j]; + y.s[i] = tab[j+1]; + } +} + +template inline void v_lut_deinterleave(const double* tab, const v_reg& idx, + v_reg& x, v_reg& y) +{ + for( int i = 0; i < n; i++ ) + { + int j = idx.s[i]; + x.s[i] = tab[j]; + y.s[i] = tab[j+1]; + } +} + +/** @brief Transpose 4x4 matrix + +Scheme: +@code +a0 {A1 A2 A3 A4} +a1 {B1 B2 B3 B4} +a2 {C1 C2 C3 C4} +a3 {D1 D2 D3 D4} +=============== +b0 {A1 B1 C1 D1} +b1 {A2 B2 C2 D2} +b2 {A3 B3 C3 D3} +b3 {A4 B4 C4 D4} +@endcode +*/ +template +inline void v_transpose4x4( v_reg<_Tp, 4>& a0, const v_reg<_Tp, 4>& a1, + const v_reg<_Tp, 4>& a2, const v_reg<_Tp, 4>& a3, + v_reg<_Tp, 4>& b0, v_reg<_Tp, 4>& b1, + v_reg<_Tp, 4>& b2, v_reg<_Tp, 4>& b3 ) +{ + b0 = v_reg<_Tp, 4>(a0.s[0], a1.s[0], a2.s[0], a3.s[0]); + b1 = v_reg<_Tp, 4>(a0.s[1], a1.s[1], a2.s[1], a3.s[1]); + b2 = v_reg<_Tp, 4>(a0.s[2], a1.s[2], a2.s[2], a3.s[2]); + b3 = v_reg<_Tp, 4>(a0.s[3], a1.s[3], a2.s[3], a3.s[3]); +} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_C_INIT_ZERO(_Tpvec, _Tp, suffix) \ +inline _Tpvec v_setzero_##suffix() { return _Tpvec::zero(); } + +//! @name Init with zero +//! @{ +//! @brief Create new vector with zero elements +OPENCV_HAL_IMPL_C_INIT_ZERO(v_uint8x16, uchar, u8) +OPENCV_HAL_IMPL_C_INIT_ZERO(v_int8x16, schar, s8) +OPENCV_HAL_IMPL_C_INIT_ZERO(v_uint16x8, ushort, u16) +OPENCV_HAL_IMPL_C_INIT_ZERO(v_int16x8, short, s16) +OPENCV_HAL_IMPL_C_INIT_ZERO(v_uint32x4, unsigned, u32) +OPENCV_HAL_IMPL_C_INIT_ZERO(v_int32x4, int, s32) +OPENCV_HAL_IMPL_C_INIT_ZERO(v_float32x4, float, f32) +OPENCV_HAL_IMPL_C_INIT_ZERO(v_float64x2, double, f64) +OPENCV_HAL_IMPL_C_INIT_ZERO(v_uint64x2, uint64, u64) +OPENCV_HAL_IMPL_C_INIT_ZERO(v_int64x2, int64, s64) +//! @} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_C_INIT_VAL(_Tpvec, _Tp, suffix) \ +inline _Tpvec v_setall_##suffix(_Tp val) { return _Tpvec::all(val); } + +//! @name Init with value +//! @{ +//! @brief Create new vector with elements set to a specific value +OPENCV_HAL_IMPL_C_INIT_VAL(v_uint8x16, uchar, u8) +OPENCV_HAL_IMPL_C_INIT_VAL(v_int8x16, schar, s8) +OPENCV_HAL_IMPL_C_INIT_VAL(v_uint16x8, ushort, u16) +OPENCV_HAL_IMPL_C_INIT_VAL(v_int16x8, short, s16) +OPENCV_HAL_IMPL_C_INIT_VAL(v_uint32x4, unsigned, u32) +OPENCV_HAL_IMPL_C_INIT_VAL(v_int32x4, int, s32) +OPENCV_HAL_IMPL_C_INIT_VAL(v_float32x4, float, f32) +OPENCV_HAL_IMPL_C_INIT_VAL(v_float64x2, double, f64) +OPENCV_HAL_IMPL_C_INIT_VAL(v_uint64x2, uint64, u64) +OPENCV_HAL_IMPL_C_INIT_VAL(v_int64x2, int64, s64) +//! @} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_C_REINTERPRET(_Tpvec, _Tp, suffix) \ +template inline _Tpvec \ + v_reinterpret_as_##suffix(const v_reg<_Tp0, n0>& a) \ +{ return a.template reinterpret_as<_Tp, _Tpvec::nlanes>(); } + +//! @name Reinterpret +//! @{ +//! @brief Convert vector to different type without modifying underlying data. +OPENCV_HAL_IMPL_C_REINTERPRET(v_uint8x16, uchar, u8) +OPENCV_HAL_IMPL_C_REINTERPRET(v_int8x16, schar, s8) +OPENCV_HAL_IMPL_C_REINTERPRET(v_uint16x8, ushort, u16) +OPENCV_HAL_IMPL_C_REINTERPRET(v_int16x8, short, s16) +OPENCV_HAL_IMPL_C_REINTERPRET(v_uint32x4, unsigned, u32) +OPENCV_HAL_IMPL_C_REINTERPRET(v_int32x4, int, s32) +OPENCV_HAL_IMPL_C_REINTERPRET(v_float32x4, float, f32) +OPENCV_HAL_IMPL_C_REINTERPRET(v_float64x2, double, f64) +OPENCV_HAL_IMPL_C_REINTERPRET(v_uint64x2, uint64, u64) +OPENCV_HAL_IMPL_C_REINTERPRET(v_int64x2, int64, s64) +//! @} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_C_SHIFTL(_Tpvec, _Tp) \ +template inline _Tpvec v_shl(const _Tpvec& a) \ +{ return a << n; } + +//! @name Left shift +//! @{ +//! @brief Shift left +OPENCV_HAL_IMPL_C_SHIFTL(v_uint16x8, ushort) +OPENCV_HAL_IMPL_C_SHIFTL(v_int16x8, short) +OPENCV_HAL_IMPL_C_SHIFTL(v_uint32x4, unsigned) +OPENCV_HAL_IMPL_C_SHIFTL(v_int32x4, int) +OPENCV_HAL_IMPL_C_SHIFTL(v_uint64x2, uint64) +OPENCV_HAL_IMPL_C_SHIFTL(v_int64x2, int64) +//! @} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_C_SHIFTR(_Tpvec, _Tp) \ +template inline _Tpvec v_shr(const _Tpvec& a) \ +{ return a >> n; } + +//! @name Right shift +//! @{ +//! @brief Shift right +OPENCV_HAL_IMPL_C_SHIFTR(v_uint16x8, ushort) +OPENCV_HAL_IMPL_C_SHIFTR(v_int16x8, short) +OPENCV_HAL_IMPL_C_SHIFTR(v_uint32x4, unsigned) +OPENCV_HAL_IMPL_C_SHIFTR(v_int32x4, int) +OPENCV_HAL_IMPL_C_SHIFTR(v_uint64x2, uint64) +OPENCV_HAL_IMPL_C_SHIFTR(v_int64x2, int64) +//! @} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_C_RSHIFTR(_Tpvec, _Tp) \ +template inline _Tpvec v_rshr(const _Tpvec& a) \ +{ \ + _Tpvec c; \ + for( int i = 0; i < _Tpvec::nlanes; i++ ) \ + c.s[i] = (_Tp)((a.s[i] + ((_Tp)1 << (n - 1))) >> n); \ + return c; \ +} + +//! @name Rounding shift +//! @{ +//! @brief Rounding shift right +OPENCV_HAL_IMPL_C_RSHIFTR(v_uint16x8, ushort) +OPENCV_HAL_IMPL_C_RSHIFTR(v_int16x8, short) +OPENCV_HAL_IMPL_C_RSHIFTR(v_uint32x4, unsigned) +OPENCV_HAL_IMPL_C_RSHIFTR(v_int32x4, int) +OPENCV_HAL_IMPL_C_RSHIFTR(v_uint64x2, uint64) +OPENCV_HAL_IMPL_C_RSHIFTR(v_int64x2, int64) +//! @} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_C_PACK(_Tpvec, _Tpnvec, _Tpn, pack_suffix, cast) \ +inline _Tpnvec v_##pack_suffix(const _Tpvec& a, const _Tpvec& b) \ +{ \ + _Tpnvec c; \ + for( int i = 0; i < _Tpvec::nlanes; i++ ) \ + { \ + c.s[i] = cast<_Tpn>(a.s[i]); \ + c.s[i+_Tpvec::nlanes] = cast<_Tpn>(b.s[i]); \ + } \ + return c; \ +} + +//! @name Pack +//! @{ +//! @brief Pack values from two vectors to one +//! +//! Return vector type have twice more elements than input vector types. Variant with _u_ suffix also +//! converts to corresponding unsigned type. +//! +//! - pack: for 16-, 32- and 64-bit integer input types +//! - pack_u: for 16- and 32-bit signed integer input types +//! +//! @note All variants except 64-bit use saturation. +OPENCV_HAL_IMPL_C_PACK(v_uint16x8, v_uint8x16, uchar, pack, saturate_cast) +OPENCV_HAL_IMPL_C_PACK(v_int16x8, v_int8x16, schar, pack, saturate_cast) +OPENCV_HAL_IMPL_C_PACK(v_uint32x4, v_uint16x8, ushort, pack, saturate_cast) +OPENCV_HAL_IMPL_C_PACK(v_int32x4, v_int16x8, short, pack, saturate_cast) +OPENCV_HAL_IMPL_C_PACK(v_uint64x2, v_uint32x4, unsigned, pack, static_cast) +OPENCV_HAL_IMPL_C_PACK(v_int64x2, v_int32x4, int, pack, static_cast) +OPENCV_HAL_IMPL_C_PACK(v_int16x8, v_uint8x16, uchar, pack_u, saturate_cast) +OPENCV_HAL_IMPL_C_PACK(v_int32x4, v_uint16x8, ushort, pack_u, saturate_cast) +//! @} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_C_RSHR_PACK(_Tpvec, _Tp, _Tpnvec, _Tpn, pack_suffix, cast) \ +template inline _Tpnvec v_rshr_##pack_suffix(const _Tpvec& a, const _Tpvec& b) \ +{ \ + _Tpnvec c; \ + for( int i = 0; i < _Tpvec::nlanes; i++ ) \ + { \ + c.s[i] = cast<_Tpn>((a.s[i] + ((_Tp)1 << (n - 1))) >> n); \ + c.s[i+_Tpvec::nlanes] = cast<_Tpn>((b.s[i] + ((_Tp)1 << (n - 1))) >> n); \ + } \ + return c; \ +} + +//! @name Pack with rounding shift +//! @{ +//! @brief Pack values from two vectors to one with rounding shift +//! +//! Values from the input vectors will be shifted right by _n_ bits with rounding, converted to narrower +//! type and returned in the result vector. Variant with _u_ suffix converts to unsigned type. +//! +//! - pack: for 16-, 32- and 64-bit integer input types +//! - pack_u: for 16- and 32-bit signed integer input types +//! +//! @note All variants except 64-bit use saturation. +OPENCV_HAL_IMPL_C_RSHR_PACK(v_uint16x8, ushort, v_uint8x16, uchar, pack, saturate_cast) +OPENCV_HAL_IMPL_C_RSHR_PACK(v_int16x8, short, v_int8x16, schar, pack, saturate_cast) +OPENCV_HAL_IMPL_C_RSHR_PACK(v_uint32x4, unsigned, v_uint16x8, ushort, pack, saturate_cast) +OPENCV_HAL_IMPL_C_RSHR_PACK(v_int32x4, int, v_int16x8, short, pack, saturate_cast) +OPENCV_HAL_IMPL_C_RSHR_PACK(v_uint64x2, uint64, v_uint32x4, unsigned, pack, static_cast) +OPENCV_HAL_IMPL_C_RSHR_PACK(v_int64x2, int64, v_int32x4, int, pack, static_cast) +OPENCV_HAL_IMPL_C_RSHR_PACK(v_int16x8, short, v_uint8x16, uchar, pack_u, saturate_cast) +OPENCV_HAL_IMPL_C_RSHR_PACK(v_int32x4, int, v_uint16x8, ushort, pack_u, saturate_cast) +//! @} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_C_PACK_STORE(_Tpvec, _Tp, _Tpnvec, _Tpn, pack_suffix, cast) \ +inline void v_##pack_suffix##_store(_Tpn* ptr, const _Tpvec& a) \ +{ \ + for( int i = 0; i < _Tpvec::nlanes; i++ ) \ + ptr[i] = cast<_Tpn>(a.s[i]); \ +} + +//! @name Pack and store +//! @{ +//! @brief Store values from the input vector into memory with pack +//! +//! Values will be stored into memory with conversion to narrower type. +//! Variant with _u_ suffix converts to corresponding unsigned type. +//! +//! - pack: for 16-, 32- and 64-bit integer input types +//! - pack_u: for 16- and 32-bit signed integer input types +//! +//! @note All variants except 64-bit use saturation. +OPENCV_HAL_IMPL_C_PACK_STORE(v_uint16x8, ushort, v_uint8x16, uchar, pack, saturate_cast) +OPENCV_HAL_IMPL_C_PACK_STORE(v_int16x8, short, v_int8x16, schar, pack, saturate_cast) +OPENCV_HAL_IMPL_C_PACK_STORE(v_uint32x4, unsigned, v_uint16x8, ushort, pack, saturate_cast) +OPENCV_HAL_IMPL_C_PACK_STORE(v_int32x4, int, v_int16x8, short, pack, saturate_cast) +OPENCV_HAL_IMPL_C_PACK_STORE(v_uint64x2, uint64, v_uint32x4, unsigned, pack, static_cast) +OPENCV_HAL_IMPL_C_PACK_STORE(v_int64x2, int64, v_int32x4, int, pack, static_cast) +OPENCV_HAL_IMPL_C_PACK_STORE(v_int16x8, short, v_uint8x16, uchar, pack_u, saturate_cast) +OPENCV_HAL_IMPL_C_PACK_STORE(v_int32x4, int, v_uint16x8, ushort, pack_u, saturate_cast) +//! @} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(_Tpvec, _Tp, _Tpnvec, _Tpn, pack_suffix, cast) \ +template inline void v_rshr_##pack_suffix##_store(_Tpn* ptr, const _Tpvec& a) \ +{ \ + for( int i = 0; i < _Tpvec::nlanes; i++ ) \ + ptr[i] = cast<_Tpn>((a.s[i] + ((_Tp)1 << (n - 1))) >> n); \ +} + +//! @name Pack and store with rounding shift +//! @{ +//! @brief Store values from the input vector into memory with pack +//! +//! Values will be shifted _n_ bits right with rounding, converted to narrower type and stored into +//! memory. Variant with _u_ suffix converts to unsigned type. +//! +//! - pack: for 16-, 32- and 64-bit integer input types +//! - pack_u: for 16- and 32-bit signed integer input types +//! +//! @note All variants except 64-bit use saturation. +OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_uint16x8, ushort, v_uint8x16, uchar, pack, saturate_cast) +OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_int16x8, short, v_int8x16, schar, pack, saturate_cast) +OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_uint32x4, unsigned, v_uint16x8, ushort, pack, saturate_cast) +OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_int32x4, int, v_int16x8, short, pack, saturate_cast) +OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_uint64x2, uint64, v_uint32x4, unsigned, pack, static_cast) +OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_int64x2, int64, v_int32x4, int, pack, static_cast) +OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_int16x8, short, v_uint8x16, uchar, pack_u, saturate_cast) +OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_int32x4, int, v_uint16x8, ushort, pack_u, saturate_cast) +//! @} + +//! @cond IGNORED +template +inline void _pack_b(_Tpm* mptr, const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) +{ + for (int i = 0; i < n; ++i) + { + mptr[i] = (_Tpm)a.s[i]; + mptr[i + n] = (_Tpm)b.s[i]; + } +} +//! @endcond + +//! @name Pack boolean values +//! @{ +//! @brief Pack boolean values from multiple vectors to one unsigned 8-bit integer vector +//! +//! @note Must provide valid boolean values to guarantee same result for all architectures. + +/** @brief +//! For 16-bit boolean values + +Scheme: +@code +a {0xFFFF 0 0 0xFFFF 0 0xFFFF 0xFFFF 0} +b {0xFFFF 0 0xFFFF 0 0 0xFFFF 0 0xFFFF} +=============== +{ + 0xFF 0 0 0xFF 0 0xFF 0xFF 0 + 0xFF 0 0xFF 0 0 0xFF 0 0xFF +} +@endcode */ + +inline v_uint8x16 v_pack_b(const v_uint16x8& a, const v_uint16x8& b) +{ + v_uint8x16 mask; + _pack_b(mask.s, a, b); + return mask; +} + +/** @overload +For 32-bit boolean values + +Scheme: +@code +a {0xFFFF.. 0 0 0xFFFF..} +b {0 0xFFFF.. 0xFFFF.. 0} +c {0xFFFF.. 0 0xFFFF.. 0} +d {0 0xFFFF.. 0 0xFFFF..} +=============== +{ + 0xFF 0 0 0xFF 0 0xFF 0xFF 0 + 0xFF 0 0xFF 0 0 0xFF 0 0xFF +} +@endcode */ + +inline v_uint8x16 v_pack_b(const v_uint32x4& a, const v_uint32x4& b, + const v_uint32x4& c, const v_uint32x4& d) +{ + v_uint8x16 mask; + _pack_b(mask.s, a, b); + _pack_b(mask.s + 8, c, d); + return mask; +} + +/** @overload +For 64-bit boolean values + +Scheme: +@code +a {0xFFFF.. 0} +b {0 0xFFFF..} +c {0xFFFF.. 0} +d {0 0xFFFF..} + +e {0xFFFF.. 0} +f {0xFFFF.. 0} +g {0 0xFFFF..} +h {0 0xFFFF..} +=============== +{ + 0xFF 0 0 0xFF 0xFF 0 0 0xFF + 0xFF 0 0xFF 0 0 0xFF 0 0xFF +} +@endcode */ +inline v_uint8x16 v_pack_b(const v_uint64x2& a, const v_uint64x2& b, const v_uint64x2& c, + const v_uint64x2& d, const v_uint64x2& e, const v_uint64x2& f, + const v_uint64x2& g, const v_uint64x2& h) +{ + v_uint8x16 mask; + _pack_b(mask.s, a, b); + _pack_b(mask.s + 4, c, d); + _pack_b(mask.s + 8, e, f); + _pack_b(mask.s + 12, g, h); + return mask; +} +//! @} + +/** @brief Matrix multiplication + +Scheme: +@code +{A0 A1 A2 A3} |V0| +{B0 B1 B2 B3} |V1| +{C0 C1 C2 C3} |V2| +{D0 D1 D2 D3} x |V3| +==================== +{R0 R1 R2 R3}, where: +R0 = A0V0 + A1V1 + A2V2 + A3V3, +R1 = B0V0 + B1V1 + B2V2 + B3V3 +... +@endcode +*/ +inline v_float32x4 v_matmul(const v_float32x4& v, const v_float32x4& m0, + const v_float32x4& m1, const v_float32x4& m2, + const v_float32x4& m3) +{ + return v_float32x4(v.s[0]*m0.s[0] + v.s[1]*m1.s[0] + v.s[2]*m2.s[0] + v.s[3]*m3.s[0], + v.s[0]*m0.s[1] + v.s[1]*m1.s[1] + v.s[2]*m2.s[1] + v.s[3]*m3.s[1], + v.s[0]*m0.s[2] + v.s[1]*m1.s[2] + v.s[2]*m2.s[2] + v.s[3]*m3.s[2], + v.s[0]*m0.s[3] + v.s[1]*m1.s[3] + v.s[2]*m2.s[3] + v.s[3]*m3.s[3]); +} + +/** @brief Matrix multiplication and add + +Scheme: +@code +{A0 A1 A2 } |V0| |D0| +{B0 B1 B2 } |V1| |D1| +{C0 C1 C2 } x |V2| + |D2| +==================== +{R0 R1 R2 R3}, where: +R0 = A0V0 + A1V1 + A2V2 + D0, +R1 = B0V0 + B1V1 + B2V2 + D1 +... +@endcode +*/ +inline v_float32x4 v_matmuladd(const v_float32x4& v, const v_float32x4& m0, + const v_float32x4& m1, const v_float32x4& m2, + const v_float32x4& m3) +{ + return v_float32x4(v.s[0]*m0.s[0] + v.s[1]*m1.s[0] + v.s[2]*m2.s[0] + m3.s[0], + v.s[0]*m0.s[1] + v.s[1]*m1.s[1] + v.s[2]*m2.s[1] + m3.s[1], + v.s[0]*m0.s[2] + v.s[1]*m1.s[2] + v.s[2]*m2.s[2] + m3.s[2], + v.s[0]*m0.s[3] + v.s[1]*m1.s[3] + v.s[2]*m2.s[3] + m3.s[3]); +} + +////// FP16 suport /////// + +inline v_reg::nlanes128> +v_load_expand(const float16_t* ptr) +{ + v_reg::nlanes128> v; + for( int i = 0; i < v.nlanes; i++ ) + { + v.s[i] = ptr[i]; + } + return v; +} + +inline void +v_pack_store(float16_t* ptr, v_reg::nlanes128>& v) +{ + for( int i = 0; i < v.nlanes; i++ ) + { + ptr[i] = float16_t(v.s[i]); + } +} + +inline void v_cleanup() {} + +//! @} + +//! @name Check SIMD support +//! @{ +//! @brief Check CPU capability of SIMD operation +static inline bool hasSIMD128() +{ + return false; +} + +//! @} + +#ifndef CV_DOXYGEN +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END +#endif +} + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/hal/intrin_forward.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/hal/intrin_forward.hpp new file mode 100644 index 0000000000000000000000000000000000000000..461855290743174351750ad30c0f3d3a3803129a --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/hal/intrin_forward.hpp @@ -0,0 +1,158 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html + +#ifndef CV__SIMD_FORWARD +#error "Need to pre-define forward width" +#endif + +namespace cv +{ + +//! @cond IGNORED + +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN + +/** Types **/ +#if CV__SIMD_FORWARD == 512 +// [todo] 512 +#error "AVX512 Not implemented yet" +#elif CV__SIMD_FORWARD == 256 +// 256 +#define __CV_VX(fun) v256_##fun +#define __CV_V_UINT8 v_uint8x32 +#define __CV_V_INT8 v_int8x32 +#define __CV_V_UINT16 v_uint16x16 +#define __CV_V_INT16 v_int16x16 +#define __CV_V_UINT32 v_uint32x8 +#define __CV_V_INT32 v_int32x8 +#define __CV_V_UINT64 v_uint64x4 +#define __CV_V_INT64 v_int64x4 +#define __CV_V_FLOAT32 v_float32x8 +#define __CV_V_FLOAT64 v_float64x4 +struct v_uint8x32; +struct v_int8x32; +struct v_uint16x16; +struct v_int16x16; +struct v_uint32x8; +struct v_int32x8; +struct v_uint64x4; +struct v_int64x4; +struct v_float32x8; +struct v_float64x4; +#else +// 128 +#define __CV_VX(fun) v_##fun +#define __CV_V_UINT8 v_uint8x16 +#define __CV_V_INT8 v_int8x16 +#define __CV_V_UINT16 v_uint16x8 +#define __CV_V_INT16 v_int16x8 +#define __CV_V_UINT32 v_uint32x4 +#define __CV_V_INT32 v_int32x4 +#define __CV_V_UINT64 v_uint64x2 +#define __CV_V_INT64 v_int64x2 +#define __CV_V_FLOAT32 v_float32x4 +#define __CV_V_FLOAT64 v_float64x2 +struct v_uint8x16; +struct v_int8x16; +struct v_uint16x8; +struct v_int16x8; +struct v_uint32x4; +struct v_int32x4; +struct v_uint64x2; +struct v_int64x2; +struct v_float32x4; +struct v_float64x2; +#endif + +/** Value reordering **/ + +// Expansion +void v_expand(const __CV_V_UINT8&, __CV_V_UINT16&, __CV_V_UINT16&); +void v_expand(const __CV_V_INT8&, __CV_V_INT16&, __CV_V_INT16&); +void v_expand(const __CV_V_UINT16&, __CV_V_UINT32&, __CV_V_UINT32&); +void v_expand(const __CV_V_INT16&, __CV_V_INT32&, __CV_V_INT32&); +void v_expand(const __CV_V_UINT32&, __CV_V_UINT64&, __CV_V_UINT64&); +void v_expand(const __CV_V_INT32&, __CV_V_INT64&, __CV_V_INT64&); +// Low Expansion +__CV_V_UINT16 v_expand_low(const __CV_V_UINT8&); +__CV_V_INT16 v_expand_low(const __CV_V_INT8&); +__CV_V_UINT32 v_expand_low(const __CV_V_UINT16&); +__CV_V_INT32 v_expand_low(const __CV_V_INT16&); +__CV_V_UINT64 v_expand_low(const __CV_V_UINT32&); +__CV_V_INT64 v_expand_low(const __CV_V_INT32&); +// High Expansion +__CV_V_UINT16 v_expand_high(const __CV_V_UINT8&); +__CV_V_INT16 v_expand_high(const __CV_V_INT8&); +__CV_V_UINT32 v_expand_high(const __CV_V_UINT16&); +__CV_V_INT32 v_expand_high(const __CV_V_INT16&); +__CV_V_UINT64 v_expand_high(const __CV_V_UINT32&); +__CV_V_INT64 v_expand_high(const __CV_V_INT32&); +// Load & Low Expansion +__CV_V_UINT16 __CV_VX(load_expand)(const uchar*); +__CV_V_INT16 __CV_VX(load_expand)(const schar*); +__CV_V_UINT32 __CV_VX(load_expand)(const ushort*); +__CV_V_INT32 __CV_VX(load_expand)(const short*); +__CV_V_UINT64 __CV_VX(load_expand)(const uint*); +__CV_V_INT64 __CV_VX(load_expand)(const int*); +// Load lower 8-bit and expand into 32-bit +__CV_V_UINT32 __CV_VX(load_expand_q)(const uchar*); +__CV_V_INT32 __CV_VX(load_expand_q)(const schar*); + +// Saturating Pack +__CV_V_UINT8 v_pack(const __CV_V_UINT16&, const __CV_V_UINT16&); +__CV_V_INT8 v_pack(const __CV_V_INT16&, const __CV_V_INT16&); +__CV_V_UINT16 v_pack(const __CV_V_UINT32&, const __CV_V_UINT32&); +__CV_V_INT16 v_pack(const __CV_V_INT32&, const __CV_V_INT32&); +// Non-saturating Pack +__CV_V_UINT32 v_pack(const __CV_V_UINT64&, const __CV_V_UINT64&); +__CV_V_INT32 v_pack(const __CV_V_INT64&, const __CV_V_INT64&); +// Pack signed integers with unsigned saturation +__CV_V_UINT8 v_pack_u(const __CV_V_INT16&, const __CV_V_INT16&); +__CV_V_UINT16 v_pack_u(const __CV_V_INT32&, const __CV_V_INT32&); + +/** Arithmetic, bitwise and comparison operations **/ + +// Non-saturating multiply +#if CV_VSX +template +Tvec v_mul_wrap(const Tvec& a, const Tvec& b); +#else +__CV_V_UINT8 v_mul_wrap(const __CV_V_UINT8&, const __CV_V_UINT8&); +__CV_V_INT8 v_mul_wrap(const __CV_V_INT8&, const __CV_V_INT8&); +__CV_V_UINT16 v_mul_wrap(const __CV_V_UINT16&, const __CV_V_UINT16&); +__CV_V_INT16 v_mul_wrap(const __CV_V_INT16&, const __CV_V_INT16&); +#endif + +// Multiply and expand +#if CV_VSX +template +void v_mul_expand(const Tvec& a, const Tvec& b, Twvec& c, Twvec& d); +#else +void v_mul_expand(const __CV_V_UINT8&, const __CV_V_UINT8&, __CV_V_UINT16&, __CV_V_UINT16&); +void v_mul_expand(const __CV_V_INT8&, const __CV_V_INT8&, __CV_V_INT16&, __CV_V_INT16&); +void v_mul_expand(const __CV_V_UINT16&, const __CV_V_UINT16&, __CV_V_UINT32&, __CV_V_UINT32&); +void v_mul_expand(const __CV_V_INT16&, const __CV_V_INT16&, __CV_V_INT32&, __CV_V_INT32&); +void v_mul_expand(const __CV_V_UINT32&, const __CV_V_UINT32&, __CV_V_UINT64&, __CV_V_UINT64&); +void v_mul_expand(const __CV_V_INT32&, const __CV_V_INT32&, __CV_V_INT64&, __CV_V_INT64&); +#endif + +/** Cleanup **/ +#undef CV__SIMD_FORWARD +#undef __CV_VX +#undef __CV_V_UINT8 +#undef __CV_V_INT8 +#undef __CV_V_UINT16 +#undef __CV_V_INT16 +#undef __CV_V_UINT32 +#undef __CV_V_INT32 +#undef __CV_V_UINT64 +#undef __CV_V_INT64 +#undef __CV_V_FLOAT32 +#undef __CV_V_FLOAT64 + +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END + +//! @endcond + +} // cv:: \ No newline at end of file diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/hal/intrin_neon.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/hal/intrin_neon.hpp new file mode 100644 index 0000000000000000000000000000000000000000..608dc97a87d806a133ad5fa851577b98b5071968 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/hal/intrin_neon.hpp @@ -0,0 +1,1697 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_HAL_INTRIN_NEON_HPP +#define OPENCV_HAL_INTRIN_NEON_HPP + +#include +#include "opencv2/core/utility.hpp" + +namespace cv +{ + +//! @cond IGNORED + +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN + +#define CV_SIMD128 1 +#if defined(__aarch64__) +#define CV_SIMD128_64F 1 +#else +#define CV_SIMD128_64F 0 +#endif + +#if CV_SIMD128_64F +#define OPENCV_HAL_IMPL_NEON_REINTERPRET(_Tpv, suffix) \ +template static inline \ +_Tpv vreinterpretq_##suffix##_f64(T a) { return (_Tpv) a; } \ +template static inline \ +float64x2_t vreinterpretq_f64_##suffix(T a) { return (float64x2_t) a; } +OPENCV_HAL_IMPL_NEON_REINTERPRET(uint8x16_t, u8) +OPENCV_HAL_IMPL_NEON_REINTERPRET(int8x16_t, s8) +OPENCV_HAL_IMPL_NEON_REINTERPRET(uint16x8_t, u16) +OPENCV_HAL_IMPL_NEON_REINTERPRET(int16x8_t, s16) +OPENCV_HAL_IMPL_NEON_REINTERPRET(uint32x4_t, u32) +OPENCV_HAL_IMPL_NEON_REINTERPRET(int32x4_t, s32) +OPENCV_HAL_IMPL_NEON_REINTERPRET(uint64x2_t, u64) +OPENCV_HAL_IMPL_NEON_REINTERPRET(int64x2_t, s64) +OPENCV_HAL_IMPL_NEON_REINTERPRET(float32x4_t, f32) +#endif + +struct v_uint8x16 +{ + typedef uchar lane_type; + enum { nlanes = 16 }; + + v_uint8x16() {} + explicit v_uint8x16(uint8x16_t v) : val(v) {} + v_uint8x16(uchar v0, uchar v1, uchar v2, uchar v3, uchar v4, uchar v5, uchar v6, uchar v7, + uchar v8, uchar v9, uchar v10, uchar v11, uchar v12, uchar v13, uchar v14, uchar v15) + { + uchar v[] = {v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15}; + val = vld1q_u8(v); + } + uchar get0() const + { + return vgetq_lane_u8(val, 0); + } + + uint8x16_t val; +}; + +struct v_int8x16 +{ + typedef schar lane_type; + enum { nlanes = 16 }; + + v_int8x16() {} + explicit v_int8x16(int8x16_t v) : val(v) {} + v_int8x16(schar v0, schar v1, schar v2, schar v3, schar v4, schar v5, schar v6, schar v7, + schar v8, schar v9, schar v10, schar v11, schar v12, schar v13, schar v14, schar v15) + { + schar v[] = {v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15}; + val = vld1q_s8(v); + } + schar get0() const + { + return vgetq_lane_s8(val, 0); + } + + int8x16_t val; +}; + +struct v_uint16x8 +{ + typedef ushort lane_type; + enum { nlanes = 8 }; + + v_uint16x8() {} + explicit v_uint16x8(uint16x8_t v) : val(v) {} + v_uint16x8(ushort v0, ushort v1, ushort v2, ushort v3, ushort v4, ushort v5, ushort v6, ushort v7) + { + ushort v[] = {v0, v1, v2, v3, v4, v5, v6, v7}; + val = vld1q_u16(v); + } + ushort get0() const + { + return vgetq_lane_u16(val, 0); + } + + uint16x8_t val; +}; + +struct v_int16x8 +{ + typedef short lane_type; + enum { nlanes = 8 }; + + v_int16x8() {} + explicit v_int16x8(int16x8_t v) : val(v) {} + v_int16x8(short v0, short v1, short v2, short v3, short v4, short v5, short v6, short v7) + { + short v[] = {v0, v1, v2, v3, v4, v5, v6, v7}; + val = vld1q_s16(v); + } + short get0() const + { + return vgetq_lane_s16(val, 0); + } + + int16x8_t val; +}; + +struct v_uint32x4 +{ + typedef unsigned lane_type; + enum { nlanes = 4 }; + + v_uint32x4() {} + explicit v_uint32x4(uint32x4_t v) : val(v) {} + v_uint32x4(unsigned v0, unsigned v1, unsigned v2, unsigned v3) + { + unsigned v[] = {v0, v1, v2, v3}; + val = vld1q_u32(v); + } + unsigned get0() const + { + return vgetq_lane_u32(val, 0); + } + + uint32x4_t val; +}; + +struct v_int32x4 +{ + typedef int lane_type; + enum { nlanes = 4 }; + + v_int32x4() {} + explicit v_int32x4(int32x4_t v) : val(v) {} + v_int32x4(int v0, int v1, int v2, int v3) + { + int v[] = {v0, v1, v2, v3}; + val = vld1q_s32(v); + } + int get0() const + { + return vgetq_lane_s32(val, 0); + } + int32x4_t val; +}; + +struct v_float32x4 +{ + typedef float lane_type; + enum { nlanes = 4 }; + + v_float32x4() {} + explicit v_float32x4(float32x4_t v) : val(v) {} + v_float32x4(float v0, float v1, float v2, float v3) + { + float v[] = {v0, v1, v2, v3}; + val = vld1q_f32(v); + } + float get0() const + { + return vgetq_lane_f32(val, 0); + } + float32x4_t val; +}; + +struct v_uint64x2 +{ + typedef uint64 lane_type; + enum { nlanes = 2 }; + + v_uint64x2() {} + explicit v_uint64x2(uint64x2_t v) : val(v) {} + v_uint64x2(uint64 v0, uint64 v1) + { + uint64 v[] = {v0, v1}; + val = vld1q_u64(v); + } + uint64 get0() const + { + return vgetq_lane_u64(val, 0); + } + uint64x2_t val; +}; + +struct v_int64x2 +{ + typedef int64 lane_type; + enum { nlanes = 2 }; + + v_int64x2() {} + explicit v_int64x2(int64x2_t v) : val(v) {} + v_int64x2(int64 v0, int64 v1) + { + int64 v[] = {v0, v1}; + val = vld1q_s64(v); + } + int64 get0() const + { + return vgetq_lane_s64(val, 0); + } + int64x2_t val; +}; + +#if CV_SIMD128_64F +struct v_float64x2 +{ + typedef double lane_type; + enum { nlanes = 2 }; + + v_float64x2() {} + explicit v_float64x2(float64x2_t v) : val(v) {} + v_float64x2(double v0, double v1) + { + double v[] = {v0, v1}; + val = vld1q_f64(v); + } + double get0() const + { + return vgetq_lane_f64(val, 0); + } + float64x2_t val; +}; +#endif + +#if CV_FP16 +// Workaround for old compilers +static inline int16x4_t vreinterpret_s16_f16(float16x4_t a) { return (int16x4_t)a; } +static inline float16x4_t vreinterpret_f16_s16(int16x4_t a) { return (float16x4_t)a; } + +static inline float16x4_t cv_vld1_f16(const void* ptr) +{ +#ifndef vld1_f16 // APPLE compiler defines vld1_f16 as macro + return vreinterpret_f16_s16(vld1_s16((const short*)ptr)); +#else + return vld1_f16((const __fp16*)ptr); +#endif +} +static inline void cv_vst1_f16(void* ptr, float16x4_t a) +{ +#ifndef vst1_f16 // APPLE compiler defines vst1_f16 as macro + vst1_s16((short*)ptr, vreinterpret_s16_f16(a)); +#else + vst1_f16((__fp16*)ptr, a); +#endif +} + +#ifndef vdup_n_f16 + #define vdup_n_f16(v) (float16x4_t){v, v, v, v} +#endif + +#endif // CV_FP16 + +#if CV_FP16 +inline v_float32x4 v128_load_fp16_f32(const short* ptr) +{ + float16x4_t a = cv_vld1_f16((const __fp16*)ptr); + return v_float32x4(vcvt_f32_f16(a)); +} + +inline void v_store_fp16(short* ptr, const v_float32x4& a) +{ + float16x4_t fp16 = vcvt_f16_f32(a.val); + cv_vst1_f16((short*)ptr, fp16); +} +#endif + +#define OPENCV_HAL_IMPL_NEON_INIT(_Tpv, _Tp, suffix) \ +inline v_##_Tpv v_setzero_##suffix() { return v_##_Tpv(vdupq_n_##suffix((_Tp)0)); } \ +inline v_##_Tpv v_setall_##suffix(_Tp v) { return v_##_Tpv(vdupq_n_##suffix(v)); } \ +inline _Tpv##_t vreinterpretq_##suffix##_##suffix(_Tpv##_t v) { return v; } \ +inline v_uint8x16 v_reinterpret_as_u8(const v_##_Tpv& v) { return v_uint8x16(vreinterpretq_u8_##suffix(v.val)); } \ +inline v_int8x16 v_reinterpret_as_s8(const v_##_Tpv& v) { return v_int8x16(vreinterpretq_s8_##suffix(v.val)); } \ +inline v_uint16x8 v_reinterpret_as_u16(const v_##_Tpv& v) { return v_uint16x8(vreinterpretq_u16_##suffix(v.val)); } \ +inline v_int16x8 v_reinterpret_as_s16(const v_##_Tpv& v) { return v_int16x8(vreinterpretq_s16_##suffix(v.val)); } \ +inline v_uint32x4 v_reinterpret_as_u32(const v_##_Tpv& v) { return v_uint32x4(vreinterpretq_u32_##suffix(v.val)); } \ +inline v_int32x4 v_reinterpret_as_s32(const v_##_Tpv& v) { return v_int32x4(vreinterpretq_s32_##suffix(v.val)); } \ +inline v_uint64x2 v_reinterpret_as_u64(const v_##_Tpv& v) { return v_uint64x2(vreinterpretq_u64_##suffix(v.val)); } \ +inline v_int64x2 v_reinterpret_as_s64(const v_##_Tpv& v) { return v_int64x2(vreinterpretq_s64_##suffix(v.val)); } \ +inline v_float32x4 v_reinterpret_as_f32(const v_##_Tpv& v) { return v_float32x4(vreinterpretq_f32_##suffix(v.val)); } + +OPENCV_HAL_IMPL_NEON_INIT(uint8x16, uchar, u8) +OPENCV_HAL_IMPL_NEON_INIT(int8x16, schar, s8) +OPENCV_HAL_IMPL_NEON_INIT(uint16x8, ushort, u16) +OPENCV_HAL_IMPL_NEON_INIT(int16x8, short, s16) +OPENCV_HAL_IMPL_NEON_INIT(uint32x4, unsigned, u32) +OPENCV_HAL_IMPL_NEON_INIT(int32x4, int, s32) +OPENCV_HAL_IMPL_NEON_INIT(uint64x2, uint64, u64) +OPENCV_HAL_IMPL_NEON_INIT(int64x2, int64, s64) +OPENCV_HAL_IMPL_NEON_INIT(float32x4, float, f32) +#if CV_SIMD128_64F +#define OPENCV_HAL_IMPL_NEON_INIT_64(_Tpv, suffix) \ +inline v_float64x2 v_reinterpret_as_f64(const v_##_Tpv& v) { return v_float64x2(vreinterpretq_f64_##suffix(v.val)); } +OPENCV_HAL_IMPL_NEON_INIT(float64x2, double, f64) +OPENCV_HAL_IMPL_NEON_INIT_64(uint8x16, u8) +OPENCV_HAL_IMPL_NEON_INIT_64(int8x16, s8) +OPENCV_HAL_IMPL_NEON_INIT_64(uint16x8, u16) +OPENCV_HAL_IMPL_NEON_INIT_64(int16x8, s16) +OPENCV_HAL_IMPL_NEON_INIT_64(uint32x4, u32) +OPENCV_HAL_IMPL_NEON_INIT_64(int32x4, s32) +OPENCV_HAL_IMPL_NEON_INIT_64(uint64x2, u64) +OPENCV_HAL_IMPL_NEON_INIT_64(int64x2, s64) +OPENCV_HAL_IMPL_NEON_INIT_64(float32x4, f32) +OPENCV_HAL_IMPL_NEON_INIT_64(float64x2, f64) +#endif + +#define OPENCV_HAL_IMPL_NEON_PACK(_Tpvec, _Tp, hreg, suffix, _Tpwvec, pack, mov, rshr) \ +inline _Tpvec v_##pack(const _Tpwvec& a, const _Tpwvec& b) \ +{ \ + hreg a1 = mov(a.val), b1 = mov(b.val); \ + return _Tpvec(vcombine_##suffix(a1, b1)); \ +} \ +inline void v_##pack##_store(_Tp* ptr, const _Tpwvec& a) \ +{ \ + hreg a1 = mov(a.val); \ + vst1_##suffix(ptr, a1); \ +} \ +template inline \ +_Tpvec v_rshr_##pack(const _Tpwvec& a, const _Tpwvec& b) \ +{ \ + hreg a1 = rshr(a.val, n); \ + hreg b1 = rshr(b.val, n); \ + return _Tpvec(vcombine_##suffix(a1, b1)); \ +} \ +template inline \ +void v_rshr_##pack##_store(_Tp* ptr, const _Tpwvec& a) \ +{ \ + hreg a1 = rshr(a.val, n); \ + vst1_##suffix(ptr, a1); \ +} + +OPENCV_HAL_IMPL_NEON_PACK(v_uint8x16, uchar, uint8x8_t, u8, v_uint16x8, pack, vqmovn_u16, vqrshrn_n_u16) +OPENCV_HAL_IMPL_NEON_PACK(v_int8x16, schar, int8x8_t, s8, v_int16x8, pack, vqmovn_s16, vqrshrn_n_s16) +OPENCV_HAL_IMPL_NEON_PACK(v_uint16x8, ushort, uint16x4_t, u16, v_uint32x4, pack, vqmovn_u32, vqrshrn_n_u32) +OPENCV_HAL_IMPL_NEON_PACK(v_int16x8, short, int16x4_t, s16, v_int32x4, pack, vqmovn_s32, vqrshrn_n_s32) +OPENCV_HAL_IMPL_NEON_PACK(v_uint32x4, unsigned, uint32x2_t, u32, v_uint64x2, pack, vmovn_u64, vrshrn_n_u64) +OPENCV_HAL_IMPL_NEON_PACK(v_int32x4, int, int32x2_t, s32, v_int64x2, pack, vmovn_s64, vrshrn_n_s64) + +OPENCV_HAL_IMPL_NEON_PACK(v_uint8x16, uchar, uint8x8_t, u8, v_int16x8, pack_u, vqmovun_s16, vqrshrun_n_s16) +OPENCV_HAL_IMPL_NEON_PACK(v_uint16x8, ushort, uint16x4_t, u16, v_int32x4, pack_u, vqmovun_s32, vqrshrun_n_s32) + +// pack boolean +inline v_uint8x16 v_pack_b(const v_uint16x8& a, const v_uint16x8& b) +{ + uint8x16_t ab = vcombine_u8(vmovn_u16(a.val), vmovn_u16(b.val)); + return v_uint8x16(ab); +} + +inline v_uint8x16 v_pack_b(const v_uint32x4& a, const v_uint32x4& b, + const v_uint32x4& c, const v_uint32x4& d) +{ + uint16x8_t nab = vcombine_u16(vmovn_u32(a.val), vmovn_u32(b.val)); + uint16x8_t ncd = vcombine_u16(vmovn_u32(c.val), vmovn_u32(d.val)); + return v_uint8x16(vcombine_u8(vmovn_u16(nab), vmovn_u16(ncd))); +} + +inline v_uint8x16 v_pack_b(const v_uint64x2& a, const v_uint64x2& b, const v_uint64x2& c, + const v_uint64x2& d, const v_uint64x2& e, const v_uint64x2& f, + const v_uint64x2& g, const v_uint64x2& h) +{ + uint32x4_t ab = vcombine_u32(vmovn_u64(a.val), vmovn_u64(b.val)); + uint32x4_t cd = vcombine_u32(vmovn_u64(c.val), vmovn_u64(d.val)); + uint32x4_t ef = vcombine_u32(vmovn_u64(e.val), vmovn_u64(f.val)); + uint32x4_t gh = vcombine_u32(vmovn_u64(g.val), vmovn_u64(h.val)); + + uint16x8_t abcd = vcombine_u16(vmovn_u32(ab), vmovn_u32(cd)); + uint16x8_t efgh = vcombine_u16(vmovn_u32(ef), vmovn_u32(gh)); + return v_uint8x16(vcombine_u8(vmovn_u16(abcd), vmovn_u16(efgh))); +} + +inline v_float32x4 v_matmul(const v_float32x4& v, const v_float32x4& m0, + const v_float32x4& m1, const v_float32x4& m2, + const v_float32x4& m3) +{ + float32x2_t vl = vget_low_f32(v.val), vh = vget_high_f32(v.val); + float32x4_t res = vmulq_lane_f32(m0.val, vl, 0); + res = vmlaq_lane_f32(res, m1.val, vl, 1); + res = vmlaq_lane_f32(res, m2.val, vh, 0); + res = vmlaq_lane_f32(res, m3.val, vh, 1); + return v_float32x4(res); +} + +inline v_float32x4 v_matmuladd(const v_float32x4& v, const v_float32x4& m0, + const v_float32x4& m1, const v_float32x4& m2, + const v_float32x4& a) +{ + float32x2_t vl = vget_low_f32(v.val), vh = vget_high_f32(v.val); + float32x4_t res = vmulq_lane_f32(m0.val, vl, 0); + res = vmlaq_lane_f32(res, m1.val, vl, 1); + res = vmlaq_lane_f32(res, m2.val, vh, 0); + res = vaddq_f32(res, a.val); + return v_float32x4(res); +} + +#define OPENCV_HAL_IMPL_NEON_BIN_OP(bin_op, _Tpvec, intrin) \ +inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b) \ +{ \ + return _Tpvec(intrin(a.val, b.val)); \ +} \ +inline _Tpvec& operator bin_op##= (_Tpvec& a, const _Tpvec& b) \ +{ \ + a.val = intrin(a.val, b.val); \ + return a; \ +} + +OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_uint8x16, vqaddq_u8) +OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_uint8x16, vqsubq_u8) +OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_int8x16, vqaddq_s8) +OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_int8x16, vqsubq_s8) +OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_uint16x8, vqaddq_u16) +OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_uint16x8, vqsubq_u16) +OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_int16x8, vqaddq_s16) +OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_int16x8, vqsubq_s16) +OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_int32x4, vaddq_s32) +OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_int32x4, vsubq_s32) +OPENCV_HAL_IMPL_NEON_BIN_OP(*, v_int32x4, vmulq_s32) +OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_uint32x4, vaddq_u32) +OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_uint32x4, vsubq_u32) +OPENCV_HAL_IMPL_NEON_BIN_OP(*, v_uint32x4, vmulq_u32) +OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_float32x4, vaddq_f32) +OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_float32x4, vsubq_f32) +OPENCV_HAL_IMPL_NEON_BIN_OP(*, v_float32x4, vmulq_f32) +OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_int64x2, vaddq_s64) +OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_int64x2, vsubq_s64) +OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_uint64x2, vaddq_u64) +OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_uint64x2, vsubq_u64) +#if CV_SIMD128_64F +OPENCV_HAL_IMPL_NEON_BIN_OP(/, v_float32x4, vdivq_f32) +OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_float64x2, vaddq_f64) +OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_float64x2, vsubq_f64) +OPENCV_HAL_IMPL_NEON_BIN_OP(*, v_float64x2, vmulq_f64) +OPENCV_HAL_IMPL_NEON_BIN_OP(/, v_float64x2, vdivq_f64) +#else +inline v_float32x4 operator / (const v_float32x4& a, const v_float32x4& b) +{ + float32x4_t reciprocal = vrecpeq_f32(b.val); + reciprocal = vmulq_f32(vrecpsq_f32(b.val, reciprocal), reciprocal); + reciprocal = vmulq_f32(vrecpsq_f32(b.val, reciprocal), reciprocal); + return v_float32x4(vmulq_f32(a.val, reciprocal)); +} +inline v_float32x4& operator /= (v_float32x4& a, const v_float32x4& b) +{ + float32x4_t reciprocal = vrecpeq_f32(b.val); + reciprocal = vmulq_f32(vrecpsq_f32(b.val, reciprocal), reciprocal); + reciprocal = vmulq_f32(vrecpsq_f32(b.val, reciprocal), reciprocal); + a.val = vmulq_f32(a.val, reciprocal); + return a; +} +#endif + +// saturating multiply 8-bit, 16-bit +#define OPENCV_HAL_IMPL_NEON_MUL_SAT(_Tpvec, _Tpwvec) \ + inline _Tpvec operator * (const _Tpvec& a, const _Tpvec& b) \ + { \ + _Tpwvec c, d; \ + v_mul_expand(a, b, c, d); \ + return v_pack(c, d); \ + } \ + inline _Tpvec& operator *= (_Tpvec& a, const _Tpvec& b) \ + { a = a * b; return a; } + +OPENCV_HAL_IMPL_NEON_MUL_SAT(v_int8x16, v_int16x8) +OPENCV_HAL_IMPL_NEON_MUL_SAT(v_uint8x16, v_uint16x8) +OPENCV_HAL_IMPL_NEON_MUL_SAT(v_int16x8, v_int32x4) +OPENCV_HAL_IMPL_NEON_MUL_SAT(v_uint16x8, v_uint32x4) + +// Multiply and expand +inline void v_mul_expand(const v_int8x16& a, const v_int8x16& b, + v_int16x8& c, v_int16x8& d) +{ + c.val = vmull_s8(vget_low_s8(a.val), vget_low_s8(b.val)); + d.val = vmull_s8(vget_high_s8(a.val), vget_high_s8(b.val)); +} + +inline void v_mul_expand(const v_uint8x16& a, const v_uint8x16& b, + v_uint16x8& c, v_uint16x8& d) +{ + c.val = vmull_u8(vget_low_u8(a.val), vget_low_u8(b.val)); + d.val = vmull_u8(vget_high_u8(a.val), vget_high_u8(b.val)); +} + +inline void v_mul_expand(const v_int16x8& a, const v_int16x8& b, + v_int32x4& c, v_int32x4& d) +{ + c.val = vmull_s16(vget_low_s16(a.val), vget_low_s16(b.val)); + d.val = vmull_s16(vget_high_s16(a.val), vget_high_s16(b.val)); +} + +inline void v_mul_expand(const v_uint16x8& a, const v_uint16x8& b, + v_uint32x4& c, v_uint32x4& d) +{ + c.val = vmull_u16(vget_low_u16(a.val), vget_low_u16(b.val)); + d.val = vmull_u16(vget_high_u16(a.val), vget_high_u16(b.val)); +} + +inline void v_mul_expand(const v_uint32x4& a, const v_uint32x4& b, + v_uint64x2& c, v_uint64x2& d) +{ + c.val = vmull_u32(vget_low_u32(a.val), vget_low_u32(b.val)); + d.val = vmull_u32(vget_high_u32(a.val), vget_high_u32(b.val)); +} + +inline v_int16x8 v_mul_hi(const v_int16x8& a, const v_int16x8& b) +{ + return v_int16x8(vcombine_s16( + vshrn_n_s32(vmull_s16( vget_low_s16(a.val), vget_low_s16(b.val)), 16), + vshrn_n_s32(vmull_s16(vget_high_s16(a.val), vget_high_s16(b.val)), 16) + )); +} +inline v_uint16x8 v_mul_hi(const v_uint16x8& a, const v_uint16x8& b) +{ + return v_uint16x8(vcombine_u16( + vshrn_n_u32(vmull_u16( vget_low_u16(a.val), vget_low_u16(b.val)), 16), + vshrn_n_u32(vmull_u16(vget_high_u16(a.val), vget_high_u16(b.val)), 16) + )); +} + +inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b) +{ + int32x4_t c = vmull_s16(vget_low_s16(a.val), vget_low_s16(b.val)); + int32x4_t d = vmull_s16(vget_high_s16(a.val), vget_high_s16(b.val)); + int32x4x2_t cd = vuzpq_s32(c, d); + return v_int32x4(vaddq_s32(cd.val[0], cd.val[1])); +} + +inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b, const v_int32x4& c) +{ + v_int32x4 s = v_dotprod(a, b); + return v_int32x4(vaddq_s32(s.val , c.val)); +} + +#define OPENCV_HAL_IMPL_NEON_LOGIC_OP(_Tpvec, suffix) \ + OPENCV_HAL_IMPL_NEON_BIN_OP(&, _Tpvec, vandq_##suffix) \ + OPENCV_HAL_IMPL_NEON_BIN_OP(|, _Tpvec, vorrq_##suffix) \ + OPENCV_HAL_IMPL_NEON_BIN_OP(^, _Tpvec, veorq_##suffix) \ + inline _Tpvec operator ~ (const _Tpvec& a) \ + { \ + return _Tpvec(vreinterpretq_##suffix##_u8(vmvnq_u8(vreinterpretq_u8_##suffix(a.val)))); \ + } + +OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_uint8x16, u8) +OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_int8x16, s8) +OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_uint16x8, u16) +OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_int16x8, s16) +OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_uint32x4, u32) +OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_int32x4, s32) +OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_uint64x2, u64) +OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_int64x2, s64) + +#define OPENCV_HAL_IMPL_NEON_FLT_BIT_OP(bin_op, intrin) \ +inline v_float32x4 operator bin_op (const v_float32x4& a, const v_float32x4& b) \ +{ \ + return v_float32x4(vreinterpretq_f32_s32(intrin(vreinterpretq_s32_f32(a.val), vreinterpretq_s32_f32(b.val)))); \ +} \ +inline v_float32x4& operator bin_op##= (v_float32x4& a, const v_float32x4& b) \ +{ \ + a.val = vreinterpretq_f32_s32(intrin(vreinterpretq_s32_f32(a.val), vreinterpretq_s32_f32(b.val))); \ + return a; \ +} + +OPENCV_HAL_IMPL_NEON_FLT_BIT_OP(&, vandq_s32) +OPENCV_HAL_IMPL_NEON_FLT_BIT_OP(|, vorrq_s32) +OPENCV_HAL_IMPL_NEON_FLT_BIT_OP(^, veorq_s32) + +inline v_float32x4 operator ~ (const v_float32x4& a) +{ + return v_float32x4(vreinterpretq_f32_s32(vmvnq_s32(vreinterpretq_s32_f32(a.val)))); +} + +#if CV_SIMD128_64F +inline v_float32x4 v_sqrt(const v_float32x4& x) +{ + return v_float32x4(vsqrtq_f32(x.val)); +} + +inline v_float32x4 v_invsqrt(const v_float32x4& x) +{ + v_float32x4 one = v_setall_f32(1.0f); + return one / v_sqrt(x); +} +#else +inline v_float32x4 v_sqrt(const v_float32x4& x) +{ + float32x4_t x1 = vmaxq_f32(x.val, vdupq_n_f32(FLT_MIN)); + float32x4_t e = vrsqrteq_f32(x1); + e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(x1, e), e), e); + e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(x1, e), e), e); + return v_float32x4(vmulq_f32(x.val, e)); +} + +inline v_float32x4 v_invsqrt(const v_float32x4& x) +{ + float32x4_t e = vrsqrteq_f32(x.val); + e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(x.val, e), e), e); + e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(x.val, e), e), e); + return v_float32x4(e); +} +#endif + +#define OPENCV_HAL_IMPL_NEON_ABS(_Tpuvec, _Tpsvec, usuffix, ssuffix) \ +inline _Tpuvec v_abs(const _Tpsvec& a) { return v_reinterpret_as_##usuffix(_Tpsvec(vabsq_##ssuffix(a.val))); } + +OPENCV_HAL_IMPL_NEON_ABS(v_uint8x16, v_int8x16, u8, s8) +OPENCV_HAL_IMPL_NEON_ABS(v_uint16x8, v_int16x8, u16, s16) +OPENCV_HAL_IMPL_NEON_ABS(v_uint32x4, v_int32x4, u32, s32) + +inline v_float32x4 v_abs(v_float32x4 x) +{ return v_float32x4(vabsq_f32(x.val)); } + +#if CV_SIMD128_64F +#define OPENCV_HAL_IMPL_NEON_DBL_BIT_OP(bin_op, intrin) \ +inline v_float64x2 operator bin_op (const v_float64x2& a, const v_float64x2& b) \ +{ \ + return v_float64x2(vreinterpretq_f64_s64(intrin(vreinterpretq_s64_f64(a.val), vreinterpretq_s64_f64(b.val)))); \ +} \ +inline v_float64x2& operator bin_op##= (v_float64x2& a, const v_float64x2& b) \ +{ \ + a.val = vreinterpretq_f64_s64(intrin(vreinterpretq_s64_f64(a.val), vreinterpretq_s64_f64(b.val))); \ + return a; \ +} + +OPENCV_HAL_IMPL_NEON_DBL_BIT_OP(&, vandq_s64) +OPENCV_HAL_IMPL_NEON_DBL_BIT_OP(|, vorrq_s64) +OPENCV_HAL_IMPL_NEON_DBL_BIT_OP(^, veorq_s64) + +inline v_float64x2 operator ~ (const v_float64x2& a) +{ + return v_float64x2(vreinterpretq_f64_s32(vmvnq_s32(vreinterpretq_s32_f64(a.val)))); +} + +inline v_float64x2 v_sqrt(const v_float64x2& x) +{ + return v_float64x2(vsqrtq_f64(x.val)); +} + +inline v_float64x2 v_invsqrt(const v_float64x2& x) +{ + v_float64x2 one = v_setall_f64(1.0f); + return one / v_sqrt(x); +} + +inline v_float64x2 v_abs(v_float64x2 x) +{ return v_float64x2(vabsq_f64(x.val)); } +#endif + +// TODO: exp, log, sin, cos + +#define OPENCV_HAL_IMPL_NEON_BIN_FUNC(_Tpvec, func, intrin) \ +inline _Tpvec func(const _Tpvec& a, const _Tpvec& b) \ +{ \ + return _Tpvec(intrin(a.val, b.val)); \ +} + +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_min, vminq_u8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_max, vmaxq_u8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int8x16, v_min, vminq_s8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int8x16, v_max, vmaxq_s8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_min, vminq_u16) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_max, vmaxq_u16) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int16x8, v_min, vminq_s16) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int16x8, v_max, vmaxq_s16) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint32x4, v_min, vminq_u32) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint32x4, v_max, vmaxq_u32) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int32x4, v_min, vminq_s32) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int32x4, v_max, vmaxq_s32) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_float32x4, v_min, vminq_f32) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_float32x4, v_max, vmaxq_f32) +#if CV_SIMD128_64F +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_float64x2, v_min, vminq_f64) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_float64x2, v_max, vmaxq_f64) +#endif + +#if CV_SIMD128_64F +inline int64x2_t vmvnq_s64(int64x2_t a) +{ + int64x2_t vx = vreinterpretq_s64_u32(vdupq_n_u32(0xFFFFFFFF)); + return veorq_s64(a, vx); +} +inline uint64x2_t vmvnq_u64(uint64x2_t a) +{ + uint64x2_t vx = vreinterpretq_u64_u32(vdupq_n_u32(0xFFFFFFFF)); + return veorq_u64(a, vx); +} +#endif +#define OPENCV_HAL_IMPL_NEON_INT_CMP_OP(_Tpvec, cast, suffix, not_suffix) \ +inline _Tpvec operator == (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(cast(vceqq_##suffix(a.val, b.val))); } \ +inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(cast(vmvnq_##not_suffix(vceqq_##suffix(a.val, b.val)))); } \ +inline _Tpvec operator < (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(cast(vcltq_##suffix(a.val, b.val))); } \ +inline _Tpvec operator > (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(cast(vcgtq_##suffix(a.val, b.val))); } \ +inline _Tpvec operator <= (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(cast(vcleq_##suffix(a.val, b.val))); } \ +inline _Tpvec operator >= (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(cast(vcgeq_##suffix(a.val, b.val))); } + +OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_uint8x16, OPENCV_HAL_NOP, u8, u8) +OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_int8x16, vreinterpretq_s8_u8, s8, u8) +OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_uint16x8, OPENCV_HAL_NOP, u16, u16) +OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_int16x8, vreinterpretq_s16_u16, s16, u16) +OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_uint32x4, OPENCV_HAL_NOP, u32, u32) +OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_int32x4, vreinterpretq_s32_u32, s32, u32) +OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_float32x4, vreinterpretq_f32_u32, f32, u32) +#if CV_SIMD128_64F +OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_uint64x2, OPENCV_HAL_NOP, u64, u64) +OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_int64x2, vreinterpretq_s64_u64, s64, u64) +OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_float64x2, vreinterpretq_f64_u64, f64, u64) +#endif + +inline v_float32x4 v_not_nan(const v_float32x4& a) +{ return v_float32x4(vreinterpretq_f32_u32(vceqq_f32(a.val, a.val))); } +#if CV_SIMD128_64F +inline v_float64x2 v_not_nan(const v_float64x2& a) +{ return v_float64x2(vreinterpretq_f64_u64(vceqq_f64(a.val, a.val))); } +#endif + +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_add_wrap, vaddq_u8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int8x16, v_add_wrap, vaddq_s8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_add_wrap, vaddq_u16) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int16x8, v_add_wrap, vaddq_s16) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_sub_wrap, vsubq_u8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int8x16, v_sub_wrap, vsubq_s8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_sub_wrap, vsubq_u16) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int16x8, v_sub_wrap, vsubq_s16) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_mul_wrap, vmulq_u8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int8x16, v_mul_wrap, vmulq_s8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_mul_wrap, vmulq_u16) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int16x8, v_mul_wrap, vmulq_s16) + +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_absdiff, vabdq_u8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_absdiff, vabdq_u16) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint32x4, v_absdiff, vabdq_u32) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_float32x4, v_absdiff, vabdq_f32) +#if CV_SIMD128_64F +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_float64x2, v_absdiff, vabdq_f64) +#endif + +/** Saturating absolute difference **/ +inline v_int8x16 v_absdiffs(const v_int8x16& a, const v_int8x16& b) +{ return v_int8x16(vqabsq_s8(vqsubq_s8(a.val, b.val))); } +inline v_int16x8 v_absdiffs(const v_int16x8& a, const v_int16x8& b) +{ return v_int16x8(vqabsq_s16(vqsubq_s16(a.val, b.val))); } + +#define OPENCV_HAL_IMPL_NEON_BIN_FUNC2(_Tpvec, _Tpvec2, cast, func, intrin) \ +inline _Tpvec2 func(const _Tpvec& a, const _Tpvec& b) \ +{ \ + return _Tpvec2(cast(intrin(a.val, b.val))); \ +} + +OPENCV_HAL_IMPL_NEON_BIN_FUNC2(v_int8x16, v_uint8x16, vreinterpretq_u8_s8, v_absdiff, vabdq_s8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC2(v_int16x8, v_uint16x8, vreinterpretq_u16_s16, v_absdiff, vabdq_s16) +OPENCV_HAL_IMPL_NEON_BIN_FUNC2(v_int32x4, v_uint32x4, vreinterpretq_u32_s32, v_absdiff, vabdq_s32) + +inline v_float32x4 v_magnitude(const v_float32x4& a, const v_float32x4& b) +{ + v_float32x4 x(vmlaq_f32(vmulq_f32(a.val, a.val), b.val, b.val)); + return v_sqrt(x); +} + +inline v_float32x4 v_sqr_magnitude(const v_float32x4& a, const v_float32x4& b) +{ + return v_float32x4(vmlaq_f32(vmulq_f32(a.val, a.val), b.val, b.val)); +} + +inline v_float32x4 v_fma(const v_float32x4& a, const v_float32x4& b, const v_float32x4& c) +{ +#if CV_SIMD128_64F + // ARMv8, which adds support for 64-bit floating-point (so CV_SIMD128_64F is defined), + // also adds FMA support both for single- and double-precision floating-point vectors + return v_float32x4(vfmaq_f32(c.val, a.val, b.val)); +#else + return v_float32x4(vmlaq_f32(c.val, a.val, b.val)); +#endif +} + +inline v_int32x4 v_fma(const v_int32x4& a, const v_int32x4& b, const v_int32x4& c) +{ + return v_int32x4(vmlaq_s32(c.val, a.val, b.val)); +} + +inline v_float32x4 v_muladd(const v_float32x4& a, const v_float32x4& b, const v_float32x4& c) +{ + return v_fma(a, b, c); +} + +inline v_int32x4 v_muladd(const v_int32x4& a, const v_int32x4& b, const v_int32x4& c) +{ + return v_fma(a, b, c); +} + +#if CV_SIMD128_64F +inline v_float64x2 v_magnitude(const v_float64x2& a, const v_float64x2& b) +{ + v_float64x2 x(vaddq_f64(vmulq_f64(a.val, a.val), vmulq_f64(b.val, b.val))); + return v_sqrt(x); +} + +inline v_float64x2 v_sqr_magnitude(const v_float64x2& a, const v_float64x2& b) +{ + return v_float64x2(vaddq_f64(vmulq_f64(a.val, a.val), vmulq_f64(b.val, b.val))); +} + +inline v_float64x2 v_fma(const v_float64x2& a, const v_float64x2& b, const v_float64x2& c) +{ + return v_float64x2(vfmaq_f64(c.val, a.val, b.val)); +} + +inline v_float64x2 v_muladd(const v_float64x2& a, const v_float64x2& b, const v_float64x2& c) +{ + return v_fma(a, b, c); +} +#endif + +// trade efficiency for convenience +#define OPENCV_HAL_IMPL_NEON_SHIFT_OP(_Tpvec, suffix, _Tps, ssuffix) \ +inline _Tpvec operator << (const _Tpvec& a, int n) \ +{ return _Tpvec(vshlq_##suffix(a.val, vdupq_n_##ssuffix((_Tps)n))); } \ +inline _Tpvec operator >> (const _Tpvec& a, int n) \ +{ return _Tpvec(vshlq_##suffix(a.val, vdupq_n_##ssuffix((_Tps)-n))); } \ +template inline _Tpvec v_shl(const _Tpvec& a) \ +{ return _Tpvec(vshlq_n_##suffix(a.val, n)); } \ +template inline _Tpvec v_shr(const _Tpvec& a) \ +{ return _Tpvec(vshrq_n_##suffix(a.val, n)); } \ +template inline _Tpvec v_rshr(const _Tpvec& a) \ +{ return _Tpvec(vrshrq_n_##suffix(a.val, n)); } + +OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_uint8x16, u8, schar, s8) +OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_int8x16, s8, schar, s8) +OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_uint16x8, u16, short, s16) +OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_int16x8, s16, short, s16) +OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_uint32x4, u32, int, s32) +OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_int32x4, s32, int, s32) +OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_uint64x2, u64, int64, s64) +OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_int64x2, s64, int64, s64) + +#define OPENCV_HAL_IMPL_NEON_ROTATE_OP(_Tpvec, suffix) \ +template inline _Tpvec v_rotate_right(const _Tpvec& a) \ +{ return _Tpvec(vextq_##suffix(a.val, vdupq_n_##suffix(0), n)); } \ +template inline _Tpvec v_rotate_left(const _Tpvec& a) \ +{ return _Tpvec(vextq_##suffix(vdupq_n_##suffix(0), a.val, _Tpvec::nlanes - n)); } \ +template<> inline _Tpvec v_rotate_left<0>(const _Tpvec& a) \ +{ return a; } \ +template inline _Tpvec v_rotate_right(const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(vextq_##suffix(a.val, b.val, n)); } \ +template inline _Tpvec v_rotate_left(const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(vextq_##suffix(b.val, a.val, _Tpvec::nlanes - n)); } \ +template<> inline _Tpvec v_rotate_left<0>(const _Tpvec& a, const _Tpvec& b) \ +{ CV_UNUSED(b); return a; } + +OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_uint8x16, u8) +OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_int8x16, s8) +OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_uint16x8, u16) +OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_int16x8, s16) +OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_uint32x4, u32) +OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_int32x4, s32) +OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_float32x4, f32) +OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_uint64x2, u64) +OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_int64x2, s64) +#if CV_SIMD128_64F +OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_float64x2, f64) +#endif + +#define OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(_Tpvec, _Tp, suffix) \ +inline _Tpvec v_load(const _Tp* ptr) \ +{ return _Tpvec(vld1q_##suffix(ptr)); } \ +inline _Tpvec v_load_aligned(const _Tp* ptr) \ +{ return _Tpvec(vld1q_##suffix(ptr)); } \ +inline _Tpvec v_load_low(const _Tp* ptr) \ +{ return _Tpvec(vcombine_##suffix(vld1_##suffix(ptr), vdup_n_##suffix((_Tp)0))); } \ +inline _Tpvec v_load_halves(const _Tp* ptr0, const _Tp* ptr1) \ +{ return _Tpvec(vcombine_##suffix(vld1_##suffix(ptr0), vld1_##suffix(ptr1))); } \ +inline void v_store(_Tp* ptr, const _Tpvec& a) \ +{ vst1q_##suffix(ptr, a.val); } \ +inline void v_store_aligned(_Tp* ptr, const _Tpvec& a) \ +{ vst1q_##suffix(ptr, a.val); } \ +inline void v_store_aligned_nocache(_Tp* ptr, const _Tpvec& a) \ +{ vst1q_##suffix(ptr, a.val); } \ +inline void v_store(_Tp* ptr, const _Tpvec& a, hal::StoreMode /*mode*/) \ +{ vst1q_##suffix(ptr, a.val); } \ +inline void v_store_low(_Tp* ptr, const _Tpvec& a) \ +{ vst1_##suffix(ptr, vget_low_##suffix(a.val)); } \ +inline void v_store_high(_Tp* ptr, const _Tpvec& a) \ +{ vst1_##suffix(ptr, vget_high_##suffix(a.val)); } + +OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_uint8x16, uchar, u8) +OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_int8x16, schar, s8) +OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_uint16x8, ushort, u16) +OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_int16x8, short, s16) +OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_uint32x4, unsigned, u32) +OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_int32x4, int, s32) +OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_uint64x2, uint64, u64) +OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_int64x2, int64, s64) +OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_float32x4, float, f32) +#if CV_SIMD128_64F +OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_float64x2, double, f64) +#endif + +#define OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(_Tpvec, _Tpnvec, scalartype, func, vectorfunc, suffix) \ +inline scalartype v_reduce_##func(const _Tpvec& a) \ +{ \ + _Tpnvec##_t a0 = vp##vectorfunc##_##suffix(vget_low_##suffix(a.val), vget_high_##suffix(a.val)); \ + a0 = vp##vectorfunc##_##suffix(a0, a0); \ + return (scalartype)vget_lane_##suffix(vp##vectorfunc##_##suffix(a0, a0),0); \ +} + +OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(v_uint16x8, uint16x4, unsigned short, sum, add, u16) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(v_uint16x8, uint16x4, unsigned short, max, max, u16) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(v_uint16x8, uint16x4, unsigned short, min, min, u16) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(v_int16x8, int16x4, short, sum, add, s16) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(v_int16x8, int16x4, short, max, max, s16) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(v_int16x8, int16x4, short, min, min, s16) + +#define OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(_Tpvec, _Tpnvec, scalartype, func, vectorfunc, suffix) \ +inline scalartype v_reduce_##func(const _Tpvec& a) \ +{ \ + _Tpnvec##_t a0 = vp##vectorfunc##_##suffix(vget_low_##suffix(a.val), vget_high_##suffix(a.val)); \ + return (scalartype)vget_lane_##suffix(vp##vectorfunc##_##suffix(a0, vget_high_##suffix(a.val)),0); \ +} + +OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_uint32x4, uint32x2, unsigned, sum, add, u32) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_uint32x4, uint32x2, unsigned, max, max, u32) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_uint32x4, uint32x2, unsigned, min, min, u32) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_int32x4, int32x2, int, sum, add, s32) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_int32x4, int32x2, int, max, max, s32) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_int32x4, int32x2, int, min, min, s32) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_float32x4, float32x2, float, sum, add, f32) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_float32x4, float32x2, float, max, max, f32) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_float32x4, float32x2, float, min, min, f32) + +#if CV_SIMD128_64F +inline double v_reduce_sum(const v_float64x2& a) +{ + return vgetq_lane_f64(a.val, 0) + vgetq_lane_f64(a.val, 1); +} +#endif + +inline v_float32x4 v_reduce_sum4(const v_float32x4& a, const v_float32x4& b, + const v_float32x4& c, const v_float32x4& d) +{ + float32x4x2_t ab = vtrnq_f32(a.val, b.val); + float32x4x2_t cd = vtrnq_f32(c.val, d.val); + + float32x4_t u0 = vaddq_f32(ab.val[0], ab.val[1]); // a0+a1 b0+b1 a2+a3 b2+b3 + float32x4_t u1 = vaddq_f32(cd.val[0], cd.val[1]); // c0+c1 d0+d1 c2+c3 d2+d3 + + float32x4_t v0 = vcombine_f32(vget_low_f32(u0), vget_low_f32(u1)); + float32x4_t v1 = vcombine_f32(vget_high_f32(u0), vget_high_f32(u1)); + + return v_float32x4(vaddq_f32(v0, v1)); +} + +inline unsigned v_reduce_sad(const v_uint8x16& a, const v_uint8x16& b) +{ + uint32x4_t t0 = vpaddlq_u16(vpaddlq_u8(vabdq_u8(a.val, b.val))); + uint32x2_t t1 = vpadd_u32(vget_low_u32(t0), vget_high_u32(t0)); + return vget_lane_u32(vpadd_u32(t1, t1), 0); +} +inline unsigned v_reduce_sad(const v_int8x16& a, const v_int8x16& b) +{ + uint32x4_t t0 = vpaddlq_u16(vpaddlq_u8(vreinterpretq_u8_s8(vabdq_s8(a.val, b.val)))); + uint32x2_t t1 = vpadd_u32(vget_low_u32(t0), vget_high_u32(t0)); + return vget_lane_u32(vpadd_u32(t1, t1), 0); +} +inline unsigned v_reduce_sad(const v_uint16x8& a, const v_uint16x8& b) +{ + uint32x4_t t0 = vpaddlq_u16(vabdq_u16(a.val, b.val)); + uint32x2_t t1 = vpadd_u32(vget_low_u32(t0), vget_high_u32(t0)); + return vget_lane_u32(vpadd_u32(t1, t1), 0); +} +inline unsigned v_reduce_sad(const v_int16x8& a, const v_int16x8& b) +{ + uint32x4_t t0 = vpaddlq_u16(vreinterpretq_u16_s16(vabdq_s16(a.val, b.val))); + uint32x2_t t1 = vpadd_u32(vget_low_u32(t0), vget_high_u32(t0)); + return vget_lane_u32(vpadd_u32(t1, t1), 0); +} +inline unsigned v_reduce_sad(const v_uint32x4& a, const v_uint32x4& b) +{ + uint32x4_t t0 = vabdq_u32(a.val, b.val); + uint32x2_t t1 = vpadd_u32(vget_low_u32(t0), vget_high_u32(t0)); + return vget_lane_u32(vpadd_u32(t1, t1), 0); +} +inline unsigned v_reduce_sad(const v_int32x4& a, const v_int32x4& b) +{ + uint32x4_t t0 = vreinterpretq_u32_s32(vabdq_s32(a.val, b.val)); + uint32x2_t t1 = vpadd_u32(vget_low_u32(t0), vget_high_u32(t0)); + return vget_lane_u32(vpadd_u32(t1, t1), 0); +} +inline float v_reduce_sad(const v_float32x4& a, const v_float32x4& b) +{ + float32x4_t t0 = vabdq_f32(a.val, b.val); + float32x2_t t1 = vpadd_f32(vget_low_f32(t0), vget_high_f32(t0)); + return vget_lane_f32(vpadd_f32(t1, t1), 0); +} + +#define OPENCV_HAL_IMPL_NEON_POPCOUNT(_Tpvec, cast) \ +inline v_uint32x4 v_popcount(const _Tpvec& a) \ +{ \ + uint8x16_t t = vcntq_u8(cast(a.val)); \ + uint16x8_t t0 = vpaddlq_u8(t); /* 16 -> 8 */ \ + uint32x4_t t1 = vpaddlq_u16(t0); /* 8 -> 4 */ \ + return v_uint32x4(t1); \ +} + +OPENCV_HAL_IMPL_NEON_POPCOUNT(v_uint8x16, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_NEON_POPCOUNT(v_uint16x8, vreinterpretq_u8_u16) +OPENCV_HAL_IMPL_NEON_POPCOUNT(v_uint32x4, vreinterpretq_u8_u32) +OPENCV_HAL_IMPL_NEON_POPCOUNT(v_int8x16, vreinterpretq_u8_s8) +OPENCV_HAL_IMPL_NEON_POPCOUNT(v_int16x8, vreinterpretq_u8_s16) +OPENCV_HAL_IMPL_NEON_POPCOUNT(v_int32x4, vreinterpretq_u8_s32) + +inline int v_signmask(const v_uint8x16& a) +{ + int8x8_t m0 = vcreate_s8(CV_BIG_UINT(0x0706050403020100)); + uint8x16_t v0 = vshlq_u8(vshrq_n_u8(a.val, 7), vcombine_s8(m0, m0)); + uint64x2_t v1 = vpaddlq_u32(vpaddlq_u16(vpaddlq_u8(v0))); + return (int)vgetq_lane_u64(v1, 0) + ((int)vgetq_lane_u64(v1, 1) << 8); +} +inline int v_signmask(const v_int8x16& a) +{ return v_signmask(v_reinterpret_as_u8(a)); } + +inline int v_signmask(const v_uint16x8& a) +{ + int16x4_t m0 = vcreate_s16(CV_BIG_UINT(0x0003000200010000)); + uint16x8_t v0 = vshlq_u16(vshrq_n_u16(a.val, 15), vcombine_s16(m0, m0)); + uint64x2_t v1 = vpaddlq_u32(vpaddlq_u16(v0)); + return (int)vgetq_lane_u64(v1, 0) + ((int)vgetq_lane_u64(v1, 1) << 4); +} +inline int v_signmask(const v_int16x8& a) +{ return v_signmask(v_reinterpret_as_u16(a)); } + +inline int v_signmask(const v_uint32x4& a) +{ + int32x2_t m0 = vcreate_s32(CV_BIG_UINT(0x0000000100000000)); + uint32x4_t v0 = vshlq_u32(vshrq_n_u32(a.val, 31), vcombine_s32(m0, m0)); + uint64x2_t v1 = vpaddlq_u32(v0); + return (int)vgetq_lane_u64(v1, 0) + ((int)vgetq_lane_u64(v1, 1) << 2); +} +inline int v_signmask(const v_int32x4& a) +{ return v_signmask(v_reinterpret_as_u32(a)); } +inline int v_signmask(const v_float32x4& a) +{ return v_signmask(v_reinterpret_as_u32(a)); } +#if CV_SIMD128_64F +inline int v_signmask(const v_uint64x2& a) +{ + int64x1_t m0 = vdup_n_s64(0); + uint64x2_t v0 = vshlq_u64(vshrq_n_u64(a.val, 63), vcombine_s64(m0, m0)); + return (int)vgetq_lane_u64(v0, 0) + ((int)vgetq_lane_u64(v0, 1) << 1); +} +inline int v_signmask(const v_float64x2& a) +{ return v_signmask(v_reinterpret_as_u64(a)); } +#endif + +#define OPENCV_HAL_IMPL_NEON_CHECK_ALLANY(_Tpvec, suffix, shift) \ +inline bool v_check_all(const v_##_Tpvec& a) \ +{ \ + _Tpvec##_t v0 = vshrq_n_##suffix(vmvnq_##suffix(a.val), shift); \ + uint64x2_t v1 = vreinterpretq_u64_##suffix(v0); \ + return (vgetq_lane_u64(v1, 0) | vgetq_lane_u64(v1, 1)) == 0; \ +} \ +inline bool v_check_any(const v_##_Tpvec& a) \ +{ \ + _Tpvec##_t v0 = vshrq_n_##suffix(a.val, shift); \ + uint64x2_t v1 = vreinterpretq_u64_##suffix(v0); \ + return (vgetq_lane_u64(v1, 0) | vgetq_lane_u64(v1, 1)) != 0; \ +} + +OPENCV_HAL_IMPL_NEON_CHECK_ALLANY(uint8x16, u8, 7) +OPENCV_HAL_IMPL_NEON_CHECK_ALLANY(uint16x8, u16, 15) +OPENCV_HAL_IMPL_NEON_CHECK_ALLANY(uint32x4, u32, 31) +#if CV_SIMD128_64F +OPENCV_HAL_IMPL_NEON_CHECK_ALLANY(uint64x2, u64, 63) +#endif + +inline bool v_check_all(const v_int8x16& a) +{ return v_check_all(v_reinterpret_as_u8(a)); } +inline bool v_check_all(const v_int16x8& a) +{ return v_check_all(v_reinterpret_as_u16(a)); } +inline bool v_check_all(const v_int32x4& a) +{ return v_check_all(v_reinterpret_as_u32(a)); } +inline bool v_check_all(const v_float32x4& a) +{ return v_check_all(v_reinterpret_as_u32(a)); } + +inline bool v_check_any(const v_int8x16& a) +{ return v_check_any(v_reinterpret_as_u8(a)); } +inline bool v_check_any(const v_int16x8& a) +{ return v_check_any(v_reinterpret_as_u16(a)); } +inline bool v_check_any(const v_int32x4& a) +{ return v_check_any(v_reinterpret_as_u32(a)); } +inline bool v_check_any(const v_float32x4& a) +{ return v_check_any(v_reinterpret_as_u32(a)); } + +#if CV_SIMD128_64F +inline bool v_check_all(const v_int64x2& a) +{ return v_check_all(v_reinterpret_as_u64(a)); } +inline bool v_check_all(const v_float64x2& a) +{ return v_check_all(v_reinterpret_as_u64(a)); } +inline bool v_check_any(const v_int64x2& a) +{ return v_check_any(v_reinterpret_as_u64(a)); } +inline bool v_check_any(const v_float64x2& a) +{ return v_check_any(v_reinterpret_as_u64(a)); } +#endif + +#define OPENCV_HAL_IMPL_NEON_SELECT(_Tpvec, suffix, usuffix) \ +inline _Tpvec v_select(const _Tpvec& mask, const _Tpvec& a, const _Tpvec& b) \ +{ \ + return _Tpvec(vbslq_##suffix(vreinterpretq_##usuffix##_##suffix(mask.val), a.val, b.val)); \ +} + +OPENCV_HAL_IMPL_NEON_SELECT(v_uint8x16, u8, u8) +OPENCV_HAL_IMPL_NEON_SELECT(v_int8x16, s8, u8) +OPENCV_HAL_IMPL_NEON_SELECT(v_uint16x8, u16, u16) +OPENCV_HAL_IMPL_NEON_SELECT(v_int16x8, s16, u16) +OPENCV_HAL_IMPL_NEON_SELECT(v_uint32x4, u32, u32) +OPENCV_HAL_IMPL_NEON_SELECT(v_int32x4, s32, u32) +OPENCV_HAL_IMPL_NEON_SELECT(v_float32x4, f32, u32) +#if CV_SIMD128_64F +OPENCV_HAL_IMPL_NEON_SELECT(v_float64x2, f64, u64) +#endif + +#define OPENCV_HAL_IMPL_NEON_EXPAND(_Tpvec, _Tpwvec, _Tp, suffix) \ +inline void v_expand(const _Tpvec& a, _Tpwvec& b0, _Tpwvec& b1) \ +{ \ + b0.val = vmovl_##suffix(vget_low_##suffix(a.val)); \ + b1.val = vmovl_##suffix(vget_high_##suffix(a.val)); \ +} \ +inline _Tpwvec v_expand_low(const _Tpvec& a) \ +{ \ + return _Tpwvec(vmovl_##suffix(vget_low_##suffix(a.val))); \ +} \ +inline _Tpwvec v_expand_high(const _Tpvec& a) \ +{ \ + return _Tpwvec(vmovl_##suffix(vget_high_##suffix(a.val))); \ +} \ +inline _Tpwvec v_load_expand(const _Tp* ptr) \ +{ \ + return _Tpwvec(vmovl_##suffix(vld1_##suffix(ptr))); \ +} + +OPENCV_HAL_IMPL_NEON_EXPAND(v_uint8x16, v_uint16x8, uchar, u8) +OPENCV_HAL_IMPL_NEON_EXPAND(v_int8x16, v_int16x8, schar, s8) +OPENCV_HAL_IMPL_NEON_EXPAND(v_uint16x8, v_uint32x4, ushort, u16) +OPENCV_HAL_IMPL_NEON_EXPAND(v_int16x8, v_int32x4, short, s16) +OPENCV_HAL_IMPL_NEON_EXPAND(v_uint32x4, v_uint64x2, uint, u32) +OPENCV_HAL_IMPL_NEON_EXPAND(v_int32x4, v_int64x2, int, s32) + +inline v_uint32x4 v_load_expand_q(const uchar* ptr) +{ + uint8x8_t v0 = vcreate_u8(*(unsigned*)ptr); + uint16x4_t v1 = vget_low_u16(vmovl_u8(v0)); + return v_uint32x4(vmovl_u16(v1)); +} + +inline v_int32x4 v_load_expand_q(const schar* ptr) +{ + int8x8_t v0 = vcreate_s8(*(unsigned*)ptr); + int16x4_t v1 = vget_low_s16(vmovl_s8(v0)); + return v_int32x4(vmovl_s16(v1)); +} + +#if defined(__aarch64__) +#define OPENCV_HAL_IMPL_NEON_UNPACKS(_Tpvec, suffix) \ +inline void v_zip(const v_##_Tpvec& a0, const v_##_Tpvec& a1, v_##_Tpvec& b0, v_##_Tpvec& b1) \ +{ \ + b0.val = vzip1q_##suffix(a0.val, a1.val); \ + b1.val = vzip2q_##suffix(a0.val, a1.val); \ +} \ +inline v_##_Tpvec v_combine_low(const v_##_Tpvec& a, const v_##_Tpvec& b) \ +{ \ + return v_##_Tpvec(vcombine_##suffix(vget_low_##suffix(a.val), vget_low_##suffix(b.val))); \ +} \ +inline v_##_Tpvec v_combine_high(const v_##_Tpvec& a, const v_##_Tpvec& b) \ +{ \ + return v_##_Tpvec(vcombine_##suffix(vget_high_##suffix(a.val), vget_high_##suffix(b.val))); \ +} \ +inline void v_recombine(const v_##_Tpvec& a, const v_##_Tpvec& b, v_##_Tpvec& c, v_##_Tpvec& d) \ +{ \ + c.val = vcombine_##suffix(vget_low_##suffix(a.val), vget_low_##suffix(b.val)); \ + d.val = vcombine_##suffix(vget_high_##suffix(a.val), vget_high_##suffix(b.val)); \ +} +#else +#define OPENCV_HAL_IMPL_NEON_UNPACKS(_Tpvec, suffix) \ +inline void v_zip(const v_##_Tpvec& a0, const v_##_Tpvec& a1, v_##_Tpvec& b0, v_##_Tpvec& b1) \ +{ \ + _Tpvec##x2_t p = vzipq_##suffix(a0.val, a1.val); \ + b0.val = p.val[0]; \ + b1.val = p.val[1]; \ +} \ +inline v_##_Tpvec v_combine_low(const v_##_Tpvec& a, const v_##_Tpvec& b) \ +{ \ + return v_##_Tpvec(vcombine_##suffix(vget_low_##suffix(a.val), vget_low_##suffix(b.val))); \ +} \ +inline v_##_Tpvec v_combine_high(const v_##_Tpvec& a, const v_##_Tpvec& b) \ +{ \ + return v_##_Tpvec(vcombine_##suffix(vget_high_##suffix(a.val), vget_high_##suffix(b.val))); \ +} \ +inline void v_recombine(const v_##_Tpvec& a, const v_##_Tpvec& b, v_##_Tpvec& c, v_##_Tpvec& d) \ +{ \ + c.val = vcombine_##suffix(vget_low_##suffix(a.val), vget_low_##suffix(b.val)); \ + d.val = vcombine_##suffix(vget_high_##suffix(a.val), vget_high_##suffix(b.val)); \ +} +#endif + +OPENCV_HAL_IMPL_NEON_UNPACKS(uint8x16, u8) +OPENCV_HAL_IMPL_NEON_UNPACKS(int8x16, s8) +OPENCV_HAL_IMPL_NEON_UNPACKS(uint16x8, u16) +OPENCV_HAL_IMPL_NEON_UNPACKS(int16x8, s16) +OPENCV_HAL_IMPL_NEON_UNPACKS(uint32x4, u32) +OPENCV_HAL_IMPL_NEON_UNPACKS(int32x4, s32) +OPENCV_HAL_IMPL_NEON_UNPACKS(float32x4, f32) +#if CV_SIMD128_64F +OPENCV_HAL_IMPL_NEON_UNPACKS(float64x2, f64) +#endif + +#define OPENCV_HAL_IMPL_NEON_EXTRACT(_Tpvec, suffix) \ +template \ +inline v_##_Tpvec v_extract(const v_##_Tpvec& a, const v_##_Tpvec& b) \ +{ \ + return v_##_Tpvec(vextq_##suffix(a.val, b.val, s)); \ +} + +OPENCV_HAL_IMPL_NEON_EXTRACT(uint8x16, u8) +OPENCV_HAL_IMPL_NEON_EXTRACT(int8x16, s8) +OPENCV_HAL_IMPL_NEON_EXTRACT(uint16x8, u16) +OPENCV_HAL_IMPL_NEON_EXTRACT(int16x8, s16) +OPENCV_HAL_IMPL_NEON_EXTRACT(uint32x4, u32) +OPENCV_HAL_IMPL_NEON_EXTRACT(int32x4, s32) +OPENCV_HAL_IMPL_NEON_EXTRACT(uint64x2, u64) +OPENCV_HAL_IMPL_NEON_EXTRACT(int64x2, s64) +OPENCV_HAL_IMPL_NEON_EXTRACT(float32x4, f32) +#if CV_SIMD128_64F +OPENCV_HAL_IMPL_NEON_EXTRACT(float64x2, f64) +#endif + +#if CV_SIMD128_64F +inline v_int32x4 v_round(const v_float32x4& a) +{ + float32x4_t a_ = a.val; + int32x4_t result; + __asm__ ("fcvtns %0.4s, %1.4s" + : "=w"(result) + : "w"(a_) + : /* No clobbers */); + return v_int32x4(result); +} +#else +inline v_int32x4 v_round(const v_float32x4& a) +{ + static const int32x4_t v_sign = vdupq_n_s32(1 << 31), + v_05 = vreinterpretq_s32_f32(vdupq_n_f32(0.5f)); + + int32x4_t v_addition = vorrq_s32(v_05, vandq_s32(v_sign, vreinterpretq_s32_f32(a.val))); + return v_int32x4(vcvtq_s32_f32(vaddq_f32(a.val, vreinterpretq_f32_s32(v_addition)))); +} +#endif +inline v_int32x4 v_floor(const v_float32x4& a) +{ + int32x4_t a1 = vcvtq_s32_f32(a.val); + uint32x4_t mask = vcgtq_f32(vcvtq_f32_s32(a1), a.val); + return v_int32x4(vaddq_s32(a1, vreinterpretq_s32_u32(mask))); +} + +inline v_int32x4 v_ceil(const v_float32x4& a) +{ + int32x4_t a1 = vcvtq_s32_f32(a.val); + uint32x4_t mask = vcgtq_f32(a.val, vcvtq_f32_s32(a1)); + return v_int32x4(vsubq_s32(a1, vreinterpretq_s32_u32(mask))); +} + +inline v_int32x4 v_trunc(const v_float32x4& a) +{ return v_int32x4(vcvtq_s32_f32(a.val)); } + +#if CV_SIMD128_64F +inline v_int32x4 v_round(const v_float64x2& a) +{ + static const int32x2_t zero = vdup_n_s32(0); + return v_int32x4(vcombine_s32(vmovn_s64(vcvtaq_s64_f64(a.val)), zero)); +} + +inline v_int32x4 v_round(const v_float64x2& a, const v_float64x2& b) +{ + return v_int32x4(vcombine_s32(vmovn_s64(vcvtaq_s64_f64(a.val)), vmovn_s64(vcvtaq_s64_f64(b.val)))); +} + +inline v_int32x4 v_floor(const v_float64x2& a) +{ + static const int32x2_t zero = vdup_n_s32(0); + int64x2_t a1 = vcvtq_s64_f64(a.val); + uint64x2_t mask = vcgtq_f64(vcvtq_f64_s64(a1), a.val); + a1 = vaddq_s64(a1, vreinterpretq_s64_u64(mask)); + return v_int32x4(vcombine_s32(vmovn_s64(a1), zero)); +} + +inline v_int32x4 v_ceil(const v_float64x2& a) +{ + static const int32x2_t zero = vdup_n_s32(0); + int64x2_t a1 = vcvtq_s64_f64(a.val); + uint64x2_t mask = vcgtq_f64(a.val, vcvtq_f64_s64(a1)); + a1 = vsubq_s64(a1, vreinterpretq_s64_u64(mask)); + return v_int32x4(vcombine_s32(vmovn_s64(a1), zero)); +} + +inline v_int32x4 v_trunc(const v_float64x2& a) +{ + static const int32x2_t zero = vdup_n_s32(0); + return v_int32x4(vcombine_s32(vmovn_s64(vcvtaq_s64_f64(a.val)), zero)); +} +#endif + +#define OPENCV_HAL_IMPL_NEON_TRANSPOSE4x4(_Tpvec, suffix) \ +inline void v_transpose4x4(const v_##_Tpvec& a0, const v_##_Tpvec& a1, \ + const v_##_Tpvec& a2, const v_##_Tpvec& a3, \ + v_##_Tpvec& b0, v_##_Tpvec& b1, \ + v_##_Tpvec& b2, v_##_Tpvec& b3) \ +{ \ + /* m00 m01 m02 m03 */ \ + /* m10 m11 m12 m13 */ \ + /* m20 m21 m22 m23 */ \ + /* m30 m31 m32 m33 */ \ + _Tpvec##x2_t t0 = vtrnq_##suffix(a0.val, a1.val); \ + _Tpvec##x2_t t1 = vtrnq_##suffix(a2.val, a3.val); \ + /* m00 m10 m02 m12 */ \ + /* m01 m11 m03 m13 */ \ + /* m20 m30 m22 m32 */ \ + /* m21 m31 m23 m33 */ \ + b0.val = vcombine_##suffix(vget_low_##suffix(t0.val[0]), vget_low_##suffix(t1.val[0])); \ + b1.val = vcombine_##suffix(vget_low_##suffix(t0.val[1]), vget_low_##suffix(t1.val[1])); \ + b2.val = vcombine_##suffix(vget_high_##suffix(t0.val[0]), vget_high_##suffix(t1.val[0])); \ + b3.val = vcombine_##suffix(vget_high_##suffix(t0.val[1]), vget_high_##suffix(t1.val[1])); \ +} + +OPENCV_HAL_IMPL_NEON_TRANSPOSE4x4(uint32x4, u32) +OPENCV_HAL_IMPL_NEON_TRANSPOSE4x4(int32x4, s32) +OPENCV_HAL_IMPL_NEON_TRANSPOSE4x4(float32x4, f32) + +#define OPENCV_HAL_IMPL_NEON_INTERLEAVED(_Tpvec, _Tp, suffix) \ +inline void v_load_deinterleave(const _Tp* ptr, v_##_Tpvec& a, v_##_Tpvec& b) \ +{ \ + _Tpvec##x2_t v = vld2q_##suffix(ptr); \ + a.val = v.val[0]; \ + b.val = v.val[1]; \ +} \ +inline void v_load_deinterleave(const _Tp* ptr, v_##_Tpvec& a, v_##_Tpvec& b, v_##_Tpvec& c) \ +{ \ + _Tpvec##x3_t v = vld3q_##suffix(ptr); \ + a.val = v.val[0]; \ + b.val = v.val[1]; \ + c.val = v.val[2]; \ +} \ +inline void v_load_deinterleave(const _Tp* ptr, v_##_Tpvec& a, v_##_Tpvec& b, \ + v_##_Tpvec& c, v_##_Tpvec& d) \ +{ \ + _Tpvec##x4_t v = vld4q_##suffix(ptr); \ + a.val = v.val[0]; \ + b.val = v.val[1]; \ + c.val = v.val[2]; \ + d.val = v.val[3]; \ +} \ +inline void v_store_interleave( _Tp* ptr, const v_##_Tpvec& a, const v_##_Tpvec& b, \ + hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \ +{ \ + _Tpvec##x2_t v; \ + v.val[0] = a.val; \ + v.val[1] = b.val; \ + vst2q_##suffix(ptr, v); \ +} \ +inline void v_store_interleave( _Tp* ptr, const v_##_Tpvec& a, const v_##_Tpvec& b, \ + const v_##_Tpvec& c, hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \ +{ \ + _Tpvec##x3_t v; \ + v.val[0] = a.val; \ + v.val[1] = b.val; \ + v.val[2] = c.val; \ + vst3q_##suffix(ptr, v); \ +} \ +inline void v_store_interleave( _Tp* ptr, const v_##_Tpvec& a, const v_##_Tpvec& b, \ + const v_##_Tpvec& c, const v_##_Tpvec& d, \ + hal::StoreMode /*mode*/=hal::STORE_UNALIGNED ) \ +{ \ + _Tpvec##x4_t v; \ + v.val[0] = a.val; \ + v.val[1] = b.val; \ + v.val[2] = c.val; \ + v.val[3] = d.val; \ + vst4q_##suffix(ptr, v); \ +} + +#define OPENCV_HAL_IMPL_NEON_INTERLEAVED_INT64(tp, suffix) \ +inline void v_load_deinterleave( const tp* ptr, v_##tp##x2& a, v_##tp##x2& b ) \ +{ \ + tp##x1_t a0 = vld1_##suffix(ptr); \ + tp##x1_t b0 = vld1_##suffix(ptr + 1); \ + tp##x1_t a1 = vld1_##suffix(ptr + 2); \ + tp##x1_t b1 = vld1_##suffix(ptr + 3); \ + a = v_##tp##x2(vcombine_##suffix(a0, a1)); \ + b = v_##tp##x2(vcombine_##suffix(b0, b1)); \ +} \ + \ +inline void v_load_deinterleave( const tp* ptr, v_##tp##x2& a, \ + v_##tp##x2& b, v_##tp##x2& c ) \ +{ \ + tp##x1_t a0 = vld1_##suffix(ptr); \ + tp##x1_t b0 = vld1_##suffix(ptr + 1); \ + tp##x1_t c0 = vld1_##suffix(ptr + 2); \ + tp##x1_t a1 = vld1_##suffix(ptr + 3); \ + tp##x1_t b1 = vld1_##suffix(ptr + 4); \ + tp##x1_t c1 = vld1_##suffix(ptr + 5); \ + a = v_##tp##x2(vcombine_##suffix(a0, a1)); \ + b = v_##tp##x2(vcombine_##suffix(b0, b1)); \ + c = v_##tp##x2(vcombine_##suffix(c0, c1)); \ +} \ + \ +inline void v_load_deinterleave( const tp* ptr, v_##tp##x2& a, v_##tp##x2& b, \ + v_##tp##x2& c, v_##tp##x2& d ) \ +{ \ + tp##x1_t a0 = vld1_##suffix(ptr); \ + tp##x1_t b0 = vld1_##suffix(ptr + 1); \ + tp##x1_t c0 = vld1_##suffix(ptr + 2); \ + tp##x1_t d0 = vld1_##suffix(ptr + 3); \ + tp##x1_t a1 = vld1_##suffix(ptr + 4); \ + tp##x1_t b1 = vld1_##suffix(ptr + 5); \ + tp##x1_t c1 = vld1_##suffix(ptr + 6); \ + tp##x1_t d1 = vld1_##suffix(ptr + 7); \ + a = v_##tp##x2(vcombine_##suffix(a0, a1)); \ + b = v_##tp##x2(vcombine_##suffix(b0, b1)); \ + c = v_##tp##x2(vcombine_##suffix(c0, c1)); \ + d = v_##tp##x2(vcombine_##suffix(d0, d1)); \ +} \ + \ +inline void v_store_interleave( tp* ptr, const v_##tp##x2& a, const v_##tp##x2& b, \ + hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \ +{ \ + vst1_##suffix(ptr, vget_low_##suffix(a.val)); \ + vst1_##suffix(ptr + 1, vget_low_##suffix(b.val)); \ + vst1_##suffix(ptr + 2, vget_high_##suffix(a.val)); \ + vst1_##suffix(ptr + 3, vget_high_##suffix(b.val)); \ +} \ + \ +inline void v_store_interleave( tp* ptr, const v_##tp##x2& a, \ + const v_##tp##x2& b, const v_##tp##x2& c, \ + hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \ +{ \ + vst1_##suffix(ptr, vget_low_##suffix(a.val)); \ + vst1_##suffix(ptr + 1, vget_low_##suffix(b.val)); \ + vst1_##suffix(ptr + 2, vget_low_##suffix(c.val)); \ + vst1_##suffix(ptr + 3, vget_high_##suffix(a.val)); \ + vst1_##suffix(ptr + 4, vget_high_##suffix(b.val)); \ + vst1_##suffix(ptr + 5, vget_high_##suffix(c.val)); \ +} \ + \ +inline void v_store_interleave( tp* ptr, const v_##tp##x2& a, const v_##tp##x2& b, \ + const v_##tp##x2& c, const v_##tp##x2& d, \ + hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \ +{ \ + vst1_##suffix(ptr, vget_low_##suffix(a.val)); \ + vst1_##suffix(ptr + 1, vget_low_##suffix(b.val)); \ + vst1_##suffix(ptr + 2, vget_low_##suffix(c.val)); \ + vst1_##suffix(ptr + 3, vget_low_##suffix(d.val)); \ + vst1_##suffix(ptr + 4, vget_high_##suffix(a.val)); \ + vst1_##suffix(ptr + 5, vget_high_##suffix(b.val)); \ + vst1_##suffix(ptr + 6, vget_high_##suffix(c.val)); \ + vst1_##suffix(ptr + 7, vget_high_##suffix(d.val)); \ +} + +OPENCV_HAL_IMPL_NEON_INTERLEAVED(uint8x16, uchar, u8) +OPENCV_HAL_IMPL_NEON_INTERLEAVED(int8x16, schar, s8) +OPENCV_HAL_IMPL_NEON_INTERLEAVED(uint16x8, ushort, u16) +OPENCV_HAL_IMPL_NEON_INTERLEAVED(int16x8, short, s16) +OPENCV_HAL_IMPL_NEON_INTERLEAVED(uint32x4, unsigned, u32) +OPENCV_HAL_IMPL_NEON_INTERLEAVED(int32x4, int, s32) +OPENCV_HAL_IMPL_NEON_INTERLEAVED(float32x4, float, f32) +#if CV_SIMD128_64F +OPENCV_HAL_IMPL_NEON_INTERLEAVED(float64x2, double, f64) +#endif + +OPENCV_HAL_IMPL_NEON_INTERLEAVED_INT64(int64, s64) +OPENCV_HAL_IMPL_NEON_INTERLEAVED_INT64(uint64, u64) + +inline v_float32x4 v_cvt_f32(const v_int32x4& a) +{ + return v_float32x4(vcvtq_f32_s32(a.val)); +} + +#if CV_SIMD128_64F +inline v_float32x4 v_cvt_f32(const v_float64x2& a) +{ + float32x2_t zero = vdup_n_f32(0.0f); + return v_float32x4(vcombine_f32(vcvt_f32_f64(a.val), zero)); +} + +inline v_float32x4 v_cvt_f32(const v_float64x2& a, const v_float64x2& b) +{ + return v_float32x4(vcombine_f32(vcvt_f32_f64(a.val), vcvt_f32_f64(b.val))); +} + +inline v_float64x2 v_cvt_f64(const v_int32x4& a) +{ + return v_float64x2(vcvt_f64_f32(vcvt_f32_s32(vget_low_s32(a.val)))); +} + +inline v_float64x2 v_cvt_f64_high(const v_int32x4& a) +{ + return v_float64x2(vcvt_f64_f32(vcvt_f32_s32(vget_high_s32(a.val)))); +} + +inline v_float64x2 v_cvt_f64(const v_float32x4& a) +{ + return v_float64x2(vcvt_f64_f32(vget_low_f32(a.val))); +} + +inline v_float64x2 v_cvt_f64_high(const v_float32x4& a) +{ + return v_float64x2(vcvt_f64_f32(vget_high_f32(a.val))); +} +#endif + +////////////// Lookup table access //////////////////// + +inline v_int32x4 v_lut(const int* tab, const v_int32x4& idxvec) +{ + int CV_DECL_ALIGNED(32) elems[4] = + { + tab[vgetq_lane_s32(idxvec.val, 0)], + tab[vgetq_lane_s32(idxvec.val, 1)], + tab[vgetq_lane_s32(idxvec.val, 2)], + tab[vgetq_lane_s32(idxvec.val, 3)] + }; + return v_int32x4(vld1q_s32(elems)); +} + +inline v_float32x4 v_lut(const float* tab, const v_int32x4& idxvec) +{ + float CV_DECL_ALIGNED(32) elems[4] = + { + tab[vgetq_lane_s32(idxvec.val, 0)], + tab[vgetq_lane_s32(idxvec.val, 1)], + tab[vgetq_lane_s32(idxvec.val, 2)], + tab[vgetq_lane_s32(idxvec.val, 3)] + }; + return v_float32x4(vld1q_f32(elems)); +} + +inline void v_lut_deinterleave(const float* tab, const v_int32x4& idxvec, v_float32x4& x, v_float32x4& y) +{ + /*int CV_DECL_ALIGNED(32) idx[4]; + v_store(idx, idxvec); + + float32x4_t xy02 = vcombine_f32(vld1_f32(tab + idx[0]), vld1_f32(tab + idx[2])); + float32x4_t xy13 = vcombine_f32(vld1_f32(tab + idx[1]), vld1_f32(tab + idx[3])); + + float32x4x2_t xxyy = vuzpq_f32(xy02, xy13); + x = v_float32x4(xxyy.val[0]); + y = v_float32x4(xxyy.val[1]);*/ + int CV_DECL_ALIGNED(32) idx[4]; + v_store_aligned(idx, idxvec); + + x = v_float32x4(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]]); + y = v_float32x4(tab[idx[0]+1], tab[idx[1]+1], tab[idx[2]+1], tab[idx[3]+1]); +} + +#if CV_SIMD128_64F +inline v_float64x2 v_lut(const double* tab, const v_int32x4& idxvec) +{ + double CV_DECL_ALIGNED(32) elems[2] = + { + tab[vgetq_lane_s32(idxvec.val, 0)], + tab[vgetq_lane_s32(idxvec.val, 1)], + }; + return v_float64x2(vld1q_f64(elems)); +} + +inline void v_lut_deinterleave(const double* tab, const v_int32x4& idxvec, v_float64x2& x, v_float64x2& y) +{ + int CV_DECL_ALIGNED(32) idx[4]; + v_store_aligned(idx, idxvec); + + x = v_float64x2(tab[idx[0]], tab[idx[1]]); + y = v_float64x2(tab[idx[0]+1], tab[idx[1]+1]); +} +#endif + +////// FP16 suport /////// +#if CV_FP16 +inline v_float32x4 v_load_expand(const float16_t* ptr) +{ + float16x4_t v = + #ifndef vld1_f16 // APPLE compiler defines vld1_f16 as macro + (float16x4_t)vld1_s16((const short*)ptr); + #else + vld1_f16((const __fp16*)ptr); + #endif + return v_float32x4(vcvt_f32_f16(v)); +} + +inline void v_pack_store(float16_t* ptr, const v_float32x4& v) +{ + float16x4_t hv = vcvt_f16_f32(v.val); + + #ifndef vst1_f16 // APPLE compiler defines vst1_f16 as macro + vst1_s16((short*)ptr, (int16x4_t)hv); + #else + vst1_f16((__fp16*)ptr, hv); + #endif +} +#else +inline v_float32x4 v_load_expand(const float16_t* ptr) +{ + const int N = 4; + float buf[N]; + for( int i = 0; i < N; i++ ) buf[i] = (float)ptr[i]; + return v_load(buf); +} + +inline void v_pack_store(float16_t* ptr, const v_float32x4& v) +{ + const int N = 4; + float buf[N]; + v_store(buf, v); + for( int i = 0; i < N; i++ ) ptr[i] = float16_t(buf[i]); +} +#endif + +inline void v_cleanup() {} + +//! @name Check SIMD support +//! @{ +//! @brief Check CPU capability of SIMD operation +static inline bool hasSIMD128() +{ + return (CV_CPU_HAS_SUPPORT_NEON) ? true : false; +} + +//! @} + +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END + +//! @endcond + +} + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/hal/intrin_sse.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/hal/intrin_sse.hpp new file mode 100644 index 0000000000000000000000000000000000000000..f7a67da1a5e614c7270ef860821ab1e22a7392ae --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/hal/intrin_sse.hpp @@ -0,0 +1,2816 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_HAL_SSE_HPP +#define OPENCV_HAL_SSE_HPP + +#include +#include "opencv2/core/utility.hpp" + +#define CV_SIMD128 1 +#define CV_SIMD128_64F 1 +#define CV_SIMD128_FP16 0 // no native operations with FP16 type. + +namespace cv +{ + +//! @cond IGNORED + +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN + +///////// Types //////////// + +struct v_uint8x16 +{ + typedef uchar lane_type; + typedef __m128i vector_type; + enum { nlanes = 16 }; + + v_uint8x16() : val(_mm_setzero_si128()) {} + explicit v_uint8x16(__m128i v) : val(v) {} + v_uint8x16(uchar v0, uchar v1, uchar v2, uchar v3, uchar v4, uchar v5, uchar v6, uchar v7, + uchar v8, uchar v9, uchar v10, uchar v11, uchar v12, uchar v13, uchar v14, uchar v15) + { + val = _mm_setr_epi8((char)v0, (char)v1, (char)v2, (char)v3, + (char)v4, (char)v5, (char)v6, (char)v7, + (char)v8, (char)v9, (char)v10, (char)v11, + (char)v12, (char)v13, (char)v14, (char)v15); + } + uchar get0() const + { + return (uchar)_mm_cvtsi128_si32(val); + } + + __m128i val; +}; + +struct v_int8x16 +{ + typedef schar lane_type; + typedef __m128i vector_type; + enum { nlanes = 16 }; + + v_int8x16() : val(_mm_setzero_si128()) {} + explicit v_int8x16(__m128i v) : val(v) {} + v_int8x16(schar v0, schar v1, schar v2, schar v3, schar v4, schar v5, schar v6, schar v7, + schar v8, schar v9, schar v10, schar v11, schar v12, schar v13, schar v14, schar v15) + { + val = _mm_setr_epi8((char)v0, (char)v1, (char)v2, (char)v3, + (char)v4, (char)v5, (char)v6, (char)v7, + (char)v8, (char)v9, (char)v10, (char)v11, + (char)v12, (char)v13, (char)v14, (char)v15); + } + schar get0() const + { + return (schar)_mm_cvtsi128_si32(val); + } + + __m128i val; +}; + +struct v_uint16x8 +{ + typedef ushort lane_type; + typedef __m128i vector_type; + enum { nlanes = 8 }; + + v_uint16x8() : val(_mm_setzero_si128()) {} + explicit v_uint16x8(__m128i v) : val(v) {} + v_uint16x8(ushort v0, ushort v1, ushort v2, ushort v3, ushort v4, ushort v5, ushort v6, ushort v7) + { + val = _mm_setr_epi16((short)v0, (short)v1, (short)v2, (short)v3, + (short)v4, (short)v5, (short)v6, (short)v7); + } + ushort get0() const + { + return (ushort)_mm_cvtsi128_si32(val); + } + + __m128i val; +}; + +struct v_int16x8 +{ + typedef short lane_type; + typedef __m128i vector_type; + enum { nlanes = 8 }; + + v_int16x8() : val(_mm_setzero_si128()) {} + explicit v_int16x8(__m128i v) : val(v) {} + v_int16x8(short v0, short v1, short v2, short v3, short v4, short v5, short v6, short v7) + { + val = _mm_setr_epi16((short)v0, (short)v1, (short)v2, (short)v3, + (short)v4, (short)v5, (short)v6, (short)v7); + } + short get0() const + { + return (short)_mm_cvtsi128_si32(val); + } + + __m128i val; +}; + +struct v_uint32x4 +{ + typedef unsigned lane_type; + typedef __m128i vector_type; + enum { nlanes = 4 }; + + v_uint32x4() : val(_mm_setzero_si128()) {} + explicit v_uint32x4(__m128i v) : val(v) {} + v_uint32x4(unsigned v0, unsigned v1, unsigned v2, unsigned v3) + { + val = _mm_setr_epi32((int)v0, (int)v1, (int)v2, (int)v3); + } + unsigned get0() const + { + return (unsigned)_mm_cvtsi128_si32(val); + } + + __m128i val; +}; + +struct v_int32x4 +{ + typedef int lane_type; + typedef __m128i vector_type; + enum { nlanes = 4 }; + + v_int32x4() : val(_mm_setzero_si128()) {} + explicit v_int32x4(__m128i v) : val(v) {} + v_int32x4(int v0, int v1, int v2, int v3) + { + val = _mm_setr_epi32(v0, v1, v2, v3); + } + int get0() const + { + return _mm_cvtsi128_si32(val); + } + + __m128i val; +}; + +struct v_float32x4 +{ + typedef float lane_type; + typedef __m128 vector_type; + enum { nlanes = 4 }; + + v_float32x4() : val(_mm_setzero_ps()) {} + explicit v_float32x4(__m128 v) : val(v) {} + v_float32x4(float v0, float v1, float v2, float v3) + { + val = _mm_setr_ps(v0, v1, v2, v3); + } + float get0() const + { + return _mm_cvtss_f32(val); + } + + __m128 val; +}; + +struct v_uint64x2 +{ + typedef uint64 lane_type; + typedef __m128i vector_type; + enum { nlanes = 2 }; + + v_uint64x2() : val(_mm_setzero_si128()) {} + explicit v_uint64x2(__m128i v) : val(v) {} + v_uint64x2(uint64 v0, uint64 v1) + { + val = _mm_setr_epi32((int)v0, (int)(v0 >> 32), (int)v1, (int)(v1 >> 32)); + } + uint64 get0() const + { + int a = _mm_cvtsi128_si32(val); + int b = _mm_cvtsi128_si32(_mm_srli_epi64(val, 32)); + return (unsigned)a | ((uint64)(unsigned)b << 32); + } + + __m128i val; +}; + +struct v_int64x2 +{ + typedef int64 lane_type; + typedef __m128i vector_type; + enum { nlanes = 2 }; + + v_int64x2() : val(_mm_setzero_si128()) {} + explicit v_int64x2(__m128i v) : val(v) {} + v_int64x2(int64 v0, int64 v1) + { + val = _mm_setr_epi32((int)v0, (int)(v0 >> 32), (int)v1, (int)(v1 >> 32)); + } + int64 get0() const + { + int a = _mm_cvtsi128_si32(val); + int b = _mm_cvtsi128_si32(_mm_srli_epi64(val, 32)); + return (int64)((unsigned)a | ((uint64)(unsigned)b << 32)); + } + + __m128i val; +}; + +struct v_float64x2 +{ + typedef double lane_type; + typedef __m128d vector_type; + enum { nlanes = 2 }; + + v_float64x2() : val(_mm_setzero_pd()) {} + explicit v_float64x2(__m128d v) : val(v) {} + v_float64x2(double v0, double v1) + { + val = _mm_setr_pd(v0, v1); + } + double get0() const + { + return _mm_cvtsd_f64(val); + } + + __m128d val; +}; + +namespace hal_sse_internal +{ + template + to_sse_type v_sse_reinterpret_as(const from_sse_type& val); + +#define OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(to_sse_type, from_sse_type, sse_cast_intrin) \ + template<> inline \ + to_sse_type v_sse_reinterpret_as(const from_sse_type& a) \ + { return sse_cast_intrin(a); } + + OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128i, __m128i, OPENCV_HAL_NOP) + OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128i, __m128, _mm_castps_si128) + OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128i, __m128d, _mm_castpd_si128) + OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128, __m128i, _mm_castsi128_ps) + OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128, __m128, OPENCV_HAL_NOP) + OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128, __m128d, _mm_castpd_ps) + OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128d, __m128i, _mm_castsi128_pd) + OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128d, __m128, _mm_castps_pd) + OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128d, __m128d, OPENCV_HAL_NOP) +} + +#define OPENCV_HAL_IMPL_SSE_INITVEC(_Tpvec, _Tp, suffix, zsuffix, ssuffix, _Tps, cast) \ +inline _Tpvec v_setzero_##suffix() { return _Tpvec(_mm_setzero_##zsuffix()); } \ +inline _Tpvec v_setall_##suffix(_Tp v) { return _Tpvec(_mm_set1_##ssuffix((_Tps)v)); } \ +template inline _Tpvec v_reinterpret_as_##suffix(const _Tpvec0& a) \ +{ return _Tpvec(cast(a.val)); } + +OPENCV_HAL_IMPL_SSE_INITVEC(v_uint8x16, uchar, u8, si128, epi8, char, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_INITVEC(v_int8x16, schar, s8, si128, epi8, char, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_INITVEC(v_uint16x8, ushort, u16, si128, epi16, short, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_INITVEC(v_int16x8, short, s16, si128, epi16, short, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_INITVEC(v_uint32x4, unsigned, u32, si128, epi32, int, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_INITVEC(v_int32x4, int, s32, si128, epi32, int, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_INITVEC(v_float32x4, float, f32, ps, ps, float, _mm_castsi128_ps) +OPENCV_HAL_IMPL_SSE_INITVEC(v_float64x2, double, f64, pd, pd, double, _mm_castsi128_pd) + +inline v_uint64x2 v_setzero_u64() { return v_uint64x2(_mm_setzero_si128()); } +inline v_int64x2 v_setzero_s64() { return v_int64x2(_mm_setzero_si128()); } +inline v_uint64x2 v_setall_u64(uint64 val) { return v_uint64x2(val, val); } +inline v_int64x2 v_setall_s64(int64 val) { return v_int64x2(val, val); } + +template inline +v_uint64x2 v_reinterpret_as_u64(const _Tpvec& a) { return v_uint64x2(a.val); } +template inline +v_int64x2 v_reinterpret_as_s64(const _Tpvec& a) { return v_int64x2(a.val); } +inline v_float32x4 v_reinterpret_as_f32(const v_uint64x2& a) +{ return v_float32x4(_mm_castsi128_ps(a.val)); } +inline v_float32x4 v_reinterpret_as_f32(const v_int64x2& a) +{ return v_float32x4(_mm_castsi128_ps(a.val)); } +inline v_float64x2 v_reinterpret_as_f64(const v_uint64x2& a) +{ return v_float64x2(_mm_castsi128_pd(a.val)); } +inline v_float64x2 v_reinterpret_as_f64(const v_int64x2& a) +{ return v_float64x2(_mm_castsi128_pd(a.val)); } + +#define OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(_Tpvec, suffix) \ +inline _Tpvec v_reinterpret_as_##suffix(const v_float32x4& a) \ +{ return _Tpvec(_mm_castps_si128(a.val)); } \ +inline _Tpvec v_reinterpret_as_##suffix(const v_float64x2& a) \ +{ return _Tpvec(_mm_castpd_si128(a.val)); } + +OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_uint8x16, u8) +OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_int8x16, s8) +OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_uint16x8, u16) +OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_int16x8, s16) +OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_uint32x4, u32) +OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_int32x4, s32) +OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_uint64x2, u64) +OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_int64x2, s64) + +inline v_float32x4 v_reinterpret_as_f32(const v_float32x4& a) {return a; } +inline v_float64x2 v_reinterpret_as_f64(const v_float64x2& a) {return a; } +inline v_float32x4 v_reinterpret_as_f32(const v_float64x2& a) {return v_float32x4(_mm_castpd_ps(a.val)); } +inline v_float64x2 v_reinterpret_as_f64(const v_float32x4& a) {return v_float64x2(_mm_castps_pd(a.val)); } + +//////////////// PACK /////////////// +inline v_uint8x16 v_pack(const v_uint16x8& a, const v_uint16x8& b) +{ + __m128i delta = _mm_set1_epi16(255); + return v_uint8x16(_mm_packus_epi16(_mm_subs_epu16(a.val, _mm_subs_epu16(a.val, delta)), + _mm_subs_epu16(b.val, _mm_subs_epu16(b.val, delta)))); +} + +inline void v_pack_store(uchar* ptr, const v_uint16x8& a) +{ + __m128i delta = _mm_set1_epi16(255); + __m128i a1 = _mm_subs_epu16(a.val, _mm_subs_epu16(a.val, delta)); + _mm_storel_epi64((__m128i*)ptr, _mm_packus_epi16(a1, a1)); +} + +inline v_uint8x16 v_pack_u(const v_int16x8& a, const v_int16x8& b) +{ return v_uint8x16(_mm_packus_epi16(a.val, b.val)); } + +inline void v_pack_u_store(uchar* ptr, const v_int16x8& a) +{ _mm_storel_epi64((__m128i*)ptr, _mm_packus_epi16(a.val, a.val)); } + +template inline +v_uint8x16 v_rshr_pack(const v_uint16x8& a, const v_uint16x8& b) +{ + // we assume that n > 0, and so the shifted 16-bit values can be treated as signed numbers. + __m128i delta = _mm_set1_epi16((short)(1 << (n-1))); + return v_uint8x16(_mm_packus_epi16(_mm_srli_epi16(_mm_adds_epu16(a.val, delta), n), + _mm_srli_epi16(_mm_adds_epu16(b.val, delta), n))); +} + +template inline +void v_rshr_pack_store(uchar* ptr, const v_uint16x8& a) +{ + __m128i delta = _mm_set1_epi16((short)(1 << (n-1))); + __m128i a1 = _mm_srli_epi16(_mm_adds_epu16(a.val, delta), n); + _mm_storel_epi64((__m128i*)ptr, _mm_packus_epi16(a1, a1)); +} + +template inline +v_uint8x16 v_rshr_pack_u(const v_int16x8& a, const v_int16x8& b) +{ + __m128i delta = _mm_set1_epi16((short)(1 << (n-1))); + return v_uint8x16(_mm_packus_epi16(_mm_srai_epi16(_mm_adds_epi16(a.val, delta), n), + _mm_srai_epi16(_mm_adds_epi16(b.val, delta), n))); +} + +template inline +void v_rshr_pack_u_store(uchar* ptr, const v_int16x8& a) +{ + __m128i delta = _mm_set1_epi16((short)(1 << (n-1))); + __m128i a1 = _mm_srai_epi16(_mm_adds_epi16(a.val, delta), n); + _mm_storel_epi64((__m128i*)ptr, _mm_packus_epi16(a1, a1)); +} + +inline v_int8x16 v_pack(const v_int16x8& a, const v_int16x8& b) +{ return v_int8x16(_mm_packs_epi16(a.val, b.val)); } + +inline void v_pack_store(schar* ptr, const v_int16x8& a) +{ _mm_storel_epi64((__m128i*)ptr, _mm_packs_epi16(a.val, a.val)); } + +template inline +v_int8x16 v_rshr_pack(const v_int16x8& a, const v_int16x8& b) +{ + // we assume that n > 0, and so the shifted 16-bit values can be treated as signed numbers. + __m128i delta = _mm_set1_epi16((short)(1 << (n-1))); + return v_int8x16(_mm_packs_epi16(_mm_srai_epi16(_mm_adds_epi16(a.val, delta), n), + _mm_srai_epi16(_mm_adds_epi16(b.val, delta), n))); +} +template inline +void v_rshr_pack_store(schar* ptr, const v_int16x8& a) +{ + // we assume that n > 0, and so the shifted 16-bit values can be treated as signed numbers. + __m128i delta = _mm_set1_epi16((short)(1 << (n-1))); + __m128i a1 = _mm_srai_epi16(_mm_adds_epi16(a.val, delta), n); + _mm_storel_epi64((__m128i*)ptr, _mm_packs_epi16(a1, a1)); +} + + +// byte-wise "mask ? a : b" +inline __m128i v_select_si128(__m128i mask, __m128i a, __m128i b) +{ +#if CV_SSE4_1 + return _mm_blendv_epi8(b, a, mask); +#else + return _mm_xor_si128(b, _mm_and_si128(_mm_xor_si128(a, b), mask)); +#endif +} + +inline v_uint16x8 v_pack(const v_uint32x4& a, const v_uint32x4& b) +{ return v_uint16x8(_v128_packs_epu32(a.val, b.val)); } + +inline void v_pack_store(ushort* ptr, const v_uint32x4& a) +{ + __m128i z = _mm_setzero_si128(), maxval32 = _mm_set1_epi32(65535), delta32 = _mm_set1_epi32(32768); + __m128i a1 = _mm_sub_epi32(v_select_si128(_mm_cmpgt_epi32(z, a.val), maxval32, a.val), delta32); + __m128i r = _mm_packs_epi32(a1, a1); + _mm_storel_epi64((__m128i*)ptr, _mm_sub_epi16(r, _mm_set1_epi16(-32768))); +} + +template inline +v_uint16x8 v_rshr_pack(const v_uint32x4& a, const v_uint32x4& b) +{ + __m128i delta = _mm_set1_epi32(1 << (n-1)), delta32 = _mm_set1_epi32(32768); + __m128i a1 = _mm_sub_epi32(_mm_srli_epi32(_mm_add_epi32(a.val, delta), n), delta32); + __m128i b1 = _mm_sub_epi32(_mm_srli_epi32(_mm_add_epi32(b.val, delta), n), delta32); + return v_uint16x8(_mm_sub_epi16(_mm_packs_epi32(a1, b1), _mm_set1_epi16(-32768))); +} + +template inline +void v_rshr_pack_store(ushort* ptr, const v_uint32x4& a) +{ + __m128i delta = _mm_set1_epi32(1 << (n-1)), delta32 = _mm_set1_epi32(32768); + __m128i a1 = _mm_sub_epi32(_mm_srli_epi32(_mm_add_epi32(a.val, delta), n), delta32); + __m128i a2 = _mm_sub_epi16(_mm_packs_epi32(a1, a1), _mm_set1_epi16(-32768)); + _mm_storel_epi64((__m128i*)ptr, a2); +} + +inline v_uint16x8 v_pack_u(const v_int32x4& a, const v_int32x4& b) +{ +#if CV_SSE4_1 + return v_uint16x8(_mm_packus_epi32(a.val, b.val)); +#else + __m128i delta32 = _mm_set1_epi32(32768); + + // preliminary saturate negative values to zero + __m128i a1 = _mm_and_si128(a.val, _mm_cmpgt_epi32(a.val, _mm_set1_epi32(0))); + __m128i b1 = _mm_and_si128(b.val, _mm_cmpgt_epi32(b.val, _mm_set1_epi32(0))); + + __m128i r = _mm_packs_epi32(_mm_sub_epi32(a1, delta32), _mm_sub_epi32(b1, delta32)); + return v_uint16x8(_mm_sub_epi16(r, _mm_set1_epi16(-32768))); +#endif +} + +inline void v_pack_u_store(ushort* ptr, const v_int32x4& a) +{ +#if CV_SSE4_1 + _mm_storel_epi64((__m128i*)ptr, _mm_packus_epi32(a.val, a.val)); +#else + __m128i delta32 = _mm_set1_epi32(32768); + __m128i a1 = _mm_sub_epi32(a.val, delta32); + __m128i r = _mm_sub_epi16(_mm_packs_epi32(a1, a1), _mm_set1_epi16(-32768)); + _mm_storel_epi64((__m128i*)ptr, r); +#endif +} + +template inline +v_uint16x8 v_rshr_pack_u(const v_int32x4& a, const v_int32x4& b) +{ +#if CV_SSE4_1 + __m128i delta = _mm_set1_epi32(1 << (n - 1)); + return v_uint16x8(_mm_packus_epi32(_mm_srai_epi32(_mm_add_epi32(a.val, delta), n), + _mm_srai_epi32(_mm_add_epi32(b.val, delta), n))); +#else + __m128i delta = _mm_set1_epi32(1 << (n-1)), delta32 = _mm_set1_epi32(32768); + __m128i a1 = _mm_sub_epi32(_mm_srai_epi32(_mm_add_epi32(a.val, delta), n), delta32); + __m128i a2 = _mm_sub_epi16(_mm_packs_epi32(a1, a1), _mm_set1_epi16(-32768)); + __m128i b1 = _mm_sub_epi32(_mm_srai_epi32(_mm_add_epi32(b.val, delta), n), delta32); + __m128i b2 = _mm_sub_epi16(_mm_packs_epi32(b1, b1), _mm_set1_epi16(-32768)); + return v_uint16x8(_mm_unpacklo_epi64(a2, b2)); +#endif +} + +template inline +void v_rshr_pack_u_store(ushort* ptr, const v_int32x4& a) +{ +#if CV_SSE4_1 + __m128i delta = _mm_set1_epi32(1 << (n - 1)); + __m128i a1 = _mm_srai_epi32(_mm_add_epi32(a.val, delta), n); + _mm_storel_epi64((__m128i*)ptr, _mm_packus_epi32(a1, a1)); +#else + __m128i delta = _mm_set1_epi32(1 << (n-1)), delta32 = _mm_set1_epi32(32768); + __m128i a1 = _mm_sub_epi32(_mm_srai_epi32(_mm_add_epi32(a.val, delta), n), delta32); + __m128i a2 = _mm_sub_epi16(_mm_packs_epi32(a1, a1), _mm_set1_epi16(-32768)); + _mm_storel_epi64((__m128i*)ptr, a2); +#endif +} + +inline v_int16x8 v_pack(const v_int32x4& a, const v_int32x4& b) +{ return v_int16x8(_mm_packs_epi32(a.val, b.val)); } + +inline void v_pack_store(short* ptr, const v_int32x4& a) +{ + _mm_storel_epi64((__m128i*)ptr, _mm_packs_epi32(a.val, a.val)); +} + +template inline +v_int16x8 v_rshr_pack(const v_int32x4& a, const v_int32x4& b) +{ + __m128i delta = _mm_set1_epi32(1 << (n-1)); + return v_int16x8(_mm_packs_epi32(_mm_srai_epi32(_mm_add_epi32(a.val, delta), n), + _mm_srai_epi32(_mm_add_epi32(b.val, delta), n))); +} + +template inline +void v_rshr_pack_store(short* ptr, const v_int32x4& a) +{ + __m128i delta = _mm_set1_epi32(1 << (n-1)); + __m128i a1 = _mm_srai_epi32(_mm_add_epi32(a.val, delta), n); + _mm_storel_epi64((__m128i*)ptr, _mm_packs_epi32(a1, a1)); +} + + +// [a0 0 | b0 0] [a1 0 | b1 0] +inline v_uint32x4 v_pack(const v_uint64x2& a, const v_uint64x2& b) +{ + __m128i v0 = _mm_unpacklo_epi32(a.val, b.val); // a0 a1 0 0 + __m128i v1 = _mm_unpackhi_epi32(a.val, b.val); // b0 b1 0 0 + return v_uint32x4(_mm_unpacklo_epi32(v0, v1)); +} + +inline void v_pack_store(unsigned* ptr, const v_uint64x2& a) +{ + __m128i a1 = _mm_shuffle_epi32(a.val, _MM_SHUFFLE(0, 2, 2, 0)); + _mm_storel_epi64((__m128i*)ptr, a1); +} + +// [a0 0 | b0 0] [a1 0 | b1 0] +inline v_int32x4 v_pack(const v_int64x2& a, const v_int64x2& b) +{ + __m128i v0 = _mm_unpacklo_epi32(a.val, b.val); // a0 a1 0 0 + __m128i v1 = _mm_unpackhi_epi32(a.val, b.val); // b0 b1 0 0 + return v_int32x4(_mm_unpacklo_epi32(v0, v1)); +} + +inline void v_pack_store(int* ptr, const v_int64x2& a) +{ + __m128i a1 = _mm_shuffle_epi32(a.val, _MM_SHUFFLE(0, 2, 2, 0)); + _mm_storel_epi64((__m128i*)ptr, a1); +} + +template inline +v_uint32x4 v_rshr_pack(const v_uint64x2& a, const v_uint64x2& b) +{ + uint64 delta = (uint64)1 << (n-1); + v_uint64x2 delta2(delta, delta); + __m128i a1 = _mm_srli_epi64(_mm_add_epi64(a.val, delta2.val), n); + __m128i b1 = _mm_srli_epi64(_mm_add_epi64(b.val, delta2.val), n); + __m128i v0 = _mm_unpacklo_epi32(a1, b1); // a0 a1 0 0 + __m128i v1 = _mm_unpackhi_epi32(a1, b1); // b0 b1 0 0 + return v_uint32x4(_mm_unpacklo_epi32(v0, v1)); +} + +template inline +void v_rshr_pack_store(unsigned* ptr, const v_uint64x2& a) +{ + uint64 delta = (uint64)1 << (n-1); + v_uint64x2 delta2(delta, delta); + __m128i a1 = _mm_srli_epi64(_mm_add_epi64(a.val, delta2.val), n); + __m128i a2 = _mm_shuffle_epi32(a1, _MM_SHUFFLE(0, 2, 2, 0)); + _mm_storel_epi64((__m128i*)ptr, a2); +} + +inline __m128i v_sign_epi64(__m128i a) +{ + return _mm_shuffle_epi32(_mm_srai_epi32(a, 31), _MM_SHUFFLE(3, 3, 1, 1)); // x m0 | x m1 +} + +inline __m128i v_srai_epi64(__m128i a, int imm) +{ + __m128i smask = v_sign_epi64(a); + return _mm_xor_si128(_mm_srli_epi64(_mm_xor_si128(a, smask), imm), smask); +} + +template inline +v_int32x4 v_rshr_pack(const v_int64x2& a, const v_int64x2& b) +{ + int64 delta = (int64)1 << (n-1); + v_int64x2 delta2(delta, delta); + __m128i a1 = v_srai_epi64(_mm_add_epi64(a.val, delta2.val), n); + __m128i b1 = v_srai_epi64(_mm_add_epi64(b.val, delta2.val), n); + __m128i v0 = _mm_unpacklo_epi32(a1, b1); // a0 a1 0 0 + __m128i v1 = _mm_unpackhi_epi32(a1, b1); // b0 b1 0 0 + return v_int32x4(_mm_unpacklo_epi32(v0, v1)); +} + +template inline +void v_rshr_pack_store(int* ptr, const v_int64x2& a) +{ + int64 delta = (int64)1 << (n-1); + v_int64x2 delta2(delta, delta); + __m128i a1 = v_srai_epi64(_mm_add_epi64(a.val, delta2.val), n); + __m128i a2 = _mm_shuffle_epi32(a1, _MM_SHUFFLE(0, 2, 2, 0)); + _mm_storel_epi64((__m128i*)ptr, a2); +} + +// pack boolean +inline v_uint8x16 v_pack_b(const v_uint16x8& a, const v_uint16x8& b) +{ + __m128i ab = _mm_packs_epi16(a.val, b.val); + return v_uint8x16(ab); +} + +inline v_uint8x16 v_pack_b(const v_uint32x4& a, const v_uint32x4& b, + const v_uint32x4& c, const v_uint32x4& d) +{ + __m128i ab = _mm_packs_epi32(a.val, b.val); + __m128i cd = _mm_packs_epi32(c.val, d.val); + return v_uint8x16(_mm_packs_epi16(ab, cd)); +} + +inline v_uint8x16 v_pack_b(const v_uint64x2& a, const v_uint64x2& b, const v_uint64x2& c, + const v_uint64x2& d, const v_uint64x2& e, const v_uint64x2& f, + const v_uint64x2& g, const v_uint64x2& h) +{ + __m128i ab = _mm_packs_epi32(a.val, b.val); + __m128i cd = _mm_packs_epi32(c.val, d.val); + __m128i ef = _mm_packs_epi32(e.val, f.val); + __m128i gh = _mm_packs_epi32(g.val, h.val); + + __m128i abcd = _mm_packs_epi32(ab, cd); + __m128i efgh = _mm_packs_epi32(ef, gh); + return v_uint8x16(_mm_packs_epi16(abcd, efgh)); +} + +inline v_float32x4 v_matmul(const v_float32x4& v, const v_float32x4& m0, + const v_float32x4& m1, const v_float32x4& m2, + const v_float32x4& m3) +{ + __m128 v0 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(0, 0, 0, 0)), m0.val); + __m128 v1 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(1, 1, 1, 1)), m1.val); + __m128 v2 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(2, 2, 2, 2)), m2.val); + __m128 v3 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(3, 3, 3, 3)), m3.val); + + return v_float32x4(_mm_add_ps(_mm_add_ps(v0, v1), _mm_add_ps(v2, v3))); +} + +inline v_float32x4 v_matmuladd(const v_float32x4& v, const v_float32x4& m0, + const v_float32x4& m1, const v_float32x4& m2, + const v_float32x4& a) +{ + __m128 v0 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(0, 0, 0, 0)), m0.val); + __m128 v1 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(1, 1, 1, 1)), m1.val); + __m128 v2 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(2, 2, 2, 2)), m2.val); + + return v_float32x4(_mm_add_ps(_mm_add_ps(v0, v1), _mm_add_ps(v2, a.val))); +} + +#define OPENCV_HAL_IMPL_SSE_BIN_OP(bin_op, _Tpvec, intrin) \ + inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b) \ + { \ + return _Tpvec(intrin(a.val, b.val)); \ + } \ + inline _Tpvec& operator bin_op##= (_Tpvec& a, const _Tpvec& b) \ + { \ + a.val = intrin(a.val, b.val); \ + return a; \ + } + +OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_uint8x16, _mm_adds_epu8) +OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_uint8x16, _mm_subs_epu8) +OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_int8x16, _mm_adds_epi8) +OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_int8x16, _mm_subs_epi8) +OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_uint16x8, _mm_adds_epu16) +OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_uint16x8, _mm_subs_epu16) +OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_int16x8, _mm_adds_epi16) +OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_int16x8, _mm_subs_epi16) +OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_uint32x4, _mm_add_epi32) +OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_uint32x4, _mm_sub_epi32) +OPENCV_HAL_IMPL_SSE_BIN_OP(*, v_uint32x4, _v128_mullo_epi32) +OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_int32x4, _mm_add_epi32) +OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_int32x4, _mm_sub_epi32) +OPENCV_HAL_IMPL_SSE_BIN_OP(*, v_int32x4, _v128_mullo_epi32) +OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_float32x4, _mm_add_ps) +OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_float32x4, _mm_sub_ps) +OPENCV_HAL_IMPL_SSE_BIN_OP(*, v_float32x4, _mm_mul_ps) +OPENCV_HAL_IMPL_SSE_BIN_OP(/, v_float32x4, _mm_div_ps) +OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_float64x2, _mm_add_pd) +OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_float64x2, _mm_sub_pd) +OPENCV_HAL_IMPL_SSE_BIN_OP(*, v_float64x2, _mm_mul_pd) +OPENCV_HAL_IMPL_SSE_BIN_OP(/, v_float64x2, _mm_div_pd) +OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_uint64x2, _mm_add_epi64) +OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_uint64x2, _mm_sub_epi64) +OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_int64x2, _mm_add_epi64) +OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_int64x2, _mm_sub_epi64) + +// saturating multiply 8-bit, 16-bit +#define OPENCV_HAL_IMPL_SSE_MUL_SAT(_Tpvec, _Tpwvec) \ + inline _Tpvec operator * (const _Tpvec& a, const _Tpvec& b) \ + { \ + _Tpwvec c, d; \ + v_mul_expand(a, b, c, d); \ + return v_pack(c, d); \ + } \ + inline _Tpvec& operator *= (_Tpvec& a, const _Tpvec& b) \ + { a = a * b; return a; } + +OPENCV_HAL_IMPL_SSE_MUL_SAT(v_uint8x16, v_uint16x8) +OPENCV_HAL_IMPL_SSE_MUL_SAT(v_int8x16, v_int16x8) +OPENCV_HAL_IMPL_SSE_MUL_SAT(v_uint16x8, v_uint32x4) +OPENCV_HAL_IMPL_SSE_MUL_SAT(v_int16x8, v_int32x4) + +// Multiply and expand +inline void v_mul_expand(const v_uint8x16& a, const v_uint8x16& b, + v_uint16x8& c, v_uint16x8& d) +{ + v_uint16x8 a0, a1, b0, b1; + v_expand(a, a0, a1); + v_expand(b, b0, b1); + c = v_mul_wrap(a0, b0); + d = v_mul_wrap(a1, b1); +} + +inline void v_mul_expand(const v_int8x16& a, const v_int8x16& b, + v_int16x8& c, v_int16x8& d) +{ + v_int16x8 a0, a1, b0, b1; + v_expand(a, a0, a1); + v_expand(b, b0, b1); + c = v_mul_wrap(a0, b0); + d = v_mul_wrap(a1, b1); +} + +inline void v_mul_expand(const v_int16x8& a, const v_int16x8& b, + v_int32x4& c, v_int32x4& d) +{ + __m128i v0 = _mm_mullo_epi16(a.val, b.val); + __m128i v1 = _mm_mulhi_epi16(a.val, b.val); + c.val = _mm_unpacklo_epi16(v0, v1); + d.val = _mm_unpackhi_epi16(v0, v1); +} + +inline void v_mul_expand(const v_uint16x8& a, const v_uint16x8& b, + v_uint32x4& c, v_uint32x4& d) +{ + __m128i v0 = _mm_mullo_epi16(a.val, b.val); + __m128i v1 = _mm_mulhi_epu16(a.val, b.val); + c.val = _mm_unpacklo_epi16(v0, v1); + d.val = _mm_unpackhi_epi16(v0, v1); +} + +inline void v_mul_expand(const v_uint32x4& a, const v_uint32x4& b, + v_uint64x2& c, v_uint64x2& d) +{ + __m128i c0 = _mm_mul_epu32(a.val, b.val); + __m128i c1 = _mm_mul_epu32(_mm_srli_epi64(a.val, 32), _mm_srli_epi64(b.val, 32)); + c.val = _mm_unpacklo_epi64(c0, c1); + d.val = _mm_unpackhi_epi64(c0, c1); +} + +inline v_int16x8 v_mul_hi(const v_int16x8& a, const v_int16x8& b) { return v_int16x8(_mm_mulhi_epi16(a.val, b.val)); } +inline v_uint16x8 v_mul_hi(const v_uint16x8& a, const v_uint16x8& b) { return v_uint16x8(_mm_mulhi_epu16(a.val, b.val)); } + +inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b) +{ + return v_int32x4(_mm_madd_epi16(a.val, b.val)); +} + +inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b, const v_int32x4& c) +{ + return v_int32x4(_mm_add_epi32(_mm_madd_epi16(a.val, b.val), c.val)); +} + +#define OPENCV_HAL_IMPL_SSE_LOGIC_OP(_Tpvec, suffix, not_const) \ + OPENCV_HAL_IMPL_SSE_BIN_OP(&, _Tpvec, _mm_and_##suffix) \ + OPENCV_HAL_IMPL_SSE_BIN_OP(|, _Tpvec, _mm_or_##suffix) \ + OPENCV_HAL_IMPL_SSE_BIN_OP(^, _Tpvec, _mm_xor_##suffix) \ + inline _Tpvec operator ~ (const _Tpvec& a) \ + { \ + return _Tpvec(_mm_xor_##suffix(a.val, not_const)); \ + } + +OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_uint8x16, si128, _mm_set1_epi32(-1)) +OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_int8x16, si128, _mm_set1_epi32(-1)) +OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_uint16x8, si128, _mm_set1_epi32(-1)) +OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_int16x8, si128, _mm_set1_epi32(-1)) +OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_uint32x4, si128, _mm_set1_epi32(-1)) +OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_int32x4, si128, _mm_set1_epi32(-1)) +OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_uint64x2, si128, _mm_set1_epi32(-1)) +OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_int64x2, si128, _mm_set1_epi32(-1)) +OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_float32x4, ps, _mm_castsi128_ps(_mm_set1_epi32(-1))) +OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_float64x2, pd, _mm_castsi128_pd(_mm_set1_epi32(-1))) + +inline v_float32x4 v_sqrt(const v_float32x4& x) +{ return v_float32x4(_mm_sqrt_ps(x.val)); } + +inline v_float32x4 v_invsqrt(const v_float32x4& x) +{ + const __m128 _0_5 = _mm_set1_ps(0.5f), _1_5 = _mm_set1_ps(1.5f); + __m128 t = x.val; + __m128 h = _mm_mul_ps(t, _0_5); + t = _mm_rsqrt_ps(t); + t = _mm_mul_ps(t, _mm_sub_ps(_1_5, _mm_mul_ps(_mm_mul_ps(t, t), h))); + return v_float32x4(t); +} + +inline v_float64x2 v_sqrt(const v_float64x2& x) +{ return v_float64x2(_mm_sqrt_pd(x.val)); } + +inline v_float64x2 v_invsqrt(const v_float64x2& x) +{ + const __m128d v_1 = _mm_set1_pd(1.); + return v_float64x2(_mm_div_pd(v_1, _mm_sqrt_pd(x.val))); +} + +#define OPENCV_HAL_IMPL_SSE_ABS_INT_FUNC(_Tpuvec, _Tpsvec, func, suffix, subWidth) \ +inline _Tpuvec v_abs(const _Tpsvec& x) \ +{ return _Tpuvec(_mm_##func##_ep##suffix(x.val, _mm_sub_ep##subWidth(_mm_setzero_si128(), x.val))); } + +OPENCV_HAL_IMPL_SSE_ABS_INT_FUNC(v_uint8x16, v_int8x16, min, u8, i8) +OPENCV_HAL_IMPL_SSE_ABS_INT_FUNC(v_uint16x8, v_int16x8, max, i16, i16) +inline v_uint32x4 v_abs(const v_int32x4& x) +{ + __m128i s = _mm_srli_epi32(x.val, 31); + __m128i f = _mm_srai_epi32(x.val, 31); + return v_uint32x4(_mm_add_epi32(_mm_xor_si128(x.val, f), s)); +} +inline v_float32x4 v_abs(const v_float32x4& x) +{ return v_float32x4(_mm_and_ps(x.val, _mm_castsi128_ps(_mm_set1_epi32(0x7fffffff)))); } +inline v_float64x2 v_abs(const v_float64x2& x) +{ + return v_float64x2(_mm_and_pd(x.val, + _mm_castsi128_pd(_mm_srli_epi64(_mm_set1_epi32(-1), 1)))); +} + +// TODO: exp, log, sin, cos + +#define OPENCV_HAL_IMPL_SSE_BIN_FUNC(_Tpvec, func, intrin) \ +inline _Tpvec func(const _Tpvec& a, const _Tpvec& b) \ +{ \ + return _Tpvec(intrin(a.val, b.val)); \ +} + +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint8x16, v_min, _mm_min_epu8) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint8x16, v_max, _mm_max_epu8) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int16x8, v_min, _mm_min_epi16) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int16x8, v_max, _mm_max_epi16) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_float32x4, v_min, _mm_min_ps) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_float32x4, v_max, _mm_max_ps) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_float64x2, v_min, _mm_min_pd) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_float64x2, v_max, _mm_max_pd) + +inline v_int8x16 v_min(const v_int8x16& a, const v_int8x16& b) +{ +#if CV_SSE4_1 + return v_int8x16(_mm_min_epi8(a.val, b.val)); +#else + __m128i delta = _mm_set1_epi8((char)-128); + return v_int8x16(_mm_xor_si128(delta, _mm_min_epu8(_mm_xor_si128(a.val, delta), + _mm_xor_si128(b.val, delta)))); +#endif +} +inline v_int8x16 v_max(const v_int8x16& a, const v_int8x16& b) +{ +#if CV_SSE4_1 + return v_int8x16(_mm_max_epi8(a.val, b.val)); +#else + __m128i delta = _mm_set1_epi8((char)-128); + return v_int8x16(_mm_xor_si128(delta, _mm_max_epu8(_mm_xor_si128(a.val, delta), + _mm_xor_si128(b.val, delta)))); +#endif +} +inline v_uint16x8 v_min(const v_uint16x8& a, const v_uint16x8& b) +{ +#if CV_SSE4_1 + return v_uint16x8(_mm_min_epu16(a.val, b.val)); +#else + return v_uint16x8(_mm_subs_epu16(a.val, _mm_subs_epu16(a.val, b.val))); +#endif +} +inline v_uint16x8 v_max(const v_uint16x8& a, const v_uint16x8& b) +{ +#if CV_SSE4_1 + return v_uint16x8(_mm_max_epu16(a.val, b.val)); +#else + return v_uint16x8(_mm_adds_epu16(_mm_subs_epu16(a.val, b.val), b.val)); +#endif +} +inline v_uint32x4 v_min(const v_uint32x4& a, const v_uint32x4& b) +{ +#if CV_SSE4_1 + return v_uint32x4(_mm_min_epu32(a.val, b.val)); +#else + __m128i delta = _mm_set1_epi32((int)0x80000000); + __m128i mask = _mm_cmpgt_epi32(_mm_xor_si128(a.val, delta), _mm_xor_si128(b.val, delta)); + return v_uint32x4(v_select_si128(mask, b.val, a.val)); +#endif +} +inline v_uint32x4 v_max(const v_uint32x4& a, const v_uint32x4& b) +{ +#if CV_SSE4_1 + return v_uint32x4(_mm_max_epu32(a.val, b.val)); +#else + __m128i delta = _mm_set1_epi32((int)0x80000000); + __m128i mask = _mm_cmpgt_epi32(_mm_xor_si128(a.val, delta), _mm_xor_si128(b.val, delta)); + return v_uint32x4(v_select_si128(mask, a.val, b.val)); +#endif +} +inline v_int32x4 v_min(const v_int32x4& a, const v_int32x4& b) +{ +#if CV_SSE4_1 + return v_int32x4(_mm_min_epi32(a.val, b.val)); +#else + return v_int32x4(v_select_si128(_mm_cmpgt_epi32(a.val, b.val), b.val, a.val)); +#endif +} +inline v_int32x4 v_max(const v_int32x4& a, const v_int32x4& b) +{ +#if CV_SSE4_1 + return v_int32x4(_mm_max_epi32(a.val, b.val)); +#else + return v_int32x4(v_select_si128(_mm_cmpgt_epi32(a.val, b.val), a.val, b.val)); +#endif +} + +#define OPENCV_HAL_IMPL_SSE_INT_CMP_OP(_Tpuvec, _Tpsvec, suffix, sbit) \ +inline _Tpuvec operator == (const _Tpuvec& a, const _Tpuvec& b) \ +{ return _Tpuvec(_mm_cmpeq_##suffix(a.val, b.val)); } \ +inline _Tpuvec operator != (const _Tpuvec& a, const _Tpuvec& b) \ +{ \ + __m128i not_mask = _mm_set1_epi32(-1); \ + return _Tpuvec(_mm_xor_si128(_mm_cmpeq_##suffix(a.val, b.val), not_mask)); \ +} \ +inline _Tpsvec operator == (const _Tpsvec& a, const _Tpsvec& b) \ +{ return _Tpsvec(_mm_cmpeq_##suffix(a.val, b.val)); } \ +inline _Tpsvec operator != (const _Tpsvec& a, const _Tpsvec& b) \ +{ \ + __m128i not_mask = _mm_set1_epi32(-1); \ + return _Tpsvec(_mm_xor_si128(_mm_cmpeq_##suffix(a.val, b.val), not_mask)); \ +} \ +inline _Tpuvec operator < (const _Tpuvec& a, const _Tpuvec& b) \ +{ \ + __m128i smask = _mm_set1_##suffix(sbit); \ + return _Tpuvec(_mm_cmpgt_##suffix(_mm_xor_si128(b.val, smask), _mm_xor_si128(a.val, smask))); \ +} \ +inline _Tpuvec operator > (const _Tpuvec& a, const _Tpuvec& b) \ +{ \ + __m128i smask = _mm_set1_##suffix(sbit); \ + return _Tpuvec(_mm_cmpgt_##suffix(_mm_xor_si128(a.val, smask), _mm_xor_si128(b.val, smask))); \ +} \ +inline _Tpuvec operator <= (const _Tpuvec& a, const _Tpuvec& b) \ +{ \ + __m128i smask = _mm_set1_##suffix(sbit); \ + __m128i not_mask = _mm_set1_epi32(-1); \ + __m128i res = _mm_cmpgt_##suffix(_mm_xor_si128(a.val, smask), _mm_xor_si128(b.val, smask)); \ + return _Tpuvec(_mm_xor_si128(res, not_mask)); \ +} \ +inline _Tpuvec operator >= (const _Tpuvec& a, const _Tpuvec& b) \ +{ \ + __m128i smask = _mm_set1_##suffix(sbit); \ + __m128i not_mask = _mm_set1_epi32(-1); \ + __m128i res = _mm_cmpgt_##suffix(_mm_xor_si128(b.val, smask), _mm_xor_si128(a.val, smask)); \ + return _Tpuvec(_mm_xor_si128(res, not_mask)); \ +} \ +inline _Tpsvec operator < (const _Tpsvec& a, const _Tpsvec& b) \ +{ \ + return _Tpsvec(_mm_cmpgt_##suffix(b.val, a.val)); \ +} \ +inline _Tpsvec operator > (const _Tpsvec& a, const _Tpsvec& b) \ +{ \ + return _Tpsvec(_mm_cmpgt_##suffix(a.val, b.val)); \ +} \ +inline _Tpsvec operator <= (const _Tpsvec& a, const _Tpsvec& b) \ +{ \ + __m128i not_mask = _mm_set1_epi32(-1); \ + return _Tpsvec(_mm_xor_si128(_mm_cmpgt_##suffix(a.val, b.val), not_mask)); \ +} \ +inline _Tpsvec operator >= (const _Tpsvec& a, const _Tpsvec& b) \ +{ \ + __m128i not_mask = _mm_set1_epi32(-1); \ + return _Tpsvec(_mm_xor_si128(_mm_cmpgt_##suffix(b.val, a.val), not_mask)); \ +} + +OPENCV_HAL_IMPL_SSE_INT_CMP_OP(v_uint8x16, v_int8x16, epi8, (char)-128) +OPENCV_HAL_IMPL_SSE_INT_CMP_OP(v_uint16x8, v_int16x8, epi16, (short)-32768) +OPENCV_HAL_IMPL_SSE_INT_CMP_OP(v_uint32x4, v_int32x4, epi32, (int)0x80000000) + +#define OPENCV_HAL_IMPL_SSE_FLT_CMP_OP(_Tpvec, suffix) \ +inline _Tpvec operator == (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(_mm_cmpeq_##suffix(a.val, b.val)); } \ +inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(_mm_cmpneq_##suffix(a.val, b.val)); } \ +inline _Tpvec operator < (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(_mm_cmplt_##suffix(a.val, b.val)); } \ +inline _Tpvec operator > (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(_mm_cmpgt_##suffix(a.val, b.val)); } \ +inline _Tpvec operator <= (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(_mm_cmple_##suffix(a.val, b.val)); } \ +inline _Tpvec operator >= (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(_mm_cmpge_##suffix(a.val, b.val)); } + +OPENCV_HAL_IMPL_SSE_FLT_CMP_OP(v_float32x4, ps) +OPENCV_HAL_IMPL_SSE_FLT_CMP_OP(v_float64x2, pd) + +#define OPENCV_HAL_IMPL_SSE_64BIT_CMP_OP(_Tpvec, cast) \ +inline _Tpvec operator == (const _Tpvec& a, const _Tpvec& b) \ +{ return cast(v_reinterpret_as_f64(a) == v_reinterpret_as_f64(b)); } \ +inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \ +{ return cast(v_reinterpret_as_f64(a) != v_reinterpret_as_f64(b)); } + +OPENCV_HAL_IMPL_SSE_64BIT_CMP_OP(v_uint64x2, v_reinterpret_as_u64) +OPENCV_HAL_IMPL_SSE_64BIT_CMP_OP(v_int64x2, v_reinterpret_as_s64) + +inline v_float32x4 v_not_nan(const v_float32x4& a) +{ return v_float32x4(_mm_cmpord_ps(a.val, a.val)); } +inline v_float64x2 v_not_nan(const v_float64x2& a) +{ return v_float64x2(_mm_cmpord_pd(a.val, a.val)); } + +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint8x16, v_add_wrap, _mm_add_epi8) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int8x16, v_add_wrap, _mm_add_epi8) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint16x8, v_add_wrap, _mm_add_epi16) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int16x8, v_add_wrap, _mm_add_epi16) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint8x16, v_sub_wrap, _mm_sub_epi8) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int8x16, v_sub_wrap, _mm_sub_epi8) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint16x8, v_sub_wrap, _mm_sub_epi16) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int16x8, v_sub_wrap, _mm_sub_epi16) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint16x8, v_mul_wrap, _mm_mullo_epi16) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int16x8, v_mul_wrap, _mm_mullo_epi16) + +inline v_uint8x16 v_mul_wrap(const v_uint8x16& a, const v_uint8x16& b) +{ + __m128i ad = _mm_srai_epi16(a.val, 8); + __m128i bd = _mm_srai_epi16(b.val, 8); + __m128i p0 = _mm_mullo_epi16(a.val, b.val); // even + __m128i p1 = _mm_slli_epi16(_mm_mullo_epi16(ad, bd), 8); // odd + const __m128i b01 = _mm_set1_epi32(0xFF00FF00); + return v_uint8x16(_v128_blendv_epi8(p0, p1, b01)); +} +inline v_int8x16 v_mul_wrap(const v_int8x16& a, const v_int8x16& b) +{ + return v_reinterpret_as_s8(v_mul_wrap(v_reinterpret_as_u8(a), v_reinterpret_as_u8(b))); +} + +/** Absolute difference **/ + +inline v_uint8x16 v_absdiff(const v_uint8x16& a, const v_uint8x16& b) +{ return v_add_wrap(a - b, b - a); } +inline v_uint16x8 v_absdiff(const v_uint16x8& a, const v_uint16x8& b) +{ return v_add_wrap(a - b, b - a); } +inline v_uint32x4 v_absdiff(const v_uint32x4& a, const v_uint32x4& b) +{ return v_max(a, b) - v_min(a, b); } + +inline v_uint8x16 v_absdiff(const v_int8x16& a, const v_int8x16& b) +{ + v_int8x16 d = v_sub_wrap(a, b); + v_int8x16 m = a < b; + return v_reinterpret_as_u8(v_sub_wrap(d ^ m, m)); +} +inline v_uint16x8 v_absdiff(const v_int16x8& a, const v_int16x8& b) +{ + return v_reinterpret_as_u16(v_sub_wrap(v_max(a, b), v_min(a, b))); +} +inline v_uint32x4 v_absdiff(const v_int32x4& a, const v_int32x4& b) +{ + v_int32x4 d = a - b; + v_int32x4 m = a < b; + return v_reinterpret_as_u32((d ^ m) - m); +} + +/** Saturating absolute difference **/ +inline v_int8x16 v_absdiffs(const v_int8x16& a, const v_int8x16& b) +{ + v_int8x16 d = a - b; + v_int8x16 m = a < b; + return (d ^ m) - m; + } +inline v_int16x8 v_absdiffs(const v_int16x8& a, const v_int16x8& b) +{ return v_max(a, b) - v_min(a, b); } + + +inline v_int32x4 v_fma(const v_int32x4& a, const v_int32x4& b, const v_int32x4& c) +{ + return a * b + c; +} + +inline v_int32x4 v_muladd(const v_int32x4& a, const v_int32x4& b, const v_int32x4& c) +{ + return v_fma(a, b, c); +} + +inline v_float32x4 v_fma(const v_float32x4& a, const v_float32x4& b, const v_float32x4& c) +{ +#if CV_FMA3 + return v_float32x4(_mm_fmadd_ps(a.val, b.val, c.val)); +#else + return v_float32x4(_mm_add_ps(_mm_mul_ps(a.val, b.val), c.val)); +#endif +} + +inline v_float64x2 v_fma(const v_float64x2& a, const v_float64x2& b, const v_float64x2& c) +{ +#if CV_FMA3 + return v_float64x2(_mm_fmadd_pd(a.val, b.val, c.val)); +#else + return v_float64x2(_mm_add_pd(_mm_mul_pd(a.val, b.val), c.val)); +#endif +} + +#define OPENCV_HAL_IMPL_SSE_MISC_FLT_OP(_Tpvec, _Tp, _Tpreg, suffix, absmask_vec) \ +inline _Tpvec v_absdiff(const _Tpvec& a, const _Tpvec& b) \ +{ \ + _Tpreg absmask = _mm_castsi128_##suffix(absmask_vec); \ + return _Tpvec(_mm_and_##suffix(_mm_sub_##suffix(a.val, b.val), absmask)); \ +} \ +inline _Tpvec v_magnitude(const _Tpvec& a, const _Tpvec& b) \ +{ \ + _Tpvec res = v_fma(a, a, b*b); \ + return _Tpvec(_mm_sqrt_##suffix(res.val)); \ +} \ +inline _Tpvec v_sqr_magnitude(const _Tpvec& a, const _Tpvec& b) \ +{ \ + return v_fma(a, a, b*b); \ +} \ +inline _Tpvec v_muladd(const _Tpvec& a, const _Tpvec& b, const _Tpvec& c) \ +{ \ + return v_fma(a, b, c); \ +} + +OPENCV_HAL_IMPL_SSE_MISC_FLT_OP(v_float32x4, float, __m128, ps, _mm_set1_epi32((int)0x7fffffff)) +OPENCV_HAL_IMPL_SSE_MISC_FLT_OP(v_float64x2, double, __m128d, pd, _mm_srli_epi64(_mm_set1_epi32(-1), 1)) + +#define OPENCV_HAL_IMPL_SSE_SHIFT_OP(_Tpuvec, _Tpsvec, suffix, srai) \ +inline _Tpuvec operator << (const _Tpuvec& a, int imm) \ +{ \ + return _Tpuvec(_mm_slli_##suffix(a.val, imm)); \ +} \ +inline _Tpsvec operator << (const _Tpsvec& a, int imm) \ +{ \ + return _Tpsvec(_mm_slli_##suffix(a.val, imm)); \ +} \ +inline _Tpuvec operator >> (const _Tpuvec& a, int imm) \ +{ \ + return _Tpuvec(_mm_srli_##suffix(a.val, imm)); \ +} \ +inline _Tpsvec operator >> (const _Tpsvec& a, int imm) \ +{ \ + return _Tpsvec(srai(a.val, imm)); \ +} \ +template \ +inline _Tpuvec v_shl(const _Tpuvec& a) \ +{ \ + return _Tpuvec(_mm_slli_##suffix(a.val, imm)); \ +} \ +template \ +inline _Tpsvec v_shl(const _Tpsvec& a) \ +{ \ + return _Tpsvec(_mm_slli_##suffix(a.val, imm)); \ +} \ +template \ +inline _Tpuvec v_shr(const _Tpuvec& a) \ +{ \ + return _Tpuvec(_mm_srli_##suffix(a.val, imm)); \ +} \ +template \ +inline _Tpsvec v_shr(const _Tpsvec& a) \ +{ \ + return _Tpsvec(srai(a.val, imm)); \ +} + +OPENCV_HAL_IMPL_SSE_SHIFT_OP(v_uint16x8, v_int16x8, epi16, _mm_srai_epi16) +OPENCV_HAL_IMPL_SSE_SHIFT_OP(v_uint32x4, v_int32x4, epi32, _mm_srai_epi32) +OPENCV_HAL_IMPL_SSE_SHIFT_OP(v_uint64x2, v_int64x2, epi64, v_srai_epi64) + +namespace hal_sse_internal +{ + template 16)), + bool is_first = (imm == 0), + bool is_half = (imm == 8), + bool is_second = (imm == 16), + bool is_other = (((imm > 0) && (imm < 8)) || ((imm > 8) && (imm < 16)))> + class v_sse_palignr_u8_class; + + template + class v_sse_palignr_u8_class; + + template + class v_sse_palignr_u8_class + { + public: + inline __m128i operator()(const __m128i& a, const __m128i&) const + { + return a; + } + }; + + template + class v_sse_palignr_u8_class + { + public: + inline __m128i operator()(const __m128i& a, const __m128i& b) const + { + return _mm_unpacklo_epi64(_mm_unpackhi_epi64(a, a), b); + } + }; + + template + class v_sse_palignr_u8_class + { + public: + inline __m128i operator()(const __m128i&, const __m128i& b) const + { + return b; + } + }; + + template + class v_sse_palignr_u8_class + { +#if CV_SSSE3 + public: + inline __m128i operator()(const __m128i& a, const __m128i& b) const + { + return _mm_alignr_epi8(b, a, imm); + } +#else + public: + inline __m128i operator()(const __m128i& a, const __m128i& b) const + { + enum { imm2 = (sizeof(__m128i) - imm) }; + return _mm_or_si128(_mm_srli_si128(a, imm), _mm_slli_si128(b, imm2)); + } +#endif + }; + + template + inline __m128i v_sse_palignr_u8(const __m128i& a, const __m128i& b) + { + CV_StaticAssert((imm >= 0) && (imm <= 16), "Invalid imm for v_sse_palignr_u8."); + return v_sse_palignr_u8_class()(a, b); + } +} + +template +inline _Tpvec v_rotate_right(const _Tpvec &a) +{ + using namespace hal_sse_internal; + enum { imm2 = (imm * sizeof(typename _Tpvec::lane_type)) }; + return _Tpvec(v_sse_reinterpret_as( + _mm_srli_si128( + v_sse_reinterpret_as<__m128i>(a.val), imm2))); +} + +template +inline _Tpvec v_rotate_left(const _Tpvec &a) +{ + using namespace hal_sse_internal; + enum { imm2 = (imm * sizeof(typename _Tpvec::lane_type)) }; + return _Tpvec(v_sse_reinterpret_as( + _mm_slli_si128( + v_sse_reinterpret_as<__m128i>(a.val), imm2))); +} + +template +inline _Tpvec v_rotate_right(const _Tpvec &a, const _Tpvec &b) +{ + using namespace hal_sse_internal; + enum { imm2 = (imm * sizeof(typename _Tpvec::lane_type)) }; + return _Tpvec(v_sse_reinterpret_as( + v_sse_palignr_u8( + v_sse_reinterpret_as<__m128i>(a.val), + v_sse_reinterpret_as<__m128i>(b.val)))); +} + +template +inline _Tpvec v_rotate_left(const _Tpvec &a, const _Tpvec &b) +{ + using namespace hal_sse_internal; + enum { imm2 = ((_Tpvec::nlanes - imm) * sizeof(typename _Tpvec::lane_type)) }; + return _Tpvec(v_sse_reinterpret_as( + v_sse_palignr_u8( + v_sse_reinterpret_as<__m128i>(b.val), + v_sse_reinterpret_as<__m128i>(a.val)))); +} + +#define OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(_Tpvec, _Tp) \ +inline _Tpvec v_load(const _Tp* ptr) \ +{ return _Tpvec(_mm_loadu_si128((const __m128i*)ptr)); } \ +inline _Tpvec v_load_aligned(const _Tp* ptr) \ +{ return _Tpvec(_mm_load_si128((const __m128i*)ptr)); } \ +inline _Tpvec v_load_low(const _Tp* ptr) \ +{ return _Tpvec(_mm_loadl_epi64((const __m128i*)ptr)); } \ +inline _Tpvec v_load_halves(const _Tp* ptr0, const _Tp* ptr1) \ +{ \ + return _Tpvec(_mm_unpacklo_epi64(_mm_loadl_epi64((const __m128i*)ptr0), \ + _mm_loadl_epi64((const __m128i*)ptr1))); \ +} \ +inline void v_store(_Tp* ptr, const _Tpvec& a) \ +{ _mm_storeu_si128((__m128i*)ptr, a.val); } \ +inline void v_store_aligned(_Tp* ptr, const _Tpvec& a) \ +{ _mm_store_si128((__m128i*)ptr, a.val); } \ +inline void v_store_aligned_nocache(_Tp* ptr, const _Tpvec& a) \ +{ _mm_stream_si128((__m128i*)ptr, a.val); } \ +inline void v_store(_Tp* ptr, const _Tpvec& a, hal::StoreMode mode) \ +{ \ + if( mode == hal::STORE_UNALIGNED ) \ + _mm_storeu_si128((__m128i*)ptr, a.val); \ + else if( mode == hal::STORE_ALIGNED_NOCACHE ) \ + _mm_stream_si128((__m128i*)ptr, a.val); \ + else \ + _mm_store_si128((__m128i*)ptr, a.val); \ +} \ +inline void v_store_low(_Tp* ptr, const _Tpvec& a) \ +{ _mm_storel_epi64((__m128i*)ptr, a.val); } \ +inline void v_store_high(_Tp* ptr, const _Tpvec& a) \ +{ _mm_storel_epi64((__m128i*)ptr, _mm_unpackhi_epi64(a.val, a.val)); } + +OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_uint8x16, uchar) +OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_int8x16, schar) +OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_uint16x8, ushort) +OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_int16x8, short) +OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_uint32x4, unsigned) +OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_int32x4, int) +OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_uint64x2, uint64) +OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_int64x2, int64) + +#define OPENCV_HAL_IMPL_SSE_LOADSTORE_FLT_OP(_Tpvec, _Tp, suffix) \ +inline _Tpvec v_load(const _Tp* ptr) \ +{ return _Tpvec(_mm_loadu_##suffix(ptr)); } \ +inline _Tpvec v_load_aligned(const _Tp* ptr) \ +{ return _Tpvec(_mm_load_##suffix(ptr)); } \ +inline _Tpvec v_load_low(const _Tp* ptr) \ +{ return _Tpvec(_mm_castsi128_##suffix(_mm_loadl_epi64((const __m128i*)ptr))); } \ +inline _Tpvec v_load_halves(const _Tp* ptr0, const _Tp* ptr1) \ +{ \ + return _Tpvec(_mm_castsi128_##suffix( \ + _mm_unpacklo_epi64(_mm_loadl_epi64((const __m128i*)ptr0), \ + _mm_loadl_epi64((const __m128i*)ptr1)))); \ +} \ +inline void v_store(_Tp* ptr, const _Tpvec& a) \ +{ _mm_storeu_##suffix(ptr, a.val); } \ +inline void v_store_aligned(_Tp* ptr, const _Tpvec& a) \ +{ _mm_store_##suffix(ptr, a.val); } \ +inline void v_store_aligned_nocache(_Tp* ptr, const _Tpvec& a) \ +{ _mm_stream_##suffix(ptr, a.val); } \ +inline void v_store(_Tp* ptr, const _Tpvec& a, hal::StoreMode mode) \ +{ \ + if( mode == hal::STORE_UNALIGNED ) \ + _mm_storeu_##suffix(ptr, a.val); \ + else if( mode == hal::STORE_ALIGNED_NOCACHE ) \ + _mm_stream_##suffix(ptr, a.val); \ + else \ + _mm_store_##suffix(ptr, a.val); \ +} \ +inline void v_store_low(_Tp* ptr, const _Tpvec& a) \ +{ _mm_storel_epi64((__m128i*)ptr, _mm_cast##suffix##_si128(a.val)); } \ +inline void v_store_high(_Tp* ptr, const _Tpvec& a) \ +{ \ + __m128i a1 = _mm_cast##suffix##_si128(a.val); \ + _mm_storel_epi64((__m128i*)ptr, _mm_unpackhi_epi64(a1, a1)); \ +} + +OPENCV_HAL_IMPL_SSE_LOADSTORE_FLT_OP(v_float32x4, float, ps) +OPENCV_HAL_IMPL_SSE_LOADSTORE_FLT_OP(v_float64x2, double, pd) + +#define OPENCV_HAL_IMPL_SSE_REDUCE_OP_8(_Tpvec, scalartype, func, suffix, sbit) \ +inline scalartype v_reduce_##func(const v_##_Tpvec& a) \ +{ \ + __m128i val = a.val; \ + val = _mm_##func##_##suffix(val, _mm_srli_si128(val,8)); \ + val = _mm_##func##_##suffix(val, _mm_srli_si128(val,4)); \ + val = _mm_##func##_##suffix(val, _mm_srli_si128(val,2)); \ + return (scalartype)_mm_cvtsi128_si32(val); \ +} \ +inline unsigned scalartype v_reduce_##func(const v_u##_Tpvec& a) \ +{ \ + __m128i val = a.val; \ + __m128i smask = _mm_set1_epi16(sbit); \ + val = _mm_xor_si128(val, smask); \ + val = _mm_##func##_##suffix(val, _mm_srli_si128(val,8)); \ + val = _mm_##func##_##suffix(val, _mm_srli_si128(val,4)); \ + val = _mm_##func##_##suffix(val, _mm_srli_si128(val,2)); \ + return (unsigned scalartype)(_mm_cvtsi128_si32(val) ^ sbit); \ +} +#define OPENCV_HAL_IMPL_SSE_REDUCE_OP_8_SUM(_Tpvec, scalartype, suffix) \ +inline scalartype v_reduce_sum(const v_##_Tpvec& a) \ +{ \ + __m128i val = a.val; \ + val = _mm_adds_epi##suffix(val, _mm_srli_si128(val, 8)); \ + val = _mm_adds_epi##suffix(val, _mm_srli_si128(val, 4)); \ + val = _mm_adds_epi##suffix(val, _mm_srli_si128(val, 2)); \ + return (scalartype)_mm_cvtsi128_si32(val); \ +} \ +inline unsigned scalartype v_reduce_sum(const v_u##_Tpvec& a) \ +{ \ + __m128i val = a.val; \ + val = _mm_adds_epu##suffix(val, _mm_srli_si128(val, 8)); \ + val = _mm_adds_epu##suffix(val, _mm_srli_si128(val, 4)); \ + val = _mm_adds_epu##suffix(val, _mm_srli_si128(val, 2)); \ + return (unsigned scalartype)_mm_cvtsi128_si32(val); \ +} +OPENCV_HAL_IMPL_SSE_REDUCE_OP_8(int16x8, short, max, epi16, (short)-32768) +OPENCV_HAL_IMPL_SSE_REDUCE_OP_8(int16x8, short, min, epi16, (short)-32768) +OPENCV_HAL_IMPL_SSE_REDUCE_OP_8_SUM(int16x8, short, 16) + +#define OPENCV_HAL_IMPL_SSE_REDUCE_OP_4_SUM(_Tpvec, scalartype, regtype, suffix, cast_from, cast_to, extract) \ +inline scalartype v_reduce_sum(const _Tpvec& a) \ +{ \ + regtype val = a.val; \ + val = _mm_add_##suffix(val, cast_to(_mm_srli_si128(cast_from(val), 8))); \ + val = _mm_add_##suffix(val, cast_to(_mm_srli_si128(cast_from(val), 4))); \ + return (scalartype)_mm_cvt##extract(val); \ +} + +#define OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(_Tpvec, scalartype, func, scalar_func) \ +inline scalartype v_reduce_##func(const _Tpvec& a) \ +{ \ + scalartype CV_DECL_ALIGNED(16) buf[4]; \ + v_store_aligned(buf, a); \ + scalartype s0 = scalar_func(buf[0], buf[1]); \ + scalartype s1 = scalar_func(buf[2], buf[3]); \ + return scalar_func(s0, s1); \ +} + +OPENCV_HAL_IMPL_SSE_REDUCE_OP_4_SUM(v_uint32x4, unsigned, __m128i, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP, si128_si32) +OPENCV_HAL_IMPL_SSE_REDUCE_OP_4_SUM(v_int32x4, int, __m128i, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP, si128_si32) +OPENCV_HAL_IMPL_SSE_REDUCE_OP_4_SUM(v_float32x4, float, __m128, ps, _mm_castps_si128, _mm_castsi128_ps, ss_f32) + +inline double v_reduce_sum(const v_float64x2& a) +{ + double CV_DECL_ALIGNED(32) idx[2]; + v_store_aligned(idx, a); + return idx[0] + idx[1]; +} + +inline v_float32x4 v_reduce_sum4(const v_float32x4& a, const v_float32x4& b, + const v_float32x4& c, const v_float32x4& d) +{ +#if CV_SSE3 + __m128 ab = _mm_hadd_ps(a.val, b.val); + __m128 cd = _mm_hadd_ps(c.val, d.val); + return v_float32x4(_mm_hadd_ps(ab, cd)); +#else + __m128 ac = _mm_add_ps(_mm_unpacklo_ps(a.val, c.val), _mm_unpackhi_ps(a.val, c.val)); + __m128 bd = _mm_add_ps(_mm_unpacklo_ps(b.val, d.val), _mm_unpackhi_ps(b.val, d.val)); + return v_float32x4(_mm_add_ps(_mm_unpacklo_ps(ac, bd), _mm_unpackhi_ps(ac, bd))); +#endif +} + +OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_uint32x4, unsigned, max, std::max) +OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_uint32x4, unsigned, min, std::min) +OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_int32x4, int, max, std::max) +OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_int32x4, int, min, std::min) +OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_float32x4, float, max, std::max) +OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_float32x4, float, min, std::min) + +inline unsigned v_reduce_sad(const v_uint8x16& a, const v_uint8x16& b) +{ + return (unsigned)_mm_cvtsi128_si32(_mm_sad_epu8(a.val, b.val)); +} +inline unsigned v_reduce_sad(const v_int8x16& a, const v_int8x16& b) +{ + __m128i half = _mm_set1_epi8(0x7f); + return (unsigned)_mm_cvtsi128_si32(_mm_sad_epu8(_mm_add_epi8(a.val, half), + _mm_add_epi8(b.val, half))); +} +inline unsigned v_reduce_sad(const v_uint16x8& a, const v_uint16x8& b) +{ + v_uint32x4 l, h; + v_expand(v_absdiff(a, b), l, h); + return v_reduce_sum(l + h); +} +inline unsigned v_reduce_sad(const v_int16x8& a, const v_int16x8& b) +{ + v_uint32x4 l, h; + v_expand(v_absdiff(a, b), l, h); + return v_reduce_sum(l + h); +} +inline unsigned v_reduce_sad(const v_uint32x4& a, const v_uint32x4& b) +{ + return v_reduce_sum(v_absdiff(a, b)); +} +inline unsigned v_reduce_sad(const v_int32x4& a, const v_int32x4& b) +{ + return v_reduce_sum(v_absdiff(a, b)); +} +inline float v_reduce_sad(const v_float32x4& a, const v_float32x4& b) +{ + return v_reduce_sum(v_absdiff(a, b)); +} + +#define OPENCV_HAL_IMPL_SSE_POPCOUNT(_Tpvec) \ +inline v_uint32x4 v_popcount(const _Tpvec& a) \ +{ \ + __m128i m1 = _mm_set1_epi32(0x55555555); \ + __m128i m2 = _mm_set1_epi32(0x33333333); \ + __m128i m4 = _mm_set1_epi32(0x0f0f0f0f); \ + __m128i p = a.val; \ + p = _mm_add_epi32(_mm_and_si128(_mm_srli_epi32(p, 1), m1), _mm_and_si128(p, m1)); \ + p = _mm_add_epi32(_mm_and_si128(_mm_srli_epi32(p, 2), m2), _mm_and_si128(p, m2)); \ + p = _mm_add_epi32(_mm_and_si128(_mm_srli_epi32(p, 4), m4), _mm_and_si128(p, m4)); \ + p = _mm_adds_epi8(p, _mm_srli_si128(p, 1)); \ + p = _mm_adds_epi8(p, _mm_srli_si128(p, 2)); \ + return v_uint32x4(_mm_and_si128(p, _mm_set1_epi32(0x000000ff))); \ +} + +OPENCV_HAL_IMPL_SSE_POPCOUNT(v_uint8x16) +OPENCV_HAL_IMPL_SSE_POPCOUNT(v_uint16x8) +OPENCV_HAL_IMPL_SSE_POPCOUNT(v_uint32x4) +OPENCV_HAL_IMPL_SSE_POPCOUNT(v_int8x16) +OPENCV_HAL_IMPL_SSE_POPCOUNT(v_int16x8) +OPENCV_HAL_IMPL_SSE_POPCOUNT(v_int32x4) + +#define OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(_Tpvec, suffix, pack_op, and_op, signmask, allmask) \ +inline int v_signmask(const _Tpvec& a) \ +{ \ + return and_op(_mm_movemask_##suffix(pack_op(a.val)), signmask); \ +} \ +inline bool v_check_all(const _Tpvec& a) \ +{ return and_op(_mm_movemask_##suffix(a.val), allmask) == allmask; } \ +inline bool v_check_any(const _Tpvec& a) \ +{ return and_op(_mm_movemask_##suffix(a.val), allmask) != 0; } + +#define OPENCV_HAL_PACKS(a) _mm_packs_epi16(a, a) +inline __m128i v_packq_epi32(__m128i a) +{ + __m128i b = _mm_packs_epi32(a, a); + return _mm_packs_epi16(b, b); +} + +OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_uint8x16, epi8, OPENCV_HAL_NOP, OPENCV_HAL_1ST, 65535, 65535) +OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_int8x16, epi8, OPENCV_HAL_NOP, OPENCV_HAL_1ST, 65535, 65535) +OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_uint16x8, epi8, OPENCV_HAL_PACKS, OPENCV_HAL_AND, 255, (int)0xaaaa) +OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_int16x8, epi8, OPENCV_HAL_PACKS, OPENCV_HAL_AND, 255, (int)0xaaaa) +OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_uint32x4, epi8, v_packq_epi32, OPENCV_HAL_AND, 15, (int)0x8888) +OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_int32x4, epi8, v_packq_epi32, OPENCV_HAL_AND, 15, (int)0x8888) +OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_float32x4, ps, OPENCV_HAL_NOP, OPENCV_HAL_1ST, 15, 15) +OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_float64x2, pd, OPENCV_HAL_NOP, OPENCV_HAL_1ST, 3, 3) + +#if CV_SSE4_1 +#define OPENCV_HAL_IMPL_SSE_SELECT(_Tpvec, cast_ret, cast, suffix) \ +inline _Tpvec v_select(const _Tpvec& mask, const _Tpvec& a, const _Tpvec& b) \ +{ \ + return _Tpvec(cast_ret(_mm_blendv_##suffix(cast(b.val), cast(a.val), cast(mask.val)))); \ +} + +OPENCV_HAL_IMPL_SSE_SELECT(v_uint8x16, OPENCV_HAL_NOP, OPENCV_HAL_NOP, epi8) +OPENCV_HAL_IMPL_SSE_SELECT(v_int8x16, OPENCV_HAL_NOP, OPENCV_HAL_NOP, epi8) +OPENCV_HAL_IMPL_SSE_SELECT(v_uint16x8, OPENCV_HAL_NOP, OPENCV_HAL_NOP, epi8) +OPENCV_HAL_IMPL_SSE_SELECT(v_int16x8, OPENCV_HAL_NOP, OPENCV_HAL_NOP, epi8) +OPENCV_HAL_IMPL_SSE_SELECT(v_uint32x4, _mm_castps_si128, _mm_castsi128_ps, ps) +OPENCV_HAL_IMPL_SSE_SELECT(v_int32x4, _mm_castps_si128, _mm_castsi128_ps, ps) +// OPENCV_HAL_IMPL_SSE_SELECT(v_uint64x2, TBD, TBD, pd) +// OPENCV_HAL_IMPL_SSE_SELECT(v_int64x2, TBD, TBD, ps) +OPENCV_HAL_IMPL_SSE_SELECT(v_float32x4, OPENCV_HAL_NOP, OPENCV_HAL_NOP, ps) +OPENCV_HAL_IMPL_SSE_SELECT(v_float64x2, OPENCV_HAL_NOP, OPENCV_HAL_NOP, pd) + +#else // CV_SSE4_1 + +#define OPENCV_HAL_IMPL_SSE_SELECT(_Tpvec, suffix) \ +inline _Tpvec v_select(const _Tpvec& mask, const _Tpvec& a, const _Tpvec& b) \ +{ \ + return _Tpvec(_mm_xor_##suffix(b.val, _mm_and_##suffix(_mm_xor_##suffix(b.val, a.val), mask.val))); \ +} + +OPENCV_HAL_IMPL_SSE_SELECT(v_uint8x16, si128) +OPENCV_HAL_IMPL_SSE_SELECT(v_int8x16, si128) +OPENCV_HAL_IMPL_SSE_SELECT(v_uint16x8, si128) +OPENCV_HAL_IMPL_SSE_SELECT(v_int16x8, si128) +OPENCV_HAL_IMPL_SSE_SELECT(v_uint32x4, si128) +OPENCV_HAL_IMPL_SSE_SELECT(v_int32x4, si128) +// OPENCV_HAL_IMPL_SSE_SELECT(v_uint64x2, si128) +// OPENCV_HAL_IMPL_SSE_SELECT(v_int64x2, si128) +OPENCV_HAL_IMPL_SSE_SELECT(v_float32x4, ps) +OPENCV_HAL_IMPL_SSE_SELECT(v_float64x2, pd) +#endif + +/* Expand */ +#define OPENCV_HAL_IMPL_SSE_EXPAND(_Tpvec, _Tpwvec, _Tp, intrin) \ + inline void v_expand(const _Tpvec& a, _Tpwvec& b0, _Tpwvec& b1) \ + { \ + b0.val = intrin(a.val); \ + b1.val = __CV_CAT(intrin, _high)(a.val); \ + } \ + inline _Tpwvec v_expand_low(const _Tpvec& a) \ + { return _Tpwvec(intrin(a.val)); } \ + inline _Tpwvec v_expand_high(const _Tpvec& a) \ + { return _Tpwvec(__CV_CAT(intrin, _high)(a.val)); } \ + inline _Tpwvec v_load_expand(const _Tp* ptr) \ + { \ + __m128i a = _mm_loadl_epi64((const __m128i*)ptr); \ + return _Tpwvec(intrin(a)); \ + } + +OPENCV_HAL_IMPL_SSE_EXPAND(v_uint8x16, v_uint16x8, uchar, _v128_cvtepu8_epi16) +OPENCV_HAL_IMPL_SSE_EXPAND(v_int8x16, v_int16x8, schar, _v128_cvtepi8_epi16) +OPENCV_HAL_IMPL_SSE_EXPAND(v_uint16x8, v_uint32x4, ushort, _v128_cvtepu16_epi32) +OPENCV_HAL_IMPL_SSE_EXPAND(v_int16x8, v_int32x4, short, _v128_cvtepi16_epi32) +OPENCV_HAL_IMPL_SSE_EXPAND(v_uint32x4, v_uint64x2, unsigned, _v128_cvtepu32_epi64) +OPENCV_HAL_IMPL_SSE_EXPAND(v_int32x4, v_int64x2, int, _v128_cvtepi32_epi64) + +#define OPENCV_HAL_IMPL_SSE_EXPAND_Q(_Tpvec, _Tp, intrin) \ + inline _Tpvec v_load_expand_q(const _Tp* ptr) \ + { \ + __m128i a = _mm_cvtsi32_si128(*(const int*)ptr); \ + return _Tpvec(intrin(a)); \ + } + +OPENCV_HAL_IMPL_SSE_EXPAND_Q(v_uint32x4, uchar, _v128_cvtepu8_epi32) +OPENCV_HAL_IMPL_SSE_EXPAND_Q(v_int32x4, schar, _v128_cvtepi8_epi32) + +#define OPENCV_HAL_IMPL_SSE_UNPACKS(_Tpvec, suffix, cast_from, cast_to) \ +inline void v_zip(const _Tpvec& a0, const _Tpvec& a1, _Tpvec& b0, _Tpvec& b1) \ +{ \ + b0.val = _mm_unpacklo_##suffix(a0.val, a1.val); \ + b1.val = _mm_unpackhi_##suffix(a0.val, a1.val); \ +} \ +inline _Tpvec v_combine_low(const _Tpvec& a, const _Tpvec& b) \ +{ \ + __m128i a1 = cast_from(a.val), b1 = cast_from(b.val); \ + return _Tpvec(cast_to(_mm_unpacklo_epi64(a1, b1))); \ +} \ +inline _Tpvec v_combine_high(const _Tpvec& a, const _Tpvec& b) \ +{ \ + __m128i a1 = cast_from(a.val), b1 = cast_from(b.val); \ + return _Tpvec(cast_to(_mm_unpackhi_epi64(a1, b1))); \ +} \ +inline void v_recombine(const _Tpvec& a, const _Tpvec& b, _Tpvec& c, _Tpvec& d) \ +{ \ + __m128i a1 = cast_from(a.val), b1 = cast_from(b.val); \ + c.val = cast_to(_mm_unpacklo_epi64(a1, b1)); \ + d.val = cast_to(_mm_unpackhi_epi64(a1, b1)); \ +} + +OPENCV_HAL_IMPL_SSE_UNPACKS(v_uint8x16, epi8, OPENCV_HAL_NOP, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_UNPACKS(v_int8x16, epi8, OPENCV_HAL_NOP, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_UNPACKS(v_uint16x8, epi16, OPENCV_HAL_NOP, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_UNPACKS(v_int16x8, epi16, OPENCV_HAL_NOP, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_UNPACKS(v_uint32x4, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_UNPACKS(v_int32x4, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_UNPACKS(v_float32x4, ps, _mm_castps_si128, _mm_castsi128_ps) +OPENCV_HAL_IMPL_SSE_UNPACKS(v_float64x2, pd, _mm_castpd_si128, _mm_castsi128_pd) + +template +inline _Tpvec v_extract(const _Tpvec& a, const _Tpvec& b) +{ + return v_rotate_right(a, b); +} + +inline v_int32x4 v_round(const v_float32x4& a) +{ return v_int32x4(_mm_cvtps_epi32(a.val)); } + +inline v_int32x4 v_floor(const v_float32x4& a) +{ + __m128i a1 = _mm_cvtps_epi32(a.val); + __m128i mask = _mm_castps_si128(_mm_cmpgt_ps(_mm_cvtepi32_ps(a1), a.val)); + return v_int32x4(_mm_add_epi32(a1, mask)); +} + +inline v_int32x4 v_ceil(const v_float32x4& a) +{ + __m128i a1 = _mm_cvtps_epi32(a.val); + __m128i mask = _mm_castps_si128(_mm_cmpgt_ps(a.val, _mm_cvtepi32_ps(a1))); + return v_int32x4(_mm_sub_epi32(a1, mask)); +} + +inline v_int32x4 v_trunc(const v_float32x4& a) +{ return v_int32x4(_mm_cvttps_epi32(a.val)); } + +inline v_int32x4 v_round(const v_float64x2& a) +{ return v_int32x4(_mm_cvtpd_epi32(a.val)); } + +inline v_int32x4 v_round(const v_float64x2& a, const v_float64x2& b) +{ + __m128i ai = _mm_cvtpd_epi32(a.val), bi = _mm_cvtpd_epi32(b.val); + return v_int32x4(_mm_unpacklo_epi64(ai, bi)); +} + +inline v_int32x4 v_floor(const v_float64x2& a) +{ + __m128i a1 = _mm_cvtpd_epi32(a.val); + __m128i mask = _mm_castpd_si128(_mm_cmpgt_pd(_mm_cvtepi32_pd(a1), a.val)); + mask = _mm_srli_si128(_mm_slli_si128(mask, 4), 8); // m0 m0 m1 m1 => m0 m1 0 0 + return v_int32x4(_mm_add_epi32(a1, mask)); +} + +inline v_int32x4 v_ceil(const v_float64x2& a) +{ + __m128i a1 = _mm_cvtpd_epi32(a.val); + __m128i mask = _mm_castpd_si128(_mm_cmpgt_pd(a.val, _mm_cvtepi32_pd(a1))); + mask = _mm_srli_si128(_mm_slli_si128(mask, 4), 8); // m0 m0 m1 m1 => m0 m1 0 0 + return v_int32x4(_mm_sub_epi32(a1, mask)); +} + +inline v_int32x4 v_trunc(const v_float64x2& a) +{ return v_int32x4(_mm_cvttpd_epi32(a.val)); } + +#define OPENCV_HAL_IMPL_SSE_TRANSPOSE4x4(_Tpvec, suffix, cast_from, cast_to) \ +inline void v_transpose4x4(const _Tpvec& a0, const _Tpvec& a1, \ + const _Tpvec& a2, const _Tpvec& a3, \ + _Tpvec& b0, _Tpvec& b1, \ + _Tpvec& b2, _Tpvec& b3) \ +{ \ + __m128i t0 = cast_from(_mm_unpacklo_##suffix(a0.val, a1.val)); \ + __m128i t1 = cast_from(_mm_unpacklo_##suffix(a2.val, a3.val)); \ + __m128i t2 = cast_from(_mm_unpackhi_##suffix(a0.val, a1.val)); \ + __m128i t3 = cast_from(_mm_unpackhi_##suffix(a2.val, a3.val)); \ +\ + b0.val = cast_to(_mm_unpacklo_epi64(t0, t1)); \ + b1.val = cast_to(_mm_unpackhi_epi64(t0, t1)); \ + b2.val = cast_to(_mm_unpacklo_epi64(t2, t3)); \ + b3.val = cast_to(_mm_unpackhi_epi64(t2, t3)); \ +} + +OPENCV_HAL_IMPL_SSE_TRANSPOSE4x4(v_uint32x4, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_TRANSPOSE4x4(v_int32x4, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_TRANSPOSE4x4(v_float32x4, ps, _mm_castps_si128, _mm_castsi128_ps) + +// load deinterleave +inline void v_load_deinterleave(const uchar* ptr, v_uint8x16& a, v_uint8x16& b) +{ + __m128i t00 = _mm_loadu_si128((const __m128i*)ptr); + __m128i t01 = _mm_loadu_si128((const __m128i*)(ptr + 16)); + + __m128i t10 = _mm_unpacklo_epi8(t00, t01); + __m128i t11 = _mm_unpackhi_epi8(t00, t01); + + __m128i t20 = _mm_unpacklo_epi8(t10, t11); + __m128i t21 = _mm_unpackhi_epi8(t10, t11); + + __m128i t30 = _mm_unpacklo_epi8(t20, t21); + __m128i t31 = _mm_unpackhi_epi8(t20, t21); + + a.val = _mm_unpacklo_epi8(t30, t31); + b.val = _mm_unpackhi_epi8(t30, t31); +} + +inline void v_load_deinterleave(const uchar* ptr, v_uint8x16& a, v_uint8x16& b, v_uint8x16& c) +{ +#if CV_SSE4_1 + const __m128i m0 = _mm_setr_epi8(0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0); + const __m128i m1 = _mm_setr_epi8(0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0); + __m128i s0 = _mm_loadu_si128((const __m128i*)ptr); + __m128i s1 = _mm_loadu_si128((const __m128i*)(ptr + 16)); + __m128i s2 = _mm_loadu_si128((const __m128i*)(ptr + 32)); + __m128i a0 = _mm_blendv_epi8(_mm_blendv_epi8(s0, s1, m0), s2, m1); + __m128i b0 = _mm_blendv_epi8(_mm_blendv_epi8(s1, s2, m0), s0, m1); + __m128i c0 = _mm_blendv_epi8(_mm_blendv_epi8(s2, s0, m0), s1, m1); + const __m128i sh_b = _mm_setr_epi8(0, 3, 6, 9, 12, 15, 2, 5, 8, 11, 14, 1, 4, 7, 10, 13); + const __m128i sh_g = _mm_setr_epi8(1, 4, 7, 10, 13, 0, 3, 6, 9, 12, 15, 2, 5, 8, 11, 14); + const __m128i sh_r = _mm_setr_epi8(2, 5, 8, 11, 14, 1, 4, 7, 10, 13, 0, 3, 6, 9, 12, 15); + a0 = _mm_shuffle_epi8(a0, sh_b); + b0 = _mm_shuffle_epi8(b0, sh_g); + c0 = _mm_shuffle_epi8(c0, sh_r); + a.val = a0; + b.val = b0; + c.val = c0; +#elif CV_SSSE3 + const __m128i m0 = _mm_setr_epi8(0, 3, 6, 9, 12, 15, 1, 4, 7, 10, 13, 2, 5, 8, 11, 14); + const __m128i m1 = _mm_alignr_epi8(m0, m0, 11); + const __m128i m2 = _mm_alignr_epi8(m0, m0, 6); + + __m128i t0 = _mm_loadu_si128((const __m128i*)ptr); + __m128i t1 = _mm_loadu_si128((const __m128i*)(ptr + 16)); + __m128i t2 = _mm_loadu_si128((const __m128i*)(ptr + 32)); + + __m128i s0 = _mm_shuffle_epi8(t0, m0); + __m128i s1 = _mm_shuffle_epi8(t1, m1); + __m128i s2 = _mm_shuffle_epi8(t2, m2); + + t0 = _mm_alignr_epi8(s1, _mm_slli_si128(s0, 10), 5); + a.val = _mm_alignr_epi8(s2, t0, 5); + + t1 = _mm_alignr_epi8(_mm_srli_si128(s1, 5), _mm_slli_si128(s0, 5), 6); + b.val = _mm_alignr_epi8(_mm_srli_si128(s2, 5), t1, 5); + + t2 = _mm_alignr_epi8(_mm_srli_si128(s2, 10), s1, 11); + c.val = _mm_alignr_epi8(t2, s0, 11); +#else + __m128i t00 = _mm_loadu_si128((const __m128i*)ptr); + __m128i t01 = _mm_loadu_si128((const __m128i*)(ptr + 16)); + __m128i t02 = _mm_loadu_si128((const __m128i*)(ptr + 32)); + + __m128i t10 = _mm_unpacklo_epi8(t00, _mm_unpackhi_epi64(t01, t01)); + __m128i t11 = _mm_unpacklo_epi8(_mm_unpackhi_epi64(t00, t00), t02); + __m128i t12 = _mm_unpacklo_epi8(t01, _mm_unpackhi_epi64(t02, t02)); + + __m128i t20 = _mm_unpacklo_epi8(t10, _mm_unpackhi_epi64(t11, t11)); + __m128i t21 = _mm_unpacklo_epi8(_mm_unpackhi_epi64(t10, t10), t12); + __m128i t22 = _mm_unpacklo_epi8(t11, _mm_unpackhi_epi64(t12, t12)); + + __m128i t30 = _mm_unpacklo_epi8(t20, _mm_unpackhi_epi64(t21, t21)); + __m128i t31 = _mm_unpacklo_epi8(_mm_unpackhi_epi64(t20, t20), t22); + __m128i t32 = _mm_unpacklo_epi8(t21, _mm_unpackhi_epi64(t22, t22)); + + a.val = _mm_unpacklo_epi8(t30, _mm_unpackhi_epi64(t31, t31)); + b.val = _mm_unpacklo_epi8(_mm_unpackhi_epi64(t30, t30), t32); + c.val = _mm_unpacklo_epi8(t31, _mm_unpackhi_epi64(t32, t32)); +#endif +} + +inline void v_load_deinterleave(const uchar* ptr, v_uint8x16& a, v_uint8x16& b, v_uint8x16& c, v_uint8x16& d) +{ + __m128i u0 = _mm_loadu_si128((const __m128i*)ptr); // a0 b0 c0 d0 a1 b1 c1 d1 ... + __m128i u1 = _mm_loadu_si128((const __m128i*)(ptr + 16)); // a4 b4 c4 d4 ... + __m128i u2 = _mm_loadu_si128((const __m128i*)(ptr + 32)); // a8 b8 c8 d8 ... + __m128i u3 = _mm_loadu_si128((const __m128i*)(ptr + 48)); // a12 b12 c12 d12 ... + + __m128i v0 = _mm_unpacklo_epi8(u0, u2); // a0 a8 b0 b8 ... + __m128i v1 = _mm_unpackhi_epi8(u0, u2); // a2 a10 b2 b10 ... + __m128i v2 = _mm_unpacklo_epi8(u1, u3); // a4 a12 b4 b12 ... + __m128i v3 = _mm_unpackhi_epi8(u1, u3); // a6 a14 b6 b14 ... + + u0 = _mm_unpacklo_epi8(v0, v2); // a0 a4 a8 a12 ... + u1 = _mm_unpacklo_epi8(v1, v3); // a2 a6 a10 a14 ... + u2 = _mm_unpackhi_epi8(v0, v2); // a1 a5 a9 a13 ... + u3 = _mm_unpackhi_epi8(v1, v3); // a3 a7 a11 a15 ... + + v0 = _mm_unpacklo_epi8(u0, u1); // a0 a2 a4 a6 ... + v1 = _mm_unpacklo_epi8(u2, u3); // a1 a3 a5 a7 ... + v2 = _mm_unpackhi_epi8(u0, u1); // c0 c2 c4 c6 ... + v3 = _mm_unpackhi_epi8(u2, u3); // c1 c3 c5 c7 ... + + a.val = _mm_unpacklo_epi8(v0, v1); + b.val = _mm_unpackhi_epi8(v0, v1); + c.val = _mm_unpacklo_epi8(v2, v3); + d.val = _mm_unpackhi_epi8(v2, v3); +} + +inline void v_load_deinterleave(const ushort* ptr, v_uint16x8& a, v_uint16x8& b) +{ + __m128i v0 = _mm_loadu_si128((__m128i*)(ptr)); // a0 b0 a1 b1 a2 b2 a3 b3 + __m128i v1 = _mm_loadu_si128((__m128i*)(ptr + 8)); // a4 b4 a5 b5 a6 b6 a7 b7 + + __m128i v2 = _mm_unpacklo_epi16(v0, v1); // a0 a4 b0 b4 a1 a5 b1 b5 + __m128i v3 = _mm_unpackhi_epi16(v0, v1); // a2 a6 b2 b6 a3 a7 b3 b7 + __m128i v4 = _mm_unpacklo_epi16(v2, v3); // a0 a2 a4 a6 b0 b2 b4 b6 + __m128i v5 = _mm_unpackhi_epi16(v2, v3); // a1 a3 a5 a7 b1 b3 b5 b7 + + a.val = _mm_unpacklo_epi16(v4, v5); // a0 a1 a2 a3 a4 a5 a6 a7 + b.val = _mm_unpackhi_epi16(v4, v5); // b0 b1 ab b3 b4 b5 b6 b7 +} + +inline void v_load_deinterleave(const ushort* ptr, v_uint16x8& a, v_uint16x8& b, v_uint16x8& c) +{ +#if CV_SSE4_1 + __m128i v0 = _mm_loadu_si128((__m128i*)(ptr)); + __m128i v1 = _mm_loadu_si128((__m128i*)(ptr + 8)); + __m128i v2 = _mm_loadu_si128((__m128i*)(ptr + 16)); + __m128i a0 = _mm_blend_epi16(_mm_blend_epi16(v0, v1, 0x92), v2, 0x24); + __m128i b0 = _mm_blend_epi16(_mm_blend_epi16(v2, v0, 0x92), v1, 0x24); + __m128i c0 = _mm_blend_epi16(_mm_blend_epi16(v1, v2, 0x92), v0, 0x24); + + const __m128i sh_a = _mm_setr_epi8(0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5, 10, 11); + const __m128i sh_b = _mm_setr_epi8(2, 3, 8, 9, 14, 15, 4, 5, 10, 11, 0, 1, 6, 7, 12, 13); + const __m128i sh_c = _mm_setr_epi8(4, 5, 10, 11, 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15); + a0 = _mm_shuffle_epi8(a0, sh_a); + b0 = _mm_shuffle_epi8(b0, sh_b); + c0 = _mm_shuffle_epi8(c0, sh_c); + + a.val = a0; + b.val = b0; + c.val = c0; +#else + __m128i t00 = _mm_loadu_si128((const __m128i*)ptr); + __m128i t01 = _mm_loadu_si128((const __m128i*)(ptr + 8)); + __m128i t02 = _mm_loadu_si128((const __m128i*)(ptr + 16)); + + __m128i t10 = _mm_unpacklo_epi16(t00, _mm_unpackhi_epi64(t01, t01)); + __m128i t11 = _mm_unpacklo_epi16(_mm_unpackhi_epi64(t00, t00), t02); + __m128i t12 = _mm_unpacklo_epi16(t01, _mm_unpackhi_epi64(t02, t02)); + + __m128i t20 = _mm_unpacklo_epi16(t10, _mm_unpackhi_epi64(t11, t11)); + __m128i t21 = _mm_unpacklo_epi16(_mm_unpackhi_epi64(t10, t10), t12); + __m128i t22 = _mm_unpacklo_epi16(t11, _mm_unpackhi_epi64(t12, t12)); + + a.val = _mm_unpacklo_epi16(t20, _mm_unpackhi_epi64(t21, t21)); + b.val = _mm_unpacklo_epi16(_mm_unpackhi_epi64(t20, t20), t22); + c.val = _mm_unpacklo_epi16(t21, _mm_unpackhi_epi64(t22, t22)); +#endif +} + +inline void v_load_deinterleave(const ushort* ptr, v_uint16x8& a, v_uint16x8& b, v_uint16x8& c, v_uint16x8& d) +{ + __m128i u0 = _mm_loadu_si128((const __m128i*)ptr); // a0 b0 c0 d0 a1 b1 c1 d1 + __m128i u1 = _mm_loadu_si128((const __m128i*)(ptr + 8)); // a2 b2 c2 d2 ... + __m128i u2 = _mm_loadu_si128((const __m128i*)(ptr + 16)); // a4 b4 c4 d4 ... + __m128i u3 = _mm_loadu_si128((const __m128i*)(ptr + 24)); // a6 b6 c6 d6 ... + + __m128i v0 = _mm_unpacklo_epi16(u0, u2); // a0 a4 b0 b4 ... + __m128i v1 = _mm_unpackhi_epi16(u0, u2); // a1 a5 b1 b5 ... + __m128i v2 = _mm_unpacklo_epi16(u1, u3); // a2 a6 b2 b6 ... + __m128i v3 = _mm_unpackhi_epi16(u1, u3); // a3 a7 b3 b7 ... + + u0 = _mm_unpacklo_epi16(v0, v2); // a0 a2 a4 a6 ... + u1 = _mm_unpacklo_epi16(v1, v3); // a1 a3 a5 a7 ... + u2 = _mm_unpackhi_epi16(v0, v2); // c0 c2 c4 c6 ... + u3 = _mm_unpackhi_epi16(v1, v3); // c1 c3 c5 c7 ... + + a.val = _mm_unpacklo_epi16(u0, u1); + b.val = _mm_unpackhi_epi16(u0, u1); + c.val = _mm_unpacklo_epi16(u2, u3); + d.val = _mm_unpackhi_epi16(u2, u3); +} + +inline void v_load_deinterleave(const unsigned* ptr, v_uint32x4& a, v_uint32x4& b) +{ + __m128i v0 = _mm_loadu_si128((__m128i*)(ptr)); // a0 b0 a1 b1 + __m128i v1 = _mm_loadu_si128((__m128i*)(ptr + 4)); // a2 b2 a3 b3 + + __m128i v2 = _mm_unpacklo_epi32(v0, v1); // a0 a2 b0 b2 + __m128i v3 = _mm_unpackhi_epi32(v0, v1); // a1 a3 b1 b3 + + a.val = _mm_unpacklo_epi32(v2, v3); // a0 a1 a2 a3 + b.val = _mm_unpackhi_epi32(v2, v3); // b0 b1 ab b3 +} + +inline void v_load_deinterleave(const unsigned* ptr, v_uint32x4& a, v_uint32x4& b, v_uint32x4& c) +{ + __m128i t00 = _mm_loadu_si128((const __m128i*)ptr); + __m128i t01 = _mm_loadu_si128((const __m128i*)(ptr + 4)); + __m128i t02 = _mm_loadu_si128((const __m128i*)(ptr + 8)); + + __m128i t10 = _mm_unpacklo_epi32(t00, _mm_unpackhi_epi64(t01, t01)); + __m128i t11 = _mm_unpacklo_epi32(_mm_unpackhi_epi64(t00, t00), t02); + __m128i t12 = _mm_unpacklo_epi32(t01, _mm_unpackhi_epi64(t02, t02)); + + a.val = _mm_unpacklo_epi32(t10, _mm_unpackhi_epi64(t11, t11)); + b.val = _mm_unpacklo_epi32(_mm_unpackhi_epi64(t10, t10), t12); + c.val = _mm_unpacklo_epi32(t11, _mm_unpackhi_epi64(t12, t12)); +} + +inline void v_load_deinterleave(const unsigned* ptr, v_uint32x4& a, v_uint32x4& b, v_uint32x4& c, v_uint32x4& d) +{ + v_uint32x4 s0(_mm_loadu_si128((const __m128i*)ptr)); // a0 b0 c0 d0 + v_uint32x4 s1(_mm_loadu_si128((const __m128i*)(ptr + 4))); // a1 b1 c1 d1 + v_uint32x4 s2(_mm_loadu_si128((const __m128i*)(ptr + 8))); // a2 b2 c2 d2 + v_uint32x4 s3(_mm_loadu_si128((const __m128i*)(ptr + 12))); // a3 b3 c3 d3 + + v_transpose4x4(s0, s1, s2, s3, a, b, c, d); +} + +inline void v_load_deinterleave(const float* ptr, v_float32x4& a, v_float32x4& b) +{ + __m128 u0 = _mm_loadu_ps(ptr); // a0 b0 a1 b1 + __m128 u1 = _mm_loadu_ps((ptr + 4)); // a2 b2 a3 b3 + + a.val = _mm_shuffle_ps(u0, u1, _MM_SHUFFLE(2, 0, 2, 0)); // a0 a1 a2 a3 + b.val = _mm_shuffle_ps(u0, u1, _MM_SHUFFLE(3, 1, 3, 1)); // b0 b1 ab b3 +} + +inline void v_load_deinterleave(const float* ptr, v_float32x4& a, v_float32x4& b, v_float32x4& c) +{ + __m128 t0 = _mm_loadu_ps(ptr + 0); + __m128 t1 = _mm_loadu_ps(ptr + 4); + __m128 t2 = _mm_loadu_ps(ptr + 8); + + __m128 at12 = _mm_shuffle_ps(t1, t2, _MM_SHUFFLE(0, 1, 0, 2)); + a.val = _mm_shuffle_ps(t0, at12, _MM_SHUFFLE(2, 0, 3, 0)); + + __m128 bt01 = _mm_shuffle_ps(t0, t1, _MM_SHUFFLE(0, 0, 0, 1)); + __m128 bt12 = _mm_shuffle_ps(t1, t2, _MM_SHUFFLE(0, 2, 0, 3)); + b.val = _mm_shuffle_ps(bt01, bt12, _MM_SHUFFLE(2, 0, 2, 0)); + + __m128 ct01 = _mm_shuffle_ps(t0, t1, _MM_SHUFFLE(0, 1, 0, 2)); + c.val = _mm_shuffle_ps(ct01, t2, _MM_SHUFFLE(3, 0, 2, 0)); +} + +inline void v_load_deinterleave(const float* ptr, v_float32x4& a, v_float32x4& b, v_float32x4& c, v_float32x4& d) +{ + __m128 t0 = _mm_loadu_ps(ptr + 0); + __m128 t1 = _mm_loadu_ps(ptr + 4); + __m128 t2 = _mm_loadu_ps(ptr + 8); + __m128 t3 = _mm_loadu_ps(ptr + 12); + __m128 t02lo = _mm_unpacklo_ps(t0, t2); + __m128 t13lo = _mm_unpacklo_ps(t1, t3); + __m128 t02hi = _mm_unpackhi_ps(t0, t2); + __m128 t13hi = _mm_unpackhi_ps(t1, t3); + a.val = _mm_unpacklo_ps(t02lo, t13lo); + b.val = _mm_unpackhi_ps(t02lo, t13lo); + c.val = _mm_unpacklo_ps(t02hi, t13hi); + d.val = _mm_unpackhi_ps(t02hi, t13hi); +} + +inline void v_load_deinterleave(const uint64 *ptr, v_uint64x2& a, v_uint64x2& b) +{ + __m128i t0 = _mm_loadu_si128((const __m128i*)ptr); + __m128i t1 = _mm_loadu_si128((const __m128i*)(ptr + 2)); + + a = v_uint64x2(_mm_unpacklo_epi64(t0, t1)); + b = v_uint64x2(_mm_unpackhi_epi64(t0, t1)); +} + +inline void v_load_deinterleave(const uint64 *ptr, v_uint64x2& a, v_uint64x2& b, v_uint64x2& c) +{ + __m128i t0 = _mm_loadu_si128((const __m128i*)ptr); // a0, b0 + __m128i t1 = _mm_loadu_si128((const __m128i*)(ptr + 2)); // c0, a1 + __m128i t2 = _mm_loadu_si128((const __m128i*)(ptr + 4)); // b1, c1 + + t1 = _mm_shuffle_epi32(t1, 0x4e); // a1, c0 + + a = v_uint64x2(_mm_unpacklo_epi64(t0, t1)); + b = v_uint64x2(_mm_unpacklo_epi64(_mm_unpackhi_epi64(t0, t0), t2)); + c = v_uint64x2(_mm_unpackhi_epi64(t1, t2)); +} + +inline void v_load_deinterleave(const uint64 *ptr, v_uint64x2& a, + v_uint64x2& b, v_uint64x2& c, v_uint64x2& d) +{ + __m128i t0 = _mm_loadu_si128((const __m128i*)ptr); // a0 b0 + __m128i t1 = _mm_loadu_si128((const __m128i*)(ptr + 2)); // c0 d0 + __m128i t2 = _mm_loadu_si128((const __m128i*)(ptr + 4)); // a1 b1 + __m128i t3 = _mm_loadu_si128((const __m128i*)(ptr + 6)); // c1 d1 + + a = v_uint64x2(_mm_unpacklo_epi64(t0, t2)); + b = v_uint64x2(_mm_unpackhi_epi64(t0, t2)); + c = v_uint64x2(_mm_unpacklo_epi64(t1, t3)); + d = v_uint64x2(_mm_unpackhi_epi64(t1, t3)); +} + +// store interleave + +inline void v_store_interleave( uchar* ptr, const v_uint8x16& a, const v_uint8x16& b, + hal::StoreMode mode = hal::STORE_UNALIGNED) +{ + __m128i v0 = _mm_unpacklo_epi8(a.val, b.val); + __m128i v1 = _mm_unpackhi_epi8(a.val, b.val); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm_stream_si128((__m128i*)(ptr), v0); + _mm_stream_si128((__m128i*)(ptr + 16), v1); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm_store_si128((__m128i*)(ptr), v0); + _mm_store_si128((__m128i*)(ptr + 16), v1); + } + else + { + _mm_storeu_si128((__m128i*)(ptr), v0); + _mm_storeu_si128((__m128i*)(ptr + 16), v1); + } +} + +inline void v_store_interleave( uchar* ptr, const v_uint8x16& a, const v_uint8x16& b, + const v_uint8x16& c, hal::StoreMode mode = hal::STORE_UNALIGNED) +{ +#if CV_SSE4_1 + const __m128i sh_a = _mm_setr_epi8(0, 11, 6, 1, 12, 7, 2, 13, 8, 3, 14, 9, 4, 15, 10, 5); + const __m128i sh_b = _mm_setr_epi8(5, 0, 11, 6, 1, 12, 7, 2, 13, 8, 3, 14, 9, 4, 15, 10); + const __m128i sh_c = _mm_setr_epi8(10, 5, 0, 11, 6, 1, 12, 7, 2, 13, 8, 3, 14, 9, 4, 15); + __m128i a0 = _mm_shuffle_epi8(a.val, sh_a); + __m128i b0 = _mm_shuffle_epi8(b.val, sh_b); + __m128i c0 = _mm_shuffle_epi8(c.val, sh_c); + + const __m128i m0 = _mm_setr_epi8(0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0); + const __m128i m1 = _mm_setr_epi8(0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0); + __m128i v0 = _mm_blendv_epi8(_mm_blendv_epi8(a0, b0, m1), c0, m0); + __m128i v1 = _mm_blendv_epi8(_mm_blendv_epi8(b0, c0, m1), a0, m0); + __m128i v2 = _mm_blendv_epi8(_mm_blendv_epi8(c0, a0, m1), b0, m0); +#elif CV_SSSE3 + const __m128i m0 = _mm_setr_epi8(0, 6, 11, 1, 7, 12, 2, 8, 13, 3, 9, 14, 4, 10, 15, 5); + const __m128i m1 = _mm_setr_epi8(5, 11, 0, 6, 12, 1, 7, 13, 2, 8, 14, 3, 9, 15, 4, 10); + const __m128i m2 = _mm_setr_epi8(10, 0, 5, 11, 1, 6, 12, 2, 7, 13, 3, 8, 14, 4, 9, 15); + + __m128i t0 = _mm_alignr_epi8(b.val, _mm_slli_si128(a.val, 10), 5); + t0 = _mm_alignr_epi8(c.val, t0, 5); + __m128i v0 = _mm_shuffle_epi8(t0, m0); + + __m128i t1 = _mm_alignr_epi8(_mm_srli_si128(b.val, 5), _mm_slli_si128(a.val, 5), 6); + t1 = _mm_alignr_epi8(_mm_srli_si128(c.val, 5), t1, 5); + __m128i v1 = _mm_shuffle_epi8(t1, m1); + + __m128i t2 = _mm_alignr_epi8(_mm_srli_si128(c.val, 10), b.val, 11); + t2 = _mm_alignr_epi8(t2, a.val, 11); + __m128i v2 = _mm_shuffle_epi8(t2, m2); +#else + __m128i z = _mm_setzero_si128(); + __m128i ab0 = _mm_unpacklo_epi8(a.val, b.val); + __m128i ab1 = _mm_unpackhi_epi8(a.val, b.val); + __m128i c0 = _mm_unpacklo_epi8(c.val, z); + __m128i c1 = _mm_unpackhi_epi8(c.val, z); + + __m128i p00 = _mm_unpacklo_epi16(ab0, c0); + __m128i p01 = _mm_unpackhi_epi16(ab0, c0); + __m128i p02 = _mm_unpacklo_epi16(ab1, c1); + __m128i p03 = _mm_unpackhi_epi16(ab1, c1); + + __m128i p10 = _mm_unpacklo_epi32(p00, p01); + __m128i p11 = _mm_unpackhi_epi32(p00, p01); + __m128i p12 = _mm_unpacklo_epi32(p02, p03); + __m128i p13 = _mm_unpackhi_epi32(p02, p03); + + __m128i p20 = _mm_unpacklo_epi64(p10, p11); + __m128i p21 = _mm_unpackhi_epi64(p10, p11); + __m128i p22 = _mm_unpacklo_epi64(p12, p13); + __m128i p23 = _mm_unpackhi_epi64(p12, p13); + + p20 = _mm_slli_si128(p20, 1); + p22 = _mm_slli_si128(p22, 1); + + __m128i p30 = _mm_slli_epi64(_mm_unpacklo_epi32(p20, p21), 8); + __m128i p31 = _mm_srli_epi64(_mm_unpackhi_epi32(p20, p21), 8); + __m128i p32 = _mm_slli_epi64(_mm_unpacklo_epi32(p22, p23), 8); + __m128i p33 = _mm_srli_epi64(_mm_unpackhi_epi32(p22, p23), 8); + + __m128i p40 = _mm_unpacklo_epi64(p30, p31); + __m128i p41 = _mm_unpackhi_epi64(p30, p31); + __m128i p42 = _mm_unpacklo_epi64(p32, p33); + __m128i p43 = _mm_unpackhi_epi64(p32, p33); + + __m128i v0 = _mm_or_si128(_mm_srli_si128(p40, 2), _mm_slli_si128(p41, 10)); + __m128i v1 = _mm_or_si128(_mm_srli_si128(p41, 6), _mm_slli_si128(p42, 6)); + __m128i v2 = _mm_or_si128(_mm_srli_si128(p42, 10), _mm_slli_si128(p43, 2)); +#endif + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm_stream_si128((__m128i*)(ptr), v0); + _mm_stream_si128((__m128i*)(ptr + 16), v1); + _mm_stream_si128((__m128i*)(ptr + 32), v2); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm_store_si128((__m128i*)(ptr), v0); + _mm_store_si128((__m128i*)(ptr + 16), v1); + _mm_store_si128((__m128i*)(ptr + 32), v2); + } + else + { + _mm_storeu_si128((__m128i*)(ptr), v0); + _mm_storeu_si128((__m128i*)(ptr + 16), v1); + _mm_storeu_si128((__m128i*)(ptr + 32), v2); + } +} + +inline void v_store_interleave( uchar* ptr, const v_uint8x16& a, const v_uint8x16& b, + const v_uint8x16& c, const v_uint8x16& d, + hal::StoreMode mode = hal::STORE_UNALIGNED) +{ + // a0 a1 a2 a3 .... + // b0 b1 b2 b3 .... + // c0 c1 c2 c3 .... + // d0 d1 d2 d3 .... + __m128i u0 = _mm_unpacklo_epi8(a.val, c.val); // a0 c0 a1 c1 ... + __m128i u1 = _mm_unpackhi_epi8(a.val, c.val); // a8 c8 a9 c9 ... + __m128i u2 = _mm_unpacklo_epi8(b.val, d.val); // b0 d0 b1 d1 ... + __m128i u3 = _mm_unpackhi_epi8(b.val, d.val); // b8 d8 b9 d9 ... + + __m128i v0 = _mm_unpacklo_epi8(u0, u2); // a0 b0 c0 d0 ... + __m128i v1 = _mm_unpackhi_epi8(u0, u2); // a4 b4 c4 d4 ... + __m128i v2 = _mm_unpacklo_epi8(u1, u3); // a8 b8 c8 d8 ... + __m128i v3 = _mm_unpackhi_epi8(u1, u3); // a12 b12 c12 d12 ... + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm_stream_si128((__m128i*)(ptr), v0); + _mm_stream_si128((__m128i*)(ptr + 16), v1); + _mm_stream_si128((__m128i*)(ptr + 32), v2); + _mm_stream_si128((__m128i*)(ptr + 48), v3); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm_store_si128((__m128i*)(ptr), v0); + _mm_store_si128((__m128i*)(ptr + 16), v1); + _mm_store_si128((__m128i*)(ptr + 32), v2); + _mm_store_si128((__m128i*)(ptr + 48), v3); + } + else + { + _mm_storeu_si128((__m128i*)(ptr), v0); + _mm_storeu_si128((__m128i*)(ptr + 16), v1); + _mm_storeu_si128((__m128i*)(ptr + 32), v2); + _mm_storeu_si128((__m128i*)(ptr + 48), v3); + } +} + +inline void v_store_interleave( ushort* ptr, const v_uint16x8& a, const v_uint16x8& b, + hal::StoreMode mode = hal::STORE_UNALIGNED) +{ + __m128i v0 = _mm_unpacklo_epi16(a.val, b.val); + __m128i v1 = _mm_unpackhi_epi16(a.val, b.val); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm_stream_si128((__m128i*)(ptr), v0); + _mm_stream_si128((__m128i*)(ptr + 8), v1); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm_store_si128((__m128i*)(ptr), v0); + _mm_store_si128((__m128i*)(ptr + 8), v1); + } + else + { + _mm_storeu_si128((__m128i*)(ptr), v0); + _mm_storeu_si128((__m128i*)(ptr + 8), v1); + } +} + +inline void v_store_interleave( ushort* ptr, const v_uint16x8& a, + const v_uint16x8& b, const v_uint16x8& c, + hal::StoreMode mode = hal::STORE_UNALIGNED) +{ +#if CV_SSE4_1 + const __m128i sh_a = _mm_setr_epi8(0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5, 10, 11); + const __m128i sh_b = _mm_setr_epi8(10, 11, 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5); + const __m128i sh_c = _mm_setr_epi8(4, 5, 10, 11, 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15); + __m128i a0 = _mm_shuffle_epi8(a.val, sh_a); + __m128i b0 = _mm_shuffle_epi8(b.val, sh_b); + __m128i c0 = _mm_shuffle_epi8(c.val, sh_c); + + __m128i v0 = _mm_blend_epi16(_mm_blend_epi16(a0, b0, 0x92), c0, 0x24); + __m128i v1 = _mm_blend_epi16(_mm_blend_epi16(c0, a0, 0x92), b0, 0x24); + __m128i v2 = _mm_blend_epi16(_mm_blend_epi16(b0, c0, 0x92), a0, 0x24); +#else + __m128i z = _mm_setzero_si128(); + __m128i ab0 = _mm_unpacklo_epi16(a.val, b.val); + __m128i ab1 = _mm_unpackhi_epi16(a.val, b.val); + __m128i c0 = _mm_unpacklo_epi16(c.val, z); + __m128i c1 = _mm_unpackhi_epi16(c.val, z); + + __m128i p10 = _mm_unpacklo_epi32(ab0, c0); + __m128i p11 = _mm_unpackhi_epi32(ab0, c0); + __m128i p12 = _mm_unpacklo_epi32(ab1, c1); + __m128i p13 = _mm_unpackhi_epi32(ab1, c1); + + __m128i p20 = _mm_unpacklo_epi64(p10, p11); + __m128i p21 = _mm_unpackhi_epi64(p10, p11); + __m128i p22 = _mm_unpacklo_epi64(p12, p13); + __m128i p23 = _mm_unpackhi_epi64(p12, p13); + + p20 = _mm_slli_si128(p20, 2); + p22 = _mm_slli_si128(p22, 2); + + __m128i p30 = _mm_unpacklo_epi64(p20, p21); + __m128i p31 = _mm_unpackhi_epi64(p20, p21); + __m128i p32 = _mm_unpacklo_epi64(p22, p23); + __m128i p33 = _mm_unpackhi_epi64(p22, p23); + + __m128i v0 = _mm_or_si128(_mm_srli_si128(p30, 2), _mm_slli_si128(p31, 10)); + __m128i v1 = _mm_or_si128(_mm_srli_si128(p31, 6), _mm_slli_si128(p32, 6)); + __m128i v2 = _mm_or_si128(_mm_srli_si128(p32, 10), _mm_slli_si128(p33, 2)); +#endif + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm_stream_si128((__m128i*)(ptr), v0); + _mm_stream_si128((__m128i*)(ptr + 8), v1); + _mm_stream_si128((__m128i*)(ptr + 16), v2); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm_store_si128((__m128i*)(ptr), v0); + _mm_store_si128((__m128i*)(ptr + 8), v1); + _mm_store_si128((__m128i*)(ptr + 16), v2); + } + else + { + _mm_storeu_si128((__m128i*)(ptr), v0); + _mm_storeu_si128((__m128i*)(ptr + 8), v1); + _mm_storeu_si128((__m128i*)(ptr + 16), v2); + } +} + +inline void v_store_interleave( ushort* ptr, const v_uint16x8& a, const v_uint16x8& b, + const v_uint16x8& c, const v_uint16x8& d, + hal::StoreMode mode = hal::STORE_UNALIGNED) +{ + // a0 a1 a2 a3 .... + // b0 b1 b2 b3 .... + // c0 c1 c2 c3 .... + // d0 d1 d2 d3 .... + __m128i u0 = _mm_unpacklo_epi16(a.val, c.val); // a0 c0 a1 c1 ... + __m128i u1 = _mm_unpackhi_epi16(a.val, c.val); // a4 c4 a5 c5 ... + __m128i u2 = _mm_unpacklo_epi16(b.val, d.val); // b0 d0 b1 d1 ... + __m128i u3 = _mm_unpackhi_epi16(b.val, d.val); // b4 d4 b5 d5 ... + + __m128i v0 = _mm_unpacklo_epi16(u0, u2); // a0 b0 c0 d0 ... + __m128i v1 = _mm_unpackhi_epi16(u0, u2); // a2 b2 c2 d2 ... + __m128i v2 = _mm_unpacklo_epi16(u1, u3); // a4 b4 c4 d4 ... + __m128i v3 = _mm_unpackhi_epi16(u1, u3); // a6 b6 c6 d6 ... + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm_stream_si128((__m128i*)(ptr), v0); + _mm_stream_si128((__m128i*)(ptr + 8), v1); + _mm_stream_si128((__m128i*)(ptr + 16), v2); + _mm_stream_si128((__m128i*)(ptr + 24), v3); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm_store_si128((__m128i*)(ptr), v0); + _mm_store_si128((__m128i*)(ptr + 8), v1); + _mm_store_si128((__m128i*)(ptr + 16), v2); + _mm_store_si128((__m128i*)(ptr + 24), v3); + } + else + { + _mm_storeu_si128((__m128i*)(ptr), v0); + _mm_storeu_si128((__m128i*)(ptr + 8), v1); + _mm_storeu_si128((__m128i*)(ptr + 16), v2); + _mm_storeu_si128((__m128i*)(ptr + 24), v3); + } +} + +inline void v_store_interleave( unsigned* ptr, const v_uint32x4& a, const v_uint32x4& b, + hal::StoreMode mode = hal::STORE_UNALIGNED) +{ + __m128i v0 = _mm_unpacklo_epi32(a.val, b.val); + __m128i v1 = _mm_unpackhi_epi32(a.val, b.val); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm_stream_si128((__m128i*)(ptr), v0); + _mm_stream_si128((__m128i*)(ptr + 4), v1); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm_store_si128((__m128i*)(ptr), v0); + _mm_store_si128((__m128i*)(ptr + 4), v1); + } + else + { + _mm_storeu_si128((__m128i*)(ptr), v0); + _mm_storeu_si128((__m128i*)(ptr + 4), v1); + } +} + +inline void v_store_interleave( unsigned* ptr, const v_uint32x4& a, const v_uint32x4& b, + const v_uint32x4& c, hal::StoreMode mode = hal::STORE_UNALIGNED) +{ + v_uint32x4 z = v_setzero_u32(), u0, u1, u2, u3; + v_transpose4x4(a, b, c, z, u0, u1, u2, u3); + + __m128i v0 = _mm_or_si128(u0.val, _mm_slli_si128(u1.val, 12)); + __m128i v1 = _mm_or_si128(_mm_srli_si128(u1.val, 4), _mm_slli_si128(u2.val, 8)); + __m128i v2 = _mm_or_si128(_mm_srli_si128(u2.val, 8), _mm_slli_si128(u3.val, 4)); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm_stream_si128((__m128i*)(ptr), v0); + _mm_stream_si128((__m128i*)(ptr + 4), v1); + _mm_stream_si128((__m128i*)(ptr + 8), v2); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm_store_si128((__m128i*)(ptr), v0); + _mm_store_si128((__m128i*)(ptr + 4), v1); + _mm_store_si128((__m128i*)(ptr + 8), v2); + } + else + { + _mm_storeu_si128((__m128i*)(ptr), v0); + _mm_storeu_si128((__m128i*)(ptr + 4), v1); + _mm_storeu_si128((__m128i*)(ptr + 8), v2); + } +} + +inline void v_store_interleave(unsigned* ptr, const v_uint32x4& a, const v_uint32x4& b, + const v_uint32x4& c, const v_uint32x4& d, + hal::StoreMode mode = hal::STORE_UNALIGNED) +{ + v_uint32x4 v0, v1, v2, v3; + v_transpose4x4(a, b, c, d, v0, v1, v2, v3); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm_stream_si128((__m128i*)(ptr), v0.val); + _mm_stream_si128((__m128i*)(ptr + 4), v1.val); + _mm_stream_si128((__m128i*)(ptr + 8), v2.val); + _mm_stream_si128((__m128i*)(ptr + 12), v3.val); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm_store_si128((__m128i*)(ptr), v0.val); + _mm_store_si128((__m128i*)(ptr + 4), v1.val); + _mm_store_si128((__m128i*)(ptr + 8), v2.val); + _mm_store_si128((__m128i*)(ptr + 12), v3.val); + } + else + { + _mm_storeu_si128((__m128i*)(ptr), v0.val); + _mm_storeu_si128((__m128i*)(ptr + 4), v1.val); + _mm_storeu_si128((__m128i*)(ptr + 8), v2.val); + _mm_storeu_si128((__m128i*)(ptr + 12), v3.val); + } +} + +// 2-channel, float only +inline void v_store_interleave(float* ptr, const v_float32x4& a, const v_float32x4& b, + hal::StoreMode mode = hal::STORE_UNALIGNED) +{ + __m128 v0 = _mm_unpacklo_ps(a.val, b.val); // a0 b0 a1 b1 + __m128 v1 = _mm_unpackhi_ps(a.val, b.val); // a2 b2 a3 b3 + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm_stream_ps(ptr, v0); + _mm_stream_ps(ptr + 4, v1); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm_store_ps(ptr, v0); + _mm_store_ps(ptr + 4, v1); + } + else + { + _mm_storeu_ps(ptr, v0); + _mm_storeu_ps(ptr + 4, v1); + } +} + +inline void v_store_interleave(float* ptr, const v_float32x4& a, const v_float32x4& b, + const v_float32x4& c, hal::StoreMode mode = hal::STORE_UNALIGNED) +{ + __m128 u0 = _mm_shuffle_ps(a.val, b.val, _MM_SHUFFLE(0, 0, 0, 0)); + __m128 u1 = _mm_shuffle_ps(c.val, a.val, _MM_SHUFFLE(1, 1, 0, 0)); + __m128 v0 = _mm_shuffle_ps(u0, u1, _MM_SHUFFLE(2, 0, 2, 0)); + __m128 u2 = _mm_shuffle_ps(b.val, c.val, _MM_SHUFFLE(1, 1, 1, 1)); + __m128 u3 = _mm_shuffle_ps(a.val, b.val, _MM_SHUFFLE(2, 2, 2, 2)); + __m128 v1 = _mm_shuffle_ps(u2, u3, _MM_SHUFFLE(2, 0, 2, 0)); + __m128 u4 = _mm_shuffle_ps(c.val, a.val, _MM_SHUFFLE(3, 3, 2, 2)); + __m128 u5 = _mm_shuffle_ps(b.val, c.val, _MM_SHUFFLE(3, 3, 3, 3)); + __m128 v2 = _mm_shuffle_ps(u4, u5, _MM_SHUFFLE(2, 0, 2, 0)); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm_stream_ps(ptr, v0); + _mm_stream_ps(ptr + 4, v1); + _mm_stream_ps(ptr + 8, v2); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm_store_ps(ptr, v0); + _mm_store_ps(ptr + 4, v1); + _mm_store_ps(ptr + 8, v2); + } + else + { + _mm_storeu_ps(ptr, v0); + _mm_storeu_ps(ptr + 4, v1); + _mm_storeu_ps(ptr + 8, v2); + } +} + +inline void v_store_interleave(float* ptr, const v_float32x4& a, const v_float32x4& b, + const v_float32x4& c, const v_float32x4& d, + hal::StoreMode mode = hal::STORE_UNALIGNED) +{ + __m128 u0 = _mm_unpacklo_ps(a.val, c.val); + __m128 u1 = _mm_unpacklo_ps(b.val, d.val); + __m128 u2 = _mm_unpackhi_ps(a.val, c.val); + __m128 u3 = _mm_unpackhi_ps(b.val, d.val); + __m128 v0 = _mm_unpacklo_ps(u0, u1); + __m128 v2 = _mm_unpacklo_ps(u2, u3); + __m128 v1 = _mm_unpackhi_ps(u0, u1); + __m128 v3 = _mm_unpackhi_ps(u2, u3); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm_stream_ps(ptr, v0); + _mm_stream_ps(ptr + 4, v1); + _mm_stream_ps(ptr + 8, v2); + _mm_stream_ps(ptr + 12, v3); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm_store_ps(ptr, v0); + _mm_store_ps(ptr + 4, v1); + _mm_store_ps(ptr + 8, v2); + _mm_store_ps(ptr + 12, v3); + } + else + { + _mm_storeu_ps(ptr, v0); + _mm_storeu_ps(ptr + 4, v1); + _mm_storeu_ps(ptr + 8, v2); + _mm_storeu_ps(ptr + 12, v3); + } +} + +inline void v_store_interleave(uint64 *ptr, const v_uint64x2& a, const v_uint64x2& b, + hal::StoreMode mode = hal::STORE_UNALIGNED) +{ + __m128i v0 = _mm_unpacklo_epi64(a.val, b.val); + __m128i v1 = _mm_unpackhi_epi64(a.val, b.val); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm_stream_si128((__m128i*)(ptr), v0); + _mm_stream_si128((__m128i*)(ptr + 2), v1); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm_store_si128((__m128i*)(ptr), v0); + _mm_store_si128((__m128i*)(ptr + 2), v1); + } + else + { + _mm_storeu_si128((__m128i*)(ptr), v0); + _mm_storeu_si128((__m128i*)(ptr + 2), v1); + } +} + +inline void v_store_interleave(uint64 *ptr, const v_uint64x2& a, const v_uint64x2& b, + const v_uint64x2& c, hal::StoreMode mode = hal::STORE_UNALIGNED) +{ + __m128i v0 = _mm_unpacklo_epi64(a.val, b.val); + __m128i v1 = _mm_unpacklo_epi64(c.val, _mm_unpackhi_epi64(a.val, a.val)); + __m128i v2 = _mm_unpackhi_epi64(b.val, c.val); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm_stream_si128((__m128i*)(ptr), v0); + _mm_stream_si128((__m128i*)(ptr + 2), v1); + _mm_stream_si128((__m128i*)(ptr + 4), v2); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm_store_si128((__m128i*)(ptr), v0); + _mm_store_si128((__m128i*)(ptr + 2), v1); + _mm_store_si128((__m128i*)(ptr + 4), v2); + } + else + { + _mm_storeu_si128((__m128i*)(ptr), v0); + _mm_storeu_si128((__m128i*)(ptr + 2), v1); + _mm_storeu_si128((__m128i*)(ptr + 4), v2); + } +} + +inline void v_store_interleave(uint64 *ptr, const v_uint64x2& a, const v_uint64x2& b, + const v_uint64x2& c, const v_uint64x2& d, + hal::StoreMode mode = hal::STORE_UNALIGNED) +{ + __m128i v0 = _mm_unpacklo_epi64(a.val, b.val); + __m128i v1 = _mm_unpacklo_epi64(c.val, d.val); + __m128i v2 = _mm_unpackhi_epi64(a.val, b.val); + __m128i v3 = _mm_unpackhi_epi64(c.val, d.val); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm_stream_si128((__m128i*)(ptr), v0); + _mm_stream_si128((__m128i*)(ptr + 2), v1); + _mm_stream_si128((__m128i*)(ptr + 4), v2); + _mm_stream_si128((__m128i*)(ptr + 6), v3); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm_store_si128((__m128i*)(ptr), v0); + _mm_store_si128((__m128i*)(ptr + 2), v1); + _mm_store_si128((__m128i*)(ptr + 4), v2); + _mm_store_si128((__m128i*)(ptr + 6), v3); + } + else + { + _mm_storeu_si128((__m128i*)(ptr), v0); + _mm_storeu_si128((__m128i*)(ptr + 2), v1); + _mm_storeu_si128((__m128i*)(ptr + 4), v2); + _mm_storeu_si128((__m128i*)(ptr + 6), v3); + } +} + +#define OPENCV_HAL_IMPL_SSE_LOADSTORE_INTERLEAVE(_Tpvec0, _Tp0, suffix0, _Tpvec1, _Tp1, suffix1) \ +inline void v_load_deinterleave( const _Tp0* ptr, _Tpvec0& a0, _Tpvec0& b0 ) \ +{ \ + _Tpvec1 a1, b1; \ + v_load_deinterleave((const _Tp1*)ptr, a1, b1); \ + a0 = v_reinterpret_as_##suffix0(a1); \ + b0 = v_reinterpret_as_##suffix0(b1); \ +} \ +inline void v_load_deinterleave( const _Tp0* ptr, _Tpvec0& a0, _Tpvec0& b0, _Tpvec0& c0 ) \ +{ \ + _Tpvec1 a1, b1, c1; \ + v_load_deinterleave((const _Tp1*)ptr, a1, b1, c1); \ + a0 = v_reinterpret_as_##suffix0(a1); \ + b0 = v_reinterpret_as_##suffix0(b1); \ + c0 = v_reinterpret_as_##suffix0(c1); \ +} \ +inline void v_load_deinterleave( const _Tp0* ptr, _Tpvec0& a0, _Tpvec0& b0, _Tpvec0& c0, _Tpvec0& d0 ) \ +{ \ + _Tpvec1 a1, b1, c1, d1; \ + v_load_deinterleave((const _Tp1*)ptr, a1, b1, c1, d1); \ + a0 = v_reinterpret_as_##suffix0(a1); \ + b0 = v_reinterpret_as_##suffix0(b1); \ + c0 = v_reinterpret_as_##suffix0(c1); \ + d0 = v_reinterpret_as_##suffix0(d1); \ +} \ +inline void v_store_interleave( _Tp0* ptr, const _Tpvec0& a0, const _Tpvec0& b0, \ + hal::StoreMode mode = hal::STORE_UNALIGNED ) \ +{ \ + _Tpvec1 a1 = v_reinterpret_as_##suffix1(a0); \ + _Tpvec1 b1 = v_reinterpret_as_##suffix1(b0); \ + v_store_interleave((_Tp1*)ptr, a1, b1, mode); \ +} \ +inline void v_store_interleave( _Tp0* ptr, const _Tpvec0& a0, const _Tpvec0& b0, \ + const _Tpvec0& c0, hal::StoreMode mode = hal::STORE_UNALIGNED ) \ +{ \ + _Tpvec1 a1 = v_reinterpret_as_##suffix1(a0); \ + _Tpvec1 b1 = v_reinterpret_as_##suffix1(b0); \ + _Tpvec1 c1 = v_reinterpret_as_##suffix1(c0); \ + v_store_interleave((_Tp1*)ptr, a1, b1, c1, mode); \ +} \ +inline void v_store_interleave( _Tp0* ptr, const _Tpvec0& a0, const _Tpvec0& b0, \ + const _Tpvec0& c0, const _Tpvec0& d0, \ + hal::StoreMode mode = hal::STORE_UNALIGNED ) \ +{ \ + _Tpvec1 a1 = v_reinterpret_as_##suffix1(a0); \ + _Tpvec1 b1 = v_reinterpret_as_##suffix1(b0); \ + _Tpvec1 c1 = v_reinterpret_as_##suffix1(c0); \ + _Tpvec1 d1 = v_reinterpret_as_##suffix1(d0); \ + v_store_interleave((_Tp1*)ptr, a1, b1, c1, d1, mode); \ +} + +OPENCV_HAL_IMPL_SSE_LOADSTORE_INTERLEAVE(v_int8x16, schar, s8, v_uint8x16, uchar, u8) +OPENCV_HAL_IMPL_SSE_LOADSTORE_INTERLEAVE(v_int16x8, short, s16, v_uint16x8, ushort, u16) +OPENCV_HAL_IMPL_SSE_LOADSTORE_INTERLEAVE(v_int32x4, int, s32, v_uint32x4, unsigned, u32) +OPENCV_HAL_IMPL_SSE_LOADSTORE_INTERLEAVE(v_int64x2, int64, s64, v_uint64x2, uint64, u64) +OPENCV_HAL_IMPL_SSE_LOADSTORE_INTERLEAVE(v_float64x2, double, f64, v_uint64x2, uint64, u64) + +inline v_float32x4 v_cvt_f32(const v_int32x4& a) +{ + return v_float32x4(_mm_cvtepi32_ps(a.val)); +} + +inline v_float32x4 v_cvt_f32(const v_float64x2& a) +{ + return v_float32x4(_mm_cvtpd_ps(a.val)); +} + +inline v_float32x4 v_cvt_f32(const v_float64x2& a, const v_float64x2& b) +{ + return v_float32x4(_mm_movelh_ps(_mm_cvtpd_ps(a.val), _mm_cvtpd_ps(b.val))); +} + +inline v_float64x2 v_cvt_f64(const v_int32x4& a) +{ + return v_float64x2(_mm_cvtepi32_pd(a.val)); +} + +inline v_float64x2 v_cvt_f64_high(const v_int32x4& a) +{ + return v_float64x2(_mm_cvtepi32_pd(_mm_srli_si128(a.val,8))); +} + +inline v_float64x2 v_cvt_f64(const v_float32x4& a) +{ + return v_float64x2(_mm_cvtps_pd(a.val)); +} + +inline v_float64x2 v_cvt_f64_high(const v_float32x4& a) +{ + return v_float64x2(_mm_cvtps_pd(_mm_movehl_ps(a.val, a.val))); +} + +#if CV_FP16 +inline v_float32x4 v128_load_fp16_f32(const short* ptr) +{ + return v_float32x4(_mm_cvtph_ps(_mm_loadu_si128((const __m128i*)ptr))); +} + +inline void v_store_fp16(short* ptr, const v_float32x4& a) +{ + __m128i fp16_value = _mm_cvtps_ph(a.val, 0); + _mm_storel_epi64((__m128i*)ptr, fp16_value); +} +#endif + +////////////// Lookup table access //////////////////// + +inline v_int32x4 v_lut(const int* tab, const v_int32x4& idxvec) +{ + int CV_DECL_ALIGNED(32) idx[4]; + v_store_aligned(idx, idxvec); + return v_int32x4(_mm_setr_epi32(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]])); +} + +inline v_float32x4 v_lut(const float* tab, const v_int32x4& idxvec) +{ + int CV_DECL_ALIGNED(32) idx[4]; + v_store_aligned(idx, idxvec); + return v_float32x4(_mm_setr_ps(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]])); +} + +inline v_float64x2 v_lut(const double* tab, const v_int32x4& idxvec) +{ + int idx[2]; + v_store_low(idx, idxvec); + return v_float64x2(_mm_setr_pd(tab[idx[0]], tab[idx[1]])); +} + +// loads pairs from the table and deinterleaves them, e.g. returns: +// x = (tab[idxvec[0], tab[idxvec[1]], tab[idxvec[2]], tab[idxvec[3]]), +// y = (tab[idxvec[0]+1], tab[idxvec[1]+1], tab[idxvec[2]+1], tab[idxvec[3]+1]) +// note that the indices are float's indices, not the float-pair indices. +// in theory, this function can be used to implement bilinear interpolation, +// when idxvec are the offsets within the image. +inline void v_lut_deinterleave(const float* tab, const v_int32x4& idxvec, v_float32x4& x, v_float32x4& y) +{ + int CV_DECL_ALIGNED(32) idx[4]; + v_store_aligned(idx, idxvec); + __m128 z = _mm_setzero_ps(); + __m128 xy01 = _mm_loadl_pi(z, (__m64*)(tab + idx[0])); + __m128 xy23 = _mm_loadl_pi(z, (__m64*)(tab + idx[2])); + xy01 = _mm_loadh_pi(xy01, (__m64*)(tab + idx[1])); + xy23 = _mm_loadh_pi(xy23, (__m64*)(tab + idx[3])); + __m128 xxyy02 = _mm_unpacklo_ps(xy01, xy23); + __m128 xxyy13 = _mm_unpackhi_ps(xy01, xy23); + x = v_float32x4(_mm_unpacklo_ps(xxyy02, xxyy13)); + y = v_float32x4(_mm_unpackhi_ps(xxyy02, xxyy13)); +} + +inline void v_lut_deinterleave(const double* tab, const v_int32x4& idxvec, v_float64x2& x, v_float64x2& y) +{ + int idx[2]; + v_store_low(idx, idxvec); + __m128d xy0 = _mm_loadu_pd(tab + idx[0]); + __m128d xy1 = _mm_loadu_pd(tab + idx[1]); + x = v_float64x2(_mm_unpacklo_pd(xy0, xy1)); + y = v_float64x2(_mm_unpackhi_pd(xy0, xy1)); +} + + +////////////// FP16 support /////////////////////////// + +inline v_float32x4 v_load_expand(const float16_t* ptr) +{ + const __m128i z = _mm_setzero_si128(), delta = _mm_set1_epi32(0x38000000); + const __m128i signmask = _mm_set1_epi32(0x80000000), maxexp = _mm_set1_epi32(0x7c000000); + const __m128 deltaf = _mm_castsi128_ps(_mm_set1_epi32(0x38800000)); + __m128i bits = _mm_unpacklo_epi16(z, _mm_loadl_epi64((const __m128i*)ptr)); // h << 16 + __m128i e = _mm_and_si128(bits, maxexp), sign = _mm_and_si128(bits, signmask); + __m128i t = _mm_add_epi32(_mm_srli_epi32(_mm_xor_si128(bits, sign), 3), delta); // ((h & 0x7fff) << 13) + delta + __m128i zt = _mm_castps_si128(_mm_sub_ps(_mm_castsi128_ps(_mm_add_epi32(t, _mm_set1_epi32(1 << 23))), deltaf)); + + t = _mm_add_epi32(t, _mm_and_si128(delta, _mm_cmpeq_epi32(maxexp, e))); + __m128i zmask = _mm_cmpeq_epi32(e, z); + __m128i ft = v_select_si128(zmask, zt, t); + return v_float32x4(_mm_castsi128_ps(_mm_or_si128(ft, sign))); +} + +inline void v_pack_store(float16_t* ptr, const v_float32x4& v) +{ + const __m128i signmask = _mm_set1_epi32(0x80000000); + const __m128i rval = _mm_set1_epi32(0x3f000000); + + __m128i t = _mm_castps_si128(v.val); + __m128i sign = _mm_srai_epi32(_mm_and_si128(t, signmask), 16); + t = _mm_andnot_si128(signmask, t); + + __m128i finitemask = _mm_cmpgt_epi32(_mm_set1_epi32(0x47800000), t); + __m128i isnan = _mm_cmpgt_epi32(t, _mm_set1_epi32(0x7f800000)); + __m128i naninf = v_select_si128(isnan, _mm_set1_epi32(0x7e00), _mm_set1_epi32(0x7c00)); + __m128i tinymask = _mm_cmpgt_epi32(_mm_set1_epi32(0x38800000), t); + __m128i tt = _mm_castps_si128(_mm_add_ps(_mm_castsi128_ps(t), _mm_castsi128_ps(rval))); + tt = _mm_sub_epi32(tt, rval); + __m128i odd = _mm_and_si128(_mm_srli_epi32(t, 13), _mm_set1_epi32(1)); + __m128i nt = _mm_add_epi32(t, _mm_set1_epi32(0xc8000fff)); + nt = _mm_srli_epi32(_mm_add_epi32(nt, odd), 13); + t = v_select_si128(tinymask, tt, nt); + t = v_select_si128(finitemask, t, naninf); + t = _mm_or_si128(t, sign); + t = _mm_packs_epi32(t, t); + _mm_storel_epi64((__m128i*)ptr, t); +} + +inline void v_cleanup() {} + +//! @name Check SIMD support +//! @{ +//! @brief Check CPU capability of SIMD operation +static inline bool hasSIMD128() +{ + return (CV_CPU_HAS_SUPPORT_SSE2) ? true : false; +} + +//! @} + +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END + +//! @endcond + +} + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/hal/intrin_sse_em.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/hal/intrin_sse_em.hpp new file mode 100644 index 0000000000000000000000000000000000000000..be2766847ca106619dc43229ba6ffdf2a8dc8f32 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/hal/intrin_sse_em.hpp @@ -0,0 +1,167 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html + +#ifndef OPENCV_HAL_INTRIN_SSE_EM_HPP +#define OPENCV_HAL_INTRIN_SSE_EM_HPP + +namespace cv +{ + +//! @cond IGNORED + +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN + +#define OPENCV_HAL_SSE_WRAP_1(fun, tp) \ + inline tp _v128_##fun(const tp& a) \ + { return _mm_##fun(a); } + +#define OPENCV_HAL_SSE_WRAP_2(fun, tp) \ + inline tp _v128_##fun(const tp& a, const tp& b) \ + { return _mm_##fun(a, b); } + +#define OPENCV_HAL_SSE_WRAP_3(fun, tp) \ + inline tp _v128_##fun(const tp& a, const tp& b, const tp& c) \ + { return _mm_##fun(a, b, c); } + +///////////////////////////// XOP ///////////////////////////// + +// [todo] define CV_XOP +#if 1 // CV_XOP +inline __m128i _v128_comgt_epu32(const __m128i& a, const __m128i& b) +{ + const __m128i delta = _mm_set1_epi32((int)0x80000000); + return _mm_cmpgt_epi32(_mm_xor_si128(a, delta), _mm_xor_si128(b, delta)); +} +// wrapping XOP +#else +OPENCV_HAL_SSE_WRAP_2(_v128_comgt_epu32, __m128i) +#endif // !CV_XOP + +///////////////////////////// SSE4.1 ///////////////////////////// + +#if !CV_SSE4_1 + +/** Swizzle **/ +inline __m128i _v128_blendv_epi8(const __m128i& a, const __m128i& b, const __m128i& mask) +{ return _mm_xor_si128(a, _mm_and_si128(_mm_xor_si128(b, a), mask)); } + +/** Convert **/ +// 8 >> 16 +inline __m128i _v128_cvtepu8_epi16(const __m128i& a) +{ + const __m128i z = _mm_setzero_si128(); + return _mm_unpacklo_epi8(a, z); +} +inline __m128i _v128_cvtepi8_epi16(const __m128i& a) +{ return _mm_srai_epi16(_mm_unpacklo_epi8(a, a), 8); } +// 8 >> 32 +inline __m128i _v128_cvtepu8_epi32(const __m128i& a) +{ + const __m128i z = _mm_setzero_si128(); + return _mm_unpacklo_epi16(_mm_unpacklo_epi8(a, z), z); +} +inline __m128i _v128_cvtepi8_epi32(const __m128i& a) +{ + __m128i r = _mm_unpacklo_epi8(a, a); + r = _mm_unpacklo_epi8(r, r); + return _mm_srai_epi32(r, 24); +} +// 16 >> 32 +inline __m128i _v128_cvtepu16_epi32(const __m128i& a) +{ + const __m128i z = _mm_setzero_si128(); + return _mm_unpacklo_epi16(a, z); +} +inline __m128i _v128_cvtepi16_epi32(const __m128i& a) +{ return _mm_srai_epi32(_mm_unpacklo_epi16(a, a), 16); } +// 32 >> 64 +inline __m128i _v128_cvtepu32_epi64(const __m128i& a) +{ + const __m128i z = _mm_setzero_si128(); + return _mm_unpacklo_epi32(a, z); +} +inline __m128i _v128_cvtepi32_epi64(const __m128i& a) +{ return _mm_unpacklo_epi32(a, _mm_srai_epi32(a, 31)); } + +/** Arithmetic **/ +inline __m128i _v128_mullo_epi32(const __m128i& a, const __m128i& b) +{ + __m128i c0 = _mm_mul_epu32(a, b); + __m128i c1 = _mm_mul_epu32(_mm_srli_epi64(a, 32), _mm_srli_epi64(b, 32)); + __m128i d0 = _mm_unpacklo_epi32(c0, c1); + __m128i d1 = _mm_unpackhi_epi32(c0, c1); + return _mm_unpacklo_epi64(d0, d1); +} + +/** Math **/ +inline __m128i _v128_min_epu32(const __m128i& a, const __m128i& b) +{ return _v128_blendv_epi8(a, b, _v128_comgt_epu32(a, b)); } + +// wrapping SSE4.1 +#else +OPENCV_HAL_SSE_WRAP_1(cvtepu8_epi16, __m128i) +OPENCV_HAL_SSE_WRAP_1(cvtepi8_epi16, __m128i) +OPENCV_HAL_SSE_WRAP_1(cvtepu8_epi32, __m128i) +OPENCV_HAL_SSE_WRAP_1(cvtepi8_epi32, __m128i) +OPENCV_HAL_SSE_WRAP_1(cvtepu16_epi32, __m128i) +OPENCV_HAL_SSE_WRAP_1(cvtepi16_epi32, __m128i) +OPENCV_HAL_SSE_WRAP_1(cvtepu32_epi64, __m128i) +OPENCV_HAL_SSE_WRAP_1(cvtepi32_epi64, __m128i) +OPENCV_HAL_SSE_WRAP_2(min_epu32, __m128i) +OPENCV_HAL_SSE_WRAP_2(mullo_epi32, __m128i) +OPENCV_HAL_SSE_WRAP_3(blendv_epi8, __m128i) +#endif // !CV_SSE4_1 + +///////////////////////////// Revolutionary ///////////////////////////// + +/** Convert **/ +// 16 << 8 +inline __m128i _v128_cvtepu8_epi16_high(const __m128i& a) +{ + const __m128i z = _mm_setzero_si128(); + return _mm_unpackhi_epi8(a, z); +} +inline __m128i _v128_cvtepi8_epi16_high(const __m128i& a) +{ return _mm_srai_epi16(_mm_unpackhi_epi8(a, a), 8); } +// 32 << 16 +inline __m128i _v128_cvtepu16_epi32_high(const __m128i& a) +{ + const __m128i z = _mm_setzero_si128(); + return _mm_unpackhi_epi16(a, z); +} +inline __m128i _v128_cvtepi16_epi32_high(const __m128i& a) +{ return _mm_srai_epi32(_mm_unpackhi_epi16(a, a), 16); } +// 64 << 32 +inline __m128i _v128_cvtepu32_epi64_high(const __m128i& a) +{ + const __m128i z = _mm_setzero_si128(); + return _mm_unpackhi_epi32(a, z); +} +inline __m128i _v128_cvtepi32_epi64_high(const __m128i& a) +{ return _mm_unpackhi_epi32(a, _mm_srai_epi32(a, 31)); } + +/** Miscellaneous **/ +inline __m128i _v128_packs_epu32(const __m128i& a, const __m128i& b) +{ + const __m128i m = _mm_set1_epi32(65535); + __m128i am = _v128_min_epu32(a, m); + __m128i bm = _v128_min_epu32(b, m); +#if CV_SSE4_1 + return _mm_packus_epi32(am, bm); +#else + const __m128i d = _mm_set1_epi32(32768), nd = _mm_set1_epi16(-32768); + am = _mm_sub_epi32(am, d); + bm = _mm_sub_epi32(bm, d); + am = _mm_packs_epi32(am, bm); + return _mm_sub_epi16(am, nd); +#endif +} + +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END + +//! @endcond + +} // cv:: + +#endif // OPENCV_HAL_INTRIN_SSE_EM_HPP \ No newline at end of file diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/hal/intrin_vsx.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/hal/intrin_vsx.hpp new file mode 100644 index 0000000000000000000000000000000000000000..fce5c444ed3fabd368b4e6b27ad0fdaba18b288d --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/hal/intrin_vsx.hpp @@ -0,0 +1,1120 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html + +#ifndef OPENCV_HAL_VSX_HPP +#define OPENCV_HAL_VSX_HPP + +#include +#include "opencv2/core/utility.hpp" + +#define CV_SIMD128 1 +#define CV_SIMD128_64F 1 + +/** + * todo: supporting half precision for power9 + * convert instractions xvcvhpsp, xvcvsphp +**/ + +namespace cv +{ + +//! @cond IGNORED + +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN + +///////// Types //////////// + +struct v_uint8x16 +{ + typedef uchar lane_type; + enum { nlanes = 16 }; + vec_uchar16 val; + + explicit v_uint8x16(const vec_uchar16& v) : val(v) + {} + v_uint8x16() : val(vec_uchar16_z) + {} + v_uint8x16(vec_bchar16 v) : val(vec_uchar16_c(v)) + {} + v_uint8x16(uchar v0, uchar v1, uchar v2, uchar v3, uchar v4, uchar v5, uchar v6, uchar v7, + uchar v8, uchar v9, uchar v10, uchar v11, uchar v12, uchar v13, uchar v14, uchar v15) + : val(vec_uchar16_set(v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15)) + {} + uchar get0() const + { return vec_extract(val, 0); } +}; + +struct v_int8x16 +{ + typedef schar lane_type; + enum { nlanes = 16 }; + vec_char16 val; + + explicit v_int8x16(const vec_char16& v) : val(v) + {} + v_int8x16() : val(vec_char16_z) + {} + v_int8x16(vec_bchar16 v) : val(vec_char16_c(v)) + {} + v_int8x16(schar v0, schar v1, schar v2, schar v3, schar v4, schar v5, schar v6, schar v7, + schar v8, schar v9, schar v10, schar v11, schar v12, schar v13, schar v14, schar v15) + : val(vec_char16_set(v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15)) + {} + schar get0() const + { return vec_extract(val, 0); } +}; + +struct v_uint16x8 +{ + typedef ushort lane_type; + enum { nlanes = 8 }; + vec_ushort8 val; + + explicit v_uint16x8(const vec_ushort8& v) : val(v) + {} + v_uint16x8() : val(vec_ushort8_z) + {} + v_uint16x8(vec_bshort8 v) : val(vec_ushort8_c(v)) + {} + v_uint16x8(ushort v0, ushort v1, ushort v2, ushort v3, ushort v4, ushort v5, ushort v6, ushort v7) + : val(vec_ushort8_set(v0, v1, v2, v3, v4, v5, v6, v7)) + {} + ushort get0() const + { return vec_extract(val, 0); } +}; + +struct v_int16x8 +{ + typedef short lane_type; + enum { nlanes = 8 }; + vec_short8 val; + + explicit v_int16x8(const vec_short8& v) : val(v) + {} + v_int16x8() : val(vec_short8_z) + {} + v_int16x8(vec_bshort8 v) : val(vec_short8_c(v)) + {} + v_int16x8(short v0, short v1, short v2, short v3, short v4, short v5, short v6, short v7) + : val(vec_short8_set(v0, v1, v2, v3, v4, v5, v6, v7)) + {} + short get0() const + { return vec_extract(val, 0); } +}; + +struct v_uint32x4 +{ + typedef unsigned lane_type; + enum { nlanes = 4 }; + vec_uint4 val; + + explicit v_uint32x4(const vec_uint4& v) : val(v) + {} + v_uint32x4() : val(vec_uint4_z) + {} + v_uint32x4(vec_bint4 v) : val(vec_uint4_c(v)) + {} + v_uint32x4(unsigned v0, unsigned v1, unsigned v2, unsigned v3) : val(vec_uint4_set(v0, v1, v2, v3)) + {} + uint get0() const + { return vec_extract(val, 0); } +}; + +struct v_int32x4 +{ + typedef int lane_type; + enum { nlanes = 4 }; + vec_int4 val; + + explicit v_int32x4(const vec_int4& v) : val(v) + {} + v_int32x4() : val(vec_int4_z) + {} + v_int32x4(vec_bint4 v) : val(vec_int4_c(v)) + {} + v_int32x4(int v0, int v1, int v2, int v3) : val(vec_int4_set(v0, v1, v2, v3)) + {} + int get0() const + { return vec_extract(val, 0); } +}; + +struct v_float32x4 +{ + typedef float lane_type; + enum { nlanes = 4 }; + vec_float4 val; + + explicit v_float32x4(const vec_float4& v) : val(v) + {} + v_float32x4() : val(vec_float4_z) + {} + v_float32x4(vec_bint4 v) : val(vec_float4_c(v)) + {} + v_float32x4(float v0, float v1, float v2, float v3) : val(vec_float4_set(v0, v1, v2, v3)) + {} + float get0() const + { return vec_extract(val, 0); } +}; + +struct v_uint64x2 +{ + typedef uint64 lane_type; + enum { nlanes = 2 }; + vec_udword2 val; + + explicit v_uint64x2(const vec_udword2& v) : val(v) + {} + v_uint64x2() : val(vec_udword2_z) + {} + v_uint64x2(vec_bdword2 v) : val(vec_udword2_c(v)) + {} + v_uint64x2(uint64 v0, uint64 v1) : val(vec_udword2_set(v0, v1)) + {} + uint64 get0() const + { return vec_extract(val, 0); } +}; + +struct v_int64x2 +{ + typedef int64 lane_type; + enum { nlanes = 2 }; + vec_dword2 val; + + explicit v_int64x2(const vec_dword2& v) : val(v) + {} + v_int64x2() : val(vec_dword2_z) + {} + v_int64x2(vec_bdword2 v) : val(vec_dword2_c(v)) + {} + v_int64x2(int64 v0, int64 v1) : val(vec_dword2_set(v0, v1)) + {} + int64 get0() const + { return vec_extract(val, 0); } +}; + +struct v_float64x2 +{ + typedef double lane_type; + enum { nlanes = 2 }; + vec_double2 val; + + explicit v_float64x2(const vec_double2& v) : val(v) + {} + v_float64x2() : val(vec_double2_z) + {} + v_float64x2(vec_bdword2 v) : val(vec_double2_c(v)) + {} + v_float64x2(double v0, double v1) : val(vec_double2_set(v0, v1)) + {} + double get0() const + { return vec_extract(val, 0); } +}; + +//////////////// Load and store operations /////////////// + +/* + * clang-5 aborted during parse "vec_xxx_c" only if it's + * inside a function template which is defined by preprocessor macro. + * + * if vec_xxx_c defined as C++ cast, clang-5 will pass it +*/ +#define OPENCV_HAL_IMPL_VSX_INITVEC(_Tpvec, _Tp, suffix, cast) \ +inline _Tpvec v_setzero_##suffix() { return _Tpvec(); } \ +inline _Tpvec v_setall_##suffix(_Tp v) { return _Tpvec(vec_splats((_Tp)v));} \ +template inline _Tpvec v_reinterpret_as_##suffix(const _Tpvec0 &a) \ +{ return _Tpvec((cast)a.val); } + +OPENCV_HAL_IMPL_VSX_INITVEC(v_uint8x16, uchar, u8, vec_uchar16) +OPENCV_HAL_IMPL_VSX_INITVEC(v_int8x16, schar, s8, vec_char16) +OPENCV_HAL_IMPL_VSX_INITVEC(v_uint16x8, ushort, u16, vec_ushort8) +OPENCV_HAL_IMPL_VSX_INITVEC(v_int16x8, short, s16, vec_short8) +OPENCV_HAL_IMPL_VSX_INITVEC(v_uint32x4, uint, u32, vec_uint4) +OPENCV_HAL_IMPL_VSX_INITVEC(v_int32x4, int, s32, vec_int4) +OPENCV_HAL_IMPL_VSX_INITVEC(v_uint64x2, uint64, u64, vec_udword2) +OPENCV_HAL_IMPL_VSX_INITVEC(v_int64x2, int64, s64, vec_dword2) +OPENCV_HAL_IMPL_VSX_INITVEC(v_float32x4, float, f32, vec_float4) +OPENCV_HAL_IMPL_VSX_INITVEC(v_float64x2, double, f64, vec_double2) + +#define OPENCV_HAL_IMPL_VSX_LOADSTORE_C(_Tpvec, _Tp, ld, ld_a, st, st_a) \ +inline _Tpvec v_load(const _Tp* ptr) \ +{ return _Tpvec(ld(0, ptr)); } \ +inline _Tpvec v_load_aligned(VSX_UNUSED(const _Tp* ptr)) \ +{ return _Tpvec(ld_a(0, ptr)); } \ +inline _Tpvec v_load_low(const _Tp* ptr) \ +{ return _Tpvec(vec_ld_l8(ptr)); } \ +inline _Tpvec v_load_halves(const _Tp* ptr0, const _Tp* ptr1) \ +{ return _Tpvec(vec_mergesqh(vec_ld_l8(ptr0), vec_ld_l8(ptr1))); } \ +inline void v_store(_Tp* ptr, const _Tpvec& a) \ +{ st(a.val, 0, ptr); } \ +inline void v_store_aligned(VSX_UNUSED(_Tp* ptr), const _Tpvec& a) \ +{ st_a(a.val, 0, ptr); } \ +inline void v_store_aligned_nocache(VSX_UNUSED(_Tp* ptr), const _Tpvec& a) \ +{ st_a(a.val, 0, ptr); } \ +inline void v_store(_Tp* ptr, const _Tpvec& a, hal::StoreMode mode) \ +{ if(mode == hal::STORE_UNALIGNED) st(a.val, 0, ptr); else st_a(a.val, 0, ptr); } \ +inline void v_store_low(_Tp* ptr, const _Tpvec& a) \ +{ vec_st_l8(a.val, ptr); } \ +inline void v_store_high(_Tp* ptr, const _Tpvec& a) \ +{ vec_st_h8(a.val, ptr); } + +#define OPENCV_HAL_IMPL_VSX_LOADSTORE(_Tpvec, _Tp) \ +OPENCV_HAL_IMPL_VSX_LOADSTORE_C(_Tpvec, _Tp, vsx_ld, vec_ld, vsx_st, vec_st) + +OPENCV_HAL_IMPL_VSX_LOADSTORE(v_uint8x16, uchar) +OPENCV_HAL_IMPL_VSX_LOADSTORE(v_int8x16, schar) +OPENCV_HAL_IMPL_VSX_LOADSTORE(v_uint16x8, ushort) +OPENCV_HAL_IMPL_VSX_LOADSTORE(v_int16x8, short) +OPENCV_HAL_IMPL_VSX_LOADSTORE(v_uint32x4, uint) +OPENCV_HAL_IMPL_VSX_LOADSTORE(v_int32x4, int) +OPENCV_HAL_IMPL_VSX_LOADSTORE(v_float32x4, float) + +OPENCV_HAL_IMPL_VSX_LOADSTORE_C(v_float64x2, double, vsx_ld, vsx_ld, vsx_st, vsx_st) +OPENCV_HAL_IMPL_VSX_LOADSTORE_C(v_uint64x2, uint64, vsx_ld2, vsx_ld2, vsx_st2, vsx_st2) +OPENCV_HAL_IMPL_VSX_LOADSTORE_C(v_int64x2, int64, vsx_ld2, vsx_ld2, vsx_st2, vsx_st2) + +//////////////// Value reordering /////////////// + +/* de&interleave */ +#define OPENCV_HAL_IMPL_VSX_INTERLEAVE(_Tp, _Tpvec) \ +inline void v_load_deinterleave(const _Tp* ptr, _Tpvec& a, _Tpvec& b) \ +{ vec_ld_deinterleave(ptr, a.val, b.val);} \ +inline void v_load_deinterleave(const _Tp* ptr, _Tpvec& a, \ + _Tpvec& b, _Tpvec& c) \ +{ vec_ld_deinterleave(ptr, a.val, b.val, c.val); } \ +inline void v_load_deinterleave(const _Tp* ptr, _Tpvec& a, _Tpvec& b, \ + _Tpvec& c, _Tpvec& d) \ +{ vec_ld_deinterleave(ptr, a.val, b.val, c.val, d.val); } \ +inline void v_store_interleave(_Tp* ptr, const _Tpvec& a, const _Tpvec& b, \ + hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \ +{ vec_st_interleave(a.val, b.val, ptr); } \ +inline void v_store_interleave(_Tp* ptr, const _Tpvec& a, \ + const _Tpvec& b, const _Tpvec& c, \ + hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \ +{ vec_st_interleave(a.val, b.val, c.val, ptr); } \ +inline void v_store_interleave(_Tp* ptr, const _Tpvec& a, const _Tpvec& b, \ + const _Tpvec& c, const _Tpvec& d, \ + hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \ +{ vec_st_interleave(a.val, b.val, c.val, d.val, ptr); } + +OPENCV_HAL_IMPL_VSX_INTERLEAVE(uchar, v_uint8x16) +OPENCV_HAL_IMPL_VSX_INTERLEAVE(schar, v_int8x16) +OPENCV_HAL_IMPL_VSX_INTERLEAVE(ushort, v_uint16x8) +OPENCV_HAL_IMPL_VSX_INTERLEAVE(short, v_int16x8) +OPENCV_HAL_IMPL_VSX_INTERLEAVE(uint, v_uint32x4) +OPENCV_HAL_IMPL_VSX_INTERLEAVE(int, v_int32x4) +OPENCV_HAL_IMPL_VSX_INTERLEAVE(float, v_float32x4) +OPENCV_HAL_IMPL_VSX_INTERLEAVE(double, v_float64x2) +OPENCV_HAL_IMPL_VSX_INTERLEAVE(int64, v_int64x2) +OPENCV_HAL_IMPL_VSX_INTERLEAVE(uint64, v_uint64x2) + +/* Expand */ +#define OPENCV_HAL_IMPL_VSX_EXPAND(_Tpvec, _Tpwvec, _Tp, fl, fh) \ +inline void v_expand(const _Tpvec& a, _Tpwvec& b0, _Tpwvec& b1) \ +{ \ + b0.val = fh(a.val); \ + b1.val = fl(a.val); \ +} \ +inline _Tpwvec v_expand_low(const _Tpvec& a) \ +{ return _Tpwvec(fh(a.val)); } \ +inline _Tpwvec v_expand_high(const _Tpvec& a) \ +{ return _Tpwvec(fl(a.val)); } \ +inline _Tpwvec v_load_expand(const _Tp* ptr) \ +{ return _Tpwvec(fh(vec_ld_l8(ptr))); } + +OPENCV_HAL_IMPL_VSX_EXPAND(v_uint8x16, v_uint16x8, uchar, vec_unpacklu, vec_unpackhu) +OPENCV_HAL_IMPL_VSX_EXPAND(v_int8x16, v_int16x8, schar, vec_unpackl, vec_unpackh) +OPENCV_HAL_IMPL_VSX_EXPAND(v_uint16x8, v_uint32x4, ushort, vec_unpacklu, vec_unpackhu) +OPENCV_HAL_IMPL_VSX_EXPAND(v_int16x8, v_int32x4, short, vec_unpackl, vec_unpackh) +OPENCV_HAL_IMPL_VSX_EXPAND(v_uint32x4, v_uint64x2, uint, vec_unpacklu, vec_unpackhu) +OPENCV_HAL_IMPL_VSX_EXPAND(v_int32x4, v_int64x2, int, vec_unpackl, vec_unpackh) + +inline v_uint32x4 v_load_expand_q(const uchar* ptr) +{ return v_uint32x4(vec_uint4_set(ptr[0], ptr[1], ptr[2], ptr[3])); } + +inline v_int32x4 v_load_expand_q(const schar* ptr) +{ return v_int32x4(vec_int4_set(ptr[0], ptr[1], ptr[2], ptr[3])); } + +/* pack */ +#define OPENCV_HAL_IMPL_VSX_PACK(_Tpvec, _Tp, _Tpwvec, _Tpvn, _Tpdel, sfnc, pkfnc, addfnc, pack) \ +inline _Tpvec v_##pack(const _Tpwvec& a, const _Tpwvec& b) \ +{ \ + return _Tpvec(pkfnc(a.val, b.val)); \ +} \ +inline void v_##pack##_store(_Tp* ptr, const _Tpwvec& a) \ +{ \ + vec_st_l8(pkfnc(a.val, a.val), ptr); \ +} \ +template \ +inline _Tpvec v_rshr_##pack(const _Tpwvec& a, const _Tpwvec& b) \ +{ \ + const __vector _Tpvn vn = vec_splats((_Tpvn)n); \ + const __vector _Tpdel delta = vec_splats((_Tpdel)((_Tpdel)1 << (n-1))); \ + return _Tpvec(pkfnc(sfnc(addfnc(a.val, delta), vn), sfnc(addfnc(b.val, delta), vn))); \ +} \ +template \ +inline void v_rshr_##pack##_store(_Tp* ptr, const _Tpwvec& a) \ +{ \ + const __vector _Tpvn vn = vec_splats((_Tpvn)n); \ + const __vector _Tpdel delta = vec_splats((_Tpdel)((_Tpdel)1 << (n-1))); \ + vec_st_l8(pkfnc(sfnc(addfnc(a.val, delta), vn), delta), ptr); \ +} + +OPENCV_HAL_IMPL_VSX_PACK(v_uint8x16, uchar, v_uint16x8, unsigned short, unsigned short, + vec_sr, vec_packs, vec_adds, pack) +OPENCV_HAL_IMPL_VSX_PACK(v_int8x16, schar, v_int16x8, unsigned short, short, + vec_sra, vec_packs, vec_adds, pack) + +OPENCV_HAL_IMPL_VSX_PACK(v_uint16x8, ushort, v_uint32x4, unsigned int, unsigned int, + vec_sr, vec_packs, vec_add, pack) +OPENCV_HAL_IMPL_VSX_PACK(v_int16x8, short, v_int32x4, unsigned int, int, + vec_sra, vec_packs, vec_add, pack) + +OPENCV_HAL_IMPL_VSX_PACK(v_uint32x4, uint, v_uint64x2, unsigned long long, unsigned long long, + vec_sr, vec_pack, vec_add, pack) +OPENCV_HAL_IMPL_VSX_PACK(v_int32x4, int, v_int64x2, unsigned long long, long long, + vec_sra, vec_pack, vec_add, pack) + +OPENCV_HAL_IMPL_VSX_PACK(v_uint8x16, uchar, v_int16x8, unsigned short, short, + vec_sra, vec_packsu, vec_adds, pack_u) +OPENCV_HAL_IMPL_VSX_PACK(v_uint16x8, ushort, v_int32x4, unsigned int, int, + vec_sra, vec_packsu, vec_add, pack_u) +// Following variant is not implemented on other platforms: +//OPENCV_HAL_IMPL_VSX_PACK(v_uint32x4, uint, v_int64x2, unsigned long long, long long, +// vec_sra, vec_packsu, vec_add, pack_u) + +// pack boolean +inline v_uint8x16 v_pack_b(const v_uint16x8& a, const v_uint16x8& b) +{ + vec_uchar16 ab = vec_pack(a.val, b.val); + return v_uint8x16(ab); +} + +inline v_uint8x16 v_pack_b(const v_uint32x4& a, const v_uint32x4& b, + const v_uint32x4& c, const v_uint32x4& d) +{ + vec_ushort8 ab = vec_pack(a.val, b.val); + vec_ushort8 cd = vec_pack(c.val, d.val); + return v_uint8x16(vec_pack(ab, cd)); +} + +inline v_uint8x16 v_pack_b(const v_uint64x2& a, const v_uint64x2& b, const v_uint64x2& c, + const v_uint64x2& d, const v_uint64x2& e, const v_uint64x2& f, + const v_uint64x2& g, const v_uint64x2& h) +{ + vec_uint4 ab = vec_pack(a.val, b.val); + vec_uint4 cd = vec_pack(c.val, d.val); + vec_uint4 ef = vec_pack(e.val, f.val); + vec_uint4 gh = vec_pack(g.val, h.val); + + vec_ushort8 abcd = vec_pack(ab, cd); + vec_ushort8 efgh = vec_pack(ef, gh); + return v_uint8x16(vec_pack(abcd, efgh)); +} + +/* Recombine */ +template +inline void v_zip(const _Tpvec& a0, const _Tpvec& a1, _Tpvec& b0, _Tpvec& b1) +{ + b0.val = vec_mergeh(a0.val, a1.val); + b1.val = vec_mergel(a0.val, a1.val); +} + +template +inline _Tpvec v_combine_high(const _Tpvec& a, const _Tpvec& b) +{ return _Tpvec(vec_mergesql(a.val, b.val)); } + +template +inline _Tpvec v_combine_low(const _Tpvec& a, const _Tpvec& b) +{ return _Tpvec(vec_mergesqh(a.val, b.val)); } + +template +inline void v_recombine(const _Tpvec& a, const _Tpvec& b, _Tpvec& c, _Tpvec& d) +{ + c.val = vec_mergesqh(a.val, b.val); + d.val = vec_mergesql(a.val, b.val); +} + +////////// Arithmetic, bitwise and comparison operations ///////// + +/* Element-wise binary and unary operations */ +/** Arithmetics **/ +#define OPENCV_HAL_IMPL_VSX_BIN_OP(bin_op, _Tpvec, intrin) \ +inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(intrin(a.val, b.val)); } \ +inline _Tpvec& operator bin_op##= (_Tpvec& a, const _Tpvec& b) \ +{ a.val = intrin(a.val, b.val); return a; } + +OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_uint8x16, vec_adds) +OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_uint8x16, vec_subs) +OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_int8x16, vec_adds) +OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_int8x16, vec_subs) +OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_uint16x8, vec_adds) +OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_uint16x8, vec_subs) +OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_int16x8, vec_adds) +OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_int16x8, vec_subs) +OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_uint32x4, vec_add) +OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_uint32x4, vec_sub) +OPENCV_HAL_IMPL_VSX_BIN_OP(*, v_uint32x4, vec_mul) +OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_int32x4, vec_add) +OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_int32x4, vec_sub) +OPENCV_HAL_IMPL_VSX_BIN_OP(*, v_int32x4, vec_mul) +OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_float32x4, vec_add) +OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_float32x4, vec_sub) +OPENCV_HAL_IMPL_VSX_BIN_OP(*, v_float32x4, vec_mul) +OPENCV_HAL_IMPL_VSX_BIN_OP(/, v_float32x4, vec_div) +OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_float64x2, vec_add) +OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_float64x2, vec_sub) +OPENCV_HAL_IMPL_VSX_BIN_OP(*, v_float64x2, vec_mul) +OPENCV_HAL_IMPL_VSX_BIN_OP(/, v_float64x2, vec_div) +OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_uint64x2, vec_add) +OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_uint64x2, vec_sub) +OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_int64x2, vec_add) +OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_int64x2, vec_sub) + +// saturating multiply +#define OPENCV_HAL_IMPL_VSX_MUL_SAT(_Tpvec, _Tpwvec) \ + inline _Tpvec operator * (const _Tpvec& a, const _Tpvec& b) \ + { \ + _Tpwvec c, d; \ + v_mul_expand(a, b, c, d); \ + return v_pack(c, d); \ + } \ + inline _Tpvec& operator *= (_Tpvec& a, const _Tpvec& b) \ + { a = a * b; return a; } + +OPENCV_HAL_IMPL_VSX_MUL_SAT(v_int8x16, v_int16x8) +OPENCV_HAL_IMPL_VSX_MUL_SAT(v_uint8x16, v_uint16x8) +OPENCV_HAL_IMPL_VSX_MUL_SAT(v_int16x8, v_int32x4) +OPENCV_HAL_IMPL_VSX_MUL_SAT(v_uint16x8, v_uint32x4) + +template +inline void v_mul_expand(const Tvec& a, const Tvec& b, Twvec& c, Twvec& d) +{ + Twvec p0 = Twvec(vec_mule(a.val, b.val)); + Twvec p1 = Twvec(vec_mulo(a.val, b.val)); + v_zip(p0, p1, c, d); +} + +inline void v_mul_expand(const v_uint32x4& a, const v_uint32x4& b, v_uint64x2& c, v_uint64x2& d) +{ + c.val = vec_mul(vec_unpackhu(a.val), vec_unpackhu(b.val)); + d.val = vec_mul(vec_unpacklu(a.val), vec_unpacklu(b.val)); +} + +inline v_int16x8 v_mul_hi(const v_int16x8& a, const v_int16x8& b) +{ + vec_int4 p0 = vec_mule(a.val, b.val); + vec_int4 p1 = vec_mulo(a.val, b.val); + static const vec_uchar16 perm = {2, 3, 18, 19, 6, 7, 22, 23, 10, 11, 26, 27, 14, 15, 30, 31}; + return v_int16x8(vec_perm(vec_short8_c(p0), vec_short8_c(p1), perm)); +} +inline v_uint16x8 v_mul_hi(const v_uint16x8& a, const v_uint16x8& b) +{ + vec_uint4 p0 = vec_mule(a.val, b.val); + vec_uint4 p1 = vec_mulo(a.val, b.val); + static const vec_uchar16 perm = {2, 3, 18, 19, 6, 7, 22, 23, 10, 11, 26, 27, 14, 15, 30, 31}; + return v_uint16x8(vec_perm(vec_ushort8_c(p0), vec_ushort8_c(p1), perm)); +} + +/** Non-saturating arithmetics **/ +#define OPENCV_HAL_IMPL_VSX_BIN_FUNC(func, intrin) \ +template \ +inline _Tpvec func(const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(intrin(a.val, b.val)); } + +OPENCV_HAL_IMPL_VSX_BIN_FUNC(v_add_wrap, vec_add) +OPENCV_HAL_IMPL_VSX_BIN_FUNC(v_sub_wrap, vec_sub) +OPENCV_HAL_IMPL_VSX_BIN_FUNC(v_mul_wrap, vec_mul) + +/** Bitwise shifts **/ +#define OPENCV_HAL_IMPL_VSX_SHIFT_OP(_Tpvec, shr, splfunc) \ +inline _Tpvec operator << (const _Tpvec& a, int imm) \ +{ return _Tpvec(vec_sl(a.val, splfunc(imm))); } \ +inline _Tpvec operator >> (const _Tpvec& a, int imm) \ +{ return _Tpvec(shr(a.val, splfunc(imm))); } \ +template inline _Tpvec v_shl(const _Tpvec& a) \ +{ return _Tpvec(vec_sl(a.val, splfunc(imm))); } \ +template inline _Tpvec v_shr(const _Tpvec& a) \ +{ return _Tpvec(shr(a.val, splfunc(imm))); } + +OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_uint8x16, vec_sr, vec_uchar16_sp) +OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_uint16x8, vec_sr, vec_ushort8_sp) +OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_uint32x4, vec_sr, vec_uint4_sp) +OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_uint64x2, vec_sr, vec_udword2_sp) +// algebraic right shift +OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_int8x16, vec_sra, vec_uchar16_sp) +OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_int16x8, vec_sra, vec_ushort8_sp) +OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_int32x4, vec_sra, vec_uint4_sp) +OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_int64x2, vec_sra, vec_udword2_sp) + +/** Bitwise logic **/ +#define OPENCV_HAL_IMPL_VSX_LOGIC_OP(_Tpvec) \ +OPENCV_HAL_IMPL_VSX_BIN_OP(&, _Tpvec, vec_and) \ +OPENCV_HAL_IMPL_VSX_BIN_OP(|, _Tpvec, vec_or) \ +OPENCV_HAL_IMPL_VSX_BIN_OP(^, _Tpvec, vec_xor) \ +inline _Tpvec operator ~ (const _Tpvec& a) \ +{ return _Tpvec(vec_not(a.val)); } + +OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_uint8x16) +OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_int8x16) +OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_uint16x8) +OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_int16x8) +OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_uint32x4) +OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_int32x4) +OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_uint64x2) +OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_int64x2) +OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_float32x4) +OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_float64x2) + +/** Bitwise select **/ +#define OPENCV_HAL_IMPL_VSX_SELECT(_Tpvec, cast) \ +inline _Tpvec v_select(const _Tpvec& mask, const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(vec_sel(b.val, a.val, cast(mask.val))); } + +OPENCV_HAL_IMPL_VSX_SELECT(v_uint8x16, vec_bchar16_c) +OPENCV_HAL_IMPL_VSX_SELECT(v_int8x16, vec_bchar16_c) +OPENCV_HAL_IMPL_VSX_SELECT(v_uint16x8, vec_bshort8_c) +OPENCV_HAL_IMPL_VSX_SELECT(v_int16x8, vec_bshort8_c) +OPENCV_HAL_IMPL_VSX_SELECT(v_uint32x4, vec_bint4_c) +OPENCV_HAL_IMPL_VSX_SELECT(v_int32x4, vec_bint4_c) +OPENCV_HAL_IMPL_VSX_SELECT(v_float32x4, vec_bint4_c) +OPENCV_HAL_IMPL_VSX_SELECT(v_float64x2, vec_bdword2_c) + +/** Comparison **/ +#define OPENCV_HAL_IMPL_VSX_INT_CMP_OP(_Tpvec) \ +inline _Tpvec operator == (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(vec_cmpeq(a.val, b.val)); } \ +inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(vec_cmpne(a.val, b.val)); } \ +inline _Tpvec operator < (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(vec_cmplt(a.val, b.val)); } \ +inline _Tpvec operator > (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(vec_cmpgt(a.val, b.val)); } \ +inline _Tpvec operator <= (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(vec_cmple(a.val, b.val)); } \ +inline _Tpvec operator >= (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(vec_cmpge(a.val, b.val)); } + +OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_uint8x16) +OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_int8x16) +OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_uint16x8) +OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_int16x8) +OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_uint32x4) +OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_int32x4) +OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_float32x4) +OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_float64x2) +OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_uint64x2) +OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_int64x2) + +inline v_float32x4 v_not_nan(const v_float32x4& a) +{ return v_float32x4(vec_cmpeq(a.val, a.val)); } +inline v_float64x2 v_not_nan(const v_float64x2& a) +{ return v_float64x2(vec_cmpeq(a.val, a.val)); } + +/** min/max **/ +OPENCV_HAL_IMPL_VSX_BIN_FUNC(v_min, vec_min) +OPENCV_HAL_IMPL_VSX_BIN_FUNC(v_max, vec_max) + +/** Rotate **/ +#define OPENCV_IMPL_VSX_ROTATE(_Tpvec, suffix, shf, cast) \ +template \ +inline _Tpvec v_rotate_##suffix(const _Tpvec& a) \ +{ \ + const int wd = imm * sizeof(typename _Tpvec::lane_type); \ + if (wd > 15) \ + return _Tpvec(); \ + return _Tpvec((cast)shf(vec_uchar16_c(a.val), vec_uchar16_sp(wd << 3))); \ +} + +#define OPENCV_IMPL_VSX_ROTATE_LR(_Tpvec, cast) \ +OPENCV_IMPL_VSX_ROTATE(_Tpvec, left, vec_slo, cast) \ +OPENCV_IMPL_VSX_ROTATE(_Tpvec, right, vec_sro, cast) + +OPENCV_IMPL_VSX_ROTATE_LR(v_uint8x16, vec_uchar16) +OPENCV_IMPL_VSX_ROTATE_LR(v_int8x16, vec_char16) +OPENCV_IMPL_VSX_ROTATE_LR(v_uint16x8, vec_ushort8) +OPENCV_IMPL_VSX_ROTATE_LR(v_int16x8, vec_short8) +OPENCV_IMPL_VSX_ROTATE_LR(v_uint32x4, vec_uint4) +OPENCV_IMPL_VSX_ROTATE_LR(v_int32x4, vec_int4) +OPENCV_IMPL_VSX_ROTATE_LR(v_float32x4, vec_float4) +OPENCV_IMPL_VSX_ROTATE_LR(v_uint64x2, vec_udword2) +OPENCV_IMPL_VSX_ROTATE_LR(v_int64x2, vec_dword2) +OPENCV_IMPL_VSX_ROTATE_LR(v_float64x2, vec_double2) + +template +inline _Tpvec v_rotate_right(const _Tpvec& a, const _Tpvec& b) +{ + enum { CV_SHIFT = 16 - imm * (sizeof(typename _Tpvec::lane_type)) }; + if (CV_SHIFT == 16) + return a; +#ifdef __IBMCPP__ + return _Tpvec(vec_sld(b.val, a.val, CV_SHIFT & 15)); +#else + return _Tpvec(vec_sld(b.val, a.val, CV_SHIFT)); +#endif +} + +template +inline _Tpvec v_rotate_left(const _Tpvec& a, const _Tpvec& b) +{ + enum { CV_SHIFT = imm * (sizeof(typename _Tpvec::lane_type)) }; + if (CV_SHIFT == 16) + return b; + return _Tpvec(vec_sld(a.val, b.val, CV_SHIFT)); +} + +#define OPENCV_IMPL_VSX_ROTATE_64_2RG(_Tpvec, suffix, rg1, rg2) \ +template \ +inline _Tpvec v_rotate_##suffix(const _Tpvec& a, const _Tpvec& b) \ +{ \ + if (imm == 1) \ + return _Tpvec(vec_permi(rg1.val, rg2.val, 2)); \ + return imm ? b : a; \ +} + +#define OPENCV_IMPL_VSX_ROTATE_64_2RG_LR(_Tpvec) \ +OPENCV_IMPL_VSX_ROTATE_64_2RG(_Tpvec, left, b, a) \ +OPENCV_IMPL_VSX_ROTATE_64_2RG(_Tpvec, right, a, b) + +OPENCV_IMPL_VSX_ROTATE_64_2RG_LR(v_float64x2) +OPENCV_IMPL_VSX_ROTATE_64_2RG_LR(v_uint64x2) +OPENCV_IMPL_VSX_ROTATE_64_2RG_LR(v_int64x2) + +/* Extract */ +template +inline _Tpvec v_extract(const _Tpvec& a, const _Tpvec& b) +{ return v_rotate_right(a, b); } + +////////// Reduce and mask ///////// + +/** Reduce **/ +inline short v_reduce_sum(const v_int16x8& a) +{ + const vec_int4 zero = vec_int4_z; + return saturate_cast(vec_extract(vec_sums(vec_sum4s(a.val, zero), zero), 3)); +} +inline ushort v_reduce_sum(const v_uint16x8& a) +{ + const vec_int4 v4 = vec_int4_c(vec_unpackhu(vec_adds(a.val, vec_sld(a.val, a.val, 8)))); + return saturate_cast(vec_extract(vec_sums(v4, vec_int4_z), 3)); +} + +#define OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(_Tpvec, _Tpvec2, scalartype, suffix, func) \ +inline scalartype v_reduce_##suffix(const _Tpvec& a) \ +{ \ + const _Tpvec2 rs = func(a.val, vec_sld(a.val, a.val, 8)); \ + return vec_extract(func(rs, vec_sld(rs, rs, 4)), 0); \ +} +OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_uint32x4, vec_uint4, uint, sum, vec_add) +OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_uint32x4, vec_uint4, uint, max, vec_max) +OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_uint32x4, vec_uint4, uint, min, vec_min) +OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_int32x4, vec_int4, int, sum, vec_add) +OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_int32x4, vec_int4, int, max, vec_max) +OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_int32x4, vec_int4, int, min, vec_min) +OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_float32x4, vec_float4, float, sum, vec_add) +OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_float32x4, vec_float4, float, max, vec_max) +OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_float32x4, vec_float4, float, min, vec_min) + +inline double v_reduce_sum(const v_float64x2& a) +{ + return vec_extract(vec_add(a.val, vec_permi(a.val, a.val, 3)), 0); +} + +#define OPENCV_HAL_IMPL_VSX_REDUCE_OP_8(_Tpvec, _Tpvec2, scalartype, suffix, func) \ +inline scalartype v_reduce_##suffix(const _Tpvec& a) \ +{ \ + _Tpvec2 rs = func(a.val, vec_sld(a.val, a.val, 8)); \ + rs = func(rs, vec_sld(rs, rs, 4)); \ + return vec_extract(func(rs, vec_sld(rs, rs, 2)), 0); \ +} +OPENCV_HAL_IMPL_VSX_REDUCE_OP_8(v_uint16x8, vec_ushort8, ushort, max, vec_max) +OPENCV_HAL_IMPL_VSX_REDUCE_OP_8(v_uint16x8, vec_ushort8, ushort, min, vec_min) +OPENCV_HAL_IMPL_VSX_REDUCE_OP_8(v_int16x8, vec_short8, short, max, vec_max) +OPENCV_HAL_IMPL_VSX_REDUCE_OP_8(v_int16x8, vec_short8, short, min, vec_min) + +inline v_float32x4 v_reduce_sum4(const v_float32x4& a, const v_float32x4& b, + const v_float32x4& c, const v_float32x4& d) +{ + vec_float4 ac = vec_add(vec_mergel(a.val, c.val), vec_mergeh(a.val, c.val)); + ac = vec_add(ac, vec_sld(ac, ac, 8)); + + vec_float4 bd = vec_add(vec_mergel(b.val, d.val), vec_mergeh(b.val, d.val)); + bd = vec_add(bd, vec_sld(bd, bd, 8)); + return v_float32x4(vec_mergeh(ac, bd)); +} + +inline unsigned v_reduce_sad(const v_uint8x16& a, const v_uint8x16& b) +{ + const vec_uint4 zero4 = vec_uint4_z; + vec_uint4 sum4 = vec_sum4s(vec_absd(a.val, b.val), zero4); + return (unsigned)vec_extract(vec_sums(vec_int4_c(sum4), vec_int4_c(zero4)), 3); +} +inline unsigned v_reduce_sad(const v_int8x16& a, const v_int8x16& b) +{ + const vec_int4 zero4 = vec_int4_z; + vec_char16 ad = vec_abss(vec_subs(a.val, b.val)); + vec_int4 sum4 = vec_sum4s(ad, zero4); + return (unsigned)vec_extract(vec_sums(sum4, zero4), 3); +} +inline unsigned v_reduce_sad(const v_uint16x8& a, const v_uint16x8& b) +{ + vec_ushort8 ad = vec_absd(a.val, b.val); + VSX_UNUSED(vec_int4) sum = vec_sums(vec_int4_c(vec_unpackhu(ad)), vec_int4_c(vec_unpacklu(ad))); + return (unsigned)vec_extract(sum, 3); +} +inline unsigned v_reduce_sad(const v_int16x8& a, const v_int16x8& b) +{ + const vec_int4 zero4 = vec_int4_z; + vec_short8 ad = vec_abss(vec_subs(a.val, b.val)); + vec_int4 sum4 = vec_sum4s(ad, zero4); + return (unsigned)vec_extract(vec_sums(sum4, zero4), 3); +} +inline unsigned v_reduce_sad(const v_uint32x4& a, const v_uint32x4& b) +{ + const vec_uint4 ad = vec_absd(a.val, b.val); + const vec_uint4 rd = vec_add(ad, vec_sld(ad, ad, 8)); + return vec_extract(vec_add(rd, vec_sld(rd, rd, 4)), 0); +} +inline unsigned v_reduce_sad(const v_int32x4& a, const v_int32x4& b) +{ + vec_int4 ad = vec_abss(vec_sub(a.val, b.val)); + return (unsigned)vec_extract(vec_sums(ad, vec_int4_z), 3); +} +inline float v_reduce_sad(const v_float32x4& a, const v_float32x4& b) +{ + const vec_float4 ad = vec_abs(vec_sub(a.val, b.val)); + const vec_float4 rd = vec_add(ad, vec_sld(ad, ad, 8)); + return vec_extract(vec_add(rd, vec_sld(rd, rd, 4)), 0); +} + +/** Popcount **/ +template +inline v_uint32x4 v_popcount(const _Tpvec& a) +{ return v_uint32x4(vec_popcntu(vec_uint4_c(a.val))); } + +/** Mask **/ +inline int v_signmask(const v_uint8x16& a) +{ + vec_uchar16 sv = vec_sr(a.val, vec_uchar16_sp(7)); + static const vec_uchar16 slm = {0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7}; + sv = vec_sl(sv, slm); + vec_uint4 sv4 = vec_sum4s(sv, vec_uint4_z); + static const vec_uint4 slm4 = {0, 0, 8, 8}; + sv4 = vec_sl(sv4, slm4); + return vec_extract(vec_sums((vec_int4) sv4, vec_int4_z), 3); +} +inline int v_signmask(const v_int8x16& a) +{ return v_signmask(v_reinterpret_as_u8(a)); } + +inline int v_signmask(const v_int16x8& a) +{ + static const vec_ushort8 slm = {0, 1, 2, 3, 4, 5, 6, 7}; + vec_short8 sv = vec_sr(a.val, vec_ushort8_sp(15)); + sv = vec_sl(sv, slm); + vec_int4 svi = vec_int4_z; + svi = vec_sums(vec_sum4s(sv, svi), svi); + return vec_extract(svi, 3); +} +inline int v_signmask(const v_uint16x8& a) +{ return v_signmask(v_reinterpret_as_s16(a)); } + +inline int v_signmask(const v_int32x4& a) +{ + static const vec_uint4 slm = {0, 1, 2, 3}; + vec_int4 sv = vec_sr(a.val, vec_uint4_sp(31)); + sv = vec_sl(sv, slm); + sv = vec_sums(sv, vec_int4_z); + return vec_extract(sv, 3); +} +inline int v_signmask(const v_uint32x4& a) +{ return v_signmask(v_reinterpret_as_s32(a)); } +inline int v_signmask(const v_float32x4& a) +{ return v_signmask(v_reinterpret_as_s32(a)); } + +inline int v_signmask(const v_int64x2& a) +{ + VSX_UNUSED(const vec_dword2) sv = vec_sr(a.val, vec_udword2_sp(63)); + return (int)vec_extract(sv, 0) | (int)vec_extract(sv, 1) << 1; +} +inline int v_signmask(const v_uint64x2& a) +{ return v_signmask(v_reinterpret_as_s64(a)); } +inline int v_signmask(const v_float64x2& a) +{ return v_signmask(v_reinterpret_as_s64(a)); } + +template +inline bool v_check_all(const _Tpvec& a) +{ return vec_all_lt(a.val, _Tpvec().val); } +inline bool v_check_all(const v_uint8x16& a) +{ return v_check_all(v_reinterpret_as_s8(a)); } +inline bool v_check_all(const v_uint16x8& a) +{ return v_check_all(v_reinterpret_as_s16(a)); } +inline bool v_check_all(const v_uint32x4& a) +{ return v_check_all(v_reinterpret_as_s32(a)); } +inline bool v_check_all(const v_float32x4& a) +{ return v_check_all(v_reinterpret_as_s32(a)); } +inline bool v_check_all(const v_float64x2& a) +{ return v_check_all(v_reinterpret_as_s64(a)); } + +template +inline bool v_check_any(const _Tpvec& a) +{ return vec_any_lt(a.val, _Tpvec().val); } +inline bool v_check_any(const v_uint8x16& a) +{ return v_check_any(v_reinterpret_as_s8(a)); } +inline bool v_check_any(const v_uint16x8& a) +{ return v_check_any(v_reinterpret_as_s16(a)); } +inline bool v_check_any(const v_uint32x4& a) +{ return v_check_any(v_reinterpret_as_s32(a)); } +inline bool v_check_any(const v_float32x4& a) +{ return v_check_any(v_reinterpret_as_s32(a)); } +inline bool v_check_any(const v_float64x2& a) +{ return v_check_any(v_reinterpret_as_s64(a)); } + +////////// Other math ///////// + +/** Some frequent operations **/ +inline v_float32x4 v_sqrt(const v_float32x4& x) +{ return v_float32x4(vec_sqrt(x.val)); } +inline v_float64x2 v_sqrt(const v_float64x2& x) +{ return v_float64x2(vec_sqrt(x.val)); } + +inline v_float32x4 v_invsqrt(const v_float32x4& x) +{ return v_float32x4(vec_rsqrt(x.val)); } +inline v_float64x2 v_invsqrt(const v_float64x2& x) +{ return v_float64x2(vec_rsqrt(x.val)); } + +#define OPENCV_HAL_IMPL_VSX_MULADD(_Tpvec) \ +inline _Tpvec v_magnitude(const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(vec_sqrt(vec_madd(a.val, a.val, vec_mul(b.val, b.val)))); } \ +inline _Tpvec v_sqr_magnitude(const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(vec_madd(a.val, a.val, vec_mul(b.val, b.val))); } \ +inline _Tpvec v_fma(const _Tpvec& a, const _Tpvec& b, const _Tpvec& c) \ +{ return _Tpvec(vec_madd(a.val, b.val, c.val)); } \ +inline _Tpvec v_muladd(const _Tpvec& a, const _Tpvec& b, const _Tpvec& c) \ +{ return _Tpvec(vec_madd(a.val, b.val, c.val)); } + +OPENCV_HAL_IMPL_VSX_MULADD(v_float32x4) +OPENCV_HAL_IMPL_VSX_MULADD(v_float64x2) + +inline v_int32x4 v_muladd(const v_int32x4& a, const v_int32x4& b, const v_int32x4& c) +{ return a * b + c; } + +// TODO: exp, log, sin, cos + +/** Absolute values **/ +inline v_uint8x16 v_abs(const v_int8x16& x) +{ return v_uint8x16(vec_uchar16_c(vec_abs(x.val))); } + +inline v_uint16x8 v_abs(const v_int16x8& x) +{ return v_uint16x8(vec_ushort8_c(vec_abs(x.val))); } + +inline v_uint32x4 v_abs(const v_int32x4& x) +{ return v_uint32x4(vec_uint4_c(vec_abs(x.val))); } + +inline v_float32x4 v_abs(const v_float32x4& x) +{ return v_float32x4(vec_abs(x.val)); } + +inline v_float64x2 v_abs(const v_float64x2& x) +{ return v_float64x2(vec_abs(x.val)); } + +/** Absolute difference **/ +// unsigned +OPENCV_HAL_IMPL_VSX_BIN_FUNC(v_absdiff, vec_absd) + +inline v_uint8x16 v_absdiff(const v_int8x16& a, const v_int8x16& b) +{ return v_reinterpret_as_u8(v_sub_wrap(v_max(a, b), v_min(a, b))); } +inline v_uint16x8 v_absdiff(const v_int16x8& a, const v_int16x8& b) +{ return v_reinterpret_as_u16(v_sub_wrap(v_max(a, b), v_min(a, b))); } +inline v_uint32x4 v_absdiff(const v_int32x4& a, const v_int32x4& b) +{ return v_reinterpret_as_u32(v_max(a, b) - v_min(a, b)); } + +inline v_float32x4 v_absdiff(const v_float32x4& a, const v_float32x4& b) +{ return v_abs(a - b); } +inline v_float64x2 v_absdiff(const v_float64x2& a, const v_float64x2& b) +{ return v_abs(a - b); } + +/** Absolute difference for signed integers **/ +inline v_int8x16 v_absdiffs(const v_int8x16& a, const v_int8x16& b) +{ return v_int8x16(vec_abss(vec_subs(a.val, b.val))); } +inline v_int16x8 v_absdiffs(const v_int16x8& a, const v_int16x8& b) +{ return v_int16x8(vec_abss(vec_subs(a.val, b.val))); } + +////////// Conversions ///////// + +/** Rounding **/ +inline v_int32x4 v_round(const v_float32x4& a) +{ return v_int32x4(vec_cts(vec_rint(a.val))); } + +inline v_int32x4 v_round(const v_float64x2& a) +{ return v_int32x4(vec_mergesqo(vec_ctso(vec_rint(a.val)), vec_int4_z)); } + +inline v_int32x4 v_round(const v_float64x2& a, const v_float64x2& b) +{ return v_int32x4(vec_mergesqo(vec_ctso(vec_rint(a.val)), vec_ctso(vec_rint(b.val)))); } + +inline v_int32x4 v_floor(const v_float32x4& a) +{ return v_int32x4(vec_cts(vec_floor(a.val))); } + +inline v_int32x4 v_floor(const v_float64x2& a) +{ return v_int32x4(vec_mergesqo(vec_ctso(vec_floor(a.val)), vec_int4_z)); } + +inline v_int32x4 v_ceil(const v_float32x4& a) +{ return v_int32x4(vec_cts(vec_ceil(a.val))); } + +inline v_int32x4 v_ceil(const v_float64x2& a) +{ return v_int32x4(vec_mergesqo(vec_ctso(vec_ceil(a.val)), vec_int4_z)); } + +inline v_int32x4 v_trunc(const v_float32x4& a) +{ return v_int32x4(vec_cts(a.val)); } + +inline v_int32x4 v_trunc(const v_float64x2& a) +{ return v_int32x4(vec_mergesqo(vec_ctso(a.val), vec_int4_z)); } + +/** To float **/ +inline v_float32x4 v_cvt_f32(const v_int32x4& a) +{ return v_float32x4(vec_ctf(a.val)); } + +inline v_float32x4 v_cvt_f32(const v_float64x2& a) +{ return v_float32x4(vec_mergesqo(vec_cvfo(a.val), vec_float4_z)); } + +inline v_float32x4 v_cvt_f32(const v_float64x2& a, const v_float64x2& b) +{ return v_float32x4(vec_mergesqo(vec_cvfo(a.val), vec_cvfo(b.val))); } + +inline v_float64x2 v_cvt_f64(const v_int32x4& a) +{ return v_float64x2(vec_ctdo(vec_mergeh(a.val, a.val))); } + +inline v_float64x2 v_cvt_f64_high(const v_int32x4& a) +{ return v_float64x2(vec_ctdo(vec_mergel(a.val, a.val))); } + +inline v_float64x2 v_cvt_f64(const v_float32x4& a) +{ return v_float64x2(vec_cvfo(vec_mergeh(a.val, a.val))); } + +inline v_float64x2 v_cvt_f64_high(const v_float32x4& a) +{ return v_float64x2(vec_cvfo(vec_mergel(a.val, a.val))); } + +////////////// Lookup table access //////////////////// + +inline v_int32x4 v_lut(const int* tab, const v_int32x4& idxvec) +{ + int CV_DECL_ALIGNED(32) idx[4]; + v_store_aligned(idx, idxvec); + return v_int32x4(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]]); +} + +inline v_float32x4 v_lut(const float* tab, const v_int32x4& idxvec) +{ + int CV_DECL_ALIGNED(32) idx[4]; + v_store_aligned(idx, idxvec); + return v_float32x4(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]]); +} + +inline v_float64x2 v_lut(const double* tab, const v_int32x4& idxvec) +{ + int CV_DECL_ALIGNED(32) idx[4]; + v_store_aligned(idx, idxvec); + return v_float64x2(tab[idx[0]], tab[idx[1]]); +} + +inline void v_lut_deinterleave(const float* tab, const v_int32x4& idxvec, v_float32x4& x, v_float32x4& y) +{ + int CV_DECL_ALIGNED(32) idx[4]; + v_store_aligned(idx, idxvec); + x = v_float32x4(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]]); + y = v_float32x4(tab[idx[0]+1], tab[idx[1]+1], tab[idx[2]+1], tab[idx[3]+1]); +} + +inline void v_lut_deinterleave(const double* tab, const v_int32x4& idxvec, v_float64x2& x, v_float64x2& y) +{ + int CV_DECL_ALIGNED(32) idx[4]; + v_store_aligned(idx, idxvec); + x = v_float64x2(tab[idx[0]], tab[idx[1]]); + y = v_float64x2(tab[idx[0]+1], tab[idx[1]+1]); +} + +/////// FP16 support //////// + +// [TODO] implement these 2 using VSX or universal intrinsics (copy from intrin_sse.cpp and adopt) +inline v_float32x4 v_load_expand(const float16_t* ptr) +{ + return v_float32x4((float)ptr[0], (float)ptr[1], (float)ptr[2], (float)ptr[3]); +} + +inline void v_pack_store(float16_t* ptr, const v_float32x4& v) +{ + float CV_DECL_ALIGNED(32) f[4]; + v_store_aligned(f, v); + ptr[0] = float16_t(f[0]); + ptr[1] = float16_t(f[1]); + ptr[2] = float16_t(f[2]); + ptr[3] = float16_t(f[3]); +} + +inline void v_cleanup() {} + + +/** Reinterpret **/ +/** its up there with load and store operations **/ + +////////// Matrix operations ///////// + +inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b) +{ return v_int32x4(vec_msum(a.val, b.val, vec_int4_z)); } + +inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b, const v_int32x4& c) +{ return v_int32x4(vec_msum(a.val, b.val, c.val)); } + +inline v_float32x4 v_matmul(const v_float32x4& v, const v_float32x4& m0, + const v_float32x4& m1, const v_float32x4& m2, + const v_float32x4& m3) +{ + const vec_float4 v0 = vec_splat(v.val, 0); + const vec_float4 v1 = vec_splat(v.val, 1); + const vec_float4 v2 = vec_splat(v.val, 2); + VSX_UNUSED(const vec_float4) v3 = vec_splat(v.val, 3); + return v_float32x4(vec_madd(v0, m0.val, vec_madd(v1, m1.val, vec_madd(v2, m2.val, vec_mul(v3, m3.val))))); +} + +inline v_float32x4 v_matmuladd(const v_float32x4& v, const v_float32x4& m0, + const v_float32x4& m1, const v_float32x4& m2, + const v_float32x4& a) +{ + const vec_float4 v0 = vec_splat(v.val, 0); + const vec_float4 v1 = vec_splat(v.val, 1); + const vec_float4 v2 = vec_splat(v.val, 2); + return v_float32x4(vec_madd(v0, m0.val, vec_madd(v1, m1.val, vec_madd(v2, m2.val, a.val)))); +} + +#define OPENCV_HAL_IMPL_VSX_TRANSPOSE4x4(_Tpvec, _Tpvec2) \ +inline void v_transpose4x4(const _Tpvec& a0, const _Tpvec& a1, \ + const _Tpvec& a2, const _Tpvec& a3, \ + _Tpvec& b0, _Tpvec& b1, _Tpvec& b2, _Tpvec& b3) \ +{ \ + _Tpvec2 a02 = vec_mergeh(a0.val, a2.val); \ + _Tpvec2 a13 = vec_mergeh(a1.val, a3.val); \ + b0.val = vec_mergeh(a02, a13); \ + b1.val = vec_mergel(a02, a13); \ + a02 = vec_mergel(a0.val, a2.val); \ + a13 = vec_mergel(a1.val, a3.val); \ + b2.val = vec_mergeh(a02, a13); \ + b3.val = vec_mergel(a02, a13); \ +} +OPENCV_HAL_IMPL_VSX_TRANSPOSE4x4(v_uint32x4, vec_uint4) +OPENCV_HAL_IMPL_VSX_TRANSPOSE4x4(v_int32x4, vec_int4) +OPENCV_HAL_IMPL_VSX_TRANSPOSE4x4(v_float32x4, vec_float4) + +//! @name Check SIMD support +//! @{ +//! @brief Check CPU capability of SIMD operation +static inline bool hasSIMD128() +{ + return (CV_CPU_HAS_SUPPORT_VSX) ? true : false; +} + +//! @} + +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END + +//! @endcond + +} + +#endif // OPENCV_HAL_VSX_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/ippasync.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/ippasync.hpp new file mode 100644 index 0000000000000000000000000000000000000000..c35d8d81a19c54b5628f547d7081c1c6c002ab77 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/ippasync.hpp @@ -0,0 +1,195 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2015, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_IPPASYNC_HPP +#define OPENCV_CORE_IPPASYNC_HPP + +#ifdef HAVE_IPP_A // this file will be removed in OpenCV 4.0 + +#include "opencv2/core.hpp" +#include +#include + +namespace cv +{ + +namespace hpp +{ + +/** @addtogroup core_ipp +This section describes conversion between OpenCV and [Intel® IPP Asynchronous +C/C++](http://software.intel.com/en-us/intel-ipp-preview) library. [Getting Started +Guide](http://registrationcenter.intel.com/irc_nas/3727/ipp_async_get_started.htm) help you to +install the library, configure header and library build paths. + */ +//! @{ + + //! convert OpenCV data type to hppDataType + inline int toHppType(const int cvType) + { + int depth = CV_MAT_DEPTH(cvType); + int hppType = depth == CV_8U ? HPP_DATA_TYPE_8U : + depth == CV_16U ? HPP_DATA_TYPE_16U : + depth == CV_16S ? HPP_DATA_TYPE_16S : + depth == CV_32S ? HPP_DATA_TYPE_32S : + depth == CV_32F ? HPP_DATA_TYPE_32F : + depth == CV_64F ? HPP_DATA_TYPE_64F : -1; + CV_Assert( hppType >= 0 ); + return hppType; + } + + //! convert hppDataType to OpenCV data type + inline int toCvType(const int hppType) + { + int cvType = hppType == HPP_DATA_TYPE_8U ? CV_8U : + hppType == HPP_DATA_TYPE_16U ? CV_16U : + hppType == HPP_DATA_TYPE_16S ? CV_16S : + hppType == HPP_DATA_TYPE_32S ? CV_32S : + hppType == HPP_DATA_TYPE_32F ? CV_32F : + hppType == HPP_DATA_TYPE_64F ? CV_64F : -1; + CV_Assert( cvType >= 0 ); + return cvType; + } + + /** @brief Convert hppiMatrix to Mat. + + This function allocates and initializes new matrix (if needed) that has the same size and type as + input matrix. Supports CV_8U, CV_16U, CV_16S, CV_32S, CV_32F, CV_64F. + @param src input hppiMatrix. + @param dst output matrix. + @param accel accelerator instance (see hpp::getHpp for the list of acceleration framework types). + @param cn number of channels. + */ + inline void copyHppToMat(hppiMatrix* src, Mat& dst, hppAccel accel, int cn) + { + hppDataType type; + hpp32u width, height; + hppStatus sts; + + if (src == NULL) + return dst.release(); + + sts = hppiInquireMatrix(src, &type, &width, &height); + + CV_Assert( sts == HPP_STATUS_NO_ERROR); + + int matType = CV_MAKETYPE(toCvType(type), cn); + + CV_Assert(width%cn == 0); + + width /= cn; + + dst.create((int)height, (int)width, (int)matType); + + size_t newSize = (size_t)(height*(hpp32u)(dst.step)); + + sts = hppiGetMatrixData(accel,src,(hpp32u)(dst.step),dst.data,&newSize); + + CV_Assert( sts == HPP_STATUS_NO_ERROR); + } + + /** @brief Create Mat from hppiMatrix. + + This function allocates and initializes the Mat that has the same size and type as input matrix. + Supports CV_8U, CV_16U, CV_16S, CV_32S, CV_32F, CV_64F. + @param src input hppiMatrix. + @param accel accelerator instance (see hpp::getHpp for the list of acceleration framework types). + @param cn number of channels. + @sa howToUseIPPAconversion, hpp::copyHppToMat, hpp::getHpp. + */ + inline Mat getMat(hppiMatrix* src, hppAccel accel, int cn) + { + Mat dst; + copyHppToMat(src, dst, accel, cn); + return dst; + } + + /** @brief Create hppiMatrix from Mat. + + This function allocates and initializes the hppiMatrix that has the same size and type as input + matrix, returns the hppiMatrix*. + + If you want to use zero-copy for GPU you should to have 4KB aligned matrix data. See details + [hppiCreateSharedMatrix](http://software.intel.com/ru-ru/node/501697). + + Supports CV_8U, CV_16U, CV_16S, CV_32S, CV_32F, CV_64F. + + @note The hppiMatrix pointer to the image buffer in system memory refers to the src.data. Control + the lifetime of the matrix and don't change its data, if there is no special need. + @param src input matrix. + @param accel accelerator instance. Supports type: + - **HPP_ACCEL_TYPE_CPU** - accelerated by optimized CPU instructions. + - **HPP_ACCEL_TYPE_GPU** - accelerated by GPU programmable units or fixed-function + accelerators. + - **HPP_ACCEL_TYPE_ANY** - any acceleration or no acceleration available. + @sa howToUseIPPAconversion, hpp::getMat + */ + inline hppiMatrix* getHpp(const Mat& src, hppAccel accel) + { + int htype = toHppType(src.type()); + int cn = src.channels(); + + CV_Assert(src.data); + hppAccelType accelType = hppQueryAccelType(accel); + + if (accelType!=HPP_ACCEL_TYPE_CPU) + { + hpp32u pitch, size; + hppQueryMatrixAllocParams(accel, src.cols*cn, src.rows, htype, &pitch, &size); + if (pitch!=0 && size!=0) + if ((int)(src.data)%4096==0 && pitch==(hpp32u)(src.step)) + { + return hppiCreateSharedMatrix(htype, src.cols*cn, src.rows, src.data, pitch, size); + } + } + + return hppiCreateMatrix(htype, src.cols*cn, src.rows, src.data, (hpp32s)(src.step));; + } + +//! @} +}} + +#endif + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/mat.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/mat.hpp new file mode 100644 index 0000000000000000000000000000000000000000..c0893b398d70e2356250af5e47b1e7ace15e5db4 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/mat.hpp @@ -0,0 +1,3701 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_MAT_HPP +#define OPENCV_CORE_MAT_HPP + +#ifndef __cplusplus +# error mat.hpp header must be compiled as C++ +#endif + +#include "opencv2/core/matx.hpp" +#include "opencv2/core/types.hpp" + +#include "opencv2/core/bufferpool.hpp" + +#ifdef CV_CXX11 +#include +#endif + +namespace cv +{ + +//! @addtogroup core_basic +//! @{ + +enum { ACCESS_READ=1<<24, ACCESS_WRITE=1<<25, + ACCESS_RW=3<<24, ACCESS_MASK=ACCESS_RW, ACCESS_FAST=1<<26 }; + +CV__DEBUG_NS_BEGIN + +class CV_EXPORTS _OutputArray; + +//////////////////////// Input/Output Array Arguments ///////////////////////////////// + +/** @brief This is the proxy class for passing read-only input arrays into OpenCV functions. + +It is defined as: +@code + typedef const _InputArray& InputArray; +@endcode +where _InputArray is a class that can be constructed from `Mat`, `Mat_`, `Matx`, +`std::vector`, `std::vector >`, `std::vector`, `std::vector >`, +`UMat`, `std::vector` or `double`. It can also be constructed from a matrix expression. + +Since this is mostly implementation-level class, and its interface may change in future versions, we +do not describe it in details. There are a few key things, though, that should be kept in mind: + +- When you see in the reference manual or in OpenCV source code a function that takes + InputArray, it means that you can actually pass `Mat`, `Matx`, `vector` etc. (see above the + complete list). +- Optional input arguments: If some of the input arrays may be empty, pass cv::noArray() (or + simply cv::Mat() as you probably did before). +- The class is designed solely for passing parameters. That is, normally you *should not* + declare class members, local and global variables of this type. +- If you want to design your own function or a class method that can operate of arrays of + multiple types, you can use InputArray (or OutputArray) for the respective parameters. Inside + a function you should use _InputArray::getMat() method to construct a matrix header for the + array (without copying data). _InputArray::kind() can be used to distinguish Mat from + `vector<>` etc., but normally it is not needed. + +Here is how you can use a function that takes InputArray : +@code + std::vector vec; + // points or a circle + for( int i = 0; i < 30; i++ ) + vec.push_back(Point2f((float)(100 + 30*cos(i*CV_PI*2/5)), + (float)(100 - 30*sin(i*CV_PI*2/5)))); + cv::transform(vec, vec, cv::Matx23f(0.707, -0.707, 10, 0.707, 0.707, 20)); +@endcode +That is, we form an STL vector containing points, and apply in-place affine transformation to the +vector using the 2x3 matrix created inline as `Matx` instance. + +Here is how such a function can be implemented (for simplicity, we implement a very specific case of +it, according to the assertion statement inside) : +@code + void myAffineTransform(InputArray _src, OutputArray _dst, InputArray _m) + { + // get Mat headers for input arrays. This is O(1) operation, + // unless _src and/or _m are matrix expressions. + Mat src = _src.getMat(), m = _m.getMat(); + CV_Assert( src.type() == CV_32FC2 && m.type() == CV_32F && m.size() == Size(3, 2) ); + + // [re]create the output array so that it has the proper size and type. + // In case of Mat it calls Mat::create, in case of STL vector it calls vector::resize. + _dst.create(src.size(), src.type()); + Mat dst = _dst.getMat(); + + for( int i = 0; i < src.rows; i++ ) + for( int j = 0; j < src.cols; j++ ) + { + Point2f pt = src.at(i, j); + dst.at(i, j) = Point2f(m.at(0, 0)*pt.x + + m.at(0, 1)*pt.y + + m.at(0, 2), + m.at(1, 0)*pt.x + + m.at(1, 1)*pt.y + + m.at(1, 2)); + } + } +@endcode +There is another related type, InputArrayOfArrays, which is currently defined as a synonym for +InputArray: +@code + typedef InputArray InputArrayOfArrays; +@endcode +It denotes function arguments that are either vectors of vectors or vectors of matrices. A separate +synonym is needed to generate Python/Java etc. wrappers properly. At the function implementation +level their use is similar, but _InputArray::getMat(idx) should be used to get header for the +idx-th component of the outer vector and _InputArray::size().area() should be used to find the +number of components (vectors/matrices) of the outer vector. + +In general, type support is limited to cv::Mat types. Other types are forbidden. +But in some cases we need to support passing of custom non-general Mat types, like arrays of cv::KeyPoint, cv::DMatch, etc. +This data is not intented to be interpreted as an image data, or processed somehow like regular cv::Mat. +To pass such custom type use rawIn() / rawOut() / rawInOut() wrappers. +Custom type is wrapped as Mat-compatible `CV_8UC` values (N = sizeof(T), N <= CV_CN_MAX). + */ +class CV_EXPORTS _InputArray +{ +public: + enum { + KIND_SHIFT = 16, + FIXED_TYPE = 0x8000 << KIND_SHIFT, + FIXED_SIZE = 0x4000 << KIND_SHIFT, + KIND_MASK = 31 << KIND_SHIFT, + + NONE = 0 << KIND_SHIFT, + MAT = 1 << KIND_SHIFT, + MATX = 2 << KIND_SHIFT, + STD_VECTOR = 3 << KIND_SHIFT, + STD_VECTOR_VECTOR = 4 << KIND_SHIFT, + STD_VECTOR_MAT = 5 << KIND_SHIFT, + EXPR = 6 << KIND_SHIFT, + OPENGL_BUFFER = 7 << KIND_SHIFT, + CUDA_HOST_MEM = 8 << KIND_SHIFT, + CUDA_GPU_MAT = 9 << KIND_SHIFT, + UMAT =10 << KIND_SHIFT, + STD_VECTOR_UMAT =11 << KIND_SHIFT, + STD_BOOL_VECTOR =12 << KIND_SHIFT, + STD_VECTOR_CUDA_GPU_MAT = 13 << KIND_SHIFT, + STD_ARRAY =14 << KIND_SHIFT, + STD_ARRAY_MAT =15 << KIND_SHIFT + }; + + _InputArray(); + _InputArray(int _flags, void* _obj); + _InputArray(const Mat& m); + _InputArray(const MatExpr& expr); + _InputArray(const std::vector& vec); + template _InputArray(const Mat_<_Tp>& m); + template _InputArray(const std::vector<_Tp>& vec); + _InputArray(const std::vector& vec); + template _InputArray(const std::vector >& vec); + _InputArray(const std::vector >&); + template _InputArray(const std::vector >& vec); + template _InputArray(const _Tp* vec, int n); + template _InputArray(const Matx<_Tp, m, n>& matx); + _InputArray(const double& val); + _InputArray(const cuda::GpuMat& d_mat); + _InputArray(const std::vector& d_mat_array); + _InputArray(const ogl::Buffer& buf); + _InputArray(const cuda::HostMem& cuda_mem); + template _InputArray(const cudev::GpuMat_<_Tp>& m); + _InputArray(const UMat& um); + _InputArray(const std::vector& umv); + +#ifdef CV_CXX_STD_ARRAY + template _InputArray(const std::array<_Tp, _Nm>& arr); + template _InputArray(const std::array& arr); +#endif + + template static _InputArray rawIn(const std::vector<_Tp>& vec); +#ifdef CV_CXX_STD_ARRAY + template static _InputArray rawIn(const std::array<_Tp, _Nm>& arr); +#endif + + Mat getMat(int idx=-1) const; + Mat getMat_(int idx=-1) const; + UMat getUMat(int idx=-1) const; + void getMatVector(std::vector& mv) const; + void getUMatVector(std::vector& umv) const; + void getGpuMatVector(std::vector& gpumv) const; + cuda::GpuMat getGpuMat() const; + ogl::Buffer getOGlBuffer() const; + + int getFlags() const; + void* getObj() const; + Size getSz() const; + + int kind() const; + int dims(int i=-1) const; + int cols(int i=-1) const; + int rows(int i=-1) const; + Size size(int i=-1) const; + int sizend(int* sz, int i=-1) const; + bool sameSize(const _InputArray& arr) const; + size_t total(int i=-1) const; + int type(int i=-1) const; + int depth(int i=-1) const; + int channels(int i=-1) const; + bool isContinuous(int i=-1) const; + bool isSubmatrix(int i=-1) const; + bool empty() const; + void copyTo(const _OutputArray& arr) const; + void copyTo(const _OutputArray& arr, const _InputArray & mask) const; + size_t offset(int i=-1) const; + size_t step(int i=-1) const; + bool isMat() const; + bool isUMat() const; + bool isMatVector() const; + bool isUMatVector() const; + bool isMatx() const; + bool isVector() const; + bool isGpuMat() const; + bool isGpuMatVector() const; + ~_InputArray(); + +protected: + int flags; + void* obj; + Size sz; + + void init(int _flags, const void* _obj); + void init(int _flags, const void* _obj, Size _sz); +}; + + +/** @brief This type is very similar to InputArray except that it is used for input/output and output function +parameters. + +Just like with InputArray, OpenCV users should not care about OutputArray, they just pass `Mat`, +`vector` etc. to the functions. The same limitation as for `InputArray`: *Do not explicitly +create OutputArray instances* applies here too. + +If you want to make your function polymorphic (i.e. accept different arrays as output parameters), +it is also not very difficult. Take the sample above as the reference. Note that +_OutputArray::create() needs to be called before _OutputArray::getMat(). This way you guarantee +that the output array is properly allocated. + +Optional output parameters. If you do not need certain output array to be computed and returned to +you, pass cv::noArray(), just like you would in the case of optional input array. At the +implementation level, use _OutputArray::needed() to check if certain output array needs to be +computed or not. + +There are several synonyms for OutputArray that are used to assist automatic Python/Java/... wrapper +generators: +@code + typedef OutputArray OutputArrayOfArrays; + typedef OutputArray InputOutputArray; + typedef OutputArray InputOutputArrayOfArrays; +@endcode + */ +class CV_EXPORTS _OutputArray : public _InputArray +{ +public: + enum + { + DEPTH_MASK_8U = 1 << CV_8U, + DEPTH_MASK_8S = 1 << CV_8S, + DEPTH_MASK_16U = 1 << CV_16U, + DEPTH_MASK_16S = 1 << CV_16S, + DEPTH_MASK_32S = 1 << CV_32S, + DEPTH_MASK_32F = 1 << CV_32F, + DEPTH_MASK_64F = 1 << CV_64F, + DEPTH_MASK_ALL = (DEPTH_MASK_64F<<1)-1, + DEPTH_MASK_ALL_BUT_8S = DEPTH_MASK_ALL & ~DEPTH_MASK_8S, + DEPTH_MASK_FLT = DEPTH_MASK_32F + DEPTH_MASK_64F + }; + + _OutputArray(); + _OutputArray(int _flags, void* _obj); + _OutputArray(Mat& m); + _OutputArray(std::vector& vec); + _OutputArray(cuda::GpuMat& d_mat); + _OutputArray(std::vector& d_mat); + _OutputArray(ogl::Buffer& buf); + _OutputArray(cuda::HostMem& cuda_mem); + template _OutputArray(cudev::GpuMat_<_Tp>& m); + template _OutputArray(std::vector<_Tp>& vec); + _OutputArray(std::vector& vec); + template _OutputArray(std::vector >& vec); + _OutputArray(std::vector >&); + template _OutputArray(std::vector >& vec); + template _OutputArray(Mat_<_Tp>& m); + template _OutputArray(_Tp* vec, int n); + template _OutputArray(Matx<_Tp, m, n>& matx); + _OutputArray(UMat& m); + _OutputArray(std::vector& vec); + + _OutputArray(const Mat& m); + _OutputArray(const std::vector& vec); + _OutputArray(const cuda::GpuMat& d_mat); + _OutputArray(const std::vector& d_mat); + _OutputArray(const ogl::Buffer& buf); + _OutputArray(const cuda::HostMem& cuda_mem); + template _OutputArray(const cudev::GpuMat_<_Tp>& m); + template _OutputArray(const std::vector<_Tp>& vec); + template _OutputArray(const std::vector >& vec); + template _OutputArray(const std::vector >& vec); + template _OutputArray(const Mat_<_Tp>& m); + template _OutputArray(const _Tp* vec, int n); + template _OutputArray(const Matx<_Tp, m, n>& matx); + _OutputArray(const UMat& m); + _OutputArray(const std::vector& vec); + +#ifdef CV_CXX_STD_ARRAY + template _OutputArray(std::array<_Tp, _Nm>& arr); + template _OutputArray(const std::array<_Tp, _Nm>& arr); + template _OutputArray(std::array& arr); + template _OutputArray(const std::array& arr); +#endif + + template static _OutputArray rawOut(std::vector<_Tp>& vec); +#ifdef CV_CXX_STD_ARRAY + template static _OutputArray rawOut(std::array<_Tp, _Nm>& arr); +#endif + + bool fixedSize() const; + bool fixedType() const; + bool needed() const; + Mat& getMatRef(int i=-1) const; + UMat& getUMatRef(int i=-1) const; + cuda::GpuMat& getGpuMatRef() const; + std::vector& getGpuMatVecRef() const; + ogl::Buffer& getOGlBufferRef() const; + cuda::HostMem& getHostMemRef() const; + void create(Size sz, int type, int i=-1, bool allowTransposed=false, int fixedDepthMask=0) const; + void create(int rows, int cols, int type, int i=-1, bool allowTransposed=false, int fixedDepthMask=0) const; + void create(int dims, const int* size, int type, int i=-1, bool allowTransposed=false, int fixedDepthMask=0) const; + void createSameSize(const _InputArray& arr, int mtype) const; + void release() const; + void clear() const; + void setTo(const _InputArray& value, const _InputArray & mask = _InputArray()) const; + + void assign(const UMat& u) const; + void assign(const Mat& m) const; + + void assign(const std::vector& v) const; + void assign(const std::vector& v) const; +}; + + +class CV_EXPORTS _InputOutputArray : public _OutputArray +{ +public: + _InputOutputArray(); + _InputOutputArray(int _flags, void* _obj); + _InputOutputArray(Mat& m); + _InputOutputArray(std::vector& vec); + _InputOutputArray(cuda::GpuMat& d_mat); + _InputOutputArray(ogl::Buffer& buf); + _InputOutputArray(cuda::HostMem& cuda_mem); + template _InputOutputArray(cudev::GpuMat_<_Tp>& m); + template _InputOutputArray(std::vector<_Tp>& vec); + _InputOutputArray(std::vector& vec); + template _InputOutputArray(std::vector >& vec); + template _InputOutputArray(std::vector >& vec); + template _InputOutputArray(Mat_<_Tp>& m); + template _InputOutputArray(_Tp* vec, int n); + template _InputOutputArray(Matx<_Tp, m, n>& matx); + _InputOutputArray(UMat& m); + _InputOutputArray(std::vector& vec); + + _InputOutputArray(const Mat& m); + _InputOutputArray(const std::vector& vec); + _InputOutputArray(const cuda::GpuMat& d_mat); + _InputOutputArray(const std::vector& d_mat); + _InputOutputArray(const ogl::Buffer& buf); + _InputOutputArray(const cuda::HostMem& cuda_mem); + template _InputOutputArray(const cudev::GpuMat_<_Tp>& m); + template _InputOutputArray(const std::vector<_Tp>& vec); + template _InputOutputArray(const std::vector >& vec); + template _InputOutputArray(const std::vector >& vec); + template _InputOutputArray(const Mat_<_Tp>& m); + template _InputOutputArray(const _Tp* vec, int n); + template _InputOutputArray(const Matx<_Tp, m, n>& matx); + _InputOutputArray(const UMat& m); + _InputOutputArray(const std::vector& vec); + +#ifdef CV_CXX_STD_ARRAY + template _InputOutputArray(std::array<_Tp, _Nm>& arr); + template _InputOutputArray(const std::array<_Tp, _Nm>& arr); + template _InputOutputArray(std::array& arr); + template _InputOutputArray(const std::array& arr); +#endif + + template static _InputOutputArray rawInOut(std::vector<_Tp>& vec); +#ifdef CV_CXX_STD_ARRAY + template _InputOutputArray rawInOut(std::array<_Tp, _Nm>& arr); +#endif + +}; + +/** Helper to wrap custom types. @see InputArray */ +template static inline _InputArray rawIn(_Tp& v); +/** Helper to wrap custom types. @see InputArray */ +template static inline _OutputArray rawOut(_Tp& v); +/** Helper to wrap custom types. @see InputArray */ +template static inline _InputOutputArray rawInOut(_Tp& v); + +CV__DEBUG_NS_END + +typedef const _InputArray& InputArray; +typedef InputArray InputArrayOfArrays; +typedef const _OutputArray& OutputArray; +typedef OutputArray OutputArrayOfArrays; +typedef const _InputOutputArray& InputOutputArray; +typedef InputOutputArray InputOutputArrayOfArrays; + +CV_EXPORTS InputOutputArray noArray(); + +/////////////////////////////////// MatAllocator ////////////////////////////////////// + +//! Usage flags for allocator +enum UMatUsageFlags +{ + USAGE_DEFAULT = 0, + + // buffer allocation policy is platform and usage specific + USAGE_ALLOCATE_HOST_MEMORY = 1 << 0, + USAGE_ALLOCATE_DEVICE_MEMORY = 1 << 1, + USAGE_ALLOCATE_SHARED_MEMORY = 1 << 2, // It is not equal to: USAGE_ALLOCATE_HOST_MEMORY | USAGE_ALLOCATE_DEVICE_MEMORY + + __UMAT_USAGE_FLAGS_32BIT = 0x7fffffff // Binary compatibility hint +}; + +struct CV_EXPORTS UMatData; + +/** @brief Custom array allocator +*/ +class CV_EXPORTS MatAllocator +{ +public: + MatAllocator() {} + virtual ~MatAllocator() {} + + // let's comment it off for now to detect and fix all the uses of allocator + //virtual void allocate(int dims, const int* sizes, int type, int*& refcount, + // uchar*& datastart, uchar*& data, size_t* step) = 0; + //virtual void deallocate(int* refcount, uchar* datastart, uchar* data) = 0; + virtual UMatData* allocate(int dims, const int* sizes, int type, + void* data, size_t* step, int flags, UMatUsageFlags usageFlags) const = 0; + virtual bool allocate(UMatData* data, int accessflags, UMatUsageFlags usageFlags) const = 0; + virtual void deallocate(UMatData* data) const = 0; + virtual void map(UMatData* data, int accessflags) const; + virtual void unmap(UMatData* data) const; + virtual void download(UMatData* data, void* dst, int dims, const size_t sz[], + const size_t srcofs[], const size_t srcstep[], + const size_t dststep[]) const; + virtual void upload(UMatData* data, const void* src, int dims, const size_t sz[], + const size_t dstofs[], const size_t dststep[], + const size_t srcstep[]) const; + virtual void copy(UMatData* srcdata, UMatData* dstdata, int dims, const size_t sz[], + const size_t srcofs[], const size_t srcstep[], + const size_t dstofs[], const size_t dststep[], bool sync) const; + + // default implementation returns DummyBufferPoolController + virtual BufferPoolController* getBufferPoolController(const char* id = NULL) const; +}; + + +//////////////////////////////// MatCommaInitializer ////////////////////////////////// + +/** @brief Comma-separated Matrix Initializer + + The class instances are usually not created explicitly. + Instead, they are created on "matrix << firstValue" operator. + + The sample below initializes 2x2 rotation matrix: + + \code + double angle = 30, a = cos(angle*CV_PI/180), b = sin(angle*CV_PI/180); + Mat R = (Mat_(2,2) << a, -b, b, a); + \endcode +*/ +template class MatCommaInitializer_ +{ +public: + //! the constructor, created by "matrix << firstValue" operator, where matrix is cv::Mat + MatCommaInitializer_(Mat_<_Tp>* _m); + //! the operator that takes the next value and put it to the matrix + template MatCommaInitializer_<_Tp>& operator , (T2 v); + //! another form of conversion operator + operator Mat_<_Tp>() const; +protected: + MatIterator_<_Tp> it; +}; + + +/////////////////////////////////////// Mat /////////////////////////////////////////// + +// note that umatdata might be allocated together +// with the matrix data, not as a separate object. +// therefore, it does not have constructor or destructor; +// it should be explicitly initialized using init(). +struct CV_EXPORTS UMatData +{ + enum { COPY_ON_MAP=1, HOST_COPY_OBSOLETE=2, + DEVICE_COPY_OBSOLETE=4, TEMP_UMAT=8, TEMP_COPIED_UMAT=24, + USER_ALLOCATED=32, DEVICE_MEM_MAPPED=64, + ASYNC_CLEANUP=128 + }; + UMatData(const MatAllocator* allocator); + ~UMatData(); + + // provide atomic access to the structure + void lock(); + void unlock(); + + bool hostCopyObsolete() const; + bool deviceCopyObsolete() const; + bool deviceMemMapped() const; + bool copyOnMap() const; + bool tempUMat() const; + bool tempCopiedUMat() const; + void markHostCopyObsolete(bool flag); + void markDeviceCopyObsolete(bool flag); + void markDeviceMemMapped(bool flag); + + const MatAllocator* prevAllocator; + const MatAllocator* currAllocator; + int urefcount; + int refcount; + uchar* data; + uchar* origdata; + size_t size; + + int flags; + void* handle; + void* userdata; + int allocatorFlags_; + int mapcount; + UMatData* originalUMatData; +}; + + +struct CV_EXPORTS MatSize +{ + explicit MatSize(int* _p); + int dims() const; + Size operator()() const; + const int& operator[](int i) const; + int& operator[](int i); + operator const int*() const; // TODO OpenCV 4.0: drop this + bool operator == (const MatSize& sz) const; + bool operator != (const MatSize& sz) const; + + int* p; +}; + +struct CV_EXPORTS MatStep +{ + MatStep(); + explicit MatStep(size_t s); + const size_t& operator[](int i) const; + size_t& operator[](int i); + operator size_t() const; + MatStep& operator = (size_t s); + + size_t* p; + size_t buf[2]; +protected: + MatStep& operator = (const MatStep&); +}; + +/** @example samples/cpp/cout_mat.cpp +An example demonstrating the serial out capabilities of cv::Mat +*/ + + /** @brief n-dimensional dense array class \anchor CVMat_Details + +The class Mat represents an n-dimensional dense numerical single-channel or multi-channel array. It +can be used to store real or complex-valued vectors and matrices, grayscale or color images, voxel +volumes, vector fields, point clouds, tensors, histograms (though, very high-dimensional histograms +may be better stored in a SparseMat ). The data layout of the array `M` is defined by the array +`M.step[]`, so that the address of element \f$(i_0,...,i_{M.dims-1})\f$, where \f$0\leq i_k= M.step[i+1]` (in fact, `M.step[i] >= M.step[i+1]*M.size[i+1]` ). This means +that 2-dimensional matrices are stored row-by-row, 3-dimensional matrices are stored plane-by-plane, +and so on. M.step[M.dims-1] is minimal and always equal to the element size M.elemSize() . + +So, the data layout in Mat is fully compatible with CvMat, IplImage, and CvMatND types from OpenCV +1.x. It is also compatible with the majority of dense array types from the standard toolkits and +SDKs, such as Numpy (ndarray), Win32 (independent device bitmaps), and others, that is, with any +array that uses *steps* (or *strides*) to compute the position of a pixel. Due to this +compatibility, it is possible to make a Mat header for user-allocated data and process it in-place +using OpenCV functions. + +There are many different ways to create a Mat object. The most popular options are listed below: + +- Use the create(nrows, ncols, type) method or the similar Mat(nrows, ncols, type[, fillValue]) +constructor. A new array of the specified size and type is allocated. type has the same meaning as +in the cvCreateMat method. For example, CV_8UC1 means a 8-bit single-channel array, CV_32FC2 +means a 2-channel (complex) floating-point array, and so on. +@code + // make a 7x7 complex matrix filled with 1+3j. + Mat M(7,7,CV_32FC2,Scalar(1,3)); + // and now turn M to a 100x60 15-channel 8-bit matrix. + // The old content will be deallocated + M.create(100,60,CV_8UC(15)); +@endcode +As noted in the introduction to this chapter, create() allocates only a new array when the shape +or type of the current array are different from the specified ones. + +- Create a multi-dimensional array: +@code + // create a 100x100x100 8-bit array + int sz[] = {100, 100, 100}; + Mat bigCube(3, sz, CV_8U, Scalar::all(0)); +@endcode +It passes the number of dimensions =1 to the Mat constructor but the created array will be +2-dimensional with the number of columns set to 1. So, Mat::dims is always \>= 2 (can also be 0 +when the array is empty). + +- Use a copy constructor or assignment operator where there can be an array or expression on the +right side (see below). As noted in the introduction, the array assignment is an O(1) operation +because it only copies the header and increases the reference counter. The Mat::clone() method can +be used to get a full (deep) copy of the array when you need it. + +- Construct a header for a part of another array. It can be a single row, single column, several +rows, several columns, rectangular region in the array (called a *minor* in algebra) or a +diagonal. Such operations are also O(1) because the new header references the same data. You can +actually modify a part of the array using this feature, for example: +@code + // add the 5-th row, multiplied by 3 to the 3rd row + M.row(3) = M.row(3) + M.row(5)*3; + // now copy the 7-th column to the 1-st column + // M.col(1) = M.col(7); // this will not work + Mat M1 = M.col(1); + M.col(7).copyTo(M1); + // create a new 320x240 image + Mat img(Size(320,240),CV_8UC3); + // select a ROI + Mat roi(img, Rect(10,10,100,100)); + // fill the ROI with (0,255,0) (which is green in RGB space); + // the original 320x240 image will be modified + roi = Scalar(0,255,0); +@endcode +Due to the additional datastart and dataend members, it is possible to compute a relative +sub-array position in the main *container* array using locateROI(): +@code + Mat A = Mat::eye(10, 10, CV_32S); + // extracts A columns, 1 (inclusive) to 3 (exclusive). + Mat B = A(Range::all(), Range(1, 3)); + // extracts B rows, 5 (inclusive) to 9 (exclusive). + // that is, C \~ A(Range(5, 9), Range(1, 3)) + Mat C = B(Range(5, 9), Range::all()); + Size size; Point ofs; + C.locateROI(size, ofs); + // size will be (width=10,height=10) and the ofs will be (x=1, y=5) +@endcode +As in case of whole matrices, if you need a deep copy, use the `clone()` method of the extracted +sub-matrices. + +- Make a header for user-allocated data. It can be useful to do the following: + -# Process "foreign" data using OpenCV (for example, when you implement a DirectShow\* filter or + a processing module for gstreamer, and so on). For example: + @code + void process_video_frame(const unsigned char* pixels, + int width, int height, int step) + { + Mat img(height, width, CV_8UC3, pixels, step); + GaussianBlur(img, img, Size(7,7), 1.5, 1.5); + } + @endcode + -# Quickly initialize small matrices and/or get a super-fast element access. + @code + double m[3][3] = {{a, b, c}, {d, e, f}, {g, h, i}}; + Mat M = Mat(3, 3, CV_64F, m).inv(); + @endcode + . + Partial yet very common cases of this *user-allocated data* case are conversions from CvMat and + IplImage to Mat. For this purpose, there is function cv::cvarrToMat taking pointers to CvMat or + IplImage and the optional flag indicating whether to copy the data or not. + @snippet samples/cpp/image.cpp iplimage + +- Use MATLAB-style array initializers, zeros(), ones(), eye(), for example: +@code + // create a double-precision identity matrix and add it to M. + M += Mat::eye(M.rows, M.cols, CV_64F); +@endcode + +- Use a comma-separated initializer: +@code + // create a 3x3 double-precision identity matrix + Mat M = (Mat_(3,3) << 1, 0, 0, 0, 1, 0, 0, 0, 1); +@endcode +With this approach, you first call a constructor of the Mat class with the proper parameters, and +then you just put `<< operator` followed by comma-separated values that can be constants, +variables, expressions, and so on. Also, note the extra parentheses required to avoid compilation +errors. + +Once the array is created, it is automatically managed via a reference-counting mechanism. If the +array header is built on top of user-allocated data, you should handle the data by yourself. The +array data is deallocated when no one points to it. If you want to release the data pointed by a +array header before the array destructor is called, use Mat::release(). + +The next important thing to learn about the array class is element access. This manual already +described how to compute an address of each array element. Normally, you are not required to use the +formula directly in the code. If you know the array element type (which can be retrieved using the +method Mat::type() ), you can access the element \f$M_{ij}\f$ of a 2-dimensional array as: +@code + M.at(i,j) += 1.f; +@endcode +assuming that `M` is a double-precision floating-point array. There are several variants of the method +at for a different number of dimensions. + +If you need to process a whole row of a 2D array, the most efficient way is to get the pointer to +the row first, and then just use the plain C operator [] : +@code + // compute sum of positive matrix elements + // (assuming that M is a double-precision matrix) + double sum=0; + for(int i = 0; i < M.rows; i++) + { + const double* Mi = M.ptr(i); + for(int j = 0; j < M.cols; j++) + sum += std::max(Mi[j], 0.); + } +@endcode +Some operations, like the one above, do not actually depend on the array shape. They just process +elements of an array one by one (or elements from multiple arrays that have the same coordinates, +for example, array addition). Such operations are called *element-wise*. It makes sense to check +whether all the input/output arrays are continuous, namely, have no gaps at the end of each row. If +yes, process them as a long single row: +@code + // compute the sum of positive matrix elements, optimized variant + double sum=0; + int cols = M.cols, rows = M.rows; + if(M.isContinuous()) + { + cols *= rows; + rows = 1; + } + for(int i = 0; i < rows; i++) + { + const double* Mi = M.ptr(i); + for(int j = 0; j < cols; j++) + sum += std::max(Mi[j], 0.); + } +@endcode +In case of the continuous matrix, the outer loop body is executed just once. So, the overhead is +smaller, which is especially noticeable in case of small matrices. + +Finally, there are STL-style iterators that are smart enough to skip gaps between successive rows: +@code + // compute sum of positive matrix elements, iterator-based variant + double sum=0; + MatConstIterator_ it = M.begin(), it_end = M.end(); + for(; it != it_end; ++it) + sum += std::max(*it, 0.); +@endcode +The matrix iterators are random-access iterators, so they can be passed to any STL algorithm, +including std::sort(). + +@note Matrix Expressions and arithmetic see MatExpr +*/ +class CV_EXPORTS Mat +{ +public: + /** + These are various constructors that form a matrix. As noted in the AutomaticAllocation, often + the default constructor is enough, and the proper matrix will be allocated by an OpenCV function. + The constructed matrix can further be assigned to another matrix or matrix expression or can be + allocated with Mat::create . In the former case, the old content is de-referenced. + */ + Mat(); + + /** @overload + @param rows Number of rows in a 2D array. + @param cols Number of columns in a 2D array. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + */ + Mat(int rows, int cols, int type); + + /** @overload + @param size 2D array size: Size(cols, rows) . In the Size() constructor, the number of rows and the + number of columns go in the reverse order. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + */ + Mat(Size size, int type); + + /** @overload + @param rows Number of rows in a 2D array. + @param cols Number of columns in a 2D array. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + @param s An optional value to initialize each matrix element with. To set all the matrix elements to + the particular value after the construction, use the assignment operator + Mat::operator=(const Scalar& value) . + */ + Mat(int rows, int cols, int type, const Scalar& s); + + /** @overload + @param size 2D array size: Size(cols, rows) . In the Size() constructor, the number of rows and the + number of columns go in the reverse order. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + @param s An optional value to initialize each matrix element with. To set all the matrix elements to + the particular value after the construction, use the assignment operator + Mat::operator=(const Scalar& value) . + */ + Mat(Size size, int type, const Scalar& s); + + /** @overload + @param ndims Array dimensionality. + @param sizes Array of integers specifying an n-dimensional array shape. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + */ + Mat(int ndims, const int* sizes, int type); + + /** @overload + @param sizes Array of integers specifying an n-dimensional array shape. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + */ + Mat(const std::vector& sizes, int type); + + /** @overload + @param ndims Array dimensionality. + @param sizes Array of integers specifying an n-dimensional array shape. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + @param s An optional value to initialize each matrix element with. To set all the matrix elements to + the particular value after the construction, use the assignment operator + Mat::operator=(const Scalar& value) . + */ + Mat(int ndims, const int* sizes, int type, const Scalar& s); + + /** @overload + @param sizes Array of integers specifying an n-dimensional array shape. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + @param s An optional value to initialize each matrix element with. To set all the matrix elements to + the particular value after the construction, use the assignment operator + Mat::operator=(const Scalar& value) . + */ + Mat(const std::vector& sizes, int type, const Scalar& s); + + + /** @overload + @param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied + by these constructors. Instead, the header pointing to m data or its sub-array is constructed and + associated with it. The reference counter, if any, is incremented. So, when you modify the matrix + formed using such a constructor, you also modify the corresponding elements of m . If you want to + have an independent copy of the sub-array, use Mat::clone() . + */ + Mat(const Mat& m); + + /** @overload + @param rows Number of rows in a 2D array. + @param cols Number of columns in a 2D array. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + @param data Pointer to the user data. Matrix constructors that take data and step parameters do not + allocate matrix data. Instead, they just initialize the matrix header that points to the specified + data, which means that no data is copied. This operation is very efficient and can be used to + process external data using OpenCV functions. The external data is not automatically deallocated, so + you should take care of it. + @param step Number of bytes each matrix row occupies. The value should include the padding bytes at + the end of each row, if any. If the parameter is missing (set to AUTO_STEP ), no padding is assumed + and the actual step is calculated as cols*elemSize(). See Mat::elemSize. + */ + Mat(int rows, int cols, int type, void* data, size_t step=AUTO_STEP); + + /** @overload + @param size 2D array size: Size(cols, rows) . In the Size() constructor, the number of rows and the + number of columns go in the reverse order. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + @param data Pointer to the user data. Matrix constructors that take data and step parameters do not + allocate matrix data. Instead, they just initialize the matrix header that points to the specified + data, which means that no data is copied. This operation is very efficient and can be used to + process external data using OpenCV functions. The external data is not automatically deallocated, so + you should take care of it. + @param step Number of bytes each matrix row occupies. The value should include the padding bytes at + the end of each row, if any. If the parameter is missing (set to AUTO_STEP ), no padding is assumed + and the actual step is calculated as cols*elemSize(). See Mat::elemSize. + */ + Mat(Size size, int type, void* data, size_t step=AUTO_STEP); + + /** @overload + @param ndims Array dimensionality. + @param sizes Array of integers specifying an n-dimensional array shape. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + @param data Pointer to the user data. Matrix constructors that take data and step parameters do not + allocate matrix data. Instead, they just initialize the matrix header that points to the specified + data, which means that no data is copied. This operation is very efficient and can be used to + process external data using OpenCV functions. The external data is not automatically deallocated, so + you should take care of it. + @param steps Array of ndims-1 steps in case of a multi-dimensional array (the last step is always + set to the element size). If not specified, the matrix is assumed to be continuous. + */ + Mat(int ndims, const int* sizes, int type, void* data, const size_t* steps=0); + + /** @overload + @param sizes Array of integers specifying an n-dimensional array shape. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + @param data Pointer to the user data. Matrix constructors that take data and step parameters do not + allocate matrix data. Instead, they just initialize the matrix header that points to the specified + data, which means that no data is copied. This operation is very efficient and can be used to + process external data using OpenCV functions. The external data is not automatically deallocated, so + you should take care of it. + @param steps Array of ndims-1 steps in case of a multi-dimensional array (the last step is always + set to the element size). If not specified, the matrix is assumed to be continuous. + */ + Mat(const std::vector& sizes, int type, void* data, const size_t* steps=0); + + /** @overload + @param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied + by these constructors. Instead, the header pointing to m data or its sub-array is constructed and + associated with it. The reference counter, if any, is incremented. So, when you modify the matrix + formed using such a constructor, you also modify the corresponding elements of m . If you want to + have an independent copy of the sub-array, use Mat::clone() . + @param rowRange Range of the m rows to take. As usual, the range start is inclusive and the range + end is exclusive. Use Range::all() to take all the rows. + @param colRange Range of the m columns to take. Use Range::all() to take all the columns. + */ + Mat(const Mat& m, const Range& rowRange, const Range& colRange=Range::all()); + + /** @overload + @param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied + by these constructors. Instead, the header pointing to m data or its sub-array is constructed and + associated with it. The reference counter, if any, is incremented. So, when you modify the matrix + formed using such a constructor, you also modify the corresponding elements of m . If you want to + have an independent copy of the sub-array, use Mat::clone() . + @param roi Region of interest. + */ + Mat(const Mat& m, const Rect& roi); + + /** @overload + @param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied + by these constructors. Instead, the header pointing to m data or its sub-array is constructed and + associated with it. The reference counter, if any, is incremented. So, when you modify the matrix + formed using such a constructor, you also modify the corresponding elements of m . If you want to + have an independent copy of the sub-array, use Mat::clone() . + @param ranges Array of selected ranges of m along each dimensionality. + */ + Mat(const Mat& m, const Range* ranges); + + /** @overload + @param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied + by these constructors. Instead, the header pointing to m data or its sub-array is constructed and + associated with it. The reference counter, if any, is incremented. So, when you modify the matrix + formed using such a constructor, you also modify the corresponding elements of m . If you want to + have an independent copy of the sub-array, use Mat::clone() . + @param ranges Array of selected ranges of m along each dimensionality. + */ + Mat(const Mat& m, const std::vector& ranges); + + /** @overload + @param vec STL vector whose elements form the matrix. The matrix has a single column and the number + of rows equal to the number of vector elements. Type of the matrix matches the type of vector + elements. The constructor can handle arbitrary types, for which there is a properly declared + DataType . This means that the vector elements must be primitive numbers or uni-type numerical + tuples of numbers. Mixed-type structures are not supported. The corresponding constructor is + explicit. Since STL vectors are not automatically converted to Mat instances, you should write + Mat(vec) explicitly. Unless you copy the data into the matrix ( copyData=true ), no new elements + will be added to the vector because it can potentially yield vector data reallocation, and, thus, + the matrix data pointer will be invalid. + @param copyData Flag to specify whether the underlying data of the STL vector should be copied + to (true) or shared with (false) the newly constructed matrix. When the data is copied, the + allocated buffer is managed using Mat reference counting mechanism. While the data is shared, + the reference counter is NULL, and you should not deallocate the data until the matrix is not + destructed. + */ + template explicit Mat(const std::vector<_Tp>& vec, bool copyData=false); + +#ifdef CV_CXX11 + /** @overload + */ + template::value>::type> + explicit Mat(const std::initializer_list<_Tp> list); + + /** @overload + */ + template explicit Mat(const std::initializer_list sizes, const std::initializer_list<_Tp> list); +#endif + +#ifdef CV_CXX_STD_ARRAY + /** @overload + */ + template explicit Mat(const std::array<_Tp, _Nm>& arr, bool copyData=false); +#endif + + /** @overload + */ + template explicit Mat(const Vec<_Tp, n>& vec, bool copyData=true); + + /** @overload + */ + template explicit Mat(const Matx<_Tp, m, n>& mtx, bool copyData=true); + + /** @overload + */ + template explicit Mat(const Point_<_Tp>& pt, bool copyData=true); + + /** @overload + */ + template explicit Mat(const Point3_<_Tp>& pt, bool copyData=true); + + /** @overload + */ + template explicit Mat(const MatCommaInitializer_<_Tp>& commaInitializer); + + //! download data from GpuMat + explicit Mat(const cuda::GpuMat& m); + + //! destructor - calls release() + ~Mat(); + + /** @brief assignment operators + + These are available assignment operators. Since they all are very different, make sure to read the + operator parameters description. + @param m Assigned, right-hand-side matrix. Matrix assignment is an O(1) operation. This means that + no data is copied but the data is shared and the reference counter, if any, is incremented. Before + assigning new data, the old data is de-referenced via Mat::release . + */ + Mat& operator = (const Mat& m); + + /** @overload + @param expr Assigned matrix expression object. As opposite to the first form of the assignment + operation, the second form can reuse already allocated matrix if it has the right size and type to + fit the matrix expression result. It is automatically handled by the real function that the matrix + expressions is expanded to. For example, C=A+B is expanded to add(A, B, C), and add takes care of + automatic C reallocation. + */ + Mat& operator = (const MatExpr& expr); + + //! retrieve UMat from Mat + UMat getUMat(int accessFlags, UMatUsageFlags usageFlags = USAGE_DEFAULT) const; + + /** @brief Creates a matrix header for the specified matrix row. + + The method makes a new header for the specified matrix row and returns it. This is an O(1) + operation, regardless of the matrix size. The underlying data of the new matrix is shared with the + original matrix. Here is the example of one of the classical basic matrix processing operations, + axpy, used by LU and many other algorithms: + @code + inline void matrix_axpy(Mat& A, int i, int j, double alpha) + { + A.row(i) += A.row(j)*alpha; + } + @endcode + @note In the current implementation, the following code does not work as expected: + @code + Mat A; + ... + A.row(i) = A.row(j); // will not work + @endcode + This happens because A.row(i) forms a temporary header that is further assigned to another header. + Remember that each of these operations is O(1), that is, no data is copied. Thus, the above + assignment is not true if you may have expected the j-th row to be copied to the i-th row. To + achieve that, you should either turn this simple assignment into an expression or use the + Mat::copyTo method: + @code + Mat A; + ... + // works, but looks a bit obscure. + A.row(i) = A.row(j) + 0; + // this is a bit longer, but the recommended method. + A.row(j).copyTo(A.row(i)); + @endcode + @param y A 0-based row index. + */ + Mat row(int y) const; + + /** @brief Creates a matrix header for the specified matrix column. + + The method makes a new header for the specified matrix column and returns it. This is an O(1) + operation, regardless of the matrix size. The underlying data of the new matrix is shared with the + original matrix. See also the Mat::row description. + @param x A 0-based column index. + */ + Mat col(int x) const; + + /** @brief Creates a matrix header for the specified row span. + + The method makes a new header for the specified row span of the matrix. Similarly to Mat::row and + Mat::col , this is an O(1) operation. + @param startrow An inclusive 0-based start index of the row span. + @param endrow An exclusive 0-based ending index of the row span. + */ + Mat rowRange(int startrow, int endrow) const; + + /** @overload + @param r Range structure containing both the start and the end indices. + */ + Mat rowRange(const Range& r) const; + + /** @brief Creates a matrix header for the specified column span. + + The method makes a new header for the specified column span of the matrix. Similarly to Mat::row and + Mat::col , this is an O(1) operation. + @param startcol An inclusive 0-based start index of the column span. + @param endcol An exclusive 0-based ending index of the column span. + */ + Mat colRange(int startcol, int endcol) const; + + /** @overload + @param r Range structure containing both the start and the end indices. + */ + Mat colRange(const Range& r) const; + + /** @brief Extracts a diagonal from a matrix + + The method makes a new header for the specified matrix diagonal. The new matrix is represented as a + single-column matrix. Similarly to Mat::row and Mat::col, this is an O(1) operation. + @param d index of the diagonal, with the following values: + - `d=0` is the main diagonal. + - `d<0` is a diagonal from the lower half. For example, d=-1 means the diagonal is set + immediately below the main one. + - `d>0` is a diagonal from the upper half. For example, d=1 means the diagonal is set + immediately above the main one. + For example: + @code + Mat m = (Mat_(3,3) << + 1,2,3, + 4,5,6, + 7,8,9); + Mat d0 = m.diag(0); + Mat d1 = m.diag(1); + Mat d_1 = m.diag(-1); + @endcode + The resulting matrices are + @code + d0 = + [1; + 5; + 9] + d1 = + [2; + 6] + d_1 = + [4; + 8] + @endcode + */ + Mat diag(int d=0) const; + + /** @brief creates a diagonal matrix + + The method creates a square diagonal matrix from specified main diagonal. + @param d One-dimensional matrix that represents the main diagonal. + */ + static Mat diag(const Mat& d); + + /** @brief Creates a full copy of the array and the underlying data. + + The method creates a full copy of the array. The original step[] is not taken into account. So, the + array copy is a continuous array occupying total()*elemSize() bytes. + */ + Mat clone() const CV_NODISCARD; + + /** @brief Copies the matrix to another one. + + The method copies the matrix data to another matrix. Before copying the data, the method invokes : + @code + m.create(this->size(), this->type()); + @endcode + so that the destination matrix is reallocated if needed. While m.copyTo(m); works flawlessly, the + function does not handle the case of a partial overlap between the source and the destination + matrices. + + When the operation mask is specified, if the Mat::create call shown above reallocates the matrix, + the newly allocated matrix is initialized with all zeros before copying the data. + @param m Destination matrix. If it does not have a proper size or type before the operation, it is + reallocated. + */ + void copyTo( OutputArray m ) const; + + /** @overload + @param m Destination matrix. If it does not have a proper size or type before the operation, it is + reallocated. + @param mask Operation mask of the same size as \*this. Its non-zero elements indicate which matrix + elements need to be copied. The mask has to be of type CV_8U and can have 1 or multiple channels. + */ + void copyTo( OutputArray m, InputArray mask ) const; + + /** @brief Converts an array to another data type with optional scaling. + + The method converts source pixel values to the target data type. saturate_cast\<\> is applied at + the end to avoid possible overflows: + + \f[m(x,y) = saturate \_ cast( \alpha (*this)(x,y) + \beta )\f] + @param m output matrix; if it does not have a proper size or type before the operation, it is + reallocated. + @param rtype desired output matrix type or, rather, the depth since the number of channels are the + same as the input has; if rtype is negative, the output matrix will have the same type as the input. + @param alpha optional scale factor. + @param beta optional delta added to the scaled values. + */ + void convertTo( OutputArray m, int rtype, double alpha=1, double beta=0 ) const; + + /** @brief Provides a functional form of convertTo. + + This is an internally used method called by the @ref MatrixExpressions engine. + @param m Destination array. + @param type Desired destination array depth (or -1 if it should be the same as the source type). + */ + void assignTo( Mat& m, int type=-1 ) const; + + /** @brief Sets all or some of the array elements to the specified value. + @param s Assigned scalar converted to the actual array type. + */ + Mat& operator = (const Scalar& s); + + /** @brief Sets all or some of the array elements to the specified value. + + This is an advanced variant of the Mat::operator=(const Scalar& s) operator. + @param value Assigned scalar converted to the actual array type. + @param mask Operation mask of the same size as \*this. Its non-zero elements indicate which matrix + elements need to be copied. The mask has to be of type CV_8U and can have 1 or multiple channels + */ + Mat& setTo(InputArray value, InputArray mask=noArray()); + + /** @brief Changes the shape and/or the number of channels of a 2D matrix without copying the data. + + The method makes a new matrix header for \*this elements. The new matrix may have a different size + and/or different number of channels. Any combination is possible if: + - No extra elements are included into the new matrix and no elements are excluded. Consequently, + the product rows\*cols\*channels() must stay the same after the transformation. + - No data is copied. That is, this is an O(1) operation. Consequently, if you change the number of + rows, or the operation changes the indices of elements row in some other way, the matrix must be + continuous. See Mat::isContinuous . + + For example, if there is a set of 3D points stored as an STL vector, and you want to represent the + points as a 3xN matrix, do the following: + @code + std::vector vec; + ... + Mat pointMat = Mat(vec). // convert vector to Mat, O(1) operation + reshape(1). // make Nx3 1-channel matrix out of Nx1 3-channel. + // Also, an O(1) operation + t(); // finally, transpose the Nx3 matrix. + // This involves copying all the elements + @endcode + @param cn New number of channels. If the parameter is 0, the number of channels remains the same. + @param rows New number of rows. If the parameter is 0, the number of rows remains the same. + */ + Mat reshape(int cn, int rows=0) const; + + /** @overload */ + Mat reshape(int cn, int newndims, const int* newsz) const; + + /** @overload */ + Mat reshape(int cn, const std::vector& newshape) const; + + /** @brief Transposes a matrix. + + The method performs matrix transposition by means of matrix expressions. It does not perform the + actual transposition but returns a temporary matrix transposition object that can be further used as + a part of more complex matrix expressions or can be assigned to a matrix: + @code + Mat A1 = A + Mat::eye(A.size(), A.type())*lambda; + Mat C = A1.t()*A1; // compute (A + lambda*I)^t * (A + lamda*I) + @endcode + */ + MatExpr t() const; + + /** @brief Inverses a matrix. + + The method performs a matrix inversion by means of matrix expressions. This means that a temporary + matrix inversion object is returned by the method and can be used further as a part of more complex + matrix expressions or can be assigned to a matrix. + @param method Matrix inversion method. One of cv::DecompTypes + */ + MatExpr inv(int method=DECOMP_LU) const; + + /** @brief Performs an element-wise multiplication or division of the two matrices. + + The method returns a temporary object encoding per-element array multiplication, with optional + scale. Note that this is not a matrix multiplication that corresponds to a simpler "\*" operator. + + Example: + @code + Mat C = A.mul(5/B); // equivalent to divide(A, B, C, 5) + @endcode + @param m Another array of the same type and the same size as \*this, or a matrix expression. + @param scale Optional scale factor. + */ + MatExpr mul(InputArray m, double scale=1) const; + + /** @brief Computes a cross-product of two 3-element vectors. + + The method computes a cross-product of two 3-element vectors. The vectors must be 3-element + floating-point vectors of the same shape and size. The result is another 3-element vector of the + same shape and type as operands. + @param m Another cross-product operand. + */ + Mat cross(InputArray m) const; + + /** @brief Computes a dot-product of two vectors. + + The method computes a dot-product of two matrices. If the matrices are not single-column or + single-row vectors, the top-to-bottom left-to-right scan ordering is used to treat them as 1D + vectors. The vectors must have the same size and type. If the matrices have more than one channel, + the dot products from all the channels are summed together. + @param m another dot-product operand. + */ + double dot(InputArray m) const; + + /** @brief Returns a zero array of the specified size and type. + + The method returns a Matlab-style zero array initializer. It can be used to quickly form a constant + array as a function parameter, part of a matrix expression, or as a matrix initializer: + @code + Mat A; + A = Mat::zeros(3, 3, CV_32F); + @endcode + In the example above, a new matrix is allocated only if A is not a 3x3 floating-point matrix. + Otherwise, the existing matrix A is filled with zeros. + @param rows Number of rows. + @param cols Number of columns. + @param type Created matrix type. + */ + static MatExpr zeros(int rows, int cols, int type); + + /** @overload + @param size Alternative to the matrix size specification Size(cols, rows) . + @param type Created matrix type. + */ + static MatExpr zeros(Size size, int type); + + /** @overload + @param ndims Array dimensionality. + @param sz Array of integers specifying the array shape. + @param type Created matrix type. + */ + static MatExpr zeros(int ndims, const int* sz, int type); + + /** @brief Returns an array of all 1's of the specified size and type. + + The method returns a Matlab-style 1's array initializer, similarly to Mat::zeros. Note that using + this method you can initialize an array with an arbitrary value, using the following Matlab idiom: + @code + Mat A = Mat::ones(100, 100, CV_8U)*3; // make 100x100 matrix filled with 3. + @endcode + The above operation does not form a 100x100 matrix of 1's and then multiply it by 3. Instead, it + just remembers the scale factor (3 in this case) and use it when actually invoking the matrix + initializer. + @note In case of multi-channels type, only the first channel will be initialized with 1's, the + others will be set to 0's. + @param rows Number of rows. + @param cols Number of columns. + @param type Created matrix type. + */ + static MatExpr ones(int rows, int cols, int type); + + /** @overload + @param size Alternative to the matrix size specification Size(cols, rows) . + @param type Created matrix type. + */ + static MatExpr ones(Size size, int type); + + /** @overload + @param ndims Array dimensionality. + @param sz Array of integers specifying the array shape. + @param type Created matrix type. + */ + static MatExpr ones(int ndims, const int* sz, int type); + + /** @brief Returns an identity matrix of the specified size and type. + + The method returns a Matlab-style identity matrix initializer, similarly to Mat::zeros. Similarly to + Mat::ones, you can use a scale operation to create a scaled identity matrix efficiently: + @code + // make a 4x4 diagonal matrix with 0.1's on the diagonal. + Mat A = Mat::eye(4, 4, CV_32F)*0.1; + @endcode + @note In case of multi-channels type, identity matrix will be initialized only for the first channel, + the others will be set to 0's + @param rows Number of rows. + @param cols Number of columns. + @param type Created matrix type. + */ + static MatExpr eye(int rows, int cols, int type); + + /** @overload + @param size Alternative matrix size specification as Size(cols, rows) . + @param type Created matrix type. + */ + static MatExpr eye(Size size, int type); + + /** @brief Allocates new array data if needed. + + This is one of the key Mat methods. Most new-style OpenCV functions and methods that produce arrays + call this method for each output array. The method uses the following algorithm: + + -# If the current array shape and the type match the new ones, return immediately. Otherwise, + de-reference the previous data by calling Mat::release. + -# Initialize the new header. + -# Allocate the new data of total()\*elemSize() bytes. + -# Allocate the new, associated with the data, reference counter and set it to 1. + + Such a scheme makes the memory management robust and efficient at the same time and helps avoid + extra typing for you. This means that usually there is no need to explicitly allocate output arrays. + That is, instead of writing: + @code + Mat color; + ... + Mat gray(color.rows, color.cols, color.depth()); + cvtColor(color, gray, COLOR_BGR2GRAY); + @endcode + you can simply write: + @code + Mat color; + ... + Mat gray; + cvtColor(color, gray, COLOR_BGR2GRAY); + @endcode + because cvtColor, as well as the most of OpenCV functions, calls Mat::create() for the output array + internally. + @param rows New number of rows. + @param cols New number of columns. + @param type New matrix type. + */ + void create(int rows, int cols, int type); + + /** @overload + @param size Alternative new matrix size specification: Size(cols, rows) + @param type New matrix type. + */ + void create(Size size, int type); + + /** @overload + @param ndims New array dimensionality. + @param sizes Array of integers specifying a new array shape. + @param type New matrix type. + */ + void create(int ndims, const int* sizes, int type); + + /** @overload + @param sizes Array of integers specifying a new array shape. + @param type New matrix type. + */ + void create(const std::vector& sizes, int type); + + /** @brief Increments the reference counter. + + The method increments the reference counter associated with the matrix data. If the matrix header + points to an external data set (see Mat::Mat ), the reference counter is NULL, and the method has no + effect in this case. Normally, to avoid memory leaks, the method should not be called explicitly. It + is called implicitly by the matrix assignment operator. The reference counter increment is an atomic + operation on the platforms that support it. Thus, it is safe to operate on the same matrices + asynchronously in different threads. + */ + void addref(); + + /** @brief Decrements the reference counter and deallocates the matrix if needed. + + The method decrements the reference counter associated with the matrix data. When the reference + counter reaches 0, the matrix data is deallocated and the data and the reference counter pointers + are set to NULL's. If the matrix header points to an external data set (see Mat::Mat ), the + reference counter is NULL, and the method has no effect in this case. + + This method can be called manually to force the matrix data deallocation. But since this method is + automatically called in the destructor, or by any other method that changes the data pointer, it is + usually not needed. The reference counter decrement and check for 0 is an atomic operation on the + platforms that support it. Thus, it is safe to operate on the same matrices asynchronously in + different threads. + */ + void release(); + + //! internal use function, consider to use 'release' method instead; deallocates the matrix data + void deallocate(); + //! internal use function; properly re-allocates _size, _step arrays + void copySize(const Mat& m); + + /** @brief Reserves space for the certain number of rows. + + The method reserves space for sz rows. If the matrix already has enough space to store sz rows, + nothing happens. If the matrix is reallocated, the first Mat::rows rows are preserved. The method + emulates the corresponding method of the STL vector class. + @param sz Number of rows. + */ + void reserve(size_t sz); + + /** @brief Reserves space for the certain number of bytes. + + The method reserves space for sz bytes. If the matrix already has enough space to store sz bytes, + nothing happens. If matrix has to be reallocated its previous content could be lost. + @param sz Number of bytes. + */ + void reserveBuffer(size_t sz); + + /** @brief Changes the number of matrix rows. + + The methods change the number of matrix rows. If the matrix is reallocated, the first + min(Mat::rows, sz) rows are preserved. The methods emulate the corresponding methods of the STL + vector class. + @param sz New number of rows. + */ + void resize(size_t sz); + + /** @overload + @param sz New number of rows. + @param s Value assigned to the newly added elements. + */ + void resize(size_t sz, const Scalar& s); + + //! internal function + void push_back_(const void* elem); + + /** @brief Adds elements to the bottom of the matrix. + + The methods add one or more elements to the bottom of the matrix. They emulate the corresponding + method of the STL vector class. When elem is Mat , its type and the number of columns must be the + same as in the container matrix. + @param elem Added element(s). + */ + template void push_back(const _Tp& elem); + + /** @overload + @param elem Added element(s). + */ + template void push_back(const Mat_<_Tp>& elem); + + /** @overload + @param elem Added element(s). + */ + template void push_back(const std::vector<_Tp>& elem); + + /** @overload + @param m Added line(s). + */ + void push_back(const Mat& m); + + /** @brief Removes elements from the bottom of the matrix. + + The method removes one or more rows from the bottom of the matrix. + @param nelems Number of removed rows. If it is greater than the total number of rows, an exception + is thrown. + */ + void pop_back(size_t nelems=1); + + /** @brief Locates the matrix header within a parent matrix. + + After you extracted a submatrix from a matrix using Mat::row, Mat::col, Mat::rowRange, + Mat::colRange, and others, the resultant submatrix points just to the part of the original big + matrix. However, each submatrix contains information (represented by datastart and dataend + fields) that helps reconstruct the original matrix size and the position of the extracted + submatrix within the original matrix. The method locateROI does exactly that. + @param wholeSize Output parameter that contains the size of the whole matrix containing *this* + as a part. + @param ofs Output parameter that contains an offset of *this* inside the whole matrix. + */ + void locateROI( Size& wholeSize, Point& ofs ) const; + + /** @brief Adjusts a submatrix size and position within the parent matrix. + + The method is complimentary to Mat::locateROI . The typical use of these functions is to determine + the submatrix position within the parent matrix and then shift the position somehow. Typically, it + can be required for filtering operations when pixels outside of the ROI should be taken into + account. When all the method parameters are positive, the ROI needs to grow in all directions by the + specified amount, for example: + @code + A.adjustROI(2, 2, 2, 2); + @endcode + In this example, the matrix size is increased by 4 elements in each direction. The matrix is shifted + by 2 elements to the left and 2 elements up, which brings in all the necessary pixels for the + filtering with the 5x5 kernel. + + adjustROI forces the adjusted ROI to be inside of the parent matrix that is boundaries of the + adjusted ROI are constrained by boundaries of the parent matrix. For example, if the submatrix A is + located in the first row of a parent matrix and you called A.adjustROI(2, 2, 2, 2) then A will not + be increased in the upward direction. + + The function is used internally by the OpenCV filtering functions, like filter2D , morphological + operations, and so on. + @param dtop Shift of the top submatrix boundary upwards. + @param dbottom Shift of the bottom submatrix boundary downwards. + @param dleft Shift of the left submatrix boundary to the left. + @param dright Shift of the right submatrix boundary to the right. + @sa copyMakeBorder + */ + Mat& adjustROI( int dtop, int dbottom, int dleft, int dright ); + + /** @brief Extracts a rectangular submatrix. + + The operators make a new header for the specified sub-array of \*this . They are the most + generalized forms of Mat::row, Mat::col, Mat::rowRange, and Mat::colRange . For example, + `A(Range(0, 10), Range::all())` is equivalent to `A.rowRange(0, 10)`. Similarly to all of the above, + the operators are O(1) operations, that is, no matrix data is copied. + @param rowRange Start and end row of the extracted submatrix. The upper boundary is not included. To + select all the rows, use Range::all(). + @param colRange Start and end column of the extracted submatrix. The upper boundary is not included. + To select all the columns, use Range::all(). + */ + Mat operator()( Range rowRange, Range colRange ) const; + + /** @overload + @param roi Extracted submatrix specified as a rectangle. + */ + Mat operator()( const Rect& roi ) const; + + /** @overload + @param ranges Array of selected ranges along each array dimension. + */ + Mat operator()( const Range* ranges ) const; + + /** @overload + @param ranges Array of selected ranges along each array dimension. + */ + Mat operator()(const std::vector& ranges) const; + + // //! converts header to CvMat; no data is copied + // operator CvMat() const; + // //! converts header to CvMatND; no data is copied + // operator CvMatND() const; + // //! converts header to IplImage; no data is copied + // operator IplImage() const; + + template operator std::vector<_Tp>() const; + template operator Vec<_Tp, n>() const; + template operator Matx<_Tp, m, n>() const; + +#ifdef CV_CXX_STD_ARRAY + template operator std::array<_Tp, _Nm>() const; +#endif + + /** @brief Reports whether the matrix is continuous or not. + + The method returns true if the matrix elements are stored continuously without gaps at the end of + each row. Otherwise, it returns false. Obviously, 1x1 or 1xN matrices are always continuous. + Matrices created with Mat::create are always continuous. But if you extract a part of the matrix + using Mat::col, Mat::diag, and so on, or constructed a matrix header for externally allocated data, + such matrices may no longer have this property. + + The continuity flag is stored as a bit in the Mat::flags field and is computed automatically when + you construct a matrix header. Thus, the continuity check is a very fast operation, though + theoretically it could be done as follows: + @code + // alternative implementation of Mat::isContinuous() + bool myCheckMatContinuity(const Mat& m) + { + //return (m.flags & Mat::CONTINUOUS_FLAG) != 0; + return m.rows == 1 || m.step == m.cols*m.elemSize(); + } + @endcode + The method is used in quite a few of OpenCV functions. The point is that element-wise operations + (such as arithmetic and logical operations, math functions, alpha blending, color space + transformations, and others) do not depend on the image geometry. Thus, if all the input and output + arrays are continuous, the functions can process them as very long single-row vectors. The example + below illustrates how an alpha-blending function can be implemented: + @code + template + void alphaBlendRGBA(const Mat& src1, const Mat& src2, Mat& dst) + { + const float alpha_scale = (float)std::numeric_limits::max(), + inv_scale = 1.f/alpha_scale; + + CV_Assert( src1.type() == src2.type() && + src1.type() == CV_MAKETYPE(traits::Depth::value, 4) && + src1.size() == src2.size()); + Size size = src1.size(); + dst.create(size, src1.type()); + + // here is the idiom: check the arrays for continuity and, + // if this is the case, + // treat the arrays as 1D vectors + if( src1.isContinuous() && src2.isContinuous() && dst.isContinuous() ) + { + size.width *= size.height; + size.height = 1; + } + size.width *= 4; + + for( int i = 0; i < size.height; i++ ) + { + // when the arrays are continuous, + // the outer loop is executed only once + const T* ptr1 = src1.ptr(i); + const T* ptr2 = src2.ptr(i); + T* dptr = dst.ptr(i); + + for( int j = 0; j < size.width; j += 4 ) + { + float alpha = ptr1[j+3]*inv_scale, beta = ptr2[j+3]*inv_scale; + dptr[j] = saturate_cast(ptr1[j]*alpha + ptr2[j]*beta); + dptr[j+1] = saturate_cast(ptr1[j+1]*alpha + ptr2[j+1]*beta); + dptr[j+2] = saturate_cast(ptr1[j+2]*alpha + ptr2[j+2]*beta); + dptr[j+3] = saturate_cast((1 - (1-alpha)*(1-beta))*alpha_scale); + } + } + } + @endcode + This approach, while being very simple, can boost the performance of a simple element-operation by + 10-20 percents, especially if the image is rather small and the operation is quite simple. + + Another OpenCV idiom in this function, a call of Mat::create for the destination array, that + allocates the destination array unless it already has the proper size and type. And while the newly + allocated arrays are always continuous, you still need to check the destination array because + Mat::create does not always allocate a new matrix. + */ + bool isContinuous() const; + + //! returns true if the matrix is a submatrix of another matrix + bool isSubmatrix() const; + + /** @brief Returns the matrix element size in bytes. + + The method returns the matrix element size in bytes. For example, if the matrix type is CV_16SC3 , + the method returns 3\*sizeof(short) or 6. + */ + size_t elemSize() const; + + /** @brief Returns the size of each matrix element channel in bytes. + + The method returns the matrix element channel size in bytes, that is, it ignores the number of + channels. For example, if the matrix type is CV_16SC3 , the method returns sizeof(short) or 2. + */ + size_t elemSize1() const; + + /** @brief Returns the type of a matrix element. + + The method returns a matrix element type. This is an identifier compatible with the CvMat type + system, like CV_16SC3 or 16-bit signed 3-channel array, and so on. + */ + int type() const; + + /** @brief Returns the depth of a matrix element. + + The method returns the identifier of the matrix element depth (the type of each individual channel). + For example, for a 16-bit signed element array, the method returns CV_16S . A complete list of + matrix types contains the following values: + - CV_8U - 8-bit unsigned integers ( 0..255 ) + - CV_8S - 8-bit signed integers ( -128..127 ) + - CV_16U - 16-bit unsigned integers ( 0..65535 ) + - CV_16S - 16-bit signed integers ( -32768..32767 ) + - CV_32S - 32-bit signed integers ( -2147483648..2147483647 ) + - CV_32F - 32-bit floating-point numbers ( -FLT_MAX..FLT_MAX, INF, NAN ) + - CV_64F - 64-bit floating-point numbers ( -DBL_MAX..DBL_MAX, INF, NAN ) + */ + int depth() const; + + /** @brief Returns the number of matrix channels. + + The method returns the number of matrix channels. + */ + int channels() const; + + /** @brief Returns a normalized step. + + The method returns a matrix step divided by Mat::elemSize1() . It can be useful to quickly access an + arbitrary matrix element. + */ + size_t step1(int i=0) const; + + /** @brief Returns true if the array has no elements. + + The method returns true if Mat::total() is 0 or if Mat::data is NULL. Because of pop_back() and + resize() methods `M.total() == 0` does not imply that `M.data == NULL`. + */ + bool empty() const; + + /** @brief Returns the total number of array elements. + + The method returns the number of array elements (a number of pixels if the array represents an + image). + */ + size_t total() const; + + /** @brief Returns the total number of array elements. + + The method returns the number of elements within a certain sub-array slice with startDim <= dim < endDim + */ + size_t total(int startDim, int endDim=INT_MAX) const; + + /** + * @param elemChannels Number of channels or number of columns the matrix should have. + * For a 2-D matrix, when the matrix has only 1 column, then it should have + * elemChannels channels; When the matrix has only 1 channel, + * then it should have elemChannels columns. + * For a 3-D matrix, it should have only one channel. Furthermore, + * if the number of planes is not one, then the number of rows + * within every plane has to be 1; if the number of rows within + * every plane is not 1, then the number of planes has to be 1. + * @param depth The depth the matrix should have. Set it to -1 when any depth is fine. + * @param requireContinuous Set it to true to require the matrix to be continuous + * @return -1 if the requirement is not satisfied. + * Otherwise, it returns the number of elements in the matrix. Note + * that an element may have multiple channels. + * + * The following code demonstrates its usage for a 2-d matrix: + * @snippet snippets/core_mat_checkVector.cpp example-2d + * + * The following code demonstrates its usage for a 3-d matrix: + * @snippet snippets/core_mat_checkVector.cpp example-3d + */ + int checkVector(int elemChannels, int depth=-1, bool requireContinuous=true) const; + + /** @brief Returns a pointer to the specified matrix row. + + The methods return `uchar*` or typed pointer to the specified matrix row. See the sample in + Mat::isContinuous to know how to use these methods. + @param i0 A 0-based row index. + */ + uchar* ptr(int i0=0); + /** @overload */ + const uchar* ptr(int i0=0) const; + + /** @overload + @param row Index along the dimension 0 + @param col Index along the dimension 1 + */ + uchar* ptr(int row, int col); + /** @overload + @param row Index along the dimension 0 + @param col Index along the dimension 1 + */ + const uchar* ptr(int row, int col) const; + + /** @overload */ + uchar* ptr(int i0, int i1, int i2); + /** @overload */ + const uchar* ptr(int i0, int i1, int i2) const; + + /** @overload */ + uchar* ptr(const int* idx); + /** @overload */ + const uchar* ptr(const int* idx) const; + /** @overload */ + template uchar* ptr(const Vec& idx); + /** @overload */ + template const uchar* ptr(const Vec& idx) const; + + /** @overload */ + template _Tp* ptr(int i0=0); + /** @overload */ + template const _Tp* ptr(int i0=0) const; + /** @overload + @param row Index along the dimension 0 + @param col Index along the dimension 1 + */ + template _Tp* ptr(int row, int col); + /** @overload + @param row Index along the dimension 0 + @param col Index along the dimension 1 + */ + template const _Tp* ptr(int row, int col) const; + /** @overload */ + template _Tp* ptr(int i0, int i1, int i2); + /** @overload */ + template const _Tp* ptr(int i0, int i1, int i2) const; + /** @overload */ + template _Tp* ptr(const int* idx); + /** @overload */ + template const _Tp* ptr(const int* idx) const; + /** @overload */ + template _Tp* ptr(const Vec& idx); + /** @overload */ + template const _Tp* ptr(const Vec& idx) const; + + /** @brief Returns a reference to the specified array element. + + The template methods return a reference to the specified array element. For the sake of higher + performance, the index range checks are only performed in the Debug configuration. + + Note that the variants with a single index (i) can be used to access elements of single-row or + single-column 2-dimensional arrays. That is, if, for example, A is a 1 x N floating-point matrix and + B is an M x 1 integer matrix, you can simply write `A.at(k+4)` and `B.at(2*i+1)` + instead of `A.at(0,k+4)` and `B.at(2*i+1,0)`, respectively. + + The example below initializes a Hilbert matrix: + @code + Mat H(100, 100, CV_64F); + for(int i = 0; i < H.rows; i++) + for(int j = 0; j < H.cols; j++) + H.at(i,j)=1./(i+j+1); + @endcode + + Keep in mind that the size identifier used in the at operator cannot be chosen at random. It depends + on the image from which you are trying to retrieve the data. The table below gives a better insight in this: + - If matrix is of type `CV_8U` then use `Mat.at(y,x)`. + - If matrix is of type `CV_8S` then use `Mat.at(y,x)`. + - If matrix is of type `CV_16U` then use `Mat.at(y,x)`. + - If matrix is of type `CV_16S` then use `Mat.at(y,x)`. + - If matrix is of type `CV_32S` then use `Mat.at(y,x)`. + - If matrix is of type `CV_32F` then use `Mat.at(y,x)`. + - If matrix is of type `CV_64F` then use `Mat.at(y,x)`. + + @param i0 Index along the dimension 0 + */ + template _Tp& at(int i0=0); + /** @overload + @param i0 Index along the dimension 0 + */ + template const _Tp& at(int i0=0) const; + /** @overload + @param row Index along the dimension 0 + @param col Index along the dimension 1 + */ + template _Tp& at(int row, int col); + /** @overload + @param row Index along the dimension 0 + @param col Index along the dimension 1 + */ + template const _Tp& at(int row, int col) const; + + /** @overload + @param i0 Index along the dimension 0 + @param i1 Index along the dimension 1 + @param i2 Index along the dimension 2 + */ + template _Tp& at(int i0, int i1, int i2); + /** @overload + @param i0 Index along the dimension 0 + @param i1 Index along the dimension 1 + @param i2 Index along the dimension 2 + */ + template const _Tp& at(int i0, int i1, int i2) const; + + /** @overload + @param idx Array of Mat::dims indices. + */ + template _Tp& at(const int* idx); + /** @overload + @param idx Array of Mat::dims indices. + */ + template const _Tp& at(const int* idx) const; + + /** @overload */ + template _Tp& at(const Vec& idx); + /** @overload */ + template const _Tp& at(const Vec& idx) const; + + /** @overload + special versions for 2D arrays (especially convenient for referencing image pixels) + @param pt Element position specified as Point(j,i) . + */ + template _Tp& at(Point pt); + /** @overload + special versions for 2D arrays (especially convenient for referencing image pixels) + @param pt Element position specified as Point(j,i) . + */ + template const _Tp& at(Point pt) const; + + /** @brief Returns the matrix iterator and sets it to the first matrix element. + + The methods return the matrix read-only or read-write iterators. The use of matrix iterators is very + similar to the use of bi-directional STL iterators. In the example below, the alpha blending + function is rewritten using the matrix iterators: + @code + template + void alphaBlendRGBA(const Mat& src1, const Mat& src2, Mat& dst) + { + typedef Vec VT; + + const float alpha_scale = (float)std::numeric_limits::max(), + inv_scale = 1.f/alpha_scale; + + CV_Assert( src1.type() == src2.type() && + src1.type() == traits::Type::value && + src1.size() == src2.size()); + Size size = src1.size(); + dst.create(size, src1.type()); + + MatConstIterator_ it1 = src1.begin(), it1_end = src1.end(); + MatConstIterator_ it2 = src2.begin(); + MatIterator_ dst_it = dst.begin(); + + for( ; it1 != it1_end; ++it1, ++it2, ++dst_it ) + { + VT pix1 = *it1, pix2 = *it2; + float alpha = pix1[3]*inv_scale, beta = pix2[3]*inv_scale; + *dst_it = VT(saturate_cast(pix1[0]*alpha + pix2[0]*beta), + saturate_cast(pix1[1]*alpha + pix2[1]*beta), + saturate_cast(pix1[2]*alpha + pix2[2]*beta), + saturate_cast((1 - (1-alpha)*(1-beta))*alpha_scale)); + } + } + @endcode + */ + template MatIterator_<_Tp> begin(); + template MatConstIterator_<_Tp> begin() const; + + /** @brief Returns the matrix iterator and sets it to the after-last matrix element. + + The methods return the matrix read-only or read-write iterators, set to the point following the last + matrix element. + */ + template MatIterator_<_Tp> end(); + template MatConstIterator_<_Tp> end() const; + + /** @brief Runs the given functor over all matrix elements in parallel. + + The operation passed as argument has to be a function pointer, a function object or a lambda(C++11). + + Example 1. All of the operations below put 0xFF the first channel of all matrix elements: + @code + Mat image(1920, 1080, CV_8UC3); + typedef cv::Point3_ Pixel; + + // first. raw pointer access. + for (int r = 0; r < image.rows; ++r) { + Pixel* ptr = image.ptr(r, 0); + const Pixel* ptr_end = ptr + image.cols; + for (; ptr != ptr_end; ++ptr) { + ptr->x = 255; + } + } + + // Using MatIterator. (Simple but there are a Iterator's overhead) + for (Pixel &p : cv::Mat_(image)) { + p.x = 255; + } + + // Parallel execution with function object. + struct Operator { + void operator ()(Pixel &pixel, const int * position) { + pixel.x = 255; + } + }; + image.forEach(Operator()); + + // Parallel execution using C++11 lambda. + image.forEach([](Pixel &p, const int * position) -> void { + p.x = 255; + }); + @endcode + Example 2. Using the pixel's position: + @code + // Creating 3D matrix (255 x 255 x 255) typed uint8_t + // and initialize all elements by the value which equals elements position. + // i.e. pixels (x,y,z) = (1,2,3) is (b,g,r) = (1,2,3). + + int sizes[] = { 255, 255, 255 }; + typedef cv::Point3_ Pixel; + + Mat_ image = Mat::zeros(3, sizes, CV_8UC3); + + image.forEach([&](Pixel& pixel, const int position[]) -> void { + pixel.x = position[0]; + pixel.y = position[1]; + pixel.z = position[2]; + }); + @endcode + */ + template void forEach(const Functor& operation); + /** @overload */ + template void forEach(const Functor& operation) const; + +#ifdef CV_CXX_MOVE_SEMANTICS + Mat(Mat&& m); + Mat& operator = (Mat&& m); +#endif + + enum { MAGIC_VAL = 0x42FF0000, AUTO_STEP = 0, CONTINUOUS_FLAG = CV_MAT_CONT_FLAG, SUBMATRIX_FLAG = CV_SUBMAT_FLAG }; + enum { MAGIC_MASK = 0xFFFF0000, TYPE_MASK = 0x00000FFF, DEPTH_MASK = 7 }; + + /*! includes several bit-fields: + - the magic signature + - continuity flag + - depth + - number of channels + */ + int flags; + //! the matrix dimensionality, >= 2 + int dims; + //! the number of rows and columns or (-1, -1) when the matrix has more than 2 dimensions + int rows, cols; + //! pointer to the data + uchar* data; + + //! helper fields used in locateROI and adjustROI + const uchar* datastart; + const uchar* dataend; + const uchar* datalimit; + + //! custom allocator + MatAllocator* allocator; + //! and the standard allocator + static MatAllocator* getStdAllocator(); + static MatAllocator* getDefaultAllocator(); + static void setDefaultAllocator(MatAllocator* allocator); + + //! internal use method: updates the continuity flag + void updateContinuityFlag(); + + //! interaction with UMat + UMatData* u; + + MatSize size; + MatStep step; + +protected: + template void forEach_impl(const Functor& operation); +}; + + +///////////////////////////////// Mat_<_Tp> //////////////////////////////////// + +/** @brief Template matrix class derived from Mat + +@code{.cpp} + template class Mat_ : public Mat + { + public: + // ... some specific methods + // and + // no new extra fields + }; +@endcode +The class `Mat_<_Tp>` is a *thin* template wrapper on top of the Mat class. It does not have any +extra data fields. Nor this class nor Mat has any virtual methods. Thus, references or pointers to +these two classes can be freely but carefully converted one to another. For example: +@code{.cpp} + // create a 100x100 8-bit matrix + Mat M(100,100,CV_8U); + // this will be compiled fine. no any data conversion will be done. + Mat_& M1 = (Mat_&)M; + // the program is likely to crash at the statement below + M1(99,99) = 1.f; +@endcode +While Mat is sufficient in most cases, Mat_ can be more convenient if you use a lot of element +access operations and if you know matrix type at the compilation time. Note that +`Mat::at(int y,int x)` and `Mat_::operator()(int y,int x)` do absolutely the same +and run at the same speed, but the latter is certainly shorter: +@code{.cpp} + Mat_ M(20,20); + for(int i = 0; i < M.rows; i++) + for(int j = 0; j < M.cols; j++) + M(i,j) = 1./(i+j+1); + Mat E, V; + eigen(M,E,V); + cout << E.at(0,0)/E.at(M.rows-1,0); +@endcode +To use Mat_ for multi-channel images/matrices, pass Vec as a Mat_ parameter: +@code{.cpp} + // allocate a 320x240 color image and fill it with green (in RGB space) + Mat_ img(240, 320, Vec3b(0,255,0)); + // now draw a diagonal white line + for(int i = 0; i < 100; i++) + img(i,i)=Vec3b(255,255,255); + // and now scramble the 2nd (red) channel of each pixel + for(int i = 0; i < img.rows; i++) + for(int j = 0; j < img.cols; j++) + img(i,j)[2] ^= (uchar)(i ^ j); +@endcode +Mat_ is fully compatible with C++11 range-based for loop. For example such loop +can be used to safely apply look-up table: +@code{.cpp} +void applyTable(Mat_& I, const uchar* const table) +{ + for(auto& pixel : I) + { + pixel = table[pixel]; + } +} +@endcode + */ +template class Mat_ : public Mat +{ +public: + typedef _Tp value_type; + typedef typename DataType<_Tp>::channel_type channel_type; + typedef MatIterator_<_Tp> iterator; + typedef MatConstIterator_<_Tp> const_iterator; + + //! default constructor + Mat_(); + //! equivalent to Mat(_rows, _cols, DataType<_Tp>::type) + Mat_(int _rows, int _cols); + //! constructor that sets each matrix element to specified value + Mat_(int _rows, int _cols, const _Tp& value); + //! equivalent to Mat(_size, DataType<_Tp>::type) + explicit Mat_(Size _size); + //! constructor that sets each matrix element to specified value + Mat_(Size _size, const _Tp& value); + //! n-dim array constructor + Mat_(int _ndims, const int* _sizes); + //! n-dim array constructor that sets each matrix element to specified value + Mat_(int _ndims, const int* _sizes, const _Tp& value); + //! copy/conversion constructor. If m is of different type, it's converted + Mat_(const Mat& m); + //! copy constructor + Mat_(const Mat_& m); + //! constructs a matrix on top of user-allocated data. step is in bytes(!!!), regardless of the type + Mat_(int _rows, int _cols, _Tp* _data, size_t _step=AUTO_STEP); + //! constructs n-dim matrix on top of user-allocated data. steps are in bytes(!!!), regardless of the type + Mat_(int _ndims, const int* _sizes, _Tp* _data, const size_t* _steps=0); + //! selects a submatrix + Mat_(const Mat_& m, const Range& rowRange, const Range& colRange=Range::all()); + //! selects a submatrix + Mat_(const Mat_& m, const Rect& roi); + //! selects a submatrix, n-dim version + Mat_(const Mat_& m, const Range* ranges); + //! selects a submatrix, n-dim version + Mat_(const Mat_& m, const std::vector& ranges); + //! from a matrix expression + explicit Mat_(const MatExpr& e); + //! makes a matrix out of Vec, std::vector, Point_ or Point3_. The matrix will have a single column + explicit Mat_(const std::vector<_Tp>& vec, bool copyData=false); + template explicit Mat_(const Vec::channel_type, n>& vec, bool copyData=true); + template explicit Mat_(const Matx::channel_type, m, n>& mtx, bool copyData=true); + explicit Mat_(const Point_::channel_type>& pt, bool copyData=true); + explicit Mat_(const Point3_::channel_type>& pt, bool copyData=true); + explicit Mat_(const MatCommaInitializer_<_Tp>& commaInitializer); + +#ifdef CV_CXX11 + Mat_(std::initializer_list<_Tp> values); + explicit Mat_(const std::initializer_list sizes, const std::initializer_list<_Tp> values); +#endif + +#ifdef CV_CXX_STD_ARRAY + template explicit Mat_(const std::array<_Tp, _Nm>& arr, bool copyData=false); +#endif + + Mat_& operator = (const Mat& m); + Mat_& operator = (const Mat_& m); + //! set all the elements to s. + Mat_& operator = (const _Tp& s); + //! assign a matrix expression + Mat_& operator = (const MatExpr& e); + + //! iterators; they are smart enough to skip gaps in the end of rows + iterator begin(); + iterator end(); + const_iterator begin() const; + const_iterator end() const; + + //! template methods for for operation over all matrix elements. + // the operations take care of skipping gaps in the end of rows (if any) + template void forEach(const Functor& operation); + template void forEach(const Functor& operation) const; + + //! equivalent to Mat::create(_rows, _cols, DataType<_Tp>::type) + void create(int _rows, int _cols); + //! equivalent to Mat::create(_size, DataType<_Tp>::type) + void create(Size _size); + //! equivalent to Mat::create(_ndims, _sizes, DatType<_Tp>::type) + void create(int _ndims, const int* _sizes); + //! equivalent to Mat::release() + void release(); + //! cross-product + Mat_ cross(const Mat_& m) const; + //! data type conversion + template operator Mat_() const; + //! overridden forms of Mat::row() etc. + Mat_ row(int y) const; + Mat_ col(int x) const; + Mat_ diag(int d=0) const; + Mat_ clone() const CV_NODISCARD; + + //! overridden forms of Mat::elemSize() etc. + size_t elemSize() const; + size_t elemSize1() const; + int type() const; + int depth() const; + int channels() const; + size_t step1(int i=0) const; + //! returns step()/sizeof(_Tp) + size_t stepT(int i=0) const; + + //! overridden forms of Mat::zeros() etc. Data type is omitted, of course + static MatExpr zeros(int rows, int cols); + static MatExpr zeros(Size size); + static MatExpr zeros(int _ndims, const int* _sizes); + static MatExpr ones(int rows, int cols); + static MatExpr ones(Size size); + static MatExpr ones(int _ndims, const int* _sizes); + static MatExpr eye(int rows, int cols); + static MatExpr eye(Size size); + + //! some more overridden methods + Mat_& adjustROI( int dtop, int dbottom, int dleft, int dright ); + Mat_ operator()( const Range& rowRange, const Range& colRange ) const; + Mat_ operator()( const Rect& roi ) const; + Mat_ operator()( const Range* ranges ) const; + Mat_ operator()(const std::vector& ranges) const; + + //! more convenient forms of row and element access operators + _Tp* operator [](int y); + const _Tp* operator [](int y) const; + + //! returns reference to the specified element + _Tp& operator ()(const int* idx); + //! returns read-only reference to the specified element + const _Tp& operator ()(const int* idx) const; + + //! returns reference to the specified element + template _Tp& operator ()(const Vec& idx); + //! returns read-only reference to the specified element + template const _Tp& operator ()(const Vec& idx) const; + + //! returns reference to the specified element (1D case) + _Tp& operator ()(int idx0); + //! returns read-only reference to the specified element (1D case) + const _Tp& operator ()(int idx0) const; + //! returns reference to the specified element (2D case) + _Tp& operator ()(int row, int col); + //! returns read-only reference to the specified element (2D case) + const _Tp& operator ()(int row, int col) const; + //! returns reference to the specified element (3D case) + _Tp& operator ()(int idx0, int idx1, int idx2); + //! returns read-only reference to the specified element (3D case) + const _Tp& operator ()(int idx0, int idx1, int idx2) const; + + _Tp& operator ()(Point pt); + const _Tp& operator ()(Point pt) const; + + //! conversion to vector. + operator std::vector<_Tp>() const; + +#ifdef CV_CXX_STD_ARRAY + //! conversion to array. + template operator std::array<_Tp, _Nm>() const; +#endif + + //! conversion to Vec + template operator Vec::channel_type, n>() const; + //! conversion to Matx + template operator Matx::channel_type, m, n>() const; + +#ifdef CV_CXX_MOVE_SEMANTICS + Mat_(Mat_&& m); + Mat_& operator = (Mat_&& m); + + Mat_(Mat&& m); + Mat_& operator = (Mat&& m); + + Mat_(MatExpr&& e); +#endif +}; + +typedef Mat_ Mat1b; +typedef Mat_ Mat2b; +typedef Mat_ Mat3b; +typedef Mat_ Mat4b; + +typedef Mat_ Mat1s; +typedef Mat_ Mat2s; +typedef Mat_ Mat3s; +typedef Mat_ Mat4s; + +typedef Mat_ Mat1w; +typedef Mat_ Mat2w; +typedef Mat_ Mat3w; +typedef Mat_ Mat4w; + +typedef Mat_ Mat1i; +typedef Mat_ Mat2i; +typedef Mat_ Mat3i; +typedef Mat_ Mat4i; + +typedef Mat_ Mat1f; +typedef Mat_ Mat2f; +typedef Mat_ Mat3f; +typedef Mat_ Mat4f; + +typedef Mat_ Mat1d; +typedef Mat_ Mat2d; +typedef Mat_ Mat3d; +typedef Mat_ Mat4d; + +/** @todo document */ +class CV_EXPORTS UMat +{ +public: + //! default constructor + UMat(UMatUsageFlags usageFlags = USAGE_DEFAULT); + //! constructs 2D matrix of the specified size and type + // (_type is CV_8UC1, CV_64FC3, CV_32SC(12) etc.) + UMat(int rows, int cols, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT); + UMat(Size size, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT); + //! constucts 2D matrix and fills it with the specified value _s. + UMat(int rows, int cols, int type, const Scalar& s, UMatUsageFlags usageFlags = USAGE_DEFAULT); + UMat(Size size, int type, const Scalar& s, UMatUsageFlags usageFlags = USAGE_DEFAULT); + + //! constructs n-dimensional matrix + UMat(int ndims, const int* sizes, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT); + UMat(int ndims, const int* sizes, int type, const Scalar& s, UMatUsageFlags usageFlags = USAGE_DEFAULT); + + //! copy constructor + UMat(const UMat& m); + + //! creates a matrix header for a part of the bigger matrix + UMat(const UMat& m, const Range& rowRange, const Range& colRange=Range::all()); + UMat(const UMat& m, const Rect& roi); + UMat(const UMat& m, const Range* ranges); + UMat(const UMat& m, const std::vector& ranges); + //! builds matrix from std::vector with or without copying the data + template explicit UMat(const std::vector<_Tp>& vec, bool copyData=false); + + //! builds matrix from cv::Vec; the data is copied by default + template explicit UMat(const Vec<_Tp, n>& vec, bool copyData=true); + //! builds matrix from cv::Matx; the data is copied by default + template explicit UMat(const Matx<_Tp, m, n>& mtx, bool copyData=true); + //! builds matrix from a 2D point + template explicit UMat(const Point_<_Tp>& pt, bool copyData=true); + //! builds matrix from a 3D point + template explicit UMat(const Point3_<_Tp>& pt, bool copyData=true); + //! builds matrix from comma initializer + template explicit UMat(const MatCommaInitializer_<_Tp>& commaInitializer); + + //! destructor - calls release() + ~UMat(); + //! assignment operators + UMat& operator = (const UMat& m); + + Mat getMat(int flags) const; + + //! returns a new matrix header for the specified row + UMat row(int y) const; + //! returns a new matrix header for the specified column + UMat col(int x) const; + //! ... for the specified row span + UMat rowRange(int startrow, int endrow) const; + UMat rowRange(const Range& r) const; + //! ... for the specified column span + UMat colRange(int startcol, int endcol) const; + UMat colRange(const Range& r) const; + //! ... for the specified diagonal + //! (d=0 - the main diagonal, + //! >0 - a diagonal from the upper half, + //! <0 - a diagonal from the lower half) + UMat diag(int d=0) const; + //! constructs a square diagonal matrix which main diagonal is vector "d" + static UMat diag(const UMat& d); + + //! returns deep copy of the matrix, i.e. the data is copied + UMat clone() const CV_NODISCARD; + //! copies the matrix content to "m". + // It calls m.create(this->size(), this->type()). + void copyTo( OutputArray m ) const; + //! copies those matrix elements to "m" that are marked with non-zero mask elements. + void copyTo( OutputArray m, InputArray mask ) const; + //! converts matrix to another datatype with optional scaling. See cvConvertScale. + void convertTo( OutputArray m, int rtype, double alpha=1, double beta=0 ) const; + + void assignTo( UMat& m, int type=-1 ) const; + + //! sets every matrix element to s + UMat& operator = (const Scalar& s); + //! sets some of the matrix elements to s, according to the mask + UMat& setTo(InputArray value, InputArray mask=noArray()); + //! creates alternative matrix header for the same data, with different + // number of channels and/or different number of rows. see cvReshape. + UMat reshape(int cn, int rows=0) const; + UMat reshape(int cn, int newndims, const int* newsz) const; + + //! matrix transposition by means of matrix expressions + UMat t() const; + //! matrix inversion by means of matrix expressions + UMat inv(int method=DECOMP_LU) const; + //! per-element matrix multiplication by means of matrix expressions + UMat mul(InputArray m, double scale=1) const; + + //! computes dot-product + double dot(InputArray m) const; + + //! Matlab-style matrix initialization + static UMat zeros(int rows, int cols, int type); + static UMat zeros(Size size, int type); + static UMat zeros(int ndims, const int* sz, int type); + static UMat ones(int rows, int cols, int type); + static UMat ones(Size size, int type); + static UMat ones(int ndims, const int* sz, int type); + static UMat eye(int rows, int cols, int type); + static UMat eye(Size size, int type); + + //! allocates new matrix data unless the matrix already has specified size and type. + // previous data is unreferenced if needed. + void create(int rows, int cols, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT); + void create(Size size, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT); + void create(int ndims, const int* sizes, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT); + void create(const std::vector& sizes, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT); + + //! increases the reference counter; use with care to avoid memleaks + void addref(); + //! decreases reference counter; + // deallocates the data when reference counter reaches 0. + void release(); + + //! deallocates the matrix data + void deallocate(); + //! internal use function; properly re-allocates _size, _step arrays + void copySize(const UMat& m); + + //! locates matrix header within a parent matrix. See below + void locateROI( Size& wholeSize, Point& ofs ) const; + //! moves/resizes the current matrix ROI inside the parent matrix. + UMat& adjustROI( int dtop, int dbottom, int dleft, int dright ); + //! extracts a rectangular sub-matrix + // (this is a generalized form of row, rowRange etc.) + UMat operator()( Range rowRange, Range colRange ) const; + UMat operator()( const Rect& roi ) const; + UMat operator()( const Range* ranges ) const; + UMat operator()(const std::vector& ranges) const; + + //! returns true iff the matrix data is continuous + // (i.e. when there are no gaps between successive rows). + // similar to CV_IS_MAT_CONT(cvmat->type) + bool isContinuous() const; + + //! returns true if the matrix is a submatrix of another matrix + bool isSubmatrix() const; + + //! returns element size in bytes, + // similar to CV_ELEM_SIZE(cvmat->type) + size_t elemSize() const; + //! returns the size of element channel in bytes. + size_t elemSize1() const; + //! returns element type, similar to CV_MAT_TYPE(cvmat->type) + int type() const; + //! returns element type, similar to CV_MAT_DEPTH(cvmat->type) + int depth() const; + //! returns element type, similar to CV_MAT_CN(cvmat->type) + int channels() const; + //! returns step/elemSize1() + size_t step1(int i=0) const; + //! returns true if matrix data is NULL + bool empty() const; + //! returns the total number of matrix elements + size_t total() const; + + //! returns N if the matrix is 1-channel (N x ptdim) or ptdim-channel (1 x N) or (N x 1); negative number otherwise + int checkVector(int elemChannels, int depth=-1, bool requireContinuous=true) const; + +#ifdef CV_CXX_MOVE_SEMANTICS + UMat(UMat&& m); + UMat& operator = (UMat&& m); +#endif + + /*! Returns the OpenCL buffer handle on which UMat operates on. + The UMat instance should be kept alive during the use of the handle to prevent the buffer to be + returned to the OpenCV buffer pool. + */ + void* handle(int accessFlags) const; + void ndoffset(size_t* ofs) const; + + enum { MAGIC_VAL = 0x42FF0000, AUTO_STEP = 0, CONTINUOUS_FLAG = CV_MAT_CONT_FLAG, SUBMATRIX_FLAG = CV_SUBMAT_FLAG }; + enum { MAGIC_MASK = 0xFFFF0000, TYPE_MASK = 0x00000FFF, DEPTH_MASK = 7 }; + + /*! includes several bit-fields: + - the magic signature + - continuity flag + - depth + - number of channels + */ + int flags; + //! the matrix dimensionality, >= 2 + int dims; + //! the number of rows and columns or (-1, -1) when the matrix has more than 2 dimensions + int rows, cols; + + //! custom allocator + MatAllocator* allocator; + UMatUsageFlags usageFlags; // usage flags for allocator + //! and the standard allocator + static MatAllocator* getStdAllocator(); + + //! internal use method: updates the continuity flag + void updateContinuityFlag(); + + // black-box container of UMat data + UMatData* u; + + // offset of the submatrix (or 0) + size_t offset; + + MatSize size; + MatStep step; + +protected: +}; + + +/////////////////////////// multi-dimensional sparse matrix ////////////////////////// + +/** @brief The class SparseMat represents multi-dimensional sparse numerical arrays. + +Such a sparse array can store elements of any type that Mat can store. *Sparse* means that only +non-zero elements are stored (though, as a result of operations on a sparse matrix, some of its +stored elements can actually become 0. It is up to you to detect such elements and delete them +using SparseMat::erase ). The non-zero elements are stored in a hash table that grows when it is +filled so that the search time is O(1) in average (regardless of whether element is there or not). +Elements can be accessed using the following methods: +- Query operations (SparseMat::ptr and the higher-level SparseMat::ref, SparseMat::value and + SparseMat::find), for example: + @code + const int dims = 5; + int size[5] = {10, 10, 10, 10, 10}; + SparseMat sparse_mat(dims, size, CV_32F); + for(int i = 0; i < 1000; i++) + { + int idx[dims]; + for(int k = 0; k < dims; k++) + idx[k] = rand() % size[k]; + sparse_mat.ref(idx) += 1.f; + } + cout << "nnz = " << sparse_mat.nzcount() << endl; + @endcode +- Sparse matrix iterators. They are similar to MatIterator but different from NAryMatIterator. + That is, the iteration loop is familiar to STL users: + @code + // prints elements of a sparse floating-point matrix + // and the sum of elements. + SparseMatConstIterator_ + it = sparse_mat.begin(), + it_end = sparse_mat.end(); + double s = 0; + int dims = sparse_mat.dims(); + for(; it != it_end; ++it) + { + // print element indices and the element value + const SparseMat::Node* n = it.node(); + printf("("); + for(int i = 0; i < dims; i++) + printf("%d%s", n->idx[i], i < dims-1 ? ", " : ")"); + printf(": %g\n", it.value()); + s += *it; + } + printf("Element sum is %g\n", s); + @endcode + If you run this loop, you will notice that elements are not enumerated in a logical order + (lexicographical, and so on). They come in the same order as they are stored in the hash table + (semi-randomly). You may collect pointers to the nodes and sort them to get the proper ordering. + Note, however, that pointers to the nodes may become invalid when you add more elements to the + matrix. This may happen due to possible buffer reallocation. +- Combination of the above 2 methods when you need to process 2 or more sparse matrices + simultaneously. For example, this is how you can compute unnormalized cross-correlation of the 2 + floating-point sparse matrices: + @code + double cross_corr(const SparseMat& a, const SparseMat& b) + { + const SparseMat *_a = &a, *_b = &b; + // if b contains less elements than a, + // it is faster to iterate through b + if(_a->nzcount() > _b->nzcount()) + std::swap(_a, _b); + SparseMatConstIterator_ it = _a->begin(), + it_end = _a->end(); + double ccorr = 0; + for(; it != it_end; ++it) + { + // take the next element from the first matrix + float avalue = *it; + const Node* anode = it.node(); + // and try to find an element with the same index in the second matrix. + // since the hash value depends only on the element index, + // reuse the hash value stored in the node + float bvalue = _b->value(anode->idx,&anode->hashval); + ccorr += avalue*bvalue; + } + return ccorr; + } + @endcode + */ +class CV_EXPORTS SparseMat +{ +public: + typedef SparseMatIterator iterator; + typedef SparseMatConstIterator const_iterator; + + enum { MAGIC_VAL=0x42FD0000, MAX_DIM=32, HASH_SCALE=0x5bd1e995, HASH_BIT=0x80000000 }; + + //! the sparse matrix header + struct CV_EXPORTS Hdr + { + Hdr(int _dims, const int* _sizes, int _type); + void clear(); + int refcount; + int dims; + int valueOffset; + size_t nodeSize; + size_t nodeCount; + size_t freeList; + std::vector pool; + std::vector hashtab; + int size[MAX_DIM]; + }; + + //! sparse matrix node - element of a hash table + struct CV_EXPORTS Node + { + //! hash value + size_t hashval; + //! index of the next node in the same hash table entry + size_t next; + //! index of the matrix element + int idx[MAX_DIM]; + }; + + /** @brief Various SparseMat constructors. + */ + SparseMat(); + + /** @overload + @param dims Array dimensionality. + @param _sizes Sparce matrix size on all dementions. + @param _type Sparse matrix data type. + */ + SparseMat(int dims, const int* _sizes, int _type); + + /** @overload + @param m Source matrix for copy constructor. If m is dense matrix (ocvMat) then it will be converted + to sparse representation. + */ + SparseMat(const SparseMat& m); + + /** @overload + @param m Source matrix for copy constructor. If m is dense matrix (ocvMat) then it will be converted + to sparse representation. + */ + explicit SparseMat(const Mat& m); + + //! the destructor + ~SparseMat(); + + //! assignment operator. This is O(1) operation, i.e. no data is copied + SparseMat& operator = (const SparseMat& m); + //! equivalent to the corresponding constructor + SparseMat& operator = (const Mat& m); + + //! creates full copy of the matrix + SparseMat clone() const CV_NODISCARD; + + //! copies all the data to the destination matrix. All the previous content of m is erased + void copyTo( SparseMat& m ) const; + //! converts sparse matrix to dense matrix. + void copyTo( Mat& m ) const; + //! multiplies all the matrix elements by the specified scale factor alpha and converts the results to the specified data type + void convertTo( SparseMat& m, int rtype, double alpha=1 ) const; + //! converts sparse matrix to dense n-dim matrix with optional type conversion and scaling. + /*! + @param [out] m - output matrix; if it does not have a proper size or type before the operation, + it is reallocated + @param [in] rtype - desired output matrix type or, rather, the depth since the number of channels + are the same as the input has; if rtype is negative, the output matrix will have the + same type as the input. + @param [in] alpha - optional scale factor + @param [in] beta - optional delta added to the scaled values + */ + void convertTo( Mat& m, int rtype, double alpha=1, double beta=0 ) const; + + // not used now + void assignTo( SparseMat& m, int type=-1 ) const; + + //! reallocates sparse matrix. + /*! + If the matrix already had the proper size and type, + it is simply cleared with clear(), otherwise, + the old matrix is released (using release()) and the new one is allocated. + */ + void create(int dims, const int* _sizes, int _type); + //! sets all the sparse matrix elements to 0, which means clearing the hash table. + void clear(); + //! manually increments the reference counter to the header. + void addref(); + // decrements the header reference counter. When the counter reaches 0, the header and all the underlying data are deallocated. + void release(); + + //! converts sparse matrix to the old-style representation; all the elements are copied. + //operator CvSparseMat*() const; + //! returns the size of each element in bytes (not including the overhead - the space occupied by SparseMat::Node elements) + size_t elemSize() const; + //! returns elemSize()/channels() + size_t elemSize1() const; + + //! returns type of sparse matrix elements + int type() const; + //! returns the depth of sparse matrix elements + int depth() const; + //! returns the number of channels + int channels() const; + + //! returns the array of sizes, or NULL if the matrix is not allocated + const int* size() const; + //! returns the size of i-th matrix dimension (or 0) + int size(int i) const; + //! returns the matrix dimensionality + int dims() const; + //! returns the number of non-zero elements (=the number of hash table nodes) + size_t nzcount() const; + + //! computes the element hash value (1D case) + size_t hash(int i0) const; + //! computes the element hash value (2D case) + size_t hash(int i0, int i1) const; + //! computes the element hash value (3D case) + size_t hash(int i0, int i1, int i2) const; + //! computes the element hash value (nD case) + size_t hash(const int* idx) const; + + //!@{ + /*! + specialized variants for 1D, 2D, 3D cases and the generic_type one for n-D case. + return pointer to the matrix element. + - if the element is there (it's non-zero), the pointer to it is returned + - if it's not there and createMissing=false, NULL pointer is returned + - if it's not there and createMissing=true, then the new element + is created and initialized with 0. Pointer to it is returned + - if the optional hashval pointer is not NULL, the element hash value is + not computed, but *hashval is taken instead. + */ + //! returns pointer to the specified element (1D case) + uchar* ptr(int i0, bool createMissing, size_t* hashval=0); + //! returns pointer to the specified element (2D case) + uchar* ptr(int i0, int i1, bool createMissing, size_t* hashval=0); + //! returns pointer to the specified element (3D case) + uchar* ptr(int i0, int i1, int i2, bool createMissing, size_t* hashval=0); + //! returns pointer to the specified element (nD case) + uchar* ptr(const int* idx, bool createMissing, size_t* hashval=0); + //!@} + + //!@{ + /*! + return read-write reference to the specified sparse matrix element. + + `ref<_Tp>(i0,...[,hashval])` is equivalent to `*(_Tp*)ptr(i0,...,true[,hashval])`. + The methods always return a valid reference. + If the element did not exist, it is created and initialiazed with 0. + */ + //! returns reference to the specified element (1D case) + template _Tp& ref(int i0, size_t* hashval=0); + //! returns reference to the specified element (2D case) + template _Tp& ref(int i0, int i1, size_t* hashval=0); + //! returns reference to the specified element (3D case) + template _Tp& ref(int i0, int i1, int i2, size_t* hashval=0); + //! returns reference to the specified element (nD case) + template _Tp& ref(const int* idx, size_t* hashval=0); + //!@} + + //!@{ + /*! + return value of the specified sparse matrix element. + + `value<_Tp>(i0,...[,hashval])` is equivalent to + @code + { const _Tp* p = find<_Tp>(i0,...[,hashval]); return p ? *p : _Tp(); } + @endcode + + That is, if the element did not exist, the methods return 0. + */ + //! returns value of the specified element (1D case) + template _Tp value(int i0, size_t* hashval=0) const; + //! returns value of the specified element (2D case) + template _Tp value(int i0, int i1, size_t* hashval=0) const; + //! returns value of the specified element (3D case) + template _Tp value(int i0, int i1, int i2, size_t* hashval=0) const; + //! returns value of the specified element (nD case) + template _Tp value(const int* idx, size_t* hashval=0) const; + //!@} + + //!@{ + /*! + Return pointer to the specified sparse matrix element if it exists + + `find<_Tp>(i0,...[,hashval])` is equivalent to `(_const Tp*)ptr(i0,...false[,hashval])`. + + If the specified element does not exist, the methods return NULL. + */ + //! returns pointer to the specified element (1D case) + template const _Tp* find(int i0, size_t* hashval=0) const; + //! returns pointer to the specified element (2D case) + template const _Tp* find(int i0, int i1, size_t* hashval=0) const; + //! returns pointer to the specified element (3D case) + template const _Tp* find(int i0, int i1, int i2, size_t* hashval=0) const; + //! returns pointer to the specified element (nD case) + template const _Tp* find(const int* idx, size_t* hashval=0) const; + //!@} + + //! erases the specified element (2D case) + void erase(int i0, int i1, size_t* hashval=0); + //! erases the specified element (3D case) + void erase(int i0, int i1, int i2, size_t* hashval=0); + //! erases the specified element (nD case) + void erase(const int* idx, size_t* hashval=0); + + //!@{ + /*! + return the sparse matrix iterator pointing to the first sparse matrix element + */ + //! returns the sparse matrix iterator at the matrix beginning + SparseMatIterator begin(); + //! returns the sparse matrix iterator at the matrix beginning + template SparseMatIterator_<_Tp> begin(); + //! returns the read-only sparse matrix iterator at the matrix beginning + SparseMatConstIterator begin() const; + //! returns the read-only sparse matrix iterator at the matrix beginning + template SparseMatConstIterator_<_Tp> begin() const; + //!@} + /*! + return the sparse matrix iterator pointing to the element following the last sparse matrix element + */ + //! returns the sparse matrix iterator at the matrix end + SparseMatIterator end(); + //! returns the read-only sparse matrix iterator at the matrix end + SparseMatConstIterator end() const; + //! returns the typed sparse matrix iterator at the matrix end + template SparseMatIterator_<_Tp> end(); + //! returns the typed read-only sparse matrix iterator at the matrix end + template SparseMatConstIterator_<_Tp> end() const; + + //! returns the value stored in the sparse martix node + template _Tp& value(Node* n); + //! returns the value stored in the sparse martix node + template const _Tp& value(const Node* n) const; + + ////////////// some internal-use methods /////////////// + Node* node(size_t nidx); + const Node* node(size_t nidx) const; + + uchar* newNode(const int* idx, size_t hashval); + void removeNode(size_t hidx, size_t nidx, size_t previdx); + void resizeHashTab(size_t newsize); + + int flags; + Hdr* hdr; +}; + + + +///////////////////////////////// SparseMat_<_Tp> //////////////////////////////////// + +/** @brief Template sparse n-dimensional array class derived from SparseMat + +SparseMat_ is a thin wrapper on top of SparseMat created in the same way as Mat_ . It simplifies +notation of some operations: +@code + int sz[] = {10, 20, 30}; + SparseMat_ M(3, sz); + ... + M.ref(1, 2, 3) = M(4, 5, 6) + M(7, 8, 9); +@endcode + */ +template class SparseMat_ : public SparseMat +{ +public: + typedef SparseMatIterator_<_Tp> iterator; + typedef SparseMatConstIterator_<_Tp> const_iterator; + + //! the default constructor + SparseMat_(); + //! the full constructor equivalent to SparseMat(dims, _sizes, DataType<_Tp>::type) + SparseMat_(int dims, const int* _sizes); + //! the copy constructor. If DataType<_Tp>.type != m.type(), the m elements are converted + SparseMat_(const SparseMat& m); + //! the copy constructor. This is O(1) operation - no data is copied + SparseMat_(const SparseMat_& m); + //! converts dense matrix to the sparse form + SparseMat_(const Mat& m); + //! converts the old-style sparse matrix to the C++ class. All the elements are copied + //SparseMat_(const CvSparseMat* m); + //! the assignment operator. If DataType<_Tp>.type != m.type(), the m elements are converted + SparseMat_& operator = (const SparseMat& m); + //! the assignment operator. This is O(1) operation - no data is copied + SparseMat_& operator = (const SparseMat_& m); + //! converts dense matrix to the sparse form + SparseMat_& operator = (const Mat& m); + + //! makes full copy of the matrix. All the elements are duplicated + SparseMat_ clone() const CV_NODISCARD; + //! equivalent to cv::SparseMat::create(dims, _sizes, DataType<_Tp>::type) + void create(int dims, const int* _sizes); + //! converts sparse matrix to the old-style CvSparseMat. All the elements are copied + //operator CvSparseMat*() const; + + //! returns type of the matrix elements + int type() const; + //! returns depth of the matrix elements + int depth() const; + //! returns the number of channels in each matrix element + int channels() const; + + //! equivalent to SparseMat::ref<_Tp>(i0, hashval) + _Tp& ref(int i0, size_t* hashval=0); + //! equivalent to SparseMat::ref<_Tp>(i0, i1, hashval) + _Tp& ref(int i0, int i1, size_t* hashval=0); + //! equivalent to SparseMat::ref<_Tp>(i0, i1, i2, hashval) + _Tp& ref(int i0, int i1, int i2, size_t* hashval=0); + //! equivalent to SparseMat::ref<_Tp>(idx, hashval) + _Tp& ref(const int* idx, size_t* hashval=0); + + //! equivalent to SparseMat::value<_Tp>(i0, hashval) + _Tp operator()(int i0, size_t* hashval=0) const; + //! equivalent to SparseMat::value<_Tp>(i0, i1, hashval) + _Tp operator()(int i0, int i1, size_t* hashval=0) const; + //! equivalent to SparseMat::value<_Tp>(i0, i1, i2, hashval) + _Tp operator()(int i0, int i1, int i2, size_t* hashval=0) const; + //! equivalent to SparseMat::value<_Tp>(idx, hashval) + _Tp operator()(const int* idx, size_t* hashval=0) const; + + //! returns sparse matrix iterator pointing to the first sparse matrix element + SparseMatIterator_<_Tp> begin(); + //! returns read-only sparse matrix iterator pointing to the first sparse matrix element + SparseMatConstIterator_<_Tp> begin() const; + //! returns sparse matrix iterator pointing to the element following the last sparse matrix element + SparseMatIterator_<_Tp> end(); + //! returns read-only sparse matrix iterator pointing to the element following the last sparse matrix element + SparseMatConstIterator_<_Tp> end() const; +}; + + + +////////////////////////////////// MatConstIterator ////////////////////////////////// + +class CV_EXPORTS MatConstIterator +{ +public: + typedef uchar* value_type; + typedef ptrdiff_t difference_type; + typedef const uchar** pointer; + typedef uchar* reference; + + typedef std::random_access_iterator_tag iterator_category; + + //! default constructor + MatConstIterator(); + //! constructor that sets the iterator to the beginning of the matrix + MatConstIterator(const Mat* _m); + //! constructor that sets the iterator to the specified element of the matrix + MatConstIterator(const Mat* _m, int _row, int _col=0); + //! constructor that sets the iterator to the specified element of the matrix + MatConstIterator(const Mat* _m, Point _pt); + //! constructor that sets the iterator to the specified element of the matrix + MatConstIterator(const Mat* _m, const int* _idx); + //! copy constructor + MatConstIterator(const MatConstIterator& it); + + //! copy operator + MatConstIterator& operator = (const MatConstIterator& it); + //! returns the current matrix element + const uchar* operator *() const; + //! returns the i-th matrix element, relative to the current + const uchar* operator [](ptrdiff_t i) const; + + //! shifts the iterator forward by the specified number of elements + MatConstIterator& operator += (ptrdiff_t ofs); + //! shifts the iterator backward by the specified number of elements + MatConstIterator& operator -= (ptrdiff_t ofs); + //! decrements the iterator + MatConstIterator& operator --(); + //! decrements the iterator + MatConstIterator operator --(int); + //! increments the iterator + MatConstIterator& operator ++(); + //! increments the iterator + MatConstIterator operator ++(int); + //! returns the current iterator position + Point pos() const; + //! returns the current iterator position + void pos(int* _idx) const; + + ptrdiff_t lpos() const; + void seek(ptrdiff_t ofs, bool relative = false); + void seek(const int* _idx, bool relative = false); + + const Mat* m; + size_t elemSize; + const uchar* ptr; + const uchar* sliceStart; + const uchar* sliceEnd; +}; + + + +////////////////////////////////// MatConstIterator_ ///////////////////////////////// + +/** @brief Matrix read-only iterator + */ +template +class MatConstIterator_ : public MatConstIterator +{ +public: + typedef _Tp value_type; + typedef ptrdiff_t difference_type; + typedef const _Tp* pointer; + typedef const _Tp& reference; + + typedef std::random_access_iterator_tag iterator_category; + + //! default constructor + MatConstIterator_(); + //! constructor that sets the iterator to the beginning of the matrix + MatConstIterator_(const Mat_<_Tp>* _m); + //! constructor that sets the iterator to the specified element of the matrix + MatConstIterator_(const Mat_<_Tp>* _m, int _row, int _col=0); + //! constructor that sets the iterator to the specified element of the matrix + MatConstIterator_(const Mat_<_Tp>* _m, Point _pt); + //! constructor that sets the iterator to the specified element of the matrix + MatConstIterator_(const Mat_<_Tp>* _m, const int* _idx); + //! copy constructor + MatConstIterator_(const MatConstIterator_& it); + + //! copy operator + MatConstIterator_& operator = (const MatConstIterator_& it); + //! returns the current matrix element + const _Tp& operator *() const; + //! returns the i-th matrix element, relative to the current + const _Tp& operator [](ptrdiff_t i) const; + + //! shifts the iterator forward by the specified number of elements + MatConstIterator_& operator += (ptrdiff_t ofs); + //! shifts the iterator backward by the specified number of elements + MatConstIterator_& operator -= (ptrdiff_t ofs); + //! decrements the iterator + MatConstIterator_& operator --(); + //! decrements the iterator + MatConstIterator_ operator --(int); + //! increments the iterator + MatConstIterator_& operator ++(); + //! increments the iterator + MatConstIterator_ operator ++(int); + //! returns the current iterator position + Point pos() const; +}; + + + +//////////////////////////////////// MatIterator_ //////////////////////////////////// + +/** @brief Matrix read-write iterator +*/ +template +class MatIterator_ : public MatConstIterator_<_Tp> +{ +public: + typedef _Tp* pointer; + typedef _Tp& reference; + + typedef std::random_access_iterator_tag iterator_category; + + //! the default constructor + MatIterator_(); + //! constructor that sets the iterator to the beginning of the matrix + MatIterator_(Mat_<_Tp>* _m); + //! constructor that sets the iterator to the specified element of the matrix + MatIterator_(Mat_<_Tp>* _m, int _row, int _col=0); + //! constructor that sets the iterator to the specified element of the matrix + MatIterator_(Mat_<_Tp>* _m, Point _pt); + //! constructor that sets the iterator to the specified element of the matrix + MatIterator_(Mat_<_Tp>* _m, const int* _idx); + //! copy constructor + MatIterator_(const MatIterator_& it); + //! copy operator + MatIterator_& operator = (const MatIterator_<_Tp>& it ); + + //! returns the current matrix element + _Tp& operator *() const; + //! returns the i-th matrix element, relative to the current + _Tp& operator [](ptrdiff_t i) const; + + //! shifts the iterator forward by the specified number of elements + MatIterator_& operator += (ptrdiff_t ofs); + //! shifts the iterator backward by the specified number of elements + MatIterator_& operator -= (ptrdiff_t ofs); + //! decrements the iterator + MatIterator_& operator --(); + //! decrements the iterator + MatIterator_ operator --(int); + //! increments the iterator + MatIterator_& operator ++(); + //! increments the iterator + MatIterator_ operator ++(int); +}; + + + +/////////////////////////////// SparseMatConstIterator /////////////////////////////// + +/** @brief Read-Only Sparse Matrix Iterator. + + Here is how to use the iterator to compute the sum of floating-point sparse matrix elements: + + \code + SparseMatConstIterator it = m.begin(), it_end = m.end(); + double s = 0; + CV_Assert( m.type() == CV_32F ); + for( ; it != it_end; ++it ) + s += it.value(); + \endcode +*/ +class CV_EXPORTS SparseMatConstIterator +{ +public: + //! the default constructor + SparseMatConstIterator(); + //! the full constructor setting the iterator to the first sparse matrix element + SparseMatConstIterator(const SparseMat* _m); + //! the copy constructor + SparseMatConstIterator(const SparseMatConstIterator& it); + + //! the assignment operator + SparseMatConstIterator& operator = (const SparseMatConstIterator& it); + + //! template method returning the current matrix element + template const _Tp& value() const; + //! returns the current node of the sparse matrix. it.node->idx is the current element index + const SparseMat::Node* node() const; + + //! moves iterator to the previous element + SparseMatConstIterator& operator --(); + //! moves iterator to the previous element + SparseMatConstIterator operator --(int); + //! moves iterator to the next element + SparseMatConstIterator& operator ++(); + //! moves iterator to the next element + SparseMatConstIterator operator ++(int); + + //! moves iterator to the element after the last element + void seekEnd(); + + const SparseMat* m; + size_t hashidx; + uchar* ptr; +}; + + + +////////////////////////////////// SparseMatIterator ///////////////////////////////// + +/** @brief Read-write Sparse Matrix Iterator + + The class is similar to cv::SparseMatConstIterator, + but can be used for in-place modification of the matrix elements. +*/ +class CV_EXPORTS SparseMatIterator : public SparseMatConstIterator +{ +public: + //! the default constructor + SparseMatIterator(); + //! the full constructor setting the iterator to the first sparse matrix element + SparseMatIterator(SparseMat* _m); + //! the full constructor setting the iterator to the specified sparse matrix element + SparseMatIterator(SparseMat* _m, const int* idx); + //! the copy constructor + SparseMatIterator(const SparseMatIterator& it); + + //! the assignment operator + SparseMatIterator& operator = (const SparseMatIterator& it); + //! returns read-write reference to the current sparse matrix element + template _Tp& value() const; + //! returns pointer to the current sparse matrix node. it.node->idx is the index of the current element (do not modify it!) + SparseMat::Node* node() const; + + //! moves iterator to the next element + SparseMatIterator& operator ++(); + //! moves iterator to the next element + SparseMatIterator operator ++(int); +}; + + + +/////////////////////////////// SparseMatConstIterator_ ////////////////////////////// + +/** @brief Template Read-Only Sparse Matrix Iterator Class. + + This is the derived from SparseMatConstIterator class that + introduces more convenient operator *() for accessing the current element. +*/ +template class SparseMatConstIterator_ : public SparseMatConstIterator +{ +public: + + typedef std::forward_iterator_tag iterator_category; + + //! the default constructor + SparseMatConstIterator_(); + //! the full constructor setting the iterator to the first sparse matrix element + SparseMatConstIterator_(const SparseMat_<_Tp>* _m); + SparseMatConstIterator_(const SparseMat* _m); + //! the copy constructor + SparseMatConstIterator_(const SparseMatConstIterator_& it); + + //! the assignment operator + SparseMatConstIterator_& operator = (const SparseMatConstIterator_& it); + //! the element access operator + const _Tp& operator *() const; + + //! moves iterator to the next element + SparseMatConstIterator_& operator ++(); + //! moves iterator to the next element + SparseMatConstIterator_ operator ++(int); +}; + + + +///////////////////////////////// SparseMatIterator_ ///////////////////////////////// + +/** @brief Template Read-Write Sparse Matrix Iterator Class. + + This is the derived from cv::SparseMatConstIterator_ class that + introduces more convenient operator *() for accessing the current element. +*/ +template class SparseMatIterator_ : public SparseMatConstIterator_<_Tp> +{ +public: + + typedef std::forward_iterator_tag iterator_category; + + //! the default constructor + SparseMatIterator_(); + //! the full constructor setting the iterator to the first sparse matrix element + SparseMatIterator_(SparseMat_<_Tp>* _m); + SparseMatIterator_(SparseMat* _m); + //! the copy constructor + SparseMatIterator_(const SparseMatIterator_& it); + + //! the assignment operator + SparseMatIterator_& operator = (const SparseMatIterator_& it); + //! returns the reference to the current element + _Tp& operator *() const; + + //! moves the iterator to the next element + SparseMatIterator_& operator ++(); + //! moves the iterator to the next element + SparseMatIterator_ operator ++(int); +}; + + + +/////////////////////////////////// NAryMatIterator ////////////////////////////////// + +/** @brief n-ary multi-dimensional array iterator. + +Use the class to implement unary, binary, and, generally, n-ary element-wise operations on +multi-dimensional arrays. Some of the arguments of an n-ary function may be continuous arrays, some +may be not. It is possible to use conventional MatIterator 's for each array but incrementing all of +the iterators after each small operations may be a big overhead. In this case consider using +NAryMatIterator to iterate through several matrices simultaneously as long as they have the same +geometry (dimensionality and all the dimension sizes are the same). On each iteration `it.planes[0]`, +`it.planes[1]`,... will be the slices of the corresponding matrices. + +The example below illustrates how you can compute a normalized and threshold 3D color histogram: +@code + void computeNormalizedColorHist(const Mat& image, Mat& hist, int N, double minProb) + { + const int histSize[] = {N, N, N}; + + // make sure that the histogram has a proper size and type + hist.create(3, histSize, CV_32F); + + // and clear it + hist = Scalar(0); + + // the loop below assumes that the image + // is a 8-bit 3-channel. check it. + CV_Assert(image.type() == CV_8UC3); + MatConstIterator_ it = image.begin(), + it_end = image.end(); + for( ; it != it_end; ++it ) + { + const Vec3b& pix = *it; + hist.at(pix[0]*N/256, pix[1]*N/256, pix[2]*N/256) += 1.f; + } + + minProb *= image.rows*image.cols; + + // initialize iterator (the style is different from STL). + // after initialization the iterator will contain + // the number of slices or planes the iterator will go through. + // it simultaneously increments iterators for several matrices + // supplied as a null terminated list of pointers + const Mat* arrays[] = {&hist, 0}; + Mat planes[1]; + NAryMatIterator itNAry(arrays, planes, 1); + double s = 0; + // iterate through the matrix. on each iteration + // itNAry.planes[i] (of type Mat) will be set to the current plane + // of the i-th n-dim matrix passed to the iterator constructor. + for(int p = 0; p < itNAry.nplanes; p++, ++itNAry) + { + threshold(itNAry.planes[0], itNAry.planes[0], minProb, 0, THRESH_TOZERO); + s += sum(itNAry.planes[0])[0]; + } + + s = 1./s; + itNAry = NAryMatIterator(arrays, planes, 1); + for(int p = 0; p < itNAry.nplanes; p++, ++itNAry) + itNAry.planes[0] *= s; + } +@endcode + */ +class CV_EXPORTS NAryMatIterator +{ +public: + //! the default constructor + NAryMatIterator(); + //! the full constructor taking arbitrary number of n-dim matrices + NAryMatIterator(const Mat** arrays, uchar** ptrs, int narrays=-1); + //! the full constructor taking arbitrary number of n-dim matrices + NAryMatIterator(const Mat** arrays, Mat* planes, int narrays=-1); + //! the separate iterator initialization method + void init(const Mat** arrays, Mat* planes, uchar** ptrs, int narrays=-1); + + //! proceeds to the next plane of every iterated matrix + NAryMatIterator& operator ++(); + //! proceeds to the next plane of every iterated matrix (postfix increment operator) + NAryMatIterator operator ++(int); + + //! the iterated arrays + const Mat** arrays; + //! the current planes + Mat* planes; + //! data pointers + uchar** ptrs; + //! the number of arrays + int narrays; + //! the number of hyper-planes that the iterator steps through + size_t nplanes; + //! the size of each segment (in elements) + size_t size; +protected: + int iterdepth; + size_t idx; +}; + + + +///////////////////////////////// Matrix Expressions ///////////////////////////////// + +class CV_EXPORTS MatOp +{ +public: + MatOp(); + virtual ~MatOp(); + + virtual bool elementWise(const MatExpr& expr) const; + virtual void assign(const MatExpr& expr, Mat& m, int type=-1) const = 0; + virtual void roi(const MatExpr& expr, const Range& rowRange, + const Range& colRange, MatExpr& res) const; + virtual void diag(const MatExpr& expr, int d, MatExpr& res) const; + virtual void augAssignAdd(const MatExpr& expr, Mat& m) const; + virtual void augAssignSubtract(const MatExpr& expr, Mat& m) const; + virtual void augAssignMultiply(const MatExpr& expr, Mat& m) const; + virtual void augAssignDivide(const MatExpr& expr, Mat& m) const; + virtual void augAssignAnd(const MatExpr& expr, Mat& m) const; + virtual void augAssignOr(const MatExpr& expr, Mat& m) const; + virtual void augAssignXor(const MatExpr& expr, Mat& m) const; + + virtual void add(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res) const; + virtual void add(const MatExpr& expr1, const Scalar& s, MatExpr& res) const; + + virtual void subtract(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res) const; + virtual void subtract(const Scalar& s, const MatExpr& expr, MatExpr& res) const; + + virtual void multiply(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res, double scale=1) const; + virtual void multiply(const MatExpr& expr1, double s, MatExpr& res) const; + + virtual void divide(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res, double scale=1) const; + virtual void divide(double s, const MatExpr& expr, MatExpr& res) const; + + virtual void abs(const MatExpr& expr, MatExpr& res) const; + + virtual void transpose(const MatExpr& expr, MatExpr& res) const; + virtual void matmul(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res) const; + virtual void invert(const MatExpr& expr, int method, MatExpr& res) const; + + virtual Size size(const MatExpr& expr) const; + virtual int type(const MatExpr& expr) const; +}; + +/** @brief Matrix expression representation +@anchor MatrixExpressions +This is a list of implemented matrix operations that can be combined in arbitrary complex +expressions (here A, B stand for matrices ( Mat ), s for a scalar ( Scalar ), alpha for a +real-valued scalar ( double )): +- Addition, subtraction, negation: `A+B`, `A-B`, `A+s`, `A-s`, `s+A`, `s-A`, `-A` +- Scaling: `A*alpha` +- Per-element multiplication and division: `A.mul(B)`, `A/B`, `alpha/A` +- Matrix multiplication: `A*B` +- Transposition: `A.t()` (means AT) +- Matrix inversion and pseudo-inversion, solving linear systems and least-squares problems: + `A.inv([method]) (~ A-1)`, `A.inv([method])*B (~ X: AX=B)` +- Comparison: `A cmpop B`, `A cmpop alpha`, `alpha cmpop A`, where *cmpop* is one of + `>`, `>=`, `==`, `!=`, `<=`, `<`. The result of comparison is an 8-bit single channel mask whose + elements are set to 255 (if the particular element or pair of elements satisfy the condition) or + 0. +- Bitwise logical operations: `A logicop B`, `A logicop s`, `s logicop A`, `~A`, where *logicop* is one of + `&`, `|`, `^`. +- Element-wise minimum and maximum: `min(A, B)`, `min(A, alpha)`, `max(A, B)`, `max(A, alpha)` +- Element-wise absolute value: `abs(A)` +- Cross-product, dot-product: `A.cross(B)`, `A.dot(B)` +- Any function of matrix or matrices and scalars that returns a matrix or a scalar, such as norm, + mean, sum, countNonZero, trace, determinant, repeat, and others. +- Matrix initializers ( Mat::eye(), Mat::zeros(), Mat::ones() ), matrix comma-separated + initializers, matrix constructors and operators that extract sub-matrices (see Mat description). +- Mat_() constructors to cast the result to the proper type. +@note Comma-separated initializers and probably some other operations may require additional +explicit Mat() or Mat_() constructor calls to resolve a possible ambiguity. + +Here are examples of matrix expressions: +@code + // compute pseudo-inverse of A, equivalent to A.inv(DECOMP_SVD) + SVD svd(A); + Mat pinvA = svd.vt.t()*Mat::diag(1./svd.w)*svd.u.t(); + + // compute the new vector of parameters in the Levenberg-Marquardt algorithm + x -= (A.t()*A + lambda*Mat::eye(A.cols,A.cols,A.type())).inv(DECOMP_CHOLESKY)*(A.t()*err); + + // sharpen image using "unsharp mask" algorithm + Mat blurred; double sigma = 1, threshold = 5, amount = 1; + GaussianBlur(img, blurred, Size(), sigma, sigma); + Mat lowContrastMask = abs(img - blurred) < threshold; + Mat sharpened = img*(1+amount) + blurred*(-amount); + img.copyTo(sharpened, lowContrastMask); +@endcode +*/ +class CV_EXPORTS MatExpr +{ +public: + MatExpr(); + explicit MatExpr(const Mat& m); + + MatExpr(const MatOp* _op, int _flags, const Mat& _a = Mat(), const Mat& _b = Mat(), + const Mat& _c = Mat(), double _alpha = 1, double _beta = 1, const Scalar& _s = Scalar()); + + operator Mat() const; + template operator Mat_<_Tp>() const; + + Size size() const; + int type() const; + + MatExpr row(int y) const; + MatExpr col(int x) const; + MatExpr diag(int d = 0) const; + MatExpr operator()( const Range& rowRange, const Range& colRange ) const; + MatExpr operator()( const Rect& roi ) const; + + MatExpr t() const; + MatExpr inv(int method = DECOMP_LU) const; + MatExpr mul(const MatExpr& e, double scale=1) const; + MatExpr mul(const Mat& m, double scale=1) const; + + Mat cross(const Mat& m) const; + double dot(const Mat& m) const; + + const MatOp* op; + int flags; + + Mat a, b, c; + double alpha, beta; + Scalar s; +}; + +//! @} core_basic + +//! @relates cv::MatExpr +//! @{ +CV_EXPORTS MatExpr operator + (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator + (const Mat& a, const Scalar& s); +CV_EXPORTS MatExpr operator + (const Scalar& s, const Mat& a); +CV_EXPORTS MatExpr operator + (const MatExpr& e, const Mat& m); +CV_EXPORTS MatExpr operator + (const Mat& m, const MatExpr& e); +CV_EXPORTS MatExpr operator + (const MatExpr& e, const Scalar& s); +CV_EXPORTS MatExpr operator + (const Scalar& s, const MatExpr& e); +CV_EXPORTS MatExpr operator + (const MatExpr& e1, const MatExpr& e2); + +CV_EXPORTS MatExpr operator - (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator - (const Mat& a, const Scalar& s); +CV_EXPORTS MatExpr operator - (const Scalar& s, const Mat& a); +CV_EXPORTS MatExpr operator - (const MatExpr& e, const Mat& m); +CV_EXPORTS MatExpr operator - (const Mat& m, const MatExpr& e); +CV_EXPORTS MatExpr operator - (const MatExpr& e, const Scalar& s); +CV_EXPORTS MatExpr operator - (const Scalar& s, const MatExpr& e); +CV_EXPORTS MatExpr operator - (const MatExpr& e1, const MatExpr& e2); + +CV_EXPORTS MatExpr operator - (const Mat& m); +CV_EXPORTS MatExpr operator - (const MatExpr& e); + +CV_EXPORTS MatExpr operator * (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator * (const Mat& a, double s); +CV_EXPORTS MatExpr operator * (double s, const Mat& a); +CV_EXPORTS MatExpr operator * (const MatExpr& e, const Mat& m); +CV_EXPORTS MatExpr operator * (const Mat& m, const MatExpr& e); +CV_EXPORTS MatExpr operator * (const MatExpr& e, double s); +CV_EXPORTS MatExpr operator * (double s, const MatExpr& e); +CV_EXPORTS MatExpr operator * (const MatExpr& e1, const MatExpr& e2); + +CV_EXPORTS MatExpr operator / (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator / (const Mat& a, double s); +CV_EXPORTS MatExpr operator / (double s, const Mat& a); +CV_EXPORTS MatExpr operator / (const MatExpr& e, const Mat& m); +CV_EXPORTS MatExpr operator / (const Mat& m, const MatExpr& e); +CV_EXPORTS MatExpr operator / (const MatExpr& e, double s); +CV_EXPORTS MatExpr operator / (double s, const MatExpr& e); +CV_EXPORTS MatExpr operator / (const MatExpr& e1, const MatExpr& e2); + +CV_EXPORTS MatExpr operator < (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator < (const Mat& a, double s); +CV_EXPORTS MatExpr operator < (double s, const Mat& a); + +CV_EXPORTS MatExpr operator <= (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator <= (const Mat& a, double s); +CV_EXPORTS MatExpr operator <= (double s, const Mat& a); + +CV_EXPORTS MatExpr operator == (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator == (const Mat& a, double s); +CV_EXPORTS MatExpr operator == (double s, const Mat& a); + +CV_EXPORTS MatExpr operator != (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator != (const Mat& a, double s); +CV_EXPORTS MatExpr operator != (double s, const Mat& a); + +CV_EXPORTS MatExpr operator >= (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator >= (const Mat& a, double s); +CV_EXPORTS MatExpr operator >= (double s, const Mat& a); + +CV_EXPORTS MatExpr operator > (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator > (const Mat& a, double s); +CV_EXPORTS MatExpr operator > (double s, const Mat& a); + +CV_EXPORTS MatExpr operator & (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator & (const Mat& a, const Scalar& s); +CV_EXPORTS MatExpr operator & (const Scalar& s, const Mat& a); + +CV_EXPORTS MatExpr operator | (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator | (const Mat& a, const Scalar& s); +CV_EXPORTS MatExpr operator | (const Scalar& s, const Mat& a); + +CV_EXPORTS MatExpr operator ^ (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator ^ (const Mat& a, const Scalar& s); +CV_EXPORTS MatExpr operator ^ (const Scalar& s, const Mat& a); + +CV_EXPORTS MatExpr operator ~(const Mat& m); + +CV_EXPORTS MatExpr min(const Mat& a, const Mat& b); +CV_EXPORTS MatExpr min(const Mat& a, double s); +CV_EXPORTS MatExpr min(double s, const Mat& a); + +CV_EXPORTS MatExpr max(const Mat& a, const Mat& b); +CV_EXPORTS MatExpr max(const Mat& a, double s); +CV_EXPORTS MatExpr max(double s, const Mat& a); + +/** @brief Calculates an absolute value of each matrix element. + +abs is a meta-function that is expanded to one of absdiff or convertScaleAbs forms: +- C = abs(A-B) is equivalent to `absdiff(A, B, C)` +- C = abs(A) is equivalent to `absdiff(A, Scalar::all(0), C)` +- C = `Mat_ >(abs(A*alpha + beta))` is equivalent to `convertScaleAbs(A, C, alpha, +beta)` + +The output matrix has the same size and the same type as the input one except for the last case, +where C is depth=CV_8U . +@param m matrix. +@sa @ref MatrixExpressions, absdiff, convertScaleAbs + */ +CV_EXPORTS MatExpr abs(const Mat& m); +/** @overload +@param e matrix expression. +*/ +CV_EXPORTS MatExpr abs(const MatExpr& e); +//! @} relates cv::MatExpr + +} // cv + +#include "opencv2/core/mat.inl.hpp" + +#endif // OPENCV_CORE_MAT_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/mat.inl.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/mat.inl.hpp new file mode 100644 index 0000000000000000000000000000000000000000..a2e7923b39340e9368dc0e02085410271532b43b --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/mat.inl.hpp @@ -0,0 +1,4027 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_MATRIX_OPERATIONS_HPP +#define OPENCV_CORE_MATRIX_OPERATIONS_HPP + +#ifndef __cplusplus +# error mat.inl.hpp header must be compiled as C++ +#endif + +#ifdef _MSC_VER +#pragma warning( push ) +#pragma warning( disable: 4127 ) +#endif + +namespace cv +{ +CV__DEBUG_NS_BEGIN + + +//! @cond IGNORED + +////////////////////////// Custom (raw) type wrapper ////////////////////////// + +template static inline +int rawType() +{ + CV_StaticAssert(sizeof(_Tp) <= CV_CN_MAX, "sizeof(_Tp) is too large"); + const int elemSize = sizeof(_Tp); + return (int)CV_MAKETYPE(CV_8U, elemSize); +} + +//////////////////////// Input/Output Arrays //////////////////////// + +inline void _InputArray::init(int _flags, const void* _obj) +{ flags = _flags; obj = (void*)_obj; } + +inline void _InputArray::init(int _flags, const void* _obj, Size _sz) +{ flags = _flags; obj = (void*)_obj; sz = _sz; } + +inline void* _InputArray::getObj() const { return obj; } +inline int _InputArray::getFlags() const { return flags; } +inline Size _InputArray::getSz() const { return sz; } + +inline _InputArray::_InputArray() { init(NONE, 0); } +inline _InputArray::_InputArray(int _flags, void* _obj) { init(_flags, _obj); } +inline _InputArray::_InputArray(const Mat& m) { init(MAT+ACCESS_READ, &m); } +inline _InputArray::_InputArray(const std::vector& vec) { init(STD_VECTOR_MAT+ACCESS_READ, &vec); } +inline _InputArray::_InputArray(const UMat& m) { init(UMAT+ACCESS_READ, &m); } +inline _InputArray::_InputArray(const std::vector& vec) { init(STD_VECTOR_UMAT+ACCESS_READ, &vec); } + +template inline +_InputArray::_InputArray(const std::vector<_Tp>& vec) +{ init(FIXED_TYPE + STD_VECTOR + traits::Type<_Tp>::value + ACCESS_READ, &vec); } + +#ifdef CV_CXX_STD_ARRAY +template inline +_InputArray::_InputArray(const std::array<_Tp, _Nm>& arr) +{ init(FIXED_TYPE + FIXED_SIZE + STD_ARRAY + traits::Type<_Tp>::value + ACCESS_READ, arr.data(), Size(1, _Nm)); } + +template inline +_InputArray::_InputArray(const std::array& arr) +{ init(STD_ARRAY_MAT + ACCESS_READ, arr.data(), Size(1, _Nm)); } +#endif + +inline +_InputArray::_InputArray(const std::vector& vec) +{ init(FIXED_TYPE + STD_BOOL_VECTOR + traits::Type::value + ACCESS_READ, &vec); } + +template inline +_InputArray::_InputArray(const std::vector >& vec) +{ init(FIXED_TYPE + STD_VECTOR_VECTOR + traits::Type<_Tp>::value + ACCESS_READ, &vec); } + +inline +_InputArray::_InputArray(const std::vector >&) +{ CV_Error(Error::StsUnsupportedFormat, "std::vector > is not supported!\n"); } + +template inline +_InputArray::_InputArray(const std::vector >& vec) +{ init(FIXED_TYPE + STD_VECTOR_MAT + traits::Type<_Tp>::value + ACCESS_READ, &vec); } + +template inline +_InputArray::_InputArray(const Matx<_Tp, m, n>& mtx) +{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_READ, &mtx, Size(n, m)); } + +template inline +_InputArray::_InputArray(const _Tp* vec, int n) +{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_READ, vec, Size(n, 1)); } + +template inline +_InputArray::_InputArray(const Mat_<_Tp>& m) +{ init(FIXED_TYPE + MAT + traits::Type<_Tp>::value + ACCESS_READ, &m); } + +inline _InputArray::_InputArray(const double& val) +{ init(FIXED_TYPE + FIXED_SIZE + MATX + CV_64F + ACCESS_READ, &val, Size(1,1)); } + +inline _InputArray::_InputArray(const MatExpr& expr) +{ init(FIXED_TYPE + FIXED_SIZE + EXPR + ACCESS_READ, &expr); } + +inline _InputArray::_InputArray(const cuda::GpuMat& d_mat) +{ init(CUDA_GPU_MAT + ACCESS_READ, &d_mat); } + +inline _InputArray::_InputArray(const std::vector& d_mat) +{ init(STD_VECTOR_CUDA_GPU_MAT + ACCESS_READ, &d_mat);} + +inline _InputArray::_InputArray(const ogl::Buffer& buf) +{ init(OPENGL_BUFFER + ACCESS_READ, &buf); } + +inline _InputArray::_InputArray(const cuda::HostMem& cuda_mem) +{ init(CUDA_HOST_MEM + ACCESS_READ, &cuda_mem); } + +template inline +_InputArray _InputArray::rawIn(const std::vector<_Tp>& vec) +{ + _InputArray v; + v.flags = _InputArray::FIXED_TYPE + _InputArray::STD_VECTOR + rawType<_Tp>() + ACCESS_READ; + v.obj = (void*)&vec; + return v; +} + +#ifdef CV_CXX_STD_ARRAY +template inline +_InputArray _InputArray::rawIn(const std::array<_Tp, _Nm>& arr) +{ + _InputArray v; + v.flags = FIXED_TYPE + FIXED_SIZE + STD_ARRAY + traits::Type<_Tp>::value + ACCESS_READ; + v.obj = (void*)arr.data(); + v.sz = Size(1, _Nm); + return v; +} +#endif + +inline _InputArray::~_InputArray() {} + +inline Mat _InputArray::getMat(int i) const +{ + if( kind() == MAT && i < 0 ) + return *(const Mat*)obj; + return getMat_(i); +} + +inline bool _InputArray::isMat() const { return kind() == _InputArray::MAT; } +inline bool _InputArray::isUMat() const { return kind() == _InputArray::UMAT; } +inline bool _InputArray::isMatVector() const { return kind() == _InputArray::STD_VECTOR_MAT; } +inline bool _InputArray::isUMatVector() const { return kind() == _InputArray::STD_VECTOR_UMAT; } +inline bool _InputArray::isMatx() const { return kind() == _InputArray::MATX; } +inline bool _InputArray::isVector() const { return kind() == _InputArray::STD_VECTOR || + kind() == _InputArray::STD_BOOL_VECTOR || + kind() == _InputArray::STD_ARRAY; } +inline bool _InputArray::isGpuMat() const { return kind() == _InputArray::CUDA_GPU_MAT; } +inline bool _InputArray::isGpuMatVector() const { return kind() == _InputArray::STD_VECTOR_CUDA_GPU_MAT; } + +//////////////////////////////////////////////////////////////////////////////////////// + +inline _OutputArray::_OutputArray() { init(ACCESS_WRITE, 0); } +inline _OutputArray::_OutputArray(int _flags, void* _obj) { init(_flags|ACCESS_WRITE, _obj); } +inline _OutputArray::_OutputArray(Mat& m) { init(MAT+ACCESS_WRITE, &m); } +inline _OutputArray::_OutputArray(std::vector& vec) { init(STD_VECTOR_MAT+ACCESS_WRITE, &vec); } +inline _OutputArray::_OutputArray(UMat& m) { init(UMAT+ACCESS_WRITE, &m); } +inline _OutputArray::_OutputArray(std::vector& vec) { init(STD_VECTOR_UMAT+ACCESS_WRITE, &vec); } + +template inline +_OutputArray::_OutputArray(std::vector<_Tp>& vec) +{ init(FIXED_TYPE + STD_VECTOR + traits::Type<_Tp>::value + ACCESS_WRITE, &vec); } + +#ifdef CV_CXX_STD_ARRAY +template inline +_OutputArray::_OutputArray(std::array<_Tp, _Nm>& arr) +{ init(FIXED_TYPE + FIXED_SIZE + STD_ARRAY + traits::Type<_Tp>::value + ACCESS_WRITE, arr.data(), Size(1, _Nm)); } + +template inline +_OutputArray::_OutputArray(std::array& arr) +{ init(STD_ARRAY_MAT + ACCESS_WRITE, arr.data(), Size(1, _Nm)); } +#endif + +inline +_OutputArray::_OutputArray(std::vector&) +{ CV_Error(Error::StsUnsupportedFormat, "std::vector cannot be an output array\n"); } + +template inline +_OutputArray::_OutputArray(std::vector >& vec) +{ init(FIXED_TYPE + STD_VECTOR_VECTOR + traits::Type<_Tp>::value + ACCESS_WRITE, &vec); } + +inline +_OutputArray::_OutputArray(std::vector >&) +{ CV_Error(Error::StsUnsupportedFormat, "std::vector > cannot be an output array\n"); } + +template inline +_OutputArray::_OutputArray(std::vector >& vec) +{ init(FIXED_TYPE + STD_VECTOR_MAT + traits::Type<_Tp>::value + ACCESS_WRITE, &vec); } + +template inline +_OutputArray::_OutputArray(Mat_<_Tp>& m) +{ init(FIXED_TYPE + MAT + traits::Type<_Tp>::value + ACCESS_WRITE, &m); } + +template inline +_OutputArray::_OutputArray(Matx<_Tp, m, n>& mtx) +{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_WRITE, &mtx, Size(n, m)); } + +template inline +_OutputArray::_OutputArray(_Tp* vec, int n) +{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_WRITE, vec, Size(n, 1)); } + +template inline +_OutputArray::_OutputArray(const std::vector<_Tp>& vec) +{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR + traits::Type<_Tp>::value + ACCESS_WRITE, &vec); } + +#ifdef CV_CXX_STD_ARRAY +template inline +_OutputArray::_OutputArray(const std::array<_Tp, _Nm>& arr) +{ init(FIXED_TYPE + FIXED_SIZE + STD_ARRAY + traits::Type<_Tp>::value + ACCESS_WRITE, arr.data(), Size(1, _Nm)); } + +template inline +_OutputArray::_OutputArray(const std::array& arr) +{ init(FIXED_SIZE + STD_ARRAY_MAT + ACCESS_WRITE, arr.data(), Size(1, _Nm)); } +#endif + +template inline +_OutputArray::_OutputArray(const std::vector >& vec) +{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_VECTOR + traits::Type<_Tp>::value + ACCESS_WRITE, &vec); } + +template inline +_OutputArray::_OutputArray(const std::vector >& vec) +{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_MAT + traits::Type<_Tp>::value + ACCESS_WRITE, &vec); } + +template inline +_OutputArray::_OutputArray(const Mat_<_Tp>& m) +{ init(FIXED_TYPE + FIXED_SIZE + MAT + traits::Type<_Tp>::value + ACCESS_WRITE, &m); } + +template inline +_OutputArray::_OutputArray(const Matx<_Tp, m, n>& mtx) +{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_WRITE, &mtx, Size(n, m)); } + +template inline +_OutputArray::_OutputArray(const _Tp* vec, int n) +{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_WRITE, vec, Size(n, 1)); } + +inline _OutputArray::_OutputArray(cuda::GpuMat& d_mat) +{ init(CUDA_GPU_MAT + ACCESS_WRITE, &d_mat); } + +inline _OutputArray::_OutputArray(std::vector& d_mat) +{ init(STD_VECTOR_CUDA_GPU_MAT + ACCESS_WRITE, &d_mat);} + +inline _OutputArray::_OutputArray(ogl::Buffer& buf) +{ init(OPENGL_BUFFER + ACCESS_WRITE, &buf); } + +inline _OutputArray::_OutputArray(cuda::HostMem& cuda_mem) +{ init(CUDA_HOST_MEM + ACCESS_WRITE, &cuda_mem); } + +inline _OutputArray::_OutputArray(const Mat& m) +{ init(FIXED_TYPE + FIXED_SIZE + MAT + ACCESS_WRITE, &m); } + +inline _OutputArray::_OutputArray(const std::vector& vec) +{ init(FIXED_SIZE + STD_VECTOR_MAT + ACCESS_WRITE, &vec); } + +inline _OutputArray::_OutputArray(const UMat& m) +{ init(FIXED_TYPE + FIXED_SIZE + UMAT + ACCESS_WRITE, &m); } + +inline _OutputArray::_OutputArray(const std::vector& vec) +{ init(FIXED_SIZE + STD_VECTOR_UMAT + ACCESS_WRITE, &vec); } + +inline _OutputArray::_OutputArray(const cuda::GpuMat& d_mat) +{ init(FIXED_TYPE + FIXED_SIZE + CUDA_GPU_MAT + ACCESS_WRITE, &d_mat); } + + +inline _OutputArray::_OutputArray(const ogl::Buffer& buf) +{ init(FIXED_TYPE + FIXED_SIZE + OPENGL_BUFFER + ACCESS_WRITE, &buf); } + +inline _OutputArray::_OutputArray(const cuda::HostMem& cuda_mem) +{ init(FIXED_TYPE + FIXED_SIZE + CUDA_HOST_MEM + ACCESS_WRITE, &cuda_mem); } + +template inline +_OutputArray _OutputArray::rawOut(std::vector<_Tp>& vec) +{ + _OutputArray v; + v.flags = _InputArray::FIXED_TYPE + _InputArray::STD_VECTOR + rawType<_Tp>() + ACCESS_WRITE; + v.obj = (void*)&vec; + return v; +} + +#ifdef CV_CXX_STD_ARRAY +template inline +_OutputArray _OutputArray::rawOut(std::array<_Tp, _Nm>& arr) +{ + _OutputArray v; + v.flags = FIXED_TYPE + FIXED_SIZE + STD_ARRAY + traits::Type<_Tp>::value + ACCESS_WRITE; + v.obj = (void*)arr.data(); + v.sz = Size(1, _Nm); + return v; +} +#endif + +/////////////////////////////////////////////////////////////////////////////////////////// + +inline _InputOutputArray::_InputOutputArray() { init(ACCESS_RW, 0); } +inline _InputOutputArray::_InputOutputArray(int _flags, void* _obj) { init(_flags|ACCESS_RW, _obj); } +inline _InputOutputArray::_InputOutputArray(Mat& m) { init(MAT+ACCESS_RW, &m); } +inline _InputOutputArray::_InputOutputArray(std::vector& vec) { init(STD_VECTOR_MAT+ACCESS_RW, &vec); } +inline _InputOutputArray::_InputOutputArray(UMat& m) { init(UMAT+ACCESS_RW, &m); } +inline _InputOutputArray::_InputOutputArray(std::vector& vec) { init(STD_VECTOR_UMAT+ACCESS_RW, &vec); } + +template inline +_InputOutputArray::_InputOutputArray(std::vector<_Tp>& vec) +{ init(FIXED_TYPE + STD_VECTOR + traits::Type<_Tp>::value + ACCESS_RW, &vec); } + +#ifdef CV_CXX_STD_ARRAY +template inline +_InputOutputArray::_InputOutputArray(std::array<_Tp, _Nm>& arr) +{ init(FIXED_TYPE + FIXED_SIZE + STD_ARRAY + traits::Type<_Tp>::value + ACCESS_RW, arr.data(), Size(1, _Nm)); } + +template inline +_InputOutputArray::_InputOutputArray(std::array& arr) +{ init(STD_ARRAY_MAT + ACCESS_RW, arr.data(), Size(1, _Nm)); } +#endif + +inline _InputOutputArray::_InputOutputArray(std::vector&) +{ CV_Error(Error::StsUnsupportedFormat, "std::vector cannot be an input/output array\n"); } + +template inline +_InputOutputArray::_InputOutputArray(std::vector >& vec) +{ init(FIXED_TYPE + STD_VECTOR_VECTOR + traits::Type<_Tp>::value + ACCESS_RW, &vec); } + +template inline +_InputOutputArray::_InputOutputArray(std::vector >& vec) +{ init(FIXED_TYPE + STD_VECTOR_MAT + traits::Type<_Tp>::value + ACCESS_RW, &vec); } + +template inline +_InputOutputArray::_InputOutputArray(Mat_<_Tp>& m) +{ init(FIXED_TYPE + MAT + traits::Type<_Tp>::value + ACCESS_RW, &m); } + +template inline +_InputOutputArray::_InputOutputArray(Matx<_Tp, m, n>& mtx) +{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_RW, &mtx, Size(n, m)); } + +template inline +_InputOutputArray::_InputOutputArray(_Tp* vec, int n) +{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_RW, vec, Size(n, 1)); } + +template inline +_InputOutputArray::_InputOutputArray(const std::vector<_Tp>& vec) +{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR + traits::Type<_Tp>::value + ACCESS_RW, &vec); } + +#ifdef CV_CXX_STD_ARRAY +template inline +_InputOutputArray::_InputOutputArray(const std::array<_Tp, _Nm>& arr) +{ init(FIXED_TYPE + FIXED_SIZE + STD_ARRAY + traits::Type<_Tp>::value + ACCESS_RW, arr.data(), Size(1, _Nm)); } + +template inline +_InputOutputArray::_InputOutputArray(const std::array& arr) +{ init(FIXED_SIZE + STD_ARRAY_MAT + ACCESS_RW, arr.data(), Size(1, _Nm)); } +#endif + +template inline +_InputOutputArray::_InputOutputArray(const std::vector >& vec) +{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_VECTOR + traits::Type<_Tp>::value + ACCESS_RW, &vec); } + +template inline +_InputOutputArray::_InputOutputArray(const std::vector >& vec) +{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_MAT + traits::Type<_Tp>::value + ACCESS_RW, &vec); } + +template inline +_InputOutputArray::_InputOutputArray(const Mat_<_Tp>& m) +{ init(FIXED_TYPE + FIXED_SIZE + MAT + traits::Type<_Tp>::value + ACCESS_RW, &m); } + +template inline +_InputOutputArray::_InputOutputArray(const Matx<_Tp, m, n>& mtx) +{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_RW, &mtx, Size(n, m)); } + +template inline +_InputOutputArray::_InputOutputArray(const _Tp* vec, int n) +{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_RW, vec, Size(n, 1)); } + +inline _InputOutputArray::_InputOutputArray(cuda::GpuMat& d_mat) +{ init(CUDA_GPU_MAT + ACCESS_RW, &d_mat); } + +inline _InputOutputArray::_InputOutputArray(ogl::Buffer& buf) +{ init(OPENGL_BUFFER + ACCESS_RW, &buf); } + +inline _InputOutputArray::_InputOutputArray(cuda::HostMem& cuda_mem) +{ init(CUDA_HOST_MEM + ACCESS_RW, &cuda_mem); } + +inline _InputOutputArray::_InputOutputArray(const Mat& m) +{ init(FIXED_TYPE + FIXED_SIZE + MAT + ACCESS_RW, &m); } + +inline _InputOutputArray::_InputOutputArray(const std::vector& vec) +{ init(FIXED_SIZE + STD_VECTOR_MAT + ACCESS_RW, &vec); } + +inline _InputOutputArray::_InputOutputArray(const UMat& m) +{ init(FIXED_TYPE + FIXED_SIZE + UMAT + ACCESS_RW, &m); } + +inline _InputOutputArray::_InputOutputArray(const std::vector& vec) +{ init(FIXED_SIZE + STD_VECTOR_UMAT + ACCESS_RW, &vec); } + +inline _InputOutputArray::_InputOutputArray(const cuda::GpuMat& d_mat) +{ init(FIXED_TYPE + FIXED_SIZE + CUDA_GPU_MAT + ACCESS_RW, &d_mat); } + +inline _InputOutputArray::_InputOutputArray(const std::vector& d_mat) +{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_CUDA_GPU_MAT + ACCESS_RW, &d_mat);} + +template<> inline _InputOutputArray::_InputOutputArray(std::vector& d_mat) +{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_CUDA_GPU_MAT + ACCESS_RW, &d_mat);} + +inline _InputOutputArray::_InputOutputArray(const ogl::Buffer& buf) +{ init(FIXED_TYPE + FIXED_SIZE + OPENGL_BUFFER + ACCESS_RW, &buf); } + +inline _InputOutputArray::_InputOutputArray(const cuda::HostMem& cuda_mem) +{ init(FIXED_TYPE + FIXED_SIZE + CUDA_HOST_MEM + ACCESS_RW, &cuda_mem); } + +template inline +_InputOutputArray _InputOutputArray::rawInOut(std::vector<_Tp>& vec) +{ + _InputOutputArray v; + v.flags = _InputArray::FIXED_TYPE + _InputArray::STD_VECTOR + rawType<_Tp>() + ACCESS_RW; + v.obj = (void*)&vec; + return v; +} + +#ifdef CV_CXX_STD_ARRAY +template inline +_InputOutputArray _InputOutputArray::rawInOut(std::array<_Tp, _Nm>& arr) +{ + _InputOutputArray v; + v.flags = FIXED_TYPE + FIXED_SIZE + STD_ARRAY + traits::Type<_Tp>::value + ACCESS_RW; + v.obj = (void*)arr.data(); + v.sz = Size(1, _Nm); + return v; +} +#endif + + +template static inline _InputArray rawIn(_Tp& v) { return _InputArray::rawIn(v); } +template static inline _OutputArray rawOut(_Tp& v) { return _OutputArray::rawOut(v); } +template static inline _InputOutputArray rawInOut(_Tp& v) { return _InputOutputArray::rawInOut(v); } + +CV__DEBUG_NS_END + +//////////////////////////////////////////// Mat ////////////////////////////////////////// + +inline +Mat::Mat() + : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), + datalimit(0), allocator(0), u(0), size(&rows), step(0) +{} + +inline +Mat::Mat(int _rows, int _cols, int _type) + : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), + datalimit(0), allocator(0), u(0), size(&rows), step(0) +{ + create(_rows, _cols, _type); +} + +inline +Mat::Mat(int _rows, int _cols, int _type, const Scalar& _s) + : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), + datalimit(0), allocator(0), u(0), size(&rows), step(0) +{ + create(_rows, _cols, _type); + *this = _s; +} + +inline +Mat::Mat(Size _sz, int _type) + : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), + datalimit(0), allocator(0), u(0), size(&rows), step(0) +{ + create( _sz.height, _sz.width, _type ); +} + +inline +Mat::Mat(Size _sz, int _type, const Scalar& _s) + : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), + datalimit(0), allocator(0), u(0), size(&rows), step(0) +{ + create(_sz.height, _sz.width, _type); + *this = _s; +} + +inline +Mat::Mat(int _dims, const int* _sz, int _type) + : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), + datalimit(0), allocator(0), u(0), size(&rows), step(0) +{ + create(_dims, _sz, _type); +} + +inline +Mat::Mat(int _dims, const int* _sz, int _type, const Scalar& _s) + : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), + datalimit(0), allocator(0), u(0), size(&rows), step(0) +{ + create(_dims, _sz, _type); + *this = _s; +} + +inline +Mat::Mat(const std::vector& _sz, int _type) + : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), + datalimit(0), allocator(0), u(0), size(&rows), step(0) +{ + create(_sz, _type); +} + +inline +Mat::Mat(const std::vector& _sz, int _type, const Scalar& _s) + : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), + datalimit(0), allocator(0), u(0), size(&rows), step(0) +{ + create(_sz, _type); + *this = _s; +} + +inline +Mat::Mat(const Mat& m) + : flags(m.flags), dims(m.dims), rows(m.rows), cols(m.cols), data(m.data), + datastart(m.datastart), dataend(m.dataend), datalimit(m.datalimit), allocator(m.allocator), + u(m.u), size(&rows), step(0) +{ + if( u ) + CV_XADD(&u->refcount, 1); + if( m.dims <= 2 ) + { + step[0] = m.step[0]; step[1] = m.step[1]; + } + else + { + dims = 0; + copySize(m); + } +} + +inline +Mat::Mat(int _rows, int _cols, int _type, void* _data, size_t _step) + : flags(MAGIC_VAL + (_type & TYPE_MASK)), dims(2), rows(_rows), cols(_cols), + data((uchar*)_data), datastart((uchar*)_data), dataend(0), datalimit(0), + allocator(0), u(0), size(&rows) +{ + CV_Assert(total() == 0 || data != NULL); + + size_t esz = CV_ELEM_SIZE(_type), esz1 = CV_ELEM_SIZE1(_type); + size_t minstep = cols * esz; + if( _step == AUTO_STEP ) + { + _step = minstep; + } + else + { + CV_DbgAssert( _step >= minstep ); + if (_step % esz1 != 0) + { + CV_Error(Error::BadStep, "Step must be a multiple of esz1"); + } + } + step[0] = _step; + step[1] = esz; + datalimit = datastart + _step * rows; + dataend = datalimit - _step + minstep; + updateContinuityFlag(); +} + +inline +Mat::Mat(Size _sz, int _type, void* _data, size_t _step) + : flags(MAGIC_VAL + (_type & TYPE_MASK)), dims(2), rows(_sz.height), cols(_sz.width), + data((uchar*)_data), datastart((uchar*)_data), dataend(0), datalimit(0), + allocator(0), u(0), size(&rows) +{ + CV_Assert(total() == 0 || data != NULL); + + size_t esz = CV_ELEM_SIZE(_type), esz1 = CV_ELEM_SIZE1(_type); + size_t minstep = cols*esz; + if( _step == AUTO_STEP ) + { + _step = minstep; + } + else + { + CV_DbgAssert( _step >= minstep ); + + if (_step % esz1 != 0) + { + CV_Error(Error::BadStep, "Step must be a multiple of esz1"); + } + } + step[0] = _step; + step[1] = esz; + datalimit = datastart + _step*rows; + dataend = datalimit - _step + minstep; + updateContinuityFlag(); +} + +template inline +Mat::Mat(const std::vector<_Tp>& vec, bool copyData) + : flags(MAGIC_VAL | traits::Type<_Tp>::value | CV_MAT_CONT_FLAG), dims(2), rows((int)vec.size()), + cols(1), data(0), datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows), step(0) +{ + if(vec.empty()) + return; + if( !copyData ) + { + step[0] = step[1] = sizeof(_Tp); + datastart = data = (uchar*)&vec[0]; + datalimit = dataend = datastart + rows * step[0]; + } + else + Mat((int)vec.size(), 1, traits::Type<_Tp>::value, (uchar*)&vec[0]).copyTo(*this); +} + +#ifdef CV_CXX11 +template inline +Mat::Mat(const std::initializer_list<_Tp> list) + : Mat() +{ + CV_Assert(list.size() != 0); + Mat((int)list.size(), 1, traits::Type<_Tp>::value, (uchar*)list.begin()).copyTo(*this); +} + +template inline +Mat::Mat(const std::initializer_list sizes, const std::initializer_list<_Tp> list) + : Mat() +{ + size_t size_total = 1; + for(auto s : sizes) + size_total *= s; + CV_Assert(list.size() != 0); + CV_Assert(size_total == list.size()); + Mat((int)sizes.size(), (int*)sizes.begin(), traits::Type<_Tp>::value, (uchar*)list.begin()).copyTo(*this); +} +#endif + +#ifdef CV_CXX_STD_ARRAY +template inline +Mat::Mat(const std::array<_Tp, _Nm>& arr, bool copyData) + : flags(MAGIC_VAL | traits::Type<_Tp>::value | CV_MAT_CONT_FLAG), dims(2), rows((int)arr.size()), + cols(1), data(0), datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows), step(0) +{ + if(arr.empty()) + return; + if( !copyData ) + { + step[0] = step[1] = sizeof(_Tp); + datastart = data = (uchar*)arr.data(); + datalimit = dataend = datastart + rows * step[0]; + } + else + Mat((int)arr.size(), 1, traits::Type<_Tp>::value, (uchar*)arr.data()).copyTo(*this); +} +#endif + +template inline +Mat::Mat(const Vec<_Tp, n>& vec, bool copyData) + : flags(MAGIC_VAL | traits::Type<_Tp>::value | CV_MAT_CONT_FLAG), dims(2), rows(n), cols(1), data(0), + datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows), step(0) +{ + if( !copyData ) + { + step[0] = step[1] = sizeof(_Tp); + datastart = data = (uchar*)vec.val; + datalimit = dataend = datastart + rows * step[0]; + } + else + Mat(n, 1, traits::Type<_Tp>::value, (void*)vec.val).copyTo(*this); +} + + +template inline +Mat::Mat(const Matx<_Tp,m,n>& M, bool copyData) + : flags(MAGIC_VAL | traits::Type<_Tp>::value | CV_MAT_CONT_FLAG), dims(2), rows(m), cols(n), data(0), + datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows), step(0) +{ + if( !copyData ) + { + step[0] = cols * sizeof(_Tp); + step[1] = sizeof(_Tp); + datastart = data = (uchar*)M.val; + datalimit = dataend = datastart + rows * step[0]; + } + else + Mat(m, n, traits::Type<_Tp>::value, (uchar*)M.val).copyTo(*this); +} + +template inline +Mat::Mat(const Point_<_Tp>& pt, bool copyData) + : flags(MAGIC_VAL | traits::Type<_Tp>::value | CV_MAT_CONT_FLAG), dims(2), rows(2), cols(1), data(0), + datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows), step(0) +{ + if( !copyData ) + { + step[0] = step[1] = sizeof(_Tp); + datastart = data = (uchar*)&pt.x; + datalimit = dataend = datastart + rows * step[0]; + } + else + { + create(2, 1, traits::Type<_Tp>::value); + ((_Tp*)data)[0] = pt.x; + ((_Tp*)data)[1] = pt.y; + } +} + +template inline +Mat::Mat(const Point3_<_Tp>& pt, bool copyData) + : flags(MAGIC_VAL | traits::Type<_Tp>::value | CV_MAT_CONT_FLAG), dims(2), rows(3), cols(1), data(0), + datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows), step(0) +{ + if( !copyData ) + { + step[0] = step[1] = sizeof(_Tp); + datastart = data = (uchar*)&pt.x; + datalimit = dataend = datastart + rows * step[0]; + } + else + { + create(3, 1, traits::Type<_Tp>::value); + ((_Tp*)data)[0] = pt.x; + ((_Tp*)data)[1] = pt.y; + ((_Tp*)data)[2] = pt.z; + } +} + +template inline +Mat::Mat(const MatCommaInitializer_<_Tp>& commaInitializer) + : flags(MAGIC_VAL | traits::Type<_Tp>::value | CV_MAT_CONT_FLAG), dims(0), rows(0), cols(0), data(0), + datastart(0), dataend(0), allocator(0), u(0), size(&rows) +{ + *this = commaInitializer.operator Mat_<_Tp>(); +} + +inline +Mat::~Mat() +{ + release(); + if( step.p != step.buf ) + fastFree(step.p); +} + +inline +Mat& Mat::operator = (const Mat& m) +{ + if( this != &m ) + { + if( m.u ) + CV_XADD(&m.u->refcount, 1); + release(); + flags = m.flags; + if( dims <= 2 && m.dims <= 2 ) + { + dims = m.dims; + rows = m.rows; + cols = m.cols; + step[0] = m.step[0]; + step[1] = m.step[1]; + } + else + copySize(m); + data = m.data; + datastart = m.datastart; + dataend = m.dataend; + datalimit = m.datalimit; + allocator = m.allocator; + u = m.u; + } + return *this; +} + +inline +Mat Mat::row(int y) const +{ + return Mat(*this, Range(y, y + 1), Range::all()); +} + +inline +Mat Mat::col(int x) const +{ + return Mat(*this, Range::all(), Range(x, x + 1)); +} + +inline +Mat Mat::rowRange(int startrow, int endrow) const +{ + return Mat(*this, Range(startrow, endrow), Range::all()); +} + +inline +Mat Mat::rowRange(const Range& r) const +{ + return Mat(*this, r, Range::all()); +} + +inline +Mat Mat::colRange(int startcol, int endcol) const +{ + return Mat(*this, Range::all(), Range(startcol, endcol)); +} + +inline +Mat Mat::colRange(const Range& r) const +{ + return Mat(*this, Range::all(), r); +} + +inline +Mat Mat::clone() const +{ + Mat m; + copyTo(m); + return m; +} + +inline +void Mat::assignTo( Mat& m, int _type ) const +{ + if( _type < 0 ) + m = *this; + else + convertTo(m, _type); +} + +inline +void Mat::create(int _rows, int _cols, int _type) +{ + _type &= TYPE_MASK; + if( dims <= 2 && rows == _rows && cols == _cols && type() == _type && data ) + return; + int sz[] = {_rows, _cols}; + create(2, sz, _type); +} + +inline +void Mat::create(Size _sz, int _type) +{ + create(_sz.height, _sz.width, _type); +} + +inline +void Mat::addref() +{ + if( u ) + CV_XADD(&u->refcount, 1); +} + +inline +void Mat::release() +{ + if( u && CV_XADD(&u->refcount, -1) == 1 ) + deallocate(); + u = NULL; + datastart = dataend = datalimit = data = 0; + for(int i = 0; i < dims; i++) + size.p[i] = 0; +#ifdef _DEBUG + flags = MAGIC_VAL; + dims = rows = cols = 0; + if(step.p != step.buf) + { + fastFree(step.p); + step.p = step.buf; + size.p = &rows; + } +#endif +} + +inline +Mat Mat::operator()( Range _rowRange, Range _colRange ) const +{ + return Mat(*this, _rowRange, _colRange); +} + +inline +Mat Mat::operator()( const Rect& roi ) const +{ + return Mat(*this, roi); +} + +inline +Mat Mat::operator()(const Range* ranges) const +{ + return Mat(*this, ranges); +} + +inline +Mat Mat::operator()(const std::vector& ranges) const +{ + return Mat(*this, ranges); +} + +inline +bool Mat::isContinuous() const +{ + return (flags & CONTINUOUS_FLAG) != 0; +} + +inline +bool Mat::isSubmatrix() const +{ + return (flags & SUBMATRIX_FLAG) != 0; +} + +inline +size_t Mat::elemSize() const +{ + size_t res = dims > 0 ? step.p[dims - 1] : 0; + CV_DbgAssert(res != 0); + return res; +} + +inline +size_t Mat::elemSize1() const +{ + return CV_ELEM_SIZE1(flags); +} + +inline +int Mat::type() const +{ + return CV_MAT_TYPE(flags); +} + +inline +int Mat::depth() const +{ + return CV_MAT_DEPTH(flags); +} + +inline +int Mat::channels() const +{ + return CV_MAT_CN(flags); +} + +inline +size_t Mat::step1(int i) const +{ + return step.p[i] / elemSize1(); +} + +inline +bool Mat::empty() const +{ + return data == 0 || total() == 0 || dims == 0; +} + +inline +size_t Mat::total() const +{ + if( dims <= 2 ) + return (size_t)rows * cols; + size_t p = 1; + for( int i = 0; i < dims; i++ ) + p *= size[i]; + return p; +} + +inline +size_t Mat::total(int startDim, int endDim) const +{ + CV_Assert( 0 <= startDim && startDim <= endDim); + size_t p = 1; + int endDim_ = endDim <= dims ? endDim : dims; + for( int i = startDim; i < endDim_; i++ ) + p *= size[i]; + return p; +} + +inline +uchar* Mat::ptr(int y) +{ + CV_DbgAssert( y == 0 || (data && dims >= 1 && (unsigned)y < (unsigned)size.p[0]) ); + return data + step.p[0] * y; +} + +inline +const uchar* Mat::ptr(int y) const +{ + CV_DbgAssert( y == 0 || (data && dims >= 1 && (unsigned)y < (unsigned)size.p[0]) ); + return data + step.p[0] * y; +} + +template inline +_Tp* Mat::ptr(int y) +{ + CV_DbgAssert( y == 0 || (data && dims >= 1 && (unsigned)y < (unsigned)size.p[0]) ); + return (_Tp*)(data + step.p[0] * y); +} + +template inline +const _Tp* Mat::ptr(int y) const +{ + CV_DbgAssert( y == 0 || (data && dims >= 1 && (unsigned)y < (unsigned)size.p[0]) ); + return (const _Tp*)(data + step.p[0] * y); +} + +inline +uchar* Mat::ptr(int i0, int i1) +{ + CV_DbgAssert(dims >= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); + return data + i0 * step.p[0] + i1 * step.p[1]; +} + +inline +const uchar* Mat::ptr(int i0, int i1) const +{ + CV_DbgAssert(dims >= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); + return data + i0 * step.p[0] + i1 * step.p[1]; +} + +template inline +_Tp* Mat::ptr(int i0, int i1) +{ + CV_DbgAssert(dims >= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); + return (_Tp*)(data + i0 * step.p[0] + i1 * step.p[1]); +} + +template inline +const _Tp* Mat::ptr(int i0, int i1) const +{ + CV_DbgAssert(dims >= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); + return (const _Tp*)(data + i0 * step.p[0] + i1 * step.p[1]); +} + +inline +uchar* Mat::ptr(int i0, int i1, int i2) +{ + CV_DbgAssert(dims >= 3); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); + CV_DbgAssert((unsigned)i2 < (unsigned)size.p[2]); + return data + i0 * step.p[0] + i1 * step.p[1] + i2 * step.p[2]; +} + +inline +const uchar* Mat::ptr(int i0, int i1, int i2) const +{ + CV_DbgAssert(dims >= 3); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); + CV_DbgAssert((unsigned)i2 < (unsigned)size.p[2]); + return data + i0 * step.p[0] + i1 * step.p[1] + i2 * step.p[2]; +} + +template inline +_Tp* Mat::ptr(int i0, int i1, int i2) +{ + CV_DbgAssert(dims >= 3); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); + CV_DbgAssert((unsigned)i2 < (unsigned)size.p[2]); + return (_Tp*)(data + i0 * step.p[0] + i1 * step.p[1] + i2 * step.p[2]); +} + +template inline +const _Tp* Mat::ptr(int i0, int i1, int i2) const +{ + CV_DbgAssert(dims >= 3); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); + CV_DbgAssert((unsigned)i2 < (unsigned)size.p[2]); + return (const _Tp*)(data + i0 * step.p[0] + i1 * step.p[1] + i2 * step.p[2]); +} + +inline +uchar* Mat::ptr(const int* idx) +{ + int i, d = dims; + uchar* p = data; + CV_DbgAssert( d >= 1 && p ); + for( i = 0; i < d; i++ ) + { + CV_DbgAssert( (unsigned)idx[i] < (unsigned)size.p[i] ); + p += idx[i] * step.p[i]; + } + return p; +} + +inline +const uchar* Mat::ptr(const int* idx) const +{ + int i, d = dims; + uchar* p = data; + CV_DbgAssert( d >= 1 && p ); + for( i = 0; i < d; i++ ) + { + CV_DbgAssert( (unsigned)idx[i] < (unsigned)size.p[i] ); + p += idx[i] * step.p[i]; + } + return p; +} + +template inline +_Tp* Mat::ptr(const int* idx) +{ + int i, d = dims; + uchar* p = data; + CV_DbgAssert( d >= 1 && p ); + for( i = 0; i < d; i++ ) + { + CV_DbgAssert( (unsigned)idx[i] < (unsigned)size.p[i] ); + p += idx[i] * step.p[i]; + } + return (_Tp*)p; +} + +template inline +const _Tp* Mat::ptr(const int* idx) const +{ + int i, d = dims; + uchar* p = data; + CV_DbgAssert( d >= 1 && p ); + for( i = 0; i < d; i++ ) + { + CV_DbgAssert( (unsigned)idx[i] < (unsigned)size.p[i] ); + p += idx[i] * step.p[i]; + } + return (const _Tp*)p; +} + +template inline +_Tp& Mat::at(int i0, int i1) +{ + CV_DbgAssert(dims <= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)(i1 * DataType<_Tp>::channels) < (unsigned)(size.p[1] * channels())); + CV_DbgAssert(CV_ELEM_SIZE1(traits::Depth<_Tp>::value) == elemSize1()); + return ((_Tp*)(data + step.p[0] * i0))[i1]; +} + +template inline +const _Tp& Mat::at(int i0, int i1) const +{ + CV_DbgAssert(dims <= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)(i1 * DataType<_Tp>::channels) < (unsigned)(size.p[1] * channels())); + CV_DbgAssert(CV_ELEM_SIZE1(traits::Depth<_Tp>::value) == elemSize1()); + return ((const _Tp*)(data + step.p[0] * i0))[i1]; +} + +template inline +_Tp& Mat::at(Point pt) +{ + CV_DbgAssert(dims <= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)pt.y < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)(pt.x * DataType<_Tp>::channels) < (unsigned)(size.p[1] * channels())); + CV_DbgAssert(CV_ELEM_SIZE1(traits::Depth<_Tp>::value) == elemSize1()); + return ((_Tp*)(data + step.p[0] * pt.y))[pt.x]; +} + +template inline +const _Tp& Mat::at(Point pt) const +{ + CV_DbgAssert(dims <= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)pt.y < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)(pt.x * DataType<_Tp>::channels) < (unsigned)(size.p[1] * channels())); + CV_DbgAssert(CV_ELEM_SIZE1(traits::Depth<_Tp>::value) == elemSize1()); + return ((const _Tp*)(data + step.p[0] * pt.y))[pt.x]; +} + +template inline +_Tp& Mat::at(int i0) +{ + CV_DbgAssert(dims <= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)(size.p[0] * size.p[1])); + CV_DbgAssert(elemSize() == sizeof(_Tp)); + if( isContinuous() || size.p[0] == 1 ) + return ((_Tp*)data)[i0]; + if( size.p[1] == 1 ) + return *(_Tp*)(data + step.p[0] * i0); + int i = i0 / cols, j = i0 - i * cols; + return ((_Tp*)(data + step.p[0] * i))[j]; +} + +template inline +const _Tp& Mat::at(int i0) const +{ + CV_DbgAssert(dims <= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)(size.p[0] * size.p[1])); + CV_DbgAssert(elemSize() == sizeof(_Tp)); + if( isContinuous() || size.p[0] == 1 ) + return ((const _Tp*)data)[i0]; + if( size.p[1] == 1 ) + return *(const _Tp*)(data + step.p[0] * i0); + int i = i0 / cols, j = i0 - i * cols; + return ((const _Tp*)(data + step.p[0] * i))[j]; +} + +template inline +_Tp& Mat::at(int i0, int i1, int i2) +{ + CV_DbgAssert( elemSize() == sizeof(_Tp) ); + return *(_Tp*)ptr(i0, i1, i2); +} + +template inline +const _Tp& Mat::at(int i0, int i1, int i2) const +{ + CV_DbgAssert( elemSize() == sizeof(_Tp) ); + return *(const _Tp*)ptr(i0, i1, i2); +} + +template inline +_Tp& Mat::at(const int* idx) +{ + CV_DbgAssert( elemSize() == sizeof(_Tp) ); + return *(_Tp*)ptr(idx); +} + +template inline +const _Tp& Mat::at(const int* idx) const +{ + CV_DbgAssert( elemSize() == sizeof(_Tp) ); + return *(const _Tp*)ptr(idx); +} + +template inline +_Tp& Mat::at(const Vec& idx) +{ + CV_DbgAssert( elemSize() == sizeof(_Tp) ); + return *(_Tp*)ptr(idx.val); +} + +template inline +const _Tp& Mat::at(const Vec& idx) const +{ + CV_DbgAssert( elemSize() == sizeof(_Tp) ); + return *(const _Tp*)ptr(idx.val); +} + +template inline +MatConstIterator_<_Tp> Mat::begin() const +{ + CV_DbgAssert( elemSize() == sizeof(_Tp) ); + return MatConstIterator_<_Tp>((const Mat_<_Tp>*)this); +} + +template inline +MatConstIterator_<_Tp> Mat::end() const +{ + CV_DbgAssert( elemSize() == sizeof(_Tp) ); + MatConstIterator_<_Tp> it((const Mat_<_Tp>*)this); + it += total(); + return it; +} + +template inline +MatIterator_<_Tp> Mat::begin() +{ + CV_DbgAssert( elemSize() == sizeof(_Tp) ); + return MatIterator_<_Tp>((Mat_<_Tp>*)this); +} + +template inline +MatIterator_<_Tp> Mat::end() +{ + CV_DbgAssert( elemSize() == sizeof(_Tp) ); + MatIterator_<_Tp> it((Mat_<_Tp>*)this); + it += total(); + return it; +} + +template inline +void Mat::forEach(const Functor& operation) { + this->forEach_impl<_Tp>(operation); +} + +template inline +void Mat::forEach(const Functor& operation) const { + // call as not const + (const_cast(this))->forEach<_Tp>(operation); +} + +template inline +Mat::operator std::vector<_Tp>() const +{ + std::vector<_Tp> v; + copyTo(v); + return v; +} + +#ifdef CV_CXX_STD_ARRAY +template inline +Mat::operator std::array<_Tp, _Nm>() const +{ + std::array<_Tp, _Nm> v; + copyTo(v); + return v; +} +#endif + +template inline +Mat::operator Vec<_Tp, n>() const +{ + CV_Assert( data && dims <= 2 && (rows == 1 || cols == 1) && + rows + cols - 1 == n && channels() == 1 ); + + if( isContinuous() && type() == traits::Type<_Tp>::value ) + return Vec<_Tp, n>((_Tp*)data); + Vec<_Tp, n> v; + Mat tmp(rows, cols, traits::Type<_Tp>::value, v.val); + convertTo(tmp, tmp.type()); + return v; +} + +template inline +Mat::operator Matx<_Tp, m, n>() const +{ + CV_Assert( data && dims <= 2 && rows == m && cols == n && channels() == 1 ); + + if( isContinuous() && type() == traits::Type<_Tp>::value ) + return Matx<_Tp, m, n>((_Tp*)data); + Matx<_Tp, m, n> mtx; + Mat tmp(rows, cols, traits::Type<_Tp>::value, mtx.val); + convertTo(tmp, tmp.type()); + return mtx; +} + +template inline +void Mat::push_back(const _Tp& elem) +{ + if( !data ) + { + *this = Mat(1, 1, traits::Type<_Tp>::value, (void*)&elem).clone(); + return; + } + CV_Assert(traits::Type<_Tp>::value == type() && cols == 1 + /* && dims == 2 (cols == 1 implies dims == 2) */); + const uchar* tmp = dataend + step[0]; + if( !isSubmatrix() && isContinuous() && tmp <= datalimit ) + { + *(_Tp*)(data + (size.p[0]++) * step.p[0]) = elem; + dataend = tmp; + } + else + push_back_(&elem); +} + +template inline +void Mat::push_back(const Mat_<_Tp>& m) +{ + push_back((const Mat&)m); +} + +template<> inline +void Mat::push_back(const MatExpr& expr) +{ + push_back(static_cast(expr)); +} + + +template inline +void Mat::push_back(const std::vector<_Tp>& v) +{ + push_back(Mat(v)); +} + +#ifdef CV_CXX_MOVE_SEMANTICS + +inline +Mat::Mat(Mat&& m) + : flags(m.flags), dims(m.dims), rows(m.rows), cols(m.cols), data(m.data), + datastart(m.datastart), dataend(m.dataend), datalimit(m.datalimit), allocator(m.allocator), + u(m.u), size(&rows) +{ + if (m.dims <= 2) // move new step/size info + { + step[0] = m.step[0]; + step[1] = m.step[1]; + } + else + { + CV_DbgAssert(m.step.p != m.step.buf); + step.p = m.step.p; + size.p = m.size.p; + m.step.p = m.step.buf; + m.size.p = &m.rows; + } + m.flags = MAGIC_VAL; m.dims = m.rows = m.cols = 0; + m.data = NULL; m.datastart = NULL; m.dataend = NULL; m.datalimit = NULL; + m.allocator = NULL; + m.u = NULL; +} + +inline +Mat& Mat::operator = (Mat&& m) +{ + if (this == &m) + return *this; + + release(); + flags = m.flags; dims = m.dims; rows = m.rows; cols = m.cols; data = m.data; + datastart = m.datastart; dataend = m.dataend; datalimit = m.datalimit; allocator = m.allocator; + u = m.u; + if (step.p != step.buf) // release self step/size + { + fastFree(step.p); + step.p = step.buf; + size.p = &rows; + } + if (m.dims <= 2) // move new step/size info + { + step[0] = m.step[0]; + step[1] = m.step[1]; + } + else + { + CV_DbgAssert(m.step.p != m.step.buf); + step.p = m.step.p; + size.p = m.size.p; + m.step.p = m.step.buf; + m.size.p = &m.rows; + } + m.flags = MAGIC_VAL; m.dims = m.rows = m.cols = 0; + m.data = NULL; m.datastart = NULL; m.dataend = NULL; m.datalimit = NULL; + m.allocator = NULL; + m.u = NULL; + return *this; +} + +#endif + + +///////////////////////////// MatSize //////////////////////////// + +inline +MatSize::MatSize(int* _p) + : p(_p) {} + +inline +int MatSize::dims() const +{ + return (p - 1)[0]; +} + +inline +Size MatSize::operator()() const +{ + CV_DbgAssert(dims() <= 2); + return Size(p[1], p[0]); +} + +inline +const int& MatSize::operator[](int i) const +{ + CV_DbgAssert(i < dims()); +#ifdef __OPENCV_BUILD + CV_DbgAssert(i >= 0); +#endif + return p[i]; +} + +inline +int& MatSize::operator[](int i) +{ + CV_DbgAssert(i < dims()); +#ifdef __OPENCV_BUILD + CV_DbgAssert(i >= 0); +#endif + return p[i]; +} + +inline +MatSize::operator const int*() const +{ + return p; +} + +inline +bool MatSize::operator == (const MatSize& sz) const +{ + int d = dims(); + int dsz = sz.dims(); + if( d != dsz ) + return false; + if( d == 2 ) + return p[0] == sz.p[0] && p[1] == sz.p[1]; + + for( int i = 0; i < d; i++ ) + if( p[i] != sz.p[i] ) + return false; + return true; +} + +inline +bool MatSize::operator != (const MatSize& sz) const +{ + return !(*this == sz); +} + + + +///////////////////////////// MatStep //////////////////////////// + +inline +MatStep::MatStep() +{ + p = buf; p[0] = p[1] = 0; +} + +inline +MatStep::MatStep(size_t s) +{ + p = buf; p[0] = s; p[1] = 0; +} + +inline +const size_t& MatStep::operator[](int i) const +{ + return p[i]; +} + +inline +size_t& MatStep::operator[](int i) +{ + return p[i]; +} + +inline MatStep::operator size_t() const +{ + CV_DbgAssert( p == buf ); + return buf[0]; +} + +inline MatStep& MatStep::operator = (size_t s) +{ + CV_DbgAssert( p == buf ); + buf[0] = s; + return *this; +} + + + +////////////////////////////// Mat_<_Tp> //////////////////////////// + +template inline +Mat_<_Tp>::Mat_() + : Mat() +{ + flags = (flags & ~CV_MAT_TYPE_MASK) | traits::Type<_Tp>::value; +} + +template inline +Mat_<_Tp>::Mat_(int _rows, int _cols) + : Mat(_rows, _cols, traits::Type<_Tp>::value) +{ +} + +template inline +Mat_<_Tp>::Mat_(int _rows, int _cols, const _Tp& value) + : Mat(_rows, _cols, traits::Type<_Tp>::value) +{ + *this = value; +} + +template inline +Mat_<_Tp>::Mat_(Size _sz) + : Mat(_sz.height, _sz.width, traits::Type<_Tp>::value) +{} + +template inline +Mat_<_Tp>::Mat_(Size _sz, const _Tp& value) + : Mat(_sz.height, _sz.width, traits::Type<_Tp>::value) +{ + *this = value; +} + +template inline +Mat_<_Tp>::Mat_(int _dims, const int* _sz) + : Mat(_dims, _sz, traits::Type<_Tp>::value) +{} + +template inline +Mat_<_Tp>::Mat_(int _dims, const int* _sz, const _Tp& _s) + : Mat(_dims, _sz, traits::Type<_Tp>::value, Scalar(_s)) +{} + +template inline +Mat_<_Tp>::Mat_(int _dims, const int* _sz, _Tp* _data, const size_t* _steps) + : Mat(_dims, _sz, traits::Type<_Tp>::value, _data, _steps) +{} + +template inline +Mat_<_Tp>::Mat_(const Mat_<_Tp>& m, const Range* ranges) + : Mat(m, ranges) +{} + +template inline +Mat_<_Tp>::Mat_(const Mat_<_Tp>& m, const std::vector& ranges) + : Mat(m, ranges) +{} + +template inline +Mat_<_Tp>::Mat_(const Mat& m) + : Mat() +{ + flags = (flags & ~CV_MAT_TYPE_MASK) | traits::Type<_Tp>::value; + *this = m; +} + +template inline +Mat_<_Tp>::Mat_(const Mat_& m) + : Mat(m) +{} + +template inline +Mat_<_Tp>::Mat_(int _rows, int _cols, _Tp* _data, size_t steps) + : Mat(_rows, _cols, traits::Type<_Tp>::value, _data, steps) +{} + +template inline +Mat_<_Tp>::Mat_(const Mat_& m, const Range& _rowRange, const Range& _colRange) + : Mat(m, _rowRange, _colRange) +{} + +template inline +Mat_<_Tp>::Mat_(const Mat_& m, const Rect& roi) + : Mat(m, roi) +{} + +template template inline +Mat_<_Tp>::Mat_(const Vec::channel_type, n>& vec, bool copyData) + : Mat(n / DataType<_Tp>::channels, 1, traits::Type<_Tp>::value, (void*)&vec) +{ + CV_Assert(n%DataType<_Tp>::channels == 0); + if( copyData ) + *this = clone(); +} + +template template inline +Mat_<_Tp>::Mat_(const Matx::channel_type, m, n>& M, bool copyData) + : Mat(m, n / DataType<_Tp>::channels, traits::Type<_Tp>::value, (void*)&M) +{ + CV_Assert(n % DataType<_Tp>::channels == 0); + if( copyData ) + *this = clone(); +} + +template inline +Mat_<_Tp>::Mat_(const Point_::channel_type>& pt, bool copyData) + : Mat(2 / DataType<_Tp>::channels, 1, traits::Type<_Tp>::value, (void*)&pt) +{ + CV_Assert(2 % DataType<_Tp>::channels == 0); + if( copyData ) + *this = clone(); +} + +template inline +Mat_<_Tp>::Mat_(const Point3_::channel_type>& pt, bool copyData) + : Mat(3 / DataType<_Tp>::channels, 1, traits::Type<_Tp>::value, (void*)&pt) +{ + CV_Assert(3 % DataType<_Tp>::channels == 0); + if( copyData ) + *this = clone(); +} + +template inline +Mat_<_Tp>::Mat_(const MatCommaInitializer_<_Tp>& commaInitializer) + : Mat(commaInitializer) +{} + +template inline +Mat_<_Tp>::Mat_(const std::vector<_Tp>& vec, bool copyData) + : Mat(vec, copyData) +{} + +#ifdef CV_CXX11 +template inline +Mat_<_Tp>::Mat_(std::initializer_list<_Tp> list) + : Mat(list) +{} + +template inline +Mat_<_Tp>::Mat_(const std::initializer_list sizes, std::initializer_list<_Tp> list) + : Mat(sizes, list) +{} +#endif + +#ifdef CV_CXX_STD_ARRAY +template template inline +Mat_<_Tp>::Mat_(const std::array<_Tp, _Nm>& arr, bool copyData) + : Mat(arr, copyData) +{} +#endif + +template inline +Mat_<_Tp>& Mat_<_Tp>::operator = (const Mat& m) +{ + if( traits::Type<_Tp>::value == m.type() ) + { + Mat::operator = (m); + return *this; + } + if( traits::Depth<_Tp>::value == m.depth() ) + { + return (*this = m.reshape(DataType<_Tp>::channels, m.dims, 0)); + } + CV_Assert(DataType<_Tp>::channels == m.channels() || m.empty()); + m.convertTo(*this, type()); + return *this; +} + +template inline +Mat_<_Tp>& Mat_<_Tp>::operator = (const Mat_& m) +{ + Mat::operator=(m); + return *this; +} + +template inline +Mat_<_Tp>& Mat_<_Tp>::operator = (const _Tp& s) +{ + typedef typename DataType<_Tp>::vec_type VT; + Mat::operator=(Scalar((const VT&)s)); + return *this; +} + +template inline +void Mat_<_Tp>::create(int _rows, int _cols) +{ + Mat::create(_rows, _cols, traits::Type<_Tp>::value); +} + +template inline +void Mat_<_Tp>::create(Size _sz) +{ + Mat::create(_sz, traits::Type<_Tp>::value); +} + +template inline +void Mat_<_Tp>::create(int _dims, const int* _sz) +{ + Mat::create(_dims, _sz, traits::Type<_Tp>::value); +} + +template inline +void Mat_<_Tp>::release() +{ + Mat::release(); +#ifdef _DEBUG + flags = (flags & ~CV_MAT_TYPE_MASK) | traits::Type<_Tp>::value; +#endif +} + +template inline +Mat_<_Tp> Mat_<_Tp>::cross(const Mat_& m) const +{ + return Mat_<_Tp>(Mat::cross(m)); +} + +template template inline +Mat_<_Tp>::operator Mat_() const +{ + return Mat_(*this); +} + +template inline +Mat_<_Tp> Mat_<_Tp>::row(int y) const +{ + return Mat_(*this, Range(y, y+1), Range::all()); +} + +template inline +Mat_<_Tp> Mat_<_Tp>::col(int x) const +{ + return Mat_(*this, Range::all(), Range(x, x+1)); +} + +template inline +Mat_<_Tp> Mat_<_Tp>::diag(int d) const +{ + return Mat_(Mat::diag(d)); +} + +template inline +Mat_<_Tp> Mat_<_Tp>::clone() const +{ + return Mat_(Mat::clone()); +} + +template inline +size_t Mat_<_Tp>::elemSize() const +{ + CV_DbgAssert( Mat::elemSize() == sizeof(_Tp) ); + return sizeof(_Tp); +} + +template inline +size_t Mat_<_Tp>::elemSize1() const +{ + CV_DbgAssert( Mat::elemSize1() == sizeof(_Tp) / DataType<_Tp>::channels ); + return sizeof(_Tp) / DataType<_Tp>::channels; +} + +template inline +int Mat_<_Tp>::type() const +{ + CV_DbgAssert( Mat::type() == traits::Type<_Tp>::value ); + return traits::Type<_Tp>::value; +} + +template inline +int Mat_<_Tp>::depth() const +{ + CV_DbgAssert( Mat::depth() == traits::Depth<_Tp>::value ); + return traits::Depth<_Tp>::value; +} + +template inline +int Mat_<_Tp>::channels() const +{ + CV_DbgAssert( Mat::channels() == DataType<_Tp>::channels ); + return DataType<_Tp>::channels; +} + +template inline +size_t Mat_<_Tp>::stepT(int i) const +{ + return step.p[i] / elemSize(); +} + +template inline +size_t Mat_<_Tp>::step1(int i) const +{ + return step.p[i] / elemSize1(); +} + +template inline +Mat_<_Tp>& Mat_<_Tp>::adjustROI( int dtop, int dbottom, int dleft, int dright ) +{ + return (Mat_<_Tp>&)(Mat::adjustROI(dtop, dbottom, dleft, dright)); +} + +template inline +Mat_<_Tp> Mat_<_Tp>::operator()( const Range& _rowRange, const Range& _colRange ) const +{ + return Mat_<_Tp>(*this, _rowRange, _colRange); +} + +template inline +Mat_<_Tp> Mat_<_Tp>::operator()( const Rect& roi ) const +{ + return Mat_<_Tp>(*this, roi); +} + +template inline +Mat_<_Tp> Mat_<_Tp>::operator()( const Range* ranges ) const +{ + return Mat_<_Tp>(*this, ranges); +} + +template inline +Mat_<_Tp> Mat_<_Tp>::operator()(const std::vector& ranges) const +{ + return Mat_<_Tp>(*this, ranges); +} + +template inline +_Tp* Mat_<_Tp>::operator [](int y) +{ + CV_DbgAssert( 0 <= y && y < size.p[0] ); + return (_Tp*)(data + y*step.p[0]); +} + +template inline +const _Tp* Mat_<_Tp>::operator [](int y) const +{ + CV_DbgAssert( 0 <= y && y < size.p[0] ); + return (const _Tp*)(data + y*step.p[0]); +} + +template inline +_Tp& Mat_<_Tp>::operator ()(int i0, int i1) +{ + CV_DbgAssert(dims <= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); + CV_DbgAssert(type() == traits::Type<_Tp>::value); + return ((_Tp*)(data + step.p[0] * i0))[i1]; +} + +template inline +const _Tp& Mat_<_Tp>::operator ()(int i0, int i1) const +{ + CV_DbgAssert(dims <= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); + CV_DbgAssert(type() == traits::Type<_Tp>::value); + return ((const _Tp*)(data + step.p[0] * i0))[i1]; +} + +template inline +_Tp& Mat_<_Tp>::operator ()(Point pt) +{ + CV_DbgAssert(dims <= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)pt.y < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)pt.x < (unsigned)size.p[1]); + CV_DbgAssert(type() == traits::Type<_Tp>::value); + return ((_Tp*)(data + step.p[0] * pt.y))[pt.x]; +} + +template inline +const _Tp& Mat_<_Tp>::operator ()(Point pt) const +{ + CV_DbgAssert(dims <= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)pt.y < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)pt.x < (unsigned)size.p[1]); + CV_DbgAssert(type() == traits::Type<_Tp>::value); + return ((const _Tp*)(data + step.p[0] * pt.y))[pt.x]; +} + +template inline +_Tp& Mat_<_Tp>::operator ()(const int* idx) +{ + return Mat::at<_Tp>(idx); +} + +template inline +const _Tp& Mat_<_Tp>::operator ()(const int* idx) const +{ + return Mat::at<_Tp>(idx); +} + +template template inline +_Tp& Mat_<_Tp>::operator ()(const Vec& idx) +{ + return Mat::at<_Tp>(idx); +} + +template template inline +const _Tp& Mat_<_Tp>::operator ()(const Vec& idx) const +{ + return Mat::at<_Tp>(idx); +} + +template inline +_Tp& Mat_<_Tp>::operator ()(int i0) +{ + return this->at<_Tp>(i0); +} + +template inline +const _Tp& Mat_<_Tp>::operator ()(int i0) const +{ + return this->at<_Tp>(i0); +} + +template inline +_Tp& Mat_<_Tp>::operator ()(int i0, int i1, int i2) +{ + return this->at<_Tp>(i0, i1, i2); +} + +template inline +const _Tp& Mat_<_Tp>::operator ()(int i0, int i1, int i2) const +{ + return this->at<_Tp>(i0, i1, i2); +} + +template inline +Mat_<_Tp>::operator std::vector<_Tp>() const +{ + std::vector<_Tp> v; + copyTo(v); + return v; +} + +#ifdef CV_CXX_STD_ARRAY +template template inline +Mat_<_Tp>::operator std::array<_Tp, _Nm>() const +{ + std::array<_Tp, _Nm> a; + copyTo(a); + return a; +} +#endif + +template template inline +Mat_<_Tp>::operator Vec::channel_type, n>() const +{ + CV_Assert(n % DataType<_Tp>::channels == 0); + +#if defined _MSC_VER + const Mat* pMat = (const Mat*)this; // workaround for MSVS <= 2012 compiler bugs (but GCC 4.6 dislikes this workaround) + return pMat->operator Vec::channel_type, n>(); +#else + return this->Mat::operator Vec::channel_type, n>(); +#endif +} + +template template inline +Mat_<_Tp>::operator Matx::channel_type, m, n>() const +{ + CV_Assert(n % DataType<_Tp>::channels == 0); + +#if defined _MSC_VER + const Mat* pMat = (const Mat*)this; // workaround for MSVS <= 2012 compiler bugs (but GCC 4.6 dislikes this workaround) + Matx::channel_type, m, n> res = pMat->operator Matx::channel_type, m, n>(); + return res; +#else + Matx::channel_type, m, n> res = this->Mat::operator Matx::channel_type, m, n>(); + return res; +#endif +} + +template inline +MatConstIterator_<_Tp> Mat_<_Tp>::begin() const +{ + return Mat::begin<_Tp>(); +} + +template inline +MatConstIterator_<_Tp> Mat_<_Tp>::end() const +{ + return Mat::end<_Tp>(); +} + +template inline +MatIterator_<_Tp> Mat_<_Tp>::begin() +{ + return Mat::begin<_Tp>(); +} + +template inline +MatIterator_<_Tp> Mat_<_Tp>::end() +{ + return Mat::end<_Tp>(); +} + +template template inline +void Mat_<_Tp>::forEach(const Functor& operation) { + Mat::forEach<_Tp, Functor>(operation); +} + +template template inline +void Mat_<_Tp>::forEach(const Functor& operation) const { + Mat::forEach<_Tp, Functor>(operation); +} + +#ifdef CV_CXX_MOVE_SEMANTICS + +template inline +Mat_<_Tp>::Mat_(Mat_&& m) + : Mat(m) +{ +} + +template inline +Mat_<_Tp>& Mat_<_Tp>::operator = (Mat_&& m) +{ + Mat::operator = (std::move(m)); + return *this; +} + +template inline +Mat_<_Tp>::Mat_(Mat&& m) + : Mat() +{ + flags = (flags & ~CV_MAT_TYPE_MASK) | traits::Type<_Tp>::value; + *this = m; +} + +template inline +Mat_<_Tp>& Mat_<_Tp>::operator = (Mat&& m) +{ + if( traits::Type<_Tp>::value == m.type() ) + { + Mat::operator = ((Mat&&)m); + return *this; + } + if( traits::Depth<_Tp>::value == m.depth() ) + { + Mat::operator = ((Mat&&)m.reshape(DataType<_Tp>::channels, m.dims, 0)); + return *this; + } + CV_DbgAssert(DataType<_Tp>::channels == m.channels()); + m.convertTo(*this, type()); + return *this; +} + +template inline +Mat_<_Tp>::Mat_(MatExpr&& e) + : Mat() +{ + flags = (flags & ~CV_MAT_TYPE_MASK) | traits::Type<_Tp>::value; + *this = Mat(e); +} + +#endif + +///////////////////////////// SparseMat ///////////////////////////// + +inline +SparseMat::SparseMat() + : flags(MAGIC_VAL), hdr(0) +{} + +inline +SparseMat::SparseMat(int _dims, const int* _sizes, int _type) + : flags(MAGIC_VAL), hdr(0) +{ + create(_dims, _sizes, _type); +} + +inline +SparseMat::SparseMat(const SparseMat& m) + : flags(m.flags), hdr(m.hdr) +{ + addref(); +} + +inline +SparseMat::~SparseMat() +{ + release(); +} + +inline +SparseMat& SparseMat::operator = (const SparseMat& m) +{ + if( this != &m ) + { + if( m.hdr ) + CV_XADD(&m.hdr->refcount, 1); + release(); + flags = m.flags; + hdr = m.hdr; + } + return *this; +} + +inline +SparseMat& SparseMat::operator = (const Mat& m) +{ + return (*this = SparseMat(m)); +} + +inline +SparseMat SparseMat::clone() const +{ + SparseMat temp; + this->copyTo(temp); + return temp; +} + +inline +void SparseMat::assignTo( SparseMat& m, int _type ) const +{ + if( _type < 0 ) + m = *this; + else + convertTo(m, _type); +} + +inline +void SparseMat::addref() +{ + if( hdr ) + CV_XADD(&hdr->refcount, 1); +} + +inline +void SparseMat::release() +{ + if( hdr && CV_XADD(&hdr->refcount, -1) == 1 ) + delete hdr; + hdr = 0; +} + +inline +size_t SparseMat::elemSize() const +{ + return CV_ELEM_SIZE(flags); +} + +inline +size_t SparseMat::elemSize1() const +{ + return CV_ELEM_SIZE1(flags); +} + +inline +int SparseMat::type() const +{ + return CV_MAT_TYPE(flags); +} + +inline +int SparseMat::depth() const +{ + return CV_MAT_DEPTH(flags); +} + +inline +int SparseMat::channels() const +{ + return CV_MAT_CN(flags); +} + +inline +const int* SparseMat::size() const +{ + return hdr ? hdr->size : 0; +} + +inline +int SparseMat::size(int i) const +{ + if( hdr ) + { + CV_DbgAssert((unsigned)i < (unsigned)hdr->dims); + return hdr->size[i]; + } + return 0; +} + +inline +int SparseMat::dims() const +{ + return hdr ? hdr->dims : 0; +} + +inline +size_t SparseMat::nzcount() const +{ + return hdr ? hdr->nodeCount : 0; +} + +inline +size_t SparseMat::hash(int i0) const +{ + return (size_t)i0; +} + +inline +size_t SparseMat::hash(int i0, int i1) const +{ + return (size_t)(unsigned)i0 * HASH_SCALE + (unsigned)i1; +} + +inline +size_t SparseMat::hash(int i0, int i1, int i2) const +{ + return ((size_t)(unsigned)i0 * HASH_SCALE + (unsigned)i1) * HASH_SCALE + (unsigned)i2; +} + +inline +size_t SparseMat::hash(const int* idx) const +{ + size_t h = (unsigned)idx[0]; + if( !hdr ) + return 0; + int d = hdr->dims; + for(int i = 1; i < d; i++ ) + h = h * HASH_SCALE + (unsigned)idx[i]; + return h; +} + +template inline +_Tp& SparseMat::ref(int i0, size_t* hashval) +{ + return *(_Tp*)((SparseMat*)this)->ptr(i0, true, hashval); +} + +template inline +_Tp& SparseMat::ref(int i0, int i1, size_t* hashval) +{ + return *(_Tp*)((SparseMat*)this)->ptr(i0, i1, true, hashval); +} + +template inline +_Tp& SparseMat::ref(int i0, int i1, int i2, size_t* hashval) +{ + return *(_Tp*)((SparseMat*)this)->ptr(i0, i1, i2, true, hashval); +} + +template inline +_Tp& SparseMat::ref(const int* idx, size_t* hashval) +{ + return *(_Tp*)((SparseMat*)this)->ptr(idx, true, hashval); +} + +template inline +_Tp SparseMat::value(int i0, size_t* hashval) const +{ + const _Tp* p = (const _Tp*)((SparseMat*)this)->ptr(i0, false, hashval); + return p ? *p : _Tp(); +} + +template inline +_Tp SparseMat::value(int i0, int i1, size_t* hashval) const +{ + const _Tp* p = (const _Tp*)((SparseMat*)this)->ptr(i0, i1, false, hashval); + return p ? *p : _Tp(); +} + +template inline +_Tp SparseMat::value(int i0, int i1, int i2, size_t* hashval) const +{ + const _Tp* p = (const _Tp*)((SparseMat*)this)->ptr(i0, i1, i2, false, hashval); + return p ? *p : _Tp(); +} + +template inline +_Tp SparseMat::value(const int* idx, size_t* hashval) const +{ + const _Tp* p = (const _Tp*)((SparseMat*)this)->ptr(idx, false, hashval); + return p ? *p : _Tp(); +} + +template inline +const _Tp* SparseMat::find(int i0, size_t* hashval) const +{ + return (const _Tp*)((SparseMat*)this)->ptr(i0, false, hashval); +} + +template inline +const _Tp* SparseMat::find(int i0, int i1, size_t* hashval) const +{ + return (const _Tp*)((SparseMat*)this)->ptr(i0, i1, false, hashval); +} + +template inline +const _Tp* SparseMat::find(int i0, int i1, int i2, size_t* hashval) const +{ + return (const _Tp*)((SparseMat*)this)->ptr(i0, i1, i2, false, hashval); +} + +template inline +const _Tp* SparseMat::find(const int* idx, size_t* hashval) const +{ + return (const _Tp*)((SparseMat*)this)->ptr(idx, false, hashval); +} + +template inline +_Tp& SparseMat::value(Node* n) +{ + return *(_Tp*)((uchar*)n + hdr->valueOffset); +} + +template inline +const _Tp& SparseMat::value(const Node* n) const +{ + return *(const _Tp*)((const uchar*)n + hdr->valueOffset); +} + +inline +SparseMat::Node* SparseMat::node(size_t nidx) +{ + return (Node*)(void*)&hdr->pool[nidx]; +} + +inline +const SparseMat::Node* SparseMat::node(size_t nidx) const +{ + return (const Node*)(const void*)&hdr->pool[nidx]; +} + +inline +SparseMatIterator SparseMat::begin() +{ + return SparseMatIterator(this); +} + +inline +SparseMatConstIterator SparseMat::begin() const +{ + return SparseMatConstIterator(this); +} + +inline +SparseMatIterator SparseMat::end() +{ + SparseMatIterator it(this); + it.seekEnd(); + return it; +} + +inline +SparseMatConstIterator SparseMat::end() const +{ + SparseMatConstIterator it(this); + it.seekEnd(); + return it; +} + +template inline +SparseMatIterator_<_Tp> SparseMat::begin() +{ + return SparseMatIterator_<_Tp>(this); +} + +template inline +SparseMatConstIterator_<_Tp> SparseMat::begin() const +{ + return SparseMatConstIterator_<_Tp>(this); +} + +template inline +SparseMatIterator_<_Tp> SparseMat::end() +{ + SparseMatIterator_<_Tp> it(this); + it.seekEnd(); + return it; +} + +template inline +SparseMatConstIterator_<_Tp> SparseMat::end() const +{ + SparseMatConstIterator_<_Tp> it(this); + it.seekEnd(); + return it; +} + + + +///////////////////////////// SparseMat_ //////////////////////////// + +template inline +SparseMat_<_Tp>::SparseMat_() +{ + flags = MAGIC_VAL | traits::Type<_Tp>::value; +} + +template inline +SparseMat_<_Tp>::SparseMat_(int _dims, const int* _sizes) + : SparseMat(_dims, _sizes, traits::Type<_Tp>::value) +{} + +template inline +SparseMat_<_Tp>::SparseMat_(const SparseMat& m) +{ + if( m.type() == traits::Type<_Tp>::value ) + *this = (const SparseMat_<_Tp>&)m; + else + m.convertTo(*this, traits::Type<_Tp>::value); +} + +template inline +SparseMat_<_Tp>::SparseMat_(const SparseMat_<_Tp>& m) +{ + this->flags = m.flags; + this->hdr = m.hdr; + if( this->hdr ) + CV_XADD(&this->hdr->refcount, 1); +} + +template inline +SparseMat_<_Tp>::SparseMat_(const Mat& m) +{ + SparseMat sm(m); + *this = sm; +} + +template inline +SparseMat_<_Tp>& SparseMat_<_Tp>::operator = (const SparseMat_<_Tp>& m) +{ + if( this != &m ) + { + if( m.hdr ) CV_XADD(&m.hdr->refcount, 1); + release(); + flags = m.flags; + hdr = m.hdr; + } + return *this; +} + +template inline +SparseMat_<_Tp>& SparseMat_<_Tp>::operator = (const SparseMat& m) +{ + if( m.type() == traits::Type<_Tp>::value ) + return (*this = (const SparseMat_<_Tp>&)m); + m.convertTo(*this, traits::Type<_Tp>::value); + return *this; +} + +template inline +SparseMat_<_Tp>& SparseMat_<_Tp>::operator = (const Mat& m) +{ + return (*this = SparseMat(m)); +} + +template inline +SparseMat_<_Tp> SparseMat_<_Tp>::clone() const +{ + SparseMat_<_Tp> m; + this->copyTo(m); + return m; +} + +template inline +void SparseMat_<_Tp>::create(int _dims, const int* _sizes) +{ + SparseMat::create(_dims, _sizes, traits::Type<_Tp>::value); +} + +template inline +int SparseMat_<_Tp>::type() const +{ + return traits::Type<_Tp>::value; +} + +template inline +int SparseMat_<_Tp>::depth() const +{ + return traits::Depth<_Tp>::value; +} + +template inline +int SparseMat_<_Tp>::channels() const +{ + return DataType<_Tp>::channels; +} + +template inline +_Tp& SparseMat_<_Tp>::ref(int i0, size_t* hashval) +{ + return SparseMat::ref<_Tp>(i0, hashval); +} + +template inline +_Tp SparseMat_<_Tp>::operator()(int i0, size_t* hashval) const +{ + return SparseMat::value<_Tp>(i0, hashval); +} + +template inline +_Tp& SparseMat_<_Tp>::ref(int i0, int i1, size_t* hashval) +{ + return SparseMat::ref<_Tp>(i0, i1, hashval); +} + +template inline +_Tp SparseMat_<_Tp>::operator()(int i0, int i1, size_t* hashval) const +{ + return SparseMat::value<_Tp>(i0, i1, hashval); +} + +template inline +_Tp& SparseMat_<_Tp>::ref(int i0, int i1, int i2, size_t* hashval) +{ + return SparseMat::ref<_Tp>(i0, i1, i2, hashval); +} + +template inline +_Tp SparseMat_<_Tp>::operator()(int i0, int i1, int i2, size_t* hashval) const +{ + return SparseMat::value<_Tp>(i0, i1, i2, hashval); +} + +template inline +_Tp& SparseMat_<_Tp>::ref(const int* idx, size_t* hashval) +{ + return SparseMat::ref<_Tp>(idx, hashval); +} + +template inline +_Tp SparseMat_<_Tp>::operator()(const int* idx, size_t* hashval) const +{ + return SparseMat::value<_Tp>(idx, hashval); +} + +template inline +SparseMatIterator_<_Tp> SparseMat_<_Tp>::begin() +{ + return SparseMatIterator_<_Tp>(this); +} + +template inline +SparseMatConstIterator_<_Tp> SparseMat_<_Tp>::begin() const +{ + return SparseMatConstIterator_<_Tp>(this); +} + +template inline +SparseMatIterator_<_Tp> SparseMat_<_Tp>::end() +{ + SparseMatIterator_<_Tp> it(this); + it.seekEnd(); + return it; +} + +template inline +SparseMatConstIterator_<_Tp> SparseMat_<_Tp>::end() const +{ + SparseMatConstIterator_<_Tp> it(this); + it.seekEnd(); + return it; +} + + + +////////////////////////// MatConstIterator ///////////////////////// + +inline +MatConstIterator::MatConstIterator() + : m(0), elemSize(0), ptr(0), sliceStart(0), sliceEnd(0) +{} + +inline +MatConstIterator::MatConstIterator(const Mat* _m) + : m(_m), elemSize(_m->elemSize()), ptr(0), sliceStart(0), sliceEnd(0) +{ + if( m && m->isContinuous() ) + { + sliceStart = m->ptr(); + sliceEnd = sliceStart + m->total()*elemSize; + } + seek((const int*)0); +} + +inline +MatConstIterator::MatConstIterator(const Mat* _m, int _row, int _col) + : m(_m), elemSize(_m->elemSize()), ptr(0), sliceStart(0), sliceEnd(0) +{ + CV_Assert(m && m->dims <= 2); + if( m->isContinuous() ) + { + sliceStart = m->ptr(); + sliceEnd = sliceStart + m->total()*elemSize; + } + int idx[] = {_row, _col}; + seek(idx); +} + +inline +MatConstIterator::MatConstIterator(const Mat* _m, Point _pt) + : m(_m), elemSize(_m->elemSize()), ptr(0), sliceStart(0), sliceEnd(0) +{ + CV_Assert(m && m->dims <= 2); + if( m->isContinuous() ) + { + sliceStart = m->ptr(); + sliceEnd = sliceStart + m->total()*elemSize; + } + int idx[] = {_pt.y, _pt.x}; + seek(idx); +} + +inline +MatConstIterator::MatConstIterator(const MatConstIterator& it) + : m(it.m), elemSize(it.elemSize), ptr(it.ptr), sliceStart(it.sliceStart), sliceEnd(it.sliceEnd) +{} + +inline +MatConstIterator& MatConstIterator::operator = (const MatConstIterator& it ) +{ + m = it.m; elemSize = it.elemSize; ptr = it.ptr; + sliceStart = it.sliceStart; sliceEnd = it.sliceEnd; + return *this; +} + +inline +const uchar* MatConstIterator::operator *() const +{ + return ptr; +} + +inline MatConstIterator& MatConstIterator::operator += (ptrdiff_t ofs) +{ + if( !m || ofs == 0 ) + return *this; + ptrdiff_t ofsb = ofs*elemSize; + ptr += ofsb; + if( ptr < sliceStart || sliceEnd <= ptr ) + { + ptr -= ofsb; + seek(ofs, true); + } + return *this; +} + +inline +MatConstIterator& MatConstIterator::operator -= (ptrdiff_t ofs) +{ + return (*this += -ofs); +} + +inline +MatConstIterator& MatConstIterator::operator --() +{ + if( m && (ptr -= elemSize) < sliceStart ) + { + ptr += elemSize; + seek(-1, true); + } + return *this; +} + +inline +MatConstIterator MatConstIterator::operator --(int) +{ + MatConstIterator b = *this; + *this += -1; + return b; +} + +inline +MatConstIterator& MatConstIterator::operator ++() +{ + if( m && (ptr += elemSize) >= sliceEnd ) + { + ptr -= elemSize; + seek(1, true); + } + return *this; +} + +inline MatConstIterator MatConstIterator::operator ++(int) +{ + MatConstIterator b = *this; + *this += 1; + return b; +} + + +static inline +bool operator == (const MatConstIterator& a, const MatConstIterator& b) +{ + return a.m == b.m && a.ptr == b.ptr; +} + +static inline +bool operator != (const MatConstIterator& a, const MatConstIterator& b) +{ + return !(a == b); +} + +static inline +bool operator < (const MatConstIterator& a, const MatConstIterator& b) +{ + return a.ptr < b.ptr; +} + +static inline +bool operator > (const MatConstIterator& a, const MatConstIterator& b) +{ + return a.ptr > b.ptr; +} + +static inline +bool operator <= (const MatConstIterator& a, const MatConstIterator& b) +{ + return a.ptr <= b.ptr; +} + +static inline +bool operator >= (const MatConstIterator& a, const MatConstIterator& b) +{ + return a.ptr >= b.ptr; +} + +static inline +ptrdiff_t operator - (const MatConstIterator& b, const MatConstIterator& a) +{ + if( a.m != b.m ) + return ((size_t)(-1) >> 1); + if( a.sliceEnd == b.sliceEnd ) + return (b.ptr - a.ptr)/static_cast(b.elemSize); + + return b.lpos() - a.lpos(); +} + +static inline +MatConstIterator operator + (const MatConstIterator& a, ptrdiff_t ofs) +{ + MatConstIterator b = a; + return b += ofs; +} + +static inline +MatConstIterator operator + (ptrdiff_t ofs, const MatConstIterator& a) +{ + MatConstIterator b = a; + return b += ofs; +} + +static inline +MatConstIterator operator - (const MatConstIterator& a, ptrdiff_t ofs) +{ + MatConstIterator b = a; + return b += -ofs; +} + + +inline +const uchar* MatConstIterator::operator [](ptrdiff_t i) const +{ + return *(*this + i); +} + + + +///////////////////////// MatConstIterator_ ///////////////////////// + +template inline +MatConstIterator_<_Tp>::MatConstIterator_() +{} + +template inline +MatConstIterator_<_Tp>::MatConstIterator_(const Mat_<_Tp>* _m) + : MatConstIterator(_m) +{} + +template inline +MatConstIterator_<_Tp>::MatConstIterator_(const Mat_<_Tp>* _m, int _row, int _col) + : MatConstIterator(_m, _row, _col) +{} + +template inline +MatConstIterator_<_Tp>::MatConstIterator_(const Mat_<_Tp>* _m, Point _pt) + : MatConstIterator(_m, _pt) +{} + +template inline +MatConstIterator_<_Tp>::MatConstIterator_(const MatConstIterator_& it) + : MatConstIterator(it) +{} + +template inline +MatConstIterator_<_Tp>& MatConstIterator_<_Tp>::operator = (const MatConstIterator_& it ) +{ + MatConstIterator::operator = (it); + return *this; +} + +template inline +const _Tp& MatConstIterator_<_Tp>::operator *() const +{ + return *(_Tp*)(this->ptr); +} + +template inline +MatConstIterator_<_Tp>& MatConstIterator_<_Tp>::operator += (ptrdiff_t ofs) +{ + MatConstIterator::operator += (ofs); + return *this; +} + +template inline +MatConstIterator_<_Tp>& MatConstIterator_<_Tp>::operator -= (ptrdiff_t ofs) +{ + return (*this += -ofs); +} + +template inline +MatConstIterator_<_Tp>& MatConstIterator_<_Tp>::operator --() +{ + MatConstIterator::operator --(); + return *this; +} + +template inline +MatConstIterator_<_Tp> MatConstIterator_<_Tp>::operator --(int) +{ + MatConstIterator_ b = *this; + MatConstIterator::operator --(); + return b; +} + +template inline +MatConstIterator_<_Tp>& MatConstIterator_<_Tp>::operator ++() +{ + MatConstIterator::operator ++(); + return *this; +} + +template inline +MatConstIterator_<_Tp> MatConstIterator_<_Tp>::operator ++(int) +{ + MatConstIterator_ b = *this; + MatConstIterator::operator ++(); + return b; +} + + +template inline +Point MatConstIterator_<_Tp>::pos() const +{ + if( !m ) + return Point(); + CV_DbgAssert( m->dims <= 2 ); + if( m->isContinuous() ) + { + ptrdiff_t ofs = (const _Tp*)ptr - (const _Tp*)m->data; + int y = (int)(ofs / m->cols); + int x = (int)(ofs - (ptrdiff_t)y * m->cols); + return Point(x, y); + } + else + { + ptrdiff_t ofs = (uchar*)ptr - m->data; + int y = (int)(ofs / m->step); + int x = (int)((ofs - y * m->step)/sizeof(_Tp)); + return Point(x, y); + } +} + + +template static inline +bool operator == (const MatConstIterator_<_Tp>& a, const MatConstIterator_<_Tp>& b) +{ + return a.m == b.m && a.ptr == b.ptr; +} + +template static inline +bool operator != (const MatConstIterator_<_Tp>& a, const MatConstIterator_<_Tp>& b) +{ + return a.m != b.m || a.ptr != b.ptr; +} + +template static inline +MatConstIterator_<_Tp> operator + (const MatConstIterator_<_Tp>& a, ptrdiff_t ofs) +{ + MatConstIterator t = (const MatConstIterator&)a + ofs; + return (MatConstIterator_<_Tp>&)t; +} + +template static inline +MatConstIterator_<_Tp> operator + (ptrdiff_t ofs, const MatConstIterator_<_Tp>& a) +{ + MatConstIterator t = (const MatConstIterator&)a + ofs; + return (MatConstIterator_<_Tp>&)t; +} + +template static inline +MatConstIterator_<_Tp> operator - (const MatConstIterator_<_Tp>& a, ptrdiff_t ofs) +{ + MatConstIterator t = (const MatConstIterator&)a - ofs; + return (MatConstIterator_<_Tp>&)t; +} + +template inline +const _Tp& MatConstIterator_<_Tp>::operator [](ptrdiff_t i) const +{ + return *(_Tp*)MatConstIterator::operator [](i); +} + + + +//////////////////////////// MatIterator_ /////////////////////////// + +template inline +MatIterator_<_Tp>::MatIterator_() + : MatConstIterator_<_Tp>() +{} + +template inline +MatIterator_<_Tp>::MatIterator_(Mat_<_Tp>* _m) + : MatConstIterator_<_Tp>(_m) +{} + +template inline +MatIterator_<_Tp>::MatIterator_(Mat_<_Tp>* _m, int _row, int _col) + : MatConstIterator_<_Tp>(_m, _row, _col) +{} + +template inline +MatIterator_<_Tp>::MatIterator_(Mat_<_Tp>* _m, Point _pt) + : MatConstIterator_<_Tp>(_m, _pt) +{} + +template inline +MatIterator_<_Tp>::MatIterator_(Mat_<_Tp>* _m, const int* _idx) + : MatConstIterator_<_Tp>(_m, _idx) +{} + +template inline +MatIterator_<_Tp>::MatIterator_(const MatIterator_& it) + : MatConstIterator_<_Tp>(it) +{} + +template inline +MatIterator_<_Tp>& MatIterator_<_Tp>::operator = (const MatIterator_<_Tp>& it ) +{ + MatConstIterator::operator = (it); + return *this; +} + +template inline +_Tp& MatIterator_<_Tp>::operator *() const +{ + return *(_Tp*)(this->ptr); +} + +template inline +MatIterator_<_Tp>& MatIterator_<_Tp>::operator += (ptrdiff_t ofs) +{ + MatConstIterator::operator += (ofs); + return *this; +} + +template inline +MatIterator_<_Tp>& MatIterator_<_Tp>::operator -= (ptrdiff_t ofs) +{ + MatConstIterator::operator += (-ofs); + return *this; +} + +template inline +MatIterator_<_Tp>& MatIterator_<_Tp>::operator --() +{ + MatConstIterator::operator --(); + return *this; +} + +template inline +MatIterator_<_Tp> MatIterator_<_Tp>::operator --(int) +{ + MatIterator_ b = *this; + MatConstIterator::operator --(); + return b; +} + +template inline +MatIterator_<_Tp>& MatIterator_<_Tp>::operator ++() +{ + MatConstIterator::operator ++(); + return *this; +} + +template inline +MatIterator_<_Tp> MatIterator_<_Tp>::operator ++(int) +{ + MatIterator_ b = *this; + MatConstIterator::operator ++(); + return b; +} + +template inline +_Tp& MatIterator_<_Tp>::operator [](ptrdiff_t i) const +{ + return *(*this + i); +} + + +template static inline +bool operator == (const MatIterator_<_Tp>& a, const MatIterator_<_Tp>& b) +{ + return a.m == b.m && a.ptr == b.ptr; +} + +template static inline +bool operator != (const MatIterator_<_Tp>& a, const MatIterator_<_Tp>& b) +{ + return a.m != b.m || a.ptr != b.ptr; +} + +template static inline +MatIterator_<_Tp> operator + (const MatIterator_<_Tp>& a, ptrdiff_t ofs) +{ + MatConstIterator t = (const MatConstIterator&)a + ofs; + return (MatIterator_<_Tp>&)t; +} + +template static inline +MatIterator_<_Tp> operator + (ptrdiff_t ofs, const MatIterator_<_Tp>& a) +{ + MatConstIterator t = (const MatConstIterator&)a + ofs; + return (MatIterator_<_Tp>&)t; +} + +template static inline +MatIterator_<_Tp> operator - (const MatIterator_<_Tp>& a, ptrdiff_t ofs) +{ + MatConstIterator t = (const MatConstIterator&)a - ofs; + return (MatIterator_<_Tp>&)t; +} + + + +/////////////////////// SparseMatConstIterator ////////////////////// + +inline +SparseMatConstIterator::SparseMatConstIterator() + : m(0), hashidx(0), ptr(0) +{} + +inline +SparseMatConstIterator::SparseMatConstIterator(const SparseMatConstIterator& it) + : m(it.m), hashidx(it.hashidx), ptr(it.ptr) +{} + +inline SparseMatConstIterator& SparseMatConstIterator::operator = (const SparseMatConstIterator& it) +{ + if( this != &it ) + { + m = it.m; + hashidx = it.hashidx; + ptr = it.ptr; + } + return *this; +} + +template inline +const _Tp& SparseMatConstIterator::value() const +{ + return *(const _Tp*)ptr; +} + +inline +const SparseMat::Node* SparseMatConstIterator::node() const +{ + return (ptr && m && m->hdr) ? (const SparseMat::Node*)(const void*)(ptr - m->hdr->valueOffset) : 0; +} + +inline +SparseMatConstIterator SparseMatConstIterator::operator ++(int) +{ + SparseMatConstIterator it = *this; + ++*this; + return it; +} + +inline +void SparseMatConstIterator::seekEnd() +{ + if( m && m->hdr ) + { + hashidx = m->hdr->hashtab.size(); + ptr = 0; + } +} + + +static inline +bool operator == (const SparseMatConstIterator& it1, const SparseMatConstIterator& it2) +{ + return it1.m == it2.m && it1.ptr == it2.ptr; +} + +static inline +bool operator != (const SparseMatConstIterator& it1, const SparseMatConstIterator& it2) +{ + return !(it1 == it2); +} + + + +///////////////////////// SparseMatIterator ///////////////////////// + +inline +SparseMatIterator::SparseMatIterator() +{} + +inline +SparseMatIterator::SparseMatIterator(SparseMat* _m) + : SparseMatConstIterator(_m) +{} + +inline +SparseMatIterator::SparseMatIterator(const SparseMatIterator& it) + : SparseMatConstIterator(it) +{} + +inline +SparseMatIterator& SparseMatIterator::operator = (const SparseMatIterator& it) +{ + (SparseMatConstIterator&)*this = it; + return *this; +} + +template inline +_Tp& SparseMatIterator::value() const +{ + return *(_Tp*)ptr; +} + +inline +SparseMat::Node* SparseMatIterator::node() const +{ + return (SparseMat::Node*)SparseMatConstIterator::node(); +} + +inline +SparseMatIterator& SparseMatIterator::operator ++() +{ + SparseMatConstIterator::operator ++(); + return *this; +} + +inline +SparseMatIterator SparseMatIterator::operator ++(int) +{ + SparseMatIterator it = *this; + ++*this; + return it; +} + + + +////////////////////// SparseMatConstIterator_ ////////////////////// + +template inline +SparseMatConstIterator_<_Tp>::SparseMatConstIterator_() +{} + +template inline +SparseMatConstIterator_<_Tp>::SparseMatConstIterator_(const SparseMat_<_Tp>* _m) + : SparseMatConstIterator(_m) +{} + +template inline +SparseMatConstIterator_<_Tp>::SparseMatConstIterator_(const SparseMat* _m) + : SparseMatConstIterator(_m) +{ + CV_Assert( _m->type() == traits::Type<_Tp>::value ); +} + +template inline +SparseMatConstIterator_<_Tp>::SparseMatConstIterator_(const SparseMatConstIterator_<_Tp>& it) + : SparseMatConstIterator(it) +{} + +template inline +SparseMatConstIterator_<_Tp>& SparseMatConstIterator_<_Tp>::operator = (const SparseMatConstIterator_<_Tp>& it) +{ + return reinterpret_cast&> + (*reinterpret_cast(this) = + reinterpret_cast(it)); +} + +template inline +const _Tp& SparseMatConstIterator_<_Tp>::operator *() const +{ + return *(const _Tp*)this->ptr; +} + +template inline +SparseMatConstIterator_<_Tp>& SparseMatConstIterator_<_Tp>::operator ++() +{ + SparseMatConstIterator::operator ++(); + return *this; +} + +template inline +SparseMatConstIterator_<_Tp> SparseMatConstIterator_<_Tp>::operator ++(int) +{ + SparseMatConstIterator_<_Tp> it = *this; + SparseMatConstIterator::operator ++(); + return it; +} + + + +///////////////////////// SparseMatIterator_ //////////////////////// + +template inline +SparseMatIterator_<_Tp>::SparseMatIterator_() +{} + +template inline +SparseMatIterator_<_Tp>::SparseMatIterator_(SparseMat_<_Tp>* _m) + : SparseMatConstIterator_<_Tp>(_m) +{} + +template inline +SparseMatIterator_<_Tp>::SparseMatIterator_(SparseMat* _m) + : SparseMatConstIterator_<_Tp>(_m) +{} + +template inline +SparseMatIterator_<_Tp>::SparseMatIterator_(const SparseMatIterator_<_Tp>& it) + : SparseMatConstIterator_<_Tp>(it) +{} + +template inline +SparseMatIterator_<_Tp>& SparseMatIterator_<_Tp>::operator = (const SparseMatIterator_<_Tp>& it) +{ + return reinterpret_cast&> + (*reinterpret_cast(this) = + reinterpret_cast(it)); +} + +template inline +_Tp& SparseMatIterator_<_Tp>::operator *() const +{ + return *(_Tp*)this->ptr; +} + +template inline +SparseMatIterator_<_Tp>& SparseMatIterator_<_Tp>::operator ++() +{ + SparseMatConstIterator::operator ++(); + return *this; +} + +template inline +SparseMatIterator_<_Tp> SparseMatIterator_<_Tp>::operator ++(int) +{ + SparseMatIterator_<_Tp> it = *this; + SparseMatConstIterator::operator ++(); + return it; +} + + + +//////////////////////// MatCommaInitializer_ /////////////////////// + +template inline +MatCommaInitializer_<_Tp>::MatCommaInitializer_(Mat_<_Tp>* _m) + : it(_m) +{} + +template template inline +MatCommaInitializer_<_Tp>& MatCommaInitializer_<_Tp>::operator , (T2 v) +{ + CV_DbgAssert( this->it < ((const Mat_<_Tp>*)this->it.m)->end() ); + *this->it = _Tp(v); + ++this->it; + return *this; +} + +template inline +MatCommaInitializer_<_Tp>::operator Mat_<_Tp>() const +{ + CV_DbgAssert( this->it == ((const Mat_<_Tp>*)this->it.m)->end() ); + return Mat_<_Tp>(*this->it.m); +} + + +template static inline +MatCommaInitializer_<_Tp> operator << (const Mat_<_Tp>& m, T2 val) +{ + MatCommaInitializer_<_Tp> commaInitializer((Mat_<_Tp>*)&m); + return (commaInitializer, val); +} + + + +///////////////////////// Matrix Expressions //////////////////////// + +inline +Mat& Mat::operator = (const MatExpr& e) +{ + e.op->assign(e, *this); + return *this; +} + +template inline +Mat_<_Tp>::Mat_(const MatExpr& e) +{ + e.op->assign(e, *this, traits::Type<_Tp>::value); +} + +template inline +Mat_<_Tp>& Mat_<_Tp>::operator = (const MatExpr& e) +{ + e.op->assign(e, *this, traits::Type<_Tp>::value); + return *this; +} + +template inline +MatExpr Mat_<_Tp>::zeros(int rows, int cols) +{ + return Mat::zeros(rows, cols, traits::Type<_Tp>::value); +} + +template inline +MatExpr Mat_<_Tp>::zeros(Size sz) +{ + return Mat::zeros(sz, traits::Type<_Tp>::value); +} + +template inline +MatExpr Mat_<_Tp>::ones(int rows, int cols) +{ + return Mat::ones(rows, cols, traits::Type<_Tp>::value); +} + +template inline +MatExpr Mat_<_Tp>::ones(Size sz) +{ + return Mat::ones(sz, traits::Type<_Tp>::value); +} + +template inline +MatExpr Mat_<_Tp>::eye(int rows, int cols) +{ + return Mat::eye(rows, cols, traits::Type<_Tp>::value); +} + +template inline +MatExpr Mat_<_Tp>::eye(Size sz) +{ + return Mat::eye(sz, traits::Type<_Tp>::value); +} + +inline +MatExpr::MatExpr() + : op(0), flags(0), a(Mat()), b(Mat()), c(Mat()), alpha(0), beta(0), s() +{} + +inline +MatExpr::MatExpr(const MatOp* _op, int _flags, const Mat& _a, const Mat& _b, + const Mat& _c, double _alpha, double _beta, const Scalar& _s) + : op(_op), flags(_flags), a(_a), b(_b), c(_c), alpha(_alpha), beta(_beta), s(_s) +{} + +inline +MatExpr::operator Mat() const +{ + Mat m; + op->assign(*this, m); + return m; +} + +template inline +MatExpr::operator Mat_<_Tp>() const +{ + Mat_<_Tp> m; + op->assign(*this, m, traits::Type<_Tp>::value); + return m; +} + + +template static inline +MatExpr min(const Mat_<_Tp>& a, const Mat_<_Tp>& b) +{ + return cv::min((const Mat&)a, (const Mat&)b); +} + +template static inline +MatExpr min(const Mat_<_Tp>& a, double s) +{ + return cv::min((const Mat&)a, s); +} + +template static inline +MatExpr min(double s, const Mat_<_Tp>& a) +{ + return cv::min((const Mat&)a, s); +} + +template static inline +MatExpr max(const Mat_<_Tp>& a, const Mat_<_Tp>& b) +{ + return cv::max((const Mat&)a, (const Mat&)b); +} + +template static inline +MatExpr max(const Mat_<_Tp>& a, double s) +{ + return cv::max((const Mat&)a, s); +} + +template static inline +MatExpr max(double s, const Mat_<_Tp>& a) +{ + return cv::max((const Mat&)a, s); +} + +template static inline +MatExpr abs(const Mat_<_Tp>& m) +{ + return cv::abs((const Mat&)m); +} + + +static inline +Mat& operator += (Mat& a, const MatExpr& b) +{ + b.op->augAssignAdd(b, a); + return a; +} + +static inline +const Mat& operator += (const Mat& a, const MatExpr& b) +{ + b.op->augAssignAdd(b, (Mat&)a); + return a; +} + +template static inline +Mat_<_Tp>& operator += (Mat_<_Tp>& a, const MatExpr& b) +{ + b.op->augAssignAdd(b, a); + return a; +} + +template static inline +const Mat_<_Tp>& operator += (const Mat_<_Tp>& a, const MatExpr& b) +{ + b.op->augAssignAdd(b, (Mat&)a); + return a; +} + +static inline +Mat& operator -= (Mat& a, const MatExpr& b) +{ + b.op->augAssignSubtract(b, a); + return a; +} + +static inline +const Mat& operator -= (const Mat& a, const MatExpr& b) +{ + b.op->augAssignSubtract(b, (Mat&)a); + return a; +} + +template static inline +Mat_<_Tp>& operator -= (Mat_<_Tp>& a, const MatExpr& b) +{ + b.op->augAssignSubtract(b, a); + return a; +} + +template static inline +const Mat_<_Tp>& operator -= (const Mat_<_Tp>& a, const MatExpr& b) +{ + b.op->augAssignSubtract(b, (Mat&)a); + return a; +} + +static inline +Mat& operator *= (Mat& a, const MatExpr& b) +{ + b.op->augAssignMultiply(b, a); + return a; +} + +static inline +const Mat& operator *= (const Mat& a, const MatExpr& b) +{ + b.op->augAssignMultiply(b, (Mat&)a); + return a; +} + +template static inline +Mat_<_Tp>& operator *= (Mat_<_Tp>& a, const MatExpr& b) +{ + b.op->augAssignMultiply(b, a); + return a; +} + +template static inline +const Mat_<_Tp>& operator *= (const Mat_<_Tp>& a, const MatExpr& b) +{ + b.op->augAssignMultiply(b, (Mat&)a); + return a; +} + +static inline +Mat& operator /= (Mat& a, const MatExpr& b) +{ + b.op->augAssignDivide(b, a); + return a; +} + +static inline +const Mat& operator /= (const Mat& a, const MatExpr& b) +{ + b.op->augAssignDivide(b, (Mat&)a); + return a; +} + +template static inline +Mat_<_Tp>& operator /= (Mat_<_Tp>& a, const MatExpr& b) +{ + b.op->augAssignDivide(b, a); + return a; +} + +template static inline +const Mat_<_Tp>& operator /= (const Mat_<_Tp>& a, const MatExpr& b) +{ + b.op->augAssignDivide(b, (Mat&)a); + return a; +} + + +//////////////////////////////// UMat //////////////////////////////// + +inline +UMat::UMat(UMatUsageFlags _usageFlags) +: flags(MAGIC_VAL), dims(0), rows(0), cols(0), allocator(0), usageFlags(_usageFlags), u(0), offset(0), size(&rows) +{} + +inline +UMat::UMat(int _rows, int _cols, int _type, UMatUsageFlags _usageFlags) +: flags(MAGIC_VAL), dims(0), rows(0), cols(0), allocator(0), usageFlags(_usageFlags), u(0), offset(0), size(&rows) +{ + create(_rows, _cols, _type); +} + +inline +UMat::UMat(int _rows, int _cols, int _type, const Scalar& _s, UMatUsageFlags _usageFlags) +: flags(MAGIC_VAL), dims(0), rows(0), cols(0), allocator(0), usageFlags(_usageFlags), u(0), offset(0), size(&rows) +{ + create(_rows, _cols, _type); + *this = _s; +} + +inline +UMat::UMat(Size _sz, int _type, UMatUsageFlags _usageFlags) +: flags(MAGIC_VAL), dims(0), rows(0), cols(0), allocator(0), usageFlags(_usageFlags), u(0), offset(0), size(&rows) +{ + create( _sz.height, _sz.width, _type ); +} + +inline +UMat::UMat(Size _sz, int _type, const Scalar& _s, UMatUsageFlags _usageFlags) +: flags(MAGIC_VAL), dims(0), rows(0), cols(0), allocator(0), usageFlags(_usageFlags), u(0), offset(0), size(&rows) +{ + create(_sz.height, _sz.width, _type); + *this = _s; +} + +inline +UMat::UMat(int _dims, const int* _sz, int _type, UMatUsageFlags _usageFlags) +: flags(MAGIC_VAL), dims(0), rows(0), cols(0), allocator(0), usageFlags(_usageFlags), u(0), offset(0), size(&rows) +{ + create(_dims, _sz, _type); +} + +inline +UMat::UMat(int _dims, const int* _sz, int _type, const Scalar& _s, UMatUsageFlags _usageFlags) +: flags(MAGIC_VAL), dims(0), rows(0), cols(0), allocator(0), usageFlags(_usageFlags), u(0), offset(0), size(&rows) +{ + create(_dims, _sz, _type); + *this = _s; +} + +inline +UMat::UMat(const UMat& m) +: flags(m.flags), dims(m.dims), rows(m.rows), cols(m.cols), allocator(m.allocator), + usageFlags(m.usageFlags), u(m.u), offset(m.offset), size(&rows) +{ + addref(); + if( m.dims <= 2 ) + { + step[0] = m.step[0]; step[1] = m.step[1]; + } + else + { + dims = 0; + copySize(m); + } +} + + +template inline +UMat::UMat(const std::vector<_Tp>& vec, bool copyData) +: flags(MAGIC_VAL | traits::Type<_Tp>::value | CV_MAT_CONT_FLAG), dims(2), rows((int)vec.size()), +cols(1), allocator(0), usageFlags(USAGE_DEFAULT), u(0), offset(0), size(&rows) +{ + if(vec.empty()) + return; + if( !copyData ) + { + // !!!TODO!!! + CV_Error(Error::StsNotImplemented, ""); + } + else + Mat((int)vec.size(), 1, traits::Type<_Tp>::value, (uchar*)&vec[0]).copyTo(*this); +} + +inline +UMat& UMat::operator = (const UMat& m) +{ + if( this != &m ) + { + const_cast(m).addref(); + release(); + flags = m.flags; + if( dims <= 2 && m.dims <= 2 ) + { + dims = m.dims; + rows = m.rows; + cols = m.cols; + step[0] = m.step[0]; + step[1] = m.step[1]; + } + else + copySize(m); + allocator = m.allocator; + if (usageFlags == USAGE_DEFAULT) + usageFlags = m.usageFlags; + u = m.u; + offset = m.offset; + } + return *this; +} + +inline +UMat UMat::row(int y) const +{ + return UMat(*this, Range(y, y + 1), Range::all()); +} + +inline +UMat UMat::col(int x) const +{ + return UMat(*this, Range::all(), Range(x, x + 1)); +} + +inline +UMat UMat::rowRange(int startrow, int endrow) const +{ + return UMat(*this, Range(startrow, endrow), Range::all()); +} + +inline +UMat UMat::rowRange(const Range& r) const +{ + return UMat(*this, r, Range::all()); +} + +inline +UMat UMat::colRange(int startcol, int endcol) const +{ + return UMat(*this, Range::all(), Range(startcol, endcol)); +} + +inline +UMat UMat::colRange(const Range& r) const +{ + return UMat(*this, Range::all(), r); +} + +inline +UMat UMat::clone() const +{ + UMat m; + copyTo(m); + return m; +} + +inline +void UMat::assignTo( UMat& m, int _type ) const +{ + if( _type < 0 ) + m = *this; + else + convertTo(m, _type); +} + +inline +void UMat::create(int _rows, int _cols, int _type, UMatUsageFlags _usageFlags) +{ + _type &= TYPE_MASK; + if( dims <= 2 && rows == _rows && cols == _cols && type() == _type && u ) + return; + int sz[] = {_rows, _cols}; + create(2, sz, _type, _usageFlags); +} + +inline +void UMat::create(Size _sz, int _type, UMatUsageFlags _usageFlags) +{ + create(_sz.height, _sz.width, _type, _usageFlags); +} + +inline +void UMat::addref() +{ + if( u ) + CV_XADD(&(u->urefcount), 1); +} + +inline void UMat::release() +{ + if( u && CV_XADD(&(u->urefcount), -1) == 1 ) + deallocate(); + for(int i = 0; i < dims; i++) + size.p[i] = 0; + u = 0; +} + +inline +UMat UMat::operator()( Range _rowRange, Range _colRange ) const +{ + return UMat(*this, _rowRange, _colRange); +} + +inline +UMat UMat::operator()( const Rect& roi ) const +{ + return UMat(*this, roi); +} + +inline +UMat UMat::operator()(const Range* ranges) const +{ + return UMat(*this, ranges); +} + +inline +UMat UMat::operator()(const std::vector& ranges) const +{ + return UMat(*this, ranges); +} + +inline +bool UMat::isContinuous() const +{ + return (flags & CONTINUOUS_FLAG) != 0; +} + +inline +bool UMat::isSubmatrix() const +{ + return (flags & SUBMATRIX_FLAG) != 0; +} + +inline +size_t UMat::elemSize() const +{ + size_t res = dims > 0 ? step.p[dims - 1] : 0; + CV_DbgAssert(res != 0); + return res; +} + +inline +size_t UMat::elemSize1() const +{ + return CV_ELEM_SIZE1(flags); +} + +inline +int UMat::type() const +{ + return CV_MAT_TYPE(flags); +} + +inline +int UMat::depth() const +{ + return CV_MAT_DEPTH(flags); +} + +inline +int UMat::channels() const +{ + return CV_MAT_CN(flags); +} + +inline +size_t UMat::step1(int i) const +{ + return step.p[i] / elemSize1(); +} + +inline +bool UMat::empty() const +{ + return u == 0 || total() == 0 || dims == 0; +} + +inline +size_t UMat::total() const +{ + if( dims <= 2 ) + return (size_t)rows * cols; + size_t p = 1; + for( int i = 0; i < dims; i++ ) + p *= size[i]; + return p; +} + +#ifdef CV_CXX_MOVE_SEMANTICS + +inline +UMat::UMat(UMat&& m) +: flags(m.flags), dims(m.dims), rows(m.rows), cols(m.cols), allocator(m.allocator), + usageFlags(m.usageFlags), u(m.u), offset(m.offset), size(&rows) +{ + if (m.dims <= 2) // move new step/size info + { + step[0] = m.step[0]; + step[1] = m.step[1]; + } + else + { + CV_DbgAssert(m.step.p != m.step.buf); + step.p = m.step.p; + size.p = m.size.p; + m.step.p = m.step.buf; + m.size.p = &m.rows; + } + m.flags = MAGIC_VAL; m.dims = m.rows = m.cols = 0; + m.allocator = NULL; + m.u = NULL; + m.offset = 0; +} + +inline +UMat& UMat::operator = (UMat&& m) +{ + if (this == &m) + return *this; + release(); + flags = m.flags; dims = m.dims; rows = m.rows; cols = m.cols; + allocator = m.allocator; usageFlags = m.usageFlags; + u = m.u; + offset = m.offset; + if (step.p != step.buf) // release self step/size + { + fastFree(step.p); + step.p = step.buf; + size.p = &rows; + } + if (m.dims <= 2) // move new step/size info + { + step[0] = m.step[0]; + step[1] = m.step[1]; + } + else + { + CV_DbgAssert(m.step.p != m.step.buf); + step.p = m.step.p; + size.p = m.size.p; + m.step.p = m.step.buf; + m.size.p = &m.rows; + } + m.flags = MAGIC_VAL; m.dims = m.rows = m.cols = 0; + m.allocator = NULL; + m.u = NULL; + m.offset = 0; + return *this; +} + +#endif + + +inline bool UMatData::hostCopyObsolete() const { return (flags & HOST_COPY_OBSOLETE) != 0; } +inline bool UMatData::deviceCopyObsolete() const { return (flags & DEVICE_COPY_OBSOLETE) != 0; } +inline bool UMatData::deviceMemMapped() const { return (flags & DEVICE_MEM_MAPPED) != 0; } +inline bool UMatData::copyOnMap() const { return (flags & COPY_ON_MAP) != 0; } +inline bool UMatData::tempUMat() const { return (flags & TEMP_UMAT) != 0; } +inline bool UMatData::tempCopiedUMat() const { return (flags & TEMP_COPIED_UMAT) == TEMP_COPIED_UMAT; } + +inline void UMatData::markDeviceMemMapped(bool flag) +{ + if(flag) + flags |= DEVICE_MEM_MAPPED; + else + flags &= ~DEVICE_MEM_MAPPED; +} + +inline void UMatData::markHostCopyObsolete(bool flag) +{ + if(flag) + flags |= HOST_COPY_OBSOLETE; + else + flags &= ~HOST_COPY_OBSOLETE; +} +inline void UMatData::markDeviceCopyObsolete(bool flag) +{ + if(flag) + flags |= DEVICE_COPY_OBSOLETE; + else + flags &= ~DEVICE_COPY_OBSOLETE; +} + +//! @endcond + +} //cv + +#ifdef _MSC_VER +#pragma warning( pop ) +#endif + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/matx.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/matx.hpp new file mode 100644 index 0000000000000000000000000000000000000000..bf3f046276a6e893dcb5138e5bef1be6417701f0 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/matx.hpp @@ -0,0 +1,1477 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_MATX_HPP +#define OPENCV_CORE_MATX_HPP + +#ifndef __cplusplus +# error matx.hpp header must be compiled as C++ +#endif + +#include "opencv2/core/cvdef.h" +#include "opencv2/core/base.hpp" +#include "opencv2/core/traits.hpp" +#include "opencv2/core/saturate.hpp" + +#ifdef CV_CXX11 +#include +#endif + +namespace cv +{ + +//! @addtogroup core_basic +//! @{ + +////////////////////////////// Small Matrix /////////////////////////// + +//! @cond IGNORED +// FIXIT Remove this (especially CV_EXPORTS modifier) +struct CV_EXPORTS Matx_AddOp { Matx_AddOp() {} Matx_AddOp(const Matx_AddOp&) {} }; +struct CV_EXPORTS Matx_SubOp { Matx_SubOp() {} Matx_SubOp(const Matx_SubOp&) {} }; +struct CV_EXPORTS Matx_ScaleOp { Matx_ScaleOp() {} Matx_ScaleOp(const Matx_ScaleOp&) {} }; +struct CV_EXPORTS Matx_MulOp { Matx_MulOp() {} Matx_MulOp(const Matx_MulOp&) {} }; +struct CV_EXPORTS Matx_DivOp { Matx_DivOp() {} Matx_DivOp(const Matx_DivOp&) {} }; +struct CV_EXPORTS Matx_MatMulOp { Matx_MatMulOp() {} Matx_MatMulOp(const Matx_MatMulOp&) {} }; +struct CV_EXPORTS Matx_TOp { Matx_TOp() {} Matx_TOp(const Matx_TOp&) {} }; +//! @endcond + +/** @brief Template class for small matrices whose type and size are known at compilation time + +If you need a more flexible type, use Mat . The elements of the matrix M are accessible using the +M(i,j) notation. Most of the common matrix operations (see also @ref MatrixExpressions ) are +available. To do an operation on Matx that is not implemented, you can easily convert the matrix to +Mat and backwards: +@code{.cpp} + Matx33f m(1, 2, 3, + 4, 5, 6, + 7, 8, 9); + cout << sum(Mat(m*m.t())) << endl; +@endcode +Except of the plain constructor which takes a list of elements, Matx can be initialized from a C-array: +@code{.cpp} + float values[] = { 1, 2, 3}; + Matx31f m(values); +@endcode +In case if C++11 features are available, std::initializer_list can be also used to initialize Matx: +@code{.cpp} + Matx31f m = { 1, 2, 3}; +@endcode + */ +template class Matx +{ +public: + enum { + rows = m, + cols = n, + channels = rows*cols, +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + depth = traits::Type<_Tp>::value, + type = CV_MAKETYPE(depth, channels), +#endif + shortdim = (m < n ? m : n) + }; + + typedef _Tp value_type; + typedef Matx<_Tp, m, n> mat_type; + typedef Matx<_Tp, shortdim, 1> diag_type; + + //! default constructor + Matx(); + + explicit Matx(_Tp v0); //!< 1x1 matrix + Matx(_Tp v0, _Tp v1); //!< 1x2 or 2x1 matrix + Matx(_Tp v0, _Tp v1, _Tp v2); //!< 1x3 or 3x1 matrix + Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3); //!< 1x4, 2x2 or 4x1 matrix + Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4); //!< 1x5 or 5x1 matrix + Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5); //!< 1x6, 2x3, 3x2 or 6x1 matrix + Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6); //!< 1x7 or 7x1 matrix + Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7); //!< 1x8, 2x4, 4x2 or 8x1 matrix + Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8); //!< 1x9, 3x3 or 9x1 matrix + Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9); //!< 1x10, 2x5 or 5x2 or 10x1 matrix + Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, + _Tp v4, _Tp v5, _Tp v6, _Tp v7, + _Tp v8, _Tp v9, _Tp v10, _Tp v11); //!< 1x12, 2x6, 3x4, 4x3, 6x2 or 12x1 matrix + Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, + _Tp v4, _Tp v5, _Tp v6, _Tp v7, + _Tp v8, _Tp v9, _Tp v10, _Tp v11, + _Tp v12, _Tp v13); //!< 1x14, 2x7, 7x2 or 14x1 matrix + Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, + _Tp v4, _Tp v5, _Tp v6, _Tp v7, + _Tp v8, _Tp v9, _Tp v10, _Tp v11, + _Tp v12, _Tp v13, _Tp v14, _Tp v15); //!< 1x16, 4x4 or 16x1 matrix + explicit Matx(const _Tp* vals); //!< initialize from a plain array + +#ifdef CV_CXX11 + Matx(std::initializer_list<_Tp>); //!< initialize from an initializer list +#endif + + static Matx all(_Tp alpha); + static Matx zeros(); + static Matx ones(); + static Matx eye(); + static Matx diag(const diag_type& d); + static Matx randu(_Tp a, _Tp b); + static Matx randn(_Tp a, _Tp b); + + //! dot product computed with the default precision + _Tp dot(const Matx<_Tp, m, n>& v) const; + + //! dot product computed in double-precision arithmetics + double ddot(const Matx<_Tp, m, n>& v) const; + + //! conversion to another data type + template operator Matx() const; + + //! change the matrix shape + template Matx<_Tp, m1, n1> reshape() const; + + //! extract part of the matrix + template Matx<_Tp, m1, n1> get_minor(int i, int j) const; + + //! extract the matrix row + Matx<_Tp, 1, n> row(int i) const; + + //! extract the matrix column + Matx<_Tp, m, 1> col(int i) const; + + //! extract the matrix diagonal + diag_type diag() const; + + //! transpose the matrix + Matx<_Tp, n, m> t() const; + + //! invert the matrix + Matx<_Tp, n, m> inv(int method=DECOMP_LU, bool *p_is_ok = NULL) const; + + //! solve linear system + template Matx<_Tp, n, l> solve(const Matx<_Tp, m, l>& rhs, int flags=DECOMP_LU) const; + Vec<_Tp, n> solve(const Vec<_Tp, m>& rhs, int method) const; + + //! multiply two matrices element-wise + Matx<_Tp, m, n> mul(const Matx<_Tp, m, n>& a) const; + + //! divide two matrices element-wise + Matx<_Tp, m, n> div(const Matx<_Tp, m, n>& a) const; + + //! element access + const _Tp& operator ()(int i, int j) const; + _Tp& operator ()(int i, int j); + + //! 1D element access + const _Tp& operator ()(int i) const; + _Tp& operator ()(int i); + + Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_AddOp); + Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_SubOp); + template Matx(const Matx<_Tp, m, n>& a, _T2 alpha, Matx_ScaleOp); + Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_MulOp); + Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_DivOp); + template Matx(const Matx<_Tp, m, l>& a, const Matx<_Tp, l, n>& b, Matx_MatMulOp); + Matx(const Matx<_Tp, n, m>& a, Matx_TOp); + + _Tp val[m*n]; //< matrix elements +}; + +typedef Matx Matx12f; +typedef Matx Matx12d; +typedef Matx Matx13f; +typedef Matx Matx13d; +typedef Matx Matx14f; +typedef Matx Matx14d; +typedef Matx Matx16f; +typedef Matx Matx16d; + +typedef Matx Matx21f; +typedef Matx Matx21d; +typedef Matx Matx31f; +typedef Matx Matx31d; +typedef Matx Matx41f; +typedef Matx Matx41d; +typedef Matx Matx61f; +typedef Matx Matx61d; + +typedef Matx Matx22f; +typedef Matx Matx22d; +typedef Matx Matx23f; +typedef Matx Matx23d; +typedef Matx Matx32f; +typedef Matx Matx32d; + +typedef Matx Matx33f; +typedef Matx Matx33d; + +typedef Matx Matx34f; +typedef Matx Matx34d; +typedef Matx Matx43f; +typedef Matx Matx43d; + +typedef Matx Matx44f; +typedef Matx Matx44d; +typedef Matx Matx66f; +typedef Matx Matx66d; + +/*! + traits +*/ +template class DataType< Matx<_Tp, m, n> > +{ +public: + typedef Matx<_Tp, m, n> value_type; + typedef Matx::work_type, m, n> work_type; + typedef _Tp channel_type; + typedef value_type vec_type; + + enum { generic_type = 0, + channels = m * n, + fmt = traits::SafeFmt::fmt + ((channels - 1) << 8) +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + ,depth = DataType::depth + ,type = CV_MAKETYPE(depth, channels) +#endif + }; +}; + +namespace traits { +template +struct Depth< Matx<_Tp, m, n> > { enum { value = Depth<_Tp>::value }; }; +template +struct Type< Matx<_Tp, m, n> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, n*m) }; }; +} // namespace + + +/** @brief Comma-separated Matrix Initializer +*/ +template class MatxCommaInitializer +{ +public: + MatxCommaInitializer(Matx<_Tp, m, n>* _mtx); + template MatxCommaInitializer<_Tp, m, n>& operator , (T2 val); + Matx<_Tp, m, n> operator *() const; + + Matx<_Tp, m, n>* dst; + int idx; +}; + +/* + Utility methods +*/ +template static double determinant(const Matx<_Tp, m, m>& a); +template static double trace(const Matx<_Tp, m, n>& a); +template static double norm(const Matx<_Tp, m, n>& M); +template static double norm(const Matx<_Tp, m, n>& M, int normType); + + + +/////////////////////// Vec (used as element of multi-channel images ///////////////////// + +/** @brief Template class for short numerical vectors, a partial case of Matx + +This template class represents short numerical vectors (of 1, 2, 3, 4 ... elements) on which you +can perform basic arithmetical operations, access individual elements using [] operator etc. The +vectors are allocated on stack, as opposite to std::valarray, std::vector, cv::Mat etc., which +elements are dynamically allocated in the heap. + +The template takes 2 parameters: +@tparam _Tp element type +@tparam cn the number of elements + +In addition to the universal notation like Vec, you can use shorter aliases +for the most popular specialized variants of Vec, e.g. Vec3f ~ Vec. + +It is possible to convert Vec\ to/from Point_, Vec\ to/from Point3_ , and Vec\ +to CvScalar or Scalar_. Use operator[] to access the elements of Vec. + +All the expected vector operations are also implemented: +- v1 = v2 + v3 +- v1 = v2 - v3 +- v1 = v2 \* scale +- v1 = scale \* v2 +- v1 = -v2 +- v1 += v2 and other augmenting operations +- v1 == v2, v1 != v2 +- norm(v1) (euclidean norm) +The Vec class is commonly used to describe pixel types of multi-channel arrays. See Mat for details. +*/ +template class Vec : public Matx<_Tp, cn, 1> +{ +public: + typedef _Tp value_type; + enum { + channels = cn, +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + depth = Matx<_Tp, cn, 1>::depth, + type = CV_MAKETYPE(depth, channels), +#endif + _dummy_enum_finalizer = 0 + }; + + //! default constructor + Vec(); + + Vec(_Tp v0); //!< 1-element vector constructor + Vec(_Tp v0, _Tp v1); //!< 2-element vector constructor + Vec(_Tp v0, _Tp v1, _Tp v2); //!< 3-element vector constructor + Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3); //!< 4-element vector constructor + Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4); //!< 5-element vector constructor + Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5); //!< 6-element vector constructor + Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6); //!< 7-element vector constructor + Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7); //!< 8-element vector constructor + Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8); //!< 9-element vector constructor + Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9); //!< 10-element vector constructor + Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9, _Tp v10, _Tp v11, _Tp v12, _Tp v13); //!< 14-element vector constructor + explicit Vec(const _Tp* values); + +#ifdef CV_CXX11 + Vec(std::initializer_list<_Tp>); +#endif + + Vec(const Vec<_Tp, cn>& v); + + static Vec all(_Tp alpha); + + //! per-element multiplication + Vec mul(const Vec<_Tp, cn>& v) const; + + //! conjugation (makes sense for complex numbers and quaternions) + Vec conj() const; + + /*! + cross product of the two 3D vectors. + + For other dimensionalities the exception is raised + */ + Vec cross(const Vec& v) const; + //! conversion to another data type + template operator Vec() const; + + /*! element access */ + const _Tp& operator [](int i) const; + _Tp& operator[](int i); + const _Tp& operator ()(int i) const; + _Tp& operator ()(int i); + + Vec(const Matx<_Tp, cn, 1>& a, const Matx<_Tp, cn, 1>& b, Matx_AddOp); + Vec(const Matx<_Tp, cn, 1>& a, const Matx<_Tp, cn, 1>& b, Matx_SubOp); + template Vec(const Matx<_Tp, cn, 1>& a, _T2 alpha, Matx_ScaleOp); +}; + +/** @name Shorter aliases for the most popular specializations of Vec + @{ +*/ +typedef Vec Vec2b; +typedef Vec Vec3b; +typedef Vec Vec4b; + +typedef Vec Vec2s; +typedef Vec Vec3s; +typedef Vec Vec4s; + +typedef Vec Vec2w; +typedef Vec Vec3w; +typedef Vec Vec4w; + +typedef Vec Vec2i; +typedef Vec Vec3i; +typedef Vec Vec4i; +typedef Vec Vec6i; +typedef Vec Vec8i; + +typedef Vec Vec2f; +typedef Vec Vec3f; +typedef Vec Vec4f; +typedef Vec Vec6f; + +typedef Vec Vec2d; +typedef Vec Vec3d; +typedef Vec Vec4d; +typedef Vec Vec6d; +/** @} */ + +/*! + traits +*/ +template class DataType< Vec<_Tp, cn> > +{ +public: + typedef Vec<_Tp, cn> value_type; + typedef Vec::work_type, cn> work_type; + typedef _Tp channel_type; + typedef value_type vec_type; + + enum { generic_type = 0, + channels = cn, + fmt = DataType::fmt + ((channels - 1) << 8), +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + depth = DataType::depth, + type = CV_MAKETYPE(depth, channels), +#endif + _dummy_enum_finalizer = 0 + }; +}; + +namespace traits { +template +struct Depth< Vec<_Tp, cn> > { enum { value = Depth<_Tp>::value }; }; +template +struct Type< Vec<_Tp, cn> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, cn) }; }; +} // namespace + + +/** @brief Comma-separated Vec Initializer +*/ +template class VecCommaInitializer : public MatxCommaInitializer<_Tp, m, 1> +{ +public: + VecCommaInitializer(Vec<_Tp, m>* _vec); + template VecCommaInitializer<_Tp, m>& operator , (T2 val); + Vec<_Tp, m> operator *() const; +}; + +template static Vec<_Tp, cn> normalize(const Vec<_Tp, cn>& v); + +//! @} core_basic + +//! @cond IGNORED + +///////////////////////////////////// helper classes ///////////////////////////////////// +namespace internal +{ + +template struct Matx_DetOp +{ + double operator ()(const Matx<_Tp, m, m>& a) const + { + Matx<_Tp, m, m> temp = a; + double p = LU(temp.val, m*sizeof(_Tp), m, 0, 0, 0); + if( p == 0 ) + return p; + for( int i = 0; i < m; i++ ) + p *= temp(i, i); + return p; + } +}; + +template struct Matx_DetOp<_Tp, 1> +{ + double operator ()(const Matx<_Tp, 1, 1>& a) const + { + return a(0,0); + } +}; + +template struct Matx_DetOp<_Tp, 2> +{ + double operator ()(const Matx<_Tp, 2, 2>& a) const + { + return a(0,0)*a(1,1) - a(0,1)*a(1,0); + } +}; + +template struct Matx_DetOp<_Tp, 3> +{ + double operator ()(const Matx<_Tp, 3, 3>& a) const + { + return a(0,0)*(a(1,1)*a(2,2) - a(2,1)*a(1,2)) - + a(0,1)*(a(1,0)*a(2,2) - a(2,0)*a(1,2)) + + a(0,2)*(a(1,0)*a(2,1) - a(2,0)*a(1,1)); + } +}; + +template Vec<_Tp, 2> inline conjugate(const Vec<_Tp, 2>& v) +{ + return Vec<_Tp, 2>(v[0], -v[1]); +} + +template Vec<_Tp, 4> inline conjugate(const Vec<_Tp, 4>& v) +{ + return Vec<_Tp, 4>(v[0], -v[1], -v[2], -v[3]); +} + +} // internal + + + +////////////////////////////////// Matx Implementation /////////////////////////////////// + +template inline +Matx<_Tp, m, n>::Matx() +{ + for(int i = 0; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp, m, n>::Matx(_Tp v0) +{ + val[0] = v0; + for(int i = 1; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1) +{ + CV_StaticAssert(channels >= 2, "Matx should have at least 2 elements."); + val[0] = v0; val[1] = v1; + for(int i = 2; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2) +{ + CV_StaticAssert(channels >= 3, "Matx should have at least 3 elements."); + val[0] = v0; val[1] = v1; val[2] = v2; + for(int i = 3; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3) +{ + CV_StaticAssert(channels >= 4, "Matx should have at least 4 elements."); + val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; + for(int i = 4; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4) +{ + CV_StaticAssert(channels >= 5, "Matx should have at least 5 elements."); + val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; val[4] = v4; + for(int i = 5; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5) +{ + CV_StaticAssert(channels >= 6, "Matx should have at least 6 elements."); + val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; + val[4] = v4; val[5] = v5; + for(int i = 6; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6) +{ + CV_StaticAssert(channels >= 7, "Matx should have at least 7 elements."); + val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; + val[4] = v4; val[5] = v5; val[6] = v6; + for(int i = 7; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7) +{ + CV_StaticAssert(channels >= 8, "Matx should have at least 8 elements."); + val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; + val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7; + for(int i = 8; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8) +{ + CV_StaticAssert(channels >= 9, "Matx should have at least 9 elements."); + val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; + val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7; + val[8] = v8; + for(int i = 9; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9) +{ + CV_StaticAssert(channels >= 10, "Matx should have at least 10 elements."); + val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; + val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7; + val[8] = v8; val[9] = v9; + for(int i = 10; i < channels; i++) val[i] = _Tp(0); +} + + +template inline +Matx<_Tp,m,n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9, _Tp v10, _Tp v11) +{ + CV_StaticAssert(channels >= 12, "Matx should have at least 12 elements."); + val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; + val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7; + val[8] = v8; val[9] = v9; val[10] = v10; val[11] = v11; + for(int i = 12; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp,m,n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9, _Tp v10, _Tp v11, _Tp v12, _Tp v13) +{ + CV_StaticAssert(channels >= 14, "Matx should have at least 14 elements."); + val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; + val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7; + val[8] = v8; val[9] = v9; val[10] = v10; val[11] = v11; + val[12] = v12; val[13] = v13; + for (int i = 14; i < channels; i++) val[i] = _Tp(0); +} + + +template inline +Matx<_Tp,m,n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9, _Tp v10, _Tp v11, _Tp v12, _Tp v13, _Tp v14, _Tp v15) +{ + CV_StaticAssert(channels >= 16, "Matx should have at least 16 elements."); + val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; + val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7; + val[8] = v8; val[9] = v9; val[10] = v10; val[11] = v11; + val[12] = v12; val[13] = v13; val[14] = v14; val[15] = v15; + for(int i = 16; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp, m, n>::Matx(const _Tp* values) +{ + for( int i = 0; i < channels; i++ ) val[i] = values[i]; +} + +#ifdef CV_CXX11 +template inline +Matx<_Tp, m, n>::Matx(std::initializer_list<_Tp> list) +{ + CV_DbgAssert(list.size() == channels); + int i = 0; + for(const auto& elem : list) + { + val[i++] = elem; + } +} +#endif + +template inline +Matx<_Tp, m, n> Matx<_Tp, m, n>::all(_Tp alpha) +{ + Matx<_Tp, m, n> M; + for( int i = 0; i < m*n; i++ ) M.val[i] = alpha; + return M; +} + +template inline +Matx<_Tp,m,n> Matx<_Tp,m,n>::zeros() +{ + return all(0); +} + +template inline +Matx<_Tp,m,n> Matx<_Tp,m,n>::ones() +{ + return all(1); +} + +template inline +Matx<_Tp,m,n> Matx<_Tp,m,n>::eye() +{ + Matx<_Tp,m,n> M; + for(int i = 0; i < shortdim; i++) + M(i,i) = 1; + return M; +} + +template inline +_Tp Matx<_Tp, m, n>::dot(const Matx<_Tp, m, n>& M) const +{ + _Tp s = 0; + for( int i = 0; i < channels; i++ ) s += val[i]*M.val[i]; + return s; +} + +template inline +double Matx<_Tp, m, n>::ddot(const Matx<_Tp, m, n>& M) const +{ + double s = 0; + for( int i = 0; i < channels; i++ ) s += (double)val[i]*M.val[i]; + return s; +} + +template inline +Matx<_Tp,m,n> Matx<_Tp,m,n>::diag(const typename Matx<_Tp,m,n>::diag_type& d) +{ + Matx<_Tp,m,n> M; + for(int i = 0; i < shortdim; i++) + M(i,i) = d(i, 0); + return M; +} + +template template +inline Matx<_Tp, m, n>::operator Matx() const +{ + Matx M; + for( int i = 0; i < m*n; i++ ) M.val[i] = saturate_cast(val[i]); + return M; +} + +template template inline +Matx<_Tp, m1, n1> Matx<_Tp, m, n>::reshape() const +{ + CV_StaticAssert(m1*n1 == m*n, "Input and destnarion matrices must have the same number of elements"); + return (const Matx<_Tp, m1, n1>&)*this; +} + +template +template inline +Matx<_Tp, m1, n1> Matx<_Tp, m, n>::get_minor(int i, int j) const +{ + CV_DbgAssert(0 <= i && i+m1 <= m && 0 <= j && j+n1 <= n); + Matx<_Tp, m1, n1> s; + for( int di = 0; di < m1; di++ ) + for( int dj = 0; dj < n1; dj++ ) + s(di, dj) = (*this)(i+di, j+dj); + return s; +} + +template inline +Matx<_Tp, 1, n> Matx<_Tp, m, n>::row(int i) const +{ + CV_DbgAssert((unsigned)i < (unsigned)m); + return Matx<_Tp, 1, n>(&val[i*n]); +} + +template inline +Matx<_Tp, m, 1> Matx<_Tp, m, n>::col(int j) const +{ + CV_DbgAssert((unsigned)j < (unsigned)n); + Matx<_Tp, m, 1> v; + for( int i = 0; i < m; i++ ) + v.val[i] = val[i*n + j]; + return v; +} + +template inline +typename Matx<_Tp, m, n>::diag_type Matx<_Tp, m, n>::diag() const +{ + diag_type d; + for( int i = 0; i < shortdim; i++ ) + d.val[i] = val[i*n + i]; + return d; +} + +template inline +const _Tp& Matx<_Tp, m, n>::operator()(int i, int j) const +{ + CV_DbgAssert( (unsigned)i < (unsigned)m && (unsigned)j < (unsigned)n ); + return this->val[i*n + j]; +} + +template inline +_Tp& Matx<_Tp, m, n>::operator ()(int i, int j) +{ + CV_DbgAssert( (unsigned)i < (unsigned)m && (unsigned)j < (unsigned)n ); + return val[i*n + j]; +} + +template inline +const _Tp& Matx<_Tp, m, n>::operator ()(int i) const +{ + CV_StaticAssert(m == 1 || n == 1, "Single index indexation requires matrix to be a column or a row"); + CV_DbgAssert( (unsigned)i < (unsigned)(m+n-1) ); + return val[i]; +} + +template inline +_Tp& Matx<_Tp, m, n>::operator ()(int i) +{ + CV_StaticAssert(m == 1 || n == 1, "Single index indexation requires matrix to be a column or a row"); + CV_DbgAssert( (unsigned)i < (unsigned)(m+n-1) ); + return val[i]; +} + +template inline +Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_AddOp) +{ + for( int i = 0; i < channels; i++ ) + val[i] = saturate_cast<_Tp>(a.val[i] + b.val[i]); +} + +template inline +Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_SubOp) +{ + for( int i = 0; i < channels; i++ ) + val[i] = saturate_cast<_Tp>(a.val[i] - b.val[i]); +} + +template template inline +Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, _T2 alpha, Matx_ScaleOp) +{ + for( int i = 0; i < channels; i++ ) + val[i] = saturate_cast<_Tp>(a.val[i] * alpha); +} + +template inline +Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_MulOp) +{ + for( int i = 0; i < channels; i++ ) + val[i] = saturate_cast<_Tp>(a.val[i] * b.val[i]); +} + +template inline +Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_DivOp) +{ + for( int i = 0; i < channels; i++ ) + val[i] = saturate_cast<_Tp>(a.val[i] / b.val[i]); +} + +template template inline +Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, l>& a, const Matx<_Tp, l, n>& b, Matx_MatMulOp) +{ + for( int i = 0; i < m; i++ ) + for( int j = 0; j < n; j++ ) + { + _Tp s = 0; + for( int k = 0; k < l; k++ ) + s += a(i, k) * b(k, j); + val[i*n + j] = s; + } +} + +template inline +Matx<_Tp,m,n>::Matx(const Matx<_Tp, n, m>& a, Matx_TOp) +{ + for( int i = 0; i < m; i++ ) + for( int j = 0; j < n; j++ ) + val[i*n + j] = a(j, i); +} + +template inline +Matx<_Tp, m, n> Matx<_Tp, m, n>::mul(const Matx<_Tp, m, n>& a) const +{ + return Matx<_Tp, m, n>(*this, a, Matx_MulOp()); +} + +template inline +Matx<_Tp, m, n> Matx<_Tp, m, n>::div(const Matx<_Tp, m, n>& a) const +{ + return Matx<_Tp, m, n>(*this, a, Matx_DivOp()); +} + +template inline +Matx<_Tp, n, m> Matx<_Tp, m, n>::t() const +{ + return Matx<_Tp, n, m>(*this, Matx_TOp()); +} + +template inline +Vec<_Tp, n> Matx<_Tp, m, n>::solve(const Vec<_Tp, m>& rhs, int method) const +{ + Matx<_Tp, n, 1> x = solve((const Matx<_Tp, m, 1>&)(rhs), method); + return (Vec<_Tp, n>&)(x); +} + +template static inline +double determinant(const Matx<_Tp, m, m>& a) +{ + return cv::internal::Matx_DetOp<_Tp, m>()(a); +} + +template static inline +double trace(const Matx<_Tp, m, n>& a) +{ + _Tp s = 0; + for( int i = 0; i < std::min(m, n); i++ ) + s += a(i,i); + return s; +} + +template static inline +double norm(const Matx<_Tp, m, n>& M) +{ + return std::sqrt(normL2Sqr<_Tp, double>(M.val, m*n)); +} + +template static inline +double norm(const Matx<_Tp, m, n>& M, int normType) +{ + switch(normType) { + case NORM_INF: + return (double)normInf<_Tp, typename DataType<_Tp>::work_type>(M.val, m*n); + case NORM_L1: + return (double)normL1<_Tp, typename DataType<_Tp>::work_type>(M.val, m*n); + case NORM_L2SQR: + return (double)normL2Sqr<_Tp, typename DataType<_Tp>::work_type>(M.val, m*n); + default: + case NORM_L2: + return std::sqrt((double)normL2Sqr<_Tp, typename DataType<_Tp>::work_type>(M.val, m*n)); + } +} + + + +//////////////////////////////// matx comma initializer ////////////////////////////////// + +template static inline +MatxCommaInitializer<_Tp, m, n> operator << (const Matx<_Tp, m, n>& mtx, _T2 val) +{ + MatxCommaInitializer<_Tp, m, n> commaInitializer((Matx<_Tp, m, n>*)&mtx); + return (commaInitializer, val); +} + +template inline +MatxCommaInitializer<_Tp, m, n>::MatxCommaInitializer(Matx<_Tp, m, n>* _mtx) + : dst(_mtx), idx(0) +{} + +template template inline +MatxCommaInitializer<_Tp, m, n>& MatxCommaInitializer<_Tp, m, n>::operator , (_T2 value) +{ + CV_DbgAssert( idx < m*n ); + dst->val[idx++] = saturate_cast<_Tp>(value); + return *this; +} + +template inline +Matx<_Tp, m, n> MatxCommaInitializer<_Tp, m, n>::operator *() const +{ + CV_DbgAssert( idx == n*m ); + return *dst; +} + + + +/////////////////////////////////// Vec Implementation /////////////////////////////////// + +template inline +Vec<_Tp, cn>::Vec() {} + +template inline +Vec<_Tp, cn>::Vec(_Tp v0) + : Matx<_Tp, cn, 1>(v0) {} + +template inline +Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1) + : Matx<_Tp, cn, 1>(v0, v1) {} + +template inline +Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2) + : Matx<_Tp, cn, 1>(v0, v1, v2) {} + +template inline +Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3) + : Matx<_Tp, cn, 1>(v0, v1, v2, v3) {} + +template inline +Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4) + : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4) {} + +template inline +Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5) + : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5) {} + +template inline +Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6) + : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6) {} + +template inline +Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7) + : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6, v7) {} + +template inline +Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8) + : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6, v7, v8) {} + +template inline +Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9) + : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6, v7, v8, v9) {} + +template inline +Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9, _Tp v10, _Tp v11, _Tp v12, _Tp v13) + : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13) {} + +template inline +Vec<_Tp, cn>::Vec(const _Tp* values) + : Matx<_Tp, cn, 1>(values) {} + +#ifdef CV_CXX11 +template inline +Vec<_Tp, cn>::Vec(std::initializer_list<_Tp> list) + : Matx<_Tp, cn, 1>(list) {} +#endif + +template inline +Vec<_Tp, cn>::Vec(const Vec<_Tp, cn>& m) + : Matx<_Tp, cn, 1>(m.val) {} + +template inline +Vec<_Tp, cn>::Vec(const Matx<_Tp, cn, 1>& a, const Matx<_Tp, cn, 1>& b, Matx_AddOp op) + : Matx<_Tp, cn, 1>(a, b, op) {} + +template inline +Vec<_Tp, cn>::Vec(const Matx<_Tp, cn, 1>& a, const Matx<_Tp, cn, 1>& b, Matx_SubOp op) + : Matx<_Tp, cn, 1>(a, b, op) {} + +template template inline +Vec<_Tp, cn>::Vec(const Matx<_Tp, cn, 1>& a, _T2 alpha, Matx_ScaleOp op) + : Matx<_Tp, cn, 1>(a, alpha, op) {} + +template inline +Vec<_Tp, cn> Vec<_Tp, cn>::all(_Tp alpha) +{ + Vec v; + for( int i = 0; i < cn; i++ ) v.val[i] = alpha; + return v; +} + +template inline +Vec<_Tp, cn> Vec<_Tp, cn>::mul(const Vec<_Tp, cn>& v) const +{ + Vec<_Tp, cn> w; + for( int i = 0; i < cn; i++ ) w.val[i] = saturate_cast<_Tp>(this->val[i]*v.val[i]); + return w; +} + +template<> inline +Vec Vec::conj() const +{ + return cv::internal::conjugate(*this); +} + +template<> inline +Vec Vec::conj() const +{ + return cv::internal::conjugate(*this); +} + +template<> inline +Vec Vec::conj() const +{ + return cv::internal::conjugate(*this); +} + +template<> inline +Vec Vec::conj() const +{ + return cv::internal::conjugate(*this); +} + +template inline +Vec<_Tp, cn> Vec<_Tp, cn>::cross(const Vec<_Tp, cn>&) const +{ + CV_StaticAssert(cn == 3, "for arbitrary-size vector there is no cross-product defined"); + return Vec<_Tp, cn>(); +} + +template<> inline +Vec Vec::cross(const Vec& v) const +{ + return Vec(this->val[1]*v.val[2] - this->val[2]*v.val[1], + this->val[2]*v.val[0] - this->val[0]*v.val[2], + this->val[0]*v.val[1] - this->val[1]*v.val[0]); +} + +template<> inline +Vec Vec::cross(const Vec& v) const +{ + return Vec(this->val[1]*v.val[2] - this->val[2]*v.val[1], + this->val[2]*v.val[0] - this->val[0]*v.val[2], + this->val[0]*v.val[1] - this->val[1]*v.val[0]); +} + +template template inline +Vec<_Tp, cn>::operator Vec() const +{ + Vec v; + for( int i = 0; i < cn; i++ ) v.val[i] = saturate_cast(this->val[i]); + return v; +} + +template inline +const _Tp& Vec<_Tp, cn>::operator [](int i) const +{ + CV_DbgAssert( (unsigned)i < (unsigned)cn ); + return this->val[i]; +} + +template inline +_Tp& Vec<_Tp, cn>::operator [](int i) +{ + CV_DbgAssert( (unsigned)i < (unsigned)cn ); + return this->val[i]; +} + +template inline +const _Tp& Vec<_Tp, cn>::operator ()(int i) const +{ + CV_DbgAssert( (unsigned)i < (unsigned)cn ); + return this->val[i]; +} + +template inline +_Tp& Vec<_Tp, cn>::operator ()(int i) +{ + CV_DbgAssert( (unsigned)i < (unsigned)cn ); + return this->val[i]; +} + +template inline +Vec<_Tp, cn> normalize(const Vec<_Tp, cn>& v) +{ + double nv = norm(v); + return v * (nv ? 1./nv : 0.); +} + + + +//////////////////////////////// vec comma initializer ////////////////////////////////// + + +template static inline +VecCommaInitializer<_Tp, cn> operator << (const Vec<_Tp, cn>& vec, _T2 val) +{ + VecCommaInitializer<_Tp, cn> commaInitializer((Vec<_Tp, cn>*)&vec); + return (commaInitializer, val); +} + +template inline +VecCommaInitializer<_Tp, cn>::VecCommaInitializer(Vec<_Tp, cn>* _vec) + : MatxCommaInitializer<_Tp, cn, 1>(_vec) +{} + +template template inline +VecCommaInitializer<_Tp, cn>& VecCommaInitializer<_Tp, cn>::operator , (_T2 value) +{ + CV_DbgAssert( this->idx < cn ); + this->dst->val[this->idx++] = saturate_cast<_Tp>(value); + return *this; +} + +template inline +Vec<_Tp, cn> VecCommaInitializer<_Tp, cn>::operator *() const +{ + CV_DbgAssert( this->idx == cn ); + return *this->dst; +} + +//! @endcond + +///////////////////////////// Matx out-of-class operators //////////////////////////////// + +//! @relates cv::Matx +//! @{ + +template static inline +Matx<_Tp1, m, n>& operator += (Matx<_Tp1, m, n>& a, const Matx<_Tp2, m, n>& b) +{ + for( int i = 0; i < m*n; i++ ) + a.val[i] = saturate_cast<_Tp1>(a.val[i] + b.val[i]); + return a; +} + +template static inline +Matx<_Tp1, m, n>& operator -= (Matx<_Tp1, m, n>& a, const Matx<_Tp2, m, n>& b) +{ + for( int i = 0; i < m*n; i++ ) + a.val[i] = saturate_cast<_Tp1>(a.val[i] - b.val[i]); + return a; +} + +template static inline +Matx<_Tp, m, n> operator + (const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b) +{ + return Matx<_Tp, m, n>(a, b, Matx_AddOp()); +} + +template static inline +Matx<_Tp, m, n> operator - (const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b) +{ + return Matx<_Tp, m, n>(a, b, Matx_SubOp()); +} + +template static inline +Matx<_Tp, m, n>& operator *= (Matx<_Tp, m, n>& a, int alpha) +{ + for( int i = 0; i < m*n; i++ ) + a.val[i] = saturate_cast<_Tp>(a.val[i] * alpha); + return a; +} + +template static inline +Matx<_Tp, m, n>& operator *= (Matx<_Tp, m, n>& a, float alpha) +{ + for( int i = 0; i < m*n; i++ ) + a.val[i] = saturate_cast<_Tp>(a.val[i] * alpha); + return a; +} + +template static inline +Matx<_Tp, m, n>& operator *= (Matx<_Tp, m, n>& a, double alpha) +{ + for( int i = 0; i < m*n; i++ ) + a.val[i] = saturate_cast<_Tp>(a.val[i] * alpha); + return a; +} + +template static inline +Matx<_Tp, m, n> operator * (const Matx<_Tp, m, n>& a, int alpha) +{ + return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Matx<_Tp, m, n> operator * (const Matx<_Tp, m, n>& a, float alpha) +{ + return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Matx<_Tp, m, n> operator * (const Matx<_Tp, m, n>& a, double alpha) +{ + return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Matx<_Tp, m, n> operator * (int alpha, const Matx<_Tp, m, n>& a) +{ + return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Matx<_Tp, m, n> operator * (float alpha, const Matx<_Tp, m, n>& a) +{ + return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Matx<_Tp, m, n> operator * (double alpha, const Matx<_Tp, m, n>& a) +{ + return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Matx<_Tp, m, n> operator - (const Matx<_Tp, m, n>& a) +{ + return Matx<_Tp, m, n>(a, -1, Matx_ScaleOp()); +} + +template static inline +Matx<_Tp, m, n> operator * (const Matx<_Tp, m, l>& a, const Matx<_Tp, l, n>& b) +{ + return Matx<_Tp, m, n>(a, b, Matx_MatMulOp()); +} + +template static inline +Vec<_Tp, m> operator * (const Matx<_Tp, m, n>& a, const Vec<_Tp, n>& b) +{ + Matx<_Tp, m, 1> c(a, b, Matx_MatMulOp()); + return (const Vec<_Tp, m>&)(c); +} + +template static inline +bool operator == (const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b) +{ + for( int i = 0; i < m*n; i++ ) + if( a.val[i] != b.val[i] ) return false; + return true; +} + +template static inline +bool operator != (const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b) +{ + return !(a == b); +} + +//! @} + +////////////////////////////// Vec out-of-class operators //////////////////////////////// + +//! @relates cv::Vec +//! @{ + +template static inline +Vec<_Tp1, cn>& operator += (Vec<_Tp1, cn>& a, const Vec<_Tp2, cn>& b) +{ + for( int i = 0; i < cn; i++ ) + a.val[i] = saturate_cast<_Tp1>(a.val[i] + b.val[i]); + return a; +} + +template static inline +Vec<_Tp1, cn>& operator -= (Vec<_Tp1, cn>& a, const Vec<_Tp2, cn>& b) +{ + for( int i = 0; i < cn; i++ ) + a.val[i] = saturate_cast<_Tp1>(a.val[i] - b.val[i]); + return a; +} + +template static inline +Vec<_Tp, cn> operator + (const Vec<_Tp, cn>& a, const Vec<_Tp, cn>& b) +{ + return Vec<_Tp, cn>(a, b, Matx_AddOp()); +} + +template static inline +Vec<_Tp, cn> operator - (const Vec<_Tp, cn>& a, const Vec<_Tp, cn>& b) +{ + return Vec<_Tp, cn>(a, b, Matx_SubOp()); +} + +template static inline +Vec<_Tp, cn>& operator *= (Vec<_Tp, cn>& a, int alpha) +{ + for( int i = 0; i < cn; i++ ) + a[i] = saturate_cast<_Tp>(a[i]*alpha); + return a; +} + +template static inline +Vec<_Tp, cn>& operator *= (Vec<_Tp, cn>& a, float alpha) +{ + for( int i = 0; i < cn; i++ ) + a[i] = saturate_cast<_Tp>(a[i]*alpha); + return a; +} + +template static inline +Vec<_Tp, cn>& operator *= (Vec<_Tp, cn>& a, double alpha) +{ + for( int i = 0; i < cn; i++ ) + a[i] = saturate_cast<_Tp>(a[i]*alpha); + return a; +} + +template static inline +Vec<_Tp, cn>& operator /= (Vec<_Tp, cn>& a, int alpha) +{ + double ialpha = 1./alpha; + for( int i = 0; i < cn; i++ ) + a[i] = saturate_cast<_Tp>(a[i]*ialpha); + return a; +} + +template static inline +Vec<_Tp, cn>& operator /= (Vec<_Tp, cn>& a, float alpha) +{ + float ialpha = 1.f/alpha; + for( int i = 0; i < cn; i++ ) + a[i] = saturate_cast<_Tp>(a[i]*ialpha); + return a; +} + +template static inline +Vec<_Tp, cn>& operator /= (Vec<_Tp, cn>& a, double alpha) +{ + double ialpha = 1./alpha; + for( int i = 0; i < cn; i++ ) + a[i] = saturate_cast<_Tp>(a[i]*ialpha); + return a; +} + +template static inline +Vec<_Tp, cn> operator * (const Vec<_Tp, cn>& a, int alpha) +{ + return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Vec<_Tp, cn> operator * (int alpha, const Vec<_Tp, cn>& a) +{ + return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Vec<_Tp, cn> operator * (const Vec<_Tp, cn>& a, float alpha) +{ + return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Vec<_Tp, cn> operator * (float alpha, const Vec<_Tp, cn>& a) +{ + return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Vec<_Tp, cn> operator * (const Vec<_Tp, cn>& a, double alpha) +{ + return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Vec<_Tp, cn> operator * (double alpha, const Vec<_Tp, cn>& a) +{ + return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Vec<_Tp, cn> operator / (const Vec<_Tp, cn>& a, int alpha) +{ + return Vec<_Tp, cn>(a, 1./alpha, Matx_ScaleOp()); +} + +template static inline +Vec<_Tp, cn> operator / (const Vec<_Tp, cn>& a, float alpha) +{ + return Vec<_Tp, cn>(a, 1.f/alpha, Matx_ScaleOp()); +} + +template static inline +Vec<_Tp, cn> operator / (const Vec<_Tp, cn>& a, double alpha) +{ + return Vec<_Tp, cn>(a, 1./alpha, Matx_ScaleOp()); +} + +template static inline +Vec<_Tp, cn> operator - (const Vec<_Tp, cn>& a) +{ + Vec<_Tp,cn> t; + for( int i = 0; i < cn; i++ ) t.val[i] = saturate_cast<_Tp>(-a.val[i]); + return t; +} + +template inline Vec<_Tp, 4> operator * (const Vec<_Tp, 4>& v1, const Vec<_Tp, 4>& v2) +{ + return Vec<_Tp, 4>(saturate_cast<_Tp>(v1[0]*v2[0] - v1[1]*v2[1] - v1[2]*v2[2] - v1[3]*v2[3]), + saturate_cast<_Tp>(v1[0]*v2[1] + v1[1]*v2[0] + v1[2]*v2[3] - v1[3]*v2[2]), + saturate_cast<_Tp>(v1[0]*v2[2] - v1[1]*v2[3] + v1[2]*v2[0] + v1[3]*v2[1]), + saturate_cast<_Tp>(v1[0]*v2[3] + v1[1]*v2[2] - v1[2]*v2[1] + v1[3]*v2[0])); +} + +template inline Vec<_Tp, 4>& operator *= (Vec<_Tp, 4>& v1, const Vec<_Tp, 4>& v2) +{ + v1 = v1 * v2; + return v1; +} + +//! @} + +} // cv + +#endif // OPENCV_CORE_MATX_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/neon_utils.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/neon_utils.hpp new file mode 100644 index 0000000000000000000000000000000000000000..573ba99ec3569e5dc13b637daf1493b7c1ad6afb --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/neon_utils.hpp @@ -0,0 +1,128 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_HAL_NEON_UTILS_HPP +#define OPENCV_HAL_NEON_UTILS_HPP + +#include "opencv2/core/cvdef.h" + +//! @addtogroup core_utils_neon +//! @{ + +#if CV_NEON + +inline int32x2_t cv_vrnd_s32_f32(float32x2_t v) +{ + static int32x2_t v_sign = vdup_n_s32(1 << 31), + v_05 = vreinterpret_s32_f32(vdup_n_f32(0.5f)); + + int32x2_t v_addition = vorr_s32(v_05, vand_s32(v_sign, vreinterpret_s32_f32(v))); + return vcvt_s32_f32(vadd_f32(v, vreinterpret_f32_s32(v_addition))); +} + +inline int32x4_t cv_vrndq_s32_f32(float32x4_t v) +{ + static int32x4_t v_sign = vdupq_n_s32(1 << 31), + v_05 = vreinterpretq_s32_f32(vdupq_n_f32(0.5f)); + + int32x4_t v_addition = vorrq_s32(v_05, vandq_s32(v_sign, vreinterpretq_s32_f32(v))); + return vcvtq_s32_f32(vaddq_f32(v, vreinterpretq_f32_s32(v_addition))); +} + +inline uint32x2_t cv_vrnd_u32_f32(float32x2_t v) +{ + static float32x2_t v_05 = vdup_n_f32(0.5f); + return vcvt_u32_f32(vadd_f32(v, v_05)); +} + +inline uint32x4_t cv_vrndq_u32_f32(float32x4_t v) +{ + static float32x4_t v_05 = vdupq_n_f32(0.5f); + return vcvtq_u32_f32(vaddq_f32(v, v_05)); +} + +inline float32x4_t cv_vrecpq_f32(float32x4_t val) +{ + float32x4_t reciprocal = vrecpeq_f32(val); + reciprocal = vmulq_f32(vrecpsq_f32(val, reciprocal), reciprocal); + reciprocal = vmulq_f32(vrecpsq_f32(val, reciprocal), reciprocal); + return reciprocal; +} + +inline float32x2_t cv_vrecp_f32(float32x2_t val) +{ + float32x2_t reciprocal = vrecpe_f32(val); + reciprocal = vmul_f32(vrecps_f32(val, reciprocal), reciprocal); + reciprocal = vmul_f32(vrecps_f32(val, reciprocal), reciprocal); + return reciprocal; +} + +inline float32x4_t cv_vrsqrtq_f32(float32x4_t val) +{ + float32x4_t e = vrsqrteq_f32(val); + e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(e, e), val), e); + e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(e, e), val), e); + return e; +} + +inline float32x2_t cv_vrsqrt_f32(float32x2_t val) +{ + float32x2_t e = vrsqrte_f32(val); + e = vmul_f32(vrsqrts_f32(vmul_f32(e, e), val), e); + e = vmul_f32(vrsqrts_f32(vmul_f32(e, e), val), e); + return e; +} + +inline float32x4_t cv_vsqrtq_f32(float32x4_t val) +{ + return cv_vrecpq_f32(cv_vrsqrtq_f32(val)); +} + +inline float32x2_t cv_vsqrt_f32(float32x2_t val) +{ + return cv_vrecp_f32(cv_vrsqrt_f32(val)); +} + +#endif + +//! @} + +#endif // OPENCV_HAL_NEON_UTILS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/ocl.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/ocl.hpp new file mode 100644 index 0000000000000000000000000000000000000000..95f0fcdd66987fb97b75905c8a4f0716e338ac2b --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/ocl.hpp @@ -0,0 +1,843 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the OpenCV Foundation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OPENCL_HPP +#define OPENCV_OPENCL_HPP + +#include "opencv2/core.hpp" + +namespace cv { namespace ocl { + +//! @addtogroup core_opencl +//! @{ + +CV_EXPORTS_W bool haveOpenCL(); +CV_EXPORTS_W bool useOpenCL(); +CV_EXPORTS_W bool haveAmdBlas(); +CV_EXPORTS_W bool haveAmdFft(); +CV_EXPORTS_W void setUseOpenCL(bool flag); +CV_EXPORTS_W void finish(); + +CV_EXPORTS bool haveSVM(); + +class CV_EXPORTS Context; +class CV_EXPORTS_W_SIMPLE Device; +class CV_EXPORTS Kernel; +class CV_EXPORTS Program; +class CV_EXPORTS ProgramSource; +class CV_EXPORTS Queue; +class CV_EXPORTS PlatformInfo; +class CV_EXPORTS Image2D; + +class CV_EXPORTS_W_SIMPLE Device +{ +public: + CV_WRAP Device(); + explicit Device(void* d); + Device(const Device& d); + Device& operator = (const Device& d); + CV_WRAP ~Device(); + + void set(void* d); + + enum + { + TYPE_DEFAULT = (1 << 0), + TYPE_CPU = (1 << 1), + TYPE_GPU = (1 << 2), + TYPE_ACCELERATOR = (1 << 3), + TYPE_DGPU = TYPE_GPU + (1 << 16), + TYPE_IGPU = TYPE_GPU + (1 << 17), + TYPE_ALL = 0xFFFFFFFF + }; + + CV_WRAP String name() const; + CV_WRAP String extensions() const; + CV_WRAP bool isExtensionSupported(const String& extensionName) const; + CV_WRAP String version() const; + CV_WRAP String vendorName() const; + CV_WRAP String OpenCL_C_Version() const; + CV_WRAP String OpenCLVersion() const; + CV_WRAP int deviceVersionMajor() const; + CV_WRAP int deviceVersionMinor() const; + CV_WRAP String driverVersion() const; + void* ptr() const; + + CV_WRAP int type() const; + + CV_WRAP int addressBits() const; + CV_WRAP bool available() const; + CV_WRAP bool compilerAvailable() const; + CV_WRAP bool linkerAvailable() const; + + enum + { + FP_DENORM=(1 << 0), + FP_INF_NAN=(1 << 1), + FP_ROUND_TO_NEAREST=(1 << 2), + FP_ROUND_TO_ZERO=(1 << 3), + FP_ROUND_TO_INF=(1 << 4), + FP_FMA=(1 << 5), + FP_SOFT_FLOAT=(1 << 6), + FP_CORRECTLY_ROUNDED_DIVIDE_SQRT=(1 << 7) + }; + CV_WRAP int doubleFPConfig() const; + CV_WRAP int singleFPConfig() const; + CV_WRAP int halfFPConfig() const; + + CV_WRAP bool endianLittle() const; + CV_WRAP bool errorCorrectionSupport() const; + + enum + { + EXEC_KERNEL=(1 << 0), + EXEC_NATIVE_KERNEL=(1 << 1) + }; + CV_WRAP int executionCapabilities() const; + + CV_WRAP size_t globalMemCacheSize() const; + + enum + { + NO_CACHE=0, + READ_ONLY_CACHE=1, + READ_WRITE_CACHE=2 + }; + CV_WRAP int globalMemCacheType() const; + CV_WRAP int globalMemCacheLineSize() const; + CV_WRAP size_t globalMemSize() const; + + CV_WRAP size_t localMemSize() const; + enum + { + NO_LOCAL_MEM=0, + LOCAL_IS_LOCAL=1, + LOCAL_IS_GLOBAL=2 + }; + CV_WRAP int localMemType() const; + CV_WRAP bool hostUnifiedMemory() const; + + CV_WRAP bool imageSupport() const; + + CV_WRAP bool imageFromBufferSupport() const; + uint imagePitchAlignment() const; + uint imageBaseAddressAlignment() const; + + /// deprecated, use isExtensionSupported() method (probably with "cl_khr_subgroups" value) + CV_WRAP bool intelSubgroupsSupport() const; + + CV_WRAP size_t image2DMaxWidth() const; + CV_WRAP size_t image2DMaxHeight() const; + + CV_WRAP size_t image3DMaxWidth() const; + CV_WRAP size_t image3DMaxHeight() const; + CV_WRAP size_t image3DMaxDepth() const; + + CV_WRAP size_t imageMaxBufferSize() const; + CV_WRAP size_t imageMaxArraySize() const; + + enum + { + UNKNOWN_VENDOR=0, + VENDOR_AMD=1, + VENDOR_INTEL=2, + VENDOR_NVIDIA=3 + }; + CV_WRAP int vendorID() const; + // FIXIT + // dev.isAMD() doesn't work for OpenCL CPU devices from AMD OpenCL platform. + // This method should use platform name instead of vendor name. + // After fix restore code in arithm.cpp: ocl_compare() + CV_WRAP inline bool isAMD() const { return vendorID() == VENDOR_AMD; } + CV_WRAP inline bool isIntel() const { return vendorID() == VENDOR_INTEL; } + CV_WRAP inline bool isNVidia() const { return vendorID() == VENDOR_NVIDIA; } + + CV_WRAP int maxClockFrequency() const; + CV_WRAP int maxComputeUnits() const; + CV_WRAP int maxConstantArgs() const; + CV_WRAP size_t maxConstantBufferSize() const; + + CV_WRAP size_t maxMemAllocSize() const; + CV_WRAP size_t maxParameterSize() const; + + CV_WRAP int maxReadImageArgs() const; + CV_WRAP int maxWriteImageArgs() const; + CV_WRAP int maxSamplers() const; + + CV_WRAP size_t maxWorkGroupSize() const; + CV_WRAP int maxWorkItemDims() const; + void maxWorkItemSizes(size_t*) const; + + CV_WRAP int memBaseAddrAlign() const; + + CV_WRAP int nativeVectorWidthChar() const; + CV_WRAP int nativeVectorWidthShort() const; + CV_WRAP int nativeVectorWidthInt() const; + CV_WRAP int nativeVectorWidthLong() const; + CV_WRAP int nativeVectorWidthFloat() const; + CV_WRAP int nativeVectorWidthDouble() const; + CV_WRAP int nativeVectorWidthHalf() const; + + CV_WRAP int preferredVectorWidthChar() const; + CV_WRAP int preferredVectorWidthShort() const; + CV_WRAP int preferredVectorWidthInt() const; + CV_WRAP int preferredVectorWidthLong() const; + CV_WRAP int preferredVectorWidthFloat() const; + CV_WRAP int preferredVectorWidthDouble() const; + CV_WRAP int preferredVectorWidthHalf() const; + + CV_WRAP size_t printfBufferSize() const; + CV_WRAP size_t profilingTimerResolution() const; + + CV_WRAP static const Device& getDefault(); + +protected: + struct Impl; + Impl* p; +}; + + +class CV_EXPORTS Context +{ +public: + Context(); + explicit Context(int dtype); + ~Context(); + Context(const Context& c); + Context& operator = (const Context& c); + + bool create(); + bool create(int dtype); + size_t ndevices() const; + const Device& device(size_t idx) const; + Program getProg(const ProgramSource& prog, + const String& buildopt, String& errmsg); + void unloadProg(Program& prog); + + static Context& getDefault(bool initialize = true); + void* ptr() const; + + friend void initializeContextFromHandle(Context& ctx, void* platform, void* context, void* device); + + bool useSVM() const; + void setUseSVM(bool enabled); + + struct Impl; + inline Impl* getImpl() const { return (Impl*)p; } +//protected: + Impl* p; +}; + +class CV_EXPORTS Platform +{ +public: + Platform(); + ~Platform(); + Platform(const Platform& p); + Platform& operator = (const Platform& p); + + void* ptr() const; + static Platform& getDefault(); + + friend void initializeContextFromHandle(Context& ctx, void* platform, void* context, void* device); +protected: + struct Impl; + Impl* p; +}; + +/** @brief Attaches OpenCL context to OpenCV +@note + OpenCV will check if available OpenCL platform has platformName name, then assign context to + OpenCV and call `clRetainContext` function. The deviceID device will be used as target device and + new command queue will be created. +@param platformName name of OpenCL platform to attach, this string is used to check if platform is available to OpenCV at runtime +@param platformID ID of platform attached context was created for +@param context OpenCL context to be attached to OpenCV +@param deviceID ID of device, must be created from attached context +*/ +CV_EXPORTS void attachContext(const String& platformName, void* platformID, void* context, void* deviceID); + +/** @brief Convert OpenCL buffer to UMat +@note + OpenCL buffer (cl_mem_buffer) should contain 2D image data, compatible with OpenCV. Memory + content is not copied from `clBuffer` to UMat. Instead, buffer handle assigned to UMat and + `clRetainMemObject` is called. +@param cl_mem_buffer source clBuffer handle +@param step num of bytes in single row +@param rows number of rows +@param cols number of cols +@param type OpenCV type of image +@param dst destination UMat +*/ +CV_EXPORTS void convertFromBuffer(void* cl_mem_buffer, size_t step, int rows, int cols, int type, UMat& dst); + +/** @brief Convert OpenCL image2d_t to UMat +@note + OpenCL `image2d_t` (cl_mem_image), should be compatible with OpenCV UMat formats. Memory content + is copied from image to UMat with `clEnqueueCopyImageToBuffer` function. +@param cl_mem_image source image2d_t handle +@param dst destination UMat +*/ +CV_EXPORTS void convertFromImage(void* cl_mem_image, UMat& dst); + +// TODO Move to internal header +void initializeContextFromHandle(Context& ctx, void* platform, void* context, void* device); + +class CV_EXPORTS Queue +{ +public: + Queue(); + explicit Queue(const Context& c, const Device& d=Device()); + ~Queue(); + Queue(const Queue& q); + Queue& operator = (const Queue& q); + + bool create(const Context& c=Context(), const Device& d=Device()); + void finish(); + void* ptr() const; + static Queue& getDefault(); + + /// @brief Returns OpenCL command queue with enable profiling mode support + const Queue& getProfilingQueue() const; + + struct Impl; friend struct Impl; + inline Impl* getImpl() const { return p; } +protected: + Impl* p; +}; + + +class CV_EXPORTS KernelArg +{ +public: + enum { LOCAL=1, READ_ONLY=2, WRITE_ONLY=4, READ_WRITE=6, CONSTANT=8, PTR_ONLY = 16, NO_SIZE=256 }; + KernelArg(int _flags, UMat* _m, int wscale=1, int iwscale=1, const void* _obj=0, size_t _sz=0); + KernelArg(); + + static KernelArg Local(size_t localMemSize) + { return KernelArg(LOCAL, 0, 1, 1, 0, localMemSize); } + static KernelArg PtrWriteOnly(const UMat& m) + { return KernelArg(PTR_ONLY+WRITE_ONLY, (UMat*)&m); } + static KernelArg PtrReadOnly(const UMat& m) + { return KernelArg(PTR_ONLY+READ_ONLY, (UMat*)&m); } + static KernelArg PtrReadWrite(const UMat& m) + { return KernelArg(PTR_ONLY+READ_WRITE, (UMat*)&m); } + static KernelArg ReadWrite(const UMat& m, int wscale=1, int iwscale=1) + { return KernelArg(READ_WRITE, (UMat*)&m, wscale, iwscale); } + static KernelArg ReadWriteNoSize(const UMat& m, int wscale=1, int iwscale=1) + { return KernelArg(READ_WRITE+NO_SIZE, (UMat*)&m, wscale, iwscale); } + static KernelArg ReadOnly(const UMat& m, int wscale=1, int iwscale=1) + { return KernelArg(READ_ONLY, (UMat*)&m, wscale, iwscale); } + static KernelArg WriteOnly(const UMat& m, int wscale=1, int iwscale=1) + { return KernelArg(WRITE_ONLY, (UMat*)&m, wscale, iwscale); } + static KernelArg ReadOnlyNoSize(const UMat& m, int wscale=1, int iwscale=1) + { return KernelArg(READ_ONLY+NO_SIZE, (UMat*)&m, wscale, iwscale); } + static KernelArg WriteOnlyNoSize(const UMat& m, int wscale=1, int iwscale=1) + { return KernelArg(WRITE_ONLY+NO_SIZE, (UMat*)&m, wscale, iwscale); } + static KernelArg Constant(const Mat& m); + template static KernelArg Constant(const _Tp* arr, size_t n) + { return KernelArg(CONSTANT, 0, 1, 1, (void*)arr, n); } + + int flags; + UMat* m; + const void* obj; + size_t sz; + int wscale, iwscale; +}; + + +class CV_EXPORTS Kernel +{ +public: + Kernel(); + Kernel(const char* kname, const Program& prog); + Kernel(const char* kname, const ProgramSource& prog, + const String& buildopts = String(), String* errmsg=0); + ~Kernel(); + Kernel(const Kernel& k); + Kernel& operator = (const Kernel& k); + + bool empty() const; + bool create(const char* kname, const Program& prog); + bool create(const char* kname, const ProgramSource& prog, + const String& buildopts, String* errmsg=0); + + int set(int i, const void* value, size_t sz); + int set(int i, const Image2D& image2D); + int set(int i, const UMat& m); + int set(int i, const KernelArg& arg); + template int set(int i, const _Tp& value) + { return set(i, &value, sizeof(value)); } + + template + Kernel& args(const _Tp0& a0) + { + set(0, a0); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1) + { + int i = set(0, a0); set(i, a1); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2) + { + int i = set(0, a0); i = set(i, a1); set(i, a2); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, + const _Tp3& a3, const _Tp4& a4) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); + i = set(i, a3); set(i, a4); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, + const _Tp3& a3, const _Tp4& a4, const _Tp5& a5) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); + i = set(i, a3); i = set(i, a4); set(i, a5); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, + const _Tp4& a4, const _Tp5& a5, const _Tp6& a6) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); + i = set(i, a4); i = set(i, a5); set(i, a6); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, + const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); + i = set(i, a4); i = set(i, a5); i = set(i, a6); set(i, a7); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, + const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, + const _Tp8& a8) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); + i = set(i, a5); i = set(i, a6); i = set(i, a7); set(i, a8); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, + const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, + const _Tp8& a8, const _Tp9& a9) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5); + i = set(i, a6); i = set(i, a7); i = set(i, a8); set(i, a9); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, + const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, + const _Tp8& a8, const _Tp9& a9, const _Tp10& a10) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5); + i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); set(i, a10); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, + const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, + const _Tp8& a8, const _Tp9& a9, const _Tp10& a10, const _Tp11& a11) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5); + i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); i = set(i, a10); set(i, a11); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, + const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, + const _Tp8& a8, const _Tp9& a9, const _Tp10& a10, const _Tp11& a11, + const _Tp12& a12) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5); + i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); i = set(i, a10); i = set(i, a11); + set(i, a12); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, + const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, + const _Tp8& a8, const _Tp9& a9, const _Tp10& a10, const _Tp11& a11, + const _Tp12& a12, const _Tp13& a13) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5); + i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); i = set(i, a10); i = set(i, a11); + i = set(i, a12); set(i, a13); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, + const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, + const _Tp8& a8, const _Tp9& a9, const _Tp10& a10, const _Tp11& a11, + const _Tp12& a12, const _Tp13& a13, const _Tp14& a14) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5); + i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); i = set(i, a10); i = set(i, a11); + i = set(i, a12); i = set(i, a13); set(i, a14); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, + const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, + const _Tp8& a8, const _Tp9& a9, const _Tp10& a10, const _Tp11& a11, + const _Tp12& a12, const _Tp13& a13, const _Tp14& a14, const _Tp15& a15) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5); + i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); i = set(i, a10); i = set(i, a11); + i = set(i, a12); i = set(i, a13); i = set(i, a14); set(i, a15); return *this; + } + /** @brief Run the OpenCL kernel. + @param dims the work problem dimensions. It is the length of globalsize and localsize. It can be either 1, 2 or 3. + @param globalsize work items for each dimension. It is not the final globalsize passed to + OpenCL. Each dimension will be adjusted to the nearest integer divisible by the corresponding + value in localsize. If localsize is NULL, it will still be adjusted depending on dims. The + adjusted values are greater than or equal to the original values. + @param localsize work-group size for each dimension. + @param sync specify whether to wait for OpenCL computation to finish before return. + @param q command queue + */ + bool run(int dims, size_t globalsize[], + size_t localsize[], bool sync, const Queue& q=Queue()); + bool runTask(bool sync, const Queue& q=Queue()); + + /** @brief Similar to synchronized run() call with returning of kernel execution time + * Separate OpenCL command queue may be used (with CL_QUEUE_PROFILING_ENABLE) + * @return Execution time in nanoseconds or negative number on error + */ + int64 runProfiling(int dims, size_t globalsize[], size_t localsize[], const Queue& q=Queue()); + + size_t workGroupSize() const; + size_t preferedWorkGroupSizeMultiple() const; + bool compileWorkGroupSize(size_t wsz[]) const; + size_t localMemSize() const; + + void* ptr() const; + struct Impl; + +protected: + Impl* p; +}; + +class CV_EXPORTS Program +{ +public: + Program(); + Program(const ProgramSource& src, + const String& buildflags, String& errmsg); + Program(const Program& prog); + + Program& operator = (const Program& prog); + ~Program(); + + bool create(const ProgramSource& src, + const String& buildflags, String& errmsg); + + void* ptr() const; + + /** + * @brief Query device-specific program binary. + * + * Returns RAW OpenCL executable binary without additional attachments. + * + * @sa ProgramSource::fromBinary + * + * @param[out] binary output buffer + */ + void getBinary(std::vector& binary) const; + + struct Impl; friend struct Impl; + inline Impl* getImpl() const { return (Impl*)p; } +protected: + Impl* p; +public: +#ifndef OPENCV_REMOVE_DEPRECATED_API + // TODO Remove this + CV_DEPRECATED bool read(const String& buf, const String& buildflags); // removed, use ProgramSource instead + CV_DEPRECATED bool write(String& buf) const; // removed, use getBinary() method instead (RAW OpenCL binary) + CV_DEPRECATED const ProgramSource& source() const; // implementation removed + CV_DEPRECATED String getPrefix() const; // deprecated, implementation replaced + CV_DEPRECATED static String getPrefix(const String& buildflags); // deprecated, implementation replaced +#endif +}; + + +class CV_EXPORTS ProgramSource +{ +public: + typedef uint64 hash_t; // deprecated + + ProgramSource(); + explicit ProgramSource(const String& module, const String& name, const String& codeStr, const String& codeHash); + explicit ProgramSource(const String& prog); // deprecated + explicit ProgramSource(const char* prog); // deprecated + ~ProgramSource(); + ProgramSource(const ProgramSource& prog); + ProgramSource& operator = (const ProgramSource& prog); + + const String& source() const; // deprecated + hash_t hash() const; // deprecated + + + /** @brief Describe OpenCL program binary. + * Do not call clCreateProgramWithBinary() and/or clBuildProgram(). + * + * Caller should guarantee binary buffer lifetime greater than ProgramSource object (and any of its copies). + * + * This kind of binary is not portable between platforms in general - it is specific to OpenCL vendor / device / driver version. + * + * @param module name of program owner module + * @param name unique name of program (module+name is used as key for OpenCL program caching) + * @param binary buffer address. See buffer lifetime requirement in description. + * @param size buffer size + * @param buildOptions additional program-related build options passed to clBuildProgram() + * @return created ProgramSource object + */ + static ProgramSource fromBinary(const String& module, const String& name, + const unsigned char* binary, const size_t size, + const cv::String& buildOptions = cv::String()); + + /** @brief Describe OpenCL program in SPIR format. + * Do not call clCreateProgramWithBinary() and/or clBuildProgram(). + * + * Supports SPIR 1.2 by default (pass '-spir-std=X.Y' in buildOptions to override this behavior) + * + * Caller should guarantee binary buffer lifetime greater than ProgramSource object (and any of its copies). + * + * Programs in this format are portable between OpenCL implementations with 'khr_spir' extension: + * https://www.khronos.org/registry/OpenCL/sdk/2.0/docs/man/xhtml/cl_khr_spir.html + * (but they are not portable between different platforms: 32-bit / 64-bit) + * + * Note: these programs can't support vendor specific extensions, like 'cl_intel_subgroups'. + * + * @param module name of program owner module + * @param name unique name of program (module+name is used as key for OpenCL program caching) + * @param binary buffer address. See buffer lifetime requirement in description. + * @param size buffer size + * @param buildOptions additional program-related build options passed to clBuildProgram() + * (these options are added automatically: '-x spir' and '-spir-std=1.2') + * @return created ProgramSource object. + */ + static ProgramSource fromSPIR(const String& module, const String& name, + const unsigned char* binary, const size_t size, + const cv::String& buildOptions = cv::String()); + + //OpenCL 2.1+ only + //static Program fromSPIRV(const String& module, const String& name, + // const unsigned char* binary, const size_t size, + // const cv::String& buildOptions = cv::String()); + + struct Impl; friend struct Impl; + inline Impl* getImpl() const { return (Impl*)p; } +protected: + Impl* p; +}; + +class CV_EXPORTS PlatformInfo +{ +public: + PlatformInfo(); + explicit PlatformInfo(void* id); + ~PlatformInfo(); + + PlatformInfo(const PlatformInfo& i); + PlatformInfo& operator =(const PlatformInfo& i); + + String name() const; + String vendor() const; + String version() const; + int deviceNumber() const; + void getDevice(Device& device, int d) const; + +protected: + struct Impl; + Impl* p; +}; + +CV_EXPORTS const char* convertTypeStr(int sdepth, int ddepth, int cn, char* buf); +CV_EXPORTS const char* typeToStr(int t); +CV_EXPORTS const char* memopTypeToStr(int t); +CV_EXPORTS const char* vecopTypeToStr(int t); +CV_EXPORTS const char* getOpenCLErrorString(int errorCode); +CV_EXPORTS String kernelToStr(InputArray _kernel, int ddepth = -1, const char * name = NULL); +CV_EXPORTS void getPlatfomsInfo(std::vector& platform_info); + + +enum OclVectorStrategy +{ + // all matrices have its own vector width + OCL_VECTOR_OWN = 0, + // all matrices have maximal vector width among all matrices + // (useful for cases when matrices have different data types) + OCL_VECTOR_MAX = 1, + + // default strategy + OCL_VECTOR_DEFAULT = OCL_VECTOR_OWN +}; + +CV_EXPORTS int predictOptimalVectorWidth(InputArray src1, InputArray src2 = noArray(), InputArray src3 = noArray(), + InputArray src4 = noArray(), InputArray src5 = noArray(), InputArray src6 = noArray(), + InputArray src7 = noArray(), InputArray src8 = noArray(), InputArray src9 = noArray(), + OclVectorStrategy strat = OCL_VECTOR_DEFAULT); + +CV_EXPORTS int checkOptimalVectorWidth(const int *vectorWidths, + InputArray src1, InputArray src2 = noArray(), InputArray src3 = noArray(), + InputArray src4 = noArray(), InputArray src5 = noArray(), InputArray src6 = noArray(), + InputArray src7 = noArray(), InputArray src8 = noArray(), InputArray src9 = noArray(), + OclVectorStrategy strat = OCL_VECTOR_DEFAULT); + +// with OCL_VECTOR_MAX strategy +CV_EXPORTS int predictOptimalVectorWidthMax(InputArray src1, InputArray src2 = noArray(), InputArray src3 = noArray(), + InputArray src4 = noArray(), InputArray src5 = noArray(), InputArray src6 = noArray(), + InputArray src7 = noArray(), InputArray src8 = noArray(), InputArray src9 = noArray()); + +CV_EXPORTS void buildOptionsAddMatrixDescription(String& buildOptions, const String& name, InputArray _m); + +class CV_EXPORTS Image2D +{ +public: + Image2D(); + + /** + @param src UMat object from which to get image properties and data + @param norm flag to enable the use of normalized channel data types + @param alias flag indicating that the image should alias the src UMat. If true, changes to the + image or src will be reflected in both objects. + */ + explicit Image2D(const UMat &src, bool norm = false, bool alias = false); + Image2D(const Image2D & i); + ~Image2D(); + + Image2D & operator = (const Image2D & i); + + /** Indicates if creating an aliased image should succeed. + Depends on the underlying platform and the dimensions of the UMat. + */ + static bool canCreateAlias(const UMat &u); + + /** Indicates if the image format is supported. + */ + static bool isFormatSupported(int depth, int cn, bool norm); + + void* ptr() const; +protected: + struct Impl; + Impl* p; +}; + +class CV_EXPORTS Timer +{ +public: + Timer(const Queue& q); + ~Timer(); + void start(); + void stop(); + + uint64 durationNS() const; //< duration in nanoseconds + +protected: + struct Impl; + Impl* const p; + +private: + Timer(const Timer&); // disabled + Timer& operator=(const Timer&); // disabled +}; + +CV_EXPORTS MatAllocator* getOpenCLAllocator(); + + +#ifdef __OPENCV_BUILD +namespace internal { + +CV_EXPORTS bool isOpenCLForced(); +#define OCL_FORCE_CHECK(condition) (cv::ocl::internal::isOpenCLForced() || (condition)) + +CV_EXPORTS bool isPerformanceCheckBypassed(); +#define OCL_PERFORMANCE_CHECK(condition) (cv::ocl::internal::isPerformanceCheckBypassed() || (condition)) + +CV_EXPORTS bool isCLBuffer(UMat& u); + +} // namespace internal +#endif + +//! @} + +}} + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/ocl_genbase.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/ocl_genbase.hpp new file mode 100644 index 0000000000000000000000000000000000000000..5334cf1f4f2db81ade82e43035396fb8ea9dc1e0 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/ocl_genbase.hpp @@ -0,0 +1,69 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the OpenCV Foundation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OPENCL_GENBASE_HPP +#define OPENCV_OPENCL_GENBASE_HPP + +//! @cond IGNORED + +namespace cv { +namespace ocl { + +class ProgramSource; + +namespace internal { + +struct CV_EXPORTS ProgramEntry +{ + const char* module; + const char* name; + const char* programCode; + const char* programHash; + ProgramSource* pProgramSource; + + operator ProgramSource& () const; +}; + +} } } // namespace + +//! @endcond + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/ocl_defs.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/ocl_defs.hpp new file mode 100644 index 0000000000000000000000000000000000000000..605a65f8d8fc42885eabc2e9e30cbb47c26f7bad --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/ocl_defs.hpp @@ -0,0 +1,75 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +// Copyright (C) 2014, Advanced Micro Devices, Inc., all rights reserved. +// Third party copyrights are property of their respective owners. + +#ifndef OPENCV_CORE_OPENCL_DEFS_HPP +#define OPENCV_CORE_OPENCL_DEFS_HPP + +#include "opencv2/core/utility.hpp" +#include "cvconfig.h" + +namespace cv { namespace ocl { +#ifdef HAVE_OPENCL +/// Call is similar to useOpenCL() but doesn't try to load OpenCL runtime or create OpenCL context +CV_EXPORTS bool isOpenCLActivated(); +#else +static inline bool isOpenCLActivated() { return false; } +#endif +}} // namespace + + +//#define CV_OPENCL_RUN_ASSERT + +#ifdef HAVE_OPENCL + +#ifdef CV_OPENCL_RUN_VERBOSE +#define CV_OCL_RUN_(condition, func, ...) \ + { \ + if (cv::ocl::isOpenCLActivated() && (condition) && func) \ + { \ + printf("%s: OpenCL implementation is running\n", CV_Func); \ + fflush(stdout); \ + CV_IMPL_ADD(CV_IMPL_OCL); \ + return __VA_ARGS__; \ + } \ + else \ + { \ + printf("%s: Plain implementation is running\n", CV_Func); \ + fflush(stdout); \ + } \ + } +#elif defined CV_OPENCL_RUN_ASSERT +#define CV_OCL_RUN_(condition, func, ...) \ + { \ + if (cv::ocl::isOpenCLActivated() && (condition)) \ + { \ + if(func) \ + { \ + CV_IMPL_ADD(CV_IMPL_OCL); \ + } \ + else \ + { \ + CV_Error(cv::Error::StsAssert, #func); \ + } \ + return __VA_ARGS__; \ + } \ + } +#else +#define CV_OCL_RUN_(condition, func, ...) \ + if (cv::ocl::isOpenCLActivated() && (condition) && func) \ + { \ + CV_IMPL_ADD(CV_IMPL_OCL); \ + return __VA_ARGS__; \ + } +#endif + +#else +#define CV_OCL_RUN_(condition, func, ...) +#endif + +#define CV_OCL_RUN(condition, func) CV_OCL_RUN_(condition, func) + +#endif // OPENCV_CORE_OPENCL_DEFS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/opencl_info.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/opencl_info.hpp new file mode 100644 index 0000000000000000000000000000000000000000..b5d37394a73670bc04d2edde1725072b9c51c523 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/opencl_info.hpp @@ -0,0 +1,198 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#include + +#include +#include + +#ifndef DUMP_CONFIG_PROPERTY +#define DUMP_CONFIG_PROPERTY(...) +#endif + +#ifndef DUMP_MESSAGE_STDOUT +#define DUMP_MESSAGE_STDOUT(...) do { std::cout << __VA_ARGS__ << std::endl; } while (false) +#endif + +namespace cv { + +namespace { +static std::string bytesToStringRepr(size_t value) +{ + size_t b = value % 1024; + value /= 1024; + + size_t kb = value % 1024; + value /= 1024; + + size_t mb = value % 1024; + value /= 1024; + + size_t gb = value; + + std::ostringstream stream; + + if (gb > 0) + stream << gb << " GB "; + if (mb > 0) + stream << mb << " MB "; + if (kb > 0) + stream << kb << " KB "; + if (b > 0) + stream << b << " B"; + + std::string s = stream.str(); + if (s[s.size() - 1] == ' ') + s = s.substr(0, s.size() - 1); + return s; +} +} // namespace + +static void dumpOpenCLInformation() +{ + using namespace cv::ocl; + + try + { + if (!haveOpenCL() || !useOpenCL()) + { + DUMP_MESSAGE_STDOUT("OpenCL is disabled"); + DUMP_CONFIG_PROPERTY("cv_ocl", "disabled"); + return; + } + + std::vector platforms; + cv::ocl::getPlatfomsInfo(platforms); + if (platforms.size() > 0) + { + DUMP_MESSAGE_STDOUT("OpenCL Platforms: "); + for (size_t i = 0; i < platforms.size(); i++) + { + const PlatformInfo* platform = &platforms[i]; + DUMP_MESSAGE_STDOUT(" " << platform->name().c_str()); + Device current_device; + for (int j = 0; j < platform->deviceNumber(); j++) + { + platform->getDevice(current_device, j); + const char* deviceTypeStr = current_device.type() == Device::TYPE_CPU + ? ("CPU") : (current_device.type() == Device::TYPE_GPU ? current_device.hostUnifiedMemory() ? "iGPU" : "dGPU" : "unknown"); + DUMP_MESSAGE_STDOUT( " " << deviceTypeStr << ": " << current_device.name().c_str() << " (" << current_device.version().c_str() << ")"); + DUMP_CONFIG_PROPERTY( cv::format("cv_ocl_platform_%d_device_%d", (int)i, (int)j ), + cv::format("(Platform=%s)(Type=%s)(Name=%s)(Version=%s)", + platform->name().c_str(), deviceTypeStr, current_device.name().c_str(), current_device.version().c_str()) ); + } + } + } + else + { + DUMP_MESSAGE_STDOUT("OpenCL is not available"); + DUMP_CONFIG_PROPERTY("cv_ocl", "not available"); + return; + } + + const Device& device = Device::getDefault(); + if (!device.available()) + CV_Error(Error::OpenCLInitError, "OpenCL device is not available"); + + DUMP_MESSAGE_STDOUT("Current OpenCL device: "); + +#if 0 + DUMP_MESSAGE_STDOUT(" Platform = " << device.getPlatform().name()); + DUMP_CONFIG_PROPERTY("cv_ocl_current_platformName", device.getPlatform().name()); +#endif + + const char* deviceTypeStr = device.type() == Device::TYPE_CPU + ? ("CPU") : (device.type() == Device::TYPE_GPU ? device.hostUnifiedMemory() ? "iGPU" : "dGPU" : "unknown"); + DUMP_MESSAGE_STDOUT(" Type = " << deviceTypeStr); + DUMP_CONFIG_PROPERTY("cv_ocl_current_deviceType", deviceTypeStr); + + DUMP_MESSAGE_STDOUT(" Name = " << device.name()); + DUMP_CONFIG_PROPERTY("cv_ocl_current_deviceName", device.name()); + + DUMP_MESSAGE_STDOUT(" Version = " << device.version()); + DUMP_CONFIG_PROPERTY("cv_ocl_current_deviceVersion", device.version()); + + DUMP_MESSAGE_STDOUT(" Driver version = " << device.driverVersion()); + DUMP_CONFIG_PROPERTY("cv_ocl_current_driverVersion", device.driverVersion()); + + DUMP_MESSAGE_STDOUT(" Address bits = " << device.addressBits()); + DUMP_CONFIG_PROPERTY("cv_ocl_current_addressBits", device.addressBits()); + + DUMP_MESSAGE_STDOUT(" Compute units = " << device.maxComputeUnits()); + DUMP_CONFIG_PROPERTY("cv_ocl_current_maxComputeUnits", device.maxComputeUnits()); + + DUMP_MESSAGE_STDOUT(" Max work group size = " << device.maxWorkGroupSize()); + DUMP_CONFIG_PROPERTY("cv_ocl_current_maxWorkGroupSize", device.maxWorkGroupSize()); + + std::string localMemorySizeStr = bytesToStringRepr(device.localMemSize()); + DUMP_MESSAGE_STDOUT(" Local memory size = " << localMemorySizeStr); + DUMP_CONFIG_PROPERTY("cv_ocl_current_localMemSize", device.localMemSize()); + + std::string maxMemAllocSizeStr = bytesToStringRepr(device.maxMemAllocSize()); + DUMP_MESSAGE_STDOUT(" Max memory allocation size = " << maxMemAllocSizeStr); + DUMP_CONFIG_PROPERTY("cv_ocl_current_maxMemAllocSize", device.maxMemAllocSize()); + + const char* doubleSupportStr = device.doubleFPConfig() > 0 ? "Yes" : "No"; + DUMP_MESSAGE_STDOUT(" Double support = " << doubleSupportStr); + DUMP_CONFIG_PROPERTY("cv_ocl_current_haveDoubleSupport", device.doubleFPConfig() > 0); + + const char* isUnifiedMemoryStr = device.hostUnifiedMemory() ? "Yes" : "No"; + DUMP_MESSAGE_STDOUT(" Host unified memory = " << isUnifiedMemoryStr); + DUMP_CONFIG_PROPERTY("cv_ocl_current_hostUnifiedMemory", device.hostUnifiedMemory()); + + DUMP_MESSAGE_STDOUT(" Device extensions:"); + String extensionsStr = device.extensions(); + size_t pos = 0; + while (pos < extensionsStr.size()) + { + size_t pos2 = extensionsStr.find(' ', pos); + if (pos2 == String::npos) + pos2 = extensionsStr.size(); + if (pos2 > pos) + { + String extensionName = extensionsStr.substr(pos, pos2 - pos); + DUMP_MESSAGE_STDOUT(" " << extensionName); + } + pos = pos2 + 1; + } + DUMP_CONFIG_PROPERTY("cv_ocl_current_extensions", extensionsStr.c_str()); + + const char* haveAmdBlasStr = haveAmdBlas() ? "Yes" : "No"; + DUMP_MESSAGE_STDOUT(" Has AMD Blas = " << haveAmdBlasStr); + DUMP_CONFIG_PROPERTY("cv_ocl_current_AmdBlas", haveAmdBlas()); + + const char* haveAmdFftStr = haveAmdFft() ? "Yes" : "No"; + DUMP_MESSAGE_STDOUT(" Has AMD Fft = " << haveAmdFftStr); + DUMP_CONFIG_PROPERTY("cv_ocl_current_AmdFft", haveAmdFft()); + + + DUMP_MESSAGE_STDOUT(" Preferred vector width char = " << device.preferredVectorWidthChar()); + DUMP_CONFIG_PROPERTY("cv_ocl_current_preferredVectorWidthChar", device.preferredVectorWidthChar()); + + DUMP_MESSAGE_STDOUT(" Preferred vector width short = " << device.preferredVectorWidthShort()); + DUMP_CONFIG_PROPERTY("cv_ocl_current_preferredVectorWidthShort", device.preferredVectorWidthShort()); + + DUMP_MESSAGE_STDOUT(" Preferred vector width int = " << device.preferredVectorWidthInt()); + DUMP_CONFIG_PROPERTY("cv_ocl_current_preferredVectorWidthInt", device.preferredVectorWidthInt()); + + DUMP_MESSAGE_STDOUT(" Preferred vector width long = " << device.preferredVectorWidthLong()); + DUMP_CONFIG_PROPERTY("cv_ocl_current_preferredVectorWidthLong", device.preferredVectorWidthLong()); + + DUMP_MESSAGE_STDOUT(" Preferred vector width float = " << device.preferredVectorWidthFloat()); + DUMP_CONFIG_PROPERTY("cv_ocl_current_preferredVectorWidthFloat", device.preferredVectorWidthFloat()); + + DUMP_MESSAGE_STDOUT(" Preferred vector width double = " << device.preferredVectorWidthDouble()); + DUMP_CONFIG_PROPERTY("cv_ocl_current_preferredVectorWidthDouble", device.preferredVectorWidthDouble()); + } + catch (...) + { + DUMP_MESSAGE_STDOUT("Exception. Can't dump OpenCL info"); + DUMP_MESSAGE_STDOUT("OpenCL device not available"); + DUMP_CONFIG_PROPERTY("cv_ocl", "not available"); + } +} +#undef DUMP_MESSAGE_STDOUT +#undef DUMP_CONFIG_PROPERTY + +} // namespace diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/opencl_svm.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/opencl_svm.hpp new file mode 100644 index 0000000000000000000000000000000000000000..7453082a67b374c01591e7480bbf62f595683fa5 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/opencl_svm.hpp @@ -0,0 +1,81 @@ +/* See LICENSE file in the root OpenCV directory */ + +#ifndef OPENCV_CORE_OPENCL_SVM_HPP +#define OPENCV_CORE_OPENCL_SVM_HPP + +// +// Internal usage only (binary compatibility is not guaranteed) +// +#ifndef __OPENCV_BUILD +#error Internal header file +#endif + +#if defined(HAVE_OPENCL) && defined(HAVE_OPENCL_SVM) +#include "runtime/opencl_core.hpp" +#include "runtime/opencl_svm_20.hpp" +#include "runtime/opencl_svm_hsa_extension.hpp" + +namespace cv { namespace ocl { namespace svm { + +struct SVMCapabilities +{ + enum Value + { + SVM_COARSE_GRAIN_BUFFER = (1 << 0), + SVM_FINE_GRAIN_BUFFER = (1 << 1), + SVM_FINE_GRAIN_SYSTEM = (1 << 2), + SVM_ATOMICS = (1 << 3), + }; + int value_; + + SVMCapabilities(int capabilities = 0) : value_(capabilities) { } + operator int() const { return value_; } + + inline bool isNoSVMSupport() const { return value_ == 0; } + inline bool isSupportCoarseGrainBuffer() const { return (value_ & SVM_COARSE_GRAIN_BUFFER) != 0; } + inline bool isSupportFineGrainBuffer() const { return (value_ & SVM_FINE_GRAIN_BUFFER) != 0; } + inline bool isSupportFineGrainSystem() const { return (value_ & SVM_FINE_GRAIN_SYSTEM) != 0; } + inline bool isSupportAtomics() const { return (value_ & SVM_ATOMICS) != 0; } +}; + +CV_EXPORTS const SVMCapabilities getSVMCapabilitites(const ocl::Context& context); + +struct SVMFunctions +{ + clSVMAllocAMD_fn fn_clSVMAlloc; + clSVMFreeAMD_fn fn_clSVMFree; + clSetKernelArgSVMPointerAMD_fn fn_clSetKernelArgSVMPointer; + //clSetKernelExecInfoAMD_fn fn_clSetKernelExecInfo; + //clEnqueueSVMFreeAMD_fn fn_clEnqueueSVMFree; + clEnqueueSVMMemcpyAMD_fn fn_clEnqueueSVMMemcpy; + clEnqueueSVMMemFillAMD_fn fn_clEnqueueSVMMemFill; + clEnqueueSVMMapAMD_fn fn_clEnqueueSVMMap; + clEnqueueSVMUnmapAMD_fn fn_clEnqueueSVMUnmap; + + inline SVMFunctions() + : fn_clSVMAlloc(NULL), fn_clSVMFree(NULL), + fn_clSetKernelArgSVMPointer(NULL), /*fn_clSetKernelExecInfo(NULL),*/ + /*fn_clEnqueueSVMFree(NULL),*/ fn_clEnqueueSVMMemcpy(NULL), fn_clEnqueueSVMMemFill(NULL), + fn_clEnqueueSVMMap(NULL), fn_clEnqueueSVMUnmap(NULL) + { + // nothing + } + + inline bool isValid() const + { + return fn_clSVMAlloc != NULL && fn_clSVMFree && fn_clSetKernelArgSVMPointer && + /*fn_clSetKernelExecInfo && fn_clEnqueueSVMFree &&*/ fn_clEnqueueSVMMemcpy && + fn_clEnqueueSVMMemFill && fn_clEnqueueSVMMap && fn_clEnqueueSVMUnmap; + } +}; + +// We should guarantee that SVMFunctions lifetime is not less than context's lifetime +CV_EXPORTS const SVMFunctions* getSVMFunctions(const ocl::Context& context); + +CV_EXPORTS bool useSVM(UMatUsageFlags usageFlags); + +}}} //namespace cv::ocl::svm +#endif + +#endif // OPENCV_CORE_OPENCL_SVM_HPP +/* End of file. */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/runtime/autogenerated/opencl_clamdblas.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/runtime/autogenerated/opencl_clamdblas.hpp new file mode 100644 index 0000000000000000000000000000000000000000..65c84935240d3618e0e984eff5102154e3b97a6c --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/runtime/autogenerated/opencl_clamdblas.hpp @@ -0,0 +1,714 @@ +// +// AUTOGENERATED, DO NOT EDIT +// +#ifndef OPENCV_CORE_OCL_RUNTIME_CLAMDBLAS_HPP +#error "Invalid usage" +#endif + +// generated by parser_clamdblas.py +#define clAmdBlasAddScratchImage clAmdBlasAddScratchImage_ +#define clAmdBlasCaxpy clAmdBlasCaxpy_ +#define clAmdBlasCcopy clAmdBlasCcopy_ +#define clAmdBlasCdotc clAmdBlasCdotc_ +#define clAmdBlasCdotu clAmdBlasCdotu_ +#define clAmdBlasCgbmv clAmdBlasCgbmv_ +#define clAmdBlasCgemm clAmdBlasCgemm_ +#define clAmdBlasCgemmEx clAmdBlasCgemmEx_ +#define clAmdBlasCgemv clAmdBlasCgemv_ +#define clAmdBlasCgemvEx clAmdBlasCgemvEx_ +#define clAmdBlasCgerc clAmdBlasCgerc_ +#define clAmdBlasCgeru clAmdBlasCgeru_ +#define clAmdBlasChbmv clAmdBlasChbmv_ +#define clAmdBlasChemm clAmdBlasChemm_ +#define clAmdBlasChemv clAmdBlasChemv_ +#define clAmdBlasCher clAmdBlasCher_ +#define clAmdBlasCher2 clAmdBlasCher2_ +#define clAmdBlasCher2k clAmdBlasCher2k_ +#define clAmdBlasCherk clAmdBlasCherk_ +#define clAmdBlasChpmv clAmdBlasChpmv_ +#define clAmdBlasChpr clAmdBlasChpr_ +#define clAmdBlasChpr2 clAmdBlasChpr2_ +#define clAmdBlasCrotg clAmdBlasCrotg_ +#define clAmdBlasCscal clAmdBlasCscal_ +#define clAmdBlasCsrot clAmdBlasCsrot_ +#define clAmdBlasCsscal clAmdBlasCsscal_ +#define clAmdBlasCswap clAmdBlasCswap_ +#define clAmdBlasCsymm clAmdBlasCsymm_ +#define clAmdBlasCsyr2k clAmdBlasCsyr2k_ +#define clAmdBlasCsyr2kEx clAmdBlasCsyr2kEx_ +#define clAmdBlasCsyrk clAmdBlasCsyrk_ +#define clAmdBlasCsyrkEx clAmdBlasCsyrkEx_ +#define clAmdBlasCtbmv clAmdBlasCtbmv_ +#define clAmdBlasCtbsv clAmdBlasCtbsv_ +#define clAmdBlasCtpmv clAmdBlasCtpmv_ +#define clAmdBlasCtpsv clAmdBlasCtpsv_ +#define clAmdBlasCtrmm clAmdBlasCtrmm_ +#define clAmdBlasCtrmmEx clAmdBlasCtrmmEx_ +#define clAmdBlasCtrmv clAmdBlasCtrmv_ +#define clAmdBlasCtrsm clAmdBlasCtrsm_ +#define clAmdBlasCtrsmEx clAmdBlasCtrsmEx_ +#define clAmdBlasCtrsv clAmdBlasCtrsv_ +#define clAmdBlasDasum clAmdBlasDasum_ +#define clAmdBlasDaxpy clAmdBlasDaxpy_ +#define clAmdBlasDcopy clAmdBlasDcopy_ +#define clAmdBlasDdot clAmdBlasDdot_ +#define clAmdBlasDgbmv clAmdBlasDgbmv_ +#define clAmdBlasDgemm clAmdBlasDgemm_ +#define clAmdBlasDgemmEx clAmdBlasDgemmEx_ +#define clAmdBlasDgemv clAmdBlasDgemv_ +#define clAmdBlasDgemvEx clAmdBlasDgemvEx_ +#define clAmdBlasDger clAmdBlasDger_ +#define clAmdBlasDnrm2 clAmdBlasDnrm2_ +#define clAmdBlasDrot clAmdBlasDrot_ +#define clAmdBlasDrotg clAmdBlasDrotg_ +#define clAmdBlasDrotm clAmdBlasDrotm_ +#define clAmdBlasDrotmg clAmdBlasDrotmg_ +#define clAmdBlasDsbmv clAmdBlasDsbmv_ +#define clAmdBlasDscal clAmdBlasDscal_ +#define clAmdBlasDspmv clAmdBlasDspmv_ +#define clAmdBlasDspr clAmdBlasDspr_ +#define clAmdBlasDspr2 clAmdBlasDspr2_ +#define clAmdBlasDswap clAmdBlasDswap_ +#define clAmdBlasDsymm clAmdBlasDsymm_ +#define clAmdBlasDsymv clAmdBlasDsymv_ +#define clAmdBlasDsymvEx clAmdBlasDsymvEx_ +#define clAmdBlasDsyr clAmdBlasDsyr_ +#define clAmdBlasDsyr2 clAmdBlasDsyr2_ +#define clAmdBlasDsyr2k clAmdBlasDsyr2k_ +#define clAmdBlasDsyr2kEx clAmdBlasDsyr2kEx_ +#define clAmdBlasDsyrk clAmdBlasDsyrk_ +#define clAmdBlasDsyrkEx clAmdBlasDsyrkEx_ +#define clAmdBlasDtbmv clAmdBlasDtbmv_ +#define clAmdBlasDtbsv clAmdBlasDtbsv_ +#define clAmdBlasDtpmv clAmdBlasDtpmv_ +#define clAmdBlasDtpsv clAmdBlasDtpsv_ +#define clAmdBlasDtrmm clAmdBlasDtrmm_ +#define clAmdBlasDtrmmEx clAmdBlasDtrmmEx_ +#define clAmdBlasDtrmv clAmdBlasDtrmv_ +#define clAmdBlasDtrsm clAmdBlasDtrsm_ +#define clAmdBlasDtrsmEx clAmdBlasDtrsmEx_ +#define clAmdBlasDtrsv clAmdBlasDtrsv_ +#define clAmdBlasDzasum clAmdBlasDzasum_ +#define clAmdBlasDznrm2 clAmdBlasDznrm2_ +#define clAmdBlasGetVersion clAmdBlasGetVersion_ +#define clAmdBlasRemoveScratchImage clAmdBlasRemoveScratchImage_ +#define clAmdBlasSasum clAmdBlasSasum_ +#define clAmdBlasSaxpy clAmdBlasSaxpy_ +#define clAmdBlasScasum clAmdBlasScasum_ +#define clAmdBlasScnrm2 clAmdBlasScnrm2_ +#define clAmdBlasScopy clAmdBlasScopy_ +#define clAmdBlasSdot clAmdBlasSdot_ +#define clAmdBlasSetup clAmdBlasSetup_ +#define clAmdBlasSgbmv clAmdBlasSgbmv_ +#define clAmdBlasSgemm clAmdBlasSgemm_ +#define clAmdBlasSgemmEx clAmdBlasSgemmEx_ +#define clAmdBlasSgemv clAmdBlasSgemv_ +#define clAmdBlasSgemvEx clAmdBlasSgemvEx_ +#define clAmdBlasSger clAmdBlasSger_ +#define clAmdBlasSnrm2 clAmdBlasSnrm2_ +#define clAmdBlasSrot clAmdBlasSrot_ +#define clAmdBlasSrotg clAmdBlasSrotg_ +#define clAmdBlasSrotm clAmdBlasSrotm_ +#define clAmdBlasSrotmg clAmdBlasSrotmg_ +#define clAmdBlasSsbmv clAmdBlasSsbmv_ +#define clAmdBlasSscal clAmdBlasSscal_ +#define clAmdBlasSspmv clAmdBlasSspmv_ +#define clAmdBlasSspr clAmdBlasSspr_ +#define clAmdBlasSspr2 clAmdBlasSspr2_ +#define clAmdBlasSswap clAmdBlasSswap_ +#define clAmdBlasSsymm clAmdBlasSsymm_ +#define clAmdBlasSsymv clAmdBlasSsymv_ +#define clAmdBlasSsymvEx clAmdBlasSsymvEx_ +#define clAmdBlasSsyr clAmdBlasSsyr_ +#define clAmdBlasSsyr2 clAmdBlasSsyr2_ +#define clAmdBlasSsyr2k clAmdBlasSsyr2k_ +#define clAmdBlasSsyr2kEx clAmdBlasSsyr2kEx_ +#define clAmdBlasSsyrk clAmdBlasSsyrk_ +#define clAmdBlasSsyrkEx clAmdBlasSsyrkEx_ +#define clAmdBlasStbmv clAmdBlasStbmv_ +#define clAmdBlasStbsv clAmdBlasStbsv_ +#define clAmdBlasStpmv clAmdBlasStpmv_ +#define clAmdBlasStpsv clAmdBlasStpsv_ +#define clAmdBlasStrmm clAmdBlasStrmm_ +#define clAmdBlasStrmmEx clAmdBlasStrmmEx_ +#define clAmdBlasStrmv clAmdBlasStrmv_ +#define clAmdBlasStrsm clAmdBlasStrsm_ +#define clAmdBlasStrsmEx clAmdBlasStrsmEx_ +#define clAmdBlasStrsv clAmdBlasStrsv_ +#define clAmdBlasTeardown clAmdBlasTeardown_ +#define clAmdBlasZaxpy clAmdBlasZaxpy_ +#define clAmdBlasZcopy clAmdBlasZcopy_ +#define clAmdBlasZdotc clAmdBlasZdotc_ +#define clAmdBlasZdotu clAmdBlasZdotu_ +#define clAmdBlasZdrot clAmdBlasZdrot_ +#define clAmdBlasZdscal clAmdBlasZdscal_ +#define clAmdBlasZgbmv clAmdBlasZgbmv_ +#define clAmdBlasZgemm clAmdBlasZgemm_ +#define clAmdBlasZgemmEx clAmdBlasZgemmEx_ +#define clAmdBlasZgemv clAmdBlasZgemv_ +#define clAmdBlasZgemvEx clAmdBlasZgemvEx_ +#define clAmdBlasZgerc clAmdBlasZgerc_ +#define clAmdBlasZgeru clAmdBlasZgeru_ +#define clAmdBlasZhbmv clAmdBlasZhbmv_ +#define clAmdBlasZhemm clAmdBlasZhemm_ +#define clAmdBlasZhemv clAmdBlasZhemv_ +#define clAmdBlasZher clAmdBlasZher_ +#define clAmdBlasZher2 clAmdBlasZher2_ +#define clAmdBlasZher2k clAmdBlasZher2k_ +#define clAmdBlasZherk clAmdBlasZherk_ +#define clAmdBlasZhpmv clAmdBlasZhpmv_ +#define clAmdBlasZhpr clAmdBlasZhpr_ +#define clAmdBlasZhpr2 clAmdBlasZhpr2_ +#define clAmdBlasZrotg clAmdBlasZrotg_ +#define clAmdBlasZscal clAmdBlasZscal_ +#define clAmdBlasZswap clAmdBlasZswap_ +#define clAmdBlasZsymm clAmdBlasZsymm_ +#define clAmdBlasZsyr2k clAmdBlasZsyr2k_ +#define clAmdBlasZsyr2kEx clAmdBlasZsyr2kEx_ +#define clAmdBlasZsyrk clAmdBlasZsyrk_ +#define clAmdBlasZsyrkEx clAmdBlasZsyrkEx_ +#define clAmdBlasZtbmv clAmdBlasZtbmv_ +#define clAmdBlasZtbsv clAmdBlasZtbsv_ +#define clAmdBlasZtpmv clAmdBlasZtpmv_ +#define clAmdBlasZtpsv clAmdBlasZtpsv_ +#define clAmdBlasZtrmm clAmdBlasZtrmm_ +#define clAmdBlasZtrmmEx clAmdBlasZtrmmEx_ +#define clAmdBlasZtrmv clAmdBlasZtrmv_ +#define clAmdBlasZtrsm clAmdBlasZtrsm_ +#define clAmdBlasZtrsmEx clAmdBlasZtrsmEx_ +#define clAmdBlasZtrsv clAmdBlasZtrsv_ +#define clAmdBlasiCamax clAmdBlasiCamax_ +#define clAmdBlasiDamax clAmdBlasiDamax_ +#define clAmdBlasiSamax clAmdBlasiSamax_ +#define clAmdBlasiZamax clAmdBlasiZamax_ + +#include + +// generated by parser_clamdblas.py +#undef clAmdBlasAddScratchImage +//#define clAmdBlasAddScratchImage clAmdBlasAddScratchImage_pfn +#undef clAmdBlasCaxpy +//#define clAmdBlasCaxpy clAmdBlasCaxpy_pfn +#undef clAmdBlasCcopy +//#define clAmdBlasCcopy clAmdBlasCcopy_pfn +#undef clAmdBlasCdotc +//#define clAmdBlasCdotc clAmdBlasCdotc_pfn +#undef clAmdBlasCdotu +//#define clAmdBlasCdotu clAmdBlasCdotu_pfn +#undef clAmdBlasCgbmv +//#define clAmdBlasCgbmv clAmdBlasCgbmv_pfn +#undef clAmdBlasCgemm +//#define clAmdBlasCgemm clAmdBlasCgemm_pfn +#undef clAmdBlasCgemmEx +#define clAmdBlasCgemmEx clAmdBlasCgemmEx_pfn +#undef clAmdBlasCgemv +//#define clAmdBlasCgemv clAmdBlasCgemv_pfn +#undef clAmdBlasCgemvEx +//#define clAmdBlasCgemvEx clAmdBlasCgemvEx_pfn +#undef clAmdBlasCgerc +//#define clAmdBlasCgerc clAmdBlasCgerc_pfn +#undef clAmdBlasCgeru +//#define clAmdBlasCgeru clAmdBlasCgeru_pfn +#undef clAmdBlasChbmv +//#define clAmdBlasChbmv clAmdBlasChbmv_pfn +#undef clAmdBlasChemm +//#define clAmdBlasChemm clAmdBlasChemm_pfn +#undef clAmdBlasChemv +//#define clAmdBlasChemv clAmdBlasChemv_pfn +#undef clAmdBlasCher +//#define clAmdBlasCher clAmdBlasCher_pfn +#undef clAmdBlasCher2 +//#define clAmdBlasCher2 clAmdBlasCher2_pfn +#undef clAmdBlasCher2k +//#define clAmdBlasCher2k clAmdBlasCher2k_pfn +#undef clAmdBlasCherk +//#define clAmdBlasCherk clAmdBlasCherk_pfn +#undef clAmdBlasChpmv +//#define clAmdBlasChpmv clAmdBlasChpmv_pfn +#undef clAmdBlasChpr +//#define clAmdBlasChpr clAmdBlasChpr_pfn +#undef clAmdBlasChpr2 +//#define clAmdBlasChpr2 clAmdBlasChpr2_pfn +#undef clAmdBlasCrotg +//#define clAmdBlasCrotg clAmdBlasCrotg_pfn +#undef clAmdBlasCscal +//#define clAmdBlasCscal clAmdBlasCscal_pfn +#undef clAmdBlasCsrot +//#define clAmdBlasCsrot clAmdBlasCsrot_pfn +#undef clAmdBlasCsscal +//#define clAmdBlasCsscal clAmdBlasCsscal_pfn +#undef clAmdBlasCswap +//#define clAmdBlasCswap clAmdBlasCswap_pfn +#undef clAmdBlasCsymm +//#define clAmdBlasCsymm clAmdBlasCsymm_pfn +#undef clAmdBlasCsyr2k +//#define clAmdBlasCsyr2k clAmdBlasCsyr2k_pfn +#undef clAmdBlasCsyr2kEx +//#define clAmdBlasCsyr2kEx clAmdBlasCsyr2kEx_pfn +#undef clAmdBlasCsyrk +//#define clAmdBlasCsyrk clAmdBlasCsyrk_pfn +#undef clAmdBlasCsyrkEx +//#define clAmdBlasCsyrkEx clAmdBlasCsyrkEx_pfn +#undef clAmdBlasCtbmv +//#define clAmdBlasCtbmv clAmdBlasCtbmv_pfn +#undef clAmdBlasCtbsv +//#define clAmdBlasCtbsv clAmdBlasCtbsv_pfn +#undef clAmdBlasCtpmv +//#define clAmdBlasCtpmv clAmdBlasCtpmv_pfn +#undef clAmdBlasCtpsv +//#define clAmdBlasCtpsv clAmdBlasCtpsv_pfn +#undef clAmdBlasCtrmm +//#define clAmdBlasCtrmm clAmdBlasCtrmm_pfn +#undef clAmdBlasCtrmmEx +//#define clAmdBlasCtrmmEx clAmdBlasCtrmmEx_pfn +#undef clAmdBlasCtrmv +//#define clAmdBlasCtrmv clAmdBlasCtrmv_pfn +#undef clAmdBlasCtrsm +//#define clAmdBlasCtrsm clAmdBlasCtrsm_pfn +#undef clAmdBlasCtrsmEx +//#define clAmdBlasCtrsmEx clAmdBlasCtrsmEx_pfn +#undef clAmdBlasCtrsv +//#define clAmdBlasCtrsv clAmdBlasCtrsv_pfn +#undef clAmdBlasDasum +//#define clAmdBlasDasum clAmdBlasDasum_pfn +#undef clAmdBlasDaxpy +//#define clAmdBlasDaxpy clAmdBlasDaxpy_pfn +#undef clAmdBlasDcopy +//#define clAmdBlasDcopy clAmdBlasDcopy_pfn +#undef clAmdBlasDdot +//#define clAmdBlasDdot clAmdBlasDdot_pfn +#undef clAmdBlasDgbmv +//#define clAmdBlasDgbmv clAmdBlasDgbmv_pfn +#undef clAmdBlasDgemm +//#define clAmdBlasDgemm clAmdBlasDgemm_pfn +#undef clAmdBlasDgemmEx +#define clAmdBlasDgemmEx clAmdBlasDgemmEx_pfn +#undef clAmdBlasDgemv +//#define clAmdBlasDgemv clAmdBlasDgemv_pfn +#undef clAmdBlasDgemvEx +//#define clAmdBlasDgemvEx clAmdBlasDgemvEx_pfn +#undef clAmdBlasDger +//#define clAmdBlasDger clAmdBlasDger_pfn +#undef clAmdBlasDnrm2 +//#define clAmdBlasDnrm2 clAmdBlasDnrm2_pfn +#undef clAmdBlasDrot +//#define clAmdBlasDrot clAmdBlasDrot_pfn +#undef clAmdBlasDrotg +//#define clAmdBlasDrotg clAmdBlasDrotg_pfn +#undef clAmdBlasDrotm +//#define clAmdBlasDrotm clAmdBlasDrotm_pfn +#undef clAmdBlasDrotmg +//#define clAmdBlasDrotmg clAmdBlasDrotmg_pfn +#undef clAmdBlasDsbmv +//#define clAmdBlasDsbmv clAmdBlasDsbmv_pfn +#undef clAmdBlasDscal +//#define clAmdBlasDscal clAmdBlasDscal_pfn +#undef clAmdBlasDspmv +//#define clAmdBlasDspmv clAmdBlasDspmv_pfn +#undef clAmdBlasDspr +//#define clAmdBlasDspr clAmdBlasDspr_pfn +#undef clAmdBlasDspr2 +//#define clAmdBlasDspr2 clAmdBlasDspr2_pfn +#undef clAmdBlasDswap +//#define clAmdBlasDswap clAmdBlasDswap_pfn +#undef clAmdBlasDsymm +//#define clAmdBlasDsymm clAmdBlasDsymm_pfn +#undef clAmdBlasDsymv +//#define clAmdBlasDsymv clAmdBlasDsymv_pfn +#undef clAmdBlasDsymvEx +//#define clAmdBlasDsymvEx clAmdBlasDsymvEx_pfn +#undef clAmdBlasDsyr +//#define clAmdBlasDsyr clAmdBlasDsyr_pfn +#undef clAmdBlasDsyr2 +//#define clAmdBlasDsyr2 clAmdBlasDsyr2_pfn +#undef clAmdBlasDsyr2k +//#define clAmdBlasDsyr2k clAmdBlasDsyr2k_pfn +#undef clAmdBlasDsyr2kEx +//#define clAmdBlasDsyr2kEx clAmdBlasDsyr2kEx_pfn +#undef clAmdBlasDsyrk +//#define clAmdBlasDsyrk clAmdBlasDsyrk_pfn +#undef clAmdBlasDsyrkEx +//#define clAmdBlasDsyrkEx clAmdBlasDsyrkEx_pfn +#undef clAmdBlasDtbmv +//#define clAmdBlasDtbmv clAmdBlasDtbmv_pfn +#undef clAmdBlasDtbsv +//#define clAmdBlasDtbsv clAmdBlasDtbsv_pfn +#undef clAmdBlasDtpmv +//#define clAmdBlasDtpmv clAmdBlasDtpmv_pfn +#undef clAmdBlasDtpsv +//#define clAmdBlasDtpsv clAmdBlasDtpsv_pfn +#undef clAmdBlasDtrmm +//#define clAmdBlasDtrmm clAmdBlasDtrmm_pfn +#undef clAmdBlasDtrmmEx +//#define clAmdBlasDtrmmEx clAmdBlasDtrmmEx_pfn +#undef clAmdBlasDtrmv +//#define clAmdBlasDtrmv clAmdBlasDtrmv_pfn +#undef clAmdBlasDtrsm +//#define clAmdBlasDtrsm clAmdBlasDtrsm_pfn +#undef clAmdBlasDtrsmEx +//#define clAmdBlasDtrsmEx clAmdBlasDtrsmEx_pfn +#undef clAmdBlasDtrsv +//#define clAmdBlasDtrsv clAmdBlasDtrsv_pfn +#undef clAmdBlasDzasum +//#define clAmdBlasDzasum clAmdBlasDzasum_pfn +#undef clAmdBlasDznrm2 +//#define clAmdBlasDznrm2 clAmdBlasDznrm2_pfn +#undef clAmdBlasGetVersion +//#define clAmdBlasGetVersion clAmdBlasGetVersion_pfn +#undef clAmdBlasRemoveScratchImage +//#define clAmdBlasRemoveScratchImage clAmdBlasRemoveScratchImage_pfn +#undef clAmdBlasSasum +//#define clAmdBlasSasum clAmdBlasSasum_pfn +#undef clAmdBlasSaxpy +//#define clAmdBlasSaxpy clAmdBlasSaxpy_pfn +#undef clAmdBlasScasum +//#define clAmdBlasScasum clAmdBlasScasum_pfn +#undef clAmdBlasScnrm2 +//#define clAmdBlasScnrm2 clAmdBlasScnrm2_pfn +#undef clAmdBlasScopy +//#define clAmdBlasScopy clAmdBlasScopy_pfn +#undef clAmdBlasSdot +//#define clAmdBlasSdot clAmdBlasSdot_pfn +#undef clAmdBlasSetup +#define clAmdBlasSetup clAmdBlasSetup_pfn +#undef clAmdBlasSgbmv +//#define clAmdBlasSgbmv clAmdBlasSgbmv_pfn +#undef clAmdBlasSgemm +//#define clAmdBlasSgemm clAmdBlasSgemm_pfn +#undef clAmdBlasSgemmEx +#define clAmdBlasSgemmEx clAmdBlasSgemmEx_pfn +#undef clAmdBlasSgemv +//#define clAmdBlasSgemv clAmdBlasSgemv_pfn +#undef clAmdBlasSgemvEx +//#define clAmdBlasSgemvEx clAmdBlasSgemvEx_pfn +#undef clAmdBlasSger +//#define clAmdBlasSger clAmdBlasSger_pfn +#undef clAmdBlasSnrm2 +//#define clAmdBlasSnrm2 clAmdBlasSnrm2_pfn +#undef clAmdBlasSrot +//#define clAmdBlasSrot clAmdBlasSrot_pfn +#undef clAmdBlasSrotg +//#define clAmdBlasSrotg clAmdBlasSrotg_pfn +#undef clAmdBlasSrotm +//#define clAmdBlasSrotm clAmdBlasSrotm_pfn +#undef clAmdBlasSrotmg +//#define clAmdBlasSrotmg clAmdBlasSrotmg_pfn +#undef clAmdBlasSsbmv +//#define clAmdBlasSsbmv clAmdBlasSsbmv_pfn +#undef clAmdBlasSscal +//#define clAmdBlasSscal clAmdBlasSscal_pfn +#undef clAmdBlasSspmv +//#define clAmdBlasSspmv clAmdBlasSspmv_pfn +#undef clAmdBlasSspr +//#define clAmdBlasSspr clAmdBlasSspr_pfn +#undef clAmdBlasSspr2 +//#define clAmdBlasSspr2 clAmdBlasSspr2_pfn +#undef clAmdBlasSswap +//#define clAmdBlasSswap clAmdBlasSswap_pfn +#undef clAmdBlasSsymm +//#define clAmdBlasSsymm clAmdBlasSsymm_pfn +#undef clAmdBlasSsymv +//#define clAmdBlasSsymv clAmdBlasSsymv_pfn +#undef clAmdBlasSsymvEx +//#define clAmdBlasSsymvEx clAmdBlasSsymvEx_pfn +#undef clAmdBlasSsyr +//#define clAmdBlasSsyr clAmdBlasSsyr_pfn +#undef clAmdBlasSsyr2 +//#define clAmdBlasSsyr2 clAmdBlasSsyr2_pfn +#undef clAmdBlasSsyr2k +//#define clAmdBlasSsyr2k clAmdBlasSsyr2k_pfn +#undef clAmdBlasSsyr2kEx +//#define clAmdBlasSsyr2kEx clAmdBlasSsyr2kEx_pfn +#undef clAmdBlasSsyrk +//#define clAmdBlasSsyrk clAmdBlasSsyrk_pfn +#undef clAmdBlasSsyrkEx +//#define clAmdBlasSsyrkEx clAmdBlasSsyrkEx_pfn +#undef clAmdBlasStbmv +//#define clAmdBlasStbmv clAmdBlasStbmv_pfn +#undef clAmdBlasStbsv +//#define clAmdBlasStbsv clAmdBlasStbsv_pfn +#undef clAmdBlasStpmv +//#define clAmdBlasStpmv clAmdBlasStpmv_pfn +#undef clAmdBlasStpsv +//#define clAmdBlasStpsv clAmdBlasStpsv_pfn +#undef clAmdBlasStrmm +//#define clAmdBlasStrmm clAmdBlasStrmm_pfn +#undef clAmdBlasStrmmEx +//#define clAmdBlasStrmmEx clAmdBlasStrmmEx_pfn +#undef clAmdBlasStrmv +//#define clAmdBlasStrmv clAmdBlasStrmv_pfn +#undef clAmdBlasStrsm +//#define clAmdBlasStrsm clAmdBlasStrsm_pfn +#undef clAmdBlasStrsmEx +//#define clAmdBlasStrsmEx clAmdBlasStrsmEx_pfn +#undef clAmdBlasStrsv +//#define clAmdBlasStrsv clAmdBlasStrsv_pfn +#undef clAmdBlasTeardown +#define clAmdBlasTeardown clAmdBlasTeardown_pfn +#undef clAmdBlasZaxpy +//#define clAmdBlasZaxpy clAmdBlasZaxpy_pfn +#undef clAmdBlasZcopy +//#define clAmdBlasZcopy clAmdBlasZcopy_pfn +#undef clAmdBlasZdotc +//#define clAmdBlasZdotc clAmdBlasZdotc_pfn +#undef clAmdBlasZdotu +//#define clAmdBlasZdotu clAmdBlasZdotu_pfn +#undef clAmdBlasZdrot +//#define clAmdBlasZdrot clAmdBlasZdrot_pfn +#undef clAmdBlasZdscal +//#define clAmdBlasZdscal clAmdBlasZdscal_pfn +#undef clAmdBlasZgbmv +//#define clAmdBlasZgbmv clAmdBlasZgbmv_pfn +#undef clAmdBlasZgemm +//#define clAmdBlasZgemm clAmdBlasZgemm_pfn +#undef clAmdBlasZgemmEx +#define clAmdBlasZgemmEx clAmdBlasZgemmEx_pfn +#undef clAmdBlasZgemv +//#define clAmdBlasZgemv clAmdBlasZgemv_pfn +#undef clAmdBlasZgemvEx +//#define clAmdBlasZgemvEx clAmdBlasZgemvEx_pfn +#undef clAmdBlasZgerc +//#define clAmdBlasZgerc clAmdBlasZgerc_pfn +#undef clAmdBlasZgeru +//#define clAmdBlasZgeru clAmdBlasZgeru_pfn +#undef clAmdBlasZhbmv +//#define clAmdBlasZhbmv clAmdBlasZhbmv_pfn +#undef clAmdBlasZhemm +//#define clAmdBlasZhemm clAmdBlasZhemm_pfn +#undef clAmdBlasZhemv +//#define clAmdBlasZhemv clAmdBlasZhemv_pfn +#undef clAmdBlasZher +//#define clAmdBlasZher clAmdBlasZher_pfn +#undef clAmdBlasZher2 +//#define clAmdBlasZher2 clAmdBlasZher2_pfn +#undef clAmdBlasZher2k +//#define clAmdBlasZher2k clAmdBlasZher2k_pfn +#undef clAmdBlasZherk +//#define clAmdBlasZherk clAmdBlasZherk_pfn +#undef clAmdBlasZhpmv +//#define clAmdBlasZhpmv clAmdBlasZhpmv_pfn +#undef clAmdBlasZhpr +//#define clAmdBlasZhpr clAmdBlasZhpr_pfn +#undef clAmdBlasZhpr2 +//#define clAmdBlasZhpr2 clAmdBlasZhpr2_pfn +#undef clAmdBlasZrotg +//#define clAmdBlasZrotg clAmdBlasZrotg_pfn +#undef clAmdBlasZscal +//#define clAmdBlasZscal clAmdBlasZscal_pfn +#undef clAmdBlasZswap +//#define clAmdBlasZswap clAmdBlasZswap_pfn +#undef clAmdBlasZsymm +//#define clAmdBlasZsymm clAmdBlasZsymm_pfn +#undef clAmdBlasZsyr2k +//#define clAmdBlasZsyr2k clAmdBlasZsyr2k_pfn +#undef clAmdBlasZsyr2kEx +//#define clAmdBlasZsyr2kEx clAmdBlasZsyr2kEx_pfn +#undef clAmdBlasZsyrk +//#define clAmdBlasZsyrk clAmdBlasZsyrk_pfn +#undef clAmdBlasZsyrkEx +//#define clAmdBlasZsyrkEx clAmdBlasZsyrkEx_pfn +#undef clAmdBlasZtbmv +//#define clAmdBlasZtbmv clAmdBlasZtbmv_pfn +#undef clAmdBlasZtbsv +//#define clAmdBlasZtbsv clAmdBlasZtbsv_pfn +#undef clAmdBlasZtpmv +//#define clAmdBlasZtpmv clAmdBlasZtpmv_pfn +#undef clAmdBlasZtpsv +//#define clAmdBlasZtpsv clAmdBlasZtpsv_pfn +#undef clAmdBlasZtrmm +//#define clAmdBlasZtrmm clAmdBlasZtrmm_pfn +#undef clAmdBlasZtrmmEx +//#define clAmdBlasZtrmmEx clAmdBlasZtrmmEx_pfn +#undef clAmdBlasZtrmv +//#define clAmdBlasZtrmv clAmdBlasZtrmv_pfn +#undef clAmdBlasZtrsm +//#define clAmdBlasZtrsm clAmdBlasZtrsm_pfn +#undef clAmdBlasZtrsmEx +//#define clAmdBlasZtrsmEx clAmdBlasZtrsmEx_pfn +#undef clAmdBlasZtrsv +//#define clAmdBlasZtrsv clAmdBlasZtrsv_pfn +#undef clAmdBlasiCamax +//#define clAmdBlasiCamax clAmdBlasiCamax_pfn +#undef clAmdBlasiDamax +//#define clAmdBlasiDamax clAmdBlasiDamax_pfn +#undef clAmdBlasiSamax +//#define clAmdBlasiSamax clAmdBlasiSamax_pfn +#undef clAmdBlasiZamax +//#define clAmdBlasiZamax clAmdBlasiZamax_pfn + +// generated by parser_clamdblas.py +//extern CL_RUNTIME_EXPORT cl_ulong (*clAmdBlasAddScratchImage)(cl_context context, size_t width, size_t height, clAmdBlasStatus* status); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCaxpy)(size_t N, cl_float2 alpha, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCcopy)(size_t N, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCdotc)(size_t N, cl_mem dotProduct, size_t offDP, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCdotu)(size_t N, cl_mem dotProduct, size_t offDP, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCgbmv)(clAmdBlasOrder order, clAmdBlasTranspose trans, size_t M, size_t N, size_t KL, size_t KU, cl_float2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_float2 beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCgemm)(clAmdBlasOrder order, clAmdBlasTranspose transA, clAmdBlasTranspose transB, size_t M, size_t N, size_t K, FloatComplex alpha, const cl_mem A, size_t lda, const cl_mem B, size_t ldb, FloatComplex beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCgemmEx)(clAmdBlasOrder order, clAmdBlasTranspose transA, clAmdBlasTranspose transB, size_t M, size_t N, size_t K, FloatComplex alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, FloatComplex beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCgemv)(clAmdBlasOrder order, clAmdBlasTranspose transA, size_t M, size_t N, FloatComplex alpha, const cl_mem A, size_t lda, const cl_mem x, size_t offx, int incx, FloatComplex beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCgemvEx)(clAmdBlasOrder order, clAmdBlasTranspose transA, size_t M, size_t N, FloatComplex alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem x, size_t offx, int incx, FloatComplex beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCgerc)(clAmdBlasOrder order, size_t M, size_t N, cl_float2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCgeru)(clAmdBlasOrder order, size_t M, size_t N, cl_float2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasChbmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, size_t K, cl_float2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_float2 beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasChemm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, size_t M, size_t N, cl_float2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_float2 beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasChemv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, FloatComplex alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, FloatComplex beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCher)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCher2)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCher2k)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, size_t N, size_t K, FloatComplex alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_float beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCherk)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, float alpha, const cl_mem A, size_t offa, size_t lda, float beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasChpmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float2 alpha, const cl_mem AP, size_t offa, const cl_mem X, size_t offx, int incx, cl_float2 beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasChpr)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasChpr2)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCrotg)(cl_mem CA, size_t offCA, cl_mem CB, size_t offCB, cl_mem C, size_t offC, cl_mem S, size_t offS, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCscal)(size_t N, cl_float2 alpha, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCsrot)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_float C, cl_float S, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCsscal)(size_t N, cl_float alpha, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCswap)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCsymm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, size_t M, size_t N, cl_float2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_float2 beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCsyr2k)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transAB, size_t N, size_t K, FloatComplex alpha, const cl_mem A, size_t lda, const cl_mem B, size_t ldb, FloatComplex beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCsyr2kEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transAB, size_t N, size_t K, FloatComplex alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, FloatComplex beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCsyrk)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, FloatComplex alpha, const cl_mem A, size_t lda, FloatComplex beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCsyrkEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, FloatComplex alpha, const cl_mem A, size_t offA, size_t lda, FloatComplex beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtbmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtbsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtpmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem AP, size_t offa, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtpsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtrmm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, FloatComplex alpha, const cl_mem A, size_t lda, cl_mem B, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtrmmEx)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, FloatComplex alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtrmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtrsm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, FloatComplex alpha, const cl_mem A, size_t lda, cl_mem B, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtrsmEx)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, FloatComplex alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtrsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDasum)(size_t N, cl_mem asum, size_t offAsum, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDaxpy)(size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDcopy)(size_t N, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDdot)(size_t N, cl_mem dotProduct, size_t offDP, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDgbmv)(clAmdBlasOrder order, clAmdBlasTranspose trans, size_t M, size_t N, size_t KL, size_t KU, cl_double alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_double beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDgemm)(clAmdBlasOrder order, clAmdBlasTranspose transA, clAmdBlasTranspose transB, size_t M, size_t N, size_t K, cl_double alpha, const cl_mem A, size_t lda, const cl_mem B, size_t ldb, cl_double beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDgemmEx)(clAmdBlasOrder order, clAmdBlasTranspose transA, clAmdBlasTranspose transB, size_t M, size_t N, size_t K, cl_double alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, cl_double beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDgemv)(clAmdBlasOrder order, clAmdBlasTranspose transA, size_t M, size_t N, cl_double alpha, const cl_mem A, size_t lda, const cl_mem x, size_t offx, int incx, cl_double beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDgemvEx)(clAmdBlasOrder order, clAmdBlasTranspose transA, size_t M, size_t N, cl_double alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem x, size_t offx, int incx, cl_double beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDger)(clAmdBlasOrder order, size_t M, size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDnrm2)(size_t N, cl_mem NRM2, size_t offNRM2, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDrot)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_double C, cl_double S, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDrotg)(cl_mem DA, size_t offDA, cl_mem DB, size_t offDB, cl_mem C, size_t offC, cl_mem S, size_t offS, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDrotm)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, const cl_mem DPARAM, size_t offDparam, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDrotmg)(cl_mem DD1, size_t offDD1, cl_mem DD2, size_t offDD2, cl_mem DX1, size_t offDX1, const cl_mem DY1, size_t offDY1, cl_mem DPARAM, size_t offDparam, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsbmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, size_t K, cl_double alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_double beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDscal)(size_t N, cl_double alpha, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDspmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double alpha, const cl_mem AP, size_t offa, const cl_mem X, size_t offx, int incx, cl_double beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDspr)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDspr2)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDswap)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsymm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, size_t M, size_t N, cl_double alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_double beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsymv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double alpha, const cl_mem A, size_t lda, const cl_mem x, size_t offx, int incx, cl_double beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsymvEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem x, size_t offx, int incx, cl_double beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsyr)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsyr2)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsyr2k)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transAB, size_t N, size_t K, cl_double alpha, const cl_mem A, size_t lda, const cl_mem B, size_t ldb, cl_double beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsyr2kEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transAB, size_t N, size_t K, cl_double alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, cl_double beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsyrk)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, cl_double alpha, const cl_mem A, size_t lda, cl_double beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsyrkEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, cl_double alpha, const cl_mem A, size_t offA, size_t lda, cl_double beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtbmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtbsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtpmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem AP, size_t offa, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtpsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtrmm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, cl_double alpha, const cl_mem A, size_t lda, cl_mem B, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtrmmEx)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, cl_double alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtrmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtrsm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, cl_double alpha, const cl_mem A, size_t lda, cl_mem B, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtrsmEx)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, cl_double alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtrsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDzasum)(size_t N, cl_mem asum, size_t offAsum, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDznrm2)(size_t N, cl_mem NRM2, size_t offNRM2, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasGetVersion)(cl_uint* major, cl_uint* minor, cl_uint* patch); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasRemoveScratchImage)(cl_ulong imageID); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSasum)(size_t N, cl_mem asum, size_t offAsum, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSaxpy)(size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasScasum)(size_t N, cl_mem asum, size_t offAsum, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasScnrm2)(size_t N, cl_mem NRM2, size_t offNRM2, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasScopy)(size_t N, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSdot)(size_t N, cl_mem dotProduct, size_t offDP, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSetup)(); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSgbmv)(clAmdBlasOrder order, clAmdBlasTranspose trans, size_t M, size_t N, size_t KL, size_t KU, cl_float alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_float beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSgemm)(clAmdBlasOrder order, clAmdBlasTranspose transA, clAmdBlasTranspose transB, size_t M, size_t N, size_t K, cl_float alpha, const cl_mem A, size_t lda, const cl_mem B, size_t ldb, cl_float beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSgemmEx)(clAmdBlasOrder order, clAmdBlasTranspose transA, clAmdBlasTranspose transB, size_t M, size_t N, size_t K, cl_float alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, cl_float beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSgemv)(clAmdBlasOrder order, clAmdBlasTranspose transA, size_t M, size_t N, cl_float alpha, const cl_mem A, size_t lda, const cl_mem x, size_t offx, int incx, cl_float beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSgemvEx)(clAmdBlasOrder order, clAmdBlasTranspose transA, size_t M, size_t N, cl_float alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem x, size_t offx, int incx, cl_float beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSger)(clAmdBlasOrder order, size_t M, size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSnrm2)(size_t N, cl_mem NRM2, size_t offNRM2, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSrot)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_float C, cl_float S, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSrotg)(cl_mem SA, size_t offSA, cl_mem SB, size_t offSB, cl_mem C, size_t offC, cl_mem S, size_t offS, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSrotm)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, const cl_mem SPARAM, size_t offSparam, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSrotmg)(cl_mem SD1, size_t offSD1, cl_mem SD2, size_t offSD2, cl_mem SX1, size_t offSX1, const cl_mem SY1, size_t offSY1, cl_mem SPARAM, size_t offSparam, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsbmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, size_t K, cl_float alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_float beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSscal)(size_t N, cl_float alpha, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSspmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float alpha, const cl_mem AP, size_t offa, const cl_mem X, size_t offx, int incx, cl_float beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSspr)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSspr2)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSswap)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsymm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, size_t M, size_t N, cl_float alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_float beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsymv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float alpha, const cl_mem A, size_t lda, const cl_mem x, size_t offx, int incx, cl_float beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsymvEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem x, size_t offx, int incx, cl_float beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsyr)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsyr2)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsyr2k)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transAB, size_t N, size_t K, cl_float alpha, const cl_mem A, size_t lda, const cl_mem B, size_t ldb, cl_float beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsyr2kEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transAB, size_t N, size_t K, cl_float alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, cl_float beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsyrk)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, cl_float alpha, const cl_mem A, size_t lda, cl_float beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsyrkEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, cl_float alpha, const cl_mem A, size_t offA, size_t lda, cl_float beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStbmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStbsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStpmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem AP, size_t offa, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStpsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStrmm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, cl_float alpha, const cl_mem A, size_t lda, cl_mem B, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStrmmEx)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, cl_float alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStrmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStrsm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, cl_float alpha, const cl_mem A, size_t lda, cl_mem B, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStrsmEx)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, cl_float alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStrsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +extern CL_RUNTIME_EXPORT void (*clAmdBlasTeardown)(); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZaxpy)(size_t N, cl_double2 alpha, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZcopy)(size_t N, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZdotc)(size_t N, cl_mem dotProduct, size_t offDP, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZdotu)(size_t N, cl_mem dotProduct, size_t offDP, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZdrot)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_double C, cl_double S, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZdscal)(size_t N, cl_double alpha, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZgbmv)(clAmdBlasOrder order, clAmdBlasTranspose trans, size_t M, size_t N, size_t KL, size_t KU, cl_double2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_double2 beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZgemm)(clAmdBlasOrder order, clAmdBlasTranspose transA, clAmdBlasTranspose transB, size_t M, size_t N, size_t K, DoubleComplex alpha, const cl_mem A, size_t lda, const cl_mem B, size_t ldb, DoubleComplex beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZgemmEx)(clAmdBlasOrder order, clAmdBlasTranspose transA, clAmdBlasTranspose transB, size_t M, size_t N, size_t K, DoubleComplex alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, DoubleComplex beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZgemv)(clAmdBlasOrder order, clAmdBlasTranspose transA, size_t M, size_t N, DoubleComplex alpha, const cl_mem A, size_t lda, const cl_mem x, size_t offx, int incx, DoubleComplex beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZgemvEx)(clAmdBlasOrder order, clAmdBlasTranspose transA, size_t M, size_t N, DoubleComplex alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem x, size_t offx, int incx, DoubleComplex beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZgerc)(clAmdBlasOrder order, size_t M, size_t N, cl_double2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZgeru)(clAmdBlasOrder order, size_t M, size_t N, cl_double2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZhbmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, size_t K, cl_double2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_double2 beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZhemm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, size_t M, size_t N, cl_double2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_double2 beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZhemv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, DoubleComplex alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, DoubleComplex beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZher)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZher2)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZher2k)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, size_t N, size_t K, DoubleComplex alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_double beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZherk)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, double alpha, const cl_mem A, size_t offa, size_t lda, double beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZhpmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double2 alpha, const cl_mem AP, size_t offa, const cl_mem X, size_t offx, int incx, cl_double2 beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZhpr)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZhpr2)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZrotg)(cl_mem CA, size_t offCA, cl_mem CB, size_t offCB, cl_mem C, size_t offC, cl_mem S, size_t offS, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZscal)(size_t N, cl_double2 alpha, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZswap)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZsymm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, size_t M, size_t N, cl_double2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_double2 beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZsyr2k)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transAB, size_t N, size_t K, DoubleComplex alpha, const cl_mem A, size_t lda, const cl_mem B, size_t ldb, DoubleComplex beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZsyr2kEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transAB, size_t N, size_t K, DoubleComplex alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, DoubleComplex beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZsyrk)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, DoubleComplex alpha, const cl_mem A, size_t lda, DoubleComplex beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZsyrkEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, DoubleComplex alpha, const cl_mem A, size_t offA, size_t lda, DoubleComplex beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtbmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtbsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtpmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem AP, size_t offa, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtpsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtrmm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, DoubleComplex alpha, const cl_mem A, size_t lda, cl_mem B, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtrmmEx)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, DoubleComplex alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtrmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtrsm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, DoubleComplex alpha, const cl_mem A, size_t lda, cl_mem B, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtrsmEx)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, DoubleComplex alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtrsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasiCamax)(size_t N, cl_mem iMax, size_t offiMax, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasiDamax)(size_t N, cl_mem iMax, size_t offiMax, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasiSamax)(size_t N, cl_mem iMax, size_t offiMax, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasiZamax)(size_t N, cl_mem iMax, size_t offiMax, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/runtime/autogenerated/opencl_clamdfft.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/runtime/autogenerated/opencl_clamdfft.hpp new file mode 100644 index 0000000000000000000000000000000000000000..1457d7eb8d69a56197b8e80430728d571282088f --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/runtime/autogenerated/opencl_clamdfft.hpp @@ -0,0 +1,142 @@ +// +// AUTOGENERATED, DO NOT EDIT +// +#ifndef OPENCV_CORE_OCL_RUNTIME_CLAMDFFT_HPP +#error "Invalid usage" +#endif + +// generated by parser_clamdfft.py +#define clAmdFftBakePlan clAmdFftBakePlan_ +#define clAmdFftCopyPlan clAmdFftCopyPlan_ +#define clAmdFftCreateDefaultPlan clAmdFftCreateDefaultPlan_ +#define clAmdFftDestroyPlan clAmdFftDestroyPlan_ +#define clAmdFftEnqueueTransform clAmdFftEnqueueTransform_ +#define clAmdFftGetLayout clAmdFftGetLayout_ +#define clAmdFftGetPlanBatchSize clAmdFftGetPlanBatchSize_ +#define clAmdFftGetPlanContext clAmdFftGetPlanContext_ +#define clAmdFftGetPlanDim clAmdFftGetPlanDim_ +#define clAmdFftGetPlanDistance clAmdFftGetPlanDistance_ +#define clAmdFftGetPlanInStride clAmdFftGetPlanInStride_ +#define clAmdFftGetPlanLength clAmdFftGetPlanLength_ +#define clAmdFftGetPlanOutStride clAmdFftGetPlanOutStride_ +#define clAmdFftGetPlanPrecision clAmdFftGetPlanPrecision_ +#define clAmdFftGetPlanScale clAmdFftGetPlanScale_ +#define clAmdFftGetPlanTransposeResult clAmdFftGetPlanTransposeResult_ +#define clAmdFftGetResultLocation clAmdFftGetResultLocation_ +#define clAmdFftGetTmpBufSize clAmdFftGetTmpBufSize_ +#define clAmdFftGetVersion clAmdFftGetVersion_ +#define clAmdFftSetLayout clAmdFftSetLayout_ +#define clAmdFftSetPlanBatchSize clAmdFftSetPlanBatchSize_ +#define clAmdFftSetPlanDim clAmdFftSetPlanDim_ +#define clAmdFftSetPlanDistance clAmdFftSetPlanDistance_ +#define clAmdFftSetPlanInStride clAmdFftSetPlanInStride_ +#define clAmdFftSetPlanLength clAmdFftSetPlanLength_ +#define clAmdFftSetPlanOutStride clAmdFftSetPlanOutStride_ +#define clAmdFftSetPlanPrecision clAmdFftSetPlanPrecision_ +#define clAmdFftSetPlanScale clAmdFftSetPlanScale_ +#define clAmdFftSetPlanTransposeResult clAmdFftSetPlanTransposeResult_ +#define clAmdFftSetResultLocation clAmdFftSetResultLocation_ +#define clAmdFftSetup clAmdFftSetup_ +#define clAmdFftTeardown clAmdFftTeardown_ + +#include + +// generated by parser_clamdfft.py +#undef clAmdFftBakePlan +#define clAmdFftBakePlan clAmdFftBakePlan_pfn +#undef clAmdFftCopyPlan +//#define clAmdFftCopyPlan clAmdFftCopyPlan_pfn +#undef clAmdFftCreateDefaultPlan +#define clAmdFftCreateDefaultPlan clAmdFftCreateDefaultPlan_pfn +#undef clAmdFftDestroyPlan +#define clAmdFftDestroyPlan clAmdFftDestroyPlan_pfn +#undef clAmdFftEnqueueTransform +#define clAmdFftEnqueueTransform clAmdFftEnqueueTransform_pfn +#undef clAmdFftGetLayout +//#define clAmdFftGetLayout clAmdFftGetLayout_pfn +#undef clAmdFftGetPlanBatchSize +//#define clAmdFftGetPlanBatchSize clAmdFftGetPlanBatchSize_pfn +#undef clAmdFftGetPlanContext +//#define clAmdFftGetPlanContext clAmdFftGetPlanContext_pfn +#undef clAmdFftGetPlanDim +//#define clAmdFftGetPlanDim clAmdFftGetPlanDim_pfn +#undef clAmdFftGetPlanDistance +//#define clAmdFftGetPlanDistance clAmdFftGetPlanDistance_pfn +#undef clAmdFftGetPlanInStride +//#define clAmdFftGetPlanInStride clAmdFftGetPlanInStride_pfn +#undef clAmdFftGetPlanLength +//#define clAmdFftGetPlanLength clAmdFftGetPlanLength_pfn +#undef clAmdFftGetPlanOutStride +//#define clAmdFftGetPlanOutStride clAmdFftGetPlanOutStride_pfn +#undef clAmdFftGetPlanPrecision +//#define clAmdFftGetPlanPrecision clAmdFftGetPlanPrecision_pfn +#undef clAmdFftGetPlanScale +//#define clAmdFftGetPlanScale clAmdFftGetPlanScale_pfn +#undef clAmdFftGetPlanTransposeResult +//#define clAmdFftGetPlanTransposeResult clAmdFftGetPlanTransposeResult_pfn +#undef clAmdFftGetResultLocation +//#define clAmdFftGetResultLocation clAmdFftGetResultLocation_pfn +#undef clAmdFftGetTmpBufSize +#define clAmdFftGetTmpBufSize clAmdFftGetTmpBufSize_pfn +#undef clAmdFftGetVersion +#define clAmdFftGetVersion clAmdFftGetVersion_pfn +#undef clAmdFftSetLayout +#define clAmdFftSetLayout clAmdFftSetLayout_pfn +#undef clAmdFftSetPlanBatchSize +#define clAmdFftSetPlanBatchSize clAmdFftSetPlanBatchSize_pfn +#undef clAmdFftSetPlanDim +//#define clAmdFftSetPlanDim clAmdFftSetPlanDim_pfn +#undef clAmdFftSetPlanDistance +#define clAmdFftSetPlanDistance clAmdFftSetPlanDistance_pfn +#undef clAmdFftSetPlanInStride +#define clAmdFftSetPlanInStride clAmdFftSetPlanInStride_pfn +#undef clAmdFftSetPlanLength +//#define clAmdFftSetPlanLength clAmdFftSetPlanLength_pfn +#undef clAmdFftSetPlanOutStride +#define clAmdFftSetPlanOutStride clAmdFftSetPlanOutStride_pfn +#undef clAmdFftSetPlanPrecision +#define clAmdFftSetPlanPrecision clAmdFftSetPlanPrecision_pfn +#undef clAmdFftSetPlanScale +#define clAmdFftSetPlanScale clAmdFftSetPlanScale_pfn +#undef clAmdFftSetPlanTransposeResult +//#define clAmdFftSetPlanTransposeResult clAmdFftSetPlanTransposeResult_pfn +#undef clAmdFftSetResultLocation +#define clAmdFftSetResultLocation clAmdFftSetResultLocation_pfn +#undef clAmdFftSetup +#define clAmdFftSetup clAmdFftSetup_pfn +#undef clAmdFftTeardown +#define clAmdFftTeardown clAmdFftTeardown_pfn + +// generated by parser_clamdfft.py +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftBakePlan)(clAmdFftPlanHandle plHandle, cl_uint numQueues, cl_command_queue* commQueueFFT, void (CL_CALLBACK* pfn_notify) (clAmdFftPlanHandle plHandle, void* user_data), void* user_data); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftCopyPlan)(clAmdFftPlanHandle* out_plHandle, cl_context new_context, clAmdFftPlanHandle in_plHandle); +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftCreateDefaultPlan)(clAmdFftPlanHandle* plHandle, cl_context context, const clAmdFftDim dim, const size_t* clLengths); +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftDestroyPlan)(clAmdFftPlanHandle* plHandle); +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftEnqueueTransform)(clAmdFftPlanHandle plHandle, clAmdFftDirection dir, cl_uint numQueuesAndEvents, cl_command_queue* commQueues, cl_uint numWaitEvents, const cl_event* waitEvents, cl_event* outEvents, cl_mem* inputBuffers, cl_mem* outputBuffers, cl_mem tmpBuffer); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetLayout)(const clAmdFftPlanHandle plHandle, clAmdFftLayout* iLayout, clAmdFftLayout* oLayout); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanBatchSize)(const clAmdFftPlanHandle plHandle, size_t* batchSize); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanContext)(const clAmdFftPlanHandle plHandle, cl_context* context); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanDim)(const clAmdFftPlanHandle plHandle, clAmdFftDim* dim, cl_uint* size); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanDistance)(const clAmdFftPlanHandle plHandle, size_t* iDist, size_t* oDist); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanInStride)(const clAmdFftPlanHandle plHandle, const clAmdFftDim dim, size_t* clStrides); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanLength)(const clAmdFftPlanHandle plHandle, const clAmdFftDim dim, size_t* clLengths); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanOutStride)(const clAmdFftPlanHandle plHandle, const clAmdFftDim dim, size_t* clStrides); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanPrecision)(const clAmdFftPlanHandle plHandle, clAmdFftPrecision* precision); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanScale)(const clAmdFftPlanHandle plHandle, clAmdFftDirection dir, cl_float* scale); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanTransposeResult)(const clAmdFftPlanHandle plHandle, clAmdFftResultTransposed* transposed); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetResultLocation)(const clAmdFftPlanHandle plHandle, clAmdFftResultLocation* placeness); +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetTmpBufSize)(const clAmdFftPlanHandle plHandle, size_t* buffersize); +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetVersion)(cl_uint* major, cl_uint* minor, cl_uint* patch); +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetLayout)(clAmdFftPlanHandle plHandle, clAmdFftLayout iLayout, clAmdFftLayout oLayout); +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetPlanBatchSize)(clAmdFftPlanHandle plHandle, size_t batchSize); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetPlanDim)(clAmdFftPlanHandle plHandle, const clAmdFftDim dim); +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetPlanDistance)(clAmdFftPlanHandle plHandle, size_t iDist, size_t oDist); +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetPlanInStride)(clAmdFftPlanHandle plHandle, const clAmdFftDim dim, size_t* clStrides); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetPlanLength)(clAmdFftPlanHandle plHandle, const clAmdFftDim dim, const size_t* clLengths); +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetPlanOutStride)(clAmdFftPlanHandle plHandle, const clAmdFftDim dim, size_t* clStrides); +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetPlanPrecision)(clAmdFftPlanHandle plHandle, clAmdFftPrecision precision); +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetPlanScale)(clAmdFftPlanHandle plHandle, clAmdFftDirection dir, cl_float scale); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetPlanTransposeResult)(clAmdFftPlanHandle plHandle, clAmdFftResultTransposed transposed); +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetResultLocation)(clAmdFftPlanHandle plHandle, clAmdFftResultLocation placeness); +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetup)(const clAmdFftSetupData* setupData); +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftTeardown)(); diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/runtime/autogenerated/opencl_core.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/runtime/autogenerated/opencl_core.hpp new file mode 100644 index 0000000000000000000000000000000000000000..fdaf469ff1cee7dada0e45311533f14a4db89d71 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/runtime/autogenerated/opencl_core.hpp @@ -0,0 +1,370 @@ +// +// AUTOGENERATED, DO NOT EDIT +// +#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_CORE_HPP +#error "Invalid usage" +#endif + +// generated by parser_cl.py +#define clBuildProgram clBuildProgram_ +#define clCompileProgram clCompileProgram_ +#define clCreateBuffer clCreateBuffer_ +#define clCreateCommandQueue clCreateCommandQueue_ +#define clCreateContext clCreateContext_ +#define clCreateContextFromType clCreateContextFromType_ +#define clCreateImage clCreateImage_ +#define clCreateImage2D clCreateImage2D_ +#define clCreateImage3D clCreateImage3D_ +#define clCreateKernel clCreateKernel_ +#define clCreateKernelsInProgram clCreateKernelsInProgram_ +#define clCreateProgramWithBinary clCreateProgramWithBinary_ +#define clCreateProgramWithBuiltInKernels clCreateProgramWithBuiltInKernels_ +#define clCreateProgramWithSource clCreateProgramWithSource_ +#define clCreateSampler clCreateSampler_ +#define clCreateSubBuffer clCreateSubBuffer_ +#define clCreateSubDevices clCreateSubDevices_ +#define clCreateUserEvent clCreateUserEvent_ +#define clEnqueueBarrier clEnqueueBarrier_ +#define clEnqueueBarrierWithWaitList clEnqueueBarrierWithWaitList_ +#define clEnqueueCopyBuffer clEnqueueCopyBuffer_ +#define clEnqueueCopyBufferRect clEnqueueCopyBufferRect_ +#define clEnqueueCopyBufferToImage clEnqueueCopyBufferToImage_ +#define clEnqueueCopyImage clEnqueueCopyImage_ +#define clEnqueueCopyImageToBuffer clEnqueueCopyImageToBuffer_ +#define clEnqueueFillBuffer clEnqueueFillBuffer_ +#define clEnqueueFillImage clEnqueueFillImage_ +#define clEnqueueMapBuffer clEnqueueMapBuffer_ +#define clEnqueueMapImage clEnqueueMapImage_ +#define clEnqueueMarker clEnqueueMarker_ +#define clEnqueueMarkerWithWaitList clEnqueueMarkerWithWaitList_ +#define clEnqueueMigrateMemObjects clEnqueueMigrateMemObjects_ +#define clEnqueueNDRangeKernel clEnqueueNDRangeKernel_ +#define clEnqueueNativeKernel clEnqueueNativeKernel_ +#define clEnqueueReadBuffer clEnqueueReadBuffer_ +#define clEnqueueReadBufferRect clEnqueueReadBufferRect_ +#define clEnqueueReadImage clEnqueueReadImage_ +#define clEnqueueTask clEnqueueTask_ +#define clEnqueueUnmapMemObject clEnqueueUnmapMemObject_ +#define clEnqueueWaitForEvents clEnqueueWaitForEvents_ +#define clEnqueueWriteBuffer clEnqueueWriteBuffer_ +#define clEnqueueWriteBufferRect clEnqueueWriteBufferRect_ +#define clEnqueueWriteImage clEnqueueWriteImage_ +#define clFinish clFinish_ +#define clFlush clFlush_ +#define clGetCommandQueueInfo clGetCommandQueueInfo_ +#define clGetContextInfo clGetContextInfo_ +#define clGetDeviceIDs clGetDeviceIDs_ +#define clGetDeviceInfo clGetDeviceInfo_ +#define clGetEventInfo clGetEventInfo_ +#define clGetEventProfilingInfo clGetEventProfilingInfo_ +#define clGetExtensionFunctionAddress clGetExtensionFunctionAddress_ +#define clGetExtensionFunctionAddressForPlatform clGetExtensionFunctionAddressForPlatform_ +#define clGetImageInfo clGetImageInfo_ +#define clGetKernelArgInfo clGetKernelArgInfo_ +#define clGetKernelInfo clGetKernelInfo_ +#define clGetKernelWorkGroupInfo clGetKernelWorkGroupInfo_ +#define clGetMemObjectInfo clGetMemObjectInfo_ +#define clGetPlatformIDs clGetPlatformIDs_ +#define clGetPlatformInfo clGetPlatformInfo_ +#define clGetProgramBuildInfo clGetProgramBuildInfo_ +#define clGetProgramInfo clGetProgramInfo_ +#define clGetSamplerInfo clGetSamplerInfo_ +#define clGetSupportedImageFormats clGetSupportedImageFormats_ +#define clLinkProgram clLinkProgram_ +#define clReleaseCommandQueue clReleaseCommandQueue_ +#define clReleaseContext clReleaseContext_ +#define clReleaseDevice clReleaseDevice_ +#define clReleaseEvent clReleaseEvent_ +#define clReleaseKernel clReleaseKernel_ +#define clReleaseMemObject clReleaseMemObject_ +#define clReleaseProgram clReleaseProgram_ +#define clReleaseSampler clReleaseSampler_ +#define clRetainCommandQueue clRetainCommandQueue_ +#define clRetainContext clRetainContext_ +#define clRetainDevice clRetainDevice_ +#define clRetainEvent clRetainEvent_ +#define clRetainKernel clRetainKernel_ +#define clRetainMemObject clRetainMemObject_ +#define clRetainProgram clRetainProgram_ +#define clRetainSampler clRetainSampler_ +#define clSetEventCallback clSetEventCallback_ +#define clSetKernelArg clSetKernelArg_ +#define clSetMemObjectDestructorCallback clSetMemObjectDestructorCallback_ +#define clSetUserEventStatus clSetUserEventStatus_ +#define clUnloadCompiler clUnloadCompiler_ +#define clUnloadPlatformCompiler clUnloadPlatformCompiler_ +#define clWaitForEvents clWaitForEvents_ + +#if defined __APPLE__ +#include +#else +#include +#endif + +// generated by parser_cl.py +#undef clBuildProgram +#define clBuildProgram clBuildProgram_pfn +#undef clCompileProgram +#define clCompileProgram clCompileProgram_pfn +#undef clCreateBuffer +#define clCreateBuffer clCreateBuffer_pfn +#undef clCreateCommandQueue +#define clCreateCommandQueue clCreateCommandQueue_pfn +#undef clCreateContext +#define clCreateContext clCreateContext_pfn +#undef clCreateContextFromType +#define clCreateContextFromType clCreateContextFromType_pfn +#undef clCreateImage +#define clCreateImage clCreateImage_pfn +#undef clCreateImage2D +#define clCreateImage2D clCreateImage2D_pfn +#undef clCreateImage3D +#define clCreateImage3D clCreateImage3D_pfn +#undef clCreateKernel +#define clCreateKernel clCreateKernel_pfn +#undef clCreateKernelsInProgram +#define clCreateKernelsInProgram clCreateKernelsInProgram_pfn +#undef clCreateProgramWithBinary +#define clCreateProgramWithBinary clCreateProgramWithBinary_pfn +#undef clCreateProgramWithBuiltInKernels +#define clCreateProgramWithBuiltInKernels clCreateProgramWithBuiltInKernels_pfn +#undef clCreateProgramWithSource +#define clCreateProgramWithSource clCreateProgramWithSource_pfn +#undef clCreateSampler +#define clCreateSampler clCreateSampler_pfn +#undef clCreateSubBuffer +#define clCreateSubBuffer clCreateSubBuffer_pfn +#undef clCreateSubDevices +#define clCreateSubDevices clCreateSubDevices_pfn +#undef clCreateUserEvent +#define clCreateUserEvent clCreateUserEvent_pfn +#undef clEnqueueBarrier +#define clEnqueueBarrier clEnqueueBarrier_pfn +#undef clEnqueueBarrierWithWaitList +#define clEnqueueBarrierWithWaitList clEnqueueBarrierWithWaitList_pfn +#undef clEnqueueCopyBuffer +#define clEnqueueCopyBuffer clEnqueueCopyBuffer_pfn +#undef clEnqueueCopyBufferRect +#define clEnqueueCopyBufferRect clEnqueueCopyBufferRect_pfn +#undef clEnqueueCopyBufferToImage +#define clEnqueueCopyBufferToImage clEnqueueCopyBufferToImage_pfn +#undef clEnqueueCopyImage +#define clEnqueueCopyImage clEnqueueCopyImage_pfn +#undef clEnqueueCopyImageToBuffer +#define clEnqueueCopyImageToBuffer clEnqueueCopyImageToBuffer_pfn +#undef clEnqueueFillBuffer +#define clEnqueueFillBuffer clEnqueueFillBuffer_pfn +#undef clEnqueueFillImage +#define clEnqueueFillImage clEnqueueFillImage_pfn +#undef clEnqueueMapBuffer +#define clEnqueueMapBuffer clEnqueueMapBuffer_pfn +#undef clEnqueueMapImage +#define clEnqueueMapImage clEnqueueMapImage_pfn +#undef clEnqueueMarker +#define clEnqueueMarker clEnqueueMarker_pfn +#undef clEnqueueMarkerWithWaitList +#define clEnqueueMarkerWithWaitList clEnqueueMarkerWithWaitList_pfn +#undef clEnqueueMigrateMemObjects +#define clEnqueueMigrateMemObjects clEnqueueMigrateMemObjects_pfn +#undef clEnqueueNDRangeKernel +#define clEnqueueNDRangeKernel clEnqueueNDRangeKernel_pfn +#undef clEnqueueNativeKernel +#define clEnqueueNativeKernel clEnqueueNativeKernel_pfn +#undef clEnqueueReadBuffer +#define clEnqueueReadBuffer clEnqueueReadBuffer_pfn +#undef clEnqueueReadBufferRect +#define clEnqueueReadBufferRect clEnqueueReadBufferRect_pfn +#undef clEnqueueReadImage +#define clEnqueueReadImage clEnqueueReadImage_pfn +#undef clEnqueueTask +#define clEnqueueTask clEnqueueTask_pfn +#undef clEnqueueUnmapMemObject +#define clEnqueueUnmapMemObject clEnqueueUnmapMemObject_pfn +#undef clEnqueueWaitForEvents +#define clEnqueueWaitForEvents clEnqueueWaitForEvents_pfn +#undef clEnqueueWriteBuffer +#define clEnqueueWriteBuffer clEnqueueWriteBuffer_pfn +#undef clEnqueueWriteBufferRect +#define clEnqueueWriteBufferRect clEnqueueWriteBufferRect_pfn +#undef clEnqueueWriteImage +#define clEnqueueWriteImage clEnqueueWriteImage_pfn +#undef clFinish +#define clFinish clFinish_pfn +#undef clFlush +#define clFlush clFlush_pfn +#undef clGetCommandQueueInfo +#define clGetCommandQueueInfo clGetCommandQueueInfo_pfn +#undef clGetContextInfo +#define clGetContextInfo clGetContextInfo_pfn +#undef clGetDeviceIDs +#define clGetDeviceIDs clGetDeviceIDs_pfn +#undef clGetDeviceInfo +#define clGetDeviceInfo clGetDeviceInfo_pfn +#undef clGetEventInfo +#define clGetEventInfo clGetEventInfo_pfn +#undef clGetEventProfilingInfo +#define clGetEventProfilingInfo clGetEventProfilingInfo_pfn +#undef clGetExtensionFunctionAddress +#define clGetExtensionFunctionAddress clGetExtensionFunctionAddress_pfn +#undef clGetExtensionFunctionAddressForPlatform +#define clGetExtensionFunctionAddressForPlatform clGetExtensionFunctionAddressForPlatform_pfn +#undef clGetImageInfo +#define clGetImageInfo clGetImageInfo_pfn +#undef clGetKernelArgInfo +#define clGetKernelArgInfo clGetKernelArgInfo_pfn +#undef clGetKernelInfo +#define clGetKernelInfo clGetKernelInfo_pfn +#undef clGetKernelWorkGroupInfo +#define clGetKernelWorkGroupInfo clGetKernelWorkGroupInfo_pfn +#undef clGetMemObjectInfo +#define clGetMemObjectInfo clGetMemObjectInfo_pfn +#undef clGetPlatformIDs +#define clGetPlatformIDs clGetPlatformIDs_pfn +#undef clGetPlatformInfo +#define clGetPlatformInfo clGetPlatformInfo_pfn +#undef clGetProgramBuildInfo +#define clGetProgramBuildInfo clGetProgramBuildInfo_pfn +#undef clGetProgramInfo +#define clGetProgramInfo clGetProgramInfo_pfn +#undef clGetSamplerInfo +#define clGetSamplerInfo clGetSamplerInfo_pfn +#undef clGetSupportedImageFormats +#define clGetSupportedImageFormats clGetSupportedImageFormats_pfn +#undef clLinkProgram +#define clLinkProgram clLinkProgram_pfn +#undef clReleaseCommandQueue +#define clReleaseCommandQueue clReleaseCommandQueue_pfn +#undef clReleaseContext +#define clReleaseContext clReleaseContext_pfn +#undef clReleaseDevice +#define clReleaseDevice clReleaseDevice_pfn +#undef clReleaseEvent +#define clReleaseEvent clReleaseEvent_pfn +#undef clReleaseKernel +#define clReleaseKernel clReleaseKernel_pfn +#undef clReleaseMemObject +#define clReleaseMemObject clReleaseMemObject_pfn +#undef clReleaseProgram +#define clReleaseProgram clReleaseProgram_pfn +#undef clReleaseSampler +#define clReleaseSampler clReleaseSampler_pfn +#undef clRetainCommandQueue +#define clRetainCommandQueue clRetainCommandQueue_pfn +#undef clRetainContext +#define clRetainContext clRetainContext_pfn +#undef clRetainDevice +#define clRetainDevice clRetainDevice_pfn +#undef clRetainEvent +#define clRetainEvent clRetainEvent_pfn +#undef clRetainKernel +#define clRetainKernel clRetainKernel_pfn +#undef clRetainMemObject +#define clRetainMemObject clRetainMemObject_pfn +#undef clRetainProgram +#define clRetainProgram clRetainProgram_pfn +#undef clRetainSampler +#define clRetainSampler clRetainSampler_pfn +#undef clSetEventCallback +#define clSetEventCallback clSetEventCallback_pfn +#undef clSetKernelArg +#define clSetKernelArg clSetKernelArg_pfn +#undef clSetMemObjectDestructorCallback +#define clSetMemObjectDestructorCallback clSetMemObjectDestructorCallback_pfn +#undef clSetUserEventStatus +#define clSetUserEventStatus clSetUserEventStatus_pfn +#undef clUnloadCompiler +#define clUnloadCompiler clUnloadCompiler_pfn +#undef clUnloadPlatformCompiler +#define clUnloadPlatformCompiler clUnloadPlatformCompiler_pfn +#undef clWaitForEvents +#define clWaitForEvents clWaitForEvents_pfn + +// generated by parser_cl.py +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clBuildProgram)(cl_program, cl_uint, const cl_device_id*, const char*, void (CL_CALLBACK*) (cl_program, void*), void*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clCompileProgram)(cl_program, cl_uint, const cl_device_id*, const char*, cl_uint, const cl_program*, const char**, void (CL_CALLBACK*) (cl_program, void*), void*); +extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateBuffer)(cl_context, cl_mem_flags, size_t, void*, cl_int*); +extern CL_RUNTIME_EXPORT cl_command_queue (CL_API_CALL*clCreateCommandQueue)(cl_context, cl_device_id, cl_command_queue_properties, cl_int*); +extern CL_RUNTIME_EXPORT cl_context (CL_API_CALL*clCreateContext)(const cl_context_properties*, cl_uint, const cl_device_id*, void (CL_CALLBACK*) (const char*, const void*, size_t, void*), void*, cl_int*); +extern CL_RUNTIME_EXPORT cl_context (CL_API_CALL*clCreateContextFromType)(const cl_context_properties*, cl_device_type, void (CL_CALLBACK*) (const char*, const void*, size_t, void*), void*, cl_int*); +extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateImage)(cl_context, cl_mem_flags, const cl_image_format*, const cl_image_desc*, void*, cl_int*); +extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateImage2D)(cl_context, cl_mem_flags, const cl_image_format*, size_t, size_t, size_t, void*, cl_int*); +extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateImage3D)(cl_context, cl_mem_flags, const cl_image_format*, size_t, size_t, size_t, size_t, size_t, void*, cl_int*); +extern CL_RUNTIME_EXPORT cl_kernel (CL_API_CALL*clCreateKernel)(cl_program, const char*, cl_int*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clCreateKernelsInProgram)(cl_program, cl_uint, cl_kernel*, cl_uint*); +extern CL_RUNTIME_EXPORT cl_program (CL_API_CALL*clCreateProgramWithBinary)(cl_context, cl_uint, const cl_device_id*, const size_t*, const unsigned char**, cl_int*, cl_int*); +extern CL_RUNTIME_EXPORT cl_program (CL_API_CALL*clCreateProgramWithBuiltInKernels)(cl_context, cl_uint, const cl_device_id*, const char*, cl_int*); +extern CL_RUNTIME_EXPORT cl_program (CL_API_CALL*clCreateProgramWithSource)(cl_context, cl_uint, const char**, const size_t*, cl_int*); +extern CL_RUNTIME_EXPORT cl_sampler (CL_API_CALL*clCreateSampler)(cl_context, cl_bool, cl_addressing_mode, cl_filter_mode, cl_int*); +extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateSubBuffer)(cl_mem, cl_mem_flags, cl_buffer_create_type, const void*, cl_int*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clCreateSubDevices)(cl_device_id, const cl_device_partition_property*, cl_uint, cl_device_id*, cl_uint*); +extern CL_RUNTIME_EXPORT cl_event (CL_API_CALL*clCreateUserEvent)(cl_context, cl_int*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueBarrier)(cl_command_queue); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueBarrierWithWaitList)(cl_command_queue, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueCopyBuffer)(cl_command_queue, cl_mem, cl_mem, size_t, size_t, size_t, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueCopyBufferRect)(cl_command_queue, cl_mem, cl_mem, const size_t*, const size_t*, const size_t*, size_t, size_t, size_t, size_t, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueCopyBufferToImage)(cl_command_queue, cl_mem, cl_mem, size_t, const size_t*, const size_t*, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueCopyImage)(cl_command_queue, cl_mem, cl_mem, const size_t*, const size_t*, const size_t*, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueCopyImageToBuffer)(cl_command_queue, cl_mem, cl_mem, const size_t*, const size_t*, size_t, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueFillBuffer)(cl_command_queue, cl_mem, const void*, size_t, size_t, size_t, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueFillImage)(cl_command_queue, cl_mem, const void*, const size_t*, const size_t*, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT void* (CL_API_CALL*clEnqueueMapBuffer)(cl_command_queue, cl_mem, cl_bool, cl_map_flags, size_t, size_t, cl_uint, const cl_event*, cl_event*, cl_int*); +extern CL_RUNTIME_EXPORT void* (CL_API_CALL*clEnqueueMapImage)(cl_command_queue, cl_mem, cl_bool, cl_map_flags, const size_t*, const size_t*, size_t*, size_t*, cl_uint, const cl_event*, cl_event*, cl_int*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueMarker)(cl_command_queue, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueMarkerWithWaitList)(cl_command_queue, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueMigrateMemObjects)(cl_command_queue, cl_uint, const cl_mem*, cl_mem_migration_flags, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueNDRangeKernel)(cl_command_queue, cl_kernel, cl_uint, const size_t*, const size_t*, const size_t*, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueNativeKernel)(cl_command_queue, void (CL_CALLBACK*) (void*), void*, size_t, cl_uint, const cl_mem*, const void**, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueReadBuffer)(cl_command_queue, cl_mem, cl_bool, size_t, size_t, void*, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueReadBufferRect)(cl_command_queue, cl_mem, cl_bool, const size_t*, const size_t*, const size_t*, size_t, size_t, size_t, size_t, void*, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueReadImage)(cl_command_queue, cl_mem, cl_bool, const size_t*, const size_t*, size_t, size_t, void*, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueTask)(cl_command_queue, cl_kernel, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueUnmapMemObject)(cl_command_queue, cl_mem, void*, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueWaitForEvents)(cl_command_queue, cl_uint, const cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueWriteBuffer)(cl_command_queue, cl_mem, cl_bool, size_t, size_t, const void*, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueWriteBufferRect)(cl_command_queue, cl_mem, cl_bool, const size_t*, const size_t*, const size_t*, size_t, size_t, size_t, size_t, const void*, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueWriteImage)(cl_command_queue, cl_mem, cl_bool, const size_t*, const size_t*, size_t, size_t, const void*, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clFinish)(cl_command_queue); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clFlush)(cl_command_queue); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetCommandQueueInfo)(cl_command_queue, cl_command_queue_info, size_t, void*, size_t*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetContextInfo)(cl_context, cl_context_info, size_t, void*, size_t*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetDeviceIDs)(cl_platform_id, cl_device_type, cl_uint, cl_device_id*, cl_uint*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetDeviceInfo)(cl_device_id, cl_device_info, size_t, void*, size_t*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetEventInfo)(cl_event, cl_event_info, size_t, void*, size_t*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetEventProfilingInfo)(cl_event, cl_profiling_info, size_t, void*, size_t*); +extern CL_RUNTIME_EXPORT void* (CL_API_CALL*clGetExtensionFunctionAddress)(const char*); +extern CL_RUNTIME_EXPORT void* (CL_API_CALL*clGetExtensionFunctionAddressForPlatform)(cl_platform_id, const char*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetImageInfo)(cl_mem, cl_image_info, size_t, void*, size_t*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetKernelArgInfo)(cl_kernel, cl_uint, cl_kernel_arg_info, size_t, void*, size_t*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetKernelInfo)(cl_kernel, cl_kernel_info, size_t, void*, size_t*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetKernelWorkGroupInfo)(cl_kernel, cl_device_id, cl_kernel_work_group_info, size_t, void*, size_t*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetMemObjectInfo)(cl_mem, cl_mem_info, size_t, void*, size_t*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetPlatformIDs)(cl_uint, cl_platform_id*, cl_uint*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetPlatformInfo)(cl_platform_id, cl_platform_info, size_t, void*, size_t*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetProgramBuildInfo)(cl_program, cl_device_id, cl_program_build_info, size_t, void*, size_t*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetProgramInfo)(cl_program, cl_program_info, size_t, void*, size_t*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetSamplerInfo)(cl_sampler, cl_sampler_info, size_t, void*, size_t*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetSupportedImageFormats)(cl_context, cl_mem_flags, cl_mem_object_type, cl_uint, cl_image_format*, cl_uint*); +extern CL_RUNTIME_EXPORT cl_program (CL_API_CALL*clLinkProgram)(cl_context, cl_uint, const cl_device_id*, const char*, cl_uint, const cl_program*, void (CL_CALLBACK*) (cl_program, void*), void*, cl_int*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseCommandQueue)(cl_command_queue); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseContext)(cl_context); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseDevice)(cl_device_id); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseEvent)(cl_event); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseKernel)(cl_kernel); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseMemObject)(cl_mem); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseProgram)(cl_program); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseSampler)(cl_sampler); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainCommandQueue)(cl_command_queue); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainContext)(cl_context); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainDevice)(cl_device_id); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainEvent)(cl_event); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainKernel)(cl_kernel); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainMemObject)(cl_mem); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainProgram)(cl_program); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainSampler)(cl_sampler); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clSetEventCallback)(cl_event, cl_int, void (CL_CALLBACK*) (cl_event, cl_int, void*), void*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clSetKernelArg)(cl_kernel, cl_uint, size_t, const void*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clSetMemObjectDestructorCallback)(cl_mem, void (CL_CALLBACK*) (cl_mem, void*), void*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clSetUserEventStatus)(cl_event, cl_int); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clUnloadCompiler)(); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clUnloadPlatformCompiler)(cl_platform_id); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clWaitForEvents)(cl_uint, const cl_event*); diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/runtime/autogenerated/opencl_core_wrappers.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/runtime/autogenerated/opencl_core_wrappers.hpp new file mode 100644 index 0000000000000000000000000000000000000000..216b22b8a85c825db0645536bbfcfe392f6b9e3f --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/runtime/autogenerated/opencl_core_wrappers.hpp @@ -0,0 +1,272 @@ +// +// AUTOGENERATED, DO NOT EDIT +// +#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_WRAPPERS_HPP +#error "Invalid usage" +#endif + +// generated by parser_cl.py +#undef clBuildProgram +#define clBuildProgram clBuildProgram_fn +inline cl_int clBuildProgram(cl_program p0, cl_uint p1, const cl_device_id* p2, const char* p3, void (CL_CALLBACK*p4) (cl_program, void*), void* p5) { return clBuildProgram_pfn(p0, p1, p2, p3, p4, p5); } +#undef clCompileProgram +#define clCompileProgram clCompileProgram_fn +inline cl_int clCompileProgram(cl_program p0, cl_uint p1, const cl_device_id* p2, const char* p3, cl_uint p4, const cl_program* p5, const char** p6, void (CL_CALLBACK*p7) (cl_program, void*), void* p8) { return clCompileProgram_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); } +#undef clCreateBuffer +#define clCreateBuffer clCreateBuffer_fn +inline cl_mem clCreateBuffer(cl_context p0, cl_mem_flags p1, size_t p2, void* p3, cl_int* p4) { return clCreateBuffer_pfn(p0, p1, p2, p3, p4); } +#undef clCreateCommandQueue +#define clCreateCommandQueue clCreateCommandQueue_fn +inline cl_command_queue clCreateCommandQueue(cl_context p0, cl_device_id p1, cl_command_queue_properties p2, cl_int* p3) { return clCreateCommandQueue_pfn(p0, p1, p2, p3); } +#undef clCreateContext +#define clCreateContext clCreateContext_fn +inline cl_context clCreateContext(const cl_context_properties* p0, cl_uint p1, const cl_device_id* p2, void (CL_CALLBACK*p3) (const char*, const void*, size_t, void*), void* p4, cl_int* p5) { return clCreateContext_pfn(p0, p1, p2, p3, p4, p5); } +#undef clCreateContextFromType +#define clCreateContextFromType clCreateContextFromType_fn +inline cl_context clCreateContextFromType(const cl_context_properties* p0, cl_device_type p1, void (CL_CALLBACK*p2) (const char*, const void*, size_t, void*), void* p3, cl_int* p4) { return clCreateContextFromType_pfn(p0, p1, p2, p3, p4); } +#undef clCreateImage +#define clCreateImage clCreateImage_fn +inline cl_mem clCreateImage(cl_context p0, cl_mem_flags p1, const cl_image_format* p2, const cl_image_desc* p3, void* p4, cl_int* p5) { return clCreateImage_pfn(p0, p1, p2, p3, p4, p5); } +#undef clCreateImage2D +#define clCreateImage2D clCreateImage2D_fn +inline cl_mem clCreateImage2D(cl_context p0, cl_mem_flags p1, const cl_image_format* p2, size_t p3, size_t p4, size_t p5, void* p6, cl_int* p7) { return clCreateImage2D_pfn(p0, p1, p2, p3, p4, p5, p6, p7); } +#undef clCreateImage3D +#define clCreateImage3D clCreateImage3D_fn +inline cl_mem clCreateImage3D(cl_context p0, cl_mem_flags p1, const cl_image_format* p2, size_t p3, size_t p4, size_t p5, size_t p6, size_t p7, void* p8, cl_int* p9) { return clCreateImage3D_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9); } +#undef clCreateKernel +#define clCreateKernel clCreateKernel_fn +inline cl_kernel clCreateKernel(cl_program p0, const char* p1, cl_int* p2) { return clCreateKernel_pfn(p0, p1, p2); } +#undef clCreateKernelsInProgram +#define clCreateKernelsInProgram clCreateKernelsInProgram_fn +inline cl_int clCreateKernelsInProgram(cl_program p0, cl_uint p1, cl_kernel* p2, cl_uint* p3) { return clCreateKernelsInProgram_pfn(p0, p1, p2, p3); } +#undef clCreateProgramWithBinary +#define clCreateProgramWithBinary clCreateProgramWithBinary_fn +inline cl_program clCreateProgramWithBinary(cl_context p0, cl_uint p1, const cl_device_id* p2, const size_t* p3, const unsigned char** p4, cl_int* p5, cl_int* p6) { return clCreateProgramWithBinary_pfn(p0, p1, p2, p3, p4, p5, p6); } +#undef clCreateProgramWithBuiltInKernels +#define clCreateProgramWithBuiltInKernels clCreateProgramWithBuiltInKernels_fn +inline cl_program clCreateProgramWithBuiltInKernels(cl_context p0, cl_uint p1, const cl_device_id* p2, const char* p3, cl_int* p4) { return clCreateProgramWithBuiltInKernels_pfn(p0, p1, p2, p3, p4); } +#undef clCreateProgramWithSource +#define clCreateProgramWithSource clCreateProgramWithSource_fn +inline cl_program clCreateProgramWithSource(cl_context p0, cl_uint p1, const char** p2, const size_t* p3, cl_int* p4) { return clCreateProgramWithSource_pfn(p0, p1, p2, p3, p4); } +#undef clCreateSampler +#define clCreateSampler clCreateSampler_fn +inline cl_sampler clCreateSampler(cl_context p0, cl_bool p1, cl_addressing_mode p2, cl_filter_mode p3, cl_int* p4) { return clCreateSampler_pfn(p0, p1, p2, p3, p4); } +#undef clCreateSubBuffer +#define clCreateSubBuffer clCreateSubBuffer_fn +inline cl_mem clCreateSubBuffer(cl_mem p0, cl_mem_flags p1, cl_buffer_create_type p2, const void* p3, cl_int* p4) { return clCreateSubBuffer_pfn(p0, p1, p2, p3, p4); } +#undef clCreateSubDevices +#define clCreateSubDevices clCreateSubDevices_fn +inline cl_int clCreateSubDevices(cl_device_id p0, const cl_device_partition_property* p1, cl_uint p2, cl_device_id* p3, cl_uint* p4) { return clCreateSubDevices_pfn(p0, p1, p2, p3, p4); } +#undef clCreateUserEvent +#define clCreateUserEvent clCreateUserEvent_fn +inline cl_event clCreateUserEvent(cl_context p0, cl_int* p1) { return clCreateUserEvent_pfn(p0, p1); } +#undef clEnqueueBarrier +#define clEnqueueBarrier clEnqueueBarrier_fn +inline cl_int clEnqueueBarrier(cl_command_queue p0) { return clEnqueueBarrier_pfn(p0); } +#undef clEnqueueBarrierWithWaitList +#define clEnqueueBarrierWithWaitList clEnqueueBarrierWithWaitList_fn +inline cl_int clEnqueueBarrierWithWaitList(cl_command_queue p0, cl_uint p1, const cl_event* p2, cl_event* p3) { return clEnqueueBarrierWithWaitList_pfn(p0, p1, p2, p3); } +#undef clEnqueueCopyBuffer +#define clEnqueueCopyBuffer clEnqueueCopyBuffer_fn +inline cl_int clEnqueueCopyBuffer(cl_command_queue p0, cl_mem p1, cl_mem p2, size_t p3, size_t p4, size_t p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueCopyBuffer_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); } +#undef clEnqueueCopyBufferRect +#define clEnqueueCopyBufferRect clEnqueueCopyBufferRect_fn +inline cl_int clEnqueueCopyBufferRect(cl_command_queue p0, cl_mem p1, cl_mem p2, const size_t* p3, const size_t* p4, const size_t* p5, size_t p6, size_t p7, size_t p8, size_t p9, cl_uint p10, const cl_event* p11, cl_event* p12) { return clEnqueueCopyBufferRect_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12); } +#undef clEnqueueCopyBufferToImage +#define clEnqueueCopyBufferToImage clEnqueueCopyBufferToImage_fn +inline cl_int clEnqueueCopyBufferToImage(cl_command_queue p0, cl_mem p1, cl_mem p2, size_t p3, const size_t* p4, const size_t* p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueCopyBufferToImage_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); } +#undef clEnqueueCopyImage +#define clEnqueueCopyImage clEnqueueCopyImage_fn +inline cl_int clEnqueueCopyImage(cl_command_queue p0, cl_mem p1, cl_mem p2, const size_t* p3, const size_t* p4, const size_t* p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueCopyImage_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); } +#undef clEnqueueCopyImageToBuffer +#define clEnqueueCopyImageToBuffer clEnqueueCopyImageToBuffer_fn +inline cl_int clEnqueueCopyImageToBuffer(cl_command_queue p0, cl_mem p1, cl_mem p2, const size_t* p3, const size_t* p4, size_t p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueCopyImageToBuffer_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); } +#undef clEnqueueFillBuffer +#define clEnqueueFillBuffer clEnqueueFillBuffer_fn +inline cl_int clEnqueueFillBuffer(cl_command_queue p0, cl_mem p1, const void* p2, size_t p3, size_t p4, size_t p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueFillBuffer_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); } +#undef clEnqueueFillImage +#define clEnqueueFillImage clEnqueueFillImage_fn +inline cl_int clEnqueueFillImage(cl_command_queue p0, cl_mem p1, const void* p2, const size_t* p3, const size_t* p4, cl_uint p5, const cl_event* p6, cl_event* p7) { return clEnqueueFillImage_pfn(p0, p1, p2, p3, p4, p5, p6, p7); } +#undef clEnqueueMapBuffer +#define clEnqueueMapBuffer clEnqueueMapBuffer_fn +inline void* clEnqueueMapBuffer(cl_command_queue p0, cl_mem p1, cl_bool p2, cl_map_flags p3, size_t p4, size_t p5, cl_uint p6, const cl_event* p7, cl_event* p8, cl_int* p9) { return clEnqueueMapBuffer_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9); } +#undef clEnqueueMapImage +#define clEnqueueMapImage clEnqueueMapImage_fn +inline void* clEnqueueMapImage(cl_command_queue p0, cl_mem p1, cl_bool p2, cl_map_flags p3, const size_t* p4, const size_t* p5, size_t* p6, size_t* p7, cl_uint p8, const cl_event* p9, cl_event* p10, cl_int* p11) { return clEnqueueMapImage_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11); } +#undef clEnqueueMarker +#define clEnqueueMarker clEnqueueMarker_fn +inline cl_int clEnqueueMarker(cl_command_queue p0, cl_event* p1) { return clEnqueueMarker_pfn(p0, p1); } +#undef clEnqueueMarkerWithWaitList +#define clEnqueueMarkerWithWaitList clEnqueueMarkerWithWaitList_fn +inline cl_int clEnqueueMarkerWithWaitList(cl_command_queue p0, cl_uint p1, const cl_event* p2, cl_event* p3) { return clEnqueueMarkerWithWaitList_pfn(p0, p1, p2, p3); } +#undef clEnqueueMigrateMemObjects +#define clEnqueueMigrateMemObjects clEnqueueMigrateMemObjects_fn +inline cl_int clEnqueueMigrateMemObjects(cl_command_queue p0, cl_uint p1, const cl_mem* p2, cl_mem_migration_flags p3, cl_uint p4, const cl_event* p5, cl_event* p6) { return clEnqueueMigrateMemObjects_pfn(p0, p1, p2, p3, p4, p5, p6); } +#undef clEnqueueNDRangeKernel +#define clEnqueueNDRangeKernel clEnqueueNDRangeKernel_fn +inline cl_int clEnqueueNDRangeKernel(cl_command_queue p0, cl_kernel p1, cl_uint p2, const size_t* p3, const size_t* p4, const size_t* p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueNDRangeKernel_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); } +#undef clEnqueueNativeKernel +#define clEnqueueNativeKernel clEnqueueNativeKernel_fn +inline cl_int clEnqueueNativeKernel(cl_command_queue p0, void (CL_CALLBACK*p1) (void*), void* p2, size_t p3, cl_uint p4, const cl_mem* p5, const void** p6, cl_uint p7, const cl_event* p8, cl_event* p9) { return clEnqueueNativeKernel_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9); } +#undef clEnqueueReadBuffer +#define clEnqueueReadBuffer clEnqueueReadBuffer_fn +inline cl_int clEnqueueReadBuffer(cl_command_queue p0, cl_mem p1, cl_bool p2, size_t p3, size_t p4, void* p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueReadBuffer_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); } +#undef clEnqueueReadBufferRect +#define clEnqueueReadBufferRect clEnqueueReadBufferRect_fn +inline cl_int clEnqueueReadBufferRect(cl_command_queue p0, cl_mem p1, cl_bool p2, const size_t* p3, const size_t* p4, const size_t* p5, size_t p6, size_t p7, size_t p8, size_t p9, void* p10, cl_uint p11, const cl_event* p12, cl_event* p13) { return clEnqueueReadBufferRect_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, p13); } +#undef clEnqueueReadImage +#define clEnqueueReadImage clEnqueueReadImage_fn +inline cl_int clEnqueueReadImage(cl_command_queue p0, cl_mem p1, cl_bool p2, const size_t* p3, const size_t* p4, size_t p5, size_t p6, void* p7, cl_uint p8, const cl_event* p9, cl_event* p10) { return clEnqueueReadImage_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, p10); } +#undef clEnqueueTask +#define clEnqueueTask clEnqueueTask_fn +inline cl_int clEnqueueTask(cl_command_queue p0, cl_kernel p1, cl_uint p2, const cl_event* p3, cl_event* p4) { return clEnqueueTask_pfn(p0, p1, p2, p3, p4); } +#undef clEnqueueUnmapMemObject +#define clEnqueueUnmapMemObject clEnqueueUnmapMemObject_fn +inline cl_int clEnqueueUnmapMemObject(cl_command_queue p0, cl_mem p1, void* p2, cl_uint p3, const cl_event* p4, cl_event* p5) { return clEnqueueUnmapMemObject_pfn(p0, p1, p2, p3, p4, p5); } +#undef clEnqueueWaitForEvents +#define clEnqueueWaitForEvents clEnqueueWaitForEvents_fn +inline cl_int clEnqueueWaitForEvents(cl_command_queue p0, cl_uint p1, const cl_event* p2) { return clEnqueueWaitForEvents_pfn(p0, p1, p2); } +#undef clEnqueueWriteBuffer +#define clEnqueueWriteBuffer clEnqueueWriteBuffer_fn +inline cl_int clEnqueueWriteBuffer(cl_command_queue p0, cl_mem p1, cl_bool p2, size_t p3, size_t p4, const void* p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueWriteBuffer_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); } +#undef clEnqueueWriteBufferRect +#define clEnqueueWriteBufferRect clEnqueueWriteBufferRect_fn +inline cl_int clEnqueueWriteBufferRect(cl_command_queue p0, cl_mem p1, cl_bool p2, const size_t* p3, const size_t* p4, const size_t* p5, size_t p6, size_t p7, size_t p8, size_t p9, const void* p10, cl_uint p11, const cl_event* p12, cl_event* p13) { return clEnqueueWriteBufferRect_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, p13); } +#undef clEnqueueWriteImage +#define clEnqueueWriteImage clEnqueueWriteImage_fn +inline cl_int clEnqueueWriteImage(cl_command_queue p0, cl_mem p1, cl_bool p2, const size_t* p3, const size_t* p4, size_t p5, size_t p6, const void* p7, cl_uint p8, const cl_event* p9, cl_event* p10) { return clEnqueueWriteImage_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, p10); } +#undef clFinish +#define clFinish clFinish_fn +inline cl_int clFinish(cl_command_queue p0) { return clFinish_pfn(p0); } +#undef clFlush +#define clFlush clFlush_fn +inline cl_int clFlush(cl_command_queue p0) { return clFlush_pfn(p0); } +#undef clGetCommandQueueInfo +#define clGetCommandQueueInfo clGetCommandQueueInfo_fn +inline cl_int clGetCommandQueueInfo(cl_command_queue p0, cl_command_queue_info p1, size_t p2, void* p3, size_t* p4) { return clGetCommandQueueInfo_pfn(p0, p1, p2, p3, p4); } +#undef clGetContextInfo +#define clGetContextInfo clGetContextInfo_fn +inline cl_int clGetContextInfo(cl_context p0, cl_context_info p1, size_t p2, void* p3, size_t* p4) { return clGetContextInfo_pfn(p0, p1, p2, p3, p4); } +#undef clGetDeviceIDs +#define clGetDeviceIDs clGetDeviceIDs_fn +inline cl_int clGetDeviceIDs(cl_platform_id p0, cl_device_type p1, cl_uint p2, cl_device_id* p3, cl_uint* p4) { return clGetDeviceIDs_pfn(p0, p1, p2, p3, p4); } +#undef clGetDeviceInfo +#define clGetDeviceInfo clGetDeviceInfo_fn +inline cl_int clGetDeviceInfo(cl_device_id p0, cl_device_info p1, size_t p2, void* p3, size_t* p4) { return clGetDeviceInfo_pfn(p0, p1, p2, p3, p4); } +#undef clGetEventInfo +#define clGetEventInfo clGetEventInfo_fn +inline cl_int clGetEventInfo(cl_event p0, cl_event_info p1, size_t p2, void* p3, size_t* p4) { return clGetEventInfo_pfn(p0, p1, p2, p3, p4); } +#undef clGetEventProfilingInfo +#define clGetEventProfilingInfo clGetEventProfilingInfo_fn +inline cl_int clGetEventProfilingInfo(cl_event p0, cl_profiling_info p1, size_t p2, void* p3, size_t* p4) { return clGetEventProfilingInfo_pfn(p0, p1, p2, p3, p4); } +#undef clGetExtensionFunctionAddress +#define clGetExtensionFunctionAddress clGetExtensionFunctionAddress_fn +inline void* clGetExtensionFunctionAddress(const char* p0) { return clGetExtensionFunctionAddress_pfn(p0); } +#undef clGetExtensionFunctionAddressForPlatform +#define clGetExtensionFunctionAddressForPlatform clGetExtensionFunctionAddressForPlatform_fn +inline void* clGetExtensionFunctionAddressForPlatform(cl_platform_id p0, const char* p1) { return clGetExtensionFunctionAddressForPlatform_pfn(p0, p1); } +#undef clGetImageInfo +#define clGetImageInfo clGetImageInfo_fn +inline cl_int clGetImageInfo(cl_mem p0, cl_image_info p1, size_t p2, void* p3, size_t* p4) { return clGetImageInfo_pfn(p0, p1, p2, p3, p4); } +#undef clGetKernelArgInfo +#define clGetKernelArgInfo clGetKernelArgInfo_fn +inline cl_int clGetKernelArgInfo(cl_kernel p0, cl_uint p1, cl_kernel_arg_info p2, size_t p3, void* p4, size_t* p5) { return clGetKernelArgInfo_pfn(p0, p1, p2, p3, p4, p5); } +#undef clGetKernelInfo +#define clGetKernelInfo clGetKernelInfo_fn +inline cl_int clGetKernelInfo(cl_kernel p0, cl_kernel_info p1, size_t p2, void* p3, size_t* p4) { return clGetKernelInfo_pfn(p0, p1, p2, p3, p4); } +#undef clGetKernelWorkGroupInfo +#define clGetKernelWorkGroupInfo clGetKernelWorkGroupInfo_fn +inline cl_int clGetKernelWorkGroupInfo(cl_kernel p0, cl_device_id p1, cl_kernel_work_group_info p2, size_t p3, void* p4, size_t* p5) { return clGetKernelWorkGroupInfo_pfn(p0, p1, p2, p3, p4, p5); } +#undef clGetMemObjectInfo +#define clGetMemObjectInfo clGetMemObjectInfo_fn +inline cl_int clGetMemObjectInfo(cl_mem p0, cl_mem_info p1, size_t p2, void* p3, size_t* p4) { return clGetMemObjectInfo_pfn(p0, p1, p2, p3, p4); } +#undef clGetPlatformIDs +#define clGetPlatformIDs clGetPlatformIDs_fn +inline cl_int clGetPlatformIDs(cl_uint p0, cl_platform_id* p1, cl_uint* p2) { return clGetPlatformIDs_pfn(p0, p1, p2); } +#undef clGetPlatformInfo +#define clGetPlatformInfo clGetPlatformInfo_fn +inline cl_int clGetPlatformInfo(cl_platform_id p0, cl_platform_info p1, size_t p2, void* p3, size_t* p4) { return clGetPlatformInfo_pfn(p0, p1, p2, p3, p4); } +#undef clGetProgramBuildInfo +#define clGetProgramBuildInfo clGetProgramBuildInfo_fn +inline cl_int clGetProgramBuildInfo(cl_program p0, cl_device_id p1, cl_program_build_info p2, size_t p3, void* p4, size_t* p5) { return clGetProgramBuildInfo_pfn(p0, p1, p2, p3, p4, p5); } +#undef clGetProgramInfo +#define clGetProgramInfo clGetProgramInfo_fn +inline cl_int clGetProgramInfo(cl_program p0, cl_program_info p1, size_t p2, void* p3, size_t* p4) { return clGetProgramInfo_pfn(p0, p1, p2, p3, p4); } +#undef clGetSamplerInfo +#define clGetSamplerInfo clGetSamplerInfo_fn +inline cl_int clGetSamplerInfo(cl_sampler p0, cl_sampler_info p1, size_t p2, void* p3, size_t* p4) { return clGetSamplerInfo_pfn(p0, p1, p2, p3, p4); } +#undef clGetSupportedImageFormats +#define clGetSupportedImageFormats clGetSupportedImageFormats_fn +inline cl_int clGetSupportedImageFormats(cl_context p0, cl_mem_flags p1, cl_mem_object_type p2, cl_uint p3, cl_image_format* p4, cl_uint* p5) { return clGetSupportedImageFormats_pfn(p0, p1, p2, p3, p4, p5); } +#undef clLinkProgram +#define clLinkProgram clLinkProgram_fn +inline cl_program clLinkProgram(cl_context p0, cl_uint p1, const cl_device_id* p2, const char* p3, cl_uint p4, const cl_program* p5, void (CL_CALLBACK*p6) (cl_program, void*), void* p7, cl_int* p8) { return clLinkProgram_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); } +#undef clReleaseCommandQueue +#define clReleaseCommandQueue clReleaseCommandQueue_fn +inline cl_int clReleaseCommandQueue(cl_command_queue p0) { return clReleaseCommandQueue_pfn(p0); } +#undef clReleaseContext +#define clReleaseContext clReleaseContext_fn +inline cl_int clReleaseContext(cl_context p0) { return clReleaseContext_pfn(p0); } +#undef clReleaseDevice +#define clReleaseDevice clReleaseDevice_fn +inline cl_int clReleaseDevice(cl_device_id p0) { return clReleaseDevice_pfn(p0); } +#undef clReleaseEvent +#define clReleaseEvent clReleaseEvent_fn +inline cl_int clReleaseEvent(cl_event p0) { return clReleaseEvent_pfn(p0); } +#undef clReleaseKernel +#define clReleaseKernel clReleaseKernel_fn +inline cl_int clReleaseKernel(cl_kernel p0) { return clReleaseKernel_pfn(p0); } +#undef clReleaseMemObject +#define clReleaseMemObject clReleaseMemObject_fn +inline cl_int clReleaseMemObject(cl_mem p0) { return clReleaseMemObject_pfn(p0); } +#undef clReleaseProgram +#define clReleaseProgram clReleaseProgram_fn +inline cl_int clReleaseProgram(cl_program p0) { return clReleaseProgram_pfn(p0); } +#undef clReleaseSampler +#define clReleaseSampler clReleaseSampler_fn +inline cl_int clReleaseSampler(cl_sampler p0) { return clReleaseSampler_pfn(p0); } +#undef clRetainCommandQueue +#define clRetainCommandQueue clRetainCommandQueue_fn +inline cl_int clRetainCommandQueue(cl_command_queue p0) { return clRetainCommandQueue_pfn(p0); } +#undef clRetainContext +#define clRetainContext clRetainContext_fn +inline cl_int clRetainContext(cl_context p0) { return clRetainContext_pfn(p0); } +#undef clRetainDevice +#define clRetainDevice clRetainDevice_fn +inline cl_int clRetainDevice(cl_device_id p0) { return clRetainDevice_pfn(p0); } +#undef clRetainEvent +#define clRetainEvent clRetainEvent_fn +inline cl_int clRetainEvent(cl_event p0) { return clRetainEvent_pfn(p0); } +#undef clRetainKernel +#define clRetainKernel clRetainKernel_fn +inline cl_int clRetainKernel(cl_kernel p0) { return clRetainKernel_pfn(p0); } +#undef clRetainMemObject +#define clRetainMemObject clRetainMemObject_fn +inline cl_int clRetainMemObject(cl_mem p0) { return clRetainMemObject_pfn(p0); } +#undef clRetainProgram +#define clRetainProgram clRetainProgram_fn +inline cl_int clRetainProgram(cl_program p0) { return clRetainProgram_pfn(p0); } +#undef clRetainSampler +#define clRetainSampler clRetainSampler_fn +inline cl_int clRetainSampler(cl_sampler p0) { return clRetainSampler_pfn(p0); } +#undef clSetEventCallback +#define clSetEventCallback clSetEventCallback_fn +inline cl_int clSetEventCallback(cl_event p0, cl_int p1, void (CL_CALLBACK*p2) (cl_event, cl_int, void*), void* p3) { return clSetEventCallback_pfn(p0, p1, p2, p3); } +#undef clSetKernelArg +#define clSetKernelArg clSetKernelArg_fn +inline cl_int clSetKernelArg(cl_kernel p0, cl_uint p1, size_t p2, const void* p3) { return clSetKernelArg_pfn(p0, p1, p2, p3); } +#undef clSetMemObjectDestructorCallback +#define clSetMemObjectDestructorCallback clSetMemObjectDestructorCallback_fn +inline cl_int clSetMemObjectDestructorCallback(cl_mem p0, void (CL_CALLBACK*p1) (cl_mem, void*), void* p2) { return clSetMemObjectDestructorCallback_pfn(p0, p1, p2); } +#undef clSetUserEventStatus +#define clSetUserEventStatus clSetUserEventStatus_fn +inline cl_int clSetUserEventStatus(cl_event p0, cl_int p1) { return clSetUserEventStatus_pfn(p0, p1); } +#undef clUnloadCompiler +#define clUnloadCompiler clUnloadCompiler_fn +inline cl_int clUnloadCompiler() { return clUnloadCompiler_pfn(); } +#undef clUnloadPlatformCompiler +#define clUnloadPlatformCompiler clUnloadPlatformCompiler_fn +inline cl_int clUnloadPlatformCompiler(cl_platform_id p0) { return clUnloadPlatformCompiler_pfn(p0); } +#undef clWaitForEvents +#define clWaitForEvents clWaitForEvents_fn +inline cl_int clWaitForEvents(cl_uint p0, const cl_event* p1) { return clWaitForEvents_pfn(p0, p1); } diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/runtime/autogenerated/opencl_gl.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/runtime/autogenerated/opencl_gl.hpp new file mode 100644 index 0000000000000000000000000000000000000000..0b12aed6c6ce9def83b9fecc15f8be14b4790363 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/runtime/autogenerated/opencl_gl.hpp @@ -0,0 +1,62 @@ +// +// AUTOGENERATED, DO NOT EDIT +// +#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_HPP +#error "Invalid usage" +#endif + +// generated by parser_cl.py +#define clCreateFromGLBuffer clCreateFromGLBuffer_ +#define clCreateFromGLRenderbuffer clCreateFromGLRenderbuffer_ +#define clCreateFromGLTexture clCreateFromGLTexture_ +#define clCreateFromGLTexture2D clCreateFromGLTexture2D_ +#define clCreateFromGLTexture3D clCreateFromGLTexture3D_ +#define clEnqueueAcquireGLObjects clEnqueueAcquireGLObjects_ +#define clEnqueueReleaseGLObjects clEnqueueReleaseGLObjects_ +#define clGetGLContextInfoKHR clGetGLContextInfoKHR_ +#define clGetGLObjectInfo clGetGLObjectInfo_ +#define clGetGLTextureInfo clGetGLTextureInfo_ + +#if defined __APPLE__ +#include +#else +#include +#endif + +// generated by parser_cl.py +#undef clCreateFromGLBuffer +#define clCreateFromGLBuffer clCreateFromGLBuffer_pfn +#undef clCreateFromGLRenderbuffer +#define clCreateFromGLRenderbuffer clCreateFromGLRenderbuffer_pfn +#undef clCreateFromGLTexture +#define clCreateFromGLTexture clCreateFromGLTexture_pfn +#undef clCreateFromGLTexture2D +#define clCreateFromGLTexture2D clCreateFromGLTexture2D_pfn +#undef clCreateFromGLTexture3D +#define clCreateFromGLTexture3D clCreateFromGLTexture3D_pfn +#undef clEnqueueAcquireGLObjects +#define clEnqueueAcquireGLObjects clEnqueueAcquireGLObjects_pfn +#undef clEnqueueReleaseGLObjects +#define clEnqueueReleaseGLObjects clEnqueueReleaseGLObjects_pfn +#undef clGetGLContextInfoKHR +#define clGetGLContextInfoKHR clGetGLContextInfoKHR_pfn +#undef clGetGLObjectInfo +#define clGetGLObjectInfo clGetGLObjectInfo_pfn +#undef clGetGLTextureInfo +#define clGetGLTextureInfo clGetGLTextureInfo_pfn + +#ifdef cl_khr_gl_sharing + +// generated by parser_cl.py +extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateFromGLBuffer)(cl_context, cl_mem_flags, cl_GLuint, int*); +extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateFromGLRenderbuffer)(cl_context, cl_mem_flags, cl_GLuint, cl_int*); +extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateFromGLTexture)(cl_context, cl_mem_flags, cl_GLenum, cl_GLint, cl_GLuint, cl_int*); +extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateFromGLTexture2D)(cl_context, cl_mem_flags, cl_GLenum, cl_GLint, cl_GLuint, cl_int*); +extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateFromGLTexture3D)(cl_context, cl_mem_flags, cl_GLenum, cl_GLint, cl_GLuint, cl_int*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueAcquireGLObjects)(cl_command_queue, cl_uint, const cl_mem*, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueReleaseGLObjects)(cl_command_queue, cl_uint, const cl_mem*, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetGLContextInfoKHR)(const cl_context_properties*, cl_gl_context_info, size_t, void*, size_t*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetGLObjectInfo)(cl_mem, cl_gl_object_type*, cl_GLuint*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetGLTextureInfo)(cl_mem, cl_gl_texture_info, size_t, void*, size_t*); + +#endif // cl_khr_gl_sharing diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/runtime/autogenerated/opencl_gl_wrappers.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/runtime/autogenerated/opencl_gl_wrappers.hpp new file mode 100644 index 0000000000000000000000000000000000000000..12f342b2e4be18b0bfb3a9fb661d47fdf8bac44c --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/runtime/autogenerated/opencl_gl_wrappers.hpp @@ -0,0 +1,42 @@ +// +// AUTOGENERATED, DO NOT EDIT +// +#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_WRAPPERS_HPP +#error "Invalid usage" +#endif + +#ifdef cl_khr_gl_sharing + +// generated by parser_cl.py +#undef clCreateFromGLBuffer +#define clCreateFromGLBuffer clCreateFromGLBuffer_fn +inline cl_mem clCreateFromGLBuffer(cl_context p0, cl_mem_flags p1, cl_GLuint p2, int* p3) { return clCreateFromGLBuffer_pfn(p0, p1, p2, p3); } +#undef clCreateFromGLRenderbuffer +#define clCreateFromGLRenderbuffer clCreateFromGLRenderbuffer_fn +inline cl_mem clCreateFromGLRenderbuffer(cl_context p0, cl_mem_flags p1, cl_GLuint p2, cl_int* p3) { return clCreateFromGLRenderbuffer_pfn(p0, p1, p2, p3); } +#undef clCreateFromGLTexture +#define clCreateFromGLTexture clCreateFromGLTexture_fn +inline cl_mem clCreateFromGLTexture(cl_context p0, cl_mem_flags p1, cl_GLenum p2, cl_GLint p3, cl_GLuint p4, cl_int* p5) { return clCreateFromGLTexture_pfn(p0, p1, p2, p3, p4, p5); } +#undef clCreateFromGLTexture2D +#define clCreateFromGLTexture2D clCreateFromGLTexture2D_fn +inline cl_mem clCreateFromGLTexture2D(cl_context p0, cl_mem_flags p1, cl_GLenum p2, cl_GLint p3, cl_GLuint p4, cl_int* p5) { return clCreateFromGLTexture2D_pfn(p0, p1, p2, p3, p4, p5); } +#undef clCreateFromGLTexture3D +#define clCreateFromGLTexture3D clCreateFromGLTexture3D_fn +inline cl_mem clCreateFromGLTexture3D(cl_context p0, cl_mem_flags p1, cl_GLenum p2, cl_GLint p3, cl_GLuint p4, cl_int* p5) { return clCreateFromGLTexture3D_pfn(p0, p1, p2, p3, p4, p5); } +#undef clEnqueueAcquireGLObjects +#define clEnqueueAcquireGLObjects clEnqueueAcquireGLObjects_fn +inline cl_int clEnqueueAcquireGLObjects(cl_command_queue p0, cl_uint p1, const cl_mem* p2, cl_uint p3, const cl_event* p4, cl_event* p5) { return clEnqueueAcquireGLObjects_pfn(p0, p1, p2, p3, p4, p5); } +#undef clEnqueueReleaseGLObjects +#define clEnqueueReleaseGLObjects clEnqueueReleaseGLObjects_fn +inline cl_int clEnqueueReleaseGLObjects(cl_command_queue p0, cl_uint p1, const cl_mem* p2, cl_uint p3, const cl_event* p4, cl_event* p5) { return clEnqueueReleaseGLObjects_pfn(p0, p1, p2, p3, p4, p5); } +#undef clGetGLContextInfoKHR +#define clGetGLContextInfoKHR clGetGLContextInfoKHR_fn +inline cl_int clGetGLContextInfoKHR(const cl_context_properties* p0, cl_gl_context_info p1, size_t p2, void* p3, size_t* p4) { return clGetGLContextInfoKHR_pfn(p0, p1, p2, p3, p4); } +#undef clGetGLObjectInfo +#define clGetGLObjectInfo clGetGLObjectInfo_fn +inline cl_int clGetGLObjectInfo(cl_mem p0, cl_gl_object_type* p1, cl_GLuint* p2) { return clGetGLObjectInfo_pfn(p0, p1, p2); } +#undef clGetGLTextureInfo +#define clGetGLTextureInfo clGetGLTextureInfo_fn +inline cl_int clGetGLTextureInfo(cl_mem p0, cl_gl_texture_info p1, size_t p2, void* p3, size_t* p4) { return clGetGLTextureInfo_pfn(p0, p1, p2, p3, p4); } + +#endif // cl_khr_gl_sharing diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/runtime/opencl_clamdblas.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/runtime/opencl_clamdblas.hpp new file mode 100644 index 0000000000000000000000000000000000000000..2ad8ac0b5db8bdd0587f60dd3ed3d2ecd28e7048 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/runtime/opencl_clamdblas.hpp @@ -0,0 +1,53 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the OpenCV Foundation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_OCL_RUNTIME_CLAMDBLAS_HPP +#define OPENCV_CORE_OCL_RUNTIME_CLAMDBLAS_HPP + +#ifdef HAVE_CLAMDBLAS + +#include "opencl_core.hpp" + +#include "autogenerated/opencl_clamdblas.hpp" + +#endif // HAVE_CLAMDBLAS + +#endif // OPENCV_CORE_OCL_RUNTIME_CLAMDBLAS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/runtime/opencl_clamdfft.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/runtime/opencl_clamdfft.hpp new file mode 100644 index 0000000000000000000000000000000000000000..a328f722fccacd8e7491abfd464b8b7c43b51074 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/runtime/opencl_clamdfft.hpp @@ -0,0 +1,53 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the OpenCV Foundation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_OCL_RUNTIME_CLAMDFFT_HPP +#define OPENCV_CORE_OCL_RUNTIME_CLAMDFFT_HPP + +#ifdef HAVE_CLAMDFFT + +#include "opencl_core.hpp" + +#include "autogenerated/opencl_clamdfft.hpp" + +#endif // HAVE_CLAMDFFT + +#endif // OPENCV_CORE_OCL_RUNTIME_CLAMDFFT_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/runtime/opencl_core.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/runtime/opencl_core.hpp new file mode 100644 index 0000000000000000000000000000000000000000..0404b3177a58f51a4137d92803198a17f8ca3ab4 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/runtime/opencl_core.hpp @@ -0,0 +1,84 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the OpenCV Foundation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_CORE_HPP +#define OPENCV_CORE_OCL_RUNTIME_OPENCL_CORE_HPP + +#ifdef HAVE_OPENCL + +#ifndef CL_RUNTIME_EXPORT +#if (defined(BUILD_SHARED_LIBS) || defined(OPENCV_CORE_SHARED)) && (defined _WIN32 || defined WINCE) && \ + !(defined(__OPENCV_BUILD) && defined(OPENCV_MODULE_IS_PART_OF_WORLD)) +#define CL_RUNTIME_EXPORT __declspec(dllimport) +#else +#define CL_RUNTIME_EXPORT +#endif +#endif + +#ifdef HAVE_OPENCL_SVM +#define clSVMAlloc clSVMAlloc_ +#define clSVMFree clSVMFree_ +#define clSetKernelArgSVMPointer clSetKernelArgSVMPointer_ +#define clSetKernelExecInfo clSetKernelExecInfo_ +#define clEnqueueSVMFree clEnqueueSVMFree_ +#define clEnqueueSVMMemcpy clEnqueueSVMMemcpy_ +#define clEnqueueSVMMemFill clEnqueueSVMMemFill_ +#define clEnqueueSVMMap clEnqueueSVMMap_ +#define clEnqueueSVMUnmap clEnqueueSVMUnmap_ +#endif + +#include "autogenerated/opencl_core.hpp" + +#ifndef CL_DEVICE_DOUBLE_FP_CONFIG +#define CL_DEVICE_DOUBLE_FP_CONFIG 0x1032 +#endif + +#ifndef CL_DEVICE_HALF_FP_CONFIG +#define CL_DEVICE_HALF_FP_CONFIG 0x1033 +#endif + +#ifndef CL_VERSION_1_2 +#define CV_REQUIRE_OPENCL_1_2_ERROR CV_Error(cv::Error::OpenCLApiCallError, "OpenCV compiled without OpenCL v1.2 support, so we can't use functionality from OpenCL v1.2") +#endif + +#endif // HAVE_OPENCL + +#endif // OPENCV_CORE_OCL_RUNTIME_OPENCL_CORE_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/runtime/opencl_core_wrappers.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/runtime/opencl_core_wrappers.hpp new file mode 100644 index 0000000000000000000000000000000000000000..38fcae9952f2838d15f7dbf79549aa9cd30bbe26 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/runtime/opencl_core_wrappers.hpp @@ -0,0 +1,47 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the OpenCV Foundation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_WRAPPERS_HPP +#define OPENCV_CORE_OCL_RUNTIME_OPENCL_WRAPPERS_HPP + +#include "autogenerated/opencl_core_wrappers.hpp" + +#endif // OPENCV_CORE_OCL_RUNTIME_OPENCL_WRAPPERS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/runtime/opencl_gl.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/runtime/opencl_gl.hpp new file mode 100644 index 0000000000000000000000000000000000000000..659c7d80581489d95c91c5ce844bc574b8c81598 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/runtime/opencl_gl.hpp @@ -0,0 +1,53 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the OpenCV Foundation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_HPP +#define OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_HPP + +#if defined HAVE_OPENCL && defined HAVE_OPENGL + +#include "opencl_core.hpp" + +#include "autogenerated/opencl_gl.hpp" + +#endif // defined HAVE_OPENCL && defined HAVE_OPENGL + +#endif // OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/runtime/opencl_gl_wrappers.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/runtime/opencl_gl_wrappers.hpp new file mode 100644 index 0000000000000000000000000000000000000000..9700004cae5ac6e11b76652ce8c78d4430e05688 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/runtime/opencl_gl_wrappers.hpp @@ -0,0 +1,47 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the OpenCV Foundation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_WRAPPERS_HPP +#define OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_WRAPPERS_HPP + +#include "autogenerated/opencl_gl_wrappers.hpp" + +#endif // OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_WRAPPERS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/runtime/opencl_svm_20.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/runtime/opencl_svm_20.hpp new file mode 100644 index 0000000000000000000000000000000000000000..9636b19b027fcc5ec2ff78c94f28b46919425fda --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/runtime/opencl_svm_20.hpp @@ -0,0 +1,48 @@ +/* See LICENSE file in the root OpenCV directory */ + +#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_2_0_HPP +#define OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_2_0_HPP + +#if defined(HAVE_OPENCL_SVM) +#include "opencl_core.hpp" + +#include "opencl_svm_definitions.hpp" + +#undef clSVMAlloc +#define clSVMAlloc clSVMAlloc_pfn +#undef clSVMFree +#define clSVMFree clSVMFree_pfn +#undef clSetKernelArgSVMPointer +#define clSetKernelArgSVMPointer clSetKernelArgSVMPointer_pfn +#undef clSetKernelExecInfo +//#define clSetKernelExecInfo clSetKernelExecInfo_pfn +#undef clEnqueueSVMFree +//#define clEnqueueSVMFree clEnqueueSVMFree_pfn +#undef clEnqueueSVMMemcpy +#define clEnqueueSVMMemcpy clEnqueueSVMMemcpy_pfn +#undef clEnqueueSVMMemFill +#define clEnqueueSVMMemFill clEnqueueSVMMemFill_pfn +#undef clEnqueueSVMMap +#define clEnqueueSVMMap clEnqueueSVMMap_pfn +#undef clEnqueueSVMUnmap +#define clEnqueueSVMUnmap clEnqueueSVMUnmap_pfn + +extern CL_RUNTIME_EXPORT void* (CL_API_CALL *clSVMAlloc)(cl_context context, cl_svm_mem_flags flags, size_t size, unsigned int alignment); +extern CL_RUNTIME_EXPORT void (CL_API_CALL *clSVMFree)(cl_context context, void* svm_pointer); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL *clSetKernelArgSVMPointer)(cl_kernel kernel, cl_uint arg_index, const void* arg_value); +//extern CL_RUNTIME_EXPORT void* (CL_API_CALL *clSetKernelExecInfo)(cl_kernel kernel, cl_kernel_exec_info param_name, size_t param_value_size, const void* param_value); +//extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL *clEnqueueSVMFree)(cl_command_queue command_queue, cl_uint num_svm_pointers, void* svm_pointers[], +// void (CL_CALLBACK *pfn_free_func)(cl_command_queue queue, cl_uint num_svm_pointers, void* svm_pointers[], void* user_data), void* user_data, +// cl_uint num_events_in_wait_list, const cl_event* event_wait_list, cl_event* event); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL *clEnqueueSVMMemcpy)(cl_command_queue command_queue, cl_bool blocking_copy, void* dst_ptr, const void* src_ptr, size_t size, + cl_uint num_events_in_wait_list, const cl_event* event_wait_list, cl_event* event); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL *clEnqueueSVMMemFill)(cl_command_queue command_queue, void* svm_ptr, const void* pattern, size_t pattern_size, size_t size, + cl_uint num_events_in_wait_list, const cl_event* event_wait_list, cl_event* event); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL *clEnqueueSVMMap)(cl_command_queue command_queue, cl_bool blocking_map, cl_map_flags map_flags, void* svm_ptr, size_t size, + cl_uint num_events_in_wait_list, const cl_event* event_wait_list, cl_event* event); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL *clEnqueueSVMUnmap)(cl_command_queue command_queue, void* svm_ptr, + cl_uint num_events_in_wait_list, const cl_event* event_wait_list, cl_event* event); + +#endif // HAVE_OPENCL_SVM + +#endif // OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_2_0_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/runtime/opencl_svm_definitions.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/runtime/opencl_svm_definitions.hpp new file mode 100644 index 0000000000000000000000000000000000000000..97c927b44d8cc6310623ad4156ff2aae7b7c193c --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/runtime/opencl_svm_definitions.hpp @@ -0,0 +1,42 @@ +/* See LICENSE file in the root OpenCV directory */ + +#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_DEFINITIONS_HPP +#define OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_DEFINITIONS_HPP + +#if defined(HAVE_OPENCL_SVM) +#if defined(CL_VERSION_2_0) + +// OpenCL 2.0 contains SVM definitions + +#else + +typedef cl_bitfield cl_device_svm_capabilities; +typedef cl_bitfield cl_svm_mem_flags; +typedef cl_uint cl_kernel_exec_info; + +// +// TODO Add real values after OpenCL 2.0 release +// + +#ifndef CL_DEVICE_SVM_CAPABILITIES +#define CL_DEVICE_SVM_CAPABILITIES 0x1053 + +#define CL_DEVICE_SVM_COARSE_GRAIN_BUFFER (1 << 0) +#define CL_DEVICE_SVM_FINE_GRAIN_BUFFER (1 << 1) +#define CL_DEVICE_SVM_FINE_GRAIN_SYSTEM (1 << 2) +#define CL_DEVICE_SVM_ATOMICS (1 << 3) +#endif + +#ifndef CL_MEM_SVM_FINE_GRAIN_BUFFER +#define CL_MEM_SVM_FINE_GRAIN_BUFFER (1 << 10) +#endif + +#ifndef CL_MEM_SVM_ATOMICS +#define CL_MEM_SVM_ATOMICS (1 << 11) +#endif + + +#endif // CL_VERSION_2_0 +#endif // HAVE_OPENCL_SVM + +#endif // OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_DEFINITIONS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/runtime/opencl_svm_hsa_extension.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/runtime/opencl_svm_hsa_extension.hpp new file mode 100644 index 0000000000000000000000000000000000000000..497bc3de7205d6bad28328772c3b20f48611d63c --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opencl/runtime/opencl_svm_hsa_extension.hpp @@ -0,0 +1,166 @@ +/* See LICENSE file in the root OpenCV directory */ + +#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_HSA_EXTENSION_HPP +#define OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_HSA_EXTENSION_HPP + +#if defined(HAVE_OPENCL_SVM) +#include "opencl_core.hpp" + +#ifndef CL_DEVICE_SVM_CAPABILITIES_AMD +// +// Part of the file is an extract from the cl_ext.h file from AMD APP SDK package. +// Below is the original copyright. +// +/******************************************************************************* + * Copyright (c) 2008-2013 The Khronos Group Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a + * copy of this software and/or associated documentation files (the + * "Materials"), to deal in the Materials without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, + * distribute, sublicense, and/or sell copies of the Materials, and to + * permit persons to whom the Materials are furnished to do so, subject to + * the following conditions: + * + * The above copyright notice and this permission notice shall be included + * in all copies or substantial portions of the Materials. + * + * THE MATERIALS ARE PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF + * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. + * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY + * CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, + * TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE + * MATERIALS OR THE USE OR OTHER DEALINGS IN THE MATERIALS. + ******************************************************************************/ + +/******************************************* + * Shared Virtual Memory (SVM) extension + *******************************************/ +typedef cl_bitfield cl_device_svm_capabilities_amd; +typedef cl_bitfield cl_svm_mem_flags_amd; +typedef cl_uint cl_kernel_exec_info_amd; + +/* cl_device_info */ +#define CL_DEVICE_SVM_CAPABILITIES_AMD 0x1053 +#define CL_DEVICE_PREFERRED_PLATFORM_ATOMIC_ALIGNMENT_AMD 0x1054 + +/* cl_device_svm_capabilities_amd */ +#define CL_DEVICE_SVM_COARSE_GRAIN_BUFFER_AMD (1 << 0) +#define CL_DEVICE_SVM_FINE_GRAIN_BUFFER_AMD (1 << 1) +#define CL_DEVICE_SVM_FINE_GRAIN_SYSTEM_AMD (1 << 2) +#define CL_DEVICE_SVM_ATOMICS_AMD (1 << 3) + +/* cl_svm_mem_flags_amd */ +#define CL_MEM_SVM_FINE_GRAIN_BUFFER_AMD (1 << 10) +#define CL_MEM_SVM_ATOMICS_AMD (1 << 11) + +/* cl_mem_info */ +#define CL_MEM_USES_SVM_POINTER_AMD 0x1109 + +/* cl_kernel_exec_info_amd */ +#define CL_KERNEL_EXEC_INFO_SVM_PTRS_AMD 0x11B6 +#define CL_KERNEL_EXEC_INFO_SVM_FINE_GRAIN_SYSTEM_AMD 0x11B7 + +/* cl_command_type */ +#define CL_COMMAND_SVM_FREE_AMD 0x1209 +#define CL_COMMAND_SVM_MEMCPY_AMD 0x120A +#define CL_COMMAND_SVM_MEMFILL_AMD 0x120B +#define CL_COMMAND_SVM_MAP_AMD 0x120C +#define CL_COMMAND_SVM_UNMAP_AMD 0x120D + +typedef CL_API_ENTRY void* +(CL_API_CALL * clSVMAllocAMD_fn)( + cl_context /* context */, + cl_svm_mem_flags_amd /* flags */, + size_t /* size */, + unsigned int /* alignment */ +) CL_EXT_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY void +(CL_API_CALL * clSVMFreeAMD_fn)( + cl_context /* context */, + void* /* svm_pointer */ +) CL_EXT_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_int +(CL_API_CALL * clEnqueueSVMFreeAMD_fn)( + cl_command_queue /* command_queue */, + cl_uint /* num_svm_pointers */, + void** /* svm_pointers */, + void (CL_CALLBACK *)( /*pfn_free_func*/ + cl_command_queue /* queue */, + cl_uint /* num_svm_pointers */, + void** /* svm_pointers */, + void* /* user_data */), + void* /* user_data */, + cl_uint /* num_events_in_wait_list */, + const cl_event* /* event_wait_list */, + cl_event* /* event */ +) CL_EXT_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_int +(CL_API_CALL * clEnqueueSVMMemcpyAMD_fn)( + cl_command_queue /* command_queue */, + cl_bool /* blocking_copy */, + void* /* dst_ptr */, + const void* /* src_ptr */, + size_t /* size */, + cl_uint /* num_events_in_wait_list */, + const cl_event* /* event_wait_list */, + cl_event* /* event */ +) CL_EXT_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_int +(CL_API_CALL * clEnqueueSVMMemFillAMD_fn)( + cl_command_queue /* command_queue */, + void* /* svm_ptr */, + const void* /* pattern */, + size_t /* pattern_size */, + size_t /* size */, + cl_uint /* num_events_in_wait_list */, + const cl_event* /* event_wait_list */, + cl_event* /* event */ +) CL_EXT_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_int +(CL_API_CALL * clEnqueueSVMMapAMD_fn)( + cl_command_queue /* command_queue */, + cl_bool /* blocking_map */, + cl_map_flags /* map_flags */, + void* /* svm_ptr */, + size_t /* size */, + cl_uint /* num_events_in_wait_list */, + const cl_event* /* event_wait_list */, + cl_event* /* event */ +) CL_EXT_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_int +(CL_API_CALL * clEnqueueSVMUnmapAMD_fn)( + cl_command_queue /* command_queue */, + void* /* svm_ptr */, + cl_uint /* num_events_in_wait_list */, + const cl_event* /* event_wait_list */, + cl_event* /* event */ +) CL_EXT_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_int +(CL_API_CALL * clSetKernelArgSVMPointerAMD_fn)( + cl_kernel /* kernel */, + cl_uint /* arg_index */, + const void * /* arg_value */ +) CL_EXT_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_int +(CL_API_CALL * clSetKernelExecInfoAMD_fn)( + cl_kernel /* kernel */, + cl_kernel_exec_info_amd /* param_name */, + size_t /* param_value_size */, + const void * /* param_value */ +) CL_EXT_SUFFIX__VERSION_1_2; + +#endif + +#endif // HAVE_OPENCL_SVM + +#endif // OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_HSA_EXTENSION_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opengl.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opengl.hpp new file mode 100644 index 0000000000000000000000000000000000000000..a6288bebe8f51cd64f2a089bc8830320845b22c5 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/opengl.hpp @@ -0,0 +1,725 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_OPENGL_HPP +#define OPENCV_CORE_OPENGL_HPP + +#ifndef __cplusplus +# error opengl.hpp header must be compiled as C++ +#endif + +#include "opencv2/core.hpp" +#include "ocl.hpp" + +namespace cv { namespace ogl { + +/** @addtogroup core_opengl +This section describes OpenGL interoperability. + +To enable OpenGL support, configure OpenCV using CMake with WITH_OPENGL=ON . Currently OpenGL is +supported only with WIN32, GTK and Qt backends on Windows and Linux (MacOS and Android are not +supported). For GTK backend gtkglext-1.0 library is required. + +To use OpenGL functionality you should first create OpenGL context (window or frame buffer). You can +do this with namedWindow function or with other OpenGL toolkit (GLUT, for example). +*/ +//! @{ + +/////////////////// OpenGL Objects /////////////////// + +/** @brief Smart pointer for OpenGL buffer object with reference counting. + +Buffer Objects are OpenGL objects that store an array of unformatted memory allocated by the OpenGL +context. These can be used to store vertex data, pixel data retrieved from images or the +framebuffer, and a variety of other things. + +ogl::Buffer has interface similar with Mat interface and represents 2D array memory. + +ogl::Buffer supports memory transfers between host and device and also can be mapped to CUDA memory. + */ +class CV_EXPORTS Buffer +{ +public: + /** @brief The target defines how you intend to use the buffer object. + */ + enum Target + { + ARRAY_BUFFER = 0x8892, //!< The buffer will be used as a source for vertex data + ELEMENT_ARRAY_BUFFER = 0x8893, //!< The buffer will be used for indices (in glDrawElements, for example) + PIXEL_PACK_BUFFER = 0x88EB, //!< The buffer will be used for reading from OpenGL textures + PIXEL_UNPACK_BUFFER = 0x88EC //!< The buffer will be used for writing to OpenGL textures + }; + + enum Access + { + READ_ONLY = 0x88B8, + WRITE_ONLY = 0x88B9, + READ_WRITE = 0x88BA + }; + + /** @brief The constructors. + + Creates empty ogl::Buffer object, creates ogl::Buffer object from existed buffer ( abufId + parameter), allocates memory for ogl::Buffer object or copies from host/device memory. + */ + Buffer(); + + /** @overload + @param arows Number of rows in a 2D array. + @param acols Number of columns in a 2D array. + @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details. + @param abufId Buffer object name. + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + Buffer(int arows, int acols, int atype, unsigned int abufId, bool autoRelease = false); + + /** @overload + @param asize 2D array size. + @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details. + @param abufId Buffer object name. + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + Buffer(Size asize, int atype, unsigned int abufId, bool autoRelease = false); + + /** @overload + @param arows Number of rows in a 2D array. + @param acols Number of columns in a 2D array. + @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details. + @param target Buffer usage. See cv::ogl::Buffer::Target . + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + Buffer(int arows, int acols, int atype, Target target = ARRAY_BUFFER, bool autoRelease = false); + + /** @overload + @param asize 2D array size. + @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details. + @param target Buffer usage. See cv::ogl::Buffer::Target . + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + Buffer(Size asize, int atype, Target target = ARRAY_BUFFER, bool autoRelease = false); + + /** @overload + @param arr Input array (host or device memory, it can be Mat , cuda::GpuMat or std::vector ). + @param target Buffer usage. See cv::ogl::Buffer::Target . + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + explicit Buffer(InputArray arr, Target target = ARRAY_BUFFER, bool autoRelease = false); + + /** @brief Allocates memory for ogl::Buffer object. + + @param arows Number of rows in a 2D array. + @param acols Number of columns in a 2D array. + @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details. + @param target Buffer usage. See cv::ogl::Buffer::Target . + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + void create(int arows, int acols, int atype, Target target = ARRAY_BUFFER, bool autoRelease = false); + + /** @overload + @param asize 2D array size. + @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details. + @param target Buffer usage. See cv::ogl::Buffer::Target . + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + void create(Size asize, int atype, Target target = ARRAY_BUFFER, bool autoRelease = false); + + /** @brief Decrements the reference counter and destroys the buffer object if needed. + + The function will call setAutoRelease(true) . + */ + void release(); + + /** @brief Sets auto release mode. + + The lifetime of the OpenGL object is tied to the lifetime of the context. If OpenGL context was + bound to a window it could be released at any time (user can close a window). If object's destructor + is called after destruction of the context it will cause an error. Thus ogl::Buffer doesn't destroy + OpenGL object in destructor by default (all OpenGL resources will be released with OpenGL context). + This function can force ogl::Buffer destructor to destroy OpenGL object. + @param flag Auto release mode (if true, release will be called in object's destructor). + */ + void setAutoRelease(bool flag); + + /** @brief Copies from host/device memory to OpenGL buffer. + @param arr Input array (host or device memory, it can be Mat , cuda::GpuMat or std::vector ). + @param target Buffer usage. See cv::ogl::Buffer::Target . + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + void copyFrom(InputArray arr, Target target = ARRAY_BUFFER, bool autoRelease = false); + + /** @overload */ + void copyFrom(InputArray arr, cuda::Stream& stream, Target target = ARRAY_BUFFER, bool autoRelease = false); + + /** @brief Copies from OpenGL buffer to host/device memory or another OpenGL buffer object. + + @param arr Destination array (host or device memory, can be Mat , cuda::GpuMat , std::vector or + ogl::Buffer ). + */ + void copyTo(OutputArray arr) const; + + /** @overload */ + void copyTo(OutputArray arr, cuda::Stream& stream) const; + + /** @brief Creates a full copy of the buffer object and the underlying data. + + @param target Buffer usage for destination buffer. + @param autoRelease Auto release mode for destination buffer. + */ + Buffer clone(Target target = ARRAY_BUFFER, bool autoRelease = false) const; + + /** @brief Binds OpenGL buffer to the specified buffer binding point. + + @param target Binding point. See cv::ogl::Buffer::Target . + */ + void bind(Target target) const; + + /** @brief Unbind any buffers from the specified binding point. + + @param target Binding point. See cv::ogl::Buffer::Target . + */ + static void unbind(Target target); + + /** @brief Maps OpenGL buffer to host memory. + + mapHost maps to the client's address space the entire data store of the buffer object. The data can + then be directly read and/or written relative to the returned pointer, depending on the specified + access policy. + + A mapped data store must be unmapped with ogl::Buffer::unmapHost before its buffer object is used. + + This operation can lead to memory transfers between host and device. + + Only one buffer object can be mapped at a time. + @param access Access policy, indicating whether it will be possible to read from, write to, or both + read from and write to the buffer object's mapped data store. The symbolic constant must be + ogl::Buffer::READ_ONLY , ogl::Buffer::WRITE_ONLY or ogl::Buffer::READ_WRITE . + */ + Mat mapHost(Access access); + + /** @brief Unmaps OpenGL buffer. + */ + void unmapHost(); + + //! map to device memory (blocking) + cuda::GpuMat mapDevice(); + void unmapDevice(); + + /** @brief Maps OpenGL buffer to CUDA device memory. + + This operation doesn't copy data. Several buffer objects can be mapped to CUDA memory at a time. + + A mapped data store must be unmapped with ogl::Buffer::unmapDevice before its buffer object is used. + */ + cuda::GpuMat mapDevice(cuda::Stream& stream); + + /** @brief Unmaps OpenGL buffer. + */ + void unmapDevice(cuda::Stream& stream); + + int rows() const; + int cols() const; + Size size() const; + bool empty() const; + + int type() const; + int depth() const; + int channels() const; + int elemSize() const; + int elemSize1() const; + + //! get OpenGL opject id + unsigned int bufId() const; + + class Impl; + +private: + Ptr impl_; + int rows_; + int cols_; + int type_; +}; + +/** @brief Smart pointer for OpenGL 2D texture memory with reference counting. + */ +class CV_EXPORTS Texture2D +{ +public: + /** @brief An Image Format describes the way that the images in Textures store their data. + */ + enum Format + { + NONE = 0, + DEPTH_COMPONENT = 0x1902, //!< Depth + RGB = 0x1907, //!< Red, Green, Blue + RGBA = 0x1908 //!< Red, Green, Blue, Alpha + }; + + /** @brief The constructors. + + Creates empty ogl::Texture2D object, allocates memory for ogl::Texture2D object or copies from + host/device memory. + */ + Texture2D(); + + /** @overload */ + Texture2D(int arows, int acols, Format aformat, unsigned int atexId, bool autoRelease = false); + + /** @overload */ + Texture2D(Size asize, Format aformat, unsigned int atexId, bool autoRelease = false); + + /** @overload + @param arows Number of rows. + @param acols Number of columns. + @param aformat Image format. See cv::ogl::Texture2D::Format . + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + Texture2D(int arows, int acols, Format aformat, bool autoRelease = false); + + /** @overload + @param asize 2D array size. + @param aformat Image format. See cv::ogl::Texture2D::Format . + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + Texture2D(Size asize, Format aformat, bool autoRelease = false); + + /** @overload + @param arr Input array (host or device memory, it can be Mat , cuda::GpuMat or ogl::Buffer ). + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + explicit Texture2D(InputArray arr, bool autoRelease = false); + + /** @brief Allocates memory for ogl::Texture2D object. + + @param arows Number of rows. + @param acols Number of columns. + @param aformat Image format. See cv::ogl::Texture2D::Format . + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + void create(int arows, int acols, Format aformat, bool autoRelease = false); + /** @overload + @param asize 2D array size. + @param aformat Image format. See cv::ogl::Texture2D::Format . + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + void create(Size asize, Format aformat, bool autoRelease = false); + + /** @brief Decrements the reference counter and destroys the texture object if needed. + + The function will call setAutoRelease(true) . + */ + void release(); + + /** @brief Sets auto release mode. + + @param flag Auto release mode (if true, release will be called in object's destructor). + + The lifetime of the OpenGL object is tied to the lifetime of the context. If OpenGL context was + bound to a window it could be released at any time (user can close a window). If object's destructor + is called after destruction of the context it will cause an error. Thus ogl::Texture2D doesn't + destroy OpenGL object in destructor by default (all OpenGL resources will be released with OpenGL + context). This function can force ogl::Texture2D destructor to destroy OpenGL object. + */ + void setAutoRelease(bool flag); + + /** @brief Copies from host/device memory to OpenGL texture. + + @param arr Input array (host or device memory, it can be Mat , cuda::GpuMat or ogl::Buffer ). + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + void copyFrom(InputArray arr, bool autoRelease = false); + + /** @brief Copies from OpenGL texture to host/device memory or another OpenGL texture object. + + @param arr Destination array (host or device memory, can be Mat , cuda::GpuMat , ogl::Buffer or + ogl::Texture2D ). + @param ddepth Destination depth. + @param autoRelease Auto release mode for destination buffer (if arr is OpenGL buffer or texture). + */ + void copyTo(OutputArray arr, int ddepth = CV_32F, bool autoRelease = false) const; + + /** @brief Binds texture to current active texture unit for GL_TEXTURE_2D target. + */ + void bind() const; + + int rows() const; + int cols() const; + Size size() const; + bool empty() const; + + Format format() const; + + //! get OpenGL opject id + unsigned int texId() const; + + class Impl; + +private: + Ptr impl_; + int rows_; + int cols_; + Format format_; +}; + +/** @brief Wrapper for OpenGL Client-Side Vertex arrays. + +ogl::Arrays stores vertex data in ogl::Buffer objects. + */ +class CV_EXPORTS Arrays +{ +public: + /** @brief Default constructor + */ + Arrays(); + + /** @brief Sets an array of vertex coordinates. + @param vertex array with vertex coordinates, can be both host and device memory. + */ + void setVertexArray(InputArray vertex); + + /** @brief Resets vertex coordinates. + */ + void resetVertexArray(); + + /** @brief Sets an array of vertex colors. + @param color array with vertex colors, can be both host and device memory. + */ + void setColorArray(InputArray color); + + /** @brief Resets vertex colors. + */ + void resetColorArray(); + + /** @brief Sets an array of vertex normals. + @param normal array with vertex normals, can be both host and device memory. + */ + void setNormalArray(InputArray normal); + + /** @brief Resets vertex normals. + */ + void resetNormalArray(); + + /** @brief Sets an array of vertex texture coordinates. + @param texCoord array with vertex texture coordinates, can be both host and device memory. + */ + void setTexCoordArray(InputArray texCoord); + + /** @brief Resets vertex texture coordinates. + */ + void resetTexCoordArray(); + + /** @brief Releases all inner buffers. + */ + void release(); + + /** @brief Sets auto release mode all inner buffers. + @param flag Auto release mode. + */ + void setAutoRelease(bool flag); + + /** @brief Binds all vertex arrays. + */ + void bind() const; + + /** @brief Returns the vertex count. + */ + int size() const; + bool empty() const; + +private: + int size_; + Buffer vertex_; + Buffer color_; + Buffer normal_; + Buffer texCoord_; +}; + +/////////////////// Render Functions /////////////////// + +//! render mode +enum RenderModes { + POINTS = 0x0000, + LINES = 0x0001, + LINE_LOOP = 0x0002, + LINE_STRIP = 0x0003, + TRIANGLES = 0x0004, + TRIANGLE_STRIP = 0x0005, + TRIANGLE_FAN = 0x0006, + QUADS = 0x0007, + QUAD_STRIP = 0x0008, + POLYGON = 0x0009 +}; + +/** @brief Render OpenGL texture or primitives. +@param tex Texture to draw. +@param wndRect Region of window, where to draw a texture (normalized coordinates). +@param texRect Region of texture to draw (normalized coordinates). + */ +CV_EXPORTS void render(const Texture2D& tex, + Rect_ wndRect = Rect_(0.0, 0.0, 1.0, 1.0), + Rect_ texRect = Rect_(0.0, 0.0, 1.0, 1.0)); + +/** @overload +@param arr Array of privitives vertices. +@param mode Render mode. One of cv::ogl::RenderModes +@param color Color for all vertices. Will be used if arr doesn't contain color array. +*/ +CV_EXPORTS void render(const Arrays& arr, int mode = POINTS, Scalar color = Scalar::all(255)); + +/** @overload +@param arr Array of privitives vertices. +@param indices Array of vertices indices (host or device memory). +@param mode Render mode. One of cv::ogl::RenderModes +@param color Color for all vertices. Will be used if arr doesn't contain color array. +*/ +CV_EXPORTS void render(const Arrays& arr, InputArray indices, int mode = POINTS, Scalar color = Scalar::all(255)); + +/////////////////// CL-GL Interoperability Functions /////////////////// + +namespace ocl { +using namespace cv::ocl; + +// TODO static functions in the Context class +/** @brief Creates OpenCL context from GL. +@return Returns reference to OpenCL Context + */ +CV_EXPORTS Context& initializeContextFromGL(); + +} // namespace cv::ogl::ocl + +/** @brief Converts InputArray to Texture2D object. +@param src - source InputArray. +@param texture - destination Texture2D object. + */ +CV_EXPORTS void convertToGLTexture2D(InputArray src, Texture2D& texture); + +/** @brief Converts Texture2D object to OutputArray. +@param texture - source Texture2D object. +@param dst - destination OutputArray. + */ +CV_EXPORTS void convertFromGLTexture2D(const Texture2D& texture, OutputArray dst); + +/** @brief Maps Buffer object to process on CL side (convert to UMat). + +Function creates CL buffer from GL one, and then constructs UMat that can be used +to process buffer data with OpenCV functions. Note that in current implementation +UMat constructed this way doesn't own corresponding GL buffer object, so it is +the user responsibility to close down CL/GL buffers relationships by explicitly +calling unmapGLBuffer() function. +@param buffer - source Buffer object. +@param accessFlags - data access flags (ACCESS_READ|ACCESS_WRITE). +@return Returns UMat object + */ +CV_EXPORTS UMat mapGLBuffer(const Buffer& buffer, int accessFlags = ACCESS_READ|ACCESS_WRITE); + +/** @brief Unmaps Buffer object (releases UMat, previously mapped from Buffer). + +Function must be called explicitly by the user for each UMat previously constructed +by the call to mapGLBuffer() function. +@param u - source UMat, created by mapGLBuffer(). + */ +CV_EXPORTS void unmapGLBuffer(UMat& u); + +//! @} +}} // namespace cv::ogl + +namespace cv { namespace cuda { + +/** @brief Sets a CUDA device and initializes it for the current thread with OpenGL interoperability. + +This function should be explicitly called after OpenGL context creation and before any CUDA calls. +@param device System index of a CUDA device starting with 0. +@ingroup core_opengl + */ +CV_EXPORTS void setGlDevice(int device = 0); + +}} + +//! @cond IGNORED + +//////////////////////////////////////////////////////////////////////// +//////////////////////////////////////////////////////////////////////// +//////////////////////////////////////////////////////////////////////// + +inline +cv::ogl::Buffer::Buffer(int arows, int acols, int atype, Target target, bool autoRelease) : rows_(0), cols_(0), type_(0) +{ + create(arows, acols, atype, target, autoRelease); +} + +inline +cv::ogl::Buffer::Buffer(Size asize, int atype, Target target, bool autoRelease) : rows_(0), cols_(0), type_(0) +{ + create(asize, atype, target, autoRelease); +} + +inline +void cv::ogl::Buffer::create(Size asize, int atype, Target target, bool autoRelease) +{ + create(asize.height, asize.width, atype, target, autoRelease); +} + +inline +int cv::ogl::Buffer::rows() const +{ + return rows_; +} + +inline +int cv::ogl::Buffer::cols() const +{ + return cols_; +} + +inline +cv::Size cv::ogl::Buffer::size() const +{ + return Size(cols_, rows_); +} + +inline +bool cv::ogl::Buffer::empty() const +{ + return rows_ == 0 || cols_ == 0; +} + +inline +int cv::ogl::Buffer::type() const +{ + return type_; +} + +inline +int cv::ogl::Buffer::depth() const +{ + return CV_MAT_DEPTH(type_); +} + +inline +int cv::ogl::Buffer::channels() const +{ + return CV_MAT_CN(type_); +} + +inline +int cv::ogl::Buffer::elemSize() const +{ + return CV_ELEM_SIZE(type_); +} + +inline +int cv::ogl::Buffer::elemSize1() const +{ + return CV_ELEM_SIZE1(type_); +} + +/////// + +inline +cv::ogl::Texture2D::Texture2D(int arows, int acols, Format aformat, bool autoRelease) : rows_(0), cols_(0), format_(NONE) +{ + create(arows, acols, aformat, autoRelease); +} + +inline +cv::ogl::Texture2D::Texture2D(Size asize, Format aformat, bool autoRelease) : rows_(0), cols_(0), format_(NONE) +{ + create(asize, aformat, autoRelease); +} + +inline +void cv::ogl::Texture2D::create(Size asize, Format aformat, bool autoRelease) +{ + create(asize.height, asize.width, aformat, autoRelease); +} + +inline +int cv::ogl::Texture2D::rows() const +{ + return rows_; +} + +inline +int cv::ogl::Texture2D::cols() const +{ + return cols_; +} + +inline +cv::Size cv::ogl::Texture2D::size() const +{ + return Size(cols_, rows_); +} + +inline +bool cv::ogl::Texture2D::empty() const +{ + return rows_ == 0 || cols_ == 0; +} + +inline +cv::ogl::Texture2D::Format cv::ogl::Texture2D::format() const +{ + return format_; +} + +/////// + +inline +cv::ogl::Arrays::Arrays() : size_(0) +{ +} + +inline +int cv::ogl::Arrays::size() const +{ + return size_; +} + +inline +bool cv::ogl::Arrays::empty() const +{ + return size_ == 0; +} + +//! @endcond + +#endif /* OPENCV_CORE_OPENGL_HPP */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/operations.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/operations.hpp new file mode 100644 index 0000000000000000000000000000000000000000..d706d9664e0b1882a6b2c77bb55823f9fc2f2568 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/operations.hpp @@ -0,0 +1,554 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_OPERATIONS_HPP +#define OPENCV_CORE_OPERATIONS_HPP + +#ifndef __cplusplus +# error operations.hpp header must be compiled as C++ +#endif + +#include + +//! @cond IGNORED + +namespace cv +{ + +////////////////////////////// Matx methods depending on core API ///////////////////////////// + +namespace internal +{ + +template struct Matx_FastInvOp +{ + bool operator()(const Matx<_Tp, m, n>& a, Matx<_Tp, n, m>& b, int method) const + { + return invert(a, b, method) != 0; + } +}; + +template struct Matx_FastInvOp<_Tp, m, m> +{ + bool operator()(const Matx<_Tp, m, m>& a, Matx<_Tp, m, m>& b, int method) const + { + if (method == DECOMP_LU || method == DECOMP_CHOLESKY) + { + Matx<_Tp, m, m> temp = a; + + // assume that b is all 0's on input => make it a unity matrix + for (int i = 0; i < m; i++) + b(i, i) = (_Tp)1; + + if (method == DECOMP_CHOLESKY) + return Cholesky(temp.val, m*sizeof(_Tp), m, b.val, m*sizeof(_Tp), m); + + return LU(temp.val, m*sizeof(_Tp), m, b.val, m*sizeof(_Tp), m) != 0; + } + else + { + return invert(a, b, method) != 0; + } + } +}; + +template struct Matx_FastInvOp<_Tp, 2, 2> +{ + bool operator()(const Matx<_Tp, 2, 2>& a, Matx<_Tp, 2, 2>& b, int /*method*/) const + { + _Tp d = (_Tp)determinant(a); + if (d == 0) + return false; + d = 1/d; + b(1,1) = a(0,0)*d; + b(0,0) = a(1,1)*d; + b(0,1) = -a(0,1)*d; + b(1,0) = -a(1,0)*d; + return true; + } +}; + +template struct Matx_FastInvOp<_Tp, 3, 3> +{ + bool operator()(const Matx<_Tp, 3, 3>& a, Matx<_Tp, 3, 3>& b, int /*method*/) const + { + _Tp d = (_Tp)determinant(a); + if (d == 0) + return false; + d = 1/d; + b(0,0) = (a(1,1) * a(2,2) - a(1,2) * a(2,1)) * d; + b(0,1) = (a(0,2) * a(2,1) - a(0,1) * a(2,2)) * d; + b(0,2) = (a(0,1) * a(1,2) - a(0,2) * a(1,1)) * d; + + b(1,0) = (a(1,2) * a(2,0) - a(1,0) * a(2,2)) * d; + b(1,1) = (a(0,0) * a(2,2) - a(0,2) * a(2,0)) * d; + b(1,2) = (a(0,2) * a(1,0) - a(0,0) * a(1,2)) * d; + + b(2,0) = (a(1,0) * a(2,1) - a(1,1) * a(2,0)) * d; + b(2,1) = (a(0,1) * a(2,0) - a(0,0) * a(2,1)) * d; + b(2,2) = (a(0,0) * a(1,1) - a(0,1) * a(1,0)) * d; + return true; + } +}; + + +template struct Matx_FastSolveOp +{ + bool operator()(const Matx<_Tp, m, l>& a, const Matx<_Tp, m, n>& b, + Matx<_Tp, l, n>& x, int method) const + { + return cv::solve(a, b, x, method); + } +}; + +template struct Matx_FastSolveOp<_Tp, m, m, n> +{ + bool operator()(const Matx<_Tp, m, m>& a, const Matx<_Tp, m, n>& b, + Matx<_Tp, m, n>& x, int method) const + { + if (method == DECOMP_LU || method == DECOMP_CHOLESKY) + { + Matx<_Tp, m, m> temp = a; + x = b; + if( method == DECOMP_CHOLESKY ) + return Cholesky(temp.val, m*sizeof(_Tp), m, x.val, n*sizeof(_Tp), n); + + return LU(temp.val, m*sizeof(_Tp), m, x.val, n*sizeof(_Tp), n) != 0; + } + else + { + return cv::solve(a, b, x, method); + } + } +}; + +template struct Matx_FastSolveOp<_Tp, 2, 2, 1> +{ + bool operator()(const Matx<_Tp, 2, 2>& a, const Matx<_Tp, 2, 1>& b, + Matx<_Tp, 2, 1>& x, int) const + { + _Tp d = (_Tp)determinant(a); + if (d == 0) + return false; + d = 1/d; + x(0) = (b(0)*a(1,1) - b(1)*a(0,1))*d; + x(1) = (b(1)*a(0,0) - b(0)*a(1,0))*d; + return true; + } +}; + +template struct Matx_FastSolveOp<_Tp, 3, 3, 1> +{ + bool operator()(const Matx<_Tp, 3, 3>& a, const Matx<_Tp, 3, 1>& b, + Matx<_Tp, 3, 1>& x, int) const + { + _Tp d = (_Tp)determinant(a); + if (d == 0) + return false; + d = 1/d; + x(0) = d*(b(0)*(a(1,1)*a(2,2) - a(1,2)*a(2,1)) - + a(0,1)*(b(1)*a(2,2) - a(1,2)*b(2)) + + a(0,2)*(b(1)*a(2,1) - a(1,1)*b(2))); + + x(1) = d*(a(0,0)*(b(1)*a(2,2) - a(1,2)*b(2)) - + b(0)*(a(1,0)*a(2,2) - a(1,2)*a(2,0)) + + a(0,2)*(a(1,0)*b(2) - b(1)*a(2,0))); + + x(2) = d*(a(0,0)*(a(1,1)*b(2) - b(1)*a(2,1)) - + a(0,1)*(a(1,0)*b(2) - b(1)*a(2,0)) + + b(0)*(a(1,0)*a(2,1) - a(1,1)*a(2,0))); + return true; + } +}; + +} // internal + +template inline +Matx<_Tp,m,n> Matx<_Tp,m,n>::randu(_Tp a, _Tp b) +{ + Matx<_Tp,m,n> M; + cv::randu(M, Scalar(a), Scalar(b)); + return M; +} + +template inline +Matx<_Tp,m,n> Matx<_Tp,m,n>::randn(_Tp a, _Tp b) +{ + Matx<_Tp,m,n> M; + cv::randn(M, Scalar(a), Scalar(b)); + return M; +} + +template inline +Matx<_Tp, n, m> Matx<_Tp, m, n>::inv(int method, bool *p_is_ok /*= NULL*/) const +{ + Matx<_Tp, n, m> b; + bool ok = cv::internal::Matx_FastInvOp<_Tp, m, n>()(*this, b, method); + if (p_is_ok) *p_is_ok = ok; + return ok ? b : Matx<_Tp, n, m>::zeros(); +} + +template template inline +Matx<_Tp, n, l> Matx<_Tp, m, n>::solve(const Matx<_Tp, m, l>& rhs, int method) const +{ + Matx<_Tp, n, l> x; + bool ok = cv::internal::Matx_FastSolveOp<_Tp, m, n, l>()(*this, rhs, x, method); + return ok ? x : Matx<_Tp, n, l>::zeros(); +} + + + +////////////////////////// Augmenting algebraic & logical operations ////////////////////////// + +#define CV_MAT_AUG_OPERATOR1(op, cvop, A, B) \ + static inline A& operator op (A& a, const B& b) { cvop; return a; } + +#define CV_MAT_AUG_OPERATOR(op, cvop, A, B) \ + CV_MAT_AUG_OPERATOR1(op, cvop, A, B) \ + CV_MAT_AUG_OPERATOR1(op, cvop, const A, B) + +#define CV_MAT_AUG_OPERATOR_T(op, cvop, A, B) \ + template CV_MAT_AUG_OPERATOR1(op, cvop, A, B) \ + template CV_MAT_AUG_OPERATOR1(op, cvop, const A, B) + +CV_MAT_AUG_OPERATOR (+=, cv::add(a,b,a), Mat, Mat) +CV_MAT_AUG_OPERATOR (+=, cv::add(a,b,a), Mat, Scalar) +CV_MAT_AUG_OPERATOR_T(+=, cv::add(a,b,a), Mat_<_Tp>, Mat) +CV_MAT_AUG_OPERATOR_T(+=, cv::add(a,b,a), Mat_<_Tp>, Scalar) +CV_MAT_AUG_OPERATOR_T(+=, cv::add(a,b,a), Mat_<_Tp>, Mat_<_Tp>) + +CV_MAT_AUG_OPERATOR (-=, cv::subtract(a,b,a), Mat, Mat) +CV_MAT_AUG_OPERATOR (-=, cv::subtract(a,b,a), Mat, Scalar) +CV_MAT_AUG_OPERATOR_T(-=, cv::subtract(a,b,a), Mat_<_Tp>, Mat) +CV_MAT_AUG_OPERATOR_T(-=, cv::subtract(a,b,a), Mat_<_Tp>, Scalar) +CV_MAT_AUG_OPERATOR_T(-=, cv::subtract(a,b,a), Mat_<_Tp>, Mat_<_Tp>) + +CV_MAT_AUG_OPERATOR (*=, cv::gemm(a, b, 1, Mat(), 0, a, 0), Mat, Mat) +CV_MAT_AUG_OPERATOR_T(*=, cv::gemm(a, b, 1, Mat(), 0, a, 0), Mat_<_Tp>, Mat) +CV_MAT_AUG_OPERATOR_T(*=, cv::gemm(a, b, 1, Mat(), 0, a, 0), Mat_<_Tp>, Mat_<_Tp>) +CV_MAT_AUG_OPERATOR (*=, a.convertTo(a, -1, b), Mat, double) +CV_MAT_AUG_OPERATOR_T(*=, a.convertTo(a, -1, b), Mat_<_Tp>, double) + +CV_MAT_AUG_OPERATOR (/=, cv::divide(a,b,a), Mat, Mat) +CV_MAT_AUG_OPERATOR_T(/=, cv::divide(a,b,a), Mat_<_Tp>, Mat) +CV_MAT_AUG_OPERATOR_T(/=, cv::divide(a,b,a), Mat_<_Tp>, Mat_<_Tp>) +CV_MAT_AUG_OPERATOR (/=, a.convertTo((Mat&)a, -1, 1./b), Mat, double) +CV_MAT_AUG_OPERATOR_T(/=, a.convertTo((Mat&)a, -1, 1./b), Mat_<_Tp>, double) + +CV_MAT_AUG_OPERATOR (&=, cv::bitwise_and(a,b,a), Mat, Mat) +CV_MAT_AUG_OPERATOR (&=, cv::bitwise_and(a,b,a), Mat, Scalar) +CV_MAT_AUG_OPERATOR_T(&=, cv::bitwise_and(a,b,a), Mat_<_Tp>, Mat) +CV_MAT_AUG_OPERATOR_T(&=, cv::bitwise_and(a,b,a), Mat_<_Tp>, Scalar) +CV_MAT_AUG_OPERATOR_T(&=, cv::bitwise_and(a,b,a), Mat_<_Tp>, Mat_<_Tp>) + +CV_MAT_AUG_OPERATOR (|=, cv::bitwise_or(a,b,a), Mat, Mat) +CV_MAT_AUG_OPERATOR (|=, cv::bitwise_or(a,b,a), Mat, Scalar) +CV_MAT_AUG_OPERATOR_T(|=, cv::bitwise_or(a,b,a), Mat_<_Tp>, Mat) +CV_MAT_AUG_OPERATOR_T(|=, cv::bitwise_or(a,b,a), Mat_<_Tp>, Scalar) +CV_MAT_AUG_OPERATOR_T(|=, cv::bitwise_or(a,b,a), Mat_<_Tp>, Mat_<_Tp>) + +CV_MAT_AUG_OPERATOR (^=, cv::bitwise_xor(a,b,a), Mat, Mat) +CV_MAT_AUG_OPERATOR (^=, cv::bitwise_xor(a,b,a), Mat, Scalar) +CV_MAT_AUG_OPERATOR_T(^=, cv::bitwise_xor(a,b,a), Mat_<_Tp>, Mat) +CV_MAT_AUG_OPERATOR_T(^=, cv::bitwise_xor(a,b,a), Mat_<_Tp>, Scalar) +CV_MAT_AUG_OPERATOR_T(^=, cv::bitwise_xor(a,b,a), Mat_<_Tp>, Mat_<_Tp>) + +#undef CV_MAT_AUG_OPERATOR_T +#undef CV_MAT_AUG_OPERATOR +#undef CV_MAT_AUG_OPERATOR1 + + + +///////////////////////////////////////////// SVD ///////////////////////////////////////////// + +inline SVD::SVD() {} +inline SVD::SVD( InputArray m, int flags ) { operator ()(m, flags); } +inline void SVD::solveZ( InputArray m, OutputArray _dst ) +{ + Mat mtx = m.getMat(); + SVD svd(mtx, (mtx.rows >= mtx.cols ? 0 : SVD::FULL_UV)); + _dst.create(svd.vt.cols, 1, svd.vt.type()); + Mat dst = _dst.getMat(); + svd.vt.row(svd.vt.rows-1).reshape(1,svd.vt.cols).copyTo(dst); +} + +template inline void + SVD::compute( const Matx<_Tp, m, n>& a, Matx<_Tp, nm, 1>& w, Matx<_Tp, m, nm>& u, Matx<_Tp, n, nm>& vt ) +{ + CV_StaticAssert( nm == MIN(m, n), "Invalid size of output vector."); + Mat _a(a, false), _u(u, false), _w(w, false), _vt(vt, false); + SVD::compute(_a, _w, _u, _vt); + CV_Assert(_w.data == (uchar*)&w.val[0] && _u.data == (uchar*)&u.val[0] && _vt.data == (uchar*)&vt.val[0]); +} + +template inline void +SVD::compute( const Matx<_Tp, m, n>& a, Matx<_Tp, nm, 1>& w ) +{ + CV_StaticAssert( nm == MIN(m, n), "Invalid size of output vector."); + Mat _a(a, false), _w(w, false); + SVD::compute(_a, _w); + CV_Assert(_w.data == (uchar*)&w.val[0]); +} + +template inline void +SVD::backSubst( const Matx<_Tp, nm, 1>& w, const Matx<_Tp, m, nm>& u, + const Matx<_Tp, n, nm>& vt, const Matx<_Tp, m, nb>& rhs, + Matx<_Tp, n, nb>& dst ) +{ + CV_StaticAssert( nm == MIN(m, n), "Invalid size of output vector."); + Mat _u(u, false), _w(w, false), _vt(vt, false), _rhs(rhs, false), _dst(dst, false); + SVD::backSubst(_w, _u, _vt, _rhs, _dst); + CV_Assert(_dst.data == (uchar*)&dst.val[0]); +} + + + +/////////////////////////////////// Multiply-with-Carry RNG /////////////////////////////////// + +inline RNG::RNG() { state = 0xffffffff; } +inline RNG::RNG(uint64 _state) { state = _state ? _state : 0xffffffff; } + +inline RNG::operator uchar() { return (uchar)next(); } +inline RNG::operator schar() { return (schar)next(); } +inline RNG::operator ushort() { return (ushort)next(); } +inline RNG::operator short() { return (short)next(); } +inline RNG::operator int() { return (int)next(); } +inline RNG::operator unsigned() { return next(); } +inline RNG::operator float() { return next()*2.3283064365386962890625e-10f; } +inline RNG::operator double() { unsigned t = next(); return (((uint64)t << 32) | next()) * 5.4210108624275221700372640043497e-20; } + +inline unsigned RNG::operator ()(unsigned N) { return (unsigned)uniform(0,N); } +inline unsigned RNG::operator ()() { return next(); } + +inline int RNG::uniform(int a, int b) { return a == b ? a : (int)(next() % (b - a) + a); } +inline float RNG::uniform(float a, float b) { return ((float)*this)*(b - a) + a; } +inline double RNG::uniform(double a, double b) { return ((double)*this)*(b - a) + a; } + +inline bool RNG::operator ==(const RNG& other) const { return state == other.state; } + +inline unsigned RNG::next() +{ + state = (uint64)(unsigned)state* /*CV_RNG_COEFF*/ 4164903690U + (unsigned)(state >> 32); + return (unsigned)state; +} + +//! returns the next unifomly-distributed random number of the specified type +template static inline _Tp randu() +{ + return (_Tp)theRNG(); +} + +///////////////////////////////// Formatted string generation ///////////////////////////////// + +/** @brief Returns a text string formatted using the printf-like expression. + +The function acts like sprintf but forms and returns an STL string. It can be used to form an error +message in the Exception constructor. +@param fmt printf-compatible formatting specifiers. + */ +CV_EXPORTS String format( const char* fmt, ... ); + +///////////////////////////////// Formatted output of cv::Mat ///////////////////////////////// + +static inline +Ptr format(InputArray mtx, int fmt) +{ + return Formatter::get(fmt)->format(mtx.getMat()); +} + +static inline +int print(Ptr fmtd, FILE* stream = stdout) +{ + int written = 0; + fmtd->reset(); + for(const char* str = fmtd->next(); str; str = fmtd->next()) + written += fputs(str, stream); + + return written; +} + +static inline +int print(const Mat& mtx, FILE* stream = stdout) +{ + return print(Formatter::get()->format(mtx), stream); +} + +static inline +int print(const UMat& mtx, FILE* stream = stdout) +{ + return print(Formatter::get()->format(mtx.getMat(ACCESS_READ)), stream); +} + +template static inline +int print(const std::vector >& vec, FILE* stream = stdout) +{ + return print(Formatter::get()->format(Mat(vec)), stream); +} + +template static inline +int print(const std::vector >& vec, FILE* stream = stdout) +{ + return print(Formatter::get()->format(Mat(vec)), stream); +} + +template static inline +int print(const Matx<_Tp, m, n>& matx, FILE* stream = stdout) +{ + return print(Formatter::get()->format(cv::Mat(matx)), stream); +} + +//! @endcond + +/****************************************************************************************\ +* Auxiliary algorithms * +\****************************************************************************************/ + +/** @brief Splits an element set into equivalency classes. + +The generic function partition implements an \f$O(N^2)\f$ algorithm for splitting a set of \f$N\f$ elements +into one or more equivalency classes, as described in + . The function returns the number of +equivalency classes. +@param _vec Set of elements stored as a vector. +@param labels Output vector of labels. It contains as many elements as vec. Each label labels[i] is +a 0-based cluster index of `vec[i]`. +@param predicate Equivalence predicate (pointer to a boolean function of two arguments or an +instance of the class that has the method bool operator()(const _Tp& a, const _Tp& b) ). The +predicate returns true when the elements are certainly in the same class, and returns false if they +may or may not be in the same class. +@ingroup core_cluster +*/ +template int +partition( const std::vector<_Tp>& _vec, std::vector& labels, + _EqPredicate predicate=_EqPredicate()) +{ + int i, j, N = (int)_vec.size(); + const _Tp* vec = &_vec[0]; + + const int PARENT=0; + const int RANK=1; + + std::vector _nodes(N*2); + int (*nodes)[2] = (int(*)[2])&_nodes[0]; + + // The first O(N) pass: create N single-vertex trees + for(i = 0; i < N; i++) + { + nodes[i][PARENT]=-1; + nodes[i][RANK] = 0; + } + + // The main O(N^2) pass: merge connected components + for( i = 0; i < N; i++ ) + { + int root = i; + + // find root + while( nodes[root][PARENT] >= 0 ) + root = nodes[root][PARENT]; + + for( j = 0; j < N; j++ ) + { + if( i == j || !predicate(vec[i], vec[j])) + continue; + int root2 = j; + + while( nodes[root2][PARENT] >= 0 ) + root2 = nodes[root2][PARENT]; + + if( root2 != root ) + { + // unite both trees + int rank = nodes[root][RANK], rank2 = nodes[root2][RANK]; + if( rank > rank2 ) + nodes[root2][PARENT] = root; + else + { + nodes[root][PARENT] = root2; + nodes[root2][RANK] += rank == rank2; + root = root2; + } + CV_Assert( nodes[root][PARENT] < 0 ); + + int k = j, parent; + + // compress the path from node2 to root + while( (parent = nodes[k][PARENT]) >= 0 ) + { + nodes[k][PARENT] = root; + k = parent; + } + + // compress the path from node to root + k = i; + while( (parent = nodes[k][PARENT]) >= 0 ) + { + nodes[k][PARENT] = root; + k = parent; + } + } + } + } + + // Final O(N) pass: enumerate classes + labels.resize(N); + int nclasses = 0; + + for( i = 0; i < N; i++ ) + { + int root = i; + while( nodes[root][PARENT] >= 0 ) + root = nodes[root][PARENT]; + // re-use the rank as the class label + if( nodes[root][RANK] >= 0 ) + nodes[root][RANK] = ~nclasses++; + labels[i] = ~nodes[root][RANK]; + } + + return nclasses; +} + +} // cv + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/optim.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/optim.hpp new file mode 100644 index 0000000000000000000000000000000000000000..c4729a9c105d61f3a43c278fd0f41660b12f4d59 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/optim.hpp @@ -0,0 +1,302 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the OpenCV Foundation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OPTIM_HPP +#define OPENCV_OPTIM_HPP + +#include "opencv2/core.hpp" + +namespace cv +{ + +/** @addtogroup core_optim +The algorithms in this section minimize or maximize function value within specified constraints or +without any constraints. +@{ +*/ + +/** @brief Basic interface for all solvers + */ +class CV_EXPORTS MinProblemSolver : public Algorithm +{ +public: + /** @brief Represents function being optimized + */ + class CV_EXPORTS Function + { + public: + virtual ~Function() {} + virtual int getDims() const = 0; + virtual double getGradientEps() const; + virtual double calc(const double* x) const = 0; + virtual void getGradient(const double* x,double* grad); + }; + + /** @brief Getter for the optimized function. + + The optimized function is represented by Function interface, which requires derivatives to + implement the calc(double*) and getDim() methods to evaluate the function. + + @return Smart-pointer to an object that implements Function interface - it represents the + function that is being optimized. It can be empty, if no function was given so far. + */ + virtual Ptr getFunction() const = 0; + + /** @brief Setter for the optimized function. + + *It should be called at least once before the call to* minimize(), as default value is not usable. + + @param f The new function to optimize. + */ + virtual void setFunction(const Ptr& f) = 0; + + /** @brief Getter for the previously set terminal criteria for this algorithm. + + @return Deep copy of the terminal criteria used at the moment. + */ + virtual TermCriteria getTermCriteria() const = 0; + + /** @brief Set terminal criteria for solver. + + This method *is not necessary* to be called before the first call to minimize(), as the default + value is sensible. + + Algorithm stops when the number of function evaluations done exceeds termcrit.maxCount, when + the function values at the vertices of simplex are within termcrit.epsilon range or simplex + becomes so small that it can enclosed in a box with termcrit.epsilon sides, whatever comes + first. + @param termcrit Terminal criteria to be used, represented as cv::TermCriteria structure. + */ + virtual void setTermCriteria(const TermCriteria& termcrit) = 0; + + /** @brief actually runs the algorithm and performs the minimization. + + The sole input parameter determines the centroid of the starting simplex (roughly, it tells + where to start), all the others (terminal criteria, initial step, function to be minimized) are + supposed to be set via the setters before the call to this method or the default values (not + always sensible) will be used. + + @param x The initial point, that will become a centroid of an initial simplex. After the algorithm + will terminate, it will be set to the point where the algorithm stops, the point of possible + minimum. + @return The value of a function at the point found. + */ + virtual double minimize(InputOutputArray x) = 0; +}; + +/** @brief This class is used to perform the non-linear non-constrained minimization of a function, + +defined on an `n`-dimensional Euclidean space, using the **Nelder-Mead method**, also known as +**downhill simplex method**. The basic idea about the method can be obtained from +. + +It should be noted, that this method, although deterministic, is rather a heuristic and therefore +may converge to a local minima, not necessary a global one. It is iterative optimization technique, +which at each step uses an information about the values of a function evaluated only at `n+1` +points, arranged as a *simplex* in `n`-dimensional space (hence the second name of the method). At +each step new point is chosen to evaluate function at, obtained value is compared with previous +ones and based on this information simplex changes it's shape , slowly moving to the local minimum. +Thus this method is using *only* function values to make decision, on contrary to, say, Nonlinear +Conjugate Gradient method (which is also implemented in optim). + +Algorithm stops when the number of function evaluations done exceeds termcrit.maxCount, when the +function values at the vertices of simplex are within termcrit.epsilon range or simplex becomes so +small that it can enclosed in a box with termcrit.epsilon sides, whatever comes first, for some +defined by user positive integer termcrit.maxCount and positive non-integer termcrit.epsilon. + +@note DownhillSolver is a derivative of the abstract interface +cv::MinProblemSolver, which in turn is derived from the Algorithm interface and is used to +encapsulate the functionality, common to all non-linear optimization algorithms in the optim +module. + +@note term criteria should meet following condition: +@code + termcrit.type == (TermCriteria::MAX_ITER + TermCriteria::EPS) && termcrit.epsilon > 0 && termcrit.maxCount > 0 +@endcode + */ +class CV_EXPORTS DownhillSolver : public MinProblemSolver +{ +public: + /** @brief Returns the initial step that will be used in downhill simplex algorithm. + + @param step Initial step that will be used in algorithm. Note, that although corresponding setter + accepts column-vectors as well as row-vectors, this method will return a row-vector. + @see DownhillSolver::setInitStep + */ + virtual void getInitStep(OutputArray step) const=0; + + /** @brief Sets the initial step that will be used in downhill simplex algorithm. + + Step, together with initial point (givin in DownhillSolver::minimize) are two `n`-dimensional + vectors that are used to determine the shape of initial simplex. Roughly said, initial point + determines the position of a simplex (it will become simplex's centroid), while step determines the + spread (size in each dimension) of a simplex. To be more precise, if \f$s,x_0\in\mathbb{R}^n\f$ are + the initial step and initial point respectively, the vertices of a simplex will be: + \f$v_0:=x_0-\frac{1}{2} s\f$ and \f$v_i:=x_0+s_i\f$ for \f$i=1,2,\dots,n\f$ where \f$s_i\f$ denotes + projections of the initial step of *n*-th coordinate (the result of projection is treated to be + vector given by \f$s_i:=e_i\cdot\left\f$, where \f$e_i\f$ form canonical basis) + + @param step Initial step that will be used in algorithm. Roughly said, it determines the spread + (size in each dimension) of an initial simplex. + */ + virtual void setInitStep(InputArray step)=0; + + /** @brief This function returns the reference to the ready-to-use DownhillSolver object. + + All the parameters are optional, so this procedure can be called even without parameters at + all. In this case, the default values will be used. As default value for terminal criteria are + the only sensible ones, MinProblemSolver::setFunction() and DownhillSolver::setInitStep() + should be called upon the obtained object, if the respective parameters were not given to + create(). Otherwise, the two ways (give parameters to createDownhillSolver() or miss them out + and call the MinProblemSolver::setFunction() and DownhillSolver::setInitStep()) are absolutely + equivalent (and will drop the same errors in the same way, should invalid input be detected). + @param f Pointer to the function that will be minimized, similarly to the one you submit via + MinProblemSolver::setFunction. + @param initStep Initial step, that will be used to construct the initial simplex, similarly to the one + you submit via MinProblemSolver::setInitStep. + @param termcrit Terminal criteria to the algorithm, similarly to the one you submit via + MinProblemSolver::setTermCriteria. + */ + static Ptr create(const Ptr& f=Ptr(), + InputArray initStep=Mat_(1,1,0.0), + TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5000,0.000001)); +}; + +/** @brief This class is used to perform the non-linear non-constrained minimization of a function +with known gradient, + +defined on an *n*-dimensional Euclidean space, using the **Nonlinear Conjugate Gradient method**. +The implementation was done based on the beautifully clear explanatory article [An Introduction to +the Conjugate Gradient Method Without the Agonizing +Pain](http://www.cs.cmu.edu/~quake-papers/painless-conjugate-gradient.pdf) by Jonathan Richard +Shewchuk. The method can be seen as an adaptation of a standard Conjugate Gradient method (see, for +example ) for numerically solving the +systems of linear equations. + +It should be noted, that this method, although deterministic, is rather a heuristic method and +therefore may converge to a local minima, not necessary a global one. What is even more disastrous, +most of its behaviour is ruled by gradient, therefore it essentially cannot distinguish between +local minima and maxima. Therefore, if it starts sufficiently near to the local maximum, it may +converge to it. Another obvious restriction is that it should be possible to compute the gradient of +a function at any point, thus it is preferable to have analytic expression for gradient and +computational burden should be born by the user. + +The latter responsibility is accompilished via the getGradient method of a +MinProblemSolver::Function interface (which represents function being optimized). This method takes +point a point in *n*-dimensional space (first argument represents the array of coordinates of that +point) and comput its gradient (it should be stored in the second argument as an array). + +@note class ConjGradSolver thus does not add any new methods to the basic MinProblemSolver interface. + +@note term criteria should meet following condition: +@code + termcrit.type == (TermCriteria::MAX_ITER + TermCriteria::EPS) && termcrit.epsilon > 0 && termcrit.maxCount > 0 + // or + termcrit.type == TermCriteria::MAX_ITER) && termcrit.maxCount > 0 +@endcode + */ +class CV_EXPORTS ConjGradSolver : public MinProblemSolver +{ +public: + /** @brief This function returns the reference to the ready-to-use ConjGradSolver object. + + All the parameters are optional, so this procedure can be called even without parameters at + all. In this case, the default values will be used. As default value for terminal criteria are + the only sensible ones, MinProblemSolver::setFunction() should be called upon the obtained + object, if the function was not given to create(). Otherwise, the two ways (submit it to + create() or miss it out and call the MinProblemSolver::setFunction()) are absolutely equivalent + (and will drop the same errors in the same way, should invalid input be detected). + @param f Pointer to the function that will be minimized, similarly to the one you submit via + MinProblemSolver::setFunction. + @param termcrit Terminal criteria to the algorithm, similarly to the one you submit via + MinProblemSolver::setTermCriteria. + */ + static Ptr create(const Ptr& f=Ptr(), + TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5000,0.000001)); +}; + +//! return codes for cv::solveLP() function +enum SolveLPResult +{ + SOLVELP_UNBOUNDED = -2, //!< problem is unbounded (target function can achieve arbitrary high values) + SOLVELP_UNFEASIBLE = -1, //!< problem is unfeasible (there are no points that satisfy all the constraints imposed) + SOLVELP_SINGLE = 0, //!< there is only one maximum for target function + SOLVELP_MULTI = 1 //!< there are multiple maxima for target function - the arbitrary one is returned +}; + +/** @brief Solve given (non-integer) linear programming problem using the Simplex Algorithm (Simplex Method). + +What we mean here by "linear programming problem" (or LP problem, for short) can be formulated as: + +\f[\mbox{Maximize } c\cdot x\\ + \mbox{Subject to:}\\ + Ax\leq b\\ + x\geq 0\f] + +Where \f$c\f$ is fixed `1`-by-`n` row-vector, \f$A\f$ is fixed `m`-by-`n` matrix, \f$b\f$ is fixed `m`-by-`1` +column vector and \f$x\f$ is an arbitrary `n`-by-`1` column vector, which satisfies the constraints. + +Simplex algorithm is one of many algorithms that are designed to handle this sort of problems +efficiently. Although it is not optimal in theoretical sense (there exist algorithms that can solve +any problem written as above in polynomial time, while simplex method degenerates to exponential +time for some special cases), it is well-studied, easy to implement and is shown to work well for +real-life purposes. + +The particular implementation is taken almost verbatim from **Introduction to Algorithms, third +edition** by T. H. Cormen, C. E. Leiserson, R. L. Rivest and Clifford Stein. In particular, the +Bland's rule is used to prevent cycling. + +@param Func This row-vector corresponds to \f$c\f$ in the LP problem formulation (see above). It should +contain 32- or 64-bit floating point numbers. As a convenience, column-vector may be also submitted, +in the latter case it is understood to correspond to \f$c^T\f$. +@param Constr `m`-by-`n+1` matrix, whose rightmost column corresponds to \f$b\f$ in formulation above +and the remaining to \f$A\f$. It should contain 32- or 64-bit floating point numbers. +@param z The solution will be returned here as a column-vector - it corresponds to \f$c\f$ in the +formulation above. It will contain 64-bit floating point numbers. +@return One of cv::SolveLPResult + */ +CV_EXPORTS_W int solveLP(const Mat& Func, const Mat& Constr, Mat& z); + +//! @} + +}// cv + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/ovx.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/ovx.hpp new file mode 100644 index 0000000000000000000000000000000000000000..8bb7d54911722ba38212bc1e082aa9281d879af4 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/ovx.hpp @@ -0,0 +1,28 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +// Copyright (C) 2016, Intel Corporation, all rights reserved. +// Third party copyrights are property of their respective owners. + +// OpenVX related definitions and declarations + +#pragma once +#ifndef OPENCV_OVX_HPP +#define OPENCV_OVX_HPP + +#include "cvdef.h" + +namespace cv +{ +/// Check if use of OpenVX is possible +CV_EXPORTS_W bool haveOpenVX(); + +/// Check if use of OpenVX is enabled +CV_EXPORTS_W bool useOpenVX(); + +/// Enable/disable use of OpenVX +CV_EXPORTS_W void setUseOpenVX(bool flag); +} // namespace cv + +#endif // OPENCV_OVX_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/persistence.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/persistence.hpp new file mode 100644 index 0000000000000000000000000000000000000000..126393fae13272d17e2114c7574a38b9a7cca6a2 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/persistence.hpp @@ -0,0 +1,1366 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_PERSISTENCE_HPP +#define OPENCV_CORE_PERSISTENCE_HPP + +#ifndef CV_DOXYGEN +/// Define to support persistence legacy formats +#define CV__LEGACY_PERSISTENCE +#endif + +#ifndef __cplusplus +# error persistence.hpp header must be compiled as C++ +#endif + +//! @addtogroup core_c +//! @{ + +/** @brief "black box" representation of the file storage associated with a file on disk. + +Several functions that are described below take CvFileStorage\* as inputs and allow the user to +save or to load hierarchical collections that consist of scalar values, standard CXCore objects +(such as matrices, sequences, graphs), and user-defined objects. + +OpenCV can read and write data in XML (), YAML () or +JSON () formats. Below is an example of 3x3 floating-point identity matrix A, +stored in XML and YAML files +using CXCore functions: +XML: +@code{.xml} + + + + 3 + 3 +
f
+ 1. 0. 0. 0. 1. 0. 0. 0. 1. + + +@endcode +YAML: +@code{.yaml} + %YAML:1.0 + A: !!opencv-matrix + rows: 3 + cols: 3 + dt: f + data: [ 1., 0., 0., 0., 1., 0., 0., 0., 1.] +@endcode +As it can be seen from the examples, XML uses nested tags to represent hierarchy, while YAML uses +indentation for that purpose (similar to the Python programming language). + +The same functions can read and write data in both formats; the particular format is determined by +the extension of the opened file, ".xml" for XML files, ".yml" or ".yaml" for YAML and ".json" for +JSON. + */ +typedef struct CvFileStorage CvFileStorage; +typedef struct CvFileNode CvFileNode; +typedef struct CvMat CvMat; +typedef struct CvMatND CvMatND; + +//! @} core_c + +#include "opencv2/core/types.hpp" +#include "opencv2/core/mat.hpp" + +namespace cv { + +/** @addtogroup core_xml + +XML/YAML/JSON file storages. {#xml_storage} +======================= +Writing to a file storage. +-------------------------- +You can store and then restore various OpenCV data structures to/from XML (), +YAML () or JSON () formats. Also, it is possible to store +and load arbitrarily complex data structures, which include OpenCV data structures, as well as +primitive data types (integer and floating-point numbers and text strings) as their elements. + +Use the following procedure to write something to XML, YAML or JSON: +-# Create new FileStorage and open it for writing. It can be done with a single call to +FileStorage::FileStorage constructor that takes a filename, or you can use the default constructor +and then call FileStorage::open. Format of the file (XML, YAML or JSON) is determined from the filename +extension (".xml", ".yml"/".yaml" and ".json", respectively) +-# Write all the data you want using the streaming operator `<<`, just like in the case of STL +streams. +-# Close the file using FileStorage::release. FileStorage destructor also closes the file. + +Here is an example: +@code + #include "opencv2/opencv.hpp" + #include + + using namespace cv; + + int main(int, char** argv) + { + FileStorage fs("test.yml", FileStorage::WRITE); + + fs << "frameCount" << 5; + time_t rawtime; time(&rawtime); + fs << "calibrationDate" << asctime(localtime(&rawtime)); + Mat cameraMatrix = (Mat_(3,3) << 1000, 0, 320, 0, 1000, 240, 0, 0, 1); + Mat distCoeffs = (Mat_(5,1) << 0.1, 0.01, -0.001, 0, 0); + fs << "cameraMatrix" << cameraMatrix << "distCoeffs" << distCoeffs; + fs << "features" << "["; + for( int i = 0; i < 3; i++ ) + { + int x = rand() % 640; + int y = rand() % 480; + uchar lbp = rand() % 256; + + fs << "{:" << "x" << x << "y" << y << "lbp" << "[:"; + for( int j = 0; j < 8; j++ ) + fs << ((lbp >> j) & 1); + fs << "]" << "}"; + } + fs << "]"; + fs.release(); + return 0; + } +@endcode +The sample above stores to YML an integer, a text string (calibration date), 2 matrices, and a custom +structure "feature", which includes feature coordinates and LBP (local binary pattern) value. Here +is output of the sample: +@code{.yaml} +%YAML:1.0 +frameCount: 5 +calibrationDate: "Fri Jun 17 14:09:29 2011\n" +cameraMatrix: !!opencv-matrix + rows: 3 + cols: 3 + dt: d + data: [ 1000., 0., 320., 0., 1000., 240., 0., 0., 1. ] +distCoeffs: !!opencv-matrix + rows: 5 + cols: 1 + dt: d + data: [ 1.0000000000000001e-01, 1.0000000000000000e-02, + -1.0000000000000000e-03, 0., 0. ] +features: + - { x:167, y:49, lbp:[ 1, 0, 0, 1, 1, 0, 1, 1 ] } + - { x:298, y:130, lbp:[ 0, 0, 0, 1, 0, 0, 1, 1 ] } + - { x:344, y:158, lbp:[ 1, 1, 0, 0, 0, 0, 1, 0 ] } +@endcode + +As an exercise, you can replace ".yml" with ".xml" or ".json" in the sample above and see, how the +corresponding XML file will look like. + +Several things can be noted by looking at the sample code and the output: + +- The produced YAML (and XML/JSON) consists of heterogeneous collections that can be nested. There are + 2 types of collections: named collections (mappings) and unnamed collections (sequences). In mappings + each element has a name and is accessed by name. This is similar to structures and std::map in + C/C++ and dictionaries in Python. In sequences elements do not have names, they are accessed by + indices. This is similar to arrays and std::vector in C/C++ and lists, tuples in Python. + "Heterogeneous" means that elements of each single collection can have different types. + + Top-level collection in YAML/XML/JSON is a mapping. Each matrix is stored as a mapping, and the matrix + elements are stored as a sequence. Then, there is a sequence of features, where each feature is + represented a mapping, and lbp value in a nested sequence. + +- When you write to a mapping (a structure), you write element name followed by its value. When you + write to a sequence, you simply write the elements one by one. OpenCV data structures (such as + cv::Mat) are written in absolutely the same way as simple C data structures - using `<<` + operator. + +- To write a mapping, you first write the special string `{` to the storage, then write the + elements as pairs (`fs << << `) and then write the closing + `}`. + +- To write a sequence, you first write the special string `[`, then write the elements, then + write the closing `]`. + +- In YAML/JSON (but not XML), mappings and sequences can be written in a compact Python-like inline + form. In the sample above matrix elements, as well as each feature, including its lbp value, is + stored in such inline form. To store a mapping/sequence in a compact form, put `:` after the + opening character, e.g. use `{:` instead of `{` and `[:` instead of `[`. When the + data is written to XML, those extra `:` are ignored. + +Reading data from a file storage. +--------------------------------- +To read the previously written XML, YAML or JSON file, do the following: +-# Open the file storage using FileStorage::FileStorage constructor or FileStorage::open method. + In the current implementation the whole file is parsed and the whole representation of file + storage is built in memory as a hierarchy of file nodes (see FileNode) + +-# Read the data you are interested in. Use FileStorage::operator [], FileNode::operator [] + and/or FileNodeIterator. + +-# Close the storage using FileStorage::release. + +Here is how to read the file created by the code sample above: +@code + FileStorage fs2("test.yml", FileStorage::READ); + + // first method: use (type) operator on FileNode. + int frameCount = (int)fs2["frameCount"]; + + String date; + // second method: use FileNode::operator >> + fs2["calibrationDate"] >> date; + + Mat cameraMatrix2, distCoeffs2; + fs2["cameraMatrix"] >> cameraMatrix2; + fs2["distCoeffs"] >> distCoeffs2; + + cout << "frameCount: " << frameCount << endl + << "calibration date: " << date << endl + << "camera matrix: " << cameraMatrix2 << endl + << "distortion coeffs: " << distCoeffs2 << endl; + + FileNode features = fs2["features"]; + FileNodeIterator it = features.begin(), it_end = features.end(); + int idx = 0; + std::vector lbpval; + + // iterate through a sequence using FileNodeIterator + for( ; it != it_end; ++it, idx++ ) + { + cout << "feature #" << idx << ": "; + cout << "x=" << (int)(*it)["x"] << ", y=" << (int)(*it)["y"] << ", lbp: ("; + // you can also easily read numerical arrays using FileNode >> std::vector operator. + (*it)["lbp"] >> lbpval; + for( int i = 0; i < (int)lbpval.size(); i++ ) + cout << " " << (int)lbpval[i]; + cout << ")" << endl; + } + fs2.release(); +@endcode + +Format specification {#format_spec} +-------------------- +`([count]{u|c|w|s|i|f|d})`... where the characters correspond to fundamental C++ types: +- `u` 8-bit unsigned number +- `c` 8-bit signed number +- `w` 16-bit unsigned number +- `s` 16-bit signed number +- `i` 32-bit signed number +- `f` single precision floating-point number +- `d` double precision floating-point number +- `r` pointer, 32 lower bits of which are written as a signed integer. The type can be used to + store structures with links between the elements. + +`count` is the optional counter of values of a given type. For example, `2if` means that each array +element is a structure of 2 integers, followed by a single-precision floating-point number. The +equivalent notations of the above specification are `iif`, `2i1f` and so forth. Other examples: `u` +means that the array consists of bytes, and `2d` means the array consists of pairs of doubles. + +@see @ref samples/cpp/filestorage.cpp +*/ + +//! @{ + +/** @example samples/cpp/filestorage.cpp +A complete example using the FileStorage interface +*/ + +////////////////////////// XML & YAML I/O ////////////////////////// + +class CV_EXPORTS FileNode; +class CV_EXPORTS FileNodeIterator; + +/** @brief XML/YAML/JSON file storage class that encapsulates all the information necessary for writing or +reading data to/from a file. + */ +class CV_EXPORTS_W FileStorage +{ +public: + //! file storage mode + enum Mode + { + READ = 0, //!< value, open the file for reading + WRITE = 1, //!< value, open the file for writing + APPEND = 2, //!< value, open the file for appending + MEMORY = 4, //!< flag, read data from source or write data to the internal buffer (which is + //!< returned by FileStorage::release) + FORMAT_MASK = (7<<3), //!< mask for format flags + FORMAT_AUTO = 0, //!< flag, auto format + FORMAT_XML = (1<<3), //!< flag, XML format + FORMAT_YAML = (2<<3), //!< flag, YAML format + FORMAT_JSON = (3<<3), //!< flag, JSON format + + BASE64 = 64, //!< flag, write rawdata in Base64 by default. (consider using WRITE_BASE64) + WRITE_BASE64 = BASE64 | WRITE, //!< flag, enable both WRITE and BASE64 + }; + enum + { + UNDEFINED = 0, + VALUE_EXPECTED = 1, + NAME_EXPECTED = 2, + INSIDE_MAP = 4 + }; + + /** @brief The constructors. + + The full constructor opens the file. Alternatively you can use the default constructor and then + call FileStorage::open. + */ + CV_WRAP FileStorage(); + + /** @overload + @copydoc open() + */ + CV_WRAP FileStorage(const String& filename, int flags, const String& encoding=String()); + + /** @overload */ + FileStorage(CvFileStorage* fs, bool owning=true); + + //! the destructor. calls release() + virtual ~FileStorage(); + + /** @brief Opens a file. + + See description of parameters in FileStorage::FileStorage. The method calls FileStorage::release + before opening the file. + @param filename Name of the file to open or the text string to read the data from. + Extension of the file (.xml, .yml/.yaml or .json) determines its format (XML, YAML or JSON + respectively). Also you can append .gz to work with compressed files, for example myHugeMatrix.xml.gz. If both + FileStorage::WRITE and FileStorage::MEMORY flags are specified, source is used just to specify + the output file format (e.g. mydata.xml, .yml etc.). A file name can also contain parameters. + You can use this format, "*?base64" (e.g. "file.json?base64" (case sensitive)), as an alternative to + FileStorage::BASE64 flag. + @param flags Mode of operation. One of FileStorage::Mode + @param encoding Encoding of the file. Note that UTF-16 XML encoding is not supported currently and + you should use 8-bit encoding instead of it. + */ + CV_WRAP virtual bool open(const String& filename, int flags, const String& encoding=String()); + + /** @brief Checks whether the file is opened. + + @returns true if the object is associated with the current file and false otherwise. It is a + good practice to call this method after you tried to open a file. + */ + CV_WRAP virtual bool isOpened() const; + + /** @brief Closes the file and releases all the memory buffers. + + Call this method after all I/O operations with the storage are finished. + */ + CV_WRAP virtual void release(); + + /** @brief Closes the file and releases all the memory buffers. + + Call this method after all I/O operations with the storage are finished. If the storage was + opened for writing data and FileStorage::WRITE was specified + */ + CV_WRAP virtual String releaseAndGetString(); + + /** @brief Returns the first element of the top-level mapping. + @returns The first element of the top-level mapping. + */ + CV_WRAP FileNode getFirstTopLevelNode() const; + + /** @brief Returns the top-level mapping + @param streamidx Zero-based index of the stream. In most cases there is only one stream in the file. + However, YAML supports multiple streams and so there can be several. + @returns The top-level mapping. + */ + CV_WRAP FileNode root(int streamidx=0) const; + + /** @brief Returns the specified element of the top-level mapping. + @param nodename Name of the file node. + @returns Node with the given name. + */ + FileNode operator[](const String& nodename) const; + + /** @overload */ + CV_WRAP_AS(getNode) FileNode operator[](const char* nodename) const; + + /** @brief Returns the obsolete C FileStorage structure. + @returns Pointer to the underlying C FileStorage structure + */ + CvFileStorage* operator *() { return fs.get(); } + + /** @overload */ + const CvFileStorage* operator *() const { return fs.get(); } + + /** @brief Writes multiple numbers. + + Writes one or more numbers of the specified format to the currently written structure. Usually it is + more convenient to use operator `<<` instead of this method. + @param fmt Specification of each array element, see @ref format_spec "format specification" + @param vec Pointer to the written array. + @param len Number of the uchar elements to write. + */ + void writeRaw( const String& fmt, const uchar* vec, size_t len ); + + /** @brief Writes the registered C structure (CvMat, CvMatND, CvSeq). + @param name Name of the written object. + @param obj Pointer to the object. + @see ocvWrite for details. + */ + void writeObj( const String& name, const void* obj ); + + /** + * @brief Simplified writing API to use with bindings. + * @param name Name of the written object + * @param val Value of the written object + */ + CV_WRAP void write(const String& name, int val); + /// @overload + CV_WRAP void write(const String& name, double val); + /// @overload + CV_WRAP void write(const String& name, const String& val); + /// @overload + CV_WRAP void write(const String& name, InputArray val); + + /** @brief Writes a comment. + + The function writes a comment into file storage. The comments are skipped when the storage is read. + @param comment The written comment, single-line or multi-line + @param append If true, the function tries to put the comment at the end of current line. + Else if the comment is multi-line, or if it does not fit at the end of the current + line, the comment starts a new line. + */ + CV_WRAP void writeComment(const String& comment, bool append = false); + + /** @brief Returns the normalized object name for the specified name of a file. + @param filename Name of a file + @returns The normalized object name. + */ + static String getDefaultObjectName(const String& filename); + + /** @brief Returns the current format. + * @returns The current format, see FileStorage::Mode + */ + CV_WRAP int getFormat() const; + + Ptr fs; //!< the underlying C FileStorage structure + String elname; //!< the currently written element + std::vector structs; //!< the stack of written structures + int state; //!< the writer state +}; + +template<> CV_EXPORTS void DefaultDeleter::operator ()(CvFileStorage* obj) const; + +/** @brief File Storage Node class. + +The node is used to store each and every element of the file storage opened for reading. When +XML/YAML file is read, it is first parsed and stored in the memory as a hierarchical collection of +nodes. Each node can be a "leaf" that is contain a single number or a string, or be a collection of +other nodes. There can be named collections (mappings) where each element has a name and it is +accessed by a name, and ordered collections (sequences) where elements do not have names but rather +accessed by index. Type of the file node can be determined using FileNode::type method. + +Note that file nodes are only used for navigating file storages opened for reading. When a file +storage is opened for writing, no data is stored in memory after it is written. + */ +class CV_EXPORTS_W_SIMPLE FileNode +{ +public: + //! type of the file storage node + enum Type + { + NONE = 0, //!< empty node + INT = 1, //!< an integer + REAL = 2, //!< floating-point number + FLOAT = REAL, //!< synonym or REAL + STR = 3, //!< text string in UTF-8 encoding + STRING = STR, //!< synonym for STR + REF = 4, //!< integer of size size_t. Typically used for storing complex dynamic structures where some elements reference the others + SEQ = 5, //!< sequence + MAP = 6, //!< mapping + TYPE_MASK = 7, + FLOW = 8, //!< compact representation of a sequence or mapping. Used only by YAML writer + USER = 16, //!< a registered object (e.g. a matrix) + EMPTY = 32, //!< empty structure (sequence or mapping) + NAMED = 64 //!< the node has a name (i.e. it is element of a mapping) + }; + /** @brief The constructors. + + These constructors are used to create a default file node, construct it from obsolete structures or + from the another file node. + */ + CV_WRAP FileNode(); + + /** @overload + @param fs Pointer to the obsolete file storage structure. + @param node File node to be used as initialization for the created file node. + */ + FileNode(const CvFileStorage* fs, const CvFileNode* node); + + /** @overload + @param node File node to be used as initialization for the created file node. + */ + FileNode(const FileNode& node); + + /** @brief Returns element of a mapping node or a sequence node. + @param nodename Name of an element in the mapping node. + @returns Returns the element with the given identifier. + */ + FileNode operator[](const String& nodename) const; + + /** @overload + @param nodename Name of an element in the mapping node. + */ + CV_WRAP_AS(getNode) FileNode operator[](const char* nodename) const; + + /** @overload + @param i Index of an element in the sequence node. + */ + CV_WRAP_AS(at) FileNode operator[](int i) const; + + /** @brief Returns keys of a mapping node. + @returns Keys of a mapping node. + */ + CV_WRAP std::vector keys() const; + + /** @brief Returns type of the node. + @returns Type of the node. See FileNode::Type + */ + CV_WRAP int type() const; + + //! returns true if the node is empty + CV_WRAP bool empty() const; + //! returns true if the node is a "none" object + CV_WRAP bool isNone() const; + //! returns true if the node is a sequence + CV_WRAP bool isSeq() const; + //! returns true if the node is a mapping + CV_WRAP bool isMap() const; + //! returns true if the node is an integer + CV_WRAP bool isInt() const; + //! returns true if the node is a floating-point number + CV_WRAP bool isReal() const; + //! returns true if the node is a text string + CV_WRAP bool isString() const; + //! returns true if the node has a name + CV_WRAP bool isNamed() const; + //! returns the node name or an empty string if the node is nameless + CV_WRAP String name() const; + //! returns the number of elements in the node, if it is a sequence or mapping, or 1 otherwise. + CV_WRAP size_t size() const; + //! returns the node content as an integer. If the node stores floating-point number, it is rounded. + operator int() const; + //! returns the node content as float + operator float() const; + //! returns the node content as double + operator double() const; + //! returns the node content as text string + operator String() const; + operator std::string() const; + + //! returns pointer to the underlying file node + CvFileNode* operator *(); + //! returns pointer to the underlying file node + const CvFileNode* operator* () const; + + //! returns iterator pointing to the first node element + FileNodeIterator begin() const; + //! returns iterator pointing to the element following the last node element + FileNodeIterator end() const; + + /** @brief Reads node elements to the buffer with the specified format. + + Usually it is more convenient to use operator `>>` instead of this method. + @param fmt Specification of each array element. See @ref format_spec "format specification" + @param vec Pointer to the destination array. + @param len Number of elements to read. If it is greater than number of remaining elements then all + of them will be read. + */ + void readRaw( const String& fmt, uchar* vec, size_t len ) const; + + //! reads the registered object and returns pointer to it + void* readObj() const; + + //! Simplified reading API to use with bindings. + CV_WRAP double real() const; + //! Simplified reading API to use with bindings. + CV_WRAP String string() const; + //! Simplified reading API to use with bindings. + CV_WRAP Mat mat() const; + + // do not use wrapper pointer classes for better efficiency + const CvFileStorage* fs; + const CvFileNode* node; +}; + + +/** @brief used to iterate through sequences and mappings. + +A standard STL notation, with node.begin(), node.end() denoting the beginning and the end of a +sequence, stored in node. See the data reading sample in the beginning of the section. + */ +class CV_EXPORTS FileNodeIterator +{ +public: + /** @brief The constructors. + + These constructors are used to create a default iterator, set it to specific element in a file node + or construct it from another iterator. + */ + FileNodeIterator(); + + /** @overload + @param fs File storage for the iterator. + @param node File node for the iterator. + @param ofs Index of the element in the node. The created iterator will point to this element. + */ + FileNodeIterator(const CvFileStorage* fs, const CvFileNode* node, size_t ofs=0); + + /** @overload + @param it Iterator to be used as initialization for the created iterator. + */ + FileNodeIterator(const FileNodeIterator& it); + + //! returns the currently observed element + FileNode operator *() const; + //! accesses the currently observed element methods + FileNode operator ->() const; + + //! moves iterator to the next node + FileNodeIterator& operator ++ (); + //! moves iterator to the next node + FileNodeIterator operator ++ (int); + //! moves iterator to the previous node + FileNodeIterator& operator -- (); + //! moves iterator to the previous node + FileNodeIterator operator -- (int); + //! moves iterator forward by the specified offset (possibly negative) + FileNodeIterator& operator += (int ofs); + //! moves iterator backward by the specified offset (possibly negative) + FileNodeIterator& operator -= (int ofs); + + /** @brief Reads node elements to the buffer with the specified format. + + Usually it is more convenient to use operator `>>` instead of this method. + @param fmt Specification of each array element. See @ref format_spec "format specification" + @param vec Pointer to the destination array. + @param maxCount Number of elements to read. If it is greater than number of remaining elements then + all of them will be read. + */ + FileNodeIterator& readRaw( const String& fmt, uchar* vec, + size_t maxCount=(size_t)INT_MAX ); + + struct SeqReader + { + int header_size; + void* seq; /* sequence, beign read; CvSeq */ + void* block; /* current block; CvSeqBlock */ + schar* ptr; /* pointer to element be read next */ + schar* block_min; /* pointer to the beginning of block */ + schar* block_max; /* pointer to the end of block */ + int delta_index;/* = seq->first->start_index */ + schar* prev_elem; /* pointer to previous element */ + }; + + const CvFileStorage* fs; + const CvFileNode* container; + SeqReader reader; + size_t remaining; +}; + +//! @} core_xml + +/////////////////// XML & YAML I/O implementation ////////////////// + +//! @relates cv::FileStorage +//! @{ + +CV_EXPORTS void write( FileStorage& fs, const String& name, int value ); +CV_EXPORTS void write( FileStorage& fs, const String& name, float value ); +CV_EXPORTS void write( FileStorage& fs, const String& name, double value ); +CV_EXPORTS void write( FileStorage& fs, const String& name, const String& value ); +CV_EXPORTS void write( FileStorage& fs, const String& name, const Mat& value ); +CV_EXPORTS void write( FileStorage& fs, const String& name, const SparseMat& value ); +#ifdef CV__LEGACY_PERSISTENCE +CV_EXPORTS void write( FileStorage& fs, const String& name, const std::vector& value); +CV_EXPORTS void write( FileStorage& fs, const String& name, const std::vector& value); +#endif + +CV_EXPORTS void writeScalar( FileStorage& fs, int value ); +CV_EXPORTS void writeScalar( FileStorage& fs, float value ); +CV_EXPORTS void writeScalar( FileStorage& fs, double value ); +CV_EXPORTS void writeScalar( FileStorage& fs, const String& value ); + +//! @} + +//! @relates cv::FileNode +//! @{ + +CV_EXPORTS void read(const FileNode& node, int& value, int default_value); +CV_EXPORTS void read(const FileNode& node, float& value, float default_value); +CV_EXPORTS void read(const FileNode& node, double& value, double default_value); +CV_EXPORTS void read(const FileNode& node, String& value, const String& default_value); +CV_EXPORTS void read(const FileNode& node, std::string& value, const std::string& default_value); +CV_EXPORTS void read(const FileNode& node, Mat& mat, const Mat& default_mat = Mat() ); +CV_EXPORTS void read(const FileNode& node, SparseMat& mat, const SparseMat& default_mat = SparseMat() ); +#ifdef CV__LEGACY_PERSISTENCE +CV_EXPORTS void read(const FileNode& node, std::vector& keypoints); +CV_EXPORTS void read(const FileNode& node, std::vector& matches); +#endif +CV_EXPORTS void read(const FileNode& node, KeyPoint& value, const KeyPoint& default_value); +CV_EXPORTS void read(const FileNode& node, DMatch& value, const DMatch& default_value); + +template static inline void read(const FileNode& node, Point_<_Tp>& value, const Point_<_Tp>& default_value) +{ + std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp; + value = temp.size() != 2 ? default_value : Point_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1])); +} + +template static inline void read(const FileNode& node, Point3_<_Tp>& value, const Point3_<_Tp>& default_value) +{ + std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp; + value = temp.size() != 3 ? default_value : Point3_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1]), + saturate_cast<_Tp>(temp[2])); +} + +template static inline void read(const FileNode& node, Size_<_Tp>& value, const Size_<_Tp>& default_value) +{ + std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp; + value = temp.size() != 2 ? default_value : Size_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1])); +} + +template static inline void read(const FileNode& node, Complex<_Tp>& value, const Complex<_Tp>& default_value) +{ + std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp; + value = temp.size() != 2 ? default_value : Complex<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1])); +} + +template static inline void read(const FileNode& node, Rect_<_Tp>& value, const Rect_<_Tp>& default_value) +{ + std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp; + value = temp.size() != 4 ? default_value : Rect_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1]), + saturate_cast<_Tp>(temp[2]), saturate_cast<_Tp>(temp[3])); +} + +template static inline void read(const FileNode& node, Vec<_Tp, cn>& value, const Vec<_Tp, cn>& default_value) +{ + std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp; + value = temp.size() != cn ? default_value : Vec<_Tp, cn>(&temp[0]); +} + +template static inline void read(const FileNode& node, Scalar_<_Tp>& value, const Scalar_<_Tp>& default_value) +{ + std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp; + value = temp.size() != 4 ? default_value : Scalar_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1]), + saturate_cast<_Tp>(temp[2]), saturate_cast<_Tp>(temp[3])); +} + +static inline void read(const FileNode& node, Range& value, const Range& default_value) +{ + Point2i temp(value.start, value.end); const Point2i default_temp = Point2i(default_value.start, default_value.end); + read(node, temp, default_temp); + value.start = temp.x; value.end = temp.y; +} + +//! @} + +/** @brief Writes string to a file storage. +@relates cv::FileStorage + */ +CV_EXPORTS FileStorage& operator << (FileStorage& fs, const String& str); + +//! @cond IGNORED + +namespace internal +{ + class CV_EXPORTS WriteStructContext + { + public: + WriteStructContext(FileStorage& _fs, const String& name, int flags, const String& typeName = String()); + ~WriteStructContext(); + private: + FileStorage* fs; + }; + + template class VecWriterProxy + { + public: + VecWriterProxy( FileStorage* _fs ) : fs(_fs) {} + void operator()(const std::vector<_Tp>& vec) const + { + size_t count = vec.size(); + for (size_t i = 0; i < count; i++) + write(*fs, vec[i]); + } + private: + FileStorage* fs; + }; + + template class VecWriterProxy<_Tp, 1> + { + public: + VecWriterProxy( FileStorage* _fs ) : fs(_fs) {} + void operator()(const std::vector<_Tp>& vec) const + { + int _fmt = traits::SafeFmt<_Tp>::fmt; + char fmt[] = { (char)((_fmt >> 8) + '1'), (char)_fmt, '\0' }; + fs->writeRaw(fmt, !vec.empty() ? (uchar*)&vec[0] : 0, vec.size() * sizeof(_Tp)); + } + private: + FileStorage* fs; + }; + + template class VecReaderProxy + { + public: + VecReaderProxy( FileNodeIterator* _it ) : it(_it) {} + void operator()(std::vector<_Tp>& vec, size_t count) const + { + count = std::min(count, it->remaining); + vec.resize(count); + for (size_t i = 0; i < count; i++, ++(*it)) + read(**it, vec[i], _Tp()); + } + private: + FileNodeIterator* it; + }; + + template class VecReaderProxy<_Tp, 1> + { + public: + VecReaderProxy( FileNodeIterator* _it ) : it(_it) {} + void operator()(std::vector<_Tp>& vec, size_t count) const + { + size_t remaining = it->remaining; + size_t cn = DataType<_Tp>::channels; + int _fmt = traits::SafeFmt<_Tp>::fmt; + CV_Assert((_fmt >> 8) < 9); + char fmt[] = { (char)((_fmt >> 8)+'1'), (char)_fmt, '\0' }; + CV_Assert((remaining % cn) == 0); + size_t remaining1 = remaining / cn; + count = count < remaining1 ? count : remaining1; + vec.resize(count); + it->readRaw(fmt, !vec.empty() ? (uchar*)&vec[0] : 0, count*sizeof(_Tp)); + } + private: + FileNodeIterator* it; + }; + +} // internal + +//! @endcond + +//! @relates cv::FileStorage +//! @{ + +template static inline +void write(FileStorage& fs, const _Tp& value) +{ + write(fs, String(), value); +} + +template<> inline +void write( FileStorage& fs, const int& value ) +{ + writeScalar(fs, value); +} + +template<> inline +void write( FileStorage& fs, const float& value ) +{ + writeScalar(fs, value); +} + +template<> inline +void write( FileStorage& fs, const double& value ) +{ + writeScalar(fs, value); +} + +template<> inline +void write( FileStorage& fs, const String& value ) +{ + writeScalar(fs, value); +} + +template static inline +void write(FileStorage& fs, const Point_<_Tp>& pt ) +{ + write(fs, pt.x); + write(fs, pt.y); +} + +template static inline +void write(FileStorage& fs, const Point3_<_Tp>& pt ) +{ + write(fs, pt.x); + write(fs, pt.y); + write(fs, pt.z); +} + +template static inline +void write(FileStorage& fs, const Size_<_Tp>& sz ) +{ + write(fs, sz.width); + write(fs, sz.height); +} + +template static inline +void write(FileStorage& fs, const Complex<_Tp>& c ) +{ + write(fs, c.re); + write(fs, c.im); +} + +template static inline +void write(FileStorage& fs, const Rect_<_Tp>& r ) +{ + write(fs, r.x); + write(fs, r.y); + write(fs, r.width); + write(fs, r.height); +} + +template static inline +void write(FileStorage& fs, const Vec<_Tp, cn>& v ) +{ + for(int i = 0; i < cn; i++) + write(fs, v.val[i]); +} + +template static inline +void write(FileStorage& fs, const Scalar_<_Tp>& s ) +{ + write(fs, s.val[0]); + write(fs, s.val[1]); + write(fs, s.val[2]); + write(fs, s.val[3]); +} + +static inline +void write(FileStorage& fs, const Range& r ) +{ + write(fs, r.start); + write(fs, r.end); +} + +template static inline +void write( FileStorage& fs, const std::vector<_Tp>& vec ) +{ + cv::internal::VecWriterProxy<_Tp, traits::SafeFmt<_Tp>::fmt != 0> w(&fs); + w(vec); +} + +template static inline +void write(FileStorage& fs, const String& name, const Point_<_Tp>& pt ) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); + write(fs, pt); +} + +template static inline +void write(FileStorage& fs, const String& name, const Point3_<_Tp>& pt ) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); + write(fs, pt); +} + +template static inline +void write(FileStorage& fs, const String& name, const Size_<_Tp>& sz ) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); + write(fs, sz); +} + +template static inline +void write(FileStorage& fs, const String& name, const Complex<_Tp>& c ) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); + write(fs, c); +} + +template static inline +void write(FileStorage& fs, const String& name, const Rect_<_Tp>& r ) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); + write(fs, r); +} + +template static inline +void write(FileStorage& fs, const String& name, const Vec<_Tp, cn>& v ) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); + write(fs, v); +} + +template static inline +void write(FileStorage& fs, const String& name, const Scalar_<_Tp>& s ) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); + write(fs, s); +} + +static inline +void write(FileStorage& fs, const String& name, const Range& r ) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); + write(fs, r); +} + +static inline +void write(FileStorage& fs, const String& name, const KeyPoint& kpt) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); + write(fs, kpt.pt.x); + write(fs, kpt.pt.y); + write(fs, kpt.size); + write(fs, kpt.angle); + write(fs, kpt.response); + write(fs, kpt.octave); + write(fs, kpt.class_id); +} + +static inline +void write(FileStorage& fs, const String& name, const DMatch& m) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); + write(fs, m.queryIdx); + write(fs, m.trainIdx); + write(fs, m.imgIdx); + write(fs, m.distance); +} + +template static inline +void write( FileStorage& fs, const String& name, const std::vector<_Tp>& vec ) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+(traits::SafeFmt<_Tp>::fmt != 0 ? FileNode::FLOW : 0)); + write(fs, vec); +} + +template static inline +void write( FileStorage& fs, const String& name, const std::vector< std::vector<_Tp> >& vec ) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ); + for(size_t i = 0; i < vec.size(); i++) + { + cv::internal::WriteStructContext ws_(fs, name, FileNode::SEQ+(traits::SafeFmt<_Tp>::fmt != 0 ? FileNode::FLOW : 0)); + write(fs, vec[i]); + } +} + +#ifdef CV__LEGACY_PERSISTENCE +// This code is not needed anymore, but it is preserved here to keep source compatibility +// Implementation is similar to templates instantiations +static inline void write(FileStorage& fs, const KeyPoint& kpt) { write(fs, String(), kpt); } +static inline void write(FileStorage& fs, const DMatch& m) { write(fs, String(), m); } +static inline void write(FileStorage& fs, const std::vector& vec) +{ + cv::internal::VecWriterProxy w(&fs); + w(vec); +} +static inline void write(FileStorage& fs, const std::vector& vec) +{ + cv::internal::VecWriterProxy w(&fs); + w(vec); + +} +#endif + +//! @} FileStorage + +//! @relates cv::FileNode +//! @{ + +static inline +void read(const FileNode& node, bool& value, bool default_value) +{ + int temp; + read(node, temp, (int)default_value); + value = temp != 0; +} + +static inline +void read(const FileNode& node, uchar& value, uchar default_value) +{ + int temp; + read(node, temp, (int)default_value); + value = saturate_cast(temp); +} + +static inline +void read(const FileNode& node, schar& value, schar default_value) +{ + int temp; + read(node, temp, (int)default_value); + value = saturate_cast(temp); +} + +static inline +void read(const FileNode& node, ushort& value, ushort default_value) +{ + int temp; + read(node, temp, (int)default_value); + value = saturate_cast(temp); +} + +static inline +void read(const FileNode& node, short& value, short default_value) +{ + int temp; + read(node, temp, (int)default_value); + value = saturate_cast(temp); +} + +template static inline +void read( FileNodeIterator& it, std::vector<_Tp>& vec, size_t maxCount = (size_t)INT_MAX ) +{ + cv::internal::VecReaderProxy<_Tp, traits::SafeFmt<_Tp>::fmt != 0> r(&it); + r(vec, maxCount); +} + +template static inline +void read( const FileNode& node, std::vector<_Tp>& vec, const std::vector<_Tp>& default_value = std::vector<_Tp>() ) +{ + if(!node.node) + vec = default_value; + else + { + FileNodeIterator it = node.begin(); + read( it, vec ); + } +} + +static inline +void read( const FileNode& node, std::vector& vec, const std::vector& default_value ) +{ + if(!node.node) + vec = default_value; + else + read(node, vec); +} + +static inline +void read( const FileNode& node, std::vector& vec, const std::vector& default_value ) +{ + if(!node.node) + vec = default_value; + else + read(node, vec); +} + +//! @} FileNode + +//! @relates cv::FileStorage +//! @{ + +/** @brief Writes data to a file storage. + */ +template static inline +FileStorage& operator << (FileStorage& fs, const _Tp& value) +{ + if( !fs.isOpened() ) + return fs; + if( fs.state == FileStorage::NAME_EXPECTED + FileStorage::INSIDE_MAP ) + CV_Error( Error::StsError, "No element name has been given" ); + write( fs, fs.elname, value ); + if( fs.state & FileStorage::INSIDE_MAP ) + fs.state = FileStorage::NAME_EXPECTED + FileStorage::INSIDE_MAP; + return fs; +} + +/** @brief Writes data to a file storage. + */ +static inline +FileStorage& operator << (FileStorage& fs, const char* str) +{ + return (fs << String(str)); +} + +/** @brief Writes data to a file storage. + */ +static inline +FileStorage& operator << (FileStorage& fs, char* value) +{ + return (fs << String(value)); +} + +//! @} FileStorage + +//! @relates cv::FileNodeIterator +//! @{ + +/** @brief Reads data from a file storage. + */ +template static inline +FileNodeIterator& operator >> (FileNodeIterator& it, _Tp& value) +{ + read( *it, value, _Tp()); + return ++it; +} + +/** @brief Reads data from a file storage. + */ +template static inline +FileNodeIterator& operator >> (FileNodeIterator& it, std::vector<_Tp>& vec) +{ + cv::internal::VecReaderProxy<_Tp, traits::SafeFmt<_Tp>::fmt != 0> r(&it); + r(vec, (size_t)INT_MAX); + return it; +} + +//! @} FileNodeIterator + +//! @relates cv::FileNode +//! @{ + +/** @brief Reads data from a file storage. + */ +template static inline +void operator >> (const FileNode& n, _Tp& value) +{ + read( n, value, _Tp()); +} + +/** @brief Reads data from a file storage. + */ +template static inline +void operator >> (const FileNode& n, std::vector<_Tp>& vec) +{ + FileNodeIterator it = n.begin(); + it >> vec; +} + +/** @brief Reads KeyPoint from a file storage. +*/ +//It needs special handling because it contains two types of fields, int & float. +static inline +void operator >> (const FileNode& n, KeyPoint& kpt) +{ + FileNodeIterator it = n.begin(); + it >> kpt.pt.x >> kpt.pt.y >> kpt.size >> kpt.angle >> kpt.response >> kpt.octave >> kpt.class_id; +} + +#ifdef CV__LEGACY_PERSISTENCE +static inline +void operator >> (const FileNode& n, std::vector& vec) +{ + read(n, vec); +} +static inline +void operator >> (const FileNode& n, std::vector& vec) +{ + read(n, vec); +} +#endif + +/** @brief Reads DMatch from a file storage. +*/ +//It needs special handling because it contains two types of fields, int & float. +static inline +void operator >> (const FileNode& n, DMatch& m) +{ + FileNodeIterator it = n.begin(); + it >> m.queryIdx >> m.trainIdx >> m.imgIdx >> m.distance; +} + +//! @} FileNode + +//! @relates cv::FileNodeIterator +//! @{ + +static inline +bool operator == (const FileNodeIterator& it1, const FileNodeIterator& it2) +{ + return it1.fs == it2.fs && it1.container == it2.container && + it1.reader.ptr == it2.reader.ptr && it1.remaining == it2.remaining; +} + +static inline +bool operator != (const FileNodeIterator& it1, const FileNodeIterator& it2) +{ + return !(it1 == it2); +} + +static inline +ptrdiff_t operator - (const FileNodeIterator& it1, const FileNodeIterator& it2) +{ + return it2.remaining - it1.remaining; +} + +static inline +bool operator < (const FileNodeIterator& it1, const FileNodeIterator& it2) +{ + return it1.remaining > it2.remaining; +} + +//! @} FileNodeIterator + +//! @cond IGNORED + +inline FileNode FileStorage::getFirstTopLevelNode() const { FileNode r = root(); FileNodeIterator it = r.begin(); return it != r.end() ? *it : FileNode(); } +inline FileNode::FileNode() : fs(0), node(0) {} +inline FileNode::FileNode(const CvFileStorage* _fs, const CvFileNode* _node) : fs(_fs), node(_node) {} +inline FileNode::FileNode(const FileNode& _node) : fs(_node.fs), node(_node.node) {} +inline bool FileNode::empty() const { return node == 0; } +inline bool FileNode::isNone() const { return type() == NONE; } +inline bool FileNode::isSeq() const { return type() == SEQ; } +inline bool FileNode::isMap() const { return type() == MAP; } +inline bool FileNode::isInt() const { return type() == INT; } +inline bool FileNode::isReal() const { return type() == REAL; } +inline bool FileNode::isString() const { return type() == STR; } +inline CvFileNode* FileNode::operator *() { return (CvFileNode*)node; } +inline const CvFileNode* FileNode::operator* () const { return node; } +inline FileNode::operator int() const { int value; read(*this, value, 0); return value; } +inline FileNode::operator float() const { float value; read(*this, value, 0.f); return value; } +inline FileNode::operator double() const { double value; read(*this, value, 0.); return value; } +inline FileNode::operator String() const { String value; read(*this, value, value); return value; } +inline double FileNode::real() const { return double(*this); } +inline String FileNode::string() const { return String(*this); } +inline Mat FileNode::mat() const { Mat value; read(*this, value, value); return value; } +inline FileNodeIterator FileNode::begin() const { return FileNodeIterator(fs, node); } +inline FileNodeIterator FileNode::end() const { return FileNodeIterator(fs, node, size()); } +inline void FileNode::readRaw( const String& fmt, uchar* vec, size_t len ) const { begin().readRaw( fmt, vec, len ); } +inline FileNode FileNodeIterator::operator *() const { return FileNode(fs, (const CvFileNode*)(const void*)reader.ptr); } +inline FileNode FileNodeIterator::operator ->() const { return FileNode(fs, (const CvFileNode*)(const void*)reader.ptr); } +inline String::String(const FileNode& fn): cstr_(0), len_(0) { read(fn, *this, *this); } + +//! @endcond + + +CV_EXPORTS void cvStartWriteRawData_Base64(::CvFileStorage * fs, const char* name, int len, const char* dt); + +CV_EXPORTS void cvWriteRawData_Base64(::CvFileStorage * fs, const void* _data, int len); + +CV_EXPORTS void cvEndWriteRawData_Base64(::CvFileStorage * fs); + +CV_EXPORTS void cvWriteMat_Base64(::CvFileStorage* fs, const char* name, const ::CvMat* mat); + +CV_EXPORTS void cvWriteMatND_Base64(::CvFileStorage* fs, const char* name, const ::CvMatND* mat); + +} // cv + +#endif // OPENCV_CORE_PERSISTENCE_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/ptr.inl.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/ptr.inl.hpp new file mode 100644 index 0000000000000000000000000000000000000000..466f6342ec523717bff6dbbba2a6f0754a335567 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/ptr.inl.hpp @@ -0,0 +1,379 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2013, NVIDIA Corporation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the copyright holders or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_PTR_INL_HPP +#define OPENCV_CORE_PTR_INL_HPP + +#include + +//! @cond IGNORED + +namespace cv { + +template +void DefaultDeleter::operator () (Y* p) const +{ + delete p; +} + +namespace detail +{ + +struct PtrOwner +{ + PtrOwner() : refCount(1) + {} + + void incRef() + { + CV_XADD(&refCount, 1); + } + + void decRef() + { + if (CV_XADD(&refCount, -1) == 1) deleteSelf(); + } + +protected: + /* This doesn't really need to be virtual, since PtrOwner is never deleted + directly, but it doesn't hurt and it helps avoid warnings. */ + virtual ~PtrOwner() + {} + + virtual void deleteSelf() = 0; + +private: + unsigned int refCount; + + // noncopyable + PtrOwner(const PtrOwner&); + PtrOwner& operator = (const PtrOwner&); +}; + +template +struct PtrOwnerImpl CV_FINAL : PtrOwner +{ + PtrOwnerImpl(Y* p, D d) : owned(p), deleter(d) + {} + + void deleteSelf() CV_OVERRIDE + { + deleter(owned); + delete this; + } + +private: + Y* owned; + D deleter; +}; + + +} + +template +Ptr::Ptr() : owner(NULL), stored(NULL) +{} + +template +template +Ptr::Ptr(Y* p) + : owner(p + ? new detail::PtrOwnerImpl >(p, DefaultDeleter()) + : NULL), + stored(p) +{} + +template +template +Ptr::Ptr(Y* p, D d) + : owner(p + ? new detail::PtrOwnerImpl(p, d) + : NULL), + stored(p) +{} + +template +Ptr::Ptr(const Ptr& o) : owner(o.owner), stored(o.stored) +{ + if (owner) owner->incRef(); +} + +template +template +Ptr::Ptr(const Ptr& o) : owner(o.owner), stored(o.stored) +{ + if (owner) owner->incRef(); +} + +template +template +Ptr::Ptr(const Ptr& o, T* p) : owner(o.owner), stored(p) +{ + if (owner) owner->incRef(); +} + +template +Ptr::~Ptr() +{ + release(); +} + +template +Ptr& Ptr::operator = (const Ptr& o) +{ + Ptr(o).swap(*this); + return *this; +} + +template +template +Ptr& Ptr::operator = (const Ptr& o) +{ + Ptr(o).swap(*this); + return *this; +} + +template +void Ptr::release() +{ + if (owner) owner->decRef(); + owner = NULL; + stored = NULL; +} + +template +template +void Ptr::reset(Y* p) +{ + Ptr(p).swap(*this); +} + +template +template +void Ptr::reset(Y* p, D d) +{ + Ptr(p, d).swap(*this); +} + +template +void Ptr::swap(Ptr& o) +{ + std::swap(owner, o.owner); + std::swap(stored, o.stored); +} + +template +T* Ptr::get() const +{ + return stored; +} + +template +typename detail::RefOrVoid::type Ptr::operator * () const +{ + return *stored; +} + +template +T* Ptr::operator -> () const +{ + return stored; +} + +template +Ptr::operator T* () const +{ + return stored; +} + + +template +bool Ptr::empty() const +{ + return !stored; +} + +template +template +Ptr Ptr::staticCast() const +{ + return Ptr(*this, static_cast(stored)); +} + +template +template +Ptr Ptr::constCast() const +{ + return Ptr(*this, const_cast(stored)); +} + +template +template +Ptr Ptr::dynamicCast() const +{ + return Ptr(*this, dynamic_cast(stored)); +} + +#ifdef CV_CXX_MOVE_SEMANTICS + +template +Ptr::Ptr(Ptr&& o) : owner(o.owner), stored(o.stored) +{ + o.owner = NULL; + o.stored = NULL; +} + +template +Ptr& Ptr::operator = (Ptr&& o) +{ + if (this == &o) + return *this; + + release(); + owner = o.owner; + stored = o.stored; + o.owner = NULL; + o.stored = NULL; + return *this; +} + +#endif + + +template +void swap(Ptr& ptr1, Ptr& ptr2){ + ptr1.swap(ptr2); +} + +template +bool operator == (const Ptr& ptr1, const Ptr& ptr2) +{ + return ptr1.get() == ptr2.get(); +} + +template +bool operator != (const Ptr& ptr1, const Ptr& ptr2) +{ + return ptr1.get() != ptr2.get(); +} + +template +Ptr makePtr() +{ + return Ptr(new T()); +} + +template +Ptr makePtr(const A1& a1) +{ + return Ptr(new T(a1)); +} + +template +Ptr makePtr(const A1& a1, const A2& a2) +{ + return Ptr(new T(a1, a2)); +} + +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3) +{ + return Ptr(new T(a1, a2, a3)); +} + +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4) +{ + return Ptr(new T(a1, a2, a3, a4)); +} + +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5) +{ + return Ptr(new T(a1, a2, a3, a4, a5)); +} + +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6) +{ + return Ptr(new T(a1, a2, a3, a4, a5, a6)); +} + +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7) +{ + return Ptr(new T(a1, a2, a3, a4, a5, a6, a7)); +} + +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8) +{ + return Ptr(new T(a1, a2, a3, a4, a5, a6, a7, a8)); +} + +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8, const A9& a9) +{ + return Ptr(new T(a1, a2, a3, a4, a5, a6, a7, a8, a9)); +} + +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8, const A9& a9, const A10& a10) +{ + return Ptr(new T(a1, a2, a3, a4, a5, a6, a7, a8, a9, a10)); +} + +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8, const A9& a9, const A10& a10, const A11& a11) +{ + return Ptr(new T(a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11)); +} + +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8, const A9& a9, const A10& a10, const A11& a11, const A12& a12) +{ + return Ptr(new T(a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12)); +} +} // namespace cv + +//! @endcond + +#endif // OPENCV_CORE_PTR_INL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/saturate.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/saturate.hpp new file mode 100644 index 0000000000000000000000000000000000000000..118599f8f97e1e3e5049857f23e3593beaf3d4b0 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/saturate.hpp @@ -0,0 +1,165 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2014, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_SATURATE_HPP +#define OPENCV_CORE_SATURATE_HPP + +#include "opencv2/core/cvdef.h" +#include "opencv2/core/fast_math.hpp" + +namespace cv +{ + +//! @addtogroup core_utils +//! @{ + +/////////////// saturate_cast (used in image & signal processing) /////////////////// + +/** @brief Template function for accurate conversion from one primitive type to another. + + The function saturate_cast resembles the standard C++ cast operations, such as static_cast\() + and others. It perform an efficient and accurate conversion from one primitive type to another + (see the introduction chapter). saturate in the name means that when the input value v is out of the + range of the target type, the result is not formed just by taking low bits of the input, but instead + the value is clipped. For example: + @code + uchar a = saturate_cast(-100); // a = 0 (UCHAR_MIN) + short b = saturate_cast(33333.33333); // b = 32767 (SHRT_MAX) + @endcode + Such clipping is done when the target type is unsigned char , signed char , unsigned short or + signed short . For 32-bit integers, no clipping is done. + + When the parameter is a floating-point value and the target type is an integer (8-, 16- or 32-bit), + the floating-point value is first rounded to the nearest integer and then clipped if needed (when + the target type is 8- or 16-bit). + + This operation is used in the simplest or most complex image processing functions in OpenCV. + + @param v Function parameter. + @sa add, subtract, multiply, divide, Mat::convertTo + */ +template static inline _Tp saturate_cast(uchar v) { return _Tp(v); } +/** @overload */ +template static inline _Tp saturate_cast(schar v) { return _Tp(v); } +/** @overload */ +template static inline _Tp saturate_cast(ushort v) { return _Tp(v); } +/** @overload */ +template static inline _Tp saturate_cast(short v) { return _Tp(v); } +/** @overload */ +template static inline _Tp saturate_cast(unsigned v) { return _Tp(v); } +/** @overload */ +template static inline _Tp saturate_cast(int v) { return _Tp(v); } +/** @overload */ +template static inline _Tp saturate_cast(float v) { return _Tp(v); } +/** @overload */ +template static inline _Tp saturate_cast(double v) { return _Tp(v); } +/** @overload */ +template static inline _Tp saturate_cast(int64 v) { return _Tp(v); } +/** @overload */ +template static inline _Tp saturate_cast(uint64 v) { return _Tp(v); } + +template<> inline uchar saturate_cast(schar v) { return (uchar)std::max((int)v, 0); } +template<> inline uchar saturate_cast(ushort v) { return (uchar)std::min((unsigned)v, (unsigned)UCHAR_MAX); } +template<> inline uchar saturate_cast(int v) { return (uchar)((unsigned)v <= UCHAR_MAX ? v : v > 0 ? UCHAR_MAX : 0); } +template<> inline uchar saturate_cast(short v) { return saturate_cast((int)v); } +template<> inline uchar saturate_cast(unsigned v) { return (uchar)std::min(v, (unsigned)UCHAR_MAX); } +template<> inline uchar saturate_cast(float v) { int iv = cvRound(v); return saturate_cast(iv); } +template<> inline uchar saturate_cast(double v) { int iv = cvRound(v); return saturate_cast(iv); } +template<> inline uchar saturate_cast(int64 v) { return (uchar)((uint64)v <= (uint64)UCHAR_MAX ? v : v > 0 ? UCHAR_MAX : 0); } +template<> inline uchar saturate_cast(uint64 v) { return (uchar)std::min(v, (uint64)UCHAR_MAX); } + +template<> inline schar saturate_cast(uchar v) { return (schar)std::min((int)v, SCHAR_MAX); } +template<> inline schar saturate_cast(ushort v) { return (schar)std::min((unsigned)v, (unsigned)SCHAR_MAX); } +template<> inline schar saturate_cast(int v) { return (schar)((unsigned)(v-SCHAR_MIN) <= (unsigned)UCHAR_MAX ? v : v > 0 ? SCHAR_MAX : SCHAR_MIN); } +template<> inline schar saturate_cast(short v) { return saturate_cast((int)v); } +template<> inline schar saturate_cast(unsigned v) { return (schar)std::min(v, (unsigned)SCHAR_MAX); } +template<> inline schar saturate_cast(float v) { int iv = cvRound(v); return saturate_cast(iv); } +template<> inline schar saturate_cast(double v) { int iv = cvRound(v); return saturate_cast(iv); } +template<> inline schar saturate_cast(int64 v) { return (schar)((uint64)((int64)v-SCHAR_MIN) <= (uint64)UCHAR_MAX ? v : v > 0 ? SCHAR_MAX : SCHAR_MIN); } +template<> inline schar saturate_cast(uint64 v) { return (schar)std::min(v, (uint64)SCHAR_MAX); } + +template<> inline ushort saturate_cast(schar v) { return (ushort)std::max((int)v, 0); } +template<> inline ushort saturate_cast(short v) { return (ushort)std::max((int)v, 0); } +template<> inline ushort saturate_cast(int v) { return (ushort)((unsigned)v <= (unsigned)USHRT_MAX ? v : v > 0 ? USHRT_MAX : 0); } +template<> inline ushort saturate_cast(unsigned v) { return (ushort)std::min(v, (unsigned)USHRT_MAX); } +template<> inline ushort saturate_cast(float v) { int iv = cvRound(v); return saturate_cast(iv); } +template<> inline ushort saturate_cast(double v) { int iv = cvRound(v); return saturate_cast(iv); } +template<> inline ushort saturate_cast(int64 v) { return (ushort)((uint64)v <= (uint64)USHRT_MAX ? v : v > 0 ? USHRT_MAX : 0); } +template<> inline ushort saturate_cast(uint64 v) { return (ushort)std::min(v, (uint64)USHRT_MAX); } + +template<> inline short saturate_cast(ushort v) { return (short)std::min((int)v, SHRT_MAX); } +template<> inline short saturate_cast(int v) { return (short)((unsigned)(v - SHRT_MIN) <= (unsigned)USHRT_MAX ? v : v > 0 ? SHRT_MAX : SHRT_MIN); } +template<> inline short saturate_cast(unsigned v) { return (short)std::min(v, (unsigned)SHRT_MAX); } +template<> inline short saturate_cast(float v) { int iv = cvRound(v); return saturate_cast(iv); } +template<> inline short saturate_cast(double v) { int iv = cvRound(v); return saturate_cast(iv); } +template<> inline short saturate_cast(int64 v) { return (short)((uint64)((int64)v - SHRT_MIN) <= (uint64)USHRT_MAX ? v : v > 0 ? SHRT_MAX : SHRT_MIN); } +template<> inline short saturate_cast(uint64 v) { return (short)std::min(v, (uint64)SHRT_MAX); } + +template<> inline int saturate_cast(unsigned v) { return (int)std::min(v, (unsigned)INT_MAX); } +template<> inline int saturate_cast(int64 v) { return (int)((uint64)(v - INT_MIN) <= (uint64)UINT_MAX ? v : v > 0 ? INT_MAX : INT_MIN); } +template<> inline int saturate_cast(uint64 v) { return (int)std::min(v, (uint64)INT_MAX); } +template<> inline int saturate_cast(float v) { return cvRound(v); } +template<> inline int saturate_cast(double v) { return cvRound(v); } + +template<> inline unsigned saturate_cast(schar v) { return (unsigned)std::max(v, (schar)0); } +template<> inline unsigned saturate_cast(short v) { return (unsigned)std::max(v, (short)0); } +template<> inline unsigned saturate_cast(int v) { return (unsigned)std::max(v, (int)0); } +template<> inline unsigned saturate_cast(int64 v) { return (unsigned)((uint64)v <= (uint64)UINT_MAX ? v : v > 0 ? UINT_MAX : 0); } +template<> inline unsigned saturate_cast(uint64 v) { return (unsigned)std::min(v, (uint64)UINT_MAX); } +// we intentionally do not clip negative numbers, to make -1 become 0xffffffff etc. +template<> inline unsigned saturate_cast(float v) { return static_cast(cvRound(v)); } +template<> inline unsigned saturate_cast(double v) { return static_cast(cvRound(v)); } + +template<> inline uint64 saturate_cast(schar v) { return (uint64)std::max(v, (schar)0); } +template<> inline uint64 saturate_cast(short v) { return (uint64)std::max(v, (short)0); } +template<> inline uint64 saturate_cast(int v) { return (uint64)std::max(v, (int)0); } +template<> inline uint64 saturate_cast(int64 v) { return (uint64)std::max(v, (int64)0); } + +template<> inline int64 saturate_cast(uint64 v) { return (int64)std::min(v, (uint64)LLONG_MAX); } + +//! @} + +} // cv + +#endif // OPENCV_CORE_SATURATE_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/softfloat.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/softfloat.hpp new file mode 100644 index 0000000000000000000000000000000000000000..547098038d8349ce3a23f72829875030b44a02e6 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/softfloat.hpp @@ -0,0 +1,514 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html + +// This file is based on files from package issued with the following license: + +/*============================================================================ + +This C header file is part of the SoftFloat IEEE Floating-Point Arithmetic +Package, Release 3c, by John R. Hauser. + +Copyright 2011, 2012, 2013, 2014, 2015, 2016, 2017 The Regents of the +University of California. All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + + 1. Redistributions of source code must retain the above copyright notice, + this list of conditions, and the following disclaimer. + + 2. Redistributions in binary form must reproduce the above copyright notice, + this list of conditions, and the following disclaimer in the documentation + and/or other materials provided with the distribution. + + 3. Neither the name of the University nor the names of its contributors may + be used to endorse or promote products derived from this software without + specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE REGENTS AND CONTRIBUTORS "AS IS", AND ANY +EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE, ARE +DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR ANY +DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND +ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +=============================================================================*/ + +#pragma once +#ifndef softfloat_h +#define softfloat_h 1 + +#include "cvdef.h" + +namespace cv +{ + +/** @addtogroup core_utils_softfloat + + [SoftFloat](http://www.jhauser.us/arithmetic/SoftFloat.html) is a software implementation + of floating-point calculations according to IEEE 754 standard. + All calculations are done in integers, that's why they are machine-independent and bit-exact. + This library can be useful in accuracy-critical parts like look-up tables generation, tests, etc. + OpenCV contains a subset of SoftFloat partially rewritten to C++. + + ### Types + + There are two basic types: @ref softfloat and @ref softdouble. + These types are binary compatible with float and double types respectively + and support conversions to/from them. + Other types from original SoftFloat library like fp16 or fp128 were thrown away + as well as quiet/signaling NaN support, on-the-fly rounding mode switch + and exception flags (though exceptions can be implemented in the future). + + ### Operations + + Both types support the following: + - Construction from signed and unsigned 32-bit and 64 integers, + float/double or raw binary representation + - Conversions between each other, to float or double and to int + using @ref cvRound, @ref cvTrunc, @ref cvFloor, @ref cvCeil or a bunch of + saturate_cast functions + - Add, subtract, multiply, divide, remainder, square root, FMA with absolute precision + - Comparison operations + - Explicit sign, exponent and significand manipulation through get/set methods, + number state indicators (isInf, isNan, isSubnormal) + - Type-specific constants like eps, minimum/maximum value, best pi approximation, etc. + - min(), max(), abs(), exp(), log() and pow() functions + +*/ +//! @{ + +struct softfloat; +struct softdouble; + +struct CV_EXPORTS softfloat +{ +public: + /** @brief Default constructor */ + softfloat() { v = 0; } + /** @brief Copy constructor */ + softfloat( const softfloat& c) { v = c.v; } + /** @brief Assign constructor */ + softfloat& operator=( const softfloat& c ) + { + if(&c != this) v = c.v; + return *this; + } + /** @brief Construct from raw + + Builds new value from raw binary representation + */ + static const softfloat fromRaw( const uint32_t a ) { softfloat x; x.v = a; return x; } + + /** @brief Construct from integer */ + explicit softfloat( const uint32_t ); + explicit softfloat( const uint64_t ); + explicit softfloat( const int32_t ); + explicit softfloat( const int64_t ); + +#ifdef CV_INT32_T_IS_LONG_INT + // for platforms with int32_t = long int + explicit softfloat( const int a ) { *this = softfloat(static_cast(a)); } +#endif + + /** @brief Construct from float */ + explicit softfloat( const float a ) { Cv32suf s; s.f = a; v = s.u; } + + /** @brief Type casts */ + operator softdouble() const; + operator float() const { Cv32suf s; s.u = v; return s.f; } + + /** @brief Basic arithmetics */ + softfloat operator + (const softfloat&) const; + softfloat operator - (const softfloat&) const; + softfloat operator * (const softfloat&) const; + softfloat operator / (const softfloat&) const; + softfloat operator - () const { softfloat x; x.v = v ^ (1U << 31); return x; } + + /** @brief Remainder operator + + A quote from original SoftFloat manual: + + > The IEEE Standard remainder operation computes the value + > a - n * b, where n is the integer closest to a / b. + > If a / b is exactly halfway between two integers, n is the even integer + > closest to a / b. The IEEE Standard’s remainder operation is always exact and so requires no rounding. + > Depending on the relative magnitudes of the operands, the remainder functions + > can take considerably longer to execute than the other SoftFloat functions. + > This is an inherent characteristic of the remainder operation itself and is not a flaw + > in the SoftFloat implementation. + */ + softfloat operator % (const softfloat&) const; + + softfloat& operator += (const softfloat& a) { *this = *this + a; return *this; } + softfloat& operator -= (const softfloat& a) { *this = *this - a; return *this; } + softfloat& operator *= (const softfloat& a) { *this = *this * a; return *this; } + softfloat& operator /= (const softfloat& a) { *this = *this / a; return *this; } + softfloat& operator %= (const softfloat& a) { *this = *this % a; return *this; } + + /** @brief Comparison operations + + - Any operation with NaN produces false + + The only exception is when x is NaN: x != y for any y. + - Positive and negative zeros are equal + */ + bool operator == ( const softfloat& ) const; + bool operator != ( const softfloat& ) const; + bool operator > ( const softfloat& ) const; + bool operator >= ( const softfloat& ) const; + bool operator < ( const softfloat& ) const; + bool operator <= ( const softfloat& ) const; + + /** @brief NaN state indicator */ + inline bool isNaN() const { return (v & 0x7fffffff) > 0x7f800000; } + /** @brief Inf state indicator */ + inline bool isInf() const { return (v & 0x7fffffff) == 0x7f800000; } + /** @brief Subnormal number indicator */ + inline bool isSubnormal() const { return ((v >> 23) & 0xFF) == 0; } + + /** @brief Get sign bit */ + inline bool getSign() const { return (v >> 31) != 0; } + /** @brief Construct a copy with new sign bit */ + inline softfloat setSign(bool sign) const { softfloat x; x.v = (v & ((1U << 31) - 1)) | ((uint32_t)sign << 31); return x; } + /** @brief Get 0-based exponent */ + inline int getExp() const { return ((v >> 23) & 0xFF) - 127; } + /** @brief Construct a copy with new 0-based exponent */ + inline softfloat setExp(int e) const { softfloat x; x.v = (v & 0x807fffff) | (((e + 127) & 0xFF) << 23 ); return x; } + + /** @brief Get a fraction part + + Returns a number 1 <= x < 2 with the same significand + */ + inline softfloat getFrac() const + { + uint_fast32_t vv = (v & 0x007fffff) | (127 << 23); + return softfloat::fromRaw(vv); + } + /** @brief Construct a copy with provided significand + + Constructs a copy of a number with significand taken from parameter + */ + inline softfloat setFrac(const softfloat& s) const + { + softfloat x; + x.v = (v & 0xff800000) | (s.v & 0x007fffff); + return x; + } + + /** @brief Zero constant */ + static softfloat zero() { return softfloat::fromRaw( 0 ); } + /** @brief Positive infinity constant */ + static softfloat inf() { return softfloat::fromRaw( 0xFF << 23 ); } + /** @brief Default NaN constant */ + static softfloat nan() { return softfloat::fromRaw( 0x7fffffff ); } + /** @brief One constant */ + static softfloat one() { return softfloat::fromRaw( 127 << 23 ); } + /** @brief Smallest normalized value */ + static softfloat min() { return softfloat::fromRaw( 0x01 << 23 ); } + /** @brief Difference between 1 and next representable value */ + static softfloat eps() { return softfloat::fromRaw( (127 - 23) << 23 ); } + /** @brief Biggest finite value */ + static softfloat max() { return softfloat::fromRaw( (0xFF << 23) - 1 ); } + /** @brief Correct pi approximation */ + static softfloat pi() { return softfloat::fromRaw( 0x40490fdb ); } + + uint32_t v; +}; + +/*---------------------------------------------------------------------------- +*----------------------------------------------------------------------------*/ + +struct CV_EXPORTS softdouble +{ +public: + /** @brief Default constructor */ + softdouble() : v(0) { } + /** @brief Copy constructor */ + softdouble( const softdouble& c) { v = c.v; } + /** @brief Assign constructor */ + softdouble& operator=( const softdouble& c ) + { + if(&c != this) v = c.v; + return *this; + } + /** @brief Construct from raw + + Builds new value from raw binary representation + */ + static softdouble fromRaw( const uint64_t a ) { softdouble x; x.v = a; return x; } + + /** @brief Construct from integer */ + explicit softdouble( const uint32_t ); + explicit softdouble( const uint64_t ); + explicit softdouble( const int32_t ); + explicit softdouble( const int64_t ); + +#ifdef CV_INT32_T_IS_LONG_INT + // for platforms with int32_t = long int + explicit softdouble( const int a ) { *this = softdouble(static_cast(a)); } +#endif + + /** @brief Construct from double */ + explicit softdouble( const double a ) { Cv64suf s; s.f = a; v = s.u; } + + /** @brief Type casts */ + operator softfloat() const; + operator double() const { Cv64suf s; s.u = v; return s.f; } + + /** @brief Basic arithmetics */ + softdouble operator + (const softdouble&) const; + softdouble operator - (const softdouble&) const; + softdouble operator * (const softdouble&) const; + softdouble operator / (const softdouble&) const; + softdouble operator - () const { softdouble x; x.v = v ^ (1ULL << 63); return x; } + + /** @brief Remainder operator + + A quote from original SoftFloat manual: + + > The IEEE Standard remainder operation computes the value + > a - n * b, where n is the integer closest to a / b. + > If a / b is exactly halfway between two integers, n is the even integer + > closest to a / b. The IEEE Standard’s remainder operation is always exact and so requires no rounding. + > Depending on the relative magnitudes of the operands, the remainder functions + > can take considerably longer to execute than the other SoftFloat functions. + > This is an inherent characteristic of the remainder operation itself and is not a flaw + > in the SoftFloat implementation. + */ + softdouble operator % (const softdouble&) const; + + softdouble& operator += (const softdouble& a) { *this = *this + a; return *this; } + softdouble& operator -= (const softdouble& a) { *this = *this - a; return *this; } + softdouble& operator *= (const softdouble& a) { *this = *this * a; return *this; } + softdouble& operator /= (const softdouble& a) { *this = *this / a; return *this; } + softdouble& operator %= (const softdouble& a) { *this = *this % a; return *this; } + + /** @brief Comparison operations + + - Any operation with NaN produces false + + The only exception is when x is NaN: x != y for any y. + - Positive and negative zeros are equal + */ + bool operator == ( const softdouble& ) const; + bool operator != ( const softdouble& ) const; + bool operator > ( const softdouble& ) const; + bool operator >= ( const softdouble& ) const; + bool operator < ( const softdouble& ) const; + bool operator <= ( const softdouble& ) const; + + /** @brief NaN state indicator */ + inline bool isNaN() const { return (v & 0x7fffffffffffffff) > 0x7ff0000000000000; } + /** @brief Inf state indicator */ + inline bool isInf() const { return (v & 0x7fffffffffffffff) == 0x7ff0000000000000; } + /** @brief Subnormal number indicator */ + inline bool isSubnormal() const { return ((v >> 52) & 0x7FF) == 0; } + + /** @brief Get sign bit */ + inline bool getSign() const { return (v >> 63) != 0; } + /** @brief Construct a copy with new sign bit */ + softdouble setSign(bool sign) const { softdouble x; x.v = (v & ((1ULL << 63) - 1)) | ((uint_fast64_t)(sign) << 63); return x; } + /** @brief Get 0-based exponent */ + inline int getExp() const { return ((v >> 52) & 0x7FF) - 1023; } + /** @brief Construct a copy with new 0-based exponent */ + inline softdouble setExp(int e) const + { + softdouble x; + x.v = (v & 0x800FFFFFFFFFFFFF) | ((uint_fast64_t)((e + 1023) & 0x7FF) << 52); + return x; + } + + /** @brief Get a fraction part + + Returns a number 1 <= x < 2 with the same significand + */ + inline softdouble getFrac() const + { + uint_fast64_t vv = (v & 0x000FFFFFFFFFFFFF) | ((uint_fast64_t)(1023) << 52); + return softdouble::fromRaw(vv); + } + /** @brief Construct a copy with provided significand + + Constructs a copy of a number with significand taken from parameter + */ + inline softdouble setFrac(const softdouble& s) const + { + softdouble x; + x.v = (v & 0xFFF0000000000000) | (s.v & 0x000FFFFFFFFFFFFF); + return x; + } + + /** @brief Zero constant */ + static softdouble zero() { return softdouble::fromRaw( 0 ); } + /** @brief Positive infinity constant */ + static softdouble inf() { return softdouble::fromRaw( (uint_fast64_t)(0x7FF) << 52 ); } + /** @brief Default NaN constant */ + static softdouble nan() { return softdouble::fromRaw( CV_BIG_INT(0x7FFFFFFFFFFFFFFF) ); } + /** @brief One constant */ + static softdouble one() { return softdouble::fromRaw( (uint_fast64_t)( 1023) << 52 ); } + /** @brief Smallest normalized value */ + static softdouble min() { return softdouble::fromRaw( (uint_fast64_t)( 0x01) << 52 ); } + /** @brief Difference between 1 and next representable value */ + static softdouble eps() { return softdouble::fromRaw( (uint_fast64_t)( 1023 - 52 ) << 52 ); } + /** @brief Biggest finite value */ + static softdouble max() { return softdouble::fromRaw( ((uint_fast64_t)(0x7FF) << 52) - 1 ); } + /** @brief Correct pi approximation */ + static softdouble pi() { return softdouble::fromRaw( CV_BIG_INT(0x400921FB54442D18) ); } + + uint64_t v; +}; + +/*---------------------------------------------------------------------------- +*----------------------------------------------------------------------------*/ + +/** @brief Fused Multiplication and Addition + +Computes (a*b)+c with single rounding +*/ +CV_EXPORTS softfloat mulAdd( const softfloat& a, const softfloat& b, const softfloat & c); +CV_EXPORTS softdouble mulAdd( const softdouble& a, const softdouble& b, const softdouble& c); + +/** @brief Square root */ +CV_EXPORTS softfloat sqrt( const softfloat& a ); +CV_EXPORTS softdouble sqrt( const softdouble& a ); +} + +/*---------------------------------------------------------------------------- +| Ported from OpenCV and added for usability +*----------------------------------------------------------------------------*/ + +/** @brief Truncates number to integer with minimum magnitude */ +CV_EXPORTS int cvTrunc(const cv::softfloat& a); +CV_EXPORTS int cvTrunc(const cv::softdouble& a); + +/** @brief Rounds a number to nearest even integer */ +CV_EXPORTS int cvRound(const cv::softfloat& a); +CV_EXPORTS int cvRound(const cv::softdouble& a); + +/** @brief Rounds a number to nearest even long long integer */ +CV_EXPORTS int64_t cvRound64(const cv::softdouble& a); + +/** @brief Rounds a number down to integer */ +CV_EXPORTS int cvFloor(const cv::softfloat& a); +CV_EXPORTS int cvFloor(const cv::softdouble& a); + +/** @brief Rounds number up to integer */ +CV_EXPORTS int cvCeil(const cv::softfloat& a); +CV_EXPORTS int cvCeil(const cv::softdouble& a); + +namespace cv +{ +/** @brief Saturate casts */ +template static inline _Tp saturate_cast(softfloat a) { return _Tp(a); } +template static inline _Tp saturate_cast(softdouble a) { return _Tp(a); } + +template<> inline uchar saturate_cast(softfloat a) { return (uchar)std::max(std::min(cvRound(a), (int)UCHAR_MAX), 0); } +template<> inline uchar saturate_cast(softdouble a) { return (uchar)std::max(std::min(cvRound(a), (int)UCHAR_MAX), 0); } + +template<> inline schar saturate_cast(softfloat a) { return (schar)std::min(std::max(cvRound(a), (int)SCHAR_MIN), (int)SCHAR_MAX); } +template<> inline schar saturate_cast(softdouble a) { return (schar)std::min(std::max(cvRound(a), (int)SCHAR_MIN), (int)SCHAR_MAX); } + +template<> inline ushort saturate_cast(softfloat a) { return (ushort)std::max(std::min(cvRound(a), (int)USHRT_MAX), 0); } +template<> inline ushort saturate_cast(softdouble a) { return (ushort)std::max(std::min(cvRound(a), (int)USHRT_MAX), 0); } + +template<> inline short saturate_cast(softfloat a) { return (short)std::min(std::max(cvRound(a), (int)SHRT_MIN), (int)SHRT_MAX); } +template<> inline short saturate_cast(softdouble a) { return (short)std::min(std::max(cvRound(a), (int)SHRT_MIN), (int)SHRT_MAX); } + +template<> inline int saturate_cast(softfloat a) { return cvRound(a); } +template<> inline int saturate_cast(softdouble a) { return cvRound(a); } + +template<> inline int64_t saturate_cast(softfloat a) { return cvRound(a); } +template<> inline int64_t saturate_cast(softdouble a) { return cvRound64(a); } + +/** @brief Saturate cast to unsigned integer and unsigned long long integer +We intentionally do not clip negative numbers, to make -1 become 0xffffffff etc. +*/ +template<> inline unsigned saturate_cast(softfloat a) { return cvRound(a); } +template<> inline unsigned saturate_cast(softdouble a) { return cvRound(a); } + +template<> inline uint64_t saturate_cast(softfloat a) { return cvRound(a); } +template<> inline uint64_t saturate_cast(softdouble a) { return cvRound64(a); } + +/** @brief Min and Max functions */ +inline softfloat min(const softfloat& a, const softfloat& b) { return (a > b) ? b : a; } +inline softdouble min(const softdouble& a, const softdouble& b) { return (a > b) ? b : a; } + +inline softfloat max(const softfloat& a, const softfloat& b) { return (a > b) ? a : b; } +inline softdouble max(const softdouble& a, const softdouble& b) { return (a > b) ? a : b; } + +/** @brief Absolute value */ +inline softfloat abs( softfloat a) { softfloat x; x.v = a.v & ((1U << 31) - 1); return x; } +inline softdouble abs( softdouble a) { softdouble x; x.v = a.v & ((1ULL << 63) - 1); return x; } + +/** @brief Exponent + +Special cases: +- exp(NaN) is NaN +- exp(-Inf) == 0 +- exp(+Inf) == +Inf +*/ +CV_EXPORTS softfloat exp( const softfloat& a); +CV_EXPORTS softdouble exp( const softdouble& a); + +/** @brief Natural logarithm + +Special cases: +- log(NaN), log(x < 0) are NaN +- log(0) == -Inf +*/ +CV_EXPORTS softfloat log( const softfloat& a ); +CV_EXPORTS softdouble log( const softdouble& a ); + +/** @brief Raising to the power + +Special cases: +- x**NaN is NaN for any x +- ( |x| == 1 )**Inf is NaN +- ( |x| > 1 )**+Inf or ( |x| < 1 )**-Inf is +Inf +- ( |x| > 1 )**-Inf or ( |x| < 1 )**+Inf is 0 +- x ** 0 == 1 for any x +- x ** 1 == 1 for any x +- NaN ** y is NaN for any other y +- Inf**(y < 0) == 0 +- Inf ** y is +Inf for any other y +- (x < 0)**y is NaN for any other y if x can't be correctly rounded to integer +- 0 ** 0 == 1 +- 0 ** (y < 0) is +Inf +- 0 ** (y > 0) is 0 +*/ +CV_EXPORTS softfloat pow( const softfloat& a, const softfloat& b); +CV_EXPORTS softdouble pow( const softdouble& a, const softdouble& b); + +/** @brief Cube root + +Special cases: +- cbrt(NaN) is NaN +- cbrt(+/-Inf) is +/-Inf +*/ +CV_EXPORTS softfloat cbrt( const softfloat& a ); + +/** @brief Sine + +Special cases: +- sin(Inf) or sin(NaN) is NaN +- sin(x) == x when sin(x) is close to zero +*/ +CV_EXPORTS softdouble sin( const softdouble& a ); + +/** @brief Cosine + * +Special cases: +- cos(Inf) or cos(NaN) is NaN +- cos(x) == +/- 1 when cos(x) is close to +/- 1 +*/ +CV_EXPORTS softdouble cos( const softdouble& a ); + +} + +//! @} + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/sse_utils.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/sse_utils.hpp new file mode 100644 index 0000000000000000000000000000000000000000..0906583ea433e289e883270d81f8641985cc3ebb --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/sse_utils.hpp @@ -0,0 +1,652 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_SSE_UTILS_HPP +#define OPENCV_CORE_SSE_UTILS_HPP + +#ifndef __cplusplus +# error sse_utils.hpp header must be compiled as C++ +#endif + +#include "opencv2/core/cvdef.h" + +//! @addtogroup core_utils_sse +//! @{ + +#if CV_SSE2 + +inline void _mm_deinterleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1) +{ + __m128i layer1_chunk0 = _mm_unpacklo_epi8(v_r0, v_g0); + __m128i layer1_chunk1 = _mm_unpackhi_epi8(v_r0, v_g0); + __m128i layer1_chunk2 = _mm_unpacklo_epi8(v_r1, v_g1); + __m128i layer1_chunk3 = _mm_unpackhi_epi8(v_r1, v_g1); + + __m128i layer2_chunk0 = _mm_unpacklo_epi8(layer1_chunk0, layer1_chunk2); + __m128i layer2_chunk1 = _mm_unpackhi_epi8(layer1_chunk0, layer1_chunk2); + __m128i layer2_chunk2 = _mm_unpacklo_epi8(layer1_chunk1, layer1_chunk3); + __m128i layer2_chunk3 = _mm_unpackhi_epi8(layer1_chunk1, layer1_chunk3); + + __m128i layer3_chunk0 = _mm_unpacklo_epi8(layer2_chunk0, layer2_chunk2); + __m128i layer3_chunk1 = _mm_unpackhi_epi8(layer2_chunk0, layer2_chunk2); + __m128i layer3_chunk2 = _mm_unpacklo_epi8(layer2_chunk1, layer2_chunk3); + __m128i layer3_chunk3 = _mm_unpackhi_epi8(layer2_chunk1, layer2_chunk3); + + __m128i layer4_chunk0 = _mm_unpacklo_epi8(layer3_chunk0, layer3_chunk2); + __m128i layer4_chunk1 = _mm_unpackhi_epi8(layer3_chunk0, layer3_chunk2); + __m128i layer4_chunk2 = _mm_unpacklo_epi8(layer3_chunk1, layer3_chunk3); + __m128i layer4_chunk3 = _mm_unpackhi_epi8(layer3_chunk1, layer3_chunk3); + + v_r0 = _mm_unpacklo_epi8(layer4_chunk0, layer4_chunk2); + v_r1 = _mm_unpackhi_epi8(layer4_chunk0, layer4_chunk2); + v_g0 = _mm_unpacklo_epi8(layer4_chunk1, layer4_chunk3); + v_g1 = _mm_unpackhi_epi8(layer4_chunk1, layer4_chunk3); +} + +inline void _mm_deinterleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, + __m128i & v_g1, __m128i & v_b0, __m128i & v_b1) +{ + __m128i layer1_chunk0 = _mm_unpacklo_epi8(v_r0, v_g1); + __m128i layer1_chunk1 = _mm_unpackhi_epi8(v_r0, v_g1); + __m128i layer1_chunk2 = _mm_unpacklo_epi8(v_r1, v_b0); + __m128i layer1_chunk3 = _mm_unpackhi_epi8(v_r1, v_b0); + __m128i layer1_chunk4 = _mm_unpacklo_epi8(v_g0, v_b1); + __m128i layer1_chunk5 = _mm_unpackhi_epi8(v_g0, v_b1); + + __m128i layer2_chunk0 = _mm_unpacklo_epi8(layer1_chunk0, layer1_chunk3); + __m128i layer2_chunk1 = _mm_unpackhi_epi8(layer1_chunk0, layer1_chunk3); + __m128i layer2_chunk2 = _mm_unpacklo_epi8(layer1_chunk1, layer1_chunk4); + __m128i layer2_chunk3 = _mm_unpackhi_epi8(layer1_chunk1, layer1_chunk4); + __m128i layer2_chunk4 = _mm_unpacklo_epi8(layer1_chunk2, layer1_chunk5); + __m128i layer2_chunk5 = _mm_unpackhi_epi8(layer1_chunk2, layer1_chunk5); + + __m128i layer3_chunk0 = _mm_unpacklo_epi8(layer2_chunk0, layer2_chunk3); + __m128i layer3_chunk1 = _mm_unpackhi_epi8(layer2_chunk0, layer2_chunk3); + __m128i layer3_chunk2 = _mm_unpacklo_epi8(layer2_chunk1, layer2_chunk4); + __m128i layer3_chunk3 = _mm_unpackhi_epi8(layer2_chunk1, layer2_chunk4); + __m128i layer3_chunk4 = _mm_unpacklo_epi8(layer2_chunk2, layer2_chunk5); + __m128i layer3_chunk5 = _mm_unpackhi_epi8(layer2_chunk2, layer2_chunk5); + + __m128i layer4_chunk0 = _mm_unpacklo_epi8(layer3_chunk0, layer3_chunk3); + __m128i layer4_chunk1 = _mm_unpackhi_epi8(layer3_chunk0, layer3_chunk3); + __m128i layer4_chunk2 = _mm_unpacklo_epi8(layer3_chunk1, layer3_chunk4); + __m128i layer4_chunk3 = _mm_unpackhi_epi8(layer3_chunk1, layer3_chunk4); + __m128i layer4_chunk4 = _mm_unpacklo_epi8(layer3_chunk2, layer3_chunk5); + __m128i layer4_chunk5 = _mm_unpackhi_epi8(layer3_chunk2, layer3_chunk5); + + v_r0 = _mm_unpacklo_epi8(layer4_chunk0, layer4_chunk3); + v_r1 = _mm_unpackhi_epi8(layer4_chunk0, layer4_chunk3); + v_g0 = _mm_unpacklo_epi8(layer4_chunk1, layer4_chunk4); + v_g1 = _mm_unpackhi_epi8(layer4_chunk1, layer4_chunk4); + v_b0 = _mm_unpacklo_epi8(layer4_chunk2, layer4_chunk5); + v_b1 = _mm_unpackhi_epi8(layer4_chunk2, layer4_chunk5); +} + +inline void _mm_deinterleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1, + __m128i & v_b0, __m128i & v_b1, __m128i & v_a0, __m128i & v_a1) +{ + __m128i layer1_chunk0 = _mm_unpacklo_epi8(v_r0, v_b0); + __m128i layer1_chunk1 = _mm_unpackhi_epi8(v_r0, v_b0); + __m128i layer1_chunk2 = _mm_unpacklo_epi8(v_r1, v_b1); + __m128i layer1_chunk3 = _mm_unpackhi_epi8(v_r1, v_b1); + __m128i layer1_chunk4 = _mm_unpacklo_epi8(v_g0, v_a0); + __m128i layer1_chunk5 = _mm_unpackhi_epi8(v_g0, v_a0); + __m128i layer1_chunk6 = _mm_unpacklo_epi8(v_g1, v_a1); + __m128i layer1_chunk7 = _mm_unpackhi_epi8(v_g1, v_a1); + + __m128i layer2_chunk0 = _mm_unpacklo_epi8(layer1_chunk0, layer1_chunk4); + __m128i layer2_chunk1 = _mm_unpackhi_epi8(layer1_chunk0, layer1_chunk4); + __m128i layer2_chunk2 = _mm_unpacklo_epi8(layer1_chunk1, layer1_chunk5); + __m128i layer2_chunk3 = _mm_unpackhi_epi8(layer1_chunk1, layer1_chunk5); + __m128i layer2_chunk4 = _mm_unpacklo_epi8(layer1_chunk2, layer1_chunk6); + __m128i layer2_chunk5 = _mm_unpackhi_epi8(layer1_chunk2, layer1_chunk6); + __m128i layer2_chunk6 = _mm_unpacklo_epi8(layer1_chunk3, layer1_chunk7); + __m128i layer2_chunk7 = _mm_unpackhi_epi8(layer1_chunk3, layer1_chunk7); + + __m128i layer3_chunk0 = _mm_unpacklo_epi8(layer2_chunk0, layer2_chunk4); + __m128i layer3_chunk1 = _mm_unpackhi_epi8(layer2_chunk0, layer2_chunk4); + __m128i layer3_chunk2 = _mm_unpacklo_epi8(layer2_chunk1, layer2_chunk5); + __m128i layer3_chunk3 = _mm_unpackhi_epi8(layer2_chunk1, layer2_chunk5); + __m128i layer3_chunk4 = _mm_unpacklo_epi8(layer2_chunk2, layer2_chunk6); + __m128i layer3_chunk5 = _mm_unpackhi_epi8(layer2_chunk2, layer2_chunk6); + __m128i layer3_chunk6 = _mm_unpacklo_epi8(layer2_chunk3, layer2_chunk7); + __m128i layer3_chunk7 = _mm_unpackhi_epi8(layer2_chunk3, layer2_chunk7); + + __m128i layer4_chunk0 = _mm_unpacklo_epi8(layer3_chunk0, layer3_chunk4); + __m128i layer4_chunk1 = _mm_unpackhi_epi8(layer3_chunk0, layer3_chunk4); + __m128i layer4_chunk2 = _mm_unpacklo_epi8(layer3_chunk1, layer3_chunk5); + __m128i layer4_chunk3 = _mm_unpackhi_epi8(layer3_chunk1, layer3_chunk5); + __m128i layer4_chunk4 = _mm_unpacklo_epi8(layer3_chunk2, layer3_chunk6); + __m128i layer4_chunk5 = _mm_unpackhi_epi8(layer3_chunk2, layer3_chunk6); + __m128i layer4_chunk6 = _mm_unpacklo_epi8(layer3_chunk3, layer3_chunk7); + __m128i layer4_chunk7 = _mm_unpackhi_epi8(layer3_chunk3, layer3_chunk7); + + v_r0 = _mm_unpacklo_epi8(layer4_chunk0, layer4_chunk4); + v_r1 = _mm_unpackhi_epi8(layer4_chunk0, layer4_chunk4); + v_g0 = _mm_unpacklo_epi8(layer4_chunk1, layer4_chunk5); + v_g1 = _mm_unpackhi_epi8(layer4_chunk1, layer4_chunk5); + v_b0 = _mm_unpacklo_epi8(layer4_chunk2, layer4_chunk6); + v_b1 = _mm_unpackhi_epi8(layer4_chunk2, layer4_chunk6); + v_a0 = _mm_unpacklo_epi8(layer4_chunk3, layer4_chunk7); + v_a1 = _mm_unpackhi_epi8(layer4_chunk3, layer4_chunk7); +} + +inline void _mm_interleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1) +{ + __m128i v_mask = _mm_set1_epi16(0x00ff); + + __m128i layer4_chunk0 = _mm_packus_epi16(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask)); + __m128i layer4_chunk2 = _mm_packus_epi16(_mm_srli_epi16(v_r0, 8), _mm_srli_epi16(v_r1, 8)); + __m128i layer4_chunk1 = _mm_packus_epi16(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask)); + __m128i layer4_chunk3 = _mm_packus_epi16(_mm_srli_epi16(v_g0, 8), _mm_srli_epi16(v_g1, 8)); + + __m128i layer3_chunk0 = _mm_packus_epi16(_mm_and_si128(layer4_chunk0, v_mask), _mm_and_si128(layer4_chunk1, v_mask)); + __m128i layer3_chunk2 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk0, 8), _mm_srli_epi16(layer4_chunk1, 8)); + __m128i layer3_chunk1 = _mm_packus_epi16(_mm_and_si128(layer4_chunk2, v_mask), _mm_and_si128(layer4_chunk3, v_mask)); + __m128i layer3_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk2, 8), _mm_srli_epi16(layer4_chunk3, 8)); + + __m128i layer2_chunk0 = _mm_packus_epi16(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask)); + __m128i layer2_chunk2 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk0, 8), _mm_srli_epi16(layer3_chunk1, 8)); + __m128i layer2_chunk1 = _mm_packus_epi16(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask)); + __m128i layer2_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk2, 8), _mm_srli_epi16(layer3_chunk3, 8)); + + __m128i layer1_chunk0 = _mm_packus_epi16(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask)); + __m128i layer1_chunk2 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk0, 8), _mm_srli_epi16(layer2_chunk1, 8)); + __m128i layer1_chunk1 = _mm_packus_epi16(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask)); + __m128i layer1_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk2, 8), _mm_srli_epi16(layer2_chunk3, 8)); + + v_r0 = _mm_packus_epi16(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask)); + v_g0 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk0, 8), _mm_srli_epi16(layer1_chunk1, 8)); + v_r1 = _mm_packus_epi16(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask)); + v_g1 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk2, 8), _mm_srli_epi16(layer1_chunk3, 8)); +} + +inline void _mm_interleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, + __m128i & v_g1, __m128i & v_b0, __m128i & v_b1) +{ + __m128i v_mask = _mm_set1_epi16(0x00ff); + + __m128i layer4_chunk0 = _mm_packus_epi16(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask)); + __m128i layer4_chunk3 = _mm_packus_epi16(_mm_srli_epi16(v_r0, 8), _mm_srli_epi16(v_r1, 8)); + __m128i layer4_chunk1 = _mm_packus_epi16(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask)); + __m128i layer4_chunk4 = _mm_packus_epi16(_mm_srli_epi16(v_g0, 8), _mm_srli_epi16(v_g1, 8)); + __m128i layer4_chunk2 = _mm_packus_epi16(_mm_and_si128(v_b0, v_mask), _mm_and_si128(v_b1, v_mask)); + __m128i layer4_chunk5 = _mm_packus_epi16(_mm_srli_epi16(v_b0, 8), _mm_srli_epi16(v_b1, 8)); + + __m128i layer3_chunk0 = _mm_packus_epi16(_mm_and_si128(layer4_chunk0, v_mask), _mm_and_si128(layer4_chunk1, v_mask)); + __m128i layer3_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk0, 8), _mm_srli_epi16(layer4_chunk1, 8)); + __m128i layer3_chunk1 = _mm_packus_epi16(_mm_and_si128(layer4_chunk2, v_mask), _mm_and_si128(layer4_chunk3, v_mask)); + __m128i layer3_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk2, 8), _mm_srli_epi16(layer4_chunk3, 8)); + __m128i layer3_chunk2 = _mm_packus_epi16(_mm_and_si128(layer4_chunk4, v_mask), _mm_and_si128(layer4_chunk5, v_mask)); + __m128i layer3_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk4, 8), _mm_srli_epi16(layer4_chunk5, 8)); + + __m128i layer2_chunk0 = _mm_packus_epi16(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask)); + __m128i layer2_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk0, 8), _mm_srli_epi16(layer3_chunk1, 8)); + __m128i layer2_chunk1 = _mm_packus_epi16(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask)); + __m128i layer2_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk2, 8), _mm_srli_epi16(layer3_chunk3, 8)); + __m128i layer2_chunk2 = _mm_packus_epi16(_mm_and_si128(layer3_chunk4, v_mask), _mm_and_si128(layer3_chunk5, v_mask)); + __m128i layer2_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk4, 8), _mm_srli_epi16(layer3_chunk5, 8)); + + __m128i layer1_chunk0 = _mm_packus_epi16(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask)); + __m128i layer1_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk0, 8), _mm_srli_epi16(layer2_chunk1, 8)); + __m128i layer1_chunk1 = _mm_packus_epi16(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask)); + __m128i layer1_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk2, 8), _mm_srli_epi16(layer2_chunk3, 8)); + __m128i layer1_chunk2 = _mm_packus_epi16(_mm_and_si128(layer2_chunk4, v_mask), _mm_and_si128(layer2_chunk5, v_mask)); + __m128i layer1_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk4, 8), _mm_srli_epi16(layer2_chunk5, 8)); + + v_r0 = _mm_packus_epi16(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask)); + v_g1 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk0, 8), _mm_srli_epi16(layer1_chunk1, 8)); + v_r1 = _mm_packus_epi16(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask)); + v_b0 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk2, 8), _mm_srli_epi16(layer1_chunk3, 8)); + v_g0 = _mm_packus_epi16(_mm_and_si128(layer1_chunk4, v_mask), _mm_and_si128(layer1_chunk5, v_mask)); + v_b1 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk4, 8), _mm_srli_epi16(layer1_chunk5, 8)); +} + +inline void _mm_interleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1, + __m128i & v_b0, __m128i & v_b1, __m128i & v_a0, __m128i & v_a1) +{ + __m128i v_mask = _mm_set1_epi16(0x00ff); + + __m128i layer4_chunk0 = _mm_packus_epi16(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask)); + __m128i layer4_chunk4 = _mm_packus_epi16(_mm_srli_epi16(v_r0, 8), _mm_srli_epi16(v_r1, 8)); + __m128i layer4_chunk1 = _mm_packus_epi16(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask)); + __m128i layer4_chunk5 = _mm_packus_epi16(_mm_srli_epi16(v_g0, 8), _mm_srli_epi16(v_g1, 8)); + __m128i layer4_chunk2 = _mm_packus_epi16(_mm_and_si128(v_b0, v_mask), _mm_and_si128(v_b1, v_mask)); + __m128i layer4_chunk6 = _mm_packus_epi16(_mm_srli_epi16(v_b0, 8), _mm_srli_epi16(v_b1, 8)); + __m128i layer4_chunk3 = _mm_packus_epi16(_mm_and_si128(v_a0, v_mask), _mm_and_si128(v_a1, v_mask)); + __m128i layer4_chunk7 = _mm_packus_epi16(_mm_srli_epi16(v_a0, 8), _mm_srli_epi16(v_a1, 8)); + + __m128i layer3_chunk0 = _mm_packus_epi16(_mm_and_si128(layer4_chunk0, v_mask), _mm_and_si128(layer4_chunk1, v_mask)); + __m128i layer3_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk0, 8), _mm_srli_epi16(layer4_chunk1, 8)); + __m128i layer3_chunk1 = _mm_packus_epi16(_mm_and_si128(layer4_chunk2, v_mask), _mm_and_si128(layer4_chunk3, v_mask)); + __m128i layer3_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk2, 8), _mm_srli_epi16(layer4_chunk3, 8)); + __m128i layer3_chunk2 = _mm_packus_epi16(_mm_and_si128(layer4_chunk4, v_mask), _mm_and_si128(layer4_chunk5, v_mask)); + __m128i layer3_chunk6 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk4, 8), _mm_srli_epi16(layer4_chunk5, 8)); + __m128i layer3_chunk3 = _mm_packus_epi16(_mm_and_si128(layer4_chunk6, v_mask), _mm_and_si128(layer4_chunk7, v_mask)); + __m128i layer3_chunk7 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk6, 8), _mm_srli_epi16(layer4_chunk7, 8)); + + __m128i layer2_chunk0 = _mm_packus_epi16(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask)); + __m128i layer2_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk0, 8), _mm_srli_epi16(layer3_chunk1, 8)); + __m128i layer2_chunk1 = _mm_packus_epi16(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask)); + __m128i layer2_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk2, 8), _mm_srli_epi16(layer3_chunk3, 8)); + __m128i layer2_chunk2 = _mm_packus_epi16(_mm_and_si128(layer3_chunk4, v_mask), _mm_and_si128(layer3_chunk5, v_mask)); + __m128i layer2_chunk6 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk4, 8), _mm_srli_epi16(layer3_chunk5, 8)); + __m128i layer2_chunk3 = _mm_packus_epi16(_mm_and_si128(layer3_chunk6, v_mask), _mm_and_si128(layer3_chunk7, v_mask)); + __m128i layer2_chunk7 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk6, 8), _mm_srli_epi16(layer3_chunk7, 8)); + + __m128i layer1_chunk0 = _mm_packus_epi16(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask)); + __m128i layer1_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk0, 8), _mm_srli_epi16(layer2_chunk1, 8)); + __m128i layer1_chunk1 = _mm_packus_epi16(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask)); + __m128i layer1_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk2, 8), _mm_srli_epi16(layer2_chunk3, 8)); + __m128i layer1_chunk2 = _mm_packus_epi16(_mm_and_si128(layer2_chunk4, v_mask), _mm_and_si128(layer2_chunk5, v_mask)); + __m128i layer1_chunk6 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk4, 8), _mm_srli_epi16(layer2_chunk5, 8)); + __m128i layer1_chunk3 = _mm_packus_epi16(_mm_and_si128(layer2_chunk6, v_mask), _mm_and_si128(layer2_chunk7, v_mask)); + __m128i layer1_chunk7 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk6, 8), _mm_srli_epi16(layer2_chunk7, 8)); + + v_r0 = _mm_packus_epi16(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask)); + v_b0 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk0, 8), _mm_srli_epi16(layer1_chunk1, 8)); + v_r1 = _mm_packus_epi16(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask)); + v_b1 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk2, 8), _mm_srli_epi16(layer1_chunk3, 8)); + v_g0 = _mm_packus_epi16(_mm_and_si128(layer1_chunk4, v_mask), _mm_and_si128(layer1_chunk5, v_mask)); + v_a0 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk4, 8), _mm_srli_epi16(layer1_chunk5, 8)); + v_g1 = _mm_packus_epi16(_mm_and_si128(layer1_chunk6, v_mask), _mm_and_si128(layer1_chunk7, v_mask)); + v_a1 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk6, 8), _mm_srli_epi16(layer1_chunk7, 8)); +} + +inline void _mm_deinterleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1) +{ + __m128i layer1_chunk0 = _mm_unpacklo_epi16(v_r0, v_g0); + __m128i layer1_chunk1 = _mm_unpackhi_epi16(v_r0, v_g0); + __m128i layer1_chunk2 = _mm_unpacklo_epi16(v_r1, v_g1); + __m128i layer1_chunk3 = _mm_unpackhi_epi16(v_r1, v_g1); + + __m128i layer2_chunk0 = _mm_unpacklo_epi16(layer1_chunk0, layer1_chunk2); + __m128i layer2_chunk1 = _mm_unpackhi_epi16(layer1_chunk0, layer1_chunk2); + __m128i layer2_chunk2 = _mm_unpacklo_epi16(layer1_chunk1, layer1_chunk3); + __m128i layer2_chunk3 = _mm_unpackhi_epi16(layer1_chunk1, layer1_chunk3); + + __m128i layer3_chunk0 = _mm_unpacklo_epi16(layer2_chunk0, layer2_chunk2); + __m128i layer3_chunk1 = _mm_unpackhi_epi16(layer2_chunk0, layer2_chunk2); + __m128i layer3_chunk2 = _mm_unpacklo_epi16(layer2_chunk1, layer2_chunk3); + __m128i layer3_chunk3 = _mm_unpackhi_epi16(layer2_chunk1, layer2_chunk3); + + v_r0 = _mm_unpacklo_epi16(layer3_chunk0, layer3_chunk2); + v_r1 = _mm_unpackhi_epi16(layer3_chunk0, layer3_chunk2); + v_g0 = _mm_unpacklo_epi16(layer3_chunk1, layer3_chunk3); + v_g1 = _mm_unpackhi_epi16(layer3_chunk1, layer3_chunk3); +} + +inline void _mm_deinterleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, + __m128i & v_g1, __m128i & v_b0, __m128i & v_b1) +{ + __m128i layer1_chunk0 = _mm_unpacklo_epi16(v_r0, v_g1); + __m128i layer1_chunk1 = _mm_unpackhi_epi16(v_r0, v_g1); + __m128i layer1_chunk2 = _mm_unpacklo_epi16(v_r1, v_b0); + __m128i layer1_chunk3 = _mm_unpackhi_epi16(v_r1, v_b0); + __m128i layer1_chunk4 = _mm_unpacklo_epi16(v_g0, v_b1); + __m128i layer1_chunk5 = _mm_unpackhi_epi16(v_g0, v_b1); + + __m128i layer2_chunk0 = _mm_unpacklo_epi16(layer1_chunk0, layer1_chunk3); + __m128i layer2_chunk1 = _mm_unpackhi_epi16(layer1_chunk0, layer1_chunk3); + __m128i layer2_chunk2 = _mm_unpacklo_epi16(layer1_chunk1, layer1_chunk4); + __m128i layer2_chunk3 = _mm_unpackhi_epi16(layer1_chunk1, layer1_chunk4); + __m128i layer2_chunk4 = _mm_unpacklo_epi16(layer1_chunk2, layer1_chunk5); + __m128i layer2_chunk5 = _mm_unpackhi_epi16(layer1_chunk2, layer1_chunk5); + + __m128i layer3_chunk0 = _mm_unpacklo_epi16(layer2_chunk0, layer2_chunk3); + __m128i layer3_chunk1 = _mm_unpackhi_epi16(layer2_chunk0, layer2_chunk3); + __m128i layer3_chunk2 = _mm_unpacklo_epi16(layer2_chunk1, layer2_chunk4); + __m128i layer3_chunk3 = _mm_unpackhi_epi16(layer2_chunk1, layer2_chunk4); + __m128i layer3_chunk4 = _mm_unpacklo_epi16(layer2_chunk2, layer2_chunk5); + __m128i layer3_chunk5 = _mm_unpackhi_epi16(layer2_chunk2, layer2_chunk5); + + v_r0 = _mm_unpacklo_epi16(layer3_chunk0, layer3_chunk3); + v_r1 = _mm_unpackhi_epi16(layer3_chunk0, layer3_chunk3); + v_g0 = _mm_unpacklo_epi16(layer3_chunk1, layer3_chunk4); + v_g1 = _mm_unpackhi_epi16(layer3_chunk1, layer3_chunk4); + v_b0 = _mm_unpacklo_epi16(layer3_chunk2, layer3_chunk5); + v_b1 = _mm_unpackhi_epi16(layer3_chunk2, layer3_chunk5); +} + +inline void _mm_deinterleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1, + __m128i & v_b0, __m128i & v_b1, __m128i & v_a0, __m128i & v_a1) +{ + __m128i layer1_chunk0 = _mm_unpacklo_epi16(v_r0, v_b0); + __m128i layer1_chunk1 = _mm_unpackhi_epi16(v_r0, v_b0); + __m128i layer1_chunk2 = _mm_unpacklo_epi16(v_r1, v_b1); + __m128i layer1_chunk3 = _mm_unpackhi_epi16(v_r1, v_b1); + __m128i layer1_chunk4 = _mm_unpacklo_epi16(v_g0, v_a0); + __m128i layer1_chunk5 = _mm_unpackhi_epi16(v_g0, v_a0); + __m128i layer1_chunk6 = _mm_unpacklo_epi16(v_g1, v_a1); + __m128i layer1_chunk7 = _mm_unpackhi_epi16(v_g1, v_a1); + + __m128i layer2_chunk0 = _mm_unpacklo_epi16(layer1_chunk0, layer1_chunk4); + __m128i layer2_chunk1 = _mm_unpackhi_epi16(layer1_chunk0, layer1_chunk4); + __m128i layer2_chunk2 = _mm_unpacklo_epi16(layer1_chunk1, layer1_chunk5); + __m128i layer2_chunk3 = _mm_unpackhi_epi16(layer1_chunk1, layer1_chunk5); + __m128i layer2_chunk4 = _mm_unpacklo_epi16(layer1_chunk2, layer1_chunk6); + __m128i layer2_chunk5 = _mm_unpackhi_epi16(layer1_chunk2, layer1_chunk6); + __m128i layer2_chunk6 = _mm_unpacklo_epi16(layer1_chunk3, layer1_chunk7); + __m128i layer2_chunk7 = _mm_unpackhi_epi16(layer1_chunk3, layer1_chunk7); + + __m128i layer3_chunk0 = _mm_unpacklo_epi16(layer2_chunk0, layer2_chunk4); + __m128i layer3_chunk1 = _mm_unpackhi_epi16(layer2_chunk0, layer2_chunk4); + __m128i layer3_chunk2 = _mm_unpacklo_epi16(layer2_chunk1, layer2_chunk5); + __m128i layer3_chunk3 = _mm_unpackhi_epi16(layer2_chunk1, layer2_chunk5); + __m128i layer3_chunk4 = _mm_unpacklo_epi16(layer2_chunk2, layer2_chunk6); + __m128i layer3_chunk5 = _mm_unpackhi_epi16(layer2_chunk2, layer2_chunk6); + __m128i layer3_chunk6 = _mm_unpacklo_epi16(layer2_chunk3, layer2_chunk7); + __m128i layer3_chunk7 = _mm_unpackhi_epi16(layer2_chunk3, layer2_chunk7); + + v_r0 = _mm_unpacklo_epi16(layer3_chunk0, layer3_chunk4); + v_r1 = _mm_unpackhi_epi16(layer3_chunk0, layer3_chunk4); + v_g0 = _mm_unpacklo_epi16(layer3_chunk1, layer3_chunk5); + v_g1 = _mm_unpackhi_epi16(layer3_chunk1, layer3_chunk5); + v_b0 = _mm_unpacklo_epi16(layer3_chunk2, layer3_chunk6); + v_b1 = _mm_unpackhi_epi16(layer3_chunk2, layer3_chunk6); + v_a0 = _mm_unpacklo_epi16(layer3_chunk3, layer3_chunk7); + v_a1 = _mm_unpackhi_epi16(layer3_chunk3, layer3_chunk7); +} + +#if CV_SSE4_1 + +inline void _mm_interleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1) +{ + __m128i v_mask = _mm_set1_epi32(0x0000ffff); + + __m128i layer3_chunk0 = _mm_packus_epi32(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask)); + __m128i layer3_chunk2 = _mm_packus_epi32(_mm_srli_epi32(v_r0, 16), _mm_srli_epi32(v_r1, 16)); + __m128i layer3_chunk1 = _mm_packus_epi32(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask)); + __m128i layer3_chunk3 = _mm_packus_epi32(_mm_srli_epi32(v_g0, 16), _mm_srli_epi32(v_g1, 16)); + + __m128i layer2_chunk0 = _mm_packus_epi32(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask)); + __m128i layer2_chunk2 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk0, 16), _mm_srli_epi32(layer3_chunk1, 16)); + __m128i layer2_chunk1 = _mm_packus_epi32(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask)); + __m128i layer2_chunk3 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk2, 16), _mm_srli_epi32(layer3_chunk3, 16)); + + __m128i layer1_chunk0 = _mm_packus_epi32(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask)); + __m128i layer1_chunk2 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk0, 16), _mm_srli_epi32(layer2_chunk1, 16)); + __m128i layer1_chunk1 = _mm_packus_epi32(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask)); + __m128i layer1_chunk3 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk2, 16), _mm_srli_epi32(layer2_chunk3, 16)); + + v_r0 = _mm_packus_epi32(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask)); + v_g0 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk0, 16), _mm_srli_epi32(layer1_chunk1, 16)); + v_r1 = _mm_packus_epi32(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask)); + v_g1 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk2, 16), _mm_srli_epi32(layer1_chunk3, 16)); +} + +inline void _mm_interleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, + __m128i & v_g1, __m128i & v_b0, __m128i & v_b1) +{ + __m128i v_mask = _mm_set1_epi32(0x0000ffff); + + __m128i layer3_chunk0 = _mm_packus_epi32(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask)); + __m128i layer3_chunk3 = _mm_packus_epi32(_mm_srli_epi32(v_r0, 16), _mm_srli_epi32(v_r1, 16)); + __m128i layer3_chunk1 = _mm_packus_epi32(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask)); + __m128i layer3_chunk4 = _mm_packus_epi32(_mm_srli_epi32(v_g0, 16), _mm_srli_epi32(v_g1, 16)); + __m128i layer3_chunk2 = _mm_packus_epi32(_mm_and_si128(v_b0, v_mask), _mm_and_si128(v_b1, v_mask)); + __m128i layer3_chunk5 = _mm_packus_epi32(_mm_srli_epi32(v_b0, 16), _mm_srli_epi32(v_b1, 16)); + + __m128i layer2_chunk0 = _mm_packus_epi32(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask)); + __m128i layer2_chunk3 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk0, 16), _mm_srli_epi32(layer3_chunk1, 16)); + __m128i layer2_chunk1 = _mm_packus_epi32(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask)); + __m128i layer2_chunk4 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk2, 16), _mm_srli_epi32(layer3_chunk3, 16)); + __m128i layer2_chunk2 = _mm_packus_epi32(_mm_and_si128(layer3_chunk4, v_mask), _mm_and_si128(layer3_chunk5, v_mask)); + __m128i layer2_chunk5 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk4, 16), _mm_srli_epi32(layer3_chunk5, 16)); + + __m128i layer1_chunk0 = _mm_packus_epi32(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask)); + __m128i layer1_chunk3 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk0, 16), _mm_srli_epi32(layer2_chunk1, 16)); + __m128i layer1_chunk1 = _mm_packus_epi32(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask)); + __m128i layer1_chunk4 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk2, 16), _mm_srli_epi32(layer2_chunk3, 16)); + __m128i layer1_chunk2 = _mm_packus_epi32(_mm_and_si128(layer2_chunk4, v_mask), _mm_and_si128(layer2_chunk5, v_mask)); + __m128i layer1_chunk5 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk4, 16), _mm_srli_epi32(layer2_chunk5, 16)); + + v_r0 = _mm_packus_epi32(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask)); + v_g1 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk0, 16), _mm_srli_epi32(layer1_chunk1, 16)); + v_r1 = _mm_packus_epi32(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask)); + v_b0 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk2, 16), _mm_srli_epi32(layer1_chunk3, 16)); + v_g0 = _mm_packus_epi32(_mm_and_si128(layer1_chunk4, v_mask), _mm_and_si128(layer1_chunk5, v_mask)); + v_b1 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk4, 16), _mm_srli_epi32(layer1_chunk5, 16)); +} + +inline void _mm_interleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1, + __m128i & v_b0, __m128i & v_b1, __m128i & v_a0, __m128i & v_a1) +{ + __m128i v_mask = _mm_set1_epi32(0x0000ffff); + + __m128i layer3_chunk0 = _mm_packus_epi32(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask)); + __m128i layer3_chunk4 = _mm_packus_epi32(_mm_srli_epi32(v_r0, 16), _mm_srli_epi32(v_r1, 16)); + __m128i layer3_chunk1 = _mm_packus_epi32(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask)); + __m128i layer3_chunk5 = _mm_packus_epi32(_mm_srli_epi32(v_g0, 16), _mm_srli_epi32(v_g1, 16)); + __m128i layer3_chunk2 = _mm_packus_epi32(_mm_and_si128(v_b0, v_mask), _mm_and_si128(v_b1, v_mask)); + __m128i layer3_chunk6 = _mm_packus_epi32(_mm_srli_epi32(v_b0, 16), _mm_srli_epi32(v_b1, 16)); + __m128i layer3_chunk3 = _mm_packus_epi32(_mm_and_si128(v_a0, v_mask), _mm_and_si128(v_a1, v_mask)); + __m128i layer3_chunk7 = _mm_packus_epi32(_mm_srli_epi32(v_a0, 16), _mm_srli_epi32(v_a1, 16)); + + __m128i layer2_chunk0 = _mm_packus_epi32(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask)); + __m128i layer2_chunk4 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk0, 16), _mm_srli_epi32(layer3_chunk1, 16)); + __m128i layer2_chunk1 = _mm_packus_epi32(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask)); + __m128i layer2_chunk5 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk2, 16), _mm_srli_epi32(layer3_chunk3, 16)); + __m128i layer2_chunk2 = _mm_packus_epi32(_mm_and_si128(layer3_chunk4, v_mask), _mm_and_si128(layer3_chunk5, v_mask)); + __m128i layer2_chunk6 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk4, 16), _mm_srli_epi32(layer3_chunk5, 16)); + __m128i layer2_chunk3 = _mm_packus_epi32(_mm_and_si128(layer3_chunk6, v_mask), _mm_and_si128(layer3_chunk7, v_mask)); + __m128i layer2_chunk7 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk6, 16), _mm_srli_epi32(layer3_chunk7, 16)); + + __m128i layer1_chunk0 = _mm_packus_epi32(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask)); + __m128i layer1_chunk4 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk0, 16), _mm_srli_epi32(layer2_chunk1, 16)); + __m128i layer1_chunk1 = _mm_packus_epi32(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask)); + __m128i layer1_chunk5 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk2, 16), _mm_srli_epi32(layer2_chunk3, 16)); + __m128i layer1_chunk2 = _mm_packus_epi32(_mm_and_si128(layer2_chunk4, v_mask), _mm_and_si128(layer2_chunk5, v_mask)); + __m128i layer1_chunk6 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk4, 16), _mm_srli_epi32(layer2_chunk5, 16)); + __m128i layer1_chunk3 = _mm_packus_epi32(_mm_and_si128(layer2_chunk6, v_mask), _mm_and_si128(layer2_chunk7, v_mask)); + __m128i layer1_chunk7 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk6, 16), _mm_srli_epi32(layer2_chunk7, 16)); + + v_r0 = _mm_packus_epi32(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask)); + v_b0 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk0, 16), _mm_srli_epi32(layer1_chunk1, 16)); + v_r1 = _mm_packus_epi32(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask)); + v_b1 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk2, 16), _mm_srli_epi32(layer1_chunk3, 16)); + v_g0 = _mm_packus_epi32(_mm_and_si128(layer1_chunk4, v_mask), _mm_and_si128(layer1_chunk5, v_mask)); + v_a0 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk4, 16), _mm_srli_epi32(layer1_chunk5, 16)); + v_g1 = _mm_packus_epi32(_mm_and_si128(layer1_chunk6, v_mask), _mm_and_si128(layer1_chunk7, v_mask)); + v_a1 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk6, 16), _mm_srli_epi32(layer1_chunk7, 16)); +} + +#endif // CV_SSE4_1 + +inline void _mm_deinterleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0, __m128 & v_g1) +{ + __m128 layer1_chunk0 = _mm_unpacklo_ps(v_r0, v_g0); + __m128 layer1_chunk1 = _mm_unpackhi_ps(v_r0, v_g0); + __m128 layer1_chunk2 = _mm_unpacklo_ps(v_r1, v_g1); + __m128 layer1_chunk3 = _mm_unpackhi_ps(v_r1, v_g1); + + __m128 layer2_chunk0 = _mm_unpacklo_ps(layer1_chunk0, layer1_chunk2); + __m128 layer2_chunk1 = _mm_unpackhi_ps(layer1_chunk0, layer1_chunk2); + __m128 layer2_chunk2 = _mm_unpacklo_ps(layer1_chunk1, layer1_chunk3); + __m128 layer2_chunk3 = _mm_unpackhi_ps(layer1_chunk1, layer1_chunk3); + + v_r0 = _mm_unpacklo_ps(layer2_chunk0, layer2_chunk2); + v_r1 = _mm_unpackhi_ps(layer2_chunk0, layer2_chunk2); + v_g0 = _mm_unpacklo_ps(layer2_chunk1, layer2_chunk3); + v_g1 = _mm_unpackhi_ps(layer2_chunk1, layer2_chunk3); +} + +inline void _mm_deinterleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0, + __m128 & v_g1, __m128 & v_b0, __m128 & v_b1) +{ + __m128 layer1_chunk0 = _mm_unpacklo_ps(v_r0, v_g1); + __m128 layer1_chunk1 = _mm_unpackhi_ps(v_r0, v_g1); + __m128 layer1_chunk2 = _mm_unpacklo_ps(v_r1, v_b0); + __m128 layer1_chunk3 = _mm_unpackhi_ps(v_r1, v_b0); + __m128 layer1_chunk4 = _mm_unpacklo_ps(v_g0, v_b1); + __m128 layer1_chunk5 = _mm_unpackhi_ps(v_g0, v_b1); + + __m128 layer2_chunk0 = _mm_unpacklo_ps(layer1_chunk0, layer1_chunk3); + __m128 layer2_chunk1 = _mm_unpackhi_ps(layer1_chunk0, layer1_chunk3); + __m128 layer2_chunk2 = _mm_unpacklo_ps(layer1_chunk1, layer1_chunk4); + __m128 layer2_chunk3 = _mm_unpackhi_ps(layer1_chunk1, layer1_chunk4); + __m128 layer2_chunk4 = _mm_unpacklo_ps(layer1_chunk2, layer1_chunk5); + __m128 layer2_chunk5 = _mm_unpackhi_ps(layer1_chunk2, layer1_chunk5); + + v_r0 = _mm_unpacklo_ps(layer2_chunk0, layer2_chunk3); + v_r1 = _mm_unpackhi_ps(layer2_chunk0, layer2_chunk3); + v_g0 = _mm_unpacklo_ps(layer2_chunk1, layer2_chunk4); + v_g1 = _mm_unpackhi_ps(layer2_chunk1, layer2_chunk4); + v_b0 = _mm_unpacklo_ps(layer2_chunk2, layer2_chunk5); + v_b1 = _mm_unpackhi_ps(layer2_chunk2, layer2_chunk5); +} + +inline void _mm_deinterleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0, __m128 & v_g1, + __m128 & v_b0, __m128 & v_b1, __m128 & v_a0, __m128 & v_a1) +{ + __m128 layer1_chunk0 = _mm_unpacklo_ps(v_r0, v_b0); + __m128 layer1_chunk1 = _mm_unpackhi_ps(v_r0, v_b0); + __m128 layer1_chunk2 = _mm_unpacklo_ps(v_r1, v_b1); + __m128 layer1_chunk3 = _mm_unpackhi_ps(v_r1, v_b1); + __m128 layer1_chunk4 = _mm_unpacklo_ps(v_g0, v_a0); + __m128 layer1_chunk5 = _mm_unpackhi_ps(v_g0, v_a0); + __m128 layer1_chunk6 = _mm_unpacklo_ps(v_g1, v_a1); + __m128 layer1_chunk7 = _mm_unpackhi_ps(v_g1, v_a1); + + __m128 layer2_chunk0 = _mm_unpacklo_ps(layer1_chunk0, layer1_chunk4); + __m128 layer2_chunk1 = _mm_unpackhi_ps(layer1_chunk0, layer1_chunk4); + __m128 layer2_chunk2 = _mm_unpacklo_ps(layer1_chunk1, layer1_chunk5); + __m128 layer2_chunk3 = _mm_unpackhi_ps(layer1_chunk1, layer1_chunk5); + __m128 layer2_chunk4 = _mm_unpacklo_ps(layer1_chunk2, layer1_chunk6); + __m128 layer2_chunk5 = _mm_unpackhi_ps(layer1_chunk2, layer1_chunk6); + __m128 layer2_chunk6 = _mm_unpacklo_ps(layer1_chunk3, layer1_chunk7); + __m128 layer2_chunk7 = _mm_unpackhi_ps(layer1_chunk3, layer1_chunk7); + + v_r0 = _mm_unpacklo_ps(layer2_chunk0, layer2_chunk4); + v_r1 = _mm_unpackhi_ps(layer2_chunk0, layer2_chunk4); + v_g0 = _mm_unpacklo_ps(layer2_chunk1, layer2_chunk5); + v_g1 = _mm_unpackhi_ps(layer2_chunk1, layer2_chunk5); + v_b0 = _mm_unpacklo_ps(layer2_chunk2, layer2_chunk6); + v_b1 = _mm_unpackhi_ps(layer2_chunk2, layer2_chunk6); + v_a0 = _mm_unpacklo_ps(layer2_chunk3, layer2_chunk7); + v_a1 = _mm_unpackhi_ps(layer2_chunk3, layer2_chunk7); +} + +inline void _mm_interleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0, __m128 & v_g1) +{ + enum { mask_lo = _MM_SHUFFLE(2, 0, 2, 0), mask_hi = _MM_SHUFFLE(3, 1, 3, 1) }; + + __m128 layer2_chunk0 = _mm_shuffle_ps(v_r0, v_r1, mask_lo); + __m128 layer2_chunk2 = _mm_shuffle_ps(v_r0, v_r1, mask_hi); + __m128 layer2_chunk1 = _mm_shuffle_ps(v_g0, v_g1, mask_lo); + __m128 layer2_chunk3 = _mm_shuffle_ps(v_g0, v_g1, mask_hi); + + __m128 layer1_chunk0 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_lo); + __m128 layer1_chunk2 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_hi); + __m128 layer1_chunk1 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_lo); + __m128 layer1_chunk3 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_hi); + + v_r0 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_lo); + v_g0 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_hi); + v_r1 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_lo); + v_g1 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_hi); +} + +inline void _mm_interleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0, + __m128 & v_g1, __m128 & v_b0, __m128 & v_b1) +{ + enum { mask_lo = _MM_SHUFFLE(2, 0, 2, 0), mask_hi = _MM_SHUFFLE(3, 1, 3, 1) }; + + __m128 layer2_chunk0 = _mm_shuffle_ps(v_r0, v_r1, mask_lo); + __m128 layer2_chunk3 = _mm_shuffle_ps(v_r0, v_r1, mask_hi); + __m128 layer2_chunk1 = _mm_shuffle_ps(v_g0, v_g1, mask_lo); + __m128 layer2_chunk4 = _mm_shuffle_ps(v_g0, v_g1, mask_hi); + __m128 layer2_chunk2 = _mm_shuffle_ps(v_b0, v_b1, mask_lo); + __m128 layer2_chunk5 = _mm_shuffle_ps(v_b0, v_b1, mask_hi); + + __m128 layer1_chunk0 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_lo); + __m128 layer1_chunk3 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_hi); + __m128 layer1_chunk1 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_lo); + __m128 layer1_chunk4 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_hi); + __m128 layer1_chunk2 = _mm_shuffle_ps(layer2_chunk4, layer2_chunk5, mask_lo); + __m128 layer1_chunk5 = _mm_shuffle_ps(layer2_chunk4, layer2_chunk5, mask_hi); + + v_r0 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_lo); + v_g1 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_hi); + v_r1 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_lo); + v_b0 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_hi); + v_g0 = _mm_shuffle_ps(layer1_chunk4, layer1_chunk5, mask_lo); + v_b1 = _mm_shuffle_ps(layer1_chunk4, layer1_chunk5, mask_hi); +} + +inline void _mm_interleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0, __m128 & v_g1, + __m128 & v_b0, __m128 & v_b1, __m128 & v_a0, __m128 & v_a1) +{ + enum { mask_lo = _MM_SHUFFLE(2, 0, 2, 0), mask_hi = _MM_SHUFFLE(3, 1, 3, 1) }; + + __m128 layer2_chunk0 = _mm_shuffle_ps(v_r0, v_r1, mask_lo); + __m128 layer2_chunk4 = _mm_shuffle_ps(v_r0, v_r1, mask_hi); + __m128 layer2_chunk1 = _mm_shuffle_ps(v_g0, v_g1, mask_lo); + __m128 layer2_chunk5 = _mm_shuffle_ps(v_g0, v_g1, mask_hi); + __m128 layer2_chunk2 = _mm_shuffle_ps(v_b0, v_b1, mask_lo); + __m128 layer2_chunk6 = _mm_shuffle_ps(v_b0, v_b1, mask_hi); + __m128 layer2_chunk3 = _mm_shuffle_ps(v_a0, v_a1, mask_lo); + __m128 layer2_chunk7 = _mm_shuffle_ps(v_a0, v_a1, mask_hi); + + __m128 layer1_chunk0 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_lo); + __m128 layer1_chunk4 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_hi); + __m128 layer1_chunk1 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_lo); + __m128 layer1_chunk5 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_hi); + __m128 layer1_chunk2 = _mm_shuffle_ps(layer2_chunk4, layer2_chunk5, mask_lo); + __m128 layer1_chunk6 = _mm_shuffle_ps(layer2_chunk4, layer2_chunk5, mask_hi); + __m128 layer1_chunk3 = _mm_shuffle_ps(layer2_chunk6, layer2_chunk7, mask_lo); + __m128 layer1_chunk7 = _mm_shuffle_ps(layer2_chunk6, layer2_chunk7, mask_hi); + + v_r0 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_lo); + v_b0 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_hi); + v_r1 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_lo); + v_b1 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_hi); + v_g0 = _mm_shuffle_ps(layer1_chunk4, layer1_chunk5, mask_lo); + v_a0 = _mm_shuffle_ps(layer1_chunk4, layer1_chunk5, mask_hi); + v_g1 = _mm_shuffle_ps(layer1_chunk6, layer1_chunk7, mask_lo); + v_a1 = _mm_shuffle_ps(layer1_chunk6, layer1_chunk7, mask_hi); +} + +#endif // CV_SSE2 + +//! @} + +#endif //OPENCV_CORE_SSE_UTILS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/traits.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/traits.hpp new file mode 100644 index 0000000000000000000000000000000000000000..6cb10f44cf59fde0dccf6768281bb4bd86d28b3d --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/traits.hpp @@ -0,0 +1,397 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_TRAITS_HPP +#define OPENCV_CORE_TRAITS_HPP + +#include "opencv2/core/cvdef.h" + +namespace cv +{ + +//#define OPENCV_TRAITS_ENABLE_DEPRECATED + +//! @addtogroup core_basic +//! @{ + +/** @brief Template "trait" class for OpenCV primitive data types. + +@note Deprecated. This is replaced by "single purpose" traits: traits::Type and traits::Depth + +A primitive OpenCV data type is one of unsigned char, bool, signed char, unsigned short, signed +short, int, float, double, or a tuple of values of one of these types, where all the values in the +tuple have the same type. Any primitive type from the list can be defined by an identifier in the +form CV_\{U|S|F}C(\), for example: uchar \~ CV_8UC1, 3-element +floating-point tuple \~ CV_32FC3, and so on. A universal OpenCV structure that is able to store a +single instance of such a primitive data type is Vec. Multiple instances of such a type can be +stored in a std::vector, Mat, Mat_, SparseMat, SparseMat_, or any other container that is able to +store Vec instances. + +The DataType class is basically used to provide a description of such primitive data types without +adding any fields or methods to the corresponding classes (and it is actually impossible to add +anything to primitive C/C++ data types). This technique is known in C++ as class traits. It is not +DataType itself that is used but its specialized versions, such as: +@code + template<> class DataType + { + typedef uchar value_type; + typedef int work_type; + typedef uchar channel_type; + enum { channel_type = CV_8U, channels = 1, fmt='u', type = CV_8U }; + }; + ... + template DataType > + { + typedef std::complex<_Tp> value_type; + typedef std::complex<_Tp> work_type; + typedef _Tp channel_type; + // DataDepth is another helper trait class + enum { depth = DataDepth<_Tp>::value, channels=2, + fmt=(channels-1)*256+DataDepth<_Tp>::fmt, + type=CV_MAKETYPE(depth, channels) }; + }; + ... +@endcode +The main purpose of this class is to convert compilation-time type information to an +OpenCV-compatible data type identifier, for example: +@code + // allocates a 30x40 floating-point matrix + Mat A(30, 40, DataType::type); + + Mat B = Mat_ >(3, 3); + // the statement below will print 6, 2 , that is depth == CV_64F, channels == 2 + cout << B.depth() << ", " << B.channels() << endl; +@endcode +So, such traits are used to tell OpenCV which data type you are working with, even if such a type is +not native to OpenCV. For example, the matrix B initialization above is compiled because OpenCV +defines the proper specialized template class DataType\ \> . This mechanism is also +useful (and used in OpenCV this way) for generic algorithms implementations. + +@note Default values were dropped to stop confusing developers about using of unsupported types (see #7599) +*/ +template class DataType +{ +public: +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + typedef _Tp value_type; + typedef value_type work_type; + typedef value_type channel_type; + typedef value_type vec_type; + enum { generic_type = 1, + depth = -1, + channels = 1, + fmt = 0, + type = CV_MAKETYPE(depth, channels) + }; +#endif +}; + +template<> class DataType +{ +public: + typedef bool value_type; + typedef int work_type; + typedef value_type channel_type; + typedef value_type vec_type; + enum { generic_type = 0, + depth = CV_8U, + channels = 1, + fmt = (int)'u', + type = CV_MAKETYPE(depth, channels) + }; +}; + +template<> class DataType +{ +public: + typedef uchar value_type; + typedef int work_type; + typedef value_type channel_type; + typedef value_type vec_type; + enum { generic_type = 0, + depth = CV_8U, + channels = 1, + fmt = (int)'u', + type = CV_MAKETYPE(depth, channels) + }; +}; + +template<> class DataType +{ +public: + typedef schar value_type; + typedef int work_type; + typedef value_type channel_type; + typedef value_type vec_type; + enum { generic_type = 0, + depth = CV_8S, + channels = 1, + fmt = (int)'c', + type = CV_MAKETYPE(depth, channels) + }; +}; + +template<> class DataType +{ +public: + typedef schar value_type; + typedef int work_type; + typedef value_type channel_type; + typedef value_type vec_type; + enum { generic_type = 0, + depth = CV_8S, + channels = 1, + fmt = (int)'c', + type = CV_MAKETYPE(depth, channels) + }; +}; + +template<> class DataType +{ +public: + typedef ushort value_type; + typedef int work_type; + typedef value_type channel_type; + typedef value_type vec_type; + enum { generic_type = 0, + depth = CV_16U, + channels = 1, + fmt = (int)'w', + type = CV_MAKETYPE(depth, channels) + }; +}; + +template<> class DataType +{ +public: + typedef short value_type; + typedef int work_type; + typedef value_type channel_type; + typedef value_type vec_type; + enum { generic_type = 0, + depth = CV_16S, + channels = 1, + fmt = (int)'s', + type = CV_MAKETYPE(depth, channels) + }; +}; + +template<> class DataType +{ +public: + typedef int value_type; + typedef value_type work_type; + typedef value_type channel_type; + typedef value_type vec_type; + enum { generic_type = 0, + depth = CV_32S, + channels = 1, + fmt = (int)'i', + type = CV_MAKETYPE(depth, channels) + }; +}; + +template<> class DataType +{ +public: + typedef float value_type; + typedef value_type work_type; + typedef value_type channel_type; + typedef value_type vec_type; + enum { generic_type = 0, + depth = CV_32F, + channels = 1, + fmt = (int)'f', + type = CV_MAKETYPE(depth, channels) + }; +}; + +template<> class DataType +{ +public: + typedef double value_type; + typedef value_type work_type; + typedef value_type channel_type; + typedef value_type vec_type; + enum { generic_type = 0, + depth = CV_64F, + channels = 1, + fmt = (int)'d', + type = CV_MAKETYPE(depth, channels) + }; +}; + + +/** @brief A helper class for cv::DataType + +The class is specialized for each fundamental numerical data type supported by OpenCV. It provides +DataDepth::value constant. +*/ +template class DataDepth +{ +public: + enum + { + value = DataType<_Tp>::depth, + fmt = DataType<_Tp>::fmt + }; +}; + + +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + +template class TypeDepth +{ +#ifdef OPENCV_TRAITS_ENABLE_LEGACY_DEFAULTS + enum { depth = CV_USRTYPE1 }; + typedef void value_type; +#endif +}; + +template<> class TypeDepth +{ + enum { depth = CV_8U }; + typedef uchar value_type; +}; + +template<> class TypeDepth +{ + enum { depth = CV_8S }; + typedef schar value_type; +}; + +template<> class TypeDepth +{ + enum { depth = CV_16U }; + typedef ushort value_type; +}; + +template<> class TypeDepth +{ + enum { depth = CV_16S }; + typedef short value_type; +}; + +template<> class TypeDepth +{ + enum { depth = CV_32S }; + typedef int value_type; +}; + +template<> class TypeDepth +{ + enum { depth = CV_32F }; + typedef float value_type; +}; + +template<> class TypeDepth +{ + enum { depth = CV_64F }; + typedef double value_type; +}; + +#endif + +//! @} + +namespace traits { + +namespace internal { +#define CV_CREATE_MEMBER_CHECK(X) \ +template class CheckMember_##X { \ + struct Fallback { int X; }; \ + struct Derived : T, Fallback { }; \ + template struct Check; \ + typedef char CV_NO[1]; \ + typedef char CV_YES[2]; \ + template static CV_NO & func(Check *); \ + template static CV_YES & func(...); \ +public: \ + typedef CheckMember_##X type; \ + enum { value = sizeof(func(0)) == sizeof(CV_YES) }; \ +}; + +CV_CREATE_MEMBER_CHECK(fmt) +CV_CREATE_MEMBER_CHECK(type) + +} // namespace internal + + +template +struct Depth +{ enum { value = DataType::depth }; }; + +template +struct Type +{ enum { value = DataType::type }; }; + +/** Similar to traits::Type but has value = -1 in case of unknown type (instead of compiler error) */ +template >::value > +struct SafeType {}; + +template +struct SafeType +{ enum { value = -1 }; }; + +template +struct SafeType +{ enum { value = Type::value }; }; + + +template >::value > +struct SafeFmt {}; + +template +struct SafeFmt +{ enum { fmt = 0 }; }; + +template +struct SafeFmt +{ enum { fmt = DataType::fmt }; }; + + +} // namespace + +} // cv + +#endif // OPENCV_CORE_TRAITS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/types.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/types.hpp new file mode 100644 index 0000000000000000000000000000000000000000..ef9ab593830ace4e94f60f5c1d52419d48e43435 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/types.hpp @@ -0,0 +1,2391 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_TYPES_HPP +#define OPENCV_CORE_TYPES_HPP + +#ifndef __cplusplus +# error types.hpp header must be compiled as C++ +#endif + +#include +#include +#include +#include + +#include "opencv2/core/cvdef.h" +#include "opencv2/core/cvstd.hpp" +#include "opencv2/core/matx.hpp" + +namespace cv +{ + +//! @addtogroup core_basic +//! @{ + +//////////////////////////////// Complex ////////////////////////////// + +/** @brief A complex number class. + + The template class is similar and compatible with std::complex, however it provides slightly + more convenient access to the real and imaginary parts using through the simple field access, as opposite + to std::complex::real() and std::complex::imag(). +*/ +template class Complex +{ +public: + + //! default constructor + Complex(); + Complex( _Tp _re, _Tp _im = 0 ); + + //! conversion to another data type + template operator Complex() const; + //! conjugation + Complex conj() const; + + _Tp re, im; //< the real and the imaginary parts +}; + +typedef Complex Complexf; +typedef Complex Complexd; + +template class DataType< Complex<_Tp> > +{ +public: + typedef Complex<_Tp> value_type; + typedef value_type work_type; + typedef _Tp channel_type; + + enum { generic_type = 0, + channels = 2, + fmt = DataType::fmt + ((channels - 1) << 8) +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + ,depth = DataType::depth + ,type = CV_MAKETYPE(depth, channels) +#endif + }; + + typedef Vec vec_type; +}; + +namespace traits { +template +struct Depth< Complex<_Tp> > { enum { value = Depth<_Tp>::value }; }; +template +struct Type< Complex<_Tp> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, 2) }; }; +} // namespace + + +//////////////////////////////// Point_ //////////////////////////////// + +/** @brief Template class for 2D points specified by its coordinates `x` and `y`. + +An instance of the class is interchangeable with C structures, CvPoint and CvPoint2D32f . There is +also a cast operator to convert point coordinates to the specified type. The conversion from +floating-point coordinates to integer coordinates is done by rounding. Commonly, the conversion +uses this operation for each of the coordinates. Besides the class members listed in the +declaration above, the following operations on points are implemented: +@code + pt1 = pt2 + pt3; + pt1 = pt2 - pt3; + pt1 = pt2 * a; + pt1 = a * pt2; + pt1 = pt2 / a; + pt1 += pt2; + pt1 -= pt2; + pt1 *= a; + pt1 /= a; + double value = norm(pt); // L2 norm + pt1 == pt2; + pt1 != pt2; +@endcode +For your convenience, the following type aliases are defined: +@code + typedef Point_ Point2i; + typedef Point2i Point; + typedef Point_ Point2f; + typedef Point_ Point2d; +@endcode +Example: +@code + Point2f a(0.3f, 0.f), b(0.f, 0.4f); + Point pt = (a + b)*10.f; + cout << pt.x << ", " << pt.y << endl; +@endcode +*/ +template class Point_ +{ +public: + typedef _Tp value_type; + + //! default constructor + Point_(); + Point_(_Tp _x, _Tp _y); + Point_(const Point_& pt); + Point_(const Size_<_Tp>& sz); + Point_(const Vec<_Tp, 2>& v); + + Point_& operator = (const Point_& pt); + //! conversion to another data type + template operator Point_<_Tp2>() const; + + //! conversion to the old-style C structures + operator Vec<_Tp, 2>() const; + + //! dot product + _Tp dot(const Point_& pt) const; + //! dot product computed in double-precision arithmetics + double ddot(const Point_& pt) const; + //! cross-product + double cross(const Point_& pt) const; + //! checks whether the point is inside the specified rectangle + bool inside(const Rect_<_Tp>& r) const; + _Tp x; //!< x coordinate of the point + _Tp y; //!< y coordinate of the point +}; + +typedef Point_ Point2i; +typedef Point_ Point2l; +typedef Point_ Point2f; +typedef Point_ Point2d; +typedef Point2i Point; + +template class DataType< Point_<_Tp> > +{ +public: + typedef Point_<_Tp> value_type; + typedef Point_::work_type> work_type; + typedef _Tp channel_type; + + enum { generic_type = 0, + channels = 2, + fmt = traits::SafeFmt::fmt + ((channels - 1) << 8) +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + ,depth = DataType::depth + ,type = CV_MAKETYPE(depth, channels) +#endif + }; + + typedef Vec vec_type; +}; + +namespace traits { +template +struct Depth< Point_<_Tp> > { enum { value = Depth<_Tp>::value }; }; +template +struct Type< Point_<_Tp> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, 2) }; }; +} // namespace + + +//////////////////////////////// Point3_ //////////////////////////////// + +/** @brief Template class for 3D points specified by its coordinates `x`, `y` and `z`. + +An instance of the class is interchangeable with the C structure CvPoint2D32f . Similarly to +Point_ , the coordinates of 3D points can be converted to another type. The vector arithmetic and +comparison operations are also supported. + +The following Point3_\<\> aliases are available: +@code + typedef Point3_ Point3i; + typedef Point3_ Point3f; + typedef Point3_ Point3d; +@endcode +@see cv::Point3i, cv::Point3f and cv::Point3d +*/ +template class Point3_ +{ +public: + typedef _Tp value_type; + + //! default constructor + Point3_(); + Point3_(_Tp _x, _Tp _y, _Tp _z); + Point3_(const Point3_& pt); + explicit Point3_(const Point_<_Tp>& pt); + Point3_(const Vec<_Tp, 3>& v); + + Point3_& operator = (const Point3_& pt); + //! conversion to another data type + template operator Point3_<_Tp2>() const; + //! conversion to cv::Vec<> +#if OPENCV_ABI_COMPATIBILITY > 300 + template operator Vec<_Tp2, 3>() const; +#else + operator Vec<_Tp, 3>() const; +#endif + + //! dot product + _Tp dot(const Point3_& pt) const; + //! dot product computed in double-precision arithmetics + double ddot(const Point3_& pt) const; + //! cross product of the 2 3D points + Point3_ cross(const Point3_& pt) const; + _Tp x; //!< x coordinate of the 3D point + _Tp y; //!< y coordinate of the 3D point + _Tp z; //!< z coordinate of the 3D point +}; + +typedef Point3_ Point3i; +typedef Point3_ Point3f; +typedef Point3_ Point3d; + +template class DataType< Point3_<_Tp> > +{ +public: + typedef Point3_<_Tp> value_type; + typedef Point3_::work_type> work_type; + typedef _Tp channel_type; + + enum { generic_type = 0, + channels = 3, + fmt = traits::SafeFmt::fmt + ((channels - 1) << 8) +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + ,depth = DataType::depth + ,type = CV_MAKETYPE(depth, channels) +#endif + }; + + typedef Vec vec_type; +}; + +namespace traits { +template +struct Depth< Point3_<_Tp> > { enum { value = Depth<_Tp>::value }; }; +template +struct Type< Point3_<_Tp> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, 3) }; }; +} // namespace + +//////////////////////////////// Size_ //////////////////////////////// + +/** @brief Template class for specifying the size of an image or rectangle. + +The class includes two members called width and height. The structure can be converted to and from +the old OpenCV structures CvSize and CvSize2D32f . The same set of arithmetic and comparison +operations as for Point_ is available. + +OpenCV defines the following Size_\<\> aliases: +@code + typedef Size_ Size2i; + typedef Size2i Size; + typedef Size_ Size2f; +@endcode +*/ +template class Size_ +{ +public: + typedef _Tp value_type; + + //! default constructor + Size_(); + Size_(_Tp _width, _Tp _height); + Size_(const Size_& sz); + Size_(const Point_<_Tp>& pt); + + Size_& operator = (const Size_& sz); + //! the area (width*height) + _Tp area() const; + //! true if empty + bool empty() const; + + //! conversion of another data type. + template operator Size_<_Tp2>() const; + + _Tp width; //!< the width + _Tp height; //!< the height +}; + +typedef Size_ Size2i; +typedef Size_ Size2l; +typedef Size_ Size2f; +typedef Size_ Size2d; +typedef Size2i Size; + +template class DataType< Size_<_Tp> > +{ +public: + typedef Size_<_Tp> value_type; + typedef Size_::work_type> work_type; + typedef _Tp channel_type; + + enum { generic_type = 0, + channels = 2, + fmt = DataType::fmt + ((channels - 1) << 8) +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + ,depth = DataType::depth + ,type = CV_MAKETYPE(depth, channels) +#endif + }; + + typedef Vec vec_type; +}; + +namespace traits { +template +struct Depth< Size_<_Tp> > { enum { value = Depth<_Tp>::value }; }; +template +struct Type< Size_<_Tp> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, 2) }; }; +} // namespace + +//////////////////////////////// Rect_ //////////////////////////////// + +/** @brief Template class for 2D rectangles + +described by the following parameters: +- Coordinates of the top-left corner. This is a default interpretation of Rect_::x and Rect_::y + in OpenCV. Though, in your algorithms you may count x and y from the bottom-left corner. +- Rectangle width and height. + +OpenCV typically assumes that the top and left boundary of the rectangle are inclusive, while the +right and bottom boundaries are not. For example, the method Rect_::contains returns true if + +\f[x \leq pt.x < x+width, + y \leq pt.y < y+height\f] + +Virtually every loop over an image ROI in OpenCV (where ROI is specified by Rect_\ ) is +implemented as: +@code + for(int y = roi.y; y < roi.y + roi.height; y++) + for(int x = roi.x; x < roi.x + roi.width; x++) + { + // ... + } +@endcode +In addition to the class members, the following operations on rectangles are implemented: +- \f$\texttt{rect} = \texttt{rect} \pm \texttt{point}\f$ (shifting a rectangle by a certain offset) +- \f$\texttt{rect} = \texttt{rect} \pm \texttt{size}\f$ (expanding or shrinking a rectangle by a + certain amount) +- rect += point, rect -= point, rect += size, rect -= size (augmenting operations) +- rect = rect1 & rect2 (rectangle intersection) +- rect = rect1 | rect2 (minimum area rectangle containing rect1 and rect2 ) +- rect &= rect1, rect |= rect1 (and the corresponding augmenting operations) +- rect == rect1, rect != rect1 (rectangle comparison) + +This is an example how the partial ordering on rectangles can be established (rect1 \f$\subseteq\f$ +rect2): +@code + template inline bool + operator <= (const Rect_<_Tp>& r1, const Rect_<_Tp>& r2) + { + return (r1 & r2) == r1; + } +@endcode +For your convenience, the Rect_\<\> alias is available: cv::Rect +*/ +template class Rect_ +{ +public: + typedef _Tp value_type; + + //! default constructor + Rect_(); + Rect_(_Tp _x, _Tp _y, _Tp _width, _Tp _height); + Rect_(const Rect_& r); + Rect_(const Point_<_Tp>& org, const Size_<_Tp>& sz); + Rect_(const Point_<_Tp>& pt1, const Point_<_Tp>& pt2); + + Rect_& operator = ( const Rect_& r ); + //! the top-left corner + Point_<_Tp> tl() const; + //! the bottom-right corner + Point_<_Tp> br() const; + + //! size (width, height) of the rectangle + Size_<_Tp> size() const; + //! area (width*height) of the rectangle + _Tp area() const; + //! true if empty + bool empty() const; + + //! conversion to another data type + template operator Rect_<_Tp2>() const; + + //! checks whether the rectangle contains the point + bool contains(const Point_<_Tp>& pt) const; + + _Tp x; //!< x coordinate of the top-left corner + _Tp y; //!< y coordinate of the top-left corner + _Tp width; //!< width of the rectangle + _Tp height; //!< height of the rectangle +}; + +typedef Rect_ Rect2i; +typedef Rect_ Rect2f; +typedef Rect_ Rect2d; +typedef Rect2i Rect; + +template class DataType< Rect_<_Tp> > +{ +public: + typedef Rect_<_Tp> value_type; + typedef Rect_::work_type> work_type; + typedef _Tp channel_type; + + enum { generic_type = 0, + channels = 4, + fmt = traits::SafeFmt::fmt + ((channels - 1) << 8) +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + ,depth = DataType::depth + ,type = CV_MAKETYPE(depth, channels) +#endif + }; + + typedef Vec vec_type; +}; + +namespace traits { +template +struct Depth< Rect_<_Tp> > { enum { value = Depth<_Tp>::value }; }; +template +struct Type< Rect_<_Tp> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, 4) }; }; +} // namespace + +///////////////////////////// RotatedRect ///////////////////////////// + +/** @brief The class represents rotated (i.e. not up-right) rectangles on a plane. + +Each rectangle is specified by the center point (mass center), length of each side (represented by +#Size2f structure) and the rotation angle in degrees. + +The sample below demonstrates how to use RotatedRect: +@snippet snippets/core_various.cpp RotatedRect_demo +![image](pics/rotatedrect.png) + +@sa CamShift, fitEllipse, minAreaRect, CvBox2D +*/ +class CV_EXPORTS RotatedRect +{ +public: + //! default constructor + RotatedRect(); + /** full constructor + @param center The rectangle mass center. + @param size Width and height of the rectangle. + @param angle The rotation angle in a clockwise direction. When the angle is 0, 90, 180, 270 etc., + the rectangle becomes an up-right rectangle. + */ + RotatedRect(const Point2f& center, const Size2f& size, float angle); + /** + Any 3 end points of the RotatedRect. They must be given in order (either clockwise or + anticlockwise). + */ + RotatedRect(const Point2f& point1, const Point2f& point2, const Point2f& point3); + + /** returns 4 vertices of the rectangle + @param pts The points array for storing rectangle vertices. The order is bottomLeft, topLeft, topRight, bottomRight. + */ + void points(Point2f pts[]) const; + //! returns the minimal up-right integer rectangle containing the rotated rectangle + Rect boundingRect() const; + //! returns the minimal (exact) floating point rectangle containing the rotated rectangle, not intended for use with images + Rect_ boundingRect2f() const; + //! returns the rectangle mass center + Point2f center; + //! returns width and height of the rectangle + Size2f size; + //! returns the rotation angle. When the angle is 0, 90, 180, 270 etc., the rectangle becomes an up-right rectangle. + float angle; +}; + +template<> class DataType< RotatedRect > +{ +public: + typedef RotatedRect value_type; + typedef value_type work_type; + typedef float channel_type; + + enum { generic_type = 0, + channels = (int)sizeof(value_type)/sizeof(channel_type), // 5 + fmt = traits::SafeFmt::fmt + ((channels - 1) << 8) +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + ,depth = DataType::depth + ,type = CV_MAKETYPE(depth, channels) +#endif + }; + + typedef Vec vec_type; +}; + +namespace traits { +template<> +struct Depth< RotatedRect > { enum { value = Depth::value }; }; +template<> +struct Type< RotatedRect > { enum { value = CV_MAKETYPE(Depth::value, (int)sizeof(RotatedRect)/sizeof(float)) }; }; +} // namespace + + +//////////////////////////////// Range ///////////////////////////////// + +/** @brief Template class specifying a continuous subsequence (slice) of a sequence. + +The class is used to specify a row or a column span in a matrix ( Mat ) and for many other purposes. +Range(a,b) is basically the same as a:b in Matlab or a..b in Python. As in Python, start is an +inclusive left boundary of the range and end is an exclusive right boundary of the range. Such a +half-opened interval is usually denoted as \f$[start,end)\f$ . + +The static method Range::all() returns a special variable that means "the whole sequence" or "the +whole range", just like " : " in Matlab or " ... " in Python. All the methods and functions in +OpenCV that take Range support this special Range::all() value. But, of course, in case of your own +custom processing, you will probably have to check and handle it explicitly: +@code + void my_function(..., const Range& r, ....) + { + if(r == Range::all()) { + // process all the data + } + else { + // process [r.start, r.end) + } + } +@endcode +*/ +class CV_EXPORTS Range +{ +public: + Range(); + Range(int _start, int _end); + int size() const; + bool empty() const; + static Range all(); + + int start, end; +}; + +template<> class DataType +{ +public: + typedef Range value_type; + typedef value_type work_type; + typedef int channel_type; + + enum { generic_type = 0, + channels = 2, + fmt = traits::SafeFmt::fmt + ((channels - 1) << 8) +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + ,depth = DataType::depth + ,type = CV_MAKETYPE(depth, channels) +#endif + }; + + typedef Vec vec_type; +}; + +namespace traits { +template<> +struct Depth< Range > { enum { value = Depth::value }; }; +template<> +struct Type< Range > { enum { value = CV_MAKETYPE(Depth::value, 2) }; }; +} // namespace + + +//////////////////////////////// Scalar_ /////////////////////////////// + +/** @brief Template class for a 4-element vector derived from Vec. + +Being derived from Vec\<_Tp, 4\> , Scalar\_ and Scalar can be used just as typical 4-element +vectors. In addition, they can be converted to/from CvScalar . The type Scalar is widely used in +OpenCV to pass pixel values. +*/ +template class Scalar_ : public Vec<_Tp, 4> +{ +public: + //! default constructor + Scalar_(); + Scalar_(_Tp v0, _Tp v1, _Tp v2=0, _Tp v3=0); + Scalar_(_Tp v0); + + template + Scalar_(const Vec<_Tp2, cn>& v); + + //! returns a scalar with all elements set to v0 + static Scalar_<_Tp> all(_Tp v0); + + //! conversion to another data type + template operator Scalar_() const; + + //! per-element product + Scalar_<_Tp> mul(const Scalar_<_Tp>& a, double scale=1 ) const; + + //! returns (v0, -v1, -v2, -v3) + Scalar_<_Tp> conj() const; + + //! returns true iff v1 == v2 == v3 == 0 + bool isReal() const; +}; + +typedef Scalar_ Scalar; + +template class DataType< Scalar_<_Tp> > +{ +public: + typedef Scalar_<_Tp> value_type; + typedef Scalar_::work_type> work_type; + typedef _Tp channel_type; + + enum { generic_type = 0, + channels = 4, + fmt = traits::SafeFmt::fmt + ((channels - 1) << 8) +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + ,depth = DataType::depth + ,type = CV_MAKETYPE(depth, channels) +#endif + }; + + typedef Vec vec_type; +}; + +namespace traits { +template +struct Depth< Scalar_<_Tp> > { enum { value = Depth<_Tp>::value }; }; +template +struct Type< Scalar_<_Tp> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, 4) }; }; +} // namespace + + +/////////////////////////////// KeyPoint //////////////////////////////// + +/** @brief Data structure for salient point detectors. + +The class instance stores a keypoint, i.e. a point feature found by one of many available keypoint +detectors, such as Harris corner detector, #FAST, %StarDetector, %SURF, %SIFT etc. + +The keypoint is characterized by the 2D position, scale (proportional to the diameter of the +neighborhood that needs to be taken into account), orientation and some other parameters. The +keypoint neighborhood is then analyzed by another algorithm that builds a descriptor (usually +represented as a feature vector). The keypoints representing the same object in different images +can then be matched using %KDTree or another method. +*/ +class CV_EXPORTS_W_SIMPLE KeyPoint +{ +public: + //! the default constructor + CV_WRAP KeyPoint(); + /** + @param _pt x & y coordinates of the keypoint + @param _size keypoint diameter + @param _angle keypoint orientation + @param _response keypoint detector response on the keypoint (that is, strength of the keypoint) + @param _octave pyramid octave in which the keypoint has been detected + @param _class_id object id + */ + KeyPoint(Point2f _pt, float _size, float _angle=-1, float _response=0, int _octave=0, int _class_id=-1); + /** + @param x x-coordinate of the keypoint + @param y y-coordinate of the keypoint + @param _size keypoint diameter + @param _angle keypoint orientation + @param _response keypoint detector response on the keypoint (that is, strength of the keypoint) + @param _octave pyramid octave in which the keypoint has been detected + @param _class_id object id + */ + CV_WRAP KeyPoint(float x, float y, float _size, float _angle=-1, float _response=0, int _octave=0, int _class_id=-1); + + size_t hash() const; + + /** + This method converts vector of keypoints to vector of points or the reverse, where each keypoint is + assigned the same size and the same orientation. + + @param keypoints Keypoints obtained from any feature detection algorithm like SIFT/SURF/ORB + @param points2f Array of (x,y) coordinates of each keypoint + @param keypointIndexes Array of indexes of keypoints to be converted to points. (Acts like a mask to + convert only specified keypoints) + */ + CV_WRAP static void convert(const std::vector& keypoints, + CV_OUT std::vector& points2f, + const std::vector& keypointIndexes=std::vector()); + /** @overload + @param points2f Array of (x,y) coordinates of each keypoint + @param keypoints Keypoints obtained from any feature detection algorithm like SIFT/SURF/ORB + @param size keypoint diameter + @param response keypoint detector response on the keypoint (that is, strength of the keypoint) + @param octave pyramid octave in which the keypoint has been detected + @param class_id object id + */ + CV_WRAP static void convert(const std::vector& points2f, + CV_OUT std::vector& keypoints, + float size=1, float response=1, int octave=0, int class_id=-1); + + /** + This method computes overlap for pair of keypoints. Overlap is the ratio between area of keypoint + regions' intersection and area of keypoint regions' union (considering keypoint region as circle). + If they don't overlap, we get zero. If they coincide at same location with same size, we get 1. + @param kp1 First keypoint + @param kp2 Second keypoint + */ + CV_WRAP static float overlap(const KeyPoint& kp1, const KeyPoint& kp2); + + CV_PROP_RW Point2f pt; //!< coordinates of the keypoints + CV_PROP_RW float size; //!< diameter of the meaningful keypoint neighborhood + CV_PROP_RW float angle; //!< computed orientation of the keypoint (-1 if not applicable); + //!< it's in [0,360) degrees and measured relative to + //!< image coordinate system, ie in clockwise. + CV_PROP_RW float response; //!< the response by which the most strong keypoints have been selected. Can be used for the further sorting or subsampling + CV_PROP_RW int octave; //!< octave (pyramid layer) from which the keypoint has been extracted + CV_PROP_RW int class_id; //!< object class (if the keypoints need to be clustered by an object they belong to) +}; + +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED +template<> class DataType +{ +public: + typedef KeyPoint value_type; + typedef float work_type; + typedef float channel_type; + + enum { generic_type = 0, + depth = DataType::depth, + channels = (int)(sizeof(value_type)/sizeof(channel_type)), // 7 + fmt = DataType::fmt + ((channels - 1) << 8), + type = CV_MAKETYPE(depth, channels) + }; + + typedef Vec vec_type; +}; +#endif + + +//////////////////////////////// DMatch ///////////////////////////////// + +/** @brief Class for matching keypoint descriptors + +query descriptor index, train descriptor index, train image index, and distance between +descriptors. +*/ +class CV_EXPORTS_W_SIMPLE DMatch +{ +public: + CV_WRAP DMatch(); + CV_WRAP DMatch(int _queryIdx, int _trainIdx, float _distance); + CV_WRAP DMatch(int _queryIdx, int _trainIdx, int _imgIdx, float _distance); + + CV_PROP_RW int queryIdx; //!< query descriptor index + CV_PROP_RW int trainIdx; //!< train descriptor index + CV_PROP_RW int imgIdx; //!< train image index + + CV_PROP_RW float distance; + + // less is better + bool operator<(const DMatch &m) const; +}; + +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED +template<> class DataType +{ +public: + typedef DMatch value_type; + typedef int work_type; + typedef int channel_type; + + enum { generic_type = 0, + depth = DataType::depth, + channels = (int)(sizeof(value_type)/sizeof(channel_type)), // 4 + fmt = DataType::fmt + ((channels - 1) << 8), + type = CV_MAKETYPE(depth, channels) + }; + + typedef Vec vec_type; +}; +#endif + + +///////////////////////////// TermCriteria ////////////////////////////// + +/** @brief The class defining termination criteria for iterative algorithms. + +You can initialize it by default constructor and then override any parameters, or the structure may +be fully initialized using the advanced variant of the constructor. +*/ +class CV_EXPORTS TermCriteria +{ +public: + /** + Criteria type, can be one of: COUNT, EPS or COUNT + EPS + */ + enum Type + { + COUNT=1, //!< the maximum number of iterations or elements to compute + MAX_ITER=COUNT, //!< ditto + EPS=2 //!< the desired accuracy or change in parameters at which the iterative algorithm stops + }; + + //! default constructor + TermCriteria(); + /** + @param type The type of termination criteria, one of TermCriteria::Type + @param maxCount The maximum number of iterations or elements to compute. + @param epsilon The desired accuracy or change in parameters at which the iterative algorithm stops. + */ + TermCriteria(int type, int maxCount, double epsilon); + + inline bool isValid() const + { + const bool isCount = (type & COUNT) && maxCount > 0; + const bool isEps = (type & EPS) && !cvIsNaN(epsilon); + return isCount || isEps; + } + + int type; //!< the type of termination criteria: COUNT, EPS or COUNT + EPS + int maxCount; //!< the maximum number of iterations/elements + double epsilon; //!< the desired accuracy +}; + + +//! @} core_basic + +///////////////////////// raster image moments ////////////////////////// + +//! @addtogroup imgproc_shape +//! @{ + +/** @brief struct returned by cv::moments + +The spatial moments \f$\texttt{Moments::m}_{ji}\f$ are computed as: + +\f[\texttt{m} _{ji}= \sum _{x,y} \left ( \texttt{array} (x,y) \cdot x^j \cdot y^i \right )\f] + +The central moments \f$\texttt{Moments::mu}_{ji}\f$ are computed as: + +\f[\texttt{mu} _{ji}= \sum _{x,y} \left ( \texttt{array} (x,y) \cdot (x - \bar{x} )^j \cdot (y - \bar{y} )^i \right )\f] + +where \f$(\bar{x}, \bar{y})\f$ is the mass center: + +\f[\bar{x} = \frac{\texttt{m}_{10}}{\texttt{m}_{00}} , \; \bar{y} = \frac{\texttt{m}_{01}}{\texttt{m}_{00}}\f] + +The normalized central moments \f$\texttt{Moments::nu}_{ij}\f$ are computed as: + +\f[\texttt{nu} _{ji}= \frac{\texttt{mu}_{ji}}{\texttt{m}_{00}^{(i+j)/2+1}} .\f] + +@note +\f$\texttt{mu}_{00}=\texttt{m}_{00}\f$, \f$\texttt{nu}_{00}=1\f$ +\f$\texttt{nu}_{10}=\texttt{mu}_{10}=\texttt{mu}_{01}=\texttt{mu}_{10}=0\f$ , hence the values are not +stored. + +The moments of a contour are defined in the same way but computed using the Green's formula (see +). So, due to a limited raster resolution, the moments +computed for a contour are slightly different from the moments computed for the same rasterized +contour. + +@note +Since the contour moments are computed using Green formula, you may get seemingly odd results for +contours with self-intersections, e.g. a zero area (m00) for butterfly-shaped contours. + */ +class CV_EXPORTS_W_MAP Moments +{ +public: + //! the default constructor + Moments(); + //! the full constructor + Moments(double m00, double m10, double m01, double m20, double m11, + double m02, double m30, double m21, double m12, double m03 ); + ////! the conversion from CvMoments + //Moments( const CvMoments& moments ); + ////! the conversion to CvMoments + //operator CvMoments() const; + + //! @name spatial moments + //! @{ + CV_PROP_RW double m00, m10, m01, m20, m11, m02, m30, m21, m12, m03; + //! @} + + //! @name central moments + //! @{ + CV_PROP_RW double mu20, mu11, mu02, mu30, mu21, mu12, mu03; + //! @} + + //! @name central normalized moments + //! @{ + CV_PROP_RW double nu20, nu11, nu02, nu30, nu21, nu12, nu03; + //! @} +}; + +template<> class DataType +{ +public: + typedef Moments value_type; + typedef double work_type; + typedef double channel_type; + + enum { generic_type = 0, + channels = (int)(sizeof(value_type)/sizeof(channel_type)), // 24 + fmt = DataType::fmt + ((channels - 1) << 8) +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + ,depth = DataType::depth + ,type = CV_MAKETYPE(depth, channels) +#endif + }; + + typedef Vec vec_type; +}; + +namespace traits { +template<> +struct Depth< Moments > { enum { value = Depth::value }; }; +template<> +struct Type< Moments > { enum { value = CV_MAKETYPE(Depth::value, (int)(sizeof(Moments)/sizeof(double))) }; }; +} // namespace + +//! @} imgproc_shape + +//! @cond IGNORED + +///////////////////////////////////////////////////////////////////////// +///////////////////////////// Implementation //////////////////////////// +///////////////////////////////////////////////////////////////////////// + +//////////////////////////////// Complex //////////////////////////////// + +template inline +Complex<_Tp>::Complex() + : re(0), im(0) {} + +template inline +Complex<_Tp>::Complex( _Tp _re, _Tp _im ) + : re(_re), im(_im) {} + +template template inline +Complex<_Tp>::operator Complex() const +{ + return Complex(saturate_cast(re), saturate_cast(im)); +} + +template inline +Complex<_Tp> Complex<_Tp>::conj() const +{ + return Complex<_Tp>(re, -im); +} + + +template static inline +bool operator == (const Complex<_Tp>& a, const Complex<_Tp>& b) +{ + return a.re == b.re && a.im == b.im; +} + +template static inline +bool operator != (const Complex<_Tp>& a, const Complex<_Tp>& b) +{ + return a.re != b.re || a.im != b.im; +} + +template static inline +Complex<_Tp> operator + (const Complex<_Tp>& a, const Complex<_Tp>& b) +{ + return Complex<_Tp>( a.re + b.re, a.im + b.im ); +} + +template static inline +Complex<_Tp>& operator += (Complex<_Tp>& a, const Complex<_Tp>& b) +{ + a.re += b.re; a.im += b.im; + return a; +} + +template static inline +Complex<_Tp> operator - (const Complex<_Tp>& a, const Complex<_Tp>& b) +{ + return Complex<_Tp>( a.re - b.re, a.im - b.im ); +} + +template static inline +Complex<_Tp>& operator -= (Complex<_Tp>& a, const Complex<_Tp>& b) +{ + a.re -= b.re; a.im -= b.im; + return a; +} + +template static inline +Complex<_Tp> operator - (const Complex<_Tp>& a) +{ + return Complex<_Tp>(-a.re, -a.im); +} + +template static inline +Complex<_Tp> operator * (const Complex<_Tp>& a, const Complex<_Tp>& b) +{ + return Complex<_Tp>( a.re*b.re - a.im*b.im, a.re*b.im + a.im*b.re ); +} + +template static inline +Complex<_Tp> operator * (const Complex<_Tp>& a, _Tp b) +{ + return Complex<_Tp>( a.re*b, a.im*b ); +} + +template static inline +Complex<_Tp> operator * (_Tp b, const Complex<_Tp>& a) +{ + return Complex<_Tp>( a.re*b, a.im*b ); +} + +template static inline +Complex<_Tp> operator + (const Complex<_Tp>& a, _Tp b) +{ + return Complex<_Tp>( a.re + b, a.im ); +} + +template static inline +Complex<_Tp> operator - (const Complex<_Tp>& a, _Tp b) +{ return Complex<_Tp>( a.re - b, a.im ); } + +template static inline +Complex<_Tp> operator + (_Tp b, const Complex<_Tp>& a) +{ + return Complex<_Tp>( a.re + b, a.im ); +} + +template static inline +Complex<_Tp> operator - (_Tp b, const Complex<_Tp>& a) +{ + return Complex<_Tp>( b - a.re, -a.im ); +} + +template static inline +Complex<_Tp>& operator += (Complex<_Tp>& a, _Tp b) +{ + a.re += b; return a; +} + +template static inline +Complex<_Tp>& operator -= (Complex<_Tp>& a, _Tp b) +{ + a.re -= b; return a; +} + +template static inline +Complex<_Tp>& operator *= (Complex<_Tp>& a, _Tp b) +{ + a.re *= b; a.im *= b; return a; +} + +template static inline +double abs(const Complex<_Tp>& a) +{ + return std::sqrt( (double)a.re*a.re + (double)a.im*a.im); +} + +template static inline +Complex<_Tp> operator / (const Complex<_Tp>& a, const Complex<_Tp>& b) +{ + double t = 1./((double)b.re*b.re + (double)b.im*b.im); + return Complex<_Tp>( (_Tp)((a.re*b.re + a.im*b.im)*t), + (_Tp)((-a.re*b.im + a.im*b.re)*t) ); +} + +template static inline +Complex<_Tp>& operator /= (Complex<_Tp>& a, const Complex<_Tp>& b) +{ + a = a / b; + return a; +} + +template static inline +Complex<_Tp> operator / (const Complex<_Tp>& a, _Tp b) +{ + _Tp t = (_Tp)1/b; + return Complex<_Tp>( a.re*t, a.im*t ); +} + +template static inline +Complex<_Tp> operator / (_Tp b, const Complex<_Tp>& a) +{ + return Complex<_Tp>(b)/a; +} + +template static inline +Complex<_Tp> operator /= (const Complex<_Tp>& a, _Tp b) +{ + _Tp t = (_Tp)1/b; + a.re *= t; a.im *= t; return a; +} + + + +//////////////////////////////// 2D Point /////////////////////////////// + +template inline +Point_<_Tp>::Point_() + : x(0), y(0) {} + +template inline +Point_<_Tp>::Point_(_Tp _x, _Tp _y) + : x(_x), y(_y) {} + +template inline +Point_<_Tp>::Point_(const Point_& pt) + : x(pt.x), y(pt.y) {} + +template inline +Point_<_Tp>::Point_(const Size_<_Tp>& sz) + : x(sz.width), y(sz.height) {} + +template inline +Point_<_Tp>::Point_(const Vec<_Tp,2>& v) + : x(v[0]), y(v[1]) {} + +template inline +Point_<_Tp>& Point_<_Tp>::operator = (const Point_& pt) +{ + x = pt.x; y = pt.y; + return *this; +} + +template template inline +Point_<_Tp>::operator Point_<_Tp2>() const +{ + return Point_<_Tp2>(saturate_cast<_Tp2>(x), saturate_cast<_Tp2>(y)); +} + +template inline +Point_<_Tp>::operator Vec<_Tp, 2>() const +{ + return Vec<_Tp, 2>(x, y); +} + +template inline +_Tp Point_<_Tp>::dot(const Point_& pt) const +{ + return saturate_cast<_Tp>(x*pt.x + y*pt.y); +} + +template inline +double Point_<_Tp>::ddot(const Point_& pt) const +{ + return (double)x*pt.x + (double)y*pt.y; +} + +template inline +double Point_<_Tp>::cross(const Point_& pt) const +{ + return (double)x*pt.y - (double)y*pt.x; +} + +template inline bool +Point_<_Tp>::inside( const Rect_<_Tp>& r ) const +{ + return r.contains(*this); +} + + +template static inline +Point_<_Tp>& operator += (Point_<_Tp>& a, const Point_<_Tp>& b) +{ + a.x += b.x; + a.y += b.y; + return a; +} + +template static inline +Point_<_Tp>& operator -= (Point_<_Tp>& a, const Point_<_Tp>& b) +{ + a.x -= b.x; + a.y -= b.y; + return a; +} + +template static inline +Point_<_Tp>& operator *= (Point_<_Tp>& a, int b) +{ + a.x = saturate_cast<_Tp>(a.x * b); + a.y = saturate_cast<_Tp>(a.y * b); + return a; +} + +template static inline +Point_<_Tp>& operator *= (Point_<_Tp>& a, float b) +{ + a.x = saturate_cast<_Tp>(a.x * b); + a.y = saturate_cast<_Tp>(a.y * b); + return a; +} + +template static inline +Point_<_Tp>& operator *= (Point_<_Tp>& a, double b) +{ + a.x = saturate_cast<_Tp>(a.x * b); + a.y = saturate_cast<_Tp>(a.y * b); + return a; +} + +template static inline +Point_<_Tp>& operator /= (Point_<_Tp>& a, int b) +{ + a.x = saturate_cast<_Tp>(a.x / b); + a.y = saturate_cast<_Tp>(a.y / b); + return a; +} + +template static inline +Point_<_Tp>& operator /= (Point_<_Tp>& a, float b) +{ + a.x = saturate_cast<_Tp>(a.x / b); + a.y = saturate_cast<_Tp>(a.y / b); + return a; +} + +template static inline +Point_<_Tp>& operator /= (Point_<_Tp>& a, double b) +{ + a.x = saturate_cast<_Tp>(a.x / b); + a.y = saturate_cast<_Tp>(a.y / b); + return a; +} + +template static inline +double norm(const Point_<_Tp>& pt) +{ + return std::sqrt((double)pt.x*pt.x + (double)pt.y*pt.y); +} + +template static inline +bool operator == (const Point_<_Tp>& a, const Point_<_Tp>& b) +{ + return a.x == b.x && a.y == b.y; +} + +template static inline +bool operator != (const Point_<_Tp>& a, const Point_<_Tp>& b) +{ + return a.x != b.x || a.y != b.y; +} + +template static inline +Point_<_Tp> operator + (const Point_<_Tp>& a, const Point_<_Tp>& b) +{ + return Point_<_Tp>( saturate_cast<_Tp>(a.x + b.x), saturate_cast<_Tp>(a.y + b.y) ); +} + +template static inline +Point_<_Tp> operator - (const Point_<_Tp>& a, const Point_<_Tp>& b) +{ + return Point_<_Tp>( saturate_cast<_Tp>(a.x - b.x), saturate_cast<_Tp>(a.y - b.y) ); +} + +template static inline +Point_<_Tp> operator - (const Point_<_Tp>& a) +{ + return Point_<_Tp>( saturate_cast<_Tp>(-a.x), saturate_cast<_Tp>(-a.y) ); +} + +template static inline +Point_<_Tp> operator * (const Point_<_Tp>& a, int b) +{ + return Point_<_Tp>( saturate_cast<_Tp>(a.x*b), saturate_cast<_Tp>(a.y*b) ); +} + +template static inline +Point_<_Tp> operator * (int a, const Point_<_Tp>& b) +{ + return Point_<_Tp>( saturate_cast<_Tp>(b.x*a), saturate_cast<_Tp>(b.y*a) ); +} + +template static inline +Point_<_Tp> operator * (const Point_<_Tp>& a, float b) +{ + return Point_<_Tp>( saturate_cast<_Tp>(a.x*b), saturate_cast<_Tp>(a.y*b) ); +} + +template static inline +Point_<_Tp> operator * (float a, const Point_<_Tp>& b) +{ + return Point_<_Tp>( saturate_cast<_Tp>(b.x*a), saturate_cast<_Tp>(b.y*a) ); +} + +template static inline +Point_<_Tp> operator * (const Point_<_Tp>& a, double b) +{ + return Point_<_Tp>( saturate_cast<_Tp>(a.x*b), saturate_cast<_Tp>(a.y*b) ); +} + +template static inline +Point_<_Tp> operator * (double a, const Point_<_Tp>& b) +{ + return Point_<_Tp>( saturate_cast<_Tp>(b.x*a), saturate_cast<_Tp>(b.y*a) ); +} + +template static inline +Point_<_Tp> operator * (const Matx<_Tp, 2, 2>& a, const Point_<_Tp>& b) +{ + Matx<_Tp, 2, 1> tmp = a * Vec<_Tp,2>(b.x, b.y); + return Point_<_Tp>(tmp.val[0], tmp.val[1]); +} + +template static inline +Point3_<_Tp> operator * (const Matx<_Tp, 3, 3>& a, const Point_<_Tp>& b) +{ + Matx<_Tp, 3, 1> tmp = a * Vec<_Tp,3>(b.x, b.y, 1); + return Point3_<_Tp>(tmp.val[0], tmp.val[1], tmp.val[2]); +} + +template static inline +Point_<_Tp> operator / (const Point_<_Tp>& a, int b) +{ + Point_<_Tp> tmp(a); + tmp /= b; + return tmp; +} + +template static inline +Point_<_Tp> operator / (const Point_<_Tp>& a, float b) +{ + Point_<_Tp> tmp(a); + tmp /= b; + return tmp; +} + +template static inline +Point_<_Tp> operator / (const Point_<_Tp>& a, double b) +{ + Point_<_Tp> tmp(a); + tmp /= b; + return tmp; +} + + +template static inline _AccTp normL2Sqr(const Point_& pt); +template static inline _AccTp normL2Sqr(const Point_& pt); +template static inline _AccTp normL2Sqr(const Point_& pt); +template static inline _AccTp normL2Sqr(const Point_& pt); + +template<> inline int normL2Sqr(const Point_& pt) { return pt.dot(pt); } +template<> inline int64 normL2Sqr(const Point_& pt) { return pt.dot(pt); } +template<> inline float normL2Sqr(const Point_& pt) { return pt.dot(pt); } +template<> inline double normL2Sqr(const Point_& pt) { return pt.dot(pt); } + +template<> inline double normL2Sqr(const Point_& pt) { return pt.ddot(pt); } +template<> inline double normL2Sqr(const Point_& pt) { return pt.ddot(pt); } + + + +//////////////////////////////// 3D Point /////////////////////////////// + +template inline +Point3_<_Tp>::Point3_() + : x(0), y(0), z(0) {} + +template inline +Point3_<_Tp>::Point3_(_Tp _x, _Tp _y, _Tp _z) + : x(_x), y(_y), z(_z) {} + +template inline +Point3_<_Tp>::Point3_(const Point3_& pt) + : x(pt.x), y(pt.y), z(pt.z) {} + +template inline +Point3_<_Tp>::Point3_(const Point_<_Tp>& pt) + : x(pt.x), y(pt.y), z(_Tp()) {} + +template inline +Point3_<_Tp>::Point3_(const Vec<_Tp, 3>& v) + : x(v[0]), y(v[1]), z(v[2]) {} + +template template inline +Point3_<_Tp>::operator Point3_<_Tp2>() const +{ + return Point3_<_Tp2>(saturate_cast<_Tp2>(x), saturate_cast<_Tp2>(y), saturate_cast<_Tp2>(z)); +} + +#if OPENCV_ABI_COMPATIBILITY > 300 +template template inline +Point3_<_Tp>::operator Vec<_Tp2, 3>() const +{ + return Vec<_Tp2, 3>(x, y, z); +} +#else +template inline +Point3_<_Tp>::operator Vec<_Tp, 3>() const +{ + return Vec<_Tp, 3>(x, y, z); +} +#endif + +template inline +Point3_<_Tp>& Point3_<_Tp>::operator = (const Point3_& pt) +{ + x = pt.x; y = pt.y; z = pt.z; + return *this; +} + +template inline +_Tp Point3_<_Tp>::dot(const Point3_& pt) const +{ + return saturate_cast<_Tp>(x*pt.x + y*pt.y + z*pt.z); +} + +template inline +double Point3_<_Tp>::ddot(const Point3_& pt) const +{ + return (double)x*pt.x + (double)y*pt.y + (double)z*pt.z; +} + +template inline +Point3_<_Tp> Point3_<_Tp>::cross(const Point3_<_Tp>& pt) const +{ + return Point3_<_Tp>(y*pt.z - z*pt.y, z*pt.x - x*pt.z, x*pt.y - y*pt.x); +} + + +template static inline +Point3_<_Tp>& operator += (Point3_<_Tp>& a, const Point3_<_Tp>& b) +{ + a.x += b.x; + a.y += b.y; + a.z += b.z; + return a; +} + +template static inline +Point3_<_Tp>& operator -= (Point3_<_Tp>& a, const Point3_<_Tp>& b) +{ + a.x -= b.x; + a.y -= b.y; + a.z -= b.z; + return a; +} + +template static inline +Point3_<_Tp>& operator *= (Point3_<_Tp>& a, int b) +{ + a.x = saturate_cast<_Tp>(a.x * b); + a.y = saturate_cast<_Tp>(a.y * b); + a.z = saturate_cast<_Tp>(a.z * b); + return a; +} + +template static inline +Point3_<_Tp>& operator *= (Point3_<_Tp>& a, float b) +{ + a.x = saturate_cast<_Tp>(a.x * b); + a.y = saturate_cast<_Tp>(a.y * b); + a.z = saturate_cast<_Tp>(a.z * b); + return a; +} + +template static inline +Point3_<_Tp>& operator *= (Point3_<_Tp>& a, double b) +{ + a.x = saturate_cast<_Tp>(a.x * b); + a.y = saturate_cast<_Tp>(a.y * b); + a.z = saturate_cast<_Tp>(a.z * b); + return a; +} + +template static inline +Point3_<_Tp>& operator /= (Point3_<_Tp>& a, int b) +{ + a.x = saturate_cast<_Tp>(a.x / b); + a.y = saturate_cast<_Tp>(a.y / b); + a.z = saturate_cast<_Tp>(a.z / b); + return a; +} + +template static inline +Point3_<_Tp>& operator /= (Point3_<_Tp>& a, float b) +{ + a.x = saturate_cast<_Tp>(a.x / b); + a.y = saturate_cast<_Tp>(a.y / b); + a.z = saturate_cast<_Tp>(a.z / b); + return a; +} + +template static inline +Point3_<_Tp>& operator /= (Point3_<_Tp>& a, double b) +{ + a.x = saturate_cast<_Tp>(a.x / b); + a.y = saturate_cast<_Tp>(a.y / b); + a.z = saturate_cast<_Tp>(a.z / b); + return a; +} + +template static inline +double norm(const Point3_<_Tp>& pt) +{ + return std::sqrt((double)pt.x*pt.x + (double)pt.y*pt.y + (double)pt.z*pt.z); +} + +template static inline +bool operator == (const Point3_<_Tp>& a, const Point3_<_Tp>& b) +{ + return a.x == b.x && a.y == b.y && a.z == b.z; +} + +template static inline +bool operator != (const Point3_<_Tp>& a, const Point3_<_Tp>& b) +{ + return a.x != b.x || a.y != b.y || a.z != b.z; +} + +template static inline +Point3_<_Tp> operator + (const Point3_<_Tp>& a, const Point3_<_Tp>& b) +{ + return Point3_<_Tp>( saturate_cast<_Tp>(a.x + b.x), saturate_cast<_Tp>(a.y + b.y), saturate_cast<_Tp>(a.z + b.z)); +} + +template static inline +Point3_<_Tp> operator - (const Point3_<_Tp>& a, const Point3_<_Tp>& b) +{ + return Point3_<_Tp>( saturate_cast<_Tp>(a.x - b.x), saturate_cast<_Tp>(a.y - b.y), saturate_cast<_Tp>(a.z - b.z)); +} + +template static inline +Point3_<_Tp> operator - (const Point3_<_Tp>& a) +{ + return Point3_<_Tp>( saturate_cast<_Tp>(-a.x), saturate_cast<_Tp>(-a.y), saturate_cast<_Tp>(-a.z) ); +} + +template static inline +Point3_<_Tp> operator * (const Point3_<_Tp>& a, int b) +{ + return Point3_<_Tp>( saturate_cast<_Tp>(a.x*b), saturate_cast<_Tp>(a.y*b), saturate_cast<_Tp>(a.z*b) ); +} + +template static inline +Point3_<_Tp> operator * (int a, const Point3_<_Tp>& b) +{ + return Point3_<_Tp>( saturate_cast<_Tp>(b.x * a), saturate_cast<_Tp>(b.y * a), saturate_cast<_Tp>(b.z * a) ); +} + +template static inline +Point3_<_Tp> operator * (const Point3_<_Tp>& a, float b) +{ + return Point3_<_Tp>( saturate_cast<_Tp>(a.x * b), saturate_cast<_Tp>(a.y * b), saturate_cast<_Tp>(a.z * b) ); +} + +template static inline +Point3_<_Tp> operator * (float a, const Point3_<_Tp>& b) +{ + return Point3_<_Tp>( saturate_cast<_Tp>(b.x * a), saturate_cast<_Tp>(b.y * a), saturate_cast<_Tp>(b.z * a) ); +} + +template static inline +Point3_<_Tp> operator * (const Point3_<_Tp>& a, double b) +{ + return Point3_<_Tp>( saturate_cast<_Tp>(a.x * b), saturate_cast<_Tp>(a.y * b), saturate_cast<_Tp>(a.z * b) ); +} + +template static inline +Point3_<_Tp> operator * (double a, const Point3_<_Tp>& b) +{ + return Point3_<_Tp>( saturate_cast<_Tp>(b.x * a), saturate_cast<_Tp>(b.y * a), saturate_cast<_Tp>(b.z * a) ); +} + +template static inline +Point3_<_Tp> operator * (const Matx<_Tp, 3, 3>& a, const Point3_<_Tp>& b) +{ + Matx<_Tp, 3, 1> tmp = a * Vec<_Tp,3>(b.x, b.y, b.z); + return Point3_<_Tp>(tmp.val[0], tmp.val[1], tmp.val[2]); +} + +template static inline +Matx<_Tp, 4, 1> operator * (const Matx<_Tp, 4, 4>& a, const Point3_<_Tp>& b) +{ + return a * Matx<_Tp, 4, 1>(b.x, b.y, b.z, 1); +} + +template static inline +Point3_<_Tp> operator / (const Point3_<_Tp>& a, int b) +{ + Point3_<_Tp> tmp(a); + tmp /= b; + return tmp; +} + +template static inline +Point3_<_Tp> operator / (const Point3_<_Tp>& a, float b) +{ + Point3_<_Tp> tmp(a); + tmp /= b; + return tmp; +} + +template static inline +Point3_<_Tp> operator / (const Point3_<_Tp>& a, double b) +{ + Point3_<_Tp> tmp(a); + tmp /= b; + return tmp; +} + + + +////////////////////////////////// Size ///////////////////////////////// + +template inline +Size_<_Tp>::Size_() + : width(0), height(0) {} + +template inline +Size_<_Tp>::Size_(_Tp _width, _Tp _height) + : width(_width), height(_height) {} + +template inline +Size_<_Tp>::Size_(const Size_& sz) + : width(sz.width), height(sz.height) {} + +template inline +Size_<_Tp>::Size_(const Point_<_Tp>& pt) + : width(pt.x), height(pt.y) {} + +template template inline +Size_<_Tp>::operator Size_<_Tp2>() const +{ + return Size_<_Tp2>(saturate_cast<_Tp2>(width), saturate_cast<_Tp2>(height)); +} + +template inline +Size_<_Tp>& Size_<_Tp>::operator = (const Size_<_Tp>& sz) +{ + width = sz.width; height = sz.height; + return *this; +} + +template inline +_Tp Size_<_Tp>::area() const +{ + const _Tp result = width * height; + CV_DbgAssert(!std::numeric_limits<_Tp>::is_integer + || width == 0 || result / width == height); // make sure the result fits in the return value + return result; +} + +template inline +bool Size_<_Tp>::empty() const +{ + return width <= 0 || height <= 0; +} + + +template static inline +Size_<_Tp>& operator *= (Size_<_Tp>& a, _Tp b) +{ + a.width *= b; + a.height *= b; + return a; +} + +template static inline +Size_<_Tp> operator * (const Size_<_Tp>& a, _Tp b) +{ + Size_<_Tp> tmp(a); + tmp *= b; + return tmp; +} + +template static inline +Size_<_Tp>& operator /= (Size_<_Tp>& a, _Tp b) +{ + a.width /= b; + a.height /= b; + return a; +} + +template static inline +Size_<_Tp> operator / (const Size_<_Tp>& a, _Tp b) +{ + Size_<_Tp> tmp(a); + tmp /= b; + return tmp; +} + +template static inline +Size_<_Tp>& operator += (Size_<_Tp>& a, const Size_<_Tp>& b) +{ + a.width += b.width; + a.height += b.height; + return a; +} + +template static inline +Size_<_Tp> operator + (const Size_<_Tp>& a, const Size_<_Tp>& b) +{ + Size_<_Tp> tmp(a); + tmp += b; + return tmp; +} + +template static inline +Size_<_Tp>& operator -= (Size_<_Tp>& a, const Size_<_Tp>& b) +{ + a.width -= b.width; + a.height -= b.height; + return a; +} + +template static inline +Size_<_Tp> operator - (const Size_<_Tp>& a, const Size_<_Tp>& b) +{ + Size_<_Tp> tmp(a); + tmp -= b; + return tmp; +} + +template static inline +bool operator == (const Size_<_Tp>& a, const Size_<_Tp>& b) +{ + return a.width == b.width && a.height == b.height; +} + +template static inline +bool operator != (const Size_<_Tp>& a, const Size_<_Tp>& b) +{ + return !(a == b); +} + + + +////////////////////////////////// Rect ///////////////////////////////// + +template inline +Rect_<_Tp>::Rect_() + : x(0), y(0), width(0), height(0) {} + +template inline +Rect_<_Tp>::Rect_(_Tp _x, _Tp _y, _Tp _width, _Tp _height) + : x(_x), y(_y), width(_width), height(_height) {} + +template inline +Rect_<_Tp>::Rect_(const Rect_<_Tp>& r) + : x(r.x), y(r.y), width(r.width), height(r.height) {} + +template inline +Rect_<_Tp>::Rect_(const Point_<_Tp>& org, const Size_<_Tp>& sz) + : x(org.x), y(org.y), width(sz.width), height(sz.height) {} + +template inline +Rect_<_Tp>::Rect_(const Point_<_Tp>& pt1, const Point_<_Tp>& pt2) +{ + x = std::min(pt1.x, pt2.x); + y = std::min(pt1.y, pt2.y); + width = std::max(pt1.x, pt2.x) - x; + height = std::max(pt1.y, pt2.y) - y; +} + +template inline +Rect_<_Tp>& Rect_<_Tp>::operator = ( const Rect_<_Tp>& r ) +{ + x = r.x; + y = r.y; + width = r.width; + height = r.height; + return *this; +} + +template inline +Point_<_Tp> Rect_<_Tp>::tl() const +{ + return Point_<_Tp>(x,y); +} + +template inline +Point_<_Tp> Rect_<_Tp>::br() const +{ + return Point_<_Tp>(x + width, y + height); +} + +template inline +Size_<_Tp> Rect_<_Tp>::size() const +{ + return Size_<_Tp>(width, height); +} + +template inline +_Tp Rect_<_Tp>::area() const +{ + const _Tp result = width * height; + CV_DbgAssert(!std::numeric_limits<_Tp>::is_integer + || width == 0 || result / width == height); // make sure the result fits in the return value + return result; +} + +template inline +bool Rect_<_Tp>::empty() const +{ + return width <= 0 || height <= 0; +} + +template template inline +Rect_<_Tp>::operator Rect_<_Tp2>() const +{ + return Rect_<_Tp2>(saturate_cast<_Tp2>(x), saturate_cast<_Tp2>(y), saturate_cast<_Tp2>(width), saturate_cast<_Tp2>(height)); +} + +template inline +bool Rect_<_Tp>::contains(const Point_<_Tp>& pt) const +{ + return x <= pt.x && pt.x < x + width && y <= pt.y && pt.y < y + height; +} + + +template static inline +Rect_<_Tp>& operator += ( Rect_<_Tp>& a, const Point_<_Tp>& b ) +{ + a.x += b.x; + a.y += b.y; + return a; +} + +template static inline +Rect_<_Tp>& operator -= ( Rect_<_Tp>& a, const Point_<_Tp>& b ) +{ + a.x -= b.x; + a.y -= b.y; + return a; +} + +template static inline +Rect_<_Tp>& operator += ( Rect_<_Tp>& a, const Size_<_Tp>& b ) +{ + a.width += b.width; + a.height += b.height; + return a; +} + +template static inline +Rect_<_Tp>& operator -= ( Rect_<_Tp>& a, const Size_<_Tp>& b ) +{ + const _Tp width = a.width - b.width; + const _Tp height = a.height - b.height; + CV_DbgAssert(width >= 0 && height >= 0); + a.width = width; + a.height = height; + return a; +} + +template static inline +Rect_<_Tp>& operator &= ( Rect_<_Tp>& a, const Rect_<_Tp>& b ) +{ + _Tp x1 = std::max(a.x, b.x); + _Tp y1 = std::max(a.y, b.y); + a.width = std::min(a.x + a.width, b.x + b.width) - x1; + a.height = std::min(a.y + a.height, b.y + b.height) - y1; + a.x = x1; + a.y = y1; + if( a.width <= 0 || a.height <= 0 ) + a = Rect(); + return a; +} + +template static inline +Rect_<_Tp>& operator |= ( Rect_<_Tp>& a, const Rect_<_Tp>& b ) +{ + if (a.empty()) { + a = b; + } + else if (!b.empty()) { + _Tp x1 = std::min(a.x, b.x); + _Tp y1 = std::min(a.y, b.y); + a.width = std::max(a.x + a.width, b.x + b.width) - x1; + a.height = std::max(a.y + a.height, b.y + b.height) - y1; + a.x = x1; + a.y = y1; + } + return a; +} + +template static inline +bool operator == (const Rect_<_Tp>& a, const Rect_<_Tp>& b) +{ + return a.x == b.x && a.y == b.y && a.width == b.width && a.height == b.height; +} + +template static inline +bool operator != (const Rect_<_Tp>& a, const Rect_<_Tp>& b) +{ + return a.x != b.x || a.y != b.y || a.width != b.width || a.height != b.height; +} + +template static inline +Rect_<_Tp> operator + (const Rect_<_Tp>& a, const Point_<_Tp>& b) +{ + return Rect_<_Tp>( a.x + b.x, a.y + b.y, a.width, a.height ); +} + +template static inline +Rect_<_Tp> operator - (const Rect_<_Tp>& a, const Point_<_Tp>& b) +{ + return Rect_<_Tp>( a.x - b.x, a.y - b.y, a.width, a.height ); +} + +template static inline +Rect_<_Tp> operator + (const Rect_<_Tp>& a, const Size_<_Tp>& b) +{ + return Rect_<_Tp>( a.x, a.y, a.width + b.width, a.height + b.height ); +} + +template static inline +Rect_<_Tp> operator - (const Rect_<_Tp>& a, const Size_<_Tp>& b) +{ + const _Tp width = a.width - b.width; + const _Tp height = a.height - b.height; + CV_DbgAssert(width >= 0 && height >= 0); + return Rect_<_Tp>( a.x, a.y, width, height ); +} + +template static inline +Rect_<_Tp> operator & (const Rect_<_Tp>& a, const Rect_<_Tp>& b) +{ + Rect_<_Tp> c = a; + return c &= b; +} + +template static inline +Rect_<_Tp> operator | (const Rect_<_Tp>& a, const Rect_<_Tp>& b) +{ + Rect_<_Tp> c = a; + return c |= b; +} + +/** + * @brief measure dissimilarity between two sample sets + * + * computes the complement of the Jaccard Index as described in . + * For rectangles this reduces to computing the intersection over the union. + */ +template static inline +double jaccardDistance(const Rect_<_Tp>& a, const Rect_<_Tp>& b) { + _Tp Aa = a.area(); + _Tp Ab = b.area(); + + if ((Aa + Ab) <= std::numeric_limits<_Tp>::epsilon()) { + // jaccard_index = 1 -> distance = 0 + return 0.0; + } + + double Aab = (a & b).area(); + // distance = 1 - jaccard_index + return 1.0 - Aab / (Aa + Ab - Aab); +} + +////////////////////////////// RotatedRect ////////////////////////////// + +inline +RotatedRect::RotatedRect() + : center(), size(), angle(0) {} + +inline +RotatedRect::RotatedRect(const Point2f& _center, const Size2f& _size, float _angle) + : center(_center), size(_size), angle(_angle) {} + + + +///////////////////////////////// Range ///////////////////////////////// + +inline +Range::Range() + : start(0), end(0) {} + +inline +Range::Range(int _start, int _end) + : start(_start), end(_end) {} + +inline +int Range::size() const +{ + return end - start; +} + +inline +bool Range::empty() const +{ + return start == end; +} + +inline +Range Range::all() +{ + return Range(INT_MIN, INT_MAX); +} + + +static inline +bool operator == (const Range& r1, const Range& r2) +{ + return r1.start == r2.start && r1.end == r2.end; +} + +static inline +bool operator != (const Range& r1, const Range& r2) +{ + return !(r1 == r2); +} + +static inline +bool operator !(const Range& r) +{ + return r.start == r.end; +} + +static inline +Range operator & (const Range& r1, const Range& r2) +{ + Range r(std::max(r1.start, r2.start), std::min(r1.end, r2.end)); + r.end = std::max(r.end, r.start); + return r; +} + +static inline +Range& operator &= (Range& r1, const Range& r2) +{ + r1 = r1 & r2; + return r1; +} + +static inline +Range operator + (const Range& r1, int delta) +{ + return Range(r1.start + delta, r1.end + delta); +} + +static inline +Range operator + (int delta, const Range& r1) +{ + return Range(r1.start + delta, r1.end + delta); +} + +static inline +Range operator - (const Range& r1, int delta) +{ + return r1 + (-delta); +} + + + +///////////////////////////////// Scalar //////////////////////////////// + +template inline +Scalar_<_Tp>::Scalar_() +{ + this->val[0] = this->val[1] = this->val[2] = this->val[3] = 0; +} + +template inline +Scalar_<_Tp>::Scalar_(_Tp v0, _Tp v1, _Tp v2, _Tp v3) +{ + this->val[0] = v0; + this->val[1] = v1; + this->val[2] = v2; + this->val[3] = v3; +} + +template template inline +Scalar_<_Tp>::Scalar_(const Vec<_Tp2, cn>& v) +{ + int i; + for( i = 0; i < (cn < 4 ? cn : 4); i++ ) + this->val[i] = cv::saturate_cast<_Tp>(v.val[i]); + for( ; i < 4; i++ ) + this->val[i] = 0; +} + +template inline +Scalar_<_Tp>::Scalar_(_Tp v0) +{ + this->val[0] = v0; + this->val[1] = this->val[2] = this->val[3] = 0; +} + +template inline +Scalar_<_Tp> Scalar_<_Tp>::all(_Tp v0) +{ + return Scalar_<_Tp>(v0, v0, v0, v0); +} + + +template inline +Scalar_<_Tp> Scalar_<_Tp>::mul(const Scalar_<_Tp>& a, double scale ) const +{ + return Scalar_<_Tp>(saturate_cast<_Tp>(this->val[0] * a.val[0] * scale), + saturate_cast<_Tp>(this->val[1] * a.val[1] * scale), + saturate_cast<_Tp>(this->val[2] * a.val[2] * scale), + saturate_cast<_Tp>(this->val[3] * a.val[3] * scale)); +} + +template inline +Scalar_<_Tp> Scalar_<_Tp>::conj() const +{ + return Scalar_<_Tp>(saturate_cast<_Tp>( this->val[0]), + saturate_cast<_Tp>(-this->val[1]), + saturate_cast<_Tp>(-this->val[2]), + saturate_cast<_Tp>(-this->val[3])); +} + +template inline +bool Scalar_<_Tp>::isReal() const +{ + return this->val[1] == 0 && this->val[2] == 0 && this->val[3] == 0; +} + + +template template inline +Scalar_<_Tp>::operator Scalar_() const +{ + return Scalar_(saturate_cast(this->val[0]), + saturate_cast(this->val[1]), + saturate_cast(this->val[2]), + saturate_cast(this->val[3])); +} + + +template static inline +Scalar_<_Tp>& operator += (Scalar_<_Tp>& a, const Scalar_<_Tp>& b) +{ + a.val[0] += b.val[0]; + a.val[1] += b.val[1]; + a.val[2] += b.val[2]; + a.val[3] += b.val[3]; + return a; +} + +template static inline +Scalar_<_Tp>& operator -= (Scalar_<_Tp>& a, const Scalar_<_Tp>& b) +{ + a.val[0] -= b.val[0]; + a.val[1] -= b.val[1]; + a.val[2] -= b.val[2]; + a.val[3] -= b.val[3]; + return a; +} + +template static inline +Scalar_<_Tp>& operator *= ( Scalar_<_Tp>& a, _Tp v ) +{ + a.val[0] *= v; + a.val[1] *= v; + a.val[2] *= v; + a.val[3] *= v; + return a; +} + +template static inline +bool operator == ( const Scalar_<_Tp>& a, const Scalar_<_Tp>& b ) +{ + return a.val[0] == b.val[0] && a.val[1] == b.val[1] && + a.val[2] == b.val[2] && a.val[3] == b.val[3]; +} + +template static inline +bool operator != ( const Scalar_<_Tp>& a, const Scalar_<_Tp>& b ) +{ + return a.val[0] != b.val[0] || a.val[1] != b.val[1] || + a.val[2] != b.val[2] || a.val[3] != b.val[3]; +} + +template static inline +Scalar_<_Tp> operator + (const Scalar_<_Tp>& a, const Scalar_<_Tp>& b) +{ + return Scalar_<_Tp>(a.val[0] + b.val[0], + a.val[1] + b.val[1], + a.val[2] + b.val[2], + a.val[3] + b.val[3]); +} + +template static inline +Scalar_<_Tp> operator - (const Scalar_<_Tp>& a, const Scalar_<_Tp>& b) +{ + return Scalar_<_Tp>(saturate_cast<_Tp>(a.val[0] - b.val[0]), + saturate_cast<_Tp>(a.val[1] - b.val[1]), + saturate_cast<_Tp>(a.val[2] - b.val[2]), + saturate_cast<_Tp>(a.val[3] - b.val[3])); +} + +template static inline +Scalar_<_Tp> operator * (const Scalar_<_Tp>& a, _Tp alpha) +{ + return Scalar_<_Tp>(a.val[0] * alpha, + a.val[1] * alpha, + a.val[2] * alpha, + a.val[3] * alpha); +} + +template static inline +Scalar_<_Tp> operator * (_Tp alpha, const Scalar_<_Tp>& a) +{ + return a*alpha; +} + +template static inline +Scalar_<_Tp> operator - (const Scalar_<_Tp>& a) +{ + return Scalar_<_Tp>(saturate_cast<_Tp>(-a.val[0]), + saturate_cast<_Tp>(-a.val[1]), + saturate_cast<_Tp>(-a.val[2]), + saturate_cast<_Tp>(-a.val[3])); +} + + +template static inline +Scalar_<_Tp> operator * (const Scalar_<_Tp>& a, const Scalar_<_Tp>& b) +{ + return Scalar_<_Tp>(saturate_cast<_Tp>(a[0]*b[0] - a[1]*b[1] - a[2]*b[2] - a[3]*b[3]), + saturate_cast<_Tp>(a[0]*b[1] + a[1]*b[0] + a[2]*b[3] - a[3]*b[2]), + saturate_cast<_Tp>(a[0]*b[2] - a[1]*b[3] + a[2]*b[0] + a[3]*b[1]), + saturate_cast<_Tp>(a[0]*b[3] + a[1]*b[2] - a[2]*b[1] + a[3]*b[0])); +} + +template static inline +Scalar_<_Tp>& operator *= (Scalar_<_Tp>& a, const Scalar_<_Tp>& b) +{ + a = a * b; + return a; +} + +template static inline +Scalar_<_Tp> operator / (const Scalar_<_Tp>& a, _Tp alpha) +{ + return Scalar_<_Tp>(a.val[0] / alpha, + a.val[1] / alpha, + a.val[2] / alpha, + a.val[3] / alpha); +} + +template static inline +Scalar_ operator / (const Scalar_& a, float alpha) +{ + float s = 1 / alpha; + return Scalar_(a.val[0] * s, a.val[1] * s, a.val[2] * s, a.val[3] * s); +} + +template static inline +Scalar_ operator / (const Scalar_& a, double alpha) +{ + double s = 1 / alpha; + return Scalar_(a.val[0] * s, a.val[1] * s, a.val[2] * s, a.val[3] * s); +} + +template static inline +Scalar_<_Tp>& operator /= (Scalar_<_Tp>& a, _Tp alpha) +{ + a = a / alpha; + return a; +} + +template static inline +Scalar_<_Tp> operator / (_Tp a, const Scalar_<_Tp>& b) +{ + _Tp s = a / (b[0]*b[0] + b[1]*b[1] + b[2]*b[2] + b[3]*b[3]); + return b.conj() * s; +} + +template static inline +Scalar_<_Tp> operator / (const Scalar_<_Tp>& a, const Scalar_<_Tp>& b) +{ + return a * ((_Tp)1 / b); +} + +template static inline +Scalar_<_Tp>& operator /= (Scalar_<_Tp>& a, const Scalar_<_Tp>& b) +{ + a = a / b; + return a; +} + +template static inline +Scalar operator * (const Matx<_Tp, 4, 4>& a, const Scalar& b) +{ + Matx c((Matx)a, b, Matx_MatMulOp()); + return reinterpret_cast(c); +} + +template<> inline +Scalar operator * (const Matx& a, const Scalar& b) +{ + Matx c(a, b, Matx_MatMulOp()); + return reinterpret_cast(c); +} + + + +//////////////////////////////// KeyPoint /////////////////////////////// + +inline +KeyPoint::KeyPoint() + : pt(0,0), size(0), angle(-1), response(0), octave(0), class_id(-1) {} + +inline +KeyPoint::KeyPoint(Point2f _pt, float _size, float _angle, float _response, int _octave, int _class_id) + : pt(_pt), size(_size), angle(_angle), response(_response), octave(_octave), class_id(_class_id) {} + +inline +KeyPoint::KeyPoint(float x, float y, float _size, float _angle, float _response, int _octave, int _class_id) + : pt(x, y), size(_size), angle(_angle), response(_response), octave(_octave), class_id(_class_id) {} + + + +///////////////////////////////// DMatch //////////////////////////////// + +inline +DMatch::DMatch() + : queryIdx(-1), trainIdx(-1), imgIdx(-1), distance(FLT_MAX) {} + +inline +DMatch::DMatch(int _queryIdx, int _trainIdx, float _distance) + : queryIdx(_queryIdx), trainIdx(_trainIdx), imgIdx(-1), distance(_distance) {} + +inline +DMatch::DMatch(int _queryIdx, int _trainIdx, int _imgIdx, float _distance) + : queryIdx(_queryIdx), trainIdx(_trainIdx), imgIdx(_imgIdx), distance(_distance) {} + +inline +bool DMatch::operator < (const DMatch &m) const +{ + return distance < m.distance; +} + + + +////////////////////////////// TermCriteria ///////////////////////////// + +inline +TermCriteria::TermCriteria() + : type(0), maxCount(0), epsilon(0) {} + +inline +TermCriteria::TermCriteria(int _type, int _maxCount, double _epsilon) + : type(_type), maxCount(_maxCount), epsilon(_epsilon) {} + +//! @endcond + +} // cv + +#endif //OPENCV_CORE_TYPES_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/types_c.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/types_c.h new file mode 100644 index 0000000000000000000000000000000000000000..5f63eb8fb43aaed60b38b7ee3e7386c2b29bbd96 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/types_c.h @@ -0,0 +1,2139 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_TYPES_H +#define OPENCV_CORE_TYPES_H + +#if !defined(__OPENCV_BUILD) && !defined(CV__DISABLE_C_API_CTORS) +#define CV__ENABLE_C_API_CTORS // enable C API ctors (must be removed) +#endif + +//#define CV__VALIDATE_UNUNITIALIZED_VARS 1 // C++11 & GCC only + +#ifdef __cplusplus + +#ifdef CV__VALIDATE_UNUNITIALIZED_VARS +#pragma GCC diagnostic ignored "-Wmissing-field-initializers" +#define CV_STRUCT_INITIALIZER {0,} +#else +#if defined(__GNUC__) && __GNUC__ == 4 // GCC 4.x warns on "= {}" initialization, fixed in GCC 5.0 +#pragma GCC diagnostic ignored "-Wmissing-field-initializers" +#endif +#define CV_STRUCT_INITIALIZER {} +#endif + +#else +#define CV_STRUCT_INITIALIZER {0} +#endif + + +#ifdef HAVE_IPL +# ifndef __IPL_H__ +# if defined _WIN32 +# include +# else +# include +# endif +# endif +#elif defined __IPL_H__ +# define HAVE_IPL +#endif + +#include "opencv2/core/cvdef.h" + +#ifndef SKIP_INCLUDES +#include +#include +#include +#include +#endif // SKIP_INCLUDES + +#if defined _WIN32 +# define CV_CDECL __cdecl +# define CV_STDCALL __stdcall +#else +# define CV_CDECL +# define CV_STDCALL +#endif + +#ifndef CV_DEFAULT +# ifdef __cplusplus +# define CV_DEFAULT(val) = val +# else +# define CV_DEFAULT(val) +# endif +#endif + +#ifndef CV_EXTERN_C_FUNCPTR +# ifdef __cplusplus +# define CV_EXTERN_C_FUNCPTR(x) extern "C" { typedef x; } +# else +# define CV_EXTERN_C_FUNCPTR(x) typedef x +# endif +#endif + +#ifndef CVAPI +# define CVAPI(rettype) CV_EXTERN_C CV_EXPORTS rettype CV_CDECL +#endif + +#ifndef CV_IMPL +# define CV_IMPL CV_EXTERN_C +#endif + +#ifdef __cplusplus +# include "opencv2/core.hpp" +#endif + +/** @addtogroup core_c + @{ +*/ + +/** @brief This is the "metatype" used *only* as a function parameter. + +It denotes that the function accepts arrays of multiple types, such as IplImage*, CvMat* or even +CvSeq* sometimes. The particular array type is determined at runtime by analyzing the first 4 +bytes of the header. In C++ interface the role of CvArr is played by InputArray and OutputArray. + */ +typedef void CvArr; + +typedef int CVStatus; + +/** @see cv::Error::Code */ +enum { + CV_StsOk= 0, /**< everything is ok */ + CV_StsBackTrace= -1, /**< pseudo error for back trace */ + CV_StsError= -2, /**< unknown /unspecified error */ + CV_StsInternal= -3, /**< internal error (bad state) */ + CV_StsNoMem= -4, /**< insufficient memory */ + CV_StsBadArg= -5, /**< function arg/param is bad */ + CV_StsBadFunc= -6, /**< unsupported function */ + CV_StsNoConv= -7, /**< iter. didn't converge */ + CV_StsAutoTrace= -8, /**< tracing */ + CV_HeaderIsNull= -9, /**< image header is NULL */ + CV_BadImageSize= -10, /**< image size is invalid */ + CV_BadOffset= -11, /**< offset is invalid */ + CV_BadDataPtr= -12, /**/ + CV_BadStep= -13, /**< image step is wrong, this may happen for a non-continuous matrix */ + CV_BadModelOrChSeq= -14, /**/ + CV_BadNumChannels= -15, /**< bad number of channels, for example, some functions accept only single channel matrices */ + CV_BadNumChannel1U= -16, /**/ + CV_BadDepth= -17, /**< input image depth is not supported by the function */ + CV_BadAlphaChannel= -18, /**/ + CV_BadOrder= -19, /**< number of dimensions is out of range */ + CV_BadOrigin= -20, /**< incorrect input origin */ + CV_BadAlign= -21, /**< incorrect input align */ + CV_BadCallBack= -22, /**/ + CV_BadTileSize= -23, /**/ + CV_BadCOI= -24, /**< input COI is not supported */ + CV_BadROISize= -25, /**< incorrect input roi */ + CV_MaskIsTiled= -26, /**/ + CV_StsNullPtr= -27, /**< null pointer */ + CV_StsVecLengthErr= -28, /**< incorrect vector length */ + CV_StsFilterStructContentErr= -29, /**< incorrect filter structure content */ + CV_StsKernelStructContentErr= -30, /**< incorrect transform kernel content */ + CV_StsFilterOffsetErr= -31, /**< incorrect filter offset value */ + CV_StsBadSize= -201, /**< the input/output structure size is incorrect */ + CV_StsDivByZero= -202, /**< division by zero */ + CV_StsInplaceNotSupported= -203, /**< in-place operation is not supported */ + CV_StsObjectNotFound= -204, /**< request can't be completed */ + CV_StsUnmatchedFormats= -205, /**< formats of input/output arrays differ */ + CV_StsBadFlag= -206, /**< flag is wrong or not supported */ + CV_StsBadPoint= -207, /**< bad CvPoint */ + CV_StsBadMask= -208, /**< bad format of mask (neither 8uC1 nor 8sC1)*/ + CV_StsUnmatchedSizes= -209, /**< sizes of input/output structures do not match */ + CV_StsUnsupportedFormat= -210, /**< the data format/type is not supported by the function*/ + CV_StsOutOfRange= -211, /**< some of parameters are out of range */ + CV_StsParseError= -212, /**< invalid syntax/structure of the parsed file */ + CV_StsNotImplemented= -213, /**< the requested function/feature is not implemented */ + CV_StsBadMemBlock= -214, /**< an allocated block has been corrupted */ + CV_StsAssert= -215, /**< assertion failed */ + CV_GpuNotSupported= -216, /**< no CUDA support */ + CV_GpuApiCallError= -217, /**< GPU API call error */ + CV_OpenGlNotSupported= -218, /**< no OpenGL support */ + CV_OpenGlApiCallError= -219, /**< OpenGL API call error */ + CV_OpenCLApiCallError= -220, /**< OpenCL API call error */ + CV_OpenCLDoubleNotSupported= -221, + CV_OpenCLInitError= -222, /**< OpenCL initialization error */ + CV_OpenCLNoAMDBlasFft= -223 +}; + +/****************************************************************************************\ +* Common macros and inline functions * +\****************************************************************************************/ + +#define CV_SWAP(a,b,t) ((t) = (a), (a) = (b), (b) = (t)) + +/** min & max without jumps */ +#define CV_IMIN(a, b) ((a) ^ (((a)^(b)) & (((a) < (b)) - 1))) + +#define CV_IMAX(a, b) ((a) ^ (((a)^(b)) & (((a) > (b)) - 1))) + +/** absolute value without jumps */ +#ifndef __cplusplus +# define CV_IABS(a) (((a) ^ ((a) < 0 ? -1 : 0)) - ((a) < 0 ? -1 : 0)) +#else +# define CV_IABS(a) abs(a) +#endif +#define CV_CMP(a,b) (((a) > (b)) - ((a) < (b))) +#define CV_SIGN(a) CV_CMP((a),0) + +#define cvInvSqrt(value) ((float)(1./sqrt(value))) +#define cvSqrt(value) ((float)sqrt(value)) + + +/*************** Random number generation *******************/ + +typedef uint64 CvRNG; + +#define CV_RNG_COEFF 4164903690U + +/** @brief Initializes a random number generator state. + +The function initializes a random number generator and returns the state. The pointer to the state +can be then passed to the cvRandInt, cvRandReal and cvRandArr functions. In the current +implementation a multiply-with-carry generator is used. +@param seed 64-bit value used to initiate a random sequence +@sa the C++ class RNG replaced CvRNG. + */ +CV_INLINE CvRNG cvRNG( int64 seed CV_DEFAULT(-1)) +{ + CvRNG rng = seed ? (uint64)seed : (uint64)(int64)-1; + return rng; +} + +/** @brief Returns a 32-bit unsigned integer and updates RNG. + +The function returns a uniformly-distributed random 32-bit unsigned integer and updates the RNG +state. It is similar to the rand() function from the C runtime library, except that OpenCV functions +always generates a 32-bit random number, regardless of the platform. +@param rng CvRNG state initialized by cvRNG. + */ +CV_INLINE unsigned cvRandInt( CvRNG* rng ) +{ + uint64 temp = *rng; + temp = (uint64)(unsigned)temp*CV_RNG_COEFF + (temp >> 32); + *rng = temp; + return (unsigned)temp; +} + +/** @brief Returns a floating-point random number and updates RNG. + +The function returns a uniformly-distributed random floating-point number between 0 and 1 (1 is not +included). +@param rng RNG state initialized by cvRNG + */ +CV_INLINE double cvRandReal( CvRNG* rng ) +{ + return cvRandInt(rng)*2.3283064365386962890625e-10 /* 2^-32 */; +} + +/****************************************************************************************\ +* Image type (IplImage) * +\****************************************************************************************/ + +#ifndef HAVE_IPL + +/* + * The following definitions (until #endif) + * is an extract from IPL headers. + * Copyright (c) 1995 Intel Corporation. + */ +#define IPL_DEPTH_SIGN 0x80000000 + +#define IPL_DEPTH_1U 1 +#define IPL_DEPTH_8U 8 +#define IPL_DEPTH_16U 16 +#define IPL_DEPTH_32F 32 + +#define IPL_DEPTH_8S (IPL_DEPTH_SIGN| 8) +#define IPL_DEPTH_16S (IPL_DEPTH_SIGN|16) +#define IPL_DEPTH_32S (IPL_DEPTH_SIGN|32) + +#define IPL_DATA_ORDER_PIXEL 0 +#define IPL_DATA_ORDER_PLANE 1 + +#define IPL_ORIGIN_TL 0 +#define IPL_ORIGIN_BL 1 + +#define IPL_ALIGN_4BYTES 4 +#define IPL_ALIGN_8BYTES 8 +#define IPL_ALIGN_16BYTES 16 +#define IPL_ALIGN_32BYTES 32 + +#define IPL_ALIGN_DWORD IPL_ALIGN_4BYTES +#define IPL_ALIGN_QWORD IPL_ALIGN_8BYTES + +#define IPL_BORDER_CONSTANT 0 +#define IPL_BORDER_REPLICATE 1 +#define IPL_BORDER_REFLECT 2 +#define IPL_BORDER_WRAP 3 + +#ifdef __cplusplus +typedef struct _IplImage IplImage; +CV_EXPORTS _IplImage cvIplImage(const cv::Mat& m); +#endif + +/** The IplImage is taken from the Intel Image Processing Library, in which the format is native. OpenCV +only supports a subset of possible IplImage formats, as outlined in the parameter list above. + +In addition to the above restrictions, OpenCV handles ROIs differently. OpenCV functions require +that the image size or ROI size of all source and destination images match exactly. On the other +hand, the Intel Image Processing Library processes the area of intersection between the source and +destination images (or ROIs), allowing them to vary independently. +*/ +typedef struct +_IplImage +{ + int nSize; /**< sizeof(IplImage) */ + int ID; /**< version (=0)*/ + int nChannels; /**< Most of OpenCV functions support 1,2,3 or 4 channels */ + int alphaChannel; /**< Ignored by OpenCV */ + int depth; /**< Pixel depth in bits: IPL_DEPTH_8U, IPL_DEPTH_8S, IPL_DEPTH_16S, + IPL_DEPTH_32S, IPL_DEPTH_32F and IPL_DEPTH_64F are supported. */ + char colorModel[4]; /**< Ignored by OpenCV */ + char channelSeq[4]; /**< ditto */ + int dataOrder; /**< 0 - interleaved color channels, 1 - separate color channels. + cvCreateImage can only create interleaved images */ + int origin; /**< 0 - top-left origin, + 1 - bottom-left origin (Windows bitmaps style). */ + int align; /**< Alignment of image rows (4 or 8). + OpenCV ignores it and uses widthStep instead. */ + int width; /**< Image width in pixels. */ + int height; /**< Image height in pixels. */ + struct _IplROI *roi; /**< Image ROI. If NULL, the whole image is selected. */ + struct _IplImage *maskROI; /**< Must be NULL. */ + void *imageId; /**< " " */ + struct _IplTileInfo *tileInfo; /**< " " */ + int imageSize; /**< Image data size in bytes + (==image->height*image->widthStep + in case of interleaved data)*/ + char *imageData; /**< Pointer to aligned image data. */ + int widthStep; /**< Size of aligned image row in bytes. */ + int BorderMode[4]; /**< Ignored by OpenCV. */ + int BorderConst[4]; /**< Ditto. */ + char *imageDataOrigin; /**< Pointer to very origin of image data + (not necessarily aligned) - + needed for correct deallocation */ + +#if defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) + _IplImage() {} + _IplImage(const cv::Mat& m) { *this = cvIplImage(m); } +#endif +} +IplImage; + +CV_INLINE IplImage cvIplImage() +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + IplImage self = CV_STRUCT_INITIALIZER; self.nSize = sizeof(IplImage); return self; +#else + return _IplImage(); +#endif +} + +typedef struct _IplTileInfo IplTileInfo; + +typedef struct _IplROI +{ + int coi; /**< 0 - no COI (all channels are selected), 1 - 0th channel is selected ...*/ + int xOffset; + int yOffset; + int width; + int height; +} +IplROI; + +typedef struct _IplConvKernel +{ + int nCols; + int nRows; + int anchorX; + int anchorY; + int *values; + int nShiftR; +} +IplConvKernel; + +typedef struct _IplConvKernelFP +{ + int nCols; + int nRows; + int anchorX; + int anchorY; + float *values; +} +IplConvKernelFP; + +#define IPL_IMAGE_HEADER 1 +#define IPL_IMAGE_DATA 2 +#define IPL_IMAGE_ROI 4 + +#endif/*HAVE_IPL*/ + +/** extra border mode */ +#define IPL_BORDER_REFLECT_101 4 +#define IPL_BORDER_TRANSPARENT 5 + +#define IPL_IMAGE_MAGIC_VAL ((int)sizeof(IplImage)) +#define CV_TYPE_NAME_IMAGE "opencv-image" + +#define CV_IS_IMAGE_HDR(img) \ + ((img) != NULL && ((const IplImage*)(img))->nSize == sizeof(IplImage)) + +#define CV_IS_IMAGE(img) \ + (CV_IS_IMAGE_HDR(img) && ((IplImage*)img)->imageData != NULL) + +/** for storing double-precision + floating point data in IplImage's */ +#define IPL_DEPTH_64F 64 + +/** get reference to pixel at (col,row), + for multi-channel images (col) should be multiplied by number of channels */ +#define CV_IMAGE_ELEM( image, elemtype, row, col ) \ + (((elemtype*)((image)->imageData + (image)->widthStep*(row)))[(col)]) + +/****************************************************************************************\ +* Matrix type (CvMat) * +\****************************************************************************************/ + +#define CV_AUTO_STEP 0x7fffffff +#define CV_WHOLE_ARR cvSlice( 0, 0x3fffffff ) + +#define CV_MAGIC_MASK 0xFFFF0000 +#define CV_MAT_MAGIC_VAL 0x42420000 +#define CV_TYPE_NAME_MAT "opencv-matrix" + +#ifdef __cplusplus +typedef struct CvMat CvMat; +CV_INLINE CvMat cvMat(const cv::Mat& m); +#endif + +/** Matrix elements are stored row by row. Element (i, j) (i - 0-based row index, j - 0-based column +index) of a matrix can be retrieved or modified using CV_MAT_ELEM macro: + + uchar pixval = CV_MAT_ELEM(grayimg, uchar, i, j) + CV_MAT_ELEM(cameraMatrix, float, 0, 2) = image.width*0.5f; + +To access multiple-channel matrices, you can use +CV_MAT_ELEM(matrix, type, i, j\*nchannels + channel_idx). + +@deprecated CvMat is now obsolete; consider using Mat instead. + */ +typedef struct CvMat +{ + int type; + int step; + + /* for internal use only */ + int* refcount; + int hdr_refcount; + + union + { + uchar* ptr; + short* s; + int* i; + float* fl; + double* db; + } data; + +#ifdef __cplusplus + union + { + int rows; + int height; + }; + + union + { + int cols; + int width; + }; +#else + int rows; + int cols; +#endif + +#if defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) + CvMat() {} + CvMat(const cv::Mat& m) { *this = cvMat(m); } +#endif +} +CvMat; + + +#define CV_IS_MAT_HDR(mat) \ + ((mat) != NULL && \ + (((const CvMat*)(mat))->type & CV_MAGIC_MASK) == CV_MAT_MAGIC_VAL && \ + ((const CvMat*)(mat))->cols > 0 && ((const CvMat*)(mat))->rows > 0) + +#define CV_IS_MAT_HDR_Z(mat) \ + ((mat) != NULL && \ + (((const CvMat*)(mat))->type & CV_MAGIC_MASK) == CV_MAT_MAGIC_VAL && \ + ((const CvMat*)(mat))->cols >= 0 && ((const CvMat*)(mat))->rows >= 0) + +#define CV_IS_MAT(mat) \ + (CV_IS_MAT_HDR(mat) && ((const CvMat*)(mat))->data.ptr != NULL) + +#define CV_IS_MASK_ARR(mat) \ + (((mat)->type & (CV_MAT_TYPE_MASK & ~CV_8SC1)) == 0) + +#define CV_ARE_TYPES_EQ(mat1, mat2) \ + ((((mat1)->type ^ (mat2)->type) & CV_MAT_TYPE_MASK) == 0) + +#define CV_ARE_CNS_EQ(mat1, mat2) \ + ((((mat1)->type ^ (mat2)->type) & CV_MAT_CN_MASK) == 0) + +#define CV_ARE_DEPTHS_EQ(mat1, mat2) \ + ((((mat1)->type ^ (mat2)->type) & CV_MAT_DEPTH_MASK) == 0) + +#define CV_ARE_SIZES_EQ(mat1, mat2) \ + ((mat1)->rows == (mat2)->rows && (mat1)->cols == (mat2)->cols) + +#define CV_IS_MAT_CONST(mat) \ + (((mat)->rows|(mat)->cols) == 1) + +#define IPL2CV_DEPTH(depth) \ + ((((CV_8U)+(CV_16U<<4)+(CV_32F<<8)+(CV_64F<<16)+(CV_8S<<20)+ \ + (CV_16S<<24)+(CV_32S<<28)) >> ((((depth) & 0xF0) >> 2) + \ + (((depth) & IPL_DEPTH_SIGN) ? 20 : 0))) & 15) + +/** Inline constructor. No data is allocated internally!!! + * (Use together with cvCreateData, or use cvCreateMat instead to + * get a matrix with allocated data): + */ +CV_INLINE CvMat cvMat( int rows, int cols, int type, void* data CV_DEFAULT(NULL)) +{ + CvMat m; + + assert( (unsigned)CV_MAT_DEPTH(type) <= CV_64F ); + type = CV_MAT_TYPE(type); + m.type = CV_MAT_MAGIC_VAL | CV_MAT_CONT_FLAG | type; + m.cols = cols; + m.rows = rows; + m.step = m.cols*CV_ELEM_SIZE(type); + m.data.ptr = (uchar*)data; + m.refcount = NULL; + m.hdr_refcount = 0; + + return m; +} + +#ifdef __cplusplus + +CV_INLINE CvMat cvMat(const cv::Mat& m) +{ + CvMat self; + CV_DbgAssert(m.dims <= 2); + self = cvMat(m.rows, m.dims == 1 ? 1 : m.cols, m.type(), m.data); + self.step = (int)m.step[0]; + self.type = (self.type & ~cv::Mat::CONTINUOUS_FLAG) | (m.flags & cv::Mat::CONTINUOUS_FLAG); + return self; +} +CV_INLINE CvMat cvMat() +{ +#if !defined(CV__ENABLE_C_API_CTORS) + CvMat self = CV_STRUCT_INITIALIZER; return self; +#else + return CvMat(); +#endif +} +CV_INLINE CvMat cvMat(const CvMat& m) +{ +#if !defined(CV__ENABLE_C_API_CTORS) + CvMat self = CV_STRUCT_INITIALIZER; memcpy(&self, &m, sizeof(self)); return self; +#else + return CvMat(m); +#endif +} + +#endif // __cplusplus + + +#define CV_MAT_ELEM_PTR_FAST( mat, row, col, pix_size ) \ + (assert( (unsigned)(row) < (unsigned)(mat).rows && \ + (unsigned)(col) < (unsigned)(mat).cols ), \ + (mat).data.ptr + (size_t)(mat).step*(row) + (pix_size)*(col)) + +#define CV_MAT_ELEM_PTR( mat, row, col ) \ + CV_MAT_ELEM_PTR_FAST( mat, row, col, CV_ELEM_SIZE((mat).type) ) + +#define CV_MAT_ELEM( mat, elemtype, row, col ) \ + (*(elemtype*)CV_MAT_ELEM_PTR_FAST( mat, row, col, sizeof(elemtype))) + +/** @brief Returns the particular element of single-channel floating-point matrix. + +The function is a fast replacement for cvGetReal2D in the case of single-channel floating-point +matrices. It is faster because it is inline, it does fewer checks for array type and array element +type, and it checks for the row and column ranges only in debug mode. +@param mat Input matrix +@param row The zero-based index of row +@param col The zero-based index of column + */ +CV_INLINE double cvmGet( const CvMat* mat, int row, int col ) +{ + int type; + + type = CV_MAT_TYPE(mat->type); + assert( (unsigned)row < (unsigned)mat->rows && + (unsigned)col < (unsigned)mat->cols ); + + if( type == CV_32FC1 ) + return ((float*)(void*)(mat->data.ptr + (size_t)mat->step*row))[col]; + else + { + assert( type == CV_64FC1 ); + return ((double*)(void*)(mat->data.ptr + (size_t)mat->step*row))[col]; + } +} + +/** @brief Sets a specific element of a single-channel floating-point matrix. + +The function is a fast replacement for cvSetReal2D in the case of single-channel floating-point +matrices. It is faster because it is inline, it does fewer checks for array type and array element +type, and it checks for the row and column ranges only in debug mode. +@param mat The matrix +@param row The zero-based index of row +@param col The zero-based index of column +@param value The new value of the matrix element + */ +CV_INLINE void cvmSet( CvMat* mat, int row, int col, double value ) +{ + int type; + type = CV_MAT_TYPE(mat->type); + assert( (unsigned)row < (unsigned)mat->rows && + (unsigned)col < (unsigned)mat->cols ); + + if( type == CV_32FC1 ) + ((float*)(void*)(mat->data.ptr + (size_t)mat->step*row))[col] = (float)value; + else + { + assert( type == CV_64FC1 ); + ((double*)(void*)(mat->data.ptr + (size_t)mat->step*row))[col] = value; + } +} + + +CV_INLINE int cvIplDepth( int type ) +{ + int depth = CV_MAT_DEPTH(type); + return CV_ELEM_SIZE1(depth)*8 | (depth == CV_8S || depth == CV_16S || + depth == CV_32S ? IPL_DEPTH_SIGN : 0); +} + + +/****************************************************************************************\ +* Multi-dimensional dense array (CvMatND) * +\****************************************************************************************/ + +#define CV_MATND_MAGIC_VAL 0x42430000 +#define CV_TYPE_NAME_MATND "opencv-nd-matrix" + +#define CV_MAX_DIM 32 + +#ifdef __cplusplus +typedef struct CvMatND CvMatND; +CV_EXPORTS CvMatND cvMatND(const cv::Mat& m); +#endif + +/** + @deprecated consider using cv::Mat instead + */ +typedef struct +CvMatND +{ + int type; + int dims; + + int* refcount; + int hdr_refcount; + + union + { + uchar* ptr; + float* fl; + double* db; + int* i; + short* s; + } data; + + struct + { + int size; + int step; + } + dim[CV_MAX_DIM]; + +#if defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) + CvMatND() {} + CvMatND(const cv::Mat& m) { *this = cvMatND(m); } +#endif +} +CvMatND; + + +CV_INLINE CvMatND cvMatND() +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvMatND self = CV_STRUCT_INITIALIZER; return self; +#else + return CvMatND(); +#endif +} + +#define CV_IS_MATND_HDR(mat) \ + ((mat) != NULL && (((const CvMatND*)(mat))->type & CV_MAGIC_MASK) == CV_MATND_MAGIC_VAL) + +#define CV_IS_MATND(mat) \ + (CV_IS_MATND_HDR(mat) && ((const CvMatND*)(mat))->data.ptr != NULL) + + +/****************************************************************************************\ +* Multi-dimensional sparse array (CvSparseMat) * +\****************************************************************************************/ + +#define CV_SPARSE_MAT_MAGIC_VAL 0x42440000 +#define CV_TYPE_NAME_SPARSE_MAT "opencv-sparse-matrix" + +struct CvSet; + +typedef struct CvSparseMat +{ + int type; + int dims; + int* refcount; + int hdr_refcount; + + struct CvSet* heap; + void** hashtable; + int hashsize; + int valoffset; + int idxoffset; + int size[CV_MAX_DIM]; + +#ifdef __cplusplus + CV_EXPORTS void copyToSparseMat(cv::SparseMat& m) const; +#endif +} +CvSparseMat; + +#ifdef __cplusplus +CV_EXPORTS CvSparseMat* cvCreateSparseMat(const cv::SparseMat& m); +#endif + +#define CV_IS_SPARSE_MAT_HDR(mat) \ + ((mat) != NULL && \ + (((const CvSparseMat*)(mat))->type & CV_MAGIC_MASK) == CV_SPARSE_MAT_MAGIC_VAL) + +#define CV_IS_SPARSE_MAT(mat) \ + CV_IS_SPARSE_MAT_HDR(mat) + +/**************** iteration through a sparse array *****************/ + +typedef struct CvSparseNode +{ + unsigned hashval; + struct CvSparseNode* next; +} +CvSparseNode; + +typedef struct CvSparseMatIterator +{ + CvSparseMat* mat; + CvSparseNode* node; + int curidx; +} +CvSparseMatIterator; + +#define CV_NODE_VAL(mat,node) ((void*)((uchar*)(node) + (mat)->valoffset)) +#define CV_NODE_IDX(mat,node) ((int*)((uchar*)(node) + (mat)->idxoffset)) + +/****************************************************************************************\ +* Histogram * +\****************************************************************************************/ + +typedef int CvHistType; + +#define CV_HIST_MAGIC_VAL 0x42450000 +#define CV_HIST_UNIFORM_FLAG (1 << 10) + +/** indicates whether bin ranges are set already or not */ +#define CV_HIST_RANGES_FLAG (1 << 11) + +#define CV_HIST_ARRAY 0 +#define CV_HIST_SPARSE 1 +#define CV_HIST_TREE CV_HIST_SPARSE + +/** should be used as a parameter only, + it turns to CV_HIST_UNIFORM_FLAG of hist->type */ +#define CV_HIST_UNIFORM 1 + +typedef struct CvHistogram +{ + int type; + CvArr* bins; + float thresh[CV_MAX_DIM][2]; /**< For uniform histograms. */ + float** thresh2; /**< For non-uniform histograms. */ + CvMatND mat; /**< Embedded matrix header for array histograms. */ +} +CvHistogram; + +#define CV_IS_HIST( hist ) \ + ((hist) != NULL && \ + (((CvHistogram*)(hist))->type & CV_MAGIC_MASK) == CV_HIST_MAGIC_VAL && \ + (hist)->bins != NULL) + +#define CV_IS_UNIFORM_HIST( hist ) \ + (((hist)->type & CV_HIST_UNIFORM_FLAG) != 0) + +#define CV_IS_SPARSE_HIST( hist ) \ + CV_IS_SPARSE_MAT((hist)->bins) + +#define CV_HIST_HAS_RANGES( hist ) \ + (((hist)->type & CV_HIST_RANGES_FLAG) != 0) + +/****************************************************************************************\ +* Other supplementary data type definitions * +\****************************************************************************************/ + +/*************************************** CvRect *****************************************/ +/** @sa Rect_ */ +typedef struct CvRect +{ + int x; + int y; + int width; + int height; + +#ifdef CV__VALIDATE_UNUNITIALIZED_VARS + CvRect() __attribute__(( warning("Non-initialized variable") )) {}; + template CvRect(const std::initializer_list<_Tp> list) + { + CV_Assert(list.size() == 0 || list.size() == 4); + x = y = width = height = 0; + if (list.size() == 4) + { + x = list.begin()[0]; y = list.begin()[1]; width = list.begin()[2]; height = list.begin()[3]; + } + }; +#elif defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) + CvRect(int _x = 0, int _y = 0, int w = 0, int h = 0): x(_x), y(_y), width(w), height(h) {} + template + CvRect(const cv::Rect_<_Tp>& r): x(cv::saturate_cast(r.x)), y(cv::saturate_cast(r.y)), width(cv::saturate_cast(r.width)), height(cv::saturate_cast(r.height)) {} +#endif +#ifdef __cplusplus + template + operator cv::Rect_<_Tp>() const { return cv::Rect_<_Tp>((_Tp)x, (_Tp)y, (_Tp)width, (_Tp)height); } +#endif +} +CvRect; + +/** constructs CvRect structure. */ +CV_INLINE CvRect cvRect( int x, int y, int width, int height ) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvRect r = {x, y, width, height}; +#else + CvRect r(x, y , width, height); +#endif + return r; +} +#ifdef __cplusplus +CV_INLINE CvRect cvRect(const cv::Rect& rc) { return cvRect(rc.x, rc.y, rc.width, rc.height); } +#endif + +CV_INLINE IplROI cvRectToROI( CvRect rect, int coi ) +{ + IplROI roi; + roi.xOffset = rect.x; + roi.yOffset = rect.y; + roi.width = rect.width; + roi.height = rect.height; + roi.coi = coi; + + return roi; +} + + +CV_INLINE CvRect cvROIToRect( IplROI roi ) +{ + return cvRect( roi.xOffset, roi.yOffset, roi.width, roi.height ); +} + +/*********************************** CvTermCriteria *************************************/ + +#define CV_TERMCRIT_ITER 1 +#define CV_TERMCRIT_NUMBER CV_TERMCRIT_ITER +#define CV_TERMCRIT_EPS 2 + +/** @sa TermCriteria + */ +typedef struct CvTermCriteria +{ + int type; /**< may be combination of + CV_TERMCRIT_ITER + CV_TERMCRIT_EPS */ + int max_iter; + double epsilon; +#if defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) + CvTermCriteria(int _type = 0, int _iter = 0, double _eps = 0) : type(_type), max_iter(_iter), epsilon(_eps) {} + CvTermCriteria(const cv::TermCriteria& t) : type(t.type), max_iter(t.maxCount), epsilon(t.epsilon) {} +#endif +#ifdef __cplusplus + operator cv::TermCriteria() const { return cv::TermCriteria(type, max_iter, epsilon); } +#endif +} +CvTermCriteria; + +CV_INLINE CvTermCriteria cvTermCriteria( int type, int max_iter, double epsilon ) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvTermCriteria t = { type, max_iter, (float)epsilon}; +#else + CvTermCriteria t(type, max_iter, epsilon); +#endif + return t; +} +#ifdef __cplusplus +CV_INLINE CvTermCriteria cvTermCriteria(const cv::TermCriteria& t) { return cvTermCriteria(t.type, t.maxCount, t.epsilon); } +#endif + + +/******************************* CvPoint and variants ***********************************/ + +typedef struct CvPoint +{ + int x; + int y; + +#ifdef CV__VALIDATE_UNUNITIALIZED_VARS + CvPoint() __attribute__(( warning("Non-initialized variable") )) {} + template CvPoint(const std::initializer_list<_Tp> list) + { + CV_Assert(list.size() == 0 || list.size() == 2); + x = y = 0; + if (list.size() == 2) + { + x = list.begin()[0]; y = list.begin()[1]; + } + }; +#elif defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) + CvPoint(int _x = 0, int _y = 0): x(_x), y(_y) {} + template + CvPoint(const cv::Point_<_Tp>& pt): x((int)pt.x), y((int)pt.y) {} +#endif +#ifdef __cplusplus + template + operator cv::Point_<_Tp>() const { return cv::Point_<_Tp>(cv::saturate_cast<_Tp>(x), cv::saturate_cast<_Tp>(y)); } +#endif +} +CvPoint; + +/** constructs CvPoint structure. */ +CV_INLINE CvPoint cvPoint( int x, int y ) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvPoint p = {x, y}; +#else + CvPoint p(x, y); +#endif + return p; +} +#ifdef __cplusplus +CV_INLINE CvPoint cvPoint(const cv::Point& pt) { return cvPoint(pt.x, pt.y); } +#endif + +typedef struct CvPoint2D32f +{ + float x; + float y; + +#ifdef CV__VALIDATE_UNUNITIALIZED_VARS + CvPoint2D32f() __attribute__(( warning("Non-initialized variable") )) {} + template CvPoint2D32f(const std::initializer_list<_Tp> list) + { + CV_Assert(list.size() == 0 || list.size() == 2); + x = y = 0; + if (list.size() == 2) + { + x = list.begin()[0]; y = list.begin()[1]; + } + }; +#elif defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) + CvPoint2D32f(float _x = 0, float _y = 0): x(_x), y(_y) {} + template + CvPoint2D32f(const cv::Point_<_Tp>& pt): x((float)pt.x), y((float)pt.y) {} +#endif +#ifdef __cplusplus + template + operator cv::Point_<_Tp>() const { return cv::Point_<_Tp>(cv::saturate_cast<_Tp>(x), cv::saturate_cast<_Tp>(y)); } +#endif +} +CvPoint2D32f; + +/** constructs CvPoint2D32f structure. */ +CV_INLINE CvPoint2D32f cvPoint2D32f( double x, double y ) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvPoint2D32f p = { (float)x, (float)y }; +#else + CvPoint2D32f p((float)x, (float)y); +#endif + return p; +} + +#ifdef __cplusplus +template +CvPoint2D32f cvPoint2D32f(const cv::Point_<_Tp>& pt) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvPoint2D32f p = { (float)pt.x, (float)pt.y }; +#else + CvPoint2D32f p((float)pt.x, (float)pt.y); +#endif + return p; +} +#endif + +/** converts CvPoint to CvPoint2D32f. */ +CV_INLINE CvPoint2D32f cvPointTo32f( CvPoint point ) +{ + return cvPoint2D32f( (float)point.x, (float)point.y ); +} + +/** converts CvPoint2D32f to CvPoint. */ +CV_INLINE CvPoint cvPointFrom32f( CvPoint2D32f point ) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvPoint ipt = { cvRound(point.x), cvRound(point.y) }; +#else + CvPoint ipt(cvRound(point.x), cvRound(point.y)); +#endif + return ipt; +} + + +typedef struct CvPoint3D32f +{ + float x; + float y; + float z; + +#ifdef CV__VALIDATE_UNUNITIALIZED_VARS + CvPoint3D32f() __attribute__(( warning("Non-initialized variable") )) {} + template CvPoint3D32f(const std::initializer_list<_Tp> list) + { + CV_Assert(list.size() == 0 || list.size() == 3); + x = y = z = 0; + if (list.size() == 3) + { + x = list.begin()[0]; y = list.begin()[1]; z = list.begin()[2]; + } + }; +#elif defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) + CvPoint3D32f(float _x = 0, float _y = 0, float _z = 0): x(_x), y(_y), z(_z) {} + template + CvPoint3D32f(const cv::Point3_<_Tp>& pt): x((float)pt.x), y((float)pt.y), z((float)pt.z) {} +#endif +#ifdef __cplusplus + template + operator cv::Point3_<_Tp>() const { return cv::Point3_<_Tp>(cv::saturate_cast<_Tp>(x), cv::saturate_cast<_Tp>(y), cv::saturate_cast<_Tp>(z)); } +#endif +} +CvPoint3D32f; + +/** constructs CvPoint3D32f structure. */ +CV_INLINE CvPoint3D32f cvPoint3D32f( double x, double y, double z ) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvPoint3D32f p = { (float)x, (float)y, (float)z }; +#else + CvPoint3D32f p((float)x, (float)y, (float)z); +#endif + return p; +} + +#ifdef __cplusplus +template +CvPoint3D32f cvPoint3D32f(const cv::Point3_<_Tp>& pt) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvPoint3D32f p = { (float)pt.x, (float)pt.y, (float)pt.z }; +#else + CvPoint3D32f p((float)pt.x, (float)pt.y, (float)pt.z); +#endif + return p; +} +#endif + + +typedef struct CvPoint2D64f +{ + double x; + double y; +#ifdef CV__VALIDATE_UNUNITIALIZED_VARS + CvPoint2D64f() __attribute__(( warning("Non-initialized variable") )) {} + template CvPoint2D64f(const std::initializer_list<_Tp> list) + { + CV_Assert(list.size() == 0 || list.size() == 2); + x = y = 0; + if (list.size() == 2) + { + x = list.begin()[0]; y = list.begin()[1]; + } + }; +#endif +} +CvPoint2D64f; + +/** constructs CvPoint2D64f structure.*/ +CV_INLINE CvPoint2D64f cvPoint2D64f( double x, double y ) +{ + CvPoint2D64f p = { x, y }; + return p; +} + + +typedef struct CvPoint3D64f +{ + double x; + double y; + double z; +#ifdef CV__VALIDATE_UNUNITIALIZED_VARS + CvPoint3D64f() __attribute__(( warning("Non-initialized variable") )) {} + template CvPoint3D64f(const std::initializer_list<_Tp> list) + { + CV_Assert(list.size() == 0 || list.size() == 3); + x = y = z = 0; + if (list.size() == 3) + { + x = list.begin()[0]; y = list.begin()[1]; z = list.begin()[2]; + } + }; +#endif +} +CvPoint3D64f; + +/** constructs CvPoint3D64f structure. */ +CV_INLINE CvPoint3D64f cvPoint3D64f( double x, double y, double z ) +{ + CvPoint3D64f p = { x, y, z }; + return p; +} + + +/******************************** CvSize's & CvBox **************************************/ + +typedef struct CvSize +{ + int width; + int height; + +#ifdef CV__VALIDATE_UNUNITIALIZED_VARS + CvSize() __attribute__(( warning("Non-initialized variable") )) {} + template CvSize(const std::initializer_list<_Tp> list) + { + CV_Assert(list.size() == 0 || list.size() == 2); + width = 0; height = 0; + if (list.size() == 2) + { + width = list.begin()[0]; height = list.begin()[1]; + } + }; +#elif defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) + CvSize(int w = 0, int h = 0): width(w), height(h) {} + template + CvSize(const cv::Size_<_Tp>& sz): width(cv::saturate_cast(sz.width)), height(cv::saturate_cast(sz.height)) {} +#endif +#ifdef __cplusplus + template + operator cv::Size_<_Tp>() const { return cv::Size_<_Tp>(cv::saturate_cast<_Tp>(width), cv::saturate_cast<_Tp>(height)); } +#endif +} +CvSize; + +/** constructs CvSize structure. */ +CV_INLINE CvSize cvSize( int width, int height ) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvSize s = { width, height }; +#else + CvSize s(width, height); +#endif + return s; +} + +#ifdef __cplusplus +CV_INLINE CvSize cvSize(const cv::Size& sz) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvSize s = { sz.width, sz.height }; +#else + CvSize s(sz.width, sz.height); +#endif + return s; +} +#endif + +typedef struct CvSize2D32f +{ + float width; + float height; + +#ifdef CV__VALIDATE_UNUNITIALIZED_VARS + CvSize2D32f() __attribute__(( warning("Non-initialized variable") )) {} + template CvSize2D32f(const std::initializer_list<_Tp> list) + { + CV_Assert(list.size() == 0 || list.size() == 2); + width = 0; height = 0; + if (list.size() == 2) + { + width = list.begin()[0]; height = list.begin()[1]; + } + }; +#elif defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) + CvSize2D32f(float w = 0, float h = 0): width(w), height(h) {} + template + CvSize2D32f(const cv::Size_<_Tp>& sz): width(cv::saturate_cast(sz.width)), height(cv::saturate_cast(sz.height)) {} +#endif +#ifdef __cplusplus + template + operator cv::Size_<_Tp>() const { return cv::Size_<_Tp>(cv::saturate_cast<_Tp>(width), cv::saturate_cast<_Tp>(height)); } +#endif +} +CvSize2D32f; + +/** constructs CvSize2D32f structure. */ +CV_INLINE CvSize2D32f cvSize2D32f( double width, double height ) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvSize2D32f s = { (float)width, (float)height }; +#else + CvSize2D32f s((float)width, (float)height); +#endif + return s; +} +#ifdef __cplusplus +template +CvSize2D32f cvSize2D32f(const cv::Size_<_Tp>& sz) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvSize2D32f s = { (float)sz.width, (float)sz.height }; +#else + CvSize2D32f s((float)sz.width, (float)sz.height); +#endif + return s; +} +#endif + +/** @sa RotatedRect + */ +typedef struct CvBox2D +{ + CvPoint2D32f center; /**< Center of the box. */ + CvSize2D32f size; /**< Box width and length. */ + float angle; /**< Angle between the horizontal axis */ + /**< and the first side (i.e. length) in degrees */ + +#if defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) + CvBox2D(CvPoint2D32f c = CvPoint2D32f(), CvSize2D32f s = CvSize2D32f(), float a = 0) : center(c), size(s), angle(a) {} + CvBox2D(const cv::RotatedRect& rr) : center(rr.center), size(rr.size), angle(rr.angle) {} +#endif +#ifdef __cplusplus + operator cv::RotatedRect() const { return cv::RotatedRect(center, size, angle); } +#endif +} +CvBox2D; + + +#ifdef __cplusplus +CV_INLINE CvBox2D cvBox2D(CvPoint2D32f c = CvPoint2D32f(), CvSize2D32f s = CvSize2D32f(), float a = 0) +{ + CvBox2D self; + self.center = c; + self.size = s; + self.angle = a; + return self; +} +CV_INLINE CvBox2D cvBox2D(const cv::RotatedRect& rr) +{ + CvBox2D self; + self.center = cvPoint2D32f(rr.center); + self.size = cvSize2D32f(rr.size); + self.angle = rr.angle; + return self; +} +#endif + + +/** Line iterator state: */ +typedef struct CvLineIterator +{ + /** Pointer to the current point: */ + uchar* ptr; + + /* Bresenham algorithm state: */ + int err; + int plus_delta; + int minus_delta; + int plus_step; + int minus_step; +} +CvLineIterator; + + + +/************************************* CvSlice ******************************************/ +#define CV_WHOLE_SEQ_END_INDEX 0x3fffffff +#define CV_WHOLE_SEQ cvSlice(0, CV_WHOLE_SEQ_END_INDEX) + +typedef struct CvSlice +{ + int start_index, end_index; + +#ifdef CV__VALIDATE_UNUNITIALIZED_VARS + CvSlice() __attribute__(( warning("Non-initialized variable") )) {} + template CvSlice(const std::initializer_list<_Tp> list) + { + CV_Assert(list.size() == 0 || list.size() == 2); + start_index = end_index = 0; + if (list.size() == 2) + { + start_index = list.begin()[0]; end_index = list.begin()[1]; + } + }; +#endif +#if defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) && !defined(__CUDACC__) + CvSlice(int start = 0, int end = 0) : start_index(start), end_index(end) {} + CvSlice(const cv::Range& r) { *this = (r.start != INT_MIN && r.end != INT_MAX) ? CvSlice(r.start, r.end) : CvSlice(0, CV_WHOLE_SEQ_END_INDEX); } + operator cv::Range() const { return (start_index == 0 && end_index == CV_WHOLE_SEQ_END_INDEX ) ? cv::Range::all() : cv::Range(start_index, end_index); } +#endif +} +CvSlice; + +CV_INLINE CvSlice cvSlice( int start, int end ) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) && !defined(__CUDACC__)) + CvSlice slice = { start, end }; +#else + CvSlice slice(start, end); +#endif + return slice; +} + +#if defined(__cplusplus) +CV_INLINE CvSlice cvSlice(const cv::Range& r) +{ + CvSlice slice = (r.start != INT_MIN && r.end != INT_MAX) ? cvSlice(r.start, r.end) : cvSlice(0, CV_WHOLE_SEQ_END_INDEX); + return slice; +} +#endif + + +/************************************* CvScalar *****************************************/ +/** @sa Scalar_ + */ +typedef struct CvScalar +{ + double val[4]; + +#ifdef CV__VALIDATE_UNUNITIALIZED_VARS + CvScalar() __attribute__(( warning("Non-initialized variable") )) {} + CvScalar(const std::initializer_list list) + { + CV_Assert(list.size() == 0 || list.size() == 4); + val[0] = val[1] = val[2] = val[3] = 0; + if (list.size() == 4) + { + val[0] = list.begin()[0]; val[1] = list.begin()[1]; val[2] = list.begin()[2]; val[3] = list.begin()[3]; + } + }; +#elif defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) + CvScalar() {} + CvScalar(double d0, double d1 = 0, double d2 = 0, double d3 = 0) { val[0] = d0; val[1] = d1; val[2] = d2; val[3] = d3; } + template + CvScalar(const cv::Scalar_<_Tp>& s) { val[0] = s.val[0]; val[1] = s.val[1]; val[2] = s.val[2]; val[3] = s.val[3]; } + template + CvScalar(const cv::Vec<_Tp, cn>& v) + { + int i; + for( i = 0; i < (cn < 4 ? cn : 4); i++ ) val[i] = v.val[i]; + for( ; i < 4; i++ ) val[i] = 0; + } +#endif +#ifdef __cplusplus + template + operator cv::Scalar_<_Tp>() const { return cv::Scalar_<_Tp>(cv::saturate_cast<_Tp>(val[0]), cv::saturate_cast<_Tp>(val[1]), cv::saturate_cast<_Tp>(val[2]), cv::saturate_cast<_Tp>(val[3])); } +#endif +} +CvScalar; + +CV_INLINE CvScalar cvScalar( double val0, double val1 CV_DEFAULT(0), + double val2 CV_DEFAULT(0), double val3 CV_DEFAULT(0)) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvScalar scalar = CV_STRUCT_INITIALIZER; +#else + CvScalar scalar; +#endif + scalar.val[0] = val0; scalar.val[1] = val1; + scalar.val[2] = val2; scalar.val[3] = val3; + return scalar; +} + +#ifdef __cplusplus +CV_INLINE CvScalar cvScalar() +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvScalar scalar = CV_STRUCT_INITIALIZER; +#else + CvScalar scalar; +#endif + scalar.val[0] = scalar.val[1] = scalar.val[2] = scalar.val[3] = 0; + return scalar; +} +CV_INLINE CvScalar cvScalar(const cv::Scalar& s) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvScalar scalar = CV_STRUCT_INITIALIZER; +#else + CvScalar scalar; +#endif + scalar.val[0] = s.val[0]; + scalar.val[1] = s.val[1]; + scalar.val[2] = s.val[2]; + scalar.val[3] = s.val[3]; + return scalar; +} +#endif + +CV_INLINE CvScalar cvRealScalar( double val0 ) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvScalar scalar = CV_STRUCT_INITIALIZER; +#else + CvScalar scalar; +#endif + scalar.val[0] = val0; + scalar.val[1] = scalar.val[2] = scalar.val[3] = 0; + return scalar; +} + +CV_INLINE CvScalar cvScalarAll( double val0123 ) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvScalar scalar = CV_STRUCT_INITIALIZER; +#else + CvScalar scalar; +#endif + scalar.val[0] = val0123; + scalar.val[1] = val0123; + scalar.val[2] = val0123; + scalar.val[3] = val0123; + return scalar; +} + +/****************************************************************************************\ +* Dynamic Data structures * +\****************************************************************************************/ + +/******************************** Memory storage ****************************************/ + +typedef struct CvMemBlock +{ + struct CvMemBlock* prev; + struct CvMemBlock* next; +} +CvMemBlock; + +#define CV_STORAGE_MAGIC_VAL 0x42890000 + +typedef struct CvMemStorage +{ + int signature; + CvMemBlock* bottom; /**< First allocated block. */ + CvMemBlock* top; /**< Current memory block - top of the stack. */ + struct CvMemStorage* parent; /**< We get new blocks from parent as needed. */ + int block_size; /**< Block size. */ + int free_space; /**< Remaining free space in current block. */ +} +CvMemStorage; + +#define CV_IS_STORAGE(storage) \ + ((storage) != NULL && \ + (((CvMemStorage*)(storage))->signature & CV_MAGIC_MASK) == CV_STORAGE_MAGIC_VAL) + + +typedef struct CvMemStoragePos +{ + CvMemBlock* top; + int free_space; +} +CvMemStoragePos; + + +/*********************************** Sequence *******************************************/ + +typedef struct CvSeqBlock +{ + struct CvSeqBlock* prev; /**< Previous sequence block. */ + struct CvSeqBlock* next; /**< Next sequence block. */ + int start_index; /**< Index of the first element in the block + */ + /**< sequence->first->start_index. */ + int count; /**< Number of elements in the block. */ + schar* data; /**< Pointer to the first element of the block. */ +} +CvSeqBlock; + + +#define CV_TREE_NODE_FIELDS(node_type) \ + int flags; /**< Miscellaneous flags. */ \ + int header_size; /**< Size of sequence header. */ \ + struct node_type* h_prev; /**< Previous sequence. */ \ + struct node_type* h_next; /**< Next sequence. */ \ + struct node_type* v_prev; /**< 2nd previous sequence. */ \ + struct node_type* v_next /**< 2nd next sequence. */ + +/** + Read/Write sequence. + Elements can be dynamically inserted to or deleted from the sequence. +*/ +#define CV_SEQUENCE_FIELDS() \ + CV_TREE_NODE_FIELDS(CvSeq); \ + int total; /**< Total number of elements. */ \ + int elem_size; /**< Size of sequence element in bytes. */ \ + schar* block_max; /**< Maximal bound of the last block. */ \ + schar* ptr; /**< Current write pointer. */ \ + int delta_elems; /**< Grow seq this many at a time. */ \ + CvMemStorage* storage; /**< Where the seq is stored. */ \ + CvSeqBlock* free_blocks; /**< Free blocks list. */ \ + CvSeqBlock* first; /**< Pointer to the first sequence block. */ + +typedef struct CvSeq +{ + CV_SEQUENCE_FIELDS() +} +CvSeq; + +#define CV_TYPE_NAME_SEQ "opencv-sequence" +#define CV_TYPE_NAME_SEQ_TREE "opencv-sequence-tree" + +/*************************************** Set ********************************************/ +/** @brief Set + Order is not preserved. There can be gaps between sequence elements. + After the element has been inserted it stays in the same place all the time. + The MSB(most-significant or sign bit) of the first field (flags) is 0 iff the element exists. +*/ +#define CV_SET_ELEM_FIELDS(elem_type) \ + int flags; \ + struct elem_type* next_free; + +typedef struct CvSetElem +{ + CV_SET_ELEM_FIELDS(CvSetElem) +} +CvSetElem; + +#define CV_SET_FIELDS() \ + CV_SEQUENCE_FIELDS() \ + CvSetElem* free_elems; \ + int active_count; + +typedef struct CvSet +{ + CV_SET_FIELDS() +} +CvSet; + + +#define CV_SET_ELEM_IDX_MASK ((1 << 26) - 1) +#define CV_SET_ELEM_FREE_FLAG (1 << (sizeof(int)*8-1)) + +/** Checks whether the element pointed by ptr belongs to a set or not */ +#define CV_IS_SET_ELEM( ptr ) (((CvSetElem*)(ptr))->flags >= 0) + +/************************************* Graph ********************************************/ + +/** @name Graph + +We represent a graph as a set of vertices. Vertices contain their adjacency lists (more exactly, +pointers to first incoming or outcoming edge (or 0 if isolated vertex)). Edges are stored in +another set. There is a singly-linked list of incoming/outcoming edges for each vertex. + +Each edge consists of: + +- Two pointers to the starting and ending vertices (vtx[0] and vtx[1] respectively). + + A graph may be oriented or not. In the latter case, edges between vertex i to vertex j are not +distinguished during search operations. + +- Two pointers to next edges for the starting and ending vertices, where next[0] points to the +next edge in the vtx[0] adjacency list and next[1] points to the next edge in the vtx[1] +adjacency list. + +@see CvGraphEdge, CvGraphVtx, CvGraphVtx2D, CvGraph +@{ +*/ +#define CV_GRAPH_EDGE_FIELDS() \ + int flags; \ + float weight; \ + struct CvGraphEdge* next[2]; \ + struct CvGraphVtx* vtx[2]; + + +#define CV_GRAPH_VERTEX_FIELDS() \ + int flags; \ + struct CvGraphEdge* first; + + +typedef struct CvGraphEdge +{ + CV_GRAPH_EDGE_FIELDS() +} +CvGraphEdge; + +typedef struct CvGraphVtx +{ + CV_GRAPH_VERTEX_FIELDS() +} +CvGraphVtx; + +typedef struct CvGraphVtx2D +{ + CV_GRAPH_VERTEX_FIELDS() + CvPoint2D32f* ptr; +} +CvGraphVtx2D; + +/** + Graph is "derived" from the set (this is set a of vertices) + and includes another set (edges) +*/ +#define CV_GRAPH_FIELDS() \ + CV_SET_FIELDS() \ + CvSet* edges; + +typedef struct CvGraph +{ + CV_GRAPH_FIELDS() +} +CvGraph; + +#define CV_TYPE_NAME_GRAPH "opencv-graph" + +/** @} */ + +/*********************************** Chain/Contour *************************************/ + +typedef struct CvChain +{ + CV_SEQUENCE_FIELDS() + CvPoint origin; +} +CvChain; + +#define CV_CONTOUR_FIELDS() \ + CV_SEQUENCE_FIELDS() \ + CvRect rect; \ + int color; \ + int reserved[3]; + +typedef struct CvContour +{ + CV_CONTOUR_FIELDS() +} +CvContour; + +typedef CvContour CvPoint2DSeq; + +/****************************************************************************************\ +* Sequence types * +\****************************************************************************************/ + +#define CV_SEQ_MAGIC_VAL 0x42990000 + +#define CV_IS_SEQ(seq) \ + ((seq) != NULL && (((CvSeq*)(seq))->flags & CV_MAGIC_MASK) == CV_SEQ_MAGIC_VAL) + +#define CV_SET_MAGIC_VAL 0x42980000 +#define CV_IS_SET(set) \ + ((set) != NULL && (((CvSeq*)(set))->flags & CV_MAGIC_MASK) == CV_SET_MAGIC_VAL) + +#define CV_SEQ_ELTYPE_BITS 12 +#define CV_SEQ_ELTYPE_MASK ((1 << CV_SEQ_ELTYPE_BITS) - 1) + +#define CV_SEQ_ELTYPE_POINT CV_32SC2 /**< (x,y) */ +#define CV_SEQ_ELTYPE_CODE CV_8UC1 /**< freeman code: 0..7 */ +#define CV_SEQ_ELTYPE_GENERIC 0 +#define CV_SEQ_ELTYPE_PTR CV_USRTYPE1 +#define CV_SEQ_ELTYPE_PPOINT CV_SEQ_ELTYPE_PTR /**< &(x,y) */ +#define CV_SEQ_ELTYPE_INDEX CV_32SC1 /**< #(x,y) */ +#define CV_SEQ_ELTYPE_GRAPH_EDGE 0 /**< &next_o, &next_d, &vtx_o, &vtx_d */ +#define CV_SEQ_ELTYPE_GRAPH_VERTEX 0 /**< first_edge, &(x,y) */ +#define CV_SEQ_ELTYPE_TRIAN_ATR 0 /**< vertex of the binary tree */ +#define CV_SEQ_ELTYPE_CONNECTED_COMP 0 /**< connected component */ +#define CV_SEQ_ELTYPE_POINT3D CV_32FC3 /**< (x,y,z) */ + +#define CV_SEQ_KIND_BITS 2 +#define CV_SEQ_KIND_MASK (((1 << CV_SEQ_KIND_BITS) - 1)<flags & CV_SEQ_ELTYPE_MASK) +#define CV_SEQ_KIND( seq ) ((seq)->flags & CV_SEQ_KIND_MASK ) + +/** flag checking */ +#define CV_IS_SEQ_INDEX( seq ) ((CV_SEQ_ELTYPE(seq) == CV_SEQ_ELTYPE_INDEX) && \ + (CV_SEQ_KIND(seq) == CV_SEQ_KIND_GENERIC)) + +#define CV_IS_SEQ_CURVE( seq ) (CV_SEQ_KIND(seq) == CV_SEQ_KIND_CURVE) +#define CV_IS_SEQ_CLOSED( seq ) (((seq)->flags & CV_SEQ_FLAG_CLOSED) != 0) +#define CV_IS_SEQ_CONVEX( seq ) 0 +#define CV_IS_SEQ_HOLE( seq ) (((seq)->flags & CV_SEQ_FLAG_HOLE) != 0) +#define CV_IS_SEQ_SIMPLE( seq ) 1 + +/** type checking macros */ +#define CV_IS_SEQ_POINT_SET( seq ) \ + ((CV_SEQ_ELTYPE(seq) == CV_32SC2 || CV_SEQ_ELTYPE(seq) == CV_32FC2)) + +#define CV_IS_SEQ_POINT_SUBSET( seq ) \ + (CV_IS_SEQ_INDEX( seq ) || CV_SEQ_ELTYPE(seq) == CV_SEQ_ELTYPE_PPOINT) + +#define CV_IS_SEQ_POLYLINE( seq ) \ + (CV_SEQ_KIND(seq) == CV_SEQ_KIND_CURVE && CV_IS_SEQ_POINT_SET(seq)) + +#define CV_IS_SEQ_POLYGON( seq ) \ + (CV_IS_SEQ_POLYLINE(seq) && CV_IS_SEQ_CLOSED(seq)) + +#define CV_IS_SEQ_CHAIN( seq ) \ + (CV_SEQ_KIND(seq) == CV_SEQ_KIND_CURVE && (seq)->elem_size == 1) + +#define CV_IS_SEQ_CONTOUR( seq ) \ + (CV_IS_SEQ_CLOSED(seq) && (CV_IS_SEQ_POLYLINE(seq) || CV_IS_SEQ_CHAIN(seq))) + +#define CV_IS_SEQ_CHAIN_CONTOUR( seq ) \ + (CV_IS_SEQ_CHAIN( seq ) && CV_IS_SEQ_CLOSED( seq )) + +#define CV_IS_SEQ_POLYGON_TREE( seq ) \ + (CV_SEQ_ELTYPE (seq) == CV_SEQ_ELTYPE_TRIAN_ATR && \ + CV_SEQ_KIND( seq ) == CV_SEQ_KIND_BIN_TREE ) + +#define CV_IS_GRAPH( seq ) \ + (CV_IS_SET(seq) && CV_SEQ_KIND((CvSet*)(seq)) == CV_SEQ_KIND_GRAPH) + +#define CV_IS_GRAPH_ORIENTED( seq ) \ + (((seq)->flags & CV_GRAPH_FLAG_ORIENTED) != 0) + +#define CV_IS_SUBDIV2D( seq ) \ + (CV_IS_SET(seq) && CV_SEQ_KIND((CvSet*)(seq)) == CV_SEQ_KIND_SUBDIV2D) + +/****************************************************************************************/ +/* Sequence writer & reader */ +/****************************************************************************************/ + +#define CV_SEQ_WRITER_FIELDS() \ + int header_size; \ + CvSeq* seq; /**< the sequence written */ \ + CvSeqBlock* block; /**< current block */ \ + schar* ptr; /**< pointer to free space */ \ + schar* block_min; /**< pointer to the beginning of block*/\ + schar* block_max; /**< pointer to the end of block */ + +typedef struct CvSeqWriter +{ + CV_SEQ_WRITER_FIELDS() +} +CvSeqWriter; + + +#define CV_SEQ_READER_FIELDS() \ + int header_size; \ + CvSeq* seq; /**< sequence, beign read */ \ + CvSeqBlock* block; /**< current block */ \ + schar* ptr; /**< pointer to element be read next */ \ + schar* block_min; /**< pointer to the beginning of block */\ + schar* block_max; /**< pointer to the end of block */ \ + int delta_index;/**< = seq->first->start_index */ \ + schar* prev_elem; /**< pointer to previous element */ + +typedef struct CvSeqReader +{ + CV_SEQ_READER_FIELDS() +} +CvSeqReader; + +/****************************************************************************************/ +/* Operations on sequences */ +/****************************************************************************************/ + +#define CV_SEQ_ELEM( seq, elem_type, index ) \ +/** assert gives some guarantee that parameter is valid */ \ +( assert(sizeof((seq)->first[0]) == sizeof(CvSeqBlock) && \ + (seq)->elem_size == sizeof(elem_type)), \ + (elem_type*)((seq)->first && (unsigned)index < \ + (unsigned)((seq)->first->count) ? \ + (seq)->first->data + (index) * sizeof(elem_type) : \ + cvGetSeqElem( (CvSeq*)(seq), (index) ))) +#define CV_GET_SEQ_ELEM( elem_type, seq, index ) CV_SEQ_ELEM( (seq), elem_type, (index) ) + +/** Add element to sequence: */ +#define CV_WRITE_SEQ_ELEM_VAR( elem_ptr, writer ) \ +{ \ + if( (writer).ptr >= (writer).block_max ) \ + { \ + cvCreateSeqBlock( &writer); \ + } \ + memcpy((writer).ptr, elem_ptr, (writer).seq->elem_size);\ + (writer).ptr += (writer).seq->elem_size; \ +} + +#define CV_WRITE_SEQ_ELEM( elem, writer ) \ +{ \ + assert( (writer).seq->elem_size == sizeof(elem)); \ + if( (writer).ptr >= (writer).block_max ) \ + { \ + cvCreateSeqBlock( &writer); \ + } \ + assert( (writer).ptr <= (writer).block_max - sizeof(elem));\ + memcpy((writer).ptr, &(elem), sizeof(elem)); \ + (writer).ptr += sizeof(elem); \ +} + + +/** Move reader position forward: */ +#define CV_NEXT_SEQ_ELEM( elem_size, reader ) \ +{ \ + if( ((reader).ptr += (elem_size)) >= (reader).block_max ) \ + { \ + cvChangeSeqBlock( &(reader), 1 ); \ + } \ +} + + +/** Move reader position backward: */ +#define CV_PREV_SEQ_ELEM( elem_size, reader ) \ +{ \ + if( ((reader).ptr -= (elem_size)) < (reader).block_min ) \ + { \ + cvChangeSeqBlock( &(reader), -1 ); \ + } \ +} + +/** Read element and move read position forward: */ +#define CV_READ_SEQ_ELEM( elem, reader ) \ +{ \ + assert( (reader).seq->elem_size == sizeof(elem)); \ + memcpy( &(elem), (reader).ptr, sizeof((elem))); \ + CV_NEXT_SEQ_ELEM( sizeof(elem), reader ) \ +} + +/** Read element and move read position backward: */ +#define CV_REV_READ_SEQ_ELEM( elem, reader ) \ +{ \ + assert( (reader).seq->elem_size == sizeof(elem)); \ + memcpy(&(elem), (reader).ptr, sizeof((elem))); \ + CV_PREV_SEQ_ELEM( sizeof(elem), reader ) \ +} + + +#define CV_READ_CHAIN_POINT( _pt, reader ) \ +{ \ + (_pt) = (reader).pt; \ + if( (reader).ptr ) \ + { \ + CV_READ_SEQ_ELEM( (reader).code, (reader)); \ + assert( ((reader).code & ~7) == 0 ); \ + (reader).pt.x += (reader).deltas[(int)(reader).code][0]; \ + (reader).pt.y += (reader).deltas[(int)(reader).code][1]; \ + } \ +} + +#define CV_CURRENT_POINT( reader ) (*((CvPoint*)((reader).ptr))) +#define CV_PREV_POINT( reader ) (*((CvPoint*)((reader).prev_elem))) + +#define CV_READ_EDGE( pt1, pt2, reader ) \ +{ \ + assert( sizeof(pt1) == sizeof(CvPoint) && \ + sizeof(pt2) == sizeof(CvPoint) && \ + reader.seq->elem_size == sizeof(CvPoint)); \ + (pt1) = CV_PREV_POINT( reader ); \ + (pt2) = CV_CURRENT_POINT( reader ); \ + (reader).prev_elem = (reader).ptr; \ + CV_NEXT_SEQ_ELEM( sizeof(CvPoint), (reader)); \ +} + +/************ Graph macros ************/ + +/** Return next graph edge for given vertex: */ +#define CV_NEXT_GRAPH_EDGE( edge, vertex ) \ + (assert((edge)->vtx[0] == (vertex) || (edge)->vtx[1] == (vertex)), \ + (edge)->next[(edge)->vtx[1] == (vertex)]) + + + +/****************************************************************************************\ +* Data structures for persistence (a.k.a serialization) functionality * +\****************************************************************************************/ + +/** "black box" file storage */ +typedef struct CvFileStorage CvFileStorage; + +/** Storage flags: */ +#define CV_STORAGE_READ 0 +#define CV_STORAGE_WRITE 1 +#define CV_STORAGE_WRITE_TEXT CV_STORAGE_WRITE +#define CV_STORAGE_WRITE_BINARY CV_STORAGE_WRITE +#define CV_STORAGE_APPEND 2 +#define CV_STORAGE_MEMORY 4 +#define CV_STORAGE_FORMAT_MASK (7<<3) +#define CV_STORAGE_FORMAT_AUTO 0 +#define CV_STORAGE_FORMAT_XML 8 +#define CV_STORAGE_FORMAT_YAML 16 +#define CV_STORAGE_FORMAT_JSON 24 +#define CV_STORAGE_BASE64 64 +#define CV_STORAGE_WRITE_BASE64 (CV_STORAGE_BASE64 | CV_STORAGE_WRITE) + +/** @brief List of attributes. : + +In the current implementation, attributes are used to pass extra parameters when writing user +objects (see cvWrite). XML attributes inside tags are not supported, aside from the object type +specification (type_id attribute). +@see cvAttrList, cvAttrValue + */ +typedef struct CvAttrList +{ + const char** attr; /**< NULL-terminated array of (attribute_name,attribute_value) pairs. */ + struct CvAttrList* next; /**< Pointer to next chunk of the attributes list. */ +} +CvAttrList; + +/** initializes CvAttrList structure */ +CV_INLINE CvAttrList cvAttrList( const char** attr CV_DEFAULT(NULL), + CvAttrList* next CV_DEFAULT(NULL) ) +{ + CvAttrList l; + l.attr = attr; + l.next = next; + + return l; +} + +struct CvTypeInfo; + +#define CV_NODE_NONE 0 +#define CV_NODE_INT 1 +#define CV_NODE_INTEGER CV_NODE_INT +#define CV_NODE_REAL 2 +#define CV_NODE_FLOAT CV_NODE_REAL +#define CV_NODE_STR 3 +#define CV_NODE_STRING CV_NODE_STR +#define CV_NODE_REF 4 /**< not used */ +#define CV_NODE_SEQ 5 +#define CV_NODE_MAP 6 +#define CV_NODE_TYPE_MASK 7 + +#define CV_NODE_TYPE(flags) ((flags) & CV_NODE_TYPE_MASK) + +/** file node flags */ +#define CV_NODE_FLOW 8 /**= CV_NODE_SEQ) +#define CV_NODE_IS_FLOW(flags) (((flags) & CV_NODE_FLOW) != 0) +#define CV_NODE_IS_EMPTY(flags) (((flags) & CV_NODE_EMPTY) != 0) +#define CV_NODE_IS_USER(flags) (((flags) & CV_NODE_USER) != 0) +#define CV_NODE_HAS_NAME(flags) (((flags) & CV_NODE_NAMED) != 0) + +#define CV_NODE_SEQ_SIMPLE 256 +#define CV_NODE_SEQ_IS_SIMPLE(seq) (((seq)->flags & CV_NODE_SEQ_SIMPLE) != 0) + +typedef struct CvString +{ + int len; + char* ptr; +} +CvString; + +/** All the keys (names) of elements in the read file storage + are stored in the hash to speed up the lookup operations: */ +typedef struct CvStringHashNode +{ + unsigned hashval; + CvString str; + struct CvStringHashNode* next; +} +CvStringHashNode; + +typedef struct CvGenericHash CvFileNodeHash; + +/** Basic element of the file storage - scalar or collection: */ +typedef struct CvFileNode +{ + int tag; + struct CvTypeInfo* info; /**< type information + (only for user-defined object, for others it is 0) */ + union + { + double f; /**< scalar floating-point number */ + int i; /**< scalar integer number */ + CvString str; /**< text string */ + CvSeq* seq; /**< sequence (ordered collection of file nodes) */ + CvFileNodeHash* map; /**< map (collection of named file nodes) */ + } data; +} +CvFileNode; + +#ifdef __cplusplus +extern "C" { +#endif +typedef int (CV_CDECL *CvIsInstanceFunc)( const void* struct_ptr ); +typedef void (CV_CDECL *CvReleaseFunc)( void** struct_dblptr ); +typedef void* (CV_CDECL *CvReadFunc)( CvFileStorage* storage, CvFileNode* node ); +typedef void (CV_CDECL *CvWriteFunc)( CvFileStorage* storage, const char* name, + const void* struct_ptr, CvAttrList attributes ); +typedef void* (CV_CDECL *CvCloneFunc)( const void* struct_ptr ); +#ifdef __cplusplus +} +#endif + +/** @brief Type information + +The structure contains information about one of the standard or user-defined types. Instances of the +type may or may not contain a pointer to the corresponding CvTypeInfo structure. In any case, there +is a way to find the type info structure for a given object using the cvTypeOf function. +Alternatively, type info can be found by type name using cvFindType, which is used when an object +is read from file storage. The user can register a new type with cvRegisterType that adds the type +information structure into the beginning of the type list. Thus, it is possible to create +specialized types from generic standard types and override the basic methods. + */ +typedef struct CvTypeInfo +{ + int flags; /**< not used */ + int header_size; /**< sizeof(CvTypeInfo) */ + struct CvTypeInfo* prev; /**< previous registered type in the list */ + struct CvTypeInfo* next; /**< next registered type in the list */ + const char* type_name; /**< type name, written to file storage */ + CvIsInstanceFunc is_instance; /**< checks if the passed object belongs to the type */ + CvReleaseFunc release; /**< releases object (memory etc.) */ + CvReadFunc read; /**< reads object from file storage */ + CvWriteFunc write; /**< writes object to file storage */ + CvCloneFunc clone; /**< creates a copy of the object */ +} +CvTypeInfo; + + +/**** System data types ******/ + +typedef struct CvPluginFuncInfo +{ + void** func_addr; + void* default_func_addr; + const char* func_names; + int search_modules; + int loaded_from; +} +CvPluginFuncInfo; + +typedef struct CvModuleInfo +{ + struct CvModuleInfo* next; + const char* name; + const char* version; + CvPluginFuncInfo* func_tab; +} +CvModuleInfo; + +/** @} */ + +#endif /*OPENCV_CORE_TYPES_H*/ + +/* End of file. */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/utility.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/utility.hpp new file mode 100644 index 0000000000000000000000000000000000000000..7a7158f5f9d5173c740c46f623b9cb723eabdcfd --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/utility.hpp @@ -0,0 +1,1358 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_UTILITY_H +#define OPENCV_CORE_UTILITY_H + +#ifndef __cplusplus +# error utility.hpp header must be compiled as C++ +#endif + +#if defined(check) +# warning Detected Apple 'check' macro definition, it can cause build conflicts. Please, include this header before any Apple headers. +#endif + +#include "opencv2/core.hpp" +#include + +#ifdef CV_CXX11 +#include +#endif + +namespace cv +{ + +#ifdef CV_COLLECT_IMPL_DATA +CV_EXPORTS void setImpl(int flags); // set implementation flags and reset storage arrays +CV_EXPORTS void addImpl(int flag, const char* func = 0); // add implementation and function name to storage arrays +// Get stored implementation flags and functions names arrays +// Each implementation entry correspond to function name entry, so you can find which implementation was executed in which function +CV_EXPORTS int getImpl(std::vector &impl, std::vector &funName); + +CV_EXPORTS bool useCollection(); // return implementation collection state +CV_EXPORTS void setUseCollection(bool flag); // set implementation collection state + +#define CV_IMPL_PLAIN 0x01 // native CPU OpenCV implementation +#define CV_IMPL_OCL 0x02 // OpenCL implementation +#define CV_IMPL_IPP 0x04 // IPP implementation +#define CV_IMPL_MT 0x10 // multithreaded implementation + +#define CV_IMPL_ADD(impl) \ + if(cv::useCollection()) \ + { \ + cv::addImpl(impl, CV_Func); \ + } +#else +#define CV_IMPL_ADD(impl) +#endif + +//! @addtogroup core_utils +//! @{ + +/** @brief Automatically Allocated Buffer Class + + The class is used for temporary buffers in functions and methods. + If a temporary buffer is usually small (a few K's of memory), + but its size depends on the parameters, it makes sense to create a small + fixed-size array on stack and use it if it's large enough. If the required buffer size + is larger than the fixed size, another buffer of sufficient size is allocated dynamically + and released after the processing. Therefore, in typical cases, when the buffer size is small, + there is no overhead associated with malloc()/free(). + At the same time, there is no limit on the size of processed data. + + This is what AutoBuffer does. The template takes 2 parameters - type of the buffer elements and + the number of stack-allocated elements. Here is how the class is used: + + \code + void my_func(const cv::Mat& m) + { + cv::AutoBuffer buf(1000); // create automatic buffer containing 1000 floats + + buf.allocate(m.rows); // if m.rows <= 1000, the pre-allocated buffer is used, + // otherwise the buffer of "m.rows" floats will be allocated + // dynamically and deallocated in cv::AutoBuffer destructor + ... + } + \endcode +*/ +template class AutoBuffer +{ +public: + typedef _Tp value_type; + + //! the default constructor + AutoBuffer(); + //! constructor taking the real buffer size + explicit AutoBuffer(size_t _size); + + //! the copy constructor + AutoBuffer(const AutoBuffer<_Tp, fixed_size>& buf); + //! the assignment operator + AutoBuffer<_Tp, fixed_size>& operator = (const AutoBuffer<_Tp, fixed_size>& buf); + + //! destructor. calls deallocate() + ~AutoBuffer(); + + //! allocates the new buffer of size _size. if the _size is small enough, stack-allocated buffer is used + void allocate(size_t _size); + //! deallocates the buffer if it was dynamically allocated + void deallocate(); + //! resizes the buffer and preserves the content + void resize(size_t _size); + //! returns the current buffer size + size_t size() const; + //! returns pointer to the real buffer, stack-allocated or heap-allocated + inline _Tp* data() { return ptr; } + //! returns read-only pointer to the real buffer, stack-allocated or heap-allocated + inline const _Tp* data() const { return ptr; } + +#if !defined(OPENCV_DISABLE_DEPRECATED_COMPATIBILITY) // use to .data() calls instead + //! returns pointer to the real buffer, stack-allocated or heap-allocated + operator _Tp* () { return ptr; } + //! returns read-only pointer to the real buffer, stack-allocated or heap-allocated + operator const _Tp* () const { return ptr; } +#else + //! returns a reference to the element at specified location. No bounds checking is performed in Release builds. + inline _Tp& operator[] (size_t i) { CV_DbgCheckLT(i, sz, "out of range"); return ptr[i]; } + //! returns a reference to the element at specified location. No bounds checking is performed in Release builds. + inline const _Tp& operator[] (size_t i) const { CV_DbgCheckLT(i, sz, "out of range"); return ptr[i]; } +#endif + +protected: + //! pointer to the real buffer, can point to buf if the buffer is small enough + _Tp* ptr; + //! size of the real buffer + size_t sz; + //! pre-allocated buffer. At least 1 element to confirm C++ standard requirements + _Tp buf[(fixed_size > 0) ? fixed_size : 1]; +}; + +/** @brief Sets/resets the break-on-error mode. + +When the break-on-error mode is set, the default error handler issues a hardware exception, which +can make debugging more convenient. + +\return the previous state + */ +CV_EXPORTS bool setBreakOnError(bool flag); + +extern "C" typedef int (*ErrorCallback)( int status, const char* func_name, + const char* err_msg, const char* file_name, + int line, void* userdata ); + + +/** @brief Sets the new error handler and the optional user data. + + The function sets the new error handler, called from cv::error(). + + \param errCallback the new error handler. If NULL, the default error handler is used. + \param userdata the optional user data pointer, passed to the callback. + \param prevUserdata the optional output parameter where the previous user data pointer is stored + + \return the previous error handler +*/ +CV_EXPORTS ErrorCallback redirectError( ErrorCallback errCallback, void* userdata=0, void** prevUserdata=0); + +CV_EXPORTS String tempfile( const char* suffix = 0); +CV_EXPORTS void glob(String pattern, std::vector& result, bool recursive = false); + +/** @brief OpenCV will try to set the number of threads for the next parallel region. + +If threads == 0, OpenCV will disable threading optimizations and run all it's functions +sequentially. Passing threads \< 0 will reset threads number to system default. This function must +be called outside of parallel region. + +OpenCV will try to run its functions with specified threads number, but some behaviour differs from +framework: +- `TBB` - User-defined parallel constructions will run with the same threads number, if + another is not specified. If later on user creates his own scheduler, OpenCV will use it. +- `OpenMP` - No special defined behaviour. +- `Concurrency` - If threads == 1, OpenCV will disable threading optimizations and run its + functions sequentially. +- `GCD` - Supports only values \<= 0. +- `C=` - No special defined behaviour. +@param nthreads Number of threads used by OpenCV. +@sa getNumThreads, getThreadNum + */ +CV_EXPORTS_W void setNumThreads(int nthreads); + +/** @brief Returns the number of threads used by OpenCV for parallel regions. + +Always returns 1 if OpenCV is built without threading support. + +The exact meaning of return value depends on the threading framework used by OpenCV library: +- `TBB` - The number of threads, that OpenCV will try to use for parallel regions. If there is + any tbb::thread_scheduler_init in user code conflicting with OpenCV, then function returns + default number of threads used by TBB library. +- `OpenMP` - An upper bound on the number of threads that could be used to form a new team. +- `Concurrency` - The number of threads, that OpenCV will try to use for parallel regions. +- `GCD` - Unsupported; returns the GCD thread pool limit (512) for compatibility. +- `C=` - The number of threads, that OpenCV will try to use for parallel regions, if before + called setNumThreads with threads \> 0, otherwise returns the number of logical CPUs, + available for the process. +@sa setNumThreads, getThreadNum + */ +CV_EXPORTS_W int getNumThreads(); + +/** @brief Returns the index of the currently executed thread within the current parallel region. Always +returns 0 if called outside of parallel region. + +@deprecated Current implementation doesn't corresponding to this documentation. + +The exact meaning of the return value depends on the threading framework used by OpenCV library: +- `TBB` - Unsupported with current 4.1 TBB release. Maybe will be supported in future. +- `OpenMP` - The thread number, within the current team, of the calling thread. +- `Concurrency` - An ID for the virtual processor that the current context is executing on (0 + for master thread and unique number for others, but not necessary 1,2,3,...). +- `GCD` - System calling thread's ID. Never returns 0 inside parallel region. +- `C=` - The index of the current parallel task. +@sa setNumThreads, getNumThreads + */ +CV_EXPORTS_W int getThreadNum(); + +/** @brief Returns full configuration time cmake output. + +Returned value is raw cmake output including version control system revision, compiler version, +compiler flags, enabled modules and third party libraries, etc. Output format depends on target +architecture. + */ +CV_EXPORTS_W const String& getBuildInformation(); + +/** @brief Returns library version string + +For example "3.4.1-dev". + +@sa getMajorVersion, getMinorVersion, getRevisionVersion +*/ +CV_EXPORTS_W String getVersionString(); + +/** @brief Returns major library version */ +CV_EXPORTS_W int getVersionMajor(); + +/** @brief Returns minor library version */ +CV_EXPORTS_W int getVersionMinor(); + +/** @brief Returns revision field of the library version */ +CV_EXPORTS_W int getVersionRevision(); + +/** @brief Returns the number of ticks. + +The function returns the number of ticks after the certain event (for example, when the machine was +turned on). It can be used to initialize RNG or to measure a function execution time by reading the +tick count before and after the function call. +@sa getTickFrequency, TickMeter + */ +CV_EXPORTS_W int64 getTickCount(); + +/** @brief Returns the number of ticks per second. + +The function returns the number of ticks per second. That is, the following code computes the +execution time in seconds: +@code + double t = (double)getTickCount(); + // do something ... + t = ((double)getTickCount() - t)/getTickFrequency(); +@endcode +@sa getTickCount, TickMeter + */ +CV_EXPORTS_W double getTickFrequency(); + +/** @brief a Class to measure passing time. + +The class computes passing time by counting the number of ticks per second. That is, the following code computes the +execution time in seconds: +@code +TickMeter tm; +tm.start(); +// do something ... +tm.stop(); +std::cout << tm.getTimeSec(); +@endcode + +It is also possible to compute the average time over multiple runs: +@code +TickMeter tm; +for (int i = 0; i < 100; i++) +{ + tm.start(); + // do something ... + tm.stop(); +} +double average_time = tm.getTimeSec() / tm.getCounter(); +std::cout << "Average time in second per iteration is: " << average_time << std::endl; +@endcode +@sa getTickCount, getTickFrequency +*/ + +class CV_EXPORTS_W TickMeter +{ +public: + //! the default constructor + CV_WRAP TickMeter() + { + reset(); + } + + /** + starts counting ticks. + */ + CV_WRAP void start() + { + startTime = cv::getTickCount(); + } + + /** + stops counting ticks. + */ + CV_WRAP void stop() + { + int64 time = cv::getTickCount(); + if (startTime == 0) + return; + ++counter; + sumTime += (time - startTime); + startTime = 0; + } + + /** + returns counted ticks. + */ + CV_WRAP int64 getTimeTicks() const + { + return sumTime; + } + + /** + returns passed time in microseconds. + */ + CV_WRAP double getTimeMicro() const + { + return getTimeMilli()*1e3; + } + + /** + returns passed time in milliseconds. + */ + CV_WRAP double getTimeMilli() const + { + return getTimeSec()*1e3; + } + + /** + returns passed time in seconds. + */ + CV_WRAP double getTimeSec() const + { + return (double)getTimeTicks() / getTickFrequency(); + } + + /** + returns internal counter value. + */ + CV_WRAP int64 getCounter() const + { + return counter; + } + + /** + resets internal values. + */ + CV_WRAP void reset() + { + startTime = 0; + sumTime = 0; + counter = 0; + } + +private: + int64 counter; + int64 sumTime; + int64 startTime; +}; + +/** @brief output operator +@code +TickMeter tm; +tm.start(); +// do something ... +tm.stop(); +std::cout << tm; +@endcode +*/ + +static inline +std::ostream& operator << (std::ostream& out, const TickMeter& tm) +{ + return out << tm.getTimeSec() << "sec"; +} + +/** @brief Returns the number of CPU ticks. + +The function returns the current number of CPU ticks on some architectures (such as x86, x64, +PowerPC). On other platforms the function is equivalent to getTickCount. It can also be used for +very accurate time measurements, as well as for RNG initialization. Note that in case of multi-CPU +systems a thread, from which getCPUTickCount is called, can be suspended and resumed at another CPU +with its own counter. So, theoretically (and practically) the subsequent calls to the function do +not necessary return the monotonously increasing values. Also, since a modern CPU varies the CPU +frequency depending on the load, the number of CPU clocks spent in some code cannot be directly +converted to time units. Therefore, getTickCount is generally a preferable solution for measuring +execution time. + */ +CV_EXPORTS_W int64 getCPUTickCount(); + +/** @brief Returns true if the specified feature is supported by the host hardware. + +The function returns true if the host hardware supports the specified feature. When user calls +setUseOptimized(false), the subsequent calls to checkHardwareSupport() will return false until +setUseOptimized(true) is called. This way user can dynamically switch on and off the optimized code +in OpenCV. +@param feature The feature of interest, one of cv::CpuFeatures + */ +CV_EXPORTS_W bool checkHardwareSupport(int feature); + +/** @brief Returns feature name by ID + +Returns empty string if feature is not defined +*/ +CV_EXPORTS_W String getHardwareFeatureName(int feature); + +/** @brief Returns list of CPU features enabled during compilation. + +Returned value is a string containing space separated list of CPU features with following markers: + +- no markers - baseline features +- prefix `*` - features enabled in dispatcher +- suffix `?` - features enabled but not available in HW + +Example: `SSE SSE2 SSE3 *SSE4.1 *SSE4.2 *FP16 *AVX *AVX2 *AVX512-SKX?` +*/ +CV_EXPORTS std::string getCPUFeaturesLine(); + +/** @brief Returns the number of logical CPUs available for the process. + */ +CV_EXPORTS_W int getNumberOfCPUs(); + + +/** @brief Aligns a pointer to the specified number of bytes. + +The function returns the aligned pointer of the same type as the input pointer: +\f[\texttt{(_Tp*)(((size_t)ptr + n-1) & -n)}\f] +@param ptr Aligned pointer. +@param n Alignment size that must be a power of two. + */ +template static inline _Tp* alignPtr(_Tp* ptr, int n=(int)sizeof(_Tp)) +{ + CV_DbgAssert((n & (n - 1)) == 0); // n is a power of 2 + return (_Tp*)(((size_t)ptr + n-1) & -n); +} + +/** @brief Aligns a buffer size to the specified number of bytes. + +The function returns the minimum number that is greater than or equal to sz and is divisible by n : +\f[\texttt{(sz + n-1) & -n}\f] +@param sz Buffer size to align. +@param n Alignment size that must be a power of two. + */ +static inline size_t alignSize(size_t sz, int n) +{ + CV_DbgAssert((n & (n - 1)) == 0); // n is a power of 2 + return (sz + n-1) & -n; +} + +/** @brief Integer division with result round up. + +Use this function instead of `ceil((float)a / b)` expressions. + +@sa alignSize +*/ +static inline int divUp(int a, unsigned int b) +{ + CV_DbgAssert(a >= 0); + return (a + b - 1) / b; +} +/** @overload */ +static inline size_t divUp(size_t a, unsigned int b) +{ + return (a + b - 1) / b; +} + +/** @brief Round first value up to the nearest multiple of second value. + +Use this function instead of `ceil((float)a / b) * b` expressions. + +@sa divUp +*/ +static inline int roundUp(int a, unsigned int b) +{ + CV_DbgAssert(a >= 0); + return a + b - 1 - (a + b -1) % b; +} +/** @overload */ +static inline size_t roundUp(size_t a, unsigned int b) +{ + return a + b - 1 - (a + b - 1) % b; +} + +/** @brief Enables or disables the optimized code. + +The function can be used to dynamically turn on and off optimized dispatched code (code that uses SSE4.2, AVX/AVX2, +and other instructions on the platforms that support it). It sets a global flag that is further +checked by OpenCV functions. Since the flag is not checked in the inner OpenCV loops, it is only +safe to call the function on the very top level in your application where you can be sure that no +other OpenCV function is currently executed. + +By default, the optimized code is enabled unless you disable it in CMake. The current status can be +retrieved using useOptimized. +@param onoff The boolean flag specifying whether the optimized code should be used (onoff=true) +or not (onoff=false). + */ +CV_EXPORTS_W void setUseOptimized(bool onoff); + +/** @brief Returns the status of optimized code usage. + +The function returns true if the optimized code is enabled. Otherwise, it returns false. + */ +CV_EXPORTS_W bool useOptimized(); + +static inline size_t getElemSize(int type) { return (size_t)CV_ELEM_SIZE(type); } + +/////////////////////////////// Parallel Primitives ////////////////////////////////// + +/** @brief Base class for parallel data processors +*/ +class CV_EXPORTS ParallelLoopBody +{ +public: + virtual ~ParallelLoopBody(); + virtual void operator() (const Range& range) const = 0; +}; + +/** @brief Parallel data processor +*/ +CV_EXPORTS void parallel_for_(const Range& range, const ParallelLoopBody& body, double nstripes=-1.); + +#ifdef CV_CXX11 +class ParallelLoopBodyLambdaWrapper : public ParallelLoopBody +{ +private: + std::function m_functor; +public: + ParallelLoopBodyLambdaWrapper(std::function functor) : + m_functor(functor) + { } + + virtual void operator() (const cv::Range& range) const CV_OVERRIDE + { + m_functor(range); + } +}; + +inline void parallel_for_(const Range& range, std::function functor, double nstripes=-1.) +{ + parallel_for_(range, ParallelLoopBodyLambdaWrapper(functor), nstripes); +} +#endif + +/////////////////////////////// forEach method of cv::Mat //////////////////////////// +template inline +void Mat::forEach_impl(const Functor& operation) { + if (false) { + operation(*reinterpret_cast<_Tp*>(0), reinterpret_cast(0)); + // If your compiler fails in this line. + // Please check that your functor signature is + // (_Tp&, const int*) <- multi-dimensional + // or (_Tp&, void*) <- in case you don't need current idx. + } + + CV_Assert(this->total() / this->size[this->dims - 1] <= INT_MAX); + const int LINES = static_cast(this->total() / this->size[this->dims - 1]); + + class PixelOperationWrapper :public ParallelLoopBody + { + public: + PixelOperationWrapper(Mat_<_Tp>* const frame, const Functor& _operation) + : mat(frame), op(_operation) {} + virtual ~PixelOperationWrapper(){} + // ! Overloaded virtual operator + // convert range call to row call. + virtual void operator()(const Range &range) const CV_OVERRIDE + { + const int DIMS = mat->dims; + const int COLS = mat->size[DIMS - 1]; + if (DIMS <= 2) { + for (int row = range.start; row < range.end; ++row) { + this->rowCall2(row, COLS); + } + } else { + std::vector idx(DIMS); /// idx is modified in this->rowCall + idx[DIMS - 2] = range.start - 1; + + for (int line_num = range.start; line_num < range.end; ++line_num) { + idx[DIMS - 2]++; + for (int i = DIMS - 2; i >= 0; --i) { + if (idx[i] >= mat->size[i]) { + idx[i - 1] += idx[i] / mat->size[i]; + idx[i] %= mat->size[i]; + continue; // carry-over; + } + else { + break; + } + } + this->rowCall(&idx[0], COLS, DIMS); + } + } + } + private: + Mat_<_Tp>* const mat; + const Functor op; + // ! Call operator for each elements in this row. + inline void rowCall(int* const idx, const int COLS, const int DIMS) const { + int &col = idx[DIMS - 1]; + col = 0; + _Tp* pixel = &(mat->template at<_Tp>(idx)); + + while (col < COLS) { + op(*pixel, const_cast(idx)); + pixel++; col++; + } + col = 0; + } + // ! Call operator for each elements in this row. 2d mat special version. + inline void rowCall2(const int row, const int COLS) const { + union Index{ + int body[2]; + operator const int*() const { + return reinterpret_cast(this); + } + int& operator[](const int i) { + return body[i]; + } + } idx = {{row, 0}}; + // Special union is needed to avoid + // "error: array subscript is above array bounds [-Werror=array-bounds]" + // when call the functor `op` such that access idx[3]. + + _Tp* pixel = &(mat->template at<_Tp>(idx)); + const _Tp* const pixel_end = pixel + COLS; + while(pixel < pixel_end) { + op(*pixel++, static_cast(idx)); + idx[1]++; + } + } + PixelOperationWrapper& operator=(const PixelOperationWrapper &) { + CV_Assert(false); + // We can not remove this implementation because Visual Studio warning C4822. + return *this; + } + }; + + parallel_for_(cv::Range(0, LINES), PixelOperationWrapper(reinterpret_cast*>(this), operation)); +} + +/////////////////////////// Synchronization Primitives /////////////////////////////// + +class CV_EXPORTS Mutex +{ +public: + Mutex(); + ~Mutex(); + Mutex(const Mutex& m); + Mutex& operator = (const Mutex& m); + + void lock(); + bool trylock(); + void unlock(); + + struct Impl; +protected: + Impl* impl; +}; + +class CV_EXPORTS AutoLock +{ +public: + AutoLock(Mutex& m) : mutex(&m) { mutex->lock(); } + ~AutoLock() { mutex->unlock(); } +protected: + Mutex* mutex; +private: + AutoLock(const AutoLock&); + AutoLock& operator = (const AutoLock&); +}; + +// TLS interface +class CV_EXPORTS TLSDataContainer +{ +protected: + TLSDataContainer(); + virtual ~TLSDataContainer(); + + void gatherData(std::vector &data) const; +#if OPENCV_ABI_COMPATIBILITY > 300 + void* getData() const; + void release(); + +private: +#else + void release(); + +public: + void* getData() const; +#endif + virtual void* createDataInstance() const = 0; + virtual void deleteDataInstance(void* pData) const = 0; + + int key_; + +public: + void cleanup(); //! Release created TLS data container objects. It is similar to release() call, but it keeps TLS container valid. +}; + +// Main TLS data class +template +class TLSData : protected TLSDataContainer +{ +public: + inline TLSData() {} + inline ~TLSData() { release(); } // Release key and delete associated data + inline T* get() const { return (T*)getData(); } // Get data associated with key + inline T& getRef() const { T* ptr = (T*)getData(); CV_Assert(ptr); return *ptr; } // Get data associated with key + + // Get data from all threads + inline void gather(std::vector &data) const + { + std::vector &dataVoid = reinterpret_cast&>(data); + gatherData(dataVoid); + } + + inline void cleanup() { TLSDataContainer::cleanup(); } + +private: + virtual void* createDataInstance() const CV_OVERRIDE {return new T;} // Wrapper to allocate data by template + virtual void deleteDataInstance(void* pData) const CV_OVERRIDE {delete (T*)pData;} // Wrapper to release data by template + + // Disable TLS copy operations + TLSData(TLSData &) {} + TLSData& operator =(const TLSData &) {return *this;} +}; + +/** @brief Designed for command line parsing + +The sample below demonstrates how to use CommandLineParser: +@code + CommandLineParser parser(argc, argv, keys); + parser.about("Application name v1.0.0"); + + if (parser.has("help")) + { + parser.printMessage(); + return 0; + } + + int N = parser.get("N"); + double fps = parser.get("fps"); + String path = parser.get("path"); + + use_time_stamp = parser.has("timestamp"); + + String img1 = parser.get(0); + String img2 = parser.get(1); + + int repeat = parser.get(2); + + if (!parser.check()) + { + parser.printErrors(); + return 0; + } +@endcode + +### Keys syntax + +The keys parameter is a string containing several blocks, each one is enclosed in curly braces and +describes one argument. Each argument contains three parts separated by the `|` symbol: + +-# argument names is a space-separated list of option synonyms (to mark argument as positional, prefix it with the `@` symbol) +-# default value will be used if the argument was not provided (can be empty) +-# help message (can be empty) + +For example: + +@code{.cpp} + const String keys = + "{help h usage ? | | print this message }" + "{@image1 | | image1 for compare }" + "{@image2 || image2 for compare }" + "{@repeat |1 | number }" + "{path |. | path to file }" + "{fps | -1.0 | fps for output video }" + "{N count |100 | count of objects }" + "{ts timestamp | | use time stamp }" + ; +} +@endcode + +Note that there are no default values for `help` and `timestamp` so we can check their presence using the `has()` method. +Arguments with default values are considered to be always present. Use the `get()` method in these cases to check their +actual value instead. + +String keys like `get("@image1")` return the empty string `""` by default - even with an empty default value. +Use the special `` default value to enforce that the returned string must not be empty. (like in `get("@image2")`) + +### Usage + +For the described keys: + +@code{.sh} + # Good call (3 positional parameters: image1, image2 and repeat; N is 200, ts is true) + $ ./app -N=200 1.png 2.jpg 19 -ts + + # Bad call + $ ./app -fps=aaa + ERRORS: + Parameter 'fps': can not convert: [aaa] to [double] +@endcode + */ +class CV_EXPORTS CommandLineParser +{ +public: + + /** @brief Constructor + + Initializes command line parser object + + @param argc number of command line arguments (from main()) + @param argv array of command line arguments (from main()) + @param keys string describing acceptable command line parameters (see class description for syntax) + */ + CommandLineParser(int argc, const char* const argv[], const String& keys); + + /** @brief Copy constructor */ + CommandLineParser(const CommandLineParser& parser); + + /** @brief Assignment operator */ + CommandLineParser& operator = (const CommandLineParser& parser); + + /** @brief Destructor */ + ~CommandLineParser(); + + /** @brief Returns application path + + This method returns the path to the executable from the command line (`argv[0]`). + + For example, if the application has been started with such a command: + @code{.sh} + $ ./bin/my-executable + @endcode + this method will return `./bin`. + */ + String getPathToApplication() const; + + /** @brief Access arguments by name + + Returns argument converted to selected type. If the argument is not known or can not be + converted to selected type, the error flag is set (can be checked with @ref check). + + For example, define: + @code{.cpp} + String keys = "{N count||}"; + @endcode + + Call: + @code{.sh} + $ ./my-app -N=20 + # or + $ ./my-app --count=20 + @endcode + + Access: + @code{.cpp} + int N = parser.get("N"); + @endcode + + @param name name of the argument + @param space_delete remove spaces from the left and right of the string + @tparam T the argument will be converted to this type if possible + + @note You can access positional arguments by their `@`-prefixed name: + @code{.cpp} + parser.get("@image"); + @endcode + */ + template + T get(const String& name, bool space_delete = true) const + { + T val = T(); + getByName(name, space_delete, ParamType::type, (void*)&val); + return val; + } + + /** @brief Access positional arguments by index + + Returns argument converted to selected type. Indexes are counted from zero. + + For example, define: + @code{.cpp} + String keys = "{@arg1||}{@arg2||}" + @endcode + + Call: + @code{.sh} + ./my-app abc qwe + @endcode + + Access arguments: + @code{.cpp} + String val_1 = parser.get(0); // returns "abc", arg1 + String val_2 = parser.get(1); // returns "qwe", arg2 + @endcode + + @param index index of the argument + @param space_delete remove spaces from the left and right of the string + @tparam T the argument will be converted to this type if possible + */ + template + T get(int index, bool space_delete = true) const + { + T val = T(); + getByIndex(index, space_delete, ParamType::type, (void*)&val); + return val; + } + + /** @brief Check if field was provided in the command line + + @param name argument name to check + */ + bool has(const String& name) const; + + /** @brief Check for parsing errors + + Returns false if error occurred while accessing the parameters (bad conversion, missing arguments, + etc.). Call @ref printErrors to print error messages list. + */ + bool check() const; + + /** @brief Set the about message + + The about message will be shown when @ref printMessage is called, right before arguments table. + */ + void about(const String& message); + + /** @brief Print help message + + This method will print standard help message containing the about message and arguments description. + + @sa about + */ + void printMessage() const; + + /** @brief Print list of errors occurred + + @sa check + */ + void printErrors() const; + +protected: + void getByName(const String& name, bool space_delete, int type, void* dst) const; + void getByIndex(int index, bool space_delete, int type, void* dst) const; + + struct Impl; + Impl* impl; +}; + +//! @} core_utils + +//! @cond IGNORED + +/////////////////////////////// AutoBuffer implementation //////////////////////////////////////// + +template inline +AutoBuffer<_Tp, fixed_size>::AutoBuffer() +{ + ptr = buf; + sz = fixed_size; +} + +template inline +AutoBuffer<_Tp, fixed_size>::AutoBuffer(size_t _size) +{ + ptr = buf; + sz = fixed_size; + allocate(_size); +} + +template inline +AutoBuffer<_Tp, fixed_size>::AutoBuffer(const AutoBuffer<_Tp, fixed_size>& abuf ) +{ + ptr = buf; + sz = fixed_size; + allocate(abuf.size()); + for( size_t i = 0; i < sz; i++ ) + ptr[i] = abuf.ptr[i]; +} + +template inline AutoBuffer<_Tp, fixed_size>& +AutoBuffer<_Tp, fixed_size>::operator = (const AutoBuffer<_Tp, fixed_size>& abuf) +{ + if( this != &abuf ) + { + deallocate(); + allocate(abuf.size()); + for( size_t i = 0; i < sz; i++ ) + ptr[i] = abuf.ptr[i]; + } + return *this; +} + +template inline +AutoBuffer<_Tp, fixed_size>::~AutoBuffer() +{ deallocate(); } + +template inline void +AutoBuffer<_Tp, fixed_size>::allocate(size_t _size) +{ + if(_size <= sz) + { + sz = _size; + return; + } + deallocate(); + sz = _size; + if(_size > fixed_size) + { + ptr = new _Tp[_size]; + } +} + +template inline void +AutoBuffer<_Tp, fixed_size>::deallocate() +{ + if( ptr != buf ) + { + delete[] ptr; + ptr = buf; + sz = fixed_size; + } +} + +template inline void +AutoBuffer<_Tp, fixed_size>::resize(size_t _size) +{ + if(_size <= sz) + { + sz = _size; + return; + } + size_t i, prevsize = sz, minsize = MIN(prevsize, _size); + _Tp* prevptr = ptr; + + ptr = _size > fixed_size ? new _Tp[_size] : buf; + sz = _size; + + if( ptr != prevptr ) + for( i = 0; i < minsize; i++ ) + ptr[i] = prevptr[i]; + for( i = prevsize; i < _size; i++ ) + ptr[i] = _Tp(); + + if( prevptr != buf ) + delete[] prevptr; +} + +template inline size_t +AutoBuffer<_Tp, fixed_size>::size() const +{ return sz; } + +template<> inline std::string CommandLineParser::get(int index, bool space_delete) const +{ + return get(index, space_delete); +} +template<> inline std::string CommandLineParser::get(const String& name, bool space_delete) const +{ + return get(name, space_delete); +} + +//! @endcond + + +// Basic Node class for tree building +template +class CV_EXPORTS Node +{ +public: + Node() + { + m_pParent = 0; + } + Node(OBJECT& payload) : m_payload(payload) + { + m_pParent = 0; + } + ~Node() + { + removeChilds(); + if (m_pParent) + { + int idx = m_pParent->findChild(this); + if (idx >= 0) + m_pParent->m_childs.erase(m_pParent->m_childs.begin() + idx); + } + } + + Node* findChild(OBJECT& payload) const + { + for(size_t i = 0; i < this->m_childs.size(); i++) + { + if(this->m_childs[i]->m_payload == payload) + return this->m_childs[i]; + } + return NULL; + } + + int findChild(Node *pNode) const + { + for (size_t i = 0; i < this->m_childs.size(); i++) + { + if(this->m_childs[i] == pNode) + return (int)i; + } + return -1; + } + + void addChild(Node *pNode) + { + if(!pNode) + return; + + CV_Assert(pNode->m_pParent == 0); + pNode->m_pParent = this; + this->m_childs.push_back(pNode); + } + + void removeChilds() + { + for(size_t i = 0; i < m_childs.size(); i++) + { + m_childs[i]->m_pParent = 0; // avoid excessive parent vector trimming + delete m_childs[i]; + } + m_childs.clear(); + } + + int getDepth() + { + int count = 0; + Node *pParent = m_pParent; + while(pParent) count++, pParent = pParent->m_pParent; + return count; + } + +public: + OBJECT m_payload; + Node* m_pParent; + std::vector*> m_childs; +}; + +// Instrumentation external interface +namespace instr +{ + +#if !defined OPENCV_ABI_CHECK + +enum TYPE +{ + TYPE_GENERAL = 0, // OpenCV API function, e.g. exported function + TYPE_MARKER, // Information marker + TYPE_WRAPPER, // Wrapper function for implementation + TYPE_FUN, // Simple function call +}; + +enum IMPL +{ + IMPL_PLAIN = 0, + IMPL_IPP, + IMPL_OPENCL, +}; + +struct NodeDataTls +{ + NodeDataTls() + { + m_ticksTotal = 0; + } + uint64 m_ticksTotal; +}; + +class CV_EXPORTS NodeData +{ +public: + NodeData(const char* funName = 0, const char* fileName = NULL, int lineNum = 0, void* retAddress = NULL, bool alwaysExpand = false, cv::instr::TYPE instrType = TYPE_GENERAL, cv::instr::IMPL implType = IMPL_PLAIN); + NodeData(NodeData &ref); + ~NodeData(); + NodeData& operator=(const NodeData&); + + cv::String m_funName; + cv::instr::TYPE m_instrType; + cv::instr::IMPL m_implType; + const char* m_fileName; + int m_lineNum; + void* m_retAddress; + bool m_alwaysExpand; + bool m_funError; + + volatile int m_counter; + volatile uint64 m_ticksTotal; + TLSData m_tls; + int m_threads; + + // No synchronization + double getTotalMs() const { return ((double)m_ticksTotal / cv::getTickFrequency()) * 1000; } + double getMeanMs() const { return (((double)m_ticksTotal/m_counter) / cv::getTickFrequency()) * 1000; } +}; +bool operator==(const NodeData& lhs, const NodeData& rhs); + +typedef Node InstrNode; + +CV_EXPORTS InstrNode* getTrace(); + +#endif // !defined OPENCV_ABI_CHECK + + +CV_EXPORTS bool useInstrumentation(); +CV_EXPORTS void setUseInstrumentation(bool flag); +CV_EXPORTS void resetTrace(); + +enum FLAGS +{ + FLAGS_NONE = 0, + FLAGS_MAPPING = 0x01, + FLAGS_EXPAND_SAME_NAMES = 0x02, +}; + +CV_EXPORTS void setFlags(FLAGS modeFlags); +static inline void setFlags(int modeFlags) { setFlags((FLAGS)modeFlags); } +CV_EXPORTS FLAGS getFlags(); + +} // namespace instr + + +namespace samples { + +//! @addtogroup core_utils_samples +// This section describes utility functions for OpenCV samples. +// +// @note Implementation of these utilities is not thread-safe. +// +//! @{ + +/** @brief Try to find requested data file + +Search directories: + +1. Directories passed via `addSamplesDataSearchPath()` +2. OPENCV_SAMPLES_DATA_PATH_HINT environment variable +3. OPENCV_SAMPLES_DATA_PATH environment variable + If parameter value is not empty and nothing is found then stop searching. +4. Detects build/install path based on: + a. current working directory (CWD) + b. and/or binary module location (opencv_core/opencv_world, doesn't work with static linkage) +5. Scan `/{,data,samples/data}` directories if build directory is detected or the current directory is in source tree. +6. Scan `/share/OpenCV` directory if install directory is detected. + +@see cv::utils::findDataFile + +@param relative_path Relative path to data file +@param required Specify "file not found" handling. + If true, function prints information message and raises cv::Exception. + If false, function returns empty result +@param silentMode Disables messages +@return Returns path (absolute or relative to the current directory) or empty string if file is not found +*/ +CV_EXPORTS_W cv::String findFile(const cv::String& relative_path, bool required = true, bool silentMode = false); + +CV_EXPORTS_W cv::String findFileOrKeep(const cv::String& relative_path, bool silentMode = false); + +inline cv::String findFileOrKeep(const cv::String& relative_path, bool silentMode) +{ + cv::String res = findFile(relative_path, false, silentMode); + if (res.empty()) + return relative_path; + return res; +} + +/** @brief Override search data path by adding new search location + +Use this only to override default behavior +Passed paths are used in LIFO order. + +@param path Path to used samples data +*/ +CV_EXPORTS_W void addSamplesDataSearchPath(const cv::String& path); + +/** @brief Append samples search data sub directory + +General usage is to add OpenCV modules name (`/modules//samples/data` -> `/samples/data` + `modules//samples/data`). +Passed subdirectories are used in LIFO order. + +@param subdir samples data sub directory +*/ +CV_EXPORTS_W void addSamplesDataSearchSubDirectory(const cv::String& subdir); + +//! @} +} // namespace samples + +namespace utils { + +CV_EXPORTS int getThreadID(); + +} // namespace + +} //namespace cv + +#ifndef DISABLE_OPENCV_24_COMPATIBILITY +#include "opencv2/core/core_c.h" +#endif + +#endif //OPENCV_CORE_UTILITY_H diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/utils/filesystem.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/utils/filesystem.hpp new file mode 100644 index 0000000000000000000000000000000000000000..00b0dd1c123e2e3bfa80f9372ecd47502eb19875 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/utils/filesystem.hpp @@ -0,0 +1,78 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#ifndef OPENCV_UTILS_FILESYSTEM_HPP +#define OPENCV_UTILS_FILESYSTEM_HPP + +namespace cv { namespace utils { namespace fs { + + +CV_EXPORTS bool exists(const cv::String& path); +CV_EXPORTS bool isDirectory(const cv::String& path); + +CV_EXPORTS void remove_all(const cv::String& path); + + +CV_EXPORTS cv::String getcwd(); + +/** @brief Converts path p to a canonical absolute path + * Symlinks are processed if there is support for them on running platform. + * + * @param path input path. Target file/directory should exist. + */ +CV_EXPORTS cv::String canonical(const cv::String& path); + +/** Join path components */ +CV_EXPORTS cv::String join(const cv::String& base, const cv::String& path); + +/** + * Generate a list of all files that match the globbing pattern. + * + * Result entries are prefixed by base directory path. + * + * @param directory base directory + * @param pattern filter pattern (based on '*'/'?' symbols). Use empty string to disable filtering and return all results + * @param[out] result result of globing. + * @param recursive scan nested directories too + * @param includeDirectories include directories into results list + */ +CV_EXPORTS void glob(const cv::String& directory, const cv::String& pattern, + CV_OUT std::vector& result, + bool recursive = false, bool includeDirectories = false); + +/** + * Generate a list of all files that match the globbing pattern. + * + * @param directory base directory + * @param pattern filter pattern (based on '*'/'?' symbols). Use empty string to disable filtering and return all results + * @param[out] result globbing result with relative paths from base directory + * @param recursive scan nested directories too + * @param includeDirectories include directories into results list + */ +CV_EXPORTS void glob_relative(const cv::String& directory, const cv::String& pattern, + CV_OUT std::vector& result, + bool recursive = false, bool includeDirectories = false); + + +CV_EXPORTS bool createDirectory(const cv::String& path); +CV_EXPORTS bool createDirectories(const cv::String& path); + +#ifdef __OPENCV_BUILD +// TODO +//CV_EXPORTS cv::String getTempDirectory(); + +/** + * @brief Returns directory to store OpenCV cache files + * Create sub-directory in common OpenCV cache directory if it doesn't exist. + * @param sub_directory_name name of sub-directory. NULL or "" value asks to return root cache directory. + * @param configuration_name optional name of configuration parameter name which overrides default behavior. + * @return Path to cache directory. Returns empty string if cache directories support is not available. Returns "disabled" if cache disabled by user. + */ +CV_EXPORTS cv::String getCacheDirectory(const char* sub_directory_name, const char* configuration_name = NULL); + +#endif + +}}} // namespace + +#endif // OPENCV_UTILS_FILESYSTEM_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/utils/logger.defines.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/utils/logger.defines.hpp new file mode 100644 index 0000000000000000000000000000000000000000..b2dfc417045f69e7586b212d3280655d11b29f57 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/utils/logger.defines.hpp @@ -0,0 +1,22 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#ifndef OPENCV_LOGGER_DEFINES_HPP +#define OPENCV_LOGGER_DEFINES_HPP + +//! @addtogroup core_logging +//! @{ + +// Supported logging levels and their semantic +#define CV_LOG_LEVEL_SILENT 0 //!< for using in setLogLevel() call +#define CV_LOG_LEVEL_FATAL 1 //!< Fatal (critical) error (unrecoverable internal error) +#define CV_LOG_LEVEL_ERROR 2 //!< Error message +#define CV_LOG_LEVEL_WARN 3 //!< Warning message +#define CV_LOG_LEVEL_INFO 4 //!< Info message +#define CV_LOG_LEVEL_DEBUG 5 //!< Debug message. Disabled in the "Release" build. +#define CV_LOG_LEVEL_VERBOSE 6 //!< Verbose (trace) messages. Requires verbosity level. Disabled in the "Release" build. + +//! @} + +#endif // OPENCV_LOGGER_DEFINES_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/utils/logger.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/utils/logger.hpp new file mode 100644 index 0000000000000000000000000000000000000000..47094f994d09e21c50611f6629f45a7ced042082 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/utils/logger.hpp @@ -0,0 +1,87 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#ifndef OPENCV_LOGGER_HPP +#define OPENCV_LOGGER_HPP + +#include +#include +#include // INT_MAX + +#include "logger.defines.hpp" + +//! @addtogroup core_logging +// This section describes OpenCV logging utilities. +// +//! @{ + +namespace cv { +namespace utils { +namespace logging { + +//! Supported logging levels and their semantic +enum LogLevel { + LOG_LEVEL_SILENT = 0, //!< for using in setLogVevel() call + LOG_LEVEL_FATAL = 1, //!< Fatal (critical) error (unrecoverable internal error) + LOG_LEVEL_ERROR = 2, //!< Error message + LOG_LEVEL_WARNING = 3, //!< Warning message + LOG_LEVEL_INFO = 4, //!< Info message + LOG_LEVEL_DEBUG = 5, //!< Debug message. Disabled in the "Release" build. + LOG_LEVEL_VERBOSE = 6, //!< Verbose (trace) messages. Requires verbosity level. Disabled in the "Release" build. +#ifndef CV_DOXYGEN + ENUM_LOG_LEVEL_FORCE_INT = INT_MAX +#endif +}; + +/** Set global logging level +@return previous logging level +*/ +CV_EXPORTS LogLevel setLogLevel(LogLevel logLevel); +/** Get global logging level */ +CV_EXPORTS LogLevel getLogLevel(); + +namespace internal { +/** Write log message */ +CV_EXPORTS void writeLogMessage(LogLevel logLevel, const char* message); +} // namespace + +/** + * \def CV_LOG_STRIP_LEVEL + * + * Define CV_LOG_STRIP_LEVEL=CV_LOG_LEVEL_[DEBUG|INFO|WARN|ERROR|FATAL|DISABLED] to compile out anything at that and before that logging level + */ +#ifndef CV_LOG_STRIP_LEVEL +# if defined NDEBUG +# define CV_LOG_STRIP_LEVEL CV_LOG_LEVEL_DEBUG +# else +# define CV_LOG_STRIP_LEVEL CV_LOG_LEVEL_VERBOSE +# endif +#endif + + +#define CV_LOG_FATAL(tag, ...) for(;;) { if (cv::utils::logging::getLogLevel() < cv::utils::logging::LOG_LEVEL_FATAL) break; std::stringstream ss; ss << __VA_ARGS__; cv::utils::logging::internal::writeLogMessage(cv::utils::logging::LOG_LEVEL_FATAL, ss.str().c_str()); break; } +#define CV_LOG_ERROR(tag, ...) for(;;) { if (cv::utils::logging::getLogLevel() < cv::utils::logging::LOG_LEVEL_ERROR) break; std::stringstream ss; ss << __VA_ARGS__; cv::utils::logging::internal::writeLogMessage(cv::utils::logging::LOG_LEVEL_ERROR, ss.str().c_str()); break; } +#define CV_LOG_WARNING(tag, ...) for(;;) { if (cv::utils::logging::getLogLevel() < cv::utils::logging::LOG_LEVEL_WARNING) break; std::stringstream ss; ss << __VA_ARGS__; cv::utils::logging::internal::writeLogMessage(cv::utils::logging::LOG_LEVEL_WARNING, ss.str().c_str()); break; } +#if CV_LOG_STRIP_LEVEL <= CV_LOG_LEVEL_INFO +#define CV_LOG_INFO(tag, ...) +#else +#define CV_LOG_INFO(tag, ...) for(;;) { if (cv::utils::logging::getLogLevel() < cv::utils::logging::LOG_LEVEL_INFO) break; std::stringstream ss; ss << __VA_ARGS__; cv::utils::logging::internal::writeLogMessage(cv::utils::logging::LOG_LEVEL_INFO, ss.str().c_str()); break; } +#endif +#if CV_LOG_STRIP_LEVEL <= CV_LOG_LEVEL_DEBUG +#define CV_LOG_DEBUG(tag, ...) +#else +#define CV_LOG_DEBUG(tag, ...) for(;;) { if (cv::utils::logging::getLogLevel() < cv::utils::logging::LOG_LEVEL_DEBUG) break; std::stringstream ss; ss << __VA_ARGS__; cv::utils::logging::internal::writeLogMessage(cv::utils::logging::LOG_LEVEL_DEBUG, ss.str().c_str()); break; } +#endif +#if CV_LOG_STRIP_LEVEL <= CV_LOG_LEVEL_VERBOSE +#define CV_LOG_VERBOSE(tag, v, ...) +#else +#define CV_LOG_VERBOSE(tag, v, ...) for(;;) { if (cv::utils::logging::getLogLevel() < cv::utils::logging::LOG_LEVEL_VERBOSE) break; std::stringstream ss; ss << "[VERB" << v << ":" << cv::utils::getThreadID() << "] " << __VA_ARGS__; cv::utils::logging::internal::writeLogMessage(cv::utils::logging::LOG_LEVEL_VERBOSE, ss.str().c_str()); break; } +#endif + + +}}} // namespace + +//! @} + +#endif // OPENCV_LOGGER_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/utils/trace.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/utils/trace.hpp new file mode 100644 index 0000000000000000000000000000000000000000..858e973a831370c17b87e3b9550b3b9a6c4873c3 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/utils/trace.hpp @@ -0,0 +1,254 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#ifndef OPENCV_TRACE_HPP +#define OPENCV_TRACE_HPP + +#include + +//! @addtogroup core_logging +// This section describes OpenCV tracing utilities. +// +//! @{ + +namespace cv { +namespace utils { +namespace trace { + +//! Macro to trace function +#define CV_TRACE_FUNCTION() + +#define CV_TRACE_FUNCTION_SKIP_NESTED() + +//! Trace code scope. +//! @note Dynamic names are not supported in this macro (on stack or heap). Use string literals here only, like "initialize". +#define CV_TRACE_REGION(name_as_static_string_literal) +//! mark completed of the current opened region and create new one +//! @note Dynamic names are not supported in this macro (on stack or heap). Use string literals here only, like "step1". +#define CV_TRACE_REGION_NEXT(name_as_static_string_literal) + +//! Macro to trace argument value +#define CV_TRACE_ARG(arg_id) + +//! Macro to trace argument value (expanded version) +#define CV_TRACE_ARG_VALUE(arg_id, arg_name, value) + +//! @cond IGNORED +#define CV_TRACE_NS cv::utils::trace + +#if !defined(OPENCV_DISABLE_TRACE) && defined(__EMSCRIPTEN__) +#define OPENCV_DISABLE_TRACE 1 +#endif + +namespace details { + +#ifndef __OPENCV_TRACE +# if defined __OPENCV_BUILD && !defined __OPENCV_TESTS && !defined __OPENCV_APPS +# define __OPENCV_TRACE 1 +# else +# define __OPENCV_TRACE 0 +# endif +#endif + +#ifndef CV_TRACE_FILENAME +# define CV_TRACE_FILENAME __FILE__ +#endif + +#ifndef CV__TRACE_FUNCTION +# if defined _MSC_VER +# define CV__TRACE_FUNCTION __FUNCSIG__ +# elif defined __GNUC__ +# define CV__TRACE_FUNCTION __PRETTY_FUNCTION__ +# else +# define CV__TRACE_FUNCTION "" +# endif +#endif + +//! Thread-local instance (usually allocated on stack) +class CV_EXPORTS Region +{ +public: + struct LocationExtraData; + struct LocationStaticStorage + { + LocationExtraData** ppExtra; //< implementation specific data + const char* name; //< region name (function name or other custom name) + const char* filename; //< source code filename + int line; //< source code line + int flags; //< flags (implementation code path: Plain, IPP, OpenCL) + }; + + Region(const LocationStaticStorage& location); + inline ~Region() + { + if (implFlags != 0) + destroy(); + CV_DbgAssert(implFlags == 0); + CV_DbgAssert(pImpl == NULL); + } + + class Impl; + Impl* pImpl; // NULL if current region is not active + int implFlags; // see RegionFlag, 0 if region is ignored + + bool isActive() const { return pImpl != NULL; } + + void destroy(); +private: + Region(const Region&); // disabled + Region& operator= (const Region&); // disabled +}; + +//! Specify region flags +enum RegionLocationFlag { + REGION_FLAG_FUNCTION = (1 << 0), //< region is function (=1) / nested named region (=0) + REGION_FLAG_APP_CODE = (1 << 1), //< region is Application code (=1) / OpenCV library code (=0) + REGION_FLAG_SKIP_NESTED = (1 << 2), //< avoid processing of nested regions + + REGION_FLAG_IMPL_IPP = (1 << 16), //< region is part of IPP code path + REGION_FLAG_IMPL_OPENCL = (2 << 16), //< region is part of OpenCL code path + REGION_FLAG_IMPL_OPENVX = (3 << 16), //< region is part of OpenVX code path + + REGION_FLAG_IMPL_MASK = (15 << 16), + + REGION_FLAG_REGION_FORCE = (1 << 30), + REGION_FLAG_REGION_NEXT = (1 << 31), //< close previous region (see #CV_TRACE_REGION_NEXT macro) + + ENUM_REGION_FLAG_FORCE_INT = INT_MAX +}; + +struct CV_EXPORTS TraceArg { +public: + struct ExtraData; + ExtraData** ppExtra; + const char* name; + int flags; +}; +/** @brief Add meta information to current region (function) + * See CV_TRACE_ARG macro + * @param arg argument information structure (global static cache) + * @param value argument value (can by dynamic string literal in case of string, static allocation is not required) + */ +CV_EXPORTS void traceArg(const TraceArg& arg, const char* value); +//! @overload +CV_EXPORTS void traceArg(const TraceArg& arg, int value); +//! @overload +CV_EXPORTS void traceArg(const TraceArg& arg, int64 value); +//! @overload +CV_EXPORTS void traceArg(const TraceArg& arg, double value); + +#define CV__TRACE_LOCATION_VARNAME(loc_id) CVAUX_CONCAT(CVAUX_CONCAT(__cv_trace_location_, loc_id), __LINE__) +#define CV__TRACE_LOCATION_EXTRA_VARNAME(loc_id) CVAUX_CONCAT(CVAUX_CONCAT(__cv_trace_location_extra_, loc_id) , __LINE__) + +#define CV__TRACE_DEFINE_LOCATION_(loc_id, name, flags) \ + static CV_TRACE_NS::details::Region::LocationExtraData* CV__TRACE_LOCATION_EXTRA_VARNAME(loc_id) = 0; \ + static const CV_TRACE_NS::details::Region::LocationStaticStorage \ + CV__TRACE_LOCATION_VARNAME(loc_id) = { &(CV__TRACE_LOCATION_EXTRA_VARNAME(loc_id)), name, CV_TRACE_FILENAME, __LINE__, flags}; + +#define CV__TRACE_DEFINE_LOCATION_FN(name, flags) CV__TRACE_DEFINE_LOCATION_(fn, name, ((flags) | CV_TRACE_NS::details::REGION_FLAG_FUNCTION)) + + +#define CV__TRACE_OPENCV_FUNCTION() \ + CV__TRACE_DEFINE_LOCATION_FN(CV__TRACE_FUNCTION, 0); \ + const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn)); + +#define CV__TRACE_OPENCV_FUNCTION_NAME(name) \ + CV__TRACE_DEFINE_LOCATION_FN(name, 0); \ + const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn)); + +#define CV__TRACE_APP_FUNCTION() \ + CV__TRACE_DEFINE_LOCATION_FN(CV__TRACE_FUNCTION, CV_TRACE_NS::details::REGION_FLAG_APP_CODE); \ + const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn)); + +#define CV__TRACE_APP_FUNCTION_NAME(name) \ + CV__TRACE_DEFINE_LOCATION_FN(name, CV_TRACE_NS::details::REGION_FLAG_APP_CODE); \ + const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn)); + + +#define CV__TRACE_OPENCV_FUNCTION_SKIP_NESTED() \ + CV__TRACE_DEFINE_LOCATION_FN(CV__TRACE_FUNCTION, CV_TRACE_NS::details::REGION_FLAG_SKIP_NESTED); \ + const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn)); + +#define CV__TRACE_OPENCV_FUNCTION_NAME_SKIP_NESTED(name) \ + CV__TRACE_DEFINE_LOCATION_FN(name, CV_TRACE_NS::details::REGION_FLAG_SKIP_NESTED); \ + const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn)); + +#define CV__TRACE_APP_FUNCTION_SKIP_NESTED() \ + CV__TRACE_DEFINE_LOCATION_FN(CV__TRACE_FUNCTION, CV_TRACE_NS::details::REGION_FLAG_SKIP_NESTED | CV_TRACE_NS::details::REGION_FLAG_APP_CODE); \ + const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn)); + + +#define CV__TRACE_REGION_(name_as_static_string_literal, flags) \ + CV__TRACE_DEFINE_LOCATION_(region, name_as_static_string_literal, flags); \ + CV_TRACE_NS::details::Region CVAUX_CONCAT(__region_, __LINE__)(CV__TRACE_LOCATION_VARNAME(region)); + +#define CV__TRACE_REGION(name_as_static_string_literal) CV__TRACE_REGION_(name_as_static_string_literal, 0) +#define CV__TRACE_REGION_NEXT(name_as_static_string_literal) CV__TRACE_REGION_(name_as_static_string_literal, CV_TRACE_NS::details::REGION_FLAG_REGION_NEXT) + +#define CV__TRACE_ARG_VARNAME(arg_id) CVAUX_CONCAT(__cv_trace_arg_ ## arg_id, __LINE__) +#define CV__TRACE_ARG_EXTRA_VARNAME(arg_id) CVAUX_CONCAT(__cv_trace_arg_extra_ ## arg_id, __LINE__) + +#define CV__TRACE_DEFINE_ARG_(arg_id, name, flags) \ + static CV_TRACE_NS::details::TraceArg::ExtraData* CV__TRACE_ARG_EXTRA_VARNAME(arg_id) = 0; \ + static const CV_TRACE_NS::details::TraceArg \ + CV__TRACE_ARG_VARNAME(arg_id) = { &(CV__TRACE_ARG_EXTRA_VARNAME(arg_id)), name, flags }; + +#define CV__TRACE_ARG_VALUE(arg_id, arg_name, value) \ + CV__TRACE_DEFINE_ARG_(arg_id, arg_name, 0); \ + CV_TRACE_NS::details::traceArg((CV__TRACE_ARG_VARNAME(arg_id)), value); + +#define CV__TRACE_ARG(arg_id) CV_TRACE_ARG_VALUE(arg_id, #arg_id, (arg_id)) + +} // namespace + +#ifndef OPENCV_DISABLE_TRACE +#undef CV_TRACE_FUNCTION +#undef CV_TRACE_FUNCTION_SKIP_NESTED +#if __OPENCV_TRACE +#define CV_TRACE_FUNCTION CV__TRACE_OPENCV_FUNCTION +#define CV_TRACE_FUNCTION_SKIP_NESTED CV__TRACE_OPENCV_FUNCTION_SKIP_NESTED +#else +#define CV_TRACE_FUNCTION CV__TRACE_APP_FUNCTION +#define CV_TRACE_FUNCTION_SKIP_NESTED CV__TRACE_APP_FUNCTION_SKIP_NESTED +#endif + +#undef CV_TRACE_REGION +#define CV_TRACE_REGION CV__TRACE_REGION + +#undef CV_TRACE_REGION_NEXT +#define CV_TRACE_REGION_NEXT CV__TRACE_REGION_NEXT + +#undef CV_TRACE_ARG_VALUE +#define CV_TRACE_ARG_VALUE(arg_id, arg_name, value) \ + if (__region_fn.isActive()) \ + { \ + CV__TRACE_ARG_VALUE(arg_id, arg_name, value); \ + } + +#undef CV_TRACE_ARG +#define CV_TRACE_ARG CV__TRACE_ARG + +#endif // OPENCV_DISABLE_TRACE + +#ifdef OPENCV_TRACE_VERBOSE +#define CV_TRACE_FUNCTION_VERBOSE CV_TRACE_FUNCTION +#define CV_TRACE_REGION_VERBOSE CV_TRACE_REGION +#define CV_TRACE_REGION_NEXT_VERBOSE CV_TRACE_REGION_NEXT +#define CV_TRACE_ARG_VALUE_VERBOSE CV_TRACE_ARG_VALUE +#define CV_TRACE_ARG_VERBOSE CV_TRACE_ARG +#else +#define CV_TRACE_FUNCTION_VERBOSE(...) +#define CV_TRACE_REGION_VERBOSE(...) +#define CV_TRACE_REGION_NEXT_VERBOSE(...) +#define CV_TRACE_ARG_VALUE_VERBOSE(...) +#define CV_TRACE_ARG_VERBOSE(...) +#endif + +//! @endcond + +}}} // namespace + +//! @} + +#endif // OPENCV_TRACE_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/va_intel.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/va_intel.hpp new file mode 100644 index 0000000000000000000000000000000000000000..f66547093e0ad69533af4cceb02bc796448f8db9 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/va_intel.hpp @@ -0,0 +1,78 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +// Copyright (C) 2015, Itseez, Inc., all rights reserved. +// Third party copyrights are property of their respective owners. + +#ifndef OPENCV_CORE_VA_INTEL_HPP +#define OPENCV_CORE_VA_INTEL_HPP + +#ifndef __cplusplus +# error va_intel.hpp header must be compiled as C++ +#endif + +#include "opencv2/core.hpp" +#include "ocl.hpp" + +#if defined(HAVE_VA) +# include "va/va.h" +#else // HAVE_VA +# if !defined(_VA_H_) + typedef void* VADisplay; + typedef unsigned int VASurfaceID; +# endif // !_VA_H_ +#endif // HAVE_VA + +namespace cv { namespace va_intel { + +/** @addtogroup core_va_intel +This section describes Intel VA-API/OpenCL (CL-VA) interoperability. + +To enable CL-VA interoperability support, configure OpenCV using CMake with WITH_VA_INTEL=ON . Currently VA-API is +supported on Linux only. You should also install Intel Media Server Studio (MSS) to use this feature. You may +have to specify the path(s) to MSS components for cmake in environment variables: + +- VA_INTEL_IOCL_ROOT for Intel OpenCL (default is "/opt/intel/opencl"). + +To use CL-VA interoperability you should first create VADisplay (libva), and then call initializeContextFromVA() +function to create OpenCL context and set up interoperability. +*/ +//! @{ + +/////////////////// CL-VA Interoperability Functions /////////////////// + +namespace ocl { +using namespace cv::ocl; + +// TODO static functions in the Context class +/** @brief Creates OpenCL context from VA. +@param display - VADisplay for which CL interop should be established. +@param tryInterop - try to set up for interoperability, if true; set up for use slow copy if false. +@return Returns reference to OpenCL Context + */ +CV_EXPORTS Context& initializeContextFromVA(VADisplay display, bool tryInterop = true); + +} // namespace cv::va_intel::ocl + +/** @brief Converts InputArray to VASurfaceID object. +@param display - VADisplay object. +@param src - source InputArray. +@param surface - destination VASurfaceID object. +@param size - size of image represented by VASurfaceID object. + */ +CV_EXPORTS void convertToVASurface(VADisplay display, InputArray src, VASurfaceID surface, Size size); + +/** @brief Converts VASurfaceID object to OutputArray. +@param display - VADisplay object. +@param surface - source VASurfaceID object. +@param size - size of image represented by VASurfaceID object. +@param dst - destination OutputArray. + */ +CV_EXPORTS void convertFromVASurface(VADisplay display, VASurfaceID surface, Size size, OutputArray dst); + +//! @} + +}} // namespace cv::va_intel + +#endif /* OPENCV_CORE_VA_INTEL_HPP */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/version.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/version.hpp new file mode 100644 index 0000000000000000000000000000000000000000..91f95eeec4439ae3941fe4dc0c4e13b55cf3f974 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/version.hpp @@ -0,0 +1,26 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#ifndef OPENCV_VERSION_HPP +#define OPENCV_VERSION_HPP + +#define CV_VERSION_MAJOR 3 +#define CV_VERSION_MINOR 4 +#define CV_VERSION_REVISION 5 +#define CV_VERSION_STATUS "" + +#define CVAUX_STR_EXP(__A) #__A +#define CVAUX_STR(__A) CVAUX_STR_EXP(__A) + +#define CVAUX_STRW_EXP(__A) L ## #__A +#define CVAUX_STRW(__A) CVAUX_STRW_EXP(__A) + +#define CV_VERSION CVAUX_STR(CV_VERSION_MAJOR) "." CVAUX_STR(CV_VERSION_MINOR) "." CVAUX_STR(CV_VERSION_REVISION) CV_VERSION_STATUS + +/* old style version constants*/ +#define CV_MAJOR_VERSION CV_VERSION_MAJOR +#define CV_MINOR_VERSION CV_VERSION_MINOR +#define CV_SUBMINOR_VERSION CV_VERSION_REVISION + +#endif // OPENCV_VERSION_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/vsx_utils.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/vsx_utils.hpp new file mode 100644 index 0000000000000000000000000000000000000000..b4e3f30562b7c4e8367d32c5eea8b8fb9e1685c6 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/vsx_utils.hpp @@ -0,0 +1,1011 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html + +#ifndef OPENCV_HAL_VSX_UTILS_HPP +#define OPENCV_HAL_VSX_UTILS_HPP + +#include "opencv2/core/cvdef.h" + +#ifndef SKIP_INCLUDES +# include +#endif + +//! @addtogroup core_utils_vsx +//! @{ +#if CV_VSX + +#define __VSX_S16__(c, v) (c){v, v, v, v, v, v, v, v, v, v, v, v, v, v, v, v} +#define __VSX_S8__(c, v) (c){v, v, v, v, v, v, v, v} +#define __VSX_S4__(c, v) (c){v, v, v, v} +#define __VSX_S2__(c, v) (c){v, v} + +typedef __vector unsigned char vec_uchar16; +#define vec_uchar16_set(...) (vec_uchar16){__VA_ARGS__} +#define vec_uchar16_sp(c) (__VSX_S16__(vec_uchar16, c)) +#define vec_uchar16_c(v) ((vec_uchar16)(v)) +#define vec_uchar16_z vec_uchar16_sp(0) + +typedef __vector signed char vec_char16; +#define vec_char16_set(...) (vec_char16){__VA_ARGS__} +#define vec_char16_sp(c) (__VSX_S16__(vec_char16, c)) +#define vec_char16_c(v) ((vec_char16)(v)) +#define vec_char16_z vec_char16_sp(0) + +typedef __vector unsigned short vec_ushort8; +#define vec_ushort8_set(...) (vec_ushort8){__VA_ARGS__} +#define vec_ushort8_sp(c) (__VSX_S8__(vec_ushort8, c)) +#define vec_ushort8_c(v) ((vec_ushort8)(v)) +#define vec_ushort8_z vec_ushort8_sp(0) + +typedef __vector signed short vec_short8; +#define vec_short8_set(...) (vec_short8){__VA_ARGS__} +#define vec_short8_sp(c) (__VSX_S8__(vec_short8, c)) +#define vec_short8_c(v) ((vec_short8)(v)) +#define vec_short8_z vec_short8_sp(0) + +typedef __vector unsigned int vec_uint4; +#define vec_uint4_set(...) (vec_uint4){__VA_ARGS__} +#define vec_uint4_sp(c) (__VSX_S4__(vec_uint4, c)) +#define vec_uint4_c(v) ((vec_uint4)(v)) +#define vec_uint4_z vec_uint4_sp(0) + +typedef __vector signed int vec_int4; +#define vec_int4_set(...) (vec_int4){__VA_ARGS__} +#define vec_int4_sp(c) (__VSX_S4__(vec_int4, c)) +#define vec_int4_c(v) ((vec_int4)(v)) +#define vec_int4_z vec_int4_sp(0) + +typedef __vector float vec_float4; +#define vec_float4_set(...) (vec_float4){__VA_ARGS__} +#define vec_float4_sp(c) (__VSX_S4__(vec_float4, c)) +#define vec_float4_c(v) ((vec_float4)(v)) +#define vec_float4_z vec_float4_sp(0) + +typedef __vector unsigned long long vec_udword2; +#define vec_udword2_set(...) (vec_udword2){__VA_ARGS__} +#define vec_udword2_sp(c) (__VSX_S2__(vec_udword2, c)) +#define vec_udword2_c(v) ((vec_udword2)(v)) +#define vec_udword2_z vec_udword2_sp(0) + +typedef __vector signed long long vec_dword2; +#define vec_dword2_set(...) (vec_dword2){__VA_ARGS__} +#define vec_dword2_sp(c) (__VSX_S2__(vec_dword2, c)) +#define vec_dword2_c(v) ((vec_dword2)(v)) +#define vec_dword2_z vec_dword2_sp(0) + +typedef __vector double vec_double2; +#define vec_double2_set(...) (vec_double2){__VA_ARGS__} +#define vec_double2_c(v) ((vec_double2)(v)) +#define vec_double2_sp(c) (__VSX_S2__(vec_double2, c)) +#define vec_double2_z vec_double2_sp(0) + +#define vec_bchar16 __vector __bool char +#define vec_bchar16_set(...) (vec_bchar16){__VA_ARGS__} +#define vec_bchar16_c(v) ((vec_bchar16)(v)) + +#define vec_bshort8 __vector __bool short +#define vec_bshort8_set(...) (vec_bshort8){__VA_ARGS__} +#define vec_bshort8_c(v) ((vec_bshort8)(v)) + +#define vec_bint4 __vector __bool int +#define vec_bint4_set(...) (vec_bint4){__VA_ARGS__} +#define vec_bint4_c(v) ((vec_bint4)(v)) + +#define vec_bdword2 __vector __bool long long +#define vec_bdword2_set(...) (vec_bdword2){__VA_ARGS__} +#define vec_bdword2_c(v) ((vec_bdword2)(v)) + +#define VSX_FINLINE(tp) extern inline tp __attribute__((always_inline)) + +#define VSX_REDIRECT_1RG(rt, rg, fnm, fn2) \ +VSX_FINLINE(rt) fnm(const rg& a) { return fn2(a); } + +#define VSX_REDIRECT_2RG(rt, rg, fnm, fn2) \ +VSX_FINLINE(rt) fnm(const rg& a, const rg& b) { return fn2(a, b); } + +/* + * GCC VSX compatibility +**/ +#if defined(__GNUG__) && !defined(__clang__) + +// inline asm helper +#define VSX_IMPL_1RG(rt, rto, rg, rgo, opc, fnm) \ +VSX_FINLINE(rt) fnm(const rg& a) \ +{ rt rs; __asm__ __volatile__(#opc" %x0,%x1" : "="#rto (rs) : #rgo (a)); return rs; } + +#define VSX_IMPL_1VRG(rt, rg, opc, fnm) \ +VSX_FINLINE(rt) fnm(const rg& a) \ +{ rt rs; __asm__ __volatile__(#opc" %0,%1" : "=v" (rs) : "v" (a)); return rs; } + +#define VSX_IMPL_2VRG_F(rt, rg, fopc, fnm) \ +VSX_FINLINE(rt) fnm(const rg& a, const rg& b) \ +{ rt rs; __asm__ __volatile__(fopc : "=v" (rs) : "v" (a), "v" (b)); return rs; } + +#define VSX_IMPL_2VRG(rt, rg, opc, fnm) VSX_IMPL_2VRG_F(rt, rg, #opc" %0,%1,%2", fnm) + +#if __GNUG__ < 7 +// up to GCC 6 vec_mul only supports precisions and llong +# ifdef vec_mul +# undef vec_mul +# endif +/* + * there's no a direct instruction for supporting 8-bit, 16-bit multiplication in ISA 2.07, + * XLC Implement it by using instruction "multiply even", "multiply odd" and "permute" +**/ +# define VSX_IMPL_MULH(Tvec, cperm) \ + VSX_FINLINE(Tvec) vec_mul(const Tvec& a, const Tvec& b) \ + { \ + static const vec_uchar16 ev_od = {cperm}; \ + return vec_perm((Tvec)vec_mule(a, b), (Tvec)vec_mulo(a, b), ev_od); \ + } + #define VSX_IMPL_MULH_P16 0, 16, 2, 18, 4, 20, 6, 22, 8, 24, 10, 26, 12, 28, 14, 30 + VSX_IMPL_MULH(vec_char16, VSX_IMPL_MULH_P16) + VSX_IMPL_MULH(vec_uchar16, VSX_IMPL_MULH_P16) + #define VSX_IMPL_MULH_P8 0, 1, 16, 17, 4, 5, 20, 21, 8, 9, 24, 25, 12, 13, 28, 29 + VSX_IMPL_MULH(vec_short8, VSX_IMPL_MULH_P8) + VSX_IMPL_MULH(vec_ushort8, VSX_IMPL_MULH_P8) + // vmuluwm can be used for unsigned or signed integers, that's what they said + VSX_IMPL_2VRG(vec_int4, vec_int4, vmuluwm, vec_mul) + VSX_IMPL_2VRG(vec_uint4, vec_uint4, vmuluwm, vec_mul) + // redirect to GCC builtin vec_mul, since it already supports precisions and llong + VSX_REDIRECT_2RG(vec_float4, vec_float4, vec_mul, __builtin_vec_mul) + VSX_REDIRECT_2RG(vec_double2, vec_double2, vec_mul, __builtin_vec_mul) + VSX_REDIRECT_2RG(vec_dword2, vec_dword2, vec_mul, __builtin_vec_mul) + VSX_REDIRECT_2RG(vec_udword2, vec_udword2, vec_mul, __builtin_vec_mul) +#endif // __GNUG__ < 7 + +#if __GNUG__ < 6 +/* + * Instruction "compare greater than or equal" in ISA 2.07 only supports single + * and double precision. + * In XLC and new versions of GCC implement integers by using instruction "greater than" and NOR. +**/ +# ifdef vec_cmpge +# undef vec_cmpge +# endif +# ifdef vec_cmple +# undef vec_cmple +# endif +# define vec_cmple(a, b) vec_cmpge(b, a) +# define VSX_IMPL_CMPGE(rt, rg, opc, fnm) \ + VSX_IMPL_2VRG_F(rt, rg, #opc" %0,%2,%1\n\t xxlnor %x0,%x0,%x0", fnm) + + VSX_IMPL_CMPGE(vec_bchar16, vec_char16, vcmpgtsb, vec_cmpge) + VSX_IMPL_CMPGE(vec_bchar16, vec_uchar16, vcmpgtub, vec_cmpge) + VSX_IMPL_CMPGE(vec_bshort8, vec_short8, vcmpgtsh, vec_cmpge) + VSX_IMPL_CMPGE(vec_bshort8, vec_ushort8, vcmpgtuh, vec_cmpge) + VSX_IMPL_CMPGE(vec_bint4, vec_int4, vcmpgtsw, vec_cmpge) + VSX_IMPL_CMPGE(vec_bint4, vec_uint4, vcmpgtuw, vec_cmpge) + VSX_IMPL_CMPGE(vec_bdword2, vec_dword2, vcmpgtsd, vec_cmpge) + VSX_IMPL_CMPGE(vec_bdword2, vec_udword2, vcmpgtud, vec_cmpge) + +// redirect to GCC builtin cmpge, since it already supports precisions + VSX_REDIRECT_2RG(vec_bint4, vec_float4, vec_cmpge, __builtin_vec_cmpge) + VSX_REDIRECT_2RG(vec_bdword2, vec_double2, vec_cmpge, __builtin_vec_cmpge) + +// up to gcc5 vec_nor doesn't support bool long long +# undef vec_nor + template + VSX_REDIRECT_2RG(T, T, vec_nor, __builtin_vec_nor) + + VSX_FINLINE(vec_bdword2) vec_nor(const vec_bdword2& a, const vec_bdword2& b) + { return vec_bdword2_c(__builtin_vec_nor(vec_dword2_c(a), vec_dword2_c(b))); } + +// vec_packs doesn't support double words in gcc4 and old versions of gcc5 +# undef vec_packs + VSX_REDIRECT_2RG(vec_char16, vec_short8, vec_packs, __builtin_vec_packs) + VSX_REDIRECT_2RG(vec_uchar16, vec_ushort8, vec_packs, __builtin_vec_packs) + VSX_REDIRECT_2RG(vec_short8, vec_int4, vec_packs, __builtin_vec_packs) + VSX_REDIRECT_2RG(vec_ushort8, vec_uint4, vec_packs, __builtin_vec_packs) + + VSX_IMPL_2VRG_F(vec_int4, vec_dword2, "vpksdss %0,%2,%1", vec_packs) + VSX_IMPL_2VRG_F(vec_uint4, vec_udword2, "vpkudus %0,%2,%1", vec_packs) +#endif // __GNUG__ < 6 + +#if __GNUG__ < 5 +// vec_xxpermdi in gcc4 missing little-endian supports just like clang +# define vec_permi(a, b, c) vec_xxpermdi(b, a, (3 ^ (((c) & 1) << 1 | (c) >> 1))) +#else +# define vec_permi vec_xxpermdi +#endif // __GNUG__ < 5 + +// shift left double by word immediate +#ifndef vec_sldw +# define vec_sldw __builtin_vsx_xxsldwi +#endif + +// vector population count +VSX_IMPL_1VRG(vec_uchar16, vec_uchar16, vpopcntb, vec_popcntu) +VSX_IMPL_1VRG(vec_uchar16, vec_char16, vpopcntb, vec_popcntu) +VSX_IMPL_1VRG(vec_ushort8, vec_ushort8, vpopcnth, vec_popcntu) +VSX_IMPL_1VRG(vec_ushort8, vec_short8, vpopcnth, vec_popcntu) +VSX_IMPL_1VRG(vec_uint4, vec_uint4, vpopcntw, vec_popcntu) +VSX_IMPL_1VRG(vec_uint4, vec_int4, vpopcntw, vec_popcntu) +VSX_IMPL_1VRG(vec_udword2, vec_udword2, vpopcntd, vec_popcntu) +VSX_IMPL_1VRG(vec_udword2, vec_dword2, vpopcntd, vec_popcntu) + +// converts between single and double-precision +VSX_REDIRECT_1RG(vec_float4, vec_double2, vec_cvfo, __builtin_vsx_xvcvdpsp) +VSX_REDIRECT_1RG(vec_double2, vec_float4, vec_cvfo, __builtin_vsx_xvcvspdp) + +// converts word and doubleword to double-precision +#ifdef vec_ctd +# undef vec_ctd +#endif +VSX_IMPL_1RG(vec_double2, wd, vec_int4, wa, xvcvsxwdp, vec_ctdo) +VSX_IMPL_1RG(vec_double2, wd, vec_uint4, wa, xvcvuxwdp, vec_ctdo) +VSX_IMPL_1RG(vec_double2, wd, vec_dword2, wi, xvcvsxddp, vec_ctd) +VSX_IMPL_1RG(vec_double2, wd, vec_udword2, wi, xvcvuxddp, vec_ctd) + +// converts word and doubleword to single-precision +#undef vec_ctf +VSX_IMPL_1RG(vec_float4, wf, vec_int4, wa, xvcvsxwsp, vec_ctf) +VSX_IMPL_1RG(vec_float4, wf, vec_uint4, wa, xvcvuxwsp, vec_ctf) +VSX_IMPL_1RG(vec_float4, wf, vec_dword2, wi, xvcvsxdsp, vec_ctfo) +VSX_IMPL_1RG(vec_float4, wf, vec_udword2, wi, xvcvuxdsp, vec_ctfo) + +// converts single and double precision to signed word +#undef vec_cts +VSX_IMPL_1RG(vec_int4, wa, vec_double2, wd, xvcvdpsxws, vec_ctso) +VSX_IMPL_1RG(vec_int4, wa, vec_float4, wf, xvcvspsxws, vec_cts) + +// converts single and double precision to unsigned word +#undef vec_ctu +VSX_IMPL_1RG(vec_uint4, wa, vec_double2, wd, xvcvdpuxws, vec_ctuo) +VSX_IMPL_1RG(vec_uint4, wa, vec_float4, wf, xvcvspuxws, vec_ctu) + +// converts single and double precision to signed doubleword +#ifdef vec_ctsl +# undef vec_ctsl +#endif +VSX_IMPL_1RG(vec_dword2, wi, vec_double2, wd, xvcvdpsxds, vec_ctsl) +VSX_IMPL_1RG(vec_dword2, wi, vec_float4, wf, xvcvspsxds, vec_ctslo) + +// converts single and double precision to unsigned doubleword +#ifdef vec_ctul +# undef vec_ctul +#endif +VSX_IMPL_1RG(vec_udword2, wi, vec_double2, wd, xvcvdpuxds, vec_ctul) +VSX_IMPL_1RG(vec_udword2, wi, vec_float4, wf, xvcvspuxds, vec_ctulo) + +// just in case if GCC doesn't define it +#ifndef vec_xl +# define vec_xl vec_vsx_ld +# define vec_xst vec_vsx_st +#endif + +#endif // GCC VSX compatibility + +/* + * CLANG VSX compatibility +**/ +#if defined(__clang__) && !defined(__IBMCPP__) + +/* + * CLANG doesn't support %x in the inline asm template which fixes register number + * when using any of the register constraints wa, wd, wf + * + * For more explanation checkout PowerPC and IBM RS6000 in https://gcc.gnu.org/onlinedocs/gcc/Machine-Constraints.html + * Also there's already an open bug https://bugs.llvm.org/show_bug.cgi?id=31837 + * + * So we're not able to use inline asm and only use built-in functions that CLANG supports + * and use __builtin_convertvector if clang missng any of vector conversions built-in functions +*/ + +// convert vector helper +#define VSX_IMPL_CONVERT(rt, rg, fnm) \ +VSX_FINLINE(rt) fnm(const rg& a) { return __builtin_convertvector(a, rt); } + +#if __clang_major__ < 5 +// implement vec_permi in a dirty way +# define VSX_IMPL_CLANG_4_PERMI(Tvec) \ + VSX_FINLINE(Tvec) vec_permi(const Tvec& a, const Tvec& b, unsigned const char c) \ + { \ + switch (c) \ + { \ + case 0: \ + return vec_mergeh(a, b); \ + case 1: \ + return vec_mergel(vec_mergeh(a, a), b); \ + case 2: \ + return vec_mergeh(vec_mergel(a, a), b); \ + default: \ + return vec_mergel(a, b); \ + } \ + } + VSX_IMPL_CLANG_4_PERMI(vec_udword2) + VSX_IMPL_CLANG_4_PERMI(vec_dword2) + VSX_IMPL_CLANG_4_PERMI(vec_double2) + +// vec_xxsldwi is missing in clang 4 +# define vec_xxsldwi(a, b, c) vec_sld(a, b, (c) * 4) +#else +// vec_xxpermdi is missing little-endian supports in clang 4 just like gcc4 +# define vec_permi(a, b, c) vec_xxpermdi(b, a, (3 ^ (((c) & 1) << 1 | (c) >> 1))) +#endif // __clang_major__ < 5 + +// shift left double by word immediate +#ifndef vec_sldw +# define vec_sldw vec_xxsldwi +#endif + +// Implement vec_rsqrt since clang only supports vec_rsqrte +#ifndef vec_rsqrt + VSX_FINLINE(vec_float4) vec_rsqrt(const vec_float4& a) + { return vec_div(vec_float4_sp(1), vec_sqrt(a)); } + + VSX_FINLINE(vec_double2) vec_rsqrt(const vec_double2& a) + { return vec_div(vec_double2_sp(1), vec_sqrt(a)); } +#endif + +// vec_promote missing support for doubleword +VSX_FINLINE(vec_dword2) vec_promote(long long a, int b) +{ + vec_dword2 ret = vec_dword2_z; + ret[b & 1] = a; + return ret; +} + +VSX_FINLINE(vec_udword2) vec_promote(unsigned long long a, int b) +{ + vec_udword2 ret = vec_udword2_z; + ret[b & 1] = a; + return ret; +} + +// vec_popcnt should return unsigned but clang has different thought just like gcc in vec_vpopcnt +#define VSX_IMPL_POPCNTU(Tvec, Tvec2, ucast) \ +VSX_FINLINE(Tvec) vec_popcntu(const Tvec2& a) \ +{ return ucast(vec_popcnt(a)); } +VSX_IMPL_POPCNTU(vec_uchar16, vec_char16, vec_uchar16_c); +VSX_IMPL_POPCNTU(vec_ushort8, vec_short8, vec_ushort8_c); +VSX_IMPL_POPCNTU(vec_uint4, vec_int4, vec_uint4_c); +// redirect unsigned types +VSX_REDIRECT_1RG(vec_uchar16, vec_uchar16, vec_popcntu, vec_popcnt) +VSX_REDIRECT_1RG(vec_ushort8, vec_ushort8, vec_popcntu, vec_popcnt) +VSX_REDIRECT_1RG(vec_uint4, vec_uint4, vec_popcntu, vec_popcnt) + +// converts between single and double precision +VSX_REDIRECT_1RG(vec_float4, vec_double2, vec_cvfo, __builtin_vsx_xvcvdpsp) +VSX_REDIRECT_1RG(vec_double2, vec_float4, vec_cvfo, __builtin_vsx_xvcvspdp) + +// converts word and doubleword to double-precision +#ifdef vec_ctd +# undef vec_ctd +#endif +VSX_REDIRECT_1RG(vec_double2, vec_int4, vec_ctdo, __builtin_vsx_xvcvsxwdp) +VSX_REDIRECT_1RG(vec_double2, vec_uint4, vec_ctdo, __builtin_vsx_xvcvuxwdp) + +VSX_IMPL_CONVERT(vec_double2, vec_dword2, vec_ctd) +VSX_IMPL_CONVERT(vec_double2, vec_udword2, vec_ctd) + +// converts word and doubleword to single-precision +#if __clang_major__ > 4 +# undef vec_ctf +#endif +VSX_IMPL_CONVERT(vec_float4, vec_int4, vec_ctf) +VSX_IMPL_CONVERT(vec_float4, vec_uint4, vec_ctf) +VSX_REDIRECT_1RG(vec_float4, vec_dword2, vec_ctfo, __builtin_vsx_xvcvsxdsp) +VSX_REDIRECT_1RG(vec_float4, vec_udword2, vec_ctfo, __builtin_vsx_xvcvuxdsp) + +// converts single and double precision to signed word +#if __clang_major__ > 4 +# undef vec_cts +#endif +VSX_REDIRECT_1RG(vec_int4, vec_double2, vec_ctso, __builtin_vsx_xvcvdpsxws) +VSX_IMPL_CONVERT(vec_int4, vec_float4, vec_cts) + +// converts single and double precision to unsigned word +#if __clang_major__ > 4 +# undef vec_ctu +#endif +VSX_REDIRECT_1RG(vec_uint4, vec_double2, vec_ctuo, __builtin_vsx_xvcvdpuxws) +VSX_IMPL_CONVERT(vec_uint4, vec_float4, vec_ctu) + +// converts single and double precision to signed doubleword +#ifdef vec_ctsl +# undef vec_ctsl +#endif +VSX_IMPL_CONVERT(vec_dword2, vec_double2, vec_ctsl) +// __builtin_convertvector unable to convert, xvcvspsxds is missing on it +VSX_FINLINE(vec_dword2) vec_ctslo(const vec_float4& a) +{ return vec_ctsl(vec_cvfo(a)); } + +// converts single and double precision to unsigned doubleword +#ifdef vec_ctul +# undef vec_ctul +#endif +VSX_IMPL_CONVERT(vec_udword2, vec_double2, vec_ctul) +// __builtin_convertvector unable to convert, xvcvspuxds is missing on it +VSX_FINLINE(vec_udword2) vec_ctulo(const vec_float4& a) +{ return vec_ctul(vec_cvfo(a)); } + +#endif // CLANG VSX compatibility + +/* + * Common GCC, CLANG compatibility +**/ +#if defined(__GNUG__) && !defined(__IBMCPP__) + +#ifdef vec_cvf +# undef vec_cvf +#endif + +#define VSX_IMPL_CONV_EVEN_4_2(rt, rg, fnm, fn2) \ +VSX_FINLINE(rt) fnm(const rg& a) \ +{ return fn2(vec_sldw(a, a, 1)); } + +VSX_IMPL_CONV_EVEN_4_2(vec_double2, vec_float4, vec_cvf, vec_cvfo) +VSX_IMPL_CONV_EVEN_4_2(vec_double2, vec_int4, vec_ctd, vec_ctdo) +VSX_IMPL_CONV_EVEN_4_2(vec_double2, vec_uint4, vec_ctd, vec_ctdo) + +VSX_IMPL_CONV_EVEN_4_2(vec_dword2, vec_float4, vec_ctsl, vec_ctslo) +VSX_IMPL_CONV_EVEN_4_2(vec_udword2, vec_float4, vec_ctul, vec_ctulo) + +#define VSX_IMPL_CONV_EVEN_2_4(rt, rg, fnm, fn2) \ +VSX_FINLINE(rt) fnm(const rg& a) \ +{ \ + rt v4 = fn2(a); \ + return vec_sldw(v4, v4, 3); \ +} + +VSX_IMPL_CONV_EVEN_2_4(vec_float4, vec_double2, vec_cvf, vec_cvfo) +VSX_IMPL_CONV_EVEN_2_4(vec_float4, vec_dword2, vec_ctf, vec_ctfo) +VSX_IMPL_CONV_EVEN_2_4(vec_float4, vec_udword2, vec_ctf, vec_ctfo) + +VSX_IMPL_CONV_EVEN_2_4(vec_int4, vec_double2, vec_cts, vec_ctso) +VSX_IMPL_CONV_EVEN_2_4(vec_uint4, vec_double2, vec_ctu, vec_ctuo) + +// Only for Eigen! +/* + * changing behavior of conversion intrinsics for gcc has effect on Eigen + * so we redfine old behavior again only on gcc, clang +*/ +#if !defined(__clang__) || __clang_major__ > 4 + // ignoring second arg since Eigen only truncates toward zero +# define VSX_IMPL_CONV_2VARIANT(rt, rg, fnm, fn2) \ + VSX_FINLINE(rt) fnm(const rg& a, int only_truncate) \ + { \ + assert(only_truncate == 0); \ + CV_UNUSED(only_truncate); \ + return fn2(a); \ + } + VSX_IMPL_CONV_2VARIANT(vec_int4, vec_float4, vec_cts, vec_cts) + VSX_IMPL_CONV_2VARIANT(vec_float4, vec_int4, vec_ctf, vec_ctf) + // define vec_cts for converting double precision to signed doubleword + // which isn't combitable with xlc but its okay since Eigen only use it for gcc + VSX_IMPL_CONV_2VARIANT(vec_dword2, vec_double2, vec_cts, vec_ctsl) +#endif // Eigen + +#endif // Common GCC, CLANG compatibility + +/* + * XLC VSX compatibility +**/ +#if defined(__IBMCPP__) + +// vector population count +#define vec_popcntu vec_popcnt + +// overload and redirect with setting second arg to zero +// since we only support conversions without the second arg +#define VSX_IMPL_OVERLOAD_Z2(rt, rg, fnm) \ +VSX_FINLINE(rt) fnm(const rg& a) { return fnm(a, 0); } + +VSX_IMPL_OVERLOAD_Z2(vec_double2, vec_int4, vec_ctd) +VSX_IMPL_OVERLOAD_Z2(vec_double2, vec_uint4, vec_ctd) +VSX_IMPL_OVERLOAD_Z2(vec_double2, vec_dword2, vec_ctd) +VSX_IMPL_OVERLOAD_Z2(vec_double2, vec_udword2, vec_ctd) + +VSX_IMPL_OVERLOAD_Z2(vec_float4, vec_int4, vec_ctf) +VSX_IMPL_OVERLOAD_Z2(vec_float4, vec_uint4, vec_ctf) +VSX_IMPL_OVERLOAD_Z2(vec_float4, vec_dword2, vec_ctf) +VSX_IMPL_OVERLOAD_Z2(vec_float4, vec_udword2, vec_ctf) + +VSX_IMPL_OVERLOAD_Z2(vec_int4, vec_double2, vec_cts) +VSX_IMPL_OVERLOAD_Z2(vec_int4, vec_float4, vec_cts) + +VSX_IMPL_OVERLOAD_Z2(vec_uint4, vec_double2, vec_ctu) +VSX_IMPL_OVERLOAD_Z2(vec_uint4, vec_float4, vec_ctu) + +VSX_IMPL_OVERLOAD_Z2(vec_dword2, vec_double2, vec_ctsl) +VSX_IMPL_OVERLOAD_Z2(vec_dword2, vec_float4, vec_ctsl) + +VSX_IMPL_OVERLOAD_Z2(vec_udword2, vec_double2, vec_ctul) +VSX_IMPL_OVERLOAD_Z2(vec_udword2, vec_float4, vec_ctul) + +// fixme: implement conversions of odd-numbered elements in a dirty way +// since xlc doesn't support VSX registers operand in inline asm. +#define VSX_IMPL_CONV_ODD_4_2(rt, rg, fnm, fn2) \ +VSX_FINLINE(rt) fnm(const rg& a) { return fn2(vec_sldw(a, a, 3)); } + +VSX_IMPL_CONV_ODD_4_2(vec_double2, vec_float4, vec_cvfo, vec_cvf) +VSX_IMPL_CONV_ODD_4_2(vec_double2, vec_int4, vec_ctdo, vec_ctd) +VSX_IMPL_CONV_ODD_4_2(vec_double2, vec_uint4, vec_ctdo, vec_ctd) + +VSX_IMPL_CONV_ODD_4_2(vec_dword2, vec_float4, vec_ctslo, vec_ctsl) +VSX_IMPL_CONV_ODD_4_2(vec_udword2, vec_float4, vec_ctulo, vec_ctul) + +#define VSX_IMPL_CONV_ODD_2_4(rt, rg, fnm, fn2) \ +VSX_FINLINE(rt) fnm(const rg& a) \ +{ \ + rt v4 = fn2(a); \ + return vec_sldw(v4, v4, 1); \ +} + +VSX_IMPL_CONV_ODD_2_4(vec_float4, vec_double2, vec_cvfo, vec_cvf) +VSX_IMPL_CONV_ODD_2_4(vec_float4, vec_dword2, vec_ctfo, vec_ctf) +VSX_IMPL_CONV_ODD_2_4(vec_float4, vec_udword2, vec_ctfo, vec_ctf) + +VSX_IMPL_CONV_ODD_2_4(vec_int4, vec_double2, vec_ctso, vec_cts) +VSX_IMPL_CONV_ODD_2_4(vec_uint4, vec_double2, vec_ctuo, vec_ctu) + +#endif // XLC VSX compatibility + +// ignore GCC warning that caused by -Wunused-but-set-variable in rare cases +#if defined(__GNUG__) && !defined(__clang__) +# define VSX_UNUSED(Tvec) Tvec __attribute__((__unused__)) +#else // CLANG, XLC +# define VSX_UNUSED(Tvec) Tvec +#endif + +// gcc can find his way in casting log int and XLC, CLANG ambiguous +#if defined(__clang__) || defined(__IBMCPP__) + VSX_FINLINE(vec_udword2) vec_splats(uint64 v) + { return vec_splats((unsigned long long) v); } + + VSX_FINLINE(vec_dword2) vec_splats(int64 v) + { return vec_splats((long long) v); } + + VSX_FINLINE(vec_udword2) vec_promote(uint64 a, int b) + { return vec_promote((unsigned long long) a, b); } + + VSX_FINLINE(vec_dword2) vec_promote(int64 a, int b) + { return vec_promote((long long) a, b); } +#endif + +/* + * implement vsx_ld(offset, pointer), vsx_st(vector, offset, pointer) + * load and set using offset depend on the pointer type + * + * implement vsx_ldf(offset, pointer), vsx_stf(vector, offset, pointer) + * load and set using offset depend on fixed bytes size + * + * Note: In clang vec_xl and vec_xst fails to load unaligned addresses + * so we are using vec_vsx_ld, vec_vsx_st instead +*/ + +#if defined(__clang__) && !defined(__IBMCPP__) +# define vsx_ldf vec_vsx_ld +# define vsx_stf vec_vsx_st +#else // GCC , XLC +# define vsx_ldf vec_xl +# define vsx_stf vec_xst +#endif + +#define VSX_OFFSET(o, p) ((o) * sizeof(*(p))) +#define vsx_ld(o, p) vsx_ldf(VSX_OFFSET(o, p), p) +#define vsx_st(v, o, p) vsx_stf(v, VSX_OFFSET(o, p), p) + +/* + * implement vsx_ld2(offset, pointer), vsx_st2(vector, offset, pointer) to load and store double words + * In GCC vec_xl and vec_xst it maps to vec_vsx_ld, vec_vsx_st which doesn't support long long + * and in CLANG we are using vec_vsx_ld, vec_vsx_st because vec_xl, vec_xst fails to load unaligned addresses + * + * In XLC vec_xl and vec_xst fail to cast int64(long int) to long long +*/ +#if (defined(__GNUG__) || defined(__clang__)) && !defined(__IBMCPP__) + VSX_FINLINE(vec_udword2) vsx_ld2(long o, const uint64* p) + { return vec_udword2_c(vsx_ldf(VSX_OFFSET(o, p), (unsigned int*)p)); } + + VSX_FINLINE(vec_dword2) vsx_ld2(long o, const int64* p) + { return vec_dword2_c(vsx_ldf(VSX_OFFSET(o, p), (int*)p)); } + + VSX_FINLINE(void) vsx_st2(const vec_udword2& vec, long o, uint64* p) + { vsx_stf(vec_uint4_c(vec), VSX_OFFSET(o, p), (unsigned int*)p); } + + VSX_FINLINE(void) vsx_st2(const vec_dword2& vec, long o, int64* p) + { vsx_stf(vec_int4_c(vec), VSX_OFFSET(o, p), (int*)p); } +#else // XLC + VSX_FINLINE(vec_udword2) vsx_ld2(long o, const uint64* p) + { return vsx_ldf(VSX_OFFSET(o, p), (unsigned long long*)p); } + + VSX_FINLINE(vec_dword2) vsx_ld2(long o, const int64* p) + { return vsx_ldf(VSX_OFFSET(o, p), (long long*)p); } + + VSX_FINLINE(void) vsx_st2(const vec_udword2& vec, long o, uint64* p) + { vsx_stf(vec, VSX_OFFSET(o, p), (unsigned long long*)p); } + + VSX_FINLINE(void) vsx_st2(const vec_dword2& vec, long o, int64* p) + { vsx_stf(vec, VSX_OFFSET(o, p), (long long*)p); } +#endif + +// Store lower 8 byte +#define vec_st_l8(v, p) *((uint64*)(p)) = vec_extract(vec_udword2_c(v), 0) + +// Store higher 8 byte +#define vec_st_h8(v, p) *((uint64*)(p)) = vec_extract(vec_udword2_c(v), 1) + +// Load 64-bits of integer data to lower part +#define VSX_IMPL_LOAD_L8(Tvec, Tp) \ +VSX_FINLINE(Tvec) vec_ld_l8(const Tp *p) \ +{ return ((Tvec)vec_promote(*((uint64*)p), 0)); } + +VSX_IMPL_LOAD_L8(vec_uchar16, uchar) +VSX_IMPL_LOAD_L8(vec_char16, schar) +VSX_IMPL_LOAD_L8(vec_ushort8, ushort) +VSX_IMPL_LOAD_L8(vec_short8, short) +VSX_IMPL_LOAD_L8(vec_uint4, uint) +VSX_IMPL_LOAD_L8(vec_int4, int) +VSX_IMPL_LOAD_L8(vec_float4, float) +VSX_IMPL_LOAD_L8(vec_udword2, uint64) +VSX_IMPL_LOAD_L8(vec_dword2, int64) +VSX_IMPL_LOAD_L8(vec_double2, double) + +// logical not +#define vec_not(a) vec_nor(a, a) + +// power9 yaya +// not equal +#ifndef vec_cmpne +# define vec_cmpne(a, b) vec_not(vec_cmpeq(a, b)) +#endif + +// absolute difference +#ifndef vec_absd +# define vec_absd(a, b) vec_sub(vec_max(a, b), vec_min(a, b)) +#endif + +/* + * Implement vec_unpacklu and vec_unpackhu + * since vec_unpackl, vec_unpackh only support signed integers +**/ +#define VSX_IMPL_UNPACKU(rt, rg, zero) \ +VSX_FINLINE(rt) vec_unpacklu(const rg& a) \ +{ return (rt)(vec_mergel(a, zero)); } \ +VSX_FINLINE(rt) vec_unpackhu(const rg& a) \ +{ return (rt)(vec_mergeh(a, zero)); } + +VSX_IMPL_UNPACKU(vec_ushort8, vec_uchar16, vec_uchar16_z) +VSX_IMPL_UNPACKU(vec_uint4, vec_ushort8, vec_ushort8_z) +VSX_IMPL_UNPACKU(vec_udword2, vec_uint4, vec_uint4_z) + +/* + * Implement vec_mergesqe and vec_mergesqo + * Merges the sequence values of even and odd elements of two vectors +*/ +#define VSX_IMPL_PERM(rt, fnm, ...) \ +VSX_FINLINE(rt) fnm(const rt& a, const rt& b) \ +{ static const vec_uchar16 perm = {__VA_ARGS__}; return vec_perm(a, b, perm); } + +// 16 +#define perm16_mergesqe 0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30 +#define perm16_mergesqo 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31 +VSX_IMPL_PERM(vec_uchar16, vec_mergesqe, perm16_mergesqe) +VSX_IMPL_PERM(vec_uchar16, vec_mergesqo, perm16_mergesqo) +VSX_IMPL_PERM(vec_char16, vec_mergesqe, perm16_mergesqe) +VSX_IMPL_PERM(vec_char16, vec_mergesqo, perm16_mergesqo) +// 8 +#define perm8_mergesqe 0, 1, 4, 5, 8, 9, 12, 13, 16, 17, 20, 21, 24, 25, 28, 29 +#define perm8_mergesqo 2, 3, 6, 7, 10, 11, 14, 15, 18, 19, 22, 23, 26, 27, 30, 31 +VSX_IMPL_PERM(vec_ushort8, vec_mergesqe, perm8_mergesqe) +VSX_IMPL_PERM(vec_ushort8, vec_mergesqo, perm8_mergesqo) +VSX_IMPL_PERM(vec_short8, vec_mergesqe, perm8_mergesqe) +VSX_IMPL_PERM(vec_short8, vec_mergesqo, perm8_mergesqo) +// 4 +#define perm4_mergesqe 0, 1, 2, 3, 8, 9, 10, 11, 16, 17, 18, 19, 24, 25, 26, 27 +#define perm4_mergesqo 4, 5, 6, 7, 12, 13, 14, 15, 20, 21, 22, 23, 28, 29, 30, 31 +VSX_IMPL_PERM(vec_uint4, vec_mergesqe, perm4_mergesqe) +VSX_IMPL_PERM(vec_uint4, vec_mergesqo, perm4_mergesqo) +VSX_IMPL_PERM(vec_int4, vec_mergesqe, perm4_mergesqe) +VSX_IMPL_PERM(vec_int4, vec_mergesqo, perm4_mergesqo) +VSX_IMPL_PERM(vec_float4, vec_mergesqe, perm4_mergesqe) +VSX_IMPL_PERM(vec_float4, vec_mergesqo, perm4_mergesqo) +// 2 +VSX_REDIRECT_2RG(vec_double2, vec_double2, vec_mergesqe, vec_mergeh) +VSX_REDIRECT_2RG(vec_double2, vec_double2, vec_mergesqo, vec_mergel) +VSX_REDIRECT_2RG(vec_dword2, vec_dword2, vec_mergesqe, vec_mergeh) +VSX_REDIRECT_2RG(vec_dword2, vec_dword2, vec_mergesqo, vec_mergel) +VSX_REDIRECT_2RG(vec_udword2, vec_udword2, vec_mergesqe, vec_mergeh) +VSX_REDIRECT_2RG(vec_udword2, vec_udword2, vec_mergesqo, vec_mergel) + +/* + * Implement vec_mergesqh and vec_mergesql + * Merges the sequence most and least significant halves of two vectors +*/ +#define VSX_IMPL_MERGESQHL(Tvec) \ +VSX_FINLINE(Tvec) vec_mergesqh(const Tvec& a, const Tvec& b) \ +{ return (Tvec)vec_mergeh(vec_udword2_c(a), vec_udword2_c(b)); } \ +VSX_FINLINE(Tvec) vec_mergesql(const Tvec& a, const Tvec& b) \ +{ return (Tvec)vec_mergel(vec_udword2_c(a), vec_udword2_c(b)); } +VSX_IMPL_MERGESQHL(vec_uchar16) +VSX_IMPL_MERGESQHL(vec_char16) +VSX_IMPL_MERGESQHL(vec_ushort8) +VSX_IMPL_MERGESQHL(vec_short8) +VSX_IMPL_MERGESQHL(vec_uint4) +VSX_IMPL_MERGESQHL(vec_int4) +VSX_IMPL_MERGESQHL(vec_float4) +VSX_REDIRECT_2RG(vec_udword2, vec_udword2, vec_mergesqh, vec_mergeh) +VSX_REDIRECT_2RG(vec_udword2, vec_udword2, vec_mergesql, vec_mergel) +VSX_REDIRECT_2RG(vec_dword2, vec_dword2, vec_mergesqh, vec_mergeh) +VSX_REDIRECT_2RG(vec_dword2, vec_dword2, vec_mergesql, vec_mergel) +VSX_REDIRECT_2RG(vec_double2, vec_double2, vec_mergesqh, vec_mergeh) +VSX_REDIRECT_2RG(vec_double2, vec_double2, vec_mergesql, vec_mergel) + + +// 2 and 4 channels interleave for all types except 2 lanes +#define VSX_IMPL_ST_INTERLEAVE(Tp, Tvec) \ +VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b, Tp* ptr) \ +{ \ + vsx_stf(vec_mergeh(a, b), 0, ptr); \ + vsx_stf(vec_mergel(a, b), 16, ptr); \ +} \ +VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b, \ + const Tvec& c, const Tvec& d, Tp* ptr) \ +{ \ + Tvec ac = vec_mergeh(a, c); \ + Tvec bd = vec_mergeh(b, d); \ + vsx_stf(vec_mergeh(ac, bd), 0, ptr); \ + vsx_stf(vec_mergel(ac, bd), 16, ptr); \ + ac = vec_mergel(a, c); \ + bd = vec_mergel(b, d); \ + vsx_stf(vec_mergeh(ac, bd), 32, ptr); \ + vsx_stf(vec_mergel(ac, bd), 48, ptr); \ +} +VSX_IMPL_ST_INTERLEAVE(uchar, vec_uchar16) +VSX_IMPL_ST_INTERLEAVE(schar, vec_char16) +VSX_IMPL_ST_INTERLEAVE(ushort, vec_ushort8) +VSX_IMPL_ST_INTERLEAVE(short, vec_short8) +VSX_IMPL_ST_INTERLEAVE(uint, vec_uint4) +VSX_IMPL_ST_INTERLEAVE(int, vec_int4) +VSX_IMPL_ST_INTERLEAVE(float, vec_float4) + +// 2 and 4 channels deinterleave for 16 lanes +#define VSX_IMPL_ST_DINTERLEAVE_8(Tp, Tvec) \ +VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b) \ +{ \ + Tvec v0 = vsx_ld(0, ptr); \ + Tvec v1 = vsx_ld(16, ptr); \ + a = vec_mergesqe(v0, v1); \ + b = vec_mergesqo(v0, v1); \ +} \ +VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b, \ + Tvec& c, Tvec& d) \ +{ \ + Tvec v0 = vsx_ld(0, ptr); \ + Tvec v1 = vsx_ld(16, ptr); \ + Tvec v2 = vsx_ld(32, ptr); \ + Tvec v3 = vsx_ld(48, ptr); \ + Tvec m0 = vec_mergesqe(v0, v1); \ + Tvec m1 = vec_mergesqe(v2, v3); \ + a = vec_mergesqe(m0, m1); \ + c = vec_mergesqo(m0, m1); \ + m0 = vec_mergesqo(v0, v1); \ + m1 = vec_mergesqo(v2, v3); \ + b = vec_mergesqe(m0, m1); \ + d = vec_mergesqo(m0, m1); \ +} +VSX_IMPL_ST_DINTERLEAVE_8(uchar, vec_uchar16) +VSX_IMPL_ST_DINTERLEAVE_8(schar, vec_char16) + +// 2 and 4 channels deinterleave for 8 lanes +#define VSX_IMPL_ST_DINTERLEAVE_16(Tp, Tvec) \ +VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b) \ +{ \ + Tvec v0 = vsx_ld(0, ptr); \ + Tvec v1 = vsx_ld(8, ptr); \ + a = vec_mergesqe(v0, v1); \ + b = vec_mergesqo(v0, v1); \ +} \ +VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b, \ + Tvec& c, Tvec& d) \ +{ \ + Tvec v0 = vsx_ld(0, ptr); \ + Tvec v1 = vsx_ld(8, ptr); \ + Tvec m0 = vec_mergeh(v0, v1); \ + Tvec m1 = vec_mergel(v0, v1); \ + Tvec ab0 = vec_mergeh(m0, m1); \ + Tvec cd0 = vec_mergel(m0, m1); \ + v0 = vsx_ld(16, ptr); \ + v1 = vsx_ld(24, ptr); \ + m0 = vec_mergeh(v0, v1); \ + m1 = vec_mergel(v0, v1); \ + Tvec ab1 = vec_mergeh(m0, m1); \ + Tvec cd1 = vec_mergel(m0, m1); \ + a = vec_mergesqh(ab0, ab1); \ + b = vec_mergesql(ab0, ab1); \ + c = vec_mergesqh(cd0, cd1); \ + d = vec_mergesql(cd0, cd1); \ +} +VSX_IMPL_ST_DINTERLEAVE_16(ushort, vec_ushort8) +VSX_IMPL_ST_DINTERLEAVE_16(short, vec_short8) + +// 2 and 4 channels deinterleave for 4 lanes +#define VSX_IMPL_ST_DINTERLEAVE_32(Tp, Tvec) \ +VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b) \ +{ \ + a = vsx_ld(0, ptr); \ + b = vsx_ld(4, ptr); \ + Tvec m0 = vec_mergeh(a, b); \ + Tvec m1 = vec_mergel(a, b); \ + a = vec_mergeh(m0, m1); \ + b = vec_mergel(m0, m1); \ +} \ +VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b, \ + Tvec& c, Tvec& d) \ +{ \ + Tvec v0 = vsx_ld(0, ptr); \ + Tvec v1 = vsx_ld(4, ptr); \ + Tvec v2 = vsx_ld(8, ptr); \ + Tvec v3 = vsx_ld(12, ptr); \ + Tvec m0 = vec_mergeh(v0, v2); \ + Tvec m1 = vec_mergeh(v1, v3); \ + a = vec_mergeh(m0, m1); \ + b = vec_mergel(m0, m1); \ + m0 = vec_mergel(v0, v2); \ + m1 = vec_mergel(v1, v3); \ + c = vec_mergeh(m0, m1); \ + d = vec_mergel(m0, m1); \ +} +VSX_IMPL_ST_DINTERLEAVE_32(uint, vec_uint4) +VSX_IMPL_ST_DINTERLEAVE_32(int, vec_int4) +VSX_IMPL_ST_DINTERLEAVE_32(float, vec_float4) + +// 2 and 4 channels interleave and deinterleave for 2 lanes +#define VSX_IMPL_ST_D_INTERLEAVE_64(Tp, Tvec, ld_func, st_func) \ +VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b, Tp* ptr) \ +{ \ + st_func(vec_mergeh(a, b), 0, ptr); \ + st_func(vec_mergel(a, b), 2, ptr); \ +} \ +VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b, \ + const Tvec& c, const Tvec& d, Tp* ptr) \ +{ \ + st_func(vec_mergeh(a, b), 0, ptr); \ + st_func(vec_mergeh(c, d), 2, ptr); \ + st_func(vec_mergel(a, b), 4, ptr); \ + st_func(vec_mergel(c, d), 6, ptr); \ +} \ +VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b) \ +{ \ + Tvec m0 = ld_func(0, ptr); \ + Tvec m1 = ld_func(2, ptr); \ + a = vec_mergeh(m0, m1); \ + b = vec_mergel(m0, m1); \ +} \ +VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b, \ + Tvec& c, Tvec& d) \ +{ \ + Tvec v0 = ld_func(0, ptr); \ + Tvec v1 = ld_func(2, ptr); \ + Tvec v2 = ld_func(4, ptr); \ + Tvec v3 = ld_func(6, ptr); \ + a = vec_mergeh(v0, v2); \ + b = vec_mergel(v0, v2); \ + c = vec_mergeh(v1, v3); \ + d = vec_mergel(v1, v3); \ +} +VSX_IMPL_ST_D_INTERLEAVE_64(int64, vec_dword2, vsx_ld2, vsx_st2) +VSX_IMPL_ST_D_INTERLEAVE_64(uint64, vec_udword2, vsx_ld2, vsx_st2) +VSX_IMPL_ST_D_INTERLEAVE_64(double, vec_double2, vsx_ld, vsx_st) + +/* 3 channels */ +#define VSX_IMPL_ST_INTERLEAVE_3CH_16(Tp, Tvec) \ +VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b, \ + const Tvec& c, Tp* ptr) \ +{ \ + static const vec_uchar16 a12 = {0, 16, 0, 1, 17, 0, 2, 18, 0, 3, 19, 0, 4, 20, 0, 5}; \ + static const vec_uchar16 a123 = {0, 1, 16, 3, 4, 17, 6, 7, 18, 9, 10, 19, 12, 13, 20, 15}; \ + vsx_st(vec_perm(vec_perm(a, b, a12), c, a123), 0, ptr); \ + static const vec_uchar16 b12 = {21, 0, 6, 22, 0, 7, 23, 0, 8, 24, 0, 9, 25, 0, 10, 26}; \ + static const vec_uchar16 b123 = {0, 21, 2, 3, 22, 5, 6, 23, 8, 9, 24, 11, 12, 25, 14, 15}; \ + vsx_st(vec_perm(vec_perm(a, b, b12), c, b123), 16, ptr); \ + static const vec_uchar16 c12 = {0, 11, 27, 0, 12, 28, 0, 13, 29, 0, 14, 30, 0, 15, 31, 0}; \ + static const vec_uchar16 c123 = {26, 1, 2, 27, 4, 5, 28, 7, 8, 29, 10, 11, 30, 13, 14, 31}; \ + vsx_st(vec_perm(vec_perm(a, b, c12), c, c123), 32, ptr); \ +} \ +VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b, Tvec& c) \ +{ \ + Tvec v1 = vsx_ld(0, ptr); \ + Tvec v2 = vsx_ld(16, ptr); \ + Tvec v3 = vsx_ld(32, ptr); \ + static const vec_uchar16 a12_perm = {0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 0, 0, 0, 0, 0}; \ + static const vec_uchar16 a123_perm = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 17, 20, 23, 26, 29}; \ + a = vec_perm(vec_perm(v1, v2, a12_perm), v3, a123_perm); \ + static const vec_uchar16 b12_perm = {1, 4, 7, 10, 13, 16, 19, 22, 25, 28, 31, 0, 0, 0, 0, 0}; \ + static const vec_uchar16 b123_perm = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 18, 21, 24, 27, 30}; \ + b = vec_perm(vec_perm(v1, v2, b12_perm), v3, b123_perm); \ + static const vec_uchar16 c12_perm = {2, 5, 8, 11, 14, 17, 20, 23, 26, 29, 0, 0, 0, 0, 0, 0}; \ + static const vec_uchar16 c123_perm = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 16, 19, 22, 25, 28, 31}; \ + c = vec_perm(vec_perm(v1, v2, c12_perm), v3, c123_perm); \ +} +VSX_IMPL_ST_INTERLEAVE_3CH_16(uchar, vec_uchar16) +VSX_IMPL_ST_INTERLEAVE_3CH_16(schar, vec_char16) + +#define VSX_IMPL_ST_INTERLEAVE_3CH_8(Tp, Tvec) \ +VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b, \ + const Tvec& c, Tp* ptr) \ +{ \ + static const vec_uchar16 a12 = {0, 1, 16, 17, 0, 0, 2, 3, 18, 19, 0, 0, 4, 5, 20, 21}; \ + static const vec_uchar16 a123 = {0, 1, 2, 3, 16, 17, 6, 7, 8, 9, 18, 19, 12, 13, 14, 15}; \ + vsx_st(vec_perm(vec_perm(a, b, a12), c, a123), 0, ptr); \ + static const vec_uchar16 b12 = {0, 0, 6, 7, 22, 23, 0, 0, 8, 9, 24, 25, 0, 0, 10, 11}; \ + static const vec_uchar16 b123 = {20, 21, 2, 3, 4, 5, 22, 23, 8, 9, 10, 11, 24, 25, 14, 15}; \ + vsx_st(vec_perm(vec_perm(a, b, b12), c, b123), 8, ptr); \ + static const vec_uchar16 c12 = {26, 27, 0, 0, 12, 13, 28, 29, 0, 0, 14, 15, 30, 31, 0, 0}; \ + static const vec_uchar16 c123 = {0, 1, 26, 27, 4, 5, 6, 7, 28, 29, 10, 11, 12, 13, 30, 31}; \ + vsx_st(vec_perm(vec_perm(a, b, c12), c, c123), 16, ptr); \ +} \ +VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b, Tvec& c) \ +{ \ + Tvec v1 = vsx_ld(0, ptr); \ + Tvec v2 = vsx_ld(8, ptr); \ + Tvec v3 = vsx_ld(16, ptr); \ + static const vec_uchar16 a12_perm = {0, 1, 6, 7, 12, 13, 18, 19, 24, 25, 30, 31, 0, 0, 0, 0}; \ + static const vec_uchar16 a123_perm = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 20, 21, 26, 27}; \ + a = vec_perm(vec_perm(v1, v2, a12_perm), v3, a123_perm); \ + static const vec_uchar16 b12_perm = {2, 3, 8, 9, 14, 15, 20, 21, 26, 27, 0, 0, 0, 0, 0, 0}; \ + static const vec_uchar16 b123_perm = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 16, 17, 22, 23, 28, 29}; \ + b = vec_perm(vec_perm(v1, v2, b12_perm), v3, b123_perm); \ + static const vec_uchar16 c12_perm = {4, 5, 10, 11, 16, 17, 22, 23, 28, 29, 0, 0, 0, 0, 0, 0}; \ + static const vec_uchar16 c123_perm = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 18, 19, 24, 25, 30, 31}; \ + c = vec_perm(vec_perm(v1, v2, c12_perm), v3, c123_perm); \ +} +VSX_IMPL_ST_INTERLEAVE_3CH_8(ushort, vec_ushort8) +VSX_IMPL_ST_INTERLEAVE_3CH_8(short, vec_short8) + +#define VSX_IMPL_ST_INTERLEAVE_3CH_4(Tp, Tvec) \ +VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b, \ + const Tvec& c, Tp* ptr) \ +{ \ + Tvec hbc = vec_mergeh(b, c); \ + static const vec_uchar16 ahbc = {0, 1, 2, 3, 16, 17, 18, 19, 20, 21, 22, 23, 4, 5, 6, 7}; \ + vsx_st(vec_perm(a, hbc, ahbc), 0, ptr); \ + Tvec lab = vec_mergel(a, b); \ + vsx_st(vec_sld(lab, hbc, 8), 4, ptr); \ + static const vec_uchar16 clab = {8, 9, 10, 11, 24, 25, 26, 27, 28, 29, 30, 31, 12, 13, 14, 15};\ + vsx_st(vec_perm(c, lab, clab), 8, ptr); \ +} \ +VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b, Tvec& c) \ +{ \ + Tvec v1 = vsx_ld(0, ptr); \ + Tvec v2 = vsx_ld(4, ptr); \ + Tvec v3 = vsx_ld(8, ptr); \ + static const vec_uchar16 flp = {0, 1, 2, 3, 12, 13, 14, 15, 16, 17, 18, 19, 28, 29, 30, 31}; \ + a = vec_perm(v1, vec_sld(v3, v2, 8), flp); \ + static const vec_uchar16 flp2 = {28, 29, 30, 31, 0, 1, 2, 3, 12, 13, 14, 15, 16, 17, 18, 19}; \ + b = vec_perm(v2, vec_sld(v1, v3, 8), flp2); \ + c = vec_perm(vec_sld(v2, v1, 8), v3, flp); \ +} +VSX_IMPL_ST_INTERLEAVE_3CH_4(uint, vec_uint4) +VSX_IMPL_ST_INTERLEAVE_3CH_4(int, vec_int4) +VSX_IMPL_ST_INTERLEAVE_3CH_4(float, vec_float4) + +#define VSX_IMPL_ST_INTERLEAVE_3CH_2(Tp, Tvec, ld_func, st_func) \ +VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b, \ + const Tvec& c, Tp* ptr) \ +{ \ + st_func(vec_mergeh(a, b), 0, ptr); \ + st_func(vec_permi(c, a, 1), 2, ptr); \ + st_func(vec_mergel(b, c), 4, ptr); \ +} \ +VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, \ + Tvec& b, Tvec& c) \ +{ \ + Tvec v1 = ld_func(0, ptr); \ + Tvec v2 = ld_func(2, ptr); \ + Tvec v3 = ld_func(4, ptr); \ + a = vec_permi(v1, v2, 1); \ + b = vec_permi(v1, v3, 2); \ + c = vec_permi(v2, v3, 1); \ +} +VSX_IMPL_ST_INTERLEAVE_3CH_2(int64, vec_dword2, vsx_ld2, vsx_st2) +VSX_IMPL_ST_INTERLEAVE_3CH_2(uint64, vec_udword2, vsx_ld2, vsx_st2) +VSX_IMPL_ST_INTERLEAVE_3CH_2(double, vec_double2, vsx_ld, vsx_st) + +#endif // CV_VSX + +//! @} + +#endif // OPENCV_HAL_VSX_UTILS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/wimage.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/wimage.hpp new file mode 100644 index 0000000000000000000000000000000000000000..c7b6efa06a70e984035cb2bc7ad050ded3c3e985 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/core/wimage.hpp @@ -0,0 +1,603 @@ +/*M////////////////////////////////////////////////////////////////////////////// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to +// this license. If you do not agree to this license, do not download, +// install, copy or use the software. +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2008, Google, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of Intel Corporation or contributors may not be used to endorse +// or promote products derived from this software without specific +// prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" +// and any express or implied warranties, including, but not limited to, the +// implied warranties of merchantability and fitness for a particular purpose +// are disclaimed. In no event shall the Intel Corporation or contributors be +// liable for any direct, indirect, incidental, special, exemplary, or +// consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +///////////////////////////////////////////////////////////////////////////////// +//M*/ + +#ifndef OPENCV_CORE_WIMAGE_HPP +#define OPENCV_CORE_WIMAGE_HPP + +#include "opencv2/core/core_c.h" + +#ifdef __cplusplus + +namespace cv { + +//! @addtogroup core +//! @{ + +template class WImage; +template class WImageBuffer; +template class WImageView; + +template class WImageC; +template class WImageBufferC; +template class WImageViewC; + +// Commonly used typedefs. +typedef WImage WImage_b; +typedef WImageView WImageView_b; +typedef WImageBuffer WImageBuffer_b; + +typedef WImageC WImage1_b; +typedef WImageViewC WImageView1_b; +typedef WImageBufferC WImageBuffer1_b; + +typedef WImageC WImage3_b; +typedef WImageViewC WImageView3_b; +typedef WImageBufferC WImageBuffer3_b; + +typedef WImage WImage_f; +typedef WImageView WImageView_f; +typedef WImageBuffer WImageBuffer_f; + +typedef WImageC WImage1_f; +typedef WImageViewC WImageView1_f; +typedef WImageBufferC WImageBuffer1_f; + +typedef WImageC WImage3_f; +typedef WImageViewC WImageView3_f; +typedef WImageBufferC WImageBuffer3_f; + +// There isn't a standard for signed and unsigned short so be more +// explicit in the typename for these cases. +typedef WImage WImage_16s; +typedef WImageView WImageView_16s; +typedef WImageBuffer WImageBuffer_16s; + +typedef WImageC WImage1_16s; +typedef WImageViewC WImageView1_16s; +typedef WImageBufferC WImageBuffer1_16s; + +typedef WImageC WImage3_16s; +typedef WImageViewC WImageView3_16s; +typedef WImageBufferC WImageBuffer3_16s; + +typedef WImage WImage_16u; +typedef WImageView WImageView_16u; +typedef WImageBuffer WImageBuffer_16u; + +typedef WImageC WImage1_16u; +typedef WImageViewC WImageView1_16u; +typedef WImageBufferC WImageBuffer1_16u; + +typedef WImageC WImage3_16u; +typedef WImageViewC WImageView3_16u; +typedef WImageBufferC WImageBuffer3_16u; + +/** @brief Image class which provides a thin layer around an IplImage. + +The goals of the class design are: + + -# All the data has explicit ownership to avoid memory leaks + -# No hidden allocations or copies for performance. + -# Easy access to OpenCV methods (which will access IPP if available) + -# Can easily treat external data as an image + -# Easy to create images which are subsets of other images + -# Fast pixel access which can take advantage of number of channels if known at compile time. + +The WImage class is the image class which provides the data accessors. The 'W' comes from the fact +that it is also a wrapper around the popular but inconvenient IplImage class. A WImage can be +constructed either using a WImageBuffer class which allocates and frees the data, or using a +WImageView class which constructs a subimage or a view into external data. The view class does no +memory management. Each class actually has two versions, one when the number of channels is known +at compile time and one when it isn't. Using the one with the number of channels specified can +provide some compile time optimizations by using the fact that the number of channels is a +constant. + +We use the convention (c,r) to refer to column c and row r with (0,0) being the upper left corner. +This is similar to standard Euclidean coordinates with the first coordinate varying in the +horizontal direction and the second coordinate varying in the vertical direction. Thus (c,r) is +usually in the domain [0, width) X [0, height) + +Example usage: +@code +WImageBuffer3_b im(5,7); // Make a 5X7 3 channel image of type uchar +WImageView3_b sub_im(im, 2,2, 3,3); // 3X3 submatrix +vector vec(10, 3.0f); +WImageView1_f user_im(&vec[0], 2, 5); // 2X5 image w/ supplied data + +im.SetZero(); // same as cvSetZero(im.Ipl()) +*im(2, 3) = 15; // Modify the element at column 2, row 3 +MySetRand(&sub_im); + +// Copy the second row into the first. This can be done with no memory +// allocation and will use SSE if IPP is available. +int w = im.Width(); +im.View(0,0, w,1).CopyFrom(im.View(0,1, w,1)); + +// Doesn't care about source of data since using WImage +void MySetRand(WImage_b* im) { // Works with any number of channels +for (int r = 0; r < im->Height(); ++r) { + float* row = im->Row(r); + for (int c = 0; c < im->Width(); ++c) { + for (int ch = 0; ch < im->Channels(); ++ch, ++row) { + *row = uchar(rand() & 255); + } + } +} +} +@endcode + +Functions that are not part of the basic image allocation, viewing, and access should come from +OpenCV, except some useful functions that are not part of OpenCV can be found in wimage_util.h +*/ +template +class WImage +{ +public: + typedef T BaseType; + + // WImage is an abstract class with no other virtual methods so make the + // destructor virtual. + virtual ~WImage() = 0; + + // Accessors + IplImage* Ipl() {return image_; } + const IplImage* Ipl() const {return image_; } + T* ImageData() { return reinterpret_cast(image_->imageData); } + const T* ImageData() const { + return reinterpret_cast(image_->imageData); + } + + int Width() const {return image_->width; } + int Height() const {return image_->height; } + + // WidthStep is the number of bytes to go to the pixel with the next y coord + int WidthStep() const {return image_->widthStep; } + + int Channels() const {return image_->nChannels; } + int ChannelSize() const {return sizeof(T); } // number of bytes per channel + + // Number of bytes per pixel + int PixelSize() const {return Channels() * ChannelSize(); } + + // Return depth type (e.g. IPL_DEPTH_8U, IPL_DEPTH_32F) which is the number + // of bits per channel and with the signed bit set. + // This is known at compile time using specializations. + int Depth() const; + + inline const T* Row(int r) const { + return reinterpret_cast(image_->imageData + r*image_->widthStep); + } + + inline T* Row(int r) { + return reinterpret_cast(image_->imageData + r*image_->widthStep); + } + + // Pixel accessors which returns a pointer to the start of the channel + inline T* operator() (int c, int r) { + return reinterpret_cast(image_->imageData + r*image_->widthStep) + + c*Channels(); + } + + inline const T* operator() (int c, int r) const { + return reinterpret_cast(image_->imageData + r*image_->widthStep) + + c*Channels(); + } + + // Copy the contents from another image which is just a convenience to cvCopy + void CopyFrom(const WImage& src) { cvCopy(src.Ipl(), image_); } + + // Set contents to zero which is just a convenient to cvSetZero + void SetZero() { cvSetZero(image_); } + + // Construct a view into a region of this image + WImageView View(int c, int r, int width, int height); + +protected: + // Disallow copy and assignment + WImage(const WImage&); + void operator=(const WImage&); + + explicit WImage(IplImage* img) : image_(img) { + assert(!img || img->depth == Depth()); + } + + void SetIpl(IplImage* image) { + assert(!image || image->depth == Depth()); + image_ = image; + } + + IplImage* image_; +}; + + +/** Image class when both the pixel type and number of channels +are known at compile time. This wrapper will speed up some of the operations +like accessing individual pixels using the () operator. +*/ +template +class WImageC : public WImage +{ +public: + typedef typename WImage::BaseType BaseType; + enum { kChannels = C }; + + explicit WImageC(IplImage* img) : WImage(img) { + assert(!img || img->nChannels == Channels()); + } + + // Construct a view into a region of this image + WImageViewC View(int c, int r, int width, int height); + + // Copy the contents from another image which is just a convenience to cvCopy + void CopyFrom(const WImageC& src) { + cvCopy(src.Ipl(), WImage::image_); + } + + // WImageC is an abstract class with no other virtual methods so make the + // destructor virtual. + virtual ~WImageC() = 0; + + int Channels() const {return C; } + +protected: + // Disallow copy and assignment + WImageC(const WImageC&); + void operator=(const WImageC&); + + void SetIpl(IplImage* image) { + assert(!image || image->depth == WImage::Depth()); + WImage::SetIpl(image); + } +}; + +/** Image class which owns the data, so it can be allocated and is always +freed. It cannot be copied but can be explicitly cloned. +*/ +template +class WImageBuffer : public WImage +{ +public: + typedef typename WImage::BaseType BaseType; + + // Default constructor which creates an object that can be + WImageBuffer() : WImage(0) {} + + WImageBuffer(int width, int height, int nchannels) : WImage(0) { + Allocate(width, height, nchannels); + } + + // Constructor which takes ownership of a given IplImage so releases + // the image on destruction. + explicit WImageBuffer(IplImage* img) : WImage(img) {} + + // Allocate an image. Does nothing if current size is the same as + // the new size. + void Allocate(int width, int height, int nchannels); + + // Set the data to point to an image, releasing the old data + void SetIpl(IplImage* img) { + ReleaseImage(); + WImage::SetIpl(img); + } + + // Clone an image which reallocates the image if of a different dimension. + void CloneFrom(const WImage& src) { + Allocate(src.Width(), src.Height(), src.Channels()); + CopyFrom(src); + } + + ~WImageBuffer() { + ReleaseImage(); + } + + // Release the image if it isn't null. + void ReleaseImage() { + if (WImage::image_) { + IplImage* image = WImage::image_; + cvReleaseImage(&image); + WImage::SetIpl(0); + } + } + + bool IsNull() const {return WImage::image_ == NULL; } + +private: + // Disallow copy and assignment + WImageBuffer(const WImageBuffer&); + void operator=(const WImageBuffer&); +}; + +/** Like a WImageBuffer class but when the number of channels is known at compile time. +*/ +template +class WImageBufferC : public WImageC +{ +public: + typedef typename WImage::BaseType BaseType; + enum { kChannels = C }; + + // Default constructor which creates an object that can be + WImageBufferC() : WImageC(0) {} + + WImageBufferC(int width, int height) : WImageC(0) { + Allocate(width, height); + } + + // Constructor which takes ownership of a given IplImage so releases + // the image on destruction. + explicit WImageBufferC(IplImage* img) : WImageC(img) {} + + // Allocate an image. Does nothing if current size is the same as + // the new size. + void Allocate(int width, int height); + + // Set the data to point to an image, releasing the old data + void SetIpl(IplImage* img) { + ReleaseImage(); + WImageC::SetIpl(img); + } + + // Clone an image which reallocates the image if of a different dimension. + void CloneFrom(const WImageC& src) { + Allocate(src.Width(), src.Height()); + CopyFrom(src); + } + + ~WImageBufferC() { + ReleaseImage(); + } + + // Release the image if it isn't null. + void ReleaseImage() { + if (WImage::image_) { + IplImage* image = WImage::image_; + cvReleaseImage(&image); + WImageC::SetIpl(0); + } + } + + bool IsNull() const {return WImage::image_ == NULL; } + +private: + // Disallow copy and assignment + WImageBufferC(const WImageBufferC&); + void operator=(const WImageBufferC&); +}; + +/** View into an image class which allows treating a subimage as an image or treating external data +as an image +*/ +template class WImageView : public WImage +{ +public: + typedef typename WImage::BaseType BaseType; + + // Construct a subimage. No checks are done that the subimage lies + // completely inside the original image. + WImageView(WImage* img, int c, int r, int width, int height); + + // Refer to external data. + // If not given width_step assumed to be same as width. + WImageView(T* data, int width, int height, int channels, int width_step = -1); + + // Refer to external data. This does NOT take ownership + // of the supplied IplImage. + WImageView(IplImage* img) : WImage(img) {} + + // Copy constructor + WImageView(const WImage& img) : WImage(0) { + header_ = *(img.Ipl()); + WImage::SetIpl(&header_); + } + + WImageView& operator=(const WImage& img) { + header_ = *(img.Ipl()); + WImage::SetIpl(&header_); + return *this; + } + +protected: + IplImage header_; +}; + + +template +class WImageViewC : public WImageC +{ +public: + typedef typename WImage::BaseType BaseType; + enum { kChannels = C }; + + // Default constructor needed for vectors of views. + WImageViewC(); + + virtual ~WImageViewC() {} + + // Construct a subimage. No checks are done that the subimage lies + // completely inside the original image. + WImageViewC(WImageC* img, + int c, int r, int width, int height); + + // Refer to external data + WImageViewC(T* data, int width, int height, int width_step = -1); + + // Refer to external data. This does NOT take ownership + // of the supplied IplImage. + WImageViewC(IplImage* img) : WImageC(img) {} + + // Copy constructor which does a shallow copy to allow multiple views + // of same data. gcc-4.1.1 gets confused if both versions of + // the constructor and assignment operator are not provided. + WImageViewC(const WImageC& img) : WImageC(0) { + header_ = *(img.Ipl()); + WImageC::SetIpl(&header_); + } + WImageViewC(const WImageViewC& img) : WImageC(0) { + header_ = *(img.Ipl()); + WImageC::SetIpl(&header_); + } + + WImageViewC& operator=(const WImageC& img) { + header_ = *(img.Ipl()); + WImageC::SetIpl(&header_); + return *this; + } + WImageViewC& operator=(const WImageViewC& img) { + header_ = *(img.Ipl()); + WImageC::SetIpl(&header_); + return *this; + } + +protected: + IplImage header_; +}; + + +// Specializations for depth +template<> +inline int WImage::Depth() const {return IPL_DEPTH_8U; } +template<> +inline int WImage::Depth() const {return IPL_DEPTH_8S; } +template<> +inline int WImage::Depth() const {return IPL_DEPTH_16S; } +template<> +inline int WImage::Depth() const {return IPL_DEPTH_16U; } +template<> +inline int WImage::Depth() const {return IPL_DEPTH_32S; } +template<> +inline int WImage::Depth() const {return IPL_DEPTH_32F; } +template<> +inline int WImage::Depth() const {return IPL_DEPTH_64F; } + +template inline WImage::~WImage() {} +template inline WImageC::~WImageC() {} + +template +inline void WImageBuffer::Allocate(int width, int height, int nchannels) +{ + if (IsNull() || WImage::Width() != width || + WImage::Height() != height || WImage::Channels() != nchannels) { + ReleaseImage(); + WImage::image_ = cvCreateImage(cvSize(width, height), + WImage::Depth(), nchannels); + } +} + +template +inline void WImageBufferC::Allocate(int width, int height) +{ + if (IsNull() || WImage::Width() != width || WImage::Height() != height) { + ReleaseImage(); + WImageC::SetIpl(cvCreateImage(cvSize(width, height),WImage::Depth(), C)); + } +} + +template +WImageView::WImageView(WImage* img, int c, int r, int width, int height) + : WImage(0) +{ + header_ = *(img->Ipl()); + header_.imageData = reinterpret_cast((*img)(c, r)); + header_.width = width; + header_.height = height; + WImage::SetIpl(&header_); +} + +template +WImageView::WImageView(T* data, int width, int height, int nchannels, int width_step) + : WImage(0) +{ + cvInitImageHeader(&header_, cvSize(width, height), WImage::Depth(), nchannels); + header_.imageData = reinterpret_cast(data); + if (width_step > 0) { + header_.widthStep = width_step; + } + WImage::SetIpl(&header_); +} + +template +WImageViewC::WImageViewC(WImageC* img, int c, int r, int width, int height) + : WImageC(0) +{ + header_ = *(img->Ipl()); + header_.imageData = reinterpret_cast((*img)(c, r)); + header_.width = width; + header_.height = height; + WImageC::SetIpl(&header_); +} + +template +WImageViewC::WImageViewC() : WImageC(0) { + cvInitImageHeader(&header_, cvSize(0, 0), WImage::Depth(), C); + header_.imageData = reinterpret_cast(0); + WImageC::SetIpl(&header_); +} + +template +WImageViewC::WImageViewC(T* data, int width, int height, int width_step) + : WImageC(0) +{ + cvInitImageHeader(&header_, cvSize(width, height), WImage::Depth(), C); + header_.imageData = reinterpret_cast(data); + if (width_step > 0) { + header_.widthStep = width_step; + } + WImageC::SetIpl(&header_); +} + +// Construct a view into a region of an image +template +WImageView WImage::View(int c, int r, int width, int height) { + return WImageView(this, c, r, width, height); +} + +template +WImageViewC WImageC::View(int c, int r, int width, int height) { + return WImageViewC(this, c, r, width, height); +} + +//! @} core + +} // end of namespace + +#endif // __cplusplus + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/cvconfig.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/cvconfig.h new file mode 100644 index 0000000000000000000000000000000000000000..b5192fb1f9619254d5c35f47c1fd1a271b00bd87 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/cvconfig.h @@ -0,0 +1,248 @@ +#ifndef OPENCV_CVCONFIG_H_INCLUDED +#define OPENCV_CVCONFIG_H_INCLUDED + +/* OpenCV compiled as static or dynamic libs */ +/* #undef BUILD_SHARED_LIBS */ + +/* OpenCV intrinsics optimized code */ +#define CV_ENABLE_INTRINSICS + +/* OpenCV additional optimized code */ +/* #undef CV_DISABLE_OPTIMIZATION */ + +/* Compile for 'real' NVIDIA GPU architectures */ +#define CUDA_ARCH_BIN "" + +/* Create PTX or BIN for 1.0 compute capability */ +/* #undef CUDA_ARCH_BIN_OR_PTX_10 */ + +/* NVIDIA GPU features are used */ +#define CUDA_ARCH_FEATURES "" + +/* Compile for 'virtual' NVIDIA PTX architectures */ +#define CUDA_ARCH_PTX "" + +/* AVFoundation video libraries */ +/* #undef HAVE_AVFOUNDATION */ + +/* V4L capturing support */ +/* #undef HAVE_CAMV4L */ + +/* V4L2 capturing support */ +#define HAVE_CAMV4L2 + +/* Carbon windowing environment */ +/* #undef HAVE_CARBON */ + +/* AMD's Basic Linear Algebra Subprograms Library*/ +/* #undef HAVE_CLAMDBLAS */ + +/* AMD's OpenCL Fast Fourier Transform Library*/ +/* #undef HAVE_CLAMDFFT */ + +/* Clp support */ +/* #undef HAVE_CLP */ + +/* Cocoa API */ +/* #undef HAVE_COCOA */ + +/* C= */ +/* #undef HAVE_CSTRIPES */ + +/* NVIDIA CUDA Basic Linear Algebra Subprograms (BLAS) API*/ +/* #undef HAVE_CUBLAS */ + +/* NVIDIA CUDA Runtime API*/ +/* #undef HAVE_CUDA */ + +/* NVIDIA CUDA Fast Fourier Transform (FFT) API*/ +/* #undef HAVE_CUFFT */ + +/* IEEE1394 capturing support */ +/* #undef HAVE_DC1394 */ + +/* IEEE1394 capturing support - libdc1394 v2.x */ +/* #undef HAVE_DC1394_2 */ + +/* DirectX */ +/* #undef HAVE_DIRECTX */ +/* #undef HAVE_DIRECTX_NV12 */ +/* #undef HAVE_D3D11 */ +/* #undef HAVE_D3D10 */ +/* #undef HAVE_D3D9 */ + +/* DirectShow Video Capture library */ +/* #undef HAVE_DSHOW */ + +/* Eigen Matrix & Linear Algebra Library */ +/* #undef HAVE_EIGEN */ + +/* FFMpeg video library */ +/* #undef HAVE_FFMPEG */ + +/* Geospatial Data Abstraction Library */ +/* #undef HAVE_GDAL */ + +/* GStreamer multimedia framework */ +/* #undef HAVE_GSTREAMER */ + +/* GTK+ 2.0 Thread support */ +/* #undef HAVE_GTHREAD */ + +/* GTK+ 2.x toolkit */ +/* #undef HAVE_GTK */ + +/* Halide support */ +/* #undef HAVE_HALIDE */ + +/* Define to 1 if you have the header file. */ +#define HAVE_INTTYPES_H 1 + +/* Intel Perceptual Computing SDK library */ +/* #undef HAVE_INTELPERC */ + +/* Intel Integrated Performance Primitives */ +/* #undef HAVE_IPP */ +/* #undef HAVE_IPP_ICV */ +/* #undef HAVE_IPP_IW */ +/* #undef HAVE_IPP_IW_LL */ + +/* JPEG-2000 codec */ +#define HAVE_JASPER + +/* IJG JPEG codec */ +#define HAVE_JPEG + +/* libpng/png.h needs to be included */ +/* #undef HAVE_LIBPNG_PNG_H */ + +/* GDCM DICOM codec */ +/* #undef HAVE_GDCM */ + +/* V4L/V4L2 capturing support via libv4l */ +/* #undef HAVE_LIBV4L */ + +/* Microsoft Media Foundation Capture library */ +/* #undef HAVE_MSMF */ + +/* NVIDIA Video Decoding API*/ +/* #undef HAVE_NVCUVID */ + +/* NVIDIA Video Encoding API*/ +/* #undef HAVE_NVCUVENC */ + +/* OpenCL Support */ +/* #undef HAVE_OPENCL */ +/* #undef HAVE_OPENCL_STATIC */ +/* #undef HAVE_OPENCL_SVM */ + +/* OpenEXR codec */ +#define HAVE_OPENEXR + +/* OpenGL support*/ +/* #undef HAVE_OPENGL */ + +/* OpenNI library */ +/* #undef HAVE_OPENNI */ + +/* OpenNI library */ +/* #undef HAVE_OPENNI2 */ + +/* PNG codec */ +#define HAVE_PNG + +/* Posix threads (pthreads) */ +#define HAVE_PTHREAD + +/* parallel_for with pthreads */ +#define HAVE_PTHREADS_PF + +/* Qt support */ +/* #undef HAVE_QT */ + +/* Qt OpenGL support */ +/* #undef HAVE_QT_OPENGL */ + +/* QuickTime video libraries */ +/* #undef HAVE_QUICKTIME */ + +/* QTKit video libraries */ +/* #undef HAVE_QTKIT */ + +/* Intel Threading Building Blocks */ +/* #undef HAVE_TBB */ + +/* TIFF codec */ +#define HAVE_TIFF + +/* Unicap video capture library */ +/* #undef HAVE_UNICAP */ + +/* Video for Windows support */ +/* #undef HAVE_VFW */ + +/* V4L2 capturing support in videoio.h */ +/* #undef HAVE_VIDEOIO */ + +/* Win32 UI */ +/* #undef HAVE_WIN32UI */ + +/* XIMEA camera support */ +/* #undef HAVE_XIMEA */ + +/* Xine video library */ +/* #undef HAVE_XINE */ + +/* Define if your processor stores words with the most significant byte + first (like Motorola and SPARC, unlike Intel and VAX). */ +/* #undef WORDS_BIGENDIAN */ + +/* gPhoto2 library */ +/* #undef HAVE_GPHOTO2 */ + +/* VA library (libva) */ +/* #undef HAVE_VA */ + +/* Intel VA-API/OpenCL */ +/* #undef HAVE_VA_INTEL */ + +/* Intel Media SDK */ +/* #undef HAVE_MFX */ + +/* Lapack */ +/* #undef HAVE_LAPACK */ + +/* Library was compiled with functions instrumentation */ +/* #undef ENABLE_INSTRUMENTATION */ + +/* OpenVX */ +/* #undef HAVE_OPENVX */ + +#if defined(HAVE_XINE) || \ + defined(HAVE_GSTREAMER) || \ + defined(HAVE_QUICKTIME) || \ + defined(HAVE_QTKIT) || \ + defined(HAVE_AVFOUNDATION) || \ + /*defined(HAVE_OPENNI) || too specialized */ \ + defined(HAVE_FFMPEG) || \ + defined(HAVE_MSMF) +#define HAVE_VIDEO_INPUT +#endif + +#if /*defined(HAVE_XINE) || */\ + defined(HAVE_GSTREAMER) || \ + defined(HAVE_QUICKTIME) || \ + defined(HAVE_QTKIT) || \ + defined(HAVE_AVFOUNDATION) || \ + defined(HAVE_FFMPEG) || \ + defined(HAVE_MSMF) +#define HAVE_VIDEO_OUTPUT +#endif + +/* OpenCV trace utilities */ +#define OPENCV_TRACE + +/* Library QR-code decoding */ +#define HAVE_QUIRC + +#endif // OPENCV_CVCONFIG_H_INCLUDED diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/dnn.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/dnn.hpp new file mode 100644 index 0000000000000000000000000000000000000000..97f2fe3ffd8cf60ec40a732768a85c3ab5a1cd73 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/dnn.hpp @@ -0,0 +1,78 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_DNN_HPP +#define OPENCV_DNN_HPP + +// This is an umbrella header to include into you project. +// We are free to change headers layout in dnn subfolder, so please include +// this header for future compatibility + + +/** @defgroup dnn Deep Neural Network module + @{ + This module contains: + - API for new layers creation, layers are building bricks of neural networks; + - set of built-in most-useful Layers; + - API to construct and modify comprehensive neural networks from layers; + - functionality for loading serialized networks models from different frameworks. + + Functionality of this module is designed only for forward pass computations (i.e. network testing). + A network training is in principle not supported. + @} +*/ +/** @example samples/dnn/classification.cpp +Check @ref tutorial_dnn_googlenet "the corresponding tutorial" for more details +*/ +/** @example samples/dnn/colorization.cpp +*/ +/** @example samples/dnn/object_detection.cpp +Check @ref tutorial_dnn_yolo "the corresponding tutorial" for more details +*/ +/** @example samples/dnn/openpose.cpp +*/ +/** @example samples/dnn/segmentation.cpp +*/ +/** @example samples/dnn/text_detection.cpp +*/ +#include + +#endif /* OPENCV_DNN_HPP */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/dnn/all_layers.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/dnn/all_layers.hpp new file mode 100644 index 0000000000000000000000000000000000000000..c6fe6d05bc80eb09b91d3cb4ec8135feb6a0a2a8 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/dnn/all_layers.hpp @@ -0,0 +1,634 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_DNN_DNN_ALL_LAYERS_HPP +#define OPENCV_DNN_DNN_ALL_LAYERS_HPP +#include + +namespace cv { +namespace dnn { +CV__DNN_EXPERIMENTAL_NS_BEGIN +//! @addtogroup dnn +//! @{ + +/** @defgroup dnnLayerList Partial List of Implemented Layers + @{ + This subsection of dnn module contains information about built-in layers and their descriptions. + + Classes listed here, in fact, provides C++ API for creating instances of built-in layers. + In addition to this way of layers instantiation, there is a more common factory API (see @ref dnnLayerFactory), it allows to create layers dynamically (by name) and register new ones. + You can use both API, but factory API is less convenient for native C++ programming and basically designed for use inside importers (see @ref readNetFromCaffe(), @ref readNetFromTorch(), @ref readNetFromTensorflow()). + + Built-in layers partially reproduce functionality of corresponding Caffe and Torch7 layers. + In particular, the following layers and Caffe importer were tested to reproduce Caffe functionality: + - Convolution + - Deconvolution + - Pooling + - InnerProduct + - TanH, ReLU, Sigmoid, BNLL, Power, AbsVal + - Softmax + - Reshape, Flatten, Slice, Split + - LRN + - MVN + - Dropout (since it does nothing on forward pass -)) +*/ + + class CV_EXPORTS BlankLayer : public Layer + { + public: + static Ptr create(const LayerParams ¶ms); + }; + + /** + * Constant layer produces the same data blob at an every forward pass. + */ + class CV_EXPORTS ConstLayer : public Layer + { + public: + static Ptr create(const LayerParams ¶ms); + }; + + //! LSTM recurrent layer + class CV_EXPORTS LSTMLayer : public Layer + { + public: + /** Creates instance of LSTM layer */ + static Ptr create(const LayerParams& params); + + /** @deprecated Use LayerParams::blobs instead. + @brief Set trained weights for LSTM layer. + + LSTM behavior on each step is defined by current input, previous output, previous cell state and learned weights. + + Let @f$x_t@f$ be current input, @f$h_t@f$ be current output, @f$c_t@f$ be current state. + Than current output and current cell state is computed as follows: + @f{eqnarray*}{ + h_t &= o_t \odot tanh(c_t), \\ + c_t &= f_t \odot c_{t-1} + i_t \odot g_t, \\ + @f} + where @f$\odot@f$ is per-element multiply operation and @f$i_t, f_t, o_t, g_t@f$ is internal gates that are computed using learned wights. + + Gates are computed as follows: + @f{eqnarray*}{ + i_t &= sigmoid&(W_{xi} x_t + W_{hi} h_{t-1} + b_i), \\ + f_t &= sigmoid&(W_{xf} x_t + W_{hf} h_{t-1} + b_f), \\ + o_t &= sigmoid&(W_{xo} x_t + W_{ho} h_{t-1} + b_o), \\ + g_t &= tanh &(W_{xg} x_t + W_{hg} h_{t-1} + b_g), \\ + @f} + where @f$W_{x?}@f$, @f$W_{h?}@f$ and @f$b_{?}@f$ are learned weights represented as matrices: + @f$W_{x?} \in R^{N_h \times N_x}@f$, @f$W_{h?} \in R^{N_h \times N_h}@f$, @f$b_? \in R^{N_h}@f$. + + For simplicity and performance purposes we use @f$ W_x = [W_{xi}; W_{xf}; W_{xo}, W_{xg}] @f$ + (i.e. @f$W_x@f$ is vertical concatenation of @f$ W_{x?} @f$), @f$ W_x \in R^{4N_h \times N_x} @f$. + The same for @f$ W_h = [W_{hi}; W_{hf}; W_{ho}, W_{hg}], W_h \in R^{4N_h \times N_h} @f$ + and for @f$ b = [b_i; b_f, b_o, b_g]@f$, @f$b \in R^{4N_h} @f$. + + @param Wh is matrix defining how previous output is transformed to internal gates (i.e. according to above mentioned notation is @f$ W_h @f$) + @param Wx is matrix defining how current input is transformed to internal gates (i.e. according to above mentioned notation is @f$ W_x @f$) + @param b is bias vector (i.e. according to above mentioned notation is @f$ b @f$) + */ + CV_DEPRECATED virtual void setWeights(const Mat &Wh, const Mat &Wx, const Mat &b) = 0; + + /** @brief Specifies shape of output blob which will be [[`T`], `N`] + @p outTailShape. + * @details If this parameter is empty or unset then @p outTailShape = [`Wh`.size(0)] will be used, + * where `Wh` is parameter from setWeights(). + */ + virtual void setOutShape(const MatShape &outTailShape = MatShape()) = 0; + + /** @deprecated Use flag `produce_cell_output` in LayerParams. + * @brief Specifies either interpret first dimension of input blob as timestamp dimenion either as sample. + * + * If flag is set to true then shape of input blob will be interpreted as [`T`, `N`, `[data dims]`] where `T` specifies number of timestamps, `N` is number of independent streams. + * In this case each forward() call will iterate through `T` timestamps and update layer's state `T` times. + * + * If flag is set to false then shape of input blob will be interpreted as [`N`, `[data dims]`]. + * In this case each forward() call will make one iteration and produce one timestamp with shape [`N`, `[out dims]`]. + */ + CV_DEPRECATED virtual void setUseTimstampsDim(bool use = true) = 0; + + /** @deprecated Use flag `use_timestamp_dim` in LayerParams. + * @brief If this flag is set to true then layer will produce @f$ c_t @f$ as second output. + * @details Shape of the second output is the same as first output. + */ + CV_DEPRECATED virtual void setProduceCellOutput(bool produce = false) = 0; + + /* In common case it use single input with @f$x_t@f$ values to compute output(s) @f$h_t@f$ (and @f$c_t@f$). + * @param input should contain packed values @f$x_t@f$ + * @param output contains computed outputs: @f$h_t@f$ (and @f$c_t@f$ if setProduceCellOutput() flag was set to true). + * + * If setUseTimstampsDim() is set to true then @p input[0] should has at least two dimensions with the following shape: [`T`, `N`, `[data dims]`], + * where `T` specifies number of timestamps, `N` is number of independent streams (i.e. @f$ x_{t_0 + t}^{stream} @f$ is stored inside @p input[0][t, stream, ...]). + * + * If setUseTimstampsDim() is set to false then @p input[0] should contain single timestamp, its shape should has form [`N`, `[data dims]`] with at least one dimension. + * (i.e. @f$ x_{t}^{stream} @f$ is stored inside @p input[0][stream, ...]). + */ + + int inputNameToIndex(String inputName) CV_OVERRIDE; + int outputNameToIndex(const String& outputName) CV_OVERRIDE; + }; + + /** @brief Classical recurrent layer + + Accepts two inputs @f$x_t@f$ and @f$h_{t-1}@f$ and compute two outputs @f$o_t@f$ and @f$h_t@f$. + + - input: should contain packed input @f$x_t@f$. + - output: should contain output @f$o_t@f$ (and @f$h_t@f$ if setProduceHiddenOutput() is set to true). + + input[0] should have shape [`T`, `N`, `data_dims`] where `T` and `N` is number of timestamps and number of independent samples of @f$x_t@f$ respectively. + + output[0] will have shape [`T`, `N`, @f$N_o@f$], where @f$N_o@f$ is number of rows in @f$ W_{xo} @f$ matrix. + + If setProduceHiddenOutput() is set to true then @p output[1] will contain a Mat with shape [`T`, `N`, @f$N_h@f$], where @f$N_h@f$ is number of rows in @f$ W_{hh} @f$ matrix. + */ + class CV_EXPORTS RNNLayer : public Layer + { + public: + /** Creates instance of RNNLayer */ + static Ptr create(const LayerParams& params); + + /** Setups learned weights. + + Recurrent-layer behavior on each step is defined by current input @f$ x_t @f$, previous state @f$ h_t @f$ and learned weights as follows: + @f{eqnarray*}{ + h_t &= tanh&(W_{hh} h_{t-1} + W_{xh} x_t + b_h), \\ + o_t &= tanh&(W_{ho} h_t + b_o), + @f} + + @param Wxh is @f$ W_{xh} @f$ matrix + @param bh is @f$ b_{h} @f$ vector + @param Whh is @f$ W_{hh} @f$ matrix + @param Who is @f$ W_{xo} @f$ matrix + @param bo is @f$ b_{o} @f$ vector + */ + virtual void setWeights(const Mat &Wxh, const Mat &bh, const Mat &Whh, const Mat &Who, const Mat &bo) = 0; + + /** @brief If this flag is set to true then layer will produce @f$ h_t @f$ as second output. + * @details Shape of the second output is the same as first output. + */ + virtual void setProduceHiddenOutput(bool produce = false) = 0; + + }; + + class CV_EXPORTS BaseConvolutionLayer : public Layer + { + public: + Size kernel, stride, pad, dilation, adjustPad; + String padMode; + int numOutput; + }; + + class CV_EXPORTS ConvolutionLayer : public BaseConvolutionLayer + { + public: + static Ptr create(const LayerParams& params); + }; + + class CV_EXPORTS DeconvolutionLayer : public BaseConvolutionLayer + { + public: + static Ptr create(const LayerParams& params); + }; + + class CV_EXPORTS LRNLayer : public Layer + { + public: + int type; + + int size; + float alpha, beta, bias; + bool normBySize; + + static Ptr create(const LayerParams& params); + }; + + class CV_EXPORTS PoolingLayer : public Layer + { + public: + int type; + Size kernel, stride; + int pad_l, pad_t, pad_r, pad_b; + CV_DEPRECATED_EXTERNAL Size pad; + bool globalPooling; + bool computeMaxIdx; + String padMode; + bool ceilMode; + // If true for average pooling with padding, divide an every output region + // by a whole kernel area. Otherwise exclude zero padded values and divide + // by number of real values. + bool avePoolPaddedArea; + // ROIPooling parameters. + Size pooledSize; + float spatialScale; + // PSROIPooling parameters. + int psRoiOutChannels; + + static Ptr create(const LayerParams& params); + }; + + class CV_EXPORTS SoftmaxLayer : public Layer + { + public: + bool logSoftMax; + + static Ptr create(const LayerParams& params); + }; + + class CV_EXPORTS InnerProductLayer : public Layer + { + public: + int axis; + static Ptr create(const LayerParams& params); + }; + + class CV_EXPORTS MVNLayer : public Layer + { + public: + float eps; + bool normVariance, acrossChannels; + + static Ptr create(const LayerParams& params); + }; + + /* Reshaping */ + + class CV_EXPORTS ReshapeLayer : public Layer + { + public: + MatShape newShapeDesc; + Range newShapeRange; + + static Ptr create(const LayerParams& params); + }; + + class CV_EXPORTS FlattenLayer : public Layer + { + public: + static Ptr create(const LayerParams ¶ms); + }; + + class CV_EXPORTS ConcatLayer : public Layer + { + public: + int axis; + /** + * @brief Add zero padding in case of concatenation of blobs with different + * spatial sizes. + * + * Details: https://github.com/torch/nn/blob/master/doc/containers.md#depthconcat + */ + bool padding; + + static Ptr create(const LayerParams ¶ms); + }; + + class CV_EXPORTS SplitLayer : public Layer + { + public: + int outputsCount; //!< Number of copies that will be produced (is ignored when negative). + + static Ptr create(const LayerParams ¶ms); + }; + + /** + * Slice layer has several modes: + * 1. Caffe mode + * @param[in] axis Axis of split operation + * @param[in] slice_point Array of split points + * + * Number of output blobs equals to number of split points plus one. The + * first blob is a slice on input from 0 to @p slice_point[0] - 1 by @p axis, + * the second output blob is a slice of input from @p slice_point[0] to + * @p slice_point[1] - 1 by @p axis and the last output blob is a slice of + * input from @p slice_point[-1] up to the end of @p axis size. + * + * 2. TensorFlow mode + * @param begin Vector of start indices + * @param size Vector of sizes + * + * More convenient numpy-like slice. One and only output blob + * is a slice `input[begin[0]:begin[0]+size[0], begin[1]:begin[1]+size[1], ...]` + * + * 3. Torch mode + * @param axis Axis of split operation + * + * Split input blob on the equal parts by @p axis. + */ + class CV_EXPORTS SliceLayer : public Layer + { + public: + /** + * @brief Vector of slice ranges. + * + * The first dimension equals number of output blobs. + * Inner vector has slice ranges for the first number of input dimensions. + */ + std::vector > sliceRanges; + int axis; + + static Ptr create(const LayerParams ¶ms); + }; + + class CV_EXPORTS PermuteLayer : public Layer + { + public: + static Ptr create(const LayerParams& params); + }; + + /** + * Permute channels of 4-dimensional input blob. + * @param group Number of groups to split input channels and pick in turns + * into output blob. + * + * \f[ groupSize = \frac{number\ of\ channels}{group} \f] + * \f[ output(n, c, h, w) = input(n, groupSize \times (c \% group) + \lfloor \frac{c}{group} \rfloor, h, w) \f] + * Read more at https://arxiv.org/pdf/1707.01083.pdf + */ + class CV_EXPORTS ShuffleChannelLayer : public Layer + { + public: + static Ptr create(const LayerParams& params); + + int group; + }; + + /** + * @brief Adds extra values for specific axes. + * @param paddings Vector of paddings in format + * @code + * [ pad_before, pad_after, // [0]th dimension + * pad_before, pad_after, // [1]st dimension + * ... + * pad_before, pad_after ] // [n]th dimension + * @endcode + * that represents number of padded values at every dimension + * starting from the first one. The rest of dimensions won't + * be padded. + * @param value Value to be padded. Defaults to zero. + * @param type Padding type: 'constant', 'reflect' + * @param input_dims Torch's parameter. If @p input_dims is not equal to the + * actual input dimensionality then the `[0]th` dimension + * is considered as a batch dimension and @p paddings are shifted + * to a one dimension. Defaults to `-1` that means padding + * corresponding to @p paddings. + */ + class CV_EXPORTS PaddingLayer : public Layer + { + public: + static Ptr create(const LayerParams& params); + }; + + /* Activations */ + class CV_EXPORTS ActivationLayer : public Layer + { + public: + virtual void forwardSlice(const float* src, float* dst, int len, + size_t outPlaneSize, int cn0, int cn1) const = 0; + }; + + class CV_EXPORTS ReLULayer : public ActivationLayer + { + public: + float negativeSlope; + + static Ptr create(const LayerParams ¶ms); + }; + + class CV_EXPORTS ReLU6Layer : public ActivationLayer + { + public: + float minValue, maxValue; + + static Ptr create(const LayerParams ¶ms); + }; + + class CV_EXPORTS ChannelsPReLULayer : public ActivationLayer + { + public: + static Ptr create(const LayerParams& params); + }; + + class CV_EXPORTS ELULayer : public ActivationLayer + { + public: + static Ptr create(const LayerParams ¶ms); + }; + + class CV_EXPORTS TanHLayer : public ActivationLayer + { + public: + static Ptr create(const LayerParams ¶ms); + }; + + class CV_EXPORTS SigmoidLayer : public ActivationLayer + { + public: + static Ptr create(const LayerParams ¶ms); + }; + + class CV_EXPORTS BNLLLayer : public ActivationLayer + { + public: + static Ptr create(const LayerParams ¶ms); + }; + + class CV_EXPORTS AbsLayer : public ActivationLayer + { + public: + static Ptr create(const LayerParams ¶ms); + }; + + class CV_EXPORTS PowerLayer : public ActivationLayer + { + public: + float power, scale, shift; + + static Ptr create(const LayerParams ¶ms); + }; + + /* Layers used in semantic segmentation */ + + class CV_EXPORTS CropLayer : public Layer + { + public: + int startAxis; + std::vector offset; + + static Ptr create(const LayerParams ¶ms); + }; + + class CV_EXPORTS EltwiseLayer : public Layer + { + public: + static Ptr create(const LayerParams ¶ms); + }; + + class CV_EXPORTS BatchNormLayer : public ActivationLayer + { + public: + bool hasWeights, hasBias; + float epsilon; + + static Ptr create(const LayerParams ¶ms); + }; + + class CV_EXPORTS MaxUnpoolLayer : public Layer + { + public: + Size poolKernel; + Size poolPad; + Size poolStride; + + static Ptr create(const LayerParams ¶ms); + }; + + class CV_EXPORTS ScaleLayer : public Layer + { + public: + bool hasBias; + int axis; + + static Ptr create(const LayerParams& params); + }; + + class CV_EXPORTS ShiftLayer : public Layer + { + public: + static Ptr create(const LayerParams& params); + }; + + class CV_EXPORTS PriorBoxLayer : public Layer + { + public: + static Ptr create(const LayerParams& params); + }; + + class CV_EXPORTS ReorgLayer : public Layer + { + public: + static Ptr create(const LayerParams& params); + }; + + class CV_EXPORTS RegionLayer : public Layer + { + public: + static Ptr create(const LayerParams& params); + }; + + class CV_EXPORTS DetectionOutputLayer : public Layer + { + public: + static Ptr create(const LayerParams& params); + }; + + /** + * @brief \f$ L_p \f$ - normalization layer. + * @param p Normalization factor. The most common `p = 1` for \f$ L_1 \f$ - + * normalization or `p = 2` for \f$ L_2 \f$ - normalization or a custom one. + * @param eps Parameter \f$ \epsilon \f$ to prevent a division by zero. + * @param across_spatial If true, normalize an input across all non-batch dimensions. + * Otherwise normalize an every channel separately. + * + * Across spatial: + * @f[ + * norm = \sqrt[p]{\epsilon + \sum_{x, y, c} |src(x, y, c)|^p } \\ + * dst(x, y, c) = \frac{ src(x, y, c) }{norm} + * @f] + * + * Channel wise normalization: + * @f[ + * norm(c) = \sqrt[p]{\epsilon + \sum_{x, y} |src(x, y, c)|^p } \\ + * dst(x, y, c) = \frac{ src(x, y, c) }{norm(c)} + * @f] + * + * Where `x, y` - spatial coordinates, `c` - channel. + * + * An every sample in the batch is normalized separately. Optionally, + * output is scaled by the trained parameters. + */ + class CV_EXPORTS NormalizeBBoxLayer : public Layer + { + public: + float pnorm, epsilon; + CV_DEPRECATED_EXTERNAL bool acrossSpatial; + + static Ptr create(const LayerParams& params); + }; + + /** + * @brief Resize input 4-dimensional blob by nearest neighbor or bilinear strategy. + * + * Layer is used to support TensorFlow's resize_nearest_neighbor and resize_bilinear ops. + */ + class CV_EXPORTS ResizeLayer : public Layer + { + public: + static Ptr create(const LayerParams& params); + }; + + /** + * @brief Bilinear resize layer from https://github.com/cdmh/deeplab-public + * + * It differs from @ref ResizeLayer in output shape and resize scales computations. + */ + class CV_EXPORTS InterpLayer : public Layer + { + public: + static Ptr create(const LayerParams& params); + }; + + class CV_EXPORTS ProposalLayer : public Layer + { + public: + static Ptr create(const LayerParams& params); + }; + + class CV_EXPORTS CropAndResizeLayer : public Layer + { + public: + static Ptr create(const LayerParams& params); + }; + +//! @} +//! @} +CV__DNN_EXPERIMENTAL_NS_END +} +} +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/dnn/dict.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/dnn/dict.hpp new file mode 100644 index 0000000000000000000000000000000000000000..60c2aa5ddb6be1e3d55f937702998f8b27851185 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/dnn/dict.hpp @@ -0,0 +1,160 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#include +#include +#include + +#include + +#ifndef OPENCV_DNN_DNN_DICT_HPP +#define OPENCV_DNN_DNN_DICT_HPP + +namespace cv { +namespace dnn { +CV__DNN_EXPERIMENTAL_NS_BEGIN +//! @addtogroup dnn +//! @{ + +/** @brief This struct stores the scalar value (or array) of one of the following type: double, cv::String or int64. + * @todo Maybe int64 is useless because double type exactly stores at least 2^52 integers. + */ +struct CV_EXPORTS_W DictValue +{ + DictValue(const DictValue &r); + DictValue(bool i) : type(Param::INT), pi(new AutoBuffer) { (*pi)[0] = i ? 1 : 0; } //!< Constructs integer scalar + DictValue(int64 i = 0) : type(Param::INT), pi(new AutoBuffer) { (*pi)[0] = i; } //!< Constructs integer scalar + CV_WRAP DictValue(int i) : type(Param::INT), pi(new AutoBuffer) { (*pi)[0] = i; } //!< Constructs integer scalar + DictValue(unsigned p) : type(Param::INT), pi(new AutoBuffer) { (*pi)[0] = p; } //!< Constructs integer scalar + CV_WRAP DictValue(double p) : type(Param::REAL), pd(new AutoBuffer) { (*pd)[0] = p; } //!< Constructs floating point scalar + CV_WRAP DictValue(const String &s) : type(Param::STRING), ps(new AutoBuffer) { (*ps)[0] = s; } //!< Constructs string scalar + DictValue(const char *s) : type(Param::STRING), ps(new AutoBuffer) { (*ps)[0] = s; } //!< @overload + + template + static DictValue arrayInt(TypeIter begin, int size); //!< Constructs integer array + template + static DictValue arrayReal(TypeIter begin, int size); //!< Constructs floating point array + template + static DictValue arrayString(TypeIter begin, int size); //!< Constructs array of strings + + template + T get(int idx = -1) const; //!< Tries to convert array element with specified index to requested type and returns its. + + int size() const; + + CV_WRAP bool isInt() const; + CV_WRAP bool isString() const; + CV_WRAP bool isReal() const; + + CV_WRAP int getIntValue(int idx = -1) const; + CV_WRAP double getRealValue(int idx = -1) const; + CV_WRAP String getStringValue(int idx = -1) const; + + DictValue &operator=(const DictValue &r); + + friend std::ostream &operator<<(std::ostream &stream, const DictValue &dictv); + + ~DictValue(); + +private: + + int type; + + union + { + AutoBuffer *pi; + AutoBuffer *pd; + AutoBuffer *ps; + void *pv; + }; + + DictValue(int _type, void *_p) : type(_type), pv(_p) {} + void release(); +}; + +/** @brief This class implements name-value dictionary, values are instances of DictValue. */ +class CV_EXPORTS Dict +{ + typedef std::map _Dict; + _Dict dict; + +public: + + //! Checks a presence of the @p key in the dictionary. + bool has(const String &key) const; + + //! If the @p key in the dictionary then returns pointer to its value, else returns NULL. + DictValue *ptr(const String &key); + + /** @overload */ + const DictValue *ptr(const String &key) const; + + //! If the @p key in the dictionary then returns its value, else an error will be generated. + const DictValue &get(const String &key) const; + + /** @overload */ + template + T get(const String &key) const; + + //! If the @p key in the dictionary then returns its value, else returns @p defaultValue. + template + T get(const String &key, const T &defaultValue) const; + + //! Sets new @p value for the @p key, or adds new key-value pair into the dictionary. + template + const T &set(const String &key, const T &value); + + //! Erase @p key from the dictionary. + void erase(const String &key); + + friend std::ostream &operator<<(std::ostream &stream, const Dict &dict); + + std::map::const_iterator begin() const; + + std::map::const_iterator end() const; +}; + +//! @} +CV__DNN_EXPERIMENTAL_NS_END +} +} + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/dnn/dnn.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/dnn/dnn.hpp new file mode 100644 index 0000000000000000000000000000000000000000..c0e84b82fa25f10d20c399b3f77352615e90dfa5 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/dnn/dnn.hpp @@ -0,0 +1,977 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_DNN_DNN_HPP +#define OPENCV_DNN_DNN_HPP + +#include +#include + +#if !defined CV_DOXYGEN && !defined CV_DNN_DONT_ADD_EXPERIMENTAL_NS +#define CV__DNN_EXPERIMENTAL_NS_BEGIN namespace experimental_dnn_34_v11 { +#define CV__DNN_EXPERIMENTAL_NS_END } +namespace cv { namespace dnn { namespace experimental_dnn_34_v11 { } using namespace experimental_dnn_34_v11; }} +#else +#define CV__DNN_EXPERIMENTAL_NS_BEGIN +#define CV__DNN_EXPERIMENTAL_NS_END +#endif + +#include + +namespace cv { +namespace dnn { +CV__DNN_EXPERIMENTAL_NS_BEGIN +//! @addtogroup dnn +//! @{ + + typedef std::vector MatShape; + + /** + * @brief Enum of computation backends supported by layers. + * @see Net::setPreferableBackend + */ + enum Backend + { + //! DNN_BACKEND_DEFAULT equals to DNN_BACKEND_INFERENCE_ENGINE if + //! OpenCV is built with Intel's Inference Engine library or + //! DNN_BACKEND_OPENCV otherwise. + DNN_BACKEND_DEFAULT, + DNN_BACKEND_HALIDE, + DNN_BACKEND_INFERENCE_ENGINE, + DNN_BACKEND_OPENCV + }; + + /** + * @brief Enum of target devices for computations. + * @see Net::setPreferableTarget + */ + enum Target + { + DNN_TARGET_CPU, + DNN_TARGET_OPENCL, + DNN_TARGET_OPENCL_FP16, + DNN_TARGET_MYRIAD, + //! FPGA device with CPU fallbacks using Inference Engine's Heterogeneous plugin. + DNN_TARGET_FPGA + }; + + CV_EXPORTS std::vector< std::pair > getAvailableBackends(); + CV_EXPORTS std::vector getAvailableTargets(Backend be); + + /** @brief This class provides all data needed to initialize layer. + * + * It includes dictionary with scalar params (which can be read by using Dict interface), + * blob params #blobs and optional meta information: #name and #type of layer instance. + */ + class CV_EXPORTS LayerParams : public Dict + { + public: + //TODO: Add ability to name blob params + std::vector blobs; //!< List of learned parameters stored as blobs. + + String name; //!< Name of the layer instance (optional, can be used internal purposes). + String type; //!< Type name which was used for creating layer by layer factory (optional). + }; + + /** + * @brief Derivatives of this class encapsulates functions of certain backends. + */ + class BackendNode + { + public: + BackendNode(int backendId); + + virtual ~BackendNode(); //!< Virtual destructor to make polymorphism. + + int backendId; //!< Backend identifier. + }; + + /** + * @brief Derivatives of this class wraps cv::Mat for different backends and targets. + */ + class BackendWrapper + { + public: + BackendWrapper(int backendId, int targetId); + + /** + * @brief Wrap cv::Mat for specific backend and target. + * @param[in] targetId Target identifier. + * @param[in] m cv::Mat for wrapping. + * + * Make CPU->GPU data transfer if it's require for the target. + */ + BackendWrapper(int targetId, const cv::Mat& m); + + /** + * @brief Make wrapper for reused cv::Mat. + * @param[in] base Wrapper of cv::Mat that will be reused. + * @param[in] shape Specific shape. + * + * Initialize wrapper from another one. It'll wrap the same host CPU + * memory and mustn't allocate memory on device(i.e. GPU). It might + * has different shape. Use in case of CPU memory reusing for reuse + * associated memory on device too. + */ + BackendWrapper(const Ptr& base, const MatShape& shape); + + virtual ~BackendWrapper(); //!< Virtual destructor to make polymorphism. + + /** + * @brief Transfer data to CPU host memory. + */ + virtual void copyToHost() = 0; + + /** + * @brief Indicate that an actual data is on CPU. + */ + virtual void setHostDirty() = 0; + + int backendId; //!< Backend identifier. + int targetId; //!< Target identifier. + }; + + class CV_EXPORTS ActivationLayer; + + /** @brief This interface class allows to build new Layers - are building blocks of networks. + * + * Each class, derived from Layer, must implement allocate() methods to declare own outputs and forward() to compute outputs. + * Also before using the new layer into networks you must register your layer by using one of @ref dnnLayerFactory "LayerFactory" macros. + */ + class CV_EXPORTS_W Layer : public Algorithm + { + public: + + //! List of learned parameters must be stored here to allow read them by using Net::getParam(). + CV_PROP_RW std::vector blobs; + + /** @brief Computes and sets internal parameters according to inputs, outputs and blobs. + * @deprecated Use Layer::finalize(InputArrayOfArrays, OutputArrayOfArrays) instead + * @param[in] input vector of already allocated input blobs + * @param[out] output vector of already allocated output blobs + * + * If this method is called after network has allocated all memory for input and output blobs + * and before inferencing. + */ + CV_DEPRECATED_EXTERNAL + virtual void finalize(const std::vector &input, std::vector &output); + + /** @brief Computes and sets internal parameters according to inputs, outputs and blobs. + * @param[in] inputs vector of already allocated input blobs + * @param[out] outputs vector of already allocated output blobs + * + * If this method is called after network has allocated all memory for input and output blobs + * and before inferencing. + */ + CV_WRAP virtual void finalize(InputArrayOfArrays inputs, OutputArrayOfArrays outputs); + + /** @brief Given the @p input blobs, computes the output @p blobs. + * @deprecated Use Layer::forward(InputArrayOfArrays, OutputArrayOfArrays, OutputArrayOfArrays) instead + * @param[in] input the input blobs. + * @param[out] output allocated output blobs, which will store results of the computation. + * @param[out] internals allocated internal blobs + */ + CV_DEPRECATED_EXTERNAL + virtual void forward(std::vector &input, std::vector &output, std::vector &internals); + + /** @brief Given the @p input blobs, computes the output @p blobs. + * @param[in] inputs the input blobs. + * @param[out] outputs allocated output blobs, which will store results of the computation. + * @param[out] internals allocated internal blobs + */ + virtual void forward(InputArrayOfArrays inputs, OutputArrayOfArrays outputs, OutputArrayOfArrays internals); + + /** @brief Given the @p input blobs, computes the output @p blobs. + * @param[in] inputs the input blobs. + * @param[out] outputs allocated output blobs, which will store results of the computation. + * @param[out] internals allocated internal blobs + */ + void forward_fallback(InputArrayOfArrays inputs, OutputArrayOfArrays outputs, OutputArrayOfArrays internals); + + /** @brief + * @overload + * @deprecated Use Layer::finalize(InputArrayOfArrays, OutputArrayOfArrays) instead + */ + CV_DEPRECATED_EXTERNAL + void finalize(const std::vector &inputs, CV_OUT std::vector &outputs); + + /** @brief + * @overload + * @deprecated Use Layer::finalize(InputArrayOfArrays, OutputArrayOfArrays) instead + */ + CV_DEPRECATED std::vector finalize(const std::vector &inputs); + + /** @brief Allocates layer and computes output. + * @deprecated This method will be removed in the future release. + */ + CV_DEPRECATED CV_WRAP void run(const std::vector &inputs, CV_OUT std::vector &outputs, + CV_IN_OUT std::vector &internals); + + /** @brief Returns index of input blob into the input array. + * @param inputName label of input blob + * + * Each layer input and output can be labeled to easily identify them using "%[.output_name]" notation. + * This method maps label of input blob to its index into input vector. + */ + virtual int inputNameToIndex(String inputName); + /** @brief Returns index of output blob in output array. + * @see inputNameToIndex() + */ + CV_WRAP virtual int outputNameToIndex(const String& outputName); + + /** + * @brief Ask layer if it support specific backend for doing computations. + * @param[in] backendId computation backend identifier. + * @see Backend + */ + virtual bool supportBackend(int backendId); + + /** + * @brief Returns Halide backend node. + * @param[in] inputs Input Halide buffers. + * @see BackendNode, BackendWrapper + * + * Input buffers should be exactly the same that will be used in forward invocations. + * Despite we can use Halide::ImageParam based on input shape only, + * it helps prevent some memory management issues (if something wrong, + * Halide tests will be failed). + */ + virtual Ptr initHalide(const std::vector > &inputs); + + virtual Ptr initInfEngine(const std::vector > &inputs); + + /** + * @brief Automatic Halide scheduling based on layer hyper-parameters. + * @param[in] node Backend node with Halide functions. + * @param[in] inputs Blobs that will be used in forward invocations. + * @param[in] outputs Blobs that will be used in forward invocations. + * @param[in] targetId Target identifier + * @see BackendNode, Target + * + * Layer don't use own Halide::Func members because we can have applied + * layers fusing. In this way the fused function should be scheduled. + */ + virtual void applyHalideScheduler(Ptr& node, + const std::vector &inputs, + const std::vector &outputs, + int targetId) const; + + /** + * @brief Implement layers fusing. + * @param[in] node Backend node of bottom layer. + * @see BackendNode + * + * Actual for graph-based backends. If layer attached successfully, + * returns non-empty cv::Ptr to node of the same backend. + * Fuse only over the last function. + */ + virtual Ptr tryAttach(const Ptr& node); + + /** + * @brief Tries to attach to the layer the subsequent activation layer, i.e. do the layer fusion in a partial case. + * @param[in] layer The subsequent activation layer. + * + * Returns true if the activation layer has been attached successfully. + */ + virtual bool setActivation(const Ptr& layer); + + /** + * @brief Try to fuse current layer with a next one + * @param[in] top Next layer to be fused. + * @returns True if fusion was performed. + */ + virtual bool tryFuse(Ptr& top); + + /** + * @brief Returns parameters of layers with channel-wise multiplication and addition. + * @param[out] scale Channel-wise multipliers. Total number of values should + * be equal to number of channels. + * @param[out] shift Channel-wise offsets. Total number of values should + * be equal to number of channels. + * + * Some layers can fuse their transformations with further layers. + * In example, convolution + batch normalization. This way base layer + * use weights from layer after it. Fused layer is skipped. + * By default, @p scale and @p shift are empty that means layer has no + * element-wise multiplications or additions. + */ + virtual void getScaleShift(Mat& scale, Mat& shift) const; + + /** + * @brief "Deattaches" all the layers, attached to particular layer. + */ + virtual void unsetAttached(); + + virtual bool getMemoryShapes(const std::vector &inputs, + const int requiredOutputs, + std::vector &outputs, + std::vector &internals) const; + virtual int64 getFLOPS(const std::vector &inputs, + const std::vector &outputs) const {CV_UNUSED(inputs); CV_UNUSED(outputs); return 0;} + + CV_PROP String name; //!< Name of the layer instance, can be used for logging or other internal purposes. + CV_PROP String type; //!< Type name which was used for creating layer by layer factory. + CV_PROP int preferableTarget; //!< prefer target for layer forwarding + + Layer(); + explicit Layer(const LayerParams ¶ms); //!< Initializes only #name, #type and #blobs fields. + void setParamsFrom(const LayerParams ¶ms); //!< Initializes only #name, #type and #blobs fields. + virtual ~Layer(); + }; + + /** @brief This class allows to create and manipulate comprehensive artificial neural networks. + * + * Neural network is presented as directed acyclic graph (DAG), where vertices are Layer instances, + * and edges specify relationships between layers inputs and outputs. + * + * Each network layer has unique integer id and unique string name inside its network. + * LayerId can store either layer name or layer id. + * + * This class supports reference counting of its instances, i. e. copies point to the same instance. + */ + class CV_EXPORTS_W_SIMPLE Net + { + public: + + CV_WRAP Net(); //!< Default constructor. + CV_WRAP ~Net(); //!< Destructor frees the net only if there aren't references to the net anymore. + + /** @brief Create a network from Intel's Model Optimizer intermediate representation. + * @param[in] xml XML configuration file with network's topology. + * @param[in] bin Binary file with trained weights. + * Networks imported from Intel's Model Optimizer are launched in Intel's Inference Engine + * backend. + */ + CV_WRAP static Net readFromModelOptimizer(const String& xml, const String& bin); + + /** Returns true if there are no layers in the network. */ + CV_WRAP bool empty() const; + + /** @brief Adds new layer to the net. + * @param name unique name of the adding layer. + * @param type typename of the adding layer (type must be registered in LayerRegister). + * @param params parameters which will be used to initialize the creating layer. + * @returns unique identifier of created layer, or -1 if a failure will happen. + */ + int addLayer(const String &name, const String &type, LayerParams ¶ms); + /** @brief Adds new layer and connects its first input to the first output of previously added layer. + * @see addLayer() + */ + int addLayerToPrev(const String &name, const String &type, LayerParams ¶ms); + + /** @brief Converts string name of the layer to the integer identifier. + * @returns id of the layer, or -1 if the layer wasn't found. + */ + CV_WRAP int getLayerId(const String &layer); + + CV_WRAP std::vector getLayerNames() const; + + /** @brief Container for strings and integers. */ + typedef DictValue LayerId; + + /** @brief Returns pointer to layer with specified id or name which the network use. */ + CV_WRAP Ptr getLayer(LayerId layerId); + + /** @brief Returns pointers to input layers of specific layer. */ + std::vector > getLayerInputs(LayerId layerId); // FIXIT: CV_WRAP + + /** @brief Connects output of the first layer to input of the second layer. + * @param outPin descriptor of the first layer output. + * @param inpPin descriptor of the second layer input. + * + * Descriptors have the following template <layer_name>[.input_number]: + * - the first part of the template layer_name is sting name of the added layer. + * If this part is empty then the network input pseudo layer will be used; + * - the second optional part of the template input_number + * is either number of the layer input, either label one. + * If this part is omitted then the first layer input will be used. + * + * @see setNetInputs(), Layer::inputNameToIndex(), Layer::outputNameToIndex() + */ + CV_WRAP void connect(String outPin, String inpPin); + + /** @brief Connects #@p outNum output of the first layer to #@p inNum input of the second layer. + * @param outLayerId identifier of the first layer + * @param outNum number of the first layer output + * @param inpLayerId identifier of the second layer + * @param inpNum number of the second layer input + */ + void connect(int outLayerId, int outNum, int inpLayerId, int inpNum); + + /** @brief Sets outputs names of the network input pseudo layer. + * + * Each net always has special own the network input pseudo layer with id=0. + * This layer stores the user blobs only and don't make any computations. + * In fact, this layer provides the only way to pass user data into the network. + * As any other layer, this layer can label its outputs and this function provides an easy way to do this. + */ + CV_WRAP void setInputsNames(const std::vector &inputBlobNames); + + /** @brief Runs forward pass to compute output of layer with name @p outputName. + * @param outputName name for layer which output is needed to get + * @return blob for first output of specified layer. + * @details By default runs forward pass for the whole network. + */ + CV_WRAP Mat forward(const String& outputName = String()); + + /** @brief Runs forward pass to compute output of layer with name @p outputName. + * @param outputBlobs contains all output blobs for specified layer. + * @param outputName name for layer which output is needed to get + * @details If @p outputName is empty, runs forward pass for the whole network. + */ + CV_WRAP void forward(OutputArrayOfArrays outputBlobs, const String& outputName = String()); + + /** @brief Runs forward pass to compute outputs of layers listed in @p outBlobNames. + * @param outputBlobs contains blobs for first outputs of specified layers. + * @param outBlobNames names for layers which outputs are needed to get + */ + CV_WRAP void forward(OutputArrayOfArrays outputBlobs, + const std::vector& outBlobNames); + + /** @brief Runs forward pass to compute outputs of layers listed in @p outBlobNames. + * @param outputBlobs contains all output blobs for each layer specified in @p outBlobNames. + * @param outBlobNames names for layers which outputs are needed to get + */ + CV_WRAP_AS(forwardAndRetrieve) void forward(CV_OUT std::vector >& outputBlobs, + const std::vector& outBlobNames); + + /** + * @brief Compile Halide layers. + * @param[in] scheduler Path to YAML file with scheduling directives. + * @see setPreferableBackend + * + * Schedule layers that support Halide backend. Then compile them for + * specific target. For layers that not represented in scheduling file + * or if no manual scheduling used at all, automatic scheduling will be applied. + */ + CV_WRAP void setHalideScheduler(const String& scheduler); + + /** + * @brief Ask network to use specific computation backend where it supported. + * @param[in] backendId backend identifier. + * @see Backend + * + * If OpenCV is compiled with Intel's Inference Engine library, DNN_BACKEND_DEFAULT + * means DNN_BACKEND_INFERENCE_ENGINE. Otherwise it equals to DNN_BACKEND_OPENCV. + */ + CV_WRAP void setPreferableBackend(int backendId); + + /** + * @brief Ask network to make computations on specific target device. + * @param[in] targetId target identifier. + * @see Target + * + * List of supported combinations backend / target: + * | | DNN_BACKEND_OPENCV | DNN_BACKEND_INFERENCE_ENGINE | DNN_BACKEND_HALIDE | + * |------------------------|--------------------|------------------------------|--------------------| + * | DNN_TARGET_CPU | + | + | + | + * | DNN_TARGET_OPENCL | + | + | + | + * | DNN_TARGET_OPENCL_FP16 | + | + | | + * | DNN_TARGET_MYRIAD | | + | | + * | DNN_TARGET_FPGA | | + | | + */ + CV_WRAP void setPreferableTarget(int targetId); + + /** @brief Sets the new input value for the network + * @param blob A new blob. Should have CV_32F or CV_8U depth. + * @param name A name of input layer. + * @param scalefactor An optional normalization scale. + * @param mean An optional mean subtraction values. + * @see connect(String, String) to know format of the descriptor. + * + * If scale or mean values are specified, a final input blob is computed + * as: + * \f[input(n,c,h,w) = scalefactor \times (blob(n,c,h,w) - mean_c)\f] + */ + CV_WRAP void setInput(InputArray blob, const String& name = "", + double scalefactor = 1.0, const Scalar& mean = Scalar()); + + /** @brief Sets the new value for the learned param of the layer. + * @param layer name or id of the layer. + * @param numParam index of the layer parameter in the Layer::blobs array. + * @param blob the new value. + * @see Layer::blobs + * @note If shape of the new blob differs from the previous shape, + * then the following forward pass may fail. + */ + CV_WRAP void setParam(LayerId layer, int numParam, const Mat &blob); + + /** @brief Returns parameter blob of the layer. + * @param layer name or id of the layer. + * @param numParam index of the layer parameter in the Layer::blobs array. + * @see Layer::blobs + */ + CV_WRAP Mat getParam(LayerId layer, int numParam = 0); + + /** @brief Returns indexes of layers with unconnected outputs. + */ + CV_WRAP std::vector getUnconnectedOutLayers() const; + + /** @brief Returns names of layers with unconnected outputs. + */ + CV_WRAP std::vector getUnconnectedOutLayersNames() const; + + /** @brief Returns input and output shapes for all layers in loaded model; + * preliminary inferencing isn't necessary. + * @param netInputShapes shapes for all input blobs in net input layer. + * @param layersIds output parameter for layer IDs. + * @param inLayersShapes output parameter for input layers shapes; + * order is the same as in layersIds + * @param outLayersShapes output parameter for output layers shapes; + * order is the same as in layersIds + */ + CV_WRAP void getLayersShapes(const std::vector& netInputShapes, + CV_OUT std::vector& layersIds, + CV_OUT std::vector >& inLayersShapes, + CV_OUT std::vector >& outLayersShapes) const; + + /** @overload */ + CV_WRAP void getLayersShapes(const MatShape& netInputShape, + CV_OUT std::vector& layersIds, + CV_OUT std::vector >& inLayersShapes, + CV_OUT std::vector >& outLayersShapes) const; + + /** @brief Returns input and output shapes for layer with specified + * id in loaded model; preliminary inferencing isn't necessary. + * @param netInputShape shape input blob in net input layer. + * @param layerId id for layer. + * @param inLayerShapes output parameter for input layers shapes; + * order is the same as in layersIds + * @param outLayerShapes output parameter for output layers shapes; + * order is the same as in layersIds + */ + void getLayerShapes(const MatShape& netInputShape, + const int layerId, + CV_OUT std::vector& inLayerShapes, + CV_OUT std::vector& outLayerShapes) const; // FIXIT: CV_WRAP + + /** @overload */ + void getLayerShapes(const std::vector& netInputShapes, + const int layerId, + CV_OUT std::vector& inLayerShapes, + CV_OUT std::vector& outLayerShapes) const; // FIXIT: CV_WRAP + + /** @brief Computes FLOP for whole loaded model with specified input shapes. + * @param netInputShapes vector of shapes for all net inputs. + * @returns computed FLOP. + */ + CV_WRAP int64 getFLOPS(const std::vector& netInputShapes) const; + /** @overload */ + CV_WRAP int64 getFLOPS(const MatShape& netInputShape) const; + /** @overload */ + CV_WRAP int64 getFLOPS(const int layerId, + const std::vector& netInputShapes) const; + /** @overload */ + CV_WRAP int64 getFLOPS(const int layerId, + const MatShape& netInputShape) const; + + /** @brief Returns list of types for layer used in model. + * @param layersTypes output parameter for returning types. + */ + CV_WRAP void getLayerTypes(CV_OUT std::vector& layersTypes) const; + + /** @brief Returns count of layers of specified type. + * @param layerType type. + * @returns count of layers + */ + CV_WRAP int getLayersCount(const String& layerType) const; + + /** @brief Computes bytes number which are required to store + * all weights and intermediate blobs for model. + * @param netInputShapes vector of shapes for all net inputs. + * @param weights output parameter to store resulting bytes for weights. + * @param blobs output parameter to store resulting bytes for intermediate blobs. + */ + void getMemoryConsumption(const std::vector& netInputShapes, + CV_OUT size_t& weights, CV_OUT size_t& blobs) const; // FIXIT: CV_WRAP + /** @overload */ + CV_WRAP void getMemoryConsumption(const MatShape& netInputShape, + CV_OUT size_t& weights, CV_OUT size_t& blobs) const; + /** @overload */ + CV_WRAP void getMemoryConsumption(const int layerId, + const std::vector& netInputShapes, + CV_OUT size_t& weights, CV_OUT size_t& blobs) const; + /** @overload */ + CV_WRAP void getMemoryConsumption(const int layerId, + const MatShape& netInputShape, + CV_OUT size_t& weights, CV_OUT size_t& blobs) const; + + /** @brief Computes bytes number which are required to store + * all weights and intermediate blobs for each layer. + * @param netInputShapes vector of shapes for all net inputs. + * @param layerIds output vector to save layer IDs. + * @param weights output parameter to store resulting bytes for weights. + * @param blobs output parameter to store resulting bytes for intermediate blobs. + */ + void getMemoryConsumption(const std::vector& netInputShapes, + CV_OUT std::vector& layerIds, + CV_OUT std::vector& weights, + CV_OUT std::vector& blobs) const; // FIXIT: CV_WRAP + /** @overload */ + void getMemoryConsumption(const MatShape& netInputShape, + CV_OUT std::vector& layerIds, + CV_OUT std::vector& weights, + CV_OUT std::vector& blobs) const; // FIXIT: CV_WRAP + + /** @brief Enables or disables layer fusion in the network. + * @param fusion true to enable the fusion, false to disable. The fusion is enabled by default. + */ + CV_WRAP void enableFusion(bool fusion); + + /** @brief Returns overall time for inference and timings (in ticks) for layers. + * Indexes in returned vector correspond to layers ids. Some layers can be fused with others, + * in this case zero ticks count will be return for that skipped layers. + * @param timings vector for tick timings for all layers. + * @return overall ticks for model inference. + */ + CV_WRAP int64 getPerfProfile(CV_OUT std::vector& timings); + + private: + struct Impl; + Ptr impl; + }; + + /** @brief Reads a network model stored in Darknet model files. + * @param cfgFile path to the .cfg file with text description of the network architecture. + * @param darknetModel path to the .weights file with learned network. + * @returns Network object that ready to do forward, throw an exception in failure cases. + * @returns Net object. + */ + CV_EXPORTS_W Net readNetFromDarknet(const String &cfgFile, const String &darknetModel = String()); + + /** @brief Reads a network model stored in Darknet model files. + * @param bufferCfg A buffer contains a content of .cfg file with text description of the network architecture. + * @param bufferModel A buffer contains a content of .weights file with learned network. + * @returns Net object. + */ + CV_EXPORTS_W Net readNetFromDarknet(const std::vector& bufferCfg, + const std::vector& bufferModel = std::vector()); + + /** @brief Reads a network model stored in Darknet model files. + * @param bufferCfg A buffer contains a content of .cfg file with text description of the network architecture. + * @param lenCfg Number of bytes to read from bufferCfg + * @param bufferModel A buffer contains a content of .weights file with learned network. + * @param lenModel Number of bytes to read from bufferModel + * @returns Net object. + */ + CV_EXPORTS Net readNetFromDarknet(const char *bufferCfg, size_t lenCfg, + const char *bufferModel = NULL, size_t lenModel = 0); + + /** @brief Reads a network model stored in Caffe framework's format. + * @param prototxt path to the .prototxt file with text description of the network architecture. + * @param caffeModel path to the .caffemodel file with learned network. + * @returns Net object. + */ + CV_EXPORTS_W Net readNetFromCaffe(const String &prototxt, const String &caffeModel = String()); + + /** @brief Reads a network model stored in Caffe model in memory. + * @param bufferProto buffer containing the content of the .prototxt file + * @param bufferModel buffer containing the content of the .caffemodel file + * @returns Net object. + */ + CV_EXPORTS_W Net readNetFromCaffe(const std::vector& bufferProto, + const std::vector& bufferModel = std::vector()); + + /** @brief Reads a network model stored in Caffe model in memory. + * @details This is an overloaded member function, provided for convenience. + * It differs from the above function only in what argument(s) it accepts. + * @param bufferProto buffer containing the content of the .prototxt file + * @param lenProto length of bufferProto + * @param bufferModel buffer containing the content of the .caffemodel file + * @param lenModel length of bufferModel + * @returns Net object. + */ + CV_EXPORTS Net readNetFromCaffe(const char *bufferProto, size_t lenProto, + const char *bufferModel = NULL, size_t lenModel = 0); + + /** @brief Reads a network model stored in TensorFlow framework's format. + * @param model path to the .pb file with binary protobuf description of the network architecture + * @param config path to the .pbtxt file that contains text graph definition in protobuf format. + * Resulting Net object is built by text graph using weights from a binary one that + * let us make it more flexible. + * @returns Net object. + */ + CV_EXPORTS_W Net readNetFromTensorflow(const String &model, const String &config = String()); + + /** @brief Reads a network model stored in TensorFlow framework's format. + * @param bufferModel buffer containing the content of the pb file + * @param bufferConfig buffer containing the content of the pbtxt file + * @returns Net object. + */ + CV_EXPORTS_W Net readNetFromTensorflow(const std::vector& bufferModel, + const std::vector& bufferConfig = std::vector()); + + /** @brief Reads a network model stored in TensorFlow framework's format. + * @details This is an overloaded member function, provided for convenience. + * It differs from the above function only in what argument(s) it accepts. + * @param bufferModel buffer containing the content of the pb file + * @param lenModel length of bufferModel + * @param bufferConfig buffer containing the content of the pbtxt file + * @param lenConfig length of bufferConfig + */ + CV_EXPORTS Net readNetFromTensorflow(const char *bufferModel, size_t lenModel, + const char *bufferConfig = NULL, size_t lenConfig = 0); + + /** + * @brief Reads a network model stored in Torch7 framework's format. + * @param model path to the file, dumped from Torch by using torch.save() function. + * @param isBinary specifies whether the network was serialized in ascii mode or binary. + * @param evaluate specifies testing phase of network. If true, it's similar to evaluate() method in Torch. + * @returns Net object. + * + * @note Ascii mode of Torch serializer is more preferable, because binary mode extensively use `long` type of C language, + * which has various bit-length on different systems. + * + * The loading file must contain serialized nn.Module object + * with importing network. Try to eliminate a custom objects from serialazing data to avoid importing errors. + * + * List of supported layers (i.e. object instances derived from Torch nn.Module class): + * - nn.Sequential + * - nn.Parallel + * - nn.Concat + * - nn.Linear + * - nn.SpatialConvolution + * - nn.SpatialMaxPooling, nn.SpatialAveragePooling + * - nn.ReLU, nn.TanH, nn.Sigmoid + * - nn.Reshape + * - nn.SoftMax, nn.LogSoftMax + * + * Also some equivalents of these classes from cunn, cudnn, and fbcunn may be successfully imported. + */ + CV_EXPORTS_W Net readNetFromTorch(const String &model, bool isBinary = true, bool evaluate = true); + + /** + * @brief Read deep learning network represented in one of the supported formats. + * @param[in] model Binary file contains trained weights. The following file + * extensions are expected for models from different frameworks: + * * `*.caffemodel` (Caffe, http://caffe.berkeleyvision.org/) + * * `*.pb` (TensorFlow, https://www.tensorflow.org/) + * * `*.t7` | `*.net` (Torch, http://torch.ch/) + * * `*.weights` (Darknet, https://pjreddie.com/darknet/) + * * `*.bin` (DLDT, https://software.intel.com/openvino-toolkit) + * @param[in] config Text file contains network configuration. It could be a + * file with the following extensions: + * * `*.prototxt` (Caffe, http://caffe.berkeleyvision.org/) + * * `*.pbtxt` (TensorFlow, https://www.tensorflow.org/) + * * `*.cfg` (Darknet, https://pjreddie.com/darknet/) + * * `*.xml` (DLDT, https://software.intel.com/openvino-toolkit) + * @param[in] framework Explicit framework name tag to determine a format. + * @returns Net object. + * + * This function automatically detects an origin framework of trained model + * and calls an appropriate function such @ref readNetFromCaffe, @ref readNetFromTensorflow, + * @ref readNetFromTorch or @ref readNetFromDarknet. An order of @p model and @p config + * arguments does not matter. + */ + CV_EXPORTS_W Net readNet(const String& model, const String& config = "", const String& framework = ""); + + /** + * @brief Read deep learning network represented in one of the supported formats. + * @details This is an overloaded member function, provided for convenience. + * It differs from the above function only in what argument(s) it accepts. + * @param[in] framework Name of origin framework. + * @param[in] bufferModel A buffer with a content of binary file with weights + * @param[in] bufferConfig A buffer with a content of text file contains network configuration. + * @returns Net object. + */ + CV_EXPORTS_W Net readNet(const String& framework, const std::vector& bufferModel, + const std::vector& bufferConfig = std::vector()); + + /** @brief Loads blob which was serialized as torch.Tensor object of Torch7 framework. + * @warning This function has the same limitations as readNetFromTorch(). + */ + CV_EXPORTS_W Mat readTorchBlob(const String &filename, bool isBinary = true); + + /** @brief Load a network from Intel's Model Optimizer intermediate representation. + * @param[in] xml XML configuration file with network's topology. + * @param[in] bin Binary file with trained weights. + * @returns Net object. + * Networks imported from Intel's Model Optimizer are launched in Intel's Inference Engine + * backend. + */ + CV_EXPORTS_W Net readNetFromModelOptimizer(const String &xml, const String &bin); + + /** @brief Reads a network model ONNX. + * @param onnxFile path to the .onnx file with text description of the network architecture. + * @returns Network object that ready to do forward, throw an exception in failure cases. + */ + CV_EXPORTS_W Net readNetFromONNX(const String &onnxFile); + + /** @brief Creates blob from .pb file. + * @param path to the .pb file with input tensor. + * @returns Mat. + */ + CV_EXPORTS_W Mat readTensorFromONNX(const String& path); + + /** @brief Creates 4-dimensional blob from image. Optionally resizes and crops @p image from center, + * subtract @p mean values, scales values by @p scalefactor, swap Blue and Red channels. + * @param image input image (with 1-, 3- or 4-channels). + * @param size spatial size for output image + * @param mean scalar with mean values which are subtracted from channels. Values are intended + * to be in (mean-R, mean-G, mean-B) order if @p image has BGR ordering and @p swapRB is true. + * @param scalefactor multiplier for @p image values. + * @param swapRB flag which indicates that swap first and last channels + * in 3-channel image is necessary. + * @param crop flag which indicates whether image will be cropped after resize or not + * @param ddepth Depth of output blob. Choose CV_32F or CV_8U. + * @details if @p crop is true, input image is resized so one side after resize is equal to corresponding + * dimension in @p size and another one is equal or larger. Then, crop from the center is performed. + * If @p crop is false, direct resize without cropping and preserving aspect ratio is performed. + * @returns 4-dimensional Mat with NCHW dimensions order. + */ + CV_EXPORTS_W Mat blobFromImage(InputArray image, double scalefactor=1.0, const Size& size = Size(), + const Scalar& mean = Scalar(), bool swapRB=false, bool crop=false, + int ddepth=CV_32F); + + /** @brief Creates 4-dimensional blob from image. + * @details This is an overloaded member function, provided for convenience. + * It differs from the above function only in what argument(s) it accepts. + */ + CV_EXPORTS void blobFromImage(InputArray image, OutputArray blob, double scalefactor=1.0, + const Size& size = Size(), const Scalar& mean = Scalar(), + bool swapRB=false, bool crop=false, int ddepth=CV_32F); + + + /** @brief Creates 4-dimensional blob from series of images. Optionally resizes and + * crops @p images from center, subtract @p mean values, scales values by @p scalefactor, + * swap Blue and Red channels. + * @param images input images (all with 1-, 3- or 4-channels). + * @param size spatial size for output image + * @param mean scalar with mean values which are subtracted from channels. Values are intended + * to be in (mean-R, mean-G, mean-B) order if @p image has BGR ordering and @p swapRB is true. + * @param scalefactor multiplier for @p images values. + * @param swapRB flag which indicates that swap first and last channels + * in 3-channel image is necessary. + * @param crop flag which indicates whether image will be cropped after resize or not + * @param ddepth Depth of output blob. Choose CV_32F or CV_8U. + * @details if @p crop is true, input image is resized so one side after resize is equal to corresponding + * dimension in @p size and another one is equal or larger. Then, crop from the center is performed. + * If @p crop is false, direct resize without cropping and preserving aspect ratio is performed. + * @returns 4-dimensional Mat with NCHW dimensions order. + */ + CV_EXPORTS_W Mat blobFromImages(InputArrayOfArrays images, double scalefactor=1.0, + Size size = Size(), const Scalar& mean = Scalar(), bool swapRB=false, bool crop=false, + int ddepth=CV_32F); + + /** @brief Creates 4-dimensional blob from series of images. + * @details This is an overloaded member function, provided for convenience. + * It differs from the above function only in what argument(s) it accepts. + */ + CV_EXPORTS void blobFromImages(InputArrayOfArrays images, OutputArray blob, + double scalefactor=1.0, Size size = Size(), + const Scalar& mean = Scalar(), bool swapRB=false, bool crop=false, + int ddepth=CV_32F); + + /** @brief Parse a 4D blob and output the images it contains as 2D arrays through a simpler data structure + * (std::vector). + * @param[in] blob_ 4 dimensional array (images, channels, height, width) in floating point precision (CV_32F) from + * which you would like to extract the images. + * @param[out] images_ array of 2D Mat containing the images extracted from the blob in floating point precision + * (CV_32F). They are non normalized neither mean added. The number of returned images equals the first dimension + * of the blob (batch size). Every image has a number of channels equals to the second dimension of the blob (depth). + */ + CV_EXPORTS_W void imagesFromBlob(const cv::Mat& blob_, OutputArrayOfArrays images_); + + /** @brief Convert all weights of Caffe network to half precision floating point. + * @param src Path to origin model from Caffe framework contains single + * precision floating point weights (usually has `.caffemodel` extension). + * @param dst Path to destination model with updated weights. + * @param layersTypes Set of layers types which parameters will be converted. + * By default, converts only Convolutional and Fully-Connected layers' + * weights. + * + * @note Shrinked model has no origin float32 weights so it can't be used + * in origin Caffe framework anymore. However the structure of data + * is taken from NVidia's Caffe fork: https://github.com/NVIDIA/caffe. + * So the resulting model may be used there. + */ + CV_EXPORTS_W void shrinkCaffeModel(const String& src, const String& dst, + const std::vector& layersTypes = std::vector()); + + /** @brief Create a text representation for a binary network stored in protocol buffer format. + * @param[in] model A path to binary network. + * @param[in] output A path to output text file to be created. + * + * @note To reduce output file size, trained weights are not included. + */ + CV_EXPORTS_W void writeTextGraph(const String& model, const String& output); + + /** @brief Performs non maximum suppression given boxes and corresponding scores. + + * @param bboxes a set of bounding boxes to apply NMS. + * @param scores a set of corresponding confidences. + * @param score_threshold a threshold used to filter boxes by score. + * @param nms_threshold a threshold used in non maximum suppression. + * @param indices the kept indices of bboxes after NMS. + * @param eta a coefficient in adaptive threshold formula: \f$nms\_threshold_{i+1}=eta\cdot nms\_threshold_i\f$. + * @param top_k if `>0`, keep at most @p top_k picked indices. + */ + CV_EXPORTS_W void NMSBoxes(const std::vector& bboxes, const std::vector& scores, + const float score_threshold, const float nms_threshold, + CV_OUT std::vector& indices, + const float eta = 1.f, const int top_k = 0); + + CV_EXPORTS_W void NMSBoxes(const std::vector& bboxes, const std::vector& scores, + const float score_threshold, const float nms_threshold, + CV_OUT std::vector& indices, + const float eta = 1.f, const int top_k = 0); + + CV_EXPORTS_AS(NMSBoxesRotated) void NMSBoxes(const std::vector& bboxes, const std::vector& scores, + const float score_threshold, const float nms_threshold, + CV_OUT std::vector& indices, + const float eta = 1.f, const int top_k = 0); + + /** @brief Release a Myriad device is binded by OpenCV. + * + * Single Myriad device cannot be shared across multiple processes which uses + * Inference Engine's Myriad plugin. + */ + CV_EXPORTS_W void resetMyriadDevice(); + +//! @} +CV__DNN_EXPERIMENTAL_NS_END +} +} + +#include +#include + +#endif /* OPENCV_DNN_DNN_HPP */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/dnn/dnn.inl.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/dnn/dnn.inl.hpp new file mode 100644 index 0000000000000000000000000000000000000000..17d4c200bde6b0be18762233044a1177ee83e630 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/dnn/dnn.inl.hpp @@ -0,0 +1,395 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_DNN_DNN_INL_HPP +#define OPENCV_DNN_DNN_INL_HPP + +#include + +namespace cv { +namespace dnn { +CV__DNN_EXPERIMENTAL_NS_BEGIN + +template +DictValue DictValue::arrayInt(TypeIter begin, int size) +{ + DictValue res(Param::INT, new AutoBuffer(size)); + for (int j = 0; j < size; begin++, j++) + (*res.pi)[j] = *begin; + return res; +} + +template +DictValue DictValue::arrayReal(TypeIter begin, int size) +{ + DictValue res(Param::REAL, new AutoBuffer(size)); + for (int j = 0; j < size; begin++, j++) + (*res.pd)[j] = *begin; + return res; +} + +template +DictValue DictValue::arrayString(TypeIter begin, int size) +{ + DictValue res(Param::STRING, new AutoBuffer(size)); + for (int j = 0; j < size; begin++, j++) + (*res.ps)[j] = *begin; + return res; +} + +template<> +inline DictValue DictValue::get(int idx) const +{ + CV_Assert(idx == -1); + return *this; +} + +template<> +inline int64 DictValue::get(int idx) const +{ + CV_Assert((idx == -1 && size() == 1) || (idx >= 0 && idx < size())); + idx = (idx == -1) ? 0 : idx; + + if (type == Param::INT) + { + return (*pi)[idx]; + } + else if (type == Param::REAL) + { + double doubleValue = (*pd)[idx]; + + double fracpart, intpart; + fracpart = std::modf(doubleValue, &intpart); + CV_Assert(fracpart == 0.0); + + return (int64)doubleValue; + } + else if (type == Param::STRING) + { + return std::atoi((*ps)[idx].c_str()); + } + else + { + CV_Assert(isInt() || isReal() || isString()); + return 0; + } +} + +template<> +inline int DictValue::get(int idx) const +{ + return (int)get(idx); +} + +inline int DictValue::getIntValue(int idx) const +{ + return (int)get(idx); +} + +template<> +inline unsigned DictValue::get(int idx) const +{ + return (unsigned)get(idx); +} + +template<> +inline bool DictValue::get(int idx) const +{ + return (get(idx) != 0); +} + +template<> +inline double DictValue::get(int idx) const +{ + CV_Assert((idx == -1 && size() == 1) || (idx >= 0 && idx < size())); + idx = (idx == -1) ? 0 : idx; + + if (type == Param::REAL) + { + return (*pd)[idx]; + } + else if (type == Param::INT) + { + return (double)(*pi)[idx]; + } + else if (type == Param::STRING) + { + return std::atof((*ps)[idx].c_str()); + } + else + { + CV_Assert(isReal() || isInt() || isString()); + return 0; + } +} + +inline double DictValue::getRealValue(int idx) const +{ + return get(idx); +} + +template<> +inline float DictValue::get(int idx) const +{ + return (float)get(idx); +} + +template<> +inline String DictValue::get(int idx) const +{ + CV_Assert(isString()); + CV_Assert((idx == -1 && ps->size() == 1) || (idx >= 0 && idx < (int)ps->size())); + return (*ps)[(idx == -1) ? 0 : idx]; +} + + +inline String DictValue::getStringValue(int idx) const +{ + return get(idx); +} + +inline void DictValue::release() +{ + switch (type) + { + case Param::INT: + delete pi; + break; + case Param::STRING: + delete ps; + break; + case Param::REAL: + delete pd; + break; + } +} + +inline DictValue::~DictValue() +{ + release(); +} + +inline DictValue & DictValue::operator=(const DictValue &r) +{ + if (&r == this) + return *this; + + if (r.type == Param::INT) + { + AutoBuffer *tmp = new AutoBuffer(*r.pi); + release(); + pi = tmp; + } + else if (r.type == Param::STRING) + { + AutoBuffer *tmp = new AutoBuffer(*r.ps); + release(); + ps = tmp; + } + else if (r.type == Param::REAL) + { + AutoBuffer *tmp = new AutoBuffer(*r.pd); + release(); + pd = tmp; + } + + type = r.type; + + return *this; +} + +inline DictValue::DictValue(const DictValue &r) +{ + type = r.type; + + if (r.type == Param::INT) + pi = new AutoBuffer(*r.pi); + else if (r.type == Param::STRING) + ps = new AutoBuffer(*r.ps); + else if (r.type == Param::REAL) + pd = new AutoBuffer(*r.pd); +} + +inline bool DictValue::isString() const +{ + return (type == Param::STRING); +} + +inline bool DictValue::isInt() const +{ + return (type == Param::INT); +} + +inline bool DictValue::isReal() const +{ + return (type == Param::REAL || type == Param::INT); +} + +inline int DictValue::size() const +{ + switch (type) + { + case Param::INT: + return (int)pi->size(); + case Param::STRING: + return (int)ps->size(); + case Param::REAL: + return (int)pd->size(); + } +#ifdef __OPENCV_BUILD + CV_Error(Error::StsInternal, ""); +#else + CV_ErrorNoReturn(Error::StsInternal, ""); +#endif +} + +inline std::ostream &operator<<(std::ostream &stream, const DictValue &dictv) +{ + int i; + + if (dictv.isInt()) + { + for (i = 0; i < dictv.size() - 1; i++) + stream << dictv.get(i) << ", "; + stream << dictv.get(i); + } + else if (dictv.isReal()) + { + for (i = 0; i < dictv.size() - 1; i++) + stream << dictv.get(i) << ", "; + stream << dictv.get(i); + } + else if (dictv.isString()) + { + for (i = 0; i < dictv.size() - 1; i++) + stream << "\"" << dictv.get(i) << "\", "; + stream << dictv.get(i); + } + + return stream; +} + +///////////////////////////////////////////////////////////////// + +inline bool Dict::has(const String &key) const +{ + return dict.count(key) != 0; +} + +inline DictValue *Dict::ptr(const String &key) +{ + _Dict::iterator i = dict.find(key); + return (i == dict.end()) ? NULL : &i->second; +} + +inline const DictValue *Dict::ptr(const String &key) const +{ + _Dict::const_iterator i = dict.find(key); + return (i == dict.end()) ? NULL : &i->second; +} + +inline const DictValue &Dict::get(const String &key) const +{ + _Dict::const_iterator i = dict.find(key); + if (i == dict.end()) + CV_Error(Error::StsObjectNotFound, "Required argument \"" + key + "\" not found into dictionary"); + return i->second; +} + +template +inline T Dict::get(const String &key) const +{ + return this->get(key).get(); +} + +template +inline T Dict::get(const String &key, const T &defaultValue) const +{ + _Dict::const_iterator i = dict.find(key); + + if (i != dict.end()) + return i->second.get(); + else + return defaultValue; +} + +template +inline const T &Dict::set(const String &key, const T &value) +{ + _Dict::iterator i = dict.find(key); + + if (i != dict.end()) + i->second = DictValue(value); + else + dict.insert(std::make_pair(key, DictValue(value))); + + return value; +} + +inline void Dict::erase(const String &key) +{ + dict.erase(key); +} + +inline std::ostream &operator<<(std::ostream &stream, const Dict &dict) +{ + Dict::_Dict::const_iterator it; + for (it = dict.dict.begin(); it != dict.dict.end(); it++) + stream << it->first << " : " << it->second << "\n"; + + return stream; +} + +inline std::map::const_iterator Dict::begin() const +{ + return dict.begin(); +} + +inline std::map::const_iterator Dict::end() const +{ + return dict.end(); +} + +CV__DNN_EXPERIMENTAL_NS_END +} +} + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/dnn/layer.details.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/dnn/layer.details.hpp new file mode 100644 index 0000000000000000000000000000000000000000..619514e8274b4513e9bf5d79cc436567bd2de78f --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/dnn/layer.details.hpp @@ -0,0 +1,78 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. +// +#ifndef OPENCV_DNN_LAYER_DETAILS_HPP +#define OPENCV_DNN_LAYER_DETAILS_HPP + +#include + +namespace cv { +namespace dnn { +CV__DNN_EXPERIMENTAL_NS_BEGIN + +/** @brief Registers layer constructor in runtime. +* @param type string, containing type name of the layer. +* @param constructorFunc pointer to the function of type LayerRegister::Constructor, which creates the layer. +* @details This macros must be placed inside the function code. +*/ +#define CV_DNN_REGISTER_LAYER_FUNC(type, constructorFunc) \ + cv::dnn::LayerFactory::registerLayer(#type, constructorFunc); + +/** @brief Registers layer class in runtime. + * @param type string, containing type name of the layer. + * @param class C++ class, derived from Layer. + * @details This macros must be placed inside the function code. + */ +#define CV_DNN_REGISTER_LAYER_CLASS(type, class) \ + cv::dnn::LayerFactory::registerLayer(#type, cv::dnn::details::_layerDynamicRegisterer); + +/** @brief Registers layer constructor on module load time. +* @param type string, containing type name of the layer. +* @param constructorFunc pointer to the function of type LayerRegister::Constructor, which creates the layer. +* @details This macros must be placed outside the function code. +*/ +#define CV_DNN_REGISTER_LAYER_FUNC_STATIC(type, constructorFunc) \ +static cv::dnn::details::_LayerStaticRegisterer __LayerStaticRegisterer_##type(#type, constructorFunc); + +/** @brief Registers layer class on module load time. + * @param type string, containing type name of the layer. + * @param class C++ class, derived from Layer. + * @details This macros must be placed outside the function code. + */ +#define CV_DNN_REGISTER_LAYER_CLASS_STATIC(type, class) \ +Ptr __LayerStaticRegisterer_func_##type(LayerParams ¶ms) \ + { return Ptr(new class(params)); } \ +static cv::dnn::details::_LayerStaticRegisterer __LayerStaticRegisterer_##type(#type, __LayerStaticRegisterer_func_##type); + +namespace details { + +template +Ptr _layerDynamicRegisterer(LayerParams ¶ms) +{ + return Ptr(LayerClass::create(params)); +} + +//allows automatically register created layer on module load time +class _LayerStaticRegisterer +{ + String type; +public: + + _LayerStaticRegisterer(const String &layerType, LayerFactory::Constructor layerConstructor) + { + this->type = layerType; + LayerFactory::registerLayer(layerType, layerConstructor); + } + + ~_LayerStaticRegisterer() + { + LayerFactory::unregisterLayer(type); + } +}; + +} // namespace +CV__DNN_EXPERIMENTAL_NS_END +}} // namespace + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/dnn/layer.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/dnn/layer.hpp new file mode 100644 index 0000000000000000000000000000000000000000..c4712b8e7ad0f13aaeb3a4e83b98bfd739dd717c --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/dnn/layer.hpp @@ -0,0 +1,85 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_DNN_LAYER_HPP +#define OPENCV_DNN_LAYER_HPP +#include + +namespace cv { +namespace dnn { +CV__DNN_EXPERIMENTAL_NS_BEGIN +//! @addtogroup dnn +//! @{ +//! +//! @defgroup dnnLayerFactory Utilities for New Layers Registration +//! @{ + +/** @brief %Layer factory allows to create instances of registered layers. */ +class CV_EXPORTS LayerFactory +{ +public: + + //! Each Layer class must provide this function to the factory + typedef Ptr(*Constructor)(LayerParams ¶ms); + + //! Registers the layer class with typename @p type and specified @p constructor. Thread-safe. + static void registerLayer(const String &type, Constructor constructor); + + //! Unregisters registered layer with specified type name. Thread-safe. + static void unregisterLayer(const String &type); + + /** @brief Creates instance of registered layer. + * @param type type name of creating layer. + * @param params parameters which will be used for layer initialization. + * @note Thread-safe. + */ + static Ptr createLayerInstance(const String &type, LayerParams& params); + +private: + LayerFactory(); +}; + +//! @} +//! @} +CV__DNN_EXPERIMENTAL_NS_END +} +} +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/dnn/shape_utils.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/dnn/shape_utils.hpp new file mode 100644 index 0000000000000000000000000000000000000000..b0ed3afc5423f5de6ebc79c800f188981352506c --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/dnn/shape_utils.hpp @@ -0,0 +1,219 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_DNN_DNN_SHAPE_UTILS_HPP +#define OPENCV_DNN_DNN_SHAPE_UTILS_HPP + +#include +#include // CV_MAX_DIM +#include +#include +#include + +namespace cv { +namespace dnn { +CV__DNN_EXPERIMENTAL_NS_BEGIN + +//Slicing + +struct _Range : public cv::Range +{ + _Range(const Range &r) : cv::Range(r) {} + _Range(int start_, int size_ = 1) : cv::Range(start_, start_ + size_) {} +}; + +static inline Mat slice(const Mat &m, const _Range &r0) +{ + Range ranges[CV_MAX_DIM]; + for (int i = 1; i < m.dims; i++) + ranges[i] = Range::all(); + ranges[0] = r0; + return m(&ranges[0]); +} + +static inline Mat slice(const Mat &m, const _Range &r0, const _Range &r1) +{ + CV_Assert(m.dims >= 2); + Range ranges[CV_MAX_DIM]; + for (int i = 2; i < m.dims; i++) + ranges[i] = Range::all(); + ranges[0] = r0; + ranges[1] = r1; + return m(&ranges[0]); +} + +static inline Mat slice(const Mat &m, const _Range &r0, const _Range &r1, const _Range &r2) +{ + CV_Assert(m.dims >= 3); + Range ranges[CV_MAX_DIM]; + for (int i = 3; i < m.dims; i++) + ranges[i] = Range::all(); + ranges[0] = r0; + ranges[1] = r1; + ranges[2] = r2; + return m(&ranges[0]); +} + +static inline Mat slice(const Mat &m, const _Range &r0, const _Range &r1, const _Range &r2, const _Range &r3) +{ + CV_Assert(m.dims >= 4); + Range ranges[CV_MAX_DIM]; + for (int i = 4; i < m.dims; i++) + ranges[i] = Range::all(); + ranges[0] = r0; + ranges[1] = r1; + ranges[2] = r2; + ranges[3] = r3; + return m(&ranges[0]); +} + +static inline Mat getPlane(const Mat &m, int n, int cn) +{ + CV_Assert(m.dims > 2); + int sz[CV_MAX_DIM]; + for(int i = 2; i < m.dims; i++) + { + sz[i-2] = m.size.p[i]; + } + return Mat(m.dims - 2, sz, m.type(), (void*)m.ptr(n, cn)); +} + +static inline MatShape shape(const int* dims, const int n) +{ + MatShape shape; + shape.assign(dims, dims + n); + return shape; +} + +static inline MatShape shape(const Mat& mat) +{ + return shape(mat.size.p, mat.dims); +} + +static inline MatShape shape(const MatSize& sz) +{ + return shape(sz.p, sz.dims()); +} + +static inline MatShape shape(const UMat& mat) +{ + return shape(mat.size.p, mat.dims); +} + +namespace {inline bool is_neg(int i) { return i < 0; }} + +static inline MatShape shape(int a0, int a1=-1, int a2=-1, int a3=-1) +{ + int dims[] = {a0, a1, a2, a3}; + MatShape s = shape(dims, 4); + s.erase(std::remove_if(s.begin(), s.end(), is_neg), s.end()); + return s; +} + +static inline int total(const MatShape& shape, int start = -1, int end = -1) +{ + if (start == -1) start = 0; + if (end == -1) end = (int)shape.size(); + + if (shape.empty()) + return 0; + + int elems = 1; + CV_Assert(start <= (int)shape.size() && end <= (int)shape.size() && + start <= end); + for(int i = start; i < end; i++) + { + elems *= shape[i]; + } + return elems; +} + +static inline MatShape concat(const MatShape& a, const MatShape& b) +{ + MatShape c = a; + c.insert(c.end(), b.begin(), b.end()); + + return c; +} + +static inline std::string toString(const MatShape& shape, const String& name = "") +{ + std::ostringstream ss; + if (!name.empty()) + ss << name << ' '; + ss << '['; + for(size_t i = 0, n = shape.size(); i < n; ++i) + ss << ' ' << shape[i]; + ss << " ]"; + return ss.str(); +} +static inline void print(const MatShape& shape, const String& name = "") +{ + std::cout << toString(shape, name) << std::endl; +} +static inline std::ostream& operator<<(std::ostream &out, const MatShape& shape) +{ + out << toString(shape); + return out; +} + +inline int clamp(int ax, int dims) +{ + return ax < 0 ? ax + dims : ax; +} + +inline int clamp(int ax, const MatShape& shape) +{ + return clamp(ax, (int)shape.size()); +} + +inline Range clamp(const Range& r, int axisSize) +{ + Range clamped(std::max(r.start, 0), + r.end > 0 ? std::min(r.end, axisSize) : axisSize + r.end + 1); + CV_Assert_N(clamped.start < clamped.end, clamped.end <= axisSize); + return clamped; +} + +CV__DNN_EXPERIMENTAL_NS_END +} +} +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/features2d.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/features2d.hpp new file mode 100644 index 0000000000000000000000000000000000000000..ee81ebe38562da7be600a5101e0d8aa1f9b60318 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/features2d.hpp @@ -0,0 +1,1428 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_FEATURES_2D_HPP +#define OPENCV_FEATURES_2D_HPP + +#include "opencv2/opencv_modules.hpp" +#include "opencv2/core.hpp" + +#ifdef HAVE_OPENCV_FLANN +#include "opencv2/flann/miniflann.hpp" +#endif + +/** + @defgroup features2d 2D Features Framework + @{ + @defgroup features2d_main Feature Detection and Description + @defgroup features2d_match Descriptor Matchers + +Matchers of keypoint descriptors in OpenCV have wrappers with a common interface that enables you to +easily switch between different algorithms solving the same problem. This section is devoted to +matching descriptors that are represented as vectors in a multidimensional space. All objects that +implement vector descriptor matchers inherit the DescriptorMatcher interface. + +@note + - An example explaining keypoint matching can be found at + opencv_source_code/samples/cpp/descriptor_extractor_matcher.cpp + - An example on descriptor matching evaluation can be found at + opencv_source_code/samples/cpp/detector_descriptor_matcher_evaluation.cpp + - An example on one to many image matching can be found at + opencv_source_code/samples/cpp/matching_to_many_images.cpp + + @defgroup features2d_draw Drawing Function of Keypoints and Matches + @defgroup features2d_category Object Categorization + +This section describes approaches based on local 2D features and used to categorize objects. + +@note + - A complete Bag-Of-Words sample can be found at + opencv_source_code/samples/cpp/bagofwords_classification.cpp + - (Python) An example using the features2D framework to perform object categorization can be + found at opencv_source_code/samples/python/find_obj.py + + @} + */ + +namespace cv +{ + +//! @addtogroup features2d +//! @{ + +// //! writes vector of keypoints to the file storage +// CV_EXPORTS void write(FileStorage& fs, const String& name, const std::vector& keypoints); +// //! reads vector of keypoints from the specified file storage node +// CV_EXPORTS void read(const FileNode& node, CV_OUT std::vector& keypoints); + +/** @brief A class filters a vector of keypoints. + + Because now it is difficult to provide a convenient interface for all usage scenarios of the + keypoints filter class, it has only several needed by now static methods. + */ +class CV_EXPORTS KeyPointsFilter +{ +public: + KeyPointsFilter(){} + + /* + * Remove keypoints within borderPixels of an image edge. + */ + static void runByImageBorder( std::vector& keypoints, Size imageSize, int borderSize ); + /* + * Remove keypoints of sizes out of range. + */ + static void runByKeypointSize( std::vector& keypoints, float minSize, + float maxSize=FLT_MAX ); + /* + * Remove keypoints from some image by mask for pixels of this image. + */ + static void runByPixelsMask( std::vector& keypoints, const Mat& mask ); + /* + * Remove duplicated keypoints. + */ + static void removeDuplicated( std::vector& keypoints ); + /* + * Remove duplicated keypoints and sort the remaining keypoints + */ + static void removeDuplicatedSorted( std::vector& keypoints ); + + /* + * Retain the specified number of the best keypoints (according to the response) + */ + static void retainBest( std::vector& keypoints, int npoints ); +}; + + +/************************************ Base Classes ************************************/ + +/** @brief Abstract base class for 2D image feature detectors and descriptor extractors +*/ +#ifdef __EMSCRIPTEN__ +class CV_EXPORTS_W Feature2D : public Algorithm +#else +class CV_EXPORTS_W Feature2D : public virtual Algorithm +#endif +{ +public: + virtual ~Feature2D(); + + /** @brief Detects keypoints in an image (first variant) or image set (second variant). + + @param image Image. + @param keypoints The detected keypoints. In the second variant of the method keypoints[i] is a set + of keypoints detected in images[i] . + @param mask Mask specifying where to look for keypoints (optional). It must be a 8-bit integer + matrix with non-zero values in the region of interest. + */ + CV_WRAP virtual void detect( InputArray image, + CV_OUT std::vector& keypoints, + InputArray mask=noArray() ); + + /** @overload + @param images Image set. + @param keypoints The detected keypoints. In the second variant of the method keypoints[i] is a set + of keypoints detected in images[i] . + @param masks Masks for each input image specifying where to look for keypoints (optional). + masks[i] is a mask for images[i]. + */ + CV_WRAP virtual void detect( InputArrayOfArrays images, + CV_OUT std::vector >& keypoints, + InputArrayOfArrays masks=noArray() ); + + /** @brief Computes the descriptors for a set of keypoints detected in an image (first variant) or image set + (second variant). + + @param image Image. + @param keypoints Input collection of keypoints. Keypoints for which a descriptor cannot be + computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint + with several dominant orientations (for each orientation). + @param descriptors Computed descriptors. In the second variant of the method descriptors[i] are + descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the + descriptor for keypoint j-th keypoint. + */ + CV_WRAP virtual void compute( InputArray image, + CV_OUT CV_IN_OUT std::vector& keypoints, + OutputArray descriptors ); + + /** @overload + + @param images Image set. + @param keypoints Input collection of keypoints. Keypoints for which a descriptor cannot be + computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint + with several dominant orientations (for each orientation). + @param descriptors Computed descriptors. In the second variant of the method descriptors[i] are + descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the + descriptor for keypoint j-th keypoint. + */ + CV_WRAP virtual void compute( InputArrayOfArrays images, + CV_OUT CV_IN_OUT std::vector >& keypoints, + OutputArrayOfArrays descriptors ); + + /** Detects keypoints and computes the descriptors */ + CV_WRAP virtual void detectAndCompute( InputArray image, InputArray mask, + CV_OUT std::vector& keypoints, + OutputArray descriptors, + bool useProvidedKeypoints=false ); + + CV_WRAP virtual int descriptorSize() const; + CV_WRAP virtual int descriptorType() const; + CV_WRAP virtual int defaultNorm() const; + + CV_WRAP void write( const String& fileName ) const; + + CV_WRAP void read( const String& fileName ); + + virtual void write( FileStorage&) const CV_OVERRIDE; + + // see corresponding cv::Algorithm method + CV_WRAP virtual void read( const FileNode&) CV_OVERRIDE; + + //! Return true if detector object is empty + CV_WRAP virtual bool empty() const CV_OVERRIDE; + CV_WRAP virtual String getDefaultName() const CV_OVERRIDE; + + // see corresponding cv::Algorithm method + CV_WRAP inline void write(const Ptr& fs, const String& name = String()) const { Algorithm::write(fs, name); } +}; + +/** Feature detectors in OpenCV have wrappers with a common interface that enables you to easily switch +between different algorithms solving the same problem. All objects that implement keypoint detectors +inherit the FeatureDetector interface. */ +typedef Feature2D FeatureDetector; + +/** Extractors of keypoint descriptors in OpenCV have wrappers with a common interface that enables you +to easily switch between different algorithms solving the same problem. This section is devoted to +computing descriptors represented as vectors in a multidimensional space. All objects that implement +the vector descriptor extractors inherit the DescriptorExtractor interface. + */ +typedef Feature2D DescriptorExtractor; + +//! @addtogroup features2d_main +//! @{ + +/** @brief Class implementing the BRISK keypoint detector and descriptor extractor, described in @cite LCS11 . + */ +class CV_EXPORTS_W BRISK : public Feature2D +{ +public: + /** @brief The BRISK constructor + + @param thresh AGAST detection threshold score. + @param octaves detection octaves. Use 0 to do single scale. + @param patternScale apply this scale to the pattern used for sampling the neighbourhood of a + keypoint. + */ + CV_WRAP static Ptr create(int thresh=30, int octaves=3, float patternScale=1.0f); + + /** @brief The BRISK constructor for a custom pattern + + @param radiusList defines the radii (in pixels) where the samples around a keypoint are taken (for + keypoint scale 1). + @param numberList defines the number of sampling points on the sampling circle. Must be the same + size as radiusList.. + @param dMax threshold for the short pairings used for descriptor formation (in pixels for keypoint + scale 1). + @param dMin threshold for the long pairings used for orientation determination (in pixels for + keypoint scale 1). + @param indexChange index remapping of the bits. */ + CV_WRAP static Ptr create(const std::vector &radiusList, const std::vector &numberList, + float dMax=5.85f, float dMin=8.2f, const std::vector& indexChange=std::vector()); + + /** @brief The BRISK constructor for a custom pattern, detection threshold and octaves + + @param thresh AGAST detection threshold score. + @param octaves detection octaves. Use 0 to do single scale. + @param radiusList defines the radii (in pixels) where the samples around a keypoint are taken (for + keypoint scale 1). + @param numberList defines the number of sampling points on the sampling circle. Must be the same + size as radiusList.. + @param dMax threshold for the short pairings used for descriptor formation (in pixels for keypoint + scale 1). + @param dMin threshold for the long pairings used for orientation determination (in pixels for + keypoint scale 1). + @param indexChange index remapping of the bits. */ + CV_WRAP static Ptr create(int thresh, int octaves, const std::vector &radiusList, + const std::vector &numberList, float dMax=5.85f, float dMin=8.2f, + const std::vector& indexChange=std::vector()); + CV_WRAP virtual String getDefaultName() const CV_OVERRIDE; +}; + +/** @brief Class implementing the ORB (*oriented BRIEF*) keypoint detector and descriptor extractor + +described in @cite RRKB11 . The algorithm uses FAST in pyramids to detect stable keypoints, selects +the strongest features using FAST or Harris response, finds their orientation using first-order +moments and computes the descriptors using BRIEF (where the coordinates of random point pairs (or +k-tuples) are rotated according to the measured orientation). + */ +class CV_EXPORTS_W ORB : public Feature2D +{ +public: + enum { kBytes = 32, HARRIS_SCORE=0, FAST_SCORE=1 }; + + /** @brief The ORB constructor + + @param nfeatures The maximum number of features to retain. + @param scaleFactor Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical + pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor + will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor + will mean that to cover certain scale range you will need more pyramid levels and so the speed + will suffer. + @param nlevels The number of pyramid levels. The smallest level will have linear size equal to + input_image_linear_size/pow(scaleFactor, nlevels - firstLevel). + @param edgeThreshold This is size of the border where the features are not detected. It should + roughly match the patchSize parameter. + @param firstLevel The level of pyramid to put source image to. Previous layers are filled + with upscaled source image. + @param WTA_K The number of points that produce each element of the oriented BRIEF descriptor. The + default value 2 means the BRIEF where we take a random point pair and compare their brightnesses, + so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3 + random points (of course, those point coordinates are random, but they are generated from the + pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel + rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such + output will occupy 2 bits, and therefore it will need a special variant of Hamming distance, + denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each + bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3). + @param scoreType The default HARRIS_SCORE means that Harris algorithm is used to rank features + (the score is written to KeyPoint::score and is used to retain best nfeatures features); + FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints, + but it is a little faster to compute. + @param patchSize size of the patch used by the oriented BRIEF descriptor. Of course, on smaller + pyramid layers the perceived image area covered by a feature will be larger. + @param fastThreshold + */ + CV_WRAP static Ptr create(int nfeatures=500, float scaleFactor=1.2f, int nlevels=8, int edgeThreshold=31, + int firstLevel=0, int WTA_K=2, int scoreType=ORB::HARRIS_SCORE, int patchSize=31, int fastThreshold=20); + + CV_WRAP virtual void setMaxFeatures(int maxFeatures) = 0; + CV_WRAP virtual int getMaxFeatures() const = 0; + + CV_WRAP virtual void setScaleFactor(double scaleFactor) = 0; + CV_WRAP virtual double getScaleFactor() const = 0; + + CV_WRAP virtual void setNLevels(int nlevels) = 0; + CV_WRAP virtual int getNLevels() const = 0; + + CV_WRAP virtual void setEdgeThreshold(int edgeThreshold) = 0; + CV_WRAP virtual int getEdgeThreshold() const = 0; + + CV_WRAP virtual void setFirstLevel(int firstLevel) = 0; + CV_WRAP virtual int getFirstLevel() const = 0; + + CV_WRAP virtual void setWTA_K(int wta_k) = 0; + CV_WRAP virtual int getWTA_K() const = 0; + + CV_WRAP virtual void setScoreType(int scoreType) = 0; + CV_WRAP virtual int getScoreType() const = 0; + + CV_WRAP virtual void setPatchSize(int patchSize) = 0; + CV_WRAP virtual int getPatchSize() const = 0; + + CV_WRAP virtual void setFastThreshold(int fastThreshold) = 0; + CV_WRAP virtual int getFastThreshold() const = 0; + CV_WRAP virtual String getDefaultName() const CV_OVERRIDE; +}; + +/** @brief Maximally stable extremal region extractor + +The class encapsulates all the parameters of the %MSER extraction algorithm (see [wiki +article](http://en.wikipedia.org/wiki/Maximally_stable_extremal_regions)). + +- there are two different implementation of %MSER: one for grey image, one for color image + +- the grey image algorithm is taken from: @cite nister2008linear ; the paper claims to be faster +than union-find method; it actually get 1.5~2m/s on my centrino L7200 1.2GHz laptop. + +- the color image algorithm is taken from: @cite forssen2007maximally ; it should be much slower +than grey image method ( 3~4 times ); the chi_table.h file is taken directly from paper's source +code which is distributed under GPL. + +- (Python) A complete example showing the use of the %MSER detector can be found at samples/python/mser.py +*/ +class CV_EXPORTS_W MSER : public Feature2D +{ +public: + /** @brief Full consturctor for %MSER detector + + @param _delta it compares \f$(size_{i}-size_{i-delta})/size_{i-delta}\f$ + @param _min_area prune the area which smaller than minArea + @param _max_area prune the area which bigger than maxArea + @param _max_variation prune the area have similar size to its children + @param _min_diversity for color image, trace back to cut off mser with diversity less than min_diversity + @param _max_evolution for color image, the evolution steps + @param _area_threshold for color image, the area threshold to cause re-initialize + @param _min_margin for color image, ignore too small margin + @param _edge_blur_size for color image, the aperture size for edge blur + */ + CV_WRAP static Ptr create( int _delta=5, int _min_area=60, int _max_area=14400, + double _max_variation=0.25, double _min_diversity=.2, + int _max_evolution=200, double _area_threshold=1.01, + double _min_margin=0.003, int _edge_blur_size=5 ); + + /** @brief Detect %MSER regions + + @param image input image (8UC1, 8UC3 or 8UC4, must be greater or equal than 3x3) + @param msers resulting list of point sets + @param bboxes resulting bounding boxes + */ + CV_WRAP virtual void detectRegions( InputArray image, + CV_OUT std::vector >& msers, + CV_OUT std::vector& bboxes ) = 0; + + CV_WRAP virtual void setDelta(int delta) = 0; + CV_WRAP virtual int getDelta() const = 0; + + CV_WRAP virtual void setMinArea(int minArea) = 0; + CV_WRAP virtual int getMinArea() const = 0; + + CV_WRAP virtual void setMaxArea(int maxArea) = 0; + CV_WRAP virtual int getMaxArea() const = 0; + + CV_WRAP virtual void setPass2Only(bool f) = 0; + CV_WRAP virtual bool getPass2Only() const = 0; + CV_WRAP virtual String getDefaultName() const CV_OVERRIDE; +}; + +/** @overload */ +CV_EXPORTS void FAST( InputArray image, CV_OUT std::vector& keypoints, + int threshold, bool nonmaxSuppression=true ); + +/** @brief Detects corners using the FAST algorithm + +@param image grayscale image where keypoints (corners) are detected. +@param keypoints keypoints detected on the image. +@param threshold threshold on difference between intensity of the central pixel and pixels of a +circle around this pixel. +@param nonmaxSuppression if true, non-maximum suppression is applied to detected corners +(keypoints). +@param type one of the three neighborhoods as defined in the paper: +FastFeatureDetector::TYPE_9_16, FastFeatureDetector::TYPE_7_12, +FastFeatureDetector::TYPE_5_8 + +Detects corners using the FAST algorithm by @cite Rosten06 . + +@note In Python API, types are given as cv2.FAST_FEATURE_DETECTOR_TYPE_5_8, +cv2.FAST_FEATURE_DETECTOR_TYPE_7_12 and cv2.FAST_FEATURE_DETECTOR_TYPE_9_16. For corner +detection, use cv2.FAST.detect() method. + */ +CV_EXPORTS void FAST( InputArray image, CV_OUT std::vector& keypoints, + int threshold, bool nonmaxSuppression, int type ); + +//! @} features2d_main + +//! @addtogroup features2d_main +//! @{ + +/** @brief Wrapping class for feature detection using the FAST method. : + */ +class CV_EXPORTS_W FastFeatureDetector : public Feature2D +{ +public: + enum + { + TYPE_5_8 = 0, TYPE_7_12 = 1, TYPE_9_16 = 2, + THRESHOLD = 10000, NONMAX_SUPPRESSION=10001, FAST_N=10002, + }; + + CV_WRAP static Ptr create( int threshold=10, + bool nonmaxSuppression=true, + int type=FastFeatureDetector::TYPE_9_16 ); + + CV_WRAP virtual void setThreshold(int threshold) = 0; + CV_WRAP virtual int getThreshold() const = 0; + + CV_WRAP virtual void setNonmaxSuppression(bool f) = 0; + CV_WRAP virtual bool getNonmaxSuppression() const = 0; + + CV_WRAP virtual void setType(int type) = 0; + CV_WRAP virtual int getType() const = 0; + CV_WRAP virtual String getDefaultName() const CV_OVERRIDE; +}; + +/** @overload */ +CV_EXPORTS void AGAST( InputArray image, CV_OUT std::vector& keypoints, + int threshold, bool nonmaxSuppression=true ); + +/** @brief Detects corners using the AGAST algorithm + +@param image grayscale image where keypoints (corners) are detected. +@param keypoints keypoints detected on the image. +@param threshold threshold on difference between intensity of the central pixel and pixels of a +circle around this pixel. +@param nonmaxSuppression if true, non-maximum suppression is applied to detected corners +(keypoints). +@param type one of the four neighborhoods as defined in the paper: +AgastFeatureDetector::AGAST_5_8, AgastFeatureDetector::AGAST_7_12d, +AgastFeatureDetector::AGAST_7_12s, AgastFeatureDetector::OAST_9_16 + +For non-Intel platforms, there is a tree optimised variant of AGAST with same numerical results. +The 32-bit binary tree tables were generated automatically from original code using perl script. +The perl script and examples of tree generation are placed in features2d/doc folder. +Detects corners using the AGAST algorithm by @cite mair2010_agast . + + */ +CV_EXPORTS void AGAST( InputArray image, CV_OUT std::vector& keypoints, + int threshold, bool nonmaxSuppression, int type ); +//! @} features2d_main + +//! @addtogroup features2d_main +//! @{ + +/** @brief Wrapping class for feature detection using the AGAST method. : + */ +class CV_EXPORTS_W AgastFeatureDetector : public Feature2D +{ +public: + enum + { + AGAST_5_8 = 0, AGAST_7_12d = 1, AGAST_7_12s = 2, OAST_9_16 = 3, + THRESHOLD = 10000, NONMAX_SUPPRESSION = 10001, + }; + + CV_WRAP static Ptr create( int threshold=10, + bool nonmaxSuppression=true, + int type=AgastFeatureDetector::OAST_9_16 ); + + CV_WRAP virtual void setThreshold(int threshold) = 0; + CV_WRAP virtual int getThreshold() const = 0; + + CV_WRAP virtual void setNonmaxSuppression(bool f) = 0; + CV_WRAP virtual bool getNonmaxSuppression() const = 0; + + CV_WRAP virtual void setType(int type) = 0; + CV_WRAP virtual int getType() const = 0; + CV_WRAP virtual String getDefaultName() const CV_OVERRIDE; +}; + +/** @brief Wrapping class for feature detection using the goodFeaturesToTrack function. : + */ +class CV_EXPORTS_W GFTTDetector : public Feature2D +{ +public: + CV_WRAP static Ptr create( int maxCorners=1000, double qualityLevel=0.01, double minDistance=1, + int blockSize=3, bool useHarrisDetector=false, double k=0.04 ); + CV_WRAP static Ptr create( int maxCorners, double qualityLevel, double minDistance, + int blockSize, int gradiantSize, bool useHarrisDetector=false, double k=0.04 ); + CV_WRAP virtual void setMaxFeatures(int maxFeatures) = 0; + CV_WRAP virtual int getMaxFeatures() const = 0; + + CV_WRAP virtual void setQualityLevel(double qlevel) = 0; + CV_WRAP virtual double getQualityLevel() const = 0; + + CV_WRAP virtual void setMinDistance(double minDistance) = 0; + CV_WRAP virtual double getMinDistance() const = 0; + + CV_WRAP virtual void setBlockSize(int blockSize) = 0; + CV_WRAP virtual int getBlockSize() const = 0; + + CV_WRAP virtual void setHarrisDetector(bool val) = 0; + CV_WRAP virtual bool getHarrisDetector() const = 0; + + CV_WRAP virtual void setK(double k) = 0; + CV_WRAP virtual double getK() const = 0; + CV_WRAP virtual String getDefaultName() const CV_OVERRIDE; +}; + +/** @brief Class for extracting blobs from an image. : + +The class implements a simple algorithm for extracting blobs from an image: + +1. Convert the source image to binary images by applying thresholding with several thresholds from + minThreshold (inclusive) to maxThreshold (exclusive) with distance thresholdStep between + neighboring thresholds. +2. Extract connected components from every binary image by findContours and calculate their + centers. +3. Group centers from several binary images by their coordinates. Close centers form one group that + corresponds to one blob, which is controlled by the minDistBetweenBlobs parameter. +4. From the groups, estimate final centers of blobs and their radiuses and return as locations and + sizes of keypoints. + +This class performs several filtrations of returned blobs. You should set filterBy\* to true/false +to turn on/off corresponding filtration. Available filtrations: + +- **By color**. This filter compares the intensity of a binary image at the center of a blob to +blobColor. If they differ, the blob is filtered out. Use blobColor = 0 to extract dark blobs +and blobColor = 255 to extract light blobs. +- **By area**. Extracted blobs have an area between minArea (inclusive) and maxArea (exclusive). +- **By circularity**. Extracted blobs have circularity +(\f$\frac{4*\pi*Area}{perimeter * perimeter}\f$) between minCircularity (inclusive) and +maxCircularity (exclusive). +- **By ratio of the minimum inertia to maximum inertia**. Extracted blobs have this ratio +between minInertiaRatio (inclusive) and maxInertiaRatio (exclusive). +- **By convexity**. Extracted blobs have convexity (area / area of blob convex hull) between +minConvexity (inclusive) and maxConvexity (exclusive). + +Default values of parameters are tuned to extract dark circular blobs. + */ +class CV_EXPORTS_W SimpleBlobDetector : public Feature2D +{ +public: + struct CV_EXPORTS_W_SIMPLE Params + { + CV_WRAP Params(); + CV_PROP_RW float thresholdStep; + CV_PROP_RW float minThreshold; + CV_PROP_RW float maxThreshold; + CV_PROP_RW size_t minRepeatability; + CV_PROP_RW float minDistBetweenBlobs; + + CV_PROP_RW bool filterByColor; + CV_PROP_RW uchar blobColor; + + CV_PROP_RW bool filterByArea; + CV_PROP_RW float minArea, maxArea; + + CV_PROP_RW bool filterByCircularity; + CV_PROP_RW float minCircularity, maxCircularity; + + CV_PROP_RW bool filterByInertia; + CV_PROP_RW float minInertiaRatio, maxInertiaRatio; + + CV_PROP_RW bool filterByConvexity; + CV_PROP_RW float minConvexity, maxConvexity; + + void read( const FileNode& fn ); + void write( FileStorage& fs ) const; + }; + + CV_WRAP static Ptr + create(const SimpleBlobDetector::Params ¶meters = SimpleBlobDetector::Params()); + CV_WRAP virtual String getDefaultName() const CV_OVERRIDE; +}; + +//! @} features2d_main + +//! @addtogroup features2d_main +//! @{ + +/** @brief Class implementing the KAZE keypoint detector and descriptor extractor, described in @cite ABD12 . + +@note AKAZE descriptor can only be used with KAZE or AKAZE keypoints .. [ABD12] KAZE Features. Pablo +F. Alcantarilla, Adrien Bartoli and Andrew J. Davison. In European Conference on Computer Vision +(ECCV), Fiorenze, Italy, October 2012. +*/ +class CV_EXPORTS_W KAZE : public Feature2D +{ +public: + enum + { + DIFF_PM_G1 = 0, + DIFF_PM_G2 = 1, + DIFF_WEICKERT = 2, + DIFF_CHARBONNIER = 3 + }; + + /** @brief The KAZE constructor + + @param extended Set to enable extraction of extended (128-byte) descriptor. + @param upright Set to enable use of upright descriptors (non rotation-invariant). + @param threshold Detector response threshold to accept point + @param nOctaves Maximum octave evolution of the image + @param nOctaveLayers Default number of sublevels per scale level + @param diffusivity Diffusivity type. DIFF_PM_G1, DIFF_PM_G2, DIFF_WEICKERT or + DIFF_CHARBONNIER + */ + CV_WRAP static Ptr create(bool extended=false, bool upright=false, + float threshold = 0.001f, + int nOctaves = 4, int nOctaveLayers = 4, + int diffusivity = KAZE::DIFF_PM_G2); + + CV_WRAP virtual void setExtended(bool extended) = 0; + CV_WRAP virtual bool getExtended() const = 0; + + CV_WRAP virtual void setUpright(bool upright) = 0; + CV_WRAP virtual bool getUpright() const = 0; + + CV_WRAP virtual void setThreshold(double threshold) = 0; + CV_WRAP virtual double getThreshold() const = 0; + + CV_WRAP virtual void setNOctaves(int octaves) = 0; + CV_WRAP virtual int getNOctaves() const = 0; + + CV_WRAP virtual void setNOctaveLayers(int octaveLayers) = 0; + CV_WRAP virtual int getNOctaveLayers() const = 0; + + CV_WRAP virtual void setDiffusivity(int diff) = 0; + CV_WRAP virtual int getDiffusivity() const = 0; + CV_WRAP virtual String getDefaultName() const CV_OVERRIDE; +}; + +/** @brief Class implementing the AKAZE keypoint detector and descriptor extractor, described in @cite ANB13. + +@details AKAZE descriptors can only be used with KAZE or AKAZE keypoints. This class is thread-safe. + +@note When you need descriptors use Feature2D::detectAndCompute, which +provides better performance. When using Feature2D::detect followed by +Feature2D::compute scale space pyramid is computed twice. + +@note AKAZE implements T-API. When image is passed as UMat some parts of the algorithm +will use OpenCL. + +@note [ANB13] Fast Explicit Diffusion for Accelerated Features in Nonlinear +Scale Spaces. Pablo F. Alcantarilla, Jesús Nuevo and Adrien Bartoli. In +British Machine Vision Conference (BMVC), Bristol, UK, September 2013. + +*/ +class CV_EXPORTS_W AKAZE : public Feature2D +{ +public: + // AKAZE descriptor type + enum + { + DESCRIPTOR_KAZE_UPRIGHT = 2, ///< Upright descriptors, not invariant to rotation + DESCRIPTOR_KAZE = 3, + DESCRIPTOR_MLDB_UPRIGHT = 4, ///< Upright descriptors, not invariant to rotation + DESCRIPTOR_MLDB = 5 + }; + + /** @brief The AKAZE constructor + + @param descriptor_type Type of the extracted descriptor: DESCRIPTOR_KAZE, + DESCRIPTOR_KAZE_UPRIGHT, DESCRIPTOR_MLDB or DESCRIPTOR_MLDB_UPRIGHT. + @param descriptor_size Size of the descriptor in bits. 0 -\> Full size + @param descriptor_channels Number of channels in the descriptor (1, 2, 3) + @param threshold Detector response threshold to accept point + @param nOctaves Maximum octave evolution of the image + @param nOctaveLayers Default number of sublevels per scale level + @param diffusivity Diffusivity type. DIFF_PM_G1, DIFF_PM_G2, DIFF_WEICKERT or + DIFF_CHARBONNIER + */ + CV_WRAP static Ptr create(int descriptor_type=AKAZE::DESCRIPTOR_MLDB, + int descriptor_size = 0, int descriptor_channels = 3, + float threshold = 0.001f, int nOctaves = 4, + int nOctaveLayers = 4, int diffusivity = KAZE::DIFF_PM_G2); + + CV_WRAP virtual void setDescriptorType(int dtype) = 0; + CV_WRAP virtual int getDescriptorType() const = 0; + + CV_WRAP virtual void setDescriptorSize(int dsize) = 0; + CV_WRAP virtual int getDescriptorSize() const = 0; + + CV_WRAP virtual void setDescriptorChannels(int dch) = 0; + CV_WRAP virtual int getDescriptorChannels() const = 0; + + CV_WRAP virtual void setThreshold(double threshold) = 0; + CV_WRAP virtual double getThreshold() const = 0; + + CV_WRAP virtual void setNOctaves(int octaves) = 0; + CV_WRAP virtual int getNOctaves() const = 0; + + CV_WRAP virtual void setNOctaveLayers(int octaveLayers) = 0; + CV_WRAP virtual int getNOctaveLayers() const = 0; + + CV_WRAP virtual void setDiffusivity(int diff) = 0; + CV_WRAP virtual int getDiffusivity() const = 0; + CV_WRAP virtual String getDefaultName() const CV_OVERRIDE; +}; + +//! @} features2d_main + +/****************************************************************************************\ +* Distance * +\****************************************************************************************/ + +template +struct CV_EXPORTS Accumulator +{ + typedef T Type; +}; + +template<> struct Accumulator { typedef float Type; }; +template<> struct Accumulator { typedef float Type; }; +template<> struct Accumulator { typedef float Type; }; +template<> struct Accumulator { typedef float Type; }; + +/* + * Squared Euclidean distance functor + */ +template +struct CV_EXPORTS SL2 +{ + enum { normType = NORM_L2SQR }; + typedef T ValueType; + typedef typename Accumulator::Type ResultType; + + ResultType operator()( const T* a, const T* b, int size ) const + { + return normL2Sqr(a, b, size); + } +}; + +/* + * Euclidean distance functor + */ +template +struct L2 +{ + enum { normType = NORM_L2 }; + typedef T ValueType; + typedef typename Accumulator::Type ResultType; + + ResultType operator()( const T* a, const T* b, int size ) const + { + return (ResultType)std::sqrt((double)normL2Sqr(a, b, size)); + } +}; + +/* + * Manhattan distance (city block distance) functor + */ +template +struct L1 +{ + enum { normType = NORM_L1 }; + typedef T ValueType; + typedef typename Accumulator::Type ResultType; + + ResultType operator()( const T* a, const T* b, int size ) const + { + return normL1(a, b, size); + } +}; + +/****************************************************************************************\ +* DescriptorMatcher * +\****************************************************************************************/ + +//! @addtogroup features2d_match +//! @{ + +/** @brief Abstract base class for matching keypoint descriptors. + +It has two groups of match methods: for matching descriptors of an image with another image or with +an image set. + */ +class CV_EXPORTS_W DescriptorMatcher : public Algorithm +{ +public: + enum + { + FLANNBASED = 1, + BRUTEFORCE = 2, + BRUTEFORCE_L1 = 3, + BRUTEFORCE_HAMMING = 4, + BRUTEFORCE_HAMMINGLUT = 5, + BRUTEFORCE_SL2 = 6 + }; + virtual ~DescriptorMatcher(); + + /** @brief Adds descriptors to train a CPU(trainDescCollectionis) or GPU(utrainDescCollectionis) descriptor + collection. + + If the collection is not empty, the new descriptors are added to existing train descriptors. + + @param descriptors Descriptors to add. Each descriptors[i] is a set of descriptors from the same + train image. + */ + CV_WRAP virtual void add( InputArrayOfArrays descriptors ); + + /** @brief Returns a constant link to the train descriptor collection trainDescCollection . + */ + CV_WRAP const std::vector& getTrainDescriptors() const; + + /** @brief Clears the train descriptor collections. + */ + CV_WRAP virtual void clear() CV_OVERRIDE; + + /** @brief Returns true if there are no train descriptors in the both collections. + */ + CV_WRAP virtual bool empty() const CV_OVERRIDE; + + /** @brief Returns true if the descriptor matcher supports masking permissible matches. + */ + CV_WRAP virtual bool isMaskSupported() const = 0; + + /** @brief Trains a descriptor matcher + + Trains a descriptor matcher (for example, the flann index). In all methods to match, the method + train() is run every time before matching. Some descriptor matchers (for example, BruteForceMatcher) + have an empty implementation of this method. Other matchers really train their inner structures (for + example, FlannBasedMatcher trains flann::Index ). + */ + CV_WRAP virtual void train(); + + /** @brief Finds the best match for each descriptor from a query set. + + @param queryDescriptors Query set of descriptors. + @param trainDescriptors Train set of descriptors. This set is not added to the train descriptors + collection stored in the class object. + @param matches Matches. If a query descriptor is masked out in mask , no match is added for this + descriptor. So, matches size may be smaller than the query descriptors count. + @param mask Mask specifying permissible matches between an input query and train matrices of + descriptors. + + In the first variant of this method, the train descriptors are passed as an input argument. In the + second variant of the method, train descriptors collection that was set by DescriptorMatcher::add is + used. Optional mask (or masks) can be passed to specify which query and training descriptors can be + matched. Namely, queryDescriptors[i] can be matched with trainDescriptors[j] only if + mask.at\(i,j) is non-zero. + */ + CV_WRAP void match( InputArray queryDescriptors, InputArray trainDescriptors, + CV_OUT std::vector& matches, InputArray mask=noArray() ) const; + + /** @brief Finds the k best matches for each descriptor from a query set. + + @param queryDescriptors Query set of descriptors. + @param trainDescriptors Train set of descriptors. This set is not added to the train descriptors + collection stored in the class object. + @param mask Mask specifying permissible matches between an input query and train matrices of + descriptors. + @param matches Matches. Each matches[i] is k or less matches for the same query descriptor. + @param k Count of best matches found per each query descriptor or less if a query descriptor has + less than k possible matches in total. + @param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is + false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, + the matches vector does not contain matches for fully masked-out query descriptors. + + These extended variants of DescriptorMatcher::match methods find several best matches for each query + descriptor. The matches are returned in the distance increasing order. See DescriptorMatcher::match + for the details about query and train descriptors. + */ + CV_WRAP void knnMatch( InputArray queryDescriptors, InputArray trainDescriptors, + CV_OUT std::vector >& matches, int k, + InputArray mask=noArray(), bool compactResult=false ) const; + + /** @brief For each query descriptor, finds the training descriptors not farther than the specified distance. + + @param queryDescriptors Query set of descriptors. + @param trainDescriptors Train set of descriptors. This set is not added to the train descriptors + collection stored in the class object. + @param matches Found matches. + @param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is + false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, + the matches vector does not contain matches for fully masked-out query descriptors. + @param maxDistance Threshold for the distance between matched descriptors. Distance means here + metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured + in Pixels)! + @param mask Mask specifying permissible matches between an input query and train matrices of + descriptors. + + For each query descriptor, the methods find such training descriptors that the distance between the + query descriptor and the training descriptor is equal or smaller than maxDistance. Found matches are + returned in the distance increasing order. + */ + CV_WRAP void radiusMatch( InputArray queryDescriptors, InputArray trainDescriptors, + CV_OUT std::vector >& matches, float maxDistance, + InputArray mask=noArray(), bool compactResult=false ) const; + + /** @overload + @param queryDescriptors Query set of descriptors. + @param matches Matches. If a query descriptor is masked out in mask , no match is added for this + descriptor. So, matches size may be smaller than the query descriptors count. + @param masks Set of masks. Each masks[i] specifies permissible matches between the input query + descriptors and stored train descriptors from the i-th image trainDescCollection[i]. + */ + CV_WRAP void match( InputArray queryDescriptors, CV_OUT std::vector& matches, + InputArrayOfArrays masks=noArray() ); + /** @overload + @param queryDescriptors Query set of descriptors. + @param matches Matches. Each matches[i] is k or less matches for the same query descriptor. + @param k Count of best matches found per each query descriptor or less if a query descriptor has + less than k possible matches in total. + @param masks Set of masks. Each masks[i] specifies permissible matches between the input query + descriptors and stored train descriptors from the i-th image trainDescCollection[i]. + @param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is + false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, + the matches vector does not contain matches for fully masked-out query descriptors. + */ + CV_WRAP void knnMatch( InputArray queryDescriptors, CV_OUT std::vector >& matches, int k, + InputArrayOfArrays masks=noArray(), bool compactResult=false ); + /** @overload + @param queryDescriptors Query set of descriptors. + @param matches Found matches. + @param maxDistance Threshold for the distance between matched descriptors. Distance means here + metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured + in Pixels)! + @param masks Set of masks. Each masks[i] specifies permissible matches between the input query + descriptors and stored train descriptors from the i-th image trainDescCollection[i]. + @param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is + false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, + the matches vector does not contain matches for fully masked-out query descriptors. + */ + CV_WRAP void radiusMatch( InputArray queryDescriptors, CV_OUT std::vector >& matches, float maxDistance, + InputArrayOfArrays masks=noArray(), bool compactResult=false ); + + + CV_WRAP void write( const String& fileName ) const + { + FileStorage fs(fileName, FileStorage::WRITE); + write(fs); + } + + CV_WRAP void read( const String& fileName ) + { + FileStorage fs(fileName, FileStorage::READ); + read(fs.root()); + } + // Reads matcher object from a file node + // see corresponding cv::Algorithm method + CV_WRAP virtual void read( const FileNode& ) CV_OVERRIDE; + // Writes matcher object to a file storage + virtual void write( FileStorage& ) const CV_OVERRIDE; + + /** @brief Clones the matcher. + + @param emptyTrainData If emptyTrainData is false, the method creates a deep copy of the object, + that is, copies both parameters and train data. If emptyTrainData is true, the method creates an + object copy with the current parameters but with empty train data. + */ + CV_WRAP virtual Ptr clone( bool emptyTrainData=false ) const = 0; + + /** @brief Creates a descriptor matcher of a given type with the default parameters (using default + constructor). + + @param descriptorMatcherType Descriptor matcher type. Now the following matcher types are + supported: + - `BruteForce` (it uses L2 ) + - `BruteForce-L1` + - `BruteForce-Hamming` + - `BruteForce-Hamming(2)` + - `FlannBased` + */ + CV_WRAP static Ptr create( const String& descriptorMatcherType ); + + CV_WRAP static Ptr create( int matcherType ); + + + // see corresponding cv::Algorithm method + CV_WRAP inline void write(const Ptr& fs, const String& name = String()) const { Algorithm::write(fs, name); } + +protected: + /** + * Class to work with descriptors from several images as with one merged matrix. + * It is used e.g. in FlannBasedMatcher. + */ + class CV_EXPORTS DescriptorCollection + { + public: + DescriptorCollection(); + DescriptorCollection( const DescriptorCollection& collection ); + virtual ~DescriptorCollection(); + + // Vector of matrices "descriptors" will be merged to one matrix "mergedDescriptors" here. + void set( const std::vector& descriptors ); + virtual void clear(); + + const Mat& getDescriptors() const; + const Mat getDescriptor( int imgIdx, int localDescIdx ) const; + const Mat getDescriptor( int globalDescIdx ) const; + void getLocalIdx( int globalDescIdx, int& imgIdx, int& localDescIdx ) const; + + int size() const; + + protected: + Mat mergedDescriptors; + std::vector startIdxs; + }; + + //! In fact the matching is implemented only by the following two methods. These methods suppose + //! that the class object has been trained already. Public match methods call these methods + //! after calling train(). + virtual void knnMatchImpl( InputArray queryDescriptors, std::vector >& matches, int k, + InputArrayOfArrays masks=noArray(), bool compactResult=false ) = 0; + virtual void radiusMatchImpl( InputArray queryDescriptors, std::vector >& matches, float maxDistance, + InputArrayOfArrays masks=noArray(), bool compactResult=false ) = 0; + + static bool isPossibleMatch( InputArray mask, int queryIdx, int trainIdx ); + static bool isMaskedOut( InputArrayOfArrays masks, int queryIdx ); + + static Mat clone_op( Mat m ) { return m.clone(); } + void checkMasks( InputArrayOfArrays masks, int queryDescriptorsCount ) const; + + //! Collection of descriptors from train images. + std::vector trainDescCollection; + std::vector utrainDescCollection; +}; + +/** @brief Brute-force descriptor matcher. + +For each descriptor in the first set, this matcher finds the closest descriptor in the second set +by trying each one. This descriptor matcher supports masking permissible matches of descriptor +sets. + */ +class CV_EXPORTS_W BFMatcher : public DescriptorMatcher +{ +public: + /** @brief Brute-force matcher constructor (obsolete). Please use BFMatcher.create() + * + * + */ + CV_WRAP BFMatcher( int normType=NORM_L2, bool crossCheck=false ); + + virtual ~BFMatcher() {} + + virtual bool isMaskSupported() const CV_OVERRIDE { return true; } + + /** @brief Brute-force matcher create method. + @param normType One of NORM_L1, NORM_L2, NORM_HAMMING, NORM_HAMMING2. L1 and L2 norms are + preferable choices for SIFT and SURF descriptors, NORM_HAMMING should be used with ORB, BRISK and + BRIEF, NORM_HAMMING2 should be used with ORB when WTA_K==3 or 4 (see ORB::ORB constructor + description). + @param crossCheck If it is false, this is will be default BFMatcher behaviour when it finds the k + nearest neighbors for each query descriptor. If crossCheck==true, then the knnMatch() method with + k=1 will only return pairs (i,j) such that for i-th query descriptor the j-th descriptor in the + matcher's collection is the nearest and vice versa, i.e. the BFMatcher will only return consistent + pairs. Such technique usually produces best results with minimal number of outliers when there are + enough matches. This is alternative to the ratio test, used by D. Lowe in SIFT paper. + */ + CV_WRAP static Ptr create( int normType=NORM_L2, bool crossCheck=false ) ; + + virtual Ptr clone( bool emptyTrainData=false ) const CV_OVERRIDE; +protected: + virtual void knnMatchImpl( InputArray queryDescriptors, std::vector >& matches, int k, + InputArrayOfArrays masks=noArray(), bool compactResult=false ) CV_OVERRIDE; + virtual void radiusMatchImpl( InputArray queryDescriptors, std::vector >& matches, float maxDistance, + InputArrayOfArrays masks=noArray(), bool compactResult=false ) CV_OVERRIDE; + + int normType; + bool crossCheck; +}; + +#if defined(HAVE_OPENCV_FLANN) || defined(CV_DOXYGEN) + +/** @brief Flann-based descriptor matcher. + +This matcher trains cv::flann::Index on a train descriptor collection and calls its nearest search +methods to find the best matches. So, this matcher may be faster when matching a large train +collection than the brute force matcher. FlannBasedMatcher does not support masking permissible +matches of descriptor sets because flann::Index does not support this. : + */ +class CV_EXPORTS_W FlannBasedMatcher : public DescriptorMatcher +{ +public: + CV_WRAP FlannBasedMatcher( const Ptr& indexParams=makePtr(), + const Ptr& searchParams=makePtr() ); + + virtual void add( InputArrayOfArrays descriptors ) CV_OVERRIDE; + virtual void clear() CV_OVERRIDE; + + // Reads matcher object from a file node + virtual void read( const FileNode& ) CV_OVERRIDE; + // Writes matcher object to a file storage + virtual void write( FileStorage& ) const CV_OVERRIDE; + + virtual void train() CV_OVERRIDE; + virtual bool isMaskSupported() const CV_OVERRIDE; + + CV_WRAP static Ptr create(); + + virtual Ptr clone( bool emptyTrainData=false ) const CV_OVERRIDE; +protected: + static void convertToDMatches( const DescriptorCollection& descriptors, + const Mat& indices, const Mat& distances, + std::vector >& matches ); + + virtual void knnMatchImpl( InputArray queryDescriptors, std::vector >& matches, int k, + InputArrayOfArrays masks=noArray(), bool compactResult=false ) CV_OVERRIDE; + virtual void radiusMatchImpl( InputArray queryDescriptors, std::vector >& matches, float maxDistance, + InputArrayOfArrays masks=noArray(), bool compactResult=false ) CV_OVERRIDE; + + Ptr indexParams; + Ptr searchParams; + Ptr flannIndex; + + DescriptorCollection mergedDescriptors; + int addedDescCount; +}; + +#endif + +//! @} features2d_match + +/****************************************************************************************\ +* Drawing functions * +\****************************************************************************************/ + +//! @addtogroup features2d_draw +//! @{ + +struct CV_EXPORTS DrawMatchesFlags +{ + enum{ DEFAULT = 0, //!< Output image matrix will be created (Mat::create), + //!< i.e. existing memory of output image may be reused. + //!< Two source image, matches and single keypoints will be drawn. + //!< For each keypoint only the center point will be drawn (without + //!< the circle around keypoint with keypoint size and orientation). + DRAW_OVER_OUTIMG = 1, //!< Output image matrix will not be created (Mat::create). + //!< Matches will be drawn on existing content of output image. + NOT_DRAW_SINGLE_POINTS = 2, //!< Single keypoints will not be drawn. + DRAW_RICH_KEYPOINTS = 4 //!< For each keypoint the circle around keypoint with keypoint size and + //!< orientation will be drawn. + }; +}; + +/** @brief Draws keypoints. + +@param image Source image. +@param keypoints Keypoints from the source image. +@param outImage Output image. Its content depends on the flags value defining what is drawn in the +output image. See possible flags bit values below. +@param color Color of keypoints. +@param flags Flags setting drawing features. Possible flags bit values are defined by +DrawMatchesFlags. See details above in drawMatches . + +@note +For Python API, flags are modified as cv2.DRAW_MATCHES_FLAGS_DEFAULT, +cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS, cv2.DRAW_MATCHES_FLAGS_DRAW_OVER_OUTIMG, +cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS + */ +CV_EXPORTS_W void drawKeypoints( InputArray image, const std::vector& keypoints, InputOutputArray outImage, + const Scalar& color=Scalar::all(-1), int flags=DrawMatchesFlags::DEFAULT ); + +/** @brief Draws the found matches of keypoints from two images. + +@param img1 First source image. +@param keypoints1 Keypoints from the first source image. +@param img2 Second source image. +@param keypoints2 Keypoints from the second source image. +@param matches1to2 Matches from the first image to the second one, which means that keypoints1[i] +has a corresponding point in keypoints2[matches[i]] . +@param outImg Output image. Its content depends on the flags value defining what is drawn in the +output image. See possible flags bit values below. +@param matchColor Color of matches (lines and connected keypoints). If matchColor==Scalar::all(-1) +, the color is generated randomly. +@param singlePointColor Color of single keypoints (circles), which means that keypoints do not +have the matches. If singlePointColor==Scalar::all(-1) , the color is generated randomly. +@param matchesMask Mask determining which matches are drawn. If the mask is empty, all matches are +drawn. +@param flags Flags setting drawing features. Possible flags bit values are defined by +DrawMatchesFlags. + +This function draws matches of keypoints from two images in the output image. Match is a line +connecting two keypoints (circles). See cv::DrawMatchesFlags. + */ +CV_EXPORTS_W void drawMatches( InputArray img1, const std::vector& keypoints1, + InputArray img2, const std::vector& keypoints2, + const std::vector& matches1to2, InputOutputArray outImg, + const Scalar& matchColor=Scalar::all(-1), const Scalar& singlePointColor=Scalar::all(-1), + const std::vector& matchesMask=std::vector(), int flags=DrawMatchesFlags::DEFAULT ); + +/** @overload */ +CV_EXPORTS_AS(drawMatchesKnn) void drawMatches( InputArray img1, const std::vector& keypoints1, + InputArray img2, const std::vector& keypoints2, + const std::vector >& matches1to2, InputOutputArray outImg, + const Scalar& matchColor=Scalar::all(-1), const Scalar& singlePointColor=Scalar::all(-1), + const std::vector >& matchesMask=std::vector >(), int flags=DrawMatchesFlags::DEFAULT ); + +//! @} features2d_draw + +/****************************************************************************************\ +* Functions to evaluate the feature detectors and [generic] descriptor extractors * +\****************************************************************************************/ + +CV_EXPORTS void evaluateFeatureDetector( const Mat& img1, const Mat& img2, const Mat& H1to2, + std::vector* keypoints1, std::vector* keypoints2, + float& repeatability, int& correspCount, + const Ptr& fdetector=Ptr() ); + +CV_EXPORTS void computeRecallPrecisionCurve( const std::vector >& matches1to2, + const std::vector >& correctMatches1to2Mask, + std::vector& recallPrecisionCurve ); + +CV_EXPORTS float getRecall( const std::vector& recallPrecisionCurve, float l_precision ); +CV_EXPORTS int getNearestPoint( const std::vector& recallPrecisionCurve, float l_precision ); + +/****************************************************************************************\ +* Bag of visual words * +\****************************************************************************************/ + +//! @addtogroup features2d_category +//! @{ + +/** @brief Abstract base class for training the *bag of visual words* vocabulary from a set of descriptors. + +For details, see, for example, *Visual Categorization with Bags of Keypoints* by Gabriella Csurka, +Christopher R. Dance, Lixin Fan, Jutta Willamowski, Cedric Bray, 2004. : + */ +class CV_EXPORTS_W BOWTrainer +{ +public: + BOWTrainer(); + virtual ~BOWTrainer(); + + /** @brief Adds descriptors to a training set. + + @param descriptors Descriptors to add to a training set. Each row of the descriptors matrix is a + descriptor. + + The training set is clustered using clustermethod to construct the vocabulary. + */ + CV_WRAP void add( const Mat& descriptors ); + + /** @brief Returns a training set of descriptors. + */ + CV_WRAP const std::vector& getDescriptors() const; + + /** @brief Returns the count of all descriptors stored in the training set. + */ + CV_WRAP int descriptorsCount() const; + + CV_WRAP virtual void clear(); + + /** @overload */ + CV_WRAP virtual Mat cluster() const = 0; + + /** @brief Clusters train descriptors. + + @param descriptors Descriptors to cluster. Each row of the descriptors matrix is a descriptor. + Descriptors are not added to the inner train descriptor set. + + The vocabulary consists of cluster centers. So, this method returns the vocabulary. In the first + variant of the method, train descriptors stored in the object are clustered. In the second variant, + input descriptors are clustered. + */ + CV_WRAP virtual Mat cluster( const Mat& descriptors ) const = 0; + +protected: + std::vector descriptors; + int size; +}; + +/** @brief kmeans -based class to train visual vocabulary using the *bag of visual words* approach. : + */ +class CV_EXPORTS_W BOWKMeansTrainer : public BOWTrainer +{ +public: + /** @brief The constructor. + + @see cv::kmeans + */ + CV_WRAP BOWKMeansTrainer( int clusterCount, const TermCriteria& termcrit=TermCriteria(), + int attempts=3, int flags=KMEANS_PP_CENTERS ); + virtual ~BOWKMeansTrainer(); + + // Returns trained vocabulary (i.e. cluster centers). + CV_WRAP virtual Mat cluster() const CV_OVERRIDE; + CV_WRAP virtual Mat cluster( const Mat& descriptors ) const CV_OVERRIDE; + +protected: + + int clusterCount; + TermCriteria termcrit; + int attempts; + int flags; +}; + +/** @brief Class to compute an image descriptor using the *bag of visual words*. + +Such a computation consists of the following steps: + +1. Compute descriptors for a given image and its keypoints set. +2. Find the nearest visual words from the vocabulary for each keypoint descriptor. +3. Compute the bag-of-words image descriptor as is a normalized histogram of vocabulary words +encountered in the image. The i-th bin of the histogram is a frequency of i-th word of the +vocabulary in the given image. + */ +class CV_EXPORTS_W BOWImgDescriptorExtractor +{ +public: + /** @brief The constructor. + + @param dextractor Descriptor extractor that is used to compute descriptors for an input image and + its keypoints. + @param dmatcher Descriptor matcher that is used to find the nearest word of the trained vocabulary + for each keypoint descriptor of the image. + */ + CV_WRAP BOWImgDescriptorExtractor( const Ptr& dextractor, + const Ptr& dmatcher ); + /** @overload */ + BOWImgDescriptorExtractor( const Ptr& dmatcher ); + virtual ~BOWImgDescriptorExtractor(); + + /** @brief Sets a visual vocabulary. + + @param vocabulary Vocabulary (can be trained using the inheritor of BOWTrainer ). Each row of the + vocabulary is a visual word (cluster center). + */ + CV_WRAP void setVocabulary( const Mat& vocabulary ); + + /** @brief Returns the set vocabulary. + */ + CV_WRAP const Mat& getVocabulary() const; + + /** @brief Computes an image descriptor using the set visual vocabulary. + + @param image Image, for which the descriptor is computed. + @param keypoints Keypoints detected in the input image. + @param imgDescriptor Computed output image descriptor. + @param pointIdxsOfClusters Indices of keypoints that belong to the cluster. This means that + pointIdxsOfClusters[i] are keypoint indices that belong to the i -th cluster (word of vocabulary) + returned if it is non-zero. + @param descriptors Descriptors of the image keypoints that are returned if they are non-zero. + */ + void compute( InputArray image, std::vector& keypoints, OutputArray imgDescriptor, + std::vector >* pointIdxsOfClusters=0, Mat* descriptors=0 ); + /** @overload + @param keypointDescriptors Computed descriptors to match with vocabulary. + @param imgDescriptor Computed output image descriptor. + @param pointIdxsOfClusters Indices of keypoints that belong to the cluster. This means that + pointIdxsOfClusters[i] are keypoint indices that belong to the i -th cluster (word of vocabulary) + returned if it is non-zero. + */ + void compute( InputArray keypointDescriptors, OutputArray imgDescriptor, + std::vector >* pointIdxsOfClusters=0 ); + // compute() is not constant because DescriptorMatcher::match is not constant + + CV_WRAP_AS(compute) void compute2( const Mat& image, std::vector& keypoints, CV_OUT Mat& imgDescriptor ) + { compute(image,keypoints,imgDescriptor); } + + /** @brief Returns an image descriptor size if the vocabulary is set. Otherwise, it returns 0. + */ + CV_WRAP int descriptorSize() const; + + /** @brief Returns an image descriptor type. + */ + CV_WRAP int descriptorType() const; + +protected: + Mat vocabulary; + Ptr dextractor; + Ptr dmatcher; +}; + +//! @} features2d_category + +//! @} features2d + +} /* namespace cv */ + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/features2d/features2d.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/features2d/features2d.hpp new file mode 100644 index 0000000000000000000000000000000000000000..e81df0ad08c1757800a26376e047b0746b67a2ae --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/features2d/features2d.hpp @@ -0,0 +1,48 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifdef __OPENCV_BUILD +#error this is a compatibility header which should not be used inside the OpenCV library +#endif + +#include "opencv2/features2d.hpp" diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/features2d/hal/interface.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/features2d/hal/interface.h new file mode 100644 index 0000000000000000000000000000000000000000..bcc6577c19f2241af17994f149c29a3fba5c5951 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/features2d/hal/interface.h @@ -0,0 +1,33 @@ +#ifndef OPENCV_FEATURE2D_HAL_INTERFACE_H +#define OPENCV_FEATURE2D_HAL_INTERFACE_H + +#include "opencv2/core/cvdef.h" +//! @addtogroup featrure2d_hal_interface +//! @{ + +//! @name Fast feature detector types +//! @sa cv::FastFeatureDetector +//! @{ +#define CV_HAL_TYPE_5_8 0 +#define CV_HAL_TYPE_7_12 1 +#define CV_HAL_TYPE_9_16 2 +//! @} + +//! @name Key point +//! @sa cv::KeyPoint +//! @{ +struct CV_EXPORTS cvhalKeyPoint +{ + float x; + float y; + float size; + float angle; + float response; + int octave; + int class_id; +}; +//! @} + +//! @} + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann.hpp new file mode 100644 index 0000000000000000000000000000000000000000..22c6ffcf19d83b16de45703864954575101cd292 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann.hpp @@ -0,0 +1,531 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_FLANN_HPP +#define OPENCV_FLANN_HPP + +#include "opencv2/core.hpp" +#include "opencv2/flann/miniflann.hpp" +#include "opencv2/flann/flann_base.hpp" + +/** +@defgroup flann Clustering and Search in Multi-Dimensional Spaces + +This section documents OpenCV's interface to the FLANN library. FLANN (Fast Library for Approximate +Nearest Neighbors) is a library that contains a collection of algorithms optimized for fast nearest +neighbor search in large datasets and for high dimensional features. More information about FLANN +can be found in @cite Muja2009 . +*/ + +namespace cvflann +{ + CV_EXPORTS flann_distance_t flann_distance_type(); + CV_DEPRECATED CV_EXPORTS void set_distance_type(flann_distance_t distance_type, int order); +} + + +namespace cv +{ +namespace flann +{ + + +//! @addtogroup flann +//! @{ + +template struct CvType {}; +template <> struct CvType { static int type() { return CV_8U; } }; +template <> struct CvType { static int type() { return CV_8S; } }; +template <> struct CvType { static int type() { return CV_16U; } }; +template <> struct CvType { static int type() { return CV_16S; } }; +template <> struct CvType { static int type() { return CV_32S; } }; +template <> struct CvType { static int type() { return CV_32F; } }; +template <> struct CvType { static int type() { return CV_64F; } }; + + +// bring the flann parameters into this namespace +using ::cvflann::get_param; +using ::cvflann::print_params; + +// bring the flann distances into this namespace +using ::cvflann::L2_Simple; +using ::cvflann::L2; +using ::cvflann::L1; +using ::cvflann::MinkowskiDistance; +using ::cvflann::MaxDistance; +using ::cvflann::HammingLUT; +using ::cvflann::Hamming; +using ::cvflann::Hamming2; +using ::cvflann::HistIntersectionDistance; +using ::cvflann::HellingerDistance; +using ::cvflann::ChiSquareDistance; +using ::cvflann::KL_Divergence; + + +/** @brief The FLANN nearest neighbor index class. This class is templated with the type of elements for which +the index is built. + */ +template +class GenericIndex +{ +public: + typedef typename Distance::ElementType ElementType; + typedef typename Distance::ResultType DistanceType; + + /** @brief Constructs a nearest neighbor search index for a given dataset. + + @param features Matrix of containing the features(points) to index. The size of the matrix is + num_features x feature_dimensionality and the data type of the elements in the matrix must + coincide with the type of the index. + @param params Structure containing the index parameters. The type of index that will be + constructed depends on the type of this parameter. See the description. + @param distance + + The method constructs a fast search structure from a set of features using the specified algorithm + with specified parameters, as defined by params. params is a reference to one of the following class + IndexParams descendants: + + - **LinearIndexParams** When passing an object of this type, the index will perform a linear, + brute-force search. : + @code + struct LinearIndexParams : public IndexParams + { + }; + @endcode + - **KDTreeIndexParams** When passing an object of this type the index constructed will consist of + a set of randomized kd-trees which will be searched in parallel. : + @code + struct KDTreeIndexParams : public IndexParams + { + KDTreeIndexParams( int trees = 4 ); + }; + @endcode + - **KMeansIndexParams** When passing an object of this type the index constructed will be a + hierarchical k-means tree. : + @code + struct KMeansIndexParams : public IndexParams + { + KMeansIndexParams( + int branching = 32, + int iterations = 11, + flann_centers_init_t centers_init = CENTERS_RANDOM, + float cb_index = 0.2 ); + }; + @endcode + - **CompositeIndexParams** When using a parameters object of this type the index created + combines the randomized kd-trees and the hierarchical k-means tree. : + @code + struct CompositeIndexParams : public IndexParams + { + CompositeIndexParams( + int trees = 4, + int branching = 32, + int iterations = 11, + flann_centers_init_t centers_init = CENTERS_RANDOM, + float cb_index = 0.2 ); + }; + @endcode + - **LshIndexParams** When using a parameters object of this type the index created uses + multi-probe LSH (by Multi-Probe LSH: Efficient Indexing for High-Dimensional Similarity Search + by Qin Lv, William Josephson, Zhe Wang, Moses Charikar, Kai Li., Proceedings of the 33rd + International Conference on Very Large Data Bases (VLDB). Vienna, Austria. September 2007) : + @code + struct LshIndexParams : public IndexParams + { + LshIndexParams( + unsigned int table_number, + unsigned int key_size, + unsigned int multi_probe_level ); + }; + @endcode + - **AutotunedIndexParams** When passing an object of this type the index created is + automatically tuned to offer the best performance, by choosing the optimal index type + (randomized kd-trees, hierarchical kmeans, linear) and parameters for the dataset provided. : + @code + struct AutotunedIndexParams : public IndexParams + { + AutotunedIndexParams( + float target_precision = 0.9, + float build_weight = 0.01, + float memory_weight = 0, + float sample_fraction = 0.1 ); + }; + @endcode + - **SavedIndexParams** This object type is used for loading a previously saved index from the + disk. : + @code + struct SavedIndexParams : public IndexParams + { + SavedIndexParams( String filename ); + }; + @endcode + */ + GenericIndex(const Mat& features, const ::cvflann::IndexParams& params, Distance distance = Distance()); + + ~GenericIndex(); + + /** @brief Performs a K-nearest neighbor search for a given query point using the index. + + @param query The query point + @param indices Vector that will contain the indices of the K-nearest neighbors found. It must have + at least knn size. + @param dists Vector that will contain the distances to the K-nearest neighbors found. It must have + at least knn size. + @param knn Number of nearest neighbors to search for. + @param params SearchParams + */ + void knnSearch(const std::vector& query, std::vector& indices, + std::vector& dists, int knn, const ::cvflann::SearchParams& params); + void knnSearch(const Mat& queries, Mat& indices, Mat& dists, int knn, const ::cvflann::SearchParams& params); + + int radiusSearch(const std::vector& query, std::vector& indices, + std::vector& dists, DistanceType radius, const ::cvflann::SearchParams& params); + int radiusSearch(const Mat& query, Mat& indices, Mat& dists, + DistanceType radius, const ::cvflann::SearchParams& params); + + void save(String filename) { nnIndex->save(filename); } + + int veclen() const { return nnIndex->veclen(); } + + int size() const { return nnIndex->size(); } + + ::cvflann::IndexParams getParameters() { return nnIndex->getParameters(); } + + CV_DEPRECATED const ::cvflann::IndexParams* getIndexParameters() { return nnIndex->getIndexParameters(); } + +private: + ::cvflann::Index* nnIndex; +}; + +//! @cond IGNORED + +#define FLANN_DISTANCE_CHECK \ + if ( ::cvflann::flann_distance_type() != cvflann::FLANN_DIST_L2) { \ + printf("[WARNING] You are using cv::flann::Index (or cv::flann::GenericIndex) and have also changed "\ + "the distance using cvflann::set_distance_type. This is no longer working as expected "\ + "(cv::flann::Index always uses L2). You should create the index templated on the distance, "\ + "for example for L1 distance use: GenericIndex< L1 > \n"); \ + } + + +template +GenericIndex::GenericIndex(const Mat& dataset, const ::cvflann::IndexParams& params, Distance distance) +{ + CV_Assert(dataset.type() == CvType::type()); + CV_Assert(dataset.isContinuous()); + ::cvflann::Matrix m_dataset((ElementType*)dataset.ptr(0), dataset.rows, dataset.cols); + + nnIndex = new ::cvflann::Index(m_dataset, params, distance); + + FLANN_DISTANCE_CHECK + + nnIndex->buildIndex(); +} + +template +GenericIndex::~GenericIndex() +{ + delete nnIndex; +} + +template +void GenericIndex::knnSearch(const std::vector& query, std::vector& indices, std::vector& dists, int knn, const ::cvflann::SearchParams& searchParams) +{ + ::cvflann::Matrix m_query((ElementType*)&query[0], 1, query.size()); + ::cvflann::Matrix m_indices(&indices[0], 1, indices.size()); + ::cvflann::Matrix m_dists(&dists[0], 1, dists.size()); + + FLANN_DISTANCE_CHECK + + nnIndex->knnSearch(m_query,m_indices,m_dists,knn,searchParams); +} + + +template +void GenericIndex::knnSearch(const Mat& queries, Mat& indices, Mat& dists, int knn, const ::cvflann::SearchParams& searchParams) +{ + CV_Assert(queries.type() == CvType::type()); + CV_Assert(queries.isContinuous()); + ::cvflann::Matrix m_queries((ElementType*)queries.ptr(0), queries.rows, queries.cols); + + CV_Assert(indices.type() == CV_32S); + CV_Assert(indices.isContinuous()); + ::cvflann::Matrix m_indices((int*)indices.ptr(0), indices.rows, indices.cols); + + CV_Assert(dists.type() == CvType::type()); + CV_Assert(dists.isContinuous()); + ::cvflann::Matrix m_dists((DistanceType*)dists.ptr(0), dists.rows, dists.cols); + + FLANN_DISTANCE_CHECK + + nnIndex->knnSearch(m_queries,m_indices,m_dists,knn, searchParams); +} + +template +int GenericIndex::radiusSearch(const std::vector& query, std::vector& indices, std::vector& dists, DistanceType radius, const ::cvflann::SearchParams& searchParams) +{ + ::cvflann::Matrix m_query((ElementType*)&query[0], 1, query.size()); + ::cvflann::Matrix m_indices(&indices[0], 1, indices.size()); + ::cvflann::Matrix m_dists(&dists[0], 1, dists.size()); + + FLANN_DISTANCE_CHECK + + return nnIndex->radiusSearch(m_query,m_indices,m_dists,radius,searchParams); +} + +template +int GenericIndex::radiusSearch(const Mat& query, Mat& indices, Mat& dists, DistanceType radius, const ::cvflann::SearchParams& searchParams) +{ + CV_Assert(query.type() == CvType::type()); + CV_Assert(query.isContinuous()); + ::cvflann::Matrix m_query((ElementType*)query.ptr(0), query.rows, query.cols); + + CV_Assert(indices.type() == CV_32S); + CV_Assert(indices.isContinuous()); + ::cvflann::Matrix m_indices((int*)indices.ptr(0), indices.rows, indices.cols); + + CV_Assert(dists.type() == CvType::type()); + CV_Assert(dists.isContinuous()); + ::cvflann::Matrix m_dists((DistanceType*)dists.ptr(0), dists.rows, dists.cols); + + FLANN_DISTANCE_CHECK + + return nnIndex->radiusSearch(m_query,m_indices,m_dists,radius,searchParams); +} + +//! @endcond + +/** + * @deprecated Use GenericIndex class instead + */ +template +class Index_ +{ +public: + typedef typename L2::ElementType ElementType; + typedef typename L2::ResultType DistanceType; + + CV_DEPRECATED Index_(const Mat& dataset, const ::cvflann::IndexParams& params) + { + printf("[WARNING] The cv::flann::Index_ class is deperecated, use cv::flann::GenericIndex instead\n"); + + CV_Assert(dataset.type() == CvType::type()); + CV_Assert(dataset.isContinuous()); + ::cvflann::Matrix m_dataset((ElementType*)dataset.ptr(0), dataset.rows, dataset.cols); + + if ( ::cvflann::flann_distance_type() == cvflann::FLANN_DIST_L2 ) { + nnIndex_L1 = NULL; + nnIndex_L2 = new ::cvflann::Index< L2 >(m_dataset, params); + } + else if ( ::cvflann::flann_distance_type() == cvflann::FLANN_DIST_L1 ) { + nnIndex_L1 = new ::cvflann::Index< L1 >(m_dataset, params); + nnIndex_L2 = NULL; + } + else { + printf("[ERROR] cv::flann::Index_ only provides backwards compatibility for the L1 and L2 distances. " + "For other distance types you must use cv::flann::GenericIndex\n"); + CV_Assert(0); + } + if (nnIndex_L1) nnIndex_L1->buildIndex(); + if (nnIndex_L2) nnIndex_L2->buildIndex(); + } + CV_DEPRECATED ~Index_() + { + if (nnIndex_L1) delete nnIndex_L1; + if (nnIndex_L2) delete nnIndex_L2; + } + + CV_DEPRECATED void knnSearch(const std::vector& query, std::vector& indices, std::vector& dists, int knn, const ::cvflann::SearchParams& searchParams) + { + ::cvflann::Matrix m_query((ElementType*)&query[0], 1, query.size()); + ::cvflann::Matrix m_indices(&indices[0], 1, indices.size()); + ::cvflann::Matrix m_dists(&dists[0], 1, dists.size()); + + if (nnIndex_L1) nnIndex_L1->knnSearch(m_query,m_indices,m_dists,knn,searchParams); + if (nnIndex_L2) nnIndex_L2->knnSearch(m_query,m_indices,m_dists,knn,searchParams); + } + CV_DEPRECATED void knnSearch(const Mat& queries, Mat& indices, Mat& dists, int knn, const ::cvflann::SearchParams& searchParams) + { + CV_Assert(queries.type() == CvType::type()); + CV_Assert(queries.isContinuous()); + ::cvflann::Matrix m_queries((ElementType*)queries.ptr(0), queries.rows, queries.cols); + + CV_Assert(indices.type() == CV_32S); + CV_Assert(indices.isContinuous()); + ::cvflann::Matrix m_indices((int*)indices.ptr(0), indices.rows, indices.cols); + + CV_Assert(dists.type() == CvType::type()); + CV_Assert(dists.isContinuous()); + ::cvflann::Matrix m_dists((DistanceType*)dists.ptr(0), dists.rows, dists.cols); + + if (nnIndex_L1) nnIndex_L1->knnSearch(m_queries,m_indices,m_dists,knn, searchParams); + if (nnIndex_L2) nnIndex_L2->knnSearch(m_queries,m_indices,m_dists,knn, searchParams); + } + + CV_DEPRECATED int radiusSearch(const std::vector& query, std::vector& indices, std::vector& dists, DistanceType radius, const ::cvflann::SearchParams& searchParams) + { + ::cvflann::Matrix m_query((ElementType*)&query[0], 1, query.size()); + ::cvflann::Matrix m_indices(&indices[0], 1, indices.size()); + ::cvflann::Matrix m_dists(&dists[0], 1, dists.size()); + + if (nnIndex_L1) return nnIndex_L1->radiusSearch(m_query,m_indices,m_dists,radius,searchParams); + if (nnIndex_L2) return nnIndex_L2->radiusSearch(m_query,m_indices,m_dists,radius,searchParams); + } + + CV_DEPRECATED int radiusSearch(const Mat& query, Mat& indices, Mat& dists, DistanceType radius, const ::cvflann::SearchParams& searchParams) + { + CV_Assert(query.type() == CvType::type()); + CV_Assert(query.isContinuous()); + ::cvflann::Matrix m_query((ElementType*)query.ptr(0), query.rows, query.cols); + + CV_Assert(indices.type() == CV_32S); + CV_Assert(indices.isContinuous()); + ::cvflann::Matrix m_indices((int*)indices.ptr(0), indices.rows, indices.cols); + + CV_Assert(dists.type() == CvType::type()); + CV_Assert(dists.isContinuous()); + ::cvflann::Matrix m_dists((DistanceType*)dists.ptr(0), dists.rows, dists.cols); + + if (nnIndex_L1) return nnIndex_L1->radiusSearch(m_query,m_indices,m_dists,radius,searchParams); + if (nnIndex_L2) return nnIndex_L2->radiusSearch(m_query,m_indices,m_dists,radius,searchParams); + } + + CV_DEPRECATED void save(String filename) + { + if (nnIndex_L1) nnIndex_L1->save(filename); + if (nnIndex_L2) nnIndex_L2->save(filename); + } + + CV_DEPRECATED int veclen() const + { + if (nnIndex_L1) return nnIndex_L1->veclen(); + if (nnIndex_L2) return nnIndex_L2->veclen(); + } + + CV_DEPRECATED int size() const + { + if (nnIndex_L1) return nnIndex_L1->size(); + if (nnIndex_L2) return nnIndex_L2->size(); + } + + CV_DEPRECATED ::cvflann::IndexParams getParameters() + { + if (nnIndex_L1) return nnIndex_L1->getParameters(); + if (nnIndex_L2) return nnIndex_L2->getParameters(); + + } + + CV_DEPRECATED const ::cvflann::IndexParams* getIndexParameters() + { + if (nnIndex_L1) return nnIndex_L1->getIndexParameters(); + if (nnIndex_L2) return nnIndex_L2->getIndexParameters(); + } + +private: + // providing backwards compatibility for L2 and L1 distances (most common) + ::cvflann::Index< L2 >* nnIndex_L2; + ::cvflann::Index< L1 >* nnIndex_L1; +}; + + +/** @brief Clusters features using hierarchical k-means algorithm. + +@param features The points to be clustered. The matrix must have elements of type +Distance::ElementType. +@param centers The centers of the clusters obtained. The matrix must have type +Distance::ResultType. The number of rows in this matrix represents the number of clusters desired, +however, because of the way the cut in the hierarchical tree is chosen, the number of clusters +computed will be the highest number of the form (branching-1)\*k+1 that's lower than the number of +clusters desired, where branching is the tree's branching factor (see description of the +KMeansIndexParams). +@param params Parameters used in the construction of the hierarchical k-means tree. +@param d Distance to be used for clustering. + +The method clusters the given feature vectors by constructing a hierarchical k-means tree and +choosing a cut in the tree that minimizes the cluster's variance. It returns the number of clusters +found. + */ +template +int hierarchicalClustering(const Mat& features, Mat& centers, const ::cvflann::KMeansIndexParams& params, + Distance d = Distance()) +{ + typedef typename Distance::ElementType ElementType; + typedef typename Distance::ResultType DistanceType; + + CV_Assert(features.type() == CvType::type()); + CV_Assert(features.isContinuous()); + ::cvflann::Matrix m_features((ElementType*)features.ptr(0), features.rows, features.cols); + + CV_Assert(centers.type() == CvType::type()); + CV_Assert(centers.isContinuous()); + ::cvflann::Matrix m_centers((DistanceType*)centers.ptr(0), centers.rows, centers.cols); + + return ::cvflann::hierarchicalClustering(m_features, m_centers, params, d); +} + +/** @deprecated +*/ +template +CV_DEPRECATED int hierarchicalClustering(const Mat& features, Mat& centers, const ::cvflann::KMeansIndexParams& params) +{ + printf("[WARNING] cv::flann::hierarchicalClustering is deprecated, use " + "cv::flann::hierarchicalClustering instead\n"); + + if ( ::cvflann::flann_distance_type() == cvflann::FLANN_DIST_L2 ) { + return hierarchicalClustering< L2 >(features, centers, params); + } + else if ( ::cvflann::flann_distance_type() == cvflann::FLANN_DIST_L1 ) { + return hierarchicalClustering< L1 >(features, centers, params); + } + else { + printf("[ERROR] cv::flann::hierarchicalClustering only provides backwards " + "compatibility for the L1 and L2 distances. " + "For other distance types you must use cv::flann::hierarchicalClustering\n"); + CV_Assert(0); + } +} + +//! @} flann + +} } // namespace cv::flann + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/all_indices.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/all_indices.h new file mode 100644 index 0000000000000000000000000000000000000000..ff53fd84c2818ba828e258620975d41843478699 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/all_indices.h @@ -0,0 +1,155 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + + +#ifndef OPENCV_FLANN_ALL_INDICES_H_ +#define OPENCV_FLANN_ALL_INDICES_H_ + +#include "general.h" + +#include "nn_index.h" +#include "kdtree_index.h" +#include "kdtree_single_index.h" +#include "kmeans_index.h" +#include "composite_index.h" +#include "linear_index.h" +#include "hierarchical_clustering_index.h" +#include "lsh_index.h" +#include "autotuned_index.h" + + +namespace cvflann +{ + +template +struct index_creator +{ + static NNIndex* create(const Matrix& dataset, const IndexParams& params, const Distance& distance) + { + flann_algorithm_t index_type = get_param(params, "algorithm"); + + NNIndex* nnIndex; + switch (index_type) { + case FLANN_INDEX_LINEAR: + nnIndex = new LinearIndex(dataset, params, distance); + break; + case FLANN_INDEX_KDTREE_SINGLE: + nnIndex = new KDTreeSingleIndex(dataset, params, distance); + break; + case FLANN_INDEX_KDTREE: + nnIndex = new KDTreeIndex(dataset, params, distance); + break; + case FLANN_INDEX_KMEANS: + nnIndex = new KMeansIndex(dataset, params, distance); + break; + case FLANN_INDEX_COMPOSITE: + nnIndex = new CompositeIndex(dataset, params, distance); + break; + case FLANN_INDEX_AUTOTUNED: + nnIndex = new AutotunedIndex(dataset, params, distance); + break; + case FLANN_INDEX_HIERARCHICAL: + nnIndex = new HierarchicalClusteringIndex(dataset, params, distance); + break; + case FLANN_INDEX_LSH: + nnIndex = new LshIndex(dataset, params, distance); + break; + default: + throw FLANNException("Unknown index type"); + } + + return nnIndex; + } +}; + +template +struct index_creator +{ + static NNIndex* create(const Matrix& dataset, const IndexParams& params, const Distance& distance) + { + flann_algorithm_t index_type = get_param(params, "algorithm"); + + NNIndex* nnIndex; + switch (index_type) { + case FLANN_INDEX_LINEAR: + nnIndex = new LinearIndex(dataset, params, distance); + break; + case FLANN_INDEX_KMEANS: + nnIndex = new KMeansIndex(dataset, params, distance); + break; + case FLANN_INDEX_HIERARCHICAL: + nnIndex = new HierarchicalClusteringIndex(dataset, params, distance); + break; + case FLANN_INDEX_LSH: + nnIndex = new LshIndex(dataset, params, distance); + break; + default: + throw FLANNException("Unknown index type"); + } + + return nnIndex; + } +}; + +template +struct index_creator +{ + static NNIndex* create(const Matrix& dataset, const IndexParams& params, const Distance& distance) + { + flann_algorithm_t index_type = get_param(params, "algorithm"); + + NNIndex* nnIndex; + switch (index_type) { + case FLANN_INDEX_LINEAR: + nnIndex = new LinearIndex(dataset, params, distance); + break; + case FLANN_INDEX_HIERARCHICAL: + nnIndex = new HierarchicalClusteringIndex(dataset, params, distance); + break; + case FLANN_INDEX_LSH: + nnIndex = new LshIndex(dataset, params, distance); + break; + default: + throw FLANNException("Unknown index type"); + } + + return nnIndex; + } +}; + +template +NNIndex* create_index_by_type(const Matrix& dataset, const IndexParams& params, const Distance& distance) +{ + return index_creator::create(dataset, params,distance); +} + +} + +#endif /* OPENCV_FLANN_ALL_INDICES_H_ */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/allocator.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/allocator.h new file mode 100644 index 0000000000000000000000000000000000000000..f347f88e9686f46888b3feaead97356a2ce045ad --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/allocator.h @@ -0,0 +1,192 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + +#ifndef OPENCV_FLANN_ALLOCATOR_H_ +#define OPENCV_FLANN_ALLOCATOR_H_ + +#include +#include + + +namespace cvflann +{ + +/** + * Allocates (using C's malloc) a generic type T. + * + * Params: + * count = number of instances to allocate. + * Returns: pointer (of type T*) to memory buffer + */ +template +T* allocate(size_t count = 1) +{ + T* mem = (T*) ::malloc(sizeof(T)*count); + return mem; +} + + +/** + * Pooled storage allocator + * + * The following routines allow for the efficient allocation of storage in + * small chunks from a specified pool. Rather than allowing each structure + * to be freed individually, an entire pool of storage is freed at once. + * This method has two advantages over just using malloc() and free(). First, + * it is far more efficient for allocating small objects, as there is + * no overhead for remembering all the information needed to free each + * object or consolidating fragmented memory. Second, the decision about + * how long to keep an object is made at the time of allocation, and there + * is no need to track down all the objects to free them. + * + */ + +const size_t WORDSIZE=16; +const size_t BLOCKSIZE=8192; + +class PooledAllocator +{ + /* We maintain memory alignment to word boundaries by requiring that all + allocations be in multiples of the machine wordsize. */ + /* Size of machine word in bytes. Must be power of 2. */ + /* Minimum number of bytes requested at a time from the system. Must be multiple of WORDSIZE. */ + + + int remaining; /* Number of bytes left in current block of storage. */ + void* base; /* Pointer to base of current block of storage. */ + void* loc; /* Current location in block to next allocate memory. */ + int blocksize; + + +public: + int usedMemory; + int wastedMemory; + + /** + Default constructor. Initializes a new pool. + */ + PooledAllocator(int blockSize = BLOCKSIZE) + { + blocksize = blockSize; + remaining = 0; + base = NULL; + loc = NULL; + + usedMemory = 0; + wastedMemory = 0; + } + + /** + * Destructor. Frees all the memory allocated in this pool. + */ + ~PooledAllocator() + { + void* prev; + + while (base != NULL) { + prev = *((void**) base); /* Get pointer to prev block. */ + ::free(base); + base = prev; + } + } + + /** + * Returns a pointer to a piece of new memory of the given size in bytes + * allocated from the pool. + */ + void* allocateMemory(int size) + { + int blockSize; + + /* Round size up to a multiple of wordsize. The following expression + only works for WORDSIZE that is a power of 2, by masking last bits of + incremented size to zero. + */ + size = (size + (WORDSIZE - 1)) & ~(WORDSIZE - 1); + + /* Check whether a new block must be allocated. Note that the first word + of a block is reserved for a pointer to the previous block. + */ + if (size > remaining) { + + wastedMemory += remaining; + + /* Allocate new storage. */ + blockSize = (size + sizeof(void*) + (WORDSIZE-1) > BLOCKSIZE) ? + size + sizeof(void*) + (WORDSIZE-1) : BLOCKSIZE; + + // use the standard C malloc to allocate memory + void* m = ::malloc(blockSize); + if (!m) { + fprintf(stderr,"Failed to allocate memory.\n"); + return NULL; + } + + /* Fill first word of new block with pointer to previous block. */ + ((void**) m)[0] = base; + base = m; + + int shift = 0; + //int shift = (WORDSIZE - ( (((size_t)m) + sizeof(void*)) & (WORDSIZE-1))) & (WORDSIZE-1); + + remaining = blockSize - sizeof(void*) - shift; + loc = ((char*)m + sizeof(void*) + shift); + } + void* rloc = loc; + loc = (char*)loc + size; + remaining -= size; + + usedMemory += size; + + return rloc; + } + + /** + * Allocates (using this pool) a generic type T. + * + * Params: + * count = number of instances to allocate. + * Returns: pointer (of type T*) to memory buffer + */ + template + T* allocate(size_t count = 1) + { + T* mem = (T*) this->allocateMemory((int)(sizeof(T)*count)); + return mem; + } + +private: + PooledAllocator(const PooledAllocator &); // copy disabled + PooledAllocator& operator=(const PooledAllocator &); // assign disabled +}; + +} + +#endif //OPENCV_FLANN_ALLOCATOR_H_ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/any.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/any.h new file mode 100644 index 0000000000000000000000000000000000000000..5b57aa3df31cfd3c5ab7e9afadba89aa9cecdcad --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/any.h @@ -0,0 +1,330 @@ +#ifndef OPENCV_FLANN_ANY_H_ +#define OPENCV_FLANN_ANY_H_ +/* + * (C) Copyright Christopher Diggins 2005-2011 + * (C) Copyright Pablo Aguilar 2005 + * (C) Copyright Kevlin Henney 2001 + * + * Distributed under the Boost Software License, Version 1.0. (See + * accompanying file LICENSE_1_0.txt or copy at + * http://www.boost.org/LICENSE_1_0.txt + * + * Adapted for FLANN by Marius Muja + */ + +#include "defines.h" +#include +#include +#include + +namespace cvflann +{ + +namespace anyimpl +{ + +struct bad_any_cast +{ +}; + +struct empty_any +{ +}; + +inline std::ostream& operator <<(std::ostream& out, const empty_any&) +{ + out << "[empty_any]"; + return out; +} + +struct base_any_policy +{ + virtual void static_delete(void** x) = 0; + virtual void copy_from_value(void const* src, void** dest) = 0; + virtual void clone(void* const* src, void** dest) = 0; + virtual void move(void* const* src, void** dest) = 0; + virtual void* get_value(void** src) = 0; + virtual const void* get_value(void* const * src) = 0; + virtual ::size_t get_size() = 0; + virtual const std::type_info& type() = 0; + virtual void print(std::ostream& out, void* const* src) = 0; + virtual ~base_any_policy() {} +}; + +template +struct typed_base_any_policy : base_any_policy +{ + virtual ::size_t get_size() CV_OVERRIDE { return sizeof(T); } + virtual const std::type_info& type() CV_OVERRIDE { return typeid(T); } + +}; + +template +struct small_any_policy CV_FINAL : typed_base_any_policy +{ + virtual void static_delete(void**) CV_OVERRIDE { } + virtual void copy_from_value(void const* src, void** dest) CV_OVERRIDE + { + new (dest) T(* reinterpret_cast(src)); + } + virtual void clone(void* const* src, void** dest) CV_OVERRIDE { *dest = *src; } + virtual void move(void* const* src, void** dest) CV_OVERRIDE { *dest = *src; } + virtual void* get_value(void** src) CV_OVERRIDE { return reinterpret_cast(src); } + virtual const void* get_value(void* const * src) CV_OVERRIDE { return reinterpret_cast(src); } + virtual void print(std::ostream& out, void* const* src) CV_OVERRIDE { out << *reinterpret_cast(src); } +}; + +template +struct big_any_policy CV_FINAL : typed_base_any_policy +{ + virtual void static_delete(void** x) CV_OVERRIDE + { + if (* x) delete (* reinterpret_cast(x)); + *x = NULL; + } + virtual void copy_from_value(void const* src, void** dest) CV_OVERRIDE + { + *dest = new T(*reinterpret_cast(src)); + } + virtual void clone(void* const* src, void** dest) CV_OVERRIDE + { + *dest = new T(**reinterpret_cast(src)); + } + virtual void move(void* const* src, void** dest) CV_OVERRIDE + { + (*reinterpret_cast(dest))->~T(); + **reinterpret_cast(dest) = **reinterpret_cast(src); + } + virtual void* get_value(void** src) CV_OVERRIDE { return *src; } + virtual const void* get_value(void* const * src) CV_OVERRIDE { return *src; } + virtual void print(std::ostream& out, void* const* src) CV_OVERRIDE { out << *reinterpret_cast(*src); } +}; + +template<> inline void big_any_policy::print(std::ostream& out, void* const* src) +{ + out << int(*reinterpret_cast(*src)); +} + +template<> inline void big_any_policy::print(std::ostream& out, void* const* src) +{ + out << int(*reinterpret_cast(*src)); +} + +template<> inline void big_any_policy::print(std::ostream& out, void* const* src) +{ + out << (*reinterpret_cast(*src)).c_str(); +} + +template +struct choose_policy +{ + typedef big_any_policy type; +}; + +template +struct choose_policy +{ + typedef small_any_policy type; +}; + +struct any; + +/// Choosing the policy for an any type is illegal, but should never happen. +/// This is designed to throw a compiler error. +template<> +struct choose_policy +{ + typedef void type; +}; + +/// Specializations for small types. +#define SMALL_POLICY(TYPE) \ + template<> \ + struct choose_policy { typedef small_any_policy type; \ + } + +SMALL_POLICY(signed char); +SMALL_POLICY(unsigned char); +SMALL_POLICY(signed short); +SMALL_POLICY(unsigned short); +SMALL_POLICY(signed int); +SMALL_POLICY(unsigned int); +SMALL_POLICY(signed long); +SMALL_POLICY(unsigned long); +SMALL_POLICY(float); +SMALL_POLICY(bool); + +#undef SMALL_POLICY + +template +class SinglePolicy +{ + SinglePolicy(); + SinglePolicy(const SinglePolicy& other); + SinglePolicy& operator=(const SinglePolicy& other); + +public: + static base_any_policy* get_policy(); + +private: + static typename choose_policy::type policy; +}; + +template +typename choose_policy::type SinglePolicy::policy; + +/// This function will return a different policy for each type. +template +inline base_any_policy* SinglePolicy::get_policy() { return &policy; } + +} // namespace anyimpl + +struct any +{ +private: + // fields + anyimpl::base_any_policy* policy; + void* object; + +public: + /// Initializing constructor. + template + any(const T& x) + : policy(anyimpl::SinglePolicy::get_policy()), object(NULL) + { + assign(x); + } + + /// Empty constructor. + any() + : policy(anyimpl::SinglePolicy::get_policy()), object(NULL) + { } + + /// Special initializing constructor for string literals. + any(const char* x) + : policy(anyimpl::SinglePolicy::get_policy()), object(NULL) + { + assign(x); + } + + /// Copy constructor. + any(const any& x) + : policy(anyimpl::SinglePolicy::get_policy()), object(NULL) + { + assign(x); + } + + /// Destructor. + ~any() + { + policy->static_delete(&object); + } + + /// Assignment function from another any. + any& assign(const any& x) + { + reset(); + policy = x.policy; + policy->clone(&x.object, &object); + return *this; + } + + /// Assignment function. + template + any& assign(const T& x) + { + reset(); + policy = anyimpl::SinglePolicy::get_policy(); + policy->copy_from_value(&x, &object); + return *this; + } + + /// Assignment operator. + template + any& operator=(const T& x) + { + return assign(x); + } + + /// Assignment operator. Template-based version above doesn't work as expected. We need regular assignment operator here. + any& operator=(const any& x) + { + return assign(x); + } + + /// Assignment operator, specialed for literal strings. + /// They have types like const char [6] which don't work as expected. + any& operator=(const char* x) + { + return assign(x); + } + + /// Utility functions + any& swap(any& x) + { + std::swap(policy, x.policy); + std::swap(object, x.object); + return *this; + } + + /// Cast operator. You can only cast to the original type. + template + T& cast() + { + if (policy->type() != typeid(T)) throw anyimpl::bad_any_cast(); + T* r = reinterpret_cast(policy->get_value(&object)); + return *r; + } + + /// Cast operator. You can only cast to the original type. + template + const T& cast() const + { + if (policy->type() != typeid(T)) throw anyimpl::bad_any_cast(); + const T* r = reinterpret_cast(policy->get_value(&object)); + return *r; + } + + /// Returns true if the any contains no value. + bool empty() const + { + return policy->type() == typeid(anyimpl::empty_any); + } + + /// Frees any allocated memory, and sets the value to NULL. + void reset() + { + policy->static_delete(&object); + policy = anyimpl::SinglePolicy::get_policy(); + } + + /// Returns true if the two types are the same. + bool compatible(const any& x) const + { + return policy->type() == x.policy->type(); + } + + /// Returns if the type is compatible with the policy + template + bool has_type() + { + return policy->type() == typeid(T); + } + + const std::type_info& type() const + { + return policy->type(); + } + + friend std::ostream& operator <<(std::ostream& out, const any& any_val); +}; + +inline std::ostream& operator <<(std::ostream& out, const any& any_val) +{ + any_val.policy->print(out,&any_val.object); + return out; +} + +} + +#endif // OPENCV_FLANN_ANY_H_ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/autotuned_index.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/autotuned_index.h new file mode 100644 index 0000000000000000000000000000000000000000..2fbc6c9afb9b732db15b0411e444f48fd90010cc --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/autotuned_index.h @@ -0,0 +1,591 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ +#ifndef OPENCV_FLANN_AUTOTUNED_INDEX_H_ +#define OPENCV_FLANN_AUTOTUNED_INDEX_H_ + +#include + +#include "general.h" +#include "nn_index.h" +#include "ground_truth.h" +#include "index_testing.h" +#include "sampling.h" +#include "kdtree_index.h" +#include "kdtree_single_index.h" +#include "kmeans_index.h" +#include "composite_index.h" +#include "linear_index.h" +#include "logger.h" + +namespace cvflann +{ + +template +NNIndex* create_index_by_type(const Matrix& dataset, const IndexParams& params, const Distance& distance); + + +struct AutotunedIndexParams : public IndexParams +{ + AutotunedIndexParams(float target_precision = 0.8, float build_weight = 0.01, float memory_weight = 0, float sample_fraction = 0.1) + { + (*this)["algorithm"] = FLANN_INDEX_AUTOTUNED; + // precision desired (used for autotuning, -1 otherwise) + (*this)["target_precision"] = target_precision; + // build tree time weighting factor + (*this)["build_weight"] = build_weight; + // index memory weighting factor + (*this)["memory_weight"] = memory_weight; + // what fraction of the dataset to use for autotuning + (*this)["sample_fraction"] = sample_fraction; + } +}; + + +template +class AutotunedIndex : public NNIndex +{ +public: + typedef typename Distance::ElementType ElementType; + typedef typename Distance::ResultType DistanceType; + + AutotunedIndex(const Matrix& inputData, const IndexParams& params = AutotunedIndexParams(), Distance d = Distance()) : + dataset_(inputData), distance_(d) + { + target_precision_ = get_param(params, "target_precision",0.8f); + build_weight_ = get_param(params,"build_weight", 0.01f); + memory_weight_ = get_param(params, "memory_weight", 0.0f); + sample_fraction_ = get_param(params,"sample_fraction", 0.1f); + bestIndex_ = NULL; + speedup_ = 0; + } + + AutotunedIndex(const AutotunedIndex&); + AutotunedIndex& operator=(const AutotunedIndex&); + + virtual ~AutotunedIndex() + { + if (bestIndex_ != NULL) { + delete bestIndex_; + bestIndex_ = NULL; + } + } + + /** + * Method responsible with building the index. + */ + virtual void buildIndex() CV_OVERRIDE + { + std::ostringstream stream; + bestParams_ = estimateBuildParams(); + print_params(bestParams_, stream); + Logger::info("----------------------------------------------------\n"); + Logger::info("Autotuned parameters:\n"); + Logger::info("%s", stream.str().c_str()); + Logger::info("----------------------------------------------------\n"); + + bestIndex_ = create_index_by_type(dataset_, bestParams_, distance_); + bestIndex_->buildIndex(); + speedup_ = estimateSearchParams(bestSearchParams_); + stream.str(std::string()); + print_params(bestSearchParams_, stream); + Logger::info("----------------------------------------------------\n"); + Logger::info("Search parameters:\n"); + Logger::info("%s", stream.str().c_str()); + Logger::info("----------------------------------------------------\n"); + } + + /** + * Saves the index to a stream + */ + virtual void saveIndex(FILE* stream) CV_OVERRIDE + { + save_value(stream, (int)bestIndex_->getType()); + bestIndex_->saveIndex(stream); + save_value(stream, get_param(bestSearchParams_, "checks")); + } + + /** + * Loads the index from a stream + */ + virtual void loadIndex(FILE* stream) CV_OVERRIDE + { + int index_type; + + load_value(stream, index_type); + IndexParams params; + params["algorithm"] = (flann_algorithm_t)index_type; + bestIndex_ = create_index_by_type(dataset_, params, distance_); + bestIndex_->loadIndex(stream); + int checks; + load_value(stream, checks); + bestSearchParams_["checks"] = checks; + } + + /** + * Method that searches for nearest-neighbors + */ + virtual void findNeighbors(ResultSet& result, const ElementType* vec, const SearchParams& searchParams) CV_OVERRIDE + { + int checks = get_param(searchParams,"checks",FLANN_CHECKS_AUTOTUNED); + if (checks == FLANN_CHECKS_AUTOTUNED) { + bestIndex_->findNeighbors(result, vec, bestSearchParams_); + } + else { + bestIndex_->findNeighbors(result, vec, searchParams); + } + } + + + IndexParams getParameters() const CV_OVERRIDE + { + return bestIndex_->getParameters(); + } + + SearchParams getSearchParameters() const + { + return bestSearchParams_; + } + + float getSpeedup() const + { + return speedup_; + } + + + /** + * Number of features in this index. + */ + virtual size_t size() const CV_OVERRIDE + { + return bestIndex_->size(); + } + + /** + * The length of each vector in this index. + */ + virtual size_t veclen() const CV_OVERRIDE + { + return bestIndex_->veclen(); + } + + /** + * The amount of memory (in bytes) this index uses. + */ + virtual int usedMemory() const CV_OVERRIDE + { + return bestIndex_->usedMemory(); + } + + /** + * Algorithm name + */ + virtual flann_algorithm_t getType() const CV_OVERRIDE + { + return FLANN_INDEX_AUTOTUNED; + } + +private: + + struct CostData + { + float searchTimeCost; + float buildTimeCost; + float memoryCost; + float totalCost; + IndexParams params; + }; + + void evaluate_kmeans(CostData& cost) + { + StartStopTimer t; + int checks; + const int nn = 1; + + Logger::info("KMeansTree using params: max_iterations=%d, branching=%d\n", + get_param(cost.params,"iterations"), + get_param(cost.params,"branching")); + KMeansIndex kmeans(sampledDataset_, cost.params, distance_); + // measure index build time + t.start(); + kmeans.buildIndex(); + t.stop(); + float buildTime = (float)t.value; + + // measure search time + float searchTime = test_index_precision(kmeans, sampledDataset_, testDataset_, gt_matches_, target_precision_, checks, distance_, nn); + + float datasetMemory = float(sampledDataset_.rows * sampledDataset_.cols * sizeof(float)); + cost.memoryCost = (kmeans.usedMemory() + datasetMemory) / datasetMemory; + cost.searchTimeCost = searchTime; + cost.buildTimeCost = buildTime; + Logger::info("KMeansTree buildTime=%g, searchTime=%g, build_weight=%g\n", buildTime, searchTime, build_weight_); + } + + + void evaluate_kdtree(CostData& cost) + { + StartStopTimer t; + int checks; + const int nn = 1; + + Logger::info("KDTree using params: trees=%d\n", get_param(cost.params,"trees")); + KDTreeIndex kdtree(sampledDataset_, cost.params, distance_); + + t.start(); + kdtree.buildIndex(); + t.stop(); + float buildTime = (float)t.value; + + //measure search time + float searchTime = test_index_precision(kdtree, sampledDataset_, testDataset_, gt_matches_, target_precision_, checks, distance_, nn); + + float datasetMemory = float(sampledDataset_.rows * sampledDataset_.cols * sizeof(float)); + cost.memoryCost = (kdtree.usedMemory() + datasetMemory) / datasetMemory; + cost.searchTimeCost = searchTime; + cost.buildTimeCost = buildTime; + Logger::info("KDTree buildTime=%g, searchTime=%g\n", buildTime, searchTime); + } + + + // struct KMeansSimpleDownhillFunctor { + // + // Autotune& autotuner; + // KMeansSimpleDownhillFunctor(Autotune& autotuner_) : autotuner(autotuner_) {} + // + // float operator()(int* params) { + // + // float maxFloat = numeric_limits::max(); + // + // if (params[0]<2) return maxFloat; + // if (params[1]<0) return maxFloat; + // + // CostData c; + // c.params["algorithm"] = KMEANS; + // c.params["centers-init"] = CENTERS_RANDOM; + // c.params["branching"] = params[0]; + // c.params["max-iterations"] = params[1]; + // + // autotuner.evaluate_kmeans(c); + // + // return c.timeCost; + // + // } + // }; + // + // struct KDTreeSimpleDownhillFunctor { + // + // Autotune& autotuner; + // KDTreeSimpleDownhillFunctor(Autotune& autotuner_) : autotuner(autotuner_) {} + // + // float operator()(int* params) { + // float maxFloat = numeric_limits::max(); + // + // if (params[0]<1) return maxFloat; + // + // CostData c; + // c.params["algorithm"] = KDTREE; + // c.params["trees"] = params[0]; + // + // autotuner.evaluate_kdtree(c); + // + // return c.timeCost; + // + // } + // }; + + + + void optimizeKMeans(std::vector& costs) + { + Logger::info("KMEANS, Step 1: Exploring parameter space\n"); + + // explore kmeans parameters space using combinations of the parameters below + int maxIterations[] = { 1, 5, 10, 15 }; + int branchingFactors[] = { 16, 32, 64, 128, 256 }; + + int kmeansParamSpaceSize = FLANN_ARRAY_LEN(maxIterations) * FLANN_ARRAY_LEN(branchingFactors); + costs.reserve(costs.size() + kmeansParamSpaceSize); + + // evaluate kmeans for all parameter combinations + for (size_t i = 0; i < FLANN_ARRAY_LEN(maxIterations); ++i) { + for (size_t j = 0; j < FLANN_ARRAY_LEN(branchingFactors); ++j) { + CostData cost; + cost.params["algorithm"] = FLANN_INDEX_KMEANS; + cost.params["centers_init"] = FLANN_CENTERS_RANDOM; + cost.params["iterations"] = maxIterations[i]; + cost.params["branching"] = branchingFactors[j]; + + evaluate_kmeans(cost); + costs.push_back(cost); + } + } + + // Logger::info("KMEANS, Step 2: simplex-downhill optimization\n"); + // + // const int n = 2; + // // choose initial simplex points as the best parameters so far + // int kmeansNMPoints[n*(n+1)]; + // float kmeansVals[n+1]; + // for (int i=0;i& costs) + { + Logger::info("KD-TREE, Step 1: Exploring parameter space\n"); + + // explore kd-tree parameters space using the parameters below + int testTrees[] = { 1, 4, 8, 16, 32 }; + + // evaluate kdtree for all parameter combinations + for (size_t i = 0; i < FLANN_ARRAY_LEN(testTrees); ++i) { + CostData cost; + cost.params["algorithm"] = FLANN_INDEX_KDTREE; + cost.params["trees"] = testTrees[i]; + + evaluate_kdtree(cost); + costs.push_back(cost); + } + + // Logger::info("KD-TREE, Step 2: simplex-downhill optimization\n"); + // + // const int n = 1; + // // choose initial simplex points as the best parameters so far + // int kdtreeNMPoints[n*(n+1)]; + // float kdtreeVals[n+1]; + // for (int i=0;i costs; + + int sampleSize = int(sample_fraction_ * dataset_.rows); + int testSampleSize = std::min(sampleSize / 10, 1000); + + Logger::info("Entering autotuning, dataset size: %d, sampleSize: %d, testSampleSize: %d, target precision: %g\n", dataset_.rows, sampleSize, testSampleSize, target_precision_); + + // For a very small dataset, it makes no sense to build any fancy index, just + // use linear search + if (testSampleSize < 10) { + Logger::info("Choosing linear, dataset too small\n"); + return LinearIndexParams(); + } + + // We use a fraction of the original dataset to speedup the autotune algorithm + sampledDataset_ = random_sample(dataset_, sampleSize); + // We use a cross-validation approach, first we sample a testset from the dataset + testDataset_ = random_sample(sampledDataset_, testSampleSize, true); + + // We compute the ground truth using linear search + Logger::info("Computing ground truth... \n"); + gt_matches_ = Matrix(new int[testDataset_.rows], testDataset_.rows, 1); + StartStopTimer t; + t.start(); + compute_ground_truth(sampledDataset_, testDataset_, gt_matches_, 0, distance_); + t.stop(); + + CostData linear_cost; + linear_cost.searchTimeCost = (float)t.value; + linear_cost.buildTimeCost = 0; + linear_cost.memoryCost = 0; + linear_cost.params["algorithm"] = FLANN_INDEX_LINEAR; + + costs.push_back(linear_cost); + + // Start parameter autotune process + Logger::info("Autotuning parameters...\n"); + + optimizeKMeans(costs); + optimizeKDTree(costs); + + float bestTimeCost = costs[0].searchTimeCost; + for (size_t i = 0; i < costs.size(); ++i) { + float timeCost = costs[i].buildTimeCost * build_weight_ + costs[i].searchTimeCost; + if (timeCost < bestTimeCost) { + bestTimeCost = timeCost; + } + } + + float bestCost = costs[0].searchTimeCost / bestTimeCost; + IndexParams bestParams = costs[0].params; + if (bestTimeCost > 0) { + for (size_t i = 0; i < costs.size(); ++i) { + float crtCost = (costs[i].buildTimeCost * build_weight_ + costs[i].searchTimeCost) / bestTimeCost + + memory_weight_ * costs[i].memoryCost; + if (crtCost < bestCost) { + bestCost = crtCost; + bestParams = costs[i].params; + } + } + } + + delete[] gt_matches_.data; + delete[] testDataset_.data; + delete[] sampledDataset_.data; + + return bestParams; + } + + + + /** + * Estimates the search time parameters needed to get the desired precision. + * Precondition: the index is built + * Postcondition: the searchParams will have the optimum params set, also the speedup obtained over linear search. + */ + float estimateSearchParams(SearchParams& searchParams) + { + const int nn = 1; + const size_t SAMPLE_COUNT = 1000; + + assert(bestIndex_ != NULL); // must have a valid index + + float speedup = 0; + + int samples = (int)std::min(dataset_.rows / 10, SAMPLE_COUNT); + if (samples > 0) { + Matrix testDataset = random_sample(dataset_, samples); + + Logger::info("Computing ground truth\n"); + + // we need to compute the ground truth first + Matrix gt_matches(new int[testDataset.rows], testDataset.rows, 1); + StartStopTimer t; + t.start(); + compute_ground_truth(dataset_, testDataset, gt_matches, 1, distance_); + t.stop(); + float linear = (float)t.value; + + int checks; + Logger::info("Estimating number of checks\n"); + + float searchTime; + float cb_index; + if (bestIndex_->getType() == FLANN_INDEX_KMEANS) { + Logger::info("KMeans algorithm, estimating cluster border factor\n"); + KMeansIndex* kmeans = (KMeansIndex*)bestIndex_; + float bestSearchTime = -1; + float best_cb_index = -1; + int best_checks = -1; + for (cb_index = 0; cb_index < 1.1f; cb_index += 0.2f) { + kmeans->set_cb_index(cb_index); + searchTime = test_index_precision(*kmeans, dataset_, testDataset, gt_matches, target_precision_, checks, distance_, nn, 1); + if ((searchTime < bestSearchTime) || (bestSearchTime == -1)) { + bestSearchTime = searchTime; + best_cb_index = cb_index; + best_checks = checks; + } + } + searchTime = bestSearchTime; + cb_index = best_cb_index; + checks = best_checks; + + kmeans->set_cb_index(best_cb_index); + Logger::info("Optimum cb_index: %g\n", cb_index); + bestParams_["cb_index"] = cb_index; + } + else { + searchTime = test_index_precision(*bestIndex_, dataset_, testDataset, gt_matches, target_precision_, checks, distance_, nn, 1); + } + + Logger::info("Required number of checks: %d \n", checks); + searchParams["checks"] = checks; + + speedup = linear / searchTime; + + delete[] gt_matches.data; + delete[] testDataset.data; + } + + return speedup; + } + +private: + NNIndex* bestIndex_; + + IndexParams bestParams_; + SearchParams bestSearchParams_; + + Matrix sampledDataset_; + Matrix testDataset_; + Matrix gt_matches_; + + float speedup_; + + /** + * The dataset used by this index + */ + const Matrix dataset_; + + /** + * Index parameters + */ + float target_precision_; + float build_weight_; + float memory_weight_; + float sample_fraction_; + + Distance distance_; + + +}; +} + +#endif /* OPENCV_FLANN_AUTOTUNED_INDEX_H_ */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/composite_index.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/composite_index.h new file mode 100644 index 0000000000000000000000000000000000000000..5e12a177bf33a7eb937890f18b8794600bc8e11a --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/composite_index.h @@ -0,0 +1,194 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + +#ifndef OPENCV_FLANN_COMPOSITE_INDEX_H_ +#define OPENCV_FLANN_COMPOSITE_INDEX_H_ + +#include "general.h" +#include "nn_index.h" +#include "kdtree_index.h" +#include "kmeans_index.h" + +namespace cvflann +{ + +/** + * Index parameters for the CompositeIndex. + */ +struct CompositeIndexParams : public IndexParams +{ + CompositeIndexParams(int trees = 4, int branching = 32, int iterations = 11, + flann_centers_init_t centers_init = FLANN_CENTERS_RANDOM, float cb_index = 0.2 ) + { + (*this)["algorithm"] = FLANN_INDEX_KMEANS; + // number of randomized trees to use (for kdtree) + (*this)["trees"] = trees; + // branching factor + (*this)["branching"] = branching; + // max iterations to perform in one kmeans clustering (kmeans tree) + (*this)["iterations"] = iterations; + // algorithm used for picking the initial cluster centers for kmeans tree + (*this)["centers_init"] = centers_init; + // cluster boundary index. Used when searching the kmeans tree + (*this)["cb_index"] = cb_index; + } +}; + + +/** + * This index builds a kd-tree index and a k-means index and performs nearest + * neighbour search both indexes. This gives a slight boost in search performance + * as some of the neighbours that are missed by one index are found by the other. + */ +template +class CompositeIndex : public NNIndex +{ +public: + typedef typename Distance::ElementType ElementType; + typedef typename Distance::ResultType DistanceType; + + /** + * Index constructor + * @param inputData dataset containing the points to index + * @param params Index parameters + * @param d Distance functor + * @return + */ + CompositeIndex(const Matrix& inputData, const IndexParams& params = CompositeIndexParams(), + Distance d = Distance()) : index_params_(params) + { + kdtree_index_ = new KDTreeIndex(inputData, params, d); + kmeans_index_ = new KMeansIndex(inputData, params, d); + + } + + CompositeIndex(const CompositeIndex&); + CompositeIndex& operator=(const CompositeIndex&); + + virtual ~CompositeIndex() + { + delete kdtree_index_; + delete kmeans_index_; + } + + /** + * @return The index type + */ + flann_algorithm_t getType() const CV_OVERRIDE + { + return FLANN_INDEX_COMPOSITE; + } + + /** + * @return Size of the index + */ + size_t size() const CV_OVERRIDE + { + return kdtree_index_->size(); + } + + /** + * \returns The dimensionality of the features in this index. + */ + size_t veclen() const CV_OVERRIDE + { + return kdtree_index_->veclen(); + } + + /** + * \returns The amount of memory (in bytes) used by the index. + */ + int usedMemory() const CV_OVERRIDE + { + return kmeans_index_->usedMemory() + kdtree_index_->usedMemory(); + } + + /** + * \brief Builds the index + */ + void buildIndex() CV_OVERRIDE + { + Logger::info("Building kmeans tree...\n"); + kmeans_index_->buildIndex(); + Logger::info("Building kdtree tree...\n"); + kdtree_index_->buildIndex(); + } + + /** + * \brief Saves the index to a stream + * \param stream The stream to save the index to + */ + void saveIndex(FILE* stream) CV_OVERRIDE + { + kmeans_index_->saveIndex(stream); + kdtree_index_->saveIndex(stream); + } + + /** + * \brief Loads the index from a stream + * \param stream The stream from which the index is loaded + */ + void loadIndex(FILE* stream) CV_OVERRIDE + { + kmeans_index_->loadIndex(stream); + kdtree_index_->loadIndex(stream); + } + + /** + * \returns The index parameters + */ + IndexParams getParameters() const CV_OVERRIDE + { + return index_params_; + } + + /** + * \brief Method that searches for nearest-neighbours + */ + void findNeighbors(ResultSet& result, const ElementType* vec, const SearchParams& searchParams) CV_OVERRIDE + { + kmeans_index_->findNeighbors(result, vec, searchParams); + kdtree_index_->findNeighbors(result, vec, searchParams); + } + +private: + /** The k-means index */ + KMeansIndex* kmeans_index_; + + /** The kd-tree index */ + KDTreeIndex* kdtree_index_; + + /** The index parameters */ + const IndexParams index_params_; +}; + +} + +#endif //OPENCV_FLANN_COMPOSITE_INDEX_H_ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/config.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/config.h new file mode 100644 index 0000000000000000000000000000000000000000..56832fd37f1f30d0f935d17cdda9caa532f7f735 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/config.h @@ -0,0 +1,38 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2011 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2011 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + + +#ifndef OPENCV_FLANN_CONFIG_H_ +#define OPENCV_FLANN_CONFIG_H_ + +#ifdef FLANN_VERSION_ +#undef FLANN_VERSION_ +#endif +#define FLANN_VERSION_ "1.6.10" + +#endif /* OPENCV_FLANN_CONFIG_H_ */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/defines.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/defines.h new file mode 100644 index 0000000000000000000000000000000000000000..6fd53c2f7882fe557f981b704558a4cab97e18be --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/defines.h @@ -0,0 +1,164 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2011 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2011 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + + +#ifndef OPENCV_FLANN_DEFINES_H_ +#define OPENCV_FLANN_DEFINES_H_ + +#include "config.h" + +#ifdef FLANN_EXPORT +#undef FLANN_EXPORT +#endif +#ifdef _WIN32 +/* win32 dll export/import directives */ + #ifdef FLANN_EXPORTS + #define FLANN_EXPORT __declspec(dllexport) + #elif defined(FLANN_STATIC) + #define FLANN_EXPORT + #else + #define FLANN_EXPORT __declspec(dllimport) + #endif +#else +/* unix needs nothing */ + #define FLANN_EXPORT +#endif + + +#undef FLANN_PLATFORM_32_BIT +#undef FLANN_PLATFORM_64_BIT +#if defined __amd64__ || defined __x86_64__ || defined _WIN64 || defined _M_X64 +#define FLANN_PLATFORM_64_BIT +#else +#define FLANN_PLATFORM_32_BIT +#endif + + +#undef FLANN_ARRAY_LEN +#define FLANN_ARRAY_LEN(a) (sizeof(a)/sizeof(a[0])) + +namespace cvflann { + +/* Nearest neighbour index algorithms */ +enum flann_algorithm_t +{ + FLANN_INDEX_LINEAR = 0, + FLANN_INDEX_KDTREE = 1, + FLANN_INDEX_KMEANS = 2, + FLANN_INDEX_COMPOSITE = 3, + FLANN_INDEX_KDTREE_SINGLE = 4, + FLANN_INDEX_HIERARCHICAL = 5, + FLANN_INDEX_LSH = 6, + FLANN_INDEX_SAVED = 254, + FLANN_INDEX_AUTOTUNED = 255, + + // deprecated constants, should use the FLANN_INDEX_* ones instead + LINEAR = 0, + KDTREE = 1, + KMEANS = 2, + COMPOSITE = 3, + KDTREE_SINGLE = 4, + SAVED = 254, + AUTOTUNED = 255 +}; + + + +enum flann_centers_init_t +{ + FLANN_CENTERS_RANDOM = 0, + FLANN_CENTERS_GONZALES = 1, + FLANN_CENTERS_KMEANSPP = 2, + FLANN_CENTERS_GROUPWISE = 3, + + // deprecated constants, should use the FLANN_CENTERS_* ones instead + CENTERS_RANDOM = 0, + CENTERS_GONZALES = 1, + CENTERS_KMEANSPP = 2 +}; + +enum flann_log_level_t +{ + FLANN_LOG_NONE = 0, + FLANN_LOG_FATAL = 1, + FLANN_LOG_ERROR = 2, + FLANN_LOG_WARN = 3, + FLANN_LOG_INFO = 4 +}; + +enum flann_distance_t +{ + FLANN_DIST_EUCLIDEAN = 1, + FLANN_DIST_L2 = 1, + FLANN_DIST_MANHATTAN = 2, + FLANN_DIST_L1 = 2, + FLANN_DIST_MINKOWSKI = 3, + FLANN_DIST_MAX = 4, + FLANN_DIST_HIST_INTERSECT = 5, + FLANN_DIST_HELLINGER = 6, + FLANN_DIST_CHI_SQUARE = 7, + FLANN_DIST_CS = 7, + FLANN_DIST_KULLBACK_LEIBLER = 8, + FLANN_DIST_KL = 8, + FLANN_DIST_HAMMING = 9, + + // deprecated constants, should use the FLANN_DIST_* ones instead + EUCLIDEAN = 1, + MANHATTAN = 2, + MINKOWSKI = 3, + MAX_DIST = 4, + HIST_INTERSECT = 5, + HELLINGER = 6, + CS = 7, + KL = 8, + KULLBACK_LEIBLER = 8 +}; + +enum flann_datatype_t +{ + FLANN_INT8 = 0, + FLANN_INT16 = 1, + FLANN_INT32 = 2, + FLANN_INT64 = 3, + FLANN_UINT8 = 4, + FLANN_UINT16 = 5, + FLANN_UINT32 = 6, + FLANN_UINT64 = 7, + FLANN_FLOAT32 = 8, + FLANN_FLOAT64 = 9 +}; + +enum +{ + FLANN_CHECKS_UNLIMITED = -1, + FLANN_CHECKS_AUTOTUNED = -2 +}; + +} + +#endif /* OPENCV_FLANN_DEFINES_H_ */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/dist.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/dist.h new file mode 100644 index 0000000000000000000000000000000000000000..eedaefff9c0c358da53fd6142c5855b6c94f8371 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/dist.h @@ -0,0 +1,905 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + +#ifndef OPENCV_FLANN_DIST_H_ +#define OPENCV_FLANN_DIST_H_ + +#include +#include +#include +#ifdef _MSC_VER +typedef unsigned __int32 uint32_t; +typedef unsigned __int64 uint64_t; +#else +#include +#endif + +#include "defines.h" + +#if defined _WIN32 && defined(_M_ARM) +# include +#endif + +#ifdef __ARM_NEON__ +# include "arm_neon.h" +#endif + +namespace cvflann +{ + +template +inline T abs(T x) { return (x<0) ? -x : x; } + +template<> +inline int abs(int x) { return ::abs(x); } + +template<> +inline float abs(float x) { return fabsf(x); } + +template<> +inline double abs(double x) { return fabs(x); } + +template +struct Accumulator { typedef T Type; }; +template<> +struct Accumulator { typedef float Type; }; +template<> +struct Accumulator { typedef float Type; }; +template<> +struct Accumulator { typedef float Type; }; +template<> +struct Accumulator { typedef float Type; }; +template<> +struct Accumulator { typedef float Type; }; +template<> +struct Accumulator { typedef float Type; }; + +#undef True +#undef False + +class True +{ +}; + +class False +{ +}; + + +/** + * Squared Euclidean distance functor. + * + * This is the simpler, unrolled version. This is preferable for + * very low dimensionality data (eg 3D points) + */ +template +struct L2_Simple +{ + typedef True is_kdtree_distance; + typedef True is_vector_space_distance; + + typedef T ElementType; + typedef typename Accumulator::Type ResultType; + + template + ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType /*worst_dist*/ = -1) const + { + ResultType result = ResultType(); + ResultType diff; + for(size_t i = 0; i < size; ++i ) { + diff = *a++ - *b++; + result += diff*diff; + } + return result; + } + + template + inline ResultType accum_dist(const U& a, const V& b, int) const + { + return (a-b)*(a-b); + } +}; + + + +/** + * Squared Euclidean distance functor, optimized version + */ +template +struct L2 +{ + typedef True is_kdtree_distance; + typedef True is_vector_space_distance; + + typedef T ElementType; + typedef typename Accumulator::Type ResultType; + + /** + * Compute the squared Euclidean distance between two vectors. + * + * This is highly optimised, with loop unrolling, as it is one + * of the most expensive inner loops. + * + * The computation of squared root at the end is omitted for + * efficiency. + */ + template + ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const + { + ResultType result = ResultType(); + ResultType diff0, diff1, diff2, diff3; + Iterator1 last = a + size; + Iterator1 lastgroup = last - 3; + + /* Process 4 items with each loop for efficiency. */ + while (a < lastgroup) { + diff0 = (ResultType)(a[0] - b[0]); + diff1 = (ResultType)(a[1] - b[1]); + diff2 = (ResultType)(a[2] - b[2]); + diff3 = (ResultType)(a[3] - b[3]); + result += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3; + a += 4; + b += 4; + + if ((worst_dist>0)&&(result>worst_dist)) { + return result; + } + } + /* Process last 0-3 pixels. Not needed for standard vector lengths. */ + while (a < last) { + diff0 = (ResultType)(*a++ - *b++); + result += diff0 * diff0; + } + return result; + } + + /** + * Partial euclidean distance, using just one dimension. This is used by the + * kd-tree when computing partial distances while traversing the tree. + * + * Squared root is omitted for efficiency. + */ + template + inline ResultType accum_dist(const U& a, const V& b, int) const + { + return (a-b)*(a-b); + } +}; + + +/* + * Manhattan distance functor, optimized version + */ +template +struct L1 +{ + typedef True is_kdtree_distance; + typedef True is_vector_space_distance; + + typedef T ElementType; + typedef typename Accumulator::Type ResultType; + + /** + * Compute the Manhattan (L_1) distance between two vectors. + * + * This is highly optimised, with loop unrolling, as it is one + * of the most expensive inner loops. + */ + template + ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const + { + ResultType result = ResultType(); + ResultType diff0, diff1, diff2, diff3; + Iterator1 last = a + size; + Iterator1 lastgroup = last - 3; + + /* Process 4 items with each loop for efficiency. */ + while (a < lastgroup) { + diff0 = (ResultType)abs(a[0] - b[0]); + diff1 = (ResultType)abs(a[1] - b[1]); + diff2 = (ResultType)abs(a[2] - b[2]); + diff3 = (ResultType)abs(a[3] - b[3]); + result += diff0 + diff1 + diff2 + diff3; + a += 4; + b += 4; + + if ((worst_dist>0)&&(result>worst_dist)) { + return result; + } + } + /* Process last 0-3 pixels. Not needed for standard vector lengths. */ + while (a < last) { + diff0 = (ResultType)abs(*a++ - *b++); + result += diff0; + } + return result; + } + + /** + * Partial distance, used by the kd-tree. + */ + template + inline ResultType accum_dist(const U& a, const V& b, int) const + { + return abs(a-b); + } +}; + + + +template +struct MinkowskiDistance +{ + typedef True is_kdtree_distance; + typedef True is_vector_space_distance; + + typedef T ElementType; + typedef typename Accumulator::Type ResultType; + + int order; + + MinkowskiDistance(int order_) : order(order_) {} + + /** + * Compute the Minkowsky (L_p) distance between two vectors. + * + * This is highly optimised, with loop unrolling, as it is one + * of the most expensive inner loops. + * + * The computation of squared root at the end is omitted for + * efficiency. + */ + template + ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const + { + ResultType result = ResultType(); + ResultType diff0, diff1, diff2, diff3; + Iterator1 last = a + size; + Iterator1 lastgroup = last - 3; + + /* Process 4 items with each loop for efficiency. */ + while (a < lastgroup) { + diff0 = (ResultType)abs(a[0] - b[0]); + diff1 = (ResultType)abs(a[1] - b[1]); + diff2 = (ResultType)abs(a[2] - b[2]); + diff3 = (ResultType)abs(a[3] - b[3]); + result += pow(diff0,order) + pow(diff1,order) + pow(diff2,order) + pow(diff3,order); + a += 4; + b += 4; + + if ((worst_dist>0)&&(result>worst_dist)) { + return result; + } + } + /* Process last 0-3 pixels. Not needed for standard vector lengths. */ + while (a < last) { + diff0 = (ResultType)abs(*a++ - *b++); + result += pow(diff0,order); + } + return result; + } + + /** + * Partial distance, used by the kd-tree. + */ + template + inline ResultType accum_dist(const U& a, const V& b, int) const + { + return pow(static_cast(abs(a-b)),order); + } +}; + + + +template +struct MaxDistance +{ + typedef False is_kdtree_distance; + typedef True is_vector_space_distance; + + typedef T ElementType; + typedef typename Accumulator::Type ResultType; + + /** + * Compute the max distance (L_infinity) between two vectors. + * + * This distance is not a valid kdtree distance, it's not dimensionwise additive. + */ + template + ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const + { + ResultType result = ResultType(); + ResultType diff0, diff1, diff2, diff3; + Iterator1 last = a + size; + Iterator1 lastgroup = last - 3; + + /* Process 4 items with each loop for efficiency. */ + while (a < lastgroup) { + diff0 = abs(a[0] - b[0]); + diff1 = abs(a[1] - b[1]); + diff2 = abs(a[2] - b[2]); + diff3 = abs(a[3] - b[3]); + if (diff0>result) {result = diff0; } + if (diff1>result) {result = diff1; } + if (diff2>result) {result = diff2; } + if (diff3>result) {result = diff3; } + a += 4; + b += 4; + + if ((worst_dist>0)&&(result>worst_dist)) { + return result; + } + } + /* Process last 0-3 pixels. Not needed for standard vector lengths. */ + while (a < last) { + diff0 = abs(*a++ - *b++); + result = (diff0>result) ? diff0 : result; + } + return result; + } + + /* This distance functor is not dimension-wise additive, which + * makes it an invalid kd-tree distance, not implementing the accum_dist method */ + +}; + +//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + +/** + * Hamming distance functor - counts the bit differences between two strings - useful for the Brief descriptor + * bit count of A exclusive XOR'ed with B + */ +struct HammingLUT +{ + typedef False is_kdtree_distance; + typedef False is_vector_space_distance; + + typedef unsigned char ElementType; + typedef int ResultType; + + /** this will count the bits in a ^ b + */ + ResultType operator()(const unsigned char* a, const unsigned char* b, size_t size) const + { + static const uchar popCountTable[] = + { + 0, 1, 1, 2, 1, 2, 2, 3, 1, 2, 2, 3, 2, 3, 3, 4, 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, + 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, + 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, + 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, + 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, + 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, + 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, + 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, 4, 5, 5, 6, 5, 6, 6, 7, 5, 6, 6, 7, 6, 7, 7, 8 + }; + ResultType result = 0; + for (size_t i = 0; i < size; i++) { + result += popCountTable[a[i] ^ b[i]]; + } + return result; + } +}; + +/** + * Hamming distance functor (pop count between two binary vectors, i.e. xor them and count the number of bits set) + * That code was taken from brief.cpp in OpenCV + */ +template +struct Hamming +{ + typedef False is_kdtree_distance; + typedef False is_vector_space_distance; + + + typedef T ElementType; + typedef int ResultType; + + template + ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType /*worst_dist*/ = -1) const + { + ResultType result = 0; +#ifdef __ARM_NEON__ + { + uint32x4_t bits = vmovq_n_u32(0); + for (size_t i = 0; i < size; i += 16) { + uint8x16_t A_vec = vld1q_u8 (a + i); + uint8x16_t B_vec = vld1q_u8 (b + i); + uint8x16_t AxorB = veorq_u8 (A_vec, B_vec); + uint8x16_t bitsSet = vcntq_u8 (AxorB); + uint16x8_t bitSet8 = vpaddlq_u8 (bitsSet); + uint32x4_t bitSet4 = vpaddlq_u16 (bitSet8); + bits = vaddq_u32(bits, bitSet4); + } + uint64x2_t bitSet2 = vpaddlq_u32 (bits); + result = vgetq_lane_s32 (vreinterpretq_s32_u64(bitSet2),0); + result += vgetq_lane_s32 (vreinterpretq_s32_u64(bitSet2),2); + } +#elif __GNUC__ + { + //for portability just use unsigned long -- and use the __builtin_popcountll (see docs for __builtin_popcountll) + typedef unsigned long long pop_t; + const size_t modulo = size % sizeof(pop_t); + const pop_t* a2 = reinterpret_cast (a); + const pop_t* b2 = reinterpret_cast (b); + const pop_t* a2_end = a2 + (size / sizeof(pop_t)); + + for (; a2 != a2_end; ++a2, ++b2) result += __builtin_popcountll((*a2) ^ (*b2)); + + if (modulo) { + //in the case where size is not dividable by sizeof(size_t) + //need to mask off the bits at the end + pop_t a_final = 0, b_final = 0; + memcpy(&a_final, a2, modulo); + memcpy(&b_final, b2, modulo); + result += __builtin_popcountll(a_final ^ b_final); + } + } +#else // NO NEON and NOT GNUC + typedef unsigned long long pop_t; + HammingLUT lut; + result = lut(reinterpret_cast (a), + reinterpret_cast (b), size * sizeof(pop_t)); +#endif + return result; + } +}; + +template +struct Hamming2 +{ + typedef False is_kdtree_distance; + typedef False is_vector_space_distance; + + typedef T ElementType; + typedef int ResultType; + + /** This is popcount_3() from: + * http://en.wikipedia.org/wiki/Hamming_weight */ + unsigned int popcnt32(uint32_t n) const + { + n -= ((n >> 1) & 0x55555555); + n = (n & 0x33333333) + ((n >> 2) & 0x33333333); + return (((n + (n >> 4))& 0xF0F0F0F)* 0x1010101) >> 24; + } + +#ifdef FLANN_PLATFORM_64_BIT + unsigned int popcnt64(uint64_t n) const + { + n -= ((n >> 1) & 0x5555555555555555); + n = (n & 0x3333333333333333) + ((n >> 2) & 0x3333333333333333); + return (((n + (n >> 4))& 0x0f0f0f0f0f0f0f0f)* 0x0101010101010101) >> 56; + } +#endif + + template + ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType /*worst_dist*/ = -1) const + { +#ifdef FLANN_PLATFORM_64_BIT + const uint64_t* pa = reinterpret_cast(a); + const uint64_t* pb = reinterpret_cast(b); + ResultType result = 0; + size /= (sizeof(uint64_t)/sizeof(unsigned char)); + for(size_t i = 0; i < size; ++i ) { + result += popcnt64(*pa ^ *pb); + ++pa; + ++pb; + } +#else + const uint32_t* pa = reinterpret_cast(a); + const uint32_t* pb = reinterpret_cast(b); + ResultType result = 0; + size /= (sizeof(uint32_t)/sizeof(unsigned char)); + for(size_t i = 0; i < size; ++i ) { + result += popcnt32(*pa ^ *pb); + ++pa; + ++pb; + } +#endif + return result; + } +}; + + + +//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + +template +struct HistIntersectionDistance +{ + typedef True is_kdtree_distance; + typedef True is_vector_space_distance; + + typedef T ElementType; + typedef typename Accumulator::Type ResultType; + + /** + * Compute the histogram intersection distance + */ + template + ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const + { + ResultType result = ResultType(); + ResultType min0, min1, min2, min3; + Iterator1 last = a + size; + Iterator1 lastgroup = last - 3; + + /* Process 4 items with each loop for efficiency. */ + while (a < lastgroup) { + min0 = (ResultType)(a[0] < b[0] ? a[0] : b[0]); + min1 = (ResultType)(a[1] < b[1] ? a[1] : b[1]); + min2 = (ResultType)(a[2] < b[2] ? a[2] : b[2]); + min3 = (ResultType)(a[3] < b[3] ? a[3] : b[3]); + result += min0 + min1 + min2 + min3; + a += 4; + b += 4; + if ((worst_dist>0)&&(result>worst_dist)) { + return result; + } + } + /* Process last 0-3 pixels. Not needed for standard vector lengths. */ + while (a < last) { + min0 = (ResultType)(*a < *b ? *a : *b); + result += min0; + ++a; + ++b; + } + return result; + } + + /** + * Partial distance, used by the kd-tree. + */ + template + inline ResultType accum_dist(const U& a, const V& b, int) const + { + return a +struct HellingerDistance +{ + typedef True is_kdtree_distance; + typedef True is_vector_space_distance; + + typedef T ElementType; + typedef typename Accumulator::Type ResultType; + + /** + * Compute the Hellinger distance + */ + template + ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType /*worst_dist*/ = -1) const + { + ResultType result = ResultType(); + ResultType diff0, diff1, diff2, diff3; + Iterator1 last = a + size; + Iterator1 lastgroup = last - 3; + + /* Process 4 items with each loop for efficiency. */ + while (a < lastgroup) { + diff0 = sqrt(static_cast(a[0])) - sqrt(static_cast(b[0])); + diff1 = sqrt(static_cast(a[1])) - sqrt(static_cast(b[1])); + diff2 = sqrt(static_cast(a[2])) - sqrt(static_cast(b[2])); + diff3 = sqrt(static_cast(a[3])) - sqrt(static_cast(b[3])); + result += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3; + a += 4; + b += 4; + } + while (a < last) { + diff0 = sqrt(static_cast(*a++)) - sqrt(static_cast(*b++)); + result += diff0 * diff0; + } + return result; + } + + /** + * Partial distance, used by the kd-tree. + */ + template + inline ResultType accum_dist(const U& a, const V& b, int) const + { + ResultType diff = sqrt(static_cast(a)) - sqrt(static_cast(b)); + return diff * diff; + } +}; + + +template +struct ChiSquareDistance +{ + typedef True is_kdtree_distance; + typedef True is_vector_space_distance; + + typedef T ElementType; + typedef typename Accumulator::Type ResultType; + + /** + * Compute the chi-square distance + */ + template + ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const + { + ResultType result = ResultType(); + ResultType sum, diff; + Iterator1 last = a + size; + + while (a < last) { + sum = (ResultType)(*a + *b); + if (sum>0) { + diff = (ResultType)(*a - *b); + result += diff*diff/sum; + } + ++a; + ++b; + + if ((worst_dist>0)&&(result>worst_dist)) { + return result; + } + } + return result; + } + + /** + * Partial distance, used by the kd-tree. + */ + template + inline ResultType accum_dist(const U& a, const V& b, int) const + { + ResultType result = ResultType(); + ResultType sum, diff; + + sum = (ResultType)(a+b); + if (sum>0) { + diff = (ResultType)(a-b); + result = diff*diff/sum; + } + return result; + } +}; + + +template +struct KL_Divergence +{ + typedef True is_kdtree_distance; + typedef True is_vector_space_distance; + + typedef T ElementType; + typedef typename Accumulator::Type ResultType; + + /** + * Compute the Kullback-Leibler divergence + */ + template + ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const + { + ResultType result = ResultType(); + Iterator1 last = a + size; + + while (a < last) { + if (* b != 0) { + ResultType ratio = (ResultType)(*a / *b); + if (ratio>0) { + result += *a * log(ratio); + } + } + ++a; + ++b; + + if ((worst_dist>0)&&(result>worst_dist)) { + return result; + } + } + return result; + } + + /** + * Partial distance, used by the kd-tree. + */ + template + inline ResultType accum_dist(const U& a, const V& b, int) const + { + ResultType result = ResultType(); + if( *b != 0 ) { + ResultType ratio = (ResultType)(a / b); + if (ratio>0) { + result = a * log(ratio); + } + } + return result; + } +}; + + + +/* + * This is a "zero iterator". It basically behaves like a zero filled + * array to all algorithms that use arrays as iterators (STL style). + * It's useful when there's a need to compute the distance between feature + * and origin it and allows for better compiler optimisation than using a + * zero-filled array. + */ +template +struct ZeroIterator +{ + + T operator*() + { + return 0; + } + + T operator[](int) + { + return 0; + } + + const ZeroIterator& operator ++() + { + return *this; + } + + ZeroIterator operator ++(int) + { + return *this; + } + + ZeroIterator& operator+=(int) + { + return *this; + } + +}; + + +/* + * Depending on processed distances, some of them are already squared (e.g. L2) + * and some are not (e.g.Hamming). In KMeans++ for instance we want to be sure + * we are working on ^2 distances, thus following templates to ensure that. + */ +template +struct squareDistance +{ + typedef typename Distance::ResultType ResultType; + ResultType operator()( ResultType dist ) { return dist*dist; } +}; + + +template +struct squareDistance, ElementType> +{ + typedef typename L2_Simple::ResultType ResultType; + ResultType operator()( ResultType dist ) { return dist; } +}; + +template +struct squareDistance, ElementType> +{ + typedef typename L2::ResultType ResultType; + ResultType operator()( ResultType dist ) { return dist; } +}; + + +template +struct squareDistance, ElementType> +{ + typedef typename MinkowskiDistance::ResultType ResultType; + ResultType operator()( ResultType dist ) { return dist; } +}; + +template +struct squareDistance, ElementType> +{ + typedef typename HellingerDistance::ResultType ResultType; + ResultType operator()( ResultType dist ) { return dist; } +}; + +template +struct squareDistance, ElementType> +{ + typedef typename ChiSquareDistance::ResultType ResultType; + ResultType operator()( ResultType dist ) { return dist; } +}; + + +template +typename Distance::ResultType ensureSquareDistance( typename Distance::ResultType dist ) +{ + typedef typename Distance::ElementType ElementType; + + squareDistance dummy; + return dummy( dist ); +} + + +/* + * ...and a template to ensure the user that he will process the normal distance, + * and not squared distance, without losing processing time calling sqrt(ensureSquareDistance) + * that will result in doing actually sqrt(dist*dist) for L1 distance for instance. + */ +template +struct simpleDistance +{ + typedef typename Distance::ResultType ResultType; + ResultType operator()( ResultType dist ) { return dist; } +}; + + +template +struct simpleDistance, ElementType> +{ + typedef typename L2_Simple::ResultType ResultType; + ResultType operator()( ResultType dist ) { return sqrt(dist); } +}; + +template +struct simpleDistance, ElementType> +{ + typedef typename L2::ResultType ResultType; + ResultType operator()( ResultType dist ) { return sqrt(dist); } +}; + + +template +struct simpleDistance, ElementType> +{ + typedef typename MinkowskiDistance::ResultType ResultType; + ResultType operator()( ResultType dist ) { return sqrt(dist); } +}; + +template +struct simpleDistance, ElementType> +{ + typedef typename HellingerDistance::ResultType ResultType; + ResultType operator()( ResultType dist ) { return sqrt(dist); } +}; + +template +struct simpleDistance, ElementType> +{ + typedef typename ChiSquareDistance::ResultType ResultType; + ResultType operator()( ResultType dist ) { return sqrt(dist); } +}; + + +template +typename Distance::ResultType ensureSimpleDistance( typename Distance::ResultType dist ) +{ + typedef typename Distance::ElementType ElementType; + + simpleDistance dummy; + return dummy( dist ); +} + +} + +#endif //OPENCV_FLANN_DIST_H_ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/dummy.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/dummy.h new file mode 100644 index 0000000000000000000000000000000000000000..d6837e53c8befc9db85a992181f2cdf72ad766d7 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/dummy.h @@ -0,0 +1,13 @@ + +#ifndef OPENCV_FLANN_DUMMY_H_ +#define OPENCV_FLANN_DUMMY_H_ + +namespace cvflann +{ + +CV_DEPRECATED inline void dummyfunc() {} + +} + + +#endif /* OPENCV_FLANN_DUMMY_H_ */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/dynamic_bitset.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/dynamic_bitset.h new file mode 100644 index 0000000000000000000000000000000000000000..923b65832197db817d488a9cffc06f65a4bc5914 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/dynamic_bitset.h @@ -0,0 +1,159 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + +/*********************************************************************** + * Author: Vincent Rabaud + *************************************************************************/ + +#ifndef OPENCV_FLANN_DYNAMIC_BITSET_H_ +#define OPENCV_FLANN_DYNAMIC_BITSET_H_ + +#ifndef FLANN_USE_BOOST +# define FLANN_USE_BOOST 0 +#endif +//#define FLANN_USE_BOOST 1 +#if FLANN_USE_BOOST +#include +typedef boost::dynamic_bitset<> DynamicBitset; +#else + +#include + +#include "dist.h" + +namespace cvflann { + +/** Class re-implementing the boost version of it + * This helps not depending on boost, it also does not do the bound checks + * and has a way to reset a block for speed + */ +class DynamicBitset +{ +public: + /** default constructor + */ + DynamicBitset() : size_(0) + { + } + + /** only constructor we use in our code + * @param sz the size of the bitset (in bits) + */ + DynamicBitset(size_t sz) + { + resize(sz); + reset(); + } + + /** Sets all the bits to 0 + */ + void clear() + { + std::fill(bitset_.begin(), bitset_.end(), 0); + } + + /** @brief checks if the bitset is empty + * @return true if the bitset is empty + */ + bool empty() const + { + return bitset_.empty(); + } + + /** set all the bits to 0 + */ + void reset() + { + std::fill(bitset_.begin(), bitset_.end(), 0); + } + + /** @brief set one bit to 0 + * @param index + */ + void reset(size_t index) + { + bitset_[index / cell_bit_size_] &= ~(size_t(1) << (index % cell_bit_size_)); + } + + /** @brief sets a specific bit to 0, and more bits too + * This function is useful when resetting a given set of bits so that the + * whole bitset ends up being 0: if that's the case, we don't care about setting + * other bits to 0 + * @param index + */ + void reset_block(size_t index) + { + bitset_[index / cell_bit_size_] = 0; + } + + /** resize the bitset so that it contains at least sz bits + * @param sz + */ + void resize(size_t sz) + { + size_ = sz; + bitset_.resize(sz / cell_bit_size_ + 1); + } + + /** set a bit to true + * @param index the index of the bit to set to 1 + */ + void set(size_t index) + { + bitset_[index / cell_bit_size_] |= size_t(1) << (index % cell_bit_size_); + } + + /** gives the number of contained bits + */ + size_t size() const + { + return size_; + } + + /** check if a bit is set + * @param index the index of the bit to check + * @return true if the bit is set + */ + bool test(size_t index) const + { + return (bitset_[index / cell_bit_size_] & (size_t(1) << (index % cell_bit_size_))) != 0; + } + +private: + std::vector bitset_; + size_t size_; + static const unsigned int cell_bit_size_ = CHAR_BIT * sizeof(size_t); +}; + +} // namespace cvflann + +#endif + +#endif // OPENCV_FLANN_DYNAMIC_BITSET_H_ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/flann.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/flann.hpp new file mode 100644 index 0000000000000000000000000000000000000000..227683f97973dc05911b00936acf91b9bec513a7 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/flann.hpp @@ -0,0 +1,48 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifdef __OPENCV_BUILD +#error this is a compatibility header which should not be used inside the OpenCV library +#endif + +#include "opencv2/flann.hpp" diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/flann_base.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/flann_base.hpp new file mode 100644 index 0000000000000000000000000000000000000000..0ffb857cc655d103f8f915d5dc1e12a7375364de --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/flann_base.hpp @@ -0,0 +1,295 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + +#ifndef OPENCV_FLANN_BASE_HPP_ +#define OPENCV_FLANN_BASE_HPP_ + +#include +#include +#include + +#include "general.h" +#include "matrix.h" +#include "params.h" +#include "saving.h" + +#include "all_indices.h" + +namespace cvflann +{ + +/** + * Sets the log level used for all flann functions + * @param level Verbosity level + */ +inline void log_verbosity(int level) +{ + if (level >= 0) { + Logger::setLevel(level); + } +} + +/** + * (Deprecated) Index parameters for creating a saved index. + */ +struct SavedIndexParams : public IndexParams +{ + SavedIndexParams(cv::String filename) + { + (* this)["algorithm"] = FLANN_INDEX_SAVED; + (*this)["filename"] = filename; + } +}; + + +template +NNIndex* load_saved_index(const Matrix& dataset, const cv::String& filename, Distance distance) +{ + typedef typename Distance::ElementType ElementType; + + FILE* fin = fopen(filename.c_str(), "rb"); + if (fin == NULL) { + return NULL; + } + IndexHeader header = load_header(fin); + if (header.data_type != Datatype::type()) { + fclose(fin); + throw FLANNException("Datatype of saved index is different than of the one to be created."); + } + if ((size_t(header.rows) != dataset.rows)||(size_t(header.cols) != dataset.cols)) { + fclose(fin); + throw FLANNException("The index saved belongs to a different dataset"); + } + + IndexParams params; + params["algorithm"] = header.index_type; + NNIndex* nnIndex = create_index_by_type(dataset, params, distance); + nnIndex->loadIndex(fin); + fclose(fin); + + return nnIndex; +} + + +template +class Index : public NNIndex +{ +public: + typedef typename Distance::ElementType ElementType; + typedef typename Distance::ResultType DistanceType; + + Index(const Matrix& features, const IndexParams& params, Distance distance = Distance() ) + : index_params_(params) + { + flann_algorithm_t index_type = get_param(params,"algorithm"); + loaded_ = false; + + if (index_type == FLANN_INDEX_SAVED) { + nnIndex_ = load_saved_index(features, get_param(params,"filename"), distance); + loaded_ = true; + } + else { + nnIndex_ = create_index_by_type(features, params, distance); + } + } + + ~Index() + { + delete nnIndex_; + } + + /** + * Builds the index. + */ + void buildIndex() CV_OVERRIDE + { + if (!loaded_) { + nnIndex_->buildIndex(); + } + } + + void save(cv::String filename) + { + FILE* fout = fopen(filename.c_str(), "wb"); + if (fout == NULL) { + throw FLANNException("Cannot open file"); + } + save_header(fout, *nnIndex_); + saveIndex(fout); + fclose(fout); + } + + /** + * \brief Saves the index to a stream + * \param stream The stream to save the index to + */ + virtual void saveIndex(FILE* stream) CV_OVERRIDE + { + nnIndex_->saveIndex(stream); + } + + /** + * \brief Loads the index from a stream + * \param stream The stream from which the index is loaded + */ + virtual void loadIndex(FILE* stream) CV_OVERRIDE + { + nnIndex_->loadIndex(stream); + } + + /** + * \returns number of features in this index. + */ + size_t veclen() const CV_OVERRIDE + { + return nnIndex_->veclen(); + } + + /** + * \returns The dimensionality of the features in this index. + */ + size_t size() const CV_OVERRIDE + { + return nnIndex_->size(); + } + + /** + * \returns The index type (kdtree, kmeans,...) + */ + flann_algorithm_t getType() const CV_OVERRIDE + { + return nnIndex_->getType(); + } + + /** + * \returns The amount of memory (in bytes) used by the index. + */ + virtual int usedMemory() const CV_OVERRIDE + { + return nnIndex_->usedMemory(); + } + + + /** + * \returns The index parameters + */ + IndexParams getParameters() const CV_OVERRIDE + { + return nnIndex_->getParameters(); + } + + /** + * \brief Perform k-nearest neighbor search + * \param[in] queries The query points for which to find the nearest neighbors + * \param[out] indices The indices of the nearest neighbors found + * \param[out] dists Distances to the nearest neighbors found + * \param[in] knn Number of nearest neighbors to return + * \param[in] params Search parameters + */ + void knnSearch(const Matrix& queries, Matrix& indices, Matrix& dists, int knn, const SearchParams& params) CV_OVERRIDE + { + nnIndex_->knnSearch(queries, indices, dists, knn, params); + } + + /** + * \brief Perform radius search + * \param[in] query The query point + * \param[out] indices The indinces of the neighbors found within the given radius + * \param[out] dists The distances to the nearest neighbors found + * \param[in] radius The radius used for search + * \param[in] params Search parameters + * \returns Number of neighbors found + */ + int radiusSearch(const Matrix& query, Matrix& indices, Matrix& dists, float radius, const SearchParams& params) CV_OVERRIDE + { + return nnIndex_->radiusSearch(query, indices, dists, radius, params); + } + + /** + * \brief Method that searches for nearest-neighbours + */ + void findNeighbors(ResultSet& result, const ElementType* vec, const SearchParams& searchParams) CV_OVERRIDE + { + nnIndex_->findNeighbors(result, vec, searchParams); + } + + /** + * \brief Returns actual index + */ + CV_DEPRECATED NNIndex* getIndex() + { + return nnIndex_; + } + + /** + * \brief Returns index parameters. + * \deprecated use getParameters() instead. + */ + CV_DEPRECATED const IndexParams* getIndexParameters() + { + return &index_params_; + } + +private: + /** Pointer to actual index class */ + NNIndex* nnIndex_; + /** Indices if the index was loaded from a file */ + bool loaded_; + /** Parameters passed to the index */ + IndexParams index_params_; + + Index(const Index &); // copy disabled + Index& operator=(const Index &); // assign disabled +}; + +/** + * Performs a hierarchical clustering of the points passed as argument and then takes a cut in the + * the clustering tree to return a flat clustering. + * @param[in] points Points to be clustered + * @param centers The computed cluster centres. Matrix should be preallocated and centers.rows is the + * number of clusters requested. + * @param params Clustering parameters (The same as for cvflann::KMeansIndex) + * @param d Distance to be used for clustering (eg: cvflann::L2) + * @return number of clusters computed (can be different than clusters.rows and is the highest number + * of the form (branching-1)*K+1 smaller than clusters.rows). + */ +template +int hierarchicalClustering(const Matrix& points, Matrix& centers, + const KMeansIndexParams& params, Distance d = Distance()) +{ + KMeansIndex kmeans(points, params, d); + kmeans.buildIndex(); + + int clusterNum = kmeans.getClusterCenters(centers); + return clusterNum; +} + +} +#endif /* OPENCV_FLANN_BASE_HPP_ */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/general.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/general.h new file mode 100644 index 0000000000000000000000000000000000000000..9d5402a6f2ff16c9bc3ea01df70e5e261272a9ec --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/general.h @@ -0,0 +1,50 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + +#ifndef OPENCV_FLANN_GENERAL_H_ +#define OPENCV_FLANN_GENERAL_H_ + +#include "opencv2/core.hpp" + +namespace cvflann +{ + +class FLANNException : public cv::Exception +{ +public: + FLANNException(const char* message) : cv::Exception(0, message, "", __FILE__, __LINE__) { } + + FLANNException(const cv::String& message) : cv::Exception(0, message, "", __FILE__, __LINE__) { } +}; + +} + + +#endif /* OPENCV_FLANN_GENERAL_H_ */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/ground_truth.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/ground_truth.h new file mode 100644 index 0000000000000000000000000000000000000000..fd8f3ae7f02f08a8ba29dd8f34673217220622f2 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/ground_truth.h @@ -0,0 +1,94 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + +#ifndef OPENCV_FLANN_GROUND_TRUTH_H_ +#define OPENCV_FLANN_GROUND_TRUTH_H_ + +#include "dist.h" +#include "matrix.h" + + +namespace cvflann +{ + +template +void find_nearest(const Matrix& dataset, typename Distance::ElementType* query, int* matches, int nn, + int skip = 0, Distance distance = Distance()) +{ + typedef typename Distance::ResultType DistanceType; + int n = nn + skip; + + std::vector match(n); + std::vector dists(n); + + dists[0] = distance(dataset[0], query, dataset.cols); + match[0] = 0; + int dcnt = 1; + + for (size_t i=1; i=1 && dists[j] +void compute_ground_truth(const Matrix& dataset, const Matrix& testset, Matrix& matches, + int skip=0, Distance d = Distance()) +{ + for (size_t i=0; i(dataset, testset[i], matches[i], (int)matches.cols, skip, d); + } +} + + +} + +#endif //OPENCV_FLANN_GROUND_TRUTH_H_ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/hdf5.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/hdf5.h new file mode 100644 index 0000000000000000000000000000000000000000..80d23b9770c36aa2d9835c1a7ef5ac4138d28441 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/hdf5.h @@ -0,0 +1,231 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + + +#ifndef OPENCV_FLANN_HDF5_H_ +#define OPENCV_FLANN_HDF5_H_ + +#include + +#include "matrix.h" + + +namespace cvflann +{ + +namespace +{ + +template +hid_t get_hdf5_type() +{ + throw FLANNException("Unsupported type for IO operations"); +} + +template<> +hid_t get_hdf5_type() { return H5T_NATIVE_CHAR; } +template<> +hid_t get_hdf5_type() { return H5T_NATIVE_UCHAR; } +template<> +hid_t get_hdf5_type() { return H5T_NATIVE_SHORT; } +template<> +hid_t get_hdf5_type() { return H5T_NATIVE_USHORT; } +template<> +hid_t get_hdf5_type() { return H5T_NATIVE_INT; } +template<> +hid_t get_hdf5_type() { return H5T_NATIVE_UINT; } +template<> +hid_t get_hdf5_type() { return H5T_NATIVE_LONG; } +template<> +hid_t get_hdf5_type() { return H5T_NATIVE_ULONG; } +template<> +hid_t get_hdf5_type() { return H5T_NATIVE_FLOAT; } +template<> +hid_t get_hdf5_type() { return H5T_NATIVE_DOUBLE; } +} + + +#define CHECK_ERROR(x,y) if ((x)<0) throw FLANNException((y)); + +template +void save_to_file(const cvflann::Matrix& dataset, const String& filename, const String& name) +{ + +#if H5Eset_auto_vers == 2 + H5Eset_auto( H5E_DEFAULT, NULL, NULL ); +#else + H5Eset_auto( NULL, NULL ); +#endif + + herr_t status; + hid_t file_id; + file_id = H5Fopen(filename.c_str(), H5F_ACC_RDWR, H5P_DEFAULT); + if (file_id < 0) { + file_id = H5Fcreate(filename.c_str(), H5F_ACC_EXCL, H5P_DEFAULT, H5P_DEFAULT); + } + CHECK_ERROR(file_id,"Error creating hdf5 file."); + + hsize_t dimsf[2]; // dataset dimensions + dimsf[0] = dataset.rows; + dimsf[1] = dataset.cols; + + hid_t space_id = H5Screate_simple(2, dimsf, NULL); + hid_t memspace_id = H5Screate_simple(2, dimsf, NULL); + + hid_t dataset_id; +#if H5Dcreate_vers == 2 + dataset_id = H5Dcreate2(file_id, name.c_str(), get_hdf5_type(), space_id, H5P_DEFAULT, H5P_DEFAULT, H5P_DEFAULT); +#else + dataset_id = H5Dcreate(file_id, name.c_str(), get_hdf5_type(), space_id, H5P_DEFAULT); +#endif + + if (dataset_id<0) { +#if H5Dopen_vers == 2 + dataset_id = H5Dopen2(file_id, name.c_str(), H5P_DEFAULT); +#else + dataset_id = H5Dopen(file_id, name.c_str()); +#endif + } + CHECK_ERROR(dataset_id,"Error creating or opening dataset in file."); + + status = H5Dwrite(dataset_id, get_hdf5_type(), memspace_id, space_id, H5P_DEFAULT, dataset.data ); + CHECK_ERROR(status, "Error writing to dataset"); + + H5Sclose(memspace_id); + H5Sclose(space_id); + H5Dclose(dataset_id); + H5Fclose(file_id); + +} + + +template +void load_from_file(cvflann::Matrix& dataset, const String& filename, const String& name) +{ + herr_t status; + hid_t file_id = H5Fopen(filename.c_str(), H5F_ACC_RDWR, H5P_DEFAULT); + CHECK_ERROR(file_id,"Error opening hdf5 file."); + + hid_t dataset_id; +#if H5Dopen_vers == 2 + dataset_id = H5Dopen2(file_id, name.c_str(), H5P_DEFAULT); +#else + dataset_id = H5Dopen(file_id, name.c_str()); +#endif + CHECK_ERROR(dataset_id,"Error opening dataset in file."); + + hid_t space_id = H5Dget_space(dataset_id); + + hsize_t dims_out[2]; + H5Sget_simple_extent_dims(space_id, dims_out, NULL); + + dataset = cvflann::Matrix(new T[dims_out[0]*dims_out[1]], dims_out[0], dims_out[1]); + + status = H5Dread(dataset_id, get_hdf5_type(), H5S_ALL, H5S_ALL, H5P_DEFAULT, dataset[0]); + CHECK_ERROR(status, "Error reading dataset"); + + H5Sclose(space_id); + H5Dclose(dataset_id); + H5Fclose(file_id); +} + + +#ifdef HAVE_MPI + +namespace mpi +{ +/** + * Loads a the hyperslice corresponding to this processor from a hdf5 file. + * @param flann_dataset Dataset where the data is loaded + * @param filename HDF5 file name + * @param name Name of dataset inside file + */ +template +void load_from_file(cvflann::Matrix& dataset, const String& filename, const String& name) +{ + MPI_Comm comm = MPI_COMM_WORLD; + MPI_Info info = MPI_INFO_NULL; + + int mpi_size, mpi_rank; + MPI_Comm_size(comm, &mpi_size); + MPI_Comm_rank(comm, &mpi_rank); + + herr_t status; + + hid_t plist_id = H5Pcreate(H5P_FILE_ACCESS); + H5Pset_fapl_mpio(plist_id, comm, info); + hid_t file_id = H5Fopen(filename.c_str(), H5F_ACC_RDWR, plist_id); + CHECK_ERROR(file_id,"Error opening hdf5 file."); + H5Pclose(plist_id); + hid_t dataset_id; +#if H5Dopen_vers == 2 + dataset_id = H5Dopen2(file_id, name.c_str(), H5P_DEFAULT); +#else + dataset_id = H5Dopen(file_id, name.c_str()); +#endif + CHECK_ERROR(dataset_id,"Error opening dataset in file."); + + hid_t space_id = H5Dget_space(dataset_id); + hsize_t dims[2]; + H5Sget_simple_extent_dims(space_id, dims, NULL); + + hsize_t count[2]; + hsize_t offset[2]; + + hsize_t item_cnt = dims[0]/mpi_size+(dims[0]%mpi_size==0 ? 0 : 1); + hsize_t cnt = (mpi_rank(), memspace_id, space_id, plist_id, dataset.data); + CHECK_ERROR(status, "Error reading dataset"); + + H5Pclose(plist_id); + H5Sclose(space_id); + H5Sclose(memspace_id); + H5Dclose(dataset_id); + H5Fclose(file_id); +} +} +#endif // HAVE_MPI +} // namespace cvflann::mpi + +#endif /* OPENCV_FLANN_HDF5_H_ */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/heap.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/heap.h new file mode 100644 index 0000000000000000000000000000000000000000..92a6ea614b47c43d77a37ca3bfe87ba9a9b5d5e7 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/heap.h @@ -0,0 +1,165 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + +#ifndef OPENCV_FLANN_HEAP_H_ +#define OPENCV_FLANN_HEAP_H_ + +#include +#include + +namespace cvflann +{ + +/** + * Priority Queue Implementation + * + * The priority queue is implemented with a heap. A heap is a complete + * (full) binary tree in which each parent is less than both of its + * children, but the order of the children is unspecified. + */ +template +class Heap +{ + + /** + * Storage array for the heap. + * Type T must be comparable. + */ + std::vector heap; + int length; + + /** + * Number of element in the heap + */ + int count; + + + +public: + /** + * Constructor. + * + * Params: + * sz = heap size + */ + + Heap(int sz) + { + length = sz; + heap.reserve(length); + count = 0; + } + + /** + * + * Returns: heap size + */ + int size() + { + return count; + } + + /** + * Tests if the heap is empty + * + * Returns: true is heap empty, false otherwise + */ + bool empty() + { + return size()==0; + } + + /** + * Clears the heap. + */ + void clear() + { + heap.clear(); + count = 0; + } + + struct CompareT + { + bool operator()(const T& t_1, const T& t_2) const + { + return t_2 < t_1; + } + }; + + /** + * Insert a new element in the heap. + * + * We select the next empty leaf node, and then keep moving any larger + * parents down until the right location is found to store this element. + * + * Params: + * value = the new element to be inserted in the heap + */ + void insert(T value) + { + /* If heap is full, then return without adding this element. */ + if (count == length) { + return; + } + + heap.push_back(value); + static CompareT compareT; + std::push_heap(heap.begin(), heap.end(), compareT); + ++count; + } + + + + /** + * Returns the node of minimum value from the heap (top of the heap). + * + * Params: + * value = out parameter used to return the min element + * Returns: false if heap empty + */ + bool popMin(T& value) + { + if (count == 0) { + return false; + } + + value = heap[0]; + static CompareT compareT; + std::pop_heap(heap.begin(), heap.end(), compareT); + heap.pop_back(); + --count; + + return true; /* Return old last node. */ + } +}; + +} + +#endif //OPENCV_FLANN_HEAP_H_ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/hierarchical_clustering_index.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/hierarchical_clustering_index.h new file mode 100644 index 0000000000000000000000000000000000000000..2a947dab6be198e35964f7b313550376f52625ba --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/hierarchical_clustering_index.h @@ -0,0 +1,848 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2011 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2011 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + +#ifndef OPENCV_FLANN_HIERARCHICAL_CLUSTERING_INDEX_H_ +#define OPENCV_FLANN_HIERARCHICAL_CLUSTERING_INDEX_H_ + +#include +#include +#include +#include +#include + +#include "general.h" +#include "nn_index.h" +#include "dist.h" +#include "matrix.h" +#include "result_set.h" +#include "heap.h" +#include "allocator.h" +#include "random.h" +#include "saving.h" + + +namespace cvflann +{ + +struct HierarchicalClusteringIndexParams : public IndexParams +{ + HierarchicalClusteringIndexParams(int branching = 32, + flann_centers_init_t centers_init = FLANN_CENTERS_RANDOM, + int trees = 4, int leaf_size = 100) + { + (*this)["algorithm"] = FLANN_INDEX_HIERARCHICAL; + // The branching factor used in the hierarchical clustering + (*this)["branching"] = branching; + // Algorithm used for picking the initial cluster centers + (*this)["centers_init"] = centers_init; + // number of parallel trees to build + (*this)["trees"] = trees; + // maximum leaf size + (*this)["leaf_size"] = leaf_size; + } +}; + + +/** + * Hierarchical index + * + * Contains a tree constructed through a hierarchical clustering + * and other information for indexing a set of points for nearest-neighbour matching. + */ +template +class HierarchicalClusteringIndex : public NNIndex +{ +public: + typedef typename Distance::ElementType ElementType; + typedef typename Distance::ResultType DistanceType; + +private: + + + typedef void (HierarchicalClusteringIndex::* centersAlgFunction)(int, int*, int, int*, int&); + + /** + * The function used for choosing the cluster centers. + */ + centersAlgFunction chooseCenters; + + + + /** + * Chooses the initial centers in the k-means clustering in a random manner. + * + * Params: + * k = number of centers + * vecs = the dataset of points + * indices = indices in the dataset + * indices_length = length of indices vector + * + */ + void chooseCentersRandom(int k, int* dsindices, int indices_length, int* centers, int& centers_length) + { + UniqueRandom r(indices_length); + + int index; + for (index=0; index=0 && rnd < n); + + centers[0] = dsindices[rnd]; + + int index; + for (index=1; indexbest_val) { + best_val = dist; + best_index = j; + } + } + if (best_index!=-1) { + centers[index] = dsindices[best_index]; + } + else { + break; + } + } + centers_length = index; + } + + + /** + * Chooses the initial centers in the k-means using the algorithm + * proposed in the KMeans++ paper: + * Arthur, David; Vassilvitskii, Sergei - k-means++: The Advantages of Careful Seeding + * + * Implementation of this function was converted from the one provided in Arthur's code. + * + * Params: + * k = number of centers + * vecs = the dataset of points + * indices = indices in the dataset + * Returns: + */ + void chooseCentersKMeanspp(int k, int* dsindices, int indices_length, int* centers, int& centers_length) + { + int n = indices_length; + + double currentPot = 0; + DistanceType* closestDistSq = new DistanceType[n]; + + // Choose one random center and set the closestDistSq values + int index = rand_int(n); + assert(index >=0 && index < n); + centers[0] = dsindices[index]; + + // Computing distance^2 will have the advantage of even higher probability further to pick new centers + // far from previous centers (and this complies to "k-means++: the advantages of careful seeding" article) + for (int i = 0; i < n; i++) { + closestDistSq[i] = distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols); + closestDistSq[i] = ensureSquareDistance( closestDistSq[i] ); + currentPot += closestDistSq[i]; + } + + + const int numLocalTries = 1; + + // Choose each center + int centerCount; + for (centerCount = 1; centerCount < k; centerCount++) { + + // Repeat several trials + double bestNewPot = -1; + int bestNewIndex = 0; + for (int localTrial = 0; localTrial < numLocalTries; localTrial++) { + + // Choose our center - have to be slightly careful to return a valid answer even accounting + // for possible rounding errors + double randVal = rand_double(currentPot); + for (index = 0; index < n-1; index++) { + if (randVal <= closestDistSq[index]) break; + else randVal -= closestDistSq[index]; + } + + // Compute the new potential + double newPot = 0; + for (int i = 0; i < n; i++) { + DistanceType dist = distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols); + newPot += std::min( ensureSquareDistance(dist), closestDistSq[i] ); + } + + // Store the best result + if ((bestNewPot < 0)||(newPot < bestNewPot)) { + bestNewPot = newPot; + bestNewIndex = index; + } + } + + // Add the appropriate center + centers[centerCount] = dsindices[bestNewIndex]; + currentPot = bestNewPot; + for (int i = 0; i < n; i++) { + DistanceType dist = distance(dataset[dsindices[i]], dataset[dsindices[bestNewIndex]], dataset.cols); + closestDistSq[i] = std::min( ensureSquareDistance(dist), closestDistSq[i] ); + } + } + + centers_length = centerCount; + + delete[] closestDistSq; + } + + + /** + * Chooses the initial centers in a way inspired by Gonzales (by Pierre-Emmanuel Viel): + * select the first point of the list as a candidate, then parse the points list. If another + * point is further than current candidate from the other centers, test if it is a good center + * of a local aggregation. If it is, replace current candidate by this point. And so on... + * + * Used with KMeansIndex that computes centers coordinates by averaging positions of clusters points, + * this doesn't make a real difference with previous methods. But used with HierarchicalClusteringIndex + * class that pick centers among existing points instead of computing the barycenters, there is a real + * improvement. + * + * Params: + * k = number of centers + * vecs = the dataset of points + * indices = indices in the dataset + * Returns: + */ + void GroupWiseCenterChooser(int k, int* dsindices, int indices_length, int* centers, int& centers_length) + { + const float kSpeedUpFactor = 1.3f; + + int n = indices_length; + + DistanceType* closestDistSq = new DistanceType[n]; + + // Choose one random center and set the closestDistSq values + int index = rand_int(n); + assert(index >=0 && index < n); + centers[0] = dsindices[index]; + + for (int i = 0; i < n; i++) { + closestDistSq[i] = distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols); + } + + + // Choose each center + int centerCount; + for (centerCount = 1; centerCount < k; centerCount++) { + + // Repeat several trials + double bestNewPot = -1; + int bestNewIndex = 0; + DistanceType furthest = 0; + for (index = 0; index < n; index++) { + + // We will test only the potential of the points further than current candidate + if( closestDistSq[index] > kSpeedUpFactor * (float)furthest ) { + + // Compute the new potential + double newPot = 0; + for (int i = 0; i < n; i++) { + newPot += std::min( distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols) + , closestDistSq[i] ); + } + + // Store the best result + if ((bestNewPot < 0)||(newPot <= bestNewPot)) { + bestNewPot = newPot; + bestNewIndex = index; + furthest = closestDistSq[index]; + } + } + } + + // Add the appropriate center + centers[centerCount] = dsindices[bestNewIndex]; + for (int i = 0; i < n; i++) { + closestDistSq[i] = std::min( distance(dataset[dsindices[i]], dataset[dsindices[bestNewIndex]], dataset.cols) + , closestDistSq[i] ); + } + } + + centers_length = centerCount; + + delete[] closestDistSq; + } + + +public: + + + /** + * Index constructor + * + * Params: + * inputData = dataset with the input features + * params = parameters passed to the hierarchical k-means algorithm + */ + HierarchicalClusteringIndex(const Matrix& inputData, const IndexParams& index_params = HierarchicalClusteringIndexParams(), + Distance d = Distance()) + : dataset(inputData), params(index_params), root(NULL), indices(NULL), distance(d) + { + memoryCounter = 0; + + size_ = dataset.rows; + veclen_ = dataset.cols; + + branching_ = get_param(params,"branching",32); + centers_init_ = get_param(params,"centers_init", FLANN_CENTERS_RANDOM); + trees_ = get_param(params,"trees",4); + leaf_size_ = get_param(params,"leaf_size",100); + + if (centers_init_==FLANN_CENTERS_RANDOM) { + chooseCenters = &HierarchicalClusteringIndex::chooseCentersRandom; + } + else if (centers_init_==FLANN_CENTERS_GONZALES) { + chooseCenters = &HierarchicalClusteringIndex::chooseCentersGonzales; + } + else if (centers_init_==FLANN_CENTERS_KMEANSPP) { + chooseCenters = &HierarchicalClusteringIndex::chooseCentersKMeanspp; + } + else if (centers_init_==FLANN_CENTERS_GROUPWISE) { + chooseCenters = &HierarchicalClusteringIndex::GroupWiseCenterChooser; + } + else { + throw FLANNException("Unknown algorithm for choosing initial centers."); + } + + trees_ = get_param(params,"trees",4); + root = new NodePtr[trees_]; + indices = new int*[trees_]; + + for (int i=0; i(); + computeClustering(root[i], indices[i], (int)size_, branching_,0); + } + } + + + flann_algorithm_t getType() const CV_OVERRIDE + { + return FLANN_INDEX_HIERARCHICAL; + } + + + void saveIndex(FILE* stream) CV_OVERRIDE + { + save_value(stream, branching_); + save_value(stream, trees_); + save_value(stream, centers_init_); + save_value(stream, leaf_size_); + save_value(stream, memoryCounter); + for (int i=0; i& result, const ElementType* vec, const SearchParams& searchParams) CV_OVERRIDE + { + + int maxChecks = get_param(searchParams,"checks",32); + + // Priority queue storing intermediate branches in the best-bin-first search + Heap* heap = new Heap((int)size_); + + std::vector checked(size_,false); + int checks = 0; + for (int i=0; ipopMin(branch) && (checks BranchSt; + + + + void save_tree(FILE* stream, NodePtr node, int num) + { + save_value(stream, *node); + if (node->childs==NULL) { + int indices_offset = (int)(node->indices - indices[num]); + save_value(stream, indices_offset); + } + else { + for(int i=0; ichilds[i], num); + } + } + } + + + void load_tree(FILE* stream, NodePtr& node, int num) + { + node = pool.allocate(); + load_value(stream, *node); + if (node->childs==NULL) { + int indices_offset; + load_value(stream, indices_offset); + node->indices = indices[num] + indices_offset; + } + else { + node->childs = pool.allocate(branching_); + for(int i=0; ichilds[i], num); + } + } + } + + + + + void computeLabels(int* dsindices, int indices_length, int* centers, int centers_length, int* labels, DistanceType& cost) + { + cost = 0; + for (int i=0; inew_dist) { + labels[i] = j; + dist = new_dist; + } + } + cost += dist; + } + } + + /** + * The method responsible with actually doing the recursive hierarchical + * clustering + * + * Params: + * node = the node to cluster + * indices = indices of the points belonging to the current node + * branching = the branching factor to use in the clustering + * + * TODO: for 1-sized clusters don't store a cluster center (it's the same as the single cluster point) + */ + void computeClustering(NodePtr node, int* dsindices, int indices_length, int branching, int level) + { + node->size = indices_length; + node->level = level; + + if (indices_length < leaf_size_) { // leaf node + node->indices = dsindices; + std::sort(node->indices,node->indices+indices_length); + node->childs = NULL; + return; + } + + std::vector centers(branching); + std::vector labels(indices_length); + + int centers_length; + (this->*chooseCenters)(branching, dsindices, indices_length, ¢ers[0], centers_length); + + if (centers_lengthindices = dsindices; + std::sort(node->indices,node->indices+indices_length); + node->childs = NULL; + return; + } + + + // assign points to clusters + DistanceType cost; + computeLabels(dsindices, indices_length, ¢ers[0], centers_length, &labels[0], cost); + + node->childs = pool.allocate(branching); + int start = 0; + int end = start; + for (int i=0; ichilds[i] = pool.allocate(); + node->childs[i]->pivot = centers[i]; + node->childs[i]->indices = NULL; + computeClustering(node->childs[i],dsindices+start, end-start, branching, level+1); + start=end; + } + } + + + + /** + * Performs one descent in the hierarchical k-means tree. The branches not + * visited are stored in a priority queue. + * + * Params: + * node = node to explore + * result = container for the k-nearest neighbors found + * vec = query points + * checks = how many points in the dataset have been checked so far + * maxChecks = maximum dataset points to checks + */ + + + void findNN(NodePtr node, ResultSet& result, const ElementType* vec, int& checks, int maxChecks, + Heap* heap, std::vector& checked) + { + if (node->childs==NULL) { + if (checks>=maxChecks) { + if (result.full()) return; + } + for (int i=0; isize; ++i) { + int index = node->indices[i]; + if (!checked[index]) { + DistanceType dist = distance(dataset[index], vec, veclen_); + result.addPoint(dist, index); + checked[index] = true; + ++checks; + } + } + } + else { + DistanceType* domain_distances = new DistanceType[branching_]; + int best_index = 0; + domain_distances[best_index] = distance(vec, dataset[node->childs[best_index]->pivot], veclen_); + for (int i=1; ichilds[i]->pivot], veclen_); + if (domain_distances[i]insert(BranchSt(node->childs[i],domain_distances[i])); + } + } + delete[] domain_distances; + findNN(node->childs[best_index],result,vec, checks, maxChecks, heap, checked); + } + } + +private: + + + /** + * The dataset used by this index + */ + const Matrix dataset; + + /** + * Parameters used by this index + */ + IndexParams params; + + + /** + * Number of features in the dataset. + */ + size_t size_; + + /** + * Length of each feature. + */ + size_t veclen_; + + /** + * The root node in the tree. + */ + NodePtr* root; + + /** + * Array of indices to vectors in the dataset. + */ + int** indices; + + + /** + * The distance + */ + Distance distance; + + /** + * Pooled memory allocator. + * + * Using a pooled memory allocator is more efficient + * than allocating memory directly when there is a large + * number small of memory allocations. + */ + PooledAllocator pool; + + /** + * Memory occupied by the index. + */ + int memoryCounter; + + /** index parameters */ + int branching_; + int trees_; + flann_centers_init_t centers_init_; + int leaf_size_; + + +}; + +} + +#endif /* OPENCV_FLANN_HIERARCHICAL_CLUSTERING_INDEX_H_ */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/index_testing.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/index_testing.h new file mode 100644 index 0000000000000000000000000000000000000000..d76400409a950a2f99d27061bc3d4a5368103ad9 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/index_testing.h @@ -0,0 +1,318 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + +#ifndef OPENCV_FLANN_INDEX_TESTING_H_ +#define OPENCV_FLANN_INDEX_TESTING_H_ + +#include +#include +#include + +#include "matrix.h" +#include "nn_index.h" +#include "result_set.h" +#include "logger.h" +#include "timer.h" + + +namespace cvflann +{ + +inline int countCorrectMatches(int* neighbors, int* groundTruth, int n) +{ + int count = 0; + for (int i=0; i +typename Distance::ResultType computeDistanceRaport(const Matrix& inputData, typename Distance::ElementType* target, + int* neighbors, int* groundTruth, int veclen, int n, const Distance& distance) +{ + typedef typename Distance::ResultType DistanceType; + + DistanceType ret = 0; + for (int i=0; i +float search_with_ground_truth(NNIndex& index, const Matrix& inputData, + const Matrix& testData, const Matrix& matches, int nn, int checks, + float& time, typename Distance::ResultType& dist, const Distance& distance, int skipMatches) +{ + typedef typename Distance::ResultType DistanceType; + + if (matches.cols resultSet(nn+skipMatches); + SearchParams searchParams(checks); + + std::vector indices(nn+skipMatches); + std::vector dists(nn+skipMatches); + int* neighbors = &indices[skipMatches]; + + int correct = 0; + DistanceType distR = 0; + StartStopTimer t; + int repeats = 0; + while (t.value<0.2) { + repeats++; + t.start(); + correct = 0; + distR = 0; + for (size_t i = 0; i < testData.rows; i++) { + resultSet.init(&indices[0], &dists[0]); + index.findNeighbors(resultSet, testData[i], searchParams); + + correct += countCorrectMatches(neighbors,matches[i], nn); + distR += computeDistanceRaport(inputData, testData[i], neighbors, matches[i], (int)testData.cols, nn, distance); + } + t.stop(); + } + time = float(t.value/repeats); + + float precicion = (float)correct/(nn*testData.rows); + + dist = distR/(testData.rows*nn); + + Logger::info("%8d %10.4g %10.5g %10.5g %10.5g\n", + checks, precicion, time, 1000.0 * time / testData.rows, dist); + + return precicion; +} + + +template +float test_index_checks(NNIndex& index, const Matrix& inputData, + const Matrix& testData, const Matrix& matches, + int checks, float& precision, const Distance& distance, int nn = 1, int skipMatches = 0) +{ + typedef typename Distance::ResultType DistanceType; + + Logger::info(" Nodes Precision(%) Time(s) Time/vec(ms) Mean dist\n"); + Logger::info("---------------------------------------------------------\n"); + + float time = 0; + DistanceType dist = 0; + precision = search_with_ground_truth(index, inputData, testData, matches, nn, checks, time, dist, distance, skipMatches); + + return time; +} + +template +float test_index_precision(NNIndex& index, const Matrix& inputData, + const Matrix& testData, const Matrix& matches, + float precision, int& checks, const Distance& distance, int nn = 1, int skipMatches = 0) +{ + typedef typename Distance::ResultType DistanceType; + const float SEARCH_EPS = 0.001f; + + Logger::info(" Nodes Precision(%) Time(s) Time/vec(ms) Mean dist\n"); + Logger::info("---------------------------------------------------------\n"); + + int c2 = 1; + float p2; + int c1 = 1; + //float p1; + float time; + DistanceType dist; + + p2 = search_with_ground_truth(index, inputData, testData, matches, nn, c2, time, dist, distance, skipMatches); + + if (p2>precision) { + Logger::info("Got as close as I can\n"); + checks = c2; + return time; + } + + while (p2SEARCH_EPS) { + Logger::info("Start linear estimation\n"); + // after we got to values in the vecinity of the desired precision + // use linear approximation get a better estimation + + cx = (c1+c2)/2; + realPrecision = search_with_ground_truth(index, inputData, testData, matches, nn, cx, time, dist, distance, skipMatches); + while (fabs(realPrecision-precision)>SEARCH_EPS) { + + if (realPrecision +void test_index_precisions(NNIndex& index, const Matrix& inputData, + const Matrix& testData, const Matrix& matches, + float* precisions, int precisions_length, const Distance& distance, int nn = 1, int skipMatches = 0, float maxTime = 0) +{ + typedef typename Distance::ResultType DistanceType; + + const float SEARCH_EPS = 0.001; + + // make sure precisions array is sorted + std::sort(precisions, precisions+precisions_length); + + int pindex = 0; + float precision = precisions[pindex]; + + Logger::info(" Nodes Precision(%) Time(s) Time/vec(ms) Mean dist\n"); + Logger::info("---------------------------------------------------------\n"); + + int c2 = 1; + float p2; + + int c1 = 1; + float p1; + + float time; + DistanceType dist; + + p2 = search_with_ground_truth(index, inputData, testData, matches, nn, c2, time, dist, distance, skipMatches); + + // if precision for 1 run down the tree is already + // better then some of the requested precisions, then + // skip those + while (precisions[pindex] 0)&&(time > maxTime)&&(p2SEARCH_EPS) { + Logger::info("Start linear estimation\n"); + // after we got to values in the vecinity of the desired precision + // use linear approximation get a better estimation + + cx = (c1+c2)/2; + realPrecision = search_with_ground_truth(index, inputData, testData, matches, nn, cx, time, dist, distance, skipMatches); + while (fabs(realPrecision-precision)>SEARCH_EPS) { + + if (realPrecision +#include +#include +#include + +#include "general.h" +#include "nn_index.h" +#include "dynamic_bitset.h" +#include "matrix.h" +#include "result_set.h" +#include "heap.h" +#include "allocator.h" +#include "random.h" +#include "saving.h" + + +namespace cvflann +{ + +struct KDTreeIndexParams : public IndexParams +{ + KDTreeIndexParams(int trees = 4) + { + (*this)["algorithm"] = FLANN_INDEX_KDTREE; + (*this)["trees"] = trees; + } +}; + + +/** + * Randomized kd-tree index + * + * Contains the k-d trees and other information for indexing a set of points + * for nearest-neighbor matching. + */ +template +class KDTreeIndex : public NNIndex +{ +public: + typedef typename Distance::ElementType ElementType; + typedef typename Distance::ResultType DistanceType; + + + /** + * KDTree constructor + * + * Params: + * inputData = dataset with the input features + * params = parameters passed to the kdtree algorithm + */ + KDTreeIndex(const Matrix& inputData, const IndexParams& params = KDTreeIndexParams(), + Distance d = Distance() ) : + dataset_(inputData), index_params_(params), distance_(d) + { + size_ = dataset_.rows; + veclen_ = dataset_.cols; + + trees_ = get_param(index_params_,"trees",4); + tree_roots_ = new NodePtr[trees_]; + + // Create a permutable array of indices to the input vectors. + vind_.resize(size_); + for (size_t i = 0; i < size_; ++i) { + vind_[i] = int(i); + } + + mean_ = new DistanceType[veclen_]; + var_ = new DistanceType[veclen_]; + } + + + KDTreeIndex(const KDTreeIndex&); + KDTreeIndex& operator=(const KDTreeIndex&); + + /** + * Standard destructor + */ + ~KDTreeIndex() + { + if (tree_roots_!=NULL) { + delete[] tree_roots_; + } + delete[] mean_; + delete[] var_; + } + + /** + * Builds the index + */ + void buildIndex() CV_OVERRIDE + { + /* Construct the randomized trees. */ + for (int i = 0; i < trees_; i++) { + /* Randomize the order of vectors to allow for unbiased sampling. */ +#ifndef OPENCV_FLANN_USE_STD_RAND + cv::randShuffle(vind_); +#else + std::random_shuffle(vind_.begin(), vind_.end()); +#endif + + tree_roots_[i] = divideTree(&vind_[0], int(size_) ); + } + } + + + flann_algorithm_t getType() const CV_OVERRIDE + { + return FLANN_INDEX_KDTREE; + } + + + void saveIndex(FILE* stream) CV_OVERRIDE + { + save_value(stream, trees_); + for (int i=0; i& result, const ElementType* vec, const SearchParams& searchParams) CV_OVERRIDE + { + int maxChecks = get_param(searchParams,"checks", 32); + float epsError = 1+get_param(searchParams,"eps",0.0f); + + if (maxChecks==FLANN_CHECKS_UNLIMITED) { + getExactNeighbors(result, vec, epsError); + } + else { + getNeighbors(result, vec, maxChecks, epsError); + } + } + + IndexParams getParameters() const CV_OVERRIDE + { + return index_params_; + } + +private: + + + /*--------------------- Internal Data Structures --------------------------*/ + struct Node + { + /** + * Dimension used for subdivision. + */ + int divfeat; + /** + * The values used for subdivision. + */ + DistanceType divval; + /** + * The child nodes. + */ + Node* child1, * child2; + }; + typedef Node* NodePtr; + typedef BranchStruct BranchSt; + typedef BranchSt* Branch; + + + + void save_tree(FILE* stream, NodePtr tree) + { + save_value(stream, *tree); + if (tree->child1!=NULL) { + save_tree(stream, tree->child1); + } + if (tree->child2!=NULL) { + save_tree(stream, tree->child2); + } + } + + + void load_tree(FILE* stream, NodePtr& tree) + { + tree = pool_.allocate(); + load_value(stream, *tree); + if (tree->child1!=NULL) { + load_tree(stream, tree->child1); + } + if (tree->child2!=NULL) { + load_tree(stream, tree->child2); + } + } + + + /** + * Create a tree node that subdivides the list of vecs from vind[first] + * to vind[last]. The routine is called recursively on each sublist. + * Place a pointer to this new tree node in the location pTree. + * + * Params: pTree = the new node to create + * first = index of the first vector + * last = index of the last vector + */ + NodePtr divideTree(int* ind, int count) + { + NodePtr node = pool_.allocate(); // allocate memory + + /* If too few exemplars remain, then make this a leaf node. */ + if ( count == 1) { + node->child1 = node->child2 = NULL; /* Mark as leaf node. */ + node->divfeat = *ind; /* Store index of this vec. */ + } + else { + int idx; + int cutfeat; + DistanceType cutval; + meanSplit(ind, count, idx, cutfeat, cutval); + + node->divfeat = cutfeat; + node->divval = cutval; + node->child1 = divideTree(ind, idx); + node->child2 = divideTree(ind+idx, count-idx); + } + + return node; + } + + + /** + * Choose which feature to use in order to subdivide this set of vectors. + * Make a random choice among those with the highest variance, and use + * its variance as the threshold value. + */ + void meanSplit(int* ind, int count, int& index, int& cutfeat, DistanceType& cutval) + { + memset(mean_,0,veclen_*sizeof(DistanceType)); + memset(var_,0,veclen_*sizeof(DistanceType)); + + /* Compute mean values. Only the first SAMPLE_MEAN values need to be + sampled to get a good estimate. + */ + int cnt = std::min((int)SAMPLE_MEAN+1, count); + for (int j = 0; j < cnt; ++j) { + ElementType* v = dataset_[ind[j]]; + for (size_t k=0; kcount/2) index = lim1; + else if (lim2 v[topind[num-1]])) { + /* Put this element at end of topind. */ + if (num < RAND_DIM) { + topind[num++] = i; /* Add to list. */ + } + else { + topind[num-1] = i; /* Replace last element. */ + } + /* Bubble end value down to right location by repeated swapping. */ + int j = num - 1; + while (j > 0 && v[topind[j]] > v[topind[j-1]]) { + std::swap(topind[j], topind[j-1]); + --j; + } + } + } + /* Select a random integer in range [0,num-1], and return that index. */ + int rnd = rand_int(num); + return (int)topind[rnd]; + } + + + /** + * Subdivide the list of points by a plane perpendicular on axe corresponding + * to the 'cutfeat' dimension at 'cutval' position. + * + * On return: + * dataset[ind[0..lim1-1]][cutfeat]cutval + */ + void planeSplit(int* ind, int count, int cutfeat, DistanceType cutval, int& lim1, int& lim2) + { + /* Move vector indices for left subtree to front of list. */ + int left = 0; + int right = count-1; + for (;; ) { + while (left<=right && dataset_[ind[left]][cutfeat]=cutval) --right; + if (left>right) break; + std::swap(ind[left], ind[right]); ++left; --right; + } + lim1 = left; + right = count-1; + for (;; ) { + while (left<=right && dataset_[ind[left]][cutfeat]<=cutval) ++left; + while (left<=right && dataset_[ind[right]][cutfeat]>cutval) --right; + if (left>right) break; + std::swap(ind[left], ind[right]); ++left; --right; + } + lim2 = left; + } + + /** + * Performs an exact nearest neighbor search. The exact search performs a full + * traversal of the tree. + */ + void getExactNeighbors(ResultSet& result, const ElementType* vec, float epsError) + { + // checkID -= 1; /* Set a different unique ID for each search. */ + + if (trees_ > 1) { + fprintf(stderr,"It doesn't make any sense to use more than one tree for exact search"); + } + if (trees_>0) { + searchLevelExact(result, vec, tree_roots_[0], 0.0, epsError); + } + assert(result.full()); + } + + /** + * Performs the approximate nearest-neighbor search. The search is approximate + * because the tree traversal is abandoned after a given number of descends in + * the tree. + */ + void getNeighbors(ResultSet& result, const ElementType* vec, int maxCheck, float epsError) + { + int i; + BranchSt branch; + + int checkCount = 0; + Heap* heap = new Heap((int)size_); + DynamicBitset checked(size_); + + /* Search once through each tree down to root. */ + for (i = 0; i < trees_; ++i) { + searchLevel(result, vec, tree_roots_[i], 0, checkCount, maxCheck, epsError, heap, checked); + } + + /* Keep searching other branches from heap until finished. */ + while ( heap->popMin(branch) && (checkCount < maxCheck || !result.full() )) { + searchLevel(result, vec, branch.node, branch.mindist, checkCount, maxCheck, epsError, heap, checked); + } + + delete heap; + + assert(result.full()); + } + + + /** + * Search starting from a given node of the tree. Based on any mismatches at + * higher levels, all exemplars below this level must have a distance of + * at least "mindistsq". + */ + void searchLevel(ResultSet& result_set, const ElementType* vec, NodePtr node, DistanceType mindist, int& checkCount, int maxCheck, + float epsError, Heap* heap, DynamicBitset& checked) + { + if (result_set.worstDist()child1 == NULL)&&(node->child2 == NULL)) { + /* Do not check same node more than once when searching multiple trees. + Once a vector is checked, we set its location in vind to the + current checkID. + */ + int index = node->divfeat; + if ( checked.test(index) || ((checkCount>=maxCheck)&& result_set.full()) ) return; + checked.set(index); + checkCount++; + + DistanceType dist = distance_(dataset_[index], vec, veclen_); + result_set.addPoint(dist,index); + + return; + } + + /* Which child branch should be taken first? */ + ElementType val = vec[node->divfeat]; + DistanceType diff = val - node->divval; + NodePtr bestChild = (diff < 0) ? node->child1 : node->child2; + NodePtr otherChild = (diff < 0) ? node->child2 : node->child1; + + /* Create a branch record for the branch not taken. Add distance + of this feature boundary (we don't attempt to correct for any + use of this feature in a parent node, which is unlikely to + happen and would have only a small effect). Don't bother + adding more branches to heap after halfway point, as cost of + adding exceeds their value. + */ + + DistanceType new_distsq = mindist + distance_.accum_dist(val, node->divval, node->divfeat); + // if (2 * checkCount < maxCheck || !result.full()) { + if ((new_distsq*epsError < result_set.worstDist())|| !result_set.full()) { + heap->insert( BranchSt(otherChild, new_distsq) ); + } + + /* Call recursively to search next level down. */ + searchLevel(result_set, vec, bestChild, mindist, checkCount, maxCheck, epsError, heap, checked); + } + + /** + * Performs an exact search in the tree starting from a node. + */ + void searchLevelExact(ResultSet& result_set, const ElementType* vec, const NodePtr node, DistanceType mindist, const float epsError) + { + /* If this is a leaf node, then do check and return. */ + if ((node->child1 == NULL)&&(node->child2 == NULL)) { + int index = node->divfeat; + DistanceType dist = distance_(dataset_[index], vec, veclen_); + result_set.addPoint(dist,index); + return; + } + + /* Which child branch should be taken first? */ + ElementType val = vec[node->divfeat]; + DistanceType diff = val - node->divval; + NodePtr bestChild = (diff < 0) ? node->child1 : node->child2; + NodePtr otherChild = (diff < 0) ? node->child2 : node->child1; + + /* Create a branch record for the branch not taken. Add distance + of this feature boundary (we don't attempt to correct for any + use of this feature in a parent node, which is unlikely to + happen and would have only a small effect). Don't bother + adding more branches to heap after halfway point, as cost of + adding exceeds their value. + */ + + DistanceType new_distsq = mindist + distance_.accum_dist(val, node->divval, node->divfeat); + + /* Call recursively to search next level down. */ + searchLevelExact(result_set, vec, bestChild, mindist, epsError); + + if (new_distsq*epsError<=result_set.worstDist()) { + searchLevelExact(result_set, vec, otherChild, new_distsq, epsError); + } + } + + +private: + + enum + { + /** + * To improve efficiency, only SAMPLE_MEAN random values are used to + * compute the mean and variance at each level when building a tree. + * A value of 100 seems to perform as well as using all values. + */ + SAMPLE_MEAN = 100, + /** + * Top random dimensions to consider + * + * When creating random trees, the dimension on which to subdivide is + * selected at random from among the top RAND_DIM dimensions with the + * highest variance. A value of 5 works well. + */ + RAND_DIM=5 + }; + + + /** + * Number of randomized trees that are used + */ + int trees_; + + /** + * Array of indices to vectors in the dataset. + */ + std::vector vind_; + + /** + * The dataset used by this index + */ + const Matrix dataset_; + + IndexParams index_params_; + + size_t size_; + size_t veclen_; + + + DistanceType* mean_; + DistanceType* var_; + + + /** + * Array of k-d trees used to find neighbours. + */ + NodePtr* tree_roots_; + + /** + * Pooled memory allocator. + * + * Using a pooled memory allocator is more efficient + * than allocating memory directly when there is a large + * number small of memory allocations. + */ + PooledAllocator pool_; + + Distance distance_; + + +}; // class KDTreeForest + +} + +#endif //OPENCV_FLANN_KDTREE_INDEX_H_ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/kdtree_single_index.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/kdtree_single_index.h new file mode 100644 index 0000000000000000000000000000000000000000..22a28d0c1a702d0e7c4f44f1af44231077ba85fd --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/kdtree_single_index.h @@ -0,0 +1,635 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + +#ifndef OPENCV_FLANN_KDTREE_SINGLE_INDEX_H_ +#define OPENCV_FLANN_KDTREE_SINGLE_INDEX_H_ + +#include +#include +#include +#include + +#include "general.h" +#include "nn_index.h" +#include "matrix.h" +#include "result_set.h" +#include "heap.h" +#include "allocator.h" +#include "random.h" +#include "saving.h" + +namespace cvflann +{ + +struct KDTreeSingleIndexParams : public IndexParams +{ + KDTreeSingleIndexParams(int leaf_max_size = 10, bool reorder = true, int dim = -1) + { + (*this)["algorithm"] = FLANN_INDEX_KDTREE_SINGLE; + (*this)["leaf_max_size"] = leaf_max_size; + (*this)["reorder"] = reorder; + (*this)["dim"] = dim; + } +}; + + +/** + * Randomized kd-tree index + * + * Contains the k-d trees and other information for indexing a set of points + * for nearest-neighbor matching. + */ +template +class KDTreeSingleIndex : public NNIndex +{ +public: + typedef typename Distance::ElementType ElementType; + typedef typename Distance::ResultType DistanceType; + + + /** + * KDTree constructor + * + * Params: + * inputData = dataset with the input features + * params = parameters passed to the kdtree algorithm + */ + KDTreeSingleIndex(const Matrix& inputData, const IndexParams& params = KDTreeSingleIndexParams(), + Distance d = Distance() ) : + dataset_(inputData), index_params_(params), distance_(d) + { + size_ = dataset_.rows; + dim_ = dataset_.cols; + root_node_ = 0; + int dim_param = get_param(params,"dim",-1); + if (dim_param>0) dim_ = dim_param; + leaf_max_size_ = get_param(params,"leaf_max_size",10); + reorder_ = get_param(params,"reorder",true); + + // Create a permutable array of indices to the input vectors. + vind_.resize(size_); + for (size_t i = 0; i < size_; i++) { + vind_[i] = (int)i; + } + } + + KDTreeSingleIndex(const KDTreeSingleIndex&); + KDTreeSingleIndex& operator=(const KDTreeSingleIndex&); + + /** + * Standard destructor + */ + ~KDTreeSingleIndex() + { + if (reorder_) delete[] data_.data; + } + + /** + * Builds the index + */ + void buildIndex() CV_OVERRIDE + { + computeBoundingBox(root_bbox_); + root_node_ = divideTree(0, (int)size_, root_bbox_ ); // construct the tree + + if (reorder_) { + delete[] data_.data; + data_ = cvflann::Matrix(new ElementType[size_*dim_], size_, dim_); + for (size_t i=0; i& queries, Matrix& indices, Matrix& dists, int knn, const SearchParams& params) CV_OVERRIDE + { + assert(queries.cols == veclen()); + assert(indices.rows >= queries.rows); + assert(dists.rows >= queries.rows); + assert(int(indices.cols) >= knn); + assert(int(dists.cols) >= knn); + + KNNSimpleResultSet resultSet(knn); + for (size_t i = 0; i < queries.rows; i++) { + resultSet.init(indices[i], dists[i]); + findNeighbors(resultSet, queries[i], params); + } + } + + IndexParams getParameters() const CV_OVERRIDE + { + return index_params_; + } + + /** + * Find set of nearest neighbors to vec. Their indices are stored inside + * the result object. + * + * Params: + * result = the result object in which the indices of the nearest-neighbors are stored + * vec = the vector for which to search the nearest neighbors + * maxCheck = the maximum number of restarts (in a best-bin-first manner) + */ + void findNeighbors(ResultSet& result, const ElementType* vec, const SearchParams& searchParams) CV_OVERRIDE + { + float epsError = 1+get_param(searchParams,"eps",0.0f); + + std::vector dists(dim_,0); + DistanceType distsq = computeInitialDistances(vec, dists); + searchLevel(result, vec, root_node_, distsq, dists, epsError); + } + +private: + + + /*--------------------- Internal Data Structures --------------------------*/ + struct Node + { + /** + * Indices of points in leaf node + */ + int left, right; + /** + * Dimension used for subdivision. + */ + int divfeat; + /** + * The values used for subdivision. + */ + DistanceType divlow, divhigh; + /** + * The child nodes. + */ + Node* child1, * child2; + }; + typedef Node* NodePtr; + + + struct Interval + { + DistanceType low, high; + }; + + typedef std::vector BoundingBox; + + typedef BranchStruct BranchSt; + typedef BranchSt* Branch; + + + + + void save_tree(FILE* stream, NodePtr tree) + { + save_value(stream, *tree); + if (tree->child1!=NULL) { + save_tree(stream, tree->child1); + } + if (tree->child2!=NULL) { + save_tree(stream, tree->child2); + } + } + + + void load_tree(FILE* stream, NodePtr& tree) + { + tree = pool_.allocate(); + load_value(stream, *tree); + if (tree->child1!=NULL) { + load_tree(stream, tree->child1); + } + if (tree->child2!=NULL) { + load_tree(stream, tree->child2); + } + } + + + void computeBoundingBox(BoundingBox& bbox) + { + bbox.resize(dim_); + for (size_t i=0; ibbox[i].high) bbox[i].high = (DistanceType)dataset_[k][i]; + } + } + } + + + /** + * Create a tree node that subdivides the list of vecs from vind[first] + * to vind[last]. The routine is called recursively on each sublist. + * Place a pointer to this new tree node in the location pTree. + * + * Params: pTree = the new node to create + * first = index of the first vector + * last = index of the last vector + */ + NodePtr divideTree(int left, int right, BoundingBox& bbox) + { + NodePtr node = pool_.allocate(); // allocate memory + + /* If too few exemplars remain, then make this a leaf node. */ + if ( (right-left) <= leaf_max_size_) { + node->child1 = node->child2 = NULL; /* Mark as leaf node. */ + node->left = left; + node->right = right; + + // compute bounding-box of leaf points + for (size_t i=0; idataset_[vind_[k]][i]) bbox[i].low=(DistanceType)dataset_[vind_[k]][i]; + if (bbox[i].highdivfeat = cutfeat; + + BoundingBox left_bbox(bbox); + left_bbox[cutfeat].high = cutval; + node->child1 = divideTree(left, left+idx, left_bbox); + + BoundingBox right_bbox(bbox); + right_bbox[cutfeat].low = cutval; + node->child2 = divideTree(left+idx, right, right_bbox); + + node->divlow = left_bbox[cutfeat].high; + node->divhigh = right_bbox[cutfeat].low; + + for (size_t i=0; imax_elem) max_elem = val; + } + } + + void middleSplit(int* ind, int count, int& index, int& cutfeat, DistanceType& cutval, const BoundingBox& bbox) + { + // find the largest span from the approximate bounding box + ElementType max_span = bbox[0].high-bbox[0].low; + cutfeat = 0; + cutval = (bbox[0].high+bbox[0].low)/2; + for (size_t i=1; imax_span) { + max_span = span; + cutfeat = i; + cutval = (bbox[i].high+bbox[i].low)/2; + } + } + + // compute exact span on the found dimension + ElementType min_elem, max_elem; + computeMinMax(ind, count, cutfeat, min_elem, max_elem); + cutval = (min_elem+max_elem)/2; + max_span = max_elem - min_elem; + + // check if a dimension of a largest span exists + size_t k = cutfeat; + for (size_t i=0; imax_span) { + computeMinMax(ind, count, i, min_elem, max_elem); + span = max_elem - min_elem; + if (span>max_span) { + max_span = span; + cutfeat = i; + cutval = (min_elem+max_elem)/2; + } + } + } + int lim1, lim2; + planeSplit(ind, count, cutfeat, cutval, lim1, lim2); + + if (lim1>count/2) index = lim1; + else if (lim2max_span) { + max_span = span; + } + } + DistanceType max_spread = -1; + cutfeat = 0; + for (size_t i=0; i(DistanceType)((1-EPS)*max_span)) { + ElementType min_elem, max_elem; + computeMinMax(ind, count, cutfeat, min_elem, max_elem); + DistanceType spread = (DistanceType)(max_elem-min_elem); + if (spread>max_spread) { + cutfeat = (int)i; + max_spread = spread; + } + } + } + // split in the middle + DistanceType split_val = (bbox[cutfeat].low+bbox[cutfeat].high)/2; + ElementType min_elem, max_elem; + computeMinMax(ind, count, cutfeat, min_elem, max_elem); + + if (split_valmax_elem) cutval = (DistanceType)max_elem; + else cutval = split_val; + + int lim1, lim2; + planeSplit(ind, count, cutfeat, cutval, lim1, lim2); + + if (lim1>count/2) index = lim1; + else if (lim2cutval + */ + void planeSplit(int* ind, int count, int cutfeat, DistanceType cutval, int& lim1, int& lim2) + { + /* Move vector indices for left subtree to front of list. */ + int left = 0; + int right = count-1; + for (;; ) { + while (left<=right && dataset_[ind[left]][cutfeat]=cutval) --right; + if (left>right) break; + std::swap(ind[left], ind[right]); ++left; --right; + } + /* If either list is empty, it means that all remaining features + * are identical. Split in the middle to maintain a balanced tree. + */ + lim1 = left; + right = count-1; + for (;; ) { + while (left<=right && dataset_[ind[left]][cutfeat]<=cutval) ++left; + while (left<=right && dataset_[ind[right]][cutfeat]>cutval) --right; + if (left>right) break; + std::swap(ind[left], ind[right]); ++left; --right; + } + lim2 = left; + } + + DistanceType computeInitialDistances(const ElementType* vec, std::vector& dists) + { + DistanceType distsq = 0.0; + + for (size_t i = 0; i < dim_; ++i) { + if (vec[i] < root_bbox_[i].low) { + dists[i] = distance_.accum_dist(vec[i], root_bbox_[i].low, (int)i); + distsq += dists[i]; + } + if (vec[i] > root_bbox_[i].high) { + dists[i] = distance_.accum_dist(vec[i], root_bbox_[i].high, (int)i); + distsq += dists[i]; + } + } + + return distsq; + } + + /** + * Performs an exact search in the tree starting from a node. + */ + void searchLevel(ResultSet& result_set, const ElementType* vec, const NodePtr node, DistanceType mindistsq, + std::vector& dists, const float epsError) + { + /* If this is a leaf node, then do check and return. */ + if ((node->child1 == NULL)&&(node->child2 == NULL)) { + DistanceType worst_dist = result_set.worstDist(); + for (int i=node->left; iright; ++i) { + int index = reorder_ ? i : vind_[i]; + DistanceType dist = distance_(vec, data_[index], dim_, worst_dist); + if (distdivfeat; + ElementType val = vec[idx]; + DistanceType diff1 = val - node->divlow; + DistanceType diff2 = val - node->divhigh; + + NodePtr bestChild; + NodePtr otherChild; + DistanceType cut_dist; + if ((diff1+diff2)<0) { + bestChild = node->child1; + otherChild = node->child2; + cut_dist = distance_.accum_dist(val, node->divhigh, idx); + } + else { + bestChild = node->child2; + otherChild = node->child1; + cut_dist = distance_.accum_dist( val, node->divlow, idx); + } + + /* Call recursively to search next level down. */ + searchLevel(result_set, vec, bestChild, mindistsq, dists, epsError); + + DistanceType dst = dists[idx]; + mindistsq = mindistsq + cut_dist - dst; + dists[idx] = cut_dist; + if (mindistsq*epsError<=result_set.worstDist()) { + searchLevel(result_set, vec, otherChild, mindistsq, dists, epsError); + } + dists[idx] = dst; + } + +private: + + /** + * The dataset used by this index + */ + const Matrix dataset_; + + IndexParams index_params_; + + int leaf_max_size_; + bool reorder_; + + + /** + * Array of indices to vectors in the dataset. + */ + std::vector vind_; + + Matrix data_; + + size_t size_; + size_t dim_; + + /** + * Array of k-d trees used to find neighbours. + */ + NodePtr root_node_; + + BoundingBox root_bbox_; + + /** + * Pooled memory allocator. + * + * Using a pooled memory allocator is more efficient + * than allocating memory directly when there is a large + * number small of memory allocations. + */ + PooledAllocator pool_; + + Distance distance_; +}; // class KDTree + +} + +#endif //OPENCV_FLANN_KDTREE_SINGLE_INDEX_H_ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/kmeans_index.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/kmeans_index.h new file mode 100644 index 0000000000000000000000000000000000000000..2f48e40785bbcc8c75f87c6f140f15f4b1f664b7 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/kmeans_index.h @@ -0,0 +1,1171 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + +#ifndef OPENCV_FLANN_KMEANS_INDEX_H_ +#define OPENCV_FLANN_KMEANS_INDEX_H_ + +#include +#include +#include +#include +#include + +#include "general.h" +#include "nn_index.h" +#include "dist.h" +#include "matrix.h" +#include "result_set.h" +#include "heap.h" +#include "allocator.h" +#include "random.h" +#include "saving.h" +#include "logger.h" + + +namespace cvflann +{ + +struct KMeansIndexParams : public IndexParams +{ + KMeansIndexParams(int branching = 32, int iterations = 11, + flann_centers_init_t centers_init = FLANN_CENTERS_RANDOM, float cb_index = 0.2 ) + { + (*this)["algorithm"] = FLANN_INDEX_KMEANS; + // branching factor + (*this)["branching"] = branching; + // max iterations to perform in one kmeans clustering (kmeans tree) + (*this)["iterations"] = iterations; + // algorithm used for picking the initial cluster centers for kmeans tree + (*this)["centers_init"] = centers_init; + // cluster boundary index. Used when searching the kmeans tree + (*this)["cb_index"] = cb_index; + } +}; + + +/** + * Hierarchical kmeans index + * + * Contains a tree constructed through a hierarchical kmeans clustering + * and other information for indexing a set of points for nearest-neighbour matching. + */ +template +class KMeansIndex : public NNIndex +{ +public: + typedef typename Distance::ElementType ElementType; + typedef typename Distance::ResultType DistanceType; + + + + typedef void (KMeansIndex::* centersAlgFunction)(int, int*, int, int*, int&); + + /** + * The function used for choosing the cluster centers. + */ + centersAlgFunction chooseCenters; + + + + /** + * Chooses the initial centers in the k-means clustering in a random manner. + * + * Params: + * k = number of centers + * vecs = the dataset of points + * indices = indices in the dataset + * indices_length = length of indices vector + * + */ + void chooseCentersRandom(int k, int* indices, int indices_length, int* centers, int& centers_length) + { + UniqueRandom r(indices_length); + + int index; + for (index=0; index=0 && rnd < n); + + centers[0] = indices[rnd]; + + int index; + for (index=1; indexbest_val) { + best_val = dist; + best_index = j; + } + } + if (best_index!=-1) { + centers[index] = indices[best_index]; + } + else { + break; + } + } + centers_length = index; + } + + + /** + * Chooses the initial centers in the k-means using the algorithm + * proposed in the KMeans++ paper: + * Arthur, David; Vassilvitskii, Sergei - k-means++: The Advantages of Careful Seeding + * + * Implementation of this function was converted from the one provided in Arthur's code. + * + * Params: + * k = number of centers + * vecs = the dataset of points + * indices = indices in the dataset + * Returns: + */ + void chooseCentersKMeanspp(int k, int* indices, int indices_length, int* centers, int& centers_length) + { + int n = indices_length; + + double currentPot = 0; + DistanceType* closestDistSq = new DistanceType[n]; + + // Choose one random center and set the closestDistSq values + int index = rand_int(n); + assert(index >=0 && index < n); + centers[0] = indices[index]; + + for (int i = 0; i < n; i++) { + closestDistSq[i] = distance_(dataset_[indices[i]], dataset_[indices[index]], dataset_.cols); + closestDistSq[i] = ensureSquareDistance( closestDistSq[i] ); + currentPot += closestDistSq[i]; + } + + + const int numLocalTries = 1; + + // Choose each center + int centerCount; + for (centerCount = 1; centerCount < k; centerCount++) { + + // Repeat several trials + double bestNewPot = -1; + int bestNewIndex = -1; + for (int localTrial = 0; localTrial < numLocalTries; localTrial++) { + + // Choose our center - have to be slightly careful to return a valid answer even accounting + // for possible rounding errors + double randVal = rand_double(currentPot); + for (index = 0; index < n-1; index++) { + if (randVal <= closestDistSq[index]) break; + else randVal -= closestDistSq[index]; + } + + // Compute the new potential + double newPot = 0; + for (int i = 0; i < n; i++) { + DistanceType dist = distance_(dataset_[indices[i]], dataset_[indices[index]], dataset_.cols); + newPot += std::min( ensureSquareDistance(dist), closestDistSq[i] ); + } + + // Store the best result + if ((bestNewPot < 0)||(newPot < bestNewPot)) { + bestNewPot = newPot; + bestNewIndex = index; + } + } + + // Add the appropriate center + centers[centerCount] = indices[bestNewIndex]; + currentPot = bestNewPot; + for (int i = 0; i < n; i++) { + DistanceType dist = distance_(dataset_[indices[i]], dataset_[indices[bestNewIndex]], dataset_.cols); + closestDistSq[i] = std::min( ensureSquareDistance(dist), closestDistSq[i] ); + } + } + + centers_length = centerCount; + + delete[] closestDistSq; + } + + + +public: + + flann_algorithm_t getType() const CV_OVERRIDE + { + return FLANN_INDEX_KMEANS; + } + + class KMeansDistanceComputer : public cv::ParallelLoopBody + { + public: + KMeansDistanceComputer(Distance _distance, const Matrix& _dataset, + const int _branching, const int* _indices, const Matrix& _dcenters, const size_t _veclen, + int* _count, int* _belongs_to, std::vector& _radiuses, bool& _converged, cv::Mutex& _mtx) + : distance(_distance) + , dataset(_dataset) + , branching(_branching) + , indices(_indices) + , dcenters(_dcenters) + , veclen(_veclen) + , count(_count) + , belongs_to(_belongs_to) + , radiuses(_radiuses) + , converged(_converged) + , mtx(_mtx) + { + } + + void operator()(const cv::Range& range) const CV_OVERRIDE + { + const int begin = range.start; + const int end = range.end; + + for( int i = begin; inew_sq_dist) { + new_centroid = j; + sq_dist = new_sq_dist; + } + } + if (sq_dist > radiuses[new_centroid]) { + radiuses[new_centroid] = sq_dist; + } + if (new_centroid != belongs_to[i]) { + count[belongs_to[i]]--; + count[new_centroid]++; + belongs_to[i] = new_centroid; + mtx.lock(); + converged = false; + mtx.unlock(); + } + } + } + + private: + Distance distance; + const Matrix& dataset; + const int branching; + const int* indices; + const Matrix& dcenters; + const size_t veclen; + int* count; + int* belongs_to; + std::vector& radiuses; + bool& converged; + cv::Mutex& mtx; + KMeansDistanceComputer& operator=( const KMeansDistanceComputer & ) { return *this; } + }; + + /** + * Index constructor + * + * Params: + * inputData = dataset with the input features + * params = parameters passed to the hierarchical k-means algorithm + */ + KMeansIndex(const Matrix& inputData, const IndexParams& params = KMeansIndexParams(), + Distance d = Distance()) + : dataset_(inputData), index_params_(params), root_(NULL), indices_(NULL), distance_(d) + { + memoryCounter_ = 0; + + size_ = dataset_.rows; + veclen_ = dataset_.cols; + + branching_ = get_param(params,"branching",32); + iterations_ = get_param(params,"iterations",11); + if (iterations_<0) { + iterations_ = (std::numeric_limits::max)(); + } + centers_init_ = get_param(params,"centers_init",FLANN_CENTERS_RANDOM); + + if (centers_init_==FLANN_CENTERS_RANDOM) { + chooseCenters = &KMeansIndex::chooseCentersRandom; + } + else if (centers_init_==FLANN_CENTERS_GONZALES) { + chooseCenters = &KMeansIndex::chooseCentersGonzales; + } + else if (centers_init_==FLANN_CENTERS_KMEANSPP) { + chooseCenters = &KMeansIndex::chooseCentersKMeanspp; + } + else { + throw FLANNException("Unknown algorithm for choosing initial centers."); + } + cb_index_ = 0.4f; + + } + + + KMeansIndex(const KMeansIndex&); + KMeansIndex& operator=(const KMeansIndex&); + + + /** + * Index destructor. + * + * Release the memory used by the index. + */ + virtual ~KMeansIndex() + { + if (root_ != NULL) { + free_centers(root_); + } + if (indices_!=NULL) { + delete[] indices_; + } + } + + /** + * Returns size of index. + */ + size_t size() const CV_OVERRIDE + { + return size_; + } + + /** + * Returns the length of an index feature. + */ + size_t veclen() const CV_OVERRIDE + { + return veclen_; + } + + + void set_cb_index( float index) + { + cb_index_ = index; + } + + /** + * Computes the inde memory usage + * Returns: memory used by the index + */ + int usedMemory() const CV_OVERRIDE + { + return pool_.usedMemory+pool_.wastedMemory+memoryCounter_; + } + + /** + * Builds the index + */ + void buildIndex() CV_OVERRIDE + { + if (branching_<2) { + throw FLANNException("Branching factor must be at least 2"); + } + + indices_ = new int[size_]; + for (size_t i=0; i(); + std::memset(root_, 0, sizeof(KMeansNode)); + + computeNodeStatistics(root_, indices_, (int)size_); + computeClustering(root_, indices_, (int)size_, branching_,0); + } + + + void saveIndex(FILE* stream) CV_OVERRIDE + { + save_value(stream, branching_); + save_value(stream, iterations_); + save_value(stream, memoryCounter_); + save_value(stream, cb_index_); + save_value(stream, *indices_, (int)size_); + + save_tree(stream, root_); + } + + + void loadIndex(FILE* stream) CV_OVERRIDE + { + load_value(stream, branching_); + load_value(stream, iterations_); + load_value(stream, memoryCounter_); + load_value(stream, cb_index_); + if (indices_!=NULL) { + delete[] indices_; + } + indices_ = new int[size_]; + load_value(stream, *indices_, size_); + + if (root_!=NULL) { + free_centers(root_); + } + load_tree(stream, root_); + + index_params_["algorithm"] = getType(); + index_params_["branching"] = branching_; + index_params_["iterations"] = iterations_; + index_params_["centers_init"] = centers_init_; + index_params_["cb_index"] = cb_index_; + + } + + + /** + * Find set of nearest neighbors to vec. Their indices are stored inside + * the result object. + * + * Params: + * result = the result object in which the indices of the nearest-neighbors are stored + * vec = the vector for which to search the nearest neighbors + * searchParams = parameters that influence the search algorithm (checks, cb_index) + */ + void findNeighbors(ResultSet& result, const ElementType* vec, const SearchParams& searchParams) CV_OVERRIDE + { + + int maxChecks = get_param(searchParams,"checks",32); + + if (maxChecks==FLANN_CHECKS_UNLIMITED) { + findExactNN(root_, result, vec); + } + else { + // Priority queue storing intermediate branches in the best-bin-first search + Heap* heap = new Heap((int)size_); + + int checks = 0; + findNN(root_, result, vec, checks, maxChecks, heap); + + BranchSt branch; + while (heap->popMin(branch) && (checks& centers) + { + int numClusters = centers.rows; + if (numClusters<1) { + throw FLANNException("Number of clusters must be at least 1"); + } + + DistanceType variance; + KMeansNodePtr* clusters = new KMeansNodePtr[numClusters]; + + int clusterCount = getMinVarianceClusters(root_, clusters, numClusters, variance); + + Logger::info("Clusters requested: %d, returning %d\n",numClusters, clusterCount); + + for (int i=0; ipivot; + for (size_t j=0; j BranchSt; + + + + + void save_tree(FILE* stream, KMeansNodePtr node) + { + save_value(stream, *node); + save_value(stream, *(node->pivot), (int)veclen_); + if (node->childs==NULL) { + int indices_offset = (int)(node->indices - indices_); + save_value(stream, indices_offset); + } + else { + for(int i=0; ichilds[i]); + } + } + } + + + void load_tree(FILE* stream, KMeansNodePtr& node) + { + node = pool_.allocate(); + load_value(stream, *node); + node->pivot = new DistanceType[veclen_]; + load_value(stream, *(node->pivot), (int)veclen_); + if (node->childs==NULL) { + int indices_offset; + load_value(stream, indices_offset); + node->indices = indices_ + indices_offset; + } + else { + node->childs = pool_.allocate(branching_); + for(int i=0; ichilds[i]); + } + } + } + + + /** + * Helper function + */ + void free_centers(KMeansNodePtr node) + { + delete[] node->pivot; + if (node->childs!=NULL) { + for (int k=0; kchilds[k]); + } + } + } + + /** + * Computes the statistics of a node (mean, radius, variance). + * + * Params: + * node = the node to use + * indices = the indices of the points belonging to the node + */ + void computeNodeStatistics(KMeansNodePtr node, int* indices, int indices_length) + { + + DistanceType radius = 0; + DistanceType variance = 0; + DistanceType* mean = new DistanceType[veclen_]; + memoryCounter_ += int(veclen_*sizeof(DistanceType)); + + memset(mean,0,veclen_*sizeof(DistanceType)); + + for (size_t i=0; i(), veclen_); + } + for (size_t j=0; j(), veclen_); + + DistanceType tmp = 0; + for (int i=0; iradius) { + radius = tmp; + } + } + + node->variance = variance; + node->radius = radius; + node->pivot = mean; + } + + + /** + * The method responsible with actually doing the recursive hierarchical + * clustering + * + * Params: + * node = the node to cluster + * indices = indices of the points belonging to the current node + * branching = the branching factor to use in the clustering + * + * TODO: for 1-sized clusters don't store a cluster center (it's the same as the single cluster point) + */ + void computeClustering(KMeansNodePtr node, int* indices, int indices_length, int branching, int level) + { + node->size = indices_length; + node->level = level; + + if (indices_length < branching) { + node->indices = indices; + std::sort(node->indices,node->indices+indices_length); + node->childs = NULL; + return; + } + + cv::AutoBuffer centers_idx_buf(branching); + int* centers_idx = (int*)centers_idx_buf; + int centers_length; + (this->*chooseCenters)(branching, indices, indices_length, centers_idx, centers_length); + + if (centers_lengthindices = indices; + std::sort(node->indices,node->indices+indices_length); + node->childs = NULL; + return; + } + + + cv::AutoBuffer dcenters_buf(branching*veclen_); + Matrix dcenters((double*)dcenters_buf,branching,veclen_); + for (int i=0; i radiuses(branching); + cv::AutoBuffer count_buf(branching); + int* count = (int*)count_buf; + for (int i=0; i belongs_to_buf(indices_length); + int* belongs_to = (int*)belongs_to_buf; + for (int i=0; inew_sq_dist) { + belongs_to[i] = j; + sq_dist = new_sq_dist; + } + } + if (sq_dist>radiuses[belongs_to[i]]) { + radiuses[belongs_to[i]] = sq_dist; + } + count[belongs_to[i]]++; + } + + bool converged = false; + int iteration = 0; + while (!converged && iterationchilds = pool_.allocate(branching); + int start = 0; + int end = start; + for (int c=0; c(), veclen_); + variance += d; + mean_radius += sqrt(d); + std::swap(indices[i],indices[end]); + std::swap(belongs_to[i],belongs_to[end]); + end++; + } + } + variance /= s; + mean_radius /= s; + variance -= distance_(centers[c], ZeroIterator(), veclen_); + + node->childs[c] = pool_.allocate(); + std::memset(node->childs[c], 0, sizeof(KMeansNode)); + node->childs[c]->radius = radiuses[c]; + node->childs[c]->pivot = centers[c]; + node->childs[c]->variance = variance; + node->childs[c]->mean_radius = mean_radius; + computeClustering(node->childs[c],indices+start, end-start, branching, level+1); + start=end; + } + + delete[] centers; + } + + + + /** + * Performs one descent in the hierarchical k-means tree. The branches not + * visited are stored in a priority queue. + * + * Params: + * node = node to explore + * result = container for the k-nearest neighbors found + * vec = query points + * checks = how many points in the dataset have been checked so far + * maxChecks = maximum dataset points to checks + */ + + + void findNN(KMeansNodePtr node, ResultSet& result, const ElementType* vec, int& checks, int maxChecks, + Heap* heap) + { + // Ignore those clusters that are too far away + { + DistanceType bsq = distance_(vec, node->pivot, veclen_); + DistanceType rsq = node->radius; + DistanceType wsq = result.worstDist(); + + DistanceType val = bsq-rsq-wsq; + DistanceType val2 = val*val-4*rsq*wsq; + + //if (val>0) { + if ((val>0)&&(val2>0)) { + return; + } + } + + if (node->childs==NULL) { + if (checks>=maxChecks) { + if (result.full()) return; + } + checks += node->size; + for (int i=0; isize; ++i) { + int index = node->indices[i]; + DistanceType dist = distance_(dataset_[index], vec, veclen_); + result.addPoint(dist, index); + } + } + else { + DistanceType* domain_distances = new DistanceType[branching_]; + int closest_center = exploreNodeBranches(node, vec, domain_distances, heap); + delete[] domain_distances; + findNN(node->childs[closest_center],result,vec, checks, maxChecks, heap); + } + } + + /** + * Helper function that computes the nearest childs of a node to a given query point. + * Params: + * node = the node + * q = the query point + * distances = array with the distances to each child node. + * Returns: + */ + int exploreNodeBranches(KMeansNodePtr node, const ElementType* q, DistanceType* domain_distances, Heap* heap) + { + + int best_index = 0; + domain_distances[best_index] = distance_(q, node->childs[best_index]->pivot, veclen_); + for (int i=1; ichilds[i]->pivot, veclen_); + if (domain_distances[i]childs[best_index]->pivot; + for (int i=0; ichilds[i]->variance; + + // float dist_to_border = getDistanceToBorder(node.childs[i].pivot,best_center,q); + // if (domain_distances[i]insert(BranchSt(node->childs[i],domain_distances[i])); + } + } + + return best_index; + } + + + /** + * Function the performs exact nearest neighbor search by traversing the entire tree. + */ + void findExactNN(KMeansNodePtr node, ResultSet& result, const ElementType* vec) + { + // Ignore those clusters that are too far away + { + DistanceType bsq = distance_(vec, node->pivot, veclen_); + DistanceType rsq = node->radius; + DistanceType wsq = result.worstDist(); + + DistanceType val = bsq-rsq-wsq; + DistanceType val2 = val*val-4*rsq*wsq; + + // if (val>0) { + if ((val>0)&&(val2>0)) { + return; + } + } + + + if (node->childs==NULL) { + for (int i=0; isize; ++i) { + int index = node->indices[i]; + DistanceType dist = distance_(dataset_[index], vec, veclen_); + result.addPoint(dist, index); + } + } + else { + int* sort_indices = new int[branching_]; + + getCenterOrdering(node, vec, sort_indices); + + for (int i=0; ichilds[sort_indices[i]],result,vec); + } + + delete[] sort_indices; + } + } + + + /** + * Helper function. + * + * I computes the order in which to traverse the child nodes of a particular node. + */ + void getCenterOrdering(KMeansNodePtr node, const ElementType* q, int* sort_indices) + { + DistanceType* domain_distances = new DistanceType[branching_]; + for (int i=0; ichilds[i]->pivot, veclen_); + + int j=0; + while (domain_distances[j]j; --k) { + domain_distances[k] = domain_distances[k-1]; + sort_indices[k] = sort_indices[k-1]; + } + domain_distances[j] = dist; + sort_indices[j] = i; + } + delete[] domain_distances; + } + + /** + * Method that computes the squared distance from the query point q + * from inside region with center c to the border between this + * region and the region with center p + */ + DistanceType getDistanceToBorder(DistanceType* p, DistanceType* c, DistanceType* q) + { + DistanceType sum = 0; + DistanceType sum2 = 0; + + for (int i=0; ivariance*root->size; + + while (clusterCount::max)(); + int splitIndex = -1; + + for (int i=0; ichilds != NULL) { + + DistanceType variance = meanVariance - clusters[i]->variance*clusters[i]->size; + + for (int j=0; jchilds[j]->variance*clusters[i]->childs[j]->size; + } + if (variance clusters_length) break; + + meanVariance = minVariance; + + // split node + KMeansNodePtr toSplit = clusters[splitIndex]; + clusters[splitIndex] = toSplit->childs[0]; + for (int i=1; ichilds[i]; + } + } + + varianceValue = meanVariance/root->size; + return clusterCount; + } + +private: + /** The branching factor used in the hierarchical k-means clustering */ + int branching_; + + /** Maximum number of iterations to use when performing k-means clustering */ + int iterations_; + + /** Algorithm for choosing the cluster centers */ + flann_centers_init_t centers_init_; + + /** + * Cluster border index. This is used in the tree search phase when determining + * the closest cluster to explore next. A zero value takes into account only + * the cluster centres, a value greater then zero also take into account the size + * of the cluster. + */ + float cb_index_; + + /** + * The dataset used by this index + */ + const Matrix dataset_; + + /** Index parameters */ + IndexParams index_params_; + + /** + * Number of features in the dataset. + */ + size_t size_; + + /** + * Length of each feature. + */ + size_t veclen_; + + /** + * The root node in the tree. + */ + KMeansNodePtr root_; + + /** + * Array of indices to vectors in the dataset. + */ + int* indices_; + + /** + * The distance + */ + Distance distance_; + + /** + * Pooled memory allocator. + */ + PooledAllocator pool_; + + /** + * Memory occupied by the index. + */ + int memoryCounter_; +}; + +} + +#endif //OPENCV_FLANN_KMEANS_INDEX_H_ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/linear_index.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/linear_index.h new file mode 100644 index 0000000000000000000000000000000000000000..ca3f44d0cbdaad8eb5c3b849f8dce65f2b7b4835 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/linear_index.h @@ -0,0 +1,132 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + +#ifndef OPENCV_FLANN_LINEAR_INDEX_H_ +#define OPENCV_FLANN_LINEAR_INDEX_H_ + +#include "general.h" +#include "nn_index.h" + +namespace cvflann +{ + +struct LinearIndexParams : public IndexParams +{ + LinearIndexParams() + { + (* this)["algorithm"] = FLANN_INDEX_LINEAR; + } +}; + +template +class LinearIndex : public NNIndex +{ +public: + + typedef typename Distance::ElementType ElementType; + typedef typename Distance::ResultType DistanceType; + + + LinearIndex(const Matrix& inputData, const IndexParams& params = LinearIndexParams(), + Distance d = Distance()) : + dataset_(inputData), index_params_(params), distance_(d) + { + } + + LinearIndex(const LinearIndex&); + LinearIndex& operator=(const LinearIndex&); + + flann_algorithm_t getType() const CV_OVERRIDE + { + return FLANN_INDEX_LINEAR; + } + + + size_t size() const CV_OVERRIDE + { + return dataset_.rows; + } + + size_t veclen() const CV_OVERRIDE + { + return dataset_.cols; + } + + + int usedMemory() const CV_OVERRIDE + { + return 0; + } + + void buildIndex() CV_OVERRIDE + { + /* nothing to do here for linear search */ + } + + void saveIndex(FILE*) CV_OVERRIDE + { + /* nothing to do here for linear search */ + } + + + void loadIndex(FILE*) CV_OVERRIDE + { + /* nothing to do here for linear search */ + + index_params_["algorithm"] = getType(); + } + + void findNeighbors(ResultSet& resultSet, const ElementType* vec, const SearchParams& /*searchParams*/) CV_OVERRIDE + { + ElementType* data = dataset_.data; + for (size_t i = 0; i < dataset_.rows; ++i, data += dataset_.cols) { + DistanceType dist = distance_(data, vec, dataset_.cols); + resultSet.addPoint(dist, (int)i); + } + } + + IndexParams getParameters() const CV_OVERRIDE + { + return index_params_; + } + +private: + /** The dataset */ + const Matrix dataset_; + /** Index parameters */ + IndexParams index_params_; + /** Index distance */ + Distance distance_; + +}; + +} + +#endif // OPENCV_FLANN_LINEAR_INDEX_H_ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/logger.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/logger.h new file mode 100644 index 0000000000000000000000000000000000000000..32618dbbb71f343647a4cb2acde5947939bb95f9 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/logger.h @@ -0,0 +1,135 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + +#ifndef OPENCV_FLANN_LOGGER_H +#define OPENCV_FLANN_LOGGER_H + +#include +#include + +#include "defines.h" + + +namespace cvflann +{ + +class Logger +{ + Logger() : stream(stdout), logLevel(FLANN_LOG_WARN) {} + + ~Logger() + { + if ((stream!=NULL)&&(stream!=stdout)) { + fclose(stream); + } + } + + static Logger& instance() + { + static Logger logger; + return logger; + } + + void _setDestination(const char* name) + { + if (name==NULL) { + stream = stdout; + } + else { +#ifdef _MSC_VER + if (fopen_s(&stream, name, "w") != 0) + stream = NULL; +#else + stream = fopen(name,"w"); +#endif + if (stream == NULL) { + stream = stdout; + } + } + } + + int _log(int level, const char* fmt, va_list arglist) + { + if (level > logLevel ) return -1; + int ret = vfprintf(stream, fmt, arglist); + return ret; + } + +public: + /** + * Sets the logging level. All messages with lower priority will be ignored. + * @param level Logging level + */ + static void setLevel(int level) { instance().logLevel = level; } + + /** + * Sets the logging destination + * @param name Filename or NULL for console + */ + static void setDestination(const char* name) { instance()._setDestination(name); } + + /** + * Print log message + * @param level Log level + * @param fmt Message format + * @return + */ + static int log(int level, const char* fmt, ...) + { + va_list arglist; + va_start(arglist, fmt); + int ret = instance()._log(level,fmt,arglist); + va_end(arglist); + return ret; + } + +#define LOG_METHOD(NAME,LEVEL) \ + static int NAME(const char* fmt, ...) \ + { \ + va_list ap; \ + va_start(ap, fmt); \ + int ret = instance()._log(LEVEL, fmt, ap); \ + va_end(ap); \ + return ret; \ + } + + LOG_METHOD(fatal, FLANN_LOG_FATAL) + LOG_METHOD(error, FLANN_LOG_ERROR) + LOG_METHOD(warn, FLANN_LOG_WARN) + LOG_METHOD(info, FLANN_LOG_INFO) + +private: + FILE* stream; + int logLevel; +}; + +} + +#endif //OPENCV_FLANN_LOGGER_H diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/lsh_index.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/lsh_index.h new file mode 100644 index 0000000000000000000000000000000000000000..42afe892acd7727f5dd312a5d275130c14a10322 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/lsh_index.h @@ -0,0 +1,392 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + +/*********************************************************************** + * Author: Vincent Rabaud + *************************************************************************/ + +#ifndef OPENCV_FLANN_LSH_INDEX_H_ +#define OPENCV_FLANN_LSH_INDEX_H_ + +#include +#include +#include +#include +#include + +#include "general.h" +#include "nn_index.h" +#include "matrix.h" +#include "result_set.h" +#include "heap.h" +#include "lsh_table.h" +#include "allocator.h" +#include "random.h" +#include "saving.h" + +namespace cvflann +{ + +struct LshIndexParams : public IndexParams +{ + LshIndexParams(unsigned int table_number = 12, unsigned int key_size = 20, unsigned int multi_probe_level = 2) + { + (* this)["algorithm"] = FLANN_INDEX_LSH; + // The number of hash tables to use + (*this)["table_number"] = table_number; + // The length of the key in the hash tables + (*this)["key_size"] = key_size; + // Number of levels to use in multi-probe (0 for standard LSH) + (*this)["multi_probe_level"] = multi_probe_level; + } +}; + +/** + * Randomized kd-tree index + * + * Contains the k-d trees and other information for indexing a set of points + * for nearest-neighbor matching. + */ +template +class LshIndex : public NNIndex +{ +public: + typedef typename Distance::ElementType ElementType; + typedef typename Distance::ResultType DistanceType; + + /** Constructor + * @param input_data dataset with the input features + * @param params parameters passed to the LSH algorithm + * @param d the distance used + */ + LshIndex(const Matrix& input_data, const IndexParams& params = LshIndexParams(), + Distance d = Distance()) : + dataset_(input_data), index_params_(params), distance_(d) + { + // cv::flann::IndexParams sets integer params as 'int', so it is used with get_param + // in place of 'unsigned int' + table_number_ = (unsigned int)get_param(index_params_,"table_number",12); + key_size_ = (unsigned int)get_param(index_params_,"key_size",20); + multi_probe_level_ = (unsigned int)get_param(index_params_,"multi_probe_level",2); + + feature_size_ = (unsigned)dataset_.cols; + fill_xor_mask(0, key_size_, multi_probe_level_, xor_masks_); + } + + + LshIndex(const LshIndex&); + LshIndex& operator=(const LshIndex&); + + /** + * Builds the index + */ + void buildIndex() CV_OVERRIDE + { + tables_.resize(table_number_); + for (unsigned int i = 0; i < table_number_; ++i) { + lsh::LshTable& table = tables_[i]; + table = lsh::LshTable(feature_size_, key_size_); + + // Add the features to the table + table.add(dataset_); + } + } + + flann_algorithm_t getType() const CV_OVERRIDE + { + return FLANN_INDEX_LSH; + } + + + void saveIndex(FILE* stream) CV_OVERRIDE + { + save_value(stream,table_number_); + save_value(stream,key_size_); + save_value(stream,multi_probe_level_); + save_value(stream, dataset_); + } + + void loadIndex(FILE* stream) CV_OVERRIDE + { + load_value(stream, table_number_); + load_value(stream, key_size_); + load_value(stream, multi_probe_level_); + load_value(stream, dataset_); + // Building the index is so fast we can afford not storing it + buildIndex(); + + index_params_["algorithm"] = getType(); + index_params_["table_number"] = table_number_; + index_params_["key_size"] = key_size_; + index_params_["multi_probe_level"] = multi_probe_level_; + } + + /** + * Returns size of index. + */ + size_t size() const CV_OVERRIDE + { + return dataset_.rows; + } + + /** + * Returns the length of an index feature. + */ + size_t veclen() const CV_OVERRIDE + { + return feature_size_; + } + + /** + * Computes the index memory usage + * Returns: memory used by the index + */ + int usedMemory() const CV_OVERRIDE + { + return (int)(dataset_.rows * sizeof(int)); + } + + + IndexParams getParameters() const CV_OVERRIDE + { + return index_params_; + } + + /** + * \brief Perform k-nearest neighbor search + * \param[in] queries The query points for which to find the nearest neighbors + * \param[out] indices The indices of the nearest neighbors found + * \param[out] dists Distances to the nearest neighbors found + * \param[in] knn Number of nearest neighbors to return + * \param[in] params Search parameters + */ + virtual void knnSearch(const Matrix& queries, Matrix& indices, Matrix& dists, int knn, const SearchParams& params) CV_OVERRIDE + { + assert(queries.cols == veclen()); + assert(indices.rows >= queries.rows); + assert(dists.rows >= queries.rows); + assert(int(indices.cols) >= knn); + assert(int(dists.cols) >= knn); + + + KNNUniqueResultSet resultSet(knn); + for (size_t i = 0; i < queries.rows; i++) { + resultSet.clear(); + std::fill_n(indices[i], knn, -1); + std::fill_n(dists[i], knn, std::numeric_limits::max()); + findNeighbors(resultSet, queries[i], params); + if (get_param(params,"sorted",true)) resultSet.sortAndCopy(indices[i], dists[i], knn); + else resultSet.copy(indices[i], dists[i], knn); + } + } + + + /** + * Find set of nearest neighbors to vec. Their indices are stored inside + * the result object. + * + * Params: + * result = the result object in which the indices of the nearest-neighbors are stored + * vec = the vector for which to search the nearest neighbors + * maxCheck = the maximum number of restarts (in a best-bin-first manner) + */ + void findNeighbors(ResultSet& result, const ElementType* vec, const SearchParams& /*searchParams*/) CV_OVERRIDE + { + getNeighbors(vec, result); + } + +private: + /** Defines the comparator on score and index + */ + typedef std::pair ScoreIndexPair; + struct SortScoreIndexPairOnSecond + { + bool operator()(const ScoreIndexPair& left, const ScoreIndexPair& right) const + { + return left.second < right.second; + } + }; + + /** Fills the different xor masks to use when getting the neighbors in multi-probe LSH + * @param key the key we build neighbors from + * @param lowest_index the lowest index of the bit set + * @param level the multi-probe level we are at + * @param xor_masks all the xor mask + */ + void fill_xor_mask(lsh::BucketKey key, int lowest_index, unsigned int level, + std::vector& xor_masks) + { + xor_masks.push_back(key); + if (level == 0) return; + for (int index = lowest_index - 1; index >= 0; --index) { + // Create a new key + lsh::BucketKey new_key = key | (1 << index); + fill_xor_mask(new_key, index, level - 1, xor_masks); + } + } + + /** Performs the approximate nearest-neighbor search. + * @param vec the feature to analyze + * @param do_radius flag indicating if we check the radius too + * @param radius the radius if it is a radius search + * @param do_k flag indicating if we limit the number of nn + * @param k_nn the number of nearest neighbors + * @param checked_average used for debugging + */ + void getNeighbors(const ElementType* vec, bool /*do_radius*/, float radius, bool do_k, unsigned int k_nn, + float& /*checked_average*/) + { + static std::vector score_index_heap; + + if (do_k) { + unsigned int worst_score = std::numeric_limits::max(); + typename std::vector >::const_iterator table = tables_.begin(); + typename std::vector >::const_iterator table_end = tables_.end(); + for (; table != table_end; ++table) { + size_t key = table->getKey(vec); + std::vector::const_iterator xor_mask = xor_masks_.begin(); + std::vector::const_iterator xor_mask_end = xor_masks_.end(); + for (; xor_mask != xor_mask_end; ++xor_mask) { + size_t sub_key = key ^ (*xor_mask); + const lsh::Bucket* bucket = table->getBucketFromKey(sub_key); + if (bucket == 0) continue; + + // Go over each descriptor index + std::vector::const_iterator training_index = bucket->begin(); + std::vector::const_iterator last_training_index = bucket->end(); + DistanceType hamming_distance; + + // Process the rest of the candidates + for (; training_index < last_training_index; ++training_index) { + hamming_distance = distance_(vec, dataset_[*training_index], dataset_.cols); + + if (hamming_distance < worst_score) { + // Insert the new element + score_index_heap.push_back(ScoreIndexPair(hamming_distance, training_index)); + std::push_heap(score_index_heap.begin(), score_index_heap.end()); + + if (score_index_heap.size() > (unsigned int)k_nn) { + // Remove the highest distance value as we have too many elements + std::pop_heap(score_index_heap.begin(), score_index_heap.end()); + score_index_heap.pop_back(); + // Keep track of the worst score + worst_score = score_index_heap.front().first; + } + } + } + } + } + } + else { + typename std::vector >::const_iterator table = tables_.begin(); + typename std::vector >::const_iterator table_end = tables_.end(); + for (; table != table_end; ++table) { + size_t key = table->getKey(vec); + std::vector::const_iterator xor_mask = xor_masks_.begin(); + std::vector::const_iterator xor_mask_end = xor_masks_.end(); + for (; xor_mask != xor_mask_end; ++xor_mask) { + size_t sub_key = key ^ (*xor_mask); + const lsh::Bucket* bucket = table->getBucketFromKey(sub_key); + if (bucket == 0) continue; + + // Go over each descriptor index + std::vector::const_iterator training_index = bucket->begin(); + std::vector::const_iterator last_training_index = bucket->end(); + DistanceType hamming_distance; + + // Process the rest of the candidates + for (; training_index < last_training_index; ++training_index) { + // Compute the Hamming distance + hamming_distance = distance_(vec, dataset_[*training_index], dataset_.cols); + if (hamming_distance < radius) score_index_heap.push_back(ScoreIndexPair(hamming_distance, training_index)); + } + } + } + } + } + + /** Performs the approximate nearest-neighbor search. + * This is a slower version than the above as it uses the ResultSet + * @param vec the feature to analyze + */ + void getNeighbors(const ElementType* vec, ResultSet& result) + { + typename std::vector >::const_iterator table = tables_.begin(); + typename std::vector >::const_iterator table_end = tables_.end(); + for (; table != table_end; ++table) { + size_t key = table->getKey(vec); + std::vector::const_iterator xor_mask = xor_masks_.begin(); + std::vector::const_iterator xor_mask_end = xor_masks_.end(); + for (; xor_mask != xor_mask_end; ++xor_mask) { + size_t sub_key = key ^ (*xor_mask); + const lsh::Bucket* bucket = table->getBucketFromKey((lsh::BucketKey)sub_key); + if (bucket == 0) continue; + + // Go over each descriptor index + std::vector::const_iterator training_index = bucket->begin(); + std::vector::const_iterator last_training_index = bucket->end(); + DistanceType hamming_distance; + + // Process the rest of the candidates + for (; training_index < last_training_index; ++training_index) { + // Compute the Hamming distance + hamming_distance = distance_(vec, dataset_[*training_index], (int)dataset_.cols); + result.addPoint(hamming_distance, *training_index); + } + } + } + } + + /** The different hash tables */ + std::vector > tables_; + + /** The data the LSH tables where built from */ + Matrix dataset_; + + /** The size of the features (as ElementType[]) */ + unsigned int feature_size_; + + IndexParams index_params_; + + /** table number */ + unsigned int table_number_; + /** key size */ + unsigned int key_size_; + /** How far should we look for neighbors in multi-probe LSH */ + unsigned int multi_probe_level_; + + /** The XOR masks to apply to a key to get the neighboring buckets */ + std::vector xor_masks_; + + Distance distance_; +}; +} + +#endif //OPENCV_FLANN_LSH_INDEX_H_ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/lsh_table.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/lsh_table.h new file mode 100644 index 0000000000000000000000000000000000000000..1db9960f84388e5174c203fbd056fab3e1504296 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/lsh_table.h @@ -0,0 +1,513 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + +/*********************************************************************** + * Author: Vincent Rabaud + *************************************************************************/ + +#ifndef OPENCV_FLANN_LSH_TABLE_H_ +#define OPENCV_FLANN_LSH_TABLE_H_ + +#include +#include +#include +#include +// TODO as soon as we use C++0x, use the code in USE_UNORDERED_MAP +#ifdef __GXX_EXPERIMENTAL_CXX0X__ +# define USE_UNORDERED_MAP 1 +#else +# define USE_UNORDERED_MAP 0 +#endif +#if USE_UNORDERED_MAP +#include +#else +#include +#endif +#include +#include + +#include "dynamic_bitset.h" +#include "matrix.h" + +namespace cvflann +{ + +namespace lsh +{ + +//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + +/** What is stored in an LSH bucket + */ +typedef uint32_t FeatureIndex; +/** The id from which we can get a bucket back in an LSH table + */ +typedef unsigned int BucketKey; + +/** A bucket in an LSH table + */ +typedef std::vector Bucket; + +//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + +/** POD for stats about an LSH table + */ +struct LshStats +{ + std::vector bucket_sizes_; + size_t n_buckets_; + size_t bucket_size_mean_; + size_t bucket_size_median_; + size_t bucket_size_min_; + size_t bucket_size_max_; + size_t bucket_size_std_dev; + /** Each contained vector contains three value: beginning/end for interval, number of elements in the bin + */ + std::vector > size_histogram_; +}; + +/** Overload the << operator for LshStats + * @param out the streams + * @param stats the stats to display + * @return the streams + */ +inline std::ostream& operator <<(std::ostream& out, const LshStats& stats) +{ + int w = 20; + out << "Lsh Table Stats:\n" << std::setw(w) << std::setiosflags(std::ios::right) << "N buckets : " + << stats.n_buckets_ << "\n" << std::setw(w) << std::setiosflags(std::ios::right) << "mean size : " + << std::setiosflags(std::ios::left) << stats.bucket_size_mean_ << "\n" << std::setw(w) + << std::setiosflags(std::ios::right) << "median size : " << stats.bucket_size_median_ << "\n" << std::setw(w) + << std::setiosflags(std::ios::right) << "min size : " << std::setiosflags(std::ios::left) + << stats.bucket_size_min_ << "\n" << std::setw(w) << std::setiosflags(std::ios::right) << "max size : " + << std::setiosflags(std::ios::left) << stats.bucket_size_max_; + + // Display the histogram + out << std::endl << std::setw(w) << std::setiosflags(std::ios::right) << "histogram : " + << std::setiosflags(std::ios::left); + for (std::vector >::const_iterator iterator = stats.size_histogram_.begin(), end = + stats.size_histogram_.end(); iterator != end; ++iterator) out << (*iterator)[0] << "-" << (*iterator)[1] << ": " << (*iterator)[2] << ", "; + + return out; +} + + +//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + +/** Lsh hash table. As its key is a sub-feature, and as usually + * the size of it is pretty small, we keep it as a continuous memory array. + * The value is an index in the corpus of features (we keep it as an unsigned + * int for pure memory reasons, it could be a size_t) + */ +template +class LshTable +{ +public: + /** A container of all the feature indices. Optimized for space + */ +#if USE_UNORDERED_MAP + typedef std::unordered_map BucketsSpace; +#else + typedef std::map BucketsSpace; +#endif + + /** A container of all the feature indices. Optimized for speed + */ + typedef std::vector BucketsSpeed; + + /** Default constructor + */ + LshTable() + { + key_size_ = 0; + feature_size_ = 0; + speed_level_ = kArray; + } + + /** Default constructor + * Create the mask and allocate the memory + * @param feature_size is the size of the feature (considered as a ElementType[]) + * @param key_size is the number of bits that are turned on in the feature + */ + LshTable(unsigned int feature_size, unsigned int key_size) + { + feature_size_ = feature_size; + (void)key_size; + std::cerr << "LSH is not implemented for that type" << std::endl; + assert(0); + } + + /** Add a feature to the table + * @param value the value to store for that feature + * @param feature the feature itself + */ + void add(unsigned int value, const ElementType* feature) + { + // Add the value to the corresponding bucket + BucketKey key = (lsh::BucketKey)getKey(feature); + + switch (speed_level_) { + case kArray: + // That means we get the buckets from an array + buckets_speed_[key].push_back(value); + break; + case kBitsetHash: + // That means we can check the bitset for the presence of a key + key_bitset_.set(key); + buckets_space_[key].push_back(value); + break; + case kHash: + { + // That means we have to check for the hash table for the presence of a key + buckets_space_[key].push_back(value); + break; + } + } + } + + /** Add a set of features to the table + * @param dataset the values to store + */ + void add(Matrix dataset) + { +#if USE_UNORDERED_MAP + buckets_space_.rehash((buckets_space_.size() + dataset.rows) * 1.2); +#endif + // Add the features to the table + for (unsigned int i = 0; i < dataset.rows; ++i) add(i, dataset[i]); + // Now that the table is full, optimize it for speed/space + optimize(); + } + + /** Get a bucket given the key + * @param key + * @return + */ + inline const Bucket* getBucketFromKey(BucketKey key) const + { + // Generate other buckets + switch (speed_level_) { + case kArray: + // That means we get the buckets from an array + return &buckets_speed_[key]; + break; + case kBitsetHash: + // That means we can check the bitset for the presence of a key + if (key_bitset_.test(key)) return &buckets_space_.find(key)->second; + else return 0; + break; + case kHash: + { + // That means we have to check for the hash table for the presence of a key + BucketsSpace::const_iterator bucket_it, bucket_end = buckets_space_.end(); + bucket_it = buckets_space_.find(key); + // Stop here if that bucket does not exist + if (bucket_it == bucket_end) return 0; + else return &bucket_it->second; + break; + } + } + return 0; + } + + /** Compute the sub-signature of a feature + */ + size_t getKey(const ElementType* /*feature*/) const + { + std::cerr << "LSH is not implemented for that type" << std::endl; + assert(0); + return 1; + } + + /** Get statistics about the table + * @return + */ + LshStats getStats() const; + +private: + /** defines the speed fo the implementation + * kArray uses a vector for storing data + * kBitsetHash uses a hash map but checks for the validity of a key with a bitset + * kHash uses a hash map only + */ + enum SpeedLevel + { + kArray, kBitsetHash, kHash + }; + + /** Initialize some variables + */ + void initialize(size_t key_size) + { + const size_t key_size_lower_bound = 1; + //a value (size_t(1) << key_size) must fit the size_t type so key_size has to be strictly less than size of size_t + const size_t key_size_upper_bound = (std::min)(sizeof(BucketKey) * CHAR_BIT + 1, sizeof(size_t) * CHAR_BIT); + if (key_size < key_size_lower_bound || key_size >= key_size_upper_bound) + { + CV_Error(cv::Error::StsBadArg, cv::format("Invalid key_size (=%d). Valid values for your system are %d <= key_size < %d.", (int)key_size, (int)key_size_lower_bound, (int)key_size_upper_bound)); + } + + speed_level_ = kHash; + key_size_ = (unsigned)key_size; + } + + /** Optimize the table for speed/space + */ + void optimize() + { + // If we are already using the fast storage, no need to do anything + if (speed_level_ == kArray) return; + + // Use an array if it will be more than half full + if (buckets_space_.size() > ((size_t(1) << key_size_) / 2)) { + speed_level_ = kArray; + // Fill the array version of it + buckets_speed_.resize(size_t(1) << key_size_); + for (BucketsSpace::const_iterator key_bucket = buckets_space_.begin(); key_bucket != buckets_space_.end(); ++key_bucket) buckets_speed_[key_bucket->first] = key_bucket->second; + + // Empty the hash table + buckets_space_.clear(); + return; + } + + // If the bitset is going to use less than 10% of the RAM of the hash map (at least 1 size_t for the key and two + // for the vector) or less than 512MB (key_size_ <= 30) + if (((std::max(buckets_space_.size(), buckets_speed_.size()) * CHAR_BIT * 3 * sizeof(BucketKey)) / 10 + >= (size_t(1) << key_size_)) || (key_size_ <= 32)) { + speed_level_ = kBitsetHash; + key_bitset_.resize(size_t(1) << key_size_); + key_bitset_.reset(); + // Try with the BucketsSpace + for (BucketsSpace::const_iterator key_bucket = buckets_space_.begin(); key_bucket != buckets_space_.end(); ++key_bucket) key_bitset_.set(key_bucket->first); + } + else { + speed_level_ = kHash; + key_bitset_.clear(); + } + } + + /** The vector of all the buckets if they are held for speed + */ + BucketsSpeed buckets_speed_; + + /** The hash table of all the buckets in case we cannot use the speed version + */ + BucketsSpace buckets_space_; + + /** What is used to store the data */ + SpeedLevel speed_level_; + + /** If the subkey is small enough, it will keep track of which subkeys are set through that bitset + * That is just a speedup so that we don't look in the hash table (which can be mush slower that checking a bitset) + */ + DynamicBitset key_bitset_; + + /** The size of the sub-signature in bits + */ + unsigned int key_size_; + + unsigned int feature_size_; + + // Members only used for the unsigned char specialization + /** The mask to apply to a feature to get the hash key + * Only used in the unsigned char case + */ + std::vector mask_; +}; + +//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// +// Specialization for unsigned char + +template<> +inline LshTable::LshTable(unsigned int feature_size, unsigned int subsignature_size) +{ + feature_size_ = feature_size; + initialize(subsignature_size); + // Allocate the mask + mask_ = std::vector((feature_size * sizeof(char) + sizeof(size_t) - 1) / sizeof(size_t), 0); + + // A bit brutal but fast to code + std::vector indices(feature_size * CHAR_BIT); + for (size_t i = 0; i < feature_size * CHAR_BIT; ++i) indices[i] = (int)i; +#ifndef OPENCV_FLANN_USE_STD_RAND + cv::randShuffle(indices); +#else + std::random_shuffle(indices.begin(), indices.end()); +#endif + + // Generate a random set of order of subsignature_size_ bits + for (unsigned int i = 0; i < key_size_; ++i) { + size_t index = indices[i]; + + // Set that bit in the mask + size_t divisor = CHAR_BIT * sizeof(size_t); + size_t idx = index / divisor; //pick the right size_t index + mask_[idx] |= size_t(1) << (index % divisor); //use modulo to find the bit offset + } + + // Set to 1 if you want to display the mask for debug +#if 0 + { + size_t bcount = 0; + BOOST_FOREACH(size_t mask_block, mask_){ + out << std::setw(sizeof(size_t) * CHAR_BIT / 4) << std::setfill('0') << std::hex << mask_block + << std::endl; + bcount += __builtin_popcountll(mask_block); + } + out << "bit count : " << std::dec << bcount << std::endl; + out << "mask size : " << mask_.size() << std::endl; + return out; + } +#endif +} + +/** Return the Subsignature of a feature + * @param feature the feature to analyze + */ +template<> +inline size_t LshTable::getKey(const unsigned char* feature) const +{ + // no need to check if T is dividable by sizeof(size_t) like in the Hamming + // distance computation as we have a mask + // FIXIT: This is bad assumption, because we reading tail bytes after of the allocated features buffer + const size_t* feature_block_ptr = reinterpret_cast ((const void*)feature); + + // Figure out the subsignature of the feature + // Given the feature ABCDEF, and the mask 001011, the output will be + // 000CEF + size_t subsignature = 0; + size_t bit_index = 1; + + for (unsigned i = 0; i < feature_size_; i += sizeof(size_t)) { + // get the mask and signature blocks + size_t feature_block; + if (i <= feature_size_ - sizeof(size_t)) + { + feature_block = *feature_block_ptr; + } + else + { + size_t tmp = 0; + memcpy(&tmp, feature_block_ptr, feature_size_ - i); // preserve bytes order + feature_block = tmp; + } + size_t mask_block = mask_[i / sizeof(size_t)]; + while (mask_block) { + // Get the lowest set bit in the mask block + size_t lowest_bit = mask_block & (-(ptrdiff_t)mask_block); + // Add it to the current subsignature if necessary + subsignature += (feature_block & lowest_bit) ? bit_index : 0; + // Reset the bit in the mask block + mask_block ^= lowest_bit; + // increment the bit index for the subsignature + bit_index <<= 1; + } + // Check the next feature block + ++feature_block_ptr; + } + return subsignature; +} + +template<> +inline LshStats LshTable::getStats() const +{ + LshStats stats; + stats.bucket_size_mean_ = 0; + if ((buckets_speed_.empty()) && (buckets_space_.empty())) { + stats.n_buckets_ = 0; + stats.bucket_size_median_ = 0; + stats.bucket_size_min_ = 0; + stats.bucket_size_max_ = 0; + return stats; + } + + if (!buckets_speed_.empty()) { + for (BucketsSpeed::const_iterator pbucket = buckets_speed_.begin(); pbucket != buckets_speed_.end(); ++pbucket) { + stats.bucket_sizes_.push_back((lsh::FeatureIndex)pbucket->size()); + stats.bucket_size_mean_ += pbucket->size(); + } + stats.bucket_size_mean_ /= buckets_speed_.size(); + stats.n_buckets_ = buckets_speed_.size(); + } + else { + for (BucketsSpace::const_iterator x = buckets_space_.begin(); x != buckets_space_.end(); ++x) { + stats.bucket_sizes_.push_back((lsh::FeatureIndex)x->second.size()); + stats.bucket_size_mean_ += x->second.size(); + } + stats.bucket_size_mean_ /= buckets_space_.size(); + stats.n_buckets_ = buckets_space_.size(); + } + + std::sort(stats.bucket_sizes_.begin(), stats.bucket_sizes_.end()); + + // BOOST_FOREACH(int size, stats.bucket_sizes_) + // std::cout << size << " "; + // std::cout << std::endl; + stats.bucket_size_median_ = stats.bucket_sizes_[stats.bucket_sizes_.size() / 2]; + stats.bucket_size_min_ = stats.bucket_sizes_.front(); + stats.bucket_size_max_ = stats.bucket_sizes_.back(); + + // TODO compute mean and std + /*float mean, stddev; + stats.bucket_size_mean_ = mean; + stats.bucket_size_std_dev = stddev;*/ + + // Include a histogram of the buckets + unsigned int bin_start = 0; + unsigned int bin_end = 20; + bool is_new_bin = true; + for (std::vector::iterator iterator = stats.bucket_sizes_.begin(), end = stats.bucket_sizes_.end(); iterator + != end; ) + if (*iterator < bin_end) { + if (is_new_bin) { + stats.size_histogram_.push_back(std::vector(3, 0)); + stats.size_histogram_.back()[0] = bin_start; + stats.size_histogram_.back()[1] = bin_end - 1; + is_new_bin = false; + } + ++stats.size_histogram_.back()[2]; + ++iterator; + } + else { + bin_start += 20; + bin_end += 20; + is_new_bin = true; + } + + return stats; +} + +// End the two namespaces +} +} + +//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + +#endif /* OPENCV_FLANN_LSH_TABLE_H_ */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/matrix.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/matrix.h new file mode 100644 index 0000000000000000000000000000000000000000..f6092d134e29242b94178f04e447be6d4e406e65 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/matrix.h @@ -0,0 +1,116 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + +#ifndef OPENCV_FLANN_DATASET_H_ +#define OPENCV_FLANN_DATASET_H_ + +#include + +#include "general.h" + +namespace cvflann +{ + +/** + * Class that implements a simple rectangular matrix stored in a memory buffer and + * provides convenient matrix-like access using the [] operators. + */ +template +class Matrix +{ +public: + typedef T type; + + size_t rows; + size_t cols; + size_t stride; + T* data; + + Matrix() : rows(0), cols(0), stride(0), data(NULL) + { + } + + Matrix(T* data_, size_t rows_, size_t cols_, size_t stride_ = 0) : + rows(rows_), cols(cols_), stride(stride_), data(data_) + { + if (stride==0) stride = cols; + } + + /** + * Convenience function for deallocating the storage data. + */ + CV_DEPRECATED void free() + { + fprintf(stderr, "The cvflann::Matrix::free() method is deprecated " + "and it does not do any memory deallocation any more. You are" + "responsible for deallocating the matrix memory (by doing" + "'delete[] matrix.data' for example)"); + } + + /** + * Operator that return a (pointer to a) row of the data. + */ + T* operator[](size_t index) const + { + return data+index*stride; + } +}; + + +class UntypedMatrix +{ +public: + size_t rows; + size_t cols; + void* data; + flann_datatype_t type; + + UntypedMatrix(void* data_, long rows_, long cols_) : + rows(rows_), cols(cols_), data(data_) + { + } + + ~UntypedMatrix() + { + } + + + template + Matrix as() + { + return Matrix((T*)data, rows, cols); + } +}; + + + +} + +#endif //OPENCV_FLANN_DATASET_H_ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/miniflann.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/miniflann.hpp new file mode 100644 index 0000000000000000000000000000000000000000..bda2ed4982666ca9c6bc922c74777d5d4540ec89 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/miniflann.hpp @@ -0,0 +1,162 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_MINIFLANN_HPP +#define OPENCV_MINIFLANN_HPP + +#include "opencv2/core.hpp" +#include "opencv2/flann/defines.h" + +namespace cv +{ + +namespace flann +{ + +struct CV_EXPORTS IndexParams +{ + IndexParams(); + ~IndexParams(); + + String getString(const String& key, const String& defaultVal=String()) const; + int getInt(const String& key, int defaultVal=-1) const; + double getDouble(const String& key, double defaultVal=-1) const; + + void setString(const String& key, const String& value); + void setInt(const String& key, int value); + void setDouble(const String& key, double value); + void setFloat(const String& key, float value); + void setBool(const String& key, bool value); + void setAlgorithm(int value); + + void getAll(std::vector& names, + std::vector& types, + std::vector& strValues, + std::vector& numValues) const; + + void* params; + +private: + IndexParams(const IndexParams &); // copy disabled + IndexParams& operator=(const IndexParams &); // assign disabled +}; + +struct CV_EXPORTS KDTreeIndexParams : public IndexParams +{ + KDTreeIndexParams(int trees=4); +}; + +struct CV_EXPORTS LinearIndexParams : public IndexParams +{ + LinearIndexParams(); +}; + +struct CV_EXPORTS CompositeIndexParams : public IndexParams +{ + CompositeIndexParams(int trees = 4, int branching = 32, int iterations = 11, + cvflann::flann_centers_init_t centers_init = cvflann::FLANN_CENTERS_RANDOM, float cb_index = 0.2f ); +}; + +struct CV_EXPORTS AutotunedIndexParams : public IndexParams +{ + AutotunedIndexParams(float target_precision = 0.8f, float build_weight = 0.01f, + float memory_weight = 0, float sample_fraction = 0.1f); +}; + +struct CV_EXPORTS HierarchicalClusteringIndexParams : public IndexParams +{ + HierarchicalClusteringIndexParams(int branching = 32, + cvflann::flann_centers_init_t centers_init = cvflann::FLANN_CENTERS_RANDOM, int trees = 4, int leaf_size = 100 ); +}; + +struct CV_EXPORTS KMeansIndexParams : public IndexParams +{ + KMeansIndexParams(int branching = 32, int iterations = 11, + cvflann::flann_centers_init_t centers_init = cvflann::FLANN_CENTERS_RANDOM, float cb_index = 0.2f ); +}; + +struct CV_EXPORTS LshIndexParams : public IndexParams +{ + LshIndexParams(int table_number, int key_size, int multi_probe_level); +}; + +struct CV_EXPORTS SavedIndexParams : public IndexParams +{ + SavedIndexParams(const String& filename); +}; + +struct CV_EXPORTS SearchParams : public IndexParams +{ + SearchParams( int checks = 32, float eps = 0, bool sorted = true ); +}; + +class CV_EXPORTS_W Index +{ +public: + CV_WRAP Index(); + CV_WRAP Index(InputArray features, const IndexParams& params, cvflann::flann_distance_t distType=cvflann::FLANN_DIST_L2); + virtual ~Index(); + + CV_WRAP virtual void build(InputArray features, const IndexParams& params, cvflann::flann_distance_t distType=cvflann::FLANN_DIST_L2); + CV_WRAP virtual void knnSearch(InputArray query, OutputArray indices, + OutputArray dists, int knn, const SearchParams& params=SearchParams()); + + CV_WRAP virtual int radiusSearch(InputArray query, OutputArray indices, + OutputArray dists, double radius, int maxResults, + const SearchParams& params=SearchParams()); + + CV_WRAP virtual void save(const String& filename) const; + CV_WRAP virtual bool load(InputArray features, const String& filename); + CV_WRAP virtual void release(); + CV_WRAP cvflann::flann_distance_t getDistance() const; + CV_WRAP cvflann::flann_algorithm_t getAlgorithm() const; + +protected: + cvflann::flann_distance_t distType; + cvflann::flann_algorithm_t algo; + int featureType; + void* index; +}; + +} } // namespace cv::flann + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/nn_index.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/nn_index.h new file mode 100644 index 0000000000000000000000000000000000000000..381d4bc3cbf79d49564f978677bd5fd1ff8aafdd --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/nn_index.h @@ -0,0 +1,177 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + +#ifndef OPENCV_FLANN_NNINDEX_H +#define OPENCV_FLANN_NNINDEX_H + +#include "general.h" +#include "matrix.h" +#include "result_set.h" +#include "params.h" + +namespace cvflann +{ + +/** + * Nearest-neighbour index base class + */ +template +class NNIndex +{ + typedef typename Distance::ElementType ElementType; + typedef typename Distance::ResultType DistanceType; + +public: + + virtual ~NNIndex() {} + + /** + * \brief Builds the index + */ + virtual void buildIndex() = 0; + + /** + * \brief Perform k-nearest neighbor search + * \param[in] queries The query points for which to find the nearest neighbors + * \param[out] indices The indices of the nearest neighbors found + * \param[out] dists Distances to the nearest neighbors found + * \param[in] knn Number of nearest neighbors to return + * \param[in] params Search parameters + */ + virtual void knnSearch(const Matrix& queries, Matrix& indices, Matrix& dists, int knn, const SearchParams& params) + { + assert(queries.cols == veclen()); + assert(indices.rows >= queries.rows); + assert(dists.rows >= queries.rows); + assert(int(indices.cols) >= knn); + assert(int(dists.cols) >= knn); + +#if 0 + KNNResultSet resultSet(knn); + for (size_t i = 0; i < queries.rows; i++) { + resultSet.init(indices[i], dists[i]); + findNeighbors(resultSet, queries[i], params); + } +#else + KNNUniqueResultSet resultSet(knn); + for (size_t i = 0; i < queries.rows; i++) { + resultSet.clear(); + findNeighbors(resultSet, queries[i], params); + if (get_param(params,"sorted",true)) resultSet.sortAndCopy(indices[i], dists[i], knn); + else resultSet.copy(indices[i], dists[i], knn); + } +#endif + } + + /** + * \brief Perform radius search + * \param[in] query The query point + * \param[out] indices The indinces of the neighbors found within the given radius + * \param[out] dists The distances to the nearest neighbors found + * \param[in] radius The radius used for search + * \param[in] params Search parameters + * \returns Number of neighbors found + */ + virtual int radiusSearch(const Matrix& query, Matrix& indices, Matrix& dists, float radius, const SearchParams& params) + { + if (query.rows != 1) { + fprintf(stderr, "I can only search one feature at a time for range search\n"); + return -1; + } + assert(query.cols == veclen()); + assert(indices.cols == dists.cols); + + int n = 0; + int* indices_ptr = NULL; + DistanceType* dists_ptr = NULL; + if (indices.cols > 0) { + n = (int)indices.cols; + indices_ptr = indices[0]; + dists_ptr = dists[0]; + } + + RadiusUniqueResultSet resultSet((DistanceType)radius); + resultSet.clear(); + findNeighbors(resultSet, query[0], params); + if (n>0) { + if (get_param(params,"sorted",true)) resultSet.sortAndCopy(indices_ptr, dists_ptr, n); + else resultSet.copy(indices_ptr, dists_ptr, n); + } + + return (int)resultSet.size(); + } + + /** + * \brief Saves the index to a stream + * \param stream The stream to save the index to + */ + virtual void saveIndex(FILE* stream) = 0; + + /** + * \brief Loads the index from a stream + * \param stream The stream from which the index is loaded + */ + virtual void loadIndex(FILE* stream) = 0; + + /** + * \returns number of features in this index. + */ + virtual size_t size() const = 0; + + /** + * \returns The dimensionality of the features in this index. + */ + virtual size_t veclen() const = 0; + + /** + * \returns The amount of memory (in bytes) used by the index. + */ + virtual int usedMemory() const = 0; + + /** + * \returns The index type (kdtree, kmeans,...) + */ + virtual flann_algorithm_t getType() const = 0; + + /** + * \returns The index parameters + */ + virtual IndexParams getParameters() const = 0; + + + /** + * \brief Method that searches for nearest-neighbours + */ + virtual void findNeighbors(ResultSet& result, const ElementType* vec, const SearchParams& searchParams) = 0; +}; + +} + +#endif //OPENCV_FLANN_NNINDEX_H diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/object_factory.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/object_factory.h new file mode 100644 index 0000000000000000000000000000000000000000..7f971c5a31d77aed5a24fd6f8e4b119eb1f11eb6 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/object_factory.h @@ -0,0 +1,91 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + +#ifndef OPENCV_FLANN_OBJECT_FACTORY_H_ +#define OPENCV_FLANN_OBJECT_FACTORY_H_ + +#include + +namespace cvflann +{ + +class CreatorNotFound +{ +}; + +template +class ObjectFactory +{ + typedef ObjectFactory ThisClass; + typedef std::map ObjectRegistry; + + // singleton class, private constructor + ObjectFactory() {} + +public: + + bool subscribe(UniqueIdType id, ObjectCreator creator) + { + if (object_registry.find(id) != object_registry.end()) return false; + + object_registry[id] = creator; + return true; + } + + bool unregister(UniqueIdType id) + { + return object_registry.erase(id) == 1; + } + + ObjectCreator create(UniqueIdType id) + { + typename ObjectRegistry::const_iterator iter = object_registry.find(id); + + if (iter == object_registry.end()) { + throw CreatorNotFound(); + } + + return iter->second; + } + + static ThisClass& instance() + { + static ThisClass the_factory; + return the_factory; + } +private: + ObjectRegistry object_registry; +}; + +} + +#endif /* OPENCV_FLANN_OBJECT_FACTORY_H_ */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/params.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/params.h new file mode 100644 index 0000000000000000000000000000000000000000..95ef4cd23bf9db1092311abf63050c73d2f88872 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/params.h @@ -0,0 +1,99 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2011 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2011 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + + +#ifndef OPENCV_FLANN_PARAMS_H_ +#define OPENCV_FLANN_PARAMS_H_ + +#include "any.h" +#include "general.h" +#include +#include + + +namespace cvflann +{ + +typedef std::map IndexParams; + +struct SearchParams : public IndexParams +{ + SearchParams(int checks = 32, float eps = 0, bool sorted = true ) + { + // how many leafs to visit when searching for neighbours (-1 for unlimited) + (*this)["checks"] = checks; + // search for eps-approximate neighbours (default: 0) + (*this)["eps"] = eps; + // only for radius search, require neighbours sorted by distance (default: true) + (*this)["sorted"] = sorted; + } +}; + + +template +T get_param(const IndexParams& params, cv::String name, const T& default_value) +{ + IndexParams::const_iterator it = params.find(name); + if (it != params.end()) { + return it->second.cast(); + } + else { + return default_value; + } +} + +template +T get_param(const IndexParams& params, cv::String name) +{ + IndexParams::const_iterator it = params.find(name); + if (it != params.end()) { + return it->second.cast(); + } + else { + throw FLANNException(cv::String("Missing parameter '")+name+cv::String("' in the parameters given")); + } +} + +inline void print_params(const IndexParams& params, std::ostream& stream) +{ + IndexParams::const_iterator it; + + for(it=params.begin(); it!=params.end(); ++it) { + stream << it->first << " : " << it->second << std::endl; + } +} + +inline void print_params(const IndexParams& params) +{ + print_params(params, std::cout); +} + +} + + +#endif /* OPENCV_FLANN_PARAMS_H_ */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/random.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/random.h new file mode 100644 index 0000000000000000000000000000000000000000..d6784747c05e660d414c9664ed79fe32fc08f38a --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/random.h @@ -0,0 +1,155 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + +#ifndef OPENCV_FLANN_RANDOM_H +#define OPENCV_FLANN_RANDOM_H + +#include +#include +#include + +#include "general.h" + +namespace cvflann +{ + +inline int rand() +{ +#ifndef OPENCV_FLANN_USE_STD_RAND +# if INT_MAX == RAND_MAX + int v = cv::theRNG().next() & INT_MAX; +# else + int v = cv::theRNG().uniform(0, RAND_MAX + 1); +# endif +#else + int v = std::rand(); +#endif // OPENCV_FLANN_USE_STD_RAND + return v; +} + +/** + * Seeds the random number generator + * @param seed Random seed + */ +inline void seed_random(unsigned int seed) +{ +#ifndef OPENCV_FLANN_USE_STD_RAND + cv::theRNG() = cv::RNG(seed); +#else + std::srand(seed); +#endif +} + +/* + * Generates a random double value. + */ +/** + * Generates a random double value. + * @param high Upper limit + * @param low Lower limit + * @return Random double value + */ +inline double rand_double(double high = 1.0, double low = 0) +{ + return low + ((high-low) * (rand() / (RAND_MAX + 1.0))); +} + +/** + * Generates a random integer value. + * @param high Upper limit + * @param low Lower limit + * @return Random integer value + */ +inline int rand_int(int high = RAND_MAX, int low = 0) +{ + return low + (int) ( double(high-low) * (rand() / (RAND_MAX + 1.0))); +} + +/** + * Random number generator that returns a distinct number from + * the [0,n) interval each time. + */ +class UniqueRandom +{ + std::vector vals_; + int size_; + int counter_; + +public: + /** + * Constructor. + * @param n Size of the interval from which to generate + * @return + */ + UniqueRandom(int n) + { + init(n); + } + + /** + * Initializes the number generator. + * @param n the size of the interval from which to generate random numbers. + */ + void init(int n) + { + // create and initialize an array of size n + vals_.resize(n); + size_ = n; + for (int i = 0; i < size_; ++i) vals_[i] = i; + + // shuffle the elements in the array +#ifndef OPENCV_FLANN_USE_STD_RAND + cv::randShuffle(vals_); +#else + std::random_shuffle(vals_.begin(), vals_.end()); +#endif + + counter_ = 0; + } + + /** + * Return a distinct random integer in greater or equal to 0 and less + * than 'n' on each call. It should be called maximum 'n' times. + * Returns: a random integer + */ + int next() + { + if (counter_ == size_) { + return -1; + } + else { + return vals_[counter_++]; + } + } +}; + +} + +#endif //OPENCV_FLANN_RANDOM_H diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/result_set.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/result_set.h new file mode 100644 index 0000000000000000000000000000000000000000..5c69ac29e717222c4fa79f62082824f0ec1eab3a --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/result_set.h @@ -0,0 +1,543 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + +#ifndef OPENCV_FLANN_RESULTSET_H +#define OPENCV_FLANN_RESULTSET_H + +#include +#include +#include +#include +#include +#include + +namespace cvflann +{ + +/* This record represents a branch point when finding neighbors in + the tree. It contains a record of the minimum distance to the query + point, as well as the node at which the search resumes. + */ + +template +struct BranchStruct +{ + T node; /* Tree node at which search resumes */ + DistanceType mindist; /* Minimum distance to query for all nodes below. */ + + BranchStruct() {} + BranchStruct(const T& aNode, DistanceType dist) : node(aNode), mindist(dist) {} + + bool operator<(const BranchStruct& rhs) const + { + return mindist +class ResultSet +{ +public: + virtual ~ResultSet() {} + + virtual bool full() const = 0; + + virtual void addPoint(DistanceType dist, int index) = 0; + + virtual DistanceType worstDist() const = 0; + +}; + +/** + * KNNSimpleResultSet does not ensure that the element it holds are unique. + * Is used in those cases where the nearest neighbour algorithm used does not + * attempt to insert the same element multiple times. + */ +template +class KNNSimpleResultSet : public ResultSet +{ + int* indices; + DistanceType* dists; + int capacity; + int count; + DistanceType worst_distance_; + +public: + KNNSimpleResultSet(int capacity_) : capacity(capacity_), count(0) + { + } + + void init(int* indices_, DistanceType* dists_) + { + indices = indices_; + dists = dists_; + count = 0; + worst_distance_ = (std::numeric_limits::max)(); + dists[capacity-1] = worst_distance_; + } + + size_t size() const + { + return count; + } + + bool full() const CV_OVERRIDE + { + return count == capacity; + } + + + void addPoint(DistanceType dist, int index) CV_OVERRIDE + { + if (dist >= worst_distance_) return; + int i; + for (i=count; i>0; --i) { +#ifdef FLANN_FIRST_MATCH + if ( (dists[i-1]>dist) || ((dist==dists[i-1])&&(indices[i-1]>index)) ) +#else + if (dists[i-1]>dist) +#endif + { + if (i +class KNNResultSet : public ResultSet +{ + int* indices; + DistanceType* dists; + int capacity; + int count; + DistanceType worst_distance_; + +public: + KNNResultSet(int capacity_) : capacity(capacity_), count(0) + { + } + + void init(int* indices_, DistanceType* dists_) + { + indices = indices_; + dists = dists_; + count = 0; + worst_distance_ = (std::numeric_limits::max)(); + dists[capacity-1] = worst_distance_; + } + + size_t size() const + { + return count; + } + + bool full() const CV_OVERRIDE + { + return count == capacity; + } + + + void addPoint(DistanceType dist, int index) CV_OVERRIDE + { + if (dist >= worst_distance_) return; + int i; + for (i = count; i > 0; --i) { +#ifdef FLANN_FIRST_MATCH + if ( (dists[i-1]<=dist) && ((dist!=dists[i-1])||(indices[i-1]<=index)) ) +#else + if (dists[i-1]<=dist) +#endif + { + // Check for duplicate indices + int j = i - 1; + while ((j >= 0) && (dists[j] == dist)) { + if (indices[j] == index) { + return; + } + --j; + } + break; + } + } + + if (count < capacity) ++count; + for (int j = count-1; j > i; --j) { + dists[j] = dists[j-1]; + indices[j] = indices[j-1]; + } + dists[i] = dist; + indices[i] = index; + worst_distance_ = dists[capacity-1]; + } + + DistanceType worstDist() const CV_OVERRIDE + { + return worst_distance_; + } +}; + + +/** + * A result-set class used when performing a radius based search. + */ +template +class RadiusResultSet : public ResultSet +{ + DistanceType radius; + int* indices; + DistanceType* dists; + size_t capacity; + size_t count; + +public: + RadiusResultSet(DistanceType radius_, int* indices_, DistanceType* dists_, int capacity_) : + radius(radius_), indices(indices_), dists(dists_), capacity(capacity_) + { + init(); + } + + ~RadiusResultSet() + { + } + + void init() + { + count = 0; + } + + size_t size() const + { + return count; + } + + bool full() const + { + return true; + } + + void addPoint(DistanceType dist, int index) + { + if (dist0)&&(count < capacity)) { + dists[count] = dist; + indices[count] = index; + } + count++; + } + } + + DistanceType worstDist() const + { + return radius; + } + +}; + +//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + +/** Class that holds the k NN neighbors + * Faster than KNNResultSet as it uses a binary heap and does not maintain two arrays + */ +template +class UniqueResultSet : public ResultSet +{ +public: + struct DistIndex + { + DistIndex(DistanceType dist, unsigned int index) : + dist_(dist), index_(index) + { + } + bool operator<(const DistIndex dist_index) const + { + return (dist_ < dist_index.dist_) || ((dist_ == dist_index.dist_) && index_ < dist_index.index_); + } + DistanceType dist_; + unsigned int index_; + }; + + /** Default cosntructor */ + UniqueResultSet() : + is_full_(false), worst_distance_(std::numeric_limits::max()) + { + } + + /** Check the status of the set + * @return true if we have k NN + */ + inline bool full() const CV_OVERRIDE + { + return is_full_; + } + + /** Remove all elements in the set + */ + virtual void clear() = 0; + + /** Copy the set to two C arrays + * @param indices pointer to a C array of indices + * @param dist pointer to a C array of distances + * @param n_neighbors the number of neighbors to copy + */ + virtual void copy(int* indices, DistanceType* dist, int n_neighbors = -1) const + { + if (n_neighbors < 0) { + for (typename std::set::const_iterator dist_index = dist_indices_.begin(), dist_index_end = + dist_indices_.end(); dist_index != dist_index_end; ++dist_index, ++indices, ++dist) { + *indices = dist_index->index_; + *dist = dist_index->dist_; + } + } + else { + int i = 0; + for (typename std::set::const_iterator dist_index = dist_indices_.begin(), dist_index_end = + dist_indices_.end(); (dist_index != dist_index_end) && (i < n_neighbors); ++dist_index, ++indices, ++dist, ++i) { + *indices = dist_index->index_; + *dist = dist_index->dist_; + } + } + } + + /** Copy the set to two C arrays but sort it according to the distance first + * @param indices pointer to a C array of indices + * @param dist pointer to a C array of distances + * @param n_neighbors the number of neighbors to copy + */ + virtual void sortAndCopy(int* indices, DistanceType* dist, int n_neighbors = -1) const + { + copy(indices, dist, n_neighbors); + } + + /** The number of neighbors in the set + * @return + */ + size_t size() const + { + return dist_indices_.size(); + } + + /** The distance of the furthest neighbor + * If we don't have enough neighbors, it returns the max possible value + * @return + */ + inline DistanceType worstDist() const CV_OVERRIDE + { + return worst_distance_; + } +protected: + /** Flag to say if the set is full */ + bool is_full_; + + /** The worst distance found so far */ + DistanceType worst_distance_; + + /** The best candidates so far */ + std::set dist_indices_; +}; + +//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + +/** Class that holds the k NN neighbors + * Faster than KNNResultSet as it uses a binary heap and does not maintain two arrays + */ +template +class KNNUniqueResultSet : public UniqueResultSet +{ +public: + /** Constructor + * @param capacity the number of neighbors to store at max + */ + KNNUniqueResultSet(unsigned int capacity) : capacity_(capacity) + { + this->is_full_ = false; + this->clear(); + } + + /** Add a possible candidate to the best neighbors + * @param dist distance for that neighbor + * @param index index of that neighbor + */ + inline void addPoint(DistanceType dist, int index) CV_OVERRIDE + { + // Don't do anything if we are worse than the worst + if (dist >= worst_distance_) return; + dist_indices_.insert(DistIndex(dist, index)); + + if (is_full_) { + if (dist_indices_.size() > capacity_) { + dist_indices_.erase(*dist_indices_.rbegin()); + worst_distance_ = dist_indices_.rbegin()->dist_; + } + } + else if (dist_indices_.size() == capacity_) { + is_full_ = true; + worst_distance_ = dist_indices_.rbegin()->dist_; + } + } + + /** Remove all elements in the set + */ + void clear() CV_OVERRIDE + { + dist_indices_.clear(); + worst_distance_ = std::numeric_limits::max(); + is_full_ = false; + } + +protected: + typedef typename UniqueResultSet::DistIndex DistIndex; + using UniqueResultSet::is_full_; + using UniqueResultSet::worst_distance_; + using UniqueResultSet::dist_indices_; + + /** The number of neighbors to keep */ + unsigned int capacity_; +}; + +//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + +/** Class that holds the radius nearest neighbors + * It is more accurate than RadiusResult as it is not limited in the number of neighbors + */ +template +class RadiusUniqueResultSet : public UniqueResultSet +{ +public: + /** Constructor + * @param radius the maximum distance of a neighbor + */ + RadiusUniqueResultSet(DistanceType radius) : + radius_(radius) + { + is_full_ = true; + } + + /** Add a possible candidate to the best neighbors + * @param dist distance for that neighbor + * @param index index of that neighbor + */ + void addPoint(DistanceType dist, int index) CV_OVERRIDE + { + if (dist <= radius_) dist_indices_.insert(DistIndex(dist, index)); + } + + /** Remove all elements in the set + */ + inline void clear() CV_OVERRIDE + { + dist_indices_.clear(); + } + + + /** Check the status of the set + * @return alwys false + */ + inline bool full() const CV_OVERRIDE + { + return true; + } + + /** The distance of the furthest neighbor + * If we don't have enough neighbors, it returns the max possible value + * @return + */ + inline DistanceType worstDist() const CV_OVERRIDE + { + return radius_; + } +private: + typedef typename UniqueResultSet::DistIndex DistIndex; + using UniqueResultSet::dist_indices_; + using UniqueResultSet::is_full_; + + /** The furthest distance a neighbor can be */ + DistanceType radius_; +}; + +//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + +/** Class that holds the k NN neighbors within a radius distance + */ +template +class KNNRadiusUniqueResultSet : public KNNUniqueResultSet +{ +public: + /** Constructor + * @param capacity the number of neighbors to store at max + * @param radius the maximum distance of a neighbor + */ + KNNRadiusUniqueResultSet(unsigned int capacity, DistanceType radius) + { + this->capacity_ = capacity; + this->radius_ = radius; + this->dist_indices_.reserve(capacity_); + this->clear(); + } + + /** Remove all elements in the set + */ + void clear() + { + dist_indices_.clear(); + worst_distance_ = radius_; + is_full_ = false; + } +private: + using KNNUniqueResultSet::dist_indices_; + using KNNUniqueResultSet::is_full_; + using KNNUniqueResultSet::worst_distance_; + + /** The maximum number of neighbors to consider */ + unsigned int capacity_; + + /** The maximum distance of a neighbor */ + DistanceType radius_; +}; +} + +#endif //OPENCV_FLANN_RESULTSET_H diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/sampling.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/sampling.h new file mode 100644 index 0000000000000000000000000000000000000000..396f177ae363bd4ddd491c84d5375fd36e8913be --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/sampling.h @@ -0,0 +1,81 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + + +#ifndef OPENCV_FLANN_SAMPLING_H_ +#define OPENCV_FLANN_SAMPLING_H_ + +#include "matrix.h" +#include "random.h" + +namespace cvflann +{ + +template +Matrix random_sample(Matrix& srcMatrix, long size, bool remove = false) +{ + Matrix newSet(new T[size * srcMatrix.cols], size,srcMatrix.cols); + + T* src,* dest; + for (long i=0; i +Matrix random_sample(const Matrix& srcMatrix, size_t size) +{ + UniqueRandom rand((int)srcMatrix.rows); + Matrix newSet(new T[size * srcMatrix.cols], size,srcMatrix.cols); + + T* src,* dest; + for (size_t i=0; i +#include + +#include "general.h" +#include "nn_index.h" + +#ifdef FLANN_SIGNATURE_ +#undef FLANN_SIGNATURE_ +#endif +#define FLANN_SIGNATURE_ "FLANN_INDEX" + +namespace cvflann +{ + +template +struct Datatype {}; +template<> +struct Datatype { static flann_datatype_t type() { return FLANN_INT8; } }; +template<> +struct Datatype { static flann_datatype_t type() { return FLANN_INT16; } }; +template<> +struct Datatype { static flann_datatype_t type() { return FLANN_INT32; } }; +template<> +struct Datatype { static flann_datatype_t type() { return FLANN_UINT8; } }; +template<> +struct Datatype { static flann_datatype_t type() { return FLANN_UINT16; } }; +template<> +struct Datatype { static flann_datatype_t type() { return FLANN_UINT32; } }; +template<> +struct Datatype { static flann_datatype_t type() { return FLANN_FLOAT32; } }; +template<> +struct Datatype { static flann_datatype_t type() { return FLANN_FLOAT64; } }; + + +/** + * Structure representing the index header. + */ +struct IndexHeader +{ + char signature[16]; + char version[16]; + flann_datatype_t data_type; + flann_algorithm_t index_type; + size_t rows; + size_t cols; +}; + +/** + * Saves index header to stream + * + * @param stream - Stream to save to + * @param index - The index to save + */ +template +void save_header(FILE* stream, const NNIndex& index) +{ + IndexHeader header; + memset(header.signature, 0, sizeof(header.signature)); + strcpy(header.signature, FLANN_SIGNATURE_); + memset(header.version, 0, sizeof(header.version)); + strcpy(header.version, FLANN_VERSION_); + header.data_type = Datatype::type(); + header.index_type = index.getType(); + header.rows = index.size(); + header.cols = index.veclen(); + + std::fwrite(&header, sizeof(header),1,stream); +} + + +/** + * + * @param stream - Stream to load from + * @return Index header + */ +inline IndexHeader load_header(FILE* stream) +{ + IndexHeader header; + size_t read_size = fread(&header,sizeof(header),1,stream); + + if (read_size!=(size_t)1) { + throw FLANNException("Invalid index file, cannot read"); + } + + if (strcmp(header.signature,FLANN_SIGNATURE_)!=0) { + throw FLANNException("Invalid index file, wrong signature"); + } + + return header; + +} + + +template +void save_value(FILE* stream, const T& value, size_t count = 1) +{ + fwrite(&value, sizeof(value),count, stream); +} + +template +void save_value(FILE* stream, const cvflann::Matrix& value) +{ + fwrite(&value, sizeof(value),1, stream); + fwrite(value.data, sizeof(T),value.rows*value.cols, stream); +} + +template +void save_value(FILE* stream, const std::vector& value) +{ + size_t size = value.size(); + fwrite(&size, sizeof(size_t), 1, stream); + fwrite(&value[0], sizeof(T), size, stream); +} + +template +void load_value(FILE* stream, T& value, size_t count = 1) +{ + size_t read_cnt = fread(&value, sizeof(value), count, stream); + if (read_cnt != count) { + throw FLANNException("Cannot read from file"); + } +} + +template +void load_value(FILE* stream, cvflann::Matrix& value) +{ + size_t read_cnt = fread(&value, sizeof(value), 1, stream); + if (read_cnt != 1) { + throw FLANNException("Cannot read from file"); + } + value.data = new T[value.rows*value.cols]; + read_cnt = fread(value.data, sizeof(T), value.rows*value.cols, stream); + if (read_cnt != (size_t)(value.rows*value.cols)) { + throw FLANNException("Cannot read from file"); + } +} + + +template +void load_value(FILE* stream, std::vector& value) +{ + size_t size; + size_t read_cnt = fread(&size, sizeof(size_t), 1, stream); + if (read_cnt!=1) { + throw FLANNException("Cannot read from file"); + } + value.resize(size); + read_cnt = fread(&value[0], sizeof(T), size, stream); + if (read_cnt != size) { + throw FLANNException("Cannot read from file"); + } +} + +} + +#endif /* OPENCV_FLANN_SAVING_H_ */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/simplex_downhill.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/simplex_downhill.h new file mode 100644 index 0000000000000000000000000000000000000000..145901ab0df09e96995bb12518425a5de406677e --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/flann/simplex_downhill.h @@ -0,0 +1,186 @@ +/*********************************************************************** + * Software License Agreement (BSD License) + * + * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. + * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. + * + * THE BSD LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + *************************************************************************/ + +#ifndef OPENCV_FLANN_SIMPLEX_DOWNHILL_H_ +#define OPENCV_FLANN_SIMPLEX_DOWNHILL_H_ + +namespace cvflann +{ + +/** + Adds val to array vals (and point to array points) and keeping the arrays sorted by vals. + */ +template +void addValue(int pos, float val, float* vals, T* point, T* points, int n) +{ + vals[pos] = val; + for (int i=0; i0 && vals[j] +float optimizeSimplexDownhill(T* points, int n, F func, float* vals = NULL ) +{ + const int MAX_ITERATIONS = 10; + + assert(n>0); + + T* p_o = new T[n]; + T* p_r = new T[n]; + T* p_e = new T[n]; + + int alpha = 1; + + int iterations = 0; + + bool ownVals = false; + if (vals == NULL) { + ownVals = true; + vals = new float[n+1]; + for (int i=0; i MAX_ITERATIONS) break; + + // compute average of simplex points (except the highest point) + for (int j=0; j=vals[0])&&(val_r=vals[n]) { + for (int i=0; i +#include "opencv2/core.hpp" +#include "opencv2/core/utility.hpp" + +namespace cvflann +{ + +/** + * A start-stop timer class. + * + * Can be used to time portions of code. + */ +class StartStopTimer +{ + int64 startTime; + +public: + /** + * Value of the timer. + */ + double value; + + + /** + * Constructor. + */ + StartStopTimer() + { + reset(); + } + + /** + * Starts the timer. + */ + void start() + { + startTime = cv::getTickCount(); + } + + /** + * Stops the timer and updates timer value. + */ + void stop() + { + int64 stopTime = cv::getTickCount(); + value += ( (double)stopTime - startTime) / cv::getTickFrequency(); + } + + /** + * Resets the timer value to 0. + */ + void reset() + { + value = 0; + } + +}; + +} + +#endif // FLANN_TIMER_H diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/highgui.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/highgui.hpp new file mode 100644 index 0000000000000000000000000000000000000000..0394c7d70d6aab81bad1e0ce5777679a0ca69a12 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/highgui.hpp @@ -0,0 +1,844 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_HIGHGUI_HPP +#define OPENCV_HIGHGUI_HPP + +#include "opencv2/core.hpp" +#ifdef HAVE_OPENCV_IMGCODECS +#include "opencv2/imgcodecs.hpp" +#endif +#ifdef HAVE_OPENCV_VIDEOIO +#include "opencv2/videoio.hpp" +#endif + +/** +@defgroup highgui High-level GUI + +While OpenCV was designed for use in full-scale applications and can be used within functionally +rich UI frameworks (such as Qt\*, WinForms\*, or Cocoa\*) or without any UI at all, sometimes there +it is required to try functionality quickly and visualize the results. This is what the HighGUI +module has been designed for. + +It provides easy interface to: + +- Create and manipulate windows that can display images and "remember" their content (no need to + handle repaint events from OS). +- Add trackbars to the windows, handle simple mouse events as well as keyboard commands. + +@{ + @defgroup highgui_opengl OpenGL support + @defgroup highgui_qt Qt New Functions + + ![image](pics/qtgui.png) + + This figure explains new functionality implemented with Qt\* GUI. The new GUI provides a statusbar, + a toolbar, and a control panel. The control panel can have trackbars and buttonbars attached to it. + If you cannot see the control panel, press Ctrl+P or right-click any Qt window and select **Display + properties window**. + + - To attach a trackbar, the window name parameter must be NULL. + + - To attach a buttonbar, a button must be created. If the last bar attached to the control panel + is a buttonbar, the new button is added to the right of the last button. If the last bar + attached to the control panel is a trackbar, or the control panel is empty, a new buttonbar is + created. Then, a new button is attached to it. + + See below the example used to generate the figure: + @code + int main(int argc, char *argv[]) + { + + int value = 50; + int value2 = 0; + + + namedWindow("main1",WINDOW_NORMAL); + namedWindow("main2",WINDOW_AUTOSIZE | CV_GUI_NORMAL); + createTrackbar( "track1", "main1", &value, 255, NULL); + + String nameb1 = "button1"; + String nameb2 = "button2"; + + createButton(nameb1,callbackButton,&nameb1,QT_CHECKBOX,1); + createButton(nameb2,callbackButton,NULL,QT_CHECKBOX,0); + createTrackbar( "track2", NULL, &value2, 255, NULL); + createButton("button5",callbackButton1,NULL,QT_RADIOBOX,0); + createButton("button6",callbackButton2,NULL,QT_RADIOBOX,1); + + setMouseCallback( "main2",on_mouse,NULL ); + + Mat img1 = imread("files/flower.jpg"); + VideoCapture video; + video.open("files/hockey.avi"); + + Mat img2,img3; + + while( waitKey(33) != 27 ) + { + img1.convertTo(img2,-1,1,value); + video >> img3; + + imshow("main1",img2); + imshow("main2",img3); + } + + destroyAllWindows(); + + return 0; + } + @endcode + + + @defgroup highgui_winrt WinRT support + + This figure explains new functionality implemented with WinRT GUI. The new GUI provides an Image control, + and a slider panel. Slider panel holds trackbars attached to it. + + Sliders are attached below the image control. Every new slider is added below the previous one. + + See below the example used to generate the figure: + @code + void sample_app::MainPage::ShowWindow() + { + static cv::String windowName("sample"); + cv::winrt_initContainer(this->cvContainer); + cv::namedWindow(windowName); // not required + + cv::Mat image = cv::imread("Assets/sample.jpg"); + cv::Mat converted = cv::Mat(image.rows, image.cols, CV_8UC4); + cv::cvtColor(image, converted, COLOR_BGR2BGRA); + cv::imshow(windowName, converted); // this will create window if it hasn't been created before + + int state = 42; + cv::TrackbarCallback callback = [](int pos, void* userdata) + { + if (pos == 0) { + cv::destroyWindow(windowName); + } + }; + cv::TrackbarCallback callbackTwin = [](int pos, void* userdata) + { + if (pos >= 70) { + cv::destroyAllWindows(); + } + }; + cv::createTrackbar("Sample trackbar", windowName, &state, 100, callback); + cv::createTrackbar("Twin brother", windowName, &state, 100, callbackTwin); + } + @endcode + + @defgroup highgui_c C API +@} +*/ + +///////////////////////// graphical user interface ////////////////////////// +namespace cv +{ + +//! @addtogroup highgui +//! @{ + +//! Flags for cv::namedWindow +enum WindowFlags { + WINDOW_NORMAL = 0x00000000, //!< the user can resize the window (no constraint) / also use to switch a fullscreen window to a normal size. + WINDOW_AUTOSIZE = 0x00000001, //!< the user cannot resize the window, the size is constrainted by the image displayed. + WINDOW_OPENGL = 0x00001000, //!< window with opengl support. + + WINDOW_FULLSCREEN = 1, //!< change the window to fullscreen. + WINDOW_FREERATIO = 0x00000100, //!< the image expends as much as it can (no ratio constraint). + WINDOW_KEEPRATIO = 0x00000000, //!< the ratio of the image is respected. + WINDOW_GUI_EXPANDED=0x00000000, //!< status bar and tool bar + WINDOW_GUI_NORMAL = 0x00000010, //!< old fashious way + }; + +//! Flags for cv::setWindowProperty / cv::getWindowProperty +enum WindowPropertyFlags { + WND_PROP_FULLSCREEN = 0, //!< fullscreen property (can be WINDOW_NORMAL or WINDOW_FULLSCREEN). + WND_PROP_AUTOSIZE = 1, //!< autosize property (can be WINDOW_NORMAL or WINDOW_AUTOSIZE). + WND_PROP_ASPECT_RATIO = 2, //!< window's aspect ration (can be set to WINDOW_FREERATIO or WINDOW_KEEPRATIO). + WND_PROP_OPENGL = 3, //!< opengl support. + WND_PROP_VISIBLE = 4 //!< checks whether the window exists and is visible + }; + +//! Mouse Events see cv::MouseCallback +enum MouseEventTypes { + EVENT_MOUSEMOVE = 0, //!< indicates that the mouse pointer has moved over the window. + EVENT_LBUTTONDOWN = 1, //!< indicates that the left mouse button is pressed. + EVENT_RBUTTONDOWN = 2, //!< indicates that the right mouse button is pressed. + EVENT_MBUTTONDOWN = 3, //!< indicates that the middle mouse button is pressed. + EVENT_LBUTTONUP = 4, //!< indicates that left mouse button is released. + EVENT_RBUTTONUP = 5, //!< indicates that right mouse button is released. + EVENT_MBUTTONUP = 6, //!< indicates that middle mouse button is released. + EVENT_LBUTTONDBLCLK = 7, //!< indicates that left mouse button is double clicked. + EVENT_RBUTTONDBLCLK = 8, //!< indicates that right mouse button is double clicked. + EVENT_MBUTTONDBLCLK = 9, //!< indicates that middle mouse button is double clicked. + EVENT_MOUSEWHEEL = 10,//!< positive and negative values mean forward and backward scrolling, respectively. + EVENT_MOUSEHWHEEL = 11 //!< positive and negative values mean right and left scrolling, respectively. + }; + +//! Mouse Event Flags see cv::MouseCallback +enum MouseEventFlags { + EVENT_FLAG_LBUTTON = 1, //!< indicates that the left mouse button is down. + EVENT_FLAG_RBUTTON = 2, //!< indicates that the right mouse button is down. + EVENT_FLAG_MBUTTON = 4, //!< indicates that the middle mouse button is down. + EVENT_FLAG_CTRLKEY = 8, //!< indicates that CTRL Key is pressed. + EVENT_FLAG_SHIFTKEY = 16,//!< indicates that SHIFT Key is pressed. + EVENT_FLAG_ALTKEY = 32 //!< indicates that ALT Key is pressed. + }; + +//! Qt font weight +enum QtFontWeights { + QT_FONT_LIGHT = 25, //!< Weight of 25 + QT_FONT_NORMAL = 50, //!< Weight of 50 + QT_FONT_DEMIBOLD = 63, //!< Weight of 63 + QT_FONT_BOLD = 75, //!< Weight of 75 + QT_FONT_BLACK = 87 //!< Weight of 87 + }; + +//! Qt font style +enum QtFontStyles { + QT_STYLE_NORMAL = 0, //!< Normal font. + QT_STYLE_ITALIC = 1, //!< Italic font. + QT_STYLE_OBLIQUE = 2 //!< Oblique font. + }; + +//! Qt "button" type +enum QtButtonTypes { + QT_PUSH_BUTTON = 0, //!< Push button. + QT_CHECKBOX = 1, //!< Checkbox button. + QT_RADIOBOX = 2, //!< Radiobox button. + QT_NEW_BUTTONBAR = 1024 //!< Button should create a new buttonbar + }; + +/** @brief Callback function for mouse events. see cv::setMouseCallback +@param event one of the cv::MouseEventTypes constants. +@param x The x-coordinate of the mouse event. +@param y The y-coordinate of the mouse event. +@param flags one of the cv::MouseEventFlags constants. +@param userdata The optional parameter. + */ +typedef void (*MouseCallback)(int event, int x, int y, int flags, void* userdata); + +/** @brief Callback function for Trackbar see cv::createTrackbar +@param pos current position of the specified trackbar. +@param userdata The optional parameter. + */ +typedef void (*TrackbarCallback)(int pos, void* userdata); + +/** @brief Callback function defined to be called every frame. See cv::setOpenGlDrawCallback +@param userdata The optional parameter. + */ +typedef void (*OpenGlDrawCallback)(void* userdata); + +/** @brief Callback function for a button created by cv::createButton +@param state current state of the button. It could be -1 for a push button, 0 or 1 for a check/radio box button. +@param userdata The optional parameter. + */ +typedef void (*ButtonCallback)(int state, void* userdata); + +/** @brief Creates a window. + +The function namedWindow creates a window that can be used as a placeholder for images and +trackbars. Created windows are referred to by their names. + +If a window with the same name already exists, the function does nothing. + +You can call cv::destroyWindow or cv::destroyAllWindows to close the window and de-allocate any associated +memory usage. For a simple program, you do not really have to call these functions because all the +resources and windows of the application are closed automatically by the operating system upon exit. + +@note + +Qt backend supports additional flags: + - **WINDOW_NORMAL or WINDOW_AUTOSIZE:** WINDOW_NORMAL enables you to resize the + window, whereas WINDOW_AUTOSIZE adjusts automatically the window size to fit the + displayed image (see imshow ), and you cannot change the window size manually. + - **WINDOW_FREERATIO or WINDOW_KEEPRATIO:** WINDOW_FREERATIO adjusts the image + with no respect to its ratio, whereas WINDOW_KEEPRATIO keeps the image ratio. + - **WINDOW_GUI_NORMAL or WINDOW_GUI_EXPANDED:** WINDOW_GUI_NORMAL is the old way to draw the window + without statusbar and toolbar, whereas WINDOW_GUI_EXPANDED is a new enhanced GUI. +By default, flags == WINDOW_AUTOSIZE | WINDOW_KEEPRATIO | WINDOW_GUI_EXPANDED + +@param winname Name of the window in the window caption that may be used as a window identifier. +@param flags Flags of the window. The supported flags are: (cv::WindowFlags) + */ +CV_EXPORTS_W void namedWindow(const String& winname, int flags = WINDOW_AUTOSIZE); + +/** @brief Destroys the specified window. + +The function destroyWindow destroys the window with the given name. + +@param winname Name of the window to be destroyed. + */ +CV_EXPORTS_W void destroyWindow(const String& winname); + +/** @brief Destroys all of the HighGUI windows. + +The function destroyAllWindows destroys all of the opened HighGUI windows. + */ +CV_EXPORTS_W void destroyAllWindows(); + +CV_EXPORTS_W int startWindowThread(); + +/** @brief Similar to #waitKey, but returns full key code. + +@note + +Key code is implementation specific and depends on used backend: QT/GTK/Win32/etc + +*/ +CV_EXPORTS_W int waitKeyEx(int delay = 0); + +/** @brief Waits for a pressed key. + +The function waitKey waits for a key event infinitely (when \f$\texttt{delay}\leq 0\f$ ) or for delay +milliseconds, when it is positive. Since the OS has a minimum time between switching threads, the +function will not wait exactly delay ms, it will wait at least delay ms, depending on what else is +running on your computer at that time. It returns the code of the pressed key or -1 if no key was +pressed before the specified time had elapsed. + +@note + +This function is the only method in HighGUI that can fetch and handle events, so it needs to be +called periodically for normal event processing unless HighGUI is used within an environment that +takes care of event processing. + +@note + +The function only works if there is at least one HighGUI window created and the window is active. +If there are several HighGUI windows, any of them can be active. + +@param delay Delay in milliseconds. 0 is the special value that means "forever". + */ +CV_EXPORTS_W int waitKey(int delay = 0); + +/** @brief Displays an image in the specified window. + +The function imshow displays an image in the specified window. If the window was created with the +cv::WINDOW_AUTOSIZE flag, the image is shown with its original size, however it is still limited by the screen resolution. +Otherwise, the image is scaled to fit the window. The function may scale the image, depending on its depth: + +- If the image is 8-bit unsigned, it is displayed as is. +- If the image is 16-bit unsigned or 32-bit integer, the pixels are divided by 256. That is, the + value range [0,255\*256] is mapped to [0,255]. +- If the image is 32-bit or 64-bit floating-point, the pixel values are multiplied by 255. That is, the + value range [0,1] is mapped to [0,255]. + +If window was created with OpenGL support, cv::imshow also support ogl::Buffer , ogl::Texture2D and +cuda::GpuMat as input. + +If the window was not created before this function, it is assumed creating a window with cv::WINDOW_AUTOSIZE. + +If you need to show an image that is bigger than the screen resolution, you will need to call namedWindow("", WINDOW_NORMAL) before the imshow. + +@note This function should be followed by cv::waitKey function which displays the image for specified +milliseconds. Otherwise, it won't display the image. For example, **waitKey(0)** will display the window +infinitely until any keypress (it is suitable for image display). **waitKey(25)** will display a frame +for 25 ms, after which display will be automatically closed. (If you put it in a loop to read +videos, it will display the video frame-by-frame) + +@note + +[__Windows Backend Only__] Pressing Ctrl+C will copy the image to the clipboard. + +[__Windows Backend Only__] Pressing Ctrl+S will show a dialog to save the image. + +@param winname Name of the window. +@param mat Image to be shown. + */ +CV_EXPORTS_W void imshow(const String& winname, InputArray mat); + +/** @brief Resizes window to the specified size + +@note + +- The specified window size is for the image area. Toolbars are not counted. +- Only windows created without cv::WINDOW_AUTOSIZE flag can be resized. + +@param winname Window name. +@param width The new window width. +@param height The new window height. + */ +CV_EXPORTS_W void resizeWindow(const String& winname, int width, int height); + +/** @overload +@param winname Window name. +@param size The new window size. +*/ +CV_EXPORTS_W void resizeWindow(const String& winname, const cv::Size& size); + +/** @brief Moves window to the specified position + +@param winname Name of the window. +@param x The new x-coordinate of the window. +@param y The new y-coordinate of the window. + */ +CV_EXPORTS_W void moveWindow(const String& winname, int x, int y); + +/** @brief Changes parameters of a window dynamically. + +The function setWindowProperty enables changing properties of a window. + +@param winname Name of the window. +@param prop_id Window property to edit. The supported operation flags are: (cv::WindowPropertyFlags) +@param prop_value New value of the window property. The supported flags are: (cv::WindowFlags) + */ +CV_EXPORTS_W void setWindowProperty(const String& winname, int prop_id, double prop_value); + +/** @brief Updates window title +@param winname Name of the window. +@param title New title. +*/ +CV_EXPORTS_W void setWindowTitle(const String& winname, const String& title); + +/** @brief Provides parameters of a window. + +The function getWindowProperty returns properties of a window. + +@param winname Name of the window. +@param prop_id Window property to retrieve. The following operation flags are available: (cv::WindowPropertyFlags) + +@sa setWindowProperty + */ +CV_EXPORTS_W double getWindowProperty(const String& winname, int prop_id); + +/** @brief Provides rectangle of image in the window. + +The function getWindowImageRect returns the client screen coordinates, width and height of the image rendering area. + +@param winname Name of the window. + +@sa resizeWindow moveWindow + */ +CV_EXPORTS_W Rect getWindowImageRect(const String& winname); + +/** @brief Sets mouse handler for the specified window + +@param winname Name of the window. +@param onMouse Mouse callback. See OpenCV samples, such as +, on how to specify and +use the callback. +@param userdata The optional parameter passed to the callback. + */ +CV_EXPORTS void setMouseCallback(const String& winname, MouseCallback onMouse, void* userdata = 0); + +/** @brief Gets the mouse-wheel motion delta, when handling mouse-wheel events cv::EVENT_MOUSEWHEEL and +cv::EVENT_MOUSEHWHEEL. + +For regular mice with a scroll-wheel, delta will be a multiple of 120. The value 120 corresponds to +a one notch rotation of the wheel or the threshold for action to be taken and one such action should +occur for each delta. Some high-precision mice with higher-resolution freely-rotating wheels may +generate smaller values. + +For cv::EVENT_MOUSEWHEEL positive and negative values mean forward and backward scrolling, +respectively. For cv::EVENT_MOUSEHWHEEL, where available, positive and negative values mean right and +left scrolling, respectively. + +With the C API, the macro CV_GET_WHEEL_DELTA(flags) can be used alternatively. + +@note + +Mouse-wheel events are currently supported only on Windows. + +@param flags The mouse callback flags parameter. + */ +CV_EXPORTS int getMouseWheelDelta(int flags); + +/** @brief Selects ROI on the given image. +Function creates a window and allows user to select a ROI using mouse. +Controls: use `space` or `enter` to finish selection, use key `c` to cancel selection (function will return the zero cv::Rect). + +@param windowName name of the window where selection process will be shown. +@param img image to select a ROI. +@param showCrosshair if true crosshair of selection rectangle will be shown. +@param fromCenter if true center of selection will match initial mouse position. In opposite case a corner of +selection rectangle will correspont to the initial mouse position. +@return selected ROI or empty rect if selection canceled. + +@note The function sets it's own mouse callback for specified window using cv::setMouseCallback(windowName, ...). +After finish of work an empty callback will be set for the used window. + */ +CV_EXPORTS_W Rect selectROI(const String& windowName, InputArray img, bool showCrosshair = true, bool fromCenter = false); + +/** @overload + */ +CV_EXPORTS_W Rect selectROI(InputArray img, bool showCrosshair = true, bool fromCenter = false); + +/** @brief Selects ROIs on the given image. +Function creates a window and allows user to select a ROIs using mouse. +Controls: use `space` or `enter` to finish current selection and start a new one, +use `esc` to terminate multiple ROI selection process. + +@param windowName name of the window where selection process will be shown. +@param img image to select a ROI. +@param boundingBoxes selected ROIs. +@param showCrosshair if true crosshair of selection rectangle will be shown. +@param fromCenter if true center of selection will match initial mouse position. In opposite case a corner of +selection rectangle will correspont to the initial mouse position. + +@note The function sets it's own mouse callback for specified window using cv::setMouseCallback(windowName, ...). +After finish of work an empty callback will be set for the used window. + */ +CV_EXPORTS_W void selectROIs(const String& windowName, InputArray img, + CV_OUT std::vector& boundingBoxes, bool showCrosshair = true, bool fromCenter = false); + +/** @brief Creates a trackbar and attaches it to the specified window. + +The function createTrackbar creates a trackbar (a slider or range control) with the specified name +and range, assigns a variable value to be a position synchronized with the trackbar and specifies +the callback function onChange to be called on the trackbar position change. The created trackbar is +displayed in the specified window winname. + +@note + +[__Qt Backend Only__] winname can be empty (or NULL) if the trackbar should be attached to the +control panel. + +Clicking the label of each trackbar enables editing the trackbar values manually. + +@param trackbarname Name of the created trackbar. +@param winname Name of the window that will be used as a parent of the created trackbar. +@param value Optional pointer to an integer variable whose value reflects the position of the +slider. Upon creation, the slider position is defined by this variable. +@param count Maximal position of the slider. The minimal position is always 0. +@param onChange Pointer to the function to be called every time the slider changes position. This +function should be prototyped as void Foo(int,void\*); , where the first parameter is the trackbar +position and the second parameter is the user data (see the next parameter). If the callback is +the NULL pointer, no callbacks are called, but only value is updated. +@param userdata User data that is passed as is to the callback. It can be used to handle trackbar +events without using global variables. + */ +CV_EXPORTS int createTrackbar(const String& trackbarname, const String& winname, + int* value, int count, + TrackbarCallback onChange = 0, + void* userdata = 0); + +/** @brief Returns the trackbar position. + +The function returns the current position of the specified trackbar. + +@note + +[__Qt Backend Only__] winname can be empty (or NULL) if the trackbar is attached to the control +panel. + +@param trackbarname Name of the trackbar. +@param winname Name of the window that is the parent of the trackbar. + */ +CV_EXPORTS_W int getTrackbarPos(const String& trackbarname, const String& winname); + +/** @brief Sets the trackbar position. + +The function sets the position of the specified trackbar in the specified window. + +@note + +[__Qt Backend Only__] winname can be empty (or NULL) if the trackbar is attached to the control +panel. + +@param trackbarname Name of the trackbar. +@param winname Name of the window that is the parent of trackbar. +@param pos New position. + */ +CV_EXPORTS_W void setTrackbarPos(const String& trackbarname, const String& winname, int pos); + +/** @brief Sets the trackbar maximum position. + +The function sets the maximum position of the specified trackbar in the specified window. + +@note + +[__Qt Backend Only__] winname can be empty (or NULL) if the trackbar is attached to the control +panel. + +@param trackbarname Name of the trackbar. +@param winname Name of the window that is the parent of trackbar. +@param maxval New maximum position. + */ +CV_EXPORTS_W void setTrackbarMax(const String& trackbarname, const String& winname, int maxval); + +/** @brief Sets the trackbar minimum position. + +The function sets the minimum position of the specified trackbar in the specified window. + +@note + +[__Qt Backend Only__] winname can be empty (or NULL) if the trackbar is attached to the control +panel. + +@param trackbarname Name of the trackbar. +@param winname Name of the window that is the parent of trackbar. +@param minval New minimum position. + */ +CV_EXPORTS_W void setTrackbarMin(const String& trackbarname, const String& winname, int minval); + +//! @addtogroup highgui_opengl OpenGL support +//! @{ + +/** @brief Displays OpenGL 2D texture in the specified window. + +@param winname Name of the window. +@param tex OpenGL 2D texture data. + */ +CV_EXPORTS void imshow(const String& winname, const ogl::Texture2D& tex); + +/** @brief Sets a callback function to be called to draw on top of displayed image. + +The function setOpenGlDrawCallback can be used to draw 3D data on the window. See the example of +callback function below: +@code + void on_opengl(void* param) + { + glLoadIdentity(); + + glTranslated(0.0, 0.0, -1.0); + + glRotatef( 55, 1, 0, 0 ); + glRotatef( 45, 0, 1, 0 ); + glRotatef( 0, 0, 0, 1 ); + + static const int coords[6][4][3] = { + { { +1, -1, -1 }, { -1, -1, -1 }, { -1, +1, -1 }, { +1, +1, -1 } }, + { { +1, +1, -1 }, { -1, +1, -1 }, { -1, +1, +1 }, { +1, +1, +1 } }, + { { +1, -1, +1 }, { +1, -1, -1 }, { +1, +1, -1 }, { +1, +1, +1 } }, + { { -1, -1, -1 }, { -1, -1, +1 }, { -1, +1, +1 }, { -1, +1, -1 } }, + { { +1, -1, +1 }, { -1, -1, +1 }, { -1, -1, -1 }, { +1, -1, -1 } }, + { { -1, -1, +1 }, { +1, -1, +1 }, { +1, +1, +1 }, { -1, +1, +1 } } + }; + + for (int i = 0; i < 6; ++i) { + glColor3ub( i*20, 100+i*10, i*42 ); + glBegin(GL_QUADS); + for (int j = 0; j < 4; ++j) { + glVertex3d(0.2 * coords[i][j][0], 0.2 * coords[i][j][1], 0.2 * coords[i][j][2]); + } + glEnd(); + } + } +@endcode + +@param winname Name of the window. +@param onOpenGlDraw Pointer to the function to be called every frame. This function should be +prototyped as void Foo(void\*) . +@param userdata Pointer passed to the callback function.(__Optional__) + */ +CV_EXPORTS void setOpenGlDrawCallback(const String& winname, OpenGlDrawCallback onOpenGlDraw, void* userdata = 0); + +/** @brief Sets the specified window as current OpenGL context. + +@param winname Name of the window. + */ +CV_EXPORTS void setOpenGlContext(const String& winname); + +/** @brief Force window to redraw its context and call draw callback ( See cv::setOpenGlDrawCallback ). + +@param winname Name of the window. + */ +CV_EXPORTS void updateWindow(const String& winname); + +//! @} highgui_opengl + +//! @addtogroup highgui_qt +//! @{ + +/** @brief QtFont available only for Qt. See cv::fontQt + */ +struct QtFont +{ + const char* nameFont; //!< Name of the font + Scalar color; //!< Color of the font. Scalar(blue_component, green_component, red_component[, alpha_component]) + int font_face; //!< See cv::QtFontStyles + const int* ascii; //!< font data and metrics + const int* greek; + const int* cyrillic; + float hscale, vscale; + float shear; //!< slope coefficient: 0 - normal, >0 - italic + int thickness; //!< See cv::QtFontWeights + float dx; //!< horizontal interval between letters + int line_type; //!< PointSize +}; + +/** @brief Creates the font to draw a text on an image. + +The function fontQt creates a cv::QtFont object. This cv::QtFont is not compatible with putText . + +A basic usage of this function is the following: : +@code + QtFont font = fontQt("Times"); + addText( img1, "Hello World !", Point(50,50), font); +@endcode + +@param nameFont Name of the font. The name should match the name of a system font (such as +*Times*). If the font is not found, a default one is used. +@param pointSize Size of the font. If not specified, equal zero or negative, the point size of the +font is set to a system-dependent default value. Generally, this is 12 points. +@param color Color of the font in BGRA where A = 255 is fully transparent. Use the macro CV_RGB +for simplicity. +@param weight Font weight. Available operation flags are : cv::QtFontWeights You can also specify a positive integer for better control. +@param style Font style. Available operation flags are : cv::QtFontStyles +@param spacing Spacing between characters. It can be negative or positive. + */ +CV_EXPORTS QtFont fontQt(const String& nameFont, int pointSize = -1, + Scalar color = Scalar::all(0), int weight = QT_FONT_NORMAL, + int style = QT_STYLE_NORMAL, int spacing = 0); + +/** @brief Draws a text on the image. + +The function addText draws *text* on the image *img* using a specific font *font* (see example cv::fontQt +) + +@param img 8-bit 3-channel image where the text should be drawn. +@param text Text to write on an image. +@param org Point(x,y) where the text should start on an image. +@param font Font to use to draw a text. + */ +CV_EXPORTS void addText( const Mat& img, const String& text, Point org, const QtFont& font); + +/** @brief Draws a text on the image. + +@param img 8-bit 3-channel image where the text should be drawn. +@param text Text to write on an image. +@param org Point(x,y) where the text should start on an image. +@param nameFont Name of the font. The name should match the name of a system font (such as +*Times*). If the font is not found, a default one is used. +@param pointSize Size of the font. If not specified, equal zero or negative, the point size of the +font is set to a system-dependent default value. Generally, this is 12 points. +@param color Color of the font in BGRA where A = 255 is fully transparent. +@param weight Font weight. Available operation flags are : cv::QtFontWeights You can also specify a positive integer for better control. +@param style Font style. Available operation flags are : cv::QtFontStyles +@param spacing Spacing between characters. It can be negative or positive. + */ +CV_EXPORTS_W void addText(const Mat& img, const String& text, Point org, const String& nameFont, int pointSize = -1, Scalar color = Scalar::all(0), + int weight = QT_FONT_NORMAL, int style = QT_STYLE_NORMAL, int spacing = 0); + +/** @brief Displays a text on a window image as an overlay for a specified duration. + +The function displayOverlay displays useful information/tips on top of the window for a certain +amount of time *delayms*. The function does not modify the image, displayed in the window, that is, +after the specified delay the original content of the window is restored. + +@param winname Name of the window. +@param text Overlay text to write on a window image. +@param delayms The period (in milliseconds), during which the overlay text is displayed. If this +function is called before the previous overlay text timed out, the timer is restarted and the text +is updated. If this value is zero, the text never disappears. + */ +CV_EXPORTS_W void displayOverlay(const String& winname, const String& text, int delayms = 0); + +/** @brief Displays a text on the window statusbar during the specified period of time. + +The function displayStatusBar displays useful information/tips on top of the window for a certain +amount of time *delayms* . This information is displayed on the window statusbar (the window must be +created with the CV_GUI_EXPANDED flags). + +@param winname Name of the window. +@param text Text to write on the window statusbar. +@param delayms Duration (in milliseconds) to display the text. If this function is called before +the previous text timed out, the timer is restarted and the text is updated. If this value is +zero, the text never disappears. + */ +CV_EXPORTS_W void displayStatusBar(const String& winname, const String& text, int delayms = 0); + +/** @brief Saves parameters of the specified window. + +The function saveWindowParameters saves size, location, flags, trackbars value, zoom and panning +location of the window windowName. + +@param windowName Name of the window. + */ +CV_EXPORTS void saveWindowParameters(const String& windowName); + +/** @brief Loads parameters of the specified window. + +The function loadWindowParameters loads size, location, flags, trackbars value, zoom and panning +location of the window windowName. + +@param windowName Name of the window. + */ +CV_EXPORTS void loadWindowParameters(const String& windowName); + +CV_EXPORTS int startLoop(int (*pt2Func)(int argc, char *argv[]), int argc, char* argv[]); + +CV_EXPORTS void stopLoop(); + +/** @brief Attaches a button to the control panel. + +The function createButton attaches a button to the control panel. Each button is added to a +buttonbar to the right of the last button. A new buttonbar is created if nothing was attached to the +control panel before, or if the last element attached to the control panel was a trackbar or if the +QT_NEW_BUTTONBAR flag is added to the type. + +See below various examples of the cv::createButton function call: : +@code + createButton(NULL,callbackButton);//create a push button "button 0", that will call callbackButton. + createButton("button2",callbackButton,NULL,QT_CHECKBOX,0); + createButton("button3",callbackButton,&value); + createButton("button5",callbackButton1,NULL,QT_RADIOBOX); + createButton("button6",callbackButton2,NULL,QT_PUSH_BUTTON,1); + createButton("button6",callbackButton2,NULL,QT_PUSH_BUTTON|QT_NEW_BUTTONBAR);// create a push button in a new row +@endcode + +@param bar_name Name of the button. +@param on_change Pointer to the function to be called every time the button changes its state. +This function should be prototyped as void Foo(int state,\*void); . *state* is the current state +of the button. It could be -1 for a push button, 0 or 1 for a check/radio box button. +@param userdata Pointer passed to the callback function. +@param type Optional type of the button. Available types are: (cv::QtButtonTypes) +@param initial_button_state Default state of the button. Use for checkbox and radiobox. Its +value could be 0 or 1. (__Optional__) +*/ +CV_EXPORTS int createButton( const String& bar_name, ButtonCallback on_change, + void* userdata = 0, int type = QT_PUSH_BUTTON, + bool initial_button_state = false); + +//! @} highgui_qt + +//! @} highgui + +} // cv + +#ifndef DISABLE_OPENCV_24_COMPATIBILITY +#include "opencv2/highgui/highgui_c.h" +#endif + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/highgui/highgui.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/highgui/highgui.hpp new file mode 100644 index 0000000000000000000000000000000000000000..160c9cf4af245e37dfc705aee5402d1cb365c46a --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/highgui/highgui.hpp @@ -0,0 +1,48 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifdef __OPENCV_BUILD +#error this is a compatibility header which should not be used inside the OpenCV library +#endif + +#include "opencv2/highgui.hpp" diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/highgui/highgui_c.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/highgui/highgui_c.h new file mode 100644 index 0000000000000000000000000000000000000000..1eb414a76cae3da5b6dcc25be1d305ec4bab77a8 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/highgui/highgui_c.h @@ -0,0 +1,260 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// Intel License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000, Intel Corporation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of Intel Corporation may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_HIGHGUI_H +#define OPENCV_HIGHGUI_H + +#include "opencv2/core/core_c.h" +#include "opencv2/imgproc/imgproc_c.h" +#ifdef HAVE_OPENCV_IMGCODECS +#include "opencv2/imgcodecs/imgcodecs_c.h" +#endif +#ifdef HAVE_OPENCV_VIDEOIO +#include "opencv2/videoio/videoio_c.h" +#endif + +#ifdef __cplusplus +extern "C" { +#endif /* __cplusplus */ + +/** @addtogroup highgui_c + @{ + */ + +/****************************************************************************************\ +* Basic GUI functions * +\****************************************************************************************/ +//YV +//-----------New for Qt +/* For font */ +enum { CV_FONT_LIGHT = 25,//QFont::Light, + CV_FONT_NORMAL = 50,//QFont::Normal, + CV_FONT_DEMIBOLD = 63,//QFont::DemiBold, + CV_FONT_BOLD = 75,//QFont::Bold, + CV_FONT_BLACK = 87 //QFont::Black +}; + +enum { CV_STYLE_NORMAL = 0,//QFont::StyleNormal, + CV_STYLE_ITALIC = 1,//QFont::StyleItalic, + CV_STYLE_OBLIQUE = 2 //QFont::StyleOblique +}; +/* ---------*/ + +//for color cvScalar(blue_component, green_component, red_component[, alpha_component]) +//and alpha= 0 <-> 0xFF (not transparent <-> transparent) +CVAPI(CvFont) cvFontQt(const char* nameFont, int pointSize CV_DEFAULT(-1), CvScalar color CV_DEFAULT(cvScalarAll(0)), int weight CV_DEFAULT(CV_FONT_NORMAL), int style CV_DEFAULT(CV_STYLE_NORMAL), int spacing CV_DEFAULT(0)); + +CVAPI(void) cvAddText(const CvArr* img, const char* text, CvPoint org, CvFont *arg2); + +CVAPI(void) cvDisplayOverlay(const char* name, const char* text, int delayms CV_DEFAULT(0)); +CVAPI(void) cvDisplayStatusBar(const char* name, const char* text, int delayms CV_DEFAULT(0)); + +CVAPI(void) cvSaveWindowParameters(const char* name); +CVAPI(void) cvLoadWindowParameters(const char* name); +CVAPI(int) cvStartLoop(int (*pt2Func)(int argc, char *argv[]), int argc, char* argv[]); +CVAPI(void) cvStopLoop( void ); + +typedef void (CV_CDECL *CvButtonCallback)(int state, void* userdata); +enum {CV_PUSH_BUTTON = 0, CV_CHECKBOX = 1, CV_RADIOBOX = 2}; +CVAPI(int) cvCreateButton( const char* button_name CV_DEFAULT(NULL),CvButtonCallback on_change CV_DEFAULT(NULL), void* userdata CV_DEFAULT(NULL) , int button_type CV_DEFAULT(CV_PUSH_BUTTON), int initial_button_state CV_DEFAULT(0)); +//---------------------- + + +/* this function is used to set some external parameters in case of X Window */ +CVAPI(int) cvInitSystem( int argc, char** argv ); + +CVAPI(int) cvStartWindowThread( void ); + +// --------- YV --------- +enum +{ + //These 3 flags are used by cvSet/GetWindowProperty + CV_WND_PROP_FULLSCREEN = 0, //to change/get window's fullscreen property + CV_WND_PROP_AUTOSIZE = 1, //to change/get window's autosize property + CV_WND_PROP_ASPECTRATIO= 2, //to change/get window's aspectratio property + CV_WND_PROP_OPENGL = 3, //to change/get window's opengl support + CV_WND_PROP_VISIBLE = 4, + + //These 2 flags are used by cvNamedWindow and cvSet/GetWindowProperty + CV_WINDOW_NORMAL = 0x00000000, //the user can resize the window (no constraint) / also use to switch a fullscreen window to a normal size + CV_WINDOW_AUTOSIZE = 0x00000001, //the user cannot resize the window, the size is constrainted by the image displayed + CV_WINDOW_OPENGL = 0x00001000, //window with opengl support + + //Those flags are only for Qt + CV_GUI_EXPANDED = 0x00000000, //status bar and tool bar + CV_GUI_NORMAL = 0x00000010, //old fashious way + + //These 3 flags are used by cvNamedWindow and cvSet/GetWindowProperty + CV_WINDOW_FULLSCREEN = 1,//change the window to fullscreen + CV_WINDOW_FREERATIO = 0x00000100,//the image expends as much as it can (no ratio constraint) + CV_WINDOW_KEEPRATIO = 0x00000000//the ration image is respected. +}; + +/* create window */ +CVAPI(int) cvNamedWindow( const char* name, int flags CV_DEFAULT(CV_WINDOW_AUTOSIZE) ); + +/* Set and Get Property of the window */ +CVAPI(void) cvSetWindowProperty(const char* name, int prop_id, double prop_value); +CVAPI(double) cvGetWindowProperty(const char* name, int prop_id); + +/* Get window image rectangle coordinates, width and height */ +CVAPI(cv::Rect)cvGetWindowImageRect(const char* name); + +/* display image within window (highgui windows remember their content) */ +CVAPI(void) cvShowImage( const char* name, const CvArr* image ); + +/* resize/move window */ +CVAPI(void) cvResizeWindow( const char* name, int width, int height ); +CVAPI(void) cvMoveWindow( const char* name, int x, int y ); + + +/* destroy window and all the trackers associated with it */ +CVAPI(void) cvDestroyWindow( const char* name ); + +CVAPI(void) cvDestroyAllWindows(void); + +/* get native window handle (HWND in case of Win32 and Widget in case of X Window) */ +CVAPI(void*) cvGetWindowHandle( const char* name ); + +/* get name of highgui window given its native handle */ +CVAPI(const char*) cvGetWindowName( void* window_handle ); + + +typedef void (CV_CDECL *CvTrackbarCallback)(int pos); + +/* create trackbar and display it on top of given window, set callback */ +CVAPI(int) cvCreateTrackbar( const char* trackbar_name, const char* window_name, + int* value, int count, CvTrackbarCallback on_change CV_DEFAULT(NULL)); + +typedef void (CV_CDECL *CvTrackbarCallback2)(int pos, void* userdata); + +CVAPI(int) cvCreateTrackbar2( const char* trackbar_name, const char* window_name, + int* value, int count, CvTrackbarCallback2 on_change, + void* userdata CV_DEFAULT(0)); + +/* retrieve or set trackbar position */ +CVAPI(int) cvGetTrackbarPos( const char* trackbar_name, const char* window_name ); +CVAPI(void) cvSetTrackbarPos( const char* trackbar_name, const char* window_name, int pos ); +CVAPI(void) cvSetTrackbarMax(const char* trackbar_name, const char* window_name, int maxval); +CVAPI(void) cvSetTrackbarMin(const char* trackbar_name, const char* window_name, int minval); + +enum +{ + CV_EVENT_MOUSEMOVE =0, + CV_EVENT_LBUTTONDOWN =1, + CV_EVENT_RBUTTONDOWN =2, + CV_EVENT_MBUTTONDOWN =3, + CV_EVENT_LBUTTONUP =4, + CV_EVENT_RBUTTONUP =5, + CV_EVENT_MBUTTONUP =6, + CV_EVENT_LBUTTONDBLCLK =7, + CV_EVENT_RBUTTONDBLCLK =8, + CV_EVENT_MBUTTONDBLCLK =9, + CV_EVENT_MOUSEWHEEL =10, + CV_EVENT_MOUSEHWHEEL =11 +}; + +enum +{ + CV_EVENT_FLAG_LBUTTON =1, + CV_EVENT_FLAG_RBUTTON =2, + CV_EVENT_FLAG_MBUTTON =4, + CV_EVENT_FLAG_CTRLKEY =8, + CV_EVENT_FLAG_SHIFTKEY =16, + CV_EVENT_FLAG_ALTKEY =32 +}; + + +#define CV_GET_WHEEL_DELTA(flags) ((short)((flags >> 16) & 0xffff)) // upper 16 bits + +typedef void (CV_CDECL *CvMouseCallback )(int event, int x, int y, int flags, void* param); + +/* assign callback for mouse events */ +CVAPI(void) cvSetMouseCallback( const char* window_name, CvMouseCallback on_mouse, + void* param CV_DEFAULT(NULL)); + +/* wait for key event infinitely (delay<=0) or for "delay" milliseconds */ +CVAPI(int) cvWaitKey(int delay CV_DEFAULT(0)); + +// OpenGL support + +typedef void (CV_CDECL *CvOpenGlDrawCallback)(void* userdata); +CVAPI(void) cvSetOpenGlDrawCallback(const char* window_name, CvOpenGlDrawCallback callback, void* userdata CV_DEFAULT(NULL)); + +CVAPI(void) cvSetOpenGlContext(const char* window_name); +CVAPI(void) cvUpdateWindow(const char* window_name); + + +/****************************************************************************************\ + +* Obsolete functions/synonyms * +\****************************************************************************************/ + +#define cvAddSearchPath(path) +#define cvvInitSystem cvInitSystem +#define cvvNamedWindow cvNamedWindow +#define cvvShowImage cvShowImage +#define cvvResizeWindow cvResizeWindow +#define cvvDestroyWindow cvDestroyWindow +#define cvvCreateTrackbar cvCreateTrackbar +#define cvvAddSearchPath cvAddSearchPath +#define cvvWaitKey(name) cvWaitKey(0) +#define cvvWaitKeyEx(name,delay) cvWaitKey(delay) +#define HG_AUTOSIZE CV_WINDOW_AUTOSIZE +#define set_preprocess_func cvSetPreprocessFuncWin32 +#define set_postprocess_func cvSetPostprocessFuncWin32 + +#if defined _WIN32 + +CVAPI(void) cvSetPreprocessFuncWin32_(const void* callback); +CVAPI(void) cvSetPostprocessFuncWin32_(const void* callback); +#define cvSetPreprocessFuncWin32(callback) cvSetPreprocessFuncWin32_((const void*)(callback)) +#define cvSetPostprocessFuncWin32(callback) cvSetPostprocessFuncWin32_((const void*)(callback)) + +#endif + +/** @} highgui_c */ + +#ifdef __cplusplus +} +#endif + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/imgcodecs.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/imgcodecs.hpp new file mode 100644 index 0000000000000000000000000000000000000000..4e79518ae16fc03fd1db0f9c280e9290ab500bbb --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/imgcodecs.hpp @@ -0,0 +1,259 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_IMGCODECS_HPP +#define OPENCV_IMGCODECS_HPP + +#include "opencv2/core.hpp" + +/** + @defgroup imgcodecs Image file reading and writing + @{ + @defgroup imgcodecs_c C API + @defgroup imgcodecs_ios iOS glue + @} +*/ + +//////////////////////////////// image codec //////////////////////////////// +namespace cv +{ + +//! @addtogroup imgcodecs +//! @{ + +//! Imread flags +enum ImreadModes { + IMREAD_UNCHANGED = -1, //!< If set, return the loaded image as is (with alpha channel, otherwise it gets cropped). + IMREAD_GRAYSCALE = 0, //!< If set, always convert image to the single channel grayscale image (codec internal conversion). + IMREAD_COLOR = 1, //!< If set, always convert image to the 3 channel BGR color image. + IMREAD_ANYDEPTH = 2, //!< If set, return 16-bit/32-bit image when the input has the corresponding depth, otherwise convert it to 8-bit. + IMREAD_ANYCOLOR = 4, //!< If set, the image is read in any possible color format. + IMREAD_LOAD_GDAL = 8, //!< If set, use the gdal driver for loading the image. + IMREAD_REDUCED_GRAYSCALE_2 = 16, //!< If set, always convert image to the single channel grayscale image and the image size reduced 1/2. + IMREAD_REDUCED_COLOR_2 = 17, //!< If set, always convert image to the 3 channel BGR color image and the image size reduced 1/2. + IMREAD_REDUCED_GRAYSCALE_4 = 32, //!< If set, always convert image to the single channel grayscale image and the image size reduced 1/4. + IMREAD_REDUCED_COLOR_4 = 33, //!< If set, always convert image to the 3 channel BGR color image and the image size reduced 1/4. + IMREAD_REDUCED_GRAYSCALE_8 = 64, //!< If set, always convert image to the single channel grayscale image and the image size reduced 1/8. + IMREAD_REDUCED_COLOR_8 = 65, //!< If set, always convert image to the 3 channel BGR color image and the image size reduced 1/8. + IMREAD_IGNORE_ORIENTATION = 128 //!< If set, do not rotate the image according to EXIF's orientation flag. + }; + +//! Imwrite flags +enum ImwriteFlags { + IMWRITE_JPEG_QUALITY = 1, //!< For JPEG, it can be a quality from 0 to 100 (the higher is the better). Default value is 95. + IMWRITE_JPEG_PROGRESSIVE = 2, //!< Enable JPEG features, 0 or 1, default is False. + IMWRITE_JPEG_OPTIMIZE = 3, //!< Enable JPEG features, 0 or 1, default is False. + IMWRITE_JPEG_RST_INTERVAL = 4, //!< JPEG restart interval, 0 - 65535, default is 0 - no restart. + IMWRITE_JPEG_LUMA_QUALITY = 5, //!< Separate luma quality level, 0 - 100, default is 0 - don't use. + IMWRITE_JPEG_CHROMA_QUALITY = 6, //!< Separate chroma quality level, 0 - 100, default is 0 - don't use. + IMWRITE_PNG_COMPRESSION = 16, //!< For PNG, it can be the compression level from 0 to 9. A higher value means a smaller size and longer compression time. If specified, strategy is changed to IMWRITE_PNG_STRATEGY_DEFAULT (Z_DEFAULT_STRATEGY). Default value is 1 (best speed setting). + IMWRITE_PNG_STRATEGY = 17, //!< One of cv::ImwritePNGFlags, default is IMWRITE_PNG_STRATEGY_RLE. + IMWRITE_PNG_BILEVEL = 18, //!< Binary level PNG, 0 or 1, default is 0. + IMWRITE_PXM_BINARY = 32, //!< For PPM, PGM, or PBM, it can be a binary format flag, 0 or 1. Default value is 1. + IMWRITE_EXR_TYPE = (3 << 4) + 0, /* 48 */ //!< override EXR storage type (FLOAT (FP32) is default) + IMWRITE_WEBP_QUALITY = 64, //!< For WEBP, it can be a quality from 1 to 100 (the higher is the better). By default (without any parameter) and for quality above 100 the lossless compression is used. + IMWRITE_PAM_TUPLETYPE = 128,//!< For PAM, sets the TUPLETYPE field to the corresponding string value that is defined for the format + IMWRITE_TIFF_RESUNIT = 256,//!< For TIFF, use to specify which DPI resolution unit to set; see libtiff documentation for valid values + IMWRITE_TIFF_XDPI = 257,//!< For TIFF, use to specify the X direction DPI + IMWRITE_TIFF_YDPI = 258 //!< For TIFF, use to specify the Y direction DPI + }; + +enum ImwriteEXRTypeFlags { + /*IMWRITE_EXR_TYPE_UNIT = 0, //!< not supported */ + IMWRITE_EXR_TYPE_HALF = 1, //!< store as HALF (FP16) + IMWRITE_EXR_TYPE_FLOAT = 2 //!< store as FP32 (default) + }; + +//! Imwrite PNG specific flags used to tune the compression algorithm. +/** These flags will be modify the way of PNG image compression and will be passed to the underlying zlib processing stage. + +- The effect of IMWRITE_PNG_STRATEGY_FILTERED is to force more Huffman coding and less string matching; it is somewhat intermediate between IMWRITE_PNG_STRATEGY_DEFAULT and IMWRITE_PNG_STRATEGY_HUFFMAN_ONLY. +- IMWRITE_PNG_STRATEGY_RLE is designed to be almost as fast as IMWRITE_PNG_STRATEGY_HUFFMAN_ONLY, but give better compression for PNG image data. +- The strategy parameter only affects the compression ratio but not the correctness of the compressed output even if it is not set appropriately. +- IMWRITE_PNG_STRATEGY_FIXED prevents the use of dynamic Huffman codes, allowing for a simpler decoder for special applications. +*/ +enum ImwritePNGFlags { + IMWRITE_PNG_STRATEGY_DEFAULT = 0, //!< Use this value for normal data. + IMWRITE_PNG_STRATEGY_FILTERED = 1, //!< Use this value for data produced by a filter (or predictor).Filtered data consists mostly of small values with a somewhat random distribution. In this case, the compression algorithm is tuned to compress them better. + IMWRITE_PNG_STRATEGY_HUFFMAN_ONLY = 2, //!< Use this value to force Huffman encoding only (no string match). + IMWRITE_PNG_STRATEGY_RLE = 3, //!< Use this value to limit match distances to one (run-length encoding). + IMWRITE_PNG_STRATEGY_FIXED = 4 //!< Using this value prevents the use of dynamic Huffman codes, allowing for a simpler decoder for special applications. + }; + +//! Imwrite PAM specific tupletype flags used to define the 'TUPETYPE' field of a PAM file. +enum ImwritePAMFlags { + IMWRITE_PAM_FORMAT_NULL = 0, + IMWRITE_PAM_FORMAT_BLACKANDWHITE = 1, + IMWRITE_PAM_FORMAT_GRAYSCALE = 2, + IMWRITE_PAM_FORMAT_GRAYSCALE_ALPHA = 3, + IMWRITE_PAM_FORMAT_RGB = 4, + IMWRITE_PAM_FORMAT_RGB_ALPHA = 5, + }; + +/** @brief Loads an image from a file. + +@anchor imread + +The function imread loads an image from the specified file and returns it. If the image cannot be +read (because of missing file, improper permissions, unsupported or invalid format), the function +returns an empty matrix ( Mat::data==NULL ). + +Currently, the following file formats are supported: + +- Windows bitmaps - \*.bmp, \*.dib (always supported) +- JPEG files - \*.jpeg, \*.jpg, \*.jpe (see the *Note* section) +- JPEG 2000 files - \*.jp2 (see the *Note* section) +- Portable Network Graphics - \*.png (see the *Note* section) +- WebP - \*.webp (see the *Note* section) +- Portable image format - \*.pbm, \*.pgm, \*.ppm \*.pxm, \*.pnm (always supported) +- Sun rasters - \*.sr, \*.ras (always supported) +- TIFF files - \*.tiff, \*.tif (see the *Note* section) +- OpenEXR Image files - \*.exr (see the *Note* section) +- Radiance HDR - \*.hdr, \*.pic (always supported) +- Raster and Vector geospatial data supported by GDAL (see the *Note* section) + +@note +- The function determines the type of an image by the content, not by the file extension. +- In the case of color images, the decoded images will have the channels stored in **B G R** order. +- When using IMREAD_GRAYSCALE, the codec's internal grayscale conversion will be used, if available. + Results may differ to the output of cvtColor() +- On Microsoft Windows\* OS and MacOSX\*, the codecs shipped with an OpenCV image (libjpeg, + libpng, libtiff, and libjasper) are used by default. So, OpenCV can always read JPEGs, PNGs, + and TIFFs. On MacOSX, there is also an option to use native MacOSX image readers. But beware + that currently these native image loaders give images with different pixel values because of + the color management embedded into MacOSX. +- On Linux\*, BSD flavors and other Unix-like open-source operating systems, OpenCV looks for + codecs supplied with an OS image. Install the relevant packages (do not forget the development + files, for example, "libjpeg-dev", in Debian\* and Ubuntu\*) to get the codec support or turn + on the OPENCV_BUILD_3RDPARTY_LIBS flag in CMake. +- In the case you set *WITH_GDAL* flag to true in CMake and @ref IMREAD_LOAD_GDAL to load the image, + then the [GDAL](http://www.gdal.org) driver will be used in order to decode the image, supporting + the following formats: [Raster](http://www.gdal.org/formats_list.html), + [Vector](http://www.gdal.org/ogr_formats.html). +- If EXIF information are embedded in the image file, the EXIF orientation will be taken into account + and thus the image will be rotated accordingly except if the flag @ref IMREAD_IGNORE_ORIENTATION is passed. +- By default number of pixels must be less than 2^30. Limit can be set using system + variable OPENCV_IO_MAX_IMAGE_PIXELS + +@param filename Name of file to be loaded. +@param flags Flag that can take values of cv::ImreadModes +*/ +CV_EXPORTS_W Mat imread( const String& filename, int flags = IMREAD_COLOR ); + +/** @brief Loads a multi-page image from a file. + +The function imreadmulti loads a multi-page image from the specified file into a vector of Mat objects. +@param filename Name of file to be loaded. +@param flags Flag that can take values of cv::ImreadModes, default with cv::IMREAD_ANYCOLOR. +@param mats A vector of Mat objects holding each page, if more than one. +@sa cv::imread +*/ +CV_EXPORTS_W bool imreadmulti(const String& filename, CV_OUT std::vector& mats, int flags = IMREAD_ANYCOLOR); + +/** @brief Saves an image to a specified file. + +The function imwrite saves the image to the specified file. The image format is chosen based on the +filename extension (see cv::imread for the list of extensions). In general, only 8-bit +single-channel or 3-channel (with 'BGR' channel order) images +can be saved using this function, with these exceptions: + +- 16-bit unsigned (CV_16U) images can be saved in the case of PNG, JPEG 2000, and TIFF formats +- 32-bit float (CV_32F) images can be saved in TIFF, OpenEXR, and Radiance HDR formats; 3-channel +(CV_32FC3) TIFF images will be saved using the LogLuv high dynamic range encoding (4 bytes per pixel) +- PNG images with an alpha channel can be saved using this function. To do this, create +8-bit (or 16-bit) 4-channel image BGRA, where the alpha channel goes last. Fully transparent pixels +should have alpha set to 0, fully opaque pixels should have alpha set to 255/65535 (see the code sample below). + +If the format, depth or channel order is different, use +Mat::convertTo and cv::cvtColor to convert it before saving. Or, use the universal FileStorage I/O +functions to save the image to XML or YAML format. + +The sample below shows how to create a BGRA image and save it to a PNG file. It also demonstrates how to set custom +compression parameters: +@include snippets/imgcodecs_imwrite.cpp +@param filename Name of the file. +@param img Image to be saved. +@param params Format-specific parameters encoded as pairs (paramId_1, paramValue_1, paramId_2, paramValue_2, ... .) see cv::ImwriteFlags +*/ +CV_EXPORTS_W bool imwrite( const String& filename, InputArray img, + const std::vector& params = std::vector()); + +/** @brief Reads an image from a buffer in memory. + +The function imdecode reads an image from the specified buffer in the memory. If the buffer is too short or +contains invalid data, the function returns an empty matrix ( Mat::data==NULL ). + +See cv::imread for the list of supported formats and flags description. + +@note In the case of color images, the decoded images will have the channels stored in **B G R** order. +@param buf Input array or vector of bytes. +@param flags The same flags as in cv::imread, see cv::ImreadModes. +*/ +CV_EXPORTS_W Mat imdecode( InputArray buf, int flags ); + +/** @overload +@param buf +@param flags +@param dst The optional output placeholder for the decoded matrix. It can save the image +reallocations when the function is called repeatedly for images of the same size. +*/ +CV_EXPORTS Mat imdecode( InputArray buf, int flags, Mat* dst); + +/** @brief Encodes an image into a memory buffer. + +The function imencode compresses the image and stores it in the memory buffer that is resized to fit the +result. See cv::imwrite for the list of supported formats and flags description. + +@param ext File extension that defines the output format. +@param img Image to be written. +@param buf Output buffer resized to fit the compressed image. +@param params Format-specific parameters. See cv::imwrite and cv::ImwriteFlags. +*/ +CV_EXPORTS_W bool imencode( const String& ext, InputArray img, + CV_OUT std::vector& buf, + const std::vector& params = std::vector()); + +//! @} imgcodecs + +} // cv + +#endif //OPENCV_IMGCODECS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/imgcodecs/imgcodecs.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/imgcodecs/imgcodecs.hpp new file mode 100644 index 0000000000000000000000000000000000000000..a3cd232645465f4c8148d1fcd3d9fde6eddddffb --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/imgcodecs/imgcodecs.hpp @@ -0,0 +1,48 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifdef __OPENCV_BUILD +#error this is a compatibility header which should not be used inside the OpenCV library +#endif + +#include "opencv2/imgcodecs.hpp" diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/imgcodecs/imgcodecs_c.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/imgcodecs/imgcodecs_c.h new file mode 100644 index 0000000000000000000000000000000000000000..c36dac33435605d126b3dd683b7edb72110e3ae8 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/imgcodecs/imgcodecs_c.h @@ -0,0 +1,149 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// Intel License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000, Intel Corporation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of Intel Corporation may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_IMGCODECS_H +#define OPENCV_IMGCODECS_H + +#include "opencv2/core/core_c.h" + +#ifdef __cplusplus +extern "C" { +#endif /* __cplusplus */ + +/** @addtogroup imgcodecs_c + @{ + */ + +enum +{ +/* 8bit, color or not */ + CV_LOAD_IMAGE_UNCHANGED =-1, +/* 8bit, gray */ + CV_LOAD_IMAGE_GRAYSCALE =0, +/* ?, color */ + CV_LOAD_IMAGE_COLOR =1, +/* any depth, ? */ + CV_LOAD_IMAGE_ANYDEPTH =2, +/* ?, any color */ + CV_LOAD_IMAGE_ANYCOLOR =4, +/* ?, no rotate */ + CV_LOAD_IMAGE_IGNORE_ORIENTATION =128 +}; + +/* load image from file + iscolor can be a combination of above flags where CV_LOAD_IMAGE_UNCHANGED + overrides the other flags + using CV_LOAD_IMAGE_ANYCOLOR alone is equivalent to CV_LOAD_IMAGE_UNCHANGED + unless CV_LOAD_IMAGE_ANYDEPTH is specified images are converted to 8bit +*/ +CVAPI(IplImage*) cvLoadImage( const char* filename, int iscolor CV_DEFAULT(CV_LOAD_IMAGE_COLOR)); +CVAPI(CvMat*) cvLoadImageM( const char* filename, int iscolor CV_DEFAULT(CV_LOAD_IMAGE_COLOR)); + +enum +{ + CV_IMWRITE_JPEG_QUALITY =1, + CV_IMWRITE_JPEG_PROGRESSIVE =2, + CV_IMWRITE_JPEG_OPTIMIZE =3, + CV_IMWRITE_JPEG_RST_INTERVAL =4, + CV_IMWRITE_JPEG_LUMA_QUALITY =5, + CV_IMWRITE_JPEG_CHROMA_QUALITY =6, + CV_IMWRITE_PNG_COMPRESSION =16, + CV_IMWRITE_PNG_STRATEGY =17, + CV_IMWRITE_PNG_BILEVEL =18, + CV_IMWRITE_PNG_STRATEGY_DEFAULT =0, + CV_IMWRITE_PNG_STRATEGY_FILTERED =1, + CV_IMWRITE_PNG_STRATEGY_HUFFMAN_ONLY =2, + CV_IMWRITE_PNG_STRATEGY_RLE =3, + CV_IMWRITE_PNG_STRATEGY_FIXED =4, + CV_IMWRITE_PXM_BINARY =32, + CV_IMWRITE_EXR_TYPE = 48, + CV_IMWRITE_WEBP_QUALITY =64, + CV_IMWRITE_PAM_TUPLETYPE = 128, + CV_IMWRITE_PAM_FORMAT_NULL = 0, + CV_IMWRITE_PAM_FORMAT_BLACKANDWHITE = 1, + CV_IMWRITE_PAM_FORMAT_GRAYSCALE = 2, + CV_IMWRITE_PAM_FORMAT_GRAYSCALE_ALPHA = 3, + CV_IMWRITE_PAM_FORMAT_RGB = 4, + CV_IMWRITE_PAM_FORMAT_RGB_ALPHA = 5, +}; + + + +/* save image to file */ +CVAPI(int) cvSaveImage( const char* filename, const CvArr* image, + const int* params CV_DEFAULT(0) ); + +/* decode image stored in the buffer */ +CVAPI(IplImage*) cvDecodeImage( const CvMat* buf, int iscolor CV_DEFAULT(CV_LOAD_IMAGE_COLOR)); +CVAPI(CvMat*) cvDecodeImageM( const CvMat* buf, int iscolor CV_DEFAULT(CV_LOAD_IMAGE_COLOR)); + +/* encode image and store the result as a byte vector (single-row 8uC1 matrix) */ +CVAPI(CvMat*) cvEncodeImage( const char* ext, const CvArr* image, + const int* params CV_DEFAULT(0) ); + +enum +{ + CV_CVTIMG_FLIP =1, + CV_CVTIMG_SWAP_RB =2 +}; + +/* utility function: convert one image to another with optional vertical flip */ +CVAPI(void) cvConvertImage( const CvArr* src, CvArr* dst, int flags CV_DEFAULT(0)); + +CVAPI(int) cvHaveImageReader(const char* filename); +CVAPI(int) cvHaveImageWriter(const char* filename); + + +/****************************************************************************************\ +* Obsolete functions/synonyms * +\****************************************************************************************/ + +#define cvvLoadImage(name) cvLoadImage((name),1) +#define cvvSaveImage cvSaveImage +#define cvvConvertImage cvConvertImage + +/** @} imgcodecs_c */ + +#ifdef __cplusplus +} +#endif + +#endif // OPENCV_IMGCODECS_H diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/imgcodecs/ios.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/imgcodecs/ios.h new file mode 100644 index 0000000000000000000000000000000000000000..a90c6d37a86c8bd6fd3c93d7b89f3c52b5ffbd96 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/imgcodecs/ios.h @@ -0,0 +1,57 @@ + +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#import +#import +#import +#import +#include "opencv2/core/core.hpp" + +//! @addtogroup imgcodecs_ios +//! @{ + +CV_EXPORTS UIImage* MatToUIImage(const cv::Mat& image); +CV_EXPORTS void UIImageToMat(const UIImage* image, + cv::Mat& m, bool alphaExist = false); + +//! @} diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/imgproc.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/imgproc.hpp new file mode 100644 index 0000000000000000000000000000000000000000..14a63f3671cc8c91516ffa36219dcc26f559c02c --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/imgproc.hpp @@ -0,0 +1,4915 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_IMGPROC_HPP +#define OPENCV_IMGPROC_HPP + +#include "opencv2/core.hpp" + +/** + @defgroup imgproc Image Processing + +This module includes image-processing functions. + + @{ + @defgroup imgproc_filter Image Filtering + +Functions and classes described in this section are used to perform various linear or non-linear +filtering operations on 2D images (represented as Mat's). It means that for each pixel location +\f$(x,y)\f$ in the source image (normally, rectangular), its neighborhood is considered and used to +compute the response. In case of a linear filter, it is a weighted sum of pixel values. In case of +morphological operations, it is the minimum or maximum values, and so on. The computed response is +stored in the destination image at the same location \f$(x,y)\f$. It means that the output image +will be of the same size as the input image. Normally, the functions support multi-channel arrays, +in which case every channel is processed independently. Therefore, the output image will also have +the same number of channels as the input one. + +Another common feature of the functions and classes described in this section is that, unlike +simple arithmetic functions, they need to extrapolate values of some non-existing pixels. For +example, if you want to smooth an image using a Gaussian \f$3 \times 3\f$ filter, then, when +processing the left-most pixels in each row, you need pixels to the left of them, that is, outside +of the image. You can let these pixels be the same as the left-most image pixels ("replicated +border" extrapolation method), or assume that all the non-existing pixels are zeros ("constant +border" extrapolation method), and so on. OpenCV enables you to specify the extrapolation method. +For details, see #BorderTypes + +@anchor filter_depths +### Depth combinations +Input depth (src.depth()) | Output depth (ddepth) +--------------------------|---------------------- +CV_8U | -1/CV_16S/CV_32F/CV_64F +CV_16U/CV_16S | -1/CV_32F/CV_64F +CV_32F | -1/CV_32F/CV_64F +CV_64F | -1/CV_64F + +@note when ddepth=-1, the output image will have the same depth as the source. + + @defgroup imgproc_transform Geometric Image Transformations + +The functions in this section perform various geometrical transformations of 2D images. They do not +change the image content but deform the pixel grid and map this deformed grid to the destination +image. In fact, to avoid sampling artifacts, the mapping is done in the reverse order, from +destination to the source. That is, for each pixel \f$(x, y)\f$ of the destination image, the +functions compute coordinates of the corresponding "donor" pixel in the source image and copy the +pixel value: + +\f[\texttt{dst} (x,y)= \texttt{src} (f_x(x,y), f_y(x,y))\f] + +In case when you specify the forward mapping \f$\left: \texttt{src} \rightarrow +\texttt{dst}\f$, the OpenCV functions first compute the corresponding inverse mapping +\f$\left: \texttt{dst} \rightarrow \texttt{src}\f$ and then use the above formula. + +The actual implementations of the geometrical transformations, from the most generic remap and to +the simplest and the fastest resize, need to solve two main problems with the above formula: + +- Extrapolation of non-existing pixels. Similarly to the filtering functions described in the +previous section, for some \f$(x,y)\f$, either one of \f$f_x(x,y)\f$, or \f$f_y(x,y)\f$, or both +of them may fall outside of the image. In this case, an extrapolation method needs to be used. +OpenCV provides the same selection of extrapolation methods as in the filtering functions. In +addition, it provides the method #BORDER_TRANSPARENT. This means that the corresponding pixels in +the destination image will not be modified at all. + +- Interpolation of pixel values. Usually \f$f_x(x,y)\f$ and \f$f_y(x,y)\f$ are floating-point +numbers. This means that \f$\left\f$ can be either an affine or perspective +transformation, or radial lens distortion correction, and so on. So, a pixel value at fractional +coordinates needs to be retrieved. In the simplest case, the coordinates can be just rounded to the +nearest integer coordinates and the corresponding pixel can be used. This is called a +nearest-neighbor interpolation. However, a better result can be achieved by using more +sophisticated [interpolation methods](http://en.wikipedia.org/wiki/Multivariate_interpolation) , +where a polynomial function is fit into some neighborhood of the computed pixel \f$(f_x(x,y), +f_y(x,y))\f$, and then the value of the polynomial at \f$(f_x(x,y), f_y(x,y))\f$ is taken as the +interpolated pixel value. In OpenCV, you can choose between several interpolation methods. See +resize for details. + +@note The geometrical transformations do not work with `CV_8S` or `CV_32S` images. + + @defgroup imgproc_misc Miscellaneous Image Transformations + @defgroup imgproc_draw Drawing Functions + +Drawing functions work with matrices/images of arbitrary depth. The boundaries of the shapes can be +rendered with antialiasing (implemented only for 8-bit images for now). All the functions include +the parameter color that uses an RGB value (that may be constructed with the Scalar constructor ) +for color images and brightness for grayscale images. For color images, the channel ordering is +normally *Blue, Green, Red*. This is what imshow, imread, and imwrite expect. So, if you form a +color using the Scalar constructor, it should look like: + +\f[\texttt{Scalar} (blue \_ component, green \_ component, red \_ component[, alpha \_ component])\f] + +If you are using your own image rendering and I/O functions, you can use any channel ordering. The +drawing functions process each channel independently and do not depend on the channel order or even +on the used color space. The whole image can be converted from BGR to RGB or to a different color +space using cvtColor . + +If a drawn figure is partially or completely outside the image, the drawing functions clip it. Also, +many drawing functions can handle pixel coordinates specified with sub-pixel accuracy. This means +that the coordinates can be passed as fixed-point numbers encoded as integers. The number of +fractional bits is specified by the shift parameter and the real point coordinates are calculated as +\f$\texttt{Point}(x,y)\rightarrow\texttt{Point2f}(x*2^{-shift},y*2^{-shift})\f$ . This feature is +especially effective when rendering antialiased shapes. + +@note The functions do not support alpha-transparency when the target image is 4-channel. In this +case, the color[3] is simply copied to the repainted pixels. Thus, if you want to paint +semi-transparent shapes, you can paint them in a separate buffer and then blend it with the main +image. + + @defgroup imgproc_color_conversions Color Space Conversions + @defgroup imgproc_colormap ColorMaps in OpenCV + +The human perception isn't built for observing fine changes in grayscale images. Human eyes are more +sensitive to observing changes between colors, so you often need to recolor your grayscale images to +get a clue about them. OpenCV now comes with various colormaps to enhance the visualization in your +computer vision application. + +In OpenCV you only need applyColorMap to apply a colormap on a given image. The following sample +code reads the path to an image from command line, applies a Jet colormap on it and shows the +result: + +@include snippets/imgproc_applyColorMap.cpp + +@see #ColormapTypes + + @defgroup imgproc_subdiv2d Planar Subdivision + +The Subdiv2D class described in this section is used to perform various planar subdivision on +a set of 2D points (represented as vector of Point2f). OpenCV subdivides a plane into triangles +using the Delaunay's algorithm, which corresponds to the dual graph of the Voronoi diagram. +In the figure below, the Delaunay's triangulation is marked with black lines and the Voronoi +diagram with red lines. + +![Delaunay triangulation (black) and Voronoi (red)](pics/delaunay_voronoi.png) + +The subdivisions can be used for the 3D piece-wise transformation of a plane, morphing, fast +location of points on the plane, building special graphs (such as NNG,RNG), and so forth. + + @defgroup imgproc_hist Histograms + @defgroup imgproc_shape Structural Analysis and Shape Descriptors + @defgroup imgproc_motion Motion Analysis and Object Tracking + @defgroup imgproc_feature Feature Detection + @defgroup imgproc_object Object Detection + @defgroup imgproc_c C API + @defgroup imgproc_hal Hardware Acceleration Layer + @{ + @defgroup imgproc_hal_functions Functions + @defgroup imgproc_hal_interface Interface + @} + @} +*/ + +namespace cv +{ + +/** @addtogroup imgproc +@{ +*/ + +//! @addtogroup imgproc_filter +//! @{ + +//! type of morphological operation +enum MorphTypes{ + MORPH_ERODE = 0, //!< see #erode + MORPH_DILATE = 1, //!< see #dilate + MORPH_OPEN = 2, //!< an opening operation + //!< \f[\texttt{dst} = \mathrm{open} ( \texttt{src} , \texttt{element} )= \mathrm{dilate} ( \mathrm{erode} ( \texttt{src} , \texttt{element} ))\f] + MORPH_CLOSE = 3, //!< a closing operation + //!< \f[\texttt{dst} = \mathrm{close} ( \texttt{src} , \texttt{element} )= \mathrm{erode} ( \mathrm{dilate} ( \texttt{src} , \texttt{element} ))\f] + MORPH_GRADIENT = 4, //!< a morphological gradient + //!< \f[\texttt{dst} = \mathrm{morph\_grad} ( \texttt{src} , \texttt{element} )= \mathrm{dilate} ( \texttt{src} , \texttt{element} )- \mathrm{erode} ( \texttt{src} , \texttt{element} )\f] + MORPH_TOPHAT = 5, //!< "top hat" + //!< \f[\texttt{dst} = \mathrm{tophat} ( \texttt{src} , \texttt{element} )= \texttt{src} - \mathrm{open} ( \texttt{src} , \texttt{element} )\f] + MORPH_BLACKHAT = 6, //!< "black hat" + //!< \f[\texttt{dst} = \mathrm{blackhat} ( \texttt{src} , \texttt{element} )= \mathrm{close} ( \texttt{src} , \texttt{element} )- \texttt{src}\f] + MORPH_HITMISS = 7 //!< "hit or miss" + //!< .- Only supported for CV_8UC1 binary images. A tutorial can be found in the documentation +}; + +//! shape of the structuring element +enum MorphShapes { + MORPH_RECT = 0, //!< a rectangular structuring element: \f[E_{ij}=1\f] + MORPH_CROSS = 1, //!< a cross-shaped structuring element: + //!< \f[E_{ij} = \fork{1}{if i=\texttt{anchor.y} or j=\texttt{anchor.x}}{0}{otherwise}\f] + MORPH_ELLIPSE = 2 //!< an elliptic structuring element, that is, a filled ellipse inscribed + //!< into the rectangle Rect(0, 0, esize.width, 0.esize.height) +}; + +//! @} imgproc_filter + +//! @addtogroup imgproc_transform +//! @{ + +//! interpolation algorithm +enum InterpolationFlags{ + /** nearest neighbor interpolation */ + INTER_NEAREST = 0, + /** bilinear interpolation */ + INTER_LINEAR = 1, + /** bicubic interpolation */ + INTER_CUBIC = 2, + /** resampling using pixel area relation. It may be a preferred method for image decimation, as + it gives moire'-free results. But when the image is zoomed, it is similar to the INTER_NEAREST + method. */ + INTER_AREA = 3, + /** Lanczos interpolation over 8x8 neighborhood */ + INTER_LANCZOS4 = 4, + /** Bit exact bilinear interpolation */ + INTER_LINEAR_EXACT = 5, + /** mask for interpolation codes */ + INTER_MAX = 7, + /** flag, fills all of the destination image pixels. If some of them correspond to outliers in the + source image, they are set to zero */ + WARP_FILL_OUTLIERS = 8, + /** flag, inverse transformation + + For example, #linearPolar or #logPolar transforms: + - flag is __not__ set: \f$dst( \rho , \phi ) = src(x,y)\f$ + - flag is set: \f$dst(x,y) = src( \rho , \phi )\f$ + */ + WARP_INVERSE_MAP = 16 +}; + +/** \brief Specify the polar mapping mode +@sa warpPolar +*/ +enum WarpPolarMode +{ + WARP_POLAR_LINEAR = 0, ///< Remaps an image to/from polar space. + WARP_POLAR_LOG = 256 ///< Remaps an image to/from semilog-polar space. +}; + +enum InterpolationMasks { + INTER_BITS = 5, + INTER_BITS2 = INTER_BITS * 2, + INTER_TAB_SIZE = 1 << INTER_BITS, + INTER_TAB_SIZE2 = INTER_TAB_SIZE * INTER_TAB_SIZE + }; + +//! @} imgproc_transform + +//! @addtogroup imgproc_misc +//! @{ + +//! Distance types for Distance Transform and M-estimators +//! @see distanceTransform, fitLine +enum DistanceTypes { + DIST_USER = -1, //!< User defined distance + DIST_L1 = 1, //!< distance = |x1-x2| + |y1-y2| + DIST_L2 = 2, //!< the simple euclidean distance + DIST_C = 3, //!< distance = max(|x1-x2|,|y1-y2|) + DIST_L12 = 4, //!< L1-L2 metric: distance = 2(sqrt(1+x*x/2) - 1)) + DIST_FAIR = 5, //!< distance = c^2(|x|/c-log(1+|x|/c)), c = 1.3998 + DIST_WELSCH = 6, //!< distance = c^2/2(1-exp(-(x/c)^2)), c = 2.9846 + DIST_HUBER = 7 //!< distance = |x| \texttt{thresh}\)}{0}{otherwise}\f] + THRESH_BINARY_INV = 1, //!< \f[\texttt{dst} (x,y) = \fork{0}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{\texttt{maxval}}{otherwise}\f] + THRESH_TRUNC = 2, //!< \f[\texttt{dst} (x,y) = \fork{\texttt{threshold}}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{\texttt{src}(x,y)}{otherwise}\f] + THRESH_TOZERO = 3, //!< \f[\texttt{dst} (x,y) = \fork{\texttt{src}(x,y)}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{0}{otherwise}\f] + THRESH_TOZERO_INV = 4, //!< \f[\texttt{dst} (x,y) = \fork{0}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{\texttt{src}(x,y)}{otherwise}\f] + THRESH_MASK = 7, + THRESH_OTSU = 8, //!< flag, use Otsu algorithm to choose the optimal threshold value + THRESH_TRIANGLE = 16 //!< flag, use Triangle algorithm to choose the optimal threshold value +}; + +//! adaptive threshold algorithm +//! @see adaptiveThreshold +enum AdaptiveThresholdTypes { + /** the threshold value \f$T(x,y)\f$ is a mean of the \f$\texttt{blockSize} \times + \texttt{blockSize}\f$ neighborhood of \f$(x, y)\f$ minus C */ + ADAPTIVE_THRESH_MEAN_C = 0, + /** the threshold value \f$T(x, y)\f$ is a weighted sum (cross-correlation with a Gaussian + window) of the \f$\texttt{blockSize} \times \texttt{blockSize}\f$ neighborhood of \f$(x, y)\f$ + minus C . The default sigma (standard deviation) is used for the specified blockSize . See + #getGaussianKernel*/ + ADAPTIVE_THRESH_GAUSSIAN_C = 1 +}; + +//! cv::undistort mode +enum UndistortTypes { + PROJ_SPHERICAL_ORTHO = 0, + PROJ_SPHERICAL_EQRECT = 1 + }; + +//! class of the pixel in GrabCut algorithm +enum GrabCutClasses { + GC_BGD = 0, //!< an obvious background pixels + GC_FGD = 1, //!< an obvious foreground (object) pixel + GC_PR_BGD = 2, //!< a possible background pixel + GC_PR_FGD = 3 //!< a possible foreground pixel +}; + +//! GrabCut algorithm flags +enum GrabCutModes { + /** The function initializes the state and the mask using the provided rectangle. After that it + runs iterCount iterations of the algorithm. */ + GC_INIT_WITH_RECT = 0, + /** The function initializes the state using the provided mask. Note that GC_INIT_WITH_RECT + and GC_INIT_WITH_MASK can be combined. Then, all the pixels outside of the ROI are + automatically initialized with GC_BGD .*/ + GC_INIT_WITH_MASK = 1, + /** The value means that the algorithm should just resume. */ + GC_EVAL = 2, + /** The value means that the algorithm should just run the grabCut algorithm (a single iteration) with the fixed model */ + GC_EVAL_FREEZE_MODEL = 3 +}; + +//! distanceTransform algorithm flags +enum DistanceTransformLabelTypes { + /** each connected component of zeros in src (as well as all the non-zero pixels closest to the + connected component) will be assigned the same label */ + DIST_LABEL_CCOMP = 0, + /** each zero pixel (and all the non-zero pixels closest to it) gets its own label. */ + DIST_LABEL_PIXEL = 1 +}; + +//! floodfill algorithm flags +enum FloodFillFlags { + /** If set, the difference between the current pixel and seed pixel is considered. Otherwise, + the difference between neighbor pixels is considered (that is, the range is floating). */ + FLOODFILL_FIXED_RANGE = 1 << 16, + /** If set, the function does not change the image ( newVal is ignored), and only fills the + mask with the value specified in bits 8-16 of flags as described above. This option only make + sense in function variants that have the mask parameter. */ + FLOODFILL_MASK_ONLY = 1 << 17 +}; + +//! @} imgproc_misc + +//! @addtogroup imgproc_shape +//! @{ + +//! connected components algorithm output formats +enum ConnectedComponentsTypes { + CC_STAT_LEFT = 0, //!< The leftmost (x) coordinate which is the inclusive start of the bounding + //!< box in the horizontal direction. + CC_STAT_TOP = 1, //!< The topmost (y) coordinate which is the inclusive start of the bounding + //!< box in the vertical direction. + CC_STAT_WIDTH = 2, //!< The horizontal size of the bounding box + CC_STAT_HEIGHT = 3, //!< The vertical size of the bounding box + CC_STAT_AREA = 4, //!< The total area (in pixels) of the connected component + CC_STAT_MAX = 5 +}; + +//! connected components algorithm +enum ConnectedComponentsAlgorithmsTypes { + CCL_WU = 0, //!< SAUF algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity + CCL_DEFAULT = -1, //!< BBDT algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity + CCL_GRANA = 1 //!< BBDT algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity +}; + +//! mode of the contour retrieval algorithm +enum RetrievalModes { + /** retrieves only the extreme outer contours. It sets `hierarchy[i][2]=hierarchy[i][3]=-1` for + all the contours. */ + RETR_EXTERNAL = 0, + /** retrieves all of the contours without establishing any hierarchical relationships. */ + RETR_LIST = 1, + /** retrieves all of the contours and organizes them into a two-level hierarchy. At the top + level, there are external boundaries of the components. At the second level, there are + boundaries of the holes. If there is another contour inside a hole of a connected component, it + is still put at the top level. */ + RETR_CCOMP = 2, + /** retrieves all of the contours and reconstructs a full hierarchy of nested contours.*/ + RETR_TREE = 3, + RETR_FLOODFILL = 4 //!< +}; + +//! the contour approximation algorithm +enum ContourApproximationModes { + /** stores absolutely all the contour points. That is, any 2 subsequent points (x1,y1) and + (x2,y2) of the contour will be either horizontal, vertical or diagonal neighbors, that is, + max(abs(x1-x2),abs(y2-y1))==1. */ + CHAIN_APPROX_NONE = 1, + /** compresses horizontal, vertical, and diagonal segments and leaves only their end points. + For example, an up-right rectangular contour is encoded with 4 points. */ + CHAIN_APPROX_SIMPLE = 2, + /** applies one of the flavors of the Teh-Chin chain approximation algorithm @cite TehChin89 */ + CHAIN_APPROX_TC89_L1 = 3, + /** applies one of the flavors of the Teh-Chin chain approximation algorithm @cite TehChin89 */ + CHAIN_APPROX_TC89_KCOS = 4 +}; + +/** @brief Shape matching methods + +\f$A\f$ denotes object1,\f$B\f$ denotes object2 + +\f$\begin{array}{l} m^A_i = \mathrm{sign} (h^A_i) \cdot \log{h^A_i} \\ m^B_i = \mathrm{sign} (h^B_i) \cdot \log{h^B_i} \end{array}\f$ + +and \f$h^A_i, h^B_i\f$ are the Hu moments of \f$A\f$ and \f$B\f$ , respectively. +*/ +enum ShapeMatchModes { + CONTOURS_MATCH_I1 =1, //!< \f[I_1(A,B) = \sum _{i=1...7} \left | \frac{1}{m^A_i} - \frac{1}{m^B_i} \right |\f] + CONTOURS_MATCH_I2 =2, //!< \f[I_2(A,B) = \sum _{i=1...7} \left | m^A_i - m^B_i \right |\f] + CONTOURS_MATCH_I3 =3 //!< \f[I_3(A,B) = \max _{i=1...7} \frac{ \left| m^A_i - m^B_i \right| }{ \left| m^A_i \right| }\f] +}; + +//! @} imgproc_shape + +//! @addtogroup imgproc_feature +//! @{ + +//! Variants of a Hough transform +enum HoughModes { + + /** classical or standard Hough transform. Every line is represented by two floating-point + numbers \f$(\rho, \theta)\f$ , where \f$\rho\f$ is a distance between (0,0) point and the line, + and \f$\theta\f$ is the angle between x-axis and the normal to the line. Thus, the matrix must + be (the created sequence will be) of CV_32FC2 type */ + HOUGH_STANDARD = 0, + /** probabilistic Hough transform (more efficient in case if the picture contains a few long + linear segments). It returns line segments rather than the whole line. Each segment is + represented by starting and ending points, and the matrix must be (the created sequence will + be) of the CV_32SC4 type. */ + HOUGH_PROBABILISTIC = 1, + /** multi-scale variant of the classical Hough transform. The lines are encoded the same way as + HOUGH_STANDARD. */ + HOUGH_MULTI_SCALE = 2, + HOUGH_GRADIENT = 3 //!< basically *21HT*, described in @cite Yuen90 +}; + +//! Variants of Line Segment %Detector +enum LineSegmentDetectorModes { + LSD_REFINE_NONE = 0, //!< No refinement applied + LSD_REFINE_STD = 1, //!< Standard refinement is applied. E.g. breaking arches into smaller straighter line approximations. + LSD_REFINE_ADV = 2 //!< Advanced refinement. Number of false alarms is calculated, lines are + //!< refined through increase of precision, decrement in size, etc. +}; + +//! @} imgproc_feature + +/** Histogram comparison methods + @ingroup imgproc_hist +*/ +enum HistCompMethods { + /** Correlation + \f[d(H_1,H_2) = \frac{\sum_I (H_1(I) - \bar{H_1}) (H_2(I) - \bar{H_2})}{\sqrt{\sum_I(H_1(I) - \bar{H_1})^2 \sum_I(H_2(I) - \bar{H_2})^2}}\f] + where + \f[\bar{H_k} = \frac{1}{N} \sum _J H_k(J)\f] + and \f$N\f$ is a total number of histogram bins. */ + HISTCMP_CORREL = 0, + /** Chi-Square + \f[d(H_1,H_2) = \sum _I \frac{\left(H_1(I)-H_2(I)\right)^2}{H_1(I)}\f] */ + HISTCMP_CHISQR = 1, + /** Intersection + \f[d(H_1,H_2) = \sum _I \min (H_1(I), H_2(I))\f] */ + HISTCMP_INTERSECT = 2, + /** Bhattacharyya distance + (In fact, OpenCV computes Hellinger distance, which is related to Bhattacharyya coefficient.) + \f[d(H_1,H_2) = \sqrt{1 - \frac{1}{\sqrt{\bar{H_1} \bar{H_2} N^2}} \sum_I \sqrt{H_1(I) \cdot H_2(I)}}\f] */ + HISTCMP_BHATTACHARYYA = 3, + HISTCMP_HELLINGER = HISTCMP_BHATTACHARYYA, //!< Synonym for HISTCMP_BHATTACHARYYA + /** Alternative Chi-Square + \f[d(H_1,H_2) = 2 * \sum _I \frac{\left(H_1(I)-H_2(I)\right)^2}{H_1(I)+H_2(I)}\f] + This alternative formula is regularly used for texture comparison. See e.g. @cite Puzicha1997 */ + HISTCMP_CHISQR_ALT = 4, + /** Kullback-Leibler divergence + \f[d(H_1,H_2) = \sum _I H_1(I) \log \left(\frac{H_1(I)}{H_2(I)}\right)\f] */ + HISTCMP_KL_DIV = 5 +}; + +/** the color conversion codes +@see @ref imgproc_color_conversions +@ingroup imgproc_color_conversions + */ +enum ColorConversionCodes { + COLOR_BGR2BGRA = 0, //!< add alpha channel to RGB or BGR image + COLOR_RGB2RGBA = COLOR_BGR2BGRA, + + COLOR_BGRA2BGR = 1, //!< remove alpha channel from RGB or BGR image + COLOR_RGBA2RGB = COLOR_BGRA2BGR, + + COLOR_BGR2RGBA = 2, //!< convert between RGB and BGR color spaces (with or without alpha channel) + COLOR_RGB2BGRA = COLOR_BGR2RGBA, + + COLOR_RGBA2BGR = 3, + COLOR_BGRA2RGB = COLOR_RGBA2BGR, + + COLOR_BGR2RGB = 4, + COLOR_RGB2BGR = COLOR_BGR2RGB, + + COLOR_BGRA2RGBA = 5, + COLOR_RGBA2BGRA = COLOR_BGRA2RGBA, + + COLOR_BGR2GRAY = 6, //!< convert between RGB/BGR and grayscale, @ref color_convert_rgb_gray "color conversions" + COLOR_RGB2GRAY = 7, + COLOR_GRAY2BGR = 8, + COLOR_GRAY2RGB = COLOR_GRAY2BGR, + COLOR_GRAY2BGRA = 9, + COLOR_GRAY2RGBA = COLOR_GRAY2BGRA, + COLOR_BGRA2GRAY = 10, + COLOR_RGBA2GRAY = 11, + + COLOR_BGR2BGR565 = 12, //!< convert between RGB/BGR and BGR565 (16-bit images) + COLOR_RGB2BGR565 = 13, + COLOR_BGR5652BGR = 14, + COLOR_BGR5652RGB = 15, + COLOR_BGRA2BGR565 = 16, + COLOR_RGBA2BGR565 = 17, + COLOR_BGR5652BGRA = 18, + COLOR_BGR5652RGBA = 19, + + COLOR_GRAY2BGR565 = 20, //!< convert between grayscale to BGR565 (16-bit images) + COLOR_BGR5652GRAY = 21, + + COLOR_BGR2BGR555 = 22, //!< convert between RGB/BGR and BGR555 (16-bit images) + COLOR_RGB2BGR555 = 23, + COLOR_BGR5552BGR = 24, + COLOR_BGR5552RGB = 25, + COLOR_BGRA2BGR555 = 26, + COLOR_RGBA2BGR555 = 27, + COLOR_BGR5552BGRA = 28, + COLOR_BGR5552RGBA = 29, + + COLOR_GRAY2BGR555 = 30, //!< convert between grayscale and BGR555 (16-bit images) + COLOR_BGR5552GRAY = 31, + + COLOR_BGR2XYZ = 32, //!< convert RGB/BGR to CIE XYZ, @ref color_convert_rgb_xyz "color conversions" + COLOR_RGB2XYZ = 33, + COLOR_XYZ2BGR = 34, + COLOR_XYZ2RGB = 35, + + COLOR_BGR2YCrCb = 36, //!< convert RGB/BGR to luma-chroma (aka YCC), @ref color_convert_rgb_ycrcb "color conversions" + COLOR_RGB2YCrCb = 37, + COLOR_YCrCb2BGR = 38, + COLOR_YCrCb2RGB = 39, + + COLOR_BGR2HSV = 40, //!< convert RGB/BGR to HSV (hue saturation value), @ref color_convert_rgb_hsv "color conversions" + COLOR_RGB2HSV = 41, + + COLOR_BGR2Lab = 44, //!< convert RGB/BGR to CIE Lab, @ref color_convert_rgb_lab "color conversions" + COLOR_RGB2Lab = 45, + + COLOR_BGR2Luv = 50, //!< convert RGB/BGR to CIE Luv, @ref color_convert_rgb_luv "color conversions" + COLOR_RGB2Luv = 51, + COLOR_BGR2HLS = 52, //!< convert RGB/BGR to HLS (hue lightness saturation), @ref color_convert_rgb_hls "color conversions" + COLOR_RGB2HLS = 53, + + COLOR_HSV2BGR = 54, //!< backward conversions to RGB/BGR + COLOR_HSV2RGB = 55, + + COLOR_Lab2BGR = 56, + COLOR_Lab2RGB = 57, + COLOR_Luv2BGR = 58, + COLOR_Luv2RGB = 59, + COLOR_HLS2BGR = 60, + COLOR_HLS2RGB = 61, + + COLOR_BGR2HSV_FULL = 66, + COLOR_RGB2HSV_FULL = 67, + COLOR_BGR2HLS_FULL = 68, + COLOR_RGB2HLS_FULL = 69, + + COLOR_HSV2BGR_FULL = 70, + COLOR_HSV2RGB_FULL = 71, + COLOR_HLS2BGR_FULL = 72, + COLOR_HLS2RGB_FULL = 73, + + COLOR_LBGR2Lab = 74, + COLOR_LRGB2Lab = 75, + COLOR_LBGR2Luv = 76, + COLOR_LRGB2Luv = 77, + + COLOR_Lab2LBGR = 78, + COLOR_Lab2LRGB = 79, + COLOR_Luv2LBGR = 80, + COLOR_Luv2LRGB = 81, + + COLOR_BGR2YUV = 82, //!< convert between RGB/BGR and YUV + COLOR_RGB2YUV = 83, + COLOR_YUV2BGR = 84, + COLOR_YUV2RGB = 85, + + //! YUV 4:2:0 family to RGB + COLOR_YUV2RGB_NV12 = 90, + COLOR_YUV2BGR_NV12 = 91, + COLOR_YUV2RGB_NV21 = 92, + COLOR_YUV2BGR_NV21 = 93, + COLOR_YUV420sp2RGB = COLOR_YUV2RGB_NV21, + COLOR_YUV420sp2BGR = COLOR_YUV2BGR_NV21, + + COLOR_YUV2RGBA_NV12 = 94, + COLOR_YUV2BGRA_NV12 = 95, + COLOR_YUV2RGBA_NV21 = 96, + COLOR_YUV2BGRA_NV21 = 97, + COLOR_YUV420sp2RGBA = COLOR_YUV2RGBA_NV21, + COLOR_YUV420sp2BGRA = COLOR_YUV2BGRA_NV21, + + COLOR_YUV2RGB_YV12 = 98, + COLOR_YUV2BGR_YV12 = 99, + COLOR_YUV2RGB_IYUV = 100, + COLOR_YUV2BGR_IYUV = 101, + COLOR_YUV2RGB_I420 = COLOR_YUV2RGB_IYUV, + COLOR_YUV2BGR_I420 = COLOR_YUV2BGR_IYUV, + COLOR_YUV420p2RGB = COLOR_YUV2RGB_YV12, + COLOR_YUV420p2BGR = COLOR_YUV2BGR_YV12, + + COLOR_YUV2RGBA_YV12 = 102, + COLOR_YUV2BGRA_YV12 = 103, + COLOR_YUV2RGBA_IYUV = 104, + COLOR_YUV2BGRA_IYUV = 105, + COLOR_YUV2RGBA_I420 = COLOR_YUV2RGBA_IYUV, + COLOR_YUV2BGRA_I420 = COLOR_YUV2BGRA_IYUV, + COLOR_YUV420p2RGBA = COLOR_YUV2RGBA_YV12, + COLOR_YUV420p2BGRA = COLOR_YUV2BGRA_YV12, + + COLOR_YUV2GRAY_420 = 106, + COLOR_YUV2GRAY_NV21 = COLOR_YUV2GRAY_420, + COLOR_YUV2GRAY_NV12 = COLOR_YUV2GRAY_420, + COLOR_YUV2GRAY_YV12 = COLOR_YUV2GRAY_420, + COLOR_YUV2GRAY_IYUV = COLOR_YUV2GRAY_420, + COLOR_YUV2GRAY_I420 = COLOR_YUV2GRAY_420, + COLOR_YUV420sp2GRAY = COLOR_YUV2GRAY_420, + COLOR_YUV420p2GRAY = COLOR_YUV2GRAY_420, + + //! YUV 4:2:2 family to RGB + COLOR_YUV2RGB_UYVY = 107, + COLOR_YUV2BGR_UYVY = 108, + //COLOR_YUV2RGB_VYUY = 109, + //COLOR_YUV2BGR_VYUY = 110, + COLOR_YUV2RGB_Y422 = COLOR_YUV2RGB_UYVY, + COLOR_YUV2BGR_Y422 = COLOR_YUV2BGR_UYVY, + COLOR_YUV2RGB_UYNV = COLOR_YUV2RGB_UYVY, + COLOR_YUV2BGR_UYNV = COLOR_YUV2BGR_UYVY, + + COLOR_YUV2RGBA_UYVY = 111, + COLOR_YUV2BGRA_UYVY = 112, + //COLOR_YUV2RGBA_VYUY = 113, + //COLOR_YUV2BGRA_VYUY = 114, + COLOR_YUV2RGBA_Y422 = COLOR_YUV2RGBA_UYVY, + COLOR_YUV2BGRA_Y422 = COLOR_YUV2BGRA_UYVY, + COLOR_YUV2RGBA_UYNV = COLOR_YUV2RGBA_UYVY, + COLOR_YUV2BGRA_UYNV = COLOR_YUV2BGRA_UYVY, + + COLOR_YUV2RGB_YUY2 = 115, + COLOR_YUV2BGR_YUY2 = 116, + COLOR_YUV2RGB_YVYU = 117, + COLOR_YUV2BGR_YVYU = 118, + COLOR_YUV2RGB_YUYV = COLOR_YUV2RGB_YUY2, + COLOR_YUV2BGR_YUYV = COLOR_YUV2BGR_YUY2, + COLOR_YUV2RGB_YUNV = COLOR_YUV2RGB_YUY2, + COLOR_YUV2BGR_YUNV = COLOR_YUV2BGR_YUY2, + + COLOR_YUV2RGBA_YUY2 = 119, + COLOR_YUV2BGRA_YUY2 = 120, + COLOR_YUV2RGBA_YVYU = 121, + COLOR_YUV2BGRA_YVYU = 122, + COLOR_YUV2RGBA_YUYV = COLOR_YUV2RGBA_YUY2, + COLOR_YUV2BGRA_YUYV = COLOR_YUV2BGRA_YUY2, + COLOR_YUV2RGBA_YUNV = COLOR_YUV2RGBA_YUY2, + COLOR_YUV2BGRA_YUNV = COLOR_YUV2BGRA_YUY2, + + COLOR_YUV2GRAY_UYVY = 123, + COLOR_YUV2GRAY_YUY2 = 124, + //CV_YUV2GRAY_VYUY = CV_YUV2GRAY_UYVY, + COLOR_YUV2GRAY_Y422 = COLOR_YUV2GRAY_UYVY, + COLOR_YUV2GRAY_UYNV = COLOR_YUV2GRAY_UYVY, + COLOR_YUV2GRAY_YVYU = COLOR_YUV2GRAY_YUY2, + COLOR_YUV2GRAY_YUYV = COLOR_YUV2GRAY_YUY2, + COLOR_YUV2GRAY_YUNV = COLOR_YUV2GRAY_YUY2, + + //! alpha premultiplication + COLOR_RGBA2mRGBA = 125, + COLOR_mRGBA2RGBA = 126, + + //! RGB to YUV 4:2:0 family + COLOR_RGB2YUV_I420 = 127, + COLOR_BGR2YUV_I420 = 128, + COLOR_RGB2YUV_IYUV = COLOR_RGB2YUV_I420, + COLOR_BGR2YUV_IYUV = COLOR_BGR2YUV_I420, + + COLOR_RGBA2YUV_I420 = 129, + COLOR_BGRA2YUV_I420 = 130, + COLOR_RGBA2YUV_IYUV = COLOR_RGBA2YUV_I420, + COLOR_BGRA2YUV_IYUV = COLOR_BGRA2YUV_I420, + COLOR_RGB2YUV_YV12 = 131, + COLOR_BGR2YUV_YV12 = 132, + COLOR_RGBA2YUV_YV12 = 133, + COLOR_BGRA2YUV_YV12 = 134, + + //! Demosaicing + COLOR_BayerBG2BGR = 46, + COLOR_BayerGB2BGR = 47, + COLOR_BayerRG2BGR = 48, + COLOR_BayerGR2BGR = 49, + + COLOR_BayerBG2RGB = COLOR_BayerRG2BGR, + COLOR_BayerGB2RGB = COLOR_BayerGR2BGR, + COLOR_BayerRG2RGB = COLOR_BayerBG2BGR, + COLOR_BayerGR2RGB = COLOR_BayerGB2BGR, + + COLOR_BayerBG2GRAY = 86, + COLOR_BayerGB2GRAY = 87, + COLOR_BayerRG2GRAY = 88, + COLOR_BayerGR2GRAY = 89, + + //! Demosaicing using Variable Number of Gradients + COLOR_BayerBG2BGR_VNG = 62, + COLOR_BayerGB2BGR_VNG = 63, + COLOR_BayerRG2BGR_VNG = 64, + COLOR_BayerGR2BGR_VNG = 65, + + COLOR_BayerBG2RGB_VNG = COLOR_BayerRG2BGR_VNG, + COLOR_BayerGB2RGB_VNG = COLOR_BayerGR2BGR_VNG, + COLOR_BayerRG2RGB_VNG = COLOR_BayerBG2BGR_VNG, + COLOR_BayerGR2RGB_VNG = COLOR_BayerGB2BGR_VNG, + + //! Edge-Aware Demosaicing + COLOR_BayerBG2BGR_EA = 135, + COLOR_BayerGB2BGR_EA = 136, + COLOR_BayerRG2BGR_EA = 137, + COLOR_BayerGR2BGR_EA = 138, + + COLOR_BayerBG2RGB_EA = COLOR_BayerRG2BGR_EA, + COLOR_BayerGB2RGB_EA = COLOR_BayerGR2BGR_EA, + COLOR_BayerRG2RGB_EA = COLOR_BayerBG2BGR_EA, + COLOR_BayerGR2RGB_EA = COLOR_BayerGB2BGR_EA, + + //! Demosaicing with alpha channel + COLOR_BayerBG2BGRA = 139, + COLOR_BayerGB2BGRA = 140, + COLOR_BayerRG2BGRA = 141, + COLOR_BayerGR2BGRA = 142, + + COLOR_BayerBG2RGBA = COLOR_BayerRG2BGRA, + COLOR_BayerGB2RGBA = COLOR_BayerGR2BGRA, + COLOR_BayerRG2RGBA = COLOR_BayerBG2BGRA, + COLOR_BayerGR2RGBA = COLOR_BayerGB2BGRA, + + COLOR_COLORCVT_MAX = 143 +}; + +//! @addtogroup imgproc_shape +//! @{ + +//! types of intersection between rectangles +enum RectanglesIntersectTypes { + INTERSECT_NONE = 0, //!< No intersection + INTERSECT_PARTIAL = 1, //!< There is a partial intersection + INTERSECT_FULL = 2 //!< One of the rectangle is fully enclosed in the other +}; + +/** @brief finds arbitrary template in the grayscale image using Generalized Hough Transform +*/ +class CV_EXPORTS GeneralizedHough : public Algorithm +{ +public: + //! set template to search + virtual void setTemplate(InputArray templ, Point templCenter = Point(-1, -1)) = 0; + virtual void setTemplate(InputArray edges, InputArray dx, InputArray dy, Point templCenter = Point(-1, -1)) = 0; + + //! find template on image + virtual void detect(InputArray image, OutputArray positions, OutputArray votes = noArray()) = 0; + virtual void detect(InputArray edges, InputArray dx, InputArray dy, OutputArray positions, OutputArray votes = noArray()) = 0; + + //! Canny low threshold. + virtual void setCannyLowThresh(int cannyLowThresh) = 0; + virtual int getCannyLowThresh() const = 0; + + //! Canny high threshold. + virtual void setCannyHighThresh(int cannyHighThresh) = 0; + virtual int getCannyHighThresh() const = 0; + + //! Minimum distance between the centers of the detected objects. + virtual void setMinDist(double minDist) = 0; + virtual double getMinDist() const = 0; + + //! Inverse ratio of the accumulator resolution to the image resolution. + virtual void setDp(double dp) = 0; + virtual double getDp() const = 0; + + //! Maximal size of inner buffers. + virtual void setMaxBufferSize(int maxBufferSize) = 0; + virtual int getMaxBufferSize() const = 0; +}; + +/** @brief finds arbitrary template in the grayscale image using Generalized Hough Transform + +Detects position only without translation and rotation @cite Ballard1981 . +*/ +class CV_EXPORTS GeneralizedHoughBallard : public GeneralizedHough +{ +public: + //! R-Table levels. + virtual void setLevels(int levels) = 0; + virtual int getLevels() const = 0; + + //! The accumulator threshold for the template centers at the detection stage. The smaller it is, the more false positions may be detected. + virtual void setVotesThreshold(int votesThreshold) = 0; + virtual int getVotesThreshold() const = 0; +}; + +/** @brief finds arbitrary template in the grayscale image using Generalized Hough Transform + +Detects position, translation and rotation @cite Guil1999 . +*/ +class CV_EXPORTS GeneralizedHoughGuil : public GeneralizedHough +{ +public: + //! Angle difference in degrees between two points in feature. + virtual void setXi(double xi) = 0; + virtual double getXi() const = 0; + + //! Feature table levels. + virtual void setLevels(int levels) = 0; + virtual int getLevels() const = 0; + + //! Maximal difference between angles that treated as equal. + virtual void setAngleEpsilon(double angleEpsilon) = 0; + virtual double getAngleEpsilon() const = 0; + + //! Minimal rotation angle to detect in degrees. + virtual void setMinAngle(double minAngle) = 0; + virtual double getMinAngle() const = 0; + + //! Maximal rotation angle to detect in degrees. + virtual void setMaxAngle(double maxAngle) = 0; + virtual double getMaxAngle() const = 0; + + //! Angle step in degrees. + virtual void setAngleStep(double angleStep) = 0; + virtual double getAngleStep() const = 0; + + //! Angle votes threshold. + virtual void setAngleThresh(int angleThresh) = 0; + virtual int getAngleThresh() const = 0; + + //! Minimal scale to detect. + virtual void setMinScale(double minScale) = 0; + virtual double getMinScale() const = 0; + + //! Maximal scale to detect. + virtual void setMaxScale(double maxScale) = 0; + virtual double getMaxScale() const = 0; + + //! Scale step. + virtual void setScaleStep(double scaleStep) = 0; + virtual double getScaleStep() const = 0; + + //! Scale votes threshold. + virtual void setScaleThresh(int scaleThresh) = 0; + virtual int getScaleThresh() const = 0; + + //! Position votes threshold. + virtual void setPosThresh(int posThresh) = 0; + virtual int getPosThresh() const = 0; +}; + +//! @} imgproc_shape + +//! @addtogroup imgproc_hist +//! @{ + +/** @brief Base class for Contrast Limited Adaptive Histogram Equalization. +*/ +class CV_EXPORTS_W CLAHE : public Algorithm +{ +public: + /** @brief Equalizes the histogram of a grayscale image using Contrast Limited Adaptive Histogram Equalization. + + @param src Source image of type CV_8UC1 or CV_16UC1. + @param dst Destination image. + */ + CV_WRAP virtual void apply(InputArray src, OutputArray dst) = 0; + + /** @brief Sets threshold for contrast limiting. + + @param clipLimit threshold value. + */ + CV_WRAP virtual void setClipLimit(double clipLimit) = 0; + + //! Returns threshold value for contrast limiting. + CV_WRAP virtual double getClipLimit() const = 0; + + /** @brief Sets size of grid for histogram equalization. Input image will be divided into + equally sized rectangular tiles. + + @param tileGridSize defines the number of tiles in row and column. + */ + CV_WRAP virtual void setTilesGridSize(Size tileGridSize) = 0; + + //!@brief Returns Size defines the number of tiles in row and column. + CV_WRAP virtual Size getTilesGridSize() const = 0; + + CV_WRAP virtual void collectGarbage() = 0; +}; + +//! @} imgproc_hist + +//! @addtogroup imgproc_subdiv2d +//! @{ + +class CV_EXPORTS_W Subdiv2D +{ +public: + /** Subdiv2D point location cases */ + enum { PTLOC_ERROR = -2, //!< Point location error + PTLOC_OUTSIDE_RECT = -1, //!< Point outside the subdivision bounding rect + PTLOC_INSIDE = 0, //!< Point inside some facet + PTLOC_VERTEX = 1, //!< Point coincides with one of the subdivision vertices + PTLOC_ON_EDGE = 2 //!< Point on some edge + }; + + /** Subdiv2D edge type navigation (see: getEdge()) */ + enum { NEXT_AROUND_ORG = 0x00, + NEXT_AROUND_DST = 0x22, + PREV_AROUND_ORG = 0x11, + PREV_AROUND_DST = 0x33, + NEXT_AROUND_LEFT = 0x13, + NEXT_AROUND_RIGHT = 0x31, + PREV_AROUND_LEFT = 0x20, + PREV_AROUND_RIGHT = 0x02 + }; + + /** creates an empty Subdiv2D object. + To create a new empty Delaunay subdivision you need to use the #initDelaunay function. + */ + CV_WRAP Subdiv2D(); + + /** @overload + + @param rect Rectangle that includes all of the 2D points that are to be added to the subdivision. + + The function creates an empty Delaunay subdivision where 2D points can be added using the function + insert() . All of the points to be added must be within the specified rectangle, otherwise a runtime + error is raised. + */ + CV_WRAP Subdiv2D(Rect rect); + + /** @brief Creates a new empty Delaunay subdivision + + @param rect Rectangle that includes all of the 2D points that are to be added to the subdivision. + + */ + CV_WRAP void initDelaunay(Rect rect); + + /** @brief Insert a single point into a Delaunay triangulation. + + @param pt Point to insert. + + The function inserts a single point into a subdivision and modifies the subdivision topology + appropriately. If a point with the same coordinates exists already, no new point is added. + @returns the ID of the point. + + @note If the point is outside of the triangulation specified rect a runtime error is raised. + */ + CV_WRAP int insert(Point2f pt); + + /** @brief Insert multiple points into a Delaunay triangulation. + + @param ptvec Points to insert. + + The function inserts a vector of points into a subdivision and modifies the subdivision topology + appropriately. + */ + CV_WRAP void insert(const std::vector& ptvec); + + /** @brief Returns the location of a point within a Delaunay triangulation. + + @param pt Point to locate. + @param edge Output edge that the point belongs to or is located to the right of it. + @param vertex Optional output vertex the input point coincides with. + + The function locates the input point within the subdivision and gives one of the triangle edges + or vertices. + + @returns an integer which specify one of the following five cases for point location: + - The point falls into some facet. The function returns #PTLOC_INSIDE and edge will contain one of + edges of the facet. + - The point falls onto the edge. The function returns #PTLOC_ON_EDGE and edge will contain this edge. + - The point coincides with one of the subdivision vertices. The function returns #PTLOC_VERTEX and + vertex will contain a pointer to the vertex. + - The point is outside the subdivision reference rectangle. The function returns #PTLOC_OUTSIDE_RECT + and no pointers are filled. + - One of input arguments is invalid. A runtime error is raised or, if silent or "parent" error + processing mode is selected, #PTLOC_ERROR is returned. + */ + CV_WRAP int locate(Point2f pt, CV_OUT int& edge, CV_OUT int& vertex); + + /** @brief Finds the subdivision vertex closest to the given point. + + @param pt Input point. + @param nearestPt Output subdivision vertex point. + + The function is another function that locates the input point within the subdivision. It finds the + subdivision vertex that is the closest to the input point. It is not necessarily one of vertices + of the facet containing the input point, though the facet (located using locate() ) is used as a + starting point. + + @returns vertex ID. + */ + CV_WRAP int findNearest(Point2f pt, CV_OUT Point2f* nearestPt = 0); + + /** @brief Returns a list of all edges. + + @param edgeList Output vector. + + The function gives each edge as a 4 numbers vector, where each two are one of the edge + vertices. i.e. org_x = v[0], org_y = v[1], dst_x = v[2], dst_y = v[3]. + */ + CV_WRAP void getEdgeList(CV_OUT std::vector& edgeList) const; + + /** @brief Returns a list of the leading edge ID connected to each triangle. + + @param leadingEdgeList Output vector. + + The function gives one edge ID for each triangle. + */ + CV_WRAP void getLeadingEdgeList(CV_OUT std::vector& leadingEdgeList) const; + + /** @brief Returns a list of all triangles. + + @param triangleList Output vector. + + The function gives each triangle as a 6 numbers vector, where each two are one of the triangle + vertices. i.e. p1_x = v[0], p1_y = v[1], p2_x = v[2], p2_y = v[3], p3_x = v[4], p3_y = v[5]. + */ + CV_WRAP void getTriangleList(CV_OUT std::vector& triangleList) const; + + /** @brief Returns a list of all Voroni facets. + + @param idx Vector of vertices IDs to consider. For all vertices you can pass empty vector. + @param facetList Output vector of the Voroni facets. + @param facetCenters Output vector of the Voroni facets center points. + + */ + CV_WRAP void getVoronoiFacetList(const std::vector& idx, CV_OUT std::vector >& facetList, + CV_OUT std::vector& facetCenters); + + /** @brief Returns vertex location from vertex ID. + + @param vertex vertex ID. + @param firstEdge Optional. The first edge ID which is connected to the vertex. + @returns vertex (x,y) + + */ + CV_WRAP Point2f getVertex(int vertex, CV_OUT int* firstEdge = 0) const; + + /** @brief Returns one of the edges related to the given edge. + + @param edge Subdivision edge ID. + @param nextEdgeType Parameter specifying which of the related edges to return. + The following values are possible: + - NEXT_AROUND_ORG next around the edge origin ( eOnext on the picture below if e is the input edge) + - NEXT_AROUND_DST next around the edge vertex ( eDnext ) + - PREV_AROUND_ORG previous around the edge origin (reversed eRnext ) + - PREV_AROUND_DST previous around the edge destination (reversed eLnext ) + - NEXT_AROUND_LEFT next around the left facet ( eLnext ) + - NEXT_AROUND_RIGHT next around the right facet ( eRnext ) + - PREV_AROUND_LEFT previous around the left facet (reversed eOnext ) + - PREV_AROUND_RIGHT previous around the right facet (reversed eDnext ) + + ![sample output](pics/quadedge.png) + + @returns edge ID related to the input edge. + */ + CV_WRAP int getEdge( int edge, int nextEdgeType ) const; + + /** @brief Returns next edge around the edge origin. + + @param edge Subdivision edge ID. + + @returns an integer which is next edge ID around the edge origin: eOnext on the + picture above if e is the input edge). + */ + CV_WRAP int nextEdge(int edge) const; + + /** @brief Returns another edge of the same quad-edge. + + @param edge Subdivision edge ID. + @param rotate Parameter specifying which of the edges of the same quad-edge as the input + one to return. The following values are possible: + - 0 - the input edge ( e on the picture below if e is the input edge) + - 1 - the rotated edge ( eRot ) + - 2 - the reversed edge (reversed e (in green)) + - 3 - the reversed rotated edge (reversed eRot (in green)) + + @returns one of the edges ID of the same quad-edge as the input edge. + */ + CV_WRAP int rotateEdge(int edge, int rotate) const; + CV_WRAP int symEdge(int edge) const; + + /** @brief Returns the edge origin. + + @param edge Subdivision edge ID. + @param orgpt Output vertex location. + + @returns vertex ID. + */ + CV_WRAP int edgeOrg(int edge, CV_OUT Point2f* orgpt = 0) const; + + /** @brief Returns the edge destination. + + @param edge Subdivision edge ID. + @param dstpt Output vertex location. + + @returns vertex ID. + */ + CV_WRAP int edgeDst(int edge, CV_OUT Point2f* dstpt = 0) const; + +protected: + int newEdge(); + void deleteEdge(int edge); + int newPoint(Point2f pt, bool isvirtual, int firstEdge = 0); + void deletePoint(int vtx); + void setEdgePoints( int edge, int orgPt, int dstPt ); + void splice( int edgeA, int edgeB ); + int connectEdges( int edgeA, int edgeB ); + void swapEdges( int edge ); + int isRightOf(Point2f pt, int edge) const; + void calcVoronoi(); + void clearVoronoi(); + void checkSubdiv() const; + + struct CV_EXPORTS Vertex + { + Vertex(); + Vertex(Point2f pt, bool _isvirtual, int _firstEdge=0); + bool isvirtual() const; + bool isfree() const; + + int firstEdge; + int type; + Point2f pt; + }; + + struct CV_EXPORTS QuadEdge + { + QuadEdge(); + QuadEdge(int edgeidx); + bool isfree() const; + + int next[4]; + int pt[4]; + }; + + //! All of the vertices + std::vector vtx; + //! All of the edges + std::vector qedges; + int freeQEdge; + int freePoint; + bool validGeometry; + + int recentEdge; + //! Top left corner of the bounding rect + Point2f topLeft; + //! Bottom right corner of the bounding rect + Point2f bottomRight; +}; + +//! @} imgproc_subdiv2d + +//! @addtogroup imgproc_feature +//! @{ + +/** @example samples/cpp/lsd_lines.cpp +An example using the LineSegmentDetector +\image html building_lsd.png "Sample output image" width=434 height=300 +*/ + +/** @brief Line segment detector class + +following the algorithm described at @cite Rafael12 . +*/ +class CV_EXPORTS_W LineSegmentDetector : public Algorithm +{ +public: + + /** @brief Finds lines in the input image. + + This is the output of the default parameters of the algorithm on the above shown image. + + ![image](pics/building_lsd.png) + + @param _image A grayscale (CV_8UC1) input image. If only a roi needs to be selected, use: + `lsd_ptr-\>detect(image(roi), lines, ...); lines += Scalar(roi.x, roi.y, roi.x, roi.y);` + @param _lines A vector of Vec4i or Vec4f elements specifying the beginning and ending point of a line. Where + Vec4i/Vec4f is (x1, y1, x2, y2), point 1 is the start, point 2 - end. Returned lines are strictly + oriented depending on the gradient. + @param width Vector of widths of the regions, where the lines are found. E.g. Width of line. + @param prec Vector of precisions with which the lines are found. + @param nfa Vector containing number of false alarms in the line region, with precision of 10%. The + bigger the value, logarithmically better the detection. + - -1 corresponds to 10 mean false alarms + - 0 corresponds to 1 mean false alarm + - 1 corresponds to 0.1 mean false alarms + This vector will be calculated only when the objects type is #LSD_REFINE_ADV. + */ + CV_WRAP virtual void detect(InputArray _image, OutputArray _lines, + OutputArray width = noArray(), OutputArray prec = noArray(), + OutputArray nfa = noArray()) = 0; + + /** @brief Draws the line segments on a given image. + @param _image The image, where the lines will be drawn. Should be bigger or equal to the image, + where the lines were found. + @param lines A vector of the lines that needed to be drawn. + */ + CV_WRAP virtual void drawSegments(InputOutputArray _image, InputArray lines) = 0; + + /** @brief Draws two groups of lines in blue and red, counting the non overlapping (mismatching) pixels. + + @param size The size of the image, where lines1 and lines2 were found. + @param lines1 The first group of lines that needs to be drawn. It is visualized in blue color. + @param lines2 The second group of lines. They visualized in red color. + @param _image Optional image, where the lines will be drawn. The image should be color(3-channel) + in order for lines1 and lines2 to be drawn in the above mentioned colors. + */ + CV_WRAP virtual int compareSegments(const Size& size, InputArray lines1, InputArray lines2, InputOutputArray _image = noArray()) = 0; + + virtual ~LineSegmentDetector() { } +}; + +/** @brief Creates a smart pointer to a LineSegmentDetector object and initializes it. + +The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want +to edit those, as to tailor it for their own application. + +@param _refine The way found lines will be refined, see #LineSegmentDetectorModes +@param _scale The scale of the image that will be used to find the lines. Range (0..1]. +@param _sigma_scale Sigma for Gaussian filter. It is computed as sigma = _sigma_scale/_scale. +@param _quant Bound to the quantization error on the gradient norm. +@param _ang_th Gradient angle tolerance in degrees. +@param _log_eps Detection threshold: -log10(NFA) \> log_eps. Used only when advance refinement +is chosen. +@param _density_th Minimal density of aligned region points in the enclosing rectangle. +@param _n_bins Number of bins in pseudo-ordering of gradient modulus. + */ +CV_EXPORTS_W Ptr createLineSegmentDetector( + int _refine = LSD_REFINE_STD, double _scale = 0.8, + double _sigma_scale = 0.6, double _quant = 2.0, double _ang_th = 22.5, + double _log_eps = 0, double _density_th = 0.7, int _n_bins = 1024); + +//! @} imgproc_feature + +//! @addtogroup imgproc_filter +//! @{ + +/** @brief Returns Gaussian filter coefficients. + +The function computes and returns the \f$\texttt{ksize} \times 1\f$ matrix of Gaussian filter +coefficients: + +\f[G_i= \alpha *e^{-(i-( \texttt{ksize} -1)/2)^2/(2* \texttt{sigma}^2)},\f] + +where \f$i=0..\texttt{ksize}-1\f$ and \f$\alpha\f$ is the scale factor chosen so that \f$\sum_i G_i=1\f$. + +Two of such generated kernels can be passed to sepFilter2D. Those functions automatically recognize +smoothing kernels (a symmetrical kernel with sum of weights equal to 1) and handle them accordingly. +You may also use the higher-level GaussianBlur. +@param ksize Aperture size. It should be odd ( \f$\texttt{ksize} \mod 2 = 1\f$ ) and positive. +@param sigma Gaussian standard deviation. If it is non-positive, it is computed from ksize as +`sigma = 0.3*((ksize-1)*0.5 - 1) + 0.8`. +@param ktype Type of filter coefficients. It can be CV_32F or CV_64F . +@sa sepFilter2D, getDerivKernels, getStructuringElement, GaussianBlur + */ +CV_EXPORTS_W Mat getGaussianKernel( int ksize, double sigma, int ktype = CV_64F ); + +/** @brief Returns filter coefficients for computing spatial image derivatives. + +The function computes and returns the filter coefficients for spatial image derivatives. When +`ksize=CV_SCHARR`, the Scharr \f$3 \times 3\f$ kernels are generated (see #Scharr). Otherwise, Sobel +kernels are generated (see #Sobel). The filters are normally passed to #sepFilter2D or to + +@param kx Output matrix of row filter coefficients. It has the type ktype . +@param ky Output matrix of column filter coefficients. It has the type ktype . +@param dx Derivative order in respect of x. +@param dy Derivative order in respect of y. +@param ksize Aperture size. It can be CV_SCHARR, 1, 3, 5, or 7. +@param normalize Flag indicating whether to normalize (scale down) the filter coefficients or not. +Theoretically, the coefficients should have the denominator \f$=2^{ksize*2-dx-dy-2}\f$. If you are +going to filter floating-point images, you are likely to use the normalized kernels. But if you +compute derivatives of an 8-bit image, store the results in a 16-bit image, and wish to preserve +all the fractional bits, you may want to set normalize=false . +@param ktype Type of filter coefficients. It can be CV_32f or CV_64F . + */ +CV_EXPORTS_W void getDerivKernels( OutputArray kx, OutputArray ky, + int dx, int dy, int ksize, + bool normalize = false, int ktype = CV_32F ); + +/** @brief Returns Gabor filter coefficients. + +For more details about gabor filter equations and parameters, see: [Gabor +Filter](http://en.wikipedia.org/wiki/Gabor_filter). + +@param ksize Size of the filter returned. +@param sigma Standard deviation of the gaussian envelope. +@param theta Orientation of the normal to the parallel stripes of a Gabor function. +@param lambd Wavelength of the sinusoidal factor. +@param gamma Spatial aspect ratio. +@param psi Phase offset. +@param ktype Type of filter coefficients. It can be CV_32F or CV_64F . + */ +CV_EXPORTS_W Mat getGaborKernel( Size ksize, double sigma, double theta, double lambd, + double gamma, double psi = CV_PI*0.5, int ktype = CV_64F ); + +//! returns "magic" border value for erosion and dilation. It is automatically transformed to Scalar::all(-DBL_MAX) for dilation. +static inline Scalar morphologyDefaultBorderValue() { return Scalar::all(DBL_MAX); } + +/** @brief Returns a structuring element of the specified size and shape for morphological operations. + +The function constructs and returns the structuring element that can be further passed to #erode, +#dilate or #morphologyEx. But you can also construct an arbitrary binary mask yourself and use it as +the structuring element. + +@param shape Element shape that could be one of #MorphShapes +@param ksize Size of the structuring element. +@param anchor Anchor position within the element. The default value \f$(-1, -1)\f$ means that the +anchor is at the center. Note that only the shape of a cross-shaped element depends on the anchor +position. In other cases the anchor just regulates how much the result of the morphological +operation is shifted. + */ +CV_EXPORTS_W Mat getStructuringElement(int shape, Size ksize, Point anchor = Point(-1,-1)); + +/** @example samples/cpp/tutorial_code/ImgProc/Smoothing/Smoothing.cpp +Sample code for simple filters +![Sample screenshot](Smoothing_Tutorial_Result_Median_Filter.jpg) +Check @ref tutorial_gausian_median_blur_bilateral_filter "the corresponding tutorial" for more details + */ + +/** @brief Blurs an image using the median filter. + +The function smoothes an image using the median filter with the \f$\texttt{ksize} \times +\texttt{ksize}\f$ aperture. Each channel of a multi-channel image is processed independently. +In-place operation is supported. + +@note The median filter uses #BORDER_REPLICATE internally to cope with border pixels, see #BorderTypes + +@param src input 1-, 3-, or 4-channel image; when ksize is 3 or 5, the image depth should be +CV_8U, CV_16U, or CV_32F, for larger aperture sizes, it can only be CV_8U. +@param dst destination array of the same size and type as src. +@param ksize aperture linear size; it must be odd and greater than 1, for example: 3, 5, 7 ... +@sa bilateralFilter, blur, boxFilter, GaussianBlur + */ +CV_EXPORTS_W void medianBlur( InputArray src, OutputArray dst, int ksize ); + +/** @brief Blurs an image using a Gaussian filter. + +The function convolves the source image with the specified Gaussian kernel. In-place filtering is +supported. + +@param src input image; the image can have any number of channels, which are processed +independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. +@param dst output image of the same size and type as src. +@param ksize Gaussian kernel size. ksize.width and ksize.height can differ but they both must be +positive and odd. Or, they can be zero's and then they are computed from sigma. +@param sigmaX Gaussian kernel standard deviation in X direction. +@param sigmaY Gaussian kernel standard deviation in Y direction; if sigmaY is zero, it is set to be +equal to sigmaX, if both sigmas are zeros, they are computed from ksize.width and ksize.height, +respectively (see #getGaussianKernel for details); to fully control the result regardless of +possible future modifications of all this semantics, it is recommended to specify all of ksize, +sigmaX, and sigmaY. +@param borderType pixel extrapolation method, see #BorderTypes + +@sa sepFilter2D, filter2D, blur, boxFilter, bilateralFilter, medianBlur + */ +CV_EXPORTS_W void GaussianBlur( InputArray src, OutputArray dst, Size ksize, + double sigmaX, double sigmaY = 0, + int borderType = BORDER_DEFAULT ); + +/** @brief Applies the bilateral filter to an image. + +The function applies bilateral filtering to the input image, as described in +http://www.dai.ed.ac.uk/CVonline/LOCAL_COPIES/MANDUCHI1/Bilateral_Filtering.html +bilateralFilter can reduce unwanted noise very well while keeping edges fairly sharp. However, it is +very slow compared to most filters. + +_Sigma values_: For simplicity, you can set the 2 sigma values to be the same. If they are small (\< +10), the filter will not have much effect, whereas if they are large (\> 150), they will have a very +strong effect, making the image look "cartoonish". + +_Filter size_: Large filters (d \> 5) are very slow, so it is recommended to use d=5 for real-time +applications, and perhaps d=9 for offline applications that need heavy noise filtering. + +This filter does not work inplace. +@param src Source 8-bit or floating-point, 1-channel or 3-channel image. +@param dst Destination image of the same size and type as src . +@param d Diameter of each pixel neighborhood that is used during filtering. If it is non-positive, +it is computed from sigmaSpace. +@param sigmaColor Filter sigma in the color space. A larger value of the parameter means that +farther colors within the pixel neighborhood (see sigmaSpace) will be mixed together, resulting +in larger areas of semi-equal color. +@param sigmaSpace Filter sigma in the coordinate space. A larger value of the parameter means that +farther pixels will influence each other as long as their colors are close enough (see sigmaColor +). When d\>0, it specifies the neighborhood size regardless of sigmaSpace. Otherwise, d is +proportional to sigmaSpace. +@param borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes + */ +CV_EXPORTS_W void bilateralFilter( InputArray src, OutputArray dst, int d, + double sigmaColor, double sigmaSpace, + int borderType = BORDER_DEFAULT ); + +/** @brief Blurs an image using the box filter. + +The function smooths an image using the kernel: + +\f[\texttt{K} = \alpha \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \end{bmatrix}\f] + +where + +\f[\alpha = \fork{\frac{1}{\texttt{ksize.width*ksize.height}}}{when \texttt{normalize=true}}{1}{otherwise}\f] + +Unnormalized box filter is useful for computing various integral characteristics over each pixel +neighborhood, such as covariance matrices of image derivatives (used in dense optical flow +algorithms, and so on). If you need to compute pixel sums over variable-size windows, use #integral. + +@param src input image. +@param dst output image of the same size and type as src. +@param ddepth the output image depth (-1 to use src.depth()). +@param ksize blurring kernel size. +@param anchor anchor point; default value Point(-1,-1) means that the anchor is at the kernel +center. +@param normalize flag, specifying whether the kernel is normalized by its area or not. +@param borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes +@sa blur, bilateralFilter, GaussianBlur, medianBlur, integral + */ +CV_EXPORTS_W void boxFilter( InputArray src, OutputArray dst, int ddepth, + Size ksize, Point anchor = Point(-1,-1), + bool normalize = true, + int borderType = BORDER_DEFAULT ); + +/** @brief Calculates the normalized sum of squares of the pixel values overlapping the filter. + +For every pixel \f$ (x, y) \f$ in the source image, the function calculates the sum of squares of those neighboring +pixel values which overlap the filter placed over the pixel \f$ (x, y) \f$. + +The unnormalized square box filter can be useful in computing local image statistics such as the the local +variance and standard deviation around the neighborhood of a pixel. + +@param src input image +@param dst output image of the same size and type as _src +@param ddepth the output image depth (-1 to use src.depth()) +@param ksize kernel size +@param anchor kernel anchor point. The default value of Point(-1, -1) denotes that the anchor is at the kernel +center. +@param normalize flag, specifying whether the kernel is to be normalized by it's area or not. +@param borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes +@sa boxFilter +*/ +CV_EXPORTS_W void sqrBoxFilter( InputArray src, OutputArray dst, int ddepth, + Size ksize, Point anchor = Point(-1, -1), + bool normalize = true, + int borderType = BORDER_DEFAULT ); + +/** @brief Blurs an image using the normalized box filter. + +The function smooths an image using the kernel: + +\f[\texttt{K} = \frac{1}{\texttt{ksize.width*ksize.height}} \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \end{bmatrix}\f] + +The call `blur(src, dst, ksize, anchor, borderType)` is equivalent to `boxFilter(src, dst, src.type(), +anchor, true, borderType)`. + +@param src input image; it can have any number of channels, which are processed independently, but +the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. +@param dst output image of the same size and type as src. +@param ksize blurring kernel size. +@param anchor anchor point; default value Point(-1,-1) means that the anchor is at the kernel +center. +@param borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes +@sa boxFilter, bilateralFilter, GaussianBlur, medianBlur + */ +CV_EXPORTS_W void blur( InputArray src, OutputArray dst, + Size ksize, Point anchor = Point(-1,-1), + int borderType = BORDER_DEFAULT ); + +/** @brief Convolves an image with the kernel. + +The function applies an arbitrary linear filter to an image. In-place operation is supported. When +the aperture is partially outside the image, the function interpolates outlier pixel values +according to the specified border mode. + +The function does actually compute correlation, not the convolution: + +\f[\texttt{dst} (x,y) = \sum _{ \stackrel{0\leq x' < \texttt{kernel.cols},}{0\leq y' < \texttt{kernel.rows}} } \texttt{kernel} (x',y')* \texttt{src} (x+x'- \texttt{anchor.x} ,y+y'- \texttt{anchor.y} )\f] + +That is, the kernel is not mirrored around the anchor point. If you need a real convolution, flip +the kernel using #flip and set the new anchor to `(kernel.cols - anchor.x - 1, kernel.rows - +anchor.y - 1)`. + +The function uses the DFT-based algorithm in case of sufficiently large kernels (~`11 x 11` or +larger) and the direct algorithm for small kernels. + +@param src input image. +@param dst output image of the same size and the same number of channels as src. +@param ddepth desired depth of the destination image, see @ref filter_depths "combinations" +@param kernel convolution kernel (or rather a correlation kernel), a single-channel floating point +matrix; if you want to apply different kernels to different channels, split the image into +separate color planes using split and process them individually. +@param anchor anchor of the kernel that indicates the relative position of a filtered point within +the kernel; the anchor should lie within the kernel; default value (-1,-1) means that the anchor +is at the kernel center. +@param delta optional value added to the filtered pixels before storing them in dst. +@param borderType pixel extrapolation method, see #BorderTypes +@sa sepFilter2D, dft, matchTemplate + */ +CV_EXPORTS_W void filter2D( InputArray src, OutputArray dst, int ddepth, + InputArray kernel, Point anchor = Point(-1,-1), + double delta = 0, int borderType = BORDER_DEFAULT ); + +/** @brief Applies a separable linear filter to an image. + +The function applies a separable linear filter to the image. That is, first, every row of src is +filtered with the 1D kernel kernelX. Then, every column of the result is filtered with the 1D +kernel kernelY. The final result shifted by delta is stored in dst . + +@param src Source image. +@param dst Destination image of the same size and the same number of channels as src . +@param ddepth Destination image depth, see @ref filter_depths "combinations" +@param kernelX Coefficients for filtering each row. +@param kernelY Coefficients for filtering each column. +@param anchor Anchor position within the kernel. The default value \f$(-1,-1)\f$ means that the anchor +is at the kernel center. +@param delta Value added to the filtered results before storing them. +@param borderType Pixel extrapolation method, see #BorderTypes +@sa filter2D, Sobel, GaussianBlur, boxFilter, blur + */ +CV_EXPORTS_W void sepFilter2D( InputArray src, OutputArray dst, int ddepth, + InputArray kernelX, InputArray kernelY, + Point anchor = Point(-1,-1), + double delta = 0, int borderType = BORDER_DEFAULT ); + +/** @example samples/cpp/tutorial_code/ImgTrans/Sobel_Demo.cpp +Sample code using Sobel and/or Scharr OpenCV functions to make a simple Edge Detector +![Sample screenshot](Sobel_Derivatives_Tutorial_Result.jpg) +Check @ref tutorial_sobel_derivatives "the corresponding tutorial" for more details +*/ + +/** @brief Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator. + +In all cases except one, the \f$\texttt{ksize} \times \texttt{ksize}\f$ separable kernel is used to +calculate the derivative. When \f$\texttt{ksize = 1}\f$, the \f$3 \times 1\f$ or \f$1 \times 3\f$ +kernel is used (that is, no Gaussian smoothing is done). `ksize = 1` can only be used for the first +or the second x- or y- derivatives. + +There is also the special value `ksize = #CV_SCHARR (-1)` that corresponds to the \f$3\times3\f$ Scharr +filter that may give more accurate results than the \f$3\times3\f$ Sobel. The Scharr aperture is + +\f[\vecthreethree{-3}{0}{3}{-10}{0}{10}{-3}{0}{3}\f] + +for the x-derivative, or transposed for the y-derivative. + +The function calculates an image derivative by convolving the image with the appropriate kernel: + +\f[\texttt{dst} = \frac{\partial^{xorder+yorder} \texttt{src}}{\partial x^{xorder} \partial y^{yorder}}\f] + +The Sobel operators combine Gaussian smoothing and differentiation, so the result is more or less +resistant to the noise. Most often, the function is called with ( xorder = 1, yorder = 0, ksize = 3) +or ( xorder = 0, yorder = 1, ksize = 3) to calculate the first x- or y- image derivative. The first +case corresponds to a kernel of: + +\f[\vecthreethree{-1}{0}{1}{-2}{0}{2}{-1}{0}{1}\f] + +The second case corresponds to a kernel of: + +\f[\vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}\f] + +@param src input image. +@param dst output image of the same size and the same number of channels as src . +@param ddepth output image depth, see @ref filter_depths "combinations"; in the case of + 8-bit input images it will result in truncated derivatives. +@param dx order of the derivative x. +@param dy order of the derivative y. +@param ksize size of the extended Sobel kernel; it must be 1, 3, 5, or 7. +@param scale optional scale factor for the computed derivative values; by default, no scaling is +applied (see #getDerivKernels for details). +@param delta optional delta value that is added to the results prior to storing them in dst. +@param borderType pixel extrapolation method, see #BorderTypes +@sa Scharr, Laplacian, sepFilter2D, filter2D, GaussianBlur, cartToPolar + */ +CV_EXPORTS_W void Sobel( InputArray src, OutputArray dst, int ddepth, + int dx, int dy, int ksize = 3, + double scale = 1, double delta = 0, + int borderType = BORDER_DEFAULT ); + +/** @brief Calculates the first order image derivative in both x and y using a Sobel operator + +Equivalent to calling: + +@code +Sobel( src, dx, CV_16SC1, 1, 0, 3 ); +Sobel( src, dy, CV_16SC1, 0, 1, 3 ); +@endcode + +@param src input image. +@param dx output image with first-order derivative in x. +@param dy output image with first-order derivative in y. +@param ksize size of Sobel kernel. It must be 3. +@param borderType pixel extrapolation method, see #BorderTypes + +@sa Sobel + */ + +CV_EXPORTS_W void spatialGradient( InputArray src, OutputArray dx, + OutputArray dy, int ksize = 3, + int borderType = BORDER_DEFAULT ); + +/** @brief Calculates the first x- or y- image derivative using Scharr operator. + +The function computes the first x- or y- spatial image derivative using the Scharr operator. The +call + +\f[\texttt{Scharr(src, dst, ddepth, dx, dy, scale, delta, borderType)}\f] + +is equivalent to + +\f[\texttt{Sobel(src, dst, ddepth, dx, dy, CV_SCHARR, scale, delta, borderType)} .\f] + +@param src input image. +@param dst output image of the same size and the same number of channels as src. +@param ddepth output image depth, see @ref filter_depths "combinations" +@param dx order of the derivative x. +@param dy order of the derivative y. +@param scale optional scale factor for the computed derivative values; by default, no scaling is +applied (see #getDerivKernels for details). +@param delta optional delta value that is added to the results prior to storing them in dst. +@param borderType pixel extrapolation method, see #BorderTypes +@sa cartToPolar + */ +CV_EXPORTS_W void Scharr( InputArray src, OutputArray dst, int ddepth, + int dx, int dy, double scale = 1, double delta = 0, + int borderType = BORDER_DEFAULT ); + +/** @example samples/cpp/laplace.cpp +An example using Laplace transformations for edge detection +*/ + +/** @brief Calculates the Laplacian of an image. + +The function calculates the Laplacian of the source image by adding up the second x and y +derivatives calculated using the Sobel operator: + +\f[\texttt{dst} = \Delta \texttt{src} = \frac{\partial^2 \texttt{src}}{\partial x^2} + \frac{\partial^2 \texttt{src}}{\partial y^2}\f] + +This is done when `ksize > 1`. When `ksize == 1`, the Laplacian is computed by filtering the image +with the following \f$3 \times 3\f$ aperture: + +\f[\vecthreethree {0}{1}{0}{1}{-4}{1}{0}{1}{0}\f] + +@param src Source image. +@param dst Destination image of the same size and the same number of channels as src . +@param ddepth Desired depth of the destination image. +@param ksize Aperture size used to compute the second-derivative filters. See #getDerivKernels for +details. The size must be positive and odd. +@param scale Optional scale factor for the computed Laplacian values. By default, no scaling is +applied. See #getDerivKernels for details. +@param delta Optional delta value that is added to the results prior to storing them in dst . +@param borderType Pixel extrapolation method, see #BorderTypes +@sa Sobel, Scharr + */ +CV_EXPORTS_W void Laplacian( InputArray src, OutputArray dst, int ddepth, + int ksize = 1, double scale = 1, double delta = 0, + int borderType = BORDER_DEFAULT ); + +//! @} imgproc_filter + +//! @addtogroup imgproc_feature +//! @{ + +/** @example samples/cpp/edge.cpp +This program demonstrates usage of the Canny edge detector + +Check @ref tutorial_canny_detector "the corresponding tutorial" for more details +*/ + +/** @brief Finds edges in an image using the Canny algorithm @cite Canny86 . + +The function finds edges in the input image and marks them in the output map edges using the +Canny algorithm. The smallest value between threshold1 and threshold2 is used for edge linking. The +largest value is used to find initial segments of strong edges. See + + +@param image 8-bit input image. +@param edges output edge map; single channels 8-bit image, which has the same size as image . +@param threshold1 first threshold for the hysteresis procedure. +@param threshold2 second threshold for the hysteresis procedure. +@param apertureSize aperture size for the Sobel operator. +@param L2gradient a flag, indicating whether a more accurate \f$L_2\f$ norm +\f$=\sqrt{(dI/dx)^2 + (dI/dy)^2}\f$ should be used to calculate the image gradient magnitude ( +L2gradient=true ), or whether the default \f$L_1\f$ norm \f$=|dI/dx|+|dI/dy|\f$ is enough ( +L2gradient=false ). + */ +CV_EXPORTS_W void Canny( InputArray image, OutputArray edges, + double threshold1, double threshold2, + int apertureSize = 3, bool L2gradient = false ); + +/** \overload + +Finds edges in an image using the Canny algorithm with custom image gradient. + +@param dx 16-bit x derivative of input image (CV_16SC1 or CV_16SC3). +@param dy 16-bit y derivative of input image (same type as dx). +@param edges output edge map; single channels 8-bit image, which has the same size as image . +@param threshold1 first threshold for the hysteresis procedure. +@param threshold2 second threshold for the hysteresis procedure. +@param L2gradient a flag, indicating whether a more accurate \f$L_2\f$ norm +\f$=\sqrt{(dI/dx)^2 + (dI/dy)^2}\f$ should be used to calculate the image gradient magnitude ( +L2gradient=true ), or whether the default \f$L_1\f$ norm \f$=|dI/dx|+|dI/dy|\f$ is enough ( +L2gradient=false ). + */ +CV_EXPORTS_W void Canny( InputArray dx, InputArray dy, + OutputArray edges, + double threshold1, double threshold2, + bool L2gradient = false ); + +/** @brief Calculates the minimal eigenvalue of gradient matrices for corner detection. + +The function is similar to cornerEigenValsAndVecs but it calculates and stores only the minimal +eigenvalue of the covariance matrix of derivatives, that is, \f$\min(\lambda_1, \lambda_2)\f$ in terms +of the formulae in the cornerEigenValsAndVecs description. + +@param src Input single-channel 8-bit or floating-point image. +@param dst Image to store the minimal eigenvalues. It has the type CV_32FC1 and the same size as +src . +@param blockSize Neighborhood size (see the details on #cornerEigenValsAndVecs ). +@param ksize Aperture parameter for the Sobel operator. +@param borderType Pixel extrapolation method. See #BorderTypes. + */ +CV_EXPORTS_W void cornerMinEigenVal( InputArray src, OutputArray dst, + int blockSize, int ksize = 3, + int borderType = BORDER_DEFAULT ); + +/** @brief Harris corner detector. + +The function runs the Harris corner detector on the image. Similarly to cornerMinEigenVal and +cornerEigenValsAndVecs , for each pixel \f$(x, y)\f$ it calculates a \f$2\times2\f$ gradient covariance +matrix \f$M^{(x,y)}\f$ over a \f$\texttt{blockSize} \times \texttt{blockSize}\f$ neighborhood. Then, it +computes the following characteristic: + +\f[\texttt{dst} (x,y) = \mathrm{det} M^{(x,y)} - k \cdot \left ( \mathrm{tr} M^{(x,y)} \right )^2\f] + +Corners in the image can be found as the local maxima of this response map. + +@param src Input single-channel 8-bit or floating-point image. +@param dst Image to store the Harris detector responses. It has the type CV_32FC1 and the same +size as src . +@param blockSize Neighborhood size (see the details on #cornerEigenValsAndVecs ). +@param ksize Aperture parameter for the Sobel operator. +@param k Harris detector free parameter. See the formula above. +@param borderType Pixel extrapolation method. See #BorderTypes. + */ +CV_EXPORTS_W void cornerHarris( InputArray src, OutputArray dst, int blockSize, + int ksize, double k, + int borderType = BORDER_DEFAULT ); + +/** @brief Calculates eigenvalues and eigenvectors of image blocks for corner detection. + +For every pixel \f$p\f$ , the function cornerEigenValsAndVecs considers a blockSize \f$\times\f$ blockSize +neighborhood \f$S(p)\f$ . It calculates the covariation matrix of derivatives over the neighborhood as: + +\f[M = \begin{bmatrix} \sum _{S(p)}(dI/dx)^2 & \sum _{S(p)}dI/dx dI/dy \\ \sum _{S(p)}dI/dx dI/dy & \sum _{S(p)}(dI/dy)^2 \end{bmatrix}\f] + +where the derivatives are computed using the Sobel operator. + +After that, it finds eigenvectors and eigenvalues of \f$M\f$ and stores them in the destination image as +\f$(\lambda_1, \lambda_2, x_1, y_1, x_2, y_2)\f$ where + +- \f$\lambda_1, \lambda_2\f$ are the non-sorted eigenvalues of \f$M\f$ +- \f$x_1, y_1\f$ are the eigenvectors corresponding to \f$\lambda_1\f$ +- \f$x_2, y_2\f$ are the eigenvectors corresponding to \f$\lambda_2\f$ + +The output of the function can be used for robust edge or corner detection. + +@param src Input single-channel 8-bit or floating-point image. +@param dst Image to store the results. It has the same size as src and the type CV_32FC(6) . +@param blockSize Neighborhood size (see details below). +@param ksize Aperture parameter for the Sobel operator. +@param borderType Pixel extrapolation method. See #BorderTypes. + +@sa cornerMinEigenVal, cornerHarris, preCornerDetect + */ +CV_EXPORTS_W void cornerEigenValsAndVecs( InputArray src, OutputArray dst, + int blockSize, int ksize, + int borderType = BORDER_DEFAULT ); + +/** @brief Calculates a feature map for corner detection. + +The function calculates the complex spatial derivative-based function of the source image + +\f[\texttt{dst} = (D_x \texttt{src} )^2 \cdot D_{yy} \texttt{src} + (D_y \texttt{src} )^2 \cdot D_{xx} \texttt{src} - 2 D_x \texttt{src} \cdot D_y \texttt{src} \cdot D_{xy} \texttt{src}\f] + +where \f$D_x\f$,\f$D_y\f$ are the first image derivatives, \f$D_{xx}\f$,\f$D_{yy}\f$ are the second image +derivatives, and \f$D_{xy}\f$ is the mixed derivative. + +The corners can be found as local maximums of the functions, as shown below: +@code + Mat corners, dilated_corners; + preCornerDetect(image, corners, 3); + // dilation with 3x3 rectangular structuring element + dilate(corners, dilated_corners, Mat(), 1); + Mat corner_mask = corners == dilated_corners; +@endcode + +@param src Source single-channel 8-bit of floating-point image. +@param dst Output image that has the type CV_32F and the same size as src . +@param ksize %Aperture size of the Sobel . +@param borderType Pixel extrapolation method. See #BorderTypes. + */ +CV_EXPORTS_W void preCornerDetect( InputArray src, OutputArray dst, int ksize, + int borderType = BORDER_DEFAULT ); + +/** @brief Refines the corner locations. + +The function iterates to find the sub-pixel accurate location of corners or radial saddle points, as +shown on the figure below. + +![image](pics/cornersubpix.png) + +Sub-pixel accurate corner locator is based on the observation that every vector from the center \f$q\f$ +to a point \f$p\f$ located within a neighborhood of \f$q\f$ is orthogonal to the image gradient at \f$p\f$ +subject to image and measurement noise. Consider the expression: + +\f[\epsilon _i = {DI_{p_i}}^T \cdot (q - p_i)\f] + +where \f${DI_{p_i}}\f$ is an image gradient at one of the points \f$p_i\f$ in a neighborhood of \f$q\f$ . The +value of \f$q\f$ is to be found so that \f$\epsilon_i\f$ is minimized. A system of equations may be set up +with \f$\epsilon_i\f$ set to zero: + +\f[\sum _i(DI_{p_i} \cdot {DI_{p_i}}^T) \cdot q - \sum _i(DI_{p_i} \cdot {DI_{p_i}}^T \cdot p_i)\f] + +where the gradients are summed within a neighborhood ("search window") of \f$q\f$ . Calling the first +gradient term \f$G\f$ and the second gradient term \f$b\f$ gives: + +\f[q = G^{-1} \cdot b\f] + +The algorithm sets the center of the neighborhood window at this new center \f$q\f$ and then iterates +until the center stays within a set threshold. + +@param image Input single-channel, 8-bit or float image. +@param corners Initial coordinates of the input corners and refined coordinates provided for +output. +@param winSize Half of the side length of the search window. For example, if winSize=Size(5,5) , +then a \f$(5*2+1) \times (5*2+1) = 11 \times 11\f$ search window is used. +@param zeroZone Half of the size of the dead region in the middle of the search zone over which +the summation in the formula below is not done. It is used sometimes to avoid possible +singularities of the autocorrelation matrix. The value of (-1,-1) indicates that there is no such +a size. +@param criteria Criteria for termination of the iterative process of corner refinement. That is, +the process of corner position refinement stops either after criteria.maxCount iterations or when +the corner position moves by less than criteria.epsilon on some iteration. + */ +CV_EXPORTS_W void cornerSubPix( InputArray image, InputOutputArray corners, + Size winSize, Size zeroZone, + TermCriteria criteria ); + +/** @brief Determines strong corners on an image. + +The function finds the most prominent corners in the image or in the specified image region, as +described in @cite Shi94 + +- Function calculates the corner quality measure at every source image pixel using the + #cornerMinEigenVal or #cornerHarris . +- Function performs a non-maximum suppression (the local maximums in *3 x 3* neighborhood are + retained). +- The corners with the minimal eigenvalue less than + \f$\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)\f$ are rejected. +- The remaining corners are sorted by the quality measure in the descending order. +- Function throws away each corner for which there is a stronger corner at a distance less than + maxDistance. + +The function can be used to initialize a point-based tracker of an object. + +@note If the function is called with different values A and B of the parameter qualityLevel , and +A \> B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector +with qualityLevel=B . + +@param image Input 8-bit or floating-point 32-bit, single-channel image. +@param corners Output vector of detected corners. +@param maxCorners Maximum number of corners to return. If there are more corners than are found, +the strongest of them is returned. `maxCorners <= 0` implies that no limit on the maximum is set +and all detected corners are returned. +@param qualityLevel Parameter characterizing the minimal accepted quality of image corners. The +parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue +(see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the +quality measure less than the product are rejected. For example, if the best corner has the +quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure +less than 15 are rejected. +@param minDistance Minimum possible Euclidean distance between the returned corners. +@param mask Optional region of interest. If the image is not empty (it needs to have the type +CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected. +@param blockSize Size of an average block for computing a derivative covariation matrix over each +pixel neighborhood. See cornerEigenValsAndVecs . +@param useHarrisDetector Parameter indicating whether to use a Harris detector (see #cornerHarris) +or #cornerMinEigenVal. +@param k Free parameter of the Harris detector. + +@sa cornerMinEigenVal, cornerHarris, calcOpticalFlowPyrLK, estimateRigidTransform, + */ + +CV_EXPORTS_W void goodFeaturesToTrack( InputArray image, OutputArray corners, + int maxCorners, double qualityLevel, double minDistance, + InputArray mask = noArray(), int blockSize = 3, + bool useHarrisDetector = false, double k = 0.04 ); + +CV_EXPORTS_W void goodFeaturesToTrack( InputArray image, OutputArray corners, + int maxCorners, double qualityLevel, double minDistance, + InputArray mask, int blockSize, + int gradientSize, bool useHarrisDetector = false, + double k = 0.04 ); +/** @example samples/cpp/tutorial_code/ImgTrans/houghlines.cpp +An example using the Hough line detector +![Sample input image](Hough_Lines_Tutorial_Original_Image.jpg) ![Output image](Hough_Lines_Tutorial_Result.jpg) +*/ + +/** @brief Finds lines in a binary image using the standard Hough transform. + +The function implements the standard or standard multi-scale Hough transform algorithm for line +detection. See for a good explanation of Hough +transform. + +@param image 8-bit, single-channel binary source image. The image may be modified by the function. +@param lines Output vector of lines. Each line is represented by a 2 or 3 element vector +\f$(\rho, \theta)\f$ or \f$(\rho, \theta, \textrm{votes})\f$ . \f$\rho\f$ is the distance from the coordinate origin \f$(0,0)\f$ (top-left corner of +the image). \f$\theta\f$ is the line rotation angle in radians ( +\f$0 \sim \textrm{vertical line}, \pi/2 \sim \textrm{horizontal line}\f$ ). +\f$\textrm{votes}\f$ is the value of accumulator. +@param rho Distance resolution of the accumulator in pixels. +@param theta Angle resolution of the accumulator in radians. +@param threshold Accumulator threshold parameter. Only those lines are returned that get enough +votes ( \f$>\texttt{threshold}\f$ ). +@param srn For the multi-scale Hough transform, it is a divisor for the distance resolution rho . +The coarse accumulator distance resolution is rho and the accurate accumulator resolution is +rho/srn . If both srn=0 and stn=0 , the classical Hough transform is used. Otherwise, both these +parameters should be positive. +@param stn For the multi-scale Hough transform, it is a divisor for the distance resolution theta. +@param min_theta For standard and multi-scale Hough transform, minimum angle to check for lines. +Must fall between 0 and max_theta. +@param max_theta For standard and multi-scale Hough transform, maximum angle to check for lines. +Must fall between min_theta and CV_PI. + */ +CV_EXPORTS_W void HoughLines( InputArray image, OutputArray lines, + double rho, double theta, int threshold, + double srn = 0, double stn = 0, + double min_theta = 0, double max_theta = CV_PI ); + +/** @brief Finds line segments in a binary image using the probabilistic Hough transform. + +The function implements the probabilistic Hough transform algorithm for line detection, described +in @cite Matas00 + +See the line detection example below: +@include snippets/imgproc_HoughLinesP.cpp +This is a sample picture the function parameters have been tuned for: + +![image](pics/building.jpg) + +And this is the output of the above program in case of the probabilistic Hough transform: + +![image](pics/houghp.png) + +@param image 8-bit, single-channel binary source image. The image may be modified by the function. +@param lines Output vector of lines. Each line is represented by a 4-element vector +\f$(x_1, y_1, x_2, y_2)\f$ , where \f$(x_1,y_1)\f$ and \f$(x_2, y_2)\f$ are the ending points of each detected +line segment. +@param rho Distance resolution of the accumulator in pixels. +@param theta Angle resolution of the accumulator in radians. +@param threshold Accumulator threshold parameter. Only those lines are returned that get enough +votes ( \f$>\texttt{threshold}\f$ ). +@param minLineLength Minimum line length. Line segments shorter than that are rejected. +@param maxLineGap Maximum allowed gap between points on the same line to link them. + +@sa LineSegmentDetector + */ +CV_EXPORTS_W void HoughLinesP( InputArray image, OutputArray lines, + double rho, double theta, int threshold, + double minLineLength = 0, double maxLineGap = 0 ); + +/** @brief Finds lines in a set of points using the standard Hough transform. + +The function finds lines in a set of points using a modification of the Hough transform. +@include snippets/imgproc_HoughLinesPointSet.cpp +@param _point Input vector of points. Each vector must be encoded as a Point vector \f$(x,y)\f$. Type must be CV_32FC2 or CV_32SC2. +@param _lines Output vector of found lines. Each vector is encoded as a vector \f$(votes, rho, theta)\f$. +The larger the value of 'votes', the higher the reliability of the Hough line. +@param lines_max Max count of hough lines. +@param threshold Accumulator threshold parameter. Only those lines are returned that get enough +votes ( \f$>\texttt{threshold}\f$ ) +@param min_rho Minimum Distance value of the accumulator in pixels. +@param max_rho Maximum Distance value of the accumulator in pixels. +@param rho_step Distance resolution of the accumulator in pixels. +@param min_theta Minimum angle value of the accumulator in radians. +@param max_theta Maximum angle value of the accumulator in radians. +@param theta_step Angle resolution of the accumulator in radians. + */ +CV_EXPORTS_W void HoughLinesPointSet( InputArray _point, OutputArray _lines, int lines_max, int threshold, + double min_rho, double max_rho, double rho_step, + double min_theta, double max_theta, double theta_step ); + +/** @example samples/cpp/tutorial_code/ImgTrans/houghcircles.cpp +An example using the Hough circle detector +*/ + +/** @brief Finds circles in a grayscale image using the Hough transform. + +The function finds circles in a grayscale image using a modification of the Hough transform. + +Example: : +@include snippets/imgproc_HoughLinesCircles.cpp + +@note Usually the function detects the centers of circles well. However, it may fail to find correct +radii. You can assist to the function by specifying the radius range ( minRadius and maxRadius ) if +you know it. Or, you may set maxRadius to a negative number to return centers only without radius +search, and find the correct radius using an additional procedure. + +@param image 8-bit, single-channel, grayscale input image. +@param circles Output vector of found circles. Each vector is encoded as 3 or 4 element +floating-point vector \f$(x, y, radius)\f$ or \f$(x, y, radius, votes)\f$ . +@param method Detection method, see #HoughModes. Currently, the only implemented method is #HOUGH_GRADIENT +@param dp Inverse ratio of the accumulator resolution to the image resolution. For example, if +dp=1 , the accumulator has the same resolution as the input image. If dp=2 , the accumulator has +half as big width and height. +@param minDist Minimum distance between the centers of the detected circles. If the parameter is +too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is +too large, some circles may be missed. +@param param1 First method-specific parameter. In case of #HOUGH_GRADIENT , it is the higher +threshold of the two passed to the Canny edge detector (the lower one is twice smaller). +@param param2 Second method-specific parameter. In case of #HOUGH_GRADIENT , it is the +accumulator threshold for the circle centers at the detection stage. The smaller it is, the more +false circles may be detected. Circles, corresponding to the larger accumulator values, will be +returned first. +@param minRadius Minimum circle radius. +@param maxRadius Maximum circle radius. If <= 0, uses the maximum image dimension. If < 0, returns +centers without finding the radius. + +@sa fitEllipse, minEnclosingCircle + */ +CV_EXPORTS_W void HoughCircles( InputArray image, OutputArray circles, + int method, double dp, double minDist, + double param1 = 100, double param2 = 100, + int minRadius = 0, int maxRadius = 0 ); + +//! @} imgproc_feature + +//! @addtogroup imgproc_filter +//! @{ + +/** @example samples/cpp/tutorial_code/ImgProc/Morphology_2.cpp +Advanced morphology Transformations sample code +![Sample screenshot](Morphology_2_Tutorial_Result.jpg) +Check @ref tutorial_opening_closing_hats "the corresponding tutorial" for more details +*/ + +/** @brief Erodes an image by using a specific structuring element. + +The function erodes the source image using the specified structuring element that determines the +shape of a pixel neighborhood over which the minimum is taken: + +\f[\texttt{dst} (x,y) = \min _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\f] + +The function supports the in-place mode. Erosion can be applied several ( iterations ) times. In +case of multi-channel images, each channel is processed independently. + +@param src input image; the number of channels can be arbitrary, but the depth should be one of +CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. +@param dst output image of the same size and type as src. +@param kernel structuring element used for erosion; if `element=Mat()`, a `3 x 3` rectangular +structuring element is used. Kernel can be created using #getStructuringElement. +@param anchor position of the anchor within the element; default value (-1, -1) means that the +anchor is at the element center. +@param iterations number of times erosion is applied. +@param borderType pixel extrapolation method, see #BorderTypes +@param borderValue border value in case of a constant border +@sa dilate, morphologyEx, getStructuringElement + */ +CV_EXPORTS_W void erode( InputArray src, OutputArray dst, InputArray kernel, + Point anchor = Point(-1,-1), int iterations = 1, + int borderType = BORDER_CONSTANT, + const Scalar& borderValue = morphologyDefaultBorderValue() ); + +/** @example samples/cpp/tutorial_code/ImgProc/Morphology_1.cpp +Erosion and Dilation sample code +![Sample Screenshot-Erosion](Morphology_1_Tutorial_Erosion_Result.jpg)![Sample Screenshot-Dilation](Morphology_1_Tutorial_Dilation_Result.jpg) +Check @ref tutorial_erosion_dilatation "the corresponding tutorial" for more details +*/ + +/** @brief Dilates an image by using a specific structuring element. + +The function dilates the source image using the specified structuring element that determines the +shape of a pixel neighborhood over which the maximum is taken: +\f[\texttt{dst} (x,y) = \max _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\f] + +The function supports the in-place mode. Dilation can be applied several ( iterations ) times. In +case of multi-channel images, each channel is processed independently. + +@param src input image; the number of channels can be arbitrary, but the depth should be one of +CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. +@param dst output image of the same size and type as src. +@param kernel structuring element used for dilation; if elemenat=Mat(), a 3 x 3 rectangular +structuring element is used. Kernel can be created using #getStructuringElement +@param anchor position of the anchor within the element; default value (-1, -1) means that the +anchor is at the element center. +@param iterations number of times dilation is applied. +@param borderType pixel extrapolation method, see #BorderTypes +@param borderValue border value in case of a constant border +@sa erode, morphologyEx, getStructuringElement + */ +CV_EXPORTS_W void dilate( InputArray src, OutputArray dst, InputArray kernel, + Point anchor = Point(-1,-1), int iterations = 1, + int borderType = BORDER_CONSTANT, + const Scalar& borderValue = morphologyDefaultBorderValue() ); + +/** @brief Performs advanced morphological transformations. + +The function cv::morphologyEx can perform advanced morphological transformations using an erosion and dilation as +basic operations. + +Any of the operations can be done in-place. In case of multi-channel images, each channel is +processed independently. + +@param src Source image. The number of channels can be arbitrary. The depth should be one of +CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. +@param dst Destination image of the same size and type as source image. +@param op Type of a morphological operation, see #MorphTypes +@param kernel Structuring element. It can be created using #getStructuringElement. +@param anchor Anchor position with the kernel. Negative values mean that the anchor is at the +kernel center. +@param iterations Number of times erosion and dilation are applied. +@param borderType Pixel extrapolation method, see #BorderTypes +@param borderValue Border value in case of a constant border. The default value has a special +meaning. +@sa dilate, erode, getStructuringElement +@note The number of iterations is the number of times erosion or dilatation operation will be applied. +For instance, an opening operation (#MORPH_OPEN) with two iterations is equivalent to apply +successively: erode -> erode -> dilate -> dilate (and not erode -> dilate -> erode -> dilate). + */ +CV_EXPORTS_W void morphologyEx( InputArray src, OutputArray dst, + int op, InputArray kernel, + Point anchor = Point(-1,-1), int iterations = 1, + int borderType = BORDER_CONSTANT, + const Scalar& borderValue = morphologyDefaultBorderValue() ); + +//! @} imgproc_filter + +//! @addtogroup imgproc_transform +//! @{ + +/** @brief Resizes an image. + +The function resize resizes the image src down to or up to the specified size. Note that the +initial dst type or size are not taken into account. Instead, the size and type are derived from +the `src`,`dsize`,`fx`, and `fy`. If you want to resize src so that it fits the pre-created dst, +you may call the function as follows: +@code + // explicitly specify dsize=dst.size(); fx and fy will be computed from that. + resize(src, dst, dst.size(), 0, 0, interpolation); +@endcode +If you want to decimate the image by factor of 2 in each direction, you can call the function this +way: +@code + // specify fx and fy and let the function compute the destination image size. + resize(src, dst, Size(), 0.5, 0.5, interpolation); +@endcode +To shrink an image, it will generally look best with #INTER_AREA interpolation, whereas to +enlarge an image, it will generally look best with c#INTER_CUBIC (slow) or #INTER_LINEAR +(faster but still looks OK). + +@param src input image. +@param dst output image; it has the size dsize (when it is non-zero) or the size computed from +src.size(), fx, and fy; the type of dst is the same as of src. +@param dsize output image size; if it equals zero, it is computed as: + \f[\texttt{dsize = Size(round(fx*src.cols), round(fy*src.rows))}\f] + Either dsize or both fx and fy must be non-zero. +@param fx scale factor along the horizontal axis; when it equals 0, it is computed as +\f[\texttt{(double)dsize.width/src.cols}\f] +@param fy scale factor along the vertical axis; when it equals 0, it is computed as +\f[\texttt{(double)dsize.height/src.rows}\f] +@param interpolation interpolation method, see #InterpolationFlags + +@sa warpAffine, warpPerspective, remap + */ +CV_EXPORTS_W void resize( InputArray src, OutputArray dst, + Size dsize, double fx = 0, double fy = 0, + int interpolation = INTER_LINEAR ); + +/** @brief Applies an affine transformation to an image. + +The function warpAffine transforms the source image using the specified matrix: + +\f[\texttt{dst} (x,y) = \texttt{src} ( \texttt{M} _{11} x + \texttt{M} _{12} y + \texttt{M} _{13}, \texttt{M} _{21} x + \texttt{M} _{22} y + \texttt{M} _{23})\f] + +when the flag #WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted +with #invertAffineTransform and then put in the formula above instead of M. The function cannot +operate in-place. + +@param src input image. +@param dst output image that has the size dsize and the same type as src . +@param M \f$2\times 3\f$ transformation matrix. +@param dsize size of the output image. +@param flags combination of interpolation methods (see #InterpolationFlags) and the optional +flag #WARP_INVERSE_MAP that means that M is the inverse transformation ( +\f$\texttt{dst}\rightarrow\texttt{src}\f$ ). +@param borderMode pixel extrapolation method (see #BorderTypes); when +borderMode=#BORDER_TRANSPARENT, it means that the pixels in the destination image corresponding to +the "outliers" in the source image are not modified by the function. +@param borderValue value used in case of a constant border; by default, it is 0. + +@sa warpPerspective, resize, remap, getRectSubPix, transform + */ +CV_EXPORTS_W void warpAffine( InputArray src, OutputArray dst, + InputArray M, Size dsize, + int flags = INTER_LINEAR, + int borderMode = BORDER_CONSTANT, + const Scalar& borderValue = Scalar()); + +/** @example samples/cpp/warpPerspective_demo.cpp +An example program shows using cv::findHomography and cv::warpPerspective for image warping +*/ + +/** @brief Applies a perspective transformation to an image. + +The function warpPerspective transforms the source image using the specified matrix: + +\f[\texttt{dst} (x,y) = \texttt{src} \left ( \frac{M_{11} x + M_{12} y + M_{13}}{M_{31} x + M_{32} y + M_{33}} , + \frac{M_{21} x + M_{22} y + M_{23}}{M_{31} x + M_{32} y + M_{33}} \right )\f] + +when the flag #WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted with invert +and then put in the formula above instead of M. The function cannot operate in-place. + +@param src input image. +@param dst output image that has the size dsize and the same type as src . +@param M \f$3\times 3\f$ transformation matrix. +@param dsize size of the output image. +@param flags combination of interpolation methods (#INTER_LINEAR or #INTER_NEAREST) and the +optional flag #WARP_INVERSE_MAP, that sets M as the inverse transformation ( +\f$\texttt{dst}\rightarrow\texttt{src}\f$ ). +@param borderMode pixel extrapolation method (#BORDER_CONSTANT or #BORDER_REPLICATE). +@param borderValue value used in case of a constant border; by default, it equals 0. + +@sa warpAffine, resize, remap, getRectSubPix, perspectiveTransform + */ +CV_EXPORTS_W void warpPerspective( InputArray src, OutputArray dst, + InputArray M, Size dsize, + int flags = INTER_LINEAR, + int borderMode = BORDER_CONSTANT, + const Scalar& borderValue = Scalar()); + +/** @brief Applies a generic geometrical transformation to an image. + +The function remap transforms the source image using the specified map: + +\f[\texttt{dst} (x,y) = \texttt{src} (map_x(x,y),map_y(x,y))\f] + +where values of pixels with non-integer coordinates are computed using one of available +interpolation methods. \f$map_x\f$ and \f$map_y\f$ can be encoded as separate floating-point maps +in \f$map_1\f$ and \f$map_2\f$ respectively, or interleaved floating-point maps of \f$(x,y)\f$ in +\f$map_1\f$, or fixed-point maps created by using convertMaps. The reason you might want to +convert from floating to fixed-point representations of a map is that they can yield much faster +(\~2x) remapping operations. In the converted case, \f$map_1\f$ contains pairs (cvFloor(x), +cvFloor(y)) and \f$map_2\f$ contains indices in a table of interpolation coefficients. + +This function cannot operate in-place. + +@param src Source image. +@param dst Destination image. It has the same size as map1 and the same type as src . +@param map1 The first map of either (x,y) points or just x values having the type CV_16SC2 , +CV_32FC1, or CV_32FC2. See convertMaps for details on converting a floating point +representation to fixed-point for speed. +@param map2 The second map of y values having the type CV_16UC1, CV_32FC1, or none (empty map +if map1 is (x,y) points), respectively. +@param interpolation Interpolation method (see #InterpolationFlags). The method #INTER_AREA is +not supported by this function. +@param borderMode Pixel extrapolation method (see #BorderTypes). When +borderMode=#BORDER_TRANSPARENT, it means that the pixels in the destination image that +corresponds to the "outliers" in the source image are not modified by the function. +@param borderValue Value used in case of a constant border. By default, it is 0. +@note +Due to current implementation limitations the size of an input and output images should be less than 32767x32767. + */ +CV_EXPORTS_W void remap( InputArray src, OutputArray dst, + InputArray map1, InputArray map2, + int interpolation, int borderMode = BORDER_CONSTANT, + const Scalar& borderValue = Scalar()); + +/** @brief Converts image transformation maps from one representation to another. + +The function converts a pair of maps for remap from one representation to another. The following +options ( (map1.type(), map2.type()) \f$\rightarrow\f$ (dstmap1.type(), dstmap2.type()) ) are +supported: + +- \f$\texttt{(CV_32FC1, CV_32FC1)} \rightarrow \texttt{(CV_16SC2, CV_16UC1)}\f$. This is the +most frequently used conversion operation, in which the original floating-point maps (see remap ) +are converted to a more compact and much faster fixed-point representation. The first output array +contains the rounded coordinates and the second array (created only when nninterpolation=false ) +contains indices in the interpolation tables. + +- \f$\texttt{(CV_32FC2)} \rightarrow \texttt{(CV_16SC2, CV_16UC1)}\f$. The same as above but +the original maps are stored in one 2-channel matrix. + +- Reverse conversion. Obviously, the reconstructed floating-point maps will not be exactly the same +as the originals. + +@param map1 The first input map of type CV_16SC2, CV_32FC1, or CV_32FC2 . +@param map2 The second input map of type CV_16UC1, CV_32FC1, or none (empty matrix), +respectively. +@param dstmap1 The first output map that has the type dstmap1type and the same size as src . +@param dstmap2 The second output map. +@param dstmap1type Type of the first output map that should be CV_16SC2, CV_32FC1, or +CV_32FC2 . +@param nninterpolation Flag indicating whether the fixed-point maps are used for the +nearest-neighbor or for a more complex interpolation. + +@sa remap, undistort, initUndistortRectifyMap + */ +CV_EXPORTS_W void convertMaps( InputArray map1, InputArray map2, + OutputArray dstmap1, OutputArray dstmap2, + int dstmap1type, bool nninterpolation = false ); + +/** @brief Calculates an affine matrix of 2D rotation. + +The function calculates the following matrix: + +\f[\begin{bmatrix} \alpha & \beta & (1- \alpha ) \cdot \texttt{center.x} - \beta \cdot \texttt{center.y} \\ - \beta & \alpha & \beta \cdot \texttt{center.x} + (1- \alpha ) \cdot \texttt{center.y} \end{bmatrix}\f] + +where + +\f[\begin{array}{l} \alpha = \texttt{scale} \cdot \cos \texttt{angle} , \\ \beta = \texttt{scale} \cdot \sin \texttt{angle} \end{array}\f] + +The transformation maps the rotation center to itself. If this is not the target, adjust the shift. + +@param center Center of the rotation in the source image. +@param angle Rotation angle in degrees. Positive values mean counter-clockwise rotation (the +coordinate origin is assumed to be the top-left corner). +@param scale Isotropic scale factor. + +@sa getAffineTransform, warpAffine, transform + */ +CV_EXPORTS_W Mat getRotationMatrix2D( Point2f center, double angle, double scale ); + +//! returns 3x3 perspective transformation for the corresponding 4 point pairs. +CV_EXPORTS Mat getPerspectiveTransform( const Point2f src[], const Point2f dst[] ); + +/** @brief Calculates an affine transform from three pairs of the corresponding points. + +The function calculates the \f$2 \times 3\f$ matrix of an affine transform so that: + +\f[\begin{bmatrix} x'_i \\ y'_i \end{bmatrix} = \texttt{map_matrix} \cdot \begin{bmatrix} x_i \\ y_i \\ 1 \end{bmatrix}\f] + +where + +\f[dst(i)=(x'_i,y'_i), src(i)=(x_i, y_i), i=0,1,2\f] + +@param src Coordinates of triangle vertices in the source image. +@param dst Coordinates of the corresponding triangle vertices in the destination image. + +@sa warpAffine, transform + */ +CV_EXPORTS Mat getAffineTransform( const Point2f src[], const Point2f dst[] ); + +/** @brief Inverts an affine transformation. + +The function computes an inverse affine transformation represented by \f$2 \times 3\f$ matrix M: + +\f[\begin{bmatrix} a_{11} & a_{12} & b_1 \\ a_{21} & a_{22} & b_2 \end{bmatrix}\f] + +The result is also a \f$2 \times 3\f$ matrix of the same type as M. + +@param M Original affine transformation. +@param iM Output reverse affine transformation. + */ +CV_EXPORTS_W void invertAffineTransform( InputArray M, OutputArray iM ); + +/** @brief Calculates a perspective transform from four pairs of the corresponding points. + +The function calculates the \f$3 \times 3\f$ matrix of a perspective transform so that: + +\f[\begin{bmatrix} t_i x'_i \\ t_i y'_i \\ t_i \end{bmatrix} = \texttt{map_matrix} \cdot \begin{bmatrix} x_i \\ y_i \\ 1 \end{bmatrix}\f] + +where + +\f[dst(i)=(x'_i,y'_i), src(i)=(x_i, y_i), i=0,1,2,3\f] + +@param src Coordinates of quadrangle vertices in the source image. +@param dst Coordinates of the corresponding quadrangle vertices in the destination image. + +@sa findHomography, warpPerspective, perspectiveTransform + */ +CV_EXPORTS_W Mat getPerspectiveTransform( InputArray src, InputArray dst ); + +CV_EXPORTS_W Mat getAffineTransform( InputArray src, InputArray dst ); + +/** @brief Retrieves a pixel rectangle from an image with sub-pixel accuracy. + +The function getRectSubPix extracts pixels from src: + +\f[patch(x, y) = src(x + \texttt{center.x} - ( \texttt{dst.cols} -1)*0.5, y + \texttt{center.y} - ( \texttt{dst.rows} -1)*0.5)\f] + +where the values of the pixels at non-integer coordinates are retrieved using bilinear +interpolation. Every channel of multi-channel images is processed independently. Also +the image should be a single channel or three channel image. While the center of the +rectangle must be inside the image, parts of the rectangle may be outside. + +@param image Source image. +@param patchSize Size of the extracted patch. +@param center Floating point coordinates of the center of the extracted rectangle within the +source image. The center must be inside the image. +@param patch Extracted patch that has the size patchSize and the same number of channels as src . +@param patchType Depth of the extracted pixels. By default, they have the same depth as src . + +@sa warpAffine, warpPerspective + */ +CV_EXPORTS_W void getRectSubPix( InputArray image, Size patchSize, + Point2f center, OutputArray patch, int patchType = -1 ); + +/** @example samples/cpp/polar_transforms.cpp +An example using the cv::linearPolar and cv::logPolar operations +*/ + +/** @brief Remaps an image to semilog-polar coordinates space. + +@deprecated This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags+WARP_POLAR_LOG); + +@internal +Transform the source image using the following transformation (See @ref polar_remaps_reference_image "Polar remaps reference image d)"): +\f[\begin{array}{l} + dst( \rho , \phi ) = src(x,y) \\ + dst.size() \leftarrow src.size() +\end{array}\f] + +where +\f[\begin{array}{l} + I = (dx,dy) = (x - center.x,y - center.y) \\ + \rho = M \cdot log_e(\texttt{magnitude} (I)) ,\\ + \phi = Kangle \cdot \texttt{angle} (I) \\ +\end{array}\f] + +and +\f[\begin{array}{l} + M = src.cols / log_e(maxRadius) \\ + Kangle = src.rows / 2\Pi \\ +\end{array}\f] + +The function emulates the human "foveal" vision and can be used for fast scale and +rotation-invariant template matching, for object tracking and so forth. +@param src Source image +@param dst Destination image. It will have same size and type as src. +@param center The transformation center; where the output precision is maximal +@param M Magnitude scale parameter. It determines the radius of the bounding circle to transform too. +@param flags A combination of interpolation methods, see #InterpolationFlags + +@note +- The function can not operate in-place. +- To calculate magnitude and angle in degrees #cartToPolar is used internally thus angles are measured from 0 to 360 with accuracy about 0.3 degrees. + +@sa cv::linearPolar +@endinternal +*/ +CV_EXPORTS_W void logPolar( InputArray src, OutputArray dst, + Point2f center, double M, int flags ); + +/** @brief Remaps an image to polar coordinates space. + +@deprecated This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags) + +@internal +Transform the source image using the following transformation (See @ref polar_remaps_reference_image "Polar remaps reference image c)"): +\f[\begin{array}{l} + dst( \rho , \phi ) = src(x,y) \\ + dst.size() \leftarrow src.size() +\end{array}\f] + +where +\f[\begin{array}{l} + I = (dx,dy) = (x - center.x,y - center.y) \\ + \rho = Kmag \cdot \texttt{magnitude} (I) ,\\ + \phi = angle \cdot \texttt{angle} (I) +\end{array}\f] + +and +\f[\begin{array}{l} + Kx = src.cols / maxRadius \\ + Ky = src.rows / 2\Pi +\end{array}\f] + + +@param src Source image +@param dst Destination image. It will have same size and type as src. +@param center The transformation center; +@param maxRadius The radius of the bounding circle to transform. It determines the inverse magnitude scale parameter too. +@param flags A combination of interpolation methods, see #InterpolationFlags + +@note +- The function can not operate in-place. +- To calculate magnitude and angle in degrees #cartToPolar is used internally thus angles are measured from 0 to 360 with accuracy about 0.3 degrees. + +@sa cv::logPolar +@endinternal +*/ +CV_EXPORTS_W void linearPolar( InputArray src, OutputArray dst, + Point2f center, double maxRadius, int flags ); + + +/** \brief Remaps an image to polar or semilog-polar coordinates space + +@anchor polar_remaps_reference_image +![Polar remaps reference](pics/polar_remap_doc.png) + +Transform the source image using the following transformation: +\f[ +dst(\rho , \phi ) = src(x,y) +\f] + +where +\f[ +\begin{array}{l} +\vec{I} = (x - center.x, \;y - center.y) \\ +\phi = Kangle \cdot \texttt{angle} (\vec{I}) \\ +\rho = \left\{\begin{matrix} +Klin \cdot \texttt{magnitude} (\vec{I}) & default \\ +Klog \cdot log_e(\texttt{magnitude} (\vec{I})) & if \; semilog \\ +\end{matrix}\right. +\end{array} +\f] + +and +\f[ +\begin{array}{l} +Kangle = dsize.height / 2\Pi \\ +Klin = dsize.width / maxRadius \\ +Klog = dsize.width / log_e(maxRadius) \\ +\end{array} +\f] + + +\par Linear vs semilog mapping + +Polar mapping can be linear or semi-log. Add one of #WarpPolarMode to `flags` to specify the polar mapping mode. + +Linear is the default mode. + +The semilog mapping emulates the human "foveal" vision that permit very high acuity on the line of sight (central vision) +in contrast to peripheral vision where acuity is minor. + +\par Option on `dsize`: + +- if both values in `dsize <=0 ` (default), +the destination image will have (almost) same area of source bounding circle: +\f[\begin{array}{l} +dsize.area \leftarrow (maxRadius^2 \cdot \Pi) \\ +dsize.width = \texttt{cvRound}(maxRadius) \\ +dsize.height = \texttt{cvRound}(maxRadius \cdot \Pi) \\ +\end{array}\f] + + +- if only `dsize.height <= 0`, +the destination image area will be proportional to the bounding circle area but scaled by `Kx * Kx`: +\f[\begin{array}{l} +dsize.height = \texttt{cvRound}(dsize.width \cdot \Pi) \\ +\end{array} +\f] + +- if both values in `dsize > 0 `, +the destination image will have the given size therefore the area of the bounding circle will be scaled to `dsize`. + + +\par Reverse mapping + +You can get reverse mapping adding #WARP_INVERSE_MAP to `flags` +\snippet polar_transforms.cpp InverseMap + +In addiction, to calculate the original coordinate from a polar mapped coordinate \f$(rho, phi)->(x, y)\f$: +\snippet polar_transforms.cpp InverseCoordinate + +@param src Source image. +@param dst Destination image. It will have same type as src. +@param dsize The destination image size (see description for valid options). +@param center The transformation center. +@param maxRadius The radius of the bounding circle to transform. It determines the inverse magnitude scale parameter too. +@param flags A combination of interpolation methods, #InterpolationFlags + #WarpPolarMode. + - Add #WARP_POLAR_LINEAR to select linear polar mapping (default) + - Add #WARP_POLAR_LOG to select semilog polar mapping + - Add #WARP_INVERSE_MAP for reverse mapping. +@note +- The function can not operate in-place. +- To calculate magnitude and angle in degrees #cartToPolar is used internally thus angles are measured from 0 to 360 with accuracy about 0.3 degrees. +- This function uses #remap. Due to current implementation limitations the size of an input and output images should be less than 32767x32767. + +@sa cv::remap +*/ +CV_EXPORTS_W void warpPolar(InputArray src, OutputArray dst, Size dsize, + Point2f center, double maxRadius, int flags); + + +//! @} imgproc_transform + +//! @addtogroup imgproc_misc +//! @{ + +/** @overload */ +CV_EXPORTS_W void integral( InputArray src, OutputArray sum, int sdepth = -1 ); + +/** @overload */ +CV_EXPORTS_AS(integral2) void integral( InputArray src, OutputArray sum, + OutputArray sqsum, int sdepth = -1, int sqdepth = -1 ); + +/** @brief Calculates the integral of an image. + +The function calculates one or more integral images for the source image as follows: + +\f[\texttt{sum} (X,Y) = \sum _{x + +Calculates the cross-power spectrum of two supplied source arrays. The arrays are padded if needed +with getOptimalDFTSize. + +The function performs the following equations: +- First it applies a Hanning window (see ) to each +image to remove possible edge effects. This window is cached until the array size changes to speed +up processing time. +- Next it computes the forward DFTs of each source array: +\f[\mathbf{G}_a = \mathcal{F}\{src_1\}, \; \mathbf{G}_b = \mathcal{F}\{src_2\}\f] +where \f$\mathcal{F}\f$ is the forward DFT. +- It then computes the cross-power spectrum of each frequency domain array: +\f[R = \frac{ \mathbf{G}_a \mathbf{G}_b^*}{|\mathbf{G}_a \mathbf{G}_b^*|}\f] +- Next the cross-correlation is converted back into the time domain via the inverse DFT: +\f[r = \mathcal{F}^{-1}\{R\}\f] +- Finally, it computes the peak location and computes a 5x5 weighted centroid around the peak to +achieve sub-pixel accuracy. +\f[(\Delta x, \Delta y) = \texttt{weightedCentroid} \{\arg \max_{(x, y)}\{r\}\}\f] +- If non-zero, the response parameter is computed as the sum of the elements of r within the 5x5 +centroid around the peak location. It is normalized to a maximum of 1 (meaning there is a single +peak) and will be smaller when there are multiple peaks. + +@param src1 Source floating point array (CV_32FC1 or CV_64FC1) +@param src2 Source floating point array (CV_32FC1 or CV_64FC1) +@param window Floating point array with windowing coefficients to reduce edge effects (optional). +@param response Signal power within the 5x5 centroid around the peak, between 0 and 1 (optional). +@returns detected phase shift (sub-pixel) between the two arrays. + +@sa dft, getOptimalDFTSize, idft, mulSpectrums createHanningWindow + */ +CV_EXPORTS_W Point2d phaseCorrelate(InputArray src1, InputArray src2, + InputArray window = noArray(), CV_OUT double* response = 0); + +/** @brief This function computes a Hanning window coefficients in two dimensions. + +See (http://en.wikipedia.org/wiki/Hann_function) and (http://en.wikipedia.org/wiki/Window_function) +for more information. + +An example is shown below: +@code + // create hanning window of size 100x100 and type CV_32F + Mat hann; + createHanningWindow(hann, Size(100, 100), CV_32F); +@endcode +@param dst Destination array to place Hann coefficients in +@param winSize The window size specifications (both width and height must be > 1) +@param type Created array type + */ +CV_EXPORTS_W void createHanningWindow(OutputArray dst, Size winSize, int type); + +//! @} imgproc_motion + +//! @addtogroup imgproc_misc +//! @{ + +/** @brief Applies a fixed-level threshold to each array element. + +The function applies fixed-level thresholding to a multiple-channel array. The function is typically +used to get a bi-level (binary) image out of a grayscale image ( #compare could be also used for +this purpose) or for removing a noise, that is, filtering out pixels with too small or too large +values. There are several types of thresholding supported by the function. They are determined by +type parameter. + +Also, the special values #THRESH_OTSU or #THRESH_TRIANGLE may be combined with one of the +above values. In these cases, the function determines the optimal threshold value using the Otsu's +or Triangle algorithm and uses it instead of the specified thresh. + +@note Currently, the Otsu's and Triangle methods are implemented only for 8-bit single-channel images. + +@param src input array (multiple-channel, 8-bit or 32-bit floating point). +@param dst output array of the same size and type and the same number of channels as src. +@param thresh threshold value. +@param maxval maximum value to use with the #THRESH_BINARY and #THRESH_BINARY_INV thresholding +types. +@param type thresholding type (see #ThresholdTypes). +@return the computed threshold value if Otsu's or Triangle methods used. + +@sa adaptiveThreshold, findContours, compare, min, max + */ +CV_EXPORTS_W double threshold( InputArray src, OutputArray dst, + double thresh, double maxval, int type ); + + +/** @brief Applies an adaptive threshold to an array. + +The function transforms a grayscale image to a binary image according to the formulae: +- **THRESH_BINARY** + \f[dst(x,y) = \fork{\texttt{maxValue}}{if \(src(x,y) > T(x,y)\)}{0}{otherwise}\f] +- **THRESH_BINARY_INV** + \f[dst(x,y) = \fork{0}{if \(src(x,y) > T(x,y)\)}{\texttt{maxValue}}{otherwise}\f] +where \f$T(x,y)\f$ is a threshold calculated individually for each pixel (see adaptiveMethod parameter). + +The function can process the image in-place. + +@param src Source 8-bit single-channel image. +@param dst Destination image of the same size and the same type as src. +@param maxValue Non-zero value assigned to the pixels for which the condition is satisfied +@param adaptiveMethod Adaptive thresholding algorithm to use, see #AdaptiveThresholdTypes. +The #BORDER_REPLICATE | #BORDER_ISOLATED is used to process boundaries. +@param thresholdType Thresholding type that must be either #THRESH_BINARY or #THRESH_BINARY_INV, +see #ThresholdTypes. +@param blockSize Size of a pixel neighborhood that is used to calculate a threshold value for the +pixel: 3, 5, 7, and so on. +@param C Constant subtracted from the mean or weighted mean (see the details below). Normally, it +is positive but may be zero or negative as well. + +@sa threshold, blur, GaussianBlur + */ +CV_EXPORTS_W void adaptiveThreshold( InputArray src, OutputArray dst, + double maxValue, int adaptiveMethod, + int thresholdType, int blockSize, double C ); + +//! @} imgproc_misc + +//! @addtogroup imgproc_filter +//! @{ + +/** @example samples/cpp/tutorial_code/ImgProc/Pyramids/Pyramids.cpp +An example using pyrDown and pyrUp functions +*/ + +/** @brief Blurs an image and downsamples it. + +By default, size of the output image is computed as `Size((src.cols+1)/2, (src.rows+1)/2)`, but in +any case, the following conditions should be satisfied: + +\f[\begin{array}{l} | \texttt{dstsize.width} *2-src.cols| \leq 2 \\ | \texttt{dstsize.height} *2-src.rows| \leq 2 \end{array}\f] + +The function performs the downsampling step of the Gaussian pyramid construction. First, it +convolves the source image with the kernel: + +\f[\frac{1}{256} \begin{bmatrix} 1 & 4 & 6 & 4 & 1 \\ 4 & 16 & 24 & 16 & 4 \\ 6 & 24 & 36 & 24 & 6 \\ 4 & 16 & 24 & 16 & 4 \\ 1 & 4 & 6 & 4 & 1 \end{bmatrix}\f] + +Then, it downsamples the image by rejecting even rows and columns. + +@param src input image. +@param dst output image; it has the specified size and the same type as src. +@param dstsize size of the output image. +@param borderType Pixel extrapolation method, see #BorderTypes (#BORDER_CONSTANT isn't supported) + */ +CV_EXPORTS_W void pyrDown( InputArray src, OutputArray dst, + const Size& dstsize = Size(), int borderType = BORDER_DEFAULT ); + +/** @brief Upsamples an image and then blurs it. + +By default, size of the output image is computed as `Size(src.cols\*2, (src.rows\*2)`, but in any +case, the following conditions should be satisfied: + +\f[\begin{array}{l} | \texttt{dstsize.width} -src.cols*2| \leq ( \texttt{dstsize.width} \mod 2) \\ | \texttt{dstsize.height} -src.rows*2| \leq ( \texttt{dstsize.height} \mod 2) \end{array}\f] + +The function performs the upsampling step of the Gaussian pyramid construction, though it can +actually be used to construct the Laplacian pyramid. First, it upsamples the source image by +injecting even zero rows and columns and then convolves the result with the same kernel as in +pyrDown multiplied by 4. + +@param src input image. +@param dst output image. It has the specified size and the same type as src . +@param dstsize size of the output image. +@param borderType Pixel extrapolation method, see #BorderTypes (only #BORDER_DEFAULT is supported) + */ +CV_EXPORTS_W void pyrUp( InputArray src, OutputArray dst, + const Size& dstsize = Size(), int borderType = BORDER_DEFAULT ); + +/** @brief Constructs the Gaussian pyramid for an image. + +The function constructs a vector of images and builds the Gaussian pyramid by recursively applying +pyrDown to the previously built pyramid layers, starting from `dst[0]==src`. + +@param src Source image. Check pyrDown for the list of supported types. +@param dst Destination vector of maxlevel+1 images of the same type as src. dst[0] will be the +same as src. dst[1] is the next pyramid layer, a smoothed and down-sized src, and so on. +@param maxlevel 0-based index of the last (the smallest) pyramid layer. It must be non-negative. +@param borderType Pixel extrapolation method, see #BorderTypes (#BORDER_CONSTANT isn't supported) + */ +CV_EXPORTS void buildPyramid( InputArray src, OutputArrayOfArrays dst, + int maxlevel, int borderType = BORDER_DEFAULT ); + +//! @} imgproc_filter + +//! @addtogroup imgproc_transform +//! @{ + +/** @brief Transforms an image to compensate for lens distortion. + +The function transforms an image to compensate radial and tangential lens distortion. + +The function is simply a combination of #initUndistortRectifyMap (with unity R ) and #remap +(with bilinear interpolation). See the former function for details of the transformation being +performed. + +Those pixels in the destination image, for which there is no correspondent pixels in the source +image, are filled with zeros (black color). + +A particular subset of the source image that will be visible in the corrected image can be regulated +by newCameraMatrix. You can use #getOptimalNewCameraMatrix to compute the appropriate +newCameraMatrix depending on your requirements. + +The camera matrix and the distortion parameters can be determined using #calibrateCamera. If +the resolution of images is different from the resolution used at the calibration stage, \f$f_x, +f_y, c_x\f$ and \f$c_y\f$ need to be scaled accordingly, while the distortion coefficients remain +the same. + +@param src Input (distorted) image. +@param dst Output (corrected) image that has the same size and type as src . +@param cameraMatrix Input camera matrix \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . +@param distCoeffs Input vector of distortion coefficients +\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ +of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed. +@param newCameraMatrix Camera matrix of the distorted image. By default, it is the same as +cameraMatrix but you may additionally scale and shift the result by using a different matrix. + */ +CV_EXPORTS_W void undistort( InputArray src, OutputArray dst, + InputArray cameraMatrix, + InputArray distCoeffs, + InputArray newCameraMatrix = noArray() ); + +/** @brief Computes the undistortion and rectification transformation map. + +The function computes the joint undistortion and rectification transformation and represents the +result in the form of maps for remap. The undistorted image looks like original, as if it is +captured with a camera using the camera matrix =newCameraMatrix and zero distortion. In case of a +monocular camera, newCameraMatrix is usually equal to cameraMatrix, or it can be computed by +#getOptimalNewCameraMatrix for a better control over scaling. In case of a stereo camera, +newCameraMatrix is normally set to P1 or P2 computed by #stereoRectify . + +Also, this new camera is oriented differently in the coordinate space, according to R. That, for +example, helps to align two heads of a stereo camera so that the epipolar lines on both images +become horizontal and have the same y- coordinate (in case of a horizontally aligned stereo camera). + +The function actually builds the maps for the inverse mapping algorithm that is used by remap. That +is, for each pixel \f$(u, v)\f$ in the destination (corrected and rectified) image, the function +computes the corresponding coordinates in the source image (that is, in the original image from +camera). The following process is applied: +\f[ +\begin{array}{l} +x \leftarrow (u - {c'}_x)/{f'}_x \\ +y \leftarrow (v - {c'}_y)/{f'}_y \\ +{[X\,Y\,W]} ^T \leftarrow R^{-1}*[x \, y \, 1]^T \\ +x' \leftarrow X/W \\ +y' \leftarrow Y/W \\ +r^2 \leftarrow x'^2 + y'^2 \\ +x'' \leftarrow x' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} ++ 2p_1 x' y' + p_2(r^2 + 2 x'^2) + s_1 r^2 + s_2 r^4\\ +y'' \leftarrow y' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} ++ p_1 (r^2 + 2 y'^2) + 2 p_2 x' y' + s_3 r^2 + s_4 r^4 \\ +s\vecthree{x'''}{y'''}{1} = +\vecthreethree{R_{33}(\tau_x, \tau_y)}{0}{-R_{13}((\tau_x, \tau_y)} +{0}{R_{33}(\tau_x, \tau_y)}{-R_{23}(\tau_x, \tau_y)} +{0}{0}{1} R(\tau_x, \tau_y) \vecthree{x''}{y''}{1}\\ +map_x(u,v) \leftarrow x''' f_x + c_x \\ +map_y(u,v) \leftarrow y''' f_y + c_y +\end{array} +\f] +where \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ +are the distortion coefficients. + +In case of a stereo camera, this function is called twice: once for each camera head, after +stereoRectify, which in its turn is called after #stereoCalibrate. But if the stereo camera +was not calibrated, it is still possible to compute the rectification transformations directly from +the fundamental matrix using #stereoRectifyUncalibrated. For each camera, the function computes +homography H as the rectification transformation in a pixel domain, not a rotation matrix R in 3D +space. R can be computed from H as +\f[\texttt{R} = \texttt{cameraMatrix} ^{-1} \cdot \texttt{H} \cdot \texttt{cameraMatrix}\f] +where cameraMatrix can be chosen arbitrarily. + +@param cameraMatrix Input camera matrix \f$A=\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . +@param distCoeffs Input vector of distortion coefficients +\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ +of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed. +@param R Optional rectification transformation in the object space (3x3 matrix). R1 or R2 , +computed by #stereoRectify can be passed here. If the matrix is empty, the identity transformation +is assumed. In cvInitUndistortMap R assumed to be an identity matrix. +@param newCameraMatrix New camera matrix \f$A'=\vecthreethree{f_x'}{0}{c_x'}{0}{f_y'}{c_y'}{0}{0}{1}\f$. +@param size Undistorted image size. +@param m1type Type of the first output map that can be CV_32FC1, CV_32FC2 or CV_16SC2, see #convertMaps +@param map1 The first output map. +@param map2 The second output map. + */ +CV_EXPORTS_W void initUndistortRectifyMap( InputArray cameraMatrix, InputArray distCoeffs, + InputArray R, InputArray newCameraMatrix, + Size size, int m1type, OutputArray map1, OutputArray map2 ); + +//! initializes maps for #remap for wide-angle +CV_EXPORTS_W float initWideAngleProjMap( InputArray cameraMatrix, InputArray distCoeffs, + Size imageSize, int destImageWidth, + int m1type, OutputArray map1, OutputArray map2, + int projType = PROJ_SPHERICAL_EQRECT, double alpha = 0); + +/** @brief Returns the default new camera matrix. + +The function returns the camera matrix that is either an exact copy of the input cameraMatrix (when +centerPrinicipalPoint=false ), or the modified one (when centerPrincipalPoint=true). + +In the latter case, the new camera matrix will be: + +\f[\begin{bmatrix} f_x && 0 && ( \texttt{imgSize.width} -1)*0.5 \\ 0 && f_y && ( \texttt{imgSize.height} -1)*0.5 \\ 0 && 0 && 1 \end{bmatrix} ,\f] + +where \f$f_x\f$ and \f$f_y\f$ are \f$(0,0)\f$ and \f$(1,1)\f$ elements of cameraMatrix, respectively. + +By default, the undistortion functions in OpenCV (see #initUndistortRectifyMap, #undistort) do not +move the principal point. However, when you work with stereo, it is important to move the principal +points in both views to the same y-coordinate (which is required by most of stereo correspondence +algorithms), and may be to the same x-coordinate too. So, you can form the new camera matrix for +each view where the principal points are located at the center. + +@param cameraMatrix Input camera matrix. +@param imgsize Camera view image size in pixels. +@param centerPrincipalPoint Location of the principal point in the new camera matrix. The +parameter indicates whether this location should be at the image center or not. + */ +CV_EXPORTS_W Mat getDefaultNewCameraMatrix( InputArray cameraMatrix, Size imgsize = Size(), + bool centerPrincipalPoint = false ); + +/** @brief Computes the ideal point coordinates from the observed point coordinates. + +The function is similar to #undistort and #initUndistortRectifyMap but it operates on a +sparse set of points instead of a raster image. Also the function performs a reverse transformation +to projectPoints. In case of a 3D object, it does not reconstruct its 3D coordinates, but for a +planar object, it does, up to a translation vector, if the proper R is specified. + +For each observed point coordinate \f$(u, v)\f$ the function computes: +\f[ +\begin{array}{l} +x^{"} \leftarrow (u - c_x)/f_x \\ +y^{"} \leftarrow (v - c_y)/f_y \\ +(x',y') = undistort(x^{"},y^{"}, \texttt{distCoeffs}) \\ +{[X\,Y\,W]} ^T \leftarrow R*[x' \, y' \, 1]^T \\ +x \leftarrow X/W \\ +y \leftarrow Y/W \\ +\text{only performed if P is specified:} \\ +u' \leftarrow x {f'}_x + {c'}_x \\ +v' \leftarrow y {f'}_y + {c'}_y +\end{array} +\f] + +where *undistort* is an approximate iterative algorithm that estimates the normalized original +point coordinates out of the normalized distorted point coordinates ("normalized" means that the +coordinates do not depend on the camera matrix). + +The function can be used for both a stereo camera head or a monocular camera (when R is empty). + +@param src Observed point coordinates, 1xN or Nx1 2-channel (CV_32FC2 or CV_64FC2). +@param dst Output ideal point coordinates after undistortion and reverse perspective +transformation. If matrix P is identity or omitted, dst will contain normalized point coordinates. +@param cameraMatrix Camera matrix \f$\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . +@param distCoeffs Input vector of distortion coefficients +\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ +of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed. +@param R Rectification transformation in the object space (3x3 matrix). R1 or R2 computed by +#stereoRectify can be passed here. If the matrix is empty, the identity transformation is used. +@param P New camera matrix (3x3) or new projection matrix (3x4) \f$\begin{bmatrix} {f'}_x & 0 & {c'}_x & t_x \\ 0 & {f'}_y & {c'}_y & t_y \\ 0 & 0 & 1 & t_z \end{bmatrix}\f$. P1 or P2 computed by +#stereoRectify can be passed here. If the matrix is empty, the identity new camera matrix is used. + */ +CV_EXPORTS_W void undistortPoints( InputArray src, OutputArray dst, + InputArray cameraMatrix, InputArray distCoeffs, + InputArray R = noArray(), InputArray P = noArray()); +/** @overload + @note Default version of #undistortPoints does 5 iterations to compute undistorted points. + + */ +CV_EXPORTS_AS(undistortPointsIter) void undistortPoints( InputArray src, OutputArray dst, + InputArray cameraMatrix, InputArray distCoeffs, + InputArray R, InputArray P, TermCriteria criteria); + +//! @} imgproc_transform + +//! @addtogroup imgproc_hist +//! @{ + +/** @example samples/cpp/demhist.cpp +An example for creating histograms of an image +*/ + +/** @brief Calculates a histogram of a set of arrays. + +The function cv::calcHist calculates the histogram of one or more arrays. The elements of a tuple used +to increment a histogram bin are taken from the corresponding input arrays at the same location. The +sample below shows how to compute a 2D Hue-Saturation histogram for a color image. : +@include snippets/imgproc_calcHist.cpp + +@param images Source arrays. They all should have the same depth, CV_8U, CV_16U or CV_32F , and the same +size. Each of them can have an arbitrary number of channels. +@param nimages Number of source images. +@param channels List of the dims channels used to compute the histogram. The first array channels +are numerated from 0 to images[0].channels()-1 , the second array channels are counted from +images[0].channels() to images[0].channels() + images[1].channels()-1, and so on. +@param mask Optional mask. If the matrix is not empty, it must be an 8-bit array of the same size +as images[i] . The non-zero mask elements mark the array elements counted in the histogram. +@param hist Output histogram, which is a dense or sparse dims -dimensional array. +@param dims Histogram dimensionality that must be positive and not greater than CV_MAX_DIMS +(equal to 32 in the current OpenCV version). +@param histSize Array of histogram sizes in each dimension. +@param ranges Array of the dims arrays of the histogram bin boundaries in each dimension. When the +histogram is uniform ( uniform =true), then for each dimension i it is enough to specify the lower +(inclusive) boundary \f$L_0\f$ of the 0-th histogram bin and the upper (exclusive) boundary +\f$U_{\texttt{histSize}[i]-1}\f$ for the last histogram bin histSize[i]-1 . That is, in case of a +uniform histogram each of ranges[i] is an array of 2 elements. When the histogram is not uniform ( +uniform=false ), then each of ranges[i] contains histSize[i]+1 elements: +\f$L_0, U_0=L_1, U_1=L_2, ..., U_{\texttt{histSize[i]}-2}=L_{\texttt{histSize[i]}-1}, U_{\texttt{histSize[i]}-1}\f$ +. The array elements, that are not between \f$L_0\f$ and \f$U_{\texttt{histSize[i]}-1}\f$ , are not +counted in the histogram. +@param uniform Flag indicating whether the histogram is uniform or not (see above). +@param accumulate Accumulation flag. If it is set, the histogram is not cleared in the beginning +when it is allocated. This feature enables you to compute a single histogram from several sets of +arrays, or to update the histogram in time. +*/ +CV_EXPORTS void calcHist( const Mat* images, int nimages, + const int* channels, InputArray mask, + OutputArray hist, int dims, const int* histSize, + const float** ranges, bool uniform = true, bool accumulate = false ); + +/** @overload + +this variant uses %SparseMat for output +*/ +CV_EXPORTS void calcHist( const Mat* images, int nimages, + const int* channels, InputArray mask, + SparseMat& hist, int dims, + const int* histSize, const float** ranges, + bool uniform = true, bool accumulate = false ); + +/** @overload */ +CV_EXPORTS_W void calcHist( InputArrayOfArrays images, + const std::vector& channels, + InputArray mask, OutputArray hist, + const std::vector& histSize, + const std::vector& ranges, + bool accumulate = false ); + +/** @brief Calculates the back projection of a histogram. + +The function cv::calcBackProject calculates the back project of the histogram. That is, similarly to +#calcHist , at each location (x, y) the function collects the values from the selected channels +in the input images and finds the corresponding histogram bin. But instead of incrementing it, the +function reads the bin value, scales it by scale , and stores in backProject(x,y) . In terms of +statistics, the function computes probability of each element value in respect with the empirical +probability distribution represented by the histogram. See how, for example, you can find and track +a bright-colored object in a scene: + +- Before tracking, show the object to the camera so that it covers almost the whole frame. +Calculate a hue histogram. The histogram may have strong maximums, corresponding to the dominant +colors in the object. + +- When tracking, calculate a back projection of a hue plane of each input video frame using that +pre-computed histogram. Threshold the back projection to suppress weak colors. It may also make +sense to suppress pixels with non-sufficient color saturation and too dark or too bright pixels. + +- Find connected components in the resulting picture and choose, for example, the largest +component. + +This is an approximate algorithm of the CamShift color object tracker. + +@param images Source arrays. They all should have the same depth, CV_8U, CV_16U or CV_32F , and the same +size. Each of them can have an arbitrary number of channels. +@param nimages Number of source images. +@param channels The list of channels used to compute the back projection. The number of channels +must match the histogram dimensionality. The first array channels are numerated from 0 to +images[0].channels()-1 , the second array channels are counted from images[0].channels() to +images[0].channels() + images[1].channels()-1, and so on. +@param hist Input histogram that can be dense or sparse. +@param backProject Destination back projection array that is a single-channel array of the same +size and depth as images[0] . +@param ranges Array of arrays of the histogram bin boundaries in each dimension. See #calcHist . +@param scale Optional scale factor for the output back projection. +@param uniform Flag indicating whether the histogram is uniform or not (see above). + +@sa calcHist, compareHist + */ +CV_EXPORTS void calcBackProject( const Mat* images, int nimages, + const int* channels, InputArray hist, + OutputArray backProject, const float** ranges, + double scale = 1, bool uniform = true ); + +/** @overload */ +CV_EXPORTS void calcBackProject( const Mat* images, int nimages, + const int* channels, const SparseMat& hist, + OutputArray backProject, const float** ranges, + double scale = 1, bool uniform = true ); + +/** @overload */ +CV_EXPORTS_W void calcBackProject( InputArrayOfArrays images, const std::vector& channels, + InputArray hist, OutputArray dst, + const std::vector& ranges, + double scale ); + +/** @brief Compares two histograms. + +The function cv::compareHist compares two dense or two sparse histograms using the specified method. + +The function returns \f$d(H_1, H_2)\f$ . + +While the function works well with 1-, 2-, 3-dimensional dense histograms, it may not be suitable +for high-dimensional sparse histograms. In such histograms, because of aliasing and sampling +problems, the coordinates of non-zero histogram bins can slightly shift. To compare such histograms +or more general sparse configurations of weighted points, consider using the #EMD function. + +@param H1 First compared histogram. +@param H2 Second compared histogram of the same size as H1 . +@param method Comparison method, see #HistCompMethods + */ +CV_EXPORTS_W double compareHist( InputArray H1, InputArray H2, int method ); + +/** @overload */ +CV_EXPORTS double compareHist( const SparseMat& H1, const SparseMat& H2, int method ); + +/** @brief Equalizes the histogram of a grayscale image. + +The function equalizes the histogram of the input image using the following algorithm: + +- Calculate the histogram \f$H\f$ for src . +- Normalize the histogram so that the sum of histogram bins is 255. +- Compute the integral of the histogram: +\f[H'_i = \sum _{0 \le j < i} H(j)\f] +- Transform the image using \f$H'\f$ as a look-up table: \f$\texttt{dst}(x,y) = H'(\texttt{src}(x,y))\f$ + +The algorithm normalizes the brightness and increases the contrast of the image. + +@param src Source 8-bit single channel image. +@param dst Destination image of the same size and type as src . + */ +CV_EXPORTS_W void equalizeHist( InputArray src, OutputArray dst ); + +/** @brief Creates a smart pointer to a cv::CLAHE class and initializes it. + +@param clipLimit Threshold for contrast limiting. +@param tileGridSize Size of grid for histogram equalization. Input image will be divided into +equally sized rectangular tiles. tileGridSize defines the number of tiles in row and column. + */ +CV_EXPORTS_W Ptr createCLAHE(double clipLimit = 40.0, Size tileGridSize = Size(8, 8)); + +/** @brief Computes the "minimal work" distance between two weighted point configurations. + +The function computes the earth mover distance and/or a lower boundary of the distance between the +two weighted point configurations. One of the applications described in @cite RubnerSept98, +@cite Rubner2000 is multi-dimensional histogram comparison for image retrieval. EMD is a transportation +problem that is solved using some modification of a simplex algorithm, thus the complexity is +exponential in the worst case, though, on average it is much faster. In the case of a real metric +the lower boundary can be calculated even faster (using linear-time algorithm) and it can be used +to determine roughly whether the two signatures are far enough so that they cannot relate to the +same object. + +@param signature1 First signature, a \f$\texttt{size1}\times \texttt{dims}+1\f$ floating-point matrix. +Each row stores the point weight followed by the point coordinates. The matrix is allowed to have +a single column (weights only) if the user-defined cost matrix is used. The weights must be +non-negative and have at least one non-zero value. +@param signature2 Second signature of the same format as signature1 , though the number of rows +may be different. The total weights may be different. In this case an extra "dummy" point is added +to either signature1 or signature2. The weights must be non-negative and have at least one non-zero +value. +@param distType Used metric. See #DistanceTypes. +@param cost User-defined \f$\texttt{size1}\times \texttt{size2}\f$ cost matrix. Also, if a cost matrix +is used, lower boundary lowerBound cannot be calculated because it needs a metric function. +@param lowerBound Optional input/output parameter: lower boundary of a distance between the two +signatures that is a distance between mass centers. The lower boundary may not be calculated if +the user-defined cost matrix is used, the total weights of point configurations are not equal, or +if the signatures consist of weights only (the signature matrices have a single column). You +**must** initialize \*lowerBound . If the calculated distance between mass centers is greater or +equal to \*lowerBound (it means that the signatures are far enough), the function does not +calculate EMD. In any case \*lowerBound is set to the calculated distance between mass centers on +return. Thus, if you want to calculate both distance between mass centers and EMD, \*lowerBound +should be set to 0. +@param flow Resultant \f$\texttt{size1} \times \texttt{size2}\f$ flow matrix: \f$\texttt{flow}_{i,j}\f$ is +a flow from \f$i\f$ -th point of signature1 to \f$j\f$ -th point of signature2 . + */ +CV_EXPORTS float EMD( InputArray signature1, InputArray signature2, + int distType, InputArray cost=noArray(), + float* lowerBound = 0, OutputArray flow = noArray() ); + +CV_EXPORTS_AS(EMD) float wrapperEMD( InputArray signature1, InputArray signature2, + int distType, InputArray cost=noArray(), + CV_IN_OUT Ptr lowerBound = Ptr(), OutputArray flow = noArray() ); + +//! @} imgproc_hist + +/** @example samples/cpp/watershed.cpp +An example using the watershed algorithm +*/ + +/** @brief Performs a marker-based image segmentation using the watershed algorithm. + +The function implements one of the variants of watershed, non-parametric marker-based segmentation +algorithm, described in @cite Meyer92 . + +Before passing the image to the function, you have to roughly outline the desired regions in the +image markers with positive (\>0) indices. So, every region is represented as one or more connected +components with the pixel values 1, 2, 3, and so on. Such markers can be retrieved from a binary +mask using #findContours and #drawContours (see the watershed.cpp demo). The markers are "seeds" of +the future image regions. All the other pixels in markers , whose relation to the outlined regions +is not known and should be defined by the algorithm, should be set to 0's. In the function output, +each pixel in markers is set to a value of the "seed" components or to -1 at boundaries between the +regions. + +@note Any two neighbor connected components are not necessarily separated by a watershed boundary +(-1's pixels); for example, they can touch each other in the initial marker image passed to the +function. + +@param image Input 8-bit 3-channel image. +@param markers Input/output 32-bit single-channel image (map) of markers. It should have the same +size as image . + +@sa findContours + +@ingroup imgproc_misc + */ +CV_EXPORTS_W void watershed( InputArray image, InputOutputArray markers ); + +//! @addtogroup imgproc_filter +//! @{ + +/** @brief Performs initial step of meanshift segmentation of an image. + +The function implements the filtering stage of meanshift segmentation, that is, the output of the +function is the filtered "posterized" image with color gradients and fine-grain texture flattened. +At every pixel (X,Y) of the input image (or down-sized input image, see below) the function executes +meanshift iterations, that is, the pixel (X,Y) neighborhood in the joint space-color hyperspace is +considered: + +\f[(x,y): X- \texttt{sp} \le x \le X+ \texttt{sp} , Y- \texttt{sp} \le y \le Y+ \texttt{sp} , ||(R,G,B)-(r,g,b)|| \le \texttt{sr}\f] + +where (R,G,B) and (r,g,b) are the vectors of color components at (X,Y) and (x,y), respectively +(though, the algorithm does not depend on the color space used, so any 3-component color space can +be used instead). Over the neighborhood the average spatial value (X',Y') and average color vector +(R',G',B') are found and they act as the neighborhood center on the next iteration: + +\f[(X,Y)~(X',Y'), (R,G,B)~(R',G',B').\f] + +After the iterations over, the color components of the initial pixel (that is, the pixel from where +the iterations started) are set to the final value (average color at the last iteration): + +\f[I(X,Y) <- (R*,G*,B*)\f] + +When maxLevel \> 0, the gaussian pyramid of maxLevel+1 levels is built, and the above procedure is +run on the smallest layer first. After that, the results are propagated to the larger layer and the +iterations are run again only on those pixels where the layer colors differ by more than sr from the +lower-resolution layer of the pyramid. That makes boundaries of color regions sharper. Note that the +results will be actually different from the ones obtained by running the meanshift procedure on the +whole original image (i.e. when maxLevel==0). + +@param src The source 8-bit, 3-channel image. +@param dst The destination image of the same format and the same size as the source. +@param sp The spatial window radius. +@param sr The color window radius. +@param maxLevel Maximum level of the pyramid for the segmentation. +@param termcrit Termination criteria: when to stop meanshift iterations. + */ +CV_EXPORTS_W void pyrMeanShiftFiltering( InputArray src, OutputArray dst, + double sp, double sr, int maxLevel = 1, + TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5,1) ); + +//! @} + +//! @addtogroup imgproc_misc +//! @{ + +/** @example samples/cpp/grabcut.cpp +An example using the GrabCut algorithm +![Sample Screenshot](grabcut_output1.jpg) +*/ + +/** @brief Runs the GrabCut algorithm. + +The function implements the [GrabCut image segmentation algorithm](http://en.wikipedia.org/wiki/GrabCut). + +@param img Input 8-bit 3-channel image. +@param mask Input/output 8-bit single-channel mask. The mask is initialized by the function when +mode is set to #GC_INIT_WITH_RECT. Its elements may have one of the #GrabCutClasses. +@param rect ROI containing a segmented object. The pixels outside of the ROI are marked as +"obvious background". The parameter is only used when mode==#GC_INIT_WITH_RECT . +@param bgdModel Temporary array for the background model. Do not modify it while you are +processing the same image. +@param fgdModel Temporary arrays for the foreground model. Do not modify it while you are +processing the same image. +@param iterCount Number of iterations the algorithm should make before returning the result. Note +that the result can be refined with further calls with mode==#GC_INIT_WITH_MASK or +mode==GC_EVAL . +@param mode Operation mode that could be one of the #GrabCutModes + */ +CV_EXPORTS_W void grabCut( InputArray img, InputOutputArray mask, Rect rect, + InputOutputArray bgdModel, InputOutputArray fgdModel, + int iterCount, int mode = GC_EVAL ); + +/** @example samples/cpp/distrans.cpp +An example on using the distance transform +*/ + +/** @brief Calculates the distance to the closest zero pixel for each pixel of the source image. + +The function cv::distanceTransform calculates the approximate or precise distance from every binary +image pixel to the nearest zero pixel. For zero image pixels, the distance will obviously be zero. + +When maskSize == #DIST_MASK_PRECISE and distanceType == #DIST_L2 , the function runs the +algorithm described in @cite Felzenszwalb04 . This algorithm is parallelized with the TBB library. + +In other cases, the algorithm @cite Borgefors86 is used. This means that for a pixel the function +finds the shortest path to the nearest zero pixel consisting of basic shifts: horizontal, vertical, +diagonal, or knight's move (the latest is available for a \f$5\times 5\f$ mask). The overall +distance is calculated as a sum of these basic distances. Since the distance function should be +symmetric, all of the horizontal and vertical shifts must have the same cost (denoted as a ), all +the diagonal shifts must have the same cost (denoted as `b`), and all knight's moves must have the +same cost (denoted as `c`). For the #DIST_C and #DIST_L1 types, the distance is calculated +precisely, whereas for #DIST_L2 (Euclidean distance) the distance can be calculated only with a +relative error (a \f$5\times 5\f$ mask gives more accurate results). For `a`,`b`, and `c`, OpenCV +uses the values suggested in the original paper: +- DIST_L1: `a = 1, b = 2` +- DIST_L2: + - `3 x 3`: `a=0.955, b=1.3693` + - `5 x 5`: `a=1, b=1.4, c=2.1969` +- DIST_C: `a = 1, b = 1` + +Typically, for a fast, coarse distance estimation #DIST_L2, a \f$3\times 3\f$ mask is used. For a +more accurate distance estimation #DIST_L2, a \f$5\times 5\f$ mask or the precise algorithm is used. +Note that both the precise and the approximate algorithms are linear on the number of pixels. + +This variant of the function does not only compute the minimum distance for each pixel \f$(x, y)\f$ +but also identifies the nearest connected component consisting of zero pixels +(labelType==#DIST_LABEL_CCOMP) or the nearest zero pixel (labelType==#DIST_LABEL_PIXEL). Index of the +component/pixel is stored in `labels(x, y)`. When labelType==#DIST_LABEL_CCOMP, the function +automatically finds connected components of zero pixels in the input image and marks them with +distinct labels. When labelType==#DIST_LABEL_CCOMP, the function scans through the input image and +marks all the zero pixels with distinct labels. + +In this mode, the complexity is still linear. That is, the function provides a very fast way to +compute the Voronoi diagram for a binary image. Currently, the second variant can use only the +approximate distance transform algorithm, i.e. maskSize=#DIST_MASK_PRECISE is not supported +yet. + +@param src 8-bit, single-channel (binary) source image. +@param dst Output image with calculated distances. It is a 8-bit or 32-bit floating-point, +single-channel image of the same size as src. +@param labels Output 2D array of labels (the discrete Voronoi diagram). It has the type +CV_32SC1 and the same size as src. +@param distanceType Type of distance, see #DistanceTypes +@param maskSize Size of the distance transform mask, see #DistanceTransformMasks. +#DIST_MASK_PRECISE is not supported by this variant. In case of the #DIST_L1 or #DIST_C distance type, +the parameter is forced to 3 because a \f$3\times 3\f$ mask gives the same result as \f$5\times +5\f$ or any larger aperture. +@param labelType Type of the label array to build, see #DistanceTransformLabelTypes. + */ +CV_EXPORTS_AS(distanceTransformWithLabels) void distanceTransform( InputArray src, OutputArray dst, + OutputArray labels, int distanceType, int maskSize, + int labelType = DIST_LABEL_CCOMP ); + +/** @overload +@param src 8-bit, single-channel (binary) source image. +@param dst Output image with calculated distances. It is a 8-bit or 32-bit floating-point, +single-channel image of the same size as src . +@param distanceType Type of distance, see #DistanceTypes +@param maskSize Size of the distance transform mask, see #DistanceTransformMasks. In case of the +#DIST_L1 or #DIST_C distance type, the parameter is forced to 3 because a \f$3\times 3\f$ mask gives +the same result as \f$5\times 5\f$ or any larger aperture. +@param dstType Type of output image. It can be CV_8U or CV_32F. Type CV_8U can be used only for +the first variant of the function and distanceType == #DIST_L1. +*/ +CV_EXPORTS_W void distanceTransform( InputArray src, OutputArray dst, + int distanceType, int maskSize, int dstType=CV_32F); + +/** @example samples/cpp/ffilldemo.cpp +An example using the FloodFill technique +*/ + +/** @overload + +variant without `mask` parameter +*/ +CV_EXPORTS int floodFill( InputOutputArray image, + Point seedPoint, Scalar newVal, CV_OUT Rect* rect = 0, + Scalar loDiff = Scalar(), Scalar upDiff = Scalar(), + int flags = 4 ); + +/** @brief Fills a connected component with the given color. + +The function cv::floodFill fills a connected component starting from the seed point with the specified +color. The connectivity is determined by the color/brightness closeness of the neighbor pixels. The +pixel at \f$(x,y)\f$ is considered to belong to the repainted domain if: + +- in case of a grayscale image and floating range +\f[\texttt{src} (x',y')- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} (x',y')+ \texttt{upDiff}\f] + + +- in case of a grayscale image and fixed range +\f[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)+ \texttt{upDiff}\f] + + +- in case of a color image and floating range +\f[\texttt{src} (x',y')_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} (x',y')_r+ \texttt{upDiff} _r,\f] +\f[\texttt{src} (x',y')_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} (x',y')_g+ \texttt{upDiff} _g\f] +and +\f[\texttt{src} (x',y')_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} (x',y')_b+ \texttt{upDiff} _b\f] + + +- in case of a color image and fixed range +\f[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r+ \texttt{upDiff} _r,\f] +\f[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g+ \texttt{upDiff} _g\f] +and +\f[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b\f] + + +where \f$src(x',y')\f$ is the value of one of pixel neighbors that is already known to belong to the +component. That is, to be added to the connected component, a color/brightness of the pixel should +be close enough to: +- Color/brightness of one of its neighbors that already belong to the connected component in case +of a floating range. +- Color/brightness of the seed point in case of a fixed range. + +Use these functions to either mark a connected component with the specified color in-place, or build +a mask and then extract the contour, or copy the region to another image, and so on. + +@param image Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the +function unless the #FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See +the details below. +@param mask Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixels +taller than image. Since this is both an input and output parameter, you must take responsibility +of initializing it. Flood-filling cannot go across non-zero pixels in the input mask. For example, +an edge detector output can be used as a mask to stop filling at edges. On output, pixels in the +mask corresponding to filled pixels in the image are set to 1 or to the a value specified in flags +as described below. Additionally, the function fills the border of the mask with ones to simplify +internal processing. It is therefore possible to use the same mask in multiple calls to the function +to make sure the filled areas do not overlap. +@param seedPoint Starting point. +@param newVal New value of the repainted domain pixels. +@param loDiff Maximal lower brightness/color difference between the currently observed pixel and +one of its neighbors belonging to the component, or a seed pixel being added to the component. +@param upDiff Maximal upper brightness/color difference between the currently observed pixel and +one of its neighbors belonging to the component, or a seed pixel being added to the component. +@param rect Optional output parameter set by the function to the minimum bounding rectangle of the +repainted domain. +@param flags Operation flags. The first 8 bits contain a connectivity value. The default value of +4 means that only the four nearest neighbor pixels (those that share an edge) are considered. A +connectivity value of 8 means that the eight nearest neighbor pixels (those that share a corner) +will be considered. The next 8 bits (8-16) contain a value between 1 and 255 with which to fill +the mask (the default value is 1). For example, 4 | ( 255 \<\< 8 ) will consider 4 nearest +neighbours and fill the mask with a value of 255. The following additional options occupy higher +bits and therefore may be further combined with the connectivity and mask fill values using +bit-wise or (|), see #FloodFillFlags. + +@note Since the mask is larger than the filled image, a pixel \f$(x, y)\f$ in image corresponds to the +pixel \f$(x+1, y+1)\f$ in the mask . + +@sa findContours + */ +CV_EXPORTS_W int floodFill( InputOutputArray image, InputOutputArray mask, + Point seedPoint, Scalar newVal, CV_OUT Rect* rect=0, + Scalar loDiff = Scalar(), Scalar upDiff = Scalar(), + int flags = 4 ); + +//! Performs linear blending of two images: +//! \f[ \texttt{dst}(i,j) = \texttt{weights1}(i,j)*\texttt{src1}(i,j) + \texttt{weights2}(i,j)*\texttt{src2}(i,j) \f] +//! @param src1 It has a type of CV_8UC(n) or CV_32FC(n), where n is a positive integer. +//! @param src2 It has the same type and size as src1. +//! @param weights1 It has a type of CV_32FC1 and the same size with src1. +//! @param weights2 It has a type of CV_32FC1 and the same size with src1. +//! @param dst It is created if it does not have the same size and type with src1. +CV_EXPORTS void blendLinear(InputArray src1, InputArray src2, InputArray weights1, InputArray weights2, OutputArray dst); + +//! @} imgproc_misc + +//! @addtogroup imgproc_color_conversions +//! @{ + +/** @brief Converts an image from one color space to another. + +The function converts an input image from one color space to another. In case of a transformation +to-from RGB color space, the order of the channels should be specified explicitly (RGB or BGR). Note +that the default color format in OpenCV is often referred to as RGB but it is actually BGR (the +bytes are reversed). So the first byte in a standard (24-bit) color image will be an 8-bit Blue +component, the second byte will be Green, and the third byte will be Red. The fourth, fifth, and +sixth bytes would then be the second pixel (Blue, then Green, then Red), and so on. + +The conventional ranges for R, G, and B channel values are: +- 0 to 255 for CV_8U images +- 0 to 65535 for CV_16U images +- 0 to 1 for CV_32F images + +In case of linear transformations, the range does not matter. But in case of a non-linear +transformation, an input RGB image should be normalized to the proper value range to get the correct +results, for example, for RGB \f$\rightarrow\f$ L\*u\*v\* transformation. For example, if you have a +32-bit floating-point image directly converted from an 8-bit image without any scaling, then it will +have the 0..255 value range instead of 0..1 assumed by the function. So, before calling #cvtColor , +you need first to scale the image down: +@code + img *= 1./255; + cvtColor(img, img, COLOR_BGR2Luv); +@endcode +If you use #cvtColor with 8-bit images, the conversion will have some information lost. For many +applications, this will not be noticeable but it is recommended to use 32-bit images in applications +that need the full range of colors or that convert an image before an operation and then convert +back. + +If conversion adds the alpha channel, its value will set to the maximum of corresponding channel +range: 255 for CV_8U, 65535 for CV_16U, 1 for CV_32F. + +@param src input image: 8-bit unsigned, 16-bit unsigned ( CV_16UC... ), or single-precision +floating-point. +@param dst output image of the same size and depth as src. +@param code color space conversion code (see #ColorConversionCodes). +@param dstCn number of channels in the destination image; if the parameter is 0, the number of the +channels is derived automatically from src and code. + +@see @ref imgproc_color_conversions + */ +CV_EXPORTS_W void cvtColor( InputArray src, OutputArray dst, int code, int dstCn = 0 ); + +/** @brief Converts an image from one color space to another where the source image is +stored in two planes. + +This function only supports YUV420 to RGB conversion as of now. + +@param src1: 8-bit image (#CV_8U) of the Y plane. +@param src2: image containing interleaved U/V plane. +@param dst: output image. +@param code: Specifies the type of conversion. It can take any of the following values: +- #COLOR_YUV2BGR_NV12 +- #COLOR_YUV2RGB_NV12 +- #COLOR_YUV2BGRA_NV12 +- #COLOR_YUV2RGBA_NV12 +- #COLOR_YUV2BGR_NV21 +- #COLOR_YUV2RGB_NV21 +- #COLOR_YUV2BGRA_NV21 +- #COLOR_YUV2RGBA_NV21 +*/ +CV_EXPORTS_W void cvtColorTwoPlane( InputArray src1, InputArray src2, OutputArray dst, int code ); + +/** @brief main function for all demosaicing processes + +@param src input image: 8-bit unsigned or 16-bit unsigned. +@param dst output image of the same size and depth as src. +@param code Color space conversion code (see the description below). +@param dstCn number of channels in the destination image; if the parameter is 0, the number of the +channels is derived automatically from src and code. + +The function can do the following transformations: + +- Demosaicing using bilinear interpolation + + #COLOR_BayerBG2BGR , #COLOR_BayerGB2BGR , #COLOR_BayerRG2BGR , #COLOR_BayerGR2BGR + + #COLOR_BayerBG2GRAY , #COLOR_BayerGB2GRAY , #COLOR_BayerRG2GRAY , #COLOR_BayerGR2GRAY + +- Demosaicing using Variable Number of Gradients. + + #COLOR_BayerBG2BGR_VNG , #COLOR_BayerGB2BGR_VNG , #COLOR_BayerRG2BGR_VNG , #COLOR_BayerGR2BGR_VNG + +- Edge-Aware Demosaicing. + + #COLOR_BayerBG2BGR_EA , #COLOR_BayerGB2BGR_EA , #COLOR_BayerRG2BGR_EA , #COLOR_BayerGR2BGR_EA + +- Demosaicing with alpha channel + + #COLOR_BayerBG2BGRA , #COLOR_BayerGB2BGRA , #COLOR_BayerRG2BGRA , #COLOR_BayerGR2BGRA + +@sa cvtColor +*/ +CV_EXPORTS_W void demosaicing(InputArray src, OutputArray dst, int code, int dstCn = 0); + +//! @} imgproc_color_conversions + +//! @addtogroup imgproc_shape +//! @{ + +/** @brief Calculates all of the moments up to the third order of a polygon or rasterized shape. + +The function computes moments, up to the 3rd order, of a vector shape or a rasterized shape. The +results are returned in the structure cv::Moments. + +@param array Raster image (single-channel, 8-bit or floating-point 2D array) or an array ( +\f$1 \times N\f$ or \f$N \times 1\f$ ) of 2D points (Point or Point2f ). +@param binaryImage If it is true, all non-zero image pixels are treated as 1's. The parameter is +used for images only. +@returns moments. + +@note Only applicable to contour moments calculations from Python bindings: Note that the numpy +type for the input array should be either np.int32 or np.float32. + +@sa contourArea, arcLength + */ +CV_EXPORTS_W Moments moments( InputArray array, bool binaryImage = false ); + +/** @brief Calculates seven Hu invariants. + +The function calculates seven Hu invariants (introduced in @cite Hu62; see also +) defined as: + +\f[\begin{array}{l} hu[0]= \eta _{20}+ \eta _{02} \\ hu[1]=( \eta _{20}- \eta _{02})^{2}+4 \eta _{11}^{2} \\ hu[2]=( \eta _{30}-3 \eta _{12})^{2}+ (3 \eta _{21}- \eta _{03})^{2} \\ hu[3]=( \eta _{30}+ \eta _{12})^{2}+ ( \eta _{21}+ \eta _{03})^{2} \\ hu[4]=( \eta _{30}-3 \eta _{12})( \eta _{30}+ \eta _{12})[( \eta _{30}+ \eta _{12})^{2}-3( \eta _{21}+ \eta _{03})^{2}]+(3 \eta _{21}- \eta _{03})( \eta _{21}+ \eta _{03})[3( \eta _{30}+ \eta _{12})^{2}-( \eta _{21}+ \eta _{03})^{2}] \\ hu[5]=( \eta _{20}- \eta _{02})[( \eta _{30}+ \eta _{12})^{2}- ( \eta _{21}+ \eta _{03})^{2}]+4 \eta _{11}( \eta _{30}+ \eta _{12})( \eta _{21}+ \eta _{03}) \\ hu[6]=(3 \eta _{21}- \eta _{03})( \eta _{21}+ \eta _{03})[3( \eta _{30}+ \eta _{12})^{2}-( \eta _{21}+ \eta _{03})^{2}]-( \eta _{30}-3 \eta _{12})( \eta _{21}+ \eta _{03})[3( \eta _{30}+ \eta _{12})^{2}-( \eta _{21}+ \eta _{03})^{2}] \\ \end{array}\f] + +where \f$\eta_{ji}\f$ stands for \f$\texttt{Moments::nu}_{ji}\f$ . + +These values are proved to be invariants to the image scale, rotation, and reflection except the +seventh one, whose sign is changed by reflection. This invariance is proved with the assumption of +infinite image resolution. In case of raster images, the computed Hu invariants for the original and +transformed images are a bit different. + +@param moments Input moments computed with moments . +@param hu Output Hu invariants. + +@sa matchShapes + */ +CV_EXPORTS void HuMoments( const Moments& moments, double hu[7] ); + +/** @overload */ +CV_EXPORTS_W void HuMoments( const Moments& m, OutputArray hu ); + +//! @} imgproc_shape + +//! @addtogroup imgproc_object +//! @{ + +//! type of the template matching operation +enum TemplateMatchModes { + TM_SQDIFF = 0, //!< \f[R(x,y)= \sum _{x',y'} (T(x',y')-I(x+x',y+y'))^2\f] + TM_SQDIFF_NORMED = 1, //!< \f[R(x,y)= \frac{\sum_{x',y'} (T(x',y')-I(x+x',y+y'))^2}{\sqrt{\sum_{x',y'}T(x',y')^2 \cdot \sum_{x',y'} I(x+x',y+y')^2}}\f] + TM_CCORR = 2, //!< \f[R(x,y)= \sum _{x',y'} (T(x',y') \cdot I(x+x',y+y'))\f] + TM_CCORR_NORMED = 3, //!< \f[R(x,y)= \frac{\sum_{x',y'} (T(x',y') \cdot I(x+x',y+y'))}{\sqrt{\sum_{x',y'}T(x',y')^2 \cdot \sum_{x',y'} I(x+x',y+y')^2}}\f] + TM_CCOEFF = 4, //!< \f[R(x,y)= \sum _{x',y'} (T'(x',y') \cdot I'(x+x',y+y'))\f] + //!< where + //!< \f[\begin{array}{l} T'(x',y')=T(x',y') - 1/(w \cdot h) \cdot \sum _{x'',y''} T(x'',y'') \\ I'(x+x',y+y')=I(x+x',y+y') - 1/(w \cdot h) \cdot \sum _{x'',y''} I(x+x'',y+y'') \end{array}\f] + TM_CCOEFF_NORMED = 5 //!< \f[R(x,y)= \frac{ \sum_{x',y'} (T'(x',y') \cdot I'(x+x',y+y')) }{ \sqrt{\sum_{x',y'}T'(x',y')^2 \cdot \sum_{x',y'} I'(x+x',y+y')^2} }\f] +}; + +/** @example samples/cpp/tutorial_code/Histograms_Matching/MatchTemplate_Demo.cpp +An example using Template Matching algorithm +*/ + +/** @brief Compares a template against overlapped image regions. + +The function slides through image , compares the overlapped patches of size \f$w \times h\f$ against +templ using the specified method and stores the comparison results in result . Here are the formulae +for the available comparison methods ( \f$I\f$ denotes image, \f$T\f$ template, \f$R\f$ result ). The summation +is done over template and/or the image patch: \f$x' = 0...w-1, y' = 0...h-1\f$ + +After the function finishes the comparison, the best matches can be found as global minimums (when +#TM_SQDIFF was used) or maximums (when #TM_CCORR or #TM_CCOEFF was used) using the +#minMaxLoc function. In case of a color image, template summation in the numerator and each sum in +the denominator is done over all of the channels and separate mean values are used for each channel. +That is, the function can take a color template and a color image. The result will still be a +single-channel image, which is easier to analyze. + +@param image Image where the search is running. It must be 8-bit or 32-bit floating-point. +@param templ Searched template. It must be not greater than the source image and have the same +data type. +@param result Map of comparison results. It must be single-channel 32-bit floating-point. If image +is \f$W \times H\f$ and templ is \f$w \times h\f$ , then result is \f$(W-w+1) \times (H-h+1)\f$ . +@param method Parameter specifying the comparison method, see #TemplateMatchModes +@param mask Mask of searched template. It must have the same datatype and size with templ. It is +not set by default. Currently, only the #TM_SQDIFF and #TM_CCORR_NORMED methods are supported. + */ +CV_EXPORTS_W void matchTemplate( InputArray image, InputArray templ, + OutputArray result, int method, InputArray mask = noArray() ); + +//! @} + +//! @addtogroup imgproc_shape +//! @{ + +/** @example samples/cpp/connected_components.cpp +This program demonstrates connected components and use of the trackbar +*/ + +/** @brief computes the connected components labeled image of boolean image + +image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0 +represents the background label. ltype specifies the output label image type, an important +consideration based on the total number of labels or alternatively the total number of pixels in +the source image. ccltype specifies the connected components labeling algorithm to use, currently +Grana (BBDT) and Wu's (SAUF) algorithms are supported, see the #ConnectedComponentsAlgorithmsTypes +for details. Note that SAUF algorithm forces a row major ordering of labels while BBDT does not. +This function uses parallel version of both Grana and Wu's algorithms if at least one allowed +parallel framework is enabled and if the rows of the image are at least twice the number returned by #getNumberOfCPUs. + +@param image the 8-bit single-channel image to be labeled +@param labels destination labeled image +@param connectivity 8 or 4 for 8-way or 4-way connectivity respectively +@param ltype output image label type. Currently CV_32S and CV_16U are supported. +@param ccltype connected components algorithm type (see the #ConnectedComponentsAlgorithmsTypes). +*/ +CV_EXPORTS_AS(connectedComponentsWithAlgorithm) int connectedComponents(InputArray image, OutputArray labels, + int connectivity, int ltype, int ccltype); + + +/** @overload + +@param image the 8-bit single-channel image to be labeled +@param labels destination labeled image +@param connectivity 8 or 4 for 8-way or 4-way connectivity respectively +@param ltype output image label type. Currently CV_32S and CV_16U are supported. +*/ +CV_EXPORTS_W int connectedComponents(InputArray image, OutputArray labels, + int connectivity = 8, int ltype = CV_32S); + + +/** @brief computes the connected components labeled image of boolean image and also produces a statistics output for each label + +image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0 +represents the background label. ltype specifies the output label image type, an important +consideration based on the total number of labels or alternatively the total number of pixels in +the source image. ccltype specifies the connected components labeling algorithm to use, currently +Grana's (BBDT) and Wu's (SAUF) algorithms are supported, see the #ConnectedComponentsAlgorithmsTypes +for details. Note that SAUF algorithm forces a row major ordering of labels while BBDT does not. +This function uses parallel version of both Grana and Wu's algorithms (statistics included) if at least one allowed +parallel framework is enabled and if the rows of the image are at least twice the number returned by #getNumberOfCPUs. + +@param image the 8-bit single-channel image to be labeled +@param labels destination labeled image +@param stats statistics output for each label, including the background label, see below for +available statistics. Statistics are accessed via stats(label, COLUMN) where COLUMN is one of +#ConnectedComponentsTypes. The data type is CV_32S. +@param centroids centroid output for each label, including the background label. Centroids are +accessed via centroids(label, 0) for x and centroids(label, 1) for y. The data type CV_64F. +@param connectivity 8 or 4 for 8-way or 4-way connectivity respectively +@param ltype output image label type. Currently CV_32S and CV_16U are supported. +@param ccltype connected components algorithm type (see #ConnectedComponentsAlgorithmsTypes). +*/ +CV_EXPORTS_AS(connectedComponentsWithStatsWithAlgorithm) int connectedComponentsWithStats(InputArray image, OutputArray labels, + OutputArray stats, OutputArray centroids, + int connectivity, int ltype, int ccltype); + +/** @overload +@param image the 8-bit single-channel image to be labeled +@param labels destination labeled image +@param stats statistics output for each label, including the background label, see below for +available statistics. Statistics are accessed via stats(label, COLUMN) where COLUMN is one of +#ConnectedComponentsTypes. The data type is CV_32S. +@param centroids centroid output for each label, including the background label. Centroids are +accessed via centroids(label, 0) for x and centroids(label, 1) for y. The data type CV_64F. +@param connectivity 8 or 4 for 8-way or 4-way connectivity respectively +@param ltype output image label type. Currently CV_32S and CV_16U are supported. +*/ +CV_EXPORTS_W int connectedComponentsWithStats(InputArray image, OutputArray labels, + OutputArray stats, OutputArray centroids, + int connectivity = 8, int ltype = CV_32S); + + +/** @brief Finds contours in a binary image. + +The function retrieves contours from the binary image using the algorithm @cite Suzuki85 . The contours +are a useful tool for shape analysis and object detection and recognition. See squares.cpp in the +OpenCV sample directory. +@note Since opencv 3.2 source image is not modified by this function. + +@param image Source, an 8-bit single-channel image. Non-zero pixels are treated as 1's. Zero +pixels remain 0's, so the image is treated as binary . You can use #compare, #inRange, #threshold , +#adaptiveThreshold, #Canny, and others to create a binary image out of a grayscale or color one. +If mode equals to #RETR_CCOMP or #RETR_FLOODFILL, the input can also be a 32-bit integer image of labels (CV_32SC1). +@param contours Detected contours. Each contour is stored as a vector of points (e.g. +std::vector >). +@param hierarchy Optional output vector (e.g. std::vector), containing information about the image topology. It has +as many elements as the number of contours. For each i-th contour contours[i], the elements +hierarchy[i][0] , hierarchy[i][1] , hierarchy[i][2] , and hierarchy[i][3] are set to 0-based indices +in contours of the next and previous contours at the same hierarchical level, the first child +contour and the parent contour, respectively. If for the contour i there are no next, previous, +parent, or nested contours, the corresponding elements of hierarchy[i] will be negative. +@param mode Contour retrieval mode, see #RetrievalModes +@param method Contour approximation method, see #ContourApproximationModes +@param offset Optional offset by which every contour point is shifted. This is useful if the +contours are extracted from the image ROI and then they should be analyzed in the whole image +context. + */ +CV_EXPORTS_W void findContours( InputOutputArray image, OutputArrayOfArrays contours, + OutputArray hierarchy, int mode, + int method, Point offset = Point()); + +/** @overload */ +CV_EXPORTS void findContours( InputOutputArray image, OutputArrayOfArrays contours, + int mode, int method, Point offset = Point()); + +/** @example samples/cpp/squares.cpp +A program using pyramid scaling, Canny, contours and contour simplification to find +squares in a list of images (pic1-6.png). Returns sequence of squares detected on the image. +*/ + +/** @example samples/tapi/squares.cpp +A program using pyramid scaling, Canny, contours and contour simplification to find +squares in the input image. +*/ + +/** @brief Approximates a polygonal curve(s) with the specified precision. + +The function cv::approxPolyDP approximates a curve or a polygon with another curve/polygon with less +vertices so that the distance between them is less or equal to the specified precision. It uses the +Douglas-Peucker algorithm + +@param curve Input vector of a 2D point stored in std::vector or Mat +@param approxCurve Result of the approximation. The type should match the type of the input curve. +@param epsilon Parameter specifying the approximation accuracy. This is the maximum distance +between the original curve and its approximation. +@param closed If true, the approximated curve is closed (its first and last vertices are +connected). Otherwise, it is not closed. + */ +CV_EXPORTS_W void approxPolyDP( InputArray curve, + OutputArray approxCurve, + double epsilon, bool closed ); + +/** @brief Calculates a contour perimeter or a curve length. + +The function computes a curve length or a closed contour perimeter. + +@param curve Input vector of 2D points, stored in std::vector or Mat. +@param closed Flag indicating whether the curve is closed or not. + */ +CV_EXPORTS_W double arcLength( InputArray curve, bool closed ); + +/** @brief Calculates the up-right bounding rectangle of a point set or non-zero pixels of gray-scale image. + +The function calculates and returns the minimal up-right bounding rectangle for the specified point set or +non-zero pixels of gray-scale image. + +@param array Input gray-scale image or 2D point set, stored in std::vector or Mat. + */ +CV_EXPORTS_W Rect boundingRect( InputArray array ); + +/** @brief Calculates a contour area. + +The function computes a contour area. Similarly to moments , the area is computed using the Green +formula. Thus, the returned area and the number of non-zero pixels, if you draw the contour using +#drawContours or #fillPoly , can be different. Also, the function will most certainly give a wrong +results for contours with self-intersections. + +Example: +@code + vector contour; + contour.push_back(Point2f(0, 0)); + contour.push_back(Point2f(10, 0)); + contour.push_back(Point2f(10, 10)); + contour.push_back(Point2f(5, 4)); + + double area0 = contourArea(contour); + vector approx; + approxPolyDP(contour, approx, 5, true); + double area1 = contourArea(approx); + + cout << "area0 =" << area0 << endl << + "area1 =" << area1 << endl << + "approx poly vertices" << approx.size() << endl; +@endcode +@param contour Input vector of 2D points (contour vertices), stored in std::vector or Mat. +@param oriented Oriented area flag. If it is true, the function returns a signed area value, +depending on the contour orientation (clockwise or counter-clockwise). Using this feature you can +determine orientation of a contour by taking the sign of an area. By default, the parameter is +false, which means that the absolute value is returned. + */ +CV_EXPORTS_W double contourArea( InputArray contour, bool oriented = false ); + +/** @brief Finds a rotated rectangle of the minimum area enclosing the input 2D point set. + +The function calculates and returns the minimum-area bounding rectangle (possibly rotated) for a +specified point set. Developer should keep in mind that the returned RotatedRect can contain negative +indices when data is close to the containing Mat element boundary. + +@param points Input vector of 2D points, stored in std::vector\<\> or Mat + */ +CV_EXPORTS_W RotatedRect minAreaRect( InputArray points ); + +/** @brief Finds the four vertices of a rotated rect. Useful to draw the rotated rectangle. + +The function finds the four vertices of a rotated rectangle. This function is useful to draw the +rectangle. In C++, instead of using this function, you can directly use RotatedRect::points method. Please +visit the @ref tutorial_bounding_rotated_ellipses "tutorial on Creating Bounding rotated boxes and ellipses for contours" for more information. + +@param box The input rotated rectangle. It may be the output of +@param points The output array of four vertices of rectangles. + */ +CV_EXPORTS_W void boxPoints(RotatedRect box, OutputArray points); + +/** @brief Finds a circle of the minimum area enclosing a 2D point set. + +The function finds the minimal enclosing circle of a 2D point set using an iterative algorithm. + +@param points Input vector of 2D points, stored in std::vector\<\> or Mat +@param center Output center of the circle. +@param radius Output radius of the circle. + */ +CV_EXPORTS_W void minEnclosingCircle( InputArray points, + CV_OUT Point2f& center, CV_OUT float& radius ); + +/** @example samples/cpp/minarea.cpp +*/ + +/** @brief Finds a triangle of minimum area enclosing a 2D point set and returns its area. + +The function finds a triangle of minimum area enclosing the given set of 2D points and returns its +area. The output for a given 2D point set is shown in the image below. 2D points are depicted in +*red* and the enclosing triangle in *yellow*. + +![Sample output of the minimum enclosing triangle function](pics/minenclosingtriangle.png) + +The implementation of the algorithm is based on O'Rourke's @cite ORourke86 and Klee and Laskowski's +@cite KleeLaskowski85 papers. O'Rourke provides a \f$\theta(n)\f$ algorithm for finding the minimal +enclosing triangle of a 2D convex polygon with n vertices. Since the #minEnclosingTriangle function +takes a 2D point set as input an additional preprocessing step of computing the convex hull of the +2D point set is required. The complexity of the #convexHull function is \f$O(n log(n))\f$ which is higher +than \f$\theta(n)\f$. Thus the overall complexity of the function is \f$O(n log(n))\f$. + +@param points Input vector of 2D points with depth CV_32S or CV_32F, stored in std::vector\<\> or Mat +@param triangle Output vector of three 2D points defining the vertices of the triangle. The depth +of the OutputArray must be CV_32F. + */ +CV_EXPORTS_W double minEnclosingTriangle( InputArray points, CV_OUT OutputArray triangle ); + +/** @brief Compares two shapes. + +The function compares two shapes. All three implemented methods use the Hu invariants (see #HuMoments) + +@param contour1 First contour or grayscale image. +@param contour2 Second contour or grayscale image. +@param method Comparison method, see #ShapeMatchModes +@param parameter Method-specific parameter (not supported now). + */ +CV_EXPORTS_W double matchShapes( InputArray contour1, InputArray contour2, + int method, double parameter ); + +/** @example samples/cpp/convexhull.cpp +An example using the convexHull functionality +*/ + +/** @brief Finds the convex hull of a point set. + +The function cv::convexHull finds the convex hull of a 2D point set using the Sklansky's algorithm @cite Sklansky82 +that has *O(N logN)* complexity in the current implementation. + +@param points Input 2D point set, stored in std::vector or Mat. +@param hull Output convex hull. It is either an integer vector of indices or vector of points. In +the first case, the hull elements are 0-based indices of the convex hull points in the original +array (since the set of convex hull points is a subset of the original point set). In the second +case, hull elements are the convex hull points themselves. +@param clockwise Orientation flag. If it is true, the output convex hull is oriented clockwise. +Otherwise, it is oriented counter-clockwise. The assumed coordinate system has its X axis pointing +to the right, and its Y axis pointing upwards. +@param returnPoints Operation flag. In case of a matrix, when the flag is true, the function +returns convex hull points. Otherwise, it returns indices of the convex hull points. When the +output array is std::vector, the flag is ignored, and the output depends on the type of the +vector: std::vector\ implies returnPoints=false, std::vector\ implies +returnPoints=true. + +@note `points` and `hull` should be different arrays, inplace processing isn't supported. + +Check @ref tutorial_hull "the corresponding tutorial" for more details. + +useful links: + +https://www.learnopencv.com/convex-hull-using-opencv-in-python-and-c/ + */ +CV_EXPORTS_W void convexHull( InputArray points, OutputArray hull, + bool clockwise = false, bool returnPoints = true ); + +/** @brief Finds the convexity defects of a contour. + +The figure below displays convexity defects of a hand contour: + +![image](pics/defects.png) + +@param contour Input contour. +@param convexhull Convex hull obtained using convexHull that should contain indices of the contour +points that make the hull. +@param convexityDefects The output vector of convexity defects. In C++ and the new Python/Java +interface each convexity defect is represented as 4-element integer vector (a.k.a. #Vec4i): +(start_index, end_index, farthest_pt_index, fixpt_depth), where indices are 0-based indices +in the original contour of the convexity defect beginning, end and the farthest point, and +fixpt_depth is fixed-point approximation (with 8 fractional bits) of the distance between the +farthest contour point and the hull. That is, to get the floating-point value of the depth will be +fixpt_depth/256.0. + */ +CV_EXPORTS_W void convexityDefects( InputArray contour, InputArray convexhull, OutputArray convexityDefects ); + +/** @brief Tests a contour convexity. + +The function tests whether the input contour is convex or not. The contour must be simple, that is, +without self-intersections. Otherwise, the function output is undefined. + +@param contour Input vector of 2D points, stored in std::vector\<\> or Mat + */ +CV_EXPORTS_W bool isContourConvex( InputArray contour ); + +//! finds intersection of two convex polygons +CV_EXPORTS_W float intersectConvexConvex( InputArray _p1, InputArray _p2, + OutputArray _p12, bool handleNested = true ); + +/** @example samples/cpp/fitellipse.cpp +An example using the fitEllipse technique +*/ + +/** @brief Fits an ellipse around a set of 2D points. + +The function calculates the ellipse that fits (in a least-squares sense) a set of 2D points best of +all. It returns the rotated rectangle in which the ellipse is inscribed. The first algorithm described by @cite Fitzgibbon95 +is used. Developer should keep in mind that it is possible that the returned +ellipse/rotatedRect data contains negative indices, due to the data points being close to the +border of the containing Mat element. + +@param points Input 2D point set, stored in std::vector\<\> or Mat + */ +CV_EXPORTS_W RotatedRect fitEllipse( InputArray points ); + +/** @brief Fits an ellipse around a set of 2D points. + + The function calculates the ellipse that fits a set of 2D points. + It returns the rotated rectangle in which the ellipse is inscribed. + The Approximate Mean Square (AMS) proposed by @cite Taubin1991 is used. + + For an ellipse, this basis set is \f$ \chi= \left(x^2, x y, y^2, x, y, 1\right) \f$, + which is a set of six free coefficients \f$ A^T=\left\{A_{\text{xx}},A_{\text{xy}},A_{\text{yy}},A_x,A_y,A_0\right\} \f$. + However, to specify an ellipse, all that is needed is five numbers; the major and minor axes lengths \f$ (a,b) \f$, + the position \f$ (x_0,y_0) \f$, and the orientation \f$ \theta \f$. This is because the basis set includes lines, + quadratics, parabolic and hyperbolic functions as well as elliptical functions as possible fits. + If the fit is found to be a parabolic or hyperbolic function then the standard #fitEllipse method is used. + The AMS method restricts the fit to parabolic, hyperbolic and elliptical curves + by imposing the condition that \f$ A^T ( D_x^T D_x + D_y^T D_y) A = 1 \f$ where + the matrices \f$ Dx \f$ and \f$ Dy \f$ are the partial derivatives of the design matrix \f$ D \f$ with + respect to x and y. The matrices are formed row by row applying the following to + each of the points in the set: + \f{align*}{ + D(i,:)&=\left\{x_i^2, x_i y_i, y_i^2, x_i, y_i, 1\right\} & + D_x(i,:)&=\left\{2 x_i,y_i,0,1,0,0\right\} & + D_y(i,:)&=\left\{0,x_i,2 y_i,0,1,0\right\} + \f} + The AMS method minimizes the cost function + \f{equation*}{ + \epsilon ^2=\frac{ A^T D^T D A }{ A^T (D_x^T D_x + D_y^T D_y) A^T } + \f} + + The minimum cost is found by solving the generalized eigenvalue problem. + + \f{equation*}{ + D^T D A = \lambda \left( D_x^T D_x + D_y^T D_y\right) A + \f} + + @param points Input 2D point set, stored in std::vector\<\> or Mat + */ +CV_EXPORTS_W RotatedRect fitEllipseAMS( InputArray points ); + + +/** @brief Fits an ellipse around a set of 2D points. + + The function calculates the ellipse that fits a set of 2D points. + It returns the rotated rectangle in which the ellipse is inscribed. + The Direct least square (Direct) method by @cite Fitzgibbon1999 is used. + + For an ellipse, this basis set is \f$ \chi= \left(x^2, x y, y^2, x, y, 1\right) \f$, + which is a set of six free coefficients \f$ A^T=\left\{A_{\text{xx}},A_{\text{xy}},A_{\text{yy}},A_x,A_y,A_0\right\} \f$. + However, to specify an ellipse, all that is needed is five numbers; the major and minor axes lengths \f$ (a,b) \f$, + the position \f$ (x_0,y_0) \f$, and the orientation \f$ \theta \f$. This is because the basis set includes lines, + quadratics, parabolic and hyperbolic functions as well as elliptical functions as possible fits. + The Direct method confines the fit to ellipses by ensuring that \f$ 4 A_{xx} A_{yy}- A_{xy}^2 > 0 \f$. + The condition imposed is that \f$ 4 A_{xx} A_{yy}- A_{xy}^2=1 \f$ which satisfies the inequality + and as the coefficients can be arbitrarily scaled is not overly restrictive. + + \f{equation*}{ + \epsilon ^2= A^T D^T D A \quad \text{with} \quad A^T C A =1 \quad \text{and} \quad C=\left(\begin{matrix} + 0 & 0 & 2 & 0 & 0 & 0 \\ + 0 & -1 & 0 & 0 & 0 & 0 \\ + 2 & 0 & 0 & 0 & 0 & 0 \\ + 0 & 0 & 0 & 0 & 0 & 0 \\ + 0 & 0 & 0 & 0 & 0 & 0 \\ + 0 & 0 & 0 & 0 & 0 & 0 + \end{matrix} \right) + \f} + + The minimum cost is found by solving the generalized eigenvalue problem. + + \f{equation*}{ + D^T D A = \lambda \left( C\right) A + \f} + + The system produces only one positive eigenvalue \f$ \lambda\f$ which is chosen as the solution + with its eigenvector \f$\mathbf{u}\f$. These are used to find the coefficients + + \f{equation*}{ + A = \sqrt{\frac{1}{\mathbf{u}^T C \mathbf{u}}} \mathbf{u} + \f} + The scaling factor guarantees that \f$A^T C A =1\f$. + + @param points Input 2D point set, stored in std::vector\<\> or Mat + */ +CV_EXPORTS_W RotatedRect fitEllipseDirect( InputArray points ); + +/** @brief Fits a line to a 2D or 3D point set. + +The function fitLine fits a line to a 2D or 3D point set by minimizing \f$\sum_i \rho(r_i)\f$ where +\f$r_i\f$ is a distance between the \f$i^{th}\f$ point, the line and \f$\rho(r)\f$ is a distance function, one +of the following: +- DIST_L2 +\f[\rho (r) = r^2/2 \quad \text{(the simplest and the fastest least-squares method)}\f] +- DIST_L1 +\f[\rho (r) = r\f] +- DIST_L12 +\f[\rho (r) = 2 \cdot ( \sqrt{1 + \frac{r^2}{2}} - 1)\f] +- DIST_FAIR +\f[\rho \left (r \right ) = C^2 \cdot \left ( \frac{r}{C} - \log{\left(1 + \frac{r}{C}\right)} \right ) \quad \text{where} \quad C=1.3998\f] +- DIST_WELSCH +\f[\rho \left (r \right ) = \frac{C^2}{2} \cdot \left ( 1 - \exp{\left(-\left(\frac{r}{C}\right)^2\right)} \right ) \quad \text{where} \quad C=2.9846\f] +- DIST_HUBER +\f[\rho (r) = \fork{r^2/2}{if \(r < C\)}{C \cdot (r-C/2)}{otherwise} \quad \text{where} \quad C=1.345\f] + +The algorithm is based on the M-estimator ( ) technique +that iteratively fits the line using the weighted least-squares algorithm. After each iteration the +weights \f$w_i\f$ are adjusted to be inversely proportional to \f$\rho(r_i)\f$ . + +@param points Input vector of 2D or 3D points, stored in std::vector\<\> or Mat. +@param line Output line parameters. In case of 2D fitting, it should be a vector of 4 elements +(like Vec4f) - (vx, vy, x0, y0), where (vx, vy) is a normalized vector collinear to the line and +(x0, y0) is a point on the line. In case of 3D fitting, it should be a vector of 6 elements (like +Vec6f) - (vx, vy, vz, x0, y0, z0), where (vx, vy, vz) is a normalized vector collinear to the line +and (x0, y0, z0) is a point on the line. +@param distType Distance used by the M-estimator, see #DistanceTypes +@param param Numerical parameter ( C ) for some types of distances. If it is 0, an optimal value +is chosen. +@param reps Sufficient accuracy for the radius (distance between the coordinate origin and the line). +@param aeps Sufficient accuracy for the angle. 0.01 would be a good default value for reps and aeps. + */ +CV_EXPORTS_W void fitLine( InputArray points, OutputArray line, int distType, + double param, double reps, double aeps ); + +/** @brief Performs a point-in-contour test. + +The function determines whether the point is inside a contour, outside, or lies on an edge (or +coincides with a vertex). It returns positive (inside), negative (outside), or zero (on an edge) +value, correspondingly. When measureDist=false , the return value is +1, -1, and 0, respectively. +Otherwise, the return value is a signed distance between the point and the nearest contour edge. + +See below a sample output of the function where each image pixel is tested against the contour: + +![sample output](pics/pointpolygon.png) + +@param contour Input contour. +@param pt Point tested against the contour. +@param measureDist If true, the function estimates the signed distance from the point to the +nearest contour edge. Otherwise, the function only checks if the point is inside a contour or not. + */ +CV_EXPORTS_W double pointPolygonTest( InputArray contour, Point2f pt, bool measureDist ); + +/** @brief Finds out if there is any intersection between two rotated rectangles. + +If there is then the vertices of the intersecting region are returned as well. + +Below are some examples of intersection configurations. The hatched pattern indicates the +intersecting region and the red vertices are returned by the function. + +![intersection examples](pics/intersection.png) + +@param rect1 First rectangle +@param rect2 Second rectangle +@param intersectingRegion The output array of the vertices of the intersecting region. It returns +at most 8 vertices. Stored as std::vector\ or cv::Mat as Mx1 of type CV_32FC2. +@returns One of #RectanglesIntersectTypes + */ +CV_EXPORTS_W int rotatedRectangleIntersection( const RotatedRect& rect1, const RotatedRect& rect2, OutputArray intersectingRegion ); + +/** @brief Creates a smart pointer to a cv::GeneralizedHoughBallard class and initializes it. +*/ +CV_EXPORTS Ptr createGeneralizedHoughBallard(); + +/** @brief Creates a smart pointer to a cv::GeneralizedHoughGuil class and initializes it. +*/ +CV_EXPORTS Ptr createGeneralizedHoughGuil(); + +//! @} imgproc_shape + +//! @addtogroup imgproc_colormap +//! @{ + +//! GNU Octave/MATLAB equivalent colormaps +enum ColormapTypes +{ + COLORMAP_AUTUMN = 0, //!< ![autumn](pics/colormaps/colorscale_autumn.jpg) + COLORMAP_BONE = 1, //!< ![bone](pics/colormaps/colorscale_bone.jpg) + COLORMAP_JET = 2, //!< ![jet](pics/colormaps/colorscale_jet.jpg) + COLORMAP_WINTER = 3, //!< ![winter](pics/colormaps/colorscale_winter.jpg) + COLORMAP_RAINBOW = 4, //!< ![rainbow](pics/colormaps/colorscale_rainbow.jpg) + COLORMAP_OCEAN = 5, //!< ![ocean](pics/colormaps/colorscale_ocean.jpg) + COLORMAP_SUMMER = 6, //!< ![summer](pics/colormaps/colorscale_summer.jpg) + COLORMAP_SPRING = 7, //!< ![spring](pics/colormaps/colorscale_spring.jpg) + COLORMAP_COOL = 8, //!< ![cool](pics/colormaps/colorscale_cool.jpg) + COLORMAP_HSV = 9, //!< ![HSV](pics/colormaps/colorscale_hsv.jpg) + COLORMAP_PINK = 10, //!< ![pink](pics/colormaps/colorscale_pink.jpg) + COLORMAP_HOT = 11, //!< ![hot](pics/colormaps/colorscale_hot.jpg) + COLORMAP_PARULA = 12 //!< ![parula](pics/colormaps/colorscale_parula.jpg) +}; + +/** @example samples/cpp/falsecolor.cpp +An example using applyColorMap function +*/ + +/** @brief Applies a GNU Octave/MATLAB equivalent colormap on a given image. + +@param src The source image, grayscale or colored of type CV_8UC1 or CV_8UC3. +@param dst The result is the colormapped source image. Note: Mat::create is called on dst. +@param colormap The colormap to apply, see #ColormapTypes +*/ +CV_EXPORTS_W void applyColorMap(InputArray src, OutputArray dst, int colormap); + +/** @brief Applies a user colormap on a given image. + +@param src The source image, grayscale or colored of type CV_8UC1 or CV_8UC3. +@param dst The result is the colormapped source image. Note: Mat::create is called on dst. +@param userColor The colormap to apply of type CV_8UC1 or CV_8UC3 and size 256 +*/ +CV_EXPORTS_W void applyColorMap(InputArray src, OutputArray dst, InputArray userColor); + +//! @} imgproc_colormap + +//! @addtogroup imgproc_draw +//! @{ + + +/** OpenCV color channel order is BGR[A] */ +#define CV_RGB(r, g, b) cv::Scalar((b), (g), (r), 0) + +/** @brief Draws a line segment connecting two points. + +The function line draws the line segment between pt1 and pt2 points in the image. The line is +clipped by the image boundaries. For non-antialiased lines with integer coordinates, the 8-connected +or 4-connected Bresenham algorithm is used. Thick lines are drawn with rounding endings. Antialiased +lines are drawn using Gaussian filtering. + +@param img Image. +@param pt1 First point of the line segment. +@param pt2 Second point of the line segment. +@param color Line color. +@param thickness Line thickness. +@param lineType Type of the line. See #LineTypes. +@param shift Number of fractional bits in the point coordinates. + */ +CV_EXPORTS_W void line(InputOutputArray img, Point pt1, Point pt2, const Scalar& color, + int thickness = 1, int lineType = LINE_8, int shift = 0); + +/** @brief Draws a arrow segment pointing from the first point to the second one. + +The function cv::arrowedLine draws an arrow between pt1 and pt2 points in the image. See also #line. + +@param img Image. +@param pt1 The point the arrow starts from. +@param pt2 The point the arrow points to. +@param color Line color. +@param thickness Line thickness. +@param line_type Type of the line. See #LineTypes +@param shift Number of fractional bits in the point coordinates. +@param tipLength The length of the arrow tip in relation to the arrow length + */ +CV_EXPORTS_W void arrowedLine(InputOutputArray img, Point pt1, Point pt2, const Scalar& color, + int thickness=1, int line_type=8, int shift=0, double tipLength=0.1); + +/** @brief Draws a simple, thick, or filled up-right rectangle. + +The function cv::rectangle draws a rectangle outline or a filled rectangle whose two opposite corners +are pt1 and pt2. + +@param img Image. +@param pt1 Vertex of the rectangle. +@param pt2 Vertex of the rectangle opposite to pt1 . +@param color Rectangle color or brightness (grayscale image). +@param thickness Thickness of lines that make up the rectangle. Negative values, like #FILLED, +mean that the function has to draw a filled rectangle. +@param lineType Type of the line. See #LineTypes +@param shift Number of fractional bits in the point coordinates. + */ +CV_EXPORTS_W void rectangle(InputOutputArray img, Point pt1, Point pt2, + const Scalar& color, int thickness = 1, + int lineType = LINE_8, int shift = 0); + +/** @overload + +use `rec` parameter as alternative specification of the drawn rectangle: `r.tl() and +r.br()-Point(1,1)` are opposite corners +*/ +CV_EXPORTS void rectangle(CV_IN_OUT Mat& img, Rect rec, + const Scalar& color, int thickness = 1, + int lineType = LINE_8, int shift = 0); + +/** @example samples/cpp/tutorial_code/ImgProc/basic_drawing/Drawing_2.cpp +An example using drawing functions +*/ + +/** @brief Draws a circle. + +The function cv::circle draws a simple or filled circle with a given center and radius. +@param img Image where the circle is drawn. +@param center Center of the circle. +@param radius Radius of the circle. +@param color Circle color. +@param thickness Thickness of the circle outline, if positive. Negative values, like #FILLED, +mean that a filled circle is to be drawn. +@param lineType Type of the circle boundary. See #LineTypes +@param shift Number of fractional bits in the coordinates of the center and in the radius value. + */ +CV_EXPORTS_W void circle(InputOutputArray img, Point center, int radius, + const Scalar& color, int thickness = 1, + int lineType = LINE_8, int shift = 0); + +/** @brief Draws a simple or thick elliptic arc or fills an ellipse sector. + +The function cv::ellipse with more parameters draws an ellipse outline, a filled ellipse, an elliptic +arc, or a filled ellipse sector. The drawing code uses general parametric form. +A piecewise-linear curve is used to approximate the elliptic arc +boundary. If you need more control of the ellipse rendering, you can retrieve the curve using +#ellipse2Poly and then render it with #polylines or fill it with #fillPoly. If you use the first +variant of the function and want to draw the whole ellipse, not an arc, pass `startAngle=0` and +`endAngle=360`. If `startAngle` is greater than `endAngle`, they are swapped. The figure below explains +the meaning of the parameters to draw the blue arc. + +![Parameters of Elliptic Arc](pics/ellipse.svg) + +@param img Image. +@param center Center of the ellipse. +@param axes Half of the size of the ellipse main axes. +@param angle Ellipse rotation angle in degrees. +@param startAngle Starting angle of the elliptic arc in degrees. +@param endAngle Ending angle of the elliptic arc in degrees. +@param color Ellipse color. +@param thickness Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that +a filled ellipse sector is to be drawn. +@param lineType Type of the ellipse boundary. See #LineTypes +@param shift Number of fractional bits in the coordinates of the center and values of axes. + */ +CV_EXPORTS_W void ellipse(InputOutputArray img, Point center, Size axes, + double angle, double startAngle, double endAngle, + const Scalar& color, int thickness = 1, + int lineType = LINE_8, int shift = 0); + +/** @overload +@param img Image. +@param box Alternative ellipse representation via RotatedRect. This means that the function draws +an ellipse inscribed in the rotated rectangle. +@param color Ellipse color. +@param thickness Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that +a filled ellipse sector is to be drawn. +@param lineType Type of the ellipse boundary. See #LineTypes +*/ +CV_EXPORTS_W void ellipse(InputOutputArray img, const RotatedRect& box, const Scalar& color, + int thickness = 1, int lineType = LINE_8); + +/* ----------------------------------------------------------------------------------------- */ +/* ADDING A SET OF PREDEFINED MARKERS WHICH COULD BE USED TO HIGHLIGHT POSITIONS IN AN IMAGE */ +/* ----------------------------------------------------------------------------------------- */ + +//! Possible set of marker types used for the cv::drawMarker function +enum MarkerTypes +{ + MARKER_CROSS = 0, //!< A crosshair marker shape + MARKER_TILTED_CROSS = 1, //!< A 45 degree tilted crosshair marker shape + MARKER_STAR = 2, //!< A star marker shape, combination of cross and tilted cross + MARKER_DIAMOND = 3, //!< A diamond marker shape + MARKER_SQUARE = 4, //!< A square marker shape + MARKER_TRIANGLE_UP = 5, //!< An upwards pointing triangle marker shape + MARKER_TRIANGLE_DOWN = 6 //!< A downwards pointing triangle marker shape +}; + +/** @brief Draws a marker on a predefined position in an image. + +The function cv::drawMarker draws a marker on a given position in the image. For the moment several +marker types are supported, see #MarkerTypes for more information. + +@param img Image. +@param position The point where the crosshair is positioned. +@param color Line color. +@param markerType The specific type of marker you want to use, see #MarkerTypes +@param thickness Line thickness. +@param line_type Type of the line, See #LineTypes +@param markerSize The length of the marker axis [default = 20 pixels] + */ +CV_EXPORTS_W void drawMarker(CV_IN_OUT Mat& img, Point position, const Scalar& color, + int markerType = MARKER_CROSS, int markerSize=20, int thickness=1, + int line_type=8); + +/* ----------------------------------------------------------------------------------------- */ +/* END OF MARKER SECTION */ +/* ----------------------------------------------------------------------------------------- */ + +/** @overload */ +CV_EXPORTS void fillConvexPoly(Mat& img, const Point* pts, int npts, + const Scalar& color, int lineType = LINE_8, + int shift = 0); + +/** @brief Fills a convex polygon. + +The function cv::fillConvexPoly draws a filled convex polygon. This function is much faster than the +function #fillPoly . It can fill not only convex polygons but any monotonic polygon without +self-intersections, that is, a polygon whose contour intersects every horizontal line (scan line) +twice at the most (though, its top-most and/or the bottom edge could be horizontal). + +@param img Image. +@param points Polygon vertices. +@param color Polygon color. +@param lineType Type of the polygon boundaries. See #LineTypes +@param shift Number of fractional bits in the vertex coordinates. + */ +CV_EXPORTS_W void fillConvexPoly(InputOutputArray img, InputArray points, + const Scalar& color, int lineType = LINE_8, + int shift = 0); + +/** @overload */ +CV_EXPORTS void fillPoly(Mat& img, const Point** pts, + const int* npts, int ncontours, + const Scalar& color, int lineType = LINE_8, int shift = 0, + Point offset = Point() ); + +/** @example samples/cpp/tutorial_code/ImgProc/basic_drawing/Drawing_1.cpp +An example using drawing functions +Check @ref tutorial_random_generator_and_text "the corresponding tutorial" for more details +*/ + +/** @brief Fills the area bounded by one or more polygons. + +The function cv::fillPoly fills an area bounded by several polygonal contours. The function can fill +complex areas, for example, areas with holes, contours with self-intersections (some of their +parts), and so forth. + +@param img Image. +@param pts Array of polygons where each polygon is represented as an array of points. +@param color Polygon color. +@param lineType Type of the polygon boundaries. See #LineTypes +@param shift Number of fractional bits in the vertex coordinates. +@param offset Optional offset of all points of the contours. + */ +CV_EXPORTS_W void fillPoly(InputOutputArray img, InputArrayOfArrays pts, + const Scalar& color, int lineType = LINE_8, int shift = 0, + Point offset = Point() ); + +/** @overload */ +CV_EXPORTS void polylines(Mat& img, const Point* const* pts, const int* npts, + int ncontours, bool isClosed, const Scalar& color, + int thickness = 1, int lineType = LINE_8, int shift = 0 ); + +/** @brief Draws several polygonal curves. + +@param img Image. +@param pts Array of polygonal curves. +@param isClosed Flag indicating whether the drawn polylines are closed or not. If they are closed, +the function draws a line from the last vertex of each curve to its first vertex. +@param color Polyline color. +@param thickness Thickness of the polyline edges. +@param lineType Type of the line segments. See #LineTypes +@param shift Number of fractional bits in the vertex coordinates. + +The function cv::polylines draws one or more polygonal curves. + */ +CV_EXPORTS_W void polylines(InputOutputArray img, InputArrayOfArrays pts, + bool isClosed, const Scalar& color, + int thickness = 1, int lineType = LINE_8, int shift = 0 ); + +/** @example samples/cpp/contours2.cpp +An example program illustrates the use of cv::findContours and cv::drawContours +\image html WindowsQtContoursOutput.png "Screenshot of the program" +*/ + +/** @example samples/cpp/segment_objects.cpp +An example using drawContours to clean up a background segmentation result +*/ + +/** @brief Draws contours outlines or filled contours. + +The function draws contour outlines in the image if \f$\texttt{thickness} \ge 0\f$ or fills the area +bounded by the contours if \f$\texttt{thickness}<0\f$ . The example below shows how to retrieve +connected components from the binary image and label them: : +@include snippets/imgproc_drawContours.cpp + +@param image Destination image. +@param contours All the input contours. Each contour is stored as a point vector. +@param contourIdx Parameter indicating a contour to draw. If it is negative, all the contours are drawn. +@param color Color of the contours. +@param thickness Thickness of lines the contours are drawn with. If it is negative (for example, +thickness=#FILLED ), the contour interiors are drawn. +@param lineType Line connectivity. See #LineTypes +@param hierarchy Optional information about hierarchy. It is only needed if you want to draw only +some of the contours (see maxLevel ). +@param maxLevel Maximal level for drawn contours. If it is 0, only the specified contour is drawn. +If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function +draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This +parameter is only taken into account when there is hierarchy available. +@param offset Optional contour shift parameter. Shift all the drawn contours by the specified +\f$\texttt{offset}=(dx,dy)\f$ . +@note When thickness=#FILLED, the function is designed to handle connected components with holes correctly +even when no hierarchy date is provided. This is done by analyzing all the outlines together +using even-odd rule. This may give incorrect results if you have a joint collection of separately retrieved +contours. In order to solve this problem, you need to call #drawContours separately for each sub-group +of contours, or iterate over the collection using contourIdx parameter. + */ +CV_EXPORTS_W void drawContours( InputOutputArray image, InputArrayOfArrays contours, + int contourIdx, const Scalar& color, + int thickness = 1, int lineType = LINE_8, + InputArray hierarchy = noArray(), + int maxLevel = INT_MAX, Point offset = Point() ); + +/** @brief Clips the line against the image rectangle. + +The function cv::clipLine calculates a part of the line segment that is entirely within the specified +rectangle. it returns false if the line segment is completely outside the rectangle. Otherwise, +it returns true . +@param imgSize Image size. The image rectangle is Rect(0, 0, imgSize.width, imgSize.height) . +@param pt1 First line point. +@param pt2 Second line point. + */ +CV_EXPORTS bool clipLine(Size imgSize, CV_IN_OUT Point& pt1, CV_IN_OUT Point& pt2); + +/** @overload +@param imgSize Image size. The image rectangle is Rect(0, 0, imgSize.width, imgSize.height) . +@param pt1 First line point. +@param pt2 Second line point. +*/ +CV_EXPORTS bool clipLine(Size2l imgSize, CV_IN_OUT Point2l& pt1, CV_IN_OUT Point2l& pt2); + +/** @overload +@param imgRect Image rectangle. +@param pt1 First line point. +@param pt2 Second line point. +*/ +CV_EXPORTS_W bool clipLine(Rect imgRect, CV_OUT CV_IN_OUT Point& pt1, CV_OUT CV_IN_OUT Point& pt2); + +/** @brief Approximates an elliptic arc with a polyline. + +The function ellipse2Poly computes the vertices of a polyline that approximates the specified +elliptic arc. It is used by #ellipse. If `arcStart` is greater than `arcEnd`, they are swapped. + +@param center Center of the arc. +@param axes Half of the size of the ellipse main axes. See #ellipse for details. +@param angle Rotation angle of the ellipse in degrees. See #ellipse for details. +@param arcStart Starting angle of the elliptic arc in degrees. +@param arcEnd Ending angle of the elliptic arc in degrees. +@param delta Angle between the subsequent polyline vertices. It defines the approximation +accuracy. +@param pts Output vector of polyline vertices. + */ +CV_EXPORTS_W void ellipse2Poly( Point center, Size axes, int angle, + int arcStart, int arcEnd, int delta, + CV_OUT std::vector& pts ); + +/** @overload +@param center Center of the arc. +@param axes Half of the size of the ellipse main axes. See #ellipse for details. +@param angle Rotation angle of the ellipse in degrees. See #ellipse for details. +@param arcStart Starting angle of the elliptic arc in degrees. +@param arcEnd Ending angle of the elliptic arc in degrees. +@param delta Angle between the subsequent polyline vertices. It defines the approximation accuracy. +@param pts Output vector of polyline vertices. +*/ +CV_EXPORTS void ellipse2Poly(Point2d center, Size2d axes, int angle, + int arcStart, int arcEnd, int delta, + CV_OUT std::vector& pts); + +/** @brief Draws a text string. + +The function cv::putText renders the specified text string in the image. Symbols that cannot be rendered +using the specified font are replaced by question marks. See #getTextSize for a text rendering code +example. + +@param img Image. +@param text Text string to be drawn. +@param org Bottom-left corner of the text string in the image. +@param fontFace Font type, see #HersheyFonts. +@param fontScale Font scale factor that is multiplied by the font-specific base size. +@param color Text color. +@param thickness Thickness of the lines used to draw a text. +@param lineType Line type. See #LineTypes +@param bottomLeftOrigin When true, the image data origin is at the bottom-left corner. Otherwise, +it is at the top-left corner. + */ +CV_EXPORTS_W void putText( InputOutputArray img, const String& text, Point org, + int fontFace, double fontScale, Scalar color, + int thickness = 1, int lineType = LINE_8, + bool bottomLeftOrigin = false ); + +/** @brief Calculates the width and height of a text string. + +The function cv::getTextSize calculates and returns the size of a box that contains the specified text. +That is, the following code renders some text, the tight box surrounding it, and the baseline: : +@code + String text = "Funny text inside the box"; + int fontFace = FONT_HERSHEY_SCRIPT_SIMPLEX; + double fontScale = 2; + int thickness = 3; + + Mat img(600, 800, CV_8UC3, Scalar::all(0)); + + int baseline=0; + Size textSize = getTextSize(text, fontFace, + fontScale, thickness, &baseline); + baseline += thickness; + + // center the text + Point textOrg((img.cols - textSize.width)/2, + (img.rows + textSize.height)/2); + + // draw the box + rectangle(img, textOrg + Point(0, baseline), + textOrg + Point(textSize.width, -textSize.height), + Scalar(0,0,255)); + // ... and the baseline first + line(img, textOrg + Point(0, thickness), + textOrg + Point(textSize.width, thickness), + Scalar(0, 0, 255)); + + // then put the text itself + putText(img, text, textOrg, fontFace, fontScale, + Scalar::all(255), thickness, 8); +@endcode + +@param text Input text string. +@param fontFace Font to use, see #HersheyFonts. +@param fontScale Font scale factor that is multiplied by the font-specific base size. +@param thickness Thickness of lines used to render the text. See #putText for details. +@param[out] baseLine y-coordinate of the baseline relative to the bottom-most text +point. +@return The size of a box that contains the specified text. + +@see putText + */ +CV_EXPORTS_W Size getTextSize(const String& text, int fontFace, + double fontScale, int thickness, + CV_OUT int* baseLine); + + +/** @brief Calculates the font-specific size to use to achieve a given height in pixels. + +@param fontFace Font to use, see cv::HersheyFonts. +@param pixelHeight Pixel height to compute the fontScale for +@param thickness Thickness of lines used to render the text.See putText for details. +@return The fontSize to use for cv::putText + +@see cv::putText +*/ +CV_EXPORTS_W double getFontScaleFromHeight(const int fontFace, + const int pixelHeight, + const int thickness = 1); + +/** @brief Line iterator + +The class is used to iterate over all the pixels on the raster line +segment connecting two specified points. + +The class LineIterator is used to get each pixel of a raster line. It +can be treated as versatile implementation of the Bresenham algorithm +where you can stop at each pixel and do some extra processing, for +example, grab pixel values along the line or draw a line with an effect +(for example, with XOR operation). + +The number of pixels along the line is stored in LineIterator::count. +The method LineIterator::pos returns the current position in the image: + +@code{.cpp} +// grabs pixels along the line (pt1, pt2) +// from 8-bit 3-channel image to the buffer +LineIterator it(img, pt1, pt2, 8); +LineIterator it2 = it; +vector buf(it.count); + +for(int i = 0; i < it.count; i++, ++it) + buf[i] = *(const Vec3b*)*it; + +// alternative way of iterating through the line +for(int i = 0; i < it2.count; i++, ++it2) +{ + Vec3b val = img.at(it2.pos()); + CV_Assert(buf[i] == val); +} +@endcode +*/ +class CV_EXPORTS LineIterator +{ +public: + /** @brief initializes the iterator + + creates iterators for the line connecting pt1 and pt2 + the line will be clipped on the image boundaries + the line is 8-connected or 4-connected + If leftToRight=true, then the iteration is always done + from the left-most point to the right most, + not to depend on the ordering of pt1 and pt2 parameters + */ + LineIterator( const Mat& img, Point pt1, Point pt2, + int connectivity = 8, bool leftToRight = false ); + /** @brief returns pointer to the current pixel + */ + uchar* operator *(); + /** @brief prefix increment operator (++it). shifts iterator to the next pixel + */ + LineIterator& operator ++(); + /** @brief postfix increment operator (it++). shifts iterator to the next pixel + */ + LineIterator operator ++(int); + /** @brief returns coordinates of the current pixel + */ + Point pos() const; + + uchar* ptr; + const uchar* ptr0; + int step, elemSize; + int err, count; + int minusDelta, plusDelta; + int minusStep, plusStep; +}; + +//! @cond IGNORED + +// === LineIterator implementation === + +inline +uchar* LineIterator::operator *() +{ + return ptr; +} + +inline +LineIterator& LineIterator::operator ++() +{ + int mask = err < 0 ? -1 : 0; + err += minusDelta + (plusDelta & mask); + ptr += minusStep + (plusStep & mask); + return *this; +} + +inline +LineIterator LineIterator::operator ++(int) +{ + LineIterator it = *this; + ++(*this); + return it; +} + +inline +Point LineIterator::pos() const +{ + Point p; + p.y = (int)((ptr - ptr0)/step); + p.x = (int)(((ptr - ptr0) - p.y*step)/elemSize); + return p; +} + +//! @endcond + +//! @} imgproc_draw + +//! @} imgproc + +} // cv + +#ifndef DISABLE_OPENCV_24_COMPATIBILITY +#include "opencv2/imgproc/imgproc_c.h" +#endif + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/imgproc/detail/distortion_model.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/imgproc/detail/distortion_model.hpp new file mode 100644 index 0000000000000000000000000000000000000000..a9c3ddec8a7febbff1adbf78a9e2d4d403819bfb --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/imgproc/detail/distortion_model.hpp @@ -0,0 +1,123 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_IMGPROC_DETAIL_DISTORTION_MODEL_HPP +#define OPENCV_IMGPROC_DETAIL_DISTORTION_MODEL_HPP + +//! @cond IGNORED + +namespace cv { namespace detail { +/** +Computes the matrix for the projection onto a tilted image sensor +\param tauX angular parameter rotation around x-axis +\param tauY angular parameter rotation around y-axis +\param matTilt if not NULL returns the matrix +\f[ +\vecthreethree{R_{33}(\tau_x, \tau_y)}{0}{-R_{13}((\tau_x, \tau_y)} +{0}{R_{33}(\tau_x, \tau_y)}{-R_{23}(\tau_x, \tau_y)} +{0}{0}{1} R(\tau_x, \tau_y) +\f] +where +\f[ +R(\tau_x, \tau_y) = +\vecthreethree{\cos(\tau_y)}{0}{-\sin(\tau_y)}{0}{1}{0}{\sin(\tau_y)}{0}{\cos(\tau_y)} +\vecthreethree{1}{0}{0}{0}{\cos(\tau_x)}{\sin(\tau_x)}{0}{-\sin(\tau_x)}{\cos(\tau_x)} = +\vecthreethree{\cos(\tau_y)}{\sin(\tau_y)\sin(\tau_x)}{-\sin(\tau_y)\cos(\tau_x)} +{0}{\cos(\tau_x)}{\sin(\tau_x)} +{\sin(\tau_y)}{-\cos(\tau_y)\sin(\tau_x)}{\cos(\tau_y)\cos(\tau_x)}. +\f] +\param dMatTiltdTauX if not NULL it returns the derivative of matTilt with +respect to \f$\tau_x\f$. +\param dMatTiltdTauY if not NULL it returns the derivative of matTilt with +respect to \f$\tau_y\f$. +\param invMatTilt if not NULL it returns the inverse of matTilt +**/ +template +void computeTiltProjectionMatrix(FLOAT tauX, + FLOAT tauY, + Matx* matTilt = 0, + Matx* dMatTiltdTauX = 0, + Matx* dMatTiltdTauY = 0, + Matx* invMatTilt = 0) +{ + FLOAT cTauX = cos(tauX); + FLOAT sTauX = sin(tauX); + FLOAT cTauY = cos(tauY); + FLOAT sTauY = sin(tauY); + Matx matRotX = Matx(1,0,0,0,cTauX,sTauX,0,-sTauX,cTauX); + Matx matRotY = Matx(cTauY,0,-sTauY,0,1,0,sTauY,0,cTauY); + Matx matRotXY = matRotY * matRotX; + Matx matProjZ = Matx(matRotXY(2,2),0,-matRotXY(0,2),0,matRotXY(2,2),-matRotXY(1,2),0,0,1); + if (matTilt) + { + // Matrix for trapezoidal distortion of tilted image sensor + *matTilt = matProjZ * matRotXY; + } + if (dMatTiltdTauX) + { + // Derivative with respect to tauX + Matx dMatRotXYdTauX = matRotY * Matx(0,0,0,0,-sTauX,cTauX,0,-cTauX,-sTauX); + Matx dMatProjZdTauX = Matx(dMatRotXYdTauX(2,2),0,-dMatRotXYdTauX(0,2), + 0,dMatRotXYdTauX(2,2),-dMatRotXYdTauX(1,2),0,0,0); + *dMatTiltdTauX = (matProjZ * dMatRotXYdTauX) + (dMatProjZdTauX * matRotXY); + } + if (dMatTiltdTauY) + { + // Derivative with respect to tauY + Matx dMatRotXYdTauY = Matx(-sTauY,0,-cTauY,0,0,0,cTauY,0,-sTauY) * matRotX; + Matx dMatProjZdTauY = Matx(dMatRotXYdTauY(2,2),0,-dMatRotXYdTauY(0,2), + 0,dMatRotXYdTauY(2,2),-dMatRotXYdTauY(1,2),0,0,0); + *dMatTiltdTauY = (matProjZ * dMatRotXYdTauY) + (dMatProjZdTauY * matRotXY); + } + if (invMatTilt) + { + FLOAT inv = 1./matRotXY(2,2); + Matx invMatProjZ = Matx(inv,0,inv*matRotXY(0,2),0,inv,inv*matRotXY(1,2),0,0,1); + *invMatTilt = matRotXY.t()*invMatProjZ; + } +} +}} // namespace detail, cv + + +//! @endcond + +#endif // OPENCV_IMGPROC_DETAIL_DISTORTION_MODEL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/imgproc/hal/hal.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/imgproc/hal/hal.hpp new file mode 100644 index 0000000000000000000000000000000000000000..a435fd6b8558ec7460cada0656c894ea26429989 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/imgproc/hal/hal.hpp @@ -0,0 +1,241 @@ +#ifndef CV_IMGPROC_HAL_HPP +#define CV_IMGPROC_HAL_HPP + +#include "opencv2/core/cvdef.h" +#include "opencv2/core/cvstd.hpp" +#include "opencv2/core/hal/interface.h" + +namespace cv { namespace hal { + +//! @addtogroup imgproc_hal_functions +//! @{ + +//--------------------------- +//! @cond IGNORED + +struct CV_EXPORTS Filter2D +{ + CV_DEPRECATED static Ptr create(uchar * , size_t , int , + int , int , + int , int , + int , int , + int , double , + int , int , + bool , bool ); + virtual void apply(uchar * , size_t , + uchar * , size_t , + int , int , + int , int , + int , int ) = 0; + virtual ~Filter2D() {} +}; + +struct CV_EXPORTS SepFilter2D +{ + CV_DEPRECATED static Ptr create(int , int , int , + uchar * , int , + uchar * , int , + int , int , + double , int ); + virtual void apply(uchar * , size_t , + uchar * , size_t , + int , int , + int , int , + int , int ) = 0; + virtual ~SepFilter2D() {} +}; + + +struct CV_EXPORTS Morph +{ + CV_DEPRECATED static Ptr create(int , int , int , int , int , + int , uchar * , size_t , + int , int , + int , int , + int , const double *, + int , bool , bool ); + virtual void apply(uchar * , size_t , uchar * , size_t , int , int , + int , int , int , int , + int , int , int , int ) = 0; + virtual ~Morph() {} +}; + +//! @endcond +//--------------------------- + +CV_EXPORTS void filter2D(int stype, int dtype, int kernel_type, + uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int full_width, int full_height, + int offset_x, int offset_y, + uchar * kernel_data, size_t kernel_step, + int kernel_width, int kernel_height, + int anchor_x, int anchor_y, + double delta, int borderType, + bool isSubmatrix); + +CV_EXPORTS void sepFilter2D(int stype, int dtype, int ktype, + uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int full_width, int full_height, + int offset_x, int offset_y, + uchar * kernelx_data, int kernelx_len, + uchar * kernely_data, int kernely_len, + int anchor_x, int anchor_y, + double delta, int borderType); + +CV_EXPORTS void morph(int op, int src_type, int dst_type, + uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int roi_width, int roi_height, int roi_x, int roi_y, + int roi_width2, int roi_height2, int roi_x2, int roi_y2, + int kernel_type, uchar * kernel_data, size_t kernel_step, + int kernel_width, int kernel_height, int anchor_x, int anchor_y, + int borderType, const double borderValue[4], + int iterations, bool isSubmatrix); + + +CV_EXPORTS void resize(int src_type, + const uchar * src_data, size_t src_step, int src_width, int src_height, + uchar * dst_data, size_t dst_step, int dst_width, int dst_height, + double inv_scale_x, double inv_scale_y, int interpolation); + +CV_EXPORTS void warpAffine(int src_type, + const uchar * src_data, size_t src_step, int src_width, int src_height, + uchar * dst_data, size_t dst_step, int dst_width, int dst_height, + const double M[6], int interpolation, int borderType, const double borderValue[4]); + +CV_EXPORTS void warpPerspectve(int src_type, + const uchar * src_data, size_t src_step, int src_width, int src_height, + uchar * dst_data, size_t dst_step, int dst_width, int dst_height, + const double M[9], int interpolation, int borderType, const double borderValue[4]); + +CV_EXPORTS void cvtBGRtoBGR(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int depth, int scn, int dcn, bool swapBlue); + +CV_EXPORTS void cvtBGRtoBGR5x5(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int scn, bool swapBlue, int greenBits); + +CV_EXPORTS void cvtBGR5x5toBGR(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int dcn, bool swapBlue, int greenBits); + +CV_EXPORTS void cvtBGRtoGray(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int depth, int scn, bool swapBlue); + +CV_EXPORTS void cvtGraytoBGR(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int depth, int dcn); + +CV_EXPORTS void cvtBGR5x5toGray(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int greenBits); + +CV_EXPORTS void cvtGraytoBGR5x5(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int greenBits); +CV_EXPORTS void cvtBGRtoYUV(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int depth, int scn, bool swapBlue, bool isCbCr); + +CV_EXPORTS void cvtYUVtoBGR(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int depth, int dcn, bool swapBlue, bool isCbCr); + +CV_EXPORTS void cvtBGRtoXYZ(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int depth, int scn, bool swapBlue); + +CV_EXPORTS void cvtXYZtoBGR(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int depth, int dcn, bool swapBlue); + +CV_EXPORTS void cvtBGRtoHSV(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int depth, int scn, bool swapBlue, bool isFullRange, bool isHSV); + +CV_EXPORTS void cvtHSVtoBGR(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int depth, int dcn, bool swapBlue, bool isFullRange, bool isHSV); + +CV_EXPORTS void cvtBGRtoLab(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int depth, int scn, bool swapBlue, bool isLab, bool srgb); + +CV_EXPORTS void cvtLabtoBGR(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int depth, int dcn, bool swapBlue, bool isLab, bool srgb); + +CV_EXPORTS void cvtTwoPlaneYUVtoBGR(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int dst_width, int dst_height, + int dcn, bool swapBlue, int uIdx); + +//! Separate Y and UV planes +CV_EXPORTS void cvtTwoPlaneYUVtoBGR(const uchar * y_data, const uchar * uv_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int dst_width, int dst_height, + int dcn, bool swapBlue, int uIdx); + +CV_EXPORTS void cvtThreePlaneYUVtoBGR(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int dst_width, int dst_height, + int dcn, bool swapBlue, int uIdx); + +CV_EXPORTS void cvtBGRtoThreePlaneYUV(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int scn, bool swapBlue, int uIdx); + +//! Separate Y and UV planes +CV_EXPORTS void cvtBGRtoTwoPlaneYUV(const uchar * src_data, size_t src_step, + uchar * y_data, uchar * uv_data, size_t dst_step, + int width, int height, + int scn, bool swapBlue, int uIdx); + +CV_EXPORTS void cvtOnePlaneYUVtoBGR(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int dcn, bool swapBlue, int uIdx, int ycn); + +CV_EXPORTS void cvtRGBAtoMultipliedRGBA(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height); + +CV_EXPORTS void cvtMultipliedRGBAtoRGBA(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height); + +CV_EXPORTS void integral(int depth, int sdepth, int sqdepth, + const uchar* src, size_t srcstep, + uchar* sum, size_t sumstep, + uchar* sqsum, size_t sqsumstep, + uchar* tilted, size_t tstep, + int width, int height, int cn); + +//! @} + +}} + +#endif // CV_IMGPROC_HAL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/imgproc/hal/interface.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/imgproc/hal/interface.h new file mode 100644 index 0000000000000000000000000000000000000000..f8dbcfe791d90fd957cace7b7700d737864990e9 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/imgproc/hal/interface.h @@ -0,0 +1,46 @@ +#ifndef OPENCV_IMGPROC_HAL_INTERFACE_H +#define OPENCV_IMGPROC_HAL_INTERFACE_H + +//! @addtogroup imgproc_hal_interface +//! @{ + +//! @name Interpolation modes +//! @sa cv::InterpolationFlags +//! @{ +#define CV_HAL_INTER_NEAREST 0 +#define CV_HAL_INTER_LINEAR 1 +#define CV_HAL_INTER_CUBIC 2 +#define CV_HAL_INTER_AREA 3 +#define CV_HAL_INTER_LANCZOS4 4 +//! @} + +//! @name Morphology operations +//! @sa cv::MorphTypes +//! @{ +#define CV_HAL_MORPH_ERODE 0 +#define CV_HAL_MORPH_DILATE 1 +//! @} + +//! @name Threshold types +//! @sa cv::ThresholdTypes +//! @{ +#define CV_HAL_THRESH_BINARY 0 +#define CV_HAL_THRESH_BINARY_INV 1 +#define CV_HAL_THRESH_TRUNC 2 +#define CV_HAL_THRESH_TOZERO 3 +#define CV_HAL_THRESH_TOZERO_INV 4 +#define CV_HAL_THRESH_MASK 7 +#define CV_HAL_THRESH_OTSU 8 +#define CV_HAL_THRESH_TRIANGLE 16 +//! @} + +//! @name Adaptive threshold algorithm +//! @sa cv::AdaptiveThresholdTypes +//! @{ +#define CV_HAL_ADAPTIVE_THRESH_MEAN_C 0 +#define CV_HAL_ADAPTIVE_THRESH_GAUSSIAN_C 1 +//! @} + +//! @} + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/imgproc/imgproc.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/imgproc/imgproc.hpp new file mode 100644 index 0000000000000000000000000000000000000000..4175bd0bc01e0189abc4c698053c00e28277ef33 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/imgproc/imgproc.hpp @@ -0,0 +1,48 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifdef __OPENCV_BUILD +#error this is a compatibility header which should not be used inside the OpenCV library +#endif + +#include "opencv2/imgproc.hpp" diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/imgproc/imgproc_c.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/imgproc/imgproc_c.h new file mode 100644 index 0000000000000000000000000000000000000000..cec0f3653ab425b86cc6840edee9d6ced8b31854 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/imgproc/imgproc_c.h @@ -0,0 +1,1210 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_IMGPROC_IMGPROC_C_H +#define OPENCV_IMGPROC_IMGPROC_C_H + +#include "opencv2/imgproc/types_c.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/** @addtogroup imgproc_c +@{ +*/ + +/*********************** Background statistics accumulation *****************************/ + +/** @brief Adds image to accumulator +@see cv::accumulate +*/ +CVAPI(void) cvAcc( const CvArr* image, CvArr* sum, + const CvArr* mask CV_DEFAULT(NULL) ); + +/** @brief Adds squared image to accumulator +@see cv::accumulateSquare +*/ +CVAPI(void) cvSquareAcc( const CvArr* image, CvArr* sqsum, + const CvArr* mask CV_DEFAULT(NULL) ); + +/** @brief Adds a product of two images to accumulator +@see cv::accumulateProduct +*/ +CVAPI(void) cvMultiplyAcc( const CvArr* image1, const CvArr* image2, CvArr* acc, + const CvArr* mask CV_DEFAULT(NULL) ); + +/** @brief Adds image to accumulator with weights: acc = acc*(1-alpha) + image*alpha +@see cv::accumulateWeighted +*/ +CVAPI(void) cvRunningAvg( const CvArr* image, CvArr* acc, double alpha, + const CvArr* mask CV_DEFAULT(NULL) ); + +/****************************************************************************************\ +* Image Processing * +\****************************************************************************************/ + +/** Copies source 2D array inside of the larger destination array and + makes a border of the specified type (IPL_BORDER_*) around the copied area. */ +CVAPI(void) cvCopyMakeBorder( const CvArr* src, CvArr* dst, CvPoint offset, + int bordertype, CvScalar value CV_DEFAULT(cvScalarAll(0))); + +/** @brief Smooths the image in one of several ways. + +@param src The source image +@param dst The destination image +@param smoothtype Type of the smoothing, see SmoothMethod_c +@param size1 The first parameter of the smoothing operation, the aperture width. Must be a +positive odd number (1, 3, 5, ...) +@param size2 The second parameter of the smoothing operation, the aperture height. Ignored by +CV_MEDIAN and CV_BILATERAL methods. In the case of simple scaled/non-scaled and Gaussian blur if +size2 is zero, it is set to size1. Otherwise it must be a positive odd number. +@param sigma1 In the case of a Gaussian parameter this parameter may specify Gaussian \f$\sigma\f$ +(standard deviation). If it is zero, it is calculated from the kernel size: +\f[\sigma = 0.3 (n/2 - 1) + 0.8 \quad \text{where} \quad n= \begin{array}{l l} \mbox{\texttt{size1} for horizontal kernel} \\ \mbox{\texttt{size2} for vertical kernel} \end{array}\f] +Using standard sigma for small kernels ( \f$3\times 3\f$ to \f$7\times 7\f$ ) gives better speed. If +sigma1 is not zero, while size1 and size2 are zeros, the kernel size is calculated from the +sigma (to provide accurate enough operation). +@param sigma2 additional parameter for bilateral filtering + +@see cv::GaussianBlur, cv::blur, cv::medianBlur, cv::bilateralFilter. + */ +CVAPI(void) cvSmooth( const CvArr* src, CvArr* dst, + int smoothtype CV_DEFAULT(CV_GAUSSIAN), + int size1 CV_DEFAULT(3), + int size2 CV_DEFAULT(0), + double sigma1 CV_DEFAULT(0), + double sigma2 CV_DEFAULT(0)); + +/** @brief Convolves an image with the kernel. + +@param src input image. +@param dst output image of the same size and the same number of channels as src. +@param kernel convolution kernel (or rather a correlation kernel), a single-channel floating point +matrix; if you want to apply different kernels to different channels, split the image into +separate color planes using split and process them individually. +@param anchor anchor of the kernel that indicates the relative position of a filtered point within +the kernel; the anchor should lie within the kernel; default value (-1,-1) means that the anchor +is at the kernel center. + +@see cv::filter2D + */ +CVAPI(void) cvFilter2D( const CvArr* src, CvArr* dst, const CvMat* kernel, + CvPoint anchor CV_DEFAULT(cvPoint(-1,-1))); + +/** @brief Finds integral image: SUM(X,Y) = sum(x \texttt{hist1}(I)\)}{\frac{\texttt{hist2}(I) \cdot \texttt{scale}}{\texttt{hist1}(I)}}{if \(\texttt{hist1}(I) \ne 0\) and \(\texttt{hist2}(I) \le \texttt{hist1}(I)\)}\f] + +@param hist1 First histogram (the divisor). +@param hist2 Second histogram. +@param dst_hist Destination histogram. +@param scale Scale factor for the destination histogram. + */ +CVAPI(void) cvCalcProbDensity( const CvHistogram* hist1, const CvHistogram* hist2, + CvHistogram* dst_hist, double scale CV_DEFAULT(255) ); + +/** @brief equalizes histogram of 8-bit single-channel image +@see cv::equalizeHist +*/ +CVAPI(void) cvEqualizeHist( const CvArr* src, CvArr* dst ); + + +/** @brief Applies distance transform to binary image +@see cv::distanceTransform +*/ +CVAPI(void) cvDistTransform( const CvArr* src, CvArr* dst, + int distance_type CV_DEFAULT(CV_DIST_L2), + int mask_size CV_DEFAULT(3), + const float* mask CV_DEFAULT(NULL), + CvArr* labels CV_DEFAULT(NULL), + int labelType CV_DEFAULT(CV_DIST_LABEL_CCOMP)); + + +/** @brief Applies fixed-level threshold to grayscale image. + + This is a basic operation applied before retrieving contours +@see cv::threshold +*/ +CVAPI(double) cvThreshold( const CvArr* src, CvArr* dst, + double threshold, double max_value, + int threshold_type ); + +/** @brief Applies adaptive threshold to grayscale image. + + The two parameters for methods CV_ADAPTIVE_THRESH_MEAN_C and + CV_ADAPTIVE_THRESH_GAUSSIAN_C are: + neighborhood size (3, 5, 7 etc.), + and a constant subtracted from mean (...,-3,-2,-1,0,1,2,3,...) +@see cv::adaptiveThreshold +*/ +CVAPI(void) cvAdaptiveThreshold( const CvArr* src, CvArr* dst, double max_value, + int adaptive_method CV_DEFAULT(CV_ADAPTIVE_THRESH_MEAN_C), + int threshold_type CV_DEFAULT(CV_THRESH_BINARY), + int block_size CV_DEFAULT(3), + double param1 CV_DEFAULT(5)); + +/** @brief Fills the connected component until the color difference gets large enough +@see cv::floodFill +*/ +CVAPI(void) cvFloodFill( CvArr* image, CvPoint seed_point, + CvScalar new_val, CvScalar lo_diff CV_DEFAULT(cvScalarAll(0)), + CvScalar up_diff CV_DEFAULT(cvScalarAll(0)), + CvConnectedComp* comp CV_DEFAULT(NULL), + int flags CV_DEFAULT(4), + CvArr* mask CV_DEFAULT(NULL)); + +/****************************************************************************************\ +* Feature detection * +\****************************************************************************************/ + +/** @brief Runs canny edge detector +@see cv::Canny +*/ +CVAPI(void) cvCanny( const CvArr* image, CvArr* edges, double threshold1, + double threshold2, int aperture_size CV_DEFAULT(3) ); + +/** @brief Calculates constraint image for corner detection + + Dx^2 * Dyy + Dxx * Dy^2 - 2 * Dx * Dy * Dxy. + Applying threshold to the result gives coordinates of corners +@see cv::preCornerDetect +*/ +CVAPI(void) cvPreCornerDetect( const CvArr* image, CvArr* corners, + int aperture_size CV_DEFAULT(3) ); + +/** @brief Calculates eigen values and vectors of 2x2 + gradient covariation matrix at every image pixel +@see cv::cornerEigenValsAndVecs +*/ +CVAPI(void) cvCornerEigenValsAndVecs( const CvArr* image, CvArr* eigenvv, + int block_size, int aperture_size CV_DEFAULT(3) ); + +/** @brief Calculates minimal eigenvalue for 2x2 gradient covariation matrix at + every image pixel +@see cv::cornerMinEigenVal +*/ +CVAPI(void) cvCornerMinEigenVal( const CvArr* image, CvArr* eigenval, + int block_size, int aperture_size CV_DEFAULT(3) ); + +/** @brief Harris corner detector: + + Calculates det(M) - k*(trace(M)^2), where M is 2x2 gradient covariation matrix for each pixel +@see cv::cornerHarris +*/ +CVAPI(void) cvCornerHarris( const CvArr* image, CvArr* harris_response, + int block_size, int aperture_size CV_DEFAULT(3), + double k CV_DEFAULT(0.04) ); + +/** @brief Adjust corner position using some sort of gradient search +@see cv::cornerSubPix +*/ +CVAPI(void) cvFindCornerSubPix( const CvArr* image, CvPoint2D32f* corners, + int count, CvSize win, CvSize zero_zone, + CvTermCriteria criteria ); + +/** @brief Finds a sparse set of points within the selected region + that seem to be easy to track +@see cv::goodFeaturesToTrack +*/ +CVAPI(void) cvGoodFeaturesToTrack( const CvArr* image, CvArr* eig_image, + CvArr* temp_image, CvPoint2D32f* corners, + int* corner_count, double quality_level, + double min_distance, + const CvArr* mask CV_DEFAULT(NULL), + int block_size CV_DEFAULT(3), + int use_harris CV_DEFAULT(0), + double k CV_DEFAULT(0.04) ); + +/** @brief Finds lines on binary image using one of several methods. + + line_storage is either memory storage or 1 x _max number of lines_ CvMat, its + number of columns is changed by the function. + method is one of CV_HOUGH_*; + rho, theta and threshold are used for each of those methods; + param1 ~ line length, param2 ~ line gap - for probabilistic, + param1 ~ srn, param2 ~ stn - for multi-scale +@see cv::HoughLines +*/ +CVAPI(CvSeq*) cvHoughLines2( CvArr* image, void* line_storage, int method, + double rho, double theta, int threshold, + double param1 CV_DEFAULT(0), double param2 CV_DEFAULT(0), + double min_theta CV_DEFAULT(0), double max_theta CV_DEFAULT(CV_PI)); + +/** @brief Finds circles in the image +@see cv::HoughCircles +*/ +CVAPI(CvSeq*) cvHoughCircles( CvArr* image, void* circle_storage, + int method, double dp, double min_dist, + double param1 CV_DEFAULT(100), + double param2 CV_DEFAULT(100), + int min_radius CV_DEFAULT(0), + int max_radius CV_DEFAULT(0)); + +/** @brief Fits a line into set of 2d or 3d points in a robust way (M-estimator technique) +@see cv::fitLine +*/ +CVAPI(void) cvFitLine( const CvArr* points, int dist_type, double param, + double reps, double aeps, float* line ); + +/****************************************************************************************\ +* Drawing * +\****************************************************************************************/ + +/****************************************************************************************\ +* Drawing functions work with images/matrices of arbitrary type. * +* For color images the channel order is BGR[A] * +* Antialiasing is supported only for 8-bit image now. * +* All the functions include parameter color that means rgb value (that may be * +* constructed with CV_RGB macro) for color images and brightness * +* for grayscale images. * +* If a drawn figure is partially or completely outside of the image, it is clipped.* +\****************************************************************************************/ + +#define CV_FILLED -1 + +#define CV_AA 16 + +/** @brief Draws 4-connected, 8-connected or antialiased line segment connecting two points +@see cv::line +*/ +CVAPI(void) cvLine( CvArr* img, CvPoint pt1, CvPoint pt2, + CvScalar color, int thickness CV_DEFAULT(1), + int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0) ); + +/** @brief Draws a rectangle given two opposite corners of the rectangle (pt1 & pt2) + + if thickness<0 (e.g. thickness == CV_FILLED), the filled box is drawn +@see cv::rectangle +*/ +CVAPI(void) cvRectangle( CvArr* img, CvPoint pt1, CvPoint pt2, + CvScalar color, int thickness CV_DEFAULT(1), + int line_type CV_DEFAULT(8), + int shift CV_DEFAULT(0)); + +/** @brief Draws a rectangle specified by a CvRect structure +@see cv::rectangle +*/ +CVAPI(void) cvRectangleR( CvArr* img, CvRect r, + CvScalar color, int thickness CV_DEFAULT(1), + int line_type CV_DEFAULT(8), + int shift CV_DEFAULT(0)); + + +/** @brief Draws a circle with specified center and radius. + + Thickness works in the same way as with cvRectangle +@see cv::circle +*/ +CVAPI(void) cvCircle( CvArr* img, CvPoint center, int radius, + CvScalar color, int thickness CV_DEFAULT(1), + int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0)); + +/** @brief Draws ellipse outline, filled ellipse, elliptic arc or filled elliptic sector + + depending on _thickness_, _start_angle_ and _end_angle_ parameters. The resultant figure + is rotated by _angle_. All the angles are in degrees +@see cv::ellipse +*/ +CVAPI(void) cvEllipse( CvArr* img, CvPoint center, CvSize axes, + double angle, double start_angle, double end_angle, + CvScalar color, int thickness CV_DEFAULT(1), + int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0)); + +CV_INLINE void cvEllipseBox( CvArr* img, CvBox2D box, CvScalar color, + int thickness CV_DEFAULT(1), + int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0) ) +{ + CvSize axes = cvSize( + cvRound(box.size.width*0.5), + cvRound(box.size.height*0.5) + ); + + cvEllipse( img, cvPointFrom32f( box.center ), axes, box.angle, + 0, 360, color, thickness, line_type, shift ); +} + +/** @brief Fills convex or monotonous polygon. +@see cv::fillConvexPoly +*/ +CVAPI(void) cvFillConvexPoly( CvArr* img, const CvPoint* pts, int npts, CvScalar color, + int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0)); + +/** @brief Fills an area bounded by one or more arbitrary polygons +@see cv::fillPoly +*/ +CVAPI(void) cvFillPoly( CvArr* img, CvPoint** pts, const int* npts, + int contours, CvScalar color, + int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0) ); + +/** @brief Draws one or more polygonal curves +@see cv::polylines +*/ +CVAPI(void) cvPolyLine( CvArr* img, CvPoint** pts, const int* npts, int contours, + int is_closed, CvScalar color, int thickness CV_DEFAULT(1), + int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0) ); + +#define cvDrawRect cvRectangle +#define cvDrawLine cvLine +#define cvDrawCircle cvCircle +#define cvDrawEllipse cvEllipse +#define cvDrawPolyLine cvPolyLine + +/** @brief Clips the line segment connecting *pt1 and *pt2 + by the rectangular window + + (0<=xptr will point to pt1 (or pt2, see left_to_right description) location in +the image. Returns the number of pixels on the line between the ending points. +@see cv::LineIterator +*/ +CVAPI(int) cvInitLineIterator( const CvArr* image, CvPoint pt1, CvPoint pt2, + CvLineIterator* line_iterator, + int connectivity CV_DEFAULT(8), + int left_to_right CV_DEFAULT(0)); + +#define CV_NEXT_LINE_POINT( line_iterator ) \ +{ \ + int _line_iterator_mask = (line_iterator).err < 0 ? -1 : 0; \ + (line_iterator).err += (line_iterator).minus_delta + \ + ((line_iterator).plus_delta & _line_iterator_mask); \ + (line_iterator).ptr += (line_iterator).minus_step + \ + ((line_iterator).plus_step & _line_iterator_mask); \ +} + + +#define CV_FONT_HERSHEY_SIMPLEX 0 +#define CV_FONT_HERSHEY_PLAIN 1 +#define CV_FONT_HERSHEY_DUPLEX 2 +#define CV_FONT_HERSHEY_COMPLEX 3 +#define CV_FONT_HERSHEY_TRIPLEX 4 +#define CV_FONT_HERSHEY_COMPLEX_SMALL 5 +#define CV_FONT_HERSHEY_SCRIPT_SIMPLEX 6 +#define CV_FONT_HERSHEY_SCRIPT_COMPLEX 7 + +#define CV_FONT_ITALIC 16 + +#define CV_FONT_VECTOR0 CV_FONT_HERSHEY_SIMPLEX + + +/** Font structure */ +typedef struct CvFont +{ + const char* nameFont; //Qt:nameFont + CvScalar color; //Qt:ColorFont -> cvScalar(blue_component, green_component, red_component[, alpha_component]) + int font_face; //Qt: bool italic /** =CV_FONT_* */ + const int* ascii; //!< font data and metrics + const int* greek; + const int* cyrillic; + float hscale, vscale; + float shear; //!< slope coefficient: 0 - normal, >0 - italic + int thickness; //!< Qt: weight /** letters thickness */ + float dx; //!< horizontal interval between letters + int line_type; //!< Qt: PointSize +} +CvFont; + +/** @brief Initializes font structure (OpenCV 1.x API). + +The function initializes the font structure that can be passed to text rendering functions. + +@param font Pointer to the font structure initialized by the function +@param font_face Font name identifier. See cv::HersheyFonts and corresponding old CV_* identifiers. +@param hscale Horizontal scale. If equal to 1.0f , the characters have the original width +depending on the font type. If equal to 0.5f , the characters are of half the original width. +@param vscale Vertical scale. If equal to 1.0f , the characters have the original height depending +on the font type. If equal to 0.5f , the characters are of half the original height. +@param shear Approximate tangent of the character slope relative to the vertical line. A zero +value means a non-italic font, 1.0f means about a 45 degree slope, etc. +@param thickness Thickness of the text strokes +@param line_type Type of the strokes, see line description + +@sa cvPutText + */ +CVAPI(void) cvInitFont( CvFont* font, int font_face, + double hscale, double vscale, + double shear CV_DEFAULT(0), + int thickness CV_DEFAULT(1), + int line_type CV_DEFAULT(8)); + +CV_INLINE CvFont cvFont( double scale, int thickness CV_DEFAULT(1) ) +{ + CvFont font; + cvInitFont( &font, CV_FONT_HERSHEY_PLAIN, scale, scale, 0, thickness, CV_AA ); + return font; +} + +/** @brief Renders text stroke with specified font and color at specified location. + CvFont should be initialized with cvInitFont +@see cvInitFont, cvGetTextSize, cvFont, cv::putText +*/ +CVAPI(void) cvPutText( CvArr* img, const char* text, CvPoint org, + const CvFont* font, CvScalar color ); + +/** @brief Calculates bounding box of text stroke (useful for alignment) +@see cv::getTextSize +*/ +CVAPI(void) cvGetTextSize( const char* text_string, const CvFont* font, + CvSize* text_size, int* baseline ); + +/** @brief Unpacks color value + +if arrtype is CV_8UC?, _color_ is treated as packed color value, otherwise the first channels +(depending on arrtype) of destination scalar are set to the same value = _color_ +*/ +CVAPI(CvScalar) cvColorToScalar( double packed_color, int arrtype ); + +/** @brief Returns the polygon points which make up the given ellipse. + +The ellipse is define by the box of size 'axes' rotated 'angle' around the 'center'. A partial +sweep of the ellipse arc can be done by spcifying arc_start and arc_end to be something other than +0 and 360, respectively. The input array 'pts' must be large enough to hold the result. The total +number of points stored into 'pts' is returned by this function. +@see cv::ellipse2Poly +*/ +CVAPI(int) cvEllipse2Poly( CvPoint center, CvSize axes, + int angle, int arc_start, int arc_end, CvPoint * pts, int delta ); + +/** @brief Draws contour outlines or filled interiors on the image +@see cv::drawContours +*/ +CVAPI(void) cvDrawContours( CvArr *img, CvSeq* contour, + CvScalar external_color, CvScalar hole_color, + int max_level, int thickness CV_DEFAULT(1), + int line_type CV_DEFAULT(8), + CvPoint offset CV_DEFAULT(cvPoint(0,0))); + +/** @} */ + +#ifdef __cplusplus +} +#endif + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/imgproc/types_c.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/imgproc/types_c.h new file mode 100644 index 0000000000000000000000000000000000000000..d3e55f576f6e93136d030285f7e753865a34ed41 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/imgproc/types_c.h @@ -0,0 +1,659 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_IMGPROC_TYPES_C_H +#define OPENCV_IMGPROC_TYPES_C_H + +#include "opencv2/core/core_c.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/** @addtogroup imgproc_c + @{ +*/ + +/** Connected component structure */ +typedef struct CvConnectedComp +{ + double area; /** DBL_EPSILON ? 1./std::sqrt(am00) : 0; + } + operator cv::Moments() const + { + return cv::Moments(m00, m10, m01, m20, m11, m02, m30, m21, m12, m03); + } +#endif +} +CvMoments; + +#ifdef __cplusplus +} // extern "C" + +CV_INLINE CvMoments cvMoments() +{ +#if !defined(CV__ENABLE_C_API_CTORS) + CvMoments self = CV_STRUCT_INITIALIZER; return self; +#else + return CvMoments(); +#endif +} + +CV_INLINE CvMoments cvMoments(const cv::Moments& m) +{ +#if !defined(CV__ENABLE_C_API_CTORS) + double am00 = std::abs(m.m00); + CvMoments self = { + m.m00, m.m10, m.m01, m.m20, m.m11, m.m02, m.m30, m.m21, m.m12, m.m03, + m.mu20, m.mu11, m.mu02, m.mu30, m.mu21, m.mu12, m.mu03, + am00 > DBL_EPSILON ? 1./std::sqrt(am00) : 0 + }; + return self; +#else + return CvMoments(m); +#endif +} + +extern "C" { +#endif // __cplusplus + +/** Hu invariants */ +typedef struct CvHuMoments +{ + double hu1, hu2, hu3, hu4, hu5, hu6, hu7; /**< Hu invariants */ +} +CvHuMoments; + +/** Template matching methods */ +enum +{ + CV_TM_SQDIFF =0, + CV_TM_SQDIFF_NORMED =1, + CV_TM_CCORR =2, + CV_TM_CCORR_NORMED =3, + CV_TM_CCOEFF =4, + CV_TM_CCOEFF_NORMED =5 +}; + +typedef float (CV_CDECL * CvDistanceFunction)( const float* a, const float* b, void* user_param ); + +/** Contour retrieval modes */ +enum +{ + CV_RETR_EXTERNAL=0, + CV_RETR_LIST=1, + CV_RETR_CCOMP=2, + CV_RETR_TREE=3, + CV_RETR_FLOODFILL=4 +}; + +/** Contour approximation methods */ +enum +{ + CV_CHAIN_CODE=0, + CV_CHAIN_APPROX_NONE=1, + CV_CHAIN_APPROX_SIMPLE=2, + CV_CHAIN_APPROX_TC89_L1=3, + CV_CHAIN_APPROX_TC89_KCOS=4, + CV_LINK_RUNS=5 +}; + +/* +Internal structure that is used for sequential retrieving contours from the image. +It supports both hierarchical and plane variants of Suzuki algorithm. +*/ +typedef struct _CvContourScanner* CvContourScanner; + +/** Freeman chain reader state */ +typedef struct CvChainPtReader +{ + CV_SEQ_READER_FIELDS() + char code; + CvPoint pt; + schar deltas[8][2]; +} +CvChainPtReader; + +/** initializes 8-element array for fast access to 3x3 neighborhood of a pixel */ +#define CV_INIT_3X3_DELTAS( deltas, step, nch ) \ + ((deltas)[0] = (nch), (deltas)[1] = -(step) + (nch), \ + (deltas)[2] = -(step), (deltas)[3] = -(step) - (nch), \ + (deltas)[4] = -(nch), (deltas)[5] = (step) - (nch), \ + (deltas)[6] = (step), (deltas)[7] = (step) + (nch)) + + +/** Contour approximation algorithms */ +enum +{ + CV_POLY_APPROX_DP = 0 +}; + +/** Shape matching methods */ +enum +{ + CV_CONTOURS_MATCH_I1 =1, //!< \f[I_1(A,B) = \sum _{i=1...7} \left | \frac{1}{m^A_i} - \frac{1}{m^B_i} \right |\f] + CV_CONTOURS_MATCH_I2 =2, //!< \f[I_2(A,B) = \sum _{i=1...7} \left | m^A_i - m^B_i \right |\f] + CV_CONTOURS_MATCH_I3 =3 //!< \f[I_3(A,B) = \max _{i=1...7} \frac{ \left| m^A_i - m^B_i \right| }{ \left| m^A_i \right| }\f] +}; + +/** Shape orientation */ +enum +{ + CV_CLOCKWISE =1, + CV_COUNTER_CLOCKWISE =2 +}; + + +/** Convexity defect */ +typedef struct CvConvexityDefect +{ + CvPoint* start; /**< point of the contour where the defect begins */ + CvPoint* end; /**< point of the contour where the defect ends */ + CvPoint* depth_point; /**< the farthest from the convex hull point within the defect */ + float depth; /**< distance between the farthest point and the convex hull */ +} CvConvexityDefect; + + +/** Histogram comparison methods */ +enum +{ + CV_COMP_CORREL =0, + CV_COMP_CHISQR =1, + CV_COMP_INTERSECT =2, + CV_COMP_BHATTACHARYYA =3, + CV_COMP_HELLINGER =CV_COMP_BHATTACHARYYA, + CV_COMP_CHISQR_ALT =4, + CV_COMP_KL_DIV =5 +}; + +/** Mask size for distance transform */ +enum +{ + CV_DIST_MASK_3 =3, + CV_DIST_MASK_5 =5, + CV_DIST_MASK_PRECISE =0 +}; + +/** Content of output label array: connected components or pixels */ +enum +{ + CV_DIST_LABEL_CCOMP = 0, + CV_DIST_LABEL_PIXEL = 1 +}; + +/** Distance types for Distance Transform and M-estimators */ +enum +{ + CV_DIST_USER =-1, /**< User defined distance */ + CV_DIST_L1 =1, /**< distance = |x1-x2| + |y1-y2| */ + CV_DIST_L2 =2, /**< the simple euclidean distance */ + CV_DIST_C =3, /**< distance = max(|x1-x2|,|y1-y2|) */ + CV_DIST_L12 =4, /**< L1-L2 metric: distance = 2(sqrt(1+x*x/2) - 1)) */ + CV_DIST_FAIR =5, /**< distance = c^2(|x|/c-log(1+|x|/c)), c = 1.3998 */ + CV_DIST_WELSCH =6, /**< distance = c^2/2(1-exp(-(x/c)^2)), c = 2.9846 */ + CV_DIST_HUBER =7 /**< distance = |x| threshold ? max_value : 0 */ + CV_THRESH_BINARY_INV =1, /**< value = value > threshold ? 0 : max_value */ + CV_THRESH_TRUNC =2, /**< value = value > threshold ? threshold : value */ + CV_THRESH_TOZERO =3, /**< value = value > threshold ? value : 0 */ + CV_THRESH_TOZERO_INV =4, /**< value = value > threshold ? 0 : value */ + CV_THRESH_MASK =7, + CV_THRESH_OTSU =8, /**< use Otsu algorithm to choose the optimal threshold value; + combine the flag with one of the above CV_THRESH_* values */ + CV_THRESH_TRIANGLE =16 /**< use Triangle algorithm to choose the optimal threshold value; + combine the flag with one of the above CV_THRESH_* values, but not + with CV_THRESH_OTSU */ +}; + +/** Adaptive threshold methods */ +enum +{ + CV_ADAPTIVE_THRESH_MEAN_C =0, + CV_ADAPTIVE_THRESH_GAUSSIAN_C =1 +}; + +/** FloodFill flags */ +enum +{ + CV_FLOODFILL_FIXED_RANGE =(1 << 16), + CV_FLOODFILL_MASK_ONLY =(1 << 17) +}; + + +/** Canny edge detector flags */ +enum +{ + CV_CANNY_L2_GRADIENT =(1 << 31) +}; + +/** Variants of a Hough transform */ +enum +{ + CV_HOUGH_STANDARD =0, + CV_HOUGH_PROBABILISTIC =1, + CV_HOUGH_MULTI_SCALE =2, + CV_HOUGH_GRADIENT =3 +}; + + +/* Fast search data structures */ +struct CvFeatureTree; +struct CvLSH; +struct CvLSHOperations; + +/** @} */ + +#ifdef __cplusplus +} +#endif + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/ml.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/ml.hpp new file mode 100644 index 0000000000000000000000000000000000000000..31e742773532b2c6d9992bb5f818bfad1384e814 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/ml.hpp @@ -0,0 +1,1961 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000, Intel Corporation, all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2014, Itseez Inc, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_ML_HPP +#define OPENCV_ML_HPP + +#ifdef __cplusplus +# include "opencv2/core.hpp" +#endif + +#ifdef __cplusplus + +#include +#include +#include + +/** + @defgroup ml Machine Learning + + The Machine Learning Library (MLL) is a set of classes and functions for statistical + classification, regression, and clustering of data. + + Most of the classification and regression algorithms are implemented as C++ classes. As the + algorithms have different sets of features (like an ability to handle missing measurements or + categorical input variables), there is a little common ground between the classes. This common + ground is defined by the class cv::ml::StatModel that all the other ML classes are derived from. + + See detailed overview here: @ref ml_intro. + */ + +namespace cv +{ + +namespace ml +{ + +//! @addtogroup ml +//! @{ + +/** @brief Variable types */ +enum VariableTypes +{ + VAR_NUMERICAL =0, //!< same as VAR_ORDERED + VAR_ORDERED =0, //!< ordered variables + VAR_CATEGORICAL =1 //!< categorical variables +}; + +/** @brief %Error types */ +enum ErrorTypes +{ + TEST_ERROR = 0, + TRAIN_ERROR = 1 +}; + +/** @brief Sample types */ +enum SampleTypes +{ + ROW_SAMPLE = 0, //!< each training sample is a row of samples + COL_SAMPLE = 1 //!< each training sample occupies a column of samples +}; + +/** @brief The structure represents the logarithmic grid range of statmodel parameters. + +It is used for optimizing statmodel accuracy by varying model parameters, the accuracy estimate +being computed by cross-validation. + */ +class CV_EXPORTS_W ParamGrid +{ +public: + /** @brief Default constructor */ + ParamGrid(); + /** @brief Constructor with parameters */ + ParamGrid(double _minVal, double _maxVal, double _logStep); + + CV_PROP_RW double minVal; //!< Minimum value of the statmodel parameter. Default value is 0. + CV_PROP_RW double maxVal; //!< Maximum value of the statmodel parameter. Default value is 0. + /** @brief Logarithmic step for iterating the statmodel parameter. + + The grid determines the following iteration sequence of the statmodel parameter values: + \f[(minVal, minVal*step, minVal*{step}^2, \dots, minVal*{logStep}^n),\f] + where \f$n\f$ is the maximal index satisfying + \f[\texttt{minVal} * \texttt{logStep} ^n < \texttt{maxVal}\f] + The grid is logarithmic, so logStep must always be greater then 1. Default value is 1. + */ + CV_PROP_RW double logStep; + + /** @brief Creates a ParamGrid Ptr that can be given to the %SVM::trainAuto method + + @param minVal minimum value of the parameter grid + @param maxVal maximum value of the parameter grid + @param logstep Logarithmic step for iterating the statmodel parameter + */ + CV_WRAP static Ptr create(double minVal=0., double maxVal=0., double logstep=1.); +}; + +/** @brief Class encapsulating training data. + +Please note that the class only specifies the interface of training data, but not implementation. +All the statistical model classes in _ml_ module accepts Ptr\ as parameter. In other +words, you can create your own class derived from TrainData and pass smart pointer to the instance +of this class into StatModel::train. + +@sa @ref ml_intro_data + */ +class CV_EXPORTS_W TrainData +{ +public: + static inline float missingValue() { return FLT_MAX; } + virtual ~TrainData(); + + CV_WRAP virtual int getLayout() const = 0; + CV_WRAP virtual int getNTrainSamples() const = 0; + CV_WRAP virtual int getNTestSamples() const = 0; + CV_WRAP virtual int getNSamples() const = 0; + CV_WRAP virtual int getNVars() const = 0; + CV_WRAP virtual int getNAllVars() const = 0; + + CV_WRAP virtual void getSample(InputArray varIdx, int sidx, float* buf) const = 0; + CV_WRAP virtual Mat getSamples() const = 0; + CV_WRAP virtual Mat getMissing() const = 0; + + /** @brief Returns matrix of train samples + + @param layout The requested layout. If it's different from the initial one, the matrix is + transposed. See ml::SampleTypes. + @param compressSamples if true, the function returns only the training samples (specified by + sampleIdx) + @param compressVars if true, the function returns the shorter training samples, containing only + the active variables. + + In current implementation the function tries to avoid physical data copying and returns the + matrix stored inside TrainData (unless the transposition or compression is needed). + */ + CV_WRAP virtual Mat getTrainSamples(int layout=ROW_SAMPLE, + bool compressSamples=true, + bool compressVars=true) const = 0; + + /** @brief Returns the vector of responses + + The function returns ordered or the original categorical responses. Usually it's used in + regression algorithms. + */ + CV_WRAP virtual Mat getTrainResponses() const = 0; + + /** @brief Returns the vector of normalized categorical responses + + The function returns vector of responses. Each response is integer from `0` to `-1`. The actual label value can be retrieved then from the class label vector, see + TrainData::getClassLabels. + */ + CV_WRAP virtual Mat getTrainNormCatResponses() const = 0; + CV_WRAP virtual Mat getTestResponses() const = 0; + CV_WRAP virtual Mat getTestNormCatResponses() const = 0; + CV_WRAP virtual Mat getResponses() const = 0; + CV_WRAP virtual Mat getNormCatResponses() const = 0; + CV_WRAP virtual Mat getSampleWeights() const = 0; + CV_WRAP virtual Mat getTrainSampleWeights() const = 0; + CV_WRAP virtual Mat getTestSampleWeights() const = 0; + CV_WRAP virtual Mat getVarIdx() const = 0; + CV_WRAP virtual Mat getVarType() const = 0; + CV_WRAP Mat getVarSymbolFlags() const; + CV_WRAP virtual int getResponseType() const = 0; + CV_WRAP virtual Mat getTrainSampleIdx() const = 0; + CV_WRAP virtual Mat getTestSampleIdx() const = 0; + CV_WRAP virtual void getValues(int vi, InputArray sidx, float* values) const = 0; + virtual void getNormCatValues(int vi, InputArray sidx, int* values) const = 0; + CV_WRAP virtual Mat getDefaultSubstValues() const = 0; + + CV_WRAP virtual int getCatCount(int vi) const = 0; + + /** @brief Returns the vector of class labels + + The function returns vector of unique labels occurred in the responses. + */ + CV_WRAP virtual Mat getClassLabels() const = 0; + + CV_WRAP virtual Mat getCatOfs() const = 0; + CV_WRAP virtual Mat getCatMap() const = 0; + + /** @brief Splits the training data into the training and test parts + @sa TrainData::setTrainTestSplitRatio + */ + CV_WRAP virtual void setTrainTestSplit(int count, bool shuffle=true) = 0; + + /** @brief Splits the training data into the training and test parts + + The function selects a subset of specified relative size and then returns it as the training + set. If the function is not called, all the data is used for training. Please, note that for + each of TrainData::getTrain\* there is corresponding TrainData::getTest\*, so that the test + subset can be retrieved and processed as well. + @sa TrainData::setTrainTestSplit + */ + CV_WRAP virtual void setTrainTestSplitRatio(double ratio, bool shuffle=true) = 0; + CV_WRAP virtual void shuffleTrainTest() = 0; + + /** @brief Returns matrix of test samples */ + CV_WRAP Mat getTestSamples() const; + + /** @brief Returns vector of symbolic names captured in loadFromCSV() */ + CV_WRAP void getNames(std::vector& names) const; + + CV_WRAP static Mat getSubVector(const Mat& vec, const Mat& idx); + + /** @brief Reads the dataset from a .csv file and returns the ready-to-use training data. + + @param filename The input file name + @param headerLineCount The number of lines in the beginning to skip; besides the header, the + function also skips empty lines and lines staring with `#` + @param responseStartIdx Index of the first output variable. If -1, the function considers the + last variable as the response + @param responseEndIdx Index of the last output variable + 1. If -1, then there is single + response variable at responseStartIdx. + @param varTypeSpec The optional text string that specifies the variables' types. It has the + format `ord[n1-n2,n3,n4-n5,...]cat[n6,n7-n8,...]`. That is, variables from `n1 to n2` + (inclusive range), `n3`, `n4 to n5` ... are considered ordered and `n6`, `n7 to n8` ... are + considered as categorical. The range `[n1..n2] + [n3] + [n4..n5] + ... + [n6] + [n7..n8]` + should cover all the variables. If varTypeSpec is not specified, then algorithm uses the + following rules: + - all input variables are considered ordered by default. If some column contains has non- + numerical values, e.g. 'apple', 'pear', 'apple', 'apple', 'mango', the corresponding + variable is considered categorical. + - if there are several output variables, they are all considered as ordered. Error is + reported when non-numerical values are used. + - if there is a single output variable, then if its values are non-numerical or are all + integers, then it's considered categorical. Otherwise, it's considered ordered. + @param delimiter The character used to separate values in each line. + @param missch The character used to specify missing measurements. It should not be a digit. + Although it's a non-numerical value, it surely does not affect the decision of whether the + variable ordered or categorical. + @note If the dataset only contains input variables and no responses, use responseStartIdx = -2 + and responseEndIdx = 0. The output variables vector will just contain zeros. + */ + static Ptr loadFromCSV(const String& filename, + int headerLineCount, + int responseStartIdx=-1, + int responseEndIdx=-1, + const String& varTypeSpec=String(), + char delimiter=',', + char missch='?'); + + /** @brief Creates training data from in-memory arrays. + + @param samples matrix of samples. It should have CV_32F type. + @param layout see ml::SampleTypes. + @param responses matrix of responses. If the responses are scalar, they should be stored as a + single row or as a single column. The matrix should have type CV_32F or CV_32S (in the + former case the responses are considered as ordered by default; in the latter case - as + categorical) + @param varIdx vector specifying which variables to use for training. It can be an integer vector + (CV_32S) containing 0-based variable indices or byte vector (CV_8U) containing a mask of + active variables. + @param sampleIdx vector specifying which samples to use for training. It can be an integer + vector (CV_32S) containing 0-based sample indices or byte vector (CV_8U) containing a mask + of training samples. + @param sampleWeights optional vector with weights for each sample. It should have CV_32F type. + @param varType optional vector of type CV_8U and size ` + + `, containing types of each input and output variable. See + ml::VariableTypes. + */ + CV_WRAP static Ptr create(InputArray samples, int layout, InputArray responses, + InputArray varIdx=noArray(), InputArray sampleIdx=noArray(), + InputArray sampleWeights=noArray(), InputArray varType=noArray()); +}; + +/** @brief Base class for statistical models in OpenCV ML. + */ +class CV_EXPORTS_W StatModel : public Algorithm +{ +public: + /** Predict options */ + enum Flags { + UPDATE_MODEL = 1, + RAW_OUTPUT=1, //!< makes the method return the raw results (the sum), not the class label + COMPRESSED_INPUT=2, + PREPROCESSED_INPUT=4 + }; + + /** @brief Returns the number of variables in training samples */ + CV_WRAP virtual int getVarCount() const = 0; + + CV_WRAP virtual bool empty() const CV_OVERRIDE; + + /** @brief Returns true if the model is trained */ + CV_WRAP virtual bool isTrained() const = 0; + /** @brief Returns true if the model is classifier */ + CV_WRAP virtual bool isClassifier() const = 0; + + /** @brief Trains the statistical model + + @param trainData training data that can be loaded from file using TrainData::loadFromCSV or + created with TrainData::create. + @param flags optional flags, depending on the model. Some of the models can be updated with the + new training samples, not completely overwritten (such as NormalBayesClassifier or ANN_MLP). + */ + CV_WRAP virtual bool train( const Ptr& trainData, int flags=0 ); + + /** @brief Trains the statistical model + + @param samples training samples + @param layout See ml::SampleTypes. + @param responses vector of responses associated with the training samples. + */ + CV_WRAP virtual bool train( InputArray samples, int layout, InputArray responses ); + + /** @brief Computes error on the training or test dataset + + @param data the training data + @param test if true, the error is computed over the test subset of the data, otherwise it's + computed over the training subset of the data. Please note that if you loaded a completely + different dataset to evaluate already trained classifier, you will probably want not to set + the test subset at all with TrainData::setTrainTestSplitRatio and specify test=false, so + that the error is computed for the whole new set. Yes, this sounds a bit confusing. + @param resp the optional output responses. + + The method uses StatModel::predict to compute the error. For regression models the error is + computed as RMS, for classifiers - as a percent of missclassified samples (0%-100%). + */ + CV_WRAP virtual float calcError( const Ptr& data, bool test, OutputArray resp ) const; + + /** @brief Predicts response(s) for the provided sample(s) + + @param samples The input samples, floating-point matrix + @param results The optional output matrix of results. + @param flags The optional flags, model-dependent. See cv::ml::StatModel::Flags. + */ + CV_WRAP virtual float predict( InputArray samples, OutputArray results=noArray(), int flags=0 ) const = 0; + + /** @brief Create and train model with default parameters + + The class must implement static `create()` method with no parameters or with all default parameter values + */ + template static Ptr<_Tp> train(const Ptr& data, int flags=0) + { + Ptr<_Tp> model = _Tp::create(); + return !model.empty() && model->train(data, flags) ? model : Ptr<_Tp>(); + } +}; + +/****************************************************************************************\ +* Normal Bayes Classifier * +\****************************************************************************************/ + +/** @brief Bayes classifier for normally distributed data. + +@sa @ref ml_intro_bayes + */ +class CV_EXPORTS_W NormalBayesClassifier : public StatModel +{ +public: + /** @brief Predicts the response for sample(s). + + The method estimates the most probable classes for input vectors. Input vectors (one or more) + are stored as rows of the matrix inputs. In case of multiple input vectors, there should be one + output vector outputs. The predicted class for a single input vector is returned by the method. + The vector outputProbs contains the output probabilities corresponding to each element of + result. + */ + CV_WRAP virtual float predictProb( InputArray inputs, OutputArray outputs, + OutputArray outputProbs, int flags=0 ) const = 0; + + /** Creates empty model + Use StatModel::train to train the model after creation. */ + CV_WRAP static Ptr create(); + + /** @brief Loads and creates a serialized NormalBayesClassifier from a file + * + * Use NormalBayesClassifier::save to serialize and store an NormalBayesClassifier to disk. + * Load the NormalBayesClassifier from this file again, by calling this function with the path to the file. + * Optionally specify the node for the file containing the classifier + * + * @param filepath path to serialized NormalBayesClassifier + * @param nodeName name of node containing the classifier + */ + CV_WRAP static Ptr load(const String& filepath , const String& nodeName = String()); +}; + +/****************************************************************************************\ +* K-Nearest Neighbour Classifier * +\****************************************************************************************/ + +/** @brief The class implements K-Nearest Neighbors model + +@sa @ref ml_intro_knn + */ +class CV_EXPORTS_W KNearest : public StatModel +{ +public: + + /** Default number of neighbors to use in predict method. */ + /** @see setDefaultK */ + CV_WRAP virtual int getDefaultK() const = 0; + /** @copybrief getDefaultK @see getDefaultK */ + CV_WRAP virtual void setDefaultK(int val) = 0; + + /** Whether classification or regression model should be trained. */ + /** @see setIsClassifier */ + CV_WRAP virtual bool getIsClassifier() const = 0; + /** @copybrief getIsClassifier @see getIsClassifier */ + CV_WRAP virtual void setIsClassifier(bool val) = 0; + + /** Parameter for KDTree implementation. */ + /** @see setEmax */ + CV_WRAP virtual int getEmax() const = 0; + /** @copybrief getEmax @see getEmax */ + CV_WRAP virtual void setEmax(int val) = 0; + + /** %Algorithm type, one of KNearest::Types. */ + /** @see setAlgorithmType */ + CV_WRAP virtual int getAlgorithmType() const = 0; + /** @copybrief getAlgorithmType @see getAlgorithmType */ + CV_WRAP virtual void setAlgorithmType(int val) = 0; + + /** @brief Finds the neighbors and predicts responses for input vectors. + + @param samples Input samples stored by rows. It is a single-precision floating-point matrix of + ` * k` size. + @param k Number of used nearest neighbors. Should be greater than 1. + @param results Vector with results of prediction (regression or classification) for each input + sample. It is a single-precision floating-point vector with `` elements. + @param neighborResponses Optional output values for corresponding neighbors. It is a single- + precision floating-point matrix of ` * k` size. + @param dist Optional output distances from the input vectors to the corresponding neighbors. It + is a single-precision floating-point matrix of ` * k` size. + + For each input vector (a row of the matrix samples), the method finds the k nearest neighbors. + In case of regression, the predicted result is a mean value of the particular vector's neighbor + responses. In case of classification, the class is determined by voting. + + For each input vector, the neighbors are sorted by their distances to the vector. + + In case of C++ interface you can use output pointers to empty matrices and the function will + allocate memory itself. + + If only a single input vector is passed, all output matrices are optional and the predicted + value is returned by the method. + + The function is parallelized with the TBB library. + */ + CV_WRAP virtual float findNearest( InputArray samples, int k, + OutputArray results, + OutputArray neighborResponses=noArray(), + OutputArray dist=noArray() ) const = 0; + + /** @brief Implementations of KNearest algorithm + */ + enum Types + { + BRUTE_FORCE=1, + KDTREE=2 + }; + + /** @brief Creates the empty model + + The static method creates empty %KNearest classifier. It should be then trained using StatModel::train method. + */ + CV_WRAP static Ptr create(); +}; + +/****************************************************************************************\ +* Support Vector Machines * +\****************************************************************************************/ + +/** @brief Support Vector Machines. + +@sa @ref ml_intro_svm + */ +class CV_EXPORTS_W SVM : public StatModel +{ +public: + + class CV_EXPORTS Kernel : public Algorithm + { + public: + virtual int getType() const = 0; + virtual void calc( int vcount, int n, const float* vecs, const float* another, float* results ) = 0; + }; + + /** Type of a %SVM formulation. + See SVM::Types. Default value is SVM::C_SVC. */ + /** @see setType */ + CV_WRAP virtual int getType() const = 0; + /** @copybrief getType @see getType */ + CV_WRAP virtual void setType(int val) = 0; + + /** Parameter \f$\gamma\f$ of a kernel function. + For SVM::POLY, SVM::RBF, SVM::SIGMOID or SVM::CHI2. Default value is 1. */ + /** @see setGamma */ + CV_WRAP virtual double getGamma() const = 0; + /** @copybrief getGamma @see getGamma */ + CV_WRAP virtual void setGamma(double val) = 0; + + /** Parameter _coef0_ of a kernel function. + For SVM::POLY or SVM::SIGMOID. Default value is 0.*/ + /** @see setCoef0 */ + CV_WRAP virtual double getCoef0() const = 0; + /** @copybrief getCoef0 @see getCoef0 */ + CV_WRAP virtual void setCoef0(double val) = 0; + + /** Parameter _degree_ of a kernel function. + For SVM::POLY. Default value is 0. */ + /** @see setDegree */ + CV_WRAP virtual double getDegree() const = 0; + /** @copybrief getDegree @see getDegree */ + CV_WRAP virtual void setDegree(double val) = 0; + + /** Parameter _C_ of a %SVM optimization problem. + For SVM::C_SVC, SVM::EPS_SVR or SVM::NU_SVR. Default value is 0. */ + /** @see setC */ + CV_WRAP virtual double getC() const = 0; + /** @copybrief getC @see getC */ + CV_WRAP virtual void setC(double val) = 0; + + /** Parameter \f$\nu\f$ of a %SVM optimization problem. + For SVM::NU_SVC, SVM::ONE_CLASS or SVM::NU_SVR. Default value is 0. */ + /** @see setNu */ + CV_WRAP virtual double getNu() const = 0; + /** @copybrief getNu @see getNu */ + CV_WRAP virtual void setNu(double val) = 0; + + /** Parameter \f$\epsilon\f$ of a %SVM optimization problem. + For SVM::EPS_SVR. Default value is 0. */ + /** @see setP */ + CV_WRAP virtual double getP() const = 0; + /** @copybrief getP @see getP */ + CV_WRAP virtual void setP(double val) = 0; + + /** Optional weights in the SVM::C_SVC problem, assigned to particular classes. + They are multiplied by _C_ so the parameter _C_ of class _i_ becomes `classWeights(i) * C`. Thus + these weights affect the misclassification penalty for different classes. The larger weight, + the larger penalty on misclassification of data from the corresponding class. Default value is + empty Mat. */ + /** @see setClassWeights */ + CV_WRAP virtual cv::Mat getClassWeights() const = 0; + /** @copybrief getClassWeights @see getClassWeights */ + CV_WRAP virtual void setClassWeights(const cv::Mat &val) = 0; + + /** Termination criteria of the iterative %SVM training procedure which solves a partial + case of constrained quadratic optimization problem. + You can specify tolerance and/or the maximum number of iterations. Default value is + `TermCriteria( TermCriteria::MAX_ITER + TermCriteria::EPS, 1000, FLT_EPSILON )`; */ + /** @see setTermCriteria */ + CV_WRAP virtual cv::TermCriteria getTermCriteria() const = 0; + /** @copybrief getTermCriteria @see getTermCriteria */ + CV_WRAP virtual void setTermCriteria(const cv::TermCriteria &val) = 0; + + /** Type of a %SVM kernel. + See SVM::KernelTypes. Default value is SVM::RBF. */ + CV_WRAP virtual int getKernelType() const = 0; + + /** Initialize with one of predefined kernels. + See SVM::KernelTypes. */ + CV_WRAP virtual void setKernel(int kernelType) = 0; + + /** Initialize with custom kernel. + See SVM::Kernel class for implementation details */ + virtual void setCustomKernel(const Ptr &_kernel) = 0; + + //! %SVM type + enum Types { + /** C-Support Vector Classification. n-class classification (n \f$\geq\f$ 2), allows + imperfect separation of classes with penalty multiplier C for outliers. */ + C_SVC=100, + /** \f$\nu\f$-Support Vector Classification. n-class classification with possible + imperfect separation. Parameter \f$\nu\f$ (in the range 0..1, the larger the value, the smoother + the decision boundary) is used instead of C. */ + NU_SVC=101, + /** Distribution Estimation (One-class %SVM). All the training data are from + the same class, %SVM builds a boundary that separates the class from the rest of the feature + space. */ + ONE_CLASS=102, + /** \f$\epsilon\f$-Support Vector Regression. The distance between feature vectors + from the training set and the fitting hyper-plane must be less than p. For outliers the + penalty multiplier C is used. */ + EPS_SVR=103, + /** \f$\nu\f$-Support Vector Regression. \f$\nu\f$ is used instead of p. + See @cite LibSVM for details. */ + NU_SVR=104 + }; + + /** @brief %SVM kernel type + + A comparison of different kernels on the following 2D test case with four classes. Four + SVM::C_SVC SVMs have been trained (one against rest) with auto_train. Evaluation on three + different kernels (SVM::CHI2, SVM::INTER, SVM::RBF). The color depicts the class with max score. + Bright means max-score \> 0, dark means max-score \< 0. + ![image](pics/SVM_Comparison.png) + */ + enum KernelTypes { + /** Returned by SVM::getKernelType in case when custom kernel has been set */ + CUSTOM=-1, + /** Linear kernel. No mapping is done, linear discrimination (or regression) is + done in the original feature space. It is the fastest option. \f$K(x_i, x_j) = x_i^T x_j\f$. */ + LINEAR=0, + /** Polynomial kernel: + \f$K(x_i, x_j) = (\gamma x_i^T x_j + coef0)^{degree}, \gamma > 0\f$. */ + POLY=1, + /** Radial basis function (RBF), a good choice in most cases. + \f$K(x_i, x_j) = e^{-\gamma ||x_i - x_j||^2}, \gamma > 0\f$. */ + RBF=2, + /** Sigmoid kernel: \f$K(x_i, x_j) = \tanh(\gamma x_i^T x_j + coef0)\f$. */ + SIGMOID=3, + /** Exponential Chi2 kernel, similar to the RBF kernel: + \f$K(x_i, x_j) = e^{-\gamma \chi^2(x_i,x_j)}, \chi^2(x_i,x_j) = (x_i-x_j)^2/(x_i+x_j), \gamma > 0\f$. */ + CHI2=4, + /** Histogram intersection kernel. A fast kernel. \f$K(x_i, x_j) = min(x_i,x_j)\f$. */ + INTER=5 + }; + + //! %SVM params type + enum ParamTypes { + C=0, + GAMMA=1, + P=2, + NU=3, + COEF=4, + DEGREE=5 + }; + + /** @brief Trains an %SVM with optimal parameters. + + @param data the training data that can be constructed using TrainData::create or + TrainData::loadFromCSV. + @param kFold Cross-validation parameter. The training set is divided into kFold subsets. One + subset is used to test the model, the others form the train set. So, the %SVM algorithm is + executed kFold times. + @param Cgrid grid for C + @param gammaGrid grid for gamma + @param pGrid grid for p + @param nuGrid grid for nu + @param coeffGrid grid for coeff + @param degreeGrid grid for degree + @param balanced If true and the problem is 2-class classification then the method creates more + balanced cross-validation subsets that is proportions between classes in subsets are close + to such proportion in the whole train dataset. + + The method trains the %SVM model automatically by choosing the optimal parameters C, gamma, p, + nu, coef0, degree. Parameters are considered optimal when the cross-validation + estimate of the test set error is minimal. + + If there is no need to optimize a parameter, the corresponding grid step should be set to any + value less than or equal to 1. For example, to avoid optimization in gamma, set `gammaGrid.step + = 0`, `gammaGrid.minVal`, `gamma_grid.maxVal` as arbitrary numbers. In this case, the value + `Gamma` is taken for gamma. + + And, finally, if the optimization in a parameter is required but the corresponding grid is + unknown, you may call the function SVM::getDefaultGrid. To generate a grid, for example, for + gamma, call `SVM::getDefaultGrid(SVM::GAMMA)`. + + This function works for the classification (SVM::C_SVC or SVM::NU_SVC) as well as for the + regression (SVM::EPS_SVR or SVM::NU_SVR). If it is SVM::ONE_CLASS, no optimization is made and + the usual %SVM with parameters specified in params is executed. + */ + virtual bool trainAuto( const Ptr& data, int kFold = 10, + ParamGrid Cgrid = getDefaultGrid(C), + ParamGrid gammaGrid = getDefaultGrid(GAMMA), + ParamGrid pGrid = getDefaultGrid(P), + ParamGrid nuGrid = getDefaultGrid(NU), + ParamGrid coeffGrid = getDefaultGrid(COEF), + ParamGrid degreeGrid = getDefaultGrid(DEGREE), + bool balanced=false) = 0; + + /** @brief Trains an %SVM with optimal parameters + + @param samples training samples + @param layout See ml::SampleTypes. + @param responses vector of responses associated with the training samples. + @param kFold Cross-validation parameter. The training set is divided into kFold subsets. One + subset is used to test the model, the others form the train set. So, the %SVM algorithm is + @param Cgrid grid for C + @param gammaGrid grid for gamma + @param pGrid grid for p + @param nuGrid grid for nu + @param coeffGrid grid for coeff + @param degreeGrid grid for degree + @param balanced If true and the problem is 2-class classification then the method creates more + balanced cross-validation subsets that is proportions between classes in subsets are close + to such proportion in the whole train dataset. + + The method trains the %SVM model automatically by choosing the optimal parameters C, gamma, p, + nu, coef0, degree. Parameters are considered optimal when the cross-validation + estimate of the test set error is minimal. + + This function only makes use of SVM::getDefaultGrid for parameter optimization and thus only + offers rudimentary parameter options. + + This function works for the classification (SVM::C_SVC or SVM::NU_SVC) as well as for the + regression (SVM::EPS_SVR or SVM::NU_SVR). If it is SVM::ONE_CLASS, no optimization is made and + the usual %SVM with parameters specified in params is executed. + */ + CV_WRAP bool trainAuto(InputArray samples, + int layout, + InputArray responses, + int kFold = 10, + Ptr Cgrid = SVM::getDefaultGridPtr(SVM::C), + Ptr gammaGrid = SVM::getDefaultGridPtr(SVM::GAMMA), + Ptr pGrid = SVM::getDefaultGridPtr(SVM::P), + Ptr nuGrid = SVM::getDefaultGridPtr(SVM::NU), + Ptr coeffGrid = SVM::getDefaultGridPtr(SVM::COEF), + Ptr degreeGrid = SVM::getDefaultGridPtr(SVM::DEGREE), + bool balanced=false); + + /** @brief Retrieves all the support vectors + + The method returns all the support vectors as a floating-point matrix, where support vectors are + stored as matrix rows. + */ + CV_WRAP virtual Mat getSupportVectors() const = 0; + + /** @brief Retrieves all the uncompressed support vectors of a linear %SVM + + The method returns all the uncompressed support vectors of a linear %SVM that the compressed + support vector, used for prediction, was derived from. They are returned in a floating-point + matrix, where the support vectors are stored as matrix rows. + */ + CV_WRAP Mat getUncompressedSupportVectors() const; + + /** @brief Retrieves the decision function + + @param i the index of the decision function. If the problem solved is regression, 1-class or + 2-class classification, then there will be just one decision function and the index should + always be 0. Otherwise, in the case of N-class classification, there will be \f$N(N-1)/2\f$ + decision functions. + @param alpha the optional output vector for weights, corresponding to different support vectors. + In the case of linear %SVM all the alpha's will be 1's. + @param svidx the optional output vector of indices of support vectors within the matrix of + support vectors (which can be retrieved by SVM::getSupportVectors). In the case of linear + %SVM each decision function consists of a single "compressed" support vector. + + The method returns rho parameter of the decision function, a scalar subtracted from the weighted + sum of kernel responses. + */ + CV_WRAP virtual double getDecisionFunction(int i, OutputArray alpha, OutputArray svidx) const = 0; + + /** @brief Generates a grid for %SVM parameters. + + @param param_id %SVM parameters IDs that must be one of the SVM::ParamTypes. The grid is + generated for the parameter with this ID. + + The function generates a grid for the specified parameter of the %SVM algorithm. The grid may be + passed to the function SVM::trainAuto. + */ + static ParamGrid getDefaultGrid( int param_id ); + + /** @brief Generates a grid for %SVM parameters. + + @param param_id %SVM parameters IDs that must be one of the SVM::ParamTypes. The grid is + generated for the parameter with this ID. + + The function generates a grid pointer for the specified parameter of the %SVM algorithm. + The grid may be passed to the function SVM::trainAuto. + */ + CV_WRAP static Ptr getDefaultGridPtr( int param_id ); + + /** Creates empty model. + Use StatModel::train to train the model. Since %SVM has several parameters, you may want to + find the best parameters for your problem, it can be done with SVM::trainAuto. */ + CV_WRAP static Ptr create(); + + /** @brief Loads and creates a serialized svm from a file + * + * Use SVM::save to serialize and store an SVM to disk. + * Load the SVM from this file again, by calling this function with the path to the file. + * + * @param filepath path to serialized svm + */ + CV_WRAP static Ptr load(const String& filepath); +}; + +/****************************************************************************************\ +* Expectation - Maximization * +\****************************************************************************************/ + +/** @brief The class implements the Expectation Maximization algorithm. + +@sa @ref ml_intro_em + */ +class CV_EXPORTS_W EM : public StatModel +{ +public: + //! Type of covariation matrices + enum Types { + /** A scaled identity matrix \f$\mu_k * I\f$. There is the only + parameter \f$\mu_k\f$ to be estimated for each matrix. The option may be used in special cases, + when the constraint is relevant, or as a first step in the optimization (for example in case + when the data is preprocessed with PCA). The results of such preliminary estimation may be + passed again to the optimization procedure, this time with + covMatType=EM::COV_MAT_DIAGONAL. */ + COV_MAT_SPHERICAL=0, + /** A diagonal matrix with positive diagonal elements. The number of + free parameters is d for each matrix. This is most commonly used option yielding good + estimation results. */ + COV_MAT_DIAGONAL=1, + /** A symmetric positively defined matrix. The number of free + parameters in each matrix is about \f$d^2/2\f$. It is not recommended to use this option, unless + there is pretty accurate initial estimation of the parameters and/or a huge number of + training samples. */ + COV_MAT_GENERIC=2, + COV_MAT_DEFAULT=COV_MAT_DIAGONAL + }; + + //! Default parameters + enum {DEFAULT_NCLUSTERS=5, DEFAULT_MAX_ITERS=100}; + + //! The initial step + enum {START_E_STEP=1, START_M_STEP=2, START_AUTO_STEP=0}; + + /** The number of mixture components in the Gaussian mixture model. + Default value of the parameter is EM::DEFAULT_NCLUSTERS=5. Some of %EM implementation could + determine the optimal number of mixtures within a specified value range, but that is not the + case in ML yet. */ + /** @see setClustersNumber */ + CV_WRAP virtual int getClustersNumber() const = 0; + /** @copybrief getClustersNumber @see getClustersNumber */ + CV_WRAP virtual void setClustersNumber(int val) = 0; + + /** Constraint on covariance matrices which defines type of matrices. + See EM::Types. */ + /** @see setCovarianceMatrixType */ + CV_WRAP virtual int getCovarianceMatrixType() const = 0; + /** @copybrief getCovarianceMatrixType @see getCovarianceMatrixType */ + CV_WRAP virtual void setCovarianceMatrixType(int val) = 0; + + /** The termination criteria of the %EM algorithm. + The %EM algorithm can be terminated by the number of iterations termCrit.maxCount (number of + M-steps) or when relative change of likelihood logarithm is less than termCrit.epsilon. Default + maximum number of iterations is EM::DEFAULT_MAX_ITERS=100. */ + /** @see setTermCriteria */ + CV_WRAP virtual TermCriteria getTermCriteria() const = 0; + /** @copybrief getTermCriteria @see getTermCriteria */ + CV_WRAP virtual void setTermCriteria(const TermCriteria &val) = 0; + + /** @brief Returns weights of the mixtures + + Returns vector with the number of elements equal to the number of mixtures. + */ + CV_WRAP virtual Mat getWeights() const = 0; + /** @brief Returns the cluster centers (means of the Gaussian mixture) + + Returns matrix with the number of rows equal to the number of mixtures and number of columns + equal to the space dimensionality. + */ + CV_WRAP virtual Mat getMeans() const = 0; + /** @brief Returns covariation matrices + + Returns vector of covariation matrices. Number of matrices is the number of gaussian mixtures, + each matrix is a square floating-point matrix NxN, where N is the space dimensionality. + */ + CV_WRAP virtual void getCovs(CV_OUT std::vector& covs) const = 0; + + /** @brief Returns posterior probabilities for the provided samples + + @param samples The input samples, floating-point matrix + @param results The optional output \f$ nSamples \times nClusters\f$ matrix of results. It contains + posterior probabilities for each sample from the input + @param flags This parameter will be ignored + */ + CV_WRAP virtual float predict( InputArray samples, OutputArray results=noArray(), int flags=0 ) const CV_OVERRIDE = 0; + + /** @brief Returns a likelihood logarithm value and an index of the most probable mixture component + for the given sample. + + @param sample A sample for classification. It should be a one-channel matrix of + \f$1 \times dims\f$ or \f$dims \times 1\f$ size. + @param probs Optional output matrix that contains posterior probabilities of each component + given the sample. It has \f$1 \times nclusters\f$ size and CV_64FC1 type. + + The method returns a two-element double vector. Zero element is a likelihood logarithm value for + the sample. First element is an index of the most probable mixture component for the given + sample. + */ + CV_WRAP virtual Vec2d predict2(InputArray sample, OutputArray probs) const = 0; + + /** @brief Estimate the Gaussian mixture parameters from a samples set. + + This variation starts with Expectation step. Initial values of the model parameters will be + estimated by the k-means algorithm. + + Unlike many of the ML models, %EM is an unsupervised learning algorithm and it does not take + responses (class labels or function values) as input. Instead, it computes the *Maximum + Likelihood Estimate* of the Gaussian mixture parameters from an input sample set, stores all the + parameters inside the structure: \f$p_{i,k}\f$ in probs, \f$a_k\f$ in means , \f$S_k\f$ in + covs[k], \f$\pi_k\f$ in weights , and optionally computes the output "class label" for each + sample: \f$\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\f$ (indices of the most + probable mixture component for each sample). + + The trained model can be used further for prediction, just like any other classifier. The + trained model is similar to the NormalBayesClassifier. + + @param samples Samples from which the Gaussian mixture model will be estimated. It should be a + one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type + it will be converted to the inner matrix of such type for the further computing. + @param logLikelihoods The optional output matrix that contains a likelihood logarithm value for + each sample. It has \f$nsamples \times 1\f$ size and CV_64FC1 type. + @param labels The optional output "class label" for each sample: + \f$\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\f$ (indices of the most probable + mixture component for each sample). It has \f$nsamples \times 1\f$ size and CV_32SC1 type. + @param probs The optional output matrix that contains posterior probabilities of each Gaussian + mixture component given the each sample. It has \f$nsamples \times nclusters\f$ size and + CV_64FC1 type. + */ + CV_WRAP virtual bool trainEM(InputArray samples, + OutputArray logLikelihoods=noArray(), + OutputArray labels=noArray(), + OutputArray probs=noArray()) = 0; + + /** @brief Estimate the Gaussian mixture parameters from a samples set. + + This variation starts with Expectation step. You need to provide initial means \f$a_k\f$ of + mixture components. Optionally you can pass initial weights \f$\pi_k\f$ and covariance matrices + \f$S_k\f$ of mixture components. + + @param samples Samples from which the Gaussian mixture model will be estimated. It should be a + one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type + it will be converted to the inner matrix of such type for the further computing. + @param means0 Initial means \f$a_k\f$ of mixture components. It is a one-channel matrix of + \f$nclusters \times dims\f$ size. If the matrix does not have CV_64F type it will be + converted to the inner matrix of such type for the further computing. + @param covs0 The vector of initial covariance matrices \f$S_k\f$ of mixture components. Each of + covariance matrices is a one-channel matrix of \f$dims \times dims\f$ size. If the matrices + do not have CV_64F type they will be converted to the inner matrices of such type for the + further computing. + @param weights0 Initial weights \f$\pi_k\f$ of mixture components. It should be a one-channel + floating-point matrix with \f$1 \times nclusters\f$ or \f$nclusters \times 1\f$ size. + @param logLikelihoods The optional output matrix that contains a likelihood logarithm value for + each sample. It has \f$nsamples \times 1\f$ size and CV_64FC1 type. + @param labels The optional output "class label" for each sample: + \f$\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\f$ (indices of the most probable + mixture component for each sample). It has \f$nsamples \times 1\f$ size and CV_32SC1 type. + @param probs The optional output matrix that contains posterior probabilities of each Gaussian + mixture component given the each sample. It has \f$nsamples \times nclusters\f$ size and + CV_64FC1 type. + */ + CV_WRAP virtual bool trainE(InputArray samples, InputArray means0, + InputArray covs0=noArray(), + InputArray weights0=noArray(), + OutputArray logLikelihoods=noArray(), + OutputArray labels=noArray(), + OutputArray probs=noArray()) = 0; + + /** @brief Estimate the Gaussian mixture parameters from a samples set. + + This variation starts with Maximization step. You need to provide initial probabilities + \f$p_{i,k}\f$ to use this option. + + @param samples Samples from which the Gaussian mixture model will be estimated. It should be a + one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type + it will be converted to the inner matrix of such type for the further computing. + @param probs0 + @param logLikelihoods The optional output matrix that contains a likelihood logarithm value for + each sample. It has \f$nsamples \times 1\f$ size and CV_64FC1 type. + @param labels The optional output "class label" for each sample: + \f$\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\f$ (indices of the most probable + mixture component for each sample). It has \f$nsamples \times 1\f$ size and CV_32SC1 type. + @param probs The optional output matrix that contains posterior probabilities of each Gaussian + mixture component given the each sample. It has \f$nsamples \times nclusters\f$ size and + CV_64FC1 type. + */ + CV_WRAP virtual bool trainM(InputArray samples, InputArray probs0, + OutputArray logLikelihoods=noArray(), + OutputArray labels=noArray(), + OutputArray probs=noArray()) = 0; + + /** Creates empty %EM model. + The model should be trained then using StatModel::train(traindata, flags) method. Alternatively, you + can use one of the EM::train\* methods or load it from file using Algorithm::load\(filename). + */ + CV_WRAP static Ptr create(); + + /** @brief Loads and creates a serialized EM from a file + * + * Use EM::save to serialize and store an EM to disk. + * Load the EM from this file again, by calling this function with the path to the file. + * Optionally specify the node for the file containing the classifier + * + * @param filepath path to serialized EM + * @param nodeName name of node containing the classifier + */ + CV_WRAP static Ptr load(const String& filepath , const String& nodeName = String()); +}; + +/****************************************************************************************\ +* Decision Tree * +\****************************************************************************************/ + +/** @brief The class represents a single decision tree or a collection of decision trees. + +The current public interface of the class allows user to train only a single decision tree, however +the class is capable of storing multiple decision trees and using them for prediction (by summing +responses or using a voting schemes), and the derived from DTrees classes (such as RTrees and Boost) +use this capability to implement decision tree ensembles. + +@sa @ref ml_intro_trees +*/ +class CV_EXPORTS_W DTrees : public StatModel +{ +public: + /** Predict options */ + enum Flags { PREDICT_AUTO=0, PREDICT_SUM=(1<<8), PREDICT_MAX_VOTE=(2<<8), PREDICT_MASK=(3<<8) }; + + /** Cluster possible values of a categorical variable into K\<=maxCategories clusters to + find a suboptimal split. + If a discrete variable, on which the training procedure tries to make a split, takes more than + maxCategories values, the precise best subset estimation may take a very long time because the + algorithm is exponential. Instead, many decision trees engines (including our implementation) + try to find sub-optimal split in this case by clustering all the samples into maxCategories + clusters that is some categories are merged together. The clustering is applied only in n \> + 2-class classification problems for categorical variables with N \> max_categories possible + values. In case of regression and 2-class classification the optimal split can be found + efficiently without employing clustering, thus the parameter is not used in these cases. + Default value is 10.*/ + /** @see setMaxCategories */ + CV_WRAP virtual int getMaxCategories() const = 0; + /** @copybrief getMaxCategories @see getMaxCategories */ + CV_WRAP virtual void setMaxCategories(int val) = 0; + + /** The maximum possible depth of the tree. + That is the training algorithms attempts to split a node while its depth is less than maxDepth. + The root node has zero depth. The actual depth may be smaller if the other termination criteria + are met (see the outline of the training procedure @ref ml_intro_trees "here"), and/or if the + tree is pruned. Default value is INT_MAX.*/ + /** @see setMaxDepth */ + CV_WRAP virtual int getMaxDepth() const = 0; + /** @copybrief getMaxDepth @see getMaxDepth */ + CV_WRAP virtual void setMaxDepth(int val) = 0; + + /** If the number of samples in a node is less than this parameter then the node will not be split. + + Default value is 10.*/ + /** @see setMinSampleCount */ + CV_WRAP virtual int getMinSampleCount() const = 0; + /** @copybrief getMinSampleCount @see getMinSampleCount */ + CV_WRAP virtual void setMinSampleCount(int val) = 0; + + /** If CVFolds \> 1 then algorithms prunes the built decision tree using K-fold + cross-validation procedure where K is equal to CVFolds. + Default value is 10.*/ + /** @see setCVFolds */ + CV_WRAP virtual int getCVFolds() const = 0; + /** @copybrief getCVFolds @see getCVFolds */ + CV_WRAP virtual void setCVFolds(int val) = 0; + + /** If true then surrogate splits will be built. + These splits allow to work with missing data and compute variable importance correctly. + Default value is false. + @note currently it's not implemented.*/ + /** @see setUseSurrogates */ + CV_WRAP virtual bool getUseSurrogates() const = 0; + /** @copybrief getUseSurrogates @see getUseSurrogates */ + CV_WRAP virtual void setUseSurrogates(bool val) = 0; + + /** If true then a pruning will be harsher. + This will make a tree more compact and more resistant to the training data noise but a bit less + accurate. Default value is true.*/ + /** @see setUse1SERule */ + CV_WRAP virtual bool getUse1SERule() const = 0; + /** @copybrief getUse1SERule @see getUse1SERule */ + CV_WRAP virtual void setUse1SERule(bool val) = 0; + + /** If true then pruned branches are physically removed from the tree. + Otherwise they are retained and it is possible to get results from the original unpruned (or + pruned less aggressively) tree. Default value is true.*/ + /** @see setTruncatePrunedTree */ + CV_WRAP virtual bool getTruncatePrunedTree() const = 0; + /** @copybrief getTruncatePrunedTree @see getTruncatePrunedTree */ + CV_WRAP virtual void setTruncatePrunedTree(bool val) = 0; + + /** Termination criteria for regression trees. + If all absolute differences between an estimated value in a node and values of train samples + in this node are less than this parameter then the node will not be split further. Default + value is 0.01f*/ + /** @see setRegressionAccuracy */ + CV_WRAP virtual float getRegressionAccuracy() const = 0; + /** @copybrief getRegressionAccuracy @see getRegressionAccuracy */ + CV_WRAP virtual void setRegressionAccuracy(float val) = 0; + + /** @brief The array of a priori class probabilities, sorted by the class label value. + + The parameter can be used to tune the decision tree preferences toward a certain class. For + example, if you want to detect some rare anomaly occurrence, the training base will likely + contain much more normal cases than anomalies, so a very good classification performance + will be achieved just by considering every case as normal. To avoid this, the priors can be + specified, where the anomaly probability is artificially increased (up to 0.5 or even + greater), so the weight of the misclassified anomalies becomes much bigger, and the tree is + adjusted properly. + + You can also think about this parameter as weights of prediction categories which determine + relative weights that you give to misclassification. That is, if the weight of the first + category is 1 and the weight of the second category is 10, then each mistake in predicting + the second category is equivalent to making 10 mistakes in predicting the first category. + Default value is empty Mat.*/ + /** @see setPriors */ + CV_WRAP virtual cv::Mat getPriors() const = 0; + /** @copybrief getPriors @see getPriors */ + CV_WRAP virtual void setPriors(const cv::Mat &val) = 0; + + /** @brief The class represents a decision tree node. + */ + class CV_EXPORTS Node + { + public: + Node(); + double value; //!< Value at the node: a class label in case of classification or estimated + //!< function value in case of regression. + int classIdx; //!< Class index normalized to 0..class_count-1 range and assigned to the + //!< node. It is used internally in classification trees and tree ensembles. + int parent; //!< Index of the parent node + int left; //!< Index of the left child node + int right; //!< Index of right child node + int defaultDir; //!< Default direction where to go (-1: left or +1: right). It helps in the + //!< case of missing values. + int split; //!< Index of the first split + }; + + /** @brief The class represents split in a decision tree. + */ + class CV_EXPORTS Split + { + public: + Split(); + int varIdx; //!< Index of variable on which the split is created. + bool inversed; //!< If true, then the inverse split rule is used (i.e. left and right + //!< branches are exchanged in the rule expressions below). + float quality; //!< The split quality, a positive number. It is used to choose the best split. + int next; //!< Index of the next split in the list of splits for the node + float c; /**< The threshold value in case of split on an ordered variable. + The rule is: + @code{.none} + if var_value < c + then next_node <- left + else next_node <- right + @endcode */ + int subsetOfs; /**< Offset of the bitset used by the split on a categorical variable. + The rule is: + @code{.none} + if bitset[var_value] == 1 + then next_node <- left + else next_node <- right + @endcode */ + }; + + /** @brief Returns indices of root nodes + */ + virtual const std::vector& getRoots() const = 0; + /** @brief Returns all the nodes + + all the node indices are indices in the returned vector + */ + virtual const std::vector& getNodes() const = 0; + /** @brief Returns all the splits + + all the split indices are indices in the returned vector + */ + virtual const std::vector& getSplits() const = 0; + /** @brief Returns all the bitsets for categorical splits + + Split::subsetOfs is an offset in the returned vector + */ + virtual const std::vector& getSubsets() const = 0; + + /** @brief Creates the empty model + + The static method creates empty decision tree with the specified parameters. It should be then + trained using train method (see StatModel::train). Alternatively, you can load the model from + file using Algorithm::load\(filename). + */ + CV_WRAP static Ptr create(); + + /** @brief Loads and creates a serialized DTrees from a file + * + * Use DTree::save to serialize and store an DTree to disk. + * Load the DTree from this file again, by calling this function with the path to the file. + * Optionally specify the node for the file containing the classifier + * + * @param filepath path to serialized DTree + * @param nodeName name of node containing the classifier + */ + CV_WRAP static Ptr load(const String& filepath , const String& nodeName = String()); +}; + +/****************************************************************************************\ +* Random Trees Classifier * +\****************************************************************************************/ + +/** @brief The class implements the random forest predictor. + +@sa @ref ml_intro_rtrees + */ +class CV_EXPORTS_W RTrees : public DTrees +{ +public: + + /** If true then variable importance will be calculated and then it can be retrieved by RTrees::getVarImportance. + Default value is false.*/ + /** @see setCalculateVarImportance */ + CV_WRAP virtual bool getCalculateVarImportance() const = 0; + /** @copybrief getCalculateVarImportance @see getCalculateVarImportance */ + CV_WRAP virtual void setCalculateVarImportance(bool val) = 0; + + /** The size of the randomly selected subset of features at each tree node and that are used + to find the best split(s). + If you set it to 0 then the size will be set to the square root of the total number of + features. Default value is 0.*/ + /** @see setActiveVarCount */ + CV_WRAP virtual int getActiveVarCount() const = 0; + /** @copybrief getActiveVarCount @see getActiveVarCount */ + CV_WRAP virtual void setActiveVarCount(int val) = 0; + + /** The termination criteria that specifies when the training algorithm stops. + Either when the specified number of trees is trained and added to the ensemble or when + sufficient accuracy (measured as OOB error) is achieved. Typically the more trees you have the + better the accuracy. However, the improvement in accuracy generally diminishes and asymptotes + pass a certain number of trees. Also to keep in mind, the number of tree increases the + prediction time linearly. Default value is TermCriteria(TermCriteria::MAX_ITERS + + TermCriteria::EPS, 50, 0.1)*/ + /** @see setTermCriteria */ + CV_WRAP virtual TermCriteria getTermCriteria() const = 0; + /** @copybrief getTermCriteria @see getTermCriteria */ + CV_WRAP virtual void setTermCriteria(const TermCriteria &val) = 0; + + /** Returns the variable importance array. + The method returns the variable importance vector, computed at the training stage when + CalculateVarImportance is set to true. If this flag was set to false, the empty matrix is + returned. + */ + CV_WRAP virtual Mat getVarImportance() const = 0; + + /** Returns the result of each individual tree in the forest. + In case the model is a regression problem, the method will return each of the trees' + results for each of the sample cases. If the model is a classifier, it will return + a Mat with samples + 1 rows, where the first row gives the class number and the + following rows return the votes each class had for each sample. + @param samples Array containing the samples for which votes will be calculated. + @param results Array where the result of the calculation will be written. + @param flags Flags for defining the type of RTrees. + */ + CV_WRAP void getVotes(InputArray samples, OutputArray results, int flags) const; + + /** Creates the empty model. + Use StatModel::train to train the model, StatModel::train to create and train the model, + Algorithm::load to load the pre-trained model. + */ + CV_WRAP static Ptr create(); + + /** @brief Loads and creates a serialized RTree from a file + * + * Use RTree::save to serialize and store an RTree to disk. + * Load the RTree from this file again, by calling this function with the path to the file. + * Optionally specify the node for the file containing the classifier + * + * @param filepath path to serialized RTree + * @param nodeName name of node containing the classifier + */ + CV_WRAP static Ptr load(const String& filepath , const String& nodeName = String()); +}; + +/****************************************************************************************\ +* Boosted tree classifier * +\****************************************************************************************/ + +/** @brief Boosted tree classifier derived from DTrees + +@sa @ref ml_intro_boost + */ +class CV_EXPORTS_W Boost : public DTrees +{ +public: + /** Type of the boosting algorithm. + See Boost::Types. Default value is Boost::REAL. */ + /** @see setBoostType */ + CV_WRAP virtual int getBoostType() const = 0; + /** @copybrief getBoostType @see getBoostType */ + CV_WRAP virtual void setBoostType(int val) = 0; + + /** The number of weak classifiers. + Default value is 100. */ + /** @see setWeakCount */ + CV_WRAP virtual int getWeakCount() const = 0; + /** @copybrief getWeakCount @see getWeakCount */ + CV_WRAP virtual void setWeakCount(int val) = 0; + + /** A threshold between 0 and 1 used to save computational time. + Samples with summary weight \f$\leq 1 - weight_trim_rate\f$ do not participate in the *next* + iteration of training. Set this parameter to 0 to turn off this functionality. Default value is 0.95.*/ + /** @see setWeightTrimRate */ + CV_WRAP virtual double getWeightTrimRate() const = 0; + /** @copybrief getWeightTrimRate @see getWeightTrimRate */ + CV_WRAP virtual void setWeightTrimRate(double val) = 0; + + /** Boosting type. + Gentle AdaBoost and Real AdaBoost are often the preferable choices. */ + enum Types { + DISCRETE=0, //!< Discrete AdaBoost. + REAL=1, //!< Real AdaBoost. It is a technique that utilizes confidence-rated predictions + //!< and works well with categorical data. + LOGIT=2, //!< LogitBoost. It can produce good regression fits. + GENTLE=3 //!< Gentle AdaBoost. It puts less weight on outlier data points and for that + //!(filename) to load the pre-trained model. */ + CV_WRAP static Ptr create(); + + /** @brief Loads and creates a serialized Boost from a file + * + * Use Boost::save to serialize and store an RTree to disk. + * Load the Boost from this file again, by calling this function with the path to the file. + * Optionally specify the node for the file containing the classifier + * + * @param filepath path to serialized Boost + * @param nodeName name of node containing the classifier + */ + CV_WRAP static Ptr load(const String& filepath , const String& nodeName = String()); +}; + +/****************************************************************************************\ +* Gradient Boosted Trees * +\****************************************************************************************/ + +/*class CV_EXPORTS_W GBTrees : public DTrees +{ +public: + struct CV_EXPORTS_W_MAP Params : public DTrees::Params + { + CV_PROP_RW int weakCount; + CV_PROP_RW int lossFunctionType; + CV_PROP_RW float subsamplePortion; + CV_PROP_RW float shrinkage; + + Params(); + Params( int lossFunctionType, int weakCount, float shrinkage, + float subsamplePortion, int maxDepth, bool useSurrogates ); + }; + + enum {SQUARED_LOSS=0, ABSOLUTE_LOSS, HUBER_LOSS=3, DEVIANCE_LOSS}; + + virtual void setK(int k) = 0; + + virtual float predictSerial( InputArray samples, + OutputArray weakResponses, int flags) const = 0; + + static Ptr create(const Params& p); +};*/ + +/****************************************************************************************\ +* Artificial Neural Networks (ANN) * +\****************************************************************************************/ + +/////////////////////////////////// Multi-Layer Perceptrons ////////////////////////////// + +/** @brief Artificial Neural Networks - Multi-Layer Perceptrons. + +Unlike many other models in ML that are constructed and trained at once, in the MLP model these +steps are separated. First, a network with the specified topology is created using the non-default +constructor or the method ANN_MLP::create. All the weights are set to zeros. Then, the network is +trained using a set of input and output vectors. The training procedure can be repeated more than +once, that is, the weights can be adjusted based on the new training data. + +Additional flags for StatModel::train are available: ANN_MLP::TrainFlags. + +@sa @ref ml_intro_ann + */ +class CV_EXPORTS_W ANN_MLP : public StatModel +{ +public: + /** Available training methods */ + enum TrainingMethods { + BACKPROP=0, //!< The back-propagation algorithm. + RPROP = 1, //!< The RPROP algorithm. See @cite RPROP93 for details. + ANNEAL = 2 //!< The simulated annealing algorithm. See @cite Kirkpatrick83 for details. + }; + + /** Sets training method and common parameters. + @param method Default value is ANN_MLP::RPROP. See ANN_MLP::TrainingMethods. + @param param1 passed to setRpropDW0 for ANN_MLP::RPROP and to setBackpropWeightScale for ANN_MLP::BACKPROP and to initialT for ANN_MLP::ANNEAL. + @param param2 passed to setRpropDWMin for ANN_MLP::RPROP and to setBackpropMomentumScale for ANN_MLP::BACKPROP and to finalT for ANN_MLP::ANNEAL. + */ + CV_WRAP virtual void setTrainMethod(int method, double param1 = 0, double param2 = 0) = 0; + + /** Returns current training method */ + CV_WRAP virtual int getTrainMethod() const = 0; + + /** Initialize the activation function for each neuron. + Currently the default and the only fully supported activation function is ANN_MLP::SIGMOID_SYM. + @param type The type of activation function. See ANN_MLP::ActivationFunctions. + @param param1 The first parameter of the activation function, \f$\alpha\f$. Default value is 0. + @param param2 The second parameter of the activation function, \f$\beta\f$. Default value is 0. + */ + CV_WRAP virtual void setActivationFunction(int type, double param1 = 0, double param2 = 0) = 0; + + /** Integer vector specifying the number of neurons in each layer including the input and output layers. + The very first element specifies the number of elements in the input layer. + The last element - number of elements in the output layer. Default value is empty Mat. + @sa getLayerSizes */ + CV_WRAP virtual void setLayerSizes(InputArray _layer_sizes) = 0; + + /** Integer vector specifying the number of neurons in each layer including the input and output layers. + The very first element specifies the number of elements in the input layer. + The last element - number of elements in the output layer. + @sa setLayerSizes */ + CV_WRAP virtual cv::Mat getLayerSizes() const = 0; + + /** Termination criteria of the training algorithm. + You can specify the maximum number of iterations (maxCount) and/or how much the error could + change between the iterations to make the algorithm continue (epsilon). Default value is + TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 1000, 0.01).*/ + /** @see setTermCriteria */ + CV_WRAP virtual TermCriteria getTermCriteria() const = 0; + /** @copybrief getTermCriteria @see getTermCriteria */ + CV_WRAP virtual void setTermCriteria(TermCriteria val) = 0; + + /** BPROP: Strength of the weight gradient term. + The recommended value is about 0.1. Default value is 0.1.*/ + /** @see setBackpropWeightScale */ + CV_WRAP virtual double getBackpropWeightScale() const = 0; + /** @copybrief getBackpropWeightScale @see getBackpropWeightScale */ + CV_WRAP virtual void setBackpropWeightScale(double val) = 0; + + /** BPROP: Strength of the momentum term (the difference between weights on the 2 previous iterations). + This parameter provides some inertia to smooth the random fluctuations of the weights. It can + vary from 0 (the feature is disabled) to 1 and beyond. The value 0.1 or so is good enough. + Default value is 0.1.*/ + /** @see setBackpropMomentumScale */ + CV_WRAP virtual double getBackpropMomentumScale() const = 0; + /** @copybrief getBackpropMomentumScale @see getBackpropMomentumScale */ + CV_WRAP virtual void setBackpropMomentumScale(double val) = 0; + + /** RPROP: Initial value \f$\Delta_0\f$ of update-values \f$\Delta_{ij}\f$. + Default value is 0.1.*/ + /** @see setRpropDW0 */ + CV_WRAP virtual double getRpropDW0() const = 0; + /** @copybrief getRpropDW0 @see getRpropDW0 */ + CV_WRAP virtual void setRpropDW0(double val) = 0; + + /** RPROP: Increase factor \f$\eta^+\f$. + It must be \>1. Default value is 1.2.*/ + /** @see setRpropDWPlus */ + CV_WRAP virtual double getRpropDWPlus() const = 0; + /** @copybrief getRpropDWPlus @see getRpropDWPlus */ + CV_WRAP virtual void setRpropDWPlus(double val) = 0; + + /** RPROP: Decrease factor \f$\eta^-\f$. + It must be \<1. Default value is 0.5.*/ + /** @see setRpropDWMinus */ + CV_WRAP virtual double getRpropDWMinus() const = 0; + /** @copybrief getRpropDWMinus @see getRpropDWMinus */ + CV_WRAP virtual void setRpropDWMinus(double val) = 0; + + /** RPROP: Update-values lower limit \f$\Delta_{min}\f$. + It must be positive. Default value is FLT_EPSILON.*/ + /** @see setRpropDWMin */ + CV_WRAP virtual double getRpropDWMin() const = 0; + /** @copybrief getRpropDWMin @see getRpropDWMin */ + CV_WRAP virtual void setRpropDWMin(double val) = 0; + + /** RPROP: Update-values upper limit \f$\Delta_{max}\f$. + It must be \>1. Default value is 50.*/ + /** @see setRpropDWMax */ + CV_WRAP virtual double getRpropDWMax() const = 0; + /** @copybrief getRpropDWMax @see getRpropDWMax */ + CV_WRAP virtual void setRpropDWMax(double val) = 0; + + /** ANNEAL: Update initial temperature. + It must be \>=0. Default value is 10.*/ + /** @see setAnnealInitialT */ + CV_WRAP double getAnnealInitialT() const; + /** @copybrief getAnnealInitialT @see getAnnealInitialT */ + CV_WRAP void setAnnealInitialT(double val); + + /** ANNEAL: Update final temperature. + It must be \>=0 and less than initialT. Default value is 0.1.*/ + /** @see setAnnealFinalT */ + CV_WRAP double getAnnealFinalT() const; + /** @copybrief getAnnealFinalT @see getAnnealFinalT */ + CV_WRAP void setAnnealFinalT(double val); + + /** ANNEAL: Update cooling ratio. + It must be \>0 and less than 1. Default value is 0.95.*/ + /** @see setAnnealCoolingRatio */ + CV_WRAP double getAnnealCoolingRatio() const; + /** @copybrief getAnnealCoolingRatio @see getAnnealCoolingRatio */ + CV_WRAP void setAnnealCoolingRatio(double val); + + /** ANNEAL: Update iteration per step. + It must be \>0 . Default value is 10.*/ + /** @see setAnnealItePerStep */ + CV_WRAP int getAnnealItePerStep() const; + /** @copybrief getAnnealItePerStep @see getAnnealItePerStep */ + CV_WRAP void setAnnealItePerStep(int val); + + /** @brief Set/initialize anneal RNG */ + void setAnnealEnergyRNG(const RNG& rng); + + /** possible activation functions */ + enum ActivationFunctions { + /** Identity function: \f$f(x)=x\f$ */ + IDENTITY = 0, + /** Symmetrical sigmoid: \f$f(x)=\beta*(1-e^{-\alpha x})/(1+e^{-\alpha x})\f$ + @note + If you are using the default sigmoid activation function with the default parameter values + fparam1=0 and fparam2=0 then the function used is y = 1.7159\*tanh(2/3 \* x), so the output + will range from [-1.7159, 1.7159], instead of [0,1].*/ + SIGMOID_SYM = 1, + /** Gaussian function: \f$f(x)=\beta e^{-\alpha x*x}\f$ */ + GAUSSIAN = 2, + /** ReLU function: \f$f(x)=max(0,x)\f$ */ + RELU = 3, + /** Leaky ReLU function: for x>0 \f$f(x)=x \f$ and x<=0 \f$f(x)=\alpha x \f$*/ + LEAKYRELU= 4 + }; + + /** Train options */ + enum TrainFlags { + /** Update the network weights, rather than compute them from scratch. In the latter case + the weights are initialized using the Nguyen-Widrow algorithm. */ + UPDATE_WEIGHTS = 1, + /** Do not normalize the input vectors. If this flag is not set, the training algorithm + normalizes each input feature independently, shifting its mean value to 0 and making the + standard deviation equal to 1. If the network is assumed to be updated frequently, the new + training data could be much different from original one. In this case, you should take care + of proper normalization. */ + NO_INPUT_SCALE = 2, + /** Do not normalize the output vectors. If the flag is not set, the training algorithm + normalizes each output feature independently, by transforming it to the certain range + depending on the used activation function. */ + NO_OUTPUT_SCALE = 4 + }; + + CV_WRAP virtual Mat getWeights(int layerIdx) const = 0; + + /** @brief Creates empty model + + Use StatModel::train to train the model, Algorithm::load\(filename) to load the pre-trained model. + Note that the train method has optional flags: ANN_MLP::TrainFlags. + */ + CV_WRAP static Ptr create(); + + /** @brief Loads and creates a serialized ANN from a file + * + * Use ANN::save to serialize and store an ANN to disk. + * Load the ANN from this file again, by calling this function with the path to the file. + * + * @param filepath path to serialized ANN + */ + CV_WRAP static Ptr load(const String& filepath); + +}; + +/****************************************************************************************\ +* Logistic Regression * +\****************************************************************************************/ + +/** @brief Implements Logistic Regression classifier. + +@sa @ref ml_intro_lr + */ +class CV_EXPORTS_W LogisticRegression : public StatModel +{ +public: + + /** Learning rate. */ + /** @see setLearningRate */ + CV_WRAP virtual double getLearningRate() const = 0; + /** @copybrief getLearningRate @see getLearningRate */ + CV_WRAP virtual void setLearningRate(double val) = 0; + + /** Number of iterations. */ + /** @see setIterations */ + CV_WRAP virtual int getIterations() const = 0; + /** @copybrief getIterations @see getIterations */ + CV_WRAP virtual void setIterations(int val) = 0; + + /** Kind of regularization to be applied. See LogisticRegression::RegKinds. */ + /** @see setRegularization */ + CV_WRAP virtual int getRegularization() const = 0; + /** @copybrief getRegularization @see getRegularization */ + CV_WRAP virtual void setRegularization(int val) = 0; + + /** Kind of training method used. See LogisticRegression::Methods. */ + /** @see setTrainMethod */ + CV_WRAP virtual int getTrainMethod() const = 0; + /** @copybrief getTrainMethod @see getTrainMethod */ + CV_WRAP virtual void setTrainMethod(int val) = 0; + + /** Specifies the number of training samples taken in each step of Mini-Batch Gradient + Descent. Will only be used if using LogisticRegression::MINI_BATCH training algorithm. It + has to take values less than the total number of training samples. */ + /** @see setMiniBatchSize */ + CV_WRAP virtual int getMiniBatchSize() const = 0; + /** @copybrief getMiniBatchSize @see getMiniBatchSize */ + CV_WRAP virtual void setMiniBatchSize(int val) = 0; + + /** Termination criteria of the algorithm. */ + /** @see setTermCriteria */ + CV_WRAP virtual TermCriteria getTermCriteria() const = 0; + /** @copybrief getTermCriteria @see getTermCriteria */ + CV_WRAP virtual void setTermCriteria(TermCriteria val) = 0; + + //! Regularization kinds + enum RegKinds { + REG_DISABLE = -1, //!< Regularization disabled + REG_L1 = 0, //!< %L1 norm + REG_L2 = 1 //!< %L2 norm + }; + + //! Training methods + enum Methods { + BATCH = 0, + MINI_BATCH = 1 //!< Set MiniBatchSize to a positive integer when using this method. + }; + + /** @brief Predicts responses for input samples and returns a float type. + + @param samples The input data for the prediction algorithm. Matrix [m x n], where each row + contains variables (features) of one object being classified. Should have data type CV_32F. + @param results Predicted labels as a column matrix of type CV_32S. + @param flags Not used. + */ + CV_WRAP virtual float predict( InputArray samples, OutputArray results=noArray(), int flags=0 ) const CV_OVERRIDE = 0; + + /** @brief This function returns the trained parameters arranged across rows. + + For a two class classifcation problem, it returns a row matrix. It returns learnt parameters of + the Logistic Regression as a matrix of type CV_32F. + */ + CV_WRAP virtual Mat get_learnt_thetas() const = 0; + + /** @brief Creates empty model. + + Creates Logistic Regression model with parameters given. + */ + CV_WRAP static Ptr create(); + + /** @brief Loads and creates a serialized LogisticRegression from a file + * + * Use LogisticRegression::save to serialize and store an LogisticRegression to disk. + * Load the LogisticRegression from this file again, by calling this function with the path to the file. + * Optionally specify the node for the file containing the classifier + * + * @param filepath path to serialized LogisticRegression + * @param nodeName name of node containing the classifier + */ + CV_WRAP static Ptr load(const String& filepath , const String& nodeName = String()); +}; + + +/****************************************************************************************\ +* Stochastic Gradient Descent SVM Classifier * +\****************************************************************************************/ + +/*! +@brief Stochastic Gradient Descent SVM classifier + +SVMSGD provides a fast and easy-to-use implementation of the SVM classifier using the Stochastic Gradient Descent approach, +as presented in @cite bottou2010large. + +The classifier has following parameters: +- model type, +- margin type, +- margin regularization (\f$\lambda\f$), +- initial step size (\f$\gamma_0\f$), +- step decreasing power (\f$c\f$), +- and termination criteria. + +The model type may have one of the following values: \ref SGD and \ref ASGD. + +- \ref SGD is the classic version of SVMSGD classifier: every next step is calculated by the formula + \f[w_{t+1} = w_t - \gamma(t) \frac{dQ_i}{dw} |_{w = w_t}\f] + where + - \f$w_t\f$ is the weights vector for decision function at step \f$t\f$, + - \f$\gamma(t)\f$ is the step size of model parameters at the iteration \f$t\f$, it is decreased on each step by the formula + \f$\gamma(t) = \gamma_0 (1 + \lambda \gamma_0 t) ^ {-c}\f$ + - \f$Q_i\f$ is the target functional from SVM task for sample with number \f$i\f$, this sample is chosen stochastically on each step of the algorithm. + +- \ref ASGD is Average Stochastic Gradient Descent SVM Classifier. ASGD classifier averages weights vector on each step of algorithm by the formula +\f$\widehat{w}_{t+1} = \frac{t}{1+t}\widehat{w}_{t} + \frac{1}{1+t}w_{t+1}\f$ + +The recommended model type is ASGD (following @cite bottou2010large). + +The margin type may have one of the following values: \ref SOFT_MARGIN or \ref HARD_MARGIN. + +- You should use \ref HARD_MARGIN type, if you have linearly separable sets. +- You should use \ref SOFT_MARGIN type, if you have non-linearly separable sets or sets with outliers. +- In the general case (if you know nothing about linear separability of your sets), use SOFT_MARGIN. + +The other parameters may be described as follows: +- Margin regularization parameter is responsible for weights decreasing at each step and for the strength of restrictions on outliers + (the less the parameter, the less probability that an outlier will be ignored). + Recommended value for SGD model is 0.0001, for ASGD model is 0.00001. + +- Initial step size parameter is the initial value for the step size \f$\gamma(t)\f$. + You will have to find the best initial step for your problem. + +- Step decreasing power is the power parameter for \f$\gamma(t)\f$ decreasing by the formula, mentioned above. + Recommended value for SGD model is 1, for ASGD model is 0.75. + +- Termination criteria can be TermCriteria::COUNT, TermCriteria::EPS or TermCriteria::COUNT + TermCriteria::EPS. + You will have to find the best termination criteria for your problem. + +Note that the parameters margin regularization, initial step size, and step decreasing power should be positive. + +To use SVMSGD algorithm do as follows: + +- first, create the SVMSGD object. The algoorithm will set optimal parameters by default, but you can set your own parameters via functions setSvmsgdType(), + setMarginType(), setMarginRegularization(), setInitialStepSize(), and setStepDecreasingPower(). + +- then the SVM model can be trained using the train features and the correspondent labels by the method train(). + +- after that, the label of a new feature vector can be predicted using the method predict(). + +@code +// Create empty object +cv::Ptr svmsgd = SVMSGD::create(); + +// Train the Stochastic Gradient Descent SVM +svmsgd->train(trainData); + +// Predict labels for the new samples +svmsgd->predict(samples, responses); +@endcode + +*/ + +class CV_EXPORTS_W SVMSGD : public cv::ml::StatModel +{ +public: + + /** SVMSGD type. + ASGD is often the preferable choice. */ + enum SvmsgdType + { + SGD, //!< Stochastic Gradient Descent + ASGD //!< Average Stochastic Gradient Descent + }; + + /** Margin type.*/ + enum MarginType + { + SOFT_MARGIN, //!< General case, suits to the case of non-linearly separable sets, allows outliers. + HARD_MARGIN //!< More accurate for the case of linearly separable sets. + }; + + /** + * @return the weights of the trained model (decision function f(x) = weights * x + shift). + */ + CV_WRAP virtual Mat getWeights() = 0; + + /** + * @return the shift of the trained model (decision function f(x) = weights * x + shift). + */ + CV_WRAP virtual float getShift() = 0; + + /** @brief Creates empty model. + * Use StatModel::train to train the model. Since %SVMSGD has several parameters, you may want to + * find the best parameters for your problem or use setOptimalParameters() to set some default parameters. + */ + CV_WRAP static Ptr create(); + + /** @brief Loads and creates a serialized SVMSGD from a file + * + * Use SVMSGD::save to serialize and store an SVMSGD to disk. + * Load the SVMSGD from this file again, by calling this function with the path to the file. + * Optionally specify the node for the file containing the classifier + * + * @param filepath path to serialized SVMSGD + * @param nodeName name of node containing the classifier + */ + CV_WRAP static Ptr load(const String& filepath , const String& nodeName = String()); + + /** @brief Function sets optimal parameters values for chosen SVM SGD model. + * @param svmsgdType is the type of SVMSGD classifier. + * @param marginType is the type of margin constraint. + */ + CV_WRAP virtual void setOptimalParameters(int svmsgdType = SVMSGD::ASGD, int marginType = SVMSGD::SOFT_MARGIN) = 0; + + /** @brief %Algorithm type, one of SVMSGD::SvmsgdType. */ + /** @see setSvmsgdType */ + CV_WRAP virtual int getSvmsgdType() const = 0; + /** @copybrief getSvmsgdType @see getSvmsgdType */ + CV_WRAP virtual void setSvmsgdType(int svmsgdType) = 0; + + /** @brief %Margin type, one of SVMSGD::MarginType. */ + /** @see setMarginType */ + CV_WRAP virtual int getMarginType() const = 0; + /** @copybrief getMarginType @see getMarginType */ + CV_WRAP virtual void setMarginType(int marginType) = 0; + + /** @brief Parameter marginRegularization of a %SVMSGD optimization problem. */ + /** @see setMarginRegularization */ + CV_WRAP virtual float getMarginRegularization() const = 0; + /** @copybrief getMarginRegularization @see getMarginRegularization */ + CV_WRAP virtual void setMarginRegularization(float marginRegularization) = 0; + + /** @brief Parameter initialStepSize of a %SVMSGD optimization problem. */ + /** @see setInitialStepSize */ + CV_WRAP virtual float getInitialStepSize() const = 0; + /** @copybrief getInitialStepSize @see getInitialStepSize */ + CV_WRAP virtual void setInitialStepSize(float InitialStepSize) = 0; + + /** @brief Parameter stepDecreasingPower of a %SVMSGD optimization problem. */ + /** @see setStepDecreasingPower */ + CV_WRAP virtual float getStepDecreasingPower() const = 0; + /** @copybrief getStepDecreasingPower @see getStepDecreasingPower */ + CV_WRAP virtual void setStepDecreasingPower(float stepDecreasingPower) = 0; + + /** @brief Termination criteria of the training algorithm. + You can specify the maximum number of iterations (maxCount) and/or how much the error could + change between the iterations to make the algorithm continue (epsilon).*/ + /** @see setTermCriteria */ + CV_WRAP virtual TermCriteria getTermCriteria() const = 0; + /** @copybrief getTermCriteria @see getTermCriteria */ + CV_WRAP virtual void setTermCriteria(const cv::TermCriteria &val) = 0; +}; + + +/****************************************************************************************\ +* Auxiliary functions declarations * +\****************************************************************************************/ + +/** @brief Generates _sample_ from multivariate normal distribution + +@param mean an average row vector +@param cov symmetric covariation matrix +@param nsamples returned samples count +@param samples returned samples array +*/ +CV_EXPORTS void randMVNormal( InputArray mean, InputArray cov, int nsamples, OutputArray samples); + +/** @brief Creates test set */ +CV_EXPORTS void createConcentricSpheresTestSet( int nsamples, int nfeatures, int nclasses, + OutputArray samples, OutputArray responses); + +/** @brief Artificial Neural Networks - Multi-Layer Perceptrons. + +@sa @ref ml_intro_ann +*/ +class CV_EXPORTS_W ANN_MLP_ANNEAL : public ANN_MLP +{ +public: + /** @see setAnnealInitialT */ + CV_WRAP virtual double getAnnealInitialT() const = 0; + /** @copybrief getAnnealInitialT @see getAnnealInitialT */ + CV_WRAP virtual void setAnnealInitialT(double val) = 0; + + /** ANNEAL: Update final temperature. + It must be \>=0 and less than initialT. Default value is 0.1.*/ + /** @see setAnnealFinalT */ + CV_WRAP virtual double getAnnealFinalT() const = 0; + /** @copybrief getAnnealFinalT @see getAnnealFinalT */ + CV_WRAP virtual void setAnnealFinalT(double val) = 0; + + /** ANNEAL: Update cooling ratio. + It must be \>0 and less than 1. Default value is 0.95.*/ + /** @see setAnnealCoolingRatio */ + CV_WRAP virtual double getAnnealCoolingRatio() const = 0; + /** @copybrief getAnnealCoolingRatio @see getAnnealCoolingRatio */ + CV_WRAP virtual void setAnnealCoolingRatio(double val) = 0; + + /** ANNEAL: Update iteration per step. + It must be \>0 . Default value is 10.*/ + /** @see setAnnealItePerStep */ + CV_WRAP virtual int getAnnealItePerStep() const = 0; + /** @copybrief getAnnealItePerStep @see getAnnealItePerStep */ + CV_WRAP virtual void setAnnealItePerStep(int val) = 0; + + /** @brief Set/initialize anneal RNG */ + virtual void setAnnealEnergyRNG(const RNG& rng) = 0; +}; + + +/****************************************************************************************\ +* Simulated annealing solver * +\****************************************************************************************/ + +#ifdef CV_DOXYGEN +/** @brief This class declares example interface for system state used in simulated annealing optimization algorithm. + +@note This class is not defined in C++ code and can't be use directly - you need your own implementation with the same methods. +*/ +struct SimulatedAnnealingSolverSystem +{ + /** Give energy value for a state of system.*/ + double energy() const; + /** Function which change the state of system (random perturbation).*/ + void changeState(); + /** Function to reverse to the previous state. Can be called once only after changeState(). */ + void reverseState(); +}; +#endif // CV_DOXYGEN + +/** @brief The class implements simulated annealing for optimization. + +@cite Kirkpatrick83 for details + +@param solverSystem optimization system (see SimulatedAnnealingSolverSystem) +@param initialTemperature initial temperature +@param finalTemperature final temperature +@param coolingRatio temperature step multiplies +@param iterationsPerStep number of iterations per temperature changing step +@param lastTemperature optional output for last used temperature +@param rngEnergy specify custom random numbers generator (cv::theRNG() by default) +*/ +template +int simulatedAnnealingSolver(SimulatedAnnealingSolverSystem& solverSystem, + double initialTemperature, double finalTemperature, double coolingRatio, + size_t iterationsPerStep, + CV_OUT double* lastTemperature = NULL, + cv::RNG& rngEnergy = cv::theRNG() +); + +//! @} ml + +} +} + +#include + +#endif // __cplusplus +#endif // OPENCV_ML_HPP + +/* End of file. */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/ml/ml.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/ml/ml.hpp new file mode 100644 index 0000000000000000000000000000000000000000..f6f9cd8f89329832d7424a9910f67ae2314598f5 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/ml/ml.hpp @@ -0,0 +1,48 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifdef __OPENCV_BUILD +#error this is a compatibility header which should not be used inside the OpenCV library +#endif + +#include "opencv2/ml.hpp" diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/ml/ml.inl.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/ml/ml.inl.hpp new file mode 100644 index 0000000000000000000000000000000000000000..dc9c78393a5a85be32b0fe187a447a01ce95e39b --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/ml/ml.inl.hpp @@ -0,0 +1,60 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#ifndef OPENCV_ML_INL_HPP +#define OPENCV_ML_INL_HPP + +namespace cv { namespace ml { + +// declared in ml.hpp +template +int simulatedAnnealingSolver(SimulatedAnnealingSolverSystem& solverSystem, + double initialTemperature, double finalTemperature, double coolingRatio, + size_t iterationsPerStep, + CV_OUT double* lastTemperature, + cv::RNG& rngEnergy +) +{ + CV_Assert(finalTemperature > 0); + CV_Assert(initialTemperature > finalTemperature); + CV_Assert(iterationsPerStep > 0); + CV_Assert(coolingRatio < 1.0f); + double Ti = initialTemperature; + double previousEnergy = solverSystem.energy(); + int exchange = 0; + while (Ti > finalTemperature) + { + for (size_t i = 0; i < iterationsPerStep; i++) + { + solverSystem.changeState(); + double newEnergy = solverSystem.energy(); + if (newEnergy < previousEnergy) + { + previousEnergy = newEnergy; + exchange++; + } + else + { + double r = rngEnergy.uniform(0.0, 1.0); + if (r < std::exp(-(newEnergy - previousEnergy) / Ti)) + { + previousEnergy = newEnergy; + exchange++; + } + else + { + solverSystem.reverseState(); + } + } + } + Ti *= coolingRatio; + } + if (lastTemperature) + *lastTemperature = Ti; + return exchange; +} + +}} //namespace + +#endif // OPENCV_ML_INL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/objdetect.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/objdetect.hpp new file mode 100644 index 0000000000000000000000000000000000000000..eac9cbaf58b78d8d51d3af2d537fb064e6118919 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/objdetect.hpp @@ -0,0 +1,683 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OBJDETECT_HPP +#define OPENCV_OBJDETECT_HPP + +#include "opencv2/core.hpp" + +/** +@defgroup objdetect Object Detection + +Haar Feature-based Cascade Classifier for Object Detection +---------------------------------------------------------- + +The object detector described below has been initially proposed by Paul Viola @cite Viola01 and +improved by Rainer Lienhart @cite Lienhart02 . + +First, a classifier (namely a *cascade of boosted classifiers working with haar-like features*) is +trained with a few hundred sample views of a particular object (i.e., a face or a car), called +positive examples, that are scaled to the same size (say, 20x20), and negative examples - arbitrary +images of the same size. + +After a classifier is trained, it can be applied to a region of interest (of the same size as used +during the training) in an input image. The classifier outputs a "1" if the region is likely to show +the object (i.e., face/car), and "0" otherwise. To search for the object in the whole image one can +move the search window across the image and check every location using the classifier. The +classifier is designed so that it can be easily "resized" in order to be able to find the objects of +interest at different sizes, which is more efficient than resizing the image itself. So, to find an +object of an unknown size in the image the scan procedure should be done several times at different +scales. + +The word "cascade" in the classifier name means that the resultant classifier consists of several +simpler classifiers (*stages*) that are applied subsequently to a region of interest until at some +stage the candidate is rejected or all the stages are passed. The word "boosted" means that the +classifiers at every stage of the cascade are complex themselves and they are built out of basic +classifiers using one of four different boosting techniques (weighted voting). Currently Discrete +Adaboost, Real Adaboost, Gentle Adaboost and Logitboost are supported. The basic classifiers are +decision-tree classifiers with at least 2 leaves. Haar-like features are the input to the basic +classifiers, and are calculated as described below. The current algorithm uses the following +Haar-like features: + +![image](pics/haarfeatures.png) + +The feature used in a particular classifier is specified by its shape (1a, 2b etc.), position within +the region of interest and the scale (this scale is not the same as the scale used at the detection +stage, though these two scales are multiplied). For example, in the case of the third line feature +(2c) the response is calculated as the difference between the sum of image pixels under the +rectangle covering the whole feature (including the two white stripes and the black stripe in the +middle) and the sum of the image pixels under the black stripe multiplied by 3 in order to +compensate for the differences in the size of areas. The sums of pixel values over a rectangular +regions are calculated rapidly using integral images (see below and the integral description). + +To see the object detector at work, have a look at the facedetect demo: + + +The following reference is for the detection part only. There is a separate application called +opencv_traincascade that can train a cascade of boosted classifiers from a set of samples. + +@note In the new C++ interface it is also possible to use LBP (local binary pattern) features in +addition to Haar-like features. .. [Viola01] Paul Viola and Michael J. Jones. Rapid Object Detection +using a Boosted Cascade of Simple Features. IEEE CVPR, 2001. The paper is available online at + + +@{ + @defgroup objdetect_c C API +@} + */ + +typedef struct CvHaarClassifierCascade CvHaarClassifierCascade; + +namespace cv +{ + +//! @addtogroup objdetect +//! @{ + +///////////////////////////// Object Detection //////////////////////////// + +//! class for grouping object candidates, detected by Cascade Classifier, HOG etc. +//! instance of the class is to be passed to cv::partition (see cxoperations.hpp) +class CV_EXPORTS SimilarRects +{ +public: + SimilarRects(double _eps) : eps(_eps) {} + inline bool operator()(const Rect& r1, const Rect& r2) const + { + double delta = eps * ((std::min)(r1.width, r2.width) + (std::min)(r1.height, r2.height)) * 0.5; + return std::abs(r1.x - r2.x) <= delta && + std::abs(r1.y - r2.y) <= delta && + std::abs(r1.x + r1.width - r2.x - r2.width) <= delta && + std::abs(r1.y + r1.height - r2.y - r2.height) <= delta; + } + double eps; +}; + +/** @brief Groups the object candidate rectangles. + +@param rectList Input/output vector of rectangles. Output vector includes retained and grouped +rectangles. (The Python list is not modified in place.) +@param groupThreshold Minimum possible number of rectangles minus 1. The threshold is used in a +group of rectangles to retain it. +@param eps Relative difference between sides of the rectangles to merge them into a group. + +The function is a wrapper for the generic function partition . It clusters all the input rectangles +using the rectangle equivalence criteria that combines rectangles with similar sizes and similar +locations. The similarity is defined by eps. When eps=0 , no clustering is done at all. If +\f$\texttt{eps}\rightarrow +\inf\f$ , all the rectangles are put in one cluster. Then, the small +clusters containing less than or equal to groupThreshold rectangles are rejected. In each other +cluster, the average rectangle is computed and put into the output rectangle list. + */ +CV_EXPORTS void groupRectangles(std::vector& rectList, int groupThreshold, double eps = 0.2); +/** @overload */ +CV_EXPORTS_W void groupRectangles(CV_IN_OUT std::vector& rectList, CV_OUT std::vector& weights, + int groupThreshold, double eps = 0.2); +/** @overload */ +CV_EXPORTS void groupRectangles(std::vector& rectList, int groupThreshold, + double eps, std::vector* weights, std::vector* levelWeights ); +/** @overload */ +CV_EXPORTS void groupRectangles(std::vector& rectList, std::vector& rejectLevels, + std::vector& levelWeights, int groupThreshold, double eps = 0.2); +/** @overload */ +CV_EXPORTS void groupRectangles_meanshift(std::vector& rectList, std::vector& foundWeights, + std::vector& foundScales, + double detectThreshold = 0.0, Size winDetSize = Size(64, 128)); + +template<> CV_EXPORTS void DefaultDeleter::operator ()(CvHaarClassifierCascade* obj) const; + +enum { CASCADE_DO_CANNY_PRUNING = 1, + CASCADE_SCALE_IMAGE = 2, + CASCADE_FIND_BIGGEST_OBJECT = 4, + CASCADE_DO_ROUGH_SEARCH = 8 + }; + +class CV_EXPORTS_W BaseCascadeClassifier : public Algorithm +{ +public: + virtual ~BaseCascadeClassifier(); + virtual bool empty() const CV_OVERRIDE = 0; + virtual bool load( const String& filename ) = 0; + virtual void detectMultiScale( InputArray image, + CV_OUT std::vector& objects, + double scaleFactor, + int minNeighbors, int flags, + Size minSize, Size maxSize ) = 0; + + virtual void detectMultiScale( InputArray image, + CV_OUT std::vector& objects, + CV_OUT std::vector& numDetections, + double scaleFactor, + int minNeighbors, int flags, + Size minSize, Size maxSize ) = 0; + + virtual void detectMultiScale( InputArray image, + CV_OUT std::vector& objects, + CV_OUT std::vector& rejectLevels, + CV_OUT std::vector& levelWeights, + double scaleFactor, + int minNeighbors, int flags, + Size minSize, Size maxSize, + bool outputRejectLevels ) = 0; + + virtual bool isOldFormatCascade() const = 0; + virtual Size getOriginalWindowSize() const = 0; + virtual int getFeatureType() const = 0; + virtual void* getOldCascade() = 0; + + class CV_EXPORTS MaskGenerator + { + public: + virtual ~MaskGenerator() {} + virtual Mat generateMask(const Mat& src)=0; + virtual void initializeMask(const Mat& /*src*/) { } + }; + virtual void setMaskGenerator(const Ptr& maskGenerator) = 0; + virtual Ptr getMaskGenerator() = 0; +}; + +/** @example facedetect.cpp +This program demonstrates usage of the Cascade classifier class +\image html Cascade_Classifier_Tutorial_Result_Haar.jpg "Sample screenshot" width=321 height=254 +*/ +/** @brief Cascade classifier class for object detection. + */ +class CV_EXPORTS_W CascadeClassifier +{ +public: + CV_WRAP CascadeClassifier(); + /** @brief Loads a classifier from a file. + + @param filename Name of the file from which the classifier is loaded. + */ + CV_WRAP CascadeClassifier(const String& filename); + ~CascadeClassifier(); + /** @brief Checks whether the classifier has been loaded. + */ + CV_WRAP bool empty() const; + /** @brief Loads a classifier from a file. + + @param filename Name of the file from which the classifier is loaded. The file may contain an old + HAAR classifier trained by the haartraining application or a new cascade classifier trained by the + traincascade application. + */ + CV_WRAP bool load( const String& filename ); + /** @brief Reads a classifier from a FileStorage node. + + @note The file may contain a new cascade classifier (trained traincascade application) only. + */ + CV_WRAP bool read( const FileNode& node ); + + /** @brief Detects objects of different sizes in the input image. The detected objects are returned as a list + of rectangles. + + @param image Matrix of the type CV_8U containing an image where objects are detected. + @param objects Vector of rectangles where each rectangle contains the detected object, the + rectangles may be partially outside the original image. + @param scaleFactor Parameter specifying how much the image size is reduced at each image scale. + @param minNeighbors Parameter specifying how many neighbors each candidate rectangle should have + to retain it. + @param flags Parameter with the same meaning for an old cascade as in the function + cvHaarDetectObjects. It is not used for a new cascade. + @param minSize Minimum possible object size. Objects smaller than that are ignored. + @param maxSize Maximum possible object size. Objects larger than that are ignored. If `maxSize == minSize` model is evaluated on single scale. + + The function is parallelized with the TBB library. + + @note + - (Python) A face detection example using cascade classifiers can be found at + opencv_source_code/samples/python/facedetect.py + */ + CV_WRAP void detectMultiScale( InputArray image, + CV_OUT std::vector& objects, + double scaleFactor = 1.1, + int minNeighbors = 3, int flags = 0, + Size minSize = Size(), + Size maxSize = Size() ); + + /** @overload + @param image Matrix of the type CV_8U containing an image where objects are detected. + @param objects Vector of rectangles where each rectangle contains the detected object, the + rectangles may be partially outside the original image. + @param numDetections Vector of detection numbers for the corresponding objects. An object's number + of detections is the number of neighboring positively classified rectangles that were joined + together to form the object. + @param scaleFactor Parameter specifying how much the image size is reduced at each image scale. + @param minNeighbors Parameter specifying how many neighbors each candidate rectangle should have + to retain it. + @param flags Parameter with the same meaning for an old cascade as in the function + cvHaarDetectObjects. It is not used for a new cascade. + @param minSize Minimum possible object size. Objects smaller than that are ignored. + @param maxSize Maximum possible object size. Objects larger than that are ignored. If `maxSize == minSize` model is evaluated on single scale. + */ + CV_WRAP_AS(detectMultiScale2) void detectMultiScale( InputArray image, + CV_OUT std::vector& objects, + CV_OUT std::vector& numDetections, + double scaleFactor=1.1, + int minNeighbors=3, int flags=0, + Size minSize=Size(), + Size maxSize=Size() ); + + /** @overload + This function allows you to retrieve the final stage decision certainty of classification. + For this, one needs to set `outputRejectLevels` on true and provide the `rejectLevels` and `levelWeights` parameter. + For each resulting detection, `levelWeights` will then contain the certainty of classification at the final stage. + This value can then be used to separate strong from weaker classifications. + + A code sample on how to use it efficiently can be found below: + @code + Mat img; + vector weights; + vector levels; + vector detections; + CascadeClassifier model("/path/to/your/model.xml"); + model.detectMultiScale(img, detections, levels, weights, 1.1, 3, 0, Size(), Size(), true); + cerr << "Detection " << detections[0] << " with weight " << weights[0] << endl; + @endcode + */ + CV_WRAP_AS(detectMultiScale3) void detectMultiScale( InputArray image, + CV_OUT std::vector& objects, + CV_OUT std::vector& rejectLevels, + CV_OUT std::vector& levelWeights, + double scaleFactor = 1.1, + int minNeighbors = 3, int flags = 0, + Size minSize = Size(), + Size maxSize = Size(), + bool outputRejectLevels = false ); + + CV_WRAP bool isOldFormatCascade() const; + CV_WRAP Size getOriginalWindowSize() const; + CV_WRAP int getFeatureType() const; + void* getOldCascade(); + + CV_WRAP static bool convert(const String& oldcascade, const String& newcascade); + + void setMaskGenerator(const Ptr& maskGenerator); + Ptr getMaskGenerator(); + + Ptr cc; +}; + +CV_EXPORTS Ptr createFaceDetectionMaskGenerator(); + +//////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector ////////////// + +//! struct for detection region of interest (ROI) +struct DetectionROI +{ + //! scale(size) of the bounding box + double scale; + //! set of requested locations to be evaluated + std::vector locations; + //! vector that will contain confidence values for each location + std::vector confidences; +}; + +/**@brief Implementation of HOG (Histogram of Oriented Gradients) descriptor and object detector. + +the HOG descriptor algorithm introduced by Navneet Dalal and Bill Triggs @cite Dalal2005 . + +useful links: + +https://hal.inria.fr/inria-00548512/document/ + +https://en.wikipedia.org/wiki/Histogram_of_oriented_gradients + +https://software.intel.com/en-us/ipp-dev-reference-histogram-of-oriented-gradients-hog-descriptor + +http://www.learnopencv.com/histogram-of-oriented-gradients + +http://www.learnopencv.com/handwritten-digits-classification-an-opencv-c-python-tutorial + + */ +struct CV_EXPORTS_W HOGDescriptor +{ +public: + enum { L2Hys = 0 //!< Default histogramNormType + }; + enum { DEFAULT_NLEVELS = 64 //!< Default nlevels value. + }; + /**@brief Creates the HOG descriptor and detector with default params. + + aqual to HOGDescriptor(Size(64,128), Size(16,16), Size(8,8), Size(8,8), 9, 1 ) + */ + CV_WRAP HOGDescriptor() : winSize(64,128), blockSize(16,16), blockStride(8,8), + cellSize(8,8), nbins(9), derivAperture(1), winSigma(-1), + histogramNormType(HOGDescriptor::L2Hys), L2HysThreshold(0.2), gammaCorrection(true), + free_coef(-1.f), nlevels(HOGDescriptor::DEFAULT_NLEVELS), signedGradient(false) + {} + + /** @overload + @param _winSize sets winSize with given value. + @param _blockSize sets blockSize with given value. + @param _blockStride sets blockStride with given value. + @param _cellSize sets cellSize with given value. + @param _nbins sets nbins with given value. + @param _derivAperture sets derivAperture with given value. + @param _winSigma sets winSigma with given value. + @param _histogramNormType sets histogramNormType with given value. + @param _L2HysThreshold sets L2HysThreshold with given value. + @param _gammaCorrection sets gammaCorrection with given value. + @param _nlevels sets nlevels with given value. + @param _signedGradient sets signedGradient with given value. + */ + CV_WRAP HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride, + Size _cellSize, int _nbins, int _derivAperture=1, double _winSigma=-1, + int _histogramNormType=HOGDescriptor::L2Hys, + double _L2HysThreshold=0.2, bool _gammaCorrection=false, + int _nlevels=HOGDescriptor::DEFAULT_NLEVELS, bool _signedGradient=false) + : winSize(_winSize), blockSize(_blockSize), blockStride(_blockStride), cellSize(_cellSize), + nbins(_nbins), derivAperture(_derivAperture), winSigma(_winSigma), + histogramNormType(_histogramNormType), L2HysThreshold(_L2HysThreshold), + gammaCorrection(_gammaCorrection), free_coef(-1.f), nlevels(_nlevels), signedGradient(_signedGradient) + {} + + /** @overload + @param filename the file name containing HOGDescriptor properties and coefficients of the trained classifier + */ + CV_WRAP HOGDescriptor(const String& filename) + { + load(filename); + } + + /** @overload + @param d the HOGDescriptor which cloned to create a new one. + */ + HOGDescriptor(const HOGDescriptor& d) + { + d.copyTo(*this); + } + + /**@brief Default destructor. + */ + virtual ~HOGDescriptor() {} + + /**@brief Returns the number of coefficients required for the classification. + */ + CV_WRAP size_t getDescriptorSize() const; + + /** @brief Checks if detector size equal to descriptor size. + */ + CV_WRAP bool checkDetectorSize() const; + + /** @brief Returns winSigma value + */ + CV_WRAP double getWinSigma() const; + + /**@example peopledetect.cpp + */ + /**@brief Sets coefficients for the linear SVM classifier. + @param _svmdetector coefficients for the linear SVM classifier. + */ + CV_WRAP virtual void setSVMDetector(InputArray _svmdetector); + + /** @brief Reads HOGDescriptor parameters from a file node. + @param fn File node + */ + virtual bool read(FileNode& fn); + + /** @brief Stores HOGDescriptor parameters in a file storage. + @param fs File storage + @param objname Object name + */ + virtual void write(FileStorage& fs, const String& objname) const; + + /** @brief loads coefficients for the linear SVM classifier from a file + @param filename Name of the file to read. + @param objname The optional name of the node to read (if empty, the first top-level node will be used). + */ + CV_WRAP virtual bool load(const String& filename, const String& objname = String()); + + /** @brief saves coefficients for the linear SVM classifier to a file + @param filename File name + @param objname Object name + */ + CV_WRAP virtual void save(const String& filename, const String& objname = String()) const; + + /** @brief clones the HOGDescriptor + @param c cloned HOGDescriptor + */ + virtual void copyTo(HOGDescriptor& c) const; + + /**@example train_HOG.cpp + */ + /** @brief Computes HOG descriptors of given image. + @param img Matrix of the type CV_8U containing an image where HOG features will be calculated. + @param descriptors Matrix of the type CV_32F + @param winStride Window stride. It must be a multiple of block stride. + @param padding Padding + @param locations Vector of Point + */ + CV_WRAP virtual void compute(InputArray img, + CV_OUT std::vector& descriptors, + Size winStride = Size(), Size padding = Size(), + const std::vector& locations = std::vector()) const; + + /** @brief Performs object detection without a multi-scale window. + @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected. + @param foundLocations Vector of point where each point contains left-top corner point of detected object boundaries. + @param weights Vector that will contain confidence values for each detected object. + @param hitThreshold Threshold for the distance between features and SVM classifying plane. + Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). + But if the free coefficient is omitted (which is allowed), you can specify it manually here. + @param winStride Window stride. It must be a multiple of block stride. + @param padding Padding + @param searchLocations Vector of Point includes set of requested locations to be evaluated. + */ + CV_WRAP virtual void detect(const Mat& img, CV_OUT std::vector& foundLocations, + CV_OUT std::vector& weights, + double hitThreshold = 0, Size winStride = Size(), + Size padding = Size(), + const std::vector& searchLocations = std::vector()) const; + + /** @brief Performs object detection without a multi-scale window. + @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected. + @param foundLocations Vector of point where each point contains left-top corner point of detected object boundaries. + @param hitThreshold Threshold for the distance between features and SVM classifying plane. + Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). + But if the free coefficient is omitted (which is allowed), you can specify it manually here. + @param winStride Window stride. It must be a multiple of block stride. + @param padding Padding + @param searchLocations Vector of Point includes locations to search. + */ + virtual void detect(const Mat& img, CV_OUT std::vector& foundLocations, + double hitThreshold = 0, Size winStride = Size(), + Size padding = Size(), + const std::vector& searchLocations=std::vector()) const; + + /** @brief Detects objects of different sizes in the input image. The detected objects are returned as a list + of rectangles. + @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected. + @param foundLocations Vector of rectangles where each rectangle contains the detected object. + @param foundWeights Vector that will contain confidence values for each detected object. + @param hitThreshold Threshold for the distance between features and SVM classifying plane. + Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). + But if the free coefficient is omitted (which is allowed), you can specify it manually here. + @param winStride Window stride. It must be a multiple of block stride. + @param padding Padding + @param scale Coefficient of the detection window increase. + @param finalThreshold Final threshold + @param useMeanshiftGrouping indicates grouping algorithm + */ + CV_WRAP virtual void detectMultiScale(InputArray img, CV_OUT std::vector& foundLocations, + CV_OUT std::vector& foundWeights, double hitThreshold = 0, + Size winStride = Size(), Size padding = Size(), double scale = 1.05, + double finalThreshold = 2.0,bool useMeanshiftGrouping = false) const; + + /** @brief Detects objects of different sizes in the input image. The detected objects are returned as a list + of rectangles. + @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected. + @param foundLocations Vector of rectangles where each rectangle contains the detected object. + @param hitThreshold Threshold for the distance between features and SVM classifying plane. + Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). + But if the free coefficient is omitted (which is allowed), you can specify it manually here. + @param winStride Window stride. It must be a multiple of block stride. + @param padding Padding + @param scale Coefficient of the detection window increase. + @param finalThreshold Final threshold + @param useMeanshiftGrouping indicates grouping algorithm + */ + virtual void detectMultiScale(InputArray img, CV_OUT std::vector& foundLocations, + double hitThreshold = 0, Size winStride = Size(), + Size padding = Size(), double scale = 1.05, + double finalThreshold = 2.0, bool useMeanshiftGrouping = false) const; + + /** @brief Computes gradients and quantized gradient orientations. + @param img Matrix contains the image to be computed + @param grad Matrix of type CV_32FC2 contains computed gradients + @param angleOfs Matrix of type CV_8UC2 contains quantized gradient orientations + @param paddingTL Padding from top-left + @param paddingBR Padding from bottom-right + */ + CV_WRAP virtual void computeGradient(const Mat& img, CV_OUT Mat& grad, CV_OUT Mat& angleOfs, + Size paddingTL = Size(), Size paddingBR = Size()) const; + + /** @brief Returns coefficients of the classifier trained for people detection (for 64x128 windows). + */ + CV_WRAP static std::vector getDefaultPeopleDetector(); + + /**@example hog.cpp + */ + /** @brief Returns coefficients of the classifier trained for people detection (for 48x96 windows). + */ + CV_WRAP static std::vector getDaimlerPeopleDetector(); + + //! Detection window size. Align to block size and block stride. Default value is Size(64,128). + CV_PROP Size winSize; + + //! Block size in pixels. Align to cell size. Default value is Size(16,16). + CV_PROP Size blockSize; + + //! Block stride. It must be a multiple of cell size. Default value is Size(8,8). + CV_PROP Size blockStride; + + //! Cell size. Default value is Size(8,8). + CV_PROP Size cellSize; + + //! Number of bins used in the calculation of histogram of gradients. Default value is 9. + CV_PROP int nbins; + + //! not documented + CV_PROP int derivAperture; + + //! Gaussian smoothing window parameter. + CV_PROP double winSigma; + + //! histogramNormType + CV_PROP int histogramNormType; + + //! L2-Hys normalization method shrinkage. + CV_PROP double L2HysThreshold; + + //! Flag to specify whether the gamma correction preprocessing is required or not. + CV_PROP bool gammaCorrection; + + //! coefficients for the linear SVM classifier. + CV_PROP std::vector svmDetector; + + //! coefficients for the linear SVM classifier used when OpenCL is enabled + UMat oclSvmDetector; + + //! not documented + float free_coef; + + //! Maximum number of detection window increases. Default value is 64 + CV_PROP int nlevels; + + //! Indicates signed gradient will be used or not + CV_PROP bool signedGradient; + + /** @brief evaluate specified ROI and return confidence value for each location + @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected. + @param locations Vector of Point + @param foundLocations Vector of Point where each Point is detected object's top-left point. + @param confidences confidences + @param hitThreshold Threshold for the distance between features and SVM classifying plane. Usually + it is 0 and should be specified in the detector coefficients (as the last free coefficient). But if + the free coefficient is omitted (which is allowed), you can specify it manually here + @param winStride winStride + @param padding padding + */ + virtual void detectROI(const cv::Mat& img, const std::vector &locations, + CV_OUT std::vector& foundLocations, CV_OUT std::vector& confidences, + double hitThreshold = 0, cv::Size winStride = Size(), + cv::Size padding = Size()) const; + + /** @brief evaluate specified ROI and return confidence value for each location in multiple scales + @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected. + @param foundLocations Vector of rectangles where each rectangle contains the detected object. + @param locations Vector of DetectionROI + @param hitThreshold Threshold for the distance between features and SVM classifying plane. Usually it is 0 and should be specified + in the detector coefficients (as the last free coefficient). But if the free coefficient is omitted (which is allowed), you can specify it manually here. + @param groupThreshold Minimum possible number of rectangles minus 1. The threshold is used in a group of rectangles to retain it. + */ + virtual void detectMultiScaleROI(const cv::Mat& img, + CV_OUT std::vector& foundLocations, + std::vector& locations, + double hitThreshold = 0, + int groupThreshold = 0) const; + + /** @brief read/parse Dalal's alt model file + @param modelfile Path of Dalal's alt model file. + */ + void readALTModel(String modelfile); + + /** @brief Groups the object candidate rectangles. + @param rectList Input/output vector of rectangles. Output vector includes retained and grouped rectangles. (The Python list is not modified in place.) + @param weights Input/output vector of weights of rectangles. Output vector includes weights of retained and grouped rectangles. (The Python list is not modified in place.) + @param groupThreshold Minimum possible number of rectangles minus 1. The threshold is used in a group of rectangles to retain it. + @param eps Relative difference between sides of the rectangles to merge them into a group. + */ + void groupRectangles(std::vector& rectList, std::vector& weights, int groupThreshold, double eps) const; +}; + +//! @} objdetect + +} + +#include "opencv2/objdetect/detection_based_tracker.hpp" + +#ifndef DISABLE_OPENCV_24_COMPATIBILITY +#include "opencv2/objdetect/objdetect_c.h" +#endif + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/objdetect/detection_based_tracker.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/objdetect/detection_based_tracker.hpp new file mode 100644 index 0000000000000000000000000000000000000000..07dd587491042d4603f4dca1fd3a24ac5bf98ffd --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/objdetect/detection_based_tracker.hpp @@ -0,0 +1,227 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OBJDETECT_DBT_HPP +#define OPENCV_OBJDETECT_DBT_HPP + +#include + +// After this condition removal update blacklist for bindings: modules/python/common.cmake +#if defined(__linux__) || defined(LINUX) || defined(__APPLE__) || defined(__ANDROID__) || \ + defined(CV_CXX11) + +#include + +namespace cv +{ + +//! @addtogroup objdetect +//! @{ + +class CV_EXPORTS DetectionBasedTracker +{ + public: + struct CV_EXPORTS Parameters + { + int maxTrackLifetime; + int minDetectionPeriod; //the minimal time between run of the big object detector (on the whole frame) in ms (1000 mean 1 sec), default=0 + + Parameters(); + }; + + class IDetector + { + public: + IDetector(): + minObjSize(96, 96), + maxObjSize(INT_MAX, INT_MAX), + minNeighbours(2), + scaleFactor(1.1f) + {} + + virtual void detect(const cv::Mat& image, std::vector& objects) = 0; + + void setMinObjectSize(const cv::Size& min) + { + minObjSize = min; + } + void setMaxObjectSize(const cv::Size& max) + { + maxObjSize = max; + } + cv::Size getMinObjectSize() const + { + return minObjSize; + } + cv::Size getMaxObjectSize() const + { + return maxObjSize; + } + float getScaleFactor() + { + return scaleFactor; + } + void setScaleFactor(float value) + { + scaleFactor = value; + } + int getMinNeighbours() + { + return minNeighbours; + } + void setMinNeighbours(int value) + { + minNeighbours = value; + } + virtual ~IDetector() {} + + protected: + cv::Size minObjSize; + cv::Size maxObjSize; + int minNeighbours; + float scaleFactor; + }; + + DetectionBasedTracker(cv::Ptr mainDetector, cv::Ptr trackingDetector, const Parameters& params); + virtual ~DetectionBasedTracker(); + + virtual bool run(); + virtual void stop(); + virtual void resetTracking(); + + virtual void process(const cv::Mat& imageGray); + + bool setParameters(const Parameters& params); + const Parameters& getParameters() const; + + + typedef std::pair Object; + virtual void getObjects(std::vector& result) const; + virtual void getObjects(std::vector& result) const; + + enum ObjectStatus + { + DETECTED_NOT_SHOWN_YET, + DETECTED, + DETECTED_TEMPORARY_LOST, + WRONG_OBJECT + }; + struct ExtObject + { + int id; + cv::Rect location; + ObjectStatus status; + ExtObject(int _id, cv::Rect _location, ObjectStatus _status) + :id(_id), location(_location), status(_status) + { + } + }; + virtual void getObjects(std::vector& result) const; + + + virtual int addObject(const cv::Rect& location); //returns id of the new object + + protected: + class SeparateDetectionWork; + cv::Ptr separateDetectionWork; + friend void* workcycleObjectDetectorFunction(void* p); + + struct InnerParameters + { + int numLastPositionsToTrack; + int numStepsToWaitBeforeFirstShow; + int numStepsToTrackWithoutDetectingIfObjectHasNotBeenShown; + int numStepsToShowWithoutDetecting; + + float coeffTrackingWindowSize; + float coeffObjectSizeToTrack; + float coeffObjectSpeedUsingInPrediction; + + InnerParameters(); + }; + Parameters parameters; + InnerParameters innerParameters; + + struct TrackedObject + { + typedef std::vector PositionsVector; + + PositionsVector lastPositions; + + int numDetectedFrames; + int numFramesNotDetected; + int id; + + TrackedObject(const cv::Rect& rect):numDetectedFrames(1), numFramesNotDetected(0) + { + lastPositions.push_back(rect); + id=getNextId(); + }; + + static int getNextId() + { + static int _id=0; + return _id++; + } + }; + + int numTrackedSteps; + std::vector trackedObjects; + + std::vector weightsPositionsSmoothing; + std::vector weightsSizesSmoothing; + + cv::Ptr cascadeForTracking; + + void updateTrackedObjects(const std::vector& detectedObjects); + cv::Rect calcTrackedObjectPositionToShow(int i) const; + cv::Rect calcTrackedObjectPositionToShow(int i, ObjectStatus& status) const; + void detectInRegion(const cv::Mat& img, const cv::Rect& r, std::vector& detectedObjectsInRegions); +}; + +//! @} objdetect + +} //end of cv namespace +#endif + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/objdetect/objdetect.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/objdetect/objdetect.hpp new file mode 100644 index 0000000000000000000000000000000000000000..3ee284f4275e3ec013df5c6c1b6b55cd7effe6de --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/objdetect/objdetect.hpp @@ -0,0 +1,48 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifdef __OPENCV_BUILD +#error this is a compatibility header which should not be used inside the OpenCV library +#endif + +#include "opencv2/objdetect.hpp" diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/objdetect/objdetect_c.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/objdetect/objdetect_c.h new file mode 100644 index 0000000000000000000000000000000000000000..67dc2f4e663274fb9be1ed7bbbaacf6bc9407414 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/objdetect/objdetect_c.h @@ -0,0 +1,166 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OBJDETECT_C_H +#define OPENCV_OBJDETECT_C_H + +#include "opencv2/core/core_c.h" + +#ifdef __cplusplus +#include +#include + +extern "C" { +#endif + +/** @addtogroup objdetect_c + @{ + */ + +/****************************************************************************************\ +* Haar-like Object Detection functions * +\****************************************************************************************/ + +#define CV_HAAR_MAGIC_VAL 0x42500000 +#define CV_TYPE_NAME_HAAR "opencv-haar-classifier" + +#define CV_IS_HAAR_CLASSIFIER( haar ) \ + ((haar) != NULL && \ + (((const CvHaarClassifierCascade*)(haar))->flags & CV_MAGIC_MASK)==CV_HAAR_MAGIC_VAL) + +#define CV_HAAR_FEATURE_MAX 3 +#define CV_HAAR_STAGE_MAX 1000 + +typedef struct CvHaarFeature +{ + int tilted; + struct + { + CvRect r; + float weight; + } rect[CV_HAAR_FEATURE_MAX]; +} CvHaarFeature; + +typedef struct CvHaarClassifier +{ + int count; + CvHaarFeature* haar_feature; + float* threshold; + int* left; + int* right; + float* alpha; +} CvHaarClassifier; + +typedef struct CvHaarStageClassifier +{ + int count; + float threshold; + CvHaarClassifier* classifier; + + int next; + int child; + int parent; +} CvHaarStageClassifier; + +typedef struct CvHidHaarClassifierCascade CvHidHaarClassifierCascade; + +typedef struct CvHaarClassifierCascade +{ + int flags; + int count; + CvSize orig_window_size; + CvSize real_window_size; + double scale; + CvHaarStageClassifier* stage_classifier; + CvHidHaarClassifierCascade* hid_cascade; +} CvHaarClassifierCascade; + +typedef struct CvAvgComp +{ + CvRect rect; + int neighbors; +} CvAvgComp; + +/* Loads haar classifier cascade from a directory. + It is obsolete: convert your cascade to xml and use cvLoad instead */ +CVAPI(CvHaarClassifierCascade*) cvLoadHaarClassifierCascade( + const char* directory, CvSize orig_window_size); + +CVAPI(void) cvReleaseHaarClassifierCascade( CvHaarClassifierCascade** cascade ); + +#define CV_HAAR_DO_CANNY_PRUNING 1 +#define CV_HAAR_SCALE_IMAGE 2 +#define CV_HAAR_FIND_BIGGEST_OBJECT 4 +#define CV_HAAR_DO_ROUGH_SEARCH 8 + +CVAPI(CvSeq*) cvHaarDetectObjects( const CvArr* image, + CvHaarClassifierCascade* cascade, CvMemStorage* storage, + double scale_factor CV_DEFAULT(1.1), + int min_neighbors CV_DEFAULT(3), int flags CV_DEFAULT(0), + CvSize min_size CV_DEFAULT(cvSize(0,0)), CvSize max_size CV_DEFAULT(cvSize(0,0))); + +/* sets images for haar classifier cascade */ +CVAPI(void) cvSetImagesForHaarClassifierCascade( CvHaarClassifierCascade* cascade, + const CvArr* sum, const CvArr* sqsum, + const CvArr* tilted_sum, double scale ); + +/* runs the cascade on the specified window */ +CVAPI(int) cvRunHaarClassifierCascade( const CvHaarClassifierCascade* cascade, + CvPoint pt, int start_stage CV_DEFAULT(0)); + +/** @} objdetect_c */ + +#ifdef __cplusplus +} + +CV_EXPORTS CvSeq* cvHaarDetectObjectsForROC( const CvArr* image, + CvHaarClassifierCascade* cascade, CvMemStorage* storage, + std::vector& rejectLevels, std::vector& levelWeightds, + double scale_factor = 1.1, + int min_neighbors = 3, int flags = 0, + CvSize min_size = cvSize(0, 0), CvSize max_size = cvSize(0, 0), + bool outputRejectLevels = false ); + +#endif + +#endif /* OPENCV_OBJDETECT_C_H */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/opencv.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/opencv.hpp new file mode 100644 index 0000000000000000000000000000000000000000..40481587f05782ae97989657dec822276096a48c --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/opencv.hpp @@ -0,0 +1,139 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009-2010, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_ALL_HPP +#define OPENCV_ALL_HPP + +// File that defines what modules where included during the build of OpenCV +// These are purely the defines of the correct HAVE_OPENCV_modulename values +#include "opencv2/opencv_modules.hpp" + +// Then the list of defines is checked to include the correct headers +// Core library is always included --> without no OpenCV functionality available +#include "opencv2/core.hpp" + +// Then the optional modules are checked +#ifdef HAVE_OPENCV_CALIB3D +#include "opencv2/calib3d.hpp" +#endif +#ifdef HAVE_OPENCV_FEATURES2D +#include "opencv2/features2d.hpp" +#endif +#ifdef HAVE_OPENCV_DNN +#include "opencv2/dnn.hpp" +#endif +#ifdef HAVE_OPENCV_FLANN +#include "opencv2/flann.hpp" +#endif +#ifdef HAVE_OPENCV_HIGHGUI +#include "opencv2/highgui.hpp" +#endif +#ifdef HAVE_OPENCV_IMGCODECS +#include "opencv2/imgcodecs.hpp" +#endif +#ifdef HAVE_OPENCV_IMGPROC +#include "opencv2/imgproc.hpp" +#endif +#ifdef HAVE_OPENCV_ML +#include "opencv2/ml.hpp" +#endif +#ifdef HAVE_OPENCV_OBJDETECT +#include "opencv2/objdetect.hpp" +#endif +#ifdef HAVE_OPENCV_PHOTO +#include "opencv2/photo.hpp" +#endif +#ifdef HAVE_OPENCV_SHAPE +#include "opencv2/shape.hpp" +#endif +#ifdef HAVE_OPENCV_STITCHING +#include "opencv2/stitching.hpp" +#endif +#ifdef HAVE_OPENCV_SUPERRES +#include "opencv2/superres.hpp" +#endif +#ifdef HAVE_OPENCV_VIDEO +#include "opencv2/video.hpp" +#endif +#ifdef HAVE_OPENCV_VIDEOIO +#include "opencv2/videoio.hpp" +#endif +#ifdef HAVE_OPENCV_VIDEOSTAB +#include "opencv2/videostab.hpp" +#endif +#ifdef HAVE_OPENCV_VIZ +#include "opencv2/viz.hpp" +#endif + +// Finally CUDA specific entries are checked and added +#ifdef HAVE_OPENCV_CUDAARITHM +#include "opencv2/cudaarithm.hpp" +#endif +#ifdef HAVE_OPENCV_CUDABGSEGM +#include "opencv2/cudabgsegm.hpp" +#endif +#ifdef HAVE_OPENCV_CUDACODEC +#include "opencv2/cudacodec.hpp" +#endif +#ifdef HAVE_OPENCV_CUDAFEATURES2D +#include "opencv2/cudafeatures2d.hpp" +#endif +#ifdef HAVE_OPENCV_CUDAFILTERS +#include "opencv2/cudafilters.hpp" +#endif +#ifdef HAVE_OPENCV_CUDAIMGPROC +#include "opencv2/cudaimgproc.hpp" +#endif +#ifdef HAVE_OPENCV_CUDAOBJDETECT +#include "opencv2/cudaobjdetect.hpp" +#endif +#ifdef HAVE_OPENCV_CUDAOPTFLOW +#include "opencv2/cudaoptflow.hpp" +#endif +#ifdef HAVE_OPENCV_CUDASTEREO +#include "opencv2/cudastereo.hpp" +#endif +#ifdef HAVE_OPENCV_CUDAWARPING +#include "opencv2/cudawarping.hpp" +#endif + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/opencv_modules.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/opencv_modules.hpp new file mode 100644 index 0000000000000000000000000000000000000000..e142b25e0ba7c3879831719f8b362f7af176d274 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/opencv_modules.hpp @@ -0,0 +1,20 @@ +/* + * ** File generated automatically, do not modify ** + * + * This file defines the list of modules available in current build configuration + * + * +*/ + +// This definition means that OpenCV is built with enabled non-free code. +// For example, patented algorithms for non-profit/non-commercial use only. +/* #undef OPENCV_ENABLE_NONFREE */ + +#define HAVE_OPENCV_CALIB3D +#define HAVE_OPENCV_CORE +#define HAVE_OPENCV_FEATURES2D +#define HAVE_OPENCV_IMGCODECS +#define HAVE_OPENCV_IMGPROC +#define HAVE_OPENCV_VIDEO + + diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/photo.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/photo.hpp new file mode 100644 index 0000000000000000000000000000000000000000..8cd1971899478c05dbd7c4b5b9a046c910850efa --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/photo.hpp @@ -0,0 +1,876 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2008-2012, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_PHOTO_HPP +#define OPENCV_PHOTO_HPP + +#include "opencv2/core.hpp" +#include "opencv2/imgproc.hpp" + +/** +@defgroup photo Computational Photography +@{ + @defgroup photo_denoise Denoising + @defgroup photo_hdr HDR imaging + +This section describes high dynamic range imaging algorithms namely tonemapping, exposure alignment, +camera calibration with multiple exposures and exposure fusion. + + @defgroup photo_clone Seamless Cloning + @defgroup photo_render Non-Photorealistic Rendering + @defgroup photo_c C API +@} + */ + +namespace cv +{ + +//! @addtogroup photo +//! @{ + +//! the inpainting algorithm +enum +{ + INPAINT_NS = 0, // Navier-Stokes algorithm + INPAINT_TELEA = 1 // A. Telea algorithm +}; + +enum +{ + NORMAL_CLONE = 1, + MIXED_CLONE = 2, + MONOCHROME_TRANSFER = 3 +}; + +enum +{ + RECURS_FILTER = 1, + NORMCONV_FILTER = 2 +}; + +/** @brief Restores the selected region in an image using the region neighborhood. + +@param src Input 8-bit, 16-bit unsigned or 32-bit float 1-channel or 8-bit 3-channel image. +@param inpaintMask Inpainting mask, 8-bit 1-channel image. Non-zero pixels indicate the area that +needs to be inpainted. +@param dst Output image with the same size and type as src . +@param inpaintRadius Radius of a circular neighborhood of each point inpainted that is considered +by the algorithm. +@param flags Inpainting method that could be one of the following: +- **INPAINT_NS** Navier-Stokes based method [Navier01] +- **INPAINT_TELEA** Method by Alexandru Telea @cite Telea04 . + +The function reconstructs the selected image area from the pixel near the area boundary. The +function may be used to remove dust and scratches from a scanned photo, or to remove undesirable +objects from still images or video. See for more details. + +@note + - An example using the inpainting technique can be found at + opencv_source_code/samples/cpp/inpaint.cpp + - (Python) An example using the inpainting technique can be found at + opencv_source_code/samples/python/inpaint.py + */ +CV_EXPORTS_W void inpaint( InputArray src, InputArray inpaintMask, + OutputArray dst, double inpaintRadius, int flags ); + +//! @addtogroup photo_denoise +//! @{ + +/** @brief Perform image denoising using Non-local Means Denoising algorithm + with several computational +optimizations. Noise expected to be a gaussian white noise + +@param src Input 8-bit 1-channel, 2-channel, 3-channel or 4-channel image. +@param dst Output image with the same size and type as src . +@param templateWindowSize Size in pixels of the template patch that is used to compute weights. +Should be odd. Recommended value 7 pixels +@param searchWindowSize Size in pixels of the window that is used to compute weighted average for +given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater +denoising time. Recommended value 21 pixels +@param h Parameter regulating filter strength. Big h value perfectly removes noise but also +removes image details, smaller h value preserves details but also preserves some noise + +This function expected to be applied to grayscale images. For colored images look at +fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored +image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting +image to CIELAB colorspace and then separately denoise L and AB components with different h +parameter. + */ +CV_EXPORTS_W void fastNlMeansDenoising( InputArray src, OutputArray dst, float h = 3, + int templateWindowSize = 7, int searchWindowSize = 21); + +/** @brief Perform image denoising using Non-local Means Denoising algorithm + with several computational +optimizations. Noise expected to be a gaussian white noise + +@param src Input 8-bit or 16-bit (only with NORM_L1) 1-channel, +2-channel, 3-channel or 4-channel image. +@param dst Output image with the same size and type as src . +@param templateWindowSize Size in pixels of the template patch that is used to compute weights. +Should be odd. Recommended value 7 pixels +@param searchWindowSize Size in pixels of the window that is used to compute weighted average for +given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater +denoising time. Recommended value 21 pixels +@param h Array of parameters regulating filter strength, either one +parameter applied to all channels or one per channel in dst. Big h value +perfectly removes noise but also removes image details, smaller h +value preserves details but also preserves some noise +@param normType Type of norm used for weight calculation. Can be either NORM_L2 or NORM_L1 + +This function expected to be applied to grayscale images. For colored images look at +fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored +image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting +image to CIELAB colorspace and then separately denoise L and AB components with different h +parameter. + */ +CV_EXPORTS_W void fastNlMeansDenoising( InputArray src, OutputArray dst, + const std::vector& h, + int templateWindowSize = 7, int searchWindowSize = 21, + int normType = NORM_L2); + +/** @brief Modification of fastNlMeansDenoising function for colored images + +@param src Input 8-bit 3-channel image. +@param dst Output image with the same size and type as src . +@param templateWindowSize Size in pixels of the template patch that is used to compute weights. +Should be odd. Recommended value 7 pixels +@param searchWindowSize Size in pixels of the window that is used to compute weighted average for +given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater +denoising time. Recommended value 21 pixels +@param h Parameter regulating filter strength for luminance component. Bigger h value perfectly +removes noise but also removes image details, smaller h value preserves details but also preserves +some noise +@param hColor The same as h but for color components. For most images value equals 10 +will be enough to remove colored noise and do not distort colors + +The function converts image to CIELAB colorspace and then separately denoise L and AB components +with given h parameters using fastNlMeansDenoising function. + */ +CV_EXPORTS_W void fastNlMeansDenoisingColored( InputArray src, OutputArray dst, + float h = 3, float hColor = 3, + int templateWindowSize = 7, int searchWindowSize = 21); + +/** @brief Modification of fastNlMeansDenoising function for images sequence where consecutive images have been +captured in small period of time. For example video. This version of the function is for grayscale +images or for manual manipulation with colorspaces. For more details see + + +@param srcImgs Input 8-bit 1-channel, 2-channel, 3-channel or +4-channel images sequence. All images should have the same type and +size. +@param imgToDenoiseIndex Target image to denoise index in srcImgs sequence +@param temporalWindowSize Number of surrounding images to use for target image denoising. Should +be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to +imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise +srcImgs[imgToDenoiseIndex] image. +@param dst Output image with the same size and type as srcImgs images. +@param templateWindowSize Size in pixels of the template patch that is used to compute weights. +Should be odd. Recommended value 7 pixels +@param searchWindowSize Size in pixels of the window that is used to compute weighted average for +given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater +denoising time. Recommended value 21 pixels +@param h Parameter regulating filter strength. Bigger h value +perfectly removes noise but also removes image details, smaller h +value preserves details but also preserves some noise + */ +CV_EXPORTS_W void fastNlMeansDenoisingMulti( InputArrayOfArrays srcImgs, OutputArray dst, + int imgToDenoiseIndex, int temporalWindowSize, + float h = 3, int templateWindowSize = 7, int searchWindowSize = 21); + +/** @brief Modification of fastNlMeansDenoising function for images sequence where consecutive images have been +captured in small period of time. For example video. This version of the function is for grayscale +images or for manual manipulation with colorspaces. For more details see + + +@param srcImgs Input 8-bit or 16-bit (only with NORM_L1) 1-channel, +2-channel, 3-channel or 4-channel images sequence. All images should +have the same type and size. +@param imgToDenoiseIndex Target image to denoise index in srcImgs sequence +@param temporalWindowSize Number of surrounding images to use for target image denoising. Should +be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to +imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise +srcImgs[imgToDenoiseIndex] image. +@param dst Output image with the same size and type as srcImgs images. +@param templateWindowSize Size in pixels of the template patch that is used to compute weights. +Should be odd. Recommended value 7 pixels +@param searchWindowSize Size in pixels of the window that is used to compute weighted average for +given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater +denoising time. Recommended value 21 pixels +@param h Array of parameters regulating filter strength, either one +parameter applied to all channels or one per channel in dst. Big h value +perfectly removes noise but also removes image details, smaller h +value preserves details but also preserves some noise +@param normType Type of norm used for weight calculation. Can be either NORM_L2 or NORM_L1 + */ +CV_EXPORTS_W void fastNlMeansDenoisingMulti( InputArrayOfArrays srcImgs, OutputArray dst, + int imgToDenoiseIndex, int temporalWindowSize, + const std::vector& h, + int templateWindowSize = 7, int searchWindowSize = 21, + int normType = NORM_L2); + +/** @brief Modification of fastNlMeansDenoisingMulti function for colored images sequences + +@param srcImgs Input 8-bit 3-channel images sequence. All images should have the same type and +size. +@param imgToDenoiseIndex Target image to denoise index in srcImgs sequence +@param temporalWindowSize Number of surrounding images to use for target image denoising. Should +be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to +imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise +srcImgs[imgToDenoiseIndex] image. +@param dst Output image with the same size and type as srcImgs images. +@param templateWindowSize Size in pixels of the template patch that is used to compute weights. +Should be odd. Recommended value 7 pixels +@param searchWindowSize Size in pixels of the window that is used to compute weighted average for +given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater +denoising time. Recommended value 21 pixels +@param h Parameter regulating filter strength for luminance component. Bigger h value perfectly +removes noise but also removes image details, smaller h value preserves details but also preserves +some noise. +@param hColor The same as h but for color components. + +The function converts images to CIELAB colorspace and then separately denoise L and AB components +with given h parameters using fastNlMeansDenoisingMulti function. + */ +CV_EXPORTS_W void fastNlMeansDenoisingColoredMulti( InputArrayOfArrays srcImgs, OutputArray dst, + int imgToDenoiseIndex, int temporalWindowSize, + float h = 3, float hColor = 3, + int templateWindowSize = 7, int searchWindowSize = 21); + +/** @brief Primal-dual algorithm is an algorithm for solving special types of variational problems (that is, +finding a function to minimize some functional). As the image denoising, in particular, may be seen +as the variational problem, primal-dual algorithm then can be used to perform denoising and this is +exactly what is implemented. + +It should be noted, that this implementation was taken from the July 2013 blog entry +@cite MA13 , which also contained (slightly more general) ready-to-use source code on Python. +Subsequently, that code was rewritten on C++ with the usage of openCV by Vadim Pisarevsky at the end +of July 2013 and finally it was slightly adapted by later authors. + +Although the thorough discussion and justification of the algorithm involved may be found in +@cite ChambolleEtAl, it might make sense to skim over it here, following @cite MA13 . To begin +with, we consider the 1-byte gray-level images as the functions from the rectangular domain of +pixels (it may be seen as set +\f$\left\{(x,y)\in\mathbb{N}\times\mathbb{N}\mid 1\leq x\leq n,\;1\leq y\leq m\right\}\f$ for some +\f$m,\;n\in\mathbb{N}\f$) into \f$\{0,1,\dots,255\}\f$. We shall denote the noised images as \f$f_i\f$ and with +this view, given some image \f$x\f$ of the same size, we may measure how bad it is by the formula + +\f[\left\|\left\|\nabla x\right\|\right\| + \lambda\sum_i\left\|\left\|x-f_i\right\|\right\|\f] + +\f$\|\|\cdot\|\|\f$ here denotes \f$L_2\f$-norm and as you see, the first addend states that we want our +image to be smooth (ideally, having zero gradient, thus being constant) and the second states that +we want our result to be close to the observations we've got. If we treat \f$x\f$ as a function, this is +exactly the functional what we seek to minimize and here the Primal-Dual algorithm comes into play. + +@param observations This array should contain one or more noised versions of the image that is to +be restored. +@param result Here the denoised image will be stored. There is no need to do pre-allocation of +storage space, as it will be automatically allocated, if necessary. +@param lambda Corresponds to \f$\lambda\f$ in the formulas above. As it is enlarged, the smooth +(blurred) images are treated more favorably than detailed (but maybe more noised) ones. Roughly +speaking, as it becomes smaller, the result will be more blur but more sever outliers will be +removed. +@param niters Number of iterations that the algorithm will run. Of course, as more iterations as +better, but it is hard to quantitatively refine this statement, so just use the default and +increase it if the results are poor. + */ +CV_EXPORTS_W void denoise_TVL1(const std::vector& observations,Mat& result, double lambda=1.0, int niters=30); + +//! @} photo_denoise + +//! @addtogroup photo_hdr +//! @{ + +enum { LDR_SIZE = 256 }; + +/** @brief Base class for tonemapping algorithms - tools that are used to map HDR image to 8-bit range. + */ +class CV_EXPORTS_W Tonemap : public Algorithm +{ +public: + /** @brief Tonemaps image + + @param src source image - 32-bit 3-channel Mat + @param dst destination image - 32-bit 3-channel Mat with values in [0, 1] range + */ + CV_WRAP virtual void process(InputArray src, OutputArray dst) = 0; + + CV_WRAP virtual float getGamma() const = 0; + CV_WRAP virtual void setGamma(float gamma) = 0; +}; + +/** @brief Creates simple linear mapper with gamma correction + +@param gamma positive value for gamma correction. Gamma value of 1.0 implies no correction, gamma +equal to 2.2f is suitable for most displays. +Generally gamma \> 1 brightens the image and gamma \< 1 darkens it. + */ +CV_EXPORTS_W Ptr createTonemap(float gamma = 1.0f); + +/** @brief Adaptive logarithmic mapping is a fast global tonemapping algorithm that scales the image in +logarithmic domain. + +Since it's a global operator the same function is applied to all the pixels, it is controlled by the +bias parameter. + +Optional saturation enhancement is possible as described in @cite FL02 . + +For more information see @cite DM03 . + */ +class CV_EXPORTS_W TonemapDrago : public Tonemap +{ +public: + + CV_WRAP virtual float getSaturation() const = 0; + CV_WRAP virtual void setSaturation(float saturation) = 0; + + CV_WRAP virtual float getBias() const = 0; + CV_WRAP virtual void setBias(float bias) = 0; +}; + +/** @brief Creates TonemapDrago object + +@param gamma gamma value for gamma correction. See createTonemap +@param saturation positive saturation enhancement value. 1.0 preserves saturation, values greater +than 1 increase saturation and values less than 1 decrease it. +@param bias value for bias function in [0, 1] range. Values from 0.7 to 0.9 usually give best +results, default value is 0.85. + */ +CV_EXPORTS_W Ptr createTonemapDrago(float gamma = 1.0f, float saturation = 1.0f, float bias = 0.85f); + +/** @brief This algorithm decomposes image into two layers: base layer and detail layer using bilateral filter +and compresses contrast of the base layer thus preserving all the details. + +This implementation uses regular bilateral filter from opencv. + +Saturation enhancement is possible as in ocvTonemapDrago. + +For more information see @cite DD02 . + */ +class CV_EXPORTS_W TonemapDurand : public Tonemap +{ +public: + + CV_WRAP virtual float getSaturation() const = 0; + CV_WRAP virtual void setSaturation(float saturation) = 0; + + CV_WRAP virtual float getContrast() const = 0; + CV_WRAP virtual void setContrast(float contrast) = 0; + + CV_WRAP virtual float getSigmaSpace() const = 0; + CV_WRAP virtual void setSigmaSpace(float sigma_space) = 0; + + CV_WRAP virtual float getSigmaColor() const = 0; + CV_WRAP virtual void setSigmaColor(float sigma_color) = 0; +}; + +/** @brief Creates TonemapDurand object + +@param gamma gamma value for gamma correction. See createTonemap +@param contrast resulting contrast on logarithmic scale, i. e. log(max / min), where max and min +are maximum and minimum luminance values of the resulting image. +@param saturation saturation enhancement value. See createTonemapDrago +@param sigma_space bilateral filter sigma in color space +@param sigma_color bilateral filter sigma in coordinate space + */ +CV_EXPORTS_W Ptr +createTonemapDurand(float gamma = 1.0f, float contrast = 4.0f, float saturation = 1.0f, float sigma_space = 2.0f, float sigma_color = 2.0f); + +/** @brief This is a global tonemapping operator that models human visual system. + +Mapping function is controlled by adaptation parameter, that is computed using light adaptation and +color adaptation. + +For more information see @cite RD05 . + */ +class CV_EXPORTS_W TonemapReinhard : public Tonemap +{ +public: + CV_WRAP virtual float getIntensity() const = 0; + CV_WRAP virtual void setIntensity(float intensity) = 0; + + CV_WRAP virtual float getLightAdaptation() const = 0; + CV_WRAP virtual void setLightAdaptation(float light_adapt) = 0; + + CV_WRAP virtual float getColorAdaptation() const = 0; + CV_WRAP virtual void setColorAdaptation(float color_adapt) = 0; +}; + +/** @brief Creates TonemapReinhard object + +@param gamma gamma value for gamma correction. See createTonemap +@param intensity result intensity in [-8, 8] range. Greater intensity produces brighter results. +@param light_adapt light adaptation in [0, 1] range. If 1 adaptation is based only on pixel +value, if 0 it's global, otherwise it's a weighted mean of this two cases. +@param color_adapt chromatic adaptation in [0, 1] range. If 1 channels are treated independently, +if 0 adaptation level is the same for each channel. + */ +CV_EXPORTS_W Ptr +createTonemapReinhard(float gamma = 1.0f, float intensity = 0.0f, float light_adapt = 1.0f, float color_adapt = 0.0f); + +/** @brief This algorithm transforms image to contrast using gradients on all levels of gaussian pyramid, +transforms contrast values to HVS response and scales the response. After this the image is +reconstructed from new contrast values. + +For more information see @cite MM06 . + */ +class CV_EXPORTS_W TonemapMantiuk : public Tonemap +{ +public: + CV_WRAP virtual float getScale() const = 0; + CV_WRAP virtual void setScale(float scale) = 0; + + CV_WRAP virtual float getSaturation() const = 0; + CV_WRAP virtual void setSaturation(float saturation) = 0; +}; + +/** @brief Creates TonemapMantiuk object + +@param gamma gamma value for gamma correction. See createTonemap +@param scale contrast scale factor. HVS response is multiplied by this parameter, thus compressing +dynamic range. Values from 0.6 to 0.9 produce best results. +@param saturation saturation enhancement value. See createTonemapDrago + */ +CV_EXPORTS_W Ptr +createTonemapMantiuk(float gamma = 1.0f, float scale = 0.7f, float saturation = 1.0f); + +/** @brief The base class for algorithms that align images of the same scene with different exposures + */ +class CV_EXPORTS_W AlignExposures : public Algorithm +{ +public: + /** @brief Aligns images + + @param src vector of input images + @param dst vector of aligned images + @param times vector of exposure time values for each image + @param response 256x1 matrix with inverse camera response function for each pixel value, it should + have the same number of channels as images. + */ + CV_WRAP virtual void process(InputArrayOfArrays src, std::vector& dst, + InputArray times, InputArray response) = 0; +}; + +/** @brief This algorithm converts images to median threshold bitmaps (1 for pixels brighter than median +luminance and 0 otherwise) and than aligns the resulting bitmaps using bit operations. + +It is invariant to exposure, so exposure values and camera response are not necessary. + +In this implementation new image regions are filled with zeros. + +For more information see @cite GW03 . + */ +class CV_EXPORTS_W AlignMTB : public AlignExposures +{ +public: + CV_WRAP virtual void process(InputArrayOfArrays src, std::vector& dst, + InputArray times, InputArray response) CV_OVERRIDE = 0; + + /** @brief Short version of process, that doesn't take extra arguments. + + @param src vector of input images + @param dst vector of aligned images + */ + CV_WRAP virtual void process(InputArrayOfArrays src, std::vector& dst) = 0; + + /** @brief Calculates shift between two images, i. e. how to shift the second image to correspond it with the + first. + + @param img0 first image + @param img1 second image + */ + CV_WRAP virtual Point calculateShift(InputArray img0, InputArray img1) = 0; + /** @brief Helper function, that shift Mat filling new regions with zeros. + + @param src input image + @param dst result image + @param shift shift value + */ + CV_WRAP virtual void shiftMat(InputArray src, OutputArray dst, const Point shift) = 0; + /** @brief Computes median threshold and exclude bitmaps of given image. + + @param img input image + @param tb median threshold bitmap + @param eb exclude bitmap + */ + CV_WRAP virtual void computeBitmaps(InputArray img, OutputArray tb, OutputArray eb) = 0; + + CV_WRAP virtual int getMaxBits() const = 0; + CV_WRAP virtual void setMaxBits(int max_bits) = 0; + + CV_WRAP virtual int getExcludeRange() const = 0; + CV_WRAP virtual void setExcludeRange(int exclude_range) = 0; + + CV_WRAP virtual bool getCut() const = 0; + CV_WRAP virtual void setCut(bool value) = 0; +}; + +/** @brief Creates AlignMTB object + +@param max_bits logarithm to the base 2 of maximal shift in each dimension. Values of 5 and 6 are +usually good enough (31 and 63 pixels shift respectively). +@param exclude_range range for exclusion bitmap that is constructed to suppress noise around the +median value. +@param cut if true cuts images, otherwise fills the new regions with zeros. + */ +CV_EXPORTS_W Ptr createAlignMTB(int max_bits = 6, int exclude_range = 4, bool cut = true); + +/** @brief The base class for camera response calibration algorithms. + */ +class CV_EXPORTS_W CalibrateCRF : public Algorithm +{ +public: + /** @brief Recovers inverse camera response. + + @param src vector of input images + @param dst 256x1 matrix with inverse camera response function + @param times vector of exposure time values for each image + */ + CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst, InputArray times) = 0; +}; + +/** @brief Inverse camera response function is extracted for each brightness value by minimizing an objective +function as linear system. Objective function is constructed using pixel values on the same position +in all images, extra term is added to make the result smoother. + +For more information see @cite DM97 . + */ +class CV_EXPORTS_W CalibrateDebevec : public CalibrateCRF +{ +public: + CV_WRAP virtual float getLambda() const = 0; + CV_WRAP virtual void setLambda(float lambda) = 0; + + CV_WRAP virtual int getSamples() const = 0; + CV_WRAP virtual void setSamples(int samples) = 0; + + CV_WRAP virtual bool getRandom() const = 0; + CV_WRAP virtual void setRandom(bool random) = 0; +}; + +/** @brief Creates CalibrateDebevec object + +@param samples number of pixel locations to use +@param lambda smoothness term weight. Greater values produce smoother results, but can alter the +response. +@param random if true sample pixel locations are chosen at random, otherwise they form a +rectangular grid. + */ +CV_EXPORTS_W Ptr createCalibrateDebevec(int samples = 70, float lambda = 10.0f, bool random = false); + +/** @brief Inverse camera response function is extracted for each brightness value by minimizing an objective +function as linear system. This algorithm uses all image pixels. + +For more information see @cite RB99 . + */ +class CV_EXPORTS_W CalibrateRobertson : public CalibrateCRF +{ +public: + CV_WRAP virtual int getMaxIter() const = 0; + CV_WRAP virtual void setMaxIter(int max_iter) = 0; + + CV_WRAP virtual float getThreshold() const = 0; + CV_WRAP virtual void setThreshold(float threshold) = 0; + + CV_WRAP virtual Mat getRadiance() const = 0; +}; + +/** @brief Creates CalibrateRobertson object + +@param max_iter maximal number of Gauss-Seidel solver iterations. +@param threshold target difference between results of two successive steps of the minimization. + */ +CV_EXPORTS_W Ptr createCalibrateRobertson(int max_iter = 30, float threshold = 0.01f); + +/** @brief The base class algorithms that can merge exposure sequence to a single image. + */ +class CV_EXPORTS_W MergeExposures : public Algorithm +{ +public: + /** @brief Merges images. + + @param src vector of input images + @param dst result image + @param times vector of exposure time values for each image + @param response 256x1 matrix with inverse camera response function for each pixel value, it should + have the same number of channels as images. + */ + CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst, + InputArray times, InputArray response) = 0; +}; + +/** @brief The resulting HDR image is calculated as weighted average of the exposures considering exposure +values and camera response. + +For more information see @cite DM97 . + */ +class CV_EXPORTS_W MergeDebevec : public MergeExposures +{ +public: + CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst, + InputArray times, InputArray response) CV_OVERRIDE = 0; + CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst, InputArray times) = 0; +}; + +/** @brief Creates MergeDebevec object + */ +CV_EXPORTS_W Ptr createMergeDebevec(); + +/** @brief Pixels are weighted using contrast, saturation and well-exposedness measures, than images are +combined using laplacian pyramids. + +The resulting image weight is constructed as weighted average of contrast, saturation and +well-exposedness measures. + +The resulting image doesn't require tonemapping and can be converted to 8-bit image by multiplying +by 255, but it's recommended to apply gamma correction and/or linear tonemapping. + +For more information see @cite MK07 . + */ +class CV_EXPORTS_W MergeMertens : public MergeExposures +{ +public: + CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst, + InputArray times, InputArray response) CV_OVERRIDE = 0; + /** @brief Short version of process, that doesn't take extra arguments. + + @param src vector of input images + @param dst result image + */ + CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst) = 0; + + CV_WRAP virtual float getContrastWeight() const = 0; + CV_WRAP virtual void setContrastWeight(float contrast_weiht) = 0; + + CV_WRAP virtual float getSaturationWeight() const = 0; + CV_WRAP virtual void setSaturationWeight(float saturation_weight) = 0; + + CV_WRAP virtual float getExposureWeight() const = 0; + CV_WRAP virtual void setExposureWeight(float exposure_weight) = 0; +}; + +/** @brief Creates MergeMertens object + +@param contrast_weight contrast measure weight. See MergeMertens. +@param saturation_weight saturation measure weight +@param exposure_weight well-exposedness measure weight + */ +CV_EXPORTS_W Ptr +createMergeMertens(float contrast_weight = 1.0f, float saturation_weight = 1.0f, float exposure_weight = 0.0f); + +/** @brief The resulting HDR image is calculated as weighted average of the exposures considering exposure +values and camera response. + +For more information see @cite RB99 . + */ +class CV_EXPORTS_W MergeRobertson : public MergeExposures +{ +public: + CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst, + InputArray times, InputArray response) CV_OVERRIDE = 0; + CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst, InputArray times) = 0; +}; + +/** @brief Creates MergeRobertson object + */ +CV_EXPORTS_W Ptr createMergeRobertson(); + +//! @} photo_hdr + +/** @brief Transforms a color image to a grayscale image. It is a basic tool in digital printing, stylized +black-and-white photograph rendering, and in many single channel image processing applications +@cite CL12 . + +@param src Input 8-bit 3-channel image. +@param grayscale Output 8-bit 1-channel image. +@param color_boost Output 8-bit 3-channel image. + +This function is to be applied on color images. + */ +CV_EXPORTS_W void decolor( InputArray src, OutputArray grayscale, OutputArray color_boost); + +//! @addtogroup photo_clone +//! @{ + +/** @example cloning_demo.cpp +An example using seamlessClone function +*/ +/** @brief Image editing tasks concern either global changes (color/intensity corrections, filters, +deformations) or local changes concerned to a selection. Here we are interested in achieving local +changes, ones that are restricted to a region manually selected (ROI), in a seamless and effortless +manner. The extent of the changes ranges from slight distortions to complete replacement by novel +content @cite PM03 . + +@param src Input 8-bit 3-channel image. +@param dst Input 8-bit 3-channel image. +@param mask Input 8-bit 1 or 3-channel image. +@param p Point in dst image where object is placed. +@param blend Output image with the same size and type as dst. +@param flags Cloning method that could be one of the following: +- **NORMAL_CLONE** The power of the method is fully expressed when inserting objects with +complex outlines into a new background +- **MIXED_CLONE** The classic method, color-based selection and alpha masking might be time +consuming and often leaves an undesirable halo. Seamless cloning, even averaged with the +original image, is not effective. Mixed seamless cloning based on a loose selection proves +effective. +- **MONOCHROME_TRANSFER** Monochrome transfer allows the user to easily replace certain features of +one object by alternative features. + */ +CV_EXPORTS_W void seamlessClone( InputArray src, InputArray dst, InputArray mask, Point p, + OutputArray blend, int flags); + +/** @brief Given an original color image, two differently colored versions of this image can be mixed +seamlessly. + +@param src Input 8-bit 3-channel image. +@param mask Input 8-bit 1 or 3-channel image. +@param dst Output image with the same size and type as src . +@param red_mul R-channel multiply factor. +@param green_mul G-channel multiply factor. +@param blue_mul B-channel multiply factor. + +Multiplication factor is between .5 to 2.5. + */ +CV_EXPORTS_W void colorChange(InputArray src, InputArray mask, OutputArray dst, float red_mul = 1.0f, + float green_mul = 1.0f, float blue_mul = 1.0f); + +/** @brief Applying an appropriate non-linear transformation to the gradient field inside the selection and +then integrating back with a Poisson solver, modifies locally the apparent illumination of an image. + +@param src Input 8-bit 3-channel image. +@param mask Input 8-bit 1 or 3-channel image. +@param dst Output image with the same size and type as src. +@param alpha Value ranges between 0-2. +@param beta Value ranges between 0-2. + +This is useful to highlight under-exposed foreground objects or to reduce specular reflections. + */ +CV_EXPORTS_W void illuminationChange(InputArray src, InputArray mask, OutputArray dst, + float alpha = 0.2f, float beta = 0.4f); + +/** @brief By retaining only the gradients at edge locations, before integrating with the Poisson solver, one +washes out the texture of the selected region, giving its contents a flat aspect. Here Canny Edge +Detector is used. + +@param src Input 8-bit 3-channel image. +@param mask Input 8-bit 1 or 3-channel image. +@param dst Output image with the same size and type as src. +@param low_threshold Range from 0 to 100. +@param high_threshold Value \> 100. +@param kernel_size The size of the Sobel kernel to be used. + +**NOTE:** + +The algorithm assumes that the color of the source image is close to that of the destination. This +assumption means that when the colors don't match, the source image color gets tinted toward the +color of the destination image. + */ +CV_EXPORTS_W void textureFlattening(InputArray src, InputArray mask, OutputArray dst, + float low_threshold = 30, float high_threshold = 45, + int kernel_size = 3); + +//! @} photo_clone + +//! @addtogroup photo_render +//! @{ + +/** @brief Filtering is the fundamental operation in image and video processing. Edge-preserving smoothing +filters are used in many different applications @cite EM11 . + +@param src Input 8-bit 3-channel image. +@param dst Output 8-bit 3-channel image. +@param flags Edge preserving filters: +- **RECURS_FILTER** = 1 +- **NORMCONV_FILTER** = 2 +@param sigma_s Range between 0 to 200. +@param sigma_r Range between 0 to 1. + */ +CV_EXPORTS_W void edgePreservingFilter(InputArray src, OutputArray dst, int flags = 1, + float sigma_s = 60, float sigma_r = 0.4f); + +/** @brief This filter enhances the details of a particular image. + +@param src Input 8-bit 3-channel image. +@param dst Output image with the same size and type as src. +@param sigma_s Range between 0 to 200. +@param sigma_r Range between 0 to 1. + */ +CV_EXPORTS_W void detailEnhance(InputArray src, OutputArray dst, float sigma_s = 10, + float sigma_r = 0.15f); + +/** @example npr_demo.cpp +An example using non-photorealistic line drawing functions +*/ +/** @brief Pencil-like non-photorealistic line drawing + +@param src Input 8-bit 3-channel image. +@param dst1 Output 8-bit 1-channel image. +@param dst2 Output image with the same size and type as src. +@param sigma_s Range between 0 to 200. +@param sigma_r Range between 0 to 1. +@param shade_factor Range between 0 to 0.1. + */ +CV_EXPORTS_W void pencilSketch(InputArray src, OutputArray dst1, OutputArray dst2, + float sigma_s = 60, float sigma_r = 0.07f, float shade_factor = 0.02f); + +/** @brief Stylization aims to produce digital imagery with a wide variety of effects not focused on +photorealism. Edge-aware filters are ideal for stylization, as they can abstract regions of low +contrast while preserving, or enhancing, high-contrast features. + +@param src Input 8-bit 3-channel image. +@param dst Output image with the same size and type as src. +@param sigma_s Range between 0 to 200. +@param sigma_r Range between 0 to 1. + */ +CV_EXPORTS_W void stylization(InputArray src, OutputArray dst, float sigma_s = 60, + float sigma_r = 0.45f); + +//! @} photo_render + +//! @} photo + +} // cv + +#ifndef DISABLE_OPENCV_24_COMPATIBILITY +#include "opencv2/photo/photo_c.h" +#endif + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/photo/cuda.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/photo/cuda.hpp new file mode 100644 index 0000000000000000000000000000000000000000..a2f38167e640fcb91a43f97d3ea9d9305dc84dcf --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/photo/cuda.hpp @@ -0,0 +1,132 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2008-2012, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_PHOTO_CUDA_HPP +#define OPENCV_PHOTO_CUDA_HPP + +#include "opencv2/core/cuda.hpp" + +namespace cv { namespace cuda { + +//! @addtogroup photo_denoise +//! @{ + +/** @brief Performs pure non local means denoising without any simplification, and thus it is not fast. + +@param src Source image. Supports only CV_8UC1, CV_8UC2 and CV_8UC3. +@param dst Destination image. +@param h Filter sigma regulating filter strength for color. +@param search_window Size of search window. +@param block_size Size of block used for computing weights. +@param borderMode Border type. See borderInterpolate for details. BORDER_REFLECT101 , +BORDER_REPLICATE , BORDER_CONSTANT , BORDER_REFLECT and BORDER_WRAP are supported for now. +@param stream Stream for the asynchronous version. + +@sa + fastNlMeansDenoising + */ +CV_EXPORTS void nonLocalMeans(InputArray src, OutputArray dst, + float h, + int search_window = 21, + int block_size = 7, + int borderMode = BORDER_DEFAULT, + Stream& stream = Stream::Null()); + +/** @brief Perform image denoising using Non-local Means Denoising algorithm + with several computational +optimizations. Noise expected to be a gaussian white noise + +@param src Input 8-bit 1-channel, 2-channel or 3-channel image. +@param dst Output image with the same size and type as src . +@param h Parameter regulating filter strength. Big h value perfectly removes noise but also +removes image details, smaller h value preserves details but also preserves some noise +@param search_window Size in pixels of the window that is used to compute weighted average for +given pixel. Should be odd. Affect performance linearly: greater search_window - greater +denoising time. Recommended value 21 pixels +@param block_size Size in pixels of the template patch that is used to compute weights. Should be +odd. Recommended value 7 pixels +@param stream Stream for the asynchronous invocations. + +This function expected to be applied to grayscale images. For colored images look at +FastNonLocalMeansDenoising::labMethod. + +@sa + fastNlMeansDenoising + */ +CV_EXPORTS void fastNlMeansDenoising(InputArray src, OutputArray dst, + float h, + int search_window = 21, + int block_size = 7, + Stream& stream = Stream::Null()); + +/** @brief Modification of fastNlMeansDenoising function for colored images + +@param src Input 8-bit 3-channel image. +@param dst Output image with the same size and type as src . +@param h_luminance Parameter regulating filter strength. Big h value perfectly removes noise but +also removes image details, smaller h value preserves details but also preserves some noise +@param photo_render float The same as h but for color components. For most images value equals 10 will be +enough to remove colored noise and do not distort colors +@param search_window Size in pixels of the window that is used to compute weighted average for +given pixel. Should be odd. Affect performance linearly: greater search_window - greater +denoising time. Recommended value 21 pixels +@param block_size Size in pixels of the template patch that is used to compute weights. Should be +odd. Recommended value 7 pixels +@param stream Stream for the asynchronous invocations. + +The function converts image to CIELAB colorspace and then separately denoise L and AB components +with given h parameters using FastNonLocalMeansDenoising::simpleMethod function. + +@sa + fastNlMeansDenoisingColored + */ +CV_EXPORTS void fastNlMeansDenoisingColored(InputArray src, OutputArray dst, + float h_luminance, float photo_render, + int search_window = 21, + int block_size = 7, + Stream& stream = Stream::Null()); + +//! @} photo + +}} // namespace cv { namespace cuda { + +#endif /* OPENCV_PHOTO_CUDA_HPP */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/photo/photo.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/photo/photo.hpp new file mode 100644 index 0000000000000000000000000000000000000000..8af5e9f0fae26070fb49d931190b08cfa09e9419 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/photo/photo.hpp @@ -0,0 +1,48 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifdef __OPENCV_BUILD +#error this is a compatibility header which should not be used inside the OpenCV library +#endif + +#include "opencv2/photo.hpp" diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/photo/photo_c.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/photo/photo_c.h new file mode 100644 index 0000000000000000000000000000000000000000..cd623c19133c4b06bf266baf6b45f3aedea0df5a --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/photo/photo_c.h @@ -0,0 +1,74 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2008-2012, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_PHOTO_C_H +#define OPENCV_PHOTO_C_H + +#include "opencv2/core/core_c.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/** @addtogroup photo_c + @{ + */ + +/* Inpainting algorithms */ +enum InpaintingModes +{ + CV_INPAINT_NS =0, + CV_INPAINT_TELEA =1 +}; + + +/* Inpaints the selected region in the image */ +CVAPI(void) cvInpaint( const CvArr* src, const CvArr* inpaint_mask, + CvArr* dst, double inpaintRange, int flags ); + +/** @} */ + +#ifdef __cplusplus +} //extern "C" +#endif + +#endif //OPENCV_PHOTO_C_H diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/shape.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/shape.hpp new file mode 100644 index 0000000000000000000000000000000000000000..f302b6bbc0f4c72f5c826d0166ba3a18168022f5 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/shape.hpp @@ -0,0 +1,57 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009-2012, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_SHAPE_HPP +#define OPENCV_SHAPE_HPP + +#include "opencv2/shape/emdL1.hpp" +#include "opencv2/shape/shape_transformer.hpp" +#include "opencv2/shape/hist_cost.hpp" +#include "opencv2/shape/shape_distance.hpp" + +/** + @defgroup shape Shape Distance and Matching + */ + +#endif + +/* End of file. */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/shape/emdL1.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/shape/emdL1.hpp new file mode 100644 index 0000000000000000000000000000000000000000..a15d68c222c50e913b878b0eda591bfc33507ba8 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/shape/emdL1.hpp @@ -0,0 +1,72 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009-2012, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_EMD_L1_HPP +#define OPENCV_EMD_L1_HPP + +#include "opencv2/core.hpp" + +namespace cv +{ +/****************************************************************************************\ +* EMDL1 Function * +\****************************************************************************************/ + +//! @addtogroup shape +//! @{ + +/** @brief Computes the "minimal work" distance between two weighted point configurations base on the papers +"EMD-L1: An efficient and Robust Algorithm for comparing histogram-based descriptors", by Haibin +Ling and Kazunori Okuda; and "The Earth Mover's Distance is the Mallows Distance: Some Insights from +Statistics", by Elizaveta Levina and Peter Bickel. + +@param signature1 First signature, a single column floating-point matrix. Each row is the value of +the histogram in each bin. +@param signature2 Second signature of the same format and size as signature1. + */ +CV_EXPORTS float EMDL1(InputArray signature1, InputArray signature2); + +//! @} + +}//namespace cv + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/shape/hist_cost.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/shape/hist_cost.hpp new file mode 100644 index 0000000000000000000000000000000000000000..21d0d6807be032af0e9b668d57b0d69c7faf2c18 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/shape/hist_cost.hpp @@ -0,0 +1,111 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_HIST_COST_HPP +#define OPENCV_HIST_COST_HPP + +#include "opencv2/imgproc.hpp" + +namespace cv +{ + +//! @addtogroup shape +//! @{ + +/** @brief Abstract base class for histogram cost algorithms. + */ +class CV_EXPORTS_W HistogramCostExtractor : public Algorithm +{ +public: + CV_WRAP virtual void buildCostMatrix(InputArray descriptors1, InputArray descriptors2, OutputArray costMatrix) = 0; + + CV_WRAP virtual void setNDummies(int nDummies) = 0; + CV_WRAP virtual int getNDummies() const = 0; + + CV_WRAP virtual void setDefaultCost(float defaultCost) = 0; + CV_WRAP virtual float getDefaultCost() const = 0; +}; + +/** @brief A norm based cost extraction. : + */ +class CV_EXPORTS_W NormHistogramCostExtractor : public HistogramCostExtractor +{ +public: + CV_WRAP virtual void setNormFlag(int flag) = 0; + CV_WRAP virtual int getNormFlag() const = 0; +}; + +CV_EXPORTS_W Ptr + createNormHistogramCostExtractor(int flag=DIST_L2, int nDummies=25, float defaultCost=0.2f); + +/** @brief An EMD based cost extraction. : + */ +class CV_EXPORTS_W EMDHistogramCostExtractor : public HistogramCostExtractor +{ +public: + CV_WRAP virtual void setNormFlag(int flag) = 0; + CV_WRAP virtual int getNormFlag() const = 0; +}; + +CV_EXPORTS_W Ptr + createEMDHistogramCostExtractor(int flag=DIST_L2, int nDummies=25, float defaultCost=0.2f); + +/** @brief An Chi based cost extraction. : + */ +class CV_EXPORTS_W ChiHistogramCostExtractor : public HistogramCostExtractor +{}; + +CV_EXPORTS_W Ptr createChiHistogramCostExtractor(int nDummies=25, float defaultCost=0.2f); + +/** @brief An EMD-L1 based cost extraction. : + */ +class CV_EXPORTS_W EMDL1HistogramCostExtractor : public HistogramCostExtractor +{}; + +CV_EXPORTS_W Ptr + createEMDL1HistogramCostExtractor(int nDummies=25, float defaultCost=0.2f); + +//! @} + +} // cv +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/shape/shape.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/shape/shape.hpp new file mode 100644 index 0000000000000000000000000000000000000000..5c4da3cefa92a75e3213988b780464fa351542e3 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/shape/shape.hpp @@ -0,0 +1,48 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifdef __OPENCV_BUILD +#error this is a compatibility header which should not be used inside the OpenCV library +#endif + +#include "opencv2/shape.hpp" diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/shape/shape_distance.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/shape/shape_distance.hpp new file mode 100644 index 0000000000000000000000000000000000000000..3a778f05258f73538b59547316e3cd1e766dcd2e --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/shape/shape_distance.hpp @@ -0,0 +1,227 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_SHAPE_SHAPE_DISTANCE_HPP +#define OPENCV_SHAPE_SHAPE_DISTANCE_HPP +#include "opencv2/core.hpp" +#include "opencv2/shape/hist_cost.hpp" +#include "opencv2/shape/shape_transformer.hpp" + +namespace cv +{ + +//! @addtogroup shape +//! @{ + +/** @example shape_example.cpp +An example using shape distance algorithm +*/ +/** @brief Abstract base class for shape distance algorithms. + */ +class CV_EXPORTS_W ShapeDistanceExtractor : public Algorithm +{ +public: + /** @brief Compute the shape distance between two shapes defined by its contours. + + @param contour1 Contour defining first shape. + @param contour2 Contour defining second shape. + */ + CV_WRAP virtual float computeDistance(InputArray contour1, InputArray contour2) = 0; +}; + +/***********************************************************************************/ +/***********************************************************************************/ +/***********************************************************************************/ +/** @brief Implementation of the Shape Context descriptor and matching algorithm + +proposed by Belongie et al. in "Shape Matching and Object Recognition Using Shape Contexts" (PAMI +2002). This implementation is packaged in a generic scheme, in order to allow you the +implementation of the common variations of the original pipeline. +*/ +class CV_EXPORTS_W ShapeContextDistanceExtractor : public ShapeDistanceExtractor +{ +public: + /** @brief Establish the number of angular bins for the Shape Context Descriptor used in the shape matching + pipeline. + + @param nAngularBins The number of angular bins in the shape context descriptor. + */ + CV_WRAP virtual void setAngularBins(int nAngularBins) = 0; + CV_WRAP virtual int getAngularBins() const = 0; + + /** @brief Establish the number of radial bins for the Shape Context Descriptor used in the shape matching + pipeline. + + @param nRadialBins The number of radial bins in the shape context descriptor. + */ + CV_WRAP virtual void setRadialBins(int nRadialBins) = 0; + CV_WRAP virtual int getRadialBins() const = 0; + + /** @brief Set the inner radius of the shape context descriptor. + + @param innerRadius The value of the inner radius. + */ + CV_WRAP virtual void setInnerRadius(float innerRadius) = 0; + CV_WRAP virtual float getInnerRadius() const = 0; + + /** @brief Set the outer radius of the shape context descriptor. + + @param outerRadius The value of the outer radius. + */ + CV_WRAP virtual void setOuterRadius(float outerRadius) = 0; + CV_WRAP virtual float getOuterRadius() const = 0; + + CV_WRAP virtual void setRotationInvariant(bool rotationInvariant) = 0; + CV_WRAP virtual bool getRotationInvariant() const = 0; + + /** @brief Set the weight of the shape context distance in the final value of the shape distance. The shape + context distance between two shapes is defined as the symmetric sum of shape context matching costs + over best matching points. The final value of the shape distance is a user-defined linear + combination of the shape context distance, an image appearance distance, and a bending energy. + + @param shapeContextWeight The weight of the shape context distance in the final distance value. + */ + CV_WRAP virtual void setShapeContextWeight(float shapeContextWeight) = 0; + CV_WRAP virtual float getShapeContextWeight() const = 0; + + /** @brief Set the weight of the Image Appearance cost in the final value of the shape distance. The image + appearance cost is defined as the sum of squared brightness differences in Gaussian windows around + corresponding image points. The final value of the shape distance is a user-defined linear + combination of the shape context distance, an image appearance distance, and a bending energy. If + this value is set to a number different from 0, is mandatory to set the images that correspond to + each shape. + + @param imageAppearanceWeight The weight of the appearance cost in the final distance value. + */ + CV_WRAP virtual void setImageAppearanceWeight(float imageAppearanceWeight) = 0; + CV_WRAP virtual float getImageAppearanceWeight() const = 0; + + /** @brief Set the weight of the Bending Energy in the final value of the shape distance. The bending energy + definition depends on what transformation is being used to align the shapes. The final value of the + shape distance is a user-defined linear combination of the shape context distance, an image + appearance distance, and a bending energy. + + @param bendingEnergyWeight The weight of the Bending Energy in the final distance value. + */ + CV_WRAP virtual void setBendingEnergyWeight(float bendingEnergyWeight) = 0; + CV_WRAP virtual float getBendingEnergyWeight() const = 0; + + /** @brief Set the images that correspond to each shape. This images are used in the calculation of the Image + Appearance cost. + + @param image1 Image corresponding to the shape defined by contours1. + @param image2 Image corresponding to the shape defined by contours2. + */ + CV_WRAP virtual void setImages(InputArray image1, InputArray image2) = 0; + CV_WRAP virtual void getImages(OutputArray image1, OutputArray image2) const = 0; + + CV_WRAP virtual void setIterations(int iterations) = 0; + CV_WRAP virtual int getIterations() const = 0; + + /** @brief Set the algorithm used for building the shape context descriptor cost matrix. + + @param comparer Smart pointer to a HistogramCostExtractor, an algorithm that defines the cost + matrix between descriptors. + */ + CV_WRAP virtual void setCostExtractor(Ptr comparer) = 0; + CV_WRAP virtual Ptr getCostExtractor() const = 0; + + /** @brief Set the value of the standard deviation for the Gaussian window for the image appearance cost. + + @param sigma Standard Deviation. + */ + CV_WRAP virtual void setStdDev(float sigma) = 0; + CV_WRAP virtual float getStdDev() const = 0; + + /** @brief Set the algorithm used for aligning the shapes. + + @param transformer Smart pointer to a ShapeTransformer, an algorithm that defines the aligning + transformation. + */ + CV_WRAP virtual void setTransformAlgorithm(Ptr transformer) = 0; + CV_WRAP virtual Ptr getTransformAlgorithm() const = 0; +}; + +/* Complete constructor */ +CV_EXPORTS_W Ptr + createShapeContextDistanceExtractor(int nAngularBins=12, int nRadialBins=4, + float innerRadius=0.2f, float outerRadius=2, int iterations=3, + const Ptr &comparer = createChiHistogramCostExtractor(), + const Ptr &transformer = createThinPlateSplineShapeTransformer()); + +/***********************************************************************************/ +/***********************************************************************************/ +/***********************************************************************************/ +/** @brief A simple Hausdorff distance measure between shapes defined by contours + +according to the paper "Comparing Images using the Hausdorff distance." by D.P. Huttenlocher, G.A. +Klanderman, and W.J. Rucklidge. (PAMI 1993). : + */ +class CV_EXPORTS_W HausdorffDistanceExtractor : public ShapeDistanceExtractor +{ +public: + /** @brief Set the norm used to compute the Hausdorff value between two shapes. It can be L1 or L2 norm. + + @param distanceFlag Flag indicating which norm is used to compute the Hausdorff distance + (NORM_L1, NORM_L2). + */ + CV_WRAP virtual void setDistanceFlag(int distanceFlag) = 0; + CV_WRAP virtual int getDistanceFlag() const = 0; + + /** @brief This method sets the rank proportion (or fractional value) that establish the Kth ranked value of + the partial Hausdorff distance. Experimentally had been shown that 0.6 is a good value to compare + shapes. + + @param rankProportion fractional value (between 0 and 1). + */ + CV_WRAP virtual void setRankProportion(float rankProportion) = 0; + CV_WRAP virtual float getRankProportion() const = 0; +}; + +/* Constructor */ +CV_EXPORTS_W Ptr createHausdorffDistanceExtractor(int distanceFlag=cv::NORM_L2, float rankProp=0.6f); + +//! @} + +} // cv +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/shape/shape_transformer.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/shape/shape_transformer.hpp new file mode 100644 index 0000000000000000000000000000000000000000..3c3ce20c12537cfca4fd60194a0e46695a48ea88 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/shape/shape_transformer.hpp @@ -0,0 +1,132 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_SHAPE_SHAPE_TRANSFORM_HPP +#define OPENCV_SHAPE_SHAPE_TRANSFORM_HPP +#include +#include "opencv2/core.hpp" +#include "opencv2/imgproc.hpp" + +namespace cv +{ + +//! @addtogroup shape +//! @{ + +/** @brief Abstract base class for shape transformation algorithms. + */ +class CV_EXPORTS_W ShapeTransformer : public Algorithm +{ +public: + /** @brief Estimate the transformation parameters of the current transformer algorithm, based on point matches. + + @param transformingShape Contour defining first shape. + @param targetShape Contour defining second shape (Target). + @param matches Standard vector of Matches between points. + */ + CV_WRAP virtual void estimateTransformation(InputArray transformingShape, InputArray targetShape, + std::vector& matches) = 0; + + /** @brief Apply a transformation, given a pre-estimated transformation parameters. + + @param input Contour (set of points) to apply the transformation. + @param output Output contour. + */ + CV_WRAP virtual float applyTransformation(InputArray input, OutputArray output=noArray()) = 0; + + /** @brief Apply a transformation, given a pre-estimated transformation parameters, to an Image. + + @param transformingImage Input image. + @param output Output image. + @param flags Image interpolation method. + @param borderMode border style. + @param borderValue border value. + */ + CV_WRAP virtual void warpImage(InputArray transformingImage, OutputArray output, + int flags=INTER_LINEAR, int borderMode=BORDER_CONSTANT, + const Scalar& borderValue=Scalar()) const = 0; +}; + +/***********************************************************************************/ +/***********************************************************************************/ + +/** @brief Definition of the transformation + +occupied in the paper "Principal Warps: Thin-Plate Splines and Decomposition of Deformations", by +F.L. Bookstein (PAMI 1989). : + */ +class CV_EXPORTS_W ThinPlateSplineShapeTransformer : public ShapeTransformer +{ +public: + /** @brief Set the regularization parameter for relaxing the exact interpolation requirements of the TPS + algorithm. + + @param beta value of the regularization parameter. + */ + CV_WRAP virtual void setRegularizationParameter(double beta) = 0; + CV_WRAP virtual double getRegularizationParameter() const = 0; +}; + +/** Complete constructor */ +CV_EXPORTS_W Ptr + createThinPlateSplineShapeTransformer(double regularizationParameter=0); + +/***********************************************************************************/ +/***********************************************************************************/ + +/** @brief Wrapper class for the OpenCV Affine Transformation algorithm. : + */ +class CV_EXPORTS_W AffineTransformer : public ShapeTransformer +{ +public: + CV_WRAP virtual void setFullAffine(bool fullAffine) = 0; + CV_WRAP virtual bool getFullAffine() const = 0; +}; + +/** Complete constructor */ +CV_EXPORTS_W Ptr createAffineTransformer(bool fullAffine); + +//! @} + +} // cv +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/stitching.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/stitching.hpp new file mode 100644 index 0000000000000000000000000000000000000000..8b1bcc1a8c08b0207ee94d02ba850828c115e1d9 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/stitching.hpp @@ -0,0 +1,321 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_STITCHING_STITCHER_HPP +#define OPENCV_STITCHING_STITCHER_HPP + +#include "opencv2/core.hpp" +#include "opencv2/features2d.hpp" +#include "opencv2/stitching/warpers.hpp" +#include "opencv2/stitching/detail/matchers.hpp" +#include "opencv2/stitching/detail/motion_estimators.hpp" +#include "opencv2/stitching/detail/exposure_compensate.hpp" +#include "opencv2/stitching/detail/seam_finders.hpp" +#include "opencv2/stitching/detail/blenders.hpp" +#include "opencv2/stitching/detail/camera.hpp" + + +#if defined(Status) +# warning Detected X11 'Status' macro definition, it can cause build conflicts. Please, include this header before any X11 headers. +#endif + + +/** +@defgroup stitching Images stitching + +This figure illustrates the stitching module pipeline implemented in the Stitcher class. Using that +class it's possible to configure/remove some steps, i.e. adjust the stitching pipeline according to +the particular needs. All building blocks from the pipeline are available in the detail namespace, +one can combine and use them separately. + +The implemented stitching pipeline is very similar to the one proposed in @cite BL07 . + +![stitching pipeline](StitchingPipeline.jpg) + +Camera models +------------- + +There are currently 2 camera models implemented in stitching pipeline. + +- _Homography model_ expecting perspective transformations between images + implemented in @ref cv::detail::BestOf2NearestMatcher cv::detail::HomographyBasedEstimator + cv::detail::BundleAdjusterReproj cv::detail::BundleAdjusterRay +- _Affine model_ expecting affine transformation with 6 DOF or 4 DOF implemented in + @ref cv::detail::AffineBestOf2NearestMatcher cv::detail::AffineBasedEstimator + cv::detail::BundleAdjusterAffine cv::detail::BundleAdjusterAffinePartial cv::AffineWarper + +Homography model is useful for creating photo panoramas captured by camera, +while affine-based model can be used to stitch scans and object captured by +specialized devices. Use @ref cv::Stitcher::create to get preconfigured pipeline for one +of those models. + +@note +Certain detailed settings of @ref cv::Stitcher might not make sense. Especially +you should not mix classes implementing affine model and classes implementing +Homography model, as they work with different transformations. + +@{ + @defgroup stitching_match Features Finding and Images Matching + @defgroup stitching_rotation Rotation Estimation + @defgroup stitching_autocalib Autocalibration + @defgroup stitching_warp Images Warping + @defgroup stitching_seam Seam Estimation + @defgroup stitching_exposure Exposure Compensation + @defgroup stitching_blend Image Blenders +@} + */ + +namespace cv { + +//! @addtogroup stitching +//! @{ + +/** @brief High level image stitcher. + +It's possible to use this class without being aware of the entire stitching pipeline. However, to +be able to achieve higher stitching stability and quality of the final images at least being +familiar with the theory is recommended. + +@note + - A basic example on image stitching can be found at + opencv_source_code/samples/cpp/stitching.cpp + - A detailed example on image stitching can be found at + opencv_source_code/samples/cpp/stitching_detailed.cpp + */ +class CV_EXPORTS_W Stitcher +{ +public: + enum { ORIG_RESOL = -1 }; + enum Status + { + OK = 0, + ERR_NEED_MORE_IMGS = 1, + ERR_HOMOGRAPHY_EST_FAIL = 2, + ERR_CAMERA_PARAMS_ADJUST_FAIL = 3 + }; + enum Mode + { + /** Mode for creating photo panoramas. Expects images under perspective + transformation and projects resulting pano to sphere. + + @sa detail::BestOf2NearestMatcher SphericalWarper + */ + PANORAMA = 0, + /** Mode for composing scans. Expects images under affine transformation does + not compensate exposure by default. + + @sa detail::AffineBestOf2NearestMatcher AffineWarper + */ + SCANS = 1, + + }; + + // Stitcher() {} + /** @brief Creates a stitcher with the default parameters. + + @param try_use_gpu Flag indicating whether GPU should be used whenever it's possible. + @return Stitcher class instance. + */ + static Stitcher createDefault(bool try_use_gpu = false); + /** @brief Creates a Stitcher configured in one of the stitching modes. + + @param mode Scenario for stitcher operation. This is usually determined by source of images + to stitch and their transformation. Default parameters will be chosen for operation in given + scenario. + @param try_use_gpu Flag indicating whether GPU should be used whenever it's possible. + @return Stitcher class instance. + */ + static Ptr create(Mode mode = PANORAMA, bool try_use_gpu = false); + + CV_WRAP double registrationResol() const { return registr_resol_; } + CV_WRAP void setRegistrationResol(double resol_mpx) { registr_resol_ = resol_mpx; } + + CV_WRAP double seamEstimationResol() const { return seam_est_resol_; } + CV_WRAP void setSeamEstimationResol(double resol_mpx) { seam_est_resol_ = resol_mpx; } + + CV_WRAP double compositingResol() const { return compose_resol_; } + CV_WRAP void setCompositingResol(double resol_mpx) { compose_resol_ = resol_mpx; } + + CV_WRAP double panoConfidenceThresh() const { return conf_thresh_; } + CV_WRAP void setPanoConfidenceThresh(double conf_thresh) { conf_thresh_ = conf_thresh; } + + CV_WRAP bool waveCorrection() const { return do_wave_correct_; } + CV_WRAP void setWaveCorrection(bool flag) { do_wave_correct_ = flag; } + + detail::WaveCorrectKind waveCorrectKind() const { return wave_correct_kind_; } + void setWaveCorrectKind(detail::WaveCorrectKind kind) { wave_correct_kind_ = kind; } + + Ptr featuresFinder() { return features_finder_; } + const Ptr featuresFinder() const { return features_finder_; } + void setFeaturesFinder(Ptr features_finder) + { features_finder_ = features_finder; } + + Ptr featuresMatcher() { return features_matcher_; } + const Ptr featuresMatcher() const { return features_matcher_; } + void setFeaturesMatcher(Ptr features_matcher) + { features_matcher_ = features_matcher; } + + const cv::UMat& matchingMask() const { return matching_mask_; } + void setMatchingMask(const cv::UMat &mask) + { + CV_Assert(mask.type() == CV_8U && mask.cols == mask.rows); + matching_mask_ = mask.clone(); + } + + Ptr bundleAdjuster() { return bundle_adjuster_; } + const Ptr bundleAdjuster() const { return bundle_adjuster_; } + void setBundleAdjuster(Ptr bundle_adjuster) + { bundle_adjuster_ = bundle_adjuster; } + + /* TODO OpenCV ABI 4.x + Ptr estimator() { return estimator_; } + const Ptr estimator() const { return estimator_; } + void setEstimator(Ptr estimator) + { estimator_ = estimator; } + */ + + Ptr warper() { return warper_; } + const Ptr warper() const { return warper_; } + void setWarper(Ptr creator) { warper_ = creator; } + + Ptr exposureCompensator() { return exposure_comp_; } + const Ptr exposureCompensator() const { return exposure_comp_; } + void setExposureCompensator(Ptr exposure_comp) + { exposure_comp_ = exposure_comp; } + + Ptr seamFinder() { return seam_finder_; } + const Ptr seamFinder() const { return seam_finder_; } + void setSeamFinder(Ptr seam_finder) { seam_finder_ = seam_finder; } + + Ptr blender() { return blender_; } + const Ptr blender() const { return blender_; } + void setBlender(Ptr b) { blender_ = b; } + + /** @overload */ + CV_WRAP Status estimateTransform(InputArrayOfArrays images); + /** @brief These functions try to match the given images and to estimate rotations of each camera. + + @note Use the functions only if you're aware of the stitching pipeline, otherwise use + Stitcher::stitch. + + @param images Input images. + @param rois Region of interest rectangles. + @return Status code. + */ + Status estimateTransform(InputArrayOfArrays images, const std::vector > &rois); + + /** @overload */ + CV_WRAP Status composePanorama(OutputArray pano); + /** @brief These functions try to compose the given images (or images stored internally from the other function + calls) into the final pano under the assumption that the image transformations were estimated + before. + + @note Use the functions only if you're aware of the stitching pipeline, otherwise use + Stitcher::stitch. + + @param images Input images. + @param pano Final pano. + @return Status code. + */ + Status composePanorama(InputArrayOfArrays images, OutputArray pano); + + /** @overload */ + CV_WRAP Status stitch(InputArrayOfArrays images, OutputArray pano); + /** @brief These functions try to stitch the given images. + + @param images Input images. + @param rois Region of interest rectangles. + @param pano Final pano. + @return Status code. + */ + Status stitch(InputArrayOfArrays images, const std::vector > &rois, OutputArray pano); + + std::vector component() const { return indices_; } + std::vector cameras() const { return cameras_; } + CV_WRAP double workScale() const { return work_scale_; } + +private: + //Stitcher() {} + + Status matchImages(); + Status estimateCameraParams(); + + double registr_resol_; + double seam_est_resol_; + double compose_resol_; + double conf_thresh_; + Ptr features_finder_; + Ptr features_matcher_; + cv::UMat matching_mask_; + Ptr bundle_adjuster_; + /* TODO OpenCV ABI 4.x + Ptr estimator_; + */ + bool do_wave_correct_; + detail::WaveCorrectKind wave_correct_kind_; + Ptr warper_; + Ptr exposure_comp_; + Ptr seam_finder_; + Ptr blender_; + + std::vector imgs_; + std::vector > rois_; + std::vector full_img_sizes_; + std::vector features_; + std::vector pairwise_matches_; + std::vector seam_est_imgs_; + std::vector indices_; + std::vector cameras_; + double work_scale_; + double seam_scale_; + double seam_work_aspect_; + double warped_image_scale_; +}; + +CV_EXPORTS_W Ptr createStitcher(bool try_use_gpu = false); +CV_EXPORTS_W Ptr createStitcherScans(bool try_use_gpu = false); + +//! @} stitching + +} // namespace cv + +#endif // OPENCV_STITCHING_STITCHER_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/stitching/detail/autocalib.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/stitching/detail/autocalib.hpp new file mode 100644 index 0000000000000000000000000000000000000000..19705e27a6617dae5e9609a5f4530f96fbf704f0 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/stitching/detail/autocalib.hpp @@ -0,0 +1,86 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_STITCHING_AUTOCALIB_HPP +#define OPENCV_STITCHING_AUTOCALIB_HPP + +#include "opencv2/core.hpp" +#include "matchers.hpp" + +namespace cv { +namespace detail { + +//! @addtogroup stitching_autocalib +//! @{ + +/** @brief Tries to estimate focal lengths from the given homography under the assumption that the camera +undergoes rotations around its centre only. + +@param H Homography. +@param f0 Estimated focal length along X axis. +@param f1 Estimated focal length along Y axis. +@param f0_ok True, if f0 was estimated successfully, false otherwise. +@param f1_ok True, if f1 was estimated successfully, false otherwise. + +See "Construction of Panoramic Image Mosaics with Global and Local Alignment" +by Heung-Yeung Shum and Richard Szeliski. + */ +void CV_EXPORTS focalsFromHomography(const Mat &H, double &f0, double &f1, bool &f0_ok, bool &f1_ok); + +/** @brief Estimates focal lengths for each given camera. + +@param features Features of images. +@param pairwise_matches Matches between all image pairs. +@param focals Estimated focal lengths for each camera. + */ +void CV_EXPORTS estimateFocal(const std::vector &features, + const std::vector &pairwise_matches, + std::vector &focals); + +bool CV_EXPORTS calibrateRotatingCamera(const std::vector &Hs, Mat &K); + +//! @} stitching_autocalib + +} // namespace detail +} // namespace cv + +#endif // OPENCV_STITCHING_AUTOCALIB_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/stitching/detail/blenders.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/stitching/detail/blenders.hpp new file mode 100644 index 0000000000000000000000000000000000000000..542f1e42fb69c17927ab79314b7066b6b9fbef8f --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/stitching/detail/blenders.hpp @@ -0,0 +1,184 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_STITCHING_BLENDERS_HPP +#define OPENCV_STITCHING_BLENDERS_HPP + +#if defined(NO) +# warning Detected Apple 'NO' macro definition, it can cause build conflicts. Please, include this header before any Apple headers. +#endif + +#include "opencv2/core.hpp" +#include "opencv2/core/cuda.hpp" + +namespace cv { +namespace detail { + +//! @addtogroup stitching_blend +//! @{ + +/** @brief Base class for all blenders. + +Simple blender which puts one image over another +*/ +class CV_EXPORTS Blender +{ +public: + virtual ~Blender() {} + + enum { NO, FEATHER, MULTI_BAND }; + static Ptr createDefault(int type, bool try_gpu = false); + + /** @brief Prepares the blender for blending. + + @param corners Source images top-left corners + @param sizes Source image sizes + */ + void prepare(const std::vector &corners, const std::vector &sizes); + /** @overload */ + virtual void prepare(Rect dst_roi); + /** @brief Processes the image. + + @param img Source image + @param mask Source image mask + @param tl Source image top-left corners + */ + virtual void feed(InputArray img, InputArray mask, Point tl); + /** @brief Blends and returns the final pano. + + @param dst Final pano + @param dst_mask Final pano mask + */ + virtual void blend(InputOutputArray dst, InputOutputArray dst_mask); + +protected: + UMat dst_, dst_mask_; + Rect dst_roi_; +}; + +/** @brief Simple blender which mixes images at its borders. + */ +class CV_EXPORTS FeatherBlender : public Blender +{ +public: + FeatherBlender(float sharpness = 0.02f); + + float sharpness() const { return sharpness_; } + void setSharpness(float val) { sharpness_ = val; } + + void prepare(Rect dst_roi) CV_OVERRIDE; + void feed(InputArray img, InputArray mask, Point tl) CV_OVERRIDE; + void blend(InputOutputArray dst, InputOutputArray dst_mask) CV_OVERRIDE; + + //! Creates weight maps for fixed set of source images by their masks and top-left corners. + //! Final image can be obtained by simple weighting of the source images. + Rect createWeightMaps(const std::vector &masks, const std::vector &corners, + std::vector &weight_maps); + +private: + float sharpness_; + UMat weight_map_; + UMat dst_weight_map_; +}; + +inline FeatherBlender::FeatherBlender(float _sharpness) { setSharpness(_sharpness); } + +/** @brief Blender which uses multi-band blending algorithm (see @cite BA83). + */ +class CV_EXPORTS MultiBandBlender : public Blender +{ +public: + MultiBandBlender(int try_gpu = false, int num_bands = 5, int weight_type = CV_32F); + + int numBands() const { return actual_num_bands_; } + void setNumBands(int val) { actual_num_bands_ = val; } + + void prepare(Rect dst_roi) CV_OVERRIDE; + void feed(InputArray img, InputArray mask, Point tl) CV_OVERRIDE; + void blend(InputOutputArray dst, InputOutputArray dst_mask) CV_OVERRIDE; + +private: + int actual_num_bands_, num_bands_; + std::vector dst_pyr_laplace_; + std::vector dst_band_weights_; + Rect dst_roi_final_; + bool can_use_gpu_; + int weight_type_; //CV_32F or CV_16S +#if defined(HAVE_OPENCV_CUDAARITHM) && defined(HAVE_OPENCV_CUDAWARPING) + std::vector gpu_dst_pyr_laplace_; + std::vector gpu_dst_band_weights_; + std::vector gpu_tl_points_; + std::vector gpu_imgs_with_border_; + std::vector > gpu_weight_pyr_gauss_vec_; + std::vector > gpu_src_pyr_laplace_vec_; + std::vector > gpu_ups_; + cuda::GpuMat gpu_dst_mask_; + cuda::GpuMat gpu_mask_; + cuda::GpuMat gpu_img_; + cuda::GpuMat gpu_weight_map_; + cuda::GpuMat gpu_add_mask_; + int gpu_feed_idx_; + bool gpu_initialized_; +#endif +}; + + +////////////////////////////////////////////////////////////////////////////// +// Auxiliary functions + +void CV_EXPORTS normalizeUsingWeightMap(InputArray weight, InputOutputArray src); + +void CV_EXPORTS createWeightMap(InputArray mask, float sharpness, InputOutputArray weight); + +void CV_EXPORTS createLaplacePyr(InputArray img, int num_levels, std::vector& pyr); +void CV_EXPORTS createLaplacePyrGpu(InputArray img, int num_levels, std::vector& pyr); + +// Restores source image +void CV_EXPORTS restoreImageFromLaplacePyr(std::vector& pyr); +void CV_EXPORTS restoreImageFromLaplacePyrGpu(std::vector& pyr); + +//! @} + +} // namespace detail +} // namespace cv + +#endif // OPENCV_STITCHING_BLENDERS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/stitching/detail/camera.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/stitching/detail/camera.hpp new file mode 100644 index 0000000000000000000000000000000000000000..07c6b5b0052e8e2b089170c007a6291f45b727e5 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/stitching/detail/camera.hpp @@ -0,0 +1,78 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_STITCHING_CAMERA_HPP +#define OPENCV_STITCHING_CAMERA_HPP + +#include "opencv2/core.hpp" + +namespace cv { +namespace detail { + +//! @addtogroup stitching +//! @{ + +/** @brief Describes camera parameters. + +@note Translation is assumed to be zero during the whole stitching pipeline. : + */ +struct CV_EXPORTS CameraParams +{ + CameraParams(); + CameraParams(const CameraParams& other); + CameraParams& operator =(const CameraParams& other); + Mat K() const; + + double focal; // Focal length + double aspect; // Aspect ratio + double ppx; // Principal point X + double ppy; // Principal point Y + Mat R; // Rotation + Mat t; // Translation +}; + +//! @} + +} // namespace detail +} // namespace cv + +#endif // #ifndef OPENCV_STITCHING_CAMERA_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/stitching/detail/exposure_compensate.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/stitching/detail/exposure_compensate.hpp new file mode 100644 index 0000000000000000000000000000000000000000..6c99407c18c20391fb503ee3eef7e164c1f1c1ce --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/stitching/detail/exposure_compensate.hpp @@ -0,0 +1,136 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_STITCHING_EXPOSURE_COMPENSATE_HPP +#define OPENCV_STITCHING_EXPOSURE_COMPENSATE_HPP + +#if defined(NO) +# warning Detected Apple 'NO' macro definition, it can cause build conflicts. Please, include this header before any Apple headers. +#endif + +#include "opencv2/core.hpp" + +namespace cv { +namespace detail { + +//! @addtogroup stitching_exposure +//! @{ + +/** @brief Base class for all exposure compensators. + */ +class CV_EXPORTS ExposureCompensator +{ +public: + virtual ~ExposureCompensator() {} + + enum { NO, GAIN, GAIN_BLOCKS }; + static Ptr createDefault(int type); + + /** + @param corners Source image top-left corners + @param images Source images + @param masks Image masks to update (second value in pair specifies the value which should be used + to detect where image is) + */ + void feed(const std::vector &corners, const std::vector &images, + const std::vector &masks); + /** @overload */ + virtual void feed(const std::vector &corners, const std::vector &images, + const std::vector > &masks) = 0; + /** @brief Compensate exposure in the specified image. + + @param index Image index + @param corner Image top-left corner + @param image Image to process + @param mask Image mask + */ + virtual void apply(int index, Point corner, InputOutputArray image, InputArray mask) = 0; +}; + +/** @brief Stub exposure compensator which does nothing. + */ +class CV_EXPORTS NoExposureCompensator : public ExposureCompensator +{ +public: + void feed(const std::vector &/*corners*/, const std::vector &/*images*/, + const std::vector > &/*masks*/) CV_OVERRIDE { } + void apply(int /*index*/, Point /*corner*/, InputOutputArray /*image*/, InputArray /*mask*/) CV_OVERRIDE { } +}; + +/** @brief Exposure compensator which tries to remove exposure related artifacts by adjusting image +intensities, see @cite BL07 and @cite WJ10 for details. + */ +class CV_EXPORTS GainCompensator : public ExposureCompensator +{ +public: + void feed(const std::vector &corners, const std::vector &images, + const std::vector > &masks) CV_OVERRIDE; + void apply(int index, Point corner, InputOutputArray image, InputArray mask) CV_OVERRIDE; + std::vector gains() const; + +private: + Mat_ gains_; +}; + +/** @brief Exposure compensator which tries to remove exposure related artifacts by adjusting image block +intensities, see @cite UES01 for details. + */ +class CV_EXPORTS BlocksGainCompensator : public ExposureCompensator +{ +public: + BlocksGainCompensator(int bl_width = 32, int bl_height = 32) + : bl_width_(bl_width), bl_height_(bl_height) {} + void feed(const std::vector &corners, const std::vector &images, + const std::vector > &masks) CV_OVERRIDE; + void apply(int index, Point corner, InputOutputArray image, InputArray mask) CV_OVERRIDE; + +private: + int bl_width_, bl_height_; + std::vector gain_maps_; +}; + +//! @} + +} // namespace detail +} // namespace cv + +#endif // OPENCV_STITCHING_EXPOSURE_COMPENSATE_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/stitching/detail/matchers.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/stitching/detail/matchers.hpp new file mode 100644 index 0000000000000000000000000000000000000000..4acebea5e11b68a5c8857f4cf1c46a42245875c0 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/stitching/detail/matchers.hpp @@ -0,0 +1,355 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_STITCHING_MATCHERS_HPP +#define OPENCV_STITCHING_MATCHERS_HPP + +#include "opencv2/core.hpp" +#include "opencv2/features2d.hpp" + +#include "opencv2/opencv_modules.hpp" + +#ifdef HAVE_OPENCV_XFEATURES2D +# include "opencv2/xfeatures2d/cuda.hpp" +#endif + +namespace cv { +namespace detail { + +//! @addtogroup stitching_match +//! @{ + +/** @brief Structure containing image keypoints and descriptors. */ +struct CV_EXPORTS ImageFeatures +{ + int img_idx; + Size img_size; + std::vector keypoints; + UMat descriptors; +}; + +/** @brief Feature finders base class */ +class CV_EXPORTS FeaturesFinder +{ +public: + virtual ~FeaturesFinder() {} + /** @overload */ + void operator ()(InputArray image, ImageFeatures &features); + /** @brief Finds features in the given image. + + @param image Source image + @param features Found features + @param rois Regions of interest + + @sa detail::ImageFeatures, Rect_ + */ + void operator ()(InputArray image, ImageFeatures &features, const std::vector &rois); + /** @brief Finds features in the given images in parallel. + + @param images Source images + @param features Found features for each image + @param rois Regions of interest for each image + + @sa detail::ImageFeatures, Rect_ + */ + void operator ()(InputArrayOfArrays images, std::vector &features, + const std::vector > &rois); + /** @overload */ + void operator ()(InputArrayOfArrays images, std::vector &features); + /** @brief Frees unused memory allocated before if there is any. */ + virtual void collectGarbage() {} + + /* TODO OpenCV ABI 4.x + reimplement this as public method similar to FeaturesMatcher and remove private function hack + @return True, if it's possible to use the same finder instance in parallel, false otherwise + bool isThreadSafe() const { return is_thread_safe_; } + */ + +protected: + /** @brief This method must implement features finding logic in order to make the wrappers + detail::FeaturesFinder::operator()_ work. + + @param image Source image + @param features Found features + + @sa detail::ImageFeatures */ + virtual void find(InputArray image, ImageFeatures &features) = 0; + /** @brief uses dynamic_cast to determine thread-safety + @return True, if it's possible to use the same finder instance in parallel, false otherwise + */ + bool isThreadSafe() const; +}; + +/** @brief SURF features finder. + +@sa detail::FeaturesFinder, SURF +*/ +class CV_EXPORTS SurfFeaturesFinder : public FeaturesFinder +{ +public: + SurfFeaturesFinder(double hess_thresh = 300., int num_octaves = 3, int num_layers = 4, + int num_octaves_descr = /*4*/3, int num_layers_descr = /*2*/4); + +private: + void find(InputArray image, ImageFeatures &features) CV_OVERRIDE; + + Ptr detector_; + Ptr extractor_; + Ptr surf; +}; + +/** @brief ORB features finder. : + +@sa detail::FeaturesFinder, ORB +*/ +class CV_EXPORTS OrbFeaturesFinder : public FeaturesFinder +{ +public: + OrbFeaturesFinder(Size _grid_size = Size(3,1), int nfeatures=1500, float scaleFactor=1.3f, int nlevels=5); + +private: + void find(InputArray image, ImageFeatures &features) CV_OVERRIDE; + + Ptr orb; + Size grid_size; +}; + +/** @brief AKAZE features finder. : + +@sa detail::FeaturesFinder, AKAZE +*/ +class CV_EXPORTS AKAZEFeaturesFinder : public detail::FeaturesFinder +{ +public: + AKAZEFeaturesFinder(int descriptor_type = AKAZE::DESCRIPTOR_MLDB, + int descriptor_size = 0, + int descriptor_channels = 3, + float threshold = 0.001f, + int nOctaves = 4, + int nOctaveLayers = 4, + int diffusivity = KAZE::DIFF_PM_G2); + +private: + void find(InputArray image, ImageFeatures &features) CV_OVERRIDE; + + Ptr akaze; +}; + +#ifdef HAVE_OPENCV_XFEATURES2D +class CV_EXPORTS SurfFeaturesFinderGpu : public FeaturesFinder +{ +public: + SurfFeaturesFinderGpu(double hess_thresh = 300., int num_octaves = 3, int num_layers = 4, + int num_octaves_descr = 4, int num_layers_descr = 2); + + void collectGarbage() CV_OVERRIDE; + +private: + void find(InputArray image, ImageFeatures &features) CV_OVERRIDE; + + cuda::GpuMat image_; + cuda::GpuMat gray_image_; + cuda::SURF_CUDA surf_; + cuda::GpuMat keypoints_; + cuda::GpuMat descriptors_; + int num_octaves_, num_layers_; + int num_octaves_descr_, num_layers_descr_; +}; +#endif + +/** @brief Structure containing information about matches between two images. + +It's assumed that there is a transformation between those images. Transformation may be +homography or affine transformation based on selected matcher. + +@sa detail::FeaturesMatcher +*/ +struct CV_EXPORTS MatchesInfo +{ + MatchesInfo(); + MatchesInfo(const MatchesInfo &other); + MatchesInfo& operator =(const MatchesInfo &other); + + int src_img_idx, dst_img_idx; //!< Images indices (optional) + std::vector matches; + std::vector inliers_mask; //!< Geometrically consistent matches mask + int num_inliers; //!< Number of geometrically consistent matches + Mat H; //!< Estimated transformation + double confidence; //!< Confidence two images are from the same panorama +}; + +/** @brief Feature matchers base class. */ +class CV_EXPORTS FeaturesMatcher +{ +public: + virtual ~FeaturesMatcher() {} + + /** @overload + @param features1 First image features + @param features2 Second image features + @param matches_info Found matches + */ + void operator ()(const ImageFeatures &features1, const ImageFeatures &features2, + MatchesInfo& matches_info) { match(features1, features2, matches_info); } + + /** @brief Performs images matching. + + @param features Features of the source images + @param pairwise_matches Found pairwise matches + @param mask Mask indicating which image pairs must be matched + + The function is parallelized with the TBB library. + + @sa detail::MatchesInfo + */ + void operator ()(const std::vector &features, std::vector &pairwise_matches, + const cv::UMat &mask = cv::UMat()); + + /** @return True, if it's possible to use the same matcher instance in parallel, false otherwise + */ + bool isThreadSafe() const { return is_thread_safe_; } + + /** @brief Frees unused memory allocated before if there is any. + */ + virtual void collectGarbage() {} + +protected: + FeaturesMatcher(bool is_thread_safe = false) : is_thread_safe_(is_thread_safe) {} + + /** @brief This method must implement matching logic in order to make the wrappers + detail::FeaturesMatcher::operator()_ work. + + @param features1 first image features + @param features2 second image features + @param matches_info found matches + */ + virtual void match(const ImageFeatures &features1, const ImageFeatures &features2, + MatchesInfo& matches_info) = 0; + + bool is_thread_safe_; +}; + +/** @brief Features matcher which finds two best matches for each feature and leaves the best one only if the +ratio between descriptor distances is greater than the threshold match_conf + +@sa detail::FeaturesMatcher + */ +class CV_EXPORTS BestOf2NearestMatcher : public FeaturesMatcher +{ +public: + /** @brief Constructs a "best of 2 nearest" matcher. + + @param try_use_gpu Should try to use GPU or not + @param match_conf Match distances ration threshold + @param num_matches_thresh1 Minimum number of matches required for the 2D projective transform + estimation used in the inliers classification step + @param num_matches_thresh2 Minimum number of matches required for the 2D projective transform + re-estimation on inliers + */ + BestOf2NearestMatcher(bool try_use_gpu = false, float match_conf = 0.3f, int num_matches_thresh1 = 6, + int num_matches_thresh2 = 6); + + void collectGarbage() CV_OVERRIDE; + +protected: + void match(const ImageFeatures &features1, const ImageFeatures &features2, MatchesInfo &matches_info) CV_OVERRIDE; + + int num_matches_thresh1_; + int num_matches_thresh2_; + Ptr impl_; +}; + +class CV_EXPORTS BestOf2NearestRangeMatcher : public BestOf2NearestMatcher +{ +public: + BestOf2NearestRangeMatcher(int range_width = 5, bool try_use_gpu = false, float match_conf = 0.3f, + int num_matches_thresh1 = 6, int num_matches_thresh2 = 6); + + void operator ()(const std::vector &features, std::vector &pairwise_matches, + const cv::UMat &mask = cv::UMat()); + + +protected: + int range_width_; +}; + +/** @brief Features matcher similar to cv::detail::BestOf2NearestMatcher which +finds two best matches for each feature and leaves the best one only if the +ratio between descriptor distances is greater than the threshold match_conf. + +Unlike cv::detail::BestOf2NearestMatcher this matcher uses affine +transformation (affine trasformation estimate will be placed in matches_info). + +@sa cv::detail::FeaturesMatcher cv::detail::BestOf2NearestMatcher + */ +class CV_EXPORTS AffineBestOf2NearestMatcher : public BestOf2NearestMatcher +{ +public: + /** @brief Constructs a "best of 2 nearest" matcher that expects affine trasformation + between images + + @param full_affine whether to use full affine transformation with 6 degress of freedom or reduced + transformation with 4 degrees of freedom using only rotation, translation and uniform scaling + @param try_use_gpu Should try to use GPU or not + @param match_conf Match distances ration threshold + @param num_matches_thresh1 Minimum number of matches required for the 2D affine transform + estimation used in the inliers classification step + + @sa cv::estimateAffine2D cv::estimateAffinePartial2D + */ + AffineBestOf2NearestMatcher(bool full_affine = false, bool try_use_gpu = false, + float match_conf = 0.3f, int num_matches_thresh1 = 6) : + BestOf2NearestMatcher(try_use_gpu, match_conf, num_matches_thresh1, num_matches_thresh1), + full_affine_(full_affine) {} + +protected: + void match(const ImageFeatures &features1, const ImageFeatures &features2, MatchesInfo &matches_info) CV_OVERRIDE; + + bool full_affine_; +}; + +//! @} stitching_match + +} // namespace detail +} // namespace cv + +#endif // OPENCV_STITCHING_MATCHERS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/stitching/detail/motion_estimators.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/stitching/detail/motion_estimators.hpp new file mode 100644 index 0000000000000000000000000000000000000000..40f12c32fc867fcd840f50e4da2aa23581136c3f --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/stitching/detail/motion_estimators.hpp @@ -0,0 +1,359 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_STITCHING_MOTION_ESTIMATORS_HPP +#define OPENCV_STITCHING_MOTION_ESTIMATORS_HPP + +#include "opencv2/core.hpp" +#include "matchers.hpp" +#include "util.hpp" +#include "camera.hpp" + +namespace cv { +namespace detail { + +//! @addtogroup stitching_rotation +//! @{ + +/** @brief Rotation estimator base class. + +It takes features of all images, pairwise matches between all images and estimates rotations of all +cameras. + +@note The coordinate system origin is implementation-dependent, but you can always normalize the +rotations in respect to the first camera, for instance. : + */ +class CV_EXPORTS Estimator +{ +public: + virtual ~Estimator() {} + + /** @brief Estimates camera parameters. + + @param features Features of images + @param pairwise_matches Pairwise matches of images + @param cameras Estimated camera parameters + @return True in case of success, false otherwise + */ + bool operator ()(const std::vector &features, + const std::vector &pairwise_matches, + std::vector &cameras) + { return estimate(features, pairwise_matches, cameras); } + +protected: + /** @brief This method must implement camera parameters estimation logic in order to make the wrapper + detail::Estimator::operator()_ work. + + @param features Features of images + @param pairwise_matches Pairwise matches of images + @param cameras Estimated camera parameters + @return True in case of success, false otherwise + */ + virtual bool estimate(const std::vector &features, + const std::vector &pairwise_matches, + std::vector &cameras) = 0; +}; + +/** @brief Homography based rotation estimator. + */ +class CV_EXPORTS HomographyBasedEstimator : public Estimator +{ +public: + HomographyBasedEstimator(bool is_focals_estimated = false) + : is_focals_estimated_(is_focals_estimated) {} + +private: + virtual bool estimate(const std::vector &features, + const std::vector &pairwise_matches, + std::vector &cameras) CV_OVERRIDE; + + bool is_focals_estimated_; +}; + +/** @brief Affine transformation based estimator. + +This estimator uses pairwise transformations estimated by matcher to estimate +final transformation for each camera. + +@sa cv::detail::HomographyBasedEstimator + */ +class CV_EXPORTS AffineBasedEstimator : public Estimator +{ +private: + virtual bool estimate(const std::vector &features, + const std::vector &pairwise_matches, + std::vector &cameras) CV_OVERRIDE; +}; + +/** @brief Base class for all camera parameters refinement methods. + */ +class CV_EXPORTS BundleAdjusterBase : public Estimator +{ +public: + const Mat refinementMask() const { return refinement_mask_.clone(); } + void setRefinementMask(const Mat &mask) + { + CV_Assert(mask.type() == CV_8U && mask.size() == Size(3, 3)); + refinement_mask_ = mask.clone(); + } + + double confThresh() const { return conf_thresh_; } + void setConfThresh(double conf_thresh) { conf_thresh_ = conf_thresh; } + + TermCriteria termCriteria() { return term_criteria_; } + void setTermCriteria(const TermCriteria& term_criteria) { term_criteria_ = term_criteria; } + +protected: + /** @brief Construct a bundle adjuster base instance. + + @param num_params_per_cam Number of parameters per camera + @param num_errs_per_measurement Number of error terms (components) per match + */ + BundleAdjusterBase(int num_params_per_cam, int num_errs_per_measurement) + : num_images_(0), total_num_matches_(0), + num_params_per_cam_(num_params_per_cam), + num_errs_per_measurement_(num_errs_per_measurement), + features_(0), pairwise_matches_(0), conf_thresh_(0) + { + setRefinementMask(Mat::ones(3, 3, CV_8U)); + setConfThresh(1.); + setTermCriteria(TermCriteria(TermCriteria::EPS + TermCriteria::COUNT, 1000, DBL_EPSILON)); + } + + // Runs bundle adjustment + virtual bool estimate(const std::vector &features, + const std::vector &pairwise_matches, + std::vector &cameras) CV_OVERRIDE; + + /** @brief Sets initial camera parameter to refine. + + @param cameras Camera parameters + */ + virtual void setUpInitialCameraParams(const std::vector &cameras) = 0; + /** @brief Gets the refined camera parameters. + + @param cameras Refined camera parameters + */ + virtual void obtainRefinedCameraParams(std::vector &cameras) const = 0; + /** @brief Calculates error vector. + + @param err Error column-vector of length total_num_matches \* num_errs_per_measurement + */ + virtual void calcError(Mat &err) = 0; + /** @brief Calculates the cost function jacobian. + + @param jac Jacobian matrix of dimensions + (total_num_matches \* num_errs_per_measurement) x (num_images \* num_params_per_cam) + */ + virtual void calcJacobian(Mat &jac) = 0; + + // 3x3 8U mask, where 0 means don't refine respective parameter, != 0 means refine + Mat refinement_mask_; + + int num_images_; + int total_num_matches_; + + int num_params_per_cam_; + int num_errs_per_measurement_; + + const ImageFeatures *features_; + const MatchesInfo *pairwise_matches_; + + // Threshold to filter out poorly matched image pairs + double conf_thresh_; + + //Levenberg-Marquardt algorithm termination criteria + TermCriteria term_criteria_; + + // Camera parameters matrix (CV_64F) + Mat cam_params_; + + // Connected images pairs + std::vector > edges_; +}; + + +/** @brief Stub bundle adjuster that does nothing. + */ +class CV_EXPORTS NoBundleAdjuster : public BundleAdjusterBase +{ +public: + NoBundleAdjuster() : BundleAdjusterBase(0, 0) {} + +private: + bool estimate(const std::vector &, const std::vector &, + std::vector &) CV_OVERRIDE + { + return true; + } + void setUpInitialCameraParams(const std::vector &) CV_OVERRIDE {} + void obtainRefinedCameraParams(std::vector &) const CV_OVERRIDE {} + void calcError(Mat &) CV_OVERRIDE {} + void calcJacobian(Mat &) CV_OVERRIDE {} +}; + + +/** @brief Implementation of the camera parameters refinement algorithm which minimizes sum of the reprojection +error squares + +It can estimate focal length, aspect ratio, principal point. +You can affect only on them via the refinement mask. + */ +class CV_EXPORTS BundleAdjusterReproj : public BundleAdjusterBase +{ +public: + BundleAdjusterReproj() : BundleAdjusterBase(7, 2) {} + +private: + void setUpInitialCameraParams(const std::vector &cameras) CV_OVERRIDE; + void obtainRefinedCameraParams(std::vector &cameras) const CV_OVERRIDE; + void calcError(Mat &err) CV_OVERRIDE; + void calcJacobian(Mat &jac) CV_OVERRIDE; + + Mat err1_, err2_; +}; + + +/** @brief Implementation of the camera parameters refinement algorithm which minimizes sum of the distances +between the rays passing through the camera center and a feature. : + +It can estimate focal length. It ignores the refinement mask for now. + */ +class CV_EXPORTS BundleAdjusterRay : public BundleAdjusterBase +{ +public: + BundleAdjusterRay() : BundleAdjusterBase(4, 3) {} + +private: + void setUpInitialCameraParams(const std::vector &cameras) CV_OVERRIDE; + void obtainRefinedCameraParams(std::vector &cameras) const CV_OVERRIDE; + void calcError(Mat &err) CV_OVERRIDE; + void calcJacobian(Mat &jac) CV_OVERRIDE; + + Mat err1_, err2_; +}; + + +/** @brief Bundle adjuster that expects affine transformation +represented in homogeneous coordinates in R for each camera param. Implements +camera parameters refinement algorithm which minimizes sum of the reprojection +error squares + +It estimates all transformation parameters. Refinement mask is ignored. + +@sa AffineBasedEstimator AffineBestOf2NearestMatcher BundleAdjusterAffinePartial + */ +class CV_EXPORTS BundleAdjusterAffine : public BundleAdjusterBase +{ +public: + BundleAdjusterAffine() : BundleAdjusterBase(6, 2) {} + +private: + void setUpInitialCameraParams(const std::vector &cameras) CV_OVERRIDE; + void obtainRefinedCameraParams(std::vector &cameras) const CV_OVERRIDE; + void calcError(Mat &err) CV_OVERRIDE; + void calcJacobian(Mat &jac) CV_OVERRIDE; + + Mat err1_, err2_; +}; + + +/** @brief Bundle adjuster that expects affine transformation with 4 DOF +represented in homogeneous coordinates in R for each camera param. Implements +camera parameters refinement algorithm which minimizes sum of the reprojection +error squares + +It estimates all transformation parameters. Refinement mask is ignored. + +@sa AffineBasedEstimator AffineBestOf2NearestMatcher BundleAdjusterAffine + */ +class CV_EXPORTS BundleAdjusterAffinePartial : public BundleAdjusterBase +{ +public: + BundleAdjusterAffinePartial() : BundleAdjusterBase(4, 2) {} + +private: + void setUpInitialCameraParams(const std::vector &cameras) CV_OVERRIDE; + void obtainRefinedCameraParams(std::vector &cameras) const CV_OVERRIDE; + void calcError(Mat &err) CV_OVERRIDE; + void calcJacobian(Mat &jac) CV_OVERRIDE; + + Mat err1_, err2_; +}; + + +enum WaveCorrectKind +{ + WAVE_CORRECT_HORIZ, + WAVE_CORRECT_VERT +}; + +/** @brief Tries to make panorama more horizontal (or vertical). + +@param rmats Camera rotation matrices. +@param kind Correction kind, see detail::WaveCorrectKind. + */ +void CV_EXPORTS waveCorrect(std::vector &rmats, WaveCorrectKind kind); + + +////////////////////////////////////////////////////////////////////////////// +// Auxiliary functions + +// Returns matches graph representation in DOT language +String CV_EXPORTS matchesGraphAsString(std::vector &pathes, std::vector &pairwise_matches, + float conf_threshold); + +std::vector CV_EXPORTS leaveBiggestComponent( + std::vector &features, + std::vector &pairwise_matches, + float conf_threshold); + +void CV_EXPORTS findMaxSpanningTree( + int num_images, const std::vector &pairwise_matches, + Graph &span_tree, std::vector ¢ers); + +//! @} stitching_rotation + +} // namespace detail +} // namespace cv + +#endif // OPENCV_STITCHING_MOTION_ESTIMATORS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/stitching/detail/seam_finders.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/stitching/detail/seam_finders.hpp new file mode 100644 index 0000000000000000000000000000000000000000..904f0ecb3bb0224a65c039b9ecf34a57e7722c9a --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/stitching/detail/seam_finders.hpp @@ -0,0 +1,285 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_STITCHING_SEAM_FINDERS_HPP +#define OPENCV_STITCHING_SEAM_FINDERS_HPP + +#include +#include "opencv2/core.hpp" +#include "opencv2/opencv_modules.hpp" + +namespace cv { +namespace detail { + +//! @addtogroup stitching_seam +//! @{ + +/** @brief Base class for a seam estimator. + */ +class CV_EXPORTS SeamFinder +{ +public: + virtual ~SeamFinder() {} + /** @brief Estimates seams. + + @param src Source images + @param corners Source image top-left corners + @param masks Source image masks to update + */ + virtual void find(const std::vector &src, const std::vector &corners, + std::vector &masks) = 0; +}; + +/** @brief Stub seam estimator which does nothing. + */ +class CV_EXPORTS NoSeamFinder : public SeamFinder +{ +public: + void find(const std::vector&, const std::vector&, std::vector&) CV_OVERRIDE {} +}; + +/** @brief Base class for all pairwise seam estimators. + */ +class CV_EXPORTS PairwiseSeamFinder : public SeamFinder +{ +public: + virtual void find(const std::vector &src, const std::vector &corners, + std::vector &masks) CV_OVERRIDE; + +protected: + void run(); + /** @brief Resolves masks intersection of two specified images in the given ROI. + + @param first First image index + @param second Second image index + @param roi Region of interest + */ + virtual void findInPair(size_t first, size_t second, Rect roi) = 0; + + std::vector images_; + std::vector sizes_; + std::vector corners_; + std::vector masks_; +}; + +/** @brief Voronoi diagram-based seam estimator. + */ +class CV_EXPORTS VoronoiSeamFinder : public PairwiseSeamFinder +{ +public: + virtual void find(const std::vector &src, const std::vector &corners, + std::vector &masks) CV_OVERRIDE; + virtual void find(const std::vector &size, const std::vector &corners, + std::vector &masks); +private: + void findInPair(size_t first, size_t second, Rect roi) CV_OVERRIDE; +}; + + +class CV_EXPORTS DpSeamFinder : public SeamFinder +{ +public: + enum CostFunction { COLOR, COLOR_GRAD }; + + DpSeamFinder(CostFunction costFunc = COLOR); + + CostFunction costFunction() const { return costFunc_; } + void setCostFunction(CostFunction val) { costFunc_ = val; } + + virtual void find(const std::vector &src, const std::vector &corners, + std::vector &masks) CV_OVERRIDE; + +private: + enum ComponentState + { + FIRST = 1, SECOND = 2, INTERS = 4, + INTERS_FIRST = INTERS | FIRST, + INTERS_SECOND = INTERS | SECOND + }; + + class ImagePairLess + { + public: + ImagePairLess(const std::vector &images, const std::vector &corners) + : src_(&images[0]), corners_(&corners[0]) {} + + bool operator() (const std::pair &l, const std::pair &r) const + { + Point c1 = corners_[l.first] + Point(src_[l.first].cols / 2, src_[l.first].rows / 2); + Point c2 = corners_[l.second] + Point(src_[l.second].cols / 2, src_[l.second].rows / 2); + int d1 = (c1 - c2).dot(c1 - c2); + + c1 = corners_[r.first] + Point(src_[r.first].cols / 2, src_[r.first].rows / 2); + c2 = corners_[r.second] + Point(src_[r.second].cols / 2, src_[r.second].rows / 2); + int d2 = (c1 - c2).dot(c1 - c2); + + return d1 < d2; + } + + private: + const Mat *src_; + const Point *corners_; + }; + + class ClosePoints + { + public: + ClosePoints(int minDist) : minDist_(minDist) {} + + bool operator() (const Point &p1, const Point &p2) const + { + int dist2 = (p1.x-p2.x) * (p1.x-p2.x) + (p1.y-p2.y) * (p1.y-p2.y); + return dist2 < minDist_ * minDist_; + } + + private: + int minDist_; + }; + + void process( + const Mat &image1, const Mat &image2, Point tl1, Point tl2, Mat &mask1, Mat &mask2); + + void findComponents(); + + void findEdges(); + + void resolveConflicts( + const Mat &image1, const Mat &image2, Point tl1, Point tl2, Mat &mask1, Mat &mask2); + + void computeGradients(const Mat &image1, const Mat &image2); + + bool hasOnlyOneNeighbor(int comp); + + bool closeToContour(int y, int x, const Mat_ &contourMask); + + bool getSeamTips(int comp1, int comp2, Point &p1, Point &p2); + + void computeCosts( + const Mat &image1, const Mat &image2, Point tl1, Point tl2, + int comp, Mat_ &costV, Mat_ &costH); + + bool estimateSeam( + const Mat &image1, const Mat &image2, Point tl1, Point tl2, int comp, + Point p1, Point p2, std::vector &seam, bool &isHorizontal); + + void updateLabelsUsingSeam( + int comp1, int comp2, const std::vector &seam, bool isHorizontalSeam); + + CostFunction costFunc_; + + // processing images pair data + Point unionTl_, unionBr_; + Size unionSize_; + Mat_ mask1_, mask2_; + Mat_ contour1mask_, contour2mask_; + Mat_ gradx1_, grady1_; + Mat_ gradx2_, grady2_; + + // components data + int ncomps_; + Mat_ labels_; + std::vector states_; + std::vector tls_, brs_; + std::vector > contours_; + std::set > edges_; +}; + +/** @brief Base class for all minimum graph-cut-based seam estimators. + */ +class CV_EXPORTS GraphCutSeamFinderBase +{ +public: + enum CostType { COST_COLOR, COST_COLOR_GRAD }; +}; + +/** @brief Minimum graph cut-based seam estimator. See details in @cite V03 . + */ +class CV_EXPORTS GraphCutSeamFinder : public GraphCutSeamFinderBase, public SeamFinder +{ +public: + GraphCutSeamFinder(int cost_type = COST_COLOR_GRAD, float terminal_cost = 10000.f, + float bad_region_penalty = 1000.f); + + ~GraphCutSeamFinder(); + + void find(const std::vector &src, const std::vector &corners, + std::vector &masks) CV_OVERRIDE; + +private: + // To avoid GCGraph dependency + class Impl; + Ptr impl_; +}; + + +#ifdef HAVE_OPENCV_CUDALEGACY +class CV_EXPORTS GraphCutSeamFinderGpu : public GraphCutSeamFinderBase, public PairwiseSeamFinder +{ +public: + GraphCutSeamFinderGpu(int cost_type = COST_COLOR_GRAD, float terminal_cost = 10000.f, + float bad_region_penalty = 1000.f) + : cost_type_(cost_type), terminal_cost_(terminal_cost), + bad_region_penalty_(bad_region_penalty) {} + + void find(const std::vector &src, const std::vector &corners, + std::vector &masks) CV_OVERRIDE; + void findInPair(size_t first, size_t second, Rect roi) CV_OVERRIDE; + +private: + void setGraphWeightsColor(const cv::Mat &img1, const cv::Mat &img2, const cv::Mat &mask1, const cv::Mat &mask2, + cv::Mat &terminals, cv::Mat &leftT, cv::Mat &rightT, cv::Mat &top, cv::Mat &bottom); + void setGraphWeightsColorGrad(const cv::Mat &img1, const cv::Mat &img2, const cv::Mat &dx1, const cv::Mat &dx2, + const cv::Mat &dy1, const cv::Mat &dy2, const cv::Mat &mask1, const cv::Mat &mask2, + cv::Mat &terminals, cv::Mat &leftT, cv::Mat &rightT, cv::Mat &top, cv::Mat &bottom); + std::vector dx_, dy_; + int cost_type_; + float terminal_cost_; + float bad_region_penalty_; +}; +#endif + +//! @} + +} // namespace detail +} // namespace cv + +#endif // OPENCV_STITCHING_SEAM_FINDERS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/stitching/detail/timelapsers.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/stitching/detail/timelapsers.hpp new file mode 100644 index 0000000000000000000000000000000000000000..74d797edf69637c58bee5255b7aca1494163a83a --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/stitching/detail/timelapsers.hpp @@ -0,0 +1,91 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + + +#ifndef OPENCV_STITCHING_TIMELAPSERS_HPP +#define OPENCV_STITCHING_TIMELAPSERS_HPP + +#include "opencv2/core.hpp" + +namespace cv { +namespace detail { + +//! @addtogroup stitching +//! @{ + +// Base Timelapser class, takes a sequence of images, applies appropriate shift, stores result in dst_. + +class CV_EXPORTS Timelapser +{ +public: + + enum {AS_IS, CROP}; + + virtual ~Timelapser() {} + + static Ptr createDefault(int type); + + virtual void initialize(const std::vector &corners, const std::vector &sizes); + virtual void process(InputArray img, InputArray mask, Point tl); + virtual const UMat& getDst() {return dst_;} + +protected: + + virtual bool test_point(Point pt); + + UMat dst_; + Rect dst_roi_; +}; + + +class CV_EXPORTS TimelapserCrop : public Timelapser +{ +public: + virtual void initialize(const std::vector &corners, const std::vector &sizes) CV_OVERRIDE; +}; + +//! @} + +} // namespace detail +} // namespace cv + +#endif // OPENCV_STITCHING_TIMELAPSERS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/stitching/detail/util.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/stitching/detail/util.hpp new file mode 100644 index 0000000000000000000000000000000000000000..78301b8558fad201a8792c19fc52d6371aa5e026 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/stitching/detail/util.hpp @@ -0,0 +1,121 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_STITCHING_UTIL_HPP +#define OPENCV_STITCHING_UTIL_HPP + +#include +#include "opencv2/core.hpp" + +namespace cv { +namespace detail { + +//! @addtogroup stitching +//! @{ + +class CV_EXPORTS DisjointSets +{ +public: + DisjointSets(int elem_count = 0) { createOneElemSets(elem_count); } + + void createOneElemSets(int elem_count); + int findSetByElem(int elem); + int mergeSets(int set1, int set2); + + std::vector parent; + std::vector size; + +private: + std::vector rank_; +}; + + +struct CV_EXPORTS GraphEdge +{ + GraphEdge(int from, int to, float weight); + bool operator <(const GraphEdge& other) const { return weight < other.weight; } + bool operator >(const GraphEdge& other) const { return weight > other.weight; } + + int from, to; + float weight; +}; + +inline GraphEdge::GraphEdge(int _from, int _to, float _weight) : from(_from), to(_to), weight(_weight) {} + + +class CV_EXPORTS Graph +{ +public: + Graph(int num_vertices = 0) { create(num_vertices); } + void create(int num_vertices) { edges_.assign(num_vertices, std::list()); } + int numVertices() const { return static_cast(edges_.size()); } + void addEdge(int from, int to, float weight); + template B forEach(B body) const; + template B walkBreadthFirst(int from, B body) const; + +private: + std::vector< std::list > edges_; +}; + + +////////////////////////////////////////////////////////////////////////////// +// Auxiliary functions + +CV_EXPORTS bool overlapRoi(Point tl1, Point tl2, Size sz1, Size sz2, Rect &roi); +CV_EXPORTS Rect resultRoi(const std::vector &corners, const std::vector &images); +CV_EXPORTS Rect resultRoi(const std::vector &corners, const std::vector &sizes); +CV_EXPORTS Rect resultRoiIntersection(const std::vector &corners, const std::vector &sizes); +CV_EXPORTS Point resultTl(const std::vector &corners); + +// Returns random 'count' element subset of the {0,1,...,size-1} set +CV_EXPORTS void selectRandomSubset(int count, int size, std::vector &subset); + +CV_EXPORTS int& stitchingLogLevel(); + +//! @} + +} // namespace detail +} // namespace cv + +#include "util_inl.hpp" + +#endif // OPENCV_STITCHING_UTIL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/stitching/detail/util_inl.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/stitching/detail/util_inl.hpp new file mode 100644 index 0000000000000000000000000000000000000000..dafab8b811802ea330af21ff6710d64256343d39 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/stitching/detail/util_inl.hpp @@ -0,0 +1,131 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_STITCHING_UTIL_INL_HPP +#define OPENCV_STITCHING_UTIL_INL_HPP + +#include +#include "opencv2/core.hpp" +#include "util.hpp" // Make your IDE see declarations + +//! @cond IGNORED + +namespace cv { +namespace detail { + +template +B Graph::forEach(B body) const +{ + for (int i = 0; i < numVertices(); ++i) + { + std::list::const_iterator edge = edges_[i].begin(); + for (; edge != edges_[i].end(); ++edge) + body(*edge); + } + return body; +} + + +template +B Graph::walkBreadthFirst(int from, B body) const +{ + std::vector was(numVertices(), false); + std::queue vertices; + + was[from] = true; + vertices.push(from); + + while (!vertices.empty()) + { + int vertex = vertices.front(); + vertices.pop(); + + std::list::const_iterator edge = edges_[vertex].begin(); + for (; edge != edges_[vertex].end(); ++edge) + { + if (!was[edge->to]) + { + body(*edge); + was[edge->to] = true; + vertices.push(edge->to); + } + } + } + + return body; +} + + +////////////////////////////////////////////////////////////////////////////// +// Some auxiliary math functions + +static inline +float normL2(const Point3f& a) +{ + return a.x * a.x + a.y * a.y + a.z * a.z; +} + + +static inline +float normL2(const Point3f& a, const Point3f& b) +{ + return normL2(a - b); +} + + +static inline +double normL2sq(const Mat &r) +{ + return r.dot(r); +} + + +static inline int sqr(int x) { return x * x; } +static inline float sqr(float x) { return x * x; } +static inline double sqr(double x) { return x * x; } + +} // namespace detail +} // namespace cv + +//! @endcond + +#endif // OPENCV_STITCHING_UTIL_INL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/stitching/detail/warpers.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/stitching/detail/warpers.hpp new file mode 100644 index 0000000000000000000000000000000000000000..1b05651f9adcbfb72ffb496f8e124cb0fc0c3717 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/stitching/detail/warpers.hpp @@ -0,0 +1,616 @@ + /*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_STITCHING_WARPERS_HPP +#define OPENCV_STITCHING_WARPERS_HPP + +#include "opencv2/core.hpp" +#include "opencv2/core/cuda.hpp" +#include "opencv2/imgproc.hpp" +#include "opencv2/opencv_modules.hpp" + +namespace cv { +namespace detail { + +//! @addtogroup stitching_warp +//! @{ + +/** @brief Rotation-only model image warper interface. + */ +class CV_EXPORTS RotationWarper +{ +public: + virtual ~RotationWarper() {} + + /** @brief Projects the image point. + + @param pt Source point + @param K Camera intrinsic parameters + @param R Camera rotation matrix + @return Projected point + */ + virtual Point2f warpPoint(const Point2f &pt, InputArray K, InputArray R) = 0; + + /** @brief Builds the projection maps according to the given camera data. + + @param src_size Source image size + @param K Camera intrinsic parameters + @param R Camera rotation matrix + @param xmap Projection map for the x axis + @param ymap Projection map for the y axis + @return Projected image minimum bounding box + */ + virtual Rect buildMaps(Size src_size, InputArray K, InputArray R, OutputArray xmap, OutputArray ymap) = 0; + + /** @brief Projects the image. + + @param src Source image + @param K Camera intrinsic parameters + @param R Camera rotation matrix + @param interp_mode Interpolation mode + @param border_mode Border extrapolation mode + @param dst Projected image + @return Project image top-left corner + */ + virtual Point warp(InputArray src, InputArray K, InputArray R, int interp_mode, int border_mode, + OutputArray dst) = 0; + + /** @brief Projects the image backward. + + @param src Projected image + @param K Camera intrinsic parameters + @param R Camera rotation matrix + @param interp_mode Interpolation mode + @param border_mode Border extrapolation mode + @param dst_size Backward-projected image size + @param dst Backward-projected image + */ + virtual void warpBackward(InputArray src, InputArray K, InputArray R, int interp_mode, int border_mode, + Size dst_size, OutputArray dst) = 0; + + /** + @param src_size Source image bounding box + @param K Camera intrinsic parameters + @param R Camera rotation matrix + @return Projected image minimum bounding box + */ + virtual Rect warpRoi(Size src_size, InputArray K, InputArray R) = 0; + + virtual float getScale() const { return 1.f; } + virtual void setScale(float) {} +}; + +/** @brief Base class for warping logic implementation. + */ +struct CV_EXPORTS ProjectorBase +{ + void setCameraParams(InputArray K = Mat::eye(3, 3, CV_32F), + InputArray R = Mat::eye(3, 3, CV_32F), + InputArray T = Mat::zeros(3, 1, CV_32F)); + + float scale; + float k[9]; + float rinv[9]; + float r_kinv[9]; + float k_rinv[9]; + float t[3]; +}; + +/** @brief Base class for rotation-based warper using a detail::ProjectorBase_ derived class. + */ +template +class CV_EXPORTS_TEMPLATE RotationWarperBase : public RotationWarper +{ +public: + Point2f warpPoint(const Point2f &pt, InputArray K, InputArray R) CV_OVERRIDE; + + Rect buildMaps(Size src_size, InputArray K, InputArray R, OutputArray xmap, OutputArray ymap) CV_OVERRIDE; + + Point warp(InputArray src, InputArray K, InputArray R, int interp_mode, int border_mode, + OutputArray dst) CV_OVERRIDE; + + void warpBackward(InputArray src, InputArray K, InputArray R, int interp_mode, int border_mode, + Size dst_size, OutputArray dst) CV_OVERRIDE; + + Rect warpRoi(Size src_size, InputArray K, InputArray R) CV_OVERRIDE; + + float getScale() const CV_OVERRIDE{ return projector_.scale; } + void setScale(float val) CV_OVERRIDE { projector_.scale = val; } + +protected: + + // Detects ROI of the destination image. It's correct for any projection. + virtual void detectResultRoi(Size src_size, Point &dst_tl, Point &dst_br); + + // Detects ROI of the destination image by walking over image border. + // Correctness for any projection isn't guaranteed. + void detectResultRoiByBorder(Size src_size, Point &dst_tl, Point &dst_br); + + P projector_; +}; + + +struct CV_EXPORTS PlaneProjector : ProjectorBase +{ + void mapForward(float x, float y, float &u, float &v); + void mapBackward(float u, float v, float &x, float &y); +}; + +/** @brief Warper that maps an image onto the z = 1 plane. + */ +class CV_EXPORTS PlaneWarper : public RotationWarperBase +{ +public: + /** @brief Construct an instance of the plane warper class. + + @param scale Projected image scale multiplier + */ + PlaneWarper(float scale = 1.f) { projector_.scale = scale; } + + Point2f warpPoint(const Point2f &pt, InputArray K, InputArray R) CV_OVERRIDE; + Point2f warpPoint(const Point2f &pt, InputArray K, InputArray R, InputArray T); + + virtual Rect buildMaps(Size src_size, InputArray K, InputArray R, InputArray T, OutputArray xmap, OutputArray ymap); + Rect buildMaps(Size src_size, InputArray K, InputArray R, OutputArray xmap, OutputArray ymap) CV_OVERRIDE; + + Point warp(InputArray src, InputArray K, InputArray R, + int interp_mode, int border_mode, OutputArray dst) CV_OVERRIDE; + virtual Point warp(InputArray src, InputArray K, InputArray R, InputArray T, int interp_mode, int border_mode, + OutputArray dst); + + Rect warpRoi(Size src_size, InputArray K, InputArray R) CV_OVERRIDE; + Rect warpRoi(Size src_size, InputArray K, InputArray R, InputArray T); + +protected: + void detectResultRoi(Size src_size, Point &dst_tl, Point &dst_br) CV_OVERRIDE; +}; + + +/** @brief Affine warper that uses rotations and translations + + Uses affine transformation in homogeneous coordinates to represent both rotation and + translation in camera rotation matrix. + */ +class CV_EXPORTS AffineWarper : public PlaneWarper +{ +public: + /** @brief Construct an instance of the affine warper class. + + @param scale Projected image scale multiplier + */ + AffineWarper(float scale = 1.f) : PlaneWarper(scale) {} + + Point2f warpPoint(const Point2f &pt, InputArray K, InputArray R) CV_OVERRIDE; + Rect buildMaps(Size src_size, InputArray K, InputArray R, OutputArray xmap, OutputArray ymap) CV_OVERRIDE; + Point warp(InputArray src, InputArray K, InputArray R, + int interp_mode, int border_mode, OutputArray dst) CV_OVERRIDE; + Rect warpRoi(Size src_size, InputArray K, InputArray R) CV_OVERRIDE; + +protected: + /** @brief Extracts rotation and translation matrices from matrix H representing + affine transformation in homogeneous coordinates + */ + void getRTfromHomogeneous(InputArray H, Mat &R, Mat &T); +}; + + +struct CV_EXPORTS SphericalProjector : ProjectorBase +{ + void mapForward(float x, float y, float &u, float &v); + void mapBackward(float u, float v, float &x, float &y); +}; + + +/** @brief Warper that maps an image onto the unit sphere located at the origin. + + Projects image onto unit sphere with origin at (0, 0, 0) and radius scale, measured in pixels. + A 360 panorama would therefore have a resulting width of 2 * scale * PI pixels. + Poles are located at (0, -1, 0) and (0, 1, 0) points. +*/ +class CV_EXPORTS SphericalWarper : public RotationWarperBase +{ +public: + /** @brief Construct an instance of the spherical warper class. + + @param scale Radius of the projected sphere, in pixels. An image spanning the + whole sphere will have a width of 2 * scale * PI pixels. + */ + SphericalWarper(float scale) { projector_.scale = scale; } + + Rect buildMaps(Size src_size, InputArray K, InputArray R, OutputArray xmap, OutputArray ymap) CV_OVERRIDE; + Point warp(InputArray src, InputArray K, InputArray R, int interp_mode, int border_mode, OutputArray dst) CV_OVERRIDE; +protected: + void detectResultRoi(Size src_size, Point &dst_tl, Point &dst_br) CV_OVERRIDE; +}; + + +struct CV_EXPORTS CylindricalProjector : ProjectorBase +{ + void mapForward(float x, float y, float &u, float &v); + void mapBackward(float u, float v, float &x, float &y); +}; + + +/** @brief Warper that maps an image onto the x\*x + z\*z = 1 cylinder. + */ +class CV_EXPORTS CylindricalWarper : public RotationWarperBase +{ +public: + /** @brief Construct an instance of the cylindrical warper class. + + @param scale Projected image scale multiplier + */ + CylindricalWarper(float scale) { projector_.scale = scale; } + + Rect buildMaps(Size src_size, InputArray K, InputArray R, OutputArray xmap, OutputArray ymap) CV_OVERRIDE; + Point warp(InputArray src, InputArray K, InputArray R, int interp_mode, int border_mode, OutputArray dst) CV_OVERRIDE; +protected: + void detectResultRoi(Size src_size, Point &dst_tl, Point &dst_br) CV_OVERRIDE + { + RotationWarperBase::detectResultRoiByBorder(src_size, dst_tl, dst_br); + } +}; + + +struct CV_EXPORTS FisheyeProjector : ProjectorBase +{ + void mapForward(float x, float y, float &u, float &v); + void mapBackward(float u, float v, float &x, float &y); +}; + + +class CV_EXPORTS FisheyeWarper : public RotationWarperBase +{ +public: + FisheyeWarper(float scale) { projector_.scale = scale; } +}; + + +struct CV_EXPORTS StereographicProjector : ProjectorBase +{ + void mapForward(float x, float y, float &u, float &v); + void mapBackward(float u, float v, float &x, float &y); +}; + + +class CV_EXPORTS StereographicWarper : public RotationWarperBase +{ +public: + StereographicWarper(float scale) { projector_.scale = scale; } +}; + + +struct CV_EXPORTS CompressedRectilinearProjector : ProjectorBase +{ + float a, b; + + void mapForward(float x, float y, float &u, float &v); + void mapBackward(float u, float v, float &x, float &y); +}; + + +class CV_EXPORTS CompressedRectilinearWarper : public RotationWarperBase +{ +public: + CompressedRectilinearWarper(float scale, float A = 1, float B = 1) + { + projector_.a = A; + projector_.b = B; + projector_.scale = scale; + } +}; + + +struct CV_EXPORTS CompressedRectilinearPortraitProjector : ProjectorBase +{ + float a, b; + + void mapForward(float x, float y, float &u, float &v); + void mapBackward(float u, float v, float &x, float &y); +}; + + +class CV_EXPORTS CompressedRectilinearPortraitWarper : public RotationWarperBase +{ +public: + CompressedRectilinearPortraitWarper(float scale, float A = 1, float B = 1) + { + projector_.a = A; + projector_.b = B; + projector_.scale = scale; + } +}; + + +struct CV_EXPORTS PaniniProjector : ProjectorBase +{ + float a, b; + + void mapForward(float x, float y, float &u, float &v); + void mapBackward(float u, float v, float &x, float &y); +}; + + +class CV_EXPORTS PaniniWarper : public RotationWarperBase +{ +public: + PaniniWarper(float scale, float A = 1, float B = 1) + { + projector_.a = A; + projector_.b = B; + projector_.scale = scale; + } +}; + + +struct CV_EXPORTS PaniniPortraitProjector : ProjectorBase +{ + float a, b; + + void mapForward(float x, float y, float &u, float &v); + void mapBackward(float u, float v, float &x, float &y); +}; + + +class CV_EXPORTS PaniniPortraitWarper : public RotationWarperBase +{ +public: + PaniniPortraitWarper(float scale, float A = 1, float B = 1) + { + projector_.a = A; + projector_.b = B; + projector_.scale = scale; + } + +}; + + +struct CV_EXPORTS MercatorProjector : ProjectorBase +{ + void mapForward(float x, float y, float &u, float &v); + void mapBackward(float u, float v, float &x, float &y); +}; + + +class CV_EXPORTS MercatorWarper : public RotationWarperBase +{ +public: + MercatorWarper(float scale) { projector_.scale = scale; } +}; + + +struct CV_EXPORTS TransverseMercatorProjector : ProjectorBase +{ + void mapForward(float x, float y, float &u, float &v); + void mapBackward(float u, float v, float &x, float &y); +}; + + +class CV_EXPORTS TransverseMercatorWarper : public RotationWarperBase +{ +public: + TransverseMercatorWarper(float scale) { projector_.scale = scale; } +}; + + +class CV_EXPORTS PlaneWarperGpu : public PlaneWarper +{ +public: + PlaneWarperGpu(float scale = 1.f) : PlaneWarper(scale) {} + + Rect buildMaps(Size src_size, InputArray K, InputArray R, OutputArray xmap, OutputArray ymap) CV_OVERRIDE + { + Rect result = buildMaps(src_size, K, R, d_xmap_, d_ymap_); + d_xmap_.download(xmap); + d_ymap_.download(ymap); + return result; + } + + Rect buildMaps(Size src_size, InputArray K, InputArray R, InputArray T, OutputArray xmap, OutputArray ymap) CV_OVERRIDE + { + Rect result = buildMaps(src_size, K, R, T, d_xmap_, d_ymap_); + d_xmap_.download(xmap); + d_ymap_.download(ymap); + return result; + } + + Point warp(InputArray src, InputArray K, InputArray R, int interp_mode, int border_mode, + OutputArray dst) CV_OVERRIDE + { + d_src_.upload(src); + Point result = warp(d_src_, K, R, interp_mode, border_mode, d_dst_); + d_dst_.download(dst); + return result; + } + + Point warp(InputArray src, InputArray K, InputArray R, InputArray T, int interp_mode, int border_mode, + OutputArray dst) CV_OVERRIDE + { + d_src_.upload(src); + Point result = warp(d_src_, K, R, T, interp_mode, border_mode, d_dst_); + d_dst_.download(dst); + return result; + } + + Rect buildMaps(Size src_size, InputArray K, InputArray R, cuda::GpuMat & xmap, cuda::GpuMat & ymap); + + Rect buildMaps(Size src_size, InputArray K, InputArray R, InputArray T, cuda::GpuMat & xmap, cuda::GpuMat & ymap); + + Point warp(const cuda::GpuMat & src, InputArray K, InputArray R, int interp_mode, int border_mode, + cuda::GpuMat & dst); + + Point warp(const cuda::GpuMat & src, InputArray K, InputArray R, InputArray T, int interp_mode, int border_mode, + cuda::GpuMat & dst); + +private: + cuda::GpuMat d_xmap_, d_ymap_, d_src_, d_dst_; +}; + + +class CV_EXPORTS SphericalWarperGpu : public SphericalWarper +{ +public: + SphericalWarperGpu(float scale) : SphericalWarper(scale) {} + + Rect buildMaps(Size src_size, InputArray K, InputArray R, OutputArray xmap, OutputArray ymap) CV_OVERRIDE + { + Rect result = buildMaps(src_size, K, R, d_xmap_, d_ymap_); + d_xmap_.download(xmap); + d_ymap_.download(ymap); + return result; + } + + Point warp(InputArray src, InputArray K, InputArray R, int interp_mode, int border_mode, + OutputArray dst) CV_OVERRIDE + { + d_src_.upload(src); + Point result = warp(d_src_, K, R, interp_mode, border_mode, d_dst_); + d_dst_.download(dst); + return result; + } + + Rect buildMaps(Size src_size, InputArray K, InputArray R, cuda::GpuMat & xmap, cuda::GpuMat & ymap); + + Point warp(const cuda::GpuMat & src, InputArray K, InputArray R, int interp_mode, int border_mode, + cuda::GpuMat & dst); + +private: + cuda::GpuMat d_xmap_, d_ymap_, d_src_, d_dst_; +}; + + +class CV_EXPORTS CylindricalWarperGpu : public CylindricalWarper +{ +public: + CylindricalWarperGpu(float scale) : CylindricalWarper(scale) {} + + Rect buildMaps(Size src_size, InputArray K, InputArray R, OutputArray xmap, OutputArray ymap) CV_OVERRIDE + { + Rect result = buildMaps(src_size, K, R, d_xmap_, d_ymap_); + d_xmap_.download(xmap); + d_ymap_.download(ymap); + return result; + } + + Point warp(InputArray src, InputArray K, InputArray R, int interp_mode, int border_mode, + OutputArray dst) CV_OVERRIDE + { + d_src_.upload(src); + Point result = warp(d_src_, K, R, interp_mode, border_mode, d_dst_); + d_dst_.download(dst); + return result; + } + + Rect buildMaps(Size src_size, InputArray K, InputArray R, cuda::GpuMat & xmap, cuda::GpuMat & ymap); + + Point warp(const cuda::GpuMat & src, InputArray K, InputArray R, int interp_mode, int border_mode, + cuda::GpuMat & dst); + +private: + cuda::GpuMat d_xmap_, d_ymap_, d_src_, d_dst_; +}; + + +struct CV_EXPORTS SphericalPortraitProjector : ProjectorBase +{ + void mapForward(float x, float y, float &u, float &v); + void mapBackward(float u, float v, float &x, float &y); +}; + + +// Projects image onto unit sphere with origin at (0, 0, 0). +// Poles are located NOT at (0, -1, 0) and (0, 1, 0) points, BUT at (1, 0, 0) and (-1, 0, 0) points. +class CV_EXPORTS SphericalPortraitWarper : public RotationWarperBase +{ +public: + SphericalPortraitWarper(float scale) { projector_.scale = scale; } + +protected: + void detectResultRoi(Size src_size, Point &dst_tl, Point &dst_br) CV_OVERRIDE; +}; + +struct CV_EXPORTS CylindricalPortraitProjector : ProjectorBase +{ + void mapForward(float x, float y, float &u, float &v); + void mapBackward(float u, float v, float &x, float &y); +}; + + +class CV_EXPORTS CylindricalPortraitWarper : public RotationWarperBase +{ +public: + CylindricalPortraitWarper(float scale) { projector_.scale = scale; } + +protected: + void detectResultRoi(Size src_size, Point &dst_tl, Point &dst_br) CV_OVERRIDE + { + RotationWarperBase::detectResultRoiByBorder(src_size, dst_tl, dst_br); + } +}; + +struct CV_EXPORTS PlanePortraitProjector : ProjectorBase +{ + void mapForward(float x, float y, float &u, float &v); + void mapBackward(float u, float v, float &x, float &y); +}; + + +class CV_EXPORTS PlanePortraitWarper : public RotationWarperBase +{ +public: + PlanePortraitWarper(float scale) { projector_.scale = scale; } + +protected: + void detectResultRoi(Size src_size, Point &dst_tl, Point &dst_br) CV_OVERRIDE + { + RotationWarperBase::detectResultRoiByBorder(src_size, dst_tl, dst_br); + } +}; + +//! @} stitching_warp + +} // namespace detail +} // namespace cv + +#include "warpers_inl.hpp" + +#endif // OPENCV_STITCHING_WARPERS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/stitching/detail/warpers_inl.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/stitching/detail/warpers_inl.hpp new file mode 100644 index 0000000000000000000000000000000000000000..f4a19d9c24ffcdb2cbb5fbd162b1a2bc2ad3c1aa --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/stitching/detail/warpers_inl.hpp @@ -0,0 +1,774 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_STITCHING_WARPERS_INL_HPP +#define OPENCV_STITCHING_WARPERS_INL_HPP + +#include "opencv2/core.hpp" +#include "warpers.hpp" // Make your IDE see declarations +#include + +//! @cond IGNORED + +namespace cv { +namespace detail { + +template +Point2f RotationWarperBase

::warpPoint(const Point2f &pt, InputArray K, InputArray R) +{ + projector_.setCameraParams(K, R); + Point2f uv; + projector_.mapForward(pt.x, pt.y, uv.x, uv.y); + return uv; +} + + +template +Rect RotationWarperBase

::buildMaps(Size src_size, InputArray K, InputArray R, OutputArray _xmap, OutputArray _ymap) +{ + projector_.setCameraParams(K, R); + + Point dst_tl, dst_br; + detectResultRoi(src_size, dst_tl, dst_br); + + _xmap.create(dst_br.y - dst_tl.y + 1, dst_br.x - dst_tl.x + 1, CV_32F); + _ymap.create(dst_br.y - dst_tl.y + 1, dst_br.x - dst_tl.x + 1, CV_32F); + + Mat xmap = _xmap.getMat(), ymap = _ymap.getMat(); + + float x, y; + for (int v = dst_tl.y; v <= dst_br.y; ++v) + { + for (int u = dst_tl.x; u <= dst_br.x; ++u) + { + projector_.mapBackward(static_cast(u), static_cast(v), x, y); + xmap.at(v - dst_tl.y, u - dst_tl.x) = x; + ymap.at(v - dst_tl.y, u - dst_tl.x) = y; + } + } + + return Rect(dst_tl, dst_br); +} + + +template +Point RotationWarperBase

::warp(InputArray src, InputArray K, InputArray R, int interp_mode, int border_mode, + OutputArray dst) +{ + UMat xmap, ymap; + Rect dst_roi = buildMaps(src.size(), K, R, xmap, ymap); + + dst.create(dst_roi.height + 1, dst_roi.width + 1, src.type()); + remap(src, dst, xmap, ymap, interp_mode, border_mode); + + return dst_roi.tl(); +} + + +template +void RotationWarperBase

::warpBackward(InputArray src, InputArray K, InputArray R, int interp_mode, int border_mode, + Size dst_size, OutputArray dst) +{ + projector_.setCameraParams(K, R); + + Point src_tl, src_br; + detectResultRoi(dst_size, src_tl, src_br); + + Size size = src.size(); + CV_Assert(src_br.x - src_tl.x + 1 == size.width && src_br.y - src_tl.y + 1 == size.height); + + Mat xmap(dst_size, CV_32F); + Mat ymap(dst_size, CV_32F); + + float u, v; + for (int y = 0; y < dst_size.height; ++y) + { + for (int x = 0; x < dst_size.width; ++x) + { + projector_.mapForward(static_cast(x), static_cast(y), u, v); + xmap.at(y, x) = u - src_tl.x; + ymap.at(y, x) = v - src_tl.y; + } + } + + dst.create(dst_size, src.type()); + remap(src, dst, xmap, ymap, interp_mode, border_mode); +} + + +template +Rect RotationWarperBase

::warpRoi(Size src_size, InputArray K, InputArray R) +{ + projector_.setCameraParams(K, R); + + Point dst_tl, dst_br; + detectResultRoi(src_size, dst_tl, dst_br); + + return Rect(dst_tl, Point(dst_br.x + 1, dst_br.y + 1)); +} + + +template +void RotationWarperBase

::detectResultRoi(Size src_size, Point &dst_tl, Point &dst_br) +{ + float tl_uf = (std::numeric_limits::max)(); + float tl_vf = (std::numeric_limits::max)(); + float br_uf = -(std::numeric_limits::max)(); + float br_vf = -(std::numeric_limits::max)(); + + float u, v; + for (int y = 0; y < src_size.height; ++y) + { + for (int x = 0; x < src_size.width; ++x) + { + projector_.mapForward(static_cast(x), static_cast(y), u, v); + tl_uf = (std::min)(tl_uf, u); tl_vf = (std::min)(tl_vf, v); + br_uf = (std::max)(br_uf, u); br_vf = (std::max)(br_vf, v); + } + } + + dst_tl.x = static_cast(tl_uf); + dst_tl.y = static_cast(tl_vf); + dst_br.x = static_cast(br_uf); + dst_br.y = static_cast(br_vf); +} + + +template +void RotationWarperBase

::detectResultRoiByBorder(Size src_size, Point &dst_tl, Point &dst_br) +{ + float tl_uf = (std::numeric_limits::max)(); + float tl_vf = (std::numeric_limits::max)(); + float br_uf = -(std::numeric_limits::max)(); + float br_vf = -(std::numeric_limits::max)(); + + float u, v; + for (float x = 0; x < src_size.width; ++x) + { + projector_.mapForward(static_cast(x), 0, u, v); + tl_uf = (std::min)(tl_uf, u); tl_vf = (std::min)(tl_vf, v); + br_uf = (std::max)(br_uf, u); br_vf = (std::max)(br_vf, v); + + projector_.mapForward(static_cast(x), static_cast(src_size.height - 1), u, v); + tl_uf = (std::min)(tl_uf, u); tl_vf = (std::min)(tl_vf, v); + br_uf = (std::max)(br_uf, u); br_vf = (std::max)(br_vf, v); + } + for (int y = 0; y < src_size.height; ++y) + { + projector_.mapForward(0, static_cast(y), u, v); + tl_uf = (std::min)(tl_uf, u); tl_vf = (std::min)(tl_vf, v); + br_uf = (std::max)(br_uf, u); br_vf = (std::max)(br_vf, v); + + projector_.mapForward(static_cast(src_size.width - 1), static_cast(y), u, v); + tl_uf = (std::min)(tl_uf, u); tl_vf = (std::min)(tl_vf, v); + br_uf = (std::max)(br_uf, u); br_vf = (std::max)(br_vf, v); + } + + dst_tl.x = static_cast(tl_uf); + dst_tl.y = static_cast(tl_vf); + dst_br.x = static_cast(br_uf); + dst_br.y = static_cast(br_vf); +} + + +inline +void PlaneProjector::mapForward(float x, float y, float &u, float &v) +{ + float x_ = r_kinv[0] * x + r_kinv[1] * y + r_kinv[2]; + float y_ = r_kinv[3] * x + r_kinv[4] * y + r_kinv[5]; + float z_ = r_kinv[6] * x + r_kinv[7] * y + r_kinv[8]; + + x_ = t[0] + x_ / z_ * (1 - t[2]); + y_ = t[1] + y_ / z_ * (1 - t[2]); + + u = scale * x_; + v = scale * y_; +} + + +inline +void PlaneProjector::mapBackward(float u, float v, float &x, float &y) +{ + u = u / scale - t[0]; + v = v / scale - t[1]; + + float z; + x = k_rinv[0] * u + k_rinv[1] * v + k_rinv[2] * (1 - t[2]); + y = k_rinv[3] * u + k_rinv[4] * v + k_rinv[5] * (1 - t[2]); + z = k_rinv[6] * u + k_rinv[7] * v + k_rinv[8] * (1 - t[2]); + + x /= z; + y /= z; +} + + +inline +void SphericalProjector::mapForward(float x, float y, float &u, float &v) +{ + float x_ = r_kinv[0] * x + r_kinv[1] * y + r_kinv[2]; + float y_ = r_kinv[3] * x + r_kinv[4] * y + r_kinv[5]; + float z_ = r_kinv[6] * x + r_kinv[7] * y + r_kinv[8]; + + u = scale * atan2f(x_, z_); + float w = y_ / sqrtf(x_ * x_ + y_ * y_ + z_ * z_); + v = scale * (static_cast(CV_PI) - acosf(w == w ? w : 0)); +} + + +inline +void SphericalProjector::mapBackward(float u, float v, float &x, float &y) +{ + u /= scale; + v /= scale; + + float sinv = sinf(static_cast(CV_PI) - v); + float x_ = sinv * sinf(u); + float y_ = cosf(static_cast(CV_PI) - v); + float z_ = sinv * cosf(u); + + float z; + x = k_rinv[0] * x_ + k_rinv[1] * y_ + k_rinv[2] * z_; + y = k_rinv[3] * x_ + k_rinv[4] * y_ + k_rinv[5] * z_; + z = k_rinv[6] * x_ + k_rinv[7] * y_ + k_rinv[8] * z_; + + if (z > 0) { x /= z; y /= z; } + else x = y = -1; +} + + +inline +void CylindricalProjector::mapForward(float x, float y, float &u, float &v) +{ + float x_ = r_kinv[0] * x + r_kinv[1] * y + r_kinv[2]; + float y_ = r_kinv[3] * x + r_kinv[4] * y + r_kinv[5]; + float z_ = r_kinv[6] * x + r_kinv[7] * y + r_kinv[8]; + + u = scale * atan2f(x_, z_); + v = scale * y_ / sqrtf(x_ * x_ + z_ * z_); +} + + +inline +void CylindricalProjector::mapBackward(float u, float v, float &x, float &y) +{ + u /= scale; + v /= scale; + + float x_ = sinf(u); + float y_ = v; + float z_ = cosf(u); + + float z; + x = k_rinv[0] * x_ + k_rinv[1] * y_ + k_rinv[2] * z_; + y = k_rinv[3] * x_ + k_rinv[4] * y_ + k_rinv[5] * z_; + z = k_rinv[6] * x_ + k_rinv[7] * y_ + k_rinv[8] * z_; + + if (z > 0) { x /= z; y /= z; } + else x = y = -1; +} + +inline +void FisheyeProjector::mapForward(float x, float y, float &u, float &v) +{ + float x_ = r_kinv[0] * x + r_kinv[1] * y + r_kinv[2]; + float y_ = r_kinv[3] * x + r_kinv[4] * y + r_kinv[5]; + float z_ = r_kinv[6] * x + r_kinv[7] * y + r_kinv[8]; + + float u_ = atan2f(x_, z_); + float v_ = (float)CV_PI - acosf(y_ / sqrtf(x_ * x_ + y_ * y_ + z_ * z_)); + + u = scale * v_ * cosf(u_); + v = scale * v_ * sinf(u_); +} + +inline +void FisheyeProjector::mapBackward(float u, float v, float &x, float &y) +{ + u /= scale; + v /= scale; + + float u_ = atan2f(v, u); + float v_ = sqrtf(u*u + v*v); + + float sinv = sinf((float)CV_PI - v_); + float x_ = sinv * sinf(u_); + float y_ = cosf((float)CV_PI - v_); + float z_ = sinv * cosf(u_); + + float z; + x = k_rinv[0] * x_ + k_rinv[1] * y_ + k_rinv[2] * z_; + y = k_rinv[3] * x_ + k_rinv[4] * y_ + k_rinv[5] * z_; + z = k_rinv[6] * x_ + k_rinv[7] * y_ + k_rinv[8] * z_; + + if (z > 0) { x /= z; y /= z; } + else x = y = -1; +} + +inline +void StereographicProjector::mapForward(float x, float y, float &u, float &v) +{ + float x_ = r_kinv[0] * x + r_kinv[1] * y + r_kinv[2]; + float y_ = r_kinv[3] * x + r_kinv[4] * y + r_kinv[5]; + float z_ = r_kinv[6] * x + r_kinv[7] * y + r_kinv[8]; + + float u_ = atan2f(x_, z_); + float v_ = (float)CV_PI - acosf(y_ / sqrtf(x_ * x_ + y_ * y_ + z_ * z_)); + + float r = sinf(v_) / (1 - cosf(v_)); + + u = scale * r * cos(u_); + v = scale * r * sin(u_); +} + +inline +void StereographicProjector::mapBackward(float u, float v, float &x, float &y) +{ + u /= scale; + v /= scale; + + float u_ = atan2f(v, u); + float r = sqrtf(u*u + v*v); + float v_ = 2 * atanf(1.f / r); + + float sinv = sinf((float)CV_PI - v_); + float x_ = sinv * sinf(u_); + float y_ = cosf((float)CV_PI - v_); + float z_ = sinv * cosf(u_); + + float z; + x = k_rinv[0] * x_ + k_rinv[1] * y_ + k_rinv[2] * z_; + y = k_rinv[3] * x_ + k_rinv[4] * y_ + k_rinv[5] * z_; + z = k_rinv[6] * x_ + k_rinv[7] * y_ + k_rinv[8] * z_; + + if (z > 0) { x /= z; y /= z; } + else x = y = -1; +} + +inline +void CompressedRectilinearProjector::mapForward(float x, float y, float &u, float &v) +{ + float x_ = r_kinv[0] * x + r_kinv[1] * y + r_kinv[2]; + float y_ = r_kinv[3] * x + r_kinv[4] * y + r_kinv[5]; + float z_ = r_kinv[6] * x + r_kinv[7] * y + r_kinv[8]; + + float u_ = atan2f(x_, z_); + float v_ = asinf(y_ / sqrtf(x_ * x_ + y_ * y_ + z_ * z_)); + + u = scale * a * tanf(u_ / a); + v = scale * b * tanf(v_) / cosf(u_); +} + +inline +void CompressedRectilinearProjector::mapBackward(float u, float v, float &x, float &y) +{ + u /= scale; + v /= scale; + + float aatg = a * atanf(u / a); + float u_ = aatg; + float v_ = atanf(v * cosf(aatg) / b); + + float cosv = cosf(v_); + float x_ = cosv * sinf(u_); + float y_ = sinf(v_); + float z_ = cosv * cosf(u_); + + float z; + x = k_rinv[0] * x_ + k_rinv[1] * y_ + k_rinv[2] * z_; + y = k_rinv[3] * x_ + k_rinv[4] * y_ + k_rinv[5] * z_; + z = k_rinv[6] * x_ + k_rinv[7] * y_ + k_rinv[8] * z_; + + if (z > 0) { x /= z; y /= z; } + else x = y = -1; +} + +inline +void CompressedRectilinearPortraitProjector::mapForward(float x, float y, float &u, float &v) +{ + float y_ = r_kinv[0] * x + r_kinv[1] * y + r_kinv[2]; + float x_ = r_kinv[3] * x + r_kinv[4] * y + r_kinv[5]; + float z_ = r_kinv[6] * x + r_kinv[7] * y + r_kinv[8]; + + float u_ = atan2f(x_, z_); + float v_ = asinf(y_ / sqrtf(x_ * x_ + y_ * y_ + z_ * z_)); + + u = - scale * a * tanf(u_ / a); + v = scale * b * tanf(v_) / cosf(u_); +} + +inline +void CompressedRectilinearPortraitProjector::mapBackward(float u, float v, float &x, float &y) +{ + u /= - scale; + v /= scale; + + float aatg = a * atanf(u / a); + float u_ = aatg; + float v_ = atanf(v * cosf( aatg ) / b); + + float cosv = cosf(v_); + float y_ = cosv * sinf(u_); + float x_ = sinf(v_); + float z_ = cosv * cosf(u_); + + float z; + x = k_rinv[0] * x_ + k_rinv[1] * y_ + k_rinv[2] * z_; + y = k_rinv[3] * x_ + k_rinv[4] * y_ + k_rinv[5] * z_; + z = k_rinv[6] * x_ + k_rinv[7] * y_ + k_rinv[8] * z_; + + if (z > 0) { x /= z; y /= z; } + else x = y = -1; +} + +inline +void PaniniProjector::mapForward(float x, float y, float &u, float &v) +{ + float x_ = r_kinv[0] * x + r_kinv[1] * y + r_kinv[2]; + float y_ = r_kinv[3] * x + r_kinv[4] * y + r_kinv[5]; + float z_ = r_kinv[6] * x + r_kinv[7] * y + r_kinv[8]; + + float u_ = atan2f(x_, z_); + float v_ = asinf(y_ / sqrtf(x_ * x_ + y_ * y_ + z_ * z_)); + + float tg = a * tanf(u_ / a); + u = scale * tg; + + float sinu = sinf(u_); + if ( fabs(sinu) < 1E-7 ) + v = scale * b * tanf(v_); + else + v = scale * b * tg * tanf(v_) / sinu; +} + +inline +void PaniniProjector::mapBackward(float u, float v, float &x, float &y) +{ + u /= scale; + v /= scale; + + float lamda = a * atanf(u / a); + float u_ = lamda; + + float v_; + if ( fabs(lamda) > 1E-7) + v_ = atanf(v * sinf(lamda) / (b * a * tanf(lamda / a))); + else + v_ = atanf(v / b); + + float cosv = cosf(v_); + float x_ = cosv * sinf(u_); + float y_ = sinf(v_); + float z_ = cosv * cosf(u_); + + float z; + x = k_rinv[0] * x_ + k_rinv[1] * y_ + k_rinv[2] * z_; + y = k_rinv[3] * x_ + k_rinv[4] * y_ + k_rinv[5] * z_; + z = k_rinv[6] * x_ + k_rinv[7] * y_ + k_rinv[8] * z_; + + if (z > 0) { x /= z; y /= z; } + else x = y = -1; +} + +inline +void PaniniPortraitProjector::mapForward(float x, float y, float &u, float &v) +{ + float y_ = r_kinv[0] * x + r_kinv[1] * y + r_kinv[2]; + float x_ = r_kinv[3] * x + r_kinv[4] * y + r_kinv[5]; + float z_ = r_kinv[6] * x + r_kinv[7] * y + r_kinv[8]; + + float u_ = atan2f(x_, z_); + float v_ = asinf(y_ / sqrtf(x_ * x_ + y_ * y_ + z_ * z_)); + + float tg = a * tanf(u_ / a); + u = - scale * tg; + + float sinu = sinf( u_ ); + if ( fabs(sinu) < 1E-7 ) + v = scale * b * tanf(v_); + else + v = scale * b * tg * tanf(v_) / sinu; +} + +inline +void PaniniPortraitProjector::mapBackward(float u, float v, float &x, float &y) +{ + u /= - scale; + v /= scale; + + float lamda = a * atanf(u / a); + float u_ = lamda; + + float v_; + if ( fabs(lamda) > 1E-7) + v_ = atanf(v * sinf(lamda) / (b * a * tanf(lamda/a))); + else + v_ = atanf(v / b); + + float cosv = cosf(v_); + float y_ = cosv * sinf(u_); + float x_ = sinf(v_); + float z_ = cosv * cosf(u_); + + float z; + x = k_rinv[0] * x_ + k_rinv[1] * y_ + k_rinv[2] * z_; + y = k_rinv[3] * x_ + k_rinv[4] * y_ + k_rinv[5] * z_; + z = k_rinv[6] * x_ + k_rinv[7] * y_ + k_rinv[8] * z_; + + if (z > 0) { x /= z; y /= z; } + else x = y = -1; +} + +inline +void MercatorProjector::mapForward(float x, float y, float &u, float &v) +{ + float x_ = r_kinv[0] * x + r_kinv[1] * y + r_kinv[2]; + float y_ = r_kinv[3] * x + r_kinv[4] * y + r_kinv[5]; + float z_ = r_kinv[6] * x + r_kinv[7] * y + r_kinv[8]; + + float u_ = atan2f(x_, z_); + float v_ = asinf(y_ / sqrtf(x_ * x_ + y_ * y_ + z_ * z_)); + + u = scale * u_; + v = scale * logf( tanf( (float)(CV_PI/4) + v_/2 ) ); +} + +inline +void MercatorProjector::mapBackward(float u, float v, float &x, float &y) +{ + u /= scale; + v /= scale; + + float v_ = atanf( sinhf(v) ); + float u_ = u; + + float cosv = cosf(v_); + float x_ = cosv * sinf(u_); + float y_ = sinf(v_); + float z_ = cosv * cosf(u_); + + float z; + x = k_rinv[0] * x_ + k_rinv[1] * y_ + k_rinv[2] * z_; + y = k_rinv[3] * x_ + k_rinv[4] * y_ + k_rinv[5] * z_; + z = k_rinv[6] * x_ + k_rinv[7] * y_ + k_rinv[8] * z_; + + if (z > 0) { x /= z; y /= z; } + else x = y = -1; +} + +inline +void TransverseMercatorProjector::mapForward(float x, float y, float &u, float &v) +{ + float x_ = r_kinv[0] * x + r_kinv[1] * y + r_kinv[2]; + float y_ = r_kinv[3] * x + r_kinv[4] * y + r_kinv[5]; + float z_ = r_kinv[6] * x + r_kinv[7] * y + r_kinv[8]; + + float u_ = atan2f(x_, z_); + float v_ = asinf(y_ / sqrtf(x_ * x_ + y_ * y_ + z_ * z_)); + + float B = cosf(v_) * sinf(u_); + + u = scale / 2 * logf( (1+B) / (1-B) ); + v = scale * atan2f(tanf(v_), cosf(u_)); +} + +inline +void TransverseMercatorProjector::mapBackward(float u, float v, float &x, float &y) +{ + u /= scale; + v /= scale; + + float v_ = asinf( sinf(v) / coshf(u) ); + float u_ = atan2f( sinhf(u), cos(v) ); + + float cosv = cosf(v_); + float x_ = cosv * sinf(u_); + float y_ = sinf(v_); + float z_ = cosv * cosf(u_); + + float z; + x = k_rinv[0] * x_ + k_rinv[1] * y_ + k_rinv[2] * z_; + y = k_rinv[3] * x_ + k_rinv[4] * y_ + k_rinv[5] * z_; + z = k_rinv[6] * x_ + k_rinv[7] * y_ + k_rinv[8] * z_; + + if (z > 0) { x /= z; y /= z; } + else x = y = -1; +} + +inline +void SphericalPortraitProjector::mapForward(float x, float y, float &u0, float &v0) +{ + float x0_ = r_kinv[0] * x + r_kinv[1] * y + r_kinv[2]; + float y0_ = r_kinv[3] * x + r_kinv[4] * y + r_kinv[5]; + float z_ = r_kinv[6] * x + r_kinv[7] * y + r_kinv[8]; + + float x_ = y0_; + float y_ = x0_; + float u, v; + + u = scale * atan2f(x_, z_); + v = scale * (static_cast(CV_PI) - acosf(y_ / sqrtf(x_ * x_ + y_ * y_ + z_ * z_))); + + u0 = -u;//v; + v0 = v;//u; +} + + +inline +void SphericalPortraitProjector::mapBackward(float u0, float v0, float &x, float &y) +{ + float u, v; + u = -u0;//v0; + v = v0;//u0; + + u /= scale; + v /= scale; + + float sinv = sinf(static_cast(CV_PI) - v); + float x0_ = sinv * sinf(u); + float y0_ = cosf(static_cast(CV_PI) - v); + float z_ = sinv * cosf(u); + + float x_ = y0_; + float y_ = x0_; + + float z; + x = k_rinv[0] * x_ + k_rinv[1] * y_ + k_rinv[2] * z_; + y = k_rinv[3] * x_ + k_rinv[4] * y_ + k_rinv[5] * z_; + z = k_rinv[6] * x_ + k_rinv[7] * y_ + k_rinv[8] * z_; + + if (z > 0) { x /= z; y /= z; } + else x = y = -1; +} + +inline +void CylindricalPortraitProjector::mapForward(float x, float y, float &u0, float &v0) +{ + float x0_ = r_kinv[0] * x + r_kinv[1] * y + r_kinv[2]; + float y0_ = r_kinv[3] * x + r_kinv[4] * y + r_kinv[5]; + float z_ = r_kinv[6] * x + r_kinv[7] * y + r_kinv[8]; + + float x_ = y0_; + float y_ = x0_; + float u, v; + + u = scale * atan2f(x_, z_); + v = scale * y_ / sqrtf(x_ * x_ + z_ * z_); + + u0 = -u;//v; + v0 = v;//u; +} + + +inline +void CylindricalPortraitProjector::mapBackward(float u0, float v0, float &x, float &y) +{ + float u, v; + u = -u0;//v0; + v = v0;//u0; + + u /= scale; + v /= scale; + + float x0_ = sinf(u); + float y0_ = v; + float z_ = cosf(u); + + float x_ = y0_; + float y_ = x0_; + + float z; + x = k_rinv[0] * x_ + k_rinv[1] * y_ + k_rinv[2] * z_; + y = k_rinv[3] * x_ + k_rinv[4] * y_ + k_rinv[5] * z_; + z = k_rinv[6] * x_ + k_rinv[7] * y_ + k_rinv[8] * z_; + + if (z > 0) { x /= z; y /= z; } + else x = y = -1; +} + +inline +void PlanePortraitProjector::mapForward(float x, float y, float &u0, float &v0) +{ + float x0_ = r_kinv[0] * x + r_kinv[1] * y + r_kinv[2]; + float y0_ = r_kinv[3] * x + r_kinv[4] * y + r_kinv[5]; + float z_ = r_kinv[6] * x + r_kinv[7] * y + r_kinv[8]; + + float x_ = y0_; + float y_ = x0_; + + x_ = t[0] + x_ / z_ * (1 - t[2]); + y_ = t[1] + y_ / z_ * (1 - t[2]); + + float u,v; + u = scale * x_; + v = scale * y_; + + u0 = -u; + v0 = v; +} + + +inline +void PlanePortraitProjector::mapBackward(float u0, float v0, float &x, float &y) +{ + float u, v; + u = -u0; + v = v0; + + u = u / scale - t[0]; + v = v / scale - t[1]; + + float z; + x = k_rinv[0] * v + k_rinv[1] * u + k_rinv[2] * (1 - t[2]); + y = k_rinv[3] * v + k_rinv[4] * u + k_rinv[5] * (1 - t[2]); + z = k_rinv[6] * v + k_rinv[7] * u + k_rinv[8] * (1 - t[2]); + + x /= z; + y /= z; +} + + +} // namespace detail +} // namespace cv + +//! @endcond + +#endif // OPENCV_STITCHING_WARPERS_INL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/stitching/warpers.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/stitching/warpers.hpp new file mode 100644 index 0000000000000000000000000000000000000000..cf7699c0991fa2fdca7bccb2c6574f4ae1e88e5b --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/stitching/warpers.hpp @@ -0,0 +1,192 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_STITCHING_WARPER_CREATORS_HPP +#define OPENCV_STITCHING_WARPER_CREATORS_HPP + +#include "opencv2/stitching/detail/warpers.hpp" + +namespace cv { + +//! @addtogroup stitching_warp +//! @{ + +/** @brief Image warper factories base class. + */ +class WarperCreator +{ +public: + virtual ~WarperCreator() {} + virtual Ptr create(float scale) const = 0; +}; + +/** @brief Plane warper factory class. + @sa detail::PlaneWarper + */ +class PlaneWarper : public WarperCreator +{ +public: + Ptr create(float scale) const CV_OVERRIDE { return makePtr(scale); } +}; + +/** @brief Affine warper factory class. + @sa detail::AffineWarper + */ +class AffineWarper : public WarperCreator +{ +public: + Ptr create(float scale) const CV_OVERRIDE { return makePtr(scale); } +}; + +/** @brief Cylindrical warper factory class. +@sa detail::CylindricalWarper +*/ +class CylindricalWarper: public WarperCreator +{ +public: + Ptr create(float scale) const CV_OVERRIDE { return makePtr(scale); } +}; + +/** @brief Spherical warper factory class */ +class SphericalWarper: public WarperCreator +{ +public: + Ptr create(float scale) const CV_OVERRIDE { return makePtr(scale); } +}; + +class FisheyeWarper : public WarperCreator +{ +public: + Ptr create(float scale) const CV_OVERRIDE { return makePtr(scale); } +}; + +class StereographicWarper: public WarperCreator +{ +public: + Ptr create(float scale) const CV_OVERRIDE { return makePtr(scale); } +}; + +class CompressedRectilinearWarper: public WarperCreator +{ + float a, b; +public: + CompressedRectilinearWarper(float A = 1, float B = 1) + { + a = A; b = B; + } + Ptr create(float scale) const CV_OVERRIDE { return makePtr(scale, a, b); } +}; + +class CompressedRectilinearPortraitWarper: public WarperCreator +{ + float a, b; +public: + CompressedRectilinearPortraitWarper(float A = 1, float B = 1) + { + a = A; b = B; + } + Ptr create(float scale) const CV_OVERRIDE { return makePtr(scale, a, b); } +}; + +class PaniniWarper: public WarperCreator +{ + float a, b; +public: + PaniniWarper(float A = 1, float B = 1) + { + a = A; b = B; + } + Ptr create(float scale) const CV_OVERRIDE { return makePtr(scale, a, b); } +}; + +class PaniniPortraitWarper: public WarperCreator +{ + float a, b; +public: + PaniniPortraitWarper(float A = 1, float B = 1) + { + a = A; b = B; + } + Ptr create(float scale) const CV_OVERRIDE { return makePtr(scale, a, b); } +}; + +class MercatorWarper: public WarperCreator +{ +public: + Ptr create(float scale) const CV_OVERRIDE { return makePtr(scale); } +}; + +class TransverseMercatorWarper: public WarperCreator +{ +public: + Ptr create(float scale) const CV_OVERRIDE { return makePtr(scale); } +}; + + + +#ifdef HAVE_OPENCV_CUDAWARPING +class PlaneWarperGpu: public WarperCreator +{ +public: + Ptr create(float scale) const CV_OVERRIDE { return makePtr(scale); } +}; + + +class CylindricalWarperGpu: public WarperCreator +{ +public: + Ptr create(float scale) const CV_OVERRIDE { return makePtr(scale); } +}; + + +class SphericalWarperGpu: public WarperCreator +{ +public: + Ptr create(float scale) const CV_OVERRIDE { return makePtr(scale); } +}; +#endif + +//! @} stitching_warp + +} // namespace cv + +#endif // OPENCV_STITCHING_WARPER_CREATORS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/superres.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/superres.hpp new file mode 100644 index 0000000000000000000000000000000000000000..16c11acd52f05a703ccec1d638e5e6323c76f2bc --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/superres.hpp @@ -0,0 +1,207 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_SUPERRES_HPP +#define OPENCV_SUPERRES_HPP + +#include "opencv2/core.hpp" +#include "opencv2/superres/optical_flow.hpp" + +/** + @defgroup superres Super Resolution + +The Super Resolution module contains a set of functions and classes that can be used to solve the +problem of resolution enhancement. There are a few methods implemented, most of them are described in +the papers @cite Farsiu03 and @cite Mitzel09 . + + */ + +namespace cv +{ + namespace superres + { + +//! @addtogroup superres +//! @{ + + class CV_EXPORTS FrameSource + { + public: + virtual ~FrameSource(); + + virtual void nextFrame(OutputArray frame) = 0; + virtual void reset() = 0; + }; + + CV_EXPORTS Ptr createFrameSource_Empty(); + + CV_EXPORTS Ptr createFrameSource_Video(const String& fileName); + CV_EXPORTS Ptr createFrameSource_Video_CUDA(const String& fileName); + + CV_EXPORTS Ptr createFrameSource_Camera(int deviceId = 0); + + /** @brief Base class for Super Resolution algorithms. + + The class is only used to define the common interface for the whole family of Super Resolution + algorithms. + */ + class CV_EXPORTS SuperResolution : public cv::Algorithm, public FrameSource + { + public: + /** @brief Set input frame source for Super Resolution algorithm. + + @param frameSource Input frame source + */ + void setInput(const Ptr& frameSource); + + /** @brief Process next frame from input and return output result. + + @param frame Output result + */ + void nextFrame(OutputArray frame) CV_OVERRIDE; + void reset() CV_OVERRIDE; + + /** @brief Clear all inner buffers. + */ + virtual void collectGarbage(); + + //! @brief Scale factor + /** @see setScale */ + virtual int getScale() const = 0; + /** @copybrief getScale @see getScale */ + virtual void setScale(int val) = 0; + + //! @brief Iterations count + /** @see setIterations */ + virtual int getIterations() const = 0; + /** @copybrief getIterations @see getIterations */ + virtual void setIterations(int val) = 0; + + //! @brief Asymptotic value of steepest descent method + /** @see setTau */ + virtual double getTau() const = 0; + /** @copybrief getTau @see getTau */ + virtual void setTau(double val) = 0; + + //! @brief Weight parameter to balance data term and smoothness term + /** @see setLabmda */ + virtual double getLabmda() const = 0; + /** @copybrief getLabmda @see getLabmda */ + virtual void setLabmda(double val) = 0; + + //! @brief Parameter of spacial distribution in Bilateral-TV + /** @see setAlpha */ + virtual double getAlpha() const = 0; + /** @copybrief getAlpha @see getAlpha */ + virtual void setAlpha(double val) = 0; + + //! @brief Kernel size of Bilateral-TV filter + /** @see setKernelSize */ + virtual int getKernelSize() const = 0; + /** @copybrief getKernelSize @see getKernelSize */ + virtual void setKernelSize(int val) = 0; + + //! @brief Gaussian blur kernel size + /** @see setBlurKernelSize */ + virtual int getBlurKernelSize() const = 0; + /** @copybrief getBlurKernelSize @see getBlurKernelSize */ + virtual void setBlurKernelSize(int val) = 0; + + //! @brief Gaussian blur sigma + /** @see setBlurSigma */ + virtual double getBlurSigma() const = 0; + /** @copybrief getBlurSigma @see getBlurSigma */ + virtual void setBlurSigma(double val) = 0; + + //! @brief Radius of the temporal search area + /** @see setTemporalAreaRadius */ + virtual int getTemporalAreaRadius() const = 0; + /** @copybrief getTemporalAreaRadius @see getTemporalAreaRadius */ + virtual void setTemporalAreaRadius(int val) = 0; + + //! @brief Dense optical flow algorithm + /** @see setOpticalFlow */ + virtual Ptr getOpticalFlow() const = 0; + /** @copybrief getOpticalFlow @see getOpticalFlow */ + virtual void setOpticalFlow(const Ptr &val) = 0; + + protected: + SuperResolution(); + + virtual void initImpl(Ptr& frameSource) = 0; + virtual void processImpl(Ptr& frameSource, OutputArray output) = 0; + + bool isUmat_; + + private: + Ptr frameSource_; + bool firstCall_; + }; + + /** @brief Create Bilateral TV-L1 Super Resolution. + + This class implements Super Resolution algorithm described in the papers @cite Farsiu03 and + @cite Mitzel09 . + + Here are important members of the class that control the algorithm, which you can set after + constructing the class instance: + + - **int scale** Scale factor. + - **int iterations** Iteration count. + - **double tau** Asymptotic value of steepest descent method. + - **double lambda** Weight parameter to balance data term and smoothness term. + - **double alpha** Parameter of spacial distribution in Bilateral-TV. + - **int btvKernelSize** Kernel size of Bilateral-TV filter. + - **int blurKernelSize** Gaussian blur kernel size. + - **double blurSigma** Gaussian blur sigma. + - **int temporalAreaRadius** Radius of the temporal search area. + - **Ptr\ opticalFlow** Dense optical flow algorithm. + */ + CV_EXPORTS Ptr createSuperResolution_BTVL1(); + CV_EXPORTS Ptr createSuperResolution_BTVL1_CUDA(); + +//! @} superres + + } +} + +#endif // OPENCV_SUPERRES_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/superres/optical_flow.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/superres/optical_flow.hpp new file mode 100644 index 0000000000000000000000000000000000000000..07e7ca9c9e9c809419e71892a0342fdd4b6a9d12 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/superres/optical_flow.hpp @@ -0,0 +1,203 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_SUPERRES_OPTICAL_FLOW_HPP +#define OPENCV_SUPERRES_OPTICAL_FLOW_HPP + +#include "opencv2/core.hpp" + +namespace cv +{ + namespace superres + { + +//! @addtogroup superres +//! @{ + + class CV_EXPORTS DenseOpticalFlowExt : public cv::Algorithm + { + public: + virtual void calc(InputArray frame0, InputArray frame1, OutputArray flow1, OutputArray flow2 = noArray()) = 0; + virtual void collectGarbage() = 0; + }; + + + class CV_EXPORTS FarnebackOpticalFlow : public virtual DenseOpticalFlowExt + { + public: + /** @see setPyrScale */ + virtual double getPyrScale() const = 0; + /** @copybrief getPyrScale @see getPyrScale */ + virtual void setPyrScale(double val) = 0; + /** @see setLevelsNumber */ + virtual int getLevelsNumber() const = 0; + /** @copybrief getLevelsNumber @see getLevelsNumber */ + virtual void setLevelsNumber(int val) = 0; + /** @see setWindowSize */ + virtual int getWindowSize() const = 0; + /** @copybrief getWindowSize @see getWindowSize */ + virtual void setWindowSize(int val) = 0; + /** @see setIterations */ + virtual int getIterations() const = 0; + /** @copybrief getIterations @see getIterations */ + virtual void setIterations(int val) = 0; + /** @see setPolyN */ + virtual int getPolyN() const = 0; + /** @copybrief getPolyN @see getPolyN */ + virtual void setPolyN(int val) = 0; + /** @see setPolySigma */ + virtual double getPolySigma() const = 0; + /** @copybrief getPolySigma @see getPolySigma */ + virtual void setPolySigma(double val) = 0; + /** @see setFlags */ + virtual int getFlags() const = 0; + /** @copybrief getFlags @see getFlags */ + virtual void setFlags(int val) = 0; + }; + CV_EXPORTS Ptr createOptFlow_Farneback(); + CV_EXPORTS Ptr createOptFlow_Farneback_CUDA(); + + +// CV_EXPORTS Ptr createOptFlow_Simple(); + + + class CV_EXPORTS DualTVL1OpticalFlow : public virtual DenseOpticalFlowExt + { + public: + /** @see setTau */ + virtual double getTau() const = 0; + /** @copybrief getTau @see getTau */ + virtual void setTau(double val) = 0; + /** @see setLambda */ + virtual double getLambda() const = 0; + /** @copybrief getLambda @see getLambda */ + virtual void setLambda(double val) = 0; + /** @see setTheta */ + virtual double getTheta() const = 0; + /** @copybrief getTheta @see getTheta */ + virtual void setTheta(double val) = 0; + /** @see setScalesNumber */ + virtual int getScalesNumber() const = 0; + /** @copybrief getScalesNumber @see getScalesNumber */ + virtual void setScalesNumber(int val) = 0; + /** @see setWarpingsNumber */ + virtual int getWarpingsNumber() const = 0; + /** @copybrief getWarpingsNumber @see getWarpingsNumber */ + virtual void setWarpingsNumber(int val) = 0; + /** @see setEpsilon */ + virtual double getEpsilon() const = 0; + /** @copybrief getEpsilon @see getEpsilon */ + virtual void setEpsilon(double val) = 0; + /** @see setIterations */ + virtual int getIterations() const = 0; + /** @copybrief getIterations @see getIterations */ + virtual void setIterations(int val) = 0; + /** @see setUseInitialFlow */ + virtual bool getUseInitialFlow() const = 0; + /** @copybrief getUseInitialFlow @see getUseInitialFlow */ + virtual void setUseInitialFlow(bool val) = 0; + }; + CV_EXPORTS Ptr createOptFlow_DualTVL1(); + CV_EXPORTS Ptr createOptFlow_DualTVL1_CUDA(); + + + class CV_EXPORTS BroxOpticalFlow : public virtual DenseOpticalFlowExt + { + public: + //! @brief Flow smoothness + /** @see setAlpha */ + virtual double getAlpha() const = 0; + /** @copybrief getAlpha @see getAlpha */ + virtual void setAlpha(double val) = 0; + //! @brief Gradient constancy importance + /** @see setGamma */ + virtual double getGamma() const = 0; + /** @copybrief getGamma @see getGamma */ + virtual void setGamma(double val) = 0; + //! @brief Pyramid scale factor + /** @see setScaleFactor */ + virtual double getScaleFactor() const = 0; + /** @copybrief getScaleFactor @see getScaleFactor */ + virtual void setScaleFactor(double val) = 0; + //! @brief Number of lagged non-linearity iterations (inner loop) + /** @see setInnerIterations */ + virtual int getInnerIterations() const = 0; + /** @copybrief getInnerIterations @see getInnerIterations */ + virtual void setInnerIterations(int val) = 0; + //! @brief Number of warping iterations (number of pyramid levels) + /** @see setOuterIterations */ + virtual int getOuterIterations() const = 0; + /** @copybrief getOuterIterations @see getOuterIterations */ + virtual void setOuterIterations(int val) = 0; + //! @brief Number of linear system solver iterations + /** @see setSolverIterations */ + virtual int getSolverIterations() const = 0; + /** @copybrief getSolverIterations @see getSolverIterations */ + virtual void setSolverIterations(int val) = 0; + }; + CV_EXPORTS Ptr createOptFlow_Brox_CUDA(); + + + class PyrLKOpticalFlow : public virtual DenseOpticalFlowExt + { + public: + /** @see setWindowSize */ + virtual int getWindowSize() const = 0; + /** @copybrief getWindowSize @see getWindowSize */ + virtual void setWindowSize(int val) = 0; + /** @see setMaxLevel */ + virtual int getMaxLevel() const = 0; + /** @copybrief getMaxLevel @see getMaxLevel */ + virtual void setMaxLevel(int val) = 0; + /** @see setIterations */ + virtual int getIterations() const = 0; + /** @copybrief getIterations @see getIterations */ + virtual void setIterations(int val) = 0; + }; + CV_EXPORTS Ptr createOptFlow_PyrLK_CUDA(); + +//! @} + + } +} + +#endif // OPENCV_SUPERRES_OPTICAL_FLOW_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/video.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/video.hpp new file mode 100644 index 0000000000000000000000000000000000000000..aa644a937a7578f562a926c836edfd2d00894e86 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/video.hpp @@ -0,0 +1,63 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_VIDEO_HPP +#define OPENCV_VIDEO_HPP + +/** + @defgroup video Video Analysis + @{ + @defgroup video_motion Motion Analysis + @defgroup video_track Object Tracking + @defgroup video_c C API + @} +*/ + +#include "opencv2/video/tracking.hpp" +#include "opencv2/video/background_segm.hpp" + +#ifndef DISABLE_OPENCV_24_COMPATIBILITY +#include "opencv2/video/tracking_c.h" +#endif + +#endif //OPENCV_VIDEO_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/video/background_segm.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/video/background_segm.hpp new file mode 100644 index 0000000000000000000000000000000000000000..e1dfa15a9a554aceee71d3a935b11201b9326b9f --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/video/background_segm.hpp @@ -0,0 +1,317 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_BACKGROUND_SEGM_HPP +#define OPENCV_BACKGROUND_SEGM_HPP + +#include "opencv2/core.hpp" + +namespace cv +{ + +//! @addtogroup video_motion +//! @{ + +/** @brief Base class for background/foreground segmentation. : + +The class is only used to define the common interface for the whole family of background/foreground +segmentation algorithms. + */ +class CV_EXPORTS_W BackgroundSubtractor : public Algorithm +{ +public: + /** @brief Computes a foreground mask. + + @param image Next video frame. + @param fgmask The output foreground mask as an 8-bit binary image. + @param learningRate The value between 0 and 1 that indicates how fast the background model is + learnt. Negative parameter value makes the algorithm to use some automatically chosen learning + rate. 0 means that the background model is not updated at all, 1 means that the background model + is completely reinitialized from the last frame. + */ + CV_WRAP virtual void apply(InputArray image, OutputArray fgmask, double learningRate=-1) = 0; + + /** @brief Computes a background image. + + @param backgroundImage The output background image. + + @note Sometimes the background image can be very blurry, as it contain the average background + statistics. + */ + CV_WRAP virtual void getBackgroundImage(OutputArray backgroundImage) const = 0; +}; + + +/** @brief Gaussian Mixture-based Background/Foreground Segmentation Algorithm. + +The class implements the Gaussian mixture model background subtraction described in @cite Zivkovic2004 +and @cite Zivkovic2006 . + */ +class CV_EXPORTS_W BackgroundSubtractorMOG2 : public BackgroundSubtractor +{ +public: + /** @brief Returns the number of last frames that affect the background model + */ + CV_WRAP virtual int getHistory() const = 0; + /** @brief Sets the number of last frames that affect the background model + */ + CV_WRAP virtual void setHistory(int history) = 0; + + /** @brief Returns the number of gaussian components in the background model + */ + CV_WRAP virtual int getNMixtures() const = 0; + /** @brief Sets the number of gaussian components in the background model. + + The model needs to be reinitalized to reserve memory. + */ + CV_WRAP virtual void setNMixtures(int nmixtures) = 0;//needs reinitialization! + + /** @brief Returns the "background ratio" parameter of the algorithm + + If a foreground pixel keeps semi-constant value for about backgroundRatio\*history frames, it's + considered background and added to the model as a center of a new component. It corresponds to TB + parameter in the paper. + */ + CV_WRAP virtual double getBackgroundRatio() const = 0; + /** @brief Sets the "background ratio" parameter of the algorithm + */ + CV_WRAP virtual void setBackgroundRatio(double ratio) = 0; + + /** @brief Returns the variance threshold for the pixel-model match + + The main threshold on the squared Mahalanobis distance to decide if the sample is well described by + the background model or not. Related to Cthr from the paper. + */ + CV_WRAP virtual double getVarThreshold() const = 0; + /** @brief Sets the variance threshold for the pixel-model match + */ + CV_WRAP virtual void setVarThreshold(double varThreshold) = 0; + + /** @brief Returns the variance threshold for the pixel-model match used for new mixture component generation + + Threshold for the squared Mahalanobis distance that helps decide when a sample is close to the + existing components (corresponds to Tg in the paper). If a pixel is not close to any component, it + is considered foreground or added as a new component. 3 sigma =\> Tg=3\*3=9 is default. A smaller Tg + value generates more components. A higher Tg value may result in a small number of components but + they can grow too large. + */ + CV_WRAP virtual double getVarThresholdGen() const = 0; + /** @brief Sets the variance threshold for the pixel-model match used for new mixture component generation + */ + CV_WRAP virtual void setVarThresholdGen(double varThresholdGen) = 0; + + /** @brief Returns the initial variance of each gaussian component + */ + CV_WRAP virtual double getVarInit() const = 0; + /** @brief Sets the initial variance of each gaussian component + */ + CV_WRAP virtual void setVarInit(double varInit) = 0; + + CV_WRAP virtual double getVarMin() const = 0; + CV_WRAP virtual void setVarMin(double varMin) = 0; + + CV_WRAP virtual double getVarMax() const = 0; + CV_WRAP virtual void setVarMax(double varMax) = 0; + + /** @brief Returns the complexity reduction threshold + + This parameter defines the number of samples needed to accept to prove the component exists. CT=0.05 + is a default value for all the samples. By setting CT=0 you get an algorithm very similar to the + standard Stauffer&Grimson algorithm. + */ + CV_WRAP virtual double getComplexityReductionThreshold() const = 0; + /** @brief Sets the complexity reduction threshold + */ + CV_WRAP virtual void setComplexityReductionThreshold(double ct) = 0; + + /** @brief Returns the shadow detection flag + + If true, the algorithm detects shadows and marks them. See createBackgroundSubtractorMOG2 for + details. + */ + CV_WRAP virtual bool getDetectShadows() const = 0; + /** @brief Enables or disables shadow detection + */ + CV_WRAP virtual void setDetectShadows(bool detectShadows) = 0; + + /** @brief Returns the shadow value + + Shadow value is the value used to mark shadows in the foreground mask. Default value is 127. Value 0 + in the mask always means background, 255 means foreground. + */ + CV_WRAP virtual int getShadowValue() const = 0; + /** @brief Sets the shadow value + */ + CV_WRAP virtual void setShadowValue(int value) = 0; + + /** @brief Returns the shadow threshold + + A shadow is detected if pixel is a darker version of the background. The shadow threshold (Tau in + the paper) is a threshold defining how much darker the shadow can be. Tau= 0.5 means that if a pixel + is more than twice darker then it is not shadow. See Prati, Mikic, Trivedi and Cucchiara, + *Detecting Moving Shadows...*, IEEE PAMI,2003. + */ + CV_WRAP virtual double getShadowThreshold() const = 0; + /** @brief Sets the shadow threshold + */ + CV_WRAP virtual void setShadowThreshold(double threshold) = 0; + + /** @brief Computes a foreground mask. + + @param image Next video frame. Floating point frame will be used without scaling and should be in range \f$[0,255]\f$. + @param fgmask The output foreground mask as an 8-bit binary image. + @param learningRate The value between 0 and 1 that indicates how fast the background model is + learnt. Negative parameter value makes the algorithm to use some automatically chosen learning + rate. 0 means that the background model is not updated at all, 1 means that the background model + is completely reinitialized from the last frame. + */ + CV_WRAP virtual void apply(InputArray image, OutputArray fgmask, double learningRate=-1) CV_OVERRIDE = 0; +}; + +/** @brief Creates MOG2 Background Subtractor + +@param history Length of the history. +@param varThreshold Threshold on the squared Mahalanobis distance between the pixel and the model +to decide whether a pixel is well described by the background model. This parameter does not +affect the background update. +@param detectShadows If true, the algorithm will detect shadows and mark them. It decreases the +speed a bit, so if you do not need this feature, set the parameter to false. + */ +CV_EXPORTS_W Ptr + createBackgroundSubtractorMOG2(int history=500, double varThreshold=16, + bool detectShadows=true); + +/** @brief K-nearest neighbours - based Background/Foreground Segmentation Algorithm. + +The class implements the K-nearest neighbours background subtraction described in @cite Zivkovic2006 . +Very efficient if number of foreground pixels is low. + */ +class CV_EXPORTS_W BackgroundSubtractorKNN : public BackgroundSubtractor +{ +public: + /** @brief Returns the number of last frames that affect the background model + */ + CV_WRAP virtual int getHistory() const = 0; + /** @brief Sets the number of last frames that affect the background model + */ + CV_WRAP virtual void setHistory(int history) = 0; + + /** @brief Returns the number of data samples in the background model + */ + CV_WRAP virtual int getNSamples() const = 0; + /** @brief Sets the number of data samples in the background model. + + The model needs to be reinitalized to reserve memory. + */ + CV_WRAP virtual void setNSamples(int _nN) = 0;//needs reinitialization! + + /** @brief Returns the threshold on the squared distance between the pixel and the sample + + The threshold on the squared distance between the pixel and the sample to decide whether a pixel is + close to a data sample. + */ + CV_WRAP virtual double getDist2Threshold() const = 0; + /** @brief Sets the threshold on the squared distance + */ + CV_WRAP virtual void setDist2Threshold(double _dist2Threshold) = 0; + + /** @brief Returns the number of neighbours, the k in the kNN. + + K is the number of samples that need to be within dist2Threshold in order to decide that that + pixel is matching the kNN background model. + */ + CV_WRAP virtual int getkNNSamples() const = 0; + /** @brief Sets the k in the kNN. How many nearest neighbours need to match. + */ + CV_WRAP virtual void setkNNSamples(int _nkNN) = 0; + + /** @brief Returns the shadow detection flag + + If true, the algorithm detects shadows and marks them. See createBackgroundSubtractorKNN for + details. + */ + CV_WRAP virtual bool getDetectShadows() const = 0; + /** @brief Enables or disables shadow detection + */ + CV_WRAP virtual void setDetectShadows(bool detectShadows) = 0; + + /** @brief Returns the shadow value + + Shadow value is the value used to mark shadows in the foreground mask. Default value is 127. Value 0 + in the mask always means background, 255 means foreground. + */ + CV_WRAP virtual int getShadowValue() const = 0; + /** @brief Sets the shadow value + */ + CV_WRAP virtual void setShadowValue(int value) = 0; + + /** @brief Returns the shadow threshold + + A shadow is detected if pixel is a darker version of the background. The shadow threshold (Tau in + the paper) is a threshold defining how much darker the shadow can be. Tau= 0.5 means that if a pixel + is more than twice darker then it is not shadow. See Prati, Mikic, Trivedi and Cucchiara, + *Detecting Moving Shadows...*, IEEE PAMI,2003. + */ + CV_WRAP virtual double getShadowThreshold() const = 0; + /** @brief Sets the shadow threshold + */ + CV_WRAP virtual void setShadowThreshold(double threshold) = 0; +}; + +/** @brief Creates KNN Background Subtractor + +@param history Length of the history. +@param dist2Threshold Threshold on the squared distance between the pixel and the sample to decide +whether a pixel is close to that sample. This parameter does not affect the background update. +@param detectShadows If true, the algorithm will detect shadows and mark them. It decreases the +speed a bit, so if you do not need this feature, set the parameter to false. + */ +CV_EXPORTS_W Ptr + createBackgroundSubtractorKNN(int history=500, double dist2Threshold=400.0, + bool detectShadows=true); + +//! @} video_motion + +} // cv + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/video/tracking.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/video/tracking.hpp new file mode 100644 index 0000000000000000000000000000000000000000..e757b0fa91025411e8e7a6fe2ad20c0d63bede2f --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/video/tracking.hpp @@ -0,0 +1,633 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_TRACKING_HPP +#define OPENCV_TRACKING_HPP + +#include "opencv2/core.hpp" +#include "opencv2/imgproc.hpp" + +namespace cv +{ + +//! @addtogroup video_track +//! @{ + +enum { OPTFLOW_USE_INITIAL_FLOW = 4, + OPTFLOW_LK_GET_MIN_EIGENVALS = 8, + OPTFLOW_FARNEBACK_GAUSSIAN = 256 + }; + +/** @brief Finds an object center, size, and orientation. + +@param probImage Back projection of the object histogram. See calcBackProject. +@param window Initial search window. +@param criteria Stop criteria for the underlying meanShift. +returns +(in old interfaces) Number of iterations CAMSHIFT took to converge +The function implements the CAMSHIFT object tracking algorithm @cite Bradski98 . First, it finds an +object center using meanShift and then adjusts the window size and finds the optimal rotation. The +function returns the rotated rectangle structure that includes the object position, size, and +orientation. The next position of the search window can be obtained with RotatedRect::boundingRect() + +See the OpenCV sample camshiftdemo.c that tracks colored objects. + +@note +- (Python) A sample explaining the camshift tracking algorithm can be found at + opencv_source_code/samples/python/camshift.py + */ +CV_EXPORTS_W RotatedRect CamShift( InputArray probImage, CV_IN_OUT Rect& window, + TermCriteria criteria ); +/** @example samples/cpp/camshiftdemo.cpp +An example using the mean-shift tracking algorithm +*/ + +/** @brief Finds an object on a back projection image. + +@param probImage Back projection of the object histogram. See calcBackProject for details. +@param window Initial search window. +@param criteria Stop criteria for the iterative search algorithm. +returns +: Number of iterations CAMSHIFT took to converge. +The function implements the iterative object search algorithm. It takes the input back projection of +an object and the initial position. The mass center in window of the back projection image is +computed and the search window center shifts to the mass center. The procedure is repeated until the +specified number of iterations criteria.maxCount is done or until the window center shifts by less +than criteria.epsilon. The algorithm is used inside CamShift and, unlike CamShift , the search +window size or orientation do not change during the search. You can simply pass the output of +calcBackProject to this function. But better results can be obtained if you pre-filter the back +projection and remove the noise. For example, you can do this by retrieving connected components +with findContours , throwing away contours with small area ( contourArea ), and rendering the +remaining contours with drawContours. + + */ +CV_EXPORTS_W int meanShift( InputArray probImage, CV_IN_OUT Rect& window, TermCriteria criteria ); + +/** @brief Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK. + +@param img 8-bit input image. +@param pyramid output pyramid. +@param winSize window size of optical flow algorithm. Must be not less than winSize argument of +calcOpticalFlowPyrLK. It is needed to calculate required padding for pyramid levels. +@param maxLevel 0-based maximal pyramid level number. +@param withDerivatives set to precompute gradients for the every pyramid level. If pyramid is +constructed without the gradients then calcOpticalFlowPyrLK will calculate them internally. +@param pyrBorder the border mode for pyramid layers. +@param derivBorder the border mode for gradients. +@param tryReuseInputImage put ROI of input image into the pyramid if possible. You can pass false +to force data copying. +@return number of levels in constructed pyramid. Can be less than maxLevel. + */ +CV_EXPORTS_W int buildOpticalFlowPyramid( InputArray img, OutputArrayOfArrays pyramid, + Size winSize, int maxLevel, bool withDerivatives = true, + int pyrBorder = BORDER_REFLECT_101, + int derivBorder = BORDER_CONSTANT, + bool tryReuseInputImage = true ); + +/** @example samples/cpp/lkdemo.cpp +An example using the Lucas-Kanade optical flow algorithm +*/ + +/** @brief Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with +pyramids. + +@param prevImg first 8-bit input image or pyramid constructed by buildOpticalFlowPyramid. +@param nextImg second input image or pyramid of the same size and the same type as prevImg. +@param prevPts vector of 2D points for which the flow needs to be found; point coordinates must be +single-precision floating-point numbers. +@param nextPts output vector of 2D points (with single-precision floating-point coordinates) +containing the calculated new positions of input features in the second image; when +OPTFLOW_USE_INITIAL_FLOW flag is passed, the vector must have the same size as in the input. +@param status output status vector (of unsigned chars); each element of the vector is set to 1 if +the flow for the corresponding features has been found, otherwise, it is set to 0. +@param err output vector of errors; each element of the vector is set to an error for the +corresponding feature, type of the error measure can be set in flags parameter; if the flow wasn't +found then the error is not defined (use the status parameter to find such cases). +@param winSize size of the search window at each pyramid level. +@param maxLevel 0-based maximal pyramid level number; if set to 0, pyramids are not used (single +level), if set to 1, two levels are used, and so on; if pyramids are passed to input then +algorithm will use as many levels as pyramids have but no more than maxLevel. +@param criteria parameter, specifying the termination criteria of the iterative search algorithm +(after the specified maximum number of iterations criteria.maxCount or when the search window +moves by less than criteria.epsilon. +@param flags operation flags: + - **OPTFLOW_USE_INITIAL_FLOW** uses initial estimations, stored in nextPts; if the flag is + not set, then prevPts is copied to nextPts and is considered the initial estimate. + - **OPTFLOW_LK_GET_MIN_EIGENVALS** use minimum eigen values as an error measure (see + minEigThreshold description); if the flag is not set, then L1 distance between patches + around the original and a moved point, divided by number of pixels in a window, is used as a + error measure. +@param minEigThreshold the algorithm calculates the minimum eigen value of a 2x2 normal matrix of +optical flow equations (this matrix is called a spatial gradient matrix in @cite Bouguet00), divided +by number of pixels in a window; if this value is less than minEigThreshold, then a corresponding +feature is filtered out and its flow is not processed, so it allows to remove bad points and get a +performance boost. + +The function implements a sparse iterative version of the Lucas-Kanade optical flow in pyramids. See +@cite Bouguet00 . The function is parallelized with the TBB library. + +@note + +- An example using the Lucas-Kanade optical flow algorithm can be found at + opencv_source_code/samples/cpp/lkdemo.cpp +- (Python) An example using the Lucas-Kanade optical flow algorithm can be found at + opencv_source_code/samples/python/lk_track.py +- (Python) An example using the Lucas-Kanade tracker for homography matching can be found at + opencv_source_code/samples/python/lk_homography.py + */ +CV_EXPORTS_W void calcOpticalFlowPyrLK( InputArray prevImg, InputArray nextImg, + InputArray prevPts, InputOutputArray nextPts, + OutputArray status, OutputArray err, + Size winSize = Size(21,21), int maxLevel = 3, + TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 0.01), + int flags = 0, double minEigThreshold = 1e-4 ); + +/** @brief Computes a dense optical flow using the Gunnar Farneback's algorithm. + +@param prev first 8-bit single-channel input image. +@param next second input image of the same size and the same type as prev. +@param flow computed flow image that has the same size as prev and type CV_32FC2. +@param pyr_scale parameter, specifying the image scale (\<1) to build pyramids for each image; +pyr_scale=0.5 means a classical pyramid, where each next layer is twice smaller than the previous +one. +@param levels number of pyramid layers including the initial image; levels=1 means that no extra +layers are created and only the original images are used. +@param winsize averaging window size; larger values increase the algorithm robustness to image +noise and give more chances for fast motion detection, but yield more blurred motion field. +@param iterations number of iterations the algorithm does at each pyramid level. +@param poly_n size of the pixel neighborhood used to find polynomial expansion in each pixel; +larger values mean that the image will be approximated with smoother surfaces, yielding more +robust algorithm and more blurred motion field, typically poly_n =5 or 7. +@param poly_sigma standard deviation of the Gaussian that is used to smooth derivatives used as a +basis for the polynomial expansion; for poly_n=5, you can set poly_sigma=1.1, for poly_n=7, a +good value would be poly_sigma=1.5. +@param flags operation flags that can be a combination of the following: + - **OPTFLOW_USE_INITIAL_FLOW** uses the input flow as an initial flow approximation. + - **OPTFLOW_FARNEBACK_GAUSSIAN** uses the Gaussian \f$\texttt{winsize}\times\texttt{winsize}\f$ + filter instead of a box filter of the same size for optical flow estimation; usually, this + option gives z more accurate flow than with a box filter, at the cost of lower speed; + normally, winsize for a Gaussian window should be set to a larger value to achieve the same + level of robustness. + +The function finds an optical flow for each prev pixel using the @cite Farneback2003 algorithm so that + +\f[\texttt{prev} (y,x) \sim \texttt{next} ( y + \texttt{flow} (y,x)[1], x + \texttt{flow} (y,x)[0])\f] + +@note + +- An example using the optical flow algorithm described by Gunnar Farneback can be found at + opencv_source_code/samples/cpp/fback.cpp +- (Python) An example using the optical flow algorithm described by Gunnar Farneback can be + found at opencv_source_code/samples/python/opt_flow.py + */ +CV_EXPORTS_W void calcOpticalFlowFarneback( InputArray prev, InputArray next, InputOutputArray flow, + double pyr_scale, int levels, int winsize, + int iterations, int poly_n, double poly_sigma, + int flags ); + +/** @brief Computes an optimal affine transformation between two 2D point sets. + +@param src First input 2D point set stored in std::vector or Mat, or an image stored in Mat. +@param dst Second input 2D point set of the same size and the same type as A, or another image. +@param fullAffine If true, the function finds an optimal affine transformation with no additional +restrictions (6 degrees of freedom). Otherwise, the class of transformations to choose from is +limited to combinations of translation, rotation, and uniform scaling (4 degrees of freedom). + +The function finds an optimal affine transform *[A|b]* (a 2 x 3 floating-point matrix) that +approximates best the affine transformation between: + +* Two point sets +* Two raster images. In this case, the function first finds some features in the src image and + finds the corresponding features in dst image. After that, the problem is reduced to the first + case. +In case of point sets, the problem is formulated as follows: you need to find a 2x2 matrix *A* and +2x1 vector *b* so that: + +\f[[A^*|b^*] = arg \min _{[A|b]} \sum _i \| \texttt{dst}[i] - A { \texttt{src}[i]}^T - b \| ^2\f] +where src[i] and dst[i] are the i-th points in src and dst, respectively +\f$[A|b]\f$ can be either arbitrary (when fullAffine=true ) or have a form of +\f[\begin{bmatrix} a_{11} & a_{12} & b_1 \\ -a_{12} & a_{11} & b_2 \end{bmatrix}\f] +when fullAffine=false. + +@sa +estimateAffine2D, estimateAffinePartial2D, getAffineTransform, getPerspectiveTransform, findHomography + */ +CV_EXPORTS_W Mat estimateRigidTransform( InputArray src, InputArray dst, bool fullAffine); +CV_EXPORTS_W Mat estimateRigidTransform( InputArray src, InputArray dst, bool fullAffine, int ransacMaxIters, double ransacGoodRatio, + int ransacSize0); + + +enum +{ + MOTION_TRANSLATION = 0, + MOTION_EUCLIDEAN = 1, + MOTION_AFFINE = 2, + MOTION_HOMOGRAPHY = 3 +}; + +/** @example samples/cpp/image_alignment.cpp +An example using the image alignment ECC algorithm +*/ + +/** @brief Finds the geometric transform (warp) between two images in terms of the ECC criterion @cite EP08 . + +@param templateImage single-channel template image; CV_8U or CV_32F array. +@param inputImage single-channel input image which should be warped with the final warpMatrix in +order to provide an image similar to templateImage, same type as temlateImage. +@param warpMatrix floating-point \f$2\times 3\f$ or \f$3\times 3\f$ mapping matrix (warp). +@param motionType parameter, specifying the type of motion: + - **MOTION_TRANSLATION** sets a translational motion model; warpMatrix is \f$2\times 3\f$ with + the first \f$2\times 2\f$ part being the unity matrix and the rest two parameters being + estimated. + - **MOTION_EUCLIDEAN** sets a Euclidean (rigid) transformation as motion model; three + parameters are estimated; warpMatrix is \f$2\times 3\f$. + - **MOTION_AFFINE** sets an affine motion model (DEFAULT); six parameters are estimated; + warpMatrix is \f$2\times 3\f$. + - **MOTION_HOMOGRAPHY** sets a homography as a motion model; eight parameters are + estimated;\`warpMatrix\` is \f$3\times 3\f$. +@param criteria parameter, specifying the termination criteria of the ECC algorithm; +criteria.epsilon defines the threshold of the increment in the correlation coefficient between two +iterations (a negative criteria.epsilon makes criteria.maxcount the only termination criterion). +Default values are shown in the declaration above. +@param inputMask An optional mask to indicate valid values of inputImage. + +The function estimates the optimum transformation (warpMatrix) with respect to ECC criterion +(@cite EP08), that is + +\f[\texttt{warpMatrix} = \texttt{warpMatrix} = \arg\max_{W} \texttt{ECC}(\texttt{templateImage}(x,y),\texttt{inputImage}(x',y'))\f] + +where + +\f[\begin{bmatrix} x' \\ y' \end{bmatrix} = W \cdot \begin{bmatrix} x \\ y \\ 1 \end{bmatrix}\f] + +(the equation holds with homogeneous coordinates for homography). It returns the final enhanced +correlation coefficient, that is the correlation coefficient between the template image and the +final warped input image. When a \f$3\times 3\f$ matrix is given with motionType =0, 1 or 2, the third +row is ignored. + +Unlike findHomography and estimateRigidTransform, the function findTransformECC implements an +area-based alignment that builds on intensity similarities. In essence, the function updates the +initial transformation that roughly aligns the images. If this information is missing, the identity +warp (unity matrix) is used as an initialization. Note that if images undergo strong +displacements/rotations, an initial transformation that roughly aligns the images is necessary +(e.g., a simple euclidean/similarity transform that allows for the images showing the same image +content approximately). Use inverse warping in the second image to take an image close to the first +one, i.e. use the flag WARP_INVERSE_MAP with warpAffine or warpPerspective. See also the OpenCV +sample image_alignment.cpp that demonstrates the use of the function. Note that the function throws +an exception if algorithm does not converges. + +@sa +estimateAffine2D, estimateAffinePartial2D, findHomography + */ +CV_EXPORTS_W double findTransformECC( InputArray templateImage, InputArray inputImage, + InputOutputArray warpMatrix, int motionType = MOTION_AFFINE, + TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 50, 0.001), + InputArray inputMask = noArray()); + +/** @example samples/cpp/kalman.cpp +An example using the standard Kalman filter +*/ + +/** @brief Kalman filter class. + +The class implements a standard Kalman filter , +@cite Welch95 . However, you can modify transitionMatrix, controlMatrix, and measurementMatrix to get +an extended Kalman filter functionality. +@note In C API when CvKalman\* kalmanFilter structure is not needed anymore, it should be released +with cvReleaseKalman(&kalmanFilter) + */ +class CV_EXPORTS_W KalmanFilter +{ +public: + CV_WRAP KalmanFilter(); + /** @overload + @param dynamParams Dimensionality of the state. + @param measureParams Dimensionality of the measurement. + @param controlParams Dimensionality of the control vector. + @param type Type of the created matrices that should be CV_32F or CV_64F. + */ + CV_WRAP KalmanFilter( int dynamParams, int measureParams, int controlParams = 0, int type = CV_32F ); + + /** @brief Re-initializes Kalman filter. The previous content is destroyed. + + @param dynamParams Dimensionality of the state. + @param measureParams Dimensionality of the measurement. + @param controlParams Dimensionality of the control vector. + @param type Type of the created matrices that should be CV_32F or CV_64F. + */ + void init( int dynamParams, int measureParams, int controlParams = 0, int type = CV_32F ); + + /** @brief Computes a predicted state. + + @param control The optional input control + */ + CV_WRAP const Mat& predict( const Mat& control = Mat() ); + + /** @brief Updates the predicted state from the measurement. + + @param measurement The measured system parameters + */ + CV_WRAP const Mat& correct( const Mat& measurement ); + + CV_PROP_RW Mat statePre; //!< predicted state (x'(k)): x(k)=A*x(k-1)+B*u(k) + CV_PROP_RW Mat statePost; //!< corrected state (x(k)): x(k)=x'(k)+K(k)*(z(k)-H*x'(k)) + CV_PROP_RW Mat transitionMatrix; //!< state transition matrix (A) + CV_PROP_RW Mat controlMatrix; //!< control matrix (B) (not used if there is no control) + CV_PROP_RW Mat measurementMatrix; //!< measurement matrix (H) + CV_PROP_RW Mat processNoiseCov; //!< process noise covariance matrix (Q) + CV_PROP_RW Mat measurementNoiseCov;//!< measurement noise covariance matrix (R) + CV_PROP_RW Mat errorCovPre; //!< priori error estimate covariance matrix (P'(k)): P'(k)=A*P(k-1)*At + Q)*/ + CV_PROP_RW Mat gain; //!< Kalman gain matrix (K(k)): K(k)=P'(k)*Ht*inv(H*P'(k)*Ht+R) + CV_PROP_RW Mat errorCovPost; //!< posteriori error estimate covariance matrix (P(k)): P(k)=(I-K(k)*H)*P'(k) + + // temporary matrices + Mat temp1; + Mat temp2; + Mat temp3; + Mat temp4; + Mat temp5; +}; + + +class CV_EXPORTS_W DenseOpticalFlow : public Algorithm +{ +public: + /** @brief Calculates an optical flow. + + @param I0 first 8-bit single-channel input image. + @param I1 second input image of the same size and the same type as prev. + @param flow computed flow image that has the same size as prev and type CV_32FC2. + */ + CV_WRAP virtual void calc( InputArray I0, InputArray I1, InputOutputArray flow ) = 0; + /** @brief Releases all inner buffers. + */ + CV_WRAP virtual void collectGarbage() = 0; +}; + +/** @brief Base interface for sparse optical flow algorithms. + */ +class CV_EXPORTS_W SparseOpticalFlow : public Algorithm +{ +public: + /** @brief Calculates a sparse optical flow. + + @param prevImg First input image. + @param nextImg Second input image of the same size and the same type as prevImg. + @param prevPts Vector of 2D points for which the flow needs to be found. + @param nextPts Output vector of 2D points containing the calculated new positions of input features in the second image. + @param status Output status vector. Each element of the vector is set to 1 if the + flow for the corresponding features has been found. Otherwise, it is set to 0. + @param err Optional output vector that contains error response for each point (inverse confidence). + */ + CV_WRAP virtual void calc(InputArray prevImg, InputArray nextImg, + InputArray prevPts, InputOutputArray nextPts, + OutputArray status, + OutputArray err = cv::noArray()) = 0; +}; + +/** @brief "Dual TV L1" Optical Flow Algorithm. + +The class implements the "Dual TV L1" optical flow algorithm described in @cite Zach2007 and +@cite Javier2012 . +Here are important members of the class that control the algorithm, which you can set after +constructing the class instance: + +- member double tau + Time step of the numerical scheme. + +- member double lambda + Weight parameter for the data term, attachment parameter. This is the most relevant + parameter, which determines the smoothness of the output. The smaller this parameter is, + the smoother the solutions we obtain. It depends on the range of motions of the images, so + its value should be adapted to each image sequence. + +- member double theta + Weight parameter for (u - v)\^2, tightness parameter. It serves as a link between the + attachment and the regularization terms. In theory, it should have a small value in order + to maintain both parts in correspondence. The method is stable for a large range of values + of this parameter. + +- member int nscales + Number of scales used to create the pyramid of images. + +- member int warps + Number of warpings per scale. Represents the number of times that I1(x+u0) and grad( + I1(x+u0) ) are computed per scale. This is a parameter that assures the stability of the + method. It also affects the running time, so it is a compromise between speed and + accuracy. + +- member double epsilon + Stopping criterion threshold used in the numerical scheme, which is a trade-off between + precision and running time. A small value will yield more accurate solutions at the + expense of a slower convergence. + +- member int iterations + Stopping criterion iterations number used in the numerical scheme. + +C. Zach, T. Pock and H. Bischof, "A Duality Based Approach for Realtime TV-L1 Optical Flow". +Javier Sanchez, Enric Meinhardt-Llopis and Gabriele Facciolo. "TV-L1 Optical Flow Estimation". +*/ +class CV_EXPORTS_W DualTVL1OpticalFlow : public DenseOpticalFlow +{ +public: + //! @brief Time step of the numerical scheme + /** @see setTau */ + CV_WRAP virtual double getTau() const = 0; + /** @copybrief getTau @see getTau */ + CV_WRAP virtual void setTau(double val) = 0; + //! @brief Weight parameter for the data term, attachment parameter + /** @see setLambda */ + CV_WRAP virtual double getLambda() const = 0; + /** @copybrief getLambda @see getLambda */ + CV_WRAP virtual void setLambda(double val) = 0; + //! @brief Weight parameter for (u - v)^2, tightness parameter + /** @see setTheta */ + CV_WRAP virtual double getTheta() const = 0; + /** @copybrief getTheta @see getTheta */ + CV_WRAP virtual void setTheta(double val) = 0; + //! @brief coefficient for additional illumination variation term + /** @see setGamma */ + CV_WRAP virtual double getGamma() const = 0; + /** @copybrief getGamma @see getGamma */ + CV_WRAP virtual void setGamma(double val) = 0; + //! @brief Number of scales used to create the pyramid of images + /** @see setScalesNumber */ + CV_WRAP virtual int getScalesNumber() const = 0; + /** @copybrief getScalesNumber @see getScalesNumber */ + CV_WRAP virtual void setScalesNumber(int val) = 0; + //! @brief Number of warpings per scale + /** @see setWarpingsNumber */ + CV_WRAP virtual int getWarpingsNumber() const = 0; + /** @copybrief getWarpingsNumber @see getWarpingsNumber */ + CV_WRAP virtual void setWarpingsNumber(int val) = 0; + //! @brief Stopping criterion threshold used in the numerical scheme, which is a trade-off between precision and running time + /** @see setEpsilon */ + CV_WRAP virtual double getEpsilon() const = 0; + /** @copybrief getEpsilon @see getEpsilon */ + CV_WRAP virtual void setEpsilon(double val) = 0; + //! @brief Inner iterations (between outlier filtering) used in the numerical scheme + /** @see setInnerIterations */ + CV_WRAP virtual int getInnerIterations() const = 0; + /** @copybrief getInnerIterations @see getInnerIterations */ + CV_WRAP virtual void setInnerIterations(int val) = 0; + //! @brief Outer iterations (number of inner loops) used in the numerical scheme + /** @see setOuterIterations */ + CV_WRAP virtual int getOuterIterations() const = 0; + /** @copybrief getOuterIterations @see getOuterIterations */ + CV_WRAP virtual void setOuterIterations(int val) = 0; + //! @brief Use initial flow + /** @see setUseInitialFlow */ + CV_WRAP virtual bool getUseInitialFlow() const = 0; + /** @copybrief getUseInitialFlow @see getUseInitialFlow */ + CV_WRAP virtual void setUseInitialFlow(bool val) = 0; + //! @brief Step between scales (<1) + /** @see setScaleStep */ + CV_WRAP virtual double getScaleStep() const = 0; + /** @copybrief getScaleStep @see getScaleStep */ + CV_WRAP virtual void setScaleStep(double val) = 0; + //! @brief Median filter kernel size (1 = no filter) (3 or 5) + /** @see setMedianFiltering */ + CV_WRAP virtual int getMedianFiltering() const = 0; + /** @copybrief getMedianFiltering @see getMedianFiltering */ + CV_WRAP virtual void setMedianFiltering(int val) = 0; + + /** @brief Creates instance of cv::DualTVL1OpticalFlow*/ + CV_WRAP static Ptr create( + double tau = 0.25, + double lambda = 0.15, + double theta = 0.3, + int nscales = 5, + int warps = 5, + double epsilon = 0.01, + int innnerIterations = 30, + int outerIterations = 10, + double scaleStep = 0.8, + double gamma = 0.0, + int medianFiltering = 5, + bool useInitialFlow = false); +}; + +/** @brief Creates instance of cv::DenseOpticalFlow +*/ +CV_EXPORTS_W Ptr createOptFlow_DualTVL1(); + +/** @brief Class computing a dense optical flow using the Gunnar Farneback's algorithm. + */ +class CV_EXPORTS_W FarnebackOpticalFlow : public DenseOpticalFlow +{ +public: + CV_WRAP virtual int getNumLevels() const = 0; + CV_WRAP virtual void setNumLevels(int numLevels) = 0; + + CV_WRAP virtual double getPyrScale() const = 0; + CV_WRAP virtual void setPyrScale(double pyrScale) = 0; + + CV_WRAP virtual bool getFastPyramids() const = 0; + CV_WRAP virtual void setFastPyramids(bool fastPyramids) = 0; + + CV_WRAP virtual int getWinSize() const = 0; + CV_WRAP virtual void setWinSize(int winSize) = 0; + + CV_WRAP virtual int getNumIters() const = 0; + CV_WRAP virtual void setNumIters(int numIters) = 0; + + CV_WRAP virtual int getPolyN() const = 0; + CV_WRAP virtual void setPolyN(int polyN) = 0; + + CV_WRAP virtual double getPolySigma() const = 0; + CV_WRAP virtual void setPolySigma(double polySigma) = 0; + + CV_WRAP virtual int getFlags() const = 0; + CV_WRAP virtual void setFlags(int flags) = 0; + + CV_WRAP static Ptr create( + int numLevels = 5, + double pyrScale = 0.5, + bool fastPyramids = false, + int winSize = 13, + int numIters = 10, + int polyN = 5, + double polySigma = 1.1, + int flags = 0); +}; + + +/** @brief Class used for calculating a sparse optical flow. + +The class can calculate an optical flow for a sparse feature set using the +iterative Lucas-Kanade method with pyramids. + +@sa calcOpticalFlowPyrLK + +*/ +class CV_EXPORTS_W SparsePyrLKOpticalFlow : public SparseOpticalFlow +{ +public: + CV_WRAP virtual Size getWinSize() const = 0; + CV_WRAP virtual void setWinSize(Size winSize) = 0; + + CV_WRAP virtual int getMaxLevel() const = 0; + CV_WRAP virtual void setMaxLevel(int maxLevel) = 0; + + CV_WRAP virtual TermCriteria getTermCriteria() const = 0; + CV_WRAP virtual void setTermCriteria(TermCriteria& crit) = 0; + + CV_WRAP virtual int getFlags() const = 0; + CV_WRAP virtual void setFlags(int flags) = 0; + + CV_WRAP virtual double getMinEigThreshold() const = 0; + CV_WRAP virtual void setMinEigThreshold(double minEigThreshold) = 0; + + CV_WRAP static Ptr create( + Size winSize = Size(21, 21), + int maxLevel = 3, TermCriteria crit = + TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 0.01), + int flags = 0, + double minEigThreshold = 1e-4); +}; + +//! @} video_track + +} // cv + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/video/tracking_c.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/video/tracking_c.h new file mode 100644 index 0000000000000000000000000000000000000000..3e32fbd0c0bb5693f7e8b4ad3a11f97be01b2e48 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/video/tracking_c.h @@ -0,0 +1,232 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_TRACKING_C_H +#define OPENCV_TRACKING_C_H + +#include "opencv2/imgproc/types_c.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/** @addtogroup video_c + @{ +*/ + +/****************************************************************************************\ +* Motion Analysis * +\****************************************************************************************/ + +/************************************ optical flow ***************************************/ + +#define CV_LKFLOW_PYR_A_READY 1 +#define CV_LKFLOW_PYR_B_READY 2 +#define CV_LKFLOW_INITIAL_GUESSES 4 +#define CV_LKFLOW_GET_MIN_EIGENVALS 8 + +/* It is Lucas & Kanade method, modified to use pyramids. + Also it does several iterations to get optical flow for + every point at every pyramid level. + Calculates optical flow between two images for certain set of points (i.e. + it is a "sparse" optical flow, which is opposite to the previous 3 methods) */ +CVAPI(void) cvCalcOpticalFlowPyrLK( const CvArr* prev, const CvArr* curr, + CvArr* prev_pyr, CvArr* curr_pyr, + const CvPoint2D32f* prev_features, + CvPoint2D32f* curr_features, + int count, + CvSize win_size, + int level, + char* status, + float* track_error, + CvTermCriteria criteria, + int flags ); + + +/* Modification of a previous sparse optical flow algorithm to calculate + affine flow */ +CVAPI(void) cvCalcAffineFlowPyrLK( const CvArr* prev, const CvArr* curr, + CvArr* prev_pyr, CvArr* curr_pyr, + const CvPoint2D32f* prev_features, + CvPoint2D32f* curr_features, + float* matrices, int count, + CvSize win_size, int level, + char* status, float* track_error, + CvTermCriteria criteria, int flags ); + +/* Estimate rigid transformation between 2 images or 2 point sets */ +CVAPI(int) cvEstimateRigidTransform( const CvArr* A, const CvArr* B, + CvMat* M, int full_affine ); + +/* Estimate optical flow for each pixel using the two-frame G. Farneback algorithm */ +CVAPI(void) cvCalcOpticalFlowFarneback( const CvArr* prev, const CvArr* next, + CvArr* flow, double pyr_scale, int levels, + int winsize, int iterations, int poly_n, + double poly_sigma, int flags ); + +/********************************* motion templates *************************************/ + +/****************************************************************************************\ +* All the motion template functions work only with single channel images. * +* Silhouette image must have depth IPL_DEPTH_8U or IPL_DEPTH_8S * +* Motion history image must have depth IPL_DEPTH_32F, * +* Gradient mask - IPL_DEPTH_8U or IPL_DEPTH_8S, * +* Motion orientation image - IPL_DEPTH_32F * +* Segmentation mask - IPL_DEPTH_32F * +* All the angles are in degrees, all the times are in milliseconds * +\****************************************************************************************/ + +/* Updates motion history image given motion silhouette */ +CVAPI(void) cvUpdateMotionHistory( const CvArr* silhouette, CvArr* mhi, + double timestamp, double duration ); + +/* Calculates gradient of the motion history image and fills + a mask indicating where the gradient is valid */ +CVAPI(void) cvCalcMotionGradient( const CvArr* mhi, CvArr* mask, CvArr* orientation, + double delta1, double delta2, + int aperture_size CV_DEFAULT(3)); + +/* Calculates average motion direction within a selected motion region + (region can be selected by setting ROIs and/or by composing a valid gradient mask + with the region mask) */ +CVAPI(double) cvCalcGlobalOrientation( const CvArr* orientation, const CvArr* mask, + const CvArr* mhi, double timestamp, + double duration ); + +/* Splits a motion history image into a few parts corresponding to separate independent motions + (e.g. left hand, right hand) */ +CVAPI(CvSeq*) cvSegmentMotion( const CvArr* mhi, CvArr* seg_mask, + CvMemStorage* storage, + double timestamp, double seg_thresh ); + +/****************************************************************************************\ +* Tracking * +\****************************************************************************************/ + +/* Implements CAMSHIFT algorithm - determines object position, size and orientation + from the object histogram back project (extension of meanshift) */ +CVAPI(int) cvCamShift( const CvArr* prob_image, CvRect window, + CvTermCriteria criteria, CvConnectedComp* comp, + CvBox2D* box CV_DEFAULT(NULL) ); + +/* Implements MeanShift algorithm - determines object position + from the object histogram back project */ +CVAPI(int) cvMeanShift( const CvArr* prob_image, CvRect window, + CvTermCriteria criteria, CvConnectedComp* comp ); + +/* +standard Kalman filter (in G. Welch' and G. Bishop's notation): + + x(k)=A*x(k-1)+B*u(k)+w(k) p(w)~N(0,Q) + z(k)=H*x(k)+v(k), p(v)~N(0,R) +*/ +typedef struct CvKalman +{ + int MP; /* number of measurement vector dimensions */ + int DP; /* number of state vector dimensions */ + int CP; /* number of control vector dimensions */ + + /* backward compatibility fields */ +#if 1 + float* PosterState; /* =state_pre->data.fl */ + float* PriorState; /* =state_post->data.fl */ + float* DynamMatr; /* =transition_matrix->data.fl */ + float* MeasurementMatr; /* =measurement_matrix->data.fl */ + float* MNCovariance; /* =measurement_noise_cov->data.fl */ + float* PNCovariance; /* =process_noise_cov->data.fl */ + float* KalmGainMatr; /* =gain->data.fl */ + float* PriorErrorCovariance;/* =error_cov_pre->data.fl */ + float* PosterErrorCovariance;/* =error_cov_post->data.fl */ + float* Temp1; /* temp1->data.fl */ + float* Temp2; /* temp2->data.fl */ +#endif + + CvMat* state_pre; /* predicted state (x'(k)): + x(k)=A*x(k-1)+B*u(k) */ + CvMat* state_post; /* corrected state (x(k)): + x(k)=x'(k)+K(k)*(z(k)-H*x'(k)) */ + CvMat* transition_matrix; /* state transition matrix (A) */ + CvMat* control_matrix; /* control matrix (B) + (it is not used if there is no control)*/ + CvMat* measurement_matrix; /* measurement matrix (H) */ + CvMat* process_noise_cov; /* process noise covariance matrix (Q) */ + CvMat* measurement_noise_cov; /* measurement noise covariance matrix (R) */ + CvMat* error_cov_pre; /* priori error estimate covariance matrix (P'(k)): + P'(k)=A*P(k-1)*At + Q)*/ + CvMat* gain; /* Kalman gain matrix (K(k)): + K(k)=P'(k)*Ht*inv(H*P'(k)*Ht+R)*/ + CvMat* error_cov_post; /* posteriori error estimate covariance matrix (P(k)): + P(k)=(I-K(k)*H)*P'(k) */ + CvMat* temp1; /* temporary matrices */ + CvMat* temp2; + CvMat* temp3; + CvMat* temp4; + CvMat* temp5; +} CvKalman; + +/* Creates Kalman filter and sets A, B, Q, R and state to some initial values */ +CVAPI(CvKalman*) cvCreateKalman( int dynam_params, int measure_params, + int control_params CV_DEFAULT(0)); + +/* Releases Kalman filter state */ +CVAPI(void) cvReleaseKalman( CvKalman** kalman); + +/* Updates Kalman filter by time (predicts future state of the system) */ +CVAPI(const CvMat*) cvKalmanPredict( CvKalman* kalman, + const CvMat* control CV_DEFAULT(NULL)); + +/* Updates Kalman filter by measurement + (corrects state of the system and internal matrices) */ +CVAPI(const CvMat*) cvKalmanCorrect( CvKalman* kalman, const CvMat* measurement ); + +#define cvKalmanUpdateByTime cvKalmanPredict +#define cvKalmanUpdateByMeasurement cvKalmanCorrect + +/** @} video_c */ + +#ifdef __cplusplus +} // extern "C" +#endif + + +#endif // OPENCV_TRACKING_C_H diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/video/video.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/video/video.hpp new file mode 100644 index 0000000000000000000000000000000000000000..8267b85d59585b2bfc4ee6c9d67a5319614a8e58 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/video/video.hpp @@ -0,0 +1,48 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifdef __OPENCV_BUILD +#error this is a compatibility header which should not be used inside the OpenCV library +#endif + +#include "opencv2/video.hpp" diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videoio.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videoio.hpp new file mode 100644 index 0000000000000000000000000000000000000000..d43e703984cf5e29b257cbe81b44c68005e07141 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videoio.hpp @@ -0,0 +1,958 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_VIDEOIO_HPP +#define OPENCV_VIDEOIO_HPP + +#include "opencv2/core.hpp" + +/** + @defgroup videoio Video I/O + + @brief Read and write video or images sequence with OpenCV + + ### See also: + - @ref videoio_overview + - Tutorials: @ref tutorial_table_of_content_videoio + @{ + @defgroup videoio_flags_base Flags for video I/O + @defgroup videoio_flags_others Additional flags for video I/O API backends + @defgroup videoio_c C API for video I/O + @defgroup videoio_ios iOS glue for video I/O + @defgroup videoio_winrt WinRT glue for video I/O + @} +*/ + +////////////////////////////////// video io ///////////////////////////////// + +typedef struct CvCapture CvCapture; +typedef struct CvVideoWriter CvVideoWriter; + +namespace cv +{ + +//! @addtogroup videoio +//! @{ + +//! @addtogroup videoio_flags_base +//! @{ + + +/** @brief %VideoCapture API backends identifier. + +Select preferred API for a capture object. +To be used in the VideoCapture::VideoCapture() constructor or VideoCapture::open() + +@note Backends are available only if they have been built with your OpenCV binaries. +See @ref videoio_overview for more information. +*/ +enum VideoCaptureAPIs { + CAP_ANY = 0, //!< Auto detect == 0 + CAP_VFW = 200, //!< Video For Windows (platform native) + CAP_V4L = 200, //!< V4L/V4L2 capturing support via libv4l + CAP_V4L2 = CAP_V4L, //!< Same as CAP_V4L + CAP_FIREWIRE = 300, //!< IEEE 1394 drivers + CAP_FIREWARE = CAP_FIREWIRE, //!< Same as CAP_FIREWIRE + CAP_IEEE1394 = CAP_FIREWIRE, //!< Same as CAP_FIREWIRE + CAP_DC1394 = CAP_FIREWIRE, //!< Same as CAP_FIREWIRE + CAP_CMU1394 = CAP_FIREWIRE, //!< Same as CAP_FIREWIRE + CAP_QT = 500, //!< QuickTime + CAP_UNICAP = 600, //!< Unicap drivers + CAP_DSHOW = 700, //!< DirectShow (via videoInput) + CAP_PVAPI = 800, //!< PvAPI, Prosilica GigE SDK + CAP_OPENNI = 900, //!< OpenNI (for Kinect) + CAP_OPENNI_ASUS = 910, //!< OpenNI (for Asus Xtion) + CAP_ANDROID = 1000, //!< Android - not used + CAP_XIAPI = 1100, //!< XIMEA Camera API + CAP_AVFOUNDATION = 1200, //!< AVFoundation framework for iOS (OS X Lion will have the same API) + CAP_GIGANETIX = 1300, //!< Smartek Giganetix GigEVisionSDK + CAP_MSMF = 1400, //!< Microsoft Media Foundation (via videoInput) + CAP_WINRT = 1410, //!< Microsoft Windows Runtime using Media Foundation + CAP_INTELPERC = 1500, //!< Intel Perceptual Computing SDK + CAP_OPENNI2 = 1600, //!< OpenNI2 (for Kinect) + CAP_OPENNI2_ASUS = 1610, //!< OpenNI2 (for Asus Xtion and Occipital Structure sensors) + CAP_GPHOTO2 = 1700, //!< gPhoto2 connection + CAP_GSTREAMER = 1800, //!< GStreamer + CAP_FFMPEG = 1900, //!< Open and record video file or stream using the FFMPEG library + CAP_IMAGES = 2000, //!< OpenCV Image Sequence (e.g. img_%02d.jpg) + CAP_ARAVIS = 2100, //!< Aravis SDK + CAP_OPENCV_MJPEG = 2200, //!< Built-in OpenCV MotionJPEG codec + CAP_INTEL_MFX = 2300, //!< Intel MediaSDK + CAP_XINE = 2400, //!< XINE engine (Linux) + }; + +/** @brief %VideoCapture generic properties identifier. + + Reading / writing properties involves many layers. Some unexpected result might happens along this chain. + Effective behaviour depends from device hardware, driver and API Backend. + @sa videoio_flags_others, VideoCapture::get(), VideoCapture::set() +*/ +enum VideoCaptureProperties { + CAP_PROP_POS_MSEC =0, //!< Current position of the video file in milliseconds. + CAP_PROP_POS_FRAMES =1, //!< 0-based index of the frame to be decoded/captured next. + CAP_PROP_POS_AVI_RATIO =2, //!< Relative position of the video file: 0=start of the film, 1=end of the film. + CAP_PROP_FRAME_WIDTH =3, //!< Width of the frames in the video stream. + CAP_PROP_FRAME_HEIGHT =4, //!< Height of the frames in the video stream. + CAP_PROP_FPS =5, //!< Frame rate. + CAP_PROP_FOURCC =6, //!< 4-character code of codec. see VideoWriter::fourcc . + CAP_PROP_FRAME_COUNT =7, //!< Number of frames in the video file. + CAP_PROP_FORMAT =8, //!< Format of the %Mat objects returned by VideoCapture::retrieve(). + CAP_PROP_MODE =9, //!< Backend-specific value indicating the current capture mode. + CAP_PROP_BRIGHTNESS =10, //!< Brightness of the image (only for those cameras that support). + CAP_PROP_CONTRAST =11, //!< Contrast of the image (only for cameras). + CAP_PROP_SATURATION =12, //!< Saturation of the image (only for cameras). + CAP_PROP_HUE =13, //!< Hue of the image (only for cameras). + CAP_PROP_GAIN =14, //!< Gain of the image (only for those cameras that support). + CAP_PROP_EXPOSURE =15, //!< Exposure (only for those cameras that support). + CAP_PROP_CONVERT_RGB =16, //!< Boolean flags indicating whether images should be converted to RGB. + CAP_PROP_WHITE_BALANCE_BLUE_U =17, //!< Currently unsupported. + CAP_PROP_RECTIFICATION =18, //!< Rectification flag for stereo cameras (note: only supported by DC1394 v 2.x backend currently). + CAP_PROP_MONOCHROME =19, + CAP_PROP_SHARPNESS =20, + CAP_PROP_AUTO_EXPOSURE =21, //!< DC1394: exposure control done by camera, user can adjust reference level using this feature. + CAP_PROP_GAMMA =22, + CAP_PROP_TEMPERATURE =23, + CAP_PROP_TRIGGER =24, + CAP_PROP_TRIGGER_DELAY =25, + CAP_PROP_WHITE_BALANCE_RED_V =26, + CAP_PROP_ZOOM =27, + CAP_PROP_FOCUS =28, + CAP_PROP_GUID =29, + CAP_PROP_ISO_SPEED =30, + CAP_PROP_BACKLIGHT =32, + CAP_PROP_PAN =33, + CAP_PROP_TILT =34, + CAP_PROP_ROLL =35, + CAP_PROP_IRIS =36, + CAP_PROP_SETTINGS =37, //!< Pop up video/camera filter dialog (note: only supported by DSHOW backend currently. The property value is ignored) + CAP_PROP_BUFFERSIZE =38, + CAP_PROP_AUTOFOCUS =39, + CAP_PROP_SAR_NUM =40, //!< Sample aspect ratio: num/den (num) + CAP_PROP_SAR_DEN =41, //!< Sample aspect ratio: num/den (den) +#ifndef CV_DOXYGEN + CV__CAP_PROP_LATEST +#endif + }; + + +/** @brief Generic camera output modes identifier. +@note Currently, these are supported through the libv4l backend only. +*/ +enum VideoCaptureModes { + CAP_MODE_BGR = 0, //!< BGR24 (default) + CAP_MODE_RGB = 1, //!< RGB24 + CAP_MODE_GRAY = 2, //!< Y8 + CAP_MODE_YUYV = 3 //!< YUYV + }; + +/** @brief %VideoWriter generic properties identifier. + @sa VideoWriter::get(), VideoWriter::set() +*/ +enum VideoWriterProperties { + VIDEOWRITER_PROP_QUALITY = 1, //!< Current quality (0..100%) of the encoded videostream. Can be adjusted dynamically in some codecs. + VIDEOWRITER_PROP_FRAMEBYTES = 2, //!< (Read-only): Size of just encoded video frame. Note that the encoding order may be different from representation order. + VIDEOWRITER_PROP_NSTRIPES = 3 //!< Number of stripes for parallel encoding. -1 for auto detection. +}; + +//! @} videoio_flags_base + +//! @addtogroup videoio_flags_others +//! @{ + +/** @name IEEE 1394 drivers + @{ +*/ + +/** @brief Modes of the IEEE 1394 controlling registers +(can be: auto, manual, auto single push, absolute Latter allowed with any other mode) +every feature can have only one mode turned on at a time +*/ +enum { CAP_PROP_DC1394_OFF = -4, //!< turn the feature off (not controlled manually nor automatically). + CAP_PROP_DC1394_MODE_MANUAL = -3, //!< set automatically when a value of the feature is set by the user. + CAP_PROP_DC1394_MODE_AUTO = -2, + CAP_PROP_DC1394_MODE_ONE_PUSH_AUTO = -1, + CAP_PROP_DC1394_MAX = 31 + }; + +//! @} IEEE 1394 drivers + +/** @name OpenNI (for Kinect) + @{ +*/ + +//! OpenNI map generators +enum { CAP_OPENNI_DEPTH_GENERATOR = 1 << 31, + CAP_OPENNI_IMAGE_GENERATOR = 1 << 30, + CAP_OPENNI_IR_GENERATOR = 1 << 29, + CAP_OPENNI_GENERATORS_MASK = CAP_OPENNI_DEPTH_GENERATOR + CAP_OPENNI_IMAGE_GENERATOR + CAP_OPENNI_IR_GENERATOR + }; + +//! Properties of cameras available through OpenNI backend +enum { CAP_PROP_OPENNI_OUTPUT_MODE = 100, + CAP_PROP_OPENNI_FRAME_MAX_DEPTH = 101, //!< In mm + CAP_PROP_OPENNI_BASELINE = 102, //!< In mm + CAP_PROP_OPENNI_FOCAL_LENGTH = 103, //!< In pixels + CAP_PROP_OPENNI_REGISTRATION = 104, //!< Flag that synchronizes the remapping depth map to image map + //!< by changing depth generator's view point (if the flag is "on") or + //!< sets this view point to its normal one (if the flag is "off"). + CAP_PROP_OPENNI_REGISTRATION_ON = CAP_PROP_OPENNI_REGISTRATION, + CAP_PROP_OPENNI_APPROX_FRAME_SYNC = 105, + CAP_PROP_OPENNI_MAX_BUFFER_SIZE = 106, + CAP_PROP_OPENNI_CIRCLE_BUFFER = 107, + CAP_PROP_OPENNI_MAX_TIME_DURATION = 108, + CAP_PROP_OPENNI_GENERATOR_PRESENT = 109, + CAP_PROP_OPENNI2_SYNC = 110, + CAP_PROP_OPENNI2_MIRROR = 111 + }; + +//! OpenNI shortcuts +enum { CAP_OPENNI_IMAGE_GENERATOR_PRESENT = CAP_OPENNI_IMAGE_GENERATOR + CAP_PROP_OPENNI_GENERATOR_PRESENT, + CAP_OPENNI_IMAGE_GENERATOR_OUTPUT_MODE = CAP_OPENNI_IMAGE_GENERATOR + CAP_PROP_OPENNI_OUTPUT_MODE, + CAP_OPENNI_DEPTH_GENERATOR_PRESENT = CAP_OPENNI_DEPTH_GENERATOR + CAP_PROP_OPENNI_GENERATOR_PRESENT, + CAP_OPENNI_DEPTH_GENERATOR_BASELINE = CAP_OPENNI_DEPTH_GENERATOR + CAP_PROP_OPENNI_BASELINE, + CAP_OPENNI_DEPTH_GENERATOR_FOCAL_LENGTH = CAP_OPENNI_DEPTH_GENERATOR + CAP_PROP_OPENNI_FOCAL_LENGTH, + CAP_OPENNI_DEPTH_GENERATOR_REGISTRATION = CAP_OPENNI_DEPTH_GENERATOR + CAP_PROP_OPENNI_REGISTRATION, + CAP_OPENNI_DEPTH_GENERATOR_REGISTRATION_ON = CAP_OPENNI_DEPTH_GENERATOR_REGISTRATION, + CAP_OPENNI_IR_GENERATOR_PRESENT = CAP_OPENNI_IR_GENERATOR + CAP_PROP_OPENNI_GENERATOR_PRESENT, + }; + +//! OpenNI data given from depth generator +enum { CAP_OPENNI_DEPTH_MAP = 0, //!< Depth values in mm (CV_16UC1) + CAP_OPENNI_POINT_CLOUD_MAP = 1, //!< XYZ in meters (CV_32FC3) + CAP_OPENNI_DISPARITY_MAP = 2, //!< Disparity in pixels (CV_8UC1) + CAP_OPENNI_DISPARITY_MAP_32F = 3, //!< Disparity in pixels (CV_32FC1) + CAP_OPENNI_VALID_DEPTH_MASK = 4, //!< CV_8UC1 + + CAP_OPENNI_BGR_IMAGE = 5, //!< Data given from RGB image generator + CAP_OPENNI_GRAY_IMAGE = 6, //!< Data given from RGB image generator + + CAP_OPENNI_IR_IMAGE = 7 //!< Data given from IR image generator + }; + +//! Supported output modes of OpenNI image generator +enum { CAP_OPENNI_VGA_30HZ = 0, + CAP_OPENNI_SXGA_15HZ = 1, + CAP_OPENNI_SXGA_30HZ = 2, + CAP_OPENNI_QVGA_30HZ = 3, + CAP_OPENNI_QVGA_60HZ = 4 + }; + +//! @} OpenNI + +/** @name GStreamer + @{ +*/ + +enum { CAP_PROP_GSTREAMER_QUEUE_LENGTH = 200 //!< Default is 1 + }; + +//! @} GStreamer + +/** @name PvAPI, Prosilica GigE SDK + @{ +*/ + +//! PVAPI +enum { CAP_PROP_PVAPI_MULTICASTIP = 300, //!< IP for enable multicast master mode. 0 for disable multicast. + CAP_PROP_PVAPI_FRAMESTARTTRIGGERMODE = 301, //!< FrameStartTriggerMode: Determines how a frame is initiated. + CAP_PROP_PVAPI_DECIMATIONHORIZONTAL = 302, //!< Horizontal sub-sampling of the image. + CAP_PROP_PVAPI_DECIMATIONVERTICAL = 303, //!< Vertical sub-sampling of the image. + CAP_PROP_PVAPI_BINNINGX = 304, //!< Horizontal binning factor. + CAP_PROP_PVAPI_BINNINGY = 305, //!< Vertical binning factor. + CAP_PROP_PVAPI_PIXELFORMAT = 306 //!< Pixel format. + }; + +//! PVAPI: FrameStartTriggerMode +enum { CAP_PVAPI_FSTRIGMODE_FREERUN = 0, //!< Freerun + CAP_PVAPI_FSTRIGMODE_SYNCIN1 = 1, //!< SyncIn1 + CAP_PVAPI_FSTRIGMODE_SYNCIN2 = 2, //!< SyncIn2 + CAP_PVAPI_FSTRIGMODE_FIXEDRATE = 3, //!< FixedRate + CAP_PVAPI_FSTRIGMODE_SOFTWARE = 4 //!< Software + }; + +//! PVAPI: DecimationHorizontal, DecimationVertical +enum { CAP_PVAPI_DECIMATION_OFF = 1, //!< Off + CAP_PVAPI_DECIMATION_2OUTOF4 = 2, //!< 2 out of 4 decimation + CAP_PVAPI_DECIMATION_2OUTOF8 = 4, //!< 2 out of 8 decimation + CAP_PVAPI_DECIMATION_2OUTOF16 = 8 //!< 2 out of 16 decimation + }; + +//! PVAPI: PixelFormat +enum { CAP_PVAPI_PIXELFORMAT_MONO8 = 1, //!< Mono8 + CAP_PVAPI_PIXELFORMAT_MONO16 = 2, //!< Mono16 + CAP_PVAPI_PIXELFORMAT_BAYER8 = 3, //!< Bayer8 + CAP_PVAPI_PIXELFORMAT_BAYER16 = 4, //!< Bayer16 + CAP_PVAPI_PIXELFORMAT_RGB24 = 5, //!< Rgb24 + CAP_PVAPI_PIXELFORMAT_BGR24 = 6, //!< Bgr24 + CAP_PVAPI_PIXELFORMAT_RGBA32 = 7, //!< Rgba32 + CAP_PVAPI_PIXELFORMAT_BGRA32 = 8, //!< Bgra32 + }; + +//! @} PvAPI + +/** @name XIMEA Camera API + @{ +*/ + +//! Properties of cameras available through XIMEA SDK backend +enum { CAP_PROP_XI_DOWNSAMPLING = 400, //!< Change image resolution by binning or skipping. + CAP_PROP_XI_DATA_FORMAT = 401, //!< Output data format. + CAP_PROP_XI_OFFSET_X = 402, //!< Horizontal offset from the origin to the area of interest (in pixels). + CAP_PROP_XI_OFFSET_Y = 403, //!< Vertical offset from the origin to the area of interest (in pixels). + CAP_PROP_XI_TRG_SOURCE = 404, //!< Defines source of trigger. + CAP_PROP_XI_TRG_SOFTWARE = 405, //!< Generates an internal trigger. PRM_TRG_SOURCE must be set to TRG_SOFTWARE. + CAP_PROP_XI_GPI_SELECTOR = 406, //!< Selects general purpose input. + CAP_PROP_XI_GPI_MODE = 407, //!< Set general purpose input mode. + CAP_PROP_XI_GPI_LEVEL = 408, //!< Get general purpose level. + CAP_PROP_XI_GPO_SELECTOR = 409, //!< Selects general purpose output. + CAP_PROP_XI_GPO_MODE = 410, //!< Set general purpose output mode. + CAP_PROP_XI_LED_SELECTOR = 411, //!< Selects camera signalling LED. + CAP_PROP_XI_LED_MODE = 412, //!< Define camera signalling LED functionality. + CAP_PROP_XI_MANUAL_WB = 413, //!< Calculates White Balance(must be called during acquisition). + CAP_PROP_XI_AUTO_WB = 414, //!< Automatic white balance. + CAP_PROP_XI_AEAG = 415, //!< Automatic exposure/gain. + CAP_PROP_XI_EXP_PRIORITY = 416, //!< Exposure priority (0.5 - exposure 50%, gain 50%). + CAP_PROP_XI_AE_MAX_LIMIT = 417, //!< Maximum limit of exposure in AEAG procedure. + CAP_PROP_XI_AG_MAX_LIMIT = 418, //!< Maximum limit of gain in AEAG procedure. + CAP_PROP_XI_AEAG_LEVEL = 419, //!< Average intensity of output signal AEAG should achieve(in %). + CAP_PROP_XI_TIMEOUT = 420, //!< Image capture timeout in milliseconds. + CAP_PROP_XI_EXPOSURE = 421, //!< Exposure time in microseconds. + CAP_PROP_XI_EXPOSURE_BURST_COUNT = 422, //!< Sets the number of times of exposure in one frame. + CAP_PROP_XI_GAIN_SELECTOR = 423, //!< Gain selector for parameter Gain allows to select different type of gains. + CAP_PROP_XI_GAIN = 424, //!< Gain in dB. + CAP_PROP_XI_DOWNSAMPLING_TYPE = 426, //!< Change image downsampling type. + CAP_PROP_XI_BINNING_SELECTOR = 427, //!< Binning engine selector. + CAP_PROP_XI_BINNING_VERTICAL = 428, //!< Vertical Binning - number of vertical photo-sensitive cells to combine together. + CAP_PROP_XI_BINNING_HORIZONTAL = 429, //!< Horizontal Binning - number of horizontal photo-sensitive cells to combine together. + CAP_PROP_XI_BINNING_PATTERN = 430, //!< Binning pattern type. + CAP_PROP_XI_DECIMATION_SELECTOR = 431, //!< Decimation engine selector. + CAP_PROP_XI_DECIMATION_VERTICAL = 432, //!< Vertical Decimation - vertical sub-sampling of the image - reduces the vertical resolution of the image by the specified vertical decimation factor. + CAP_PROP_XI_DECIMATION_HORIZONTAL = 433, //!< Horizontal Decimation - horizontal sub-sampling of the image - reduces the horizontal resolution of the image by the specified vertical decimation factor. + CAP_PROP_XI_DECIMATION_PATTERN = 434, //!< Decimation pattern type. + CAP_PROP_XI_TEST_PATTERN_GENERATOR_SELECTOR = 587, //!< Selects which test pattern generator is controlled by the TestPattern feature. + CAP_PROP_XI_TEST_PATTERN = 588, //!< Selects which test pattern type is generated by the selected generator. + CAP_PROP_XI_IMAGE_DATA_FORMAT = 435, //!< Output data format. + CAP_PROP_XI_SHUTTER_TYPE = 436, //!< Change sensor shutter type(CMOS sensor). + CAP_PROP_XI_SENSOR_TAPS = 437, //!< Number of taps. + CAP_PROP_XI_AEAG_ROI_OFFSET_X = 439, //!< Automatic exposure/gain ROI offset X. + CAP_PROP_XI_AEAG_ROI_OFFSET_Y = 440, //!< Automatic exposure/gain ROI offset Y. + CAP_PROP_XI_AEAG_ROI_WIDTH = 441, //!< Automatic exposure/gain ROI Width. + CAP_PROP_XI_AEAG_ROI_HEIGHT = 442, //!< Automatic exposure/gain ROI Height. + CAP_PROP_XI_BPC = 445, //!< Correction of bad pixels. + CAP_PROP_XI_WB_KR = 448, //!< White balance red coefficient. + CAP_PROP_XI_WB_KG = 449, //!< White balance green coefficient. + CAP_PROP_XI_WB_KB = 450, //!< White balance blue coefficient. + CAP_PROP_XI_WIDTH = 451, //!< Width of the Image provided by the device (in pixels). + CAP_PROP_XI_HEIGHT = 452, //!< Height of the Image provided by the device (in pixels). + CAP_PROP_XI_REGION_SELECTOR = 589, //!< Selects Region in Multiple ROI which parameters are set by width, height, ... ,region mode. + CAP_PROP_XI_REGION_MODE = 595, //!< Activates/deactivates Region selected by Region Selector. + CAP_PROP_XI_LIMIT_BANDWIDTH = 459, //!< Set/get bandwidth(datarate)(in Megabits). + CAP_PROP_XI_SENSOR_DATA_BIT_DEPTH = 460, //!< Sensor output data bit depth. + CAP_PROP_XI_OUTPUT_DATA_BIT_DEPTH = 461, //!< Device output data bit depth. + CAP_PROP_XI_IMAGE_DATA_BIT_DEPTH = 462, //!< bitdepth of data returned by function xiGetImage. + CAP_PROP_XI_OUTPUT_DATA_PACKING = 463, //!< Device output data packing (or grouping) enabled. Packing could be enabled if output_data_bit_depth > 8 and packing capability is available. + CAP_PROP_XI_OUTPUT_DATA_PACKING_TYPE = 464, //!< Data packing type. Some cameras supports only specific packing type. + CAP_PROP_XI_IS_COOLED = 465, //!< Returns 1 for cameras that support cooling. + CAP_PROP_XI_COOLING = 466, //!< Start camera cooling. + CAP_PROP_XI_TARGET_TEMP = 467, //!< Set sensor target temperature for cooling. + CAP_PROP_XI_CHIP_TEMP = 468, //!< Camera sensor temperature. + CAP_PROP_XI_HOUS_TEMP = 469, //!< Camera housing temperature. + CAP_PROP_XI_HOUS_BACK_SIDE_TEMP = 590, //!< Camera housing back side temperature. + CAP_PROP_XI_SENSOR_BOARD_TEMP = 596, //!< Camera sensor board temperature. + CAP_PROP_XI_CMS = 470, //!< Mode of color management system. + CAP_PROP_XI_APPLY_CMS = 471, //!< Enable applying of CMS profiles to xiGetImage (see XI_PRM_INPUT_CMS_PROFILE, XI_PRM_OUTPUT_CMS_PROFILE). + CAP_PROP_XI_IMAGE_IS_COLOR = 474, //!< Returns 1 for color cameras. + CAP_PROP_XI_COLOR_FILTER_ARRAY = 475, //!< Returns color filter array type of RAW data. + CAP_PROP_XI_GAMMAY = 476, //!< Luminosity gamma. + CAP_PROP_XI_GAMMAC = 477, //!< Chromaticity gamma. + CAP_PROP_XI_SHARPNESS = 478, //!< Sharpness Strength. + CAP_PROP_XI_CC_MATRIX_00 = 479, //!< Color Correction Matrix element [0][0]. + CAP_PROP_XI_CC_MATRIX_01 = 480, //!< Color Correction Matrix element [0][1]. + CAP_PROP_XI_CC_MATRIX_02 = 481, //!< Color Correction Matrix element [0][2]. + CAP_PROP_XI_CC_MATRIX_03 = 482, //!< Color Correction Matrix element [0][3]. + CAP_PROP_XI_CC_MATRIX_10 = 483, //!< Color Correction Matrix element [1][0]. + CAP_PROP_XI_CC_MATRIX_11 = 484, //!< Color Correction Matrix element [1][1]. + CAP_PROP_XI_CC_MATRIX_12 = 485, //!< Color Correction Matrix element [1][2]. + CAP_PROP_XI_CC_MATRIX_13 = 486, //!< Color Correction Matrix element [1][3]. + CAP_PROP_XI_CC_MATRIX_20 = 487, //!< Color Correction Matrix element [2][0]. + CAP_PROP_XI_CC_MATRIX_21 = 488, //!< Color Correction Matrix element [2][1]. + CAP_PROP_XI_CC_MATRIX_22 = 489, //!< Color Correction Matrix element [2][2]. + CAP_PROP_XI_CC_MATRIX_23 = 490, //!< Color Correction Matrix element [2][3]. + CAP_PROP_XI_CC_MATRIX_30 = 491, //!< Color Correction Matrix element [3][0]. + CAP_PROP_XI_CC_MATRIX_31 = 492, //!< Color Correction Matrix element [3][1]. + CAP_PROP_XI_CC_MATRIX_32 = 493, //!< Color Correction Matrix element [3][2]. + CAP_PROP_XI_CC_MATRIX_33 = 494, //!< Color Correction Matrix element [3][3]. + CAP_PROP_XI_DEFAULT_CC_MATRIX = 495, //!< Set default Color Correction Matrix. + CAP_PROP_XI_TRG_SELECTOR = 498, //!< Selects the type of trigger. + CAP_PROP_XI_ACQ_FRAME_BURST_COUNT = 499, //!< Sets number of frames acquired by burst. This burst is used only if trigger is set to FrameBurstStart. + CAP_PROP_XI_DEBOUNCE_EN = 507, //!< Enable/Disable debounce to selected GPI. + CAP_PROP_XI_DEBOUNCE_T0 = 508, //!< Debounce time (x * 10us). + CAP_PROP_XI_DEBOUNCE_T1 = 509, //!< Debounce time (x * 10us). + CAP_PROP_XI_DEBOUNCE_POL = 510, //!< Debounce polarity (pol = 1 t0 - falling edge, t1 - rising edge). + CAP_PROP_XI_LENS_MODE = 511, //!< Status of lens control interface. This shall be set to XI_ON before any Lens operations. + CAP_PROP_XI_LENS_APERTURE_VALUE = 512, //!< Current lens aperture value in stops. Examples: 2.8, 4, 5.6, 8, 11. + CAP_PROP_XI_LENS_FOCUS_MOVEMENT_VALUE = 513, //!< Lens current focus movement value to be used by XI_PRM_LENS_FOCUS_MOVE in motor steps. + CAP_PROP_XI_LENS_FOCUS_MOVE = 514, //!< Moves lens focus motor by steps set in XI_PRM_LENS_FOCUS_MOVEMENT_VALUE. + CAP_PROP_XI_LENS_FOCUS_DISTANCE = 515, //!< Lens focus distance in cm. + CAP_PROP_XI_LENS_FOCAL_LENGTH = 516, //!< Lens focal distance in mm. + CAP_PROP_XI_LENS_FEATURE_SELECTOR = 517, //!< Selects the current feature which is accessible by XI_PRM_LENS_FEATURE. + CAP_PROP_XI_LENS_FEATURE = 518, //!< Allows access to lens feature value currently selected by XI_PRM_LENS_FEATURE_SELECTOR. + CAP_PROP_XI_DEVICE_MODEL_ID = 521, //!< Returns device model id. + CAP_PROP_XI_DEVICE_SN = 522, //!< Returns device serial number. + CAP_PROP_XI_IMAGE_DATA_FORMAT_RGB32_ALPHA = 529, //!< The alpha channel of RGB32 output image format. + CAP_PROP_XI_IMAGE_PAYLOAD_SIZE = 530, //!< Buffer size in bytes sufficient for output image returned by xiGetImage. + CAP_PROP_XI_TRANSPORT_PIXEL_FORMAT = 531, //!< Current format of pixels on transport layer. + CAP_PROP_XI_SENSOR_CLOCK_FREQ_HZ = 532, //!< Sensor clock frequency in Hz. + CAP_PROP_XI_SENSOR_CLOCK_FREQ_INDEX = 533, //!< Sensor clock frequency index. Sensor with selected frequencies have possibility to set the frequency only by this index. + CAP_PROP_XI_SENSOR_OUTPUT_CHANNEL_COUNT = 534, //!< Number of output channels from sensor used for data transfer. + CAP_PROP_XI_FRAMERATE = 535, //!< Define framerate in Hz. + CAP_PROP_XI_COUNTER_SELECTOR = 536, //!< Select counter. + CAP_PROP_XI_COUNTER_VALUE = 537, //!< Counter status. + CAP_PROP_XI_ACQ_TIMING_MODE = 538, //!< Type of sensor frames timing. + CAP_PROP_XI_AVAILABLE_BANDWIDTH = 539, //!< Calculate and returns available interface bandwidth(int Megabits). + CAP_PROP_XI_BUFFER_POLICY = 540, //!< Data move policy. + CAP_PROP_XI_LUT_EN = 541, //!< Activates LUT. + CAP_PROP_XI_LUT_INDEX = 542, //!< Control the index (offset) of the coefficient to access in the LUT. + CAP_PROP_XI_LUT_VALUE = 543, //!< Value at entry LUTIndex of the LUT. + CAP_PROP_XI_TRG_DELAY = 544, //!< Specifies the delay in microseconds (us) to apply after the trigger reception before activating it. + CAP_PROP_XI_TS_RST_MODE = 545, //!< Defines how time stamp reset engine will be armed. + CAP_PROP_XI_TS_RST_SOURCE = 546, //!< Defines which source will be used for timestamp reset. Writing this parameter will trigger settings of engine (arming). + CAP_PROP_XI_IS_DEVICE_EXIST = 547, //!< Returns 1 if camera connected and works properly. + CAP_PROP_XI_ACQ_BUFFER_SIZE = 548, //!< Acquisition buffer size in buffer_size_unit. Default bytes. + CAP_PROP_XI_ACQ_BUFFER_SIZE_UNIT = 549, //!< Acquisition buffer size unit in bytes. Default 1. E.g. Value 1024 means that buffer_size is in KiBytes. + CAP_PROP_XI_ACQ_TRANSPORT_BUFFER_SIZE = 550, //!< Acquisition transport buffer size in bytes. + CAP_PROP_XI_BUFFERS_QUEUE_SIZE = 551, //!< Queue of field/frame buffers. + CAP_PROP_XI_ACQ_TRANSPORT_BUFFER_COMMIT = 552, //!< Number of buffers to commit to low level. + CAP_PROP_XI_RECENT_FRAME = 553, //!< GetImage returns most recent frame. + CAP_PROP_XI_DEVICE_RESET = 554, //!< Resets the camera to default state. + CAP_PROP_XI_COLUMN_FPN_CORRECTION = 555, //!< Correction of column FPN. + CAP_PROP_XI_ROW_FPN_CORRECTION = 591, //!< Correction of row FPN. + CAP_PROP_XI_SENSOR_MODE = 558, //!< Current sensor mode. Allows to select sensor mode by one integer. Setting of this parameter affects: image dimensions and downsampling. + CAP_PROP_XI_HDR = 559, //!< Enable High Dynamic Range feature. + CAP_PROP_XI_HDR_KNEEPOINT_COUNT = 560, //!< The number of kneepoints in the PWLR. + CAP_PROP_XI_HDR_T1 = 561, //!< Position of first kneepoint(in % of XI_PRM_EXPOSURE). + CAP_PROP_XI_HDR_T2 = 562, //!< Position of second kneepoint (in % of XI_PRM_EXPOSURE). + CAP_PROP_XI_KNEEPOINT1 = 563, //!< Value of first kneepoint (% of sensor saturation). + CAP_PROP_XI_KNEEPOINT2 = 564, //!< Value of second kneepoint (% of sensor saturation). + CAP_PROP_XI_IMAGE_BLACK_LEVEL = 565, //!< Last image black level counts. Can be used for Offline processing to recall it. + CAP_PROP_XI_HW_REVISION = 571, //!< Returns hardware revision number. + CAP_PROP_XI_DEBUG_LEVEL = 572, //!< Set debug level. + CAP_PROP_XI_AUTO_BANDWIDTH_CALCULATION = 573, //!< Automatic bandwidth calculation. + CAP_PROP_XI_FFS_FILE_ID = 594, //!< File number. + CAP_PROP_XI_FFS_FILE_SIZE = 580, //!< Size of file. + CAP_PROP_XI_FREE_FFS_SIZE = 581, //!< Size of free camera FFS. + CAP_PROP_XI_USED_FFS_SIZE = 582, //!< Size of used camera FFS. + CAP_PROP_XI_FFS_ACCESS_KEY = 583, //!< Setting of key enables file operations on some cameras. + CAP_PROP_XI_SENSOR_FEATURE_SELECTOR = 585, //!< Selects the current feature which is accessible by XI_PRM_SENSOR_FEATURE_VALUE. + CAP_PROP_XI_SENSOR_FEATURE_VALUE = 586, //!< Allows access to sensor feature value currently selected by XI_PRM_SENSOR_FEATURE_SELECTOR. + }; + +//! @} XIMEA + +/** @name AVFoundation framework for iOS + OS X Lion will have the same API + @{ +*/ + +//! Properties of cameras available through AVFOUNDATION backend +enum { CAP_PROP_IOS_DEVICE_FOCUS = 9001, + CAP_PROP_IOS_DEVICE_EXPOSURE = 9002, + CAP_PROP_IOS_DEVICE_FLASH = 9003, + CAP_PROP_IOS_DEVICE_WHITEBALANCE = 9004, + CAP_PROP_IOS_DEVICE_TORCH = 9005 + }; + +/** @name Smartek Giganetix GigEVisionSDK + @{ +*/ + +//! Properties of cameras available through Smartek Giganetix Ethernet Vision backend +/* --- Vladimir Litvinenko (litvinenko.vladimir@gmail.com) --- */ +enum { CAP_PROP_GIGA_FRAME_OFFSET_X = 10001, + CAP_PROP_GIGA_FRAME_OFFSET_Y = 10002, + CAP_PROP_GIGA_FRAME_WIDTH_MAX = 10003, + CAP_PROP_GIGA_FRAME_HEIGH_MAX = 10004, + CAP_PROP_GIGA_FRAME_SENS_WIDTH = 10005, + CAP_PROP_GIGA_FRAME_SENS_HEIGH = 10006 + }; + +//! @} Smartek + +/** @name Intel Perceptual Computing SDK + @{ +*/ +enum { CAP_PROP_INTELPERC_PROFILE_COUNT = 11001, + CAP_PROP_INTELPERC_PROFILE_IDX = 11002, + CAP_PROP_INTELPERC_DEPTH_LOW_CONFIDENCE_VALUE = 11003, + CAP_PROP_INTELPERC_DEPTH_SATURATION_VALUE = 11004, + CAP_PROP_INTELPERC_DEPTH_CONFIDENCE_THRESHOLD = 11005, + CAP_PROP_INTELPERC_DEPTH_FOCAL_LENGTH_HORZ = 11006, + CAP_PROP_INTELPERC_DEPTH_FOCAL_LENGTH_VERT = 11007 + }; + +//! Intel Perceptual Streams +enum { CAP_INTELPERC_DEPTH_GENERATOR = 1 << 29, + CAP_INTELPERC_IMAGE_GENERATOR = 1 << 28, + CAP_INTELPERC_GENERATORS_MASK = CAP_INTELPERC_DEPTH_GENERATOR + CAP_INTELPERC_IMAGE_GENERATOR + }; + +enum { CAP_INTELPERC_DEPTH_MAP = 0, //!< Each pixel is a 16-bit integer. The value indicates the distance from an object to the camera's XY plane or the Cartesian depth. + CAP_INTELPERC_UVDEPTH_MAP = 1, //!< Each pixel contains two 32-bit floating point values in the range of 0-1, representing the mapping of depth coordinates to the color coordinates. + CAP_INTELPERC_IR_MAP = 2, //!< Each pixel is a 16-bit integer. The value indicates the intensity of the reflected laser beam. + CAP_INTELPERC_IMAGE = 3 + }; + +//! @} Intel Perceptual + +/** @name gPhoto2 connection + @{ +*/ + +/** @brief gPhoto2 properties + +If `propertyId` is less than 0 then work on widget with that __additive inversed__ camera setting ID +Get IDs by using CAP_PROP_GPHOTO2_WIDGET_ENUMERATE. +@see CvCaptureCAM_GPHOTO2 for more info +*/ +enum { CAP_PROP_GPHOTO2_PREVIEW = 17001, //!< Capture only preview from liveview mode. + CAP_PROP_GPHOTO2_WIDGET_ENUMERATE = 17002, //!< Readonly, returns (const char *). + CAP_PROP_GPHOTO2_RELOAD_CONFIG = 17003, //!< Trigger, only by set. Reload camera settings. + CAP_PROP_GPHOTO2_RELOAD_ON_CHANGE = 17004, //!< Reload all settings on set. + CAP_PROP_GPHOTO2_COLLECT_MSGS = 17005, //!< Collect messages with details. + CAP_PROP_GPHOTO2_FLUSH_MSGS = 17006, //!< Readonly, returns (const char *). + CAP_PROP_SPEED = 17007, //!< Exposure speed. Can be readonly, depends on camera program. + CAP_PROP_APERTURE = 17008, //!< Aperture. Can be readonly, depends on camera program. + CAP_PROP_EXPOSUREPROGRAM = 17009, //!< Camera exposure program. + CAP_PROP_VIEWFINDER = 17010 //!< Enter liveview mode. + }; + +//! @} gPhoto2 + + +/** @name Images backend + @{ +*/ + +/** @brief Images backend properties + +*/ +enum { CAP_PROP_IMAGES_BASE = 18000, + CAP_PROP_IMAGES_LAST = 19000 // excluding + }; + +//! @} Images + +//! @} videoio_flags_others + + +class IVideoCapture; + +/** @brief Class for video capturing from video files, image sequences or cameras. + +The class provides C++ API for capturing video from cameras or for reading video files and image sequences. + +Here is how the class can be used: +@include samples/cpp/videocapture_basic.cpp + +@note In @ref videoio_c "C API" the black-box structure `CvCapture` is used instead of %VideoCapture. +@note +- (C++) A basic sample on using the %VideoCapture interface can be found at + `OPENCV_SOURCE_CODE/samples/cpp/videocapture_starter.cpp` +- (Python) A basic sample on using the %VideoCapture interface can be found at + `OPENCV_SOURCE_CODE/samples/python/video.py` +- (Python) A multi threaded video processing sample can be found at + `OPENCV_SOURCE_CODE/samples/python/video_threaded.py` +- (Python) %VideoCapture sample showcasing some features of the Video4Linux2 backend + `OPENCV_SOURCE_CODE/samples/python/video_v4l2.py` + */ +class CV_EXPORTS_W VideoCapture +{ +public: + /** @brief Default constructor + @note In @ref videoio_c "C API", when you finished working with video, release CvCapture structure with + cvReleaseCapture(), or use Ptr\ that calls cvReleaseCapture() automatically in the + destructor. + */ + CV_WRAP VideoCapture(); + + /** @overload + @brief Open video file or a capturing device or a IP video stream for video capturing + + Same as VideoCapture(const String& filename, int apiPreference) but using default Capture API backends + */ + CV_WRAP VideoCapture(const String& filename); + + /** @overload + @brief Open video file or a capturing device or a IP video stream for video capturing with API Preference + + @param filename it can be: + - name of video file (eg. `video.avi`) + - or image sequence (eg. `img_%02d.jpg`, which will read samples like `img_00.jpg, img_01.jpg, img_02.jpg, ...`) + - or URL of video stream (eg. `protocol://host:port/script_name?script_params|auth`). + Note that each video stream or IP camera feed has its own URL scheme. Please refer to the + documentation of source stream to know the right URL. + @param apiPreference preferred Capture API backends to use. Can be used to enforce a specific reader + implementation if multiple are available: e.g. cv::CAP_FFMPEG or cv::CAP_IMAGES or cv::CAP_DSHOW. + @sa The list of supported API backends cv::VideoCaptureAPIs + */ + CV_WRAP VideoCapture(const String& filename, int apiPreference); + + /** @overload + @brief Open a camera for video capturing + + @param index camera_id + domain_offset (CAP_*) id of the video capturing device to open. To open default camera using default backend just pass 0. + Use a `domain_offset` to enforce a specific reader implementation if multiple are available like cv::CAP_FFMPEG or cv::CAP_IMAGES or cv::CAP_DSHOW. + e.g. to open Camera 1 using the MS Media Foundation API use `index = 1 + cv::CAP_MSMF` + + @sa The list of supported API backends cv::VideoCaptureAPIs + */ + CV_WRAP VideoCapture(int index); + + /** @brief Default destructor + + The method first calls VideoCapture::release to close the already opened file or camera. + */ + virtual ~VideoCapture(); + + /** @brief Open video file or a capturing device or a IP video stream for video capturing + + @overload + + Parameters are same as the constructor VideoCapture(const String& filename) + @return `true` if the file has been successfully opened + + The method first calls VideoCapture::release to close the already opened file or camera. + */ + CV_WRAP virtual bool open(const String& filename); + + /** @brief Open a camera for video capturing + + @overload + + Parameters are same as the constructor VideoCapture(int index) + @return `true` if the camera has been successfully opened. + + The method first calls VideoCapture::release to close the already opened file or camera. + */ + CV_WRAP virtual bool open(int index); + + /** @brief Open a camera for video capturing + + @overload + + Parameters are similar as the constructor VideoCapture(int index),except it takes an additional argument apiPreference. + Definitely, is same as open(int index) where `index=cameraNum + apiPreference` + @return `true` if the camera has been successfully opened. + */ + CV_WRAP bool open(int cameraNum, int apiPreference); + + /** @brief Returns true if video capturing has been initialized already. + + If the previous call to VideoCapture constructor or VideoCapture::open() succeeded, the method returns + true. + */ + CV_WRAP virtual bool isOpened() const; + + /** @brief Closes video file or capturing device. + + The method is automatically called by subsequent VideoCapture::open and by VideoCapture + destructor. + + The C function also deallocates memory and clears \*capture pointer. + */ + CV_WRAP virtual void release(); + + /** @brief Grabs the next frame from video file or capturing device. + + @return `true` (non-zero) in the case of success. + + The method/function grabs the next frame from video file or camera and returns true (non-zero) in + the case of success. + + The primary use of the function is in multi-camera environments, especially when the cameras do not + have hardware synchronization. That is, you call VideoCapture::grab() for each camera and after that + call the slower method VideoCapture::retrieve() to decode and get frame from each camera. This way + the overhead on demosaicing or motion jpeg decompression etc. is eliminated and the retrieved frames + from different cameras will be closer in time. + + Also, when a connected camera is multi-head (for example, a stereo camera or a Kinect device), the + correct way of retrieving data from it is to call VideoCapture::grab() first and then call + VideoCapture::retrieve() one or more times with different values of the channel parameter. + + @ref tutorial_kinect_openni + */ + CV_WRAP virtual bool grab(); + + /** @brief Decodes and returns the grabbed video frame. + + @param [out] image the video frame is returned here. If no frames has been grabbed the image will be empty. + @param flag it could be a frame index or a driver specific flag + @return `false` if no frames has been grabbed + + The method decodes and returns the just grabbed frame. If no frames has been grabbed + (camera has been disconnected, or there are no more frames in video file), the method returns false + and the function returns an empty image (with %cv::Mat, test it with Mat::empty()). + + @sa read() + + @note In @ref videoio_c "C API", functions cvRetrieveFrame() and cv.RetrieveFrame() return image stored inside the video + capturing structure. It is not allowed to modify or release the image! You can copy the frame using + :ocvcvCloneImage and then do whatever you want with the copy. + */ + CV_WRAP virtual bool retrieve(OutputArray image, int flag = 0); + + /** @brief Stream operator to read the next video frame. + @sa read() + */ + virtual VideoCapture& operator >> (CV_OUT Mat& image); + + /** @overload + @sa read() + */ + virtual VideoCapture& operator >> (CV_OUT UMat& image); + + /** @brief Grabs, decodes and returns the next video frame. + + @param [out] image the video frame is returned here. If no frames has been grabbed the image will be empty. + @return `false` if no frames has been grabbed + + The method/function combines VideoCapture::grab() and VideoCapture::retrieve() in one call. This is the + most convenient method for reading video files or capturing data from decode and returns the just + grabbed frame. If no frames has been grabbed (camera has been disconnected, or there are no more + frames in video file), the method returns false and the function returns empty image (with %cv::Mat, test it with Mat::empty()). + + @note In @ref videoio_c "C API", functions cvRetrieveFrame() and cv.RetrieveFrame() return image stored inside the video + capturing structure. It is not allowed to modify or release the image! You can copy the frame using + :ocvcvCloneImage and then do whatever you want with the copy. + */ + CV_WRAP virtual bool read(OutputArray image); + + /** @brief Sets a property in the VideoCapture. + + @param propId Property identifier from cv::VideoCaptureProperties (eg. cv::CAP_PROP_POS_MSEC, cv::CAP_PROP_POS_FRAMES, ...) + or one from @ref videoio_flags_others + @param value Value of the property. + @return `true` if the property is supported by backend used by the VideoCapture instance. + @note Even if it returns `true` this doesn't ensure that the property + value has been accepted by the capture device. See note in VideoCapture::get() + */ + CV_WRAP virtual bool set(int propId, double value); + + /** @brief Returns the specified VideoCapture property + + @param propId Property identifier from cv::VideoCaptureProperties (eg. cv::CAP_PROP_POS_MSEC, cv::CAP_PROP_POS_FRAMES, ...) + or one from @ref videoio_flags_others + @return Value for the specified property. Value 0 is returned when querying a property that is + not supported by the backend used by the VideoCapture instance. + + @note Reading / writing properties involves many layers. Some unexpected result might happens + along this chain. + @code {.txt} + `VideoCapture -> API Backend -> Operating System -> Device Driver -> Device Hardware` + @endcode + The returned value might be different from what really used by the device or it could be encoded + using device dependent rules (eg. steps or percentage). Effective behaviour depends from device + driver and API Backend + + */ + CV_WRAP virtual double get(int propId) const; + + /** @brief Open video file or a capturing device or a IP video stream for video capturing with API Preference + + @overload + + Parameters are same as the constructor VideoCapture(const String& filename, int apiPreference) + @return `true` if the file has been successfully opened + + The method first calls VideoCapture::release to close the already opened file or camera. + */ + CV_WRAP virtual bool open(const String& filename, int apiPreference); + +protected: + Ptr cap; + Ptr icap; +}; + +class IVideoWriter; + +/** @example videowriter_basic.cpp +An example using VideoCapture and VideoWriter class + */ +/** @brief Video writer class. + +The class provides C++ API for writing video files or image sequences. + */ +class CV_EXPORTS_W VideoWriter +{ +public: + /** @brief Default constructors + + The constructors/functions initialize video writers. + - On Linux FFMPEG is used to write videos; + - On Windows FFMPEG or VFW is used; + - On MacOSX QTKit is used. + */ + CV_WRAP VideoWriter(); + + /** @overload + @param filename Name of the output video file. + @param fourcc 4-character code of codec used to compress the frames. For example, + VideoWriter::fourcc('P','I','M','1') is a MPEG-1 codec, VideoWriter::fourcc('M','J','P','G') is a + motion-jpeg codec etc. List of codes can be obtained at [Video Codecs by + FOURCC](http://www.fourcc.org/codecs.php) page. FFMPEG backend with MP4 container natively uses + other values as fourcc code: see [ObjectType](http://www.mp4ra.org/codecs.html), + so you may receive a warning message from OpenCV about fourcc code conversion. + @param fps Framerate of the created video stream. + @param frameSize Size of the video frames. + @param isColor If it is not zero, the encoder will expect and encode color frames, otherwise it + will work with grayscale frames (the flag is currently supported on Windows only). + + @b Tips: + - With some backends `fourcc=-1` pops up the codec selection dialog from the system. + - To save image sequence use a proper filename (eg. `img_%02d.jpg`) and `fourcc=0` + OR `fps=0`. Use uncompressed image format (eg. `img_%02d.BMP`) to save raw frames. + - Most codecs are lossy. If you want lossless video file you need to use a lossless codecs + (eg. FFMPEG FFV1, Huffman HFYU, Lagarith LAGS, etc...) + - If FFMPEG is enabled, using `codec=0; fps=0;` you can create an uncompressed (raw) video file. + */ + CV_WRAP VideoWriter(const String& filename, int fourcc, double fps, + Size frameSize, bool isColor = true); + + /** @overload + The `apiPreference` parameter allows to specify API backends to use. Can be used to enforce a specific reader implementation + if multiple are available: e.g. cv::CAP_FFMPEG or cv::CAP_GSTREAMER. + */ + CV_WRAP VideoWriter(const String& filename, int apiPreference, int fourcc, double fps, + Size frameSize, bool isColor = true); + + /** @brief Default destructor + + The method first calls VideoWriter::release to close the already opened file. + */ + virtual ~VideoWriter(); + + /** @brief Initializes or reinitializes video writer. + + The method opens video writer. Parameters are the same as in the constructor + VideoWriter::VideoWriter. + @return `true` if video writer has been successfully initialized + + The method first calls VideoWriter::release to close the already opened file. + */ + CV_WRAP virtual bool open(const String& filename, int fourcc, double fps, + Size frameSize, bool isColor = true); + + /** @overload + */ + CV_WRAP bool open(const String& filename, int apiPreference, int fourcc, double fps, + Size frameSize, bool isColor = true); + + /** @brief Returns true if video writer has been successfully initialized. + */ + CV_WRAP virtual bool isOpened() const; + + /** @brief Closes the video writer. + + The method is automatically called by subsequent VideoWriter::open and by the VideoWriter + destructor. + */ + CV_WRAP virtual void release(); + + /** @brief Stream operator to write the next video frame. + @sa write + */ + virtual VideoWriter& operator << (const Mat& image); + + /** @brief Writes the next video frame + + @param image The written frame + + The function/method writes the specified image to video file. It must have the same size as has + been specified when opening the video writer. + */ + CV_WRAP virtual void write(const Mat& image); + + /** @brief Sets a property in the VideoWriter. + + @param propId Property identifier from cv::VideoWriterProperties (eg. cv::VIDEOWRITER_PROP_QUALITY) + or one of @ref videoio_flags_others + + @param value Value of the property. + @return `true` if the property is supported by the backend used by the VideoWriter instance. + */ + CV_WRAP virtual bool set(int propId, double value); + + /** @brief Returns the specified VideoWriter property + + @param propId Property identifier from cv::VideoWriterProperties (eg. cv::VIDEOWRITER_PROP_QUALITY) + or one of @ref videoio_flags_others + + @return Value for the specified property. Value 0 is returned when querying a property that is + not supported by the backend used by the VideoWriter instance. + */ + CV_WRAP virtual double get(int propId) const; + + /** @brief Concatenates 4 chars to a fourcc code + + @return a fourcc code + + This static method constructs the fourcc code of the codec to be used in the constructor + VideoWriter::VideoWriter or VideoWriter::open. + */ + CV_WRAP static int fourcc(char c1, char c2, char c3, char c4); + +protected: + Ptr writer; + Ptr iwriter; + + static Ptr create(const String& filename, int fourcc, double fps, + Size frameSize, bool isColor = true); +}; + +template<> CV_EXPORTS void DefaultDeleter::operator ()(CvCapture* obj) const; +template<> CV_EXPORTS void DefaultDeleter::operator ()(CvVideoWriter* obj) const; + +//! @} videoio + +} // cv + +#endif //OPENCV_VIDEOIO_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videoio/cap_ios.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videoio/cap_ios.h new file mode 100644 index 0000000000000000000000000000000000000000..0691420cf76b80f2e0ec641531b8bd821da74f30 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videoio/cap_ios.h @@ -0,0 +1,150 @@ +/* For iOS video I/O + * by Eduard Feicho on 29/07/12 + * Copyright 2012. All rights reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * 1. Redistributions of source code must retain the above copyright notice, + * this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright notice, + * this list of conditions and the following disclaimer in the documentation + * and/or other materials provided with the distribution. + * 3. The name of the author may not be used to endorse or promote products + * derived from this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR IMPLIED + * WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF + * MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO + * EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; + * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, + * WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR + * OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF + * ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + * + */ + +#import +#import +#import +#import +#include "opencv2/core.hpp" + +//! @addtogroup videoio_ios +//! @{ + +/////////////////////////////////////// CvAbstractCamera ///////////////////////////////////// + +@class CvAbstractCamera; + +CV_EXPORTS @interface CvAbstractCamera : NSObject +{ + UIDeviceOrientation currentDeviceOrientation; + + BOOL cameraAvailable; +} + +@property (nonatomic, strong) AVCaptureSession* captureSession; +@property (nonatomic, strong) AVCaptureConnection* videoCaptureConnection; + +@property (nonatomic, readonly) BOOL running; +@property (nonatomic, readonly) BOOL captureSessionLoaded; + +@property (nonatomic, assign) int defaultFPS; +@property (nonatomic, readonly) AVCaptureVideoPreviewLayer *captureVideoPreviewLayer; +@property (nonatomic, assign) AVCaptureDevicePosition defaultAVCaptureDevicePosition; +@property (nonatomic, assign) AVCaptureVideoOrientation defaultAVCaptureVideoOrientation; +@property (nonatomic, assign) BOOL useAVCaptureVideoPreviewLayer; +@property (nonatomic, strong) NSString *const defaultAVCaptureSessionPreset; + +@property (nonatomic, assign) int imageWidth; +@property (nonatomic, assign) int imageHeight; + +@property (nonatomic, strong) UIView* parentView; + +- (void)start; +- (void)stop; +- (void)switchCameras; + +- (id)initWithParentView:(UIView*)parent; + +- (void)createCaptureOutput; +- (void)createVideoPreviewLayer; +- (void)updateOrientation; + +- (void)lockFocus; +- (void)unlockFocus; +- (void)lockExposure; +- (void)unlockExposure; +- (void)lockBalance; +- (void)unlockBalance; + +@end + +///////////////////////////////// CvVideoCamera /////////////////////////////////////////// + +@class CvVideoCamera; + +CV_EXPORTS @protocol CvVideoCameraDelegate + +#ifdef __cplusplus +// delegate method for processing image frames +- (void)processImage:(cv::Mat&)image; +#endif + +@end + +CV_EXPORTS @interface CvVideoCamera : CvAbstractCamera +{ + AVCaptureVideoDataOutput *videoDataOutput; + + dispatch_queue_t videoDataOutputQueue; + CALayer *customPreviewLayer; + + CMTime lastSampleTime; + +} + +@property (nonatomic, weak) id delegate; +@property (nonatomic, assign) BOOL grayscaleMode; + +@property (nonatomic, assign) BOOL recordVideo; +@property (nonatomic, assign) BOOL rotateVideo; +@property (nonatomic, strong) AVAssetWriterInput* recordAssetWriterInput; +@property (nonatomic, strong) AVAssetWriterInputPixelBufferAdaptor* recordPixelBufferAdaptor; +@property (nonatomic, strong) AVAssetWriter* recordAssetWriter; + +- (void)adjustLayoutToInterfaceOrientation:(UIInterfaceOrientation)interfaceOrientation; +- (void)layoutPreviewLayer; +- (void)saveVideo; +- (NSURL *)videoFileURL; +- (NSString *)videoFileString; + + +@end + +///////////////////////////////// CvPhotoCamera /////////////////////////////////////////// + +@class CvPhotoCamera; + +CV_EXPORTS @protocol CvPhotoCameraDelegate + +- (void)photoCamera:(CvPhotoCamera*)photoCamera capturedImage:(UIImage *)image; +- (void)photoCameraCancel:(CvPhotoCamera*)photoCamera; + +@end + +CV_EXPORTS @interface CvPhotoCamera : CvAbstractCamera +{ + AVCaptureStillImageOutput *stillImageOutput; +} + +@property (nonatomic, weak) id delegate; + +- (void)takePicture; + +@end + +//! @} videoio_ios diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videoio/videoio.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videoio/videoio.hpp new file mode 100644 index 0000000000000000000000000000000000000000..ec84cf7a687444710f20a531b03ef88859d8a109 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videoio/videoio.hpp @@ -0,0 +1,48 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifdef __OPENCV_BUILD +#error this is a compatibility header which should not be used inside the OpenCV library +#endif + +#include "opencv2/videoio.hpp" diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videoio/videoio_c.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videoio/videoio_c.h new file mode 100644 index 0000000000000000000000000000000000000000..32f6ec7eafa2293363323e06274e523f22dbc251 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videoio/videoio_c.h @@ -0,0 +1,587 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// Intel License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000, Intel Corporation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of Intel Corporation may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_VIDEOIO_H +#define OPENCV_VIDEOIO_H + +#include "opencv2/core/core_c.h" + +#ifdef __cplusplus +extern "C" { +#endif /* __cplusplus */ + +/** + @addtogroup videoio_c + @{ +*/ + +/****************************************************************************************\ +* Working with Video Files and Cameras * +\****************************************************************************************/ + +/** @brief "black box" capture structure + +In C++ use cv::VideoCapture +*/ +typedef struct CvCapture CvCapture; + +/** @brief start capturing frames from video file +*/ +CVAPI(CvCapture*) cvCreateFileCapture( const char* filename ); + +/** @brief start capturing frames from video file. allows specifying a preferred API to use +*/ +CVAPI(CvCapture*) cvCreateFileCaptureWithPreference( const char* filename , int apiPreference); + +enum +{ + CV_CAP_ANY =0, // autodetect + + CV_CAP_MIL =100, // MIL proprietary drivers + + CV_CAP_VFW =200, // platform native + CV_CAP_V4L =200, + CV_CAP_V4L2 =200, + + CV_CAP_FIREWARE =300, // IEEE 1394 drivers + CV_CAP_FIREWIRE =300, + CV_CAP_IEEE1394 =300, + CV_CAP_DC1394 =300, + CV_CAP_CMU1394 =300, + + CV_CAP_STEREO =400, // TYZX proprietary drivers + CV_CAP_TYZX =400, + CV_TYZX_LEFT =400, + CV_TYZX_RIGHT =401, + CV_TYZX_COLOR =402, + CV_TYZX_Z =403, + + CV_CAP_QT =500, // QuickTime + + CV_CAP_UNICAP =600, // Unicap drivers + + CV_CAP_DSHOW =700, // DirectShow (via videoInput) + CV_CAP_MSMF =1400, // Microsoft Media Foundation (via videoInput) + + CV_CAP_PVAPI =800, // PvAPI, Prosilica GigE SDK + + CV_CAP_OPENNI =900, // OpenNI (for Kinect) + CV_CAP_OPENNI_ASUS =910, // OpenNI (for Asus Xtion) + + CV_CAP_ANDROID =1000, // Android - not used + CV_CAP_ANDROID_BACK =CV_CAP_ANDROID+99, // Android back camera - not used + CV_CAP_ANDROID_FRONT =CV_CAP_ANDROID+98, // Android front camera - not used + + CV_CAP_XIAPI =1100, // XIMEA Camera API + + CV_CAP_AVFOUNDATION = 1200, // AVFoundation framework for iOS (OS X Lion will have the same API) + + CV_CAP_GIGANETIX = 1300, // Smartek Giganetix GigEVisionSDK + + CV_CAP_INTELPERC = 1500, // Intel Perceptual Computing + + CV_CAP_OPENNI2 = 1600, // OpenNI2 (for Kinect) + CV_CAP_GPHOTO2 = 1700, + CV_CAP_GSTREAMER = 1800, // GStreamer + CV_CAP_FFMPEG = 1900, // FFMPEG + CV_CAP_IMAGES = 2000, // OpenCV Image Sequence (e.g. img_%02d.jpg) + + CV_CAP_ARAVIS = 2100 // Aravis GigE SDK +}; + +/** @brief start capturing frames from camera: index = camera_index + domain_offset (CV_CAP_*) +*/ +CVAPI(CvCapture*) cvCreateCameraCapture( int index ); + +/** @brief grab a frame, return 1 on success, 0 on fail. + + this function is thought to be fast +*/ +CVAPI(int) cvGrabFrame( CvCapture* capture ); + +/** @brief get the frame grabbed with cvGrabFrame(..) + + This function may apply some frame processing like + frame decompression, flipping etc. + @warning !!!DO NOT RELEASE or MODIFY the retrieved frame!!! +*/ +CVAPI(IplImage*) cvRetrieveFrame( CvCapture* capture, int streamIdx CV_DEFAULT(0) ); + +/** @brief Just a combination of cvGrabFrame and cvRetrieveFrame + + @warning !!!DO NOT RELEASE or MODIFY the retrieved frame!!! +*/ +CVAPI(IplImage*) cvQueryFrame( CvCapture* capture ); + +/** @brief stop capturing/reading and free resources +*/ +CVAPI(void) cvReleaseCapture( CvCapture** capture ); + +enum +{ + // modes of the controlling registers (can be: auto, manual, auto single push, absolute Latter allowed with any other mode) + // every feature can have only one mode turned on at a time + CV_CAP_PROP_DC1394_OFF = -4, //turn the feature off (not controlled manually nor automatically) + CV_CAP_PROP_DC1394_MODE_MANUAL = -3, //set automatically when a value of the feature is set by the user + CV_CAP_PROP_DC1394_MODE_AUTO = -2, + CV_CAP_PROP_DC1394_MODE_ONE_PUSH_AUTO = -1, + CV_CAP_PROP_POS_MSEC =0, + CV_CAP_PROP_POS_FRAMES =1, + CV_CAP_PROP_POS_AVI_RATIO =2, + CV_CAP_PROP_FRAME_WIDTH =3, + CV_CAP_PROP_FRAME_HEIGHT =4, + CV_CAP_PROP_FPS =5, + CV_CAP_PROP_FOURCC =6, + CV_CAP_PROP_FRAME_COUNT =7, + CV_CAP_PROP_FORMAT =8, + CV_CAP_PROP_MODE =9, + CV_CAP_PROP_BRIGHTNESS =10, + CV_CAP_PROP_CONTRAST =11, + CV_CAP_PROP_SATURATION =12, + CV_CAP_PROP_HUE =13, + CV_CAP_PROP_GAIN =14, + CV_CAP_PROP_EXPOSURE =15, + CV_CAP_PROP_CONVERT_RGB =16, + CV_CAP_PROP_WHITE_BALANCE_BLUE_U =17, + CV_CAP_PROP_RECTIFICATION =18, + CV_CAP_PROP_MONOCHROME =19, + CV_CAP_PROP_SHARPNESS =20, + CV_CAP_PROP_AUTO_EXPOSURE =21, // exposure control done by camera, + // user can adjust reference level + // using this feature + CV_CAP_PROP_GAMMA =22, + CV_CAP_PROP_TEMPERATURE =23, + CV_CAP_PROP_TRIGGER =24, + CV_CAP_PROP_TRIGGER_DELAY =25, + CV_CAP_PROP_WHITE_BALANCE_RED_V =26, + CV_CAP_PROP_ZOOM =27, + CV_CAP_PROP_FOCUS =28, + CV_CAP_PROP_GUID =29, + CV_CAP_PROP_ISO_SPEED =30, + CV_CAP_PROP_MAX_DC1394 =31, + CV_CAP_PROP_BACKLIGHT =32, + CV_CAP_PROP_PAN =33, + CV_CAP_PROP_TILT =34, + CV_CAP_PROP_ROLL =35, + CV_CAP_PROP_IRIS =36, + CV_CAP_PROP_SETTINGS =37, + CV_CAP_PROP_BUFFERSIZE =38, + CV_CAP_PROP_AUTOFOCUS =39, + CV_CAP_PROP_SAR_NUM =40, + CV_CAP_PROP_SAR_DEN =41, + + CV_CAP_PROP_AUTOGRAB =1024, // property for videoio class CvCapture_Android only + CV_CAP_PROP_SUPPORTED_PREVIEW_SIZES_STRING=1025, // readonly, tricky property, returns cpnst char* indeed + CV_CAP_PROP_PREVIEW_FORMAT=1026, // readonly, tricky property, returns cpnst char* indeed + + // OpenNI map generators + CV_CAP_OPENNI_DEPTH_GENERATOR = 1 << 31, + CV_CAP_OPENNI_IMAGE_GENERATOR = 1 << 30, + CV_CAP_OPENNI_IR_GENERATOR = 1 << 29, + CV_CAP_OPENNI_GENERATORS_MASK = CV_CAP_OPENNI_DEPTH_GENERATOR + CV_CAP_OPENNI_IMAGE_GENERATOR + CV_CAP_OPENNI_IR_GENERATOR, + + // Properties of cameras available through OpenNI interfaces + CV_CAP_PROP_OPENNI_OUTPUT_MODE = 100, + CV_CAP_PROP_OPENNI_FRAME_MAX_DEPTH = 101, // in mm + CV_CAP_PROP_OPENNI_BASELINE = 102, // in mm + CV_CAP_PROP_OPENNI_FOCAL_LENGTH = 103, // in pixels + CV_CAP_PROP_OPENNI_REGISTRATION = 104, // flag + CV_CAP_PROP_OPENNI_REGISTRATION_ON = CV_CAP_PROP_OPENNI_REGISTRATION, // flag that synchronizes the remapping depth map to image map + // by changing depth generator's view point (if the flag is "on") or + // sets this view point to its normal one (if the flag is "off"). + CV_CAP_PROP_OPENNI_APPROX_FRAME_SYNC = 105, + CV_CAP_PROP_OPENNI_MAX_BUFFER_SIZE = 106, + CV_CAP_PROP_OPENNI_CIRCLE_BUFFER = 107, + CV_CAP_PROP_OPENNI_MAX_TIME_DURATION = 108, + + CV_CAP_PROP_OPENNI_GENERATOR_PRESENT = 109, + CV_CAP_PROP_OPENNI2_SYNC = 110, + CV_CAP_PROP_OPENNI2_MIRROR = 111, + + CV_CAP_OPENNI_IMAGE_GENERATOR_PRESENT = CV_CAP_OPENNI_IMAGE_GENERATOR + CV_CAP_PROP_OPENNI_GENERATOR_PRESENT, + CV_CAP_OPENNI_IMAGE_GENERATOR_OUTPUT_MODE = CV_CAP_OPENNI_IMAGE_GENERATOR + CV_CAP_PROP_OPENNI_OUTPUT_MODE, + CV_CAP_OPENNI_DEPTH_GENERATOR_PRESENT = CV_CAP_OPENNI_DEPTH_GENERATOR + CV_CAP_PROP_OPENNI_GENERATOR_PRESENT, + CV_CAP_OPENNI_DEPTH_GENERATOR_BASELINE = CV_CAP_OPENNI_DEPTH_GENERATOR + CV_CAP_PROP_OPENNI_BASELINE, + CV_CAP_OPENNI_DEPTH_GENERATOR_FOCAL_LENGTH = CV_CAP_OPENNI_DEPTH_GENERATOR + CV_CAP_PROP_OPENNI_FOCAL_LENGTH, + CV_CAP_OPENNI_DEPTH_GENERATOR_REGISTRATION = CV_CAP_OPENNI_DEPTH_GENERATOR + CV_CAP_PROP_OPENNI_REGISTRATION, + CV_CAP_OPENNI_DEPTH_GENERATOR_REGISTRATION_ON = CV_CAP_OPENNI_DEPTH_GENERATOR_REGISTRATION, + CV_CAP_OPENNI_IR_GENERATOR_PRESENT = CV_CAP_OPENNI_IR_GENERATOR + CV_CAP_PROP_OPENNI_GENERATOR_PRESENT, + + // Properties of cameras available through GStreamer interface + CV_CAP_GSTREAMER_QUEUE_LENGTH = 200, // default is 1 + + // PVAPI + CV_CAP_PROP_PVAPI_MULTICASTIP = 300, // ip for anable multicast master mode. 0 for disable multicast + CV_CAP_PROP_PVAPI_FRAMESTARTTRIGGERMODE = 301, // FrameStartTriggerMode: Determines how a frame is initiated + CV_CAP_PROP_PVAPI_DECIMATIONHORIZONTAL = 302, // Horizontal sub-sampling of the image + CV_CAP_PROP_PVAPI_DECIMATIONVERTICAL = 303, // Vertical sub-sampling of the image + CV_CAP_PROP_PVAPI_BINNINGX = 304, // Horizontal binning factor + CV_CAP_PROP_PVAPI_BINNINGY = 305, // Vertical binning factor + CV_CAP_PROP_PVAPI_PIXELFORMAT = 306, // Pixel format + + // Properties of cameras available through XIMEA SDK interface + CV_CAP_PROP_XI_DOWNSAMPLING = 400, // Change image resolution by binning or skipping. + CV_CAP_PROP_XI_DATA_FORMAT = 401, // Output data format. + CV_CAP_PROP_XI_OFFSET_X = 402, // Horizontal offset from the origin to the area of interest (in pixels). + CV_CAP_PROP_XI_OFFSET_Y = 403, // Vertical offset from the origin to the area of interest (in pixels). + CV_CAP_PROP_XI_TRG_SOURCE = 404, // Defines source of trigger. + CV_CAP_PROP_XI_TRG_SOFTWARE = 405, // Generates an internal trigger. PRM_TRG_SOURCE must be set to TRG_SOFTWARE. + CV_CAP_PROP_XI_GPI_SELECTOR = 406, // Selects general purpose input + CV_CAP_PROP_XI_GPI_MODE = 407, // Set general purpose input mode + CV_CAP_PROP_XI_GPI_LEVEL = 408, // Get general purpose level + CV_CAP_PROP_XI_GPO_SELECTOR = 409, // Selects general purpose output + CV_CAP_PROP_XI_GPO_MODE = 410, // Set general purpose output mode + CV_CAP_PROP_XI_LED_SELECTOR = 411, // Selects camera signalling LED + CV_CAP_PROP_XI_LED_MODE = 412, // Define camera signalling LED functionality + CV_CAP_PROP_XI_MANUAL_WB = 413, // Calculates White Balance(must be called during acquisition) + CV_CAP_PROP_XI_AUTO_WB = 414, // Automatic white balance + CV_CAP_PROP_XI_AEAG = 415, // Automatic exposure/gain + CV_CAP_PROP_XI_EXP_PRIORITY = 416, // Exposure priority (0.5 - exposure 50%, gain 50%). + CV_CAP_PROP_XI_AE_MAX_LIMIT = 417, // Maximum limit of exposure in AEAG procedure + CV_CAP_PROP_XI_AG_MAX_LIMIT = 418, // Maximum limit of gain in AEAG procedure + CV_CAP_PROP_XI_AEAG_LEVEL = 419, // Average intensity of output signal AEAG should achieve(in %) + CV_CAP_PROP_XI_TIMEOUT = 420, // Image capture timeout in milliseconds + CV_CAP_PROP_XI_EXPOSURE = 421, // Exposure time in microseconds + CV_CAP_PROP_XI_EXPOSURE_BURST_COUNT = 422, // Sets the number of times of exposure in one frame. + CV_CAP_PROP_XI_GAIN_SELECTOR = 423, // Gain selector for parameter Gain allows to select different type of gains. + CV_CAP_PROP_XI_GAIN = 424, // Gain in dB + CV_CAP_PROP_XI_DOWNSAMPLING_TYPE = 426, // Change image downsampling type. + CV_CAP_PROP_XI_BINNING_SELECTOR = 427, // Binning engine selector. + CV_CAP_PROP_XI_BINNING_VERTICAL = 428, // Vertical Binning - number of vertical photo-sensitive cells to combine together. + CV_CAP_PROP_XI_BINNING_HORIZONTAL = 429, // Horizontal Binning - number of horizontal photo-sensitive cells to combine together. + CV_CAP_PROP_XI_BINNING_PATTERN = 430, // Binning pattern type. + CV_CAP_PROP_XI_DECIMATION_SELECTOR = 431, // Decimation engine selector. + CV_CAP_PROP_XI_DECIMATION_VERTICAL = 432, // Vertical Decimation - vertical sub-sampling of the image - reduces the vertical resolution of the image by the specified vertical decimation factor. + CV_CAP_PROP_XI_DECIMATION_HORIZONTAL = 433, // Horizontal Decimation - horizontal sub-sampling of the image - reduces the horizontal resolution of the image by the specified vertical decimation factor. + CV_CAP_PROP_XI_DECIMATION_PATTERN = 434, // Decimation pattern type. + CV_CAP_PROP_XI_TEST_PATTERN_GENERATOR_SELECTOR = 587, // Selects which test pattern generator is controlled by the TestPattern feature. + CV_CAP_PROP_XI_TEST_PATTERN = 588, // Selects which test pattern type is generated by the selected generator. + CV_CAP_PROP_XI_IMAGE_DATA_FORMAT = 435, // Output data format. + CV_CAP_PROP_XI_SHUTTER_TYPE = 436, // Change sensor shutter type(CMOS sensor). + CV_CAP_PROP_XI_SENSOR_TAPS = 437, // Number of taps + CV_CAP_PROP_XI_AEAG_ROI_OFFSET_X = 439, // Automatic exposure/gain ROI offset X + CV_CAP_PROP_XI_AEAG_ROI_OFFSET_Y = 440, // Automatic exposure/gain ROI offset Y + CV_CAP_PROP_XI_AEAG_ROI_WIDTH = 441, // Automatic exposure/gain ROI Width + CV_CAP_PROP_XI_AEAG_ROI_HEIGHT = 442, // Automatic exposure/gain ROI Height + CV_CAP_PROP_XI_BPC = 445, // Correction of bad pixels + CV_CAP_PROP_XI_WB_KR = 448, // White balance red coefficient + CV_CAP_PROP_XI_WB_KG = 449, // White balance green coefficient + CV_CAP_PROP_XI_WB_KB = 450, // White balance blue coefficient + CV_CAP_PROP_XI_WIDTH = 451, // Width of the Image provided by the device (in pixels). + CV_CAP_PROP_XI_HEIGHT = 452, // Height of the Image provided by the device (in pixels). + CV_CAP_PROP_XI_REGION_SELECTOR = 589, // Selects Region in Multiple ROI which parameters are set by width, height, ... ,region mode + CV_CAP_PROP_XI_REGION_MODE = 595, // Activates/deactivates Region selected by Region Selector + CV_CAP_PROP_XI_LIMIT_BANDWIDTH = 459, // Set/get bandwidth(datarate)(in Megabits) + CV_CAP_PROP_XI_SENSOR_DATA_BIT_DEPTH = 460, // Sensor output data bit depth. + CV_CAP_PROP_XI_OUTPUT_DATA_BIT_DEPTH = 461, // Device output data bit depth. + CV_CAP_PROP_XI_IMAGE_DATA_BIT_DEPTH = 462, // bitdepth of data returned by function xiGetImage + CV_CAP_PROP_XI_OUTPUT_DATA_PACKING = 463, // Device output data packing (or grouping) enabled. Packing could be enabled if output_data_bit_depth > 8 and packing capability is available. + CV_CAP_PROP_XI_OUTPUT_DATA_PACKING_TYPE = 464, // Data packing type. Some cameras supports only specific packing type. + CV_CAP_PROP_XI_IS_COOLED = 465, // Returns 1 for cameras that support cooling. + CV_CAP_PROP_XI_COOLING = 466, // Start camera cooling. + CV_CAP_PROP_XI_TARGET_TEMP = 467, // Set sensor target temperature for cooling. + CV_CAP_PROP_XI_CHIP_TEMP = 468, // Camera sensor temperature + CV_CAP_PROP_XI_HOUS_TEMP = 469, // Camera housing tepmerature + CV_CAP_PROP_XI_HOUS_BACK_SIDE_TEMP = 590, // Camera housing back side tepmerature + CV_CAP_PROP_XI_SENSOR_BOARD_TEMP = 596, // Camera sensor board temperature + CV_CAP_PROP_XI_CMS = 470, // Mode of color management system. + CV_CAP_PROP_XI_APPLY_CMS = 471, // Enable applying of CMS profiles to xiGetImage (see XI_PRM_INPUT_CMS_PROFILE, XI_PRM_OUTPUT_CMS_PROFILE). + CV_CAP_PROP_XI_IMAGE_IS_COLOR = 474, // Returns 1 for color cameras. + CV_CAP_PROP_XI_COLOR_FILTER_ARRAY = 475, // Returns color filter array type of RAW data. + CV_CAP_PROP_XI_GAMMAY = 476, // Luminosity gamma + CV_CAP_PROP_XI_GAMMAC = 477, // Chromaticity gamma + CV_CAP_PROP_XI_SHARPNESS = 478, // Sharpness Strength + CV_CAP_PROP_XI_CC_MATRIX_00 = 479, // Color Correction Matrix element [0][0] + CV_CAP_PROP_XI_CC_MATRIX_01 = 480, // Color Correction Matrix element [0][1] + CV_CAP_PROP_XI_CC_MATRIX_02 = 481, // Color Correction Matrix element [0][2] + CV_CAP_PROP_XI_CC_MATRIX_03 = 482, // Color Correction Matrix element [0][3] + CV_CAP_PROP_XI_CC_MATRIX_10 = 483, // Color Correction Matrix element [1][0] + CV_CAP_PROP_XI_CC_MATRIX_11 = 484, // Color Correction Matrix element [1][1] + CV_CAP_PROP_XI_CC_MATRIX_12 = 485, // Color Correction Matrix element [1][2] + CV_CAP_PROP_XI_CC_MATRIX_13 = 486, // Color Correction Matrix element [1][3] + CV_CAP_PROP_XI_CC_MATRIX_20 = 487, // Color Correction Matrix element [2][0] + CV_CAP_PROP_XI_CC_MATRIX_21 = 488, // Color Correction Matrix element [2][1] + CV_CAP_PROP_XI_CC_MATRIX_22 = 489, // Color Correction Matrix element [2][2] + CV_CAP_PROP_XI_CC_MATRIX_23 = 490, // Color Correction Matrix element [2][3] + CV_CAP_PROP_XI_CC_MATRIX_30 = 491, // Color Correction Matrix element [3][0] + CV_CAP_PROP_XI_CC_MATRIX_31 = 492, // Color Correction Matrix element [3][1] + CV_CAP_PROP_XI_CC_MATRIX_32 = 493, // Color Correction Matrix element [3][2] + CV_CAP_PROP_XI_CC_MATRIX_33 = 494, // Color Correction Matrix element [3][3] + CV_CAP_PROP_XI_DEFAULT_CC_MATRIX = 495, // Set default Color Correction Matrix + CV_CAP_PROP_XI_TRG_SELECTOR = 498, // Selects the type of trigger. + CV_CAP_PROP_XI_ACQ_FRAME_BURST_COUNT = 499, // Sets number of frames acquired by burst. This burst is used only if trigger is set to FrameBurstStart + CV_CAP_PROP_XI_DEBOUNCE_EN = 507, // Enable/Disable debounce to selected GPI + CV_CAP_PROP_XI_DEBOUNCE_T0 = 508, // Debounce time (x * 10us) + CV_CAP_PROP_XI_DEBOUNCE_T1 = 509, // Debounce time (x * 10us) + CV_CAP_PROP_XI_DEBOUNCE_POL = 510, // Debounce polarity (pol = 1 t0 - falling edge, t1 - rising edge) + CV_CAP_PROP_XI_LENS_MODE = 511, // Status of lens control interface. This shall be set to XI_ON before any Lens operations. + CV_CAP_PROP_XI_LENS_APERTURE_VALUE = 512, // Current lens aperture value in stops. Examples: 2.8, 4, 5.6, 8, 11 + CV_CAP_PROP_XI_LENS_FOCUS_MOVEMENT_VALUE = 513, // Lens current focus movement value to be used by XI_PRM_LENS_FOCUS_MOVE in motor steps. + CV_CAP_PROP_XI_LENS_FOCUS_MOVE = 514, // Moves lens focus motor by steps set in XI_PRM_LENS_FOCUS_MOVEMENT_VALUE. + CV_CAP_PROP_XI_LENS_FOCUS_DISTANCE = 515, // Lens focus distance in cm. + CV_CAP_PROP_XI_LENS_FOCAL_LENGTH = 516, // Lens focal distance in mm. + CV_CAP_PROP_XI_LENS_FEATURE_SELECTOR = 517, // Selects the current feature which is accessible by XI_PRM_LENS_FEATURE. + CV_CAP_PROP_XI_LENS_FEATURE = 518, // Allows access to lens feature value currently selected by XI_PRM_LENS_FEATURE_SELECTOR. + CV_CAP_PROP_XI_DEVICE_MODEL_ID = 521, // Return device model id + CV_CAP_PROP_XI_DEVICE_SN = 522, // Return device serial number + CV_CAP_PROP_XI_IMAGE_DATA_FORMAT_RGB32_ALPHA = 529, // The alpha channel of RGB32 output image format. + CV_CAP_PROP_XI_IMAGE_PAYLOAD_SIZE = 530, // Buffer size in bytes sufficient for output image returned by xiGetImage + CV_CAP_PROP_XI_TRANSPORT_PIXEL_FORMAT = 531, // Current format of pixels on transport layer. + CV_CAP_PROP_XI_SENSOR_CLOCK_FREQ_HZ = 532, // Sensor clock frequency in Hz. + CV_CAP_PROP_XI_SENSOR_CLOCK_FREQ_INDEX = 533, // Sensor clock frequency index. Sensor with selected frequencies have possibility to set the frequency only by this index. + CV_CAP_PROP_XI_SENSOR_OUTPUT_CHANNEL_COUNT = 534, // Number of output channels from sensor used for data transfer. + CV_CAP_PROP_XI_FRAMERATE = 535, // Define framerate in Hz + CV_CAP_PROP_XI_COUNTER_SELECTOR = 536, // Select counter + CV_CAP_PROP_XI_COUNTER_VALUE = 537, // Counter status + CV_CAP_PROP_XI_ACQ_TIMING_MODE = 538, // Type of sensor frames timing. + CV_CAP_PROP_XI_AVAILABLE_BANDWIDTH = 539, // Calculate and return available interface bandwidth(int Megabits) + CV_CAP_PROP_XI_BUFFER_POLICY = 540, // Data move policy + CV_CAP_PROP_XI_LUT_EN = 541, // Activates LUT. + CV_CAP_PROP_XI_LUT_INDEX = 542, // Control the index (offset) of the coefficient to access in the LUT. + CV_CAP_PROP_XI_LUT_VALUE = 543, // Value at entry LUTIndex of the LUT + CV_CAP_PROP_XI_TRG_DELAY = 544, // Specifies the delay in microseconds (us) to apply after the trigger reception before activating it. + CV_CAP_PROP_XI_TS_RST_MODE = 545, // Defines how time stamp reset engine will be armed + CV_CAP_PROP_XI_TS_RST_SOURCE = 546, // Defines which source will be used for timestamp reset. Writing this parameter will trigger settings of engine (arming) + CV_CAP_PROP_XI_IS_DEVICE_EXIST = 547, // Returns 1 if camera connected and works properly. + CV_CAP_PROP_XI_ACQ_BUFFER_SIZE = 548, // Acquisition buffer size in buffer_size_unit. Default bytes. + CV_CAP_PROP_XI_ACQ_BUFFER_SIZE_UNIT = 549, // Acquisition buffer size unit in bytes. Default 1. E.g. Value 1024 means that buffer_size is in KiBytes + CV_CAP_PROP_XI_ACQ_TRANSPORT_BUFFER_SIZE = 550, // Acquisition transport buffer size in bytes + CV_CAP_PROP_XI_BUFFERS_QUEUE_SIZE = 551, // Queue of field/frame buffers + CV_CAP_PROP_XI_ACQ_TRANSPORT_BUFFER_COMMIT = 552, // Number of buffers to commit to low level + CV_CAP_PROP_XI_RECENT_FRAME = 553, // GetImage returns most recent frame + CV_CAP_PROP_XI_DEVICE_RESET = 554, // Resets the camera to default state. + CV_CAP_PROP_XI_COLUMN_FPN_CORRECTION = 555, // Correction of column FPN + CV_CAP_PROP_XI_ROW_FPN_CORRECTION = 591, // Correction of row FPN + CV_CAP_PROP_XI_SENSOR_MODE = 558, // Current sensor mode. Allows to select sensor mode by one integer. Setting of this parameter affects: image dimensions and downsampling. + CV_CAP_PROP_XI_HDR = 559, // Enable High Dynamic Range feature. + CV_CAP_PROP_XI_HDR_KNEEPOINT_COUNT = 560, // The number of kneepoints in the PWLR. + CV_CAP_PROP_XI_HDR_T1 = 561, // position of first kneepoint(in % of XI_PRM_EXPOSURE) + CV_CAP_PROP_XI_HDR_T2 = 562, // position of second kneepoint (in % of XI_PRM_EXPOSURE) + CV_CAP_PROP_XI_KNEEPOINT1 = 563, // value of first kneepoint (% of sensor saturation) + CV_CAP_PROP_XI_KNEEPOINT2 = 564, // value of second kneepoint (% of sensor saturation) + CV_CAP_PROP_XI_IMAGE_BLACK_LEVEL = 565, // Last image black level counts. Can be used for Offline processing to recall it. + CV_CAP_PROP_XI_HW_REVISION = 571, // Returns hardware revision number. + CV_CAP_PROP_XI_DEBUG_LEVEL = 572, // Set debug level + CV_CAP_PROP_XI_AUTO_BANDWIDTH_CALCULATION = 573, // Automatic bandwidth calculation, + CV_CAP_PROP_XI_FFS_FILE_ID = 594, // File number. + CV_CAP_PROP_XI_FFS_FILE_SIZE = 580, // Size of file. + CV_CAP_PROP_XI_FREE_FFS_SIZE = 581, // Size of free camera FFS. + CV_CAP_PROP_XI_USED_FFS_SIZE = 582, // Size of used camera FFS. + CV_CAP_PROP_XI_FFS_ACCESS_KEY = 583, // Setting of key enables file operations on some cameras. + CV_CAP_PROP_XI_SENSOR_FEATURE_SELECTOR = 585, // Selects the current feature which is accessible by XI_PRM_SENSOR_FEATURE_VALUE. + CV_CAP_PROP_XI_SENSOR_FEATURE_VALUE = 586, // Allows access to sensor feature value currently selected by XI_PRM_SENSOR_FEATURE_SELECTOR. + + + // Properties for Android cameras + CV_CAP_PROP_ANDROID_FLASH_MODE = 8001, + CV_CAP_PROP_ANDROID_FOCUS_MODE = 8002, + CV_CAP_PROP_ANDROID_WHITE_BALANCE = 8003, + CV_CAP_PROP_ANDROID_ANTIBANDING = 8004, + CV_CAP_PROP_ANDROID_FOCAL_LENGTH = 8005, + CV_CAP_PROP_ANDROID_FOCUS_DISTANCE_NEAR = 8006, + CV_CAP_PROP_ANDROID_FOCUS_DISTANCE_OPTIMAL = 8007, + CV_CAP_PROP_ANDROID_FOCUS_DISTANCE_FAR = 8008, + CV_CAP_PROP_ANDROID_EXPOSE_LOCK = 8009, + CV_CAP_PROP_ANDROID_WHITEBALANCE_LOCK = 8010, + + // Properties of cameras available through AVFOUNDATION interface + CV_CAP_PROP_IOS_DEVICE_FOCUS = 9001, + CV_CAP_PROP_IOS_DEVICE_EXPOSURE = 9002, + CV_CAP_PROP_IOS_DEVICE_FLASH = 9003, + CV_CAP_PROP_IOS_DEVICE_WHITEBALANCE = 9004, + CV_CAP_PROP_IOS_DEVICE_TORCH = 9005, + + // Properties of cameras available through Smartek Giganetix Ethernet Vision interface + /* --- Vladimir Litvinenko (litvinenko.vladimir@gmail.com) --- */ + CV_CAP_PROP_GIGA_FRAME_OFFSET_X = 10001, + CV_CAP_PROP_GIGA_FRAME_OFFSET_Y = 10002, + CV_CAP_PROP_GIGA_FRAME_WIDTH_MAX = 10003, + CV_CAP_PROP_GIGA_FRAME_HEIGH_MAX = 10004, + CV_CAP_PROP_GIGA_FRAME_SENS_WIDTH = 10005, + CV_CAP_PROP_GIGA_FRAME_SENS_HEIGH = 10006, + + CV_CAP_PROP_INTELPERC_PROFILE_COUNT = 11001, + CV_CAP_PROP_INTELPERC_PROFILE_IDX = 11002, + CV_CAP_PROP_INTELPERC_DEPTH_LOW_CONFIDENCE_VALUE = 11003, + CV_CAP_PROP_INTELPERC_DEPTH_SATURATION_VALUE = 11004, + CV_CAP_PROP_INTELPERC_DEPTH_CONFIDENCE_THRESHOLD = 11005, + CV_CAP_PROP_INTELPERC_DEPTH_FOCAL_LENGTH_HORZ = 11006, + CV_CAP_PROP_INTELPERC_DEPTH_FOCAL_LENGTH_VERT = 11007, + + // Intel PerC streams + CV_CAP_INTELPERC_DEPTH_GENERATOR = 1 << 29, + CV_CAP_INTELPERC_IMAGE_GENERATOR = 1 << 28, + CV_CAP_INTELPERC_GENERATORS_MASK = CV_CAP_INTELPERC_DEPTH_GENERATOR + CV_CAP_INTELPERC_IMAGE_GENERATOR +}; + +// Generic camera output modes. +// Currently, these are supported through the libv4l interface only. +enum +{ + CV_CAP_MODE_BGR = 0, // BGR24 (default) + CV_CAP_MODE_RGB = 1, // RGB24 + CV_CAP_MODE_GRAY = 2, // Y8 + CV_CAP_MODE_YUYV = 3 // YUYV +}; + +enum +{ + // Data given from depth generator. + CV_CAP_OPENNI_DEPTH_MAP = 0, // Depth values in mm (CV_16UC1) + CV_CAP_OPENNI_POINT_CLOUD_MAP = 1, // XYZ in meters (CV_32FC3) + CV_CAP_OPENNI_DISPARITY_MAP = 2, // Disparity in pixels (CV_8UC1) + CV_CAP_OPENNI_DISPARITY_MAP_32F = 3, // Disparity in pixels (CV_32FC1) + CV_CAP_OPENNI_VALID_DEPTH_MASK = 4, // CV_8UC1 + + // Data given from RGB image generator. + CV_CAP_OPENNI_BGR_IMAGE = 5, + CV_CAP_OPENNI_GRAY_IMAGE = 6, + + // Data given from IR image generator. + CV_CAP_OPENNI_IR_IMAGE = 7 +}; + +// Supported output modes of OpenNI image generator +enum +{ + CV_CAP_OPENNI_VGA_30HZ = 0, + CV_CAP_OPENNI_SXGA_15HZ = 1, + CV_CAP_OPENNI_SXGA_30HZ = 2, + CV_CAP_OPENNI_QVGA_30HZ = 3, + CV_CAP_OPENNI_QVGA_60HZ = 4 +}; + +enum +{ + CV_CAP_INTELPERC_DEPTH_MAP = 0, // Each pixel is a 16-bit integer. The value indicates the distance from an object to the camera's XY plane or the Cartesian depth. + CV_CAP_INTELPERC_UVDEPTH_MAP = 1, // Each pixel contains two 32-bit floating point values in the range of 0-1, representing the mapping of depth coordinates to the color coordinates. + CV_CAP_INTELPERC_IR_MAP = 2, // Each pixel is a 16-bit integer. The value indicates the intensity of the reflected laser beam. + CV_CAP_INTELPERC_IMAGE = 3 +}; + +// gPhoto2 properties, if propertyId is less than 0 then work on widget with that __additive inversed__ camera setting ID +// Get IDs by using CAP_PROP_GPHOTO2_WIDGET_ENUMERATE. +// @see CvCaptureCAM_GPHOTO2 for more info +enum +{ + CV_CAP_PROP_GPHOTO2_PREVIEW = 17001, // Capture only preview from liveview mode. + CV_CAP_PROP_GPHOTO2_WIDGET_ENUMERATE = 17002, // Readonly, returns (const char *). + CV_CAP_PROP_GPHOTO2_RELOAD_CONFIG = 17003, // Trigger, only by set. Reload camera settings. + CV_CAP_PROP_GPHOTO2_RELOAD_ON_CHANGE = 17004, // Reload all settings on set. + CV_CAP_PROP_GPHOTO2_COLLECT_MSGS = 17005, // Collect messages with details. + CV_CAP_PROP_GPHOTO2_FLUSH_MSGS = 17006, // Readonly, returns (const char *). + CV_CAP_PROP_SPEED = 17007, // Exposure speed. Can be readonly, depends on camera program. + CV_CAP_PROP_APERTURE = 17008, // Aperture. Can be readonly, depends on camera program. + CV_CAP_PROP_EXPOSUREPROGRAM = 17009, // Camera exposure program. + CV_CAP_PROP_VIEWFINDER = 17010 // Enter liveview mode. +}; + +/** @brief retrieve capture properties +*/ +CVAPI(double) cvGetCaptureProperty( CvCapture* capture, int property_id ); +/** @brief set capture properties +*/ +CVAPI(int) cvSetCaptureProperty( CvCapture* capture, int property_id, double value ); + +/** @brief Return the type of the capturer (eg, ::CV_CAP_VFW, ::CV_CAP_UNICAP) + +It is unknown if created with ::CV_CAP_ANY +*/ +CVAPI(int) cvGetCaptureDomain( CvCapture* capture); + +/** @brief "black box" video file writer structure + +In C++ use cv::VideoWriter +*/ +typedef struct CvVideoWriter CvVideoWriter; + +//! Macro to construct the fourcc code of the codec. Same as CV_FOURCC() +#define CV_FOURCC_MACRO(c1, c2, c3, c4) (((c1) & 255) + (((c2) & 255) << 8) + (((c3) & 255) << 16) + (((c4) & 255) << 24)) + +/** @brief Constructs the fourcc code of the codec function + +Simply call it with 4 chars fourcc code like `CV_FOURCC('I', 'Y', 'U', 'V')` + +List of codes can be obtained at [Video Codecs by FOURCC](http://www.fourcc.org/codecs.php) page. +FFMPEG backend with MP4 container natively uses other values as fourcc code: +see [ObjectType](http://www.mp4ra.org/codecs.html). +*/ +CV_INLINE int CV_FOURCC(char c1, char c2, char c3, char c4) +{ + return CV_FOURCC_MACRO(c1, c2, c3, c4); +} + +//! (Windows only) Open Codec Selection Dialog +#define CV_FOURCC_PROMPT -1 +//! (Linux only) Use default codec for specified filename +#define CV_FOURCC_DEFAULT CV_FOURCC('I', 'Y', 'U', 'V') + +/** @brief initialize video file writer +*/ +CVAPI(CvVideoWriter*) cvCreateVideoWriter( const char* filename, int fourcc, + double fps, CvSize frame_size, + int is_color CV_DEFAULT(1)); + +/** @brief write frame to video file +*/ +CVAPI(int) cvWriteFrame( CvVideoWriter* writer, const IplImage* image ); + +/** @brief close video file writer +*/ +CVAPI(void) cvReleaseVideoWriter( CvVideoWriter** writer ); + +// *************************************************************************************** +//! @name Obsolete functions/synonyms +//! @{ +#define cvCaptureFromCAM cvCreateCameraCapture //!< @deprecated use cvCreateCameraCapture() instead +#define cvCaptureFromFile cvCreateFileCapture //!< @deprecated use cvCreateFileCapture() instead +#define cvCaptureFromAVI cvCaptureFromFile //!< @deprecated use cvCreateFileCapture() instead +#define cvCreateAVIWriter cvCreateVideoWriter //!< @deprecated use cvCreateVideoWriter() instead +#define cvWriteToAVI cvWriteFrame //!< @deprecated use cvWriteFrame() instead +//! @} Obsolete... + +//! @} videoio_c + +#ifdef __cplusplus +} +#endif + +#endif //OPENCV_VIDEOIO_H diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videostab.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videostab.hpp new file mode 100644 index 0000000000000000000000000000000000000000..ca3f5adef2b9162cfe47b68b2f4790025188401d --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videostab.hpp @@ -0,0 +1,81 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_VIDEOSTAB_HPP +#define OPENCV_VIDEOSTAB_HPP + +/** + @defgroup videostab Video Stabilization + +The video stabilization module contains a set of functions and classes that can be used to solve the +problem of video stabilization. There are a few methods implemented, most of them are described in +the papers @cite OF06 and @cite G11 . However, there are some extensions and deviations from the original +paper methods. + +### References + + 1. "Full-Frame Video Stabilization with Motion Inpainting" + Yasuyuki Matsushita, Eyal Ofek, Weina Ge, Xiaoou Tang, Senior Member, and Heung-Yeung Shum + 2. "Auto-Directed Video Stabilization with Robust L1 Optimal Camera Paths" + Matthias Grundmann, Vivek Kwatra, Irfan Essa + + @{ + @defgroup videostab_motion Global Motion Estimation + +The video stabilization module contains a set of functions and classes for global motion estimation +between point clouds or between images. In the last case features are extracted and matched +internally. For the sake of convenience the motion estimation functions are wrapped into classes. +Both the functions and the classes are available. + + @defgroup videostab_marching Fast Marching Method + +The Fast Marching Method @cite Telea04 is used in of the video stabilization routines to do motion and +color inpainting. The method is implemented is a flexible way and it's made public for other users. + + @} + +*/ + +#include "opencv2/videostab/stabilizer.hpp" +#include "opencv2/videostab/ring_buffer.hpp" + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videostab/deblurring.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videostab/deblurring.hpp new file mode 100644 index 0000000000000000000000000000000000000000..c6656401eba6304b67fd34383a70310c7de8c923 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videostab/deblurring.hpp @@ -0,0 +1,116 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_VIDEOSTAB_DEBLURRING_HPP +#define OPENCV_VIDEOSTAB_DEBLURRING_HPP + +#include +#include "opencv2/core.hpp" + +namespace cv +{ +namespace videostab +{ + +//! @addtogroup videostab +//! @{ + +CV_EXPORTS float calcBlurriness(const Mat &frame); + +class CV_EXPORTS DeblurerBase +{ +public: + DeblurerBase() : radius_(0), frames_(0), motions_(0), blurrinessRates_(0) {} + + virtual ~DeblurerBase() {} + + virtual void setRadius(int val) { radius_ = val; } + virtual int radius() const { return radius_; } + + virtual void deblur(int idx, Mat &frame) = 0; + + + // data from stabilizer + + virtual void setFrames(const std::vector &val) { frames_ = &val; } + virtual const std::vector& frames() const { return *frames_; } + + virtual void setMotions(const std::vector &val) { motions_ = &val; } + virtual const std::vector& motions() const { return *motions_; } + + virtual void setBlurrinessRates(const std::vector &val) { blurrinessRates_ = &val; } + virtual const std::vector& blurrinessRates() const { return *blurrinessRates_; } + +protected: + int radius_; + const std::vector *frames_; + const std::vector *motions_; + const std::vector *blurrinessRates_; +}; + +class CV_EXPORTS NullDeblurer : public DeblurerBase +{ +public: + virtual void deblur(int /*idx*/, Mat &/*frame*/) CV_OVERRIDE {} +}; + +class CV_EXPORTS WeightingDeblurer : public DeblurerBase +{ +public: + WeightingDeblurer(); + + void setSensitivity(float val) { sensitivity_ = val; } + float sensitivity() const { return sensitivity_; } + + virtual void deblur(int idx, Mat &frame) CV_OVERRIDE; + +private: + float sensitivity_; + Mat_ bSum_, gSum_, rSum_, wSum_; +}; + +//! @} + +} // namespace videostab +} // namespace cv + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videostab/fast_marching.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videostab/fast_marching.hpp new file mode 100644 index 0000000000000000000000000000000000000000..43f8e4a7299dc114d323b7f32fb3fbd69df1c05c --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videostab/fast_marching.hpp @@ -0,0 +1,121 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_VIDEOSTAB_FAST_MARCHING_HPP +#define OPENCV_VIDEOSTAB_FAST_MARCHING_HPP + +#include +#include +#include +#include "opencv2/core.hpp" + +namespace cv +{ +namespace videostab +{ + +//! @addtogroup videostab_marching +//! @{ + +/** @brief Describes the Fast Marching Method implementation. + + See http://iwi.eldoc.ub.rug.nl/FILES/root/2004/JGraphToolsTelea/2004JGraphToolsTelea.pdf + */ +class CV_EXPORTS FastMarchingMethod +{ +public: + FastMarchingMethod() : inf_(1e6f), size_(0) {} + + /** @brief Template method that runs the Fast Marching Method. + + @param mask Image mask. 0 value indicates that the pixel value must be inpainted, 255 indicates + that the pixel value is known, other values aren't acceptable. + @param inpaint Inpainting functor that overloads void operator ()(int x, int y). + @return Inpainting functor. + */ + template + Inpaint run(const Mat &mask, Inpaint inpaint); + + /** + @return Distance map that's created during working of the method. + */ + Mat distanceMap() const { return dist_; } + +private: + enum { INSIDE = 0, BAND = 1, KNOWN = 255 }; + + struct DXY + { + float dist; + int x, y; + + DXY() : dist(0), x(0), y(0) {} + DXY(float _dist, int _x, int _y) : dist(_dist), x(_x), y(_y) {} + bool operator <(const DXY &dxy) const { return dist < dxy.dist; } + }; + + float solve(int x1, int y1, int x2, int y2) const; + int& indexOf(const DXY &dxy) { return index_(dxy.y, dxy.x); } + + void heapUp(int idx); + void heapDown(int idx); + void heapAdd(const DXY &dxy); + void heapRemoveMin(); + + float inf_; + + cv::Mat_ flag_; // flag map + cv::Mat_ dist_; // distance map + + cv::Mat_ index_; // index of point in the narrow band + std::vector narrowBand_; // narrow band heap + int size_; // narrow band size +}; + +//! @} + +} // namespace videostab +} // namespace cv + +#include "fast_marching_inl.hpp" + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videostab/fast_marching_inl.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videostab/fast_marching_inl.hpp new file mode 100644 index 0000000000000000000000000000000000000000..fdd488aac80b6a1c43b70b9ae3ce08a0e173a28c --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videostab/fast_marching_inl.hpp @@ -0,0 +1,165 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_VIDEOSTAB_FAST_MARCHING_INL_HPP +#define OPENCV_VIDEOSTAB_FAST_MARCHING_INL_HPP + +#include "opencv2/videostab/fast_marching.hpp" + +namespace cv +{ +namespace videostab +{ + +template +Inpaint FastMarchingMethod::run(const cv::Mat &mask, Inpaint inpaint) +{ + using namespace cv; + + CV_Assert(mask.type() == CV_8U); + + static const int lut[4][2] = {{-1,0}, {0,-1}, {1,0}, {0,1}}; + + mask.copyTo(flag_); + flag_.create(mask.size()); + dist_.create(mask.size()); + index_.create(mask.size()); + narrowBand_.clear(); + size_ = 0; + + // init + for (int y = 0; y < flag_.rows; ++y) + { + for (int x = 0; x < flag_.cols; ++x) + { + if (flag_(y,x) == KNOWN) + dist_(y,x) = 0.f; + else + { + int n = 0; + int nunknown = 0; + + for (int i = 0; i < 4; ++i) + { + int xn = x + lut[i][0]; + int yn = y + lut[i][1]; + + if (xn >= 0 && xn < flag_.cols && yn >= 0 && yn < flag_.rows) + { + n++; + if (flag_(yn,xn) != KNOWN) + nunknown++; + } + } + + if (n>0 && nunknown == n) + { + dist_(y,x) = inf_; + flag_(y,x) = INSIDE; + } + else + { + dist_(y,x) = 0.f; + flag_(y,x) = BAND; + inpaint(x, y); + + narrowBand_.push_back(DXY(0.f,x,y)); + index_(y,x) = size_++; + } + } + } + } + + // make heap + for (int i = size_/2-1; i >= 0; --i) + heapDown(i); + + // main cycle + while (size_ > 0) + { + int x = narrowBand_[0].x; + int y = narrowBand_[0].y; + heapRemoveMin(); + + flag_(y,x) = KNOWN; + for (int n = 0; n < 4; ++n) + { + int xn = x + lut[n][0]; + int yn = y + lut[n][1]; + + if (xn >= 0 && xn < flag_.cols && yn >= 0 && yn < flag_.rows && flag_(yn,xn) != KNOWN) + { + dist_(yn,xn) = std::min(std::min(solve(xn-1, yn, xn, yn-1), solve(xn+1, yn, xn, yn-1)), + std::min(solve(xn-1, yn, xn, yn+1), solve(xn+1, yn, xn, yn+1))); + + if (flag_(yn,xn) == INSIDE) + { + flag_(yn,xn) = BAND; + inpaint(xn, yn); + heapAdd(DXY(dist_(yn,xn),xn,yn)); + } + else + { + int i = index_(yn,xn); + if (dist_(yn,xn) < narrowBand_[i].dist) + { + narrowBand_[i].dist = dist_(yn,xn); + heapUp(i); + } + // works better if it's commented out + /*else if (dist(yn,xn) > narrowBand[i].dist) + { + narrowBand[i].dist = dist(yn,xn); + heapDown(i); + }*/ + } + } + } + } + + return inpaint; +} + +} // namespace videostab +} // namespace cv + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videostab/frame_source.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videostab/frame_source.hpp new file mode 100644 index 0000000000000000000000000000000000000000..171c637c8eba0abebb5540e89bfd1dd0a11086c2 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videostab/frame_source.hpp @@ -0,0 +1,94 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_VIDEOSTAB_FRAME_SOURCE_HPP +#define OPENCV_VIDEOSTAB_FRAME_SOURCE_HPP + +#include +#include "opencv2/core.hpp" + +namespace cv +{ +namespace videostab +{ + +//! @addtogroup videostab +//! @{ + +class CV_EXPORTS IFrameSource +{ +public: + virtual ~IFrameSource() {} + virtual void reset() = 0; + virtual Mat nextFrame() = 0; +}; + +class CV_EXPORTS NullFrameSource : public IFrameSource +{ +public: + virtual void reset() CV_OVERRIDE {} + virtual Mat nextFrame() CV_OVERRIDE { return Mat(); } +}; + +class CV_EXPORTS VideoFileSource : public IFrameSource +{ +public: + VideoFileSource(const String &path, bool volatileFrame = false); + + virtual void reset() CV_OVERRIDE; + virtual Mat nextFrame() CV_OVERRIDE; + + int width(); + int height(); + int count(); + double fps(); + +private: + Ptr impl; +}; + +//! @} + +} // namespace videostab +} // namespace cv + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videostab/global_motion.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videostab/global_motion.hpp new file mode 100644 index 0000000000000000000000000000000000000000..fedca2cf5eaaf79e437297f234697217eb295065 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videostab/global_motion.hpp @@ -0,0 +1,300 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_VIDEOSTAB_GLOBAL_MOTION_HPP +#define OPENCV_VIDEOSTAB_GLOBAL_MOTION_HPP + +#include +#include +#include "opencv2/core.hpp" +#include "opencv2/features2d.hpp" +#include "opencv2/opencv_modules.hpp" +#include "opencv2/videostab/optical_flow.hpp" +#include "opencv2/videostab/motion_core.hpp" +#include "opencv2/videostab/outlier_rejection.hpp" + +#ifdef HAVE_OPENCV_CUDAIMGPROC +# include "opencv2/cudaimgproc.hpp" +#endif + +namespace cv +{ +namespace videostab +{ + +//! @addtogroup videostab_motion +//! @{ + +/** @brief Estimates best global motion between two 2D point clouds in the least-squares sense. + +@note Works in-place and changes input point arrays. + +@param points0 Source set of 2D points (32F). +@param points1 Destination set of 2D points (32F). +@param model Motion model (up to MM_AFFINE). +@param rmse Final root-mean-square error. +@return 3x3 2D transformation matrix (32F). + */ +CV_EXPORTS Mat estimateGlobalMotionLeastSquares( + InputOutputArray points0, InputOutputArray points1, int model = MM_AFFINE, + float *rmse = 0); + +/** @brief Estimates best global motion between two 2D point clouds robustly (using RANSAC method). + +@param points0 Source set of 2D points (32F). +@param points1 Destination set of 2D points (32F). +@param model Motion model. See cv::videostab::MotionModel. +@param params RANSAC method parameters. See videostab::RansacParams. +@param rmse Final root-mean-square error. +@param ninliers Final number of inliers. + */ +CV_EXPORTS Mat estimateGlobalMotionRansac( + InputArray points0, InputArray points1, int model = MM_AFFINE, + const RansacParams ¶ms = RansacParams::default2dMotion(MM_AFFINE), + float *rmse = 0, int *ninliers = 0); + +/** @brief Base class for all global motion estimation methods. + */ +class CV_EXPORTS MotionEstimatorBase +{ +public: + virtual ~MotionEstimatorBase() {} + + /** @brief Sets motion model. + + @param val Motion model. See cv::videostab::MotionModel. + */ + virtual void setMotionModel(MotionModel val) { motionModel_ = val; } + + /** + @return Motion model. See cv::videostab::MotionModel. + */ + virtual MotionModel motionModel() const { return motionModel_; } + + /** @brief Estimates global motion between two 2D point clouds. + + @param points0 Source set of 2D points (32F). + @param points1 Destination set of 2D points (32F). + @param ok Indicates whether motion was estimated successfully. + @return 3x3 2D transformation matrix (32F). + */ + virtual Mat estimate(InputArray points0, InputArray points1, bool *ok = 0) = 0; + +protected: + MotionEstimatorBase(MotionModel model) { setMotionModel(model); } + +private: + MotionModel motionModel_; +}; + +/** @brief Describes a robust RANSAC-based global 2D motion estimation method which minimizes L2 error. + */ +class CV_EXPORTS MotionEstimatorRansacL2 : public MotionEstimatorBase +{ +public: + MotionEstimatorRansacL2(MotionModel model = MM_AFFINE); + + void setRansacParams(const RansacParams &val) { ransacParams_ = val; } + RansacParams ransacParams() const { return ransacParams_; } + + void setMinInlierRatio(float val) { minInlierRatio_ = val; } + float minInlierRatio() const { return minInlierRatio_; } + + virtual Mat estimate(InputArray points0, InputArray points1, bool *ok = 0) CV_OVERRIDE; + +private: + RansacParams ransacParams_; + float minInlierRatio_; +}; + +/** @brief Describes a global 2D motion estimation method which minimizes L1 error. + +@note To be able to use this method you must build OpenCV with CLP library support. : + */ +class CV_EXPORTS MotionEstimatorL1 : public MotionEstimatorBase +{ +public: + MotionEstimatorL1(MotionModel model = MM_AFFINE); + + virtual Mat estimate(InputArray points0, InputArray points1, bool *ok = 0) CV_OVERRIDE; + +private: + std::vector obj_, collb_, colub_; + std::vector elems_, rowlb_, rowub_; + std::vector rows_, cols_; + + void set(int row, int col, double coef) + { + rows_.push_back(row); + cols_.push_back(col); + elems_.push_back(coef); + } +}; + +/** @brief Base class for global 2D motion estimation methods which take frames as input. + */ +class CV_EXPORTS ImageMotionEstimatorBase +{ +public: + virtual ~ImageMotionEstimatorBase() {} + + virtual void setMotionModel(MotionModel val) { motionModel_ = val; } + virtual MotionModel motionModel() const { return motionModel_; } + + virtual Mat estimate(const Mat &frame0, const Mat &frame1, bool *ok = 0) = 0; + +protected: + ImageMotionEstimatorBase(MotionModel model) { setMotionModel(model); } + +private: + MotionModel motionModel_; +}; + +class CV_EXPORTS FromFileMotionReader : public ImageMotionEstimatorBase +{ +public: + FromFileMotionReader(const String &path); + + virtual Mat estimate(const Mat &frame0, const Mat &frame1, bool *ok = 0) CV_OVERRIDE; + +private: + std::ifstream file_; +}; + +class CV_EXPORTS ToFileMotionWriter : public ImageMotionEstimatorBase +{ +public: + ToFileMotionWriter(const String &path, Ptr estimator); + + virtual void setMotionModel(MotionModel val) CV_OVERRIDE { motionEstimator_->setMotionModel(val); } + virtual MotionModel motionModel() const CV_OVERRIDE { return motionEstimator_->motionModel(); } + + virtual Mat estimate(const Mat &frame0, const Mat &frame1, bool *ok = 0) CV_OVERRIDE; + +private: + std::ofstream file_; + Ptr motionEstimator_; +}; + +/** @brief Describes a global 2D motion estimation method which uses keypoints detection and optical flow for +matching. + */ +class CV_EXPORTS KeypointBasedMotionEstimator : public ImageMotionEstimatorBase +{ +public: + KeypointBasedMotionEstimator(Ptr estimator); + + virtual void setMotionModel(MotionModel val) CV_OVERRIDE { motionEstimator_->setMotionModel(val); } + virtual MotionModel motionModel() const CV_OVERRIDE { return motionEstimator_->motionModel(); } + + void setDetector(Ptr val) { detector_ = val; } + Ptr detector() const { return detector_; } + + void setOpticalFlowEstimator(Ptr val) { optFlowEstimator_ = val; } + Ptr opticalFlowEstimator() const { return optFlowEstimator_; } + + void setOutlierRejector(Ptr val) { outlierRejector_ = val; } + Ptr outlierRejector() const { return outlierRejector_; } + + virtual Mat estimate(const Mat &frame0, const Mat &frame1, bool *ok = 0) CV_OVERRIDE; + Mat estimate(InputArray frame0, InputArray frame1, bool *ok = 0); + +private: + Ptr motionEstimator_; + Ptr detector_; + Ptr optFlowEstimator_; + Ptr outlierRejector_; + + std::vector status_; + std::vector keypointsPrev_; + std::vector pointsPrev_, points_; + std::vector pointsPrevGood_, pointsGood_; +}; + +#if defined(HAVE_OPENCV_CUDAIMGPROC) && defined(HAVE_OPENCV_CUDAOPTFLOW) + +class CV_EXPORTS KeypointBasedMotionEstimatorGpu : public ImageMotionEstimatorBase +{ +public: + KeypointBasedMotionEstimatorGpu(Ptr estimator); + + virtual void setMotionModel(MotionModel val) CV_OVERRIDE { motionEstimator_->setMotionModel(val); } + virtual MotionModel motionModel() const CV_OVERRIDE { return motionEstimator_->motionModel(); } + + void setOutlierRejector(Ptr val) { outlierRejector_ = val; } + Ptr outlierRejector() const { return outlierRejector_; } + + virtual Mat estimate(const Mat &frame0, const Mat &frame1, bool *ok = 0) CV_OVERRIDE; + Mat estimate(const cuda::GpuMat &frame0, const cuda::GpuMat &frame1, bool *ok = 0); + +private: + Ptr motionEstimator_; + Ptr detector_; + SparsePyrLkOptFlowEstimatorGpu optFlowEstimator_; + Ptr outlierRejector_; + + cuda::GpuMat frame0_, grayFrame0_, frame1_; + cuda::GpuMat pointsPrev_, points_; + cuda::GpuMat status_; + + Mat hostPointsPrev_, hostPoints_; + std::vector hostPointsPrevTmp_, hostPointsTmp_; + std::vector rejectionStatus_; +}; + +#endif // defined(HAVE_OPENCV_CUDAIMGPROC) && defined(HAVE_OPENCV_CUDAOPTFLOW) + +/** @brief Computes motion between two frames assuming that all the intermediate motions are known. + +@param from Source frame index. +@param to Destination frame index. +@param motions Pair-wise motions. motions[i] denotes motion from the frame i to the frame i+1 +@return Motion from the Source frame to the Destination frame. + */ +CV_EXPORTS Mat getMotion(int from, int to, const std::vector &motions); + +//! @} + +} // namespace videostab +} // namespace cv + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videostab/inpainting.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videostab/inpainting.hpp new file mode 100644 index 0000000000000000000000000000000000000000..9c123f02acbabcdece88dc7a84ee61b59eb8ec3d --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videostab/inpainting.hpp @@ -0,0 +1,212 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_VIDEOSTAB_INPAINTINT_HPP +#define OPENCV_VIDEOSTAB_INPAINTINT_HPP + +#include +#include "opencv2/core.hpp" +#include "opencv2/videostab/optical_flow.hpp" +#include "opencv2/videostab/fast_marching.hpp" +#include "opencv2/videostab/global_motion.hpp" +#include "opencv2/photo.hpp" + +namespace cv +{ +namespace videostab +{ + +//! @addtogroup videostab +//! @{ + +class CV_EXPORTS InpainterBase +{ +public: + InpainterBase() + : radius_(0), motionModel_(MM_UNKNOWN), frames_(0), motions_(0), + stabilizedFrames_(0), stabilizationMotions_(0) {} + + virtual ~InpainterBase() {} + + virtual void setRadius(int val) { radius_ = val; } + virtual int radius() const { return radius_; } + + virtual void setMotionModel(MotionModel val) { motionModel_ = val; } + virtual MotionModel motionModel() const { return motionModel_; } + + virtual void inpaint(int idx, Mat &frame, Mat &mask) = 0; + + + // data from stabilizer + + virtual void setFrames(const std::vector &val) { frames_ = &val; } + virtual const std::vector& frames() const { return *frames_; } + + virtual void setMotions(const std::vector &val) { motions_ = &val; } + virtual const std::vector& motions() const { return *motions_; } + + virtual void setStabilizedFrames(const std::vector &val) { stabilizedFrames_ = &val; } + virtual const std::vector& stabilizedFrames() const { return *stabilizedFrames_; } + + virtual void setStabilizationMotions(const std::vector &val) { stabilizationMotions_ = &val; } + virtual const std::vector& stabilizationMotions() const { return *stabilizationMotions_; } + +protected: + int radius_; + MotionModel motionModel_; + const std::vector *frames_; + const std::vector *motions_; + const std::vector *stabilizedFrames_; + const std::vector *stabilizationMotions_; +}; + +class CV_EXPORTS NullInpainter : public InpainterBase +{ +public: + virtual void inpaint(int /*idx*/, Mat &/*frame*/, Mat &/*mask*/) CV_OVERRIDE {} +}; + +class CV_EXPORTS InpaintingPipeline : public InpainterBase +{ +public: + void pushBack(Ptr inpainter) { inpainters_.push_back(inpainter); } + bool empty() const { return inpainters_.empty(); } + + virtual void setRadius(int val) CV_OVERRIDE; + virtual void setMotionModel(MotionModel val) CV_OVERRIDE; + virtual void setFrames(const std::vector &val) CV_OVERRIDE; + virtual void setMotions(const std::vector &val) CV_OVERRIDE; + virtual void setStabilizedFrames(const std::vector &val) CV_OVERRIDE; + virtual void setStabilizationMotions(const std::vector &val) CV_OVERRIDE; + + virtual void inpaint(int idx, Mat &frame, Mat &mask) CV_OVERRIDE; + +private: + std::vector > inpainters_; +}; + +class CV_EXPORTS ConsistentMosaicInpainter : public InpainterBase +{ +public: + ConsistentMosaicInpainter(); + + void setStdevThresh(float val) { stdevThresh_ = val; } + float stdevThresh() const { return stdevThresh_; } + + virtual void inpaint(int idx, Mat &frame, Mat &mask) CV_OVERRIDE; + +private: + float stdevThresh_; +}; + +class CV_EXPORTS MotionInpainter : public InpainterBase +{ +public: + MotionInpainter(); + + void setOptFlowEstimator(Ptr val) { optFlowEstimator_ = val; } + Ptr optFlowEstimator() const { return optFlowEstimator_; } + + void setFlowErrorThreshold(float val) { flowErrorThreshold_ = val; } + float flowErrorThreshold() const { return flowErrorThreshold_; } + + void setDistThreshold(float val) { distThresh_ = val; } + float distThresh() const { return distThresh_; } + + void setBorderMode(int val) { borderMode_ = val; } + int borderMode() const { return borderMode_; } + + virtual void inpaint(int idx, Mat &frame, Mat &mask) CV_OVERRIDE; + +private: + FastMarchingMethod fmm_; + Ptr optFlowEstimator_; + float flowErrorThreshold_; + float distThresh_; + int borderMode_; + + Mat frame1_, transformedFrame1_; + Mat_ grayFrame_, transformedGrayFrame1_; + Mat_ mask1_, transformedMask1_; + Mat_ flowX_, flowY_, flowErrors_; + Mat_ flowMask_; +}; + +class CV_EXPORTS ColorAverageInpainter : public InpainterBase +{ +public: + virtual void inpaint(int idx, Mat &frame, Mat &mask) CV_OVERRIDE; + +private: + FastMarchingMethod fmm_; +}; + +class CV_EXPORTS ColorInpainter : public InpainterBase +{ +public: + ColorInpainter(int method = INPAINT_TELEA, double radius = 2.); + + virtual void inpaint(int idx, Mat &frame, Mat &mask) CV_OVERRIDE; + +private: + int method_; + double radius_; + Mat invMask_; +}; + +inline ColorInpainter::ColorInpainter(int _method, double _radius) + : method_(_method), radius_(_radius) {} + +CV_EXPORTS void calcFlowMask( + const Mat &flowX, const Mat &flowY, const Mat &errors, float maxError, + const Mat &mask0, const Mat &mask1, Mat &flowMask); + +CV_EXPORTS void completeFrameAccordingToFlow( + const Mat &flowMask, const Mat &flowX, const Mat &flowY, const Mat &frame1, const Mat &mask1, + float distThresh, Mat& frame0, Mat &mask0); + +//! @} + +} // namespace videostab +} // namespace cv + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videostab/log.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videostab/log.hpp new file mode 100644 index 0000000000000000000000000000000000000000..73e704996d9057b75055d0129ce04c126f7ae8d8 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videostab/log.hpp @@ -0,0 +1,80 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_VIDEOSTAB_LOG_HPP +#define OPENCV_VIDEOSTAB_LOG_HPP + +#include "opencv2/core.hpp" + +namespace cv +{ +namespace videostab +{ + +//! @addtogroup videostab +//! @{ + +class CV_EXPORTS ILog +{ +public: + virtual ~ILog() {} + virtual void print(const char *format, ...) = 0; +}; + +class CV_EXPORTS NullLog : public ILog +{ +public: + virtual void print(const char * /*format*/, ...) CV_OVERRIDE {} +}; + +class CV_EXPORTS LogToStdout : public ILog +{ +public: + virtual void print(const char *format, ...) CV_OVERRIDE; +}; + +//! @} + +} // namespace videostab +} // namespace cv + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videostab/motion_core.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videostab/motion_core.hpp new file mode 100644 index 0000000000000000000000000000000000000000..4525cc7b3cea946607bbc2fbdb4363a88d175ce8 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videostab/motion_core.hpp @@ -0,0 +1,129 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_VIDEOSTAB_MOTION_CORE_HPP +#define OPENCV_VIDEOSTAB_MOTION_CORE_HPP + +#include +#include "opencv2/core.hpp" + +namespace cv +{ +namespace videostab +{ + +//! @addtogroup videostab_motion +//! @{ + +/** @brief Describes motion model between two point clouds. + */ +enum MotionModel +{ + MM_TRANSLATION = 0, + MM_TRANSLATION_AND_SCALE = 1, + MM_ROTATION = 2, + MM_RIGID = 3, + MM_SIMILARITY = 4, + MM_AFFINE = 5, + MM_HOMOGRAPHY = 6, + MM_UNKNOWN = 7 +}; + +/** @brief Describes RANSAC method parameters. + */ +struct CV_EXPORTS RansacParams +{ + int size; //!< subset size + float thresh; //!< max error to classify as inlier + float eps; //!< max outliers ratio + float prob; //!< probability of success + + RansacParams() : size(0), thresh(0), eps(0), prob(0) {} + /** @brief Constructor + @param size Subset size. + @param thresh Maximum re-projection error value to classify as inlier. + @param eps Maximum ratio of incorrect correspondences. + @param prob Required success probability. + */ + RansacParams(int size, float thresh, float eps, float prob); + + /** + @return Number of iterations that'll be performed by RANSAC method. + */ + int niters() const + { + return static_cast( + std::ceil(std::log(1 - prob) / std::log(1 - std::pow(1 - eps, size)))); + } + + /** + @param model Motion model. See cv::videostab::MotionModel. + @return Default RANSAC method parameters for the given motion model. + */ + static RansacParams default2dMotion(MotionModel model) + { + CV_Assert(model < MM_UNKNOWN); + if (model == MM_TRANSLATION) + return RansacParams(1, 0.5f, 0.5f, 0.99f); + if (model == MM_TRANSLATION_AND_SCALE) + return RansacParams(2, 0.5f, 0.5f, 0.99f); + if (model == MM_ROTATION) + return RansacParams(1, 0.5f, 0.5f, 0.99f); + if (model == MM_RIGID) + return RansacParams(2, 0.5f, 0.5f, 0.99f); + if (model == MM_SIMILARITY) + return RansacParams(2, 0.5f, 0.5f, 0.99f); + if (model == MM_AFFINE) + return RansacParams(3, 0.5f, 0.5f, 0.99f); + return RansacParams(4, 0.5f, 0.5f, 0.99f); + } +}; + +inline RansacParams::RansacParams(int _size, float _thresh, float _eps, float _prob) + : size(_size), thresh(_thresh), eps(_eps), prob(_prob) {} + +//! @} + +} // namespace videostab +} // namespace cv + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videostab/motion_stabilizing.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videostab/motion_stabilizing.hpp new file mode 100644 index 0000000000000000000000000000000000000000..c50095bb12c24ec0136093a4ac8231b34fb30796 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videostab/motion_stabilizing.hpp @@ -0,0 +1,174 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_VIDEOSTAB_MOTION_STABILIZING_HPP +#define OPENCV_VIDEOSTAB_MOTION_STABILIZING_HPP + +#include +#include +#include "opencv2/core.hpp" +#include "opencv2/videostab/global_motion.hpp" + +namespace cv +{ +namespace videostab +{ + +//! @addtogroup videostab_motion +//! @{ + +class CV_EXPORTS IMotionStabilizer +{ +public: + virtual ~IMotionStabilizer() {} + + //! assumes that [0, size-1) is in or equals to [range.first, range.second) + virtual void stabilize( + int size, const std::vector &motions, std::pair range, + Mat *stabilizationMotions) = 0; +}; + +class CV_EXPORTS MotionStabilizationPipeline : public IMotionStabilizer +{ +public: + void pushBack(Ptr stabilizer) { stabilizers_.push_back(stabilizer); } + bool empty() const { return stabilizers_.empty(); } + + virtual void stabilize( + int size, const std::vector &motions, std::pair range, + Mat *stabilizationMotions) CV_OVERRIDE; + +private: + std::vector > stabilizers_; +}; + +class CV_EXPORTS MotionFilterBase : public IMotionStabilizer +{ +public: + virtual ~MotionFilterBase() {} + + virtual Mat stabilize( + int idx, const std::vector &motions, std::pair range) = 0; + + virtual void stabilize( + int size, const std::vector &motions, std::pair range, + Mat *stabilizationMotions) CV_OVERRIDE; +}; + +class CV_EXPORTS GaussianMotionFilter : public MotionFilterBase +{ +public: + GaussianMotionFilter(int radius = 15, float stdev = -1.f); + + void setParams(int radius, float stdev = -1.f); + int radius() const { return radius_; } + float stdev() const { return stdev_; } + + virtual Mat stabilize( + int idx, const std::vector &motions, std::pair range) CV_OVERRIDE; + +private: + int radius_; + float stdev_; + std::vector weight_; +}; + +inline GaussianMotionFilter::GaussianMotionFilter(int _radius, float _stdev) { setParams(_radius, _stdev); } + +class CV_EXPORTS LpMotionStabilizer : public IMotionStabilizer +{ +public: + LpMotionStabilizer(MotionModel model = MM_SIMILARITY); + + void setMotionModel(MotionModel val) { model_ = val; } + MotionModel motionModel() const { return model_; } + + void setFrameSize(Size val) { frameSize_ = val; } + Size frameSize() const { return frameSize_; } + + void setTrimRatio(float val) { trimRatio_ = val; } + float trimRatio() const { return trimRatio_; } + + void setWeight1(float val) { w1_ = val; } + float weight1() const { return w1_; } + + void setWeight2(float val) { w2_ = val; } + float weight2() const { return w2_; } + + void setWeight3(float val) { w3_ = val; } + float weight3() const { return w3_; } + + void setWeight4(float val) { w4_ = val; } + float weight4() const { return w4_; } + + virtual void stabilize( + int size, const std::vector &motions, std::pair range, + Mat *stabilizationMotions) CV_OVERRIDE; + +private: + MotionModel model_; + Size frameSize_; + float trimRatio_; + float w1_, w2_, w3_, w4_; + + std::vector obj_, collb_, colub_; + std::vector rows_, cols_; + std::vector elems_, rowlb_, rowub_; + + void set(int row, int col, double coef) + { + rows_.push_back(row); + cols_.push_back(col); + elems_.push_back(coef); + } +}; + +CV_EXPORTS Mat ensureInclusionConstraint(const Mat &M, Size size, float trimRatio); + +CV_EXPORTS float estimateOptimalTrimRatio(const Mat &M, Size size); + +//! @} + +} // namespace videostab +} // namespace + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videostab/optical_flow.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videostab/optical_flow.hpp new file mode 100644 index 0000000000000000000000000000000000000000..5e06941d5bd3ef311d94d7c873aba7f02ddeddcb --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videostab/optical_flow.hpp @@ -0,0 +1,150 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_VIDEOSTAB_OPTICAL_FLOW_HPP +#define OPENCV_VIDEOSTAB_OPTICAL_FLOW_HPP + +#include "opencv2/core.hpp" +#include "opencv2/opencv_modules.hpp" + +#ifdef HAVE_OPENCV_CUDAOPTFLOW + #include "opencv2/cudaoptflow.hpp" +#endif + +namespace cv +{ +namespace videostab +{ + +//! @addtogroup videostab +//! @{ + +class CV_EXPORTS ISparseOptFlowEstimator +{ +public: + virtual ~ISparseOptFlowEstimator() {} + virtual void run( + InputArray frame0, InputArray frame1, InputArray points0, InputOutputArray points1, + OutputArray status, OutputArray errors) = 0; +}; + +class CV_EXPORTS IDenseOptFlowEstimator +{ +public: + virtual ~IDenseOptFlowEstimator() {} + virtual void run( + InputArray frame0, InputArray frame1, InputOutputArray flowX, InputOutputArray flowY, + OutputArray errors) = 0; +}; + +class CV_EXPORTS PyrLkOptFlowEstimatorBase +{ +public: + PyrLkOptFlowEstimatorBase() { setWinSize(Size(21, 21)); setMaxLevel(3); } + + virtual void setWinSize(Size val) { winSize_ = val; } + virtual Size winSize() const { return winSize_; } + + virtual void setMaxLevel(int val) { maxLevel_ = val; } + virtual int maxLevel() const { return maxLevel_; } + virtual ~PyrLkOptFlowEstimatorBase() {} + +protected: + Size winSize_; + int maxLevel_; +}; + +class CV_EXPORTS SparsePyrLkOptFlowEstimator + : public PyrLkOptFlowEstimatorBase, public ISparseOptFlowEstimator +{ +public: + virtual void run( + InputArray frame0, InputArray frame1, InputArray points0, InputOutputArray points1, + OutputArray status, OutputArray errors) CV_OVERRIDE; +}; + +#ifdef HAVE_OPENCV_CUDAOPTFLOW + +class CV_EXPORTS SparsePyrLkOptFlowEstimatorGpu + : public PyrLkOptFlowEstimatorBase, public ISparseOptFlowEstimator +{ +public: + SparsePyrLkOptFlowEstimatorGpu(); + + virtual void run( + InputArray frame0, InputArray frame1, InputArray points0, InputOutputArray points1, + OutputArray status, OutputArray errors) CV_OVERRIDE; + + void run(const cuda::GpuMat &frame0, const cuda::GpuMat &frame1, const cuda::GpuMat &points0, cuda::GpuMat &points1, + cuda::GpuMat &status, cuda::GpuMat &errors); + + void run(const cuda::GpuMat &frame0, const cuda::GpuMat &frame1, const cuda::GpuMat &points0, cuda::GpuMat &points1, + cuda::GpuMat &status); + +private: + Ptr optFlowEstimator_; + cuda::GpuMat frame0_, frame1_, points0_, points1_, status_, errors_; +}; + +class CV_EXPORTS DensePyrLkOptFlowEstimatorGpu + : public PyrLkOptFlowEstimatorBase, public IDenseOptFlowEstimator +{ +public: + DensePyrLkOptFlowEstimatorGpu(); + + virtual void run( + InputArray frame0, InputArray frame1, InputOutputArray flowX, InputOutputArray flowY, + OutputArray errors) CV_OVERRIDE; + +private: + Ptr optFlowEstimator_; + cuda::GpuMat frame0_, frame1_, flowX_, flowY_, errors_; +}; + +#endif + +//! @} + +} // namespace videostab +} // namespace cv + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videostab/outlier_rejection.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videostab/outlier_rejection.hpp new file mode 100644 index 0000000000000000000000000000000000000000..1d29896bf3f41d761bec90eff55d07b7d747a0a6 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videostab/outlier_rejection.hpp @@ -0,0 +1,101 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_VIDEOSTAB_OUTLIER_REJECTION_HPP +#define OPENCV_VIDEOSTAB_OUTLIER_REJECTION_HPP + +#include +#include "opencv2/core.hpp" +#include "opencv2/videostab/motion_core.hpp" + +namespace cv +{ +namespace videostab +{ + +//! @addtogroup videostab +//! @{ + +class CV_EXPORTS IOutlierRejector +{ +public: + virtual ~IOutlierRejector() {} + + virtual void process( + Size frameSize, InputArray points0, InputArray points1, OutputArray mask) = 0; +}; + +class CV_EXPORTS NullOutlierRejector : public IOutlierRejector +{ +public: + virtual void process( + Size frameSize, InputArray points0, InputArray points1, OutputArray mask) CV_OVERRIDE; +}; + +class CV_EXPORTS TranslationBasedLocalOutlierRejector : public IOutlierRejector +{ +public: + TranslationBasedLocalOutlierRejector(); + + void setCellSize(Size val) { cellSize_ = val; } + Size cellSize() const { return cellSize_; } + + void setRansacParams(RansacParams val) { ransacParams_ = val; } + RansacParams ransacParams() const { return ransacParams_; } + + virtual void process( + Size frameSize, InputArray points0, InputArray points1, OutputArray mask) CV_OVERRIDE; + +private: + Size cellSize_; + RansacParams ransacParams_; + + typedef std::vector Cell; + std::vector grid_; +}; + +//! @} + +} // namespace videostab +} // namespace cv + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videostab/ring_buffer.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videostab/ring_buffer.hpp new file mode 100644 index 0000000000000000000000000000000000000000..55d52444bc55d5cc726ccc967c9c45a43d5fc031 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videostab/ring_buffer.hpp @@ -0,0 +1,72 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_VIDEOSTAB_RING_BUFFER_HPP +#define OPENCV_VIDEOSTAB_RING_BUFFER_HPP + +#include +#include "opencv2/imgproc.hpp" + +namespace cv +{ +namespace videostab +{ + +//! @addtogroup videostab +//! @{ + +template inline T& at(int idx, std::vector &items) +{ + return items[cv::borderInterpolate(idx, static_cast(items.size()), cv::BORDER_WRAP)]; +} + +template inline const T& at(int idx, const std::vector &items) +{ + return items[cv::borderInterpolate(idx, static_cast(items.size()), cv::BORDER_WRAP)]; +} + +//! @} + +} // namespace videostab +} // namespace cv + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videostab/stabilizer.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videostab/stabilizer.hpp new file mode 100644 index 0000000000000000000000000000000000000000..634a0aa9c61d5affec2e72e1515ea1603bc6a71b --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videostab/stabilizer.hpp @@ -0,0 +1,200 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_VIDEOSTAB_STABILIZER_HPP +#define OPENCV_VIDEOSTAB_STABILIZER_HPP + +#include +#include +#include "opencv2/core.hpp" +#include "opencv2/imgproc.hpp" +#include "opencv2/videostab/global_motion.hpp" +#include "opencv2/videostab/motion_stabilizing.hpp" +#include "opencv2/videostab/frame_source.hpp" +#include "opencv2/videostab/log.hpp" +#include "opencv2/videostab/inpainting.hpp" +#include "opencv2/videostab/deblurring.hpp" +#include "opencv2/videostab/wobble_suppression.hpp" + +namespace cv +{ +namespace videostab +{ + +//! @addtogroup videostab +//! @{ + +class CV_EXPORTS StabilizerBase +{ +public: + virtual ~StabilizerBase() {} + + void setLog(Ptr ilog) { log_ = ilog; } + Ptr log() const { return log_; } + + void setRadius(int val) { radius_ = val; } + int radius() const { return radius_; } + + void setFrameSource(Ptr val) { frameSource_ = val; } + Ptr frameSource() const { return frameSource_; } + + void setMotionEstimator(Ptr val) { motionEstimator_ = val; } + Ptr motionEstimator() const { return motionEstimator_; } + + void setDeblurer(Ptr val) { deblurer_ = val; } + Ptr deblurrer() const { return deblurer_; } + + void setTrimRatio(float val) { trimRatio_ = val; } + float trimRatio() const { return trimRatio_; } + + void setCorrectionForInclusion(bool val) { doCorrectionForInclusion_ = val; } + bool doCorrectionForInclusion() const { return doCorrectionForInclusion_; } + + void setBorderMode(int val) { borderMode_ = val; } + int borderMode() const { return borderMode_; } + + void setInpainter(Ptr val) { inpainter_ = val; } + Ptr inpainter() const { return inpainter_; } + +protected: + StabilizerBase(); + + void reset(); + Mat nextStabilizedFrame(); + bool doOneIteration(); + virtual void setUp(const Mat &firstFrame); + virtual Mat estimateMotion() = 0; + virtual Mat estimateStabilizationMotion() = 0; + void stabilizeFrame(); + virtual Mat postProcessFrame(const Mat &frame); + void logProcessingTime(); + + Ptr log_; + Ptr frameSource_; + Ptr motionEstimator_; + Ptr deblurer_; + Ptr inpainter_; + int radius_; + float trimRatio_; + bool doCorrectionForInclusion_; + int borderMode_; + + Size frameSize_; + Mat frameMask_; + int curPos_; + int curStabilizedPos_; + bool doDeblurring_; + Mat preProcessedFrame_; + bool doInpainting_; + Mat inpaintingMask_; + Mat finalFrame_; + std::vector frames_; + std::vector motions_; // motions_[i] is the motion from i-th to i+1-th frame + std::vector blurrinessRates_; + std::vector stabilizedFrames_; + std::vector stabilizedMasks_; + std::vector stabilizationMotions_; + clock_t processingStartTime_; +}; + +class CV_EXPORTS OnePassStabilizer : public StabilizerBase, public IFrameSource +{ +public: + OnePassStabilizer(); + + void setMotionFilter(Ptr val) { motionFilter_ = val; } + Ptr motionFilter() const { return motionFilter_; } + + virtual void reset() CV_OVERRIDE; + virtual Mat nextFrame() CV_OVERRIDE { return nextStabilizedFrame(); } + +protected: + virtual void setUp(const Mat &firstFrame) CV_OVERRIDE; + virtual Mat estimateMotion() CV_OVERRIDE; + virtual Mat estimateStabilizationMotion() CV_OVERRIDE; + virtual Mat postProcessFrame(const Mat &frame) CV_OVERRIDE; + + Ptr motionFilter_; +}; + +class CV_EXPORTS TwoPassStabilizer : public StabilizerBase, public IFrameSource +{ +public: + TwoPassStabilizer(); + + void setMotionStabilizer(Ptr val) { motionStabilizer_ = val; } + Ptr motionStabilizer() const { return motionStabilizer_; } + + void setWobbleSuppressor(Ptr val) { wobbleSuppressor_ = val; } + Ptr wobbleSuppressor() const { return wobbleSuppressor_; } + + void setEstimateTrimRatio(bool val) { mustEstTrimRatio_ = val; } + bool mustEstimateTrimaRatio() const { return mustEstTrimRatio_; } + + virtual void reset() CV_OVERRIDE; + virtual Mat nextFrame() CV_OVERRIDE; + +protected: + void runPrePassIfNecessary(); + + virtual void setUp(const Mat &firstFrame) CV_OVERRIDE; + virtual Mat estimateMotion() CV_OVERRIDE; + virtual Mat estimateStabilizationMotion() CV_OVERRIDE; + virtual Mat postProcessFrame(const Mat &frame) CV_OVERRIDE; + + Ptr motionStabilizer_; + Ptr wobbleSuppressor_; + bool mustEstTrimRatio_; + + int frameCount_; + bool isPrePassDone_; + bool doWobbleSuppression_; + std::vector motions2_; + Mat suppressedFrame_; +}; + +//! @} + +} // namespace videostab +} // namespace cv + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videostab/wobble_suppression.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videostab/wobble_suppression.hpp new file mode 100644 index 0000000000000000000000000000000000000000..d60ae6dd098480b373769962b31c17c1fc61501c --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/include/opencv2/videostab/wobble_suppression.hpp @@ -0,0 +1,140 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_VIDEOSTAB_WOBBLE_SUPPRESSION_HPP +#define OPENCV_VIDEOSTAB_WOBBLE_SUPPRESSION_HPP + +#include +#include "opencv2/core.hpp" +#include "opencv2/core/cuda.hpp" +#include "opencv2/videostab/global_motion.hpp" +#include "opencv2/videostab/log.hpp" + +namespace cv +{ +namespace videostab +{ + +//! @addtogroup videostab +//! @{ + +class CV_EXPORTS WobbleSuppressorBase +{ +public: + WobbleSuppressorBase(); + + virtual ~WobbleSuppressorBase() {} + + void setMotionEstimator(Ptr val) { motionEstimator_ = val; } + Ptr motionEstimator() const { return motionEstimator_; } + + virtual void suppress(int idx, const Mat &frame, Mat &result) = 0; + + + // data from stabilizer + + virtual void setFrameCount(int val) { frameCount_ = val; } + virtual int frameCount() const { return frameCount_; } + + virtual void setMotions(const std::vector &val) { motions_ = &val; } + virtual const std::vector& motions() const { return *motions_; } + + virtual void setMotions2(const std::vector &val) { motions2_ = &val; } + virtual const std::vector& motions2() const { return *motions2_; } + + virtual void setStabilizationMotions(const std::vector &val) { stabilizationMotions_ = &val; } + virtual const std::vector& stabilizationMotions() const { return *stabilizationMotions_; } + +protected: + Ptr motionEstimator_; + int frameCount_; + const std::vector *motions_; + const std::vector *motions2_; + const std::vector *stabilizationMotions_; +}; + +class CV_EXPORTS NullWobbleSuppressor : public WobbleSuppressorBase +{ +public: + virtual void suppress(int idx, const Mat &frame, Mat &result) CV_OVERRIDE; +}; + +class CV_EXPORTS MoreAccurateMotionWobbleSuppressorBase : public WobbleSuppressorBase +{ +public: + virtual void setPeriod(int val) { period_ = val; } + virtual int period() const { return period_; } + +protected: + MoreAccurateMotionWobbleSuppressorBase() { setPeriod(30); } + + int period_; +}; + +class CV_EXPORTS MoreAccurateMotionWobbleSuppressor : public MoreAccurateMotionWobbleSuppressorBase +{ +public: + virtual void suppress(int idx, const Mat &frame, Mat &result) CV_OVERRIDE; + +private: + Mat_ mapx_, mapy_; +}; + +#if defined(HAVE_OPENCV_CUDAWARPING) +class CV_EXPORTS MoreAccurateMotionWobbleSuppressorGpu : public MoreAccurateMotionWobbleSuppressorBase +{ +public: + void suppress(int idx, const cuda::GpuMat &frame, cuda::GpuMat &result); + virtual void suppress(int idx, const Mat &frame, Mat &result) CV_OVERRIDE; + +private: + cuda::GpuMat frameDevice_, resultDevice_; + cuda::GpuMat mapx_, mapy_; +}; +#endif + +//! @} + +} // namespace videostab +} // namespace cv + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/lib/libopencv_calib3d.a b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/lib/libopencv_calib3d.a new file mode 100644 index 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b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/3rdparty/lib/libquirc.a new file mode 100644 index 0000000000000000000000000000000000000000..3fb099f63750eb1f78abae18be338b909f8e6f26 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/3rdparty/lib/libquirc.a @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:685d749f2778af64f13dcd8f86352ecb5fde24fc0489c495426843c08ce6ff8d +size 26536 diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/3rdparty/lib/libtegra_hal.a b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/3rdparty/lib/libtegra_hal.a new file mode 100644 index 0000000000000000000000000000000000000000..563bdacb9ba1da13d9da2b1d66ddc7b4a40535c1 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/3rdparty/lib/libtegra_hal.a @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ce566713feea2db10740d1147f824fb83b03f3a80a890ce3ec1f3425405e331b +size 1122644 diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/3rdparty/lib/libzlib.a b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/3rdparty/lib/libzlib.a new file mode 100644 index 0000000000000000000000000000000000000000..fb600a4ab3e1aace903718dfb46266bd459b97f2 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/3rdparty/lib/libzlib.a @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5d07a1233c16e7429e048203d2435509af084b5bb6e5e331389910d411e03858 +size 160746 diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/OpenCVConfig-version.cmake b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/OpenCVConfig-version.cmake new file mode 100644 index 0000000000000000000000000000000000000000..f8be90d0b64ad0ff98b823e89d10f5399908a262 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/OpenCVConfig-version.cmake @@ -0,0 +1,15 @@ +set(OpenCV_VERSION 3.4.5) +set(PACKAGE_VERSION ${OpenCV_VERSION}) + +set(PACKAGE_VERSION_EXACT False) +set(PACKAGE_VERSION_COMPATIBLE False) + +if(PACKAGE_FIND_VERSION VERSION_EQUAL PACKAGE_VERSION) + set(PACKAGE_VERSION_EXACT True) + set(PACKAGE_VERSION_COMPATIBLE True) +endif() + +if(PACKAGE_FIND_VERSION_MAJOR EQUAL 3 + AND PACKAGE_FIND_VERSION VERSION_LESS PACKAGE_VERSION) + set(PACKAGE_VERSION_COMPATIBLE True) +endif() diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/OpenCVConfig.cmake b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/OpenCVConfig.cmake new file mode 100644 index 0000000000000000000000000000000000000000..efc56ff317c571db635429a861d9fff82af93c6f --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/OpenCVConfig.cmake @@ -0,0 +1,315 @@ +# =================================================================================== +# The OpenCV CMake configuration file +# +# ** File generated automatically, do not modify ** +# +# Usage from an external project: +# In your CMakeLists.txt, add these lines: +# +# find_package(OpenCV REQUIRED) +# include_directories(${OpenCV_INCLUDE_DIRS}) # Not needed for CMake >= 2.8.11 +# target_link_libraries(MY_TARGET_NAME ${OpenCV_LIBS}) +# +# Or you can search for specific OpenCV modules: +# +# find_package(OpenCV REQUIRED core videoio) +# +# You can also mark OpenCV components as optional: + +# find_package(OpenCV REQUIRED core OPTIONAL_COMPONENTS viz) +# +# If the module is found then OPENCV__FOUND is set to TRUE. +# +# This file will define the following variables: +# - OpenCV_LIBS : The list of all imported targets for OpenCV modules. +# - OpenCV_INCLUDE_DIRS : The OpenCV include directories. +# - OpenCV_COMPUTE_CAPABILITIES : The version of compute capability. +# - OpenCV_ANDROID_NATIVE_API_LEVEL : Minimum required level of Android API. +# - OpenCV_VERSION : The version of this OpenCV build: "3.4.5" +# - OpenCV_VERSION_MAJOR : Major version part of OpenCV_VERSION: "3" +# - OpenCV_VERSION_MINOR : Minor version part of OpenCV_VERSION: "4" +# - OpenCV_VERSION_PATCH : Patch version part of OpenCV_VERSION: "5" +# - OpenCV_VERSION_STATUS : Development status of this build: "" +# +# Advanced variables: +# - OpenCV_SHARED : Use OpenCV as shared library +# - OpenCV_INSTALL_PATH : OpenCV location +# - OpenCV_LIB_COMPONENTS : Present OpenCV modules list +# - OpenCV_USE_MANGLED_PATHS : Mangled OpenCV path flag +# +# Deprecated variables: +# - OpenCV_VERSION_TWEAK : Always "0" +# +# =================================================================================== + +# ====================================================== +# Version variables: +# ====================================================== +SET(OpenCV_VERSION 3.4.5) +SET(OpenCV_VERSION_MAJOR 3) +SET(OpenCV_VERSION_MINOR 4) +SET(OpenCV_VERSION_PATCH 5) +SET(OpenCV_VERSION_TWEAK 0) +SET(OpenCV_VERSION_STATUS "") + +include(FindPackageHandleStandardArgs) + +if(NOT CMAKE_VERSION VERSION_LESS 2.8.8 + AND OpenCV_FIND_COMPONENTS # prevent excessive output +) + # HANDLE_COMPONENTS was introduced in CMake 2.8.8 + list(APPEND _OpenCV_FPHSA_ARGS HANDLE_COMPONENTS) + # The missing components will be handled by the FindPackageHandleStandardArgs + # module. + set(_OpenCV_HANDLE_COMPONENTS_MANUALLY FALSE) +else() + # The missing components will be handled by this config. + set(_OpenCV_HANDLE_COMPONENTS_MANUALLY TRUE) +endif() + +# Extract directory name from full path of the file currently being processed. +# Note that CMake 2.8.3 introduced CMAKE_CURRENT_LIST_DIR. We reimplement it +# for older versions of CMake to support these as well. +if(CMAKE_VERSION VERSION_LESS "2.8.3") + get_filename_component(CMAKE_CURRENT_LIST_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH) +endif() + +# Extract the directory where *this* file has been installed (determined at cmake run-time) +# Get the absolute path with no ../.. relative marks, to eliminate implicit linker warnings +set(OpenCV_CONFIG_PATH "${CMAKE_CURRENT_LIST_DIR}") +get_filename_component(OpenCV_INSTALL_PATH "${OpenCV_CONFIG_PATH}/../../" REALPATH) + +# Search packages for host system instead of packages for target system. +# in case of cross compilation this macro should be defined by toolchain file +if(NOT COMMAND find_host_package) + macro(find_host_package) + find_package(${ARGN}) + endmacro() +endif() +if(NOT COMMAND find_host_program) + macro(find_host_program) + find_program(${ARGN}) + endmacro() +endif() + + + + + + + + +# Some additional settings are required if OpenCV is built as static libs +set(OpenCV_SHARED OFF) + +# Enables mangled install paths, that help with side by side installs +set(OpenCV_USE_MANGLED_PATHS FALSE) + +set(OpenCV_LIB_COMPONENTS opencv_calib3d;opencv_core;opencv_features2d;opencv_imgcodecs;opencv_imgproc;opencv_video) +set(OpenCV_INCLUDE_DIRS "${OpenCV_INSTALL_PATH}/include" "${OpenCV_INSTALL_PATH}/include/opencv") + +if(NOT TARGET opencv_core) + include(${CMAKE_CURRENT_LIST_DIR}/OpenCVModules${OpenCV_MODULES_SUFFIX}.cmake) +endif() + +if(NOT CMAKE_VERSION VERSION_LESS "2.8.11") + # Target property INTERFACE_INCLUDE_DIRECTORIES available since 2.8.11: + # * http://www.cmake.org/cmake/help/v2.8.11/cmake.html#prop_tgt:INTERFACE_INCLUDE_DIRECTORIES + foreach(__component ${OpenCV_LIB_COMPONENTS}) + if(TARGET ${__component}) + set_target_properties( + ${__component} + PROPERTIES + INTERFACE_INCLUDE_DIRECTORIES "${OpenCV_INCLUDE_DIRS}" + ) + endif() + endforeach() +endif() + + +if(NOT DEFINED OPENCV_MAP_IMPORTED_CONFIG) + if(CMAKE_GENERATOR MATCHES "Visual Studio" OR MSVC) + # OpenCV supports Debug and Release builds only. + # But MSVS has 'RelWithDebInfo' 'MinSizeRel' configurations for applications. + # By default CMake maps these configuration on the first available (Debug) which is wrong. + # Non-Debug build of Application can't be used with OpenCV Debug build (ABI mismatch problem) + # Add mapping of RelWithDebInfo and MinSizeRel to Release here + set(OPENCV_MAP_IMPORTED_CONFIG "RELWITHDEBINFO=!Release;MINSIZEREL=!Release") + endif() +endif() +set(__remap_warnings "") +macro(ocv_map_imported_config target) + if(DEFINED OPENCV_MAP_IMPORTED_CONFIG) # list, "RELWITHDEBINFO=Release;MINSIZEREL=Release" + get_target_property(__available_configurations ${target} IMPORTED_CONFIGURATIONS) + foreach(remap ${OPENCV_MAP_IMPORTED_CONFIG}) + if(remap MATCHES "^(.+)=(!?)([^!]+)$") + set(__remap_config "${CMAKE_MATCH_1}") + set(__final_config "${CMAKE_MATCH_3}") + set(__force_flag "${CMAKE_MATCH_2}") + string(TOUPPER "${__final_config}" __final_config_upper) + string(TOUPPER "${__remap_config}" __remap_config_upper) + if(";${__available_configurations};" MATCHES ";${__remap_config_upper};" AND NOT "${__force_flag}" STREQUAL "!") + # configuration already exists, skip remap + set(__remap_warnings "${__remap_warnings}... Configuration already exists ${__remap_config} (skip mapping ${__remap_config} => ${__final_config}) (available configurations: ${__available_configurations})\n") + continue() + endif() + if(__available_configurations AND NOT ";${__available_configurations};" MATCHES ";${__final_config_upper};") + # skip, configuration is not available + if(NOT "${__force_flag}" STREQUAL "!") + set(__remap_warnings "${__remap_warnings}... Configuration is not available '${__final_config}' for ${target}, build may fail (available configurations: ${__available_configurations})\n") + endif() + endif() + set_target_properties(${target} PROPERTIES + MAP_IMPORTED_CONFIG_${__remap_config} "${__final_config}" + ) + else() + message(WARNING "Invalid entry of OPENCV_MAP_IMPORTED_CONFIG: '${remap}' (${OPENCV_MAP_IMPORTED_CONFIG})") + endif() + endforeach() + endif() +endmacro() + + +# ============================================================== +# Form list of modules (components) to find +# ============================================================== +if(NOT OpenCV_FIND_COMPONENTS) + set(OpenCV_FIND_COMPONENTS ${OpenCV_LIB_COMPONENTS}) + list(REMOVE_ITEM OpenCV_FIND_COMPONENTS opencv_java) + if(GTest_FOUND OR GTEST_FOUND) + list(REMOVE_ITEM OpenCV_FIND_COMPONENTS opencv_ts) + endif() +endif() + +set(OpenCV_WORLD_COMPONENTS ) + +# expand short module names and see if requested components exist +foreach(__cvcomponent ${OpenCV_FIND_COMPONENTS}) + # Store the name of the original component so we can set the + # OpenCV__FOUND variable which can be checked by the user. + set (__original_cvcomponent ${__cvcomponent}) + if(NOT __cvcomponent MATCHES "^opencv_") + set(__cvcomponent opencv_${__cvcomponent}) + endif() + list(FIND OpenCV_LIB_COMPONENTS ${__cvcomponent} __cvcomponentIdx) + if(__cvcomponentIdx LESS 0) + if(_OpenCV_HANDLE_COMPONENTS_MANUALLY) + # Either the component is required or the user did not set any components at + # all. In the latter case, the OpenCV_FIND_REQUIRED_ variable + # will not be defined since it is not set by this config. So let's assume + # the implicitly set components are always required. + if(NOT DEFINED OpenCV_FIND_REQUIRED_${__original_cvcomponent} OR + OpenCV_FIND_REQUIRED_${__original_cvcomponent}) + message(FATAL_ERROR "${__cvcomponent} is required but was not found") + elseif(NOT OpenCV_FIND_QUIETLY) + # The component was marked as optional using OPTIONAL_COMPONENTS + message(WARNING "Optional component ${__cvcomponent} was not found") + endif() + endif(_OpenCV_HANDLE_COMPONENTS_MANUALLY) + #indicate that module is NOT found + string(TOUPPER "${__cvcomponent}" __cvcomponentUP) + set(${__cvcomponentUP}_FOUND "${__cvcomponentUP}_FOUND-NOTFOUND") + set(OpenCV_${__original_cvcomponent}_FOUND FALSE) + else() + # Not using list(APPEND) here, because OpenCV_LIBS may not exist yet. + # Also not clearing OpenCV_LIBS anywhere, so that multiple calls + # to find_package(OpenCV) with different component lists add up. + set(OpenCV_LIBS ${OpenCV_LIBS} "${__cvcomponent}") + #indicate that module is found + string(TOUPPER "${__cvcomponent}" __cvcomponentUP) + set(${__cvcomponentUP}_FOUND 1) + set(OpenCV_${__original_cvcomponent}_FOUND TRUE) + endif() + if(OpenCV_SHARED AND ";${OpenCV_WORLD_COMPONENTS};" MATCHES ";${__cvcomponent};" AND NOT TARGET ${__cvcomponent}) + get_target_property(__implib_dbg opencv_world IMPORTED_IMPLIB_DEBUG) + get_target_property(__implib_release opencv_world IMPORTED_IMPLIB_RELEASE) + get_target_property(__location_dbg opencv_world IMPORTED_LOCATION_DEBUG) + get_target_property(__location_release opencv_world IMPORTED_LOCATION_RELEASE) + get_target_property(__include_dir opencv_world INTERFACE_INCLUDE_DIRECTORIES) + add_library(${__cvcomponent} SHARED IMPORTED) + set_target_properties(${__cvcomponent} PROPERTIES INTERFACE_INCLUDE_DIRECTORIES "${__include_dir}") + if(__location_dbg) + set_property(TARGET ${__cvcomponent} APPEND PROPERTY IMPORTED_CONFIGURATIONS DEBUG) + set_target_properties(${__cvcomponent} PROPERTIES + IMPORTED_IMPLIB_DEBUG "${__implib_dbg}" + IMPORTED_LINK_INTERFACE_LIBRARIES_DEBUG "" + IMPORTED_LOCATION_DEBUG "${__location_dbg}" + ) + endif() + if(__location_release) + set_property(TARGET ${__cvcomponent} APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) + set_target_properties(${__cvcomponent} PROPERTIES + IMPORTED_IMPLIB_RELEASE "${__implib_release}" + IMPORTED_LINK_INTERFACE_LIBRARIES_RELEASE "" + IMPORTED_LOCATION_RELEASE "${__location_release}" + ) + endif() + endif() + if(TARGET ${__cvcomponent}) + ocv_map_imported_config(${__cvcomponent}) + endif() +endforeach() + +if(__remap_warnings AND NOT OpenCV_FIND_QUIETLY) + message("OpenCV: configurations remap warnings:\n${__remap_warnings}OpenCV: Check variable OPENCV_MAP_IMPORTED_CONFIG=${OPENCV_MAP_IMPORTED_CONFIG}") +endif() + +# ============================================================== +# Compatibility stuff +# ============================================================== +set(OpenCV_LIBRARIES ${OpenCV_LIBS}) + +# +# Some macros for samples +# +macro(ocv_check_dependencies) + set(OCV_DEPENDENCIES_FOUND TRUE) + foreach(d ${ARGN}) + if(NOT TARGET ${d}) + message(WARNING "OpenCV: Can't resolve dependency: ${d}") + set(OCV_DEPENDENCIES_FOUND FALSE) + break() + endif() + endforeach() +endmacro() + +# adds include directories in such way that directories from the OpenCV source tree go first +function(ocv_include_directories) + set(__add_before "") + file(TO_CMAKE_PATH "${OpenCV_INSTALL_PATH}" __baseDir) + foreach(dir ${ARGN}) + get_filename_component(__abs_dir "${dir}" ABSOLUTE) + if("${__abs_dir}" MATCHES "^${__baseDir}") + list(APPEND __add_before "${dir}") + else() + include_directories(AFTER SYSTEM "${dir}") + endif() + endforeach() + include_directories(BEFORE ${__add_before}) +endfunction() + +macro(ocv_include_modules) + include_directories(BEFORE "${OpenCV_INCLUDE_DIRS}") +endmacro() + +macro(ocv_include_modules_recurse) + include_directories(BEFORE "${OpenCV_INCLUDE_DIRS}") +endmacro() + +macro(ocv_target_link_libraries) + target_link_libraries(${ARGN}) +endmacro() + +# remove all matching elements from the list +macro(ocv_list_filterout lst regex) + foreach(item ${${lst}}) + if(item MATCHES "${regex}") + list(REMOVE_ITEM ${lst} "${item}") + endif() + endforeach() +endmacro() + +# We do not actually need REQUIRED_VARS to be checked for. Just use the +# installation directory for the status. +find_package_handle_standard_args(OpenCV REQUIRED_VARS OpenCV_INSTALL_PATH + VERSION_VAR OpenCV_VERSION ${_OpenCV_FPHSA_ARGS}) diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/OpenCVModules-release.cmake b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/OpenCVModules-release.cmake new file mode 100644 index 0000000000000000000000000000000000000000..f9e275c462183a6e0c0c94433b5ea2654bb5eb84 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/OpenCVModules-release.cmake @@ -0,0 +1,169 @@ +#---------------------------------------------------------------- +# Generated CMake target import file for configuration "Release". +#---------------------------------------------------------------- + +# Commands may need to know the format version. +set(CMAKE_IMPORT_FILE_VERSION 1) + +# Import target "zlib" for configuration "Release" +set_property(TARGET zlib APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(zlib PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "C" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/share/OpenCV/3rdparty/lib/libzlib.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS zlib ) +list(APPEND _IMPORT_CHECK_FILES_FOR_zlib "${_IMPORT_PREFIX}/share/OpenCV/3rdparty/lib/libzlib.a" ) + +# Import target "libjpeg-turbo" for configuration "Release" +set_property(TARGET libjpeg-turbo APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(libjpeg-turbo PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "C" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/share/OpenCV/3rdparty/lib/liblibjpeg-turbo.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS libjpeg-turbo ) +list(APPEND _IMPORT_CHECK_FILES_FOR_libjpeg-turbo "${_IMPORT_PREFIX}/share/OpenCV/3rdparty/lib/liblibjpeg-turbo.a" ) + +# Import target "libtiff" for configuration "Release" +set_property(TARGET libtiff APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(libtiff PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "C;CXX" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/share/OpenCV/3rdparty/lib/liblibtiff.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS libtiff ) +list(APPEND _IMPORT_CHECK_FILES_FOR_libtiff "${_IMPORT_PREFIX}/share/OpenCV/3rdparty/lib/liblibtiff.a" ) + +# Import target "libwebp" for configuration "Release" +set_property(TARGET libwebp APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(libwebp PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "C" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/share/OpenCV/3rdparty/lib/liblibwebp.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS libwebp ) +list(APPEND _IMPORT_CHECK_FILES_FOR_libwebp "${_IMPORT_PREFIX}/share/OpenCV/3rdparty/lib/liblibwebp.a" ) + +# Import target "libjasper" for configuration "Release" +set_property(TARGET libjasper APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(libjasper PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "C" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/share/OpenCV/3rdparty/lib/liblibjasper.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS libjasper ) +list(APPEND _IMPORT_CHECK_FILES_FOR_libjasper "${_IMPORT_PREFIX}/share/OpenCV/3rdparty/lib/liblibjasper.a" ) + +# Import target "libpng" for configuration "Release" +set_property(TARGET libpng APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(libpng PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "ASM;C" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/share/OpenCV/3rdparty/lib/liblibpng.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS libpng ) +list(APPEND _IMPORT_CHECK_FILES_FOR_libpng "${_IMPORT_PREFIX}/share/OpenCV/3rdparty/lib/liblibpng.a" ) + +# Import target "IlmImf" for configuration "Release" +set_property(TARGET IlmImf APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(IlmImf PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/share/OpenCV/3rdparty/lib/libIlmImf.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS IlmImf ) +list(APPEND _IMPORT_CHECK_FILES_FOR_IlmImf "${_IMPORT_PREFIX}/share/OpenCV/3rdparty/lib/libIlmImf.a" ) + +# Import target "libprotobuf" for configuration "Release" +set_property(TARGET libprotobuf APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(libprotobuf PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/share/OpenCV/3rdparty/lib/liblibprotobuf.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS libprotobuf ) +list(APPEND _IMPORT_CHECK_FILES_FOR_libprotobuf "${_IMPORT_PREFIX}/share/OpenCV/3rdparty/lib/liblibprotobuf.a" ) + +# Import target "quirc" for configuration "Release" +set_property(TARGET quirc APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(quirc PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "C" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/share/OpenCV/3rdparty/lib/libquirc.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS quirc ) +list(APPEND _IMPORT_CHECK_FILES_FOR_quirc "${_IMPORT_PREFIX}/share/OpenCV/3rdparty/lib/libquirc.a" ) + +# Import target "tegra_hal" for configuration "Release" +set_property(TARGET tegra_hal APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(tegra_hal PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/share/OpenCV/3rdparty/lib/libtegra_hal.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS tegra_hal ) +list(APPEND _IMPORT_CHECK_FILES_FOR_tegra_hal "${_IMPORT_PREFIX}/share/OpenCV/3rdparty/lib/libtegra_hal.a" ) + +# Import target "opencv_core" for configuration "Release" +set_property(TARGET opencv_core APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(opencv_core PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/lib/libopencv_core.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS opencv_core ) +list(APPEND _IMPORT_CHECK_FILES_FOR_opencv_core "${_IMPORT_PREFIX}/lib/libopencv_core.a" ) + +# Import target "opencv_imgproc" for configuration "Release" +set_property(TARGET opencv_imgproc APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(opencv_imgproc PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/lib/libopencv_imgproc.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS opencv_imgproc ) +list(APPEND _IMPORT_CHECK_FILES_FOR_opencv_imgproc "${_IMPORT_PREFIX}/lib/libopencv_imgproc.a" ) + +# Import target "opencv_video" for configuration "Release" +set_property(TARGET opencv_video APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(opencv_video PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/lib/libopencv_video.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS opencv_video ) +list(APPEND _IMPORT_CHECK_FILES_FOR_opencv_video "${_IMPORT_PREFIX}/lib/libopencv_video.a" ) + +# Import target "opencv_features2d" for configuration "Release" +set_property(TARGET opencv_features2d APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(opencv_features2d PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/lib/libopencv_features2d.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS opencv_features2d ) +list(APPEND _IMPORT_CHECK_FILES_FOR_opencv_features2d "${_IMPORT_PREFIX}/lib/libopencv_features2d.a" ) + +# Import target "opencv_imgcodecs" for configuration "Release" +set_property(TARGET opencv_imgcodecs APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(opencv_imgcodecs PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/lib/libopencv_imgcodecs.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS opencv_imgcodecs ) +list(APPEND _IMPORT_CHECK_FILES_FOR_opencv_imgcodecs "${_IMPORT_PREFIX}/lib/libopencv_imgcodecs.a" ) + +# Import target "opencv_calib3d" for configuration "Release" +set_property(TARGET opencv_calib3d APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(opencv_calib3d PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/lib/libopencv_calib3d.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS opencv_calib3d ) +list(APPEND _IMPORT_CHECK_FILES_FOR_opencv_calib3d "${_IMPORT_PREFIX}/lib/libopencv_calib3d.a" ) + +# Commands beyond this point should not need to know the version. +set(CMAKE_IMPORT_FILE_VERSION) diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/OpenCVModules.cmake b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/OpenCVModules.cmake new file mode 100644 index 0000000000000000000000000000000000000000..55aa2db299500b55ca6484422ab75943ec9768bd --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/OpenCVModules.cmake @@ -0,0 +1,174 @@ +# Generated by CMake + +if("${CMAKE_MAJOR_VERSION}.${CMAKE_MINOR_VERSION}" LESS 2.5) + message(FATAL_ERROR "CMake >= 2.6.0 required") +endif() +cmake_policy(PUSH) +cmake_policy(VERSION 2.6) +#---------------------------------------------------------------- +# Generated CMake target import file. +#---------------------------------------------------------------- + +# Commands may need to know the format version. +set(CMAKE_IMPORT_FILE_VERSION 1) + +# Protect against multiple inclusion, which would fail when already imported targets are added once more. +set(_targetsDefined) +set(_targetsNotDefined) +set(_expectedTargets) +foreach(_expectedTarget zlib libjpeg-turbo libtiff libwebp libjasper libpng IlmImf libprotobuf quirc tegra_hal opencv_core opencv_imgproc opencv_video opencv_features2d opencv_imgcodecs opencv_calib3d) + list(APPEND _expectedTargets ${_expectedTarget}) + if(NOT TARGET ${_expectedTarget}) + list(APPEND _targetsNotDefined ${_expectedTarget}) + endif() + if(TARGET ${_expectedTarget}) + list(APPEND _targetsDefined ${_expectedTarget}) + endif() +endforeach() +if("${_targetsDefined}" STREQUAL "${_expectedTargets}") + unset(_targetsDefined) + unset(_targetsNotDefined) + unset(_expectedTargets) + set(CMAKE_IMPORT_FILE_VERSION) + cmake_policy(POP) + return() +endif() +if(NOT "${_targetsDefined}" STREQUAL "") + message(FATAL_ERROR "Some (but not all) targets in this export set were already defined.\nTargets Defined: ${_targetsDefined}\nTargets not yet defined: ${_targetsNotDefined}\n") +endif() +unset(_targetsDefined) +unset(_targetsNotDefined) +unset(_expectedTargets) + + +# Compute the installation prefix relative to this file. +get_filename_component(_IMPORT_PREFIX "${CMAKE_CURRENT_LIST_FILE}" PATH) +get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH) +get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH) +if(_IMPORT_PREFIX STREQUAL "/") + set(_IMPORT_PREFIX "") +endif() + +# Create imported target zlib +add_library(zlib STATIC IMPORTED) + +# Create imported target libjpeg-turbo +add_library(libjpeg-turbo STATIC IMPORTED) + +# Create imported target libtiff +add_library(libtiff STATIC IMPORTED) + +set_target_properties(libtiff PROPERTIES + INTERFACE_LINK_LIBRARIES "zlib" +) + +# Create imported target libwebp +add_library(libwebp STATIC IMPORTED) + +# Create imported target libjasper +add_library(libjasper STATIC IMPORTED) + +# Create imported target libpng +add_library(libpng STATIC IMPORTED) + +set_target_properties(libpng PROPERTIES + INTERFACE_LINK_LIBRARIES "zlib" +) + +# Create imported target IlmImf +add_library(IlmImf STATIC IMPORTED) + +set_target_properties(IlmImf PROPERTIES + INTERFACE_LINK_LIBRARIES "zlib" +) + +# Create imported target libprotobuf +add_library(libprotobuf STATIC IMPORTED) + +# Create imported target quirc +add_library(quirc STATIC IMPORTED) + +# Create imported target tegra_hal +add_library(tegra_hal STATIC IMPORTED) + +# Create imported target opencv_core +add_library(opencv_core STATIC IMPORTED) + +set_target_properties(opencv_core PROPERTIES + INTERFACE_LINK_LIBRARIES "\$;\$;\$;\$;\$;\$;\$" +) + +# Create imported target opencv_imgproc +add_library(opencv_imgproc STATIC IMPORTED) + +set_target_properties(opencv_imgproc PROPERTIES + INTERFACE_LINK_LIBRARIES "opencv_core;\$;\$;\$;\$;\$" +) + +# Create imported target opencv_video +add_library(opencv_video STATIC IMPORTED) + +set_target_properties(opencv_video PROPERTIES + INTERFACE_LINK_LIBRARIES "opencv_core;opencv_imgproc;\$;\$;\$;\$;\$" +) + +# Create imported target opencv_features2d +add_library(opencv_features2d STATIC IMPORTED) + +set_target_properties(opencv_features2d PROPERTIES + INTERFACE_LINK_LIBRARIES "opencv_core;opencv_imgproc;\$;\$;\$;\$;\$" +) + +# Create imported target opencv_imgcodecs +add_library(opencv_imgcodecs STATIC IMPORTED) + +set_target_properties(opencv_imgcodecs PROPERTIES + INTERFACE_LINK_LIBRARIES "opencv_core;opencv_imgproc;\$;\$;\$;\$;\$;\$;\$;\$;\$;\$;\$;\$" +) + +# Create imported target opencv_calib3d +add_library(opencv_calib3d STATIC IMPORTED) + +set_target_properties(opencv_calib3d PROPERTIES + INTERFACE_LINK_LIBRARIES "opencv_core;opencv_imgproc;opencv_features2d;\$;\$;\$;\$;\$" +) + +if(CMAKE_VERSION VERSION_LESS 2.8.12) + message(FATAL_ERROR "This file relies on consumers using CMake 2.8.12 or greater.") +endif() + +# Load information for each installed configuration. +get_filename_component(_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH) +file(GLOB CONFIG_FILES "${_DIR}/OpenCVModules-*.cmake") +foreach(f ${CONFIG_FILES}) + include(${f}) +endforeach() + +# Cleanup temporary variables. +set(_IMPORT_PREFIX) + +# Loop over all imported files and verify that they actually exist +foreach(target ${_IMPORT_CHECK_TARGETS} ) + foreach(file ${_IMPORT_CHECK_FILES_FOR_${target}} ) + if(NOT EXISTS "${file}" ) + message(FATAL_ERROR "The imported target \"${target}\" references the file + \"${file}\" +but this file does not exist. Possible reasons include: +* The file was deleted, renamed, or moved to another location. +* An install or uninstall procedure did not complete successfully. +* The installation package was faulty and contained + \"${CMAKE_CURRENT_LIST_FILE}\" +but not all the files it references. +") + endif() + endforeach() + unset(_IMPORT_CHECK_FILES_FOR_${target}) +endforeach() +unset(_IMPORT_CHECK_TARGETS) + +# This file does not depend on other imported targets which have +# been exported from the same project but in a separate export set. + +# Commands beyond this point should not need to know the version. +set(CMAKE_IMPORT_FILE_VERSION) +cmake_policy(POP) diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/haarcascades/haarcascade_eye.xml b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/haarcascades/haarcascade_eye.xml new file mode 100644 index 0000000000000000000000000000000000000000..b21e3b93d74b5130b5a1323be9fc46017ab0e8c7 --- /dev/null +++ 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a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/SoftFloat-COPYING.txt b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/SoftFloat-COPYING.txt new file mode 100644 index 0000000000000000000000000000000000000000..e16aa0fd58c12d2da71a95650be9a23b87ef46e0 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/SoftFloat-COPYING.txt @@ -0,0 +1,36 @@ + +License for Berkeley SoftFloat Release 3c + +John R. Hauser +2017 February 10 + +The following applies to the whole of SoftFloat Release 3c as well as to +each source file individually. + +Copyright 2011, 2012, 2013, 2014, 2015, 2016, 2017 The Regents of the +University of California. All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + + 1. Redistributions of source code must retain the above copyright notice, + this list of conditions, and the following disclaimer. + + 2. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions, and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + 3. Neither the name of the University nor the names of its contributors + may be used to endorse or promote products derived from this software + without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE REGENTS AND CONTRIBUTORS "AS IS", AND ANY +EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE, ARE +DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR ANY +DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND +ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF +THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/jasper-LICENSE b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/jasper-LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..f817ef43a55a70eb1646b07bf254218fee97ee20 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/jasper-LICENSE @@ -0,0 +1,51 @@ +JasPer License Version 2.0 + +Copyright (c) 2001-2006 Michael David Adams +Copyright (c) 1999-2000 Image Power, Inc. +Copyright (c) 1999-2000 The University of British Columbia + +All rights reserved. + +Permission is hereby granted, free of charge, to any person (the +"User") obtaining a copy of this software and associated documentation +files (the "Software"), to deal in the Software without restriction, +including without limitation the rights to use, copy, modify, merge, +publish, distribute, and/or sell copies of the Software, and to permit +persons to whom the Software is furnished to do so, subject to the +following conditions: + +1. The above copyright notices and this permission notice (which +includes the disclaimer below) shall be included in all copies or +substantial portions of the Software. + +2. The name of a copyright holder shall not be used to endorse or +promote products derived from the Software without specific prior +written permission. + +THIS DISCLAIMER OF WARRANTY CONSTITUTES AN ESSENTIAL PART OF THIS +LICENSE. NO USE OF THE SOFTWARE IS AUTHORIZED HEREUNDER EXCEPT UNDER +THIS DISCLAIMER. THE SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS +"AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING +BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A +PARTICULAR PURPOSE AND NONINFRINGEMENT OF THIRD PARTY RIGHTS. IN NO +EVENT SHALL THE COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, OR ANY SPECIAL +INDIRECT OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING +FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, +NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION +WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. NO ASSURANCES ARE +PROVIDED BY THE COPYRIGHT HOLDERS THAT THE SOFTWARE DOES NOT INFRINGE +THE PATENT OR OTHER INTELLECTUAL PROPERTY RIGHTS OF ANY OTHER ENTITY. +EACH COPYRIGHT HOLDER DISCLAIMS ANY LIABILITY TO THE USER FOR CLAIMS +BROUGHT BY ANY OTHER ENTITY BASED ON INFRINGEMENT OF INTELLECTUAL +PROPERTY RIGHTS OR OTHERWISE. AS A CONDITION TO EXERCISING THE RIGHTS +GRANTED HEREUNDER, EACH USER HEREBY ASSUMES SOLE RESPONSIBILITY TO SECURE +ANY OTHER INTELLECTUAL PROPERTY RIGHTS NEEDED, IF ANY. THE SOFTWARE +IS NOT FAULT-TOLERANT AND IS NOT INTENDED FOR USE IN MISSION-CRITICAL +SYSTEMS, SUCH AS THOSE USED IN THE OPERATION OF NUCLEAR FACILITIES, +AIRCRAFT NAVIGATION OR COMMUNICATION SYSTEMS, AIR TRAFFIC CONTROL +SYSTEMS, DIRECT LIFE SUPPORT MACHINES, OR WEAPONS SYSTEMS, IN WHICH +THE FAILURE OF THE SOFTWARE OR SYSTEM COULD LEAD DIRECTLY TO DEATH, +PERSONAL INJURY, OR SEVERE PHYSICAL OR ENVIRONMENTAL DAMAGE ("HIGH +RISK ACTIVITIES"). THE COPYRIGHT HOLDERS SPECIFICALLY DISCLAIM ANY +EXPRESS OR IMPLIED WARRANTY OF FITNESS FOR HIGH RISK ACTIVITIES. + diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/jasper-README b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/jasper-README new file mode 100644 index 0000000000000000000000000000000000000000..88fd2f10bd4d4809a53cc8076668d71829c249ad --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/jasper-README @@ -0,0 +1,22 @@ +JasPer Readme +************* + +This is the source code distribution for JasPer. JasPer is a collection +of software (i.e., a library and application programs) for the coding +and manipulation of images. This software can handle image data in a +variety of formats. One such format supported by JasPer is the JPEG-2000 +format defined in ISO/IEC 15444-1. + +The complete licensing terms for the JasPer software can be found in +the file named "LICENSE" in the top level directory of this software +distribution. Any use of this software contrary to the terms of the +license is strictly prohibited. The changes made to the software +since the last release are described in the file "NEWS". Detailed +documentation on the JasPer software can be found in the JasPer Software +Reference Manual. This manual is located in the "doc" directory, and +includes useful information such as: 1) how to build, install, and use +the software, 2) how to submit report bugs, and 3) where to find +additional information about the software. + +Enjoy! :) + diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/jasper-copyright b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/jasper-copyright new file mode 100644 index 0000000000000000000000000000000000000000..88a55abcad68c9a21328907ca6b64f816f2d7265 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/jasper-copyright @@ -0,0 +1,62 @@ +This package was debianized by Christopher L Cheney on +Fri, 22 Aug 2003 01:33:34 -0500. + +The current maintainer is Roland Stigge + +It was downloaded from http://www.ece.uvic.ca/~mdadams/jasper/ + +Upstream Author: Michael Adams + +License: + +JasPer License Version 2.0 + +Copyright (c) 1999-2000 Image Power, Inc. +Copyright (c) 1999-2000 The University of British Columbia +Copyright (c) 2001-2003 Michael David Adams + +All rights reserved. + +Permission is hereby granted, free of charge, to any person (the +"User") obtaining a copy of this software and associated documentation +files (the "Software"), to deal in the Software without restriction, +including without limitation the rights to use, copy, modify, merge, +publish, distribute, and/or sell copies of the Software, and to permit +persons to whom the Software is furnished to do so, subject to the +following conditions: + +1. The above copyright notices and this permission notice (which +includes the disclaimer below) shall be included in all copies or +substantial portions of the Software. + +2. The name of a copyright holder shall not be used to endorse or +promote products derived from the Software without specific prior +written permission. + +THIS DISCLAIMER OF WARRANTY CONSTITUTES AN ESSENTIAL PART OF THIS +LICENSE. NO USE OF THE SOFTWARE IS AUTHORIZED HEREUNDER EXCEPT UNDER +THIS DISCLAIMER. THE SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS +"AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING +BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A +PARTICULAR PURPOSE AND NONINFRINGEMENT OF THIRD PARTY RIGHTS. IN NO +EVENT SHALL THE COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, OR ANY SPECIAL +INDIRECT OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING +FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, +NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION +WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. NO ASSURANCES ARE +PROVIDED BY THE COPYRIGHT HOLDERS THAT THE SOFTWARE DOES NOT INFRINGE +THE PATENT OR OTHER INTELLECTUAL PROPERTY RIGHTS OF ANY OTHER ENTITY. +EACH COPYRIGHT HOLDER DISCLAIMS ANY LIABILITY TO THE USER FOR CLAIMS +BROUGHT BY ANY OTHER ENTITY BASED ON INFRINGEMENT OF INTELLECTUAL +PROPERTY RIGHTS OR OTHERWISE. AS A CONDITION TO EXERCISING THE RIGHTS +GRANTED HEREUNDER, EACH USER HEREBY ASSUMES SOLE RESPONSIBILITY TO SECURE +ANY OTHER INTELLECTUAL PROPERTY RIGHTS NEEDED, IF ANY. THE SOFTWARE +IS NOT FAULT-TOLERANT AND IS NOT INTENDED FOR USE IN MISSION-CRITICAL +SYSTEMS, SUCH AS THOSE USED IN THE OPERATION OF NUCLEAR FACILITIES, +AIRCRAFT NAVIGATION OR COMMUNICATION SYSTEMS, AIR TRAFFIC CONTROL +SYSTEMS, DIRECT LIFE SUPPORT MACHINES, OR WEAPONS SYSTEMS, IN WHICH +THE FAILURE OF THE SOFTWARE OR SYSTEM COULD LEAD DIRECTLY TO DEATH, +PERSONAL INJURY, OR SEVERE PHYSICAL OR ENVIRONMENTAL DAMAGE ("HIGH +RISK ACTIVITIES"). THE COPYRIGHT HOLDERS SPECIFICALLY DISCLAIM ANY +EXPRESS OR IMPLIED WARRANTY OF FITNESS FOR HIGH RISK ACTIVITIES. + diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/libjpeg-turbo-LICENSE.md b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/libjpeg-turbo-LICENSE.md new file mode 100644 index 0000000000000000000000000000000000000000..0572390635d944af818ebe132019722650e3ac0b --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/libjpeg-turbo-LICENSE.md @@ -0,0 +1,139 @@ +libjpeg-turbo Licenses +====================== + +libjpeg-turbo is covered by three compatible BSD-style open source licenses: + +- The IJG (Independent JPEG Group) License, which is listed in + [README.ijg](README.ijg) + + This license applies to the libjpeg API library and associated programs + (any code inherited from libjpeg, and any modifications to that code.) + +- The Modified (3-clause) BSD License, which is listed below + + This license covers the TurboJPEG API library and associated programs. + +- The zlib License, which is listed below + + This license is a subset of the other two, and it covers the libjpeg-turbo + SIMD extensions. + + +Complying with the libjpeg-turbo Licenses +========================================= + +This section provides a roll-up of the libjpeg-turbo licensing terms, to the +best of our understanding. + +1. If you are distributing a modified version of the libjpeg-turbo source, + then: + + 1. You cannot alter or remove any existing copyright or license notices + from the source. + + **Origin** + - Clause 1 of the IJG License + - Clause 1 of the Modified BSD License + - Clauses 1 and 3 of the zlib License + + 2. You must add your own copyright notice to the header of each source + file you modified, so others can tell that you modified that file (if + there is not an existing copyright header in that file, then you can + simply add a notice stating that you modified the file.) + + **Origin** + - Clause 1 of the IJG License + - Clause 2 of the zlib License + + 3. You must include the IJG README file, and you must not alter any of the + copyright or license text in that file. + + **Origin** + - Clause 1 of the IJG License + +2. If you are distributing only libjpeg-turbo binaries without the source, or + if you are distributing an application that statically links with + libjpeg-turbo, then: + + 1. Your product documentation must include a message stating: + + This software is based in part on the work of the Independent JPEG + Group. + + **Origin** + - Clause 2 of the IJG license + + 2. If your binary distribution includes or uses the TurboJPEG API, then + your product documentation must include the text of the Modified BSD + License. + + **Origin** + - Clause 2 of the Modified BSD License + +3. You cannot use the name of the IJG or The libjpeg-turbo Project or the + contributors thereof in advertising, publicity, etc. + + **Origin** + - IJG License + - Clause 3 of the Modified BSD License + +4. The IJG and The libjpeg-turbo Project do not warrant libjpeg-turbo to be + free of defects, nor do we accept any liability for undesirable + consequences resulting from your use of the software. + + **Origin** + - IJG License + - Modified BSD License + - zlib License + + +The Modified (3-clause) BSD License +=================================== + +Copyright (C)\ \. All Rights Reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +- Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. +- Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. +- Neither the name of the libjpeg-turbo Project nor the names of its + contributors may be used to endorse or promote products derived from this + software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS", +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE +LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +POSSIBILITY OF SUCH DAMAGE. + + +The zlib License +================ + +Copyright (C) \, \. + +This software is provided 'as-is', without any express or implied +warranty. In no event will the authors be held liable for any damages +arising from the use of this software. + +Permission is granted to anyone to use this software for any purpose, +including commercial applications, and to alter it and redistribute it +freely, subject to the following restrictions: + +1. The origin of this software must not be misrepresented; you must not + claim that you wrote the original software. If you use this software + in a product, an acknowledgment in the product documentation would be + appreciated but is not required. +2. Altered source versions must be plainly marked as such, and must not be + misrepresented as being the original software. +3. This notice may not be removed or altered from any source distribution. diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/libjpeg-turbo-README.ijg b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/libjpeg-turbo-README.ijg new file mode 100644 index 0000000000000000000000000000000000000000..9c450ceb07ce9a43325e6524b0b2da017fea8db3 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/libjpeg-turbo-README.ijg @@ -0,0 +1,279 @@ +libjpeg-turbo note: This file has been modified by The libjpeg-turbo Project +to include only information relevant to libjpeg-turbo, to wordsmith certain +sections, and to remove impolitic language that existed in the libjpeg v8 +README. It is included only for reference. Please see README.md for +information specific to libjpeg-turbo. + + +The Independent JPEG Group's JPEG software +========================================== + +This distribution contains a release of the Independent JPEG Group's free JPEG +software. You are welcome to redistribute this software and to use it for any +purpose, subject to the conditions under LEGAL ISSUES, below. + +This software is the work of Tom Lane, Guido Vollbeding, Philip Gladstone, +Bill Allombert, Jim Boucher, Lee Crocker, Bob Friesenhahn, Ben Jackson, +Julian Minguillon, Luis Ortiz, George Phillips, Davide Rossi, Ge' Weijers, +and other members of the Independent JPEG Group. + +IJG is not affiliated with the ISO/IEC JTC1/SC29/WG1 standards committee +(also known as JPEG, together with ITU-T SG16). + + +DOCUMENTATION ROADMAP +===================== + +This file contains the following sections: + +OVERVIEW General description of JPEG and the IJG software. +LEGAL ISSUES Copyright, lack of warranty, terms of distribution. +REFERENCES Where to learn more about JPEG. +ARCHIVE LOCATIONS Where to find newer versions of this software. +FILE FORMAT WARS Software *not* to get. +TO DO Plans for future IJG releases. + +Other documentation files in the distribution are: + +User documentation: + usage.txt Usage instructions for cjpeg, djpeg, jpegtran, + rdjpgcom, and wrjpgcom. + *.1 Unix-style man pages for programs (same info as usage.txt). + wizard.txt Advanced usage instructions for JPEG wizards only. + change.log Version-to-version change highlights. +Programmer and internal documentation: + libjpeg.txt How to use the JPEG library in your own programs. + example.c Sample code for calling the JPEG library. + structure.txt Overview of the JPEG library's internal structure. + coderules.txt Coding style rules --- please read if you contribute code. + +Please read at least usage.txt. Some information can also be found in the JPEG +FAQ (Frequently Asked Questions) article. See ARCHIVE LOCATIONS below to find +out where to obtain the FAQ article. + +If you want to understand how the JPEG code works, we suggest reading one or +more of the REFERENCES, then looking at the documentation files (in roughly +the order listed) before diving into the code. + + +OVERVIEW +======== + +This package contains C software to implement JPEG image encoding, decoding, +and transcoding. JPEG (pronounced "jay-peg") is a standardized compression +method for full-color and grayscale images. JPEG's strong suit is compressing +photographic images or other types of images that have smooth color and +brightness transitions between neighboring pixels. Images with sharp lines or +other abrupt features may not compress well with JPEG, and a higher JPEG +quality may have to be used to avoid visible compression artifacts with such +images. + +JPEG is lossy, meaning that the output pixels are not necessarily identical to +the input pixels. However, on photographic content and other "smooth" images, +very good compression ratios can be obtained with no visible compression +artifacts, and extremely high compression ratios are possible if you are +willing to sacrifice image quality (by reducing the "quality" setting in the +compressor.) + +This software implements JPEG baseline, extended-sequential, and progressive +compression processes. Provision is made for supporting all variants of these +processes, although some uncommon parameter settings aren't implemented yet. +We have made no provision for supporting the hierarchical or lossless +processes defined in the standard. + +We provide a set of library routines for reading and writing JPEG image files, +plus two sample applications "cjpeg" and "djpeg", which use the library to +perform conversion between JPEG and some other popular image file formats. +The library is intended to be reused in other applications. + +In order to support file conversion and viewing software, we have included +considerable functionality beyond the bare JPEG coding/decoding capability; +for example, the color quantization modules are not strictly part of JPEG +decoding, but they are essential for output to colormapped file formats or +colormapped displays. These extra functions can be compiled out of the +library if not required for a particular application. + +We have also included "jpegtran", a utility for lossless transcoding between +different JPEG processes, and "rdjpgcom" and "wrjpgcom", two simple +applications for inserting and extracting textual comments in JFIF files. + +The emphasis in designing this software has been on achieving portability and +flexibility, while also making it fast enough to be useful. In particular, +the software is not intended to be read as a tutorial on JPEG. (See the +REFERENCES section for introductory material.) Rather, it is intended to +be reliable, portable, industrial-strength code. We do not claim to have +achieved that goal in every aspect of the software, but we strive for it. + +We welcome the use of this software as a component of commercial products. +No royalty is required, but we do ask for an acknowledgement in product +documentation, as described under LEGAL ISSUES. + + +LEGAL ISSUES +============ + +In plain English: + +1. We don't promise that this software works. (But if you find any bugs, + please let us know!) +2. You can use this software for whatever you want. You don't have to pay us. +3. You may not pretend that you wrote this software. If you use it in a + program, you must acknowledge somewhere in your documentation that + you've used the IJG code. + +In legalese: + +The authors make NO WARRANTY or representation, either express or implied, +with respect to this software, its quality, accuracy, merchantability, or +fitness for a particular purpose. This software is provided "AS IS", and you, +its user, assume the entire risk as to its quality and accuracy. + +This software is copyright (C) 1991-2016, Thomas G. Lane, Guido Vollbeding. +All Rights Reserved except as specified below. + +Permission is hereby granted to use, copy, modify, and distribute this +software (or portions thereof) for any purpose, without fee, subject to these +conditions: +(1) If any part of the source code for this software is distributed, then this +README file must be included, with this copyright and no-warranty notice +unaltered; and any additions, deletions, or changes to the original files +must be clearly indicated in accompanying documentation. +(2) If only executable code is distributed, then the accompanying +documentation must state that "this software is based in part on the work of +the Independent JPEG Group". +(3) Permission for use of this software is granted only if the user accepts +full responsibility for any undesirable consequences; the authors accept +NO LIABILITY for damages of any kind. + +These conditions apply to any software derived from or based on the IJG code, +not just to the unmodified library. If you use our work, you ought to +acknowledge us. + +Permission is NOT granted for the use of any IJG author's name or company name +in advertising or publicity relating to this software or products derived from +it. This software may be referred to only as "the Independent JPEG Group's +software". + +We specifically permit and encourage the use of this software as the basis of +commercial products, provided that all warranty or liability claims are +assumed by the product vendor. + + +The Unix configuration script "configure" was produced with GNU Autoconf. +It is copyright by the Free Software Foundation but is freely distributable. +The same holds for its supporting scripts (config.guess, config.sub, +ltmain.sh). Another support script, install-sh, is copyright by X Consortium +but is also freely distributable. + +The IJG distribution formerly included code to read and write GIF files. +To avoid entanglement with the Unisys LZW patent (now expired), GIF reading +support has been removed altogether, and the GIF writer has been simplified +to produce "uncompressed GIFs". This technique does not use the LZW +algorithm; the resulting GIF files are larger than usual, but are readable +by all standard GIF decoders. + +We are required to state that + "The Graphics Interchange Format(c) is the Copyright property of + CompuServe Incorporated. GIF(sm) is a Service Mark property of + CompuServe Incorporated." + + +REFERENCES +========== + +We recommend reading one or more of these references before trying to +understand the innards of the JPEG software. + +The best short technical introduction to the JPEG compression algorithm is + Wallace, Gregory K. "The JPEG Still Picture Compression Standard", + Communications of the ACM, April 1991 (vol. 34 no. 4), pp. 30-44. +(Adjacent articles in that issue discuss MPEG motion picture compression, +applications of JPEG, and related topics.) If you don't have the CACM issue +handy, a PDF file containing a revised version of Wallace's article is +available at http://www.ijg.org/files/Wallace.JPEG.pdf. The file (actually +a preprint for an article that appeared in IEEE Trans. Consumer Electronics) +omits the sample images that appeared in CACM, but it includes corrections +and some added material. Note: the Wallace article is copyright ACM and IEEE, +and it may not be used for commercial purposes. + +A somewhat less technical, more leisurely introduction to JPEG can be found in +"The Data Compression Book" by Mark Nelson and Jean-loup Gailly, published by +M&T Books (New York), 2nd ed. 1996, ISBN 1-55851-434-1. This book provides +good explanations and example C code for a multitude of compression methods +including JPEG. It is an excellent source if you are comfortable reading C +code but don't know much about data compression in general. The book's JPEG +sample code is far from industrial-strength, but when you are ready to look +at a full implementation, you've got one here... + +The best currently available description of JPEG is the textbook "JPEG Still +Image Data Compression Standard" by William B. Pennebaker and Joan L. +Mitchell, published by Van Nostrand Reinhold, 1993, ISBN 0-442-01272-1. +Price US$59.95, 638 pp. The book includes the complete text of the ISO JPEG +standards (DIS 10918-1 and draft DIS 10918-2). + +The original JPEG standard is divided into two parts, Part 1 being the actual +specification, while Part 2 covers compliance testing methods. Part 1 is +titled "Digital Compression and Coding of Continuous-tone Still Images, +Part 1: Requirements and guidelines" and has document numbers ISO/IEC IS +10918-1, ITU-T T.81. Part 2 is titled "Digital Compression and Coding of +Continuous-tone Still Images, Part 2: Compliance testing" and has document +numbers ISO/IEC IS 10918-2, ITU-T T.83. + +The JPEG standard does not specify all details of an interchangeable file +format. For the omitted details we follow the "JFIF" conventions, revision +1.02. JFIF 1.02 has been adopted as an Ecma International Technical Report +and thus received a formal publication status. It is available as a free +download in PDF format from +http://www.ecma-international.org/publications/techreports/E-TR-098.htm. +A PostScript version of the JFIF document is available at +http://www.ijg.org/files/jfif.ps.gz. There is also a plain text version at +http://www.ijg.org/files/jfif.txt.gz, but it is missing the figures. + +The TIFF 6.0 file format specification can be obtained by FTP from +ftp://ftp.sgi.com/graphics/tiff/TIFF6.ps.gz. The JPEG incorporation scheme +found in the TIFF 6.0 spec of 3-June-92 has a number of serious problems. +IJG does not recommend use of the TIFF 6.0 design (TIFF Compression tag 6). +Instead, we recommend the JPEG design proposed by TIFF Technical Note #2 +(Compression tag 7). Copies of this Note can be obtained from +http://www.ijg.org/files/. It is expected that the next revision +of the TIFF spec will replace the 6.0 JPEG design with the Note's design. +Although IJG's own code does not support TIFF/JPEG, the free libtiff library +uses our library to implement TIFF/JPEG per the Note. + + +ARCHIVE LOCATIONS +================= + +The "official" archive site for this software is www.ijg.org. +The most recent released version can always be found there in +directory "files". + +The JPEG FAQ (Frequently Asked Questions) article is a source of some +general information about JPEG. +It is available on the World Wide Web at http://www.faqs.org/faqs/jpeg-faq/ +and other news.answers archive sites, including the official news.answers +archive at rtfm.mit.edu: ftp://rtfm.mit.edu/pub/usenet/news.answers/jpeg-faq/. +If you don't have Web or FTP access, send e-mail to mail-server@rtfm.mit.edu +with body + send usenet/news.answers/jpeg-faq/part1 + send usenet/news.answers/jpeg-faq/part2 + + +FILE FORMAT WARS +================ + +The ISO/IEC JTC1/SC29/WG1 standards committee (also known as JPEG, together +with ITU-T SG16) currently promotes different formats containing the name +"JPEG" which are incompatible with original DCT-based JPEG. IJG therefore does +not support these formats (see REFERENCES). Indeed, one of the original +reasons for developing this free software was to help force convergence on +common, interoperable format standards for JPEG files. +Don't use an incompatible file format! +(In any case, our decoder will remain capable of reading existing JPEG +image files indefinitely.) + + +TO DO +===== + +Please send bug reports, offers of help, etc. to jpeg-info@jpegclub.org. diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/libjpeg-turbo-README.md b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/libjpeg-turbo-README.md new file mode 100644 index 0000000000000000000000000000000000000000..74e6eac45a78b7c4817b2c85373c1673dfb3d5a2 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/libjpeg-turbo-README.md @@ -0,0 +1,341 @@ +Background +========== + +libjpeg-turbo is a JPEG image codec that uses SIMD instructions (MMX, SSE2, +NEON, AltiVec) to accelerate baseline JPEG compression and decompression on +x86, x86-64, ARM, and PowerPC systems. On such systems, libjpeg-turbo is +generally 2-6x as fast as libjpeg, all else being equal. On other types of +systems, libjpeg-turbo can still outperform libjpeg by a significant amount, by +virtue of its highly-optimized Huffman coding routines. In many cases, the +performance of libjpeg-turbo rivals that of proprietary high-speed JPEG codecs. + +libjpeg-turbo implements both the traditional libjpeg API as well as the less +powerful but more straightforward TurboJPEG API. libjpeg-turbo also features +colorspace extensions that allow it to compress from/decompress to 32-bit and +big-endian pixel buffers (RGBX, XBGR, etc.), as well as a full-featured Java +interface. + +libjpeg-turbo was originally based on libjpeg/SIMD, an MMX-accelerated +derivative of libjpeg v6b developed by Miyasaka Masaru. The TigerVNC and +VirtualGL projects made numerous enhancements to the codec in 2009, and in +early 2010, libjpeg-turbo spun off into an independent project, with the goal +of making high-speed JPEG compression/decompression technology available to a +broader range of users and developers. + + +License +======= + +libjpeg-turbo is covered by three compatible BSD-style open source licenses. +Refer to [LICENSE.md](LICENSE.md) for a roll-up of license terms. + + +Building libjpeg-turbo +====================== + +Refer to [BUILDING.md](BUILDING.md) for complete instructions. + + +Using libjpeg-turbo +=================== + +libjpeg-turbo includes two APIs that can be used to compress and decompress +JPEG images: + +- **TurboJPEG API**
+ This API provides an easy-to-use interface for compressing and decompressing + JPEG images in memory. It also provides some functionality that would not be + straightforward to achieve using the underlying libjpeg API, such as + generating planar YUV images and performing multiple simultaneous lossless + transforms on an image. The Java interface for libjpeg-turbo is written on + top of the TurboJPEG API. + +- **libjpeg API**
+ This is the de facto industry-standard API for compressing and decompressing + JPEG images. It is more difficult to use than the TurboJPEG API but also + more powerful. The libjpeg API implementation in libjpeg-turbo is both + API/ABI-compatible and mathematically compatible with libjpeg v6b. It can + also optionally be configured to be API/ABI-compatible with libjpeg v7 and v8 + (see below.) + +There is no significant performance advantage to either API when both are used +to perform similar operations. + +Colorspace Extensions +--------------------- + +libjpeg-turbo includes extensions that allow JPEG images to be compressed +directly from (and decompressed directly to) buffers that use BGR, BGRX, +RGBX, XBGR, and XRGB pixel ordering. This is implemented with ten new +colorspace constants: + + JCS_EXT_RGB /* red/green/blue */ + JCS_EXT_RGBX /* red/green/blue/x */ + JCS_EXT_BGR /* blue/green/red */ + JCS_EXT_BGRX /* blue/green/red/x */ + JCS_EXT_XBGR /* x/blue/green/red */ + JCS_EXT_XRGB /* x/red/green/blue */ + JCS_EXT_RGBA /* red/green/blue/alpha */ + JCS_EXT_BGRA /* blue/green/red/alpha */ + JCS_EXT_ABGR /* alpha/blue/green/red */ + JCS_EXT_ARGB /* alpha/red/green/blue */ + +Setting `cinfo.in_color_space` (compression) or `cinfo.out_color_space` +(decompression) to one of these values will cause libjpeg-turbo to read the +red, green, and blue values from (or write them to) the appropriate position in +the pixel when compressing from/decompressing to an RGB buffer. + +Your application can check for the existence of these extensions at compile +time with: + + #ifdef JCS_EXTENSIONS + +At run time, attempting to use these extensions with a libjpeg implementation +that does not support them will result in a "Bogus input colorspace" error. +Applications can trap this error in order to test whether run-time support is +available for the colorspace extensions. + +When using the RGBX, BGRX, XBGR, and XRGB colorspaces during decompression, the +X byte is undefined, and in order to ensure the best performance, libjpeg-turbo +can set that byte to whatever value it wishes. If an application expects the X +byte to be used as an alpha channel, then it should specify `JCS_EXT_RGBA`, +`JCS_EXT_BGRA`, `JCS_EXT_ABGR`, or `JCS_EXT_ARGB`. When these colorspace +constants are used, the X byte is guaranteed to be 0xFF, which is interpreted +as opaque. + +Your application can check for the existence of the alpha channel colorspace +extensions at compile time with: + + #ifdef JCS_ALPHA_EXTENSIONS + +[jcstest.c](jcstest.c), located in the libjpeg-turbo source tree, demonstrates +how to check for the existence of the colorspace extensions at compile time and +run time. + +libjpeg v7 and v8 API/ABI Emulation +----------------------------------- + +With libjpeg v7 and v8, new features were added that necessitated extending the +compression and decompression structures. Unfortunately, due to the exposed +nature of those structures, extending them also necessitated breaking backward +ABI compatibility with previous libjpeg releases. Thus, programs that were +built to use libjpeg v7 or v8 did not work with libjpeg-turbo, since it is +based on the libjpeg v6b code base. Although libjpeg v7 and v8 are not +as widely used as v6b, enough programs (including a few Linux distros) made +the switch that there was a demand to emulate the libjpeg v7 and v8 ABIs +in libjpeg-turbo. It should be noted, however, that this feature was added +primarily so that applications that had already been compiled to use libjpeg +v7+ could take advantage of accelerated baseline JPEG encoding/decoding +without recompiling. libjpeg-turbo does not claim to support all of the +libjpeg v7+ features, nor to produce identical output to libjpeg v7+ in all +cases (see below.) + +By passing an argument of `--with-jpeg7` or `--with-jpeg8` to `configure`, or +an argument of `-DWITH_JPEG7=1` or `-DWITH_JPEG8=1` to `cmake`, you can build a +version of libjpeg-turbo that emulates the libjpeg v7 or v8 ABI, so that +programs that are built against libjpeg v7 or v8 can be run with libjpeg-turbo. +The following section describes which libjpeg v7+ features are supported and +which aren't. + +### Support for libjpeg v7 and v8 Features + +#### Fully supported + +- **libjpeg: IDCT scaling extensions in decompressor**
+ libjpeg-turbo supports IDCT scaling with scaling factors of 1/8, 1/4, 3/8, + 1/2, 5/8, 3/4, 7/8, 9/8, 5/4, 11/8, 3/2, 13/8, 7/4, 15/8, and 2/1 (only 1/4 + and 1/2 are SIMD-accelerated.) + +- **libjpeg: Arithmetic coding** + +- **libjpeg: In-memory source and destination managers**
+ See notes below. + +- **cjpeg: Separate quality settings for luminance and chrominance**
+ Note that the libpjeg v7+ API was extended to accommodate this feature only + for convenience purposes. It has always been possible to implement this + feature with libjpeg v6b (see rdswitch.c for an example.) + +- **cjpeg: 32-bit BMP support** + +- **cjpeg: `-rgb` option** + +- **jpegtran: Lossless cropping** + +- **jpegtran: `-perfect` option** + +- **jpegtran: Forcing width/height when performing lossless crop** + +- **rdjpgcom: `-raw` option** + +- **rdjpgcom: Locale awareness** + + +#### Not supported + +NOTE: As of this writing, extensive research has been conducted into the +usefulness of DCT scaling as a means of data reduction and SmartScale as a +means of quality improvement. The reader is invited to peruse the research at + and draw his/her own conclusions, +but it is the general belief of our project that these features have not +demonstrated sufficient usefulness to justify inclusion in libjpeg-turbo. + +- **libjpeg: DCT scaling in compressor**
+ `cinfo.scale_num` and `cinfo.scale_denom` are silently ignored. + There is no technical reason why DCT scaling could not be supported when + emulating the libjpeg v7+ API/ABI, but without the SmartScale extension (see + below), only scaling factors of 1/2, 8/15, 4/7, 8/13, 2/3, 8/11, 4/5, and + 8/9 would be available, which is of limited usefulness. + +- **libjpeg: SmartScale**
+ `cinfo.block_size` is silently ignored. + SmartScale is an extension to the JPEG format that allows for DCT block + sizes other than 8x8. Providing support for this new format would be + feasible (particularly without full acceleration.) However, until/unless + the format becomes either an official industry standard or, at minimum, an + accepted solution in the community, we are hesitant to implement it, as + there is no sense of whether or how it might change in the future. It is + our belief that SmartScale has not demonstrated sufficient usefulness as a + lossless format nor as a means of quality enhancement, and thus our primary + interest in providing this feature would be as a means of supporting + additional DCT scaling factors. + +- **libjpeg: Fancy downsampling in compressor**
+ `cinfo.do_fancy_downsampling` is silently ignored. + This requires the DCT scaling feature, which is not supported. + +- **jpegtran: Scaling**
+ This requires both the DCT scaling and SmartScale features, which are not + supported. + +- **Lossless RGB JPEG files**
+ This requires the SmartScale feature, which is not supported. + +### What About libjpeg v9? + +libjpeg v9 introduced yet another field to the JPEG compression structure +(`color_transform`), thus making the ABI backward incompatible with that of +libjpeg v8. This new field was introduced solely for the purpose of supporting +lossless SmartScale encoding. Furthermore, there was actually no reason to +extend the API in this manner, as the color transform could have just as easily +been activated by way of a new JPEG colorspace constant, thus preserving +backward ABI compatibility. + +Our research (see link above) has shown that lossless SmartScale does not +generally accomplish anything that can't already be accomplished better with +existing, standard lossless formats. Therefore, at this time it is our belief +that there is not sufficient technical justification for software projects to +upgrade from libjpeg v8 to libjpeg v9, and thus there is not sufficient +technical justification for us to emulate the libjpeg v9 ABI. + +In-Memory Source/Destination Managers +------------------------------------- + +By default, libjpeg-turbo 1.3 and later includes the `jpeg_mem_src()` and +`jpeg_mem_dest()` functions, even when not emulating the libjpeg v8 API/ABI. +Previously, it was necessary to build libjpeg-turbo from source with libjpeg v8 +API/ABI emulation in order to use the in-memory source/destination managers, +but several projects requested that those functions be included when emulating +the libjpeg v6b API/ABI as well. This allows the use of those functions by +programs that need them, without breaking ABI compatibility for programs that +don't, and it allows those functions to be provided in the "official" +libjpeg-turbo binaries. + +Those who are concerned about maintaining strict conformance with the libjpeg +v6b or v7 API can pass an argument of `--without-mem-srcdst` to `configure` or +an argument of `-DWITH_MEM_SRCDST=0` to `cmake` prior to building +libjpeg-turbo. This will restore the pre-1.3 behavior, in which +`jpeg_mem_src()` and `jpeg_mem_dest()` are only included when emulating the +libjpeg v8 API/ABI. + +On Un*x systems, including the in-memory source/destination managers changes +the dynamic library version from 62.1.0 to 62.2.0 if using libjpeg v6b API/ABI +emulation and from 7.1.0 to 7.2.0 if using libjpeg v7 API/ABI emulation. + +Note that, on most Un*x systems, the dynamic linker will not look for a +function in a library until that function is actually used. Thus, if a program +is built against libjpeg-turbo 1.3+ and uses `jpeg_mem_src()` or +`jpeg_mem_dest()`, that program will not fail if run against an older version +of libjpeg-turbo or against libjpeg v7- until the program actually tries to +call `jpeg_mem_src()` or `jpeg_mem_dest()`. Such is not the case on Windows. +If a program is built against the libjpeg-turbo 1.3+ DLL and uses +`jpeg_mem_src()` or `jpeg_mem_dest()`, then it must use the libjpeg-turbo 1.3+ +DLL at run time. + +Both cjpeg and djpeg have been extended to allow testing the in-memory +source/destination manager functions. See their respective man pages for more +details. + + +Mathematical Compatibility +========================== + +For the most part, libjpeg-turbo should produce identical output to libjpeg +v6b. The one exception to this is when using the floating point DCT/IDCT, in +which case the outputs of libjpeg v6b and libjpeg-turbo can differ for the +following reasons: + +- The SSE/SSE2 floating point DCT implementation in libjpeg-turbo is ever so + slightly more accurate than the implementation in libjpeg v6b, but not by + any amount perceptible to human vision (generally in the range of 0.01 to + 0.08 dB gain in PNSR.) + +- When not using the SIMD extensions, libjpeg-turbo uses the more accurate + (and slightly faster) floating point IDCT algorithm introduced in libjpeg + v8a as opposed to the algorithm used in libjpeg v6b. It should be noted, + however, that this algorithm basically brings the accuracy of the floating + point IDCT in line with the accuracy of the slow integer IDCT. The floating + point DCT/IDCT algorithms are mainly a legacy feature, and they do not + produce significantly more accuracy than the slow integer algorithms (to put + numbers on this, the typical difference in PNSR between the two algorithms + is less than 0.10 dB, whereas changing the quality level by 1 in the upper + range of the quality scale is typically more like a 1.0 dB difference.) + +- If the floating point algorithms in libjpeg-turbo are not implemented using + SIMD instructions on a particular platform, then the accuracy of the + floating point DCT/IDCT can depend on the compiler settings. + +While libjpeg-turbo does emulate the libjpeg v8 API/ABI, under the hood it is +still using the same algorithms as libjpeg v6b, so there are several specific +cases in which libjpeg-turbo cannot be expected to produce the same output as +libjpeg v8: + +- When decompressing using scaling factors of 1/2 and 1/4, because libjpeg v8 + implements those scaling algorithms differently than libjpeg v6b does, and + libjpeg-turbo's SIMD extensions are based on the libjpeg v6b behavior. + +- When using chrominance subsampling, because libjpeg v8 implements this + with its DCT/IDCT scaling algorithms rather than with a separate + downsampling/upsampling algorithm. In our testing, the subsampled/upsampled + output of libjpeg v8 is less accurate than that of libjpeg v6b for this + reason. + +- When decompressing using a scaling factor > 1 and merged (AKA "non-fancy" or + "non-smooth") chrominance upsampling, because libjpeg v8 does not support + merged upsampling with scaling factors > 1. + + +Performance Pitfalls +==================== + +Restart Markers +--------------- + +The optimized Huffman decoder in libjpeg-turbo does not handle restart markers +in a way that makes the rest of the libjpeg infrastructure happy, so it is +necessary to use the slow Huffman decoder when decompressing a JPEG image that +has restart markers. This can cause the decompression performance to drop by +as much as 20%, but the performance will still be much greater than that of +libjpeg. Many consumer packages, such as PhotoShop, use restart markers when +generating JPEG images, so images generated by those programs will experience +this issue. + +Fast Integer Forward DCT at High Quality Levels +----------------------------------------------- + +The algorithm used by the SIMD-accelerated quantization function cannot produce +correct results whenever the fast integer forward DCT is used along with a JPEG +quality of 98-100. Thus, libjpeg-turbo must use the non-SIMD quantization +function in those cases. This causes performance to drop by as much as 40%. +It is therefore strongly advised that you use the slow integer forward DCT +whenever encoding images with a JPEG quality of 98 or higher. diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/libpng-LICENSE b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/libpng-LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..6ee9c8f5548223ab91d4600840d2597bd5cdbe9f --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/libpng-LICENSE @@ -0,0 +1,133 @@ + +This copy of the libpng notices is provided for your convenience. In case of +any discrepancy between this copy and the notices in the file png.h that is +included in the libpng distribution, the latter shall prevail. + +COPYRIGHT NOTICE, DISCLAIMER, and LICENSE: + +If you modify libpng you may insert additional notices immediately following +this sentence. + +This code is released under the libpng license. + +libpng versions 1.0.7, July 1, 2000 through 1.6.35, July 15, 2018 are +Copyright (c) 2000-2002, 2004, 2006-2018 Glenn Randers-Pehrson, are +derived from libpng-1.0.6, and are distributed according to the same +disclaimer and license as libpng-1.0.6 with the following individuals +added to the list of Contributing Authors: + + Simon-Pierre Cadieux + Eric S. Raymond + Mans Rullgard + Cosmin Truta + Gilles Vollant + James Yu + Mandar Sahastrabuddhe + Google Inc. + Vadim Barkov + +and with the following additions to the disclaimer: + + There is no warranty against interference with your enjoyment of the + library or against infringement. There is no warranty that our + efforts or the library will fulfill any of your particular purposes + or needs. This library is provided with all faults, and the entire + risk of satisfactory quality, performance, accuracy, and effort is with + the user. + +Some files in the "contrib" directory and some configure-generated +files that are distributed with libpng have other copyright owners and +are released under other open source licenses. + +libpng versions 0.97, January 1998, through 1.0.6, March 20, 2000, are +Copyright (c) 1998-2000 Glenn Randers-Pehrson, are derived from +libpng-0.96, and are distributed according to the same disclaimer and +license as libpng-0.96, with the following individuals added to the list +of Contributing Authors: + + Tom Lane + Glenn Randers-Pehrson + Willem van Schaik + +libpng versions 0.89, June 1996, through 0.96, May 1997, are +Copyright (c) 1996-1997 Andreas Dilger, are derived from libpng-0.88, +and are distributed according to the same disclaimer and license as +libpng-0.88, with the following individuals added to the list of +Contributing Authors: + + John Bowler + Kevin Bracey + Sam Bushell + Magnus Holmgren + Greg Roelofs + Tom Tanner + +Some files in the "scripts" directory have other copyright owners +but are released under this license. + +libpng versions 0.5, May 1995, through 0.88, January 1996, are +Copyright (c) 1995-1996 Guy Eric Schalnat, Group 42, Inc. + +For the purposes of this copyright and license, "Contributing Authors" +is defined as the following set of individuals: + + Andreas Dilger + Dave Martindale + Guy Eric Schalnat + Paul Schmidt + Tim Wegner + +The PNG Reference Library is supplied "AS IS". The Contributing Authors +and Group 42, Inc. disclaim all warranties, expressed or implied, +including, without limitation, the warranties of merchantability and of +fitness for any purpose. The Contributing Authors and Group 42, Inc. +assume no liability for direct, indirect, incidental, special, exemplary, +or consequential damages, which may result from the use of the PNG +Reference Library, even if advised of the possibility of such damage. + +Permission is hereby granted to use, copy, modify, and distribute this +source code, or portions hereof, for any purpose, without fee, subject +to the following restrictions: + + 1. The origin of this source code must not be misrepresented. + + 2. Altered versions must be plainly marked as such and must not + be misrepresented as being the original source. + + 3. This Copyright notice may not be removed or altered from any + source or altered source distribution. + +The Contributing Authors and Group 42, Inc. specifically permit, without +fee, and encourage the use of this source code as a component to +supporting the PNG file format in commercial products. If you use this +source code in a product, acknowledgment is not required but would be +appreciated. + +END OF COPYRIGHT NOTICE, DISCLAIMER, and LICENSE. + +TRADEMARK: + +The name "libpng" has not been registered by the Copyright owner +as a trademark in any jurisdiction. However, because libpng has +been distributed and maintained world-wide, continually since 1995, +the Copyright owner claims "common-law trademark protection" in any +jurisdiction where common-law trademark is recognized. + +OSI CERTIFICATION: + +Libpng is OSI Certified Open Source Software. OSI Certified Open Source is +a certification mark of the Open Source Initiative. OSI has not addressed +the additional disclaimers inserted at version 1.0.7. + +EXPORT CONTROL: + +The Copyright owner believes that the Export Control Classification +Number (ECCN) for libpng is EAR99, which means not subject to export +controls or International Traffic in Arms Regulations (ITAR) because +it is open source, publicly available software, that does not contain +any encryption software. See the EAR, paragraphs 734.3(b)(3) and +734.7(b). + +Glenn Randers-Pehrson +glennrp at users.sourceforge.net +July 15, 2018 diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/libpng-README b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/libpng-README new file mode 100644 index 0000000000000000000000000000000000000000..f098b27f3971aec2dd120678d09c5aac707704d1 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/libpng-README @@ -0,0 +1,222 @@ +README for libpng version 1.6.35 - July 15, 2018 (shared library 16.0) +See the note about version numbers near the top of png.h + +See INSTALL for instructions on how to install libpng. + +Libpng comes in several distribution formats. Get libpng-*.tar.gz or +libpng-*.tar.xz or if you want UNIX-style line endings in the text files, +or lpng*.7z or lpng*.zip if you want DOS-style line endings. + +Version 0.89 was the first official release of libpng. Don't let the +fact that it's the first release fool you. The libpng library has been in +extensive use and testing since mid-1995. By late 1997 it had +finally gotten to the stage where there hadn't been significant +changes to the API in some time, and people have a bad feeling about +libraries with versions < 1.0. Version 1.0.0 was released in +March 1998. + +**** +Note that some of the changes to the png_info structure render this +version of the library binary incompatible with libpng-0.89 or +earlier versions if you are using a shared library. The type of the +"filler" parameter for png_set_filler() has changed from png_byte to +png_uint_32, which will affect shared-library applications that use +this function. + +To avoid problems with changes to the internals of the png info_struct, +new APIs have been made available in 0.95 to avoid direct application +access to info_ptr. These functions are the png_set_ and +png_get_ functions. These functions should be used when +accessing/storing the info_struct data, rather than manipulating it +directly, to avoid such problems in the future. + +It is important to note that the APIs did not make current programs +that access the info struct directly incompatible with the new +library, through libpng-1.2.x. In libpng-1.4.x, which was meant to +be a transitional release, members of the png_struct and the +info_struct can still be accessed, but the compiler will issue a +warning about deprecated usage. Since libpng-1.5.0, direct access +to these structs is not allowed, and the definitions of the structs +reside in private pngstruct.h and pnginfo.h header files that are not +accessible to applications. It is strongly suggested that new +programs use the new APIs (as shown in example.c and pngtest.c), and +older programs be converted to the new format, to facilitate upgrades +in the future. +**** + +Additions since 0.90 include the ability to compile libpng as a +Windows DLL, and new APIs for accessing data in the info struct. +Experimental functions include the ability to set weighting and cost +factors for row filter selection, direct reads of integers from buffers +on big-endian processors that support misaligned data access, faster +methods of doing alpha composition, and more accurate 16->8 bit color +conversion. + +The additions since 0.89 include the ability to read from a PNG stream +which has had some (or all) of the signature bytes read by the calling +application. This also allows the reading of embedded PNG streams that +do not have the PNG file signature. As well, it is now possible to set +the library action on the detection of chunk CRC errors. It is possible +to set different actions based on whether the CRC error occurred in a +critical or an ancillary chunk. + +The changes made to the library, and bugs fixed are based on discussions +on the PNG-implement mailing list and not on material submitted +privately to Guy, Andreas, or Glenn. They will forward any good +suggestions to the list. + +For a detailed description on using libpng, read libpng-manual.txt. For +examples of libpng in a program, see example.c and pngtest.c. For usage +information and restrictions (what little they are) on libpng, see +png.h. For a description on using zlib (the compression library used by +libpng) and zlib's restrictions, see zlib.h + +I have included a general makefile, as well as several machine and +compiler specific ones, but you may have to modify one for your own needs. + +You should use zlib 1.0.4 or later to run this, but it MAY work with +versions as old as zlib 0.95. Even so, there are bugs in older zlib +versions which can cause the output of invalid compression streams for +some images. You will definitely need zlib 1.0.4 or later if you are +taking advantage of the MS-DOS "far" structure allocation for the small +and medium memory models. You should also note that zlib is a +compression library that is useful for more things than just PNG files. +You can use zlib as a drop-in replacement for fread() and fwrite() if +you are so inclined. + +zlib should be available at the same place that libpng is, or at zlib.net. + +You may also want a copy of the PNG specification. It is available +as an RFC, a W3C Recommendation, and an ISO/IEC Standard. You can find +these at http://www.libpng.org/pub/png/pngdocs.html . + +This code is currently being archived at libpng.sourceforge.io in the +[DOWNLOAD] area, and at http://libpng.download/src . If you +can't find it in any of those places, e-mail me, and I'll help you find it. + +I am not a lawyer, but I believe that the Export Control Classification +Number (ECCN) for libpng is EAR99, which means not subject to export +controls or International Traffic in Arms Regulations (ITAR) because it +is open source, publicly available software, that does not contain any +encryption software. See the EAR, paragraphs 734.3(b)(3) and 734.7(b). + +If you have any code changes, requests, problems, etc., please e-mail +them to me. Also, I'd appreciate any make files or project files, +and any modifications you needed to make to get libpng to compile, +along with a #define variable to tell what compiler/system you are on. +If you needed to add transformations to libpng, or wish libpng would +provide the image in a different way, drop me a note (and code, if +possible), so I can consider supporting the transformation. +Finally, if you get any warning messages when compiling libpng +(note: not zlib), and they are easy to fix, I'd appreciate the +fix. Please mention "libpng" somewhere in the subject line. Thanks. + +This release was created and will be supported by myself (of course +based in a large way on Guy's and Andreas' earlier work), and the PNG +development group. + +Send comments/corrections/commendations to png-mng-implement at +lists.sourceforge.net (subscription required; visit +https://lists.sourceforge.net/lists/listinfo/png-mng-implement +to subscribe). + +You can't reach Guy, the original libpng author, at the addresses +given in previous versions of this document. He and Andreas will +read mail addressed to the png-implement list, however. + +Please do not send general questions about PNG. Send them to +png-mng-misc at lists.sf.net (subscription required; visit +https://lists.sourceforge.net/lists/listinfo/png-mng-misc to +subscribe). If you have a question about something +in the PNG specification that is related to using libpng, send it +to me. Send me any questions that start with "I was using libpng, +and ...". If in doubt, send questions to me. I'll bounce them +to others, if necessary. + +Please do not send suggestions on how to change PNG. We have +been discussing PNG for twenty years now, and it is official and +finished. If you have suggestions for libpng, however, I'll +gladly listen. Even if your suggestion is not used immediately, +it may be used later. + +Files in this distribution: + + ANNOUNCE => Announcement of this version, with recent changes + CHANGES => Description of changes between libpng versions + KNOWNBUG => List of known bugs and deficiencies + LICENSE => License to use and redistribute libpng + README => This file + TODO => Things not implemented in the current library + Y2KINFO => Statement of Y2K compliance + example.c => Example code for using libpng functions + libpng.3 => manual page for libpng (includes libpng-manual.txt) + libpng-manual.txt => Description of libpng and its functions + libpngpf.3 => manual page for libpng's private functions + png.5 => manual page for the PNG format + png.c => Basic interface functions common to library + png.h => Library function and interface declarations (public) + pngpriv.h => Library function and interface declarations (private) + pngconf.h => System specific library configuration (public) + pngstruct.h => png_struct declaration (private) + pnginfo.h => png_info struct declaration (private) + pngdebug.h => debugging macros (private) + pngerror.c => Error/warning message I/O functions + pngget.c => Functions for retrieving info from struct + pngmem.c => Memory handling functions + pngbar.png => PNG logo, 88x31 + pngnow.png => PNG logo, 98x31 + pngpread.c => Progressive reading functions + pngread.c => Read data/helper high-level functions + pngrio.c => Lowest-level data read I/O functions + pngrtran.c => Read data transformation functions + pngrutil.c => Read data utility functions + pngset.c => Functions for storing data into the info_struct + pngtest.c => Library test program + pngtest.png => Library test sample image + pngtrans.c => Common data transformation functions + pngwio.c => Lowest-level write I/O functions + pngwrite.c => High-level write functions + pngwtran.c => Write data transformations + pngwutil.c => Write utility functions + arm => Contains optimized code for the ARM platform + powerpc => Contains optimized code for the PowerPC platform + contrib => Contributions + arm-neon => Optimized code for ARM-NEON platform + powerpc-vsx => Optimized code for POWERPC-VSX platform + examples => Example programs + gregbook => source code for PNG reading and writing, from + Greg Roelofs' "PNG: The Definitive Guide", + O'Reilly, 1999 + libtests => Test programs + mips-msa => Optimized code for MIPS-MSA platform + pngminim => Minimal decoder, encoder, and progressive decoder + programs demonstrating use of pngusr.dfa + pngminus => Simple pnm2png and png2pnm programs + pngsuite => Test images + testpngs + tools => Various tools + visupng => Contains a MSVC workspace for VisualPng + intel => Optimized code for INTEL-SSE2 platform + mips => Optimized code for MIPS platform + projects => Contains project files and workspaces for + building a DLL + owatcom => Contains a WATCOM project for building libpng + visualc71 => Contains a Microsoft Visual C++ (MSVC) + workspace for building libpng and zlib + vstudio => Contains a Microsoft Visual C++ (MSVC) + workspace for building libpng and zlib + scripts => Directory containing scripts for building libpng: + (see scripts/README.txt for the list of scripts) + +Good luck, and happy coding. + +-Glenn Randers-Pehrson (current maintainer, since 1998) + Internet: glennrp at users.sourceforge.net + +-Andreas Eric Dilger (former maintainer, 1996-1997) + Internet: adilger at enel.ucalgary.ca + Web: http://www-mddsp.enel.ucalgary.ca/People/adilger/ + +-Guy Eric Schalnat (original author and former maintainer, 1995-1996) + (formerly of Group 42, Inc) + Internet: gschal at infinet.com diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/libpng-opencv-libpng.patch b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/libpng-opencv-libpng.patch new file mode 100644 index 0000000000000000000000000000000000000000..e760e8d89f87af6c6924cfd452f626f97ae47163 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/libpng-opencv-libpng.patch @@ -0,0 +1,38 @@ +--- a/3rdparty/libpng/pngpriv.h ++++ b/3rdparty/libpng/pngpriv.h +@@ -214,7 +214,7 @@ + # endif + #endif + +-#if PNG_INTEL_SSE_OPT > 0 ++#if defined(PNG_INTEL_SSE_OPT) && PNG_INTEL_SSE_OPT > 0 + # ifndef PNG_INTEL_SSE_IMPLEMENTATION + # if defined(__SSE4_1__) || defined(__AVX__) + /* We are not actually using AVX, but checking for AVX is the best +@@ -547,7 +547,7 @@ + + /* Memory model/platform independent fns */ + #ifndef PNG_ABORT +-# ifdef _WINDOWS_ ++# if defined(_WINDOWS_) && !defined(WINRT) + # define PNG_ABORT() ExitProcess(0) + # else + # define PNG_ABORT() abort() +@@ -1340,7 +1340,7 @@ PNG_INTERNAL_FUNCTION(void,png_read_filter_row_paeth4_vsx,(png_row_infop + row_info, png_bytep row, png_const_bytep prev_row),PNG_EMPTY); + #endif + +-#if PNG_INTEL_SSE_IMPLEMENTATION > 0 ++#if defined(PNG_INTEL_SSE_IMPLEMENTATION) && PNG_INTEL_SSE_IMPLEMENTATION > 0 + PNG_INTERNAL_FUNCTION(void,png_read_filter_row_sub3_sse2,(png_row_infop + row_info, png_bytep row, png_const_bytep prev_row),PNG_EMPTY); + PNG_INTERNAL_FUNCTION(void,png_read_filter_row_sub4_sse2,(png_row_infop +@@ -2099,7 +2099,7 @@ PNG_INTERNAL_FUNCTION(void, png_init_filter_functions_msa, + (png_structp png_ptr, unsigned int bpp), PNG_EMPTY); + #endif + +-# if PNG_INTEL_SSE_IMPLEMENTATION > 0 ++# if defined(PNG_INTEL_SSE_IMPLEMENTATION) && PNG_INTEL_SSE_IMPLEMENTATION > 0 + PNG_INTERNAL_FUNCTION(void, png_init_filter_functions_sse2, + (png_structp png_ptr, unsigned int bpp), PNG_EMPTY); + # endif \ No newline at end of file diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/libtiff-COPYRIGHT b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/libtiff-COPYRIGHT new file mode 100644 index 0000000000000000000000000000000000000000..8282186151e224ac83d9990805062ff7b8effa00 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/libtiff-COPYRIGHT @@ -0,0 +1,21 @@ +Copyright (c) 1988-1997 Sam Leffler +Copyright (c) 1991-1997 Silicon Graphics, Inc. + +Permission to use, copy, modify, distribute, and sell this software and +its documentation for any purpose is hereby granted without fee, provided +that (i) the above copyright notices and this permission notice appear in +all copies of the software and related documentation, and (ii) the names of +Sam Leffler and Silicon Graphics may not be used in any advertising or +publicity relating to the software without the specific, prior written +permission of Sam Leffler and Silicon Graphics. + +THE SOFTWARE IS PROVIDED "AS-IS" AND WITHOUT WARRANTY OF ANY KIND, +EXPRESS, IMPLIED OR OTHERWISE, INCLUDING WITHOUT LIMITATION, ANY +WARRANTY OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. + +IN NO EVENT SHALL SAM LEFFLER OR SILICON GRAPHICS BE LIABLE FOR +ANY SPECIAL, INCIDENTAL, INDIRECT OR CONSEQUENTIAL DAMAGES OF ANY KIND, +OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, +WHETHER OR NOT ADVISED OF THE POSSIBILITY OF DAMAGE, AND ON ANY THEORY OF +LIABILITY, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE +OF THIS SOFTWARE. diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/opencl-headers-LICENSE.txt b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/opencl-headers-LICENSE.txt new file mode 100644 index 0000000000000000000000000000000000000000..020ce65fcac2a60e44dab1626fa4924dec17ea23 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/opencl-headers-LICENSE.txt @@ -0,0 +1,25 @@ +Copyright (c) 2008-2015 The Khronos Group Inc. + +Permission is hereby granted, free of charge, to any person obtaining a +copy of this software and/or associated documentation files (the +"Materials"), to deal in the Materials without restriction, including +without limitation the rights to use, copy, modify, merge, publish, +distribute, sublicense, and/or sell copies of the Materials, and to +permit persons to whom the Materials are furnished to do so, subject to +the following conditions: + +The above copyright notice and this permission notice shall be included +in all copies or substantial portions of the Materials. + +MODIFICATIONS TO THIS FILE MAY MEAN IT NO LONGER ACCURATELY REFLECTS +KHRONOS STANDARDS. THE UNMODIFIED, NORMATIVE VERSIONS OF KHRONOS +SPECIFICATIONS AND HEADER INFORMATION ARE LOCATED AT + https://www.khronos.org/registry/ + +THE MATERIALS ARE PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, +EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. +IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY +CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, +TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE +MATERIALS OR THE USE OR OTHER DEALINGS IN THE MATERIALS. diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/openexr-AUTHORS.ilmbase b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/openexr-AUTHORS.ilmbase new file mode 100644 index 0000000000000000000000000000000000000000..51c1f9cea5329ed8e8cd4ce118a1a6a16db49b7a --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/openexr-AUTHORS.ilmbase @@ -0,0 +1,21 @@ +Developers: +----------- + +Florian Kainz +Rod Bogart +Drew Hess +Bill Anderson +Wojciech Jarosz + +Contributors: +------------- + +Rito Trevino +Josh Pines +Christian Rouet + +Win32 build system: +------------------- + +Nick Porcino +Kimball Thurston diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/openexr-AUTHORS.openexr b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/openexr-AUTHORS.openexr new file mode 100644 index 0000000000000000000000000000000000000000..2926b13e837b2a84dfa827dcc222d495185e2ae0 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/openexr-AUTHORS.openexr @@ -0,0 +1,41 @@ +Developers: +----------- + +Florian Kainz +Rod Bogart +Drew Hess +Paul Schneider +Bill Anderson +Wojciech Jarosz +Andrew Kunz + +Contributors: +------------- + +Simon Green +Rito Trevino +Josh Pines +Christian Rouet +Rodrigo Damazio +Greg Ward +Joseph Goldstone +Loren Carpenter, Pixar Animation Studios + +Win32 build system: +------------------- + +Nick Porcino +Kimball Thurston + +Win32 port contributors: +------------------------ + +Dustin Graves +Jukka Liimatta +Baumann Konstantin +Daniel Koch +E. Scott Larsen +stephan mantler +Andreas Kahler +Frank Jargstorff +Lutz Latta diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/openexr-LICENSE b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/openexr-LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..6372750d3cc40ba4dd12c57b2ac9026a2d7d54b9 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/openexr-LICENSE @@ -0,0 +1,34 @@ +Copyright (c) 2006, Industrial Light & Magic, a division of Lucasfilm +Entertainment Company Ltd. Portions contributed and copyright held by +others as indicated. All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are +met: + + * Redistributions of source code must retain the above + copyright notice, this list of conditions and the following + disclaimer. + + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided with + the distribution. + + * Neither the name of Industrial Light & Magic nor the names of + any other contributors to this software may be used to endorse or + promote products derived from this software without specific prior + written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS +IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, +THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR +PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR +CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, +EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, +PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR +PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF +LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING +NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/openexr-fix_msvc2013_errors.patch b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/openexr-fix_msvc2013_errors.patch new file mode 100644 index 0000000000000000000000000000000000000000..0ce106f13b5778d8cdb364074309c6d18ff61e29 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/openexr-fix_msvc2013_errors.patch @@ -0,0 +1,72 @@ +diff --git a/3rdparty/openexr/IlmImf/ImfAcesFile.cpp b/3rdparty/openexr/IlmImf/ImfAcesFile.cpp +index de4bf83..9418b9d 100644 +--- a/3rdparty/openexr/IlmImf/ImfAcesFile.cpp ++++ b/3rdparty/openexr/IlmImf/ImfAcesFile.cpp +@@ -42,6 +42,7 @@ + #include + #include + #include ++#include // for std::max() + + using namespace std; + using namespace Imath; +diff --git a/3rdparty/openexr/IlmImf/ImfOutputFile.cpp b/3rdparty/openexr/IlmImf/ImfOutputFile.cpp +index 8831ec9..e69b92b 100644 +--- a/3rdparty/openexr/IlmImf/ImfOutputFile.cpp ++++ b/3rdparty/openexr/IlmImf/ImfOutputFile.cpp +@@ -58,6 +58,7 @@ + #include + #include + #include ++#include // for std::max() + + + namespace Imf { +diff --git a/3rdparty/openexr/IlmImf/ImfScanLineInputFile.cpp b/3rdparty/openexr/IlmImf/ImfScanLineInputFile.cpp +index f7a12a3..5d8b522 100644 +--- a/3rdparty/openexr/IlmImf/ImfScanLineInputFile.cpp ++++ b/3rdparty/openexr/IlmImf/ImfScanLineInputFile.cpp +@@ -56,6 +56,7 @@ + #include + #include + #include ++#include // for std::max() + + + namespace Imf { +diff --git a/3rdparty/openexr/IlmImf/ImfTiledMisc.cpp b/3rdparty/openexr/IlmImf/ImfTiledMisc.cpp +index 57f52f1..9588e78 100644 +--- a/3rdparty/openexr/IlmImf/ImfTiledMisc.cpp ++++ b/3rdparty/openexr/IlmImf/ImfTiledMisc.cpp +@@ -43,6 +43,7 @@ + #include "Iex.h" + #include + #include ++#include // for std::max() + + + namespace Imf { +diff --git a/3rdparty/openexr/IlmImf/ImfTiledOutputFile.cpp b/3rdparty/openexr/IlmImf/ImfTiledOutputFile.cpp +index 0882106..0bc3cb3 100644 +--- a/3rdparty/openexr/IlmImf/ImfTiledOutputFile.cpp ++++ b/3rdparty/openexr/IlmImf/ImfTiledOutputFile.cpp +@@ -63,6 +63,7 @@ + #include + #include + #include ++#include // for std::max() + + + namespace Imf { +diff --git a/3rdparty/openexr/Imath/ImathMatrixAlgo.cpp b/3rdparty/openexr/Imath/ImathMatrixAlgo.cpp +index f0d2ed6..7ddc649 100644 +--- a/3rdparty/openexr/Imath/ImathMatrixAlgo.cpp ++++ b/3rdparty/openexr/Imath/ImathMatrixAlgo.cpp +@@ -44,6 +44,7 @@ + + #include "ImathMatrixAlgo.h" + #include ++#include // for std::max() + + #if defined(OPENEXR_DLL) + #define EXPORT_CONST __declspec(dllexport) diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/protobuf-LICENSE b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/protobuf-LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..f028c82324202241b99522ed311d1c369c1eee66 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/protobuf-LICENSE @@ -0,0 +1,42 @@ +This license applies to all parts of Protocol Buffers except the following: + + - Atomicops support for generic gcc, located in + src/google/protobuf/stubs/atomicops_internals_generic_gcc.h. + This file is copyrighted by Red Hat Inc. + + - Atomicops support for AIX/POWER, located in + src/google/protobuf/stubs/atomicops_internals_power.h. + This file is copyrighted by Bloomberg Finance LP. + +Copyright 2014, Google Inc. All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are +met: + + * Redistributions of source code must retain the above copyright +notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above +copyright notice, this list of conditions and the following disclaimer +in the documentation and/or other materials provided with the +distribution. + * Neither the name of Google Inc. nor the names of its +contributors may be used to endorse or promote products derived from +this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +Code generated by the Protocol Buffer compiler is owned by the owner +of the input file used when generating it. This code is not +standalone and requires a support library to be linked with it. This +support library is itself covered by the above license. diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/protobuf-README.md b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/protobuf-README.md new file mode 100644 index 0000000000000000000000000000000000000000..e0edb75f580ec43c1e7abf2aa99aeb6a8d01001f --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/protobuf-README.md @@ -0,0 +1,3 @@ +Project: Protocol Buffers - Google's data interchange format +Source code: https://github.com/google/protobuf +Version: 3.5.1 diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/quirc-LICENSE b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/quirc-LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..d47c0262c2f2a785b6b0f5a87f940251352decc9 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/quirc-LICENSE @@ -0,0 +1,16 @@ +quirc -- QR-code recognition library +Copyright (C) 2010-2012 Daniel Beer + +Permission to use, copy, modify, and/or distribute this software for +any purpose with or without fee is hereby granted, provided that the +above copyright notice and this permission notice appear in all +copies. + +THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL +WARRANTIES WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE +AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL +DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR +PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER +TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR +PERFORMANCE OF THIS SOFTWARE. diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/zlib-README b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/zlib-README new file mode 100644 index 0000000000000000000000000000000000000000..51106de4753292ad59de03de9e634e6814eeb7a2 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/licenses/zlib-README @@ -0,0 +1,115 @@ +ZLIB DATA COMPRESSION LIBRARY + +zlib 1.2.11 is a general purpose data compression library. All the code is +thread safe. The data format used by the zlib library is described by RFCs +(Request for Comments) 1950 to 1952 in the files +http://tools.ietf.org/html/rfc1950 (zlib format), rfc1951 (deflate format) and +rfc1952 (gzip format). + +All functions of the compression library are documented in the file zlib.h +(volunteer to write man pages welcome, contact zlib@gzip.org). A usage example +of the library is given in the file test/example.c which also tests that +the library is working correctly. Another example is given in the file +test/minigzip.c. The compression library itself is composed of all source +files in the root directory. + +To compile all files and run the test program, follow the instructions given at +the top of Makefile.in. In short "./configure; make test", and if that goes +well, "make install" should work for most flavors of Unix. For Windows, use +one of the special makefiles in win32/ or contrib/vstudio/ . For VMS, use +make_vms.com. + +Questions about zlib should be sent to , or to Gilles Vollant + for the Windows DLL version. The zlib home page is +http://zlib.net/ . Before reporting a problem, please check this site to +verify that you have the latest version of zlib; otherwise get the latest +version and check whether the problem still exists or not. + +PLEASE read the zlib FAQ http://zlib.net/zlib_faq.html before asking for help. + +Mark Nelson wrote an article about zlib for the Jan. 1997 +issue of Dr. Dobb's Journal; a copy of the article is available at +http://marknelson.us/1997/01/01/zlib-engine/ . + +The changes made in version 1.2.11 are documented in the file ChangeLog. + +Unsupported third party contributions are provided in directory contrib/ . + +zlib is available in Java using the java.util.zip package, documented at +http://java.sun.com/developer/technicalArticles/Programming/compression/ . + +A Perl interface to zlib written by Paul Marquess is available +at CPAN (Comprehensive Perl Archive Network) sites, including +http://search.cpan.org/~pmqs/IO-Compress-Zlib/ . + +A Python interface to zlib written by A.M. Kuchling is +available in Python 1.5 and later versions, see +http://docs.python.org/library/zlib.html . + +zlib is built into tcl: http://wiki.tcl.tk/4610 . + +An experimental package to read and write files in .zip format, written on top +of zlib by Gilles Vollant , is available in the +contrib/minizip directory of zlib. + + +Notes for some targets: + +- For Windows DLL versions, please see win32/DLL_FAQ.txt + +- For 64-bit Irix, deflate.c must be compiled without any optimization. With + -O, one libpng test fails. The test works in 32 bit mode (with the -n32 + compiler flag). The compiler bug has been reported to SGI. + +- zlib doesn't work with gcc 2.6.3 on a DEC 3000/300LX under OSF/1 2.1 it works + when compiled with cc. + +- On Digital Unix 4.0D (formely OSF/1) on AlphaServer, the cc option -std1 is + necessary to get gzprintf working correctly. This is done by configure. + +- zlib doesn't work on HP-UX 9.05 with some versions of /bin/cc. It works with + other compilers. Use "make test" to check your compiler. + +- gzdopen is not supported on RISCOS or BEOS. + +- For PalmOs, see http://palmzlib.sourceforge.net/ + + +Acknowledgments: + + The deflate format used by zlib was defined by Phil Katz. The deflate and + zlib specifications were written by L. Peter Deutsch. Thanks to all the + people who reported problems and suggested various improvements in zlib; they + are too numerous to cite here. + +Copyright notice: + + (C) 1995-2017 Jean-loup Gailly and Mark Adler + + This software is provided 'as-is', without any express or implied + warranty. In no event will the authors be held liable for any damages + arising from the use of this software. + + Permission is granted to anyone to use this software for any purpose, + including commercial applications, and to alter it and redistribute it + freely, subject to the following restrictions: + + 1. The origin of this software must not be misrepresented; you must not + claim that you wrote the original software. If you use this software + in a product, an acknowledgment in the product documentation would be + appreciated but is not required. + 2. Altered source versions must be plainly marked as such, and must not be + misrepresented as being the original software. + 3. This notice may not be removed or altered from any source distribution. + + Jean-loup Gailly Mark Adler + jloup@gzip.org madler@alumni.caltech.edu + +If you use the zlib library in a product, we would appreciate *not* receiving +lengthy legal documents to sign. The sources are provided for free but without +warranty of any kind. The library has been entirely written by Jean-loup +Gailly and Mark Adler; it does not include third-party code. + +If you redistribute modified sources, we would appreciate that you include in +the file ChangeLog history information documenting your changes. Please read +the FAQ for more information on the distribution of modified source versions. diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/valgrind.supp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/valgrind.supp new file mode 100644 index 0000000000000000000000000000000000000000..7a4341467f2fd9beda7f5ac006ba5ff2d126c75f --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/valgrind.supp @@ -0,0 +1,221 @@ +{ + OpenCV-IPP static init + Memcheck:Cond + fun:ippicvGetCpuFeatures + fun:ippicvStaticInit +} + +{ + OpenCV-getInitializationMutex + Memcheck:Leak + ... + fun:_ZN2cv22getInitializationMutexEv +} + +{ + OpenCV-SingletonBuffer + Memcheck:Leak + ... + fun:_ZN2cv20allocSingletonBufferEm +} + +{ + OpenCV-getStdAllocator + Memcheck:Leak + ... + fun:_ZN2cv3Mat15getStdAllocatorEv +} + +{ + OpenCV-getOpenCLAllocator + Memcheck:Leak + ... + fun:_ZN2cv3ocl18getOpenCLAllocatorEv +} + +{ + OpenCV-getCoreTlsData + Memcheck:Leak + fun:_Znwm + fun:_ZN2cv14getCoreTlsDataEv +} + +{ + OpenCV-TLS-getTlsStorage + Memcheck:Leak + ... + fun:_ZN2cvL13getTlsStorageEv +} + +{ + OpenCV-TLS-getData() + Memcheck:Leak + ... + fun:*setData* + fun:_ZNK2cv16TLSDataContainer7getDataEv +} + +{ + OpenCV-parallel_for-reconfigure + Memcheck:Leak + ... + fun:_ZN2cv10ThreadPool12reconfigure_Ej +} + +{ + OpenCV-parallel_for-instance + Memcheck:Leak + fun:_Znwm + fun:*instance* + ... + fun:_ZN2cv13parallel_for_ERKNS_5RangeERKNS_16ParallelLoopBodyEd +} + +{ + OpenCV-parallel_for-setNumThreads() + Memcheck:Leak + ... + fun:_ZN2cv13setNumThreadsEi +} + +{ + OpenCV-parallel_for-getNumThreads() + Memcheck:Leak + ... + fun:_ZN2cv13getNumThreadsEv +} + +{ + OpenCV-getIPPSingelton + Memcheck:Leak + ... + fun:_ZN2cv3ippL15getIPPSingeltonEv +} + +{ + OpenCV-getGlobalMatOpInitializer + Memcheck:Leak + fun:_Znwm + fun:_ZN2cvL25getGlobalMatOpInitializerEv +} + +{ + OpenCV-CoreTLSData + Memcheck:Leak + ... + fun:_ZNK2cv7TLSDataINS_11CoreTLSDataEE3getEv +} + +{ + OpenCV-getThreadID() + Memcheck:Leak + ... + fun:_ZN2cv5utils11getThreadIDEv +} + +{ + OpenCV-ThreadID + Memcheck:Leak + fun:_Znwm + fun:_ZNK2cv7TLSDataINS_12_GLOBAL__N_18ThreadIDEE18createDataInstanceEv +} + +{ + OpenCV-ThreadID-TLS + Memcheck:Leak + fun:_Znwm + fun:getThreadIDTLS +} + +{ + OpenCV-CoreTLS + Memcheck:Leak + fun:_Znwm + fun:_ZNK2cv7TLSDataINS_11CoreTLSDataEE18createDataInstanceEv +} + +{ + OpenCV-UMatDataAutoLockerTLS + Memcheck:Leak + ... + fun:_ZN2cvL21getUMatDataAutoLockerEv +} + +{ + OpenCV-haveOpenCL + Memcheck:Leak + ... + fun:_ZN2cv3ocl10haveOpenCLEv +} + +{ + OpenCV-DNN-getLayerFactoryMutex + Memcheck:Leak + ... + fun:_ZN2cv3dnn*L20getLayerFactoryMutexEv +} + +{ + OpenCV-ocl::Context + Memcheck:Leak + ... + fun:_ZN2cv3ocl7Context10getDefaultEb +} + +{ + OpenCV-ocl::Device + Memcheck:Leak + ... + fun:_ZN2cv3ocl6Device10getDefaultEv +} + +{ + OpenCV-ocl::Queue + Memcheck:Leak + ... + fun:_ZN2cv3ocl5Queue6createERKNS0_7ContextERKNS0_6DeviceE +} + +{ + OpenCV-ocl::Program + Memcheck:Leak + ... + fun:_ZN2cv3ocl6Kernel6createEPKcRKNS0_7ProgramE +} + +{ + OpenCV-ocl::ProgramEntry + Memcheck:Leak + ... + fun:_ZNK2cv3ocl8internal12ProgramEntrycvRNS0_13ProgramSourceEEv +} + +{ + OpenCV-ocl::Context::getProg + Memcheck:Leak + ... + fun:_ZN2cv3ocl7Context7getProgERKNS0_13ProgramSourceERKNS_6StringERS5_ +} + +{ + OpenCV-getTraceManager() + Memcheck:Leak + ... + fun:getTraceManagerCallOnce +} + +{ + OpenCV-ITT + Memcheck:Leak + ... + fun:__itt_*create* +} + +{ + OpenCV-FFmpeg-swsscale + Memcheck:Addr16 + ... + fun:sws_scale + fun:_ZN20CvVideoWriter_FFMPEG10writeFrameEPKhiiiii + fun:cvWriteFrame_FFMPEG +} diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/valgrind_3rdparty.supp b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/valgrind_3rdparty.supp new file mode 100644 index 0000000000000000000000000000000000000000..50811d112d37a8ae6007bfc81a7b16fb9a980f9b --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/valgrind_3rdparty.supp @@ -0,0 +1,122 @@ +{ + IPP static init + Memcheck:Cond + fun:ippicvGetCpuFeatures + fun:ippicvStaticInit +} + +{ + TBB - allocate_via_handler_v3 issue + Memcheck:Leak + fun:malloc + fun:_ZN3tbb8internal23allocate_via_handler_v3Em +} + +{ + GTest + Memcheck:Cond + fun:_ZN7testing8internal11CmpHelperLEIddEENS_15AssertionResultEPKcS4_RKT_RKT0_ +} + +{ + OpenCL + Memcheck:Cond + ... + obj:**/libOpenCL.so* +} + +{ + OpenCL-Intel + Memcheck:Cond + ... + obj:**/libigdrcl.so +} + +{ + OpenCL-Intel + Memcheck:Leak + ... + obj:*/libigdrcl.so* +} + +{ + OpenCL + Memcheck:Param + ioctl(generic) + ... + fun:clGetPlatformIDs +} + +{ + OpenCL-Init + Memcheck:Leak + ... + fun:clGetPlatformIDs +} + +{ + glib + Memcheck:Leak + fun:*alloc + obj:*/libglib* +} + +{ + gcrypt + Memcheck:Leak + ... + obj:*/libgcrypt* +} + +{ + p11-kit + Memcheck:Leak + fun:*alloc + obj:*/libp11-kit* +} + +{ + gobject + Memcheck:Leak + fun:*alloc + ... + obj:*/libgobject* +} + +{ + tasn + Memcheck:Leak + fun:*alloc + obj:*/libtasn*.so* +} + +{ + dl_init + Memcheck:Leak + ... + fun:_dl_init +} + +{ + dl_open + Memcheck:Leak + ... + fun:_dl_open +} + +{ + GDAL + Memcheck:Leak + fun:*alloc + ... + obj:/usr/lib/libgdal.so.1.17.1 +} + +{ + FFMPEG-sws_scale + Memcheck:Addr16 + ... + fun:sws_scale + ... + fun:cvWriteFrame_FFMPEG +} diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/bin/opencv_version b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/bin/opencv_version new file mode 100755 index 0000000000000000000000000000000000000000..0994753b7943d9ab0789bdffc4481246c9817d91 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/bin/opencv_version @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b995c483ab1551f5b4a59d9adcc591973e7f90d19f2c5db586451ef815e07e43 +size 1211172 diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/bin/setup_vars_opencv3.sh b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/bin/setup_vars_opencv3.sh new file mode 100644 index 0000000000000000000000000000000000000000..384f8cad431a5610d843f00ce4a6ff0ed26715ce --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/bin/setup_vars_opencv3.sh @@ -0,0 +1,20 @@ +#!/bin/bash + +SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null && pwd )" + +[[ ! "${OPENCV_QUIET}" ]] && ( echo "Setting vars for OpenCV 3.4.5" ) +export LD_LIBRARY_PATH="$SCRIPT_DIR/../lib:$LD_LIBRARY_PATH" + +if [[ ! "$OPENCV_SKIP_PYTHON" ]]; then + PYTHONPATH_OPENCV="$SCRIPT_DIR/python_loader_is_not_installed" + [[ ! "${OPENCV_QUIET}" ]] && ( echo "Append PYTHONPATH: ${PYTHONPATH_OPENCV}" ) + export PYTHONPATH="${PYTHONPATH_OPENCV}:$PYTHONPATH" +fi + +# Don't exec in "sourced" mode +if [[ "${BASH_SOURCE[0]}" == "${0}" ]]; then + if [[ $# -ne 0 ]]; then + [[ ! "${OPENCV_QUIET}" && "${OPENCV_VERBOSE}" ]] && ( echo "Executing: $*" ) + exec "$@" + fi +fi diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv/cv.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv/cv.h new file mode 100644 index 0000000000000000000000000000000000000000..19a74e297cfb58d8bfd0d2d5bbec9de46e55f975 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv/cv.h @@ -0,0 +1,73 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OLD_CV_H +#define OPENCV_OLD_CV_H + +#if defined(_MSC_VER) + #define CV_DO_PRAGMA(x) __pragma(x) + #define __CVSTR2__(x) #x + #define __CVSTR1__(x) __CVSTR2__(x) + #define __CVMSVCLOC__ __FILE__ "("__CVSTR1__(__LINE__)") : " + #define CV_MSG_PRAGMA(_msg) CV_DO_PRAGMA(message (__CVMSVCLOC__ _msg)) +#elif defined(__GNUC__) + #define CV_DO_PRAGMA(x) _Pragma (#x) + #define CV_MSG_PRAGMA(_msg) CV_DO_PRAGMA(message (_msg)) +#else + #define CV_DO_PRAGMA(x) + #define CV_MSG_PRAGMA(_msg) +#endif +#define CV_WARNING(x) CV_MSG_PRAGMA("Warning: " #x) + +//CV_WARNING("This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module") + +#include "opencv2/core/core_c.h" +#include "opencv2/imgproc/imgproc_c.h" +#include "opencv2/photo/photo_c.h" +#include "opencv2/video/tracking_c.h" +#include "opencv2/objdetect/objdetect_c.h" + +#if !defined(CV_IMPL) +#define CV_IMPL extern "C" +#endif //CV_IMPL + +#endif // __OPENCV_OLD_CV_H_ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv/cv.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv/cv.hpp new file mode 100644 index 0000000000000000000000000000000000000000..867395644b4f44d0c1290de73c5285e71d3a1e70 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv/cv.hpp @@ -0,0 +1,60 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OLD_CV_HPP +#define OPENCV_OLD_CV_HPP + +//#if defined(__GNUC__) +//#warning "This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module" +//#endif + +#include "cv.h" +#include "opencv2/core.hpp" +#include "opencv2/imgproc.hpp" +#include "opencv2/photo.hpp" +#include "opencv2/video.hpp" +#include "opencv2/highgui.hpp" +#include "opencv2/features2d.hpp" +#include "opencv2/calib3d.hpp" +#include "opencv2/objdetect.hpp" + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv/cvaux.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv/cvaux.h new file mode 100644 index 0000000000000000000000000000000000000000..c0367cc25b765d545ab12e463295d34793c2f7e9 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv/cvaux.h @@ -0,0 +1,57 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// Intel License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000, Intel Corporation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of Intel Corporation may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OLD_AUX_H +#define OPENCV_OLD_AUX_H + +//#if defined(__GNUC__) +//#warning "This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module" +//#endif + +#include "opencv2/core/core_c.h" +#include "opencv2/imgproc/imgproc_c.h" +#include "opencv2/photo/photo_c.h" +#include "opencv2/video/tracking_c.h" +#include "opencv2/objdetect/objdetect_c.h" + +#endif + +/* End of file. */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv/cvaux.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv/cvaux.hpp new file mode 100644 index 0000000000000000000000000000000000000000..4888eef282ecb2ef5e0e5ebf88b745aec3eb70c7 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv/cvaux.hpp @@ -0,0 +1,52 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// Intel License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000, Intel Corporation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of Intel Corporation may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OLD_AUX_HPP +#define OPENCV_OLD_AUX_HPP + +//#if defined(__GNUC__) +//#warning "This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module" +//#endif + +#include "cvaux.h" +#include "opencv2/core/utility.hpp" + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv/cvwimage.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv/cvwimage.h new file mode 100644 index 0000000000000000000000000000000000000000..ec0ab141d9974866f29ff1a84961a50b67176b1c --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv/cvwimage.h @@ -0,0 +1,46 @@ +/////////////////////////////////////////////////////////////////////////////// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to +// this license. If you do not agree to this license, do not download, +// install, copy or use the software. +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2008, Google, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of Intel Corporation or contributors may not be used to endorse +// or promote products derived from this software without specific +// prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" +// and any express or implied warranties, including, but not limited to, the +// implied warranties of merchantability and fitness for a particular purpose +// are disclaimed. In no event shall the Intel Corporation or contributors be +// liable for any direct, indirect, incidental, special, exemplary, or +// consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. + + +#ifndef OPENCV_OLD_WIMAGE_HPP +#define OPENCV_OLD_WIMAGE_HPP + +#include "opencv2/core/wimage.hpp" + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv/cxcore.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv/cxcore.h new file mode 100644 index 0000000000000000000000000000000000000000..dc070c77d2890a4443ebcb776ac978712c92e0ec --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv/cxcore.h @@ -0,0 +1,52 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OLD_CXCORE_H +#define OPENCV_OLD_CXCORE_H + +//#if defined(__GNUC__) +//#warning "This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module" +//#endif + +#include "opencv2/core/core_c.h" + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv/cxcore.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv/cxcore.hpp new file mode 100644 index 0000000000000000000000000000000000000000..c371677c933fdd520af741bc95825c71d14a5508 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv/cxcore.hpp @@ -0,0 +1,53 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OLD_CXCORE_HPP +#define OPENCV_OLD_CXCORE_HPP + +//#if defined(__GNUC__) +//#warning "This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module" +//#endif + +#include "cxcore.h" +#include "opencv2/core.hpp" + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv/cxeigen.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv/cxeigen.hpp new file mode 100644 index 0000000000000000000000000000000000000000..1d3df914ac95f387775587a692b1fc06e01da953 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv/cxeigen.hpp @@ -0,0 +1,48 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OLD_EIGEN_HPP +#define OPENCV_OLD_EIGEN_HPP + +#include "opencv2/core/eigen.hpp" + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv/cxmisc.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv/cxmisc.h new file mode 100644 index 0000000000000000000000000000000000000000..9b9bc8204bd810f5c57bc6c93552c4684af63b93 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv/cxmisc.h @@ -0,0 +1,8 @@ +#ifndef OPENCV_OLD_CXMISC_H +#define OPENCV_OLD_CXMISC_H + +#ifdef __cplusplus +# include "opencv2/core/utility.hpp" +#endif + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv/highgui.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv/highgui.h new file mode 100644 index 0000000000000000000000000000000000000000..69b394e08ace38c92a2ea35a5e9f7c5eca22ae80 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv/highgui.h @@ -0,0 +1,48 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// Intel License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000, Intel Corporation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of Intel Corporation may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OLD_HIGHGUI_H +#define OPENCV_OLD_HIGHGUI_H + +#include "opencv2/core/core_c.h" +#include "opencv2/highgui/highgui_c.h" + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv/ml.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv/ml.h new file mode 100644 index 0000000000000000000000000000000000000000..0c376bacb9ab5ef9167f54b688ce474f2a92ac2e --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv/ml.h @@ -0,0 +1,47 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// Intel License Agreement +// +// Copyright (C) 2000, Intel Corporation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of Intel Corporation may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OLD_ML_H +#define OPENCV_OLD_ML_H + +#include "opencv2/core/core_c.h" +#include "opencv2/ml.hpp" + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/calib3d.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/calib3d.hpp new file mode 100644 index 0000000000000000000000000000000000000000..c5b3ffd4d3ca5aa5a3dbedd1472a771e1dc9662f --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/calib3d.hpp @@ -0,0 +1,2479 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CALIB3D_HPP +#define OPENCV_CALIB3D_HPP + +#include "opencv2/core.hpp" +#include "opencv2/features2d.hpp" +#include "opencv2/core/affine.hpp" + +/** + @defgroup calib3d Camera Calibration and 3D Reconstruction + +The functions in this section use a so-called pinhole camera model. In this model, a scene view is +formed by projecting 3D points into the image plane using a perspective transformation. + +\f[s \; m' = A [R|t] M'\f] + +or + +\f[s \vecthree{u}{v}{1} = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1} +\begin{bmatrix} +r_{11} & r_{12} & r_{13} & t_1 \\ +r_{21} & r_{22} & r_{23} & t_2 \\ +r_{31} & r_{32} & r_{33} & t_3 +\end{bmatrix} +\begin{bmatrix} +X \\ +Y \\ +Z \\ +1 +\end{bmatrix}\f] + +where: + +- \f$(X, Y, Z)\f$ are the coordinates of a 3D point in the world coordinate space +- \f$(u, v)\f$ are the coordinates of the projection point in pixels +- \f$A\f$ is a camera matrix, or a matrix of intrinsic parameters +- \f$(cx, cy)\f$ is a principal point that is usually at the image center +- \f$fx, fy\f$ are the focal lengths expressed in pixel units. + +Thus, if an image from the camera is scaled by a factor, all of these parameters should be scaled +(multiplied/divided, respectively) by the same factor. The matrix of intrinsic parameters does not +depend on the scene viewed. So, once estimated, it can be re-used as long as the focal length is +fixed (in case of zoom lens). The joint rotation-translation matrix \f$[R|t]\f$ is called a matrix of +extrinsic parameters. It is used to describe the camera motion around a static scene, or vice versa, +rigid motion of an object in front of a still camera. That is, \f$[R|t]\f$ translates coordinates of a +point \f$(X, Y, Z)\f$ to a coordinate system, fixed with respect to the camera. The transformation above +is equivalent to the following (when \f$z \ne 0\f$ ): + +\f[\begin{array}{l} +\vecthree{x}{y}{z} = R \vecthree{X}{Y}{Z} + t \\ +x' = x/z \\ +y' = y/z \\ +u = f_x*x' + c_x \\ +v = f_y*y' + c_y +\end{array}\f] + +The following figure illustrates the pinhole camera model. + +![Pinhole camera model](pics/pinhole_camera_model.png) + +Real lenses usually have some distortion, mostly radial distortion and slight tangential distortion. +So, the above model is extended as: + +\f[\begin{array}{l} +\vecthree{x}{y}{z} = R \vecthree{X}{Y}{Z} + t \\ +x' = x/z \\ +y' = y/z \\ +x'' = x' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} + 2 p_1 x' y' + p_2(r^2 + 2 x'^2) + s_1 r^2 + s_2 r^4 \\ +y'' = y' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} + p_1 (r^2 + 2 y'^2) + 2 p_2 x' y' + s_3 r^2 + s_4 r^4 \\ +\text{where} \quad r^2 = x'^2 + y'^2 \\ +u = f_x*x'' + c_x \\ +v = f_y*y'' + c_y +\end{array}\f] + +\f$k_1\f$, \f$k_2\f$, \f$k_3\f$, \f$k_4\f$, \f$k_5\f$, and \f$k_6\f$ are radial distortion coefficients. \f$p_1\f$ and \f$p_2\f$ are +tangential distortion coefficients. \f$s_1\f$, \f$s_2\f$, \f$s_3\f$, and \f$s_4\f$, are the thin prism distortion +coefficients. Higher-order coefficients are not considered in OpenCV. + +The next figures show two common types of radial distortion: barrel distortion (typically \f$ k_1 < 0 \f$) and pincushion distortion (typically \f$ k_1 > 0 \f$). + +![](pics/distortion_examples.png) +![](pics/distortion_examples2.png) + +In some cases the image sensor may be tilted in order to focus an oblique plane in front of the +camera (Scheimpfug condition). This can be useful for particle image velocimetry (PIV) or +triangulation with a laser fan. The tilt causes a perspective distortion of \f$x''\f$ and +\f$y''\f$. This distortion can be modelled in the following way, see e.g. @cite Louhichi07. + +\f[\begin{array}{l} +s\vecthree{x'''}{y'''}{1} = +\vecthreethree{R_{33}(\tau_x, \tau_y)}{0}{-R_{13}(\tau_x, \tau_y)} +{0}{R_{33}(\tau_x, \tau_y)}{-R_{23}(\tau_x, \tau_y)} +{0}{0}{1} R(\tau_x, \tau_y) \vecthree{x''}{y''}{1}\\ +u = f_x*x''' + c_x \\ +v = f_y*y''' + c_y +\end{array}\f] + +where the matrix \f$R(\tau_x, \tau_y)\f$ is defined by two rotations with angular parameter \f$\tau_x\f$ +and \f$\tau_y\f$, respectively, + +\f[ +R(\tau_x, \tau_y) = +\vecthreethree{\cos(\tau_y)}{0}{-\sin(\tau_y)}{0}{1}{0}{\sin(\tau_y)}{0}{\cos(\tau_y)} +\vecthreethree{1}{0}{0}{0}{\cos(\tau_x)}{\sin(\tau_x)}{0}{-\sin(\tau_x)}{\cos(\tau_x)} = +\vecthreethree{\cos(\tau_y)}{\sin(\tau_y)\sin(\tau_x)}{-\sin(\tau_y)\cos(\tau_x)} +{0}{\cos(\tau_x)}{\sin(\tau_x)} +{\sin(\tau_y)}{-\cos(\tau_y)\sin(\tau_x)}{\cos(\tau_y)\cos(\tau_x)}. +\f] + +In the functions below the coefficients are passed or returned as + +\f[(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f] + +vector. That is, if the vector contains four elements, it means that \f$k_3=0\f$ . The distortion +coefficients do not depend on the scene viewed. Thus, they also belong to the intrinsic camera +parameters. And they remain the same regardless of the captured image resolution. If, for example, a +camera has been calibrated on images of 320 x 240 resolution, absolutely the same distortion +coefficients can be used for 640 x 480 images from the same camera while \f$f_x\f$, \f$f_y\f$, \f$c_x\f$, and +\f$c_y\f$ need to be scaled appropriately. + +The functions below use the above model to do the following: + +- Project 3D points to the image plane given intrinsic and extrinsic parameters. +- Compute extrinsic parameters given intrinsic parameters, a few 3D points, and their +projections. +- Estimate intrinsic and extrinsic camera parameters from several views of a known calibration +pattern (every view is described by several 3D-2D point correspondences). +- Estimate the relative position and orientation of the stereo camera "heads" and compute the +*rectification* transformation that makes the camera optical axes parallel. + +@note + - A calibration sample for 3 cameras in horizontal position can be found at + opencv_source_code/samples/cpp/3calibration.cpp + - A calibration sample based on a sequence of images can be found at + opencv_source_code/samples/cpp/calibration.cpp + - A calibration sample in order to do 3D reconstruction can be found at + opencv_source_code/samples/cpp/build3dmodel.cpp + - A calibration sample of an artificially generated camera and chessboard patterns can be + found at opencv_source_code/samples/cpp/calibration_artificial.cpp + - A calibration example on stereo calibration can be found at + opencv_source_code/samples/cpp/stereo_calib.cpp + - A calibration example on stereo matching can be found at + opencv_source_code/samples/cpp/stereo_match.cpp + - (Python) A camera calibration sample can be found at + opencv_source_code/samples/python/calibrate.py + + @{ + @defgroup calib3d_fisheye Fisheye camera model + + Definitions: Let P be a point in 3D of coordinates X in the world reference frame (stored in the + matrix X) The coordinate vector of P in the camera reference frame is: + + \f[Xc = R X + T\f] + + where R is the rotation matrix corresponding to the rotation vector om: R = rodrigues(om); call x, y + and z the 3 coordinates of Xc: + + \f[x = Xc_1 \\ y = Xc_2 \\ z = Xc_3\f] + + The pinhole projection coordinates of P is [a; b] where + + \f[a = x / z \ and \ b = y / z \\ r^2 = a^2 + b^2 \\ \theta = atan(r)\f] + + Fisheye distortion: + + \f[\theta_d = \theta (1 + k_1 \theta^2 + k_2 \theta^4 + k_3 \theta^6 + k_4 \theta^8)\f] + + The distorted point coordinates are [x'; y'] where + + \f[x' = (\theta_d / r) a \\ y' = (\theta_d / r) b \f] + + Finally, conversion into pixel coordinates: The final pixel coordinates vector [u; v] where: + + \f[u = f_x (x' + \alpha y') + c_x \\ + v = f_y y' + c_y\f] + + @defgroup calib3d_c C API + + @} + */ + +namespace cv +{ + +//! @addtogroup calib3d +//! @{ + +//! type of the robust estimation algorithm +enum { LMEDS = 4, //!< least-median of squares algorithm + RANSAC = 8, //!< RANSAC algorithm + RHO = 16 //!< RHO algorithm + }; + +enum { SOLVEPNP_ITERATIVE = 0, + SOLVEPNP_EPNP = 1, //!< EPnP: Efficient Perspective-n-Point Camera Pose Estimation @cite lepetit2009epnp + SOLVEPNP_P3P = 2, //!< Complete Solution Classification for the Perspective-Three-Point Problem @cite gao2003complete + SOLVEPNP_DLS = 3, //!< A Direct Least-Squares (DLS) Method for PnP @cite hesch2011direct + SOLVEPNP_UPNP = 4, //!< Exhaustive Linearization for Robust Camera Pose and Focal Length Estimation @cite penate2013exhaustive + SOLVEPNP_AP3P = 5, //!< An Efficient Algebraic Solution to the Perspective-Three-Point Problem @cite Ke17 + SOLVEPNP_MAX_COUNT //!< Used for count +}; + +enum { CALIB_CB_ADAPTIVE_THRESH = 1, + CALIB_CB_NORMALIZE_IMAGE = 2, + CALIB_CB_FILTER_QUADS = 4, + CALIB_CB_FAST_CHECK = 8 + }; + +enum { CALIB_CB_SYMMETRIC_GRID = 1, + CALIB_CB_ASYMMETRIC_GRID = 2, + CALIB_CB_CLUSTERING = 4 + }; + +enum { CALIB_USE_INTRINSIC_GUESS = 0x00001, + CALIB_FIX_ASPECT_RATIO = 0x00002, + CALIB_FIX_PRINCIPAL_POINT = 0x00004, + CALIB_ZERO_TANGENT_DIST = 0x00008, + CALIB_FIX_FOCAL_LENGTH = 0x00010, + CALIB_FIX_K1 = 0x00020, + CALIB_FIX_K2 = 0x00040, + CALIB_FIX_K3 = 0x00080, + CALIB_FIX_K4 = 0x00800, + CALIB_FIX_K5 = 0x01000, + CALIB_FIX_K6 = 0x02000, + CALIB_RATIONAL_MODEL = 0x04000, + CALIB_THIN_PRISM_MODEL = 0x08000, + CALIB_FIX_S1_S2_S3_S4 = 0x10000, + CALIB_TILTED_MODEL = 0x40000, + CALIB_FIX_TAUX_TAUY = 0x80000, + CALIB_USE_QR = 0x100000, //!< use QR instead of SVD decomposition for solving. Faster but potentially less precise + CALIB_FIX_TANGENT_DIST = 0x200000, + // only for stereo + CALIB_FIX_INTRINSIC = 0x00100, + CALIB_SAME_FOCAL_LENGTH = 0x00200, + // for stereo rectification + CALIB_ZERO_DISPARITY = 0x00400, + CALIB_USE_LU = (1 << 17), //!< use LU instead of SVD decomposition for solving. much faster but potentially less precise + CALIB_USE_EXTRINSIC_GUESS = (1 << 22), //!< for stereoCalibrate + }; + +//! the algorithm for finding fundamental matrix +enum { FM_7POINT = 1, //!< 7-point algorithm + FM_8POINT = 2, //!< 8-point algorithm + FM_LMEDS = 4, //!< least-median algorithm. 7-point algorithm is used. + FM_RANSAC = 8 //!< RANSAC algorithm. It needs at least 15 points. 7-point algorithm is used. + }; + + + +/** @brief Converts a rotation matrix to a rotation vector or vice versa. + +@param src Input rotation vector (3x1 or 1x3) or rotation matrix (3x3). +@param dst Output rotation matrix (3x3) or rotation vector (3x1 or 1x3), respectively. +@param jacobian Optional output Jacobian matrix, 3x9 or 9x3, which is a matrix of partial +derivatives of the output array components with respect to the input array components. + +\f[\begin{array}{l} \theta \leftarrow norm(r) \\ r \leftarrow r/ \theta \\ R = \cos{\theta} I + (1- \cos{\theta} ) r r^T + \sin{\theta} \vecthreethree{0}{-r_z}{r_y}{r_z}{0}{-r_x}{-r_y}{r_x}{0} \end{array}\f] + +Inverse transformation can be also done easily, since + +\f[\sin ( \theta ) \vecthreethree{0}{-r_z}{r_y}{r_z}{0}{-r_x}{-r_y}{r_x}{0} = \frac{R - R^T}{2}\f] + +A rotation vector is a convenient and most compact representation of a rotation matrix (since any +rotation matrix has just 3 degrees of freedom). The representation is used in the global 3D geometry +optimization procedures like calibrateCamera, stereoCalibrate, or solvePnP . + */ +CV_EXPORTS_W void Rodrigues( InputArray src, OutputArray dst, OutputArray jacobian = noArray() ); + +/** @example samples/cpp/tutorial_code/features2D/Homography/pose_from_homography.cpp +An example program about pose estimation from coplanar points + +Check @ref tutorial_homography "the corresponding tutorial" for more details +*/ + +/** @brief Finds a perspective transformation between two planes. + +@param srcPoints Coordinates of the points in the original plane, a matrix of the type CV_32FC2 +or vector\ . +@param dstPoints Coordinates of the points in the target plane, a matrix of the type CV_32FC2 or +a vector\ . +@param method Method used to compute a homography matrix. The following methods are possible: +- **0** - a regular method using all the points, i.e., the least squares method +- **RANSAC** - RANSAC-based robust method +- **LMEDS** - Least-Median robust method +- **RHO** - PROSAC-based robust method +@param ransacReprojThreshold Maximum allowed reprojection error to treat a point pair as an inlier +(used in the RANSAC and RHO methods only). That is, if +\f[\| \texttt{dstPoints} _i - \texttt{convertPointsHomogeneous} ( \texttt{H} * \texttt{srcPoints} _i) \|_2 > \texttt{ransacReprojThreshold}\f] +then the point \f$i\f$ is considered as an outlier. If srcPoints and dstPoints are measured in pixels, +it usually makes sense to set this parameter somewhere in the range of 1 to 10. +@param mask Optional output mask set by a robust method ( RANSAC or LMEDS ). Note that the input +mask values are ignored. +@param maxIters The maximum number of RANSAC iterations. +@param confidence Confidence level, between 0 and 1. + +The function finds and returns the perspective transformation \f$H\f$ between the source and the +destination planes: + +\f[s_i \vecthree{x'_i}{y'_i}{1} \sim H \vecthree{x_i}{y_i}{1}\f] + +so that the back-projection error + +\f[\sum _i \left ( x'_i- \frac{h_{11} x_i + h_{12} y_i + h_{13}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2+ \left ( y'_i- \frac{h_{21} x_i + h_{22} y_i + h_{23}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2\f] + +is minimized. If the parameter method is set to the default value 0, the function uses all the point +pairs to compute an initial homography estimate with a simple least-squares scheme. + +However, if not all of the point pairs ( \f$srcPoints_i\f$, \f$dstPoints_i\f$ ) fit the rigid perspective +transformation (that is, there are some outliers), this initial estimate will be poor. In this case, +you can use one of the three robust methods. The methods RANSAC, LMeDS and RHO try many different +random subsets of the corresponding point pairs (of four pairs each, collinear pairs are discarded), estimate the homography matrix +using this subset and a simple least-squares algorithm, and then compute the quality/goodness of the +computed homography (which is the number of inliers for RANSAC or the least median re-projection error for +LMeDS). The best subset is then used to produce the initial estimate of the homography matrix and +the mask of inliers/outliers. + +Regardless of the method, robust or not, the computed homography matrix is refined further (using +inliers only in case of a robust method) with the Levenberg-Marquardt method to reduce the +re-projection error even more. + +The methods RANSAC and RHO can handle practically any ratio of outliers but need a threshold to +distinguish inliers from outliers. The method LMeDS does not need any threshold but it works +correctly only when there are more than 50% of inliers. Finally, if there are no outliers and the +noise is rather small, use the default method (method=0). + +The function is used to find initial intrinsic and extrinsic matrices. Homography matrix is +determined up to a scale. Thus, it is normalized so that \f$h_{33}=1\f$. Note that whenever an \f$H\f$ matrix +cannot be estimated, an empty one will be returned. + +@sa +getAffineTransform, estimateAffine2D, estimateAffinePartial2D, getPerspectiveTransform, warpPerspective, +perspectiveTransform + */ +CV_EXPORTS_W Mat findHomography( InputArray srcPoints, InputArray dstPoints, + int method = 0, double ransacReprojThreshold = 3, + OutputArray mask=noArray(), const int maxIters = 2000, + const double confidence = 0.995); + +/** @overload */ +CV_EXPORTS Mat findHomography( InputArray srcPoints, InputArray dstPoints, + OutputArray mask, int method = 0, double ransacReprojThreshold = 3 ); + +/** @brief Computes an RQ decomposition of 3x3 matrices. + +@param src 3x3 input matrix. +@param mtxR Output 3x3 upper-triangular matrix. +@param mtxQ Output 3x3 orthogonal matrix. +@param Qx Optional output 3x3 rotation matrix around x-axis. +@param Qy Optional output 3x3 rotation matrix around y-axis. +@param Qz Optional output 3x3 rotation matrix around z-axis. + +The function computes a RQ decomposition using the given rotations. This function is used in +decomposeProjectionMatrix to decompose the left 3x3 submatrix of a projection matrix into a camera +and a rotation matrix. + +It optionally returns three rotation matrices, one for each axis, and the three Euler angles in +degrees (as the return value) that could be used in OpenGL. Note, there is always more than one +sequence of rotations about the three principal axes that results in the same orientation of an +object, e.g. see @cite Slabaugh . Returned tree rotation matrices and corresponding three Euler angles +are only one of the possible solutions. + */ +CV_EXPORTS_W Vec3d RQDecomp3x3( InputArray src, OutputArray mtxR, OutputArray mtxQ, + OutputArray Qx = noArray(), + OutputArray Qy = noArray(), + OutputArray Qz = noArray()); + +/** @brief Decomposes a projection matrix into a rotation matrix and a camera matrix. + +@param projMatrix 3x4 input projection matrix P. +@param cameraMatrix Output 3x3 camera matrix K. +@param rotMatrix Output 3x3 external rotation matrix R. +@param transVect Output 4x1 translation vector T. +@param rotMatrixX Optional 3x3 rotation matrix around x-axis. +@param rotMatrixY Optional 3x3 rotation matrix around y-axis. +@param rotMatrixZ Optional 3x3 rotation matrix around z-axis. +@param eulerAngles Optional three-element vector containing three Euler angles of rotation in +degrees. + +The function computes a decomposition of a projection matrix into a calibration and a rotation +matrix and the position of a camera. + +It optionally returns three rotation matrices, one for each axis, and three Euler angles that could +be used in OpenGL. Note, there is always more than one sequence of rotations about the three +principal axes that results in the same orientation of an object, e.g. see @cite Slabaugh . Returned +tree rotation matrices and corresponding three Euler angles are only one of the possible solutions. + +The function is based on RQDecomp3x3 . + */ +CV_EXPORTS_W void decomposeProjectionMatrix( InputArray projMatrix, OutputArray cameraMatrix, + OutputArray rotMatrix, OutputArray transVect, + OutputArray rotMatrixX = noArray(), + OutputArray rotMatrixY = noArray(), + OutputArray rotMatrixZ = noArray(), + OutputArray eulerAngles =noArray() ); + +/** @brief Computes partial derivatives of the matrix product for each multiplied matrix. + +@param A First multiplied matrix. +@param B Second multiplied matrix. +@param dABdA First output derivative matrix d(A\*B)/dA of size +\f$\texttt{A.rows*B.cols} \times {A.rows*A.cols}\f$ . +@param dABdB Second output derivative matrix d(A\*B)/dB of size +\f$\texttt{A.rows*B.cols} \times {B.rows*B.cols}\f$ . + +The function computes partial derivatives of the elements of the matrix product \f$A*B\f$ with regard to +the elements of each of the two input matrices. The function is used to compute the Jacobian +matrices in stereoCalibrate but can also be used in any other similar optimization function. + */ +CV_EXPORTS_W void matMulDeriv( InputArray A, InputArray B, OutputArray dABdA, OutputArray dABdB ); + +/** @brief Combines two rotation-and-shift transformations. + +@param rvec1 First rotation vector. +@param tvec1 First translation vector. +@param rvec2 Second rotation vector. +@param tvec2 Second translation vector. +@param rvec3 Output rotation vector of the superposition. +@param tvec3 Output translation vector of the superposition. +@param dr3dr1 +@param dr3dt1 +@param dr3dr2 +@param dr3dt2 +@param dt3dr1 +@param dt3dt1 +@param dt3dr2 +@param dt3dt2 Optional output derivatives of rvec3 or tvec3 with regard to rvec1, rvec2, tvec1 and +tvec2, respectively. + +The functions compute: + +\f[\begin{array}{l} \texttt{rvec3} = \mathrm{rodrigues} ^{-1} \left ( \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \mathrm{rodrigues} ( \texttt{rvec1} ) \right ) \\ \texttt{tvec3} = \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \texttt{tvec1} + \texttt{tvec2} \end{array} ,\f] + +where \f$\mathrm{rodrigues}\f$ denotes a rotation vector to a rotation matrix transformation, and +\f$\mathrm{rodrigues}^{-1}\f$ denotes the inverse transformation. See Rodrigues for details. + +Also, the functions can compute the derivatives of the output vectors with regards to the input +vectors (see matMulDeriv ). The functions are used inside stereoCalibrate but can also be used in +your own code where Levenberg-Marquardt or another gradient-based solver is used to optimize a +function that contains a matrix multiplication. + */ +CV_EXPORTS_W void composeRT( InputArray rvec1, InputArray tvec1, + InputArray rvec2, InputArray tvec2, + OutputArray rvec3, OutputArray tvec3, + OutputArray dr3dr1 = noArray(), OutputArray dr3dt1 = noArray(), + OutputArray dr3dr2 = noArray(), OutputArray dr3dt2 = noArray(), + OutputArray dt3dr1 = noArray(), OutputArray dt3dt1 = noArray(), + OutputArray dt3dr2 = noArray(), OutputArray dt3dt2 = noArray() ); + +/** @brief Projects 3D points to an image plane. + +@param objectPoints Array of object points, 3xN/Nx3 1-channel or 1xN/Nx1 3-channel (or +vector\ ), where N is the number of points in the view. +@param rvec Rotation vector. See Rodrigues for details. +@param tvec Translation vector. +@param cameraMatrix Camera matrix \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$ . +@param distCoeffs Input vector of distortion coefficients +\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of +4, 5, 8, 12 or 14 elements. If the vector is empty, the zero distortion coefficients are assumed. +@param imagePoints Output array of image points, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel, or +vector\ . +@param jacobian Optional output 2Nx(10+\) jacobian matrix of derivatives of image +points with respect to components of the rotation vector, translation vector, focal lengths, +coordinates of the principal point and the distortion coefficients. In the old interface different +components of the jacobian are returned via different output parameters. +@param aspectRatio Optional "fixed aspect ratio" parameter. If the parameter is not 0, the +function assumes that the aspect ratio (*fx/fy*) is fixed and correspondingly adjusts the jacobian +matrix. + +The function computes projections of 3D points to the image plane given intrinsic and extrinsic +camera parameters. Optionally, the function computes Jacobians - matrices of partial derivatives of +image points coordinates (as functions of all the input parameters) with respect to the particular +parameters, intrinsic and/or extrinsic. The Jacobians are used during the global optimization in +calibrateCamera, solvePnP, and stereoCalibrate . The function itself can also be used to compute a +re-projection error given the current intrinsic and extrinsic parameters. + +@note By setting rvec=tvec=(0,0,0) or by setting cameraMatrix to a 3x3 identity matrix, or by +passing zero distortion coefficients, you can get various useful partial cases of the function. This +means that you can compute the distorted coordinates for a sparse set of points or apply a +perspective transformation (and also compute the derivatives) in the ideal zero-distortion setup. + */ +CV_EXPORTS_W void projectPoints( InputArray objectPoints, + InputArray rvec, InputArray tvec, + InputArray cameraMatrix, InputArray distCoeffs, + OutputArray imagePoints, + OutputArray jacobian = noArray(), + double aspectRatio = 0 ); + +/** @example samples/cpp/tutorial_code/features2D/Homography/homography_from_camera_displacement.cpp +An example program about homography from the camera displacement + +Check @ref tutorial_homography "the corresponding tutorial" for more details +*/ + +/** @brief Finds an object pose from 3D-2D point correspondences. + +@param objectPoints Array of object points in the object coordinate space, Nx3 1-channel or +1xN/Nx1 3-channel, where N is the number of points. vector\ can be also passed here. +@param imagePoints Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, +where N is the number of points. vector\ can be also passed here. +@param cameraMatrix Input camera matrix \f$A = \vecthreethree{fx}{0}{cx}{0}{fy}{cy}{0}{0}{1}\f$ . +@param distCoeffs Input vector of distortion coefficients +\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of +4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are +assumed. +@param rvec Output rotation vector (see @ref Rodrigues ) that, together with tvec , brings points from +the model coordinate system to the camera coordinate system. +@param tvec Output translation vector. +@param useExtrinsicGuess Parameter used for #SOLVEPNP_ITERATIVE. If true (1), the function uses +the provided rvec and tvec values as initial approximations of the rotation and translation +vectors, respectively, and further optimizes them. +@param flags Method for solving a PnP problem: +- **SOLVEPNP_ITERATIVE** Iterative method is based on Levenberg-Marquardt optimization. In +this case the function finds such a pose that minimizes reprojection error, that is the sum +of squared distances between the observed projections imagePoints and the projected (using +projectPoints ) objectPoints . +- **SOLVEPNP_P3P** Method is based on the paper of X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang +"Complete Solution Classification for the Perspective-Three-Point Problem" (@cite gao2003complete). +In this case the function requires exactly four object and image points. +- **SOLVEPNP_AP3P** Method is based on the paper of T. Ke, S. Roumeliotis +"An Efficient Algebraic Solution to the Perspective-Three-Point Problem" (@cite Ke17). +In this case the function requires exactly four object and image points. +- **SOLVEPNP_EPNP** Method has been introduced by F.Moreno-Noguer, V.Lepetit and P.Fua in the +paper "EPnP: Efficient Perspective-n-Point Camera Pose Estimation" (@cite lepetit2009epnp). +- **SOLVEPNP_DLS** Method is based on the paper of Joel A. Hesch and Stergios I. Roumeliotis. +"A Direct Least-Squares (DLS) Method for PnP" (@cite hesch2011direct). +- **SOLVEPNP_UPNP** Method is based on the paper of A.Penate-Sanchez, J.Andrade-Cetto, +F.Moreno-Noguer. "Exhaustive Linearization for Robust Camera Pose and Focal Length +Estimation" (@cite penate2013exhaustive). In this case the function also estimates the parameters \f$f_x\f$ and \f$f_y\f$ +assuming that both have the same value. Then the cameraMatrix is updated with the estimated +focal length. +- **SOLVEPNP_AP3P** Method is based on the paper of Tong Ke and Stergios I. Roumeliotis. +"An Efficient Algebraic Solution to the Perspective-Three-Point Problem" (@cite Ke17). In this case the +function requires exactly four object and image points. + +The function estimates the object pose given a set of object points, their corresponding image +projections, as well as the camera matrix and the distortion coefficients, see the figure below +(more precisely, the X-axis of the camera frame is pointing to the right, the Y-axis downward +and the Z-axis forward). + +![](pnp.jpg) + +Points expressed in the world frame \f$ \bf{X}_w \f$ are projected into the image plane \f$ \left[ u, v \right] \f$ +using the perspective projection model \f$ \Pi \f$ and the camera intrinsic parameters matrix \f$ \bf{A} \f$: + +\f[ + \begin{align*} + \begin{bmatrix} + u \\ + v \\ + 1 + \end{bmatrix} &= + \bf{A} \hspace{0.1em} \Pi \hspace{0.2em} ^{c}\bf{M}_w + \begin{bmatrix} + X_{w} \\ + Y_{w} \\ + Z_{w} \\ + 1 + \end{bmatrix} \\ + \begin{bmatrix} + u \\ + v \\ + 1 + \end{bmatrix} &= + \begin{bmatrix} + f_x & 0 & c_x \\ + 0 & f_y & c_y \\ + 0 & 0 & 1 + \end{bmatrix} + \begin{bmatrix} + 1 & 0 & 0 & 0 \\ + 0 & 1 & 0 & 0 \\ + 0 & 0 & 1 & 0 + \end{bmatrix} + \begin{bmatrix} + r_{11} & r_{12} & r_{13} & t_x \\ + r_{21} & r_{22} & r_{23} & t_y \\ + r_{31} & r_{32} & r_{33} & t_z \\ + 0 & 0 & 0 & 1 + \end{bmatrix} + \begin{bmatrix} + X_{w} \\ + Y_{w} \\ + Z_{w} \\ + 1 + \end{bmatrix} + \end{align*} +\f] + +The estimated pose is thus the rotation (`rvec`) and the translation (`tvec`) vectors that allow to transform +a 3D point expressed in the world frame into the camera frame: + +\f[ + \begin{align*} + \begin{bmatrix} + X_c \\ + Y_c \\ + Z_c \\ + 1 + \end{bmatrix} &= + \hspace{0.2em} ^{c}\bf{M}_w + \begin{bmatrix} + X_{w} \\ + Y_{w} \\ + Z_{w} \\ + 1 + \end{bmatrix} \\ + \begin{bmatrix} + X_c \\ + Y_c \\ + Z_c \\ + 1 + \end{bmatrix} &= + \begin{bmatrix} + r_{11} & r_{12} & r_{13} & t_x \\ + r_{21} & r_{22} & r_{23} & t_y \\ + r_{31} & r_{32} & r_{33} & t_z \\ + 0 & 0 & 0 & 1 + \end{bmatrix} + \begin{bmatrix} + X_{w} \\ + Y_{w} \\ + Z_{w} \\ + 1 + \end{bmatrix} + \end{align*} +\f] + +@note + - An example of how to use solvePnP for planar augmented reality can be found at + opencv_source_code/samples/python/plane_ar.py + - If you are using Python: + - Numpy array slices won't work as input because solvePnP requires contiguous + arrays (enforced by the assertion using cv::Mat::checkVector() around line 55 of + modules/calib3d/src/solvepnp.cpp version 2.4.9) + - The P3P algorithm requires image points to be in an array of shape (N,1,2) due + to its calling of cv::undistortPoints (around line 75 of modules/calib3d/src/solvepnp.cpp version 2.4.9) + which requires 2-channel information. + - Thus, given some data D = np.array(...) where D.shape = (N,M), in order to use a subset of + it as, e.g., imagePoints, one must effectively copy it into a new array: imagePoints = + np.ascontiguousarray(D[:,:2]).reshape((N,1,2)) + - The methods **SOLVEPNP_DLS** and **SOLVEPNP_UPNP** cannot be used as the current implementations are + unstable and sometimes give completely wrong results. If you pass one of these two + flags, **SOLVEPNP_EPNP** method will be used instead. + - The minimum number of points is 4 in the general case. In the case of **SOLVEPNP_P3P** and **SOLVEPNP_AP3P** + methods, it is required to use exactly 4 points (the first 3 points are used to estimate all the solutions + of the P3P problem, the last one is used to retain the best solution that minimizes the reprojection error). + - With **SOLVEPNP_ITERATIVE** method and `useExtrinsicGuess=true`, the minimum number of points is 3 (3 points + are sufficient to compute a pose but there are up to 4 solutions). The initial solution should be close to the + global solution to converge. + */ +CV_EXPORTS_W bool solvePnP( InputArray objectPoints, InputArray imagePoints, + InputArray cameraMatrix, InputArray distCoeffs, + OutputArray rvec, OutputArray tvec, + bool useExtrinsicGuess = false, int flags = SOLVEPNP_ITERATIVE ); + +/** @brief Finds an object pose from 3D-2D point correspondences using the RANSAC scheme. + +@param objectPoints Array of object points in the object coordinate space, Nx3 1-channel or +1xN/Nx1 3-channel, where N is the number of points. vector\ can be also passed here. +@param imagePoints Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, +where N is the number of points. vector\ can be also passed here. +@param cameraMatrix Input camera matrix \f$A = \vecthreethree{fx}{0}{cx}{0}{fy}{cy}{0}{0}{1}\f$ . +@param distCoeffs Input vector of distortion coefficients +\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of +4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are +assumed. +@param rvec Output rotation vector (see Rodrigues ) that, together with tvec , brings points from +the model coordinate system to the camera coordinate system. +@param tvec Output translation vector. +@param useExtrinsicGuess Parameter used for SOLVEPNP_ITERATIVE. If true (1), the function uses +the provided rvec and tvec values as initial approximations of the rotation and translation +vectors, respectively, and further optimizes them. +@param iterationsCount Number of iterations. +@param reprojectionError Inlier threshold value used by the RANSAC procedure. The parameter value +is the maximum allowed distance between the observed and computed point projections to consider it +an inlier. +@param confidence The probability that the algorithm produces a useful result. +@param inliers Output vector that contains indices of inliers in objectPoints and imagePoints . +@param flags Method for solving a PnP problem (see solvePnP ). + +The function estimates an object pose given a set of object points, their corresponding image +projections, as well as the camera matrix and the distortion coefficients. This function finds such +a pose that minimizes reprojection error, that is, the sum of squared distances between the observed +projections imagePoints and the projected (using projectPoints ) objectPoints. The use of RANSAC +makes the function resistant to outliers. + +@note + - An example of how to use solvePNPRansac for object detection can be found at + opencv_source_code/samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/ + - The default method used to estimate the camera pose for the Minimal Sample Sets step + is #SOLVEPNP_EPNP. Exceptions are: + - if you choose #SOLVEPNP_P3P or #SOLVEPNP_AP3P, these methods will be used. + - if the number of input points is equal to 4, #SOLVEPNP_P3P is used. + - The method used to estimate the camera pose using all the inliers is defined by the + flags parameters unless it is equal to #SOLVEPNP_P3P or #SOLVEPNP_AP3P. In this case, + the method #SOLVEPNP_EPNP will be used instead. + */ +CV_EXPORTS_W bool solvePnPRansac( InputArray objectPoints, InputArray imagePoints, + InputArray cameraMatrix, InputArray distCoeffs, + OutputArray rvec, OutputArray tvec, + bool useExtrinsicGuess = false, int iterationsCount = 100, + float reprojectionError = 8.0, double confidence = 0.99, + OutputArray inliers = noArray(), int flags = SOLVEPNP_ITERATIVE ); +/** @brief Finds an object pose from 3 3D-2D point correspondences. + +@param objectPoints Array of object points in the object coordinate space, 3x3 1-channel or +1x3/3x1 3-channel. vector\ can be also passed here. +@param imagePoints Array of corresponding image points, 3x2 1-channel or 1x3/3x1 2-channel. + vector\ can be also passed here. +@param cameraMatrix Input camera matrix \f$A = \vecthreethree{fx}{0}{cx}{0}{fy}{cy}{0}{0}{1}\f$ . +@param distCoeffs Input vector of distortion coefficients +\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of +4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are +assumed. +@param rvecs Output rotation vectors (see Rodrigues ) that, together with tvecs , brings points from +the model coordinate system to the camera coordinate system. A P3P problem has up to 4 solutions. +@param tvecs Output translation vectors. +@param flags Method for solving a P3P problem: +- **SOLVEPNP_P3P** Method is based on the paper of X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang +"Complete Solution Classification for the Perspective-Three-Point Problem" (@cite gao2003complete). +- **SOLVEPNP_AP3P** Method is based on the paper of Tong Ke and Stergios I. Roumeliotis. +"An Efficient Algebraic Solution to the Perspective-Three-Point Problem" (@cite Ke17). + +The function estimates the object pose given 3 object points, their corresponding image +projections, as well as the camera matrix and the distortion coefficients. + */ +CV_EXPORTS_W int solveP3P( InputArray objectPoints, InputArray imagePoints, + InputArray cameraMatrix, InputArray distCoeffs, + OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, + int flags ); + +/** @brief Finds an initial camera matrix from 3D-2D point correspondences. + +@param objectPoints Vector of vectors of the calibration pattern points in the calibration pattern +coordinate space. In the old interface all the per-view vectors are concatenated. See +calibrateCamera for details. +@param imagePoints Vector of vectors of the projections of the calibration pattern points. In the +old interface all the per-view vectors are concatenated. +@param imageSize Image size in pixels used to initialize the principal point. +@param aspectRatio If it is zero or negative, both \f$f_x\f$ and \f$f_y\f$ are estimated independently. +Otherwise, \f$f_x = f_y * \texttt{aspectRatio}\f$ . + +The function estimates and returns an initial camera matrix for the camera calibration process. +Currently, the function only supports planar calibration patterns, which are patterns where each +object point has z-coordinate =0. + */ +CV_EXPORTS_W Mat initCameraMatrix2D( InputArrayOfArrays objectPoints, + InputArrayOfArrays imagePoints, + Size imageSize, double aspectRatio = 1.0 ); + +/** @brief Finds the positions of internal corners of the chessboard. + +@param image Source chessboard view. It must be an 8-bit grayscale or color image. +@param patternSize Number of inner corners per a chessboard row and column +( patternSize = cvSize(points_per_row,points_per_colum) = cvSize(columns,rows) ). +@param corners Output array of detected corners. +@param flags Various operation flags that can be zero or a combination of the following values: +- **CALIB_CB_ADAPTIVE_THRESH** Use adaptive thresholding to convert the image to black +and white, rather than a fixed threshold level (computed from the average image brightness). +- **CALIB_CB_NORMALIZE_IMAGE** Normalize the image gamma with equalizeHist before +applying fixed or adaptive thresholding. +- **CALIB_CB_FILTER_QUADS** Use additional criteria (like contour area, perimeter, +square-like shape) to filter out false quads extracted at the contour retrieval stage. +- **CALIB_CB_FAST_CHECK** Run a fast check on the image that looks for chessboard corners, +and shortcut the call if none is found. This can drastically speed up the call in the +degenerate condition when no chessboard is observed. + +The function attempts to determine whether the input image is a view of the chessboard pattern and +locate the internal chessboard corners. The function returns a non-zero value if all of the corners +are found and they are placed in a certain order (row by row, left to right in every row). +Otherwise, if the function fails to find all the corners or reorder them, it returns 0. For example, +a regular chessboard has 8 x 8 squares and 7 x 7 internal corners, that is, points where the black +squares touch each other. The detected coordinates are approximate, and to determine their positions +more accurately, the function calls cornerSubPix. You also may use the function cornerSubPix with +different parameters if returned coordinates are not accurate enough. + +Sample usage of detecting and drawing chessboard corners: : +@code + Size patternsize(8,6); //interior number of corners + Mat gray = ....; //source image + vector corners; //this will be filled by the detected corners + + //CALIB_CB_FAST_CHECK saves a lot of time on images + //that do not contain any chessboard corners + bool patternfound = findChessboardCorners(gray, patternsize, corners, + CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE + + CALIB_CB_FAST_CHECK); + + if(patternfound) + cornerSubPix(gray, corners, Size(11, 11), Size(-1, -1), + TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 30, 0.1)); + + drawChessboardCorners(img, patternsize, Mat(corners), patternfound); +@endcode +@note The function requires white space (like a square-thick border, the wider the better) around +the board to make the detection more robust in various environments. Otherwise, if there is no +border and the background is dark, the outer black squares cannot be segmented properly and so the +square grouping and ordering algorithm fails. + */ +CV_EXPORTS_W bool findChessboardCorners( InputArray image, Size patternSize, OutputArray corners, + int flags = CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE ); + +//! finds subpixel-accurate positions of the chessboard corners +CV_EXPORTS bool find4QuadCornerSubpix( InputArray img, InputOutputArray corners, Size region_size ); + +/** @brief Renders the detected chessboard corners. + +@param image Destination image. It must be an 8-bit color image. +@param patternSize Number of inner corners per a chessboard row and column +(patternSize = cv::Size(points_per_row,points_per_column)). +@param corners Array of detected corners, the output of findChessboardCorners. +@param patternWasFound Parameter indicating whether the complete board was found or not. The +return value of findChessboardCorners should be passed here. + +The function draws individual chessboard corners detected either as red circles if the board was not +found, or as colored corners connected with lines if the board was found. + */ +CV_EXPORTS_W void drawChessboardCorners( InputOutputArray image, Size patternSize, + InputArray corners, bool patternWasFound ); + +/** @brief Draw axes of the world/object coordinate system from pose estimation. @sa solvePnP + +@param image Input/output image. It must have 1 or 3 channels. The number of channels is not altered. +@param cameraMatrix Input 3x3 floating-point matrix of camera intrinsic parameters. +\f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ +@param distCoeffs Input vector of distortion coefficients +\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of +4, 5, 8, 12 or 14 elements. If the vector is empty, the zero distortion coefficients are assumed. +@param rvec Rotation vector (see @ref Rodrigues ) that, together with tvec , brings points from +the model coordinate system to the camera coordinate system. +@param tvec Translation vector. +@param length Length of the painted axes in the same unit than tvec (usually in meters). +@param thickness Line thickness of the painted axes. + +This function draws the axes of the world/object coordinate system w.r.t. to the camera frame. +OX is drawn in red, OY in green and OZ in blue. + */ +CV_EXPORTS_W void drawFrameAxes(InputOutputArray image, InputArray cameraMatrix, InputArray distCoeffs, + InputArray rvec, InputArray tvec, float length, int thickness=3); + +struct CV_EXPORTS_W_SIMPLE CirclesGridFinderParameters +{ + CV_WRAP CirclesGridFinderParameters(); + CV_PROP_RW cv::Size2f densityNeighborhoodSize; + CV_PROP_RW float minDensity; + CV_PROP_RW int kmeansAttempts; + CV_PROP_RW int minDistanceToAddKeypoint; + CV_PROP_RW int keypointScale; + CV_PROP_RW float minGraphConfidence; + CV_PROP_RW float vertexGain; + CV_PROP_RW float vertexPenalty; + CV_PROP_RW float existingVertexGain; + CV_PROP_RW float edgeGain; + CV_PROP_RW float edgePenalty; + CV_PROP_RW float convexHullFactor; + CV_PROP_RW float minRNGEdgeSwitchDist; + + enum GridType + { + SYMMETRIC_GRID, ASYMMETRIC_GRID + }; + GridType gridType; +}; + +struct CV_EXPORTS_W_SIMPLE CirclesGridFinderParameters2 : public CirclesGridFinderParameters +{ + CV_WRAP CirclesGridFinderParameters2(); + + CV_PROP_RW float squareSize; //!< Distance between two adjacent points. Used by CALIB_CB_CLUSTERING. + CV_PROP_RW float maxRectifiedDistance; //!< Max deviation from predicion. Used by CALIB_CB_CLUSTERING. +}; + +/** @brief Finds centers in the grid of circles. + +@param image grid view of input circles; it must be an 8-bit grayscale or color image. +@param patternSize number of circles per row and column +( patternSize = Size(points_per_row, points_per_colum) ). +@param centers output array of detected centers. +@param flags various operation flags that can be one of the following values: +- **CALIB_CB_SYMMETRIC_GRID** uses symmetric pattern of circles. +- **CALIB_CB_ASYMMETRIC_GRID** uses asymmetric pattern of circles. +- **CALIB_CB_CLUSTERING** uses a special algorithm for grid detection. It is more robust to +perspective distortions but much more sensitive to background clutter. +@param blobDetector feature detector that finds blobs like dark circles on light background. +@param parameters struct for finding circles in a grid pattern. + +The function attempts to determine whether the input image contains a grid of circles. If it is, the +function locates centers of the circles. The function returns a non-zero value if all of the centers +have been found and they have been placed in a certain order (row by row, left to right in every +row). Otherwise, if the function fails to find all the corners or reorder them, it returns 0. + +Sample usage of detecting and drawing the centers of circles: : +@code + Size patternsize(7,7); //number of centers + Mat gray = ....; //source image + vector centers; //this will be filled by the detected centers + + bool patternfound = findCirclesGrid(gray, patternsize, centers); + + drawChessboardCorners(img, patternsize, Mat(centers), patternfound); +@endcode +@note The function requires white space (like a square-thick border, the wider the better) around +the board to make the detection more robust in various environments. + */ +CV_EXPORTS_W bool findCirclesGrid( InputArray image, Size patternSize, + OutputArray centers, int flags, + const Ptr &blobDetector, + CirclesGridFinderParameters parameters); + +/** @overload */ +CV_EXPORTS_W bool findCirclesGrid2( InputArray image, Size patternSize, + OutputArray centers, int flags, + const Ptr &blobDetector, + CirclesGridFinderParameters2 parameters); + +/** @overload */ +CV_EXPORTS_W bool findCirclesGrid( InputArray image, Size patternSize, + OutputArray centers, int flags = CALIB_CB_SYMMETRIC_GRID, + const Ptr &blobDetector = SimpleBlobDetector::create()); + +/** @brief Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern. + +@param objectPoints In the new interface it is a vector of vectors of calibration pattern points in +the calibration pattern coordinate space (e.g. std::vector>). The outer +vector contains as many elements as the number of the pattern views. If the same calibration pattern +is shown in each view and it is fully visible, all the vectors will be the same. Although, it is +possible to use partially occluded patterns, or even different patterns in different views. Then, +the vectors will be different. The points are 3D, but since they are in a pattern coordinate system, +then, if the rig is planar, it may make sense to put the model to a XY coordinate plane so that +Z-coordinate of each input object point is 0. +In the old interface all the vectors of object points from different views are concatenated +together. +@param imagePoints In the new interface it is a vector of vectors of the projections of calibration +pattern points (e.g. std::vector>). imagePoints.size() and +objectPoints.size() and imagePoints[i].size() must be equal to objectPoints[i].size() for each i. +In the old interface all the vectors of object points from different views are concatenated +together. +@param imageSize Size of the image used only to initialize the intrinsic camera matrix. +@param cameraMatrix Output 3x3 floating-point camera matrix +\f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . If CV\_CALIB\_USE\_INTRINSIC\_GUESS +and/or CALIB_FIX_ASPECT_RATIO are specified, some or all of fx, fy, cx, cy must be +initialized before calling the function. +@param distCoeffs Output vector of distortion coefficients +\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of +4, 5, 8, 12 or 14 elements. +@param rvecs Output vector of rotation vectors (see Rodrigues ) estimated for each pattern view +(e.g. std::vector>). That is, each k-th rotation vector together with the corresponding +k-th translation vector (see the next output parameter description) brings the calibration pattern +from the model coordinate space (in which object points are specified) to the world coordinate +space, that is, a real position of the calibration pattern in the k-th pattern view (k=0.. *M* -1). +@param tvecs Output vector of translation vectors estimated for each pattern view. +@param stdDeviationsIntrinsics Output vector of standard deviations estimated for intrinsic parameters. + Order of deviations values: +\f$(f_x, f_y, c_x, c_y, k_1, k_2, p_1, p_2, k_3, k_4, k_5, k_6 , s_1, s_2, s_3, + s_4, \tau_x, \tau_y)\f$ If one of parameters is not estimated, it's deviation is equals to zero. +@param stdDeviationsExtrinsics Output vector of standard deviations estimated for extrinsic parameters. + Order of deviations values: \f$(R_1, T_1, \dotsc , R_M, T_M)\f$ where M is number of pattern views, + \f$R_i, T_i\f$ are concatenated 1x3 vectors. + @param perViewErrors Output vector of the RMS re-projection error estimated for each pattern view. +@param flags Different flags that may be zero or a combination of the following values: +- **CALIB_USE_INTRINSIC_GUESS** cameraMatrix contains valid initial values of +fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image +center ( imageSize is used), and focal distances are computed in a least-squares fashion. +Note, that if intrinsic parameters are known, there is no need to use this function just to +estimate extrinsic parameters. Use solvePnP instead. +- **CALIB_FIX_PRINCIPAL_POINT** The principal point is not changed during the global +optimization. It stays at the center or at a different location specified when +CALIB_USE_INTRINSIC_GUESS is set too. +- **CALIB_FIX_ASPECT_RATIO** The functions considers only fy as a free parameter. The +ratio fx/fy stays the same as in the input cameraMatrix . When +CALIB_USE_INTRINSIC_GUESS is not set, the actual input values of fx and fy are +ignored, only their ratio is computed and used further. +- **CALIB_ZERO_TANGENT_DIST** Tangential distortion coefficients \f$(p_1, p_2)\f$ are set +to zeros and stay zero. +- **CALIB_FIX_K1,...,CALIB_FIX_K6** The corresponding radial distortion +coefficient is not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is +set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. +- **CALIB_RATIONAL_MODEL** Coefficients k4, k5, and k6 are enabled. To provide the +backward compatibility, this extra flag should be explicitly specified to make the +calibration function use the rational model and return 8 coefficients. If the flag is not +set, the function computes and returns only 5 distortion coefficients. +- **CALIB_THIN_PRISM_MODEL** Coefficients s1, s2, s3 and s4 are enabled. To provide the +backward compatibility, this extra flag should be explicitly specified to make the +calibration function use the thin prism model and return 12 coefficients. If the flag is not +set, the function computes and returns only 5 distortion coefficients. +- **CALIB_FIX_S1_S2_S3_S4** The thin prism distortion coefficients are not changed during +the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the +supplied distCoeffs matrix is used. Otherwise, it is set to 0. +- **CALIB_TILTED_MODEL** Coefficients tauX and tauY are enabled. To provide the +backward compatibility, this extra flag should be explicitly specified to make the +calibration function use the tilted sensor model and return 14 coefficients. If the flag is not +set, the function computes and returns only 5 distortion coefficients. +- **CALIB_FIX_TAUX_TAUY** The coefficients of the tilted sensor model are not changed during +the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the +supplied distCoeffs matrix is used. Otherwise, it is set to 0. +@param criteria Termination criteria for the iterative optimization algorithm. + +@return the overall RMS re-projection error. + +The function estimates the intrinsic camera parameters and extrinsic parameters for each of the +views. The algorithm is based on @cite Zhang2000 and @cite BouguetMCT . The coordinates of 3D object +points and their corresponding 2D projections in each view must be specified. That may be achieved +by using an object with a known geometry and easily detectable feature points. Such an object is +called a calibration rig or calibration pattern, and OpenCV has built-in support for a chessboard as +a calibration rig (see findChessboardCorners ). Currently, initialization of intrinsic parameters +(when CALIB_USE_INTRINSIC_GUESS is not set) is only implemented for planar calibration +patterns (where Z-coordinates of the object points must be all zeros). 3D calibration rigs can also +be used as long as initial cameraMatrix is provided. + +The algorithm performs the following steps: + +- Compute the initial intrinsic parameters (the option only available for planar calibration + patterns) or read them from the input parameters. The distortion coefficients are all set to + zeros initially unless some of CALIB_FIX_K? are specified. + +- Estimate the initial camera pose as if the intrinsic parameters have been already known. This is + done using solvePnP . + +- Run the global Levenberg-Marquardt optimization algorithm to minimize the reprojection error, + that is, the total sum of squared distances between the observed feature points imagePoints and + the projected (using the current estimates for camera parameters and the poses) object points + objectPoints. See projectPoints for details. + +@note + If you use a non-square (=non-NxN) grid and findChessboardCorners for calibration, and + calibrateCamera returns bad values (zero distortion coefficients, an image center very far from + (w/2-0.5,h/2-0.5), and/or large differences between \f$f_x\f$ and \f$f_y\f$ (ratios of 10:1 or more)), + then you have probably used patternSize=cvSize(rows,cols) instead of using + patternSize=cvSize(cols,rows) in findChessboardCorners . + +@sa + findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate, undistort + */ +CV_EXPORTS_AS(calibrateCameraExtended) double calibrateCamera( InputArrayOfArrays objectPoints, + InputArrayOfArrays imagePoints, Size imageSize, + InputOutputArray cameraMatrix, InputOutputArray distCoeffs, + OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, + OutputArray stdDeviationsIntrinsics, + OutputArray stdDeviationsExtrinsics, + OutputArray perViewErrors, + int flags = 0, TermCriteria criteria = TermCriteria( + TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON) ); + +/** @overload double calibrateCamera( InputArrayOfArrays objectPoints, + InputArrayOfArrays imagePoints, Size imageSize, + InputOutputArray cameraMatrix, InputOutputArray distCoeffs, + OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, + OutputArray stdDeviations, OutputArray perViewErrors, + int flags = 0, TermCriteria criteria = TermCriteria( + TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON) ) + */ +CV_EXPORTS_W double calibrateCamera( InputArrayOfArrays objectPoints, + InputArrayOfArrays imagePoints, Size imageSize, + InputOutputArray cameraMatrix, InputOutputArray distCoeffs, + OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, + int flags = 0, TermCriteria criteria = TermCriteria( + TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON) ); + +/** @brief Computes useful camera characteristics from the camera matrix. + +@param cameraMatrix Input camera matrix that can be estimated by calibrateCamera or +stereoCalibrate . +@param imageSize Input image size in pixels. +@param apertureWidth Physical width in mm of the sensor. +@param apertureHeight Physical height in mm of the sensor. +@param fovx Output field of view in degrees along the horizontal sensor axis. +@param fovy Output field of view in degrees along the vertical sensor axis. +@param focalLength Focal length of the lens in mm. +@param principalPoint Principal point in mm. +@param aspectRatio \f$f_y/f_x\f$ + +The function computes various useful camera characteristics from the previously estimated camera +matrix. + +@note + Do keep in mind that the unity measure 'mm' stands for whatever unit of measure one chooses for + the chessboard pitch (it can thus be any value). + */ +CV_EXPORTS_W void calibrationMatrixValues( InputArray cameraMatrix, Size imageSize, + double apertureWidth, double apertureHeight, + CV_OUT double& fovx, CV_OUT double& fovy, + CV_OUT double& focalLength, CV_OUT Point2d& principalPoint, + CV_OUT double& aspectRatio ); + +/** @brief Calibrates the stereo camera. + +@param objectPoints Vector of vectors of the calibration pattern points. +@param imagePoints1 Vector of vectors of the projections of the calibration pattern points, +observed by the first camera. +@param imagePoints2 Vector of vectors of the projections of the calibration pattern points, +observed by the second camera. +@param cameraMatrix1 Input/output first camera matrix: +\f$\vecthreethree{f_x^{(j)}}{0}{c_x^{(j)}}{0}{f_y^{(j)}}{c_y^{(j)}}{0}{0}{1}\f$ , \f$j = 0,\, 1\f$ . If +any of CALIB_USE_INTRINSIC_GUESS , CALIB_FIX_ASPECT_RATIO , +CALIB_FIX_INTRINSIC , or CALIB_FIX_FOCAL_LENGTH are specified, some or all of the +matrix components must be initialized. See the flags description for details. +@param distCoeffs1 Input/output vector of distortion coefficients +\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of +4, 5, 8, 12 or 14 elements. The output vector length depends on the flags. +@param cameraMatrix2 Input/output second camera matrix. The parameter is similar to cameraMatrix1 +@param distCoeffs2 Input/output lens distortion coefficients for the second camera. The parameter +is similar to distCoeffs1 . +@param imageSize Size of the image used only to initialize intrinsic camera matrix. +@param R Output rotation matrix between the 1st and the 2nd camera coordinate systems. +@param T Output translation vector between the coordinate systems of the cameras. +@param E Output essential matrix. +@param F Output fundamental matrix. +@param perViewErrors Output vector of the RMS re-projection error estimated for each pattern view. +@param flags Different flags that may be zero or a combination of the following values: +- **CALIB_FIX_INTRINSIC** Fix cameraMatrix? and distCoeffs? so that only R, T, E , and F +matrices are estimated. +- **CALIB_USE_INTRINSIC_GUESS** Optimize some or all of the intrinsic parameters +according to the specified flags. Initial values are provided by the user. +- **CALIB_USE_EXTRINSIC_GUESS** R, T contain valid initial values that are optimized further. +Otherwise R, T are initialized to the median value of the pattern views (each dimension separately). +- **CALIB_FIX_PRINCIPAL_POINT** Fix the principal points during the optimization. +- **CALIB_FIX_FOCAL_LENGTH** Fix \f$f^{(j)}_x\f$ and \f$f^{(j)}_y\f$ . +- **CALIB_FIX_ASPECT_RATIO** Optimize \f$f^{(j)}_y\f$ . Fix the ratio \f$f^{(j)}_x/f^{(j)}_y\f$ +. +- **CALIB_SAME_FOCAL_LENGTH** Enforce \f$f^{(0)}_x=f^{(1)}_x\f$ and \f$f^{(0)}_y=f^{(1)}_y\f$ . +- **CALIB_ZERO_TANGENT_DIST** Set tangential distortion coefficients for each camera to +zeros and fix there. +- **CALIB_FIX_K1,...,CALIB_FIX_K6** Do not change the corresponding radial +distortion coefficient during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, +the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. +- **CALIB_RATIONAL_MODEL** Enable coefficients k4, k5, and k6. To provide the backward +compatibility, this extra flag should be explicitly specified to make the calibration +function use the rational model and return 8 coefficients. If the flag is not set, the +function computes and returns only 5 distortion coefficients. +- **CALIB_THIN_PRISM_MODEL** Coefficients s1, s2, s3 and s4 are enabled. To provide the +backward compatibility, this extra flag should be explicitly specified to make the +calibration function use the thin prism model and return 12 coefficients. If the flag is not +set, the function computes and returns only 5 distortion coefficients. +- **CALIB_FIX_S1_S2_S3_S4** The thin prism distortion coefficients are not changed during +the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the +supplied distCoeffs matrix is used. Otherwise, it is set to 0. +- **CALIB_TILTED_MODEL** Coefficients tauX and tauY are enabled. To provide the +backward compatibility, this extra flag should be explicitly specified to make the +calibration function use the tilted sensor model and return 14 coefficients. If the flag is not +set, the function computes and returns only 5 distortion coefficients. +- **CALIB_FIX_TAUX_TAUY** The coefficients of the tilted sensor model are not changed during +the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the +supplied distCoeffs matrix is used. Otherwise, it is set to 0. +@param criteria Termination criteria for the iterative optimization algorithm. + +The function estimates transformation between two cameras making a stereo pair. If you have a stereo +camera where the relative position and orientation of two cameras is fixed, and if you computed +poses of an object relative to the first camera and to the second camera, (R1, T1) and (R2, T2), +respectively (this can be done with solvePnP ), then those poses definitely relate to each other. +This means that, given ( \f$R_1\f$,\f$T_1\f$ ), it should be possible to compute ( \f$R_2\f$,\f$T_2\f$ ). You only +need to know the position and orientation of the second camera relative to the first camera. This is +what the described function does. It computes ( \f$R\f$,\f$T\f$ ) so that: + +\f[R_2=R*R_1\f] +\f[T_2=R*T_1 + T,\f] + +Optionally, it computes the essential matrix E: + +\f[E= \vecthreethree{0}{-T_2}{T_1}{T_2}{0}{-T_0}{-T_1}{T_0}{0} *R\f] + +where \f$T_i\f$ are components of the translation vector \f$T\f$ : \f$T=[T_0, T_1, T_2]^T\f$ . And the function +can also compute the fundamental matrix F: + +\f[F = cameraMatrix2^{-T} E cameraMatrix1^{-1}\f] + +Besides the stereo-related information, the function can also perform a full calibration of each of +two cameras. However, due to the high dimensionality of the parameter space and noise in the input +data, the function can diverge from the correct solution. If the intrinsic parameters can be +estimated with high accuracy for each of the cameras individually (for example, using +calibrateCamera ), you are recommended to do so and then pass CALIB_FIX_INTRINSIC flag to the +function along with the computed intrinsic parameters. Otherwise, if all the parameters are +estimated at once, it makes sense to restrict some parameters, for example, pass +CALIB_SAME_FOCAL_LENGTH and CALIB_ZERO_TANGENT_DIST flags, which is usually a +reasonable assumption. + +Similarly to calibrateCamera , the function minimizes the total re-projection error for all the +points in all the available views from both cameras. The function returns the final value of the +re-projection error. + */ +CV_EXPORTS_AS(stereoCalibrateExtended) double stereoCalibrate( InputArrayOfArrays objectPoints, + InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2, + InputOutputArray cameraMatrix1, InputOutputArray distCoeffs1, + InputOutputArray cameraMatrix2, InputOutputArray distCoeffs2, + Size imageSize, InputOutputArray R,InputOutputArray T, OutputArray E, OutputArray F, + OutputArray perViewErrors, int flags = CALIB_FIX_INTRINSIC, + TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 1e-6) ); + +/// @overload +CV_EXPORTS_W double stereoCalibrate( InputArrayOfArrays objectPoints, + InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2, + InputOutputArray cameraMatrix1, InputOutputArray distCoeffs1, + InputOutputArray cameraMatrix2, InputOutputArray distCoeffs2, + Size imageSize, OutputArray R,OutputArray T, OutputArray E, OutputArray F, + int flags = CALIB_FIX_INTRINSIC, + TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 1e-6) ); + +/** @brief Computes rectification transforms for each head of a calibrated stereo camera. + +@param cameraMatrix1 First camera matrix. +@param distCoeffs1 First camera distortion parameters. +@param cameraMatrix2 Second camera matrix. +@param distCoeffs2 Second camera distortion parameters. +@param imageSize Size of the image used for stereo calibration. +@param R Rotation matrix between the coordinate systems of the first and the second cameras. +@param T Translation vector between coordinate systems of the cameras. +@param R1 Output 3x3 rectification transform (rotation matrix) for the first camera. +@param R2 Output 3x3 rectification transform (rotation matrix) for the second camera. +@param P1 Output 3x4 projection matrix in the new (rectified) coordinate systems for the first +camera. +@param P2 Output 3x4 projection matrix in the new (rectified) coordinate systems for the second +camera. +@param Q Output \f$4 \times 4\f$ disparity-to-depth mapping matrix (see reprojectImageTo3D ). +@param flags Operation flags that may be zero or CALIB_ZERO_DISPARITY . If the flag is set, +the function makes the principal points of each camera have the same pixel coordinates in the +rectified views. And if the flag is not set, the function may still shift the images in the +horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the +useful image area. +@param alpha Free scaling parameter. If it is -1 or absent, the function performs the default +scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified +images are zoomed and shifted so that only valid pixels are visible (no black areas after +rectification). alpha=1 means that the rectified image is decimated and shifted so that all the +pixels from the original images from the cameras are retained in the rectified images (no source +image pixels are lost). Obviously, any intermediate value yields an intermediate result between +those two extreme cases. +@param newImageSize New image resolution after rectification. The same size should be passed to +initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) +is passed (default), it is set to the original imageSize . Setting it to larger value can help you +preserve details in the original image, especially when there is a big radial distortion. +@param validPixROI1 Optional output rectangles inside the rectified images where all the pixels +are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller +(see the picture below). +@param validPixROI2 Optional output rectangles inside the rectified images where all the pixels +are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller +(see the picture below). + +The function computes the rotation matrices for each camera that (virtually) make both camera image +planes the same plane. Consequently, this makes all the epipolar lines parallel and thus simplifies +the dense stereo correspondence problem. The function takes the matrices computed by stereoCalibrate +as input. As output, it provides two rotation matrices and also two projection matrices in the new +coordinates. The function distinguishes the following two cases: + +- **Horizontal stereo**: the first and the second camera views are shifted relative to each other + mainly along the x axis (with possible small vertical shift). In the rectified images, the + corresponding epipolar lines in the left and right cameras are horizontal and have the same + y-coordinate. P1 and P2 look like: + + \f[\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\f] + + \f[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x*f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\f] + + where \f$T_x\f$ is a horizontal shift between the cameras and \f$cx_1=cx_2\f$ if + CALIB_ZERO_DISPARITY is set. + +- **Vertical stereo**: the first and the second camera views are shifted relative to each other + mainly in vertical direction (and probably a bit in the horizontal direction too). The epipolar + lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like: + + \f[\texttt{P1} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\f] + + \f[\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y*f \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\f] + + where \f$T_y\f$ is a vertical shift between the cameras and \f$cy_1=cy_2\f$ if CALIB_ZERO_DISPARITY is + set. + +As you can see, the first three columns of P1 and P2 will effectively be the new "rectified" camera +matrices. The matrices, together with R1 and R2 , can then be passed to initUndistortRectifyMap to +initialize the rectification map for each camera. + +See below the screenshot from the stereo_calib.cpp sample. Some red horizontal lines pass through +the corresponding image regions. This means that the images are well rectified, which is what most +stereo correspondence algorithms rely on. The green rectangles are roi1 and roi2 . You see that +their interiors are all valid pixels. + +![image](pics/stereo_undistort.jpg) + */ +CV_EXPORTS_W void stereoRectify( InputArray cameraMatrix1, InputArray distCoeffs1, + InputArray cameraMatrix2, InputArray distCoeffs2, + Size imageSize, InputArray R, InputArray T, + OutputArray R1, OutputArray R2, + OutputArray P1, OutputArray P2, + OutputArray Q, int flags = CALIB_ZERO_DISPARITY, + double alpha = -1, Size newImageSize = Size(), + CV_OUT Rect* validPixROI1 = 0, CV_OUT Rect* validPixROI2 = 0 ); + +/** @brief Computes a rectification transform for an uncalibrated stereo camera. + +@param points1 Array of feature points in the first image. +@param points2 The corresponding points in the second image. The same formats as in +findFundamentalMat are supported. +@param F Input fundamental matrix. It can be computed from the same set of point pairs using +findFundamentalMat . +@param imgSize Size of the image. +@param H1 Output rectification homography matrix for the first image. +@param H2 Output rectification homography matrix for the second image. +@param threshold Optional threshold used to filter out the outliers. If the parameter is greater +than zero, all the point pairs that do not comply with the epipolar geometry (that is, the points +for which \f$|\texttt{points2[i]}^T*\texttt{F}*\texttt{points1[i]}|>\texttt{threshold}\f$ ) are +rejected prior to computing the homographies. Otherwise, all the points are considered inliers. + +The function computes the rectification transformations without knowing intrinsic parameters of the +cameras and their relative position in the space, which explains the suffix "uncalibrated". Another +related difference from stereoRectify is that the function outputs not the rectification +transformations in the object (3D) space, but the planar perspective transformations encoded by the +homography matrices H1 and H2 . The function implements the algorithm @cite Hartley99 . + +@note + While the algorithm does not need to know the intrinsic parameters of the cameras, it heavily + depends on the epipolar geometry. Therefore, if the camera lenses have a significant distortion, + it would be better to correct it before computing the fundamental matrix and calling this + function. For example, distortion coefficients can be estimated for each head of stereo camera + separately by using calibrateCamera . Then, the images can be corrected using undistort , or + just the point coordinates can be corrected with undistortPoints . + */ +CV_EXPORTS_W bool stereoRectifyUncalibrated( InputArray points1, InputArray points2, + InputArray F, Size imgSize, + OutputArray H1, OutputArray H2, + double threshold = 5 ); + +//! computes the rectification transformations for 3-head camera, where all the heads are on the same line. +CV_EXPORTS_W float rectify3Collinear( InputArray cameraMatrix1, InputArray distCoeffs1, + InputArray cameraMatrix2, InputArray distCoeffs2, + InputArray cameraMatrix3, InputArray distCoeffs3, + InputArrayOfArrays imgpt1, InputArrayOfArrays imgpt3, + Size imageSize, InputArray R12, InputArray T12, + InputArray R13, InputArray T13, + OutputArray R1, OutputArray R2, OutputArray R3, + OutputArray P1, OutputArray P2, OutputArray P3, + OutputArray Q, double alpha, Size newImgSize, + CV_OUT Rect* roi1, CV_OUT Rect* roi2, int flags ); + +/** @brief Returns the new camera matrix based on the free scaling parameter. + +@param cameraMatrix Input camera matrix. +@param distCoeffs Input vector of distortion coefficients +\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of +4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are +assumed. +@param imageSize Original image size. +@param alpha Free scaling parameter between 0 (when all the pixels in the undistorted image are +valid) and 1 (when all the source image pixels are retained in the undistorted image). See +stereoRectify for details. +@param newImgSize Image size after rectification. By default, it is set to imageSize . +@param validPixROI Optional output rectangle that outlines all-good-pixels region in the +undistorted image. See roi1, roi2 description in stereoRectify . +@param centerPrincipalPoint Optional flag that indicates whether in the new camera matrix the +principal point should be at the image center or not. By default, the principal point is chosen to +best fit a subset of the source image (determined by alpha) to the corrected image. +@return new_camera_matrix Output new camera matrix. + +The function computes and returns the optimal new camera matrix based on the free scaling parameter. +By varying this parameter, you may retrieve only sensible pixels alpha=0 , keep all the original +image pixels if there is valuable information in the corners alpha=1 , or get something in between. +When alpha\>0 , the undistorted result is likely to have some black pixels corresponding to +"virtual" pixels outside of the captured distorted image. The original camera matrix, distortion +coefficients, the computed new camera matrix, and newImageSize should be passed to +initUndistortRectifyMap to produce the maps for remap . + */ +CV_EXPORTS_W Mat getOptimalNewCameraMatrix( InputArray cameraMatrix, InputArray distCoeffs, + Size imageSize, double alpha, Size newImgSize = Size(), + CV_OUT Rect* validPixROI = 0, + bool centerPrincipalPoint = false); + +/** @brief Converts points from Euclidean to homogeneous space. + +@param src Input vector of N-dimensional points. +@param dst Output vector of N+1-dimensional points. + +The function converts points from Euclidean to homogeneous space by appending 1's to the tuple of +point coordinates. That is, each point (x1, x2, ..., xn) is converted to (x1, x2, ..., xn, 1). + */ +CV_EXPORTS_W void convertPointsToHomogeneous( InputArray src, OutputArray dst ); + +/** @brief Converts points from homogeneous to Euclidean space. + +@param src Input vector of N-dimensional points. +@param dst Output vector of N-1-dimensional points. + +The function converts points homogeneous to Euclidean space using perspective projection. That is, +each point (x1, x2, ... x(n-1), xn) is converted to (x1/xn, x2/xn, ..., x(n-1)/xn). When xn=0, the +output point coordinates will be (0,0,0,...). + */ +CV_EXPORTS_W void convertPointsFromHomogeneous( InputArray src, OutputArray dst ); + +/** @brief Converts points to/from homogeneous coordinates. + +@param src Input array or vector of 2D, 3D, or 4D points. +@param dst Output vector of 2D, 3D, or 4D points. + +The function converts 2D or 3D points from/to homogeneous coordinates by calling either +convertPointsToHomogeneous or convertPointsFromHomogeneous. + +@note The function is obsolete. Use one of the previous two functions instead. + */ +CV_EXPORTS void convertPointsHomogeneous( InputArray src, OutputArray dst ); + +/** @brief Calculates a fundamental matrix from the corresponding points in two images. + +@param points1 Array of N points from the first image. The point coordinates should be +floating-point (single or double precision). +@param points2 Array of the second image points of the same size and format as points1 . +@param method Method for computing a fundamental matrix. +- **CV_FM_7POINT** for a 7-point algorithm. \f$N = 7\f$ +- **CV_FM_8POINT** for an 8-point algorithm. \f$N \ge 8\f$ +- **CV_FM_RANSAC** for the RANSAC algorithm. \f$N \ge 8\f$ +- **CV_FM_LMEDS** for the LMedS algorithm. \f$N \ge 8\f$ +@param ransacReprojThreshold Parameter used only for RANSAC. It is the maximum distance from a point to an epipolar +line in pixels, beyond which the point is considered an outlier and is not used for computing the +final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the +point localization, image resolution, and the image noise. +@param confidence Parameter used for the RANSAC and LMedS methods only. It specifies a desirable level +of confidence (probability) that the estimated matrix is correct. +@param mask + +The epipolar geometry is described by the following equation: + +\f[[p_2; 1]^T F [p_1; 1] = 0\f] + +where \f$F\f$ is a fundamental matrix, \f$p_1\f$ and \f$p_2\f$ are corresponding points in the first and the +second images, respectively. + +The function calculates the fundamental matrix using one of four methods listed above and returns +the found fundamental matrix. Normally just one matrix is found. But in case of the 7-point +algorithm, the function may return up to 3 solutions ( \f$9 \times 3\f$ matrix that stores all 3 +matrices sequentially). + +The calculated fundamental matrix may be passed further to computeCorrespondEpilines that finds the +epipolar lines corresponding to the specified points. It can also be passed to +stereoRectifyUncalibrated to compute the rectification transformation. : +@code + // Example. Estimation of fundamental matrix using the RANSAC algorithm + int point_count = 100; + vector points1(point_count); + vector points2(point_count); + + // initialize the points here ... + for( int i = 0; i < point_count; i++ ) + { + points1[i] = ...; + points2[i] = ...; + } + + Mat fundamental_matrix = + findFundamentalMat(points1, points2, FM_RANSAC, 3, 0.99); +@endcode + */ +CV_EXPORTS_W Mat findFundamentalMat( InputArray points1, InputArray points2, + int method = FM_RANSAC, + double ransacReprojThreshold = 3., double confidence = 0.99, + OutputArray mask = noArray() ); + +/** @overload */ +CV_EXPORTS Mat findFundamentalMat( InputArray points1, InputArray points2, + OutputArray mask, int method = FM_RANSAC, + double ransacReprojThreshold = 3., double confidence = 0.99 ); + +/** @brief Calculates an essential matrix from the corresponding points in two images. + +@param points1 Array of N (N \>= 5) 2D points from the first image. The point coordinates should +be floating-point (single or double precision). +@param points2 Array of the second image points of the same size and format as points1 . +@param cameraMatrix Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . +Note that this function assumes that points1 and points2 are feature points from cameras with the +same camera matrix. +@param method Method for computing an essential matrix. +- **RANSAC** for the RANSAC algorithm. +- **LMEDS** for the LMedS algorithm. +@param prob Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of +confidence (probability) that the estimated matrix is correct. +@param threshold Parameter used for RANSAC. It is the maximum distance from a point to an epipolar +line in pixels, beyond which the point is considered an outlier and is not used for computing the +final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the +point localization, image resolution, and the image noise. +@param mask Output array of N elements, every element of which is set to 0 for outliers and to 1 +for the other points. The array is computed only in the RANSAC and LMedS methods. + +This function estimates essential matrix based on the five-point algorithm solver in @cite Nister03 . +@cite SteweniusCFS is also a related. The epipolar geometry is described by the following equation: + +\f[[p_2; 1]^T K^{-T} E K^{-1} [p_1; 1] = 0\f] + +where \f$E\f$ is an essential matrix, \f$p_1\f$ and \f$p_2\f$ are corresponding points in the first and the +second images, respectively. The result of this function may be passed further to +decomposeEssentialMat or recoverPose to recover the relative pose between cameras. + */ +CV_EXPORTS_W Mat findEssentialMat( InputArray points1, InputArray points2, + InputArray cameraMatrix, int method = RANSAC, + double prob = 0.999, double threshold = 1.0, + OutputArray mask = noArray() ); + +/** @overload +@param points1 Array of N (N \>= 5) 2D points from the first image. The point coordinates should +be floating-point (single or double precision). +@param points2 Array of the second image points of the same size and format as points1 . +@param focal focal length of the camera. Note that this function assumes that points1 and points2 +are feature points from cameras with same focal length and principal point. +@param pp principal point of the camera. +@param method Method for computing a fundamental matrix. +- **RANSAC** for the RANSAC algorithm. +- **LMEDS** for the LMedS algorithm. +@param threshold Parameter used for RANSAC. It is the maximum distance from a point to an epipolar +line in pixels, beyond which the point is considered an outlier and is not used for computing the +final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the +point localization, image resolution, and the image noise. +@param prob Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of +confidence (probability) that the estimated matrix is correct. +@param mask Output array of N elements, every element of which is set to 0 for outliers and to 1 +for the other points. The array is computed only in the RANSAC and LMedS methods. + +This function differs from the one above that it computes camera matrix from focal length and +principal point: + +\f[K = +\begin{bmatrix} +f & 0 & x_{pp} \\ +0 & f & y_{pp} \\ +0 & 0 & 1 +\end{bmatrix}\f] + */ +CV_EXPORTS_W Mat findEssentialMat( InputArray points1, InputArray points2, + double focal = 1.0, Point2d pp = Point2d(0, 0), + int method = RANSAC, double prob = 0.999, + double threshold = 1.0, OutputArray mask = noArray() ); + +/** @brief Decompose an essential matrix to possible rotations and translation. + +@param E The input essential matrix. +@param R1 One possible rotation matrix. +@param R2 Another possible rotation matrix. +@param t One possible translation. + +This function decompose an essential matrix E using svd decomposition @cite HartleyZ00 . Generally 4 +possible poses exists for a given E. They are \f$[R_1, t]\f$, \f$[R_1, -t]\f$, \f$[R_2, t]\f$, \f$[R_2, -t]\f$. By +decomposing E, you can only get the direction of the translation, so the function returns unit t. + */ +CV_EXPORTS_W void decomposeEssentialMat( InputArray E, OutputArray R1, OutputArray R2, OutputArray t ); + +/** @brief Recover relative camera rotation and translation from an estimated essential matrix and the +corresponding points in two images, using cheirality check. Returns the number of inliers which pass +the check. + +@param E The input essential matrix. +@param points1 Array of N 2D points from the first image. The point coordinates should be +floating-point (single or double precision). +@param points2 Array of the second image points of the same size and format as points1 . +@param cameraMatrix Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . +Note that this function assumes that points1 and points2 are feature points from cameras with the +same camera matrix. +@param R Recovered relative rotation. +@param t Recovered relative translation. +@param mask Input/output mask for inliers in points1 and points2. +: If it is not empty, then it marks inliers in points1 and points2 for then given essential +matrix E. Only these inliers will be used to recover pose. In the output mask only inliers +which pass the cheirality check. +This function decomposes an essential matrix using decomposeEssentialMat and then verifies possible +pose hypotheses by doing cheirality check. The cheirality check basically means that the +triangulated 3D points should have positive depth. Some details can be found in @cite Nister03 . + +This function can be used to process output E and mask from findEssentialMat. In this scenario, +points1 and points2 are the same input for findEssentialMat. : +@code + // Example. Estimation of fundamental matrix using the RANSAC algorithm + int point_count = 100; + vector points1(point_count); + vector points2(point_count); + + // initialize the points here ... + for( int i = 0; i < point_count; i++ ) + { + points1[i] = ...; + points2[i] = ...; + } + + // cametra matrix with both focal lengths = 1, and principal point = (0, 0) + Mat cameraMatrix = Mat::eye(3, 3, CV_64F); + + Mat E, R, t, mask; + + E = findEssentialMat(points1, points2, cameraMatrix, RANSAC, 0.999, 1.0, mask); + recoverPose(E, points1, points2, cameraMatrix, R, t, mask); +@endcode + */ +CV_EXPORTS_W int recoverPose( InputArray E, InputArray points1, InputArray points2, + InputArray cameraMatrix, OutputArray R, OutputArray t, + InputOutputArray mask = noArray() ); + +/** @overload +@param E The input essential matrix. +@param points1 Array of N 2D points from the first image. The point coordinates should be +floating-point (single or double precision). +@param points2 Array of the second image points of the same size and format as points1 . +@param R Recovered relative rotation. +@param t Recovered relative translation. +@param focal Focal length of the camera. Note that this function assumes that points1 and points2 +are feature points from cameras with same focal length and principal point. +@param pp principal point of the camera. +@param mask Input/output mask for inliers in points1 and points2. +: If it is not empty, then it marks inliers in points1 and points2 for then given essential +matrix E. Only these inliers will be used to recover pose. In the output mask only inliers +which pass the cheirality check. + +This function differs from the one above that it computes camera matrix from focal length and +principal point: + +\f[K = +\begin{bmatrix} +f & 0 & x_{pp} \\ +0 & f & y_{pp} \\ +0 & 0 & 1 +\end{bmatrix}\f] + */ +CV_EXPORTS_W int recoverPose( InputArray E, InputArray points1, InputArray points2, + OutputArray R, OutputArray t, + double focal = 1.0, Point2d pp = Point2d(0, 0), + InputOutputArray mask = noArray() ); + +/** @overload +@param E The input essential matrix. +@param points1 Array of N 2D points from the first image. The point coordinates should be +floating-point (single or double precision). +@param points2 Array of the second image points of the same size and format as points1. +@param cameraMatrix Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . +Note that this function assumes that points1 and points2 are feature points from cameras with the +same camera matrix. +@param R Recovered relative rotation. +@param t Recovered relative translation. +@param distanceThresh threshold distance which is used to filter out far away points (i.e. infinite points). +@param mask Input/output mask for inliers in points1 and points2. +: If it is not empty, then it marks inliers in points1 and points2 for then given essential +matrix E. Only these inliers will be used to recover pose. In the output mask only inliers +which pass the cheirality check. +@param triangulatedPoints 3d points which were reconstructed by triangulation. + */ + +CV_EXPORTS_W int recoverPose( InputArray E, InputArray points1, InputArray points2, + InputArray cameraMatrix, OutputArray R, OutputArray t, double distanceThresh, InputOutputArray mask = noArray(), + OutputArray triangulatedPoints = noArray()); + +/** @brief For points in an image of a stereo pair, computes the corresponding epilines in the other image. + +@param points Input points. \f$N \times 1\f$ or \f$1 \times N\f$ matrix of type CV_32FC2 or +vector\ . +@param whichImage Index of the image (1 or 2) that contains the points . +@param F Fundamental matrix that can be estimated using findFundamentalMat or stereoRectify . +@param lines Output vector of the epipolar lines corresponding to the points in the other image. +Each line \f$ax + by + c=0\f$ is encoded by 3 numbers \f$(a, b, c)\f$ . + +For every point in one of the two images of a stereo pair, the function finds the equation of the +corresponding epipolar line in the other image. + +From the fundamental matrix definition (see findFundamentalMat ), line \f$l^{(2)}_i\f$ in the second +image for the point \f$p^{(1)}_i\f$ in the first image (when whichImage=1 ) is computed as: + +\f[l^{(2)}_i = F p^{(1)}_i\f] + +And vice versa, when whichImage=2, \f$l^{(1)}_i\f$ is computed from \f$p^{(2)}_i\f$ as: + +\f[l^{(1)}_i = F^T p^{(2)}_i\f] + +Line coefficients are defined up to a scale. They are normalized so that \f$a_i^2+b_i^2=1\f$ . + */ +CV_EXPORTS_W void computeCorrespondEpilines( InputArray points, int whichImage, + InputArray F, OutputArray lines ); + +/** @brief Reconstructs points by triangulation. + +@param projMatr1 3x4 projection matrix of the first camera. +@param projMatr2 3x4 projection matrix of the second camera. +@param projPoints1 2xN array of feature points in the first image. In case of c++ version it can +be also a vector of feature points or two-channel matrix of size 1xN or Nx1. +@param projPoints2 2xN array of corresponding points in the second image. In case of c++ version +it can be also a vector of feature points or two-channel matrix of size 1xN or Nx1. +@param points4D 4xN array of reconstructed points in homogeneous coordinates. + +The function reconstructs 3-dimensional points (in homogeneous coordinates) by using their +observations with a stereo camera. Projections matrices can be obtained from stereoRectify. + +@note + Keep in mind that all input data should be of float type in order for this function to work. + +@sa + reprojectImageTo3D + */ +CV_EXPORTS_W void triangulatePoints( InputArray projMatr1, InputArray projMatr2, + InputArray projPoints1, InputArray projPoints2, + OutputArray points4D ); + +/** @brief Refines coordinates of corresponding points. + +@param F 3x3 fundamental matrix. +@param points1 1xN array containing the first set of points. +@param points2 1xN array containing the second set of points. +@param newPoints1 The optimized points1. +@param newPoints2 The optimized points2. + +The function implements the Optimal Triangulation Method (see Multiple View Geometry for details). +For each given point correspondence points1[i] \<-\> points2[i], and a fundamental matrix F, it +computes the corrected correspondences newPoints1[i] \<-\> newPoints2[i] that minimize the geometric +error \f$d(points1[i], newPoints1[i])^2 + d(points2[i],newPoints2[i])^2\f$ (where \f$d(a,b)\f$ is the +geometric distance between points \f$a\f$ and \f$b\f$ ) subject to the epipolar constraint +\f$newPoints2^T * F * newPoints1 = 0\f$ . + */ +CV_EXPORTS_W void correctMatches( InputArray F, InputArray points1, InputArray points2, + OutputArray newPoints1, OutputArray newPoints2 ); + +/** @brief Filters off small noise blobs (speckles) in the disparity map + +@param img The input 16-bit signed disparity image +@param newVal The disparity value used to paint-off the speckles +@param maxSpeckleSize The maximum speckle size to consider it a speckle. Larger blobs are not +affected by the algorithm +@param maxDiff Maximum difference between neighbor disparity pixels to put them into the same +blob. Note that since StereoBM, StereoSGBM and may be other algorithms return a fixed-point +disparity map, where disparity values are multiplied by 16, this scale factor should be taken into +account when specifying this parameter value. +@param buf The optional temporary buffer to avoid memory allocation within the function. + */ +CV_EXPORTS_W void filterSpeckles( InputOutputArray img, double newVal, + int maxSpeckleSize, double maxDiff, + InputOutputArray buf = noArray() ); + +//! computes valid disparity ROI from the valid ROIs of the rectified images (that are returned by cv::stereoRectify()) +CV_EXPORTS_W Rect getValidDisparityROI( Rect roi1, Rect roi2, + int minDisparity, int numberOfDisparities, + int SADWindowSize ); + +//! validates disparity using the left-right check. The matrix "cost" should be computed by the stereo correspondence algorithm +CV_EXPORTS_W void validateDisparity( InputOutputArray disparity, InputArray cost, + int minDisparity, int numberOfDisparities, + int disp12MaxDisp = 1 ); + +/** @brief Reprojects a disparity image to 3D space. + +@param disparity Input single-channel 8-bit unsigned, 16-bit signed, 32-bit signed or 32-bit +floating-point disparity image. If 16-bit signed format is used, the values are assumed to have no +fractional bits. +@param _3dImage Output 3-channel floating-point image of the same size as disparity . Each +element of _3dImage(x,y) contains 3D coordinates of the point (x,y) computed from the disparity +map. +@param Q \f$4 \times 4\f$ perspective transformation matrix that can be obtained with stereoRectify. +@param handleMissingValues Indicates, whether the function should handle missing values (i.e. +points where the disparity was not computed). If handleMissingValues=true, then pixels with the +minimal disparity that corresponds to the outliers (see StereoMatcher::compute ) are transformed +to 3D points with a very large Z value (currently set to 10000). +@param ddepth The optional output array depth. If it is -1, the output image will have CV_32F +depth. ddepth can also be set to CV_16S, CV_32S or CV_32F. + +The function transforms a single-channel disparity map to a 3-channel image representing a 3D +surface. That is, for each pixel (x,y) and the corresponding disparity d=disparity(x,y) , it +computes: + +\f[\begin{array}{l} [X \; Y \; Z \; W]^T = \texttt{Q} *[x \; y \; \texttt{disparity} (x,y) \; 1]^T \\ \texttt{\_3dImage} (x,y) = (X/W, \; Y/W, \; Z/W) \end{array}\f] + +The matrix Q can be an arbitrary \f$4 \times 4\f$ matrix (for example, the one computed by +stereoRectify). To reproject a sparse set of points {(x,y,d),...} to 3D space, use +perspectiveTransform . + */ +CV_EXPORTS_W void reprojectImageTo3D( InputArray disparity, + OutputArray _3dImage, InputArray Q, + bool handleMissingValues = false, + int ddepth = -1 ); + +/** @brief Calculates the Sampson Distance between two points. + +The function cv::sampsonDistance calculates and returns the first order approximation of the geometric error as: +\f[ +sd( \texttt{pt1} , \texttt{pt2} )= +\frac{(\texttt{pt2}^t \cdot \texttt{F} \cdot \texttt{pt1})^2} +{((\texttt{F} \cdot \texttt{pt1})(0))^2 + +((\texttt{F} \cdot \texttt{pt1})(1))^2 + +((\texttt{F}^t \cdot \texttt{pt2})(0))^2 + +((\texttt{F}^t \cdot \texttt{pt2})(1))^2} +\f] +The fundamental matrix may be calculated using the cv::findFundamentalMat function. See @cite HartleyZ00 11.4.3 for details. +@param pt1 first homogeneous 2d point +@param pt2 second homogeneous 2d point +@param F fundamental matrix +@return The computed Sampson distance. +*/ +CV_EXPORTS_W double sampsonDistance(InputArray pt1, InputArray pt2, InputArray F); + +/** @brief Computes an optimal affine transformation between two 3D point sets. + +It computes +\f[ +\begin{bmatrix} +x\\ +y\\ +z\\ +\end{bmatrix} += +\begin{bmatrix} +a_{11} & a_{12} & a_{13}\\ +a_{21} & a_{22} & a_{23}\\ +a_{31} & a_{32} & a_{33}\\ +\end{bmatrix} +\begin{bmatrix} +X\\ +Y\\ +Z\\ +\end{bmatrix} ++ +\begin{bmatrix} +b_1\\ +b_2\\ +b_3\\ +\end{bmatrix} +\f] + +@param src First input 3D point set containing \f$(X,Y,Z)\f$. +@param dst Second input 3D point set containing \f$(x,y,z)\f$. +@param out Output 3D affine transformation matrix \f$3 \times 4\f$ of the form +\f[ +\begin{bmatrix} +a_{11} & a_{12} & a_{13} & b_1\\ +a_{21} & a_{22} & a_{23} & b_2\\ +a_{31} & a_{32} & a_{33} & b_3\\ +\end{bmatrix} +\f] +@param inliers Output vector indicating which points are inliers (1-inlier, 0-outlier). +@param ransacThreshold Maximum reprojection error in the RANSAC algorithm to consider a point as +an inlier. +@param confidence Confidence level, between 0 and 1, for the estimated transformation. Anything +between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation +significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation. + +The function estimates an optimal 3D affine transformation between two 3D point sets using the +RANSAC algorithm. + */ +CV_EXPORTS_W int estimateAffine3D(InputArray src, InputArray dst, + OutputArray out, OutputArray inliers, + double ransacThreshold = 3, double confidence = 0.99); + +/** @brief Computes an optimal affine transformation between two 2D point sets. + +It computes +\f[ +\begin{bmatrix} +x\\ +y\\ +\end{bmatrix} += +\begin{bmatrix} +a_{11} & a_{12}\\ +a_{21} & a_{22}\\ +\end{bmatrix} +\begin{bmatrix} +X\\ +Y\\ +\end{bmatrix} ++ +\begin{bmatrix} +b_1\\ +b_2\\ +\end{bmatrix} +\f] + +@param from First input 2D point set containing \f$(X,Y)\f$. +@param to Second input 2D point set containing \f$(x,y)\f$. +@param inliers Output vector indicating which points are inliers (1-inlier, 0-outlier). +@param method Robust method used to compute transformation. The following methods are possible: +- cv::RANSAC - RANSAC-based robust method +- cv::LMEDS - Least-Median robust method +RANSAC is the default method. +@param ransacReprojThreshold Maximum reprojection error in the RANSAC algorithm to consider +a point as an inlier. Applies only to RANSAC. +@param maxIters The maximum number of robust method iterations. +@param confidence Confidence level, between 0 and 1, for the estimated transformation. Anything +between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation +significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation. +@param refineIters Maximum number of iterations of refining algorithm (Levenberg-Marquardt). +Passing 0 will disable refining, so the output matrix will be output of robust method. + +@return Output 2D affine transformation matrix \f$2 \times 3\f$ or empty matrix if transformation +could not be estimated. The returned matrix has the following form: +\f[ +\begin{bmatrix} +a_{11} & a_{12} & b_1\\ +a_{21} & a_{22} & b_2\\ +\end{bmatrix} +\f] + +The function estimates an optimal 2D affine transformation between two 2D point sets using the +selected robust algorithm. + +The computed transformation is then refined further (using only inliers) with the +Levenberg-Marquardt method to reduce the re-projection error even more. + +@note +The RANSAC method can handle practically any ratio of outliers but needs a threshold to +distinguish inliers from outliers. The method LMeDS does not need any threshold but it works +correctly only when there are more than 50% of inliers. + +@sa estimateAffinePartial2D, getAffineTransform +*/ +CV_EXPORTS_W cv::Mat estimateAffine2D(InputArray from, InputArray to, OutputArray inliers = noArray(), + int method = RANSAC, double ransacReprojThreshold = 3, + size_t maxIters = 2000, double confidence = 0.99, + size_t refineIters = 10); + +/** @brief Computes an optimal limited affine transformation with 4 degrees of freedom between +two 2D point sets. + +@param from First input 2D point set. +@param to Second input 2D point set. +@param inliers Output vector indicating which points are inliers. +@param method Robust method used to compute transformation. The following methods are possible: +- cv::RANSAC - RANSAC-based robust method +- cv::LMEDS - Least-Median robust method +RANSAC is the default method. +@param ransacReprojThreshold Maximum reprojection error in the RANSAC algorithm to consider +a point as an inlier. Applies only to RANSAC. +@param maxIters The maximum number of robust method iterations. +@param confidence Confidence level, between 0 and 1, for the estimated transformation. Anything +between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation +significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation. +@param refineIters Maximum number of iterations of refining algorithm (Levenberg-Marquardt). +Passing 0 will disable refining, so the output matrix will be output of robust method. + +@return Output 2D affine transformation (4 degrees of freedom) matrix \f$2 \times 3\f$ or +empty matrix if transformation could not be estimated. + +The function estimates an optimal 2D affine transformation with 4 degrees of freedom limited to +combinations of translation, rotation, and uniform scaling. Uses the selected algorithm for robust +estimation. + +The computed transformation is then refined further (using only inliers) with the +Levenberg-Marquardt method to reduce the re-projection error even more. + +Estimated transformation matrix is: +\f[ \begin{bmatrix} \cos(\theta) \cdot s & -\sin(\theta) \cdot s & t_x \\ + \sin(\theta) \cdot s & \cos(\theta) \cdot s & t_y +\end{bmatrix} \f] +Where \f$ \theta \f$ is the rotation angle, \f$ s \f$ the scaling factor and \f$ t_x, t_y \f$ are +translations in \f$ x, y \f$ axes respectively. + +@note +The RANSAC method can handle practically any ratio of outliers but need a threshold to +distinguish inliers from outliers. The method LMeDS does not need any threshold but it works +correctly only when there are more than 50% of inliers. + +@sa estimateAffine2D, getAffineTransform +*/ +CV_EXPORTS_W cv::Mat estimateAffinePartial2D(InputArray from, InputArray to, OutputArray inliers = noArray(), + int method = RANSAC, double ransacReprojThreshold = 3, + size_t maxIters = 2000, double confidence = 0.99, + size_t refineIters = 10); + +/** @example samples/cpp/tutorial_code/features2D/Homography/decompose_homography.cpp +An example program with homography decomposition. + +Check @ref tutorial_homography "the corresponding tutorial" for more details. +*/ + +/** @brief Decompose a homography matrix to rotation(s), translation(s) and plane normal(s). + +@param H The input homography matrix between two images. +@param K The input intrinsic camera calibration matrix. +@param rotations Array of rotation matrices. +@param translations Array of translation matrices. +@param normals Array of plane normal matrices. + +This function extracts relative camera motion between two views observing a planar object from the +homography H induced by the plane. The intrinsic camera matrix K must also be provided. The function +may return up to four mathematical solution sets. At least two of the solutions may further be +invalidated if point correspondences are available by applying positive depth constraint (all points +must be in front of the camera). The decomposition method is described in detail in @cite Malis . + */ +CV_EXPORTS_W int decomposeHomographyMat(InputArray H, + InputArray K, + OutputArrayOfArrays rotations, + OutputArrayOfArrays translations, + OutputArrayOfArrays normals); + +/** @brief Filters homography decompositions based on additional information. + +@param rotations Vector of rotation matrices. +@param normals Vector of plane normal matrices. +@param beforePoints Vector of (rectified) visible reference points before the homography is applied +@param afterPoints Vector of (rectified) visible reference points after the homography is applied +@param possibleSolutions Vector of int indices representing the viable solution set after filtering +@param pointsMask optional Mat/Vector of 8u type representing the mask for the inliers as given by the findHomography function + +This function is intended to filter the output of the decomposeHomographyMat based on additional +information as described in @cite Malis . The summary of the method: the decomposeHomographyMat function +returns 2 unique solutions and their "opposites" for a total of 4 solutions. If we have access to the +sets of points visible in the camera frame before and after the homography transformation is applied, +we can determine which are the true potential solutions and which are the opposites by verifying which +homographies are consistent with all visible reference points being in front of the camera. The inputs +are left unchanged; the filtered solution set is returned as indices into the existing one. + +*/ +CV_EXPORTS_W void filterHomographyDecompByVisibleRefpoints(InputArrayOfArrays rotations, + InputArrayOfArrays normals, + InputArray beforePoints, + InputArray afterPoints, + OutputArray possibleSolutions, + InputArray pointsMask = noArray()); + +/** @brief The base class for stereo correspondence algorithms. + */ +class CV_EXPORTS_W StereoMatcher : public Algorithm +{ +public: + enum { DISP_SHIFT = 4, + DISP_SCALE = (1 << DISP_SHIFT) + }; + + /** @brief Computes disparity map for the specified stereo pair + + @param left Left 8-bit single-channel image. + @param right Right image of the same size and the same type as the left one. + @param disparity Output disparity map. It has the same size as the input images. Some algorithms, + like StereoBM or StereoSGBM compute 16-bit fixed-point disparity map (where each disparity value + has 4 fractional bits), whereas other algorithms output 32-bit floating-point disparity map. + */ + CV_WRAP virtual void compute( InputArray left, InputArray right, + OutputArray disparity ) = 0; + + CV_WRAP virtual int getMinDisparity() const = 0; + CV_WRAP virtual void setMinDisparity(int minDisparity) = 0; + + CV_WRAP virtual int getNumDisparities() const = 0; + CV_WRAP virtual void setNumDisparities(int numDisparities) = 0; + + CV_WRAP virtual int getBlockSize() const = 0; + CV_WRAP virtual void setBlockSize(int blockSize) = 0; + + CV_WRAP virtual int getSpeckleWindowSize() const = 0; + CV_WRAP virtual void setSpeckleWindowSize(int speckleWindowSize) = 0; + + CV_WRAP virtual int getSpeckleRange() const = 0; + CV_WRAP virtual void setSpeckleRange(int speckleRange) = 0; + + CV_WRAP virtual int getDisp12MaxDiff() const = 0; + CV_WRAP virtual void setDisp12MaxDiff(int disp12MaxDiff) = 0; +}; + + +/** @brief Class for computing stereo correspondence using the block matching algorithm, introduced and +contributed to OpenCV by K. Konolige. + */ +class CV_EXPORTS_W StereoBM : public StereoMatcher +{ +public: + enum { PREFILTER_NORMALIZED_RESPONSE = 0, + PREFILTER_XSOBEL = 1 + }; + + CV_WRAP virtual int getPreFilterType() const = 0; + CV_WRAP virtual void setPreFilterType(int preFilterType) = 0; + + CV_WRAP virtual int getPreFilterSize() const = 0; + CV_WRAP virtual void setPreFilterSize(int preFilterSize) = 0; + + CV_WRAP virtual int getPreFilterCap() const = 0; + CV_WRAP virtual void setPreFilterCap(int preFilterCap) = 0; + + CV_WRAP virtual int getTextureThreshold() const = 0; + CV_WRAP virtual void setTextureThreshold(int textureThreshold) = 0; + + CV_WRAP virtual int getUniquenessRatio() const = 0; + CV_WRAP virtual void setUniquenessRatio(int uniquenessRatio) = 0; + + CV_WRAP virtual int getSmallerBlockSize() const = 0; + CV_WRAP virtual void setSmallerBlockSize(int blockSize) = 0; + + CV_WRAP virtual Rect getROI1() const = 0; + CV_WRAP virtual void setROI1(Rect roi1) = 0; + + CV_WRAP virtual Rect getROI2() const = 0; + CV_WRAP virtual void setROI2(Rect roi2) = 0; + + /** @brief Creates StereoBM object + + @param numDisparities the disparity search range. For each pixel algorithm will find the best + disparity from 0 (default minimum disparity) to numDisparities. The search range can then be + shifted by changing the minimum disparity. + @param blockSize the linear size of the blocks compared by the algorithm. The size should be odd + (as the block is centered at the current pixel). Larger block size implies smoother, though less + accurate disparity map. Smaller block size gives more detailed disparity map, but there is higher + chance for algorithm to find a wrong correspondence. + + The function create StereoBM object. You can then call StereoBM::compute() to compute disparity for + a specific stereo pair. + */ + CV_WRAP static Ptr create(int numDisparities = 0, int blockSize = 21); +}; + +/** @brief The class implements the modified H. Hirschmuller algorithm @cite HH08 that differs from the original +one as follows: + +- By default, the algorithm is single-pass, which means that you consider only 5 directions +instead of 8. Set mode=StereoSGBM::MODE_HH in createStereoSGBM to run the full variant of the +algorithm but beware that it may consume a lot of memory. +- The algorithm matches blocks, not individual pixels. Though, setting blockSize=1 reduces the +blocks to single pixels. +- Mutual information cost function is not implemented. Instead, a simpler Birchfield-Tomasi +sub-pixel metric from @cite BT98 is used. Though, the color images are supported as well. +- Some pre- and post- processing steps from K. Konolige algorithm StereoBM are included, for +example: pre-filtering (StereoBM::PREFILTER_XSOBEL type) and post-filtering (uniqueness +check, quadratic interpolation and speckle filtering). + +@note + - (Python) An example illustrating the use of the StereoSGBM matching algorithm can be found + at opencv_source_code/samples/python/stereo_match.py + */ +class CV_EXPORTS_W StereoSGBM : public StereoMatcher +{ +public: + enum + { + MODE_SGBM = 0, + MODE_HH = 1, + MODE_SGBM_3WAY = 2, + MODE_HH4 = 3 + }; + + CV_WRAP virtual int getPreFilterCap() const = 0; + CV_WRAP virtual void setPreFilterCap(int preFilterCap) = 0; + + CV_WRAP virtual int getUniquenessRatio() const = 0; + CV_WRAP virtual void setUniquenessRatio(int uniquenessRatio) = 0; + + CV_WRAP virtual int getP1() const = 0; + CV_WRAP virtual void setP1(int P1) = 0; + + CV_WRAP virtual int getP2() const = 0; + CV_WRAP virtual void setP2(int P2) = 0; + + CV_WRAP virtual int getMode() const = 0; + CV_WRAP virtual void setMode(int mode) = 0; + + /** @brief Creates StereoSGBM object + + @param minDisparity Minimum possible disparity value. Normally, it is zero but sometimes + rectification algorithms can shift images, so this parameter needs to be adjusted accordingly. + @param numDisparities Maximum disparity minus minimum disparity. The value is always greater than + zero. In the current implementation, this parameter must be divisible by 16. + @param blockSize Matched block size. It must be an odd number \>=1 . Normally, it should be + somewhere in the 3..11 range. + @param P1 The first parameter controlling the disparity smoothness. See below. + @param P2 The second parameter controlling the disparity smoothness. The larger the values are, + the smoother the disparity is. P1 is the penalty on the disparity change by plus or minus 1 + between neighbor pixels. P2 is the penalty on the disparity change by more than 1 between neighbor + pixels. The algorithm requires P2 \> P1 . See stereo_match.cpp sample where some reasonably good + P1 and P2 values are shown (like 8\*number_of_image_channels\*SADWindowSize\*SADWindowSize and + 32\*number_of_image_channels\*SADWindowSize\*SADWindowSize , respectively). + @param disp12MaxDiff Maximum allowed difference (in integer pixel units) in the left-right + disparity check. Set it to a non-positive value to disable the check. + @param preFilterCap Truncation value for the prefiltered image pixels. The algorithm first + computes x-derivative at each pixel and clips its value by [-preFilterCap, preFilterCap] interval. + The result values are passed to the Birchfield-Tomasi pixel cost function. + @param uniquenessRatio Margin in percentage by which the best (minimum) computed cost function + value should "win" the second best value to consider the found match correct. Normally, a value + within the 5-15 range is good enough. + @param speckleWindowSize Maximum size of smooth disparity regions to consider their noise speckles + and invalidate. Set it to 0 to disable speckle filtering. Otherwise, set it somewhere in the + 50-200 range. + @param speckleRange Maximum disparity variation within each connected component. If you do speckle + filtering, set the parameter to a positive value, it will be implicitly multiplied by 16. + Normally, 1 or 2 is good enough. + @param mode Set it to StereoSGBM::MODE_HH to run the full-scale two-pass dynamic programming + algorithm. It will consume O(W\*H\*numDisparities) bytes, which is large for 640x480 stereo and + huge for HD-size pictures. By default, it is set to false . + + The first constructor initializes StereoSGBM with all the default parameters. So, you only have to + set StereoSGBM::numDisparities at minimum. The second constructor enables you to set each parameter + to a custom value. + */ + CV_WRAP static Ptr create(int minDisparity = 0, int numDisparities = 16, int blockSize = 3, + int P1 = 0, int P2 = 0, int disp12MaxDiff = 0, + int preFilterCap = 0, int uniquenessRatio = 0, + int speckleWindowSize = 0, int speckleRange = 0, + int mode = StereoSGBM::MODE_SGBM); +}; + +//! @} calib3d + +/** @brief The methods in this namespace use a so-called fisheye camera model. + @ingroup calib3d_fisheye +*/ +namespace fisheye +{ +//! @addtogroup calib3d_fisheye +//! @{ + + enum{ + CALIB_USE_INTRINSIC_GUESS = 1 << 0, + CALIB_RECOMPUTE_EXTRINSIC = 1 << 1, + CALIB_CHECK_COND = 1 << 2, + CALIB_FIX_SKEW = 1 << 3, + CALIB_FIX_K1 = 1 << 4, + CALIB_FIX_K2 = 1 << 5, + CALIB_FIX_K3 = 1 << 6, + CALIB_FIX_K4 = 1 << 7, + CALIB_FIX_INTRINSIC = 1 << 8, + CALIB_FIX_PRINCIPAL_POINT = 1 << 9 + }; + + /** @brief Projects points using fisheye model + + @param objectPoints Array of object points, 1xN/Nx1 3-channel (or vector\ ), where N is + the number of points in the view. + @param imagePoints Output array of image points, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel, or + vector\. + @param affine + @param K Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$. + @param D Input vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$. + @param alpha The skew coefficient. + @param jacobian Optional output 2Nx15 jacobian matrix of derivatives of image points with respect + to components of the focal lengths, coordinates of the principal point, distortion coefficients, + rotation vector, translation vector, and the skew. In the old interface different components of + the jacobian are returned via different output parameters. + + The function computes projections of 3D points to the image plane given intrinsic and extrinsic + camera parameters. Optionally, the function computes Jacobians - matrices of partial derivatives of + image points coordinates (as functions of all the input parameters) with respect to the particular + parameters, intrinsic and/or extrinsic. + */ + CV_EXPORTS void projectPoints(InputArray objectPoints, OutputArray imagePoints, const Affine3d& affine, + InputArray K, InputArray D, double alpha = 0, OutputArray jacobian = noArray()); + + /** @overload */ + CV_EXPORTS_W void projectPoints(InputArray objectPoints, OutputArray imagePoints, InputArray rvec, InputArray tvec, + InputArray K, InputArray D, double alpha = 0, OutputArray jacobian = noArray()); + + /** @brief Distorts 2D points using fisheye model. + + @param undistorted Array of object points, 1xN/Nx1 2-channel (or vector\ ), where N is + the number of points in the view. + @param K Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$. + @param D Input vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$. + @param alpha The skew coefficient. + @param distorted Output array of image points, 1xN/Nx1 2-channel, or vector\ . + + Note that the function assumes the camera matrix of the undistorted points to be identity. + This means if you want to transform back points undistorted with undistortPoints() you have to + multiply them with \f$P^{-1}\f$. + */ + CV_EXPORTS_W void distortPoints(InputArray undistorted, OutputArray distorted, InputArray K, InputArray D, double alpha = 0); + + /** @brief Undistorts 2D points using fisheye model + + @param distorted Array of object points, 1xN/Nx1 2-channel (or vector\ ), where N is the + number of points in the view. + @param K Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$. + @param D Input vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$. + @param R Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 + 1-channel or 1x1 3-channel + @param P New camera matrix (3x3) or new projection matrix (3x4) + @param undistorted Output array of image points, 1xN/Nx1 2-channel, or vector\ . + */ + CV_EXPORTS_W void undistortPoints(InputArray distorted, OutputArray undistorted, + InputArray K, InputArray D, InputArray R = noArray(), InputArray P = noArray()); + + /** @brief Computes undistortion and rectification maps for image transform by cv::remap(). If D is empty zero + distortion is used, if R or P is empty identity matrixes are used. + + @param K Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$. + @param D Input vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$. + @param R Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 + 1-channel or 1x1 3-channel + @param P New camera matrix (3x3) or new projection matrix (3x4) + @param size Undistorted image size. + @param m1type Type of the first output map that can be CV_32FC1 or CV_16SC2 . See convertMaps() + for details. + @param map1 The first output map. + @param map2 The second output map. + */ + CV_EXPORTS_W void initUndistortRectifyMap(InputArray K, InputArray D, InputArray R, InputArray P, + const cv::Size& size, int m1type, OutputArray map1, OutputArray map2); + + /** @brief Transforms an image to compensate for fisheye lens distortion. + + @param distorted image with fisheye lens distortion. + @param undistorted Output image with compensated fisheye lens distortion. + @param K Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$. + @param D Input vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$. + @param Knew Camera matrix of the distorted image. By default, it is the identity matrix but you + may additionally scale and shift the result by using a different matrix. + @param new_size + + The function transforms an image to compensate radial and tangential lens distortion. + + The function is simply a combination of fisheye::initUndistortRectifyMap (with unity R ) and remap + (with bilinear interpolation). See the former function for details of the transformation being + performed. + + See below the results of undistortImage. + - a\) result of undistort of perspective camera model (all possible coefficients (k_1, k_2, k_3, + k_4, k_5, k_6) of distortion were optimized under calibration) + - b\) result of fisheye::undistortImage of fisheye camera model (all possible coefficients (k_1, k_2, + k_3, k_4) of fisheye distortion were optimized under calibration) + - c\) original image was captured with fisheye lens + + Pictures a) and b) almost the same. But if we consider points of image located far from the center + of image, we can notice that on image a) these points are distorted. + + ![image](pics/fisheye_undistorted.jpg) + */ + CV_EXPORTS_W void undistortImage(InputArray distorted, OutputArray undistorted, + InputArray K, InputArray D, InputArray Knew = cv::noArray(), const Size& new_size = Size()); + + /** @brief Estimates new camera matrix for undistortion or rectification. + + @param K Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$. + @param image_size + @param D Input vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$. + @param R Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 + 1-channel or 1x1 3-channel + @param P New camera matrix (3x3) or new projection matrix (3x4) + @param balance Sets the new focal length in range between the min focal length and the max focal + length. Balance is in range of [0, 1]. + @param new_size + @param fov_scale Divisor for new focal length. + */ + CV_EXPORTS_W void estimateNewCameraMatrixForUndistortRectify(InputArray K, InputArray D, const Size &image_size, InputArray R, + OutputArray P, double balance = 0.0, const Size& new_size = Size(), double fov_scale = 1.0); + + /** @brief Performs camera calibaration + + @param objectPoints vector of vectors of calibration pattern points in the calibration pattern + coordinate space. + @param imagePoints vector of vectors of the projections of calibration pattern points. + imagePoints.size() and objectPoints.size() and imagePoints[i].size() must be equal to + objectPoints[i].size() for each i. + @param image_size Size of the image used only to initialize the intrinsic camera matrix. + @param K Output 3x3 floating-point camera matrix + \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . If + fisheye::CALIB_USE_INTRINSIC_GUESS/ is specified, some or all of fx, fy, cx, cy must be + initialized before calling the function. + @param D Output vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$. + @param rvecs Output vector of rotation vectors (see Rodrigues ) estimated for each pattern view. + That is, each k-th rotation vector together with the corresponding k-th translation vector (see + the next output parameter description) brings the calibration pattern from the model coordinate + space (in which object points are specified) to the world coordinate space, that is, a real + position of the calibration pattern in the k-th pattern view (k=0.. *M* -1). + @param tvecs Output vector of translation vectors estimated for each pattern view. + @param flags Different flags that may be zero or a combination of the following values: + - **fisheye::CALIB_USE_INTRINSIC_GUESS** cameraMatrix contains valid initial values of + fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image + center ( imageSize is used), and focal distances are computed in a least-squares fashion. + - **fisheye::CALIB_RECOMPUTE_EXTRINSIC** Extrinsic will be recomputed after each iteration + of intrinsic optimization. + - **fisheye::CALIB_CHECK_COND** The functions will check validity of condition number. + - **fisheye::CALIB_FIX_SKEW** Skew coefficient (alpha) is set to zero and stay zero. + - **fisheye::CALIB_FIX_K1..fisheye::CALIB_FIX_K4** Selected distortion coefficients + are set to zeros and stay zero. + - **fisheye::CALIB_FIX_PRINCIPAL_POINT** The principal point is not changed during the global +optimization. It stays at the center or at a different location specified when CALIB_USE_INTRINSIC_GUESS is set too. + @param criteria Termination criteria for the iterative optimization algorithm. + */ + CV_EXPORTS_W double calibrate(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, const Size& image_size, + InputOutputArray K, InputOutputArray D, OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, int flags = 0, + TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 100, DBL_EPSILON)); + + /** @brief Stereo rectification for fisheye camera model + + @param K1 First camera matrix. + @param D1 First camera distortion parameters. + @param K2 Second camera matrix. + @param D2 Second camera distortion parameters. + @param imageSize Size of the image used for stereo calibration. + @param R Rotation matrix between the coordinate systems of the first and the second + cameras. + @param tvec Translation vector between coordinate systems of the cameras. + @param R1 Output 3x3 rectification transform (rotation matrix) for the first camera. + @param R2 Output 3x3 rectification transform (rotation matrix) for the second camera. + @param P1 Output 3x4 projection matrix in the new (rectified) coordinate systems for the first + camera. + @param P2 Output 3x4 projection matrix in the new (rectified) coordinate systems for the second + camera. + @param Q Output \f$4 \times 4\f$ disparity-to-depth mapping matrix (see reprojectImageTo3D ). + @param flags Operation flags that may be zero or CALIB_ZERO_DISPARITY . If the flag is set, + the function makes the principal points of each camera have the same pixel coordinates in the + rectified views. And if the flag is not set, the function may still shift the images in the + horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the + useful image area. + @param newImageSize New image resolution after rectification. The same size should be passed to + initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) + is passed (default), it is set to the original imageSize . Setting it to larger value can help you + preserve details in the original image, especially when there is a big radial distortion. + @param balance Sets the new focal length in range between the min focal length and the max focal + length. Balance is in range of [0, 1]. + @param fov_scale Divisor for new focal length. + */ + CV_EXPORTS_W void stereoRectify(InputArray K1, InputArray D1, InputArray K2, InputArray D2, const Size &imageSize, InputArray R, InputArray tvec, + OutputArray R1, OutputArray R2, OutputArray P1, OutputArray P2, OutputArray Q, int flags, const Size &newImageSize = Size(), + double balance = 0.0, double fov_scale = 1.0); + + /** @brief Performs stereo calibration + + @param objectPoints Vector of vectors of the calibration pattern points. + @param imagePoints1 Vector of vectors of the projections of the calibration pattern points, + observed by the first camera. + @param imagePoints2 Vector of vectors of the projections of the calibration pattern points, + observed by the second camera. + @param K1 Input/output first camera matrix: + \f$\vecthreethree{f_x^{(j)}}{0}{c_x^{(j)}}{0}{f_y^{(j)}}{c_y^{(j)}}{0}{0}{1}\f$ , \f$j = 0,\, 1\f$ . If + any of fisheye::CALIB_USE_INTRINSIC_GUESS , fisheye::CALIB_FIX_INTRINSIC are specified, + some or all of the matrix components must be initialized. + @param D1 Input/output vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$ of 4 elements. + @param K2 Input/output second camera matrix. The parameter is similar to K1 . + @param D2 Input/output lens distortion coefficients for the second camera. The parameter is + similar to D1 . + @param imageSize Size of the image used only to initialize intrinsic camera matrix. + @param R Output rotation matrix between the 1st and the 2nd camera coordinate systems. + @param T Output translation vector between the coordinate systems of the cameras. + @param flags Different flags that may be zero or a combination of the following values: + - **fisheye::CALIB_FIX_INTRINSIC** Fix K1, K2? and D1, D2? so that only R, T matrices + are estimated. + - **fisheye::CALIB_USE_INTRINSIC_GUESS** K1, K2 contains valid initial values of + fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image + center (imageSize is used), and focal distances are computed in a least-squares fashion. + - **fisheye::CALIB_RECOMPUTE_EXTRINSIC** Extrinsic will be recomputed after each iteration + of intrinsic optimization. + - **fisheye::CALIB_CHECK_COND** The functions will check validity of condition number. + - **fisheye::CALIB_FIX_SKEW** Skew coefficient (alpha) is set to zero and stay zero. + - **fisheye::CALIB_FIX_K1..4** Selected distortion coefficients are set to zeros and stay + zero. + @param criteria Termination criteria for the iterative optimization algorithm. + */ + CV_EXPORTS_W double stereoCalibrate(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2, + InputOutputArray K1, InputOutputArray D1, InputOutputArray K2, InputOutputArray D2, Size imageSize, + OutputArray R, OutputArray T, int flags = fisheye::CALIB_FIX_INTRINSIC, + TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 100, DBL_EPSILON)); + +//! @} calib3d_fisheye +} // end namespace fisheye + +} //end namespace cv + +#ifndef DISABLE_OPENCV_24_COMPATIBILITY +#include "opencv2/calib3d/calib3d_c.h" +#endif + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/calib3d/calib3d.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/calib3d/calib3d.hpp new file mode 100644 index 0000000000000000000000000000000000000000..b3da45edd59c0a0807abb834e0e4216522316443 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/calib3d/calib3d.hpp @@ -0,0 +1,48 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifdef __OPENCV_BUILD +#error this is a compatibility header which should not be used inside the OpenCV library +#endif + +#include "opencv2/calib3d.hpp" diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/calib3d/calib3d_c.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/calib3d/calib3d_c.h new file mode 100644 index 0000000000000000000000000000000000000000..8ec6390d70e3064debb62ad7301ba31707f6339c --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/calib3d/calib3d_c.h @@ -0,0 +1,427 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CALIB3D_C_H +#define OPENCV_CALIB3D_C_H + +#include "opencv2/core/core_c.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/** @addtogroup calib3d_c + @{ + */ + +/****************************************************************************************\ +* Camera Calibration, Pose Estimation and Stereo * +\****************************************************************************************/ + +typedef struct CvPOSITObject CvPOSITObject; + +/* Allocates and initializes CvPOSITObject structure before doing cvPOSIT */ +CVAPI(CvPOSITObject*) cvCreatePOSITObject( CvPoint3D32f* points, int point_count ); + + +/* Runs POSIT (POSe from ITeration) algorithm for determining 3d position of + an object given its model and projection in a weak-perspective case */ +CVAPI(void) cvPOSIT( CvPOSITObject* posit_object, CvPoint2D32f* image_points, + double focal_length, CvTermCriteria criteria, + float* rotation_matrix, float* translation_vector); + +/* Releases CvPOSITObject structure */ +CVAPI(void) cvReleasePOSITObject( CvPOSITObject** posit_object ); + +/* updates the number of RANSAC iterations */ +CVAPI(int) cvRANSACUpdateNumIters( double p, double err_prob, + int model_points, int max_iters ); + +CVAPI(void) cvConvertPointsHomogeneous( const CvMat* src, CvMat* dst ); + +/* Calculates fundamental matrix given a set of corresponding points */ +#define CV_FM_7POINT 1 +#define CV_FM_8POINT 2 + +#define CV_LMEDS 4 +#define CV_RANSAC 8 + +#define CV_FM_LMEDS_ONLY CV_LMEDS +#define CV_FM_RANSAC_ONLY CV_RANSAC +#define CV_FM_LMEDS CV_LMEDS +#define CV_FM_RANSAC CV_RANSAC + +enum +{ + CV_ITERATIVE = 0, + CV_EPNP = 1, // F.Moreno-Noguer, V.Lepetit and P.Fua "EPnP: Efficient Perspective-n-Point Camera Pose Estimation" + CV_P3P = 2, // X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang; "Complete Solution Classification for the Perspective-Three-Point Problem" + CV_DLS = 3 // Joel A. Hesch and Stergios I. Roumeliotis. "A Direct Least-Squares (DLS) Method for PnP" +}; + +CVAPI(int) cvFindFundamentalMat( const CvMat* points1, const CvMat* points2, + CvMat* fundamental_matrix, + int method CV_DEFAULT(CV_FM_RANSAC), + double param1 CV_DEFAULT(3.), double param2 CV_DEFAULT(0.99), + CvMat* status CV_DEFAULT(NULL) ); + +/* For each input point on one of images + computes parameters of the corresponding + epipolar line on the other image */ +CVAPI(void) cvComputeCorrespondEpilines( const CvMat* points, + int which_image, + const CvMat* fundamental_matrix, + CvMat* correspondent_lines ); + +/* Triangulation functions */ + +CVAPI(void) cvTriangulatePoints(CvMat* projMatr1, CvMat* projMatr2, + CvMat* projPoints1, CvMat* projPoints2, + CvMat* points4D); + +CVAPI(void) cvCorrectMatches(CvMat* F, CvMat* points1, CvMat* points2, + CvMat* new_points1, CvMat* new_points2); + + +/* Computes the optimal new camera matrix according to the free scaling parameter alpha: + alpha=0 - only valid pixels will be retained in the undistorted image + alpha=1 - all the source image pixels will be retained in the undistorted image +*/ +CVAPI(void) cvGetOptimalNewCameraMatrix( const CvMat* camera_matrix, + const CvMat* dist_coeffs, + CvSize image_size, double alpha, + CvMat* new_camera_matrix, + CvSize new_imag_size CV_DEFAULT(cvSize(0,0)), + CvRect* valid_pixel_ROI CV_DEFAULT(0), + int center_principal_point CV_DEFAULT(0)); + +/* Converts rotation vector to rotation matrix or vice versa */ +CVAPI(int) cvRodrigues2( const CvMat* src, CvMat* dst, + CvMat* jacobian CV_DEFAULT(0) ); + +/* Finds perspective transformation between the object plane and image (view) plane */ +CVAPI(int) cvFindHomography( const CvMat* src_points, + const CvMat* dst_points, + CvMat* homography, + int method CV_DEFAULT(0), + double ransacReprojThreshold CV_DEFAULT(3), + CvMat* mask CV_DEFAULT(0), + int maxIters CV_DEFAULT(2000), + double confidence CV_DEFAULT(0.995)); + +/* Computes RQ decomposition for 3x3 matrices */ +CVAPI(void) cvRQDecomp3x3( const CvMat *matrixM, CvMat *matrixR, CvMat *matrixQ, + CvMat *matrixQx CV_DEFAULT(NULL), + CvMat *matrixQy CV_DEFAULT(NULL), + CvMat *matrixQz CV_DEFAULT(NULL), + CvPoint3D64f *eulerAngles CV_DEFAULT(NULL)); + +/* Computes projection matrix decomposition */ +CVAPI(void) cvDecomposeProjectionMatrix( const CvMat *projMatr, CvMat *calibMatr, + CvMat *rotMatr, CvMat *posVect, + CvMat *rotMatrX CV_DEFAULT(NULL), + CvMat *rotMatrY CV_DEFAULT(NULL), + CvMat *rotMatrZ CV_DEFAULT(NULL), + CvPoint3D64f *eulerAngles CV_DEFAULT(NULL)); + +/* Computes d(AB)/dA and d(AB)/dB */ +CVAPI(void) cvCalcMatMulDeriv( const CvMat* A, const CvMat* B, CvMat* dABdA, CvMat* dABdB ); + +/* Computes r3 = rodrigues(rodrigues(r2)*rodrigues(r1)), + t3 = rodrigues(r2)*t1 + t2 and the respective derivatives */ +CVAPI(void) cvComposeRT( const CvMat* _rvec1, const CvMat* _tvec1, + const CvMat* _rvec2, const CvMat* _tvec2, + CvMat* _rvec3, CvMat* _tvec3, + CvMat* dr3dr1 CV_DEFAULT(0), CvMat* dr3dt1 CV_DEFAULT(0), + CvMat* dr3dr2 CV_DEFAULT(0), CvMat* dr3dt2 CV_DEFAULT(0), + CvMat* dt3dr1 CV_DEFAULT(0), CvMat* dt3dt1 CV_DEFAULT(0), + CvMat* dt3dr2 CV_DEFAULT(0), CvMat* dt3dt2 CV_DEFAULT(0) ); + +/* Projects object points to the view plane using + the specified extrinsic and intrinsic camera parameters */ +CVAPI(void) cvProjectPoints2( const CvMat* object_points, const CvMat* rotation_vector, + const CvMat* translation_vector, const CvMat* camera_matrix, + const CvMat* distortion_coeffs, CvMat* image_points, + CvMat* dpdrot CV_DEFAULT(NULL), CvMat* dpdt CV_DEFAULT(NULL), + CvMat* dpdf CV_DEFAULT(NULL), CvMat* dpdc CV_DEFAULT(NULL), + CvMat* dpddist CV_DEFAULT(NULL), + double aspect_ratio CV_DEFAULT(0)); + +/* Finds extrinsic camera parameters from + a few known corresponding point pairs and intrinsic parameters */ +CVAPI(void) cvFindExtrinsicCameraParams2( const CvMat* object_points, + const CvMat* image_points, + const CvMat* camera_matrix, + const CvMat* distortion_coeffs, + CvMat* rotation_vector, + CvMat* translation_vector, + int use_extrinsic_guess CV_DEFAULT(0) ); + +/* Computes initial estimate of the intrinsic camera parameters + in case of planar calibration target (e.g. chessboard) */ +CVAPI(void) cvInitIntrinsicParams2D( const CvMat* object_points, + const CvMat* image_points, + const CvMat* npoints, CvSize image_size, + CvMat* camera_matrix, + double aspect_ratio CV_DEFAULT(1.) ); + +#define CV_CALIB_CB_ADAPTIVE_THRESH 1 +#define CV_CALIB_CB_NORMALIZE_IMAGE 2 +#define CV_CALIB_CB_FILTER_QUADS 4 +#define CV_CALIB_CB_FAST_CHECK 8 + +// Performs a fast check if a chessboard is in the input image. This is a workaround to +// a problem of cvFindChessboardCorners being slow on images with no chessboard +// - src: input image +// - size: chessboard size +// Returns 1 if a chessboard can be in this image and findChessboardCorners should be called, +// 0 if there is no chessboard, -1 in case of error +CVAPI(int) cvCheckChessboard(IplImage* src, CvSize size); + + /* Detects corners on a chessboard calibration pattern */ +CVAPI(int) cvFindChessboardCorners( const void* image, CvSize pattern_size, + CvPoint2D32f* corners, + int* corner_count CV_DEFAULT(NULL), + int flags CV_DEFAULT(CV_CALIB_CB_ADAPTIVE_THRESH+CV_CALIB_CB_NORMALIZE_IMAGE) ); + +/* Draws individual chessboard corners or the whole chessboard detected */ +CVAPI(void) cvDrawChessboardCorners( CvArr* image, CvSize pattern_size, + CvPoint2D32f* corners, + int count, int pattern_was_found ); + +#define CV_CALIB_USE_INTRINSIC_GUESS 1 +#define CV_CALIB_FIX_ASPECT_RATIO 2 +#define CV_CALIB_FIX_PRINCIPAL_POINT 4 +#define CV_CALIB_ZERO_TANGENT_DIST 8 +#define CV_CALIB_FIX_FOCAL_LENGTH 16 +#define CV_CALIB_FIX_K1 32 +#define CV_CALIB_FIX_K2 64 +#define CV_CALIB_FIX_K3 128 +#define CV_CALIB_FIX_K4 2048 +#define CV_CALIB_FIX_K5 4096 +#define CV_CALIB_FIX_K6 8192 +#define CV_CALIB_RATIONAL_MODEL 16384 +#define CV_CALIB_THIN_PRISM_MODEL 32768 +#define CV_CALIB_FIX_S1_S2_S3_S4 65536 +#define CV_CALIB_TILTED_MODEL 262144 +#define CV_CALIB_FIX_TAUX_TAUY 524288 +#define CV_CALIB_FIX_TANGENT_DIST 2097152 + +#define CV_CALIB_NINTRINSIC 18 + +/* Finds intrinsic and extrinsic camera parameters + from a few views of known calibration pattern */ +CVAPI(double) cvCalibrateCamera2( const CvMat* object_points, + const CvMat* image_points, + const CvMat* point_counts, + CvSize image_size, + CvMat* camera_matrix, + CvMat* distortion_coeffs, + CvMat* rotation_vectors CV_DEFAULT(NULL), + CvMat* translation_vectors CV_DEFAULT(NULL), + int flags CV_DEFAULT(0), + CvTermCriteria term_crit CV_DEFAULT(cvTermCriteria( + CV_TERMCRIT_ITER+CV_TERMCRIT_EPS,30,DBL_EPSILON)) ); + +/* Computes various useful characteristics of the camera from the data computed by + cvCalibrateCamera2 */ +CVAPI(void) cvCalibrationMatrixValues( const CvMat *camera_matrix, + CvSize image_size, + double aperture_width CV_DEFAULT(0), + double aperture_height CV_DEFAULT(0), + double *fovx CV_DEFAULT(NULL), + double *fovy CV_DEFAULT(NULL), + double *focal_length CV_DEFAULT(NULL), + CvPoint2D64f *principal_point CV_DEFAULT(NULL), + double *pixel_aspect_ratio CV_DEFAULT(NULL)); + +#define CV_CALIB_FIX_INTRINSIC 256 +#define CV_CALIB_SAME_FOCAL_LENGTH 512 + +/* Computes the transformation from one camera coordinate system to another one + from a few correspondent views of the same calibration target. Optionally, calibrates + both cameras */ +CVAPI(double) cvStereoCalibrate( const CvMat* object_points, const CvMat* image_points1, + const CvMat* image_points2, const CvMat* npoints, + CvMat* camera_matrix1, CvMat* dist_coeffs1, + CvMat* camera_matrix2, CvMat* dist_coeffs2, + CvSize image_size, CvMat* R, CvMat* T, + CvMat* E CV_DEFAULT(0), CvMat* F CV_DEFAULT(0), + int flags CV_DEFAULT(CV_CALIB_FIX_INTRINSIC), + CvTermCriteria term_crit CV_DEFAULT(cvTermCriteria( + CV_TERMCRIT_ITER+CV_TERMCRIT_EPS,30,1e-6)) ); + +#define CV_CALIB_ZERO_DISPARITY 1024 + +/* Computes 3D rotations (+ optional shift) for each camera coordinate system to make both + views parallel (=> to make all the epipolar lines horizontal or vertical) */ +CVAPI(void) cvStereoRectify( const CvMat* camera_matrix1, const CvMat* camera_matrix2, + const CvMat* dist_coeffs1, const CvMat* dist_coeffs2, + CvSize image_size, const CvMat* R, const CvMat* T, + CvMat* R1, CvMat* R2, CvMat* P1, CvMat* P2, + CvMat* Q CV_DEFAULT(0), + int flags CV_DEFAULT(CV_CALIB_ZERO_DISPARITY), + double alpha CV_DEFAULT(-1), + CvSize new_image_size CV_DEFAULT(cvSize(0,0)), + CvRect* valid_pix_ROI1 CV_DEFAULT(0), + CvRect* valid_pix_ROI2 CV_DEFAULT(0)); + +/* Computes rectification transformations for uncalibrated pair of images using a set + of point correspondences */ +CVAPI(int) cvStereoRectifyUncalibrated( const CvMat* points1, const CvMat* points2, + const CvMat* F, CvSize img_size, + CvMat* H1, CvMat* H2, + double threshold CV_DEFAULT(5)); + + + +/* stereo correspondence parameters and functions */ + +#define CV_STEREO_BM_NORMALIZED_RESPONSE 0 +#define CV_STEREO_BM_XSOBEL 1 + +/* Block matching algorithm structure */ +typedef struct CvStereoBMState +{ + // pre-filtering (normalization of input images) + int preFilterType; // =CV_STEREO_BM_NORMALIZED_RESPONSE now + int preFilterSize; // averaging window size: ~5x5..21x21 + int preFilterCap; // the output of pre-filtering is clipped by [-preFilterCap,preFilterCap] + + // correspondence using Sum of Absolute Difference (SAD) + int SADWindowSize; // ~5x5..21x21 + int minDisparity; // minimum disparity (can be negative) + int numberOfDisparities; // maximum disparity - minimum disparity (> 0) + + // post-filtering + int textureThreshold; // the disparity is only computed for pixels + // with textured enough neighborhood + int uniquenessRatio; // accept the computed disparity d* only if + // SAD(d) >= SAD(d*)*(1 + uniquenessRatio/100.) + // for any d != d*+/-1 within the search range. + int speckleWindowSize; // disparity variation window + int speckleRange; // acceptable range of variation in window + + int trySmallerWindows; // if 1, the results may be more accurate, + // at the expense of slower processing + CvRect roi1, roi2; + int disp12MaxDiff; + + // temporary buffers + CvMat* preFilteredImg0; + CvMat* preFilteredImg1; + CvMat* slidingSumBuf; + CvMat* cost; + CvMat* disp; +} CvStereoBMState; + +#define CV_STEREO_BM_BASIC 0 +#define CV_STEREO_BM_FISH_EYE 1 +#define CV_STEREO_BM_NARROW 2 + +CVAPI(CvStereoBMState*) cvCreateStereoBMState(int preset CV_DEFAULT(CV_STEREO_BM_BASIC), + int numberOfDisparities CV_DEFAULT(0)); + +CVAPI(void) cvReleaseStereoBMState( CvStereoBMState** state ); + +CVAPI(void) cvFindStereoCorrespondenceBM( const CvArr* left, const CvArr* right, + CvArr* disparity, CvStereoBMState* state ); + +CVAPI(CvRect) cvGetValidDisparityROI( CvRect roi1, CvRect roi2, int minDisparity, + int numberOfDisparities, int SADWindowSize ); + +CVAPI(void) cvValidateDisparity( CvArr* disparity, const CvArr* cost, + int minDisparity, int numberOfDisparities, + int disp12MaxDiff CV_DEFAULT(1) ); + +/* Reprojects the computed disparity image to the 3D space using the specified 4x4 matrix */ +CVAPI(void) cvReprojectImageTo3D( const CvArr* disparityImage, + CvArr* _3dImage, const CvMat* Q, + int handleMissingValues CV_DEFAULT(0) ); + +/** @} calib3d_c */ + +#ifdef __cplusplus +} // extern "C" + +////////////////////////////////////////////////////////////////////////////////////////// +class CV_EXPORTS CvLevMarq +{ +public: + CvLevMarq(); + CvLevMarq( int nparams, int nerrs, CvTermCriteria criteria= + cvTermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER,30,DBL_EPSILON), + bool completeSymmFlag=false ); + ~CvLevMarq(); + void init( int nparams, int nerrs, CvTermCriteria criteria= + cvTermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER,30,DBL_EPSILON), + bool completeSymmFlag=false ); + bool update( const CvMat*& param, CvMat*& J, CvMat*& err ); + bool updateAlt( const CvMat*& param, CvMat*& JtJ, CvMat*& JtErr, double*& errNorm ); + + void clear(); + void step(); + enum { DONE=0, STARTED=1, CALC_J=2, CHECK_ERR=3 }; + + cv::Ptr mask; + cv::Ptr prevParam; + cv::Ptr param; + cv::Ptr J; + cv::Ptr err; + cv::Ptr JtJ; + cv::Ptr JtJN; + cv::Ptr JtErr; + cv::Ptr JtJV; + cv::Ptr JtJW; + double prevErrNorm, errNorm; + int lambdaLg10; + CvTermCriteria criteria; + int state; + int iters; + bool completeSymmFlag; + int solveMethod; +}; + +#endif + +#endif /* OPENCV_CALIB3D_C_H */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core.hpp new file mode 100644 index 0000000000000000000000000000000000000000..089c0db3342048c008487c00b8125e070d75f6a0 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core.hpp @@ -0,0 +1,3285 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2015, Intel Corporation, all rights reserved. +// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved. +// Copyright (C) 2015, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_HPP +#define OPENCV_CORE_HPP + +#ifndef __cplusplus +# error core.hpp header must be compiled as C++ +#endif + +#include "opencv2/core/cvdef.h" +#include "opencv2/core/version.hpp" +#include "opencv2/core/base.hpp" +#include "opencv2/core/cvstd.hpp" +#include "opencv2/core/traits.hpp" +#include "opencv2/core/matx.hpp" +#include "opencv2/core/types.hpp" +#include "opencv2/core/mat.hpp" +#include "opencv2/core/persistence.hpp" + +/** +@defgroup core Core functionality +@{ + @defgroup core_basic Basic structures + @defgroup core_c C structures and operations + @{ + @defgroup core_c_glue Connections with C++ + @} + @defgroup core_array Operations on arrays + @defgroup core_xml XML/YAML Persistence + @defgroup core_cluster Clustering + @defgroup core_utils Utility and system functions and macros + @{ + @defgroup core_utils_sse SSE utilities + @defgroup core_utils_neon NEON utilities + @defgroup core_utils_softfloat Softfloat support + @defgroup core_utils_samples Utility functions for OpenCV samples + @} + @defgroup core_opengl OpenGL interoperability + @defgroup core_ipp Intel IPP Asynchronous C/C++ Converters + @defgroup core_optim Optimization Algorithms + @defgroup core_directx DirectX interoperability + @defgroup core_eigen Eigen support + @defgroup core_opencl OpenCL support + @defgroup core_va_intel Intel VA-API/OpenCL (CL-VA) interoperability + @defgroup core_hal Hardware Acceleration Layer + @{ + @defgroup core_hal_functions Functions + @defgroup core_hal_interface Interface + @defgroup core_hal_intrin Universal intrinsics + @{ + @defgroup core_hal_intrin_impl Private implementation helpers + @} + @} +@} + */ + +namespace cv { + +//! @addtogroup core_utils +//! @{ + +/*! @brief Class passed to an error. + +This class encapsulates all or almost all necessary +information about the error happened in the program. The exception is +usually constructed and thrown implicitly via CV_Error and CV_Error_ macros. +@see error + */ +class CV_EXPORTS Exception : public std::exception +{ +public: + /*! + Default constructor + */ + Exception(); + /*! + Full constructor. Normally the constructor is not called explicitly. + Instead, the macros CV_Error(), CV_Error_() and CV_Assert() are used. + */ + Exception(int _code, const String& _err, const String& _func, const String& _file, int _line); + virtual ~Exception() throw(); + + /*! + \return the error description and the context as a text string. + */ + virtual const char *what() const throw() CV_OVERRIDE; + void formatMessage(); + + String msg; ///< the formatted error message + + int code; ///< error code @see CVStatus + String err; ///< error description + String func; ///< function name. Available only when the compiler supports getting it + String file; ///< source file name where the error has occurred + int line; ///< line number in the source file where the error has occurred +}; + +/*! @brief Signals an error and raises the exception. + +By default the function prints information about the error to stderr, +then it either stops if cv::setBreakOnError() had been called before or raises the exception. +It is possible to alternate error processing by using #redirectError(). +@param exc the exception raisen. +@deprecated drop this version + */ +CV_EXPORTS void error( const Exception& exc ); + +enum SortFlags { SORT_EVERY_ROW = 0, //!< each matrix row is sorted independently + SORT_EVERY_COLUMN = 1, //!< each matrix column is sorted + //!< independently; this flag and the previous one are + //!< mutually exclusive. + SORT_ASCENDING = 0, //!< each matrix row is sorted in the ascending + //!< order. + SORT_DESCENDING = 16 //!< each matrix row is sorted in the + //!< descending order; this flag and the previous one are also + //!< mutually exclusive. + }; + +//! @} core_utils + +//! @addtogroup core +//! @{ + +//! Covariation flags +enum CovarFlags { + /** The output covariance matrix is calculated as: + \f[\texttt{scale} \cdot [ \texttt{vects} [0]- \texttt{mean} , \texttt{vects} [1]- \texttt{mean} ,...]^T \cdot [ \texttt{vects} [0]- \texttt{mean} , \texttt{vects} [1]- \texttt{mean} ,...],\f] + The covariance matrix will be nsamples x nsamples. Such an unusual covariance matrix is used + for fast PCA of a set of very large vectors (see, for example, the EigenFaces technique for + face recognition). Eigenvalues of this "scrambled" matrix match the eigenvalues of the true + covariance matrix. The "true" eigenvectors can be easily calculated from the eigenvectors of + the "scrambled" covariance matrix. */ + COVAR_SCRAMBLED = 0, + /**The output covariance matrix is calculated as: + \f[\texttt{scale} \cdot [ \texttt{vects} [0]- \texttt{mean} , \texttt{vects} [1]- \texttt{mean} ,...] \cdot [ \texttt{vects} [0]- \texttt{mean} , \texttt{vects} [1]- \texttt{mean} ,...]^T,\f] + covar will be a square matrix of the same size as the total number of elements in each input + vector. One and only one of #COVAR_SCRAMBLED and #COVAR_NORMAL must be specified.*/ + COVAR_NORMAL = 1, + /** If the flag is specified, the function does not calculate mean from + the input vectors but, instead, uses the passed mean vector. This is useful if mean has been + pre-calculated or known in advance, or if the covariance matrix is calculated by parts. In + this case, mean is not a mean vector of the input sub-set of vectors but rather the mean + vector of the whole set.*/ + COVAR_USE_AVG = 2, + /** If the flag is specified, the covariance matrix is scaled. In the + "normal" mode, scale is 1./nsamples . In the "scrambled" mode, scale is the reciprocal of the + total number of elements in each input vector. By default (if the flag is not specified), the + covariance matrix is not scaled ( scale=1 ).*/ + COVAR_SCALE = 4, + /** If the flag is + specified, all the input vectors are stored as rows of the samples matrix. mean should be a + single-row vector in this case.*/ + COVAR_ROWS = 8, + /** If the flag is + specified, all the input vectors are stored as columns of the samples matrix. mean should be a + single-column vector in this case.*/ + COVAR_COLS = 16 +}; + +//! k-Means flags +enum KmeansFlags { + /** Select random initial centers in each attempt.*/ + KMEANS_RANDOM_CENTERS = 0, + /** Use kmeans++ center initialization by Arthur and Vassilvitskii [Arthur2007].*/ + KMEANS_PP_CENTERS = 2, + /** During the first (and possibly the only) attempt, use the + user-supplied labels instead of computing them from the initial centers. For the second and + further attempts, use the random or semi-random centers. Use one of KMEANS_\*_CENTERS flag + to specify the exact method.*/ + KMEANS_USE_INITIAL_LABELS = 1 +}; + +//! type of line +enum LineTypes { + FILLED = -1, + LINE_4 = 4, //!< 4-connected line + LINE_8 = 8, //!< 8-connected line + LINE_AA = 16 //!< antialiased line +}; + +//! Only a subset of Hershey fonts are supported +enum HersheyFonts { + FONT_HERSHEY_SIMPLEX = 0, //!< normal size sans-serif font + FONT_HERSHEY_PLAIN = 1, //!< small size sans-serif font + FONT_HERSHEY_DUPLEX = 2, //!< normal size sans-serif font (more complex than FONT_HERSHEY_SIMPLEX) + FONT_HERSHEY_COMPLEX = 3, //!< normal size serif font + FONT_HERSHEY_TRIPLEX = 4, //!< normal size serif font (more complex than FONT_HERSHEY_COMPLEX) + FONT_HERSHEY_COMPLEX_SMALL = 5, //!< smaller version of FONT_HERSHEY_COMPLEX + FONT_HERSHEY_SCRIPT_SIMPLEX = 6, //!< hand-writing style font + FONT_HERSHEY_SCRIPT_COMPLEX = 7, //!< more complex variant of FONT_HERSHEY_SCRIPT_SIMPLEX + FONT_ITALIC = 16 //!< flag for italic font +}; + +enum ReduceTypes { REDUCE_SUM = 0, //!< the output is the sum of all rows/columns of the matrix. + REDUCE_AVG = 1, //!< the output is the mean vector of all rows/columns of the matrix. + REDUCE_MAX = 2, //!< the output is the maximum (column/row-wise) of all rows/columns of the matrix. + REDUCE_MIN = 3 //!< the output is the minimum (column/row-wise) of all rows/columns of the matrix. + }; + + +/** @brief Swaps two matrices +*/ +CV_EXPORTS void swap(Mat& a, Mat& b); +/** @overload */ +CV_EXPORTS void swap( UMat& a, UMat& b ); + +//! @} core + +//! @addtogroup core_array +//! @{ + +/** @brief Computes the source location of an extrapolated pixel. + +The function computes and returns the coordinate of a donor pixel corresponding to the specified +extrapolated pixel when using the specified extrapolation border mode. For example, if you use +cv::BORDER_WRAP mode in the horizontal direction, cv::BORDER_REFLECT_101 in the vertical direction and +want to compute value of the "virtual" pixel Point(-5, 100) in a floating-point image img , it +looks like: +@code{.cpp} + float val = img.at(borderInterpolate(100, img.rows, cv::BORDER_REFLECT_101), + borderInterpolate(-5, img.cols, cv::BORDER_WRAP)); +@endcode +Normally, the function is not called directly. It is used inside filtering functions and also in +copyMakeBorder. +@param p 0-based coordinate of the extrapolated pixel along one of the axes, likely \<0 or \>= len +@param len Length of the array along the corresponding axis. +@param borderType Border type, one of the #BorderTypes, except for #BORDER_TRANSPARENT and +#BORDER_ISOLATED . When borderType==#BORDER_CONSTANT , the function always returns -1, regardless +of p and len. + +@sa copyMakeBorder +*/ +CV_EXPORTS_W int borderInterpolate(int p, int len, int borderType); + +/** @example samples/cpp/tutorial_code/ImgTrans/copyMakeBorder_demo.cpp +An example using copyMakeBorder function. +Check @ref tutorial_copyMakeBorder "the corresponding tutorial" for more details +*/ + +/** @brief Forms a border around an image. + +The function copies the source image into the middle of the destination image. The areas to the +left, to the right, above and below the copied source image will be filled with extrapolated +pixels. This is not what filtering functions based on it do (they extrapolate pixels on-fly), but +what other more complex functions, including your own, may do to simplify image boundary handling. + +The function supports the mode when src is already in the middle of dst . In this case, the +function does not copy src itself but simply constructs the border, for example: + +@code{.cpp} + // let border be the same in all directions + int border=2; + // constructs a larger image to fit both the image and the border + Mat gray_buf(rgb.rows + border*2, rgb.cols + border*2, rgb.depth()); + // select the middle part of it w/o copying data + Mat gray(gray_canvas, Rect(border, border, rgb.cols, rgb.rows)); + // convert image from RGB to grayscale + cvtColor(rgb, gray, COLOR_RGB2GRAY); + // form a border in-place + copyMakeBorder(gray, gray_buf, border, border, + border, border, BORDER_REPLICATE); + // now do some custom filtering ... + ... +@endcode +@note When the source image is a part (ROI) of a bigger image, the function will try to use the +pixels outside of the ROI to form a border. To disable this feature and always do extrapolation, as +if src was not a ROI, use borderType | #BORDER_ISOLATED. + +@param src Source image. +@param dst Destination image of the same type as src and the size Size(src.cols+left+right, +src.rows+top+bottom) . +@param top +@param bottom +@param left +@param right Parameter specifying how many pixels in each direction from the source image rectangle +to extrapolate. For example, top=1, bottom=1, left=1, right=1 mean that 1 pixel-wide border needs +to be built. +@param borderType Border type. See borderInterpolate for details. +@param value Border value if borderType==BORDER_CONSTANT . + +@sa borderInterpolate +*/ +CV_EXPORTS_W void copyMakeBorder(InputArray src, OutputArray dst, + int top, int bottom, int left, int right, + int borderType, const Scalar& value = Scalar() ); + +/** @brief Calculates the per-element sum of two arrays or an array and a scalar. + +The function add calculates: +- Sum of two arrays when both input arrays have the same size and the same number of channels: +\f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) + \texttt{src2}(I)) \quad \texttt{if mask}(I) \ne0\f] +- Sum of an array and a scalar when src2 is constructed from Scalar or has the same number of +elements as `src1.channels()`: +\f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) + \texttt{src2} ) \quad \texttt{if mask}(I) \ne0\f] +- Sum of a scalar and an array when src1 is constructed from Scalar or has the same number of +elements as `src2.channels()`: +\f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1} + \texttt{src2}(I) ) \quad \texttt{if mask}(I) \ne0\f] +where `I` is a multi-dimensional index of array elements. In case of multi-channel arrays, each +channel is processed independently. + +The first function in the list above can be replaced with matrix expressions: +@code{.cpp} + dst = src1 + src2; + dst += src1; // equivalent to add(dst, src1, dst); +@endcode +The input arrays and the output array can all have the same or different depths. For example, you +can add a 16-bit unsigned array to a 8-bit signed array and store the sum as a 32-bit +floating-point array. Depth of the output array is determined by the dtype parameter. In the second +and third cases above, as well as in the first case, when src1.depth() == src2.depth(), dtype can +be set to the default -1. In this case, the output array will have the same depth as the input +array, be it src1, src2 or both. +@note Saturation is not applied when the output array has the depth CV_32S. You may even get +result of an incorrect sign in the case of overflow. +@param src1 first input array or a scalar. +@param src2 second input array or a scalar. +@param dst output array that has the same size and number of channels as the input array(s); the +depth is defined by dtype or src1/src2. +@param mask optional operation mask - 8-bit single channel array, that specifies elements of the +output array to be changed. +@param dtype optional depth of the output array (see the discussion below). +@sa subtract, addWeighted, scaleAdd, Mat::convertTo +*/ +CV_EXPORTS_W void add(InputArray src1, InputArray src2, OutputArray dst, + InputArray mask = noArray(), int dtype = -1); + +/** @brief Calculates the per-element difference between two arrays or array and a scalar. + +The function subtract calculates: +- Difference between two arrays, when both input arrays have the same size and the same number of +channels: + \f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) - \texttt{src2}(I)) \quad \texttt{if mask}(I) \ne0\f] +- Difference between an array and a scalar, when src2 is constructed from Scalar or has the same +number of elements as `src1.channels()`: + \f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) - \texttt{src2} ) \quad \texttt{if mask}(I) \ne0\f] +- Difference between a scalar and an array, when src1 is constructed from Scalar or has the same +number of elements as `src2.channels()`: + \f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1} - \texttt{src2}(I) ) \quad \texttt{if mask}(I) \ne0\f] +- The reverse difference between a scalar and an array in the case of `SubRS`: + \f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src2} - \texttt{src1}(I) ) \quad \texttt{if mask}(I) \ne0\f] +where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each +channel is processed independently. + +The first function in the list above can be replaced with matrix expressions: +@code{.cpp} + dst = src1 - src2; + dst -= src1; // equivalent to subtract(dst, src1, dst); +@endcode +The input arrays and the output array can all have the same or different depths. For example, you +can subtract to 8-bit unsigned arrays and store the difference in a 16-bit signed array. Depth of +the output array is determined by dtype parameter. In the second and third cases above, as well as +in the first case, when src1.depth() == src2.depth(), dtype can be set to the default -1. In this +case the output array will have the same depth as the input array, be it src1, src2 or both. +@note Saturation is not applied when the output array has the depth CV_32S. You may even get +result of an incorrect sign in the case of overflow. +@param src1 first input array or a scalar. +@param src2 second input array or a scalar. +@param dst output array of the same size and the same number of channels as the input array. +@param mask optional operation mask; this is an 8-bit single channel array that specifies elements +of the output array to be changed. +@param dtype optional depth of the output array +@sa add, addWeighted, scaleAdd, Mat::convertTo + */ +CV_EXPORTS_W void subtract(InputArray src1, InputArray src2, OutputArray dst, + InputArray mask = noArray(), int dtype = -1); + + +/** @brief Calculates the per-element scaled product of two arrays. + +The function multiply calculates the per-element product of two arrays: + +\f[\texttt{dst} (I)= \texttt{saturate} ( \texttt{scale} \cdot \texttt{src1} (I) \cdot \texttt{src2} (I))\f] + +There is also a @ref MatrixExpressions -friendly variant of the first function. See Mat::mul . + +For a not-per-element matrix product, see gemm . + +@note Saturation is not applied when the output array has the depth +CV_32S. You may even get result of an incorrect sign in the case of +overflow. +@param src1 first input array. +@param src2 second input array of the same size and the same type as src1. +@param dst output array of the same size and type as src1. +@param scale optional scale factor. +@param dtype optional depth of the output array +@sa add, subtract, divide, scaleAdd, addWeighted, accumulate, accumulateProduct, accumulateSquare, +Mat::convertTo +*/ +CV_EXPORTS_W void multiply(InputArray src1, InputArray src2, + OutputArray dst, double scale = 1, int dtype = -1); + +/** @brief Performs per-element division of two arrays or a scalar by an array. + +The function cv::divide divides one array by another: +\f[\texttt{dst(I) = saturate(src1(I)*scale/src2(I))}\f] +or a scalar by an array when there is no src1 : +\f[\texttt{dst(I) = saturate(scale/src2(I))}\f] + +When src2(I) is zero, dst(I) will also be zero. Different channels of +multi-channel arrays are processed independently. + +@note Saturation is not applied when the output array has the depth CV_32S. You may even get +result of an incorrect sign in the case of overflow. +@param src1 first input array. +@param src2 second input array of the same size and type as src1. +@param scale scalar factor. +@param dst output array of the same size and type as src2. +@param dtype optional depth of the output array; if -1, dst will have depth src2.depth(), but in +case of an array-by-array division, you can only pass -1 when src1.depth()==src2.depth(). +@sa multiply, add, subtract +*/ +CV_EXPORTS_W void divide(InputArray src1, InputArray src2, OutputArray dst, + double scale = 1, int dtype = -1); + +/** @overload */ +CV_EXPORTS_W void divide(double scale, InputArray src2, + OutputArray dst, int dtype = -1); + +/** @brief Calculates the sum of a scaled array and another array. + +The function scaleAdd is one of the classical primitive linear algebra operations, known as DAXPY +or SAXPY in [BLAS](http://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms). It calculates +the sum of a scaled array and another array: +\f[\texttt{dst} (I)= \texttt{scale} \cdot \texttt{src1} (I) + \texttt{src2} (I)\f] +The function can also be emulated with a matrix expression, for example: +@code{.cpp} + Mat A(3, 3, CV_64F); + ... + A.row(0) = A.row(1)*2 + A.row(2); +@endcode +@param src1 first input array. +@param alpha scale factor for the first array. +@param src2 second input array of the same size and type as src1. +@param dst output array of the same size and type as src1. +@sa add, addWeighted, subtract, Mat::dot, Mat::convertTo +*/ +CV_EXPORTS_W void scaleAdd(InputArray src1, double alpha, InputArray src2, OutputArray dst); + +/** @example samples/cpp/tutorial_code/HighGUI/AddingImagesTrackbar.cpp +Check @ref tutorial_trackbar "the corresponding tutorial" for more details +*/ + +/** @brief Calculates the weighted sum of two arrays. + +The function addWeighted calculates the weighted sum of two arrays as follows: +\f[\texttt{dst} (I)= \texttt{saturate} ( \texttt{src1} (I)* \texttt{alpha} + \texttt{src2} (I)* \texttt{beta} + \texttt{gamma} )\f] +where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each +channel is processed independently. +The function can be replaced with a matrix expression: +@code{.cpp} + dst = src1*alpha + src2*beta + gamma; +@endcode +@note Saturation is not applied when the output array has the depth CV_32S. You may even get +result of an incorrect sign in the case of overflow. +@param src1 first input array. +@param alpha weight of the first array elements. +@param src2 second input array of the same size and channel number as src1. +@param beta weight of the second array elements. +@param gamma scalar added to each sum. +@param dst output array that has the same size and number of channels as the input arrays. +@param dtype optional depth of the output array; when both input arrays have the same depth, dtype +can be set to -1, which will be equivalent to src1.depth(). +@sa add, subtract, scaleAdd, Mat::convertTo +*/ +CV_EXPORTS_W void addWeighted(InputArray src1, double alpha, InputArray src2, + double beta, double gamma, OutputArray dst, int dtype = -1); + +/** @brief Scales, calculates absolute values, and converts the result to 8-bit. + +On each element of the input array, the function convertScaleAbs +performs three operations sequentially: scaling, taking an absolute +value, conversion to an unsigned 8-bit type: +\f[\texttt{dst} (I)= \texttt{saturate\_cast} (| \texttt{src} (I)* \texttt{alpha} + \texttt{beta} |)\f] +In case of multi-channel arrays, the function processes each channel +independently. When the output is not 8-bit, the operation can be +emulated by calling the Mat::convertTo method (or by using matrix +expressions) and then by calculating an absolute value of the result. +For example: +@code{.cpp} + Mat_ A(30,30); + randu(A, Scalar(-100), Scalar(100)); + Mat_ B = A*5 + 3; + B = abs(B); + // Mat_ B = abs(A*5+3) will also do the job, + // but it will allocate a temporary matrix +@endcode +@param src input array. +@param dst output array. +@param alpha optional scale factor. +@param beta optional delta added to the scaled values. +@sa Mat::convertTo, cv::abs(const Mat&) +*/ +CV_EXPORTS_W void convertScaleAbs(InputArray src, OutputArray dst, + double alpha = 1, double beta = 0); + +/** @brief Converts an array to half precision floating number. + +This function converts FP32 (single precision floating point) from/to FP16 (half precision floating point). CV_16S format is used to represent FP16 data. +There are two use modes (src -> dst): CV_32F -> CV_16S and CV_16S -> CV_32F. The input array has to have type of CV_32F or +CV_16S to represent the bit depth. If the input array is neither of them, the function will raise an error. +The format of half precision floating point is defined in IEEE 754-2008. + +@param src input array. +@param dst output array. +*/ +CV_EXPORTS_W void convertFp16(InputArray src, OutputArray dst); + +/** @brief Performs a look-up table transform of an array. + +The function LUT fills the output array with values from the look-up table. Indices of the entries +are taken from the input array. That is, the function processes each element of src as follows: +\f[\texttt{dst} (I) \leftarrow \texttt{lut(src(I) + d)}\f] +where +\f[d = \fork{0}{if \(\texttt{src}\) has depth \(\texttt{CV_8U}\)}{128}{if \(\texttt{src}\) has depth \(\texttt{CV_8S}\)}\f] +@param src input array of 8-bit elements. +@param lut look-up table of 256 elements; in case of multi-channel input array, the table should +either have a single channel (in this case the same table is used for all channels) or the same +number of channels as in the input array. +@param dst output array of the same size and number of channels as src, and the same depth as lut. +@sa convertScaleAbs, Mat::convertTo +*/ +CV_EXPORTS_W void LUT(InputArray src, InputArray lut, OutputArray dst); + +/** @brief Calculates the sum of array elements. + +The function cv::sum calculates and returns the sum of array elements, +independently for each channel. +@param src input array that must have from 1 to 4 channels. +@sa countNonZero, mean, meanStdDev, norm, minMaxLoc, reduce +*/ +CV_EXPORTS_AS(sumElems) Scalar sum(InputArray src); + +/** @brief Counts non-zero array elements. + +The function returns the number of non-zero elements in src : +\f[\sum _{I: \; \texttt{src} (I) \ne0 } 1\f] +@param src single-channel array. +@sa mean, meanStdDev, norm, minMaxLoc, calcCovarMatrix +*/ +CV_EXPORTS_W int countNonZero( InputArray src ); + +/** @brief Returns the list of locations of non-zero pixels + +Given a binary matrix (likely returned from an operation such +as threshold(), compare(), >, ==, etc, return all of +the non-zero indices as a cv::Mat or std::vector (x,y) +For example: +@code{.cpp} + cv::Mat binaryImage; // input, binary image + cv::Mat locations; // output, locations of non-zero pixels + cv::findNonZero(binaryImage, locations); + + // access pixel coordinates + Point pnt = locations.at(i); +@endcode +or +@code{.cpp} + cv::Mat binaryImage; // input, binary image + vector locations; // output, locations of non-zero pixels + cv::findNonZero(binaryImage, locations); + + // access pixel coordinates + Point pnt = locations[i]; +@endcode +@param src single-channel array (type CV_8UC1) +@param idx the output array, type of cv::Mat or std::vector, corresponding to non-zero indices in the input +*/ +CV_EXPORTS_W void findNonZero( InputArray src, OutputArray idx ); + +/** @brief Calculates an average (mean) of array elements. + +The function cv::mean calculates the mean value M of array elements, +independently for each channel, and return it: +\f[\begin{array}{l} N = \sum _{I: \; \texttt{mask} (I) \ne 0} 1 \\ M_c = \left ( \sum _{I: \; \texttt{mask} (I) \ne 0}{ \texttt{mtx} (I)_c} \right )/N \end{array}\f] +When all the mask elements are 0's, the function returns Scalar::all(0) +@param src input array that should have from 1 to 4 channels so that the result can be stored in +Scalar_ . +@param mask optional operation mask. +@sa countNonZero, meanStdDev, norm, minMaxLoc +*/ +CV_EXPORTS_W Scalar mean(InputArray src, InputArray mask = noArray()); + +/** Calculates a mean and standard deviation of array elements. + +The function cv::meanStdDev calculates the mean and the standard deviation M +of array elements independently for each channel and returns it via the +output parameters: +\f[\begin{array}{l} N = \sum _{I, \texttt{mask} (I) \ne 0} 1 \\ \texttt{mean} _c = \frac{\sum_{ I: \; \texttt{mask}(I) \ne 0} \texttt{src} (I)_c}{N} \\ \texttt{stddev} _c = \sqrt{\frac{\sum_{ I: \; \texttt{mask}(I) \ne 0} \left ( \texttt{src} (I)_c - \texttt{mean} _c \right )^2}{N}} \end{array}\f] +When all the mask elements are 0's, the function returns +mean=stddev=Scalar::all(0). +@note The calculated standard deviation is only the diagonal of the +complete normalized covariance matrix. If the full matrix is needed, you +can reshape the multi-channel array M x N to the single-channel array +M\*N x mtx.channels() (only possible when the matrix is continuous) and +then pass the matrix to calcCovarMatrix . +@param src input array that should have from 1 to 4 channels so that the results can be stored in +Scalar_ 's. +@param mean output parameter: calculated mean value. +@param stddev output parameter: calculated standard deviation. +@param mask optional operation mask. +@sa countNonZero, mean, norm, minMaxLoc, calcCovarMatrix +*/ +CV_EXPORTS_W void meanStdDev(InputArray src, OutputArray mean, OutputArray stddev, + InputArray mask=noArray()); + +/** @brief Calculates the absolute norm of an array. + +This version of #norm calculates the absolute norm of src1. The type of norm to calculate is specified using #NormTypes. + +As example for one array consider the function \f$r(x)= \begin{pmatrix} x \\ 1-x \end{pmatrix}, x \in [-1;1]\f$. +The \f$ L_{1}, L_{2} \f$ and \f$ L_{\infty} \f$ norm for the sample value \f$r(-1) = \begin{pmatrix} -1 \\ 2 \end{pmatrix}\f$ +is calculated as follows +\f{align*} + \| r(-1) \|_{L_1} &= |-1| + |2| = 3 \\ + \| r(-1) \|_{L_2} &= \sqrt{(-1)^{2} + (2)^{2}} = \sqrt{5} \\ + \| r(-1) \|_{L_\infty} &= \max(|-1|,|2|) = 2 +\f} +and for \f$r(0.5) = \begin{pmatrix} 0.5 \\ 0.5 \end{pmatrix}\f$ the calculation is +\f{align*} + \| r(0.5) \|_{L_1} &= |0.5| + |0.5| = 1 \\ + \| r(0.5) \|_{L_2} &= \sqrt{(0.5)^{2} + (0.5)^{2}} = \sqrt{0.5} \\ + \| r(0.5) \|_{L_\infty} &= \max(|0.5|,|0.5|) = 0.5. +\f} +The following graphic shows all values for the three norm functions \f$\| r(x) \|_{L_1}, \| r(x) \|_{L_2}\f$ and \f$\| r(x) \|_{L_\infty}\f$. +It is notable that the \f$ L_{1} \f$ norm forms the upper and the \f$ L_{\infty} \f$ norm forms the lower border for the example function \f$ r(x) \f$. +![Graphs for the different norm functions from the above example](pics/NormTypes_OneArray_1-2-INF.png) + +When the mask parameter is specified and it is not empty, the norm is + +If normType is not specified, #NORM_L2 is used. +calculated only over the region specified by the mask. + +Multi-channel input arrays are treated as single-channel arrays, that is, +the results for all channels are combined. + +Hamming norms can only be calculated with CV_8U depth arrays. + +@param src1 first input array. +@param normType type of the norm (see #NormTypes). +@param mask optional operation mask; it must have the same size as src1 and CV_8UC1 type. +*/ +CV_EXPORTS_W double norm(InputArray src1, int normType = NORM_L2, InputArray mask = noArray()); + +/** @brief Calculates an absolute difference norm or a relative difference norm. + +This version of cv::norm calculates the absolute difference norm +or the relative difference norm of arrays src1 and src2. +The type of norm to calculate is specified using #NormTypes. + +@param src1 first input array. +@param src2 second input array of the same size and the same type as src1. +@param normType type of the norm (see #NormTypes). +@param mask optional operation mask; it must have the same size as src1 and CV_8UC1 type. +*/ +CV_EXPORTS_W double norm(InputArray src1, InputArray src2, + int normType = NORM_L2, InputArray mask = noArray()); +/** @overload +@param src first input array. +@param normType type of the norm (see #NormTypes). +*/ +CV_EXPORTS double norm( const SparseMat& src, int normType ); + +/** @brief Computes the Peak Signal-to-Noise Ratio (PSNR) image quality metric. + +This function calculates the Peak Signal-to-Noise Ratio (PSNR) image quality metric in decibels (dB), between two input arrays src1 and src2. Arrays must have depth CV_8U. + +The PSNR is calculated as follows: + +\f[ +\texttt{PSNR} = 10 \cdot \log_{10}{\left( \frac{R^2}{MSE} \right) } +\f] + +where R is the maximum integer value of depth CV_8U (255) and MSE is the mean squared error between the two arrays. + +@param src1 first input array. +@param src2 second input array of the same size as src1. + + */ +CV_EXPORTS_W double PSNR(InputArray src1, InputArray src2); + +/** @brief naive nearest neighbor finder + +see http://en.wikipedia.org/wiki/Nearest_neighbor_search +@todo document + */ +CV_EXPORTS_W void batchDistance(InputArray src1, InputArray src2, + OutputArray dist, int dtype, OutputArray nidx, + int normType = NORM_L2, int K = 0, + InputArray mask = noArray(), int update = 0, + bool crosscheck = false); + +/** @brief Normalizes the norm or value range of an array. + +The function cv::normalize normalizes scale and shift the input array elements so that +\f[\| \texttt{dst} \| _{L_p}= \texttt{alpha}\f] +(where p=Inf, 1 or 2) when normType=NORM_INF, NORM_L1, or NORM_L2, respectively; or so that +\f[\min _I \texttt{dst} (I)= \texttt{alpha} , \, \, \max _I \texttt{dst} (I)= \texttt{beta}\f] + +when normType=NORM_MINMAX (for dense arrays only). The optional mask specifies a sub-array to be +normalized. This means that the norm or min-n-max are calculated over the sub-array, and then this +sub-array is modified to be normalized. If you want to only use the mask to calculate the norm or +min-max but modify the whole array, you can use norm and Mat::convertTo. + +In case of sparse matrices, only the non-zero values are analyzed and transformed. Because of this, +the range transformation for sparse matrices is not allowed since it can shift the zero level. + +Possible usage with some positive example data: +@code{.cpp} + vector positiveData = { 2.0, 8.0, 10.0 }; + vector normalizedData_l1, normalizedData_l2, normalizedData_inf, normalizedData_minmax; + + // Norm to probability (total count) + // sum(numbers) = 20.0 + // 2.0 0.1 (2.0/20.0) + // 8.0 0.4 (8.0/20.0) + // 10.0 0.5 (10.0/20.0) + normalize(positiveData, normalizedData_l1, 1.0, 0.0, NORM_L1); + + // Norm to unit vector: ||positiveData|| = 1.0 + // 2.0 0.15 + // 8.0 0.62 + // 10.0 0.77 + normalize(positiveData, normalizedData_l2, 1.0, 0.0, NORM_L2); + + // Norm to max element + // 2.0 0.2 (2.0/10.0) + // 8.0 0.8 (8.0/10.0) + // 10.0 1.0 (10.0/10.0) + normalize(positiveData, normalizedData_inf, 1.0, 0.0, NORM_INF); + + // Norm to range [0.0;1.0] + // 2.0 0.0 (shift to left border) + // 8.0 0.75 (6.0/8.0) + // 10.0 1.0 (shift to right border) + normalize(positiveData, normalizedData_minmax, 1.0, 0.0, NORM_MINMAX); +@endcode + +@param src input array. +@param dst output array of the same size as src . +@param alpha norm value to normalize to or the lower range boundary in case of the range +normalization. +@param beta upper range boundary in case of the range normalization; it is not used for the norm +normalization. +@param norm_type normalization type (see cv::NormTypes). +@param dtype when negative, the output array has the same type as src; otherwise, it has the same +number of channels as src and the depth =CV_MAT_DEPTH(dtype). +@param mask optional operation mask. +@sa norm, Mat::convertTo, SparseMat::convertTo +*/ +CV_EXPORTS_W void normalize( InputArray src, InputOutputArray dst, double alpha = 1, double beta = 0, + int norm_type = NORM_L2, int dtype = -1, InputArray mask = noArray()); + +/** @overload +@param src input array. +@param dst output array of the same size as src . +@param alpha norm value to normalize to or the lower range boundary in case of the range +normalization. +@param normType normalization type (see cv::NormTypes). +*/ +CV_EXPORTS void normalize( const SparseMat& src, SparseMat& dst, double alpha, int normType ); + +/** @brief Finds the global minimum and maximum in an array. + +The function cv::minMaxLoc finds the minimum and maximum element values and their positions. The +extremums are searched across the whole array or, if mask is not an empty array, in the specified +array region. + +The function do not work with multi-channel arrays. If you need to find minimum or maximum +elements across all the channels, use Mat::reshape first to reinterpret the array as +single-channel. Or you may extract the particular channel using either extractImageCOI , or +mixChannels , or split . +@param src input single-channel array. +@param minVal pointer to the returned minimum value; NULL is used if not required. +@param maxVal pointer to the returned maximum value; NULL is used if not required. +@param minLoc pointer to the returned minimum location (in 2D case); NULL is used if not required. +@param maxLoc pointer to the returned maximum location (in 2D case); NULL is used if not required. +@param mask optional mask used to select a sub-array. +@sa max, min, compare, inRange, extractImageCOI, mixChannels, split, Mat::reshape +*/ +CV_EXPORTS_W void minMaxLoc(InputArray src, CV_OUT double* minVal, + CV_OUT double* maxVal = 0, CV_OUT Point* minLoc = 0, + CV_OUT Point* maxLoc = 0, InputArray mask = noArray()); + + +/** @brief Finds the global minimum and maximum in an array + +The function cv::minMaxIdx finds the minimum and maximum element values and their positions. The +extremums are searched across the whole array or, if mask is not an empty array, in the specified +array region. The function does not work with multi-channel arrays. If you need to find minimum or +maximum elements across all the channels, use Mat::reshape first to reinterpret the array as +single-channel. Or you may extract the particular channel using either extractImageCOI , or +mixChannels , or split . In case of a sparse matrix, the minimum is found among non-zero elements +only. +@note When minIdx is not NULL, it must have at least 2 elements (as well as maxIdx), even if src is +a single-row or single-column matrix. In OpenCV (following MATLAB) each array has at least 2 +dimensions, i.e. single-column matrix is Mx1 matrix (and therefore minIdx/maxIdx will be +(i1,0)/(i2,0)) and single-row matrix is 1xN matrix (and therefore minIdx/maxIdx will be +(0,j1)/(0,j2)). +@param src input single-channel array. +@param minVal pointer to the returned minimum value; NULL is used if not required. +@param maxVal pointer to the returned maximum value; NULL is used if not required. +@param minIdx pointer to the returned minimum location (in nD case); NULL is used if not required; +Otherwise, it must point to an array of src.dims elements, the coordinates of the minimum element +in each dimension are stored there sequentially. +@param maxIdx pointer to the returned maximum location (in nD case). NULL is used if not required. +@param mask specified array region +*/ +CV_EXPORTS void minMaxIdx(InputArray src, double* minVal, double* maxVal = 0, + int* minIdx = 0, int* maxIdx = 0, InputArray mask = noArray()); + +/** @overload +@param a input single-channel array. +@param minVal pointer to the returned minimum value; NULL is used if not required. +@param maxVal pointer to the returned maximum value; NULL is used if not required. +@param minIdx pointer to the returned minimum location (in nD case); NULL is used if not required; +Otherwise, it must point to an array of src.dims elements, the coordinates of the minimum element +in each dimension are stored there sequentially. +@param maxIdx pointer to the returned maximum location (in nD case). NULL is used if not required. +*/ +CV_EXPORTS void minMaxLoc(const SparseMat& a, double* minVal, + double* maxVal, int* minIdx = 0, int* maxIdx = 0); + +/** @brief Reduces a matrix to a vector. + +The function #reduce reduces the matrix to a vector by treating the matrix rows/columns as a set of +1D vectors and performing the specified operation on the vectors until a single row/column is +obtained. For example, the function can be used to compute horizontal and vertical projections of a +raster image. In case of #REDUCE_MAX and #REDUCE_MIN , the output image should have the same type as the source one. +In case of #REDUCE_SUM and #REDUCE_AVG , the output may have a larger element bit-depth to preserve accuracy. +And multi-channel arrays are also supported in these two reduction modes. + +The following code demonstrates its usage for a single channel matrix. +@snippet snippets/core_reduce.cpp example + +And the following code demonstrates its usage for a two-channel matrix. +@snippet snippets/core_reduce.cpp example2 + +@param src input 2D matrix. +@param dst output vector. Its size and type is defined by dim and dtype parameters. +@param dim dimension index along which the matrix is reduced. 0 means that the matrix is reduced to +a single row. 1 means that the matrix is reduced to a single column. +@param rtype reduction operation that could be one of #ReduceTypes +@param dtype when negative, the output vector will have the same type as the input matrix, +otherwise, its type will be CV_MAKE_TYPE(CV_MAT_DEPTH(dtype), src.channels()). +@sa repeat +*/ +CV_EXPORTS_W void reduce(InputArray src, OutputArray dst, int dim, int rtype, int dtype = -1); + +/** @brief Creates one multi-channel array out of several single-channel ones. + +The function cv::merge merges several arrays to make a single multi-channel array. That is, each +element of the output array will be a concatenation of the elements of the input arrays, where +elements of i-th input array are treated as mv[i].channels()-element vectors. + +The function cv::split does the reverse operation. If you need to shuffle channels in some other +advanced way, use cv::mixChannels. + +The following example shows how to merge 3 single channel matrices into a single 3-channel matrix. +@snippet snippets/core_merge.cpp example + +@param mv input array of matrices to be merged; all the matrices in mv must have the same +size and the same depth. +@param count number of input matrices when mv is a plain C array; it must be greater than zero. +@param dst output array of the same size and the same depth as mv[0]; The number of channels will +be equal to the parameter count. +@sa mixChannels, split, Mat::reshape +*/ +CV_EXPORTS void merge(const Mat* mv, size_t count, OutputArray dst); + +/** @overload +@param mv input vector of matrices to be merged; all the matrices in mv must have the same +size and the same depth. +@param dst output array of the same size and the same depth as mv[0]; The number of channels will +be the total number of channels in the matrix array. + */ +CV_EXPORTS_W void merge(InputArrayOfArrays mv, OutputArray dst); + +/** @brief Divides a multi-channel array into several single-channel arrays. + +The function cv::split splits a multi-channel array into separate single-channel arrays: +\f[\texttt{mv} [c](I) = \texttt{src} (I)_c\f] +If you need to extract a single channel or do some other sophisticated channel permutation, use +mixChannels . + +The following example demonstrates how to split a 3-channel matrix into 3 single channel matrices. +@snippet snippets/core_split.cpp example + +@param src input multi-channel array. +@param mvbegin output array; the number of arrays must match src.channels(); the arrays themselves are +reallocated, if needed. +@sa merge, mixChannels, cvtColor +*/ +CV_EXPORTS void split(const Mat& src, Mat* mvbegin); + +/** @overload +@param m input multi-channel array. +@param mv output vector of arrays; the arrays themselves are reallocated, if needed. +*/ +CV_EXPORTS_W void split(InputArray m, OutputArrayOfArrays mv); + +/** @brief Copies specified channels from input arrays to the specified channels of +output arrays. + +The function cv::mixChannels provides an advanced mechanism for shuffling image channels. + +cv::split,cv::merge,cv::extractChannel,cv::insertChannel and some forms of cv::cvtColor are partial cases of cv::mixChannels. + +In the example below, the code splits a 4-channel BGRA image into a 3-channel BGR (with B and R +channels swapped) and a separate alpha-channel image: +@code{.cpp} + Mat bgra( 100, 100, CV_8UC4, Scalar(255,0,0,255) ); + Mat bgr( bgra.rows, bgra.cols, CV_8UC3 ); + Mat alpha( bgra.rows, bgra.cols, CV_8UC1 ); + + // forming an array of matrices is a quite efficient operation, + // because the matrix data is not copied, only the headers + Mat out[] = { bgr, alpha }; + // bgra[0] -> bgr[2], bgra[1] -> bgr[1], + // bgra[2] -> bgr[0], bgra[3] -> alpha[0] + int from_to[] = { 0,2, 1,1, 2,0, 3,3 }; + mixChannels( &bgra, 1, out, 2, from_to, 4 ); +@endcode +@note Unlike many other new-style C++ functions in OpenCV (see the introduction section and +Mat::create ), cv::mixChannels requires the output arrays to be pre-allocated before calling the +function. +@param src input array or vector of matrices; all of the matrices must have the same size and the +same depth. +@param nsrcs number of matrices in `src`. +@param dst output array or vector of matrices; all the matrices **must be allocated**; their size and +depth must be the same as in `src[0]`. +@param ndsts number of matrices in `dst`. +@param fromTo array of index pairs specifying which channels are copied and where; fromTo[k\*2] is +a 0-based index of the input channel in src, fromTo[k\*2+1] is an index of the output channel in +dst; the continuous channel numbering is used: the first input image channels are indexed from 0 to +src[0].channels()-1, the second input image channels are indexed from src[0].channels() to +src[0].channels() + src[1].channels()-1, and so on, the same scheme is used for the output image +channels; as a special case, when fromTo[k\*2] is negative, the corresponding output channel is +filled with zero . +@param npairs number of index pairs in `fromTo`. +@sa split, merge, extractChannel, insertChannel, cvtColor +*/ +CV_EXPORTS void mixChannels(const Mat* src, size_t nsrcs, Mat* dst, size_t ndsts, + const int* fromTo, size_t npairs); + +/** @overload +@param src input array or vector of matrices; all of the matrices must have the same size and the +same depth. +@param dst output array or vector of matrices; all the matrices **must be allocated**; their size and +depth must be the same as in src[0]. +@param fromTo array of index pairs specifying which channels are copied and where; fromTo[k\*2] is +a 0-based index of the input channel in src, fromTo[k\*2+1] is an index of the output channel in +dst; the continuous channel numbering is used: the first input image channels are indexed from 0 to +src[0].channels()-1, the second input image channels are indexed from src[0].channels() to +src[0].channels() + src[1].channels()-1, and so on, the same scheme is used for the output image +channels; as a special case, when fromTo[k\*2] is negative, the corresponding output channel is +filled with zero . +@param npairs number of index pairs in fromTo. +*/ +CV_EXPORTS void mixChannels(InputArrayOfArrays src, InputOutputArrayOfArrays dst, + const int* fromTo, size_t npairs); + +/** @overload +@param src input array or vector of matrices; all of the matrices must have the same size and the +same depth. +@param dst output array or vector of matrices; all the matrices **must be allocated**; their size and +depth must be the same as in src[0]. +@param fromTo array of index pairs specifying which channels are copied and where; fromTo[k\*2] is +a 0-based index of the input channel in src, fromTo[k\*2+1] is an index of the output channel in +dst; the continuous channel numbering is used: the first input image channels are indexed from 0 to +src[0].channels()-1, the second input image channels are indexed from src[0].channels() to +src[0].channels() + src[1].channels()-1, and so on, the same scheme is used for the output image +channels; as a special case, when fromTo[k\*2] is negative, the corresponding output channel is +filled with zero . +*/ +CV_EXPORTS_W void mixChannels(InputArrayOfArrays src, InputOutputArrayOfArrays dst, + const std::vector& fromTo); + +/** @brief Extracts a single channel from src (coi is 0-based index) +@param src input array +@param dst output array +@param coi index of channel to extract +@sa mixChannels, split +*/ +CV_EXPORTS_W void extractChannel(InputArray src, OutputArray dst, int coi); + +/** @brief Inserts a single channel to dst (coi is 0-based index) +@param src input array +@param dst output array +@param coi index of channel for insertion +@sa mixChannels, merge +*/ +CV_EXPORTS_W void insertChannel(InputArray src, InputOutputArray dst, int coi); + +/** @brief Flips a 2D array around vertical, horizontal, or both axes. + +The function cv::flip flips the array in one of three different ways (row +and column indices are 0-based): +\f[\texttt{dst} _{ij} = +\left\{ +\begin{array}{l l} +\texttt{src} _{\texttt{src.rows}-i-1,j} & if\; \texttt{flipCode} = 0 \\ +\texttt{src} _{i, \texttt{src.cols} -j-1} & if\; \texttt{flipCode} > 0 \\ +\texttt{src} _{ \texttt{src.rows} -i-1, \texttt{src.cols} -j-1} & if\; \texttt{flipCode} < 0 \\ +\end{array} +\right.\f] +The example scenarios of using the function are the following: +* Vertical flipping of the image (flipCode == 0) to switch between + top-left and bottom-left image origin. This is a typical operation + in video processing on Microsoft Windows\* OS. +* Horizontal flipping of the image with the subsequent horizontal + shift and absolute difference calculation to check for a + vertical-axis symmetry (flipCode \> 0). +* Simultaneous horizontal and vertical flipping of the image with + the subsequent shift and absolute difference calculation to check + for a central symmetry (flipCode \< 0). +* Reversing the order of point arrays (flipCode \> 0 or + flipCode == 0). +@param src input array. +@param dst output array of the same size and type as src. +@param flipCode a flag to specify how to flip the array; 0 means +flipping around the x-axis and positive value (for example, 1) means +flipping around y-axis. Negative value (for example, -1) means flipping +around both axes. +@sa transpose , repeat , completeSymm +*/ +CV_EXPORTS_W void flip(InputArray src, OutputArray dst, int flipCode); + +enum RotateFlags { + ROTATE_90_CLOCKWISE = 0, //! A = (cv::Mat_(3, 2) << 1, 4, + 2, 5, + 3, 6); + cv::Mat_ B = (cv::Mat_(3, 2) << 7, 10, + 8, 11, + 9, 12); + + cv::Mat C; + cv::hconcat(A, B, C); + //C: + //[1, 4, 7, 10; + // 2, 5, 8, 11; + // 3, 6, 9, 12] + @endcode + @param src1 first input array to be considered for horizontal concatenation. + @param src2 second input array to be considered for horizontal concatenation. + @param dst output array. It has the same number of rows and depth as the src1 and src2, and the sum of cols of the src1 and src2. + */ +CV_EXPORTS void hconcat(InputArray src1, InputArray src2, OutputArray dst); +/** @overload + @code{.cpp} + std::vector matrices = { cv::Mat(4, 1, CV_8UC1, cv::Scalar(1)), + cv::Mat(4, 1, CV_8UC1, cv::Scalar(2)), + cv::Mat(4, 1, CV_8UC1, cv::Scalar(3)),}; + + cv::Mat out; + cv::hconcat( matrices, out ); + //out: + //[1, 2, 3; + // 1, 2, 3; + // 1, 2, 3; + // 1, 2, 3] + @endcode + @param src input array or vector of matrices. all of the matrices must have the same number of rows and the same depth. + @param dst output array. It has the same number of rows and depth as the src, and the sum of cols of the src. +same depth. + */ +CV_EXPORTS_W void hconcat(InputArrayOfArrays src, OutputArray dst); + +/** @brief Applies vertical concatenation to given matrices. + +The function vertically concatenates two or more cv::Mat matrices (with the same number of cols). +@code{.cpp} + cv::Mat matArray[] = { cv::Mat(1, 4, CV_8UC1, cv::Scalar(1)), + cv::Mat(1, 4, CV_8UC1, cv::Scalar(2)), + cv::Mat(1, 4, CV_8UC1, cv::Scalar(3)),}; + + cv::Mat out; + cv::vconcat( matArray, 3, out ); + //out: + //[1, 1, 1, 1; + // 2, 2, 2, 2; + // 3, 3, 3, 3] +@endcode +@param src input array or vector of matrices. all of the matrices must have the same number of cols and the same depth. +@param nsrc number of matrices in src. +@param dst output array. It has the same number of cols and depth as the src, and the sum of rows of the src. +@sa cv::hconcat(const Mat*, size_t, OutputArray), @sa cv::hconcat(InputArrayOfArrays, OutputArray) and @sa cv::hconcat(InputArray, InputArray, OutputArray) +*/ +CV_EXPORTS void vconcat(const Mat* src, size_t nsrc, OutputArray dst); +/** @overload + @code{.cpp} + cv::Mat_ A = (cv::Mat_(3, 2) << 1, 7, + 2, 8, + 3, 9); + cv::Mat_ B = (cv::Mat_(3, 2) << 4, 10, + 5, 11, + 6, 12); + + cv::Mat C; + cv::vconcat(A, B, C); + //C: + //[1, 7; + // 2, 8; + // 3, 9; + // 4, 10; + // 5, 11; + // 6, 12] + @endcode + @param src1 first input array to be considered for vertical concatenation. + @param src2 second input array to be considered for vertical concatenation. + @param dst output array. It has the same number of cols and depth as the src1 and src2, and the sum of rows of the src1 and src2. + */ +CV_EXPORTS void vconcat(InputArray src1, InputArray src2, OutputArray dst); +/** @overload + @code{.cpp} + std::vector matrices = { cv::Mat(1, 4, CV_8UC1, cv::Scalar(1)), + cv::Mat(1, 4, CV_8UC1, cv::Scalar(2)), + cv::Mat(1, 4, CV_8UC1, cv::Scalar(3)),}; + + cv::Mat out; + cv::vconcat( matrices, out ); + //out: + //[1, 1, 1, 1; + // 2, 2, 2, 2; + // 3, 3, 3, 3] + @endcode + @param src input array or vector of matrices. all of the matrices must have the same number of cols and the same depth + @param dst output array. It has the same number of cols and depth as the src, and the sum of rows of the src. +same depth. + */ +CV_EXPORTS_W void vconcat(InputArrayOfArrays src, OutputArray dst); + +/** @brief computes bitwise conjunction of the two arrays (dst = src1 & src2) +Calculates the per-element bit-wise conjunction of two arrays or an +array and a scalar. + +The function cv::bitwise_and calculates the per-element bit-wise logical conjunction for: +* Two arrays when src1 and src2 have the same size: + \f[\texttt{dst} (I) = \texttt{src1} (I) \wedge \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f] +* An array and a scalar when src2 is constructed from Scalar or has + the same number of elements as `src1.channels()`: + \f[\texttt{dst} (I) = \texttt{src1} (I) \wedge \texttt{src2} \quad \texttt{if mask} (I) \ne0\f] +* A scalar and an array when src1 is constructed from Scalar or has + the same number of elements as `src2.channels()`: + \f[\texttt{dst} (I) = \texttt{src1} \wedge \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f] +In case of floating-point arrays, their machine-specific bit +representations (usually IEEE754-compliant) are used for the operation. +In case of multi-channel arrays, each channel is processed +independently. In the second and third cases above, the scalar is first +converted to the array type. +@param src1 first input array or a scalar. +@param src2 second input array or a scalar. +@param dst output array that has the same size and type as the input +arrays. +@param mask optional operation mask, 8-bit single channel array, that +specifies elements of the output array to be changed. +*/ +CV_EXPORTS_W void bitwise_and(InputArray src1, InputArray src2, + OutputArray dst, InputArray mask = noArray()); + +/** @brief Calculates the per-element bit-wise disjunction of two arrays or an +array and a scalar. + +The function cv::bitwise_or calculates the per-element bit-wise logical disjunction for: +* Two arrays when src1 and src2 have the same size: + \f[\texttt{dst} (I) = \texttt{src1} (I) \vee \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f] +* An array and a scalar when src2 is constructed from Scalar or has + the same number of elements as `src1.channels()`: + \f[\texttt{dst} (I) = \texttt{src1} (I) \vee \texttt{src2} \quad \texttt{if mask} (I) \ne0\f] +* A scalar and an array when src1 is constructed from Scalar or has + the same number of elements as `src2.channels()`: + \f[\texttt{dst} (I) = \texttt{src1} \vee \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f] +In case of floating-point arrays, their machine-specific bit +representations (usually IEEE754-compliant) are used for the operation. +In case of multi-channel arrays, each channel is processed +independently. In the second and third cases above, the scalar is first +converted to the array type. +@param src1 first input array or a scalar. +@param src2 second input array or a scalar. +@param dst output array that has the same size and type as the input +arrays. +@param mask optional operation mask, 8-bit single channel array, that +specifies elements of the output array to be changed. +*/ +CV_EXPORTS_W void bitwise_or(InputArray src1, InputArray src2, + OutputArray dst, InputArray mask = noArray()); + +/** @brief Calculates the per-element bit-wise "exclusive or" operation on two +arrays or an array and a scalar. + +The function cv::bitwise_xor calculates the per-element bit-wise logical "exclusive-or" +operation for: +* Two arrays when src1 and src2 have the same size: + \f[\texttt{dst} (I) = \texttt{src1} (I) \oplus \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f] +* An array and a scalar when src2 is constructed from Scalar or has + the same number of elements as `src1.channels()`: + \f[\texttt{dst} (I) = \texttt{src1} (I) \oplus \texttt{src2} \quad \texttt{if mask} (I) \ne0\f] +* A scalar and an array when src1 is constructed from Scalar or has + the same number of elements as `src2.channels()`: + \f[\texttt{dst} (I) = \texttt{src1} \oplus \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f] +In case of floating-point arrays, their machine-specific bit +representations (usually IEEE754-compliant) are used for the operation. +In case of multi-channel arrays, each channel is processed +independently. In the 2nd and 3rd cases above, the scalar is first +converted to the array type. +@param src1 first input array or a scalar. +@param src2 second input array or a scalar. +@param dst output array that has the same size and type as the input +arrays. +@param mask optional operation mask, 8-bit single channel array, that +specifies elements of the output array to be changed. +*/ +CV_EXPORTS_W void bitwise_xor(InputArray src1, InputArray src2, + OutputArray dst, InputArray mask = noArray()); + +/** @brief Inverts every bit of an array. + +The function cv::bitwise_not calculates per-element bit-wise inversion of the input +array: +\f[\texttt{dst} (I) = \neg \texttt{src} (I)\f] +In case of a floating-point input array, its machine-specific bit +representation (usually IEEE754-compliant) is used for the operation. In +case of multi-channel arrays, each channel is processed independently. +@param src input array. +@param dst output array that has the same size and type as the input +array. +@param mask optional operation mask, 8-bit single channel array, that +specifies elements of the output array to be changed. +*/ +CV_EXPORTS_W void bitwise_not(InputArray src, OutputArray dst, + InputArray mask = noArray()); + +/** @brief Calculates the per-element absolute difference between two arrays or between an array and a scalar. + +The function cv::absdiff calculates: +* Absolute difference between two arrays when they have the same + size and type: + \f[\texttt{dst}(I) = \texttt{saturate} (| \texttt{src1}(I) - \texttt{src2}(I)|)\f] +* Absolute difference between an array and a scalar when the second + array is constructed from Scalar or has as many elements as the + number of channels in `src1`: + \f[\texttt{dst}(I) = \texttt{saturate} (| \texttt{src1}(I) - \texttt{src2} |)\f] +* Absolute difference between a scalar and an array when the first + array is constructed from Scalar or has as many elements as the + number of channels in `src2`: + \f[\texttt{dst}(I) = \texttt{saturate} (| \texttt{src1} - \texttt{src2}(I) |)\f] + where I is a multi-dimensional index of array elements. In case of + multi-channel arrays, each channel is processed independently. +@note Saturation is not applied when the arrays have the depth CV_32S. +You may even get a negative value in the case of overflow. +@param src1 first input array or a scalar. +@param src2 second input array or a scalar. +@param dst output array that has the same size and type as input arrays. +@sa cv::abs(const Mat&) +*/ +CV_EXPORTS_W void absdiff(InputArray src1, InputArray src2, OutputArray dst); + +/** @brief Checks if array elements lie between the elements of two other arrays. + +The function checks the range as follows: +- For every element of a single-channel input array: + \f[\texttt{dst} (I)= \texttt{lowerb} (I)_0 \leq \texttt{src} (I)_0 \leq \texttt{upperb} (I)_0\f] +- For two-channel arrays: + \f[\texttt{dst} (I)= \texttt{lowerb} (I)_0 \leq \texttt{src} (I)_0 \leq \texttt{upperb} (I)_0 \land \texttt{lowerb} (I)_1 \leq \texttt{src} (I)_1 \leq \texttt{upperb} (I)_1\f] +- and so forth. + +That is, dst (I) is set to 255 (all 1 -bits) if src (I) is within the +specified 1D, 2D, 3D, ... box and 0 otherwise. + +When the lower and/or upper boundary parameters are scalars, the indexes +(I) at lowerb and upperb in the above formulas should be omitted. +@param src first input array. +@param lowerb inclusive lower boundary array or a scalar. +@param upperb inclusive upper boundary array or a scalar. +@param dst output array of the same size as src and CV_8U type. +*/ +CV_EXPORTS_W void inRange(InputArray src, InputArray lowerb, + InputArray upperb, OutputArray dst); + +/** @brief Performs the per-element comparison of two arrays or an array and scalar value. + +The function compares: +* Elements of two arrays when src1 and src2 have the same size: + \f[\texttt{dst} (I) = \texttt{src1} (I) \,\texttt{cmpop}\, \texttt{src2} (I)\f] +* Elements of src1 with a scalar src2 when src2 is constructed from + Scalar or has a single element: + \f[\texttt{dst} (I) = \texttt{src1}(I) \,\texttt{cmpop}\, \texttt{src2}\f] +* src1 with elements of src2 when src1 is constructed from Scalar or + has a single element: + \f[\texttt{dst} (I) = \texttt{src1} \,\texttt{cmpop}\, \texttt{src2} (I)\f] +When the comparison result is true, the corresponding element of output +array is set to 255. The comparison operations can be replaced with the +equivalent matrix expressions: +@code{.cpp} + Mat dst1 = src1 >= src2; + Mat dst2 = src1 < 8; + ... +@endcode +@param src1 first input array or a scalar; when it is an array, it must have a single channel. +@param src2 second input array or a scalar; when it is an array, it must have a single channel. +@param dst output array of type ref CV_8U that has the same size and the same number of channels as + the input arrays. +@param cmpop a flag, that specifies correspondence between the arrays (cv::CmpTypes) +@sa checkRange, min, max, threshold +*/ +CV_EXPORTS_W void compare(InputArray src1, InputArray src2, OutputArray dst, int cmpop); + +/** @brief Calculates per-element minimum of two arrays or an array and a scalar. + +The function cv::min calculates the per-element minimum of two arrays: +\f[\texttt{dst} (I)= \min ( \texttt{src1} (I), \texttt{src2} (I))\f] +or array and a scalar: +\f[\texttt{dst} (I)= \min ( \texttt{src1} (I), \texttt{value} )\f] +@param src1 first input array. +@param src2 second input array of the same size and type as src1. +@param dst output array of the same size and type as src1. +@sa max, compare, inRange, minMaxLoc +*/ +CV_EXPORTS_W void min(InputArray src1, InputArray src2, OutputArray dst); +/** @overload +needed to avoid conflicts with const _Tp& std::min(const _Tp&, const _Tp&, _Compare) +*/ +CV_EXPORTS void min(const Mat& src1, const Mat& src2, Mat& dst); +/** @overload +needed to avoid conflicts with const _Tp& std::min(const _Tp&, const _Tp&, _Compare) +*/ +CV_EXPORTS void min(const UMat& src1, const UMat& src2, UMat& dst); + +/** @brief Calculates per-element maximum of two arrays or an array and a scalar. + +The function cv::max calculates the per-element maximum of two arrays: +\f[\texttt{dst} (I)= \max ( \texttt{src1} (I), \texttt{src2} (I))\f] +or array and a scalar: +\f[\texttt{dst} (I)= \max ( \texttt{src1} (I), \texttt{value} )\f] +@param src1 first input array. +@param src2 second input array of the same size and type as src1 . +@param dst output array of the same size and type as src1. +@sa min, compare, inRange, minMaxLoc, @ref MatrixExpressions +*/ +CV_EXPORTS_W void max(InputArray src1, InputArray src2, OutputArray dst); +/** @overload +needed to avoid conflicts with const _Tp& std::min(const _Tp&, const _Tp&, _Compare) +*/ +CV_EXPORTS void max(const Mat& src1, const Mat& src2, Mat& dst); +/** @overload +needed to avoid conflicts with const _Tp& std::min(const _Tp&, const _Tp&, _Compare) +*/ +CV_EXPORTS void max(const UMat& src1, const UMat& src2, UMat& dst); + +/** @brief Calculates a square root of array elements. + +The function cv::sqrt calculates a square root of each input array element. +In case of multi-channel arrays, each channel is processed +independently. The accuracy is approximately the same as of the built-in +std::sqrt . +@param src input floating-point array. +@param dst output array of the same size and type as src. +*/ +CV_EXPORTS_W void sqrt(InputArray src, OutputArray dst); + +/** @brief Raises every array element to a power. + +The function cv::pow raises every element of the input array to power : +\f[\texttt{dst} (I) = \fork{\texttt{src}(I)^{power}}{if \(\texttt{power}\) is integer}{|\texttt{src}(I)|^{power}}{otherwise}\f] + +So, for a non-integer power exponent, the absolute values of input array +elements are used. However, it is possible to get true values for +negative values using some extra operations. In the example below, +computing the 5th root of array src shows: +@code{.cpp} + Mat mask = src < 0; + pow(src, 1./5, dst); + subtract(Scalar::all(0), dst, dst, mask); +@endcode +For some values of power, such as integer values, 0.5 and -0.5, +specialized faster algorithms are used. + +Special values (NaN, Inf) are not handled. +@param src input array. +@param power exponent of power. +@param dst output array of the same size and type as src. +@sa sqrt, exp, log, cartToPolar, polarToCart +*/ +CV_EXPORTS_W void pow(InputArray src, double power, OutputArray dst); + +/** @brief Calculates the exponent of every array element. + +The function cv::exp calculates the exponent of every element of the input +array: +\f[\texttt{dst} [I] = e^{ src(I) }\f] + +The maximum relative error is about 7e-6 for single-precision input and +less than 1e-10 for double-precision input. Currently, the function +converts denormalized values to zeros on output. Special values (NaN, +Inf) are not handled. +@param src input array. +@param dst output array of the same size and type as src. +@sa log , cartToPolar , polarToCart , phase , pow , sqrt , magnitude +*/ +CV_EXPORTS_W void exp(InputArray src, OutputArray dst); + +/** @brief Calculates the natural logarithm of every array element. + +The function cv::log calculates the natural logarithm of every element of the input array: +\f[\texttt{dst} (I) = \log (\texttt{src}(I)) \f] + +Output on zero, negative and special (NaN, Inf) values is undefined. + +@param src input array. +@param dst output array of the same size and type as src . +@sa exp, cartToPolar, polarToCart, phase, pow, sqrt, magnitude +*/ +CV_EXPORTS_W void log(InputArray src, OutputArray dst); + +/** @brief Calculates x and y coordinates of 2D vectors from their magnitude and angle. + +The function cv::polarToCart calculates the Cartesian coordinates of each 2D +vector represented by the corresponding elements of magnitude and angle: +\f[\begin{array}{l} \texttt{x} (I) = \texttt{magnitude} (I) \cos ( \texttt{angle} (I)) \\ \texttt{y} (I) = \texttt{magnitude} (I) \sin ( \texttt{angle} (I)) \\ \end{array}\f] + +The relative accuracy of the estimated coordinates is about 1e-6. +@param magnitude input floating-point array of magnitudes of 2D vectors; +it can be an empty matrix (=Mat()), in this case, the function assumes +that all the magnitudes are =1; if it is not empty, it must have the +same size and type as angle. +@param angle input floating-point array of angles of 2D vectors. +@param x output array of x-coordinates of 2D vectors; it has the same +size and type as angle. +@param y output array of y-coordinates of 2D vectors; it has the same +size and type as angle. +@param angleInDegrees when true, the input angles are measured in +degrees, otherwise, they are measured in radians. +@sa cartToPolar, magnitude, phase, exp, log, pow, sqrt +*/ +CV_EXPORTS_W void polarToCart(InputArray magnitude, InputArray angle, + OutputArray x, OutputArray y, bool angleInDegrees = false); + +/** @brief Calculates the magnitude and angle of 2D vectors. + +The function cv::cartToPolar calculates either the magnitude, angle, or both +for every 2D vector (x(I),y(I)): +\f[\begin{array}{l} \texttt{magnitude} (I)= \sqrt{\texttt{x}(I)^2+\texttt{y}(I)^2} , \\ \texttt{angle} (I)= \texttt{atan2} ( \texttt{y} (I), \texttt{x} (I))[ \cdot180 / \pi ] \end{array}\f] + +The angles are calculated with accuracy about 0.3 degrees. For the point +(0,0), the angle is set to 0. +@param x array of x-coordinates; this must be a single-precision or +double-precision floating-point array. +@param y array of y-coordinates, that must have the same size and same type as x. +@param magnitude output array of magnitudes of the same size and type as x. +@param angle output array of angles that has the same size and type as +x; the angles are measured in radians (from 0 to 2\*Pi) or in degrees (0 to 360 degrees). +@param angleInDegrees a flag, indicating whether the angles are measured +in radians (which is by default), or in degrees. +@sa Sobel, Scharr +*/ +CV_EXPORTS_W void cartToPolar(InputArray x, InputArray y, + OutputArray magnitude, OutputArray angle, + bool angleInDegrees = false); + +/** @brief Calculates the rotation angle of 2D vectors. + +The function cv::phase calculates the rotation angle of each 2D vector that +is formed from the corresponding elements of x and y : +\f[\texttt{angle} (I) = \texttt{atan2} ( \texttt{y} (I), \texttt{x} (I))\f] + +The angle estimation accuracy is about 0.3 degrees. When x(I)=y(I)=0 , +the corresponding angle(I) is set to 0. +@param x input floating-point array of x-coordinates of 2D vectors. +@param y input array of y-coordinates of 2D vectors; it must have the +same size and the same type as x. +@param angle output array of vector angles; it has the same size and +same type as x . +@param angleInDegrees when true, the function calculates the angle in +degrees, otherwise, they are measured in radians. +*/ +CV_EXPORTS_W void phase(InputArray x, InputArray y, OutputArray angle, + bool angleInDegrees = false); + +/** @brief Calculates the magnitude of 2D vectors. + +The function cv::magnitude calculates the magnitude of 2D vectors formed +from the corresponding elements of x and y arrays: +\f[\texttt{dst} (I) = \sqrt{\texttt{x}(I)^2 + \texttt{y}(I)^2}\f] +@param x floating-point array of x-coordinates of the vectors. +@param y floating-point array of y-coordinates of the vectors; it must +have the same size as x. +@param magnitude output array of the same size and type as x. +@sa cartToPolar, polarToCart, phase, sqrt +*/ +CV_EXPORTS_W void magnitude(InputArray x, InputArray y, OutputArray magnitude); + +/** @brief Checks every element of an input array for invalid values. + +The function cv::checkRange checks that every array element is neither NaN nor infinite. When minVal \> +-DBL_MAX and maxVal \< DBL_MAX, the function also checks that each value is between minVal and +maxVal. In case of multi-channel arrays, each channel is processed independently. If some values +are out of range, position of the first outlier is stored in pos (when pos != NULL). Then, the +function either returns false (when quiet=true) or throws an exception. +@param a input array. +@param quiet a flag, indicating whether the functions quietly return false when the array elements +are out of range or they throw an exception. +@param pos optional output parameter, when not NULL, must be a pointer to array of src.dims +elements. +@param minVal inclusive lower boundary of valid values range. +@param maxVal exclusive upper boundary of valid values range. +*/ +CV_EXPORTS_W bool checkRange(InputArray a, bool quiet = true, CV_OUT Point* pos = 0, + double minVal = -DBL_MAX, double maxVal = DBL_MAX); + +/** @brief converts NaN's to the given number +*/ +CV_EXPORTS_W void patchNaNs(InputOutputArray a, double val = 0); + +/** @brief Performs generalized matrix multiplication. + +The function cv::gemm performs generalized matrix multiplication similar to the +gemm functions in BLAS level 3. For example, +`gemm(src1, src2, alpha, src3, beta, dst, GEMM_1_T + GEMM_3_T)` +corresponds to +\f[\texttt{dst} = \texttt{alpha} \cdot \texttt{src1} ^T \cdot \texttt{src2} + \texttt{beta} \cdot \texttt{src3} ^T\f] + +In case of complex (two-channel) data, performed a complex matrix +multiplication. + +The function can be replaced with a matrix expression. For example, the +above call can be replaced with: +@code{.cpp} + dst = alpha*src1.t()*src2 + beta*src3.t(); +@endcode +@param src1 first multiplied input matrix that could be real(CV_32FC1, +CV_64FC1) or complex(CV_32FC2, CV_64FC2). +@param src2 second multiplied input matrix of the same type as src1. +@param alpha weight of the matrix product. +@param src3 third optional delta matrix added to the matrix product; it +should have the same type as src1 and src2. +@param beta weight of src3. +@param dst output matrix; it has the proper size and the same type as +input matrices. +@param flags operation flags (cv::GemmFlags) +@sa mulTransposed , transform +*/ +CV_EXPORTS_W void gemm(InputArray src1, InputArray src2, double alpha, + InputArray src3, double beta, OutputArray dst, int flags = 0); + +/** @brief Calculates the product of a matrix and its transposition. + +The function cv::mulTransposed calculates the product of src and its +transposition: +\f[\texttt{dst} = \texttt{scale} ( \texttt{src} - \texttt{delta} )^T ( \texttt{src} - \texttt{delta} )\f] +if aTa=true , and +\f[\texttt{dst} = \texttt{scale} ( \texttt{src} - \texttt{delta} ) ( \texttt{src} - \texttt{delta} )^T\f] +otherwise. The function is used to calculate the covariance matrix. With +zero delta, it can be used as a faster substitute for general matrix +product A\*B when B=A' +@param src input single-channel matrix. Note that unlike gemm, the +function can multiply not only floating-point matrices. +@param dst output square matrix. +@param aTa Flag specifying the multiplication ordering. See the +description below. +@param delta Optional delta matrix subtracted from src before the +multiplication. When the matrix is empty ( delta=noArray() ), it is +assumed to be zero, that is, nothing is subtracted. If it has the same +size as src , it is simply subtracted. Otherwise, it is "repeated" (see +repeat ) to cover the full src and then subtracted. Type of the delta +matrix, when it is not empty, must be the same as the type of created +output matrix. See the dtype parameter description below. +@param scale Optional scale factor for the matrix product. +@param dtype Optional type of the output matrix. When it is negative, +the output matrix will have the same type as src . Otherwise, it will be +type=CV_MAT_DEPTH(dtype) that should be either CV_32F or CV_64F . +@sa calcCovarMatrix, gemm, repeat, reduce +*/ +CV_EXPORTS_W void mulTransposed( InputArray src, OutputArray dst, bool aTa, + InputArray delta = noArray(), + double scale = 1, int dtype = -1 ); + +/** @brief Transposes a matrix. + +The function cv::transpose transposes the matrix src : +\f[\texttt{dst} (i,j) = \texttt{src} (j,i)\f] +@note No complex conjugation is done in case of a complex matrix. It +should be done separately if needed. +@param src input array. +@param dst output array of the same type as src. +*/ +CV_EXPORTS_W void transpose(InputArray src, OutputArray dst); + +/** @brief Performs the matrix transformation of every array element. + +The function cv::transform performs the matrix transformation of every +element of the array src and stores the results in dst : +\f[\texttt{dst} (I) = \texttt{m} \cdot \texttt{src} (I)\f] +(when m.cols=src.channels() ), or +\f[\texttt{dst} (I) = \texttt{m} \cdot [ \texttt{src} (I); 1]\f] +(when m.cols=src.channels()+1 ) + +Every element of the N -channel array src is interpreted as N -element +vector that is transformed using the M x N or M x (N+1) matrix m to +M-element vector - the corresponding element of the output array dst . + +The function may be used for geometrical transformation of +N -dimensional points, arbitrary linear color space transformation (such +as various kinds of RGB to YUV transforms), shuffling the image +channels, and so forth. +@param src input array that must have as many channels (1 to 4) as +m.cols or m.cols-1. +@param dst output array of the same size and depth as src; it has as +many channels as m.rows. +@param m transformation 2x2 or 2x3 floating-point matrix. +@sa perspectiveTransform, getAffineTransform, estimateAffine2D, warpAffine, warpPerspective +*/ +CV_EXPORTS_W void transform(InputArray src, OutputArray dst, InputArray m ); + +/** @brief Performs the perspective matrix transformation of vectors. + +The function cv::perspectiveTransform transforms every element of src by +treating it as a 2D or 3D vector, in the following way: +\f[(x, y, z) \rightarrow (x'/w, y'/w, z'/w)\f] +where +\f[(x', y', z', w') = \texttt{mat} \cdot \begin{bmatrix} x & y & z & 1 \end{bmatrix}\f] +and +\f[w = \fork{w'}{if \(w' \ne 0\)}{\infty}{otherwise}\f] + +Here a 3D vector transformation is shown. In case of a 2D vector +transformation, the z component is omitted. + +@note The function transforms a sparse set of 2D or 3D vectors. If you +want to transform an image using perspective transformation, use +warpPerspective . If you have an inverse problem, that is, you want to +compute the most probable perspective transformation out of several +pairs of corresponding points, you can use getPerspectiveTransform or +findHomography . +@param src input two-channel or three-channel floating-point array; each +element is a 2D/3D vector to be transformed. +@param dst output array of the same size and type as src. +@param m 3x3 or 4x4 floating-point transformation matrix. +@sa transform, warpPerspective, getPerspectiveTransform, findHomography +*/ +CV_EXPORTS_W void perspectiveTransform(InputArray src, OutputArray dst, InputArray m ); + +/** @brief Copies the lower or the upper half of a square matrix to its another half. + +The function cv::completeSymm copies the lower or the upper half of a square matrix to +its another half. The matrix diagonal remains unchanged: + - \f$\texttt{m}_{ij}=\texttt{m}_{ji}\f$ for \f$i > j\f$ if + lowerToUpper=false + - \f$\texttt{m}_{ij}=\texttt{m}_{ji}\f$ for \f$i < j\f$ if + lowerToUpper=true + +@param m input-output floating-point square matrix. +@param lowerToUpper operation flag; if true, the lower half is copied to +the upper half. Otherwise, the upper half is copied to the lower half. +@sa flip, transpose +*/ +CV_EXPORTS_W void completeSymm(InputOutputArray m, bool lowerToUpper = false); + +/** @brief Initializes a scaled identity matrix. + +The function cv::setIdentity initializes a scaled identity matrix: +\f[\texttt{mtx} (i,j)= \fork{\texttt{value}}{ if \(i=j\)}{0}{otherwise}\f] + +The function can also be emulated using the matrix initializers and the +matrix expressions: +@code + Mat A = Mat::eye(4, 3, CV_32F)*5; + // A will be set to [[5, 0, 0], [0, 5, 0], [0, 0, 5], [0, 0, 0]] +@endcode +@param mtx matrix to initialize (not necessarily square). +@param s value to assign to diagonal elements. +@sa Mat::zeros, Mat::ones, Mat::setTo, Mat::operator= +*/ +CV_EXPORTS_W void setIdentity(InputOutputArray mtx, const Scalar& s = Scalar(1)); + +/** @brief Returns the determinant of a square floating-point matrix. + +The function cv::determinant calculates and returns the determinant of the +specified matrix. For small matrices ( mtx.cols=mtx.rows\<=3 ), the +direct method is used. For larger matrices, the function uses LU +factorization with partial pivoting. + +For symmetric positively-determined matrices, it is also possible to use +eigen decomposition to calculate the determinant. +@param mtx input matrix that must have CV_32FC1 or CV_64FC1 type and +square size. +@sa trace, invert, solve, eigen, @ref MatrixExpressions +*/ +CV_EXPORTS_W double determinant(InputArray mtx); + +/** @brief Returns the trace of a matrix. + +The function cv::trace returns the sum of the diagonal elements of the +matrix mtx . +\f[\mathrm{tr} ( \texttt{mtx} ) = \sum _i \texttt{mtx} (i,i)\f] +@param mtx input matrix. +*/ +CV_EXPORTS_W Scalar trace(InputArray mtx); + +/** @brief Finds the inverse or pseudo-inverse of a matrix. + +The function cv::invert inverts the matrix src and stores the result in dst +. When the matrix src is singular or non-square, the function calculates +the pseudo-inverse matrix (the dst matrix) so that norm(src\*dst - I) is +minimal, where I is an identity matrix. + +In case of the #DECOMP_LU method, the function returns non-zero value if +the inverse has been successfully calculated and 0 if src is singular. + +In case of the #DECOMP_SVD method, the function returns the inverse +condition number of src (the ratio of the smallest singular value to the +largest singular value) and 0 if src is singular. The SVD method +calculates a pseudo-inverse matrix if src is singular. + +Similarly to #DECOMP_LU, the method #DECOMP_CHOLESKY works only with +non-singular square matrices that should also be symmetrical and +positively defined. In this case, the function stores the inverted +matrix in dst and returns non-zero. Otherwise, it returns 0. + +@param src input floating-point M x N matrix. +@param dst output matrix of N x M size and the same type as src. +@param flags inversion method (cv::DecompTypes) +@sa solve, SVD +*/ +CV_EXPORTS_W double invert(InputArray src, OutputArray dst, int flags = DECOMP_LU); + +/** @brief Solves one or more linear systems or least-squares problems. + +The function cv::solve solves a linear system or least-squares problem (the +latter is possible with SVD or QR methods, or by specifying the flag +#DECOMP_NORMAL ): +\f[\texttt{dst} = \arg \min _X \| \texttt{src1} \cdot \texttt{X} - \texttt{src2} \|\f] + +If #DECOMP_LU or #DECOMP_CHOLESKY method is used, the function returns 1 +if src1 (or \f$\texttt{src1}^T\texttt{src1}\f$ ) is non-singular. Otherwise, +it returns 0. In the latter case, dst is not valid. Other methods find a +pseudo-solution in case of a singular left-hand side part. + +@note If you want to find a unity-norm solution of an under-defined +singular system \f$\texttt{src1}\cdot\texttt{dst}=0\f$ , the function solve +will not do the work. Use SVD::solveZ instead. + +@param src1 input matrix on the left-hand side of the system. +@param src2 input matrix on the right-hand side of the system. +@param dst output solution. +@param flags solution (matrix inversion) method (#DecompTypes) +@sa invert, SVD, eigen +*/ +CV_EXPORTS_W bool solve(InputArray src1, InputArray src2, + OutputArray dst, int flags = DECOMP_LU); + +/** @brief Sorts each row or each column of a matrix. + +The function cv::sort sorts each matrix row or each matrix column in +ascending or descending order. So you should pass two operation flags to +get desired behaviour. If you want to sort matrix rows or columns +lexicographically, you can use STL std::sort generic function with the +proper comparison predicate. + +@param src input single-channel array. +@param dst output array of the same size and type as src. +@param flags operation flags, a combination of #SortFlags +@sa sortIdx, randShuffle +*/ +CV_EXPORTS_W void sort(InputArray src, OutputArray dst, int flags); + +/** @brief Sorts each row or each column of a matrix. + +The function cv::sortIdx sorts each matrix row or each matrix column in the +ascending or descending order. So you should pass two operation flags to +get desired behaviour. Instead of reordering the elements themselves, it +stores the indices of sorted elements in the output array. For example: +@code + Mat A = Mat::eye(3,3,CV_32F), B; + sortIdx(A, B, SORT_EVERY_ROW + SORT_ASCENDING); + // B will probably contain + // (because of equal elements in A some permutations are possible): + // [[1, 2, 0], [0, 2, 1], [0, 1, 2]] +@endcode +@param src input single-channel array. +@param dst output integer array of the same size as src. +@param flags operation flags that could be a combination of cv::SortFlags +@sa sort, randShuffle +*/ +CV_EXPORTS_W void sortIdx(InputArray src, OutputArray dst, int flags); + +/** @brief Finds the real roots of a cubic equation. + +The function solveCubic finds the real roots of a cubic equation: +- if coeffs is a 4-element vector: +\f[\texttt{coeffs} [0] x^3 + \texttt{coeffs} [1] x^2 + \texttt{coeffs} [2] x + \texttt{coeffs} [3] = 0\f] +- if coeffs is a 3-element vector: +\f[x^3 + \texttt{coeffs} [0] x^2 + \texttt{coeffs} [1] x + \texttt{coeffs} [2] = 0\f] + +The roots are stored in the roots array. +@param coeffs equation coefficients, an array of 3 or 4 elements. +@param roots output array of real roots that has 1 or 3 elements. +@return number of real roots. It can be 0, 1 or 2. +*/ +CV_EXPORTS_W int solveCubic(InputArray coeffs, OutputArray roots); + +/** @brief Finds the real or complex roots of a polynomial equation. + +The function cv::solvePoly finds real and complex roots of a polynomial equation: +\f[\texttt{coeffs} [n] x^{n} + \texttt{coeffs} [n-1] x^{n-1} + ... + \texttt{coeffs} [1] x + \texttt{coeffs} [0] = 0\f] +@param coeffs array of polynomial coefficients. +@param roots output (complex) array of roots. +@param maxIters maximum number of iterations the algorithm does. +*/ +CV_EXPORTS_W double solvePoly(InputArray coeffs, OutputArray roots, int maxIters = 300); + +/** @brief Calculates eigenvalues and eigenvectors of a symmetric matrix. + +The function cv::eigen calculates just eigenvalues, or eigenvalues and eigenvectors of the symmetric +matrix src: +@code + src*eigenvectors.row(i).t() = eigenvalues.at(i)*eigenvectors.row(i).t() +@endcode + +@note Use cv::eigenNonSymmetric for calculation of real eigenvalues and eigenvectors of non-symmetric matrix. + +@param src input matrix that must have CV_32FC1 or CV_64FC1 type, square size and be symmetrical +(src ^T^ == src). +@param eigenvalues output vector of eigenvalues of the same type as src; the eigenvalues are stored +in the descending order. +@param eigenvectors output matrix of eigenvectors; it has the same size and type as src; the +eigenvectors are stored as subsequent matrix rows, in the same order as the corresponding +eigenvalues. +@sa eigenNonSymmetric, completeSymm , PCA +*/ +CV_EXPORTS_W bool eigen(InputArray src, OutputArray eigenvalues, + OutputArray eigenvectors = noArray()); + +/** @brief Calculates eigenvalues and eigenvectors of a non-symmetric matrix (real eigenvalues only). + +@note Assumes real eigenvalues. + +The function calculates eigenvalues and eigenvectors (optional) of the square matrix src: +@code + src*eigenvectors.row(i).t() = eigenvalues.at(i)*eigenvectors.row(i).t() +@endcode + +@param src input matrix (CV_32FC1 or CV_64FC1 type). +@param eigenvalues output vector of eigenvalues (type is the same type as src). +@param eigenvectors output matrix of eigenvectors (type is the same type as src). The eigenvectors are stored as subsequent matrix rows, in the same order as the corresponding eigenvalues. +@sa eigen +*/ +CV_EXPORTS_W void eigenNonSymmetric(InputArray src, OutputArray eigenvalues, + OutputArray eigenvectors); + +/** @brief Calculates the covariance matrix of a set of vectors. + +The function cv::calcCovarMatrix calculates the covariance matrix and, optionally, the mean vector of +the set of input vectors. +@param samples samples stored as separate matrices +@param nsamples number of samples +@param covar output covariance matrix of the type ctype and square size. +@param mean input or output (depending on the flags) array as the average value of the input vectors. +@param flags operation flags as a combination of #CovarFlags +@param ctype type of the matrixl; it equals 'CV_64F' by default. +@sa PCA, mulTransposed, Mahalanobis +@todo InputArrayOfArrays +*/ +CV_EXPORTS void calcCovarMatrix( const Mat* samples, int nsamples, Mat& covar, Mat& mean, + int flags, int ctype = CV_64F); + +/** @overload +@note use #COVAR_ROWS or #COVAR_COLS flag +@param samples samples stored as rows/columns of a single matrix. +@param covar output covariance matrix of the type ctype and square size. +@param mean input or output (depending on the flags) array as the average value of the input vectors. +@param flags operation flags as a combination of #CovarFlags +@param ctype type of the matrixl; it equals 'CV_64F' by default. +*/ +CV_EXPORTS_W void calcCovarMatrix( InputArray samples, OutputArray covar, + InputOutputArray mean, int flags, int ctype = CV_64F); + +/** wrap PCA::operator() */ +CV_EXPORTS_W void PCACompute(InputArray data, InputOutputArray mean, + OutputArray eigenvectors, int maxComponents = 0); + +/** wrap PCA::operator() and add eigenvalues output parameter */ +CV_EXPORTS_AS(PCACompute2) void PCACompute(InputArray data, InputOutputArray mean, + OutputArray eigenvectors, OutputArray eigenvalues, + int maxComponents = 0); + +/** wrap PCA::operator() */ +CV_EXPORTS_W void PCACompute(InputArray data, InputOutputArray mean, + OutputArray eigenvectors, double retainedVariance); + +/** wrap PCA::operator() and add eigenvalues output parameter */ +CV_EXPORTS_AS(PCACompute2) void PCACompute(InputArray data, InputOutputArray mean, + OutputArray eigenvectors, OutputArray eigenvalues, + double retainedVariance); + +/** wrap PCA::project */ +CV_EXPORTS_W void PCAProject(InputArray data, InputArray mean, + InputArray eigenvectors, OutputArray result); + +/** wrap PCA::backProject */ +CV_EXPORTS_W void PCABackProject(InputArray data, InputArray mean, + InputArray eigenvectors, OutputArray result); + +/** wrap SVD::compute */ +CV_EXPORTS_W void SVDecomp( InputArray src, OutputArray w, OutputArray u, OutputArray vt, int flags = 0 ); + +/** wrap SVD::backSubst */ +CV_EXPORTS_W void SVBackSubst( InputArray w, InputArray u, InputArray vt, + InputArray rhs, OutputArray dst ); + +/** @brief Calculates the Mahalanobis distance between two vectors. + +The function cv::Mahalanobis calculates and returns the weighted distance between two vectors: +\f[d( \texttt{vec1} , \texttt{vec2} )= \sqrt{\sum_{i,j}{\texttt{icovar(i,j)}\cdot(\texttt{vec1}(I)-\texttt{vec2}(I))\cdot(\texttt{vec1(j)}-\texttt{vec2(j)})} }\f] +The covariance matrix may be calculated using the #calcCovarMatrix function and then inverted using +the invert function (preferably using the #DECOMP_SVD method, as the most accurate). +@param v1 first 1D input vector. +@param v2 second 1D input vector. +@param icovar inverse covariance matrix. +*/ +CV_EXPORTS_W double Mahalanobis(InputArray v1, InputArray v2, InputArray icovar); + +/** @brief Performs a forward or inverse Discrete Fourier transform of a 1D or 2D floating-point array. + +The function cv::dft performs one of the following: +- Forward the Fourier transform of a 1D vector of N elements: + \f[Y = F^{(N)} \cdot X,\f] + where \f$F^{(N)}_{jk}=\exp(-2\pi i j k/N)\f$ and \f$i=\sqrt{-1}\f$ +- Inverse the Fourier transform of a 1D vector of N elements: + \f[\begin{array}{l} X'= \left (F^{(N)} \right )^{-1} \cdot Y = \left (F^{(N)} \right )^* \cdot y \\ X = (1/N) \cdot X, \end{array}\f] + where \f$F^*=\left(\textrm{Re}(F^{(N)})-\textrm{Im}(F^{(N)})\right)^T\f$ +- Forward the 2D Fourier transform of a M x N matrix: + \f[Y = F^{(M)} \cdot X \cdot F^{(N)}\f] +- Inverse the 2D Fourier transform of a M x N matrix: + \f[\begin{array}{l} X'= \left (F^{(M)} \right )^* \cdot Y \cdot \left (F^{(N)} \right )^* \\ X = \frac{1}{M \cdot N} \cdot X' \end{array}\f] + +In case of real (single-channel) data, the output spectrum of the forward Fourier transform or input +spectrum of the inverse Fourier transform can be represented in a packed format called *CCS* +(complex-conjugate-symmetrical). It was borrowed from IPL (Intel\* Image Processing Library). Here +is how 2D *CCS* spectrum looks: +\f[\begin{bmatrix} Re Y_{0,0} & Re Y_{0,1} & Im Y_{0,1} & Re Y_{0,2} & Im Y_{0,2} & \cdots & Re Y_{0,N/2-1} & Im Y_{0,N/2-1} & Re Y_{0,N/2} \\ Re Y_{1,0} & Re Y_{1,1} & Im Y_{1,1} & Re Y_{1,2} & Im Y_{1,2} & \cdots & Re Y_{1,N/2-1} & Im Y_{1,N/2-1} & Re Y_{1,N/2} \\ Im Y_{1,0} & Re Y_{2,1} & Im Y_{2,1} & Re Y_{2,2} & Im Y_{2,2} & \cdots & Re Y_{2,N/2-1} & Im Y_{2,N/2-1} & Im Y_{1,N/2} \\ \hdotsfor{9} \\ Re Y_{M/2-1,0} & Re Y_{M-3,1} & Im Y_{M-3,1} & \hdotsfor{3} & Re Y_{M-3,N/2-1} & Im Y_{M-3,N/2-1}& Re Y_{M/2-1,N/2} \\ Im Y_{M/2-1,0} & Re Y_{M-2,1} & Im Y_{M-2,1} & \hdotsfor{3} & Re Y_{M-2,N/2-1} & Im Y_{M-2,N/2-1}& Im Y_{M/2-1,N/2} \\ Re Y_{M/2,0} & Re Y_{M-1,1} & Im Y_{M-1,1} & \hdotsfor{3} & Re Y_{M-1,N/2-1} & Im Y_{M-1,N/2-1}& Re Y_{M/2,N/2} \end{bmatrix}\f] + +In case of 1D transform of a real vector, the output looks like the first row of the matrix above. + +So, the function chooses an operation mode depending on the flags and size of the input array: +- If #DFT_ROWS is set or the input array has a single row or single column, the function + performs a 1D forward or inverse transform of each row of a matrix when #DFT_ROWS is set. + Otherwise, it performs a 2D transform. +- If the input array is real and #DFT_INVERSE is not set, the function performs a forward 1D or + 2D transform: + - When #DFT_COMPLEX_OUTPUT is set, the output is a complex matrix of the same size as + input. + - When #DFT_COMPLEX_OUTPUT is not set, the output is a real matrix of the same size as + input. In case of 2D transform, it uses the packed format as shown above. In case of a + single 1D transform, it looks like the first row of the matrix above. In case of + multiple 1D transforms (when using the #DFT_ROWS flag), each row of the output matrix + looks like the first row of the matrix above. +- If the input array is complex and either #DFT_INVERSE or #DFT_REAL_OUTPUT are not set, the + output is a complex array of the same size as input. The function performs a forward or + inverse 1D or 2D transform of the whole input array or each row of the input array + independently, depending on the flags DFT_INVERSE and DFT_ROWS. +- When #DFT_INVERSE is set and the input array is real, or it is complex but #DFT_REAL_OUTPUT + is set, the output is a real array of the same size as input. The function performs a 1D or 2D + inverse transformation of the whole input array or each individual row, depending on the flags + #DFT_INVERSE and #DFT_ROWS. + +If #DFT_SCALE is set, the scaling is done after the transformation. + +Unlike dct , the function supports arrays of arbitrary size. But only those arrays are processed +efficiently, whose sizes can be factorized in a product of small prime numbers (2, 3, and 5 in the +current implementation). Such an efficient DFT size can be calculated using the getOptimalDFTSize +method. + +The sample below illustrates how to calculate a DFT-based convolution of two 2D real arrays: +@code + void convolveDFT(InputArray A, InputArray B, OutputArray C) + { + // reallocate the output array if needed + C.create(abs(A.rows - B.rows)+1, abs(A.cols - B.cols)+1, A.type()); + Size dftSize; + // calculate the size of DFT transform + dftSize.width = getOptimalDFTSize(A.cols + B.cols - 1); + dftSize.height = getOptimalDFTSize(A.rows + B.rows - 1); + + // allocate temporary buffers and initialize them with 0's + Mat tempA(dftSize, A.type(), Scalar::all(0)); + Mat tempB(dftSize, B.type(), Scalar::all(0)); + + // copy A and B to the top-left corners of tempA and tempB, respectively + Mat roiA(tempA, Rect(0,0,A.cols,A.rows)); + A.copyTo(roiA); + Mat roiB(tempB, Rect(0,0,B.cols,B.rows)); + B.copyTo(roiB); + + // now transform the padded A & B in-place; + // use "nonzeroRows" hint for faster processing + dft(tempA, tempA, 0, A.rows); + dft(tempB, tempB, 0, B.rows); + + // multiply the spectrums; + // the function handles packed spectrum representations well + mulSpectrums(tempA, tempB, tempA); + + // transform the product back from the frequency domain. + // Even though all the result rows will be non-zero, + // you need only the first C.rows of them, and thus you + // pass nonzeroRows == C.rows + dft(tempA, tempA, DFT_INVERSE + DFT_SCALE, C.rows); + + // now copy the result back to C. + tempA(Rect(0, 0, C.cols, C.rows)).copyTo(C); + + // all the temporary buffers will be deallocated automatically + } +@endcode +To optimize this sample, consider the following approaches: +- Since nonzeroRows != 0 is passed to the forward transform calls and since A and B are copied to + the top-left corners of tempA and tempB, respectively, it is not necessary to clear the whole + tempA and tempB. It is only necessary to clear the tempA.cols - A.cols ( tempB.cols - B.cols) + rightmost columns of the matrices. +- This DFT-based convolution does not have to be applied to the whole big arrays, especially if B + is significantly smaller than A or vice versa. Instead, you can calculate convolution by parts. + To do this, you need to split the output array C into multiple tiles. For each tile, estimate + which parts of A and B are required to calculate convolution in this tile. If the tiles in C are + too small, the speed will decrease a lot because of repeated work. In the ultimate case, when + each tile in C is a single pixel, the algorithm becomes equivalent to the naive convolution + algorithm. If the tiles are too big, the temporary arrays tempA and tempB become too big and + there is also a slowdown because of bad cache locality. So, there is an optimal tile size + somewhere in the middle. +- If different tiles in C can be calculated in parallel and, thus, the convolution is done by + parts, the loop can be threaded. + +All of the above improvements have been implemented in #matchTemplate and #filter2D . Therefore, by +using them, you can get the performance even better than with the above theoretically optimal +implementation. Though, those two functions actually calculate cross-correlation, not convolution, +so you need to "flip" the second convolution operand B vertically and horizontally using flip . +@note +- An example using the discrete fourier transform can be found at + opencv_source_code/samples/cpp/dft.cpp +- (Python) An example using the dft functionality to perform Wiener deconvolution can be found + at opencv_source/samples/python/deconvolution.py +- (Python) An example rearranging the quadrants of a Fourier image can be found at + opencv_source/samples/python/dft.py +@param src input array that could be real or complex. +@param dst output array whose size and type depends on the flags . +@param flags transformation flags, representing a combination of the #DftFlags +@param nonzeroRows when the parameter is not zero, the function assumes that only the first +nonzeroRows rows of the input array (#DFT_INVERSE is not set) or only the first nonzeroRows of the +output array (#DFT_INVERSE is set) contain non-zeros, thus, the function can handle the rest of the +rows more efficiently and save some time; this technique is very useful for calculating array +cross-correlation or convolution using DFT. +@sa dct , getOptimalDFTSize , mulSpectrums, filter2D , matchTemplate , flip , cartToPolar , +magnitude , phase +*/ +CV_EXPORTS_W void dft(InputArray src, OutputArray dst, int flags = 0, int nonzeroRows = 0); + +/** @brief Calculates the inverse Discrete Fourier Transform of a 1D or 2D array. + +idft(src, dst, flags) is equivalent to dft(src, dst, flags | #DFT_INVERSE) . +@note None of dft and idft scales the result by default. So, you should pass #DFT_SCALE to one of +dft or idft explicitly to make these transforms mutually inverse. +@sa dft, dct, idct, mulSpectrums, getOptimalDFTSize +@param src input floating-point real or complex array. +@param dst output array whose size and type depend on the flags. +@param flags operation flags (see dft and #DftFlags). +@param nonzeroRows number of dst rows to process; the rest of the rows have undefined content (see +the convolution sample in dft description. +*/ +CV_EXPORTS_W void idft(InputArray src, OutputArray dst, int flags = 0, int nonzeroRows = 0); + +/** @brief Performs a forward or inverse discrete Cosine transform of 1D or 2D array. + +The function cv::dct performs a forward or inverse discrete Cosine transform (DCT) of a 1D or 2D +floating-point array: +- Forward Cosine transform of a 1D vector of N elements: + \f[Y = C^{(N)} \cdot X\f] + where + \f[C^{(N)}_{jk}= \sqrt{\alpha_j/N} \cos \left ( \frac{\pi(2k+1)j}{2N} \right )\f] + and + \f$\alpha_0=1\f$, \f$\alpha_j=2\f$ for *j \> 0*. +- Inverse Cosine transform of a 1D vector of N elements: + \f[X = \left (C^{(N)} \right )^{-1} \cdot Y = \left (C^{(N)} \right )^T \cdot Y\f] + (since \f$C^{(N)}\f$ is an orthogonal matrix, \f$C^{(N)} \cdot \left(C^{(N)}\right)^T = I\f$ ) +- Forward 2D Cosine transform of M x N matrix: + \f[Y = C^{(N)} \cdot X \cdot \left (C^{(N)} \right )^T\f] +- Inverse 2D Cosine transform of M x N matrix: + \f[X = \left (C^{(N)} \right )^T \cdot X \cdot C^{(N)}\f] + +The function chooses the mode of operation by looking at the flags and size of the input array: +- If (flags & #DCT_INVERSE) == 0 , the function does a forward 1D or 2D transform. Otherwise, it + is an inverse 1D or 2D transform. +- If (flags & #DCT_ROWS) != 0 , the function performs a 1D transform of each row. +- If the array is a single column or a single row, the function performs a 1D transform. +- If none of the above is true, the function performs a 2D transform. + +@note Currently dct supports even-size arrays (2, 4, 6 ...). For data analysis and approximation, you +can pad the array when necessary. +Also, the function performance depends very much, and not monotonically, on the array size (see +getOptimalDFTSize ). In the current implementation DCT of a vector of size N is calculated via DFT +of a vector of size N/2 . Thus, the optimal DCT size N1 \>= N can be calculated as: +@code + size_t getOptimalDCTSize(size_t N) { return 2*getOptimalDFTSize((N+1)/2); } + N1 = getOptimalDCTSize(N); +@endcode +@param src input floating-point array. +@param dst output array of the same size and type as src . +@param flags transformation flags as a combination of cv::DftFlags (DCT_*) +@sa dft , getOptimalDFTSize , idct +*/ +CV_EXPORTS_W void dct(InputArray src, OutputArray dst, int flags = 0); + +/** @brief Calculates the inverse Discrete Cosine Transform of a 1D or 2D array. + +idct(src, dst, flags) is equivalent to dct(src, dst, flags | DCT_INVERSE). +@param src input floating-point single-channel array. +@param dst output array of the same size and type as src. +@param flags operation flags. +@sa dct, dft, idft, getOptimalDFTSize +*/ +CV_EXPORTS_W void idct(InputArray src, OutputArray dst, int flags = 0); + +/** @brief Performs the per-element multiplication of two Fourier spectrums. + +The function cv::mulSpectrums performs the per-element multiplication of the two CCS-packed or complex +matrices that are results of a real or complex Fourier transform. + +The function, together with dft and idft , may be used to calculate convolution (pass conjB=false ) +or correlation (pass conjB=true ) of two arrays rapidly. When the arrays are complex, they are +simply multiplied (per element) with an optional conjugation of the second-array elements. When the +arrays are real, they are assumed to be CCS-packed (see dft for details). +@param a first input array. +@param b second input array of the same size and type as src1 . +@param c output array of the same size and type as src1 . +@param flags operation flags; currently, the only supported flag is cv::DFT_ROWS, which indicates that +each row of src1 and src2 is an independent 1D Fourier spectrum. If you do not want to use this flag, then simply add a `0` as value. +@param conjB optional flag that conjugates the second input array before the multiplication (true) +or not (false). +*/ +CV_EXPORTS_W void mulSpectrums(InputArray a, InputArray b, OutputArray c, + int flags, bool conjB = false); + +/** @brief Returns the optimal DFT size for a given vector size. + +DFT performance is not a monotonic function of a vector size. Therefore, when you calculate +convolution of two arrays or perform the spectral analysis of an array, it usually makes sense to +pad the input data with zeros to get a bit larger array that can be transformed much faster than the +original one. Arrays whose size is a power-of-two (2, 4, 8, 16, 32, ...) are the fastest to process. +Though, the arrays whose size is a product of 2's, 3's, and 5's (for example, 300 = 5\*5\*3\*2\*2) +are also processed quite efficiently. + +The function cv::getOptimalDFTSize returns the minimum number N that is greater than or equal to vecsize +so that the DFT of a vector of size N can be processed efficiently. In the current implementation N += 2 ^p^ \* 3 ^q^ \* 5 ^r^ for some integer p, q, r. + +The function returns a negative number if vecsize is too large (very close to INT_MAX ). + +While the function cannot be used directly to estimate the optimal vector size for DCT transform +(since the current DCT implementation supports only even-size vectors), it can be easily processed +as getOptimalDFTSize((vecsize+1)/2)\*2. +@param vecsize vector size. +@sa dft , dct , idft , idct , mulSpectrums +*/ +CV_EXPORTS_W int getOptimalDFTSize(int vecsize); + +/** @brief Returns the default random number generator. + +The function cv::theRNG returns the default random number generator. For each thread, there is a +separate random number generator, so you can use the function safely in multi-thread environments. +If you just need to get a single random number using this generator or initialize an array, you can +use randu or randn instead. But if you are going to generate many random numbers inside a loop, it +is much faster to use this function to retrieve the generator and then use RNG::operator _Tp() . +@sa RNG, randu, randn +*/ +CV_EXPORTS RNG& theRNG(); + +/** @brief Sets state of default random number generator. + +The function cv::setRNGSeed sets state of default random number generator to custom value. +@param seed new state for default random number generator +@sa RNG, randu, randn +*/ +CV_EXPORTS_W void setRNGSeed(int seed); + +/** @brief Generates a single uniformly-distributed random number or an array of random numbers. + +Non-template variant of the function fills the matrix dst with uniformly-distributed +random numbers from the specified range: +\f[\texttt{low} _c \leq \texttt{dst} (I)_c < \texttt{high} _c\f] +@param dst output array of random numbers; the array must be pre-allocated. +@param low inclusive lower boundary of the generated random numbers. +@param high exclusive upper boundary of the generated random numbers. +@sa RNG, randn, theRNG +*/ +CV_EXPORTS_W void randu(InputOutputArray dst, InputArray low, InputArray high); + +/** @brief Fills the array with normally distributed random numbers. + +The function cv::randn fills the matrix dst with normally distributed random numbers with the specified +mean vector and the standard deviation matrix. The generated random numbers are clipped to fit the +value range of the output array data type. +@param dst output array of random numbers; the array must be pre-allocated and have 1 to 4 channels. +@param mean mean value (expectation) of the generated random numbers. +@param stddev standard deviation of the generated random numbers; it can be either a vector (in +which case a diagonal standard deviation matrix is assumed) or a square matrix. +@sa RNG, randu +*/ +CV_EXPORTS_W void randn(InputOutputArray dst, InputArray mean, InputArray stddev); + +/** @brief Shuffles the array elements randomly. + +The function cv::randShuffle shuffles the specified 1D array by randomly choosing pairs of elements and +swapping them. The number of such swap operations will be dst.rows\*dst.cols\*iterFactor . +@param dst input/output numerical 1D array. +@param iterFactor scale factor that determines the number of random swap operations (see the details +below). +@param rng optional random number generator used for shuffling; if it is zero, theRNG () is used +instead. +@sa RNG, sort +*/ +CV_EXPORTS_W void randShuffle(InputOutputArray dst, double iterFactor = 1., RNG* rng = 0); + +/** @brief Principal Component Analysis + +The class is used to calculate a special basis for a set of vectors. The +basis will consist of eigenvectors of the covariance matrix calculated +from the input set of vectors. The class %PCA can also transform +vectors to/from the new coordinate space defined by the basis. Usually, +in this new coordinate system, each vector from the original set (and +any linear combination of such vectors) can be quite accurately +approximated by taking its first few components, corresponding to the +eigenvectors of the largest eigenvalues of the covariance matrix. +Geometrically it means that you calculate a projection of the vector to +a subspace formed by a few eigenvectors corresponding to the dominant +eigenvalues of the covariance matrix. And usually such a projection is +very close to the original vector. So, you can represent the original +vector from a high-dimensional space with a much shorter vector +consisting of the projected vector's coordinates in the subspace. Such a +transformation is also known as Karhunen-Loeve Transform, or KLT. +See http://en.wikipedia.org/wiki/Principal_component_analysis + +The sample below is the function that takes two matrices. The first +function stores a set of vectors (a row per vector) that is used to +calculate PCA. The second function stores another "test" set of vectors +(a row per vector). First, these vectors are compressed with PCA, then +reconstructed back, and then the reconstruction error norm is computed +and printed for each vector. : + +@code{.cpp} +using namespace cv; + +PCA compressPCA(const Mat& pcaset, int maxComponents, + const Mat& testset, Mat& compressed) +{ + PCA pca(pcaset, // pass the data + Mat(), // we do not have a pre-computed mean vector, + // so let the PCA engine to compute it + PCA::DATA_AS_ROW, // indicate that the vectors + // are stored as matrix rows + // (use PCA::DATA_AS_COL if the vectors are + // the matrix columns) + maxComponents // specify, how many principal components to retain + ); + // if there is no test data, just return the computed basis, ready-to-use + if( !testset.data ) + return pca; + CV_Assert( testset.cols == pcaset.cols ); + + compressed.create(testset.rows, maxComponents, testset.type()); + + Mat reconstructed; + for( int i = 0; i < testset.rows; i++ ) + { + Mat vec = testset.row(i), coeffs = compressed.row(i), reconstructed; + // compress the vector, the result will be stored + // in the i-th row of the output matrix + pca.project(vec, coeffs); + // and then reconstruct it + pca.backProject(coeffs, reconstructed); + // and measure the error + printf("%d. diff = %g\n", i, norm(vec, reconstructed, NORM_L2)); + } + return pca; +} +@endcode +@sa calcCovarMatrix, mulTransposed, SVD, dft, dct +*/ +class CV_EXPORTS PCA +{ +public: + enum Flags { DATA_AS_ROW = 0, //!< indicates that the input samples are stored as matrix rows + DATA_AS_COL = 1, //!< indicates that the input samples are stored as matrix columns + USE_AVG = 2 //! + }; + + /** @brief default constructor + + The default constructor initializes an empty %PCA structure. The other + constructors initialize the structure and call PCA::operator()(). + */ + PCA(); + + /** @overload + @param data input samples stored as matrix rows or matrix columns. + @param mean optional mean value; if the matrix is empty (@c noArray()), + the mean is computed from the data. + @param flags operation flags; currently the parameter is only used to + specify the data layout (PCA::Flags) + @param maxComponents maximum number of components that %PCA should + retain; by default, all the components are retained. + */ + PCA(InputArray data, InputArray mean, int flags, int maxComponents = 0); + + /** @overload + @param data input samples stored as matrix rows or matrix columns. + @param mean optional mean value; if the matrix is empty (noArray()), + the mean is computed from the data. + @param flags operation flags; currently the parameter is only used to + specify the data layout (PCA::Flags) + @param retainedVariance Percentage of variance that PCA should retain. + Using this parameter will let the PCA decided how many components to + retain but it will always keep at least 2. + */ + PCA(InputArray data, InputArray mean, int flags, double retainedVariance); + + /** @brief performs %PCA + + The operator performs %PCA of the supplied dataset. It is safe to reuse + the same PCA structure for multiple datasets. That is, if the structure + has been previously used with another dataset, the existing internal + data is reclaimed and the new @ref eigenvalues, @ref eigenvectors and @ref + mean are allocated and computed. + + The computed @ref eigenvalues are sorted from the largest to the smallest and + the corresponding @ref eigenvectors are stored as eigenvectors rows. + + @param data input samples stored as the matrix rows or as the matrix + columns. + @param mean optional mean value; if the matrix is empty (noArray()), + the mean is computed from the data. + @param flags operation flags; currently the parameter is only used to + specify the data layout. (Flags) + @param maxComponents maximum number of components that PCA should + retain; by default, all the components are retained. + */ + PCA& operator()(InputArray data, InputArray mean, int flags, int maxComponents = 0); + + /** @overload + @param data input samples stored as the matrix rows or as the matrix + columns. + @param mean optional mean value; if the matrix is empty (noArray()), + the mean is computed from the data. + @param flags operation flags; currently the parameter is only used to + specify the data layout. (PCA::Flags) + @param retainedVariance Percentage of variance that %PCA should retain. + Using this parameter will let the %PCA decided how many components to + retain but it will always keep at least 2. + */ + PCA& operator()(InputArray data, InputArray mean, int flags, double retainedVariance); + + /** @brief Projects vector(s) to the principal component subspace. + + The methods project one or more vectors to the principal component + subspace, where each vector projection is represented by coefficients in + the principal component basis. The first form of the method returns the + matrix that the second form writes to the result. So the first form can + be used as a part of expression while the second form can be more + efficient in a processing loop. + @param vec input vector(s); must have the same dimensionality and the + same layout as the input data used at %PCA phase, that is, if + DATA_AS_ROW are specified, then `vec.cols==data.cols` + (vector dimensionality) and `vec.rows` is the number of vectors to + project, and the same is true for the PCA::DATA_AS_COL case. + */ + Mat project(InputArray vec) const; + + /** @overload + @param vec input vector(s); must have the same dimensionality and the + same layout as the input data used at PCA phase, that is, if + DATA_AS_ROW are specified, then `vec.cols==data.cols` + (vector dimensionality) and `vec.rows` is the number of vectors to + project, and the same is true for the PCA::DATA_AS_COL case. + @param result output vectors; in case of PCA::DATA_AS_COL, the + output matrix has as many columns as the number of input vectors, this + means that `result.cols==vec.cols` and the number of rows match the + number of principal components (for example, `maxComponents` parameter + passed to the constructor). + */ + void project(InputArray vec, OutputArray result) const; + + /** @brief Reconstructs vectors from their PC projections. + + The methods are inverse operations to PCA::project. They take PC + coordinates of projected vectors and reconstruct the original vectors. + Unless all the principal components have been retained, the + reconstructed vectors are different from the originals. But typically, + the difference is small if the number of components is large enough (but + still much smaller than the original vector dimensionality). As a + result, PCA is used. + @param vec coordinates of the vectors in the principal component + subspace, the layout and size are the same as of PCA::project output + vectors. + */ + Mat backProject(InputArray vec) const; + + /** @overload + @param vec coordinates of the vectors in the principal component + subspace, the layout and size are the same as of PCA::project output + vectors. + @param result reconstructed vectors; the layout and size are the same as + of PCA::project input vectors. + */ + void backProject(InputArray vec, OutputArray result) const; + + /** @brief write PCA objects + + Writes @ref eigenvalues @ref eigenvectors and @ref mean to specified FileStorage + */ + void write(FileStorage& fs) const; + + /** @brief load PCA objects + + Loads @ref eigenvalues @ref eigenvectors and @ref mean from specified FileNode + */ + void read(const FileNode& fn); + + Mat eigenvectors; //!< eigenvectors of the covariation matrix + Mat eigenvalues; //!< eigenvalues of the covariation matrix + Mat mean; //!< mean value subtracted before the projection and added after the back projection +}; + +/** @example samples/cpp/pca.cpp +An example using %PCA for dimensionality reduction while maintaining an amount of variance +*/ + +/** @example samples/cpp/tutorial_code/ml/introduction_to_pca/introduction_to_pca.cpp +Check @ref tutorial_introduction_to_pca "the corresponding tutorial" for more details +*/ + +/** +@brief Linear Discriminant Analysis +@todo document this class +*/ +class CV_EXPORTS LDA +{ +public: + /** @brief constructor + Initializes a LDA with num_components (default 0). + */ + explicit LDA(int num_components = 0); + + /** Initializes and performs a Discriminant Analysis with Fisher's + Optimization Criterion on given data in src and corresponding labels + in labels. If 0 (or less) number of components are given, they are + automatically determined for given data in computation. + */ + LDA(InputArrayOfArrays src, InputArray labels, int num_components = 0); + + /** Serializes this object to a given filename. + */ + void save(const String& filename) const; + + /** Deserializes this object from a given filename. + */ + void load(const String& filename); + + /** Serializes this object to a given cv::FileStorage. + */ + void save(FileStorage& fs) const; + + /** Deserializes this object from a given cv::FileStorage. + */ + void load(const FileStorage& node); + + /** destructor + */ + ~LDA(); + + /** Compute the discriminants for data in src (row aligned) and labels. + */ + void compute(InputArrayOfArrays src, InputArray labels); + + /** Projects samples into the LDA subspace. + src may be one or more row aligned samples. + */ + Mat project(InputArray src); + + /** Reconstructs projections from the LDA subspace. + src may be one or more row aligned projections. + */ + Mat reconstruct(InputArray src); + + /** Returns the eigenvectors of this LDA. + */ + Mat eigenvectors() const { return _eigenvectors; } + + /** Returns the eigenvalues of this LDA. + */ + Mat eigenvalues() const { return _eigenvalues; } + + static Mat subspaceProject(InputArray W, InputArray mean, InputArray src); + static Mat subspaceReconstruct(InputArray W, InputArray mean, InputArray src); + +protected: + bool _dataAsRow; // unused, but needed for 3.0 ABI compatibility. + int _num_components; + Mat _eigenvectors; + Mat _eigenvalues; + void lda(InputArrayOfArrays src, InputArray labels); +}; + +/** @brief Singular Value Decomposition + +Class for computing Singular Value Decomposition of a floating-point +matrix. The Singular Value Decomposition is used to solve least-square +problems, under-determined linear systems, invert matrices, compute +condition numbers, and so on. + +If you want to compute a condition number of a matrix or an absolute value of +its determinant, you do not need `u` and `vt`. You can pass +flags=SVD::NO_UV|... . Another flag SVD::FULL_UV indicates that full-size u +and vt must be computed, which is not necessary most of the time. + +@sa invert, solve, eigen, determinant +*/ +class CV_EXPORTS SVD +{ +public: + enum Flags { + /** allow the algorithm to modify the decomposed matrix; it can save space and speed up + processing. currently ignored. */ + MODIFY_A = 1, + /** indicates that only a vector of singular values `w` is to be processed, while u and vt + will be set to empty matrices */ + NO_UV = 2, + /** when the matrix is not square, by default the algorithm produces u and vt matrices of + sufficiently large size for the further A reconstruction; if, however, FULL_UV flag is + specified, u and vt will be full-size square orthogonal matrices.*/ + FULL_UV = 4 + }; + + /** @brief the default constructor + + initializes an empty SVD structure + */ + SVD(); + + /** @overload + initializes an empty SVD structure and then calls SVD::operator() + @param src decomposed matrix. The depth has to be CV_32F or CV_64F. + @param flags operation flags (SVD::Flags) + */ + SVD( InputArray src, int flags = 0 ); + + /** @brief the operator that performs SVD. The previously allocated u, w and vt are released. + + The operator performs the singular value decomposition of the supplied + matrix. The u,`vt` , and the vector of singular values w are stored in + the structure. The same SVD structure can be reused many times with + different matrices. Each time, if needed, the previous u,`vt` , and w + are reclaimed and the new matrices are created, which is all handled by + Mat::create. + @param src decomposed matrix. The depth has to be CV_32F or CV_64F. + @param flags operation flags (SVD::Flags) + */ + SVD& operator ()( InputArray src, int flags = 0 ); + + /** @brief decomposes matrix and stores the results to user-provided matrices + + The methods/functions perform SVD of matrix. Unlike SVD::SVD constructor + and SVD::operator(), they store the results to the user-provided + matrices: + + @code{.cpp} + Mat A, w, u, vt; + SVD::compute(A, w, u, vt); + @endcode + + @param src decomposed matrix. The depth has to be CV_32F or CV_64F. + @param w calculated singular values + @param u calculated left singular vectors + @param vt transposed matrix of right singular vectors + @param flags operation flags - see SVD::Flags. + */ + static void compute( InputArray src, OutputArray w, + OutputArray u, OutputArray vt, int flags = 0 ); + + /** @overload + computes singular values of a matrix + @param src decomposed matrix. The depth has to be CV_32F or CV_64F. + @param w calculated singular values + @param flags operation flags - see SVD::Flags. + */ + static void compute( InputArray src, OutputArray w, int flags = 0 ); + + /** @brief performs back substitution + */ + static void backSubst( InputArray w, InputArray u, + InputArray vt, InputArray rhs, + OutputArray dst ); + + /** @brief solves an under-determined singular linear system + + The method finds a unit-length solution x of a singular linear system + A\*x = 0. Depending on the rank of A, there can be no solutions, a + single solution or an infinite number of solutions. In general, the + algorithm solves the following problem: + \f[dst = \arg \min _{x: \| x \| =1} \| src \cdot x \|\f] + @param src left-hand-side matrix. + @param dst found solution. + */ + static void solveZ( InputArray src, OutputArray dst ); + + /** @brief performs a singular value back substitution. + + The method calculates a back substitution for the specified right-hand + side: + + \f[\texttt{x} = \texttt{vt} ^T \cdot diag( \texttt{w} )^{-1} \cdot \texttt{u} ^T \cdot \texttt{rhs} \sim \texttt{A} ^{-1} \cdot \texttt{rhs}\f] + + Using this technique you can either get a very accurate solution of the + convenient linear system, or the best (in the least-squares terms) + pseudo-solution of an overdetermined linear system. + + @param rhs right-hand side of a linear system (u\*w\*v')\*dst = rhs to + be solved, where A has been previously decomposed. + + @param dst found solution of the system. + + @note Explicit SVD with the further back substitution only makes sense + if you need to solve many linear systems with the same left-hand side + (for example, src ). If all you need is to solve a single system + (possibly with multiple rhs immediately available), simply call solve + add pass #DECOMP_SVD there. It does absolutely the same thing. + */ + void backSubst( InputArray rhs, OutputArray dst ) const; + + /** @todo document */ + template static + void compute( const Matx<_Tp, m, n>& a, Matx<_Tp, nm, 1>& w, Matx<_Tp, m, nm>& u, Matx<_Tp, n, nm>& vt ); + + /** @todo document */ + template static + void compute( const Matx<_Tp, m, n>& a, Matx<_Tp, nm, 1>& w ); + + /** @todo document */ + template static + void backSubst( const Matx<_Tp, nm, 1>& w, const Matx<_Tp, m, nm>& u, const Matx<_Tp, n, nm>& vt, const Matx<_Tp, m, nb>& rhs, Matx<_Tp, n, nb>& dst ); + + Mat u, w, vt; +}; + +/** @brief Random Number Generator + +Random number generator. It encapsulates the state (currently, a 64-bit +integer) and has methods to return scalar random values and to fill +arrays with random values. Currently it supports uniform and Gaussian +(normal) distributions. The generator uses Multiply-With-Carry +algorithm, introduced by G. Marsaglia ( + ). +Gaussian-distribution random numbers are generated using the Ziggurat +algorithm ( ), +introduced by G. Marsaglia and W. W. Tsang. +*/ +class CV_EXPORTS RNG +{ +public: + enum { UNIFORM = 0, + NORMAL = 1 + }; + + /** @brief constructor + + These are the RNG constructors. The first form sets the state to some + pre-defined value, equal to 2\*\*32-1 in the current implementation. The + second form sets the state to the specified value. If you passed state=0 + , the constructor uses the above default value instead to avoid the + singular random number sequence, consisting of all zeros. + */ + RNG(); + /** @overload + @param state 64-bit value used to initialize the RNG. + */ + RNG(uint64 state); + /**The method updates the state using the MWC algorithm and returns the + next 32-bit random number.*/ + unsigned next(); + + /**Each of the methods updates the state using the MWC algorithm and + returns the next random number of the specified type. In case of integer + types, the returned number is from the available value range for the + specified type. In case of floating-point types, the returned value is + from [0,1) range. + */ + operator uchar(); + /** @overload */ + operator schar(); + /** @overload */ + operator ushort(); + /** @overload */ + operator short(); + /** @overload */ + operator unsigned(); + /** @overload */ + operator int(); + /** @overload */ + operator float(); + /** @overload */ + operator double(); + + /** @brief returns a random integer sampled uniformly from [0, N). + + The methods transform the state using the MWC algorithm and return the + next random number. The first form is equivalent to RNG::next . The + second form returns the random number modulo N , which means that the + result is in the range [0, N) . + */ + unsigned operator ()(); + /** @overload + @param N upper non-inclusive boundary of the returned random number. + */ + unsigned operator ()(unsigned N); + + /** @brief returns uniformly distributed integer random number from [a,b) range + + The methods transform the state using the MWC algorithm and return the + next uniformly-distributed random number of the specified type, deduced + from the input parameter type, from the range [a, b) . There is a nuance + illustrated by the following sample: + + @code{.cpp} + RNG rng; + + // always produces 0 + double a = rng.uniform(0, 1); + + // produces double from [0, 1) + double a1 = rng.uniform((double)0, (double)1); + + // produces float from [0, 1) + float b = rng.uniform(0.f, 1.f); + + // produces double from [0, 1) + double c = rng.uniform(0., 1.); + + // may cause compiler error because of ambiguity: + // RNG::uniform(0, (int)0.999999)? or RNG::uniform((double)0, 0.99999)? + double d = rng.uniform(0, 0.999999); + @endcode + + The compiler does not take into account the type of the variable to + which you assign the result of RNG::uniform . The only thing that + matters to the compiler is the type of a and b parameters. So, if you + want a floating-point random number, but the range boundaries are + integer numbers, either put dots in the end, if they are constants, or + use explicit type cast operators, as in the a1 initialization above. + @param a lower inclusive boundary of the returned random number. + @param b upper non-inclusive boundary of the returned random number. + */ + int uniform(int a, int b); + /** @overload */ + float uniform(float a, float b); + /** @overload */ + double uniform(double a, double b); + + /** @brief Fills arrays with random numbers. + + @param mat 2D or N-dimensional matrix; currently matrices with more than + 4 channels are not supported by the methods, use Mat::reshape as a + possible workaround. + @param distType distribution type, RNG::UNIFORM or RNG::NORMAL. + @param a first distribution parameter; in case of the uniform + distribution, this is an inclusive lower boundary, in case of the normal + distribution, this is a mean value. + @param b second distribution parameter; in case of the uniform + distribution, this is a non-inclusive upper boundary, in case of the + normal distribution, this is a standard deviation (diagonal of the + standard deviation matrix or the full standard deviation matrix). + @param saturateRange pre-saturation flag; for uniform distribution only; + if true, the method will first convert a and b to the acceptable value + range (according to the mat datatype) and then will generate uniformly + distributed random numbers within the range [saturate(a), saturate(b)), + if saturateRange=false, the method will generate uniformly distributed + random numbers in the original range [a, b) and then will saturate them, + it means, for example, that + theRNG().fill(mat_8u, RNG::UNIFORM, -DBL_MAX, DBL_MAX) will likely + produce array mostly filled with 0's and 255's, since the range (0, 255) + is significantly smaller than [-DBL_MAX, DBL_MAX). + + Each of the methods fills the matrix with the random values from the + specified distribution. As the new numbers are generated, the RNG state + is updated accordingly. In case of multiple-channel images, every + channel is filled independently, which means that RNG cannot generate + samples from the multi-dimensional Gaussian distribution with + non-diagonal covariance matrix directly. To do that, the method + generates samples from multi-dimensional standard Gaussian distribution + with zero mean and identity covariation matrix, and then transforms them + using transform to get samples from the specified Gaussian distribution. + */ + void fill( InputOutputArray mat, int distType, InputArray a, InputArray b, bool saturateRange = false ); + + /** @brief Returns the next random number sampled from the Gaussian distribution + @param sigma standard deviation of the distribution. + + The method transforms the state using the MWC algorithm and returns the + next random number from the Gaussian distribution N(0,sigma) . That is, + the mean value of the returned random numbers is zero and the standard + deviation is the specified sigma . + */ + double gaussian(double sigma); + + uint64 state; + + bool operator ==(const RNG& other) const; +}; + +/** @brief Mersenne Twister random number generator + +Inspired by http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/MT2002/CODES/mt19937ar.c +@todo document +*/ +class CV_EXPORTS RNG_MT19937 +{ +public: + RNG_MT19937(); + RNG_MT19937(unsigned s); + void seed(unsigned s); + + unsigned next(); + + operator int(); + operator unsigned(); + operator float(); + operator double(); + + unsigned operator ()(unsigned N); + unsigned operator ()(); + + /** @brief returns uniformly distributed integer random number from [a,b) range*/ + int uniform(int a, int b); + /** @brief returns uniformly distributed floating-point random number from [a,b) range*/ + float uniform(float a, float b); + /** @brief returns uniformly distributed double-precision floating-point random number from [a,b) range*/ + double uniform(double a, double b); + +private: + enum PeriodParameters {N = 624, M = 397}; + unsigned state[N]; + int mti; +}; + +//! @} core_array + +//! @addtogroup core_cluster +//! @{ + +/** @example samples/cpp/kmeans.cpp +An example on K-means clustering +*/ + +/** @brief Finds centers of clusters and groups input samples around the clusters. + +The function kmeans implements a k-means algorithm that finds the centers of cluster_count clusters +and groups the input samples around the clusters. As an output, \f$\texttt{bestLabels}_i\f$ contains a +0-based cluster index for the sample stored in the \f$i^{th}\f$ row of the samples matrix. + +@note +- (Python) An example on K-means clustering can be found at + opencv_source_code/samples/python/kmeans.py +@param data Data for clustering. An array of N-Dimensional points with float coordinates is needed. +Examples of this array can be: +- Mat points(count, 2, CV_32F); +- Mat points(count, 1, CV_32FC2); +- Mat points(1, count, CV_32FC2); +- std::vector\ points(sampleCount); +@param K Number of clusters to split the set by. +@param bestLabels Input/output integer array that stores the cluster indices for every sample. +@param criteria The algorithm termination criteria, that is, the maximum number of iterations and/or +the desired accuracy. The accuracy is specified as criteria.epsilon. As soon as each of the cluster +centers moves by less than criteria.epsilon on some iteration, the algorithm stops. +@param attempts Flag to specify the number of times the algorithm is executed using different +initial labellings. The algorithm returns the labels that yield the best compactness (see the last +function parameter). +@param flags Flag that can take values of cv::KmeansFlags +@param centers Output matrix of the cluster centers, one row per each cluster center. +@return The function returns the compactness measure that is computed as +\f[\sum _i \| \texttt{samples} _i - \texttt{centers} _{ \texttt{labels} _i} \| ^2\f] +after every attempt. The best (minimum) value is chosen and the corresponding labels and the +compactness value are returned by the function. Basically, you can use only the core of the +function, set the number of attempts to 1, initialize labels each time using a custom algorithm, +pass them with the ( flags = #KMEANS_USE_INITIAL_LABELS ) flag, and then choose the best +(most-compact) clustering. +*/ +CV_EXPORTS_W double kmeans( InputArray data, int K, InputOutputArray bestLabels, + TermCriteria criteria, int attempts, + int flags, OutputArray centers = noArray() ); + +//! @} core_cluster + +//! @addtogroup core_basic +//! @{ + +/////////////////////////////// Formatted output of cv::Mat /////////////////////////// + +/** @todo document */ +class CV_EXPORTS Formatted +{ +public: + virtual const char* next() = 0; + virtual void reset() = 0; + virtual ~Formatted(); +}; + +/** @todo document */ +class CV_EXPORTS Formatter +{ +public: + enum { FMT_DEFAULT = 0, + FMT_MATLAB = 1, + FMT_CSV = 2, + FMT_PYTHON = 3, + FMT_NUMPY = 4, + FMT_C = 5 + }; + + virtual ~Formatter(); + + virtual Ptr format(const Mat& mtx) const = 0; + + virtual void set32fPrecision(int p = 8) = 0; + virtual void set64fPrecision(int p = 16) = 0; + virtual void setMultiline(bool ml = true) = 0; + + static Ptr get(int fmt = FMT_DEFAULT); + +}; + +static inline +String& operator << (String& out, Ptr fmtd) +{ + fmtd->reset(); + for(const char* str = fmtd->next(); str; str = fmtd->next()) + out += cv::String(str); + return out; +} + +static inline +String& operator << (String& out, const Mat& mtx) +{ + return out << Formatter::get()->format(mtx); +} + +//////////////////////////////////////// Algorithm //////////////////////////////////// + +class CV_EXPORTS Algorithm; + +template struct ParamType {}; + + +/** @brief This is a base class for all more or less complex algorithms in OpenCV + +especially for classes of algorithms, for which there can be multiple implementations. The examples +are stereo correspondence (for which there are algorithms like block matching, semi-global block +matching, graph-cut etc.), background subtraction (which can be done using mixture-of-gaussians +models, codebook-based algorithm etc.), optical flow (block matching, Lucas-Kanade, Horn-Schunck +etc.). + +Here is example of SimpleBlobDetector use in your application via Algorithm interface: +@snippet snippets/core_various.cpp Algorithm +*/ +class CV_EXPORTS_W Algorithm +{ +public: + Algorithm(); + virtual ~Algorithm(); + + /** @brief Clears the algorithm state + */ + CV_WRAP virtual void clear() {} + + /** @brief Stores algorithm parameters in a file storage + */ + virtual void write(FileStorage& fs) const { CV_UNUSED(fs); } + + /** @brief simplified API for language bindings + * @overload + */ + CV_WRAP void write(const Ptr& fs, const String& name = String()) const; + + /** @brief Reads algorithm parameters from a file storage + */ + CV_WRAP virtual void read(const FileNode& fn) { CV_UNUSED(fn); } + + /** @brief Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read + */ + CV_WRAP virtual bool empty() const { return false; } + + /** @brief Reads algorithm from the file node + + This is static template method of Algorithm. It's usage is following (in the case of SVM): + @code + cv::FileStorage fsRead("example.xml", FileStorage::READ); + Ptr svm = Algorithm::read(fsRead.root()); + @endcode + In order to make this method work, the derived class must overwrite Algorithm::read(const + FileNode& fn) and also have static create() method without parameters + (or with all the optional parameters) + */ + template static Ptr<_Tp> read(const FileNode& fn) + { + Ptr<_Tp> obj = _Tp::create(); + obj->read(fn); + return !obj->empty() ? obj : Ptr<_Tp>(); + } + + /** @brief Loads algorithm from the file + + @param filename Name of the file to read. + @param objname The optional name of the node to read (if empty, the first top-level node will be used) + + This is static template method of Algorithm. It's usage is following (in the case of SVM): + @code + Ptr svm = Algorithm::load("my_svm_model.xml"); + @endcode + In order to make this method work, the derived class must overwrite Algorithm::read(const + FileNode& fn). + */ + template static Ptr<_Tp> load(const String& filename, const String& objname=String()) + { + FileStorage fs(filename, FileStorage::READ); + CV_Assert(fs.isOpened()); + FileNode fn = objname.empty() ? fs.getFirstTopLevelNode() : fs[objname]; + if (fn.empty()) return Ptr<_Tp>(); + Ptr<_Tp> obj = _Tp::create(); + obj->read(fn); + return !obj->empty() ? obj : Ptr<_Tp>(); + } + + /** @brief Loads algorithm from a String + + @param strModel The string variable containing the model you want to load. + @param objname The optional name of the node to read (if empty, the first top-level node will be used) + + This is static template method of Algorithm. It's usage is following (in the case of SVM): + @code + Ptr svm = Algorithm::loadFromString(myStringModel); + @endcode + */ + template static Ptr<_Tp> loadFromString(const String& strModel, const String& objname=String()) + { + FileStorage fs(strModel, FileStorage::READ + FileStorage::MEMORY); + FileNode fn = objname.empty() ? fs.getFirstTopLevelNode() : fs[objname]; + Ptr<_Tp> obj = _Tp::create(); + obj->read(fn); + return !obj->empty() ? obj : Ptr<_Tp>(); + } + + /** Saves the algorithm to a file. + In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs). */ + CV_WRAP virtual void save(const String& filename) const; + + /** Returns the algorithm string identifier. + This string is used as top level xml/yml node tag when the object is saved to a file or string. */ + CV_WRAP virtual String getDefaultName() const; + +protected: + void writeFormat(FileStorage& fs) const; +}; + +struct Param { + enum { INT=0, BOOLEAN=1, REAL=2, STRING=3, MAT=4, MAT_VECTOR=5, ALGORITHM=6, FLOAT=7, + UNSIGNED_INT=8, UINT64=9, UCHAR=11, SCALAR=12 }; +}; + + + +template<> struct ParamType +{ + typedef bool const_param_type; + typedef bool member_type; + + enum { type = Param::BOOLEAN }; +}; + +template<> struct ParamType +{ + typedef int const_param_type; + typedef int member_type; + + enum { type = Param::INT }; +}; + +template<> struct ParamType +{ + typedef double const_param_type; + typedef double member_type; + + enum { type = Param::REAL }; +}; + +template<> struct ParamType +{ + typedef const String& const_param_type; + typedef String member_type; + + enum { type = Param::STRING }; +}; + +template<> struct ParamType +{ + typedef const Mat& const_param_type; + typedef Mat member_type; + + enum { type = Param::MAT }; +}; + +template<> struct ParamType > +{ + typedef const std::vector& const_param_type; + typedef std::vector member_type; + + enum { type = Param::MAT_VECTOR }; +}; + +template<> struct ParamType +{ + typedef const Ptr& const_param_type; + typedef Ptr member_type; + + enum { type = Param::ALGORITHM }; +}; + +template<> struct ParamType +{ + typedef float const_param_type; + typedef float member_type; + + enum { type = Param::FLOAT }; +}; + +template<> struct ParamType +{ + typedef unsigned const_param_type; + typedef unsigned member_type; + + enum { type = Param::UNSIGNED_INT }; +}; + +template<> struct ParamType +{ + typedef uint64 const_param_type; + typedef uint64 member_type; + + enum { type = Param::UINT64 }; +}; + +template<> struct ParamType +{ + typedef uchar const_param_type; + typedef uchar member_type; + + enum { type = Param::UCHAR }; +}; + +template<> struct ParamType +{ + typedef const Scalar& const_param_type; + typedef Scalar member_type; + + enum { type = Param::SCALAR }; +}; + +//! @} core_basic + +} //namespace cv + +#include "opencv2/core/operations.hpp" +#include "opencv2/core/cvstd.inl.hpp" +#include "opencv2/core/utility.hpp" +#include "opencv2/core/optim.hpp" +#include "opencv2/core/ovx.hpp" + +#endif /*OPENCV_CORE_HPP*/ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/affine.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/affine.hpp new file mode 100644 index 0000000000000000000000000000000000000000..7e2ed307858323ffbb4fa95c7c1275b9932c0574 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/affine.hpp @@ -0,0 +1,678 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_AFFINE3_HPP +#define OPENCV_CORE_AFFINE3_HPP + +#ifdef __cplusplus + +#include + +namespace cv +{ + +//! @addtogroup core +//! @{ + + /** @brief Affine transform + * + * It represents a 4x4 homogeneous transformation matrix \f$T\f$ + * + * \f[T = + * \begin{bmatrix} + * R & t\\ + * 0 & 1\\ + * \end{bmatrix} + * \f] + * + * where \f$R\f$ is a 3x3 rotation matrix and \f$t\f$ is a 3x1 translation vector. + * + * You can specify \f$R\f$ either by a 3x3 rotation matrix or by a 3x1 rotation vector, + * which is converted to a 3x3 rotation matrix by the Rodrigues formula. + * + * To construct a matrix \f$T\f$ representing first rotation around the axis \f$r\f$ with rotation + * angle \f$|r|\f$ in radian (right hand rule) and then translation by the vector \f$t\f$, you can use + * + * @code + * cv::Vec3f r, t; + * cv::Affine3f T(r, t); + * @endcode + * + * If you already have the rotation matrix \f$R\f$, then you can use + * + * @code + * cv::Matx33f R; + * cv::Affine3f T(R, t); + * @endcode + * + * To extract the rotation matrix \f$R\f$ from \f$T\f$, use + * + * @code + * cv::Matx33f R = T.rotation(); + * @endcode + * + * To extract the translation vector \f$t\f$ from \f$T\f$, use + * + * @code + * cv::Vec3f t = T.translation(); + * @endcode + * + * To extract the rotation vector \f$r\f$ from \f$T\f$, use + * + * @code + * cv::Vec3f r = T.rvec(); + * @endcode + * + * Note that since the mapping from rotation vectors to rotation matrices + * is many to one. The returned rotation vector is not necessarily the one + * you used before to set the matrix. + * + * If you have two transformations \f$T = T_1 * T_2\f$, use + * + * @code + * cv::Affine3f T, T1, T2; + * T = T2.concatenate(T1); + * @endcode + * + * To get the inverse transform of \f$T\f$, use + * + * @code + * cv::Affine3f T, T_inv; + * T_inv = T.inv(); + * @endcode + * + */ + template + class Affine3 + { + public: + typedef T float_type; + typedef Matx Mat3; + typedef Matx Mat4; + typedef Vec Vec3; + + //! Default constructor. It represents a 4x4 identity matrix. + Affine3(); + + //! Augmented affine matrix + Affine3(const Mat4& affine); + + /** + * The resulting 4x4 matrix is + * + * \f[ + * \begin{bmatrix} + * R & t\\ + * 0 & 1\\ + * \end{bmatrix} + * \f] + * + * @param R 3x3 rotation matrix. + * @param t 3x1 translation vector. + */ + Affine3(const Mat3& R, const Vec3& t = Vec3::all(0)); + + /** + * Rodrigues vector. + * + * The last row of the current matrix is set to [0,0,0,1]. + * + * @param rvec 3x1 rotation vector. Its direction indicates the rotation axis and its length + * indicates the rotation angle in radian (using right hand rule). + * @param t 3x1 translation vector. + */ + Affine3(const Vec3& rvec, const Vec3& t = Vec3::all(0)); + + /** + * Combines all constructors above. Supports 4x4, 3x4, 3x3, 1x3, 3x1 sizes of data matrix. + * + * The last row of the current matrix is set to [0,0,0,1] when data is not 4x4. + * + * @param data 1-channel matrix. + * when it is 4x4, it is copied to the current matrix and t is not used. + * When it is 3x4, it is copied to the upper part 3x4 of the current matrix and t is not used. + * When it is 3x3, it is copied to the upper left 3x3 part of the current matrix. + * When it is 3x1 or 1x3, it is treated as a rotation vector and the Rodrigues formula is used + * to compute a 3x3 rotation matrix. + * @param t 3x1 translation vector. It is used only when data is neither 4x4 nor 3x4. + */ + explicit Affine3(const Mat& data, const Vec3& t = Vec3::all(0)); + + //! From 16-element array + explicit Affine3(const float_type* vals); + + //! Create an 4x4 identity transform + static Affine3 Identity(); + + /** + * Rotation matrix. + * + * Copy the rotation matrix to the upper left 3x3 part of the current matrix. + * The remaining elements of the current matrix are not changed. + * + * @param R 3x3 rotation matrix. + * + */ + void rotation(const Mat3& R); + + /** + * Rodrigues vector. + * + * It sets the upper left 3x3 part of the matrix. The remaining part is unaffected. + * + * @param rvec 3x1 rotation vector. The direction indicates the rotation axis and + * its length indicates the rotation angle in radian (using the right thumb convention). + */ + void rotation(const Vec3& rvec); + + /** + * Combines rotation methods above. Supports 3x3, 1x3, 3x1 sizes of data matrix. + * + * It sets the upper left 3x3 part of the matrix. The remaining part is unaffected. + * + * @param data 1-channel matrix. + * When it is a 3x3 matrix, it sets the upper left 3x3 part of the current matrix. + * When it is a 1x3 or 3x1 matrix, it is used as a rotation vector. The Rodrigues formula + * is used to compute the rotation matrix and sets the upper left 3x3 part of the current matrix. + */ + void rotation(const Mat& data); + + /** + * Copy the 3x3 matrix L to the upper left part of the current matrix + * + * It sets the upper left 3x3 part of the matrix. The remaining part is unaffected. + * + * @param L 3x3 matrix. + */ + void linear(const Mat3& L); + + /** + * Copy t to the first three elements of the last column of the current matrix + * + * It sets the upper right 3x1 part of the matrix. The remaining part is unaffected. + * + * @param t 3x1 translation vector. + */ + void translation(const Vec3& t); + + //! @return the upper left 3x3 part + Mat3 rotation() const; + + //! @return the upper left 3x3 part + Mat3 linear() const; + + //! @return the upper right 3x1 part + Vec3 translation() const; + + //! Rodrigues vector. + //! @return a vector representing the upper left 3x3 rotation matrix of the current matrix. + //! @warning Since the mapping between rotation vectors and rotation matrices is many to one, + //! this function returns only one rotation vector that represents the current rotation matrix, + //! which is not necessarily the same one set by `rotation(const Vec3& rvec)`. + Vec3 rvec() const; + + //! @return the inverse of the current matrix. + Affine3 inv(int method = cv::DECOMP_SVD) const; + + //! a.rotate(R) is equivalent to Affine(R, 0) * a; + Affine3 rotate(const Mat3& R) const; + + //! a.rotate(rvec) is equivalent to Affine(rvec, 0) * a; + Affine3 rotate(const Vec3& rvec) const; + + //! a.translate(t) is equivalent to Affine(E, t) * a, where E is an identity matrix + Affine3 translate(const Vec3& t) const; + + //! a.concatenate(affine) is equivalent to affine * a; + Affine3 concatenate(const Affine3& affine) const; + + template operator Affine3() const; + + template Affine3 cast() const; + + Mat4 matrix; + +#if defined EIGEN_WORLD_VERSION && defined EIGEN_GEOMETRY_MODULE_H + Affine3(const Eigen::Transform& affine); + Affine3(const Eigen::Transform& affine); + operator Eigen::Transform() const; + operator Eigen::Transform() const; +#endif + }; + + template static + Affine3 operator*(const Affine3& affine1, const Affine3& affine2); + + //! V is a 3-element vector with member fields x, y and z + template static + V operator*(const Affine3& affine, const V& vector); + + typedef Affine3 Affine3f; + typedef Affine3 Affine3d; + + static Vec3f operator*(const Affine3f& affine, const Vec3f& vector); + static Vec3d operator*(const Affine3d& affine, const Vec3d& vector); + + template class DataType< Affine3<_Tp> > + { + public: + typedef Affine3<_Tp> value_type; + typedef Affine3::work_type> work_type; + typedef _Tp channel_type; + + enum { generic_type = 0, + channels = 16, + fmt = traits::SafeFmt::fmt + ((channels - 1) << 8) +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + ,depth = DataType::depth + ,type = CV_MAKETYPE(depth, channels) +#endif + }; + + typedef Vec vec_type; + }; + + namespace traits { + template + struct Depth< Affine3<_Tp> > { enum { value = Depth<_Tp>::value }; }; + template + struct Type< Affine3<_Tp> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, 16) }; }; + } // namespace + +//! @} core + +} + +//! @cond IGNORED + +/////////////////////////////////////////////////////////////////////////////////// +// Implementation + +template inline +cv::Affine3::Affine3() + : matrix(Mat4::eye()) +{} + +template inline +cv::Affine3::Affine3(const Mat4& affine) + : matrix(affine) +{} + +template inline +cv::Affine3::Affine3(const Mat3& R, const Vec3& t) +{ + rotation(R); + translation(t); + matrix.val[12] = matrix.val[13] = matrix.val[14] = 0; + matrix.val[15] = 1; +} + +template inline +cv::Affine3::Affine3(const Vec3& _rvec, const Vec3& t) +{ + rotation(_rvec); + translation(t); + matrix.val[12] = matrix.val[13] = matrix.val[14] = 0; + matrix.val[15] = 1; +} + +template inline +cv::Affine3::Affine3(const cv::Mat& data, const Vec3& t) +{ + CV_Assert(data.type() == cv::traits::Type::value); + CV_Assert(data.channels() == 1); + + if (data.cols == 4 && data.rows == 4) + { + data.copyTo(matrix); + return; + } + else if (data.cols == 4 && data.rows == 3) + { + rotation(data(Rect(0, 0, 3, 3))); + translation(data(Rect(3, 0, 1, 3))); + } + else + { + rotation(data); + translation(t); + } + + matrix.val[12] = matrix.val[13] = matrix.val[14] = 0; + matrix.val[15] = 1; +} + +template inline +cv::Affine3::Affine3(const float_type* vals) : matrix(vals) +{} + +template inline +cv::Affine3 cv::Affine3::Identity() +{ + return Affine3(cv::Affine3::Mat4::eye()); +} + +template inline +void cv::Affine3::rotation(const Mat3& R) +{ + linear(R); +} + +template inline +void cv::Affine3::rotation(const Vec3& _rvec) +{ + double theta = norm(_rvec); + + if (theta < DBL_EPSILON) + rotation(Mat3::eye()); + else + { + double c = std::cos(theta); + double s = std::sin(theta); + double c1 = 1. - c; + double itheta = (theta != 0) ? 1./theta : 0.; + + Point3_ r = _rvec*itheta; + + Mat3 rrt( r.x*r.x, r.x*r.y, r.x*r.z, r.x*r.y, r.y*r.y, r.y*r.z, r.x*r.z, r.y*r.z, r.z*r.z ); + Mat3 r_x( 0, -r.z, r.y, r.z, 0, -r.x, -r.y, r.x, 0 ); + + // R = cos(theta)*I + (1 - cos(theta))*r*rT + sin(theta)*[r_x] + // where [r_x] is [0 -rz ry; rz 0 -rx; -ry rx 0] + Mat3 R = c*Mat3::eye() + c1*rrt + s*r_x; + + rotation(R); + } +} + +//Combines rotation methods above. Supports 3x3, 1x3, 3x1 sizes of data matrix; +template inline +void cv::Affine3::rotation(const cv::Mat& data) +{ + CV_Assert(data.type() == cv::traits::Type::value); + CV_Assert(data.channels() == 1); + + if (data.cols == 3 && data.rows == 3) + { + Mat3 R; + data.copyTo(R); + rotation(R); + } + else if ((data.cols == 3 && data.rows == 1) || (data.cols == 1 && data.rows == 3)) + { + Vec3 _rvec; + data.reshape(1, 3).copyTo(_rvec); + rotation(_rvec); + } + else + CV_Error(Error::StsError, "Input matrix can only be 3x3, 1x3 or 3x1"); +} + +template inline +void cv::Affine3::linear(const Mat3& L) +{ + matrix.val[0] = L.val[0]; matrix.val[1] = L.val[1]; matrix.val[ 2] = L.val[2]; + matrix.val[4] = L.val[3]; matrix.val[5] = L.val[4]; matrix.val[ 6] = L.val[5]; + matrix.val[8] = L.val[6]; matrix.val[9] = L.val[7]; matrix.val[10] = L.val[8]; +} + +template inline +void cv::Affine3::translation(const Vec3& t) +{ + matrix.val[3] = t[0]; matrix.val[7] = t[1]; matrix.val[11] = t[2]; +} + +template inline +typename cv::Affine3::Mat3 cv::Affine3::rotation() const +{ + return linear(); +} + +template inline +typename cv::Affine3::Mat3 cv::Affine3::linear() const +{ + typename cv::Affine3::Mat3 R; + R.val[0] = matrix.val[0]; R.val[1] = matrix.val[1]; R.val[2] = matrix.val[ 2]; + R.val[3] = matrix.val[4]; R.val[4] = matrix.val[5]; R.val[5] = matrix.val[ 6]; + R.val[6] = matrix.val[8]; R.val[7] = matrix.val[9]; R.val[8] = matrix.val[10]; + return R; +} + +template inline +typename cv::Affine3::Vec3 cv::Affine3::translation() const +{ + return Vec3(matrix.val[3], matrix.val[7], matrix.val[11]); +} + +template inline +typename cv::Affine3::Vec3 cv::Affine3::rvec() const +{ + cv::Vec3d w; + cv::Matx33d u, vt, R = rotation(); + cv::SVD::compute(R, w, u, vt, cv::SVD::FULL_UV + cv::SVD::MODIFY_A); + R = u * vt; + + double rx = R.val[7] - R.val[5]; + double ry = R.val[2] - R.val[6]; + double rz = R.val[3] - R.val[1]; + + double s = std::sqrt((rx*rx + ry*ry + rz*rz)*0.25); + double c = (R.val[0] + R.val[4] + R.val[8] - 1) * 0.5; + c = c > 1.0 ? 1.0 : c < -1.0 ? -1.0 : c; + double theta = acos(c); + + if( s < 1e-5 ) + { + if( c > 0 ) + rx = ry = rz = 0; + else + { + double t; + t = (R.val[0] + 1) * 0.5; + rx = std::sqrt(std::max(t, 0.0)); + t = (R.val[4] + 1) * 0.5; + ry = std::sqrt(std::max(t, 0.0)) * (R.val[1] < 0 ? -1.0 : 1.0); + t = (R.val[8] + 1) * 0.5; + rz = std::sqrt(std::max(t, 0.0)) * (R.val[2] < 0 ? -1.0 : 1.0); + + if( fabs(rx) < fabs(ry) && fabs(rx) < fabs(rz) && (R.val[5] > 0) != (ry*rz > 0) ) + rz = -rz; + theta /= std::sqrt(rx*rx + ry*ry + rz*rz); + rx *= theta; + ry *= theta; + rz *= theta; + } + } + else + { + double vth = 1/(2*s); + vth *= theta; + rx *= vth; ry *= vth; rz *= vth; + } + + return cv::Vec3d(rx, ry, rz); +} + +template inline +cv::Affine3 cv::Affine3::inv(int method) const +{ + return matrix.inv(method); +} + +template inline +cv::Affine3 cv::Affine3::rotate(const Mat3& R) const +{ + Mat3 Lc = linear(); + Vec3 tc = translation(); + Mat4 result; + result.val[12] = result.val[13] = result.val[14] = 0; + result.val[15] = 1; + + for(int j = 0; j < 3; ++j) + { + for(int i = 0; i < 3; ++i) + { + float_type value = 0; + for(int k = 0; k < 3; ++k) + value += R(j, k) * Lc(k, i); + result(j, i) = value; + } + + result(j, 3) = R.row(j).dot(tc.t()); + } + return result; +} + +template inline +cv::Affine3 cv::Affine3::rotate(const Vec3& _rvec) const +{ + return rotate(Affine3f(_rvec).rotation()); +} + +template inline +cv::Affine3 cv::Affine3::translate(const Vec3& t) const +{ + Mat4 m = matrix; + m.val[ 3] += t[0]; + m.val[ 7] += t[1]; + m.val[11] += t[2]; + return m; +} + +template inline +cv::Affine3 cv::Affine3::concatenate(const Affine3& affine) const +{ + return (*this).rotate(affine.rotation()).translate(affine.translation()); +} + +template template inline +cv::Affine3::operator Affine3() const +{ + return Affine3(matrix); +} + +template template inline +cv::Affine3 cv::Affine3::cast() const +{ + return Affine3(matrix); +} + +template inline +cv::Affine3 cv::operator*(const cv::Affine3& affine1, const cv::Affine3& affine2) +{ + return affine2.concatenate(affine1); +} + +template inline +V cv::operator*(const cv::Affine3& affine, const V& v) +{ + const typename Affine3::Mat4& m = affine.matrix; + + V r; + r.x = m.val[0] * v.x + m.val[1] * v.y + m.val[ 2] * v.z + m.val[ 3]; + r.y = m.val[4] * v.x + m.val[5] * v.y + m.val[ 6] * v.z + m.val[ 7]; + r.z = m.val[8] * v.x + m.val[9] * v.y + m.val[10] * v.z + m.val[11]; + return r; +} + +static inline +cv::Vec3f cv::operator*(const cv::Affine3f& affine, const cv::Vec3f& v) +{ + const cv::Matx44f& m = affine.matrix; + cv::Vec3f r; + r.val[0] = m.val[0] * v[0] + m.val[1] * v[1] + m.val[ 2] * v[2] + m.val[ 3]; + r.val[1] = m.val[4] * v[0] + m.val[5] * v[1] + m.val[ 6] * v[2] + m.val[ 7]; + r.val[2] = m.val[8] * v[0] + m.val[9] * v[1] + m.val[10] * v[2] + m.val[11]; + return r; +} + +static inline +cv::Vec3d cv::operator*(const cv::Affine3d& affine, const cv::Vec3d& v) +{ + const cv::Matx44d& m = affine.matrix; + cv::Vec3d r; + r.val[0] = m.val[0] * v[0] + m.val[1] * v[1] + m.val[ 2] * v[2] + m.val[ 3]; + r.val[1] = m.val[4] * v[0] + m.val[5] * v[1] + m.val[ 6] * v[2] + m.val[ 7]; + r.val[2] = m.val[8] * v[0] + m.val[9] * v[1] + m.val[10] * v[2] + m.val[11]; + return r; +} + + + +#if defined EIGEN_WORLD_VERSION && defined EIGEN_GEOMETRY_MODULE_H + +template inline +cv::Affine3::Affine3(const Eigen::Transform& affine) +{ + cv::Mat(4, 4, cv::traits::Type::value, affine.matrix().data()).copyTo(matrix); +} + +template inline +cv::Affine3::Affine3(const Eigen::Transform& affine) +{ + Eigen::Transform a = affine; + cv::Mat(4, 4, cv::traits::Type::value, a.matrix().data()).copyTo(matrix); +} + +template inline +cv::Affine3::operator Eigen::Transform() const +{ + Eigen::Transform r; + cv::Mat hdr(4, 4, cv::traits::Type::value, r.matrix().data()); + cv::Mat(matrix, false).copyTo(hdr); + return r; +} + +template inline +cv::Affine3::operator Eigen::Transform() const +{ + return this->operator Eigen::Transform(); +} + +#endif /* defined EIGEN_WORLD_VERSION && defined EIGEN_GEOMETRY_MODULE_H */ + +//! @endcond + +#endif /* __cplusplus */ + +#endif /* OPENCV_CORE_AFFINE3_HPP */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/base.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/base.hpp new file mode 100644 index 0000000000000000000000000000000000000000..31cd7a8202acfb0e001ee0346caff7233f459e23 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/base.hpp @@ -0,0 +1,707 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2014, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_BASE_HPP +#define OPENCV_CORE_BASE_HPP + +#ifndef __cplusplus +# error base.hpp header must be compiled as C++ +#endif + +#include "opencv2/opencv_modules.hpp" + +#include +#include + +#include "opencv2/core/cvdef.h" +#include "opencv2/core/cvstd.hpp" + +namespace cv +{ + +//! @addtogroup core_utils +//! @{ + +namespace Error { +//! error codes +enum Code { + StsOk= 0, //!< everything is ok + StsBackTrace= -1, //!< pseudo error for back trace + StsError= -2, //!< unknown /unspecified error + StsInternal= -3, //!< internal error (bad state) + StsNoMem= -4, //!< insufficient memory + StsBadArg= -5, //!< function arg/param is bad + StsBadFunc= -6, //!< unsupported function + StsNoConv= -7, //!< iteration didn't converge + StsAutoTrace= -8, //!< tracing + HeaderIsNull= -9, //!< image header is NULL + BadImageSize= -10, //!< image size is invalid + BadOffset= -11, //!< offset is invalid + BadDataPtr= -12, //!< + BadStep= -13, //!< image step is wrong, this may happen for a non-continuous matrix. + BadModelOrChSeq= -14, //!< + BadNumChannels= -15, //!< bad number of channels, for example, some functions accept only single channel matrices. + BadNumChannel1U= -16, //!< + BadDepth= -17, //!< input image depth is not supported by the function + BadAlphaChannel= -18, //!< + BadOrder= -19, //!< number of dimensions is out of range + BadOrigin= -20, //!< incorrect input origin + BadAlign= -21, //!< incorrect input align + BadCallBack= -22, //!< + BadTileSize= -23, //!< + BadCOI= -24, //!< input COI is not supported + BadROISize= -25, //!< incorrect input roi + MaskIsTiled= -26, //!< + StsNullPtr= -27, //!< null pointer + StsVecLengthErr= -28, //!< incorrect vector length + StsFilterStructContentErr= -29, //!< incorrect filter structure content + StsKernelStructContentErr= -30, //!< incorrect transform kernel content + StsFilterOffsetErr= -31, //!< incorrect filter offset value + StsBadSize= -201, //!< the input/output structure size is incorrect + StsDivByZero= -202, //!< division by zero + StsInplaceNotSupported= -203, //!< in-place operation is not supported + StsObjectNotFound= -204, //!< request can't be completed + StsUnmatchedFormats= -205, //!< formats of input/output arrays differ + StsBadFlag= -206, //!< flag is wrong or not supported + StsBadPoint= -207, //!< bad CvPoint + StsBadMask= -208, //!< bad format of mask (neither 8uC1 nor 8sC1) + StsUnmatchedSizes= -209, //!< sizes of input/output structures do not match + StsUnsupportedFormat= -210, //!< the data format/type is not supported by the function + StsOutOfRange= -211, //!< some of parameters are out of range + StsParseError= -212, //!< invalid syntax/structure of the parsed file + StsNotImplemented= -213, //!< the requested function/feature is not implemented + StsBadMemBlock= -214, //!< an allocated block has been corrupted + StsAssert= -215, //!< assertion failed + GpuNotSupported= -216, //!< no CUDA support + GpuApiCallError= -217, //!< GPU API call error + OpenGlNotSupported= -218, //!< no OpenGL support + OpenGlApiCallError= -219, //!< OpenGL API call error + OpenCLApiCallError= -220, //!< OpenCL API call error + OpenCLDoubleNotSupported= -221, + OpenCLInitError= -222, //!< OpenCL initialization error + OpenCLNoAMDBlasFft= -223 +}; +} //Error + +//! @} core_utils + +//! @addtogroup core_array +//! @{ + +//! matrix decomposition types +enum DecompTypes { + /** Gaussian elimination with the optimal pivot element chosen. */ + DECOMP_LU = 0, + /** singular value decomposition (SVD) method; the system can be over-defined and/or the matrix + src1 can be singular */ + DECOMP_SVD = 1, + /** eigenvalue decomposition; the matrix src1 must be symmetrical */ + DECOMP_EIG = 2, + /** Cholesky \f$LL^T\f$ factorization; the matrix src1 must be symmetrical and positively + defined */ + DECOMP_CHOLESKY = 3, + /** QR factorization; the system can be over-defined and/or the matrix src1 can be singular */ + DECOMP_QR = 4, + /** while all the previous flags are mutually exclusive, this flag can be used together with + any of the previous; it means that the normal equations + \f$\texttt{src1}^T\cdot\texttt{src1}\cdot\texttt{dst}=\texttt{src1}^T\texttt{src2}\f$ are + solved instead of the original system + \f$\texttt{src1}\cdot\texttt{dst}=\texttt{src2}\f$ */ + DECOMP_NORMAL = 16 +}; + +/** norm types + +src1 and src2 denote input arrays. +*/ + +enum NormTypes { + /** + \f[ + norm = \forkthree + {\|\texttt{src1}\|_{L_{\infty}} = \max _I | \texttt{src1} (I)|}{if \(\texttt{normType} = \texttt{NORM_INF}\) } + {\|\texttt{src1}-\texttt{src2}\|_{L_{\infty}} = \max _I | \texttt{src1} (I) - \texttt{src2} (I)|}{if \(\texttt{normType} = \texttt{NORM_INF}\) } + {\frac{\|\texttt{src1}-\texttt{src2}\|_{L_{\infty}} }{\|\texttt{src2}\|_{L_{\infty}} }}{if \(\texttt{normType} = \texttt{NORM_RELATIVE | NORM_INF}\) } + \f] + */ + NORM_INF = 1, + /** + \f[ + norm = \forkthree + {\| \texttt{src1} \| _{L_1} = \sum _I | \texttt{src1} (I)|}{if \(\texttt{normType} = \texttt{NORM_L1}\)} + { \| \texttt{src1} - \texttt{src2} \| _{L_1} = \sum _I | \texttt{src1} (I) - \texttt{src2} (I)|}{if \(\texttt{normType} = \texttt{NORM_L1}\) } + { \frac{\|\texttt{src1}-\texttt{src2}\|_{L_1} }{\|\texttt{src2}\|_{L_1}} }{if \(\texttt{normType} = \texttt{NORM_RELATIVE | NORM_L1}\) } + \f]*/ + NORM_L1 = 2, + /** + \f[ + norm = \forkthree + { \| \texttt{src1} \| _{L_2} = \sqrt{\sum_I \texttt{src1}(I)^2} }{if \(\texttt{normType} = \texttt{NORM_L2}\) } + { \| \texttt{src1} - \texttt{src2} \| _{L_2} = \sqrt{\sum_I (\texttt{src1}(I) - \texttt{src2}(I))^2} }{if \(\texttt{normType} = \texttt{NORM_L2}\) } + { \frac{\|\texttt{src1}-\texttt{src2}\|_{L_2} }{\|\texttt{src2}\|_{L_2}} }{if \(\texttt{normType} = \texttt{NORM_RELATIVE | NORM_L2}\) } + \f] + */ + NORM_L2 = 4, + /** + \f[ + norm = \forkthree + { \| \texttt{src1} \| _{L_2} ^{2} = \sum_I \texttt{src1}(I)^2} {if \(\texttt{normType} = \texttt{NORM_L2SQR}\)} + { \| \texttt{src1} - \texttt{src2} \| _{L_2} ^{2} = \sum_I (\texttt{src1}(I) - \texttt{src2}(I))^2 }{if \(\texttt{normType} = \texttt{NORM_L2SQR}\) } + { \left(\frac{\|\texttt{src1}-\texttt{src2}\|_{L_2} }{\|\texttt{src2}\|_{L_2}}\right)^2 }{if \(\texttt{normType} = \texttt{NORM_RELATIVE | NORM_L2}\) } + \f] + */ + NORM_L2SQR = 5, + /** + In the case of one input array, calculates the Hamming distance of the array from zero, + In the case of two input arrays, calculates the Hamming distance between the arrays. + */ + NORM_HAMMING = 6, + /** + Similar to NORM_HAMMING, but in the calculation, each two bits of the input sequence will + be added and treated as a single bit to be used in the same calculation as NORM_HAMMING. + */ + NORM_HAMMING2 = 7, + NORM_TYPE_MASK = 7, //!< bit-mask which can be used to separate norm type from norm flags + NORM_RELATIVE = 8, //!< flag + NORM_MINMAX = 32 //!< flag + }; + +//! comparison types +enum CmpTypes { CMP_EQ = 0, //!< src1 is equal to src2. + CMP_GT = 1, //!< src1 is greater than src2. + CMP_GE = 2, //!< src1 is greater than or equal to src2. + CMP_LT = 3, //!< src1 is less than src2. + CMP_LE = 4, //!< src1 is less than or equal to src2. + CMP_NE = 5 //!< src1 is unequal to src2. + }; + +//! generalized matrix multiplication flags +enum GemmFlags { GEMM_1_T = 1, //!< transposes src1 + GEMM_2_T = 2, //!< transposes src2 + GEMM_3_T = 4 //!< transposes src3 + }; + +enum DftFlags { + /** performs an inverse 1D or 2D transform instead of the default forward + transform. */ + DFT_INVERSE = 1, + /** scales the result: divide it by the number of array elements. Normally, it is + combined with DFT_INVERSE. */ + DFT_SCALE = 2, + /** performs a forward or inverse transform of every individual row of the input + matrix; this flag enables you to transform multiple vectors simultaneously and can be used to + decrease the overhead (which is sometimes several times larger than the processing itself) to + perform 3D and higher-dimensional transformations and so forth.*/ + DFT_ROWS = 4, + /** performs a forward transformation of 1D or 2D real array; the result, + though being a complex array, has complex-conjugate symmetry (*CCS*, see the function + description below for details), and such an array can be packed into a real array of the same + size as input, which is the fastest option and which is what the function does by default; + however, you may wish to get a full complex array (for simpler spectrum analysis, and so on) - + pass the flag to enable the function to produce a full-size complex output array. */ + DFT_COMPLEX_OUTPUT = 16, + /** performs an inverse transformation of a 1D or 2D complex array; the + result is normally a complex array of the same size, however, if the input array has + conjugate-complex symmetry (for example, it is a result of forward transformation with + DFT_COMPLEX_OUTPUT flag), the output is a real array; while the function itself does not + check whether the input is symmetrical or not, you can pass the flag and then the function + will assume the symmetry and produce the real output array (note that when the input is packed + into a real array and inverse transformation is executed, the function treats the input as a + packed complex-conjugate symmetrical array, and the output will also be a real array). */ + DFT_REAL_OUTPUT = 32, + /** specifies that input is complex input. If this flag is set, the input must have 2 channels. + On the other hand, for backwards compatibility reason, if input has 2 channels, input is + already considered complex. */ + DFT_COMPLEX_INPUT = 64, + /** performs an inverse 1D or 2D transform instead of the default forward transform. */ + DCT_INVERSE = DFT_INVERSE, + /** performs a forward or inverse transform of every individual row of the input + matrix. This flag enables you to transform multiple vectors simultaneously and can be used to + decrease the overhead (which is sometimes several times larger than the processing itself) to + perform 3D and higher-dimensional transforms and so forth.*/ + DCT_ROWS = DFT_ROWS +}; + +//! Various border types, image boundaries are denoted with `|` +//! @see borderInterpolate, copyMakeBorder +enum BorderTypes { + BORDER_CONSTANT = 0, //!< `iiiiii|abcdefgh|iiiiiii` with some specified `i` + BORDER_REPLICATE = 1, //!< `aaaaaa|abcdefgh|hhhhhhh` + BORDER_REFLECT = 2, //!< `fedcba|abcdefgh|hgfedcb` + BORDER_WRAP = 3, //!< `cdefgh|abcdefgh|abcdefg` + BORDER_REFLECT_101 = 4, //!< `gfedcb|abcdefgh|gfedcba` + BORDER_TRANSPARENT = 5, //!< `uvwxyz|abcdefgh|ijklmno` + + BORDER_REFLECT101 = BORDER_REFLECT_101, //!< same as BORDER_REFLECT_101 + BORDER_DEFAULT = BORDER_REFLECT_101, //!< same as BORDER_REFLECT_101 + BORDER_ISOLATED = 16 //!< do not look outside of ROI +}; + +//! @} core_array + +//! @addtogroup core_utils +//! @{ + +/*! @brief Signals an error and raises the exception. + +By default the function prints information about the error to stderr, +then it either stops if setBreakOnError() had been called before or raises the exception. +It is possible to alternate error processing by using redirectError(). +@param _code - error code (Error::Code) +@param _err - error description +@param _func - function name. Available only when the compiler supports getting it +@param _file - source file name where the error has occurred +@param _line - line number in the source file where the error has occurred +@see CV_Error, CV_Error_, CV_Assert, CV_DbgAssert + */ +CV_EXPORTS void error(int _code, const String& _err, const char* _func, const char* _file, int _line); + +#ifdef __GNUC__ +# if defined __clang__ || defined __APPLE__ +# pragma GCC diagnostic push +# pragma GCC diagnostic ignored "-Winvalid-noreturn" +# endif +#endif + +/** same as cv::error, but does not return */ +CV_INLINE CV_NORETURN void errorNoReturn(int _code, const String& _err, const char* _func, const char* _file, int _line) +{ + error(_code, _err, _func, _file, _line); +#ifdef __GNUC__ +# if !defined __clang__ && !defined __APPLE__ + // this suppresses this warning: "noreturn" function does return [enabled by default] + __builtin_trap(); + // or use infinite loop: for (;;) {} +# endif +#endif +} +#ifdef __GNUC__ +# if defined __clang__ || defined __APPLE__ +# pragma GCC diagnostic pop +# endif +#endif + +#ifdef CV_STATIC_ANALYSIS + +// In practice, some macro are not processed correctly (noreturn is not detected). +// We need to use simplified definition for them. +#define CV_Error(...) do { abort(); } while (0) +#define CV_Error_( code, args ) do { cv::format args; abort(); } while (0) +#define CV_Assert( expr ) do { if (!(expr)) abort(); } while (0) +#define CV_ErrorNoReturn CV_Error +#define CV_ErrorNoReturn_ CV_Error_ + +#else // CV_STATIC_ANALYSIS + +/** @brief Call the error handler. + +Currently, the error handler prints the error code and the error message to the standard +error stream `stderr`. In the Debug configuration, it then provokes memory access violation, so that +the execution stack and all the parameters can be analyzed by the debugger. In the Release +configuration, the exception is thrown. + +@param code one of Error::Code +@param msg error message +*/ +#define CV_Error( code, msg ) cv::error( code, msg, CV_Func, __FILE__, __LINE__ ) + +/** @brief Call the error handler. + +This macro can be used to construct an error message on-fly to include some dynamic information, +for example: +@code + // note the extra parentheses around the formatted text message + CV_Error_(Error::StsOutOfRange, + ("the value at (%d, %d)=%g is out of range", badPt.x, badPt.y, badValue)); +@endcode +@param code one of Error::Code +@param args printf-like formatted error message in parentheses +*/ +#define CV_Error_( code, args ) cv::error( code, cv::format args, CV_Func, __FILE__, __LINE__ ) + +/** @brief Checks a condition at runtime and throws exception if it fails + +The macros CV_Assert (and CV_DbgAssert(expr)) evaluate the specified expression. If it is 0, the macros +raise an error (see cv::error). The macro CV_Assert checks the condition in both Debug and Release +configurations while CV_DbgAssert is only retained in the Debug configuration. +*/ +#define CV_Assert( expr ) do { if(!!(expr)) ; else cv::error( cv::Error::StsAssert, #expr, CV_Func, __FILE__, __LINE__ ); } while(0) + +//! @cond IGNORED +#define CV__ErrorNoReturn( code, msg ) cv::errorNoReturn( code, msg, CV_Func, __FILE__, __LINE__ ) +#define CV__ErrorNoReturn_( code, args ) cv::errorNoReturn( code, cv::format args, CV_Func, __FILE__, __LINE__ ) +#ifdef __OPENCV_BUILD +#undef CV_Error +#define CV_Error CV__ErrorNoReturn +#undef CV_Error_ +#define CV_Error_ CV__ErrorNoReturn_ +#undef CV_Assert +#define CV_Assert( expr ) do { if(!!(expr)) ; else cv::errorNoReturn( cv::Error::StsAssert, #expr, CV_Func, __FILE__, __LINE__ ); } while(0) +#else +// backward compatibility +#define CV_ErrorNoReturn CV__ErrorNoReturn +#define CV_ErrorNoReturn_ CV__ErrorNoReturn_ +#endif +//! @endcond + +#endif // CV_STATIC_ANALYSIS + +//! @cond IGNORED + +#if defined OPENCV_FORCE_MULTIARG_ASSERT_CHECK && defined CV_STATIC_ANALYSIS +#warning "OPENCV_FORCE_MULTIARG_ASSERT_CHECK can't be used with CV_STATIC_ANALYSIS" +#undef OPENCV_FORCE_MULTIARG_ASSERT_CHECK +#endif + +#ifdef OPENCV_FORCE_MULTIARG_ASSERT_CHECK +#define CV_Assert_1( expr ) do { if(!!(expr)) ; else cv::error( cv::Error::StsAssert, #expr, CV_Func, __FILE__, __LINE__ ); } while(0) +#else +#define CV_Assert_1 CV_Assert +#endif +#define CV_Assert_2( expr1, expr2 ) CV_Assert_1(expr1); CV_Assert_1(expr2) +#define CV_Assert_3( expr1, expr2, expr3 ) CV_Assert_2(expr1, expr2); CV_Assert_1(expr3) +#define CV_Assert_4( expr1, expr2, expr3, expr4 ) CV_Assert_3(expr1, expr2, expr3); CV_Assert_1(expr4) +#define CV_Assert_5( expr1, expr2, expr3, expr4, expr5 ) CV_Assert_4(expr1, expr2, expr3, expr4); CV_Assert_1(expr5) +#define CV_Assert_6( expr1, expr2, expr3, expr4, expr5, expr6 ) CV_Assert_5(expr1, expr2, expr3, expr4, expr5); CV_Assert_1(expr6) +#define CV_Assert_7( expr1, expr2, expr3, expr4, expr5, expr6, expr7 ) CV_Assert_6(expr1, expr2, expr3, expr4, expr5, expr6 ); CV_Assert_1(expr7) +#define CV_Assert_8( expr1, expr2, expr3, expr4, expr5, expr6, expr7, expr8 ) CV_Assert_7(expr1, expr2, expr3, expr4, expr5, expr6, expr7 ); CV_Assert_1(expr8) +#define CV_Assert_9( expr1, expr2, expr3, expr4, expr5, expr6, expr7, expr8, expr9 ) CV_Assert_8(expr1, expr2, expr3, expr4, expr5, expr6, expr7, expr8 ); CV_Assert_1(expr9) +#define CV_Assert_10( expr1, expr2, expr3, expr4, expr5, expr6, expr7, expr8, expr9, expr10 ) CV_Assert_9(expr1, expr2, expr3, expr4, expr5, expr6, expr7, expr8, expr9 ); CV_Assert_1(expr10) + +#define CV_Assert_N(...) do { __CV_CAT(CV_Assert_, __CV_VA_NUM_ARGS(__VA_ARGS__)) (__VA_ARGS__); } while(0) + +#ifdef OPENCV_FORCE_MULTIARG_ASSERT_CHECK +#undef CV_Assert +#define CV_Assert CV_Assert_N +#endif +//! @endcond + +#if defined _DEBUG || defined CV_STATIC_ANALYSIS +# define CV_DbgAssert(expr) CV_Assert(expr) +#else +/** replaced with CV_Assert(expr) in Debug configuration */ +# define CV_DbgAssert(expr) +#endif + +/* + * Hamming distance functor - counts the bit differences between two strings - useful for the Brief descriptor + * bit count of A exclusive XOR'ed with B + */ +struct CV_EXPORTS Hamming +{ + enum { normType = NORM_HAMMING }; + typedef unsigned char ValueType; + typedef int ResultType; + + /** this will count the bits in a ^ b + */ + ResultType operator()( const unsigned char* a, const unsigned char* b, int size ) const; +}; + +typedef Hamming HammingLUT; + +/////////////////////////////////// inline norms //////////////////////////////////// + +template inline _Tp cv_abs(_Tp x) { return std::abs(x); } +inline int cv_abs(uchar x) { return x; } +inline int cv_abs(schar x) { return std::abs(x); } +inline int cv_abs(ushort x) { return x; } +inline int cv_abs(short x) { return std::abs(x); } + +template static inline +_AccTp normL2Sqr(const _Tp* a, int n) +{ + _AccTp s = 0; + int i=0; +#if CV_ENABLE_UNROLLED + for( ; i <= n - 4; i += 4 ) + { + _AccTp v0 = a[i], v1 = a[i+1], v2 = a[i+2], v3 = a[i+3]; + s += v0*v0 + v1*v1 + v2*v2 + v3*v3; + } +#endif + for( ; i < n; i++ ) + { + _AccTp v = a[i]; + s += v*v; + } + return s; +} + +template static inline +_AccTp normL1(const _Tp* a, int n) +{ + _AccTp s = 0; + int i = 0; +#if CV_ENABLE_UNROLLED + for(; i <= n - 4; i += 4 ) + { + s += (_AccTp)cv_abs(a[i]) + (_AccTp)cv_abs(a[i+1]) + + (_AccTp)cv_abs(a[i+2]) + (_AccTp)cv_abs(a[i+3]); + } +#endif + for( ; i < n; i++ ) + s += cv_abs(a[i]); + return s; +} + +template static inline +_AccTp normInf(const _Tp* a, int n) +{ + _AccTp s = 0; + for( int i = 0; i < n; i++ ) + s = std::max(s, (_AccTp)cv_abs(a[i])); + return s; +} + +template static inline +_AccTp normL2Sqr(const _Tp* a, const _Tp* b, int n) +{ + _AccTp s = 0; + int i= 0; +#if CV_ENABLE_UNROLLED + for(; i <= n - 4; i += 4 ) + { + _AccTp v0 = _AccTp(a[i] - b[i]), v1 = _AccTp(a[i+1] - b[i+1]), v2 = _AccTp(a[i+2] - b[i+2]), v3 = _AccTp(a[i+3] - b[i+3]); + s += v0*v0 + v1*v1 + v2*v2 + v3*v3; + } +#endif + for( ; i < n; i++ ) + { + _AccTp v = _AccTp(a[i] - b[i]); + s += v*v; + } + return s; +} + +static inline float normL2Sqr(const float* a, const float* b, int n) +{ + float s = 0.f; + for( int i = 0; i < n; i++ ) + { + float v = a[i] - b[i]; + s += v*v; + } + return s; +} + +template static inline +_AccTp normL1(const _Tp* a, const _Tp* b, int n) +{ + _AccTp s = 0; + int i= 0; +#if CV_ENABLE_UNROLLED + for(; i <= n - 4; i += 4 ) + { + _AccTp v0 = _AccTp(a[i] - b[i]), v1 = _AccTp(a[i+1] - b[i+1]), v2 = _AccTp(a[i+2] - b[i+2]), v3 = _AccTp(a[i+3] - b[i+3]); + s += std::abs(v0) + std::abs(v1) + std::abs(v2) + std::abs(v3); + } +#endif + for( ; i < n; i++ ) + { + _AccTp v = _AccTp(a[i] - b[i]); + s += std::abs(v); + } + return s; +} + +inline float normL1(const float* a, const float* b, int n) +{ + float s = 0.f; + for( int i = 0; i < n; i++ ) + { + s += std::abs(a[i] - b[i]); + } + return s; +} + +inline int normL1(const uchar* a, const uchar* b, int n) +{ + int s = 0; + for( int i = 0; i < n; i++ ) + { + s += std::abs(a[i] - b[i]); + } + return s; +} + +template static inline +_AccTp normInf(const _Tp* a, const _Tp* b, int n) +{ + _AccTp s = 0; + for( int i = 0; i < n; i++ ) + { + _AccTp v0 = a[i] - b[i]; + s = std::max(s, std::abs(v0)); + } + return s; +} + +/** @brief Computes the cube root of an argument. + + The function cubeRoot computes \f$\sqrt[3]{\texttt{val}}\f$. Negative arguments are handled correctly. + NaN and Inf are not handled. The accuracy approaches the maximum possible accuracy for + single-precision data. + @param val A function argument. + */ +CV_EXPORTS_W float cubeRoot(float val); + +/** @brief Calculates the angle of a 2D vector in degrees. + + The function fastAtan2 calculates the full-range angle of an input 2D vector. The angle is measured + in degrees and varies from 0 to 360 degrees. The accuracy is about 0.3 degrees. + @param x x-coordinate of the vector. + @param y y-coordinate of the vector. + */ +CV_EXPORTS_W float fastAtan2(float y, float x); + +/** proxy for hal::LU */ +CV_EXPORTS int LU(float* A, size_t astep, int m, float* b, size_t bstep, int n); +/** proxy for hal::LU */ +CV_EXPORTS int LU(double* A, size_t astep, int m, double* b, size_t bstep, int n); +/** proxy for hal::Cholesky */ +CV_EXPORTS bool Cholesky(float* A, size_t astep, int m, float* b, size_t bstep, int n); +/** proxy for hal::Cholesky */ +CV_EXPORTS bool Cholesky(double* A, size_t astep, int m, double* b, size_t bstep, int n); + +////////////////// forward declarations for important OpenCV types ////////////////// + +//! @cond IGNORED + +template class Vec; +template class Matx; + +template class Complex; +template class Point_; +template class Point3_; +template class Size_; +template class Rect_; +template class Scalar_; + +class CV_EXPORTS RotatedRect; +class CV_EXPORTS Range; +class CV_EXPORTS TermCriteria; +class CV_EXPORTS KeyPoint; +class CV_EXPORTS DMatch; +class CV_EXPORTS RNG; + +class CV_EXPORTS Mat; +class CV_EXPORTS MatExpr; + +class CV_EXPORTS UMat; + +class CV_EXPORTS SparseMat; +typedef Mat MatND; + +template class Mat_; +template class SparseMat_; + +class CV_EXPORTS MatConstIterator; +class CV_EXPORTS SparseMatIterator; +class CV_EXPORTS SparseMatConstIterator; +template class MatIterator_; +template class MatConstIterator_; +template class SparseMatIterator_; +template class SparseMatConstIterator_; + +namespace ogl +{ + class CV_EXPORTS Buffer; + class CV_EXPORTS Texture2D; + class CV_EXPORTS Arrays; +} + +namespace cuda +{ + class CV_EXPORTS GpuMat; + class CV_EXPORTS HostMem; + class CV_EXPORTS Stream; + class CV_EXPORTS Event; +} + +namespace cudev +{ + template class GpuMat_; +} + +namespace ipp +{ +#if OPENCV_ABI_COMPATIBILITY > 300 +CV_EXPORTS unsigned long long getIppFeatures(); +#else +CV_EXPORTS int getIppFeatures(); +#endif +CV_EXPORTS void setIppStatus(int status, const char * const funcname = NULL, const char * const filename = NULL, + int line = 0); +CV_EXPORTS int getIppStatus(); +CV_EXPORTS String getIppErrorLocation(); +CV_EXPORTS_W bool useIPP(); +CV_EXPORTS_W void setUseIPP(bool flag); +CV_EXPORTS_W String getIppVersion(); + +// IPP Not-Exact mode. This function may force use of IPP then both IPP and OpenCV provide proper results +// but have internal accuracy differences which have too much direct or indirect impact on accuracy tests. +CV_EXPORTS_W bool useIPP_NotExact(); +CV_EXPORTS_W void setUseIPP_NotExact(bool flag); +#if OPENCV_ABI_COMPATIBILITY < 400 +CV_EXPORTS_W bool useIPP_NE(); +CV_EXPORTS_W void setUseIPP_NE(bool flag); +#endif + +} // ipp + +//! @endcond + +//! @} core_utils + + + + +} // cv + +#include "opencv2/core/neon_utils.hpp" +#include "opencv2/core/vsx_utils.hpp" +#include "opencv2/core/check.hpp" + +#endif //OPENCV_CORE_BASE_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/bindings_utils.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/bindings_utils.hpp new file mode 100644 index 0000000000000000000000000000000000000000..c1123f2b0e3fa8b712961e1bd818f8c37b9beaa6 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/bindings_utils.hpp @@ -0,0 +1,23 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#ifndef OPENCV_CORE_BINDINGS_UTILS_HPP +#define OPENCV_CORE_BINDINGS_UTILS_HPP + +namespace cv { namespace utils { +//! @addtogroup core_utils +//! @{ + +CV_EXPORTS_W String dumpInputArray(InputArray argument); + +CV_EXPORTS_W String dumpInputArrayOfArrays(InputArrayOfArrays argument); + +CV_EXPORTS_W String dumpInputOutputArray(InputOutputArray argument); + +CV_EXPORTS_W String dumpInputOutputArrayOfArrays(InputOutputArrayOfArrays argument); + +//! @} +}} // namespace + +#endif // OPENCV_CORE_BINDINGS_UTILS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/bufferpool.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/bufferpool.hpp new file mode 100644 index 0000000000000000000000000000000000000000..4698e5da167d6a3b6a9ef7672faa3542b23fa662 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/bufferpool.hpp @@ -0,0 +1,40 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. +// +// Copyright (C) 2014, Advanced Micro Devices, Inc., all rights reserved. + +#ifndef OPENCV_CORE_BUFFER_POOL_HPP +#define OPENCV_CORE_BUFFER_POOL_HPP + +#ifdef _MSC_VER +#pragma warning(push) +#pragma warning(disable: 4265) +#endif + +namespace cv +{ + +//! @addtogroup core +//! @{ + +class BufferPoolController +{ +protected: + ~BufferPoolController() { } +public: + virtual size_t getReservedSize() const = 0; + virtual size_t getMaxReservedSize() const = 0; + virtual void setMaxReservedSize(size_t size) = 0; + virtual void freeAllReservedBuffers() = 0; +}; + +//! @} + +} + +#ifdef _MSC_VER +#pragma warning(pop) +#endif + +#endif // OPENCV_CORE_BUFFER_POOL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/check.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/check.hpp new file mode 100644 index 0000000000000000000000000000000000000000..bf441383e84059ae41263e9208018ea976df0774 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/check.hpp @@ -0,0 +1,157 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#ifndef OPENCV_CORE_CHECK_HPP +#define OPENCV_CORE_CHECK_HPP + +#include + +namespace cv { + +/** Returns string of cv::Mat depth value: CV_8U -> "CV_8U" or "" */ +CV_EXPORTS const char* depthToString(int depth); + +/** Returns string of cv::Mat depth value: CV_8UC3 -> "CV_8UC3" or "" */ +CV_EXPORTS const String typeToString(int type); + + +//! @cond IGNORED +namespace detail { + +/** Returns string of cv::Mat depth value: CV_8U -> "CV_8U" or NULL */ +CV_EXPORTS const char* depthToString_(int depth); + +/** Returns string of cv::Mat depth value: CV_8UC3 -> "CV_8UC3" or cv::String() */ +CV_EXPORTS const cv::String typeToString_(int type); + +enum TestOp { + TEST_CUSTOM = 0, + TEST_EQ = 1, + TEST_NE = 2, + TEST_LE = 3, + TEST_LT = 4, + TEST_GE = 5, + TEST_GT = 6, + CV__LAST_TEST_OP +}; + +struct CheckContext { + const char* func; + const char* file; + int line; + enum TestOp testOp; + const char* message; + const char* p1_str; + const char* p2_str; +}; + +#ifndef CV__CHECK_FILENAME +# define CV__CHECK_FILENAME __FILE__ +#endif + +#ifndef CV__CHECK_FUNCTION +# if defined _MSC_VER +# define CV__CHECK_FUNCTION __FUNCSIG__ +# elif defined __GNUC__ +# define CV__CHECK_FUNCTION __PRETTY_FUNCTION__ +# else +# define CV__CHECK_FUNCTION "" +# endif +#endif + +#define CV__CHECK_LOCATION_VARNAME(id) CVAUX_CONCAT(CVAUX_CONCAT(__cv_check_, id), __LINE__) +#define CV__DEFINE_CHECK_CONTEXT(id, message, testOp, p1_str, p2_str) \ + static const cv::detail::CheckContext CV__CHECK_LOCATION_VARNAME(id) = \ + { CV__CHECK_FUNCTION, CV__CHECK_FILENAME, __LINE__, testOp, message, p1_str, p2_str } + +CV_EXPORTS void CV_NORETURN check_failed_auto(const int v1, const int v2, const CheckContext& ctx); +CV_EXPORTS void CV_NORETURN check_failed_auto(const size_t v1, const size_t v2, const CheckContext& ctx); +CV_EXPORTS void CV_NORETURN check_failed_auto(const float v1, const float v2, const CheckContext& ctx); +CV_EXPORTS void CV_NORETURN check_failed_auto(const double v1, const double v2, const CheckContext& ctx); +CV_EXPORTS void CV_NORETURN check_failed_MatDepth(const int v1, const int v2, const CheckContext& ctx); +CV_EXPORTS void CV_NORETURN check_failed_MatType(const int v1, const int v2, const CheckContext& ctx); +CV_EXPORTS void CV_NORETURN check_failed_MatChannels(const int v1, const int v2, const CheckContext& ctx); + +CV_EXPORTS void CV_NORETURN check_failed_auto(const int v, const CheckContext& ctx); +CV_EXPORTS void CV_NORETURN check_failed_auto(const size_t v, const CheckContext& ctx); +CV_EXPORTS void CV_NORETURN check_failed_auto(const float v, const CheckContext& ctx); +CV_EXPORTS void CV_NORETURN check_failed_auto(const double v, const CheckContext& ctx); +CV_EXPORTS void CV_NORETURN check_failed_MatDepth(const int v, const CheckContext& ctx); +CV_EXPORTS void CV_NORETURN check_failed_MatType(const int v, const CheckContext& ctx); +CV_EXPORTS void CV_NORETURN check_failed_MatChannels(const int v, const CheckContext& ctx); + + +#define CV__TEST_EQ(v1, v2) ((v1) == (v2)) +#define CV__TEST_NE(v1, v2) ((v1) != (v2)) +#define CV__TEST_LE(v1, v2) ((v1) <= (v2)) +#define CV__TEST_LT(v1, v2) ((v1) < (v2)) +#define CV__TEST_GE(v1, v2) ((v1) >= (v2)) +#define CV__TEST_GT(v1, v2) ((v1) > (v2)) + +#define CV__CHECK(id, op, type, v1, v2, v1_str, v2_str, msg_str) do { \ + if(CV__TEST_##op((v1), (v2))) ; else { \ + CV__DEFINE_CHECK_CONTEXT(id, msg_str, cv::detail::TEST_ ## op, v1_str, v2_str); \ + cv::detail::check_failed_ ## type((v1), (v2), CV__CHECK_LOCATION_VARNAME(id)); \ + } \ +} while (0) + +#define CV__CHECK_CUSTOM_TEST(id, type, v, test_expr, v_str, test_expr_str, msg_str) do { \ + if(!!(test_expr)) ; else { \ + CV__DEFINE_CHECK_CONTEXT(id, msg_str, cv::detail::TEST_CUSTOM, v_str, test_expr_str); \ + cv::detail::check_failed_ ## type((v), CV__CHECK_LOCATION_VARNAME(id)); \ + } \ +} while (0) + +} // namespace +//! @endcond + + +/// Supported values of these types: int, float, double +#define CV_CheckEQ(v1, v2, msg) CV__CHECK(_, EQ, auto, v1, v2, #v1, #v2, msg) +#define CV_CheckNE(v1, v2, msg) CV__CHECK(_, NE, auto, v1, v2, #v1, #v2, msg) +#define CV_CheckLE(v1, v2, msg) CV__CHECK(_, LE, auto, v1, v2, #v1, #v2, msg) +#define CV_CheckLT(v1, v2, msg) CV__CHECK(_, LT, auto, v1, v2, #v1, #v2, msg) +#define CV_CheckGE(v1, v2, msg) CV__CHECK(_, GE, auto, v1, v2, #v1, #v2, msg) +#define CV_CheckGT(v1, v2, msg) CV__CHECK(_, GT, auto, v1, v2, #v1, #v2, msg) + +/// Check with additional "decoding" of type values in error message +#define CV_CheckTypeEQ(t1, t2, msg) CV__CHECK(_, EQ, MatType, t1, t2, #t1, #t2, msg) +/// Check with additional "decoding" of depth values in error message +#define CV_CheckDepthEQ(d1, d2, msg) CV__CHECK(_, EQ, MatDepth, d1, d2, #d1, #d2, msg) + +#define CV_CheckChannelsEQ(c1, c2, msg) CV__CHECK(_, EQ, MatChannels, c1, c2, #c1, #c2, msg) + +/// Example: type == CV_8UC1 || type == CV_8UC3 +#define CV_CheckType(t, test_expr, msg) CV__CHECK_CUSTOM_TEST(_, MatType, t, (test_expr), #t, #test_expr, msg) + +/// Example: depth == CV_32F || depth == CV_64F +#define CV_CheckDepth(t, test_expr, msg) CV__CHECK_CUSTOM_TEST(_, MatDepth, t, (test_expr), #t, #test_expr, msg) + +/// Example: v == A || v == B +#define CV_Check(v, test_expr, msg) CV__CHECK_CUSTOM_TEST(_, auto, v, (test_expr), #v, #test_expr, msg) + +/// Some complex conditions: CV_Check(src2, src2.empty() || (src2.type() == src1.type() && src2.size() == src1.size()), "src2 should have same size/type as src1") +// TODO define pretty-printers + +#ifndef NDEBUG +#define CV_DbgCheck(v, test_expr, msg) CV__CHECK_CUSTOM_TEST(_, auto, v, (test_expr), #v, #test_expr, msg) +#define CV_DbgCheckEQ(v1, v2, msg) CV__CHECK(_, EQ, auto, v1, v2, #v1, #v2, msg) +#define CV_DbgCheckNE(v1, v2, msg) CV__CHECK(_, NE, auto, v1, v2, #v1, #v2, msg) +#define CV_DbgCheckLE(v1, v2, msg) CV__CHECK(_, LE, auto, v1, v2, #v1, #v2, msg) +#define CV_DbgCheckLT(v1, v2, msg) CV__CHECK(_, LT, auto, v1, v2, #v1, #v2, msg) +#define CV_DbgCheckGE(v1, v2, msg) CV__CHECK(_, GE, auto, v1, v2, #v1, #v2, msg) +#define CV_DbgCheckGT(v1, v2, msg) CV__CHECK(_, GT, auto, v1, v2, #v1, #v2, msg) +#else +#define CV_DbgCheck(v, test_expr, msg) do { } while (0) +#define CV_DbgCheckEQ(v1, v2, msg) do { } while (0) +#define CV_DbgCheckNE(v1, v2, msg) do { } while (0) +#define CV_DbgCheckLE(v1, v2, msg) do { } while (0) +#define CV_DbgCheckLT(v1, v2, msg) do { } while (0) +#define CV_DbgCheckGE(v1, v2, msg) do { } while (0) +#define CV_DbgCheckGT(v1, v2, msg) do { } while (0) +#endif + +} // namespace + +#endif // OPENCV_CORE_CHECK_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/core.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/core.hpp new file mode 100644 index 0000000000000000000000000000000000000000..438918359be44fcf2489dc4f10617385fe3770e9 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/core.hpp @@ -0,0 +1,48 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifdef __OPENCV_BUILD +#error this is a compatibility header which should not be used inside the OpenCV library +#endif + +#include "opencv2/core.hpp" diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/core_c.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/core_c.h new file mode 100644 index 0000000000000000000000000000000000000000..e5fe516d264e30060360087b472e1eaec6cd0e44 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/core_c.h @@ -0,0 +1,3175 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + + +#ifndef OPENCV_CORE_C_H +#define OPENCV_CORE_C_H + +#include "opencv2/core/types_c.h" + +#ifdef __cplusplus +# ifdef _MSC_VER +/* disable warning C4190: 'function' has C-linkage specified, but returns UDT 'typename' + which is incompatible with C + + It is OK to disable it because we only extend few plain structures with + C++ construrtors for simpler interoperability with C++ API of the library +*/ +# pragma warning(disable:4190) +# elif defined __clang__ && __clang_major__ >= 3 +# pragma GCC diagnostic ignored "-Wreturn-type-c-linkage" +# endif +#endif + +#ifdef __cplusplus +extern "C" { +#endif + +/** @addtogroup core_c + @{ +*/ + +/****************************************************************************************\ +* Array allocation, deallocation, initialization and access to elements * +\****************************************************************************************/ + +/** `malloc` wrapper. + If there is no enough memory, the function + (as well as other OpenCV functions that call cvAlloc) + raises an error. */ +CVAPI(void*) cvAlloc( size_t size ); + +/** `free` wrapper. + Here and further all the memory releasing functions + (that all call cvFree) take double pointer in order to + to clear pointer to the data after releasing it. + Passing pointer to NULL pointer is Ok: nothing happens in this case +*/ +CVAPI(void) cvFree_( void* ptr ); +#define cvFree(ptr) (cvFree_(*(ptr)), *(ptr)=0) + +/** @brief Creates an image header but does not allocate the image data. + +@param size Image width and height +@param depth Image depth (see cvCreateImage ) +@param channels Number of channels (see cvCreateImage ) + */ +CVAPI(IplImage*) cvCreateImageHeader( CvSize size, int depth, int channels ); + +/** @brief Initializes an image header that was previously allocated. + +The returned IplImage\* points to the initialized header. +@param image Image header to initialize +@param size Image width and height +@param depth Image depth (see cvCreateImage ) +@param channels Number of channels (see cvCreateImage ) +@param origin Top-left IPL_ORIGIN_TL or bottom-left IPL_ORIGIN_BL +@param align Alignment for image rows, typically 4 or 8 bytes + */ +CVAPI(IplImage*) cvInitImageHeader( IplImage* image, CvSize size, int depth, + int channels, int origin CV_DEFAULT(0), + int align CV_DEFAULT(4)); + +/** @brief Creates an image header and allocates the image data. + +This function call is equivalent to the following code: +@code + header = cvCreateImageHeader(size, depth, channels); + cvCreateData(header); +@endcode +@param size Image width and height +@param depth Bit depth of image elements. See IplImage for valid depths. +@param channels Number of channels per pixel. See IplImage for details. This function only creates +images with interleaved channels. + */ +CVAPI(IplImage*) cvCreateImage( CvSize size, int depth, int channels ); + +/** @brief Deallocates an image header. + +This call is an analogue of : +@code + if(image ) + { + iplDeallocate(*image, IPL_IMAGE_HEADER | IPL_IMAGE_ROI); + *image = 0; + } +@endcode +but it does not use IPL functions by default (see the CV_TURN_ON_IPL_COMPATIBILITY macro). +@param image Double pointer to the image header + */ +CVAPI(void) cvReleaseImageHeader( IplImage** image ); + +/** @brief Deallocates the image header and the image data. + +This call is a shortened form of : +@code + if(*image ) + { + cvReleaseData(*image); + cvReleaseImageHeader(image); + } +@endcode +@param image Double pointer to the image header +*/ +CVAPI(void) cvReleaseImage( IplImage** image ); + +/** Creates a copy of IPL image (widthStep may differ) */ +CVAPI(IplImage*) cvCloneImage( const IplImage* image ); + +/** @brief Sets the channel of interest in an IplImage. + +If the ROI is set to NULL and the coi is *not* 0, the ROI is allocated. Most OpenCV functions do +*not* support the COI setting, so to process an individual image/matrix channel one may copy (via +cvCopy or cvSplit) the channel to a separate image/matrix, process it and then copy the result +back (via cvCopy or cvMerge) if needed. +@param image A pointer to the image header +@param coi The channel of interest. 0 - all channels are selected, 1 - first channel is selected, +etc. Note that the channel indices become 1-based. + */ +CVAPI(void) cvSetImageCOI( IplImage* image, int coi ); + +/** @brief Returns the index of the channel of interest. + +Returns the channel of interest of in an IplImage. Returned values correspond to the coi in +cvSetImageCOI. +@param image A pointer to the image header + */ +CVAPI(int) cvGetImageCOI( const IplImage* image ); + +/** @brief Sets an image Region Of Interest (ROI) for a given rectangle. + +If the original image ROI was NULL and the rect is not the whole image, the ROI structure is +allocated. + +Most OpenCV functions support the use of ROI and treat the image rectangle as a separate image. For +example, all of the pixel coordinates are counted from the top-left (or bottom-left) corner of the +ROI, not the original image. +@param image A pointer to the image header +@param rect The ROI rectangle + */ +CVAPI(void) cvSetImageROI( IplImage* image, CvRect rect ); + +/** @brief Resets the image ROI to include the entire image and releases the ROI structure. + +This produces a similar result to the following, but in addition it releases the ROI structure. : +@code + cvSetImageROI(image, cvRect(0, 0, image->width, image->height )); + cvSetImageCOI(image, 0); +@endcode +@param image A pointer to the image header + */ +CVAPI(void) cvResetImageROI( IplImage* image ); + +/** @brief Returns the image ROI. + +If there is no ROI set, cvRect(0,0,image-\>width,image-\>height) is returned. +@param image A pointer to the image header + */ +CVAPI(CvRect) cvGetImageROI( const IplImage* image ); + +/** @brief Creates a matrix header but does not allocate the matrix data. + +The function allocates a new matrix header and returns a pointer to it. The matrix data can then be +allocated using cvCreateData or set explicitly to user-allocated data via cvSetData. +@param rows Number of rows in the matrix +@param cols Number of columns in the matrix +@param type Type of the matrix elements, see cvCreateMat + */ +CVAPI(CvMat*) cvCreateMatHeader( int rows, int cols, int type ); + +#define CV_AUTOSTEP 0x7fffffff + +/** @brief Initializes a pre-allocated matrix header. + +This function is often used to process raw data with OpenCV matrix functions. For example, the +following code computes the matrix product of two matrices, stored as ordinary arrays: +@code + double a[] = { 1, 2, 3, 4, + 5, 6, 7, 8, + 9, 10, 11, 12 }; + + double b[] = { 1, 5, 9, + 2, 6, 10, + 3, 7, 11, + 4, 8, 12 }; + + double c[9]; + CvMat Ma, Mb, Mc ; + + cvInitMatHeader(&Ma, 3, 4, CV_64FC1, a); + cvInitMatHeader(&Mb, 4, 3, CV_64FC1, b); + cvInitMatHeader(&Mc, 3, 3, CV_64FC1, c); + + cvMatMulAdd(&Ma, &Mb, 0, &Mc); + // the c array now contains the product of a (3x4) and b (4x3) +@endcode +@param mat A pointer to the matrix header to be initialized +@param rows Number of rows in the matrix +@param cols Number of columns in the matrix +@param type Type of the matrix elements, see cvCreateMat . +@param data Optional: data pointer assigned to the matrix header +@param step Optional: full row width in bytes of the assigned data. By default, the minimal +possible step is used which assumes there are no gaps between subsequent rows of the matrix. + */ +CVAPI(CvMat*) cvInitMatHeader( CvMat* mat, int rows, int cols, + int type, void* data CV_DEFAULT(NULL), + int step CV_DEFAULT(CV_AUTOSTEP) ); + +/** @brief Creates a matrix header and allocates the matrix data. + +The function call is equivalent to the following code: +@code + CvMat* mat = cvCreateMatHeader(rows, cols, type); + cvCreateData(mat); +@endcode +@param rows Number of rows in the matrix +@param cols Number of columns in the matrix +@param type The type of the matrix elements in the form +CV_\\C\ , where S=signed, U=unsigned, F=float. For +example, CV _ 8UC1 means the elements are 8-bit unsigned and the there is 1 channel, and CV _ +32SC2 means the elements are 32-bit signed and there are 2 channels. + */ +CVAPI(CvMat*) cvCreateMat( int rows, int cols, int type ); + +/** @brief Deallocates a matrix. + +The function decrements the matrix data reference counter and deallocates matrix header. If the data +reference counter is 0, it also deallocates the data. : +@code + if(*mat ) + cvDecRefData(*mat); + cvFree((void**)mat); +@endcode +@param mat Double pointer to the matrix + */ +CVAPI(void) cvReleaseMat( CvMat** mat ); + +/** @brief Decrements an array data reference counter. + +The function decrements the data reference counter in a CvMat or CvMatND if the reference counter + +pointer is not NULL. If the counter reaches zero, the data is deallocated. In the current +implementation the reference counter is not NULL only if the data was allocated using the +cvCreateData function. The counter will be NULL in other cases such as: external data was assigned +to the header using cvSetData, header is part of a larger matrix or image, or the header was +converted from an image or n-dimensional matrix header. +@param arr Pointer to an array header + */ +CV_INLINE void cvDecRefData( CvArr* arr ) +{ + if( CV_IS_MAT( arr )) + { + CvMat* mat = (CvMat*)arr; + mat->data.ptr = NULL; + if( mat->refcount != NULL && --*mat->refcount == 0 ) + cvFree( &mat->refcount ); + mat->refcount = NULL; + } + else if( CV_IS_MATND( arr )) + { + CvMatND* mat = (CvMatND*)arr; + mat->data.ptr = NULL; + if( mat->refcount != NULL && --*mat->refcount == 0 ) + cvFree( &mat->refcount ); + mat->refcount = NULL; + } +} + +/** @brief Increments array data reference counter. + +The function increments CvMat or CvMatND data reference counter and returns the new counter value if +the reference counter pointer is not NULL, otherwise it returns zero. +@param arr Array header + */ +CV_INLINE int cvIncRefData( CvArr* arr ) +{ + int refcount = 0; + if( CV_IS_MAT( arr )) + { + CvMat* mat = (CvMat*)arr; + if( mat->refcount != NULL ) + refcount = ++*mat->refcount; + } + else if( CV_IS_MATND( arr )) + { + CvMatND* mat = (CvMatND*)arr; + if( mat->refcount != NULL ) + refcount = ++*mat->refcount; + } + return refcount; +} + + +/** Creates an exact copy of the input matrix (except, may be, step value) */ +CVAPI(CvMat*) cvCloneMat( const CvMat* mat ); + + +/** @brief Returns matrix header corresponding to the rectangular sub-array of input image or matrix. + +The function returns header, corresponding to a specified rectangle of the input array. In other + +words, it allows the user to treat a rectangular part of input array as a stand-alone array. ROI is +taken into account by the function so the sub-array of ROI is actually extracted. +@param arr Input array +@param submat Pointer to the resultant sub-array header +@param rect Zero-based coordinates of the rectangle of interest + */ +CVAPI(CvMat*) cvGetSubRect( const CvArr* arr, CvMat* submat, CvRect rect ); +#define cvGetSubArr cvGetSubRect + +/** @brief Returns array row or row span. + +The function returns the header, corresponding to a specified row/row span of the input array. +cvGetRow(arr, submat, row) is a shortcut for cvGetRows(arr, submat, row, row+1). +@param arr Input array +@param submat Pointer to the resulting sub-array header +@param start_row Zero-based index of the starting row (inclusive) of the span +@param end_row Zero-based index of the ending row (exclusive) of the span +@param delta_row Index step in the row span. That is, the function extracts every delta_row -th +row from start_row and up to (but not including) end_row . + */ +CVAPI(CvMat*) cvGetRows( const CvArr* arr, CvMat* submat, + int start_row, int end_row, + int delta_row CV_DEFAULT(1)); + +/** @overload +@param arr Input array +@param submat Pointer to the resulting sub-array header +@param row Zero-based index of the selected row +*/ +CV_INLINE CvMat* cvGetRow( const CvArr* arr, CvMat* submat, int row ) +{ + return cvGetRows( arr, submat, row, row + 1, 1 ); +} + + +/** @brief Returns one of more array columns. + +The function returns the header, corresponding to a specified column span of the input array. That + +is, no data is copied. Therefore, any modifications of the submatrix will affect the original array. +If you need to copy the columns, use cvCloneMat. cvGetCol(arr, submat, col) is a shortcut for +cvGetCols(arr, submat, col, col+1). +@param arr Input array +@param submat Pointer to the resulting sub-array header +@param start_col Zero-based index of the starting column (inclusive) of the span +@param end_col Zero-based index of the ending column (exclusive) of the span + */ +CVAPI(CvMat*) cvGetCols( const CvArr* arr, CvMat* submat, + int start_col, int end_col ); + +/** @overload +@param arr Input array +@param submat Pointer to the resulting sub-array header +@param col Zero-based index of the selected column +*/ +CV_INLINE CvMat* cvGetCol( const CvArr* arr, CvMat* submat, int col ) +{ + return cvGetCols( arr, submat, col, col + 1 ); +} + +/** @brief Returns one of array diagonals. + +The function returns the header, corresponding to a specified diagonal of the input array. +@param arr Input array +@param submat Pointer to the resulting sub-array header +@param diag Index of the array diagonal. Zero value corresponds to the main diagonal, -1 +corresponds to the diagonal above the main, 1 corresponds to the diagonal below the main, and so +forth. + */ +CVAPI(CvMat*) cvGetDiag( const CvArr* arr, CvMat* submat, + int diag CV_DEFAULT(0)); + +/** low-level scalar <-> raw data conversion functions */ +CVAPI(void) cvScalarToRawData( const CvScalar* scalar, void* data, int type, + int extend_to_12 CV_DEFAULT(0) ); + +CVAPI(void) cvRawDataToScalar( const void* data, int type, CvScalar* scalar ); + +/** @brief Creates a new matrix header but does not allocate the matrix data. + +The function allocates a header for a multi-dimensional dense array. The array data can further be +allocated using cvCreateData or set explicitly to user-allocated data via cvSetData. +@param dims Number of array dimensions +@param sizes Array of dimension sizes +@param type Type of array elements, see cvCreateMat + */ +CVAPI(CvMatND*) cvCreateMatNDHeader( int dims, const int* sizes, int type ); + +/** @brief Creates the header and allocates the data for a multi-dimensional dense array. + +This function call is equivalent to the following code: +@code + CvMatND* mat = cvCreateMatNDHeader(dims, sizes, type); + cvCreateData(mat); +@endcode +@param dims Number of array dimensions. This must not exceed CV_MAX_DIM (32 by default, but can be +changed at build time). +@param sizes Array of dimension sizes. +@param type Type of array elements, see cvCreateMat . + */ +CVAPI(CvMatND*) cvCreateMatND( int dims, const int* sizes, int type ); + +/** @brief Initializes a pre-allocated multi-dimensional array header. + +@param mat A pointer to the array header to be initialized +@param dims The number of array dimensions +@param sizes An array of dimension sizes +@param type Type of array elements, see cvCreateMat +@param data Optional data pointer assigned to the matrix header + */ +CVAPI(CvMatND*) cvInitMatNDHeader( CvMatND* mat, int dims, const int* sizes, + int type, void* data CV_DEFAULT(NULL) ); + +/** @brief Deallocates a multi-dimensional array. + +The function decrements the array data reference counter and releases the array header. If the +reference counter reaches 0, it also deallocates the data. : +@code + if(*mat ) + cvDecRefData(*mat); + cvFree((void**)mat); +@endcode +@param mat Double pointer to the array + */ +CV_INLINE void cvReleaseMatND( CvMatND** mat ) +{ + cvReleaseMat( (CvMat**)mat ); +} + +/** Creates a copy of CvMatND (except, may be, steps) */ +CVAPI(CvMatND*) cvCloneMatND( const CvMatND* mat ); + +/** @brief Creates sparse array. + +The function allocates a multi-dimensional sparse array. Initially the array contain no elements, +that is PtrND and other related functions will return 0 for every index. +@param dims Number of array dimensions. In contrast to the dense matrix, the number of dimensions is +practically unlimited (up to \f$2^{16}\f$ ). +@param sizes Array of dimension sizes +@param type Type of array elements. The same as for CvMat + */ +CVAPI(CvSparseMat*) cvCreateSparseMat( int dims, const int* sizes, int type ); + +/** @brief Deallocates sparse array. + +The function releases the sparse array and clears the array pointer upon exit. +@param mat Double pointer to the array + */ +CVAPI(void) cvReleaseSparseMat( CvSparseMat** mat ); + +/** Creates a copy of CvSparseMat (except, may be, zero items) */ +CVAPI(CvSparseMat*) cvCloneSparseMat( const CvSparseMat* mat ); + +/** @brief Initializes sparse array elements iterator. + +The function initializes iterator of sparse array elements and returns pointer to the first element, +or NULL if the array is empty. +@param mat Input array +@param mat_iterator Initialized iterator + */ +CVAPI(CvSparseNode*) cvInitSparseMatIterator( const CvSparseMat* mat, + CvSparseMatIterator* mat_iterator ); + +/** @brief Returns the next sparse matrix element + +The function moves iterator to the next sparse matrix element and returns pointer to it. In the +current version there is no any particular order of the elements, because they are stored in the +hash table. The sample below demonstrates how to iterate through the sparse matrix: +@code + // print all the non-zero sparse matrix elements and compute their sum + double sum = 0; + int i, dims = cvGetDims(sparsemat); + CvSparseMatIterator it; + CvSparseNode* node = cvInitSparseMatIterator(sparsemat, &it); + + for(; node != 0; node = cvGetNextSparseNode(&it)) + { + int* idx = CV_NODE_IDX(array, node); + float val = *(float*)CV_NODE_VAL(array, node); + printf("M"); + for(i = 0; i < dims; i++ ) + printf("[%d]", idx[i]); + printf("=%g\n", val); + + sum += val; + } + + printf("nTotal sum = %g\n", sum); +@endcode +@param mat_iterator Sparse array iterator + */ +CV_INLINE CvSparseNode* cvGetNextSparseNode( CvSparseMatIterator* mat_iterator ) +{ + if( mat_iterator->node->next ) + return mat_iterator->node = mat_iterator->node->next; + else + { + int idx; + for( idx = ++mat_iterator->curidx; idx < mat_iterator->mat->hashsize; idx++ ) + { + CvSparseNode* node = (CvSparseNode*)mat_iterator->mat->hashtable[idx]; + if( node ) + { + mat_iterator->curidx = idx; + return mat_iterator->node = node; + } + } + return NULL; + } +} + + +#define CV_MAX_ARR 10 + +/** matrix iterator: used for n-ary operations on dense arrays */ +typedef struct CvNArrayIterator +{ + int count; /**< number of arrays */ + int dims; /**< number of dimensions to iterate */ + CvSize size; /**< maximal common linear size: { width = size, height = 1 } */ + uchar* ptr[CV_MAX_ARR]; /**< pointers to the array slices */ + int stack[CV_MAX_DIM]; /**< for internal use */ + CvMatND* hdr[CV_MAX_ARR]; /**< pointers to the headers of the + matrices that are processed */ +} +CvNArrayIterator; + +#define CV_NO_DEPTH_CHECK 1 +#define CV_NO_CN_CHECK 2 +#define CV_NO_SIZE_CHECK 4 + +/** initializes iterator that traverses through several arrays simulteneously + (the function together with cvNextArraySlice is used for + N-ari element-wise operations) */ +CVAPI(int) cvInitNArrayIterator( int count, CvArr** arrs, + const CvArr* mask, CvMatND* stubs, + CvNArrayIterator* array_iterator, + int flags CV_DEFAULT(0) ); + +/** returns zero value if iteration is finished, non-zero (slice length) otherwise */ +CVAPI(int) cvNextNArraySlice( CvNArrayIterator* array_iterator ); + + +/** @brief Returns type of array elements. + +The function returns type of the array elements. In the case of IplImage the type is converted to +CvMat-like representation. For example, if the image has been created as: +@code + IplImage* img = cvCreateImage(cvSize(640, 480), IPL_DEPTH_8U, 3); +@endcode +The code cvGetElemType(img) will return CV_8UC3. +@param arr Input array + */ +CVAPI(int) cvGetElemType( const CvArr* arr ); + +/** @brief Return number of array dimensions + +The function returns the array dimensionality and the array of dimension sizes. In the case of +IplImage or CvMat it always returns 2 regardless of number of image/matrix rows. For example, the +following code calculates total number of array elements: +@code + int sizes[CV_MAX_DIM]; + int i, total = 1; + int dims = cvGetDims(arr, size); + for(i = 0; i < dims; i++ ) + total *= sizes[i]; +@endcode +@param arr Input array +@param sizes Optional output vector of the array dimension sizes. For 2d arrays the number of rows +(height) goes first, number of columns (width) next. + */ +CVAPI(int) cvGetDims( const CvArr* arr, int* sizes CV_DEFAULT(NULL) ); + + +/** @brief Returns array size along the specified dimension. + +@param arr Input array +@param index Zero-based dimension index (for matrices 0 means number of rows, 1 means number of +columns; for images 0 means height, 1 means width) + */ +CVAPI(int) cvGetDimSize( const CvArr* arr, int index ); + + +/** @brief Return pointer to a particular array element. + +The functions return a pointer to a specific array element. Number of array dimension should match +to the number of indices passed to the function except for cvPtr1D function that can be used for +sequential access to 1D, 2D or nD dense arrays. + +The functions can be used for sparse arrays as well - if the requested node does not exist they +create it and set it to zero. + +All these as well as other functions accessing array elements ( cvGetND , cvGetRealND , cvSet +, cvSetND , cvSetRealND ) raise an error in case if the element index is out of range. +@param arr Input array +@param idx0 The first zero-based component of the element index +@param type Optional output parameter: type of matrix elements + */ +CVAPI(uchar*) cvPtr1D( const CvArr* arr, int idx0, int* type CV_DEFAULT(NULL)); +/** @overload */ +CVAPI(uchar*) cvPtr2D( const CvArr* arr, int idx0, int idx1, int* type CV_DEFAULT(NULL) ); +/** @overload */ +CVAPI(uchar*) cvPtr3D( const CvArr* arr, int idx0, int idx1, int idx2, + int* type CV_DEFAULT(NULL)); +/** @overload +@param arr Input array +@param idx Array of the element indices +@param type Optional output parameter: type of matrix elements +@param create_node Optional input parameter for sparse matrices. Non-zero value of the parameter +means that the requested element is created if it does not exist already. +@param precalc_hashval Optional input parameter for sparse matrices. If the pointer is not NULL, +the function does not recalculate the node hash value, but takes it from the specified location. +It is useful for speeding up pair-wise operations (TODO: provide an example) +*/ +CVAPI(uchar*) cvPtrND( const CvArr* arr, const int* idx, int* type CV_DEFAULT(NULL), + int create_node CV_DEFAULT(1), + unsigned* precalc_hashval CV_DEFAULT(NULL)); + +/** @brief Return a specific array element. + +The functions return a specific array element. In the case of a sparse array the functions return 0 +if the requested node does not exist (no new node is created by the functions). +@param arr Input array +@param idx0 The first zero-based component of the element index + */ +CVAPI(CvScalar) cvGet1D( const CvArr* arr, int idx0 ); +/** @overload */ +CVAPI(CvScalar) cvGet2D( const CvArr* arr, int idx0, int idx1 ); +/** @overload */ +CVAPI(CvScalar) cvGet3D( const CvArr* arr, int idx0, int idx1, int idx2 ); +/** @overload +@param arr Input array +@param idx Array of the element indices +*/ +CVAPI(CvScalar) cvGetND( const CvArr* arr, const int* idx ); + +/** @brief Return a specific element of single-channel 1D, 2D, 3D or nD array. + +Returns a specific element of a single-channel array. If the array has multiple channels, a runtime +error is raised. Note that Get?D functions can be used safely for both single-channel and +multiple-channel arrays though they are a bit slower. + +In the case of a sparse array the functions return 0 if the requested node does not exist (no new +node is created by the functions). +@param arr Input array. Must have a single channel. +@param idx0 The first zero-based component of the element index + */ +CVAPI(double) cvGetReal1D( const CvArr* arr, int idx0 ); +/** @overload */ +CVAPI(double) cvGetReal2D( const CvArr* arr, int idx0, int idx1 ); +/** @overload */ +CVAPI(double) cvGetReal3D( const CvArr* arr, int idx0, int idx1, int idx2 ); +/** @overload +@param arr Input array. Must have a single channel. +@param idx Array of the element indices +*/ +CVAPI(double) cvGetRealND( const CvArr* arr, const int* idx ); + +/** @brief Change the particular array element. + +The functions assign the new value to a particular array element. In the case of a sparse array the +functions create the node if it does not exist yet. +@param arr Input array +@param idx0 The first zero-based component of the element index +@param value The assigned value + */ +CVAPI(void) cvSet1D( CvArr* arr, int idx0, CvScalar value ); +/** @overload */ +CVAPI(void) cvSet2D( CvArr* arr, int idx0, int idx1, CvScalar value ); +/** @overload */ +CVAPI(void) cvSet3D( CvArr* arr, int idx0, int idx1, int idx2, CvScalar value ); +/** @overload +@param arr Input array +@param idx Array of the element indices +@param value The assigned value +*/ +CVAPI(void) cvSetND( CvArr* arr, const int* idx, CvScalar value ); + +/** @brief Change a specific array element. + +The functions assign a new value to a specific element of a single-channel array. If the array has +multiple channels, a runtime error is raised. Note that the Set\*D function can be used safely for +both single-channel and multiple-channel arrays, though they are a bit slower. + +In the case of a sparse array the functions create the node if it does not yet exist. +@param arr Input array +@param idx0 The first zero-based component of the element index +@param value The assigned value + */ +CVAPI(void) cvSetReal1D( CvArr* arr, int idx0, double value ); +/** @overload */ +CVAPI(void) cvSetReal2D( CvArr* arr, int idx0, int idx1, double value ); +/** @overload */ +CVAPI(void) cvSetReal3D( CvArr* arr, int idx0, + int idx1, int idx2, double value ); +/** @overload +@param arr Input array +@param idx Array of the element indices +@param value The assigned value +*/ +CVAPI(void) cvSetRealND( CvArr* arr, const int* idx, double value ); + +/** clears element of ND dense array, + in case of sparse arrays it deletes the specified node */ +CVAPI(void) cvClearND( CvArr* arr, const int* idx ); + +/** @brief Returns matrix header for arbitrary array. + +The function returns a matrix header for the input array that can be a matrix - CvMat, an image - +IplImage, or a multi-dimensional dense array - CvMatND (the third option is allowed only if +allowND != 0) . In the case of matrix the function simply returns the input pointer. In the case of +IplImage\* or CvMatND it initializes the header structure with parameters of the current image ROI +and returns &header. Because COI is not supported by CvMat, it is returned separately. + +The function provides an easy way to handle both types of arrays - IplImage and CvMat using the same +code. Input array must have non-zero data pointer, otherwise the function will report an error. + +@note If the input array is IplImage with planar data layout and COI set, the function returns the +pointer to the selected plane and COI == 0. This feature allows user to process IplImage structures +with planar data layout, even though OpenCV does not support such images. +@param arr Input array +@param header Pointer to CvMat structure used as a temporary buffer +@param coi Optional output parameter for storing COI +@param allowND If non-zero, the function accepts multi-dimensional dense arrays (CvMatND\*) and +returns 2D matrix (if CvMatND has two dimensions) or 1D matrix (when CvMatND has 1 dimension or +more than 2 dimensions). The CvMatND array must be continuous. +@sa cvGetImage, cvarrToMat. + */ +CVAPI(CvMat*) cvGetMat( const CvArr* arr, CvMat* header, + int* coi CV_DEFAULT(NULL), + int allowND CV_DEFAULT(0)); + +/** @brief Returns image header for arbitrary array. + +The function returns the image header for the input array that can be a matrix (CvMat) or image +(IplImage). In the case of an image the function simply returns the input pointer. In the case of +CvMat it initializes an image_header structure with the parameters of the input matrix. Note that +if we transform IplImage to CvMat using cvGetMat and then transform CvMat back to IplImage using +this function, we will get different headers if the ROI is set in the original image. +@param arr Input array +@param image_header Pointer to IplImage structure used as a temporary buffer + */ +CVAPI(IplImage*) cvGetImage( const CvArr* arr, IplImage* image_header ); + + +/** @brief Changes the shape of a multi-dimensional array without copying the data. + +The function is an advanced version of cvReshape that can work with multi-dimensional arrays as +well (though it can work with ordinary images and matrices) and change the number of dimensions. + +Below are the two samples from the cvReshape description rewritten using cvReshapeMatND: +@code + IplImage* color_img = cvCreateImage(cvSize(320,240), IPL_DEPTH_8U, 3); + IplImage gray_img_hdr, *gray_img; + gray_img = (IplImage*)cvReshapeMatND(color_img, sizeof(gray_img_hdr), &gray_img_hdr, 1, 0, 0); + ... + int size[] = { 2, 2, 2 }; + CvMatND* mat = cvCreateMatND(3, size, CV_32F); + CvMat row_header, *row; + row = (CvMat*)cvReshapeMatND(mat, sizeof(row_header), &row_header, 0, 1, 0); +@endcode +In C, the header file for this function includes a convenient macro cvReshapeND that does away with +the sizeof_header parameter. So, the lines containing the call to cvReshapeMatND in the examples +may be replaced as follow: +@code + gray_img = (IplImage*)cvReshapeND(color_img, &gray_img_hdr, 1, 0, 0); + ... + row = (CvMat*)cvReshapeND(mat, &row_header, 0, 1, 0); +@endcode +@param arr Input array +@param sizeof_header Size of output header to distinguish between IplImage, CvMat and CvMatND +output headers +@param header Output header to be filled +@param new_cn New number of channels. new_cn = 0 means that the number of channels remains +unchanged. +@param new_dims New number of dimensions. new_dims = 0 means that the number of dimensions +remains the same. +@param new_sizes Array of new dimension sizes. Only new_dims-1 values are used, because the +total number of elements must remain the same. Thus, if new_dims = 1, new_sizes array is not +used. + */ +CVAPI(CvArr*) cvReshapeMatND( const CvArr* arr, + int sizeof_header, CvArr* header, + int new_cn, int new_dims, int* new_sizes ); + +#define cvReshapeND( arr, header, new_cn, new_dims, new_sizes ) \ + cvReshapeMatND( (arr), sizeof(*(header)), (header), \ + (new_cn), (new_dims), (new_sizes)) + +/** @brief Changes shape of matrix/image without copying data. + +The function initializes the CvMat header so that it points to the same data as the original array +but has a different shape - different number of channels, different number of rows, or both. + +The following example code creates one image buffer and two image headers, the first is for a +320x240x3 image and the second is for a 960x240x1 image: +@code + IplImage* color_img = cvCreateImage(cvSize(320,240), IPL_DEPTH_8U, 3); + CvMat gray_mat_hdr; + IplImage gray_img_hdr, *gray_img; + cvReshape(color_img, &gray_mat_hdr, 1); + gray_img = cvGetImage(&gray_mat_hdr, &gray_img_hdr); +@endcode +And the next example converts a 3x3 matrix to a single 1x9 vector: +@code + CvMat* mat = cvCreateMat(3, 3, CV_32F); + CvMat row_header, *row; + row = cvReshape(mat, &row_header, 0, 1); +@endcode +@param arr Input array +@param header Output header to be filled +@param new_cn New number of channels. 'new_cn = 0' means that the number of channels remains +unchanged. +@param new_rows New number of rows. 'new_rows = 0' means that the number of rows remains +unchanged unless it needs to be changed according to new_cn value. +*/ +CVAPI(CvMat*) cvReshape( const CvArr* arr, CvMat* header, + int new_cn, int new_rows CV_DEFAULT(0) ); + +/** Repeats source 2d array several times in both horizontal and + vertical direction to fill destination array */ +CVAPI(void) cvRepeat( const CvArr* src, CvArr* dst ); + +/** @brief Allocates array data + +The function allocates image, matrix or multi-dimensional dense array data. Note that in the case of +matrix types OpenCV allocation functions are used. In the case of IplImage they are used unless +CV_TURN_ON_IPL_COMPATIBILITY() has been called before. In the latter case IPL functions are used +to allocate the data. +@param arr Array header + */ +CVAPI(void) cvCreateData( CvArr* arr ); + +/** @brief Releases array data. + +The function releases the array data. In the case of CvMat or CvMatND it simply calls +cvDecRefData(), that is the function can not deallocate external data. See also the note to +cvCreateData . +@param arr Array header + */ +CVAPI(void) cvReleaseData( CvArr* arr ); + +/** @brief Assigns user data to the array header. + +The function assigns user data to the array header. Header should be initialized before using +cvCreateMatHeader, cvCreateImageHeader, cvCreateMatNDHeader, cvInitMatHeader, +cvInitImageHeader or cvInitMatNDHeader. +@param arr Array header +@param data User data +@param step Full row length in bytes + */ +CVAPI(void) cvSetData( CvArr* arr, void* data, int step ); + +/** @brief Retrieves low-level information about the array. + +The function fills output variables with low-level information about the array data. All output + +parameters are optional, so some of the pointers may be set to NULL. If the array is IplImage with +ROI set, the parameters of ROI are returned. + +The following example shows how to get access to array elements. It computes absolute values of the +array elements : +@code + float* data; + int step; + CvSize size; + + cvGetRawData(array, (uchar**)&data, &step, &size); + step /= sizeof(data[0]); + + for(int y = 0; y < size.height; y++, data += step ) + for(int x = 0; x < size.width; x++ ) + data[x] = (float)fabs(data[x]); +@endcode +@param arr Array header +@param data Output pointer to the whole image origin or ROI origin if ROI is set +@param step Output full row length in bytes +@param roi_size Output ROI size + */ +CVAPI(void) cvGetRawData( const CvArr* arr, uchar** data, + int* step CV_DEFAULT(NULL), + CvSize* roi_size CV_DEFAULT(NULL)); + +/** @brief Returns size of matrix or image ROI. + +The function returns number of rows (CvSize::height) and number of columns (CvSize::width) of the +input matrix or image. In the case of image the size of ROI is returned. +@param arr array header + */ +CVAPI(CvSize) cvGetSize( const CvArr* arr ); + +/** @brief Copies one array to another. + +The function copies selected elements from an input array to an output array: + +\f[\texttt{dst} (I)= \texttt{src} (I) \quad \text{if} \quad \texttt{mask} (I) \ne 0.\f] + +If any of the passed arrays is of IplImage type, then its ROI and COI fields are used. Both arrays +must have the same type, the same number of dimensions, and the same size. The function can also +copy sparse arrays (mask is not supported in this case). +@param src The source array +@param dst The destination array +@param mask Operation mask, 8-bit single channel array; specifies elements of the destination array +to be changed + */ +CVAPI(void) cvCopy( const CvArr* src, CvArr* dst, + const CvArr* mask CV_DEFAULT(NULL) ); + +/** @brief Sets every element of an array to a given value. + +The function copies the scalar value to every selected element of the destination array: +\f[\texttt{arr} (I)= \texttt{value} \quad \text{if} \quad \texttt{mask} (I) \ne 0\f] +If array arr is of IplImage type, then is ROI used, but COI must not be set. +@param arr The destination array +@param value Fill value +@param mask Operation mask, 8-bit single channel array; specifies elements of the destination +array to be changed + */ +CVAPI(void) cvSet( CvArr* arr, CvScalar value, + const CvArr* mask CV_DEFAULT(NULL) ); + +/** @brief Clears the array. + +The function clears the array. In the case of dense arrays (CvMat, CvMatND or IplImage), +cvZero(array) is equivalent to cvSet(array,cvScalarAll(0),0). In the case of sparse arrays all the +elements are removed. +@param arr Array to be cleared + */ +CVAPI(void) cvSetZero( CvArr* arr ); +#define cvZero cvSetZero + + +/** Splits a multi-channel array into the set of single-channel arrays or + extracts particular [color] plane */ +CVAPI(void) cvSplit( const CvArr* src, CvArr* dst0, CvArr* dst1, + CvArr* dst2, CvArr* dst3 ); + +/** Merges a set of single-channel arrays into the single multi-channel array + or inserts one particular [color] plane to the array */ +CVAPI(void) cvMerge( const CvArr* src0, const CvArr* src1, + const CvArr* src2, const CvArr* src3, + CvArr* dst ); + +/** Copies several channels from input arrays to + certain channels of output arrays */ +CVAPI(void) cvMixChannels( const CvArr** src, int src_count, + CvArr** dst, int dst_count, + const int* from_to, int pair_count ); + +/** @brief Converts one array to another with optional linear transformation. + +The function has several different purposes, and thus has several different names. It copies one +array to another with optional scaling, which is performed first, and/or optional type conversion, +performed after: + +\f[\texttt{dst} (I) = \texttt{scale} \texttt{src} (I) + ( \texttt{shift} _0, \texttt{shift} _1,...)\f] + +All the channels of multi-channel arrays are processed independently. + +The type of conversion is done with rounding and saturation, that is if the result of scaling + +conversion can not be represented exactly by a value of the destination array element type, it is +set to the nearest representable value on the real axis. +@param src Source array +@param dst Destination array +@param scale Scale factor +@param shift Value added to the scaled source array elements + */ +CVAPI(void) cvConvertScale( const CvArr* src, CvArr* dst, + double scale CV_DEFAULT(1), + double shift CV_DEFAULT(0) ); +#define cvCvtScale cvConvertScale +#define cvScale cvConvertScale +#define cvConvert( src, dst ) cvConvertScale( (src), (dst), 1, 0 ) + + +/** Performs linear transformation on every source array element, + stores absolute value of the result: + dst(x,y,c) = abs(scale*src(x,y,c)+shift). + destination array must have 8u type. + In other cases one may use cvConvertScale + cvAbsDiffS */ +CVAPI(void) cvConvertScaleAbs( const CvArr* src, CvArr* dst, + double scale CV_DEFAULT(1), + double shift CV_DEFAULT(0) ); +#define cvCvtScaleAbs cvConvertScaleAbs + + +/** checks termination criteria validity and + sets eps to default_eps (if it is not set), + max_iter to default_max_iters (if it is not set) +*/ +CVAPI(CvTermCriteria) cvCheckTermCriteria( CvTermCriteria criteria, + double default_eps, + int default_max_iters ); + +/****************************************************************************************\ +* Arithmetic, logic and comparison operations * +\****************************************************************************************/ + +/** dst(mask) = src1(mask) + src2(mask) */ +CVAPI(void) cvAdd( const CvArr* src1, const CvArr* src2, CvArr* dst, + const CvArr* mask CV_DEFAULT(NULL)); + +/** dst(mask) = src(mask) + value */ +CVAPI(void) cvAddS( const CvArr* src, CvScalar value, CvArr* dst, + const CvArr* mask CV_DEFAULT(NULL)); + +/** dst(mask) = src1(mask) - src2(mask) */ +CVAPI(void) cvSub( const CvArr* src1, const CvArr* src2, CvArr* dst, + const CvArr* mask CV_DEFAULT(NULL)); + +/** dst(mask) = src(mask) - value = src(mask) + (-value) */ +CV_INLINE void cvSubS( const CvArr* src, CvScalar value, CvArr* dst, + const CvArr* mask CV_DEFAULT(NULL)) +{ + cvAddS( src, cvScalar( -value.val[0], -value.val[1], -value.val[2], -value.val[3]), + dst, mask ); +} + +/** dst(mask) = value - src(mask) */ +CVAPI(void) cvSubRS( const CvArr* src, CvScalar value, CvArr* dst, + const CvArr* mask CV_DEFAULT(NULL)); + +/** dst(idx) = src1(idx) * src2(idx) * scale + (scaled element-wise multiplication of 2 arrays) */ +CVAPI(void) cvMul( const CvArr* src1, const CvArr* src2, + CvArr* dst, double scale CV_DEFAULT(1) ); + +/** element-wise division/inversion with scaling: + dst(idx) = src1(idx) * scale / src2(idx) + or dst(idx) = scale / src2(idx) if src1 == 0 */ +CVAPI(void) cvDiv( const CvArr* src1, const CvArr* src2, + CvArr* dst, double scale CV_DEFAULT(1)); + +/** dst = src1 * scale + src2 */ +CVAPI(void) cvScaleAdd( const CvArr* src1, CvScalar scale, + const CvArr* src2, CvArr* dst ); +#define cvAXPY( A, real_scalar, B, C ) cvScaleAdd(A, cvRealScalar(real_scalar), B, C) + +/** dst = src1 * alpha + src2 * beta + gamma */ +CVAPI(void) cvAddWeighted( const CvArr* src1, double alpha, + const CvArr* src2, double beta, + double gamma, CvArr* dst ); + +/** @brief Calculates the dot product of two arrays in Euclidean metrics. + +The function calculates and returns the Euclidean dot product of two arrays. + +\f[src1 \bullet src2 = \sum _I ( \texttt{src1} (I) \texttt{src2} (I))\f] + +In the case of multiple channel arrays, the results for all channels are accumulated. In particular, +cvDotProduct(a,a) where a is a complex vector, will return \f$||\texttt{a}||^2\f$. The function can +process multi-dimensional arrays, row by row, layer by layer, and so on. +@param src1 The first source array +@param src2 The second source array + */ +CVAPI(double) cvDotProduct( const CvArr* src1, const CvArr* src2 ); + +/** dst(idx) = src1(idx) & src2(idx) */ +CVAPI(void) cvAnd( const CvArr* src1, const CvArr* src2, + CvArr* dst, const CvArr* mask CV_DEFAULT(NULL)); + +/** dst(idx) = src(idx) & value */ +CVAPI(void) cvAndS( const CvArr* src, CvScalar value, + CvArr* dst, const CvArr* mask CV_DEFAULT(NULL)); + +/** dst(idx) = src1(idx) | src2(idx) */ +CVAPI(void) cvOr( const CvArr* src1, const CvArr* src2, + CvArr* dst, const CvArr* mask CV_DEFAULT(NULL)); + +/** dst(idx) = src(idx) | value */ +CVAPI(void) cvOrS( const CvArr* src, CvScalar value, + CvArr* dst, const CvArr* mask CV_DEFAULT(NULL)); + +/** dst(idx) = src1(idx) ^ src2(idx) */ +CVAPI(void) cvXor( const CvArr* src1, const CvArr* src2, + CvArr* dst, const CvArr* mask CV_DEFAULT(NULL)); + +/** dst(idx) = src(idx) ^ value */ +CVAPI(void) cvXorS( const CvArr* src, CvScalar value, + CvArr* dst, const CvArr* mask CV_DEFAULT(NULL)); + +/** dst(idx) = ~src(idx) */ +CVAPI(void) cvNot( const CvArr* src, CvArr* dst ); + +/** dst(idx) = lower(idx) <= src(idx) < upper(idx) */ +CVAPI(void) cvInRange( const CvArr* src, const CvArr* lower, + const CvArr* upper, CvArr* dst ); + +/** dst(idx) = lower <= src(idx) < upper */ +CVAPI(void) cvInRangeS( const CvArr* src, CvScalar lower, + CvScalar upper, CvArr* dst ); + +#define CV_CMP_EQ 0 +#define CV_CMP_GT 1 +#define CV_CMP_GE 2 +#define CV_CMP_LT 3 +#define CV_CMP_LE 4 +#define CV_CMP_NE 5 + +/** The comparison operation support single-channel arrays only. + Destination image should be 8uC1 or 8sC1 */ + +/** dst(idx) = src1(idx) _cmp_op_ src2(idx) */ +CVAPI(void) cvCmp( const CvArr* src1, const CvArr* src2, CvArr* dst, int cmp_op ); + +/** dst(idx) = src1(idx) _cmp_op_ value */ +CVAPI(void) cvCmpS( const CvArr* src, double value, CvArr* dst, int cmp_op ); + +/** dst(idx) = min(src1(idx),src2(idx)) */ +CVAPI(void) cvMin( const CvArr* src1, const CvArr* src2, CvArr* dst ); + +/** dst(idx) = max(src1(idx),src2(idx)) */ +CVAPI(void) cvMax( const CvArr* src1, const CvArr* src2, CvArr* dst ); + +/** dst(idx) = min(src(idx),value) */ +CVAPI(void) cvMinS( const CvArr* src, double value, CvArr* dst ); + +/** dst(idx) = max(src(idx),value) */ +CVAPI(void) cvMaxS( const CvArr* src, double value, CvArr* dst ); + +/** dst(x,y,c) = abs(src1(x,y,c) - src2(x,y,c)) */ +CVAPI(void) cvAbsDiff( const CvArr* src1, const CvArr* src2, CvArr* dst ); + +/** dst(x,y,c) = abs(src(x,y,c) - value(c)) */ +CVAPI(void) cvAbsDiffS( const CvArr* src, CvArr* dst, CvScalar value ); +#define cvAbs( src, dst ) cvAbsDiffS( (src), (dst), cvScalarAll(0)) + +/****************************************************************************************\ +* Math operations * +\****************************************************************************************/ + +/** Does cartesian->polar coordinates conversion. + Either of output components (magnitude or angle) is optional */ +CVAPI(void) cvCartToPolar( const CvArr* x, const CvArr* y, + CvArr* magnitude, CvArr* angle CV_DEFAULT(NULL), + int angle_in_degrees CV_DEFAULT(0)); + +/** Does polar->cartesian coordinates conversion. + Either of output components (magnitude or angle) is optional. + If magnitude is missing it is assumed to be all 1's */ +CVAPI(void) cvPolarToCart( const CvArr* magnitude, const CvArr* angle, + CvArr* x, CvArr* y, + int angle_in_degrees CV_DEFAULT(0)); + +/** Does powering: dst(idx) = src(idx)^power */ +CVAPI(void) cvPow( const CvArr* src, CvArr* dst, double power ); + +/** Does exponention: dst(idx) = exp(src(idx)). + Overflow is not handled yet. Underflow is handled. + Maximal relative error is ~7e-6 for single-precision input */ +CVAPI(void) cvExp( const CvArr* src, CvArr* dst ); + +/** Calculates natural logarithms: dst(idx) = log(abs(src(idx))). + Logarithm of 0 gives large negative number(~-700) + Maximal relative error is ~3e-7 for single-precision output +*/ +CVAPI(void) cvLog( const CvArr* src, CvArr* dst ); + +/** Fast arctangent calculation */ +CVAPI(float) cvFastArctan( float y, float x ); + +/** Fast cubic root calculation */ +CVAPI(float) cvCbrt( float value ); + +#define CV_CHECK_RANGE 1 +#define CV_CHECK_QUIET 2 +/** Checks array values for NaNs, Infs or simply for too large numbers + (if CV_CHECK_RANGE is set). If CV_CHECK_QUIET is set, + no runtime errors is raised (function returns zero value in case of "bad" values). + Otherwise cvError is called */ +CVAPI(int) cvCheckArr( const CvArr* arr, int flags CV_DEFAULT(0), + double min_val CV_DEFAULT(0), double max_val CV_DEFAULT(0)); +#define cvCheckArray cvCheckArr + +#define CV_RAND_UNI 0 +#define CV_RAND_NORMAL 1 + +/** @brief Fills an array with random numbers and updates the RNG state. + +The function fills the destination array with uniformly or normally distributed random numbers. +@param rng CvRNG state initialized by cvRNG +@param arr The destination array +@param dist_type Distribution type +> - **CV_RAND_UNI** uniform distribution +> - **CV_RAND_NORMAL** normal or Gaussian distribution +@param param1 The first parameter of the distribution. In the case of a uniform distribution it is +the inclusive lower boundary of the random numbers range. In the case of a normal distribution it +is the mean value of the random numbers. +@param param2 The second parameter of the distribution. In the case of a uniform distribution it +is the exclusive upper boundary of the random numbers range. In the case of a normal distribution +it is the standard deviation of the random numbers. +@sa randu, randn, RNG::fill. + */ +CVAPI(void) cvRandArr( CvRNG* rng, CvArr* arr, int dist_type, + CvScalar param1, CvScalar param2 ); + +CVAPI(void) cvRandShuffle( CvArr* mat, CvRNG* rng, + double iter_factor CV_DEFAULT(1.)); + +#define CV_SORT_EVERY_ROW 0 +#define CV_SORT_EVERY_COLUMN 1 +#define CV_SORT_ASCENDING 0 +#define CV_SORT_DESCENDING 16 + +CVAPI(void) cvSort( const CvArr* src, CvArr* dst CV_DEFAULT(NULL), + CvArr* idxmat CV_DEFAULT(NULL), + int flags CV_DEFAULT(0)); + +/** Finds real roots of a cubic equation */ +CVAPI(int) cvSolveCubic( const CvMat* coeffs, CvMat* roots ); + +/** Finds all real and complex roots of a polynomial equation */ +CVAPI(void) cvSolvePoly(const CvMat* coeffs, CvMat *roots2, + int maxiter CV_DEFAULT(20), int fig CV_DEFAULT(100)); + +/****************************************************************************************\ +* Matrix operations * +\****************************************************************************************/ + +/** @brief Calculates the cross product of two 3D vectors. + +The function calculates the cross product of two 3D vectors: +\f[\texttt{dst} = \texttt{src1} \times \texttt{src2}\f] +or: +\f[\begin{array}{l} \texttt{dst} _1 = \texttt{src1} _2 \texttt{src2} _3 - \texttt{src1} _3 \texttt{src2} _2 \\ \texttt{dst} _2 = \texttt{src1} _3 \texttt{src2} _1 - \texttt{src1} _1 \texttt{src2} _3 \\ \texttt{dst} _3 = \texttt{src1} _1 \texttt{src2} _2 - \texttt{src1} _2 \texttt{src2} _1 \end{array}\f] +@param src1 The first source vector +@param src2 The second source vector +@param dst The destination vector + */ +CVAPI(void) cvCrossProduct( const CvArr* src1, const CvArr* src2, CvArr* dst ); + +/** Matrix transform: dst = A*B + C, C is optional */ +#define cvMatMulAdd( src1, src2, src3, dst ) cvGEMM( (src1), (src2), 1., (src3), 1., (dst), 0 ) +#define cvMatMul( src1, src2, dst ) cvMatMulAdd( (src1), (src2), NULL, (dst)) + +#define CV_GEMM_A_T 1 +#define CV_GEMM_B_T 2 +#define CV_GEMM_C_T 4 +/** Extended matrix transform: + dst = alpha*op(A)*op(B) + beta*op(C), where op(X) is X or X^T */ +CVAPI(void) cvGEMM( const CvArr* src1, const CvArr* src2, double alpha, + const CvArr* src3, double beta, CvArr* dst, + int tABC CV_DEFAULT(0)); +#define cvMatMulAddEx cvGEMM + +/** Transforms each element of source array and stores + resultant vectors in destination array */ +CVAPI(void) cvTransform( const CvArr* src, CvArr* dst, + const CvMat* transmat, + const CvMat* shiftvec CV_DEFAULT(NULL)); +#define cvMatMulAddS cvTransform + +/** Does perspective transform on every element of input array */ +CVAPI(void) cvPerspectiveTransform( const CvArr* src, CvArr* dst, + const CvMat* mat ); + +/** Calculates (A-delta)*(A-delta)^T (order=0) or (A-delta)^T*(A-delta) (order=1) */ +CVAPI(void) cvMulTransposed( const CvArr* src, CvArr* dst, int order, + const CvArr* delta CV_DEFAULT(NULL), + double scale CV_DEFAULT(1.) ); + +/** Tranposes matrix. Square matrices can be transposed in-place */ +CVAPI(void) cvTranspose( const CvArr* src, CvArr* dst ); +#define cvT cvTranspose + +/** Completes the symmetric matrix from the lower (LtoR=0) or from the upper (LtoR!=0) part */ +CVAPI(void) cvCompleteSymm( CvMat* matrix, int LtoR CV_DEFAULT(0) ); + +/** Mirror array data around horizontal (flip=0), + vertical (flip=1) or both(flip=-1) axises: + cvFlip(src) flips images vertically and sequences horizontally (inplace) */ +CVAPI(void) cvFlip( const CvArr* src, CvArr* dst CV_DEFAULT(NULL), + int flip_mode CV_DEFAULT(0)); +#define cvMirror cvFlip + + +#define CV_SVD_MODIFY_A 1 +#define CV_SVD_U_T 2 +#define CV_SVD_V_T 4 + +/** Performs Singular Value Decomposition of a matrix */ +CVAPI(void) cvSVD( CvArr* A, CvArr* W, CvArr* U CV_DEFAULT(NULL), + CvArr* V CV_DEFAULT(NULL), int flags CV_DEFAULT(0)); + +/** Performs Singular Value Back Substitution (solves A*X = B): + flags must be the same as in cvSVD */ +CVAPI(void) cvSVBkSb( const CvArr* W, const CvArr* U, + const CvArr* V, const CvArr* B, + CvArr* X, int flags ); + +#define CV_LU 0 +#define CV_SVD 1 +#define CV_SVD_SYM 2 +#define CV_CHOLESKY 3 +#define CV_QR 4 +#define CV_NORMAL 16 + +/** Inverts matrix */ +CVAPI(double) cvInvert( const CvArr* src, CvArr* dst, + int method CV_DEFAULT(CV_LU)); +#define cvInv cvInvert + +/** Solves linear system (src1)*(dst) = (src2) + (returns 0 if src1 is a singular and CV_LU method is used) */ +CVAPI(int) cvSolve( const CvArr* src1, const CvArr* src2, CvArr* dst, + int method CV_DEFAULT(CV_LU)); + +/** Calculates determinant of input matrix */ +CVAPI(double) cvDet( const CvArr* mat ); + +/** Calculates trace of the matrix (sum of elements on the main diagonal) */ +CVAPI(CvScalar) cvTrace( const CvArr* mat ); + +/** Finds eigen values and vectors of a symmetric matrix */ +CVAPI(void) cvEigenVV( CvArr* mat, CvArr* evects, CvArr* evals, + double eps CV_DEFAULT(0), + int lowindex CV_DEFAULT(-1), + int highindex CV_DEFAULT(-1)); + +///* Finds selected eigen values and vectors of a symmetric matrix */ +//CVAPI(void) cvSelectedEigenVV( CvArr* mat, CvArr* evects, CvArr* evals, +// int lowindex, int highindex ); + +/** Makes an identity matrix (mat_ij = i == j) */ +CVAPI(void) cvSetIdentity( CvArr* mat, CvScalar value CV_DEFAULT(cvRealScalar(1)) ); + +/** Fills matrix with given range of numbers */ +CVAPI(CvArr*) cvRange( CvArr* mat, double start, double end ); + +/** @anchor core_c_CovarFlags +@name Flags for cvCalcCovarMatrix +@see cvCalcCovarMatrix + @{ +*/ + +/** flag for cvCalcCovarMatrix, transpose([v1-avg, v2-avg,...]) * [v1-avg,v2-avg,...] */ +#define CV_COVAR_SCRAMBLED 0 + +/** flag for cvCalcCovarMatrix, [v1-avg, v2-avg,...] * transpose([v1-avg,v2-avg,...]) */ +#define CV_COVAR_NORMAL 1 + +/** flag for cvCalcCovarMatrix, do not calc average (i.e. mean vector) - use the input vector instead + (useful for calculating covariance matrix by parts) */ +#define CV_COVAR_USE_AVG 2 + +/** flag for cvCalcCovarMatrix, scale the covariance matrix coefficients by number of the vectors */ +#define CV_COVAR_SCALE 4 + +/** flag for cvCalcCovarMatrix, all the input vectors are stored in a single matrix, as its rows */ +#define CV_COVAR_ROWS 8 + +/** flag for cvCalcCovarMatrix, all the input vectors are stored in a single matrix, as its columns */ +#define CV_COVAR_COLS 16 + +/** @} */ + +/** Calculates covariation matrix for a set of vectors +@see @ref core_c_CovarFlags "flags" +*/ +CVAPI(void) cvCalcCovarMatrix( const CvArr** vects, int count, + CvArr* cov_mat, CvArr* avg, int flags ); + +#define CV_PCA_DATA_AS_ROW 0 +#define CV_PCA_DATA_AS_COL 1 +#define CV_PCA_USE_AVG 2 +CVAPI(void) cvCalcPCA( const CvArr* data, CvArr* mean, + CvArr* eigenvals, CvArr* eigenvects, int flags ); + +CVAPI(void) cvProjectPCA( const CvArr* data, const CvArr* mean, + const CvArr* eigenvects, CvArr* result ); + +CVAPI(void) cvBackProjectPCA( const CvArr* proj, const CvArr* mean, + const CvArr* eigenvects, CvArr* result ); + +/** Calculates Mahalanobis(weighted) distance */ +CVAPI(double) cvMahalanobis( const CvArr* vec1, const CvArr* vec2, const CvArr* mat ); +#define cvMahalonobis cvMahalanobis + +/****************************************************************************************\ +* Array Statistics * +\****************************************************************************************/ + +/** Finds sum of array elements */ +CVAPI(CvScalar) cvSum( const CvArr* arr ); + +/** Calculates number of non-zero pixels */ +CVAPI(int) cvCountNonZero( const CvArr* arr ); + +/** Calculates mean value of array elements */ +CVAPI(CvScalar) cvAvg( const CvArr* arr, const CvArr* mask CV_DEFAULT(NULL) ); + +/** Calculates mean and standard deviation of pixel values */ +CVAPI(void) cvAvgSdv( const CvArr* arr, CvScalar* mean, CvScalar* std_dev, + const CvArr* mask CV_DEFAULT(NULL) ); + +/** Finds global minimum, maximum and their positions */ +CVAPI(void) cvMinMaxLoc( const CvArr* arr, double* min_val, double* max_val, + CvPoint* min_loc CV_DEFAULT(NULL), + CvPoint* max_loc CV_DEFAULT(NULL), + const CvArr* mask CV_DEFAULT(NULL) ); + +/** @anchor core_c_NormFlags + @name Flags for cvNorm and cvNormalize + @{ +*/ +#define CV_C 1 +#define CV_L1 2 +#define CV_L2 4 +#define CV_NORM_MASK 7 +#define CV_RELATIVE 8 +#define CV_DIFF 16 +#define CV_MINMAX 32 + +#define CV_DIFF_C (CV_DIFF | CV_C) +#define CV_DIFF_L1 (CV_DIFF | CV_L1) +#define CV_DIFF_L2 (CV_DIFF | CV_L2) +#define CV_RELATIVE_C (CV_RELATIVE | CV_C) +#define CV_RELATIVE_L1 (CV_RELATIVE | CV_L1) +#define CV_RELATIVE_L2 (CV_RELATIVE | CV_L2) +/** @} */ + +/** Finds norm, difference norm or relative difference norm for an array (or two arrays) +@see ref core_c_NormFlags "flags" +*/ +CVAPI(double) cvNorm( const CvArr* arr1, const CvArr* arr2 CV_DEFAULT(NULL), + int norm_type CV_DEFAULT(CV_L2), + const CvArr* mask CV_DEFAULT(NULL) ); + +/** @see ref core_c_NormFlags "flags" */ +CVAPI(void) cvNormalize( const CvArr* src, CvArr* dst, + double a CV_DEFAULT(1.), double b CV_DEFAULT(0.), + int norm_type CV_DEFAULT(CV_L2), + const CvArr* mask CV_DEFAULT(NULL) ); + +/** @anchor core_c_ReduceFlags + @name Flags for cvReduce + @{ +*/ +#define CV_REDUCE_SUM 0 +#define CV_REDUCE_AVG 1 +#define CV_REDUCE_MAX 2 +#define CV_REDUCE_MIN 3 +/** @} */ + +/** @see @ref core_c_ReduceFlags "flags" */ +CVAPI(void) cvReduce( const CvArr* src, CvArr* dst, int dim CV_DEFAULT(-1), + int op CV_DEFAULT(CV_REDUCE_SUM) ); + +/****************************************************************************************\ +* Discrete Linear Transforms and Related Functions * +\****************************************************************************************/ + +/** @anchor core_c_DftFlags + @name Flags for cvDFT, cvDCT and cvMulSpectrums + @{ + */ +#define CV_DXT_FORWARD 0 +#define CV_DXT_INVERSE 1 +#define CV_DXT_SCALE 2 /**< divide result by size of array */ +#define CV_DXT_INV_SCALE (CV_DXT_INVERSE + CV_DXT_SCALE) +#define CV_DXT_INVERSE_SCALE CV_DXT_INV_SCALE +#define CV_DXT_ROWS 4 /**< transform each row individually */ +#define CV_DXT_MUL_CONJ 8 /**< conjugate the second argument of cvMulSpectrums */ +/** @} */ + +/** Discrete Fourier Transform: + complex->complex, + real->ccs (forward), + ccs->real (inverse) +@see core_c_DftFlags "flags" +*/ +CVAPI(void) cvDFT( const CvArr* src, CvArr* dst, int flags, + int nonzero_rows CV_DEFAULT(0) ); +#define cvFFT cvDFT + +/** Multiply results of DFTs: DFT(X)*DFT(Y) or DFT(X)*conj(DFT(Y)) +@see core_c_DftFlags "flags" +*/ +CVAPI(void) cvMulSpectrums( const CvArr* src1, const CvArr* src2, + CvArr* dst, int flags ); + +/** Finds optimal DFT vector size >= size0 */ +CVAPI(int) cvGetOptimalDFTSize( int size0 ); + +/** Discrete Cosine Transform +@see core_c_DftFlags "flags" +*/ +CVAPI(void) cvDCT( const CvArr* src, CvArr* dst, int flags ); + +/****************************************************************************************\ +* Dynamic data structures * +\****************************************************************************************/ + +/** Calculates length of sequence slice (with support of negative indices). */ +CVAPI(int) cvSliceLength( CvSlice slice, const CvSeq* seq ); + + +/** Creates new memory storage. + block_size == 0 means that default, + somewhat optimal size, is used (currently, it is 64K) */ +CVAPI(CvMemStorage*) cvCreateMemStorage( int block_size CV_DEFAULT(0)); + + +/** Creates a memory storage that will borrow memory blocks from parent storage */ +CVAPI(CvMemStorage*) cvCreateChildMemStorage( CvMemStorage* parent ); + + +/** Releases memory storage. All the children of a parent must be released before + the parent. A child storage returns all the blocks to parent when it is released */ +CVAPI(void) cvReleaseMemStorage( CvMemStorage** storage ); + + +/** Clears memory storage. This is the only way(!!!) (besides cvRestoreMemStoragePos) + to reuse memory allocated for the storage - cvClearSeq,cvClearSet ... + do not free any memory. + A child storage returns all the blocks to the parent when it is cleared */ +CVAPI(void) cvClearMemStorage( CvMemStorage* storage ); + +/** Remember a storage "free memory" position */ +CVAPI(void) cvSaveMemStoragePos( const CvMemStorage* storage, CvMemStoragePos* pos ); + +/** Restore a storage "free memory" position */ +CVAPI(void) cvRestoreMemStoragePos( CvMemStorage* storage, CvMemStoragePos* pos ); + +/** Allocates continuous buffer of the specified size in the storage */ +CVAPI(void*) cvMemStorageAlloc( CvMemStorage* storage, size_t size ); + +/** Allocates string in memory storage */ +CVAPI(CvString) cvMemStorageAllocString( CvMemStorage* storage, const char* ptr, + int len CV_DEFAULT(-1) ); + +/** Creates new empty sequence that will reside in the specified storage */ +CVAPI(CvSeq*) cvCreateSeq( int seq_flags, size_t header_size, + size_t elem_size, CvMemStorage* storage ); + +/** Changes default size (granularity) of sequence blocks. + The default size is ~1Kbyte */ +CVAPI(void) cvSetSeqBlockSize( CvSeq* seq, int delta_elems ); + + +/** Adds new element to the end of sequence. Returns pointer to the element */ +CVAPI(schar*) cvSeqPush( CvSeq* seq, const void* element CV_DEFAULT(NULL)); + + +/** Adds new element to the beginning of sequence. Returns pointer to it */ +CVAPI(schar*) cvSeqPushFront( CvSeq* seq, const void* element CV_DEFAULT(NULL)); + + +/** Removes the last element from sequence and optionally saves it */ +CVAPI(void) cvSeqPop( CvSeq* seq, void* element CV_DEFAULT(NULL)); + + +/** Removes the first element from sequence and optioanally saves it */ +CVAPI(void) cvSeqPopFront( CvSeq* seq, void* element CV_DEFAULT(NULL)); + + +#define CV_FRONT 1 +#define CV_BACK 0 +/** Adds several new elements to the end of sequence */ +CVAPI(void) cvSeqPushMulti( CvSeq* seq, const void* elements, + int count, int in_front CV_DEFAULT(0) ); + +/** Removes several elements from the end of sequence and optionally saves them */ +CVAPI(void) cvSeqPopMulti( CvSeq* seq, void* elements, + int count, int in_front CV_DEFAULT(0) ); + +/** Inserts a new element in the middle of sequence. + cvSeqInsert(seq,0,elem) == cvSeqPushFront(seq,elem) */ +CVAPI(schar*) cvSeqInsert( CvSeq* seq, int before_index, + const void* element CV_DEFAULT(NULL)); + +/** Removes specified sequence element */ +CVAPI(void) cvSeqRemove( CvSeq* seq, int index ); + + +/** Removes all the elements from the sequence. The freed memory + can be reused later only by the same sequence unless cvClearMemStorage + or cvRestoreMemStoragePos is called */ +CVAPI(void) cvClearSeq( CvSeq* seq ); + + +/** Retrieves pointer to specified sequence element. + Negative indices are supported and mean counting from the end + (e.g -1 means the last sequence element) */ +CVAPI(schar*) cvGetSeqElem( const CvSeq* seq, int index ); + +/** Calculates index of the specified sequence element. + Returns -1 if element does not belong to the sequence */ +CVAPI(int) cvSeqElemIdx( const CvSeq* seq, const void* element, + CvSeqBlock** block CV_DEFAULT(NULL) ); + +/** Initializes sequence writer. The new elements will be added to the end of sequence */ +CVAPI(void) cvStartAppendToSeq( CvSeq* seq, CvSeqWriter* writer ); + + +/** Combination of cvCreateSeq and cvStartAppendToSeq */ +CVAPI(void) cvStartWriteSeq( int seq_flags, int header_size, + int elem_size, CvMemStorage* storage, + CvSeqWriter* writer ); + +/** Closes sequence writer, updates sequence header and returns pointer + to the resultant sequence + (which may be useful if the sequence was created using cvStartWriteSeq)) +*/ +CVAPI(CvSeq*) cvEndWriteSeq( CvSeqWriter* writer ); + + +/** Updates sequence header. May be useful to get access to some of previously + written elements via cvGetSeqElem or sequence reader */ +CVAPI(void) cvFlushSeqWriter( CvSeqWriter* writer ); + + +/** Initializes sequence reader. + The sequence can be read in forward or backward direction */ +CVAPI(void) cvStartReadSeq( const CvSeq* seq, CvSeqReader* reader, + int reverse CV_DEFAULT(0) ); + + +/** Returns current sequence reader position (currently observed sequence element) */ +CVAPI(int) cvGetSeqReaderPos( CvSeqReader* reader ); + + +/** Changes sequence reader position. It may seek to an absolute or + to relative to the current position */ +CVAPI(void) cvSetSeqReaderPos( CvSeqReader* reader, int index, + int is_relative CV_DEFAULT(0)); + +/** Copies sequence content to a continuous piece of memory */ +CVAPI(void*) cvCvtSeqToArray( const CvSeq* seq, void* elements, + CvSlice slice CV_DEFAULT(CV_WHOLE_SEQ) ); + +/** Creates sequence header for array. + After that all the operations on sequences that do not alter the content + can be applied to the resultant sequence */ +CVAPI(CvSeq*) cvMakeSeqHeaderForArray( int seq_type, int header_size, + int elem_size, void* elements, int total, + CvSeq* seq, CvSeqBlock* block ); + +/** Extracts sequence slice (with or without copying sequence elements) */ +CVAPI(CvSeq*) cvSeqSlice( const CvSeq* seq, CvSlice slice, + CvMemStorage* storage CV_DEFAULT(NULL), + int copy_data CV_DEFAULT(0)); + +CV_INLINE CvSeq* cvCloneSeq( const CvSeq* seq, CvMemStorage* storage CV_DEFAULT(NULL)) +{ + return cvSeqSlice( seq, CV_WHOLE_SEQ, storage, 1 ); +} + +/** Removes sequence slice */ +CVAPI(void) cvSeqRemoveSlice( CvSeq* seq, CvSlice slice ); + +/** Inserts a sequence or array into another sequence */ +CVAPI(void) cvSeqInsertSlice( CvSeq* seq, int before_index, const CvArr* from_arr ); + +/** a < b ? -1 : a > b ? 1 : 0 */ +typedef int (CV_CDECL* CvCmpFunc)(const void* a, const void* b, void* userdata ); + +/** Sorts sequence in-place given element comparison function */ +CVAPI(void) cvSeqSort( CvSeq* seq, CvCmpFunc func, void* userdata CV_DEFAULT(NULL) ); + +/** Finds element in a [sorted] sequence */ +CVAPI(schar*) cvSeqSearch( CvSeq* seq, const void* elem, CvCmpFunc func, + int is_sorted, int* elem_idx, + void* userdata CV_DEFAULT(NULL) ); + +/** Reverses order of sequence elements in-place */ +CVAPI(void) cvSeqInvert( CvSeq* seq ); + +/** Splits sequence into one or more equivalence classes using the specified criteria */ +CVAPI(int) cvSeqPartition( const CvSeq* seq, CvMemStorage* storage, + CvSeq** labels, CvCmpFunc is_equal, void* userdata ); + +/************ Internal sequence functions ************/ +CVAPI(void) cvChangeSeqBlock( void* reader, int direction ); +CVAPI(void) cvCreateSeqBlock( CvSeqWriter* writer ); + + +/** Creates a new set */ +CVAPI(CvSet*) cvCreateSet( int set_flags, int header_size, + int elem_size, CvMemStorage* storage ); + +/** Adds new element to the set and returns pointer to it */ +CVAPI(int) cvSetAdd( CvSet* set_header, CvSetElem* elem CV_DEFAULT(NULL), + CvSetElem** inserted_elem CV_DEFAULT(NULL) ); + +/** Fast variant of cvSetAdd */ +CV_INLINE CvSetElem* cvSetNew( CvSet* set_header ) +{ + CvSetElem* elem = set_header->free_elems; + if( elem ) + { + set_header->free_elems = elem->next_free; + elem->flags = elem->flags & CV_SET_ELEM_IDX_MASK; + set_header->active_count++; + } + else + cvSetAdd( set_header, NULL, &elem ); + return elem; +} + +/** Removes set element given its pointer */ +CV_INLINE void cvSetRemoveByPtr( CvSet* set_header, void* elem ) +{ + CvSetElem* _elem = (CvSetElem*)elem; + assert( _elem->flags >= 0 /*&& (elem->flags & CV_SET_ELEM_IDX_MASK) < set_header->total*/ ); + _elem->next_free = set_header->free_elems; + _elem->flags = (_elem->flags & CV_SET_ELEM_IDX_MASK) | CV_SET_ELEM_FREE_FLAG; + set_header->free_elems = _elem; + set_header->active_count--; +} + +/** Removes element from the set by its index */ +CVAPI(void) cvSetRemove( CvSet* set_header, int index ); + +/** Returns a set element by index. If the element doesn't belong to the set, + NULL is returned */ +CV_INLINE CvSetElem* cvGetSetElem( const CvSet* set_header, int idx ) +{ + CvSetElem* elem = (CvSetElem*)(void *)cvGetSeqElem( (CvSeq*)set_header, idx ); + return elem && CV_IS_SET_ELEM( elem ) ? elem : 0; +} + +/** Removes all the elements from the set */ +CVAPI(void) cvClearSet( CvSet* set_header ); + +/** Creates new graph */ +CVAPI(CvGraph*) cvCreateGraph( int graph_flags, int header_size, + int vtx_size, int edge_size, + CvMemStorage* storage ); + +/** Adds new vertex to the graph */ +CVAPI(int) cvGraphAddVtx( CvGraph* graph, const CvGraphVtx* vtx CV_DEFAULT(NULL), + CvGraphVtx** inserted_vtx CV_DEFAULT(NULL) ); + + +/** Removes vertex from the graph together with all incident edges */ +CVAPI(int) cvGraphRemoveVtx( CvGraph* graph, int index ); +CVAPI(int) cvGraphRemoveVtxByPtr( CvGraph* graph, CvGraphVtx* vtx ); + + +/** Link two vertices specified by indices or pointers if they + are not connected or return pointer to already existing edge + connecting the vertices. + Functions return 1 if a new edge was created, 0 otherwise */ +CVAPI(int) cvGraphAddEdge( CvGraph* graph, + int start_idx, int end_idx, + const CvGraphEdge* edge CV_DEFAULT(NULL), + CvGraphEdge** inserted_edge CV_DEFAULT(NULL) ); + +CVAPI(int) cvGraphAddEdgeByPtr( CvGraph* graph, + CvGraphVtx* start_vtx, CvGraphVtx* end_vtx, + const CvGraphEdge* edge CV_DEFAULT(NULL), + CvGraphEdge** inserted_edge CV_DEFAULT(NULL) ); + +/** Remove edge connecting two vertices */ +CVAPI(void) cvGraphRemoveEdge( CvGraph* graph, int start_idx, int end_idx ); +CVAPI(void) cvGraphRemoveEdgeByPtr( CvGraph* graph, CvGraphVtx* start_vtx, + CvGraphVtx* end_vtx ); + +/** Find edge connecting two vertices */ +CVAPI(CvGraphEdge*) cvFindGraphEdge( const CvGraph* graph, int start_idx, int end_idx ); +CVAPI(CvGraphEdge*) cvFindGraphEdgeByPtr( const CvGraph* graph, + const CvGraphVtx* start_vtx, + const CvGraphVtx* end_vtx ); +#define cvGraphFindEdge cvFindGraphEdge +#define cvGraphFindEdgeByPtr cvFindGraphEdgeByPtr + +/** Remove all vertices and edges from the graph */ +CVAPI(void) cvClearGraph( CvGraph* graph ); + + +/** Count number of edges incident to the vertex */ +CVAPI(int) cvGraphVtxDegree( const CvGraph* graph, int vtx_idx ); +CVAPI(int) cvGraphVtxDegreeByPtr( const CvGraph* graph, const CvGraphVtx* vtx ); + + +/** Retrieves graph vertex by given index */ +#define cvGetGraphVtx( graph, idx ) (CvGraphVtx*)cvGetSetElem((CvSet*)(graph), (idx)) + +/** Retrieves index of a graph vertex given its pointer */ +#define cvGraphVtxIdx( graph, vtx ) ((vtx)->flags & CV_SET_ELEM_IDX_MASK) + +/** Retrieves index of a graph edge given its pointer */ +#define cvGraphEdgeIdx( graph, edge ) ((edge)->flags & CV_SET_ELEM_IDX_MASK) + +#define cvGraphGetVtxCount( graph ) ((graph)->active_count) +#define cvGraphGetEdgeCount( graph ) ((graph)->edges->active_count) + +#define CV_GRAPH_VERTEX 1 +#define CV_GRAPH_TREE_EDGE 2 +#define CV_GRAPH_BACK_EDGE 4 +#define CV_GRAPH_FORWARD_EDGE 8 +#define CV_GRAPH_CROSS_EDGE 16 +#define CV_GRAPH_ANY_EDGE 30 +#define CV_GRAPH_NEW_TREE 32 +#define CV_GRAPH_BACKTRACKING 64 +#define CV_GRAPH_OVER -1 + +#define CV_GRAPH_ALL_ITEMS -1 + +/** flags for graph vertices and edges */ +#define CV_GRAPH_ITEM_VISITED_FLAG (1 << 30) +#define CV_IS_GRAPH_VERTEX_VISITED(vtx) \ + (((CvGraphVtx*)(vtx))->flags & CV_GRAPH_ITEM_VISITED_FLAG) +#define CV_IS_GRAPH_EDGE_VISITED(edge) \ + (((CvGraphEdge*)(edge))->flags & CV_GRAPH_ITEM_VISITED_FLAG) +#define CV_GRAPH_SEARCH_TREE_NODE_FLAG (1 << 29) +#define CV_GRAPH_FORWARD_EDGE_FLAG (1 << 28) + +typedef struct CvGraphScanner +{ + CvGraphVtx* vtx; /* current graph vertex (or current edge origin) */ + CvGraphVtx* dst; /* current graph edge destination vertex */ + CvGraphEdge* edge; /* current edge */ + + CvGraph* graph; /* the graph */ + CvSeq* stack; /* the graph vertex stack */ + int index; /* the lower bound of certainly visited vertices */ + int mask; /* event mask */ +} +CvGraphScanner; + +/** Creates new graph scanner. */ +CVAPI(CvGraphScanner*) cvCreateGraphScanner( CvGraph* graph, + CvGraphVtx* vtx CV_DEFAULT(NULL), + int mask CV_DEFAULT(CV_GRAPH_ALL_ITEMS)); + +/** Releases graph scanner. */ +CVAPI(void) cvReleaseGraphScanner( CvGraphScanner** scanner ); + +/** Get next graph element */ +CVAPI(int) cvNextGraphItem( CvGraphScanner* scanner ); + +/** Creates a copy of graph */ +CVAPI(CvGraph*) cvCloneGraph( const CvGraph* graph, CvMemStorage* storage ); + + +/** Does look-up transformation. Elements of the source array + (that should be 8uC1 or 8sC1) are used as indexes in lutarr 256-element table */ +CVAPI(void) cvLUT( const CvArr* src, CvArr* dst, const CvArr* lut ); + + +/******************* Iteration through the sequence tree *****************/ +typedef struct CvTreeNodeIterator +{ + const void* node; + int level; + int max_level; +} +CvTreeNodeIterator; + +CVAPI(void) cvInitTreeNodeIterator( CvTreeNodeIterator* tree_iterator, + const void* first, int max_level ); +CVAPI(void*) cvNextTreeNode( CvTreeNodeIterator* tree_iterator ); +CVAPI(void*) cvPrevTreeNode( CvTreeNodeIterator* tree_iterator ); + +/** Inserts sequence into tree with specified "parent" sequence. + If parent is equal to frame (e.g. the most external contour), + then added contour will have null pointer to parent. */ +CVAPI(void) cvInsertNodeIntoTree( void* node, void* parent, void* frame ); + +/** Removes contour from tree (together with the contour children). */ +CVAPI(void) cvRemoveNodeFromTree( void* node, void* frame ); + +/** Gathers pointers to all the sequences, + accessible from the `first`, to the single sequence */ +CVAPI(CvSeq*) cvTreeToNodeSeq( const void* first, int header_size, + CvMemStorage* storage ); + +/** The function implements the K-means algorithm for clustering an array of sample + vectors in a specified number of classes */ +#define CV_KMEANS_USE_INITIAL_LABELS 1 +CVAPI(int) cvKMeans2( const CvArr* samples, int cluster_count, CvArr* labels, + CvTermCriteria termcrit, int attempts CV_DEFAULT(1), + CvRNG* rng CV_DEFAULT(0), int flags CV_DEFAULT(0), + CvArr* _centers CV_DEFAULT(0), double* compactness CV_DEFAULT(0) ); + +/****************************************************************************************\ +* System functions * +\****************************************************************************************/ + +/** Loads optimized functions from IPP, MKL etc. or switches back to pure C code */ +CVAPI(int) cvUseOptimized( int on_off ); + +typedef IplImage* (CV_STDCALL* Cv_iplCreateImageHeader) + (int,int,int,char*,char*,int,int,int,int,int, + IplROI*,IplImage*,void*,IplTileInfo*); +typedef void (CV_STDCALL* Cv_iplAllocateImageData)(IplImage*,int,int); +typedef void (CV_STDCALL* Cv_iplDeallocate)(IplImage*,int); +typedef IplROI* (CV_STDCALL* Cv_iplCreateROI)(int,int,int,int,int); +typedef IplImage* (CV_STDCALL* Cv_iplCloneImage)(const IplImage*); + +/** @brief Makes OpenCV use IPL functions for allocating IplImage and IplROI structures. + +Normally, the function is not called directly. Instead, a simple macro +CV_TURN_ON_IPL_COMPATIBILITY() is used that calls cvSetIPLAllocators and passes there pointers +to IPL allocation functions. : +@code + ... + CV_TURN_ON_IPL_COMPATIBILITY() + ... +@endcode +@param create_header pointer to a function, creating IPL image header. +@param allocate_data pointer to a function, allocating IPL image data. +@param deallocate pointer to a function, deallocating IPL image. +@param create_roi pointer to a function, creating IPL image ROI (i.e. Region of Interest). +@param clone_image pointer to a function, cloning an IPL image. + */ +CVAPI(void) cvSetIPLAllocators( Cv_iplCreateImageHeader create_header, + Cv_iplAllocateImageData allocate_data, + Cv_iplDeallocate deallocate, + Cv_iplCreateROI create_roi, + Cv_iplCloneImage clone_image ); + +#define CV_TURN_ON_IPL_COMPATIBILITY() \ + cvSetIPLAllocators( iplCreateImageHeader, iplAllocateImage, \ + iplDeallocate, iplCreateROI, iplCloneImage ) + +/****************************************************************************************\ +* Data Persistence * +\****************************************************************************************/ + +/********************************** High-level functions ********************************/ + +/** @brief Opens file storage for reading or writing data. + +The function opens file storage for reading or writing data. In the latter case, a new file is +created or an existing file is rewritten. The type of the read or written file is determined by the +filename extension: .xml for XML, .yml or .yaml for YAML and .json for JSON. + +At the same time, it also supports adding parameters like "example.xml?base64". + +The function returns a pointer to the CvFileStorage structure. +If the file cannot be opened then the function returns NULL. +@param filename Name of the file associated with the storage +@param memstorage Memory storage used for temporary data and for +: storing dynamic structures, such as CvSeq or CvGraph . If it is NULL, a temporary memory + storage is created and used. +@param flags Can be one of the following: +> - **CV_STORAGE_READ** the storage is open for reading +> - **CV_STORAGE_WRITE** the storage is open for writing + (use **CV_STORAGE_WRITE | CV_STORAGE_WRITE_BASE64** to write rawdata in Base64) +@param encoding + */ +CVAPI(CvFileStorage*) cvOpenFileStorage( const char* filename, CvMemStorage* memstorage, + int flags, const char* encoding CV_DEFAULT(NULL) ); + +/** @brief Releases file storage. + +The function closes the file associated with the storage and releases all the temporary structures. +It must be called after all I/O operations with the storage are finished. +@param fs Double pointer to the released file storage + */ +CVAPI(void) cvReleaseFileStorage( CvFileStorage** fs ); + +/** returns attribute value or 0 (NULL) if there is no such attribute */ +CVAPI(const char*) cvAttrValue( const CvAttrList* attr, const char* attr_name ); + +/** @brief Starts writing a new structure. + +The function starts writing a compound structure (collection) that can be a sequence or a map. After +all the structure fields, which can be scalars or structures, are written, cvEndWriteStruct should +be called. The function can be used to group some objects or to implement the write function for a +some user object (see CvTypeInfo). +@param fs File storage +@param name Name of the written structure. The structure can be accessed by this name when the +storage is read. +@param struct_flags A combination one of the following values: +- **CV_NODE_SEQ** the written structure is a sequence (see discussion of CvFileStorage ), + that is, its elements do not have a name. +- **CV_NODE_MAP** the written structure is a map (see discussion of CvFileStorage ), that + is, all its elements have names. +One and only one of the two above flags must be specified +- **CV_NODE_FLOW** the optional flag that makes sense only for YAML streams. It means that + the structure is written as a flow (not as a block), which is more compact. It is + recommended to use this flag for structures or arrays whose elements are all scalars. +@param type_name Optional parameter - the object type name. In + case of XML it is written as a type_id attribute of the structure opening tag. In the case of + YAML it is written after a colon following the structure name (see the example in + CvFileStorage description). In case of JSON it is written as a name/value pair. + Mainly it is used with user objects. When the storage is read, the + encoded type name is used to determine the object type (see CvTypeInfo and cvFindType ). +@param attributes This parameter is not used in the current implementation + */ +CVAPI(void) cvStartWriteStruct( CvFileStorage* fs, const char* name, + int struct_flags, const char* type_name CV_DEFAULT(NULL), + CvAttrList attributes CV_DEFAULT(cvAttrList())); + +/** @brief Finishes writing to a file node collection. +@param fs File storage +@sa cvStartWriteStruct. + */ +CVAPI(void) cvEndWriteStruct( CvFileStorage* fs ); + +/** @brief Writes an integer value. + +The function writes a single integer value (with or without a name) to the file storage. +@param fs File storage +@param name Name of the written value. Should be NULL if and only if the parent structure is a +sequence. +@param value The written value + */ +CVAPI(void) cvWriteInt( CvFileStorage* fs, const char* name, int value ); + +/** @brief Writes a floating-point value. + +The function writes a single floating-point value (with or without a name) to file storage. Special +values are encoded as follows: NaN (Not A Number) as .NaN, infinity as +.Inf or -.Inf. + +The following example shows how to use the low-level writing functions to store custom structures, +such as termination criteria, without registering a new type. : +@code + void write_termcriteria( CvFileStorage* fs, const char* struct_name, + CvTermCriteria* termcrit ) + { + cvStartWriteStruct( fs, struct_name, CV_NODE_MAP, NULL, cvAttrList(0,0)); + cvWriteComment( fs, "termination criteria", 1 ); // just a description + if( termcrit->type & CV_TERMCRIT_ITER ) + cvWriteInteger( fs, "max_iterations", termcrit->max_iter ); + if( termcrit->type & CV_TERMCRIT_EPS ) + cvWriteReal( fs, "accuracy", termcrit->epsilon ); + cvEndWriteStruct( fs ); + } +@endcode +@param fs File storage +@param name Name of the written value. Should be NULL if and only if the parent structure is a +sequence. +@param value The written value +*/ +CVAPI(void) cvWriteReal( CvFileStorage* fs, const char* name, double value ); + +/** @brief Writes a text string. + +The function writes a text string to file storage. +@param fs File storage +@param name Name of the written string . Should be NULL if and only if the parent structure is a +sequence. +@param str The written text string +@param quote If non-zero, the written string is put in quotes, regardless of whether they are +required. Otherwise, if the flag is zero, quotes are used only when they are required (e.g. when +the string starts with a digit or contains spaces). + */ +CVAPI(void) cvWriteString( CvFileStorage* fs, const char* name, + const char* str, int quote CV_DEFAULT(0) ); + +/** @brief Writes a comment. + +The function writes a comment into file storage. The comments are skipped when the storage is read. +@param fs File storage +@param comment The written comment, single-line or multi-line +@param eol_comment If non-zero, the function tries to put the comment at the end of current line. +If the flag is zero, if the comment is multi-line, or if it does not fit at the end of the current +line, the comment starts a new line. + */ +CVAPI(void) cvWriteComment( CvFileStorage* fs, const char* comment, + int eol_comment ); + +/** @brief Writes an object to file storage. + +The function writes an object to file storage. First, the appropriate type info is found using +cvTypeOf. Then, the write method associated with the type info is called. + +Attributes are used to customize the writing procedure. The standard types support the following +attributes (all the dt attributes have the same format as in cvWriteRawData): + +-# CvSeq + - **header_dt** description of user fields of the sequence header that follow CvSeq, or + CvChain (if the sequence is a Freeman chain) or CvContour (if the sequence is a contour or + point sequence) + - **dt** description of the sequence elements. + - **recursive** if the attribute is present and is not equal to "0" or "false", the whole + tree of sequences (contours) is stored. +-# CvGraph + - **header_dt** description of user fields of the graph header that follows CvGraph; + - **vertex_dt** description of user fields of graph vertices + - **edge_dt** description of user fields of graph edges (note that the edge weight is + always written, so there is no need to specify it explicitly) + +Below is the code that creates the YAML file shown in the CvFileStorage description: +@code + #include "cxcore.h" + + int main( int argc, char** argv ) + { + CvMat* mat = cvCreateMat( 3, 3, CV_32F ); + CvFileStorage* fs = cvOpenFileStorage( "example.yml", 0, CV_STORAGE_WRITE ); + + cvSetIdentity( mat ); + cvWrite( fs, "A", mat, cvAttrList(0,0) ); + + cvReleaseFileStorage( &fs ); + cvReleaseMat( &mat ); + return 0; + } +@endcode +@param fs File storage +@param name Name of the written object. Should be NULL if and only if the parent structure is a +sequence. +@param ptr Pointer to the object +@param attributes The attributes of the object. They are specific for each particular type (see +the discussion below). + */ +CVAPI(void) cvWrite( CvFileStorage* fs, const char* name, const void* ptr, + CvAttrList attributes CV_DEFAULT(cvAttrList())); + +/** @brief Starts the next stream. + +The function finishes the currently written stream and starts the next stream. In the case of XML +the file with multiple streams looks like this: +@code{.xml} + + + + + + + ... +@endcode +The YAML file will look like this: +@code{.yaml} + %YAML 1.0 + # stream #1 data + ... + --- + # stream #2 data +@endcode +This is useful for concatenating files or for resuming the writing process. +@param fs File storage + */ +CVAPI(void) cvStartNextStream( CvFileStorage* fs ); + +/** @brief Writes multiple numbers. + +The function writes an array, whose elements consist of single or multiple numbers. The function +call can be replaced with a loop containing a few cvWriteInt and cvWriteReal calls, but a single +call is more efficient. Note that because none of the elements have a name, they should be written +to a sequence rather than a map. +@param fs File storage +@param src Pointer to the written array +@param len Number of the array elements to write +@param dt Specification of each array element, see @ref format_spec "format specification" + */ +CVAPI(void) cvWriteRawData( CvFileStorage* fs, const void* src, + int len, const char* dt ); + +/** @brief Writes multiple numbers in Base64. + +If either CV_STORAGE_WRITE_BASE64 or cv::FileStorage::WRITE_BASE64 is used, +this function will be the same as cvWriteRawData. If neither, the main +difference is that it outputs a sequence in Base64 encoding rather than +in plain text. + +This function can only be used to write a sequence with a type "binary". + +@param fs File storage +@param src Pointer to the written array +@param len Number of the array elements to write +@param dt Specification of each array element, see @ref format_spec "format specification" +*/ +CVAPI(void) cvWriteRawDataBase64( CvFileStorage* fs, const void* src, + int len, const char* dt ); + +/** @brief Returns a unique pointer for a given name. + +The function returns a unique pointer for each particular file node name. This pointer can be then +passed to the cvGetFileNode function that is faster than cvGetFileNodeByName because it compares +text strings by comparing pointers rather than the strings' content. + +Consider the following example where an array of points is encoded as a sequence of 2-entry maps: +@code + points: + - { x: 10, y: 10 } + - { x: 20, y: 20 } + - { x: 30, y: 30 } + # ... +@endcode +Then, it is possible to get hashed "x" and "y" pointers to speed up decoding of the points. : +@code + #include "cxcore.h" + + int main( int argc, char** argv ) + { + CvFileStorage* fs = cvOpenFileStorage( "points.yml", 0, CV_STORAGE_READ ); + CvStringHashNode* x_key = cvGetHashedNode( fs, "x", -1, 1 ); + CvStringHashNode* y_key = cvGetHashedNode( fs, "y", -1, 1 ); + CvFileNode* points = cvGetFileNodeByName( fs, 0, "points" ); + + if( CV_NODE_IS_SEQ(points->tag) ) + { + CvSeq* seq = points->data.seq; + int i, total = seq->total; + CvSeqReader reader; + cvStartReadSeq( seq, &reader, 0 ); + for( i = 0; i < total; i++ ) + { + CvFileNode* pt = (CvFileNode*)reader.ptr; + #if 1 // faster variant + CvFileNode* xnode = cvGetFileNode( fs, pt, x_key, 0 ); + CvFileNode* ynode = cvGetFileNode( fs, pt, y_key, 0 ); + assert( xnode && CV_NODE_IS_INT(xnode->tag) && + ynode && CV_NODE_IS_INT(ynode->tag)); + int x = xnode->data.i; // or x = cvReadInt( xnode, 0 ); + int y = ynode->data.i; // or y = cvReadInt( ynode, 0 ); + #elif 1 // slower variant; does not use x_key & y_key + CvFileNode* xnode = cvGetFileNodeByName( fs, pt, "x" ); + CvFileNode* ynode = cvGetFileNodeByName( fs, pt, "y" ); + assert( xnode && CV_NODE_IS_INT(xnode->tag) && + ynode && CV_NODE_IS_INT(ynode->tag)); + int x = xnode->data.i; // or x = cvReadInt( xnode, 0 ); + int y = ynode->data.i; // or y = cvReadInt( ynode, 0 ); + #else // the slowest yet the easiest to use variant + int x = cvReadIntByName( fs, pt, "x", 0 ); + int y = cvReadIntByName( fs, pt, "y", 0 ); + #endif + CV_NEXT_SEQ_ELEM( seq->elem_size, reader ); + printf(" + } + } + cvReleaseFileStorage( &fs ); + return 0; + } +@endcode +Please note that whatever method of accessing a map you are using, it is still much slower than +using plain sequences; for example, in the above example, it is more efficient to encode the points +as pairs of integers in a single numeric sequence. +@param fs File storage +@param name Literal node name +@param len Length of the name (if it is known apriori), or -1 if it needs to be calculated +@param create_missing Flag that specifies, whether an absent key should be added into the hash table +*/ +CVAPI(CvStringHashNode*) cvGetHashedKey( CvFileStorage* fs, const char* name, + int len CV_DEFAULT(-1), + int create_missing CV_DEFAULT(0)); + +/** @brief Retrieves one of the top-level nodes of the file storage. + +The function returns one of the top-level file nodes. The top-level nodes do not have a name, they +correspond to the streams that are stored one after another in the file storage. If the index is out +of range, the function returns a NULL pointer, so all the top-level nodes can be iterated by +subsequent calls to the function with stream_index=0,1,..., until the NULL pointer is returned. +This function can be used as a base for recursive traversal of the file storage. +@param fs File storage +@param stream_index Zero-based index of the stream. See cvStartNextStream . In most cases, +there is only one stream in the file; however, there can be several. + */ +CVAPI(CvFileNode*) cvGetRootFileNode( const CvFileStorage* fs, + int stream_index CV_DEFAULT(0) ); + +/** @brief Finds a node in a map or file storage. + +The function finds a file node. It is a faster version of cvGetFileNodeByName (see +cvGetHashedKey discussion). Also, the function can insert a new node, if it is not in the map yet. +@param fs File storage +@param map The parent map. If it is NULL, the function searches a top-level node. If both map and +key are NULLs, the function returns the root file node - a map that contains top-level nodes. +@param key Unique pointer to the node name, retrieved with cvGetHashedKey +@param create_missing Flag that specifies whether an absent node should be added to the map + */ +CVAPI(CvFileNode*) cvGetFileNode( CvFileStorage* fs, CvFileNode* map, + const CvStringHashNode* key, + int create_missing CV_DEFAULT(0) ); + +/** @brief Finds a node in a map or file storage. + +The function finds a file node by name. The node is searched either in map or, if the pointer is +NULL, among the top-level file storage nodes. Using this function for maps and cvGetSeqElem (or +sequence reader) for sequences, it is possible to navigate through the file storage. To speed up +multiple queries for a certain key (e.g., in the case of an array of structures) one may use a +combination of cvGetHashedKey and cvGetFileNode. +@param fs File storage +@param map The parent map. If it is NULL, the function searches in all the top-level nodes +(streams), starting with the first one. +@param name The file node name + */ +CVAPI(CvFileNode*) cvGetFileNodeByName( const CvFileStorage* fs, + const CvFileNode* map, + const char* name ); + +/** @brief Retrieves an integer value from a file node. + +The function returns an integer that is represented by the file node. If the file node is NULL, the +default_value is returned (thus, it is convenient to call the function right after cvGetFileNode +without checking for a NULL pointer). If the file node has type CV_NODE_INT, then node-\>data.i is +returned. If the file node has type CV_NODE_REAL, then node-\>data.f is converted to an integer +and returned. Otherwise the error is reported. +@param node File node +@param default_value The value that is returned if node is NULL + */ +CV_INLINE int cvReadInt( const CvFileNode* node, int default_value CV_DEFAULT(0) ) +{ + return !node ? default_value : + CV_NODE_IS_INT(node->tag) ? node->data.i : + CV_NODE_IS_REAL(node->tag) ? cvRound(node->data.f) : 0x7fffffff; +} + +/** @brief Finds a file node and returns its value. + +The function is a simple superposition of cvGetFileNodeByName and cvReadInt. +@param fs File storage +@param map The parent map. If it is NULL, the function searches a top-level node. +@param name The node name +@param default_value The value that is returned if the file node is not found + */ +CV_INLINE int cvReadIntByName( const CvFileStorage* fs, const CvFileNode* map, + const char* name, int default_value CV_DEFAULT(0) ) +{ + return cvReadInt( cvGetFileNodeByName( fs, map, name ), default_value ); +} + +/** @brief Retrieves a floating-point value from a file node. + +The function returns a floating-point value that is represented by the file node. If the file node +is NULL, the default_value is returned (thus, it is convenient to call the function right after +cvGetFileNode without checking for a NULL pointer). If the file node has type CV_NODE_REAL , +then node-\>data.f is returned. If the file node has type CV_NODE_INT , then node-:math:\>data.f +is converted to floating-point and returned. Otherwise the result is not determined. +@param node File node +@param default_value The value that is returned if node is NULL + */ +CV_INLINE double cvReadReal( const CvFileNode* node, double default_value CV_DEFAULT(0.) ) +{ + return !node ? default_value : + CV_NODE_IS_INT(node->tag) ? (double)node->data.i : + CV_NODE_IS_REAL(node->tag) ? node->data.f : 1e300; +} + +/** @brief Finds a file node and returns its value. + +The function is a simple superposition of cvGetFileNodeByName and cvReadReal . +@param fs File storage +@param map The parent map. If it is NULL, the function searches a top-level node. +@param name The node name +@param default_value The value that is returned if the file node is not found + */ +CV_INLINE double cvReadRealByName( const CvFileStorage* fs, const CvFileNode* map, + const char* name, double default_value CV_DEFAULT(0.) ) +{ + return cvReadReal( cvGetFileNodeByName( fs, map, name ), default_value ); +} + +/** @brief Retrieves a text string from a file node. + +The function returns a text string that is represented by the file node. If the file node is NULL, +the default_value is returned (thus, it is convenient to call the function right after +cvGetFileNode without checking for a NULL pointer). If the file node has type CV_NODE_STR , then +node-:math:\>data.str.ptr is returned. Otherwise the result is not determined. +@param node File node +@param default_value The value that is returned if node is NULL + */ +CV_INLINE const char* cvReadString( const CvFileNode* node, + const char* default_value CV_DEFAULT(NULL) ) +{ + return !node ? default_value : CV_NODE_IS_STRING(node->tag) ? node->data.str.ptr : 0; +} + +/** @brief Finds a file node by its name and returns its value. + +The function is a simple superposition of cvGetFileNodeByName and cvReadString . +@param fs File storage +@param map The parent map. If it is NULL, the function searches a top-level node. +@param name The node name +@param default_value The value that is returned if the file node is not found + */ +CV_INLINE const char* cvReadStringByName( const CvFileStorage* fs, const CvFileNode* map, + const char* name, const char* default_value CV_DEFAULT(NULL) ) +{ + return cvReadString( cvGetFileNodeByName( fs, map, name ), default_value ); +} + + +/** @brief Decodes an object and returns a pointer to it. + +The function decodes a user object (creates an object in a native representation from the file +storage subtree) and returns it. The object to be decoded must be an instance of a registered type +that supports the read method (see CvTypeInfo). The type of the object is determined by the type +name that is encoded in the file. If the object is a dynamic structure, it is created either in +memory storage and passed to cvOpenFileStorage or, if a NULL pointer was passed, in temporary +memory storage, which is released when cvReleaseFileStorage is called. Otherwise, if the object is +not a dynamic structure, it is created in a heap and should be released with a specialized function +or by using the generic cvRelease. +@param fs File storage +@param node The root object node +@param attributes Unused parameter + */ +CVAPI(void*) cvRead( CvFileStorage* fs, CvFileNode* node, + CvAttrList* attributes CV_DEFAULT(NULL)); + +/** @brief Finds an object by name and decodes it. + +The function is a simple superposition of cvGetFileNodeByName and cvRead. +@param fs File storage +@param map The parent map. If it is NULL, the function searches a top-level node. +@param name The node name +@param attributes Unused parameter + */ +CV_INLINE void* cvReadByName( CvFileStorage* fs, const CvFileNode* map, + const char* name, CvAttrList* attributes CV_DEFAULT(NULL) ) +{ + return cvRead( fs, cvGetFileNodeByName( fs, map, name ), attributes ); +} + + +/** @brief Initializes the file node sequence reader. + +The function initializes the sequence reader to read data from a file node. The initialized reader +can be then passed to cvReadRawDataSlice. +@param fs File storage +@param src The file node (a sequence) to read numbers from +@param reader Pointer to the sequence reader + */ +CVAPI(void) cvStartReadRawData( const CvFileStorage* fs, const CvFileNode* src, + CvSeqReader* reader ); + +/** @brief Initializes file node sequence reader. + +The function reads one or more elements from the file node, representing a sequence, to a +user-specified array. The total number of read sequence elements is a product of total and the +number of components in each array element. For example, if dt=2if, the function will read total\*3 +sequence elements. As with any sequence, some parts of the file node sequence can be skipped or read +repeatedly by repositioning the reader using cvSetSeqReaderPos. +@param fs File storage +@param reader The sequence reader. Initialize it with cvStartReadRawData . +@param count The number of elements to read +@param dst Pointer to the destination array +@param dt Specification of each array element. It has the same format as in cvWriteRawData . + */ +CVAPI(void) cvReadRawDataSlice( const CvFileStorage* fs, CvSeqReader* reader, + int count, void* dst, const char* dt ); + +/** @brief Reads multiple numbers. + +The function reads elements from a file node that represents a sequence of scalars. +@param fs File storage +@param src The file node (a sequence) to read numbers from +@param dst Pointer to the destination array +@param dt Specification of each array element. It has the same format as in cvWriteRawData . + */ +CVAPI(void) cvReadRawData( const CvFileStorage* fs, const CvFileNode* src, + void* dst, const char* dt ); + +/** @brief Writes a file node to another file storage. + +The function writes a copy of a file node to file storage. Possible applications of the function are +merging several file storages into one and conversion between XML, YAML and JSON formats. +@param fs Destination file storage +@param new_node_name New name of the file node in the destination file storage. To keep the +existing name, use cvcvGetFileNodeName +@param node The written node +@param embed If the written node is a collection and this parameter is not zero, no extra level of +hierarchy is created. Instead, all the elements of node are written into the currently written +structure. Of course, map elements can only be embedded into another map, and sequence elements +can only be embedded into another sequence. + */ +CVAPI(void) cvWriteFileNode( CvFileStorage* fs, const char* new_node_name, + const CvFileNode* node, int embed ); + +/** @brief Returns the name of a file node. + +The function returns the name of a file node or NULL, if the file node does not have a name or if +node is NULL. +@param node File node + */ +CVAPI(const char*) cvGetFileNodeName( const CvFileNode* node ); + +/*********************************** Adding own types ***********************************/ + +/** @brief Registers a new type. + +The function registers a new type, which is described by info . The function creates a copy of the +structure, so the user should delete it after calling the function. +@param info Type info structure + */ +CVAPI(void) cvRegisterType( const CvTypeInfo* info ); + +/** @brief Unregisters the type. + +The function unregisters a type with a specified name. If the name is unknown, it is possible to +locate the type info by an instance of the type using cvTypeOf or by iterating the type list, +starting from cvFirstType, and then calling cvUnregisterType(info-\>typeName). +@param type_name Name of an unregistered type + */ +CVAPI(void) cvUnregisterType( const char* type_name ); + +/** @brief Returns the beginning of a type list. + +The function returns the first type in the list of registered types. Navigation through the list can +be done via the prev and next fields of the CvTypeInfo structure. + */ +CVAPI(CvTypeInfo*) cvFirstType(void); + +/** @brief Finds a type by its name. + +The function finds a registered type by its name. It returns NULL if there is no type with the +specified name. +@param type_name Type name + */ +CVAPI(CvTypeInfo*) cvFindType( const char* type_name ); + +/** @brief Returns the type of an object. + +The function finds the type of a given object. It iterates through the list of registered types and +calls the is_instance function/method for every type info structure with that object until one of +them returns non-zero or until the whole list has been traversed. In the latter case, the function +returns NULL. +@param struct_ptr The object pointer + */ +CVAPI(CvTypeInfo*) cvTypeOf( const void* struct_ptr ); + +/** @brief Releases an object. + +The function finds the type of a given object and calls release with the double pointer. +@param struct_ptr Double pointer to the object + */ +CVAPI(void) cvRelease( void** struct_ptr ); + +/** @brief Makes a clone of an object. + +The function finds the type of a given object and calls clone with the passed object. Of course, if +you know the object type, for example, struct_ptr is CvMat\*, it is faster to call the specific +function, like cvCloneMat. +@param struct_ptr The object to clone + */ +CVAPI(void*) cvClone( const void* struct_ptr ); + +/** @brief Saves an object to a file. + +The function saves an object to a file. It provides a simple interface to cvWrite . +@param filename File name +@param struct_ptr Object to save +@param name Optional object name. If it is NULL, the name will be formed from filename . +@param comment Optional comment to put in the beginning of the file +@param attributes Optional attributes passed to cvWrite + */ +CVAPI(void) cvSave( const char* filename, const void* struct_ptr, + const char* name CV_DEFAULT(NULL), + const char* comment CV_DEFAULT(NULL), + CvAttrList attributes CV_DEFAULT(cvAttrList())); + +/** @brief Loads an object from a file. + +The function loads an object from a file. It basically reads the specified file, find the first +top-level node and calls cvRead for that node. If the file node does not have type information or +the type information can not be found by the type name, the function returns NULL. After the object +is loaded, the file storage is closed and all the temporary buffers are deleted. Thus, to load a +dynamic structure, such as a sequence, contour, or graph, one should pass a valid memory storage +destination to the function. +@param filename File name +@param memstorage Memory storage for dynamic structures, such as CvSeq or CvGraph . It is not used +for matrices or images. +@param name Optional object name. If it is NULL, the first top-level object in the storage will be +loaded. +@param real_name Optional output parameter that will contain the name of the loaded object +(useful if name=NULL ) + */ +CVAPI(void*) cvLoad( const char* filename, + CvMemStorage* memstorage CV_DEFAULT(NULL), + const char* name CV_DEFAULT(NULL), + const char** real_name CV_DEFAULT(NULL) ); + +/*********************************** Measuring Execution Time ***************************/ + +/** helper functions for RNG initialization and accurate time measurement: + uses internal clock counter on x86 */ +CVAPI(int64) cvGetTickCount( void ); +CVAPI(double) cvGetTickFrequency( void ); + +/*********************************** CPU capabilities ***********************************/ + +CVAPI(int) cvCheckHardwareSupport(int feature); + +/*********************************** Multi-Threading ************************************/ + +/** retrieve/set the number of threads used in OpenMP implementations */ +CVAPI(int) cvGetNumThreads( void ); +CVAPI(void) cvSetNumThreads( int threads CV_DEFAULT(0) ); +/** get index of the thread being executed */ +CVAPI(int) cvGetThreadNum( void ); + + +/********************************** Error Handling **************************************/ + +/** Get current OpenCV error status */ +CVAPI(int) cvGetErrStatus( void ); + +/** Sets error status silently */ +CVAPI(void) cvSetErrStatus( int status ); + +#define CV_ErrModeLeaf 0 /* Print error and exit program */ +#define CV_ErrModeParent 1 /* Print error and continue */ +#define CV_ErrModeSilent 2 /* Don't print and continue */ + +/** Retrieves current error processing mode */ +CVAPI(int) cvGetErrMode( void ); + +/** Sets error processing mode, returns previously used mode */ +CVAPI(int) cvSetErrMode( int mode ); + +/** Sets error status and performs some additional actions (displaying message box, + writing message to stderr, terminating application etc.) + depending on the current error mode */ +CVAPI(void) cvError( int status, const char* func_name, + const char* err_msg, const char* file_name, int line ); + +/** Retrieves textual description of the error given its code */ +CVAPI(const char*) cvErrorStr( int status ); + +/** Retrieves detailed information about the last error occurred */ +CVAPI(int) cvGetErrInfo( const char** errcode_desc, const char** description, + const char** filename, int* line ); + +/** Maps IPP error codes to the counterparts from OpenCV */ +CVAPI(int) cvErrorFromIppStatus( int ipp_status ); + +typedef int (CV_CDECL *CvErrorCallback)( int status, const char* func_name, + const char* err_msg, const char* file_name, int line, void* userdata ); + +/** Assigns a new error-handling function */ +CVAPI(CvErrorCallback) cvRedirectError( CvErrorCallback error_handler, + void* userdata CV_DEFAULT(NULL), + void** prev_userdata CV_DEFAULT(NULL) ); + +/** Output nothing */ +CVAPI(int) cvNulDevReport( int status, const char* func_name, const char* err_msg, + const char* file_name, int line, void* userdata ); + +/** Output to console(fprintf(stderr,...)) */ +CVAPI(int) cvStdErrReport( int status, const char* func_name, const char* err_msg, + const char* file_name, int line, void* userdata ); + +/** Output to MessageBox(WIN32) */ +CVAPI(int) cvGuiBoxReport( int status, const char* func_name, const char* err_msg, + const char* file_name, int line, void* userdata ); + +#define OPENCV_ERROR(status,func,context) \ +cvError((status),(func),(context),__FILE__,__LINE__) + +#define OPENCV_ASSERT(expr,func,context) \ +{if (! (expr)) \ +{OPENCV_ERROR(CV_StsInternal,(func),(context));}} + +#define OPENCV_CALL( Func ) \ +{ \ +Func; \ +} + + +/** CV_FUNCNAME macro defines icvFuncName constant which is used by CV_ERROR macro */ +#ifdef CV_NO_FUNC_NAMES +#define CV_FUNCNAME( Name ) +#define cvFuncName "" +#else +#define CV_FUNCNAME( Name ) \ +static char cvFuncName[] = Name +#endif + + +/** + CV_ERROR macro unconditionally raises error with passed code and message. + After raising error, control will be transferred to the exit label. + */ +#define CV_ERROR( Code, Msg ) \ +{ \ + cvError( (Code), cvFuncName, Msg, __FILE__, __LINE__ ); \ + __CV_EXIT__; \ +} + +/** + CV_CHECK macro checks error status after CV (or IPL) + function call. If error detected, control will be transferred to the exit + label. + */ +#define CV_CHECK() \ +{ \ + if( cvGetErrStatus() < 0 ) \ + CV_ERROR( CV_StsBackTrace, "Inner function failed." ); \ +} + + +/** + CV_CALL macro calls CV (or IPL) function, checks error status and + signals a error if the function failed. Useful in "parent node" + error processing mode + */ +#define CV_CALL( Func ) \ +{ \ + Func; \ + CV_CHECK(); \ +} + + +/** Runtime assertion macro */ +#define CV_ASSERT( Condition ) \ +{ \ + if( !(Condition) ) \ + CV_ERROR( CV_StsInternal, "Assertion: " #Condition " failed" ); \ +} + +#define __CV_BEGIN__ { +#define __CV_END__ goto exit; exit: ; } +#define __CV_EXIT__ goto exit + +/** @} core_c */ + +#ifdef __cplusplus +} // extern "C" +#endif + +#ifdef __cplusplus + +//! @addtogroup core_c_glue +//! @{ + +//! class for automatic module/RTTI data registration/unregistration +struct CV_EXPORTS CvType +{ + CvType( const char* type_name, + CvIsInstanceFunc is_instance, CvReleaseFunc release=0, + CvReadFunc read=0, CvWriteFunc write=0, CvCloneFunc clone=0 ); + ~CvType(); + CvTypeInfo* info; + + static CvTypeInfo* first; + static CvTypeInfo* last; +}; + +//! @} + +#include "opencv2/core/utility.hpp" + +namespace cv +{ + +//! @addtogroup core_c_glue +//! @{ + +/////////////////////////////////////////// glue /////////////////////////////////////////// + +//! converts array (CvMat or IplImage) to cv::Mat +CV_EXPORTS Mat cvarrToMat(const CvArr* arr, bool copyData=false, + bool allowND=true, int coiMode=0, + AutoBuffer* buf=0); + +static inline Mat cvarrToMatND(const CvArr* arr, bool copyData=false, int coiMode=0) +{ + return cvarrToMat(arr, copyData, true, coiMode); +} + + +//! extracts Channel of Interest from CvMat or IplImage and makes cv::Mat out of it. +CV_EXPORTS void extractImageCOI(const CvArr* arr, OutputArray coiimg, int coi=-1); +//! inserts single-channel cv::Mat into a multi-channel CvMat or IplImage +CV_EXPORTS void insertImageCOI(InputArray coiimg, CvArr* arr, int coi=-1); + + + +////// specialized implementations of DefaultDeleter::operator() for classic OpenCV types ////// + +template<> CV_EXPORTS void DefaultDeleter::operator ()(CvMat* obj) const; +template<> CV_EXPORTS void DefaultDeleter::operator ()(IplImage* obj) const; +template<> CV_EXPORTS void DefaultDeleter::operator ()(CvMatND* obj) const; +template<> CV_EXPORTS void DefaultDeleter::operator ()(CvSparseMat* obj) const; +template<> CV_EXPORTS void DefaultDeleter::operator ()(CvMemStorage* obj) const; + +////////////// convenient wrappers for operating old-style dynamic structures ////////////// + +template class SeqIterator; + +typedef Ptr MemStorage; + +/*! + Template Sequence Class derived from CvSeq + + The class provides more convenient access to sequence elements, + STL-style operations and iterators. + + \note The class is targeted for simple data types, + i.e. no constructors or destructors + are called for the sequence elements. +*/ +template class Seq +{ +public: + typedef SeqIterator<_Tp> iterator; + typedef SeqIterator<_Tp> const_iterator; + + //! the default constructor + Seq(); + //! the constructor for wrapping CvSeq structure. The real element type in CvSeq should match _Tp. + Seq(const CvSeq* seq); + //! creates the empty sequence that resides in the specified storage + Seq(MemStorage& storage, int headerSize = sizeof(CvSeq)); + //! returns read-write reference to the specified element + _Tp& operator [](int idx); + //! returns read-only reference to the specified element + const _Tp& operator[](int idx) const; + //! returns iterator pointing to the beginning of the sequence + SeqIterator<_Tp> begin() const; + //! returns iterator pointing to the element following the last sequence element + SeqIterator<_Tp> end() const; + //! returns the number of elements in the sequence + size_t size() const; + //! returns the type of sequence elements (CV_8UC1 ... CV_64FC(CV_CN_MAX) ...) + int type() const; + //! returns the depth of sequence elements (CV_8U ... CV_64F) + int depth() const; + //! returns the number of channels in each sequence element + int channels() const; + //! returns the size of each sequence element + size_t elemSize() const; + //! returns index of the specified sequence element + size_t index(const _Tp& elem) const; + //! appends the specified element to the end of the sequence + void push_back(const _Tp& elem); + //! appends the specified element to the front of the sequence + void push_front(const _Tp& elem); + //! appends zero or more elements to the end of the sequence + void push_back(const _Tp* elems, size_t count); + //! appends zero or more elements to the front of the sequence + void push_front(const _Tp* elems, size_t count); + //! inserts the specified element to the specified position + void insert(int idx, const _Tp& elem); + //! inserts zero or more elements to the specified position + void insert(int idx, const _Tp* elems, size_t count); + //! removes element at the specified position + void remove(int idx); + //! removes the specified subsequence + void remove(const Range& r); + + //! returns reference to the first sequence element + _Tp& front(); + //! returns read-only reference to the first sequence element + const _Tp& front() const; + //! returns reference to the last sequence element + _Tp& back(); + //! returns read-only reference to the last sequence element + const _Tp& back() const; + //! returns true iff the sequence contains no elements + bool empty() const; + + //! removes all the elements from the sequence + void clear(); + //! removes the first element from the sequence + void pop_front(); + //! removes the last element from the sequence + void pop_back(); + //! removes zero or more elements from the beginning of the sequence + void pop_front(_Tp* elems, size_t count); + //! removes zero or more elements from the end of the sequence + void pop_back(_Tp* elems, size_t count); + + //! copies the whole sequence or the sequence slice to the specified vector + void copyTo(std::vector<_Tp>& vec, const Range& range=Range::all()) const; + //! returns the vector containing all the sequence elements + operator std::vector<_Tp>() const; + + CvSeq* seq; +}; + + +/*! + STL-style Sequence Iterator inherited from the CvSeqReader structure +*/ +template class SeqIterator : public CvSeqReader +{ +public: + //! the default constructor + SeqIterator(); + //! the constructor setting the iterator to the beginning or to the end of the sequence + SeqIterator(const Seq<_Tp>& seq, bool seekEnd=false); + //! positions the iterator within the sequence + void seek(size_t pos); + //! reports the current iterator position + size_t tell() const; + //! returns reference to the current sequence element + _Tp& operator *(); + //! returns read-only reference to the current sequence element + const _Tp& operator *() const; + //! moves iterator to the next sequence element + SeqIterator& operator ++(); + //! moves iterator to the next sequence element + SeqIterator operator ++(int) const; + //! moves iterator to the previous sequence element + SeqIterator& operator --(); + //! moves iterator to the previous sequence element + SeqIterator operator --(int) const; + + //! moves iterator forward by the specified offset (possibly negative) + SeqIterator& operator +=(int); + //! moves iterator backward by the specified offset (possibly negative) + SeqIterator& operator -=(int); + + // this is index of the current element module seq->total*2 + // (to distinguish between 0 and seq->total) + int index; +}; + + + +// bridge C++ => C Seq API +CV_EXPORTS schar* seqPush( CvSeq* seq, const void* element=0); +CV_EXPORTS schar* seqPushFront( CvSeq* seq, const void* element=0); +CV_EXPORTS void seqPop( CvSeq* seq, void* element=0); +CV_EXPORTS void seqPopFront( CvSeq* seq, void* element=0); +CV_EXPORTS void seqPopMulti( CvSeq* seq, void* elements, + int count, int in_front=0 ); +CV_EXPORTS void seqRemove( CvSeq* seq, int index ); +CV_EXPORTS void clearSeq( CvSeq* seq ); +CV_EXPORTS schar* getSeqElem( const CvSeq* seq, int index ); +CV_EXPORTS void seqRemoveSlice( CvSeq* seq, CvSlice slice ); +CV_EXPORTS void seqInsertSlice( CvSeq* seq, int before_index, const CvArr* from_arr ); + +template inline Seq<_Tp>::Seq() : seq(0) {} +template inline Seq<_Tp>::Seq( const CvSeq* _seq ) : seq((CvSeq*)_seq) +{ + CV_Assert(!_seq || _seq->elem_size == sizeof(_Tp)); +} + +template inline Seq<_Tp>::Seq( MemStorage& storage, + int headerSize ) +{ + CV_Assert(headerSize >= (int)sizeof(CvSeq)); + seq = cvCreateSeq(DataType<_Tp>::type, headerSize, sizeof(_Tp), storage); +} + +template inline _Tp& Seq<_Tp>::operator [](int idx) +{ return *(_Tp*)getSeqElem(seq, idx); } + +template inline const _Tp& Seq<_Tp>::operator [](int idx) const +{ return *(_Tp*)getSeqElem(seq, idx); } + +template inline SeqIterator<_Tp> Seq<_Tp>::begin() const +{ return SeqIterator<_Tp>(*this); } + +template inline SeqIterator<_Tp> Seq<_Tp>::end() const +{ return SeqIterator<_Tp>(*this, true); } + +template inline size_t Seq<_Tp>::size() const +{ return seq ? seq->total : 0; } + +template inline int Seq<_Tp>::type() const +{ return seq ? CV_MAT_TYPE(seq->flags) : 0; } + +template inline int Seq<_Tp>::depth() const +{ return seq ? CV_MAT_DEPTH(seq->flags) : 0; } + +template inline int Seq<_Tp>::channels() const +{ return seq ? CV_MAT_CN(seq->flags) : 0; } + +template inline size_t Seq<_Tp>::elemSize() const +{ return seq ? seq->elem_size : 0; } + +template inline size_t Seq<_Tp>::index(const _Tp& elem) const +{ return cvSeqElemIdx(seq, &elem); } + +template inline void Seq<_Tp>::push_back(const _Tp& elem) +{ cvSeqPush(seq, &elem); } + +template inline void Seq<_Tp>::push_front(const _Tp& elem) +{ cvSeqPushFront(seq, &elem); } + +template inline void Seq<_Tp>::push_back(const _Tp* elem, size_t count) +{ cvSeqPushMulti(seq, elem, (int)count, 0); } + +template inline void Seq<_Tp>::push_front(const _Tp* elem, size_t count) +{ cvSeqPushMulti(seq, elem, (int)count, 1); } + +template inline _Tp& Seq<_Tp>::back() +{ return *(_Tp*)getSeqElem(seq, -1); } + +template inline const _Tp& Seq<_Tp>::back() const +{ return *(const _Tp*)getSeqElem(seq, -1); } + +template inline _Tp& Seq<_Tp>::front() +{ return *(_Tp*)getSeqElem(seq, 0); } + +template inline const _Tp& Seq<_Tp>::front() const +{ return *(const _Tp*)getSeqElem(seq, 0); } + +template inline bool Seq<_Tp>::empty() const +{ return !seq || seq->total == 0; } + +template inline void Seq<_Tp>::clear() +{ if(seq) clearSeq(seq); } + +template inline void Seq<_Tp>::pop_back() +{ seqPop(seq); } + +template inline void Seq<_Tp>::pop_front() +{ seqPopFront(seq); } + +template inline void Seq<_Tp>::pop_back(_Tp* elem, size_t count) +{ seqPopMulti(seq, elem, (int)count, 0); } + +template inline void Seq<_Tp>::pop_front(_Tp* elem, size_t count) +{ seqPopMulti(seq, elem, (int)count, 1); } + +template inline void Seq<_Tp>::insert(int idx, const _Tp& elem) +{ seqInsert(seq, idx, &elem); } + +template inline void Seq<_Tp>::insert(int idx, const _Tp* elems, size_t count) +{ + CvMat m = cvMat(1, count, DataType<_Tp>::type, elems); + seqInsertSlice(seq, idx, &m); +} + +template inline void Seq<_Tp>::remove(int idx) +{ seqRemove(seq, idx); } + +template inline void Seq<_Tp>::remove(const Range& r) +{ seqRemoveSlice(seq, cvSlice(r.start, r.end)); } + +template inline void Seq<_Tp>::copyTo(std::vector<_Tp>& vec, const Range& range) const +{ + size_t len = !seq ? 0 : range == Range::all() ? seq->total : range.end - range.start; + vec.resize(len); + if( seq && len ) + cvCvtSeqToArray(seq, &vec[0], cvSlice(range)); +} + +template inline Seq<_Tp>::operator std::vector<_Tp>() const +{ + std::vector<_Tp> vec; + copyTo(vec); + return vec; +} + +template inline SeqIterator<_Tp>::SeqIterator() +{ memset(this, 0, sizeof(*this)); } + +template inline SeqIterator<_Tp>::SeqIterator(const Seq<_Tp>& _seq, bool seekEnd) +{ + cvStartReadSeq(_seq.seq, this); + index = seekEnd ? _seq.seq->total : 0; +} + +template inline void SeqIterator<_Tp>::seek(size_t pos) +{ + cvSetSeqReaderPos(this, (int)pos, false); + index = pos; +} + +template inline size_t SeqIterator<_Tp>::tell() const +{ return index; } + +template inline _Tp& SeqIterator<_Tp>::operator *() +{ return *(_Tp*)ptr; } + +template inline const _Tp& SeqIterator<_Tp>::operator *() const +{ return *(const _Tp*)ptr; } + +template inline SeqIterator<_Tp>& SeqIterator<_Tp>::operator ++() +{ + CV_NEXT_SEQ_ELEM(sizeof(_Tp), *this); + if( ++index >= seq->total*2 ) + index = 0; + return *this; +} + +template inline SeqIterator<_Tp> SeqIterator<_Tp>::operator ++(int) const +{ + SeqIterator<_Tp> it = *this; + ++*this; + return it; +} + +template inline SeqIterator<_Tp>& SeqIterator<_Tp>::operator --() +{ + CV_PREV_SEQ_ELEM(sizeof(_Tp), *this); + if( --index < 0 ) + index = seq->total*2-1; + return *this; +} + +template inline SeqIterator<_Tp> SeqIterator<_Tp>::operator --(int) const +{ + SeqIterator<_Tp> it = *this; + --*this; + return it; +} + +template inline SeqIterator<_Tp>& SeqIterator<_Tp>::operator +=(int delta) +{ + cvSetSeqReaderPos(this, delta, 1); + index += delta; + int n = seq->total*2; + if( index < 0 ) + index += n; + if( index >= n ) + index -= n; + return *this; +} + +template inline SeqIterator<_Tp>& SeqIterator<_Tp>::operator -=(int delta) +{ + return (*this += -delta); +} + +template inline ptrdiff_t operator - (const SeqIterator<_Tp>& a, + const SeqIterator<_Tp>& b) +{ + ptrdiff_t delta = a.index - b.index, n = a.seq->total; + if( delta > n || delta < -n ) + delta += delta < 0 ? n : -n; + return delta; +} + +template inline bool operator == (const SeqIterator<_Tp>& a, + const SeqIterator<_Tp>& b) +{ + return a.seq == b.seq && a.index == b.index; +} + +template inline bool operator != (const SeqIterator<_Tp>& a, + const SeqIterator<_Tp>& b) +{ + return !(a == b); +} + +//! @} + +} // cv + +#endif + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda.hpp new file mode 100644 index 0000000000000000000000000000000000000000..820aba71ecb7276fc6a08aff3d50598b85daa11e --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda.hpp @@ -0,0 +1,1049 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_CUDA_HPP +#define OPENCV_CORE_CUDA_HPP + +#ifndef __cplusplus +# error cuda.hpp header must be compiled as C++ +#endif + +#include "opencv2/core.hpp" +#include "opencv2/core/cuda_types.hpp" + +/** + @defgroup cuda CUDA-accelerated Computer Vision + @{ + @defgroup cudacore Core part + @{ + @defgroup cudacore_init Initialization and Information + @defgroup cudacore_struct Data Structures + @} + @} + */ + +namespace cv { namespace cuda { + +//! @addtogroup cudacore_struct +//! @{ + +//=================================================================================== +// GpuMat +//=================================================================================== + +/** @brief Base storage class for GPU memory with reference counting. + +Its interface matches the Mat interface with the following limitations: + +- no arbitrary dimensions support (only 2D) +- no functions that return references to their data (because references on GPU are not valid for + CPU) +- no expression templates technique support + +Beware that the latter limitation may lead to overloaded matrix operators that cause memory +allocations. The GpuMat class is convertible to cuda::PtrStepSz and cuda::PtrStep so it can be +passed directly to the kernel. + +@note In contrast with Mat, in most cases GpuMat::isContinuous() == false . This means that rows are +aligned to a size depending on the hardware. Single-row GpuMat is always a continuous matrix. + +@note You are not recommended to leave static or global GpuMat variables allocated, that is, to rely +on its destructor. The destruction order of such variables and CUDA context is undefined. GPU memory +release function returns error if the CUDA context has been destroyed before. + +Some member functions are described as a "Blocking Call" while some are described as a +"Non-Blocking Call". Blocking functions are synchronous to host. It is guaranteed that the GPU +operation is finished when the function returns. However, non-blocking functions are asynchronous to +host. Those functions may return even if the GPU operation is not finished. + +Compared to their blocking counterpart, non-blocking functions accept Stream as an additional +argument. If a non-default stream is passed, the GPU operation may overlap with operations in other +streams. + +@sa Mat + */ +class CV_EXPORTS GpuMat +{ +public: + class CV_EXPORTS Allocator + { + public: + virtual ~Allocator() {} + + // allocator must fill data, step and refcount fields + virtual bool allocate(GpuMat* mat, int rows, int cols, size_t elemSize) = 0; + virtual void free(GpuMat* mat) = 0; + }; + + //! default allocator + static Allocator* defaultAllocator(); + static void setDefaultAllocator(Allocator* allocator); + + //! default constructor + explicit GpuMat(Allocator* allocator = defaultAllocator()); + + //! constructs GpuMat of the specified size and type + GpuMat(int rows, int cols, int type, Allocator* allocator = defaultAllocator()); + GpuMat(Size size, int type, Allocator* allocator = defaultAllocator()); + + //! constucts GpuMat and fills it with the specified value _s + GpuMat(int rows, int cols, int type, Scalar s, Allocator* allocator = defaultAllocator()); + GpuMat(Size size, int type, Scalar s, Allocator* allocator = defaultAllocator()); + + //! copy constructor + GpuMat(const GpuMat& m); + + //! constructor for GpuMat headers pointing to user-allocated data + GpuMat(int rows, int cols, int type, void* data, size_t step = Mat::AUTO_STEP); + GpuMat(Size size, int type, void* data, size_t step = Mat::AUTO_STEP); + + //! creates a GpuMat header for a part of the bigger matrix + GpuMat(const GpuMat& m, Range rowRange, Range colRange); + GpuMat(const GpuMat& m, Rect roi); + + //! builds GpuMat from host memory (Blocking call) + explicit GpuMat(InputArray arr, Allocator* allocator = defaultAllocator()); + + //! destructor - calls release() + ~GpuMat(); + + //! assignment operators + GpuMat& operator =(const GpuMat& m); + + //! allocates new GpuMat data unless the GpuMat already has specified size and type + void create(int rows, int cols, int type); + void create(Size size, int type); + + //! decreases reference counter, deallocate the data when reference counter reaches 0 + void release(); + + //! swaps with other smart pointer + void swap(GpuMat& mat); + + /** @brief Performs data upload to GpuMat (Blocking call) + + This function copies data from host memory to device memory. As being a blocking call, it is + guaranteed that the copy operation is finished when this function returns. + */ + void upload(InputArray arr); + + /** @brief Performs data upload to GpuMat (Non-Blocking call) + + This function copies data from host memory to device memory. As being a non-blocking call, this + function may return even if the copy operation is not finished. + + The copy operation may be overlapped with operations in other non-default streams if \p stream is + not the default stream and \p dst is HostMem allocated with HostMem::PAGE_LOCKED option. + */ + void upload(InputArray arr, Stream& stream); + + /** @brief Performs data download from GpuMat (Blocking call) + + This function copies data from device memory to host memory. As being a blocking call, it is + guaranteed that the copy operation is finished when this function returns. + */ + void download(OutputArray dst) const; + + /** @brief Performs data download from GpuMat (Non-Blocking call) + + This function copies data from device memory to host memory. As being a non-blocking call, this + function may return even if the copy operation is not finished. + + The copy operation may be overlapped with operations in other non-default streams if \p stream is + not the default stream and \p dst is HostMem allocated with HostMem::PAGE_LOCKED option. + */ + void download(OutputArray dst, Stream& stream) const; + + //! returns deep copy of the GpuMat, i.e. the data is copied + GpuMat clone() const; + + //! copies the GpuMat content to device memory (Blocking call) + void copyTo(OutputArray dst) const; + + //! copies the GpuMat content to device memory (Non-Blocking call) + void copyTo(OutputArray dst, Stream& stream) const; + + //! copies those GpuMat elements to "m" that are marked with non-zero mask elements (Blocking call) + void copyTo(OutputArray dst, InputArray mask) const; + + //! copies those GpuMat elements to "m" that are marked with non-zero mask elements (Non-Blocking call) + void copyTo(OutputArray dst, InputArray mask, Stream& stream) const; + + //! sets some of the GpuMat elements to s (Blocking call) + GpuMat& setTo(Scalar s); + + //! sets some of the GpuMat elements to s (Non-Blocking call) + GpuMat& setTo(Scalar s, Stream& stream); + + //! sets some of the GpuMat elements to s, according to the mask (Blocking call) + GpuMat& setTo(Scalar s, InputArray mask); + + //! sets some of the GpuMat elements to s, according to the mask (Non-Blocking call) + GpuMat& setTo(Scalar s, InputArray mask, Stream& stream); + + //! converts GpuMat to another datatype (Blocking call) + void convertTo(OutputArray dst, int rtype) const; + + //! converts GpuMat to another datatype (Non-Blocking call) + void convertTo(OutputArray dst, int rtype, Stream& stream) const; + + //! converts GpuMat to another datatype with scaling (Blocking call) + void convertTo(OutputArray dst, int rtype, double alpha, double beta = 0.0) const; + + //! converts GpuMat to another datatype with scaling (Non-Blocking call) + void convertTo(OutputArray dst, int rtype, double alpha, Stream& stream) const; + + //! converts GpuMat to another datatype with scaling (Non-Blocking call) + void convertTo(OutputArray dst, int rtype, double alpha, double beta, Stream& stream) const; + + void assignTo(GpuMat& m, int type=-1) const; + + //! returns pointer to y-th row + uchar* ptr(int y = 0); + const uchar* ptr(int y = 0) const; + + //! template version of the above method + template _Tp* ptr(int y = 0); + template const _Tp* ptr(int y = 0) const; + + template operator PtrStepSz<_Tp>() const; + template operator PtrStep<_Tp>() const; + + //! returns a new GpuMat header for the specified row + GpuMat row(int y) const; + + //! returns a new GpuMat header for the specified column + GpuMat col(int x) const; + + //! ... for the specified row span + GpuMat rowRange(int startrow, int endrow) const; + GpuMat rowRange(Range r) const; + + //! ... for the specified column span + GpuMat colRange(int startcol, int endcol) const; + GpuMat colRange(Range r) const; + + //! extracts a rectangular sub-GpuMat (this is a generalized form of row, rowRange etc.) + GpuMat operator ()(Range rowRange, Range colRange) const; + GpuMat operator ()(Rect roi) const; + + //! creates alternative GpuMat header for the same data, with different + //! number of channels and/or different number of rows + GpuMat reshape(int cn, int rows = 0) const; + + //! locates GpuMat header within a parent GpuMat + void locateROI(Size& wholeSize, Point& ofs) const; + + //! moves/resizes the current GpuMat ROI inside the parent GpuMat + GpuMat& adjustROI(int dtop, int dbottom, int dleft, int dright); + + //! returns true iff the GpuMat data is continuous + //! (i.e. when there are no gaps between successive rows) + bool isContinuous() const; + + //! returns element size in bytes + size_t elemSize() const; + + //! returns the size of element channel in bytes + size_t elemSize1() const; + + //! returns element type + int type() const; + + //! returns element type + int depth() const; + + //! returns number of channels + int channels() const; + + //! returns step/elemSize1() + size_t step1() const; + + //! returns GpuMat size : width == number of columns, height == number of rows + Size size() const; + + //! returns true if GpuMat data is NULL + bool empty() const; + + //! internal use method: updates the continuity flag + void updateContinuityFlag(); + + /*! includes several bit-fields: + - the magic signature + - continuity flag + - depth + - number of channels + */ + int flags; + + //! the number of rows and columns + int rows, cols; + + //! a distance between successive rows in bytes; includes the gap if any + size_t step; + + //! pointer to the data + uchar* data; + + //! pointer to the reference counter; + //! when GpuMat points to user-allocated data, the pointer is NULL + int* refcount; + + //! helper fields used in locateROI and adjustROI + uchar* datastart; + const uchar* dataend; + + //! allocator + Allocator* allocator; +}; + +/** @brief Creates a continuous matrix. + +@param rows Row count. +@param cols Column count. +@param type Type of the matrix. +@param arr Destination matrix. This parameter changes only if it has a proper type and area ( +\f$\texttt{rows} \times \texttt{cols}\f$ ). + +Matrix is called continuous if its elements are stored continuously, that is, without gaps at the +end of each row. + */ +CV_EXPORTS void createContinuous(int rows, int cols, int type, OutputArray arr); + +/** @brief Ensures that the size of a matrix is big enough and the matrix has a proper type. + +@param rows Minimum desired number of rows. +@param cols Minimum desired number of columns. +@param type Desired matrix type. +@param arr Destination matrix. + +The function does not reallocate memory if the matrix has proper attributes already. + */ +CV_EXPORTS void ensureSizeIsEnough(int rows, int cols, int type, OutputArray arr); + +/** @brief BufferPool for use with CUDA streams + +BufferPool utilizes Stream's allocator to create new buffers for GpuMat's. It is +only useful when enabled with #setBufferPoolUsage. + +@code + setBufferPoolUsage(true); +@endcode + +@note #setBufferPoolUsage must be called \em before any Stream declaration. + +Users may specify custom allocator for Stream and may implement their own stream based +functions utilizing the same underlying GPU memory management. + +If custom allocator is not specified, BufferPool utilizes StackAllocator by +default. StackAllocator allocates a chunk of GPU device memory beforehand, +and when GpuMat is declared later on, it is given the pre-allocated memory. +This kind of strategy reduces the number of calls for memory allocating APIs +such as cudaMalloc or cudaMallocPitch. + +Below is an example that utilizes BufferPool with StackAllocator: + +@code + #include + + using namespace cv; + using namespace cv::cuda + + int main() + { + setBufferPoolUsage(true); // Tell OpenCV that we are going to utilize BufferPool + setBufferPoolConfig(getDevice(), 1024 * 1024 * 64, 2); // Allocate 64 MB, 2 stacks (default is 10 MB, 5 stacks) + + Stream stream1, stream2; // Each stream uses 1 stack + BufferPool pool1(stream1), pool2(stream2); + + GpuMat d_src1 = pool1.getBuffer(4096, 4096, CV_8UC1); // 16MB + GpuMat d_dst1 = pool1.getBuffer(4096, 4096, CV_8UC3); // 48MB, pool1 is now full + + GpuMat d_src2 = pool2.getBuffer(1024, 1024, CV_8UC1); // 1MB + GpuMat d_dst2 = pool2.getBuffer(1024, 1024, CV_8UC3); // 3MB + + cvtColor(d_src1, d_dst1, CV_GRAY2BGR, 0, stream1); + cvtColor(d_src2, d_dst2, CV_GRAY2BGR, 0, stream2); + } +@endcode + +If we allocate another GpuMat on pool1 in the above example, it will be carried out by +the DefaultAllocator since the stack for pool1 is full. + +@code + GpuMat d_add1 = pool1.getBuffer(1024, 1024, CV_8UC1); // Stack for pool1 is full, memory is allocated with DefaultAllocator +@endcode + +If a third stream is declared in the above example, allocating with #getBuffer +within that stream will also be carried out by the DefaultAllocator because we've run out of +stacks. + +@code + Stream stream3; // Only 2 stacks were allocated, we've run out of stacks + BufferPool pool3(stream3); + GpuMat d_src3 = pool3.getBuffer(1024, 1024, CV_8UC1); // Memory is allocated with DefaultAllocator +@endcode + +@warning When utilizing StackAllocator, deallocation order is important. + +Just like a stack, deallocation must be done in LIFO order. Below is an example of +erroneous usage that violates LIFO rule. If OpenCV is compiled in Debug mode, this +sample code will emit CV_Assert error. + +@code + int main() + { + setBufferPoolUsage(true); // Tell OpenCV that we are going to utilize BufferPool + Stream stream; // A default size (10 MB) stack is allocated to this stream + BufferPool pool(stream); + + GpuMat mat1 = pool.getBuffer(1024, 1024, CV_8UC1); // Allocate mat1 (1MB) + GpuMat mat2 = pool.getBuffer(1024, 1024, CV_8UC1); // Allocate mat2 (1MB) + + mat1.release(); // erroneous usage : mat2 must be deallocated before mat1 + } +@endcode + +Since C++ local variables are destroyed in the reverse order of construction, +the code sample below satisfies the LIFO rule. Local GpuMat's are deallocated +and the corresponding memory is automatically returned to the pool for later usage. + +@code + int main() + { + setBufferPoolUsage(true); // Tell OpenCV that we are going to utilize BufferPool + setBufferPoolConfig(getDevice(), 1024 * 1024 * 64, 2); // Allocate 64 MB, 2 stacks (default is 10 MB, 5 stacks) + + Stream stream1, stream2; // Each stream uses 1 stack + BufferPool pool1(stream1), pool2(stream2); + + for (int i = 0; i < 10; i++) + { + GpuMat d_src1 = pool1.getBuffer(4096, 4096, CV_8UC1); // 16MB + GpuMat d_dst1 = pool1.getBuffer(4096, 4096, CV_8UC3); // 48MB, pool1 is now full + + GpuMat d_src2 = pool2.getBuffer(1024, 1024, CV_8UC1); // 1MB + GpuMat d_dst2 = pool2.getBuffer(1024, 1024, CV_8UC3); // 3MB + + d_src1.setTo(Scalar(i), stream1); + d_src2.setTo(Scalar(i), stream2); + + cvtColor(d_src1, d_dst1, CV_GRAY2BGR, 0, stream1); + cvtColor(d_src2, d_dst2, CV_GRAY2BGR, 0, stream2); + // The order of destruction of the local variables is: + // d_dst2 => d_src2 => d_dst1 => d_src1 + // LIFO rule is satisfied, this code runs without error + } + } +@endcode + */ +class CV_EXPORTS BufferPool +{ +public: + + //! Gets the BufferPool for the given stream. + explicit BufferPool(Stream& stream); + + //! Allocates a new GpuMat of given size and type. + GpuMat getBuffer(int rows, int cols, int type); + + //! Allocates a new GpuMat of given size and type. + GpuMat getBuffer(Size size, int type) { return getBuffer(size.height, size.width, type); } + + //! Returns the allocator associated with the stream. + Ptr getAllocator() const { return allocator_; } + +private: + Ptr allocator_; +}; + +//! BufferPool management (must be called before Stream creation) +CV_EXPORTS void setBufferPoolUsage(bool on); +CV_EXPORTS void setBufferPoolConfig(int deviceId, size_t stackSize, int stackCount); + +//=================================================================================== +// HostMem +//=================================================================================== + +/** @brief Class with reference counting wrapping special memory type allocation functions from CUDA. + +Its interface is also Mat-like but with additional memory type parameters. + +- **PAGE_LOCKED** sets a page locked memory type used commonly for fast and asynchronous + uploading/downloading data from/to GPU. +- **SHARED** specifies a zero copy memory allocation that enables mapping the host memory to GPU + address space, if supported. +- **WRITE_COMBINED** sets the write combined buffer that is not cached by CPU. Such buffers are + used to supply GPU with data when GPU only reads it. The advantage is a better CPU cache + utilization. + +@note Allocation size of such memory types is usually limited. For more details, see *CUDA 2.2 +Pinned Memory APIs* document or *CUDA C Programming Guide*. + */ +class CV_EXPORTS HostMem +{ +public: + enum AllocType { PAGE_LOCKED = 1, SHARED = 2, WRITE_COMBINED = 4 }; + + static MatAllocator* getAllocator(AllocType alloc_type = PAGE_LOCKED); + + explicit HostMem(AllocType alloc_type = PAGE_LOCKED); + + HostMem(const HostMem& m); + + HostMem(int rows, int cols, int type, AllocType alloc_type = PAGE_LOCKED); + HostMem(Size size, int type, AllocType alloc_type = PAGE_LOCKED); + + //! creates from host memory with coping data + explicit HostMem(InputArray arr, AllocType alloc_type = PAGE_LOCKED); + + ~HostMem(); + + HostMem& operator =(const HostMem& m); + + //! swaps with other smart pointer + void swap(HostMem& b); + + //! returns deep copy of the matrix, i.e. the data is copied + HostMem clone() const; + + //! allocates new matrix data unless the matrix already has specified size and type. + void create(int rows, int cols, int type); + void create(Size size, int type); + + //! creates alternative HostMem header for the same data, with different + //! number of channels and/or different number of rows + HostMem reshape(int cn, int rows = 0) const; + + //! decrements reference counter and released memory if needed. + void release(); + + //! returns matrix header with disabled reference counting for HostMem data. + Mat createMatHeader() const; + + /** @brief Maps CPU memory to GPU address space and creates the cuda::GpuMat header without reference counting + for it. + + This can be done only if memory was allocated with the SHARED flag and if it is supported by the + hardware. Laptops often share video and CPU memory, so address spaces can be mapped, which + eliminates an extra copy. + */ + GpuMat createGpuMatHeader() const; + + // Please see cv::Mat for descriptions + bool isContinuous() const; + size_t elemSize() const; + size_t elemSize1() const; + int type() const; + int depth() const; + int channels() const; + size_t step1() const; + Size size() const; + bool empty() const; + + // Please see cv::Mat for descriptions + int flags; + int rows, cols; + size_t step; + + uchar* data; + int* refcount; + + uchar* datastart; + const uchar* dataend; + + AllocType alloc_type; +}; + +/** @brief Page-locks the memory of matrix and maps it for the device(s). + +@param m Input matrix. + */ +CV_EXPORTS void registerPageLocked(Mat& m); + +/** @brief Unmaps the memory of matrix and makes it pageable again. + +@param m Input matrix. + */ +CV_EXPORTS void unregisterPageLocked(Mat& m); + +//=================================================================================== +// Stream +//=================================================================================== + +/** @brief This class encapsulates a queue of asynchronous calls. + +@note Currently, you may face problems if an operation is enqueued twice with different data. Some +functions use the constant GPU memory, and next call may update the memory before the previous one +has been finished. But calling different operations asynchronously is safe because each operation +has its own constant buffer. Memory copy/upload/download/set operations to the buffers you hold are +also safe. + +@note The Stream class is not thread-safe. Please use different Stream objects for different CPU threads. + +@code +void thread1() +{ + cv::cuda::Stream stream1; + cv::cuda::func1(..., stream1); +} + +void thread2() +{ + cv::cuda::Stream stream2; + cv::cuda::func2(..., stream2); +} +@endcode + +@note By default all CUDA routines are launched in Stream::Null() object, if the stream is not specified by user. +In multi-threading environment the stream objects must be passed explicitly (see previous note). + */ +class CV_EXPORTS Stream +{ + typedef void (Stream::*bool_type)() const; + void this_type_does_not_support_comparisons() const {} + +public: + typedef void (*StreamCallback)(int status, void* userData); + + //! creates a new asynchronous stream + Stream(); + + //! creates a new asynchronous stream with custom allocator + Stream(const Ptr& allocator); + + /** @brief Returns true if the current stream queue is finished. Otherwise, it returns false. + */ + bool queryIfComplete() const; + + /** @brief Blocks the current CPU thread until all operations in the stream are complete. + */ + void waitForCompletion(); + + /** @brief Makes a compute stream wait on an event. + */ + void waitEvent(const Event& event); + + /** @brief Adds a callback to be called on the host after all currently enqueued items in the stream have + completed. + + @note Callbacks must not make any CUDA API calls. Callbacks must not perform any synchronization + that may depend on outstanding device work or other callbacks that are not mandated to run earlier. + Callbacks without a mandated order (in independent streams) execute in undefined order and may be + serialized. + */ + void enqueueHostCallback(StreamCallback callback, void* userData); + + //! return Stream object for default CUDA stream + static Stream& Null(); + + //! returns true if stream object is not default (!= 0) + operator bool_type() const; + + class Impl; + +private: + Ptr impl_; + Stream(const Ptr& impl); + + friend struct StreamAccessor; + friend class BufferPool; + friend class DefaultDeviceInitializer; +}; + +class CV_EXPORTS Event +{ +public: + enum CreateFlags + { + DEFAULT = 0x00, /**< Default event flag */ + BLOCKING_SYNC = 0x01, /**< Event uses blocking synchronization */ + DISABLE_TIMING = 0x02, /**< Event will not record timing data */ + INTERPROCESS = 0x04 /**< Event is suitable for interprocess use. DisableTiming must be set */ + }; + + explicit Event(CreateFlags flags = DEFAULT); + + //! records an event + void record(Stream& stream = Stream::Null()); + + //! queries an event's status + bool queryIfComplete() const; + + //! waits for an event to complete + void waitForCompletion(); + + //! computes the elapsed time between events + static float elapsedTime(const Event& start, const Event& end); + + class Impl; + +private: + Ptr impl_; + Event(const Ptr& impl); + + friend struct EventAccessor; +}; + +//! @} cudacore_struct + +//=================================================================================== +// Initialization & Info +//=================================================================================== + +//! @addtogroup cudacore_init +//! @{ + +/** @brief Returns the number of installed CUDA-enabled devices. + +Use this function before any other CUDA functions calls. If OpenCV is compiled without CUDA support, +this function returns 0. If the CUDA driver is not installed, or is incompatible, this function +returns -1. + */ +CV_EXPORTS int getCudaEnabledDeviceCount(); + +/** @brief Sets a device and initializes it for the current thread. + +@param device System index of a CUDA device starting with 0. + +If the call of this function is omitted, a default device is initialized at the fist CUDA usage. + */ +CV_EXPORTS void setDevice(int device); + +/** @brief Returns the current device index set by cuda::setDevice or initialized by default. + */ +CV_EXPORTS int getDevice(); + +/** @brief Explicitly destroys and cleans up all resources associated with the current device in the current +process. + +Any subsequent API call to this device will reinitialize the device. + */ +CV_EXPORTS void resetDevice(); + +/** @brief Enumeration providing CUDA computing features. + */ +enum FeatureSet +{ + FEATURE_SET_COMPUTE_10 = 10, + FEATURE_SET_COMPUTE_11 = 11, + FEATURE_SET_COMPUTE_12 = 12, + FEATURE_SET_COMPUTE_13 = 13, + FEATURE_SET_COMPUTE_20 = 20, + FEATURE_SET_COMPUTE_21 = 21, + FEATURE_SET_COMPUTE_30 = 30, + FEATURE_SET_COMPUTE_32 = 32, + FEATURE_SET_COMPUTE_35 = 35, + FEATURE_SET_COMPUTE_50 = 50, + + GLOBAL_ATOMICS = FEATURE_SET_COMPUTE_11, + SHARED_ATOMICS = FEATURE_SET_COMPUTE_12, + NATIVE_DOUBLE = FEATURE_SET_COMPUTE_13, + WARP_SHUFFLE_FUNCTIONS = FEATURE_SET_COMPUTE_30, + DYNAMIC_PARALLELISM = FEATURE_SET_COMPUTE_35 +}; + +//! checks whether current device supports the given feature +CV_EXPORTS bool deviceSupports(FeatureSet feature_set); + +/** @brief Class providing a set of static methods to check what NVIDIA\* card architecture the CUDA module was +built for. + +According to the CUDA C Programming Guide Version 3.2: "PTX code produced for some specific compute +capability can always be compiled to binary code of greater or equal compute capability". + */ +class CV_EXPORTS TargetArchs +{ +public: + /** @brief The following method checks whether the module was built with the support of the given feature: + + @param feature_set Features to be checked. See :ocvcuda::FeatureSet. + */ + static bool builtWith(FeatureSet feature_set); + + /** @brief There is a set of methods to check whether the module contains intermediate (PTX) or binary CUDA + code for the given architecture(s): + + @param major Major compute capability version. + @param minor Minor compute capability version. + */ + static bool has(int major, int minor); + static bool hasPtx(int major, int minor); + static bool hasBin(int major, int minor); + + static bool hasEqualOrLessPtx(int major, int minor); + static bool hasEqualOrGreater(int major, int minor); + static bool hasEqualOrGreaterPtx(int major, int minor); + static bool hasEqualOrGreaterBin(int major, int minor); +}; + +/** @brief Class providing functionality for querying the specified GPU properties. + */ +class CV_EXPORTS DeviceInfo +{ +public: + //! creates DeviceInfo object for the current GPU + DeviceInfo(); + + /** @brief The constructors. + + @param device_id System index of the CUDA device starting with 0. + + Constructs the DeviceInfo object for the specified device. If device_id parameter is missed, it + constructs an object for the current device. + */ + DeviceInfo(int device_id); + + /** @brief Returns system index of the CUDA device starting with 0. + */ + int deviceID() const; + + //! ASCII string identifying device + const char* name() const; + + //! global memory available on device in bytes + size_t totalGlobalMem() const; + + //! shared memory available per block in bytes + size_t sharedMemPerBlock() const; + + //! 32-bit registers available per block + int regsPerBlock() const; + + //! warp size in threads + int warpSize() const; + + //! maximum pitch in bytes allowed by memory copies + size_t memPitch() const; + + //! maximum number of threads per block + int maxThreadsPerBlock() const; + + //! maximum size of each dimension of a block + Vec3i maxThreadsDim() const; + + //! maximum size of each dimension of a grid + Vec3i maxGridSize() const; + + //! clock frequency in kilohertz + int clockRate() const; + + //! constant memory available on device in bytes + size_t totalConstMem() const; + + //! major compute capability + int majorVersion() const; + + //! minor compute capability + int minorVersion() const; + + //! alignment requirement for textures + size_t textureAlignment() const; + + //! pitch alignment requirement for texture references bound to pitched memory + size_t texturePitchAlignment() const; + + //! number of multiprocessors on device + int multiProcessorCount() const; + + //! specified whether there is a run time limit on kernels + bool kernelExecTimeoutEnabled() const; + + //! device is integrated as opposed to discrete + bool integrated() const; + + //! device can map host memory with cudaHostAlloc/cudaHostGetDevicePointer + bool canMapHostMemory() const; + + enum ComputeMode + { + ComputeModeDefault, /**< default compute mode (Multiple threads can use cudaSetDevice with this device) */ + ComputeModeExclusive, /**< compute-exclusive-thread mode (Only one thread in one process will be able to use cudaSetDevice with this device) */ + ComputeModeProhibited, /**< compute-prohibited mode (No threads can use cudaSetDevice with this device) */ + ComputeModeExclusiveProcess /**< compute-exclusive-process mode (Many threads in one process will be able to use cudaSetDevice with this device) */ + }; + + //! compute mode + ComputeMode computeMode() const; + + //! maximum 1D texture size + int maxTexture1D() const; + + //! maximum 1D mipmapped texture size + int maxTexture1DMipmap() const; + + //! maximum size for 1D textures bound to linear memory + int maxTexture1DLinear() const; + + //! maximum 2D texture dimensions + Vec2i maxTexture2D() const; + + //! maximum 2D mipmapped texture dimensions + Vec2i maxTexture2DMipmap() const; + + //! maximum dimensions (width, height, pitch) for 2D textures bound to pitched memory + Vec3i maxTexture2DLinear() const; + + //! maximum 2D texture dimensions if texture gather operations have to be performed + Vec2i maxTexture2DGather() const; + + //! maximum 3D texture dimensions + Vec3i maxTexture3D() const; + + //! maximum Cubemap texture dimensions + int maxTextureCubemap() const; + + //! maximum 1D layered texture dimensions + Vec2i maxTexture1DLayered() const; + + //! maximum 2D layered texture dimensions + Vec3i maxTexture2DLayered() const; + + //! maximum Cubemap layered texture dimensions + Vec2i maxTextureCubemapLayered() const; + + //! maximum 1D surface size + int maxSurface1D() const; + + //! maximum 2D surface dimensions + Vec2i maxSurface2D() const; + + //! maximum 3D surface dimensions + Vec3i maxSurface3D() const; + + //! maximum 1D layered surface dimensions + Vec2i maxSurface1DLayered() const; + + //! maximum 2D layered surface dimensions + Vec3i maxSurface2DLayered() const; + + //! maximum Cubemap surface dimensions + int maxSurfaceCubemap() const; + + //! maximum Cubemap layered surface dimensions + Vec2i maxSurfaceCubemapLayered() const; + + //! alignment requirements for surfaces + size_t surfaceAlignment() const; + + //! device can possibly execute multiple kernels concurrently + bool concurrentKernels() const; + + //! device has ECC support enabled + bool ECCEnabled() const; + + //! PCI bus ID of the device + int pciBusID() const; + + //! PCI device ID of the device + int pciDeviceID() const; + + //! PCI domain ID of the device + int pciDomainID() const; + + //! true if device is a Tesla device using TCC driver, false otherwise + bool tccDriver() const; + + //! number of asynchronous engines + int asyncEngineCount() const; + + //! device shares a unified address space with the host + bool unifiedAddressing() const; + + //! peak memory clock frequency in kilohertz + int memoryClockRate() const; + + //! global memory bus width in bits + int memoryBusWidth() const; + + //! size of L2 cache in bytes + int l2CacheSize() const; + + //! maximum resident threads per multiprocessor + int maxThreadsPerMultiProcessor() const; + + //! gets free and total device memory + void queryMemory(size_t& totalMemory, size_t& freeMemory) const; + size_t freeMemory() const; + size_t totalMemory() const; + + /** @brief Provides information on CUDA feature support. + + @param feature_set Features to be checked. See cuda::FeatureSet. + + This function returns true if the device has the specified CUDA feature. Otherwise, it returns false + */ + bool supports(FeatureSet feature_set) const; + + /** @brief Checks the CUDA module and device compatibility. + + This function returns true if the CUDA module can be run on the specified device. Otherwise, it + returns false . + */ + bool isCompatible() const; + +private: + int device_id_; +}; + +CV_EXPORTS void printCudaDeviceInfo(int device); +CV_EXPORTS void printShortCudaDeviceInfo(int device); + +/** @brief Converts an array to half precision floating number. + +@param _src input array. +@param _dst output array. +@param stream Stream for the asynchronous version. +@sa convertFp16 +*/ +CV_EXPORTS void convertFp16(InputArray _src, OutputArray _dst, Stream& stream = Stream::Null()); + +//! @} cudacore_init + +}} // namespace cv { namespace cuda { + + +#include "opencv2/core/cuda.inl.hpp" + +#endif /* OPENCV_CORE_CUDA_HPP */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda.inl.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda.inl.hpp new file mode 100644 index 0000000000000000000000000000000000000000..35ae2e49d77f331cca5d6cb13a48f3a6885c2ab7 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda.inl.hpp @@ -0,0 +1,631 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_CUDAINL_HPP +#define OPENCV_CORE_CUDAINL_HPP + +#include "opencv2/core/cuda.hpp" + +//! @cond IGNORED + +namespace cv { namespace cuda { + +//=================================================================================== +// GpuMat +//=================================================================================== + +inline +GpuMat::GpuMat(Allocator* allocator_) + : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_) +{} + +inline +GpuMat::GpuMat(int rows_, int cols_, int type_, Allocator* allocator_) + : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_) +{ + if (rows_ > 0 && cols_ > 0) + create(rows_, cols_, type_); +} + +inline +GpuMat::GpuMat(Size size_, int type_, Allocator* allocator_) + : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_) +{ + if (size_.height > 0 && size_.width > 0) + create(size_.height, size_.width, type_); +} + +inline +GpuMat::GpuMat(int rows_, int cols_, int type_, Scalar s_, Allocator* allocator_) + : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_) +{ + if (rows_ > 0 && cols_ > 0) + { + create(rows_, cols_, type_); + setTo(s_); + } +} + +inline +GpuMat::GpuMat(Size size_, int type_, Scalar s_, Allocator* allocator_) + : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_) +{ + if (size_.height > 0 && size_.width > 0) + { + create(size_.height, size_.width, type_); + setTo(s_); + } +} + +inline +GpuMat::GpuMat(const GpuMat& m) + : flags(m.flags), rows(m.rows), cols(m.cols), step(m.step), data(m.data), refcount(m.refcount), datastart(m.datastart), dataend(m.dataend), allocator(m.allocator) +{ + if (refcount) + CV_XADD(refcount, 1); +} + +inline +GpuMat::GpuMat(InputArray arr, Allocator* allocator_) : + flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_) +{ + upload(arr); +} + +inline +GpuMat::~GpuMat() +{ + release(); +} + +inline +GpuMat& GpuMat::operator =(const GpuMat& m) +{ + if (this != &m) + { + GpuMat temp(m); + swap(temp); + } + + return *this; +} + +inline +void GpuMat::create(Size size_, int type_) +{ + create(size_.height, size_.width, type_); +} + +inline +void GpuMat::swap(GpuMat& b) +{ + std::swap(flags, b.flags); + std::swap(rows, b.rows); + std::swap(cols, b.cols); + std::swap(step, b.step); + std::swap(data, b.data); + std::swap(datastart, b.datastart); + std::swap(dataend, b.dataend); + std::swap(refcount, b.refcount); + std::swap(allocator, b.allocator); +} + +inline +GpuMat GpuMat::clone() const +{ + GpuMat m; + copyTo(m); + return m; +} + +inline +void GpuMat::copyTo(OutputArray dst, InputArray mask) const +{ + copyTo(dst, mask, Stream::Null()); +} + +inline +GpuMat& GpuMat::setTo(Scalar s) +{ + return setTo(s, Stream::Null()); +} + +inline +GpuMat& GpuMat::setTo(Scalar s, InputArray mask) +{ + return setTo(s, mask, Stream::Null()); +} + +inline +void GpuMat::convertTo(OutputArray dst, int rtype) const +{ + convertTo(dst, rtype, Stream::Null()); +} + +inline +void GpuMat::convertTo(OutputArray dst, int rtype, double alpha, double beta) const +{ + convertTo(dst, rtype, alpha, beta, Stream::Null()); +} + +inline +void GpuMat::convertTo(OutputArray dst, int rtype, double alpha, Stream& stream) const +{ + convertTo(dst, rtype, alpha, 0.0, stream); +} + +inline +void GpuMat::assignTo(GpuMat& m, int _type) const +{ + if (_type < 0) + m = *this; + else + convertTo(m, _type); +} + +inline +uchar* GpuMat::ptr(int y) +{ + CV_DbgAssert( (unsigned)y < (unsigned)rows ); + return data + step * y; +} + +inline +const uchar* GpuMat::ptr(int y) const +{ + CV_DbgAssert( (unsigned)y < (unsigned)rows ); + return data + step * y; +} + +template inline +_Tp* GpuMat::ptr(int y) +{ + return (_Tp*)ptr(y); +} + +template inline +const _Tp* GpuMat::ptr(int y) const +{ + return (const _Tp*)ptr(y); +} + +template inline +GpuMat::operator PtrStepSz() const +{ + return PtrStepSz(rows, cols, (T*)data, step); +} + +template inline +GpuMat::operator PtrStep() const +{ + return PtrStep((T*)data, step); +} + +inline +GpuMat GpuMat::row(int y) const +{ + return GpuMat(*this, Range(y, y+1), Range::all()); +} + +inline +GpuMat GpuMat::col(int x) const +{ + return GpuMat(*this, Range::all(), Range(x, x+1)); +} + +inline +GpuMat GpuMat::rowRange(int startrow, int endrow) const +{ + return GpuMat(*this, Range(startrow, endrow), Range::all()); +} + +inline +GpuMat GpuMat::rowRange(Range r) const +{ + return GpuMat(*this, r, Range::all()); +} + +inline +GpuMat GpuMat::colRange(int startcol, int endcol) const +{ + return GpuMat(*this, Range::all(), Range(startcol, endcol)); +} + +inline +GpuMat GpuMat::colRange(Range r) const +{ + return GpuMat(*this, Range::all(), r); +} + +inline +GpuMat GpuMat::operator ()(Range rowRange_, Range colRange_) const +{ + return GpuMat(*this, rowRange_, colRange_); +} + +inline +GpuMat GpuMat::operator ()(Rect roi) const +{ + return GpuMat(*this, roi); +} + +inline +bool GpuMat::isContinuous() const +{ + return (flags & Mat::CONTINUOUS_FLAG) != 0; +} + +inline +size_t GpuMat::elemSize() const +{ + return CV_ELEM_SIZE(flags); +} + +inline +size_t GpuMat::elemSize1() const +{ + return CV_ELEM_SIZE1(flags); +} + +inline +int GpuMat::type() const +{ + return CV_MAT_TYPE(flags); +} + +inline +int GpuMat::depth() const +{ + return CV_MAT_DEPTH(flags); +} + +inline +int GpuMat::channels() const +{ + return CV_MAT_CN(flags); +} + +inline +size_t GpuMat::step1() const +{ + return step / elemSize1(); +} + +inline +Size GpuMat::size() const +{ + return Size(cols, rows); +} + +inline +bool GpuMat::empty() const +{ + return data == 0; +} + +static inline +GpuMat createContinuous(int rows, int cols, int type) +{ + GpuMat m; + createContinuous(rows, cols, type, m); + return m; +} + +static inline +void createContinuous(Size size, int type, OutputArray arr) +{ + createContinuous(size.height, size.width, type, arr); +} + +static inline +GpuMat createContinuous(Size size, int type) +{ + GpuMat m; + createContinuous(size, type, m); + return m; +} + +static inline +void ensureSizeIsEnough(Size size, int type, OutputArray arr) +{ + ensureSizeIsEnough(size.height, size.width, type, arr); +} + +static inline +void swap(GpuMat& a, GpuMat& b) +{ + a.swap(b); +} + +//=================================================================================== +// HostMem +//=================================================================================== + +inline +HostMem::HostMem(AllocType alloc_type_) + : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), alloc_type(alloc_type_) +{ +} + +inline +HostMem::HostMem(const HostMem& m) + : flags(m.flags), rows(m.rows), cols(m.cols), step(m.step), data(m.data), refcount(m.refcount), datastart(m.datastart), dataend(m.dataend), alloc_type(m.alloc_type) +{ + if( refcount ) + CV_XADD(refcount, 1); +} + +inline +HostMem::HostMem(int rows_, int cols_, int type_, AllocType alloc_type_) + : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), alloc_type(alloc_type_) +{ + if (rows_ > 0 && cols_ > 0) + create(rows_, cols_, type_); +} + +inline +HostMem::HostMem(Size size_, int type_, AllocType alloc_type_) + : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), alloc_type(alloc_type_) +{ + if (size_.height > 0 && size_.width > 0) + create(size_.height, size_.width, type_); +} + +inline +HostMem::HostMem(InputArray arr, AllocType alloc_type_) + : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), alloc_type(alloc_type_) +{ + arr.getMat().copyTo(*this); +} + +inline +HostMem::~HostMem() +{ + release(); +} + +inline +HostMem& HostMem::operator =(const HostMem& m) +{ + if (this != &m) + { + HostMem temp(m); + swap(temp); + } + + return *this; +} + +inline +void HostMem::swap(HostMem& b) +{ + std::swap(flags, b.flags); + std::swap(rows, b.rows); + std::swap(cols, b.cols); + std::swap(step, b.step); + std::swap(data, b.data); + std::swap(datastart, b.datastart); + std::swap(dataend, b.dataend); + std::swap(refcount, b.refcount); + std::swap(alloc_type, b.alloc_type); +} + +inline +HostMem HostMem::clone() const +{ + HostMem m(size(), type(), alloc_type); + createMatHeader().copyTo(m); + return m; +} + +inline +void HostMem::create(Size size_, int type_) +{ + create(size_.height, size_.width, type_); +} + +inline +Mat HostMem::createMatHeader() const +{ + return Mat(size(), type(), data, step); +} + +inline +bool HostMem::isContinuous() const +{ + return (flags & Mat::CONTINUOUS_FLAG) != 0; +} + +inline +size_t HostMem::elemSize() const +{ + return CV_ELEM_SIZE(flags); +} + +inline +size_t HostMem::elemSize1() const +{ + return CV_ELEM_SIZE1(flags); +} + +inline +int HostMem::type() const +{ + return CV_MAT_TYPE(flags); +} + +inline +int HostMem::depth() const +{ + return CV_MAT_DEPTH(flags); +} + +inline +int HostMem::channels() const +{ + return CV_MAT_CN(flags); +} + +inline +size_t HostMem::step1() const +{ + return step / elemSize1(); +} + +inline +Size HostMem::size() const +{ + return Size(cols, rows); +} + +inline +bool HostMem::empty() const +{ + return data == 0; +} + +static inline +void swap(HostMem& a, HostMem& b) +{ + a.swap(b); +} + +//=================================================================================== +// Stream +//=================================================================================== + +inline +Stream::Stream(const Ptr& impl) + : impl_(impl) +{ +} + +//=================================================================================== +// Event +//=================================================================================== + +inline +Event::Event(const Ptr& impl) + : impl_(impl) +{ +} + +//=================================================================================== +// Initialization & Info +//=================================================================================== + +inline +bool TargetArchs::has(int major, int minor) +{ + return hasPtx(major, minor) || hasBin(major, minor); +} + +inline +bool TargetArchs::hasEqualOrGreater(int major, int minor) +{ + return hasEqualOrGreaterPtx(major, minor) || hasEqualOrGreaterBin(major, minor); +} + +inline +DeviceInfo::DeviceInfo() +{ + device_id_ = getDevice(); +} + +inline +DeviceInfo::DeviceInfo(int device_id) +{ + CV_Assert( device_id >= 0 && device_id < getCudaEnabledDeviceCount() ); + device_id_ = device_id; +} + +inline +int DeviceInfo::deviceID() const +{ + return device_id_; +} + +inline +size_t DeviceInfo::freeMemory() const +{ + size_t _totalMemory = 0, _freeMemory = 0; + queryMemory(_totalMemory, _freeMemory); + return _freeMemory; +} + +inline +size_t DeviceInfo::totalMemory() const +{ + size_t _totalMemory = 0, _freeMemory = 0; + queryMemory(_totalMemory, _freeMemory); + return _totalMemory; +} + +inline +bool DeviceInfo::supports(FeatureSet feature_set) const +{ + int version = majorVersion() * 10 + minorVersion(); + return version >= feature_set; +} + + +}} // namespace cv { namespace cuda { + +//=================================================================================== +// Mat +//=================================================================================== + +namespace cv { + +inline +Mat::Mat(const cuda::GpuMat& m) + : flags(0), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows) +{ + m.download(*this); +} + +} + +//! @endcond + +#endif // OPENCV_CORE_CUDAINL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/block.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/block.hpp new file mode 100644 index 0000000000000000000000000000000000000000..c277f0ea9c411b6763ba04d6dec56644eb25479a --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/block.hpp @@ -0,0 +1,211 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_DEVICE_BLOCK_HPP +#define OPENCV_CUDA_DEVICE_BLOCK_HPP + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + struct Block + { + static __device__ __forceinline__ unsigned int id() + { + return blockIdx.x; + } + + static __device__ __forceinline__ unsigned int stride() + { + return blockDim.x * blockDim.y * blockDim.z; + } + + static __device__ __forceinline__ void sync() + { + __syncthreads(); + } + + static __device__ __forceinline__ int flattenedThreadId() + { + return threadIdx.z * blockDim.x * blockDim.y + threadIdx.y * blockDim.x + threadIdx.x; + } + + template + static __device__ __forceinline__ void fill(It beg, It end, const T& value) + { + int STRIDE = stride(); + It t = beg + flattenedThreadId(); + + for(; t < end; t += STRIDE) + *t = value; + } + + template + static __device__ __forceinline__ void yota(OutIt beg, OutIt end, T value) + { + int STRIDE = stride(); + int tid = flattenedThreadId(); + value += tid; + + for(OutIt t = beg + tid; t < end; t += STRIDE, value += STRIDE) + *t = value; + } + + template + static __device__ __forceinline__ void copy(InIt beg, InIt end, OutIt out) + { + int STRIDE = stride(); + InIt t = beg + flattenedThreadId(); + OutIt o = out + (t - beg); + + for(; t < end; t += STRIDE, o += STRIDE) + *o = *t; + } + + template + static __device__ __forceinline__ void transform(InIt beg, InIt end, OutIt out, UnOp op) + { + int STRIDE = stride(); + InIt t = beg + flattenedThreadId(); + OutIt o = out + (t - beg); + + for(; t < end; t += STRIDE, o += STRIDE) + *o = op(*t); + } + + template + static __device__ __forceinline__ void transform(InIt1 beg1, InIt1 end1, InIt2 beg2, OutIt out, BinOp op) + { + int STRIDE = stride(); + InIt1 t1 = beg1 + flattenedThreadId(); + InIt2 t2 = beg2 + flattenedThreadId(); + OutIt o = out + (t1 - beg1); + + for(; t1 < end1; t1 += STRIDE, t2 += STRIDE, o += STRIDE) + *o = op(*t1, *t2); + } + + template + static __device__ __forceinline__ void reduce(volatile T* buffer, BinOp op) + { + int tid = flattenedThreadId(); + T val = buffer[tid]; + + if (CTA_SIZE >= 1024) { if (tid < 512) buffer[tid] = val = op(val, buffer[tid + 512]); __syncthreads(); } + if (CTA_SIZE >= 512) { if (tid < 256) buffer[tid] = val = op(val, buffer[tid + 256]); __syncthreads(); } + if (CTA_SIZE >= 256) { if (tid < 128) buffer[tid] = val = op(val, buffer[tid + 128]); __syncthreads(); } + if (CTA_SIZE >= 128) { if (tid < 64) buffer[tid] = val = op(val, buffer[tid + 64]); __syncthreads(); } + + if (tid < 32) + { + if (CTA_SIZE >= 64) { buffer[tid] = val = op(val, buffer[tid + 32]); } + if (CTA_SIZE >= 32) { buffer[tid] = val = op(val, buffer[tid + 16]); } + if (CTA_SIZE >= 16) { buffer[tid] = val = op(val, buffer[tid + 8]); } + if (CTA_SIZE >= 8) { buffer[tid] = val = op(val, buffer[tid + 4]); } + if (CTA_SIZE >= 4) { buffer[tid] = val = op(val, buffer[tid + 2]); } + if (CTA_SIZE >= 2) { buffer[tid] = val = op(val, buffer[tid + 1]); } + } + } + + template + static __device__ __forceinline__ T reduce(volatile T* buffer, T init, BinOp op) + { + int tid = flattenedThreadId(); + T val = buffer[tid] = init; + __syncthreads(); + + if (CTA_SIZE >= 1024) { if (tid < 512) buffer[tid] = val = op(val, buffer[tid + 512]); __syncthreads(); } + if (CTA_SIZE >= 512) { if (tid < 256) buffer[tid] = val = op(val, buffer[tid + 256]); __syncthreads(); } + if (CTA_SIZE >= 256) { if (tid < 128) buffer[tid] = val = op(val, buffer[tid + 128]); __syncthreads(); } + if (CTA_SIZE >= 128) { if (tid < 64) buffer[tid] = val = op(val, buffer[tid + 64]); __syncthreads(); } + + if (tid < 32) + { + if (CTA_SIZE >= 64) { buffer[tid] = val = op(val, buffer[tid + 32]); } + if (CTA_SIZE >= 32) { buffer[tid] = val = op(val, buffer[tid + 16]); } + if (CTA_SIZE >= 16) { buffer[tid] = val = op(val, buffer[tid + 8]); } + if (CTA_SIZE >= 8) { buffer[tid] = val = op(val, buffer[tid + 4]); } + if (CTA_SIZE >= 4) { buffer[tid] = val = op(val, buffer[tid + 2]); } + if (CTA_SIZE >= 2) { buffer[tid] = val = op(val, buffer[tid + 1]); } + } + __syncthreads(); + return buffer[0]; + } + + template + static __device__ __forceinline__ void reduce_n(T* data, unsigned int n, BinOp op) + { + int ftid = flattenedThreadId(); + int sft = stride(); + + if (sft < n) + { + for (unsigned int i = sft + ftid; i < n; i += sft) + data[ftid] = op(data[ftid], data[i]); + + __syncthreads(); + + n = sft; + } + + while (n > 1) + { + unsigned int half = n/2; + + if (ftid < half) + data[ftid] = op(data[ftid], data[n - ftid - 1]); + + __syncthreads(); + + n = n - half; + } + } + }; +}}} + +//! @endcond + +#endif /* OPENCV_CUDA_DEVICE_BLOCK_HPP */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/border_interpolate.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/border_interpolate.hpp new file mode 100644 index 0000000000000000000000000000000000000000..874f705baf0249644f0b92ea31cfa2cb31b4ae19 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/border_interpolate.hpp @@ -0,0 +1,722 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_BORDER_INTERPOLATE_HPP +#define OPENCV_CUDA_BORDER_INTERPOLATE_HPP + +#include "saturate_cast.hpp" +#include "vec_traits.hpp" +#include "vec_math.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + ////////////////////////////////////////////////////////////// + // BrdConstant + + template struct BrdRowConstant + { + typedef D result_type; + + explicit __host__ __device__ __forceinline__ BrdRowConstant(int width_, const D& val_ = VecTraits::all(0)) : width(width_), val(val_) {} + + template __device__ __forceinline__ D at_low(int x, const T* data) const + { + return x >= 0 ? saturate_cast(data[x]) : val; + } + + template __device__ __forceinline__ D at_high(int x, const T* data) const + { + return x < width ? saturate_cast(data[x]) : val; + } + + template __device__ __forceinline__ D at(int x, const T* data) const + { + return (x >= 0 && x < width) ? saturate_cast(data[x]) : val; + } + + int width; + D val; + }; + + template struct BrdColConstant + { + typedef D result_type; + + explicit __host__ __device__ __forceinline__ BrdColConstant(int height_, const D& val_ = VecTraits::all(0)) : height(height_), val(val_) {} + + template __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const + { + return y >= 0 ? saturate_cast(*(const T*)((const char*)data + y * step)) : val; + } + + template __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const + { + return y < height ? saturate_cast(*(const T*)((const char*)data + y * step)) : val; + } + + template __device__ __forceinline__ D at(int y, const T* data, size_t step) const + { + return (y >= 0 && y < height) ? saturate_cast(*(const T*)((const char*)data + y * step)) : val; + } + + int height; + D val; + }; + + template struct BrdConstant + { + typedef D result_type; + + __host__ __device__ __forceinline__ BrdConstant(int height_, int width_, const D& val_ = VecTraits::all(0)) : height(height_), width(width_), val(val_) + { + } + + template __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const + { + return (x >= 0 && x < width && y >= 0 && y < height) ? saturate_cast(((const T*)((const uchar*)data + y * step))[x]) : val; + } + + template __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const + { + return (x >= 0 && x < width && y >= 0 && y < height) ? saturate_cast(src(y, x)) : val; + } + + int height; + int width; + D val; + }; + + ////////////////////////////////////////////////////////////// + // BrdReplicate + + template struct BrdRowReplicate + { + typedef D result_type; + + explicit __host__ __device__ __forceinline__ BrdRowReplicate(int width) : last_col(width - 1) {} + template __host__ __device__ __forceinline__ BrdRowReplicate(int width, U) : last_col(width - 1) {} + + __device__ __forceinline__ int idx_col_low(int x) const + { + return ::max(x, 0); + } + + __device__ __forceinline__ int idx_col_high(int x) const + { + return ::min(x, last_col); + } + + __device__ __forceinline__ int idx_col(int x) const + { + return idx_col_low(idx_col_high(x)); + } + + template __device__ __forceinline__ D at_low(int x, const T* data) const + { + return saturate_cast(data[idx_col_low(x)]); + } + + template __device__ __forceinline__ D at_high(int x, const T* data) const + { + return saturate_cast(data[idx_col_high(x)]); + } + + template __device__ __forceinline__ D at(int x, const T* data) const + { + return saturate_cast(data[idx_col(x)]); + } + + int last_col; + }; + + template struct BrdColReplicate + { + typedef D result_type; + + explicit __host__ __device__ __forceinline__ BrdColReplicate(int height) : last_row(height - 1) {} + template __host__ __device__ __forceinline__ BrdColReplicate(int height, U) : last_row(height - 1) {} + + __device__ __forceinline__ int idx_row_low(int y) const + { + return ::max(y, 0); + } + + __device__ __forceinline__ int idx_row_high(int y) const + { + return ::min(y, last_row); + } + + __device__ __forceinline__ int idx_row(int y) const + { + return idx_row_low(idx_row_high(y)); + } + + template __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const + { + return saturate_cast(*(const T*)((const char*)data + idx_row_low(y) * step)); + } + + template __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const + { + return saturate_cast(*(const T*)((const char*)data + idx_row_high(y) * step)); + } + + template __device__ __forceinline__ D at(int y, const T* data, size_t step) const + { + return saturate_cast(*(const T*)((const char*)data + idx_row(y) * step)); + } + + int last_row; + }; + + template struct BrdReplicate + { + typedef D result_type; + + __host__ __device__ __forceinline__ BrdReplicate(int height, int width) : last_row(height - 1), last_col(width - 1) {} + template __host__ __device__ __forceinline__ BrdReplicate(int height, int width, U) : last_row(height - 1), last_col(width - 1) {} + + __device__ __forceinline__ int idx_row_low(int y) const + { + return ::max(y, 0); + } + + __device__ __forceinline__ int idx_row_high(int y) const + { + return ::min(y, last_row); + } + + __device__ __forceinline__ int idx_row(int y) const + { + return idx_row_low(idx_row_high(y)); + } + + __device__ __forceinline__ int idx_col_low(int x) const + { + return ::max(x, 0); + } + + __device__ __forceinline__ int idx_col_high(int x) const + { + return ::min(x, last_col); + } + + __device__ __forceinline__ int idx_col(int x) const + { + return idx_col_low(idx_col_high(x)); + } + + template __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const + { + return saturate_cast(((const T*)((const char*)data + idx_row(y) * step))[idx_col(x)]); + } + + template __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const + { + return saturate_cast(src(idx_row(y), idx_col(x))); + } + + int last_row; + int last_col; + }; + + ////////////////////////////////////////////////////////////// + // BrdReflect101 + + template struct BrdRowReflect101 + { + typedef D result_type; + + explicit __host__ __device__ __forceinline__ BrdRowReflect101(int width) : last_col(width - 1) {} + template __host__ __device__ __forceinline__ BrdRowReflect101(int width, U) : last_col(width - 1) {} + + __device__ __forceinline__ int idx_col_low(int x) const + { + return ::abs(x) % (last_col + 1); + } + + __device__ __forceinline__ int idx_col_high(int x) const + { + return ::abs(last_col - ::abs(last_col - x)) % (last_col + 1); + } + + __device__ __forceinline__ int idx_col(int x) const + { + return idx_col_low(idx_col_high(x)); + } + + template __device__ __forceinline__ D at_low(int x, const T* data) const + { + return saturate_cast(data[idx_col_low(x)]); + } + + template __device__ __forceinline__ D at_high(int x, const T* data) const + { + return saturate_cast(data[idx_col_high(x)]); + } + + template __device__ __forceinline__ D at(int x, const T* data) const + { + return saturate_cast(data[idx_col(x)]); + } + + int last_col; + }; + + template struct BrdColReflect101 + { + typedef D result_type; + + explicit __host__ __device__ __forceinline__ BrdColReflect101(int height) : last_row(height - 1) {} + template __host__ __device__ __forceinline__ BrdColReflect101(int height, U) : last_row(height - 1) {} + + __device__ __forceinline__ int idx_row_low(int y) const + { + return ::abs(y) % (last_row + 1); + } + + __device__ __forceinline__ int idx_row_high(int y) const + { + return ::abs(last_row - ::abs(last_row - y)) % (last_row + 1); + } + + __device__ __forceinline__ int idx_row(int y) const + { + return idx_row_low(idx_row_high(y)); + } + + template __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const + { + return saturate_cast(*(const D*)((const char*)data + idx_row_low(y) * step)); + } + + template __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const + { + return saturate_cast(*(const D*)((const char*)data + idx_row_high(y) * step)); + } + + template __device__ __forceinline__ D at(int y, const T* data, size_t step) const + { + return saturate_cast(*(const D*)((const char*)data + idx_row(y) * step)); + } + + int last_row; + }; + + template struct BrdReflect101 + { + typedef D result_type; + + __host__ __device__ __forceinline__ BrdReflect101(int height, int width) : last_row(height - 1), last_col(width - 1) {} + template __host__ __device__ __forceinline__ BrdReflect101(int height, int width, U) : last_row(height - 1), last_col(width - 1) {} + + __device__ __forceinline__ int idx_row_low(int y) const + { + return ::abs(y) % (last_row + 1); + } + + __device__ __forceinline__ int idx_row_high(int y) const + { + return ::abs(last_row - ::abs(last_row - y)) % (last_row + 1); + } + + __device__ __forceinline__ int idx_row(int y) const + { + return idx_row_low(idx_row_high(y)); + } + + __device__ __forceinline__ int idx_col_low(int x) const + { + return ::abs(x) % (last_col + 1); + } + + __device__ __forceinline__ int idx_col_high(int x) const + { + return ::abs(last_col - ::abs(last_col - x)) % (last_col + 1); + } + + __device__ __forceinline__ int idx_col(int x) const + { + return idx_col_low(idx_col_high(x)); + } + + template __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const + { + return saturate_cast(((const T*)((const char*)data + idx_row(y) * step))[idx_col(x)]); + } + + template __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const + { + return saturate_cast(src(idx_row(y), idx_col(x))); + } + + int last_row; + int last_col; + }; + + ////////////////////////////////////////////////////////////// + // BrdReflect + + template struct BrdRowReflect + { + typedef D result_type; + + explicit __host__ __device__ __forceinline__ BrdRowReflect(int width) : last_col(width - 1) {} + template __host__ __device__ __forceinline__ BrdRowReflect(int width, U) : last_col(width - 1) {} + + __device__ __forceinline__ int idx_col_low(int x) const + { + return (::abs(x) - (x < 0)) % (last_col + 1); + } + + __device__ __forceinline__ int idx_col_high(int x) const + { + return ::abs(last_col - ::abs(last_col - x) + (x > last_col)) % (last_col + 1); + } + + __device__ __forceinline__ int idx_col(int x) const + { + return idx_col_high(::abs(x) - (x < 0)); + } + + template __device__ __forceinline__ D at_low(int x, const T* data) const + { + return saturate_cast(data[idx_col_low(x)]); + } + + template __device__ __forceinline__ D at_high(int x, const T* data) const + { + return saturate_cast(data[idx_col_high(x)]); + } + + template __device__ __forceinline__ D at(int x, const T* data) const + { + return saturate_cast(data[idx_col(x)]); + } + + int last_col; + }; + + template struct BrdColReflect + { + typedef D result_type; + + explicit __host__ __device__ __forceinline__ BrdColReflect(int height) : last_row(height - 1) {} + template __host__ __device__ __forceinline__ BrdColReflect(int height, U) : last_row(height - 1) {} + + __device__ __forceinline__ int idx_row_low(int y) const + { + return (::abs(y) - (y < 0)) % (last_row + 1); + } + + __device__ __forceinline__ int idx_row_high(int y) const + { + return ::abs(last_row - ::abs(last_row - y) + (y > last_row)) % (last_row + 1); + } + + __device__ __forceinline__ int idx_row(int y) const + { + return idx_row_high(::abs(y) - (y < 0)); + } + + template __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const + { + return saturate_cast(*(const D*)((const char*)data + idx_row_low(y) * step)); + } + + template __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const + { + return saturate_cast(*(const D*)((const char*)data + idx_row_high(y) * step)); + } + + template __device__ __forceinline__ D at(int y, const T* data, size_t step) const + { + return saturate_cast(*(const D*)((const char*)data + idx_row(y) * step)); + } + + int last_row; + }; + + template struct BrdReflect + { + typedef D result_type; + + __host__ __device__ __forceinline__ BrdReflect(int height, int width) : last_row(height - 1), last_col(width - 1) {} + template __host__ __device__ __forceinline__ BrdReflect(int height, int width, U) : last_row(height - 1), last_col(width - 1) {} + + __device__ __forceinline__ int idx_row_low(int y) const + { + return (::abs(y) - (y < 0)) % (last_row + 1); + } + + __device__ __forceinline__ int idx_row_high(int y) const + { + return /*::abs*/(last_row - ::abs(last_row - y) + (y > last_row)) /*% (last_row + 1)*/; + } + + __device__ __forceinline__ int idx_row(int y) const + { + return idx_row_low(idx_row_high(y)); + } + + __device__ __forceinline__ int idx_col_low(int x) const + { + return (::abs(x) - (x < 0)) % (last_col + 1); + } + + __device__ __forceinline__ int idx_col_high(int x) const + { + return (last_col - ::abs(last_col - x) + (x > last_col)); + } + + __device__ __forceinline__ int idx_col(int x) const + { + return idx_col_low(idx_col_high(x)); + } + + template __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const + { + return saturate_cast(((const T*)((const char*)data + idx_row(y) * step))[idx_col(x)]); + } + + template __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const + { + return saturate_cast(src(idx_row(y), idx_col(x))); + } + + int last_row; + int last_col; + }; + + ////////////////////////////////////////////////////////////// + // BrdWrap + + template struct BrdRowWrap + { + typedef D result_type; + + explicit __host__ __device__ __forceinline__ BrdRowWrap(int width_) : width(width_) {} + template __host__ __device__ __forceinline__ BrdRowWrap(int width_, U) : width(width_) {} + + __device__ __forceinline__ int idx_col_low(int x) const + { + return (x >= 0) * x + (x < 0) * (x - ((x - width + 1) / width) * width); + } + + __device__ __forceinline__ int idx_col_high(int x) const + { + return (x < width) * x + (x >= width) * (x % width); + } + + __device__ __forceinline__ int idx_col(int x) const + { + return idx_col_high(idx_col_low(x)); + } + + template __device__ __forceinline__ D at_low(int x, const T* data) const + { + return saturate_cast(data[idx_col_low(x)]); + } + + template __device__ __forceinline__ D at_high(int x, const T* data) const + { + return saturate_cast(data[idx_col_high(x)]); + } + + template __device__ __forceinline__ D at(int x, const T* data) const + { + return saturate_cast(data[idx_col(x)]); + } + + int width; + }; + + template struct BrdColWrap + { + typedef D result_type; + + explicit __host__ __device__ __forceinline__ BrdColWrap(int height_) : height(height_) {} + template __host__ __device__ __forceinline__ BrdColWrap(int height_, U) : height(height_) {} + + __device__ __forceinline__ int idx_row_low(int y) const + { + return (y >= 0) * y + (y < 0) * (y - ((y - height + 1) / height) * height); + } + + __device__ __forceinline__ int idx_row_high(int y) const + { + return (y < height) * y + (y >= height) * (y % height); + } + + __device__ __forceinline__ int idx_row(int y) const + { + return idx_row_high(idx_row_low(y)); + } + + template __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const + { + return saturate_cast(*(const D*)((const char*)data + idx_row_low(y) * step)); + } + + template __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const + { + return saturate_cast(*(const D*)((const char*)data + idx_row_high(y) * step)); + } + + template __device__ __forceinline__ D at(int y, const T* data, size_t step) const + { + return saturate_cast(*(const D*)((const char*)data + idx_row(y) * step)); + } + + int height; + }; + + template struct BrdWrap + { + typedef D result_type; + + __host__ __device__ __forceinline__ BrdWrap(int height_, int width_) : + height(height_), width(width_) + { + } + template + __host__ __device__ __forceinline__ BrdWrap(int height_, int width_, U) : + height(height_), width(width_) + { + } + + __device__ __forceinline__ int idx_row_low(int y) const + { + return (y >= 0) ? y : (y - ((y - height + 1) / height) * height); + } + + __device__ __forceinline__ int idx_row_high(int y) const + { + return (y < height) ? y : (y % height); + } + + __device__ __forceinline__ int idx_row(int y) const + { + return idx_row_high(idx_row_low(y)); + } + + __device__ __forceinline__ int idx_col_low(int x) const + { + return (x >= 0) ? x : (x - ((x - width + 1) / width) * width); + } + + __device__ __forceinline__ int idx_col_high(int x) const + { + return (x < width) ? x : (x % width); + } + + __device__ __forceinline__ int idx_col(int x) const + { + return idx_col_high(idx_col_low(x)); + } + + template __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const + { + return saturate_cast(((const T*)((const char*)data + idx_row(y) * step))[idx_col(x)]); + } + + template __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const + { + return saturate_cast(src(idx_row(y), idx_col(x))); + } + + int height; + int width; + }; + + ////////////////////////////////////////////////////////////// + // BorderReader + + template struct BorderReader + { + typedef typename B::result_type elem_type; + typedef typename Ptr2D::index_type index_type; + + __host__ __device__ __forceinline__ BorderReader(const Ptr2D& ptr_, const B& b_) : ptr(ptr_), b(b_) {} + + __device__ __forceinline__ elem_type operator ()(index_type y, index_type x) const + { + return b.at(y, x, ptr); + } + + Ptr2D ptr; + B b; + }; + + // under win32 there is some bug with templated types that passed as kernel parameters + // with this specialization all works fine + template struct BorderReader< Ptr2D, BrdConstant > + { + typedef typename BrdConstant::result_type elem_type; + typedef typename Ptr2D::index_type index_type; + + __host__ __device__ __forceinline__ BorderReader(const Ptr2D& src_, const BrdConstant& b) : + src(src_), height(b.height), width(b.width), val(b.val) + { + } + + __device__ __forceinline__ D operator ()(index_type y, index_type x) const + { + return (x >= 0 && x < width && y >= 0 && y < height) ? saturate_cast(src(y, x)) : val; + } + + Ptr2D src; + int height; + int width; + D val; + }; +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_BORDER_INTERPOLATE_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/color.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/color.hpp new file mode 100644 index 0000000000000000000000000000000000000000..dcce280214826d3495bb89699be3197822085090 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/color.hpp @@ -0,0 +1,309 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_COLOR_HPP +#define OPENCV_CUDA_COLOR_HPP + +#include "detail/color_detail.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + // All OPENCV_CUDA_IMPLEMENT_*_TRAITS(ColorSpace1_to_ColorSpace2, ...) macros implements + // template class ColorSpace1_to_ColorSpace2_traits + // { + // typedef ... functor_type; + // static __host__ __device__ functor_type create_functor(); + // }; + + OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgr_to_rgb, 3, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgr_to_bgra, 3, 4, 0) + OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgr_to_rgba, 3, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgra_to_bgr, 4, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgra_to_rgb, 4, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgra_to_rgba, 4, 4, 2) + + #undef OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS + + OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(bgr_to_bgr555, 3, 0, 5) + OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(bgr_to_bgr565, 3, 0, 6) + OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(rgb_to_bgr555, 3, 2, 5) + OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(rgb_to_bgr565, 3, 2, 6) + OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(bgra_to_bgr555, 4, 0, 5) + OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(bgra_to_bgr565, 4, 0, 6) + OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(rgba_to_bgr555, 4, 2, 5) + OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(rgba_to_bgr565, 4, 2, 6) + + #undef OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS + + OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr555_to_rgb, 3, 2, 5) + OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr565_to_rgb, 3, 2, 6) + OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr555_to_bgr, 3, 0, 5) + OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr565_to_bgr, 3, 0, 6) + OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr555_to_rgba, 4, 2, 5) + OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr565_to_rgba, 4, 2, 6) + OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr555_to_bgra, 4, 0, 5) + OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr565_to_bgra, 4, 0, 6) + + #undef OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS + + OPENCV_CUDA_IMPLEMENT_GRAY2RGB_TRAITS(gray_to_bgr, 3) + OPENCV_CUDA_IMPLEMENT_GRAY2RGB_TRAITS(gray_to_bgra, 4) + + #undef OPENCV_CUDA_IMPLEMENT_GRAY2RGB_TRAITS + + OPENCV_CUDA_IMPLEMENT_GRAY2RGB5x5_TRAITS(gray_to_bgr555, 5) + OPENCV_CUDA_IMPLEMENT_GRAY2RGB5x5_TRAITS(gray_to_bgr565, 6) + + #undef OPENCV_CUDA_IMPLEMENT_GRAY2RGB5x5_TRAITS + + OPENCV_CUDA_IMPLEMENT_RGB5x52GRAY_TRAITS(bgr555_to_gray, 5) + OPENCV_CUDA_IMPLEMENT_RGB5x52GRAY_TRAITS(bgr565_to_gray, 6) + + #undef OPENCV_CUDA_IMPLEMENT_RGB5x52GRAY_TRAITS + + OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS(rgb_to_gray, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS(bgr_to_gray, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS(rgba_to_gray, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS(bgra_to_gray, 4, 0) + + #undef OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS + + OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(rgb_to_yuv, 3, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(rgba_to_yuv, 4, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(rgb_to_yuv4, 3, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(rgba_to_yuv4, 4, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(bgr_to_yuv, 3, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(bgra_to_yuv, 4, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(bgr_to_yuv4, 3, 4, 0) + OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(bgra_to_yuv4, 4, 4, 0) + + #undef OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS + + OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv_to_rgb, 3, 3, 2) + OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv_to_rgba, 3, 4, 2) + OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv4_to_rgb, 4, 3, 2) + OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv4_to_rgba, 4, 4, 2) + OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv_to_bgr, 3, 3, 0) + OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv_to_bgra, 3, 4, 0) + OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv4_to_bgr, 4, 3, 0) + OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv4_to_bgra, 4, 4, 0) + + #undef OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS + + OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(rgb_to_YCrCb, 3, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(rgba_to_YCrCb, 4, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(rgb_to_YCrCb4, 3, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(rgba_to_YCrCb4, 4, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(bgr_to_YCrCb, 3, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(bgra_to_YCrCb, 4, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(bgr_to_YCrCb4, 3, 4, 0) + OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(bgra_to_YCrCb4, 4, 4, 0) + + #undef OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS + + OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb_to_rgb, 3, 3, 2) + OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb_to_rgba, 3, 4, 2) + OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb4_to_rgb, 4, 3, 2) + OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb4_to_rgba, 4, 4, 2) + OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb_to_bgr, 3, 3, 0) + OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb_to_bgra, 3, 4, 0) + OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb4_to_bgr, 4, 3, 0) + OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb4_to_bgra, 4, 4, 0) + + #undef OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS + + OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(rgb_to_xyz, 3, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(rgba_to_xyz, 4, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(rgb_to_xyz4, 3, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(rgba_to_xyz4, 4, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(bgr_to_xyz, 3, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(bgra_to_xyz, 4, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(bgr_to_xyz4, 3, 4, 0) + OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(bgra_to_xyz4, 4, 4, 0) + + #undef OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS + + OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz_to_rgb, 3, 3, 2) + OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz4_to_rgb, 4, 3, 2) + OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz_to_rgba, 3, 4, 2) + OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz4_to_rgba, 4, 4, 2) + OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz_to_bgr, 3, 3, 0) + OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz4_to_bgr, 4, 3, 0) + OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz_to_bgra, 3, 4, 0) + OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz4_to_bgra, 4, 4, 0) + + #undef OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS + + OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(rgb_to_hsv, 3, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(rgba_to_hsv, 4, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(rgb_to_hsv4, 3, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(rgba_to_hsv4, 4, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(bgr_to_hsv, 3, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(bgra_to_hsv, 4, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(bgr_to_hsv4, 3, 4, 0) + OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(bgra_to_hsv4, 4, 4, 0) + + #undef OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS + + OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv_to_rgb, 3, 3, 2) + OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv_to_rgba, 3, 4, 2) + OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv4_to_rgb, 4, 3, 2) + OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv4_to_rgba, 4, 4, 2) + OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv_to_bgr, 3, 3, 0) + OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv_to_bgra, 3, 4, 0) + OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv4_to_bgr, 4, 3, 0) + OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv4_to_bgra, 4, 4, 0) + + #undef OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS + + OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(rgb_to_hls, 3, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(rgba_to_hls, 4, 3, 2) + OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(rgb_to_hls4, 3, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(rgba_to_hls4, 4, 4, 2) + OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(bgr_to_hls, 3, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(bgra_to_hls, 4, 3, 0) + OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(bgr_to_hls4, 3, 4, 0) + OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(bgra_to_hls4, 4, 4, 0) + + #undef OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS + + OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls_to_rgb, 3, 3, 2) + OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls_to_rgba, 3, 4, 2) + OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls4_to_rgb, 4, 3, 2) + OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls4_to_rgba, 4, 4, 2) + OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls_to_bgr, 3, 3, 0) + OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls_to_bgra, 3, 4, 0) + OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls4_to_bgr, 4, 3, 0) + OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls4_to_bgra, 4, 4, 0) + + #undef OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS + + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(rgb_to_lab, 3, 3, true, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(rgba_to_lab, 4, 3, true, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(rgb_to_lab4, 3, 4, true, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(rgba_to_lab4, 4, 4, true, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(bgr_to_lab, 3, 3, true, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(bgra_to_lab, 4, 3, true, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(bgr_to_lab4, 3, 4, true, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(bgra_to_lab4, 4, 4, true, 0) + + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lrgb_to_lab, 3, 3, false, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lrgba_to_lab, 4, 3, false, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lrgb_to_lab4, 3, 4, false, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lrgba_to_lab4, 4, 4, false, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lbgr_to_lab, 3, 3, false, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lbgra_to_lab, 4, 3, false, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lbgr_to_lab4, 3, 4, false, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lbgra_to_lab4, 4, 4, false, 0) + + #undef OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS + + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_rgb, 3, 3, true, 2) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_rgb, 4, 3, true, 2) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_rgba, 3, 4, true, 2) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_rgba, 4, 4, true, 2) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_bgr, 3, 3, true, 0) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_bgr, 4, 3, true, 0) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_bgra, 3, 4, true, 0) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_bgra, 4, 4, true, 0) + + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_lrgb, 3, 3, false, 2) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_lrgb, 4, 3, false, 2) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_lrgba, 3, 4, false, 2) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_lrgba, 4, 4, false, 2) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_lbgr, 3, 3, false, 0) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_lbgr, 4, 3, false, 0) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_lbgra, 3, 4, false, 0) + OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_lbgra, 4, 4, false, 0) + + #undef OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS + + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(rgb_to_luv, 3, 3, true, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(rgba_to_luv, 4, 3, true, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(rgb_to_luv4, 3, 4, true, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(rgba_to_luv4, 4, 4, true, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(bgr_to_luv, 3, 3, true, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(bgra_to_luv, 4, 3, true, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(bgr_to_luv4, 3, 4, true, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(bgra_to_luv4, 4, 4, true, 0) + + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lrgb_to_luv, 3, 3, false, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lrgba_to_luv, 4, 3, false, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lrgb_to_luv4, 3, 4, false, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lrgba_to_luv4, 4, 4, false, 2) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lbgr_to_luv, 3, 3, false, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lbgra_to_luv, 4, 3, false, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lbgr_to_luv4, 3, 4, false, 0) + OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lbgra_to_luv4, 4, 4, false, 0) + + #undef OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS + + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_rgb, 3, 3, true, 2) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_rgb, 4, 3, true, 2) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_rgba, 3, 4, true, 2) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_rgba, 4, 4, true, 2) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_bgr, 3, 3, true, 0) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_bgr, 4, 3, true, 0) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_bgra, 3, 4, true, 0) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_bgra, 4, 4, true, 0) + + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_lrgb, 3, 3, false, 2) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_lrgb, 4, 3, false, 2) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_lrgba, 3, 4, false, 2) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_lrgba, 4, 4, false, 2) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_lbgr, 3, 3, false, 0) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_lbgr, 4, 3, false, 0) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_lbgra, 3, 4, false, 0) + OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_lbgra, 4, 4, false, 0) + + #undef OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_COLOR_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/common.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/common.hpp new file mode 100644 index 0000000000000000000000000000000000000000..14b1f3f5aecd40562c3b8b9ad06d05098fa3da30 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/common.hpp @@ -0,0 +1,109 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_COMMON_HPP +#define OPENCV_CUDA_COMMON_HPP + +#include +#include "opencv2/core/cuda_types.hpp" +#include "opencv2/core/cvdef.h" +#include "opencv2/core/base.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +#ifndef CV_PI_F + #ifndef CV_PI + #define CV_PI_F 3.14159265f + #else + #define CV_PI_F ((float)CV_PI) + #endif +#endif + +namespace cv { namespace cuda { + static inline void checkCudaError(cudaError_t err, const char* file, const int line, const char* func) + { + if (cudaSuccess != err) + cv::error(cv::Error::GpuApiCallError, cudaGetErrorString(err), func, file, line); + } +}} + +#ifndef cudaSafeCall + #define cudaSafeCall(expr) cv::cuda::checkCudaError(expr, __FILE__, __LINE__, CV_Func) +#endif + +namespace cv { namespace cuda +{ + template static inline bool isAligned(const T* ptr, size_t size) + { + return reinterpret_cast(ptr) % size == 0; + } + + static inline bool isAligned(size_t step, size_t size) + { + return step % size == 0; + } +}} + +namespace cv { namespace cuda +{ + namespace device + { + __host__ __device__ __forceinline__ int divUp(int total, int grain) + { + return (total + grain - 1) / grain; + } + + template inline void bindTexture(const textureReference* tex, const PtrStepSz& img) + { + cudaChannelFormatDesc desc = cudaCreateChannelDesc(); + cudaSafeCall( cudaBindTexture2D(0, tex, img.ptr(), &desc, img.cols, img.rows, img.step) ); + } + } +}} + +//! @endcond + +#endif // OPENCV_CUDA_COMMON_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/datamov_utils.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/datamov_utils.hpp new file mode 100644 index 0000000000000000000000000000000000000000..6820d0fd64de9090dd9f02a296ea3de0a3884ef1 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/datamov_utils.hpp @@ -0,0 +1,113 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_DATAMOV_UTILS_HPP +#define OPENCV_CUDA_DATAMOV_UTILS_HPP + +#include "common.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 200 + + // for Fermi memory space is detected automatically + template struct ForceGlob + { + __device__ __forceinline__ static void Load(const T* ptr, int offset, T& val) { val = ptr[offset]; } + }; + + #else // __CUDA_ARCH__ >= 200 + + #if defined(_WIN64) || defined(__LP64__) + // 64-bit register modifier for inlined asm + #define OPENCV_CUDA_ASM_PTR "l" + #else + // 32-bit register modifier for inlined asm + #define OPENCV_CUDA_ASM_PTR "r" + #endif + + template struct ForceGlob; + + #define OPENCV_CUDA_DEFINE_FORCE_GLOB(base_type, ptx_type, reg_mod) \ + template <> struct ForceGlob \ + { \ + __device__ __forceinline__ static void Load(const base_type* ptr, int offset, base_type& val) \ + { \ + asm("ld.global."#ptx_type" %0, [%1];" : "="#reg_mod(val) : OPENCV_CUDA_ASM_PTR(ptr + offset)); \ + } \ + }; + + #define OPENCV_CUDA_DEFINE_FORCE_GLOB_B(base_type, ptx_type) \ + template <> struct ForceGlob \ + { \ + __device__ __forceinline__ static void Load(const base_type* ptr, int offset, base_type& val) \ + { \ + asm("ld.global."#ptx_type" %0, [%1];" : "=r"(*reinterpret_cast(&val)) : OPENCV_CUDA_ASM_PTR(ptr + offset)); \ + } \ + }; + + OPENCV_CUDA_DEFINE_FORCE_GLOB_B(uchar, u8) + OPENCV_CUDA_DEFINE_FORCE_GLOB_B(schar, s8) + OPENCV_CUDA_DEFINE_FORCE_GLOB_B(char, b8) + OPENCV_CUDA_DEFINE_FORCE_GLOB (ushort, u16, h) + OPENCV_CUDA_DEFINE_FORCE_GLOB (short, s16, h) + OPENCV_CUDA_DEFINE_FORCE_GLOB (uint, u32, r) + OPENCV_CUDA_DEFINE_FORCE_GLOB (int, s32, r) + OPENCV_CUDA_DEFINE_FORCE_GLOB (float, f32, f) + OPENCV_CUDA_DEFINE_FORCE_GLOB (double, f64, d) + + #undef OPENCV_CUDA_DEFINE_FORCE_GLOB + #undef OPENCV_CUDA_DEFINE_FORCE_GLOB_B + #undef OPENCV_CUDA_ASM_PTR + + #endif // __CUDA_ARCH__ >= 200 +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_DATAMOV_UTILS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/detail/color_detail.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/detail/color_detail.hpp new file mode 100644 index 0000000000000000000000000000000000000000..bfb40550dcda7b48b2ebdf7342447468dfa350ce --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/detail/color_detail.hpp @@ -0,0 +1,1980 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_COLOR_DETAIL_HPP +#define OPENCV_CUDA_COLOR_DETAIL_HPP + +#include "../common.hpp" +#include "../vec_traits.hpp" +#include "../saturate_cast.hpp" +#include "../limits.hpp" +#include "../functional.hpp" + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + #ifndef CV_DESCALE + #define CV_DESCALE(x, n) (((x) + (1 << ((n)-1))) >> (n)) + #endif + + namespace color_detail + { + template struct ColorChannel + { + typedef float worktype_f; + static __device__ __forceinline__ T max() { return numeric_limits::max(); } + static __device__ __forceinline__ T half() { return (T)(max()/2 + 1); } + }; + + template<> struct ColorChannel + { + typedef float worktype_f; + static __device__ __forceinline__ float max() { return 1.f; } + static __device__ __forceinline__ float half() { return 0.5f; } + }; + + template static __device__ __forceinline__ void setAlpha(typename TypeVec::vec_type& vec, T val) + { + } + + template static __device__ __forceinline__ void setAlpha(typename TypeVec::vec_type& vec, T val) + { + vec.w = val; + } + + template static __device__ __forceinline__ T getAlpha(const typename TypeVec::vec_type& vec) + { + return ColorChannel::max(); + } + + template static __device__ __forceinline__ T getAlpha(const typename TypeVec::vec_type& vec) + { + return vec.w; + } + + enum + { + yuv_shift = 14, + xyz_shift = 12, + R2Y = 4899, + G2Y = 9617, + B2Y = 1868, + BLOCK_SIZE = 256 + }; + } + +////////////////// Various 3/4-channel to 3/4-channel RGB transformations ///////////////// + + namespace color_detail + { + template struct RGB2RGB + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + dst.x = (&src.x)[bidx]; + dst.y = src.y; + dst.z = (&src.x)[bidx^2]; + setAlpha(dst, getAlpha(src)); + + return dst; + } + + __host__ __device__ __forceinline__ RGB2RGB() {} + __host__ __device__ __forceinline__ RGB2RGB(const RGB2RGB&) {} + }; + + template <> struct RGB2RGB : unary_function + { + __device__ uint operator()(uint src) const + { + uint dst = 0; + + dst |= (0xffu & (src >> 16)); + dst |= (0xffu & (src >> 8)) << 8; + dst |= (0xffu & (src)) << 16; + dst |= (0xffu & (src >> 24)) << 24; + + return dst; + } + + __host__ __device__ __forceinline__ RGB2RGB() {} + __host__ __device__ __forceinline__ RGB2RGB(const RGB2RGB&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(name, scn, dcn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + +/////////// Transforming 16-bit (565 or 555) RGB to/from 24/32-bit (888[8]) RGB ////////// + + namespace color_detail + { + template struct RGB2RGB5x5Converter; + template struct RGB2RGB5x5Converter<6, bidx> + { + static __device__ __forceinline__ ushort cvt(const uchar3& src) + { + return (ushort)(((&src.x)[bidx] >> 3) | ((src.y & ~3) << 3) | (((&src.x)[bidx^2] & ~7) << 8)); + } + + static __device__ __forceinline__ ushort cvt(uint src) + { + uint b = 0xffu & (src >> (bidx * 8)); + uint g = 0xffu & (src >> 8); + uint r = 0xffu & (src >> ((bidx ^ 2) * 8)); + return (ushort)((b >> 3) | ((g & ~3) << 3) | ((r & ~7) << 8)); + } + }; + + template struct RGB2RGB5x5Converter<5, bidx> + { + static __device__ __forceinline__ ushort cvt(const uchar3& src) + { + return (ushort)(((&src.x)[bidx] >> 3) | ((src.y & ~7) << 2) | (((&src.x)[bidx^2] & ~7) << 7)); + } + + static __device__ __forceinline__ ushort cvt(uint src) + { + uint b = 0xffu & (src >> (bidx * 8)); + uint g = 0xffu & (src >> 8); + uint r = 0xffu & (src >> ((bidx ^ 2) * 8)); + uint a = 0xffu & (src >> 24); + return (ushort)((b >> 3) | ((g & ~7) << 2) | ((r & ~7) << 7) | (a * 0x8000)); + } + }; + + template struct RGB2RGB5x5; + + template struct RGB2RGB5x5<3, bidx,green_bits> : unary_function + { + __device__ __forceinline__ ushort operator()(const uchar3& src) const + { + return RGB2RGB5x5Converter::cvt(src); + } + + __host__ __device__ __forceinline__ RGB2RGB5x5() {} + __host__ __device__ __forceinline__ RGB2RGB5x5(const RGB2RGB5x5&) {} + }; + + template struct RGB2RGB5x5<4, bidx,green_bits> : unary_function + { + __device__ __forceinline__ ushort operator()(uint src) const + { + return RGB2RGB5x5Converter::cvt(src); + } + + __host__ __device__ __forceinline__ RGB2RGB5x5() {} + __host__ __device__ __forceinline__ RGB2RGB5x5(const RGB2RGB5x5&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(name, scn, bidx, green_bits) \ + struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2RGB5x5 functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + + namespace color_detail + { + template struct RGB5x52RGBConverter; + + template struct RGB5x52RGBConverter<5, bidx> + { + static __device__ __forceinline__ void cvt(uint src, uchar3& dst) + { + (&dst.x)[bidx] = src << 3; + dst.y = (src >> 2) & ~7; + (&dst.x)[bidx ^ 2] = (src >> 7) & ~7; + } + + static __device__ __forceinline__ void cvt(uint src, uint& dst) + { + dst = 0; + + dst |= (0xffu & (src << 3)) << (bidx * 8); + dst |= (0xffu & ((src >> 2) & ~7)) << 8; + dst |= (0xffu & ((src >> 7) & ~7)) << ((bidx ^ 2) * 8); + dst |= ((src & 0x8000) * 0xffu) << 24; + } + }; + + template struct RGB5x52RGBConverter<6, bidx> + { + static __device__ __forceinline__ void cvt(uint src, uchar3& dst) + { + (&dst.x)[bidx] = src << 3; + dst.y = (src >> 3) & ~3; + (&dst.x)[bidx ^ 2] = (src >> 8) & ~7; + } + + static __device__ __forceinline__ void cvt(uint src, uint& dst) + { + dst = 0xffu << 24; + + dst |= (0xffu & (src << 3)) << (bidx * 8); + dst |= (0xffu &((src >> 3) & ~3)) << 8; + dst |= (0xffu & ((src >> 8) & ~7)) << ((bidx ^ 2) * 8); + } + }; + + template struct RGB5x52RGB; + + template struct RGB5x52RGB<3, bidx, green_bits> : unary_function + { + __device__ __forceinline__ uchar3 operator()(ushort src) const + { + uchar3 dst; + RGB5x52RGBConverter::cvt(src, dst); + return dst; + } + __host__ __device__ __forceinline__ RGB5x52RGB() {} + __host__ __device__ __forceinline__ RGB5x52RGB(const RGB5x52RGB&) {} + + }; + + template struct RGB5x52RGB<4, bidx, green_bits> : unary_function + { + __device__ __forceinline__ uint operator()(ushort src) const + { + uint dst; + RGB5x52RGBConverter::cvt(src, dst); + return dst; + } + __host__ __device__ __forceinline__ RGB5x52RGB() {} + __host__ __device__ __forceinline__ RGB5x52RGB(const RGB5x52RGB&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(name, dcn, bidx, green_bits) \ + struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB5x52RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + +///////////////////////////////// Grayscale to Color //////////////////////////////// + + namespace color_detail + { + template struct Gray2RGB : unary_function::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator()(T src) const + { + typename TypeVec::vec_type dst; + + dst.z = dst.y = dst.x = src; + setAlpha(dst, ColorChannel::max()); + + return dst; + } + __host__ __device__ __forceinline__ Gray2RGB() {} + __host__ __device__ __forceinline__ Gray2RGB(const Gray2RGB&) {} + }; + + template <> struct Gray2RGB : unary_function + { + __device__ __forceinline__ uint operator()(uint src) const + { + uint dst = 0xffu << 24; + + dst |= src; + dst |= src << 8; + dst |= src << 16; + + return dst; + } + __host__ __device__ __forceinline__ Gray2RGB() {} + __host__ __device__ __forceinline__ Gray2RGB(const Gray2RGB&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_GRAY2RGB_TRAITS(name, dcn) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::Gray2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + + namespace color_detail + { + template struct Gray2RGB5x5Converter; + template<> struct Gray2RGB5x5Converter<6> + { + static __device__ __forceinline__ ushort cvt(uint t) + { + return (ushort)((t >> 3) | ((t & ~3) << 3) | ((t & ~7) << 8)); + } + }; + + template<> struct Gray2RGB5x5Converter<5> + { + static __device__ __forceinline__ ushort cvt(uint t) + { + t >>= 3; + return (ushort)(t | (t << 5) | (t << 10)); + } + }; + + template struct Gray2RGB5x5 : unary_function + { + __device__ __forceinline__ ushort operator()(uint src) const + { + return Gray2RGB5x5Converter::cvt(src); + } + + __host__ __device__ __forceinline__ Gray2RGB5x5() {} + __host__ __device__ __forceinline__ Gray2RGB5x5(const Gray2RGB5x5&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_GRAY2RGB5x5_TRAITS(name, green_bits) \ + struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::Gray2RGB5x5 functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + +///////////////////////////////// Color to Grayscale //////////////////////////////// + + namespace color_detail + { + template struct RGB5x52GrayConverter; + template <> struct RGB5x52GrayConverter<6> + { + static __device__ __forceinline__ uchar cvt(uint t) + { + return (uchar)CV_DESCALE(((t << 3) & 0xf8) * B2Y + ((t >> 3) & 0xfc) * G2Y + ((t >> 8) & 0xf8) * R2Y, yuv_shift); + } + }; + + template <> struct RGB5x52GrayConverter<5> + { + static __device__ __forceinline__ uchar cvt(uint t) + { + return (uchar)CV_DESCALE(((t << 3) & 0xf8) * B2Y + ((t >> 2) & 0xf8) * G2Y + ((t >> 7) & 0xf8) * R2Y, yuv_shift); + } + }; + + template struct RGB5x52Gray : unary_function + { + __device__ __forceinline__ uchar operator()(uint src) const + { + return RGB5x52GrayConverter::cvt(src); + } + __host__ __device__ __forceinline__ RGB5x52Gray() {} + __host__ __device__ __forceinline__ RGB5x52Gray(const RGB5x52Gray&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB5x52GRAY_TRAITS(name, green_bits) \ + struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB5x52Gray functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + + namespace color_detail + { + template static __device__ __forceinline__ T RGB2GrayConvert(const T* src) + { + return (T)CV_DESCALE((unsigned)(src[bidx] * B2Y + src[1] * G2Y + src[bidx^2] * R2Y), yuv_shift); + } + + template static __device__ __forceinline__ uchar RGB2GrayConvert(uint src) + { + uint b = 0xffu & (src >> (bidx * 8)); + uint g = 0xffu & (src >> 8); + uint r = 0xffu & (src >> ((bidx ^ 2) * 8)); + return CV_DESCALE((uint)(b * B2Y + g * G2Y + r * R2Y), yuv_shift); + } + + template static __device__ __forceinline__ float RGB2GrayConvert(const float* src) + { + return src[bidx] * 0.114f + src[1] * 0.587f + src[bidx^2] * 0.299f; + } + + template struct RGB2Gray : unary_function::vec_type, T> + { + __device__ __forceinline__ T operator()(const typename TypeVec::vec_type& src) const + { + return RGB2GrayConvert(&src.x); + } + __host__ __device__ __forceinline__ RGB2Gray() {} + __host__ __device__ __forceinline__ RGB2Gray(const RGB2Gray&) {} + }; + + template struct RGB2Gray : unary_function + { + __device__ __forceinline__ uchar operator()(uint src) const + { + return RGB2GrayConvert(src); + } + __host__ __device__ __forceinline__ RGB2Gray() {} + __host__ __device__ __forceinline__ RGB2Gray(const RGB2Gray&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS(name, scn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2Gray functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + +///////////////////////////////////// RGB <-> YUV ////////////////////////////////////// + + namespace color_detail + { + __constant__ float c_RGB2YUVCoeffs_f[5] = { 0.114f, 0.587f, 0.299f, 0.492f, 0.877f }; + __constant__ int c_RGB2YUVCoeffs_i[5] = { B2Y, G2Y, R2Y, 8061, 14369 }; + + template static __device__ void RGB2YUVConvert(const T* src, D& dst) + { + const int delta = ColorChannel::half() * (1 << yuv_shift); + + const int Y = CV_DESCALE(src[0] * c_RGB2YUVCoeffs_i[bidx^2] + src[1] * c_RGB2YUVCoeffs_i[1] + src[2] * c_RGB2YUVCoeffs_i[bidx], yuv_shift); + const int Cr = CV_DESCALE((src[bidx^2] - Y) * c_RGB2YUVCoeffs_i[3] + delta, yuv_shift); + const int Cb = CV_DESCALE((src[bidx] - Y) * c_RGB2YUVCoeffs_i[4] + delta, yuv_shift); + + dst.x = saturate_cast(Y); + dst.y = saturate_cast(Cr); + dst.z = saturate_cast(Cb); + } + + template static __device__ __forceinline__ void RGB2YUVConvert(const float* src, D& dst) + { + dst.x = src[0] * c_RGB2YUVCoeffs_f[bidx^2] + src[1] * c_RGB2YUVCoeffs_f[1] + src[2] * c_RGB2YUVCoeffs_f[bidx]; + dst.y = (src[bidx^2] - dst.x) * c_RGB2YUVCoeffs_f[3] + ColorChannel::half(); + dst.z = (src[bidx] - dst.x) * c_RGB2YUVCoeffs_f[4] + ColorChannel::half(); + } + + template struct RGB2YUV + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + RGB2YUVConvert(&src.x, dst); + return dst; + } + __host__ __device__ __forceinline__ RGB2YUV() {} + __host__ __device__ __forceinline__ RGB2YUV(const RGB2YUV&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(name, scn, dcn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2YUV functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + + namespace color_detail + { + __constant__ float c_YUV2RGBCoeffs_f[5] = { 2.032f, -0.395f, -0.581f, 1.140f }; + __constant__ int c_YUV2RGBCoeffs_i[5] = { 33292, -6472, -9519, 18678 }; + + template static __device__ void YUV2RGBConvert(const T& src, D* dst) + { + const int b = src.x + CV_DESCALE((src.z - ColorChannel::half()) * c_YUV2RGBCoeffs_i[3], yuv_shift); + + const int g = src.x + CV_DESCALE((src.z - ColorChannel::half()) * c_YUV2RGBCoeffs_i[2] + + (src.y - ColorChannel::half()) * c_YUV2RGBCoeffs_i[1], yuv_shift); + + const int r = src.x + CV_DESCALE((src.y - ColorChannel::half()) * c_YUV2RGBCoeffs_i[0], yuv_shift); + + dst[bidx] = saturate_cast(b); + dst[1] = saturate_cast(g); + dst[bidx^2] = saturate_cast(r); + } + + template static __device__ uint YUV2RGBConvert(uint src) + { + const int x = 0xff & (src); + const int y = 0xff & (src >> 8); + const int z = 0xff & (src >> 16); + + const int b = x + CV_DESCALE((z - ColorChannel::half()) * c_YUV2RGBCoeffs_i[3], yuv_shift); + + const int g = x + CV_DESCALE((z - ColorChannel::half()) * c_YUV2RGBCoeffs_i[2] + + (y - ColorChannel::half()) * c_YUV2RGBCoeffs_i[1], yuv_shift); + + const int r = x + CV_DESCALE((y - ColorChannel::half()) * c_YUV2RGBCoeffs_i[0], yuv_shift); + + uint dst = 0xffu << 24; + + dst |= saturate_cast(b) << (bidx * 8); + dst |= saturate_cast(g) << 8; + dst |= saturate_cast(r) << ((bidx ^ 2) * 8); + + return dst; + } + + template static __device__ __forceinline__ void YUV2RGBConvert(const T& src, float* dst) + { + dst[bidx] = src.x + (src.z - ColorChannel::half()) * c_YUV2RGBCoeffs_f[3]; + + dst[1] = src.x + (src.z - ColorChannel::half()) * c_YUV2RGBCoeffs_f[2] + + (src.y - ColorChannel::half()) * c_YUV2RGBCoeffs_f[1]; + + dst[bidx^2] = src.x + (src.y - ColorChannel::half()) * c_YUV2RGBCoeffs_f[0]; + } + + template struct YUV2RGB + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + YUV2RGBConvert(src, &dst.x); + setAlpha(dst, ColorChannel::max()); + + return dst; + } + __host__ __device__ __forceinline__ YUV2RGB() {} + __host__ __device__ __forceinline__ YUV2RGB(const YUV2RGB&) {} + }; + + template struct YUV2RGB : unary_function + { + __device__ __forceinline__ uint operator ()(uint src) const + { + return YUV2RGBConvert(src); + } + __host__ __device__ __forceinline__ YUV2RGB() {} + __host__ __device__ __forceinline__ YUV2RGB(const YUV2RGB&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(name, scn, dcn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::YUV2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + +///////////////////////////////////// RGB <-> YCrCb ////////////////////////////////////// + + namespace color_detail + { + __constant__ float c_RGB2YCrCbCoeffs_f[5] = {0.299f, 0.587f, 0.114f, 0.713f, 0.564f}; + __constant__ int c_RGB2YCrCbCoeffs_i[5] = {R2Y, G2Y, B2Y, 11682, 9241}; + + template static __device__ void RGB2YCrCbConvert(const T* src, D& dst) + { + const int delta = ColorChannel::half() * (1 << yuv_shift); + + const int Y = CV_DESCALE(src[0] * c_RGB2YCrCbCoeffs_i[bidx^2] + src[1] * c_RGB2YCrCbCoeffs_i[1] + src[2] * c_RGB2YCrCbCoeffs_i[bidx], yuv_shift); + const int Cr = CV_DESCALE((src[bidx^2] - Y) * c_RGB2YCrCbCoeffs_i[3] + delta, yuv_shift); + const int Cb = CV_DESCALE((src[bidx] - Y) * c_RGB2YCrCbCoeffs_i[4] + delta, yuv_shift); + + dst.x = saturate_cast(Y); + dst.y = saturate_cast(Cr); + dst.z = saturate_cast(Cb); + } + + template static __device__ uint RGB2YCrCbConvert(uint src) + { + const int delta = ColorChannel::half() * (1 << yuv_shift); + + const int Y = CV_DESCALE((0xffu & src) * c_RGB2YCrCbCoeffs_i[bidx^2] + (0xffu & (src >> 8)) * c_RGB2YCrCbCoeffs_i[1] + (0xffu & (src >> 16)) * c_RGB2YCrCbCoeffs_i[bidx], yuv_shift); + const int Cr = CV_DESCALE(((0xffu & (src >> ((bidx ^ 2) * 8))) - Y) * c_RGB2YCrCbCoeffs_i[3] + delta, yuv_shift); + const int Cb = CV_DESCALE(((0xffu & (src >> (bidx * 8))) - Y) * c_RGB2YCrCbCoeffs_i[4] + delta, yuv_shift); + + uint dst = 0; + + dst |= saturate_cast(Y); + dst |= saturate_cast(Cr) << 8; + dst |= saturate_cast(Cb) << 16; + + return dst; + } + + template static __device__ __forceinline__ void RGB2YCrCbConvert(const float* src, D& dst) + { + dst.x = src[0] * c_RGB2YCrCbCoeffs_f[bidx^2] + src[1] * c_RGB2YCrCbCoeffs_f[1] + src[2] * c_RGB2YCrCbCoeffs_f[bidx]; + dst.y = (src[bidx^2] - dst.x) * c_RGB2YCrCbCoeffs_f[3] + ColorChannel::half(); + dst.z = (src[bidx] - dst.x) * c_RGB2YCrCbCoeffs_f[4] + ColorChannel::half(); + } + + template struct RGB2YCrCb + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + RGB2YCrCbConvert(&src.x, dst); + return dst; + } + __host__ __device__ __forceinline__ RGB2YCrCb() {} + __host__ __device__ __forceinline__ RGB2YCrCb(const RGB2YCrCb&) {} + }; + + template struct RGB2YCrCb : unary_function + { + __device__ __forceinline__ uint operator ()(uint src) const + { + return RGB2YCrCbConvert(src); + } + + __host__ __device__ __forceinline__ RGB2YCrCb() {} + __host__ __device__ __forceinline__ RGB2YCrCb(const RGB2YCrCb&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(name, scn, dcn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2YCrCb functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + + namespace color_detail + { + __constant__ float c_YCrCb2RGBCoeffs_f[5] = {1.403f, -0.714f, -0.344f, 1.773f}; + __constant__ int c_YCrCb2RGBCoeffs_i[5] = {22987, -11698, -5636, 29049}; + + template static __device__ void YCrCb2RGBConvert(const T& src, D* dst) + { + const int b = src.x + CV_DESCALE((src.z - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[3], yuv_shift); + const int g = src.x + CV_DESCALE((src.z - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[2] + (src.y - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[1], yuv_shift); + const int r = src.x + CV_DESCALE((src.y - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[0], yuv_shift); + + dst[bidx] = saturate_cast(b); + dst[1] = saturate_cast(g); + dst[bidx^2] = saturate_cast(r); + } + + template static __device__ uint YCrCb2RGBConvert(uint src) + { + const int x = 0xff & (src); + const int y = 0xff & (src >> 8); + const int z = 0xff & (src >> 16); + + const int b = x + CV_DESCALE((z - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[3], yuv_shift); + const int g = x + CV_DESCALE((z - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[2] + (y - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[1], yuv_shift); + const int r = x + CV_DESCALE((y - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[0], yuv_shift); + + uint dst = 0xffu << 24; + + dst |= saturate_cast(b) << (bidx * 8); + dst |= saturate_cast(g) << 8; + dst |= saturate_cast(r) << ((bidx ^ 2) * 8); + + return dst; + } + + template __device__ __forceinline__ void YCrCb2RGBConvert(const T& src, float* dst) + { + dst[bidx] = src.x + (src.z - ColorChannel::half()) * c_YCrCb2RGBCoeffs_f[3]; + dst[1] = src.x + (src.z - ColorChannel::half()) * c_YCrCb2RGBCoeffs_f[2] + (src.y - ColorChannel::half()) * c_YCrCb2RGBCoeffs_f[1]; + dst[bidx^2] = src.x + (src.y - ColorChannel::half()) * c_YCrCb2RGBCoeffs_f[0]; + } + + template struct YCrCb2RGB + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + YCrCb2RGBConvert(src, &dst.x); + setAlpha(dst, ColorChannel::max()); + + return dst; + } + __host__ __device__ __forceinline__ YCrCb2RGB() {} + __host__ __device__ __forceinline__ YCrCb2RGB(const YCrCb2RGB&) {} + }; + + template struct YCrCb2RGB : unary_function + { + __device__ __forceinline__ uint operator ()(uint src) const + { + return YCrCb2RGBConvert(src); + } + __host__ __device__ __forceinline__ YCrCb2RGB() {} + __host__ __device__ __forceinline__ YCrCb2RGB(const YCrCb2RGB&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(name, scn, dcn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::YCrCb2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + +////////////////////////////////////// RGB <-> XYZ /////////////////////////////////////// + + namespace color_detail + { + __constant__ float c_RGB2XYZ_D65f[9] = { 0.412453f, 0.357580f, 0.180423f, 0.212671f, 0.715160f, 0.072169f, 0.019334f, 0.119193f, 0.950227f }; + __constant__ int c_RGB2XYZ_D65i[9] = { 1689, 1465, 739, 871, 2929, 296, 79, 488, 3892 }; + + template static __device__ __forceinline__ void RGB2XYZConvert(const T* src, D& dst) + { + dst.z = saturate_cast(CV_DESCALE(src[bidx^2] * c_RGB2XYZ_D65i[6] + src[1] * c_RGB2XYZ_D65i[7] + src[bidx] * c_RGB2XYZ_D65i[8], xyz_shift)); + dst.x = saturate_cast(CV_DESCALE(src[bidx^2] * c_RGB2XYZ_D65i[0] + src[1] * c_RGB2XYZ_D65i[1] + src[bidx] * c_RGB2XYZ_D65i[2], xyz_shift)); + dst.y = saturate_cast(CV_DESCALE(src[bidx^2] * c_RGB2XYZ_D65i[3] + src[1] * c_RGB2XYZ_D65i[4] + src[bidx] * c_RGB2XYZ_D65i[5], xyz_shift)); + } + + template static __device__ __forceinline__ uint RGB2XYZConvert(uint src) + { + const uint b = 0xffu & (src >> (bidx * 8)); + const uint g = 0xffu & (src >> 8); + const uint r = 0xffu & (src >> ((bidx ^ 2) * 8)); + + const uint x = saturate_cast(CV_DESCALE(r * c_RGB2XYZ_D65i[0] + g * c_RGB2XYZ_D65i[1] + b * c_RGB2XYZ_D65i[2], xyz_shift)); + const uint y = saturate_cast(CV_DESCALE(r * c_RGB2XYZ_D65i[3] + g * c_RGB2XYZ_D65i[4] + b * c_RGB2XYZ_D65i[5], xyz_shift)); + const uint z = saturate_cast(CV_DESCALE(r * c_RGB2XYZ_D65i[6] + g * c_RGB2XYZ_D65i[7] + b * c_RGB2XYZ_D65i[8], xyz_shift)); + + uint dst = 0; + + dst |= x; + dst |= y << 8; + dst |= z << 16; + + return dst; + } + + template static __device__ __forceinline__ void RGB2XYZConvert(const float* src, D& dst) + { + dst.x = src[bidx^2] * c_RGB2XYZ_D65f[0] + src[1] * c_RGB2XYZ_D65f[1] + src[bidx] * c_RGB2XYZ_D65f[2]; + dst.y = src[bidx^2] * c_RGB2XYZ_D65f[3] + src[1] * c_RGB2XYZ_D65f[4] + src[bidx] * c_RGB2XYZ_D65f[5]; + dst.z = src[bidx^2] * c_RGB2XYZ_D65f[6] + src[1] * c_RGB2XYZ_D65f[7] + src[bidx] * c_RGB2XYZ_D65f[8]; + } + + template struct RGB2XYZ + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + RGB2XYZConvert(&src.x, dst); + + return dst; + } + __host__ __device__ __forceinline__ RGB2XYZ() {} + __host__ __device__ __forceinline__ RGB2XYZ(const RGB2XYZ&) {} + }; + + template struct RGB2XYZ : unary_function + { + __device__ __forceinline__ uint operator()(uint src) const + { + return RGB2XYZConvert(src); + } + __host__ __device__ __forceinline__ RGB2XYZ() {} + __host__ __device__ __forceinline__ RGB2XYZ(const RGB2XYZ&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(name, scn, dcn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2XYZ functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + + namespace color_detail + { + __constant__ float c_XYZ2sRGB_D65f[9] = { 3.240479f, -1.53715f, -0.498535f, -0.969256f, 1.875991f, 0.041556f, 0.055648f, -0.204043f, 1.057311f }; + __constant__ int c_XYZ2sRGB_D65i[9] = { 13273, -6296, -2042, -3970, 7684, 170, 228, -836, 4331 }; + + template static __device__ __forceinline__ void XYZ2RGBConvert(const T& src, D* dst) + { + dst[bidx^2] = saturate_cast(CV_DESCALE(src.x * c_XYZ2sRGB_D65i[0] + src.y * c_XYZ2sRGB_D65i[1] + src.z * c_XYZ2sRGB_D65i[2], xyz_shift)); + dst[1] = saturate_cast(CV_DESCALE(src.x * c_XYZ2sRGB_D65i[3] + src.y * c_XYZ2sRGB_D65i[4] + src.z * c_XYZ2sRGB_D65i[5], xyz_shift)); + dst[bidx] = saturate_cast(CV_DESCALE(src.x * c_XYZ2sRGB_D65i[6] + src.y * c_XYZ2sRGB_D65i[7] + src.z * c_XYZ2sRGB_D65i[8], xyz_shift)); + } + + template static __device__ __forceinline__ uint XYZ2RGBConvert(uint src) + { + const int x = 0xff & src; + const int y = 0xff & (src >> 8); + const int z = 0xff & (src >> 16); + + const uint r = saturate_cast(CV_DESCALE(x * c_XYZ2sRGB_D65i[0] + y * c_XYZ2sRGB_D65i[1] + z * c_XYZ2sRGB_D65i[2], xyz_shift)); + const uint g = saturate_cast(CV_DESCALE(x * c_XYZ2sRGB_D65i[3] + y * c_XYZ2sRGB_D65i[4] + z * c_XYZ2sRGB_D65i[5], xyz_shift)); + const uint b = saturate_cast(CV_DESCALE(x * c_XYZ2sRGB_D65i[6] + y * c_XYZ2sRGB_D65i[7] + z * c_XYZ2sRGB_D65i[8], xyz_shift)); + + uint dst = 0xffu << 24; + + dst |= b << (bidx * 8); + dst |= g << 8; + dst |= r << ((bidx ^ 2) * 8); + + return dst; + } + + template static __device__ __forceinline__ void XYZ2RGBConvert(const T& src, float* dst) + { + dst[bidx^2] = src.x * c_XYZ2sRGB_D65f[0] + src.y * c_XYZ2sRGB_D65f[1] + src.z * c_XYZ2sRGB_D65f[2]; + dst[1] = src.x * c_XYZ2sRGB_D65f[3] + src.y * c_XYZ2sRGB_D65f[4] + src.z * c_XYZ2sRGB_D65f[5]; + dst[bidx] = src.x * c_XYZ2sRGB_D65f[6] + src.y * c_XYZ2sRGB_D65f[7] + src.z * c_XYZ2sRGB_D65f[8]; + } + + template struct XYZ2RGB + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + XYZ2RGBConvert(src, &dst.x); + setAlpha(dst, ColorChannel::max()); + + return dst; + } + __host__ __device__ __forceinline__ XYZ2RGB() {} + __host__ __device__ __forceinline__ XYZ2RGB(const XYZ2RGB&) {} + }; + + template struct XYZ2RGB : unary_function + { + __device__ __forceinline__ uint operator()(uint src) const + { + return XYZ2RGBConvert(src); + } + __host__ __device__ __forceinline__ XYZ2RGB() {} + __host__ __device__ __forceinline__ XYZ2RGB(const XYZ2RGB&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(name, scn, dcn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::XYZ2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + +////////////////////////////////////// RGB <-> HSV /////////////////////////////////////// + + namespace color_detail + { + __constant__ int c_HsvDivTable [256] = {0, 1044480, 522240, 348160, 261120, 208896, 174080, 149211, 130560, 116053, 104448, 94953, 87040, 80345, 74606, 69632, 65280, 61440, 58027, 54973, 52224, 49737, 47476, 45412, 43520, 41779, 40172, 38684, 37303, 36017, 34816, 33693, 32640, 31651, 30720, 29842, 29013, 28229, 27486, 26782, 26112, 25475, 24869, 24290, 23738, 23211, 22706, 22223, 21760, 21316, 20890, 20480, 20086, 19707, 19342, 18991, 18651, 18324, 18008, 17703, 17408, 17123, 16846, 16579, 16320, 16069, 15825, 15589, 15360, 15137, 14921, 14711, 14507, 14308, 14115, 13926, 13743, 13565, 13391, 13221, 13056, 12895, 12738, 12584, 12434, 12288, 12145, 12006, 11869, 11736, 11605, 11478, 11353, 11231, 11111, 10995, 10880, 10768, 10658, 10550, 10445, 10341, 10240, 10141, 10043, 9947, 9854, 9761, 9671, 9582, 9495, 9410, 9326, 9243, 9162, 9082, 9004, 8927, 8852, 8777, 8704, 8632, 8561, 8492, 8423, 8356, 8290, 8224, 8160, 8097, 8034, 7973, 7913, 7853, 7795, 7737, 7680, 7624, 7569, 7514, 7461, 7408, 7355, 7304, 7253, 7203, 7154, 7105, 7057, 7010, 6963, 6917, 6872, 6827, 6782, 6739, 6695, 6653, 6611, 6569, 6528, 6487, 6447, 6408, 6369, 6330, 6292, 6254, 6217, 6180, 6144, 6108, 6073, 6037, 6003, 5968, 5935, 5901, 5868, 5835, 5803, 5771, 5739, 5708, 5677, 5646, 5615, 5585, 5556, 5526, 5497, 5468, 5440, 5412, 5384, 5356, 5329, 5302, 5275, 5249, 5222, 5196, 5171, 5145, 5120, 5095, 5070, 5046, 5022, 4998, 4974, 4950, 4927, 4904, 4881, 4858, 4836, 4813, 4791, 4769, 4748, 4726, 4705, 4684, 4663, 4642, 4622, 4601, 4581, 4561, 4541, 4522, 4502, 4483, 4464, 4445, 4426, 4407, 4389, 4370, 4352, 4334, 4316, 4298, 4281, 4263, 4246, 4229, 4212, 4195, 4178, 4161, 4145, 4128, 4112, 4096}; + __constant__ int c_HsvDivTable180[256] = {0, 122880, 61440, 40960, 30720, 24576, 20480, 17554, 15360, 13653, 12288, 11171, 10240, 9452, 8777, 8192, 7680, 7228, 6827, 6467, 6144, 5851, 5585, 5343, 5120, 4915, 4726, 4551, 4389, 4237, 4096, 3964, 3840, 3724, 3614, 3511, 3413, 3321, 3234, 3151, 3072, 2997, 2926, 2858, 2793, 2731, 2671, 2614, 2560, 2508, 2458, 2409, 2363, 2318, 2276, 2234, 2194, 2156, 2119, 2083, 2048, 2014, 1982, 1950, 1920, 1890, 1862, 1834, 1807, 1781, 1755, 1731, 1707, 1683, 1661, 1638, 1617, 1596, 1575, 1555, 1536, 1517, 1499, 1480, 1463, 1446, 1429, 1412, 1396, 1381, 1365, 1350, 1336, 1321, 1307, 1293, 1280, 1267, 1254, 1241, 1229, 1217, 1205, 1193, 1182, 1170, 1159, 1148, 1138, 1127, 1117, 1107, 1097, 1087, 1078, 1069, 1059, 1050, 1041, 1033, 1024, 1016, 1007, 999, 991, 983, 975, 968, 960, 953, 945, 938, 931, 924, 917, 910, 904, 897, 890, 884, 878, 871, 865, 859, 853, 847, 842, 836, 830, 825, 819, 814, 808, 803, 798, 793, 788, 783, 778, 773, 768, 763, 759, 754, 749, 745, 740, 736, 731, 727, 723, 719, 714, 710, 706, 702, 698, 694, 690, 686, 683, 679, 675, 671, 668, 664, 661, 657, 654, 650, 647, 643, 640, 637, 633, 630, 627, 624, 621, 617, 614, 611, 608, 605, 602, 599, 597, 594, 591, 588, 585, 582, 580, 577, 574, 572, 569, 566, 564, 561, 559, 556, 554, 551, 549, 546, 544, 541, 539, 537, 534, 532, 530, 527, 525, 523, 521, 518, 516, 514, 512, 510, 508, 506, 504, 502, 500, 497, 495, 493, 492, 490, 488, 486, 484, 482}; + __constant__ int c_HsvDivTable256[256] = {0, 174763, 87381, 58254, 43691, 34953, 29127, 24966, 21845, 19418, 17476, 15888, 14564, 13443, 12483, 11651, 10923, 10280, 9709, 9198, 8738, 8322, 7944, 7598, 7282, 6991, 6722, 6473, 6242, 6026, 5825, 5638, 5461, 5296, 5140, 4993, 4855, 4723, 4599, 4481, 4369, 4263, 4161, 4064, 3972, 3884, 3799, 3718, 3641, 3567, 3495, 3427, 3361, 3297, 3236, 3178, 3121, 3066, 3013, 2962, 2913, 2865, 2819, 2774, 2731, 2689, 2648, 2608, 2570, 2533, 2497, 2461, 2427, 2394, 2362, 2330, 2300, 2270, 2241, 2212, 2185, 2158, 2131, 2106, 2081, 2056, 2032, 2009, 1986, 1964, 1942, 1920, 1900, 1879, 1859, 1840, 1820, 1802, 1783, 1765, 1748, 1730, 1713, 1697, 1680, 1664, 1649, 1633, 1618, 1603, 1589, 1574, 1560, 1547, 1533, 1520, 1507, 1494, 1481, 1469, 1456, 1444, 1432, 1421, 1409, 1398, 1387, 1376, 1365, 1355, 1344, 1334, 1324, 1314, 1304, 1295, 1285, 1276, 1266, 1257, 1248, 1239, 1231, 1222, 1214, 1205, 1197, 1189, 1181, 1173, 1165, 1157, 1150, 1142, 1135, 1128, 1120, 1113, 1106, 1099, 1092, 1085, 1079, 1072, 1066, 1059, 1053, 1046, 1040, 1034, 1028, 1022, 1016, 1010, 1004, 999, 993, 987, 982, 976, 971, 966, 960, 955, 950, 945, 940, 935, 930, 925, 920, 915, 910, 906, 901, 896, 892, 887, 883, 878, 874, 869, 865, 861, 857, 853, 848, 844, 840, 836, 832, 828, 824, 820, 817, 813, 809, 805, 802, 798, 794, 791, 787, 784, 780, 777, 773, 770, 767, 763, 760, 757, 753, 750, 747, 744, 741, 737, 734, 731, 728, 725, 722, 719, 716, 713, 710, 708, 705, 702, 699, 696, 694, 691, 688, 685}; + + template static __device__ void RGB2HSVConvert(const uchar* src, D& dst) + { + const int hsv_shift = 12; + const int* hdiv_table = hr == 180 ? c_HsvDivTable180 : c_HsvDivTable256; + + int b = src[bidx], g = src[1], r = src[bidx^2]; + int h, s, v = b; + int vmin = b, diff; + int vr, vg; + + v = ::max(v, g); + v = ::max(v, r); + vmin = ::min(vmin, g); + vmin = ::min(vmin, r); + + diff = v - vmin; + vr = (v == r) * -1; + vg = (v == g) * -1; + + s = (diff * c_HsvDivTable[v] + (1 << (hsv_shift-1))) >> hsv_shift; + h = (vr & (g - b)) + (~vr & ((vg & (b - r + 2 * diff)) + ((~vg) & (r - g + 4 * diff)))); + h = (h * hdiv_table[diff] + (1 << (hsv_shift-1))) >> hsv_shift; + h += (h < 0) * hr; + + dst.x = saturate_cast(h); + dst.y = (uchar)s; + dst.z = (uchar)v; + } + + template static __device__ uint RGB2HSVConvert(uint src) + { + const int hsv_shift = 12; + const int* hdiv_table = hr == 180 ? c_HsvDivTable180 : c_HsvDivTable256; + + const int b = 0xff & (src >> (bidx * 8)); + const int g = 0xff & (src >> 8); + const int r = 0xff & (src >> ((bidx ^ 2) * 8)); + + int h, s, v = b; + int vmin = b, diff; + int vr, vg; + + v = ::max(v, g); + v = ::max(v, r); + vmin = ::min(vmin, g); + vmin = ::min(vmin, r); + + diff = v - vmin; + vr = (v == r) * -1; + vg = (v == g) * -1; + + s = (diff * c_HsvDivTable[v] + (1 << (hsv_shift-1))) >> hsv_shift; + h = (vr & (g - b)) + (~vr & ((vg & (b - r + 2 * diff)) + ((~vg) & (r - g + 4 * diff)))); + h = (h * hdiv_table[diff] + (1 << (hsv_shift-1))) >> hsv_shift; + h += (h < 0) * hr; + + uint dst = 0; + + dst |= saturate_cast(h); + dst |= (0xffu & s) << 8; + dst |= (0xffu & v) << 16; + + return dst; + } + + template static __device__ void RGB2HSVConvert(const float* src, D& dst) + { + const float hscale = hr * (1.f / 360.f); + + float b = src[bidx], g = src[1], r = src[bidx^2]; + float h, s, v; + + float vmin, diff; + + v = vmin = r; + v = fmax(v, g); + v = fmax(v, b); + vmin = fmin(vmin, g); + vmin = fmin(vmin, b); + + diff = v - vmin; + s = diff / (float)(::fabs(v) + numeric_limits::epsilon()); + diff = (float)(60. / (diff + numeric_limits::epsilon())); + + h = (v == r) * (g - b) * diff; + h += (v != r && v == g) * ((b - r) * diff + 120.f); + h += (v != r && v != g) * ((r - g) * diff + 240.f); + h += (h < 0) * 360.f; + + dst.x = h * hscale; + dst.y = s; + dst.z = v; + } + + template struct RGB2HSV + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + RGB2HSVConvert(&src.x, dst); + + return dst; + } + __host__ __device__ __forceinline__ RGB2HSV() {} + __host__ __device__ __forceinline__ RGB2HSV(const RGB2HSV&) {} + }; + + template struct RGB2HSV : unary_function + { + __device__ __forceinline__ uint operator()(uint src) const + { + return RGB2HSVConvert(src); + } + __host__ __device__ __forceinline__ RGB2HSV() {} + __host__ __device__ __forceinline__ RGB2HSV(const RGB2HSV&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(name, scn, dcn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2HSV functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template struct name ## _full_traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2HSV functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template <> struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2HSV functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template <> struct name ## _full_traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2HSV functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + + namespace color_detail + { + __constant__ int c_HsvSectorData[6][3] = { {1,3,0}, {1,0,2}, {3,0,1}, {0,2,1}, {0,1,3}, {2,1,0} }; + + template static __device__ void HSV2RGBConvert(const T& src, float* dst) + { + const float hscale = 6.f / hr; + + float h = src.x, s = src.y, v = src.z; + float b = v, g = v, r = v; + + if (s != 0) + { + h *= hscale; + + if( h < 0 ) + do h += 6; while( h < 0 ); + else if( h >= 6 ) + do h -= 6; while( h >= 6 ); + + int sector = __float2int_rd(h); + h -= sector; + + if ( (unsigned)sector >= 6u ) + { + sector = 0; + h = 0.f; + } + + float tab[4]; + tab[0] = v; + tab[1] = v * (1.f - s); + tab[2] = v * (1.f - s * h); + tab[3] = v * (1.f - s * (1.f - h)); + + b = tab[c_HsvSectorData[sector][0]]; + g = tab[c_HsvSectorData[sector][1]]; + r = tab[c_HsvSectorData[sector][2]]; + } + + dst[bidx] = b; + dst[1] = g; + dst[bidx^2] = r; + } + + template static __device__ void HSV2RGBConvert(const T& src, uchar* dst) + { + float3 buf; + + buf.x = src.x; + buf.y = src.y * (1.f / 255.f); + buf.z = src.z * (1.f / 255.f); + + HSV2RGBConvert(buf, &buf.x); + + dst[0] = saturate_cast(buf.x * 255.f); + dst[1] = saturate_cast(buf.y * 255.f); + dst[2] = saturate_cast(buf.z * 255.f); + } + + template static __device__ uint HSV2RGBConvert(uint src) + { + float3 buf; + + buf.x = src & 0xff; + buf.y = ((src >> 8) & 0xff) * (1.f/255.f); + buf.z = ((src >> 16) & 0xff) * (1.f/255.f); + + HSV2RGBConvert(buf, &buf.x); + + uint dst = 0xffu << 24; + + dst |= saturate_cast(buf.x * 255.f); + dst |= saturate_cast(buf.y * 255.f) << 8; + dst |= saturate_cast(buf.z * 255.f) << 16; + + return dst; + } + + template struct HSV2RGB + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + HSV2RGBConvert(src, &dst.x); + setAlpha(dst, ColorChannel::max()); + + return dst; + } + __host__ __device__ __forceinline__ HSV2RGB() {} + __host__ __device__ __forceinline__ HSV2RGB(const HSV2RGB&) {} + }; + + template struct HSV2RGB : unary_function + { + __device__ __forceinline__ uint operator()(uint src) const + { + return HSV2RGBConvert(src); + } + __host__ __device__ __forceinline__ HSV2RGB() {} + __host__ __device__ __forceinline__ HSV2RGB(const HSV2RGB&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(name, scn, dcn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::HSV2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template struct name ## _full_traits \ + { \ + typedef ::cv::cuda::device::color_detail::HSV2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template <> struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::HSV2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template <> struct name ## _full_traits \ + { \ + typedef ::cv::cuda::device::color_detail::HSV2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + +/////////////////////////////////////// RGB <-> HLS //////////////////////////////////////// + + namespace color_detail + { + template static __device__ void RGB2HLSConvert(const float* src, D& dst) + { + const float hscale = hr * (1.f / 360.f); + + float b = src[bidx], g = src[1], r = src[bidx^2]; + float h = 0.f, s = 0.f, l; + float vmin, vmax, diff; + + vmax = vmin = r; + vmax = fmax(vmax, g); + vmax = fmax(vmax, b); + vmin = fmin(vmin, g); + vmin = fmin(vmin, b); + + diff = vmax - vmin; + l = (vmax + vmin) * 0.5f; + + if (diff > numeric_limits::epsilon()) + { + s = (l < 0.5f) * diff / (vmax + vmin); + s += (l >= 0.5f) * diff / (2.0f - vmax - vmin); + + diff = 60.f / diff; + + h = (vmax == r) * (g - b) * diff; + h += (vmax != r && vmax == g) * ((b - r) * diff + 120.f); + h += (vmax != r && vmax != g) * ((r - g) * diff + 240.f); + h += (h < 0.f) * 360.f; + } + + dst.x = h * hscale; + dst.y = l; + dst.z = s; + } + + template static __device__ void RGB2HLSConvert(const uchar* src, D& dst) + { + float3 buf; + + buf.x = src[0] * (1.f / 255.f); + buf.y = src[1] * (1.f / 255.f); + buf.z = src[2] * (1.f / 255.f); + + RGB2HLSConvert(&buf.x, buf); + + dst.x = saturate_cast(buf.x); + dst.y = saturate_cast(buf.y*255.f); + dst.z = saturate_cast(buf.z*255.f); + } + + template static __device__ uint RGB2HLSConvert(uint src) + { + float3 buf; + + buf.x = (0xff & src) * (1.f / 255.f); + buf.y = (0xff & (src >> 8)) * (1.f / 255.f); + buf.z = (0xff & (src >> 16)) * (1.f / 255.f); + + RGB2HLSConvert(&buf.x, buf); + + uint dst = 0xffu << 24; + + dst |= saturate_cast(buf.x); + dst |= saturate_cast(buf.y * 255.f) << 8; + dst |= saturate_cast(buf.z * 255.f) << 16; + + return dst; + } + + template struct RGB2HLS + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + RGB2HLSConvert(&src.x, dst); + + return dst; + } + __host__ __device__ __forceinline__ RGB2HLS() {} + __host__ __device__ __forceinline__ RGB2HLS(const RGB2HLS&) {} + }; + + template struct RGB2HLS : unary_function + { + __device__ __forceinline__ uint operator()(uint src) const + { + return RGB2HLSConvert(src); + } + __host__ __device__ __forceinline__ RGB2HLS() {} + __host__ __device__ __forceinline__ RGB2HLS(const RGB2HLS&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(name, scn, dcn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2HLS functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template struct name ## _full_traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2HLS functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template <> struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2HLS functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template <> struct name ## _full_traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2HLS functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + + namespace color_detail + { + __constant__ int c_HlsSectorData[6][3] = { {1,3,0}, {1,0,2}, {3,0,1}, {0,2,1}, {0,1,3}, {2,1,0} }; + + template static __device__ void HLS2RGBConvert(const T& src, float* dst) + { + const float hscale = 6.0f / hr; + + float h = src.x, l = src.y, s = src.z; + float b = l, g = l, r = l; + + if (s != 0) + { + float p2 = (l <= 0.5f) * l * (1 + s); + p2 += (l > 0.5f) * (l + s - l * s); + float p1 = 2 * l - p2; + + h *= hscale; + + if( h < 0 ) + do h += 6; while( h < 0 ); + else if( h >= 6 ) + do h -= 6; while( h >= 6 ); + + int sector; + sector = __float2int_rd(h); + + h -= sector; + + float tab[4]; + tab[0] = p2; + tab[1] = p1; + tab[2] = p1 + (p2 - p1) * (1 - h); + tab[3] = p1 + (p2 - p1) * h; + + b = tab[c_HlsSectorData[sector][0]]; + g = tab[c_HlsSectorData[sector][1]]; + r = tab[c_HlsSectorData[sector][2]]; + } + + dst[bidx] = b; + dst[1] = g; + dst[bidx^2] = r; + } + + template static __device__ void HLS2RGBConvert(const T& src, uchar* dst) + { + float3 buf; + + buf.x = src.x; + buf.y = src.y * (1.f / 255.f); + buf.z = src.z * (1.f / 255.f); + + HLS2RGBConvert(buf, &buf.x); + + dst[0] = saturate_cast(buf.x * 255.f); + dst[1] = saturate_cast(buf.y * 255.f); + dst[2] = saturate_cast(buf.z * 255.f); + } + + template static __device__ uint HLS2RGBConvert(uint src) + { + float3 buf; + + buf.x = 0xff & src; + buf.y = (0xff & (src >> 8)) * (1.f / 255.f); + buf.z = (0xff & (src >> 16)) * (1.f / 255.f); + + HLS2RGBConvert(buf, &buf.x); + + uint dst = 0xffu << 24; + + dst |= saturate_cast(buf.x * 255.f); + dst |= saturate_cast(buf.y * 255.f) << 8; + dst |= saturate_cast(buf.z * 255.f) << 16; + + return dst; + } + + template struct HLS2RGB + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + HLS2RGBConvert(src, &dst.x); + setAlpha(dst, ColorChannel::max()); + + return dst; + } + __host__ __device__ __forceinline__ HLS2RGB() {} + __host__ __device__ __forceinline__ HLS2RGB(const HLS2RGB&) {} + }; + + template struct HLS2RGB : unary_function + { + __device__ __forceinline__ uint operator()(uint src) const + { + return HLS2RGBConvert(src); + } + __host__ __device__ __forceinline__ HLS2RGB() {} + __host__ __device__ __forceinline__ HLS2RGB(const HLS2RGB&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(name, scn, dcn, bidx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::HLS2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template struct name ## _full_traits \ + { \ + typedef ::cv::cuda::device::color_detail::HLS2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template <> struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::HLS2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; \ + template <> struct name ## _full_traits \ + { \ + typedef ::cv::cuda::device::color_detail::HLS2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + +///////////////////////////////////// RGB <-> Lab ///////////////////////////////////// + + namespace color_detail + { + enum + { + LAB_CBRT_TAB_SIZE = 1024, + GAMMA_TAB_SIZE = 1024, + lab_shift = xyz_shift, + gamma_shift = 3, + lab_shift2 = (lab_shift + gamma_shift), + LAB_CBRT_TAB_SIZE_B = (256 * 3 / 2 * (1 << gamma_shift)) + }; + + __constant__ ushort c_sRGBGammaTab_b[] = {0,1,1,2,2,3,4,4,5,6,6,7,8,8,9,10,11,11,12,13,14,15,16,17,19,20,21,22,24,25,26,28,29,31,33,34,36,38,40,41,43,45,47,49,51,54,56,58,60,63,65,68,70,73,75,78,81,83,86,89,92,95,98,101,105,108,111,115,118,121,125,129,132,136,140,144,147,151,155,160,164,168,172,176,181,185,190,194,199,204,209,213,218,223,228,233,239,244,249,255,260,265,271,277,282,288,294,300,306,312,318,324,331,337,343,350,356,363,370,376,383,390,397,404,411,418,426,433,440,448,455,463,471,478,486,494,502,510,518,527,535,543,552,560,569,578,586,595,604,613,622,631,641,650,659,669,678,688,698,707,717,727,737,747,757,768,778,788,799,809,820,831,842,852,863,875,886,897,908,920,931,943,954,966,978,990,1002,1014,1026,1038,1050,1063,1075,1088,1101,1113,1126,1139,1152,1165,1178,1192,1205,1218,1232,1245,1259,1273,1287,1301,1315,1329,1343,1357,1372,1386,1401,1415,1430,1445,1460,1475,1490,1505,1521,1536,1551,1567,1583,1598,1614,1630,1646,1662,1678,1695,1711,1728,1744,1761,1778,1794,1811,1828,1846,1863,1880,1897,1915,1933,1950,1968,1986,2004,2022,2040}; + + __device__ __forceinline__ int LabCbrt_b(int i) + { + float x = i * (1.f / (255.f * (1 << gamma_shift))); + return (1 << lab_shift2) * (x < 0.008856f ? x * 7.787f + 0.13793103448275862f : ::cbrtf(x)); + } + + template + __device__ __forceinline__ void RGB2LabConvert_b(const T& src, D& dst) + { + const int Lscale = (116 * 255 + 50) / 100; + const int Lshift = -((16 * 255 * (1 << lab_shift2) + 50) / 100); + + int B = blueIdx == 0 ? src.x : src.z; + int G = src.y; + int R = blueIdx == 0 ? src.z : src.x; + + if (srgb) + { + B = c_sRGBGammaTab_b[B]; + G = c_sRGBGammaTab_b[G]; + R = c_sRGBGammaTab_b[R]; + } + else + { + B <<= 3; + G <<= 3; + R <<= 3; + } + + int fX = LabCbrt_b(CV_DESCALE(B * 778 + G * 1541 + R * 1777, lab_shift)); + int fY = LabCbrt_b(CV_DESCALE(B * 296 + G * 2929 + R * 871, lab_shift)); + int fZ = LabCbrt_b(CV_DESCALE(B * 3575 + G * 448 + R * 73, lab_shift)); + + int L = CV_DESCALE(Lscale * fY + Lshift, lab_shift2); + int a = CV_DESCALE(500 * (fX - fY) + 128 * (1 << lab_shift2), lab_shift2); + int b = CV_DESCALE(200 * (fY - fZ) + 128 * (1 << lab_shift2), lab_shift2); + + dst.x = saturate_cast(L); + dst.y = saturate_cast(a); + dst.z = saturate_cast(b); + } + + __device__ __forceinline__ float splineInterpolate(float x, const float* tab, int n) + { + int ix = ::min(::max(int(x), 0), n-1); + x -= ix; + tab += ix * 4; + return ((tab[3] * x + tab[2]) * x + tab[1]) * x + tab[0]; + } + + __constant__ float c_sRGBGammaTab[] = 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+ + template + __device__ __forceinline__ void RGB2LabConvert_f(const T& src, D& dst) + { + const float _1_3 = 1.0f / 3.0f; + const float _a = 16.0f / 116.0f; + + float B = blueIdx == 0 ? src.x : src.z; + float G = src.y; + float R = blueIdx == 0 ? src.z : src.x; + + if (srgb) + { + B = splineInterpolate(B * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE); + G = splineInterpolate(G * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE); + R = splineInterpolate(R * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE); + } + + float X = B * 0.189828f + G * 0.376219f + R * 0.433953f; + float Y = B * 0.072169f + G * 0.715160f + R * 0.212671f; + float Z = B * 0.872766f + G * 0.109477f + R * 0.017758f; + + float FX = X > 0.008856f ? ::powf(X, _1_3) : (7.787f * X + _a); + float FY = Y > 0.008856f ? ::powf(Y, _1_3) : (7.787f * Y + _a); + float FZ = Z > 0.008856f ? ::powf(Z, _1_3) : (7.787f * Z + _a); + + float L = Y > 0.008856f ? (116.f * FY - 16.f) : (903.3f * Y); + float a = 500.f * (FX - FY); + float b = 200.f * (FY - FZ); + + dst.x = L; + dst.y = a; + dst.z = b; + } + + template struct RGB2Lab; + template + struct RGB2Lab + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + RGB2LabConvert_b(src, dst); + + return dst; + } + __host__ __device__ __forceinline__ RGB2Lab() {} + __host__ __device__ __forceinline__ RGB2Lab(const RGB2Lab&) {} + }; + template + struct RGB2Lab + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + RGB2LabConvert_f(src, dst); + + return dst; + } + __host__ __device__ __forceinline__ RGB2Lab() {} + __host__ __device__ __forceinline__ RGB2Lab(const RGB2Lab&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(name, scn, dcn, srgb, blueIdx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2Lab functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + + namespace color_detail + { + __constant__ float c_sRGBInvGammaTab[] = 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+ + template + __device__ __forceinline__ void Lab2RGBConvert_f(const T& src, D& dst) + { + const float lThresh = 0.008856f * 903.3f; + const float fThresh = 7.787f * 0.008856f + 16.0f / 116.0f; + + float Y, fy; + + if (src.x <= lThresh) + { + Y = src.x / 903.3f; + fy = 7.787f * Y + 16.0f / 116.0f; + } + else + { + fy = (src.x + 16.0f) / 116.0f; + Y = fy * fy * fy; + } + + float X = src.y / 500.0f + fy; + float Z = fy - src.z / 200.0f; + + if (X <= fThresh) + X = (X - 16.0f / 116.0f) / 7.787f; + else + X = X * X * X; + + if (Z <= fThresh) + Z = (Z - 16.0f / 116.0f) / 7.787f; + else + Z = Z * Z * Z; + + float B = 0.052891f * X - 0.204043f * Y + 1.151152f * Z; + float G = -0.921235f * X + 1.875991f * Y + 0.045244f * Z; + float R = 3.079933f * X - 1.537150f * Y - 0.542782f * Z; + + if (srgb) + { + B = splineInterpolate(B * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE); + G = splineInterpolate(G * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE); + R = splineInterpolate(R * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE); + } + + dst.x = blueIdx == 0 ? B : R; + dst.y = G; + dst.z = blueIdx == 0 ? R : B; + setAlpha(dst, ColorChannel::max()); + } + + template + __device__ __forceinline__ void Lab2RGBConvert_b(const T& src, D& dst) + { + float3 srcf, dstf; + + srcf.x = src.x * (100.f / 255.f); + srcf.y = src.y - 128; + srcf.z = src.z - 128; + + Lab2RGBConvert_f(srcf, dstf); + + dst.x = saturate_cast(dstf.x * 255.f); + dst.y = saturate_cast(dstf.y * 255.f); + dst.z = saturate_cast(dstf.z * 255.f); + setAlpha(dst, ColorChannel::max()); + } + + template struct Lab2RGB; + template + struct Lab2RGB + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + Lab2RGBConvert_b(src, dst); + + return dst; + } + __host__ __device__ __forceinline__ Lab2RGB() {} + __host__ __device__ __forceinline__ Lab2RGB(const Lab2RGB&) {} + }; + template + struct Lab2RGB + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + Lab2RGBConvert_f(src, dst); + + return dst; + } + __host__ __device__ __forceinline__ Lab2RGB() {} + __host__ __device__ __forceinline__ Lab2RGB(const Lab2RGB&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(name, scn, dcn, srgb, blueIdx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::Lab2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + +///////////////////////////////////// RGB <-> Luv ///////////////////////////////////// + + namespace color_detail + { + __constant__ float c_LabCbrtTab[] = 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+ + template + __device__ __forceinline__ void RGB2LuvConvert_f(const T& src, D& dst) + { + const float _d = 1.f / (0.950456f + 15 + 1.088754f * 3); + const float _un = 13 * (4 * 0.950456f * _d); + const float _vn = 13 * (9 * _d); + + float B = blueIdx == 0 ? src.x : src.z; + float G = src.y; + float R = blueIdx == 0 ? src.z : src.x; + + if (srgb) + { + B = splineInterpolate(B * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE); + G = splineInterpolate(G * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE); + R = splineInterpolate(R * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE); + } + + float X = R * 0.412453f + G * 0.357580f + B * 0.180423f; + float Y = R * 0.212671f + G * 0.715160f + B * 0.072169f; + float Z = R * 0.019334f + G * 0.119193f + B * 0.950227f; + + float L = splineInterpolate(Y * (LAB_CBRT_TAB_SIZE / 1.5f), c_LabCbrtTab, LAB_CBRT_TAB_SIZE); + L = 116.f * L - 16.f; + + const float d = (4 * 13) / ::fmaxf(X + 15 * Y + 3 * Z, numeric_limits::epsilon()); + float u = L * (X * d - _un); + float v = L * ((9 * 0.25f) * Y * d - _vn); + + dst.x = L; + dst.y = u; + dst.z = v; + } + + template + __device__ __forceinline__ void RGB2LuvConvert_b(const T& src, D& dst) + { + float3 srcf, dstf; + + srcf.x = src.x * (1.f / 255.f); + srcf.y = src.y * (1.f / 255.f); + srcf.z = src.z * (1.f / 255.f); + + RGB2LuvConvert_f(srcf, dstf); + + dst.x = saturate_cast(dstf.x * 2.55f); + dst.y = saturate_cast(dstf.y * 0.72033898305084743f + 96.525423728813564f); + dst.z = saturate_cast(dstf.z * 0.9732824427480916f + 136.259541984732824f); + } + + template struct RGB2Luv; + template + struct RGB2Luv + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + RGB2LuvConvert_b(src, dst); + + return dst; + } + __host__ __device__ __forceinline__ RGB2Luv() {} + __host__ __device__ __forceinline__ RGB2Luv(const RGB2Luv&) {} + }; + template + struct RGB2Luv + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + RGB2LuvConvert_f(src, dst); + + return dst; + } + __host__ __device__ __forceinline__ RGB2Luv() {} + __host__ __device__ __forceinline__ RGB2Luv(const RGB2Luv&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(name, scn, dcn, srgb, blueIdx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::RGB2Luv functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + + namespace color_detail + { + template + __device__ __forceinline__ void Luv2RGBConvert_f(const T& src, D& dst) + { + const float _d = 1.f / (0.950456f + 15 + 1.088754f * 3); + const float _un = 4 * 0.950456f * _d; + const float _vn = 9 * _d; + + float L = src.x; + float u = src.y; + float v = src.z; + + float Y = (L + 16.f) * (1.f / 116.f); + Y = Y * Y * Y; + + float d = (1.f / 13.f) / L; + u = u * d + _un; + v = v * d + _vn; + + float iv = 1.f / v; + float X = 2.25f * u * Y * iv; + float Z = (12 - 3 * u - 20 * v) * Y * 0.25f * iv; + + float B = 0.055648f * X - 0.204043f * Y + 1.057311f * Z; + float G = -0.969256f * X + 1.875991f * Y + 0.041556f * Z; + float R = 3.240479f * X - 1.537150f * Y - 0.498535f * Z; + + if (srgb) + { + B = splineInterpolate(B * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE); + G = splineInterpolate(G * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE); + R = splineInterpolate(R * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE); + } + + dst.x = blueIdx == 0 ? B : R; + dst.y = G; + dst.z = blueIdx == 0 ? R : B; + setAlpha(dst, ColorChannel::max()); + } + + template + __device__ __forceinline__ void Luv2RGBConvert_b(const T& src, D& dst) + { + float3 srcf, dstf; + + srcf.x = src.x * (100.f / 255.f); + srcf.y = src.y * 1.388235294117647f - 134.f; + srcf.z = src.z * 1.027450980392157f - 140.f; + + Luv2RGBConvert_f(srcf, dstf); + + dst.x = saturate_cast(dstf.x * 255.f); + dst.y = saturate_cast(dstf.y * 255.f); + dst.z = saturate_cast(dstf.z * 255.f); + setAlpha(dst, ColorChannel::max()); + } + + template struct Luv2RGB; + template + struct Luv2RGB + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + Luv2RGBConvert_b(src, dst); + + return dst; + } + __host__ __device__ __forceinline__ Luv2RGB() {} + __host__ __device__ __forceinline__ Luv2RGB(const Luv2RGB&) {} + }; + template + struct Luv2RGB + : unary_function::vec_type, typename TypeVec::vec_type> + { + __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const + { + typename TypeVec::vec_type dst; + + Luv2RGBConvert_f(src, dst); + + return dst; + } + __host__ __device__ __forceinline__ Luv2RGB() {} + __host__ __device__ __forceinline__ Luv2RGB(const Luv2RGB&) {} + }; + } + +#define OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(name, scn, dcn, srgb, blueIdx) \ + template struct name ## _traits \ + { \ + typedef ::cv::cuda::device::color_detail::Luv2RGB functor_type; \ + static __host__ __device__ __forceinline__ functor_type create_functor() \ + { \ + return functor_type(); \ + } \ + }; + + #undef CV_DESCALE + +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_COLOR_DETAIL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/detail/reduce.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/detail/reduce.hpp new file mode 100644 index 0000000000000000000000000000000000000000..8af20b0dc86d76232220c5111bbdf5a2bc31748c --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/detail/reduce.hpp @@ -0,0 +1,365 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_REDUCE_DETAIL_HPP +#define OPENCV_CUDA_REDUCE_DETAIL_HPP + +#include +#include "../warp.hpp" +#include "../warp_shuffle.hpp" + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + namespace reduce_detail + { + template struct GetType; + template struct GetType + { + typedef T type; + }; + template struct GetType + { + typedef T type; + }; + template struct GetType + { + typedef T type; + }; + + template + struct For + { + template + static __device__ void loadToSmem(const PointerTuple& smem, const ValTuple& val, unsigned int tid) + { + thrust::get(smem)[tid] = thrust::get(val); + + For::loadToSmem(smem, val, tid); + } + template + static __device__ void loadFromSmem(const PointerTuple& smem, const ValTuple& val, unsigned int tid) + { + thrust::get(val) = thrust::get(smem)[tid]; + + For::loadFromSmem(smem, val, tid); + } + + template + static __device__ void merge(const PointerTuple& smem, const ValTuple& val, unsigned int tid, unsigned int delta, const OpTuple& op) + { + typename GetType::type>::type reg = thrust::get(smem)[tid + delta]; + thrust::get(smem)[tid] = thrust::get(val) = thrust::get(op)(thrust::get(val), reg); + + For::merge(smem, val, tid, delta, op); + } + template + static __device__ void mergeShfl(const ValTuple& val, unsigned int delta, unsigned int width, const OpTuple& op) + { + typename GetType::type>::type reg = shfl_down(thrust::get(val), delta, width); + thrust::get(val) = thrust::get(op)(thrust::get(val), reg); + + For::mergeShfl(val, delta, width, op); + } + }; + template + struct For + { + template + static __device__ void loadToSmem(const PointerTuple&, const ValTuple&, unsigned int) + { + } + template + static __device__ void loadFromSmem(const PointerTuple&, const ValTuple&, unsigned int) + { + } + + template + static __device__ void merge(const PointerTuple&, const ValTuple&, unsigned int, unsigned int, const OpTuple&) + { + } + template + static __device__ void mergeShfl(const ValTuple&, unsigned int, unsigned int, const OpTuple&) + { + } + }; + + template + __device__ __forceinline__ void loadToSmem(volatile T* smem, T& val, unsigned int tid) + { + smem[tid] = val; + } + template + __device__ __forceinline__ void loadFromSmem(volatile T* smem, T& val, unsigned int tid) + { + val = smem[tid]; + } + template + __device__ __forceinline__ void loadToSmem(const thrust::tuple& smem, + const thrust::tuple& val, + unsigned int tid) + { + For<0, thrust::tuple_size >::value>::loadToSmem(smem, val, tid); + } + template + __device__ __forceinline__ void loadFromSmem(const thrust::tuple& smem, + const thrust::tuple& val, + unsigned int tid) + { + For<0, thrust::tuple_size >::value>::loadFromSmem(smem, val, tid); + } + + template + __device__ __forceinline__ void merge(volatile T* smem, T& val, unsigned int tid, unsigned int delta, const Op& op) + { + T reg = smem[tid + delta]; + smem[tid] = val = op(val, reg); + } + template + __device__ __forceinline__ void mergeShfl(T& val, unsigned int delta, unsigned int width, const Op& op) + { + T reg = shfl_down(val, delta, width); + val = op(val, reg); + } + template + __device__ __forceinline__ void merge(const thrust::tuple& smem, + const thrust::tuple& val, + unsigned int tid, + unsigned int delta, + const thrust::tuple& op) + { + For<0, thrust::tuple_size >::value>::merge(smem, val, tid, delta, op); + } + template + __device__ __forceinline__ void mergeShfl(const thrust::tuple& val, + unsigned int delta, + unsigned int width, + const thrust::tuple& op) + { + For<0, thrust::tuple_size >::value>::mergeShfl(val, delta, width, op); + } + + template struct Generic + { + template + static __device__ void reduce(Pointer smem, Reference val, unsigned int tid, Op op) + { + loadToSmem(smem, val, tid); + if (N >= 32) + __syncthreads(); + + if (N >= 2048) + { + if (tid < 1024) + merge(smem, val, tid, 1024, op); + + __syncthreads(); + } + if (N >= 1024) + { + if (tid < 512) + merge(smem, val, tid, 512, op); + + __syncthreads(); + } + if (N >= 512) + { + if (tid < 256) + merge(smem, val, tid, 256, op); + + __syncthreads(); + } + if (N >= 256) + { + if (tid < 128) + merge(smem, val, tid, 128, op); + + __syncthreads(); + } + if (N >= 128) + { + if (tid < 64) + merge(smem, val, tid, 64, op); + + __syncthreads(); + } + if (N >= 64) + { + if (tid < 32) + merge(smem, val, tid, 32, op); + } + + if (tid < 16) + { + merge(smem, val, tid, 16, op); + merge(smem, val, tid, 8, op); + merge(smem, val, tid, 4, op); + merge(smem, val, tid, 2, op); + merge(smem, val, tid, 1, op); + } + } + }; + + template + struct Unroll + { + static __device__ void loopShfl(Reference val, Op op, unsigned int N) + { + mergeShfl(val, I, N, op); + Unroll::loopShfl(val, op, N); + } + static __device__ void loop(Pointer smem, Reference val, unsigned int tid, Op op) + { + merge(smem, val, tid, I, op); + Unroll::loop(smem, val, tid, op); + } + }; + template + struct Unroll<0, Pointer, Reference, Op> + { + static __device__ void loopShfl(Reference, Op, unsigned int) + { + } + static __device__ void loop(Pointer, Reference, unsigned int, Op) + { + } + }; + + template struct WarpOptimized + { + template + static __device__ void reduce(Pointer smem, Reference val, unsigned int tid, Op op) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + CV_UNUSED(smem); + CV_UNUSED(tid); + + Unroll::loopShfl(val, op, N); + #else + loadToSmem(smem, val, tid); + + if (tid < N / 2) + Unroll::loop(smem, val, tid, op); + #endif + } + }; + + template struct GenericOptimized32 + { + enum { M = N / 32 }; + + template + static __device__ void reduce(Pointer smem, Reference val, unsigned int tid, Op op) + { + const unsigned int laneId = Warp::laneId(); + + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + Unroll<16, Pointer, Reference, Op>::loopShfl(val, op, warpSize); + + if (laneId == 0) + loadToSmem(smem, val, tid / 32); + #else + loadToSmem(smem, val, tid); + + if (laneId < 16) + Unroll<16, Pointer, Reference, Op>::loop(smem, val, tid, op); + + __syncthreads(); + + if (laneId == 0) + loadToSmem(smem, val, tid / 32); + #endif + + __syncthreads(); + + loadFromSmem(smem, val, tid); + + if (tid < 32) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + Unroll::loopShfl(val, op, M); + #else + Unroll::loop(smem, val, tid, op); + #endif + } + } + }; + + template struct StaticIf; + template struct StaticIf + { + typedef T1 type; + }; + template struct StaticIf + { + typedef T2 type; + }; + + template struct IsPowerOf2 + { + enum { value = ((N != 0) && !(N & (N - 1))) }; + }; + + template struct Dispatcher + { + typedef typename StaticIf< + (N <= 32) && IsPowerOf2::value, + WarpOptimized, + typename StaticIf< + (N <= 1024) && IsPowerOf2::value, + GenericOptimized32, + Generic + >::type + >::type reductor; + }; + } +}}} + +//! @endcond + +#endif // OPENCV_CUDA_REDUCE_DETAIL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/detail/reduce_key_val.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/detail/reduce_key_val.hpp new file mode 100644 index 0000000000000000000000000000000000000000..df37c173be64bed1331f49260388acceea6de56b --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/detail/reduce_key_val.hpp @@ -0,0 +1,502 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_PRED_VAL_REDUCE_DETAIL_HPP +#define OPENCV_CUDA_PRED_VAL_REDUCE_DETAIL_HPP + +#include +#include "../warp.hpp" +#include "../warp_shuffle.hpp" + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + namespace reduce_key_val_detail + { + template struct GetType; + template struct GetType + { + typedef T type; + }; + template struct GetType + { + typedef T type; + }; + template struct GetType + { + typedef T type; + }; + + template + struct For + { + template + static __device__ void loadToSmem(const PointerTuple& smem, const ReferenceTuple& data, unsigned int tid) + { + thrust::get(smem)[tid] = thrust::get(data); + + For::loadToSmem(smem, data, tid); + } + template + static __device__ void loadFromSmem(const PointerTuple& smem, const ReferenceTuple& data, unsigned int tid) + { + thrust::get(data) = thrust::get(smem)[tid]; + + For::loadFromSmem(smem, data, tid); + } + + template + static __device__ void copyShfl(const ReferenceTuple& val, unsigned int delta, int width) + { + thrust::get(val) = shfl_down(thrust::get(val), delta, width); + + For::copyShfl(val, delta, width); + } + template + static __device__ void copy(const PointerTuple& svals, const ReferenceTuple& val, unsigned int tid, unsigned int delta) + { + thrust::get(svals)[tid] = thrust::get(val) = thrust::get(svals)[tid + delta]; + + For::copy(svals, val, tid, delta); + } + + template + static __device__ void mergeShfl(const KeyReferenceTuple& key, const ValReferenceTuple& val, const CmpTuple& cmp, unsigned int delta, int width) + { + typename GetType::type>::type reg = shfl_down(thrust::get(key), delta, width); + + if (thrust::get(cmp)(reg, thrust::get(key))) + { + thrust::get(key) = reg; + thrust::get(val) = shfl_down(thrust::get(val), delta, width); + } + + For::mergeShfl(key, val, cmp, delta, width); + } + template + static __device__ void merge(const KeyPointerTuple& skeys, const KeyReferenceTuple& key, + const ValPointerTuple& svals, const ValReferenceTuple& val, + const CmpTuple& cmp, + unsigned int tid, unsigned int delta) + { + typename GetType::type>::type reg = thrust::get(skeys)[tid + delta]; + + if (thrust::get(cmp)(reg, thrust::get(key))) + { + thrust::get(skeys)[tid] = thrust::get(key) = reg; + thrust::get(svals)[tid] = thrust::get(val) = thrust::get(svals)[tid + delta]; + } + + For::merge(skeys, key, svals, val, cmp, tid, delta); + } + }; + template + struct For + { + template + static __device__ void loadToSmem(const PointerTuple&, const ReferenceTuple&, unsigned int) + { + } + template + static __device__ void loadFromSmem(const PointerTuple&, const ReferenceTuple&, unsigned int) + { + } + + template + static __device__ void copyShfl(const ReferenceTuple&, unsigned int, int) + { + } + template + static __device__ void copy(const PointerTuple&, const ReferenceTuple&, unsigned int, unsigned int) + { + } + + template + static __device__ void mergeShfl(const KeyReferenceTuple&, const ValReferenceTuple&, const CmpTuple&, unsigned int, int) + { + } + template + static __device__ void merge(const KeyPointerTuple&, const KeyReferenceTuple&, + const ValPointerTuple&, const ValReferenceTuple&, + const CmpTuple&, + unsigned int, unsigned int) + { + } + }; + + ////////////////////////////////////////////////////// + // loadToSmem + + template + __device__ __forceinline__ void loadToSmem(volatile T* smem, T& data, unsigned int tid) + { + smem[tid] = data; + } + template + __device__ __forceinline__ void loadFromSmem(volatile T* smem, T& data, unsigned int tid) + { + data = smem[tid]; + } + template + __device__ __forceinline__ void loadToSmem(const thrust::tuple& smem, + const thrust::tuple& data, + unsigned int tid) + { + For<0, thrust::tuple_size >::value>::loadToSmem(smem, data, tid); + } + template + __device__ __forceinline__ void loadFromSmem(const thrust::tuple& smem, + const thrust::tuple& data, + unsigned int tid) + { + For<0, thrust::tuple_size >::value>::loadFromSmem(smem, data, tid); + } + + ////////////////////////////////////////////////////// + // copyVals + + template + __device__ __forceinline__ void copyValsShfl(V& val, unsigned int delta, int width) + { + val = shfl_down(val, delta, width); + } + template + __device__ __forceinline__ void copyVals(volatile V* svals, V& val, unsigned int tid, unsigned int delta) + { + svals[tid] = val = svals[tid + delta]; + } + template + __device__ __forceinline__ void copyValsShfl(const thrust::tuple& val, + unsigned int delta, + int width) + { + For<0, thrust::tuple_size >::value>::copyShfl(val, delta, width); + } + template + __device__ __forceinline__ void copyVals(const thrust::tuple& svals, + const thrust::tuple& val, + unsigned int tid, unsigned int delta) + { + For<0, thrust::tuple_size >::value>::copy(svals, val, tid, delta); + } + + ////////////////////////////////////////////////////// + // merge + + template + __device__ __forceinline__ void mergeShfl(K& key, V& val, const Cmp& cmp, unsigned int delta, int width) + { + K reg = shfl_down(key, delta, width); + + if (cmp(reg, key)) + { + key = reg; + copyValsShfl(val, delta, width); + } + } + template + __device__ __forceinline__ void merge(volatile K* skeys, K& key, volatile V* svals, V& val, const Cmp& cmp, unsigned int tid, unsigned int delta) + { + K reg = skeys[tid + delta]; + + if (cmp(reg, key)) + { + skeys[tid] = key = reg; + copyVals(svals, val, tid, delta); + } + } + template + __device__ __forceinline__ void mergeShfl(K& key, + const thrust::tuple& val, + const Cmp& cmp, + unsigned int delta, int width) + { + K reg = shfl_down(key, delta, width); + + if (cmp(reg, key)) + { + key = reg; + copyValsShfl(val, delta, width); + } + } + template + __device__ __forceinline__ void merge(volatile K* skeys, K& key, + const thrust::tuple& svals, + const thrust::tuple& val, + const Cmp& cmp, unsigned int tid, unsigned int delta) + { + K reg = skeys[tid + delta]; + + if (cmp(reg, key)) + { + skeys[tid] = key = reg; + copyVals(svals, val, tid, delta); + } + } + template + __device__ __forceinline__ void mergeShfl(const thrust::tuple& key, + const thrust::tuple& val, + const thrust::tuple& cmp, + unsigned int delta, int width) + { + For<0, thrust::tuple_size >::value>::mergeShfl(key, val, cmp, delta, width); + } + template + __device__ __forceinline__ void merge(const thrust::tuple& skeys, + const thrust::tuple& key, + const thrust::tuple& svals, + const thrust::tuple& val, + const thrust::tuple& cmp, + unsigned int tid, unsigned int delta) + { + For<0, thrust::tuple_size >::value>::merge(skeys, key, svals, val, cmp, tid, delta); + } + + ////////////////////////////////////////////////////// + // Generic + + template struct Generic + { + template + static __device__ void reduce(KP skeys, KR key, VP svals, VR val, unsigned int tid, Cmp cmp) + { + loadToSmem(skeys, key, tid); + loadValsToSmem(svals, val, tid); + if (N >= 32) + __syncthreads(); + + if (N >= 2048) + { + if (tid < 1024) + merge(skeys, key, svals, val, cmp, tid, 1024); + + __syncthreads(); + } + if (N >= 1024) + { + if (tid < 512) + merge(skeys, key, svals, val, cmp, tid, 512); + + __syncthreads(); + } + if (N >= 512) + { + if (tid < 256) + merge(skeys, key, svals, val, cmp, tid, 256); + + __syncthreads(); + } + if (N >= 256) + { + if (tid < 128) + merge(skeys, key, svals, val, cmp, tid, 128); + + __syncthreads(); + } + if (N >= 128) + { + if (tid < 64) + merge(skeys, key, svals, val, cmp, tid, 64); + + __syncthreads(); + } + if (N >= 64) + { + if (tid < 32) + merge(skeys, key, svals, val, cmp, tid, 32); + } + + if (tid < 16) + { + merge(skeys, key, svals, val, cmp, tid, 16); + merge(skeys, key, svals, val, cmp, tid, 8); + merge(skeys, key, svals, val, cmp, tid, 4); + merge(skeys, key, svals, val, cmp, tid, 2); + merge(skeys, key, svals, val, cmp, tid, 1); + } + } + }; + + template + struct Unroll + { + static __device__ void loopShfl(KR key, VR val, Cmp cmp, unsigned int N) + { + mergeShfl(key, val, cmp, I, N); + Unroll::loopShfl(key, val, cmp, N); + } + static __device__ void loop(KP skeys, KR key, VP svals, VR val, unsigned int tid, Cmp cmp) + { + merge(skeys, key, svals, val, cmp, tid, I); + Unroll::loop(skeys, key, svals, val, tid, cmp); + } + }; + template + struct Unroll<0, KP, KR, VP, VR, Cmp> + { + static __device__ void loopShfl(KR, VR, Cmp, unsigned int) + { + } + static __device__ void loop(KP, KR, VP, VR, unsigned int, Cmp) + { + } + }; + + template struct WarpOptimized + { + template + static __device__ void reduce(KP skeys, KR key, VP svals, VR val, unsigned int tid, Cmp cmp) + { + #if 0 // __CUDA_ARCH__ >= 300 + CV_UNUSED(skeys); + CV_UNUSED(svals); + CV_UNUSED(tid); + + Unroll::loopShfl(key, val, cmp, N); + #else + loadToSmem(skeys, key, tid); + loadToSmem(svals, val, tid); + + if (tid < N / 2) + Unroll::loop(skeys, key, svals, val, tid, cmp); + #endif + } + }; + + template struct GenericOptimized32 + { + enum { M = N / 32 }; + + template + static __device__ void reduce(KP skeys, KR key, VP svals, VR val, unsigned int tid, Cmp cmp) + { + const unsigned int laneId = Warp::laneId(); + + #if 0 // __CUDA_ARCH__ >= 300 + Unroll<16, KP, KR, VP, VR, Cmp>::loopShfl(key, val, cmp, warpSize); + + if (laneId == 0) + { + loadToSmem(skeys, key, tid / 32); + loadToSmem(svals, val, tid / 32); + } + #else + loadToSmem(skeys, key, tid); + loadToSmem(svals, val, tid); + + if (laneId < 16) + Unroll<16, KP, KR, VP, VR, Cmp>::loop(skeys, key, svals, val, tid, cmp); + + __syncthreads(); + + if (laneId == 0) + { + loadToSmem(skeys, key, tid / 32); + loadToSmem(svals, val, tid / 32); + } + #endif + + __syncthreads(); + + loadFromSmem(skeys, key, tid); + + if (tid < 32) + { + #if 0 // __CUDA_ARCH__ >= 300 + loadFromSmem(svals, val, tid); + + Unroll::loopShfl(key, val, cmp, M); + #else + Unroll::loop(skeys, key, svals, val, tid, cmp); + #endif + } + } + }; + + template struct StaticIf; + template struct StaticIf + { + typedef T1 type; + }; + template struct StaticIf + { + typedef T2 type; + }; + + template struct IsPowerOf2 + { + enum { value = ((N != 0) && !(N & (N - 1))) }; + }; + + template struct Dispatcher + { + typedef typename StaticIf< + (N <= 32) && IsPowerOf2::value, + WarpOptimized, + typename StaticIf< + (N <= 1024) && IsPowerOf2::value, + GenericOptimized32, + Generic + >::type + >::type reductor; + }; + } +}}} + +//! @endcond + +#endif // OPENCV_CUDA_PRED_VAL_REDUCE_DETAIL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/detail/transform_detail.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/detail/transform_detail.hpp new file mode 100644 index 0000000000000000000000000000000000000000..1919848827543461c62aeacc90b41c8c105fa5ea --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/detail/transform_detail.hpp @@ -0,0 +1,392 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_TRANSFORM_DETAIL_HPP +#define OPENCV_CUDA_TRANSFORM_DETAIL_HPP + +#include "../common.hpp" +#include "../vec_traits.hpp" +#include "../functional.hpp" + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + namespace transform_detail + { + //! Read Write Traits + + template struct UnaryReadWriteTraits + { + typedef typename TypeVec::vec_type read_type; + typedef typename TypeVec::vec_type write_type; + }; + + template struct BinaryReadWriteTraits + { + typedef typename TypeVec::vec_type read_type1; + typedef typename TypeVec::vec_type read_type2; + typedef typename TypeVec::vec_type write_type; + }; + + //! Transform kernels + + template struct OpUnroller; + template <> struct OpUnroller<1> + { + template + static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, UnOp& op, int x_shifted, int y) + { + if (mask(y, x_shifted)) + dst.x = op(src.x); + } + + template + static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, BinOp& op, int x_shifted, int y) + { + if (mask(y, x_shifted)) + dst.x = op(src1.x, src2.x); + } + }; + template <> struct OpUnroller<2> + { + template + static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, UnOp& op, int x_shifted, int y) + { + if (mask(y, x_shifted)) + dst.x = op(src.x); + if (mask(y, x_shifted + 1)) + dst.y = op(src.y); + } + + template + static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, BinOp& op, int x_shifted, int y) + { + if (mask(y, x_shifted)) + dst.x = op(src1.x, src2.x); + if (mask(y, x_shifted + 1)) + dst.y = op(src1.y, src2.y); + } + }; + template <> struct OpUnroller<3> + { + template + static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, const UnOp& op, int x_shifted, int y) + { + if (mask(y, x_shifted)) + dst.x = op(src.x); + if (mask(y, x_shifted + 1)) + dst.y = op(src.y); + if (mask(y, x_shifted + 2)) + dst.z = op(src.z); + } + + template + static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, const BinOp& op, int x_shifted, int y) + { + if (mask(y, x_shifted)) + dst.x = op(src1.x, src2.x); + if (mask(y, x_shifted + 1)) + dst.y = op(src1.y, src2.y); + if (mask(y, x_shifted + 2)) + dst.z = op(src1.z, src2.z); + } + }; + template <> struct OpUnroller<4> + { + template + static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, const UnOp& op, int x_shifted, int y) + { + if (mask(y, x_shifted)) + dst.x = op(src.x); + if (mask(y, x_shifted + 1)) + dst.y = op(src.y); + if (mask(y, x_shifted + 2)) + dst.z = op(src.z); + if (mask(y, x_shifted + 3)) + dst.w = op(src.w); + } + + template + static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, const BinOp& op, int x_shifted, int y) + { + if (mask(y, x_shifted)) + dst.x = op(src1.x, src2.x); + if (mask(y, x_shifted + 1)) + dst.y = op(src1.y, src2.y); + if (mask(y, x_shifted + 2)) + dst.z = op(src1.z, src2.z); + if (mask(y, x_shifted + 3)) + dst.w = op(src1.w, src2.w); + } + }; + template <> struct OpUnroller<8> + { + template + static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, const UnOp& op, int x_shifted, int y) + { + if (mask(y, x_shifted)) + dst.a0 = op(src.a0); + if (mask(y, x_shifted + 1)) + dst.a1 = op(src.a1); + if (mask(y, x_shifted + 2)) + dst.a2 = op(src.a2); + if (mask(y, x_shifted + 3)) + dst.a3 = op(src.a3); + if (mask(y, x_shifted + 4)) + dst.a4 = op(src.a4); + if (mask(y, x_shifted + 5)) + dst.a5 = op(src.a5); + if (mask(y, x_shifted + 6)) + dst.a6 = op(src.a6); + if (mask(y, x_shifted + 7)) + dst.a7 = op(src.a7); + } + + template + static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, const BinOp& op, int x_shifted, int y) + { + if (mask(y, x_shifted)) + dst.a0 = op(src1.a0, src2.a0); + if (mask(y, x_shifted + 1)) + dst.a1 = op(src1.a1, src2.a1); + if (mask(y, x_shifted + 2)) + dst.a2 = op(src1.a2, src2.a2); + if (mask(y, x_shifted + 3)) + dst.a3 = op(src1.a3, src2.a3); + if (mask(y, x_shifted + 4)) + dst.a4 = op(src1.a4, src2.a4); + if (mask(y, x_shifted + 5)) + dst.a5 = op(src1.a5, src2.a5); + if (mask(y, x_shifted + 6)) + dst.a6 = op(src1.a6, src2.a6); + if (mask(y, x_shifted + 7)) + dst.a7 = op(src1.a7, src2.a7); + } + }; + + template + static __global__ void transformSmart(const PtrStepSz src_, PtrStep dst_, const Mask mask, const UnOp op) + { + typedef TransformFunctorTraits ft; + typedef typename UnaryReadWriteTraits::read_type read_type; + typedef typename UnaryReadWriteTraits::write_type write_type; + + const int x = threadIdx.x + blockIdx.x * blockDim.x; + const int y = threadIdx.y + blockIdx.y * blockDim.y; + const int x_shifted = x * ft::smart_shift; + + if (y < src_.rows) + { + const T* src = src_.ptr(y); + D* dst = dst_.ptr(y); + + if (x_shifted + ft::smart_shift - 1 < src_.cols) + { + const read_type src_n_el = ((const read_type*)src)[x]; + OpUnroller::unroll(src_n_el, ((write_type*)dst)[x], mask, op, x_shifted, y); + } + else + { + for (int real_x = x_shifted; real_x < src_.cols; ++real_x) + { + if (mask(y, real_x)) + dst[real_x] = op(src[real_x]); + } + } + } + } + + template + __global__ static void transformSimple(const PtrStepSz src, PtrStep dst, const Mask mask, const UnOp op) + { + const int x = blockDim.x * blockIdx.x + threadIdx.x; + const int y = blockDim.y * blockIdx.y + threadIdx.y; + + if (x < src.cols && y < src.rows && mask(y, x)) + { + dst.ptr(y)[x] = op(src.ptr(y)[x]); + } + } + + template + static __global__ void transformSmart(const PtrStepSz src1_, const PtrStep src2_, PtrStep dst_, + const Mask mask, const BinOp op) + { + typedef TransformFunctorTraits ft; + typedef typename BinaryReadWriteTraits::read_type1 read_type1; + typedef typename BinaryReadWriteTraits::read_type2 read_type2; + typedef typename BinaryReadWriteTraits::write_type write_type; + + const int x = threadIdx.x + blockIdx.x * blockDim.x; + const int y = threadIdx.y + blockIdx.y * blockDim.y; + const int x_shifted = x * ft::smart_shift; + + if (y < src1_.rows) + { + const T1* src1 = src1_.ptr(y); + const T2* src2 = src2_.ptr(y); + D* dst = dst_.ptr(y); + + if (x_shifted + ft::smart_shift - 1 < src1_.cols) + { + const read_type1 src1_n_el = ((const read_type1*)src1)[x]; + const read_type2 src2_n_el = ((const read_type2*)src2)[x]; + + OpUnroller::unroll(src1_n_el, src2_n_el, ((write_type*)dst)[x], mask, op, x_shifted, y); + } + else + { + for (int real_x = x_shifted; real_x < src1_.cols; ++real_x) + { + if (mask(y, real_x)) + dst[real_x] = op(src1[real_x], src2[real_x]); + } + } + } + } + + template + static __global__ void transformSimple(const PtrStepSz src1, const PtrStep src2, PtrStep dst, + const Mask mask, const BinOp op) + { + const int x = blockDim.x * blockIdx.x + threadIdx.x; + const int y = blockDim.y * blockIdx.y + threadIdx.y; + + if (x < src1.cols && y < src1.rows && mask(y, x)) + { + const T1 src1_data = src1.ptr(y)[x]; + const T2 src2_data = src2.ptr(y)[x]; + dst.ptr(y)[x] = op(src1_data, src2_data); + } + } + + template struct TransformDispatcher; + template<> struct TransformDispatcher + { + template + static void call(PtrStepSz src, PtrStepSz dst, UnOp op, Mask mask, cudaStream_t stream) + { + typedef TransformFunctorTraits ft; + + const dim3 threads(ft::simple_block_dim_x, ft::simple_block_dim_y, 1); + const dim3 grid(divUp(src.cols, threads.x), divUp(src.rows, threads.y), 1); + + transformSimple<<>>(src, dst, mask, op); + cudaSafeCall( cudaGetLastError() ); + + if (stream == 0) + cudaSafeCall( cudaDeviceSynchronize() ); + } + + template + static void call(PtrStepSz src1, PtrStepSz src2, PtrStepSz dst, BinOp op, Mask mask, cudaStream_t stream) + { + typedef TransformFunctorTraits ft; + + const dim3 threads(ft::simple_block_dim_x, ft::simple_block_dim_y, 1); + const dim3 grid(divUp(src1.cols, threads.x), divUp(src1.rows, threads.y), 1); + + transformSimple<<>>(src1, src2, dst, mask, op); + cudaSafeCall( cudaGetLastError() ); + + if (stream == 0) + cudaSafeCall( cudaDeviceSynchronize() ); + } + }; + template<> struct TransformDispatcher + { + template + static void call(PtrStepSz src, PtrStepSz dst, UnOp op, Mask mask, cudaStream_t stream) + { + typedef TransformFunctorTraits ft; + + CV_StaticAssert(ft::smart_shift != 1, ""); + + if (!isAligned(src.data, ft::smart_shift * sizeof(T)) || !isAligned(src.step, ft::smart_shift * sizeof(T)) || + !isAligned(dst.data, ft::smart_shift * sizeof(D)) || !isAligned(dst.step, ft::smart_shift * sizeof(D))) + { + TransformDispatcher::call(src, dst, op, mask, stream); + return; + } + + const dim3 threads(ft::smart_block_dim_x, ft::smart_block_dim_y, 1); + const dim3 grid(divUp(src.cols, threads.x * ft::smart_shift), divUp(src.rows, threads.y), 1); + + transformSmart<<>>(src, dst, mask, op); + cudaSafeCall( cudaGetLastError() ); + + if (stream == 0) + cudaSafeCall( cudaDeviceSynchronize() ); + } + + template + static void call(PtrStepSz src1, PtrStepSz src2, PtrStepSz dst, BinOp op, Mask mask, cudaStream_t stream) + { + typedef TransformFunctorTraits ft; + + CV_StaticAssert(ft::smart_shift != 1, ""); + + if (!isAligned(src1.data, ft::smart_shift * sizeof(T1)) || !isAligned(src1.step, ft::smart_shift * sizeof(T1)) || + !isAligned(src2.data, ft::smart_shift * sizeof(T2)) || !isAligned(src2.step, ft::smart_shift * sizeof(T2)) || + !isAligned(dst.data, ft::smart_shift * sizeof(D)) || !isAligned(dst.step, ft::smart_shift * sizeof(D))) + { + TransformDispatcher::call(src1, src2, dst, op, mask, stream); + return; + } + + const dim3 threads(ft::smart_block_dim_x, ft::smart_block_dim_y, 1); + const dim3 grid(divUp(src1.cols, threads.x * ft::smart_shift), divUp(src1.rows, threads.y), 1); + + transformSmart<<>>(src1, src2, dst, mask, op); + cudaSafeCall( cudaGetLastError() ); + + if (stream == 0) + cudaSafeCall( cudaDeviceSynchronize() ); + } + }; + } // namespace transform_detail +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_TRANSFORM_DETAIL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/detail/type_traits_detail.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/detail/type_traits_detail.hpp new file mode 100644 index 0000000000000000000000000000000000000000..a78bd2c0d858f10d785cabef7258ac4ed656586f --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/detail/type_traits_detail.hpp @@ -0,0 +1,191 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_TYPE_TRAITS_DETAIL_HPP +#define OPENCV_CUDA_TYPE_TRAITS_DETAIL_HPP + +#include "../common.hpp" +#include "../vec_traits.hpp" + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + namespace type_traits_detail + { + template struct Select { typedef T1 type; }; + template struct Select { typedef T2 type; }; + + template struct IsSignedIntergral { enum {value = 0}; }; + template <> struct IsSignedIntergral { enum {value = 1}; }; + template <> struct IsSignedIntergral { enum {value = 1}; }; + template <> struct IsSignedIntergral { enum {value = 1}; }; + template <> struct IsSignedIntergral { enum {value = 1}; }; + template <> struct IsSignedIntergral { enum {value = 1}; }; + template <> struct IsSignedIntergral { enum {value = 1}; }; + + template struct IsUnsignedIntegral { enum {value = 0}; }; + template <> struct IsUnsignedIntegral { enum {value = 1}; }; + template <> struct IsUnsignedIntegral { enum {value = 1}; }; + template <> struct IsUnsignedIntegral { enum {value = 1}; }; + template <> struct IsUnsignedIntegral { enum {value = 1}; }; + template <> struct IsUnsignedIntegral { enum {value = 1}; }; + template <> struct IsUnsignedIntegral { enum {value = 1}; }; + + template struct IsIntegral { enum {value = IsSignedIntergral::value || IsUnsignedIntegral::value}; }; + template <> struct IsIntegral { enum {value = 1}; }; + template <> struct IsIntegral { enum {value = 1}; }; + + template struct IsFloat { enum {value = 0}; }; + template <> struct IsFloat { enum {value = 1}; }; + template <> struct IsFloat { enum {value = 1}; }; + + template struct IsVec { enum {value = 0}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + template <> struct IsVec { enum {value = 1}; }; + + template struct AddParameterType { typedef const U& type; }; + template struct AddParameterType { typedef U& type; }; + template <> struct AddParameterType { typedef void type; }; + + template struct ReferenceTraits + { + enum { value = false }; + typedef U type; + }; + template struct ReferenceTraits + { + enum { value = true }; + typedef U type; + }; + + template struct PointerTraits + { + enum { value = false }; + typedef void type; + }; + template struct PointerTraits + { + enum { value = true }; + typedef U type; + }; + template struct PointerTraits + { + enum { value = true }; + typedef U type; + }; + + template struct UnConst + { + typedef U type; + enum { value = 0 }; + }; + template struct UnConst + { + typedef U type; + enum { value = 1 }; + }; + template struct UnConst + { + typedef U& type; + enum { value = 1 }; + }; + + template struct UnVolatile + { + typedef U type; + enum { value = 0 }; + }; + template struct UnVolatile + { + typedef U type; + enum { value = 1 }; + }; + template struct UnVolatile + { + typedef U& type; + enum { value = 1 }; + }; + } // namespace type_traits_detail +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_TYPE_TRAITS_DETAIL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/detail/vec_distance_detail.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/detail/vec_distance_detail.hpp new file mode 100644 index 0000000000000000000000000000000000000000..8283a99560d995f34a768fb40b0c4a33d5bcfee2 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/detail/vec_distance_detail.hpp @@ -0,0 +1,121 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_VEC_DISTANCE_DETAIL_HPP +#define OPENCV_CUDA_VEC_DISTANCE_DETAIL_HPP + +#include "../datamov_utils.hpp" + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + namespace vec_distance_detail + { + template struct UnrollVecDiffCached + { + template + static __device__ void calcCheck(const T1* vecCached, const T2* vecGlob, int len, Dist& dist, int ind) + { + if (ind < len) + { + T1 val1 = *vecCached++; + + T2 val2; + ForceGlob::Load(vecGlob, ind, val2); + + dist.reduceIter(val1, val2); + + UnrollVecDiffCached::calcCheck(vecCached, vecGlob, len, dist, ind + THREAD_DIM); + } + } + + template + static __device__ void calcWithoutCheck(const T1* vecCached, const T2* vecGlob, Dist& dist) + { + T1 val1 = *vecCached++; + + T2 val2; + ForceGlob::Load(vecGlob, 0, val2); + vecGlob += THREAD_DIM; + + dist.reduceIter(val1, val2); + + UnrollVecDiffCached::calcWithoutCheck(vecCached, vecGlob, dist); + } + }; + template struct UnrollVecDiffCached + { + template + static __device__ __forceinline__ void calcCheck(const T1*, const T2*, int, Dist&, int) + { + } + + template + static __device__ __forceinline__ void calcWithoutCheck(const T1*, const T2*, Dist&) + { + } + }; + + template struct VecDiffCachedCalculator; + template struct VecDiffCachedCalculator + { + template + static __device__ __forceinline__ void calc(const T1* vecCached, const T2* vecGlob, int len, Dist& dist, int tid) + { + UnrollVecDiffCached::calcCheck(vecCached, vecGlob, len, dist, tid); + } + }; + template struct VecDiffCachedCalculator + { + template + static __device__ __forceinline__ void calc(const T1* vecCached, const T2* vecGlob, int len, Dist& dist, int tid) + { + UnrollVecDiffCached::calcWithoutCheck(vecCached, vecGlob + tid, dist); + } + }; + } // namespace vec_distance_detail +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_VEC_DISTANCE_DETAIL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/dynamic_smem.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/dynamic_smem.hpp new file mode 100644 index 0000000000000000000000000000000000000000..42570c6830c41bb53e7f9802f2a430f5181218c0 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/dynamic_smem.hpp @@ -0,0 +1,88 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_DYNAMIC_SMEM_HPP +#define OPENCV_CUDA_DYNAMIC_SMEM_HPP + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + template struct DynamicSharedMem + { + __device__ __forceinline__ operator T*() + { + extern __shared__ int __smem[]; + return (T*)__smem; + } + + __device__ __forceinline__ operator const T*() const + { + extern __shared__ int __smem[]; + return (T*)__smem; + } + }; + + // specialize for double to avoid unaligned memory access compile errors + template<> struct DynamicSharedMem + { + __device__ __forceinline__ operator double*() + { + extern __shared__ double __smem_d[]; + return (double*)__smem_d; + } + + __device__ __forceinline__ operator const double*() const + { + extern __shared__ double __smem_d[]; + return (double*)__smem_d; + } + }; +}}} + +//! @endcond + +#endif // OPENCV_CUDA_DYNAMIC_SMEM_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/emulation.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/emulation.hpp new file mode 100644 index 0000000000000000000000000000000000000000..17dc1171a237e2f1979ad2f782e79c3a9b7f9d53 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/emulation.hpp @@ -0,0 +1,269 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_EMULATION_HPP_ +#define OPENCV_CUDA_EMULATION_HPP_ + +#include "common.hpp" +#include "warp_reduce.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + struct Emulation + { + + static __device__ __forceinline__ int syncthreadsOr(int pred) + { +#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ < 200) + // just campilation stab + return 0; +#else + return __syncthreads_or(pred); +#endif + } + + template + static __forceinline__ __device__ int Ballot(int predicate) + { +#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ >= 200) + return __ballot(predicate); +#else + __shared__ volatile int cta_buffer[CTA_SIZE]; + + int tid = threadIdx.x; + cta_buffer[tid] = predicate ? (1 << (tid & 31)) : 0; + return warp_reduce(cta_buffer); +#endif + } + + struct smem + { + enum { TAG_MASK = (1U << ( (sizeof(unsigned int) << 3) - 5U)) - 1U }; + + template + static __device__ __forceinline__ T atomicInc(T* address, T val) + { +#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ < 120) + T count; + unsigned int tag = threadIdx.x << ( (sizeof(unsigned int) << 3) - 5U); + do + { + count = *address & TAG_MASK; + count = tag | (count + 1); + *address = count; + } while (*address != count); + + return (count & TAG_MASK) - 1; +#else + return ::atomicInc(address, val); +#endif + } + + template + static __device__ __forceinline__ T atomicAdd(T* address, T val) + { +#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ < 120) + T count; + unsigned int tag = threadIdx.x << ( (sizeof(unsigned int) << 3) - 5U); + do + { + count = *address & TAG_MASK; + count = tag | (count + val); + *address = count; + } while (*address != count); + + return (count & TAG_MASK) - val; +#else + return ::atomicAdd(address, val); +#endif + } + + template + static __device__ __forceinline__ T atomicMin(T* address, T val) + { +#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ < 120) + T count = ::min(*address, val); + do + { + *address = count; + } while (*address > count); + + return count; +#else + return ::atomicMin(address, val); +#endif + } + }; // struct cmem + + struct glob + { + static __device__ __forceinline__ int atomicAdd(int* address, int val) + { + return ::atomicAdd(address, val); + } + static __device__ __forceinline__ unsigned int atomicAdd(unsigned int* address, unsigned int val) + { + return ::atomicAdd(address, val); + } + static __device__ __forceinline__ float atomicAdd(float* address, float val) + { + #if __CUDA_ARCH__ >= 200 + return ::atomicAdd(address, val); + #else + int* address_as_i = (int*) address; + int old = *address_as_i, assumed; + do { + assumed = old; + old = ::atomicCAS(address_as_i, assumed, + __float_as_int(val + __int_as_float(assumed))); + } while (assumed != old); + return __int_as_float(old); + #endif + } + static __device__ __forceinline__ double atomicAdd(double* address, double val) + { + #if __CUDA_ARCH__ >= 130 + unsigned long long int* address_as_ull = (unsigned long long int*) address; + unsigned long long int old = *address_as_ull, assumed; + do { + assumed = old; + old = ::atomicCAS(address_as_ull, assumed, + __double_as_longlong(val + __longlong_as_double(assumed))); + } while (assumed != old); + return __longlong_as_double(old); + #else + CV_UNUSED(address); + CV_UNUSED(val); + return 0.0; + #endif + } + + static __device__ __forceinline__ int atomicMin(int* address, int val) + { + return ::atomicMin(address, val); + } + static __device__ __forceinline__ float atomicMin(float* address, float val) + { + #if __CUDA_ARCH__ >= 120 + int* address_as_i = (int*) address; + int old = *address_as_i, assumed; + do { + assumed = old; + old = ::atomicCAS(address_as_i, assumed, + __float_as_int(::fminf(val, __int_as_float(assumed)))); + } while (assumed != old); + return __int_as_float(old); + #else + CV_UNUSED(address); + CV_UNUSED(val); + return 0.0f; + #endif + } + static __device__ __forceinline__ double atomicMin(double* address, double val) + { + #if __CUDA_ARCH__ >= 130 + unsigned long long int* address_as_ull = (unsigned long long int*) address; + unsigned long long int old = *address_as_ull, assumed; + do { + assumed = old; + old = ::atomicCAS(address_as_ull, assumed, + __double_as_longlong(::fmin(val, __longlong_as_double(assumed)))); + } while (assumed != old); + return __longlong_as_double(old); + #else + CV_UNUSED(address); + CV_UNUSED(val); + return 0.0; + #endif + } + + static __device__ __forceinline__ int atomicMax(int* address, int val) + { + return ::atomicMax(address, val); + } + static __device__ __forceinline__ float atomicMax(float* address, float val) + { + #if __CUDA_ARCH__ >= 120 + int* address_as_i = (int*) address; + int old = *address_as_i, assumed; + do { + assumed = old; + old = ::atomicCAS(address_as_i, assumed, + __float_as_int(::fmaxf(val, __int_as_float(assumed)))); + } while (assumed != old); + return __int_as_float(old); + #else + CV_UNUSED(address); + CV_UNUSED(val); + return 0.0f; + #endif + } + static __device__ __forceinline__ double atomicMax(double* address, double val) + { + #if __CUDA_ARCH__ >= 130 + unsigned long long int* address_as_ull = (unsigned long long int*) address; + unsigned long long int old = *address_as_ull, assumed; + do { + assumed = old; + old = ::atomicCAS(address_as_ull, assumed, + __double_as_longlong(::fmax(val, __longlong_as_double(assumed)))); + } while (assumed != old); + return __longlong_as_double(old); + #else + CV_UNUSED(address); + CV_UNUSED(val); + return 0.0; + #endif + } + }; + }; //struct Emulation +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif /* OPENCV_CUDA_EMULATION_HPP_ */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/filters.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/filters.hpp new file mode 100644 index 0000000000000000000000000000000000000000..bb94212299a7f2ae5a2fa9fe7d38323adddfb3c3 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/filters.hpp @@ -0,0 +1,286 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_FILTERS_HPP +#define OPENCV_CUDA_FILTERS_HPP + +#include "saturate_cast.hpp" +#include "vec_traits.hpp" +#include "vec_math.hpp" +#include "type_traits.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + template struct PointFilter + { + typedef typename Ptr2D::elem_type elem_type; + typedef float index_type; + + explicit __host__ __device__ __forceinline__ PointFilter(const Ptr2D& src_, float fx = 0.f, float fy = 0.f) + : src(src_) + { + CV_UNUSED(fx); + CV_UNUSED(fy); + } + + __device__ __forceinline__ elem_type operator ()(float y, float x) const + { + return src(__float2int_rz(y), __float2int_rz(x)); + } + + Ptr2D src; + }; + + template struct LinearFilter + { + typedef typename Ptr2D::elem_type elem_type; + typedef float index_type; + + explicit __host__ __device__ __forceinline__ LinearFilter(const Ptr2D& src_, float fx = 0.f, float fy = 0.f) + : src(src_) + { + CV_UNUSED(fx); + CV_UNUSED(fy); + } + __device__ __forceinline__ elem_type operator ()(float y, float x) const + { + typedef typename TypeVec::cn>::vec_type work_type; + + work_type out = VecTraits::all(0); + + const int x1 = __float2int_rd(x); + const int y1 = __float2int_rd(y); + const int x2 = x1 + 1; + const int y2 = y1 + 1; + + elem_type src_reg = src(y1, x1); + out = out + src_reg * ((x2 - x) * (y2 - y)); + + src_reg = src(y1, x2); + out = out + src_reg * ((x - x1) * (y2 - y)); + + src_reg = src(y2, x1); + out = out + src_reg * ((x2 - x) * (y - y1)); + + src_reg = src(y2, x2); + out = out + src_reg * ((x - x1) * (y - y1)); + + return saturate_cast(out); + } + + Ptr2D src; + }; + + template struct CubicFilter + { + typedef typename Ptr2D::elem_type elem_type; + typedef float index_type; + typedef typename TypeVec::cn>::vec_type work_type; + + explicit __host__ __device__ __forceinline__ CubicFilter(const Ptr2D& src_, float fx = 0.f, float fy = 0.f) + : src(src_) + { + CV_UNUSED(fx); + CV_UNUSED(fy); + } + + static __device__ __forceinline__ float bicubicCoeff(float x_) + { + float x = fabsf(x_); + if (x <= 1.0f) + { + return x * x * (1.5f * x - 2.5f) + 1.0f; + } + else if (x < 2.0f) + { + return x * (x * (-0.5f * x + 2.5f) - 4.0f) + 2.0f; + } + else + { + return 0.0f; + } + } + + __device__ elem_type operator ()(float y, float x) const + { + const float xmin = ::ceilf(x - 2.0f); + const float xmax = ::floorf(x + 2.0f); + + const float ymin = ::ceilf(y - 2.0f); + const float ymax = ::floorf(y + 2.0f); + + work_type sum = VecTraits::all(0); + float wsum = 0.0f; + + for (float cy = ymin; cy <= ymax; cy += 1.0f) + { + for (float cx = xmin; cx <= xmax; cx += 1.0f) + { + const float w = bicubicCoeff(x - cx) * bicubicCoeff(y - cy); + sum = sum + w * src(__float2int_rd(cy), __float2int_rd(cx)); + wsum += w; + } + } + + work_type res = (!wsum)? VecTraits::all(0) : sum / wsum; + + return saturate_cast(res); + } + + Ptr2D src; + }; + // for integer scaling + template struct IntegerAreaFilter + { + typedef typename Ptr2D::elem_type elem_type; + typedef float index_type; + + explicit __host__ __device__ __forceinline__ IntegerAreaFilter(const Ptr2D& src_, float scale_x_, float scale_y_) + : src(src_), scale_x(scale_x_), scale_y(scale_y_), scale(1.f / (scale_x * scale_y)) {} + + __device__ __forceinline__ elem_type operator ()(float y, float x) const + { + float fsx1 = x * scale_x; + float fsx2 = fsx1 + scale_x; + + int sx1 = __float2int_ru(fsx1); + int sx2 = __float2int_rd(fsx2); + + float fsy1 = y * scale_y; + float fsy2 = fsy1 + scale_y; + + int sy1 = __float2int_ru(fsy1); + int sy2 = __float2int_rd(fsy2); + + typedef typename TypeVec::cn>::vec_type work_type; + work_type out = VecTraits::all(0.f); + + for(int dy = sy1; dy < sy2; ++dy) + for(int dx = sx1; dx < sx2; ++dx) + { + out = out + src(dy, dx) * scale; + } + + return saturate_cast(out); + } + + Ptr2D src; + float scale_x, scale_y ,scale; + }; + + template struct AreaFilter + { + typedef typename Ptr2D::elem_type elem_type; + typedef float index_type; + + explicit __host__ __device__ __forceinline__ AreaFilter(const Ptr2D& src_, float scale_x_, float scale_y_) + : src(src_), scale_x(scale_x_), scale_y(scale_y_){} + + __device__ __forceinline__ elem_type operator ()(float y, float x) const + { + float fsx1 = x * scale_x; + float fsx2 = fsx1 + scale_x; + + int sx1 = __float2int_ru(fsx1); + int sx2 = __float2int_rd(fsx2); + + float fsy1 = y * scale_y; + float fsy2 = fsy1 + scale_y; + + int sy1 = __float2int_ru(fsy1); + int sy2 = __float2int_rd(fsy2); + + float scale = 1.f / (fminf(scale_x, src.width - fsx1) * fminf(scale_y, src.height - fsy1)); + + typedef typename TypeVec::cn>::vec_type work_type; + work_type out = VecTraits::all(0.f); + + for (int dy = sy1; dy < sy2; ++dy) + { + for (int dx = sx1; dx < sx2; ++dx) + out = out + src(dy, dx) * scale; + + if (sx1 > fsx1) + out = out + src(dy, (sx1 -1) ) * ((sx1 - fsx1) * scale); + + if (sx2 < fsx2) + out = out + src(dy, sx2) * ((fsx2 -sx2) * scale); + } + + if (sy1 > fsy1) + for (int dx = sx1; dx < sx2; ++dx) + out = out + src( (sy1 - 1) , dx) * ((sy1 -fsy1) * scale); + + if (sy2 < fsy2) + for (int dx = sx1; dx < sx2; ++dx) + out = out + src(sy2, dx) * ((fsy2 -sy2) * scale); + + if ((sy1 > fsy1) && (sx1 > fsx1)) + out = out + src( (sy1 - 1) , (sx1 - 1)) * ((sy1 -fsy1) * (sx1 -fsx1) * scale); + + if ((sy1 > fsy1) && (sx2 < fsx2)) + out = out + src( (sy1 - 1) , sx2) * ((sy1 -fsy1) * (fsx2 -sx2) * scale); + + if ((sy2 < fsy2) && (sx2 < fsx2)) + out = out + src(sy2, sx2) * ((fsy2 -sy2) * (fsx2 -sx2) * scale); + + if ((sy2 < fsy2) && (sx1 > fsx1)) + out = out + src(sy2, (sx1 - 1)) * ((fsy2 -sy2) * (sx1 -fsx1) * scale); + + return saturate_cast(out); + } + + Ptr2D src; + float scale_x, scale_y; + int width, haight; + }; +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_FILTERS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/funcattrib.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/funcattrib.hpp new file mode 100644 index 0000000000000000000000000000000000000000..f58208048882076cb4748706491c46462833ad44 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/funcattrib.hpp @@ -0,0 +1,79 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_DEVICE_FUNCATTRIB_HPP +#define OPENCV_CUDA_DEVICE_FUNCATTRIB_HPP + +#include + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + template + void printFuncAttrib(Func& func) + { + + cudaFuncAttributes attrs; + cudaFuncGetAttributes(&attrs, func); + + printf("=== Function stats ===\n"); + printf("Name: \n"); + printf("sharedSizeBytes = %d\n", attrs.sharedSizeBytes); + printf("constSizeBytes = %d\n", attrs.constSizeBytes); + printf("localSizeBytes = %d\n", attrs.localSizeBytes); + printf("maxThreadsPerBlock = %d\n", attrs.maxThreadsPerBlock); + printf("numRegs = %d\n", attrs.numRegs); + printf("ptxVersion = %d\n", attrs.ptxVersion); + printf("binaryVersion = %d\n", attrs.binaryVersion); + printf("\n"); + fflush(stdout); + } +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif /* OPENCV_CUDA_DEVICE_FUNCATTRIB_HPP */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/functional.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/functional.hpp new file mode 100644 index 0000000000000000000000000000000000000000..4944381ddaa82591f158f87cd41b3784c2cf1787 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/functional.hpp @@ -0,0 +1,811 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_FUNCTIONAL_HPP +#define OPENCV_CUDA_FUNCTIONAL_HPP + +#include +#include "saturate_cast.hpp" +#include "vec_traits.hpp" +#include "type_traits.hpp" +#include "device_functions.h" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + // Function Objects +#ifdef CV_CXX11 + template struct unary_function + { + typedef Argument argument_type; + typedef Result result_type; + }; + template struct binary_function + { + typedef Argument1 first_argument_type; + typedef Argument2 second_argument_type; + typedef Result result_type; + }; +#else + template struct unary_function : public std::unary_function {}; + template struct binary_function : public std::binary_function {}; +#endif + + // Arithmetic Operations + template struct plus : binary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a + b; + } + __host__ __device__ __forceinline__ plus() {} + __host__ __device__ __forceinline__ plus(const plus&) {} + }; + + template struct minus : binary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a - b; + } + __host__ __device__ __forceinline__ minus() {} + __host__ __device__ __forceinline__ minus(const minus&) {} + }; + + template struct multiplies : binary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a * b; + } + __host__ __device__ __forceinline__ multiplies() {} + __host__ __device__ __forceinline__ multiplies(const multiplies&) {} + }; + + template struct divides : binary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a / b; + } + __host__ __device__ __forceinline__ divides() {} + __host__ __device__ __forceinline__ divides(const divides&) {} + }; + + template struct modulus : binary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a % b; + } + __host__ __device__ __forceinline__ modulus() {} + __host__ __device__ __forceinline__ modulus(const modulus&) {} + }; + + template struct negate : unary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a) const + { + return -a; + } + __host__ __device__ __forceinline__ negate() {} + __host__ __device__ __forceinline__ negate(const negate&) {} + }; + + // Comparison Operations + template struct equal_to : binary_function + { + __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a == b; + } + __host__ __device__ __forceinline__ equal_to() {} + __host__ __device__ __forceinline__ equal_to(const equal_to&) {} + }; + + template struct not_equal_to : binary_function + { + __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a != b; + } + __host__ __device__ __forceinline__ not_equal_to() {} + __host__ __device__ __forceinline__ not_equal_to(const not_equal_to&) {} + }; + + template struct greater : binary_function + { + __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a > b; + } + __host__ __device__ __forceinline__ greater() {} + __host__ __device__ __forceinline__ greater(const greater&) {} + }; + + template struct less : binary_function + { + __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a < b; + } + __host__ __device__ __forceinline__ less() {} + __host__ __device__ __forceinline__ less(const less&) {} + }; + + template struct greater_equal : binary_function + { + __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a >= b; + } + __host__ __device__ __forceinline__ greater_equal() {} + __host__ __device__ __forceinline__ greater_equal(const greater_equal&) {} + }; + + template struct less_equal : binary_function + { + __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a <= b; + } + __host__ __device__ __forceinline__ less_equal() {} + __host__ __device__ __forceinline__ less_equal(const less_equal&) {} + }; + + // Logical Operations + template struct logical_and : binary_function + { + __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a && b; + } + __host__ __device__ __forceinline__ logical_and() {} + __host__ __device__ __forceinline__ logical_and(const logical_and&) {} + }; + + template struct logical_or : binary_function + { + __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a || b; + } + __host__ __device__ __forceinline__ logical_or() {} + __host__ __device__ __forceinline__ logical_or(const logical_or&) {} + }; + + template struct logical_not : unary_function + { + __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a) const + { + return !a; + } + __host__ __device__ __forceinline__ logical_not() {} + __host__ __device__ __forceinline__ logical_not(const logical_not&) {} + }; + + // Bitwise Operations + template struct bit_and : binary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a & b; + } + __host__ __device__ __forceinline__ bit_and() {} + __host__ __device__ __forceinline__ bit_and(const bit_and&) {} + }; + + template struct bit_or : binary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a | b; + } + __host__ __device__ __forceinline__ bit_or() {} + __host__ __device__ __forceinline__ bit_or(const bit_or&) {} + }; + + template struct bit_xor : binary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, + typename TypeTraits::ParameterType b) const + { + return a ^ b; + } + __host__ __device__ __forceinline__ bit_xor() {} + __host__ __device__ __forceinline__ bit_xor(const bit_xor&) {} + }; + + template struct bit_not : unary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType v) const + { + return ~v; + } + __host__ __device__ __forceinline__ bit_not() {} + __host__ __device__ __forceinline__ bit_not(const bit_not&) {} + }; + + // Generalized Identity Operations + template struct identity : unary_function + { + __device__ __forceinline__ typename TypeTraits::ParameterType operator()(typename TypeTraits::ParameterType x) const + { + return x; + } + __host__ __device__ __forceinline__ identity() {} + __host__ __device__ __forceinline__ identity(const identity&) {} + }; + + template struct project1st : binary_function + { + __device__ __forceinline__ typename TypeTraits::ParameterType operator()(typename TypeTraits::ParameterType lhs, typename TypeTraits::ParameterType rhs) const + { + return lhs; + } + __host__ __device__ __forceinline__ project1st() {} + __host__ __device__ __forceinline__ project1st(const project1st&) {} + }; + + template struct project2nd : binary_function + { + __device__ __forceinline__ typename TypeTraits::ParameterType operator()(typename TypeTraits::ParameterType lhs, typename TypeTraits::ParameterType rhs) const + { + return rhs; + } + __host__ __device__ __forceinline__ project2nd() {} + __host__ __device__ __forceinline__ project2nd(const project2nd&) {} + }; + + // Min/Max Operations + +#define OPENCV_CUDA_IMPLEMENT_MINMAX(name, type, op) \ + template <> struct name : binary_function \ + { \ + __device__ __forceinline__ type operator()(type lhs, type rhs) const {return op(lhs, rhs);} \ + __host__ __device__ __forceinline__ name() {}\ + __host__ __device__ __forceinline__ name(const name&) {}\ + }; + + template struct maximum : binary_function + { + __device__ __forceinline__ T operator()(typename TypeTraits::ParameterType lhs, typename TypeTraits::ParameterType rhs) const + { + return max(lhs, rhs); + } + __host__ __device__ __forceinline__ maximum() {} + __host__ __device__ __forceinline__ maximum(const maximum&) {} + }; + + OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, uchar, ::max) + OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, schar, ::max) + OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, char, ::max) + OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, ushort, ::max) + OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, short, ::max) + OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, int, ::max) + OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, uint, ::max) + OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, float, ::fmax) + OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, double, ::fmax) + + template struct minimum : binary_function + { + __device__ __forceinline__ T operator()(typename TypeTraits::ParameterType lhs, typename TypeTraits::ParameterType rhs) const + { + return min(lhs, rhs); + } + __host__ __device__ __forceinline__ minimum() {} + __host__ __device__ __forceinline__ minimum(const minimum&) {} + }; + + OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, uchar, ::min) + OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, schar, ::min) + OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, char, ::min) + OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, ushort, ::min) + OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, short, ::min) + OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, int, ::min) + OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, uint, ::min) + OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, float, ::fmin) + OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, double, ::fmin) + +#undef OPENCV_CUDA_IMPLEMENT_MINMAX + + // Math functions + + template struct abs_func : unary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType x) const + { + return abs(x); + } + + __host__ __device__ __forceinline__ abs_func() {} + __host__ __device__ __forceinline__ abs_func(const abs_func&) {} + }; + template <> struct abs_func : unary_function + { + __device__ __forceinline__ unsigned char operator ()(unsigned char x) const + { + return x; + } + + __host__ __device__ __forceinline__ abs_func() {} + __host__ __device__ __forceinline__ abs_func(const abs_func&) {} + }; + template <> struct abs_func : unary_function + { + __device__ __forceinline__ signed char operator ()(signed char x) const + { + return ::abs((int)x); + } + + __host__ __device__ __forceinline__ abs_func() {} + __host__ __device__ __forceinline__ abs_func(const abs_func&) {} + }; + template <> struct abs_func : unary_function + { + __device__ __forceinline__ char operator ()(char x) const + { + return ::abs((int)x); + } + + __host__ __device__ __forceinline__ abs_func() {} + __host__ __device__ __forceinline__ abs_func(const abs_func&) {} + }; + template <> struct abs_func : unary_function + { + __device__ __forceinline__ unsigned short operator ()(unsigned short x) const + { + return x; + } + + __host__ __device__ __forceinline__ abs_func() {} + __host__ __device__ __forceinline__ abs_func(const abs_func&) {} + }; + template <> struct abs_func : unary_function + { + __device__ __forceinline__ short operator ()(short x) const + { + return ::abs((int)x); + } + + __host__ __device__ __forceinline__ abs_func() {} + __host__ __device__ __forceinline__ abs_func(const abs_func&) {} + }; + template <> struct abs_func : unary_function + { + __device__ __forceinline__ unsigned int operator ()(unsigned int x) const + { + return x; + } + + __host__ __device__ __forceinline__ abs_func() {} + __host__ __device__ __forceinline__ abs_func(const abs_func&) {} + }; + template <> struct abs_func : unary_function + { + __device__ __forceinline__ int operator ()(int x) const + { + return ::abs(x); + } + + __host__ __device__ __forceinline__ abs_func() {} + __host__ __device__ __forceinline__ abs_func(const abs_func&) {} + }; + template <> struct abs_func : unary_function + { + __device__ __forceinline__ float operator ()(float x) const + { + return ::fabsf(x); + } + + __host__ __device__ __forceinline__ abs_func() {} + __host__ __device__ __forceinline__ abs_func(const abs_func&) {} + }; + template <> struct abs_func : unary_function + { + __device__ __forceinline__ double operator ()(double x) const + { + return ::fabs(x); + } + + __host__ __device__ __forceinline__ abs_func() {} + __host__ __device__ __forceinline__ abs_func(const abs_func&) {} + }; + +#define OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(name, func) \ + template struct name ## _func : unary_function \ + { \ + __device__ __forceinline__ float operator ()(typename TypeTraits::ParameterType v) const \ + { \ + return func ## f(v); \ + } \ + __host__ __device__ __forceinline__ name ## _func() {} \ + __host__ __device__ __forceinline__ name ## _func(const name ## _func&) {} \ + }; \ + template <> struct name ## _func : unary_function \ + { \ + __device__ __forceinline__ double operator ()(double v) const \ + { \ + return func(v); \ + } \ + __host__ __device__ __forceinline__ name ## _func() {} \ + __host__ __device__ __forceinline__ name ## _func(const name ## _func&) {} \ + }; + +#define OPENCV_CUDA_IMPLEMENT_BIN_FUNCTOR(name, func) \ + template struct name ## _func : binary_function \ + { \ + __device__ __forceinline__ float operator ()(typename TypeTraits::ParameterType v1, typename TypeTraits::ParameterType v2) const \ + { \ + return func ## f(v1, v2); \ + } \ + __host__ __device__ __forceinline__ name ## _func() {} \ + __host__ __device__ __forceinline__ name ## _func(const name ## _func&) {} \ + }; \ + template <> struct name ## _func : binary_function \ + { \ + __device__ __forceinline__ double operator ()(double v1, double v2) const \ + { \ + return func(v1, v2); \ + } \ + __host__ __device__ __forceinline__ name ## _func() {} \ + __host__ __device__ __forceinline__ name ## _func(const name ## _func&) {} \ + }; + + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(sqrt, ::sqrt) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(exp, ::exp) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(exp2, ::exp2) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(exp10, ::exp10) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(log, ::log) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(log2, ::log2) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(log10, ::log10) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(sin, ::sin) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(cos, ::cos) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(tan, ::tan) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(asin, ::asin) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(acos, ::acos) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(atan, ::atan) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(sinh, ::sinh) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(cosh, ::cosh) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(tanh, ::tanh) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(asinh, ::asinh) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(acosh, ::acosh) + OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(atanh, ::atanh) + + OPENCV_CUDA_IMPLEMENT_BIN_FUNCTOR(hypot, ::hypot) + OPENCV_CUDA_IMPLEMENT_BIN_FUNCTOR(atan2, ::atan2) + OPENCV_CUDA_IMPLEMENT_BIN_FUNCTOR(pow, ::pow) + + #undef OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR + #undef OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR_NO_DOUBLE + #undef OPENCV_CUDA_IMPLEMENT_BIN_FUNCTOR + + template struct hypot_sqr_func : binary_function + { + __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType src1, typename TypeTraits::ParameterType src2) const + { + return src1 * src1 + src2 * src2; + } + __host__ __device__ __forceinline__ hypot_sqr_func() {} + __host__ __device__ __forceinline__ hypot_sqr_func(const hypot_sqr_func&) {} + }; + + // Saturate Cast Functor + template struct saturate_cast_func : unary_function + { + __device__ __forceinline__ D operator ()(typename TypeTraits::ParameterType v) const + { + return saturate_cast(v); + } + __host__ __device__ __forceinline__ saturate_cast_func() {} + __host__ __device__ __forceinline__ saturate_cast_func(const saturate_cast_func&) {} + }; + + // Threshold Functors + template struct thresh_binary_func : unary_function + { + __host__ __device__ __forceinline__ thresh_binary_func(T thresh_, T maxVal_) : thresh(thresh_), maxVal(maxVal_) {} + + __device__ __forceinline__ T operator()(typename TypeTraits::ParameterType src) const + { + return (src > thresh) * maxVal; + } + + __host__ __device__ __forceinline__ thresh_binary_func() {} + __host__ __device__ __forceinline__ thresh_binary_func(const thresh_binary_func& other) + : thresh(other.thresh), maxVal(other.maxVal) {} + + T thresh; + T maxVal; + }; + + template struct thresh_binary_inv_func : unary_function + { + __host__ __device__ __forceinline__ thresh_binary_inv_func(T thresh_, T maxVal_) : thresh(thresh_), maxVal(maxVal_) {} + + __device__ __forceinline__ T operator()(typename TypeTraits::ParameterType src) const + { + return (src <= thresh) * maxVal; + } + + __host__ __device__ __forceinline__ thresh_binary_inv_func() {} + __host__ __device__ __forceinline__ thresh_binary_inv_func(const thresh_binary_inv_func& other) + : thresh(other.thresh), maxVal(other.maxVal) {} + + T thresh; + T maxVal; + }; + + template struct thresh_trunc_func : unary_function + { + explicit __host__ __device__ __forceinline__ thresh_trunc_func(T thresh_, T maxVal_ = 0) : thresh(thresh_) {CV_UNUSED(maxVal_);} + + __device__ __forceinline__ T operator()(typename TypeTraits::ParameterType src) const + { + return minimum()(src, thresh); + } + + __host__ __device__ __forceinline__ thresh_trunc_func() {} + __host__ __device__ __forceinline__ thresh_trunc_func(const thresh_trunc_func& other) + : thresh(other.thresh) {} + + T thresh; + }; + + template struct thresh_to_zero_func : unary_function + { + explicit __host__ __device__ __forceinline__ thresh_to_zero_func(T thresh_, T maxVal_ = 0) : thresh(thresh_) {CV_UNUSED(maxVal_);} + + __device__ __forceinline__ T operator()(typename TypeTraits::ParameterType src) const + { + return (src > thresh) * src; + } + + __host__ __device__ __forceinline__ thresh_to_zero_func() {} + __host__ __device__ __forceinline__ thresh_to_zero_func(const thresh_to_zero_func& other) + : thresh(other.thresh) {} + + T thresh; + }; + + template struct thresh_to_zero_inv_func : unary_function + { + explicit __host__ __device__ __forceinline__ thresh_to_zero_inv_func(T thresh_, T maxVal_ = 0) : thresh(thresh_) {CV_UNUSED(maxVal_);} + + __device__ __forceinline__ T operator()(typename TypeTraits::ParameterType src) const + { + return (src <= thresh) * src; + } + + __host__ __device__ __forceinline__ thresh_to_zero_inv_func() {} + __host__ __device__ __forceinline__ thresh_to_zero_inv_func(const thresh_to_zero_inv_func& other) + : thresh(other.thresh) {} + + T thresh; + }; + + // Function Object Adaptors + template struct unary_negate : unary_function + { + explicit __host__ __device__ __forceinline__ unary_negate(const Predicate& p) : pred(p) {} + + __device__ __forceinline__ bool operator()(typename TypeTraits::ParameterType x) const + { + return !pred(x); + } + + __host__ __device__ __forceinline__ unary_negate() {} + __host__ __device__ __forceinline__ unary_negate(const unary_negate& other) : pred(other.pred) {} + + Predicate pred; + }; + + template __host__ __device__ __forceinline__ unary_negate not1(const Predicate& pred) + { + return unary_negate(pred); + } + + template struct binary_negate : binary_function + { + explicit __host__ __device__ __forceinline__ binary_negate(const Predicate& p) : pred(p) {} + + __device__ __forceinline__ bool operator()(typename TypeTraits::ParameterType x, + typename TypeTraits::ParameterType y) const + { + return !pred(x,y); + } + + __host__ __device__ __forceinline__ binary_negate() {} + __host__ __device__ __forceinline__ binary_negate(const binary_negate& other) : pred(other.pred) {} + + Predicate pred; + }; + + template __host__ __device__ __forceinline__ binary_negate not2(const BinaryPredicate& pred) + { + return binary_negate(pred); + } + + template struct binder1st : unary_function + { + __host__ __device__ __forceinline__ binder1st(const Op& op_, const typename Op::first_argument_type& arg1_) : op(op_), arg1(arg1_) {} + + __device__ __forceinline__ typename Op::result_type operator ()(typename TypeTraits::ParameterType a) const + { + return op(arg1, a); + } + + __host__ __device__ __forceinline__ binder1st() {} + __host__ __device__ __forceinline__ binder1st(const binder1st& other) : op(other.op), arg1(other.arg1) {} + + Op op; + typename Op::first_argument_type arg1; + }; + + template __host__ __device__ __forceinline__ binder1st bind1st(const Op& op, const T& x) + { + return binder1st(op, typename Op::first_argument_type(x)); + } + + template struct binder2nd : unary_function + { + __host__ __device__ __forceinline__ binder2nd(const Op& op_, const typename Op::second_argument_type& arg2_) : op(op_), arg2(arg2_) {} + + __forceinline__ __device__ typename Op::result_type operator ()(typename TypeTraits::ParameterType a) const + { + return op(a, arg2); + } + + __host__ __device__ __forceinline__ binder2nd() {} + __host__ __device__ __forceinline__ binder2nd(const binder2nd& other) : op(other.op), arg2(other.arg2) {} + + Op op; + typename Op::second_argument_type arg2; + }; + + template __host__ __device__ __forceinline__ binder2nd bind2nd(const Op& op, const T& x) + { + return binder2nd(op, typename Op::second_argument_type(x)); + } + + // Functor Traits + template struct IsUnaryFunction + { + typedef char Yes; + struct No {Yes a[2];}; + + template static Yes check(unary_function); + static No check(...); + + static F makeF(); + + enum { value = (sizeof(check(makeF())) == sizeof(Yes)) }; + }; + + template struct IsBinaryFunction + { + typedef char Yes; + struct No {Yes a[2];}; + + template static Yes check(binary_function); + static No check(...); + + static F makeF(); + + enum { value = (sizeof(check(makeF())) == sizeof(Yes)) }; + }; + + namespace functional_detail + { + template struct UnOpShift { enum { shift = 1 }; }; + template struct UnOpShift { enum { shift = 4 }; }; + template struct UnOpShift { enum { shift = 2 }; }; + + template struct DefaultUnaryShift + { + enum { shift = UnOpShift::shift }; + }; + + template struct BinOpShift { enum { shift = 1 }; }; + template struct BinOpShift { enum { shift = 4 }; }; + template struct BinOpShift { enum { shift = 2 }; }; + + template struct DefaultBinaryShift + { + enum { shift = BinOpShift::shift }; + }; + + template ::value> struct ShiftDispatcher; + template struct ShiftDispatcher + { + enum { shift = DefaultUnaryShift::shift }; + }; + template struct ShiftDispatcher + { + enum { shift = DefaultBinaryShift::shift }; + }; + } + + template struct DefaultTransformShift + { + enum { shift = functional_detail::ShiftDispatcher::shift }; + }; + + template struct DefaultTransformFunctorTraits + { + enum { simple_block_dim_x = 16 }; + enum { simple_block_dim_y = 16 }; + + enum { smart_block_dim_x = 16 }; + enum { smart_block_dim_y = 16 }; + enum { smart_shift = DefaultTransformShift::shift }; + }; + + template struct TransformFunctorTraits : DefaultTransformFunctorTraits {}; + +#define OPENCV_CUDA_TRANSFORM_FUNCTOR_TRAITS(type) \ + template <> struct TransformFunctorTraits< type > : DefaultTransformFunctorTraits< type > +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_FUNCTIONAL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/limits.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/limits.hpp new file mode 100644 index 0000000000000000000000000000000000000000..7e15ed629a395ae7a79e46883bd8f0d67eebb8ee --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/limits.hpp @@ -0,0 +1,128 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_LIMITS_HPP +#define OPENCV_CUDA_LIMITS_HPP + +#include +#include +#include "common.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ +template struct numeric_limits; + +template <> struct numeric_limits +{ + __device__ __forceinline__ static bool min() { return false; } + __device__ __forceinline__ static bool max() { return true; } + static const bool is_signed = false; +}; + +template <> struct numeric_limits +{ + __device__ __forceinline__ static signed char min() { return SCHAR_MIN; } + __device__ __forceinline__ static signed char max() { return SCHAR_MAX; } + static const bool is_signed = true; +}; + +template <> struct numeric_limits +{ + __device__ __forceinline__ static unsigned char min() { return 0; } + __device__ __forceinline__ static unsigned char max() { return UCHAR_MAX; } + static const bool is_signed = false; +}; + +template <> struct numeric_limits +{ + __device__ __forceinline__ static short min() { return SHRT_MIN; } + __device__ __forceinline__ static short max() { return SHRT_MAX; } + static const bool is_signed = true; +}; + +template <> struct numeric_limits +{ + __device__ __forceinline__ static unsigned short min() { return 0; } + __device__ __forceinline__ static unsigned short max() { return USHRT_MAX; } + static const bool is_signed = false; +}; + +template <> struct numeric_limits +{ + __device__ __forceinline__ static int min() { return INT_MIN; } + __device__ __forceinline__ static int max() { return INT_MAX; } + static const bool is_signed = true; +}; + +template <> struct numeric_limits +{ + __device__ __forceinline__ static unsigned int min() { return 0; } + __device__ __forceinline__ static unsigned int max() { return UINT_MAX; } + static const bool is_signed = false; +}; + +template <> struct numeric_limits +{ + __device__ __forceinline__ static float min() { return FLT_MIN; } + __device__ __forceinline__ static float max() { return FLT_MAX; } + __device__ __forceinline__ static float epsilon() { return FLT_EPSILON; } + static const bool is_signed = true; +}; + +template <> struct numeric_limits +{ + __device__ __forceinline__ static double min() { return DBL_MIN; } + __device__ __forceinline__ static double max() { return DBL_MAX; } + __device__ __forceinline__ static double epsilon() { return DBL_EPSILON; } + static const bool is_signed = true; +}; +}}} // namespace cv { namespace cuda { namespace cudev { + +//! @endcond + +#endif // OPENCV_CUDA_LIMITS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/reduce.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/reduce.hpp new file mode 100644 index 0000000000000000000000000000000000000000..5de365081789d6be1b6e5b35f3648fa07e091650 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/reduce.hpp @@ -0,0 +1,209 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_REDUCE_HPP +#define OPENCV_CUDA_REDUCE_HPP + +#ifndef THRUST_DEBUG // eliminate -Wundef warning +#define THRUST_DEBUG 0 +#endif + +#include +#include "detail/reduce.hpp" +#include "detail/reduce_key_val.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + template + __device__ __forceinline__ void reduce(volatile T* smem, T& val, unsigned int tid, const Op& op) + { + reduce_detail::Dispatcher::reductor::template reduce(smem, val, tid, op); + } + template + __device__ __forceinline__ void reduce(const thrust::tuple& smem, + const thrust::tuple& val, + unsigned int tid, + const thrust::tuple& op) + { + reduce_detail::Dispatcher::reductor::template reduce< + const thrust::tuple&, + const thrust::tuple&, + const thrust::tuple&>(smem, val, tid, op); + } + + template + __device__ __forceinline__ void reduceKeyVal(volatile K* skeys, K& key, volatile V* svals, V& val, unsigned int tid, const Cmp& cmp) + { + reduce_key_val_detail::Dispatcher::reductor::template reduce(skeys, key, svals, val, tid, cmp); + } + template + __device__ __forceinline__ void reduceKeyVal(volatile K* skeys, K& key, + const thrust::tuple& svals, + const thrust::tuple& val, + unsigned int tid, const Cmp& cmp) + { + reduce_key_val_detail::Dispatcher::reductor::template reduce&, + const thrust::tuple&, + const Cmp&>(skeys, key, svals, val, tid, cmp); + } + template + __device__ __forceinline__ void reduceKeyVal(const thrust::tuple& skeys, + const thrust::tuple& key, + const thrust::tuple& svals, + const thrust::tuple& val, + unsigned int tid, + const thrust::tuple& cmp) + { + reduce_key_val_detail::Dispatcher::reductor::template reduce< + const thrust::tuple&, + const thrust::tuple&, + const thrust::tuple&, + const thrust::tuple&, + const thrust::tuple& + >(skeys, key, svals, val, tid, cmp); + } + + // smem_tuple + + template + __device__ __forceinline__ + thrust::tuple + smem_tuple(T0* t0) + { + return thrust::make_tuple((volatile T0*) t0); + } + + template + __device__ __forceinline__ + thrust::tuple + smem_tuple(T0* t0, T1* t1) + { + return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1); + } + + template + __device__ __forceinline__ + thrust::tuple + smem_tuple(T0* t0, T1* t1, T2* t2) + { + return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2); + } + + template + __device__ __forceinline__ + thrust::tuple + smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3) + { + return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3); + } + + template + __device__ __forceinline__ + thrust::tuple + smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4) + { + return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4); + } + + template + __device__ __forceinline__ + thrust::tuple + smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5) + { + return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5); + } + + template + __device__ __forceinline__ + thrust::tuple + smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5, T6* t6) + { + return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5, (volatile T6*) t6); + } + + template + __device__ __forceinline__ + thrust::tuple + smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5, T6* t6, T7* t7) + { + return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5, (volatile T6*) t6, (volatile T7*) t7); + } + + template + __device__ __forceinline__ + thrust::tuple + smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5, T6* t6, T7* t7, T8* t8) + { + return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5, (volatile T6*) t6, (volatile T7*) t7, (volatile T8*) t8); + } + + template + __device__ __forceinline__ + thrust::tuple + smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5, T6* t6, T7* t7, T8* t8, T9* t9) + { + return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5, (volatile T6*) t6, (volatile T7*) t7, (volatile T8*) t8, (volatile T9*) t9); + } +}}} + +//! @endcond + +#endif // OPENCV_CUDA_REDUCE_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/saturate_cast.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/saturate_cast.hpp new file mode 100644 index 0000000000000000000000000000000000000000..c3a3d1cb8338375118930fc7abf918e57a041ceb --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/saturate_cast.hpp @@ -0,0 +1,292 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_SATURATE_CAST_HPP +#define OPENCV_CUDA_SATURATE_CAST_HPP + +#include "common.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + template __device__ __forceinline__ _Tp saturate_cast(uchar v) { return _Tp(v); } + template __device__ __forceinline__ _Tp saturate_cast(schar v) { return _Tp(v); } + template __device__ __forceinline__ _Tp saturate_cast(ushort v) { return _Tp(v); } + template __device__ __forceinline__ _Tp saturate_cast(short v) { return _Tp(v); } + template __device__ __forceinline__ _Tp saturate_cast(uint v) { return _Tp(v); } + template __device__ __forceinline__ _Tp saturate_cast(int v) { return _Tp(v); } + template __device__ __forceinline__ _Tp saturate_cast(float v) { return _Tp(v); } + template __device__ __forceinline__ _Tp saturate_cast(double v) { return _Tp(v); } + + template<> __device__ __forceinline__ uchar saturate_cast(schar v) + { + uint res = 0; + int vi = v; + asm("cvt.sat.u8.s8 %0, %1;" : "=r"(res) : "r"(vi)); + return res; + } + template<> __device__ __forceinline__ uchar saturate_cast(short v) + { + uint res = 0; + asm("cvt.sat.u8.s16 %0, %1;" : "=r"(res) : "h"(v)); + return res; + } + template<> __device__ __forceinline__ uchar saturate_cast(ushort v) + { + uint res = 0; + asm("cvt.sat.u8.u16 %0, %1;" : "=r"(res) : "h"(v)); + return res; + } + template<> __device__ __forceinline__ uchar saturate_cast(int v) + { + uint res = 0; + asm("cvt.sat.u8.s32 %0, %1;" : "=r"(res) : "r"(v)); + return res; + } + template<> __device__ __forceinline__ uchar saturate_cast(uint v) + { + uint res = 0; + asm("cvt.sat.u8.u32 %0, %1;" : "=r"(res) : "r"(v)); + return res; + } + template<> __device__ __forceinline__ uchar saturate_cast(float v) + { + uint res = 0; + asm("cvt.rni.sat.u8.f32 %0, %1;" : "=r"(res) : "f"(v)); + return res; + } + template<> __device__ __forceinline__ uchar saturate_cast(double v) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130 + uint res = 0; + asm("cvt.rni.sat.u8.f64 %0, %1;" : "=r"(res) : "d"(v)); + return res; + #else + return saturate_cast((float)v); + #endif + } + + template<> __device__ __forceinline__ schar saturate_cast(uchar v) + { + uint res = 0; + uint vi = v; + asm("cvt.sat.s8.u8 %0, %1;" : "=r"(res) : "r"(vi)); + return res; + } + template<> __device__ __forceinline__ schar saturate_cast(short v) + { + uint res = 0; + asm("cvt.sat.s8.s16 %0, %1;" : "=r"(res) : "h"(v)); + return res; + } + template<> __device__ __forceinline__ schar saturate_cast(ushort v) + { + uint res = 0; + asm("cvt.sat.s8.u16 %0, %1;" : "=r"(res) : "h"(v)); + return res; + } + template<> __device__ __forceinline__ schar saturate_cast(int v) + { + uint res = 0; + asm("cvt.sat.s8.s32 %0, %1;" : "=r"(res) : "r"(v)); + return res; + } + template<> __device__ __forceinline__ schar saturate_cast(uint v) + { + uint res = 0; + asm("cvt.sat.s8.u32 %0, %1;" : "=r"(res) : "r"(v)); + return res; + } + template<> __device__ __forceinline__ schar saturate_cast(float v) + { + uint res = 0; + asm("cvt.rni.sat.s8.f32 %0, %1;" : "=r"(res) : "f"(v)); + return res; + } + template<> __device__ __forceinline__ schar saturate_cast(double v) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130 + uint res = 0; + asm("cvt.rni.sat.s8.f64 %0, %1;" : "=r"(res) : "d"(v)); + return res; + #else + return saturate_cast((float)v); + #endif + } + + template<> __device__ __forceinline__ ushort saturate_cast(schar v) + { + ushort res = 0; + int vi = v; + asm("cvt.sat.u16.s8 %0, %1;" : "=h"(res) : "r"(vi)); + return res; + } + template<> __device__ __forceinline__ ushort saturate_cast(short v) + { + ushort res = 0; + asm("cvt.sat.u16.s16 %0, %1;" : "=h"(res) : "h"(v)); + return res; + } + template<> __device__ __forceinline__ ushort saturate_cast(int v) + { + ushort res = 0; + asm("cvt.sat.u16.s32 %0, %1;" : "=h"(res) : "r"(v)); + return res; + } + template<> __device__ __forceinline__ ushort saturate_cast(uint v) + { + ushort res = 0; + asm("cvt.sat.u16.u32 %0, %1;" : "=h"(res) : "r"(v)); + return res; + } + template<> __device__ __forceinline__ ushort saturate_cast(float v) + { + ushort res = 0; + asm("cvt.rni.sat.u16.f32 %0, %1;" : "=h"(res) : "f"(v)); + return res; + } + template<> __device__ __forceinline__ ushort saturate_cast(double v) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130 + ushort res = 0; + asm("cvt.rni.sat.u16.f64 %0, %1;" : "=h"(res) : "d"(v)); + return res; + #else + return saturate_cast((float)v); + #endif + } + + template<> __device__ __forceinline__ short saturate_cast(ushort v) + { + short res = 0; + asm("cvt.sat.s16.u16 %0, %1;" : "=h"(res) : "h"(v)); + return res; + } + template<> __device__ __forceinline__ short saturate_cast(int v) + { + short res = 0; + asm("cvt.sat.s16.s32 %0, %1;" : "=h"(res) : "r"(v)); + return res; + } + template<> __device__ __forceinline__ short saturate_cast(uint v) + { + short res = 0; + asm("cvt.sat.s16.u32 %0, %1;" : "=h"(res) : "r"(v)); + return res; + } + template<> __device__ __forceinline__ short saturate_cast(float v) + { + short res = 0; + asm("cvt.rni.sat.s16.f32 %0, %1;" : "=h"(res) : "f"(v)); + return res; + } + template<> __device__ __forceinline__ short saturate_cast(double v) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130 + short res = 0; + asm("cvt.rni.sat.s16.f64 %0, %1;" : "=h"(res) : "d"(v)); + return res; + #else + return saturate_cast((float)v); + #endif + } + + template<> __device__ __forceinline__ int saturate_cast(uint v) + { + int res = 0; + asm("cvt.sat.s32.u32 %0, %1;" : "=r"(res) : "r"(v)); + return res; + } + template<> __device__ __forceinline__ int saturate_cast(float v) + { + return __float2int_rn(v); + } + template<> __device__ __forceinline__ int saturate_cast(double v) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130 + return __double2int_rn(v); + #else + return saturate_cast((float)v); + #endif + } + + template<> __device__ __forceinline__ uint saturate_cast(schar v) + { + uint res = 0; + int vi = v; + asm("cvt.sat.u32.s8 %0, %1;" : "=r"(res) : "r"(vi)); + return res; + } + template<> __device__ __forceinline__ uint saturate_cast(short v) + { + uint res = 0; + asm("cvt.sat.u32.s16 %0, %1;" : "=r"(res) : "h"(v)); + return res; + } + template<> __device__ __forceinline__ uint saturate_cast(int v) + { + uint res = 0; + asm("cvt.sat.u32.s32 %0, %1;" : "=r"(res) : "r"(v)); + return res; + } + template<> __device__ __forceinline__ uint saturate_cast(float v) + { + return __float2uint_rn(v); + } + template<> __device__ __forceinline__ uint saturate_cast(double v) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130 + return __double2uint_rn(v); + #else + return saturate_cast((float)v); + #endif + } +}}} + +//! @endcond + +#endif /* OPENCV_CUDA_SATURATE_CAST_HPP */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/scan.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/scan.hpp new file mode 100644 index 0000000000000000000000000000000000000000..e128fb0962efb20ecaa6b16c545b16d5917549f4 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/scan.hpp @@ -0,0 +1,258 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_SCAN_HPP +#define OPENCV_CUDA_SCAN_HPP + +#include "opencv2/core/cuda/common.hpp" +#include "opencv2/core/cuda/utility.hpp" +#include "opencv2/core/cuda/warp.hpp" +#include "opencv2/core/cuda/warp_shuffle.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + enum ScanKind { EXCLUSIVE = 0, INCLUSIVE = 1 }; + + template struct WarpScan + { + __device__ __forceinline__ WarpScan() {} + __device__ __forceinline__ WarpScan(const WarpScan& other) { CV_UNUSED(other); } + + __device__ __forceinline__ T operator()( volatile T *ptr , const unsigned int idx) + { + const unsigned int lane = idx & 31; + F op; + + if ( lane >= 1) ptr [idx ] = op(ptr [idx - 1], ptr [idx]); + if ( lane >= 2) ptr [idx ] = op(ptr [idx - 2], ptr [idx]); + if ( lane >= 4) ptr [idx ] = op(ptr [idx - 4], ptr [idx]); + if ( lane >= 8) ptr [idx ] = op(ptr [idx - 8], ptr [idx]); + if ( lane >= 16) ptr [idx ] = op(ptr [idx - 16], ptr [idx]); + + if( Kind == INCLUSIVE ) + return ptr [idx]; + else + return (lane > 0) ? ptr [idx - 1] : 0; + } + + __device__ __forceinline__ unsigned int index(const unsigned int tid) + { + return tid; + } + + __device__ __forceinline__ void init(volatile T *ptr){} + + static const int warp_offset = 0; + + typedef WarpScan merge; + }; + + template struct WarpScanNoComp + { + __device__ __forceinline__ WarpScanNoComp() {} + __device__ __forceinline__ WarpScanNoComp(const WarpScanNoComp& other) { CV_UNUSED(other); } + + __device__ __forceinline__ T operator()( volatile T *ptr , const unsigned int idx) + { + const unsigned int lane = threadIdx.x & 31; + F op; + + ptr [idx ] = op(ptr [idx - 1], ptr [idx]); + ptr [idx ] = op(ptr [idx - 2], ptr [idx]); + ptr [idx ] = op(ptr [idx - 4], ptr [idx]); + ptr [idx ] = op(ptr [idx - 8], ptr [idx]); + ptr [idx ] = op(ptr [idx - 16], ptr [idx]); + + if( Kind == INCLUSIVE ) + return ptr [idx]; + else + return (lane > 0) ? ptr [idx - 1] : 0; + } + + __device__ __forceinline__ unsigned int index(const unsigned int tid) + { + return (tid >> warp_log) * warp_smem_stride + 16 + (tid & warp_mask); + } + + __device__ __forceinline__ void init(volatile T *ptr) + { + ptr[threadIdx.x] = 0; + } + + static const int warp_smem_stride = 32 + 16 + 1; + static const int warp_offset = 16; + static const int warp_log = 5; + static const int warp_mask = 31; + + typedef WarpScanNoComp merge; + }; + + template struct BlockScan + { + __device__ __forceinline__ BlockScan() {} + __device__ __forceinline__ BlockScan(const BlockScan& other) { CV_UNUSED(other); } + + __device__ __forceinline__ T operator()(volatile T *ptr) + { + const unsigned int tid = threadIdx.x; + const unsigned int lane = tid & warp_mask; + const unsigned int warp = tid >> warp_log; + + Sc scan; + typename Sc::merge merge_scan; + const unsigned int idx = scan.index(tid); + + T val = scan(ptr, idx); + __syncthreads (); + + if( warp == 0) + scan.init(ptr); + __syncthreads (); + + if( lane == 31 ) + ptr [scan.warp_offset + warp ] = (Kind == INCLUSIVE) ? val : ptr [idx]; + __syncthreads (); + + if( warp == 0 ) + merge_scan(ptr, idx); + __syncthreads(); + + if ( warp > 0) + val = ptr [scan.warp_offset + warp - 1] + val; + __syncthreads (); + + ptr[idx] = val; + __syncthreads (); + + return val ; + } + + static const int warp_log = 5; + static const int warp_mask = 31; + }; + + template + __device__ T warpScanInclusive(T idata, volatile T* s_Data, unsigned int tid) + { + #if __CUDA_ARCH__ >= 300 + const unsigned int laneId = cv::cuda::device::Warp::laneId(); + + // scan on shuffl functions + #pragma unroll + for (int i = 1; i <= (OPENCV_CUDA_WARP_SIZE / 2); i *= 2) + { + const T n = cv::cuda::device::shfl_up(idata, i); + if (laneId >= i) + idata += n; + } + + return idata; + #else + unsigned int pos = 2 * tid - (tid & (OPENCV_CUDA_WARP_SIZE - 1)); + s_Data[pos] = 0; + pos += OPENCV_CUDA_WARP_SIZE; + s_Data[pos] = idata; + + s_Data[pos] += s_Data[pos - 1]; + s_Data[pos] += s_Data[pos - 2]; + s_Data[pos] += s_Data[pos - 4]; + s_Data[pos] += s_Data[pos - 8]; + s_Data[pos] += s_Data[pos - 16]; + + return s_Data[pos]; + #endif + } + + template + __device__ __forceinline__ T warpScanExclusive(T idata, volatile T* s_Data, unsigned int tid) + { + return warpScanInclusive(idata, s_Data, tid) - idata; + } + + template + __device__ T blockScanInclusive(T idata, volatile T* s_Data, unsigned int tid) + { + if (tiNumScanThreads > OPENCV_CUDA_WARP_SIZE) + { + //Bottom-level inclusive warp scan + T warpResult = warpScanInclusive(idata, s_Data, tid); + + //Save top elements of each warp for exclusive warp scan + //sync to wait for warp scans to complete (because s_Data is being overwritten) + __syncthreads(); + if ((tid & (OPENCV_CUDA_WARP_SIZE - 1)) == (OPENCV_CUDA_WARP_SIZE - 1)) + { + s_Data[tid >> OPENCV_CUDA_LOG_WARP_SIZE] = warpResult; + } + + //wait for warp scans to complete + __syncthreads(); + + if (tid < (tiNumScanThreads / OPENCV_CUDA_WARP_SIZE) ) + { + //grab top warp elements + T val = s_Data[tid]; + //calculate exclusive scan and write back to shared memory + s_Data[tid] = warpScanExclusive(val, s_Data, tid); + } + + //return updated warp scans with exclusive scan results + __syncthreads(); + + return warpResult + s_Data[tid >> OPENCV_CUDA_LOG_WARP_SIZE]; + } + else + { + return warpScanInclusive(idata, s_Data, tid); + } + } +}}} + +//! @endcond + +#endif // OPENCV_CUDA_SCAN_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/simd_functions.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/simd_functions.hpp new file mode 100644 index 0000000000000000000000000000000000000000..3d8c2e0d8e56ae109e1f7ab839aec46b492c556c --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/simd_functions.hpp @@ -0,0 +1,869 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +/* + * Copyright (c) 2013 NVIDIA Corporation. All rights reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * Redistributions of source code must retain the above copyright notice, + * this list of conditions and the following disclaimer. + * + * Redistributions in binary form must reproduce the above copyright notice, + * this list of conditions and the following disclaimer in the documentation + * and/or other materials provided with the distribution. + * + * Neither the name of NVIDIA Corporation nor the names of its contributors + * may be used to endorse or promote products derived from this software + * without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + * ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE + * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + * POSSIBILITY OF SUCH DAMAGE. + */ + +#ifndef OPENCV_CUDA_SIMD_FUNCTIONS_HPP +#define OPENCV_CUDA_SIMD_FUNCTIONS_HPP + +#include "common.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + // 2 + + static __device__ __forceinline__ unsigned int vadd2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vadd2.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #elif __CUDA_ARCH__ >= 200 + asm("vadd.u32.u32.u32.sat %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vadd.u32.u32.u32.sat %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int s; + s = a ^ b; // sum bits + r = a + b; // actual sum + s = s ^ r; // determine carry-ins for each bit position + s = s & 0x00010000; // carry-in to high word (= carry-out from low word) + r = r - s; // subtract out carry-out from low word + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vsub2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vsub2.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #elif __CUDA_ARCH__ >= 200 + asm("vsub.u32.u32.u32.sat %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vsub.u32.u32.u32.sat %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int s; + s = a ^ b; // sum bits + r = a - b; // actual sum + s = s ^ r; // determine carry-ins for each bit position + s = s & 0x00010000; // borrow to high word + r = r + s; // compensate for borrow from low word + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vabsdiff2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vabsdiff2.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #elif __CUDA_ARCH__ >= 200 + asm("vabsdiff.u32.u32.u32.sat %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vabsdiff.u32.u32.u32.sat %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int s, t, u, v; + s = a & 0x0000ffff; // extract low halfword + r = b & 0x0000ffff; // extract low halfword + u = ::max(r, s); // maximum of low halfwords + v = ::min(r, s); // minimum of low halfwords + s = a & 0xffff0000; // extract high halfword + r = b & 0xffff0000; // extract high halfword + t = ::max(r, s); // maximum of high halfwords + s = ::min(r, s); // minimum of high halfwords + r = u | t; // maximum of both halfwords + s = v | s; // minimum of both halfwords + r = r - s; // |a - b| = max(a,b) - min(a,b); + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vavg2(unsigned int a, unsigned int b) + { + unsigned int r, s; + + // HAKMEM #23: a + b = 2 * (a & b) + (a ^ b) ==> + // (a + b) / 2 = (a & b) + ((a ^ b) >> 1) + s = a ^ b; + r = a & b; + s = s & 0xfffefffe; // ensure shift doesn't cross halfword boundaries + s = s >> 1; + s = r + s; + + return s; + } + + static __device__ __forceinline__ unsigned int vavrg2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vavrg2.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + // HAKMEM #23: a + b = 2 * (a | b) - (a ^ b) ==> + // (a + b + 1) / 2 = (a | b) - ((a ^ b) >> 1) + unsigned int s; + s = a ^ b; + r = a | b; + s = s & 0xfffefffe; // ensure shift doesn't cross half-word boundaries + s = s >> 1; + r = r - s; + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vseteq2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vset2.u32.u32.eq %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + // inspired by Alan Mycroft's null-byte detection algorithm: + // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) + unsigned int c; + r = a ^ b; // 0x0000 if a == b + c = r | 0x80008000; // set msbs, to catch carry out + r = r ^ c; // extract msbs, msb = 1 if r < 0x8000 + c = c - 0x00010001; // msb = 0, if r was 0x0000 or 0x8000 + c = r & ~c; // msb = 1, if r was 0x0000 + r = c >> 15; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmpeq2(unsigned int a, unsigned int b) + { + unsigned int r, c; + + #if __CUDA_ARCH__ >= 300 + r = vseteq2(a, b); + c = r << 16; // convert bool + r = c - r; // into mask + #else + // inspired by Alan Mycroft's null-byte detection algorithm: + // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) + r = a ^ b; // 0x0000 if a == b + c = r | 0x80008000; // set msbs, to catch carry out + r = r ^ c; // extract msbs, msb = 1 if r < 0x8000 + c = c - 0x00010001; // msb = 0, if r was 0x0000 or 0x8000 + c = r & ~c; // msb = 1, if r was 0x0000 + r = c >> 15; // convert + r = c - r; // msbs to + r = c | r; // mask + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vsetge2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vset2.u32.u32.ge %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int c; + asm("not.b32 %0, %0;" : "+r"(b)); + c = vavrg2(a, b); // (a + ~b + 1) / 2 = (a - b) / 2 + c = c & 0x80008000; // msb = carry-outs + r = c >> 15; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmpge2(unsigned int a, unsigned int b) + { + unsigned int r, c; + + #if __CUDA_ARCH__ >= 300 + r = vsetge2(a, b); + c = r << 16; // convert bool + r = c - r; // into mask + #else + asm("not.b32 %0, %0;" : "+r"(b)); + c = vavrg2(a, b); // (a + ~b + 1) / 2 = (a - b) / 2 + c = c & 0x80008000; // msb = carry-outs + r = c >> 15; // convert + r = c - r; // msbs to + r = c | r; // mask + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vsetgt2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vset2.u32.u32.gt %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int c; + asm("not.b32 %0, %0;" : "+r"(b)); + c = vavg2(a, b); // (a + ~b) / 2 = (a - b) / 2 [rounded down] + c = c & 0x80008000; // msbs = carry-outs + r = c >> 15; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmpgt2(unsigned int a, unsigned int b) + { + unsigned int r, c; + + #if __CUDA_ARCH__ >= 300 + r = vsetgt2(a, b); + c = r << 16; // convert bool + r = c - r; // into mask + #else + asm("not.b32 %0, %0;" : "+r"(b)); + c = vavg2(a, b); // (a + ~b) / 2 = (a - b) / 2 [rounded down] + c = c & 0x80008000; // msbs = carry-outs + r = c >> 15; // convert + r = c - r; // msbs to + r = c | r; // mask + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vsetle2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vset2.u32.u32.le %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int c; + asm("not.b32 %0, %0;" : "+r"(a)); + c = vavrg2(a, b); // (b + ~a + 1) / 2 = (b - a) / 2 + c = c & 0x80008000; // msb = carry-outs + r = c >> 15; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmple2(unsigned int a, unsigned int b) + { + unsigned int r, c; + + #if __CUDA_ARCH__ >= 300 + r = vsetle2(a, b); + c = r << 16; // convert bool + r = c - r; // into mask + #else + asm("not.b32 %0, %0;" : "+r"(a)); + c = vavrg2(a, b); // (b + ~a + 1) / 2 = (b - a) / 2 + c = c & 0x80008000; // msb = carry-outs + r = c >> 15; // convert + r = c - r; // msbs to + r = c | r; // mask + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vsetlt2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vset2.u32.u32.lt %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int c; + asm("not.b32 %0, %0;" : "+r"(a)); + c = vavg2(a, b); // (b + ~a) / 2 = (b - a) / 2 [rounded down] + c = c & 0x80008000; // msb = carry-outs + r = c >> 15; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmplt2(unsigned int a, unsigned int b) + { + unsigned int r, c; + + #if __CUDA_ARCH__ >= 300 + r = vsetlt2(a, b); + c = r << 16; // convert bool + r = c - r; // into mask + #else + asm("not.b32 %0, %0;" : "+r"(a)); + c = vavg2(a, b); // (b + ~a) / 2 = (b - a) / 2 [rounded down] + c = c & 0x80008000; // msb = carry-outs + r = c >> 15; // convert + r = c - r; // msbs to + r = c | r; // mask + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vsetne2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm ("vset2.u32.u32.ne %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + // inspired by Alan Mycroft's null-byte detection algorithm: + // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) + unsigned int c; + r = a ^ b; // 0x0000 if a == b + c = r | 0x80008000; // set msbs, to catch carry out + c = c - 0x00010001; // msb = 0, if r was 0x0000 or 0x8000 + c = r | c; // msb = 1, if r was not 0x0000 + c = c & 0x80008000; // extract msbs + r = c >> 15; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmpne2(unsigned int a, unsigned int b) + { + unsigned int r, c; + + #if __CUDA_ARCH__ >= 300 + r = vsetne2(a, b); + c = r << 16; // convert bool + r = c - r; // into mask + #else + // inspired by Alan Mycroft's null-byte detection algorithm: + // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) + r = a ^ b; // 0x0000 if a == b + c = r | 0x80008000; // set msbs, to catch carry out + c = c - 0x00010001; // msb = 0, if r was 0x0000 or 0x8000 + c = r | c; // msb = 1, if r was not 0x0000 + c = c & 0x80008000; // extract msbs + r = c >> 15; // convert + r = c - r; // msbs to + r = c | r; // mask + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vmax2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vmax2.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #elif __CUDA_ARCH__ >= 200 + asm("vmax.u32.u32.u32 %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vmax.u32.u32.u32 %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int s, t, u; + r = a & 0x0000ffff; // extract low halfword + s = b & 0x0000ffff; // extract low halfword + t = ::max(r, s); // maximum of low halfwords + r = a & 0xffff0000; // extract high halfword + s = b & 0xffff0000; // extract high halfword + u = ::max(r, s); // maximum of high halfwords + r = t | u; // combine halfword maximums + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vmin2(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vmin2.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #elif __CUDA_ARCH__ >= 200 + asm("vmin.u32.u32.u32 %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vmin.u32.u32.u32 %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int s, t, u; + r = a & 0x0000ffff; // extract low halfword + s = b & 0x0000ffff; // extract low halfword + t = ::min(r, s); // minimum of low halfwords + r = a & 0xffff0000; // extract high halfword + s = b & 0xffff0000; // extract high halfword + u = ::min(r, s); // minimum of high halfwords + r = t | u; // combine halfword minimums + #endif + + return r; + } + + // 4 + + static __device__ __forceinline__ unsigned int vadd4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vadd4.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #elif __CUDA_ARCH__ >= 200 + asm("vadd.u32.u32.u32.sat %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vadd.u32.u32.u32.sat %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vadd.u32.u32.u32.sat %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vadd.u32.u32.u32.sat %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int s, t; + s = a ^ b; // sum bits + r = a & 0x7f7f7f7f; // clear msbs + t = b & 0x7f7f7f7f; // clear msbs + s = s & 0x80808080; // msb sum bits + r = r + t; // add without msbs, record carry-out in msbs + r = r ^ s; // sum of msb sum and carry-in bits, w/o carry-out + #endif /* __CUDA_ARCH__ >= 300 */ + + return r; + } + + static __device__ __forceinline__ unsigned int vsub4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vsub4.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #elif __CUDA_ARCH__ >= 200 + asm("vsub.u32.u32.u32.sat %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vsub.u32.u32.u32.sat %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vsub.u32.u32.u32.sat %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vsub.u32.u32.u32.sat %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int s, t; + s = a ^ ~b; // inverted sum bits + r = a | 0x80808080; // set msbs + t = b & 0x7f7f7f7f; // clear msbs + s = s & 0x80808080; // inverted msb sum bits + r = r - t; // subtract w/o msbs, record inverted borrows in msb + r = r ^ s; // combine inverted msb sum bits and borrows + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vavg4(unsigned int a, unsigned int b) + { + unsigned int r, s; + + // HAKMEM #23: a + b = 2 * (a & b) + (a ^ b) ==> + // (a + b) / 2 = (a & b) + ((a ^ b) >> 1) + s = a ^ b; + r = a & b; + s = s & 0xfefefefe; // ensure following shift doesn't cross byte boundaries + s = s >> 1; + s = r + s; + + return s; + } + + static __device__ __forceinline__ unsigned int vavrg4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vavrg4.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + // HAKMEM #23: a + b = 2 * (a | b) - (a ^ b) ==> + // (a + b + 1) / 2 = (a | b) - ((a ^ b) >> 1) + unsigned int c; + c = a ^ b; + r = a | b; + c = c & 0xfefefefe; // ensure following shift doesn't cross byte boundaries + c = c >> 1; + r = r - c; + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vseteq4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vset4.u32.u32.eq %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + // inspired by Alan Mycroft's null-byte detection algorithm: + // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) + unsigned int c; + r = a ^ b; // 0x00 if a == b + c = r | 0x80808080; // set msbs, to catch carry out + r = r ^ c; // extract msbs, msb = 1 if r < 0x80 + c = c - 0x01010101; // msb = 0, if r was 0x00 or 0x80 + c = r & ~c; // msb = 1, if r was 0x00 + r = c >> 7; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmpeq4(unsigned int a, unsigned int b) + { + unsigned int r, t; + + #if __CUDA_ARCH__ >= 300 + r = vseteq4(a, b); + t = r << 8; // convert bool + r = t - r; // to mask + #else + // inspired by Alan Mycroft's null-byte detection algorithm: + // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) + t = a ^ b; // 0x00 if a == b + r = t | 0x80808080; // set msbs, to catch carry out + t = t ^ r; // extract msbs, msb = 1 if t < 0x80 + r = r - 0x01010101; // msb = 0, if t was 0x00 or 0x80 + r = t & ~r; // msb = 1, if t was 0x00 + t = r >> 7; // build mask + t = r - t; // from + r = t | r; // msbs + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vsetle4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vset4.u32.u32.le %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int c; + asm("not.b32 %0, %0;" : "+r"(a)); + c = vavrg4(a, b); // (b + ~a + 1) / 2 = (b - a) / 2 + c = c & 0x80808080; // msb = carry-outs + r = c >> 7; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmple4(unsigned int a, unsigned int b) + { + unsigned int r, c; + + #if __CUDA_ARCH__ >= 300 + r = vsetle4(a, b); + c = r << 8; // convert bool + r = c - r; // to mask + #else + asm("not.b32 %0, %0;" : "+r"(a)); + c = vavrg4(a, b); // (b + ~a + 1) / 2 = (b - a) / 2 + c = c & 0x80808080; // msbs = carry-outs + r = c >> 7; // convert + r = c - r; // msbs to + r = c | r; // mask + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vsetlt4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vset4.u32.u32.lt %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int c; + asm("not.b32 %0, %0;" : "+r"(a)); + c = vavg4(a, b); // (b + ~a) / 2 = (b - a) / 2 [rounded down] + c = c & 0x80808080; // msb = carry-outs + r = c >> 7; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmplt4(unsigned int a, unsigned int b) + { + unsigned int r, c; + + #if __CUDA_ARCH__ >= 300 + r = vsetlt4(a, b); + c = r << 8; // convert bool + r = c - r; // to mask + #else + asm("not.b32 %0, %0;" : "+r"(a)); + c = vavg4(a, b); // (b + ~a) / 2 = (b - a) / 2 [rounded down] + c = c & 0x80808080; // msbs = carry-outs + r = c >> 7; // convert + r = c - r; // msbs to + r = c | r; // mask + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vsetge4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vset4.u32.u32.ge %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int c; + asm("not.b32 %0, %0;" : "+r"(b)); + c = vavrg4(a, b); // (a + ~b + 1) / 2 = (a - b) / 2 + c = c & 0x80808080; // msb = carry-outs + r = c >> 7; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmpge4(unsigned int a, unsigned int b) + { + unsigned int r, s; + + #if __CUDA_ARCH__ >= 300 + r = vsetge4(a, b); + s = r << 8; // convert bool + r = s - r; // to mask + #else + asm ("not.b32 %0,%0;" : "+r"(b)); + r = vavrg4 (a, b); // (a + ~b + 1) / 2 = (a - b) / 2 + r = r & 0x80808080; // msb = carry-outs + s = r >> 7; // build mask + s = r - s; // from + r = s | r; // msbs + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vsetgt4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vset4.u32.u32.gt %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int c; + asm("not.b32 %0, %0;" : "+r"(b)); + c = vavg4(a, b); // (a + ~b) / 2 = (a - b) / 2 [rounded down] + c = c & 0x80808080; // msb = carry-outs + r = c >> 7; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmpgt4(unsigned int a, unsigned int b) + { + unsigned int r, c; + + #if __CUDA_ARCH__ >= 300 + r = vsetgt4(a, b); + c = r << 8; // convert bool + r = c - r; // to mask + #else + asm("not.b32 %0, %0;" : "+r"(b)); + c = vavg4(a, b); // (a + ~b) / 2 = (a - b) / 2 [rounded down] + c = c & 0x80808080; // msb = carry-outs + r = c >> 7; // convert + r = c - r; // msbs to + r = c | r; // mask + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vsetne4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vset4.u32.u32.ne %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + // inspired by Alan Mycroft's null-byte detection algorithm: + // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) + unsigned int c; + r = a ^ b; // 0x00 if a == b + c = r | 0x80808080; // set msbs, to catch carry out + c = c - 0x01010101; // msb = 0, if r was 0x00 or 0x80 + c = r | c; // msb = 1, if r was not 0x00 + c = c & 0x80808080; // extract msbs + r = c >> 7; // convert to bool + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vcmpne4(unsigned int a, unsigned int b) + { + unsigned int r, c; + + #if __CUDA_ARCH__ >= 300 + r = vsetne4(a, b); + c = r << 8; // convert bool + r = c - r; // to mask + #else + // inspired by Alan Mycroft's null-byte detection algorithm: + // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) + r = a ^ b; // 0x00 if a == b + c = r | 0x80808080; // set msbs, to catch carry out + c = c - 0x01010101; // msb = 0, if r was 0x00 or 0x80 + c = r | c; // msb = 1, if r was not 0x00 + c = c & 0x80808080; // extract msbs + r = c >> 7; // convert + r = c - r; // msbs to + r = c | r; // mask + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vabsdiff4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vabsdiff4.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #elif __CUDA_ARCH__ >= 200 + asm("vabsdiff.u32.u32.u32.sat %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vabsdiff.u32.u32.u32.sat %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vabsdiff.u32.u32.u32.sat %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vabsdiff.u32.u32.u32.sat %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int s; + s = vcmpge4(a, b); // mask = 0xff if a >= b + r = a ^ b; // + s = (r & s) ^ b; // select a when a >= b, else select b => max(a,b) + r = s ^ r; // select a when b >= a, else select b => min(a,b) + r = s - r; // |a - b| = max(a,b) - min(a,b); + #endif + + return r; + } + + static __device__ __forceinline__ unsigned int vmax4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vmax4.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #elif __CUDA_ARCH__ >= 200 + asm("vmax.u32.u32.u32 %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vmax.u32.u32.u32 %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vmax.u32.u32.u32 %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vmax.u32.u32.u32 %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int s; + s = vcmpge4(a, b); // mask = 0xff if a >= b + r = a & s; // select a when b >= a + s = b & ~s; // select b when b < a + r = r | s; // combine byte selections + #endif + + return r; // byte-wise unsigned maximum + } + + static __device__ __forceinline__ unsigned int vmin4(unsigned int a, unsigned int b) + { + unsigned int r = 0; + + #if __CUDA_ARCH__ >= 300 + asm("vmin4.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #elif __CUDA_ARCH__ >= 200 + asm("vmin.u32.u32.u32 %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vmin.u32.u32.u32 %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vmin.u32.u32.u32 %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + asm("vmin.u32.u32.u32 %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); + #else + unsigned int s; + s = vcmpge4(b, a); // mask = 0xff if a >= b + r = a & s; // select a when b >= a + s = b & ~s; // select b when b < a + r = r | s; // combine byte selections + #endif + + return r; + } +}}} + +//! @endcond + +#endif // OPENCV_CUDA_SIMD_FUNCTIONS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/transform.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/transform.hpp new file mode 100644 index 0000000000000000000000000000000000000000..42aa6ea170f21f95fc1e3702c2c410dbd3cc45e7 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/transform.hpp @@ -0,0 +1,75 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_TRANSFORM_HPP +#define OPENCV_CUDA_TRANSFORM_HPP + +#include "common.hpp" +#include "utility.hpp" +#include "detail/transform_detail.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + template + static inline void transform(PtrStepSz src, PtrStepSz dst, UnOp op, const Mask& mask, cudaStream_t stream) + { + typedef TransformFunctorTraits ft; + transform_detail::TransformDispatcher::cn == 1 && VecTraits::cn == 1 && ft::smart_shift != 1>::call(src, dst, op, mask, stream); + } + + template + static inline void transform(PtrStepSz src1, PtrStepSz src2, PtrStepSz dst, BinOp op, const Mask& mask, cudaStream_t stream) + { + typedef TransformFunctorTraits ft; + transform_detail::TransformDispatcher::cn == 1 && VecTraits::cn == 1 && VecTraits::cn == 1 && ft::smart_shift != 1>::call(src1, src2, dst, op, mask, stream); + } +}}} + +//! @endcond + +#endif // OPENCV_CUDA_TRANSFORM_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/type_traits.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/type_traits.hpp new file mode 100644 index 0000000000000000000000000000000000000000..8b7a3fd16832df41f6bdb47a06569f6265ccad9d --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/type_traits.hpp @@ -0,0 +1,90 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_TYPE_TRAITS_HPP +#define OPENCV_CUDA_TYPE_TRAITS_HPP + +#include "detail/type_traits_detail.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + template struct IsSimpleParameter + { + enum {value = type_traits_detail::IsIntegral::value || type_traits_detail::IsFloat::value || + type_traits_detail::PointerTraits::type>::value}; + }; + + template struct TypeTraits + { + typedef typename type_traits_detail::UnConst::type NonConstType; + typedef typename type_traits_detail::UnVolatile::type NonVolatileType; + typedef typename type_traits_detail::UnVolatile::type>::type UnqualifiedType; + typedef typename type_traits_detail::PointerTraits::type PointeeType; + typedef typename type_traits_detail::ReferenceTraits::type ReferredType; + + enum { isConst = type_traits_detail::UnConst::value }; + enum { isVolatile = type_traits_detail::UnVolatile::value }; + + enum { isReference = type_traits_detail::ReferenceTraits::value }; + enum { isPointer = type_traits_detail::PointerTraits::type>::value }; + + enum { isUnsignedInt = type_traits_detail::IsUnsignedIntegral::value }; + enum { isSignedInt = type_traits_detail::IsSignedIntergral::value }; + enum { isIntegral = type_traits_detail::IsIntegral::value }; + enum { isFloat = type_traits_detail::IsFloat::value }; + enum { isArith = isIntegral || isFloat }; + enum { isVec = type_traits_detail::IsVec::value }; + + typedef typename type_traits_detail::Select::value, + T, typename type_traits_detail::AddParameterType::type>::type ParameterType; + }; +}}} + +//! @endcond + +#endif // OPENCV_CUDA_TYPE_TRAITS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/utility.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/utility.hpp new file mode 100644 index 0000000000000000000000000000000000000000..7f5db48a500f6e8b0399c04ad05d79a0a0137741 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/utility.hpp @@ -0,0 +1,230 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_UTILITY_HPP +#define OPENCV_CUDA_UTILITY_HPP + +#include "saturate_cast.hpp" +#include "datamov_utils.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + struct CV_EXPORTS ThrustAllocator + { + typedef uchar value_type; + virtual ~ThrustAllocator(); + virtual __device__ __host__ uchar* allocate(size_t numBytes) = 0; + virtual __device__ __host__ void deallocate(uchar* ptr, size_t numBytes) = 0; + static ThrustAllocator& getAllocator(); + static void setAllocator(ThrustAllocator* allocator); + }; + #define OPENCV_CUDA_LOG_WARP_SIZE (5) + #define OPENCV_CUDA_WARP_SIZE (1 << OPENCV_CUDA_LOG_WARP_SIZE) + #define OPENCV_CUDA_LOG_MEM_BANKS ((__CUDA_ARCH__ >= 200) ? 5 : 4) // 32 banks on fermi, 16 on tesla + #define OPENCV_CUDA_MEM_BANKS (1 << OPENCV_CUDA_LOG_MEM_BANKS) + + /////////////////////////////////////////////////////////////////////////////// + // swap + + template void __device__ __host__ __forceinline__ swap(T& a, T& b) + { + const T temp = a; + a = b; + b = temp; + } + + /////////////////////////////////////////////////////////////////////////////// + // Mask Reader + + struct SingleMask + { + explicit __host__ __device__ __forceinline__ SingleMask(PtrStepb mask_) : mask(mask_) {} + __host__ __device__ __forceinline__ SingleMask(const SingleMask& mask_): mask(mask_.mask){} + + __device__ __forceinline__ bool operator()(int y, int x) const + { + return mask.ptr(y)[x] != 0; + } + + PtrStepb mask; + }; + + struct SingleMaskChannels + { + __host__ __device__ __forceinline__ SingleMaskChannels(PtrStepb mask_, int channels_) + : mask(mask_), channels(channels_) {} + __host__ __device__ __forceinline__ SingleMaskChannels(const SingleMaskChannels& mask_) + :mask(mask_.mask), channels(mask_.channels){} + + __device__ __forceinline__ bool operator()(int y, int x) const + { + return mask.ptr(y)[x / channels] != 0; + } + + PtrStepb mask; + int channels; + }; + + struct MaskCollection + { + explicit __host__ __device__ __forceinline__ MaskCollection(PtrStepb* maskCollection_) + : maskCollection(maskCollection_) {} + + __device__ __forceinline__ MaskCollection(const MaskCollection& masks_) + : maskCollection(masks_.maskCollection), curMask(masks_.curMask){} + + __device__ __forceinline__ void next() + { + curMask = *maskCollection++; + } + __device__ __forceinline__ void setMask(int z) + { + curMask = maskCollection[z]; + } + + __device__ __forceinline__ bool operator()(int y, int x) const + { + uchar val; + return curMask.data == 0 || (ForceGlob::Load(curMask.ptr(y), x, val), (val != 0)); + } + + const PtrStepb* maskCollection; + PtrStepb curMask; + }; + + struct WithOutMask + { + __host__ __device__ __forceinline__ WithOutMask(){} + __host__ __device__ __forceinline__ WithOutMask(const WithOutMask&){} + + __device__ __forceinline__ void next() const + { + } + __device__ __forceinline__ void setMask(int) const + { + } + + __device__ __forceinline__ bool operator()(int, int) const + { + return true; + } + + __device__ __forceinline__ bool operator()(int, int, int) const + { + return true; + } + + static __device__ __forceinline__ bool check(int, int) + { + return true; + } + + static __device__ __forceinline__ bool check(int, int, int) + { + return true; + } + }; + + /////////////////////////////////////////////////////////////////////////////// + // Solve linear system + + // solve 2x2 linear system Ax=b + template __device__ __forceinline__ bool solve2x2(const T A[2][2], const T b[2], T x[2]) + { + T det = A[0][0] * A[1][1] - A[1][0] * A[0][1]; + + if (det != 0) + { + double invdet = 1.0 / det; + + x[0] = saturate_cast(invdet * (b[0] * A[1][1] - b[1] * A[0][1])); + + x[1] = saturate_cast(invdet * (A[0][0] * b[1] - A[1][0] * b[0])); + + return true; + } + + return false; + } + + // solve 3x3 linear system Ax=b + template __device__ __forceinline__ bool solve3x3(const T A[3][3], const T b[3], T x[3]) + { + T det = A[0][0] * (A[1][1] * A[2][2] - A[1][2] * A[2][1]) + - A[0][1] * (A[1][0] * A[2][2] - A[1][2] * A[2][0]) + + A[0][2] * (A[1][0] * A[2][1] - A[1][1] * A[2][0]); + + if (det != 0) + { + double invdet = 1.0 / det; + + x[0] = saturate_cast(invdet * + (b[0] * (A[1][1] * A[2][2] - A[1][2] * A[2][1]) - + A[0][1] * (b[1] * A[2][2] - A[1][2] * b[2] ) + + A[0][2] * (b[1] * A[2][1] - A[1][1] * b[2] ))); + + x[1] = saturate_cast(invdet * + (A[0][0] * (b[1] * A[2][2] - A[1][2] * b[2] ) - + b[0] * (A[1][0] * A[2][2] - A[1][2] * A[2][0]) + + A[0][2] * (A[1][0] * b[2] - b[1] * A[2][0]))); + + x[2] = saturate_cast(invdet * + (A[0][0] * (A[1][1] * b[2] - b[1] * A[2][1]) - + A[0][1] * (A[1][0] * b[2] - b[1] * A[2][0]) + + b[0] * (A[1][0] * A[2][1] - A[1][1] * A[2][0]))); + + return true; + } + + return false; + } +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_UTILITY_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/vec_distance.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/vec_distance.hpp new file mode 100644 index 0000000000000000000000000000000000000000..ef6e51087d33e8fd859c63be04515a7e99fd6afb --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/vec_distance.hpp @@ -0,0 +1,232 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_VEC_DISTANCE_HPP +#define OPENCV_CUDA_VEC_DISTANCE_HPP + +#include "reduce.hpp" +#include "functional.hpp" +#include "detail/vec_distance_detail.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + template struct L1Dist + { + typedef int value_type; + typedef int result_type; + + __device__ __forceinline__ L1Dist() : mySum(0) {} + + __device__ __forceinline__ void reduceIter(int val1, int val2) + { + mySum = __sad(val1, val2, mySum); + } + + template __device__ __forceinline__ void reduceAll(int* smem, int tid) + { + reduce(smem, mySum, tid, plus()); + } + + __device__ __forceinline__ operator int() const + { + return mySum; + } + + int mySum; + }; + template <> struct L1Dist + { + typedef float value_type; + typedef float result_type; + + __device__ __forceinline__ L1Dist() : mySum(0.0f) {} + + __device__ __forceinline__ void reduceIter(float val1, float val2) + { + mySum += ::fabs(val1 - val2); + } + + template __device__ __forceinline__ void reduceAll(float* smem, int tid) + { + reduce(smem, mySum, tid, plus()); + } + + __device__ __forceinline__ operator float() const + { + return mySum; + } + + float mySum; + }; + + struct L2Dist + { + typedef float value_type; + typedef float result_type; + + __device__ __forceinline__ L2Dist() : mySum(0.0f) {} + + __device__ __forceinline__ void reduceIter(float val1, float val2) + { + float reg = val1 - val2; + mySum += reg * reg; + } + + template __device__ __forceinline__ void reduceAll(float* smem, int tid) + { + reduce(smem, mySum, tid, plus()); + } + + __device__ __forceinline__ operator float() const + { + return sqrtf(mySum); + } + + float mySum; + }; + + struct HammingDist + { + typedef int value_type; + typedef int result_type; + + __device__ __forceinline__ HammingDist() : mySum(0) {} + + __device__ __forceinline__ void reduceIter(int val1, int val2) + { + mySum += __popc(val1 ^ val2); + } + + template __device__ __forceinline__ void reduceAll(int* smem, int tid) + { + reduce(smem, mySum, tid, plus()); + } + + __device__ __forceinline__ operator int() const + { + return mySum; + } + + int mySum; + }; + + // calc distance between two vectors in global memory + template + __device__ void calcVecDiffGlobal(const T1* vec1, const T2* vec2, int len, Dist& dist, typename Dist::result_type* smem, int tid) + { + for (int i = tid; i < len; i += THREAD_DIM) + { + T1 val1; + ForceGlob::Load(vec1, i, val1); + + T2 val2; + ForceGlob::Load(vec2, i, val2); + + dist.reduceIter(val1, val2); + } + + dist.reduceAll(smem, tid); + } + + // calc distance between two vectors, first vector is cached in register or shared memory, second vector is in global memory + template + __device__ __forceinline__ void calcVecDiffCached(const T1* vecCached, const T2* vecGlob, int len, Dist& dist, typename Dist::result_type* smem, int tid) + { + vec_distance_detail::VecDiffCachedCalculator::calc(vecCached, vecGlob, len, dist, tid); + + dist.reduceAll(smem, tid); + } + + // calc distance between two vectors in global memory + template struct VecDiffGlobal + { + explicit __device__ __forceinline__ VecDiffGlobal(const T1* vec1_, int = 0, void* = 0, int = 0, int = 0) + { + vec1 = vec1_; + } + + template + __device__ __forceinline__ void calc(const T2* vec2, int len, Dist& dist, typename Dist::result_type* smem, int tid) const + { + calcVecDiffGlobal(vec1, vec2, len, dist, smem, tid); + } + + const T1* vec1; + }; + + // calc distance between two vectors, first vector is cached in register memory, second vector is in global memory + template struct VecDiffCachedRegister + { + template __device__ __forceinline__ VecDiffCachedRegister(const T1* vec1, int len, U* smem, int glob_tid, int tid) + { + if (glob_tid < len) + smem[glob_tid] = vec1[glob_tid]; + __syncthreads(); + + U* vec1ValsPtr = vec1Vals; + + #pragma unroll + for (int i = tid; i < MAX_LEN; i += THREAD_DIM) + *vec1ValsPtr++ = smem[i]; + + __syncthreads(); + } + + template + __device__ __forceinline__ void calc(const T2* vec2, int len, Dist& dist, typename Dist::result_type* smem, int tid) const + { + calcVecDiffCached(vec1Vals, vec2, len, dist, smem, tid); + } + + U vec1Vals[MAX_LEN / THREAD_DIM]; + }; +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_VEC_DISTANCE_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/vec_math.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/vec_math.hpp new file mode 100644 index 0000000000000000000000000000000000000000..9085b92397d8db583a3890d688a1fe8b7bf72152 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/vec_math.hpp @@ -0,0 +1,930 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_VECMATH_HPP +#define OPENCV_CUDA_VECMATH_HPP + +#include "vec_traits.hpp" +#include "saturate_cast.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + +// saturate_cast + +namespace vec_math_detail +{ + template struct SatCastHelper; + template struct SatCastHelper<1, VecD> + { + template static __device__ __forceinline__ VecD cast(const VecS& v) + { + typedef typename VecTraits::elem_type D; + return VecTraits::make(saturate_cast(v.x)); + } + }; + template struct SatCastHelper<2, VecD> + { + template static __device__ __forceinline__ VecD cast(const VecS& v) + { + typedef typename VecTraits::elem_type D; + return VecTraits::make(saturate_cast(v.x), saturate_cast(v.y)); + } + }; + template struct SatCastHelper<3, VecD> + { + template static __device__ __forceinline__ VecD cast(const VecS& v) + { + typedef typename VecTraits::elem_type D; + return VecTraits::make(saturate_cast(v.x), saturate_cast(v.y), saturate_cast(v.z)); + } + }; + template struct SatCastHelper<4, VecD> + { + template static __device__ __forceinline__ VecD cast(const VecS& v) + { + typedef typename VecTraits::elem_type D; + return VecTraits::make(saturate_cast(v.x), saturate_cast(v.y), saturate_cast(v.z), saturate_cast(v.w)); + } + }; + + template static __device__ __forceinline__ VecD saturate_cast_helper(const VecS& v) + { + return SatCastHelper::cn, VecD>::cast(v); + } +} + +template static __device__ __forceinline__ T saturate_cast(const uchar1& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const char1& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const ushort1& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const short1& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const uint1& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const int1& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const float1& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const double1& v) {return vec_math_detail::saturate_cast_helper(v);} + +template static __device__ __forceinline__ T saturate_cast(const uchar2& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const char2& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const ushort2& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const short2& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const uint2& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const int2& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const float2& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const double2& v) {return vec_math_detail::saturate_cast_helper(v);} + +template static __device__ __forceinline__ T saturate_cast(const uchar3& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const char3& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const ushort3& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const short3& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const uint3& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const int3& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const float3& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const double3& v) {return vec_math_detail::saturate_cast_helper(v);} + +template static __device__ __forceinline__ T saturate_cast(const uchar4& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const char4& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const ushort4& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const short4& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const uint4& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const int4& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const float4& v) {return vec_math_detail::saturate_cast_helper(v);} +template static __device__ __forceinline__ T saturate_cast(const double4& v) {return vec_math_detail::saturate_cast_helper(v);} + +// unary operators + +#define CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(op, input_type, output_type) \ + __device__ __forceinline__ output_type ## 1 operator op(const input_type ## 1 & a) \ + { \ + return VecTraits::make(op (a.x)); \ + } \ + __device__ __forceinline__ output_type ## 2 operator op(const input_type ## 2 & a) \ + { \ + return VecTraits::make(op (a.x), op (a.y)); \ + } \ + __device__ __forceinline__ output_type ## 3 operator op(const input_type ## 3 & a) \ + { \ + return VecTraits::make(op (a.x), op (a.y), op (a.z)); \ + } \ + __device__ __forceinline__ output_type ## 4 operator op(const input_type ## 4 & a) \ + { \ + return VecTraits::make(op (a.x), op (a.y), op (a.z), op (a.w)); \ + } + +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, char, char) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, short, short) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, int, int) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, char, uchar) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, ushort, uchar) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, short, uchar) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, int, uchar) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, uint, uchar) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, float, uchar) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, double, uchar) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, char, char) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, ushort, ushort) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, short, short) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, int, int) +CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, uint, uint) + +#undef CV_CUDEV_IMPLEMENT_VEC_UNARY_OP + +// unary functions + +#define CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(func_name, func, input_type, output_type) \ + __device__ __forceinline__ output_type ## 1 func_name(const input_type ## 1 & a) \ + { \ + return VecTraits::make(func (a.x)); \ + } \ + __device__ __forceinline__ output_type ## 2 func_name(const input_type ## 2 & a) \ + { \ + return VecTraits::make(func (a.x), func (a.y)); \ + } \ + __device__ __forceinline__ output_type ## 3 func_name(const input_type ## 3 & a) \ + { \ + return VecTraits::make(func (a.x), func (a.y), func (a.z)); \ + } \ + __device__ __forceinline__ output_type ## 4 func_name(const input_type ## 4 & a) \ + { \ + return VecTraits::make(func (a.x), func (a.y), func (a.z), func (a.w)); \ + } + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, /*::abs*/, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::abs, char, char) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, /*::abs*/, ushort, ushort) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::abs, short, short) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::abs, int, int) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, /*::abs*/, uint, uint) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::fabsf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::fabs, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrt, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::exp, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::log, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sin, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cos, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tan, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asin, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acos, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atan, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinh, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::cosh, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanh, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinh, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acosh, double, double) + +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, char, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, short, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, int, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, float, float) +CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanh, double, double) + +#undef CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC + +// binary operators (vec & vec) + +#define CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(op, input_type, output_type) \ + __device__ __forceinline__ output_type ## 1 operator op(const input_type ## 1 & a, const input_type ## 1 & b) \ + { \ + return VecTraits::make(a.x op b.x); \ + } \ + __device__ __forceinline__ output_type ## 2 operator op(const input_type ## 2 & a, const input_type ## 2 & b) \ + { \ + return VecTraits::make(a.x op b.x, a.y op b.y); \ + } \ + __device__ __forceinline__ output_type ## 3 operator op(const input_type ## 3 & a, const input_type ## 3 & b) \ + { \ + return VecTraits::make(a.x op b.x, a.y op b.y, a.z op b.z); \ + } \ + __device__ __forceinline__ output_type ## 4 operator op(const input_type ## 4 & a, const input_type ## 4 & b) \ + { \ + return VecTraits::make(a.x op b.x, a.y op b.y, a.z op b.z, a.w op b.w); \ + } + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, uchar, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, char, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, ushort, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, short, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, int, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, uint, uint) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, float, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, double, double) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, uchar, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, char, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, ushort, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, short, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, int, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, uint, uint) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, float, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, double, double) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, uchar, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, char, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, ushort, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, short, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, int, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, uint, uint) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, float, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, double, double) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, uchar, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, char, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, ushort, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, short, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, int, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, uint, uint) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, float, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, double, double) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, char, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, ushort, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, short, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, int, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, uint, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, float, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, double, uchar) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, char, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, ushort, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, short, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, int, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, uint, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, float, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, double, uchar) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, char, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, ushort, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, short, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, int, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, uint, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, float, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, double, uchar) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, char, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, ushort, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, short, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, int, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, uint, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, float, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, double, uchar) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, char, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, ushort, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, short, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, int, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, uint, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, float, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, double, uchar) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, char, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, ushort, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, short, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, int, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, uint, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, float, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, double, uchar) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, char, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, ushort, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, short, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, int, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, uint, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, float, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, double, uchar) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, char, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, ushort, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, short, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, int, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, uint, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, float, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, double, uchar) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, char, char) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, ushort, ushort) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, short, short) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, int, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, uint, uint) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, char, char) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, ushort, ushort) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, short, short) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, int, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, uint, uint) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, char, char) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, ushort, ushort) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, short, short) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, int, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, uint, uint) + +#undef CV_CUDEV_IMPLEMENT_VEC_BINARY_OP + +// binary operators (vec & scalar) + +#define CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(op, input_type, scalar_type, output_type) \ + __device__ __forceinline__ output_type ## 1 operator op(const input_type ## 1 & a, scalar_type s) \ + { \ + return VecTraits::make(a.x op s); \ + } \ + __device__ __forceinline__ output_type ## 1 operator op(scalar_type s, const input_type ## 1 & b) \ + { \ + return VecTraits::make(s op b.x); \ + } \ + __device__ __forceinline__ output_type ## 2 operator op(const input_type ## 2 & a, scalar_type s) \ + { \ + return VecTraits::make(a.x op s, a.y op s); \ + } \ + __device__ __forceinline__ output_type ## 2 operator op(scalar_type s, const input_type ## 2 & b) \ + { \ + return VecTraits::make(s op b.x, s op b.y); \ + } \ + __device__ __forceinline__ output_type ## 3 operator op(const input_type ## 3 & a, scalar_type s) \ + { \ + return VecTraits::make(a.x op s, a.y op s, a.z op s); \ + } \ + __device__ __forceinline__ output_type ## 3 operator op(scalar_type s, const input_type ## 3 & b) \ + { \ + return VecTraits::make(s op b.x, s op b.y, s op b.z); \ + } \ + __device__ __forceinline__ output_type ## 4 operator op(const input_type ## 4 & a, scalar_type s) \ + { \ + return VecTraits::make(a.x op s, a.y op s, a.z op s, a.w op s); \ + } \ + __device__ __forceinline__ output_type ## 4 operator op(scalar_type s, const input_type ## 4 & b) \ + { \ + return VecTraits::make(s op b.x, s op b.y, s op b.z, s op b.w); \ + } + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uchar, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uchar, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uchar, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, char, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, char, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, char, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, ushort, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, ushort, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, ushort, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, short, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, short, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, short, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, int, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, int, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, int, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uint, uint, uint) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uint, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uint, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, float, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, float, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, double, double, double) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uchar, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uchar, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uchar, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, char, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, char, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, char, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, ushort, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, ushort, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, ushort, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, short, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, short, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, short, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, int, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, int, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, int, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uint, uint, uint) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uint, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uint, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, float, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, float, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, double, double, double) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uchar, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uchar, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uchar, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, char, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, char, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, char, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, ushort, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, ushort, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, ushort, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, short, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, short, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, short, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, int, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, int, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, int, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uint, uint, uint) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uint, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uint, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, float, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, float, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, double, double, double) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uchar, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uchar, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uchar, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, char, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, char, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, char, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, ushort, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, ushort, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, ushort, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, short, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, short, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, short, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, int, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, int, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, int, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uint, uint, uint) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uint, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uint, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, float, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, float, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, double, double, double) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, char, char, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, ushort, ushort, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, short, short, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, int, int, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, uint, uint, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, float, float, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, double, double, uchar) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, char, char, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, ushort, ushort, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, short, short, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, int, int, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, uint, uint, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, float, float, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, double, double, uchar) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, char, char, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, ushort, ushort, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, short, short, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, int, int, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, uint, uint, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, float, float, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, double, double, uchar) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, char, char, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, ushort, ushort, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, short, short, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, int, int, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, uint, uint, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, float, float, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, double, double, uchar) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, char, char, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, ushort, ushort, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, short, short, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, int, int, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, uint, uint, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, float, float, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, double, double, uchar) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, char, char, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, ushort, ushort, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, short, short, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, int, int, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, uint, uint, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, float, float, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, double, double, uchar) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, char, char, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, ushort, ushort, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, short, short, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, int, int, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, uint, uint, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, float, float, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, double, double, uchar) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, char, char, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, ushort, ushort, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, short, short, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, int, int, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, uint, uint, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, float, float, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, double, double, uchar) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, char, char, char) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, ushort, ushort, ushort) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, short, short, short) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, int, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, uint, uint, uint) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, char, char, char) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, ushort, ushort, ushort) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, short, short, short) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, int, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, uint, uint, uint) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, char, char, char) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, ushort, ushort, ushort) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, short, short, short) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, int, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, uint, uint, uint) + +#undef CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP + +// binary function (vec & vec) + +#define CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(func_name, func, input_type, output_type) \ + __device__ __forceinline__ output_type ## 1 func_name(const input_type ## 1 & a, const input_type ## 1 & b) \ + { \ + return VecTraits::make(func (a.x, b.x)); \ + } \ + __device__ __forceinline__ output_type ## 2 func_name(const input_type ## 2 & a, const input_type ## 2 & b) \ + { \ + return VecTraits::make(func (a.x, b.x), func (a.y, b.y)); \ + } \ + __device__ __forceinline__ output_type ## 3 func_name(const input_type ## 3 & a, const input_type ## 3 & b) \ + { \ + return VecTraits::make(func (a.x, b.x), func (a.y, b.y), func (a.z, b.z)); \ + } \ + __device__ __forceinline__ output_type ## 4 func_name(const input_type ## 4 & a, const input_type ## 4 & b) \ + { \ + return VecTraits::make(func (a.x, b.x), func (a.y, b.y), func (a.z, b.z), func (a.w, b.w)); \ + } + +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, char, char) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, ushort, ushort) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, short, short) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, uint, uint) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, int, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::fmaxf, float, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::fmax, double, double) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, uchar, uchar) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, char, char) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, ushort, ushort) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, short, short) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, uint, uint) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, int, int) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::fminf, float, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::fmin, double, double) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, char, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, short, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, uint, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, int, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, float, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypot, double, double) + +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, uchar, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, char, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, ushort, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, short, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, uint, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, int, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, float, float) +CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2, double, double) + +#undef CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC + +// binary function (vec & scalar) + +#define CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(func_name, func, input_type, scalar_type, output_type) \ + __device__ __forceinline__ output_type ## 1 func_name(const input_type ## 1 & a, scalar_type s) \ + { \ + return VecTraits::make(func ((output_type) a.x, (output_type) s)); \ + } \ + __device__ __forceinline__ output_type ## 1 func_name(scalar_type s, const input_type ## 1 & b) \ + { \ + return VecTraits::make(func ((output_type) s, (output_type) b.x)); \ + } \ + __device__ __forceinline__ output_type ## 2 func_name(const input_type ## 2 & a, scalar_type s) \ + { \ + return VecTraits::make(func ((output_type) a.x, (output_type) s), func ((output_type) a.y, (output_type) s)); \ + } \ + __device__ __forceinline__ output_type ## 2 func_name(scalar_type s, const input_type ## 2 & b) \ + { \ + return VecTraits::make(func ((output_type) s, (output_type) b.x), func ((output_type) s, (output_type) b.y)); \ + } \ + __device__ __forceinline__ output_type ## 3 func_name(const input_type ## 3 & a, scalar_type s) \ + { \ + return VecTraits::make(func ((output_type) a.x, (output_type) s), func ((output_type) a.y, (output_type) s), func ((output_type) a.z, (output_type) s)); \ + } \ + __device__ __forceinline__ output_type ## 3 func_name(scalar_type s, const input_type ## 3 & b) \ + { \ + return VecTraits::make(func ((output_type) s, (output_type) b.x), func ((output_type) s, (output_type) b.y), func ((output_type) s, (output_type) b.z)); \ + } \ + __device__ __forceinline__ output_type ## 4 func_name(const input_type ## 4 & a, scalar_type s) \ + { \ + return VecTraits::make(func ((output_type) a.x, (output_type) s), func ((output_type) a.y, (output_type) s), func ((output_type) a.z, (output_type) s), func ((output_type) a.w, (output_type) s)); \ + } \ + __device__ __forceinline__ output_type ## 4 func_name(scalar_type s, const input_type ## 4 & b) \ + { \ + return VecTraits::make(func ((output_type) s, (output_type) b.x), func ((output_type) s, (output_type) b.y), func ((output_type) s, (output_type) b.z), func ((output_type) s, (output_type) b.w)); \ + } + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, uchar, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, uchar, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, char, char, char) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, char, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, char, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, ushort, ushort, ushort) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, ushort, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, ushort, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, short, short, short) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, short, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, short, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, uint, uint, uint) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, uint, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, uint, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, int, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, int, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, int, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, float, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, float, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, double, double, double) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, uchar, uchar, uchar) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, uchar, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, uchar, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, char, char, char) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, char, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, char, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, ushort, ushort, ushort) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, ushort, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, ushort, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, short, short, short) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, short, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, short, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, uint, uint, uint) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, uint, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, uint, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, int, int, int) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, int, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, int, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, float, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, float, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, double, double, double) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, uchar, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, uchar, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, char, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, char, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, ushort, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, ushort, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, short, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, short, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, uint, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, uint, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, int, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, int, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, float, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, float, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, double, double, double) + +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, uchar, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, uchar, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, char, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, char, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, ushort, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, ushort, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, short, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, short, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, uint, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, uint, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, int, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, int, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, float, float, float) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, float, double, double) +CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, double, double, double) + +#undef CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC + +}}} // namespace cv { namespace cuda { namespace device + +//! @endcond + +#endif // OPENCV_CUDA_VECMATH_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/vec_traits.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/vec_traits.hpp new file mode 100644 index 0000000000000000000000000000000000000000..b5ff281a0b8a3b7c5a6a481fa7880b7f24e4786f --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/vec_traits.hpp @@ -0,0 +1,288 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_VEC_TRAITS_HPP +#define OPENCV_CUDA_VEC_TRAITS_HPP + +#include "common.hpp" + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + template struct TypeVec; + + struct __align__(8) uchar8 + { + uchar a0, a1, a2, a3, a4, a5, a6, a7; + }; + static __host__ __device__ __forceinline__ uchar8 make_uchar8(uchar a0, uchar a1, uchar a2, uchar a3, uchar a4, uchar a5, uchar a6, uchar a7) + { + uchar8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; + return val; + } + struct __align__(8) char8 + { + schar a0, a1, a2, a3, a4, a5, a6, a7; + }; + static __host__ __device__ __forceinline__ char8 make_char8(schar a0, schar a1, schar a2, schar a3, schar a4, schar a5, schar a6, schar a7) + { + char8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; + return val; + } + struct __align__(16) ushort8 + { + ushort a0, a1, a2, a3, a4, a5, a6, a7; + }; + static __host__ __device__ __forceinline__ ushort8 make_ushort8(ushort a0, ushort a1, ushort a2, ushort a3, ushort a4, ushort a5, ushort a6, ushort a7) + { + ushort8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; + return val; + } + struct __align__(16) short8 + { + short a0, a1, a2, a3, a4, a5, a6, a7; + }; + static __host__ __device__ __forceinline__ short8 make_short8(short a0, short a1, short a2, short a3, short a4, short a5, short a6, short a7) + { + short8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; + return val; + } + struct __align__(32) uint8 + { + uint a0, a1, a2, a3, a4, a5, a6, a7; + }; + static __host__ __device__ __forceinline__ uint8 make_uint8(uint a0, uint a1, uint a2, uint a3, uint a4, uint a5, uint a6, uint a7) + { + uint8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; + return val; + } + struct __align__(32) int8 + { + int a0, a1, a2, a3, a4, a5, a6, a7; + }; + static __host__ __device__ __forceinline__ int8 make_int8(int a0, int a1, int a2, int a3, int a4, int a5, int a6, int a7) + { + int8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; + return val; + } + struct __align__(32) float8 + { + float a0, a1, a2, a3, a4, a5, a6, a7; + }; + static __host__ __device__ __forceinline__ float8 make_float8(float a0, float a1, float a2, float a3, float a4, float a5, float a6, float a7) + { + float8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; + return val; + } + struct double8 + { + double a0, a1, a2, a3, a4, a5, a6, a7; + }; + static __host__ __device__ __forceinline__ double8 make_double8(double a0, double a1, double a2, double a3, double a4, double a5, double a6, double a7) + { + double8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; + return val; + } + +#define OPENCV_CUDA_IMPLEMENT_TYPE_VEC(type) \ + template<> struct TypeVec { typedef type vec_type; }; \ + template<> struct TypeVec { typedef type ## 1 vec_type; }; \ + template<> struct TypeVec { typedef type ## 2 vec_type; }; \ + template<> struct TypeVec { typedef type ## 2 vec_type; }; \ + template<> struct TypeVec { typedef type ## 3 vec_type; }; \ + template<> struct TypeVec { typedef type ## 3 vec_type; }; \ + template<> struct TypeVec { typedef type ## 4 vec_type; }; \ + template<> struct TypeVec { typedef type ## 4 vec_type; }; \ + template<> struct TypeVec { typedef type ## 8 vec_type; }; \ + template<> struct TypeVec { typedef type ## 8 vec_type; }; + + OPENCV_CUDA_IMPLEMENT_TYPE_VEC(uchar) + OPENCV_CUDA_IMPLEMENT_TYPE_VEC(char) + OPENCV_CUDA_IMPLEMENT_TYPE_VEC(ushort) + OPENCV_CUDA_IMPLEMENT_TYPE_VEC(short) + OPENCV_CUDA_IMPLEMENT_TYPE_VEC(int) + OPENCV_CUDA_IMPLEMENT_TYPE_VEC(uint) + OPENCV_CUDA_IMPLEMENT_TYPE_VEC(float) + OPENCV_CUDA_IMPLEMENT_TYPE_VEC(double) + + #undef OPENCV_CUDA_IMPLEMENT_TYPE_VEC + + template<> struct TypeVec { typedef schar vec_type; }; + template<> struct TypeVec { typedef char2 vec_type; }; + template<> struct TypeVec { typedef char3 vec_type; }; + template<> struct TypeVec { typedef char4 vec_type; }; + template<> struct TypeVec { typedef char8 vec_type; }; + + template<> struct TypeVec { typedef uchar vec_type; }; + template<> struct TypeVec { typedef uchar2 vec_type; }; + template<> struct TypeVec { typedef uchar3 vec_type; }; + template<> struct TypeVec { typedef uchar4 vec_type; }; + template<> struct TypeVec { typedef uchar8 vec_type; }; + + template struct VecTraits; + +#define OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(type) \ + template<> struct VecTraits \ + { \ + typedef type elem_type; \ + enum {cn=1}; \ + static __device__ __host__ __forceinline__ type all(type v) {return v;} \ + static __device__ __host__ __forceinline__ type make(type x) {return x;} \ + static __device__ __host__ __forceinline__ type make(const type* v) {return *v;} \ + }; \ + template<> struct VecTraits \ + { \ + typedef type elem_type; \ + enum {cn=1}; \ + static __device__ __host__ __forceinline__ type ## 1 all(type v) {return make_ ## type ## 1(v);} \ + static __device__ __host__ __forceinline__ type ## 1 make(type x) {return make_ ## type ## 1(x);} \ + static __device__ __host__ __forceinline__ type ## 1 make(const type* v) {return make_ ## type ## 1(*v);} \ + }; \ + template<> struct VecTraits \ + { \ + typedef type elem_type; \ + enum {cn=2}; \ + static __device__ __host__ __forceinline__ type ## 2 all(type v) {return make_ ## type ## 2(v, v);} \ + static __device__ __host__ __forceinline__ type ## 2 make(type x, type y) {return make_ ## type ## 2(x, y);} \ + static __device__ __host__ __forceinline__ type ## 2 make(const type* v) {return make_ ## type ## 2(v[0], v[1]);} \ + }; \ + template<> struct VecTraits \ + { \ + typedef type elem_type; \ + enum {cn=3}; \ + static __device__ __host__ __forceinline__ type ## 3 all(type v) {return make_ ## type ## 3(v, v, v);} \ + static __device__ __host__ __forceinline__ type ## 3 make(type x, type y, type z) {return make_ ## type ## 3(x, y, z);} \ + static __device__ __host__ __forceinline__ type ## 3 make(const type* v) {return make_ ## type ## 3(v[0], v[1], v[2]);} \ + }; \ + template<> struct VecTraits \ + { \ + typedef type elem_type; \ + enum {cn=4}; \ + static __device__ __host__ __forceinline__ type ## 4 all(type v) {return make_ ## type ## 4(v, v, v, v);} \ + static __device__ __host__ __forceinline__ type ## 4 make(type x, type y, type z, type w) {return make_ ## type ## 4(x, y, z, w);} \ + static __device__ __host__ __forceinline__ type ## 4 make(const type* v) {return make_ ## type ## 4(v[0], v[1], v[2], v[3]);} \ + }; \ + template<> struct VecTraits \ + { \ + typedef type elem_type; \ + enum {cn=8}; \ + static __device__ __host__ __forceinline__ type ## 8 all(type v) {return make_ ## type ## 8(v, v, v, v, v, v, v, v);} \ + static __device__ __host__ __forceinline__ type ## 8 make(type a0, type a1, type a2, type a3, type a4, type a5, type a6, type a7) {return make_ ## type ## 8(a0, a1, a2, a3, a4, a5, a6, a7);} \ + static __device__ __host__ __forceinline__ type ## 8 make(const type* v) {return make_ ## type ## 8(v[0], v[1], v[2], v[3], v[4], v[5], v[6], v[7]);} \ + }; + + OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(uchar) + OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(ushort) + OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(short) + OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(int) + OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(uint) + OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(float) + OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(double) + + #undef OPENCV_CUDA_IMPLEMENT_VEC_TRAITS + + template<> struct VecTraits + { + typedef char elem_type; + enum {cn=1}; + static __device__ __host__ __forceinline__ char all(char v) {return v;} + static __device__ __host__ __forceinline__ char make(char x) {return x;} + static __device__ __host__ __forceinline__ char make(const char* x) {return *x;} + }; + template<> struct VecTraits + { + typedef schar elem_type; + enum {cn=1}; + static __device__ __host__ __forceinline__ schar all(schar v) {return v;} + static __device__ __host__ __forceinline__ schar make(schar x) {return x;} + static __device__ __host__ __forceinline__ schar make(const schar* x) {return *x;} + }; + template<> struct VecTraits + { + typedef schar elem_type; + enum {cn=1}; + static __device__ __host__ __forceinline__ char1 all(schar v) {return make_char1(v);} + static __device__ __host__ __forceinline__ char1 make(schar x) {return make_char1(x);} + static __device__ __host__ __forceinline__ char1 make(const schar* v) {return make_char1(v[0]);} + }; + template<> struct VecTraits + { + typedef schar elem_type; + enum {cn=2}; + static __device__ __host__ __forceinline__ char2 all(schar v) {return make_char2(v, v);} + static __device__ __host__ __forceinline__ char2 make(schar x, schar y) {return make_char2(x, y);} + static __device__ __host__ __forceinline__ char2 make(const schar* v) {return make_char2(v[0], v[1]);} + }; + template<> struct VecTraits + { + typedef schar elem_type; + enum {cn=3}; + static __device__ __host__ __forceinline__ char3 all(schar v) {return make_char3(v, v, v);} + static __device__ __host__ __forceinline__ char3 make(schar x, schar y, schar z) {return make_char3(x, y, z);} + static __device__ __host__ __forceinline__ char3 make(const schar* v) {return make_char3(v[0], v[1], v[2]);} + }; + template<> struct VecTraits + { + typedef schar elem_type; + enum {cn=4}; + static __device__ __host__ __forceinline__ char4 all(schar v) {return make_char4(v, v, v, v);} + static __device__ __host__ __forceinline__ char4 make(schar x, schar y, schar z, schar w) {return make_char4(x, y, z, w);} + static __device__ __host__ __forceinline__ char4 make(const schar* v) {return make_char4(v[0], v[1], v[2], v[3]);} + }; + template<> struct VecTraits + { + typedef schar elem_type; + enum {cn=8}; + static __device__ __host__ __forceinline__ char8 all(schar v) {return make_char8(v, v, v, v, v, v, v, v);} + static __device__ __host__ __forceinline__ char8 make(schar a0, schar a1, schar a2, schar a3, schar a4, schar a5, schar a6, schar a7) {return make_char8(a0, a1, a2, a3, a4, a5, a6, a7);} + static __device__ __host__ __forceinline__ char8 make(const schar* v) {return make_char8(v[0], v[1], v[2], v[3], v[4], v[5], v[6], v[7]);} + }; +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif // OPENCV_CUDA_VEC_TRAITS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/warp.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/warp.hpp new file mode 100644 index 0000000000000000000000000000000000000000..8af7e6a212afd45cc9ddd00c26010875f55d80bf --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/warp.hpp @@ -0,0 +1,139 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_DEVICE_WARP_HPP +#define OPENCV_CUDA_DEVICE_WARP_HPP + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + struct Warp + { + enum + { + LOG_WARP_SIZE = 5, + WARP_SIZE = 1 << LOG_WARP_SIZE, + STRIDE = WARP_SIZE + }; + + /** \brief Returns the warp lane ID of the calling thread. */ + static __device__ __forceinline__ unsigned int laneId() + { + unsigned int ret; + asm("mov.u32 %0, %%laneid;" : "=r"(ret) ); + return ret; + } + + template + static __device__ __forceinline__ void fill(It beg, It end, const T& value) + { + for(It t = beg + laneId(); t < end; t += STRIDE) + *t = value; + } + + template + static __device__ __forceinline__ OutIt copy(InIt beg, InIt end, OutIt out) + { + for(InIt t = beg + laneId(); t < end; t += STRIDE, out += STRIDE) + *out = *t; + return out; + } + + template + static __device__ __forceinline__ OutIt transform(InIt beg, InIt end, OutIt out, UnOp op) + { + for(InIt t = beg + laneId(); t < end; t += STRIDE, out += STRIDE) + *out = op(*t); + return out; + } + + template + static __device__ __forceinline__ OutIt transform(InIt1 beg1, InIt1 end1, InIt2 beg2, OutIt out, BinOp op) + { + unsigned int lane = laneId(); + + InIt1 t1 = beg1 + lane; + InIt2 t2 = beg2 + lane; + for(; t1 < end1; t1 += STRIDE, t2 += STRIDE, out += STRIDE) + *out = op(*t1, *t2); + return out; + } + + template + static __device__ __forceinline__ T reduce(volatile T *ptr, BinOp op) + { + const unsigned int lane = laneId(); + + if (lane < 16) + { + T partial = ptr[lane]; + + ptr[lane] = partial = op(partial, ptr[lane + 16]); + ptr[lane] = partial = op(partial, ptr[lane + 8]); + ptr[lane] = partial = op(partial, ptr[lane + 4]); + ptr[lane] = partial = op(partial, ptr[lane + 2]); + ptr[lane] = partial = op(partial, ptr[lane + 1]); + } + + return *ptr; + } + + template + static __device__ __forceinline__ void yota(OutIt beg, OutIt end, T value) + { + unsigned int lane = laneId(); + value += lane; + + for(OutIt t = beg + lane; t < end; t += STRIDE, value += STRIDE) + *t = value; + } + }; +}}} // namespace cv { namespace cuda { namespace cudev + +//! @endcond + +#endif /* OPENCV_CUDA_DEVICE_WARP_HPP */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/warp_reduce.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/warp_reduce.hpp new file mode 100644 index 0000000000000000000000000000000000000000..530303d2495fe878340b80bf1a4b330ed7aadc5f --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/warp_reduce.hpp @@ -0,0 +1,76 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_WARP_REDUCE_HPP__ +#define OPENCV_CUDA_WARP_REDUCE_HPP__ + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ + template + __device__ __forceinline__ T warp_reduce(volatile T *ptr , const unsigned int tid = threadIdx.x) + { + const unsigned int lane = tid & 31; // index of thread in warp (0..31) + + if (lane < 16) + { + T partial = ptr[tid]; + + ptr[tid] = partial = partial + ptr[tid + 16]; + ptr[tid] = partial = partial + ptr[tid + 8]; + ptr[tid] = partial = partial + ptr[tid + 4]; + ptr[tid] = partial = partial + ptr[tid + 2]; + ptr[tid] = partial = partial + ptr[tid + 1]; + } + + return ptr[tid - lane]; + } +}}} // namespace cv { namespace cuda { namespace cudev { + +//! @endcond + +#endif /* OPENCV_CUDA_WARP_REDUCE_HPP__ */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/warp_shuffle.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/warp_shuffle.hpp new file mode 100644 index 0000000000000000000000000000000000000000..0da54aee99bf82f0946b64ed39d3bb9bceaf5d7b --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda/warp_shuffle.hpp @@ -0,0 +1,162 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CUDA_WARP_SHUFFLE_HPP +#define OPENCV_CUDA_WARP_SHUFFLE_HPP + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +namespace cv { namespace cuda { namespace device +{ +#if __CUDACC_VER_MAJOR__ >= 9 +# define __shfl(x, y, z) __shfl_sync(0xFFFFFFFFU, x, y, z) +# define __shfl_up(x, y, z) __shfl_up_sync(0xFFFFFFFFU, x, y, z) +# define __shfl_down(x, y, z) __shfl_down_sync(0xFFFFFFFFU, x, y, z) +#endif + template + __device__ __forceinline__ T shfl(T val, int srcLane, int width = warpSize) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + return __shfl(val, srcLane, width); + #else + return T(); + #endif + } + __device__ __forceinline__ unsigned int shfl(unsigned int val, int srcLane, int width = warpSize) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + return (unsigned int) __shfl((int) val, srcLane, width); + #else + return 0; + #endif + } + __device__ __forceinline__ double shfl(double val, int srcLane, int width = warpSize) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + int lo = __double2loint(val); + int hi = __double2hiint(val); + + lo = __shfl(lo, srcLane, width); + hi = __shfl(hi, srcLane, width); + + return __hiloint2double(hi, lo); + #else + return 0.0; + #endif + } + + template + __device__ __forceinline__ T shfl_down(T val, unsigned int delta, int width = warpSize) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + return __shfl_down(val, delta, width); + #else + return T(); + #endif + } + __device__ __forceinline__ unsigned int shfl_down(unsigned int val, unsigned int delta, int width = warpSize) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + return (unsigned int) __shfl_down((int) val, delta, width); + #else + return 0; + #endif + } + __device__ __forceinline__ double shfl_down(double val, unsigned int delta, int width = warpSize) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + int lo = __double2loint(val); + int hi = __double2hiint(val); + + lo = __shfl_down(lo, delta, width); + hi = __shfl_down(hi, delta, width); + + return __hiloint2double(hi, lo); + #else + return 0.0; + #endif + } + + template + __device__ __forceinline__ T shfl_up(T val, unsigned int delta, int width = warpSize) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + return __shfl_up(val, delta, width); + #else + return T(); + #endif + } + __device__ __forceinline__ unsigned int shfl_up(unsigned int val, unsigned int delta, int width = warpSize) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + return (unsigned int) __shfl_up((int) val, delta, width); + #else + return 0; + #endif + } + __device__ __forceinline__ double shfl_up(double val, unsigned int delta, int width = warpSize) + { + #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 + int lo = __double2loint(val); + int hi = __double2hiint(val); + + lo = __shfl_up(lo, delta, width); + hi = __shfl_up(hi, delta, width); + + return __hiloint2double(hi, lo); + #else + return 0.0; + #endif + } +}}} + +# undef __shfl +# undef __shfl_up +# undef __shfl_down + +//! @endcond + +#endif // OPENCV_CUDA_WARP_SHUFFLE_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda_stream_accessor.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda_stream_accessor.hpp new file mode 100644 index 0000000000000000000000000000000000000000..deaf356fff4311ffddcc388ee28106d0b5459f3f --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda_stream_accessor.hpp @@ -0,0 +1,86 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_CUDA_STREAM_ACCESSOR_HPP +#define OPENCV_CORE_CUDA_STREAM_ACCESSOR_HPP + +#ifndef __cplusplus +# error cuda_stream_accessor.hpp header must be compiled as C++ +#endif + +/** @file cuda_stream_accessor.hpp + * This is only header file that depends on CUDA Runtime API. All other headers are independent. + */ + +#include +#include "opencv2/core/cuda.hpp" + +namespace cv +{ + namespace cuda + { + +//! @addtogroup cudacore_struct +//! @{ + + /** @brief Class that enables getting cudaStream_t from cuda::Stream + */ + struct StreamAccessor + { + CV_EXPORTS static cudaStream_t getStream(const Stream& stream); + CV_EXPORTS static Stream wrapStream(cudaStream_t stream); + }; + + /** @brief Class that enables getting cudaEvent_t from cuda::Event + */ + struct EventAccessor + { + CV_EXPORTS static cudaEvent_t getEvent(const Event& event); + CV_EXPORTS static Event wrapEvent(cudaEvent_t event); + }; + +//! @} + + } +} + +#endif /* OPENCV_CORE_CUDA_STREAM_ACCESSOR_HPP */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda_types.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda_types.hpp new file mode 100644 index 0000000000000000000000000000000000000000..e2647c0455b1e501cf96b7badee7cfc6c5e18eff --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cuda_types.hpp @@ -0,0 +1,142 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_CUDA_TYPES_HPP +#define OPENCV_CORE_CUDA_TYPES_HPP + +#ifndef __cplusplus +# error cuda_types.hpp header must be compiled as C++ +#endif + +#if defined(__OPENCV_BUILD) && defined(__clang__) +#pragma clang diagnostic ignored "-Winconsistent-missing-override" +#endif +#if defined(__OPENCV_BUILD) && defined(__GNUC__) && __GNUC__ >= 5 +#pragma GCC diagnostic ignored "-Wsuggest-override" +#endif + +/** @file + * @deprecated Use @ref cudev instead. + */ + +//! @cond IGNORED + +#ifdef __CUDACC__ + #define __CV_CUDA_HOST_DEVICE__ __host__ __device__ __forceinline__ +#else + #define __CV_CUDA_HOST_DEVICE__ +#endif + +namespace cv +{ + namespace cuda + { + + // Simple lightweight structures that encapsulates information about an image on device. + // It is intended to pass to nvcc-compiled code. GpuMat depends on headers that nvcc can't compile + + template struct DevPtr + { + typedef T elem_type; + typedef int index_type; + + enum { elem_size = sizeof(elem_type) }; + + T* data; + + __CV_CUDA_HOST_DEVICE__ DevPtr() : data(0) {} + __CV_CUDA_HOST_DEVICE__ DevPtr(T* data_) : data(data_) {} + + __CV_CUDA_HOST_DEVICE__ size_t elemSize() const { return elem_size; } + __CV_CUDA_HOST_DEVICE__ operator T*() { return data; } + __CV_CUDA_HOST_DEVICE__ operator const T*() const { return data; } + }; + + template struct PtrSz : public DevPtr + { + __CV_CUDA_HOST_DEVICE__ PtrSz() : size(0) {} + __CV_CUDA_HOST_DEVICE__ PtrSz(T* data_, size_t size_) : DevPtr(data_), size(size_) {} + + size_t size; + }; + + template struct PtrStep : public DevPtr + { + __CV_CUDA_HOST_DEVICE__ PtrStep() : step(0) {} + __CV_CUDA_HOST_DEVICE__ PtrStep(T* data_, size_t step_) : DevPtr(data_), step(step_) {} + + size_t step; + + __CV_CUDA_HOST_DEVICE__ T* ptr(int y = 0) { return ( T*)( ( char*)DevPtr::data + y * step); } + __CV_CUDA_HOST_DEVICE__ const T* ptr(int y = 0) const { return (const T*)( (const char*)DevPtr::data + y * step); } + + __CV_CUDA_HOST_DEVICE__ T& operator ()(int y, int x) { return ptr(y)[x]; } + __CV_CUDA_HOST_DEVICE__ const T& operator ()(int y, int x) const { return ptr(y)[x]; } + }; + + template struct PtrStepSz : public PtrStep + { + __CV_CUDA_HOST_DEVICE__ PtrStepSz() : cols(0), rows(0) {} + __CV_CUDA_HOST_DEVICE__ PtrStepSz(int rows_, int cols_, T* data_, size_t step_) + : PtrStep(data_, step_), cols(cols_), rows(rows_) {} + + template + explicit PtrStepSz(const PtrStepSz& d) : PtrStep((T*)d.data, d.step), cols(d.cols), rows(d.rows){} + + int cols; + int rows; + }; + + typedef PtrStepSz PtrStepSzb; + typedef PtrStepSz PtrStepSzf; + typedef PtrStepSz PtrStepSzi; + + typedef PtrStep PtrStepb; + typedef PtrStep PtrStepf; + typedef PtrStep PtrStepi; + + } +} + +//! @endcond + +#endif /* OPENCV_CORE_CUDA_TYPES_HPP */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cv_cpu_dispatch.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cv_cpu_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..57aa0ce2fb2b4978b59dbab2b6332f11b37128f6 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cv_cpu_dispatch.h @@ -0,0 +1,247 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#if defined __OPENCV_BUILD \ + +#include "cv_cpu_config.h" +#include "cv_cpu_helper.h" + +#ifdef CV_CPU_DISPATCH_MODE +#define CV_CPU_OPTIMIZATION_NAMESPACE __CV_CAT(opt_, CV_CPU_DISPATCH_MODE) +#define CV_CPU_OPTIMIZATION_NAMESPACE_BEGIN namespace __CV_CAT(opt_, CV_CPU_DISPATCH_MODE) { +#define CV_CPU_OPTIMIZATION_NAMESPACE_END } +#else +#define CV_CPU_OPTIMIZATION_NAMESPACE cpu_baseline +#define CV_CPU_OPTIMIZATION_NAMESPACE_BEGIN namespace cpu_baseline { +#define CV_CPU_OPTIMIZATION_NAMESPACE_END } +#endif + + +#define __CV_CPU_DISPATCH_CHAIN_END(fn, args, mode, ...) /* done */ +#define __CV_CPU_DISPATCH(fn, args, mode, ...) __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) +#define __CV_CPU_DISPATCH_EXPAND(fn, args, ...) __CV_EXPAND(__CV_CPU_DISPATCH(fn, args, __VA_ARGS__)) +#define CV_CPU_DISPATCH(fn, args, ...) __CV_CPU_DISPATCH_EXPAND(fn, args, __VA_ARGS__, END) // expand macros + + +#if defined CV_ENABLE_INTRINSICS \ + && !defined CV_DISABLE_OPTIMIZATION \ + && !defined __CUDACC__ /* do not include SSE/AVX/NEON headers for NVCC compiler */ \ + +#ifdef CV_CPU_COMPILE_SSE2 +# include +# define CV_MMX 1 +# define CV_SSE 1 +# define CV_SSE2 1 +#endif +#ifdef CV_CPU_COMPILE_SSE3 +# include +# define CV_SSE3 1 +#endif +#ifdef CV_CPU_COMPILE_SSSE3 +# include +# define CV_SSSE3 1 +#endif +#ifdef CV_CPU_COMPILE_SSE4_1 +# include +# define CV_SSE4_1 1 +#endif +#ifdef CV_CPU_COMPILE_SSE4_2 +# include +# define CV_SSE4_2 1 +#endif +#ifdef CV_CPU_COMPILE_POPCNT +# ifdef _MSC_VER +# include +# if defined(_M_X64) +# define CV_POPCNT_U64 _mm_popcnt_u64 +# endif +# define CV_POPCNT_U32 _mm_popcnt_u32 +# else +# include +# if defined(__x86_64__) +# define CV_POPCNT_U64 __builtin_popcountll +# endif +# define CV_POPCNT_U32 __builtin_popcount +# endif +# define CV_POPCNT 1 +#endif +#ifdef CV_CPU_COMPILE_AVX +# include +# define CV_AVX 1 +#endif +#ifdef CV_CPU_COMPILE_FP16 +# if defined(__arm__) || defined(__aarch64__) || defined(_M_ARM) +# include +# else +# include +# endif +# define CV_FP16 1 +#endif +#ifdef CV_CPU_COMPILE_AVX2 +# include +# define CV_AVX2 1 +#endif +#ifdef CV_CPU_COMPILE_AVX_512F +# include +# define CV_AVX_512F 1 +#endif +#ifdef CV_CPU_COMPILE_AVX512_SKX +# include +# define CV_AVX512_SKX 1 +#endif +#ifdef CV_CPU_COMPILE_FMA3 +# define CV_FMA3 1 +#endif + +#if defined _WIN32 && defined(_M_ARM) +# include +# include +# define CV_NEON 1 +#elif defined(__ARM_NEON__) || (defined (__ARM_NEON) && defined(__aarch64__)) +# include +# define CV_NEON 1 +#endif + +#if defined(__ARM_NEON__) || defined(__aarch64__) +# include +#endif + +#ifdef CV_CPU_COMPILE_VSX +# include +# undef vector +# undef pixel +# undef bool +# define CV_VSX 1 +#endif + +#ifdef CV_CPU_COMPILE_VSX3 +# define CV_VSX3 1 +#endif + +#endif // CV_ENABLE_INTRINSICS && !CV_DISABLE_OPTIMIZATION && !__CUDACC__ + +#if defined CV_CPU_COMPILE_AVX && !defined CV_CPU_BASELINE_COMPILE_AVX +struct VZeroUpperGuard { +#ifdef __GNUC__ + __attribute__((always_inline)) +#endif + inline ~VZeroUpperGuard() { _mm256_zeroupper(); } +}; +#define __CV_AVX_GUARD VZeroUpperGuard __vzeroupper_guard; CV_UNUSED(__vzeroupper_guard); +#endif + +#ifdef __CV_AVX_GUARD +#define CV_AVX_GUARD __CV_AVX_GUARD +#else +#define CV_AVX_GUARD +#endif + +#endif // __OPENCV_BUILD + + + +#if !defined __OPENCV_BUILD /* Compatibility code */ \ + && !defined __CUDACC__ /* do not include SSE/AVX/NEON headers for NVCC compiler */ +#if defined __SSE2__ || defined _M_X64 || (defined _M_IX86_FP && _M_IX86_FP >= 2) +# include +# define CV_MMX 1 +# define CV_SSE 1 +# define CV_SSE2 1 +#elif defined _WIN32 && defined(_M_ARM) +# include +# include +# define CV_NEON 1 +#elif defined(__ARM_NEON__) || (defined (__ARM_NEON) && defined(__aarch64__)) +# include +# define CV_NEON 1 +#elif defined(__VSX__) && defined(__PPC64__) && defined(__LITTLE_ENDIAN__) +# include +# undef vector +# undef pixel +# undef bool +# define CV_VSX 1 +#endif + +#endif // !__OPENCV_BUILD && !__CUDACC (Compatibility code) + + + +#ifndef CV_MMX +# define CV_MMX 0 +#endif +#ifndef CV_SSE +# define CV_SSE 0 +#endif +#ifndef CV_SSE2 +# define CV_SSE2 0 +#endif +#ifndef CV_SSE3 +# define CV_SSE3 0 +#endif +#ifndef CV_SSSE3 +# define CV_SSSE3 0 +#endif +#ifndef CV_SSE4_1 +# define CV_SSE4_1 0 +#endif +#ifndef CV_SSE4_2 +# define CV_SSE4_2 0 +#endif +#ifndef CV_POPCNT +# define CV_POPCNT 0 +#endif +#ifndef CV_AVX +# define CV_AVX 0 +#endif +#ifndef CV_FP16 +# define CV_FP16 0 +#endif +#ifndef CV_AVX2 +# define CV_AVX2 0 +#endif +#ifndef CV_FMA3 +# define CV_FMA3 0 +#endif +#ifndef CV_AVX_512F +# define CV_AVX_512F 0 +#endif +#ifndef CV_AVX_512BW +# define CV_AVX_512BW 0 +#endif +#ifndef CV_AVX_512CD +# define CV_AVX_512CD 0 +#endif +#ifndef CV_AVX_512DQ +# define CV_AVX_512DQ 0 +#endif +#ifndef CV_AVX_512ER +# define CV_AVX_512ER 0 +#endif +#ifndef CV_AVX_512IFMA512 +# define CV_AVX_512IFMA512 0 +#endif +#ifndef CV_AVX_512PF +# define CV_AVX_512PF 0 +#endif +#ifndef CV_AVX_512VBMI +# define CV_AVX_512VBMI 0 +#endif +#ifndef CV_AVX_512VL +# define CV_AVX_512VL 0 +#endif +#ifndef CV_AVX512_SKX +# define CV_AVX512_SKX 0 +#endif + +#ifndef CV_NEON +# define CV_NEON 0 +#endif + +#ifndef CV_VSX +# define CV_VSX 0 +#endif + +#ifndef CV_VSX3 +# define CV_VSX3 0 +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cv_cpu_helper.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cv_cpu_helper.h new file mode 100644 index 0000000000000000000000000000000000000000..ad1339796d8b4cccd8cd794ed4eb1d2ea59a9f4f --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cv_cpu_helper.h @@ -0,0 +1,340 @@ +// AUTOGENERATED, DO NOT EDIT + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSE +# define CV_TRY_SSE 1 +# define CV_CPU_FORCE_SSE 1 +# define CV_CPU_HAS_SUPPORT_SSE 1 +# define CV_CPU_CALL_SSE(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_SSE_(fn, args) return (opt_SSE::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSE +# define CV_TRY_SSE 1 +# define CV_CPU_FORCE_SSE 0 +# define CV_CPU_HAS_SUPPORT_SSE (cv::checkHardwareSupport(CV_CPU_SSE)) +# define CV_CPU_CALL_SSE(fn, args) if (CV_CPU_HAS_SUPPORT_SSE) return (opt_SSE::fn args) +# define CV_CPU_CALL_SSE_(fn, args) if (CV_CPU_HAS_SUPPORT_SSE) return (opt_SSE::fn args) +#else +# define CV_TRY_SSE 0 +# define CV_CPU_FORCE_SSE 0 +# define CV_CPU_HAS_SUPPORT_SSE 0 +# define CV_CPU_CALL_SSE(fn, args) +# define CV_CPU_CALL_SSE_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_SSE(fn, args, mode, ...) CV_CPU_CALL_SSE(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSE2 +# define CV_TRY_SSE2 1 +# define CV_CPU_FORCE_SSE2 1 +# define CV_CPU_HAS_SUPPORT_SSE2 1 +# define CV_CPU_CALL_SSE2(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_SSE2_(fn, args) return (opt_SSE2::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSE2 +# define CV_TRY_SSE2 1 +# define CV_CPU_FORCE_SSE2 0 +# define CV_CPU_HAS_SUPPORT_SSE2 (cv::checkHardwareSupport(CV_CPU_SSE2)) +# define CV_CPU_CALL_SSE2(fn, args) if (CV_CPU_HAS_SUPPORT_SSE2) return (opt_SSE2::fn args) +# define CV_CPU_CALL_SSE2_(fn, args) if (CV_CPU_HAS_SUPPORT_SSE2) return (opt_SSE2::fn args) +#else +# define CV_TRY_SSE2 0 +# define CV_CPU_FORCE_SSE2 0 +# define CV_CPU_HAS_SUPPORT_SSE2 0 +# define CV_CPU_CALL_SSE2(fn, args) +# define CV_CPU_CALL_SSE2_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_SSE2(fn, args, mode, ...) CV_CPU_CALL_SSE2(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSE3 +# define CV_TRY_SSE3 1 +# define CV_CPU_FORCE_SSE3 1 +# define CV_CPU_HAS_SUPPORT_SSE3 1 +# define CV_CPU_CALL_SSE3(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_SSE3_(fn, args) return (opt_SSE3::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSE3 +# define CV_TRY_SSE3 1 +# define CV_CPU_FORCE_SSE3 0 +# define CV_CPU_HAS_SUPPORT_SSE3 (cv::checkHardwareSupport(CV_CPU_SSE3)) +# define CV_CPU_CALL_SSE3(fn, args) if (CV_CPU_HAS_SUPPORT_SSE3) return (opt_SSE3::fn args) +# define CV_CPU_CALL_SSE3_(fn, args) if (CV_CPU_HAS_SUPPORT_SSE3) return (opt_SSE3::fn args) +#else +# define CV_TRY_SSE3 0 +# define CV_CPU_FORCE_SSE3 0 +# define CV_CPU_HAS_SUPPORT_SSE3 0 +# define CV_CPU_CALL_SSE3(fn, args) +# define CV_CPU_CALL_SSE3_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_SSE3(fn, args, mode, ...) CV_CPU_CALL_SSE3(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSSE3 +# define CV_TRY_SSSE3 1 +# define CV_CPU_FORCE_SSSE3 1 +# define CV_CPU_HAS_SUPPORT_SSSE3 1 +# define CV_CPU_CALL_SSSE3(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_SSSE3_(fn, args) return (opt_SSSE3::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSSE3 +# define CV_TRY_SSSE3 1 +# define CV_CPU_FORCE_SSSE3 0 +# define CV_CPU_HAS_SUPPORT_SSSE3 (cv::checkHardwareSupport(CV_CPU_SSSE3)) +# define CV_CPU_CALL_SSSE3(fn, args) if (CV_CPU_HAS_SUPPORT_SSSE3) return (opt_SSSE3::fn args) +# define CV_CPU_CALL_SSSE3_(fn, args) if (CV_CPU_HAS_SUPPORT_SSSE3) return (opt_SSSE3::fn args) +#else +# define CV_TRY_SSSE3 0 +# define CV_CPU_FORCE_SSSE3 0 +# define CV_CPU_HAS_SUPPORT_SSSE3 0 +# define CV_CPU_CALL_SSSE3(fn, args) +# define CV_CPU_CALL_SSSE3_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_SSSE3(fn, args, mode, ...) CV_CPU_CALL_SSSE3(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSE4_1 +# define CV_TRY_SSE4_1 1 +# define CV_CPU_FORCE_SSE4_1 1 +# define CV_CPU_HAS_SUPPORT_SSE4_1 1 +# define CV_CPU_CALL_SSE4_1(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_SSE4_1_(fn, args) return (opt_SSE4_1::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSE4_1 +# define CV_TRY_SSE4_1 1 +# define CV_CPU_FORCE_SSE4_1 0 +# define CV_CPU_HAS_SUPPORT_SSE4_1 (cv::checkHardwareSupport(CV_CPU_SSE4_1)) +# define CV_CPU_CALL_SSE4_1(fn, args) if (CV_CPU_HAS_SUPPORT_SSE4_1) return (opt_SSE4_1::fn args) +# define CV_CPU_CALL_SSE4_1_(fn, args) if (CV_CPU_HAS_SUPPORT_SSE4_1) return (opt_SSE4_1::fn args) +#else +# define CV_TRY_SSE4_1 0 +# define CV_CPU_FORCE_SSE4_1 0 +# define CV_CPU_HAS_SUPPORT_SSE4_1 0 +# define CV_CPU_CALL_SSE4_1(fn, args) +# define CV_CPU_CALL_SSE4_1_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_SSE4_1(fn, args, mode, ...) CV_CPU_CALL_SSE4_1(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSE4_2 +# define CV_TRY_SSE4_2 1 +# define CV_CPU_FORCE_SSE4_2 1 +# define CV_CPU_HAS_SUPPORT_SSE4_2 1 +# define CV_CPU_CALL_SSE4_2(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_SSE4_2_(fn, args) return (opt_SSE4_2::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSE4_2 +# define CV_TRY_SSE4_2 1 +# define CV_CPU_FORCE_SSE4_2 0 +# define CV_CPU_HAS_SUPPORT_SSE4_2 (cv::checkHardwareSupport(CV_CPU_SSE4_2)) +# define CV_CPU_CALL_SSE4_2(fn, args) if (CV_CPU_HAS_SUPPORT_SSE4_2) return (opt_SSE4_2::fn args) +# define CV_CPU_CALL_SSE4_2_(fn, args) if (CV_CPU_HAS_SUPPORT_SSE4_2) return (opt_SSE4_2::fn args) +#else +# define CV_TRY_SSE4_2 0 +# define CV_CPU_FORCE_SSE4_2 0 +# define CV_CPU_HAS_SUPPORT_SSE4_2 0 +# define CV_CPU_CALL_SSE4_2(fn, args) +# define CV_CPU_CALL_SSE4_2_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_SSE4_2(fn, args, mode, ...) CV_CPU_CALL_SSE4_2(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_POPCNT +# define CV_TRY_POPCNT 1 +# define CV_CPU_FORCE_POPCNT 1 +# define CV_CPU_HAS_SUPPORT_POPCNT 1 +# define CV_CPU_CALL_POPCNT(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_POPCNT_(fn, args) return (opt_POPCNT::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_POPCNT +# define CV_TRY_POPCNT 1 +# define CV_CPU_FORCE_POPCNT 0 +# define CV_CPU_HAS_SUPPORT_POPCNT (cv::checkHardwareSupport(CV_CPU_POPCNT)) +# define CV_CPU_CALL_POPCNT(fn, args) if (CV_CPU_HAS_SUPPORT_POPCNT) return (opt_POPCNT::fn args) +# define CV_CPU_CALL_POPCNT_(fn, args) if (CV_CPU_HAS_SUPPORT_POPCNT) return (opt_POPCNT::fn args) +#else +# define CV_TRY_POPCNT 0 +# define CV_CPU_FORCE_POPCNT 0 +# define CV_CPU_HAS_SUPPORT_POPCNT 0 +# define CV_CPU_CALL_POPCNT(fn, args) +# define CV_CPU_CALL_POPCNT_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_POPCNT(fn, args, mode, ...) CV_CPU_CALL_POPCNT(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX +# define CV_TRY_AVX 1 +# define CV_CPU_FORCE_AVX 1 +# define CV_CPU_HAS_SUPPORT_AVX 1 +# define CV_CPU_CALL_AVX(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_AVX_(fn, args) return (opt_AVX::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX +# define CV_TRY_AVX 1 +# define CV_CPU_FORCE_AVX 0 +# define CV_CPU_HAS_SUPPORT_AVX (cv::checkHardwareSupport(CV_CPU_AVX)) +# define CV_CPU_CALL_AVX(fn, args) if (CV_CPU_HAS_SUPPORT_AVX) return (opt_AVX::fn args) +# define CV_CPU_CALL_AVX_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX) return (opt_AVX::fn args) +#else +# define CV_TRY_AVX 0 +# define CV_CPU_FORCE_AVX 0 +# define CV_CPU_HAS_SUPPORT_AVX 0 +# define CV_CPU_CALL_AVX(fn, args) +# define CV_CPU_CALL_AVX_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_AVX(fn, args, mode, ...) CV_CPU_CALL_AVX(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_FP16 +# define CV_TRY_FP16 1 +# define CV_CPU_FORCE_FP16 1 +# define CV_CPU_HAS_SUPPORT_FP16 1 +# define CV_CPU_CALL_FP16(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_FP16_(fn, args) return (opt_FP16::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_FP16 +# define CV_TRY_FP16 1 +# define CV_CPU_FORCE_FP16 0 +# define CV_CPU_HAS_SUPPORT_FP16 (cv::checkHardwareSupport(CV_CPU_FP16)) +# define CV_CPU_CALL_FP16(fn, args) if (CV_CPU_HAS_SUPPORT_FP16) return (opt_FP16::fn args) +# define CV_CPU_CALL_FP16_(fn, args) if (CV_CPU_HAS_SUPPORT_FP16) return (opt_FP16::fn args) +#else +# define CV_TRY_FP16 0 +# define CV_CPU_FORCE_FP16 0 +# define CV_CPU_HAS_SUPPORT_FP16 0 +# define CV_CPU_CALL_FP16(fn, args) +# define CV_CPU_CALL_FP16_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_FP16(fn, args, mode, ...) CV_CPU_CALL_FP16(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX2 +# define CV_TRY_AVX2 1 +# define CV_CPU_FORCE_AVX2 1 +# define CV_CPU_HAS_SUPPORT_AVX2 1 +# define CV_CPU_CALL_AVX2(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_AVX2_(fn, args) return (opt_AVX2::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX2 +# define CV_TRY_AVX2 1 +# define CV_CPU_FORCE_AVX2 0 +# define CV_CPU_HAS_SUPPORT_AVX2 (cv::checkHardwareSupport(CV_CPU_AVX2)) +# define CV_CPU_CALL_AVX2(fn, args) if (CV_CPU_HAS_SUPPORT_AVX2) return (opt_AVX2::fn args) +# define CV_CPU_CALL_AVX2_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX2) return (opt_AVX2::fn args) +#else +# define CV_TRY_AVX2 0 +# define CV_CPU_FORCE_AVX2 0 +# define CV_CPU_HAS_SUPPORT_AVX2 0 +# define CV_CPU_CALL_AVX2(fn, args) +# define CV_CPU_CALL_AVX2_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_AVX2(fn, args, mode, ...) CV_CPU_CALL_AVX2(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_FMA3 +# define CV_TRY_FMA3 1 +# define CV_CPU_FORCE_FMA3 1 +# define CV_CPU_HAS_SUPPORT_FMA3 1 +# define CV_CPU_CALL_FMA3(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_FMA3_(fn, args) return (opt_FMA3::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_FMA3 +# define CV_TRY_FMA3 1 +# define CV_CPU_FORCE_FMA3 0 +# define CV_CPU_HAS_SUPPORT_FMA3 (cv::checkHardwareSupport(CV_CPU_FMA3)) +# define CV_CPU_CALL_FMA3(fn, args) if (CV_CPU_HAS_SUPPORT_FMA3) return (opt_FMA3::fn args) +# define CV_CPU_CALL_FMA3_(fn, args) if (CV_CPU_HAS_SUPPORT_FMA3) return (opt_FMA3::fn args) +#else +# define CV_TRY_FMA3 0 +# define CV_CPU_FORCE_FMA3 0 +# define CV_CPU_HAS_SUPPORT_FMA3 0 +# define CV_CPU_CALL_FMA3(fn, args) +# define CV_CPU_CALL_FMA3_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_FMA3(fn, args, mode, ...) CV_CPU_CALL_FMA3(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX_512F +# define CV_TRY_AVX_512F 1 +# define CV_CPU_FORCE_AVX_512F 1 +# define CV_CPU_HAS_SUPPORT_AVX_512F 1 +# define CV_CPU_CALL_AVX_512F(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_AVX_512F_(fn, args) return (opt_AVX_512F::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX_512F +# define CV_TRY_AVX_512F 1 +# define CV_CPU_FORCE_AVX_512F 0 +# define CV_CPU_HAS_SUPPORT_AVX_512F (cv::checkHardwareSupport(CV_CPU_AVX_512F)) +# define CV_CPU_CALL_AVX_512F(fn, args) if (CV_CPU_HAS_SUPPORT_AVX_512F) return (opt_AVX_512F::fn args) +# define CV_CPU_CALL_AVX_512F_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX_512F) return (opt_AVX_512F::fn args) +#else +# define CV_TRY_AVX_512F 0 +# define CV_CPU_FORCE_AVX_512F 0 +# define CV_CPU_HAS_SUPPORT_AVX_512F 0 +# define CV_CPU_CALL_AVX_512F(fn, args) +# define CV_CPU_CALL_AVX_512F_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_AVX_512F(fn, args, mode, ...) CV_CPU_CALL_AVX_512F(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX512_SKX +# define CV_TRY_AVX512_SKX 1 +# define CV_CPU_FORCE_AVX512_SKX 1 +# define CV_CPU_HAS_SUPPORT_AVX512_SKX 1 +# define CV_CPU_CALL_AVX512_SKX(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_AVX512_SKX_(fn, args) return (opt_AVX512_SKX::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX512_SKX +# define CV_TRY_AVX512_SKX 1 +# define CV_CPU_FORCE_AVX512_SKX 0 +# define CV_CPU_HAS_SUPPORT_AVX512_SKX (cv::checkHardwareSupport(CV_CPU_AVX512_SKX)) +# define CV_CPU_CALL_AVX512_SKX(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_SKX) return (opt_AVX512_SKX::fn args) +# define CV_CPU_CALL_AVX512_SKX_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_SKX) return (opt_AVX512_SKX::fn args) +#else +# define CV_TRY_AVX512_SKX 0 +# define CV_CPU_FORCE_AVX512_SKX 0 +# define CV_CPU_HAS_SUPPORT_AVX512_SKX 0 +# define CV_CPU_CALL_AVX512_SKX(fn, args) +# define CV_CPU_CALL_AVX512_SKX_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_AVX512_SKX(fn, args, mode, ...) CV_CPU_CALL_AVX512_SKX(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_NEON +# define CV_TRY_NEON 1 +# define CV_CPU_FORCE_NEON 1 +# define CV_CPU_HAS_SUPPORT_NEON 1 +# define CV_CPU_CALL_NEON(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_NEON_(fn, args) return (opt_NEON::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_NEON +# define CV_TRY_NEON 1 +# define CV_CPU_FORCE_NEON 0 +# define CV_CPU_HAS_SUPPORT_NEON (cv::checkHardwareSupport(CV_CPU_NEON)) +# define CV_CPU_CALL_NEON(fn, args) if (CV_CPU_HAS_SUPPORT_NEON) return (opt_NEON::fn args) +# define CV_CPU_CALL_NEON_(fn, args) if (CV_CPU_HAS_SUPPORT_NEON) return (opt_NEON::fn args) +#else +# define CV_TRY_NEON 0 +# define CV_CPU_FORCE_NEON 0 +# define CV_CPU_HAS_SUPPORT_NEON 0 +# define CV_CPU_CALL_NEON(fn, args) +# define CV_CPU_CALL_NEON_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_NEON(fn, args, mode, ...) CV_CPU_CALL_NEON(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_VSX +# define CV_TRY_VSX 1 +# define CV_CPU_FORCE_VSX 1 +# define CV_CPU_HAS_SUPPORT_VSX 1 +# define CV_CPU_CALL_VSX(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_VSX_(fn, args) return (opt_VSX::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_VSX +# define CV_TRY_VSX 1 +# define CV_CPU_FORCE_VSX 0 +# define CV_CPU_HAS_SUPPORT_VSX (cv::checkHardwareSupport(CV_CPU_VSX)) +# define CV_CPU_CALL_VSX(fn, args) if (CV_CPU_HAS_SUPPORT_VSX) return (opt_VSX::fn args) +# define CV_CPU_CALL_VSX_(fn, args) if (CV_CPU_HAS_SUPPORT_VSX) return (opt_VSX::fn args) +#else +# define CV_TRY_VSX 0 +# define CV_CPU_FORCE_VSX 0 +# define CV_CPU_HAS_SUPPORT_VSX 0 +# define CV_CPU_CALL_VSX(fn, args) +# define CV_CPU_CALL_VSX_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_VSX(fn, args, mode, ...) CV_CPU_CALL_VSX(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_VSX3 +# define CV_TRY_VSX3 1 +# define CV_CPU_FORCE_VSX3 1 +# define CV_CPU_HAS_SUPPORT_VSX3 1 +# define CV_CPU_CALL_VSX3(fn, args) return (cpu_baseline::fn args) +# define CV_CPU_CALL_VSX3_(fn, args) return (opt_VSX3::fn args) +#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_VSX3 +# define CV_TRY_VSX3 1 +# define CV_CPU_FORCE_VSX3 0 +# define CV_CPU_HAS_SUPPORT_VSX3 (cv::checkHardwareSupport(CV_CPU_VSX3)) +# define CV_CPU_CALL_VSX3(fn, args) if (CV_CPU_HAS_SUPPORT_VSX3) return (opt_VSX3::fn args) +# define CV_CPU_CALL_VSX3_(fn, args) if (CV_CPU_HAS_SUPPORT_VSX3) return (opt_VSX3::fn args) +#else +# define CV_TRY_VSX3 0 +# define CV_CPU_FORCE_VSX3 0 +# define CV_CPU_HAS_SUPPORT_VSX3 0 +# define CV_CPU_CALL_VSX3(fn, args) +# define CV_CPU_CALL_VSX3_(fn, args) +#endif +#define __CV_CPU_DISPATCH_CHAIN_VSX3(fn, args, mode, ...) CV_CPU_CALL_VSX3(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) + +#define CV_CPU_CALL_BASELINE(fn, args) return (cpu_baseline::fn args) +#define __CV_CPU_DISPATCH_CHAIN_BASELINE(fn, args, mode, ...) CV_CPU_CALL_BASELINE(fn, args) /* last in sequence */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cvdef.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cvdef.h new file mode 100644 index 0000000000000000000000000000000000000000..1e8a697e90ae28175928b3ebe6a4793457c144bf --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cvdef.h @@ -0,0 +1,753 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_CVDEF_H +#define OPENCV_CORE_CVDEF_H + +//! @addtogroup core_utils +//! @{ + +#if !defined CV_DOXYGEN && !defined CV_IGNORE_DEBUG_BUILD_GUARD +#if (defined(_MSC_VER) && (defined(DEBUG) || defined(_DEBUG))) || \ + (defined(_GLIBCXX_DEBUG) || defined(_GLIBCXX_DEBUG_PEDANTIC)) +// Guard to prevent using of binary incompatible binaries / runtimes +// https://github.com/opencv/opencv/pull/9161 +#define CV__DEBUG_NS_BEGIN namespace debug_build_guard { +#define CV__DEBUG_NS_END } +namespace cv { namespace debug_build_guard { } using namespace debug_build_guard; } +#endif +#endif + +#ifndef CV__DEBUG_NS_BEGIN +#define CV__DEBUG_NS_BEGIN +#define CV__DEBUG_NS_END +#endif + + +#ifdef __OPENCV_BUILD +#include "cvconfig.h" +#endif + +#ifndef __CV_EXPAND +#define __CV_EXPAND(x) x +#endif + +#ifndef __CV_CAT +#define __CV_CAT__(x, y) x ## y +#define __CV_CAT_(x, y) __CV_CAT__(x, y) +#define __CV_CAT(x, y) __CV_CAT_(x, y) +#endif + +#define __CV_VA_NUM_ARGS_HELPER(_1, _2, _3, _4, _5, _6, _7, _8, _9, _10, N, ...) N +#define __CV_VA_NUM_ARGS(...) __CV_VA_NUM_ARGS_HELPER(__VA_ARGS__, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0) + +#if defined __GNUC__ +#define CV_Func __func__ +#elif defined _MSC_VER +#define CV_Func __FUNCTION__ +#else +#define CV_Func "" +#endif + +//! @cond IGNORED + +//////////////// static assert ///////////////// +#define CVAUX_CONCAT_EXP(a, b) a##b +#define CVAUX_CONCAT(a, b) CVAUX_CONCAT_EXP(a,b) + +#if defined(__clang__) +# ifndef __has_extension +# define __has_extension __has_feature /* compatibility, for older versions of clang */ +# endif +# if __has_extension(cxx_static_assert) +# define CV_StaticAssert(condition, reason) static_assert((condition), reason " " #condition) +# elif __has_extension(c_static_assert) +# define CV_StaticAssert(condition, reason) _Static_assert((condition), reason " " #condition) +# endif +#elif defined(__GNUC__) +# if (defined(__GXX_EXPERIMENTAL_CXX0X__) || __cplusplus >= 201103L) +# define CV_StaticAssert(condition, reason) static_assert((condition), reason " " #condition) +# endif +#elif defined(_MSC_VER) +# if _MSC_VER >= 1600 /* MSVC 10 */ +# define CV_StaticAssert(condition, reason) static_assert((condition), reason " " #condition) +# endif +#endif +#ifndef CV_StaticAssert +# if !defined(__clang__) && defined(__GNUC__) && (__GNUC__*100 + __GNUC_MINOR__ > 302) +# define CV_StaticAssert(condition, reason) ({ extern int __attribute__((error("CV_StaticAssert: " reason " " #condition))) CV_StaticAssert(); ((condition) ? 0 : CV_StaticAssert()); }) +# else + template struct CV_StaticAssert_failed; + template <> struct CV_StaticAssert_failed { enum { val = 1 }; }; + template struct CV_StaticAssert_test {}; +# define CV_StaticAssert(condition, reason)\ + typedef cv::CV_StaticAssert_test< sizeof(cv::CV_StaticAssert_failed< static_cast(condition) >) > CVAUX_CONCAT(CV_StaticAssert_failed_at_, __LINE__) +# endif +#endif + +// Suppress warning "-Wdeprecated-declarations" / C4996 +#if defined(_MSC_VER) + #define CV_DO_PRAGMA(x) __pragma(x) +#elif defined(__GNUC__) + #define CV_DO_PRAGMA(x) _Pragma (#x) +#else + #define CV_DO_PRAGMA(x) +#endif + +#ifdef _MSC_VER +#define CV_SUPPRESS_DEPRECATED_START \ + CV_DO_PRAGMA(warning(push)) \ + CV_DO_PRAGMA(warning(disable: 4996)) +#define CV_SUPPRESS_DEPRECATED_END CV_DO_PRAGMA(warning(pop)) +#elif defined (__clang__) || ((__GNUC__) && (__GNUC__*100 + __GNUC_MINOR__ > 405)) +#define CV_SUPPRESS_DEPRECATED_START \ + CV_DO_PRAGMA(GCC diagnostic push) \ + CV_DO_PRAGMA(GCC diagnostic ignored "-Wdeprecated-declarations") +#define CV_SUPPRESS_DEPRECATED_END CV_DO_PRAGMA(GCC diagnostic pop) +#else +#define CV_SUPPRESS_DEPRECATED_START +#define CV_SUPPRESS_DEPRECATED_END +#endif + +#define CV_UNUSED(name) (void)name + +#if defined __GNUC__ && !defined __EXCEPTIONS +#define CV_TRY +#define CV_CATCH(A, B) for (A B; false; ) +#define CV_CATCH_ALL if (false) +#define CV_THROW(A) abort() +#define CV_RETHROW() abort() +#else +#define CV_TRY try +#define CV_CATCH(A, B) catch(const A & B) +#define CV_CATCH_ALL catch(...) +#define CV_THROW(A) throw A +#define CV_RETHROW() throw +#endif + +//! @endcond + +// undef problematic defines sometimes defined by system headers (windows.h in particular) +#undef small +#undef min +#undef max +#undef abs +#undef Complex + +#include +#include "opencv2/core/hal/interface.h" + +#if defined __ICL +# define CV_ICC __ICL +#elif defined __ICC +# define CV_ICC __ICC +#elif defined __ECL +# define CV_ICC __ECL +#elif defined __ECC +# define CV_ICC __ECC +#elif defined __INTEL_COMPILER +# define CV_ICC __INTEL_COMPILER +#endif + +#ifndef CV_INLINE +# if defined __cplusplus +# define CV_INLINE static inline +# elif defined _MSC_VER +# define CV_INLINE __inline +# else +# define CV_INLINE static +# endif +#endif + +#if defined CV_DISABLE_OPTIMIZATION || (defined CV_ICC && !defined CV_ENABLE_UNROLLED) +# define CV_ENABLE_UNROLLED 0 +#else +# define CV_ENABLE_UNROLLED 1 +#endif + +#ifdef __GNUC__ +# define CV_DECL_ALIGNED(x) __attribute__ ((aligned (x))) +#elif defined _MSC_VER +# define CV_DECL_ALIGNED(x) __declspec(align(x)) +#else +# define CV_DECL_ALIGNED(x) +#endif + +/* CPU features and intrinsics support */ +#define CV_CPU_NONE 0 +#define CV_CPU_MMX 1 +#define CV_CPU_SSE 2 +#define CV_CPU_SSE2 3 +#define CV_CPU_SSE3 4 +#define CV_CPU_SSSE3 5 +#define CV_CPU_SSE4_1 6 +#define CV_CPU_SSE4_2 7 +#define CV_CPU_POPCNT 8 +#define CV_CPU_FP16 9 +#define CV_CPU_AVX 10 +#define CV_CPU_AVX2 11 +#define CV_CPU_FMA3 12 + +#define CV_CPU_AVX_512F 13 +#define CV_CPU_AVX_512BW 14 +#define CV_CPU_AVX_512CD 15 +#define CV_CPU_AVX_512DQ 16 +#define CV_CPU_AVX_512ER 17 +#define CV_CPU_AVX_512IFMA512 18 // deprecated +#define CV_CPU_AVX_512IFMA 18 +#define CV_CPU_AVX_512PF 19 +#define CV_CPU_AVX_512VBMI 20 +#define CV_CPU_AVX_512VL 21 + +#define CV_CPU_NEON 100 + +#define CV_CPU_VSX 200 +#define CV_CPU_VSX3 201 + +// CPU features groups +#define CV_CPU_AVX512_SKX 256 + +// when adding to this list remember to update the following enum +#define CV_HARDWARE_MAX_FEATURE 512 + +/** @brief Available CPU features. +*/ +enum CpuFeatures { + CPU_MMX = 1, + CPU_SSE = 2, + CPU_SSE2 = 3, + CPU_SSE3 = 4, + CPU_SSSE3 = 5, + CPU_SSE4_1 = 6, + CPU_SSE4_2 = 7, + CPU_POPCNT = 8, + CPU_FP16 = 9, + CPU_AVX = 10, + CPU_AVX2 = 11, + CPU_FMA3 = 12, + + CPU_AVX_512F = 13, + CPU_AVX_512BW = 14, + CPU_AVX_512CD = 15, + CPU_AVX_512DQ = 16, + CPU_AVX_512ER = 17, + CPU_AVX_512IFMA512 = 18, // deprecated + CPU_AVX_512IFMA = 18, + CPU_AVX_512PF = 19, + CPU_AVX_512VBMI = 20, + CPU_AVX_512VL = 21, + + CPU_NEON = 100, + + CPU_VSX = 200, + CPU_VSX3 = 201, + + CPU_AVX512_SKX = 256, //!< Skylake-X with AVX-512F/CD/BW/DQ/VL + + CPU_MAX_FEATURE = 512 // see CV_HARDWARE_MAX_FEATURE +}; + + +#include "cv_cpu_dispatch.h" + + +/* fundamental constants */ +#define CV_PI 3.1415926535897932384626433832795 +#define CV_2PI 6.283185307179586476925286766559 +#define CV_LOG2 0.69314718055994530941723212145818 + +#if defined __ARM_FP16_FORMAT_IEEE \ + && !defined __CUDACC__ +# define CV_FP16_TYPE 1 +#else +# define CV_FP16_TYPE 0 +#endif + +typedef union Cv16suf +{ + short i; + ushort u; +#if CV_FP16_TYPE + __fp16 h; +#endif +} +Cv16suf; + +typedef union Cv32suf +{ + int i; + unsigned u; + float f; +} +Cv32suf; + +typedef union Cv64suf +{ + int64 i; + uint64 u; + double f; +} +Cv64suf; + +#define OPENCV_ABI_COMPATIBILITY 300 + +#ifdef __OPENCV_BUILD +# define DISABLE_OPENCV_24_COMPATIBILITY +# define OPENCV_DISABLE_DEPRECATED_COMPATIBILITY +#endif + +#ifdef CVAPI_EXPORTS +# if (defined _WIN32 || defined WINCE || defined __CYGWIN__) +# define CV_EXPORTS __declspec(dllexport) +# elif defined __GNUC__ && __GNUC__ >= 4 +# define CV_EXPORTS __attribute__ ((visibility ("default"))) +# endif +#endif + +#ifndef CV_EXPORTS +# define CV_EXPORTS +#endif + +#ifdef _MSC_VER +# define CV_EXPORTS_TEMPLATE +#else +# define CV_EXPORTS_TEMPLATE CV_EXPORTS +#endif + +#ifndef CV_DEPRECATED +# if defined(__GNUC__) +# define CV_DEPRECATED __attribute__ ((deprecated)) +# elif defined(_MSC_VER) +# define CV_DEPRECATED __declspec(deprecated) +# else +# define CV_DEPRECATED +# endif +#endif + +#ifndef CV_DEPRECATED_EXTERNAL +# if defined(__OPENCV_BUILD) +# define CV_DEPRECATED_EXTERNAL /* nothing */ +# else +# define CV_DEPRECATED_EXTERNAL CV_DEPRECATED +# endif +#endif + + +#ifndef CV_EXTERN_C +# ifdef __cplusplus +# define CV_EXTERN_C extern "C" +# else +# define CV_EXTERN_C +# endif +#endif + +/* special informative macros for wrapper generators */ +#define CV_EXPORTS_W CV_EXPORTS +#define CV_EXPORTS_W_SIMPLE CV_EXPORTS +#define CV_EXPORTS_AS(synonym) CV_EXPORTS +#define CV_EXPORTS_W_MAP CV_EXPORTS +#define CV_IN_OUT +#define CV_OUT +#define CV_PROP +#define CV_PROP_RW +#define CV_WRAP +#define CV_WRAP_AS(synonym) + +/****************************************************************************************\ +* Matrix type (Mat) * +\****************************************************************************************/ + +#define CV_MAT_CN_MASK ((CV_CN_MAX - 1) << CV_CN_SHIFT) +#define CV_MAT_CN(flags) ((((flags) & CV_MAT_CN_MASK) >> CV_CN_SHIFT) + 1) +#define CV_MAT_TYPE_MASK (CV_DEPTH_MAX*CV_CN_MAX - 1) +#define CV_MAT_TYPE(flags) ((flags) & CV_MAT_TYPE_MASK) +#define CV_MAT_CONT_FLAG_SHIFT 14 +#define CV_MAT_CONT_FLAG (1 << CV_MAT_CONT_FLAG_SHIFT) +#define CV_IS_MAT_CONT(flags) ((flags) & CV_MAT_CONT_FLAG) +#define CV_IS_CONT_MAT CV_IS_MAT_CONT +#define CV_SUBMAT_FLAG_SHIFT 15 +#define CV_SUBMAT_FLAG (1 << CV_SUBMAT_FLAG_SHIFT) +#define CV_IS_SUBMAT(flags) ((flags) & CV_MAT_SUBMAT_FLAG) + +/** Size of each channel item, + 0x8442211 = 1000 0100 0100 0010 0010 0001 0001 ~ array of sizeof(arr_type_elem) */ +#define CV_ELEM_SIZE1(type) \ + ((((sizeof(size_t)<<28)|0x8442211) >> CV_MAT_DEPTH(type)*4) & 15) + +/** 0x3a50 = 11 10 10 01 01 00 00 ~ array of log2(sizeof(arr_type_elem)) */ +#define CV_ELEM_SIZE(type) \ + (CV_MAT_CN(type) << ((((sizeof(size_t)/4+1)*16384|0x3a50) >> CV_MAT_DEPTH(type)*2) & 3)) + +#ifndef MIN +# define MIN(a,b) ((a) > (b) ? (b) : (a)) +#endif + +#ifndef MAX +# define MAX(a,b) ((a) < (b) ? (b) : (a)) +#endif + +/****************************************************************************************\ +* static analysys * +\****************************************************************************************/ + +// In practice, some macro are not processed correctly (noreturn is not detected). +// We need to use simplified definition for them. +#ifndef CV_STATIC_ANALYSIS +# if defined(__KLOCWORK__) || defined(__clang_analyzer__) || defined(__COVERITY__) +# define CV_STATIC_ANALYSIS 1 +# endif +#else +# if defined(CV_STATIC_ANALYSIS) && !(__CV_CAT(1, CV_STATIC_ANALYSIS) == 1) // defined and not empty +# if 0 == CV_STATIC_ANALYSIS +# undef CV_STATIC_ANALYSIS +# endif +# endif +#endif + +/****************************************************************************************\ +* Thread sanitizer * +\****************************************************************************************/ +#ifndef CV_THREAD_SANITIZER +# if defined(__has_feature) +# if __has_feature(thread_sanitizer) +# define CV_THREAD_SANITIZER +# endif +# endif +#endif + +/****************************************************************************************\ +* exchange-add operation for atomic operations on reference counters * +\****************************************************************************************/ + +#ifdef CV_XADD + // allow to use user-defined macro +#elif defined __GNUC__ || defined __clang__ +# if defined __clang__ && __clang_major__ >= 3 && !defined __ANDROID__ && !defined __EMSCRIPTEN__ && !defined(__CUDACC__) +# ifdef __ATOMIC_ACQ_REL +# define CV_XADD(addr, delta) __c11_atomic_fetch_add((_Atomic(int)*)(addr), delta, __ATOMIC_ACQ_REL) +# else +# define CV_XADD(addr, delta) __atomic_fetch_add((_Atomic(int)*)(addr), delta, 4) +# endif +# else +# if defined __ATOMIC_ACQ_REL && !defined __clang__ + // version for gcc >= 4.7 +# define CV_XADD(addr, delta) (int)__atomic_fetch_add((unsigned*)(addr), (unsigned)(delta), __ATOMIC_ACQ_REL) +# else +# define CV_XADD(addr, delta) (int)__sync_fetch_and_add((unsigned*)(addr), (unsigned)(delta)) +# endif +# endif +#elif defined _MSC_VER && !defined RC_INVOKED +# include +# define CV_XADD(addr, delta) (int)_InterlockedExchangeAdd((long volatile*)addr, delta) +#else + CV_INLINE CV_XADD(int* addr, int delta) { int tmp = *addr; *addr += delta; return tmp; } +#endif + + +/****************************************************************************************\ +* CV_NORETURN attribute * +\****************************************************************************************/ + +#ifndef CV_NORETURN +# if defined(__GNUC__) +# define CV_NORETURN __attribute__((__noreturn__)) +# elif defined(_MSC_VER) && (_MSC_VER >= 1300) +# define CV_NORETURN __declspec(noreturn) +# else +# define CV_NORETURN /* nothing by default */ +# endif +#endif + + +/****************************************************************************************\ +* CV_NODISCARD attribute * +* encourages the compiler to issue a warning if the return value is discarded (C++17) * +\****************************************************************************************/ +#ifndef CV_NODISCARD +# if defined(__GNUC__) +# define CV_NODISCARD __attribute__((__warn_unused_result__)) // at least available with GCC 3.4 +# elif defined(__clang__) && defined(__has_attribute) +# if __has_attribute(__warn_unused_result__) +# define CV_NODISCARD __attribute__((__warn_unused_result__)) +# endif +# endif +#endif +#ifndef CV_NODISCARD +# define CV_NODISCARD /* nothing by default */ +#endif + + +/****************************************************************************************\ +* C++ 11 * +\****************************************************************************************/ +#ifndef CV_CXX11 +# if __cplusplus >= 201103L || (defined(_MSC_VER) && _MSC_VER >= 1800) +# define CV_CXX11 1 +# endif +#else +# if CV_CXX11 == 0 +# undef CV_CXX11 +# endif +#endif + + +/****************************************************************************************\ +* C++ Move semantics * +\****************************************************************************************/ + +#ifndef CV_CXX_MOVE_SEMANTICS +# if __cplusplus >= 201103L || defined(__GXX_EXPERIMENTAL_CXX0X__) || (defined(_MSC_VER) && _MSC_VER >= 1600) +# define CV_CXX_MOVE_SEMANTICS 1 +# elif defined(__clang) +# if __has_feature(cxx_rvalue_references) +# define CV_CXX_MOVE_SEMANTICS 1 +# endif +# endif +#else +# if CV_CXX_MOVE_SEMANTICS == 0 +# undef CV_CXX_MOVE_SEMANTICS +# endif +#endif + +/****************************************************************************************\ +* C++11 std::array * +\****************************************************************************************/ + +#ifndef CV_CXX_STD_ARRAY +# if __cplusplus >= 201103L || (defined(_MSC_VER) && _MSC_VER >= 1900/*MSVS 2015*/) +# define CV_CXX_STD_ARRAY 1 +# include +# endif +#else +# if CV_CXX_STD_ARRAY == 0 +# undef CV_CXX_STD_ARRAY +# endif +#endif + + +/****************************************************************************************\ +* C++11 override / final * +\****************************************************************************************/ + +#ifndef CV_OVERRIDE +# ifdef CV_CXX11 +# define CV_OVERRIDE override +# endif +#endif +#ifndef CV_OVERRIDE +# define CV_OVERRIDE +#endif + +#ifndef CV_FINAL +# ifdef CV_CXX11 +# define CV_FINAL final +# endif +#endif +#ifndef CV_FINAL +# define CV_FINAL +#endif + + + +// Integer types portatibility +#ifdef OPENCV_STDINT_HEADER +#include OPENCV_STDINT_HEADER +#elif defined(__cplusplus) +#if defined(_MSC_VER) && _MSC_VER < 1600 /* MSVS 2010 */ +namespace cv { +typedef signed char int8_t; +typedef unsigned char uint8_t; +typedef signed short int16_t; +typedef unsigned short uint16_t; +typedef signed int int32_t; +typedef unsigned int uint32_t; +typedef signed __int64 int64_t; +typedef unsigned __int64 uint64_t; +} +#elif defined(_MSC_VER) || __cplusplus >= 201103L +#include +namespace cv { +using std::int8_t; +using std::uint8_t; +using std::int16_t; +using std::uint16_t; +using std::int32_t; +using std::uint32_t; +using std::int64_t; +using std::uint64_t; +} +#else +#include +namespace cv { +typedef ::int8_t int8_t; +typedef ::uint8_t uint8_t; +typedef ::int16_t int16_t; +typedef ::uint16_t uint16_t; +typedef ::int32_t int32_t; +typedef ::uint32_t uint32_t; +typedef ::int64_t int64_t; +typedef ::uint64_t uint64_t; +} +#endif +#else // pure C +#include +#endif + +#ifdef __cplusplus +namespace cv +{ + +class float16_t +{ +public: +#if CV_FP16_TYPE + + float16_t() {} + explicit float16_t(float x) { h = (__fp16)x; } + operator float() const { return (float)h; } + static float16_t fromBits(ushort w) + { + Cv16suf u; + u.u = w; + float16_t result; + result.h = u.h; + return result; + } + static float16_t zero() + { + float16_t result; + result.h = (__fp16)0; + return result; + } + ushort bits() const + { + Cv16suf u; + u.h = h; + return u.u; + } +protected: + __fp16 h; + +#else + float16_t() {} + explicit float16_t(float x) + { + #if CV_AVX2 + __m128 v = _mm_load_ss(&x); + w = (ushort)_mm_cvtsi128_si32(_mm_cvtps_ph(v, 0)); + #else + Cv32suf in; + in.f = x; + unsigned sign = in.u & 0x80000000; + in.u ^= sign; + + if( in.u >= 0x47800000 ) + w = (ushort)(in.u > 0x7f800000 ? 0x7e00 : 0x7c00); + else + { + if (in.u < 0x38800000) + { + in.f += 0.5f; + w = (ushort)(in.u - 0x3f000000); + } + else + { + unsigned t = in.u + 0xc8000fff; + w = (ushort)((t + ((in.u >> 13) & 1)) >> 13); + } + } + + w = (ushort)(w | (sign >> 16)); + #endif + } + + operator float() const + { + #if CV_AVX2 + float f; + _mm_store_ss(&f, _mm_cvtph_ps(_mm_cvtsi32_si128(w))); + return f; + #else + Cv32suf out; + + unsigned t = ((w & 0x7fff) << 13) + 0x38000000; + unsigned sign = (w & 0x8000) << 16; + unsigned e = w & 0x7c00; + + out.u = t + (1 << 23); + out.u = (e >= 0x7c00 ? t + 0x38000000 : + e == 0 ? (out.f -= 6.103515625e-05f, out.u) : t) | sign; + return out.f; + #endif + } + + static float16_t fromBits(ushort b) + { + float16_t result; + result.w = b; + return result; + } + static float16_t zero() + { + float16_t result; + result.w = (ushort)0; + return result; + } + ushort bits() const { return w; } +protected: + ushort w; + +#endif +}; + +} +#endif + +//! @} + +#ifndef __cplusplus +#include "opencv2/core/fast_math.hpp" // define cvRound(double) +#endif + +#endif // OPENCV_CORE_CVDEF_H diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cvstd.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cvstd.hpp new file mode 100644 index 0000000000000000000000000000000000000000..0a3f553ab89f986dac83bbdc46dbdb36e6c0a7c7 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cvstd.hpp @@ -0,0 +1,1040 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_CVSTD_HPP +#define OPENCV_CORE_CVSTD_HPP + +#ifndef __cplusplus +# error cvstd.hpp header must be compiled as C++ +#endif + +#include "opencv2/core/cvdef.h" +#include +#include +#include + +#include + +// import useful primitives from stl +# include +# include +# include //for abs(int) +# include + +namespace cv +{ + static inline uchar abs(uchar a) { return a; } + static inline ushort abs(ushort a) { return a; } + static inline unsigned abs(unsigned a) { return a; } + static inline uint64 abs(uint64 a) { return a; } + + using std::min; + using std::max; + using std::abs; + using std::swap; + using std::sqrt; + using std::exp; + using std::pow; + using std::log; +} + +namespace cv { + +//! @addtogroup core_utils +//! @{ + +//////////////////////////// memory management functions //////////////////////////// + +/** @brief Allocates an aligned memory buffer. + +The function allocates the buffer of the specified size and returns it. When the buffer size is 16 +bytes or more, the returned buffer is aligned to 16 bytes. +@param bufSize Allocated buffer size. + */ +CV_EXPORTS void* fastMalloc(size_t bufSize); + +/** @brief Deallocates a memory buffer. + +The function deallocates the buffer allocated with fastMalloc . If NULL pointer is passed, the +function does nothing. C version of the function clears the pointer *pptr* to avoid problems with +double memory deallocation. +@param ptr Pointer to the allocated buffer. + */ +CV_EXPORTS void fastFree(void* ptr); + +/*! + The STL-compilant memory Allocator based on cv::fastMalloc() and cv::fastFree() +*/ +template class Allocator +{ +public: + typedef _Tp value_type; + typedef value_type* pointer; + typedef const value_type* const_pointer; + typedef value_type& reference; + typedef const value_type& const_reference; + typedef size_t size_type; + typedef ptrdiff_t difference_type; + template class rebind { typedef Allocator other; }; + + explicit Allocator() {} + ~Allocator() {} + explicit Allocator(Allocator const&) {} + template + explicit Allocator(Allocator const&) {} + + // address + pointer address(reference r) { return &r; } + const_pointer address(const_reference r) { return &r; } + + pointer allocate(size_type count, const void* =0) { return reinterpret_cast(fastMalloc(count * sizeof (_Tp))); } + void deallocate(pointer p, size_type) { fastFree(p); } + + void construct(pointer p, const _Tp& v) { new(static_cast(p)) _Tp(v); } + void destroy(pointer p) { p->~_Tp(); } + + size_type max_size() const { return cv::max(static_cast<_Tp>(-1)/sizeof(_Tp), 1); } +}; + +//! @} core_utils + +//! @cond IGNORED + +namespace detail +{ + +// Metafunction to avoid taking a reference to void. +template +struct RefOrVoid { typedef T& type; }; + +template<> +struct RefOrVoid{ typedef void type; }; + +template<> +struct RefOrVoid{ typedef const void type; }; + +template<> +struct RefOrVoid{ typedef volatile void type; }; + +template<> +struct RefOrVoid{ typedef const volatile void type; }; + +// This class would be private to Ptr, if it didn't have to be a non-template. +struct PtrOwner; + +} + +template +struct DefaultDeleter +{ + void operator () (Y* p) const; +}; + +//! @endcond + +//! @addtogroup core_basic +//! @{ + +/** @brief Template class for smart pointers with shared ownership + +A Ptr\ pretends to be a pointer to an object of type T. Unlike an ordinary pointer, however, the +object will be automatically cleaned up once all Ptr instances pointing to it are destroyed. + +Ptr is similar to boost::shared_ptr that is part of the Boost library +() and std::shared_ptr from +the [C++11](http://en.wikipedia.org/wiki/C++11) standard. + +This class provides the following advantages: +- Default constructor, copy constructor, and assignment operator for an arbitrary C++ class or C + structure. For some objects, like files, windows, mutexes, sockets, and others, a copy + constructor or an assignment operator are difficult to define. For some other objects, like + complex classifiers in OpenCV, copy constructors are absent and not easy to implement. Finally, + some of complex OpenCV and your own data structures may be written in C. However, copy + constructors and default constructors can simplify programming a lot. Besides, they are often + required (for example, by STL containers). By using a Ptr to such an object instead of the + object itself, you automatically get all of the necessary constructors and the assignment + operator. +- *O(1)* complexity of the above-mentioned operations. While some structures, like std::vector, + provide a copy constructor and an assignment operator, the operations may take a considerable + amount of time if the data structures are large. But if the structures are put into a Ptr, the + overhead is small and independent of the data size. +- Automatic and customizable cleanup, even for C structures. See the example below with FILE\*. +- Heterogeneous collections of objects. The standard STL and most other C++ and OpenCV containers + can store only objects of the same type and the same size. The classical solution to store + objects of different types in the same container is to store pointers to the base class (Base\*) + instead but then you lose the automatic memory management. Again, by using Ptr\ instead + of raw pointers, you can solve the problem. + +A Ptr is said to *own* a pointer - that is, for each Ptr there is a pointer that will be deleted +once all Ptr instances that own it are destroyed. The owned pointer may be null, in which case +nothing is deleted. Each Ptr also *stores* a pointer. The stored pointer is the pointer the Ptr +pretends to be; that is, the one you get when you use Ptr::get or the conversion to T\*. It's +usually the same as the owned pointer, but if you use casts or the general shared-ownership +constructor, the two may diverge: the Ptr will still own the original pointer, but will itself point +to something else. + +The owned pointer is treated as a black box. The only thing Ptr needs to know about it is how to +delete it. This knowledge is encapsulated in the *deleter* - an auxiliary object that is associated +with the owned pointer and shared between all Ptr instances that own it. The default deleter is an +instance of DefaultDeleter, which uses the standard C++ delete operator; as such it will work with +any pointer allocated with the standard new operator. + +However, if the pointer must be deleted in a different way, you must specify a custom deleter upon +Ptr construction. A deleter is simply a callable object that accepts the pointer as its sole +argument. For example, if you want to wrap FILE, you may do so as follows: +@code + Ptr f(fopen("myfile.txt", "w"), fclose); + if(!f) throw ...; + fprintf(f, ....); + ... + // the file will be closed automatically by f's destructor. +@endcode +Alternatively, if you want all pointers of a particular type to be deleted the same way, you can +specialize DefaultDeleter::operator() for that type, like this: +@code + namespace cv { + template<> void DefaultDeleter::operator ()(FILE * obj) const + { + fclose(obj); + } + } +@endcode +For convenience, the following types from the OpenCV C API already have such a specialization that +calls the appropriate release function: +- CvCapture +- CvFileStorage +- CvHaarClassifierCascade +- CvMat +- CvMatND +- CvMemStorage +- CvSparseMat +- CvVideoWriter +- IplImage +@note The shared ownership mechanism is implemented with reference counting. As such, cyclic +ownership (e.g. when object a contains a Ptr to object b, which contains a Ptr to object a) will +lead to all involved objects never being cleaned up. Avoid such situations. +@note It is safe to concurrently read (but not write) a Ptr instance from multiple threads and +therefore it is normally safe to use it in multi-threaded applications. The same is true for Mat and +other C++ OpenCV classes that use internal reference counts. +*/ +template +struct Ptr +{ + /** Generic programming support. */ + typedef T element_type; + + /** The default constructor creates a null Ptr - one that owns and stores a null pointer. + */ + Ptr(); + + /** + If p is null, these are equivalent to the default constructor. + Otherwise, these constructors assume ownership of p - that is, the created Ptr owns and stores p + and assumes it is the sole owner of it. Don't use them if p is already owned by another Ptr, or + else p will get deleted twice. + With the first constructor, DefaultDeleter\() becomes the associated deleter (so p will + eventually be deleted with the standard delete operator). Y must be a complete type at the point + of invocation. + With the second constructor, d becomes the associated deleter. + Y\* must be convertible to T\*. + @param p Pointer to own. + @note It is often easier to use makePtr instead. + */ + template +#ifdef DISABLE_OPENCV_24_COMPATIBILITY + explicit +#endif + Ptr(Y* p); + + /** @overload + @param d Deleter to use for the owned pointer. + @param p Pointer to own. + */ + template + Ptr(Y* p, D d); + + /** + These constructors create a Ptr that shares ownership with another Ptr - that is, own the same + pointer as o. + With the first two, the same pointer is stored, as well; for the second, Y\* must be convertible + to T\*. + With the third, p is stored, and Y may be any type. This constructor allows to have completely + unrelated owned and stored pointers, and should be used with care to avoid confusion. A relatively + benign use is to create a non-owning Ptr, like this: + @code + ptr = Ptr(Ptr(), dont_delete_me); // owns nothing; will not delete the pointer. + @endcode + @param o Ptr to share ownership with. + */ + Ptr(const Ptr& o); + + /** @overload + @param o Ptr to share ownership with. + */ + template + Ptr(const Ptr& o); + + /** @overload + @param o Ptr to share ownership with. + @param p Pointer to store. + */ + template + Ptr(const Ptr& o, T* p); + + /** The destructor is equivalent to calling Ptr::release. */ + ~Ptr(); + + /** + Assignment replaces the current Ptr instance with one that owns and stores same pointers as o and + then destroys the old instance. + @param o Ptr to share ownership with. + */ + Ptr& operator = (const Ptr& o); + + /** @overload */ + template + Ptr& operator = (const Ptr& o); + + /** If no other Ptr instance owns the owned pointer, deletes it with the associated deleter. Then sets + both the owned and the stored pointers to NULL. + */ + void release(); + + /** + `ptr.reset(...)` is equivalent to `ptr = Ptr(...)`. + @param p Pointer to own. + */ + template + void reset(Y* p); + + /** @overload + @param d Deleter to use for the owned pointer. + @param p Pointer to own. + */ + template + void reset(Y* p, D d); + + /** + Swaps the owned and stored pointers (and deleters, if any) of this and o. + @param o Ptr to swap with. + */ + void swap(Ptr& o); + + /** Returns the stored pointer. */ + T* get() const; + + /** Ordinary pointer emulation. */ + typename detail::RefOrVoid::type operator * () const; + + /** Ordinary pointer emulation. */ + T* operator -> () const; + + /** Equivalent to get(). */ + operator T* () const; + + /** ptr.empty() is equivalent to `!ptr.get()`. */ + bool empty() const; + + /** Returns a Ptr that owns the same pointer as this, and stores the same + pointer as this, except converted via static_cast to Y*. + */ + template + Ptr staticCast() const; + + /** Ditto for const_cast. */ + template + Ptr constCast() const; + + /** Ditto for dynamic_cast. */ + template + Ptr dynamicCast() const; + +#ifdef CV_CXX_MOVE_SEMANTICS + Ptr(Ptr&& o); + Ptr& operator = (Ptr&& o); +#endif + +private: + detail::PtrOwner* owner; + T* stored; + + template + friend struct Ptr; // have to do this for the cross-type copy constructor +}; + +/** Equivalent to ptr1.swap(ptr2). Provided to help write generic algorithms. */ +template +void swap(Ptr& ptr1, Ptr& ptr2); + +/** Return whether ptr1.get() and ptr2.get() are equal and not equal, respectively. */ +template +bool operator == (const Ptr& ptr1, const Ptr& ptr2); +template +bool operator != (const Ptr& ptr1, const Ptr& ptr2); + +/** `makePtr(...)` is equivalent to `Ptr(new T(...))`. It is shorter than the latter, and it's +marginally safer than using a constructor or Ptr::reset, since it ensures that the owned pointer +is new and thus not owned by any other Ptr instance. +Unfortunately, perfect forwarding is impossible to implement in C++03, and so makePtr is limited +to constructors of T that have up to 10 arguments, none of which are non-const references. + */ +template +Ptr makePtr(); +/** @overload */ +template +Ptr makePtr(const A1& a1); +/** @overload */ +template +Ptr makePtr(const A1& a1, const A2& a2); +/** @overload */ +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3); +/** @overload */ +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4); +/** @overload */ +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5); +/** @overload */ +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6); +/** @overload */ +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7); +/** @overload */ +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8); +/** @overload */ +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8, const A9& a9); +/** @overload */ +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8, const A9& a9, const A10& a10); + +//////////////////////////////// string class //////////////////////////////// + +class CV_EXPORTS FileNode; //for string constructor from FileNode + +class CV_EXPORTS String +{ +public: + typedef char value_type; + typedef char& reference; + typedef const char& const_reference; + typedef char* pointer; + typedef const char* const_pointer; + typedef ptrdiff_t difference_type; + typedef size_t size_type; + typedef char* iterator; + typedef const char* const_iterator; + + static const size_t npos = size_t(-1); + + String(); + String(const String& str); + String(const String& str, size_t pos, size_t len = npos); + String(const char* s); + String(const char* s, size_t n); + String(size_t n, char c); + String(const char* first, const char* last); + template String(Iterator first, Iterator last); + explicit String(const FileNode& fn); + ~String(); + + String& operator=(const String& str); + String& operator=(const char* s); + String& operator=(char c); + + String& operator+=(const String& str); + String& operator+=(const char* s); + String& operator+=(char c); + + size_t size() const; + size_t length() const; + + char operator[](size_t idx) const; + char operator[](int idx) const; + + const char* begin() const; + const char* end() const; + + const char* c_str() const; + + bool empty() const; + void clear(); + + int compare(const char* s) const; + int compare(const String& str) const; + + void swap(String& str); + String substr(size_t pos = 0, size_t len = npos) const; + + size_t find(const char* s, size_t pos, size_t n) const; + size_t find(char c, size_t pos = 0) const; + size_t find(const String& str, size_t pos = 0) const; + size_t find(const char* s, size_t pos = 0) const; + + size_t rfind(const char* s, size_t pos, size_t n) const; + size_t rfind(char c, size_t pos = npos) const; + size_t rfind(const String& str, size_t pos = npos) const; + size_t rfind(const char* s, size_t pos = npos) const; + + size_t find_first_of(const char* s, size_t pos, size_t n) const; + size_t find_first_of(char c, size_t pos = 0) const; + size_t find_first_of(const String& str, size_t pos = 0) const; + size_t find_first_of(const char* s, size_t pos = 0) const; + + size_t find_last_of(const char* s, size_t pos, size_t n) const; + size_t find_last_of(char c, size_t pos = npos) const; + size_t find_last_of(const String& str, size_t pos = npos) const; + size_t find_last_of(const char* s, size_t pos = npos) const; + + friend String operator+ (const String& lhs, const String& rhs); + friend String operator+ (const String& lhs, const char* rhs); + friend String operator+ (const char* lhs, const String& rhs); + friend String operator+ (const String& lhs, char rhs); + friend String operator+ (char lhs, const String& rhs); + + String toLowerCase() const; + + String(const std::string& str); + String(const std::string& str, size_t pos, size_t len = npos); + String& operator=(const std::string& str); + String& operator+=(const std::string& str); + operator std::string() const; + + friend String operator+ (const String& lhs, const std::string& rhs); + friend String operator+ (const std::string& lhs, const String& rhs); + +private: + char* cstr_; + size_t len_; + + char* allocate(size_t len); // len without trailing 0 + void deallocate(); + + String(int); // disabled and invalid. Catch invalid usages like, commandLineParser.has(0) problem +}; + +//! @} core_basic + +////////////////////////// cv::String implementation ///////////////////////// + +//! @cond IGNORED + +inline +String::String() + : cstr_(0), len_(0) +{} + +inline +String::String(const String& str) + : cstr_(str.cstr_), len_(str.len_) +{ + if (cstr_) + CV_XADD(((int*)cstr_)-1, 1); +} + +inline +String::String(const String& str, size_t pos, size_t len) + : cstr_(0), len_(0) +{ + pos = min(pos, str.len_); + len = min(str.len_ - pos, len); + if (!len) return; + if (len == str.len_) + { + CV_XADD(((int*)str.cstr_)-1, 1); + cstr_ = str.cstr_; + len_ = str.len_; + return; + } + memcpy(allocate(len), str.cstr_ + pos, len); +} + +inline +String::String(const char* s) + : cstr_(0), len_(0) +{ + if (!s) return; + size_t len = strlen(s); + if (!len) return; + memcpy(allocate(len), s, len); +} + +inline +String::String(const char* s, size_t n) + : cstr_(0), len_(0) +{ + if (!n) return; + if (!s) return; + memcpy(allocate(n), s, n); +} + +inline +String::String(size_t n, char c) + : cstr_(0), len_(0) +{ + if (!n) return; + memset(allocate(n), c, n); +} + +inline +String::String(const char* first, const char* last) + : cstr_(0), len_(0) +{ + size_t len = (size_t)(last - first); + if (!len) return; + memcpy(allocate(len), first, len); +} + +template inline +String::String(Iterator first, Iterator last) + : cstr_(0), len_(0) +{ + size_t len = (size_t)(last - first); + if (!len) return; + char* str = allocate(len); + while (first != last) + { + *str++ = *first; + ++first; + } +} + +inline +String::~String() +{ + deallocate(); +} + +inline +String& String::operator=(const String& str) +{ + if (&str == this) return *this; + + deallocate(); + if (str.cstr_) CV_XADD(((int*)str.cstr_)-1, 1); + cstr_ = str.cstr_; + len_ = str.len_; + return *this; +} + +inline +String& String::operator=(const char* s) +{ + deallocate(); + if (!s) return *this; + size_t len = strlen(s); + if (len) memcpy(allocate(len), s, len); + return *this; +} + +inline +String& String::operator=(char c) +{ + deallocate(); + allocate(1)[0] = c; + return *this; +} + +inline +String& String::operator+=(const String& str) +{ + *this = *this + str; + return *this; +} + +inline +String& String::operator+=(const char* s) +{ + *this = *this + s; + return *this; +} + +inline +String& String::operator+=(char c) +{ + *this = *this + c; + return *this; +} + +inline +size_t String::size() const +{ + return len_; +} + +inline +size_t String::length() const +{ + return len_; +} + +inline +char String::operator[](size_t idx) const +{ + return cstr_[idx]; +} + +inline +char String::operator[](int idx) const +{ + return cstr_[idx]; +} + +inline +const char* String::begin() const +{ + return cstr_; +} + +inline +const char* String::end() const +{ + return len_ ? cstr_ + len_ : NULL; +} + +inline +bool String::empty() const +{ + return len_ == 0; +} + +inline +const char* String::c_str() const +{ + return cstr_ ? cstr_ : ""; +} + +inline +void String::swap(String& str) +{ + cv::swap(cstr_, str.cstr_); + cv::swap(len_, str.len_); +} + +inline +void String::clear() +{ + deallocate(); +} + +inline +int String::compare(const char* s) const +{ + if (cstr_ == s) return 0; + return strcmp(c_str(), s); +} + +inline +int String::compare(const String& str) const +{ + if (cstr_ == str.cstr_) return 0; + return strcmp(c_str(), str.c_str()); +} + +inline +String String::substr(size_t pos, size_t len) const +{ + return String(*this, pos, len); +} + +inline +size_t String::find(const char* s, size_t pos, size_t n) const +{ + if (n == 0 || pos + n > len_) return npos; + const char* lmax = cstr_ + len_ - n; + for (const char* i = cstr_ + pos; i <= lmax; ++i) + { + size_t j = 0; + while (j < n && s[j] == i[j]) ++j; + if (j == n) return (size_t)(i - cstr_); + } + return npos; +} + +inline +size_t String::find(char c, size_t pos) const +{ + return find(&c, pos, 1); +} + +inline +size_t String::find(const String& str, size_t pos) const +{ + return find(str.c_str(), pos, str.len_); +} + +inline +size_t String::find(const char* s, size_t pos) const +{ + if (pos >= len_ || !s[0]) return npos; + const char* lmax = cstr_ + len_; + for (const char* i = cstr_ + pos; i < lmax; ++i) + { + size_t j = 0; + while (s[j] && s[j] == i[j]) + { if(i + j >= lmax) return npos; + ++j; + } + if (!s[j]) return (size_t)(i - cstr_); + } + return npos; +} + +inline +size_t String::rfind(const char* s, size_t pos, size_t n) const +{ + if (n > len_) return npos; + if (pos > len_ - n) pos = len_ - n; + for (const char* i = cstr_ + pos; i >= cstr_; --i) + { + size_t j = 0; + while (j < n && s[j] == i[j]) ++j; + if (j == n) return (size_t)(i - cstr_); + } + return npos; +} + +inline +size_t String::rfind(char c, size_t pos) const +{ + return rfind(&c, pos, 1); +} + +inline +size_t String::rfind(const String& str, size_t pos) const +{ + return rfind(str.c_str(), pos, str.len_); +} + +inline +size_t String::rfind(const char* s, size_t pos) const +{ + return rfind(s, pos, strlen(s)); +} + +inline +size_t String::find_first_of(const char* s, size_t pos, size_t n) const +{ + if (n == 0 || pos + n > len_) return npos; + const char* lmax = cstr_ + len_; + for (const char* i = cstr_ + pos; i < lmax; ++i) + { + for (size_t j = 0; j < n; ++j) + if (s[j] == *i) + return (size_t)(i - cstr_); + } + return npos; +} + +inline +size_t String::find_first_of(char c, size_t pos) const +{ + return find_first_of(&c, pos, 1); +} + +inline +size_t String::find_first_of(const String& str, size_t pos) const +{ + return find_first_of(str.c_str(), pos, str.len_); +} + +inline +size_t String::find_first_of(const char* s, size_t pos) const +{ + if (len_ == 0) return npos; + if (pos >= len_ || !s[0]) return npos; + const char* lmax = cstr_ + len_; + for (const char* i = cstr_ + pos; i < lmax; ++i) + { + for (size_t j = 0; s[j]; ++j) + if (s[j] == *i) + return (size_t)(i - cstr_); + } + return npos; +} + +inline +size_t String::find_last_of(const char* s, size_t pos, size_t n) const +{ + if (len_ == 0) return npos; + if (pos >= len_) pos = len_ - 1; + for (const char* i = cstr_ + pos; i >= cstr_; --i) + { + for (size_t j = 0; j < n; ++j) + if (s[j] == *i) + return (size_t)(i - cstr_); + } + return npos; +} + +inline +size_t String::find_last_of(char c, size_t pos) const +{ + return find_last_of(&c, pos, 1); +} + +inline +size_t String::find_last_of(const String& str, size_t pos) const +{ + return find_last_of(str.c_str(), pos, str.len_); +} + +inline +size_t String::find_last_of(const char* s, size_t pos) const +{ + if (len_ == 0) return npos; + if (pos >= len_) pos = len_ - 1; + for (const char* i = cstr_ + pos; i >= cstr_; --i) + { + for (size_t j = 0; s[j]; ++j) + if (s[j] == *i) + return (size_t)(i - cstr_); + } + return npos; +} + +inline +String String::toLowerCase() const +{ + if (!cstr_) + return String(); + String res(cstr_, len_); + for (size_t i = 0; i < len_; ++i) + res.cstr_[i] = (char) ::tolower(cstr_[i]); + + return res; +} + +//! @endcond + +// ************************* cv::String non-member functions ************************* + +//! @relates cv::String +//! @{ + +inline +String operator + (const String& lhs, const String& rhs) +{ + String s; + s.allocate(lhs.len_ + rhs.len_); + if (lhs.len_) memcpy(s.cstr_, lhs.cstr_, lhs.len_); + if (rhs.len_) memcpy(s.cstr_ + lhs.len_, rhs.cstr_, rhs.len_); + return s; +} + +inline +String operator + (const String& lhs, const char* rhs) +{ + String s; + size_t rhslen = strlen(rhs); + s.allocate(lhs.len_ + rhslen); + if (lhs.len_) memcpy(s.cstr_, lhs.cstr_, lhs.len_); + if (rhslen) memcpy(s.cstr_ + lhs.len_, rhs, rhslen); + return s; +} + +inline +String operator + (const char* lhs, const String& rhs) +{ + String s; + size_t lhslen = strlen(lhs); + s.allocate(lhslen + rhs.len_); + if (lhslen) memcpy(s.cstr_, lhs, lhslen); + if (rhs.len_) memcpy(s.cstr_ + lhslen, rhs.cstr_, rhs.len_); + return s; +} + +inline +String operator + (const String& lhs, char rhs) +{ + String s; + s.allocate(lhs.len_ + 1); + if (lhs.len_) memcpy(s.cstr_, lhs.cstr_, lhs.len_); + s.cstr_[lhs.len_] = rhs; + return s; +} + +inline +String operator + (char lhs, const String& rhs) +{ + String s; + s.allocate(rhs.len_ + 1); + s.cstr_[0] = lhs; + if (rhs.len_) memcpy(s.cstr_ + 1, rhs.cstr_, rhs.len_); + return s; +} + +static inline bool operator== (const String& lhs, const String& rhs) { return 0 == lhs.compare(rhs); } +static inline bool operator== (const char* lhs, const String& rhs) { return 0 == rhs.compare(lhs); } +static inline bool operator== (const String& lhs, const char* rhs) { return 0 == lhs.compare(rhs); } +static inline bool operator!= (const String& lhs, const String& rhs) { return 0 != lhs.compare(rhs); } +static inline bool operator!= (const char* lhs, const String& rhs) { return 0 != rhs.compare(lhs); } +static inline bool operator!= (const String& lhs, const char* rhs) { return 0 != lhs.compare(rhs); } +static inline bool operator< (const String& lhs, const String& rhs) { return lhs.compare(rhs) < 0; } +static inline bool operator< (const char* lhs, const String& rhs) { return rhs.compare(lhs) > 0; } +static inline bool operator< (const String& lhs, const char* rhs) { return lhs.compare(rhs) < 0; } +static inline bool operator<= (const String& lhs, const String& rhs) { return lhs.compare(rhs) <= 0; } +static inline bool operator<= (const char* lhs, const String& rhs) { return rhs.compare(lhs) >= 0; } +static inline bool operator<= (const String& lhs, const char* rhs) { return lhs.compare(rhs) <= 0; } +static inline bool operator> (const String& lhs, const String& rhs) { return lhs.compare(rhs) > 0; } +static inline bool operator> (const char* lhs, const String& rhs) { return rhs.compare(lhs) < 0; } +static inline bool operator> (const String& lhs, const char* rhs) { return lhs.compare(rhs) > 0; } +static inline bool operator>= (const String& lhs, const String& rhs) { return lhs.compare(rhs) >= 0; } +static inline bool operator>= (const char* lhs, const String& rhs) { return rhs.compare(lhs) <= 0; } +static inline bool operator>= (const String& lhs, const char* rhs) { return lhs.compare(rhs) >= 0; } + +//! @} relates cv::String + +} // cv + +namespace std +{ + static inline void swap(cv::String& a, cv::String& b) { a.swap(b); } +} + +#include "opencv2/core/ptr.inl.hpp" + +#endif //OPENCV_CORE_CVSTD_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cvstd.inl.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cvstd.inl.hpp new file mode 100644 index 0000000000000000000000000000000000000000..ed37cacb300e13d17aed7d1441e7bb77eed365b5 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/cvstd.inl.hpp @@ -0,0 +1,285 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_CVSTDINL_HPP +#define OPENCV_CORE_CVSTDINL_HPP + +#include +#include + +//! @cond IGNORED + +#ifdef _MSC_VER +#pragma warning( push ) +#pragma warning( disable: 4127 ) +#endif + +namespace cv +{ + +template class DataType< std::complex<_Tp> > +{ +public: + typedef std::complex<_Tp> value_type; + typedef value_type work_type; + typedef _Tp channel_type; + + enum { generic_type = 0, + depth = DataType::depth, + channels = 2, + fmt = DataType::fmt + ((channels - 1) << 8), + type = CV_MAKETYPE(depth, channels) }; + + typedef Vec vec_type; +}; + +inline +String::String(const std::string& str) + : cstr_(0), len_(0) +{ + size_t len = str.size(); + if (len) memcpy(allocate(len), str.c_str(), len); +} + +inline +String::String(const std::string& str, size_t pos, size_t len) + : cstr_(0), len_(0) +{ + size_t strlen = str.size(); + pos = min(pos, strlen); + len = min(strlen - pos, len); + if (!len) return; + memcpy(allocate(len), str.c_str() + pos, len); +} + +inline +String& String::operator = (const std::string& str) +{ + deallocate(); + size_t len = str.size(); + if (len) memcpy(allocate(len), str.c_str(), len); + return *this; +} + +inline +String& String::operator += (const std::string& str) +{ + *this = *this + str; + return *this; +} + +inline +String::operator std::string() const +{ + return std::string(cstr_, len_); +} + +inline +String operator + (const String& lhs, const std::string& rhs) +{ + String s; + size_t rhslen = rhs.size(); + s.allocate(lhs.len_ + rhslen); + if (lhs.len_) memcpy(s.cstr_, lhs.cstr_, lhs.len_); + if (rhslen) memcpy(s.cstr_ + lhs.len_, rhs.c_str(), rhslen); + return s; +} + +inline +String operator + (const std::string& lhs, const String& rhs) +{ + String s; + size_t lhslen = lhs.size(); + s.allocate(lhslen + rhs.len_); + if (lhslen) memcpy(s.cstr_, lhs.c_str(), lhslen); + if (rhs.len_) memcpy(s.cstr_ + lhslen, rhs.cstr_, rhs.len_); + return s; +} + +inline +FileNode::operator std::string() const +{ + String value; + read(*this, value, value); + return value; +} + +template<> inline +void operator >> (const FileNode& n, std::string& value) +{ + read(n, value, std::string()); +} + +template<> inline +FileStorage& operator << (FileStorage& fs, const std::string& value) +{ + return fs << cv::String(value); +} + +static inline +std::ostream& operator << (std::ostream& os, const String& str) +{ + return os << str.c_str(); +} + +static inline +std::ostream& operator << (std::ostream& out, Ptr fmtd) +{ + fmtd->reset(); + for(const char* str = fmtd->next(); str; str = fmtd->next()) + out << str; + return out; +} + +static inline +std::ostream& operator << (std::ostream& out, const Mat& mtx) +{ + return out << Formatter::get()->format(mtx); +} + +static inline +std::ostream& operator << (std::ostream& out, const UMat& m) +{ + return out << m.getMat(ACCESS_READ); +} + +template static inline +std::ostream& operator << (std::ostream& out, const Complex<_Tp>& c) +{ + return out << "(" << c.re << "," << c.im << ")"; +} + +template static inline +std::ostream& operator << (std::ostream& out, const std::vector >& vec) +{ + return out << Formatter::get()->format(Mat(vec)); +} + + +template static inline +std::ostream& operator << (std::ostream& out, const std::vector >& vec) +{ + return out << Formatter::get()->format(Mat(vec)); +} + + +template static inline +std::ostream& operator << (std::ostream& out, const Matx<_Tp, m, n>& matx) +{ + return out << Formatter::get()->format(Mat(matx)); +} + +template static inline +std::ostream& operator << (std::ostream& out, const Point_<_Tp>& p) +{ + out << "[" << p.x << ", " << p.y << "]"; + return out; +} + +template static inline +std::ostream& operator << (std::ostream& out, const Point3_<_Tp>& p) +{ + out << "[" << p.x << ", " << p.y << ", " << p.z << "]"; + return out; +} + +template static inline +std::ostream& operator << (std::ostream& out, const Vec<_Tp, n>& vec) +{ + out << "["; + if (cv::traits::Depth<_Tp>::value <= CV_32S) + { + for (int i = 0; i < n - 1; ++i) { + out << (int)vec[i] << ", "; + } + out << (int)vec[n-1] << "]"; + } + else + { + for (int i = 0; i < n - 1; ++i) { + out << vec[i] << ", "; + } + out << vec[n-1] << "]"; + } + + return out; +} + +template static inline +std::ostream& operator << (std::ostream& out, const Size_<_Tp>& size) +{ + return out << "[" << size.width << " x " << size.height << "]"; +} + +template static inline +std::ostream& operator << (std::ostream& out, const Rect_<_Tp>& rect) +{ + return out << "[" << rect.width << " x " << rect.height << " from (" << rect.x << ", " << rect.y << ")]"; +} + +static inline std::ostream& operator << (std::ostream& out, const MatSize& msize) +{ + int i, dims = msize.dims(); + for( i = 0; i < dims; i++ ) + { + out << msize[i]; + if( i < dims-1 ) + out << " x "; + } + return out; +} + +static inline std::ostream &operator<< (std::ostream &s, cv::Range &r) +{ + return s << "[" << r.start << " : " << r.end << ")"; +} + +} // cv + +#ifdef _MSC_VER +#pragma warning( pop ) +#endif + +//! @endcond + +#endif // OPENCV_CORE_CVSTDINL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/directx.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/directx.hpp new file mode 100644 index 0000000000000000000000000000000000000000..056a85a1bc996f1483b681273887fc25840f7251 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/directx.hpp @@ -0,0 +1,184 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors as is and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the copyright holders or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_DIRECTX_HPP +#define OPENCV_CORE_DIRECTX_HPP + +#include "mat.hpp" +#include "ocl.hpp" + +#if !defined(__d3d11_h__) +struct ID3D11Device; +struct ID3D11Texture2D; +#endif + +#if !defined(__d3d10_h__) +struct ID3D10Device; +struct ID3D10Texture2D; +#endif + +#if !defined(_D3D9_H_) +struct IDirect3DDevice9; +struct IDirect3DDevice9Ex; +struct IDirect3DSurface9; +#endif + + +namespace cv { namespace directx { + +namespace ocl { +using namespace cv::ocl; + +//! @addtogroup core_directx +// This section describes OpenCL and DirectX interoperability. +// +// To enable DirectX support, configure OpenCV using CMake with WITH_DIRECTX=ON . Note, DirectX is +// supported only on Windows. +// +// To use OpenCL functionality you should first initialize OpenCL context from DirectX resource. +// +//! @{ + +// TODO static functions in the Context class +//! @brief Creates OpenCL context from D3D11 device +// +//! @param pD3D11Device - pointer to D3D11 device +//! @return Returns reference to OpenCL Context +CV_EXPORTS Context& initializeContextFromD3D11Device(ID3D11Device* pD3D11Device); + +//! @brief Creates OpenCL context from D3D10 device +// +//! @param pD3D10Device - pointer to D3D10 device +//! @return Returns reference to OpenCL Context +CV_EXPORTS Context& initializeContextFromD3D10Device(ID3D10Device* pD3D10Device); + +//! @brief Creates OpenCL context from Direct3DDevice9Ex device +// +//! @param pDirect3DDevice9Ex - pointer to Direct3DDevice9Ex device +//! @return Returns reference to OpenCL Context +CV_EXPORTS Context& initializeContextFromDirect3DDevice9Ex(IDirect3DDevice9Ex* pDirect3DDevice9Ex); + +//! @brief Creates OpenCL context from Direct3DDevice9 device +// +//! @param pDirect3DDevice9 - pointer to Direct3Device9 device +//! @return Returns reference to OpenCL Context +CV_EXPORTS Context& initializeContextFromDirect3DDevice9(IDirect3DDevice9* pDirect3DDevice9); + +//! @} + +} // namespace cv::directx::ocl + +//! @addtogroup core_directx +//! @{ + +//! @brief Converts InputArray to ID3D11Texture2D. If destination texture format is DXGI_FORMAT_NV12 then +//! input UMat expected to be in BGR format and data will be downsampled and color-converted to NV12. +// +//! @note Note: Destination texture must be allocated by application. Function does memory copy from src to +//! pD3D11Texture2D +// +//! @param src - source InputArray +//! @param pD3D11Texture2D - destination D3D11 texture +CV_EXPORTS void convertToD3D11Texture2D(InputArray src, ID3D11Texture2D* pD3D11Texture2D); + +//! @brief Converts ID3D11Texture2D to OutputArray. If input texture format is DXGI_FORMAT_NV12 then +//! data will be upsampled and color-converted to BGR format. +// +//! @note Note: Destination matrix will be re-allocated if it has not enough memory to match texture size. +//! function does memory copy from pD3D11Texture2D to dst +// +//! @param pD3D11Texture2D - source D3D11 texture +//! @param dst - destination OutputArray +CV_EXPORTS void convertFromD3D11Texture2D(ID3D11Texture2D* pD3D11Texture2D, OutputArray dst); + +//! @brief Converts InputArray to ID3D10Texture2D +// +//! @note Note: function does memory copy from src to +//! pD3D10Texture2D +// +//! @param src - source InputArray +//! @param pD3D10Texture2D - destination D3D10 texture +CV_EXPORTS void convertToD3D10Texture2D(InputArray src, ID3D10Texture2D* pD3D10Texture2D); + +//! @brief Converts ID3D10Texture2D to OutputArray +// +//! @note Note: function does memory copy from pD3D10Texture2D +//! to dst +// +//! @param pD3D10Texture2D - source D3D10 texture +//! @param dst - destination OutputArray +CV_EXPORTS void convertFromD3D10Texture2D(ID3D10Texture2D* pD3D10Texture2D, OutputArray dst); + +//! @brief Converts InputArray to IDirect3DSurface9 +// +//! @note Note: function does memory copy from src to +//! pDirect3DSurface9 +// +//! @param src - source InputArray +//! @param pDirect3DSurface9 - destination D3D10 texture +//! @param surfaceSharedHandle - shared handle +CV_EXPORTS void convertToDirect3DSurface9(InputArray src, IDirect3DSurface9* pDirect3DSurface9, void* surfaceSharedHandle = NULL); + +//! @brief Converts IDirect3DSurface9 to OutputArray +// +//! @note Note: function does memory copy from pDirect3DSurface9 +//! to dst +// +//! @param pDirect3DSurface9 - source D3D10 texture +//! @param dst - destination OutputArray +//! @param surfaceSharedHandle - shared handle +CV_EXPORTS void convertFromDirect3DSurface9(IDirect3DSurface9* pDirect3DSurface9, OutputArray dst, void* surfaceSharedHandle = NULL); + +//! @brief Get OpenCV type from DirectX type +//! @param iDXGI_FORMAT - enum DXGI_FORMAT for D3D10/D3D11 +//! @return OpenCV type or -1 if there is no equivalent +CV_EXPORTS int getTypeFromDXGI_FORMAT(const int iDXGI_FORMAT); // enum DXGI_FORMAT for D3D10/D3D11 + +//! @brief Get OpenCV type from DirectX type +//! @param iD3DFORMAT - enum D3DTYPE for D3D9 +//! @return OpenCV type or -1 if there is no equivalent +CV_EXPORTS int getTypeFromD3DFORMAT(const int iD3DFORMAT); // enum D3DTYPE for D3D9 + +//! @} + +} } // namespace cv::directx + +#endif // OPENCV_CORE_DIRECTX_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/eigen.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/eigen.hpp new file mode 100644 index 0000000000000000000000000000000000000000..741648edb8fb12e6597d75691fd0019f07ded093 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/eigen.hpp @@ -0,0 +1,280 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + + +#ifndef OPENCV_CORE_EIGEN_HPP +#define OPENCV_CORE_EIGEN_HPP + +#include "opencv2/core.hpp" + +#if defined _MSC_VER && _MSC_VER >= 1200 +#pragma warning( disable: 4714 ) //__forceinline is not inlined +#pragma warning( disable: 4127 ) //conditional expression is constant +#pragma warning( disable: 4244 ) //conversion from '__int64' to 'int', possible loss of data +#endif + +namespace cv +{ + +//! @addtogroup core_eigen +//! @{ + +template static inline +void eigen2cv( const Eigen::Matrix<_Tp, _rows, _cols, _options, _maxRows, _maxCols>& src, OutputArray dst ) +{ + if( !(src.Flags & Eigen::RowMajorBit) ) + { + Mat _src(src.cols(), src.rows(), traits::Type<_Tp>::value, + (void*)src.data(), src.outerStride()*sizeof(_Tp)); + transpose(_src, dst); + } + else + { + Mat _src(src.rows(), src.cols(), traits::Type<_Tp>::value, + (void*)src.data(), src.outerStride()*sizeof(_Tp)); + _src.copyTo(dst); + } +} + +// Matx case +template static inline +void eigen2cv( const Eigen::Matrix<_Tp, _rows, _cols, _options, _maxRows, _maxCols>& src, + Matx<_Tp, _rows, _cols>& dst ) +{ + if( !(src.Flags & Eigen::RowMajorBit) ) + { + dst = Matx<_Tp, _cols, _rows>(static_cast(src.data())).t(); + } + else + { + dst = Matx<_Tp, _rows, _cols>(static_cast(src.data())); + } +} + +template static inline +void cv2eigen( const Mat& src, + Eigen::Matrix<_Tp, _rows, _cols, _options, _maxRows, _maxCols>& dst ) +{ + CV_DbgAssert(src.rows == _rows && src.cols == _cols); + if( !(dst.Flags & Eigen::RowMajorBit) ) + { + const Mat _dst(src.cols, src.rows, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + if( src.type() == _dst.type() ) + transpose(src, _dst); + else if( src.cols == src.rows ) + { + src.convertTo(_dst, _dst.type()); + transpose(_dst, _dst); + } + else + Mat(src.t()).convertTo(_dst, _dst.type()); + } + else + { + const Mat _dst(src.rows, src.cols, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + src.convertTo(_dst, _dst.type()); + } +} + +// Matx case +template static inline +void cv2eigen( const Matx<_Tp, _rows, _cols>& src, + Eigen::Matrix<_Tp, _rows, _cols, _options, _maxRows, _maxCols>& dst ) +{ + if( !(dst.Flags & Eigen::RowMajorBit) ) + { + const Mat _dst(_cols, _rows, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + transpose(src, _dst); + } + else + { + const Mat _dst(_rows, _cols, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + Mat(src).copyTo(_dst); + } +} + +template static inline +void cv2eigen( const Mat& src, + Eigen::Matrix<_Tp, Eigen::Dynamic, Eigen::Dynamic>& dst ) +{ + dst.resize(src.rows, src.cols); + if( !(dst.Flags & Eigen::RowMajorBit) ) + { + const Mat _dst(src.cols, src.rows, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + if( src.type() == _dst.type() ) + transpose(src, _dst); + else if( src.cols == src.rows ) + { + src.convertTo(_dst, _dst.type()); + transpose(_dst, _dst); + } + else + Mat(src.t()).convertTo(_dst, _dst.type()); + } + else + { + const Mat _dst(src.rows, src.cols, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + src.convertTo(_dst, _dst.type()); + } +} + +// Matx case +template static inline +void cv2eigen( const Matx<_Tp, _rows, _cols>& src, + Eigen::Matrix<_Tp, Eigen::Dynamic, Eigen::Dynamic>& dst ) +{ + dst.resize(_rows, _cols); + if( !(dst.Flags & Eigen::RowMajorBit) ) + { + const Mat _dst(_cols, _rows, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + transpose(src, _dst); + } + else + { + const Mat _dst(_rows, _cols, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + Mat(src).copyTo(_dst); + } +} + +template static inline +void cv2eigen( const Mat& src, + Eigen::Matrix<_Tp, Eigen::Dynamic, 1>& dst ) +{ + CV_Assert(src.cols == 1); + dst.resize(src.rows); + + if( !(dst.Flags & Eigen::RowMajorBit) ) + { + const Mat _dst(src.cols, src.rows, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + if( src.type() == _dst.type() ) + transpose(src, _dst); + else + Mat(src.t()).convertTo(_dst, _dst.type()); + } + else + { + const Mat _dst(src.rows, src.cols, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + src.convertTo(_dst, _dst.type()); + } +} + +// Matx case +template static inline +void cv2eigen( const Matx<_Tp, _rows, 1>& src, + Eigen::Matrix<_Tp, Eigen::Dynamic, 1>& dst ) +{ + dst.resize(_rows); + + if( !(dst.Flags & Eigen::RowMajorBit) ) + { + const Mat _dst(1, _rows, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + transpose(src, _dst); + } + else + { + const Mat _dst(_rows, 1, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + src.copyTo(_dst); + } +} + + +template static inline +void cv2eigen( const Mat& src, + Eigen::Matrix<_Tp, 1, Eigen::Dynamic>& dst ) +{ + CV_Assert(src.rows == 1); + dst.resize(src.cols); + if( !(dst.Flags & Eigen::RowMajorBit) ) + { + const Mat _dst(src.cols, src.rows, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + if( src.type() == _dst.type() ) + transpose(src, _dst); + else + Mat(src.t()).convertTo(_dst, _dst.type()); + } + else + { + const Mat _dst(src.rows, src.cols, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + src.convertTo(_dst, _dst.type()); + } +} + +//Matx +template static inline +void cv2eigen( const Matx<_Tp, 1, _cols>& src, + Eigen::Matrix<_Tp, 1, Eigen::Dynamic>& dst ) +{ + dst.resize(_cols); + if( !(dst.Flags & Eigen::RowMajorBit) ) + { + const Mat _dst(_cols, 1, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + transpose(src, _dst); + } + else + { + const Mat _dst(1, _cols, traits::Type<_Tp>::value, + dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); + Mat(src).copyTo(_dst); + } +} + +//! @} + +} // cv + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/fast_math.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/fast_math.hpp new file mode 100644 index 0000000000000000000000000000000000000000..d9ea28e6325a8e76df473d22371617b3ce324f64 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/fast_math.hpp @@ -0,0 +1,271 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_FAST_MATH_HPP +#define OPENCV_CORE_FAST_MATH_HPP + +#include "opencv2/core/cvdef.h" + +#if ((defined _MSC_VER && defined _M_X64) || (defined __GNUC__ && defined __x86_64__ \ + && defined __SSE2__ && !defined __APPLE__)) && !defined(__CUDACC__) +#include +#endif + + +//! @addtogroup core_utils +//! @{ + +/****************************************************************************************\ +* fast math * +\****************************************************************************************/ + +#ifdef __cplusplus +# include +#else +# ifdef __BORLANDC__ +# include +# else +# include +# endif +#endif + +#ifdef HAVE_TEGRA_OPTIMIZATION +# include "tegra_round.hpp" +#endif + +#if defined __GNUC__ && defined __arm__ && (defined __ARM_PCS_VFP || defined __ARM_VFPV3__ || defined __ARM_NEON__) && !defined __SOFTFP__ && !defined(__CUDACC__) + // 1. general scheme + #define ARM_ROUND(_value, _asm_string) \ + int res; \ + float temp; \ + CV_UNUSED(temp); \ + __asm__(_asm_string : [res] "=r" (res), [temp] "=w" (temp) : [value] "w" (_value)); \ + return res + // 2. version for double + #ifdef __clang__ + #define ARM_ROUND_DBL(value) ARM_ROUND(value, "vcvtr.s32.f64 %[temp], %[value] \n vmov %[res], %[temp]") + #else + #define ARM_ROUND_DBL(value) ARM_ROUND(value, "vcvtr.s32.f64 %[temp], %P[value] \n vmov %[res], %[temp]") + #endif + // 3. version for float + #define ARM_ROUND_FLT(value) ARM_ROUND(value, "vcvtr.s32.f32 %[temp], %[value]\n vmov %[res], %[temp]") +#endif + +/** @brief Rounds floating-point number to the nearest integer + + @param value floating-point number. If the value is outside of INT_MIN ... INT_MAX range, the + result is not defined. + */ +CV_INLINE int +cvRound( double value ) +{ +#if ((defined _MSC_VER && defined _M_X64) || (defined __GNUC__ && defined __x86_64__ \ + && defined __SSE2__ && !defined __APPLE__) || CV_SSE2) && !defined(__CUDACC__) + __m128d t = _mm_set_sd( value ); + return _mm_cvtsd_si32(t); +#elif defined _MSC_VER && defined _M_IX86 + int t; + __asm + { + fld value; + fistp t; + } + return t; +#elif ((defined _MSC_VER && defined _M_ARM) || defined CV_ICC || \ + defined __GNUC__) && defined HAVE_TEGRA_OPTIMIZATION + TEGRA_ROUND_DBL(value); +#elif defined CV_ICC || defined __GNUC__ +# if defined ARM_ROUND_DBL + ARM_ROUND_DBL(value); +# else + return (int)lrint(value); +# endif +#else + /* it's ok if round does not comply with IEEE754 standard; + the tests should allow +/-1 difference when the tested functions use round */ + return (int)(value + (value >= 0 ? 0.5 : -0.5)); +#endif +} + + +/** @brief Rounds floating-point number to the nearest integer not larger than the original. + + The function computes an integer i such that: + \f[i \le \texttt{value} < i+1\f] + @param value floating-point number. If the value is outside of INT_MIN ... INT_MAX range, the + result is not defined. + */ +CV_INLINE int cvFloor( double value ) +{ + int i = (int)value; + return i - (i > value); +} + +/** @brief Rounds floating-point number to the nearest integer not smaller than the original. + + The function computes an integer i such that: + \f[i \le \texttt{value} < i+1\f] + @param value floating-point number. If the value is outside of INT_MIN ... INT_MAX range, the + result is not defined. + */ +CV_INLINE int cvCeil( double value ) +{ + int i = (int)value; + return i + (i < value); +} + +/** @brief Determines if the argument is Not A Number. + + @param value The input floating-point value + + The function returns 1 if the argument is Not A Number (as defined by IEEE754 standard), 0 + otherwise. */ +CV_INLINE int cvIsNaN( double value ) +{ + Cv64suf ieee754; + ieee754.f = value; + return ((unsigned)(ieee754.u >> 32) & 0x7fffffff) + + ((unsigned)ieee754.u != 0) > 0x7ff00000; +} + +/** @brief Determines if the argument is Infinity. + + @param value The input floating-point value + + The function returns 1 if the argument is a plus or minus infinity (as defined by IEEE754 standard) + and 0 otherwise. */ +CV_INLINE int cvIsInf( double value ) +{ + Cv64suf ieee754; + ieee754.f = value; + return ((unsigned)(ieee754.u >> 32) & 0x7fffffff) == 0x7ff00000 && + (unsigned)ieee754.u == 0; +} + +#ifdef __cplusplus + +/** @overload */ +CV_INLINE int cvRound(float value) +{ +#if ((defined _MSC_VER && defined _M_X64) || (defined __GNUC__ && defined __x86_64__ \ + && defined __SSE2__ && !defined __APPLE__) || CV_SSE2) && !defined(__CUDACC__) + __m128 t = _mm_set_ss( value ); + return _mm_cvtss_si32(t); +#elif defined _MSC_VER && defined _M_IX86 + int t; + __asm + { + fld value; + fistp t; + } + return t; +#elif ((defined _MSC_VER && defined _M_ARM) || defined CV_ICC || \ + defined __GNUC__) && defined HAVE_TEGRA_OPTIMIZATION + TEGRA_ROUND_FLT(value); +#elif defined CV_ICC || defined __GNUC__ +# if defined ARM_ROUND_FLT + ARM_ROUND_FLT(value); +# else + return (int)lrintf(value); +# endif +#else + /* it's ok if round does not comply with IEEE754 standard; + the tests should allow +/-1 difference when the tested functions use round */ + return (int)(value + (value >= 0 ? 0.5f : -0.5f)); +#endif +} + +/** @overload */ +CV_INLINE int cvRound( int value ) +{ + return value; +} + +/** @overload */ +CV_INLINE int cvFloor( float value ) +{ + int i = (int)value; + return i - (i > value); +} + +/** @overload */ +CV_INLINE int cvFloor( int value ) +{ + return value; +} + +/** @overload */ +CV_INLINE int cvCeil( float value ) +{ + int i = (int)value; + return i + (i < value); +} + +/** @overload */ +CV_INLINE int cvCeil( int value ) +{ + return value; +} + +/** @overload */ +CV_INLINE int cvIsNaN( float value ) +{ + Cv32suf ieee754; + ieee754.f = value; + return (ieee754.u & 0x7fffffff) > 0x7f800000; +} + +/** @overload */ +CV_INLINE int cvIsInf( float value ) +{ + Cv32suf ieee754; + ieee754.f = value; + return (ieee754.u & 0x7fffffff) == 0x7f800000; +} + +#endif // __cplusplus + +//! @} core_utils + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/hal/hal.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/hal/hal.hpp new file mode 100644 index 0000000000000000000000000000000000000000..68900ec4281028f445b44b603790181d9731f7b3 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/hal/hal.hpp @@ -0,0 +1,250 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_HAL_HPP +#define OPENCV_HAL_HPP + +#include "opencv2/core/cvdef.h" +#include "opencv2/core/cvstd.hpp" +#include "opencv2/core/hal/interface.h" + +namespace cv { namespace hal { + +//! @addtogroup core_hal_functions +//! @{ + +CV_EXPORTS int normHamming(const uchar* a, int n); +CV_EXPORTS int normHamming(const uchar* a, const uchar* b, int n); + +CV_EXPORTS int normHamming(const uchar* a, int n, int cellSize); +CV_EXPORTS int normHamming(const uchar* a, const uchar* b, int n, int cellSize); + +CV_EXPORTS int LU32f(float* A, size_t astep, int m, float* b, size_t bstep, int n); +CV_EXPORTS int LU64f(double* A, size_t astep, int m, double* b, size_t bstep, int n); +CV_EXPORTS bool Cholesky32f(float* A, size_t astep, int m, float* b, size_t bstep, int n); +CV_EXPORTS bool Cholesky64f(double* A, size_t astep, int m, double* b, size_t bstep, int n); +CV_EXPORTS void SVD32f(float* At, size_t astep, float* W, float* U, size_t ustep, float* Vt, size_t vstep, int m, int n, int flags); +CV_EXPORTS void SVD64f(double* At, size_t astep, double* W, double* U, size_t ustep, double* Vt, size_t vstep, int m, int n, int flags); +CV_EXPORTS int QR32f(float* A, size_t astep, int m, int n, int k, float* b, size_t bstep, float* hFactors); +CV_EXPORTS int QR64f(double* A, size_t astep, int m, int n, int k, double* b, size_t bstep, double* hFactors); + +CV_EXPORTS void gemm32f(const float* src1, size_t src1_step, const float* src2, size_t src2_step, + float alpha, const float* src3, size_t src3_step, float beta, float* dst, size_t dst_step, + int m_a, int n_a, int n_d, int flags); +CV_EXPORTS void gemm64f(const double* src1, size_t src1_step, const double* src2, size_t src2_step, + double alpha, const double* src3, size_t src3_step, double beta, double* dst, size_t dst_step, + int m_a, int n_a, int n_d, int flags); +CV_EXPORTS void gemm32fc(const float* src1, size_t src1_step, const float* src2, size_t src2_step, + float alpha, const float* src3, size_t src3_step, float beta, float* dst, size_t dst_step, + int m_a, int n_a, int n_d, int flags); +CV_EXPORTS void gemm64fc(const double* src1, size_t src1_step, const double* src2, size_t src2_step, + double alpha, const double* src3, size_t src3_step, double beta, double* dst, size_t dst_step, + int m_a, int n_a, int n_d, int flags); + +CV_EXPORTS int normL1_(const uchar* a, const uchar* b, int n); +CV_EXPORTS float normL1_(const float* a, const float* b, int n); +CV_EXPORTS float normL2Sqr_(const float* a, const float* b, int n); + +CV_EXPORTS void exp32f(const float* src, float* dst, int n); +CV_EXPORTS void exp64f(const double* src, double* dst, int n); +CV_EXPORTS void log32f(const float* src, float* dst, int n); +CV_EXPORTS void log64f(const double* src, double* dst, int n); + +CV_EXPORTS void fastAtan32f(const float* y, const float* x, float* dst, int n, bool angleInDegrees); +CV_EXPORTS void fastAtan64f(const double* y, const double* x, double* dst, int n, bool angleInDegrees); +CV_EXPORTS void magnitude32f(const float* x, const float* y, float* dst, int n); +CV_EXPORTS void magnitude64f(const double* x, const double* y, double* dst, int n); +CV_EXPORTS void sqrt32f(const float* src, float* dst, int len); +CV_EXPORTS void sqrt64f(const double* src, double* dst, int len); +CV_EXPORTS void invSqrt32f(const float* src, float* dst, int len); +CV_EXPORTS void invSqrt64f(const double* src, double* dst, int len); + +CV_EXPORTS void split8u(const uchar* src, uchar** dst, int len, int cn ); +CV_EXPORTS void split16u(const ushort* src, ushort** dst, int len, int cn ); +CV_EXPORTS void split32s(const int* src, int** dst, int len, int cn ); +CV_EXPORTS void split64s(const int64* src, int64** dst, int len, int cn ); + +CV_EXPORTS void merge8u(const uchar** src, uchar* dst, int len, int cn ); +CV_EXPORTS void merge16u(const ushort** src, ushort* dst, int len, int cn ); +CV_EXPORTS void merge32s(const int** src, int* dst, int len, int cn ); +CV_EXPORTS void merge64s(const int64** src, int64* dst, int len, int cn ); + +CV_EXPORTS void add8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void add8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void add16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void add16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void add32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void add32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void add64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* ); + +CV_EXPORTS void sub8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void sub8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void sub16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void sub16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void sub32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void sub32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void sub64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* ); + +CV_EXPORTS void max8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void max8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void max16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void max16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void max32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void max32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void max64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* ); + +CV_EXPORTS void min8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void min8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void min16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void min16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void min32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void min32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void min64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* ); + +CV_EXPORTS void absdiff8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void absdiff8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void absdiff16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void absdiff16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void absdiff32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void absdiff32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void absdiff64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* ); + +CV_EXPORTS void and8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void or8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void xor8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); +CV_EXPORTS void not8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); + +CV_EXPORTS void cmp8u(const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop); +CV_EXPORTS void cmp8s(const schar* src1, size_t step1, const schar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop); +CV_EXPORTS void cmp16u(const ushort* src1, size_t step1, const ushort* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop); +CV_EXPORTS void cmp16s(const short* src1, size_t step1, const short* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop); +CV_EXPORTS void cmp32s(const int* src1, size_t step1, const int* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop); +CV_EXPORTS void cmp32f(const float* src1, size_t step1, const float* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop); +CV_EXPORTS void cmp64f(const double* src1, size_t step1, const double* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop); + +CV_EXPORTS void mul8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void mul8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void mul16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void mul16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void mul32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void mul32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void mul64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* scale); + +CV_EXPORTS void div8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void div8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void div16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void div16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void div32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void div32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void div64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* scale); + +CV_EXPORTS void recip8u( const uchar *, size_t, const uchar * src2, size_t step2, uchar* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void recip8s( const schar *, size_t, const schar * src2, size_t step2, schar* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void recip16u( const ushort *, size_t, const ushort * src2, size_t step2, ushort* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void recip16s( const short *, size_t, const short * src2, size_t step2, short* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void recip32s( const int *, size_t, const int * src2, size_t step2, int* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void recip32f( const float *, size_t, const float * src2, size_t step2, float* dst, size_t step, int width, int height, void* scale); +CV_EXPORTS void recip64f( const double *, size_t, const double * src2, size_t step2, double* dst, size_t step, int width, int height, void* scale); + +CV_EXPORTS void addWeighted8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _scalars ); +CV_EXPORTS void addWeighted8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* scalars ); +CV_EXPORTS void addWeighted16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* scalars ); +CV_EXPORTS void addWeighted16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* scalars ); +CV_EXPORTS void addWeighted32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* scalars ); +CV_EXPORTS void addWeighted32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* scalars ); +CV_EXPORTS void addWeighted64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* scalars ); + +struct CV_EXPORTS DFT1D +{ + static Ptr create(int len, int count, int depth, int flags, bool * useBuffer = 0); + virtual void apply(const uchar *src, uchar *dst) = 0; + virtual ~DFT1D() {} +}; + +struct CV_EXPORTS DFT2D +{ + static Ptr create(int width, int height, int depth, + int src_channels, int dst_channels, + int flags, int nonzero_rows = 0); + virtual void apply(const uchar *src_data, size_t src_step, uchar *dst_data, size_t dst_step) = 0; + virtual ~DFT2D() {} +}; + +struct CV_EXPORTS DCT2D +{ + static Ptr create(int width, int height, int depth, int flags); + virtual void apply(const uchar *src_data, size_t src_step, uchar *dst_data, size_t dst_step) = 0; + virtual ~DCT2D() {} +}; + +//! @} core_hal + +//============================================================================= +// for binary compatibility with 3.0 + +//! @cond IGNORED + +CV_EXPORTS int LU(float* A, size_t astep, int m, float* b, size_t bstep, int n); +CV_EXPORTS int LU(double* A, size_t astep, int m, double* b, size_t bstep, int n); +CV_EXPORTS bool Cholesky(float* A, size_t astep, int m, float* b, size_t bstep, int n); +CV_EXPORTS bool Cholesky(double* A, size_t astep, int m, double* b, size_t bstep, int n); + +CV_EXPORTS void exp(const float* src, float* dst, int n); +CV_EXPORTS void exp(const double* src, double* dst, int n); +CV_EXPORTS void log(const float* src, float* dst, int n); +CV_EXPORTS void log(const double* src, double* dst, int n); + +CV_EXPORTS void fastAtan2(const float* y, const float* x, float* dst, int n, bool angleInDegrees); +CV_EXPORTS void magnitude(const float* x, const float* y, float* dst, int n); +CV_EXPORTS void magnitude(const double* x, const double* y, double* dst, int n); +CV_EXPORTS void sqrt(const float* src, float* dst, int len); +CV_EXPORTS void sqrt(const double* src, double* dst, int len); +CV_EXPORTS void invSqrt(const float* src, float* dst, int len); +CV_EXPORTS void invSqrt(const double* src, double* dst, int len); + +//! @endcond + +}} //cv::hal + +#endif //OPENCV_HAL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/hal/interface.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/hal/interface.h new file mode 100644 index 0000000000000000000000000000000000000000..8f640254c373728d46d3e4c32ebb15518c6f9b9a --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/hal/interface.h @@ -0,0 +1,182 @@ +#ifndef OPENCV_CORE_HAL_INTERFACE_H +#define OPENCV_CORE_HAL_INTERFACE_H + +//! @addtogroup core_hal_interface +//! @{ + +//! @name Return codes +//! @{ +#define CV_HAL_ERROR_OK 0 +#define CV_HAL_ERROR_NOT_IMPLEMENTED 1 +#define CV_HAL_ERROR_UNKNOWN -1 +//! @} + +#ifdef __cplusplus +#include +#else +#include +#include +#endif + +//! @name Data types +//! primitive types +//! - schar - signed 1 byte integer +//! - uchar - unsigned 1 byte integer +//! - short - signed 2 byte integer +//! - ushort - unsigned 2 byte integer +//! - int - signed 4 byte integer +//! - uint - unsigned 4 byte integer +//! - int64 - signed 8 byte integer +//! - uint64 - unsigned 8 byte integer +//! @{ +#if !defined _MSC_VER && !defined __BORLANDC__ +# if defined __cplusplus && __cplusplus >= 201103L && !defined __APPLE__ +# include +# ifdef __NEWLIB__ + typedef unsigned int uint; +# else + typedef std::uint32_t uint; +# endif +# else +# include + typedef uint32_t uint; +# endif +#else + typedef unsigned uint; +#endif + +typedef signed char schar; + +#ifndef __IPL_H__ + typedef unsigned char uchar; + typedef unsigned short ushort; +#endif + +#if defined _MSC_VER || defined __BORLANDC__ + typedef __int64 int64; + typedef unsigned __int64 uint64; +# define CV_BIG_INT(n) n##I64 +# define CV_BIG_UINT(n) n##UI64 +#else + typedef int64_t int64; + typedef uint64_t uint64; +# define CV_BIG_INT(n) n##LL +# define CV_BIG_UINT(n) n##ULL +#endif + +#define CV_CN_MAX 512 +#define CV_CN_SHIFT 3 +#define CV_DEPTH_MAX (1 << CV_CN_SHIFT) + +#define CV_8U 0 +#define CV_8S 1 +#define CV_16U 2 +#define CV_16S 3 +#define CV_32S 4 +#define CV_32F 5 +#define CV_64F 6 +#define CV_USRTYPE1 7 + +#define CV_MAT_DEPTH_MASK (CV_DEPTH_MAX - 1) +#define CV_MAT_DEPTH(flags) ((flags) & CV_MAT_DEPTH_MASK) + +#define CV_MAKETYPE(depth,cn) (CV_MAT_DEPTH(depth) + (((cn)-1) << CV_CN_SHIFT)) +#define CV_MAKE_TYPE CV_MAKETYPE + +#define CV_8UC1 CV_MAKETYPE(CV_8U,1) +#define CV_8UC2 CV_MAKETYPE(CV_8U,2) +#define CV_8UC3 CV_MAKETYPE(CV_8U,3) +#define CV_8UC4 CV_MAKETYPE(CV_8U,4) +#define CV_8UC(n) CV_MAKETYPE(CV_8U,(n)) + +#define CV_8SC1 CV_MAKETYPE(CV_8S,1) +#define CV_8SC2 CV_MAKETYPE(CV_8S,2) +#define CV_8SC3 CV_MAKETYPE(CV_8S,3) +#define CV_8SC4 CV_MAKETYPE(CV_8S,4) +#define CV_8SC(n) CV_MAKETYPE(CV_8S,(n)) + +#define CV_16UC1 CV_MAKETYPE(CV_16U,1) +#define CV_16UC2 CV_MAKETYPE(CV_16U,2) +#define CV_16UC3 CV_MAKETYPE(CV_16U,3) +#define CV_16UC4 CV_MAKETYPE(CV_16U,4) +#define CV_16UC(n) CV_MAKETYPE(CV_16U,(n)) + +#define CV_16SC1 CV_MAKETYPE(CV_16S,1) +#define CV_16SC2 CV_MAKETYPE(CV_16S,2) +#define CV_16SC3 CV_MAKETYPE(CV_16S,3) +#define CV_16SC4 CV_MAKETYPE(CV_16S,4) +#define CV_16SC(n) CV_MAKETYPE(CV_16S,(n)) + +#define CV_32SC1 CV_MAKETYPE(CV_32S,1) +#define CV_32SC2 CV_MAKETYPE(CV_32S,2) +#define CV_32SC3 CV_MAKETYPE(CV_32S,3) +#define CV_32SC4 CV_MAKETYPE(CV_32S,4) +#define CV_32SC(n) CV_MAKETYPE(CV_32S,(n)) + +#define CV_32FC1 CV_MAKETYPE(CV_32F,1) +#define CV_32FC2 CV_MAKETYPE(CV_32F,2) +#define CV_32FC3 CV_MAKETYPE(CV_32F,3) +#define CV_32FC4 CV_MAKETYPE(CV_32F,4) +#define CV_32FC(n) CV_MAKETYPE(CV_32F,(n)) + +#define CV_64FC1 CV_MAKETYPE(CV_64F,1) +#define CV_64FC2 CV_MAKETYPE(CV_64F,2) +#define CV_64FC3 CV_MAKETYPE(CV_64F,3) +#define CV_64FC4 CV_MAKETYPE(CV_64F,4) +#define CV_64FC(n) CV_MAKETYPE(CV_64F,(n)) +//! @} + +//! @name Comparison operation +//! @sa cv::CmpTypes +//! @{ +#define CV_HAL_CMP_EQ 0 +#define CV_HAL_CMP_GT 1 +#define CV_HAL_CMP_GE 2 +#define CV_HAL_CMP_LT 3 +#define CV_HAL_CMP_LE 4 +#define CV_HAL_CMP_NE 5 +//! @} + +//! @name Border processing modes +//! @sa cv::BorderTypes +//! @{ +#define CV_HAL_BORDER_CONSTANT 0 +#define CV_HAL_BORDER_REPLICATE 1 +#define CV_HAL_BORDER_REFLECT 2 +#define CV_HAL_BORDER_WRAP 3 +#define CV_HAL_BORDER_REFLECT_101 4 +#define CV_HAL_BORDER_TRANSPARENT 5 +#define CV_HAL_BORDER_ISOLATED 16 +//! @} + +//! @name DFT flags +//! @{ +#define CV_HAL_DFT_INVERSE 1 +#define CV_HAL_DFT_SCALE 2 +#define CV_HAL_DFT_ROWS 4 +#define CV_HAL_DFT_COMPLEX_OUTPUT 16 +#define CV_HAL_DFT_REAL_OUTPUT 32 +#define CV_HAL_DFT_TWO_STAGE 64 +#define CV_HAL_DFT_STAGE_COLS 128 +#define CV_HAL_DFT_IS_CONTINUOUS 512 +#define CV_HAL_DFT_IS_INPLACE 1024 +//! @} + +//! @name SVD flags +//! @{ +#define CV_HAL_SVD_NO_UV 1 +#define CV_HAL_SVD_SHORT_UV 2 +#define CV_HAL_SVD_MODIFY_A 4 +#define CV_HAL_SVD_FULL_UV 8 +//! @} + +//! @name Gemm flags +//! @{ +#define CV_HAL_GEMM_1_T 1 +#define CV_HAL_GEMM_2_T 2 +#define CV_HAL_GEMM_3_T 4 +//! @} + +//! @} + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/hal/intrin.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/hal/intrin.hpp new file mode 100644 index 0000000000000000000000000000000000000000..ef74176f3389a47644c92ea34fc65a1c8634e681 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/hal/intrin.hpp @@ -0,0 +1,420 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_HAL_INTRIN_HPP +#define OPENCV_HAL_INTRIN_HPP + +#include +#include +#include +#include "opencv2/core/cvdef.h" + +#define OPENCV_HAL_ADD(a, b) ((a) + (b)) +#define OPENCV_HAL_AND(a, b) ((a) & (b)) +#define OPENCV_HAL_NOP(a) (a) +#define OPENCV_HAL_1ST(a, b) (a) + +// unlike HAL API, which is in cv::hal, +// we put intrinsics into cv namespace to make its +// access from within opencv code more accessible +namespace cv { + +namespace hal { + +enum StoreMode +{ + STORE_UNALIGNED = 0, + STORE_ALIGNED = 1, + STORE_ALIGNED_NOCACHE = 2 +}; + +} + +template struct V_TypeTraits +{ +}; + +#define CV_INTRIN_DEF_TYPE_TRAITS(type, int_type_, uint_type_, abs_type_, w_type_, q_type_, sum_type_, nlanes128_) \ + template<> struct V_TypeTraits \ + { \ + typedef type value_type; \ + typedef int_type_ int_type; \ + typedef abs_type_ abs_type; \ + typedef uint_type_ uint_type; \ + typedef w_type_ w_type; \ + typedef q_type_ q_type; \ + typedef sum_type_ sum_type; \ + enum { nlanes128 = nlanes128_ }; \ + \ + static inline int_type reinterpret_int(type x) \ + { \ + union { type l; int_type i; } v; \ + v.l = x; \ + return v.i; \ + } \ + \ + static inline type reinterpret_from_int(int_type x) \ + { \ + union { type l; int_type i; } v; \ + v.i = x; \ + return v.l; \ + } \ + } + +CV_INTRIN_DEF_TYPE_TRAITS(uchar, schar, uchar, uchar, ushort, unsigned, unsigned, 16); +CV_INTRIN_DEF_TYPE_TRAITS(schar, schar, uchar, uchar, short, int, int, 16); +CV_INTRIN_DEF_TYPE_TRAITS(ushort, short, ushort, ushort, unsigned, uint64, unsigned, 8); +CV_INTRIN_DEF_TYPE_TRAITS(short, short, ushort, ushort, int, int64, int, 8); +CV_INTRIN_DEF_TYPE_TRAITS(unsigned, int, unsigned, unsigned, uint64, void, unsigned, 4); +CV_INTRIN_DEF_TYPE_TRAITS(int, int, unsigned, unsigned, int64, void, int, 4); +CV_INTRIN_DEF_TYPE_TRAITS(float, int, unsigned, float, double, void, float, 4); +CV_INTRIN_DEF_TYPE_TRAITS(uint64, int64, uint64, uint64, void, void, uint64, 2); +CV_INTRIN_DEF_TYPE_TRAITS(int64, int64, uint64, uint64, void, void, int64, 2); +CV_INTRIN_DEF_TYPE_TRAITS(double, int64, uint64, double, void, void, double, 2); + +#ifndef CV_DOXYGEN + +#ifdef CV_CPU_DISPATCH_MODE + #define CV_CPU_OPTIMIZATION_HAL_NAMESPACE __CV_CAT(hal_, CV_CPU_DISPATCH_MODE) + #define CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN namespace __CV_CAT(hal_, CV_CPU_DISPATCH_MODE) { + #define CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END } +#else + #define CV_CPU_OPTIMIZATION_HAL_NAMESPACE hal_baseline + #define CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN namespace hal_baseline { + #define CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END } +#endif + +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END +using namespace CV_CPU_OPTIMIZATION_HAL_NAMESPACE; +#endif +} + +#ifdef CV_DOXYGEN +# undef CV_AVX2 +# undef CV_SSE2 +# undef CV_NEON +# undef CV_VSX +# undef CV_FP16 +#endif + +#if CV_SSE2 || CV_NEON || CV_VSX +#define CV__SIMD_FORWARD 128 +#include "opencv2/core/hal/intrin_forward.hpp" +#endif + +#if CV_SSE2 + +#include "opencv2/core/hal/intrin_sse_em.hpp" +#include "opencv2/core/hal/intrin_sse.hpp" + +#elif CV_NEON + +#include "opencv2/core/hal/intrin_neon.hpp" + +#elif CV_VSX + +#include "opencv2/core/hal/intrin_vsx.hpp" + +#else + +#define CV_SIMD128_CPP 1 +#include "opencv2/core/hal/intrin_cpp.hpp" + +#endif + +// AVX2 can be used together with SSE2, so +// we define those two sets of intrinsics at once. +// Most of the intrinsics do not conflict (the proper overloaded variant is +// resolved by the argument types, e.g. v_float32x4 ~ SSE2, v_float32x8 ~ AVX2), +// but some of AVX2 intrinsics get v256_ prefix instead of v_, e.g. v256_load() vs v_load(). +// Correspondingly, the wide intrinsics (which are mapped to the "widest" +// available instruction set) will get vx_ prefix +// (and will be mapped to v256_ counterparts) (e.g. vx_load() => v256_load()) +#if CV_AVX2 + +#define CV__SIMD_FORWARD 256 +#include "opencv2/core/hal/intrin_forward.hpp" +#include "opencv2/core/hal/intrin_avx.hpp" + +#endif + +//! @cond IGNORED + +namespace cv { + +#ifndef CV_DOXYGEN +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN +#endif + +#ifndef CV_SIMD128 +#define CV_SIMD128 0 +#endif + +#ifndef CV_SIMD128_64F +#define CV_SIMD128_64F 0 +#endif + +#ifndef CV_SIMD256 +#define CV_SIMD256 0 +#endif + +#ifndef CV_SIMD256_64F +#define CV_SIMD256_64F 0 +#endif + +#ifndef CV_SIMD512 +#define CV_SIMD512 0 +#endif + +#ifndef CV_SIMD512_64F +#define CV_SIMD512_64F 0 +#endif + +#ifndef CV_SIMD128_FP16 +#define CV_SIMD128_FP16 0 +#endif + +#ifndef CV_SIMD256_FP16 +#define CV_SIMD256_FP16 0 +#endif + +#ifndef CV_SIMD512_FP16 +#define CV_SIMD512_FP16 0 +#endif + +//================================================================================================== + +#define CV_INTRIN_DEFINE_WIDE_INTRIN(typ, vtyp, short_typ, prefix, loadsfx) \ + inline vtyp vx_setall_##short_typ(typ v) { return prefix##_setall_##short_typ(v); } \ + inline vtyp vx_setzero_##short_typ() { return prefix##_setzero_##short_typ(); } \ + inline vtyp vx_##loadsfx(const typ* ptr) { return prefix##_##loadsfx(ptr); } \ + inline vtyp vx_##loadsfx##_aligned(const typ* ptr) { return prefix##_##loadsfx##_aligned(ptr); } \ + inline vtyp vx_##loadsfx##_low(const typ* ptr) { return prefix##_##loadsfx##_low(ptr); } \ + inline vtyp vx_##loadsfx##_halves(const typ* ptr0, const typ* ptr1) { return prefix##_##loadsfx##_halves(ptr0, ptr1); } \ + inline void vx_store(typ* ptr, const vtyp& v) { return v_store(ptr, v); } \ + inline void vx_store_aligned(typ* ptr, const vtyp& v) { return v_store_aligned(ptr, v); } + +#define CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND(typ, wtyp, prefix) \ + inline wtyp vx_load_expand(const typ* ptr) { return prefix##_load_expand(ptr); } + +#define CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND_Q(typ, qtyp, prefix) \ + inline qtyp vx_load_expand_q(const typ* ptr) { return prefix##_load_expand_q(ptr); } + +#define CV_INTRIN_DEFINE_WIDE_INTRIN_WITH_EXPAND(typ, vtyp, short_typ, wtyp, qtyp, prefix, loadsfx) \ + CV_INTRIN_DEFINE_WIDE_INTRIN(typ, vtyp, short_typ, prefix, loadsfx) \ + CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND(typ, wtyp, prefix) \ + CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND_Q(typ, qtyp, prefix) + +#define CV_INTRIN_DEFINE_WIDE_INTRIN_ALL_TYPES(prefix) \ + CV_INTRIN_DEFINE_WIDE_INTRIN_WITH_EXPAND(uchar, v_uint8, u8, v_uint16, v_uint32, prefix, load) \ + CV_INTRIN_DEFINE_WIDE_INTRIN_WITH_EXPAND(schar, v_int8, s8, v_int16, v_int32, prefix, load) \ + CV_INTRIN_DEFINE_WIDE_INTRIN(ushort, v_uint16, u16, prefix, load) \ + CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND(ushort, v_uint32, prefix) \ + CV_INTRIN_DEFINE_WIDE_INTRIN(short, v_int16, s16, prefix, load) \ + CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND(short, v_int32, prefix) \ + CV_INTRIN_DEFINE_WIDE_INTRIN(int, v_int32, s32, prefix, load) \ + CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND(int, v_int64, prefix) \ + CV_INTRIN_DEFINE_WIDE_INTRIN(unsigned, v_uint32, u32, prefix, load) \ + CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND(unsigned, v_uint64, prefix) \ + CV_INTRIN_DEFINE_WIDE_INTRIN(float, v_float32, f32, prefix, load) \ + CV_INTRIN_DEFINE_WIDE_INTRIN(int64, v_int64, s64, prefix, load) \ + CV_INTRIN_DEFINE_WIDE_INTRIN(uint64, v_uint64, u64, prefix, load) \ + CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND(float16_t, v_float32, prefix) + +template struct V_RegTraits +{ +}; + +#define CV_DEF_REG_TRAITS(prefix, _reg, lane_type, suffix, _u_reg, _w_reg, _q_reg, _int_reg, _round_reg) \ + template<> struct V_RegTraits<_reg> \ + { \ + typedef _reg reg; \ + typedef _u_reg u_reg; \ + typedef _w_reg w_reg; \ + typedef _q_reg q_reg; \ + typedef _int_reg int_reg; \ + typedef _round_reg round_reg; \ + } + +#if CV_SIMD128 || CV_SIMD128_CPP + CV_DEF_REG_TRAITS(v, v_uint8x16, uchar, u8, v_uint8x16, v_uint16x8, v_uint32x4, v_int8x16, void); + CV_DEF_REG_TRAITS(v, v_int8x16, schar, s8, v_uint8x16, v_int16x8, v_int32x4, v_int8x16, void); + CV_DEF_REG_TRAITS(v, v_uint16x8, ushort, u16, v_uint16x8, v_uint32x4, v_uint64x2, v_int16x8, void); + CV_DEF_REG_TRAITS(v, v_int16x8, short, s16, v_uint16x8, v_int32x4, v_int64x2, v_int16x8, void); + CV_DEF_REG_TRAITS(v, v_uint32x4, unsigned, u32, v_uint32x4, v_uint64x2, void, v_int32x4, void); + CV_DEF_REG_TRAITS(v, v_int32x4, int, s32, v_uint32x4, v_int64x2, void, v_int32x4, void); +#if CV_SIMD128_64F + CV_DEF_REG_TRAITS(v, v_float32x4, float, f32, v_float32x4, v_float64x2, void, v_int32x4, v_int32x4); +#else + CV_DEF_REG_TRAITS(v, v_float32x4, float, f32, v_float32x4, void, void, v_int32x4, v_int32x4); +#endif + CV_DEF_REG_TRAITS(v, v_uint64x2, uint64, u64, v_uint64x2, void, void, v_int64x2, void); + CV_DEF_REG_TRAITS(v, v_int64x2, int64, s64, v_uint64x2, void, void, v_int64x2, void); +#if CV_SIMD128_64F + CV_DEF_REG_TRAITS(v, v_float64x2, double, f64, v_float64x2, void, void, v_int64x2, v_int32x4); +#endif +#endif + +#if CV_SIMD256 + CV_DEF_REG_TRAITS(v256, v_uint8x32, uchar, u8, v_uint8x32, v_uint16x16, v_uint32x8, v_int8x32, void); + CV_DEF_REG_TRAITS(v256, v_int8x32, schar, s8, v_uint8x32, v_int16x16, v_int32x8, v_int8x32, void); + CV_DEF_REG_TRAITS(v256, v_uint16x16, ushort, u16, v_uint16x16, v_uint32x8, v_uint64x4, v_int16x16, void); + CV_DEF_REG_TRAITS(v256, v_int16x16, short, s16, v_uint16x16, v_int32x8, v_int64x4, v_int16x16, void); + CV_DEF_REG_TRAITS(v256, v_uint32x8, unsigned, u32, v_uint32x8, v_uint64x4, void, v_int32x8, void); + CV_DEF_REG_TRAITS(v256, v_int32x8, int, s32, v_uint32x8, v_int64x4, void, v_int32x8, void); + CV_DEF_REG_TRAITS(v256, v_float32x8, float, f32, v_float32x8, v_float64x4, void, v_int32x8, v_int32x8); + CV_DEF_REG_TRAITS(v256, v_uint64x4, uint64, u64, v_uint64x4, void, void, v_int64x4, void); + CV_DEF_REG_TRAITS(v256, v_int64x4, int64, s64, v_uint64x4, void, void, v_int64x4, void); + CV_DEF_REG_TRAITS(v256, v_float64x4, double, f64, v_float64x4, void, void, v_int64x4, v_int32x8); +#endif + +#if CV_SIMD512 && (!defined(CV__SIMD_FORCE_WIDTH) || CV__SIMD_FORCE_WIDTH == 512) +#define CV__SIMD_NAMESPACE simd512 +namespace CV__SIMD_NAMESPACE { + #define CV_SIMD 1 + #define CV_SIMD_64F CV_SIMD512_64F + #define CV_SIMD_WIDTH 64 + // TODO typedef v_uint8 / v_int32 / etc types here +} // namespace +using namespace CV__SIMD_NAMESPACE; +#elif CV_SIMD256 && (!defined(CV__SIMD_FORCE_WIDTH) || CV__SIMD_FORCE_WIDTH == 256) +#define CV__SIMD_NAMESPACE simd256 +namespace CV__SIMD_NAMESPACE { + #define CV_SIMD 1 + #define CV_SIMD_64F CV_SIMD256_64F + #define CV_SIMD_FP16 CV_SIMD256_FP16 + #define CV_SIMD_WIDTH 32 + typedef v_uint8x32 v_uint8; + typedef v_int8x32 v_int8; + typedef v_uint16x16 v_uint16; + typedef v_int16x16 v_int16; + typedef v_uint32x8 v_uint32; + typedef v_int32x8 v_int32; + typedef v_uint64x4 v_uint64; + typedef v_int64x4 v_int64; + typedef v_float32x8 v_float32; + #if CV_SIMD256_64F + typedef v_float64x4 v_float64; + #endif + CV_INTRIN_DEFINE_WIDE_INTRIN_ALL_TYPES(v256) + CV_INTRIN_DEFINE_WIDE_INTRIN(double, v_float64, f64, v256, load) + inline void vx_cleanup() { v256_cleanup(); } +} // namespace +using namespace CV__SIMD_NAMESPACE; +#elif (CV_SIMD128 || CV_SIMD128_CPP) && (!defined(CV__SIMD_FORCE_WIDTH) || CV__SIMD_FORCE_WIDTH == 128) +#define CV__SIMD_NAMESPACE simd128 +namespace CV__SIMD_NAMESPACE { + #define CV_SIMD CV_SIMD128 + #define CV_SIMD_64F CV_SIMD128_64F + #define CV_SIMD_WIDTH 16 + typedef v_uint8x16 v_uint8; + typedef v_int8x16 v_int8; + typedef v_uint16x8 v_uint16; + typedef v_int16x8 v_int16; + typedef v_uint32x4 v_uint32; + typedef v_int32x4 v_int32; + typedef v_uint64x2 v_uint64; + typedef v_int64x2 v_int64; + typedef v_float32x4 v_float32; + #if CV_SIMD128_64F + typedef v_float64x2 v_float64; + #endif + CV_INTRIN_DEFINE_WIDE_INTRIN_ALL_TYPES(v) + #if CV_SIMD128_64F + CV_INTRIN_DEFINE_WIDE_INTRIN(double, v_float64, f64, v, load) + #endif + inline void vx_cleanup() { v_cleanup(); } +} // namespace +using namespace CV__SIMD_NAMESPACE; +#endif + +inline unsigned int trailingZeros32(unsigned int value) { +#if defined(_MSC_VER) +#if (_MSC_VER < 1700) || defined(_M_ARM) + unsigned long index = 0; + _BitScanForward(&index, value); + return (unsigned int)index; +#elif defined(__clang__) + // clang-cl doesn't export _tzcnt_u32 for non BMI systems + return value ? __builtin_ctz(value) : 32; +#else + return _tzcnt_u32(value); +#endif +#elif defined(__GNUC__) || defined(__GNUG__) + return __builtin_ctz(value); +#elif defined(__ICC) || defined(__INTEL_COMPILER) + return _bit_scan_forward(value); +#elif defined(__clang__) + return llvm.cttz.i32(value, true); +#else + static const int MultiplyDeBruijnBitPosition[32] = { + 0, 1, 28, 2, 29, 14, 24, 3, 30, 22, 20, 15, 25, 17, 4, 8, + 31, 27, 13, 23, 21, 19, 16, 7, 26, 12, 18, 6, 11, 5, 10, 9 }; + return MultiplyDeBruijnBitPosition[((uint32_t)((value & -value) * 0x077CB531U)) >> 27]; +#endif +} + +#ifndef CV_DOXYGEN +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END +#endif + +#ifndef CV_SIMD_64F +#define CV_SIMD_64F 0 +#endif + +#ifndef CV_SIMD_FP16 +#define CV_SIMD_FP16 0 //!< Defined to 1 on native support of operations with float16x8_t / float16x16_t (SIMD256) types +#endif + + +#ifndef CV_SIMD +#define CV_SIMD 0 +#endif + +} // cv:: + +//! @endcond + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/hal/intrin_avx.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/hal/intrin_avx.hpp new file mode 100644 index 0000000000000000000000000000000000000000..c3797d67c1031bcc5d27f546b49734766a1e6e4d --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/hal/intrin_avx.hpp @@ -0,0 +1,2628 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html + +#ifndef OPENCV_HAL_INTRIN_AVX_HPP +#define OPENCV_HAL_INTRIN_AVX_HPP + +#define CV_SIMD256 1 +#define CV_SIMD256_64F 1 +#define CV_SIMD256_FP16 0 // no native operations with FP16 type. Only load/store from float32x8 are available (if CV_FP16 == 1) + +namespace cv +{ + +//! @cond IGNORED + +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN + +///////// Utils //////////// + +inline __m256i _v256_combine(const __m128i& lo, const __m128i& hi) +{ return _mm256_inserti128_si256(_mm256_castsi128_si256(lo), hi, 1); } + +inline __m256 _v256_combine(const __m128& lo, const __m128& hi) +{ return _mm256_insertf128_ps(_mm256_castps128_ps256(lo), hi, 1); } + +inline __m256d _v256_combine(const __m128d& lo, const __m128d& hi) +{ return _mm256_insertf128_pd(_mm256_castpd128_pd256(lo), hi, 1); } + +inline int _v_cvtsi256_si32(const __m256i& a) +{ return _mm_cvtsi128_si32(_mm256_castsi256_si128(a)); } + +inline __m256i _v256_shuffle_odd_64(const __m256i& v) +{ return _mm256_permute4x64_epi64(v, _MM_SHUFFLE(3, 1, 2, 0)); } + +inline __m256d _v256_shuffle_odd_64(const __m256d& v) +{ return _mm256_permute4x64_pd(v, _MM_SHUFFLE(3, 1, 2, 0)); } + +template +inline __m256i _v256_permute2x128(const __m256i& a, const __m256i& b) +{ return _mm256_permute2x128_si256(a, b, imm); } + +template +inline __m256 _v256_permute2x128(const __m256& a, const __m256& b) +{ return _mm256_permute2f128_ps(a, b, imm); } + +template +inline __m256d _v256_permute2x128(const __m256d& a, const __m256d& b) +{ return _mm256_permute2f128_pd(a, b, imm); } + +template +inline _Tpvec v256_permute2x128(const _Tpvec& a, const _Tpvec& b) +{ return _Tpvec(_v256_permute2x128(a.val, b.val)); } + +template +inline __m256i _v256_permute4x64(const __m256i& a) +{ return _mm256_permute4x64_epi64(a, imm); } + +template +inline __m256d _v256_permute4x64(const __m256d& a) +{ return _mm256_permute4x64_pd(a, imm); } + +template +inline _Tpvec v256_permute4x64(const _Tpvec& a) +{ return _Tpvec(_v256_permute4x64(a.val)); } + +inline __m128i _v256_extract_high(const __m256i& v) +{ return _mm256_extracti128_si256(v, 1); } + +inline __m128 _v256_extract_high(const __m256& v) +{ return _mm256_extractf128_ps(v, 1); } + +inline __m128d _v256_extract_high(const __m256d& v) +{ return _mm256_extractf128_pd(v, 1); } + +inline __m128i _v256_extract_low(const __m256i& v) +{ return _mm256_castsi256_si128(v); } + +inline __m128 _v256_extract_low(const __m256& v) +{ return _mm256_castps256_ps128(v); } + +inline __m128d _v256_extract_low(const __m256d& v) +{ return _mm256_castpd256_pd128(v); } + +inline __m256i _v256_packs_epu32(const __m256i& a, const __m256i& b) +{ + const __m256i m = _mm256_set1_epi32(65535); + __m256i am = _mm256_min_epu32(a, m); + __m256i bm = _mm256_min_epu32(b, m); + return _mm256_packus_epi32(am, bm); +} + +///////// Types //////////// + +struct v_uint8x32 +{ + typedef uchar lane_type; + enum { nlanes = 32 }; + __m256i val; + + explicit v_uint8x32(__m256i v) : val(v) {} + v_uint8x32(uchar v0, uchar v1, uchar v2, uchar v3, + uchar v4, uchar v5, uchar v6, uchar v7, + uchar v8, uchar v9, uchar v10, uchar v11, + uchar v12, uchar v13, uchar v14, uchar v15, + uchar v16, uchar v17, uchar v18, uchar v19, + uchar v20, uchar v21, uchar v22, uchar v23, + uchar v24, uchar v25, uchar v26, uchar v27, + uchar v28, uchar v29, uchar v30, uchar v31) + { + val = _mm256_setr_epi8((char)v0, (char)v1, (char)v2, (char)v3, + (char)v4, (char)v5, (char)v6 , (char)v7, (char)v8, (char)v9, + (char)v10, (char)v11, (char)v12, (char)v13, (char)v14, (char)v15, + (char)v16, (char)v17, (char)v18, (char)v19, (char)v20, (char)v21, + (char)v22, (char)v23, (char)v24, (char)v25, (char)v26, (char)v27, + (char)v28, (char)v29, (char)v30, (char)v31); + } + v_uint8x32() : val(_mm256_setzero_si256()) {} + uchar get0() const { return (uchar)_v_cvtsi256_si32(val); } +}; + +struct v_int8x32 +{ + typedef schar lane_type; + enum { nlanes = 32 }; + __m256i val; + + explicit v_int8x32(__m256i v) : val(v) {} + v_int8x32(schar v0, schar v1, schar v2, schar v3, + schar v4, schar v5, schar v6, schar v7, + schar v8, schar v9, schar v10, schar v11, + schar v12, schar v13, schar v14, schar v15, + schar v16, schar v17, schar v18, schar v19, + schar v20, schar v21, schar v22, schar v23, + schar v24, schar v25, schar v26, schar v27, + schar v28, schar v29, schar v30, schar v31) + { + val = _mm256_setr_epi8(v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, + v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, + v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31); + } + v_int8x32() : val(_mm256_setzero_si256()) {} + schar get0() const { return (schar)_v_cvtsi256_si32(val); } +}; + +struct v_uint16x16 +{ + typedef ushort lane_type; + enum { nlanes = 16 }; + __m256i val; + + explicit v_uint16x16(__m256i v) : val(v) {} + v_uint16x16(ushort v0, ushort v1, ushort v2, ushort v3, + ushort v4, ushort v5, ushort v6, ushort v7, + ushort v8, ushort v9, ushort v10, ushort v11, + ushort v12, ushort v13, ushort v14, ushort v15) + { + val = _mm256_setr_epi16((short)v0, (short)v1, (short)v2, (short)v3, + (short)v4, (short)v5, (short)v6, (short)v7, (short)v8, (short)v9, + (short)v10, (short)v11, (short)v12, (short)v13, (short)v14, (short)v15); + } + v_uint16x16() : val(_mm256_setzero_si256()) {} + ushort get0() const { return (ushort)_v_cvtsi256_si32(val); } +}; + +struct v_int16x16 +{ + typedef short lane_type; + enum { nlanes = 16 }; + __m256i val; + + explicit v_int16x16(__m256i v) : val(v) {} + v_int16x16(short v0, short v1, short v2, short v3, + short v4, short v5, short v6, short v7, + short v8, short v9, short v10, short v11, + short v12, short v13, short v14, short v15) + { + val = _mm256_setr_epi16(v0, v1, v2, v3, v4, v5, v6, v7, + v8, v9, v10, v11, v12, v13, v14, v15); + } + v_int16x16() : val(_mm256_setzero_si256()) {} + short get0() const { return (short)_v_cvtsi256_si32(val); } +}; + +struct v_uint32x8 +{ + typedef unsigned lane_type; + enum { nlanes = 8 }; + __m256i val; + + explicit v_uint32x8(__m256i v) : val(v) {} + v_uint32x8(unsigned v0, unsigned v1, unsigned v2, unsigned v3, + unsigned v4, unsigned v5, unsigned v6, unsigned v7) + { + val = _mm256_setr_epi32((unsigned)v0, (unsigned)v1, (unsigned)v2, + (unsigned)v3, (unsigned)v4, (unsigned)v5, (unsigned)v6, (unsigned)v7); + } + v_uint32x8() : val(_mm256_setzero_si256()) {} + unsigned get0() const { return (unsigned)_v_cvtsi256_si32(val); } +}; + +struct v_int32x8 +{ + typedef int lane_type; + enum { nlanes = 8 }; + __m256i val; + + explicit v_int32x8(__m256i v) : val(v) {} + v_int32x8(int v0, int v1, int v2, int v3, + int v4, int v5, int v6, int v7) + { + val = _mm256_setr_epi32(v0, v1, v2, v3, v4, v5, v6, v7); + } + v_int32x8() : val(_mm256_setzero_si256()) {} + int get0() const { return _v_cvtsi256_si32(val); } +}; + +struct v_float32x8 +{ + typedef float lane_type; + enum { nlanes = 8 }; + __m256 val; + + explicit v_float32x8(__m256 v) : val(v) {} + v_float32x8(float v0, float v1, float v2, float v3, + float v4, float v5, float v6, float v7) + { + val = _mm256_setr_ps(v0, v1, v2, v3, v4, v5, v6, v7); + } + v_float32x8() : val(_mm256_setzero_ps()) {} + float get0() const { return _mm_cvtss_f32(_mm256_castps256_ps128(val)); } +}; + +struct v_uint64x4 +{ + typedef uint64 lane_type; + enum { nlanes = 4 }; + __m256i val; + + explicit v_uint64x4(__m256i v) : val(v) {} + v_uint64x4(uint64 v0, uint64 v1, uint64 v2, uint64 v3) + { val = _mm256_setr_epi64x((int64)v0, (int64)v1, (int64)v2, (int64)v3); } + v_uint64x4() : val(_mm256_setzero_si256()) {} + uint64 get0() const + { + #if defined __x86_64__ || defined _M_X64 + return (uint64)_mm_cvtsi128_si64(_mm256_castsi256_si128(val)); + #else + int a = _mm_cvtsi128_si32(_mm256_castsi256_si128(val)); + int b = _mm_cvtsi128_si32(_mm256_castsi256_si128(_mm256_srli_epi64(val, 32))); + return (unsigned)a | ((uint64)(unsigned)b << 32); + #endif + } +}; + +struct v_int64x4 +{ + typedef int64 lane_type; + enum { nlanes = 4 }; + __m256i val; + + explicit v_int64x4(__m256i v) : val(v) {} + v_int64x4(int64 v0, int64 v1, int64 v2, int64 v3) + { val = _mm256_setr_epi64x(v0, v1, v2, v3); } + v_int64x4() : val(_mm256_setzero_si256()) {} + + int64 get0() const + { + #if defined __x86_64__ || defined _M_X64 + return (int64)_mm_cvtsi128_si64(_mm256_castsi256_si128(val)); + #else + int a = _mm_cvtsi128_si32(_mm256_castsi256_si128(val)); + int b = _mm_cvtsi128_si32(_mm256_castsi256_si128(_mm256_srli_epi64(val, 32))); + return (int64)((unsigned)a | ((uint64)(unsigned)b << 32)); + #endif + } +}; + +struct v_float64x4 +{ + typedef double lane_type; + enum { nlanes = 4 }; + __m256d val; + + explicit v_float64x4(__m256d v) : val(v) {} + v_float64x4(double v0, double v1, double v2, double v3) + { val = _mm256_setr_pd(v0, v1, v2, v3); } + v_float64x4() : val(_mm256_setzero_pd()) {} + double get0() const { return _mm_cvtsd_f64(_mm256_castpd256_pd128(val)); } +}; + +//////////////// Load and store operations /////////////// + +#define OPENCV_HAL_IMPL_AVX_LOADSTORE(_Tpvec, _Tp) \ + inline _Tpvec v256_load(const _Tp* ptr) \ + { return _Tpvec(_mm256_loadu_si256((const __m256i*)ptr)); } \ + inline _Tpvec v256_load_aligned(const _Tp* ptr) \ + { return _Tpvec(_mm256_load_si256((const __m256i*)ptr)); } \ + inline _Tpvec v256_load_low(const _Tp* ptr) \ + { \ + __m128i v128 = _mm_loadu_si128((const __m128i*)ptr); \ + return _Tpvec(_mm256_castsi128_si256(v128)); \ + } \ + inline _Tpvec v256_load_halves(const _Tp* ptr0, const _Tp* ptr1) \ + { \ + __m128i vlo = _mm_loadu_si128((const __m128i*)ptr0); \ + __m128i vhi = _mm_loadu_si128((const __m128i*)ptr1); \ + return _Tpvec(_v256_combine(vlo, vhi)); \ + } \ + inline void v_store(_Tp* ptr, const _Tpvec& a) \ + { _mm256_storeu_si256((__m256i*)ptr, a.val); } \ + inline void v_store_aligned(_Tp* ptr, const _Tpvec& a) \ + { _mm256_store_si256((__m256i*)ptr, a.val); } \ + inline void v_store_aligned_nocache(_Tp* ptr, const _Tpvec& a) \ + { _mm256_stream_si256((__m256i*)ptr, a.val); } \ + inline void v_store(_Tp* ptr, const _Tpvec& a, hal::StoreMode mode) \ + { \ + if( mode == hal::STORE_UNALIGNED ) \ + _mm256_storeu_si256((__m256i*)ptr, a.val); \ + else if( mode == hal::STORE_ALIGNED_NOCACHE ) \ + _mm256_stream_si256((__m256i*)ptr, a.val); \ + else \ + _mm256_store_si256((__m256i*)ptr, a.val); \ + } \ + inline void v_store_low(_Tp* ptr, const _Tpvec& a) \ + { _mm_storeu_si128((__m128i*)ptr, _v256_extract_low(a.val)); } \ + inline void v_store_high(_Tp* ptr, const _Tpvec& a) \ + { _mm_storeu_si128((__m128i*)ptr, _v256_extract_high(a.val)); } + +OPENCV_HAL_IMPL_AVX_LOADSTORE(v_uint8x32, uchar) +OPENCV_HAL_IMPL_AVX_LOADSTORE(v_int8x32, schar) +OPENCV_HAL_IMPL_AVX_LOADSTORE(v_uint16x16, ushort) +OPENCV_HAL_IMPL_AVX_LOADSTORE(v_int16x16, short) +OPENCV_HAL_IMPL_AVX_LOADSTORE(v_uint32x8, unsigned) +OPENCV_HAL_IMPL_AVX_LOADSTORE(v_int32x8, int) +OPENCV_HAL_IMPL_AVX_LOADSTORE(v_uint64x4, uint64) +OPENCV_HAL_IMPL_AVX_LOADSTORE(v_int64x4, int64) + +#define OPENCV_HAL_IMPL_AVX_LOADSTORE_FLT(_Tpvec, _Tp, suffix, halfreg) \ + inline _Tpvec v256_load(const _Tp* ptr) \ + { return _Tpvec(_mm256_loadu_##suffix(ptr)); } \ + inline _Tpvec v256_load_aligned(const _Tp* ptr) \ + { return _Tpvec(_mm256_load_##suffix(ptr)); } \ + inline _Tpvec v256_load_low(const _Tp* ptr) \ + { \ + return _Tpvec(_mm256_cast##suffix##128_##suffix##256 \ + (_mm_loadu_##suffix(ptr))); \ + } \ + inline _Tpvec v256_load_halves(const _Tp* ptr0, const _Tp* ptr1) \ + { \ + halfreg vlo = _mm_loadu_##suffix(ptr0); \ + halfreg vhi = _mm_loadu_##suffix(ptr1); \ + return _Tpvec(_v256_combine(vlo, vhi)); \ + } \ + inline void v_store(_Tp* ptr, const _Tpvec& a) \ + { _mm256_storeu_##suffix(ptr, a.val); } \ + inline void v_store_aligned(_Tp* ptr, const _Tpvec& a) \ + { _mm256_store_##suffix(ptr, a.val); } \ + inline void v_store_aligned_nocache(_Tp* ptr, const _Tpvec& a) \ + { _mm256_stream_##suffix(ptr, a.val); } \ + inline void v_store(_Tp* ptr, const _Tpvec& a, hal::StoreMode mode) \ + { \ + if( mode == hal::STORE_UNALIGNED ) \ + _mm256_storeu_##suffix(ptr, a.val); \ + else if( mode == hal::STORE_ALIGNED_NOCACHE ) \ + _mm256_stream_##suffix(ptr, a.val); \ + else \ + _mm256_store_##suffix(ptr, a.val); \ + } \ + inline void v_store_low(_Tp* ptr, const _Tpvec& a) \ + { _mm_storeu_##suffix(ptr, _v256_extract_low(a.val)); } \ + inline void v_store_high(_Tp* ptr, const _Tpvec& a) \ + { _mm_storeu_##suffix(ptr, _v256_extract_high(a.val)); } + +OPENCV_HAL_IMPL_AVX_LOADSTORE_FLT(v_float32x8, float, ps, __m128) +OPENCV_HAL_IMPL_AVX_LOADSTORE_FLT(v_float64x4, double, pd, __m128d) + +#define OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, _Tpvecf, suffix, cast) \ + inline _Tpvec v_reinterpret_as_##suffix(const _Tpvecf& a) \ + { return _Tpvec(cast(a.val)); } + +#define OPENCV_HAL_IMPL_AVX_INIT(_Tpvec, _Tp, suffix, ssuffix, ctype_s) \ + inline _Tpvec v256_setzero_##suffix() \ + { return _Tpvec(_mm256_setzero_si256()); } \ + inline _Tpvec v256_setall_##suffix(_Tp v) \ + { return _Tpvec(_mm256_set1_##ssuffix((ctype_s)v)); } \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_uint8x32, suffix, OPENCV_HAL_NOP) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_int8x32, suffix, OPENCV_HAL_NOP) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_uint16x16, suffix, OPENCV_HAL_NOP) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_int16x16, suffix, OPENCV_HAL_NOP) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_uint32x8, suffix, OPENCV_HAL_NOP) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_int32x8, suffix, OPENCV_HAL_NOP) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_uint64x4, suffix, OPENCV_HAL_NOP) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_int64x4, suffix, OPENCV_HAL_NOP) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_float32x8, suffix, _mm256_castps_si256) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_float64x4, suffix, _mm256_castpd_si256) + +OPENCV_HAL_IMPL_AVX_INIT(v_uint8x32, uchar, u8, epi8, char) +OPENCV_HAL_IMPL_AVX_INIT(v_int8x32, schar, s8, epi8, char) +OPENCV_HAL_IMPL_AVX_INIT(v_uint16x16, ushort, u16, epi16, short) +OPENCV_HAL_IMPL_AVX_INIT(v_int16x16, short, s16, epi16, short) +OPENCV_HAL_IMPL_AVX_INIT(v_uint32x8, unsigned, u32, epi32, int) +OPENCV_HAL_IMPL_AVX_INIT(v_int32x8, int, s32, epi32, int) +OPENCV_HAL_IMPL_AVX_INIT(v_uint64x4, uint64, u64, epi64x, int64) +OPENCV_HAL_IMPL_AVX_INIT(v_int64x4, int64, s64, epi64x, int64) + +#define OPENCV_HAL_IMPL_AVX_INIT_FLT(_Tpvec, _Tp, suffix, zsuffix, cast) \ + inline _Tpvec v256_setzero_##suffix() \ + { return _Tpvec(_mm256_setzero_##zsuffix()); } \ + inline _Tpvec v256_setall_##suffix(_Tp v) \ + { return _Tpvec(_mm256_set1_##zsuffix(v)); } \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_uint8x32, suffix, cast) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_int8x32, suffix, cast) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_uint16x16, suffix, cast) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_int16x16, suffix, cast) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_uint32x8, suffix, cast) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_int32x8, suffix, cast) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_uint64x4, suffix, cast) \ + OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_int64x4, suffix, cast) + +OPENCV_HAL_IMPL_AVX_INIT_FLT(v_float32x8, float, f32, ps, _mm256_castsi256_ps) +OPENCV_HAL_IMPL_AVX_INIT_FLT(v_float64x4, double, f64, pd, _mm256_castsi256_pd) + +inline v_float32x8 v_reinterpret_as_f32(const v_float32x8& a) +{ return a; } +inline v_float32x8 v_reinterpret_as_f32(const v_float64x4& a) +{ return v_float32x8(_mm256_castpd_ps(a.val)); } + +inline v_float64x4 v_reinterpret_as_f64(const v_float64x4& a) +{ return a; } +inline v_float64x4 v_reinterpret_as_f64(const v_float32x8& a) +{ return v_float64x4(_mm256_castps_pd(a.val)); } + +#if CV_FP16 +inline v_float32x8 v256_load_fp16_f32(const short* ptr) +{ + return v_float32x8(_mm256_cvtph_ps(_mm_loadu_si128((const __m128i*)ptr))); +} + +inline void v_store_fp16(short* ptr, const v_float32x8& a) +{ + __m128i fp16_value = _mm256_cvtps_ph(a.val, 0); + _mm_store_si128((__m128i*)ptr, fp16_value); +} +#endif + +/* Recombine */ +/*#define OPENCV_HAL_IMPL_AVX_COMBINE(_Tpvec, perm) \ + inline _Tpvec v_combine_low(const _Tpvec& a, const _Tpvec& b) \ + { return _Tpvec(perm(a.val, b.val, 0x20)); } \ + inline _Tpvec v_combine_high(const _Tpvec& a, const _Tpvec& b) \ + { return _Tpvec(perm(a.val, b.val, 0x31)); } \ + inline void v_recombine(const _Tpvec& a, const _Tpvec& b, \ + _Tpvec& c, _Tpvec& d) \ + { c = v_combine_low(a, b); d = v_combine_high(a, b); } + +#define OPENCV_HAL_IMPL_AVX_UNPACKS(_Tpvec, suffix) \ + OPENCV_HAL_IMPL_AVX_COMBINE(_Tpvec, _mm256_permute2x128_si256) \ + inline void v_zip(const _Tpvec& a0, const _Tpvec& a1, \ + _Tpvec& b0, _Tpvec& b1) \ + { \ + __m256i v0 = _v256_shuffle_odd_64(a0.val); \ + __m256i v1 = _v256_shuffle_odd_64(a1.val); \ + b0.val = _mm256_unpacklo_##suffix(v0, v1); \ + b1.val = _mm256_unpackhi_##suffix(v0, v1); \ + } + +OPENCV_HAL_IMPL_AVX_UNPACKS(v_uint8x32, epi8) +OPENCV_HAL_IMPL_AVX_UNPACKS(v_int8x32, epi8) +OPENCV_HAL_IMPL_AVX_UNPACKS(v_uint16x16, epi16) +OPENCV_HAL_IMPL_AVX_UNPACKS(v_int16x16, epi16) +OPENCV_HAL_IMPL_AVX_UNPACKS(v_uint32x8, epi32) +OPENCV_HAL_IMPL_AVX_UNPACKS(v_int32x8, epi32) +OPENCV_HAL_IMPL_AVX_UNPACKS(v_uint64x4, epi64) +OPENCV_HAL_IMPL_AVX_UNPACKS(v_int64x4, epi64) +OPENCV_HAL_IMPL_AVX_COMBINE(v_float32x8, _mm256_permute2f128_ps) +OPENCV_HAL_IMPL_AVX_COMBINE(v_float64x4, _mm256_permute2f128_pd) + +inline void v_zip(const v_float32x8& a0, const v_float32x8& a1, v_float32x8& b0, v_float32x8& b1) +{ + __m256 v0 = _mm256_unpacklo_ps(a0.val, a1.val); + __m256 v1 = _mm256_unpackhi_ps(a0.val, a1.val); + v_recombine(v_float32x8(v0), v_float32x8(v1), b0, b1); +} + +inline void v_zip(const v_float64x4& a0, const v_float64x4& a1, v_float64x4& b0, v_float64x4& b1) +{ + __m256d v0 = _v_shuffle_odd_64(a0.val); + __m256d v1 = _v_shuffle_odd_64(a1.val); + b0.val = _mm256_unpacklo_pd(v0, v1); + b1.val = _mm256_unpackhi_pd(v0, v1); +}*/ + +//////////////// Variant Value reordering /////////////// + +// unpacks +#define OPENCV_HAL_IMPL_AVX_UNPACK(_Tpvec, suffix) \ + inline _Tpvec v256_unpacklo(const _Tpvec& a, const _Tpvec& b) \ + { return _Tpvec(_mm256_unpacklo_##suffix(a.val, b.val)); } \ + inline _Tpvec v256_unpackhi(const _Tpvec& a, const _Tpvec& b) \ + { return _Tpvec(_mm256_unpackhi_##suffix(a.val, b.val)); } + +OPENCV_HAL_IMPL_AVX_UNPACK(v_uint8x32, epi8) +OPENCV_HAL_IMPL_AVX_UNPACK(v_int8x32, epi8) +OPENCV_HAL_IMPL_AVX_UNPACK(v_uint16x16, epi16) +OPENCV_HAL_IMPL_AVX_UNPACK(v_int16x16, epi16) +OPENCV_HAL_IMPL_AVX_UNPACK(v_uint32x8, epi32) +OPENCV_HAL_IMPL_AVX_UNPACK(v_int32x8, epi32) +OPENCV_HAL_IMPL_AVX_UNPACK(v_uint64x4, epi64) +OPENCV_HAL_IMPL_AVX_UNPACK(v_int64x4, epi64) +OPENCV_HAL_IMPL_AVX_UNPACK(v_float32x8, ps) +OPENCV_HAL_IMPL_AVX_UNPACK(v_float64x4, pd) + +// blend +#define OPENCV_HAL_IMPL_AVX_BLEND(_Tpvec, suffix) \ + template \ + inline _Tpvec v256_blend(const _Tpvec& a, const _Tpvec& b) \ + { return _Tpvec(_mm256_blend_##suffix(a.val, b.val, m)); } + +OPENCV_HAL_IMPL_AVX_BLEND(v_uint16x16, epi16) +OPENCV_HAL_IMPL_AVX_BLEND(v_int16x16, epi16) +OPENCV_HAL_IMPL_AVX_BLEND(v_uint32x8, epi32) +OPENCV_HAL_IMPL_AVX_BLEND(v_int32x8, epi32) +OPENCV_HAL_IMPL_AVX_BLEND(v_float32x8, ps) +OPENCV_HAL_IMPL_AVX_BLEND(v_float64x4, pd) + +template +inline v_uint64x4 v256_blend(const v_uint64x4& a, const v_uint64x4& b) +{ + enum {M0 = m}; + enum {M1 = (M0 | (M0 << 2)) & 0x33}; + enum {M2 = (M1 | (M1 << 1)) & 0x55}; + enum {MM = M2 | (M2 << 1)}; + return v_uint64x4(_mm256_blend_epi32(a.val, b.val, MM)); +} +template +inline v_int64x4 v256_blend(const v_int64x4& a, const v_int64x4& b) +{ return v_int64x4(v256_blend(v_uint64x4(a.val), v_uint64x4(b.val)).val); } + +// shuffle +// todo: emluate 64bit +#define OPENCV_HAL_IMPL_AVX_SHUFFLE(_Tpvec, intrin) \ + template \ + inline _Tpvec v256_shuffle(const _Tpvec& a) \ + { return _Tpvec(_mm256_##intrin(a.val, m)); } + +OPENCV_HAL_IMPL_AVX_SHUFFLE(v_uint32x8, shuffle_epi32) +OPENCV_HAL_IMPL_AVX_SHUFFLE(v_int32x8, shuffle_epi32) +OPENCV_HAL_IMPL_AVX_SHUFFLE(v_float32x8, permute_ps) +OPENCV_HAL_IMPL_AVX_SHUFFLE(v_float64x4, permute_pd) + +template +inline void v256_zip(const _Tpvec& a, const _Tpvec& b, _Tpvec& ab0, _Tpvec& ab1) +{ + ab0 = v256_unpacklo(a, b); + ab1 = v256_unpackhi(a, b); +} + +template +inline _Tpvec v256_combine_diagonal(const _Tpvec& a, const _Tpvec& b) +{ return _Tpvec(_mm256_blend_epi32(a.val, b.val, 0xf0)); } + +inline v_float32x8 v256_combine_diagonal(const v_float32x8& a, const v_float32x8& b) +{ return v256_blend<0xf0>(a, b); } + +inline v_float64x4 v256_combine_diagonal(const v_float64x4& a, const v_float64x4& b) +{ return v256_blend<0xc>(a, b); } + +template +inline _Tpvec v256_alignr_128(const _Tpvec& a, const _Tpvec& b) +{ return v256_permute2x128<0x21>(a, b); } + +template +inline _Tpvec v256_alignr_64(const _Tpvec& a, const _Tpvec& b) +{ return _Tpvec(_mm256_alignr_epi8(a.val, b.val, 8)); } +inline v_float64x4 v256_alignr_64(const v_float64x4& a, const v_float64x4& b) +{ return v_float64x4(_mm256_shuffle_pd(b.val, a.val, _MM_SHUFFLE(0, 0, 1, 1))); } +// todo: emulate float32 + +template +inline _Tpvec v256_swap_halves(const _Tpvec& a) +{ return v256_permute2x128<1>(a, a); } + +template +inline _Tpvec v256_reverse_64(const _Tpvec& a) +{ return v256_permute4x64<_MM_SHUFFLE(0, 1, 2, 3)>(a); } + +// ZIP +#define OPENCV_HAL_IMPL_AVX_ZIP(_Tpvec) \ + inline _Tpvec v_combine_low(const _Tpvec& a, const _Tpvec& b) \ + { return v256_permute2x128<0x20>(a, b); } \ + inline _Tpvec v_combine_high(const _Tpvec& a, const _Tpvec& b) \ + { return v256_permute2x128<0x31>(a, b); } \ + inline void v_recombine(const _Tpvec& a, const _Tpvec& b, \ + _Tpvec& c, _Tpvec& d) \ + { \ + _Tpvec a1b0 = v256_alignr_128(a, b); \ + c = v256_combine_diagonal(a, a1b0); \ + d = v256_combine_diagonal(a1b0, b); \ + } \ + inline void v_zip(const _Tpvec& a, const _Tpvec& b, \ + _Tpvec& ab0, _Tpvec& ab1) \ + { \ + _Tpvec ab0ab2, ab1ab3; \ + v256_zip(a, b, ab0ab2, ab1ab3); \ + v_recombine(ab0ab2, ab1ab3, ab0, ab1); \ + } + +OPENCV_HAL_IMPL_AVX_ZIP(v_uint8x32) +OPENCV_HAL_IMPL_AVX_ZIP(v_int8x32) +OPENCV_HAL_IMPL_AVX_ZIP(v_uint16x16) +OPENCV_HAL_IMPL_AVX_ZIP(v_int16x16) +OPENCV_HAL_IMPL_AVX_ZIP(v_uint32x8) +OPENCV_HAL_IMPL_AVX_ZIP(v_int32x8) +OPENCV_HAL_IMPL_AVX_ZIP(v_uint64x4) +OPENCV_HAL_IMPL_AVX_ZIP(v_int64x4) +OPENCV_HAL_IMPL_AVX_ZIP(v_float32x8) +OPENCV_HAL_IMPL_AVX_ZIP(v_float64x4) + +////////// Arithmetic, bitwise and comparison operations ///////// + +/* Element-wise binary and unary operations */ + +/** Arithmetics **/ +#define OPENCV_HAL_IMPL_AVX_BIN_OP(bin_op, _Tpvec, intrin) \ + inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b) \ + { return _Tpvec(intrin(a.val, b.val)); } \ + inline _Tpvec& operator bin_op##= (_Tpvec& a, const _Tpvec& b) \ + { a.val = intrin(a.val, b.val); return a; } + +OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_uint8x32, _mm256_adds_epu8) +OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_uint8x32, _mm256_subs_epu8) +OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_int8x32, _mm256_adds_epi8) +OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_int8x32, _mm256_subs_epi8) +OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_uint16x16, _mm256_adds_epu16) +OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_uint16x16, _mm256_subs_epu16) +OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_int16x16, _mm256_adds_epi16) +OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_int16x16, _mm256_subs_epi16) +OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_uint32x8, _mm256_add_epi32) +OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_uint32x8, _mm256_sub_epi32) +OPENCV_HAL_IMPL_AVX_BIN_OP(*, v_uint32x8, _mm256_mullo_epi32) +OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_int32x8, _mm256_add_epi32) +OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_int32x8, _mm256_sub_epi32) +OPENCV_HAL_IMPL_AVX_BIN_OP(*, v_int32x8, _mm256_mullo_epi32) +OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_uint64x4, _mm256_add_epi64) +OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_uint64x4, _mm256_sub_epi64) +OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_int64x4, _mm256_add_epi64) +OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_int64x4, _mm256_sub_epi64) + +OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_float32x8, _mm256_add_ps) +OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_float32x8, _mm256_sub_ps) +OPENCV_HAL_IMPL_AVX_BIN_OP(*, v_float32x8, _mm256_mul_ps) +OPENCV_HAL_IMPL_AVX_BIN_OP(/, v_float32x8, _mm256_div_ps) +OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_float64x4, _mm256_add_pd) +OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_float64x4, _mm256_sub_pd) +OPENCV_HAL_IMPL_AVX_BIN_OP(*, v_float64x4, _mm256_mul_pd) +OPENCV_HAL_IMPL_AVX_BIN_OP(/, v_float64x4, _mm256_div_pd) + +// saturating multiply 8-bit, 16-bit +inline v_uint8x32 operator * (const v_uint8x32& a, const v_uint8x32& b) +{ + v_uint16x16 c, d; + v_mul_expand(a, b, c, d); + return v_pack(c, d); +} +inline v_int8x32 operator * (const v_int8x32& a, const v_int8x32& b) +{ + v_int16x16 c, d; + v_mul_expand(a, b, c, d); + return v_pack(c, d); +} +inline v_uint16x16 operator * (const v_uint16x16& a, const v_uint16x16& b) +{ + __m256i pl = _mm256_mullo_epi16(a.val, b.val); + __m256i ph = _mm256_mulhi_epu16(a.val, b.val); + __m256i p0 = _mm256_unpacklo_epi16(pl, ph); + __m256i p1 = _mm256_unpackhi_epi16(pl, ph); + return v_uint16x16(_v256_packs_epu32(p0, p1)); +} +inline v_int16x16 operator * (const v_int16x16& a, const v_int16x16& b) +{ + __m256i pl = _mm256_mullo_epi16(a.val, b.val); + __m256i ph = _mm256_mulhi_epi16(a.val, b.val); + __m256i p0 = _mm256_unpacklo_epi16(pl, ph); + __m256i p1 = _mm256_unpackhi_epi16(pl, ph); + return v_int16x16(_mm256_packs_epi32(p0, p1)); +} +inline v_uint8x32& operator *= (v_uint8x32& a, const v_uint8x32& b) +{ a = a * b; return a; } +inline v_int8x32& operator *= (v_int8x32& a, const v_int8x32& b) +{ a = a * b; return a; } +inline v_uint16x16& operator *= (v_uint16x16& a, const v_uint16x16& b) +{ a = a * b; return a; } +inline v_int16x16& operator *= (v_int16x16& a, const v_int16x16& b) +{ a = a * b; return a; } + +/** Non-saturating arithmetics **/ +#define OPENCV_HAL_IMPL_AVX_BIN_FUNC(func, _Tpvec, intrin) \ + inline _Tpvec func(const _Tpvec& a, const _Tpvec& b) \ + { return _Tpvec(intrin(a.val, b.val)); } + +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_add_wrap, v_uint8x32, _mm256_add_epi8) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_add_wrap, v_int8x32, _mm256_add_epi8) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_add_wrap, v_uint16x16, _mm256_add_epi16) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_add_wrap, v_int16x16, _mm256_add_epi16) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_sub_wrap, v_uint8x32, _mm256_sub_epi8) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_sub_wrap, v_int8x32, _mm256_sub_epi8) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_sub_wrap, v_uint16x16, _mm256_sub_epi16) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_sub_wrap, v_int16x16, _mm256_sub_epi16) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_mul_wrap, v_uint16x16, _mm256_mullo_epi16) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_mul_wrap, v_int16x16, _mm256_mullo_epi16) + +inline v_uint8x32 v_mul_wrap(const v_uint8x32& a, const v_uint8x32& b) +{ + __m256i ad = _mm256_srai_epi16(a.val, 8); + __m256i bd = _mm256_srai_epi16(b.val, 8); + __m256i p0 = _mm256_mullo_epi16(a.val, b.val); // even + __m256i p1 = _mm256_slli_epi16(_mm256_mullo_epi16(ad, bd), 8); // odd + + const __m256i b01 = _mm256_set1_epi32(0xFF00FF00); + return v_uint8x32(_mm256_blendv_epi8(p0, p1, b01)); +} +inline v_int8x32 v_mul_wrap(const v_int8x32& a, const v_int8x32& b) +{ + return v_reinterpret_as_s8(v_mul_wrap(v_reinterpret_as_u8(a), v_reinterpret_as_u8(b))); +} + +// Multiply and expand +inline void v_mul_expand(const v_uint8x32& a, const v_uint8x32& b, + v_uint16x16& c, v_uint16x16& d) +{ + v_uint16x16 a0, a1, b0, b1; + v_expand(a, a0, a1); + v_expand(b, b0, b1); + c = v_mul_wrap(a0, b0); + d = v_mul_wrap(a1, b1); +} + +inline void v_mul_expand(const v_int8x32& a, const v_int8x32& b, + v_int16x16& c, v_int16x16& d) +{ + v_int16x16 a0, a1, b0, b1; + v_expand(a, a0, a1); + v_expand(b, b0, b1); + c = v_mul_wrap(a0, b0); + d = v_mul_wrap(a1, b1); +} + +inline void v_mul_expand(const v_int16x16& a, const v_int16x16& b, + v_int32x8& c, v_int32x8& d) +{ + v_int16x16 vhi = v_int16x16(_mm256_mulhi_epi16(a.val, b.val)); + + v_int16x16 v0, v1; + v_zip(v_mul_wrap(a, b), vhi, v0, v1); + + c = v_reinterpret_as_s32(v0); + d = v_reinterpret_as_s32(v1); +} + +inline void v_mul_expand(const v_uint16x16& a, const v_uint16x16& b, + v_uint32x8& c, v_uint32x8& d) +{ + v_uint16x16 vhi = v_uint16x16(_mm256_mulhi_epu16(a.val, b.val)); + + v_uint16x16 v0, v1; + v_zip(v_mul_wrap(a, b), vhi, v0, v1); + + c = v_reinterpret_as_u32(v0); + d = v_reinterpret_as_u32(v1); +} + +inline void v_mul_expand(const v_uint32x8& a, const v_uint32x8& b, + v_uint64x4& c, v_uint64x4& d) +{ + __m256i v0 = _mm256_mul_epu32(a.val, b.val); + __m256i v1 = _mm256_mul_epu32(_mm256_srli_epi64(a.val, 32), _mm256_srli_epi64(b.val, 32)); + v_zip(v_uint64x4(v0), v_uint64x4(v1), c, d); +} + +inline v_int16x16 v_mul_hi(const v_int16x16& a, const v_int16x16& b) { return v_int16x16(_mm256_mulhi_epi16(a.val, b.val)); } +inline v_uint16x16 v_mul_hi(const v_uint16x16& a, const v_uint16x16& b) { return v_uint16x16(_mm256_mulhi_epu16(a.val, b.val)); } + +/** Bitwise shifts **/ +#define OPENCV_HAL_IMPL_AVX_SHIFT_OP(_Tpuvec, _Tpsvec, suffix, srai) \ + inline _Tpuvec operator << (const _Tpuvec& a, int imm) \ + { return _Tpuvec(_mm256_slli_##suffix(a.val, imm)); } \ + inline _Tpsvec operator << (const _Tpsvec& a, int imm) \ + { return _Tpsvec(_mm256_slli_##suffix(a.val, imm)); } \ + inline _Tpuvec operator >> (const _Tpuvec& a, int imm) \ + { return _Tpuvec(_mm256_srli_##suffix(a.val, imm)); } \ + inline _Tpsvec operator >> (const _Tpsvec& a, int imm) \ + { return _Tpsvec(srai(a.val, imm)); } \ + template \ + inline _Tpuvec v_shl(const _Tpuvec& a) \ + { return _Tpuvec(_mm256_slli_##suffix(a.val, imm)); } \ + template \ + inline _Tpsvec v_shl(const _Tpsvec& a) \ + { return _Tpsvec(_mm256_slli_##suffix(a.val, imm)); } \ + template \ + inline _Tpuvec v_shr(const _Tpuvec& a) \ + { return _Tpuvec(_mm256_srli_##suffix(a.val, imm)); } \ + template \ + inline _Tpsvec v_shr(const _Tpsvec& a) \ + { return _Tpsvec(srai(a.val, imm)); } + +OPENCV_HAL_IMPL_AVX_SHIFT_OP(v_uint16x16, v_int16x16, epi16, _mm256_srai_epi16) +OPENCV_HAL_IMPL_AVX_SHIFT_OP(v_uint32x8, v_int32x8, epi32, _mm256_srai_epi32) + +inline __m256i _mm256_srai_epi64xx(const __m256i a, int imm) +{ + __m256i d = _mm256_set1_epi64x((int64)1 << 63); + __m256i r = _mm256_srli_epi64(_mm256_add_epi64(a, d), imm); + return _mm256_sub_epi64(r, _mm256_srli_epi64(d, imm)); +} +OPENCV_HAL_IMPL_AVX_SHIFT_OP(v_uint64x4, v_int64x4, epi64, _mm256_srai_epi64xx) + + +/** Bitwise logic **/ +#define OPENCV_HAL_IMPL_AVX_LOGIC_OP(_Tpvec, suffix, not_const) \ + OPENCV_HAL_IMPL_AVX_BIN_OP(&, _Tpvec, _mm256_and_##suffix) \ + OPENCV_HAL_IMPL_AVX_BIN_OP(|, _Tpvec, _mm256_or_##suffix) \ + OPENCV_HAL_IMPL_AVX_BIN_OP(^, _Tpvec, _mm256_xor_##suffix) \ + inline _Tpvec operator ~ (const _Tpvec& a) \ + { return _Tpvec(_mm256_xor_##suffix(a.val, not_const)); } + +OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_uint8x32, si256, _mm256_set1_epi32(-1)) +OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_int8x32, si256, _mm256_set1_epi32(-1)) +OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_uint16x16, si256, _mm256_set1_epi32(-1)) +OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_int16x16, si256, _mm256_set1_epi32(-1)) +OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_uint32x8, si256, _mm256_set1_epi32(-1)) +OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_int32x8, si256, _mm256_set1_epi32(-1)) +OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_uint64x4, si256, _mm256_set1_epi64x(-1)) +OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_int64x4, si256, _mm256_set1_epi64x(-1)) +OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_float32x8, ps, _mm256_castsi256_ps(_mm256_set1_epi32(-1))) +OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_float64x4, pd, _mm256_castsi256_pd(_mm256_set1_epi32(-1))) + +/** Select **/ +#define OPENCV_HAL_IMPL_AVX_SELECT(_Tpvec, suffix) \ + inline _Tpvec v_select(const _Tpvec& mask, const _Tpvec& a, const _Tpvec& b) \ + { return _Tpvec(_mm256_blendv_##suffix(b.val, a.val, mask.val)); } + +OPENCV_HAL_IMPL_AVX_SELECT(v_uint8x32, epi8) +OPENCV_HAL_IMPL_AVX_SELECT(v_int8x32, epi8) +OPENCV_HAL_IMPL_AVX_SELECT(v_uint16x16, epi8) +OPENCV_HAL_IMPL_AVX_SELECT(v_int16x16, epi8) +OPENCV_HAL_IMPL_AVX_SELECT(v_uint32x8, epi8) +OPENCV_HAL_IMPL_AVX_SELECT(v_int32x8, epi8) +OPENCV_HAL_IMPL_AVX_SELECT(v_float32x8, ps) +OPENCV_HAL_IMPL_AVX_SELECT(v_float64x4, pd) + +/** Comparison **/ +#define OPENCV_HAL_IMPL_AVX_CMP_OP_OV(_Tpvec) \ + inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \ + { return ~(a == b); } \ + inline _Tpvec operator < (const _Tpvec& a, const _Tpvec& b) \ + { return b > a; } \ + inline _Tpvec operator >= (const _Tpvec& a, const _Tpvec& b) \ + { return ~(a < b); } \ + inline _Tpvec operator <= (const _Tpvec& a, const _Tpvec& b) \ + { return b >= a; } + +#define OPENCV_HAL_IMPL_AVX_CMP_OP_INT(_Tpuvec, _Tpsvec, suffix, sbit) \ + inline _Tpuvec operator == (const _Tpuvec& a, const _Tpuvec& b) \ + { return _Tpuvec(_mm256_cmpeq_##suffix(a.val, b.val)); } \ + inline _Tpuvec operator > (const _Tpuvec& a, const _Tpuvec& b) \ + { \ + __m256i smask = _mm256_set1_##suffix(sbit); \ + return _Tpuvec(_mm256_cmpgt_##suffix( \ + _mm256_xor_si256(a.val, smask), \ + _mm256_xor_si256(b.val, smask))); \ + } \ + inline _Tpsvec operator == (const _Tpsvec& a, const _Tpsvec& b) \ + { return _Tpsvec(_mm256_cmpeq_##suffix(a.val, b.val)); } \ + inline _Tpsvec operator > (const _Tpsvec& a, const _Tpsvec& b) \ + { return _Tpsvec(_mm256_cmpgt_##suffix(a.val, b.val)); } \ + OPENCV_HAL_IMPL_AVX_CMP_OP_OV(_Tpuvec) \ + OPENCV_HAL_IMPL_AVX_CMP_OP_OV(_Tpsvec) + +OPENCV_HAL_IMPL_AVX_CMP_OP_INT(v_uint8x32, v_int8x32, epi8, (char)-128) +OPENCV_HAL_IMPL_AVX_CMP_OP_INT(v_uint16x16, v_int16x16, epi16, (short)-32768) +OPENCV_HAL_IMPL_AVX_CMP_OP_INT(v_uint32x8, v_int32x8, epi32, (int)0x80000000) + +#define OPENCV_HAL_IMPL_AVX_CMP_OP_64BIT(_Tpvec) \ + inline _Tpvec operator == (const _Tpvec& a, const _Tpvec& b) \ + { return _Tpvec(_mm256_cmpeq_epi64(a.val, b.val)); } \ + inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \ + { return ~(a == b); } + +OPENCV_HAL_IMPL_AVX_CMP_OP_64BIT(v_uint64x4) +OPENCV_HAL_IMPL_AVX_CMP_OP_64BIT(v_int64x4) + +#define OPENCV_HAL_IMPL_AVX_CMP_FLT(bin_op, imm8, _Tpvec, suffix) \ + inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b) \ + { return _Tpvec(_mm256_cmp_##suffix(a.val, b.val, imm8)); } + +#define OPENCV_HAL_IMPL_AVX_CMP_OP_FLT(_Tpvec, suffix) \ + OPENCV_HAL_IMPL_AVX_CMP_FLT(==, _CMP_EQ_OQ, _Tpvec, suffix) \ + OPENCV_HAL_IMPL_AVX_CMP_FLT(!=, _CMP_NEQ_OQ, _Tpvec, suffix) \ + OPENCV_HAL_IMPL_AVX_CMP_FLT(<, _CMP_LT_OQ, _Tpvec, suffix) \ + OPENCV_HAL_IMPL_AVX_CMP_FLT(>, _CMP_GT_OQ, _Tpvec, suffix) \ + OPENCV_HAL_IMPL_AVX_CMP_FLT(<=, _CMP_LE_OQ, _Tpvec, suffix) \ + OPENCV_HAL_IMPL_AVX_CMP_FLT(>=, _CMP_GE_OQ, _Tpvec, suffix) + +OPENCV_HAL_IMPL_AVX_CMP_OP_FLT(v_float32x8, ps) +OPENCV_HAL_IMPL_AVX_CMP_OP_FLT(v_float64x4, pd) + +inline v_float32x8 v_not_nan(const v_float32x8& a) +{ return v_float32x8(_mm256_cmp_ps(a.val, a.val, _CMP_ORD_Q)); } +inline v_float64x4 v_not_nan(const v_float64x4& a) +{ return v_float64x4(_mm256_cmp_pd(a.val, a.val, _CMP_ORD_Q)); } + +/** min/max **/ +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_min, v_uint8x32, _mm256_min_epu8) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_max, v_uint8x32, _mm256_max_epu8) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_min, v_int8x32, _mm256_min_epi8) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_max, v_int8x32, _mm256_max_epi8) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_min, v_uint16x16, _mm256_min_epu16) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_max, v_uint16x16, _mm256_max_epu16) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_min, v_int16x16, _mm256_min_epi16) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_max, v_int16x16, _mm256_max_epi16) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_min, v_uint32x8, _mm256_min_epu32) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_max, v_uint32x8, _mm256_max_epu32) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_min, v_int32x8, _mm256_min_epi32) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_max, v_int32x8, _mm256_max_epi32) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_min, v_float32x8, _mm256_min_ps) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_max, v_float32x8, _mm256_max_ps) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_min, v_float64x4, _mm256_min_pd) +OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_max, v_float64x4, _mm256_max_pd) + +/** Rotate **/ +template +inline v_uint8x32 v_rotate_left(const v_uint8x32& a, const v_uint8x32& b) +{ + enum {IMM_R = (16 - imm) & 0xFF}; + enum {IMM_R2 = (32 - imm) & 0xFF}; + + if (imm == 0) return a; + if (imm == 32) return b; + if (imm > 32) return v_uint8x32(); + + __m256i swap = _mm256_permute2x128_si256(a.val, b.val, 0x03); + if (imm == 16) return v_uint8x32(swap); + if (imm < 16) return v_uint8x32(_mm256_alignr_epi8(a.val, swap, IMM_R)); + return v_uint8x32(_mm256_alignr_epi8(swap, b.val, IMM_R2)); // imm < 32 +} + +template +inline v_uint8x32 v_rotate_right(const v_uint8x32& a, const v_uint8x32& b) +{ + enum {IMM_L = (imm - 16) & 0xFF}; + + if (imm == 0) return a; + if (imm == 32) return b; + if (imm > 32) return v_uint8x32(); + + __m256i swap = _mm256_permute2x128_si256(a.val, b.val, 0x21); + if (imm == 16) return v_uint8x32(swap); + if (imm < 16) return v_uint8x32(_mm256_alignr_epi8(swap, a.val, imm)); + return v_uint8x32(_mm256_alignr_epi8(b.val, swap, IMM_L)); +} + +template +inline v_uint8x32 v_rotate_left(const v_uint8x32& a) +{ + enum {IMM_L = (imm - 16) & 0xFF}; + enum {IMM_R = (16 - imm) & 0xFF}; + + if (imm == 0) return a; + if (imm > 32) return v_uint8x32(); + + // ESAC control[3] ? [127:0] = 0 + __m256i swapz = _mm256_permute2x128_si256(a.val, a.val, _MM_SHUFFLE(0, 0, 2, 0)); + if (imm == 16) return v_uint8x32(swapz); + if (imm < 16) return v_uint8x32(_mm256_alignr_epi8(a.val, swapz, IMM_R)); + return v_uint8x32(_mm256_slli_si256(swapz, IMM_L)); +} + +template +inline v_uint8x32 v_rotate_right(const v_uint8x32& a) +{ + enum {IMM_L = (imm - 16) & 0xFF}; + + if (imm == 0) return a; + if (imm > 32) return v_uint8x32(); + + // ESAC control[3] ? [127:0] = 0 + __m256i swapz = _mm256_permute2x128_si256(a.val, a.val, _MM_SHUFFLE(2, 0, 0, 1)); + if (imm == 16) return v_uint8x32(swapz); + if (imm < 16) return v_uint8x32(_mm256_alignr_epi8(swapz, a.val, imm)); + return v_uint8x32(_mm256_srli_si256(swapz, IMM_L)); +} + +#define OPENCV_HAL_IMPL_AVX_ROTATE_CAST(intrin, _Tpvec, cast) \ + template \ + inline _Tpvec intrin(const _Tpvec& a, const _Tpvec& b) \ + { \ + enum {IMMxW = imm * sizeof(typename _Tpvec::lane_type)}; \ + v_uint8x32 ret = intrin(v_reinterpret_as_u8(a), \ + v_reinterpret_as_u8(b)); \ + return _Tpvec(cast(ret.val)); \ + } \ + template \ + inline _Tpvec intrin(const _Tpvec& a) \ + { \ + enum {IMMxW = imm * sizeof(typename _Tpvec::lane_type)}; \ + v_uint8x32 ret = intrin(v_reinterpret_as_u8(a)); \ + return _Tpvec(cast(ret.val)); \ + } + +#define OPENCV_HAL_IMPL_AVX_ROTATE(_Tpvec) \ + OPENCV_HAL_IMPL_AVX_ROTATE_CAST(v_rotate_left, _Tpvec, OPENCV_HAL_NOP) \ + OPENCV_HAL_IMPL_AVX_ROTATE_CAST(v_rotate_right, _Tpvec, OPENCV_HAL_NOP) + +OPENCV_HAL_IMPL_AVX_ROTATE(v_int8x32) +OPENCV_HAL_IMPL_AVX_ROTATE(v_uint16x16) +OPENCV_HAL_IMPL_AVX_ROTATE(v_int16x16) +OPENCV_HAL_IMPL_AVX_ROTATE(v_uint32x8) +OPENCV_HAL_IMPL_AVX_ROTATE(v_int32x8) +OPENCV_HAL_IMPL_AVX_ROTATE(v_uint64x4) +OPENCV_HAL_IMPL_AVX_ROTATE(v_int64x4) + +OPENCV_HAL_IMPL_AVX_ROTATE_CAST(v_rotate_left, v_float32x8, _mm256_castsi256_ps) +OPENCV_HAL_IMPL_AVX_ROTATE_CAST(v_rotate_right, v_float32x8, _mm256_castsi256_ps) +OPENCV_HAL_IMPL_AVX_ROTATE_CAST(v_rotate_left, v_float64x4, _mm256_castsi256_pd) +OPENCV_HAL_IMPL_AVX_ROTATE_CAST(v_rotate_right, v_float64x4, _mm256_castsi256_pd) + +////////// Reduce and mask ///////// + +/** Reduce **/ +#define OPENCV_HAL_IMPL_AVX_REDUCE_16(_Tpvec, sctype, func, intrin) \ + inline sctype v_reduce_##func(const _Tpvec& a) \ + { \ + __m128i v0 = _v256_extract_low(a.val); \ + __m128i v1 = _v256_extract_high(a.val); \ + v0 = intrin(v0, v1); \ + v0 = intrin(v0, _mm_srli_si128(v0, 8)); \ + v0 = intrin(v0, _mm_srli_si128(v0, 4)); \ + v0 = intrin(v0, _mm_srli_si128(v0, 2)); \ + return (sctype) _mm_cvtsi128_si32(v0); \ + } + +OPENCV_HAL_IMPL_AVX_REDUCE_16(v_uint16x16, ushort, min, _mm_min_epu16) +OPENCV_HAL_IMPL_AVX_REDUCE_16(v_int16x16, short, min, _mm_min_epi16) +OPENCV_HAL_IMPL_AVX_REDUCE_16(v_uint16x16, ushort, max, _mm_max_epu16) +OPENCV_HAL_IMPL_AVX_REDUCE_16(v_int16x16, short, max, _mm_max_epi16) + +#define OPENCV_HAL_IMPL_AVX_REDUCE_8(_Tpvec, sctype, func, intrin) \ + inline sctype v_reduce_##func(const _Tpvec& a) \ + { \ + __m128i v0 = _v256_extract_low(a.val); \ + __m128i v1 = _v256_extract_high(a.val); \ + v0 = intrin(v0, v1); \ + v0 = intrin(v0, _mm_srli_si128(v0, 8)); \ + v0 = intrin(v0, _mm_srli_si128(v0, 4)); \ + return (sctype) _mm_cvtsi128_si32(v0); \ + } + +OPENCV_HAL_IMPL_AVX_REDUCE_8(v_uint32x8, unsigned, min, _mm_min_epu32) +OPENCV_HAL_IMPL_AVX_REDUCE_8(v_int32x8, int, min, _mm_min_epi32) +OPENCV_HAL_IMPL_AVX_REDUCE_8(v_uint32x8, unsigned, max, _mm_max_epu32) +OPENCV_HAL_IMPL_AVX_REDUCE_8(v_int32x8, int, max, _mm_max_epi32) + +#define OPENCV_HAL_IMPL_AVX_REDUCE_FLT(func, intrin) \ + inline float v_reduce_##func(const v_float32x8& a) \ + { \ + __m128 v0 = _v256_extract_low(a.val); \ + __m128 v1 = _v256_extract_high(a.val); \ + v0 = intrin(v0, v1); \ + v0 = intrin(v0, _mm_permute_ps(v0, _MM_SHUFFLE(0, 0, 3, 2))); \ + v0 = intrin(v0, _mm_permute_ps(v0, _MM_SHUFFLE(0, 0, 0, 3))); \ + return _mm_cvtss_f32(v0); \ + } + +OPENCV_HAL_IMPL_AVX_REDUCE_FLT(min, _mm_min_ps) +OPENCV_HAL_IMPL_AVX_REDUCE_FLT(max, _mm_max_ps) + +inline ushort v_reduce_sum(const v_uint16x16& a) +{ + __m128i a0 = _v256_extract_low(a.val); + __m128i a1 = _v256_extract_high(a.val); + + __m128i s0 = _mm_adds_epu16(a0, a1); + s0 = _mm_adds_epu16(s0, _mm_srli_si128(s0, 8)); + s0 = _mm_adds_epu16(s0, _mm_srli_si128(s0, 4)); + s0 = _mm_adds_epu16(s0, _mm_srli_si128(s0, 2)); + + return (ushort)_mm_cvtsi128_si32(s0); +} + +inline short v_reduce_sum(const v_int16x16& a) +{ + __m256i s0 = _mm256_hadds_epi16(a.val, a.val); + s0 = _mm256_hadds_epi16(s0, s0); + s0 = _mm256_hadds_epi16(s0, s0); + + __m128i s1 = _v256_extract_high(s0); + s1 = _mm_adds_epi16(_v256_extract_low(s0), s1); + + return (short)_mm_cvtsi128_si32(s1); +} + +inline int v_reduce_sum(const v_int32x8& a) +{ + __m256i s0 = _mm256_hadd_epi32(a.val, a.val); + s0 = _mm256_hadd_epi32(s0, s0); + + __m128i s1 = _v256_extract_high(s0); + s1 = _mm_add_epi32(_v256_extract_low(s0), s1); + + return _mm_cvtsi128_si32(s1); +} + +inline unsigned v_reduce_sum(const v_uint32x8& a) +{ return v_reduce_sum(v_reinterpret_as_s32(a)); } + +inline float v_reduce_sum(const v_float32x8& a) +{ + __m256 s0 = _mm256_hadd_ps(a.val, a.val); + s0 = _mm256_hadd_ps(s0, s0); + + __m128 s1 = _v256_extract_high(s0); + s1 = _mm_add_ps(_v256_extract_low(s0), s1); + + return _mm_cvtss_f32(s1); +} + +inline double v_reduce_sum(const v_float64x4& a) +{ + __m256d s0 = _mm256_hadd_pd(a.val, a.val); + return _mm_cvtsd_f64(_mm_add_pd(_v256_extract_low(s0), _v256_extract_high(s0))); +} + +inline v_float32x8 v_reduce_sum4(const v_float32x8& a, const v_float32x8& b, + const v_float32x8& c, const v_float32x8& d) +{ + __m256 ab = _mm256_hadd_ps(a.val, b.val); + __m256 cd = _mm256_hadd_ps(c.val, d.val); + return v_float32x8(_mm256_hadd_ps(ab, cd)); +} + +inline unsigned v_reduce_sad(const v_uint8x32& a, const v_uint8x32& b) +{ + return (unsigned)_v_cvtsi256_si32(_mm256_sad_epu8(a.val, b.val)); +} +inline unsigned v_reduce_sad(const v_int8x32& a, const v_int8x32& b) +{ + __m256i half = _mm256_set1_epi8(0x7f); + return (unsigned)_v_cvtsi256_si32(_mm256_sad_epu8(_mm256_add_epi8(a.val, half), _mm256_add_epi8(b.val, half))); +} +inline unsigned v_reduce_sad(const v_uint16x16& a, const v_uint16x16& b) +{ + v_uint32x8 l, h; + v_expand(v_add_wrap(a - b, b - a), l, h); + return v_reduce_sum(l + h); +} +inline unsigned v_reduce_sad(const v_int16x16& a, const v_int16x16& b) +{ + v_uint32x8 l, h; + v_expand(v_reinterpret_as_u16(v_sub_wrap(v_max(a, b), v_min(a, b))), l, h); + return v_reduce_sum(l + h); +} +inline unsigned v_reduce_sad(const v_uint32x8& a, const v_uint32x8& b) +{ + return v_reduce_sum(v_max(a, b) - v_min(a, b)); +} +inline unsigned v_reduce_sad(const v_int32x8& a, const v_int32x8& b) +{ + v_int32x8 m = a < b; + return v_reduce_sum(v_reinterpret_as_u32(((a - b) ^ m) - m)); +} +inline float v_reduce_sad(const v_float32x8& a, const v_float32x8& b) +{ + return v_reduce_sum((a - b) & v_float32x8(_mm256_castsi256_ps(_mm256_set1_epi32(0x7fffffff)))); +} + +/** Popcount **/ +#define OPENCV_HAL_IMPL_AVX_POPCOUNT(_Tpvec) \ + inline v_uint32x8 v_popcount(const _Tpvec& a) \ + { \ + const v_uint32x8 m1 = v256_setall_u32(0x55555555); \ + const v_uint32x8 m2 = v256_setall_u32(0x33333333); \ + const v_uint32x8 m4 = v256_setall_u32(0x0f0f0f0f); \ + v_uint32x8 p = v_reinterpret_as_u32(a); \ + p = ((p >> 1) & m1) + (p & m1); \ + p = ((p >> 2) & m2) + (p & m2); \ + p = ((p >> 4) & m4) + (p & m4); \ + p.val = _mm256_sad_epu8(p.val, _mm256_setzero_si256()); \ + return p; \ + } + +OPENCV_HAL_IMPL_AVX_POPCOUNT(v_uint8x32) +OPENCV_HAL_IMPL_AVX_POPCOUNT(v_int8x32) +OPENCV_HAL_IMPL_AVX_POPCOUNT(v_uint16x16) +OPENCV_HAL_IMPL_AVX_POPCOUNT(v_int16x16) +OPENCV_HAL_IMPL_AVX_POPCOUNT(v_uint32x8) +OPENCV_HAL_IMPL_AVX_POPCOUNT(v_int32x8) + +/** Mask **/ +inline int v_signmask(const v_int8x32& a) +{ return _mm256_movemask_epi8(a.val); } +inline int v_signmask(const v_uint8x32& a) +{ return v_signmask(v_reinterpret_as_s8(a)); } + +inline int v_signmask(const v_int16x16& a) +{ + v_int8x32 v = v_int8x32(_mm256_packs_epi16(a.val, a.val)); + return v_signmask(v) & 255; +} +inline int v_signmask(const v_uint16x16& a) +{ return v_signmask(v_reinterpret_as_s16(a)); } + +inline int v_signmask(const v_int32x8& a) +{ + __m256i a16 = _mm256_packs_epi32(a.val, a.val); + v_int8x32 v = v_int8x32(_mm256_packs_epi16(a16, a16)); + return v_signmask(v) & 15; +} +inline int v_signmask(const v_uint32x8& a) +{ return v_signmask(v_reinterpret_as_s32(a)); } + +inline int v_signmask(const v_float32x8& a) +{ return _mm256_movemask_ps(a.val); } +inline int v_signmask(const v_float64x4& a) +{ return _mm256_movemask_pd(a.val); } + +/** Checks **/ +#define OPENCV_HAL_IMPL_AVX_CHECK(_Tpvec, and_op, allmask) \ + inline bool v_check_all(const _Tpvec& a) \ + { \ + int mask = v_signmask(v_reinterpret_as_s8(a)); \ + return and_op(mask, allmask) == allmask; \ + } \ + inline bool v_check_any(const _Tpvec& a) \ + { \ + int mask = v_signmask(v_reinterpret_as_s8(a)); \ + return and_op(mask, allmask) != 0; \ + } + +OPENCV_HAL_IMPL_AVX_CHECK(v_uint8x32, OPENCV_HAL_1ST, -1) +OPENCV_HAL_IMPL_AVX_CHECK(v_int8x32, OPENCV_HAL_1ST, -1) +OPENCV_HAL_IMPL_AVX_CHECK(v_uint16x16, OPENCV_HAL_AND, (int)0xaaaa) +OPENCV_HAL_IMPL_AVX_CHECK(v_int16x16, OPENCV_HAL_AND, (int)0xaaaa) +OPENCV_HAL_IMPL_AVX_CHECK(v_uint32x8, OPENCV_HAL_AND, (int)0x8888) +OPENCV_HAL_IMPL_AVX_CHECK(v_int32x8, OPENCV_HAL_AND, (int)0x8888) + +#define OPENCV_HAL_IMPL_AVX_CHECK_FLT(_Tpvec, allmask) \ + inline bool v_check_all(const _Tpvec& a) \ + { \ + int mask = v_signmask(a); \ + return mask == allmask; \ + } \ + inline bool v_check_any(const _Tpvec& a) \ + { \ + int mask = v_signmask(a); \ + return mask != 0; \ + } + +OPENCV_HAL_IMPL_AVX_CHECK_FLT(v_float32x8, 255) +OPENCV_HAL_IMPL_AVX_CHECK_FLT(v_float64x4, 15) + + +////////// Other math ///////// + +/** Some frequent operations **/ +#define OPENCV_HAL_IMPL_AVX_MULADD(_Tpvec, suffix) \ + inline _Tpvec v_fma(const _Tpvec& a, const _Tpvec& b, const _Tpvec& c) \ + { return _Tpvec(_mm256_fmadd_##suffix(a.val, b.val, c.val)); } \ + inline _Tpvec v_muladd(const _Tpvec& a, const _Tpvec& b, const _Tpvec& c) \ + { return _Tpvec(_mm256_fmadd_##suffix(a.val, b.val, c.val)); } \ + inline _Tpvec v_sqrt(const _Tpvec& x) \ + { return _Tpvec(_mm256_sqrt_##suffix(x.val)); } \ + inline _Tpvec v_sqr_magnitude(const _Tpvec& a, const _Tpvec& b) \ + { return v_fma(a, a, b * b); } \ + inline _Tpvec v_magnitude(const _Tpvec& a, const _Tpvec& b) \ + { return v_sqrt(v_fma(a, a, b*b)); } + +OPENCV_HAL_IMPL_AVX_MULADD(v_float32x8, ps) +OPENCV_HAL_IMPL_AVX_MULADD(v_float64x4, pd) + +inline v_int32x8 v_fma(const v_int32x8& a, const v_int32x8& b, const v_int32x8& c) +{ + return a * b + c; +} + +inline v_int32x8 v_muladd(const v_int32x8& a, const v_int32x8& b, const v_int32x8& c) +{ + return v_fma(a, b, c); +} + +inline v_float32x8 v_invsqrt(const v_float32x8& x) +{ + v_float32x8 half = x * v256_setall_f32(0.5); + v_float32x8 t = v_float32x8(_mm256_rsqrt_ps(x.val)); + // todo: _mm256_fnmsub_ps + t *= v256_setall_f32(1.5) - ((t * t) * half); + return t; +} + +inline v_float64x4 v_invsqrt(const v_float64x4& x) +{ + return v256_setall_f64(1.) / v_sqrt(x); +} + +/** Absolute values **/ +#define OPENCV_HAL_IMPL_AVX_ABS(_Tpvec, suffix) \ + inline v_u##_Tpvec v_abs(const v_##_Tpvec& x) \ + { return v_u##_Tpvec(_mm256_abs_##suffix(x.val)); } + +OPENCV_HAL_IMPL_AVX_ABS(int8x32, epi8) +OPENCV_HAL_IMPL_AVX_ABS(int16x16, epi16) +OPENCV_HAL_IMPL_AVX_ABS(int32x8, epi32) + +inline v_float32x8 v_abs(const v_float32x8& x) +{ return x & v_float32x8(_mm256_castsi256_ps(_mm256_set1_epi32(0x7fffffff))); } +inline v_float64x4 v_abs(const v_float64x4& x) +{ return x & v_float64x4(_mm256_castsi256_pd(_mm256_srli_epi64(_mm256_set1_epi64x(-1), 1))); } + +/** Absolute difference **/ +inline v_uint8x32 v_absdiff(const v_uint8x32& a, const v_uint8x32& b) +{ return v_add_wrap(a - b, b - a); } +inline v_uint16x16 v_absdiff(const v_uint16x16& a, const v_uint16x16& b) +{ return v_add_wrap(a - b, b - a); } +inline v_uint32x8 v_absdiff(const v_uint32x8& a, const v_uint32x8& b) +{ return v_max(a, b) - v_min(a, b); } + +inline v_uint8x32 v_absdiff(const v_int8x32& a, const v_int8x32& b) +{ + v_int8x32 d = v_sub_wrap(a, b); + v_int8x32 m = a < b; + return v_reinterpret_as_u8(v_sub_wrap(d ^ m, m)); +} + +inline v_uint16x16 v_absdiff(const v_int16x16& a, const v_int16x16& b) +{ return v_reinterpret_as_u16(v_sub_wrap(v_max(a, b), v_min(a, b))); } + +inline v_uint32x8 v_absdiff(const v_int32x8& a, const v_int32x8& b) +{ + v_int32x8 d = a - b; + v_int32x8 m = a < b; + return v_reinterpret_as_u32((d ^ m) - m); +} + +inline v_float32x8 v_absdiff(const v_float32x8& a, const v_float32x8& b) +{ return v_abs(a - b); } + +inline v_float64x4 v_absdiff(const v_float64x4& a, const v_float64x4& b) +{ return v_abs(a - b); } + +/** Saturating absolute difference **/ +inline v_int8x32 v_absdiffs(const v_int8x32& a, const v_int8x32& b) +{ + v_int8x32 d = a - b; + v_int8x32 m = a < b; + return (d ^ m) - m; +} +inline v_int16x16 v_absdiffs(const v_int16x16& a, const v_int16x16& b) +{ return v_max(a, b) - v_min(a, b); } + +////////// Conversions ///////// + +/** Rounding **/ +inline v_int32x8 v_round(const v_float32x8& a) +{ return v_int32x8(_mm256_cvtps_epi32(a.val)); } + +inline v_int32x8 v_round(const v_float64x4& a) +{ return v_int32x8(_mm256_castsi128_si256(_mm256_cvtpd_epi32(a.val))); } + +inline v_int32x8 v_round(const v_float64x4& a, const v_float64x4& b) +{ + __m128i ai = _mm256_cvtpd_epi32(a.val), bi = _mm256_cvtpd_epi32(b.val); + return v_int32x8(_v256_combine(ai, bi)); +} + +inline v_int32x8 v_trunc(const v_float32x8& a) +{ return v_int32x8(_mm256_cvttps_epi32(a.val)); } + +inline v_int32x8 v_trunc(const v_float64x4& a) +{ return v_int32x8(_mm256_castsi128_si256(_mm256_cvttpd_epi32(a.val))); } + +inline v_int32x8 v_floor(const v_float32x8& a) +{ return v_int32x8(_mm256_cvttps_epi32(_mm256_floor_ps(a.val))); } + +inline v_int32x8 v_floor(const v_float64x4& a) +{ return v_trunc(v_float64x4(_mm256_floor_pd(a.val))); } + +inline v_int32x8 v_ceil(const v_float32x8& a) +{ return v_int32x8(_mm256_cvttps_epi32(_mm256_ceil_ps(a.val))); } + +inline v_int32x8 v_ceil(const v_float64x4& a) +{ return v_trunc(v_float64x4(_mm256_ceil_pd(a.val))); } + +/** To float **/ +inline v_float32x8 v_cvt_f32(const v_int32x8& a) +{ return v_float32x8(_mm256_cvtepi32_ps(a.val)); } + +inline v_float32x8 v_cvt_f32(const v_float64x4& a) +{ return v_float32x8(_mm256_castps128_ps256(_mm256_cvtpd_ps(a.val))); } + +inline v_float32x8 v_cvt_f32(const v_float64x4& a, const v_float64x4& b) +{ + __m128 af = _mm256_cvtpd_ps(a.val), bf = _mm256_cvtpd_ps(b.val); + return v_float32x8(_mm256_insertf128_ps(_mm256_castps128_ps256(af), bf, 1)); +} + +inline v_float64x4 v_cvt_f64(const v_int32x8& a) +{ return v_float64x4(_mm256_cvtepi32_pd(_v256_extract_low(a.val))); } + +inline v_float64x4 v_cvt_f64_high(const v_int32x8& a) +{ return v_float64x4(_mm256_cvtepi32_pd(_v256_extract_high(a.val))); } + +inline v_float64x4 v_cvt_f64(const v_float32x8& a) +{ return v_float64x4(_mm256_cvtps_pd(_v256_extract_low(a.val))); } + +inline v_float64x4 v_cvt_f64_high(const v_float32x8& a) +{ return v_float64x4(_mm256_cvtps_pd(_v256_extract_high(a.val))); } + +////////////// Lookup table access //////////////////// + +inline v_int32x8 v_lut(const int* tab, const v_int32x8& idxvec) +{ + return v_int32x8(_mm256_i32gather_epi32(tab, idxvec.val, 4)); +} + +inline v_uint32x8 v_lut(const unsigned* tab, const v_int32x8& idxvec) +{ + return v_reinterpret_as_u32(v_lut((const int *)tab, idxvec)); +} + +inline v_float32x8 v_lut(const float* tab, const v_int32x8& idxvec) +{ + return v_float32x8(_mm256_i32gather_ps(tab, idxvec.val, 4)); +} + +inline v_float64x4 v_lut(const double* tab, const v_int32x8& idxvec) +{ + return v_float64x4(_mm256_i32gather_pd(tab, _mm256_castsi256_si128(idxvec.val), 8)); +} + +inline void v_lut_deinterleave(const float* tab, const v_int32x8& idxvec, v_float32x8& x, v_float32x8& y) +{ + int CV_DECL_ALIGNED(32) idx[8]; + v_store_aligned(idx, idxvec); + __m128 z = _mm_setzero_ps(); + __m128 xy01, xy45, xy23, xy67; + xy01 = _mm_loadl_pi(z, (const __m64*)(tab + idx[0])); + xy01 = _mm_loadh_pi(xy01, (const __m64*)(tab + idx[1])); + xy45 = _mm_loadl_pi(z, (const __m64*)(tab + idx[4])); + xy45 = _mm_loadh_pi(xy45, (const __m64*)(tab + idx[5])); + __m256 xy0145 = _v256_combine(xy01, xy45); + xy23 = _mm_loadl_pi(z, (const __m64*)(tab + idx[2])); + xy23 = _mm_loadh_pi(xy23, (const __m64*)(tab + idx[3])); + xy67 = _mm_loadl_pi(z, (const __m64*)(tab + idx[6])); + xy67 = _mm_loadh_pi(xy67, (const __m64*)(tab + idx[7])); + __m256 xy2367 = _v256_combine(xy23, xy67); + + __m256 xxyy0145 = _mm256_unpacklo_ps(xy0145, xy2367); + __m256 xxyy2367 = _mm256_unpackhi_ps(xy0145, xy2367); + + x = v_float32x8(_mm256_unpacklo_ps(xxyy0145, xxyy2367)); + y = v_float32x8(_mm256_unpackhi_ps(xxyy0145, xxyy2367)); +} + +inline void v_lut_deinterleave(const double* tab, const v_int32x8& idxvec, v_float64x4& x, v_float64x4& y) +{ + int CV_DECL_ALIGNED(32) idx[4]; + v_store_low(idx, idxvec); + __m128d xy0 = _mm_loadu_pd(tab + idx[0]); + __m128d xy2 = _mm_loadu_pd(tab + idx[2]); + __m128d xy1 = _mm_loadu_pd(tab + idx[1]); + __m128d xy3 = _mm_loadu_pd(tab + idx[3]); + __m256d xy02 = _v256_combine(xy0, xy2); + __m256d xy13 = _v256_combine(xy1, xy3); + + x = v_float64x4(_mm256_unpacklo_pd(xy02, xy13)); + y = v_float64x4(_mm256_unpackhi_pd(xy02, xy13)); +} + +////////// Matrix operations ///////// + +inline v_int32x8 v_dotprod(const v_int16x16& a, const v_int16x16& b) +{ return v_int32x8(_mm256_madd_epi16(a.val, b.val)); } + +inline v_int32x8 v_dotprod(const v_int16x16& a, const v_int16x16& b, const v_int32x8& c) +{ return v_dotprod(a, b) + c; } + +#define OPENCV_HAL_AVX_SPLAT2_PS(a, im) \ + v_float32x8(_mm256_permute_ps(a.val, _MM_SHUFFLE(im, im, im, im))) + +inline v_float32x8 v_matmul(const v_float32x8& v, const v_float32x8& m0, + const v_float32x8& m1, const v_float32x8& m2, + const v_float32x8& m3) +{ + v_float32x8 v04 = OPENCV_HAL_AVX_SPLAT2_PS(v, 0); + v_float32x8 v15 = OPENCV_HAL_AVX_SPLAT2_PS(v, 1); + v_float32x8 v26 = OPENCV_HAL_AVX_SPLAT2_PS(v, 2); + v_float32x8 v37 = OPENCV_HAL_AVX_SPLAT2_PS(v, 3); + return v_fma(v04, m0, v_fma(v15, m1, v_fma(v26, m2, v37 * m3))); +} + +inline v_float32x8 v_matmuladd(const v_float32x8& v, const v_float32x8& m0, + const v_float32x8& m1, const v_float32x8& m2, + const v_float32x8& a) +{ + v_float32x8 v04 = OPENCV_HAL_AVX_SPLAT2_PS(v, 0); + v_float32x8 v15 = OPENCV_HAL_AVX_SPLAT2_PS(v, 1); + v_float32x8 v26 = OPENCV_HAL_AVX_SPLAT2_PS(v, 2); + return v_fma(v04, m0, v_fma(v15, m1, v_fma(v26, m2, a))); +} + +#define OPENCV_HAL_IMPL_AVX_TRANSPOSE4x4(_Tpvec, suffix, cast_from, cast_to) \ + inline void v_transpose4x4(const _Tpvec& a0, const _Tpvec& a1, \ + const _Tpvec& a2, const _Tpvec& a3, \ + _Tpvec& b0, _Tpvec& b1, _Tpvec& b2, _Tpvec& b3) \ + { \ + __m256i t0 = cast_from(_mm256_unpacklo_##suffix(a0.val, a1.val)); \ + __m256i t1 = cast_from(_mm256_unpacklo_##suffix(a2.val, a3.val)); \ + __m256i t2 = cast_from(_mm256_unpackhi_##suffix(a0.val, a1.val)); \ + __m256i t3 = cast_from(_mm256_unpackhi_##suffix(a2.val, a3.val)); \ + b0.val = cast_to(_mm256_unpacklo_epi64(t0, t1)); \ + b1.val = cast_to(_mm256_unpackhi_epi64(t0, t1)); \ + b2.val = cast_to(_mm256_unpacklo_epi64(t2, t3)); \ + b3.val = cast_to(_mm256_unpackhi_epi64(t2, t3)); \ + } + +OPENCV_HAL_IMPL_AVX_TRANSPOSE4x4(v_uint32x8, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_AVX_TRANSPOSE4x4(v_int32x8, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_AVX_TRANSPOSE4x4(v_float32x8, ps, _mm256_castps_si256, _mm256_castsi256_ps) + +//////////////// Value reordering /////////////// + +/* Expand */ +#define OPENCV_HAL_IMPL_AVX_EXPAND(_Tpvec, _Tpwvec, _Tp, intrin) \ + inline void v_expand(const _Tpvec& a, _Tpwvec& b0, _Tpwvec& b1) \ + { \ + b0.val = intrin(_v256_extract_low(a.val)); \ + b1.val = intrin(_v256_extract_high(a.val)); \ + } \ + inline _Tpwvec v_expand_low(const _Tpvec& a) \ + { return _Tpwvec(intrin(_v256_extract_low(a.val))); } \ + inline _Tpwvec v_expand_high(const _Tpvec& a) \ + { return _Tpwvec(intrin(_v256_extract_high(a.val))); } \ + inline _Tpwvec v256_load_expand(const _Tp* ptr) \ + { \ + __m128i a = _mm_loadu_si128((const __m128i*)ptr); \ + return _Tpwvec(intrin(a)); \ + } + +OPENCV_HAL_IMPL_AVX_EXPAND(v_uint8x32, v_uint16x16, uchar, _mm256_cvtepu8_epi16) +OPENCV_HAL_IMPL_AVX_EXPAND(v_int8x32, v_int16x16, schar, _mm256_cvtepi8_epi16) +OPENCV_HAL_IMPL_AVX_EXPAND(v_uint16x16, v_uint32x8, ushort, _mm256_cvtepu16_epi32) +OPENCV_HAL_IMPL_AVX_EXPAND(v_int16x16, v_int32x8, short, _mm256_cvtepi16_epi32) +OPENCV_HAL_IMPL_AVX_EXPAND(v_uint32x8, v_uint64x4, unsigned, _mm256_cvtepu32_epi64) +OPENCV_HAL_IMPL_AVX_EXPAND(v_int32x8, v_int64x4, int, _mm256_cvtepi32_epi64) + +#define OPENCV_HAL_IMPL_AVX_EXPAND_Q(_Tpvec, _Tp, intrin) \ + inline _Tpvec v256_load_expand_q(const _Tp* ptr) \ + { \ + __m128i a = _mm_loadl_epi64((const __m128i*)ptr); \ + return _Tpvec(intrin(a)); \ + } + +OPENCV_HAL_IMPL_AVX_EXPAND_Q(v_uint32x8, uchar, _mm256_cvtepu8_epi32) +OPENCV_HAL_IMPL_AVX_EXPAND_Q(v_int32x8, schar, _mm256_cvtepi8_epi32) + +/* pack */ +// 16 +inline v_int8x32 v_pack(const v_int16x16& a, const v_int16x16& b) +{ return v_int8x32(_v256_shuffle_odd_64(_mm256_packs_epi16(a.val, b.val))); } + +inline v_uint8x32 v_pack(const v_uint16x16& a, const v_uint16x16& b) +{ + __m256i t = _mm256_set1_epi16(255); + __m256i a1 = _mm256_min_epu16(a.val, t); + __m256i b1 = _mm256_min_epu16(b.val, t); + return v_uint8x32(_v256_shuffle_odd_64(_mm256_packus_epi16(a1, b1))); +} + +inline v_uint8x32 v_pack_u(const v_int16x16& a, const v_int16x16& b) +{ + return v_uint8x32(_v256_shuffle_odd_64(_mm256_packus_epi16(a.val, b.val))); +} + +inline void v_pack_store(schar* ptr, const v_int16x16& a) +{ v_store_low(ptr, v_pack(a, a)); } + +inline void v_pack_store(uchar* ptr, const v_uint16x16& a) +{ + const __m256i m = _mm256_set1_epi16(255); + __m256i am = _mm256_min_epu16(a.val, m); + am = _v256_shuffle_odd_64(_mm256_packus_epi16(am, am)); + v_store_low(ptr, v_uint8x32(am)); +} + +inline void v_pack_u_store(uchar* ptr, const v_int16x16& a) +{ v_store_low(ptr, v_pack_u(a, a)); } + +template inline +v_uint8x32 v_rshr_pack(const v_uint16x16& a, const v_uint16x16& b) +{ + // we assume that n > 0, and so the shifted 16-bit values can be treated as signed numbers. + v_uint16x16 delta = v256_setall_u16((short)(1 << (n-1))); + return v_pack_u(v_reinterpret_as_s16((a + delta) >> n), + v_reinterpret_as_s16((b + delta) >> n)); +} + +template inline +void v_rshr_pack_store(uchar* ptr, const v_uint16x16& a) +{ + v_uint16x16 delta = v256_setall_u16((short)(1 << (n-1))); + v_pack_u_store(ptr, v_reinterpret_as_s16((a + delta) >> n)); +} + +template inline +v_uint8x32 v_rshr_pack_u(const v_int16x16& a, const v_int16x16& b) +{ + v_int16x16 delta = v256_setall_s16((short)(1 << (n-1))); + return v_pack_u((a + delta) >> n, (b + delta) >> n); +} + +template inline +void v_rshr_pack_u_store(uchar* ptr, const v_int16x16& a) +{ + v_int16x16 delta = v256_setall_s16((short)(1 << (n-1))); + v_pack_u_store(ptr, (a + delta) >> n); +} + +template inline +v_int8x32 v_rshr_pack(const v_int16x16& a, const v_int16x16& b) +{ + v_int16x16 delta = v256_setall_s16((short)(1 << (n-1))); + return v_pack((a + delta) >> n, (b + delta) >> n); +} + +template inline +void v_rshr_pack_store(schar* ptr, const v_int16x16& a) +{ + v_int16x16 delta = v256_setall_s16((short)(1 << (n-1))); + v_pack_store(ptr, (a + delta) >> n); +} + +// 32 +inline v_int16x16 v_pack(const v_int32x8& a, const v_int32x8& b) +{ return v_int16x16(_v256_shuffle_odd_64(_mm256_packs_epi32(a.val, b.val))); } + +inline v_uint16x16 v_pack(const v_uint32x8& a, const v_uint32x8& b) +{ return v_uint16x16(_v256_shuffle_odd_64(_v256_packs_epu32(a.val, b.val))); } + +inline v_uint16x16 v_pack_u(const v_int32x8& a, const v_int32x8& b) +{ return v_uint16x16(_v256_shuffle_odd_64(_mm256_packus_epi32(a.val, b.val))); } + +inline void v_pack_store(short* ptr, const v_int32x8& a) +{ v_store_low(ptr, v_pack(a, a)); } + +inline void v_pack_store(ushort* ptr, const v_uint32x8& a) +{ + const __m256i m = _mm256_set1_epi32(65535); + __m256i am = _mm256_min_epu32(a.val, m); + am = _v256_shuffle_odd_64(_mm256_packus_epi32(am, am)); + v_store_low(ptr, v_uint16x16(am)); +} + +inline void v_pack_u_store(ushort* ptr, const v_int32x8& a) +{ v_store_low(ptr, v_pack_u(a, a)); } + + +template inline +v_uint16x16 v_rshr_pack(const v_uint32x8& a, const v_uint32x8& b) +{ + // we assume that n > 0, and so the shifted 32-bit values can be treated as signed numbers. + v_uint32x8 delta = v256_setall_u32(1 << (n-1)); + return v_pack_u(v_reinterpret_as_s32((a + delta) >> n), + v_reinterpret_as_s32((b + delta) >> n)); +} + +template inline +void v_rshr_pack_store(ushort* ptr, const v_uint32x8& a) +{ + v_uint32x8 delta = v256_setall_u32(1 << (n-1)); + v_pack_u_store(ptr, v_reinterpret_as_s32((a + delta) >> n)); +} + +template inline +v_uint16x16 v_rshr_pack_u(const v_int32x8& a, const v_int32x8& b) +{ + v_int32x8 delta = v256_setall_s32(1 << (n-1)); + return v_pack_u((a + delta) >> n, (b + delta) >> n); +} + +template inline +void v_rshr_pack_u_store(ushort* ptr, const v_int32x8& a) +{ + v_int32x8 delta = v256_setall_s32(1 << (n-1)); + v_pack_u_store(ptr, (a + delta) >> n); +} + +template inline +v_int16x16 v_rshr_pack(const v_int32x8& a, const v_int32x8& b) +{ + v_int32x8 delta = v256_setall_s32(1 << (n-1)); + return v_pack((a + delta) >> n, (b + delta) >> n); +} + +template inline +void v_rshr_pack_store(short* ptr, const v_int32x8& a) +{ + v_int32x8 delta = v256_setall_s32(1 << (n-1)); + v_pack_store(ptr, (a + delta) >> n); +} + +// 64 +// Non-saturating pack +inline v_uint32x8 v_pack(const v_uint64x4& a, const v_uint64x4& b) +{ + __m256i a0 = _mm256_shuffle_epi32(a.val, _MM_SHUFFLE(0, 0, 2, 0)); + __m256i b0 = _mm256_shuffle_epi32(b.val, _MM_SHUFFLE(0, 0, 2, 0)); + __m256i ab = _mm256_unpacklo_epi64(a0, b0); // a0, a1, b0, b1, a2, a3, b2, b3 + return v_uint32x8(_v256_shuffle_odd_64(ab)); +} + +inline v_int32x8 v_pack(const v_int64x4& a, const v_int64x4& b) +{ return v_reinterpret_as_s32(v_pack(v_reinterpret_as_u64(a), v_reinterpret_as_u64(b))); } + +inline void v_pack_store(unsigned* ptr, const v_uint64x4& a) +{ + __m256i a0 = _mm256_shuffle_epi32(a.val, _MM_SHUFFLE(0, 0, 2, 0)); + v_store_low(ptr, v_uint32x8(_v256_shuffle_odd_64(a0))); +} + +inline void v_pack_store(int* ptr, const v_int64x4& b) +{ v_pack_store((unsigned*)ptr, v_reinterpret_as_u64(b)); } + +template inline +v_uint32x8 v_rshr_pack(const v_uint64x4& a, const v_uint64x4& b) +{ + v_uint64x4 delta = v256_setall_u64((uint64)1 << (n-1)); + return v_pack((a + delta) >> n, (b + delta) >> n); +} + +template inline +void v_rshr_pack_store(unsigned* ptr, const v_uint64x4& a) +{ + v_uint64x4 delta = v256_setall_u64((uint64)1 << (n-1)); + v_pack_store(ptr, (a + delta) >> n); +} + +template inline +v_int32x8 v_rshr_pack(const v_int64x4& a, const v_int64x4& b) +{ + v_int64x4 delta = v256_setall_s64((int64)1 << (n-1)); + return v_pack((a + delta) >> n, (b + delta) >> n); +} + +template inline +void v_rshr_pack_store(int* ptr, const v_int64x4& a) +{ + v_int64x4 delta = v256_setall_s64((int64)1 << (n-1)); + v_pack_store(ptr, (a + delta) >> n); +} + +// pack boolean +inline v_uint8x32 v_pack_b(const v_uint16x16& a, const v_uint16x16& b) +{ + __m256i ab = _mm256_packs_epi16(a.val, b.val); + return v_uint8x32(_v256_shuffle_odd_64(ab)); +} + +inline v_uint8x32 v_pack_b(const v_uint32x8& a, const v_uint32x8& b, + const v_uint32x8& c, const v_uint32x8& d) +{ + __m256i ab = _mm256_packs_epi32(a.val, b.val); + __m256i cd = _mm256_packs_epi32(c.val, d.val); + + __m256i abcd = _v256_shuffle_odd_64(_mm256_packs_epi16(ab, cd)); + return v_uint8x32(_mm256_shuffle_epi32(abcd, _MM_SHUFFLE(3, 1, 2, 0))); +} + +inline v_uint8x32 v_pack_b(const v_uint64x4& a, const v_uint64x4& b, const v_uint64x4& c, + const v_uint64x4& d, const v_uint64x4& e, const v_uint64x4& f, + const v_uint64x4& g, const v_uint64x4& h) +{ + __m256i ab = _mm256_packs_epi32(a.val, b.val); + __m256i cd = _mm256_packs_epi32(c.val, d.val); + __m256i ef = _mm256_packs_epi32(e.val, f.val); + __m256i gh = _mm256_packs_epi32(g.val, h.val); + + __m256i abcd = _mm256_packs_epi32(ab, cd); + __m256i efgh = _mm256_packs_epi32(ef, gh); + __m256i pkall = _v256_shuffle_odd_64(_mm256_packs_epi16(abcd, efgh)); + + __m256i rev = _mm256_alignr_epi8(pkall, pkall, 8); + return v_uint8x32(_mm256_unpacklo_epi16(pkall, rev)); +} + +/* Recombine */ +// its up there with load and store operations + +/* Extract */ +#define OPENCV_HAL_IMPL_AVX_EXTRACT(_Tpvec) \ + template \ + inline _Tpvec v_extract(const _Tpvec& a, const _Tpvec& b) \ + { return v_rotate_right(a, b); } + +OPENCV_HAL_IMPL_AVX_EXTRACT(v_uint8x32) +OPENCV_HAL_IMPL_AVX_EXTRACT(v_int8x32) +OPENCV_HAL_IMPL_AVX_EXTRACT(v_uint16x16) +OPENCV_HAL_IMPL_AVX_EXTRACT(v_int16x16) +OPENCV_HAL_IMPL_AVX_EXTRACT(v_uint32x8) +OPENCV_HAL_IMPL_AVX_EXTRACT(v_int32x8) +OPENCV_HAL_IMPL_AVX_EXTRACT(v_uint64x4) +OPENCV_HAL_IMPL_AVX_EXTRACT(v_int64x4) +OPENCV_HAL_IMPL_AVX_EXTRACT(v_float32x8) +OPENCV_HAL_IMPL_AVX_EXTRACT(v_float64x4) + + +///////////////////// load deinterleave ///////////////////////////// + +inline void v_load_deinterleave( const uchar* ptr, v_uint8x32& a, v_uint8x32& b ) +{ + __m256i ab0 = _mm256_loadu_si256((const __m256i*)ptr); + __m256i ab1 = _mm256_loadu_si256((const __m256i*)(ptr + 32)); + + const __m256i sh = _mm256_setr_epi8(0, 2, 4, 6, 8, 10, 12, 14, 1, 3, 5, 7, 9, 11, 13, 15, + 0, 2, 4, 6, 8, 10, 12, 14, 1, 3, 5, 7, 9, 11, 13, 15); + __m256i p0 = _mm256_shuffle_epi8(ab0, sh); + __m256i p1 = _mm256_shuffle_epi8(ab1, sh); + __m256i pl = _mm256_permute2x128_si256(p0, p1, 0 + 2*16); + __m256i ph = _mm256_permute2x128_si256(p0, p1, 1 + 3*16); + __m256i a0 = _mm256_unpacklo_epi64(pl, ph); + __m256i b0 = _mm256_unpackhi_epi64(pl, ph); + a = v_uint8x32(a0); + b = v_uint8x32(b0); +} + +inline void v_load_deinterleave( const ushort* ptr, v_uint16x16& a, v_uint16x16& b ) +{ + __m256i ab0 = _mm256_loadu_si256((const __m256i*)ptr); + __m256i ab1 = _mm256_loadu_si256((const __m256i*)(ptr + 16)); + + const __m256i sh = _mm256_setr_epi8(0, 1, 4, 5, 8, 9, 12, 13, 2, 3, 6, 7, 10, 11, 14, 15, + 0, 1, 4, 5, 8, 9, 12, 13, 2, 3, 6, 7, 10, 11, 14, 15); + __m256i p0 = _mm256_shuffle_epi8(ab0, sh); + __m256i p1 = _mm256_shuffle_epi8(ab1, sh); + __m256i pl = _mm256_permute2x128_si256(p0, p1, 0 + 2*16); + __m256i ph = _mm256_permute2x128_si256(p0, p1, 1 + 3*16); + __m256i a0 = _mm256_unpacklo_epi64(pl, ph); + __m256i b0 = _mm256_unpackhi_epi64(pl, ph); + a = v_uint16x16(a0); + b = v_uint16x16(b0); +} + +inline void v_load_deinterleave( const unsigned* ptr, v_uint32x8& a, v_uint32x8& b ) +{ + __m256i ab0 = _mm256_loadu_si256((const __m256i*)ptr); + __m256i ab1 = _mm256_loadu_si256((const __m256i*)(ptr + 8)); + + const int sh = 0+2*4+1*16+3*64; + __m256i p0 = _mm256_shuffle_epi32(ab0, sh); + __m256i p1 = _mm256_shuffle_epi32(ab1, sh); + __m256i pl = _mm256_permute2x128_si256(p0, p1, 0 + 2*16); + __m256i ph = _mm256_permute2x128_si256(p0, p1, 1 + 3*16); + __m256i a0 = _mm256_unpacklo_epi64(pl, ph); + __m256i b0 = _mm256_unpackhi_epi64(pl, ph); + a = v_uint32x8(a0); + b = v_uint32x8(b0); +} + +inline void v_load_deinterleave( const uint64* ptr, v_uint64x4& a, v_uint64x4& b ) +{ + __m256i ab0 = _mm256_loadu_si256((const __m256i*)ptr); + __m256i ab1 = _mm256_loadu_si256((const __m256i*)(ptr + 4)); + + __m256i pl = _mm256_permute2x128_si256(ab0, ab1, 0 + 2*16); + __m256i ph = _mm256_permute2x128_si256(ab0, ab1, 1 + 3*16); + __m256i a0 = _mm256_unpacklo_epi64(pl, ph); + __m256i b0 = _mm256_unpackhi_epi64(pl, ph); + a = v_uint64x4(a0); + b = v_uint64x4(b0); +} + +inline void v_load_deinterleave( const uchar* ptr, v_uint8x32& b, v_uint8x32& g, v_uint8x32& r ) +{ + __m256i bgr0 = _mm256_loadu_si256((const __m256i*)ptr); + __m256i bgr1 = _mm256_loadu_si256((const __m256i*)(ptr + 32)); + __m256i bgr2 = _mm256_loadu_si256((const __m256i*)(ptr + 64)); + + __m256i s02_low = _mm256_permute2x128_si256(bgr0, bgr2, 0 + 2*16); + __m256i s02_high = _mm256_permute2x128_si256(bgr0, bgr2, 1 + 3*16); + + const __m256i m0 = _mm256_setr_epi8(0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, + 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0); + const __m256i m1 = _mm256_setr_epi8(0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, + -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1); + + __m256i b0 = _mm256_blendv_epi8(_mm256_blendv_epi8(s02_low, s02_high, m0), bgr1, m1); + __m256i g0 = _mm256_blendv_epi8(_mm256_blendv_epi8(s02_high, s02_low, m1), bgr1, m0); + __m256i r0 = _mm256_blendv_epi8(_mm256_blendv_epi8(bgr1, s02_low, m0), s02_high, m1); + + const __m256i + sh_b = _mm256_setr_epi8(0, 3, 6, 9, 12, 15, 2, 5, 8, 11, 14, 1, 4, 7, 10, 13, + 0, 3, 6, 9, 12, 15, 2, 5, 8, 11, 14, 1, 4, 7, 10, 13), + sh_g = _mm256_setr_epi8(1, 4, 7, 10, 13, 0, 3, 6, 9, 12, 15, 2, 5, 8, 11, 14, + 1, 4, 7, 10, 13, 0, 3, 6, 9, 12, 15, 2, 5, 8, 11, 14), + sh_r = _mm256_setr_epi8(2, 5, 8, 11, 14, 1, 4, 7, 10, 13, 0, 3, 6, 9, 12, 15, + 2, 5, 8, 11, 14, 1, 4, 7, 10, 13, 0, 3, 6, 9, 12, 15); + b0 = _mm256_shuffle_epi8(b0, sh_b); + g0 = _mm256_shuffle_epi8(g0, sh_g); + r0 = _mm256_shuffle_epi8(r0, sh_r); + + b = v_uint8x32(b0); + g = v_uint8x32(g0); + r = v_uint8x32(r0); +} + +inline void v_load_deinterleave( const ushort* ptr, v_uint16x16& b, v_uint16x16& g, v_uint16x16& r ) +{ + __m256i bgr0 = _mm256_loadu_si256((const __m256i*)ptr); + __m256i bgr1 = _mm256_loadu_si256((const __m256i*)(ptr + 16)); + __m256i bgr2 = _mm256_loadu_si256((const __m256i*)(ptr + 32)); + + __m256i s02_low = _mm256_permute2x128_si256(bgr0, bgr2, 0 + 2*16); + __m256i s02_high = _mm256_permute2x128_si256(bgr0, bgr2, 1 + 3*16); + + const __m256i m0 = _mm256_setr_epi8(0, 0, -1, -1, 0, 0, 0, 0, -1, -1, 0, 0, 0, 0, -1, -1, + 0, 0, 0, 0, -1, -1, 0, 0, 0, 0, -1, -1, 0, 0, 0, 0); + const __m256i m1 = _mm256_setr_epi8(0, 0, 0, 0, -1, -1, 0, 0, 0, 0, -1, -1, 0, 0, 0, 0, + -1, -1, 0, 0, 0, 0, -1, -1, 0, 0, 0, 0, -1, -1, 0, 0); + __m256i b0 = _mm256_blendv_epi8(_mm256_blendv_epi8(s02_low, s02_high, m0), bgr1, m1); + __m256i g0 = _mm256_blendv_epi8(_mm256_blendv_epi8(bgr1, s02_low, m0), s02_high, m1); + __m256i r0 = _mm256_blendv_epi8(_mm256_blendv_epi8(s02_high, s02_low, m1), bgr1, m0); + const __m256i sh_b = _mm256_setr_epi8(0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5, 10, 11, + 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5, 10, 11); + const __m256i sh_g = _mm256_setr_epi8(2, 3, 8, 9, 14, 15, 4, 5, 10, 11, 0, 1, 6, 7, 12, 13, + 2, 3, 8, 9, 14, 15, 4, 5, 10, 11, 0, 1, 6, 7, 12, 13); + const __m256i sh_r = _mm256_setr_epi8(4, 5, 10, 11, 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, + 4, 5, 10, 11, 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15); + b0 = _mm256_shuffle_epi8(b0, sh_b); + g0 = _mm256_shuffle_epi8(g0, sh_g); + r0 = _mm256_shuffle_epi8(r0, sh_r); + + b = v_uint16x16(b0); + g = v_uint16x16(g0); + r = v_uint16x16(r0); +} + +inline void v_load_deinterleave( const unsigned* ptr, v_uint32x8& b, v_uint32x8& g, v_uint32x8& r ) +{ + __m256i bgr0 = _mm256_loadu_si256((const __m256i*)ptr); + __m256i bgr1 = _mm256_loadu_si256((const __m256i*)(ptr + 8)); + __m256i bgr2 = _mm256_loadu_si256((const __m256i*)(ptr + 16)); + + __m256i s02_low = _mm256_permute2x128_si256(bgr0, bgr2, 0 + 2*16); + __m256i s02_high = _mm256_permute2x128_si256(bgr0, bgr2, 1 + 3*16); + + __m256i b0 = _mm256_blend_epi32(_mm256_blend_epi32(s02_low, s02_high, 0x24), bgr1, 0x92); + __m256i g0 = _mm256_blend_epi32(_mm256_blend_epi32(s02_high, s02_low, 0x92), bgr1, 0x24); + __m256i r0 = _mm256_blend_epi32(_mm256_blend_epi32(bgr1, s02_low, 0x24), s02_high, 0x92); + + b0 = _mm256_shuffle_epi32(b0, 0x6c); + g0 = _mm256_shuffle_epi32(g0, 0xb1); + r0 = _mm256_shuffle_epi32(r0, 0xc6); + + b = v_uint32x8(b0); + g = v_uint32x8(g0); + r = v_uint32x8(r0); +} + +inline void v_load_deinterleave( const uint64* ptr, v_uint64x4& b, v_uint64x4& g, v_uint64x4& r ) +{ + __m256i bgr0 = _mm256_loadu_si256((const __m256i*)ptr); + __m256i bgr1 = _mm256_loadu_si256((const __m256i*)(ptr + 4)); + __m256i bgr2 = _mm256_loadu_si256((const __m256i*)(ptr + 8)); + + __m256i s01 = _mm256_blend_epi32(bgr0, bgr1, 0xf0); + __m256i s12 = _mm256_blend_epi32(bgr1, bgr2, 0xf0); + __m256i s20r = _mm256_permute4x64_epi64(_mm256_blend_epi32(bgr2, bgr0, 0xf0), 0x1b); + __m256i b0 = _mm256_unpacklo_epi64(s01, s20r); + __m256i g0 = _mm256_alignr_epi8(s12, s01, 8); + __m256i r0 = _mm256_unpackhi_epi64(s20r, s12); + + b = v_uint64x4(b0); + g = v_uint64x4(g0); + r = v_uint64x4(r0); +} + +inline void v_load_deinterleave( const uchar* ptr, v_uint8x32& b, v_uint8x32& g, v_uint8x32& r, v_uint8x32& a ) +{ + __m256i bgr0 = _mm256_loadu_si256((const __m256i*)ptr); + __m256i bgr1 = _mm256_loadu_si256((const __m256i*)(ptr + 32)); + __m256i bgr2 = _mm256_loadu_si256((const __m256i*)(ptr + 64)); + __m256i bgr3 = _mm256_loadu_si256((const __m256i*)(ptr + 96)); + const __m256i sh = _mm256_setr_epi8(0, 4, 8, 12, 1, 5, 9, 13, 2, 6, 10, 14, 3, 7, 11, 15, + 0, 4, 8, 12, 1, 5, 9, 13, 2, 6, 10, 14, 3, 7, 11, 15); + + __m256i p0 = _mm256_shuffle_epi8(bgr0, sh); + __m256i p1 = _mm256_shuffle_epi8(bgr1, sh); + __m256i p2 = _mm256_shuffle_epi8(bgr2, sh); + __m256i p3 = _mm256_shuffle_epi8(bgr3, sh); + + __m256i p01l = _mm256_unpacklo_epi32(p0, p1); + __m256i p01h = _mm256_unpackhi_epi32(p0, p1); + __m256i p23l = _mm256_unpacklo_epi32(p2, p3); + __m256i p23h = _mm256_unpackhi_epi32(p2, p3); + + __m256i pll = _mm256_permute2x128_si256(p01l, p23l, 0 + 2*16); + __m256i plh = _mm256_permute2x128_si256(p01l, p23l, 1 + 3*16); + __m256i phl = _mm256_permute2x128_si256(p01h, p23h, 0 + 2*16); + __m256i phh = _mm256_permute2x128_si256(p01h, p23h, 1 + 3*16); + + __m256i b0 = _mm256_unpacklo_epi32(pll, plh); + __m256i g0 = _mm256_unpackhi_epi32(pll, plh); + __m256i r0 = _mm256_unpacklo_epi32(phl, phh); + __m256i a0 = _mm256_unpackhi_epi32(phl, phh); + + b = v_uint8x32(b0); + g = v_uint8x32(g0); + r = v_uint8x32(r0); + a = v_uint8x32(a0); +} + +inline void v_load_deinterleave( const ushort* ptr, v_uint16x16& b, v_uint16x16& g, v_uint16x16& r, v_uint16x16& a ) +{ + __m256i bgr0 = _mm256_loadu_si256((const __m256i*)ptr); + __m256i bgr1 = _mm256_loadu_si256((const __m256i*)(ptr + 16)); + __m256i bgr2 = _mm256_loadu_si256((const __m256i*)(ptr + 32)); + __m256i bgr3 = _mm256_loadu_si256((const __m256i*)(ptr + 48)); + const __m256i sh = _mm256_setr_epi8(0, 1, 8, 9, 2, 3, 10, 11, 4, 5, 12, 13, 6, 7, 14, 15, + 0, 1, 8, 9, 2, 3, 10, 11, 4, 5, 12, 13, 6, 7, 14, 15); + __m256i p0 = _mm256_shuffle_epi8(bgr0, sh); + __m256i p1 = _mm256_shuffle_epi8(bgr1, sh); + __m256i p2 = _mm256_shuffle_epi8(bgr2, sh); + __m256i p3 = _mm256_shuffle_epi8(bgr3, sh); + + __m256i p01l = _mm256_unpacklo_epi32(p0, p1); + __m256i p01h = _mm256_unpackhi_epi32(p0, p1); + __m256i p23l = _mm256_unpacklo_epi32(p2, p3); + __m256i p23h = _mm256_unpackhi_epi32(p2, p3); + + __m256i pll = _mm256_permute2x128_si256(p01l, p23l, 0 + 2*16); + __m256i plh = _mm256_permute2x128_si256(p01l, p23l, 1 + 3*16); + __m256i phl = _mm256_permute2x128_si256(p01h, p23h, 0 + 2*16); + __m256i phh = _mm256_permute2x128_si256(p01h, p23h, 1 + 3*16); + + __m256i b0 = _mm256_unpacklo_epi32(pll, plh); + __m256i g0 = _mm256_unpackhi_epi32(pll, plh); + __m256i r0 = _mm256_unpacklo_epi32(phl, phh); + __m256i a0 = _mm256_unpackhi_epi32(phl, phh); + + b = v_uint16x16(b0); + g = v_uint16x16(g0); + r = v_uint16x16(r0); + a = v_uint16x16(a0); +} + +inline void v_load_deinterleave( const unsigned* ptr, v_uint32x8& b, v_uint32x8& g, v_uint32x8& r, v_uint32x8& a ) +{ + __m256i p0 = _mm256_loadu_si256((const __m256i*)ptr); + __m256i p1 = _mm256_loadu_si256((const __m256i*)(ptr + 8)); + __m256i p2 = _mm256_loadu_si256((const __m256i*)(ptr + 16)); + __m256i p3 = _mm256_loadu_si256((const __m256i*)(ptr + 24)); + + __m256i p01l = _mm256_unpacklo_epi32(p0, p1); + __m256i p01h = _mm256_unpackhi_epi32(p0, p1); + __m256i p23l = _mm256_unpacklo_epi32(p2, p3); + __m256i p23h = _mm256_unpackhi_epi32(p2, p3); + + __m256i pll = _mm256_permute2x128_si256(p01l, p23l, 0 + 2*16); + __m256i plh = _mm256_permute2x128_si256(p01l, p23l, 1 + 3*16); + __m256i phl = _mm256_permute2x128_si256(p01h, p23h, 0 + 2*16); + __m256i phh = _mm256_permute2x128_si256(p01h, p23h, 1 + 3*16); + + __m256i b0 = _mm256_unpacklo_epi32(pll, plh); + __m256i g0 = _mm256_unpackhi_epi32(pll, plh); + __m256i r0 = _mm256_unpacklo_epi32(phl, phh); + __m256i a0 = _mm256_unpackhi_epi32(phl, phh); + + b = v_uint32x8(b0); + g = v_uint32x8(g0); + r = v_uint32x8(r0); + a = v_uint32x8(a0); +} + +inline void v_load_deinterleave( const uint64* ptr, v_uint64x4& b, v_uint64x4& g, v_uint64x4& r, v_uint64x4& a ) +{ + __m256i bgra0 = _mm256_loadu_si256((const __m256i*)ptr); + __m256i bgra1 = _mm256_loadu_si256((const __m256i*)(ptr + 4)); + __m256i bgra2 = _mm256_loadu_si256((const __m256i*)(ptr + 8)); + __m256i bgra3 = _mm256_loadu_si256((const __m256i*)(ptr + 12)); + + __m256i l02 = _mm256_permute2x128_si256(bgra0, bgra2, 0 + 2*16); + __m256i h02 = _mm256_permute2x128_si256(bgra0, bgra2, 1 + 3*16); + __m256i l13 = _mm256_permute2x128_si256(bgra1, bgra3, 0 + 2*16); + __m256i h13 = _mm256_permute2x128_si256(bgra1, bgra3, 1 + 3*16); + + __m256i b0 = _mm256_unpacklo_epi64(l02, l13); + __m256i g0 = _mm256_unpackhi_epi64(l02, l13); + __m256i r0 = _mm256_unpacklo_epi64(h02, h13); + __m256i a0 = _mm256_unpackhi_epi64(h02, h13); + + b = v_uint64x4(b0); + g = v_uint64x4(g0); + r = v_uint64x4(r0); + a = v_uint64x4(a0); +} + +///////////////////////////// store interleave ///////////////////////////////////// + +inline void v_store_interleave( uchar* ptr, const v_uint8x32& x, const v_uint8x32& y, + hal::StoreMode mode=hal::STORE_UNALIGNED ) +{ + __m256i xy_l = _mm256_unpacklo_epi8(x.val, y.val); + __m256i xy_h = _mm256_unpackhi_epi8(x.val, y.val); + + __m256i xy0 = _mm256_permute2x128_si256(xy_l, xy_h, 0 + 2*16); + __m256i xy1 = _mm256_permute2x128_si256(xy_l, xy_h, 1 + 3*16); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm256_stream_si256((__m256i*)ptr, xy0); + _mm256_stream_si256((__m256i*)(ptr + 32), xy1); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm256_store_si256((__m256i*)ptr, xy0); + _mm256_store_si256((__m256i*)(ptr + 32), xy1); + } + else + { + _mm256_storeu_si256((__m256i*)ptr, xy0); + _mm256_storeu_si256((__m256i*)(ptr + 32), xy1); + } +} + +inline void v_store_interleave( ushort* ptr, const v_uint16x16& x, const v_uint16x16& y, + hal::StoreMode mode=hal::STORE_UNALIGNED ) +{ + __m256i xy_l = _mm256_unpacklo_epi16(x.val, y.val); + __m256i xy_h = _mm256_unpackhi_epi16(x.val, y.val); + + __m256i xy0 = _mm256_permute2x128_si256(xy_l, xy_h, 0 + 2*16); + __m256i xy1 = _mm256_permute2x128_si256(xy_l, xy_h, 1 + 3*16); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm256_stream_si256((__m256i*)ptr, xy0); + _mm256_stream_si256((__m256i*)(ptr + 16), xy1); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm256_store_si256((__m256i*)ptr, xy0); + _mm256_store_si256((__m256i*)(ptr + 16), xy1); + } + else + { + _mm256_storeu_si256((__m256i*)ptr, xy0); + _mm256_storeu_si256((__m256i*)(ptr + 16), xy1); + } +} + +inline void v_store_interleave( unsigned* ptr, const v_uint32x8& x, const v_uint32x8& y, + hal::StoreMode mode=hal::STORE_UNALIGNED ) +{ + __m256i xy_l = _mm256_unpacklo_epi32(x.val, y.val); + __m256i xy_h = _mm256_unpackhi_epi32(x.val, y.val); + + __m256i xy0 = _mm256_permute2x128_si256(xy_l, xy_h, 0 + 2*16); + __m256i xy1 = _mm256_permute2x128_si256(xy_l, xy_h, 1 + 3*16); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm256_stream_si256((__m256i*)ptr, xy0); + _mm256_stream_si256((__m256i*)(ptr + 8), xy1); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm256_store_si256((__m256i*)ptr, xy0); + _mm256_store_si256((__m256i*)(ptr + 8), xy1); + } + else + { + _mm256_storeu_si256((__m256i*)ptr, xy0); + _mm256_storeu_si256((__m256i*)(ptr + 8), xy1); + } +} + +inline void v_store_interleave( uint64* ptr, const v_uint64x4& x, const v_uint64x4& y, + hal::StoreMode mode=hal::STORE_UNALIGNED ) +{ + __m256i xy_l = _mm256_unpacklo_epi64(x.val, y.val); + __m256i xy_h = _mm256_unpackhi_epi64(x.val, y.val); + + __m256i xy0 = _mm256_permute2x128_si256(xy_l, xy_h, 0 + 2*16); + __m256i xy1 = _mm256_permute2x128_si256(xy_l, xy_h, 1 + 3*16); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm256_stream_si256((__m256i*)ptr, xy0); + _mm256_stream_si256((__m256i*)(ptr + 4), xy1); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm256_store_si256((__m256i*)ptr, xy0); + _mm256_store_si256((__m256i*)(ptr + 4), xy1); + } + else + { + _mm256_storeu_si256((__m256i*)ptr, xy0); + _mm256_storeu_si256((__m256i*)(ptr + 4), xy1); + } +} + +inline void v_store_interleave( uchar* ptr, const v_uint8x32& b, const v_uint8x32& g, const v_uint8x32& r, + hal::StoreMode mode=hal::STORE_UNALIGNED ) +{ + const __m256i sh_b = _mm256_setr_epi8( + 0, 11, 6, 1, 12, 7, 2, 13, 8, 3, 14, 9, 4, 15, 10, 5, + 0, 11, 6, 1, 12, 7, 2, 13, 8, 3, 14, 9, 4, 15, 10, 5); + const __m256i sh_g = _mm256_setr_epi8( + 5, 0, 11, 6, 1, 12, 7, 2, 13, 8, 3, 14, 9, 4, 15, 10, + 5, 0, 11, 6, 1, 12, 7, 2, 13, 8, 3, 14, 9, 4, 15, 10); + const __m256i sh_r = _mm256_setr_epi8( + 10, 5, 0, 11, 6, 1, 12, 7, 2, 13, 8, 3, 14, 9, 4, 15, + 10, 5, 0, 11, 6, 1, 12, 7, 2, 13, 8, 3, 14, 9, 4, 15); + + __m256i b0 = _mm256_shuffle_epi8(b.val, sh_b); + __m256i g0 = _mm256_shuffle_epi8(g.val, sh_g); + __m256i r0 = _mm256_shuffle_epi8(r.val, sh_r); + + const __m256i m0 = _mm256_setr_epi8(0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, + 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0); + const __m256i m1 = _mm256_setr_epi8(0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, + 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0); + + __m256i p0 = _mm256_blendv_epi8(_mm256_blendv_epi8(b0, g0, m0), r0, m1); + __m256i p1 = _mm256_blendv_epi8(_mm256_blendv_epi8(g0, r0, m0), b0, m1); + __m256i p2 = _mm256_blendv_epi8(_mm256_blendv_epi8(r0, b0, m0), g0, m1); + + __m256i bgr0 = _mm256_permute2x128_si256(p0, p1, 0 + 2*16); + __m256i bgr1 = _mm256_permute2x128_si256(p2, p0, 0 + 3*16); + __m256i bgr2 = _mm256_permute2x128_si256(p1, p2, 1 + 3*16); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm256_stream_si256((__m256i*)ptr, bgr0); + _mm256_stream_si256((__m256i*)(ptr + 32), bgr1); + _mm256_stream_si256((__m256i*)(ptr + 64), bgr2); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm256_store_si256((__m256i*)ptr, bgr0); + _mm256_store_si256((__m256i*)(ptr + 32), bgr1); + _mm256_store_si256((__m256i*)(ptr + 64), bgr2); + } + else + { + _mm256_storeu_si256((__m256i*)ptr, bgr0); + _mm256_storeu_si256((__m256i*)(ptr + 32), bgr1); + _mm256_storeu_si256((__m256i*)(ptr + 64), bgr2); + } +} + +inline void v_store_interleave( ushort* ptr, const v_uint16x16& b, const v_uint16x16& g, const v_uint16x16& r, + hal::StoreMode mode=hal::STORE_UNALIGNED ) +{ + const __m256i sh_b = _mm256_setr_epi8( + 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5, 10, 11, + 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5, 10, 11); + const __m256i sh_g = _mm256_setr_epi8( + 10, 11, 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5, + 10, 11, 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5); + const __m256i sh_r = _mm256_setr_epi8( + 4, 5, 10, 11, 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, + 4, 5, 10, 11, 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15); + + __m256i b0 = _mm256_shuffle_epi8(b.val, sh_b); + __m256i g0 = _mm256_shuffle_epi8(g.val, sh_g); + __m256i r0 = _mm256_shuffle_epi8(r.val, sh_r); + + const __m256i m0 = _mm256_setr_epi8(0, 0, -1, -1, 0, 0, 0, 0, -1, -1, 0, 0, 0, 0, -1, -1, + 0, 0, 0, 0, -1, -1, 0, 0, 0, 0, -1, -1, 0, 0, 0, 0); + const __m256i m1 = _mm256_setr_epi8(0, 0, 0, 0, -1, -1, 0, 0, 0, 0, -1, -1, 0, 0, 0, 0, + -1, -1, 0, 0, 0, 0, -1, -1, 0, 0, 0, 0, -1, -1, 0, 0); + + __m256i p0 = _mm256_blendv_epi8(_mm256_blendv_epi8(b0, g0, m0), r0, m1); + __m256i p1 = _mm256_blendv_epi8(_mm256_blendv_epi8(g0, r0, m0), b0, m1); + __m256i p2 = _mm256_blendv_epi8(_mm256_blendv_epi8(r0, b0, m0), g0, m1); + + __m256i bgr0 = _mm256_permute2x128_si256(p0, p2, 0 + 2*16); + //__m256i bgr1 = p1; + __m256i bgr2 = _mm256_permute2x128_si256(p0, p2, 1 + 3*16); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm256_stream_si256((__m256i*)ptr, bgr0); + _mm256_stream_si256((__m256i*)(ptr + 16), p1); + _mm256_stream_si256((__m256i*)(ptr + 32), bgr2); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm256_store_si256((__m256i*)ptr, bgr0); + _mm256_store_si256((__m256i*)(ptr + 16), p1); + _mm256_store_si256((__m256i*)(ptr + 32), bgr2); + } + else + { + _mm256_storeu_si256((__m256i*)ptr, bgr0); + _mm256_storeu_si256((__m256i*)(ptr + 16), p1); + _mm256_storeu_si256((__m256i*)(ptr + 32), bgr2); + } +} + +inline void v_store_interleave( unsigned* ptr, const v_uint32x8& b, const v_uint32x8& g, const v_uint32x8& r, + hal::StoreMode mode=hal::STORE_UNALIGNED ) +{ + __m256i b0 = _mm256_shuffle_epi32(b.val, 0x6c); + __m256i g0 = _mm256_shuffle_epi32(g.val, 0xb1); + __m256i r0 = _mm256_shuffle_epi32(r.val, 0xc6); + + __m256i p0 = _mm256_blend_epi32(_mm256_blend_epi32(b0, g0, 0x92), r0, 0x24); + __m256i p1 = _mm256_blend_epi32(_mm256_blend_epi32(g0, r0, 0x92), b0, 0x24); + __m256i p2 = _mm256_blend_epi32(_mm256_blend_epi32(r0, b0, 0x92), g0, 0x24); + + __m256i bgr0 = _mm256_permute2x128_si256(p0, p1, 0 + 2*16); + //__m256i bgr1 = p2; + __m256i bgr2 = _mm256_permute2x128_si256(p0, p1, 1 + 3*16); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm256_stream_si256((__m256i*)ptr, bgr0); + _mm256_stream_si256((__m256i*)(ptr + 8), p2); + _mm256_stream_si256((__m256i*)(ptr + 16), bgr2); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm256_store_si256((__m256i*)ptr, bgr0); + _mm256_store_si256((__m256i*)(ptr + 8), p2); + _mm256_store_si256((__m256i*)(ptr + 16), bgr2); + } + else + { + _mm256_storeu_si256((__m256i*)ptr, bgr0); + _mm256_storeu_si256((__m256i*)(ptr + 8), p2); + _mm256_storeu_si256((__m256i*)(ptr + 16), bgr2); + } +} + +inline void v_store_interleave( uint64* ptr, const v_uint64x4& b, const v_uint64x4& g, const v_uint64x4& r, + hal::StoreMode mode=hal::STORE_UNALIGNED ) +{ + __m256i s01 = _mm256_unpacklo_epi64(b.val, g.val); + __m256i s12 = _mm256_unpackhi_epi64(g.val, r.val); + __m256i s20 = _mm256_blend_epi32(r.val, b.val, 0xcc); + + __m256i bgr0 = _mm256_permute2x128_si256(s01, s20, 0 + 2*16); + __m256i bgr1 = _mm256_blend_epi32(s01, s12, 0x0f); + __m256i bgr2 = _mm256_permute2x128_si256(s20, s12, 1 + 3*16); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm256_stream_si256((__m256i*)ptr, bgr0); + _mm256_stream_si256((__m256i*)(ptr + 4), bgr1); + _mm256_stream_si256((__m256i*)(ptr + 8), bgr2); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm256_store_si256((__m256i*)ptr, bgr0); + _mm256_store_si256((__m256i*)(ptr + 4), bgr1); + _mm256_store_si256((__m256i*)(ptr + 8), bgr2); + } + else + { + _mm256_storeu_si256((__m256i*)ptr, bgr0); + _mm256_storeu_si256((__m256i*)(ptr + 4), bgr1); + _mm256_storeu_si256((__m256i*)(ptr + 8), bgr2); + } +} + +inline void v_store_interleave( uchar* ptr, const v_uint8x32& b, const v_uint8x32& g, + const v_uint8x32& r, const v_uint8x32& a, + hal::StoreMode mode=hal::STORE_UNALIGNED ) +{ + __m256i bg0 = _mm256_unpacklo_epi8(b.val, g.val); + __m256i bg1 = _mm256_unpackhi_epi8(b.val, g.val); + __m256i ra0 = _mm256_unpacklo_epi8(r.val, a.val); + __m256i ra1 = _mm256_unpackhi_epi8(r.val, a.val); + + __m256i bgra0_ = _mm256_unpacklo_epi16(bg0, ra0); + __m256i bgra1_ = _mm256_unpackhi_epi16(bg0, ra0); + __m256i bgra2_ = _mm256_unpacklo_epi16(bg1, ra1); + __m256i bgra3_ = _mm256_unpackhi_epi16(bg1, ra1); + + __m256i bgra0 = _mm256_permute2x128_si256(bgra0_, bgra1_, 0 + 2*16); + __m256i bgra2 = _mm256_permute2x128_si256(bgra0_, bgra1_, 1 + 3*16); + __m256i bgra1 = _mm256_permute2x128_si256(bgra2_, bgra3_, 0 + 2*16); + __m256i bgra3 = _mm256_permute2x128_si256(bgra2_, bgra3_, 1 + 3*16); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm256_stream_si256((__m256i*)ptr, bgra0); + _mm256_stream_si256((__m256i*)(ptr + 32), bgra1); + _mm256_stream_si256((__m256i*)(ptr + 64), bgra2); + _mm256_stream_si256((__m256i*)(ptr + 96), bgra3); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm256_store_si256((__m256i*)ptr, bgra0); + _mm256_store_si256((__m256i*)(ptr + 32), bgra1); + _mm256_store_si256((__m256i*)(ptr + 64), bgra2); + _mm256_store_si256((__m256i*)(ptr + 96), bgra3); + } + else + { + _mm256_storeu_si256((__m256i*)ptr, bgra0); + _mm256_storeu_si256((__m256i*)(ptr + 32), bgra1); + _mm256_storeu_si256((__m256i*)(ptr + 64), bgra2); + _mm256_storeu_si256((__m256i*)(ptr + 96), bgra3); + } +} + +inline void v_store_interleave( ushort* ptr, const v_uint16x16& b, const v_uint16x16& g, + const v_uint16x16& r, const v_uint16x16& a, + hal::StoreMode mode=hal::STORE_UNALIGNED ) +{ + __m256i bg0 = _mm256_unpacklo_epi16(b.val, g.val); + __m256i bg1 = _mm256_unpackhi_epi16(b.val, g.val); + __m256i ra0 = _mm256_unpacklo_epi16(r.val, a.val); + __m256i ra1 = _mm256_unpackhi_epi16(r.val, a.val); + + __m256i bgra0_ = _mm256_unpacklo_epi32(bg0, ra0); + __m256i bgra1_ = _mm256_unpackhi_epi32(bg0, ra0); + __m256i bgra2_ = _mm256_unpacklo_epi32(bg1, ra1); + __m256i bgra3_ = _mm256_unpackhi_epi32(bg1, ra1); + + __m256i bgra0 = _mm256_permute2x128_si256(bgra0_, bgra1_, 0 + 2*16); + __m256i bgra2 = _mm256_permute2x128_si256(bgra0_, bgra1_, 1 + 3*16); + __m256i bgra1 = _mm256_permute2x128_si256(bgra2_, bgra3_, 0 + 2*16); + __m256i bgra3 = _mm256_permute2x128_si256(bgra2_, bgra3_, 1 + 3*16); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm256_stream_si256((__m256i*)ptr, bgra0); + _mm256_stream_si256((__m256i*)(ptr + 16), bgra1); + _mm256_stream_si256((__m256i*)(ptr + 32), bgra2); + _mm256_stream_si256((__m256i*)(ptr + 48), bgra3); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm256_store_si256((__m256i*)ptr, bgra0); + _mm256_store_si256((__m256i*)(ptr + 16), bgra1); + _mm256_store_si256((__m256i*)(ptr + 32), bgra2); + _mm256_store_si256((__m256i*)(ptr + 48), bgra3); + } + else + { + _mm256_storeu_si256((__m256i*)ptr, bgra0); + _mm256_storeu_si256((__m256i*)(ptr + 16), bgra1); + _mm256_storeu_si256((__m256i*)(ptr + 32), bgra2); + _mm256_storeu_si256((__m256i*)(ptr + 48), bgra3); + } +} + +inline void v_store_interleave( unsigned* ptr, const v_uint32x8& b, const v_uint32x8& g, + const v_uint32x8& r, const v_uint32x8& a, + hal::StoreMode mode=hal::STORE_UNALIGNED ) +{ + __m256i bg0 = _mm256_unpacklo_epi32(b.val, g.val); + __m256i bg1 = _mm256_unpackhi_epi32(b.val, g.val); + __m256i ra0 = _mm256_unpacklo_epi32(r.val, a.val); + __m256i ra1 = _mm256_unpackhi_epi32(r.val, a.val); + + __m256i bgra0_ = _mm256_unpacklo_epi64(bg0, ra0); + __m256i bgra1_ = _mm256_unpackhi_epi64(bg0, ra0); + __m256i bgra2_ = _mm256_unpacklo_epi64(bg1, ra1); + __m256i bgra3_ = _mm256_unpackhi_epi64(bg1, ra1); + + __m256i bgra0 = _mm256_permute2x128_si256(bgra0_, bgra1_, 0 + 2*16); + __m256i bgra2 = _mm256_permute2x128_si256(bgra0_, bgra1_, 1 + 3*16); + __m256i bgra1 = _mm256_permute2x128_si256(bgra2_, bgra3_, 0 + 2*16); + __m256i bgra3 = _mm256_permute2x128_si256(bgra2_, bgra3_, 1 + 3*16); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm256_stream_si256((__m256i*)ptr, bgra0); + _mm256_stream_si256((__m256i*)(ptr + 8), bgra1); + _mm256_stream_si256((__m256i*)(ptr + 16), bgra2); + _mm256_stream_si256((__m256i*)(ptr + 24), bgra3); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm256_store_si256((__m256i*)ptr, bgra0); + _mm256_store_si256((__m256i*)(ptr + 8), bgra1); + _mm256_store_si256((__m256i*)(ptr + 16), bgra2); + _mm256_store_si256((__m256i*)(ptr + 24), bgra3); + } + else + { + _mm256_storeu_si256((__m256i*)ptr, bgra0); + _mm256_storeu_si256((__m256i*)(ptr + 8), bgra1); + _mm256_storeu_si256((__m256i*)(ptr + 16), bgra2); + _mm256_storeu_si256((__m256i*)(ptr + 24), bgra3); + } +} + +inline void v_store_interleave( uint64* ptr, const v_uint64x4& b, const v_uint64x4& g, + const v_uint64x4& r, const v_uint64x4& a, + hal::StoreMode mode=hal::STORE_UNALIGNED ) +{ + __m256i bg0 = _mm256_unpacklo_epi64(b.val, g.val); + __m256i bg1 = _mm256_unpackhi_epi64(b.val, g.val); + __m256i ra0 = _mm256_unpacklo_epi64(r.val, a.val); + __m256i ra1 = _mm256_unpackhi_epi64(r.val, a.val); + + __m256i bgra0 = _mm256_permute2x128_si256(bg0, ra0, 0 + 2*16); + __m256i bgra1 = _mm256_permute2x128_si256(bg1, ra1, 0 + 2*16); + __m256i bgra2 = _mm256_permute2x128_si256(bg0, ra0, 1 + 3*16); + __m256i bgra3 = _mm256_permute2x128_si256(bg1, ra1, 1 + 3*16); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm256_stream_si256((__m256i*)ptr, bgra0); + _mm256_stream_si256((__m256i*)(ptr + 4), bgra1); + _mm256_stream_si256((__m256i*)(ptr + 8), bgra2); + _mm256_stream_si256((__m256i*)(ptr + 12), bgra3); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm256_store_si256((__m256i*)ptr, bgra0); + _mm256_store_si256((__m256i*)(ptr + 4), bgra1); + _mm256_store_si256((__m256i*)(ptr + 8), bgra2); + _mm256_store_si256((__m256i*)(ptr + 12), bgra3); + } + else + { + _mm256_storeu_si256((__m256i*)ptr, bgra0); + _mm256_storeu_si256((__m256i*)(ptr + 4), bgra1); + _mm256_storeu_si256((__m256i*)(ptr + 8), bgra2); + _mm256_storeu_si256((__m256i*)(ptr + 12), bgra3); + } +} + +#define OPENCV_HAL_IMPL_AVX_LOADSTORE_INTERLEAVE(_Tpvec0, _Tp0, suffix0, _Tpvec1, _Tp1, suffix1) \ +inline void v_load_deinterleave( const _Tp0* ptr, _Tpvec0& a0, _Tpvec0& b0 ) \ +{ \ + _Tpvec1 a1, b1; \ + v_load_deinterleave((const _Tp1*)ptr, a1, b1); \ + a0 = v_reinterpret_as_##suffix0(a1); \ + b0 = v_reinterpret_as_##suffix0(b1); \ +} \ +inline void v_load_deinterleave( const _Tp0* ptr, _Tpvec0& a0, _Tpvec0& b0, _Tpvec0& c0 ) \ +{ \ + _Tpvec1 a1, b1, c1; \ + v_load_deinterleave((const _Tp1*)ptr, a1, b1, c1); \ + a0 = v_reinterpret_as_##suffix0(a1); \ + b0 = v_reinterpret_as_##suffix0(b1); \ + c0 = v_reinterpret_as_##suffix0(c1); \ +} \ +inline void v_load_deinterleave( const _Tp0* ptr, _Tpvec0& a0, _Tpvec0& b0, _Tpvec0& c0, _Tpvec0& d0 ) \ +{ \ + _Tpvec1 a1, b1, c1, d1; \ + v_load_deinterleave((const _Tp1*)ptr, a1, b1, c1, d1); \ + a0 = v_reinterpret_as_##suffix0(a1); \ + b0 = v_reinterpret_as_##suffix0(b1); \ + c0 = v_reinterpret_as_##suffix0(c1); \ + d0 = v_reinterpret_as_##suffix0(d1); \ +} \ +inline void v_store_interleave( _Tp0* ptr, const _Tpvec0& a0, const _Tpvec0& b0, \ + hal::StoreMode mode=hal::STORE_UNALIGNED ) \ +{ \ + _Tpvec1 a1 = v_reinterpret_as_##suffix1(a0); \ + _Tpvec1 b1 = v_reinterpret_as_##suffix1(b0); \ + v_store_interleave((_Tp1*)ptr, a1, b1, mode); \ +} \ +inline void v_store_interleave( _Tp0* ptr, const _Tpvec0& a0, const _Tpvec0& b0, const _Tpvec0& c0, \ + hal::StoreMode mode=hal::STORE_UNALIGNED ) \ +{ \ + _Tpvec1 a1 = v_reinterpret_as_##suffix1(a0); \ + _Tpvec1 b1 = v_reinterpret_as_##suffix1(b0); \ + _Tpvec1 c1 = v_reinterpret_as_##suffix1(c0); \ + v_store_interleave((_Tp1*)ptr, a1, b1, c1, mode); \ +} \ +inline void v_store_interleave( _Tp0* ptr, const _Tpvec0& a0, const _Tpvec0& b0, \ + const _Tpvec0& c0, const _Tpvec0& d0, \ + hal::StoreMode mode=hal::STORE_UNALIGNED ) \ +{ \ + _Tpvec1 a1 = v_reinterpret_as_##suffix1(a0); \ + _Tpvec1 b1 = v_reinterpret_as_##suffix1(b0); \ + _Tpvec1 c1 = v_reinterpret_as_##suffix1(c0); \ + _Tpvec1 d1 = v_reinterpret_as_##suffix1(d0); \ + v_store_interleave((_Tp1*)ptr, a1, b1, c1, d1, mode); \ +} + +OPENCV_HAL_IMPL_AVX_LOADSTORE_INTERLEAVE(v_int8x32, schar, s8, v_uint8x32, uchar, u8) +OPENCV_HAL_IMPL_AVX_LOADSTORE_INTERLEAVE(v_int16x16, short, s16, v_uint16x16, ushort, u16) +OPENCV_HAL_IMPL_AVX_LOADSTORE_INTERLEAVE(v_int32x8, int, s32, v_uint32x8, unsigned, u32) +OPENCV_HAL_IMPL_AVX_LOADSTORE_INTERLEAVE(v_float32x8, float, f32, v_uint32x8, unsigned, u32) +OPENCV_HAL_IMPL_AVX_LOADSTORE_INTERLEAVE(v_int64x4, int64, s64, v_uint64x4, uint64, u64) +OPENCV_HAL_IMPL_AVX_LOADSTORE_INTERLEAVE(v_float64x4, double, f64, v_uint64x4, uint64, u64) + +// FP16 +inline v_float32x8 v256_load_expand(const float16_t* ptr) +{ + return v_float32x8(_mm256_cvtph_ps(_mm_loadu_si128((const __m128i*)ptr))); +} + +inline void v_pack_store(float16_t* ptr, const v_float32x8& a) +{ + __m128i ah = _mm256_cvtps_ph(a.val, 0); + _mm_storeu_si128((__m128i*)ptr, ah); +} + +inline void v256_cleanup() { _mm256_zeroall(); } + +//! @name Check SIMD256 support +//! @{ +//! @brief Check CPU capability of SIMD operation +static inline bool hasSIMD256() +{ + return (CV_CPU_HAS_SUPPORT_AVX2) ? true : false; +} +//! @} + +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END + +//! @endcond + +} // cv:: + +#endif // OPENCV_HAL_INTRIN_AVX_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/hal/intrin_cpp.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/hal/intrin_cpp.hpp new file mode 100644 index 0000000000000000000000000000000000000000..65a01f31d7183a25e92e8b6b4a78802e33501f22 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/hal/intrin_cpp.hpp @@ -0,0 +1,2310 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_HAL_INTRIN_CPP_HPP +#define OPENCV_HAL_INTRIN_CPP_HPP + +#include +#include +#include +#include "opencv2/core/saturate.hpp" + +namespace cv +{ + +#ifndef CV_DOXYGEN +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN +#endif + +/** @addtogroup core_hal_intrin + +"Universal intrinsics" is a types and functions set intended to simplify vectorization of code on +different platforms. Currently there are two supported SIMD extensions: __SSE/SSE2__ on x86 +architectures and __NEON__ on ARM architectures, both allow working with 128 bit registers +containing packed values of different types. In case when there is no SIMD extension available +during compilation, fallback C++ implementation of intrinsics will be chosen and code will work as +expected although it could be slower. + +### Types + +There are several types representing 128-bit register as a vector of packed values, each type is +implemented as a structure based on a one SIMD register. + +- cv::v_uint8x16 and cv::v_int8x16: sixteen 8-bit integer values (unsigned/signed) - char +- cv::v_uint16x8 and cv::v_int16x8: eight 16-bit integer values (unsigned/signed) - short +- cv::v_uint32x4 and cv::v_int32x4: four 32-bit integer values (unsgined/signed) - int +- cv::v_uint64x2 and cv::v_int64x2: two 64-bit integer values (unsigned/signed) - int64 +- cv::v_float32x4: four 32-bit floating point values (signed) - float +- cv::v_float64x2: two 64-bit floating point valies (signed) - double + +@note +cv::v_float64x2 is not implemented in NEON variant, if you want to use this type, don't forget to +check the CV_SIMD128_64F preprocessor definition: +@code +#if CV_SIMD128_64F +//... +#endif +@endcode + +### Load and store operations + +These operations allow to set contents of the register explicitly or by loading it from some memory +block and to save contents of the register to memory block. + +- Constructors: +@ref v_reg::v_reg(const _Tp *ptr) "from memory", +@ref v_reg::v_reg(_Tp s0, _Tp s1) "from two values", ... +- Other create methods: +@ref v_setall_s8, @ref v_setall_u8, ..., +@ref v_setzero_u8, @ref v_setzero_s8, ... +- Memory operations: +@ref v_load, @ref v_load_aligned, @ref v_load_low, @ref v_load_halves, +@ref v_store, @ref v_store_aligned, +@ref v_store_high, @ref v_store_low + +### Value reordering + +These operations allow to reorder or recombine elements in one or multiple vectors. + +- Interleave, deinterleave (2, 3 and 4 channels): @ref v_load_deinterleave, @ref v_store_interleave +- Expand: @ref v_load_expand, @ref v_load_expand_q, @ref v_expand, @ref v_expand_low, @ref v_expand_high +- Pack: @ref v_pack, @ref v_pack_u, @ref v_pack_b, @ref v_rshr_pack, @ref v_rshr_pack_u, +@ref v_pack_store, @ref v_pack_u_store, @ref v_rshr_pack_store, @ref v_rshr_pack_u_store +- Recombine: @ref v_zip, @ref v_recombine, @ref v_combine_low, @ref v_combine_high +- Extract: @ref v_extract + + +### Arithmetic, bitwise and comparison operations + +Element-wise binary and unary operations. + +- Arithmetics: +@ref operator +(const v_reg &a, const v_reg &b) "+", +@ref operator -(const v_reg &a, const v_reg &b) "-", +@ref operator *(const v_reg &a, const v_reg &b) "*", +@ref operator /(const v_reg &a, const v_reg &b) "/", +@ref v_mul_expand + +- Non-saturating arithmetics: @ref v_add_wrap, @ref v_sub_wrap + +- Bitwise shifts: +@ref operator <<(const v_reg &a, int s) "<<", +@ref operator >>(const v_reg &a, int s) ">>", +@ref v_shl, @ref v_shr + +- Bitwise logic: +@ref operator&(const v_reg &a, const v_reg &b) "&", +@ref operator |(const v_reg &a, const v_reg &b) "|", +@ref operator ^(const v_reg &a, const v_reg &b) "^", +@ref operator ~(const v_reg &a) "~" + +- Comparison: +@ref operator >(const v_reg &a, const v_reg &b) ">", +@ref operator >=(const v_reg &a, const v_reg &b) ">=", +@ref operator <(const v_reg &a, const v_reg &b) "<", +@ref operator <=(const v_reg &a, const v_reg &b) "<=", +@ref operator==(const v_reg &a, const v_reg &b) "==", +@ref operator !=(const v_reg &a, const v_reg &b) "!=" + +- min/max: @ref v_min, @ref v_max + +### Reduce and mask + +Most of these operations return only one value. + +- Reduce: @ref v_reduce_min, @ref v_reduce_max, @ref v_reduce_sum, @ref v_popcount +- Mask: @ref v_signmask, @ref v_check_all, @ref v_check_any, @ref v_select + +### Other math + +- Some frequent operations: @ref v_sqrt, @ref v_invsqrt, @ref v_magnitude, @ref v_sqr_magnitude +- Absolute values: @ref v_abs, @ref v_absdiff, @ref v_absdiffs + +### Conversions + +Different type conversions and casts: + +- Rounding: @ref v_round, @ref v_floor, @ref v_ceil, @ref v_trunc, +- To float: @ref v_cvt_f32, @ref v_cvt_f64 +- Reinterpret: @ref v_reinterpret_as_u8, @ref v_reinterpret_as_s8, ... + +### Matrix operations + +In these operations vectors represent matrix rows/columns: @ref v_dotprod, @ref v_matmul, @ref v_transpose4x4 + +### Usability + +Most operations are implemented only for some subset of the available types, following matrices +shows the applicability of different operations to the types. + +Regular integers: + +| Operations\\Types | uint 8x16 | int 8x16 | uint 16x8 | int 16x8 | uint 32x4 | int 32x4 | +|-------------------|:-:|:-:|:-:|:-:|:-:|:-:| +|load, store | x | x | x | x | x | x | +|interleave | x | x | x | x | x | x | +|expand | x | x | x | x | x | x | +|expand_low | x | x | x | x | x | x | +|expand_high | x | x | x | x | x | x | +|expand_q | x | x | | | | | +|add, sub | x | x | x | x | x | x | +|add_wrap, sub_wrap | x | x | x | x | | | +|mul_wrap | x | x | x | x | | | +|mul | x | x | x | x | x | x | +|mul_expand | x | x | x | x | x | | +|compare | x | x | x | x | x | x | +|shift | | | x | x | x | x | +|dotprod | | | | x | | | +|logical | x | x | x | x | x | x | +|min, max | x | x | x | x | x | x | +|absdiff | x | x | x | x | x | x | +|absdiffs | | x | | x | | | +|reduce | | | | | x | x | +|mask | x | x | x | x | x | x | +|pack | x | x | x | x | x | x | +|pack_u | x | | x | | | | +|pack_b | x | | | | | | +|unpack | x | x | x | x | x | x | +|extract | x | x | x | x | x | x | +|rotate (lanes) | x | x | x | x | x | x | +|cvt_flt32 | | | | | | x | +|cvt_flt64 | | | | | | x | +|transpose4x4 | | | | | x | x | + +Big integers: + +| Operations\\Types | uint 64x2 | int 64x2 | +|-------------------|:-:|:-:| +|load, store | x | x | +|add, sub | x | x | +|shift | x | x | +|logical | x | x | +|extract | x | x | +|rotate (lanes) | x | x | + +Floating point: + +| Operations\\Types | float 32x4 | float 64x2 | +|-------------------|:-:|:-:| +|load, store | x | x | +|interleave | x | | +|add, sub | x | x | +|mul | x | x | +|div | x | x | +|compare | x | x | +|min, max | x | x | +|absdiff | x | x | +|reduce | x | | +|mask | x | x | +|unpack | x | x | +|cvt_flt32 | | x | +|cvt_flt64 | x | | +|sqrt, abs | x | x | +|float math | x | x | +|transpose4x4 | x | | +|extract | x | x | +|rotate (lanes) | x | x | + + @{ */ + +template struct v_reg +{ +//! @cond IGNORED + typedef _Tp lane_type; + enum { nlanes = n }; +// !@endcond + + /** @brief Constructor + + Initializes register with data from memory + @param ptr pointer to memory block with data for register */ + explicit v_reg(const _Tp* ptr) { for( int i = 0; i < n; i++ ) s[i] = ptr[i]; } + + /** @brief Constructor + + Initializes register with two 64-bit values */ + v_reg(_Tp s0, _Tp s1) { s[0] = s0; s[1] = s1; } + + /** @brief Constructor + + Initializes register with four 32-bit values */ + v_reg(_Tp s0, _Tp s1, _Tp s2, _Tp s3) { s[0] = s0; s[1] = s1; s[2] = s2; s[3] = s3; } + + /** @brief Constructor + + Initializes register with eight 16-bit values */ + v_reg(_Tp s0, _Tp s1, _Tp s2, _Tp s3, + _Tp s4, _Tp s5, _Tp s6, _Tp s7) + { + s[0] = s0; s[1] = s1; s[2] = s2; s[3] = s3; + s[4] = s4; s[5] = s5; s[6] = s6; s[7] = s7; + } + + /** @brief Constructor + + Initializes register with sixteen 8-bit values */ + v_reg(_Tp s0, _Tp s1, _Tp s2, _Tp s3, + _Tp s4, _Tp s5, _Tp s6, _Tp s7, + _Tp s8, _Tp s9, _Tp s10, _Tp s11, + _Tp s12, _Tp s13, _Tp s14, _Tp s15) + { + s[0] = s0; s[1] = s1; s[2] = s2; s[3] = s3; + s[4] = s4; s[5] = s5; s[6] = s6; s[7] = s7; + s[8] = s8; s[9] = s9; s[10] = s10; s[11] = s11; + s[12] = s12; s[13] = s13; s[14] = s14; s[15] = s15; + } + + /** @brief Default constructor + + Does not initialize anything*/ + v_reg() {} + + /** @brief Copy constructor */ + v_reg(const v_reg<_Tp, n> & r) + { + for( int i = 0; i < n; i++ ) + s[i] = r.s[i]; + } + /** @brief Access first value + + Returns value of the first lane according to register type, for example: + @code{.cpp} + v_int32x4 r(1, 2, 3, 4); + int v = r.get0(); // returns 1 + v_uint64x2 r(1, 2); + uint64_t v = r.get0(); // returns 1 + @endcode + */ + _Tp get0() const { return s[0]; } + +//! @cond IGNORED + _Tp get(const int i) const { return s[i]; } + v_reg<_Tp, n> high() const + { + v_reg<_Tp, n> c; + int i; + for( i = 0; i < n/2; i++ ) + { + c.s[i] = s[i+(n/2)]; + c.s[i+(n/2)] = 0; + } + return c; + } + + static v_reg<_Tp, n> zero() + { + v_reg<_Tp, n> c; + for( int i = 0; i < n; i++ ) + c.s[i] = (_Tp)0; + return c; + } + + static v_reg<_Tp, n> all(_Tp s) + { + v_reg<_Tp, n> c; + for( int i = 0; i < n; i++ ) + c.s[i] = s; + return c; + } + + template v_reg<_Tp2, n2> reinterpret_as() const + { + size_t bytes = std::min(sizeof(_Tp2)*n2, sizeof(_Tp)*n); + v_reg<_Tp2, n2> c; + std::memcpy(&c.s[0], &s[0], bytes); + return c; + } + + _Tp s[n]; +//! @endcond +}; + +/** @brief Sixteen 8-bit unsigned integer values */ +typedef v_reg v_uint8x16; +/** @brief Sixteen 8-bit signed integer values */ +typedef v_reg v_int8x16; +/** @brief Eight 16-bit unsigned integer values */ +typedef v_reg v_uint16x8; +/** @brief Eight 16-bit signed integer values */ +typedef v_reg v_int16x8; +/** @brief Four 32-bit unsigned integer values */ +typedef v_reg v_uint32x4; +/** @brief Four 32-bit signed integer values */ +typedef v_reg v_int32x4; +/** @brief Four 32-bit floating point values (single precision) */ +typedef v_reg v_float32x4; +/** @brief Two 64-bit floating point values (double precision) */ +typedef v_reg v_float64x2; +/** @brief Two 64-bit unsigned integer values */ +typedef v_reg v_uint64x2; +/** @brief Two 64-bit signed integer values */ +typedef v_reg v_int64x2; + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_BIN_OP(bin_op) \ +template inline v_reg<_Tp, n> \ + operator bin_op (const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ +{ \ + v_reg<_Tp, n> c; \ + for( int i = 0; i < n; i++ ) \ + c.s[i] = saturate_cast<_Tp>(a.s[i] bin_op b.s[i]); \ + return c; \ +} \ +template inline v_reg<_Tp, n>& \ + operator bin_op##= (v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ +{ \ + for( int i = 0; i < n; i++ ) \ + a.s[i] = saturate_cast<_Tp>(a.s[i] bin_op b.s[i]); \ + return a; \ +} + +/** @brief Add values + +For all types. */ +OPENCV_HAL_IMPL_BIN_OP(+) + +/** @brief Subtract values + +For all types. */ +OPENCV_HAL_IMPL_BIN_OP(-) + +/** @brief Multiply values + +For 16- and 32-bit integer types and floating types. */ +OPENCV_HAL_IMPL_BIN_OP(*) + +/** @brief Divide values + +For floating types only. */ +OPENCV_HAL_IMPL_BIN_OP(/) + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_BIT_OP(bit_op) \ +template inline v_reg<_Tp, n> operator bit_op \ + (const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ +{ \ + v_reg<_Tp, n> c; \ + typedef typename V_TypeTraits<_Tp>::int_type itype; \ + for( int i = 0; i < n; i++ ) \ + c.s[i] = V_TypeTraits<_Tp>::reinterpret_from_int((itype)(V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) bit_op \ + V_TypeTraits<_Tp>::reinterpret_int(b.s[i]))); \ + return c; \ +} \ +template inline v_reg<_Tp, n>& operator \ + bit_op##= (v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ +{ \ + typedef typename V_TypeTraits<_Tp>::int_type itype; \ + for( int i = 0; i < n; i++ ) \ + a.s[i] = V_TypeTraits<_Tp>::reinterpret_from_int((itype)(V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) bit_op \ + V_TypeTraits<_Tp>::reinterpret_int(b.s[i]))); \ + return a; \ +} + +/** @brief Bitwise AND + +Only for integer types. */ +OPENCV_HAL_IMPL_BIT_OP(&) + +/** @brief Bitwise OR + +Only for integer types. */ +OPENCV_HAL_IMPL_BIT_OP(|) + +/** @brief Bitwise XOR + +Only for integer types.*/ +OPENCV_HAL_IMPL_BIT_OP(^) + +/** @brief Bitwise NOT + +Only for integer types.*/ +template inline v_reg<_Tp, n> operator ~ (const v_reg<_Tp, n>& a) +{ + v_reg<_Tp, n> c; + for( int i = 0; i < n; i++ ) + { + c.s[i] = V_TypeTraits<_Tp>::reinterpret_from_int(~V_TypeTraits<_Tp>::reinterpret_int(a.s[i])); + } + return c; +} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_MATH_FUNC(func, cfunc, _Tp2) \ +template inline v_reg<_Tp2, n> func(const v_reg<_Tp, n>& a) \ +{ \ + v_reg<_Tp2, n> c; \ + for( int i = 0; i < n; i++ ) \ + c.s[i] = cfunc(a.s[i]); \ + return c; \ +} + +/** @brief Square root of elements + +Only for floating point types.*/ +OPENCV_HAL_IMPL_MATH_FUNC(v_sqrt, std::sqrt, _Tp) + +//! @cond IGNORED +OPENCV_HAL_IMPL_MATH_FUNC(v_sin, std::sin, _Tp) +OPENCV_HAL_IMPL_MATH_FUNC(v_cos, std::cos, _Tp) +OPENCV_HAL_IMPL_MATH_FUNC(v_exp, std::exp, _Tp) +OPENCV_HAL_IMPL_MATH_FUNC(v_log, std::log, _Tp) +//! @endcond + +/** @brief Absolute value of elements + +Only for floating point types.*/ +OPENCV_HAL_IMPL_MATH_FUNC(v_abs, (typename V_TypeTraits<_Tp>::abs_type)std::abs, + typename V_TypeTraits<_Tp>::abs_type) + +/** @brief Round elements + +Only for floating point types.*/ +OPENCV_HAL_IMPL_MATH_FUNC(v_round, cvRound, int) + +/** @brief Floor elements + +Only for floating point types.*/ +OPENCV_HAL_IMPL_MATH_FUNC(v_floor, cvFloor, int) + +/** @brief Ceil elements + +Only for floating point types.*/ +OPENCV_HAL_IMPL_MATH_FUNC(v_ceil, cvCeil, int) + +/** @brief Truncate elements + +Only for floating point types.*/ +OPENCV_HAL_IMPL_MATH_FUNC(v_trunc, int, int) + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_MINMAX_FUNC(func, cfunc) \ +template inline v_reg<_Tp, n> func(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ +{ \ + v_reg<_Tp, n> c; \ + for( int i = 0; i < n; i++ ) \ + c.s[i] = cfunc(a.s[i], b.s[i]); \ + return c; \ +} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_REDUCE_MINMAX_FUNC(func, cfunc) \ +template inline _Tp func(const v_reg<_Tp, n>& a) \ +{ \ + _Tp c = a.s[0]; \ + for( int i = 1; i < n; i++ ) \ + c = cfunc(c, a.s[i]); \ + return c; \ +} + +/** @brief Choose min values for each pair + +Scheme: +@code +{A1 A2 ...} +{B1 B2 ...} +-------------- +{min(A1,B1) min(A2,B2) ...} +@endcode +For all types except 64-bit integer. */ +OPENCV_HAL_IMPL_MINMAX_FUNC(v_min, std::min) + +/** @brief Choose max values for each pair + +Scheme: +@code +{A1 A2 ...} +{B1 B2 ...} +-------------- +{max(A1,B1) max(A2,B2) ...} +@endcode +For all types except 64-bit integer. */ +OPENCV_HAL_IMPL_MINMAX_FUNC(v_max, std::max) + +/** @brief Find one min value + +Scheme: +@code +{A1 A2 A3 ...} => min(A1,A2,A3,...) +@endcode +For 32-bit integer and 32-bit floating point types. */ +OPENCV_HAL_IMPL_REDUCE_MINMAX_FUNC(v_reduce_min, std::min) + +/** @brief Find one max value + +Scheme: +@code +{A1 A2 A3 ...} => max(A1,A2,A3,...) +@endcode +For 32-bit integer and 32-bit floating point types. */ +OPENCV_HAL_IMPL_REDUCE_MINMAX_FUNC(v_reduce_max, std::max) + +static const unsigned char popCountTable[] = +{ + 0, 1, 1, 2, 1, 2, 2, 3, 1, 2, 2, 3, 2, 3, 3, 4, + 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, + 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, + 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, + 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, + 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, + 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, + 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, + 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, + 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, + 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, + 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, + 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, + 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, + 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, + 4, 5, 5, 6, 5, 6, 6, 7, 5, 6, 6, 7, 6, 7, 7, 8, +}; +/** @brief Count the 1 bits in the vector and return 4 values + +Scheme: +@code +{A1 A2 A3 ...} => popcount(A1) +@endcode +Any types but result will be in v_uint32x4*/ +template inline v_uint32x4 v_popcount(const v_reg<_Tp, n>& a) +{ + v_uint8x16 b; + b = v_reinterpret_as_u8(a); + for( int i = 0; i < v_uint8x16::nlanes; i++ ) + { + b.s[i] = popCountTable[b.s[i]]; + } + v_uint32x4 c; + for( int i = 0; i < v_uint32x4::nlanes; i++ ) + { + c.s[i] = b.s[i*4] + b.s[i*4+1] + b.s[i*4+2] + b.s[i*4+3]; + } + return c; +} + + +//! @cond IGNORED +template +inline void v_minmax( const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b, + v_reg<_Tp, n>& minval, v_reg<_Tp, n>& maxval ) +{ + for( int i = 0; i < n; i++ ) + { + minval.s[i] = std::min(a.s[i], b.s[i]); + maxval.s[i] = std::max(a.s[i], b.s[i]); + } +} +//! @endcond + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_CMP_OP(cmp_op) \ +template \ +inline v_reg<_Tp, n> operator cmp_op(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ +{ \ + typedef typename V_TypeTraits<_Tp>::int_type itype; \ + v_reg<_Tp, n> c; \ + for( int i = 0; i < n; i++ ) \ + c.s[i] = V_TypeTraits<_Tp>::reinterpret_from_int((itype)-(int)(a.s[i] cmp_op b.s[i])); \ + return c; \ +} + +/** @brief Less-than comparison + +For all types except 64-bit integer values. */ +OPENCV_HAL_IMPL_CMP_OP(<) + +/** @brief Greater-than comparison + +For all types except 64-bit integer values. */ +OPENCV_HAL_IMPL_CMP_OP(>) + +/** @brief Less-than or equal comparison + +For all types except 64-bit integer values. */ +OPENCV_HAL_IMPL_CMP_OP(<=) + +/** @brief Greater-than or equal comparison + +For all types except 64-bit integer values. */ +OPENCV_HAL_IMPL_CMP_OP(>=) + +/** @brief Equal comparison + +For all types except 64-bit integer values. */ +OPENCV_HAL_IMPL_CMP_OP(==) + +/** @brief Not equal comparison + +For all types except 64-bit integer values. */ +OPENCV_HAL_IMPL_CMP_OP(!=) + +template +inline v_reg v_not_nan(const v_reg& a) +{ + typedef typename V_TypeTraits::int_type itype; + v_reg c; + for (int i = 0; i < n; i++) + c.s[i] = V_TypeTraits::reinterpret_from_int((itype)-(int)(a.s[i] == a.s[i])); + return c; +} +template +inline v_reg v_not_nan(const v_reg& a) +{ + typedef typename V_TypeTraits::int_type itype; + v_reg c; + for (int i = 0; i < n; i++) + c.s[i] = V_TypeTraits::reinterpret_from_int((itype)-(int)(a.s[i] == a.s[i])); + return c; +} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_ARITHM_OP(func, bin_op, cast_op, _Tp2) \ +template \ +inline v_reg<_Tp2, n> func(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ +{ \ + typedef _Tp2 rtype; \ + v_reg c; \ + for( int i = 0; i < n; i++ ) \ + c.s[i] = cast_op(a.s[i] bin_op b.s[i]); \ + return c; \ +} + +/** @brief Add values without saturation + +For 8- and 16-bit integer values. */ +OPENCV_HAL_IMPL_ARITHM_OP(v_add_wrap, +, (_Tp), _Tp) + +/** @brief Subtract values without saturation + +For 8- and 16-bit integer values. */ +OPENCV_HAL_IMPL_ARITHM_OP(v_sub_wrap, -, (_Tp), _Tp) + +/** @brief Multiply values without saturation + +For 8- and 16-bit integer values. */ +OPENCV_HAL_IMPL_ARITHM_OP(v_mul_wrap, *, (_Tp), _Tp) + +//! @cond IGNORED +template inline T _absdiff(T a, T b) +{ + return a > b ? a - b : b - a; +} +//! @endcond + +/** @brief Absolute difference + +Returns \f$ |a - b| \f$ converted to corresponding unsigned type. +Example: +@code{.cpp} +v_int32x4 a, b; // {1, 2, 3, 4} and {4, 3, 2, 1} +v_uint32x4 c = v_absdiff(a, b); // result is {3, 1, 1, 3} +@endcode +For 8-, 16-, 32-bit integer source types. */ +template +inline v_reg::abs_type, n> v_absdiff(const v_reg<_Tp, n>& a, const v_reg<_Tp, n> & b) +{ + typedef typename V_TypeTraits<_Tp>::abs_type rtype; + v_reg c; + const rtype mask = (rtype)(std::numeric_limits<_Tp>::is_signed ? (1 << (sizeof(rtype)*8 - 1)) : 0); + for( int i = 0; i < n; i++ ) + { + rtype ua = a.s[i] ^ mask; + rtype ub = b.s[i] ^ mask; + c.s[i] = _absdiff(ua, ub); + } + return c; +} + +/** @overload + +For 32-bit floating point values */ +inline v_float32x4 v_absdiff(const v_float32x4& a, const v_float32x4& b) +{ + v_float32x4 c; + for( int i = 0; i < c.nlanes; i++ ) + c.s[i] = _absdiff(a.s[i], b.s[i]); + return c; +} + +/** @overload + +For 64-bit floating point values */ +inline v_float64x2 v_absdiff(const v_float64x2& a, const v_float64x2& b) +{ + v_float64x2 c; + for( int i = 0; i < c.nlanes; i++ ) + c.s[i] = _absdiff(a.s[i], b.s[i]); + return c; +} + +/** @brief Saturating absolute difference + +Returns \f$ saturate(|a - b|) \f$ . +For 8-, 16-bit signed integer source types. */ +template +inline v_reg<_Tp, n> v_absdiffs(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) +{ + v_reg<_Tp, n> c; + for( int i = 0; i < n; i++) + c.s[i] = saturate_cast<_Tp>(std::abs(a.s[i] - b.s[i])); + return c; +} + +/** @brief Inversed square root + +Returns \f$ 1/sqrt(a) \f$ +For floating point types only. */ +template +inline v_reg<_Tp, n> v_invsqrt(const v_reg<_Tp, n>& a) +{ + v_reg<_Tp, n> c; + for( int i = 0; i < n; i++ ) + c.s[i] = 1.f/std::sqrt(a.s[i]); + return c; +} + +/** @brief Magnitude + +Returns \f$ sqrt(a^2 + b^2) \f$ +For floating point types only. */ +template +inline v_reg<_Tp, n> v_magnitude(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) +{ + v_reg<_Tp, n> c; + for( int i = 0; i < n; i++ ) + c.s[i] = std::sqrt(a.s[i]*a.s[i] + b.s[i]*b.s[i]); + return c; +} + +/** @brief Square of the magnitude + +Returns \f$ a^2 + b^2 \f$ +For floating point types only. */ +template +inline v_reg<_Tp, n> v_sqr_magnitude(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) +{ + v_reg<_Tp, n> c; + for( int i = 0; i < n; i++ ) + c.s[i] = a.s[i]*a.s[i] + b.s[i]*b.s[i]; + return c; +} + +/** @brief Multiply and add + + Returns \f$ a*b + c \f$ + For floating point types and signed 32bit int only. */ +template +inline v_reg<_Tp, n> v_fma(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b, + const v_reg<_Tp, n>& c) +{ + v_reg<_Tp, n> d; + for( int i = 0; i < n; i++ ) + d.s[i] = a.s[i]*b.s[i] + c.s[i]; + return d; +} + +/** @brief A synonym for v_fma */ +template +inline v_reg<_Tp, n> v_muladd(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b, + const v_reg<_Tp, n>& c) +{ + return v_fma(a, b, c); +} + +/** @brief Dot product of elements + +Multiply values in two registers and sum adjacent result pairs. +Scheme: +@code + {A1 A2 ...} // 16-bit +x {B1 B2 ...} // 16-bit +------------- +{A1B1+A2B2 ...} // 32-bit +@endcode +Implemented only for 16-bit signed source type (v_int16x8). +*/ +template inline v_reg::w_type, n/2> + v_dotprod(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) +{ + typedef typename V_TypeTraits<_Tp>::w_type w_type; + v_reg c; + for( int i = 0; i < (n/2); i++ ) + c.s[i] = (w_type)a.s[i*2]*b.s[i*2] + (w_type)a.s[i*2+1]*b.s[i*2+1]; + return c; +} + +/** @brief Dot product of elements + +Same as cv::v_dotprod, but add a third element to the sum of adjacent pairs. +Scheme: +@code + {A1 A2 ...} // 16-bit +x {B1 B2 ...} // 16-bit +------------- + {A1B1+A2B2+C1 ...} // 32-bit + +@endcode +Implemented only for 16-bit signed source type (v_int16x8). +*/ +template inline v_reg::w_type, n/2> + v_dotprod(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b, const v_reg::w_type, n / 2>& c) +{ + typedef typename V_TypeTraits<_Tp>::w_type w_type; + v_reg s; + for( int i = 0; i < (n/2); i++ ) + s.s[i] = (w_type)a.s[i*2]*b.s[i*2] + (w_type)a.s[i*2+1]*b.s[i*2+1] + c.s[i]; + return s; +} + +/** @brief Multiply and expand + +Multiply values two registers and store results in two registers with wider pack type. +Scheme: +@code + {A B C D} // 32-bit +x {E F G H} // 32-bit +--------------- +{AE BF} // 64-bit + {CG DH} // 64-bit +@endcode +Example: +@code{.cpp} +v_uint32x4 a, b; // {1,2,3,4} and {2,2,2,2} +v_uint64x2 c, d; // results +v_mul_expand(a, b, c, d); // c, d = {2,4}, {6, 8} +@endcode +Implemented only for 16- and unsigned 32-bit source types (v_int16x8, v_uint16x8, v_uint32x4). +*/ +template inline void v_mul_expand(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b, + v_reg::w_type, n/2>& c, + v_reg::w_type, n/2>& d) +{ + typedef typename V_TypeTraits<_Tp>::w_type w_type; + for( int i = 0; i < (n/2); i++ ) + { + c.s[i] = (w_type)a.s[i]*b.s[i]; + d.s[i] = (w_type)a.s[i+(n/2)]*b.s[i+(n/2)]; + } +} + +/** @brief Multiply and extract high part + +Multiply values two registers and store high part of the results. +Implemented only for 16-bit source types (v_int16x8, v_uint16x8). Returns \f$ a*b >> 16 \f$ +*/ +template inline v_reg<_Tp, n> v_mul_hi(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) +{ + typedef typename V_TypeTraits<_Tp>::w_type w_type; + v_reg<_Tp, n> c; + for (int i = 0; i < n; i++) + c.s[i] = (_Tp)(((w_type)a.s[i] * b.s[i]) >> sizeof(_Tp)*8); + return c; +} + +//! @cond IGNORED +template inline void v_hsum(const v_reg<_Tp, n>& a, + v_reg::w_type, n/2>& c) +{ + typedef typename V_TypeTraits<_Tp>::w_type w_type; + for( int i = 0; i < (n/2); i++ ) + { + c.s[i] = (w_type)a.s[i*2] + a.s[i*2+1]; + } +} +//! @endcond + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_SHIFT_OP(shift_op) \ +template inline v_reg<_Tp, n> operator shift_op(const v_reg<_Tp, n>& a, int imm) \ +{ \ + v_reg<_Tp, n> c; \ + for( int i = 0; i < n; i++ ) \ + c.s[i] = (_Tp)(a.s[i] shift_op imm); \ + return c; \ +} + +/** @brief Bitwise shift left + +For 16-, 32- and 64-bit integer values. */ +OPENCV_HAL_IMPL_SHIFT_OP(<< ) + +/** @brief Bitwise shift right + +For 16-, 32- and 64-bit integer values. */ +OPENCV_HAL_IMPL_SHIFT_OP(>> ) + +/** @brief Element shift left among vector + +For all type */ +#define OPENCV_HAL_IMPL_ROTATE_SHIFT_OP(suffix,opA,opB) \ +template inline v_reg<_Tp, n> v_rotate_##suffix(const v_reg<_Tp, n>& a) \ +{ \ + v_reg<_Tp, n> b; \ + for (int i = 0; i < n; i++) \ + { \ + int sIndex = i opA imm; \ + if (0 <= sIndex && sIndex < n) \ + { \ + b.s[i] = a.s[sIndex]; \ + } \ + else \ + { \ + b.s[i] = 0; \ + } \ + } \ + return b; \ +} \ +template inline v_reg<_Tp, n> v_rotate_##suffix(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ +{ \ + v_reg<_Tp, n> c; \ + for (int i = 0; i < n; i++) \ + { \ + int aIndex = i opA imm; \ + int bIndex = i opA imm opB n; \ + if (0 <= bIndex && bIndex < n) \ + { \ + c.s[i] = b.s[bIndex]; \ + } \ + else if (0 <= aIndex && aIndex < n) \ + { \ + c.s[i] = a.s[aIndex]; \ + } \ + else \ + { \ + c.s[i] = 0; \ + } \ + } \ + return c; \ +} + +OPENCV_HAL_IMPL_ROTATE_SHIFT_OP(left, -, +) +OPENCV_HAL_IMPL_ROTATE_SHIFT_OP(right, +, -) + +/** @brief Sum packed values + +Scheme: +@code +{A1 A2 A3 ...} => sum{A1,A2,A3,...} +@endcode +For 32-bit integer and 32-bit floating point types.*/ +template inline typename V_TypeTraits<_Tp>::sum_type v_reduce_sum(const v_reg<_Tp, n>& a) +{ + typename V_TypeTraits<_Tp>::sum_type c = a.s[0]; + for( int i = 1; i < n; i++ ) + c += a.s[i]; + return c; +} + +/** @brief Sums all elements of each input vector, returns the vector of sums + + Scheme: + @code + result[0] = a[0] + a[1] + a[2] + a[3] + result[1] = b[0] + b[1] + b[2] + b[3] + result[2] = c[0] + c[1] + c[2] + c[3] + result[3] = d[0] + d[1] + d[2] + d[3] + @endcode +*/ +inline v_float32x4 v_reduce_sum4(const v_float32x4& a, const v_float32x4& b, + const v_float32x4& c, const v_float32x4& d) +{ + v_float32x4 r; + r.s[0] = a.s[0] + a.s[1] + a.s[2] + a.s[3]; + r.s[1] = b.s[0] + b.s[1] + b.s[2] + b.s[3]; + r.s[2] = c.s[0] + c.s[1] + c.s[2] + c.s[3]; + r.s[3] = d.s[0] + d.s[1] + d.s[2] + d.s[3]; + return r; +} + +/** @brief Sum absolute differences of values + +Scheme: +@code +{A1 A2 A3 ...} {B1 B2 B3 ...} => sum{ABS(A1-B1),abs(A2-B2),abs(A3-B3),...} +@endcode +For all types except 64-bit types.*/ +template inline typename V_TypeTraits< typename V_TypeTraits<_Tp>::abs_type >::sum_type v_reduce_sad(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) +{ + typename V_TypeTraits< typename V_TypeTraits<_Tp>::abs_type >::sum_type c = _absdiff(a.s[0], b.s[0]); + for (int i = 1; i < n; i++) + c += _absdiff(a.s[i], b.s[i]); + return c; +} + +/** @brief Get negative values mask + +Returned value is a bit mask with bits set to 1 on places corresponding to negative packed values indexes. +Example: +@code{.cpp} +v_int32x4 r; // set to {-1, -1, 1, 1} +int mask = v_signmask(r); // mask = 3 <== 00000000 00000000 00000000 00000011 +@endcode +For all types except 64-bit. */ +template inline int v_signmask(const v_reg<_Tp, n>& a) +{ + int mask = 0; + for( int i = 0; i < n; i++ ) + mask |= (V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) < 0) << i; + return mask; +} + +/** @brief Check if all packed values are less than zero + +Unsigned values will be casted to signed: `uchar 254 => char -2`. +For all types except 64-bit. */ +template inline bool v_check_all(const v_reg<_Tp, n>& a) +{ + for( int i = 0; i < n; i++ ) + if( V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) >= 0 ) + return false; + return true; +} + +/** @brief Check if any of packed values is less than zero + +Unsigned values will be casted to signed: `uchar 254 => char -2`. +For all types except 64-bit. */ +template inline bool v_check_any(const v_reg<_Tp, n>& a) +{ + for( int i = 0; i < n; i++ ) + if( V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) < 0 ) + return true; + return false; +} + +/** @brief Per-element select (blend operation) + +Return value will be built by combining values _a_ and _b_ using the following scheme: + result[i] = mask[i] ? a[i] : b[i]; + +@note: _mask_ element values are restricted to these values: +- 0: select element from _b_ +- 0xff/0xffff/etc: select element from _a_ +(fully compatible with bitwise-based operator) +*/ +template inline v_reg<_Tp, n> v_select(const v_reg<_Tp, n>& mask, + const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) +{ + typedef V_TypeTraits<_Tp> Traits; + typedef typename Traits::int_type int_type; + v_reg<_Tp, n> c; + for( int i = 0; i < n; i++ ) + { + int_type m = Traits::reinterpret_int(mask.s[i]); + CV_DbgAssert(m == 0 || m == (~(int_type)0)); // restrict mask values: 0 or 0xff/0xffff/etc + c.s[i] = m ? a.s[i] : b.s[i]; + } + return c; +} + +/** @brief Expand values to the wider pack type + +Copy contents of register to two registers with 2x wider pack type. +Scheme: +@code + int32x4 int64x2 int64x2 +{A B C D} ==> {A B} , {C D} +@endcode */ +template inline void v_expand(const v_reg<_Tp, n>& a, + v_reg::w_type, n/2>& b0, + v_reg::w_type, n/2>& b1) +{ + for( int i = 0; i < (n/2); i++ ) + { + b0.s[i] = a.s[i]; + b1.s[i] = a.s[i+(n/2)]; + } +} + +/** @brief Expand lower values to the wider pack type + +Same as cv::v_expand, but return lower half of the vector. + +Scheme: +@code + int32x4 int64x2 +{A B C D} ==> {A B} +@endcode */ +template +inline v_reg::w_type, n/2> +v_expand_low(const v_reg<_Tp, n>& a) +{ + v_reg::w_type, n/2> b; + for( int i = 0; i < (n/2); i++ ) + b.s[i] = a.s[i]; + return b; +} + +/** @brief Expand higher values to the wider pack type + +Same as cv::v_expand_low, but expand higher half of the vector instead. + +Scheme: +@code + int32x4 int64x2 +{A B C D} ==> {C D} +@endcode */ +template +inline v_reg::w_type, n/2> +v_expand_high(const v_reg<_Tp, n>& a) +{ + v_reg::w_type, n/2> b; + for( int i = 0; i < (n/2); i++ ) + b.s[i] = a.s[i+(n/2)]; + return b; +} + +//! @cond IGNORED +template inline v_reg::int_type, n> + v_reinterpret_as_int(const v_reg<_Tp, n>& a) +{ + v_reg::int_type, n> c; + for( int i = 0; i < n; i++ ) + c.s[i] = V_TypeTraits<_Tp>::reinterpret_int(a.s[i]); + return c; +} + +template inline v_reg::uint_type, n> + v_reinterpret_as_uint(const v_reg<_Tp, n>& a) +{ + v_reg::uint_type, n> c; + for( int i = 0; i < n; i++ ) + c.s[i] = V_TypeTraits<_Tp>::reinterpret_uint(a.s[i]); + return c; +} +//! @endcond + +/** @brief Interleave two vectors + +Scheme: +@code + {A1 A2 A3 A4} + {B1 B2 B3 B4} +--------------- + {A1 B1 A2 B2} and {A3 B3 A4 B4} +@endcode +For all types except 64-bit. +*/ +template inline void v_zip( const v_reg<_Tp, n>& a0, const v_reg<_Tp, n>& a1, + v_reg<_Tp, n>& b0, v_reg<_Tp, n>& b1 ) +{ + int i; + for( i = 0; i < n/2; i++ ) + { + b0.s[i*2] = a0.s[i]; + b0.s[i*2+1] = a1.s[i]; + } + for( ; i < n; i++ ) + { + b1.s[i*2-n] = a0.s[i]; + b1.s[i*2-n+1] = a1.s[i]; + } +} + +/** @brief Load register contents from memory + +@param ptr pointer to memory block with data +@return register object + +@note Returned type will be detected from passed pointer type, for example uchar ==> cv::v_uint8x16, int ==> cv::v_int32x4, etc. + */ +template +inline v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128> v_load(const _Tp* ptr) +{ + return v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128>(ptr); +} + +/** @brief Load register contents from memory (aligned) + +similar to cv::v_load, but source memory block should be aligned (to 16-byte boundary) + */ +template +inline v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128> v_load_aligned(const _Tp* ptr) +{ + return v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128>(ptr); +} + +/** @brief Load 64-bits of data to lower part (high part is undefined). + +@param ptr memory block containing data for first half (0..n/2) + +@code{.cpp} +int lo[2] = { 1, 2 }; +v_int32x4 r = v_load_low(lo); +@endcode + */ +template +inline v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128> v_load_low(const _Tp* ptr) +{ + v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128> c; + for( int i = 0; i < c.nlanes/2; i++ ) + { + c.s[i] = ptr[i]; + } + return c; +} + +/** @brief Load register contents from two memory blocks + +@param loptr memory block containing data for first half (0..n/2) +@param hiptr memory block containing data for second half (n/2..n) + +@code{.cpp} +int lo[2] = { 1, 2 }, hi[2] = { 3, 4 }; +v_int32x4 r = v_load_halves(lo, hi); +@endcode + */ +template +inline v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128> v_load_halves(const _Tp* loptr, const _Tp* hiptr) +{ + v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128> c; + for( int i = 0; i < c.nlanes/2; i++ ) + { + c.s[i] = loptr[i]; + c.s[i+c.nlanes/2] = hiptr[i]; + } + return c; +} + +/** @brief Load register contents from memory with double expand + +Same as cv::v_load, but result pack type will be 2x wider than memory type. + +@code{.cpp} +short buf[4] = {1, 2, 3, 4}; // type is int16 +v_int32x4 r = v_load_expand(buf); // r = {1, 2, 3, 4} - type is int32 +@endcode +For 8-, 16-, 32-bit integer source types. */ +template +inline v_reg::w_type, V_TypeTraits<_Tp>::nlanes128 / 2> +v_load_expand(const _Tp* ptr) +{ + typedef typename V_TypeTraits<_Tp>::w_type w_type; + v_reg::nlanes128> c; + for( int i = 0; i < c.nlanes; i++ ) + { + c.s[i] = ptr[i]; + } + return c; +} + +/** @brief Load register contents from memory with quad expand + +Same as cv::v_load_expand, but result type is 4 times wider than source. +@code{.cpp} +char buf[4] = {1, 2, 3, 4}; // type is int8 +v_int32x4 r = v_load_q(buf); // r = {1, 2, 3, 4} - type is int32 +@endcode +For 8-bit integer source types. */ +template +inline v_reg::q_type, V_TypeTraits<_Tp>::nlanes128 / 4> +v_load_expand_q(const _Tp* ptr) +{ + typedef typename V_TypeTraits<_Tp>::q_type q_type; + v_reg::nlanes128> c; + for( int i = 0; i < c.nlanes; i++ ) + { + c.s[i] = ptr[i]; + } + return c; +} + +/** @brief Load and deinterleave (2 channels) + +Load data from memory deinterleave and store to 2 registers. +Scheme: +@code +{A1 B1 A2 B2 ...} ==> {A1 A2 ...}, {B1 B2 ...} +@endcode +For all types except 64-bit. */ +template inline void v_load_deinterleave(const _Tp* ptr, v_reg<_Tp, n>& a, + v_reg<_Tp, n>& b) +{ + int i, i2; + for( i = i2 = 0; i < n; i++, i2 += 2 ) + { + a.s[i] = ptr[i2]; + b.s[i] = ptr[i2+1]; + } +} + +/** @brief Load and deinterleave (3 channels) + +Load data from memory deinterleave and store to 3 registers. +Scheme: +@code +{A1 B1 C1 A2 B2 C2 ...} ==> {A1 A2 ...}, {B1 B2 ...}, {C1 C2 ...} +@endcode +For all types except 64-bit. */ +template inline void v_load_deinterleave(const _Tp* ptr, v_reg<_Tp, n>& a, + v_reg<_Tp, n>& b, v_reg<_Tp, n>& c) +{ + int i, i3; + for( i = i3 = 0; i < n; i++, i3 += 3 ) + { + a.s[i] = ptr[i3]; + b.s[i] = ptr[i3+1]; + c.s[i] = ptr[i3+2]; + } +} + +/** @brief Load and deinterleave (4 channels) + +Load data from memory deinterleave and store to 4 registers. +Scheme: +@code +{A1 B1 C1 D1 A2 B2 C2 D2 ...} ==> {A1 A2 ...}, {B1 B2 ...}, {C1 C2 ...}, {D1 D2 ...} +@endcode +For all types except 64-bit. */ +template +inline void v_load_deinterleave(const _Tp* ptr, v_reg<_Tp, n>& a, + v_reg<_Tp, n>& b, v_reg<_Tp, n>& c, + v_reg<_Tp, n>& d) +{ + int i, i4; + for( i = i4 = 0; i < n; i++, i4 += 4 ) + { + a.s[i] = ptr[i4]; + b.s[i] = ptr[i4+1]; + c.s[i] = ptr[i4+2]; + d.s[i] = ptr[i4+3]; + } +} + +/** @brief Interleave and store (2 channels) + +Interleave and store data from 2 registers to memory. +Scheme: +@code +{A1 A2 ...}, {B1 B2 ...} ==> {A1 B1 A2 B2 ...} +@endcode +For all types except 64-bit. */ +template +inline void v_store_interleave( _Tp* ptr, const v_reg<_Tp, n>& a, + const v_reg<_Tp, n>& b, + hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) +{ + int i, i2; + for( i = i2 = 0; i < n; i++, i2 += 2 ) + { + ptr[i2] = a.s[i]; + ptr[i2+1] = b.s[i]; + } +} + +/** @brief Interleave and store (3 channels) + +Interleave and store data from 3 registers to memory. +Scheme: +@code +{A1 A2 ...}, {B1 B2 ...}, {C1 C2 ...} ==> {A1 B1 C1 A2 B2 C2 ...} +@endcode +For all types except 64-bit. */ +template +inline void v_store_interleave( _Tp* ptr, const v_reg<_Tp, n>& a, + const v_reg<_Tp, n>& b, const v_reg<_Tp, n>& c, + hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) +{ + int i, i3; + for( i = i3 = 0; i < n; i++, i3 += 3 ) + { + ptr[i3] = a.s[i]; + ptr[i3+1] = b.s[i]; + ptr[i3+2] = c.s[i]; + } +} + +/** @brief Interleave and store (4 channels) + +Interleave and store data from 4 registers to memory. +Scheme: +@code +{A1 A2 ...}, {B1 B2 ...}, {C1 C2 ...}, {D1 D2 ...} ==> {A1 B1 C1 D1 A2 B2 C2 D2 ...} +@endcode +For all types except 64-bit. */ +template inline void v_store_interleave( _Tp* ptr, const v_reg<_Tp, n>& a, + const v_reg<_Tp, n>& b, const v_reg<_Tp, n>& c, + const v_reg<_Tp, n>& d, + hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) +{ + int i, i4; + for( i = i4 = 0; i < n; i++, i4 += 4 ) + { + ptr[i4] = a.s[i]; + ptr[i4+1] = b.s[i]; + ptr[i4+2] = c.s[i]; + ptr[i4+3] = d.s[i]; + } +} + +/** @brief Store data to memory + +Store register contents to memory. +Scheme: +@code + REG {A B C D} ==> MEM {A B C D} +@endcode +Pointer can be unaligned. */ +template +inline void v_store(_Tp* ptr, const v_reg<_Tp, n>& a) +{ + for( int i = 0; i < n; i++ ) + ptr[i] = a.s[i]; +} + +/** @brief Store data to memory (lower half) + +Store lower half of register contents to memory. +Scheme: +@code + REG {A B C D} ==> MEM {A B} +@endcode */ +template +inline void v_store_low(_Tp* ptr, const v_reg<_Tp, n>& a) +{ + for( int i = 0; i < (n/2); i++ ) + ptr[i] = a.s[i]; +} + +/** @brief Store data to memory (higher half) + +Store higher half of register contents to memory. +Scheme: +@code + REG {A B C D} ==> MEM {C D} +@endcode */ +template +inline void v_store_high(_Tp* ptr, const v_reg<_Tp, n>& a) +{ + for( int i = 0; i < (n/2); i++ ) + ptr[i] = a.s[i+(n/2)]; +} + +/** @brief Store data to memory (aligned) + +Store register contents to memory. +Scheme: +@code + REG {A B C D} ==> MEM {A B C D} +@endcode +Pointer __should__ be aligned by 16-byte boundary. */ +template +inline void v_store_aligned(_Tp* ptr, const v_reg<_Tp, n>& a) +{ + for( int i = 0; i < n; i++ ) + ptr[i] = a.s[i]; +} + +template +inline void v_store_aligned_nocache(_Tp* ptr, const v_reg<_Tp, n>& a) +{ + for( int i = 0; i < n; i++ ) + ptr[i] = a.s[i]; +} + +template +inline void v_store_aligned(_Tp* ptr, const v_reg<_Tp, n>& a, hal::StoreMode /*mode*/) +{ + for( int i = 0; i < n; i++ ) + ptr[i] = a.s[i]; +} + +/** @brief Combine vector from first elements of two vectors + +Scheme: +@code + {A1 A2 A3 A4} + {B1 B2 B3 B4} +--------------- + {A1 A2 B1 B2} +@endcode +For all types except 64-bit. */ +template +inline v_reg<_Tp, n> v_combine_low(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) +{ + v_reg<_Tp, n> c; + for( int i = 0; i < (n/2); i++ ) + { + c.s[i] = a.s[i]; + c.s[i+(n/2)] = b.s[i]; + } + return c; +} + +/** @brief Combine vector from last elements of two vectors + +Scheme: +@code + {A1 A2 A3 A4} + {B1 B2 B3 B4} +--------------- + {A3 A4 B3 B4} +@endcode +For all types except 64-bit. */ +template +inline v_reg<_Tp, n> v_combine_high(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) +{ + v_reg<_Tp, n> c; + for( int i = 0; i < (n/2); i++ ) + { + c.s[i] = a.s[i+(n/2)]; + c.s[i+(n/2)] = b.s[i+(n/2)]; + } + return c; +} + +/** @brief Combine two vectors from lower and higher parts of two other vectors + +@code{.cpp} +low = cv::v_combine_low(a, b); +high = cv::v_combine_high(a, b); +@endcode */ +template +inline void v_recombine(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b, + v_reg<_Tp, n>& low, v_reg<_Tp, n>& high) +{ + for( int i = 0; i < (n/2); i++ ) + { + low.s[i] = a.s[i]; + low.s[i+(n/2)] = b.s[i]; + high.s[i] = a.s[i+(n/2)]; + high.s[i+(n/2)] = b.s[i+(n/2)]; + } +} + +/** @brief Vector extract + +Scheme: +@code + {A1 A2 A3 A4} + {B1 B2 B3 B4} +======================== +shift = 1 {A2 A3 A4 B1} +shift = 2 {A3 A4 B1 B2} +shift = 3 {A4 B1 B2 B3} +@endcode +Restriction: 0 <= shift < nlanes + +Usage: +@code +v_int32x4 a, b, c; +c = v_extract<2>(a, b); +@endcode +For all types. */ +template +inline v_reg<_Tp, n> v_extract(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) +{ + v_reg<_Tp, n> r; + const int shift = n - s; + int i = 0; + for (; i < shift; ++i) + r.s[i] = a.s[i+s]; + for (; i < n; ++i) + r.s[i] = b.s[i-shift]; + return r; +} + +/** @brief Round + +Rounds each value. Input type is float vector ==> output type is int vector.*/ +template inline v_reg v_round(const v_reg& a) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + c.s[i] = cvRound(a.s[i]); + return c; +} + +/** @overload */ +template inline v_reg v_round(const v_reg& a, const v_reg& b) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + { + c.s[i] = cvRound(a.s[i]); + c.s[i+n] = cvRound(b.s[i]); + } + return c; +} + +/** @brief Floor + +Floor each value. Input type is float vector ==> output type is int vector.*/ +template inline v_reg v_floor(const v_reg& a) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + c.s[i] = cvFloor(a.s[i]); + return c; +} + +/** @brief Ceil + +Ceil each value. Input type is float vector ==> output type is int vector.*/ +template inline v_reg v_ceil(const v_reg& a) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + c.s[i] = cvCeil(a.s[i]); + return c; +} + +/** @brief Trunc + +Truncate each value. Input type is float vector ==> output type is int vector.*/ +template inline v_reg v_trunc(const v_reg& a) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + c.s[i] = (int)(a.s[i]); + return c; +} + +/** @overload */ +template inline v_reg v_round(const v_reg& a) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + { + c.s[i] = cvRound(a.s[i]); + c.s[i+n] = 0; + } + return c; +} + +/** @overload */ +template inline v_reg v_floor(const v_reg& a) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + { + c.s[i] = cvFloor(a.s[i]); + c.s[i+n] = 0; + } + return c; +} + +/** @overload */ +template inline v_reg v_ceil(const v_reg& a) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + { + c.s[i] = cvCeil(a.s[i]); + c.s[i+n] = 0; + } + return c; +} + +/** @overload */ +template inline v_reg v_trunc(const v_reg& a) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + { + c.s[i] = cvCeil(a.s[i]); + c.s[i+n] = 0; + } + return c; +} + +/** @brief Convert to float + +Supported input type is cv::v_int32x4. */ +template inline v_reg v_cvt_f32(const v_reg& a) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + c.s[i] = (float)a.s[i]; + return c; +} + +template inline v_reg v_cvt_f32(const v_reg& a, const v_reg& b) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + { + c.s[i] = (float)a.s[i]; + c.s[i+n] = (float)b.s[i]; + } + return c; +} + +/** @brief Convert to double + +Supported input type is cv::v_int32x4. */ +template inline v_reg v_cvt_f64(const v_reg& a) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + c.s[i] = (double)a.s[i]; + return c; +} + +/** @brief Convert to double + +Supported input type is cv::v_float32x4. */ +template inline v_reg v_cvt_f64(const v_reg& a) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + c.s[i] = (double)a.s[i]; + return c; +} + +template inline v_reg v_lut(const int* tab, const v_reg& idx) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + c.s[i] = tab[idx.s[i]]; + return c; +} + +template inline v_reg v_lut(const float* tab, const v_reg& idx) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + c.s[i] = tab[idx.s[i]]; + return c; +} + +template inline v_reg v_lut(const double* tab, const v_reg& idx) +{ + v_reg c; + for( int i = 0; i < n; i++ ) + c.s[i] = tab[idx.s[i]]; + return c; +} + +template inline void v_lut_deinterleave(const float* tab, const v_reg& idx, + v_reg& x, v_reg& y) +{ + for( int i = 0; i < n; i++ ) + { + int j = idx.s[i]; + x.s[i] = tab[j]; + y.s[i] = tab[j+1]; + } +} + +template inline void v_lut_deinterleave(const double* tab, const v_reg& idx, + v_reg& x, v_reg& y) +{ + for( int i = 0; i < n; i++ ) + { + int j = idx.s[i]; + x.s[i] = tab[j]; + y.s[i] = tab[j+1]; + } +} + +/** @brief Transpose 4x4 matrix + +Scheme: +@code +a0 {A1 A2 A3 A4} +a1 {B1 B2 B3 B4} +a2 {C1 C2 C3 C4} +a3 {D1 D2 D3 D4} +=============== +b0 {A1 B1 C1 D1} +b1 {A2 B2 C2 D2} +b2 {A3 B3 C3 D3} +b3 {A4 B4 C4 D4} +@endcode +*/ +template +inline void v_transpose4x4( v_reg<_Tp, 4>& a0, const v_reg<_Tp, 4>& a1, + const v_reg<_Tp, 4>& a2, const v_reg<_Tp, 4>& a3, + v_reg<_Tp, 4>& b0, v_reg<_Tp, 4>& b1, + v_reg<_Tp, 4>& b2, v_reg<_Tp, 4>& b3 ) +{ + b0 = v_reg<_Tp, 4>(a0.s[0], a1.s[0], a2.s[0], a3.s[0]); + b1 = v_reg<_Tp, 4>(a0.s[1], a1.s[1], a2.s[1], a3.s[1]); + b2 = v_reg<_Tp, 4>(a0.s[2], a1.s[2], a2.s[2], a3.s[2]); + b3 = v_reg<_Tp, 4>(a0.s[3], a1.s[3], a2.s[3], a3.s[3]); +} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_C_INIT_ZERO(_Tpvec, _Tp, suffix) \ +inline _Tpvec v_setzero_##suffix() { return _Tpvec::zero(); } + +//! @name Init with zero +//! @{ +//! @brief Create new vector with zero elements +OPENCV_HAL_IMPL_C_INIT_ZERO(v_uint8x16, uchar, u8) +OPENCV_HAL_IMPL_C_INIT_ZERO(v_int8x16, schar, s8) +OPENCV_HAL_IMPL_C_INIT_ZERO(v_uint16x8, ushort, u16) +OPENCV_HAL_IMPL_C_INIT_ZERO(v_int16x8, short, s16) +OPENCV_HAL_IMPL_C_INIT_ZERO(v_uint32x4, unsigned, u32) +OPENCV_HAL_IMPL_C_INIT_ZERO(v_int32x4, int, s32) +OPENCV_HAL_IMPL_C_INIT_ZERO(v_float32x4, float, f32) +OPENCV_HAL_IMPL_C_INIT_ZERO(v_float64x2, double, f64) +OPENCV_HAL_IMPL_C_INIT_ZERO(v_uint64x2, uint64, u64) +OPENCV_HAL_IMPL_C_INIT_ZERO(v_int64x2, int64, s64) +//! @} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_C_INIT_VAL(_Tpvec, _Tp, suffix) \ +inline _Tpvec v_setall_##suffix(_Tp val) { return _Tpvec::all(val); } + +//! @name Init with value +//! @{ +//! @brief Create new vector with elements set to a specific value +OPENCV_HAL_IMPL_C_INIT_VAL(v_uint8x16, uchar, u8) +OPENCV_HAL_IMPL_C_INIT_VAL(v_int8x16, schar, s8) +OPENCV_HAL_IMPL_C_INIT_VAL(v_uint16x8, ushort, u16) +OPENCV_HAL_IMPL_C_INIT_VAL(v_int16x8, short, s16) +OPENCV_HAL_IMPL_C_INIT_VAL(v_uint32x4, unsigned, u32) +OPENCV_HAL_IMPL_C_INIT_VAL(v_int32x4, int, s32) +OPENCV_HAL_IMPL_C_INIT_VAL(v_float32x4, float, f32) +OPENCV_HAL_IMPL_C_INIT_VAL(v_float64x2, double, f64) +OPENCV_HAL_IMPL_C_INIT_VAL(v_uint64x2, uint64, u64) +OPENCV_HAL_IMPL_C_INIT_VAL(v_int64x2, int64, s64) +//! @} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_C_REINTERPRET(_Tpvec, _Tp, suffix) \ +template inline _Tpvec \ + v_reinterpret_as_##suffix(const v_reg<_Tp0, n0>& a) \ +{ return a.template reinterpret_as<_Tp, _Tpvec::nlanes>(); } + +//! @name Reinterpret +//! @{ +//! @brief Convert vector to different type without modifying underlying data. +OPENCV_HAL_IMPL_C_REINTERPRET(v_uint8x16, uchar, u8) +OPENCV_HAL_IMPL_C_REINTERPRET(v_int8x16, schar, s8) +OPENCV_HAL_IMPL_C_REINTERPRET(v_uint16x8, ushort, u16) +OPENCV_HAL_IMPL_C_REINTERPRET(v_int16x8, short, s16) +OPENCV_HAL_IMPL_C_REINTERPRET(v_uint32x4, unsigned, u32) +OPENCV_HAL_IMPL_C_REINTERPRET(v_int32x4, int, s32) +OPENCV_HAL_IMPL_C_REINTERPRET(v_float32x4, float, f32) +OPENCV_HAL_IMPL_C_REINTERPRET(v_float64x2, double, f64) +OPENCV_HAL_IMPL_C_REINTERPRET(v_uint64x2, uint64, u64) +OPENCV_HAL_IMPL_C_REINTERPRET(v_int64x2, int64, s64) +//! @} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_C_SHIFTL(_Tpvec, _Tp) \ +template inline _Tpvec v_shl(const _Tpvec& a) \ +{ return a << n; } + +//! @name Left shift +//! @{ +//! @brief Shift left +OPENCV_HAL_IMPL_C_SHIFTL(v_uint16x8, ushort) +OPENCV_HAL_IMPL_C_SHIFTL(v_int16x8, short) +OPENCV_HAL_IMPL_C_SHIFTL(v_uint32x4, unsigned) +OPENCV_HAL_IMPL_C_SHIFTL(v_int32x4, int) +OPENCV_HAL_IMPL_C_SHIFTL(v_uint64x2, uint64) +OPENCV_HAL_IMPL_C_SHIFTL(v_int64x2, int64) +//! @} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_C_SHIFTR(_Tpvec, _Tp) \ +template inline _Tpvec v_shr(const _Tpvec& a) \ +{ return a >> n; } + +//! @name Right shift +//! @{ +//! @brief Shift right +OPENCV_HAL_IMPL_C_SHIFTR(v_uint16x8, ushort) +OPENCV_HAL_IMPL_C_SHIFTR(v_int16x8, short) +OPENCV_HAL_IMPL_C_SHIFTR(v_uint32x4, unsigned) +OPENCV_HAL_IMPL_C_SHIFTR(v_int32x4, int) +OPENCV_HAL_IMPL_C_SHIFTR(v_uint64x2, uint64) +OPENCV_HAL_IMPL_C_SHIFTR(v_int64x2, int64) +//! @} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_C_RSHIFTR(_Tpvec, _Tp) \ +template inline _Tpvec v_rshr(const _Tpvec& a) \ +{ \ + _Tpvec c; \ + for( int i = 0; i < _Tpvec::nlanes; i++ ) \ + c.s[i] = (_Tp)((a.s[i] + ((_Tp)1 << (n - 1))) >> n); \ + return c; \ +} + +//! @name Rounding shift +//! @{ +//! @brief Rounding shift right +OPENCV_HAL_IMPL_C_RSHIFTR(v_uint16x8, ushort) +OPENCV_HAL_IMPL_C_RSHIFTR(v_int16x8, short) +OPENCV_HAL_IMPL_C_RSHIFTR(v_uint32x4, unsigned) +OPENCV_HAL_IMPL_C_RSHIFTR(v_int32x4, int) +OPENCV_HAL_IMPL_C_RSHIFTR(v_uint64x2, uint64) +OPENCV_HAL_IMPL_C_RSHIFTR(v_int64x2, int64) +//! @} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_C_PACK(_Tpvec, _Tpnvec, _Tpn, pack_suffix, cast) \ +inline _Tpnvec v_##pack_suffix(const _Tpvec& a, const _Tpvec& b) \ +{ \ + _Tpnvec c; \ + for( int i = 0; i < _Tpvec::nlanes; i++ ) \ + { \ + c.s[i] = cast<_Tpn>(a.s[i]); \ + c.s[i+_Tpvec::nlanes] = cast<_Tpn>(b.s[i]); \ + } \ + return c; \ +} + +//! @name Pack +//! @{ +//! @brief Pack values from two vectors to one +//! +//! Return vector type have twice more elements than input vector types. Variant with _u_ suffix also +//! converts to corresponding unsigned type. +//! +//! - pack: for 16-, 32- and 64-bit integer input types +//! - pack_u: for 16- and 32-bit signed integer input types +//! +//! @note All variants except 64-bit use saturation. +OPENCV_HAL_IMPL_C_PACK(v_uint16x8, v_uint8x16, uchar, pack, saturate_cast) +OPENCV_HAL_IMPL_C_PACK(v_int16x8, v_int8x16, schar, pack, saturate_cast) +OPENCV_HAL_IMPL_C_PACK(v_uint32x4, v_uint16x8, ushort, pack, saturate_cast) +OPENCV_HAL_IMPL_C_PACK(v_int32x4, v_int16x8, short, pack, saturate_cast) +OPENCV_HAL_IMPL_C_PACK(v_uint64x2, v_uint32x4, unsigned, pack, static_cast) +OPENCV_HAL_IMPL_C_PACK(v_int64x2, v_int32x4, int, pack, static_cast) +OPENCV_HAL_IMPL_C_PACK(v_int16x8, v_uint8x16, uchar, pack_u, saturate_cast) +OPENCV_HAL_IMPL_C_PACK(v_int32x4, v_uint16x8, ushort, pack_u, saturate_cast) +//! @} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_C_RSHR_PACK(_Tpvec, _Tp, _Tpnvec, _Tpn, pack_suffix, cast) \ +template inline _Tpnvec v_rshr_##pack_suffix(const _Tpvec& a, const _Tpvec& b) \ +{ \ + _Tpnvec c; \ + for( int i = 0; i < _Tpvec::nlanes; i++ ) \ + { \ + c.s[i] = cast<_Tpn>((a.s[i] + ((_Tp)1 << (n - 1))) >> n); \ + c.s[i+_Tpvec::nlanes] = cast<_Tpn>((b.s[i] + ((_Tp)1 << (n - 1))) >> n); \ + } \ + return c; \ +} + +//! @name Pack with rounding shift +//! @{ +//! @brief Pack values from two vectors to one with rounding shift +//! +//! Values from the input vectors will be shifted right by _n_ bits with rounding, converted to narrower +//! type and returned in the result vector. Variant with _u_ suffix converts to unsigned type. +//! +//! - pack: for 16-, 32- and 64-bit integer input types +//! - pack_u: for 16- and 32-bit signed integer input types +//! +//! @note All variants except 64-bit use saturation. +OPENCV_HAL_IMPL_C_RSHR_PACK(v_uint16x8, ushort, v_uint8x16, uchar, pack, saturate_cast) +OPENCV_HAL_IMPL_C_RSHR_PACK(v_int16x8, short, v_int8x16, schar, pack, saturate_cast) +OPENCV_HAL_IMPL_C_RSHR_PACK(v_uint32x4, unsigned, v_uint16x8, ushort, pack, saturate_cast) +OPENCV_HAL_IMPL_C_RSHR_PACK(v_int32x4, int, v_int16x8, short, pack, saturate_cast) +OPENCV_HAL_IMPL_C_RSHR_PACK(v_uint64x2, uint64, v_uint32x4, unsigned, pack, static_cast) +OPENCV_HAL_IMPL_C_RSHR_PACK(v_int64x2, int64, v_int32x4, int, pack, static_cast) +OPENCV_HAL_IMPL_C_RSHR_PACK(v_int16x8, short, v_uint8x16, uchar, pack_u, saturate_cast) +OPENCV_HAL_IMPL_C_RSHR_PACK(v_int32x4, int, v_uint16x8, ushort, pack_u, saturate_cast) +//! @} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_C_PACK_STORE(_Tpvec, _Tp, _Tpnvec, _Tpn, pack_suffix, cast) \ +inline void v_##pack_suffix##_store(_Tpn* ptr, const _Tpvec& a) \ +{ \ + for( int i = 0; i < _Tpvec::nlanes; i++ ) \ + ptr[i] = cast<_Tpn>(a.s[i]); \ +} + +//! @name Pack and store +//! @{ +//! @brief Store values from the input vector into memory with pack +//! +//! Values will be stored into memory with conversion to narrower type. +//! Variant with _u_ suffix converts to corresponding unsigned type. +//! +//! - pack: for 16-, 32- and 64-bit integer input types +//! - pack_u: for 16- and 32-bit signed integer input types +//! +//! @note All variants except 64-bit use saturation. +OPENCV_HAL_IMPL_C_PACK_STORE(v_uint16x8, ushort, v_uint8x16, uchar, pack, saturate_cast) +OPENCV_HAL_IMPL_C_PACK_STORE(v_int16x8, short, v_int8x16, schar, pack, saturate_cast) +OPENCV_HAL_IMPL_C_PACK_STORE(v_uint32x4, unsigned, v_uint16x8, ushort, pack, saturate_cast) +OPENCV_HAL_IMPL_C_PACK_STORE(v_int32x4, int, v_int16x8, short, pack, saturate_cast) +OPENCV_HAL_IMPL_C_PACK_STORE(v_uint64x2, uint64, v_uint32x4, unsigned, pack, static_cast) +OPENCV_HAL_IMPL_C_PACK_STORE(v_int64x2, int64, v_int32x4, int, pack, static_cast) +OPENCV_HAL_IMPL_C_PACK_STORE(v_int16x8, short, v_uint8x16, uchar, pack_u, saturate_cast) +OPENCV_HAL_IMPL_C_PACK_STORE(v_int32x4, int, v_uint16x8, ushort, pack_u, saturate_cast) +//! @} + +//! @brief Helper macro +//! @ingroup core_hal_intrin_impl +#define OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(_Tpvec, _Tp, _Tpnvec, _Tpn, pack_suffix, cast) \ +template inline void v_rshr_##pack_suffix##_store(_Tpn* ptr, const _Tpvec& a) \ +{ \ + for( int i = 0; i < _Tpvec::nlanes; i++ ) \ + ptr[i] = cast<_Tpn>((a.s[i] + ((_Tp)1 << (n - 1))) >> n); \ +} + +//! @name Pack and store with rounding shift +//! @{ +//! @brief Store values from the input vector into memory with pack +//! +//! Values will be shifted _n_ bits right with rounding, converted to narrower type and stored into +//! memory. Variant with _u_ suffix converts to unsigned type. +//! +//! - pack: for 16-, 32- and 64-bit integer input types +//! - pack_u: for 16- and 32-bit signed integer input types +//! +//! @note All variants except 64-bit use saturation. +OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_uint16x8, ushort, v_uint8x16, uchar, pack, saturate_cast) +OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_int16x8, short, v_int8x16, schar, pack, saturate_cast) +OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_uint32x4, unsigned, v_uint16x8, ushort, pack, saturate_cast) +OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_int32x4, int, v_int16x8, short, pack, saturate_cast) +OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_uint64x2, uint64, v_uint32x4, unsigned, pack, static_cast) +OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_int64x2, int64, v_int32x4, int, pack, static_cast) +OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_int16x8, short, v_uint8x16, uchar, pack_u, saturate_cast) +OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_int32x4, int, v_uint16x8, ushort, pack_u, saturate_cast) +//! @} + +//! @cond IGNORED +template +inline void _pack_b(_Tpm* mptr, const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) +{ + for (int i = 0; i < n; ++i) + { + mptr[i] = (_Tpm)a.s[i]; + mptr[i + n] = (_Tpm)b.s[i]; + } +} +//! @endcond + +//! @name Pack boolean values +//! @{ +//! @brief Pack boolean values from multiple vectors to one unsigned 8-bit integer vector +//! +//! @note Must provide valid boolean values to guarantee same result for all architectures. + +/** @brief +//! For 16-bit boolean values + +Scheme: +@code +a {0xFFFF 0 0 0xFFFF 0 0xFFFF 0xFFFF 0} +b {0xFFFF 0 0xFFFF 0 0 0xFFFF 0 0xFFFF} +=============== +{ + 0xFF 0 0 0xFF 0 0xFF 0xFF 0 + 0xFF 0 0xFF 0 0 0xFF 0 0xFF +} +@endcode */ + +inline v_uint8x16 v_pack_b(const v_uint16x8& a, const v_uint16x8& b) +{ + v_uint8x16 mask; + _pack_b(mask.s, a, b); + return mask; +} + +/** @overload +For 32-bit boolean values + +Scheme: +@code +a {0xFFFF.. 0 0 0xFFFF..} +b {0 0xFFFF.. 0xFFFF.. 0} +c {0xFFFF.. 0 0xFFFF.. 0} +d {0 0xFFFF.. 0 0xFFFF..} +=============== +{ + 0xFF 0 0 0xFF 0 0xFF 0xFF 0 + 0xFF 0 0xFF 0 0 0xFF 0 0xFF +} +@endcode */ + +inline v_uint8x16 v_pack_b(const v_uint32x4& a, const v_uint32x4& b, + const v_uint32x4& c, const v_uint32x4& d) +{ + v_uint8x16 mask; + _pack_b(mask.s, a, b); + _pack_b(mask.s + 8, c, d); + return mask; +} + +/** @overload +For 64-bit boolean values + +Scheme: +@code +a {0xFFFF.. 0} +b {0 0xFFFF..} +c {0xFFFF.. 0} +d {0 0xFFFF..} + +e {0xFFFF.. 0} +f {0xFFFF.. 0} +g {0 0xFFFF..} +h {0 0xFFFF..} +=============== +{ + 0xFF 0 0 0xFF 0xFF 0 0 0xFF + 0xFF 0 0xFF 0 0 0xFF 0 0xFF +} +@endcode */ +inline v_uint8x16 v_pack_b(const v_uint64x2& a, const v_uint64x2& b, const v_uint64x2& c, + const v_uint64x2& d, const v_uint64x2& e, const v_uint64x2& f, + const v_uint64x2& g, const v_uint64x2& h) +{ + v_uint8x16 mask; + _pack_b(mask.s, a, b); + _pack_b(mask.s + 4, c, d); + _pack_b(mask.s + 8, e, f); + _pack_b(mask.s + 12, g, h); + return mask; +} +//! @} + +/** @brief Matrix multiplication + +Scheme: +@code +{A0 A1 A2 A3} |V0| +{B0 B1 B2 B3} |V1| +{C0 C1 C2 C3} |V2| +{D0 D1 D2 D3} x |V3| +==================== +{R0 R1 R2 R3}, where: +R0 = A0V0 + A1V1 + A2V2 + A3V3, +R1 = B0V0 + B1V1 + B2V2 + B3V3 +... +@endcode +*/ +inline v_float32x4 v_matmul(const v_float32x4& v, const v_float32x4& m0, + const v_float32x4& m1, const v_float32x4& m2, + const v_float32x4& m3) +{ + return v_float32x4(v.s[0]*m0.s[0] + v.s[1]*m1.s[0] + v.s[2]*m2.s[0] + v.s[3]*m3.s[0], + v.s[0]*m0.s[1] + v.s[1]*m1.s[1] + v.s[2]*m2.s[1] + v.s[3]*m3.s[1], + v.s[0]*m0.s[2] + v.s[1]*m1.s[2] + v.s[2]*m2.s[2] + v.s[3]*m3.s[2], + v.s[0]*m0.s[3] + v.s[1]*m1.s[3] + v.s[2]*m2.s[3] + v.s[3]*m3.s[3]); +} + +/** @brief Matrix multiplication and add + +Scheme: +@code +{A0 A1 A2 } |V0| |D0| +{B0 B1 B2 } |V1| |D1| +{C0 C1 C2 } x |V2| + |D2| +==================== +{R0 R1 R2 R3}, where: +R0 = A0V0 + A1V1 + A2V2 + D0, +R1 = B0V0 + B1V1 + B2V2 + D1 +... +@endcode +*/ +inline v_float32x4 v_matmuladd(const v_float32x4& v, const v_float32x4& m0, + const v_float32x4& m1, const v_float32x4& m2, + const v_float32x4& m3) +{ + return v_float32x4(v.s[0]*m0.s[0] + v.s[1]*m1.s[0] + v.s[2]*m2.s[0] + m3.s[0], + v.s[0]*m0.s[1] + v.s[1]*m1.s[1] + v.s[2]*m2.s[1] + m3.s[1], + v.s[0]*m0.s[2] + v.s[1]*m1.s[2] + v.s[2]*m2.s[2] + m3.s[2], + v.s[0]*m0.s[3] + v.s[1]*m1.s[3] + v.s[2]*m2.s[3] + m3.s[3]); +} + +////// FP16 suport /////// + +inline v_reg::nlanes128> +v_load_expand(const float16_t* ptr) +{ + v_reg::nlanes128> v; + for( int i = 0; i < v.nlanes; i++ ) + { + v.s[i] = ptr[i]; + } + return v; +} + +inline void +v_pack_store(float16_t* ptr, v_reg::nlanes128>& v) +{ + for( int i = 0; i < v.nlanes; i++ ) + { + ptr[i] = float16_t(v.s[i]); + } +} + +inline void v_cleanup() {} + +//! @} + +//! @name Check SIMD support +//! @{ +//! @brief Check CPU capability of SIMD operation +static inline bool hasSIMD128() +{ + return false; +} + +//! @} + +#ifndef CV_DOXYGEN +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END +#endif +} + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/hal/intrin_forward.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/hal/intrin_forward.hpp new file mode 100644 index 0000000000000000000000000000000000000000..461855290743174351750ad30c0f3d3a3803129a --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/hal/intrin_forward.hpp @@ -0,0 +1,158 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html + +#ifndef CV__SIMD_FORWARD +#error "Need to pre-define forward width" +#endif + +namespace cv +{ + +//! @cond IGNORED + +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN + +/** Types **/ +#if CV__SIMD_FORWARD == 512 +// [todo] 512 +#error "AVX512 Not implemented yet" +#elif CV__SIMD_FORWARD == 256 +// 256 +#define __CV_VX(fun) v256_##fun +#define __CV_V_UINT8 v_uint8x32 +#define __CV_V_INT8 v_int8x32 +#define __CV_V_UINT16 v_uint16x16 +#define __CV_V_INT16 v_int16x16 +#define __CV_V_UINT32 v_uint32x8 +#define __CV_V_INT32 v_int32x8 +#define __CV_V_UINT64 v_uint64x4 +#define __CV_V_INT64 v_int64x4 +#define __CV_V_FLOAT32 v_float32x8 +#define __CV_V_FLOAT64 v_float64x4 +struct v_uint8x32; +struct v_int8x32; +struct v_uint16x16; +struct v_int16x16; +struct v_uint32x8; +struct v_int32x8; +struct v_uint64x4; +struct v_int64x4; +struct v_float32x8; +struct v_float64x4; +#else +// 128 +#define __CV_VX(fun) v_##fun +#define __CV_V_UINT8 v_uint8x16 +#define __CV_V_INT8 v_int8x16 +#define __CV_V_UINT16 v_uint16x8 +#define __CV_V_INT16 v_int16x8 +#define __CV_V_UINT32 v_uint32x4 +#define __CV_V_INT32 v_int32x4 +#define __CV_V_UINT64 v_uint64x2 +#define __CV_V_INT64 v_int64x2 +#define __CV_V_FLOAT32 v_float32x4 +#define __CV_V_FLOAT64 v_float64x2 +struct v_uint8x16; +struct v_int8x16; +struct v_uint16x8; +struct v_int16x8; +struct v_uint32x4; +struct v_int32x4; +struct v_uint64x2; +struct v_int64x2; +struct v_float32x4; +struct v_float64x2; +#endif + +/** Value reordering **/ + +// Expansion +void v_expand(const __CV_V_UINT8&, __CV_V_UINT16&, __CV_V_UINT16&); +void v_expand(const __CV_V_INT8&, __CV_V_INT16&, __CV_V_INT16&); +void v_expand(const __CV_V_UINT16&, __CV_V_UINT32&, __CV_V_UINT32&); +void v_expand(const __CV_V_INT16&, __CV_V_INT32&, __CV_V_INT32&); +void v_expand(const __CV_V_UINT32&, __CV_V_UINT64&, __CV_V_UINT64&); +void v_expand(const __CV_V_INT32&, __CV_V_INT64&, __CV_V_INT64&); +// Low Expansion +__CV_V_UINT16 v_expand_low(const __CV_V_UINT8&); +__CV_V_INT16 v_expand_low(const __CV_V_INT8&); +__CV_V_UINT32 v_expand_low(const __CV_V_UINT16&); +__CV_V_INT32 v_expand_low(const __CV_V_INT16&); +__CV_V_UINT64 v_expand_low(const __CV_V_UINT32&); +__CV_V_INT64 v_expand_low(const __CV_V_INT32&); +// High Expansion +__CV_V_UINT16 v_expand_high(const __CV_V_UINT8&); +__CV_V_INT16 v_expand_high(const __CV_V_INT8&); +__CV_V_UINT32 v_expand_high(const __CV_V_UINT16&); +__CV_V_INT32 v_expand_high(const __CV_V_INT16&); +__CV_V_UINT64 v_expand_high(const __CV_V_UINT32&); +__CV_V_INT64 v_expand_high(const __CV_V_INT32&); +// Load & Low Expansion +__CV_V_UINT16 __CV_VX(load_expand)(const uchar*); +__CV_V_INT16 __CV_VX(load_expand)(const schar*); +__CV_V_UINT32 __CV_VX(load_expand)(const ushort*); +__CV_V_INT32 __CV_VX(load_expand)(const short*); +__CV_V_UINT64 __CV_VX(load_expand)(const uint*); +__CV_V_INT64 __CV_VX(load_expand)(const int*); +// Load lower 8-bit and expand into 32-bit +__CV_V_UINT32 __CV_VX(load_expand_q)(const uchar*); +__CV_V_INT32 __CV_VX(load_expand_q)(const schar*); + +// Saturating Pack +__CV_V_UINT8 v_pack(const __CV_V_UINT16&, const __CV_V_UINT16&); +__CV_V_INT8 v_pack(const __CV_V_INT16&, const __CV_V_INT16&); +__CV_V_UINT16 v_pack(const __CV_V_UINT32&, const __CV_V_UINT32&); +__CV_V_INT16 v_pack(const __CV_V_INT32&, const __CV_V_INT32&); +// Non-saturating Pack +__CV_V_UINT32 v_pack(const __CV_V_UINT64&, const __CV_V_UINT64&); +__CV_V_INT32 v_pack(const __CV_V_INT64&, const __CV_V_INT64&); +// Pack signed integers with unsigned saturation +__CV_V_UINT8 v_pack_u(const __CV_V_INT16&, const __CV_V_INT16&); +__CV_V_UINT16 v_pack_u(const __CV_V_INT32&, const __CV_V_INT32&); + +/** Arithmetic, bitwise and comparison operations **/ + +// Non-saturating multiply +#if CV_VSX +template +Tvec v_mul_wrap(const Tvec& a, const Tvec& b); +#else +__CV_V_UINT8 v_mul_wrap(const __CV_V_UINT8&, const __CV_V_UINT8&); +__CV_V_INT8 v_mul_wrap(const __CV_V_INT8&, const __CV_V_INT8&); +__CV_V_UINT16 v_mul_wrap(const __CV_V_UINT16&, const __CV_V_UINT16&); +__CV_V_INT16 v_mul_wrap(const __CV_V_INT16&, const __CV_V_INT16&); +#endif + +// Multiply and expand +#if CV_VSX +template +void v_mul_expand(const Tvec& a, const Tvec& b, Twvec& c, Twvec& d); +#else +void v_mul_expand(const __CV_V_UINT8&, const __CV_V_UINT8&, __CV_V_UINT16&, __CV_V_UINT16&); +void v_mul_expand(const __CV_V_INT8&, const __CV_V_INT8&, __CV_V_INT16&, __CV_V_INT16&); +void v_mul_expand(const __CV_V_UINT16&, const __CV_V_UINT16&, __CV_V_UINT32&, __CV_V_UINT32&); +void v_mul_expand(const __CV_V_INT16&, const __CV_V_INT16&, __CV_V_INT32&, __CV_V_INT32&); +void v_mul_expand(const __CV_V_UINT32&, const __CV_V_UINT32&, __CV_V_UINT64&, __CV_V_UINT64&); +void v_mul_expand(const __CV_V_INT32&, const __CV_V_INT32&, __CV_V_INT64&, __CV_V_INT64&); +#endif + +/** Cleanup **/ +#undef CV__SIMD_FORWARD +#undef __CV_VX +#undef __CV_V_UINT8 +#undef __CV_V_INT8 +#undef __CV_V_UINT16 +#undef __CV_V_INT16 +#undef __CV_V_UINT32 +#undef __CV_V_INT32 +#undef __CV_V_UINT64 +#undef __CV_V_INT64 +#undef __CV_V_FLOAT32 +#undef __CV_V_FLOAT64 + +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END + +//! @endcond + +} // cv:: \ No newline at end of file diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/hal/intrin_neon.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/hal/intrin_neon.hpp new file mode 100644 index 0000000000000000000000000000000000000000..608dc97a87d806a133ad5fa851577b98b5071968 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/hal/intrin_neon.hpp @@ -0,0 +1,1697 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_HAL_INTRIN_NEON_HPP +#define OPENCV_HAL_INTRIN_NEON_HPP + +#include +#include "opencv2/core/utility.hpp" + +namespace cv +{ + +//! @cond IGNORED + +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN + +#define CV_SIMD128 1 +#if defined(__aarch64__) +#define CV_SIMD128_64F 1 +#else +#define CV_SIMD128_64F 0 +#endif + +#if CV_SIMD128_64F +#define OPENCV_HAL_IMPL_NEON_REINTERPRET(_Tpv, suffix) \ +template static inline \ +_Tpv vreinterpretq_##suffix##_f64(T a) { return (_Tpv) a; } \ +template static inline \ +float64x2_t vreinterpretq_f64_##suffix(T a) { return (float64x2_t) a; } +OPENCV_HAL_IMPL_NEON_REINTERPRET(uint8x16_t, u8) +OPENCV_HAL_IMPL_NEON_REINTERPRET(int8x16_t, s8) +OPENCV_HAL_IMPL_NEON_REINTERPRET(uint16x8_t, u16) +OPENCV_HAL_IMPL_NEON_REINTERPRET(int16x8_t, s16) +OPENCV_HAL_IMPL_NEON_REINTERPRET(uint32x4_t, u32) +OPENCV_HAL_IMPL_NEON_REINTERPRET(int32x4_t, s32) +OPENCV_HAL_IMPL_NEON_REINTERPRET(uint64x2_t, u64) +OPENCV_HAL_IMPL_NEON_REINTERPRET(int64x2_t, s64) +OPENCV_HAL_IMPL_NEON_REINTERPRET(float32x4_t, f32) +#endif + +struct v_uint8x16 +{ + typedef uchar lane_type; + enum { nlanes = 16 }; + + v_uint8x16() {} + explicit v_uint8x16(uint8x16_t v) : val(v) {} + v_uint8x16(uchar v0, uchar v1, uchar v2, uchar v3, uchar v4, uchar v5, uchar v6, uchar v7, + uchar v8, uchar v9, uchar v10, uchar v11, uchar v12, uchar v13, uchar v14, uchar v15) + { + uchar v[] = {v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15}; + val = vld1q_u8(v); + } + uchar get0() const + { + return vgetq_lane_u8(val, 0); + } + + uint8x16_t val; +}; + +struct v_int8x16 +{ + typedef schar lane_type; + enum { nlanes = 16 }; + + v_int8x16() {} + explicit v_int8x16(int8x16_t v) : val(v) {} + v_int8x16(schar v0, schar v1, schar v2, schar v3, schar v4, schar v5, schar v6, schar v7, + schar v8, schar v9, schar v10, schar v11, schar v12, schar v13, schar v14, schar v15) + { + schar v[] = {v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15}; + val = vld1q_s8(v); + } + schar get0() const + { + return vgetq_lane_s8(val, 0); + } + + int8x16_t val; +}; + +struct v_uint16x8 +{ + typedef ushort lane_type; + enum { nlanes = 8 }; + + v_uint16x8() {} + explicit v_uint16x8(uint16x8_t v) : val(v) {} + v_uint16x8(ushort v0, ushort v1, ushort v2, ushort v3, ushort v4, ushort v5, ushort v6, ushort v7) + { + ushort v[] = {v0, v1, v2, v3, v4, v5, v6, v7}; + val = vld1q_u16(v); + } + ushort get0() const + { + return vgetq_lane_u16(val, 0); + } + + uint16x8_t val; +}; + +struct v_int16x8 +{ + typedef short lane_type; + enum { nlanes = 8 }; + + v_int16x8() {} + explicit v_int16x8(int16x8_t v) : val(v) {} + v_int16x8(short v0, short v1, short v2, short v3, short v4, short v5, short v6, short v7) + { + short v[] = {v0, v1, v2, v3, v4, v5, v6, v7}; + val = vld1q_s16(v); + } + short get0() const + { + return vgetq_lane_s16(val, 0); + } + + int16x8_t val; +}; + +struct v_uint32x4 +{ + typedef unsigned lane_type; + enum { nlanes = 4 }; + + v_uint32x4() {} + explicit v_uint32x4(uint32x4_t v) : val(v) {} + v_uint32x4(unsigned v0, unsigned v1, unsigned v2, unsigned v3) + { + unsigned v[] = {v0, v1, v2, v3}; + val = vld1q_u32(v); + } + unsigned get0() const + { + return vgetq_lane_u32(val, 0); + } + + uint32x4_t val; +}; + +struct v_int32x4 +{ + typedef int lane_type; + enum { nlanes = 4 }; + + v_int32x4() {} + explicit v_int32x4(int32x4_t v) : val(v) {} + v_int32x4(int v0, int v1, int v2, int v3) + { + int v[] = {v0, v1, v2, v3}; + val = vld1q_s32(v); + } + int get0() const + { + return vgetq_lane_s32(val, 0); + } + int32x4_t val; +}; + +struct v_float32x4 +{ + typedef float lane_type; + enum { nlanes = 4 }; + + v_float32x4() {} + explicit v_float32x4(float32x4_t v) : val(v) {} + v_float32x4(float v0, float v1, float v2, float v3) + { + float v[] = {v0, v1, v2, v3}; + val = vld1q_f32(v); + } + float get0() const + { + return vgetq_lane_f32(val, 0); + } + float32x4_t val; +}; + +struct v_uint64x2 +{ + typedef uint64 lane_type; + enum { nlanes = 2 }; + + v_uint64x2() {} + explicit v_uint64x2(uint64x2_t v) : val(v) {} + v_uint64x2(uint64 v0, uint64 v1) + { + uint64 v[] = {v0, v1}; + val = vld1q_u64(v); + } + uint64 get0() const + { + return vgetq_lane_u64(val, 0); + } + uint64x2_t val; +}; + +struct v_int64x2 +{ + typedef int64 lane_type; + enum { nlanes = 2 }; + + v_int64x2() {} + explicit v_int64x2(int64x2_t v) : val(v) {} + v_int64x2(int64 v0, int64 v1) + { + int64 v[] = {v0, v1}; + val = vld1q_s64(v); + } + int64 get0() const + { + return vgetq_lane_s64(val, 0); + } + int64x2_t val; +}; + +#if CV_SIMD128_64F +struct v_float64x2 +{ + typedef double lane_type; + enum { nlanes = 2 }; + + v_float64x2() {} + explicit v_float64x2(float64x2_t v) : val(v) {} + v_float64x2(double v0, double v1) + { + double v[] = {v0, v1}; + val = vld1q_f64(v); + } + double get0() const + { + return vgetq_lane_f64(val, 0); + } + float64x2_t val; +}; +#endif + +#if CV_FP16 +// Workaround for old compilers +static inline int16x4_t vreinterpret_s16_f16(float16x4_t a) { return (int16x4_t)a; } +static inline float16x4_t vreinterpret_f16_s16(int16x4_t a) { return (float16x4_t)a; } + +static inline float16x4_t cv_vld1_f16(const void* ptr) +{ +#ifndef vld1_f16 // APPLE compiler defines vld1_f16 as macro + return vreinterpret_f16_s16(vld1_s16((const short*)ptr)); +#else + return vld1_f16((const __fp16*)ptr); +#endif +} +static inline void cv_vst1_f16(void* ptr, float16x4_t a) +{ +#ifndef vst1_f16 // APPLE compiler defines vst1_f16 as macro + vst1_s16((short*)ptr, vreinterpret_s16_f16(a)); +#else + vst1_f16((__fp16*)ptr, a); +#endif +} + +#ifndef vdup_n_f16 + #define vdup_n_f16(v) (float16x4_t){v, v, v, v} +#endif + +#endif // CV_FP16 + +#if CV_FP16 +inline v_float32x4 v128_load_fp16_f32(const short* ptr) +{ + float16x4_t a = cv_vld1_f16((const __fp16*)ptr); + return v_float32x4(vcvt_f32_f16(a)); +} + +inline void v_store_fp16(short* ptr, const v_float32x4& a) +{ + float16x4_t fp16 = vcvt_f16_f32(a.val); + cv_vst1_f16((short*)ptr, fp16); +} +#endif + +#define OPENCV_HAL_IMPL_NEON_INIT(_Tpv, _Tp, suffix) \ +inline v_##_Tpv v_setzero_##suffix() { return v_##_Tpv(vdupq_n_##suffix((_Tp)0)); } \ +inline v_##_Tpv v_setall_##suffix(_Tp v) { return v_##_Tpv(vdupq_n_##suffix(v)); } \ +inline _Tpv##_t vreinterpretq_##suffix##_##suffix(_Tpv##_t v) { return v; } \ +inline v_uint8x16 v_reinterpret_as_u8(const v_##_Tpv& v) { return v_uint8x16(vreinterpretq_u8_##suffix(v.val)); } \ +inline v_int8x16 v_reinterpret_as_s8(const v_##_Tpv& v) { return v_int8x16(vreinterpretq_s8_##suffix(v.val)); } \ +inline v_uint16x8 v_reinterpret_as_u16(const v_##_Tpv& v) { return v_uint16x8(vreinterpretq_u16_##suffix(v.val)); } \ +inline v_int16x8 v_reinterpret_as_s16(const v_##_Tpv& v) { return v_int16x8(vreinterpretq_s16_##suffix(v.val)); } \ +inline v_uint32x4 v_reinterpret_as_u32(const v_##_Tpv& v) { return v_uint32x4(vreinterpretq_u32_##suffix(v.val)); } \ +inline v_int32x4 v_reinterpret_as_s32(const v_##_Tpv& v) { return v_int32x4(vreinterpretq_s32_##suffix(v.val)); } \ +inline v_uint64x2 v_reinterpret_as_u64(const v_##_Tpv& v) { return v_uint64x2(vreinterpretq_u64_##suffix(v.val)); } \ +inline v_int64x2 v_reinterpret_as_s64(const v_##_Tpv& v) { return v_int64x2(vreinterpretq_s64_##suffix(v.val)); } \ +inline v_float32x4 v_reinterpret_as_f32(const v_##_Tpv& v) { return v_float32x4(vreinterpretq_f32_##suffix(v.val)); } + +OPENCV_HAL_IMPL_NEON_INIT(uint8x16, uchar, u8) +OPENCV_HAL_IMPL_NEON_INIT(int8x16, schar, s8) +OPENCV_HAL_IMPL_NEON_INIT(uint16x8, ushort, u16) +OPENCV_HAL_IMPL_NEON_INIT(int16x8, short, s16) +OPENCV_HAL_IMPL_NEON_INIT(uint32x4, unsigned, u32) +OPENCV_HAL_IMPL_NEON_INIT(int32x4, int, s32) +OPENCV_HAL_IMPL_NEON_INIT(uint64x2, uint64, u64) +OPENCV_HAL_IMPL_NEON_INIT(int64x2, int64, s64) +OPENCV_HAL_IMPL_NEON_INIT(float32x4, float, f32) +#if CV_SIMD128_64F +#define OPENCV_HAL_IMPL_NEON_INIT_64(_Tpv, suffix) \ +inline v_float64x2 v_reinterpret_as_f64(const v_##_Tpv& v) { return v_float64x2(vreinterpretq_f64_##suffix(v.val)); } +OPENCV_HAL_IMPL_NEON_INIT(float64x2, double, f64) +OPENCV_HAL_IMPL_NEON_INIT_64(uint8x16, u8) +OPENCV_HAL_IMPL_NEON_INIT_64(int8x16, s8) +OPENCV_HAL_IMPL_NEON_INIT_64(uint16x8, u16) +OPENCV_HAL_IMPL_NEON_INIT_64(int16x8, s16) +OPENCV_HAL_IMPL_NEON_INIT_64(uint32x4, u32) +OPENCV_HAL_IMPL_NEON_INIT_64(int32x4, s32) +OPENCV_HAL_IMPL_NEON_INIT_64(uint64x2, u64) +OPENCV_HAL_IMPL_NEON_INIT_64(int64x2, s64) +OPENCV_HAL_IMPL_NEON_INIT_64(float32x4, f32) +OPENCV_HAL_IMPL_NEON_INIT_64(float64x2, f64) +#endif + +#define OPENCV_HAL_IMPL_NEON_PACK(_Tpvec, _Tp, hreg, suffix, _Tpwvec, pack, mov, rshr) \ +inline _Tpvec v_##pack(const _Tpwvec& a, const _Tpwvec& b) \ +{ \ + hreg a1 = mov(a.val), b1 = mov(b.val); \ + return _Tpvec(vcombine_##suffix(a1, b1)); \ +} \ +inline void v_##pack##_store(_Tp* ptr, const _Tpwvec& a) \ +{ \ + hreg a1 = mov(a.val); \ + vst1_##suffix(ptr, a1); \ +} \ +template inline \ +_Tpvec v_rshr_##pack(const _Tpwvec& a, const _Tpwvec& b) \ +{ \ + hreg a1 = rshr(a.val, n); \ + hreg b1 = rshr(b.val, n); \ + return _Tpvec(vcombine_##suffix(a1, b1)); \ +} \ +template inline \ +void v_rshr_##pack##_store(_Tp* ptr, const _Tpwvec& a) \ +{ \ + hreg a1 = rshr(a.val, n); \ + vst1_##suffix(ptr, a1); \ +} + +OPENCV_HAL_IMPL_NEON_PACK(v_uint8x16, uchar, uint8x8_t, u8, v_uint16x8, pack, vqmovn_u16, vqrshrn_n_u16) +OPENCV_HAL_IMPL_NEON_PACK(v_int8x16, schar, int8x8_t, s8, v_int16x8, pack, vqmovn_s16, vqrshrn_n_s16) +OPENCV_HAL_IMPL_NEON_PACK(v_uint16x8, ushort, uint16x4_t, u16, v_uint32x4, pack, vqmovn_u32, vqrshrn_n_u32) +OPENCV_HAL_IMPL_NEON_PACK(v_int16x8, short, int16x4_t, s16, v_int32x4, pack, vqmovn_s32, vqrshrn_n_s32) +OPENCV_HAL_IMPL_NEON_PACK(v_uint32x4, unsigned, uint32x2_t, u32, v_uint64x2, pack, vmovn_u64, vrshrn_n_u64) +OPENCV_HAL_IMPL_NEON_PACK(v_int32x4, int, int32x2_t, s32, v_int64x2, pack, vmovn_s64, vrshrn_n_s64) + +OPENCV_HAL_IMPL_NEON_PACK(v_uint8x16, uchar, uint8x8_t, u8, v_int16x8, pack_u, vqmovun_s16, vqrshrun_n_s16) +OPENCV_HAL_IMPL_NEON_PACK(v_uint16x8, ushort, uint16x4_t, u16, v_int32x4, pack_u, vqmovun_s32, vqrshrun_n_s32) + +// pack boolean +inline v_uint8x16 v_pack_b(const v_uint16x8& a, const v_uint16x8& b) +{ + uint8x16_t ab = vcombine_u8(vmovn_u16(a.val), vmovn_u16(b.val)); + return v_uint8x16(ab); +} + +inline v_uint8x16 v_pack_b(const v_uint32x4& a, const v_uint32x4& b, + const v_uint32x4& c, const v_uint32x4& d) +{ + uint16x8_t nab = vcombine_u16(vmovn_u32(a.val), vmovn_u32(b.val)); + uint16x8_t ncd = vcombine_u16(vmovn_u32(c.val), vmovn_u32(d.val)); + return v_uint8x16(vcombine_u8(vmovn_u16(nab), vmovn_u16(ncd))); +} + +inline v_uint8x16 v_pack_b(const v_uint64x2& a, const v_uint64x2& b, const v_uint64x2& c, + const v_uint64x2& d, const v_uint64x2& e, const v_uint64x2& f, + const v_uint64x2& g, const v_uint64x2& h) +{ + uint32x4_t ab = vcombine_u32(vmovn_u64(a.val), vmovn_u64(b.val)); + uint32x4_t cd = vcombine_u32(vmovn_u64(c.val), vmovn_u64(d.val)); + uint32x4_t ef = vcombine_u32(vmovn_u64(e.val), vmovn_u64(f.val)); + uint32x4_t gh = vcombine_u32(vmovn_u64(g.val), vmovn_u64(h.val)); + + uint16x8_t abcd = vcombine_u16(vmovn_u32(ab), vmovn_u32(cd)); + uint16x8_t efgh = vcombine_u16(vmovn_u32(ef), vmovn_u32(gh)); + return v_uint8x16(vcombine_u8(vmovn_u16(abcd), vmovn_u16(efgh))); +} + +inline v_float32x4 v_matmul(const v_float32x4& v, const v_float32x4& m0, + const v_float32x4& m1, const v_float32x4& m2, + const v_float32x4& m3) +{ + float32x2_t vl = vget_low_f32(v.val), vh = vget_high_f32(v.val); + float32x4_t res = vmulq_lane_f32(m0.val, vl, 0); + res = vmlaq_lane_f32(res, m1.val, vl, 1); + res = vmlaq_lane_f32(res, m2.val, vh, 0); + res = vmlaq_lane_f32(res, m3.val, vh, 1); + return v_float32x4(res); +} + +inline v_float32x4 v_matmuladd(const v_float32x4& v, const v_float32x4& m0, + const v_float32x4& m1, const v_float32x4& m2, + const v_float32x4& a) +{ + float32x2_t vl = vget_low_f32(v.val), vh = vget_high_f32(v.val); + float32x4_t res = vmulq_lane_f32(m0.val, vl, 0); + res = vmlaq_lane_f32(res, m1.val, vl, 1); + res = vmlaq_lane_f32(res, m2.val, vh, 0); + res = vaddq_f32(res, a.val); + return v_float32x4(res); +} + +#define OPENCV_HAL_IMPL_NEON_BIN_OP(bin_op, _Tpvec, intrin) \ +inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b) \ +{ \ + return _Tpvec(intrin(a.val, b.val)); \ +} \ +inline _Tpvec& operator bin_op##= (_Tpvec& a, const _Tpvec& b) \ +{ \ + a.val = intrin(a.val, b.val); \ + return a; \ +} + +OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_uint8x16, vqaddq_u8) +OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_uint8x16, vqsubq_u8) +OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_int8x16, vqaddq_s8) +OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_int8x16, vqsubq_s8) +OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_uint16x8, vqaddq_u16) +OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_uint16x8, vqsubq_u16) +OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_int16x8, vqaddq_s16) +OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_int16x8, vqsubq_s16) +OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_int32x4, vaddq_s32) +OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_int32x4, vsubq_s32) +OPENCV_HAL_IMPL_NEON_BIN_OP(*, v_int32x4, vmulq_s32) +OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_uint32x4, vaddq_u32) +OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_uint32x4, vsubq_u32) +OPENCV_HAL_IMPL_NEON_BIN_OP(*, v_uint32x4, vmulq_u32) +OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_float32x4, vaddq_f32) +OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_float32x4, vsubq_f32) +OPENCV_HAL_IMPL_NEON_BIN_OP(*, v_float32x4, vmulq_f32) +OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_int64x2, vaddq_s64) +OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_int64x2, vsubq_s64) +OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_uint64x2, vaddq_u64) +OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_uint64x2, vsubq_u64) +#if CV_SIMD128_64F +OPENCV_HAL_IMPL_NEON_BIN_OP(/, v_float32x4, vdivq_f32) +OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_float64x2, vaddq_f64) +OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_float64x2, vsubq_f64) +OPENCV_HAL_IMPL_NEON_BIN_OP(*, v_float64x2, vmulq_f64) +OPENCV_HAL_IMPL_NEON_BIN_OP(/, v_float64x2, vdivq_f64) +#else +inline v_float32x4 operator / (const v_float32x4& a, const v_float32x4& b) +{ + float32x4_t reciprocal = vrecpeq_f32(b.val); + reciprocal = vmulq_f32(vrecpsq_f32(b.val, reciprocal), reciprocal); + reciprocal = vmulq_f32(vrecpsq_f32(b.val, reciprocal), reciprocal); + return v_float32x4(vmulq_f32(a.val, reciprocal)); +} +inline v_float32x4& operator /= (v_float32x4& a, const v_float32x4& b) +{ + float32x4_t reciprocal = vrecpeq_f32(b.val); + reciprocal = vmulq_f32(vrecpsq_f32(b.val, reciprocal), reciprocal); + reciprocal = vmulq_f32(vrecpsq_f32(b.val, reciprocal), reciprocal); + a.val = vmulq_f32(a.val, reciprocal); + return a; +} +#endif + +// saturating multiply 8-bit, 16-bit +#define OPENCV_HAL_IMPL_NEON_MUL_SAT(_Tpvec, _Tpwvec) \ + inline _Tpvec operator * (const _Tpvec& a, const _Tpvec& b) \ + { \ + _Tpwvec c, d; \ + v_mul_expand(a, b, c, d); \ + return v_pack(c, d); \ + } \ + inline _Tpvec& operator *= (_Tpvec& a, const _Tpvec& b) \ + { a = a * b; return a; } + +OPENCV_HAL_IMPL_NEON_MUL_SAT(v_int8x16, v_int16x8) +OPENCV_HAL_IMPL_NEON_MUL_SAT(v_uint8x16, v_uint16x8) +OPENCV_HAL_IMPL_NEON_MUL_SAT(v_int16x8, v_int32x4) +OPENCV_HAL_IMPL_NEON_MUL_SAT(v_uint16x8, v_uint32x4) + +// Multiply and expand +inline void v_mul_expand(const v_int8x16& a, const v_int8x16& b, + v_int16x8& c, v_int16x8& d) +{ + c.val = vmull_s8(vget_low_s8(a.val), vget_low_s8(b.val)); + d.val = vmull_s8(vget_high_s8(a.val), vget_high_s8(b.val)); +} + +inline void v_mul_expand(const v_uint8x16& a, const v_uint8x16& b, + v_uint16x8& c, v_uint16x8& d) +{ + c.val = vmull_u8(vget_low_u8(a.val), vget_low_u8(b.val)); + d.val = vmull_u8(vget_high_u8(a.val), vget_high_u8(b.val)); +} + +inline void v_mul_expand(const v_int16x8& a, const v_int16x8& b, + v_int32x4& c, v_int32x4& d) +{ + c.val = vmull_s16(vget_low_s16(a.val), vget_low_s16(b.val)); + d.val = vmull_s16(vget_high_s16(a.val), vget_high_s16(b.val)); +} + +inline void v_mul_expand(const v_uint16x8& a, const v_uint16x8& b, + v_uint32x4& c, v_uint32x4& d) +{ + c.val = vmull_u16(vget_low_u16(a.val), vget_low_u16(b.val)); + d.val = vmull_u16(vget_high_u16(a.val), vget_high_u16(b.val)); +} + +inline void v_mul_expand(const v_uint32x4& a, const v_uint32x4& b, + v_uint64x2& c, v_uint64x2& d) +{ + c.val = vmull_u32(vget_low_u32(a.val), vget_low_u32(b.val)); + d.val = vmull_u32(vget_high_u32(a.val), vget_high_u32(b.val)); +} + +inline v_int16x8 v_mul_hi(const v_int16x8& a, const v_int16x8& b) +{ + return v_int16x8(vcombine_s16( + vshrn_n_s32(vmull_s16( vget_low_s16(a.val), vget_low_s16(b.val)), 16), + vshrn_n_s32(vmull_s16(vget_high_s16(a.val), vget_high_s16(b.val)), 16) + )); +} +inline v_uint16x8 v_mul_hi(const v_uint16x8& a, const v_uint16x8& b) +{ + return v_uint16x8(vcombine_u16( + vshrn_n_u32(vmull_u16( vget_low_u16(a.val), vget_low_u16(b.val)), 16), + vshrn_n_u32(vmull_u16(vget_high_u16(a.val), vget_high_u16(b.val)), 16) + )); +} + +inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b) +{ + int32x4_t c = vmull_s16(vget_low_s16(a.val), vget_low_s16(b.val)); + int32x4_t d = vmull_s16(vget_high_s16(a.val), vget_high_s16(b.val)); + int32x4x2_t cd = vuzpq_s32(c, d); + return v_int32x4(vaddq_s32(cd.val[0], cd.val[1])); +} + +inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b, const v_int32x4& c) +{ + v_int32x4 s = v_dotprod(a, b); + return v_int32x4(vaddq_s32(s.val , c.val)); +} + +#define OPENCV_HAL_IMPL_NEON_LOGIC_OP(_Tpvec, suffix) \ + OPENCV_HAL_IMPL_NEON_BIN_OP(&, _Tpvec, vandq_##suffix) \ + OPENCV_HAL_IMPL_NEON_BIN_OP(|, _Tpvec, vorrq_##suffix) \ + OPENCV_HAL_IMPL_NEON_BIN_OP(^, _Tpvec, veorq_##suffix) \ + inline _Tpvec operator ~ (const _Tpvec& a) \ + { \ + return _Tpvec(vreinterpretq_##suffix##_u8(vmvnq_u8(vreinterpretq_u8_##suffix(a.val)))); \ + } + +OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_uint8x16, u8) +OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_int8x16, s8) +OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_uint16x8, u16) +OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_int16x8, s16) +OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_uint32x4, u32) +OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_int32x4, s32) +OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_uint64x2, u64) +OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_int64x2, s64) + +#define OPENCV_HAL_IMPL_NEON_FLT_BIT_OP(bin_op, intrin) \ +inline v_float32x4 operator bin_op (const v_float32x4& a, const v_float32x4& b) \ +{ \ + return v_float32x4(vreinterpretq_f32_s32(intrin(vreinterpretq_s32_f32(a.val), vreinterpretq_s32_f32(b.val)))); \ +} \ +inline v_float32x4& operator bin_op##= (v_float32x4& a, const v_float32x4& b) \ +{ \ + a.val = vreinterpretq_f32_s32(intrin(vreinterpretq_s32_f32(a.val), vreinterpretq_s32_f32(b.val))); \ + return a; \ +} + +OPENCV_HAL_IMPL_NEON_FLT_BIT_OP(&, vandq_s32) +OPENCV_HAL_IMPL_NEON_FLT_BIT_OP(|, vorrq_s32) +OPENCV_HAL_IMPL_NEON_FLT_BIT_OP(^, veorq_s32) + +inline v_float32x4 operator ~ (const v_float32x4& a) +{ + return v_float32x4(vreinterpretq_f32_s32(vmvnq_s32(vreinterpretq_s32_f32(a.val)))); +} + +#if CV_SIMD128_64F +inline v_float32x4 v_sqrt(const v_float32x4& x) +{ + return v_float32x4(vsqrtq_f32(x.val)); +} + +inline v_float32x4 v_invsqrt(const v_float32x4& x) +{ + v_float32x4 one = v_setall_f32(1.0f); + return one / v_sqrt(x); +} +#else +inline v_float32x4 v_sqrt(const v_float32x4& x) +{ + float32x4_t x1 = vmaxq_f32(x.val, vdupq_n_f32(FLT_MIN)); + float32x4_t e = vrsqrteq_f32(x1); + e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(x1, e), e), e); + e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(x1, e), e), e); + return v_float32x4(vmulq_f32(x.val, e)); +} + +inline v_float32x4 v_invsqrt(const v_float32x4& x) +{ + float32x4_t e = vrsqrteq_f32(x.val); + e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(x.val, e), e), e); + e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(x.val, e), e), e); + return v_float32x4(e); +} +#endif + +#define OPENCV_HAL_IMPL_NEON_ABS(_Tpuvec, _Tpsvec, usuffix, ssuffix) \ +inline _Tpuvec v_abs(const _Tpsvec& a) { return v_reinterpret_as_##usuffix(_Tpsvec(vabsq_##ssuffix(a.val))); } + +OPENCV_HAL_IMPL_NEON_ABS(v_uint8x16, v_int8x16, u8, s8) +OPENCV_HAL_IMPL_NEON_ABS(v_uint16x8, v_int16x8, u16, s16) +OPENCV_HAL_IMPL_NEON_ABS(v_uint32x4, v_int32x4, u32, s32) + +inline v_float32x4 v_abs(v_float32x4 x) +{ return v_float32x4(vabsq_f32(x.val)); } + +#if CV_SIMD128_64F +#define OPENCV_HAL_IMPL_NEON_DBL_BIT_OP(bin_op, intrin) \ +inline v_float64x2 operator bin_op (const v_float64x2& a, const v_float64x2& b) \ +{ \ + return v_float64x2(vreinterpretq_f64_s64(intrin(vreinterpretq_s64_f64(a.val), vreinterpretq_s64_f64(b.val)))); \ +} \ +inline v_float64x2& operator bin_op##= (v_float64x2& a, const v_float64x2& b) \ +{ \ + a.val = vreinterpretq_f64_s64(intrin(vreinterpretq_s64_f64(a.val), vreinterpretq_s64_f64(b.val))); \ + return a; \ +} + +OPENCV_HAL_IMPL_NEON_DBL_BIT_OP(&, vandq_s64) +OPENCV_HAL_IMPL_NEON_DBL_BIT_OP(|, vorrq_s64) +OPENCV_HAL_IMPL_NEON_DBL_BIT_OP(^, veorq_s64) + +inline v_float64x2 operator ~ (const v_float64x2& a) +{ + return v_float64x2(vreinterpretq_f64_s32(vmvnq_s32(vreinterpretq_s32_f64(a.val)))); +} + +inline v_float64x2 v_sqrt(const v_float64x2& x) +{ + return v_float64x2(vsqrtq_f64(x.val)); +} + +inline v_float64x2 v_invsqrt(const v_float64x2& x) +{ + v_float64x2 one = v_setall_f64(1.0f); + return one / v_sqrt(x); +} + +inline v_float64x2 v_abs(v_float64x2 x) +{ return v_float64x2(vabsq_f64(x.val)); } +#endif + +// TODO: exp, log, sin, cos + +#define OPENCV_HAL_IMPL_NEON_BIN_FUNC(_Tpvec, func, intrin) \ +inline _Tpvec func(const _Tpvec& a, const _Tpvec& b) \ +{ \ + return _Tpvec(intrin(a.val, b.val)); \ +} + +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_min, vminq_u8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_max, vmaxq_u8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int8x16, v_min, vminq_s8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int8x16, v_max, vmaxq_s8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_min, vminq_u16) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_max, vmaxq_u16) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int16x8, v_min, vminq_s16) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int16x8, v_max, vmaxq_s16) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint32x4, v_min, vminq_u32) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint32x4, v_max, vmaxq_u32) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int32x4, v_min, vminq_s32) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int32x4, v_max, vmaxq_s32) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_float32x4, v_min, vminq_f32) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_float32x4, v_max, vmaxq_f32) +#if CV_SIMD128_64F +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_float64x2, v_min, vminq_f64) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_float64x2, v_max, vmaxq_f64) +#endif + +#if CV_SIMD128_64F +inline int64x2_t vmvnq_s64(int64x2_t a) +{ + int64x2_t vx = vreinterpretq_s64_u32(vdupq_n_u32(0xFFFFFFFF)); + return veorq_s64(a, vx); +} +inline uint64x2_t vmvnq_u64(uint64x2_t a) +{ + uint64x2_t vx = vreinterpretq_u64_u32(vdupq_n_u32(0xFFFFFFFF)); + return veorq_u64(a, vx); +} +#endif +#define OPENCV_HAL_IMPL_NEON_INT_CMP_OP(_Tpvec, cast, suffix, not_suffix) \ +inline _Tpvec operator == (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(cast(vceqq_##suffix(a.val, b.val))); } \ +inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(cast(vmvnq_##not_suffix(vceqq_##suffix(a.val, b.val)))); } \ +inline _Tpvec operator < (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(cast(vcltq_##suffix(a.val, b.val))); } \ +inline _Tpvec operator > (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(cast(vcgtq_##suffix(a.val, b.val))); } \ +inline _Tpvec operator <= (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(cast(vcleq_##suffix(a.val, b.val))); } \ +inline _Tpvec operator >= (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(cast(vcgeq_##suffix(a.val, b.val))); } + +OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_uint8x16, OPENCV_HAL_NOP, u8, u8) +OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_int8x16, vreinterpretq_s8_u8, s8, u8) +OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_uint16x8, OPENCV_HAL_NOP, u16, u16) +OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_int16x8, vreinterpretq_s16_u16, s16, u16) +OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_uint32x4, OPENCV_HAL_NOP, u32, u32) +OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_int32x4, vreinterpretq_s32_u32, s32, u32) +OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_float32x4, vreinterpretq_f32_u32, f32, u32) +#if CV_SIMD128_64F +OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_uint64x2, OPENCV_HAL_NOP, u64, u64) +OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_int64x2, vreinterpretq_s64_u64, s64, u64) +OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_float64x2, vreinterpretq_f64_u64, f64, u64) +#endif + +inline v_float32x4 v_not_nan(const v_float32x4& a) +{ return v_float32x4(vreinterpretq_f32_u32(vceqq_f32(a.val, a.val))); } +#if CV_SIMD128_64F +inline v_float64x2 v_not_nan(const v_float64x2& a) +{ return v_float64x2(vreinterpretq_f64_u64(vceqq_f64(a.val, a.val))); } +#endif + +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_add_wrap, vaddq_u8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int8x16, v_add_wrap, vaddq_s8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_add_wrap, vaddq_u16) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int16x8, v_add_wrap, vaddq_s16) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_sub_wrap, vsubq_u8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int8x16, v_sub_wrap, vsubq_s8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_sub_wrap, vsubq_u16) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int16x8, v_sub_wrap, vsubq_s16) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_mul_wrap, vmulq_u8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int8x16, v_mul_wrap, vmulq_s8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_mul_wrap, vmulq_u16) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int16x8, v_mul_wrap, vmulq_s16) + +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_absdiff, vabdq_u8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_absdiff, vabdq_u16) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint32x4, v_absdiff, vabdq_u32) +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_float32x4, v_absdiff, vabdq_f32) +#if CV_SIMD128_64F +OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_float64x2, v_absdiff, vabdq_f64) +#endif + +/** Saturating absolute difference **/ +inline v_int8x16 v_absdiffs(const v_int8x16& a, const v_int8x16& b) +{ return v_int8x16(vqabsq_s8(vqsubq_s8(a.val, b.val))); } +inline v_int16x8 v_absdiffs(const v_int16x8& a, const v_int16x8& b) +{ return v_int16x8(vqabsq_s16(vqsubq_s16(a.val, b.val))); } + +#define OPENCV_HAL_IMPL_NEON_BIN_FUNC2(_Tpvec, _Tpvec2, cast, func, intrin) \ +inline _Tpvec2 func(const _Tpvec& a, const _Tpvec& b) \ +{ \ + return _Tpvec2(cast(intrin(a.val, b.val))); \ +} + +OPENCV_HAL_IMPL_NEON_BIN_FUNC2(v_int8x16, v_uint8x16, vreinterpretq_u8_s8, v_absdiff, vabdq_s8) +OPENCV_HAL_IMPL_NEON_BIN_FUNC2(v_int16x8, v_uint16x8, vreinterpretq_u16_s16, v_absdiff, vabdq_s16) +OPENCV_HAL_IMPL_NEON_BIN_FUNC2(v_int32x4, v_uint32x4, vreinterpretq_u32_s32, v_absdiff, vabdq_s32) + +inline v_float32x4 v_magnitude(const v_float32x4& a, const v_float32x4& b) +{ + v_float32x4 x(vmlaq_f32(vmulq_f32(a.val, a.val), b.val, b.val)); + return v_sqrt(x); +} + +inline v_float32x4 v_sqr_magnitude(const v_float32x4& a, const v_float32x4& b) +{ + return v_float32x4(vmlaq_f32(vmulq_f32(a.val, a.val), b.val, b.val)); +} + +inline v_float32x4 v_fma(const v_float32x4& a, const v_float32x4& b, const v_float32x4& c) +{ +#if CV_SIMD128_64F + // ARMv8, which adds support for 64-bit floating-point (so CV_SIMD128_64F is defined), + // also adds FMA support both for single- and double-precision floating-point vectors + return v_float32x4(vfmaq_f32(c.val, a.val, b.val)); +#else + return v_float32x4(vmlaq_f32(c.val, a.val, b.val)); +#endif +} + +inline v_int32x4 v_fma(const v_int32x4& a, const v_int32x4& b, const v_int32x4& c) +{ + return v_int32x4(vmlaq_s32(c.val, a.val, b.val)); +} + +inline v_float32x4 v_muladd(const v_float32x4& a, const v_float32x4& b, const v_float32x4& c) +{ + return v_fma(a, b, c); +} + +inline v_int32x4 v_muladd(const v_int32x4& a, const v_int32x4& b, const v_int32x4& c) +{ + return v_fma(a, b, c); +} + +#if CV_SIMD128_64F +inline v_float64x2 v_magnitude(const v_float64x2& a, const v_float64x2& b) +{ + v_float64x2 x(vaddq_f64(vmulq_f64(a.val, a.val), vmulq_f64(b.val, b.val))); + return v_sqrt(x); +} + +inline v_float64x2 v_sqr_magnitude(const v_float64x2& a, const v_float64x2& b) +{ + return v_float64x2(vaddq_f64(vmulq_f64(a.val, a.val), vmulq_f64(b.val, b.val))); +} + +inline v_float64x2 v_fma(const v_float64x2& a, const v_float64x2& b, const v_float64x2& c) +{ + return v_float64x2(vfmaq_f64(c.val, a.val, b.val)); +} + +inline v_float64x2 v_muladd(const v_float64x2& a, const v_float64x2& b, const v_float64x2& c) +{ + return v_fma(a, b, c); +} +#endif + +// trade efficiency for convenience +#define OPENCV_HAL_IMPL_NEON_SHIFT_OP(_Tpvec, suffix, _Tps, ssuffix) \ +inline _Tpvec operator << (const _Tpvec& a, int n) \ +{ return _Tpvec(vshlq_##suffix(a.val, vdupq_n_##ssuffix((_Tps)n))); } \ +inline _Tpvec operator >> (const _Tpvec& a, int n) \ +{ return _Tpvec(vshlq_##suffix(a.val, vdupq_n_##ssuffix((_Tps)-n))); } \ +template inline _Tpvec v_shl(const _Tpvec& a) \ +{ return _Tpvec(vshlq_n_##suffix(a.val, n)); } \ +template inline _Tpvec v_shr(const _Tpvec& a) \ +{ return _Tpvec(vshrq_n_##suffix(a.val, n)); } \ +template inline _Tpvec v_rshr(const _Tpvec& a) \ +{ return _Tpvec(vrshrq_n_##suffix(a.val, n)); } + +OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_uint8x16, u8, schar, s8) +OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_int8x16, s8, schar, s8) +OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_uint16x8, u16, short, s16) +OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_int16x8, s16, short, s16) +OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_uint32x4, u32, int, s32) +OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_int32x4, s32, int, s32) +OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_uint64x2, u64, int64, s64) +OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_int64x2, s64, int64, s64) + +#define OPENCV_HAL_IMPL_NEON_ROTATE_OP(_Tpvec, suffix) \ +template inline _Tpvec v_rotate_right(const _Tpvec& a) \ +{ return _Tpvec(vextq_##suffix(a.val, vdupq_n_##suffix(0), n)); } \ +template inline _Tpvec v_rotate_left(const _Tpvec& a) \ +{ return _Tpvec(vextq_##suffix(vdupq_n_##suffix(0), a.val, _Tpvec::nlanes - n)); } \ +template<> inline _Tpvec v_rotate_left<0>(const _Tpvec& a) \ +{ return a; } \ +template inline _Tpvec v_rotate_right(const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(vextq_##suffix(a.val, b.val, n)); } \ +template inline _Tpvec v_rotate_left(const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(vextq_##suffix(b.val, a.val, _Tpvec::nlanes - n)); } \ +template<> inline _Tpvec v_rotate_left<0>(const _Tpvec& a, const _Tpvec& b) \ +{ CV_UNUSED(b); return a; } + +OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_uint8x16, u8) +OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_int8x16, s8) +OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_uint16x8, u16) +OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_int16x8, s16) +OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_uint32x4, u32) +OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_int32x4, s32) +OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_float32x4, f32) +OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_uint64x2, u64) +OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_int64x2, s64) +#if CV_SIMD128_64F +OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_float64x2, f64) +#endif + +#define OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(_Tpvec, _Tp, suffix) \ +inline _Tpvec v_load(const _Tp* ptr) \ +{ return _Tpvec(vld1q_##suffix(ptr)); } \ +inline _Tpvec v_load_aligned(const _Tp* ptr) \ +{ return _Tpvec(vld1q_##suffix(ptr)); } \ +inline _Tpvec v_load_low(const _Tp* ptr) \ +{ return _Tpvec(vcombine_##suffix(vld1_##suffix(ptr), vdup_n_##suffix((_Tp)0))); } \ +inline _Tpvec v_load_halves(const _Tp* ptr0, const _Tp* ptr1) \ +{ return _Tpvec(vcombine_##suffix(vld1_##suffix(ptr0), vld1_##suffix(ptr1))); } \ +inline void v_store(_Tp* ptr, const _Tpvec& a) \ +{ vst1q_##suffix(ptr, a.val); } \ +inline void v_store_aligned(_Tp* ptr, const _Tpvec& a) \ +{ vst1q_##suffix(ptr, a.val); } \ +inline void v_store_aligned_nocache(_Tp* ptr, const _Tpvec& a) \ +{ vst1q_##suffix(ptr, a.val); } \ +inline void v_store(_Tp* ptr, const _Tpvec& a, hal::StoreMode /*mode*/) \ +{ vst1q_##suffix(ptr, a.val); } \ +inline void v_store_low(_Tp* ptr, const _Tpvec& a) \ +{ vst1_##suffix(ptr, vget_low_##suffix(a.val)); } \ +inline void v_store_high(_Tp* ptr, const _Tpvec& a) \ +{ vst1_##suffix(ptr, vget_high_##suffix(a.val)); } + +OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_uint8x16, uchar, u8) +OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_int8x16, schar, s8) +OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_uint16x8, ushort, u16) +OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_int16x8, short, s16) +OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_uint32x4, unsigned, u32) +OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_int32x4, int, s32) +OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_uint64x2, uint64, u64) +OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_int64x2, int64, s64) +OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_float32x4, float, f32) +#if CV_SIMD128_64F +OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_float64x2, double, f64) +#endif + +#define OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(_Tpvec, _Tpnvec, scalartype, func, vectorfunc, suffix) \ +inline scalartype v_reduce_##func(const _Tpvec& a) \ +{ \ + _Tpnvec##_t a0 = vp##vectorfunc##_##suffix(vget_low_##suffix(a.val), vget_high_##suffix(a.val)); \ + a0 = vp##vectorfunc##_##suffix(a0, a0); \ + return (scalartype)vget_lane_##suffix(vp##vectorfunc##_##suffix(a0, a0),0); \ +} + +OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(v_uint16x8, uint16x4, unsigned short, sum, add, u16) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(v_uint16x8, uint16x4, unsigned short, max, max, u16) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(v_uint16x8, uint16x4, unsigned short, min, min, u16) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(v_int16x8, int16x4, short, sum, add, s16) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(v_int16x8, int16x4, short, max, max, s16) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(v_int16x8, int16x4, short, min, min, s16) + +#define OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(_Tpvec, _Tpnvec, scalartype, func, vectorfunc, suffix) \ +inline scalartype v_reduce_##func(const _Tpvec& a) \ +{ \ + _Tpnvec##_t a0 = vp##vectorfunc##_##suffix(vget_low_##suffix(a.val), vget_high_##suffix(a.val)); \ + return (scalartype)vget_lane_##suffix(vp##vectorfunc##_##suffix(a0, vget_high_##suffix(a.val)),0); \ +} + +OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_uint32x4, uint32x2, unsigned, sum, add, u32) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_uint32x4, uint32x2, unsigned, max, max, u32) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_uint32x4, uint32x2, unsigned, min, min, u32) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_int32x4, int32x2, int, sum, add, s32) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_int32x4, int32x2, int, max, max, s32) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_int32x4, int32x2, int, min, min, s32) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_float32x4, float32x2, float, sum, add, f32) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_float32x4, float32x2, float, max, max, f32) +OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_float32x4, float32x2, float, min, min, f32) + +#if CV_SIMD128_64F +inline double v_reduce_sum(const v_float64x2& a) +{ + return vgetq_lane_f64(a.val, 0) + vgetq_lane_f64(a.val, 1); +} +#endif + +inline v_float32x4 v_reduce_sum4(const v_float32x4& a, const v_float32x4& b, + const v_float32x4& c, const v_float32x4& d) +{ + float32x4x2_t ab = vtrnq_f32(a.val, b.val); + float32x4x2_t cd = vtrnq_f32(c.val, d.val); + + float32x4_t u0 = vaddq_f32(ab.val[0], ab.val[1]); // a0+a1 b0+b1 a2+a3 b2+b3 + float32x4_t u1 = vaddq_f32(cd.val[0], cd.val[1]); // c0+c1 d0+d1 c2+c3 d2+d3 + + float32x4_t v0 = vcombine_f32(vget_low_f32(u0), vget_low_f32(u1)); + float32x4_t v1 = vcombine_f32(vget_high_f32(u0), vget_high_f32(u1)); + + return v_float32x4(vaddq_f32(v0, v1)); +} + +inline unsigned v_reduce_sad(const v_uint8x16& a, const v_uint8x16& b) +{ + uint32x4_t t0 = vpaddlq_u16(vpaddlq_u8(vabdq_u8(a.val, b.val))); + uint32x2_t t1 = vpadd_u32(vget_low_u32(t0), vget_high_u32(t0)); + return vget_lane_u32(vpadd_u32(t1, t1), 0); +} +inline unsigned v_reduce_sad(const v_int8x16& a, const v_int8x16& b) +{ + uint32x4_t t0 = vpaddlq_u16(vpaddlq_u8(vreinterpretq_u8_s8(vabdq_s8(a.val, b.val)))); + uint32x2_t t1 = vpadd_u32(vget_low_u32(t0), vget_high_u32(t0)); + return vget_lane_u32(vpadd_u32(t1, t1), 0); +} +inline unsigned v_reduce_sad(const v_uint16x8& a, const v_uint16x8& b) +{ + uint32x4_t t0 = vpaddlq_u16(vabdq_u16(a.val, b.val)); + uint32x2_t t1 = vpadd_u32(vget_low_u32(t0), vget_high_u32(t0)); + return vget_lane_u32(vpadd_u32(t1, t1), 0); +} +inline unsigned v_reduce_sad(const v_int16x8& a, const v_int16x8& b) +{ + uint32x4_t t0 = vpaddlq_u16(vreinterpretq_u16_s16(vabdq_s16(a.val, b.val))); + uint32x2_t t1 = vpadd_u32(vget_low_u32(t0), vget_high_u32(t0)); + return vget_lane_u32(vpadd_u32(t1, t1), 0); +} +inline unsigned v_reduce_sad(const v_uint32x4& a, const v_uint32x4& b) +{ + uint32x4_t t0 = vabdq_u32(a.val, b.val); + uint32x2_t t1 = vpadd_u32(vget_low_u32(t0), vget_high_u32(t0)); + return vget_lane_u32(vpadd_u32(t1, t1), 0); +} +inline unsigned v_reduce_sad(const v_int32x4& a, const v_int32x4& b) +{ + uint32x4_t t0 = vreinterpretq_u32_s32(vabdq_s32(a.val, b.val)); + uint32x2_t t1 = vpadd_u32(vget_low_u32(t0), vget_high_u32(t0)); + return vget_lane_u32(vpadd_u32(t1, t1), 0); +} +inline float v_reduce_sad(const v_float32x4& a, const v_float32x4& b) +{ + float32x4_t t0 = vabdq_f32(a.val, b.val); + float32x2_t t1 = vpadd_f32(vget_low_f32(t0), vget_high_f32(t0)); + return vget_lane_f32(vpadd_f32(t1, t1), 0); +} + +#define OPENCV_HAL_IMPL_NEON_POPCOUNT(_Tpvec, cast) \ +inline v_uint32x4 v_popcount(const _Tpvec& a) \ +{ \ + uint8x16_t t = vcntq_u8(cast(a.val)); \ + uint16x8_t t0 = vpaddlq_u8(t); /* 16 -> 8 */ \ + uint32x4_t t1 = vpaddlq_u16(t0); /* 8 -> 4 */ \ + return v_uint32x4(t1); \ +} + +OPENCV_HAL_IMPL_NEON_POPCOUNT(v_uint8x16, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_NEON_POPCOUNT(v_uint16x8, vreinterpretq_u8_u16) +OPENCV_HAL_IMPL_NEON_POPCOUNT(v_uint32x4, vreinterpretq_u8_u32) +OPENCV_HAL_IMPL_NEON_POPCOUNT(v_int8x16, vreinterpretq_u8_s8) +OPENCV_HAL_IMPL_NEON_POPCOUNT(v_int16x8, vreinterpretq_u8_s16) +OPENCV_HAL_IMPL_NEON_POPCOUNT(v_int32x4, vreinterpretq_u8_s32) + +inline int v_signmask(const v_uint8x16& a) +{ + int8x8_t m0 = vcreate_s8(CV_BIG_UINT(0x0706050403020100)); + uint8x16_t v0 = vshlq_u8(vshrq_n_u8(a.val, 7), vcombine_s8(m0, m0)); + uint64x2_t v1 = vpaddlq_u32(vpaddlq_u16(vpaddlq_u8(v0))); + return (int)vgetq_lane_u64(v1, 0) + ((int)vgetq_lane_u64(v1, 1) << 8); +} +inline int v_signmask(const v_int8x16& a) +{ return v_signmask(v_reinterpret_as_u8(a)); } + +inline int v_signmask(const v_uint16x8& a) +{ + int16x4_t m0 = vcreate_s16(CV_BIG_UINT(0x0003000200010000)); + uint16x8_t v0 = vshlq_u16(vshrq_n_u16(a.val, 15), vcombine_s16(m0, m0)); + uint64x2_t v1 = vpaddlq_u32(vpaddlq_u16(v0)); + return (int)vgetq_lane_u64(v1, 0) + ((int)vgetq_lane_u64(v1, 1) << 4); +} +inline int v_signmask(const v_int16x8& a) +{ return v_signmask(v_reinterpret_as_u16(a)); } + +inline int v_signmask(const v_uint32x4& a) +{ + int32x2_t m0 = vcreate_s32(CV_BIG_UINT(0x0000000100000000)); + uint32x4_t v0 = vshlq_u32(vshrq_n_u32(a.val, 31), vcombine_s32(m0, m0)); + uint64x2_t v1 = vpaddlq_u32(v0); + return (int)vgetq_lane_u64(v1, 0) + ((int)vgetq_lane_u64(v1, 1) << 2); +} +inline int v_signmask(const v_int32x4& a) +{ return v_signmask(v_reinterpret_as_u32(a)); } +inline int v_signmask(const v_float32x4& a) +{ return v_signmask(v_reinterpret_as_u32(a)); } +#if CV_SIMD128_64F +inline int v_signmask(const v_uint64x2& a) +{ + int64x1_t m0 = vdup_n_s64(0); + uint64x2_t v0 = vshlq_u64(vshrq_n_u64(a.val, 63), vcombine_s64(m0, m0)); + return (int)vgetq_lane_u64(v0, 0) + ((int)vgetq_lane_u64(v0, 1) << 1); +} +inline int v_signmask(const v_float64x2& a) +{ return v_signmask(v_reinterpret_as_u64(a)); } +#endif + +#define OPENCV_HAL_IMPL_NEON_CHECK_ALLANY(_Tpvec, suffix, shift) \ +inline bool v_check_all(const v_##_Tpvec& a) \ +{ \ + _Tpvec##_t v0 = vshrq_n_##suffix(vmvnq_##suffix(a.val), shift); \ + uint64x2_t v1 = vreinterpretq_u64_##suffix(v0); \ + return (vgetq_lane_u64(v1, 0) | vgetq_lane_u64(v1, 1)) == 0; \ +} \ +inline bool v_check_any(const v_##_Tpvec& a) \ +{ \ + _Tpvec##_t v0 = vshrq_n_##suffix(a.val, shift); \ + uint64x2_t v1 = vreinterpretq_u64_##suffix(v0); \ + return (vgetq_lane_u64(v1, 0) | vgetq_lane_u64(v1, 1)) != 0; \ +} + +OPENCV_HAL_IMPL_NEON_CHECK_ALLANY(uint8x16, u8, 7) +OPENCV_HAL_IMPL_NEON_CHECK_ALLANY(uint16x8, u16, 15) +OPENCV_HAL_IMPL_NEON_CHECK_ALLANY(uint32x4, u32, 31) +#if CV_SIMD128_64F +OPENCV_HAL_IMPL_NEON_CHECK_ALLANY(uint64x2, u64, 63) +#endif + +inline bool v_check_all(const v_int8x16& a) +{ return v_check_all(v_reinterpret_as_u8(a)); } +inline bool v_check_all(const v_int16x8& a) +{ return v_check_all(v_reinterpret_as_u16(a)); } +inline bool v_check_all(const v_int32x4& a) +{ return v_check_all(v_reinterpret_as_u32(a)); } +inline bool v_check_all(const v_float32x4& a) +{ return v_check_all(v_reinterpret_as_u32(a)); } + +inline bool v_check_any(const v_int8x16& a) +{ return v_check_any(v_reinterpret_as_u8(a)); } +inline bool v_check_any(const v_int16x8& a) +{ return v_check_any(v_reinterpret_as_u16(a)); } +inline bool v_check_any(const v_int32x4& a) +{ return v_check_any(v_reinterpret_as_u32(a)); } +inline bool v_check_any(const v_float32x4& a) +{ return v_check_any(v_reinterpret_as_u32(a)); } + +#if CV_SIMD128_64F +inline bool v_check_all(const v_int64x2& a) +{ return v_check_all(v_reinterpret_as_u64(a)); } +inline bool v_check_all(const v_float64x2& a) +{ return v_check_all(v_reinterpret_as_u64(a)); } +inline bool v_check_any(const v_int64x2& a) +{ return v_check_any(v_reinterpret_as_u64(a)); } +inline bool v_check_any(const v_float64x2& a) +{ return v_check_any(v_reinterpret_as_u64(a)); } +#endif + +#define OPENCV_HAL_IMPL_NEON_SELECT(_Tpvec, suffix, usuffix) \ +inline _Tpvec v_select(const _Tpvec& mask, const _Tpvec& a, const _Tpvec& b) \ +{ \ + return _Tpvec(vbslq_##suffix(vreinterpretq_##usuffix##_##suffix(mask.val), a.val, b.val)); \ +} + +OPENCV_HAL_IMPL_NEON_SELECT(v_uint8x16, u8, u8) +OPENCV_HAL_IMPL_NEON_SELECT(v_int8x16, s8, u8) +OPENCV_HAL_IMPL_NEON_SELECT(v_uint16x8, u16, u16) +OPENCV_HAL_IMPL_NEON_SELECT(v_int16x8, s16, u16) +OPENCV_HAL_IMPL_NEON_SELECT(v_uint32x4, u32, u32) +OPENCV_HAL_IMPL_NEON_SELECT(v_int32x4, s32, u32) +OPENCV_HAL_IMPL_NEON_SELECT(v_float32x4, f32, u32) +#if CV_SIMD128_64F +OPENCV_HAL_IMPL_NEON_SELECT(v_float64x2, f64, u64) +#endif + +#define OPENCV_HAL_IMPL_NEON_EXPAND(_Tpvec, _Tpwvec, _Tp, suffix) \ +inline void v_expand(const _Tpvec& a, _Tpwvec& b0, _Tpwvec& b1) \ +{ \ + b0.val = vmovl_##suffix(vget_low_##suffix(a.val)); \ + b1.val = vmovl_##suffix(vget_high_##suffix(a.val)); \ +} \ +inline _Tpwvec v_expand_low(const _Tpvec& a) \ +{ \ + return _Tpwvec(vmovl_##suffix(vget_low_##suffix(a.val))); \ +} \ +inline _Tpwvec v_expand_high(const _Tpvec& a) \ +{ \ + return _Tpwvec(vmovl_##suffix(vget_high_##suffix(a.val))); \ +} \ +inline _Tpwvec v_load_expand(const _Tp* ptr) \ +{ \ + return _Tpwvec(vmovl_##suffix(vld1_##suffix(ptr))); \ +} + +OPENCV_HAL_IMPL_NEON_EXPAND(v_uint8x16, v_uint16x8, uchar, u8) +OPENCV_HAL_IMPL_NEON_EXPAND(v_int8x16, v_int16x8, schar, s8) +OPENCV_HAL_IMPL_NEON_EXPAND(v_uint16x8, v_uint32x4, ushort, u16) +OPENCV_HAL_IMPL_NEON_EXPAND(v_int16x8, v_int32x4, short, s16) +OPENCV_HAL_IMPL_NEON_EXPAND(v_uint32x4, v_uint64x2, uint, u32) +OPENCV_HAL_IMPL_NEON_EXPAND(v_int32x4, v_int64x2, int, s32) + +inline v_uint32x4 v_load_expand_q(const uchar* ptr) +{ + uint8x8_t v0 = vcreate_u8(*(unsigned*)ptr); + uint16x4_t v1 = vget_low_u16(vmovl_u8(v0)); + return v_uint32x4(vmovl_u16(v1)); +} + +inline v_int32x4 v_load_expand_q(const schar* ptr) +{ + int8x8_t v0 = vcreate_s8(*(unsigned*)ptr); + int16x4_t v1 = vget_low_s16(vmovl_s8(v0)); + return v_int32x4(vmovl_s16(v1)); +} + +#if defined(__aarch64__) +#define OPENCV_HAL_IMPL_NEON_UNPACKS(_Tpvec, suffix) \ +inline void v_zip(const v_##_Tpvec& a0, const v_##_Tpvec& a1, v_##_Tpvec& b0, v_##_Tpvec& b1) \ +{ \ + b0.val = vzip1q_##suffix(a0.val, a1.val); \ + b1.val = vzip2q_##suffix(a0.val, a1.val); \ +} \ +inline v_##_Tpvec v_combine_low(const v_##_Tpvec& a, const v_##_Tpvec& b) \ +{ \ + return v_##_Tpvec(vcombine_##suffix(vget_low_##suffix(a.val), vget_low_##suffix(b.val))); \ +} \ +inline v_##_Tpvec v_combine_high(const v_##_Tpvec& a, const v_##_Tpvec& b) \ +{ \ + return v_##_Tpvec(vcombine_##suffix(vget_high_##suffix(a.val), vget_high_##suffix(b.val))); \ +} \ +inline void v_recombine(const v_##_Tpvec& a, const v_##_Tpvec& b, v_##_Tpvec& c, v_##_Tpvec& d) \ +{ \ + c.val = vcombine_##suffix(vget_low_##suffix(a.val), vget_low_##suffix(b.val)); \ + d.val = vcombine_##suffix(vget_high_##suffix(a.val), vget_high_##suffix(b.val)); \ +} +#else +#define OPENCV_HAL_IMPL_NEON_UNPACKS(_Tpvec, suffix) \ +inline void v_zip(const v_##_Tpvec& a0, const v_##_Tpvec& a1, v_##_Tpvec& b0, v_##_Tpvec& b1) \ +{ \ + _Tpvec##x2_t p = vzipq_##suffix(a0.val, a1.val); \ + b0.val = p.val[0]; \ + b1.val = p.val[1]; \ +} \ +inline v_##_Tpvec v_combine_low(const v_##_Tpvec& a, const v_##_Tpvec& b) \ +{ \ + return v_##_Tpvec(vcombine_##suffix(vget_low_##suffix(a.val), vget_low_##suffix(b.val))); \ +} \ +inline v_##_Tpvec v_combine_high(const v_##_Tpvec& a, const v_##_Tpvec& b) \ +{ \ + return v_##_Tpvec(vcombine_##suffix(vget_high_##suffix(a.val), vget_high_##suffix(b.val))); \ +} \ +inline void v_recombine(const v_##_Tpvec& a, const v_##_Tpvec& b, v_##_Tpvec& c, v_##_Tpvec& d) \ +{ \ + c.val = vcombine_##suffix(vget_low_##suffix(a.val), vget_low_##suffix(b.val)); \ + d.val = vcombine_##suffix(vget_high_##suffix(a.val), vget_high_##suffix(b.val)); \ +} +#endif + +OPENCV_HAL_IMPL_NEON_UNPACKS(uint8x16, u8) +OPENCV_HAL_IMPL_NEON_UNPACKS(int8x16, s8) +OPENCV_HAL_IMPL_NEON_UNPACKS(uint16x8, u16) +OPENCV_HAL_IMPL_NEON_UNPACKS(int16x8, s16) +OPENCV_HAL_IMPL_NEON_UNPACKS(uint32x4, u32) +OPENCV_HAL_IMPL_NEON_UNPACKS(int32x4, s32) +OPENCV_HAL_IMPL_NEON_UNPACKS(float32x4, f32) +#if CV_SIMD128_64F +OPENCV_HAL_IMPL_NEON_UNPACKS(float64x2, f64) +#endif + +#define OPENCV_HAL_IMPL_NEON_EXTRACT(_Tpvec, suffix) \ +template \ +inline v_##_Tpvec v_extract(const v_##_Tpvec& a, const v_##_Tpvec& b) \ +{ \ + return v_##_Tpvec(vextq_##suffix(a.val, b.val, s)); \ +} + +OPENCV_HAL_IMPL_NEON_EXTRACT(uint8x16, u8) +OPENCV_HAL_IMPL_NEON_EXTRACT(int8x16, s8) +OPENCV_HAL_IMPL_NEON_EXTRACT(uint16x8, u16) +OPENCV_HAL_IMPL_NEON_EXTRACT(int16x8, s16) +OPENCV_HAL_IMPL_NEON_EXTRACT(uint32x4, u32) +OPENCV_HAL_IMPL_NEON_EXTRACT(int32x4, s32) +OPENCV_HAL_IMPL_NEON_EXTRACT(uint64x2, u64) +OPENCV_HAL_IMPL_NEON_EXTRACT(int64x2, s64) +OPENCV_HAL_IMPL_NEON_EXTRACT(float32x4, f32) +#if CV_SIMD128_64F +OPENCV_HAL_IMPL_NEON_EXTRACT(float64x2, f64) +#endif + +#if CV_SIMD128_64F +inline v_int32x4 v_round(const v_float32x4& a) +{ + float32x4_t a_ = a.val; + int32x4_t result; + __asm__ ("fcvtns %0.4s, %1.4s" + : "=w"(result) + : "w"(a_) + : /* No clobbers */); + return v_int32x4(result); +} +#else +inline v_int32x4 v_round(const v_float32x4& a) +{ + static const int32x4_t v_sign = vdupq_n_s32(1 << 31), + v_05 = vreinterpretq_s32_f32(vdupq_n_f32(0.5f)); + + int32x4_t v_addition = vorrq_s32(v_05, vandq_s32(v_sign, vreinterpretq_s32_f32(a.val))); + return v_int32x4(vcvtq_s32_f32(vaddq_f32(a.val, vreinterpretq_f32_s32(v_addition)))); +} +#endif +inline v_int32x4 v_floor(const v_float32x4& a) +{ + int32x4_t a1 = vcvtq_s32_f32(a.val); + uint32x4_t mask = vcgtq_f32(vcvtq_f32_s32(a1), a.val); + return v_int32x4(vaddq_s32(a1, vreinterpretq_s32_u32(mask))); +} + +inline v_int32x4 v_ceil(const v_float32x4& a) +{ + int32x4_t a1 = vcvtq_s32_f32(a.val); + uint32x4_t mask = vcgtq_f32(a.val, vcvtq_f32_s32(a1)); + return v_int32x4(vsubq_s32(a1, vreinterpretq_s32_u32(mask))); +} + +inline v_int32x4 v_trunc(const v_float32x4& a) +{ return v_int32x4(vcvtq_s32_f32(a.val)); } + +#if CV_SIMD128_64F +inline v_int32x4 v_round(const v_float64x2& a) +{ + static const int32x2_t zero = vdup_n_s32(0); + return v_int32x4(vcombine_s32(vmovn_s64(vcvtaq_s64_f64(a.val)), zero)); +} + +inline v_int32x4 v_round(const v_float64x2& a, const v_float64x2& b) +{ + return v_int32x4(vcombine_s32(vmovn_s64(vcvtaq_s64_f64(a.val)), vmovn_s64(vcvtaq_s64_f64(b.val)))); +} + +inline v_int32x4 v_floor(const v_float64x2& a) +{ + static const int32x2_t zero = vdup_n_s32(0); + int64x2_t a1 = vcvtq_s64_f64(a.val); + uint64x2_t mask = vcgtq_f64(vcvtq_f64_s64(a1), a.val); + a1 = vaddq_s64(a1, vreinterpretq_s64_u64(mask)); + return v_int32x4(vcombine_s32(vmovn_s64(a1), zero)); +} + +inline v_int32x4 v_ceil(const v_float64x2& a) +{ + static const int32x2_t zero = vdup_n_s32(0); + int64x2_t a1 = vcvtq_s64_f64(a.val); + uint64x2_t mask = vcgtq_f64(a.val, vcvtq_f64_s64(a1)); + a1 = vsubq_s64(a1, vreinterpretq_s64_u64(mask)); + return v_int32x4(vcombine_s32(vmovn_s64(a1), zero)); +} + +inline v_int32x4 v_trunc(const v_float64x2& a) +{ + static const int32x2_t zero = vdup_n_s32(0); + return v_int32x4(vcombine_s32(vmovn_s64(vcvtaq_s64_f64(a.val)), zero)); +} +#endif + +#define OPENCV_HAL_IMPL_NEON_TRANSPOSE4x4(_Tpvec, suffix) \ +inline void v_transpose4x4(const v_##_Tpvec& a0, const v_##_Tpvec& a1, \ + const v_##_Tpvec& a2, const v_##_Tpvec& a3, \ + v_##_Tpvec& b0, v_##_Tpvec& b1, \ + v_##_Tpvec& b2, v_##_Tpvec& b3) \ +{ \ + /* m00 m01 m02 m03 */ \ + /* m10 m11 m12 m13 */ \ + /* m20 m21 m22 m23 */ \ + /* m30 m31 m32 m33 */ \ + _Tpvec##x2_t t0 = vtrnq_##suffix(a0.val, a1.val); \ + _Tpvec##x2_t t1 = vtrnq_##suffix(a2.val, a3.val); \ + /* m00 m10 m02 m12 */ \ + /* m01 m11 m03 m13 */ \ + /* m20 m30 m22 m32 */ \ + /* m21 m31 m23 m33 */ \ + b0.val = vcombine_##suffix(vget_low_##suffix(t0.val[0]), vget_low_##suffix(t1.val[0])); \ + b1.val = vcombine_##suffix(vget_low_##suffix(t0.val[1]), vget_low_##suffix(t1.val[1])); \ + b2.val = vcombine_##suffix(vget_high_##suffix(t0.val[0]), vget_high_##suffix(t1.val[0])); \ + b3.val = vcombine_##suffix(vget_high_##suffix(t0.val[1]), vget_high_##suffix(t1.val[1])); \ +} + +OPENCV_HAL_IMPL_NEON_TRANSPOSE4x4(uint32x4, u32) +OPENCV_HAL_IMPL_NEON_TRANSPOSE4x4(int32x4, s32) +OPENCV_HAL_IMPL_NEON_TRANSPOSE4x4(float32x4, f32) + +#define OPENCV_HAL_IMPL_NEON_INTERLEAVED(_Tpvec, _Tp, suffix) \ +inline void v_load_deinterleave(const _Tp* ptr, v_##_Tpvec& a, v_##_Tpvec& b) \ +{ \ + _Tpvec##x2_t v = vld2q_##suffix(ptr); \ + a.val = v.val[0]; \ + b.val = v.val[1]; \ +} \ +inline void v_load_deinterleave(const _Tp* ptr, v_##_Tpvec& a, v_##_Tpvec& b, v_##_Tpvec& c) \ +{ \ + _Tpvec##x3_t v = vld3q_##suffix(ptr); \ + a.val = v.val[0]; \ + b.val = v.val[1]; \ + c.val = v.val[2]; \ +} \ +inline void v_load_deinterleave(const _Tp* ptr, v_##_Tpvec& a, v_##_Tpvec& b, \ + v_##_Tpvec& c, v_##_Tpvec& d) \ +{ \ + _Tpvec##x4_t v = vld4q_##suffix(ptr); \ + a.val = v.val[0]; \ + b.val = v.val[1]; \ + c.val = v.val[2]; \ + d.val = v.val[3]; \ +} \ +inline void v_store_interleave( _Tp* ptr, const v_##_Tpvec& a, const v_##_Tpvec& b, \ + hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \ +{ \ + _Tpvec##x2_t v; \ + v.val[0] = a.val; \ + v.val[1] = b.val; \ + vst2q_##suffix(ptr, v); \ +} \ +inline void v_store_interleave( _Tp* ptr, const v_##_Tpvec& a, const v_##_Tpvec& b, \ + const v_##_Tpvec& c, hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \ +{ \ + _Tpvec##x3_t v; \ + v.val[0] = a.val; \ + v.val[1] = b.val; \ + v.val[2] = c.val; \ + vst3q_##suffix(ptr, v); \ +} \ +inline void v_store_interleave( _Tp* ptr, const v_##_Tpvec& a, const v_##_Tpvec& b, \ + const v_##_Tpvec& c, const v_##_Tpvec& d, \ + hal::StoreMode /*mode*/=hal::STORE_UNALIGNED ) \ +{ \ + _Tpvec##x4_t v; \ + v.val[0] = a.val; \ + v.val[1] = b.val; \ + v.val[2] = c.val; \ + v.val[3] = d.val; \ + vst4q_##suffix(ptr, v); \ +} + +#define OPENCV_HAL_IMPL_NEON_INTERLEAVED_INT64(tp, suffix) \ +inline void v_load_deinterleave( const tp* ptr, v_##tp##x2& a, v_##tp##x2& b ) \ +{ \ + tp##x1_t a0 = vld1_##suffix(ptr); \ + tp##x1_t b0 = vld1_##suffix(ptr + 1); \ + tp##x1_t a1 = vld1_##suffix(ptr + 2); \ + tp##x1_t b1 = vld1_##suffix(ptr + 3); \ + a = v_##tp##x2(vcombine_##suffix(a0, a1)); \ + b = v_##tp##x2(vcombine_##suffix(b0, b1)); \ +} \ + \ +inline void v_load_deinterleave( const tp* ptr, v_##tp##x2& a, \ + v_##tp##x2& b, v_##tp##x2& c ) \ +{ \ + tp##x1_t a0 = vld1_##suffix(ptr); \ + tp##x1_t b0 = vld1_##suffix(ptr + 1); \ + tp##x1_t c0 = vld1_##suffix(ptr + 2); \ + tp##x1_t a1 = vld1_##suffix(ptr + 3); \ + tp##x1_t b1 = vld1_##suffix(ptr + 4); \ + tp##x1_t c1 = vld1_##suffix(ptr + 5); \ + a = v_##tp##x2(vcombine_##suffix(a0, a1)); \ + b = v_##tp##x2(vcombine_##suffix(b0, b1)); \ + c = v_##tp##x2(vcombine_##suffix(c0, c1)); \ +} \ + \ +inline void v_load_deinterleave( const tp* ptr, v_##tp##x2& a, v_##tp##x2& b, \ + v_##tp##x2& c, v_##tp##x2& d ) \ +{ \ + tp##x1_t a0 = vld1_##suffix(ptr); \ + tp##x1_t b0 = vld1_##suffix(ptr + 1); \ + tp##x1_t c0 = vld1_##suffix(ptr + 2); \ + tp##x1_t d0 = vld1_##suffix(ptr + 3); \ + tp##x1_t a1 = vld1_##suffix(ptr + 4); \ + tp##x1_t b1 = vld1_##suffix(ptr + 5); \ + tp##x1_t c1 = vld1_##suffix(ptr + 6); \ + tp##x1_t d1 = vld1_##suffix(ptr + 7); \ + a = v_##tp##x2(vcombine_##suffix(a0, a1)); \ + b = v_##tp##x2(vcombine_##suffix(b0, b1)); \ + c = v_##tp##x2(vcombine_##suffix(c0, c1)); \ + d = v_##tp##x2(vcombine_##suffix(d0, d1)); \ +} \ + \ +inline void v_store_interleave( tp* ptr, const v_##tp##x2& a, const v_##tp##x2& b, \ + hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \ +{ \ + vst1_##suffix(ptr, vget_low_##suffix(a.val)); \ + vst1_##suffix(ptr + 1, vget_low_##suffix(b.val)); \ + vst1_##suffix(ptr + 2, vget_high_##suffix(a.val)); \ + vst1_##suffix(ptr + 3, vget_high_##suffix(b.val)); \ +} \ + \ +inline void v_store_interleave( tp* ptr, const v_##tp##x2& a, \ + const v_##tp##x2& b, const v_##tp##x2& c, \ + hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \ +{ \ + vst1_##suffix(ptr, vget_low_##suffix(a.val)); \ + vst1_##suffix(ptr + 1, vget_low_##suffix(b.val)); \ + vst1_##suffix(ptr + 2, vget_low_##suffix(c.val)); \ + vst1_##suffix(ptr + 3, vget_high_##suffix(a.val)); \ + vst1_##suffix(ptr + 4, vget_high_##suffix(b.val)); \ + vst1_##suffix(ptr + 5, vget_high_##suffix(c.val)); \ +} \ + \ +inline void v_store_interleave( tp* ptr, const v_##tp##x2& a, const v_##tp##x2& b, \ + const v_##tp##x2& c, const v_##tp##x2& d, \ + hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \ +{ \ + vst1_##suffix(ptr, vget_low_##suffix(a.val)); \ + vst1_##suffix(ptr + 1, vget_low_##suffix(b.val)); \ + vst1_##suffix(ptr + 2, vget_low_##suffix(c.val)); \ + vst1_##suffix(ptr + 3, vget_low_##suffix(d.val)); \ + vst1_##suffix(ptr + 4, vget_high_##suffix(a.val)); \ + vst1_##suffix(ptr + 5, vget_high_##suffix(b.val)); \ + vst1_##suffix(ptr + 6, vget_high_##suffix(c.val)); \ + vst1_##suffix(ptr + 7, vget_high_##suffix(d.val)); \ +} + +OPENCV_HAL_IMPL_NEON_INTERLEAVED(uint8x16, uchar, u8) +OPENCV_HAL_IMPL_NEON_INTERLEAVED(int8x16, schar, s8) +OPENCV_HAL_IMPL_NEON_INTERLEAVED(uint16x8, ushort, u16) +OPENCV_HAL_IMPL_NEON_INTERLEAVED(int16x8, short, s16) +OPENCV_HAL_IMPL_NEON_INTERLEAVED(uint32x4, unsigned, u32) +OPENCV_HAL_IMPL_NEON_INTERLEAVED(int32x4, int, s32) +OPENCV_HAL_IMPL_NEON_INTERLEAVED(float32x4, float, f32) +#if CV_SIMD128_64F +OPENCV_HAL_IMPL_NEON_INTERLEAVED(float64x2, double, f64) +#endif + +OPENCV_HAL_IMPL_NEON_INTERLEAVED_INT64(int64, s64) +OPENCV_HAL_IMPL_NEON_INTERLEAVED_INT64(uint64, u64) + +inline v_float32x4 v_cvt_f32(const v_int32x4& a) +{ + return v_float32x4(vcvtq_f32_s32(a.val)); +} + +#if CV_SIMD128_64F +inline v_float32x4 v_cvt_f32(const v_float64x2& a) +{ + float32x2_t zero = vdup_n_f32(0.0f); + return v_float32x4(vcombine_f32(vcvt_f32_f64(a.val), zero)); +} + +inline v_float32x4 v_cvt_f32(const v_float64x2& a, const v_float64x2& b) +{ + return v_float32x4(vcombine_f32(vcvt_f32_f64(a.val), vcvt_f32_f64(b.val))); +} + +inline v_float64x2 v_cvt_f64(const v_int32x4& a) +{ + return v_float64x2(vcvt_f64_f32(vcvt_f32_s32(vget_low_s32(a.val)))); +} + +inline v_float64x2 v_cvt_f64_high(const v_int32x4& a) +{ + return v_float64x2(vcvt_f64_f32(vcvt_f32_s32(vget_high_s32(a.val)))); +} + +inline v_float64x2 v_cvt_f64(const v_float32x4& a) +{ + return v_float64x2(vcvt_f64_f32(vget_low_f32(a.val))); +} + +inline v_float64x2 v_cvt_f64_high(const v_float32x4& a) +{ + return v_float64x2(vcvt_f64_f32(vget_high_f32(a.val))); +} +#endif + +////////////// Lookup table access //////////////////// + +inline v_int32x4 v_lut(const int* tab, const v_int32x4& idxvec) +{ + int CV_DECL_ALIGNED(32) elems[4] = + { + tab[vgetq_lane_s32(idxvec.val, 0)], + tab[vgetq_lane_s32(idxvec.val, 1)], + tab[vgetq_lane_s32(idxvec.val, 2)], + tab[vgetq_lane_s32(idxvec.val, 3)] + }; + return v_int32x4(vld1q_s32(elems)); +} + +inline v_float32x4 v_lut(const float* tab, const v_int32x4& idxvec) +{ + float CV_DECL_ALIGNED(32) elems[4] = + { + tab[vgetq_lane_s32(idxvec.val, 0)], + tab[vgetq_lane_s32(idxvec.val, 1)], + tab[vgetq_lane_s32(idxvec.val, 2)], + tab[vgetq_lane_s32(idxvec.val, 3)] + }; + return v_float32x4(vld1q_f32(elems)); +} + +inline void v_lut_deinterleave(const float* tab, const v_int32x4& idxvec, v_float32x4& x, v_float32x4& y) +{ + /*int CV_DECL_ALIGNED(32) idx[4]; + v_store(idx, idxvec); + + float32x4_t xy02 = vcombine_f32(vld1_f32(tab + idx[0]), vld1_f32(tab + idx[2])); + float32x4_t xy13 = vcombine_f32(vld1_f32(tab + idx[1]), vld1_f32(tab + idx[3])); + + float32x4x2_t xxyy = vuzpq_f32(xy02, xy13); + x = v_float32x4(xxyy.val[0]); + y = v_float32x4(xxyy.val[1]);*/ + int CV_DECL_ALIGNED(32) idx[4]; + v_store_aligned(idx, idxvec); + + x = v_float32x4(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]]); + y = v_float32x4(tab[idx[0]+1], tab[idx[1]+1], tab[idx[2]+1], tab[idx[3]+1]); +} + +#if CV_SIMD128_64F +inline v_float64x2 v_lut(const double* tab, const v_int32x4& idxvec) +{ + double CV_DECL_ALIGNED(32) elems[2] = + { + tab[vgetq_lane_s32(idxvec.val, 0)], + tab[vgetq_lane_s32(idxvec.val, 1)], + }; + return v_float64x2(vld1q_f64(elems)); +} + +inline void v_lut_deinterleave(const double* tab, const v_int32x4& idxvec, v_float64x2& x, v_float64x2& y) +{ + int CV_DECL_ALIGNED(32) idx[4]; + v_store_aligned(idx, idxvec); + + x = v_float64x2(tab[idx[0]], tab[idx[1]]); + y = v_float64x2(tab[idx[0]+1], tab[idx[1]+1]); +} +#endif + +////// FP16 suport /////// +#if CV_FP16 +inline v_float32x4 v_load_expand(const float16_t* ptr) +{ + float16x4_t v = + #ifndef vld1_f16 // APPLE compiler defines vld1_f16 as macro + (float16x4_t)vld1_s16((const short*)ptr); + #else + vld1_f16((const __fp16*)ptr); + #endif + return v_float32x4(vcvt_f32_f16(v)); +} + +inline void v_pack_store(float16_t* ptr, const v_float32x4& v) +{ + float16x4_t hv = vcvt_f16_f32(v.val); + + #ifndef vst1_f16 // APPLE compiler defines vst1_f16 as macro + vst1_s16((short*)ptr, (int16x4_t)hv); + #else + vst1_f16((__fp16*)ptr, hv); + #endif +} +#else +inline v_float32x4 v_load_expand(const float16_t* ptr) +{ + const int N = 4; + float buf[N]; + for( int i = 0; i < N; i++ ) buf[i] = (float)ptr[i]; + return v_load(buf); +} + +inline void v_pack_store(float16_t* ptr, const v_float32x4& v) +{ + const int N = 4; + float buf[N]; + v_store(buf, v); + for( int i = 0; i < N; i++ ) ptr[i] = float16_t(buf[i]); +} +#endif + +inline void v_cleanup() {} + +//! @name Check SIMD support +//! @{ +//! @brief Check CPU capability of SIMD operation +static inline bool hasSIMD128() +{ + return (CV_CPU_HAS_SUPPORT_NEON) ? true : false; +} + +//! @} + +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END + +//! @endcond + +} + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/hal/intrin_sse.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/hal/intrin_sse.hpp new file mode 100644 index 0000000000000000000000000000000000000000..f7a67da1a5e614c7270ef860821ab1e22a7392ae --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/hal/intrin_sse.hpp @@ -0,0 +1,2816 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_HAL_SSE_HPP +#define OPENCV_HAL_SSE_HPP + +#include +#include "opencv2/core/utility.hpp" + +#define CV_SIMD128 1 +#define CV_SIMD128_64F 1 +#define CV_SIMD128_FP16 0 // no native operations with FP16 type. + +namespace cv +{ + +//! @cond IGNORED + +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN + +///////// Types //////////// + +struct v_uint8x16 +{ + typedef uchar lane_type; + typedef __m128i vector_type; + enum { nlanes = 16 }; + + v_uint8x16() : val(_mm_setzero_si128()) {} + explicit v_uint8x16(__m128i v) : val(v) {} + v_uint8x16(uchar v0, uchar v1, uchar v2, uchar v3, uchar v4, uchar v5, uchar v6, uchar v7, + uchar v8, uchar v9, uchar v10, uchar v11, uchar v12, uchar v13, uchar v14, uchar v15) + { + val = _mm_setr_epi8((char)v0, (char)v1, (char)v2, (char)v3, + (char)v4, (char)v5, (char)v6, (char)v7, + (char)v8, (char)v9, (char)v10, (char)v11, + (char)v12, (char)v13, (char)v14, (char)v15); + } + uchar get0() const + { + return (uchar)_mm_cvtsi128_si32(val); + } + + __m128i val; +}; + +struct v_int8x16 +{ + typedef schar lane_type; + typedef __m128i vector_type; + enum { nlanes = 16 }; + + v_int8x16() : val(_mm_setzero_si128()) {} + explicit v_int8x16(__m128i v) : val(v) {} + v_int8x16(schar v0, schar v1, schar v2, schar v3, schar v4, schar v5, schar v6, schar v7, + schar v8, schar v9, schar v10, schar v11, schar v12, schar v13, schar v14, schar v15) + { + val = _mm_setr_epi8((char)v0, (char)v1, (char)v2, (char)v3, + (char)v4, (char)v5, (char)v6, (char)v7, + (char)v8, (char)v9, (char)v10, (char)v11, + (char)v12, (char)v13, (char)v14, (char)v15); + } + schar get0() const + { + return (schar)_mm_cvtsi128_si32(val); + } + + __m128i val; +}; + +struct v_uint16x8 +{ + typedef ushort lane_type; + typedef __m128i vector_type; + enum { nlanes = 8 }; + + v_uint16x8() : val(_mm_setzero_si128()) {} + explicit v_uint16x8(__m128i v) : val(v) {} + v_uint16x8(ushort v0, ushort v1, ushort v2, ushort v3, ushort v4, ushort v5, ushort v6, ushort v7) + { + val = _mm_setr_epi16((short)v0, (short)v1, (short)v2, (short)v3, + (short)v4, (short)v5, (short)v6, (short)v7); + } + ushort get0() const + { + return (ushort)_mm_cvtsi128_si32(val); + } + + __m128i val; +}; + +struct v_int16x8 +{ + typedef short lane_type; + typedef __m128i vector_type; + enum { nlanes = 8 }; + + v_int16x8() : val(_mm_setzero_si128()) {} + explicit v_int16x8(__m128i v) : val(v) {} + v_int16x8(short v0, short v1, short v2, short v3, short v4, short v5, short v6, short v7) + { + val = _mm_setr_epi16((short)v0, (short)v1, (short)v2, (short)v3, + (short)v4, (short)v5, (short)v6, (short)v7); + } + short get0() const + { + return (short)_mm_cvtsi128_si32(val); + } + + __m128i val; +}; + +struct v_uint32x4 +{ + typedef unsigned lane_type; + typedef __m128i vector_type; + enum { nlanes = 4 }; + + v_uint32x4() : val(_mm_setzero_si128()) {} + explicit v_uint32x4(__m128i v) : val(v) {} + v_uint32x4(unsigned v0, unsigned v1, unsigned v2, unsigned v3) + { + val = _mm_setr_epi32((int)v0, (int)v1, (int)v2, (int)v3); + } + unsigned get0() const + { + return (unsigned)_mm_cvtsi128_si32(val); + } + + __m128i val; +}; + +struct v_int32x4 +{ + typedef int lane_type; + typedef __m128i vector_type; + enum { nlanes = 4 }; + + v_int32x4() : val(_mm_setzero_si128()) {} + explicit v_int32x4(__m128i v) : val(v) {} + v_int32x4(int v0, int v1, int v2, int v3) + { + val = _mm_setr_epi32(v0, v1, v2, v3); + } + int get0() const + { + return _mm_cvtsi128_si32(val); + } + + __m128i val; +}; + +struct v_float32x4 +{ + typedef float lane_type; + typedef __m128 vector_type; + enum { nlanes = 4 }; + + v_float32x4() : val(_mm_setzero_ps()) {} + explicit v_float32x4(__m128 v) : val(v) {} + v_float32x4(float v0, float v1, float v2, float v3) + { + val = _mm_setr_ps(v0, v1, v2, v3); + } + float get0() const + { + return _mm_cvtss_f32(val); + } + + __m128 val; +}; + +struct v_uint64x2 +{ + typedef uint64 lane_type; + typedef __m128i vector_type; + enum { nlanes = 2 }; + + v_uint64x2() : val(_mm_setzero_si128()) {} + explicit v_uint64x2(__m128i v) : val(v) {} + v_uint64x2(uint64 v0, uint64 v1) + { + val = _mm_setr_epi32((int)v0, (int)(v0 >> 32), (int)v1, (int)(v1 >> 32)); + } + uint64 get0() const + { + int a = _mm_cvtsi128_si32(val); + int b = _mm_cvtsi128_si32(_mm_srli_epi64(val, 32)); + return (unsigned)a | ((uint64)(unsigned)b << 32); + } + + __m128i val; +}; + +struct v_int64x2 +{ + typedef int64 lane_type; + typedef __m128i vector_type; + enum { nlanes = 2 }; + + v_int64x2() : val(_mm_setzero_si128()) {} + explicit v_int64x2(__m128i v) : val(v) {} + v_int64x2(int64 v0, int64 v1) + { + val = _mm_setr_epi32((int)v0, (int)(v0 >> 32), (int)v1, (int)(v1 >> 32)); + } + int64 get0() const + { + int a = _mm_cvtsi128_si32(val); + int b = _mm_cvtsi128_si32(_mm_srli_epi64(val, 32)); + return (int64)((unsigned)a | ((uint64)(unsigned)b << 32)); + } + + __m128i val; +}; + +struct v_float64x2 +{ + typedef double lane_type; + typedef __m128d vector_type; + enum { nlanes = 2 }; + + v_float64x2() : val(_mm_setzero_pd()) {} + explicit v_float64x2(__m128d v) : val(v) {} + v_float64x2(double v0, double v1) + { + val = _mm_setr_pd(v0, v1); + } + double get0() const + { + return _mm_cvtsd_f64(val); + } + + __m128d val; +}; + +namespace hal_sse_internal +{ + template + to_sse_type v_sse_reinterpret_as(const from_sse_type& val); + +#define OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(to_sse_type, from_sse_type, sse_cast_intrin) \ + template<> inline \ + to_sse_type v_sse_reinterpret_as(const from_sse_type& a) \ + { return sse_cast_intrin(a); } + + OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128i, __m128i, OPENCV_HAL_NOP) + OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128i, __m128, _mm_castps_si128) + OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128i, __m128d, _mm_castpd_si128) + OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128, __m128i, _mm_castsi128_ps) + OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128, __m128, OPENCV_HAL_NOP) + OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128, __m128d, _mm_castpd_ps) + OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128d, __m128i, _mm_castsi128_pd) + OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128d, __m128, _mm_castps_pd) + OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128d, __m128d, OPENCV_HAL_NOP) +} + +#define OPENCV_HAL_IMPL_SSE_INITVEC(_Tpvec, _Tp, suffix, zsuffix, ssuffix, _Tps, cast) \ +inline _Tpvec v_setzero_##suffix() { return _Tpvec(_mm_setzero_##zsuffix()); } \ +inline _Tpvec v_setall_##suffix(_Tp v) { return _Tpvec(_mm_set1_##ssuffix((_Tps)v)); } \ +template inline _Tpvec v_reinterpret_as_##suffix(const _Tpvec0& a) \ +{ return _Tpvec(cast(a.val)); } + +OPENCV_HAL_IMPL_SSE_INITVEC(v_uint8x16, uchar, u8, si128, epi8, char, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_INITVEC(v_int8x16, schar, s8, si128, epi8, char, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_INITVEC(v_uint16x8, ushort, u16, si128, epi16, short, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_INITVEC(v_int16x8, short, s16, si128, epi16, short, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_INITVEC(v_uint32x4, unsigned, u32, si128, epi32, int, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_INITVEC(v_int32x4, int, s32, si128, epi32, int, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_INITVEC(v_float32x4, float, f32, ps, ps, float, _mm_castsi128_ps) +OPENCV_HAL_IMPL_SSE_INITVEC(v_float64x2, double, f64, pd, pd, double, _mm_castsi128_pd) + +inline v_uint64x2 v_setzero_u64() { return v_uint64x2(_mm_setzero_si128()); } +inline v_int64x2 v_setzero_s64() { return v_int64x2(_mm_setzero_si128()); } +inline v_uint64x2 v_setall_u64(uint64 val) { return v_uint64x2(val, val); } +inline v_int64x2 v_setall_s64(int64 val) { return v_int64x2(val, val); } + +template inline +v_uint64x2 v_reinterpret_as_u64(const _Tpvec& a) { return v_uint64x2(a.val); } +template inline +v_int64x2 v_reinterpret_as_s64(const _Tpvec& a) { return v_int64x2(a.val); } +inline v_float32x4 v_reinterpret_as_f32(const v_uint64x2& a) +{ return v_float32x4(_mm_castsi128_ps(a.val)); } +inline v_float32x4 v_reinterpret_as_f32(const v_int64x2& a) +{ return v_float32x4(_mm_castsi128_ps(a.val)); } +inline v_float64x2 v_reinterpret_as_f64(const v_uint64x2& a) +{ return v_float64x2(_mm_castsi128_pd(a.val)); } +inline v_float64x2 v_reinterpret_as_f64(const v_int64x2& a) +{ return v_float64x2(_mm_castsi128_pd(a.val)); } + +#define OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(_Tpvec, suffix) \ +inline _Tpvec v_reinterpret_as_##suffix(const v_float32x4& a) \ +{ return _Tpvec(_mm_castps_si128(a.val)); } \ +inline _Tpvec v_reinterpret_as_##suffix(const v_float64x2& a) \ +{ return _Tpvec(_mm_castpd_si128(a.val)); } + +OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_uint8x16, u8) +OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_int8x16, s8) +OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_uint16x8, u16) +OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_int16x8, s16) +OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_uint32x4, u32) +OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_int32x4, s32) +OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_uint64x2, u64) +OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_int64x2, s64) + +inline v_float32x4 v_reinterpret_as_f32(const v_float32x4& a) {return a; } +inline v_float64x2 v_reinterpret_as_f64(const v_float64x2& a) {return a; } +inline v_float32x4 v_reinterpret_as_f32(const v_float64x2& a) {return v_float32x4(_mm_castpd_ps(a.val)); } +inline v_float64x2 v_reinterpret_as_f64(const v_float32x4& a) {return v_float64x2(_mm_castps_pd(a.val)); } + +//////////////// PACK /////////////// +inline v_uint8x16 v_pack(const v_uint16x8& a, const v_uint16x8& b) +{ + __m128i delta = _mm_set1_epi16(255); + return v_uint8x16(_mm_packus_epi16(_mm_subs_epu16(a.val, _mm_subs_epu16(a.val, delta)), + _mm_subs_epu16(b.val, _mm_subs_epu16(b.val, delta)))); +} + +inline void v_pack_store(uchar* ptr, const v_uint16x8& a) +{ + __m128i delta = _mm_set1_epi16(255); + __m128i a1 = _mm_subs_epu16(a.val, _mm_subs_epu16(a.val, delta)); + _mm_storel_epi64((__m128i*)ptr, _mm_packus_epi16(a1, a1)); +} + +inline v_uint8x16 v_pack_u(const v_int16x8& a, const v_int16x8& b) +{ return v_uint8x16(_mm_packus_epi16(a.val, b.val)); } + +inline void v_pack_u_store(uchar* ptr, const v_int16x8& a) +{ _mm_storel_epi64((__m128i*)ptr, _mm_packus_epi16(a.val, a.val)); } + +template inline +v_uint8x16 v_rshr_pack(const v_uint16x8& a, const v_uint16x8& b) +{ + // we assume that n > 0, and so the shifted 16-bit values can be treated as signed numbers. + __m128i delta = _mm_set1_epi16((short)(1 << (n-1))); + return v_uint8x16(_mm_packus_epi16(_mm_srli_epi16(_mm_adds_epu16(a.val, delta), n), + _mm_srli_epi16(_mm_adds_epu16(b.val, delta), n))); +} + +template inline +void v_rshr_pack_store(uchar* ptr, const v_uint16x8& a) +{ + __m128i delta = _mm_set1_epi16((short)(1 << (n-1))); + __m128i a1 = _mm_srli_epi16(_mm_adds_epu16(a.val, delta), n); + _mm_storel_epi64((__m128i*)ptr, _mm_packus_epi16(a1, a1)); +} + +template inline +v_uint8x16 v_rshr_pack_u(const v_int16x8& a, const v_int16x8& b) +{ + __m128i delta = _mm_set1_epi16((short)(1 << (n-1))); + return v_uint8x16(_mm_packus_epi16(_mm_srai_epi16(_mm_adds_epi16(a.val, delta), n), + _mm_srai_epi16(_mm_adds_epi16(b.val, delta), n))); +} + +template inline +void v_rshr_pack_u_store(uchar* ptr, const v_int16x8& a) +{ + __m128i delta = _mm_set1_epi16((short)(1 << (n-1))); + __m128i a1 = _mm_srai_epi16(_mm_adds_epi16(a.val, delta), n); + _mm_storel_epi64((__m128i*)ptr, _mm_packus_epi16(a1, a1)); +} + +inline v_int8x16 v_pack(const v_int16x8& a, const v_int16x8& b) +{ return v_int8x16(_mm_packs_epi16(a.val, b.val)); } + +inline void v_pack_store(schar* ptr, const v_int16x8& a) +{ _mm_storel_epi64((__m128i*)ptr, _mm_packs_epi16(a.val, a.val)); } + +template inline +v_int8x16 v_rshr_pack(const v_int16x8& a, const v_int16x8& b) +{ + // we assume that n > 0, and so the shifted 16-bit values can be treated as signed numbers. + __m128i delta = _mm_set1_epi16((short)(1 << (n-1))); + return v_int8x16(_mm_packs_epi16(_mm_srai_epi16(_mm_adds_epi16(a.val, delta), n), + _mm_srai_epi16(_mm_adds_epi16(b.val, delta), n))); +} +template inline +void v_rshr_pack_store(schar* ptr, const v_int16x8& a) +{ + // we assume that n > 0, and so the shifted 16-bit values can be treated as signed numbers. + __m128i delta = _mm_set1_epi16((short)(1 << (n-1))); + __m128i a1 = _mm_srai_epi16(_mm_adds_epi16(a.val, delta), n); + _mm_storel_epi64((__m128i*)ptr, _mm_packs_epi16(a1, a1)); +} + + +// byte-wise "mask ? a : b" +inline __m128i v_select_si128(__m128i mask, __m128i a, __m128i b) +{ +#if CV_SSE4_1 + return _mm_blendv_epi8(b, a, mask); +#else + return _mm_xor_si128(b, _mm_and_si128(_mm_xor_si128(a, b), mask)); +#endif +} + +inline v_uint16x8 v_pack(const v_uint32x4& a, const v_uint32x4& b) +{ return v_uint16x8(_v128_packs_epu32(a.val, b.val)); } + +inline void v_pack_store(ushort* ptr, const v_uint32x4& a) +{ + __m128i z = _mm_setzero_si128(), maxval32 = _mm_set1_epi32(65535), delta32 = _mm_set1_epi32(32768); + __m128i a1 = _mm_sub_epi32(v_select_si128(_mm_cmpgt_epi32(z, a.val), maxval32, a.val), delta32); + __m128i r = _mm_packs_epi32(a1, a1); + _mm_storel_epi64((__m128i*)ptr, _mm_sub_epi16(r, _mm_set1_epi16(-32768))); +} + +template inline +v_uint16x8 v_rshr_pack(const v_uint32x4& a, const v_uint32x4& b) +{ + __m128i delta = _mm_set1_epi32(1 << (n-1)), delta32 = _mm_set1_epi32(32768); + __m128i a1 = _mm_sub_epi32(_mm_srli_epi32(_mm_add_epi32(a.val, delta), n), delta32); + __m128i b1 = _mm_sub_epi32(_mm_srli_epi32(_mm_add_epi32(b.val, delta), n), delta32); + return v_uint16x8(_mm_sub_epi16(_mm_packs_epi32(a1, b1), _mm_set1_epi16(-32768))); +} + +template inline +void v_rshr_pack_store(ushort* ptr, const v_uint32x4& a) +{ + __m128i delta = _mm_set1_epi32(1 << (n-1)), delta32 = _mm_set1_epi32(32768); + __m128i a1 = _mm_sub_epi32(_mm_srli_epi32(_mm_add_epi32(a.val, delta), n), delta32); + __m128i a2 = _mm_sub_epi16(_mm_packs_epi32(a1, a1), _mm_set1_epi16(-32768)); + _mm_storel_epi64((__m128i*)ptr, a2); +} + +inline v_uint16x8 v_pack_u(const v_int32x4& a, const v_int32x4& b) +{ +#if CV_SSE4_1 + return v_uint16x8(_mm_packus_epi32(a.val, b.val)); +#else + __m128i delta32 = _mm_set1_epi32(32768); + + // preliminary saturate negative values to zero + __m128i a1 = _mm_and_si128(a.val, _mm_cmpgt_epi32(a.val, _mm_set1_epi32(0))); + __m128i b1 = _mm_and_si128(b.val, _mm_cmpgt_epi32(b.val, _mm_set1_epi32(0))); + + __m128i r = _mm_packs_epi32(_mm_sub_epi32(a1, delta32), _mm_sub_epi32(b1, delta32)); + return v_uint16x8(_mm_sub_epi16(r, _mm_set1_epi16(-32768))); +#endif +} + +inline void v_pack_u_store(ushort* ptr, const v_int32x4& a) +{ +#if CV_SSE4_1 + _mm_storel_epi64((__m128i*)ptr, _mm_packus_epi32(a.val, a.val)); +#else + __m128i delta32 = _mm_set1_epi32(32768); + __m128i a1 = _mm_sub_epi32(a.val, delta32); + __m128i r = _mm_sub_epi16(_mm_packs_epi32(a1, a1), _mm_set1_epi16(-32768)); + _mm_storel_epi64((__m128i*)ptr, r); +#endif +} + +template inline +v_uint16x8 v_rshr_pack_u(const v_int32x4& a, const v_int32x4& b) +{ +#if CV_SSE4_1 + __m128i delta = _mm_set1_epi32(1 << (n - 1)); + return v_uint16x8(_mm_packus_epi32(_mm_srai_epi32(_mm_add_epi32(a.val, delta), n), + _mm_srai_epi32(_mm_add_epi32(b.val, delta), n))); +#else + __m128i delta = _mm_set1_epi32(1 << (n-1)), delta32 = _mm_set1_epi32(32768); + __m128i a1 = _mm_sub_epi32(_mm_srai_epi32(_mm_add_epi32(a.val, delta), n), delta32); + __m128i a2 = _mm_sub_epi16(_mm_packs_epi32(a1, a1), _mm_set1_epi16(-32768)); + __m128i b1 = _mm_sub_epi32(_mm_srai_epi32(_mm_add_epi32(b.val, delta), n), delta32); + __m128i b2 = _mm_sub_epi16(_mm_packs_epi32(b1, b1), _mm_set1_epi16(-32768)); + return v_uint16x8(_mm_unpacklo_epi64(a2, b2)); +#endif +} + +template inline +void v_rshr_pack_u_store(ushort* ptr, const v_int32x4& a) +{ +#if CV_SSE4_1 + __m128i delta = _mm_set1_epi32(1 << (n - 1)); + __m128i a1 = _mm_srai_epi32(_mm_add_epi32(a.val, delta), n); + _mm_storel_epi64((__m128i*)ptr, _mm_packus_epi32(a1, a1)); +#else + __m128i delta = _mm_set1_epi32(1 << (n-1)), delta32 = _mm_set1_epi32(32768); + __m128i a1 = _mm_sub_epi32(_mm_srai_epi32(_mm_add_epi32(a.val, delta), n), delta32); + __m128i a2 = _mm_sub_epi16(_mm_packs_epi32(a1, a1), _mm_set1_epi16(-32768)); + _mm_storel_epi64((__m128i*)ptr, a2); +#endif +} + +inline v_int16x8 v_pack(const v_int32x4& a, const v_int32x4& b) +{ return v_int16x8(_mm_packs_epi32(a.val, b.val)); } + +inline void v_pack_store(short* ptr, const v_int32x4& a) +{ + _mm_storel_epi64((__m128i*)ptr, _mm_packs_epi32(a.val, a.val)); +} + +template inline +v_int16x8 v_rshr_pack(const v_int32x4& a, const v_int32x4& b) +{ + __m128i delta = _mm_set1_epi32(1 << (n-1)); + return v_int16x8(_mm_packs_epi32(_mm_srai_epi32(_mm_add_epi32(a.val, delta), n), + _mm_srai_epi32(_mm_add_epi32(b.val, delta), n))); +} + +template inline +void v_rshr_pack_store(short* ptr, const v_int32x4& a) +{ + __m128i delta = _mm_set1_epi32(1 << (n-1)); + __m128i a1 = _mm_srai_epi32(_mm_add_epi32(a.val, delta), n); + _mm_storel_epi64((__m128i*)ptr, _mm_packs_epi32(a1, a1)); +} + + +// [a0 0 | b0 0] [a1 0 | b1 0] +inline v_uint32x4 v_pack(const v_uint64x2& a, const v_uint64x2& b) +{ + __m128i v0 = _mm_unpacklo_epi32(a.val, b.val); // a0 a1 0 0 + __m128i v1 = _mm_unpackhi_epi32(a.val, b.val); // b0 b1 0 0 + return v_uint32x4(_mm_unpacklo_epi32(v0, v1)); +} + +inline void v_pack_store(unsigned* ptr, const v_uint64x2& a) +{ + __m128i a1 = _mm_shuffle_epi32(a.val, _MM_SHUFFLE(0, 2, 2, 0)); + _mm_storel_epi64((__m128i*)ptr, a1); +} + +// [a0 0 | b0 0] [a1 0 | b1 0] +inline v_int32x4 v_pack(const v_int64x2& a, const v_int64x2& b) +{ + __m128i v0 = _mm_unpacklo_epi32(a.val, b.val); // a0 a1 0 0 + __m128i v1 = _mm_unpackhi_epi32(a.val, b.val); // b0 b1 0 0 + return v_int32x4(_mm_unpacklo_epi32(v0, v1)); +} + +inline void v_pack_store(int* ptr, const v_int64x2& a) +{ + __m128i a1 = _mm_shuffle_epi32(a.val, _MM_SHUFFLE(0, 2, 2, 0)); + _mm_storel_epi64((__m128i*)ptr, a1); +} + +template inline +v_uint32x4 v_rshr_pack(const v_uint64x2& a, const v_uint64x2& b) +{ + uint64 delta = (uint64)1 << (n-1); + v_uint64x2 delta2(delta, delta); + __m128i a1 = _mm_srli_epi64(_mm_add_epi64(a.val, delta2.val), n); + __m128i b1 = _mm_srli_epi64(_mm_add_epi64(b.val, delta2.val), n); + __m128i v0 = _mm_unpacklo_epi32(a1, b1); // a0 a1 0 0 + __m128i v1 = _mm_unpackhi_epi32(a1, b1); // b0 b1 0 0 + return v_uint32x4(_mm_unpacklo_epi32(v0, v1)); +} + +template inline +void v_rshr_pack_store(unsigned* ptr, const v_uint64x2& a) +{ + uint64 delta = (uint64)1 << (n-1); + v_uint64x2 delta2(delta, delta); + __m128i a1 = _mm_srli_epi64(_mm_add_epi64(a.val, delta2.val), n); + __m128i a2 = _mm_shuffle_epi32(a1, _MM_SHUFFLE(0, 2, 2, 0)); + _mm_storel_epi64((__m128i*)ptr, a2); +} + +inline __m128i v_sign_epi64(__m128i a) +{ + return _mm_shuffle_epi32(_mm_srai_epi32(a, 31), _MM_SHUFFLE(3, 3, 1, 1)); // x m0 | x m1 +} + +inline __m128i v_srai_epi64(__m128i a, int imm) +{ + __m128i smask = v_sign_epi64(a); + return _mm_xor_si128(_mm_srli_epi64(_mm_xor_si128(a, smask), imm), smask); +} + +template inline +v_int32x4 v_rshr_pack(const v_int64x2& a, const v_int64x2& b) +{ + int64 delta = (int64)1 << (n-1); + v_int64x2 delta2(delta, delta); + __m128i a1 = v_srai_epi64(_mm_add_epi64(a.val, delta2.val), n); + __m128i b1 = v_srai_epi64(_mm_add_epi64(b.val, delta2.val), n); + __m128i v0 = _mm_unpacklo_epi32(a1, b1); // a0 a1 0 0 + __m128i v1 = _mm_unpackhi_epi32(a1, b1); // b0 b1 0 0 + return v_int32x4(_mm_unpacklo_epi32(v0, v1)); +} + +template inline +void v_rshr_pack_store(int* ptr, const v_int64x2& a) +{ + int64 delta = (int64)1 << (n-1); + v_int64x2 delta2(delta, delta); + __m128i a1 = v_srai_epi64(_mm_add_epi64(a.val, delta2.val), n); + __m128i a2 = _mm_shuffle_epi32(a1, _MM_SHUFFLE(0, 2, 2, 0)); + _mm_storel_epi64((__m128i*)ptr, a2); +} + +// pack boolean +inline v_uint8x16 v_pack_b(const v_uint16x8& a, const v_uint16x8& b) +{ + __m128i ab = _mm_packs_epi16(a.val, b.val); + return v_uint8x16(ab); +} + +inline v_uint8x16 v_pack_b(const v_uint32x4& a, const v_uint32x4& b, + const v_uint32x4& c, const v_uint32x4& d) +{ + __m128i ab = _mm_packs_epi32(a.val, b.val); + __m128i cd = _mm_packs_epi32(c.val, d.val); + return v_uint8x16(_mm_packs_epi16(ab, cd)); +} + +inline v_uint8x16 v_pack_b(const v_uint64x2& a, const v_uint64x2& b, const v_uint64x2& c, + const v_uint64x2& d, const v_uint64x2& e, const v_uint64x2& f, + const v_uint64x2& g, const v_uint64x2& h) +{ + __m128i ab = _mm_packs_epi32(a.val, b.val); + __m128i cd = _mm_packs_epi32(c.val, d.val); + __m128i ef = _mm_packs_epi32(e.val, f.val); + __m128i gh = _mm_packs_epi32(g.val, h.val); + + __m128i abcd = _mm_packs_epi32(ab, cd); + __m128i efgh = _mm_packs_epi32(ef, gh); + return v_uint8x16(_mm_packs_epi16(abcd, efgh)); +} + +inline v_float32x4 v_matmul(const v_float32x4& v, const v_float32x4& m0, + const v_float32x4& m1, const v_float32x4& m2, + const v_float32x4& m3) +{ + __m128 v0 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(0, 0, 0, 0)), m0.val); + __m128 v1 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(1, 1, 1, 1)), m1.val); + __m128 v2 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(2, 2, 2, 2)), m2.val); + __m128 v3 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(3, 3, 3, 3)), m3.val); + + return v_float32x4(_mm_add_ps(_mm_add_ps(v0, v1), _mm_add_ps(v2, v3))); +} + +inline v_float32x4 v_matmuladd(const v_float32x4& v, const v_float32x4& m0, + const v_float32x4& m1, const v_float32x4& m2, + const v_float32x4& a) +{ + __m128 v0 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(0, 0, 0, 0)), m0.val); + __m128 v1 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(1, 1, 1, 1)), m1.val); + __m128 v2 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(2, 2, 2, 2)), m2.val); + + return v_float32x4(_mm_add_ps(_mm_add_ps(v0, v1), _mm_add_ps(v2, a.val))); +} + +#define OPENCV_HAL_IMPL_SSE_BIN_OP(bin_op, _Tpvec, intrin) \ + inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b) \ + { \ + return _Tpvec(intrin(a.val, b.val)); \ + } \ + inline _Tpvec& operator bin_op##= (_Tpvec& a, const _Tpvec& b) \ + { \ + a.val = intrin(a.val, b.val); \ + return a; \ + } + +OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_uint8x16, _mm_adds_epu8) +OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_uint8x16, _mm_subs_epu8) +OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_int8x16, _mm_adds_epi8) +OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_int8x16, _mm_subs_epi8) +OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_uint16x8, _mm_adds_epu16) +OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_uint16x8, _mm_subs_epu16) +OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_int16x8, _mm_adds_epi16) +OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_int16x8, _mm_subs_epi16) +OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_uint32x4, _mm_add_epi32) +OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_uint32x4, _mm_sub_epi32) +OPENCV_HAL_IMPL_SSE_BIN_OP(*, v_uint32x4, _v128_mullo_epi32) +OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_int32x4, _mm_add_epi32) +OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_int32x4, _mm_sub_epi32) +OPENCV_HAL_IMPL_SSE_BIN_OP(*, v_int32x4, _v128_mullo_epi32) +OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_float32x4, _mm_add_ps) +OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_float32x4, _mm_sub_ps) +OPENCV_HAL_IMPL_SSE_BIN_OP(*, v_float32x4, _mm_mul_ps) +OPENCV_HAL_IMPL_SSE_BIN_OP(/, v_float32x4, _mm_div_ps) +OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_float64x2, _mm_add_pd) +OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_float64x2, _mm_sub_pd) +OPENCV_HAL_IMPL_SSE_BIN_OP(*, v_float64x2, _mm_mul_pd) +OPENCV_HAL_IMPL_SSE_BIN_OP(/, v_float64x2, _mm_div_pd) +OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_uint64x2, _mm_add_epi64) +OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_uint64x2, _mm_sub_epi64) +OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_int64x2, _mm_add_epi64) +OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_int64x2, _mm_sub_epi64) + +// saturating multiply 8-bit, 16-bit +#define OPENCV_HAL_IMPL_SSE_MUL_SAT(_Tpvec, _Tpwvec) \ + inline _Tpvec operator * (const _Tpvec& a, const _Tpvec& b) \ + { \ + _Tpwvec c, d; \ + v_mul_expand(a, b, c, d); \ + return v_pack(c, d); \ + } \ + inline _Tpvec& operator *= (_Tpvec& a, const _Tpvec& b) \ + { a = a * b; return a; } + +OPENCV_HAL_IMPL_SSE_MUL_SAT(v_uint8x16, v_uint16x8) +OPENCV_HAL_IMPL_SSE_MUL_SAT(v_int8x16, v_int16x8) +OPENCV_HAL_IMPL_SSE_MUL_SAT(v_uint16x8, v_uint32x4) +OPENCV_HAL_IMPL_SSE_MUL_SAT(v_int16x8, v_int32x4) + +// Multiply and expand +inline void v_mul_expand(const v_uint8x16& a, const v_uint8x16& b, + v_uint16x8& c, v_uint16x8& d) +{ + v_uint16x8 a0, a1, b0, b1; + v_expand(a, a0, a1); + v_expand(b, b0, b1); + c = v_mul_wrap(a0, b0); + d = v_mul_wrap(a1, b1); +} + +inline void v_mul_expand(const v_int8x16& a, const v_int8x16& b, + v_int16x8& c, v_int16x8& d) +{ + v_int16x8 a0, a1, b0, b1; + v_expand(a, a0, a1); + v_expand(b, b0, b1); + c = v_mul_wrap(a0, b0); + d = v_mul_wrap(a1, b1); +} + +inline void v_mul_expand(const v_int16x8& a, const v_int16x8& b, + v_int32x4& c, v_int32x4& d) +{ + __m128i v0 = _mm_mullo_epi16(a.val, b.val); + __m128i v1 = _mm_mulhi_epi16(a.val, b.val); + c.val = _mm_unpacklo_epi16(v0, v1); + d.val = _mm_unpackhi_epi16(v0, v1); +} + +inline void v_mul_expand(const v_uint16x8& a, const v_uint16x8& b, + v_uint32x4& c, v_uint32x4& d) +{ + __m128i v0 = _mm_mullo_epi16(a.val, b.val); + __m128i v1 = _mm_mulhi_epu16(a.val, b.val); + c.val = _mm_unpacklo_epi16(v0, v1); + d.val = _mm_unpackhi_epi16(v0, v1); +} + +inline void v_mul_expand(const v_uint32x4& a, const v_uint32x4& b, + v_uint64x2& c, v_uint64x2& d) +{ + __m128i c0 = _mm_mul_epu32(a.val, b.val); + __m128i c1 = _mm_mul_epu32(_mm_srli_epi64(a.val, 32), _mm_srli_epi64(b.val, 32)); + c.val = _mm_unpacklo_epi64(c0, c1); + d.val = _mm_unpackhi_epi64(c0, c1); +} + +inline v_int16x8 v_mul_hi(const v_int16x8& a, const v_int16x8& b) { return v_int16x8(_mm_mulhi_epi16(a.val, b.val)); } +inline v_uint16x8 v_mul_hi(const v_uint16x8& a, const v_uint16x8& b) { return v_uint16x8(_mm_mulhi_epu16(a.val, b.val)); } + +inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b) +{ + return v_int32x4(_mm_madd_epi16(a.val, b.val)); +} + +inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b, const v_int32x4& c) +{ + return v_int32x4(_mm_add_epi32(_mm_madd_epi16(a.val, b.val), c.val)); +} + +#define OPENCV_HAL_IMPL_SSE_LOGIC_OP(_Tpvec, suffix, not_const) \ + OPENCV_HAL_IMPL_SSE_BIN_OP(&, _Tpvec, _mm_and_##suffix) \ + OPENCV_HAL_IMPL_SSE_BIN_OP(|, _Tpvec, _mm_or_##suffix) \ + OPENCV_HAL_IMPL_SSE_BIN_OP(^, _Tpvec, _mm_xor_##suffix) \ + inline _Tpvec operator ~ (const _Tpvec& a) \ + { \ + return _Tpvec(_mm_xor_##suffix(a.val, not_const)); \ + } + +OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_uint8x16, si128, _mm_set1_epi32(-1)) +OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_int8x16, si128, _mm_set1_epi32(-1)) +OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_uint16x8, si128, _mm_set1_epi32(-1)) +OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_int16x8, si128, _mm_set1_epi32(-1)) +OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_uint32x4, si128, _mm_set1_epi32(-1)) +OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_int32x4, si128, _mm_set1_epi32(-1)) +OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_uint64x2, si128, _mm_set1_epi32(-1)) +OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_int64x2, si128, _mm_set1_epi32(-1)) +OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_float32x4, ps, _mm_castsi128_ps(_mm_set1_epi32(-1))) +OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_float64x2, pd, _mm_castsi128_pd(_mm_set1_epi32(-1))) + +inline v_float32x4 v_sqrt(const v_float32x4& x) +{ return v_float32x4(_mm_sqrt_ps(x.val)); } + +inline v_float32x4 v_invsqrt(const v_float32x4& x) +{ + const __m128 _0_5 = _mm_set1_ps(0.5f), _1_5 = _mm_set1_ps(1.5f); + __m128 t = x.val; + __m128 h = _mm_mul_ps(t, _0_5); + t = _mm_rsqrt_ps(t); + t = _mm_mul_ps(t, _mm_sub_ps(_1_5, _mm_mul_ps(_mm_mul_ps(t, t), h))); + return v_float32x4(t); +} + +inline v_float64x2 v_sqrt(const v_float64x2& x) +{ return v_float64x2(_mm_sqrt_pd(x.val)); } + +inline v_float64x2 v_invsqrt(const v_float64x2& x) +{ + const __m128d v_1 = _mm_set1_pd(1.); + return v_float64x2(_mm_div_pd(v_1, _mm_sqrt_pd(x.val))); +} + +#define OPENCV_HAL_IMPL_SSE_ABS_INT_FUNC(_Tpuvec, _Tpsvec, func, suffix, subWidth) \ +inline _Tpuvec v_abs(const _Tpsvec& x) \ +{ return _Tpuvec(_mm_##func##_ep##suffix(x.val, _mm_sub_ep##subWidth(_mm_setzero_si128(), x.val))); } + +OPENCV_HAL_IMPL_SSE_ABS_INT_FUNC(v_uint8x16, v_int8x16, min, u8, i8) +OPENCV_HAL_IMPL_SSE_ABS_INT_FUNC(v_uint16x8, v_int16x8, max, i16, i16) +inline v_uint32x4 v_abs(const v_int32x4& x) +{ + __m128i s = _mm_srli_epi32(x.val, 31); + __m128i f = _mm_srai_epi32(x.val, 31); + return v_uint32x4(_mm_add_epi32(_mm_xor_si128(x.val, f), s)); +} +inline v_float32x4 v_abs(const v_float32x4& x) +{ return v_float32x4(_mm_and_ps(x.val, _mm_castsi128_ps(_mm_set1_epi32(0x7fffffff)))); } +inline v_float64x2 v_abs(const v_float64x2& x) +{ + return v_float64x2(_mm_and_pd(x.val, + _mm_castsi128_pd(_mm_srli_epi64(_mm_set1_epi32(-1), 1)))); +} + +// TODO: exp, log, sin, cos + +#define OPENCV_HAL_IMPL_SSE_BIN_FUNC(_Tpvec, func, intrin) \ +inline _Tpvec func(const _Tpvec& a, const _Tpvec& b) \ +{ \ + return _Tpvec(intrin(a.val, b.val)); \ +} + +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint8x16, v_min, _mm_min_epu8) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint8x16, v_max, _mm_max_epu8) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int16x8, v_min, _mm_min_epi16) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int16x8, v_max, _mm_max_epi16) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_float32x4, v_min, _mm_min_ps) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_float32x4, v_max, _mm_max_ps) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_float64x2, v_min, _mm_min_pd) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_float64x2, v_max, _mm_max_pd) + +inline v_int8x16 v_min(const v_int8x16& a, const v_int8x16& b) +{ +#if CV_SSE4_1 + return v_int8x16(_mm_min_epi8(a.val, b.val)); +#else + __m128i delta = _mm_set1_epi8((char)-128); + return v_int8x16(_mm_xor_si128(delta, _mm_min_epu8(_mm_xor_si128(a.val, delta), + _mm_xor_si128(b.val, delta)))); +#endif +} +inline v_int8x16 v_max(const v_int8x16& a, const v_int8x16& b) +{ +#if CV_SSE4_1 + return v_int8x16(_mm_max_epi8(a.val, b.val)); +#else + __m128i delta = _mm_set1_epi8((char)-128); + return v_int8x16(_mm_xor_si128(delta, _mm_max_epu8(_mm_xor_si128(a.val, delta), + _mm_xor_si128(b.val, delta)))); +#endif +} +inline v_uint16x8 v_min(const v_uint16x8& a, const v_uint16x8& b) +{ +#if CV_SSE4_1 + return v_uint16x8(_mm_min_epu16(a.val, b.val)); +#else + return v_uint16x8(_mm_subs_epu16(a.val, _mm_subs_epu16(a.val, b.val))); +#endif +} +inline v_uint16x8 v_max(const v_uint16x8& a, const v_uint16x8& b) +{ +#if CV_SSE4_1 + return v_uint16x8(_mm_max_epu16(a.val, b.val)); +#else + return v_uint16x8(_mm_adds_epu16(_mm_subs_epu16(a.val, b.val), b.val)); +#endif +} +inline v_uint32x4 v_min(const v_uint32x4& a, const v_uint32x4& b) +{ +#if CV_SSE4_1 + return v_uint32x4(_mm_min_epu32(a.val, b.val)); +#else + __m128i delta = _mm_set1_epi32((int)0x80000000); + __m128i mask = _mm_cmpgt_epi32(_mm_xor_si128(a.val, delta), _mm_xor_si128(b.val, delta)); + return v_uint32x4(v_select_si128(mask, b.val, a.val)); +#endif +} +inline v_uint32x4 v_max(const v_uint32x4& a, const v_uint32x4& b) +{ +#if CV_SSE4_1 + return v_uint32x4(_mm_max_epu32(a.val, b.val)); +#else + __m128i delta = _mm_set1_epi32((int)0x80000000); + __m128i mask = _mm_cmpgt_epi32(_mm_xor_si128(a.val, delta), _mm_xor_si128(b.val, delta)); + return v_uint32x4(v_select_si128(mask, a.val, b.val)); +#endif +} +inline v_int32x4 v_min(const v_int32x4& a, const v_int32x4& b) +{ +#if CV_SSE4_1 + return v_int32x4(_mm_min_epi32(a.val, b.val)); +#else + return v_int32x4(v_select_si128(_mm_cmpgt_epi32(a.val, b.val), b.val, a.val)); +#endif +} +inline v_int32x4 v_max(const v_int32x4& a, const v_int32x4& b) +{ +#if CV_SSE4_1 + return v_int32x4(_mm_max_epi32(a.val, b.val)); +#else + return v_int32x4(v_select_si128(_mm_cmpgt_epi32(a.val, b.val), a.val, b.val)); +#endif +} + +#define OPENCV_HAL_IMPL_SSE_INT_CMP_OP(_Tpuvec, _Tpsvec, suffix, sbit) \ +inline _Tpuvec operator == (const _Tpuvec& a, const _Tpuvec& b) \ +{ return _Tpuvec(_mm_cmpeq_##suffix(a.val, b.val)); } \ +inline _Tpuvec operator != (const _Tpuvec& a, const _Tpuvec& b) \ +{ \ + __m128i not_mask = _mm_set1_epi32(-1); \ + return _Tpuvec(_mm_xor_si128(_mm_cmpeq_##suffix(a.val, b.val), not_mask)); \ +} \ +inline _Tpsvec operator == (const _Tpsvec& a, const _Tpsvec& b) \ +{ return _Tpsvec(_mm_cmpeq_##suffix(a.val, b.val)); } \ +inline _Tpsvec operator != (const _Tpsvec& a, const _Tpsvec& b) \ +{ \ + __m128i not_mask = _mm_set1_epi32(-1); \ + return _Tpsvec(_mm_xor_si128(_mm_cmpeq_##suffix(a.val, b.val), not_mask)); \ +} \ +inline _Tpuvec operator < (const _Tpuvec& a, const _Tpuvec& b) \ +{ \ + __m128i smask = _mm_set1_##suffix(sbit); \ + return _Tpuvec(_mm_cmpgt_##suffix(_mm_xor_si128(b.val, smask), _mm_xor_si128(a.val, smask))); \ +} \ +inline _Tpuvec operator > (const _Tpuvec& a, const _Tpuvec& b) \ +{ \ + __m128i smask = _mm_set1_##suffix(sbit); \ + return _Tpuvec(_mm_cmpgt_##suffix(_mm_xor_si128(a.val, smask), _mm_xor_si128(b.val, smask))); \ +} \ +inline _Tpuvec operator <= (const _Tpuvec& a, const _Tpuvec& b) \ +{ \ + __m128i smask = _mm_set1_##suffix(sbit); \ + __m128i not_mask = _mm_set1_epi32(-1); \ + __m128i res = _mm_cmpgt_##suffix(_mm_xor_si128(a.val, smask), _mm_xor_si128(b.val, smask)); \ + return _Tpuvec(_mm_xor_si128(res, not_mask)); \ +} \ +inline _Tpuvec operator >= (const _Tpuvec& a, const _Tpuvec& b) \ +{ \ + __m128i smask = _mm_set1_##suffix(sbit); \ + __m128i not_mask = _mm_set1_epi32(-1); \ + __m128i res = _mm_cmpgt_##suffix(_mm_xor_si128(b.val, smask), _mm_xor_si128(a.val, smask)); \ + return _Tpuvec(_mm_xor_si128(res, not_mask)); \ +} \ +inline _Tpsvec operator < (const _Tpsvec& a, const _Tpsvec& b) \ +{ \ + return _Tpsvec(_mm_cmpgt_##suffix(b.val, a.val)); \ +} \ +inline _Tpsvec operator > (const _Tpsvec& a, const _Tpsvec& b) \ +{ \ + return _Tpsvec(_mm_cmpgt_##suffix(a.val, b.val)); \ +} \ +inline _Tpsvec operator <= (const _Tpsvec& a, const _Tpsvec& b) \ +{ \ + __m128i not_mask = _mm_set1_epi32(-1); \ + return _Tpsvec(_mm_xor_si128(_mm_cmpgt_##suffix(a.val, b.val), not_mask)); \ +} \ +inline _Tpsvec operator >= (const _Tpsvec& a, const _Tpsvec& b) \ +{ \ + __m128i not_mask = _mm_set1_epi32(-1); \ + return _Tpsvec(_mm_xor_si128(_mm_cmpgt_##suffix(b.val, a.val), not_mask)); \ +} + +OPENCV_HAL_IMPL_SSE_INT_CMP_OP(v_uint8x16, v_int8x16, epi8, (char)-128) +OPENCV_HAL_IMPL_SSE_INT_CMP_OP(v_uint16x8, v_int16x8, epi16, (short)-32768) +OPENCV_HAL_IMPL_SSE_INT_CMP_OP(v_uint32x4, v_int32x4, epi32, (int)0x80000000) + +#define OPENCV_HAL_IMPL_SSE_FLT_CMP_OP(_Tpvec, suffix) \ +inline _Tpvec operator == (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(_mm_cmpeq_##suffix(a.val, b.val)); } \ +inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(_mm_cmpneq_##suffix(a.val, b.val)); } \ +inline _Tpvec operator < (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(_mm_cmplt_##suffix(a.val, b.val)); } \ +inline _Tpvec operator > (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(_mm_cmpgt_##suffix(a.val, b.val)); } \ +inline _Tpvec operator <= (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(_mm_cmple_##suffix(a.val, b.val)); } \ +inline _Tpvec operator >= (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(_mm_cmpge_##suffix(a.val, b.val)); } + +OPENCV_HAL_IMPL_SSE_FLT_CMP_OP(v_float32x4, ps) +OPENCV_HAL_IMPL_SSE_FLT_CMP_OP(v_float64x2, pd) + +#define OPENCV_HAL_IMPL_SSE_64BIT_CMP_OP(_Tpvec, cast) \ +inline _Tpvec operator == (const _Tpvec& a, const _Tpvec& b) \ +{ return cast(v_reinterpret_as_f64(a) == v_reinterpret_as_f64(b)); } \ +inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \ +{ return cast(v_reinterpret_as_f64(a) != v_reinterpret_as_f64(b)); } + +OPENCV_HAL_IMPL_SSE_64BIT_CMP_OP(v_uint64x2, v_reinterpret_as_u64) +OPENCV_HAL_IMPL_SSE_64BIT_CMP_OP(v_int64x2, v_reinterpret_as_s64) + +inline v_float32x4 v_not_nan(const v_float32x4& a) +{ return v_float32x4(_mm_cmpord_ps(a.val, a.val)); } +inline v_float64x2 v_not_nan(const v_float64x2& a) +{ return v_float64x2(_mm_cmpord_pd(a.val, a.val)); } + +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint8x16, v_add_wrap, _mm_add_epi8) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int8x16, v_add_wrap, _mm_add_epi8) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint16x8, v_add_wrap, _mm_add_epi16) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int16x8, v_add_wrap, _mm_add_epi16) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint8x16, v_sub_wrap, _mm_sub_epi8) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int8x16, v_sub_wrap, _mm_sub_epi8) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint16x8, v_sub_wrap, _mm_sub_epi16) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int16x8, v_sub_wrap, _mm_sub_epi16) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint16x8, v_mul_wrap, _mm_mullo_epi16) +OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int16x8, v_mul_wrap, _mm_mullo_epi16) + +inline v_uint8x16 v_mul_wrap(const v_uint8x16& a, const v_uint8x16& b) +{ + __m128i ad = _mm_srai_epi16(a.val, 8); + __m128i bd = _mm_srai_epi16(b.val, 8); + __m128i p0 = _mm_mullo_epi16(a.val, b.val); // even + __m128i p1 = _mm_slli_epi16(_mm_mullo_epi16(ad, bd), 8); // odd + const __m128i b01 = _mm_set1_epi32(0xFF00FF00); + return v_uint8x16(_v128_blendv_epi8(p0, p1, b01)); +} +inline v_int8x16 v_mul_wrap(const v_int8x16& a, const v_int8x16& b) +{ + return v_reinterpret_as_s8(v_mul_wrap(v_reinterpret_as_u8(a), v_reinterpret_as_u8(b))); +} + +/** Absolute difference **/ + +inline v_uint8x16 v_absdiff(const v_uint8x16& a, const v_uint8x16& b) +{ return v_add_wrap(a - b, b - a); } +inline v_uint16x8 v_absdiff(const v_uint16x8& a, const v_uint16x8& b) +{ return v_add_wrap(a - b, b - a); } +inline v_uint32x4 v_absdiff(const v_uint32x4& a, const v_uint32x4& b) +{ return v_max(a, b) - v_min(a, b); } + +inline v_uint8x16 v_absdiff(const v_int8x16& a, const v_int8x16& b) +{ + v_int8x16 d = v_sub_wrap(a, b); + v_int8x16 m = a < b; + return v_reinterpret_as_u8(v_sub_wrap(d ^ m, m)); +} +inline v_uint16x8 v_absdiff(const v_int16x8& a, const v_int16x8& b) +{ + return v_reinterpret_as_u16(v_sub_wrap(v_max(a, b), v_min(a, b))); +} +inline v_uint32x4 v_absdiff(const v_int32x4& a, const v_int32x4& b) +{ + v_int32x4 d = a - b; + v_int32x4 m = a < b; + return v_reinterpret_as_u32((d ^ m) - m); +} + +/** Saturating absolute difference **/ +inline v_int8x16 v_absdiffs(const v_int8x16& a, const v_int8x16& b) +{ + v_int8x16 d = a - b; + v_int8x16 m = a < b; + return (d ^ m) - m; + } +inline v_int16x8 v_absdiffs(const v_int16x8& a, const v_int16x8& b) +{ return v_max(a, b) - v_min(a, b); } + + +inline v_int32x4 v_fma(const v_int32x4& a, const v_int32x4& b, const v_int32x4& c) +{ + return a * b + c; +} + +inline v_int32x4 v_muladd(const v_int32x4& a, const v_int32x4& b, const v_int32x4& c) +{ + return v_fma(a, b, c); +} + +inline v_float32x4 v_fma(const v_float32x4& a, const v_float32x4& b, const v_float32x4& c) +{ +#if CV_FMA3 + return v_float32x4(_mm_fmadd_ps(a.val, b.val, c.val)); +#else + return v_float32x4(_mm_add_ps(_mm_mul_ps(a.val, b.val), c.val)); +#endif +} + +inline v_float64x2 v_fma(const v_float64x2& a, const v_float64x2& b, const v_float64x2& c) +{ +#if CV_FMA3 + return v_float64x2(_mm_fmadd_pd(a.val, b.val, c.val)); +#else + return v_float64x2(_mm_add_pd(_mm_mul_pd(a.val, b.val), c.val)); +#endif +} + +#define OPENCV_HAL_IMPL_SSE_MISC_FLT_OP(_Tpvec, _Tp, _Tpreg, suffix, absmask_vec) \ +inline _Tpvec v_absdiff(const _Tpvec& a, const _Tpvec& b) \ +{ \ + _Tpreg absmask = _mm_castsi128_##suffix(absmask_vec); \ + return _Tpvec(_mm_and_##suffix(_mm_sub_##suffix(a.val, b.val), absmask)); \ +} \ +inline _Tpvec v_magnitude(const _Tpvec& a, const _Tpvec& b) \ +{ \ + _Tpvec res = v_fma(a, a, b*b); \ + return _Tpvec(_mm_sqrt_##suffix(res.val)); \ +} \ +inline _Tpvec v_sqr_magnitude(const _Tpvec& a, const _Tpvec& b) \ +{ \ + return v_fma(a, a, b*b); \ +} \ +inline _Tpvec v_muladd(const _Tpvec& a, const _Tpvec& b, const _Tpvec& c) \ +{ \ + return v_fma(a, b, c); \ +} + +OPENCV_HAL_IMPL_SSE_MISC_FLT_OP(v_float32x4, float, __m128, ps, _mm_set1_epi32((int)0x7fffffff)) +OPENCV_HAL_IMPL_SSE_MISC_FLT_OP(v_float64x2, double, __m128d, pd, _mm_srli_epi64(_mm_set1_epi32(-1), 1)) + +#define OPENCV_HAL_IMPL_SSE_SHIFT_OP(_Tpuvec, _Tpsvec, suffix, srai) \ +inline _Tpuvec operator << (const _Tpuvec& a, int imm) \ +{ \ + return _Tpuvec(_mm_slli_##suffix(a.val, imm)); \ +} \ +inline _Tpsvec operator << (const _Tpsvec& a, int imm) \ +{ \ + return _Tpsvec(_mm_slli_##suffix(a.val, imm)); \ +} \ +inline _Tpuvec operator >> (const _Tpuvec& a, int imm) \ +{ \ + return _Tpuvec(_mm_srli_##suffix(a.val, imm)); \ +} \ +inline _Tpsvec operator >> (const _Tpsvec& a, int imm) \ +{ \ + return _Tpsvec(srai(a.val, imm)); \ +} \ +template \ +inline _Tpuvec v_shl(const _Tpuvec& a) \ +{ \ + return _Tpuvec(_mm_slli_##suffix(a.val, imm)); \ +} \ +template \ +inline _Tpsvec v_shl(const _Tpsvec& a) \ +{ \ + return _Tpsvec(_mm_slli_##suffix(a.val, imm)); \ +} \ +template \ +inline _Tpuvec v_shr(const _Tpuvec& a) \ +{ \ + return _Tpuvec(_mm_srli_##suffix(a.val, imm)); \ +} \ +template \ +inline _Tpsvec v_shr(const _Tpsvec& a) \ +{ \ + return _Tpsvec(srai(a.val, imm)); \ +} + +OPENCV_HAL_IMPL_SSE_SHIFT_OP(v_uint16x8, v_int16x8, epi16, _mm_srai_epi16) +OPENCV_HAL_IMPL_SSE_SHIFT_OP(v_uint32x4, v_int32x4, epi32, _mm_srai_epi32) +OPENCV_HAL_IMPL_SSE_SHIFT_OP(v_uint64x2, v_int64x2, epi64, v_srai_epi64) + +namespace hal_sse_internal +{ + template 16)), + bool is_first = (imm == 0), + bool is_half = (imm == 8), + bool is_second = (imm == 16), + bool is_other = (((imm > 0) && (imm < 8)) || ((imm > 8) && (imm < 16)))> + class v_sse_palignr_u8_class; + + template + class v_sse_palignr_u8_class; + + template + class v_sse_palignr_u8_class + { + public: + inline __m128i operator()(const __m128i& a, const __m128i&) const + { + return a; + } + }; + + template + class v_sse_palignr_u8_class + { + public: + inline __m128i operator()(const __m128i& a, const __m128i& b) const + { + return _mm_unpacklo_epi64(_mm_unpackhi_epi64(a, a), b); + } + }; + + template + class v_sse_palignr_u8_class + { + public: + inline __m128i operator()(const __m128i&, const __m128i& b) const + { + return b; + } + }; + + template + class v_sse_palignr_u8_class + { +#if CV_SSSE3 + public: + inline __m128i operator()(const __m128i& a, const __m128i& b) const + { + return _mm_alignr_epi8(b, a, imm); + } +#else + public: + inline __m128i operator()(const __m128i& a, const __m128i& b) const + { + enum { imm2 = (sizeof(__m128i) - imm) }; + return _mm_or_si128(_mm_srli_si128(a, imm), _mm_slli_si128(b, imm2)); + } +#endif + }; + + template + inline __m128i v_sse_palignr_u8(const __m128i& a, const __m128i& b) + { + CV_StaticAssert((imm >= 0) && (imm <= 16), "Invalid imm for v_sse_palignr_u8."); + return v_sse_palignr_u8_class()(a, b); + } +} + +template +inline _Tpvec v_rotate_right(const _Tpvec &a) +{ + using namespace hal_sse_internal; + enum { imm2 = (imm * sizeof(typename _Tpvec::lane_type)) }; + return _Tpvec(v_sse_reinterpret_as( + _mm_srli_si128( + v_sse_reinterpret_as<__m128i>(a.val), imm2))); +} + +template +inline _Tpvec v_rotate_left(const _Tpvec &a) +{ + using namespace hal_sse_internal; + enum { imm2 = (imm * sizeof(typename _Tpvec::lane_type)) }; + return _Tpvec(v_sse_reinterpret_as( + _mm_slli_si128( + v_sse_reinterpret_as<__m128i>(a.val), imm2))); +} + +template +inline _Tpvec v_rotate_right(const _Tpvec &a, const _Tpvec &b) +{ + using namespace hal_sse_internal; + enum { imm2 = (imm * sizeof(typename _Tpvec::lane_type)) }; + return _Tpvec(v_sse_reinterpret_as( + v_sse_palignr_u8( + v_sse_reinterpret_as<__m128i>(a.val), + v_sse_reinterpret_as<__m128i>(b.val)))); +} + +template +inline _Tpvec v_rotate_left(const _Tpvec &a, const _Tpvec &b) +{ + using namespace hal_sse_internal; + enum { imm2 = ((_Tpvec::nlanes - imm) * sizeof(typename _Tpvec::lane_type)) }; + return _Tpvec(v_sse_reinterpret_as( + v_sse_palignr_u8( + v_sse_reinterpret_as<__m128i>(b.val), + v_sse_reinterpret_as<__m128i>(a.val)))); +} + +#define OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(_Tpvec, _Tp) \ +inline _Tpvec v_load(const _Tp* ptr) \ +{ return _Tpvec(_mm_loadu_si128((const __m128i*)ptr)); } \ +inline _Tpvec v_load_aligned(const _Tp* ptr) \ +{ return _Tpvec(_mm_load_si128((const __m128i*)ptr)); } \ +inline _Tpvec v_load_low(const _Tp* ptr) \ +{ return _Tpvec(_mm_loadl_epi64((const __m128i*)ptr)); } \ +inline _Tpvec v_load_halves(const _Tp* ptr0, const _Tp* ptr1) \ +{ \ + return _Tpvec(_mm_unpacklo_epi64(_mm_loadl_epi64((const __m128i*)ptr0), \ + _mm_loadl_epi64((const __m128i*)ptr1))); \ +} \ +inline void v_store(_Tp* ptr, const _Tpvec& a) \ +{ _mm_storeu_si128((__m128i*)ptr, a.val); } \ +inline void v_store_aligned(_Tp* ptr, const _Tpvec& a) \ +{ _mm_store_si128((__m128i*)ptr, a.val); } \ +inline void v_store_aligned_nocache(_Tp* ptr, const _Tpvec& a) \ +{ _mm_stream_si128((__m128i*)ptr, a.val); } \ +inline void v_store(_Tp* ptr, const _Tpvec& a, hal::StoreMode mode) \ +{ \ + if( mode == hal::STORE_UNALIGNED ) \ + _mm_storeu_si128((__m128i*)ptr, a.val); \ + else if( mode == hal::STORE_ALIGNED_NOCACHE ) \ + _mm_stream_si128((__m128i*)ptr, a.val); \ + else \ + _mm_store_si128((__m128i*)ptr, a.val); \ +} \ +inline void v_store_low(_Tp* ptr, const _Tpvec& a) \ +{ _mm_storel_epi64((__m128i*)ptr, a.val); } \ +inline void v_store_high(_Tp* ptr, const _Tpvec& a) \ +{ _mm_storel_epi64((__m128i*)ptr, _mm_unpackhi_epi64(a.val, a.val)); } + +OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_uint8x16, uchar) +OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_int8x16, schar) +OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_uint16x8, ushort) +OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_int16x8, short) +OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_uint32x4, unsigned) +OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_int32x4, int) +OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_uint64x2, uint64) +OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_int64x2, int64) + +#define OPENCV_HAL_IMPL_SSE_LOADSTORE_FLT_OP(_Tpvec, _Tp, suffix) \ +inline _Tpvec v_load(const _Tp* ptr) \ +{ return _Tpvec(_mm_loadu_##suffix(ptr)); } \ +inline _Tpvec v_load_aligned(const _Tp* ptr) \ +{ return _Tpvec(_mm_load_##suffix(ptr)); } \ +inline _Tpvec v_load_low(const _Tp* ptr) \ +{ return _Tpvec(_mm_castsi128_##suffix(_mm_loadl_epi64((const __m128i*)ptr))); } \ +inline _Tpvec v_load_halves(const _Tp* ptr0, const _Tp* ptr1) \ +{ \ + return _Tpvec(_mm_castsi128_##suffix( \ + _mm_unpacklo_epi64(_mm_loadl_epi64((const __m128i*)ptr0), \ + _mm_loadl_epi64((const __m128i*)ptr1)))); \ +} \ +inline void v_store(_Tp* ptr, const _Tpvec& a) \ +{ _mm_storeu_##suffix(ptr, a.val); } \ +inline void v_store_aligned(_Tp* ptr, const _Tpvec& a) \ +{ _mm_store_##suffix(ptr, a.val); } \ +inline void v_store_aligned_nocache(_Tp* ptr, const _Tpvec& a) \ +{ _mm_stream_##suffix(ptr, a.val); } \ +inline void v_store(_Tp* ptr, const _Tpvec& a, hal::StoreMode mode) \ +{ \ + if( mode == hal::STORE_UNALIGNED ) \ + _mm_storeu_##suffix(ptr, a.val); \ + else if( mode == hal::STORE_ALIGNED_NOCACHE ) \ + _mm_stream_##suffix(ptr, a.val); \ + else \ + _mm_store_##suffix(ptr, a.val); \ +} \ +inline void v_store_low(_Tp* ptr, const _Tpvec& a) \ +{ _mm_storel_epi64((__m128i*)ptr, _mm_cast##suffix##_si128(a.val)); } \ +inline void v_store_high(_Tp* ptr, const _Tpvec& a) \ +{ \ + __m128i a1 = _mm_cast##suffix##_si128(a.val); \ + _mm_storel_epi64((__m128i*)ptr, _mm_unpackhi_epi64(a1, a1)); \ +} + +OPENCV_HAL_IMPL_SSE_LOADSTORE_FLT_OP(v_float32x4, float, ps) +OPENCV_HAL_IMPL_SSE_LOADSTORE_FLT_OP(v_float64x2, double, pd) + +#define OPENCV_HAL_IMPL_SSE_REDUCE_OP_8(_Tpvec, scalartype, func, suffix, sbit) \ +inline scalartype v_reduce_##func(const v_##_Tpvec& a) \ +{ \ + __m128i val = a.val; \ + val = _mm_##func##_##suffix(val, _mm_srli_si128(val,8)); \ + val = _mm_##func##_##suffix(val, _mm_srli_si128(val,4)); \ + val = _mm_##func##_##suffix(val, _mm_srli_si128(val,2)); \ + return (scalartype)_mm_cvtsi128_si32(val); \ +} \ +inline unsigned scalartype v_reduce_##func(const v_u##_Tpvec& a) \ +{ \ + __m128i val = a.val; \ + __m128i smask = _mm_set1_epi16(sbit); \ + val = _mm_xor_si128(val, smask); \ + val = _mm_##func##_##suffix(val, _mm_srli_si128(val,8)); \ + val = _mm_##func##_##suffix(val, _mm_srli_si128(val,4)); \ + val = _mm_##func##_##suffix(val, _mm_srli_si128(val,2)); \ + return (unsigned scalartype)(_mm_cvtsi128_si32(val) ^ sbit); \ +} +#define OPENCV_HAL_IMPL_SSE_REDUCE_OP_8_SUM(_Tpvec, scalartype, suffix) \ +inline scalartype v_reduce_sum(const v_##_Tpvec& a) \ +{ \ + __m128i val = a.val; \ + val = _mm_adds_epi##suffix(val, _mm_srli_si128(val, 8)); \ + val = _mm_adds_epi##suffix(val, _mm_srli_si128(val, 4)); \ + val = _mm_adds_epi##suffix(val, _mm_srli_si128(val, 2)); \ + return (scalartype)_mm_cvtsi128_si32(val); \ +} \ +inline unsigned scalartype v_reduce_sum(const v_u##_Tpvec& a) \ +{ \ + __m128i val = a.val; \ + val = _mm_adds_epu##suffix(val, _mm_srli_si128(val, 8)); \ + val = _mm_adds_epu##suffix(val, _mm_srli_si128(val, 4)); \ + val = _mm_adds_epu##suffix(val, _mm_srli_si128(val, 2)); \ + return (unsigned scalartype)_mm_cvtsi128_si32(val); \ +} +OPENCV_HAL_IMPL_SSE_REDUCE_OP_8(int16x8, short, max, epi16, (short)-32768) +OPENCV_HAL_IMPL_SSE_REDUCE_OP_8(int16x8, short, min, epi16, (short)-32768) +OPENCV_HAL_IMPL_SSE_REDUCE_OP_8_SUM(int16x8, short, 16) + +#define OPENCV_HAL_IMPL_SSE_REDUCE_OP_4_SUM(_Tpvec, scalartype, regtype, suffix, cast_from, cast_to, extract) \ +inline scalartype v_reduce_sum(const _Tpvec& a) \ +{ \ + regtype val = a.val; \ + val = _mm_add_##suffix(val, cast_to(_mm_srli_si128(cast_from(val), 8))); \ + val = _mm_add_##suffix(val, cast_to(_mm_srli_si128(cast_from(val), 4))); \ + return (scalartype)_mm_cvt##extract(val); \ +} + +#define OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(_Tpvec, scalartype, func, scalar_func) \ +inline scalartype v_reduce_##func(const _Tpvec& a) \ +{ \ + scalartype CV_DECL_ALIGNED(16) buf[4]; \ + v_store_aligned(buf, a); \ + scalartype s0 = scalar_func(buf[0], buf[1]); \ + scalartype s1 = scalar_func(buf[2], buf[3]); \ + return scalar_func(s0, s1); \ +} + +OPENCV_HAL_IMPL_SSE_REDUCE_OP_4_SUM(v_uint32x4, unsigned, __m128i, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP, si128_si32) +OPENCV_HAL_IMPL_SSE_REDUCE_OP_4_SUM(v_int32x4, int, __m128i, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP, si128_si32) +OPENCV_HAL_IMPL_SSE_REDUCE_OP_4_SUM(v_float32x4, float, __m128, ps, _mm_castps_si128, _mm_castsi128_ps, ss_f32) + +inline double v_reduce_sum(const v_float64x2& a) +{ + double CV_DECL_ALIGNED(32) idx[2]; + v_store_aligned(idx, a); + return idx[0] + idx[1]; +} + +inline v_float32x4 v_reduce_sum4(const v_float32x4& a, const v_float32x4& b, + const v_float32x4& c, const v_float32x4& d) +{ +#if CV_SSE3 + __m128 ab = _mm_hadd_ps(a.val, b.val); + __m128 cd = _mm_hadd_ps(c.val, d.val); + return v_float32x4(_mm_hadd_ps(ab, cd)); +#else + __m128 ac = _mm_add_ps(_mm_unpacklo_ps(a.val, c.val), _mm_unpackhi_ps(a.val, c.val)); + __m128 bd = _mm_add_ps(_mm_unpacklo_ps(b.val, d.val), _mm_unpackhi_ps(b.val, d.val)); + return v_float32x4(_mm_add_ps(_mm_unpacklo_ps(ac, bd), _mm_unpackhi_ps(ac, bd))); +#endif +} + +OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_uint32x4, unsigned, max, std::max) +OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_uint32x4, unsigned, min, std::min) +OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_int32x4, int, max, std::max) +OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_int32x4, int, min, std::min) +OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_float32x4, float, max, std::max) +OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_float32x4, float, min, std::min) + +inline unsigned v_reduce_sad(const v_uint8x16& a, const v_uint8x16& b) +{ + return (unsigned)_mm_cvtsi128_si32(_mm_sad_epu8(a.val, b.val)); +} +inline unsigned v_reduce_sad(const v_int8x16& a, const v_int8x16& b) +{ + __m128i half = _mm_set1_epi8(0x7f); + return (unsigned)_mm_cvtsi128_si32(_mm_sad_epu8(_mm_add_epi8(a.val, half), + _mm_add_epi8(b.val, half))); +} +inline unsigned v_reduce_sad(const v_uint16x8& a, const v_uint16x8& b) +{ + v_uint32x4 l, h; + v_expand(v_absdiff(a, b), l, h); + return v_reduce_sum(l + h); +} +inline unsigned v_reduce_sad(const v_int16x8& a, const v_int16x8& b) +{ + v_uint32x4 l, h; + v_expand(v_absdiff(a, b), l, h); + return v_reduce_sum(l + h); +} +inline unsigned v_reduce_sad(const v_uint32x4& a, const v_uint32x4& b) +{ + return v_reduce_sum(v_absdiff(a, b)); +} +inline unsigned v_reduce_sad(const v_int32x4& a, const v_int32x4& b) +{ + return v_reduce_sum(v_absdiff(a, b)); +} +inline float v_reduce_sad(const v_float32x4& a, const v_float32x4& b) +{ + return v_reduce_sum(v_absdiff(a, b)); +} + +#define OPENCV_HAL_IMPL_SSE_POPCOUNT(_Tpvec) \ +inline v_uint32x4 v_popcount(const _Tpvec& a) \ +{ \ + __m128i m1 = _mm_set1_epi32(0x55555555); \ + __m128i m2 = _mm_set1_epi32(0x33333333); \ + __m128i m4 = _mm_set1_epi32(0x0f0f0f0f); \ + __m128i p = a.val; \ + p = _mm_add_epi32(_mm_and_si128(_mm_srli_epi32(p, 1), m1), _mm_and_si128(p, m1)); \ + p = _mm_add_epi32(_mm_and_si128(_mm_srli_epi32(p, 2), m2), _mm_and_si128(p, m2)); \ + p = _mm_add_epi32(_mm_and_si128(_mm_srli_epi32(p, 4), m4), _mm_and_si128(p, m4)); \ + p = _mm_adds_epi8(p, _mm_srli_si128(p, 1)); \ + p = _mm_adds_epi8(p, _mm_srli_si128(p, 2)); \ + return v_uint32x4(_mm_and_si128(p, _mm_set1_epi32(0x000000ff))); \ +} + +OPENCV_HAL_IMPL_SSE_POPCOUNT(v_uint8x16) +OPENCV_HAL_IMPL_SSE_POPCOUNT(v_uint16x8) +OPENCV_HAL_IMPL_SSE_POPCOUNT(v_uint32x4) +OPENCV_HAL_IMPL_SSE_POPCOUNT(v_int8x16) +OPENCV_HAL_IMPL_SSE_POPCOUNT(v_int16x8) +OPENCV_HAL_IMPL_SSE_POPCOUNT(v_int32x4) + +#define OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(_Tpvec, suffix, pack_op, and_op, signmask, allmask) \ +inline int v_signmask(const _Tpvec& a) \ +{ \ + return and_op(_mm_movemask_##suffix(pack_op(a.val)), signmask); \ +} \ +inline bool v_check_all(const _Tpvec& a) \ +{ return and_op(_mm_movemask_##suffix(a.val), allmask) == allmask; } \ +inline bool v_check_any(const _Tpvec& a) \ +{ return and_op(_mm_movemask_##suffix(a.val), allmask) != 0; } + +#define OPENCV_HAL_PACKS(a) _mm_packs_epi16(a, a) +inline __m128i v_packq_epi32(__m128i a) +{ + __m128i b = _mm_packs_epi32(a, a); + return _mm_packs_epi16(b, b); +} + +OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_uint8x16, epi8, OPENCV_HAL_NOP, OPENCV_HAL_1ST, 65535, 65535) +OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_int8x16, epi8, OPENCV_HAL_NOP, OPENCV_HAL_1ST, 65535, 65535) +OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_uint16x8, epi8, OPENCV_HAL_PACKS, OPENCV_HAL_AND, 255, (int)0xaaaa) +OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_int16x8, epi8, OPENCV_HAL_PACKS, OPENCV_HAL_AND, 255, (int)0xaaaa) +OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_uint32x4, epi8, v_packq_epi32, OPENCV_HAL_AND, 15, (int)0x8888) +OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_int32x4, epi8, v_packq_epi32, OPENCV_HAL_AND, 15, (int)0x8888) +OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_float32x4, ps, OPENCV_HAL_NOP, OPENCV_HAL_1ST, 15, 15) +OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_float64x2, pd, OPENCV_HAL_NOP, OPENCV_HAL_1ST, 3, 3) + +#if CV_SSE4_1 +#define OPENCV_HAL_IMPL_SSE_SELECT(_Tpvec, cast_ret, cast, suffix) \ +inline _Tpvec v_select(const _Tpvec& mask, const _Tpvec& a, const _Tpvec& b) \ +{ \ + return _Tpvec(cast_ret(_mm_blendv_##suffix(cast(b.val), cast(a.val), cast(mask.val)))); \ +} + +OPENCV_HAL_IMPL_SSE_SELECT(v_uint8x16, OPENCV_HAL_NOP, OPENCV_HAL_NOP, epi8) +OPENCV_HAL_IMPL_SSE_SELECT(v_int8x16, OPENCV_HAL_NOP, OPENCV_HAL_NOP, epi8) +OPENCV_HAL_IMPL_SSE_SELECT(v_uint16x8, OPENCV_HAL_NOP, OPENCV_HAL_NOP, epi8) +OPENCV_HAL_IMPL_SSE_SELECT(v_int16x8, OPENCV_HAL_NOP, OPENCV_HAL_NOP, epi8) +OPENCV_HAL_IMPL_SSE_SELECT(v_uint32x4, _mm_castps_si128, _mm_castsi128_ps, ps) +OPENCV_HAL_IMPL_SSE_SELECT(v_int32x4, _mm_castps_si128, _mm_castsi128_ps, ps) +// OPENCV_HAL_IMPL_SSE_SELECT(v_uint64x2, TBD, TBD, pd) +// OPENCV_HAL_IMPL_SSE_SELECT(v_int64x2, TBD, TBD, ps) +OPENCV_HAL_IMPL_SSE_SELECT(v_float32x4, OPENCV_HAL_NOP, OPENCV_HAL_NOP, ps) +OPENCV_HAL_IMPL_SSE_SELECT(v_float64x2, OPENCV_HAL_NOP, OPENCV_HAL_NOP, pd) + +#else // CV_SSE4_1 + +#define OPENCV_HAL_IMPL_SSE_SELECT(_Tpvec, suffix) \ +inline _Tpvec v_select(const _Tpvec& mask, const _Tpvec& a, const _Tpvec& b) \ +{ \ + return _Tpvec(_mm_xor_##suffix(b.val, _mm_and_##suffix(_mm_xor_##suffix(b.val, a.val), mask.val))); \ +} + +OPENCV_HAL_IMPL_SSE_SELECT(v_uint8x16, si128) +OPENCV_HAL_IMPL_SSE_SELECT(v_int8x16, si128) +OPENCV_HAL_IMPL_SSE_SELECT(v_uint16x8, si128) +OPENCV_HAL_IMPL_SSE_SELECT(v_int16x8, si128) +OPENCV_HAL_IMPL_SSE_SELECT(v_uint32x4, si128) +OPENCV_HAL_IMPL_SSE_SELECT(v_int32x4, si128) +// OPENCV_HAL_IMPL_SSE_SELECT(v_uint64x2, si128) +// OPENCV_HAL_IMPL_SSE_SELECT(v_int64x2, si128) +OPENCV_HAL_IMPL_SSE_SELECT(v_float32x4, ps) +OPENCV_HAL_IMPL_SSE_SELECT(v_float64x2, pd) +#endif + +/* Expand */ +#define OPENCV_HAL_IMPL_SSE_EXPAND(_Tpvec, _Tpwvec, _Tp, intrin) \ + inline void v_expand(const _Tpvec& a, _Tpwvec& b0, _Tpwvec& b1) \ + { \ + b0.val = intrin(a.val); \ + b1.val = __CV_CAT(intrin, _high)(a.val); \ + } \ + inline _Tpwvec v_expand_low(const _Tpvec& a) \ + { return _Tpwvec(intrin(a.val)); } \ + inline _Tpwvec v_expand_high(const _Tpvec& a) \ + { return _Tpwvec(__CV_CAT(intrin, _high)(a.val)); } \ + inline _Tpwvec v_load_expand(const _Tp* ptr) \ + { \ + __m128i a = _mm_loadl_epi64((const __m128i*)ptr); \ + return _Tpwvec(intrin(a)); \ + } + +OPENCV_HAL_IMPL_SSE_EXPAND(v_uint8x16, v_uint16x8, uchar, _v128_cvtepu8_epi16) +OPENCV_HAL_IMPL_SSE_EXPAND(v_int8x16, v_int16x8, schar, _v128_cvtepi8_epi16) +OPENCV_HAL_IMPL_SSE_EXPAND(v_uint16x8, v_uint32x4, ushort, _v128_cvtepu16_epi32) +OPENCV_HAL_IMPL_SSE_EXPAND(v_int16x8, v_int32x4, short, _v128_cvtepi16_epi32) +OPENCV_HAL_IMPL_SSE_EXPAND(v_uint32x4, v_uint64x2, unsigned, _v128_cvtepu32_epi64) +OPENCV_HAL_IMPL_SSE_EXPAND(v_int32x4, v_int64x2, int, _v128_cvtepi32_epi64) + +#define OPENCV_HAL_IMPL_SSE_EXPAND_Q(_Tpvec, _Tp, intrin) \ + inline _Tpvec v_load_expand_q(const _Tp* ptr) \ + { \ + __m128i a = _mm_cvtsi32_si128(*(const int*)ptr); \ + return _Tpvec(intrin(a)); \ + } + +OPENCV_HAL_IMPL_SSE_EXPAND_Q(v_uint32x4, uchar, _v128_cvtepu8_epi32) +OPENCV_HAL_IMPL_SSE_EXPAND_Q(v_int32x4, schar, _v128_cvtepi8_epi32) + +#define OPENCV_HAL_IMPL_SSE_UNPACKS(_Tpvec, suffix, cast_from, cast_to) \ +inline void v_zip(const _Tpvec& a0, const _Tpvec& a1, _Tpvec& b0, _Tpvec& b1) \ +{ \ + b0.val = _mm_unpacklo_##suffix(a0.val, a1.val); \ + b1.val = _mm_unpackhi_##suffix(a0.val, a1.val); \ +} \ +inline _Tpvec v_combine_low(const _Tpvec& a, const _Tpvec& b) \ +{ \ + __m128i a1 = cast_from(a.val), b1 = cast_from(b.val); \ + return _Tpvec(cast_to(_mm_unpacklo_epi64(a1, b1))); \ +} \ +inline _Tpvec v_combine_high(const _Tpvec& a, const _Tpvec& b) \ +{ \ + __m128i a1 = cast_from(a.val), b1 = cast_from(b.val); \ + return _Tpvec(cast_to(_mm_unpackhi_epi64(a1, b1))); \ +} \ +inline void v_recombine(const _Tpvec& a, const _Tpvec& b, _Tpvec& c, _Tpvec& d) \ +{ \ + __m128i a1 = cast_from(a.val), b1 = cast_from(b.val); \ + c.val = cast_to(_mm_unpacklo_epi64(a1, b1)); \ + d.val = cast_to(_mm_unpackhi_epi64(a1, b1)); \ +} + +OPENCV_HAL_IMPL_SSE_UNPACKS(v_uint8x16, epi8, OPENCV_HAL_NOP, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_UNPACKS(v_int8x16, epi8, OPENCV_HAL_NOP, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_UNPACKS(v_uint16x8, epi16, OPENCV_HAL_NOP, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_UNPACKS(v_int16x8, epi16, OPENCV_HAL_NOP, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_UNPACKS(v_uint32x4, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_UNPACKS(v_int32x4, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_UNPACKS(v_float32x4, ps, _mm_castps_si128, _mm_castsi128_ps) +OPENCV_HAL_IMPL_SSE_UNPACKS(v_float64x2, pd, _mm_castpd_si128, _mm_castsi128_pd) + +template +inline _Tpvec v_extract(const _Tpvec& a, const _Tpvec& b) +{ + return v_rotate_right(a, b); +} + +inline v_int32x4 v_round(const v_float32x4& a) +{ return v_int32x4(_mm_cvtps_epi32(a.val)); } + +inline v_int32x4 v_floor(const v_float32x4& a) +{ + __m128i a1 = _mm_cvtps_epi32(a.val); + __m128i mask = _mm_castps_si128(_mm_cmpgt_ps(_mm_cvtepi32_ps(a1), a.val)); + return v_int32x4(_mm_add_epi32(a1, mask)); +} + +inline v_int32x4 v_ceil(const v_float32x4& a) +{ + __m128i a1 = _mm_cvtps_epi32(a.val); + __m128i mask = _mm_castps_si128(_mm_cmpgt_ps(a.val, _mm_cvtepi32_ps(a1))); + return v_int32x4(_mm_sub_epi32(a1, mask)); +} + +inline v_int32x4 v_trunc(const v_float32x4& a) +{ return v_int32x4(_mm_cvttps_epi32(a.val)); } + +inline v_int32x4 v_round(const v_float64x2& a) +{ return v_int32x4(_mm_cvtpd_epi32(a.val)); } + +inline v_int32x4 v_round(const v_float64x2& a, const v_float64x2& b) +{ + __m128i ai = _mm_cvtpd_epi32(a.val), bi = _mm_cvtpd_epi32(b.val); + return v_int32x4(_mm_unpacklo_epi64(ai, bi)); +} + +inline v_int32x4 v_floor(const v_float64x2& a) +{ + __m128i a1 = _mm_cvtpd_epi32(a.val); + __m128i mask = _mm_castpd_si128(_mm_cmpgt_pd(_mm_cvtepi32_pd(a1), a.val)); + mask = _mm_srli_si128(_mm_slli_si128(mask, 4), 8); // m0 m0 m1 m1 => m0 m1 0 0 + return v_int32x4(_mm_add_epi32(a1, mask)); +} + +inline v_int32x4 v_ceil(const v_float64x2& a) +{ + __m128i a1 = _mm_cvtpd_epi32(a.val); + __m128i mask = _mm_castpd_si128(_mm_cmpgt_pd(a.val, _mm_cvtepi32_pd(a1))); + mask = _mm_srli_si128(_mm_slli_si128(mask, 4), 8); // m0 m0 m1 m1 => m0 m1 0 0 + return v_int32x4(_mm_sub_epi32(a1, mask)); +} + +inline v_int32x4 v_trunc(const v_float64x2& a) +{ return v_int32x4(_mm_cvttpd_epi32(a.val)); } + +#define OPENCV_HAL_IMPL_SSE_TRANSPOSE4x4(_Tpvec, suffix, cast_from, cast_to) \ +inline void v_transpose4x4(const _Tpvec& a0, const _Tpvec& a1, \ + const _Tpvec& a2, const _Tpvec& a3, \ + _Tpvec& b0, _Tpvec& b1, \ + _Tpvec& b2, _Tpvec& b3) \ +{ \ + __m128i t0 = cast_from(_mm_unpacklo_##suffix(a0.val, a1.val)); \ + __m128i t1 = cast_from(_mm_unpacklo_##suffix(a2.val, a3.val)); \ + __m128i t2 = cast_from(_mm_unpackhi_##suffix(a0.val, a1.val)); \ + __m128i t3 = cast_from(_mm_unpackhi_##suffix(a2.val, a3.val)); \ +\ + b0.val = cast_to(_mm_unpacklo_epi64(t0, t1)); \ + b1.val = cast_to(_mm_unpackhi_epi64(t0, t1)); \ + b2.val = cast_to(_mm_unpacklo_epi64(t2, t3)); \ + b3.val = cast_to(_mm_unpackhi_epi64(t2, t3)); \ +} + +OPENCV_HAL_IMPL_SSE_TRANSPOSE4x4(v_uint32x4, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_TRANSPOSE4x4(v_int32x4, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP) +OPENCV_HAL_IMPL_SSE_TRANSPOSE4x4(v_float32x4, ps, _mm_castps_si128, _mm_castsi128_ps) + +// load deinterleave +inline void v_load_deinterleave(const uchar* ptr, v_uint8x16& a, v_uint8x16& b) +{ + __m128i t00 = _mm_loadu_si128((const __m128i*)ptr); + __m128i t01 = _mm_loadu_si128((const __m128i*)(ptr + 16)); + + __m128i t10 = _mm_unpacklo_epi8(t00, t01); + __m128i t11 = _mm_unpackhi_epi8(t00, t01); + + __m128i t20 = _mm_unpacklo_epi8(t10, t11); + __m128i t21 = _mm_unpackhi_epi8(t10, t11); + + __m128i t30 = _mm_unpacklo_epi8(t20, t21); + __m128i t31 = _mm_unpackhi_epi8(t20, t21); + + a.val = _mm_unpacklo_epi8(t30, t31); + b.val = _mm_unpackhi_epi8(t30, t31); +} + +inline void v_load_deinterleave(const uchar* ptr, v_uint8x16& a, v_uint8x16& b, v_uint8x16& c) +{ +#if CV_SSE4_1 + const __m128i m0 = _mm_setr_epi8(0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0); + const __m128i m1 = _mm_setr_epi8(0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0); + __m128i s0 = _mm_loadu_si128((const __m128i*)ptr); + __m128i s1 = _mm_loadu_si128((const __m128i*)(ptr + 16)); + __m128i s2 = _mm_loadu_si128((const __m128i*)(ptr + 32)); + __m128i a0 = _mm_blendv_epi8(_mm_blendv_epi8(s0, s1, m0), s2, m1); + __m128i b0 = _mm_blendv_epi8(_mm_blendv_epi8(s1, s2, m0), s0, m1); + __m128i c0 = _mm_blendv_epi8(_mm_blendv_epi8(s2, s0, m0), s1, m1); + const __m128i sh_b = _mm_setr_epi8(0, 3, 6, 9, 12, 15, 2, 5, 8, 11, 14, 1, 4, 7, 10, 13); + const __m128i sh_g = _mm_setr_epi8(1, 4, 7, 10, 13, 0, 3, 6, 9, 12, 15, 2, 5, 8, 11, 14); + const __m128i sh_r = _mm_setr_epi8(2, 5, 8, 11, 14, 1, 4, 7, 10, 13, 0, 3, 6, 9, 12, 15); + a0 = _mm_shuffle_epi8(a0, sh_b); + b0 = _mm_shuffle_epi8(b0, sh_g); + c0 = _mm_shuffle_epi8(c0, sh_r); + a.val = a0; + b.val = b0; + c.val = c0; +#elif CV_SSSE3 + const __m128i m0 = _mm_setr_epi8(0, 3, 6, 9, 12, 15, 1, 4, 7, 10, 13, 2, 5, 8, 11, 14); + const __m128i m1 = _mm_alignr_epi8(m0, m0, 11); + const __m128i m2 = _mm_alignr_epi8(m0, m0, 6); + + __m128i t0 = _mm_loadu_si128((const __m128i*)ptr); + __m128i t1 = _mm_loadu_si128((const __m128i*)(ptr + 16)); + __m128i t2 = _mm_loadu_si128((const __m128i*)(ptr + 32)); + + __m128i s0 = _mm_shuffle_epi8(t0, m0); + __m128i s1 = _mm_shuffle_epi8(t1, m1); + __m128i s2 = _mm_shuffle_epi8(t2, m2); + + t0 = _mm_alignr_epi8(s1, _mm_slli_si128(s0, 10), 5); + a.val = _mm_alignr_epi8(s2, t0, 5); + + t1 = _mm_alignr_epi8(_mm_srli_si128(s1, 5), _mm_slli_si128(s0, 5), 6); + b.val = _mm_alignr_epi8(_mm_srli_si128(s2, 5), t1, 5); + + t2 = _mm_alignr_epi8(_mm_srli_si128(s2, 10), s1, 11); + c.val = _mm_alignr_epi8(t2, s0, 11); +#else + __m128i t00 = _mm_loadu_si128((const __m128i*)ptr); + __m128i t01 = _mm_loadu_si128((const __m128i*)(ptr + 16)); + __m128i t02 = _mm_loadu_si128((const __m128i*)(ptr + 32)); + + __m128i t10 = _mm_unpacklo_epi8(t00, _mm_unpackhi_epi64(t01, t01)); + __m128i t11 = _mm_unpacklo_epi8(_mm_unpackhi_epi64(t00, t00), t02); + __m128i t12 = _mm_unpacklo_epi8(t01, _mm_unpackhi_epi64(t02, t02)); + + __m128i t20 = _mm_unpacklo_epi8(t10, _mm_unpackhi_epi64(t11, t11)); + __m128i t21 = _mm_unpacklo_epi8(_mm_unpackhi_epi64(t10, t10), t12); + __m128i t22 = _mm_unpacklo_epi8(t11, _mm_unpackhi_epi64(t12, t12)); + + __m128i t30 = _mm_unpacklo_epi8(t20, _mm_unpackhi_epi64(t21, t21)); + __m128i t31 = _mm_unpacklo_epi8(_mm_unpackhi_epi64(t20, t20), t22); + __m128i t32 = _mm_unpacklo_epi8(t21, _mm_unpackhi_epi64(t22, t22)); + + a.val = _mm_unpacklo_epi8(t30, _mm_unpackhi_epi64(t31, t31)); + b.val = _mm_unpacklo_epi8(_mm_unpackhi_epi64(t30, t30), t32); + c.val = _mm_unpacklo_epi8(t31, _mm_unpackhi_epi64(t32, t32)); +#endif +} + +inline void v_load_deinterleave(const uchar* ptr, v_uint8x16& a, v_uint8x16& b, v_uint8x16& c, v_uint8x16& d) +{ + __m128i u0 = _mm_loadu_si128((const __m128i*)ptr); // a0 b0 c0 d0 a1 b1 c1 d1 ... + __m128i u1 = _mm_loadu_si128((const __m128i*)(ptr + 16)); // a4 b4 c4 d4 ... + __m128i u2 = _mm_loadu_si128((const __m128i*)(ptr + 32)); // a8 b8 c8 d8 ... + __m128i u3 = _mm_loadu_si128((const __m128i*)(ptr + 48)); // a12 b12 c12 d12 ... + + __m128i v0 = _mm_unpacklo_epi8(u0, u2); // a0 a8 b0 b8 ... + __m128i v1 = _mm_unpackhi_epi8(u0, u2); // a2 a10 b2 b10 ... + __m128i v2 = _mm_unpacklo_epi8(u1, u3); // a4 a12 b4 b12 ... + __m128i v3 = _mm_unpackhi_epi8(u1, u3); // a6 a14 b6 b14 ... + + u0 = _mm_unpacklo_epi8(v0, v2); // a0 a4 a8 a12 ... + u1 = _mm_unpacklo_epi8(v1, v3); // a2 a6 a10 a14 ... + u2 = _mm_unpackhi_epi8(v0, v2); // a1 a5 a9 a13 ... + u3 = _mm_unpackhi_epi8(v1, v3); // a3 a7 a11 a15 ... + + v0 = _mm_unpacklo_epi8(u0, u1); // a0 a2 a4 a6 ... + v1 = _mm_unpacklo_epi8(u2, u3); // a1 a3 a5 a7 ... + v2 = _mm_unpackhi_epi8(u0, u1); // c0 c2 c4 c6 ... + v3 = _mm_unpackhi_epi8(u2, u3); // c1 c3 c5 c7 ... + + a.val = _mm_unpacklo_epi8(v0, v1); + b.val = _mm_unpackhi_epi8(v0, v1); + c.val = _mm_unpacklo_epi8(v2, v3); + d.val = _mm_unpackhi_epi8(v2, v3); +} + +inline void v_load_deinterleave(const ushort* ptr, v_uint16x8& a, v_uint16x8& b) +{ + __m128i v0 = _mm_loadu_si128((__m128i*)(ptr)); // a0 b0 a1 b1 a2 b2 a3 b3 + __m128i v1 = _mm_loadu_si128((__m128i*)(ptr + 8)); // a4 b4 a5 b5 a6 b6 a7 b7 + + __m128i v2 = _mm_unpacklo_epi16(v0, v1); // a0 a4 b0 b4 a1 a5 b1 b5 + __m128i v3 = _mm_unpackhi_epi16(v0, v1); // a2 a6 b2 b6 a3 a7 b3 b7 + __m128i v4 = _mm_unpacklo_epi16(v2, v3); // a0 a2 a4 a6 b0 b2 b4 b6 + __m128i v5 = _mm_unpackhi_epi16(v2, v3); // a1 a3 a5 a7 b1 b3 b5 b7 + + a.val = _mm_unpacklo_epi16(v4, v5); // a0 a1 a2 a3 a4 a5 a6 a7 + b.val = _mm_unpackhi_epi16(v4, v5); // b0 b1 ab b3 b4 b5 b6 b7 +} + +inline void v_load_deinterleave(const ushort* ptr, v_uint16x8& a, v_uint16x8& b, v_uint16x8& c) +{ +#if CV_SSE4_1 + __m128i v0 = _mm_loadu_si128((__m128i*)(ptr)); + __m128i v1 = _mm_loadu_si128((__m128i*)(ptr + 8)); + __m128i v2 = _mm_loadu_si128((__m128i*)(ptr + 16)); + __m128i a0 = _mm_blend_epi16(_mm_blend_epi16(v0, v1, 0x92), v2, 0x24); + __m128i b0 = _mm_blend_epi16(_mm_blend_epi16(v2, v0, 0x92), v1, 0x24); + __m128i c0 = _mm_blend_epi16(_mm_blend_epi16(v1, v2, 0x92), v0, 0x24); + + const __m128i sh_a = _mm_setr_epi8(0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5, 10, 11); + const __m128i sh_b = _mm_setr_epi8(2, 3, 8, 9, 14, 15, 4, 5, 10, 11, 0, 1, 6, 7, 12, 13); + const __m128i sh_c = _mm_setr_epi8(4, 5, 10, 11, 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15); + a0 = _mm_shuffle_epi8(a0, sh_a); + b0 = _mm_shuffle_epi8(b0, sh_b); + c0 = _mm_shuffle_epi8(c0, sh_c); + + a.val = a0; + b.val = b0; + c.val = c0; +#else + __m128i t00 = _mm_loadu_si128((const __m128i*)ptr); + __m128i t01 = _mm_loadu_si128((const __m128i*)(ptr + 8)); + __m128i t02 = _mm_loadu_si128((const __m128i*)(ptr + 16)); + + __m128i t10 = _mm_unpacklo_epi16(t00, _mm_unpackhi_epi64(t01, t01)); + __m128i t11 = _mm_unpacklo_epi16(_mm_unpackhi_epi64(t00, t00), t02); + __m128i t12 = _mm_unpacklo_epi16(t01, _mm_unpackhi_epi64(t02, t02)); + + __m128i t20 = _mm_unpacklo_epi16(t10, _mm_unpackhi_epi64(t11, t11)); + __m128i t21 = _mm_unpacklo_epi16(_mm_unpackhi_epi64(t10, t10), t12); + __m128i t22 = _mm_unpacklo_epi16(t11, _mm_unpackhi_epi64(t12, t12)); + + a.val = _mm_unpacklo_epi16(t20, _mm_unpackhi_epi64(t21, t21)); + b.val = _mm_unpacklo_epi16(_mm_unpackhi_epi64(t20, t20), t22); + c.val = _mm_unpacklo_epi16(t21, _mm_unpackhi_epi64(t22, t22)); +#endif +} + +inline void v_load_deinterleave(const ushort* ptr, v_uint16x8& a, v_uint16x8& b, v_uint16x8& c, v_uint16x8& d) +{ + __m128i u0 = _mm_loadu_si128((const __m128i*)ptr); // a0 b0 c0 d0 a1 b1 c1 d1 + __m128i u1 = _mm_loadu_si128((const __m128i*)(ptr + 8)); // a2 b2 c2 d2 ... + __m128i u2 = _mm_loadu_si128((const __m128i*)(ptr + 16)); // a4 b4 c4 d4 ... + __m128i u3 = _mm_loadu_si128((const __m128i*)(ptr + 24)); // a6 b6 c6 d6 ... + + __m128i v0 = _mm_unpacklo_epi16(u0, u2); // a0 a4 b0 b4 ... + __m128i v1 = _mm_unpackhi_epi16(u0, u2); // a1 a5 b1 b5 ... + __m128i v2 = _mm_unpacklo_epi16(u1, u3); // a2 a6 b2 b6 ... + __m128i v3 = _mm_unpackhi_epi16(u1, u3); // a3 a7 b3 b7 ... + + u0 = _mm_unpacklo_epi16(v0, v2); // a0 a2 a4 a6 ... + u1 = _mm_unpacklo_epi16(v1, v3); // a1 a3 a5 a7 ... + u2 = _mm_unpackhi_epi16(v0, v2); // c0 c2 c4 c6 ... + u3 = _mm_unpackhi_epi16(v1, v3); // c1 c3 c5 c7 ... + + a.val = _mm_unpacklo_epi16(u0, u1); + b.val = _mm_unpackhi_epi16(u0, u1); + c.val = _mm_unpacklo_epi16(u2, u3); + d.val = _mm_unpackhi_epi16(u2, u3); +} + +inline void v_load_deinterleave(const unsigned* ptr, v_uint32x4& a, v_uint32x4& b) +{ + __m128i v0 = _mm_loadu_si128((__m128i*)(ptr)); // a0 b0 a1 b1 + __m128i v1 = _mm_loadu_si128((__m128i*)(ptr + 4)); // a2 b2 a3 b3 + + __m128i v2 = _mm_unpacklo_epi32(v0, v1); // a0 a2 b0 b2 + __m128i v3 = _mm_unpackhi_epi32(v0, v1); // a1 a3 b1 b3 + + a.val = _mm_unpacklo_epi32(v2, v3); // a0 a1 a2 a3 + b.val = _mm_unpackhi_epi32(v2, v3); // b0 b1 ab b3 +} + +inline void v_load_deinterleave(const unsigned* ptr, v_uint32x4& a, v_uint32x4& b, v_uint32x4& c) +{ + __m128i t00 = _mm_loadu_si128((const __m128i*)ptr); + __m128i t01 = _mm_loadu_si128((const __m128i*)(ptr + 4)); + __m128i t02 = _mm_loadu_si128((const __m128i*)(ptr + 8)); + + __m128i t10 = _mm_unpacklo_epi32(t00, _mm_unpackhi_epi64(t01, t01)); + __m128i t11 = _mm_unpacklo_epi32(_mm_unpackhi_epi64(t00, t00), t02); + __m128i t12 = _mm_unpacklo_epi32(t01, _mm_unpackhi_epi64(t02, t02)); + + a.val = _mm_unpacklo_epi32(t10, _mm_unpackhi_epi64(t11, t11)); + b.val = _mm_unpacklo_epi32(_mm_unpackhi_epi64(t10, t10), t12); + c.val = _mm_unpacklo_epi32(t11, _mm_unpackhi_epi64(t12, t12)); +} + +inline void v_load_deinterleave(const unsigned* ptr, v_uint32x4& a, v_uint32x4& b, v_uint32x4& c, v_uint32x4& d) +{ + v_uint32x4 s0(_mm_loadu_si128((const __m128i*)ptr)); // a0 b0 c0 d0 + v_uint32x4 s1(_mm_loadu_si128((const __m128i*)(ptr + 4))); // a1 b1 c1 d1 + v_uint32x4 s2(_mm_loadu_si128((const __m128i*)(ptr + 8))); // a2 b2 c2 d2 + v_uint32x4 s3(_mm_loadu_si128((const __m128i*)(ptr + 12))); // a3 b3 c3 d3 + + v_transpose4x4(s0, s1, s2, s3, a, b, c, d); +} + +inline void v_load_deinterleave(const float* ptr, v_float32x4& a, v_float32x4& b) +{ + __m128 u0 = _mm_loadu_ps(ptr); // a0 b0 a1 b1 + __m128 u1 = _mm_loadu_ps((ptr + 4)); // a2 b2 a3 b3 + + a.val = _mm_shuffle_ps(u0, u1, _MM_SHUFFLE(2, 0, 2, 0)); // a0 a1 a2 a3 + b.val = _mm_shuffle_ps(u0, u1, _MM_SHUFFLE(3, 1, 3, 1)); // b0 b1 ab b3 +} + +inline void v_load_deinterleave(const float* ptr, v_float32x4& a, v_float32x4& b, v_float32x4& c) +{ + __m128 t0 = _mm_loadu_ps(ptr + 0); + __m128 t1 = _mm_loadu_ps(ptr + 4); + __m128 t2 = _mm_loadu_ps(ptr + 8); + + __m128 at12 = _mm_shuffle_ps(t1, t2, _MM_SHUFFLE(0, 1, 0, 2)); + a.val = _mm_shuffle_ps(t0, at12, _MM_SHUFFLE(2, 0, 3, 0)); + + __m128 bt01 = _mm_shuffle_ps(t0, t1, _MM_SHUFFLE(0, 0, 0, 1)); + __m128 bt12 = _mm_shuffle_ps(t1, t2, _MM_SHUFFLE(0, 2, 0, 3)); + b.val = _mm_shuffle_ps(bt01, bt12, _MM_SHUFFLE(2, 0, 2, 0)); + + __m128 ct01 = _mm_shuffle_ps(t0, t1, _MM_SHUFFLE(0, 1, 0, 2)); + c.val = _mm_shuffle_ps(ct01, t2, _MM_SHUFFLE(3, 0, 2, 0)); +} + +inline void v_load_deinterleave(const float* ptr, v_float32x4& a, v_float32x4& b, v_float32x4& c, v_float32x4& d) +{ + __m128 t0 = _mm_loadu_ps(ptr + 0); + __m128 t1 = _mm_loadu_ps(ptr + 4); + __m128 t2 = _mm_loadu_ps(ptr + 8); + __m128 t3 = _mm_loadu_ps(ptr + 12); + __m128 t02lo = _mm_unpacklo_ps(t0, t2); + __m128 t13lo = _mm_unpacklo_ps(t1, t3); + __m128 t02hi = _mm_unpackhi_ps(t0, t2); + __m128 t13hi = _mm_unpackhi_ps(t1, t3); + a.val = _mm_unpacklo_ps(t02lo, t13lo); + b.val = _mm_unpackhi_ps(t02lo, t13lo); + c.val = _mm_unpacklo_ps(t02hi, t13hi); + d.val = _mm_unpackhi_ps(t02hi, t13hi); +} + +inline void v_load_deinterleave(const uint64 *ptr, v_uint64x2& a, v_uint64x2& b) +{ + __m128i t0 = _mm_loadu_si128((const __m128i*)ptr); + __m128i t1 = _mm_loadu_si128((const __m128i*)(ptr + 2)); + + a = v_uint64x2(_mm_unpacklo_epi64(t0, t1)); + b = v_uint64x2(_mm_unpackhi_epi64(t0, t1)); +} + +inline void v_load_deinterleave(const uint64 *ptr, v_uint64x2& a, v_uint64x2& b, v_uint64x2& c) +{ + __m128i t0 = _mm_loadu_si128((const __m128i*)ptr); // a0, b0 + __m128i t1 = _mm_loadu_si128((const __m128i*)(ptr + 2)); // c0, a1 + __m128i t2 = _mm_loadu_si128((const __m128i*)(ptr + 4)); // b1, c1 + + t1 = _mm_shuffle_epi32(t1, 0x4e); // a1, c0 + + a = v_uint64x2(_mm_unpacklo_epi64(t0, t1)); + b = v_uint64x2(_mm_unpacklo_epi64(_mm_unpackhi_epi64(t0, t0), t2)); + c = v_uint64x2(_mm_unpackhi_epi64(t1, t2)); +} + +inline void v_load_deinterleave(const uint64 *ptr, v_uint64x2& a, + v_uint64x2& b, v_uint64x2& c, v_uint64x2& d) +{ + __m128i t0 = _mm_loadu_si128((const __m128i*)ptr); // a0 b0 + __m128i t1 = _mm_loadu_si128((const __m128i*)(ptr + 2)); // c0 d0 + __m128i t2 = _mm_loadu_si128((const __m128i*)(ptr + 4)); // a1 b1 + __m128i t3 = _mm_loadu_si128((const __m128i*)(ptr + 6)); // c1 d1 + + a = v_uint64x2(_mm_unpacklo_epi64(t0, t2)); + b = v_uint64x2(_mm_unpackhi_epi64(t0, t2)); + c = v_uint64x2(_mm_unpacklo_epi64(t1, t3)); + d = v_uint64x2(_mm_unpackhi_epi64(t1, t3)); +} + +// store interleave + +inline void v_store_interleave( uchar* ptr, const v_uint8x16& a, const v_uint8x16& b, + hal::StoreMode mode = hal::STORE_UNALIGNED) +{ + __m128i v0 = _mm_unpacklo_epi8(a.val, b.val); + __m128i v1 = _mm_unpackhi_epi8(a.val, b.val); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm_stream_si128((__m128i*)(ptr), v0); + _mm_stream_si128((__m128i*)(ptr + 16), v1); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm_store_si128((__m128i*)(ptr), v0); + _mm_store_si128((__m128i*)(ptr + 16), v1); + } + else + { + _mm_storeu_si128((__m128i*)(ptr), v0); + _mm_storeu_si128((__m128i*)(ptr + 16), v1); + } +} + +inline void v_store_interleave( uchar* ptr, const v_uint8x16& a, const v_uint8x16& b, + const v_uint8x16& c, hal::StoreMode mode = hal::STORE_UNALIGNED) +{ +#if CV_SSE4_1 + const __m128i sh_a = _mm_setr_epi8(0, 11, 6, 1, 12, 7, 2, 13, 8, 3, 14, 9, 4, 15, 10, 5); + const __m128i sh_b = _mm_setr_epi8(5, 0, 11, 6, 1, 12, 7, 2, 13, 8, 3, 14, 9, 4, 15, 10); + const __m128i sh_c = _mm_setr_epi8(10, 5, 0, 11, 6, 1, 12, 7, 2, 13, 8, 3, 14, 9, 4, 15); + __m128i a0 = _mm_shuffle_epi8(a.val, sh_a); + __m128i b0 = _mm_shuffle_epi8(b.val, sh_b); + __m128i c0 = _mm_shuffle_epi8(c.val, sh_c); + + const __m128i m0 = _mm_setr_epi8(0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0); + const __m128i m1 = _mm_setr_epi8(0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0); + __m128i v0 = _mm_blendv_epi8(_mm_blendv_epi8(a0, b0, m1), c0, m0); + __m128i v1 = _mm_blendv_epi8(_mm_blendv_epi8(b0, c0, m1), a0, m0); + __m128i v2 = _mm_blendv_epi8(_mm_blendv_epi8(c0, a0, m1), b0, m0); +#elif CV_SSSE3 + const __m128i m0 = _mm_setr_epi8(0, 6, 11, 1, 7, 12, 2, 8, 13, 3, 9, 14, 4, 10, 15, 5); + const __m128i m1 = _mm_setr_epi8(5, 11, 0, 6, 12, 1, 7, 13, 2, 8, 14, 3, 9, 15, 4, 10); + const __m128i m2 = _mm_setr_epi8(10, 0, 5, 11, 1, 6, 12, 2, 7, 13, 3, 8, 14, 4, 9, 15); + + __m128i t0 = _mm_alignr_epi8(b.val, _mm_slli_si128(a.val, 10), 5); + t0 = _mm_alignr_epi8(c.val, t0, 5); + __m128i v0 = _mm_shuffle_epi8(t0, m0); + + __m128i t1 = _mm_alignr_epi8(_mm_srli_si128(b.val, 5), _mm_slli_si128(a.val, 5), 6); + t1 = _mm_alignr_epi8(_mm_srli_si128(c.val, 5), t1, 5); + __m128i v1 = _mm_shuffle_epi8(t1, m1); + + __m128i t2 = _mm_alignr_epi8(_mm_srli_si128(c.val, 10), b.val, 11); + t2 = _mm_alignr_epi8(t2, a.val, 11); + __m128i v2 = _mm_shuffle_epi8(t2, m2); +#else + __m128i z = _mm_setzero_si128(); + __m128i ab0 = _mm_unpacklo_epi8(a.val, b.val); + __m128i ab1 = _mm_unpackhi_epi8(a.val, b.val); + __m128i c0 = _mm_unpacklo_epi8(c.val, z); + __m128i c1 = _mm_unpackhi_epi8(c.val, z); + + __m128i p00 = _mm_unpacklo_epi16(ab0, c0); + __m128i p01 = _mm_unpackhi_epi16(ab0, c0); + __m128i p02 = _mm_unpacklo_epi16(ab1, c1); + __m128i p03 = _mm_unpackhi_epi16(ab1, c1); + + __m128i p10 = _mm_unpacklo_epi32(p00, p01); + __m128i p11 = _mm_unpackhi_epi32(p00, p01); + __m128i p12 = _mm_unpacklo_epi32(p02, p03); + __m128i p13 = _mm_unpackhi_epi32(p02, p03); + + __m128i p20 = _mm_unpacklo_epi64(p10, p11); + __m128i p21 = _mm_unpackhi_epi64(p10, p11); + __m128i p22 = _mm_unpacklo_epi64(p12, p13); + __m128i p23 = _mm_unpackhi_epi64(p12, p13); + + p20 = _mm_slli_si128(p20, 1); + p22 = _mm_slli_si128(p22, 1); + + __m128i p30 = _mm_slli_epi64(_mm_unpacklo_epi32(p20, p21), 8); + __m128i p31 = _mm_srli_epi64(_mm_unpackhi_epi32(p20, p21), 8); + __m128i p32 = _mm_slli_epi64(_mm_unpacklo_epi32(p22, p23), 8); + __m128i p33 = _mm_srli_epi64(_mm_unpackhi_epi32(p22, p23), 8); + + __m128i p40 = _mm_unpacklo_epi64(p30, p31); + __m128i p41 = _mm_unpackhi_epi64(p30, p31); + __m128i p42 = _mm_unpacklo_epi64(p32, p33); + __m128i p43 = _mm_unpackhi_epi64(p32, p33); + + __m128i v0 = _mm_or_si128(_mm_srli_si128(p40, 2), _mm_slli_si128(p41, 10)); + __m128i v1 = _mm_or_si128(_mm_srli_si128(p41, 6), _mm_slli_si128(p42, 6)); + __m128i v2 = _mm_or_si128(_mm_srli_si128(p42, 10), _mm_slli_si128(p43, 2)); +#endif + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm_stream_si128((__m128i*)(ptr), v0); + _mm_stream_si128((__m128i*)(ptr + 16), v1); + _mm_stream_si128((__m128i*)(ptr + 32), v2); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm_store_si128((__m128i*)(ptr), v0); + _mm_store_si128((__m128i*)(ptr + 16), v1); + _mm_store_si128((__m128i*)(ptr + 32), v2); + } + else + { + _mm_storeu_si128((__m128i*)(ptr), v0); + _mm_storeu_si128((__m128i*)(ptr + 16), v1); + _mm_storeu_si128((__m128i*)(ptr + 32), v2); + } +} + +inline void v_store_interleave( uchar* ptr, const v_uint8x16& a, const v_uint8x16& b, + const v_uint8x16& c, const v_uint8x16& d, + hal::StoreMode mode = hal::STORE_UNALIGNED) +{ + // a0 a1 a2 a3 .... + // b0 b1 b2 b3 .... + // c0 c1 c2 c3 .... + // d0 d1 d2 d3 .... + __m128i u0 = _mm_unpacklo_epi8(a.val, c.val); // a0 c0 a1 c1 ... + __m128i u1 = _mm_unpackhi_epi8(a.val, c.val); // a8 c8 a9 c9 ... + __m128i u2 = _mm_unpacklo_epi8(b.val, d.val); // b0 d0 b1 d1 ... + __m128i u3 = _mm_unpackhi_epi8(b.val, d.val); // b8 d8 b9 d9 ... + + __m128i v0 = _mm_unpacklo_epi8(u0, u2); // a0 b0 c0 d0 ... + __m128i v1 = _mm_unpackhi_epi8(u0, u2); // a4 b4 c4 d4 ... + __m128i v2 = _mm_unpacklo_epi8(u1, u3); // a8 b8 c8 d8 ... + __m128i v3 = _mm_unpackhi_epi8(u1, u3); // a12 b12 c12 d12 ... + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm_stream_si128((__m128i*)(ptr), v0); + _mm_stream_si128((__m128i*)(ptr + 16), v1); + _mm_stream_si128((__m128i*)(ptr + 32), v2); + _mm_stream_si128((__m128i*)(ptr + 48), v3); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm_store_si128((__m128i*)(ptr), v0); + _mm_store_si128((__m128i*)(ptr + 16), v1); + _mm_store_si128((__m128i*)(ptr + 32), v2); + _mm_store_si128((__m128i*)(ptr + 48), v3); + } + else + { + _mm_storeu_si128((__m128i*)(ptr), v0); + _mm_storeu_si128((__m128i*)(ptr + 16), v1); + _mm_storeu_si128((__m128i*)(ptr + 32), v2); + _mm_storeu_si128((__m128i*)(ptr + 48), v3); + } +} + +inline void v_store_interleave( ushort* ptr, const v_uint16x8& a, const v_uint16x8& b, + hal::StoreMode mode = hal::STORE_UNALIGNED) +{ + __m128i v0 = _mm_unpacklo_epi16(a.val, b.val); + __m128i v1 = _mm_unpackhi_epi16(a.val, b.val); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm_stream_si128((__m128i*)(ptr), v0); + _mm_stream_si128((__m128i*)(ptr + 8), v1); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm_store_si128((__m128i*)(ptr), v0); + _mm_store_si128((__m128i*)(ptr + 8), v1); + } + else + { + _mm_storeu_si128((__m128i*)(ptr), v0); + _mm_storeu_si128((__m128i*)(ptr + 8), v1); + } +} + +inline void v_store_interleave( ushort* ptr, const v_uint16x8& a, + const v_uint16x8& b, const v_uint16x8& c, + hal::StoreMode mode = hal::STORE_UNALIGNED) +{ +#if CV_SSE4_1 + const __m128i sh_a = _mm_setr_epi8(0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5, 10, 11); + const __m128i sh_b = _mm_setr_epi8(10, 11, 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5); + const __m128i sh_c = _mm_setr_epi8(4, 5, 10, 11, 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15); + __m128i a0 = _mm_shuffle_epi8(a.val, sh_a); + __m128i b0 = _mm_shuffle_epi8(b.val, sh_b); + __m128i c0 = _mm_shuffle_epi8(c.val, sh_c); + + __m128i v0 = _mm_blend_epi16(_mm_blend_epi16(a0, b0, 0x92), c0, 0x24); + __m128i v1 = _mm_blend_epi16(_mm_blend_epi16(c0, a0, 0x92), b0, 0x24); + __m128i v2 = _mm_blend_epi16(_mm_blend_epi16(b0, c0, 0x92), a0, 0x24); +#else + __m128i z = _mm_setzero_si128(); + __m128i ab0 = _mm_unpacklo_epi16(a.val, b.val); + __m128i ab1 = _mm_unpackhi_epi16(a.val, b.val); + __m128i c0 = _mm_unpacklo_epi16(c.val, z); + __m128i c1 = _mm_unpackhi_epi16(c.val, z); + + __m128i p10 = _mm_unpacklo_epi32(ab0, c0); + __m128i p11 = _mm_unpackhi_epi32(ab0, c0); + __m128i p12 = _mm_unpacklo_epi32(ab1, c1); + __m128i p13 = _mm_unpackhi_epi32(ab1, c1); + + __m128i p20 = _mm_unpacklo_epi64(p10, p11); + __m128i p21 = _mm_unpackhi_epi64(p10, p11); + __m128i p22 = _mm_unpacklo_epi64(p12, p13); + __m128i p23 = _mm_unpackhi_epi64(p12, p13); + + p20 = _mm_slli_si128(p20, 2); + p22 = _mm_slli_si128(p22, 2); + + __m128i p30 = _mm_unpacklo_epi64(p20, p21); + __m128i p31 = _mm_unpackhi_epi64(p20, p21); + __m128i p32 = _mm_unpacklo_epi64(p22, p23); + __m128i p33 = _mm_unpackhi_epi64(p22, p23); + + __m128i v0 = _mm_or_si128(_mm_srli_si128(p30, 2), _mm_slli_si128(p31, 10)); + __m128i v1 = _mm_or_si128(_mm_srli_si128(p31, 6), _mm_slli_si128(p32, 6)); + __m128i v2 = _mm_or_si128(_mm_srli_si128(p32, 10), _mm_slli_si128(p33, 2)); +#endif + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm_stream_si128((__m128i*)(ptr), v0); + _mm_stream_si128((__m128i*)(ptr + 8), v1); + _mm_stream_si128((__m128i*)(ptr + 16), v2); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm_store_si128((__m128i*)(ptr), v0); + _mm_store_si128((__m128i*)(ptr + 8), v1); + _mm_store_si128((__m128i*)(ptr + 16), v2); + } + else + { + _mm_storeu_si128((__m128i*)(ptr), v0); + _mm_storeu_si128((__m128i*)(ptr + 8), v1); + _mm_storeu_si128((__m128i*)(ptr + 16), v2); + } +} + +inline void v_store_interleave( ushort* ptr, const v_uint16x8& a, const v_uint16x8& b, + const v_uint16x8& c, const v_uint16x8& d, + hal::StoreMode mode = hal::STORE_UNALIGNED) +{ + // a0 a1 a2 a3 .... + // b0 b1 b2 b3 .... + // c0 c1 c2 c3 .... + // d0 d1 d2 d3 .... + __m128i u0 = _mm_unpacklo_epi16(a.val, c.val); // a0 c0 a1 c1 ... + __m128i u1 = _mm_unpackhi_epi16(a.val, c.val); // a4 c4 a5 c5 ... + __m128i u2 = _mm_unpacklo_epi16(b.val, d.val); // b0 d0 b1 d1 ... + __m128i u3 = _mm_unpackhi_epi16(b.val, d.val); // b4 d4 b5 d5 ... + + __m128i v0 = _mm_unpacklo_epi16(u0, u2); // a0 b0 c0 d0 ... + __m128i v1 = _mm_unpackhi_epi16(u0, u2); // a2 b2 c2 d2 ... + __m128i v2 = _mm_unpacklo_epi16(u1, u3); // a4 b4 c4 d4 ... + __m128i v3 = _mm_unpackhi_epi16(u1, u3); // a6 b6 c6 d6 ... + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm_stream_si128((__m128i*)(ptr), v0); + _mm_stream_si128((__m128i*)(ptr + 8), v1); + _mm_stream_si128((__m128i*)(ptr + 16), v2); + _mm_stream_si128((__m128i*)(ptr + 24), v3); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm_store_si128((__m128i*)(ptr), v0); + _mm_store_si128((__m128i*)(ptr + 8), v1); + _mm_store_si128((__m128i*)(ptr + 16), v2); + _mm_store_si128((__m128i*)(ptr + 24), v3); + } + else + { + _mm_storeu_si128((__m128i*)(ptr), v0); + _mm_storeu_si128((__m128i*)(ptr + 8), v1); + _mm_storeu_si128((__m128i*)(ptr + 16), v2); + _mm_storeu_si128((__m128i*)(ptr + 24), v3); + } +} + +inline void v_store_interleave( unsigned* ptr, const v_uint32x4& a, const v_uint32x4& b, + hal::StoreMode mode = hal::STORE_UNALIGNED) +{ + __m128i v0 = _mm_unpacklo_epi32(a.val, b.val); + __m128i v1 = _mm_unpackhi_epi32(a.val, b.val); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm_stream_si128((__m128i*)(ptr), v0); + _mm_stream_si128((__m128i*)(ptr + 4), v1); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm_store_si128((__m128i*)(ptr), v0); + _mm_store_si128((__m128i*)(ptr + 4), v1); + } + else + { + _mm_storeu_si128((__m128i*)(ptr), v0); + _mm_storeu_si128((__m128i*)(ptr + 4), v1); + } +} + +inline void v_store_interleave( unsigned* ptr, const v_uint32x4& a, const v_uint32x4& b, + const v_uint32x4& c, hal::StoreMode mode = hal::STORE_UNALIGNED) +{ + v_uint32x4 z = v_setzero_u32(), u0, u1, u2, u3; + v_transpose4x4(a, b, c, z, u0, u1, u2, u3); + + __m128i v0 = _mm_or_si128(u0.val, _mm_slli_si128(u1.val, 12)); + __m128i v1 = _mm_or_si128(_mm_srli_si128(u1.val, 4), _mm_slli_si128(u2.val, 8)); + __m128i v2 = _mm_or_si128(_mm_srli_si128(u2.val, 8), _mm_slli_si128(u3.val, 4)); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm_stream_si128((__m128i*)(ptr), v0); + _mm_stream_si128((__m128i*)(ptr + 4), v1); + _mm_stream_si128((__m128i*)(ptr + 8), v2); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm_store_si128((__m128i*)(ptr), v0); + _mm_store_si128((__m128i*)(ptr + 4), v1); + _mm_store_si128((__m128i*)(ptr + 8), v2); + } + else + { + _mm_storeu_si128((__m128i*)(ptr), v0); + _mm_storeu_si128((__m128i*)(ptr + 4), v1); + _mm_storeu_si128((__m128i*)(ptr + 8), v2); + } +} + +inline void v_store_interleave(unsigned* ptr, const v_uint32x4& a, const v_uint32x4& b, + const v_uint32x4& c, const v_uint32x4& d, + hal::StoreMode mode = hal::STORE_UNALIGNED) +{ + v_uint32x4 v0, v1, v2, v3; + v_transpose4x4(a, b, c, d, v0, v1, v2, v3); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm_stream_si128((__m128i*)(ptr), v0.val); + _mm_stream_si128((__m128i*)(ptr + 4), v1.val); + _mm_stream_si128((__m128i*)(ptr + 8), v2.val); + _mm_stream_si128((__m128i*)(ptr + 12), v3.val); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm_store_si128((__m128i*)(ptr), v0.val); + _mm_store_si128((__m128i*)(ptr + 4), v1.val); + _mm_store_si128((__m128i*)(ptr + 8), v2.val); + _mm_store_si128((__m128i*)(ptr + 12), v3.val); + } + else + { + _mm_storeu_si128((__m128i*)(ptr), v0.val); + _mm_storeu_si128((__m128i*)(ptr + 4), v1.val); + _mm_storeu_si128((__m128i*)(ptr + 8), v2.val); + _mm_storeu_si128((__m128i*)(ptr + 12), v3.val); + } +} + +// 2-channel, float only +inline void v_store_interleave(float* ptr, const v_float32x4& a, const v_float32x4& b, + hal::StoreMode mode = hal::STORE_UNALIGNED) +{ + __m128 v0 = _mm_unpacklo_ps(a.val, b.val); // a0 b0 a1 b1 + __m128 v1 = _mm_unpackhi_ps(a.val, b.val); // a2 b2 a3 b3 + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm_stream_ps(ptr, v0); + _mm_stream_ps(ptr + 4, v1); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm_store_ps(ptr, v0); + _mm_store_ps(ptr + 4, v1); + } + else + { + _mm_storeu_ps(ptr, v0); + _mm_storeu_ps(ptr + 4, v1); + } +} + +inline void v_store_interleave(float* ptr, const v_float32x4& a, const v_float32x4& b, + const v_float32x4& c, hal::StoreMode mode = hal::STORE_UNALIGNED) +{ + __m128 u0 = _mm_shuffle_ps(a.val, b.val, _MM_SHUFFLE(0, 0, 0, 0)); + __m128 u1 = _mm_shuffle_ps(c.val, a.val, _MM_SHUFFLE(1, 1, 0, 0)); + __m128 v0 = _mm_shuffle_ps(u0, u1, _MM_SHUFFLE(2, 0, 2, 0)); + __m128 u2 = _mm_shuffle_ps(b.val, c.val, _MM_SHUFFLE(1, 1, 1, 1)); + __m128 u3 = _mm_shuffle_ps(a.val, b.val, _MM_SHUFFLE(2, 2, 2, 2)); + __m128 v1 = _mm_shuffle_ps(u2, u3, _MM_SHUFFLE(2, 0, 2, 0)); + __m128 u4 = _mm_shuffle_ps(c.val, a.val, _MM_SHUFFLE(3, 3, 2, 2)); + __m128 u5 = _mm_shuffle_ps(b.val, c.val, _MM_SHUFFLE(3, 3, 3, 3)); + __m128 v2 = _mm_shuffle_ps(u4, u5, _MM_SHUFFLE(2, 0, 2, 0)); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm_stream_ps(ptr, v0); + _mm_stream_ps(ptr + 4, v1); + _mm_stream_ps(ptr + 8, v2); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm_store_ps(ptr, v0); + _mm_store_ps(ptr + 4, v1); + _mm_store_ps(ptr + 8, v2); + } + else + { + _mm_storeu_ps(ptr, v0); + _mm_storeu_ps(ptr + 4, v1); + _mm_storeu_ps(ptr + 8, v2); + } +} + +inline void v_store_interleave(float* ptr, const v_float32x4& a, const v_float32x4& b, + const v_float32x4& c, const v_float32x4& d, + hal::StoreMode mode = hal::STORE_UNALIGNED) +{ + __m128 u0 = _mm_unpacklo_ps(a.val, c.val); + __m128 u1 = _mm_unpacklo_ps(b.val, d.val); + __m128 u2 = _mm_unpackhi_ps(a.val, c.val); + __m128 u3 = _mm_unpackhi_ps(b.val, d.val); + __m128 v0 = _mm_unpacklo_ps(u0, u1); + __m128 v2 = _mm_unpacklo_ps(u2, u3); + __m128 v1 = _mm_unpackhi_ps(u0, u1); + __m128 v3 = _mm_unpackhi_ps(u2, u3); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm_stream_ps(ptr, v0); + _mm_stream_ps(ptr + 4, v1); + _mm_stream_ps(ptr + 8, v2); + _mm_stream_ps(ptr + 12, v3); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm_store_ps(ptr, v0); + _mm_store_ps(ptr + 4, v1); + _mm_store_ps(ptr + 8, v2); + _mm_store_ps(ptr + 12, v3); + } + else + { + _mm_storeu_ps(ptr, v0); + _mm_storeu_ps(ptr + 4, v1); + _mm_storeu_ps(ptr + 8, v2); + _mm_storeu_ps(ptr + 12, v3); + } +} + +inline void v_store_interleave(uint64 *ptr, const v_uint64x2& a, const v_uint64x2& b, + hal::StoreMode mode = hal::STORE_UNALIGNED) +{ + __m128i v0 = _mm_unpacklo_epi64(a.val, b.val); + __m128i v1 = _mm_unpackhi_epi64(a.val, b.val); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm_stream_si128((__m128i*)(ptr), v0); + _mm_stream_si128((__m128i*)(ptr + 2), v1); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm_store_si128((__m128i*)(ptr), v0); + _mm_store_si128((__m128i*)(ptr + 2), v1); + } + else + { + _mm_storeu_si128((__m128i*)(ptr), v0); + _mm_storeu_si128((__m128i*)(ptr + 2), v1); + } +} + +inline void v_store_interleave(uint64 *ptr, const v_uint64x2& a, const v_uint64x2& b, + const v_uint64x2& c, hal::StoreMode mode = hal::STORE_UNALIGNED) +{ + __m128i v0 = _mm_unpacklo_epi64(a.val, b.val); + __m128i v1 = _mm_unpacklo_epi64(c.val, _mm_unpackhi_epi64(a.val, a.val)); + __m128i v2 = _mm_unpackhi_epi64(b.val, c.val); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm_stream_si128((__m128i*)(ptr), v0); + _mm_stream_si128((__m128i*)(ptr + 2), v1); + _mm_stream_si128((__m128i*)(ptr + 4), v2); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm_store_si128((__m128i*)(ptr), v0); + _mm_store_si128((__m128i*)(ptr + 2), v1); + _mm_store_si128((__m128i*)(ptr + 4), v2); + } + else + { + _mm_storeu_si128((__m128i*)(ptr), v0); + _mm_storeu_si128((__m128i*)(ptr + 2), v1); + _mm_storeu_si128((__m128i*)(ptr + 4), v2); + } +} + +inline void v_store_interleave(uint64 *ptr, const v_uint64x2& a, const v_uint64x2& b, + const v_uint64x2& c, const v_uint64x2& d, + hal::StoreMode mode = hal::STORE_UNALIGNED) +{ + __m128i v0 = _mm_unpacklo_epi64(a.val, b.val); + __m128i v1 = _mm_unpacklo_epi64(c.val, d.val); + __m128i v2 = _mm_unpackhi_epi64(a.val, b.val); + __m128i v3 = _mm_unpackhi_epi64(c.val, d.val); + + if( mode == hal::STORE_ALIGNED_NOCACHE ) + { + _mm_stream_si128((__m128i*)(ptr), v0); + _mm_stream_si128((__m128i*)(ptr + 2), v1); + _mm_stream_si128((__m128i*)(ptr + 4), v2); + _mm_stream_si128((__m128i*)(ptr + 6), v3); + } + else if( mode == hal::STORE_ALIGNED ) + { + _mm_store_si128((__m128i*)(ptr), v0); + _mm_store_si128((__m128i*)(ptr + 2), v1); + _mm_store_si128((__m128i*)(ptr + 4), v2); + _mm_store_si128((__m128i*)(ptr + 6), v3); + } + else + { + _mm_storeu_si128((__m128i*)(ptr), v0); + _mm_storeu_si128((__m128i*)(ptr + 2), v1); + _mm_storeu_si128((__m128i*)(ptr + 4), v2); + _mm_storeu_si128((__m128i*)(ptr + 6), v3); + } +} + +#define OPENCV_HAL_IMPL_SSE_LOADSTORE_INTERLEAVE(_Tpvec0, _Tp0, suffix0, _Tpvec1, _Tp1, suffix1) \ +inline void v_load_deinterleave( const _Tp0* ptr, _Tpvec0& a0, _Tpvec0& b0 ) \ +{ \ + _Tpvec1 a1, b1; \ + v_load_deinterleave((const _Tp1*)ptr, a1, b1); \ + a0 = v_reinterpret_as_##suffix0(a1); \ + b0 = v_reinterpret_as_##suffix0(b1); \ +} \ +inline void v_load_deinterleave( const _Tp0* ptr, _Tpvec0& a0, _Tpvec0& b0, _Tpvec0& c0 ) \ +{ \ + _Tpvec1 a1, b1, c1; \ + v_load_deinterleave((const _Tp1*)ptr, a1, b1, c1); \ + a0 = v_reinterpret_as_##suffix0(a1); \ + b0 = v_reinterpret_as_##suffix0(b1); \ + c0 = v_reinterpret_as_##suffix0(c1); \ +} \ +inline void v_load_deinterleave( const _Tp0* ptr, _Tpvec0& a0, _Tpvec0& b0, _Tpvec0& c0, _Tpvec0& d0 ) \ +{ \ + _Tpvec1 a1, b1, c1, d1; \ + v_load_deinterleave((const _Tp1*)ptr, a1, b1, c1, d1); \ + a0 = v_reinterpret_as_##suffix0(a1); \ + b0 = v_reinterpret_as_##suffix0(b1); \ + c0 = v_reinterpret_as_##suffix0(c1); \ + d0 = v_reinterpret_as_##suffix0(d1); \ +} \ +inline void v_store_interleave( _Tp0* ptr, const _Tpvec0& a0, const _Tpvec0& b0, \ + hal::StoreMode mode = hal::STORE_UNALIGNED ) \ +{ \ + _Tpvec1 a1 = v_reinterpret_as_##suffix1(a0); \ + _Tpvec1 b1 = v_reinterpret_as_##suffix1(b0); \ + v_store_interleave((_Tp1*)ptr, a1, b1, mode); \ +} \ +inline void v_store_interleave( _Tp0* ptr, const _Tpvec0& a0, const _Tpvec0& b0, \ + const _Tpvec0& c0, hal::StoreMode mode = hal::STORE_UNALIGNED ) \ +{ \ + _Tpvec1 a1 = v_reinterpret_as_##suffix1(a0); \ + _Tpvec1 b1 = v_reinterpret_as_##suffix1(b0); \ + _Tpvec1 c1 = v_reinterpret_as_##suffix1(c0); \ + v_store_interleave((_Tp1*)ptr, a1, b1, c1, mode); \ +} \ +inline void v_store_interleave( _Tp0* ptr, const _Tpvec0& a0, const _Tpvec0& b0, \ + const _Tpvec0& c0, const _Tpvec0& d0, \ + hal::StoreMode mode = hal::STORE_UNALIGNED ) \ +{ \ + _Tpvec1 a1 = v_reinterpret_as_##suffix1(a0); \ + _Tpvec1 b1 = v_reinterpret_as_##suffix1(b0); \ + _Tpvec1 c1 = v_reinterpret_as_##suffix1(c0); \ + _Tpvec1 d1 = v_reinterpret_as_##suffix1(d0); \ + v_store_interleave((_Tp1*)ptr, a1, b1, c1, d1, mode); \ +} + +OPENCV_HAL_IMPL_SSE_LOADSTORE_INTERLEAVE(v_int8x16, schar, s8, v_uint8x16, uchar, u8) +OPENCV_HAL_IMPL_SSE_LOADSTORE_INTERLEAVE(v_int16x8, short, s16, v_uint16x8, ushort, u16) +OPENCV_HAL_IMPL_SSE_LOADSTORE_INTERLEAVE(v_int32x4, int, s32, v_uint32x4, unsigned, u32) +OPENCV_HAL_IMPL_SSE_LOADSTORE_INTERLEAVE(v_int64x2, int64, s64, v_uint64x2, uint64, u64) +OPENCV_HAL_IMPL_SSE_LOADSTORE_INTERLEAVE(v_float64x2, double, f64, v_uint64x2, uint64, u64) + +inline v_float32x4 v_cvt_f32(const v_int32x4& a) +{ + return v_float32x4(_mm_cvtepi32_ps(a.val)); +} + +inline v_float32x4 v_cvt_f32(const v_float64x2& a) +{ + return v_float32x4(_mm_cvtpd_ps(a.val)); +} + +inline v_float32x4 v_cvt_f32(const v_float64x2& a, const v_float64x2& b) +{ + return v_float32x4(_mm_movelh_ps(_mm_cvtpd_ps(a.val), _mm_cvtpd_ps(b.val))); +} + +inline v_float64x2 v_cvt_f64(const v_int32x4& a) +{ + return v_float64x2(_mm_cvtepi32_pd(a.val)); +} + +inline v_float64x2 v_cvt_f64_high(const v_int32x4& a) +{ + return v_float64x2(_mm_cvtepi32_pd(_mm_srli_si128(a.val,8))); +} + +inline v_float64x2 v_cvt_f64(const v_float32x4& a) +{ + return v_float64x2(_mm_cvtps_pd(a.val)); +} + +inline v_float64x2 v_cvt_f64_high(const v_float32x4& a) +{ + return v_float64x2(_mm_cvtps_pd(_mm_movehl_ps(a.val, a.val))); +} + +#if CV_FP16 +inline v_float32x4 v128_load_fp16_f32(const short* ptr) +{ + return v_float32x4(_mm_cvtph_ps(_mm_loadu_si128((const __m128i*)ptr))); +} + +inline void v_store_fp16(short* ptr, const v_float32x4& a) +{ + __m128i fp16_value = _mm_cvtps_ph(a.val, 0); + _mm_storel_epi64((__m128i*)ptr, fp16_value); +} +#endif + +////////////// Lookup table access //////////////////// + +inline v_int32x4 v_lut(const int* tab, const v_int32x4& idxvec) +{ + int CV_DECL_ALIGNED(32) idx[4]; + v_store_aligned(idx, idxvec); + return v_int32x4(_mm_setr_epi32(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]])); +} + +inline v_float32x4 v_lut(const float* tab, const v_int32x4& idxvec) +{ + int CV_DECL_ALIGNED(32) idx[4]; + v_store_aligned(idx, idxvec); + return v_float32x4(_mm_setr_ps(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]])); +} + +inline v_float64x2 v_lut(const double* tab, const v_int32x4& idxvec) +{ + int idx[2]; + v_store_low(idx, idxvec); + return v_float64x2(_mm_setr_pd(tab[idx[0]], tab[idx[1]])); +} + +// loads pairs from the table and deinterleaves them, e.g. returns: +// x = (tab[idxvec[0], tab[idxvec[1]], tab[idxvec[2]], tab[idxvec[3]]), +// y = (tab[idxvec[0]+1], tab[idxvec[1]+1], tab[idxvec[2]+1], tab[idxvec[3]+1]) +// note that the indices are float's indices, not the float-pair indices. +// in theory, this function can be used to implement bilinear interpolation, +// when idxvec are the offsets within the image. +inline void v_lut_deinterleave(const float* tab, const v_int32x4& idxvec, v_float32x4& x, v_float32x4& y) +{ + int CV_DECL_ALIGNED(32) idx[4]; + v_store_aligned(idx, idxvec); + __m128 z = _mm_setzero_ps(); + __m128 xy01 = _mm_loadl_pi(z, (__m64*)(tab + idx[0])); + __m128 xy23 = _mm_loadl_pi(z, (__m64*)(tab + idx[2])); + xy01 = _mm_loadh_pi(xy01, (__m64*)(tab + idx[1])); + xy23 = _mm_loadh_pi(xy23, (__m64*)(tab + idx[3])); + __m128 xxyy02 = _mm_unpacklo_ps(xy01, xy23); + __m128 xxyy13 = _mm_unpackhi_ps(xy01, xy23); + x = v_float32x4(_mm_unpacklo_ps(xxyy02, xxyy13)); + y = v_float32x4(_mm_unpackhi_ps(xxyy02, xxyy13)); +} + +inline void v_lut_deinterleave(const double* tab, const v_int32x4& idxvec, v_float64x2& x, v_float64x2& y) +{ + int idx[2]; + v_store_low(idx, idxvec); + __m128d xy0 = _mm_loadu_pd(tab + idx[0]); + __m128d xy1 = _mm_loadu_pd(tab + idx[1]); + x = v_float64x2(_mm_unpacklo_pd(xy0, xy1)); + y = v_float64x2(_mm_unpackhi_pd(xy0, xy1)); +} + + +////////////// FP16 support /////////////////////////// + +inline v_float32x4 v_load_expand(const float16_t* ptr) +{ + const __m128i z = _mm_setzero_si128(), delta = _mm_set1_epi32(0x38000000); + const __m128i signmask = _mm_set1_epi32(0x80000000), maxexp = _mm_set1_epi32(0x7c000000); + const __m128 deltaf = _mm_castsi128_ps(_mm_set1_epi32(0x38800000)); + __m128i bits = _mm_unpacklo_epi16(z, _mm_loadl_epi64((const __m128i*)ptr)); // h << 16 + __m128i e = _mm_and_si128(bits, maxexp), sign = _mm_and_si128(bits, signmask); + __m128i t = _mm_add_epi32(_mm_srli_epi32(_mm_xor_si128(bits, sign), 3), delta); // ((h & 0x7fff) << 13) + delta + __m128i zt = _mm_castps_si128(_mm_sub_ps(_mm_castsi128_ps(_mm_add_epi32(t, _mm_set1_epi32(1 << 23))), deltaf)); + + t = _mm_add_epi32(t, _mm_and_si128(delta, _mm_cmpeq_epi32(maxexp, e))); + __m128i zmask = _mm_cmpeq_epi32(e, z); + __m128i ft = v_select_si128(zmask, zt, t); + return v_float32x4(_mm_castsi128_ps(_mm_or_si128(ft, sign))); +} + +inline void v_pack_store(float16_t* ptr, const v_float32x4& v) +{ + const __m128i signmask = _mm_set1_epi32(0x80000000); + const __m128i rval = _mm_set1_epi32(0x3f000000); + + __m128i t = _mm_castps_si128(v.val); + __m128i sign = _mm_srai_epi32(_mm_and_si128(t, signmask), 16); + t = _mm_andnot_si128(signmask, t); + + __m128i finitemask = _mm_cmpgt_epi32(_mm_set1_epi32(0x47800000), t); + __m128i isnan = _mm_cmpgt_epi32(t, _mm_set1_epi32(0x7f800000)); + __m128i naninf = v_select_si128(isnan, _mm_set1_epi32(0x7e00), _mm_set1_epi32(0x7c00)); + __m128i tinymask = _mm_cmpgt_epi32(_mm_set1_epi32(0x38800000), t); + __m128i tt = _mm_castps_si128(_mm_add_ps(_mm_castsi128_ps(t), _mm_castsi128_ps(rval))); + tt = _mm_sub_epi32(tt, rval); + __m128i odd = _mm_and_si128(_mm_srli_epi32(t, 13), _mm_set1_epi32(1)); + __m128i nt = _mm_add_epi32(t, _mm_set1_epi32(0xc8000fff)); + nt = _mm_srli_epi32(_mm_add_epi32(nt, odd), 13); + t = v_select_si128(tinymask, tt, nt); + t = v_select_si128(finitemask, t, naninf); + t = _mm_or_si128(t, sign); + t = _mm_packs_epi32(t, t); + _mm_storel_epi64((__m128i*)ptr, t); +} + +inline void v_cleanup() {} + +//! @name Check SIMD support +//! @{ +//! @brief Check CPU capability of SIMD operation +static inline bool hasSIMD128() +{ + return (CV_CPU_HAS_SUPPORT_SSE2) ? true : false; +} + +//! @} + +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END + +//! @endcond + +} + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/hal/intrin_sse_em.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/hal/intrin_sse_em.hpp new file mode 100644 index 0000000000000000000000000000000000000000..be2766847ca106619dc43229ba6ffdf2a8dc8f32 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/hal/intrin_sse_em.hpp @@ -0,0 +1,167 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html + +#ifndef OPENCV_HAL_INTRIN_SSE_EM_HPP +#define OPENCV_HAL_INTRIN_SSE_EM_HPP + +namespace cv +{ + +//! @cond IGNORED + +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN + +#define OPENCV_HAL_SSE_WRAP_1(fun, tp) \ + inline tp _v128_##fun(const tp& a) \ + { return _mm_##fun(a); } + +#define OPENCV_HAL_SSE_WRAP_2(fun, tp) \ + inline tp _v128_##fun(const tp& a, const tp& b) \ + { return _mm_##fun(a, b); } + +#define OPENCV_HAL_SSE_WRAP_3(fun, tp) \ + inline tp _v128_##fun(const tp& a, const tp& b, const tp& c) \ + { return _mm_##fun(a, b, c); } + +///////////////////////////// XOP ///////////////////////////// + +// [todo] define CV_XOP +#if 1 // CV_XOP +inline __m128i _v128_comgt_epu32(const __m128i& a, const __m128i& b) +{ + const __m128i delta = _mm_set1_epi32((int)0x80000000); + return _mm_cmpgt_epi32(_mm_xor_si128(a, delta), _mm_xor_si128(b, delta)); +} +// wrapping XOP +#else +OPENCV_HAL_SSE_WRAP_2(_v128_comgt_epu32, __m128i) +#endif // !CV_XOP + +///////////////////////////// SSE4.1 ///////////////////////////// + +#if !CV_SSE4_1 + +/** Swizzle **/ +inline __m128i _v128_blendv_epi8(const __m128i& a, const __m128i& b, const __m128i& mask) +{ return _mm_xor_si128(a, _mm_and_si128(_mm_xor_si128(b, a), mask)); } + +/** Convert **/ +// 8 >> 16 +inline __m128i _v128_cvtepu8_epi16(const __m128i& a) +{ + const __m128i z = _mm_setzero_si128(); + return _mm_unpacklo_epi8(a, z); +} +inline __m128i _v128_cvtepi8_epi16(const __m128i& a) +{ return _mm_srai_epi16(_mm_unpacklo_epi8(a, a), 8); } +// 8 >> 32 +inline __m128i _v128_cvtepu8_epi32(const __m128i& a) +{ + const __m128i z = _mm_setzero_si128(); + return _mm_unpacklo_epi16(_mm_unpacklo_epi8(a, z), z); +} +inline __m128i _v128_cvtepi8_epi32(const __m128i& a) +{ + __m128i r = _mm_unpacklo_epi8(a, a); + r = _mm_unpacklo_epi8(r, r); + return _mm_srai_epi32(r, 24); +} +// 16 >> 32 +inline __m128i _v128_cvtepu16_epi32(const __m128i& a) +{ + const __m128i z = _mm_setzero_si128(); + return _mm_unpacklo_epi16(a, z); +} +inline __m128i _v128_cvtepi16_epi32(const __m128i& a) +{ return _mm_srai_epi32(_mm_unpacklo_epi16(a, a), 16); } +// 32 >> 64 +inline __m128i _v128_cvtepu32_epi64(const __m128i& a) +{ + const __m128i z = _mm_setzero_si128(); + return _mm_unpacklo_epi32(a, z); +} +inline __m128i _v128_cvtepi32_epi64(const __m128i& a) +{ return _mm_unpacklo_epi32(a, _mm_srai_epi32(a, 31)); } + +/** Arithmetic **/ +inline __m128i _v128_mullo_epi32(const __m128i& a, const __m128i& b) +{ + __m128i c0 = _mm_mul_epu32(a, b); + __m128i c1 = _mm_mul_epu32(_mm_srli_epi64(a, 32), _mm_srli_epi64(b, 32)); + __m128i d0 = _mm_unpacklo_epi32(c0, c1); + __m128i d1 = _mm_unpackhi_epi32(c0, c1); + return _mm_unpacklo_epi64(d0, d1); +} + +/** Math **/ +inline __m128i _v128_min_epu32(const __m128i& a, const __m128i& b) +{ return _v128_blendv_epi8(a, b, _v128_comgt_epu32(a, b)); } + +// wrapping SSE4.1 +#else +OPENCV_HAL_SSE_WRAP_1(cvtepu8_epi16, __m128i) +OPENCV_HAL_SSE_WRAP_1(cvtepi8_epi16, __m128i) +OPENCV_HAL_SSE_WRAP_1(cvtepu8_epi32, __m128i) +OPENCV_HAL_SSE_WRAP_1(cvtepi8_epi32, __m128i) +OPENCV_HAL_SSE_WRAP_1(cvtepu16_epi32, __m128i) +OPENCV_HAL_SSE_WRAP_1(cvtepi16_epi32, __m128i) +OPENCV_HAL_SSE_WRAP_1(cvtepu32_epi64, __m128i) +OPENCV_HAL_SSE_WRAP_1(cvtepi32_epi64, __m128i) +OPENCV_HAL_SSE_WRAP_2(min_epu32, __m128i) +OPENCV_HAL_SSE_WRAP_2(mullo_epi32, __m128i) +OPENCV_HAL_SSE_WRAP_3(blendv_epi8, __m128i) +#endif // !CV_SSE4_1 + +///////////////////////////// Revolutionary ///////////////////////////// + +/** Convert **/ +// 16 << 8 +inline __m128i _v128_cvtepu8_epi16_high(const __m128i& a) +{ + const __m128i z = _mm_setzero_si128(); + return _mm_unpackhi_epi8(a, z); +} +inline __m128i _v128_cvtepi8_epi16_high(const __m128i& a) +{ return _mm_srai_epi16(_mm_unpackhi_epi8(a, a), 8); } +// 32 << 16 +inline __m128i _v128_cvtepu16_epi32_high(const __m128i& a) +{ + const __m128i z = _mm_setzero_si128(); + return _mm_unpackhi_epi16(a, z); +} +inline __m128i _v128_cvtepi16_epi32_high(const __m128i& a) +{ return _mm_srai_epi32(_mm_unpackhi_epi16(a, a), 16); } +// 64 << 32 +inline __m128i _v128_cvtepu32_epi64_high(const __m128i& a) +{ + const __m128i z = _mm_setzero_si128(); + return _mm_unpackhi_epi32(a, z); +} +inline __m128i _v128_cvtepi32_epi64_high(const __m128i& a) +{ return _mm_unpackhi_epi32(a, _mm_srai_epi32(a, 31)); } + +/** Miscellaneous **/ +inline __m128i _v128_packs_epu32(const __m128i& a, const __m128i& b) +{ + const __m128i m = _mm_set1_epi32(65535); + __m128i am = _v128_min_epu32(a, m); + __m128i bm = _v128_min_epu32(b, m); +#if CV_SSE4_1 + return _mm_packus_epi32(am, bm); +#else + const __m128i d = _mm_set1_epi32(32768), nd = _mm_set1_epi16(-32768); + am = _mm_sub_epi32(am, d); + bm = _mm_sub_epi32(bm, d); + am = _mm_packs_epi32(am, bm); + return _mm_sub_epi16(am, nd); +#endif +} + +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END + +//! @endcond + +} // cv:: + +#endif // OPENCV_HAL_INTRIN_SSE_EM_HPP \ No newline at end of file diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/hal/intrin_vsx.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/hal/intrin_vsx.hpp new file mode 100644 index 0000000000000000000000000000000000000000..fce5c444ed3fabd368b4e6b27ad0fdaba18b288d --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/hal/intrin_vsx.hpp @@ -0,0 +1,1120 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html + +#ifndef OPENCV_HAL_VSX_HPP +#define OPENCV_HAL_VSX_HPP + +#include +#include "opencv2/core/utility.hpp" + +#define CV_SIMD128 1 +#define CV_SIMD128_64F 1 + +/** + * todo: supporting half precision for power9 + * convert instractions xvcvhpsp, xvcvsphp +**/ + +namespace cv +{ + +//! @cond IGNORED + +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN + +///////// Types //////////// + +struct v_uint8x16 +{ + typedef uchar lane_type; + enum { nlanes = 16 }; + vec_uchar16 val; + + explicit v_uint8x16(const vec_uchar16& v) : val(v) + {} + v_uint8x16() : val(vec_uchar16_z) + {} + v_uint8x16(vec_bchar16 v) : val(vec_uchar16_c(v)) + {} + v_uint8x16(uchar v0, uchar v1, uchar v2, uchar v3, uchar v4, uchar v5, uchar v6, uchar v7, + uchar v8, uchar v9, uchar v10, uchar v11, uchar v12, uchar v13, uchar v14, uchar v15) + : val(vec_uchar16_set(v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15)) + {} + uchar get0() const + { return vec_extract(val, 0); } +}; + +struct v_int8x16 +{ + typedef schar lane_type; + enum { nlanes = 16 }; + vec_char16 val; + + explicit v_int8x16(const vec_char16& v) : val(v) + {} + v_int8x16() : val(vec_char16_z) + {} + v_int8x16(vec_bchar16 v) : val(vec_char16_c(v)) + {} + v_int8x16(schar v0, schar v1, schar v2, schar v3, schar v4, schar v5, schar v6, schar v7, + schar v8, schar v9, schar v10, schar v11, schar v12, schar v13, schar v14, schar v15) + : val(vec_char16_set(v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15)) + {} + schar get0() const + { return vec_extract(val, 0); } +}; + +struct v_uint16x8 +{ + typedef ushort lane_type; + enum { nlanes = 8 }; + vec_ushort8 val; + + explicit v_uint16x8(const vec_ushort8& v) : val(v) + {} + v_uint16x8() : val(vec_ushort8_z) + {} + v_uint16x8(vec_bshort8 v) : val(vec_ushort8_c(v)) + {} + v_uint16x8(ushort v0, ushort v1, ushort v2, ushort v3, ushort v4, ushort v5, ushort v6, ushort v7) + : val(vec_ushort8_set(v0, v1, v2, v3, v4, v5, v6, v7)) + {} + ushort get0() const + { return vec_extract(val, 0); } +}; + +struct v_int16x8 +{ + typedef short lane_type; + enum { nlanes = 8 }; + vec_short8 val; + + explicit v_int16x8(const vec_short8& v) : val(v) + {} + v_int16x8() : val(vec_short8_z) + {} + v_int16x8(vec_bshort8 v) : val(vec_short8_c(v)) + {} + v_int16x8(short v0, short v1, short v2, short v3, short v4, short v5, short v6, short v7) + : val(vec_short8_set(v0, v1, v2, v3, v4, v5, v6, v7)) + {} + short get0() const + { return vec_extract(val, 0); } +}; + +struct v_uint32x4 +{ + typedef unsigned lane_type; + enum { nlanes = 4 }; + vec_uint4 val; + + explicit v_uint32x4(const vec_uint4& v) : val(v) + {} + v_uint32x4() : val(vec_uint4_z) + {} + v_uint32x4(vec_bint4 v) : val(vec_uint4_c(v)) + {} + v_uint32x4(unsigned v0, unsigned v1, unsigned v2, unsigned v3) : val(vec_uint4_set(v0, v1, v2, v3)) + {} + uint get0() const + { return vec_extract(val, 0); } +}; + +struct v_int32x4 +{ + typedef int lane_type; + enum { nlanes = 4 }; + vec_int4 val; + + explicit v_int32x4(const vec_int4& v) : val(v) + {} + v_int32x4() : val(vec_int4_z) + {} + v_int32x4(vec_bint4 v) : val(vec_int4_c(v)) + {} + v_int32x4(int v0, int v1, int v2, int v3) : val(vec_int4_set(v0, v1, v2, v3)) + {} + int get0() const + { return vec_extract(val, 0); } +}; + +struct v_float32x4 +{ + typedef float lane_type; + enum { nlanes = 4 }; + vec_float4 val; + + explicit v_float32x4(const vec_float4& v) : val(v) + {} + v_float32x4() : val(vec_float4_z) + {} + v_float32x4(vec_bint4 v) : val(vec_float4_c(v)) + {} + v_float32x4(float v0, float v1, float v2, float v3) : val(vec_float4_set(v0, v1, v2, v3)) + {} + float get0() const + { return vec_extract(val, 0); } +}; + +struct v_uint64x2 +{ + typedef uint64 lane_type; + enum { nlanes = 2 }; + vec_udword2 val; + + explicit v_uint64x2(const vec_udword2& v) : val(v) + {} + v_uint64x2() : val(vec_udword2_z) + {} + v_uint64x2(vec_bdword2 v) : val(vec_udword2_c(v)) + {} + v_uint64x2(uint64 v0, uint64 v1) : val(vec_udword2_set(v0, v1)) + {} + uint64 get0() const + { return vec_extract(val, 0); } +}; + +struct v_int64x2 +{ + typedef int64 lane_type; + enum { nlanes = 2 }; + vec_dword2 val; + + explicit v_int64x2(const vec_dword2& v) : val(v) + {} + v_int64x2() : val(vec_dword2_z) + {} + v_int64x2(vec_bdword2 v) : val(vec_dword2_c(v)) + {} + v_int64x2(int64 v0, int64 v1) : val(vec_dword2_set(v0, v1)) + {} + int64 get0() const + { return vec_extract(val, 0); } +}; + +struct v_float64x2 +{ + typedef double lane_type; + enum { nlanes = 2 }; + vec_double2 val; + + explicit v_float64x2(const vec_double2& v) : val(v) + {} + v_float64x2() : val(vec_double2_z) + {} + v_float64x2(vec_bdword2 v) : val(vec_double2_c(v)) + {} + v_float64x2(double v0, double v1) : val(vec_double2_set(v0, v1)) + {} + double get0() const + { return vec_extract(val, 0); } +}; + +//////////////// Load and store operations /////////////// + +/* + * clang-5 aborted during parse "vec_xxx_c" only if it's + * inside a function template which is defined by preprocessor macro. + * + * if vec_xxx_c defined as C++ cast, clang-5 will pass it +*/ +#define OPENCV_HAL_IMPL_VSX_INITVEC(_Tpvec, _Tp, suffix, cast) \ +inline _Tpvec v_setzero_##suffix() { return _Tpvec(); } \ +inline _Tpvec v_setall_##suffix(_Tp v) { return _Tpvec(vec_splats((_Tp)v));} \ +template inline _Tpvec v_reinterpret_as_##suffix(const _Tpvec0 &a) \ +{ return _Tpvec((cast)a.val); } + +OPENCV_HAL_IMPL_VSX_INITVEC(v_uint8x16, uchar, u8, vec_uchar16) +OPENCV_HAL_IMPL_VSX_INITVEC(v_int8x16, schar, s8, vec_char16) +OPENCV_HAL_IMPL_VSX_INITVEC(v_uint16x8, ushort, u16, vec_ushort8) +OPENCV_HAL_IMPL_VSX_INITVEC(v_int16x8, short, s16, vec_short8) +OPENCV_HAL_IMPL_VSX_INITVEC(v_uint32x4, uint, u32, vec_uint4) +OPENCV_HAL_IMPL_VSX_INITVEC(v_int32x4, int, s32, vec_int4) +OPENCV_HAL_IMPL_VSX_INITVEC(v_uint64x2, uint64, u64, vec_udword2) +OPENCV_HAL_IMPL_VSX_INITVEC(v_int64x2, int64, s64, vec_dword2) +OPENCV_HAL_IMPL_VSX_INITVEC(v_float32x4, float, f32, vec_float4) +OPENCV_HAL_IMPL_VSX_INITVEC(v_float64x2, double, f64, vec_double2) + +#define OPENCV_HAL_IMPL_VSX_LOADSTORE_C(_Tpvec, _Tp, ld, ld_a, st, st_a) \ +inline _Tpvec v_load(const _Tp* ptr) \ +{ return _Tpvec(ld(0, ptr)); } \ +inline _Tpvec v_load_aligned(VSX_UNUSED(const _Tp* ptr)) \ +{ return _Tpvec(ld_a(0, ptr)); } \ +inline _Tpvec v_load_low(const _Tp* ptr) \ +{ return _Tpvec(vec_ld_l8(ptr)); } \ +inline _Tpvec v_load_halves(const _Tp* ptr0, const _Tp* ptr1) \ +{ return _Tpvec(vec_mergesqh(vec_ld_l8(ptr0), vec_ld_l8(ptr1))); } \ +inline void v_store(_Tp* ptr, const _Tpvec& a) \ +{ st(a.val, 0, ptr); } \ +inline void v_store_aligned(VSX_UNUSED(_Tp* ptr), const _Tpvec& a) \ +{ st_a(a.val, 0, ptr); } \ +inline void v_store_aligned_nocache(VSX_UNUSED(_Tp* ptr), const _Tpvec& a) \ +{ st_a(a.val, 0, ptr); } \ +inline void v_store(_Tp* ptr, const _Tpvec& a, hal::StoreMode mode) \ +{ if(mode == hal::STORE_UNALIGNED) st(a.val, 0, ptr); else st_a(a.val, 0, ptr); } \ +inline void v_store_low(_Tp* ptr, const _Tpvec& a) \ +{ vec_st_l8(a.val, ptr); } \ +inline void v_store_high(_Tp* ptr, const _Tpvec& a) \ +{ vec_st_h8(a.val, ptr); } + +#define OPENCV_HAL_IMPL_VSX_LOADSTORE(_Tpvec, _Tp) \ +OPENCV_HAL_IMPL_VSX_LOADSTORE_C(_Tpvec, _Tp, vsx_ld, vec_ld, vsx_st, vec_st) + +OPENCV_HAL_IMPL_VSX_LOADSTORE(v_uint8x16, uchar) +OPENCV_HAL_IMPL_VSX_LOADSTORE(v_int8x16, schar) +OPENCV_HAL_IMPL_VSX_LOADSTORE(v_uint16x8, ushort) +OPENCV_HAL_IMPL_VSX_LOADSTORE(v_int16x8, short) +OPENCV_HAL_IMPL_VSX_LOADSTORE(v_uint32x4, uint) +OPENCV_HAL_IMPL_VSX_LOADSTORE(v_int32x4, int) +OPENCV_HAL_IMPL_VSX_LOADSTORE(v_float32x4, float) + +OPENCV_HAL_IMPL_VSX_LOADSTORE_C(v_float64x2, double, vsx_ld, vsx_ld, vsx_st, vsx_st) +OPENCV_HAL_IMPL_VSX_LOADSTORE_C(v_uint64x2, uint64, vsx_ld2, vsx_ld2, vsx_st2, vsx_st2) +OPENCV_HAL_IMPL_VSX_LOADSTORE_C(v_int64x2, int64, vsx_ld2, vsx_ld2, vsx_st2, vsx_st2) + +//////////////// Value reordering /////////////// + +/* de&interleave */ +#define OPENCV_HAL_IMPL_VSX_INTERLEAVE(_Tp, _Tpvec) \ +inline void v_load_deinterleave(const _Tp* ptr, _Tpvec& a, _Tpvec& b) \ +{ vec_ld_deinterleave(ptr, a.val, b.val);} \ +inline void v_load_deinterleave(const _Tp* ptr, _Tpvec& a, \ + _Tpvec& b, _Tpvec& c) \ +{ vec_ld_deinterleave(ptr, a.val, b.val, c.val); } \ +inline void v_load_deinterleave(const _Tp* ptr, _Tpvec& a, _Tpvec& b, \ + _Tpvec& c, _Tpvec& d) \ +{ vec_ld_deinterleave(ptr, a.val, b.val, c.val, d.val); } \ +inline void v_store_interleave(_Tp* ptr, const _Tpvec& a, const _Tpvec& b, \ + hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \ +{ vec_st_interleave(a.val, b.val, ptr); } \ +inline void v_store_interleave(_Tp* ptr, const _Tpvec& a, \ + const _Tpvec& b, const _Tpvec& c, \ + hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \ +{ vec_st_interleave(a.val, b.val, c.val, ptr); } \ +inline void v_store_interleave(_Tp* ptr, const _Tpvec& a, const _Tpvec& b, \ + const _Tpvec& c, const _Tpvec& d, \ + hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \ +{ vec_st_interleave(a.val, b.val, c.val, d.val, ptr); } + +OPENCV_HAL_IMPL_VSX_INTERLEAVE(uchar, v_uint8x16) +OPENCV_HAL_IMPL_VSX_INTERLEAVE(schar, v_int8x16) +OPENCV_HAL_IMPL_VSX_INTERLEAVE(ushort, v_uint16x8) +OPENCV_HAL_IMPL_VSX_INTERLEAVE(short, v_int16x8) +OPENCV_HAL_IMPL_VSX_INTERLEAVE(uint, v_uint32x4) +OPENCV_HAL_IMPL_VSX_INTERLEAVE(int, v_int32x4) +OPENCV_HAL_IMPL_VSX_INTERLEAVE(float, v_float32x4) +OPENCV_HAL_IMPL_VSX_INTERLEAVE(double, v_float64x2) +OPENCV_HAL_IMPL_VSX_INTERLEAVE(int64, v_int64x2) +OPENCV_HAL_IMPL_VSX_INTERLEAVE(uint64, v_uint64x2) + +/* Expand */ +#define OPENCV_HAL_IMPL_VSX_EXPAND(_Tpvec, _Tpwvec, _Tp, fl, fh) \ +inline void v_expand(const _Tpvec& a, _Tpwvec& b0, _Tpwvec& b1) \ +{ \ + b0.val = fh(a.val); \ + b1.val = fl(a.val); \ +} \ +inline _Tpwvec v_expand_low(const _Tpvec& a) \ +{ return _Tpwvec(fh(a.val)); } \ +inline _Tpwvec v_expand_high(const _Tpvec& a) \ +{ return _Tpwvec(fl(a.val)); } \ +inline _Tpwvec v_load_expand(const _Tp* ptr) \ +{ return _Tpwvec(fh(vec_ld_l8(ptr))); } + +OPENCV_HAL_IMPL_VSX_EXPAND(v_uint8x16, v_uint16x8, uchar, vec_unpacklu, vec_unpackhu) +OPENCV_HAL_IMPL_VSX_EXPAND(v_int8x16, v_int16x8, schar, vec_unpackl, vec_unpackh) +OPENCV_HAL_IMPL_VSX_EXPAND(v_uint16x8, v_uint32x4, ushort, vec_unpacklu, vec_unpackhu) +OPENCV_HAL_IMPL_VSX_EXPAND(v_int16x8, v_int32x4, short, vec_unpackl, vec_unpackh) +OPENCV_HAL_IMPL_VSX_EXPAND(v_uint32x4, v_uint64x2, uint, vec_unpacklu, vec_unpackhu) +OPENCV_HAL_IMPL_VSX_EXPAND(v_int32x4, v_int64x2, int, vec_unpackl, vec_unpackh) + +inline v_uint32x4 v_load_expand_q(const uchar* ptr) +{ return v_uint32x4(vec_uint4_set(ptr[0], ptr[1], ptr[2], ptr[3])); } + +inline v_int32x4 v_load_expand_q(const schar* ptr) +{ return v_int32x4(vec_int4_set(ptr[0], ptr[1], ptr[2], ptr[3])); } + +/* pack */ +#define OPENCV_HAL_IMPL_VSX_PACK(_Tpvec, _Tp, _Tpwvec, _Tpvn, _Tpdel, sfnc, pkfnc, addfnc, pack) \ +inline _Tpvec v_##pack(const _Tpwvec& a, const _Tpwvec& b) \ +{ \ + return _Tpvec(pkfnc(a.val, b.val)); \ +} \ +inline void v_##pack##_store(_Tp* ptr, const _Tpwvec& a) \ +{ \ + vec_st_l8(pkfnc(a.val, a.val), ptr); \ +} \ +template \ +inline _Tpvec v_rshr_##pack(const _Tpwvec& a, const _Tpwvec& b) \ +{ \ + const __vector _Tpvn vn = vec_splats((_Tpvn)n); \ + const __vector _Tpdel delta = vec_splats((_Tpdel)((_Tpdel)1 << (n-1))); \ + return _Tpvec(pkfnc(sfnc(addfnc(a.val, delta), vn), sfnc(addfnc(b.val, delta), vn))); \ +} \ +template \ +inline void v_rshr_##pack##_store(_Tp* ptr, const _Tpwvec& a) \ +{ \ + const __vector _Tpvn vn = vec_splats((_Tpvn)n); \ + const __vector _Tpdel delta = vec_splats((_Tpdel)((_Tpdel)1 << (n-1))); \ + vec_st_l8(pkfnc(sfnc(addfnc(a.val, delta), vn), delta), ptr); \ +} + +OPENCV_HAL_IMPL_VSX_PACK(v_uint8x16, uchar, v_uint16x8, unsigned short, unsigned short, + vec_sr, vec_packs, vec_adds, pack) +OPENCV_HAL_IMPL_VSX_PACK(v_int8x16, schar, v_int16x8, unsigned short, short, + vec_sra, vec_packs, vec_adds, pack) + +OPENCV_HAL_IMPL_VSX_PACK(v_uint16x8, ushort, v_uint32x4, unsigned int, unsigned int, + vec_sr, vec_packs, vec_add, pack) +OPENCV_HAL_IMPL_VSX_PACK(v_int16x8, short, v_int32x4, unsigned int, int, + vec_sra, vec_packs, vec_add, pack) + +OPENCV_HAL_IMPL_VSX_PACK(v_uint32x4, uint, v_uint64x2, unsigned long long, unsigned long long, + vec_sr, vec_pack, vec_add, pack) +OPENCV_HAL_IMPL_VSX_PACK(v_int32x4, int, v_int64x2, unsigned long long, long long, + vec_sra, vec_pack, vec_add, pack) + +OPENCV_HAL_IMPL_VSX_PACK(v_uint8x16, uchar, v_int16x8, unsigned short, short, + vec_sra, vec_packsu, vec_adds, pack_u) +OPENCV_HAL_IMPL_VSX_PACK(v_uint16x8, ushort, v_int32x4, unsigned int, int, + vec_sra, vec_packsu, vec_add, pack_u) +// Following variant is not implemented on other platforms: +//OPENCV_HAL_IMPL_VSX_PACK(v_uint32x4, uint, v_int64x2, unsigned long long, long long, +// vec_sra, vec_packsu, vec_add, pack_u) + +// pack boolean +inline v_uint8x16 v_pack_b(const v_uint16x8& a, const v_uint16x8& b) +{ + vec_uchar16 ab = vec_pack(a.val, b.val); + return v_uint8x16(ab); +} + +inline v_uint8x16 v_pack_b(const v_uint32x4& a, const v_uint32x4& b, + const v_uint32x4& c, const v_uint32x4& d) +{ + vec_ushort8 ab = vec_pack(a.val, b.val); + vec_ushort8 cd = vec_pack(c.val, d.val); + return v_uint8x16(vec_pack(ab, cd)); +} + +inline v_uint8x16 v_pack_b(const v_uint64x2& a, const v_uint64x2& b, const v_uint64x2& c, + const v_uint64x2& d, const v_uint64x2& e, const v_uint64x2& f, + const v_uint64x2& g, const v_uint64x2& h) +{ + vec_uint4 ab = vec_pack(a.val, b.val); + vec_uint4 cd = vec_pack(c.val, d.val); + vec_uint4 ef = vec_pack(e.val, f.val); + vec_uint4 gh = vec_pack(g.val, h.val); + + vec_ushort8 abcd = vec_pack(ab, cd); + vec_ushort8 efgh = vec_pack(ef, gh); + return v_uint8x16(vec_pack(abcd, efgh)); +} + +/* Recombine */ +template +inline void v_zip(const _Tpvec& a0, const _Tpvec& a1, _Tpvec& b0, _Tpvec& b1) +{ + b0.val = vec_mergeh(a0.val, a1.val); + b1.val = vec_mergel(a0.val, a1.val); +} + +template +inline _Tpvec v_combine_high(const _Tpvec& a, const _Tpvec& b) +{ return _Tpvec(vec_mergesql(a.val, b.val)); } + +template +inline _Tpvec v_combine_low(const _Tpvec& a, const _Tpvec& b) +{ return _Tpvec(vec_mergesqh(a.val, b.val)); } + +template +inline void v_recombine(const _Tpvec& a, const _Tpvec& b, _Tpvec& c, _Tpvec& d) +{ + c.val = vec_mergesqh(a.val, b.val); + d.val = vec_mergesql(a.val, b.val); +} + +////////// Arithmetic, bitwise and comparison operations ///////// + +/* Element-wise binary and unary operations */ +/** Arithmetics **/ +#define OPENCV_HAL_IMPL_VSX_BIN_OP(bin_op, _Tpvec, intrin) \ +inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(intrin(a.val, b.val)); } \ +inline _Tpvec& operator bin_op##= (_Tpvec& a, const _Tpvec& b) \ +{ a.val = intrin(a.val, b.val); return a; } + +OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_uint8x16, vec_adds) +OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_uint8x16, vec_subs) +OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_int8x16, vec_adds) +OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_int8x16, vec_subs) +OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_uint16x8, vec_adds) +OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_uint16x8, vec_subs) +OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_int16x8, vec_adds) +OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_int16x8, vec_subs) +OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_uint32x4, vec_add) +OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_uint32x4, vec_sub) +OPENCV_HAL_IMPL_VSX_BIN_OP(*, v_uint32x4, vec_mul) +OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_int32x4, vec_add) +OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_int32x4, vec_sub) +OPENCV_HAL_IMPL_VSX_BIN_OP(*, v_int32x4, vec_mul) +OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_float32x4, vec_add) +OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_float32x4, vec_sub) +OPENCV_HAL_IMPL_VSX_BIN_OP(*, v_float32x4, vec_mul) +OPENCV_HAL_IMPL_VSX_BIN_OP(/, v_float32x4, vec_div) +OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_float64x2, vec_add) +OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_float64x2, vec_sub) +OPENCV_HAL_IMPL_VSX_BIN_OP(*, v_float64x2, vec_mul) +OPENCV_HAL_IMPL_VSX_BIN_OP(/, v_float64x2, vec_div) +OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_uint64x2, vec_add) +OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_uint64x2, vec_sub) +OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_int64x2, vec_add) +OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_int64x2, vec_sub) + +// saturating multiply +#define OPENCV_HAL_IMPL_VSX_MUL_SAT(_Tpvec, _Tpwvec) \ + inline _Tpvec operator * (const _Tpvec& a, const _Tpvec& b) \ + { \ + _Tpwvec c, d; \ + v_mul_expand(a, b, c, d); \ + return v_pack(c, d); \ + } \ + inline _Tpvec& operator *= (_Tpvec& a, const _Tpvec& b) \ + { a = a * b; return a; } + +OPENCV_HAL_IMPL_VSX_MUL_SAT(v_int8x16, v_int16x8) +OPENCV_HAL_IMPL_VSX_MUL_SAT(v_uint8x16, v_uint16x8) +OPENCV_HAL_IMPL_VSX_MUL_SAT(v_int16x8, v_int32x4) +OPENCV_HAL_IMPL_VSX_MUL_SAT(v_uint16x8, v_uint32x4) + +template +inline void v_mul_expand(const Tvec& a, const Tvec& b, Twvec& c, Twvec& d) +{ + Twvec p0 = Twvec(vec_mule(a.val, b.val)); + Twvec p1 = Twvec(vec_mulo(a.val, b.val)); + v_zip(p0, p1, c, d); +} + +inline void v_mul_expand(const v_uint32x4& a, const v_uint32x4& b, v_uint64x2& c, v_uint64x2& d) +{ + c.val = vec_mul(vec_unpackhu(a.val), vec_unpackhu(b.val)); + d.val = vec_mul(vec_unpacklu(a.val), vec_unpacklu(b.val)); +} + +inline v_int16x8 v_mul_hi(const v_int16x8& a, const v_int16x8& b) +{ + vec_int4 p0 = vec_mule(a.val, b.val); + vec_int4 p1 = vec_mulo(a.val, b.val); + static const vec_uchar16 perm = {2, 3, 18, 19, 6, 7, 22, 23, 10, 11, 26, 27, 14, 15, 30, 31}; + return v_int16x8(vec_perm(vec_short8_c(p0), vec_short8_c(p1), perm)); +} +inline v_uint16x8 v_mul_hi(const v_uint16x8& a, const v_uint16x8& b) +{ + vec_uint4 p0 = vec_mule(a.val, b.val); + vec_uint4 p1 = vec_mulo(a.val, b.val); + static const vec_uchar16 perm = {2, 3, 18, 19, 6, 7, 22, 23, 10, 11, 26, 27, 14, 15, 30, 31}; + return v_uint16x8(vec_perm(vec_ushort8_c(p0), vec_ushort8_c(p1), perm)); +} + +/** Non-saturating arithmetics **/ +#define OPENCV_HAL_IMPL_VSX_BIN_FUNC(func, intrin) \ +template \ +inline _Tpvec func(const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(intrin(a.val, b.val)); } + +OPENCV_HAL_IMPL_VSX_BIN_FUNC(v_add_wrap, vec_add) +OPENCV_HAL_IMPL_VSX_BIN_FUNC(v_sub_wrap, vec_sub) +OPENCV_HAL_IMPL_VSX_BIN_FUNC(v_mul_wrap, vec_mul) + +/** Bitwise shifts **/ +#define OPENCV_HAL_IMPL_VSX_SHIFT_OP(_Tpvec, shr, splfunc) \ +inline _Tpvec operator << (const _Tpvec& a, int imm) \ +{ return _Tpvec(vec_sl(a.val, splfunc(imm))); } \ +inline _Tpvec operator >> (const _Tpvec& a, int imm) \ +{ return _Tpvec(shr(a.val, splfunc(imm))); } \ +template inline _Tpvec v_shl(const _Tpvec& a) \ +{ return _Tpvec(vec_sl(a.val, splfunc(imm))); } \ +template inline _Tpvec v_shr(const _Tpvec& a) \ +{ return _Tpvec(shr(a.val, splfunc(imm))); } + +OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_uint8x16, vec_sr, vec_uchar16_sp) +OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_uint16x8, vec_sr, vec_ushort8_sp) +OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_uint32x4, vec_sr, vec_uint4_sp) +OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_uint64x2, vec_sr, vec_udword2_sp) +// algebraic right shift +OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_int8x16, vec_sra, vec_uchar16_sp) +OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_int16x8, vec_sra, vec_ushort8_sp) +OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_int32x4, vec_sra, vec_uint4_sp) +OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_int64x2, vec_sra, vec_udword2_sp) + +/** Bitwise logic **/ +#define OPENCV_HAL_IMPL_VSX_LOGIC_OP(_Tpvec) \ +OPENCV_HAL_IMPL_VSX_BIN_OP(&, _Tpvec, vec_and) \ +OPENCV_HAL_IMPL_VSX_BIN_OP(|, _Tpvec, vec_or) \ +OPENCV_HAL_IMPL_VSX_BIN_OP(^, _Tpvec, vec_xor) \ +inline _Tpvec operator ~ (const _Tpvec& a) \ +{ return _Tpvec(vec_not(a.val)); } + +OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_uint8x16) +OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_int8x16) +OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_uint16x8) +OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_int16x8) +OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_uint32x4) +OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_int32x4) +OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_uint64x2) +OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_int64x2) +OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_float32x4) +OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_float64x2) + +/** Bitwise select **/ +#define OPENCV_HAL_IMPL_VSX_SELECT(_Tpvec, cast) \ +inline _Tpvec v_select(const _Tpvec& mask, const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(vec_sel(b.val, a.val, cast(mask.val))); } + +OPENCV_HAL_IMPL_VSX_SELECT(v_uint8x16, vec_bchar16_c) +OPENCV_HAL_IMPL_VSX_SELECT(v_int8x16, vec_bchar16_c) +OPENCV_HAL_IMPL_VSX_SELECT(v_uint16x8, vec_bshort8_c) +OPENCV_HAL_IMPL_VSX_SELECT(v_int16x8, vec_bshort8_c) +OPENCV_HAL_IMPL_VSX_SELECT(v_uint32x4, vec_bint4_c) +OPENCV_HAL_IMPL_VSX_SELECT(v_int32x4, vec_bint4_c) +OPENCV_HAL_IMPL_VSX_SELECT(v_float32x4, vec_bint4_c) +OPENCV_HAL_IMPL_VSX_SELECT(v_float64x2, vec_bdword2_c) + +/** Comparison **/ +#define OPENCV_HAL_IMPL_VSX_INT_CMP_OP(_Tpvec) \ +inline _Tpvec operator == (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(vec_cmpeq(a.val, b.val)); } \ +inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(vec_cmpne(a.val, b.val)); } \ +inline _Tpvec operator < (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(vec_cmplt(a.val, b.val)); } \ +inline _Tpvec operator > (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(vec_cmpgt(a.val, b.val)); } \ +inline _Tpvec operator <= (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(vec_cmple(a.val, b.val)); } \ +inline _Tpvec operator >= (const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(vec_cmpge(a.val, b.val)); } + +OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_uint8x16) +OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_int8x16) +OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_uint16x8) +OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_int16x8) +OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_uint32x4) +OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_int32x4) +OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_float32x4) +OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_float64x2) +OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_uint64x2) +OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_int64x2) + +inline v_float32x4 v_not_nan(const v_float32x4& a) +{ return v_float32x4(vec_cmpeq(a.val, a.val)); } +inline v_float64x2 v_not_nan(const v_float64x2& a) +{ return v_float64x2(vec_cmpeq(a.val, a.val)); } + +/** min/max **/ +OPENCV_HAL_IMPL_VSX_BIN_FUNC(v_min, vec_min) +OPENCV_HAL_IMPL_VSX_BIN_FUNC(v_max, vec_max) + +/** Rotate **/ +#define OPENCV_IMPL_VSX_ROTATE(_Tpvec, suffix, shf, cast) \ +template \ +inline _Tpvec v_rotate_##suffix(const _Tpvec& a) \ +{ \ + const int wd = imm * sizeof(typename _Tpvec::lane_type); \ + if (wd > 15) \ + return _Tpvec(); \ + return _Tpvec((cast)shf(vec_uchar16_c(a.val), vec_uchar16_sp(wd << 3))); \ +} + +#define OPENCV_IMPL_VSX_ROTATE_LR(_Tpvec, cast) \ +OPENCV_IMPL_VSX_ROTATE(_Tpvec, left, vec_slo, cast) \ +OPENCV_IMPL_VSX_ROTATE(_Tpvec, right, vec_sro, cast) + +OPENCV_IMPL_VSX_ROTATE_LR(v_uint8x16, vec_uchar16) +OPENCV_IMPL_VSX_ROTATE_LR(v_int8x16, vec_char16) +OPENCV_IMPL_VSX_ROTATE_LR(v_uint16x8, vec_ushort8) +OPENCV_IMPL_VSX_ROTATE_LR(v_int16x8, vec_short8) +OPENCV_IMPL_VSX_ROTATE_LR(v_uint32x4, vec_uint4) +OPENCV_IMPL_VSX_ROTATE_LR(v_int32x4, vec_int4) +OPENCV_IMPL_VSX_ROTATE_LR(v_float32x4, vec_float4) +OPENCV_IMPL_VSX_ROTATE_LR(v_uint64x2, vec_udword2) +OPENCV_IMPL_VSX_ROTATE_LR(v_int64x2, vec_dword2) +OPENCV_IMPL_VSX_ROTATE_LR(v_float64x2, vec_double2) + +template +inline _Tpvec v_rotate_right(const _Tpvec& a, const _Tpvec& b) +{ + enum { CV_SHIFT = 16 - imm * (sizeof(typename _Tpvec::lane_type)) }; + if (CV_SHIFT == 16) + return a; +#ifdef __IBMCPP__ + return _Tpvec(vec_sld(b.val, a.val, CV_SHIFT & 15)); +#else + return _Tpvec(vec_sld(b.val, a.val, CV_SHIFT)); +#endif +} + +template +inline _Tpvec v_rotate_left(const _Tpvec& a, const _Tpvec& b) +{ + enum { CV_SHIFT = imm * (sizeof(typename _Tpvec::lane_type)) }; + if (CV_SHIFT == 16) + return b; + return _Tpvec(vec_sld(a.val, b.val, CV_SHIFT)); +} + +#define OPENCV_IMPL_VSX_ROTATE_64_2RG(_Tpvec, suffix, rg1, rg2) \ +template \ +inline _Tpvec v_rotate_##suffix(const _Tpvec& a, const _Tpvec& b) \ +{ \ + if (imm == 1) \ + return _Tpvec(vec_permi(rg1.val, rg2.val, 2)); \ + return imm ? b : a; \ +} + +#define OPENCV_IMPL_VSX_ROTATE_64_2RG_LR(_Tpvec) \ +OPENCV_IMPL_VSX_ROTATE_64_2RG(_Tpvec, left, b, a) \ +OPENCV_IMPL_VSX_ROTATE_64_2RG(_Tpvec, right, a, b) + +OPENCV_IMPL_VSX_ROTATE_64_2RG_LR(v_float64x2) +OPENCV_IMPL_VSX_ROTATE_64_2RG_LR(v_uint64x2) +OPENCV_IMPL_VSX_ROTATE_64_2RG_LR(v_int64x2) + +/* Extract */ +template +inline _Tpvec v_extract(const _Tpvec& a, const _Tpvec& b) +{ return v_rotate_right(a, b); } + +////////// Reduce and mask ///////// + +/** Reduce **/ +inline short v_reduce_sum(const v_int16x8& a) +{ + const vec_int4 zero = vec_int4_z; + return saturate_cast(vec_extract(vec_sums(vec_sum4s(a.val, zero), zero), 3)); +} +inline ushort v_reduce_sum(const v_uint16x8& a) +{ + const vec_int4 v4 = vec_int4_c(vec_unpackhu(vec_adds(a.val, vec_sld(a.val, a.val, 8)))); + return saturate_cast(vec_extract(vec_sums(v4, vec_int4_z), 3)); +} + +#define OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(_Tpvec, _Tpvec2, scalartype, suffix, func) \ +inline scalartype v_reduce_##suffix(const _Tpvec& a) \ +{ \ + const _Tpvec2 rs = func(a.val, vec_sld(a.val, a.val, 8)); \ + return vec_extract(func(rs, vec_sld(rs, rs, 4)), 0); \ +} +OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_uint32x4, vec_uint4, uint, sum, vec_add) +OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_uint32x4, vec_uint4, uint, max, vec_max) +OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_uint32x4, vec_uint4, uint, min, vec_min) +OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_int32x4, vec_int4, int, sum, vec_add) +OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_int32x4, vec_int4, int, max, vec_max) +OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_int32x4, vec_int4, int, min, vec_min) +OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_float32x4, vec_float4, float, sum, vec_add) +OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_float32x4, vec_float4, float, max, vec_max) +OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_float32x4, vec_float4, float, min, vec_min) + +inline double v_reduce_sum(const v_float64x2& a) +{ + return vec_extract(vec_add(a.val, vec_permi(a.val, a.val, 3)), 0); +} + +#define OPENCV_HAL_IMPL_VSX_REDUCE_OP_8(_Tpvec, _Tpvec2, scalartype, suffix, func) \ +inline scalartype v_reduce_##suffix(const _Tpvec& a) \ +{ \ + _Tpvec2 rs = func(a.val, vec_sld(a.val, a.val, 8)); \ + rs = func(rs, vec_sld(rs, rs, 4)); \ + return vec_extract(func(rs, vec_sld(rs, rs, 2)), 0); \ +} +OPENCV_HAL_IMPL_VSX_REDUCE_OP_8(v_uint16x8, vec_ushort8, ushort, max, vec_max) +OPENCV_HAL_IMPL_VSX_REDUCE_OP_8(v_uint16x8, vec_ushort8, ushort, min, vec_min) +OPENCV_HAL_IMPL_VSX_REDUCE_OP_8(v_int16x8, vec_short8, short, max, vec_max) +OPENCV_HAL_IMPL_VSX_REDUCE_OP_8(v_int16x8, vec_short8, short, min, vec_min) + +inline v_float32x4 v_reduce_sum4(const v_float32x4& a, const v_float32x4& b, + const v_float32x4& c, const v_float32x4& d) +{ + vec_float4 ac = vec_add(vec_mergel(a.val, c.val), vec_mergeh(a.val, c.val)); + ac = vec_add(ac, vec_sld(ac, ac, 8)); + + vec_float4 bd = vec_add(vec_mergel(b.val, d.val), vec_mergeh(b.val, d.val)); + bd = vec_add(bd, vec_sld(bd, bd, 8)); + return v_float32x4(vec_mergeh(ac, bd)); +} + +inline unsigned v_reduce_sad(const v_uint8x16& a, const v_uint8x16& b) +{ + const vec_uint4 zero4 = vec_uint4_z; + vec_uint4 sum4 = vec_sum4s(vec_absd(a.val, b.val), zero4); + return (unsigned)vec_extract(vec_sums(vec_int4_c(sum4), vec_int4_c(zero4)), 3); +} +inline unsigned v_reduce_sad(const v_int8x16& a, const v_int8x16& b) +{ + const vec_int4 zero4 = vec_int4_z; + vec_char16 ad = vec_abss(vec_subs(a.val, b.val)); + vec_int4 sum4 = vec_sum4s(ad, zero4); + return (unsigned)vec_extract(vec_sums(sum4, zero4), 3); +} +inline unsigned v_reduce_sad(const v_uint16x8& a, const v_uint16x8& b) +{ + vec_ushort8 ad = vec_absd(a.val, b.val); + VSX_UNUSED(vec_int4) sum = vec_sums(vec_int4_c(vec_unpackhu(ad)), vec_int4_c(vec_unpacklu(ad))); + return (unsigned)vec_extract(sum, 3); +} +inline unsigned v_reduce_sad(const v_int16x8& a, const v_int16x8& b) +{ + const vec_int4 zero4 = vec_int4_z; + vec_short8 ad = vec_abss(vec_subs(a.val, b.val)); + vec_int4 sum4 = vec_sum4s(ad, zero4); + return (unsigned)vec_extract(vec_sums(sum4, zero4), 3); +} +inline unsigned v_reduce_sad(const v_uint32x4& a, const v_uint32x4& b) +{ + const vec_uint4 ad = vec_absd(a.val, b.val); + const vec_uint4 rd = vec_add(ad, vec_sld(ad, ad, 8)); + return vec_extract(vec_add(rd, vec_sld(rd, rd, 4)), 0); +} +inline unsigned v_reduce_sad(const v_int32x4& a, const v_int32x4& b) +{ + vec_int4 ad = vec_abss(vec_sub(a.val, b.val)); + return (unsigned)vec_extract(vec_sums(ad, vec_int4_z), 3); +} +inline float v_reduce_sad(const v_float32x4& a, const v_float32x4& b) +{ + const vec_float4 ad = vec_abs(vec_sub(a.val, b.val)); + const vec_float4 rd = vec_add(ad, vec_sld(ad, ad, 8)); + return vec_extract(vec_add(rd, vec_sld(rd, rd, 4)), 0); +} + +/** Popcount **/ +template +inline v_uint32x4 v_popcount(const _Tpvec& a) +{ return v_uint32x4(vec_popcntu(vec_uint4_c(a.val))); } + +/** Mask **/ +inline int v_signmask(const v_uint8x16& a) +{ + vec_uchar16 sv = vec_sr(a.val, vec_uchar16_sp(7)); + static const vec_uchar16 slm = {0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7}; + sv = vec_sl(sv, slm); + vec_uint4 sv4 = vec_sum4s(sv, vec_uint4_z); + static const vec_uint4 slm4 = {0, 0, 8, 8}; + sv4 = vec_sl(sv4, slm4); + return vec_extract(vec_sums((vec_int4) sv4, vec_int4_z), 3); +} +inline int v_signmask(const v_int8x16& a) +{ return v_signmask(v_reinterpret_as_u8(a)); } + +inline int v_signmask(const v_int16x8& a) +{ + static const vec_ushort8 slm = {0, 1, 2, 3, 4, 5, 6, 7}; + vec_short8 sv = vec_sr(a.val, vec_ushort8_sp(15)); + sv = vec_sl(sv, slm); + vec_int4 svi = vec_int4_z; + svi = vec_sums(vec_sum4s(sv, svi), svi); + return vec_extract(svi, 3); +} +inline int v_signmask(const v_uint16x8& a) +{ return v_signmask(v_reinterpret_as_s16(a)); } + +inline int v_signmask(const v_int32x4& a) +{ + static const vec_uint4 slm = {0, 1, 2, 3}; + vec_int4 sv = vec_sr(a.val, vec_uint4_sp(31)); + sv = vec_sl(sv, slm); + sv = vec_sums(sv, vec_int4_z); + return vec_extract(sv, 3); +} +inline int v_signmask(const v_uint32x4& a) +{ return v_signmask(v_reinterpret_as_s32(a)); } +inline int v_signmask(const v_float32x4& a) +{ return v_signmask(v_reinterpret_as_s32(a)); } + +inline int v_signmask(const v_int64x2& a) +{ + VSX_UNUSED(const vec_dword2) sv = vec_sr(a.val, vec_udword2_sp(63)); + return (int)vec_extract(sv, 0) | (int)vec_extract(sv, 1) << 1; +} +inline int v_signmask(const v_uint64x2& a) +{ return v_signmask(v_reinterpret_as_s64(a)); } +inline int v_signmask(const v_float64x2& a) +{ return v_signmask(v_reinterpret_as_s64(a)); } + +template +inline bool v_check_all(const _Tpvec& a) +{ return vec_all_lt(a.val, _Tpvec().val); } +inline bool v_check_all(const v_uint8x16& a) +{ return v_check_all(v_reinterpret_as_s8(a)); } +inline bool v_check_all(const v_uint16x8& a) +{ return v_check_all(v_reinterpret_as_s16(a)); } +inline bool v_check_all(const v_uint32x4& a) +{ return v_check_all(v_reinterpret_as_s32(a)); } +inline bool v_check_all(const v_float32x4& a) +{ return v_check_all(v_reinterpret_as_s32(a)); } +inline bool v_check_all(const v_float64x2& a) +{ return v_check_all(v_reinterpret_as_s64(a)); } + +template +inline bool v_check_any(const _Tpvec& a) +{ return vec_any_lt(a.val, _Tpvec().val); } +inline bool v_check_any(const v_uint8x16& a) +{ return v_check_any(v_reinterpret_as_s8(a)); } +inline bool v_check_any(const v_uint16x8& a) +{ return v_check_any(v_reinterpret_as_s16(a)); } +inline bool v_check_any(const v_uint32x4& a) +{ return v_check_any(v_reinterpret_as_s32(a)); } +inline bool v_check_any(const v_float32x4& a) +{ return v_check_any(v_reinterpret_as_s32(a)); } +inline bool v_check_any(const v_float64x2& a) +{ return v_check_any(v_reinterpret_as_s64(a)); } + +////////// Other math ///////// + +/** Some frequent operations **/ +inline v_float32x4 v_sqrt(const v_float32x4& x) +{ return v_float32x4(vec_sqrt(x.val)); } +inline v_float64x2 v_sqrt(const v_float64x2& x) +{ return v_float64x2(vec_sqrt(x.val)); } + +inline v_float32x4 v_invsqrt(const v_float32x4& x) +{ return v_float32x4(vec_rsqrt(x.val)); } +inline v_float64x2 v_invsqrt(const v_float64x2& x) +{ return v_float64x2(vec_rsqrt(x.val)); } + +#define OPENCV_HAL_IMPL_VSX_MULADD(_Tpvec) \ +inline _Tpvec v_magnitude(const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(vec_sqrt(vec_madd(a.val, a.val, vec_mul(b.val, b.val)))); } \ +inline _Tpvec v_sqr_magnitude(const _Tpvec& a, const _Tpvec& b) \ +{ return _Tpvec(vec_madd(a.val, a.val, vec_mul(b.val, b.val))); } \ +inline _Tpvec v_fma(const _Tpvec& a, const _Tpvec& b, const _Tpvec& c) \ +{ return _Tpvec(vec_madd(a.val, b.val, c.val)); } \ +inline _Tpvec v_muladd(const _Tpvec& a, const _Tpvec& b, const _Tpvec& c) \ +{ return _Tpvec(vec_madd(a.val, b.val, c.val)); } + +OPENCV_HAL_IMPL_VSX_MULADD(v_float32x4) +OPENCV_HAL_IMPL_VSX_MULADD(v_float64x2) + +inline v_int32x4 v_muladd(const v_int32x4& a, const v_int32x4& b, const v_int32x4& c) +{ return a * b + c; } + +// TODO: exp, log, sin, cos + +/** Absolute values **/ +inline v_uint8x16 v_abs(const v_int8x16& x) +{ return v_uint8x16(vec_uchar16_c(vec_abs(x.val))); } + +inline v_uint16x8 v_abs(const v_int16x8& x) +{ return v_uint16x8(vec_ushort8_c(vec_abs(x.val))); } + +inline v_uint32x4 v_abs(const v_int32x4& x) +{ return v_uint32x4(vec_uint4_c(vec_abs(x.val))); } + +inline v_float32x4 v_abs(const v_float32x4& x) +{ return v_float32x4(vec_abs(x.val)); } + +inline v_float64x2 v_abs(const v_float64x2& x) +{ return v_float64x2(vec_abs(x.val)); } + +/** Absolute difference **/ +// unsigned +OPENCV_HAL_IMPL_VSX_BIN_FUNC(v_absdiff, vec_absd) + +inline v_uint8x16 v_absdiff(const v_int8x16& a, const v_int8x16& b) +{ return v_reinterpret_as_u8(v_sub_wrap(v_max(a, b), v_min(a, b))); } +inline v_uint16x8 v_absdiff(const v_int16x8& a, const v_int16x8& b) +{ return v_reinterpret_as_u16(v_sub_wrap(v_max(a, b), v_min(a, b))); } +inline v_uint32x4 v_absdiff(const v_int32x4& a, const v_int32x4& b) +{ return v_reinterpret_as_u32(v_max(a, b) - v_min(a, b)); } + +inline v_float32x4 v_absdiff(const v_float32x4& a, const v_float32x4& b) +{ return v_abs(a - b); } +inline v_float64x2 v_absdiff(const v_float64x2& a, const v_float64x2& b) +{ return v_abs(a - b); } + +/** Absolute difference for signed integers **/ +inline v_int8x16 v_absdiffs(const v_int8x16& a, const v_int8x16& b) +{ return v_int8x16(vec_abss(vec_subs(a.val, b.val))); } +inline v_int16x8 v_absdiffs(const v_int16x8& a, const v_int16x8& b) +{ return v_int16x8(vec_abss(vec_subs(a.val, b.val))); } + +////////// Conversions ///////// + +/** Rounding **/ +inline v_int32x4 v_round(const v_float32x4& a) +{ return v_int32x4(vec_cts(vec_rint(a.val))); } + +inline v_int32x4 v_round(const v_float64x2& a) +{ return v_int32x4(vec_mergesqo(vec_ctso(vec_rint(a.val)), vec_int4_z)); } + +inline v_int32x4 v_round(const v_float64x2& a, const v_float64x2& b) +{ return v_int32x4(vec_mergesqo(vec_ctso(vec_rint(a.val)), vec_ctso(vec_rint(b.val)))); } + +inline v_int32x4 v_floor(const v_float32x4& a) +{ return v_int32x4(vec_cts(vec_floor(a.val))); } + +inline v_int32x4 v_floor(const v_float64x2& a) +{ return v_int32x4(vec_mergesqo(vec_ctso(vec_floor(a.val)), vec_int4_z)); } + +inline v_int32x4 v_ceil(const v_float32x4& a) +{ return v_int32x4(vec_cts(vec_ceil(a.val))); } + +inline v_int32x4 v_ceil(const v_float64x2& a) +{ return v_int32x4(vec_mergesqo(vec_ctso(vec_ceil(a.val)), vec_int4_z)); } + +inline v_int32x4 v_trunc(const v_float32x4& a) +{ return v_int32x4(vec_cts(a.val)); } + +inline v_int32x4 v_trunc(const v_float64x2& a) +{ return v_int32x4(vec_mergesqo(vec_ctso(a.val), vec_int4_z)); } + +/** To float **/ +inline v_float32x4 v_cvt_f32(const v_int32x4& a) +{ return v_float32x4(vec_ctf(a.val)); } + +inline v_float32x4 v_cvt_f32(const v_float64x2& a) +{ return v_float32x4(vec_mergesqo(vec_cvfo(a.val), vec_float4_z)); } + +inline v_float32x4 v_cvt_f32(const v_float64x2& a, const v_float64x2& b) +{ return v_float32x4(vec_mergesqo(vec_cvfo(a.val), vec_cvfo(b.val))); } + +inline v_float64x2 v_cvt_f64(const v_int32x4& a) +{ return v_float64x2(vec_ctdo(vec_mergeh(a.val, a.val))); } + +inline v_float64x2 v_cvt_f64_high(const v_int32x4& a) +{ return v_float64x2(vec_ctdo(vec_mergel(a.val, a.val))); } + +inline v_float64x2 v_cvt_f64(const v_float32x4& a) +{ return v_float64x2(vec_cvfo(vec_mergeh(a.val, a.val))); } + +inline v_float64x2 v_cvt_f64_high(const v_float32x4& a) +{ return v_float64x2(vec_cvfo(vec_mergel(a.val, a.val))); } + +////////////// Lookup table access //////////////////// + +inline v_int32x4 v_lut(const int* tab, const v_int32x4& idxvec) +{ + int CV_DECL_ALIGNED(32) idx[4]; + v_store_aligned(idx, idxvec); + return v_int32x4(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]]); +} + +inline v_float32x4 v_lut(const float* tab, const v_int32x4& idxvec) +{ + int CV_DECL_ALIGNED(32) idx[4]; + v_store_aligned(idx, idxvec); + return v_float32x4(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]]); +} + +inline v_float64x2 v_lut(const double* tab, const v_int32x4& idxvec) +{ + int CV_DECL_ALIGNED(32) idx[4]; + v_store_aligned(idx, idxvec); + return v_float64x2(tab[idx[0]], tab[idx[1]]); +} + +inline void v_lut_deinterleave(const float* tab, const v_int32x4& idxvec, v_float32x4& x, v_float32x4& y) +{ + int CV_DECL_ALIGNED(32) idx[4]; + v_store_aligned(idx, idxvec); + x = v_float32x4(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]]); + y = v_float32x4(tab[idx[0]+1], tab[idx[1]+1], tab[idx[2]+1], tab[idx[3]+1]); +} + +inline void v_lut_deinterleave(const double* tab, const v_int32x4& idxvec, v_float64x2& x, v_float64x2& y) +{ + int CV_DECL_ALIGNED(32) idx[4]; + v_store_aligned(idx, idxvec); + x = v_float64x2(tab[idx[0]], tab[idx[1]]); + y = v_float64x2(tab[idx[0]+1], tab[idx[1]+1]); +} + +/////// FP16 support //////// + +// [TODO] implement these 2 using VSX or universal intrinsics (copy from intrin_sse.cpp and adopt) +inline v_float32x4 v_load_expand(const float16_t* ptr) +{ + return v_float32x4((float)ptr[0], (float)ptr[1], (float)ptr[2], (float)ptr[3]); +} + +inline void v_pack_store(float16_t* ptr, const v_float32x4& v) +{ + float CV_DECL_ALIGNED(32) f[4]; + v_store_aligned(f, v); + ptr[0] = float16_t(f[0]); + ptr[1] = float16_t(f[1]); + ptr[2] = float16_t(f[2]); + ptr[3] = float16_t(f[3]); +} + +inline void v_cleanup() {} + + +/** Reinterpret **/ +/** its up there with load and store operations **/ + +////////// Matrix operations ///////// + +inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b) +{ return v_int32x4(vec_msum(a.val, b.val, vec_int4_z)); } + +inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b, const v_int32x4& c) +{ return v_int32x4(vec_msum(a.val, b.val, c.val)); } + +inline v_float32x4 v_matmul(const v_float32x4& v, const v_float32x4& m0, + const v_float32x4& m1, const v_float32x4& m2, + const v_float32x4& m3) +{ + const vec_float4 v0 = vec_splat(v.val, 0); + const vec_float4 v1 = vec_splat(v.val, 1); + const vec_float4 v2 = vec_splat(v.val, 2); + VSX_UNUSED(const vec_float4) v3 = vec_splat(v.val, 3); + return v_float32x4(vec_madd(v0, m0.val, vec_madd(v1, m1.val, vec_madd(v2, m2.val, vec_mul(v3, m3.val))))); +} + +inline v_float32x4 v_matmuladd(const v_float32x4& v, const v_float32x4& m0, + const v_float32x4& m1, const v_float32x4& m2, + const v_float32x4& a) +{ + const vec_float4 v0 = vec_splat(v.val, 0); + const vec_float4 v1 = vec_splat(v.val, 1); + const vec_float4 v2 = vec_splat(v.val, 2); + return v_float32x4(vec_madd(v0, m0.val, vec_madd(v1, m1.val, vec_madd(v2, m2.val, a.val)))); +} + +#define OPENCV_HAL_IMPL_VSX_TRANSPOSE4x4(_Tpvec, _Tpvec2) \ +inline void v_transpose4x4(const _Tpvec& a0, const _Tpvec& a1, \ + const _Tpvec& a2, const _Tpvec& a3, \ + _Tpvec& b0, _Tpvec& b1, _Tpvec& b2, _Tpvec& b3) \ +{ \ + _Tpvec2 a02 = vec_mergeh(a0.val, a2.val); \ + _Tpvec2 a13 = vec_mergeh(a1.val, a3.val); \ + b0.val = vec_mergeh(a02, a13); \ + b1.val = vec_mergel(a02, a13); \ + a02 = vec_mergel(a0.val, a2.val); \ + a13 = vec_mergel(a1.val, a3.val); \ + b2.val = vec_mergeh(a02, a13); \ + b3.val = vec_mergel(a02, a13); \ +} +OPENCV_HAL_IMPL_VSX_TRANSPOSE4x4(v_uint32x4, vec_uint4) +OPENCV_HAL_IMPL_VSX_TRANSPOSE4x4(v_int32x4, vec_int4) +OPENCV_HAL_IMPL_VSX_TRANSPOSE4x4(v_float32x4, vec_float4) + +//! @name Check SIMD support +//! @{ +//! @brief Check CPU capability of SIMD operation +static inline bool hasSIMD128() +{ + return (CV_CPU_HAS_SUPPORT_VSX) ? true : false; +} + +//! @} + +CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END + +//! @endcond + +} + +#endif // OPENCV_HAL_VSX_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/ippasync.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/ippasync.hpp new file mode 100644 index 0000000000000000000000000000000000000000..c35d8d81a19c54b5628f547d7081c1c6c002ab77 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/ippasync.hpp @@ -0,0 +1,195 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2015, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_IPPASYNC_HPP +#define OPENCV_CORE_IPPASYNC_HPP + +#ifdef HAVE_IPP_A // this file will be removed in OpenCV 4.0 + +#include "opencv2/core.hpp" +#include +#include + +namespace cv +{ + +namespace hpp +{ + +/** @addtogroup core_ipp +This section describes conversion between OpenCV and [Intel® IPP Asynchronous +C/C++](http://software.intel.com/en-us/intel-ipp-preview) library. [Getting Started +Guide](http://registrationcenter.intel.com/irc_nas/3727/ipp_async_get_started.htm) help you to +install the library, configure header and library build paths. + */ +//! @{ + + //! convert OpenCV data type to hppDataType + inline int toHppType(const int cvType) + { + int depth = CV_MAT_DEPTH(cvType); + int hppType = depth == CV_8U ? HPP_DATA_TYPE_8U : + depth == CV_16U ? HPP_DATA_TYPE_16U : + depth == CV_16S ? HPP_DATA_TYPE_16S : + depth == CV_32S ? HPP_DATA_TYPE_32S : + depth == CV_32F ? HPP_DATA_TYPE_32F : + depth == CV_64F ? HPP_DATA_TYPE_64F : -1; + CV_Assert( hppType >= 0 ); + return hppType; + } + + //! convert hppDataType to OpenCV data type + inline int toCvType(const int hppType) + { + int cvType = hppType == HPP_DATA_TYPE_8U ? CV_8U : + hppType == HPP_DATA_TYPE_16U ? CV_16U : + hppType == HPP_DATA_TYPE_16S ? CV_16S : + hppType == HPP_DATA_TYPE_32S ? CV_32S : + hppType == HPP_DATA_TYPE_32F ? CV_32F : + hppType == HPP_DATA_TYPE_64F ? CV_64F : -1; + CV_Assert( cvType >= 0 ); + return cvType; + } + + /** @brief Convert hppiMatrix to Mat. + + This function allocates and initializes new matrix (if needed) that has the same size and type as + input matrix. Supports CV_8U, CV_16U, CV_16S, CV_32S, CV_32F, CV_64F. + @param src input hppiMatrix. + @param dst output matrix. + @param accel accelerator instance (see hpp::getHpp for the list of acceleration framework types). + @param cn number of channels. + */ + inline void copyHppToMat(hppiMatrix* src, Mat& dst, hppAccel accel, int cn) + { + hppDataType type; + hpp32u width, height; + hppStatus sts; + + if (src == NULL) + return dst.release(); + + sts = hppiInquireMatrix(src, &type, &width, &height); + + CV_Assert( sts == HPP_STATUS_NO_ERROR); + + int matType = CV_MAKETYPE(toCvType(type), cn); + + CV_Assert(width%cn == 0); + + width /= cn; + + dst.create((int)height, (int)width, (int)matType); + + size_t newSize = (size_t)(height*(hpp32u)(dst.step)); + + sts = hppiGetMatrixData(accel,src,(hpp32u)(dst.step),dst.data,&newSize); + + CV_Assert( sts == HPP_STATUS_NO_ERROR); + } + + /** @brief Create Mat from hppiMatrix. + + This function allocates and initializes the Mat that has the same size and type as input matrix. + Supports CV_8U, CV_16U, CV_16S, CV_32S, CV_32F, CV_64F. + @param src input hppiMatrix. + @param accel accelerator instance (see hpp::getHpp for the list of acceleration framework types). + @param cn number of channels. + @sa howToUseIPPAconversion, hpp::copyHppToMat, hpp::getHpp. + */ + inline Mat getMat(hppiMatrix* src, hppAccel accel, int cn) + { + Mat dst; + copyHppToMat(src, dst, accel, cn); + return dst; + } + + /** @brief Create hppiMatrix from Mat. + + This function allocates and initializes the hppiMatrix that has the same size and type as input + matrix, returns the hppiMatrix*. + + If you want to use zero-copy for GPU you should to have 4KB aligned matrix data. See details + [hppiCreateSharedMatrix](http://software.intel.com/ru-ru/node/501697). + + Supports CV_8U, CV_16U, CV_16S, CV_32S, CV_32F, CV_64F. + + @note The hppiMatrix pointer to the image buffer in system memory refers to the src.data. Control + the lifetime of the matrix and don't change its data, if there is no special need. + @param src input matrix. + @param accel accelerator instance. Supports type: + - **HPP_ACCEL_TYPE_CPU** - accelerated by optimized CPU instructions. + - **HPP_ACCEL_TYPE_GPU** - accelerated by GPU programmable units or fixed-function + accelerators. + - **HPP_ACCEL_TYPE_ANY** - any acceleration or no acceleration available. + @sa howToUseIPPAconversion, hpp::getMat + */ + inline hppiMatrix* getHpp(const Mat& src, hppAccel accel) + { + int htype = toHppType(src.type()); + int cn = src.channels(); + + CV_Assert(src.data); + hppAccelType accelType = hppQueryAccelType(accel); + + if (accelType!=HPP_ACCEL_TYPE_CPU) + { + hpp32u pitch, size; + hppQueryMatrixAllocParams(accel, src.cols*cn, src.rows, htype, &pitch, &size); + if (pitch!=0 && size!=0) + if ((int)(src.data)%4096==0 && pitch==(hpp32u)(src.step)) + { + return hppiCreateSharedMatrix(htype, src.cols*cn, src.rows, src.data, pitch, size); + } + } + + return hppiCreateMatrix(htype, src.cols*cn, src.rows, src.data, (hpp32s)(src.step));; + } + +//! @} +}} + +#endif + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/mat.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/mat.hpp new file mode 100644 index 0000000000000000000000000000000000000000..c0893b398d70e2356250af5e47b1e7ace15e5db4 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/mat.hpp @@ -0,0 +1,3701 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_MAT_HPP +#define OPENCV_CORE_MAT_HPP + +#ifndef __cplusplus +# error mat.hpp header must be compiled as C++ +#endif + +#include "opencv2/core/matx.hpp" +#include "opencv2/core/types.hpp" + +#include "opencv2/core/bufferpool.hpp" + +#ifdef CV_CXX11 +#include +#endif + +namespace cv +{ + +//! @addtogroup core_basic +//! @{ + +enum { ACCESS_READ=1<<24, ACCESS_WRITE=1<<25, + ACCESS_RW=3<<24, ACCESS_MASK=ACCESS_RW, ACCESS_FAST=1<<26 }; + +CV__DEBUG_NS_BEGIN + +class CV_EXPORTS _OutputArray; + +//////////////////////// Input/Output Array Arguments ///////////////////////////////// + +/** @brief This is the proxy class for passing read-only input arrays into OpenCV functions. + +It is defined as: +@code + typedef const _InputArray& InputArray; +@endcode +where _InputArray is a class that can be constructed from `Mat`, `Mat_`, `Matx`, +`std::vector`, `std::vector >`, `std::vector`, `std::vector >`, +`UMat`, `std::vector` or `double`. It can also be constructed from a matrix expression. + +Since this is mostly implementation-level class, and its interface may change in future versions, we +do not describe it in details. There are a few key things, though, that should be kept in mind: + +- When you see in the reference manual or in OpenCV source code a function that takes + InputArray, it means that you can actually pass `Mat`, `Matx`, `vector` etc. (see above the + complete list). +- Optional input arguments: If some of the input arrays may be empty, pass cv::noArray() (or + simply cv::Mat() as you probably did before). +- The class is designed solely for passing parameters. That is, normally you *should not* + declare class members, local and global variables of this type. +- If you want to design your own function or a class method that can operate of arrays of + multiple types, you can use InputArray (or OutputArray) for the respective parameters. Inside + a function you should use _InputArray::getMat() method to construct a matrix header for the + array (without copying data). _InputArray::kind() can be used to distinguish Mat from + `vector<>` etc., but normally it is not needed. + +Here is how you can use a function that takes InputArray : +@code + std::vector vec; + // points or a circle + for( int i = 0; i < 30; i++ ) + vec.push_back(Point2f((float)(100 + 30*cos(i*CV_PI*2/5)), + (float)(100 - 30*sin(i*CV_PI*2/5)))); + cv::transform(vec, vec, cv::Matx23f(0.707, -0.707, 10, 0.707, 0.707, 20)); +@endcode +That is, we form an STL vector containing points, and apply in-place affine transformation to the +vector using the 2x3 matrix created inline as `Matx` instance. + +Here is how such a function can be implemented (for simplicity, we implement a very specific case of +it, according to the assertion statement inside) : +@code + void myAffineTransform(InputArray _src, OutputArray _dst, InputArray _m) + { + // get Mat headers for input arrays. This is O(1) operation, + // unless _src and/or _m are matrix expressions. + Mat src = _src.getMat(), m = _m.getMat(); + CV_Assert( src.type() == CV_32FC2 && m.type() == CV_32F && m.size() == Size(3, 2) ); + + // [re]create the output array so that it has the proper size and type. + // In case of Mat it calls Mat::create, in case of STL vector it calls vector::resize. + _dst.create(src.size(), src.type()); + Mat dst = _dst.getMat(); + + for( int i = 0; i < src.rows; i++ ) + for( int j = 0; j < src.cols; j++ ) + { + Point2f pt = src.at(i, j); + dst.at(i, j) = Point2f(m.at(0, 0)*pt.x + + m.at(0, 1)*pt.y + + m.at(0, 2), + m.at(1, 0)*pt.x + + m.at(1, 1)*pt.y + + m.at(1, 2)); + } + } +@endcode +There is another related type, InputArrayOfArrays, which is currently defined as a synonym for +InputArray: +@code + typedef InputArray InputArrayOfArrays; +@endcode +It denotes function arguments that are either vectors of vectors or vectors of matrices. A separate +synonym is needed to generate Python/Java etc. wrappers properly. At the function implementation +level their use is similar, but _InputArray::getMat(idx) should be used to get header for the +idx-th component of the outer vector and _InputArray::size().area() should be used to find the +number of components (vectors/matrices) of the outer vector. + +In general, type support is limited to cv::Mat types. Other types are forbidden. +But in some cases we need to support passing of custom non-general Mat types, like arrays of cv::KeyPoint, cv::DMatch, etc. +This data is not intented to be interpreted as an image data, or processed somehow like regular cv::Mat. +To pass such custom type use rawIn() / rawOut() / rawInOut() wrappers. +Custom type is wrapped as Mat-compatible `CV_8UC` values (N = sizeof(T), N <= CV_CN_MAX). + */ +class CV_EXPORTS _InputArray +{ +public: + enum { + KIND_SHIFT = 16, + FIXED_TYPE = 0x8000 << KIND_SHIFT, + FIXED_SIZE = 0x4000 << KIND_SHIFT, + KIND_MASK = 31 << KIND_SHIFT, + + NONE = 0 << KIND_SHIFT, + MAT = 1 << KIND_SHIFT, + MATX = 2 << KIND_SHIFT, + STD_VECTOR = 3 << KIND_SHIFT, + STD_VECTOR_VECTOR = 4 << KIND_SHIFT, + STD_VECTOR_MAT = 5 << KIND_SHIFT, + EXPR = 6 << KIND_SHIFT, + OPENGL_BUFFER = 7 << KIND_SHIFT, + CUDA_HOST_MEM = 8 << KIND_SHIFT, + CUDA_GPU_MAT = 9 << KIND_SHIFT, + UMAT =10 << KIND_SHIFT, + STD_VECTOR_UMAT =11 << KIND_SHIFT, + STD_BOOL_VECTOR =12 << KIND_SHIFT, + STD_VECTOR_CUDA_GPU_MAT = 13 << KIND_SHIFT, + STD_ARRAY =14 << KIND_SHIFT, + STD_ARRAY_MAT =15 << KIND_SHIFT + }; + + _InputArray(); + _InputArray(int _flags, void* _obj); + _InputArray(const Mat& m); + _InputArray(const MatExpr& expr); + _InputArray(const std::vector& vec); + template _InputArray(const Mat_<_Tp>& m); + template _InputArray(const std::vector<_Tp>& vec); + _InputArray(const std::vector& vec); + template _InputArray(const std::vector >& vec); + _InputArray(const std::vector >&); + template _InputArray(const std::vector >& vec); + template _InputArray(const _Tp* vec, int n); + template _InputArray(const Matx<_Tp, m, n>& matx); + _InputArray(const double& val); + _InputArray(const cuda::GpuMat& d_mat); + _InputArray(const std::vector& d_mat_array); + _InputArray(const ogl::Buffer& buf); + _InputArray(const cuda::HostMem& cuda_mem); + template _InputArray(const cudev::GpuMat_<_Tp>& m); + _InputArray(const UMat& um); + _InputArray(const std::vector& umv); + +#ifdef CV_CXX_STD_ARRAY + template _InputArray(const std::array<_Tp, _Nm>& arr); + template _InputArray(const std::array& arr); +#endif + + template static _InputArray rawIn(const std::vector<_Tp>& vec); +#ifdef CV_CXX_STD_ARRAY + template static _InputArray rawIn(const std::array<_Tp, _Nm>& arr); +#endif + + Mat getMat(int idx=-1) const; + Mat getMat_(int idx=-1) const; + UMat getUMat(int idx=-1) const; + void getMatVector(std::vector& mv) const; + void getUMatVector(std::vector& umv) const; + void getGpuMatVector(std::vector& gpumv) const; + cuda::GpuMat getGpuMat() const; + ogl::Buffer getOGlBuffer() const; + + int getFlags() const; + void* getObj() const; + Size getSz() const; + + int kind() const; + int dims(int i=-1) const; + int cols(int i=-1) const; + int rows(int i=-1) const; + Size size(int i=-1) const; + int sizend(int* sz, int i=-1) const; + bool sameSize(const _InputArray& arr) const; + size_t total(int i=-1) const; + int type(int i=-1) const; + int depth(int i=-1) const; + int channels(int i=-1) const; + bool isContinuous(int i=-1) const; + bool isSubmatrix(int i=-1) const; + bool empty() const; + void copyTo(const _OutputArray& arr) const; + void copyTo(const _OutputArray& arr, const _InputArray & mask) const; + size_t offset(int i=-1) const; + size_t step(int i=-1) const; + bool isMat() const; + bool isUMat() const; + bool isMatVector() const; + bool isUMatVector() const; + bool isMatx() const; + bool isVector() const; + bool isGpuMat() const; + bool isGpuMatVector() const; + ~_InputArray(); + +protected: + int flags; + void* obj; + Size sz; + + void init(int _flags, const void* _obj); + void init(int _flags, const void* _obj, Size _sz); +}; + + +/** @brief This type is very similar to InputArray except that it is used for input/output and output function +parameters. + +Just like with InputArray, OpenCV users should not care about OutputArray, they just pass `Mat`, +`vector` etc. to the functions. The same limitation as for `InputArray`: *Do not explicitly +create OutputArray instances* applies here too. + +If you want to make your function polymorphic (i.e. accept different arrays as output parameters), +it is also not very difficult. Take the sample above as the reference. Note that +_OutputArray::create() needs to be called before _OutputArray::getMat(). This way you guarantee +that the output array is properly allocated. + +Optional output parameters. If you do not need certain output array to be computed and returned to +you, pass cv::noArray(), just like you would in the case of optional input array. At the +implementation level, use _OutputArray::needed() to check if certain output array needs to be +computed or not. + +There are several synonyms for OutputArray that are used to assist automatic Python/Java/... wrapper +generators: +@code + typedef OutputArray OutputArrayOfArrays; + typedef OutputArray InputOutputArray; + typedef OutputArray InputOutputArrayOfArrays; +@endcode + */ +class CV_EXPORTS _OutputArray : public _InputArray +{ +public: + enum + { + DEPTH_MASK_8U = 1 << CV_8U, + DEPTH_MASK_8S = 1 << CV_8S, + DEPTH_MASK_16U = 1 << CV_16U, + DEPTH_MASK_16S = 1 << CV_16S, + DEPTH_MASK_32S = 1 << CV_32S, + DEPTH_MASK_32F = 1 << CV_32F, + DEPTH_MASK_64F = 1 << CV_64F, + DEPTH_MASK_ALL = (DEPTH_MASK_64F<<1)-1, + DEPTH_MASK_ALL_BUT_8S = DEPTH_MASK_ALL & ~DEPTH_MASK_8S, + DEPTH_MASK_FLT = DEPTH_MASK_32F + DEPTH_MASK_64F + }; + + _OutputArray(); + _OutputArray(int _flags, void* _obj); + _OutputArray(Mat& m); + _OutputArray(std::vector& vec); + _OutputArray(cuda::GpuMat& d_mat); + _OutputArray(std::vector& d_mat); + _OutputArray(ogl::Buffer& buf); + _OutputArray(cuda::HostMem& cuda_mem); + template _OutputArray(cudev::GpuMat_<_Tp>& m); + template _OutputArray(std::vector<_Tp>& vec); + _OutputArray(std::vector& vec); + template _OutputArray(std::vector >& vec); + _OutputArray(std::vector >&); + template _OutputArray(std::vector >& vec); + template _OutputArray(Mat_<_Tp>& m); + template _OutputArray(_Tp* vec, int n); + template _OutputArray(Matx<_Tp, m, n>& matx); + _OutputArray(UMat& m); + _OutputArray(std::vector& vec); + + _OutputArray(const Mat& m); + _OutputArray(const std::vector& vec); + _OutputArray(const cuda::GpuMat& d_mat); + _OutputArray(const std::vector& d_mat); + _OutputArray(const ogl::Buffer& buf); + _OutputArray(const cuda::HostMem& cuda_mem); + template _OutputArray(const cudev::GpuMat_<_Tp>& m); + template _OutputArray(const std::vector<_Tp>& vec); + template _OutputArray(const std::vector >& vec); + template _OutputArray(const std::vector >& vec); + template _OutputArray(const Mat_<_Tp>& m); + template _OutputArray(const _Tp* vec, int n); + template _OutputArray(const Matx<_Tp, m, n>& matx); + _OutputArray(const UMat& m); + _OutputArray(const std::vector& vec); + +#ifdef CV_CXX_STD_ARRAY + template _OutputArray(std::array<_Tp, _Nm>& arr); + template _OutputArray(const std::array<_Tp, _Nm>& arr); + template _OutputArray(std::array& arr); + template _OutputArray(const std::array& arr); +#endif + + template static _OutputArray rawOut(std::vector<_Tp>& vec); +#ifdef CV_CXX_STD_ARRAY + template static _OutputArray rawOut(std::array<_Tp, _Nm>& arr); +#endif + + bool fixedSize() const; + bool fixedType() const; + bool needed() const; + Mat& getMatRef(int i=-1) const; + UMat& getUMatRef(int i=-1) const; + cuda::GpuMat& getGpuMatRef() const; + std::vector& getGpuMatVecRef() const; + ogl::Buffer& getOGlBufferRef() const; + cuda::HostMem& getHostMemRef() const; + void create(Size sz, int type, int i=-1, bool allowTransposed=false, int fixedDepthMask=0) const; + void create(int rows, int cols, int type, int i=-1, bool allowTransposed=false, int fixedDepthMask=0) const; + void create(int dims, const int* size, int type, int i=-1, bool allowTransposed=false, int fixedDepthMask=0) const; + void createSameSize(const _InputArray& arr, int mtype) const; + void release() const; + void clear() const; + void setTo(const _InputArray& value, const _InputArray & mask = _InputArray()) const; + + void assign(const UMat& u) const; + void assign(const Mat& m) const; + + void assign(const std::vector& v) const; + void assign(const std::vector& v) const; +}; + + +class CV_EXPORTS _InputOutputArray : public _OutputArray +{ +public: + _InputOutputArray(); + _InputOutputArray(int _flags, void* _obj); + _InputOutputArray(Mat& m); + _InputOutputArray(std::vector& vec); + _InputOutputArray(cuda::GpuMat& d_mat); + _InputOutputArray(ogl::Buffer& buf); + _InputOutputArray(cuda::HostMem& cuda_mem); + template _InputOutputArray(cudev::GpuMat_<_Tp>& m); + template _InputOutputArray(std::vector<_Tp>& vec); + _InputOutputArray(std::vector& vec); + template _InputOutputArray(std::vector >& vec); + template _InputOutputArray(std::vector >& vec); + template _InputOutputArray(Mat_<_Tp>& m); + template _InputOutputArray(_Tp* vec, int n); + template _InputOutputArray(Matx<_Tp, m, n>& matx); + _InputOutputArray(UMat& m); + _InputOutputArray(std::vector& vec); + + _InputOutputArray(const Mat& m); + _InputOutputArray(const std::vector& vec); + _InputOutputArray(const cuda::GpuMat& d_mat); + _InputOutputArray(const std::vector& d_mat); + _InputOutputArray(const ogl::Buffer& buf); + _InputOutputArray(const cuda::HostMem& cuda_mem); + template _InputOutputArray(const cudev::GpuMat_<_Tp>& m); + template _InputOutputArray(const std::vector<_Tp>& vec); + template _InputOutputArray(const std::vector >& vec); + template _InputOutputArray(const std::vector >& vec); + template _InputOutputArray(const Mat_<_Tp>& m); + template _InputOutputArray(const _Tp* vec, int n); + template _InputOutputArray(const Matx<_Tp, m, n>& matx); + _InputOutputArray(const UMat& m); + _InputOutputArray(const std::vector& vec); + +#ifdef CV_CXX_STD_ARRAY + template _InputOutputArray(std::array<_Tp, _Nm>& arr); + template _InputOutputArray(const std::array<_Tp, _Nm>& arr); + template _InputOutputArray(std::array& arr); + template _InputOutputArray(const std::array& arr); +#endif + + template static _InputOutputArray rawInOut(std::vector<_Tp>& vec); +#ifdef CV_CXX_STD_ARRAY + template _InputOutputArray rawInOut(std::array<_Tp, _Nm>& arr); +#endif + +}; + +/** Helper to wrap custom types. @see InputArray */ +template static inline _InputArray rawIn(_Tp& v); +/** Helper to wrap custom types. @see InputArray */ +template static inline _OutputArray rawOut(_Tp& v); +/** Helper to wrap custom types. @see InputArray */ +template static inline _InputOutputArray rawInOut(_Tp& v); + +CV__DEBUG_NS_END + +typedef const _InputArray& InputArray; +typedef InputArray InputArrayOfArrays; +typedef const _OutputArray& OutputArray; +typedef OutputArray OutputArrayOfArrays; +typedef const _InputOutputArray& InputOutputArray; +typedef InputOutputArray InputOutputArrayOfArrays; + +CV_EXPORTS InputOutputArray noArray(); + +/////////////////////////////////// MatAllocator ////////////////////////////////////// + +//! Usage flags for allocator +enum UMatUsageFlags +{ + USAGE_DEFAULT = 0, + + // buffer allocation policy is platform and usage specific + USAGE_ALLOCATE_HOST_MEMORY = 1 << 0, + USAGE_ALLOCATE_DEVICE_MEMORY = 1 << 1, + USAGE_ALLOCATE_SHARED_MEMORY = 1 << 2, // It is not equal to: USAGE_ALLOCATE_HOST_MEMORY | USAGE_ALLOCATE_DEVICE_MEMORY + + __UMAT_USAGE_FLAGS_32BIT = 0x7fffffff // Binary compatibility hint +}; + +struct CV_EXPORTS UMatData; + +/** @brief Custom array allocator +*/ +class CV_EXPORTS MatAllocator +{ +public: + MatAllocator() {} + virtual ~MatAllocator() {} + + // let's comment it off for now to detect and fix all the uses of allocator + //virtual void allocate(int dims, const int* sizes, int type, int*& refcount, + // uchar*& datastart, uchar*& data, size_t* step) = 0; + //virtual void deallocate(int* refcount, uchar* datastart, uchar* data) = 0; + virtual UMatData* allocate(int dims, const int* sizes, int type, + void* data, size_t* step, int flags, UMatUsageFlags usageFlags) const = 0; + virtual bool allocate(UMatData* data, int accessflags, UMatUsageFlags usageFlags) const = 0; + virtual void deallocate(UMatData* data) const = 0; + virtual void map(UMatData* data, int accessflags) const; + virtual void unmap(UMatData* data) const; + virtual void download(UMatData* data, void* dst, int dims, const size_t sz[], + const size_t srcofs[], const size_t srcstep[], + const size_t dststep[]) const; + virtual void upload(UMatData* data, const void* src, int dims, const size_t sz[], + const size_t dstofs[], const size_t dststep[], + const size_t srcstep[]) const; + virtual void copy(UMatData* srcdata, UMatData* dstdata, int dims, const size_t sz[], + const size_t srcofs[], const size_t srcstep[], + const size_t dstofs[], const size_t dststep[], bool sync) const; + + // default implementation returns DummyBufferPoolController + virtual BufferPoolController* getBufferPoolController(const char* id = NULL) const; +}; + + +//////////////////////////////// MatCommaInitializer ////////////////////////////////// + +/** @brief Comma-separated Matrix Initializer + + The class instances are usually not created explicitly. + Instead, they are created on "matrix << firstValue" operator. + + The sample below initializes 2x2 rotation matrix: + + \code + double angle = 30, a = cos(angle*CV_PI/180), b = sin(angle*CV_PI/180); + Mat R = (Mat_(2,2) << a, -b, b, a); + \endcode +*/ +template class MatCommaInitializer_ +{ +public: + //! the constructor, created by "matrix << firstValue" operator, where matrix is cv::Mat + MatCommaInitializer_(Mat_<_Tp>* _m); + //! the operator that takes the next value and put it to the matrix + template MatCommaInitializer_<_Tp>& operator , (T2 v); + //! another form of conversion operator + operator Mat_<_Tp>() const; +protected: + MatIterator_<_Tp> it; +}; + + +/////////////////////////////////////// Mat /////////////////////////////////////////// + +// note that umatdata might be allocated together +// with the matrix data, not as a separate object. +// therefore, it does not have constructor or destructor; +// it should be explicitly initialized using init(). +struct CV_EXPORTS UMatData +{ + enum { COPY_ON_MAP=1, HOST_COPY_OBSOLETE=2, + DEVICE_COPY_OBSOLETE=4, TEMP_UMAT=8, TEMP_COPIED_UMAT=24, + USER_ALLOCATED=32, DEVICE_MEM_MAPPED=64, + ASYNC_CLEANUP=128 + }; + UMatData(const MatAllocator* allocator); + ~UMatData(); + + // provide atomic access to the structure + void lock(); + void unlock(); + + bool hostCopyObsolete() const; + bool deviceCopyObsolete() const; + bool deviceMemMapped() const; + bool copyOnMap() const; + bool tempUMat() const; + bool tempCopiedUMat() const; + void markHostCopyObsolete(bool flag); + void markDeviceCopyObsolete(bool flag); + void markDeviceMemMapped(bool flag); + + const MatAllocator* prevAllocator; + const MatAllocator* currAllocator; + int urefcount; + int refcount; + uchar* data; + uchar* origdata; + size_t size; + + int flags; + void* handle; + void* userdata; + int allocatorFlags_; + int mapcount; + UMatData* originalUMatData; +}; + + +struct CV_EXPORTS MatSize +{ + explicit MatSize(int* _p); + int dims() const; + Size operator()() const; + const int& operator[](int i) const; + int& operator[](int i); + operator const int*() const; // TODO OpenCV 4.0: drop this + bool operator == (const MatSize& sz) const; + bool operator != (const MatSize& sz) const; + + int* p; +}; + +struct CV_EXPORTS MatStep +{ + MatStep(); + explicit MatStep(size_t s); + const size_t& operator[](int i) const; + size_t& operator[](int i); + operator size_t() const; + MatStep& operator = (size_t s); + + size_t* p; + size_t buf[2]; +protected: + MatStep& operator = (const MatStep&); +}; + +/** @example samples/cpp/cout_mat.cpp +An example demonstrating the serial out capabilities of cv::Mat +*/ + + /** @brief n-dimensional dense array class \anchor CVMat_Details + +The class Mat represents an n-dimensional dense numerical single-channel or multi-channel array. It +can be used to store real or complex-valued vectors and matrices, grayscale or color images, voxel +volumes, vector fields, point clouds, tensors, histograms (though, very high-dimensional histograms +may be better stored in a SparseMat ). The data layout of the array `M` is defined by the array +`M.step[]`, so that the address of element \f$(i_0,...,i_{M.dims-1})\f$, where \f$0\leq i_k= M.step[i+1]` (in fact, `M.step[i] >= M.step[i+1]*M.size[i+1]` ). This means +that 2-dimensional matrices are stored row-by-row, 3-dimensional matrices are stored plane-by-plane, +and so on. M.step[M.dims-1] is minimal and always equal to the element size M.elemSize() . + +So, the data layout in Mat is fully compatible with CvMat, IplImage, and CvMatND types from OpenCV +1.x. It is also compatible with the majority of dense array types from the standard toolkits and +SDKs, such as Numpy (ndarray), Win32 (independent device bitmaps), and others, that is, with any +array that uses *steps* (or *strides*) to compute the position of a pixel. Due to this +compatibility, it is possible to make a Mat header for user-allocated data and process it in-place +using OpenCV functions. + +There are many different ways to create a Mat object. The most popular options are listed below: + +- Use the create(nrows, ncols, type) method or the similar Mat(nrows, ncols, type[, fillValue]) +constructor. A new array of the specified size and type is allocated. type has the same meaning as +in the cvCreateMat method. For example, CV_8UC1 means a 8-bit single-channel array, CV_32FC2 +means a 2-channel (complex) floating-point array, and so on. +@code + // make a 7x7 complex matrix filled with 1+3j. + Mat M(7,7,CV_32FC2,Scalar(1,3)); + // and now turn M to a 100x60 15-channel 8-bit matrix. + // The old content will be deallocated + M.create(100,60,CV_8UC(15)); +@endcode +As noted in the introduction to this chapter, create() allocates only a new array when the shape +or type of the current array are different from the specified ones. + +- Create a multi-dimensional array: +@code + // create a 100x100x100 8-bit array + int sz[] = {100, 100, 100}; + Mat bigCube(3, sz, CV_8U, Scalar::all(0)); +@endcode +It passes the number of dimensions =1 to the Mat constructor but the created array will be +2-dimensional with the number of columns set to 1. So, Mat::dims is always \>= 2 (can also be 0 +when the array is empty). + +- Use a copy constructor or assignment operator where there can be an array or expression on the +right side (see below). As noted in the introduction, the array assignment is an O(1) operation +because it only copies the header and increases the reference counter. The Mat::clone() method can +be used to get a full (deep) copy of the array when you need it. + +- Construct a header for a part of another array. It can be a single row, single column, several +rows, several columns, rectangular region in the array (called a *minor* in algebra) or a +diagonal. Such operations are also O(1) because the new header references the same data. You can +actually modify a part of the array using this feature, for example: +@code + // add the 5-th row, multiplied by 3 to the 3rd row + M.row(3) = M.row(3) + M.row(5)*3; + // now copy the 7-th column to the 1-st column + // M.col(1) = M.col(7); // this will not work + Mat M1 = M.col(1); + M.col(7).copyTo(M1); + // create a new 320x240 image + Mat img(Size(320,240),CV_8UC3); + // select a ROI + Mat roi(img, Rect(10,10,100,100)); + // fill the ROI with (0,255,0) (which is green in RGB space); + // the original 320x240 image will be modified + roi = Scalar(0,255,0); +@endcode +Due to the additional datastart and dataend members, it is possible to compute a relative +sub-array position in the main *container* array using locateROI(): +@code + Mat A = Mat::eye(10, 10, CV_32S); + // extracts A columns, 1 (inclusive) to 3 (exclusive). + Mat B = A(Range::all(), Range(1, 3)); + // extracts B rows, 5 (inclusive) to 9 (exclusive). + // that is, C \~ A(Range(5, 9), Range(1, 3)) + Mat C = B(Range(5, 9), Range::all()); + Size size; Point ofs; + C.locateROI(size, ofs); + // size will be (width=10,height=10) and the ofs will be (x=1, y=5) +@endcode +As in case of whole matrices, if you need a deep copy, use the `clone()` method of the extracted +sub-matrices. + +- Make a header for user-allocated data. It can be useful to do the following: + -# Process "foreign" data using OpenCV (for example, when you implement a DirectShow\* filter or + a processing module for gstreamer, and so on). For example: + @code + void process_video_frame(const unsigned char* pixels, + int width, int height, int step) + { + Mat img(height, width, CV_8UC3, pixels, step); + GaussianBlur(img, img, Size(7,7), 1.5, 1.5); + } + @endcode + -# Quickly initialize small matrices and/or get a super-fast element access. + @code + double m[3][3] = {{a, b, c}, {d, e, f}, {g, h, i}}; + Mat M = Mat(3, 3, CV_64F, m).inv(); + @endcode + . + Partial yet very common cases of this *user-allocated data* case are conversions from CvMat and + IplImage to Mat. For this purpose, there is function cv::cvarrToMat taking pointers to CvMat or + IplImage and the optional flag indicating whether to copy the data or not. + @snippet samples/cpp/image.cpp iplimage + +- Use MATLAB-style array initializers, zeros(), ones(), eye(), for example: +@code + // create a double-precision identity matrix and add it to M. + M += Mat::eye(M.rows, M.cols, CV_64F); +@endcode + +- Use a comma-separated initializer: +@code + // create a 3x3 double-precision identity matrix + Mat M = (Mat_(3,3) << 1, 0, 0, 0, 1, 0, 0, 0, 1); +@endcode +With this approach, you first call a constructor of the Mat class with the proper parameters, and +then you just put `<< operator` followed by comma-separated values that can be constants, +variables, expressions, and so on. Also, note the extra parentheses required to avoid compilation +errors. + +Once the array is created, it is automatically managed via a reference-counting mechanism. If the +array header is built on top of user-allocated data, you should handle the data by yourself. The +array data is deallocated when no one points to it. If you want to release the data pointed by a +array header before the array destructor is called, use Mat::release(). + +The next important thing to learn about the array class is element access. This manual already +described how to compute an address of each array element. Normally, you are not required to use the +formula directly in the code. If you know the array element type (which can be retrieved using the +method Mat::type() ), you can access the element \f$M_{ij}\f$ of a 2-dimensional array as: +@code + M.at(i,j) += 1.f; +@endcode +assuming that `M` is a double-precision floating-point array. There are several variants of the method +at for a different number of dimensions. + +If you need to process a whole row of a 2D array, the most efficient way is to get the pointer to +the row first, and then just use the plain C operator [] : +@code + // compute sum of positive matrix elements + // (assuming that M is a double-precision matrix) + double sum=0; + for(int i = 0; i < M.rows; i++) + { + const double* Mi = M.ptr(i); + for(int j = 0; j < M.cols; j++) + sum += std::max(Mi[j], 0.); + } +@endcode +Some operations, like the one above, do not actually depend on the array shape. They just process +elements of an array one by one (or elements from multiple arrays that have the same coordinates, +for example, array addition). Such operations are called *element-wise*. It makes sense to check +whether all the input/output arrays are continuous, namely, have no gaps at the end of each row. If +yes, process them as a long single row: +@code + // compute the sum of positive matrix elements, optimized variant + double sum=0; + int cols = M.cols, rows = M.rows; + if(M.isContinuous()) + { + cols *= rows; + rows = 1; + } + for(int i = 0; i < rows; i++) + { + const double* Mi = M.ptr(i); + for(int j = 0; j < cols; j++) + sum += std::max(Mi[j], 0.); + } +@endcode +In case of the continuous matrix, the outer loop body is executed just once. So, the overhead is +smaller, which is especially noticeable in case of small matrices. + +Finally, there are STL-style iterators that are smart enough to skip gaps between successive rows: +@code + // compute sum of positive matrix elements, iterator-based variant + double sum=0; + MatConstIterator_ it = M.begin(), it_end = M.end(); + for(; it != it_end; ++it) + sum += std::max(*it, 0.); +@endcode +The matrix iterators are random-access iterators, so they can be passed to any STL algorithm, +including std::sort(). + +@note Matrix Expressions and arithmetic see MatExpr +*/ +class CV_EXPORTS Mat +{ +public: + /** + These are various constructors that form a matrix. As noted in the AutomaticAllocation, often + the default constructor is enough, and the proper matrix will be allocated by an OpenCV function. + The constructed matrix can further be assigned to another matrix or matrix expression or can be + allocated with Mat::create . In the former case, the old content is de-referenced. + */ + Mat(); + + /** @overload + @param rows Number of rows in a 2D array. + @param cols Number of columns in a 2D array. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + */ + Mat(int rows, int cols, int type); + + /** @overload + @param size 2D array size: Size(cols, rows) . In the Size() constructor, the number of rows and the + number of columns go in the reverse order. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + */ + Mat(Size size, int type); + + /** @overload + @param rows Number of rows in a 2D array. + @param cols Number of columns in a 2D array. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + @param s An optional value to initialize each matrix element with. To set all the matrix elements to + the particular value after the construction, use the assignment operator + Mat::operator=(const Scalar& value) . + */ + Mat(int rows, int cols, int type, const Scalar& s); + + /** @overload + @param size 2D array size: Size(cols, rows) . In the Size() constructor, the number of rows and the + number of columns go in the reverse order. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + @param s An optional value to initialize each matrix element with. To set all the matrix elements to + the particular value after the construction, use the assignment operator + Mat::operator=(const Scalar& value) . + */ + Mat(Size size, int type, const Scalar& s); + + /** @overload + @param ndims Array dimensionality. + @param sizes Array of integers specifying an n-dimensional array shape. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + */ + Mat(int ndims, const int* sizes, int type); + + /** @overload + @param sizes Array of integers specifying an n-dimensional array shape. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + */ + Mat(const std::vector& sizes, int type); + + /** @overload + @param ndims Array dimensionality. + @param sizes Array of integers specifying an n-dimensional array shape. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + @param s An optional value to initialize each matrix element with. To set all the matrix elements to + the particular value after the construction, use the assignment operator + Mat::operator=(const Scalar& value) . + */ + Mat(int ndims, const int* sizes, int type, const Scalar& s); + + /** @overload + @param sizes Array of integers specifying an n-dimensional array shape. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + @param s An optional value to initialize each matrix element with. To set all the matrix elements to + the particular value after the construction, use the assignment operator + Mat::operator=(const Scalar& value) . + */ + Mat(const std::vector& sizes, int type, const Scalar& s); + + + /** @overload + @param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied + by these constructors. Instead, the header pointing to m data or its sub-array is constructed and + associated with it. The reference counter, if any, is incremented. So, when you modify the matrix + formed using such a constructor, you also modify the corresponding elements of m . If you want to + have an independent copy of the sub-array, use Mat::clone() . + */ + Mat(const Mat& m); + + /** @overload + @param rows Number of rows in a 2D array. + @param cols Number of columns in a 2D array. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + @param data Pointer to the user data. Matrix constructors that take data and step parameters do not + allocate matrix data. Instead, they just initialize the matrix header that points to the specified + data, which means that no data is copied. This operation is very efficient and can be used to + process external data using OpenCV functions. The external data is not automatically deallocated, so + you should take care of it. + @param step Number of bytes each matrix row occupies. The value should include the padding bytes at + the end of each row, if any. If the parameter is missing (set to AUTO_STEP ), no padding is assumed + and the actual step is calculated as cols*elemSize(). See Mat::elemSize. + */ + Mat(int rows, int cols, int type, void* data, size_t step=AUTO_STEP); + + /** @overload + @param size 2D array size: Size(cols, rows) . In the Size() constructor, the number of rows and the + number of columns go in the reverse order. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + @param data Pointer to the user data. Matrix constructors that take data and step parameters do not + allocate matrix data. Instead, they just initialize the matrix header that points to the specified + data, which means that no data is copied. This operation is very efficient and can be used to + process external data using OpenCV functions. The external data is not automatically deallocated, so + you should take care of it. + @param step Number of bytes each matrix row occupies. The value should include the padding bytes at + the end of each row, if any. If the parameter is missing (set to AUTO_STEP ), no padding is assumed + and the actual step is calculated as cols*elemSize(). See Mat::elemSize. + */ + Mat(Size size, int type, void* data, size_t step=AUTO_STEP); + + /** @overload + @param ndims Array dimensionality. + @param sizes Array of integers specifying an n-dimensional array shape. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + @param data Pointer to the user data. Matrix constructors that take data and step parameters do not + allocate matrix data. Instead, they just initialize the matrix header that points to the specified + data, which means that no data is copied. This operation is very efficient and can be used to + process external data using OpenCV functions. The external data is not automatically deallocated, so + you should take care of it. + @param steps Array of ndims-1 steps in case of a multi-dimensional array (the last step is always + set to the element size). If not specified, the matrix is assumed to be continuous. + */ + Mat(int ndims, const int* sizes, int type, void* data, const size_t* steps=0); + + /** @overload + @param sizes Array of integers specifying an n-dimensional array shape. + @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or + CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. + @param data Pointer to the user data. Matrix constructors that take data and step parameters do not + allocate matrix data. Instead, they just initialize the matrix header that points to the specified + data, which means that no data is copied. This operation is very efficient and can be used to + process external data using OpenCV functions. The external data is not automatically deallocated, so + you should take care of it. + @param steps Array of ndims-1 steps in case of a multi-dimensional array (the last step is always + set to the element size). If not specified, the matrix is assumed to be continuous. + */ + Mat(const std::vector& sizes, int type, void* data, const size_t* steps=0); + + /** @overload + @param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied + by these constructors. Instead, the header pointing to m data or its sub-array is constructed and + associated with it. The reference counter, if any, is incremented. So, when you modify the matrix + formed using such a constructor, you also modify the corresponding elements of m . If you want to + have an independent copy of the sub-array, use Mat::clone() . + @param rowRange Range of the m rows to take. As usual, the range start is inclusive and the range + end is exclusive. Use Range::all() to take all the rows. + @param colRange Range of the m columns to take. Use Range::all() to take all the columns. + */ + Mat(const Mat& m, const Range& rowRange, const Range& colRange=Range::all()); + + /** @overload + @param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied + by these constructors. Instead, the header pointing to m data or its sub-array is constructed and + associated with it. The reference counter, if any, is incremented. So, when you modify the matrix + formed using such a constructor, you also modify the corresponding elements of m . If you want to + have an independent copy of the sub-array, use Mat::clone() . + @param roi Region of interest. + */ + Mat(const Mat& m, const Rect& roi); + + /** @overload + @param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied + by these constructors. Instead, the header pointing to m data or its sub-array is constructed and + associated with it. The reference counter, if any, is incremented. So, when you modify the matrix + formed using such a constructor, you also modify the corresponding elements of m . If you want to + have an independent copy of the sub-array, use Mat::clone() . + @param ranges Array of selected ranges of m along each dimensionality. + */ + Mat(const Mat& m, const Range* ranges); + + /** @overload + @param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied + by these constructors. Instead, the header pointing to m data or its sub-array is constructed and + associated with it. The reference counter, if any, is incremented. So, when you modify the matrix + formed using such a constructor, you also modify the corresponding elements of m . If you want to + have an independent copy of the sub-array, use Mat::clone() . + @param ranges Array of selected ranges of m along each dimensionality. + */ + Mat(const Mat& m, const std::vector& ranges); + + /** @overload + @param vec STL vector whose elements form the matrix. The matrix has a single column and the number + of rows equal to the number of vector elements. Type of the matrix matches the type of vector + elements. The constructor can handle arbitrary types, for which there is a properly declared + DataType . This means that the vector elements must be primitive numbers or uni-type numerical + tuples of numbers. Mixed-type structures are not supported. The corresponding constructor is + explicit. Since STL vectors are not automatically converted to Mat instances, you should write + Mat(vec) explicitly. Unless you copy the data into the matrix ( copyData=true ), no new elements + will be added to the vector because it can potentially yield vector data reallocation, and, thus, + the matrix data pointer will be invalid. + @param copyData Flag to specify whether the underlying data of the STL vector should be copied + to (true) or shared with (false) the newly constructed matrix. When the data is copied, the + allocated buffer is managed using Mat reference counting mechanism. While the data is shared, + the reference counter is NULL, and you should not deallocate the data until the matrix is not + destructed. + */ + template explicit Mat(const std::vector<_Tp>& vec, bool copyData=false); + +#ifdef CV_CXX11 + /** @overload + */ + template::value>::type> + explicit Mat(const std::initializer_list<_Tp> list); + + /** @overload + */ + template explicit Mat(const std::initializer_list sizes, const std::initializer_list<_Tp> list); +#endif + +#ifdef CV_CXX_STD_ARRAY + /** @overload + */ + template explicit Mat(const std::array<_Tp, _Nm>& arr, bool copyData=false); +#endif + + /** @overload + */ + template explicit Mat(const Vec<_Tp, n>& vec, bool copyData=true); + + /** @overload + */ + template explicit Mat(const Matx<_Tp, m, n>& mtx, bool copyData=true); + + /** @overload + */ + template explicit Mat(const Point_<_Tp>& pt, bool copyData=true); + + /** @overload + */ + template explicit Mat(const Point3_<_Tp>& pt, bool copyData=true); + + /** @overload + */ + template explicit Mat(const MatCommaInitializer_<_Tp>& commaInitializer); + + //! download data from GpuMat + explicit Mat(const cuda::GpuMat& m); + + //! destructor - calls release() + ~Mat(); + + /** @brief assignment operators + + These are available assignment operators. Since they all are very different, make sure to read the + operator parameters description. + @param m Assigned, right-hand-side matrix. Matrix assignment is an O(1) operation. This means that + no data is copied but the data is shared and the reference counter, if any, is incremented. Before + assigning new data, the old data is de-referenced via Mat::release . + */ + Mat& operator = (const Mat& m); + + /** @overload + @param expr Assigned matrix expression object. As opposite to the first form of the assignment + operation, the second form can reuse already allocated matrix if it has the right size and type to + fit the matrix expression result. It is automatically handled by the real function that the matrix + expressions is expanded to. For example, C=A+B is expanded to add(A, B, C), and add takes care of + automatic C reallocation. + */ + Mat& operator = (const MatExpr& expr); + + //! retrieve UMat from Mat + UMat getUMat(int accessFlags, UMatUsageFlags usageFlags = USAGE_DEFAULT) const; + + /** @brief Creates a matrix header for the specified matrix row. + + The method makes a new header for the specified matrix row and returns it. This is an O(1) + operation, regardless of the matrix size. The underlying data of the new matrix is shared with the + original matrix. Here is the example of one of the classical basic matrix processing operations, + axpy, used by LU and many other algorithms: + @code + inline void matrix_axpy(Mat& A, int i, int j, double alpha) + { + A.row(i) += A.row(j)*alpha; + } + @endcode + @note In the current implementation, the following code does not work as expected: + @code + Mat A; + ... + A.row(i) = A.row(j); // will not work + @endcode + This happens because A.row(i) forms a temporary header that is further assigned to another header. + Remember that each of these operations is O(1), that is, no data is copied. Thus, the above + assignment is not true if you may have expected the j-th row to be copied to the i-th row. To + achieve that, you should either turn this simple assignment into an expression or use the + Mat::copyTo method: + @code + Mat A; + ... + // works, but looks a bit obscure. + A.row(i) = A.row(j) + 0; + // this is a bit longer, but the recommended method. + A.row(j).copyTo(A.row(i)); + @endcode + @param y A 0-based row index. + */ + Mat row(int y) const; + + /** @brief Creates a matrix header for the specified matrix column. + + The method makes a new header for the specified matrix column and returns it. This is an O(1) + operation, regardless of the matrix size. The underlying data of the new matrix is shared with the + original matrix. See also the Mat::row description. + @param x A 0-based column index. + */ + Mat col(int x) const; + + /** @brief Creates a matrix header for the specified row span. + + The method makes a new header for the specified row span of the matrix. Similarly to Mat::row and + Mat::col , this is an O(1) operation. + @param startrow An inclusive 0-based start index of the row span. + @param endrow An exclusive 0-based ending index of the row span. + */ + Mat rowRange(int startrow, int endrow) const; + + /** @overload + @param r Range structure containing both the start and the end indices. + */ + Mat rowRange(const Range& r) const; + + /** @brief Creates a matrix header for the specified column span. + + The method makes a new header for the specified column span of the matrix. Similarly to Mat::row and + Mat::col , this is an O(1) operation. + @param startcol An inclusive 0-based start index of the column span. + @param endcol An exclusive 0-based ending index of the column span. + */ + Mat colRange(int startcol, int endcol) const; + + /** @overload + @param r Range structure containing both the start and the end indices. + */ + Mat colRange(const Range& r) const; + + /** @brief Extracts a diagonal from a matrix + + The method makes a new header for the specified matrix diagonal. The new matrix is represented as a + single-column matrix. Similarly to Mat::row and Mat::col, this is an O(1) operation. + @param d index of the diagonal, with the following values: + - `d=0` is the main diagonal. + - `d<0` is a diagonal from the lower half. For example, d=-1 means the diagonal is set + immediately below the main one. + - `d>0` is a diagonal from the upper half. For example, d=1 means the diagonal is set + immediately above the main one. + For example: + @code + Mat m = (Mat_(3,3) << + 1,2,3, + 4,5,6, + 7,8,9); + Mat d0 = m.diag(0); + Mat d1 = m.diag(1); + Mat d_1 = m.diag(-1); + @endcode + The resulting matrices are + @code + d0 = + [1; + 5; + 9] + d1 = + [2; + 6] + d_1 = + [4; + 8] + @endcode + */ + Mat diag(int d=0) const; + + /** @brief creates a diagonal matrix + + The method creates a square diagonal matrix from specified main diagonal. + @param d One-dimensional matrix that represents the main diagonal. + */ + static Mat diag(const Mat& d); + + /** @brief Creates a full copy of the array and the underlying data. + + The method creates a full copy of the array. The original step[] is not taken into account. So, the + array copy is a continuous array occupying total()*elemSize() bytes. + */ + Mat clone() const CV_NODISCARD; + + /** @brief Copies the matrix to another one. + + The method copies the matrix data to another matrix. Before copying the data, the method invokes : + @code + m.create(this->size(), this->type()); + @endcode + so that the destination matrix is reallocated if needed. While m.copyTo(m); works flawlessly, the + function does not handle the case of a partial overlap between the source and the destination + matrices. + + When the operation mask is specified, if the Mat::create call shown above reallocates the matrix, + the newly allocated matrix is initialized with all zeros before copying the data. + @param m Destination matrix. If it does not have a proper size or type before the operation, it is + reallocated. + */ + void copyTo( OutputArray m ) const; + + /** @overload + @param m Destination matrix. If it does not have a proper size or type before the operation, it is + reallocated. + @param mask Operation mask of the same size as \*this. Its non-zero elements indicate which matrix + elements need to be copied. The mask has to be of type CV_8U and can have 1 or multiple channels. + */ + void copyTo( OutputArray m, InputArray mask ) const; + + /** @brief Converts an array to another data type with optional scaling. + + The method converts source pixel values to the target data type. saturate_cast\<\> is applied at + the end to avoid possible overflows: + + \f[m(x,y) = saturate \_ cast( \alpha (*this)(x,y) + \beta )\f] + @param m output matrix; if it does not have a proper size or type before the operation, it is + reallocated. + @param rtype desired output matrix type or, rather, the depth since the number of channels are the + same as the input has; if rtype is negative, the output matrix will have the same type as the input. + @param alpha optional scale factor. + @param beta optional delta added to the scaled values. + */ + void convertTo( OutputArray m, int rtype, double alpha=1, double beta=0 ) const; + + /** @brief Provides a functional form of convertTo. + + This is an internally used method called by the @ref MatrixExpressions engine. + @param m Destination array. + @param type Desired destination array depth (or -1 if it should be the same as the source type). + */ + void assignTo( Mat& m, int type=-1 ) const; + + /** @brief Sets all or some of the array elements to the specified value. + @param s Assigned scalar converted to the actual array type. + */ + Mat& operator = (const Scalar& s); + + /** @brief Sets all or some of the array elements to the specified value. + + This is an advanced variant of the Mat::operator=(const Scalar& s) operator. + @param value Assigned scalar converted to the actual array type. + @param mask Operation mask of the same size as \*this. Its non-zero elements indicate which matrix + elements need to be copied. The mask has to be of type CV_8U and can have 1 or multiple channels + */ + Mat& setTo(InputArray value, InputArray mask=noArray()); + + /** @brief Changes the shape and/or the number of channels of a 2D matrix without copying the data. + + The method makes a new matrix header for \*this elements. The new matrix may have a different size + and/or different number of channels. Any combination is possible if: + - No extra elements are included into the new matrix and no elements are excluded. Consequently, + the product rows\*cols\*channels() must stay the same after the transformation. + - No data is copied. That is, this is an O(1) operation. Consequently, if you change the number of + rows, or the operation changes the indices of elements row in some other way, the matrix must be + continuous. See Mat::isContinuous . + + For example, if there is a set of 3D points stored as an STL vector, and you want to represent the + points as a 3xN matrix, do the following: + @code + std::vector vec; + ... + Mat pointMat = Mat(vec). // convert vector to Mat, O(1) operation + reshape(1). // make Nx3 1-channel matrix out of Nx1 3-channel. + // Also, an O(1) operation + t(); // finally, transpose the Nx3 matrix. + // This involves copying all the elements + @endcode + @param cn New number of channels. If the parameter is 0, the number of channels remains the same. + @param rows New number of rows. If the parameter is 0, the number of rows remains the same. + */ + Mat reshape(int cn, int rows=0) const; + + /** @overload */ + Mat reshape(int cn, int newndims, const int* newsz) const; + + /** @overload */ + Mat reshape(int cn, const std::vector& newshape) const; + + /** @brief Transposes a matrix. + + The method performs matrix transposition by means of matrix expressions. It does not perform the + actual transposition but returns a temporary matrix transposition object that can be further used as + a part of more complex matrix expressions or can be assigned to a matrix: + @code + Mat A1 = A + Mat::eye(A.size(), A.type())*lambda; + Mat C = A1.t()*A1; // compute (A + lambda*I)^t * (A + lamda*I) + @endcode + */ + MatExpr t() const; + + /** @brief Inverses a matrix. + + The method performs a matrix inversion by means of matrix expressions. This means that a temporary + matrix inversion object is returned by the method and can be used further as a part of more complex + matrix expressions or can be assigned to a matrix. + @param method Matrix inversion method. One of cv::DecompTypes + */ + MatExpr inv(int method=DECOMP_LU) const; + + /** @brief Performs an element-wise multiplication or division of the two matrices. + + The method returns a temporary object encoding per-element array multiplication, with optional + scale. Note that this is not a matrix multiplication that corresponds to a simpler "\*" operator. + + Example: + @code + Mat C = A.mul(5/B); // equivalent to divide(A, B, C, 5) + @endcode + @param m Another array of the same type and the same size as \*this, or a matrix expression. + @param scale Optional scale factor. + */ + MatExpr mul(InputArray m, double scale=1) const; + + /** @brief Computes a cross-product of two 3-element vectors. + + The method computes a cross-product of two 3-element vectors. The vectors must be 3-element + floating-point vectors of the same shape and size. The result is another 3-element vector of the + same shape and type as operands. + @param m Another cross-product operand. + */ + Mat cross(InputArray m) const; + + /** @brief Computes a dot-product of two vectors. + + The method computes a dot-product of two matrices. If the matrices are not single-column or + single-row vectors, the top-to-bottom left-to-right scan ordering is used to treat them as 1D + vectors. The vectors must have the same size and type. If the matrices have more than one channel, + the dot products from all the channels are summed together. + @param m another dot-product operand. + */ + double dot(InputArray m) const; + + /** @brief Returns a zero array of the specified size and type. + + The method returns a Matlab-style zero array initializer. It can be used to quickly form a constant + array as a function parameter, part of a matrix expression, or as a matrix initializer: + @code + Mat A; + A = Mat::zeros(3, 3, CV_32F); + @endcode + In the example above, a new matrix is allocated only if A is not a 3x3 floating-point matrix. + Otherwise, the existing matrix A is filled with zeros. + @param rows Number of rows. + @param cols Number of columns. + @param type Created matrix type. + */ + static MatExpr zeros(int rows, int cols, int type); + + /** @overload + @param size Alternative to the matrix size specification Size(cols, rows) . + @param type Created matrix type. + */ + static MatExpr zeros(Size size, int type); + + /** @overload + @param ndims Array dimensionality. + @param sz Array of integers specifying the array shape. + @param type Created matrix type. + */ + static MatExpr zeros(int ndims, const int* sz, int type); + + /** @brief Returns an array of all 1's of the specified size and type. + + The method returns a Matlab-style 1's array initializer, similarly to Mat::zeros. Note that using + this method you can initialize an array with an arbitrary value, using the following Matlab idiom: + @code + Mat A = Mat::ones(100, 100, CV_8U)*3; // make 100x100 matrix filled with 3. + @endcode + The above operation does not form a 100x100 matrix of 1's and then multiply it by 3. Instead, it + just remembers the scale factor (3 in this case) and use it when actually invoking the matrix + initializer. + @note In case of multi-channels type, only the first channel will be initialized with 1's, the + others will be set to 0's. + @param rows Number of rows. + @param cols Number of columns. + @param type Created matrix type. + */ + static MatExpr ones(int rows, int cols, int type); + + /** @overload + @param size Alternative to the matrix size specification Size(cols, rows) . + @param type Created matrix type. + */ + static MatExpr ones(Size size, int type); + + /** @overload + @param ndims Array dimensionality. + @param sz Array of integers specifying the array shape. + @param type Created matrix type. + */ + static MatExpr ones(int ndims, const int* sz, int type); + + /** @brief Returns an identity matrix of the specified size and type. + + The method returns a Matlab-style identity matrix initializer, similarly to Mat::zeros. Similarly to + Mat::ones, you can use a scale operation to create a scaled identity matrix efficiently: + @code + // make a 4x4 diagonal matrix with 0.1's on the diagonal. + Mat A = Mat::eye(4, 4, CV_32F)*0.1; + @endcode + @note In case of multi-channels type, identity matrix will be initialized only for the first channel, + the others will be set to 0's + @param rows Number of rows. + @param cols Number of columns. + @param type Created matrix type. + */ + static MatExpr eye(int rows, int cols, int type); + + /** @overload + @param size Alternative matrix size specification as Size(cols, rows) . + @param type Created matrix type. + */ + static MatExpr eye(Size size, int type); + + /** @brief Allocates new array data if needed. + + This is one of the key Mat methods. Most new-style OpenCV functions and methods that produce arrays + call this method for each output array. The method uses the following algorithm: + + -# If the current array shape and the type match the new ones, return immediately. Otherwise, + de-reference the previous data by calling Mat::release. + -# Initialize the new header. + -# Allocate the new data of total()\*elemSize() bytes. + -# Allocate the new, associated with the data, reference counter and set it to 1. + + Such a scheme makes the memory management robust and efficient at the same time and helps avoid + extra typing for you. This means that usually there is no need to explicitly allocate output arrays. + That is, instead of writing: + @code + Mat color; + ... + Mat gray(color.rows, color.cols, color.depth()); + cvtColor(color, gray, COLOR_BGR2GRAY); + @endcode + you can simply write: + @code + Mat color; + ... + Mat gray; + cvtColor(color, gray, COLOR_BGR2GRAY); + @endcode + because cvtColor, as well as the most of OpenCV functions, calls Mat::create() for the output array + internally. + @param rows New number of rows. + @param cols New number of columns. + @param type New matrix type. + */ + void create(int rows, int cols, int type); + + /** @overload + @param size Alternative new matrix size specification: Size(cols, rows) + @param type New matrix type. + */ + void create(Size size, int type); + + /** @overload + @param ndims New array dimensionality. + @param sizes Array of integers specifying a new array shape. + @param type New matrix type. + */ + void create(int ndims, const int* sizes, int type); + + /** @overload + @param sizes Array of integers specifying a new array shape. + @param type New matrix type. + */ + void create(const std::vector& sizes, int type); + + /** @brief Increments the reference counter. + + The method increments the reference counter associated with the matrix data. If the matrix header + points to an external data set (see Mat::Mat ), the reference counter is NULL, and the method has no + effect in this case. Normally, to avoid memory leaks, the method should not be called explicitly. It + is called implicitly by the matrix assignment operator. The reference counter increment is an atomic + operation on the platforms that support it. Thus, it is safe to operate on the same matrices + asynchronously in different threads. + */ + void addref(); + + /** @brief Decrements the reference counter and deallocates the matrix if needed. + + The method decrements the reference counter associated with the matrix data. When the reference + counter reaches 0, the matrix data is deallocated and the data and the reference counter pointers + are set to NULL's. If the matrix header points to an external data set (see Mat::Mat ), the + reference counter is NULL, and the method has no effect in this case. + + This method can be called manually to force the matrix data deallocation. But since this method is + automatically called in the destructor, or by any other method that changes the data pointer, it is + usually not needed. The reference counter decrement and check for 0 is an atomic operation on the + platforms that support it. Thus, it is safe to operate on the same matrices asynchronously in + different threads. + */ + void release(); + + //! internal use function, consider to use 'release' method instead; deallocates the matrix data + void deallocate(); + //! internal use function; properly re-allocates _size, _step arrays + void copySize(const Mat& m); + + /** @brief Reserves space for the certain number of rows. + + The method reserves space for sz rows. If the matrix already has enough space to store sz rows, + nothing happens. If the matrix is reallocated, the first Mat::rows rows are preserved. The method + emulates the corresponding method of the STL vector class. + @param sz Number of rows. + */ + void reserve(size_t sz); + + /** @brief Reserves space for the certain number of bytes. + + The method reserves space for sz bytes. If the matrix already has enough space to store sz bytes, + nothing happens. If matrix has to be reallocated its previous content could be lost. + @param sz Number of bytes. + */ + void reserveBuffer(size_t sz); + + /** @brief Changes the number of matrix rows. + + The methods change the number of matrix rows. If the matrix is reallocated, the first + min(Mat::rows, sz) rows are preserved. The methods emulate the corresponding methods of the STL + vector class. + @param sz New number of rows. + */ + void resize(size_t sz); + + /** @overload + @param sz New number of rows. + @param s Value assigned to the newly added elements. + */ + void resize(size_t sz, const Scalar& s); + + //! internal function + void push_back_(const void* elem); + + /** @brief Adds elements to the bottom of the matrix. + + The methods add one or more elements to the bottom of the matrix. They emulate the corresponding + method of the STL vector class. When elem is Mat , its type and the number of columns must be the + same as in the container matrix. + @param elem Added element(s). + */ + template void push_back(const _Tp& elem); + + /** @overload + @param elem Added element(s). + */ + template void push_back(const Mat_<_Tp>& elem); + + /** @overload + @param elem Added element(s). + */ + template void push_back(const std::vector<_Tp>& elem); + + /** @overload + @param m Added line(s). + */ + void push_back(const Mat& m); + + /** @brief Removes elements from the bottom of the matrix. + + The method removes one or more rows from the bottom of the matrix. + @param nelems Number of removed rows. If it is greater than the total number of rows, an exception + is thrown. + */ + void pop_back(size_t nelems=1); + + /** @brief Locates the matrix header within a parent matrix. + + After you extracted a submatrix from a matrix using Mat::row, Mat::col, Mat::rowRange, + Mat::colRange, and others, the resultant submatrix points just to the part of the original big + matrix. However, each submatrix contains information (represented by datastart and dataend + fields) that helps reconstruct the original matrix size and the position of the extracted + submatrix within the original matrix. The method locateROI does exactly that. + @param wholeSize Output parameter that contains the size of the whole matrix containing *this* + as a part. + @param ofs Output parameter that contains an offset of *this* inside the whole matrix. + */ + void locateROI( Size& wholeSize, Point& ofs ) const; + + /** @brief Adjusts a submatrix size and position within the parent matrix. + + The method is complimentary to Mat::locateROI . The typical use of these functions is to determine + the submatrix position within the parent matrix and then shift the position somehow. Typically, it + can be required for filtering operations when pixels outside of the ROI should be taken into + account. When all the method parameters are positive, the ROI needs to grow in all directions by the + specified amount, for example: + @code + A.adjustROI(2, 2, 2, 2); + @endcode + In this example, the matrix size is increased by 4 elements in each direction. The matrix is shifted + by 2 elements to the left and 2 elements up, which brings in all the necessary pixels for the + filtering with the 5x5 kernel. + + adjustROI forces the adjusted ROI to be inside of the parent matrix that is boundaries of the + adjusted ROI are constrained by boundaries of the parent matrix. For example, if the submatrix A is + located in the first row of a parent matrix and you called A.adjustROI(2, 2, 2, 2) then A will not + be increased in the upward direction. + + The function is used internally by the OpenCV filtering functions, like filter2D , morphological + operations, and so on. + @param dtop Shift of the top submatrix boundary upwards. + @param dbottom Shift of the bottom submatrix boundary downwards. + @param dleft Shift of the left submatrix boundary to the left. + @param dright Shift of the right submatrix boundary to the right. + @sa copyMakeBorder + */ + Mat& adjustROI( int dtop, int dbottom, int dleft, int dright ); + + /** @brief Extracts a rectangular submatrix. + + The operators make a new header for the specified sub-array of \*this . They are the most + generalized forms of Mat::row, Mat::col, Mat::rowRange, and Mat::colRange . For example, + `A(Range(0, 10), Range::all())` is equivalent to `A.rowRange(0, 10)`. Similarly to all of the above, + the operators are O(1) operations, that is, no matrix data is copied. + @param rowRange Start and end row of the extracted submatrix. The upper boundary is not included. To + select all the rows, use Range::all(). + @param colRange Start and end column of the extracted submatrix. The upper boundary is not included. + To select all the columns, use Range::all(). + */ + Mat operator()( Range rowRange, Range colRange ) const; + + /** @overload + @param roi Extracted submatrix specified as a rectangle. + */ + Mat operator()( const Rect& roi ) const; + + /** @overload + @param ranges Array of selected ranges along each array dimension. + */ + Mat operator()( const Range* ranges ) const; + + /** @overload + @param ranges Array of selected ranges along each array dimension. + */ + Mat operator()(const std::vector& ranges) const; + + // //! converts header to CvMat; no data is copied + // operator CvMat() const; + // //! converts header to CvMatND; no data is copied + // operator CvMatND() const; + // //! converts header to IplImage; no data is copied + // operator IplImage() const; + + template operator std::vector<_Tp>() const; + template operator Vec<_Tp, n>() const; + template operator Matx<_Tp, m, n>() const; + +#ifdef CV_CXX_STD_ARRAY + template operator std::array<_Tp, _Nm>() const; +#endif + + /** @brief Reports whether the matrix is continuous or not. + + The method returns true if the matrix elements are stored continuously without gaps at the end of + each row. Otherwise, it returns false. Obviously, 1x1 or 1xN matrices are always continuous. + Matrices created with Mat::create are always continuous. But if you extract a part of the matrix + using Mat::col, Mat::diag, and so on, or constructed a matrix header for externally allocated data, + such matrices may no longer have this property. + + The continuity flag is stored as a bit in the Mat::flags field and is computed automatically when + you construct a matrix header. Thus, the continuity check is a very fast operation, though + theoretically it could be done as follows: + @code + // alternative implementation of Mat::isContinuous() + bool myCheckMatContinuity(const Mat& m) + { + //return (m.flags & Mat::CONTINUOUS_FLAG) != 0; + return m.rows == 1 || m.step == m.cols*m.elemSize(); + } + @endcode + The method is used in quite a few of OpenCV functions. The point is that element-wise operations + (such as arithmetic and logical operations, math functions, alpha blending, color space + transformations, and others) do not depend on the image geometry. Thus, if all the input and output + arrays are continuous, the functions can process them as very long single-row vectors. The example + below illustrates how an alpha-blending function can be implemented: + @code + template + void alphaBlendRGBA(const Mat& src1, const Mat& src2, Mat& dst) + { + const float alpha_scale = (float)std::numeric_limits::max(), + inv_scale = 1.f/alpha_scale; + + CV_Assert( src1.type() == src2.type() && + src1.type() == CV_MAKETYPE(traits::Depth::value, 4) && + src1.size() == src2.size()); + Size size = src1.size(); + dst.create(size, src1.type()); + + // here is the idiom: check the arrays for continuity and, + // if this is the case, + // treat the arrays as 1D vectors + if( src1.isContinuous() && src2.isContinuous() && dst.isContinuous() ) + { + size.width *= size.height; + size.height = 1; + } + size.width *= 4; + + for( int i = 0; i < size.height; i++ ) + { + // when the arrays are continuous, + // the outer loop is executed only once + const T* ptr1 = src1.ptr(i); + const T* ptr2 = src2.ptr(i); + T* dptr = dst.ptr(i); + + for( int j = 0; j < size.width; j += 4 ) + { + float alpha = ptr1[j+3]*inv_scale, beta = ptr2[j+3]*inv_scale; + dptr[j] = saturate_cast(ptr1[j]*alpha + ptr2[j]*beta); + dptr[j+1] = saturate_cast(ptr1[j+1]*alpha + ptr2[j+1]*beta); + dptr[j+2] = saturate_cast(ptr1[j+2]*alpha + ptr2[j+2]*beta); + dptr[j+3] = saturate_cast((1 - (1-alpha)*(1-beta))*alpha_scale); + } + } + } + @endcode + This approach, while being very simple, can boost the performance of a simple element-operation by + 10-20 percents, especially if the image is rather small and the operation is quite simple. + + Another OpenCV idiom in this function, a call of Mat::create for the destination array, that + allocates the destination array unless it already has the proper size and type. And while the newly + allocated arrays are always continuous, you still need to check the destination array because + Mat::create does not always allocate a new matrix. + */ + bool isContinuous() const; + + //! returns true if the matrix is a submatrix of another matrix + bool isSubmatrix() const; + + /** @brief Returns the matrix element size in bytes. + + The method returns the matrix element size in bytes. For example, if the matrix type is CV_16SC3 , + the method returns 3\*sizeof(short) or 6. + */ + size_t elemSize() const; + + /** @brief Returns the size of each matrix element channel in bytes. + + The method returns the matrix element channel size in bytes, that is, it ignores the number of + channels. For example, if the matrix type is CV_16SC3 , the method returns sizeof(short) or 2. + */ + size_t elemSize1() const; + + /** @brief Returns the type of a matrix element. + + The method returns a matrix element type. This is an identifier compatible with the CvMat type + system, like CV_16SC3 or 16-bit signed 3-channel array, and so on. + */ + int type() const; + + /** @brief Returns the depth of a matrix element. + + The method returns the identifier of the matrix element depth (the type of each individual channel). + For example, for a 16-bit signed element array, the method returns CV_16S . A complete list of + matrix types contains the following values: + - CV_8U - 8-bit unsigned integers ( 0..255 ) + - CV_8S - 8-bit signed integers ( -128..127 ) + - CV_16U - 16-bit unsigned integers ( 0..65535 ) + - CV_16S - 16-bit signed integers ( -32768..32767 ) + - CV_32S - 32-bit signed integers ( -2147483648..2147483647 ) + - CV_32F - 32-bit floating-point numbers ( -FLT_MAX..FLT_MAX, INF, NAN ) + - CV_64F - 64-bit floating-point numbers ( -DBL_MAX..DBL_MAX, INF, NAN ) + */ + int depth() const; + + /** @brief Returns the number of matrix channels. + + The method returns the number of matrix channels. + */ + int channels() const; + + /** @brief Returns a normalized step. + + The method returns a matrix step divided by Mat::elemSize1() . It can be useful to quickly access an + arbitrary matrix element. + */ + size_t step1(int i=0) const; + + /** @brief Returns true if the array has no elements. + + The method returns true if Mat::total() is 0 or if Mat::data is NULL. Because of pop_back() and + resize() methods `M.total() == 0` does not imply that `M.data == NULL`. + */ + bool empty() const; + + /** @brief Returns the total number of array elements. + + The method returns the number of array elements (a number of pixels if the array represents an + image). + */ + size_t total() const; + + /** @brief Returns the total number of array elements. + + The method returns the number of elements within a certain sub-array slice with startDim <= dim < endDim + */ + size_t total(int startDim, int endDim=INT_MAX) const; + + /** + * @param elemChannels Number of channels or number of columns the matrix should have. + * For a 2-D matrix, when the matrix has only 1 column, then it should have + * elemChannels channels; When the matrix has only 1 channel, + * then it should have elemChannels columns. + * For a 3-D matrix, it should have only one channel. Furthermore, + * if the number of planes is not one, then the number of rows + * within every plane has to be 1; if the number of rows within + * every plane is not 1, then the number of planes has to be 1. + * @param depth The depth the matrix should have. Set it to -1 when any depth is fine. + * @param requireContinuous Set it to true to require the matrix to be continuous + * @return -1 if the requirement is not satisfied. + * Otherwise, it returns the number of elements in the matrix. Note + * that an element may have multiple channels. + * + * The following code demonstrates its usage for a 2-d matrix: + * @snippet snippets/core_mat_checkVector.cpp example-2d + * + * The following code demonstrates its usage for a 3-d matrix: + * @snippet snippets/core_mat_checkVector.cpp example-3d + */ + int checkVector(int elemChannels, int depth=-1, bool requireContinuous=true) const; + + /** @brief Returns a pointer to the specified matrix row. + + The methods return `uchar*` or typed pointer to the specified matrix row. See the sample in + Mat::isContinuous to know how to use these methods. + @param i0 A 0-based row index. + */ + uchar* ptr(int i0=0); + /** @overload */ + const uchar* ptr(int i0=0) const; + + /** @overload + @param row Index along the dimension 0 + @param col Index along the dimension 1 + */ + uchar* ptr(int row, int col); + /** @overload + @param row Index along the dimension 0 + @param col Index along the dimension 1 + */ + const uchar* ptr(int row, int col) const; + + /** @overload */ + uchar* ptr(int i0, int i1, int i2); + /** @overload */ + const uchar* ptr(int i0, int i1, int i2) const; + + /** @overload */ + uchar* ptr(const int* idx); + /** @overload */ + const uchar* ptr(const int* idx) const; + /** @overload */ + template uchar* ptr(const Vec& idx); + /** @overload */ + template const uchar* ptr(const Vec& idx) const; + + /** @overload */ + template _Tp* ptr(int i0=0); + /** @overload */ + template const _Tp* ptr(int i0=0) const; + /** @overload + @param row Index along the dimension 0 + @param col Index along the dimension 1 + */ + template _Tp* ptr(int row, int col); + /** @overload + @param row Index along the dimension 0 + @param col Index along the dimension 1 + */ + template const _Tp* ptr(int row, int col) const; + /** @overload */ + template _Tp* ptr(int i0, int i1, int i2); + /** @overload */ + template const _Tp* ptr(int i0, int i1, int i2) const; + /** @overload */ + template _Tp* ptr(const int* idx); + /** @overload */ + template const _Tp* ptr(const int* idx) const; + /** @overload */ + template _Tp* ptr(const Vec& idx); + /** @overload */ + template const _Tp* ptr(const Vec& idx) const; + + /** @brief Returns a reference to the specified array element. + + The template methods return a reference to the specified array element. For the sake of higher + performance, the index range checks are only performed in the Debug configuration. + + Note that the variants with a single index (i) can be used to access elements of single-row or + single-column 2-dimensional arrays. That is, if, for example, A is a 1 x N floating-point matrix and + B is an M x 1 integer matrix, you can simply write `A.at(k+4)` and `B.at(2*i+1)` + instead of `A.at(0,k+4)` and `B.at(2*i+1,0)`, respectively. + + The example below initializes a Hilbert matrix: + @code + Mat H(100, 100, CV_64F); + for(int i = 0; i < H.rows; i++) + for(int j = 0; j < H.cols; j++) + H.at(i,j)=1./(i+j+1); + @endcode + + Keep in mind that the size identifier used in the at operator cannot be chosen at random. It depends + on the image from which you are trying to retrieve the data. The table below gives a better insight in this: + - If matrix is of type `CV_8U` then use `Mat.at(y,x)`. + - If matrix is of type `CV_8S` then use `Mat.at(y,x)`. + - If matrix is of type `CV_16U` then use `Mat.at(y,x)`. + - If matrix is of type `CV_16S` then use `Mat.at(y,x)`. + - If matrix is of type `CV_32S` then use `Mat.at(y,x)`. + - If matrix is of type `CV_32F` then use `Mat.at(y,x)`. + - If matrix is of type `CV_64F` then use `Mat.at(y,x)`. + + @param i0 Index along the dimension 0 + */ + template _Tp& at(int i0=0); + /** @overload + @param i0 Index along the dimension 0 + */ + template const _Tp& at(int i0=0) const; + /** @overload + @param row Index along the dimension 0 + @param col Index along the dimension 1 + */ + template _Tp& at(int row, int col); + /** @overload + @param row Index along the dimension 0 + @param col Index along the dimension 1 + */ + template const _Tp& at(int row, int col) const; + + /** @overload + @param i0 Index along the dimension 0 + @param i1 Index along the dimension 1 + @param i2 Index along the dimension 2 + */ + template _Tp& at(int i0, int i1, int i2); + /** @overload + @param i0 Index along the dimension 0 + @param i1 Index along the dimension 1 + @param i2 Index along the dimension 2 + */ + template const _Tp& at(int i0, int i1, int i2) const; + + /** @overload + @param idx Array of Mat::dims indices. + */ + template _Tp& at(const int* idx); + /** @overload + @param idx Array of Mat::dims indices. + */ + template const _Tp& at(const int* idx) const; + + /** @overload */ + template _Tp& at(const Vec& idx); + /** @overload */ + template const _Tp& at(const Vec& idx) const; + + /** @overload + special versions for 2D arrays (especially convenient for referencing image pixels) + @param pt Element position specified as Point(j,i) . + */ + template _Tp& at(Point pt); + /** @overload + special versions for 2D arrays (especially convenient for referencing image pixels) + @param pt Element position specified as Point(j,i) . + */ + template const _Tp& at(Point pt) const; + + /** @brief Returns the matrix iterator and sets it to the first matrix element. + + The methods return the matrix read-only or read-write iterators. The use of matrix iterators is very + similar to the use of bi-directional STL iterators. In the example below, the alpha blending + function is rewritten using the matrix iterators: + @code + template + void alphaBlendRGBA(const Mat& src1, const Mat& src2, Mat& dst) + { + typedef Vec VT; + + const float alpha_scale = (float)std::numeric_limits::max(), + inv_scale = 1.f/alpha_scale; + + CV_Assert( src1.type() == src2.type() && + src1.type() == traits::Type::value && + src1.size() == src2.size()); + Size size = src1.size(); + dst.create(size, src1.type()); + + MatConstIterator_ it1 = src1.begin(), it1_end = src1.end(); + MatConstIterator_ it2 = src2.begin(); + MatIterator_ dst_it = dst.begin(); + + for( ; it1 != it1_end; ++it1, ++it2, ++dst_it ) + { + VT pix1 = *it1, pix2 = *it2; + float alpha = pix1[3]*inv_scale, beta = pix2[3]*inv_scale; + *dst_it = VT(saturate_cast(pix1[0]*alpha + pix2[0]*beta), + saturate_cast(pix1[1]*alpha + pix2[1]*beta), + saturate_cast(pix1[2]*alpha + pix2[2]*beta), + saturate_cast((1 - (1-alpha)*(1-beta))*alpha_scale)); + } + } + @endcode + */ + template MatIterator_<_Tp> begin(); + template MatConstIterator_<_Tp> begin() const; + + /** @brief Returns the matrix iterator and sets it to the after-last matrix element. + + The methods return the matrix read-only or read-write iterators, set to the point following the last + matrix element. + */ + template MatIterator_<_Tp> end(); + template MatConstIterator_<_Tp> end() const; + + /** @brief Runs the given functor over all matrix elements in parallel. + + The operation passed as argument has to be a function pointer, a function object or a lambda(C++11). + + Example 1. All of the operations below put 0xFF the first channel of all matrix elements: + @code + Mat image(1920, 1080, CV_8UC3); + typedef cv::Point3_ Pixel; + + // first. raw pointer access. + for (int r = 0; r < image.rows; ++r) { + Pixel* ptr = image.ptr(r, 0); + const Pixel* ptr_end = ptr + image.cols; + for (; ptr != ptr_end; ++ptr) { + ptr->x = 255; + } + } + + // Using MatIterator. (Simple but there are a Iterator's overhead) + for (Pixel &p : cv::Mat_(image)) { + p.x = 255; + } + + // Parallel execution with function object. + struct Operator { + void operator ()(Pixel &pixel, const int * position) { + pixel.x = 255; + } + }; + image.forEach(Operator()); + + // Parallel execution using C++11 lambda. + image.forEach([](Pixel &p, const int * position) -> void { + p.x = 255; + }); + @endcode + Example 2. Using the pixel's position: + @code + // Creating 3D matrix (255 x 255 x 255) typed uint8_t + // and initialize all elements by the value which equals elements position. + // i.e. pixels (x,y,z) = (1,2,3) is (b,g,r) = (1,2,3). + + int sizes[] = { 255, 255, 255 }; + typedef cv::Point3_ Pixel; + + Mat_ image = Mat::zeros(3, sizes, CV_8UC3); + + image.forEach([&](Pixel& pixel, const int position[]) -> void { + pixel.x = position[0]; + pixel.y = position[1]; + pixel.z = position[2]; + }); + @endcode + */ + template void forEach(const Functor& operation); + /** @overload */ + template void forEach(const Functor& operation) const; + +#ifdef CV_CXX_MOVE_SEMANTICS + Mat(Mat&& m); + Mat& operator = (Mat&& m); +#endif + + enum { MAGIC_VAL = 0x42FF0000, AUTO_STEP = 0, CONTINUOUS_FLAG = CV_MAT_CONT_FLAG, SUBMATRIX_FLAG = CV_SUBMAT_FLAG }; + enum { MAGIC_MASK = 0xFFFF0000, TYPE_MASK = 0x00000FFF, DEPTH_MASK = 7 }; + + /*! includes several bit-fields: + - the magic signature + - continuity flag + - depth + - number of channels + */ + int flags; + //! the matrix dimensionality, >= 2 + int dims; + //! the number of rows and columns or (-1, -1) when the matrix has more than 2 dimensions + int rows, cols; + //! pointer to the data + uchar* data; + + //! helper fields used in locateROI and adjustROI + const uchar* datastart; + const uchar* dataend; + const uchar* datalimit; + + //! custom allocator + MatAllocator* allocator; + //! and the standard allocator + static MatAllocator* getStdAllocator(); + static MatAllocator* getDefaultAllocator(); + static void setDefaultAllocator(MatAllocator* allocator); + + //! internal use method: updates the continuity flag + void updateContinuityFlag(); + + //! interaction with UMat + UMatData* u; + + MatSize size; + MatStep step; + +protected: + template void forEach_impl(const Functor& operation); +}; + + +///////////////////////////////// Mat_<_Tp> //////////////////////////////////// + +/** @brief Template matrix class derived from Mat + +@code{.cpp} + template class Mat_ : public Mat + { + public: + // ... some specific methods + // and + // no new extra fields + }; +@endcode +The class `Mat_<_Tp>` is a *thin* template wrapper on top of the Mat class. It does not have any +extra data fields. Nor this class nor Mat has any virtual methods. Thus, references or pointers to +these two classes can be freely but carefully converted one to another. For example: +@code{.cpp} + // create a 100x100 8-bit matrix + Mat M(100,100,CV_8U); + // this will be compiled fine. no any data conversion will be done. + Mat_& M1 = (Mat_&)M; + // the program is likely to crash at the statement below + M1(99,99) = 1.f; +@endcode +While Mat is sufficient in most cases, Mat_ can be more convenient if you use a lot of element +access operations and if you know matrix type at the compilation time. Note that +`Mat::at(int y,int x)` and `Mat_::operator()(int y,int x)` do absolutely the same +and run at the same speed, but the latter is certainly shorter: +@code{.cpp} + Mat_ M(20,20); + for(int i = 0; i < M.rows; i++) + for(int j = 0; j < M.cols; j++) + M(i,j) = 1./(i+j+1); + Mat E, V; + eigen(M,E,V); + cout << E.at(0,0)/E.at(M.rows-1,0); +@endcode +To use Mat_ for multi-channel images/matrices, pass Vec as a Mat_ parameter: +@code{.cpp} + // allocate a 320x240 color image and fill it with green (in RGB space) + Mat_ img(240, 320, Vec3b(0,255,0)); + // now draw a diagonal white line + for(int i = 0; i < 100; i++) + img(i,i)=Vec3b(255,255,255); + // and now scramble the 2nd (red) channel of each pixel + for(int i = 0; i < img.rows; i++) + for(int j = 0; j < img.cols; j++) + img(i,j)[2] ^= (uchar)(i ^ j); +@endcode +Mat_ is fully compatible with C++11 range-based for loop. For example such loop +can be used to safely apply look-up table: +@code{.cpp} +void applyTable(Mat_& I, const uchar* const table) +{ + for(auto& pixel : I) + { + pixel = table[pixel]; + } +} +@endcode + */ +template class Mat_ : public Mat +{ +public: + typedef _Tp value_type; + typedef typename DataType<_Tp>::channel_type channel_type; + typedef MatIterator_<_Tp> iterator; + typedef MatConstIterator_<_Tp> const_iterator; + + //! default constructor + Mat_(); + //! equivalent to Mat(_rows, _cols, DataType<_Tp>::type) + Mat_(int _rows, int _cols); + //! constructor that sets each matrix element to specified value + Mat_(int _rows, int _cols, const _Tp& value); + //! equivalent to Mat(_size, DataType<_Tp>::type) + explicit Mat_(Size _size); + //! constructor that sets each matrix element to specified value + Mat_(Size _size, const _Tp& value); + //! n-dim array constructor + Mat_(int _ndims, const int* _sizes); + //! n-dim array constructor that sets each matrix element to specified value + Mat_(int _ndims, const int* _sizes, const _Tp& value); + //! copy/conversion constructor. If m is of different type, it's converted + Mat_(const Mat& m); + //! copy constructor + Mat_(const Mat_& m); + //! constructs a matrix on top of user-allocated data. step is in bytes(!!!), regardless of the type + Mat_(int _rows, int _cols, _Tp* _data, size_t _step=AUTO_STEP); + //! constructs n-dim matrix on top of user-allocated data. steps are in bytes(!!!), regardless of the type + Mat_(int _ndims, const int* _sizes, _Tp* _data, const size_t* _steps=0); + //! selects a submatrix + Mat_(const Mat_& m, const Range& rowRange, const Range& colRange=Range::all()); + //! selects a submatrix + Mat_(const Mat_& m, const Rect& roi); + //! selects a submatrix, n-dim version + Mat_(const Mat_& m, const Range* ranges); + //! selects a submatrix, n-dim version + Mat_(const Mat_& m, const std::vector& ranges); + //! from a matrix expression + explicit Mat_(const MatExpr& e); + //! makes a matrix out of Vec, std::vector, Point_ or Point3_. The matrix will have a single column + explicit Mat_(const std::vector<_Tp>& vec, bool copyData=false); + template explicit Mat_(const Vec::channel_type, n>& vec, bool copyData=true); + template explicit Mat_(const Matx::channel_type, m, n>& mtx, bool copyData=true); + explicit Mat_(const Point_::channel_type>& pt, bool copyData=true); + explicit Mat_(const Point3_::channel_type>& pt, bool copyData=true); + explicit Mat_(const MatCommaInitializer_<_Tp>& commaInitializer); + +#ifdef CV_CXX11 + Mat_(std::initializer_list<_Tp> values); + explicit Mat_(const std::initializer_list sizes, const std::initializer_list<_Tp> values); +#endif + +#ifdef CV_CXX_STD_ARRAY + template explicit Mat_(const std::array<_Tp, _Nm>& arr, bool copyData=false); +#endif + + Mat_& operator = (const Mat& m); + Mat_& operator = (const Mat_& m); + //! set all the elements to s. + Mat_& operator = (const _Tp& s); + //! assign a matrix expression + Mat_& operator = (const MatExpr& e); + + //! iterators; they are smart enough to skip gaps in the end of rows + iterator begin(); + iterator end(); + const_iterator begin() const; + const_iterator end() const; + + //! template methods for for operation over all matrix elements. + // the operations take care of skipping gaps in the end of rows (if any) + template void forEach(const Functor& operation); + template void forEach(const Functor& operation) const; + + //! equivalent to Mat::create(_rows, _cols, DataType<_Tp>::type) + void create(int _rows, int _cols); + //! equivalent to Mat::create(_size, DataType<_Tp>::type) + void create(Size _size); + //! equivalent to Mat::create(_ndims, _sizes, DatType<_Tp>::type) + void create(int _ndims, const int* _sizes); + //! equivalent to Mat::release() + void release(); + //! cross-product + Mat_ cross(const Mat_& m) const; + //! data type conversion + template operator Mat_() const; + //! overridden forms of Mat::row() etc. + Mat_ row(int y) const; + Mat_ col(int x) const; + Mat_ diag(int d=0) const; + Mat_ clone() const CV_NODISCARD; + + //! overridden forms of Mat::elemSize() etc. + size_t elemSize() const; + size_t elemSize1() const; + int type() const; + int depth() const; + int channels() const; + size_t step1(int i=0) const; + //! returns step()/sizeof(_Tp) + size_t stepT(int i=0) const; + + //! overridden forms of Mat::zeros() etc. Data type is omitted, of course + static MatExpr zeros(int rows, int cols); + static MatExpr zeros(Size size); + static MatExpr zeros(int _ndims, const int* _sizes); + static MatExpr ones(int rows, int cols); + static MatExpr ones(Size size); + static MatExpr ones(int _ndims, const int* _sizes); + static MatExpr eye(int rows, int cols); + static MatExpr eye(Size size); + + //! some more overridden methods + Mat_& adjustROI( int dtop, int dbottom, int dleft, int dright ); + Mat_ operator()( const Range& rowRange, const Range& colRange ) const; + Mat_ operator()( const Rect& roi ) const; + Mat_ operator()( const Range* ranges ) const; + Mat_ operator()(const std::vector& ranges) const; + + //! more convenient forms of row and element access operators + _Tp* operator [](int y); + const _Tp* operator [](int y) const; + + //! returns reference to the specified element + _Tp& operator ()(const int* idx); + //! returns read-only reference to the specified element + const _Tp& operator ()(const int* idx) const; + + //! returns reference to the specified element + template _Tp& operator ()(const Vec& idx); + //! returns read-only reference to the specified element + template const _Tp& operator ()(const Vec& idx) const; + + //! returns reference to the specified element (1D case) + _Tp& operator ()(int idx0); + //! returns read-only reference to the specified element (1D case) + const _Tp& operator ()(int idx0) const; + //! returns reference to the specified element (2D case) + _Tp& operator ()(int row, int col); + //! returns read-only reference to the specified element (2D case) + const _Tp& operator ()(int row, int col) const; + //! returns reference to the specified element (3D case) + _Tp& operator ()(int idx0, int idx1, int idx2); + //! returns read-only reference to the specified element (3D case) + const _Tp& operator ()(int idx0, int idx1, int idx2) const; + + _Tp& operator ()(Point pt); + const _Tp& operator ()(Point pt) const; + + //! conversion to vector. + operator std::vector<_Tp>() const; + +#ifdef CV_CXX_STD_ARRAY + //! conversion to array. + template operator std::array<_Tp, _Nm>() const; +#endif + + //! conversion to Vec + template operator Vec::channel_type, n>() const; + //! conversion to Matx + template operator Matx::channel_type, m, n>() const; + +#ifdef CV_CXX_MOVE_SEMANTICS + Mat_(Mat_&& m); + Mat_& operator = (Mat_&& m); + + Mat_(Mat&& m); + Mat_& operator = (Mat&& m); + + Mat_(MatExpr&& e); +#endif +}; + +typedef Mat_ Mat1b; +typedef Mat_ Mat2b; +typedef Mat_ Mat3b; +typedef Mat_ Mat4b; + +typedef Mat_ Mat1s; +typedef Mat_ Mat2s; +typedef Mat_ Mat3s; +typedef Mat_ Mat4s; + +typedef Mat_ Mat1w; +typedef Mat_ Mat2w; +typedef Mat_ Mat3w; +typedef Mat_ Mat4w; + +typedef Mat_ Mat1i; +typedef Mat_ Mat2i; +typedef Mat_ Mat3i; +typedef Mat_ Mat4i; + +typedef Mat_ Mat1f; +typedef Mat_ Mat2f; +typedef Mat_ Mat3f; +typedef Mat_ Mat4f; + +typedef Mat_ Mat1d; +typedef Mat_ Mat2d; +typedef Mat_ Mat3d; +typedef Mat_ Mat4d; + +/** @todo document */ +class CV_EXPORTS UMat +{ +public: + //! default constructor + UMat(UMatUsageFlags usageFlags = USAGE_DEFAULT); + //! constructs 2D matrix of the specified size and type + // (_type is CV_8UC1, CV_64FC3, CV_32SC(12) etc.) + UMat(int rows, int cols, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT); + UMat(Size size, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT); + //! constucts 2D matrix and fills it with the specified value _s. + UMat(int rows, int cols, int type, const Scalar& s, UMatUsageFlags usageFlags = USAGE_DEFAULT); + UMat(Size size, int type, const Scalar& s, UMatUsageFlags usageFlags = USAGE_DEFAULT); + + //! constructs n-dimensional matrix + UMat(int ndims, const int* sizes, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT); + UMat(int ndims, const int* sizes, int type, const Scalar& s, UMatUsageFlags usageFlags = USAGE_DEFAULT); + + //! copy constructor + UMat(const UMat& m); + + //! creates a matrix header for a part of the bigger matrix + UMat(const UMat& m, const Range& rowRange, const Range& colRange=Range::all()); + UMat(const UMat& m, const Rect& roi); + UMat(const UMat& m, const Range* ranges); + UMat(const UMat& m, const std::vector& ranges); + //! builds matrix from std::vector with or without copying the data + template explicit UMat(const std::vector<_Tp>& vec, bool copyData=false); + + //! builds matrix from cv::Vec; the data is copied by default + template explicit UMat(const Vec<_Tp, n>& vec, bool copyData=true); + //! builds matrix from cv::Matx; the data is copied by default + template explicit UMat(const Matx<_Tp, m, n>& mtx, bool copyData=true); + //! builds matrix from a 2D point + template explicit UMat(const Point_<_Tp>& pt, bool copyData=true); + //! builds matrix from a 3D point + template explicit UMat(const Point3_<_Tp>& pt, bool copyData=true); + //! builds matrix from comma initializer + template explicit UMat(const MatCommaInitializer_<_Tp>& commaInitializer); + + //! destructor - calls release() + ~UMat(); + //! assignment operators + UMat& operator = (const UMat& m); + + Mat getMat(int flags) const; + + //! returns a new matrix header for the specified row + UMat row(int y) const; + //! returns a new matrix header for the specified column + UMat col(int x) const; + //! ... for the specified row span + UMat rowRange(int startrow, int endrow) const; + UMat rowRange(const Range& r) const; + //! ... for the specified column span + UMat colRange(int startcol, int endcol) const; + UMat colRange(const Range& r) const; + //! ... for the specified diagonal + //! (d=0 - the main diagonal, + //! >0 - a diagonal from the upper half, + //! <0 - a diagonal from the lower half) + UMat diag(int d=0) const; + //! constructs a square diagonal matrix which main diagonal is vector "d" + static UMat diag(const UMat& d); + + //! returns deep copy of the matrix, i.e. the data is copied + UMat clone() const CV_NODISCARD; + //! copies the matrix content to "m". + // It calls m.create(this->size(), this->type()). + void copyTo( OutputArray m ) const; + //! copies those matrix elements to "m" that are marked with non-zero mask elements. + void copyTo( OutputArray m, InputArray mask ) const; + //! converts matrix to another datatype with optional scaling. See cvConvertScale. + void convertTo( OutputArray m, int rtype, double alpha=1, double beta=0 ) const; + + void assignTo( UMat& m, int type=-1 ) const; + + //! sets every matrix element to s + UMat& operator = (const Scalar& s); + //! sets some of the matrix elements to s, according to the mask + UMat& setTo(InputArray value, InputArray mask=noArray()); + //! creates alternative matrix header for the same data, with different + // number of channels and/or different number of rows. see cvReshape. + UMat reshape(int cn, int rows=0) const; + UMat reshape(int cn, int newndims, const int* newsz) const; + + //! matrix transposition by means of matrix expressions + UMat t() const; + //! matrix inversion by means of matrix expressions + UMat inv(int method=DECOMP_LU) const; + //! per-element matrix multiplication by means of matrix expressions + UMat mul(InputArray m, double scale=1) const; + + //! computes dot-product + double dot(InputArray m) const; + + //! Matlab-style matrix initialization + static UMat zeros(int rows, int cols, int type); + static UMat zeros(Size size, int type); + static UMat zeros(int ndims, const int* sz, int type); + static UMat ones(int rows, int cols, int type); + static UMat ones(Size size, int type); + static UMat ones(int ndims, const int* sz, int type); + static UMat eye(int rows, int cols, int type); + static UMat eye(Size size, int type); + + //! allocates new matrix data unless the matrix already has specified size and type. + // previous data is unreferenced if needed. + void create(int rows, int cols, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT); + void create(Size size, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT); + void create(int ndims, const int* sizes, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT); + void create(const std::vector& sizes, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT); + + //! increases the reference counter; use with care to avoid memleaks + void addref(); + //! decreases reference counter; + // deallocates the data when reference counter reaches 0. + void release(); + + //! deallocates the matrix data + void deallocate(); + //! internal use function; properly re-allocates _size, _step arrays + void copySize(const UMat& m); + + //! locates matrix header within a parent matrix. See below + void locateROI( Size& wholeSize, Point& ofs ) const; + //! moves/resizes the current matrix ROI inside the parent matrix. + UMat& adjustROI( int dtop, int dbottom, int dleft, int dright ); + //! extracts a rectangular sub-matrix + // (this is a generalized form of row, rowRange etc.) + UMat operator()( Range rowRange, Range colRange ) const; + UMat operator()( const Rect& roi ) const; + UMat operator()( const Range* ranges ) const; + UMat operator()(const std::vector& ranges) const; + + //! returns true iff the matrix data is continuous + // (i.e. when there are no gaps between successive rows). + // similar to CV_IS_MAT_CONT(cvmat->type) + bool isContinuous() const; + + //! returns true if the matrix is a submatrix of another matrix + bool isSubmatrix() const; + + //! returns element size in bytes, + // similar to CV_ELEM_SIZE(cvmat->type) + size_t elemSize() const; + //! returns the size of element channel in bytes. + size_t elemSize1() const; + //! returns element type, similar to CV_MAT_TYPE(cvmat->type) + int type() const; + //! returns element type, similar to CV_MAT_DEPTH(cvmat->type) + int depth() const; + //! returns element type, similar to CV_MAT_CN(cvmat->type) + int channels() const; + //! returns step/elemSize1() + size_t step1(int i=0) const; + //! returns true if matrix data is NULL + bool empty() const; + //! returns the total number of matrix elements + size_t total() const; + + //! returns N if the matrix is 1-channel (N x ptdim) or ptdim-channel (1 x N) or (N x 1); negative number otherwise + int checkVector(int elemChannels, int depth=-1, bool requireContinuous=true) const; + +#ifdef CV_CXX_MOVE_SEMANTICS + UMat(UMat&& m); + UMat& operator = (UMat&& m); +#endif + + /*! Returns the OpenCL buffer handle on which UMat operates on. + The UMat instance should be kept alive during the use of the handle to prevent the buffer to be + returned to the OpenCV buffer pool. + */ + void* handle(int accessFlags) const; + void ndoffset(size_t* ofs) const; + + enum { MAGIC_VAL = 0x42FF0000, AUTO_STEP = 0, CONTINUOUS_FLAG = CV_MAT_CONT_FLAG, SUBMATRIX_FLAG = CV_SUBMAT_FLAG }; + enum { MAGIC_MASK = 0xFFFF0000, TYPE_MASK = 0x00000FFF, DEPTH_MASK = 7 }; + + /*! includes several bit-fields: + - the magic signature + - continuity flag + - depth + - number of channels + */ + int flags; + //! the matrix dimensionality, >= 2 + int dims; + //! the number of rows and columns or (-1, -1) when the matrix has more than 2 dimensions + int rows, cols; + + //! custom allocator + MatAllocator* allocator; + UMatUsageFlags usageFlags; // usage flags for allocator + //! and the standard allocator + static MatAllocator* getStdAllocator(); + + //! internal use method: updates the continuity flag + void updateContinuityFlag(); + + // black-box container of UMat data + UMatData* u; + + // offset of the submatrix (or 0) + size_t offset; + + MatSize size; + MatStep step; + +protected: +}; + + +/////////////////////////// multi-dimensional sparse matrix ////////////////////////// + +/** @brief The class SparseMat represents multi-dimensional sparse numerical arrays. + +Such a sparse array can store elements of any type that Mat can store. *Sparse* means that only +non-zero elements are stored (though, as a result of operations on a sparse matrix, some of its +stored elements can actually become 0. It is up to you to detect such elements and delete them +using SparseMat::erase ). The non-zero elements are stored in a hash table that grows when it is +filled so that the search time is O(1) in average (regardless of whether element is there or not). +Elements can be accessed using the following methods: +- Query operations (SparseMat::ptr and the higher-level SparseMat::ref, SparseMat::value and + SparseMat::find), for example: + @code + const int dims = 5; + int size[5] = {10, 10, 10, 10, 10}; + SparseMat sparse_mat(dims, size, CV_32F); + for(int i = 0; i < 1000; i++) + { + int idx[dims]; + for(int k = 0; k < dims; k++) + idx[k] = rand() % size[k]; + sparse_mat.ref(idx) += 1.f; + } + cout << "nnz = " << sparse_mat.nzcount() << endl; + @endcode +- Sparse matrix iterators. They are similar to MatIterator but different from NAryMatIterator. + That is, the iteration loop is familiar to STL users: + @code + // prints elements of a sparse floating-point matrix + // and the sum of elements. + SparseMatConstIterator_ + it = sparse_mat.begin(), + it_end = sparse_mat.end(); + double s = 0; + int dims = sparse_mat.dims(); + for(; it != it_end; ++it) + { + // print element indices and the element value + const SparseMat::Node* n = it.node(); + printf("("); + for(int i = 0; i < dims; i++) + printf("%d%s", n->idx[i], i < dims-1 ? ", " : ")"); + printf(": %g\n", it.value()); + s += *it; + } + printf("Element sum is %g\n", s); + @endcode + If you run this loop, you will notice that elements are not enumerated in a logical order + (lexicographical, and so on). They come in the same order as they are stored in the hash table + (semi-randomly). You may collect pointers to the nodes and sort them to get the proper ordering. + Note, however, that pointers to the nodes may become invalid when you add more elements to the + matrix. This may happen due to possible buffer reallocation. +- Combination of the above 2 methods when you need to process 2 or more sparse matrices + simultaneously. For example, this is how you can compute unnormalized cross-correlation of the 2 + floating-point sparse matrices: + @code + double cross_corr(const SparseMat& a, const SparseMat& b) + { + const SparseMat *_a = &a, *_b = &b; + // if b contains less elements than a, + // it is faster to iterate through b + if(_a->nzcount() > _b->nzcount()) + std::swap(_a, _b); + SparseMatConstIterator_ it = _a->begin(), + it_end = _a->end(); + double ccorr = 0; + for(; it != it_end; ++it) + { + // take the next element from the first matrix + float avalue = *it; + const Node* anode = it.node(); + // and try to find an element with the same index in the second matrix. + // since the hash value depends only on the element index, + // reuse the hash value stored in the node + float bvalue = _b->value(anode->idx,&anode->hashval); + ccorr += avalue*bvalue; + } + return ccorr; + } + @endcode + */ +class CV_EXPORTS SparseMat +{ +public: + typedef SparseMatIterator iterator; + typedef SparseMatConstIterator const_iterator; + + enum { MAGIC_VAL=0x42FD0000, MAX_DIM=32, HASH_SCALE=0x5bd1e995, HASH_BIT=0x80000000 }; + + //! the sparse matrix header + struct CV_EXPORTS Hdr + { + Hdr(int _dims, const int* _sizes, int _type); + void clear(); + int refcount; + int dims; + int valueOffset; + size_t nodeSize; + size_t nodeCount; + size_t freeList; + std::vector pool; + std::vector hashtab; + int size[MAX_DIM]; + }; + + //! sparse matrix node - element of a hash table + struct CV_EXPORTS Node + { + //! hash value + size_t hashval; + //! index of the next node in the same hash table entry + size_t next; + //! index of the matrix element + int idx[MAX_DIM]; + }; + + /** @brief Various SparseMat constructors. + */ + SparseMat(); + + /** @overload + @param dims Array dimensionality. + @param _sizes Sparce matrix size on all dementions. + @param _type Sparse matrix data type. + */ + SparseMat(int dims, const int* _sizes, int _type); + + /** @overload + @param m Source matrix for copy constructor. If m is dense matrix (ocvMat) then it will be converted + to sparse representation. + */ + SparseMat(const SparseMat& m); + + /** @overload + @param m Source matrix for copy constructor. If m is dense matrix (ocvMat) then it will be converted + to sparse representation. + */ + explicit SparseMat(const Mat& m); + + //! the destructor + ~SparseMat(); + + //! assignment operator. This is O(1) operation, i.e. no data is copied + SparseMat& operator = (const SparseMat& m); + //! equivalent to the corresponding constructor + SparseMat& operator = (const Mat& m); + + //! creates full copy of the matrix + SparseMat clone() const CV_NODISCARD; + + //! copies all the data to the destination matrix. All the previous content of m is erased + void copyTo( SparseMat& m ) const; + //! converts sparse matrix to dense matrix. + void copyTo( Mat& m ) const; + //! multiplies all the matrix elements by the specified scale factor alpha and converts the results to the specified data type + void convertTo( SparseMat& m, int rtype, double alpha=1 ) const; + //! converts sparse matrix to dense n-dim matrix with optional type conversion and scaling. + /*! + @param [out] m - output matrix; if it does not have a proper size or type before the operation, + it is reallocated + @param [in] rtype - desired output matrix type or, rather, the depth since the number of channels + are the same as the input has; if rtype is negative, the output matrix will have the + same type as the input. + @param [in] alpha - optional scale factor + @param [in] beta - optional delta added to the scaled values + */ + void convertTo( Mat& m, int rtype, double alpha=1, double beta=0 ) const; + + // not used now + void assignTo( SparseMat& m, int type=-1 ) const; + + //! reallocates sparse matrix. + /*! + If the matrix already had the proper size and type, + it is simply cleared with clear(), otherwise, + the old matrix is released (using release()) and the new one is allocated. + */ + void create(int dims, const int* _sizes, int _type); + //! sets all the sparse matrix elements to 0, which means clearing the hash table. + void clear(); + //! manually increments the reference counter to the header. + void addref(); + // decrements the header reference counter. When the counter reaches 0, the header and all the underlying data are deallocated. + void release(); + + //! converts sparse matrix to the old-style representation; all the elements are copied. + //operator CvSparseMat*() const; + //! returns the size of each element in bytes (not including the overhead - the space occupied by SparseMat::Node elements) + size_t elemSize() const; + //! returns elemSize()/channels() + size_t elemSize1() const; + + //! returns type of sparse matrix elements + int type() const; + //! returns the depth of sparse matrix elements + int depth() const; + //! returns the number of channels + int channels() const; + + //! returns the array of sizes, or NULL if the matrix is not allocated + const int* size() const; + //! returns the size of i-th matrix dimension (or 0) + int size(int i) const; + //! returns the matrix dimensionality + int dims() const; + //! returns the number of non-zero elements (=the number of hash table nodes) + size_t nzcount() const; + + //! computes the element hash value (1D case) + size_t hash(int i0) const; + //! computes the element hash value (2D case) + size_t hash(int i0, int i1) const; + //! computes the element hash value (3D case) + size_t hash(int i0, int i1, int i2) const; + //! computes the element hash value (nD case) + size_t hash(const int* idx) const; + + //!@{ + /*! + specialized variants for 1D, 2D, 3D cases and the generic_type one for n-D case. + return pointer to the matrix element. + - if the element is there (it's non-zero), the pointer to it is returned + - if it's not there and createMissing=false, NULL pointer is returned + - if it's not there and createMissing=true, then the new element + is created and initialized with 0. Pointer to it is returned + - if the optional hashval pointer is not NULL, the element hash value is + not computed, but *hashval is taken instead. + */ + //! returns pointer to the specified element (1D case) + uchar* ptr(int i0, bool createMissing, size_t* hashval=0); + //! returns pointer to the specified element (2D case) + uchar* ptr(int i0, int i1, bool createMissing, size_t* hashval=0); + //! returns pointer to the specified element (3D case) + uchar* ptr(int i0, int i1, int i2, bool createMissing, size_t* hashval=0); + //! returns pointer to the specified element (nD case) + uchar* ptr(const int* idx, bool createMissing, size_t* hashval=0); + //!@} + + //!@{ + /*! + return read-write reference to the specified sparse matrix element. + + `ref<_Tp>(i0,...[,hashval])` is equivalent to `*(_Tp*)ptr(i0,...,true[,hashval])`. + The methods always return a valid reference. + If the element did not exist, it is created and initialiazed with 0. + */ + //! returns reference to the specified element (1D case) + template _Tp& ref(int i0, size_t* hashval=0); + //! returns reference to the specified element (2D case) + template _Tp& ref(int i0, int i1, size_t* hashval=0); + //! returns reference to the specified element (3D case) + template _Tp& ref(int i0, int i1, int i2, size_t* hashval=0); + //! returns reference to the specified element (nD case) + template _Tp& ref(const int* idx, size_t* hashval=0); + //!@} + + //!@{ + /*! + return value of the specified sparse matrix element. + + `value<_Tp>(i0,...[,hashval])` is equivalent to + @code + { const _Tp* p = find<_Tp>(i0,...[,hashval]); return p ? *p : _Tp(); } + @endcode + + That is, if the element did not exist, the methods return 0. + */ + //! returns value of the specified element (1D case) + template _Tp value(int i0, size_t* hashval=0) const; + //! returns value of the specified element (2D case) + template _Tp value(int i0, int i1, size_t* hashval=0) const; + //! returns value of the specified element (3D case) + template _Tp value(int i0, int i1, int i2, size_t* hashval=0) const; + //! returns value of the specified element (nD case) + template _Tp value(const int* idx, size_t* hashval=0) const; + //!@} + + //!@{ + /*! + Return pointer to the specified sparse matrix element if it exists + + `find<_Tp>(i0,...[,hashval])` is equivalent to `(_const Tp*)ptr(i0,...false[,hashval])`. + + If the specified element does not exist, the methods return NULL. + */ + //! returns pointer to the specified element (1D case) + template const _Tp* find(int i0, size_t* hashval=0) const; + //! returns pointer to the specified element (2D case) + template const _Tp* find(int i0, int i1, size_t* hashval=0) const; + //! returns pointer to the specified element (3D case) + template const _Tp* find(int i0, int i1, int i2, size_t* hashval=0) const; + //! returns pointer to the specified element (nD case) + template const _Tp* find(const int* idx, size_t* hashval=0) const; + //!@} + + //! erases the specified element (2D case) + void erase(int i0, int i1, size_t* hashval=0); + //! erases the specified element (3D case) + void erase(int i0, int i1, int i2, size_t* hashval=0); + //! erases the specified element (nD case) + void erase(const int* idx, size_t* hashval=0); + + //!@{ + /*! + return the sparse matrix iterator pointing to the first sparse matrix element + */ + //! returns the sparse matrix iterator at the matrix beginning + SparseMatIterator begin(); + //! returns the sparse matrix iterator at the matrix beginning + template SparseMatIterator_<_Tp> begin(); + //! returns the read-only sparse matrix iterator at the matrix beginning + SparseMatConstIterator begin() const; + //! returns the read-only sparse matrix iterator at the matrix beginning + template SparseMatConstIterator_<_Tp> begin() const; + //!@} + /*! + return the sparse matrix iterator pointing to the element following the last sparse matrix element + */ + //! returns the sparse matrix iterator at the matrix end + SparseMatIterator end(); + //! returns the read-only sparse matrix iterator at the matrix end + SparseMatConstIterator end() const; + //! returns the typed sparse matrix iterator at the matrix end + template SparseMatIterator_<_Tp> end(); + //! returns the typed read-only sparse matrix iterator at the matrix end + template SparseMatConstIterator_<_Tp> end() const; + + //! returns the value stored in the sparse martix node + template _Tp& value(Node* n); + //! returns the value stored in the sparse martix node + template const _Tp& value(const Node* n) const; + + ////////////// some internal-use methods /////////////// + Node* node(size_t nidx); + const Node* node(size_t nidx) const; + + uchar* newNode(const int* idx, size_t hashval); + void removeNode(size_t hidx, size_t nidx, size_t previdx); + void resizeHashTab(size_t newsize); + + int flags; + Hdr* hdr; +}; + + + +///////////////////////////////// SparseMat_<_Tp> //////////////////////////////////// + +/** @brief Template sparse n-dimensional array class derived from SparseMat + +SparseMat_ is a thin wrapper on top of SparseMat created in the same way as Mat_ . It simplifies +notation of some operations: +@code + int sz[] = {10, 20, 30}; + SparseMat_ M(3, sz); + ... + M.ref(1, 2, 3) = M(4, 5, 6) + M(7, 8, 9); +@endcode + */ +template class SparseMat_ : public SparseMat +{ +public: + typedef SparseMatIterator_<_Tp> iterator; + typedef SparseMatConstIterator_<_Tp> const_iterator; + + //! the default constructor + SparseMat_(); + //! the full constructor equivalent to SparseMat(dims, _sizes, DataType<_Tp>::type) + SparseMat_(int dims, const int* _sizes); + //! the copy constructor. If DataType<_Tp>.type != m.type(), the m elements are converted + SparseMat_(const SparseMat& m); + //! the copy constructor. This is O(1) operation - no data is copied + SparseMat_(const SparseMat_& m); + //! converts dense matrix to the sparse form + SparseMat_(const Mat& m); + //! converts the old-style sparse matrix to the C++ class. All the elements are copied + //SparseMat_(const CvSparseMat* m); + //! the assignment operator. If DataType<_Tp>.type != m.type(), the m elements are converted + SparseMat_& operator = (const SparseMat& m); + //! the assignment operator. This is O(1) operation - no data is copied + SparseMat_& operator = (const SparseMat_& m); + //! converts dense matrix to the sparse form + SparseMat_& operator = (const Mat& m); + + //! makes full copy of the matrix. All the elements are duplicated + SparseMat_ clone() const CV_NODISCARD; + //! equivalent to cv::SparseMat::create(dims, _sizes, DataType<_Tp>::type) + void create(int dims, const int* _sizes); + //! converts sparse matrix to the old-style CvSparseMat. All the elements are copied + //operator CvSparseMat*() const; + + //! returns type of the matrix elements + int type() const; + //! returns depth of the matrix elements + int depth() const; + //! returns the number of channels in each matrix element + int channels() const; + + //! equivalent to SparseMat::ref<_Tp>(i0, hashval) + _Tp& ref(int i0, size_t* hashval=0); + //! equivalent to SparseMat::ref<_Tp>(i0, i1, hashval) + _Tp& ref(int i0, int i1, size_t* hashval=0); + //! equivalent to SparseMat::ref<_Tp>(i0, i1, i2, hashval) + _Tp& ref(int i0, int i1, int i2, size_t* hashval=0); + //! equivalent to SparseMat::ref<_Tp>(idx, hashval) + _Tp& ref(const int* idx, size_t* hashval=0); + + //! equivalent to SparseMat::value<_Tp>(i0, hashval) + _Tp operator()(int i0, size_t* hashval=0) const; + //! equivalent to SparseMat::value<_Tp>(i0, i1, hashval) + _Tp operator()(int i0, int i1, size_t* hashval=0) const; + //! equivalent to SparseMat::value<_Tp>(i0, i1, i2, hashval) + _Tp operator()(int i0, int i1, int i2, size_t* hashval=0) const; + //! equivalent to SparseMat::value<_Tp>(idx, hashval) + _Tp operator()(const int* idx, size_t* hashval=0) const; + + //! returns sparse matrix iterator pointing to the first sparse matrix element + SparseMatIterator_<_Tp> begin(); + //! returns read-only sparse matrix iterator pointing to the first sparse matrix element + SparseMatConstIterator_<_Tp> begin() const; + //! returns sparse matrix iterator pointing to the element following the last sparse matrix element + SparseMatIterator_<_Tp> end(); + //! returns read-only sparse matrix iterator pointing to the element following the last sparse matrix element + SparseMatConstIterator_<_Tp> end() const; +}; + + + +////////////////////////////////// MatConstIterator ////////////////////////////////// + +class CV_EXPORTS MatConstIterator +{ +public: + typedef uchar* value_type; + typedef ptrdiff_t difference_type; + typedef const uchar** pointer; + typedef uchar* reference; + + typedef std::random_access_iterator_tag iterator_category; + + //! default constructor + MatConstIterator(); + //! constructor that sets the iterator to the beginning of the matrix + MatConstIterator(const Mat* _m); + //! constructor that sets the iterator to the specified element of the matrix + MatConstIterator(const Mat* _m, int _row, int _col=0); + //! constructor that sets the iterator to the specified element of the matrix + MatConstIterator(const Mat* _m, Point _pt); + //! constructor that sets the iterator to the specified element of the matrix + MatConstIterator(const Mat* _m, const int* _idx); + //! copy constructor + MatConstIterator(const MatConstIterator& it); + + //! copy operator + MatConstIterator& operator = (const MatConstIterator& it); + //! returns the current matrix element + const uchar* operator *() const; + //! returns the i-th matrix element, relative to the current + const uchar* operator [](ptrdiff_t i) const; + + //! shifts the iterator forward by the specified number of elements + MatConstIterator& operator += (ptrdiff_t ofs); + //! shifts the iterator backward by the specified number of elements + MatConstIterator& operator -= (ptrdiff_t ofs); + //! decrements the iterator + MatConstIterator& operator --(); + //! decrements the iterator + MatConstIterator operator --(int); + //! increments the iterator + MatConstIterator& operator ++(); + //! increments the iterator + MatConstIterator operator ++(int); + //! returns the current iterator position + Point pos() const; + //! returns the current iterator position + void pos(int* _idx) const; + + ptrdiff_t lpos() const; + void seek(ptrdiff_t ofs, bool relative = false); + void seek(const int* _idx, bool relative = false); + + const Mat* m; + size_t elemSize; + const uchar* ptr; + const uchar* sliceStart; + const uchar* sliceEnd; +}; + + + +////////////////////////////////// MatConstIterator_ ///////////////////////////////// + +/** @brief Matrix read-only iterator + */ +template +class MatConstIterator_ : public MatConstIterator +{ +public: + typedef _Tp value_type; + typedef ptrdiff_t difference_type; + typedef const _Tp* pointer; + typedef const _Tp& reference; + + typedef std::random_access_iterator_tag iterator_category; + + //! default constructor + MatConstIterator_(); + //! constructor that sets the iterator to the beginning of the matrix + MatConstIterator_(const Mat_<_Tp>* _m); + //! constructor that sets the iterator to the specified element of the matrix + MatConstIterator_(const Mat_<_Tp>* _m, int _row, int _col=0); + //! constructor that sets the iterator to the specified element of the matrix + MatConstIterator_(const Mat_<_Tp>* _m, Point _pt); + //! constructor that sets the iterator to the specified element of the matrix + MatConstIterator_(const Mat_<_Tp>* _m, const int* _idx); + //! copy constructor + MatConstIterator_(const MatConstIterator_& it); + + //! copy operator + MatConstIterator_& operator = (const MatConstIterator_& it); + //! returns the current matrix element + const _Tp& operator *() const; + //! returns the i-th matrix element, relative to the current + const _Tp& operator [](ptrdiff_t i) const; + + //! shifts the iterator forward by the specified number of elements + MatConstIterator_& operator += (ptrdiff_t ofs); + //! shifts the iterator backward by the specified number of elements + MatConstIterator_& operator -= (ptrdiff_t ofs); + //! decrements the iterator + MatConstIterator_& operator --(); + //! decrements the iterator + MatConstIterator_ operator --(int); + //! increments the iterator + MatConstIterator_& operator ++(); + //! increments the iterator + MatConstIterator_ operator ++(int); + //! returns the current iterator position + Point pos() const; +}; + + + +//////////////////////////////////// MatIterator_ //////////////////////////////////// + +/** @brief Matrix read-write iterator +*/ +template +class MatIterator_ : public MatConstIterator_<_Tp> +{ +public: + typedef _Tp* pointer; + typedef _Tp& reference; + + typedef std::random_access_iterator_tag iterator_category; + + //! the default constructor + MatIterator_(); + //! constructor that sets the iterator to the beginning of the matrix + MatIterator_(Mat_<_Tp>* _m); + //! constructor that sets the iterator to the specified element of the matrix + MatIterator_(Mat_<_Tp>* _m, int _row, int _col=0); + //! constructor that sets the iterator to the specified element of the matrix + MatIterator_(Mat_<_Tp>* _m, Point _pt); + //! constructor that sets the iterator to the specified element of the matrix + MatIterator_(Mat_<_Tp>* _m, const int* _idx); + //! copy constructor + MatIterator_(const MatIterator_& it); + //! copy operator + MatIterator_& operator = (const MatIterator_<_Tp>& it ); + + //! returns the current matrix element + _Tp& operator *() const; + //! returns the i-th matrix element, relative to the current + _Tp& operator [](ptrdiff_t i) const; + + //! shifts the iterator forward by the specified number of elements + MatIterator_& operator += (ptrdiff_t ofs); + //! shifts the iterator backward by the specified number of elements + MatIterator_& operator -= (ptrdiff_t ofs); + //! decrements the iterator + MatIterator_& operator --(); + //! decrements the iterator + MatIterator_ operator --(int); + //! increments the iterator + MatIterator_& operator ++(); + //! increments the iterator + MatIterator_ operator ++(int); +}; + + + +/////////////////////////////// SparseMatConstIterator /////////////////////////////// + +/** @brief Read-Only Sparse Matrix Iterator. + + Here is how to use the iterator to compute the sum of floating-point sparse matrix elements: + + \code + SparseMatConstIterator it = m.begin(), it_end = m.end(); + double s = 0; + CV_Assert( m.type() == CV_32F ); + for( ; it != it_end; ++it ) + s += it.value(); + \endcode +*/ +class CV_EXPORTS SparseMatConstIterator +{ +public: + //! the default constructor + SparseMatConstIterator(); + //! the full constructor setting the iterator to the first sparse matrix element + SparseMatConstIterator(const SparseMat* _m); + //! the copy constructor + SparseMatConstIterator(const SparseMatConstIterator& it); + + //! the assignment operator + SparseMatConstIterator& operator = (const SparseMatConstIterator& it); + + //! template method returning the current matrix element + template const _Tp& value() const; + //! returns the current node of the sparse matrix. it.node->idx is the current element index + const SparseMat::Node* node() const; + + //! moves iterator to the previous element + SparseMatConstIterator& operator --(); + //! moves iterator to the previous element + SparseMatConstIterator operator --(int); + //! moves iterator to the next element + SparseMatConstIterator& operator ++(); + //! moves iterator to the next element + SparseMatConstIterator operator ++(int); + + //! moves iterator to the element after the last element + void seekEnd(); + + const SparseMat* m; + size_t hashidx; + uchar* ptr; +}; + + + +////////////////////////////////// SparseMatIterator ///////////////////////////////// + +/** @brief Read-write Sparse Matrix Iterator + + The class is similar to cv::SparseMatConstIterator, + but can be used for in-place modification of the matrix elements. +*/ +class CV_EXPORTS SparseMatIterator : public SparseMatConstIterator +{ +public: + //! the default constructor + SparseMatIterator(); + //! the full constructor setting the iterator to the first sparse matrix element + SparseMatIterator(SparseMat* _m); + //! the full constructor setting the iterator to the specified sparse matrix element + SparseMatIterator(SparseMat* _m, const int* idx); + //! the copy constructor + SparseMatIterator(const SparseMatIterator& it); + + //! the assignment operator + SparseMatIterator& operator = (const SparseMatIterator& it); + //! returns read-write reference to the current sparse matrix element + template _Tp& value() const; + //! returns pointer to the current sparse matrix node. it.node->idx is the index of the current element (do not modify it!) + SparseMat::Node* node() const; + + //! moves iterator to the next element + SparseMatIterator& operator ++(); + //! moves iterator to the next element + SparseMatIterator operator ++(int); +}; + + + +/////////////////////////////// SparseMatConstIterator_ ////////////////////////////// + +/** @brief Template Read-Only Sparse Matrix Iterator Class. + + This is the derived from SparseMatConstIterator class that + introduces more convenient operator *() for accessing the current element. +*/ +template class SparseMatConstIterator_ : public SparseMatConstIterator +{ +public: + + typedef std::forward_iterator_tag iterator_category; + + //! the default constructor + SparseMatConstIterator_(); + //! the full constructor setting the iterator to the first sparse matrix element + SparseMatConstIterator_(const SparseMat_<_Tp>* _m); + SparseMatConstIterator_(const SparseMat* _m); + //! the copy constructor + SparseMatConstIterator_(const SparseMatConstIterator_& it); + + //! the assignment operator + SparseMatConstIterator_& operator = (const SparseMatConstIterator_& it); + //! the element access operator + const _Tp& operator *() const; + + //! moves iterator to the next element + SparseMatConstIterator_& operator ++(); + //! moves iterator to the next element + SparseMatConstIterator_ operator ++(int); +}; + + + +///////////////////////////////// SparseMatIterator_ ///////////////////////////////// + +/** @brief Template Read-Write Sparse Matrix Iterator Class. + + This is the derived from cv::SparseMatConstIterator_ class that + introduces more convenient operator *() for accessing the current element. +*/ +template class SparseMatIterator_ : public SparseMatConstIterator_<_Tp> +{ +public: + + typedef std::forward_iterator_tag iterator_category; + + //! the default constructor + SparseMatIterator_(); + //! the full constructor setting the iterator to the first sparse matrix element + SparseMatIterator_(SparseMat_<_Tp>* _m); + SparseMatIterator_(SparseMat* _m); + //! the copy constructor + SparseMatIterator_(const SparseMatIterator_& it); + + //! the assignment operator + SparseMatIterator_& operator = (const SparseMatIterator_& it); + //! returns the reference to the current element + _Tp& operator *() const; + + //! moves the iterator to the next element + SparseMatIterator_& operator ++(); + //! moves the iterator to the next element + SparseMatIterator_ operator ++(int); +}; + + + +/////////////////////////////////// NAryMatIterator ////////////////////////////////// + +/** @brief n-ary multi-dimensional array iterator. + +Use the class to implement unary, binary, and, generally, n-ary element-wise operations on +multi-dimensional arrays. Some of the arguments of an n-ary function may be continuous arrays, some +may be not. It is possible to use conventional MatIterator 's for each array but incrementing all of +the iterators after each small operations may be a big overhead. In this case consider using +NAryMatIterator to iterate through several matrices simultaneously as long as they have the same +geometry (dimensionality and all the dimension sizes are the same). On each iteration `it.planes[0]`, +`it.planes[1]`,... will be the slices of the corresponding matrices. + +The example below illustrates how you can compute a normalized and threshold 3D color histogram: +@code + void computeNormalizedColorHist(const Mat& image, Mat& hist, int N, double minProb) + { + const int histSize[] = {N, N, N}; + + // make sure that the histogram has a proper size and type + hist.create(3, histSize, CV_32F); + + // and clear it + hist = Scalar(0); + + // the loop below assumes that the image + // is a 8-bit 3-channel. check it. + CV_Assert(image.type() == CV_8UC3); + MatConstIterator_ it = image.begin(), + it_end = image.end(); + for( ; it != it_end; ++it ) + { + const Vec3b& pix = *it; + hist.at(pix[0]*N/256, pix[1]*N/256, pix[2]*N/256) += 1.f; + } + + minProb *= image.rows*image.cols; + + // initialize iterator (the style is different from STL). + // after initialization the iterator will contain + // the number of slices or planes the iterator will go through. + // it simultaneously increments iterators for several matrices + // supplied as a null terminated list of pointers + const Mat* arrays[] = {&hist, 0}; + Mat planes[1]; + NAryMatIterator itNAry(arrays, planes, 1); + double s = 0; + // iterate through the matrix. on each iteration + // itNAry.planes[i] (of type Mat) will be set to the current plane + // of the i-th n-dim matrix passed to the iterator constructor. + for(int p = 0; p < itNAry.nplanes; p++, ++itNAry) + { + threshold(itNAry.planes[0], itNAry.planes[0], minProb, 0, THRESH_TOZERO); + s += sum(itNAry.planes[0])[0]; + } + + s = 1./s; + itNAry = NAryMatIterator(arrays, planes, 1); + for(int p = 0; p < itNAry.nplanes; p++, ++itNAry) + itNAry.planes[0] *= s; + } +@endcode + */ +class CV_EXPORTS NAryMatIterator +{ +public: + //! the default constructor + NAryMatIterator(); + //! the full constructor taking arbitrary number of n-dim matrices + NAryMatIterator(const Mat** arrays, uchar** ptrs, int narrays=-1); + //! the full constructor taking arbitrary number of n-dim matrices + NAryMatIterator(const Mat** arrays, Mat* planes, int narrays=-1); + //! the separate iterator initialization method + void init(const Mat** arrays, Mat* planes, uchar** ptrs, int narrays=-1); + + //! proceeds to the next plane of every iterated matrix + NAryMatIterator& operator ++(); + //! proceeds to the next plane of every iterated matrix (postfix increment operator) + NAryMatIterator operator ++(int); + + //! the iterated arrays + const Mat** arrays; + //! the current planes + Mat* planes; + //! data pointers + uchar** ptrs; + //! the number of arrays + int narrays; + //! the number of hyper-planes that the iterator steps through + size_t nplanes; + //! the size of each segment (in elements) + size_t size; +protected: + int iterdepth; + size_t idx; +}; + + + +///////////////////////////////// Matrix Expressions ///////////////////////////////// + +class CV_EXPORTS MatOp +{ +public: + MatOp(); + virtual ~MatOp(); + + virtual bool elementWise(const MatExpr& expr) const; + virtual void assign(const MatExpr& expr, Mat& m, int type=-1) const = 0; + virtual void roi(const MatExpr& expr, const Range& rowRange, + const Range& colRange, MatExpr& res) const; + virtual void diag(const MatExpr& expr, int d, MatExpr& res) const; + virtual void augAssignAdd(const MatExpr& expr, Mat& m) const; + virtual void augAssignSubtract(const MatExpr& expr, Mat& m) const; + virtual void augAssignMultiply(const MatExpr& expr, Mat& m) const; + virtual void augAssignDivide(const MatExpr& expr, Mat& m) const; + virtual void augAssignAnd(const MatExpr& expr, Mat& m) const; + virtual void augAssignOr(const MatExpr& expr, Mat& m) const; + virtual void augAssignXor(const MatExpr& expr, Mat& m) const; + + virtual void add(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res) const; + virtual void add(const MatExpr& expr1, const Scalar& s, MatExpr& res) const; + + virtual void subtract(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res) const; + virtual void subtract(const Scalar& s, const MatExpr& expr, MatExpr& res) const; + + virtual void multiply(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res, double scale=1) const; + virtual void multiply(const MatExpr& expr1, double s, MatExpr& res) const; + + virtual void divide(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res, double scale=1) const; + virtual void divide(double s, const MatExpr& expr, MatExpr& res) const; + + virtual void abs(const MatExpr& expr, MatExpr& res) const; + + virtual void transpose(const MatExpr& expr, MatExpr& res) const; + virtual void matmul(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res) const; + virtual void invert(const MatExpr& expr, int method, MatExpr& res) const; + + virtual Size size(const MatExpr& expr) const; + virtual int type(const MatExpr& expr) const; +}; + +/** @brief Matrix expression representation +@anchor MatrixExpressions +This is a list of implemented matrix operations that can be combined in arbitrary complex +expressions (here A, B stand for matrices ( Mat ), s for a scalar ( Scalar ), alpha for a +real-valued scalar ( double )): +- Addition, subtraction, negation: `A+B`, `A-B`, `A+s`, `A-s`, `s+A`, `s-A`, `-A` +- Scaling: `A*alpha` +- Per-element multiplication and division: `A.mul(B)`, `A/B`, `alpha/A` +- Matrix multiplication: `A*B` +- Transposition: `A.t()` (means AT) +- Matrix inversion and pseudo-inversion, solving linear systems and least-squares problems: + `A.inv([method]) (~ A-1)`, `A.inv([method])*B (~ X: AX=B)` +- Comparison: `A cmpop B`, `A cmpop alpha`, `alpha cmpop A`, where *cmpop* is one of + `>`, `>=`, `==`, `!=`, `<=`, `<`. The result of comparison is an 8-bit single channel mask whose + elements are set to 255 (if the particular element or pair of elements satisfy the condition) or + 0. +- Bitwise logical operations: `A logicop B`, `A logicop s`, `s logicop A`, `~A`, where *logicop* is one of + `&`, `|`, `^`. +- Element-wise minimum and maximum: `min(A, B)`, `min(A, alpha)`, `max(A, B)`, `max(A, alpha)` +- Element-wise absolute value: `abs(A)` +- Cross-product, dot-product: `A.cross(B)`, `A.dot(B)` +- Any function of matrix or matrices and scalars that returns a matrix or a scalar, such as norm, + mean, sum, countNonZero, trace, determinant, repeat, and others. +- Matrix initializers ( Mat::eye(), Mat::zeros(), Mat::ones() ), matrix comma-separated + initializers, matrix constructors and operators that extract sub-matrices (see Mat description). +- Mat_() constructors to cast the result to the proper type. +@note Comma-separated initializers and probably some other operations may require additional +explicit Mat() or Mat_() constructor calls to resolve a possible ambiguity. + +Here are examples of matrix expressions: +@code + // compute pseudo-inverse of A, equivalent to A.inv(DECOMP_SVD) + SVD svd(A); + Mat pinvA = svd.vt.t()*Mat::diag(1./svd.w)*svd.u.t(); + + // compute the new vector of parameters in the Levenberg-Marquardt algorithm + x -= (A.t()*A + lambda*Mat::eye(A.cols,A.cols,A.type())).inv(DECOMP_CHOLESKY)*(A.t()*err); + + // sharpen image using "unsharp mask" algorithm + Mat blurred; double sigma = 1, threshold = 5, amount = 1; + GaussianBlur(img, blurred, Size(), sigma, sigma); + Mat lowContrastMask = abs(img - blurred) < threshold; + Mat sharpened = img*(1+amount) + blurred*(-amount); + img.copyTo(sharpened, lowContrastMask); +@endcode +*/ +class CV_EXPORTS MatExpr +{ +public: + MatExpr(); + explicit MatExpr(const Mat& m); + + MatExpr(const MatOp* _op, int _flags, const Mat& _a = Mat(), const Mat& _b = Mat(), + const Mat& _c = Mat(), double _alpha = 1, double _beta = 1, const Scalar& _s = Scalar()); + + operator Mat() const; + template operator Mat_<_Tp>() const; + + Size size() const; + int type() const; + + MatExpr row(int y) const; + MatExpr col(int x) const; + MatExpr diag(int d = 0) const; + MatExpr operator()( const Range& rowRange, const Range& colRange ) const; + MatExpr operator()( const Rect& roi ) const; + + MatExpr t() const; + MatExpr inv(int method = DECOMP_LU) const; + MatExpr mul(const MatExpr& e, double scale=1) const; + MatExpr mul(const Mat& m, double scale=1) const; + + Mat cross(const Mat& m) const; + double dot(const Mat& m) const; + + const MatOp* op; + int flags; + + Mat a, b, c; + double alpha, beta; + Scalar s; +}; + +//! @} core_basic + +//! @relates cv::MatExpr +//! @{ +CV_EXPORTS MatExpr operator + (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator + (const Mat& a, const Scalar& s); +CV_EXPORTS MatExpr operator + (const Scalar& s, const Mat& a); +CV_EXPORTS MatExpr operator + (const MatExpr& e, const Mat& m); +CV_EXPORTS MatExpr operator + (const Mat& m, const MatExpr& e); +CV_EXPORTS MatExpr operator + (const MatExpr& e, const Scalar& s); +CV_EXPORTS MatExpr operator + (const Scalar& s, const MatExpr& e); +CV_EXPORTS MatExpr operator + (const MatExpr& e1, const MatExpr& e2); + +CV_EXPORTS MatExpr operator - (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator - (const Mat& a, const Scalar& s); +CV_EXPORTS MatExpr operator - (const Scalar& s, const Mat& a); +CV_EXPORTS MatExpr operator - (const MatExpr& e, const Mat& m); +CV_EXPORTS MatExpr operator - (const Mat& m, const MatExpr& e); +CV_EXPORTS MatExpr operator - (const MatExpr& e, const Scalar& s); +CV_EXPORTS MatExpr operator - (const Scalar& s, const MatExpr& e); +CV_EXPORTS MatExpr operator - (const MatExpr& e1, const MatExpr& e2); + +CV_EXPORTS MatExpr operator - (const Mat& m); +CV_EXPORTS MatExpr operator - (const MatExpr& e); + +CV_EXPORTS MatExpr operator * (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator * (const Mat& a, double s); +CV_EXPORTS MatExpr operator * (double s, const Mat& a); +CV_EXPORTS MatExpr operator * (const MatExpr& e, const Mat& m); +CV_EXPORTS MatExpr operator * (const Mat& m, const MatExpr& e); +CV_EXPORTS MatExpr operator * (const MatExpr& e, double s); +CV_EXPORTS MatExpr operator * (double s, const MatExpr& e); +CV_EXPORTS MatExpr operator * (const MatExpr& e1, const MatExpr& e2); + +CV_EXPORTS MatExpr operator / (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator / (const Mat& a, double s); +CV_EXPORTS MatExpr operator / (double s, const Mat& a); +CV_EXPORTS MatExpr operator / (const MatExpr& e, const Mat& m); +CV_EXPORTS MatExpr operator / (const Mat& m, const MatExpr& e); +CV_EXPORTS MatExpr operator / (const MatExpr& e, double s); +CV_EXPORTS MatExpr operator / (double s, const MatExpr& e); +CV_EXPORTS MatExpr operator / (const MatExpr& e1, const MatExpr& e2); + +CV_EXPORTS MatExpr operator < (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator < (const Mat& a, double s); +CV_EXPORTS MatExpr operator < (double s, const Mat& a); + +CV_EXPORTS MatExpr operator <= (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator <= (const Mat& a, double s); +CV_EXPORTS MatExpr operator <= (double s, const Mat& a); + +CV_EXPORTS MatExpr operator == (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator == (const Mat& a, double s); +CV_EXPORTS MatExpr operator == (double s, const Mat& a); + +CV_EXPORTS MatExpr operator != (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator != (const Mat& a, double s); +CV_EXPORTS MatExpr operator != (double s, const Mat& a); + +CV_EXPORTS MatExpr operator >= (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator >= (const Mat& a, double s); +CV_EXPORTS MatExpr operator >= (double s, const Mat& a); + +CV_EXPORTS MatExpr operator > (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator > (const Mat& a, double s); +CV_EXPORTS MatExpr operator > (double s, const Mat& a); + +CV_EXPORTS MatExpr operator & (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator & (const Mat& a, const Scalar& s); +CV_EXPORTS MatExpr operator & (const Scalar& s, const Mat& a); + +CV_EXPORTS MatExpr operator | (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator | (const Mat& a, const Scalar& s); +CV_EXPORTS MatExpr operator | (const Scalar& s, const Mat& a); + +CV_EXPORTS MatExpr operator ^ (const Mat& a, const Mat& b); +CV_EXPORTS MatExpr operator ^ (const Mat& a, const Scalar& s); +CV_EXPORTS MatExpr operator ^ (const Scalar& s, const Mat& a); + +CV_EXPORTS MatExpr operator ~(const Mat& m); + +CV_EXPORTS MatExpr min(const Mat& a, const Mat& b); +CV_EXPORTS MatExpr min(const Mat& a, double s); +CV_EXPORTS MatExpr min(double s, const Mat& a); + +CV_EXPORTS MatExpr max(const Mat& a, const Mat& b); +CV_EXPORTS MatExpr max(const Mat& a, double s); +CV_EXPORTS MatExpr max(double s, const Mat& a); + +/** @brief Calculates an absolute value of each matrix element. + +abs is a meta-function that is expanded to one of absdiff or convertScaleAbs forms: +- C = abs(A-B) is equivalent to `absdiff(A, B, C)` +- C = abs(A) is equivalent to `absdiff(A, Scalar::all(0), C)` +- C = `Mat_ >(abs(A*alpha + beta))` is equivalent to `convertScaleAbs(A, C, alpha, +beta)` + +The output matrix has the same size and the same type as the input one except for the last case, +where C is depth=CV_8U . +@param m matrix. +@sa @ref MatrixExpressions, absdiff, convertScaleAbs + */ +CV_EXPORTS MatExpr abs(const Mat& m); +/** @overload +@param e matrix expression. +*/ +CV_EXPORTS MatExpr abs(const MatExpr& e); +//! @} relates cv::MatExpr + +} // cv + +#include "opencv2/core/mat.inl.hpp" + +#endif // OPENCV_CORE_MAT_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/mat.inl.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/mat.inl.hpp new file mode 100644 index 0000000000000000000000000000000000000000..a2e7923b39340e9368dc0e02085410271532b43b --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/mat.inl.hpp @@ -0,0 +1,4027 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_MATRIX_OPERATIONS_HPP +#define OPENCV_CORE_MATRIX_OPERATIONS_HPP + +#ifndef __cplusplus +# error mat.inl.hpp header must be compiled as C++ +#endif + +#ifdef _MSC_VER +#pragma warning( push ) +#pragma warning( disable: 4127 ) +#endif + +namespace cv +{ +CV__DEBUG_NS_BEGIN + + +//! @cond IGNORED + +////////////////////////// Custom (raw) type wrapper ////////////////////////// + +template static inline +int rawType() +{ + CV_StaticAssert(sizeof(_Tp) <= CV_CN_MAX, "sizeof(_Tp) is too large"); + const int elemSize = sizeof(_Tp); + return (int)CV_MAKETYPE(CV_8U, elemSize); +} + +//////////////////////// Input/Output Arrays //////////////////////// + +inline void _InputArray::init(int _flags, const void* _obj) +{ flags = _flags; obj = (void*)_obj; } + +inline void _InputArray::init(int _flags, const void* _obj, Size _sz) +{ flags = _flags; obj = (void*)_obj; sz = _sz; } + +inline void* _InputArray::getObj() const { return obj; } +inline int _InputArray::getFlags() const { return flags; } +inline Size _InputArray::getSz() const { return sz; } + +inline _InputArray::_InputArray() { init(NONE, 0); } +inline _InputArray::_InputArray(int _flags, void* _obj) { init(_flags, _obj); } +inline _InputArray::_InputArray(const Mat& m) { init(MAT+ACCESS_READ, &m); } +inline _InputArray::_InputArray(const std::vector& vec) { init(STD_VECTOR_MAT+ACCESS_READ, &vec); } +inline _InputArray::_InputArray(const UMat& m) { init(UMAT+ACCESS_READ, &m); } +inline _InputArray::_InputArray(const std::vector& vec) { init(STD_VECTOR_UMAT+ACCESS_READ, &vec); } + +template inline +_InputArray::_InputArray(const std::vector<_Tp>& vec) +{ init(FIXED_TYPE + STD_VECTOR + traits::Type<_Tp>::value + ACCESS_READ, &vec); } + +#ifdef CV_CXX_STD_ARRAY +template inline +_InputArray::_InputArray(const std::array<_Tp, _Nm>& arr) +{ init(FIXED_TYPE + FIXED_SIZE + STD_ARRAY + traits::Type<_Tp>::value + ACCESS_READ, arr.data(), Size(1, _Nm)); } + +template inline +_InputArray::_InputArray(const std::array& arr) +{ init(STD_ARRAY_MAT + ACCESS_READ, arr.data(), Size(1, _Nm)); } +#endif + +inline +_InputArray::_InputArray(const std::vector& vec) +{ init(FIXED_TYPE + STD_BOOL_VECTOR + traits::Type::value + ACCESS_READ, &vec); } + +template inline +_InputArray::_InputArray(const std::vector >& vec) +{ init(FIXED_TYPE + STD_VECTOR_VECTOR + traits::Type<_Tp>::value + ACCESS_READ, &vec); } + +inline +_InputArray::_InputArray(const std::vector >&) +{ CV_Error(Error::StsUnsupportedFormat, "std::vector > is not supported!\n"); } + +template inline +_InputArray::_InputArray(const std::vector >& vec) +{ init(FIXED_TYPE + STD_VECTOR_MAT + traits::Type<_Tp>::value + ACCESS_READ, &vec); } + +template inline +_InputArray::_InputArray(const Matx<_Tp, m, n>& mtx) +{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_READ, &mtx, Size(n, m)); } + +template inline +_InputArray::_InputArray(const _Tp* vec, int n) +{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_READ, vec, Size(n, 1)); } + +template inline +_InputArray::_InputArray(const Mat_<_Tp>& m) +{ init(FIXED_TYPE + MAT + traits::Type<_Tp>::value + ACCESS_READ, &m); } + +inline _InputArray::_InputArray(const double& val) +{ init(FIXED_TYPE + FIXED_SIZE + MATX + CV_64F + ACCESS_READ, &val, Size(1,1)); } + +inline _InputArray::_InputArray(const MatExpr& expr) +{ init(FIXED_TYPE + FIXED_SIZE + EXPR + ACCESS_READ, &expr); } + +inline _InputArray::_InputArray(const cuda::GpuMat& d_mat) +{ init(CUDA_GPU_MAT + ACCESS_READ, &d_mat); } + +inline _InputArray::_InputArray(const std::vector& d_mat) +{ init(STD_VECTOR_CUDA_GPU_MAT + ACCESS_READ, &d_mat);} + +inline _InputArray::_InputArray(const ogl::Buffer& buf) +{ init(OPENGL_BUFFER + ACCESS_READ, &buf); } + +inline _InputArray::_InputArray(const cuda::HostMem& cuda_mem) +{ init(CUDA_HOST_MEM + ACCESS_READ, &cuda_mem); } + +template inline +_InputArray _InputArray::rawIn(const std::vector<_Tp>& vec) +{ + _InputArray v; + v.flags = _InputArray::FIXED_TYPE + _InputArray::STD_VECTOR + rawType<_Tp>() + ACCESS_READ; + v.obj = (void*)&vec; + return v; +} + +#ifdef CV_CXX_STD_ARRAY +template inline +_InputArray _InputArray::rawIn(const std::array<_Tp, _Nm>& arr) +{ + _InputArray v; + v.flags = FIXED_TYPE + FIXED_SIZE + STD_ARRAY + traits::Type<_Tp>::value + ACCESS_READ; + v.obj = (void*)arr.data(); + v.sz = Size(1, _Nm); + return v; +} +#endif + +inline _InputArray::~_InputArray() {} + +inline Mat _InputArray::getMat(int i) const +{ + if( kind() == MAT && i < 0 ) + return *(const Mat*)obj; + return getMat_(i); +} + +inline bool _InputArray::isMat() const { return kind() == _InputArray::MAT; } +inline bool _InputArray::isUMat() const { return kind() == _InputArray::UMAT; } +inline bool _InputArray::isMatVector() const { return kind() == _InputArray::STD_VECTOR_MAT; } +inline bool _InputArray::isUMatVector() const { return kind() == _InputArray::STD_VECTOR_UMAT; } +inline bool _InputArray::isMatx() const { return kind() == _InputArray::MATX; } +inline bool _InputArray::isVector() const { return kind() == _InputArray::STD_VECTOR || + kind() == _InputArray::STD_BOOL_VECTOR || + kind() == _InputArray::STD_ARRAY; } +inline bool _InputArray::isGpuMat() const { return kind() == _InputArray::CUDA_GPU_MAT; } +inline bool _InputArray::isGpuMatVector() const { return kind() == _InputArray::STD_VECTOR_CUDA_GPU_MAT; } + +//////////////////////////////////////////////////////////////////////////////////////// + +inline _OutputArray::_OutputArray() { init(ACCESS_WRITE, 0); } +inline _OutputArray::_OutputArray(int _flags, void* _obj) { init(_flags|ACCESS_WRITE, _obj); } +inline _OutputArray::_OutputArray(Mat& m) { init(MAT+ACCESS_WRITE, &m); } +inline _OutputArray::_OutputArray(std::vector& vec) { init(STD_VECTOR_MAT+ACCESS_WRITE, &vec); } +inline _OutputArray::_OutputArray(UMat& m) { init(UMAT+ACCESS_WRITE, &m); } +inline _OutputArray::_OutputArray(std::vector& vec) { init(STD_VECTOR_UMAT+ACCESS_WRITE, &vec); } + +template inline +_OutputArray::_OutputArray(std::vector<_Tp>& vec) +{ init(FIXED_TYPE + STD_VECTOR + traits::Type<_Tp>::value + ACCESS_WRITE, &vec); } + +#ifdef CV_CXX_STD_ARRAY +template inline +_OutputArray::_OutputArray(std::array<_Tp, _Nm>& arr) +{ init(FIXED_TYPE + FIXED_SIZE + STD_ARRAY + traits::Type<_Tp>::value + ACCESS_WRITE, arr.data(), Size(1, _Nm)); } + +template inline +_OutputArray::_OutputArray(std::array& arr) +{ init(STD_ARRAY_MAT + ACCESS_WRITE, arr.data(), Size(1, _Nm)); } +#endif + +inline +_OutputArray::_OutputArray(std::vector&) +{ CV_Error(Error::StsUnsupportedFormat, "std::vector cannot be an output array\n"); } + +template inline +_OutputArray::_OutputArray(std::vector >& vec) +{ init(FIXED_TYPE + STD_VECTOR_VECTOR + traits::Type<_Tp>::value + ACCESS_WRITE, &vec); } + +inline +_OutputArray::_OutputArray(std::vector >&) +{ CV_Error(Error::StsUnsupportedFormat, "std::vector > cannot be an output array\n"); } + +template inline +_OutputArray::_OutputArray(std::vector >& vec) +{ init(FIXED_TYPE + STD_VECTOR_MAT + traits::Type<_Tp>::value + ACCESS_WRITE, &vec); } + +template inline +_OutputArray::_OutputArray(Mat_<_Tp>& m) +{ init(FIXED_TYPE + MAT + traits::Type<_Tp>::value + ACCESS_WRITE, &m); } + +template inline +_OutputArray::_OutputArray(Matx<_Tp, m, n>& mtx) +{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_WRITE, &mtx, Size(n, m)); } + +template inline +_OutputArray::_OutputArray(_Tp* vec, int n) +{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_WRITE, vec, Size(n, 1)); } + +template inline +_OutputArray::_OutputArray(const std::vector<_Tp>& vec) +{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR + traits::Type<_Tp>::value + ACCESS_WRITE, &vec); } + +#ifdef CV_CXX_STD_ARRAY +template inline +_OutputArray::_OutputArray(const std::array<_Tp, _Nm>& arr) +{ init(FIXED_TYPE + FIXED_SIZE + STD_ARRAY + traits::Type<_Tp>::value + ACCESS_WRITE, arr.data(), Size(1, _Nm)); } + +template inline +_OutputArray::_OutputArray(const std::array& arr) +{ init(FIXED_SIZE + STD_ARRAY_MAT + ACCESS_WRITE, arr.data(), Size(1, _Nm)); } +#endif + +template inline +_OutputArray::_OutputArray(const std::vector >& vec) +{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_VECTOR + traits::Type<_Tp>::value + ACCESS_WRITE, &vec); } + +template inline +_OutputArray::_OutputArray(const std::vector >& vec) +{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_MAT + traits::Type<_Tp>::value + ACCESS_WRITE, &vec); } + +template inline +_OutputArray::_OutputArray(const Mat_<_Tp>& m) +{ init(FIXED_TYPE + FIXED_SIZE + MAT + traits::Type<_Tp>::value + ACCESS_WRITE, &m); } + +template inline +_OutputArray::_OutputArray(const Matx<_Tp, m, n>& mtx) +{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_WRITE, &mtx, Size(n, m)); } + +template inline +_OutputArray::_OutputArray(const _Tp* vec, int n) +{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_WRITE, vec, Size(n, 1)); } + +inline _OutputArray::_OutputArray(cuda::GpuMat& d_mat) +{ init(CUDA_GPU_MAT + ACCESS_WRITE, &d_mat); } + +inline _OutputArray::_OutputArray(std::vector& d_mat) +{ init(STD_VECTOR_CUDA_GPU_MAT + ACCESS_WRITE, &d_mat);} + +inline _OutputArray::_OutputArray(ogl::Buffer& buf) +{ init(OPENGL_BUFFER + ACCESS_WRITE, &buf); } + +inline _OutputArray::_OutputArray(cuda::HostMem& cuda_mem) +{ init(CUDA_HOST_MEM + ACCESS_WRITE, &cuda_mem); } + +inline _OutputArray::_OutputArray(const Mat& m) +{ init(FIXED_TYPE + FIXED_SIZE + MAT + ACCESS_WRITE, &m); } + +inline _OutputArray::_OutputArray(const std::vector& vec) +{ init(FIXED_SIZE + STD_VECTOR_MAT + ACCESS_WRITE, &vec); } + +inline _OutputArray::_OutputArray(const UMat& m) +{ init(FIXED_TYPE + FIXED_SIZE + UMAT + ACCESS_WRITE, &m); } + +inline _OutputArray::_OutputArray(const std::vector& vec) +{ init(FIXED_SIZE + STD_VECTOR_UMAT + ACCESS_WRITE, &vec); } + +inline _OutputArray::_OutputArray(const cuda::GpuMat& d_mat) +{ init(FIXED_TYPE + FIXED_SIZE + CUDA_GPU_MAT + ACCESS_WRITE, &d_mat); } + + +inline _OutputArray::_OutputArray(const ogl::Buffer& buf) +{ init(FIXED_TYPE + FIXED_SIZE + OPENGL_BUFFER + ACCESS_WRITE, &buf); } + +inline _OutputArray::_OutputArray(const cuda::HostMem& cuda_mem) +{ init(FIXED_TYPE + FIXED_SIZE + CUDA_HOST_MEM + ACCESS_WRITE, &cuda_mem); } + +template inline +_OutputArray _OutputArray::rawOut(std::vector<_Tp>& vec) +{ + _OutputArray v; + v.flags = _InputArray::FIXED_TYPE + _InputArray::STD_VECTOR + rawType<_Tp>() + ACCESS_WRITE; + v.obj = (void*)&vec; + return v; +} + +#ifdef CV_CXX_STD_ARRAY +template inline +_OutputArray _OutputArray::rawOut(std::array<_Tp, _Nm>& arr) +{ + _OutputArray v; + v.flags = FIXED_TYPE + FIXED_SIZE + STD_ARRAY + traits::Type<_Tp>::value + ACCESS_WRITE; + v.obj = (void*)arr.data(); + v.sz = Size(1, _Nm); + return v; +} +#endif + +/////////////////////////////////////////////////////////////////////////////////////////// + +inline _InputOutputArray::_InputOutputArray() { init(ACCESS_RW, 0); } +inline _InputOutputArray::_InputOutputArray(int _flags, void* _obj) { init(_flags|ACCESS_RW, _obj); } +inline _InputOutputArray::_InputOutputArray(Mat& m) { init(MAT+ACCESS_RW, &m); } +inline _InputOutputArray::_InputOutputArray(std::vector& vec) { init(STD_VECTOR_MAT+ACCESS_RW, &vec); } +inline _InputOutputArray::_InputOutputArray(UMat& m) { init(UMAT+ACCESS_RW, &m); } +inline _InputOutputArray::_InputOutputArray(std::vector& vec) { init(STD_VECTOR_UMAT+ACCESS_RW, &vec); } + +template inline +_InputOutputArray::_InputOutputArray(std::vector<_Tp>& vec) +{ init(FIXED_TYPE + STD_VECTOR + traits::Type<_Tp>::value + ACCESS_RW, &vec); } + +#ifdef CV_CXX_STD_ARRAY +template inline +_InputOutputArray::_InputOutputArray(std::array<_Tp, _Nm>& arr) +{ init(FIXED_TYPE + FIXED_SIZE + STD_ARRAY + traits::Type<_Tp>::value + ACCESS_RW, arr.data(), Size(1, _Nm)); } + +template inline +_InputOutputArray::_InputOutputArray(std::array& arr) +{ init(STD_ARRAY_MAT + ACCESS_RW, arr.data(), Size(1, _Nm)); } +#endif + +inline _InputOutputArray::_InputOutputArray(std::vector&) +{ CV_Error(Error::StsUnsupportedFormat, "std::vector cannot be an input/output array\n"); } + +template inline +_InputOutputArray::_InputOutputArray(std::vector >& vec) +{ init(FIXED_TYPE + STD_VECTOR_VECTOR + traits::Type<_Tp>::value + ACCESS_RW, &vec); } + +template inline +_InputOutputArray::_InputOutputArray(std::vector >& vec) +{ init(FIXED_TYPE + STD_VECTOR_MAT + traits::Type<_Tp>::value + ACCESS_RW, &vec); } + +template inline +_InputOutputArray::_InputOutputArray(Mat_<_Tp>& m) +{ init(FIXED_TYPE + MAT + traits::Type<_Tp>::value + ACCESS_RW, &m); } + +template inline +_InputOutputArray::_InputOutputArray(Matx<_Tp, m, n>& mtx) +{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_RW, &mtx, Size(n, m)); } + +template inline +_InputOutputArray::_InputOutputArray(_Tp* vec, int n) +{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_RW, vec, Size(n, 1)); } + +template inline +_InputOutputArray::_InputOutputArray(const std::vector<_Tp>& vec) +{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR + traits::Type<_Tp>::value + ACCESS_RW, &vec); } + +#ifdef CV_CXX_STD_ARRAY +template inline +_InputOutputArray::_InputOutputArray(const std::array<_Tp, _Nm>& arr) +{ init(FIXED_TYPE + FIXED_SIZE + STD_ARRAY + traits::Type<_Tp>::value + ACCESS_RW, arr.data(), Size(1, _Nm)); } + +template inline +_InputOutputArray::_InputOutputArray(const std::array& arr) +{ init(FIXED_SIZE + STD_ARRAY_MAT + ACCESS_RW, arr.data(), Size(1, _Nm)); } +#endif + +template inline +_InputOutputArray::_InputOutputArray(const std::vector >& vec) +{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_VECTOR + traits::Type<_Tp>::value + ACCESS_RW, &vec); } + +template inline +_InputOutputArray::_InputOutputArray(const std::vector >& vec) +{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_MAT + traits::Type<_Tp>::value + ACCESS_RW, &vec); } + +template inline +_InputOutputArray::_InputOutputArray(const Mat_<_Tp>& m) +{ init(FIXED_TYPE + FIXED_SIZE + MAT + traits::Type<_Tp>::value + ACCESS_RW, &m); } + +template inline +_InputOutputArray::_InputOutputArray(const Matx<_Tp, m, n>& mtx) +{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_RW, &mtx, Size(n, m)); } + +template inline +_InputOutputArray::_InputOutputArray(const _Tp* vec, int n) +{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_RW, vec, Size(n, 1)); } + +inline _InputOutputArray::_InputOutputArray(cuda::GpuMat& d_mat) +{ init(CUDA_GPU_MAT + ACCESS_RW, &d_mat); } + +inline _InputOutputArray::_InputOutputArray(ogl::Buffer& buf) +{ init(OPENGL_BUFFER + ACCESS_RW, &buf); } + +inline _InputOutputArray::_InputOutputArray(cuda::HostMem& cuda_mem) +{ init(CUDA_HOST_MEM + ACCESS_RW, &cuda_mem); } + +inline _InputOutputArray::_InputOutputArray(const Mat& m) +{ init(FIXED_TYPE + FIXED_SIZE + MAT + ACCESS_RW, &m); } + +inline _InputOutputArray::_InputOutputArray(const std::vector& vec) +{ init(FIXED_SIZE + STD_VECTOR_MAT + ACCESS_RW, &vec); } + +inline _InputOutputArray::_InputOutputArray(const UMat& m) +{ init(FIXED_TYPE + FIXED_SIZE + UMAT + ACCESS_RW, &m); } + +inline _InputOutputArray::_InputOutputArray(const std::vector& vec) +{ init(FIXED_SIZE + STD_VECTOR_UMAT + ACCESS_RW, &vec); } + +inline _InputOutputArray::_InputOutputArray(const cuda::GpuMat& d_mat) +{ init(FIXED_TYPE + FIXED_SIZE + CUDA_GPU_MAT + ACCESS_RW, &d_mat); } + +inline _InputOutputArray::_InputOutputArray(const std::vector& d_mat) +{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_CUDA_GPU_MAT + ACCESS_RW, &d_mat);} + +template<> inline _InputOutputArray::_InputOutputArray(std::vector& d_mat) +{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_CUDA_GPU_MAT + ACCESS_RW, &d_mat);} + +inline _InputOutputArray::_InputOutputArray(const ogl::Buffer& buf) +{ init(FIXED_TYPE + FIXED_SIZE + OPENGL_BUFFER + ACCESS_RW, &buf); } + +inline _InputOutputArray::_InputOutputArray(const cuda::HostMem& cuda_mem) +{ init(FIXED_TYPE + FIXED_SIZE + CUDA_HOST_MEM + ACCESS_RW, &cuda_mem); } + +template inline +_InputOutputArray _InputOutputArray::rawInOut(std::vector<_Tp>& vec) +{ + _InputOutputArray v; + v.flags = _InputArray::FIXED_TYPE + _InputArray::STD_VECTOR + rawType<_Tp>() + ACCESS_RW; + v.obj = (void*)&vec; + return v; +} + +#ifdef CV_CXX_STD_ARRAY +template inline +_InputOutputArray _InputOutputArray::rawInOut(std::array<_Tp, _Nm>& arr) +{ + _InputOutputArray v; + v.flags = FIXED_TYPE + FIXED_SIZE + STD_ARRAY + traits::Type<_Tp>::value + ACCESS_RW; + v.obj = (void*)arr.data(); + v.sz = Size(1, _Nm); + return v; +} +#endif + + +template static inline _InputArray rawIn(_Tp& v) { return _InputArray::rawIn(v); } +template static inline _OutputArray rawOut(_Tp& v) { return _OutputArray::rawOut(v); } +template static inline _InputOutputArray rawInOut(_Tp& v) { return _InputOutputArray::rawInOut(v); } + +CV__DEBUG_NS_END + +//////////////////////////////////////////// Mat ////////////////////////////////////////// + +inline +Mat::Mat() + : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), + datalimit(0), allocator(0), u(0), size(&rows), step(0) +{} + +inline +Mat::Mat(int _rows, int _cols, int _type) + : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), + datalimit(0), allocator(0), u(0), size(&rows), step(0) +{ + create(_rows, _cols, _type); +} + +inline +Mat::Mat(int _rows, int _cols, int _type, const Scalar& _s) + : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), + datalimit(0), allocator(0), u(0), size(&rows), step(0) +{ + create(_rows, _cols, _type); + *this = _s; +} + +inline +Mat::Mat(Size _sz, int _type) + : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), + datalimit(0), allocator(0), u(0), size(&rows), step(0) +{ + create( _sz.height, _sz.width, _type ); +} + +inline +Mat::Mat(Size _sz, int _type, const Scalar& _s) + : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), + datalimit(0), allocator(0), u(0), size(&rows), step(0) +{ + create(_sz.height, _sz.width, _type); + *this = _s; +} + +inline +Mat::Mat(int _dims, const int* _sz, int _type) + : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), + datalimit(0), allocator(0), u(0), size(&rows), step(0) +{ + create(_dims, _sz, _type); +} + +inline +Mat::Mat(int _dims, const int* _sz, int _type, const Scalar& _s) + : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), + datalimit(0), allocator(0), u(0), size(&rows), step(0) +{ + create(_dims, _sz, _type); + *this = _s; +} + +inline +Mat::Mat(const std::vector& _sz, int _type) + : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), + datalimit(0), allocator(0), u(0), size(&rows), step(0) +{ + create(_sz, _type); +} + +inline +Mat::Mat(const std::vector& _sz, int _type, const Scalar& _s) + : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), + datalimit(0), allocator(0), u(0), size(&rows), step(0) +{ + create(_sz, _type); + *this = _s; +} + +inline +Mat::Mat(const Mat& m) + : flags(m.flags), dims(m.dims), rows(m.rows), cols(m.cols), data(m.data), + datastart(m.datastart), dataend(m.dataend), datalimit(m.datalimit), allocator(m.allocator), + u(m.u), size(&rows), step(0) +{ + if( u ) + CV_XADD(&u->refcount, 1); + if( m.dims <= 2 ) + { + step[0] = m.step[0]; step[1] = m.step[1]; + } + else + { + dims = 0; + copySize(m); + } +} + +inline +Mat::Mat(int _rows, int _cols, int _type, void* _data, size_t _step) + : flags(MAGIC_VAL + (_type & TYPE_MASK)), dims(2), rows(_rows), cols(_cols), + data((uchar*)_data), datastart((uchar*)_data), dataend(0), datalimit(0), + allocator(0), u(0), size(&rows) +{ + CV_Assert(total() == 0 || data != NULL); + + size_t esz = CV_ELEM_SIZE(_type), esz1 = CV_ELEM_SIZE1(_type); + size_t minstep = cols * esz; + if( _step == AUTO_STEP ) + { + _step = minstep; + } + else + { + CV_DbgAssert( _step >= minstep ); + if (_step % esz1 != 0) + { + CV_Error(Error::BadStep, "Step must be a multiple of esz1"); + } + } + step[0] = _step; + step[1] = esz; + datalimit = datastart + _step * rows; + dataend = datalimit - _step + minstep; + updateContinuityFlag(); +} + +inline +Mat::Mat(Size _sz, int _type, void* _data, size_t _step) + : flags(MAGIC_VAL + (_type & TYPE_MASK)), dims(2), rows(_sz.height), cols(_sz.width), + data((uchar*)_data), datastart((uchar*)_data), dataend(0), datalimit(0), + allocator(0), u(0), size(&rows) +{ + CV_Assert(total() == 0 || data != NULL); + + size_t esz = CV_ELEM_SIZE(_type), esz1 = CV_ELEM_SIZE1(_type); + size_t minstep = cols*esz; + if( _step == AUTO_STEP ) + { + _step = minstep; + } + else + { + CV_DbgAssert( _step >= minstep ); + + if (_step % esz1 != 0) + { + CV_Error(Error::BadStep, "Step must be a multiple of esz1"); + } + } + step[0] = _step; + step[1] = esz; + datalimit = datastart + _step*rows; + dataend = datalimit - _step + minstep; + updateContinuityFlag(); +} + +template inline +Mat::Mat(const std::vector<_Tp>& vec, bool copyData) + : flags(MAGIC_VAL | traits::Type<_Tp>::value | CV_MAT_CONT_FLAG), dims(2), rows((int)vec.size()), + cols(1), data(0), datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows), step(0) +{ + if(vec.empty()) + return; + if( !copyData ) + { + step[0] = step[1] = sizeof(_Tp); + datastart = data = (uchar*)&vec[0]; + datalimit = dataend = datastart + rows * step[0]; + } + else + Mat((int)vec.size(), 1, traits::Type<_Tp>::value, (uchar*)&vec[0]).copyTo(*this); +} + +#ifdef CV_CXX11 +template inline +Mat::Mat(const std::initializer_list<_Tp> list) + : Mat() +{ + CV_Assert(list.size() != 0); + Mat((int)list.size(), 1, traits::Type<_Tp>::value, (uchar*)list.begin()).copyTo(*this); +} + +template inline +Mat::Mat(const std::initializer_list sizes, const std::initializer_list<_Tp> list) + : Mat() +{ + size_t size_total = 1; + for(auto s : sizes) + size_total *= s; + CV_Assert(list.size() != 0); + CV_Assert(size_total == list.size()); + Mat((int)sizes.size(), (int*)sizes.begin(), traits::Type<_Tp>::value, (uchar*)list.begin()).copyTo(*this); +} +#endif + +#ifdef CV_CXX_STD_ARRAY +template inline +Mat::Mat(const std::array<_Tp, _Nm>& arr, bool copyData) + : flags(MAGIC_VAL | traits::Type<_Tp>::value | CV_MAT_CONT_FLAG), dims(2), rows((int)arr.size()), + cols(1), data(0), datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows), step(0) +{ + if(arr.empty()) + return; + if( !copyData ) + { + step[0] = step[1] = sizeof(_Tp); + datastart = data = (uchar*)arr.data(); + datalimit = dataend = datastart + rows * step[0]; + } + else + Mat((int)arr.size(), 1, traits::Type<_Tp>::value, (uchar*)arr.data()).copyTo(*this); +} +#endif + +template inline +Mat::Mat(const Vec<_Tp, n>& vec, bool copyData) + : flags(MAGIC_VAL | traits::Type<_Tp>::value | CV_MAT_CONT_FLAG), dims(2), rows(n), cols(1), data(0), + datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows), step(0) +{ + if( !copyData ) + { + step[0] = step[1] = sizeof(_Tp); + datastart = data = (uchar*)vec.val; + datalimit = dataend = datastart + rows * step[0]; + } + else + Mat(n, 1, traits::Type<_Tp>::value, (void*)vec.val).copyTo(*this); +} + + +template inline +Mat::Mat(const Matx<_Tp,m,n>& M, bool copyData) + : flags(MAGIC_VAL | traits::Type<_Tp>::value | CV_MAT_CONT_FLAG), dims(2), rows(m), cols(n), data(0), + datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows), step(0) +{ + if( !copyData ) + { + step[0] = cols * sizeof(_Tp); + step[1] = sizeof(_Tp); + datastart = data = (uchar*)M.val; + datalimit = dataend = datastart + rows * step[0]; + } + else + Mat(m, n, traits::Type<_Tp>::value, (uchar*)M.val).copyTo(*this); +} + +template inline +Mat::Mat(const Point_<_Tp>& pt, bool copyData) + : flags(MAGIC_VAL | traits::Type<_Tp>::value | CV_MAT_CONT_FLAG), dims(2), rows(2), cols(1), data(0), + datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows), step(0) +{ + if( !copyData ) + { + step[0] = step[1] = sizeof(_Tp); + datastart = data = (uchar*)&pt.x; + datalimit = dataend = datastart + rows * step[0]; + } + else + { + create(2, 1, traits::Type<_Tp>::value); + ((_Tp*)data)[0] = pt.x; + ((_Tp*)data)[1] = pt.y; + } +} + +template inline +Mat::Mat(const Point3_<_Tp>& pt, bool copyData) + : flags(MAGIC_VAL | traits::Type<_Tp>::value | CV_MAT_CONT_FLAG), dims(2), rows(3), cols(1), data(0), + datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows), step(0) +{ + if( !copyData ) + { + step[0] = step[1] = sizeof(_Tp); + datastart = data = (uchar*)&pt.x; + datalimit = dataend = datastart + rows * step[0]; + } + else + { + create(3, 1, traits::Type<_Tp>::value); + ((_Tp*)data)[0] = pt.x; + ((_Tp*)data)[1] = pt.y; + ((_Tp*)data)[2] = pt.z; + } +} + +template inline +Mat::Mat(const MatCommaInitializer_<_Tp>& commaInitializer) + : flags(MAGIC_VAL | traits::Type<_Tp>::value | CV_MAT_CONT_FLAG), dims(0), rows(0), cols(0), data(0), + datastart(0), dataend(0), allocator(0), u(0), size(&rows) +{ + *this = commaInitializer.operator Mat_<_Tp>(); +} + +inline +Mat::~Mat() +{ + release(); + if( step.p != step.buf ) + fastFree(step.p); +} + +inline +Mat& Mat::operator = (const Mat& m) +{ + if( this != &m ) + { + if( m.u ) + CV_XADD(&m.u->refcount, 1); + release(); + flags = m.flags; + if( dims <= 2 && m.dims <= 2 ) + { + dims = m.dims; + rows = m.rows; + cols = m.cols; + step[0] = m.step[0]; + step[1] = m.step[1]; + } + else + copySize(m); + data = m.data; + datastart = m.datastart; + dataend = m.dataend; + datalimit = m.datalimit; + allocator = m.allocator; + u = m.u; + } + return *this; +} + +inline +Mat Mat::row(int y) const +{ + return Mat(*this, Range(y, y + 1), Range::all()); +} + +inline +Mat Mat::col(int x) const +{ + return Mat(*this, Range::all(), Range(x, x + 1)); +} + +inline +Mat Mat::rowRange(int startrow, int endrow) const +{ + return Mat(*this, Range(startrow, endrow), Range::all()); +} + +inline +Mat Mat::rowRange(const Range& r) const +{ + return Mat(*this, r, Range::all()); +} + +inline +Mat Mat::colRange(int startcol, int endcol) const +{ + return Mat(*this, Range::all(), Range(startcol, endcol)); +} + +inline +Mat Mat::colRange(const Range& r) const +{ + return Mat(*this, Range::all(), r); +} + +inline +Mat Mat::clone() const +{ + Mat m; + copyTo(m); + return m; +} + +inline +void Mat::assignTo( Mat& m, int _type ) const +{ + if( _type < 0 ) + m = *this; + else + convertTo(m, _type); +} + +inline +void Mat::create(int _rows, int _cols, int _type) +{ + _type &= TYPE_MASK; + if( dims <= 2 && rows == _rows && cols == _cols && type() == _type && data ) + return; + int sz[] = {_rows, _cols}; + create(2, sz, _type); +} + +inline +void Mat::create(Size _sz, int _type) +{ + create(_sz.height, _sz.width, _type); +} + +inline +void Mat::addref() +{ + if( u ) + CV_XADD(&u->refcount, 1); +} + +inline +void Mat::release() +{ + if( u && CV_XADD(&u->refcount, -1) == 1 ) + deallocate(); + u = NULL; + datastart = dataend = datalimit = data = 0; + for(int i = 0; i < dims; i++) + size.p[i] = 0; +#ifdef _DEBUG + flags = MAGIC_VAL; + dims = rows = cols = 0; + if(step.p != step.buf) + { + fastFree(step.p); + step.p = step.buf; + size.p = &rows; + } +#endif +} + +inline +Mat Mat::operator()( Range _rowRange, Range _colRange ) const +{ + return Mat(*this, _rowRange, _colRange); +} + +inline +Mat Mat::operator()( const Rect& roi ) const +{ + return Mat(*this, roi); +} + +inline +Mat Mat::operator()(const Range* ranges) const +{ + return Mat(*this, ranges); +} + +inline +Mat Mat::operator()(const std::vector& ranges) const +{ + return Mat(*this, ranges); +} + +inline +bool Mat::isContinuous() const +{ + return (flags & CONTINUOUS_FLAG) != 0; +} + +inline +bool Mat::isSubmatrix() const +{ + return (flags & SUBMATRIX_FLAG) != 0; +} + +inline +size_t Mat::elemSize() const +{ + size_t res = dims > 0 ? step.p[dims - 1] : 0; + CV_DbgAssert(res != 0); + return res; +} + +inline +size_t Mat::elemSize1() const +{ + return CV_ELEM_SIZE1(flags); +} + +inline +int Mat::type() const +{ + return CV_MAT_TYPE(flags); +} + +inline +int Mat::depth() const +{ + return CV_MAT_DEPTH(flags); +} + +inline +int Mat::channels() const +{ + return CV_MAT_CN(flags); +} + +inline +size_t Mat::step1(int i) const +{ + return step.p[i] / elemSize1(); +} + +inline +bool Mat::empty() const +{ + return data == 0 || total() == 0 || dims == 0; +} + +inline +size_t Mat::total() const +{ + if( dims <= 2 ) + return (size_t)rows * cols; + size_t p = 1; + for( int i = 0; i < dims; i++ ) + p *= size[i]; + return p; +} + +inline +size_t Mat::total(int startDim, int endDim) const +{ + CV_Assert( 0 <= startDim && startDim <= endDim); + size_t p = 1; + int endDim_ = endDim <= dims ? endDim : dims; + for( int i = startDim; i < endDim_; i++ ) + p *= size[i]; + return p; +} + +inline +uchar* Mat::ptr(int y) +{ + CV_DbgAssert( y == 0 || (data && dims >= 1 && (unsigned)y < (unsigned)size.p[0]) ); + return data + step.p[0] * y; +} + +inline +const uchar* Mat::ptr(int y) const +{ + CV_DbgAssert( y == 0 || (data && dims >= 1 && (unsigned)y < (unsigned)size.p[0]) ); + return data + step.p[0] * y; +} + +template inline +_Tp* Mat::ptr(int y) +{ + CV_DbgAssert( y == 0 || (data && dims >= 1 && (unsigned)y < (unsigned)size.p[0]) ); + return (_Tp*)(data + step.p[0] * y); +} + +template inline +const _Tp* Mat::ptr(int y) const +{ + CV_DbgAssert( y == 0 || (data && dims >= 1 && (unsigned)y < (unsigned)size.p[0]) ); + return (const _Tp*)(data + step.p[0] * y); +} + +inline +uchar* Mat::ptr(int i0, int i1) +{ + CV_DbgAssert(dims >= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); + return data + i0 * step.p[0] + i1 * step.p[1]; +} + +inline +const uchar* Mat::ptr(int i0, int i1) const +{ + CV_DbgAssert(dims >= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); + return data + i0 * step.p[0] + i1 * step.p[1]; +} + +template inline +_Tp* Mat::ptr(int i0, int i1) +{ + CV_DbgAssert(dims >= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); + return (_Tp*)(data + i0 * step.p[0] + i1 * step.p[1]); +} + +template inline +const _Tp* Mat::ptr(int i0, int i1) const +{ + CV_DbgAssert(dims >= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); + return (const _Tp*)(data + i0 * step.p[0] + i1 * step.p[1]); +} + +inline +uchar* Mat::ptr(int i0, int i1, int i2) +{ + CV_DbgAssert(dims >= 3); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); + CV_DbgAssert((unsigned)i2 < (unsigned)size.p[2]); + return data + i0 * step.p[0] + i1 * step.p[1] + i2 * step.p[2]; +} + +inline +const uchar* Mat::ptr(int i0, int i1, int i2) const +{ + CV_DbgAssert(dims >= 3); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); + CV_DbgAssert((unsigned)i2 < (unsigned)size.p[2]); + return data + i0 * step.p[0] + i1 * step.p[1] + i2 * step.p[2]; +} + +template inline +_Tp* Mat::ptr(int i0, int i1, int i2) +{ + CV_DbgAssert(dims >= 3); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); + CV_DbgAssert((unsigned)i2 < (unsigned)size.p[2]); + return (_Tp*)(data + i0 * step.p[0] + i1 * step.p[1] + i2 * step.p[2]); +} + +template inline +const _Tp* Mat::ptr(int i0, int i1, int i2) const +{ + CV_DbgAssert(dims >= 3); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); + CV_DbgAssert((unsigned)i2 < (unsigned)size.p[2]); + return (const _Tp*)(data + i0 * step.p[0] + i1 * step.p[1] + i2 * step.p[2]); +} + +inline +uchar* Mat::ptr(const int* idx) +{ + int i, d = dims; + uchar* p = data; + CV_DbgAssert( d >= 1 && p ); + for( i = 0; i < d; i++ ) + { + CV_DbgAssert( (unsigned)idx[i] < (unsigned)size.p[i] ); + p += idx[i] * step.p[i]; + } + return p; +} + +inline +const uchar* Mat::ptr(const int* idx) const +{ + int i, d = dims; + uchar* p = data; + CV_DbgAssert( d >= 1 && p ); + for( i = 0; i < d; i++ ) + { + CV_DbgAssert( (unsigned)idx[i] < (unsigned)size.p[i] ); + p += idx[i] * step.p[i]; + } + return p; +} + +template inline +_Tp* Mat::ptr(const int* idx) +{ + int i, d = dims; + uchar* p = data; + CV_DbgAssert( d >= 1 && p ); + for( i = 0; i < d; i++ ) + { + CV_DbgAssert( (unsigned)idx[i] < (unsigned)size.p[i] ); + p += idx[i] * step.p[i]; + } + return (_Tp*)p; +} + +template inline +const _Tp* Mat::ptr(const int* idx) const +{ + int i, d = dims; + uchar* p = data; + CV_DbgAssert( d >= 1 && p ); + for( i = 0; i < d; i++ ) + { + CV_DbgAssert( (unsigned)idx[i] < (unsigned)size.p[i] ); + p += idx[i] * step.p[i]; + } + return (const _Tp*)p; +} + +template inline +_Tp& Mat::at(int i0, int i1) +{ + CV_DbgAssert(dims <= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)(i1 * DataType<_Tp>::channels) < (unsigned)(size.p[1] * channels())); + CV_DbgAssert(CV_ELEM_SIZE1(traits::Depth<_Tp>::value) == elemSize1()); + return ((_Tp*)(data + step.p[0] * i0))[i1]; +} + +template inline +const _Tp& Mat::at(int i0, int i1) const +{ + CV_DbgAssert(dims <= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)(i1 * DataType<_Tp>::channels) < (unsigned)(size.p[1] * channels())); + CV_DbgAssert(CV_ELEM_SIZE1(traits::Depth<_Tp>::value) == elemSize1()); + return ((const _Tp*)(data + step.p[0] * i0))[i1]; +} + +template inline +_Tp& Mat::at(Point pt) +{ + CV_DbgAssert(dims <= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)pt.y < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)(pt.x * DataType<_Tp>::channels) < (unsigned)(size.p[1] * channels())); + CV_DbgAssert(CV_ELEM_SIZE1(traits::Depth<_Tp>::value) == elemSize1()); + return ((_Tp*)(data + step.p[0] * pt.y))[pt.x]; +} + +template inline +const _Tp& Mat::at(Point pt) const +{ + CV_DbgAssert(dims <= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)pt.y < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)(pt.x * DataType<_Tp>::channels) < (unsigned)(size.p[1] * channels())); + CV_DbgAssert(CV_ELEM_SIZE1(traits::Depth<_Tp>::value) == elemSize1()); + return ((const _Tp*)(data + step.p[0] * pt.y))[pt.x]; +} + +template inline +_Tp& Mat::at(int i0) +{ + CV_DbgAssert(dims <= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)(size.p[0] * size.p[1])); + CV_DbgAssert(elemSize() == sizeof(_Tp)); + if( isContinuous() || size.p[0] == 1 ) + return ((_Tp*)data)[i0]; + if( size.p[1] == 1 ) + return *(_Tp*)(data + step.p[0] * i0); + int i = i0 / cols, j = i0 - i * cols; + return ((_Tp*)(data + step.p[0] * i))[j]; +} + +template inline +const _Tp& Mat::at(int i0) const +{ + CV_DbgAssert(dims <= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)(size.p[0] * size.p[1])); + CV_DbgAssert(elemSize() == sizeof(_Tp)); + if( isContinuous() || size.p[0] == 1 ) + return ((const _Tp*)data)[i0]; + if( size.p[1] == 1 ) + return *(const _Tp*)(data + step.p[0] * i0); + int i = i0 / cols, j = i0 - i * cols; + return ((const _Tp*)(data + step.p[0] * i))[j]; +} + +template inline +_Tp& Mat::at(int i0, int i1, int i2) +{ + CV_DbgAssert( elemSize() == sizeof(_Tp) ); + return *(_Tp*)ptr(i0, i1, i2); +} + +template inline +const _Tp& Mat::at(int i0, int i1, int i2) const +{ + CV_DbgAssert( elemSize() == sizeof(_Tp) ); + return *(const _Tp*)ptr(i0, i1, i2); +} + +template inline +_Tp& Mat::at(const int* idx) +{ + CV_DbgAssert( elemSize() == sizeof(_Tp) ); + return *(_Tp*)ptr(idx); +} + +template inline +const _Tp& Mat::at(const int* idx) const +{ + CV_DbgAssert( elemSize() == sizeof(_Tp) ); + return *(const _Tp*)ptr(idx); +} + +template inline +_Tp& Mat::at(const Vec& idx) +{ + CV_DbgAssert( elemSize() == sizeof(_Tp) ); + return *(_Tp*)ptr(idx.val); +} + +template inline +const _Tp& Mat::at(const Vec& idx) const +{ + CV_DbgAssert( elemSize() == sizeof(_Tp) ); + return *(const _Tp*)ptr(idx.val); +} + +template inline +MatConstIterator_<_Tp> Mat::begin() const +{ + CV_DbgAssert( elemSize() == sizeof(_Tp) ); + return MatConstIterator_<_Tp>((const Mat_<_Tp>*)this); +} + +template inline +MatConstIterator_<_Tp> Mat::end() const +{ + CV_DbgAssert( elemSize() == sizeof(_Tp) ); + MatConstIterator_<_Tp> it((const Mat_<_Tp>*)this); + it += total(); + return it; +} + +template inline +MatIterator_<_Tp> Mat::begin() +{ + CV_DbgAssert( elemSize() == sizeof(_Tp) ); + return MatIterator_<_Tp>((Mat_<_Tp>*)this); +} + +template inline +MatIterator_<_Tp> Mat::end() +{ + CV_DbgAssert( elemSize() == sizeof(_Tp) ); + MatIterator_<_Tp> it((Mat_<_Tp>*)this); + it += total(); + return it; +} + +template inline +void Mat::forEach(const Functor& operation) { + this->forEach_impl<_Tp>(operation); +} + +template inline +void Mat::forEach(const Functor& operation) const { + // call as not const + (const_cast(this))->forEach<_Tp>(operation); +} + +template inline +Mat::operator std::vector<_Tp>() const +{ + std::vector<_Tp> v; + copyTo(v); + return v; +} + +#ifdef CV_CXX_STD_ARRAY +template inline +Mat::operator std::array<_Tp, _Nm>() const +{ + std::array<_Tp, _Nm> v; + copyTo(v); + return v; +} +#endif + +template inline +Mat::operator Vec<_Tp, n>() const +{ + CV_Assert( data && dims <= 2 && (rows == 1 || cols == 1) && + rows + cols - 1 == n && channels() == 1 ); + + if( isContinuous() && type() == traits::Type<_Tp>::value ) + return Vec<_Tp, n>((_Tp*)data); + Vec<_Tp, n> v; + Mat tmp(rows, cols, traits::Type<_Tp>::value, v.val); + convertTo(tmp, tmp.type()); + return v; +} + +template inline +Mat::operator Matx<_Tp, m, n>() const +{ + CV_Assert( data && dims <= 2 && rows == m && cols == n && channels() == 1 ); + + if( isContinuous() && type() == traits::Type<_Tp>::value ) + return Matx<_Tp, m, n>((_Tp*)data); + Matx<_Tp, m, n> mtx; + Mat tmp(rows, cols, traits::Type<_Tp>::value, mtx.val); + convertTo(tmp, tmp.type()); + return mtx; +} + +template inline +void Mat::push_back(const _Tp& elem) +{ + if( !data ) + { + *this = Mat(1, 1, traits::Type<_Tp>::value, (void*)&elem).clone(); + return; + } + CV_Assert(traits::Type<_Tp>::value == type() && cols == 1 + /* && dims == 2 (cols == 1 implies dims == 2) */); + const uchar* tmp = dataend + step[0]; + if( !isSubmatrix() && isContinuous() && tmp <= datalimit ) + { + *(_Tp*)(data + (size.p[0]++) * step.p[0]) = elem; + dataend = tmp; + } + else + push_back_(&elem); +} + +template inline +void Mat::push_back(const Mat_<_Tp>& m) +{ + push_back((const Mat&)m); +} + +template<> inline +void Mat::push_back(const MatExpr& expr) +{ + push_back(static_cast(expr)); +} + + +template inline +void Mat::push_back(const std::vector<_Tp>& v) +{ + push_back(Mat(v)); +} + +#ifdef CV_CXX_MOVE_SEMANTICS + +inline +Mat::Mat(Mat&& m) + : flags(m.flags), dims(m.dims), rows(m.rows), cols(m.cols), data(m.data), + datastart(m.datastart), dataend(m.dataend), datalimit(m.datalimit), allocator(m.allocator), + u(m.u), size(&rows) +{ + if (m.dims <= 2) // move new step/size info + { + step[0] = m.step[0]; + step[1] = m.step[1]; + } + else + { + CV_DbgAssert(m.step.p != m.step.buf); + step.p = m.step.p; + size.p = m.size.p; + m.step.p = m.step.buf; + m.size.p = &m.rows; + } + m.flags = MAGIC_VAL; m.dims = m.rows = m.cols = 0; + m.data = NULL; m.datastart = NULL; m.dataend = NULL; m.datalimit = NULL; + m.allocator = NULL; + m.u = NULL; +} + +inline +Mat& Mat::operator = (Mat&& m) +{ + if (this == &m) + return *this; + + release(); + flags = m.flags; dims = m.dims; rows = m.rows; cols = m.cols; data = m.data; + datastart = m.datastart; dataend = m.dataend; datalimit = m.datalimit; allocator = m.allocator; + u = m.u; + if (step.p != step.buf) // release self step/size + { + fastFree(step.p); + step.p = step.buf; + size.p = &rows; + } + if (m.dims <= 2) // move new step/size info + { + step[0] = m.step[0]; + step[1] = m.step[1]; + } + else + { + CV_DbgAssert(m.step.p != m.step.buf); + step.p = m.step.p; + size.p = m.size.p; + m.step.p = m.step.buf; + m.size.p = &m.rows; + } + m.flags = MAGIC_VAL; m.dims = m.rows = m.cols = 0; + m.data = NULL; m.datastart = NULL; m.dataend = NULL; m.datalimit = NULL; + m.allocator = NULL; + m.u = NULL; + return *this; +} + +#endif + + +///////////////////////////// MatSize //////////////////////////// + +inline +MatSize::MatSize(int* _p) + : p(_p) {} + +inline +int MatSize::dims() const +{ + return (p - 1)[0]; +} + +inline +Size MatSize::operator()() const +{ + CV_DbgAssert(dims() <= 2); + return Size(p[1], p[0]); +} + +inline +const int& MatSize::operator[](int i) const +{ + CV_DbgAssert(i < dims()); +#ifdef __OPENCV_BUILD + CV_DbgAssert(i >= 0); +#endif + return p[i]; +} + +inline +int& MatSize::operator[](int i) +{ + CV_DbgAssert(i < dims()); +#ifdef __OPENCV_BUILD + CV_DbgAssert(i >= 0); +#endif + return p[i]; +} + +inline +MatSize::operator const int*() const +{ + return p; +} + +inline +bool MatSize::operator == (const MatSize& sz) const +{ + int d = dims(); + int dsz = sz.dims(); + if( d != dsz ) + return false; + if( d == 2 ) + return p[0] == sz.p[0] && p[1] == sz.p[1]; + + for( int i = 0; i < d; i++ ) + if( p[i] != sz.p[i] ) + return false; + return true; +} + +inline +bool MatSize::operator != (const MatSize& sz) const +{ + return !(*this == sz); +} + + + +///////////////////////////// MatStep //////////////////////////// + +inline +MatStep::MatStep() +{ + p = buf; p[0] = p[1] = 0; +} + +inline +MatStep::MatStep(size_t s) +{ + p = buf; p[0] = s; p[1] = 0; +} + +inline +const size_t& MatStep::operator[](int i) const +{ + return p[i]; +} + +inline +size_t& MatStep::operator[](int i) +{ + return p[i]; +} + +inline MatStep::operator size_t() const +{ + CV_DbgAssert( p == buf ); + return buf[0]; +} + +inline MatStep& MatStep::operator = (size_t s) +{ + CV_DbgAssert( p == buf ); + buf[0] = s; + return *this; +} + + + +////////////////////////////// Mat_<_Tp> //////////////////////////// + +template inline +Mat_<_Tp>::Mat_() + : Mat() +{ + flags = (flags & ~CV_MAT_TYPE_MASK) | traits::Type<_Tp>::value; +} + +template inline +Mat_<_Tp>::Mat_(int _rows, int _cols) + : Mat(_rows, _cols, traits::Type<_Tp>::value) +{ +} + +template inline +Mat_<_Tp>::Mat_(int _rows, int _cols, const _Tp& value) + : Mat(_rows, _cols, traits::Type<_Tp>::value) +{ + *this = value; +} + +template inline +Mat_<_Tp>::Mat_(Size _sz) + : Mat(_sz.height, _sz.width, traits::Type<_Tp>::value) +{} + +template inline +Mat_<_Tp>::Mat_(Size _sz, const _Tp& value) + : Mat(_sz.height, _sz.width, traits::Type<_Tp>::value) +{ + *this = value; +} + +template inline +Mat_<_Tp>::Mat_(int _dims, const int* _sz) + : Mat(_dims, _sz, traits::Type<_Tp>::value) +{} + +template inline +Mat_<_Tp>::Mat_(int _dims, const int* _sz, const _Tp& _s) + : Mat(_dims, _sz, traits::Type<_Tp>::value, Scalar(_s)) +{} + +template inline +Mat_<_Tp>::Mat_(int _dims, const int* _sz, _Tp* _data, const size_t* _steps) + : Mat(_dims, _sz, traits::Type<_Tp>::value, _data, _steps) +{} + +template inline +Mat_<_Tp>::Mat_(const Mat_<_Tp>& m, const Range* ranges) + : Mat(m, ranges) +{} + +template inline +Mat_<_Tp>::Mat_(const Mat_<_Tp>& m, const std::vector& ranges) + : Mat(m, ranges) +{} + +template inline +Mat_<_Tp>::Mat_(const Mat& m) + : Mat() +{ + flags = (flags & ~CV_MAT_TYPE_MASK) | traits::Type<_Tp>::value; + *this = m; +} + +template inline +Mat_<_Tp>::Mat_(const Mat_& m) + : Mat(m) +{} + +template inline +Mat_<_Tp>::Mat_(int _rows, int _cols, _Tp* _data, size_t steps) + : Mat(_rows, _cols, traits::Type<_Tp>::value, _data, steps) +{} + +template inline +Mat_<_Tp>::Mat_(const Mat_& m, const Range& _rowRange, const Range& _colRange) + : Mat(m, _rowRange, _colRange) +{} + +template inline +Mat_<_Tp>::Mat_(const Mat_& m, const Rect& roi) + : Mat(m, roi) +{} + +template template inline +Mat_<_Tp>::Mat_(const Vec::channel_type, n>& vec, bool copyData) + : Mat(n / DataType<_Tp>::channels, 1, traits::Type<_Tp>::value, (void*)&vec) +{ + CV_Assert(n%DataType<_Tp>::channels == 0); + if( copyData ) + *this = clone(); +} + +template template inline +Mat_<_Tp>::Mat_(const Matx::channel_type, m, n>& M, bool copyData) + : Mat(m, n / DataType<_Tp>::channels, traits::Type<_Tp>::value, (void*)&M) +{ + CV_Assert(n % DataType<_Tp>::channels == 0); + if( copyData ) + *this = clone(); +} + +template inline +Mat_<_Tp>::Mat_(const Point_::channel_type>& pt, bool copyData) + : Mat(2 / DataType<_Tp>::channels, 1, traits::Type<_Tp>::value, (void*)&pt) +{ + CV_Assert(2 % DataType<_Tp>::channels == 0); + if( copyData ) + *this = clone(); +} + +template inline +Mat_<_Tp>::Mat_(const Point3_::channel_type>& pt, bool copyData) + : Mat(3 / DataType<_Tp>::channels, 1, traits::Type<_Tp>::value, (void*)&pt) +{ + CV_Assert(3 % DataType<_Tp>::channels == 0); + if( copyData ) + *this = clone(); +} + +template inline +Mat_<_Tp>::Mat_(const MatCommaInitializer_<_Tp>& commaInitializer) + : Mat(commaInitializer) +{} + +template inline +Mat_<_Tp>::Mat_(const std::vector<_Tp>& vec, bool copyData) + : Mat(vec, copyData) +{} + +#ifdef CV_CXX11 +template inline +Mat_<_Tp>::Mat_(std::initializer_list<_Tp> list) + : Mat(list) +{} + +template inline +Mat_<_Tp>::Mat_(const std::initializer_list sizes, std::initializer_list<_Tp> list) + : Mat(sizes, list) +{} +#endif + +#ifdef CV_CXX_STD_ARRAY +template template inline +Mat_<_Tp>::Mat_(const std::array<_Tp, _Nm>& arr, bool copyData) + : Mat(arr, copyData) +{} +#endif + +template inline +Mat_<_Tp>& Mat_<_Tp>::operator = (const Mat& m) +{ + if( traits::Type<_Tp>::value == m.type() ) + { + Mat::operator = (m); + return *this; + } + if( traits::Depth<_Tp>::value == m.depth() ) + { + return (*this = m.reshape(DataType<_Tp>::channels, m.dims, 0)); + } + CV_Assert(DataType<_Tp>::channels == m.channels() || m.empty()); + m.convertTo(*this, type()); + return *this; +} + +template inline +Mat_<_Tp>& Mat_<_Tp>::operator = (const Mat_& m) +{ + Mat::operator=(m); + return *this; +} + +template inline +Mat_<_Tp>& Mat_<_Tp>::operator = (const _Tp& s) +{ + typedef typename DataType<_Tp>::vec_type VT; + Mat::operator=(Scalar((const VT&)s)); + return *this; +} + +template inline +void Mat_<_Tp>::create(int _rows, int _cols) +{ + Mat::create(_rows, _cols, traits::Type<_Tp>::value); +} + +template inline +void Mat_<_Tp>::create(Size _sz) +{ + Mat::create(_sz, traits::Type<_Tp>::value); +} + +template inline +void Mat_<_Tp>::create(int _dims, const int* _sz) +{ + Mat::create(_dims, _sz, traits::Type<_Tp>::value); +} + +template inline +void Mat_<_Tp>::release() +{ + Mat::release(); +#ifdef _DEBUG + flags = (flags & ~CV_MAT_TYPE_MASK) | traits::Type<_Tp>::value; +#endif +} + +template inline +Mat_<_Tp> Mat_<_Tp>::cross(const Mat_& m) const +{ + return Mat_<_Tp>(Mat::cross(m)); +} + +template template inline +Mat_<_Tp>::operator Mat_() const +{ + return Mat_(*this); +} + +template inline +Mat_<_Tp> Mat_<_Tp>::row(int y) const +{ + return Mat_(*this, Range(y, y+1), Range::all()); +} + +template inline +Mat_<_Tp> Mat_<_Tp>::col(int x) const +{ + return Mat_(*this, Range::all(), Range(x, x+1)); +} + +template inline +Mat_<_Tp> Mat_<_Tp>::diag(int d) const +{ + return Mat_(Mat::diag(d)); +} + +template inline +Mat_<_Tp> Mat_<_Tp>::clone() const +{ + return Mat_(Mat::clone()); +} + +template inline +size_t Mat_<_Tp>::elemSize() const +{ + CV_DbgAssert( Mat::elemSize() == sizeof(_Tp) ); + return sizeof(_Tp); +} + +template inline +size_t Mat_<_Tp>::elemSize1() const +{ + CV_DbgAssert( Mat::elemSize1() == sizeof(_Tp) / DataType<_Tp>::channels ); + return sizeof(_Tp) / DataType<_Tp>::channels; +} + +template inline +int Mat_<_Tp>::type() const +{ + CV_DbgAssert( Mat::type() == traits::Type<_Tp>::value ); + return traits::Type<_Tp>::value; +} + +template inline +int Mat_<_Tp>::depth() const +{ + CV_DbgAssert( Mat::depth() == traits::Depth<_Tp>::value ); + return traits::Depth<_Tp>::value; +} + +template inline +int Mat_<_Tp>::channels() const +{ + CV_DbgAssert( Mat::channels() == DataType<_Tp>::channels ); + return DataType<_Tp>::channels; +} + +template inline +size_t Mat_<_Tp>::stepT(int i) const +{ + return step.p[i] / elemSize(); +} + +template inline +size_t Mat_<_Tp>::step1(int i) const +{ + return step.p[i] / elemSize1(); +} + +template inline +Mat_<_Tp>& Mat_<_Tp>::adjustROI( int dtop, int dbottom, int dleft, int dright ) +{ + return (Mat_<_Tp>&)(Mat::adjustROI(dtop, dbottom, dleft, dright)); +} + +template inline +Mat_<_Tp> Mat_<_Tp>::operator()( const Range& _rowRange, const Range& _colRange ) const +{ + return Mat_<_Tp>(*this, _rowRange, _colRange); +} + +template inline +Mat_<_Tp> Mat_<_Tp>::operator()( const Rect& roi ) const +{ + return Mat_<_Tp>(*this, roi); +} + +template inline +Mat_<_Tp> Mat_<_Tp>::operator()( const Range* ranges ) const +{ + return Mat_<_Tp>(*this, ranges); +} + +template inline +Mat_<_Tp> Mat_<_Tp>::operator()(const std::vector& ranges) const +{ + return Mat_<_Tp>(*this, ranges); +} + +template inline +_Tp* Mat_<_Tp>::operator [](int y) +{ + CV_DbgAssert( 0 <= y && y < size.p[0] ); + return (_Tp*)(data + y*step.p[0]); +} + +template inline +const _Tp* Mat_<_Tp>::operator [](int y) const +{ + CV_DbgAssert( 0 <= y && y < size.p[0] ); + return (const _Tp*)(data + y*step.p[0]); +} + +template inline +_Tp& Mat_<_Tp>::operator ()(int i0, int i1) +{ + CV_DbgAssert(dims <= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); + CV_DbgAssert(type() == traits::Type<_Tp>::value); + return ((_Tp*)(data + step.p[0] * i0))[i1]; +} + +template inline +const _Tp& Mat_<_Tp>::operator ()(int i0, int i1) const +{ + CV_DbgAssert(dims <= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); + CV_DbgAssert(type() == traits::Type<_Tp>::value); + return ((const _Tp*)(data + step.p[0] * i0))[i1]; +} + +template inline +_Tp& Mat_<_Tp>::operator ()(Point pt) +{ + CV_DbgAssert(dims <= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)pt.y < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)pt.x < (unsigned)size.p[1]); + CV_DbgAssert(type() == traits::Type<_Tp>::value); + return ((_Tp*)(data + step.p[0] * pt.y))[pt.x]; +} + +template inline +const _Tp& Mat_<_Tp>::operator ()(Point pt) const +{ + CV_DbgAssert(dims <= 2); + CV_DbgAssert(data); + CV_DbgAssert((unsigned)pt.y < (unsigned)size.p[0]); + CV_DbgAssert((unsigned)pt.x < (unsigned)size.p[1]); + CV_DbgAssert(type() == traits::Type<_Tp>::value); + return ((const _Tp*)(data + step.p[0] * pt.y))[pt.x]; +} + +template inline +_Tp& Mat_<_Tp>::operator ()(const int* idx) +{ + return Mat::at<_Tp>(idx); +} + +template inline +const _Tp& Mat_<_Tp>::operator ()(const int* idx) const +{ + return Mat::at<_Tp>(idx); +} + +template template inline +_Tp& Mat_<_Tp>::operator ()(const Vec& idx) +{ + return Mat::at<_Tp>(idx); +} + +template template inline +const _Tp& Mat_<_Tp>::operator ()(const Vec& idx) const +{ + return Mat::at<_Tp>(idx); +} + +template inline +_Tp& Mat_<_Tp>::operator ()(int i0) +{ + return this->at<_Tp>(i0); +} + +template inline +const _Tp& Mat_<_Tp>::operator ()(int i0) const +{ + return this->at<_Tp>(i0); +} + +template inline +_Tp& Mat_<_Tp>::operator ()(int i0, int i1, int i2) +{ + return this->at<_Tp>(i0, i1, i2); +} + +template inline +const _Tp& Mat_<_Tp>::operator ()(int i0, int i1, int i2) const +{ + return this->at<_Tp>(i0, i1, i2); +} + +template inline +Mat_<_Tp>::operator std::vector<_Tp>() const +{ + std::vector<_Tp> v; + copyTo(v); + return v; +} + +#ifdef CV_CXX_STD_ARRAY +template template inline +Mat_<_Tp>::operator std::array<_Tp, _Nm>() const +{ + std::array<_Tp, _Nm> a; + copyTo(a); + return a; +} +#endif + +template template inline +Mat_<_Tp>::operator Vec::channel_type, n>() const +{ + CV_Assert(n % DataType<_Tp>::channels == 0); + +#if defined _MSC_VER + const Mat* pMat = (const Mat*)this; // workaround for MSVS <= 2012 compiler bugs (but GCC 4.6 dislikes this workaround) + return pMat->operator Vec::channel_type, n>(); +#else + return this->Mat::operator Vec::channel_type, n>(); +#endif +} + +template template inline +Mat_<_Tp>::operator Matx::channel_type, m, n>() const +{ + CV_Assert(n % DataType<_Tp>::channels == 0); + +#if defined _MSC_VER + const Mat* pMat = (const Mat*)this; // workaround for MSVS <= 2012 compiler bugs (but GCC 4.6 dislikes this workaround) + Matx::channel_type, m, n> res = pMat->operator Matx::channel_type, m, n>(); + return res; +#else + Matx::channel_type, m, n> res = this->Mat::operator Matx::channel_type, m, n>(); + return res; +#endif +} + +template inline +MatConstIterator_<_Tp> Mat_<_Tp>::begin() const +{ + return Mat::begin<_Tp>(); +} + +template inline +MatConstIterator_<_Tp> Mat_<_Tp>::end() const +{ + return Mat::end<_Tp>(); +} + +template inline +MatIterator_<_Tp> Mat_<_Tp>::begin() +{ + return Mat::begin<_Tp>(); +} + +template inline +MatIterator_<_Tp> Mat_<_Tp>::end() +{ + return Mat::end<_Tp>(); +} + +template template inline +void Mat_<_Tp>::forEach(const Functor& operation) { + Mat::forEach<_Tp, Functor>(operation); +} + +template template inline +void Mat_<_Tp>::forEach(const Functor& operation) const { + Mat::forEach<_Tp, Functor>(operation); +} + +#ifdef CV_CXX_MOVE_SEMANTICS + +template inline +Mat_<_Tp>::Mat_(Mat_&& m) + : Mat(m) +{ +} + +template inline +Mat_<_Tp>& Mat_<_Tp>::operator = (Mat_&& m) +{ + Mat::operator = (std::move(m)); + return *this; +} + +template inline +Mat_<_Tp>::Mat_(Mat&& m) + : Mat() +{ + flags = (flags & ~CV_MAT_TYPE_MASK) | traits::Type<_Tp>::value; + *this = m; +} + +template inline +Mat_<_Tp>& Mat_<_Tp>::operator = (Mat&& m) +{ + if( traits::Type<_Tp>::value == m.type() ) + { + Mat::operator = ((Mat&&)m); + return *this; + } + if( traits::Depth<_Tp>::value == m.depth() ) + { + Mat::operator = ((Mat&&)m.reshape(DataType<_Tp>::channels, m.dims, 0)); + return *this; + } + CV_DbgAssert(DataType<_Tp>::channels == m.channels()); + m.convertTo(*this, type()); + return *this; +} + +template inline +Mat_<_Tp>::Mat_(MatExpr&& e) + : Mat() +{ + flags = (flags & ~CV_MAT_TYPE_MASK) | traits::Type<_Tp>::value; + *this = Mat(e); +} + +#endif + +///////////////////////////// SparseMat ///////////////////////////// + +inline +SparseMat::SparseMat() + : flags(MAGIC_VAL), hdr(0) +{} + +inline +SparseMat::SparseMat(int _dims, const int* _sizes, int _type) + : flags(MAGIC_VAL), hdr(0) +{ + create(_dims, _sizes, _type); +} + +inline +SparseMat::SparseMat(const SparseMat& m) + : flags(m.flags), hdr(m.hdr) +{ + addref(); +} + +inline +SparseMat::~SparseMat() +{ + release(); +} + +inline +SparseMat& SparseMat::operator = (const SparseMat& m) +{ + if( this != &m ) + { + if( m.hdr ) + CV_XADD(&m.hdr->refcount, 1); + release(); + flags = m.flags; + hdr = m.hdr; + } + return *this; +} + +inline +SparseMat& SparseMat::operator = (const Mat& m) +{ + return (*this = SparseMat(m)); +} + +inline +SparseMat SparseMat::clone() const +{ + SparseMat temp; + this->copyTo(temp); + return temp; +} + +inline +void SparseMat::assignTo( SparseMat& m, int _type ) const +{ + if( _type < 0 ) + m = *this; + else + convertTo(m, _type); +} + +inline +void SparseMat::addref() +{ + if( hdr ) + CV_XADD(&hdr->refcount, 1); +} + +inline +void SparseMat::release() +{ + if( hdr && CV_XADD(&hdr->refcount, -1) == 1 ) + delete hdr; + hdr = 0; +} + +inline +size_t SparseMat::elemSize() const +{ + return CV_ELEM_SIZE(flags); +} + +inline +size_t SparseMat::elemSize1() const +{ + return CV_ELEM_SIZE1(flags); +} + +inline +int SparseMat::type() const +{ + return CV_MAT_TYPE(flags); +} + +inline +int SparseMat::depth() const +{ + return CV_MAT_DEPTH(flags); +} + +inline +int SparseMat::channels() const +{ + return CV_MAT_CN(flags); +} + +inline +const int* SparseMat::size() const +{ + return hdr ? hdr->size : 0; +} + +inline +int SparseMat::size(int i) const +{ + if( hdr ) + { + CV_DbgAssert((unsigned)i < (unsigned)hdr->dims); + return hdr->size[i]; + } + return 0; +} + +inline +int SparseMat::dims() const +{ + return hdr ? hdr->dims : 0; +} + +inline +size_t SparseMat::nzcount() const +{ + return hdr ? hdr->nodeCount : 0; +} + +inline +size_t SparseMat::hash(int i0) const +{ + return (size_t)i0; +} + +inline +size_t SparseMat::hash(int i0, int i1) const +{ + return (size_t)(unsigned)i0 * HASH_SCALE + (unsigned)i1; +} + +inline +size_t SparseMat::hash(int i0, int i1, int i2) const +{ + return ((size_t)(unsigned)i0 * HASH_SCALE + (unsigned)i1) * HASH_SCALE + (unsigned)i2; +} + +inline +size_t SparseMat::hash(const int* idx) const +{ + size_t h = (unsigned)idx[0]; + if( !hdr ) + return 0; + int d = hdr->dims; + for(int i = 1; i < d; i++ ) + h = h * HASH_SCALE + (unsigned)idx[i]; + return h; +} + +template inline +_Tp& SparseMat::ref(int i0, size_t* hashval) +{ + return *(_Tp*)((SparseMat*)this)->ptr(i0, true, hashval); +} + +template inline +_Tp& SparseMat::ref(int i0, int i1, size_t* hashval) +{ + return *(_Tp*)((SparseMat*)this)->ptr(i0, i1, true, hashval); +} + +template inline +_Tp& SparseMat::ref(int i0, int i1, int i2, size_t* hashval) +{ + return *(_Tp*)((SparseMat*)this)->ptr(i0, i1, i2, true, hashval); +} + +template inline +_Tp& SparseMat::ref(const int* idx, size_t* hashval) +{ + return *(_Tp*)((SparseMat*)this)->ptr(idx, true, hashval); +} + +template inline +_Tp SparseMat::value(int i0, size_t* hashval) const +{ + const _Tp* p = (const _Tp*)((SparseMat*)this)->ptr(i0, false, hashval); + return p ? *p : _Tp(); +} + +template inline +_Tp SparseMat::value(int i0, int i1, size_t* hashval) const +{ + const _Tp* p = (const _Tp*)((SparseMat*)this)->ptr(i0, i1, false, hashval); + return p ? *p : _Tp(); +} + +template inline +_Tp SparseMat::value(int i0, int i1, int i2, size_t* hashval) const +{ + const _Tp* p = (const _Tp*)((SparseMat*)this)->ptr(i0, i1, i2, false, hashval); + return p ? *p : _Tp(); +} + +template inline +_Tp SparseMat::value(const int* idx, size_t* hashval) const +{ + const _Tp* p = (const _Tp*)((SparseMat*)this)->ptr(idx, false, hashval); + return p ? *p : _Tp(); +} + +template inline +const _Tp* SparseMat::find(int i0, size_t* hashval) const +{ + return (const _Tp*)((SparseMat*)this)->ptr(i0, false, hashval); +} + +template inline +const _Tp* SparseMat::find(int i0, int i1, size_t* hashval) const +{ + return (const _Tp*)((SparseMat*)this)->ptr(i0, i1, false, hashval); +} + +template inline +const _Tp* SparseMat::find(int i0, int i1, int i2, size_t* hashval) const +{ + return (const _Tp*)((SparseMat*)this)->ptr(i0, i1, i2, false, hashval); +} + +template inline +const _Tp* SparseMat::find(const int* idx, size_t* hashval) const +{ + return (const _Tp*)((SparseMat*)this)->ptr(idx, false, hashval); +} + +template inline +_Tp& SparseMat::value(Node* n) +{ + return *(_Tp*)((uchar*)n + hdr->valueOffset); +} + +template inline +const _Tp& SparseMat::value(const Node* n) const +{ + return *(const _Tp*)((const uchar*)n + hdr->valueOffset); +} + +inline +SparseMat::Node* SparseMat::node(size_t nidx) +{ + return (Node*)(void*)&hdr->pool[nidx]; +} + +inline +const SparseMat::Node* SparseMat::node(size_t nidx) const +{ + return (const Node*)(const void*)&hdr->pool[nidx]; +} + +inline +SparseMatIterator SparseMat::begin() +{ + return SparseMatIterator(this); +} + +inline +SparseMatConstIterator SparseMat::begin() const +{ + return SparseMatConstIterator(this); +} + +inline +SparseMatIterator SparseMat::end() +{ + SparseMatIterator it(this); + it.seekEnd(); + return it; +} + +inline +SparseMatConstIterator SparseMat::end() const +{ + SparseMatConstIterator it(this); + it.seekEnd(); + return it; +} + +template inline +SparseMatIterator_<_Tp> SparseMat::begin() +{ + return SparseMatIterator_<_Tp>(this); +} + +template inline +SparseMatConstIterator_<_Tp> SparseMat::begin() const +{ + return SparseMatConstIterator_<_Tp>(this); +} + +template inline +SparseMatIterator_<_Tp> SparseMat::end() +{ + SparseMatIterator_<_Tp> it(this); + it.seekEnd(); + return it; +} + +template inline +SparseMatConstIterator_<_Tp> SparseMat::end() const +{ + SparseMatConstIterator_<_Tp> it(this); + it.seekEnd(); + return it; +} + + + +///////////////////////////// SparseMat_ //////////////////////////// + +template inline +SparseMat_<_Tp>::SparseMat_() +{ + flags = MAGIC_VAL | traits::Type<_Tp>::value; +} + +template inline +SparseMat_<_Tp>::SparseMat_(int _dims, const int* _sizes) + : SparseMat(_dims, _sizes, traits::Type<_Tp>::value) +{} + +template inline +SparseMat_<_Tp>::SparseMat_(const SparseMat& m) +{ + if( m.type() == traits::Type<_Tp>::value ) + *this = (const SparseMat_<_Tp>&)m; + else + m.convertTo(*this, traits::Type<_Tp>::value); +} + +template inline +SparseMat_<_Tp>::SparseMat_(const SparseMat_<_Tp>& m) +{ + this->flags = m.flags; + this->hdr = m.hdr; + if( this->hdr ) + CV_XADD(&this->hdr->refcount, 1); +} + +template inline +SparseMat_<_Tp>::SparseMat_(const Mat& m) +{ + SparseMat sm(m); + *this = sm; +} + +template inline +SparseMat_<_Tp>& SparseMat_<_Tp>::operator = (const SparseMat_<_Tp>& m) +{ + if( this != &m ) + { + if( m.hdr ) CV_XADD(&m.hdr->refcount, 1); + release(); + flags = m.flags; + hdr = m.hdr; + } + return *this; +} + +template inline +SparseMat_<_Tp>& SparseMat_<_Tp>::operator = (const SparseMat& m) +{ + if( m.type() == traits::Type<_Tp>::value ) + return (*this = (const SparseMat_<_Tp>&)m); + m.convertTo(*this, traits::Type<_Tp>::value); + return *this; +} + +template inline +SparseMat_<_Tp>& SparseMat_<_Tp>::operator = (const Mat& m) +{ + return (*this = SparseMat(m)); +} + +template inline +SparseMat_<_Tp> SparseMat_<_Tp>::clone() const +{ + SparseMat_<_Tp> m; + this->copyTo(m); + return m; +} + +template inline +void SparseMat_<_Tp>::create(int _dims, const int* _sizes) +{ + SparseMat::create(_dims, _sizes, traits::Type<_Tp>::value); +} + +template inline +int SparseMat_<_Tp>::type() const +{ + return traits::Type<_Tp>::value; +} + +template inline +int SparseMat_<_Tp>::depth() const +{ + return traits::Depth<_Tp>::value; +} + +template inline +int SparseMat_<_Tp>::channels() const +{ + return DataType<_Tp>::channels; +} + +template inline +_Tp& SparseMat_<_Tp>::ref(int i0, size_t* hashval) +{ + return SparseMat::ref<_Tp>(i0, hashval); +} + +template inline +_Tp SparseMat_<_Tp>::operator()(int i0, size_t* hashval) const +{ + return SparseMat::value<_Tp>(i0, hashval); +} + +template inline +_Tp& SparseMat_<_Tp>::ref(int i0, int i1, size_t* hashval) +{ + return SparseMat::ref<_Tp>(i0, i1, hashval); +} + +template inline +_Tp SparseMat_<_Tp>::operator()(int i0, int i1, size_t* hashval) const +{ + return SparseMat::value<_Tp>(i0, i1, hashval); +} + +template inline +_Tp& SparseMat_<_Tp>::ref(int i0, int i1, int i2, size_t* hashval) +{ + return SparseMat::ref<_Tp>(i0, i1, i2, hashval); +} + +template inline +_Tp SparseMat_<_Tp>::operator()(int i0, int i1, int i2, size_t* hashval) const +{ + return SparseMat::value<_Tp>(i0, i1, i2, hashval); +} + +template inline +_Tp& SparseMat_<_Tp>::ref(const int* idx, size_t* hashval) +{ + return SparseMat::ref<_Tp>(idx, hashval); +} + +template inline +_Tp SparseMat_<_Tp>::operator()(const int* idx, size_t* hashval) const +{ + return SparseMat::value<_Tp>(idx, hashval); +} + +template inline +SparseMatIterator_<_Tp> SparseMat_<_Tp>::begin() +{ + return SparseMatIterator_<_Tp>(this); +} + +template inline +SparseMatConstIterator_<_Tp> SparseMat_<_Tp>::begin() const +{ + return SparseMatConstIterator_<_Tp>(this); +} + +template inline +SparseMatIterator_<_Tp> SparseMat_<_Tp>::end() +{ + SparseMatIterator_<_Tp> it(this); + it.seekEnd(); + return it; +} + +template inline +SparseMatConstIterator_<_Tp> SparseMat_<_Tp>::end() const +{ + SparseMatConstIterator_<_Tp> it(this); + it.seekEnd(); + return it; +} + + + +////////////////////////// MatConstIterator ///////////////////////// + +inline +MatConstIterator::MatConstIterator() + : m(0), elemSize(0), ptr(0), sliceStart(0), sliceEnd(0) +{} + +inline +MatConstIterator::MatConstIterator(const Mat* _m) + : m(_m), elemSize(_m->elemSize()), ptr(0), sliceStart(0), sliceEnd(0) +{ + if( m && m->isContinuous() ) + { + sliceStart = m->ptr(); + sliceEnd = sliceStart + m->total()*elemSize; + } + seek((const int*)0); +} + +inline +MatConstIterator::MatConstIterator(const Mat* _m, int _row, int _col) + : m(_m), elemSize(_m->elemSize()), ptr(0), sliceStart(0), sliceEnd(0) +{ + CV_Assert(m && m->dims <= 2); + if( m->isContinuous() ) + { + sliceStart = m->ptr(); + sliceEnd = sliceStart + m->total()*elemSize; + } + int idx[] = {_row, _col}; + seek(idx); +} + +inline +MatConstIterator::MatConstIterator(const Mat* _m, Point _pt) + : m(_m), elemSize(_m->elemSize()), ptr(0), sliceStart(0), sliceEnd(0) +{ + CV_Assert(m && m->dims <= 2); + if( m->isContinuous() ) + { + sliceStart = m->ptr(); + sliceEnd = sliceStart + m->total()*elemSize; + } + int idx[] = {_pt.y, _pt.x}; + seek(idx); +} + +inline +MatConstIterator::MatConstIterator(const MatConstIterator& it) + : m(it.m), elemSize(it.elemSize), ptr(it.ptr), sliceStart(it.sliceStart), sliceEnd(it.sliceEnd) +{} + +inline +MatConstIterator& MatConstIterator::operator = (const MatConstIterator& it ) +{ + m = it.m; elemSize = it.elemSize; ptr = it.ptr; + sliceStart = it.sliceStart; sliceEnd = it.sliceEnd; + return *this; +} + +inline +const uchar* MatConstIterator::operator *() const +{ + return ptr; +} + +inline MatConstIterator& MatConstIterator::operator += (ptrdiff_t ofs) +{ + if( !m || ofs == 0 ) + return *this; + ptrdiff_t ofsb = ofs*elemSize; + ptr += ofsb; + if( ptr < sliceStart || sliceEnd <= ptr ) + { + ptr -= ofsb; + seek(ofs, true); + } + return *this; +} + +inline +MatConstIterator& MatConstIterator::operator -= (ptrdiff_t ofs) +{ + return (*this += -ofs); +} + +inline +MatConstIterator& MatConstIterator::operator --() +{ + if( m && (ptr -= elemSize) < sliceStart ) + { + ptr += elemSize; + seek(-1, true); + } + return *this; +} + +inline +MatConstIterator MatConstIterator::operator --(int) +{ + MatConstIterator b = *this; + *this += -1; + return b; +} + +inline +MatConstIterator& MatConstIterator::operator ++() +{ + if( m && (ptr += elemSize) >= sliceEnd ) + { + ptr -= elemSize; + seek(1, true); + } + return *this; +} + +inline MatConstIterator MatConstIterator::operator ++(int) +{ + MatConstIterator b = *this; + *this += 1; + return b; +} + + +static inline +bool operator == (const MatConstIterator& a, const MatConstIterator& b) +{ + return a.m == b.m && a.ptr == b.ptr; +} + +static inline +bool operator != (const MatConstIterator& a, const MatConstIterator& b) +{ + return !(a == b); +} + +static inline +bool operator < (const MatConstIterator& a, const MatConstIterator& b) +{ + return a.ptr < b.ptr; +} + +static inline +bool operator > (const MatConstIterator& a, const MatConstIterator& b) +{ + return a.ptr > b.ptr; +} + +static inline +bool operator <= (const MatConstIterator& a, const MatConstIterator& b) +{ + return a.ptr <= b.ptr; +} + +static inline +bool operator >= (const MatConstIterator& a, const MatConstIterator& b) +{ + return a.ptr >= b.ptr; +} + +static inline +ptrdiff_t operator - (const MatConstIterator& b, const MatConstIterator& a) +{ + if( a.m != b.m ) + return ((size_t)(-1) >> 1); + if( a.sliceEnd == b.sliceEnd ) + return (b.ptr - a.ptr)/static_cast(b.elemSize); + + return b.lpos() - a.lpos(); +} + +static inline +MatConstIterator operator + (const MatConstIterator& a, ptrdiff_t ofs) +{ + MatConstIterator b = a; + return b += ofs; +} + +static inline +MatConstIterator operator + (ptrdiff_t ofs, const MatConstIterator& a) +{ + MatConstIterator b = a; + return b += ofs; +} + +static inline +MatConstIterator operator - (const MatConstIterator& a, ptrdiff_t ofs) +{ + MatConstIterator b = a; + return b += -ofs; +} + + +inline +const uchar* MatConstIterator::operator [](ptrdiff_t i) const +{ + return *(*this + i); +} + + + +///////////////////////// MatConstIterator_ ///////////////////////// + +template inline +MatConstIterator_<_Tp>::MatConstIterator_() +{} + +template inline +MatConstIterator_<_Tp>::MatConstIterator_(const Mat_<_Tp>* _m) + : MatConstIterator(_m) +{} + +template inline +MatConstIterator_<_Tp>::MatConstIterator_(const Mat_<_Tp>* _m, int _row, int _col) + : MatConstIterator(_m, _row, _col) +{} + +template inline +MatConstIterator_<_Tp>::MatConstIterator_(const Mat_<_Tp>* _m, Point _pt) + : MatConstIterator(_m, _pt) +{} + +template inline +MatConstIterator_<_Tp>::MatConstIterator_(const MatConstIterator_& it) + : MatConstIterator(it) +{} + +template inline +MatConstIterator_<_Tp>& MatConstIterator_<_Tp>::operator = (const MatConstIterator_& it ) +{ + MatConstIterator::operator = (it); + return *this; +} + +template inline +const _Tp& MatConstIterator_<_Tp>::operator *() const +{ + return *(_Tp*)(this->ptr); +} + +template inline +MatConstIterator_<_Tp>& MatConstIterator_<_Tp>::operator += (ptrdiff_t ofs) +{ + MatConstIterator::operator += (ofs); + return *this; +} + +template inline +MatConstIterator_<_Tp>& MatConstIterator_<_Tp>::operator -= (ptrdiff_t ofs) +{ + return (*this += -ofs); +} + +template inline +MatConstIterator_<_Tp>& MatConstIterator_<_Tp>::operator --() +{ + MatConstIterator::operator --(); + return *this; +} + +template inline +MatConstIterator_<_Tp> MatConstIterator_<_Tp>::operator --(int) +{ + MatConstIterator_ b = *this; + MatConstIterator::operator --(); + return b; +} + +template inline +MatConstIterator_<_Tp>& MatConstIterator_<_Tp>::operator ++() +{ + MatConstIterator::operator ++(); + return *this; +} + +template inline +MatConstIterator_<_Tp> MatConstIterator_<_Tp>::operator ++(int) +{ + MatConstIterator_ b = *this; + MatConstIterator::operator ++(); + return b; +} + + +template inline +Point MatConstIterator_<_Tp>::pos() const +{ + if( !m ) + return Point(); + CV_DbgAssert( m->dims <= 2 ); + if( m->isContinuous() ) + { + ptrdiff_t ofs = (const _Tp*)ptr - (const _Tp*)m->data; + int y = (int)(ofs / m->cols); + int x = (int)(ofs - (ptrdiff_t)y * m->cols); + return Point(x, y); + } + else + { + ptrdiff_t ofs = (uchar*)ptr - m->data; + int y = (int)(ofs / m->step); + int x = (int)((ofs - y * m->step)/sizeof(_Tp)); + return Point(x, y); + } +} + + +template static inline +bool operator == (const MatConstIterator_<_Tp>& a, const MatConstIterator_<_Tp>& b) +{ + return a.m == b.m && a.ptr == b.ptr; +} + +template static inline +bool operator != (const MatConstIterator_<_Tp>& a, const MatConstIterator_<_Tp>& b) +{ + return a.m != b.m || a.ptr != b.ptr; +} + +template static inline +MatConstIterator_<_Tp> operator + (const MatConstIterator_<_Tp>& a, ptrdiff_t ofs) +{ + MatConstIterator t = (const MatConstIterator&)a + ofs; + return (MatConstIterator_<_Tp>&)t; +} + +template static inline +MatConstIterator_<_Tp> operator + (ptrdiff_t ofs, const MatConstIterator_<_Tp>& a) +{ + MatConstIterator t = (const MatConstIterator&)a + ofs; + return (MatConstIterator_<_Tp>&)t; +} + +template static inline +MatConstIterator_<_Tp> operator - (const MatConstIterator_<_Tp>& a, ptrdiff_t ofs) +{ + MatConstIterator t = (const MatConstIterator&)a - ofs; + return (MatConstIterator_<_Tp>&)t; +} + +template inline +const _Tp& MatConstIterator_<_Tp>::operator [](ptrdiff_t i) const +{ + return *(_Tp*)MatConstIterator::operator [](i); +} + + + +//////////////////////////// MatIterator_ /////////////////////////// + +template inline +MatIterator_<_Tp>::MatIterator_() + : MatConstIterator_<_Tp>() +{} + +template inline +MatIterator_<_Tp>::MatIterator_(Mat_<_Tp>* _m) + : MatConstIterator_<_Tp>(_m) +{} + +template inline +MatIterator_<_Tp>::MatIterator_(Mat_<_Tp>* _m, int _row, int _col) + : MatConstIterator_<_Tp>(_m, _row, _col) +{} + +template inline +MatIterator_<_Tp>::MatIterator_(Mat_<_Tp>* _m, Point _pt) + : MatConstIterator_<_Tp>(_m, _pt) +{} + +template inline +MatIterator_<_Tp>::MatIterator_(Mat_<_Tp>* _m, const int* _idx) + : MatConstIterator_<_Tp>(_m, _idx) +{} + +template inline +MatIterator_<_Tp>::MatIterator_(const MatIterator_& it) + : MatConstIterator_<_Tp>(it) +{} + +template inline +MatIterator_<_Tp>& MatIterator_<_Tp>::operator = (const MatIterator_<_Tp>& it ) +{ + MatConstIterator::operator = (it); + return *this; +} + +template inline +_Tp& MatIterator_<_Tp>::operator *() const +{ + return *(_Tp*)(this->ptr); +} + +template inline +MatIterator_<_Tp>& MatIterator_<_Tp>::operator += (ptrdiff_t ofs) +{ + MatConstIterator::operator += (ofs); + return *this; +} + +template inline +MatIterator_<_Tp>& MatIterator_<_Tp>::operator -= (ptrdiff_t ofs) +{ + MatConstIterator::operator += (-ofs); + return *this; +} + +template inline +MatIterator_<_Tp>& MatIterator_<_Tp>::operator --() +{ + MatConstIterator::operator --(); + return *this; +} + +template inline +MatIterator_<_Tp> MatIterator_<_Tp>::operator --(int) +{ + MatIterator_ b = *this; + MatConstIterator::operator --(); + return b; +} + +template inline +MatIterator_<_Tp>& MatIterator_<_Tp>::operator ++() +{ + MatConstIterator::operator ++(); + return *this; +} + +template inline +MatIterator_<_Tp> MatIterator_<_Tp>::operator ++(int) +{ + MatIterator_ b = *this; + MatConstIterator::operator ++(); + return b; +} + +template inline +_Tp& MatIterator_<_Tp>::operator [](ptrdiff_t i) const +{ + return *(*this + i); +} + + +template static inline +bool operator == (const MatIterator_<_Tp>& a, const MatIterator_<_Tp>& b) +{ + return a.m == b.m && a.ptr == b.ptr; +} + +template static inline +bool operator != (const MatIterator_<_Tp>& a, const MatIterator_<_Tp>& b) +{ + return a.m != b.m || a.ptr != b.ptr; +} + +template static inline +MatIterator_<_Tp> operator + (const MatIterator_<_Tp>& a, ptrdiff_t ofs) +{ + MatConstIterator t = (const MatConstIterator&)a + ofs; + return (MatIterator_<_Tp>&)t; +} + +template static inline +MatIterator_<_Tp> operator + (ptrdiff_t ofs, const MatIterator_<_Tp>& a) +{ + MatConstIterator t = (const MatConstIterator&)a + ofs; + return (MatIterator_<_Tp>&)t; +} + +template static inline +MatIterator_<_Tp> operator - (const MatIterator_<_Tp>& a, ptrdiff_t ofs) +{ + MatConstIterator t = (const MatConstIterator&)a - ofs; + return (MatIterator_<_Tp>&)t; +} + + + +/////////////////////// SparseMatConstIterator ////////////////////// + +inline +SparseMatConstIterator::SparseMatConstIterator() + : m(0), hashidx(0), ptr(0) +{} + +inline +SparseMatConstIterator::SparseMatConstIterator(const SparseMatConstIterator& it) + : m(it.m), hashidx(it.hashidx), ptr(it.ptr) +{} + +inline SparseMatConstIterator& SparseMatConstIterator::operator = (const SparseMatConstIterator& it) +{ + if( this != &it ) + { + m = it.m; + hashidx = it.hashidx; + ptr = it.ptr; + } + return *this; +} + +template inline +const _Tp& SparseMatConstIterator::value() const +{ + return *(const _Tp*)ptr; +} + +inline +const SparseMat::Node* SparseMatConstIterator::node() const +{ + return (ptr && m && m->hdr) ? (const SparseMat::Node*)(const void*)(ptr - m->hdr->valueOffset) : 0; +} + +inline +SparseMatConstIterator SparseMatConstIterator::operator ++(int) +{ + SparseMatConstIterator it = *this; + ++*this; + return it; +} + +inline +void SparseMatConstIterator::seekEnd() +{ + if( m && m->hdr ) + { + hashidx = m->hdr->hashtab.size(); + ptr = 0; + } +} + + +static inline +bool operator == (const SparseMatConstIterator& it1, const SparseMatConstIterator& it2) +{ + return it1.m == it2.m && it1.ptr == it2.ptr; +} + +static inline +bool operator != (const SparseMatConstIterator& it1, const SparseMatConstIterator& it2) +{ + return !(it1 == it2); +} + + + +///////////////////////// SparseMatIterator ///////////////////////// + +inline +SparseMatIterator::SparseMatIterator() +{} + +inline +SparseMatIterator::SparseMatIterator(SparseMat* _m) + : SparseMatConstIterator(_m) +{} + +inline +SparseMatIterator::SparseMatIterator(const SparseMatIterator& it) + : SparseMatConstIterator(it) +{} + +inline +SparseMatIterator& SparseMatIterator::operator = (const SparseMatIterator& it) +{ + (SparseMatConstIterator&)*this = it; + return *this; +} + +template inline +_Tp& SparseMatIterator::value() const +{ + return *(_Tp*)ptr; +} + +inline +SparseMat::Node* SparseMatIterator::node() const +{ + return (SparseMat::Node*)SparseMatConstIterator::node(); +} + +inline +SparseMatIterator& SparseMatIterator::operator ++() +{ + SparseMatConstIterator::operator ++(); + return *this; +} + +inline +SparseMatIterator SparseMatIterator::operator ++(int) +{ + SparseMatIterator it = *this; + ++*this; + return it; +} + + + +////////////////////// SparseMatConstIterator_ ////////////////////// + +template inline +SparseMatConstIterator_<_Tp>::SparseMatConstIterator_() +{} + +template inline +SparseMatConstIterator_<_Tp>::SparseMatConstIterator_(const SparseMat_<_Tp>* _m) + : SparseMatConstIterator(_m) +{} + +template inline +SparseMatConstIterator_<_Tp>::SparseMatConstIterator_(const SparseMat* _m) + : SparseMatConstIterator(_m) +{ + CV_Assert( _m->type() == traits::Type<_Tp>::value ); +} + +template inline +SparseMatConstIterator_<_Tp>::SparseMatConstIterator_(const SparseMatConstIterator_<_Tp>& it) + : SparseMatConstIterator(it) +{} + +template inline +SparseMatConstIterator_<_Tp>& SparseMatConstIterator_<_Tp>::operator = (const SparseMatConstIterator_<_Tp>& it) +{ + return reinterpret_cast&> + (*reinterpret_cast(this) = + reinterpret_cast(it)); +} + +template inline +const _Tp& SparseMatConstIterator_<_Tp>::operator *() const +{ + return *(const _Tp*)this->ptr; +} + +template inline +SparseMatConstIterator_<_Tp>& SparseMatConstIterator_<_Tp>::operator ++() +{ + SparseMatConstIterator::operator ++(); + return *this; +} + +template inline +SparseMatConstIterator_<_Tp> SparseMatConstIterator_<_Tp>::operator ++(int) +{ + SparseMatConstIterator_<_Tp> it = *this; + SparseMatConstIterator::operator ++(); + return it; +} + + + +///////////////////////// SparseMatIterator_ //////////////////////// + +template inline +SparseMatIterator_<_Tp>::SparseMatIterator_() +{} + +template inline +SparseMatIterator_<_Tp>::SparseMatIterator_(SparseMat_<_Tp>* _m) + : SparseMatConstIterator_<_Tp>(_m) +{} + +template inline +SparseMatIterator_<_Tp>::SparseMatIterator_(SparseMat* _m) + : SparseMatConstIterator_<_Tp>(_m) +{} + +template inline +SparseMatIterator_<_Tp>::SparseMatIterator_(const SparseMatIterator_<_Tp>& it) + : SparseMatConstIterator_<_Tp>(it) +{} + +template inline +SparseMatIterator_<_Tp>& SparseMatIterator_<_Tp>::operator = (const SparseMatIterator_<_Tp>& it) +{ + return reinterpret_cast&> + (*reinterpret_cast(this) = + reinterpret_cast(it)); +} + +template inline +_Tp& SparseMatIterator_<_Tp>::operator *() const +{ + return *(_Tp*)this->ptr; +} + +template inline +SparseMatIterator_<_Tp>& SparseMatIterator_<_Tp>::operator ++() +{ + SparseMatConstIterator::operator ++(); + return *this; +} + +template inline +SparseMatIterator_<_Tp> SparseMatIterator_<_Tp>::operator ++(int) +{ + SparseMatIterator_<_Tp> it = *this; + SparseMatConstIterator::operator ++(); + return it; +} + + + +//////////////////////// MatCommaInitializer_ /////////////////////// + +template inline +MatCommaInitializer_<_Tp>::MatCommaInitializer_(Mat_<_Tp>* _m) + : it(_m) +{} + +template template inline +MatCommaInitializer_<_Tp>& MatCommaInitializer_<_Tp>::operator , (T2 v) +{ + CV_DbgAssert( this->it < ((const Mat_<_Tp>*)this->it.m)->end() ); + *this->it = _Tp(v); + ++this->it; + return *this; +} + +template inline +MatCommaInitializer_<_Tp>::operator Mat_<_Tp>() const +{ + CV_DbgAssert( this->it == ((const Mat_<_Tp>*)this->it.m)->end() ); + return Mat_<_Tp>(*this->it.m); +} + + +template static inline +MatCommaInitializer_<_Tp> operator << (const Mat_<_Tp>& m, T2 val) +{ + MatCommaInitializer_<_Tp> commaInitializer((Mat_<_Tp>*)&m); + return (commaInitializer, val); +} + + + +///////////////////////// Matrix Expressions //////////////////////// + +inline +Mat& Mat::operator = (const MatExpr& e) +{ + e.op->assign(e, *this); + return *this; +} + +template inline +Mat_<_Tp>::Mat_(const MatExpr& e) +{ + e.op->assign(e, *this, traits::Type<_Tp>::value); +} + +template inline +Mat_<_Tp>& Mat_<_Tp>::operator = (const MatExpr& e) +{ + e.op->assign(e, *this, traits::Type<_Tp>::value); + return *this; +} + +template inline +MatExpr Mat_<_Tp>::zeros(int rows, int cols) +{ + return Mat::zeros(rows, cols, traits::Type<_Tp>::value); +} + +template inline +MatExpr Mat_<_Tp>::zeros(Size sz) +{ + return Mat::zeros(sz, traits::Type<_Tp>::value); +} + +template inline +MatExpr Mat_<_Tp>::ones(int rows, int cols) +{ + return Mat::ones(rows, cols, traits::Type<_Tp>::value); +} + +template inline +MatExpr Mat_<_Tp>::ones(Size sz) +{ + return Mat::ones(sz, traits::Type<_Tp>::value); +} + +template inline +MatExpr Mat_<_Tp>::eye(int rows, int cols) +{ + return Mat::eye(rows, cols, traits::Type<_Tp>::value); +} + +template inline +MatExpr Mat_<_Tp>::eye(Size sz) +{ + return Mat::eye(sz, traits::Type<_Tp>::value); +} + +inline +MatExpr::MatExpr() + : op(0), flags(0), a(Mat()), b(Mat()), c(Mat()), alpha(0), beta(0), s() +{} + +inline +MatExpr::MatExpr(const MatOp* _op, int _flags, const Mat& _a, const Mat& _b, + const Mat& _c, double _alpha, double _beta, const Scalar& _s) + : op(_op), flags(_flags), a(_a), b(_b), c(_c), alpha(_alpha), beta(_beta), s(_s) +{} + +inline +MatExpr::operator Mat() const +{ + Mat m; + op->assign(*this, m); + return m; +} + +template inline +MatExpr::operator Mat_<_Tp>() const +{ + Mat_<_Tp> m; + op->assign(*this, m, traits::Type<_Tp>::value); + return m; +} + + +template static inline +MatExpr min(const Mat_<_Tp>& a, const Mat_<_Tp>& b) +{ + return cv::min((const Mat&)a, (const Mat&)b); +} + +template static inline +MatExpr min(const Mat_<_Tp>& a, double s) +{ + return cv::min((const Mat&)a, s); +} + +template static inline +MatExpr min(double s, const Mat_<_Tp>& a) +{ + return cv::min((const Mat&)a, s); +} + +template static inline +MatExpr max(const Mat_<_Tp>& a, const Mat_<_Tp>& b) +{ + return cv::max((const Mat&)a, (const Mat&)b); +} + +template static inline +MatExpr max(const Mat_<_Tp>& a, double s) +{ + return cv::max((const Mat&)a, s); +} + +template static inline +MatExpr max(double s, const Mat_<_Tp>& a) +{ + return cv::max((const Mat&)a, s); +} + +template static inline +MatExpr abs(const Mat_<_Tp>& m) +{ + return cv::abs((const Mat&)m); +} + + +static inline +Mat& operator += (Mat& a, const MatExpr& b) +{ + b.op->augAssignAdd(b, a); + return a; +} + +static inline +const Mat& operator += (const Mat& a, const MatExpr& b) +{ + b.op->augAssignAdd(b, (Mat&)a); + return a; +} + +template static inline +Mat_<_Tp>& operator += (Mat_<_Tp>& a, const MatExpr& b) +{ + b.op->augAssignAdd(b, a); + return a; +} + +template static inline +const Mat_<_Tp>& operator += (const Mat_<_Tp>& a, const MatExpr& b) +{ + b.op->augAssignAdd(b, (Mat&)a); + return a; +} + +static inline +Mat& operator -= (Mat& a, const MatExpr& b) +{ + b.op->augAssignSubtract(b, a); + return a; +} + +static inline +const Mat& operator -= (const Mat& a, const MatExpr& b) +{ + b.op->augAssignSubtract(b, (Mat&)a); + return a; +} + +template static inline +Mat_<_Tp>& operator -= (Mat_<_Tp>& a, const MatExpr& b) +{ + b.op->augAssignSubtract(b, a); + return a; +} + +template static inline +const Mat_<_Tp>& operator -= (const Mat_<_Tp>& a, const MatExpr& b) +{ + b.op->augAssignSubtract(b, (Mat&)a); + return a; +} + +static inline +Mat& operator *= (Mat& a, const MatExpr& b) +{ + b.op->augAssignMultiply(b, a); + return a; +} + +static inline +const Mat& operator *= (const Mat& a, const MatExpr& b) +{ + b.op->augAssignMultiply(b, (Mat&)a); + return a; +} + +template static inline +Mat_<_Tp>& operator *= (Mat_<_Tp>& a, const MatExpr& b) +{ + b.op->augAssignMultiply(b, a); + return a; +} + +template static inline +const Mat_<_Tp>& operator *= (const Mat_<_Tp>& a, const MatExpr& b) +{ + b.op->augAssignMultiply(b, (Mat&)a); + return a; +} + +static inline +Mat& operator /= (Mat& a, const MatExpr& b) +{ + b.op->augAssignDivide(b, a); + return a; +} + +static inline +const Mat& operator /= (const Mat& a, const MatExpr& b) +{ + b.op->augAssignDivide(b, (Mat&)a); + return a; +} + +template static inline +Mat_<_Tp>& operator /= (Mat_<_Tp>& a, const MatExpr& b) +{ + b.op->augAssignDivide(b, a); + return a; +} + +template static inline +const Mat_<_Tp>& operator /= (const Mat_<_Tp>& a, const MatExpr& b) +{ + b.op->augAssignDivide(b, (Mat&)a); + return a; +} + + +//////////////////////////////// UMat //////////////////////////////// + +inline +UMat::UMat(UMatUsageFlags _usageFlags) +: flags(MAGIC_VAL), dims(0), rows(0), cols(0), allocator(0), usageFlags(_usageFlags), u(0), offset(0), size(&rows) +{} + +inline +UMat::UMat(int _rows, int _cols, int _type, UMatUsageFlags _usageFlags) +: flags(MAGIC_VAL), dims(0), rows(0), cols(0), allocator(0), usageFlags(_usageFlags), u(0), offset(0), size(&rows) +{ + create(_rows, _cols, _type); +} + +inline +UMat::UMat(int _rows, int _cols, int _type, const Scalar& _s, UMatUsageFlags _usageFlags) +: flags(MAGIC_VAL), dims(0), rows(0), cols(0), allocator(0), usageFlags(_usageFlags), u(0), offset(0), size(&rows) +{ + create(_rows, _cols, _type); + *this = _s; +} + +inline +UMat::UMat(Size _sz, int _type, UMatUsageFlags _usageFlags) +: flags(MAGIC_VAL), dims(0), rows(0), cols(0), allocator(0), usageFlags(_usageFlags), u(0), offset(0), size(&rows) +{ + create( _sz.height, _sz.width, _type ); +} + +inline +UMat::UMat(Size _sz, int _type, const Scalar& _s, UMatUsageFlags _usageFlags) +: flags(MAGIC_VAL), dims(0), rows(0), cols(0), allocator(0), usageFlags(_usageFlags), u(0), offset(0), size(&rows) +{ + create(_sz.height, _sz.width, _type); + *this = _s; +} + +inline +UMat::UMat(int _dims, const int* _sz, int _type, UMatUsageFlags _usageFlags) +: flags(MAGIC_VAL), dims(0), rows(0), cols(0), allocator(0), usageFlags(_usageFlags), u(0), offset(0), size(&rows) +{ + create(_dims, _sz, _type); +} + +inline +UMat::UMat(int _dims, const int* _sz, int _type, const Scalar& _s, UMatUsageFlags _usageFlags) +: flags(MAGIC_VAL), dims(0), rows(0), cols(0), allocator(0), usageFlags(_usageFlags), u(0), offset(0), size(&rows) +{ + create(_dims, _sz, _type); + *this = _s; +} + +inline +UMat::UMat(const UMat& m) +: flags(m.flags), dims(m.dims), rows(m.rows), cols(m.cols), allocator(m.allocator), + usageFlags(m.usageFlags), u(m.u), offset(m.offset), size(&rows) +{ + addref(); + if( m.dims <= 2 ) + { + step[0] = m.step[0]; step[1] = m.step[1]; + } + else + { + dims = 0; + copySize(m); + } +} + + +template inline +UMat::UMat(const std::vector<_Tp>& vec, bool copyData) +: flags(MAGIC_VAL | traits::Type<_Tp>::value | CV_MAT_CONT_FLAG), dims(2), rows((int)vec.size()), +cols(1), allocator(0), usageFlags(USAGE_DEFAULT), u(0), offset(0), size(&rows) +{ + if(vec.empty()) + return; + if( !copyData ) + { + // !!!TODO!!! + CV_Error(Error::StsNotImplemented, ""); + } + else + Mat((int)vec.size(), 1, traits::Type<_Tp>::value, (uchar*)&vec[0]).copyTo(*this); +} + +inline +UMat& UMat::operator = (const UMat& m) +{ + if( this != &m ) + { + const_cast(m).addref(); + release(); + flags = m.flags; + if( dims <= 2 && m.dims <= 2 ) + { + dims = m.dims; + rows = m.rows; + cols = m.cols; + step[0] = m.step[0]; + step[1] = m.step[1]; + } + else + copySize(m); + allocator = m.allocator; + if (usageFlags == USAGE_DEFAULT) + usageFlags = m.usageFlags; + u = m.u; + offset = m.offset; + } + return *this; +} + +inline +UMat UMat::row(int y) const +{ + return UMat(*this, Range(y, y + 1), Range::all()); +} + +inline +UMat UMat::col(int x) const +{ + return UMat(*this, Range::all(), Range(x, x + 1)); +} + +inline +UMat UMat::rowRange(int startrow, int endrow) const +{ + return UMat(*this, Range(startrow, endrow), Range::all()); +} + +inline +UMat UMat::rowRange(const Range& r) const +{ + return UMat(*this, r, Range::all()); +} + +inline +UMat UMat::colRange(int startcol, int endcol) const +{ + return UMat(*this, Range::all(), Range(startcol, endcol)); +} + +inline +UMat UMat::colRange(const Range& r) const +{ + return UMat(*this, Range::all(), r); +} + +inline +UMat UMat::clone() const +{ + UMat m; + copyTo(m); + return m; +} + +inline +void UMat::assignTo( UMat& m, int _type ) const +{ + if( _type < 0 ) + m = *this; + else + convertTo(m, _type); +} + +inline +void UMat::create(int _rows, int _cols, int _type, UMatUsageFlags _usageFlags) +{ + _type &= TYPE_MASK; + if( dims <= 2 && rows == _rows && cols == _cols && type() == _type && u ) + return; + int sz[] = {_rows, _cols}; + create(2, sz, _type, _usageFlags); +} + +inline +void UMat::create(Size _sz, int _type, UMatUsageFlags _usageFlags) +{ + create(_sz.height, _sz.width, _type, _usageFlags); +} + +inline +void UMat::addref() +{ + if( u ) + CV_XADD(&(u->urefcount), 1); +} + +inline void UMat::release() +{ + if( u && CV_XADD(&(u->urefcount), -1) == 1 ) + deallocate(); + for(int i = 0; i < dims; i++) + size.p[i] = 0; + u = 0; +} + +inline +UMat UMat::operator()( Range _rowRange, Range _colRange ) const +{ + return UMat(*this, _rowRange, _colRange); +} + +inline +UMat UMat::operator()( const Rect& roi ) const +{ + return UMat(*this, roi); +} + +inline +UMat UMat::operator()(const Range* ranges) const +{ + return UMat(*this, ranges); +} + +inline +UMat UMat::operator()(const std::vector& ranges) const +{ + return UMat(*this, ranges); +} + +inline +bool UMat::isContinuous() const +{ + return (flags & CONTINUOUS_FLAG) != 0; +} + +inline +bool UMat::isSubmatrix() const +{ + return (flags & SUBMATRIX_FLAG) != 0; +} + +inline +size_t UMat::elemSize() const +{ + size_t res = dims > 0 ? step.p[dims - 1] : 0; + CV_DbgAssert(res != 0); + return res; +} + +inline +size_t UMat::elemSize1() const +{ + return CV_ELEM_SIZE1(flags); +} + +inline +int UMat::type() const +{ + return CV_MAT_TYPE(flags); +} + +inline +int UMat::depth() const +{ + return CV_MAT_DEPTH(flags); +} + +inline +int UMat::channels() const +{ + return CV_MAT_CN(flags); +} + +inline +size_t UMat::step1(int i) const +{ + return step.p[i] / elemSize1(); +} + +inline +bool UMat::empty() const +{ + return u == 0 || total() == 0 || dims == 0; +} + +inline +size_t UMat::total() const +{ + if( dims <= 2 ) + return (size_t)rows * cols; + size_t p = 1; + for( int i = 0; i < dims; i++ ) + p *= size[i]; + return p; +} + +#ifdef CV_CXX_MOVE_SEMANTICS + +inline +UMat::UMat(UMat&& m) +: flags(m.flags), dims(m.dims), rows(m.rows), cols(m.cols), allocator(m.allocator), + usageFlags(m.usageFlags), u(m.u), offset(m.offset), size(&rows) +{ + if (m.dims <= 2) // move new step/size info + { + step[0] = m.step[0]; + step[1] = m.step[1]; + } + else + { + CV_DbgAssert(m.step.p != m.step.buf); + step.p = m.step.p; + size.p = m.size.p; + m.step.p = m.step.buf; + m.size.p = &m.rows; + } + m.flags = MAGIC_VAL; m.dims = m.rows = m.cols = 0; + m.allocator = NULL; + m.u = NULL; + m.offset = 0; +} + +inline +UMat& UMat::operator = (UMat&& m) +{ + if (this == &m) + return *this; + release(); + flags = m.flags; dims = m.dims; rows = m.rows; cols = m.cols; + allocator = m.allocator; usageFlags = m.usageFlags; + u = m.u; + offset = m.offset; + if (step.p != step.buf) // release self step/size + { + fastFree(step.p); + step.p = step.buf; + size.p = &rows; + } + if (m.dims <= 2) // move new step/size info + { + step[0] = m.step[0]; + step[1] = m.step[1]; + } + else + { + CV_DbgAssert(m.step.p != m.step.buf); + step.p = m.step.p; + size.p = m.size.p; + m.step.p = m.step.buf; + m.size.p = &m.rows; + } + m.flags = MAGIC_VAL; m.dims = m.rows = m.cols = 0; + m.allocator = NULL; + m.u = NULL; + m.offset = 0; + return *this; +} + +#endif + + +inline bool UMatData::hostCopyObsolete() const { return (flags & HOST_COPY_OBSOLETE) != 0; } +inline bool UMatData::deviceCopyObsolete() const { return (flags & DEVICE_COPY_OBSOLETE) != 0; } +inline bool UMatData::deviceMemMapped() const { return (flags & DEVICE_MEM_MAPPED) != 0; } +inline bool UMatData::copyOnMap() const { return (flags & COPY_ON_MAP) != 0; } +inline bool UMatData::tempUMat() const { return (flags & TEMP_UMAT) != 0; } +inline bool UMatData::tempCopiedUMat() const { return (flags & TEMP_COPIED_UMAT) == TEMP_COPIED_UMAT; } + +inline void UMatData::markDeviceMemMapped(bool flag) +{ + if(flag) + flags |= DEVICE_MEM_MAPPED; + else + flags &= ~DEVICE_MEM_MAPPED; +} + +inline void UMatData::markHostCopyObsolete(bool flag) +{ + if(flag) + flags |= HOST_COPY_OBSOLETE; + else + flags &= ~HOST_COPY_OBSOLETE; +} +inline void UMatData::markDeviceCopyObsolete(bool flag) +{ + if(flag) + flags |= DEVICE_COPY_OBSOLETE; + else + flags &= ~DEVICE_COPY_OBSOLETE; +} + +//! @endcond + +} //cv + +#ifdef _MSC_VER +#pragma warning( pop ) +#endif + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/matx.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/matx.hpp new file mode 100644 index 0000000000000000000000000000000000000000..bf3f046276a6e893dcb5138e5bef1be6417701f0 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/matx.hpp @@ -0,0 +1,1477 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_MATX_HPP +#define OPENCV_CORE_MATX_HPP + +#ifndef __cplusplus +# error matx.hpp header must be compiled as C++ +#endif + +#include "opencv2/core/cvdef.h" +#include "opencv2/core/base.hpp" +#include "opencv2/core/traits.hpp" +#include "opencv2/core/saturate.hpp" + +#ifdef CV_CXX11 +#include +#endif + +namespace cv +{ + +//! @addtogroup core_basic +//! @{ + +////////////////////////////// Small Matrix /////////////////////////// + +//! @cond IGNORED +// FIXIT Remove this (especially CV_EXPORTS modifier) +struct CV_EXPORTS Matx_AddOp { Matx_AddOp() {} Matx_AddOp(const Matx_AddOp&) {} }; +struct CV_EXPORTS Matx_SubOp { Matx_SubOp() {} Matx_SubOp(const Matx_SubOp&) {} }; +struct CV_EXPORTS Matx_ScaleOp { Matx_ScaleOp() {} Matx_ScaleOp(const Matx_ScaleOp&) {} }; +struct CV_EXPORTS Matx_MulOp { Matx_MulOp() {} Matx_MulOp(const Matx_MulOp&) {} }; +struct CV_EXPORTS Matx_DivOp { Matx_DivOp() {} Matx_DivOp(const Matx_DivOp&) {} }; +struct CV_EXPORTS Matx_MatMulOp { Matx_MatMulOp() {} Matx_MatMulOp(const Matx_MatMulOp&) {} }; +struct CV_EXPORTS Matx_TOp { Matx_TOp() {} Matx_TOp(const Matx_TOp&) {} }; +//! @endcond + +/** @brief Template class for small matrices whose type and size are known at compilation time + +If you need a more flexible type, use Mat . The elements of the matrix M are accessible using the +M(i,j) notation. Most of the common matrix operations (see also @ref MatrixExpressions ) are +available. To do an operation on Matx that is not implemented, you can easily convert the matrix to +Mat and backwards: +@code{.cpp} + Matx33f m(1, 2, 3, + 4, 5, 6, + 7, 8, 9); + cout << sum(Mat(m*m.t())) << endl; +@endcode +Except of the plain constructor which takes a list of elements, Matx can be initialized from a C-array: +@code{.cpp} + float values[] = { 1, 2, 3}; + Matx31f m(values); +@endcode +In case if C++11 features are available, std::initializer_list can be also used to initialize Matx: +@code{.cpp} + Matx31f m = { 1, 2, 3}; +@endcode + */ +template class Matx +{ +public: + enum { + rows = m, + cols = n, + channels = rows*cols, +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + depth = traits::Type<_Tp>::value, + type = CV_MAKETYPE(depth, channels), +#endif + shortdim = (m < n ? m : n) + }; + + typedef _Tp value_type; + typedef Matx<_Tp, m, n> mat_type; + typedef Matx<_Tp, shortdim, 1> diag_type; + + //! default constructor + Matx(); + + explicit Matx(_Tp v0); //!< 1x1 matrix + Matx(_Tp v0, _Tp v1); //!< 1x2 or 2x1 matrix + Matx(_Tp v0, _Tp v1, _Tp v2); //!< 1x3 or 3x1 matrix + Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3); //!< 1x4, 2x2 or 4x1 matrix + Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4); //!< 1x5 or 5x1 matrix + Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5); //!< 1x6, 2x3, 3x2 or 6x1 matrix + Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6); //!< 1x7 or 7x1 matrix + Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7); //!< 1x8, 2x4, 4x2 or 8x1 matrix + Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8); //!< 1x9, 3x3 or 9x1 matrix + Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9); //!< 1x10, 2x5 or 5x2 or 10x1 matrix + Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, + _Tp v4, _Tp v5, _Tp v6, _Tp v7, + _Tp v8, _Tp v9, _Tp v10, _Tp v11); //!< 1x12, 2x6, 3x4, 4x3, 6x2 or 12x1 matrix + Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, + _Tp v4, _Tp v5, _Tp v6, _Tp v7, + _Tp v8, _Tp v9, _Tp v10, _Tp v11, + _Tp v12, _Tp v13); //!< 1x14, 2x7, 7x2 or 14x1 matrix + Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, + _Tp v4, _Tp v5, _Tp v6, _Tp v7, + _Tp v8, _Tp v9, _Tp v10, _Tp v11, + _Tp v12, _Tp v13, _Tp v14, _Tp v15); //!< 1x16, 4x4 or 16x1 matrix + explicit Matx(const _Tp* vals); //!< initialize from a plain array + +#ifdef CV_CXX11 + Matx(std::initializer_list<_Tp>); //!< initialize from an initializer list +#endif + + static Matx all(_Tp alpha); + static Matx zeros(); + static Matx ones(); + static Matx eye(); + static Matx diag(const diag_type& d); + static Matx randu(_Tp a, _Tp b); + static Matx randn(_Tp a, _Tp b); + + //! dot product computed with the default precision + _Tp dot(const Matx<_Tp, m, n>& v) const; + + //! dot product computed in double-precision arithmetics + double ddot(const Matx<_Tp, m, n>& v) const; + + //! conversion to another data type + template operator Matx() const; + + //! change the matrix shape + template Matx<_Tp, m1, n1> reshape() const; + + //! extract part of the matrix + template Matx<_Tp, m1, n1> get_minor(int i, int j) const; + + //! extract the matrix row + Matx<_Tp, 1, n> row(int i) const; + + //! extract the matrix column + Matx<_Tp, m, 1> col(int i) const; + + //! extract the matrix diagonal + diag_type diag() const; + + //! transpose the matrix + Matx<_Tp, n, m> t() const; + + //! invert the matrix + Matx<_Tp, n, m> inv(int method=DECOMP_LU, bool *p_is_ok = NULL) const; + + //! solve linear system + template Matx<_Tp, n, l> solve(const Matx<_Tp, m, l>& rhs, int flags=DECOMP_LU) const; + Vec<_Tp, n> solve(const Vec<_Tp, m>& rhs, int method) const; + + //! multiply two matrices element-wise + Matx<_Tp, m, n> mul(const Matx<_Tp, m, n>& a) const; + + //! divide two matrices element-wise + Matx<_Tp, m, n> div(const Matx<_Tp, m, n>& a) const; + + //! element access + const _Tp& operator ()(int i, int j) const; + _Tp& operator ()(int i, int j); + + //! 1D element access + const _Tp& operator ()(int i) const; + _Tp& operator ()(int i); + + Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_AddOp); + Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_SubOp); + template Matx(const Matx<_Tp, m, n>& a, _T2 alpha, Matx_ScaleOp); + Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_MulOp); + Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_DivOp); + template Matx(const Matx<_Tp, m, l>& a, const Matx<_Tp, l, n>& b, Matx_MatMulOp); + Matx(const Matx<_Tp, n, m>& a, Matx_TOp); + + _Tp val[m*n]; //< matrix elements +}; + +typedef Matx Matx12f; +typedef Matx Matx12d; +typedef Matx Matx13f; +typedef Matx Matx13d; +typedef Matx Matx14f; +typedef Matx Matx14d; +typedef Matx Matx16f; +typedef Matx Matx16d; + +typedef Matx Matx21f; +typedef Matx Matx21d; +typedef Matx Matx31f; +typedef Matx Matx31d; +typedef Matx Matx41f; +typedef Matx Matx41d; +typedef Matx Matx61f; +typedef Matx Matx61d; + +typedef Matx Matx22f; +typedef Matx Matx22d; +typedef Matx Matx23f; +typedef Matx Matx23d; +typedef Matx Matx32f; +typedef Matx Matx32d; + +typedef Matx Matx33f; +typedef Matx Matx33d; + +typedef Matx Matx34f; +typedef Matx Matx34d; +typedef Matx Matx43f; +typedef Matx Matx43d; + +typedef Matx Matx44f; +typedef Matx Matx44d; +typedef Matx Matx66f; +typedef Matx Matx66d; + +/*! + traits +*/ +template class DataType< Matx<_Tp, m, n> > +{ +public: + typedef Matx<_Tp, m, n> value_type; + typedef Matx::work_type, m, n> work_type; + typedef _Tp channel_type; + typedef value_type vec_type; + + enum { generic_type = 0, + channels = m * n, + fmt = traits::SafeFmt::fmt + ((channels - 1) << 8) +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + ,depth = DataType::depth + ,type = CV_MAKETYPE(depth, channels) +#endif + }; +}; + +namespace traits { +template +struct Depth< Matx<_Tp, m, n> > { enum { value = Depth<_Tp>::value }; }; +template +struct Type< Matx<_Tp, m, n> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, n*m) }; }; +} // namespace + + +/** @brief Comma-separated Matrix Initializer +*/ +template class MatxCommaInitializer +{ +public: + MatxCommaInitializer(Matx<_Tp, m, n>* _mtx); + template MatxCommaInitializer<_Tp, m, n>& operator , (T2 val); + Matx<_Tp, m, n> operator *() const; + + Matx<_Tp, m, n>* dst; + int idx; +}; + +/* + Utility methods +*/ +template static double determinant(const Matx<_Tp, m, m>& a); +template static double trace(const Matx<_Tp, m, n>& a); +template static double norm(const Matx<_Tp, m, n>& M); +template static double norm(const Matx<_Tp, m, n>& M, int normType); + + + +/////////////////////// Vec (used as element of multi-channel images ///////////////////// + +/** @brief Template class for short numerical vectors, a partial case of Matx + +This template class represents short numerical vectors (of 1, 2, 3, 4 ... elements) on which you +can perform basic arithmetical operations, access individual elements using [] operator etc. The +vectors are allocated on stack, as opposite to std::valarray, std::vector, cv::Mat etc., which +elements are dynamically allocated in the heap. + +The template takes 2 parameters: +@tparam _Tp element type +@tparam cn the number of elements + +In addition to the universal notation like Vec, you can use shorter aliases +for the most popular specialized variants of Vec, e.g. Vec3f ~ Vec. + +It is possible to convert Vec\ to/from Point_, Vec\ to/from Point3_ , and Vec\ +to CvScalar or Scalar_. Use operator[] to access the elements of Vec. + +All the expected vector operations are also implemented: +- v1 = v2 + v3 +- v1 = v2 - v3 +- v1 = v2 \* scale +- v1 = scale \* v2 +- v1 = -v2 +- v1 += v2 and other augmenting operations +- v1 == v2, v1 != v2 +- norm(v1) (euclidean norm) +The Vec class is commonly used to describe pixel types of multi-channel arrays. See Mat for details. +*/ +template class Vec : public Matx<_Tp, cn, 1> +{ +public: + typedef _Tp value_type; + enum { + channels = cn, +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + depth = Matx<_Tp, cn, 1>::depth, + type = CV_MAKETYPE(depth, channels), +#endif + _dummy_enum_finalizer = 0 + }; + + //! default constructor + Vec(); + + Vec(_Tp v0); //!< 1-element vector constructor + Vec(_Tp v0, _Tp v1); //!< 2-element vector constructor + Vec(_Tp v0, _Tp v1, _Tp v2); //!< 3-element vector constructor + Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3); //!< 4-element vector constructor + Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4); //!< 5-element vector constructor + Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5); //!< 6-element vector constructor + Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6); //!< 7-element vector constructor + Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7); //!< 8-element vector constructor + Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8); //!< 9-element vector constructor + Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9); //!< 10-element vector constructor + Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9, _Tp v10, _Tp v11, _Tp v12, _Tp v13); //!< 14-element vector constructor + explicit Vec(const _Tp* values); + +#ifdef CV_CXX11 + Vec(std::initializer_list<_Tp>); +#endif + + Vec(const Vec<_Tp, cn>& v); + + static Vec all(_Tp alpha); + + //! per-element multiplication + Vec mul(const Vec<_Tp, cn>& v) const; + + //! conjugation (makes sense for complex numbers and quaternions) + Vec conj() const; + + /*! + cross product of the two 3D vectors. + + For other dimensionalities the exception is raised + */ + Vec cross(const Vec& v) const; + //! conversion to another data type + template operator Vec() const; + + /*! element access */ + const _Tp& operator [](int i) const; + _Tp& operator[](int i); + const _Tp& operator ()(int i) const; + _Tp& operator ()(int i); + + Vec(const Matx<_Tp, cn, 1>& a, const Matx<_Tp, cn, 1>& b, Matx_AddOp); + Vec(const Matx<_Tp, cn, 1>& a, const Matx<_Tp, cn, 1>& b, Matx_SubOp); + template Vec(const Matx<_Tp, cn, 1>& a, _T2 alpha, Matx_ScaleOp); +}; + +/** @name Shorter aliases for the most popular specializations of Vec + @{ +*/ +typedef Vec Vec2b; +typedef Vec Vec3b; +typedef Vec Vec4b; + +typedef Vec Vec2s; +typedef Vec Vec3s; +typedef Vec Vec4s; + +typedef Vec Vec2w; +typedef Vec Vec3w; +typedef Vec Vec4w; + +typedef Vec Vec2i; +typedef Vec Vec3i; +typedef Vec Vec4i; +typedef Vec Vec6i; +typedef Vec Vec8i; + +typedef Vec Vec2f; +typedef Vec Vec3f; +typedef Vec Vec4f; +typedef Vec Vec6f; + +typedef Vec Vec2d; +typedef Vec Vec3d; +typedef Vec Vec4d; +typedef Vec Vec6d; +/** @} */ + +/*! + traits +*/ +template class DataType< Vec<_Tp, cn> > +{ +public: + typedef Vec<_Tp, cn> value_type; + typedef Vec::work_type, cn> work_type; + typedef _Tp channel_type; + typedef value_type vec_type; + + enum { generic_type = 0, + channels = cn, + fmt = DataType::fmt + ((channels - 1) << 8), +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + depth = DataType::depth, + type = CV_MAKETYPE(depth, channels), +#endif + _dummy_enum_finalizer = 0 + }; +}; + +namespace traits { +template +struct Depth< Vec<_Tp, cn> > { enum { value = Depth<_Tp>::value }; }; +template +struct Type< Vec<_Tp, cn> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, cn) }; }; +} // namespace + + +/** @brief Comma-separated Vec Initializer +*/ +template class VecCommaInitializer : public MatxCommaInitializer<_Tp, m, 1> +{ +public: + VecCommaInitializer(Vec<_Tp, m>* _vec); + template VecCommaInitializer<_Tp, m>& operator , (T2 val); + Vec<_Tp, m> operator *() const; +}; + +template static Vec<_Tp, cn> normalize(const Vec<_Tp, cn>& v); + +//! @} core_basic + +//! @cond IGNORED + +///////////////////////////////////// helper classes ///////////////////////////////////// +namespace internal +{ + +template struct Matx_DetOp +{ + double operator ()(const Matx<_Tp, m, m>& a) const + { + Matx<_Tp, m, m> temp = a; + double p = LU(temp.val, m*sizeof(_Tp), m, 0, 0, 0); + if( p == 0 ) + return p; + for( int i = 0; i < m; i++ ) + p *= temp(i, i); + return p; + } +}; + +template struct Matx_DetOp<_Tp, 1> +{ + double operator ()(const Matx<_Tp, 1, 1>& a) const + { + return a(0,0); + } +}; + +template struct Matx_DetOp<_Tp, 2> +{ + double operator ()(const Matx<_Tp, 2, 2>& a) const + { + return a(0,0)*a(1,1) - a(0,1)*a(1,0); + } +}; + +template struct Matx_DetOp<_Tp, 3> +{ + double operator ()(const Matx<_Tp, 3, 3>& a) const + { + return a(0,0)*(a(1,1)*a(2,2) - a(2,1)*a(1,2)) - + a(0,1)*(a(1,0)*a(2,2) - a(2,0)*a(1,2)) + + a(0,2)*(a(1,0)*a(2,1) - a(2,0)*a(1,1)); + } +}; + +template Vec<_Tp, 2> inline conjugate(const Vec<_Tp, 2>& v) +{ + return Vec<_Tp, 2>(v[0], -v[1]); +} + +template Vec<_Tp, 4> inline conjugate(const Vec<_Tp, 4>& v) +{ + return Vec<_Tp, 4>(v[0], -v[1], -v[2], -v[3]); +} + +} // internal + + + +////////////////////////////////// Matx Implementation /////////////////////////////////// + +template inline +Matx<_Tp, m, n>::Matx() +{ + for(int i = 0; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp, m, n>::Matx(_Tp v0) +{ + val[0] = v0; + for(int i = 1; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1) +{ + CV_StaticAssert(channels >= 2, "Matx should have at least 2 elements."); + val[0] = v0; val[1] = v1; + for(int i = 2; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2) +{ + CV_StaticAssert(channels >= 3, "Matx should have at least 3 elements."); + val[0] = v0; val[1] = v1; val[2] = v2; + for(int i = 3; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3) +{ + CV_StaticAssert(channels >= 4, "Matx should have at least 4 elements."); + val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; + for(int i = 4; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4) +{ + CV_StaticAssert(channels >= 5, "Matx should have at least 5 elements."); + val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; val[4] = v4; + for(int i = 5; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5) +{ + CV_StaticAssert(channels >= 6, "Matx should have at least 6 elements."); + val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; + val[4] = v4; val[5] = v5; + for(int i = 6; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6) +{ + CV_StaticAssert(channels >= 7, "Matx should have at least 7 elements."); + val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; + val[4] = v4; val[5] = v5; val[6] = v6; + for(int i = 7; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7) +{ + CV_StaticAssert(channels >= 8, "Matx should have at least 8 elements."); + val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; + val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7; + for(int i = 8; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8) +{ + CV_StaticAssert(channels >= 9, "Matx should have at least 9 elements."); + val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; + val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7; + val[8] = v8; + for(int i = 9; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9) +{ + CV_StaticAssert(channels >= 10, "Matx should have at least 10 elements."); + val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; + val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7; + val[8] = v8; val[9] = v9; + for(int i = 10; i < channels; i++) val[i] = _Tp(0); +} + + +template inline +Matx<_Tp,m,n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9, _Tp v10, _Tp v11) +{ + CV_StaticAssert(channels >= 12, "Matx should have at least 12 elements."); + val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; + val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7; + val[8] = v8; val[9] = v9; val[10] = v10; val[11] = v11; + for(int i = 12; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp,m,n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9, _Tp v10, _Tp v11, _Tp v12, _Tp v13) +{ + CV_StaticAssert(channels >= 14, "Matx should have at least 14 elements."); + val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; + val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7; + val[8] = v8; val[9] = v9; val[10] = v10; val[11] = v11; + val[12] = v12; val[13] = v13; + for (int i = 14; i < channels; i++) val[i] = _Tp(0); +} + + +template inline +Matx<_Tp,m,n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9, _Tp v10, _Tp v11, _Tp v12, _Tp v13, _Tp v14, _Tp v15) +{ + CV_StaticAssert(channels >= 16, "Matx should have at least 16 elements."); + val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; + val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7; + val[8] = v8; val[9] = v9; val[10] = v10; val[11] = v11; + val[12] = v12; val[13] = v13; val[14] = v14; val[15] = v15; + for(int i = 16; i < channels; i++) val[i] = _Tp(0); +} + +template inline +Matx<_Tp, m, n>::Matx(const _Tp* values) +{ + for( int i = 0; i < channels; i++ ) val[i] = values[i]; +} + +#ifdef CV_CXX11 +template inline +Matx<_Tp, m, n>::Matx(std::initializer_list<_Tp> list) +{ + CV_DbgAssert(list.size() == channels); + int i = 0; + for(const auto& elem : list) + { + val[i++] = elem; + } +} +#endif + +template inline +Matx<_Tp, m, n> Matx<_Tp, m, n>::all(_Tp alpha) +{ + Matx<_Tp, m, n> M; + for( int i = 0; i < m*n; i++ ) M.val[i] = alpha; + return M; +} + +template inline +Matx<_Tp,m,n> Matx<_Tp,m,n>::zeros() +{ + return all(0); +} + +template inline +Matx<_Tp,m,n> Matx<_Tp,m,n>::ones() +{ + return all(1); +} + +template inline +Matx<_Tp,m,n> Matx<_Tp,m,n>::eye() +{ + Matx<_Tp,m,n> M; + for(int i = 0; i < shortdim; i++) + M(i,i) = 1; + return M; +} + +template inline +_Tp Matx<_Tp, m, n>::dot(const Matx<_Tp, m, n>& M) const +{ + _Tp s = 0; + for( int i = 0; i < channels; i++ ) s += val[i]*M.val[i]; + return s; +} + +template inline +double Matx<_Tp, m, n>::ddot(const Matx<_Tp, m, n>& M) const +{ + double s = 0; + for( int i = 0; i < channels; i++ ) s += (double)val[i]*M.val[i]; + return s; +} + +template inline +Matx<_Tp,m,n> Matx<_Tp,m,n>::diag(const typename Matx<_Tp,m,n>::diag_type& d) +{ + Matx<_Tp,m,n> M; + for(int i = 0; i < shortdim; i++) + M(i,i) = d(i, 0); + return M; +} + +template template +inline Matx<_Tp, m, n>::operator Matx() const +{ + Matx M; + for( int i = 0; i < m*n; i++ ) M.val[i] = saturate_cast(val[i]); + return M; +} + +template template inline +Matx<_Tp, m1, n1> Matx<_Tp, m, n>::reshape() const +{ + CV_StaticAssert(m1*n1 == m*n, "Input and destnarion matrices must have the same number of elements"); + return (const Matx<_Tp, m1, n1>&)*this; +} + +template +template inline +Matx<_Tp, m1, n1> Matx<_Tp, m, n>::get_minor(int i, int j) const +{ + CV_DbgAssert(0 <= i && i+m1 <= m && 0 <= j && j+n1 <= n); + Matx<_Tp, m1, n1> s; + for( int di = 0; di < m1; di++ ) + for( int dj = 0; dj < n1; dj++ ) + s(di, dj) = (*this)(i+di, j+dj); + return s; +} + +template inline +Matx<_Tp, 1, n> Matx<_Tp, m, n>::row(int i) const +{ + CV_DbgAssert((unsigned)i < (unsigned)m); + return Matx<_Tp, 1, n>(&val[i*n]); +} + +template inline +Matx<_Tp, m, 1> Matx<_Tp, m, n>::col(int j) const +{ + CV_DbgAssert((unsigned)j < (unsigned)n); + Matx<_Tp, m, 1> v; + for( int i = 0; i < m; i++ ) + v.val[i] = val[i*n + j]; + return v; +} + +template inline +typename Matx<_Tp, m, n>::diag_type Matx<_Tp, m, n>::diag() const +{ + diag_type d; + for( int i = 0; i < shortdim; i++ ) + d.val[i] = val[i*n + i]; + return d; +} + +template inline +const _Tp& Matx<_Tp, m, n>::operator()(int i, int j) const +{ + CV_DbgAssert( (unsigned)i < (unsigned)m && (unsigned)j < (unsigned)n ); + return this->val[i*n + j]; +} + +template inline +_Tp& Matx<_Tp, m, n>::operator ()(int i, int j) +{ + CV_DbgAssert( (unsigned)i < (unsigned)m && (unsigned)j < (unsigned)n ); + return val[i*n + j]; +} + +template inline +const _Tp& Matx<_Tp, m, n>::operator ()(int i) const +{ + CV_StaticAssert(m == 1 || n == 1, "Single index indexation requires matrix to be a column or a row"); + CV_DbgAssert( (unsigned)i < (unsigned)(m+n-1) ); + return val[i]; +} + +template inline +_Tp& Matx<_Tp, m, n>::operator ()(int i) +{ + CV_StaticAssert(m == 1 || n == 1, "Single index indexation requires matrix to be a column or a row"); + CV_DbgAssert( (unsigned)i < (unsigned)(m+n-1) ); + return val[i]; +} + +template inline +Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_AddOp) +{ + for( int i = 0; i < channels; i++ ) + val[i] = saturate_cast<_Tp>(a.val[i] + b.val[i]); +} + +template inline +Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_SubOp) +{ + for( int i = 0; i < channels; i++ ) + val[i] = saturate_cast<_Tp>(a.val[i] - b.val[i]); +} + +template template inline +Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, _T2 alpha, Matx_ScaleOp) +{ + for( int i = 0; i < channels; i++ ) + val[i] = saturate_cast<_Tp>(a.val[i] * alpha); +} + +template inline +Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_MulOp) +{ + for( int i = 0; i < channels; i++ ) + val[i] = saturate_cast<_Tp>(a.val[i] * b.val[i]); +} + +template inline +Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_DivOp) +{ + for( int i = 0; i < channels; i++ ) + val[i] = saturate_cast<_Tp>(a.val[i] / b.val[i]); +} + +template template inline +Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, l>& a, const Matx<_Tp, l, n>& b, Matx_MatMulOp) +{ + for( int i = 0; i < m; i++ ) + for( int j = 0; j < n; j++ ) + { + _Tp s = 0; + for( int k = 0; k < l; k++ ) + s += a(i, k) * b(k, j); + val[i*n + j] = s; + } +} + +template inline +Matx<_Tp,m,n>::Matx(const Matx<_Tp, n, m>& a, Matx_TOp) +{ + for( int i = 0; i < m; i++ ) + for( int j = 0; j < n; j++ ) + val[i*n + j] = a(j, i); +} + +template inline +Matx<_Tp, m, n> Matx<_Tp, m, n>::mul(const Matx<_Tp, m, n>& a) const +{ + return Matx<_Tp, m, n>(*this, a, Matx_MulOp()); +} + +template inline +Matx<_Tp, m, n> Matx<_Tp, m, n>::div(const Matx<_Tp, m, n>& a) const +{ + return Matx<_Tp, m, n>(*this, a, Matx_DivOp()); +} + +template inline +Matx<_Tp, n, m> Matx<_Tp, m, n>::t() const +{ + return Matx<_Tp, n, m>(*this, Matx_TOp()); +} + +template inline +Vec<_Tp, n> Matx<_Tp, m, n>::solve(const Vec<_Tp, m>& rhs, int method) const +{ + Matx<_Tp, n, 1> x = solve((const Matx<_Tp, m, 1>&)(rhs), method); + return (Vec<_Tp, n>&)(x); +} + +template static inline +double determinant(const Matx<_Tp, m, m>& a) +{ + return cv::internal::Matx_DetOp<_Tp, m>()(a); +} + +template static inline +double trace(const Matx<_Tp, m, n>& a) +{ + _Tp s = 0; + for( int i = 0; i < std::min(m, n); i++ ) + s += a(i,i); + return s; +} + +template static inline +double norm(const Matx<_Tp, m, n>& M) +{ + return std::sqrt(normL2Sqr<_Tp, double>(M.val, m*n)); +} + +template static inline +double norm(const Matx<_Tp, m, n>& M, int normType) +{ + switch(normType) { + case NORM_INF: + return (double)normInf<_Tp, typename DataType<_Tp>::work_type>(M.val, m*n); + case NORM_L1: + return (double)normL1<_Tp, typename DataType<_Tp>::work_type>(M.val, m*n); + case NORM_L2SQR: + return (double)normL2Sqr<_Tp, typename DataType<_Tp>::work_type>(M.val, m*n); + default: + case NORM_L2: + return std::sqrt((double)normL2Sqr<_Tp, typename DataType<_Tp>::work_type>(M.val, m*n)); + } +} + + + +//////////////////////////////// matx comma initializer ////////////////////////////////// + +template static inline +MatxCommaInitializer<_Tp, m, n> operator << (const Matx<_Tp, m, n>& mtx, _T2 val) +{ + MatxCommaInitializer<_Tp, m, n> commaInitializer((Matx<_Tp, m, n>*)&mtx); + return (commaInitializer, val); +} + +template inline +MatxCommaInitializer<_Tp, m, n>::MatxCommaInitializer(Matx<_Tp, m, n>* _mtx) + : dst(_mtx), idx(0) +{} + +template template inline +MatxCommaInitializer<_Tp, m, n>& MatxCommaInitializer<_Tp, m, n>::operator , (_T2 value) +{ + CV_DbgAssert( idx < m*n ); + dst->val[idx++] = saturate_cast<_Tp>(value); + return *this; +} + +template inline +Matx<_Tp, m, n> MatxCommaInitializer<_Tp, m, n>::operator *() const +{ + CV_DbgAssert( idx == n*m ); + return *dst; +} + + + +/////////////////////////////////// Vec Implementation /////////////////////////////////// + +template inline +Vec<_Tp, cn>::Vec() {} + +template inline +Vec<_Tp, cn>::Vec(_Tp v0) + : Matx<_Tp, cn, 1>(v0) {} + +template inline +Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1) + : Matx<_Tp, cn, 1>(v0, v1) {} + +template inline +Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2) + : Matx<_Tp, cn, 1>(v0, v1, v2) {} + +template inline +Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3) + : Matx<_Tp, cn, 1>(v0, v1, v2, v3) {} + +template inline +Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4) + : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4) {} + +template inline +Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5) + : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5) {} + +template inline +Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6) + : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6) {} + +template inline +Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7) + : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6, v7) {} + +template inline +Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8) + : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6, v7, v8) {} + +template inline +Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9) + : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6, v7, v8, v9) {} + +template inline +Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9, _Tp v10, _Tp v11, _Tp v12, _Tp v13) + : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13) {} + +template inline +Vec<_Tp, cn>::Vec(const _Tp* values) + : Matx<_Tp, cn, 1>(values) {} + +#ifdef CV_CXX11 +template inline +Vec<_Tp, cn>::Vec(std::initializer_list<_Tp> list) + : Matx<_Tp, cn, 1>(list) {} +#endif + +template inline +Vec<_Tp, cn>::Vec(const Vec<_Tp, cn>& m) + : Matx<_Tp, cn, 1>(m.val) {} + +template inline +Vec<_Tp, cn>::Vec(const Matx<_Tp, cn, 1>& a, const Matx<_Tp, cn, 1>& b, Matx_AddOp op) + : Matx<_Tp, cn, 1>(a, b, op) {} + +template inline +Vec<_Tp, cn>::Vec(const Matx<_Tp, cn, 1>& a, const Matx<_Tp, cn, 1>& b, Matx_SubOp op) + : Matx<_Tp, cn, 1>(a, b, op) {} + +template template inline +Vec<_Tp, cn>::Vec(const Matx<_Tp, cn, 1>& a, _T2 alpha, Matx_ScaleOp op) + : Matx<_Tp, cn, 1>(a, alpha, op) {} + +template inline +Vec<_Tp, cn> Vec<_Tp, cn>::all(_Tp alpha) +{ + Vec v; + for( int i = 0; i < cn; i++ ) v.val[i] = alpha; + return v; +} + +template inline +Vec<_Tp, cn> Vec<_Tp, cn>::mul(const Vec<_Tp, cn>& v) const +{ + Vec<_Tp, cn> w; + for( int i = 0; i < cn; i++ ) w.val[i] = saturate_cast<_Tp>(this->val[i]*v.val[i]); + return w; +} + +template<> inline +Vec Vec::conj() const +{ + return cv::internal::conjugate(*this); +} + +template<> inline +Vec Vec::conj() const +{ + return cv::internal::conjugate(*this); +} + +template<> inline +Vec Vec::conj() const +{ + return cv::internal::conjugate(*this); +} + +template<> inline +Vec Vec::conj() const +{ + return cv::internal::conjugate(*this); +} + +template inline +Vec<_Tp, cn> Vec<_Tp, cn>::cross(const Vec<_Tp, cn>&) const +{ + CV_StaticAssert(cn == 3, "for arbitrary-size vector there is no cross-product defined"); + return Vec<_Tp, cn>(); +} + +template<> inline +Vec Vec::cross(const Vec& v) const +{ + return Vec(this->val[1]*v.val[2] - this->val[2]*v.val[1], + this->val[2]*v.val[0] - this->val[0]*v.val[2], + this->val[0]*v.val[1] - this->val[1]*v.val[0]); +} + +template<> inline +Vec Vec::cross(const Vec& v) const +{ + return Vec(this->val[1]*v.val[2] - this->val[2]*v.val[1], + this->val[2]*v.val[0] - this->val[0]*v.val[2], + this->val[0]*v.val[1] - this->val[1]*v.val[0]); +} + +template template inline +Vec<_Tp, cn>::operator Vec() const +{ + Vec v; + for( int i = 0; i < cn; i++ ) v.val[i] = saturate_cast(this->val[i]); + return v; +} + +template inline +const _Tp& Vec<_Tp, cn>::operator [](int i) const +{ + CV_DbgAssert( (unsigned)i < (unsigned)cn ); + return this->val[i]; +} + +template inline +_Tp& Vec<_Tp, cn>::operator [](int i) +{ + CV_DbgAssert( (unsigned)i < (unsigned)cn ); + return this->val[i]; +} + +template inline +const _Tp& Vec<_Tp, cn>::operator ()(int i) const +{ + CV_DbgAssert( (unsigned)i < (unsigned)cn ); + return this->val[i]; +} + +template inline +_Tp& Vec<_Tp, cn>::operator ()(int i) +{ + CV_DbgAssert( (unsigned)i < (unsigned)cn ); + return this->val[i]; +} + +template inline +Vec<_Tp, cn> normalize(const Vec<_Tp, cn>& v) +{ + double nv = norm(v); + return v * (nv ? 1./nv : 0.); +} + + + +//////////////////////////////// vec comma initializer ////////////////////////////////// + + +template static inline +VecCommaInitializer<_Tp, cn> operator << (const Vec<_Tp, cn>& vec, _T2 val) +{ + VecCommaInitializer<_Tp, cn> commaInitializer((Vec<_Tp, cn>*)&vec); + return (commaInitializer, val); +} + +template inline +VecCommaInitializer<_Tp, cn>::VecCommaInitializer(Vec<_Tp, cn>* _vec) + : MatxCommaInitializer<_Tp, cn, 1>(_vec) +{} + +template template inline +VecCommaInitializer<_Tp, cn>& VecCommaInitializer<_Tp, cn>::operator , (_T2 value) +{ + CV_DbgAssert( this->idx < cn ); + this->dst->val[this->idx++] = saturate_cast<_Tp>(value); + return *this; +} + +template inline +Vec<_Tp, cn> VecCommaInitializer<_Tp, cn>::operator *() const +{ + CV_DbgAssert( this->idx == cn ); + return *this->dst; +} + +//! @endcond + +///////////////////////////// Matx out-of-class operators //////////////////////////////// + +//! @relates cv::Matx +//! @{ + +template static inline +Matx<_Tp1, m, n>& operator += (Matx<_Tp1, m, n>& a, const Matx<_Tp2, m, n>& b) +{ + for( int i = 0; i < m*n; i++ ) + a.val[i] = saturate_cast<_Tp1>(a.val[i] + b.val[i]); + return a; +} + +template static inline +Matx<_Tp1, m, n>& operator -= (Matx<_Tp1, m, n>& a, const Matx<_Tp2, m, n>& b) +{ + for( int i = 0; i < m*n; i++ ) + a.val[i] = saturate_cast<_Tp1>(a.val[i] - b.val[i]); + return a; +} + +template static inline +Matx<_Tp, m, n> operator + (const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b) +{ + return Matx<_Tp, m, n>(a, b, Matx_AddOp()); +} + +template static inline +Matx<_Tp, m, n> operator - (const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b) +{ + return Matx<_Tp, m, n>(a, b, Matx_SubOp()); +} + +template static inline +Matx<_Tp, m, n>& operator *= (Matx<_Tp, m, n>& a, int alpha) +{ + for( int i = 0; i < m*n; i++ ) + a.val[i] = saturate_cast<_Tp>(a.val[i] * alpha); + return a; +} + +template static inline +Matx<_Tp, m, n>& operator *= (Matx<_Tp, m, n>& a, float alpha) +{ + for( int i = 0; i < m*n; i++ ) + a.val[i] = saturate_cast<_Tp>(a.val[i] * alpha); + return a; +} + +template static inline +Matx<_Tp, m, n>& operator *= (Matx<_Tp, m, n>& a, double alpha) +{ + for( int i = 0; i < m*n; i++ ) + a.val[i] = saturate_cast<_Tp>(a.val[i] * alpha); + return a; +} + +template static inline +Matx<_Tp, m, n> operator * (const Matx<_Tp, m, n>& a, int alpha) +{ + return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Matx<_Tp, m, n> operator * (const Matx<_Tp, m, n>& a, float alpha) +{ + return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Matx<_Tp, m, n> operator * (const Matx<_Tp, m, n>& a, double alpha) +{ + return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Matx<_Tp, m, n> operator * (int alpha, const Matx<_Tp, m, n>& a) +{ + return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Matx<_Tp, m, n> operator * (float alpha, const Matx<_Tp, m, n>& a) +{ + return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Matx<_Tp, m, n> operator * (double alpha, const Matx<_Tp, m, n>& a) +{ + return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Matx<_Tp, m, n> operator - (const Matx<_Tp, m, n>& a) +{ + return Matx<_Tp, m, n>(a, -1, Matx_ScaleOp()); +} + +template static inline +Matx<_Tp, m, n> operator * (const Matx<_Tp, m, l>& a, const Matx<_Tp, l, n>& b) +{ + return Matx<_Tp, m, n>(a, b, Matx_MatMulOp()); +} + +template static inline +Vec<_Tp, m> operator * (const Matx<_Tp, m, n>& a, const Vec<_Tp, n>& b) +{ + Matx<_Tp, m, 1> c(a, b, Matx_MatMulOp()); + return (const Vec<_Tp, m>&)(c); +} + +template static inline +bool operator == (const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b) +{ + for( int i = 0; i < m*n; i++ ) + if( a.val[i] != b.val[i] ) return false; + return true; +} + +template static inline +bool operator != (const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b) +{ + return !(a == b); +} + +//! @} + +////////////////////////////// Vec out-of-class operators //////////////////////////////// + +//! @relates cv::Vec +//! @{ + +template static inline +Vec<_Tp1, cn>& operator += (Vec<_Tp1, cn>& a, const Vec<_Tp2, cn>& b) +{ + for( int i = 0; i < cn; i++ ) + a.val[i] = saturate_cast<_Tp1>(a.val[i] + b.val[i]); + return a; +} + +template static inline +Vec<_Tp1, cn>& operator -= (Vec<_Tp1, cn>& a, const Vec<_Tp2, cn>& b) +{ + for( int i = 0; i < cn; i++ ) + a.val[i] = saturate_cast<_Tp1>(a.val[i] - b.val[i]); + return a; +} + +template static inline +Vec<_Tp, cn> operator + (const Vec<_Tp, cn>& a, const Vec<_Tp, cn>& b) +{ + return Vec<_Tp, cn>(a, b, Matx_AddOp()); +} + +template static inline +Vec<_Tp, cn> operator - (const Vec<_Tp, cn>& a, const Vec<_Tp, cn>& b) +{ + return Vec<_Tp, cn>(a, b, Matx_SubOp()); +} + +template static inline +Vec<_Tp, cn>& operator *= (Vec<_Tp, cn>& a, int alpha) +{ + for( int i = 0; i < cn; i++ ) + a[i] = saturate_cast<_Tp>(a[i]*alpha); + return a; +} + +template static inline +Vec<_Tp, cn>& operator *= (Vec<_Tp, cn>& a, float alpha) +{ + for( int i = 0; i < cn; i++ ) + a[i] = saturate_cast<_Tp>(a[i]*alpha); + return a; +} + +template static inline +Vec<_Tp, cn>& operator *= (Vec<_Tp, cn>& a, double alpha) +{ + for( int i = 0; i < cn; i++ ) + a[i] = saturate_cast<_Tp>(a[i]*alpha); + return a; +} + +template static inline +Vec<_Tp, cn>& operator /= (Vec<_Tp, cn>& a, int alpha) +{ + double ialpha = 1./alpha; + for( int i = 0; i < cn; i++ ) + a[i] = saturate_cast<_Tp>(a[i]*ialpha); + return a; +} + +template static inline +Vec<_Tp, cn>& operator /= (Vec<_Tp, cn>& a, float alpha) +{ + float ialpha = 1.f/alpha; + for( int i = 0; i < cn; i++ ) + a[i] = saturate_cast<_Tp>(a[i]*ialpha); + return a; +} + +template static inline +Vec<_Tp, cn>& operator /= (Vec<_Tp, cn>& a, double alpha) +{ + double ialpha = 1./alpha; + for( int i = 0; i < cn; i++ ) + a[i] = saturate_cast<_Tp>(a[i]*ialpha); + return a; +} + +template static inline +Vec<_Tp, cn> operator * (const Vec<_Tp, cn>& a, int alpha) +{ + return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Vec<_Tp, cn> operator * (int alpha, const Vec<_Tp, cn>& a) +{ + return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Vec<_Tp, cn> operator * (const Vec<_Tp, cn>& a, float alpha) +{ + return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Vec<_Tp, cn> operator * (float alpha, const Vec<_Tp, cn>& a) +{ + return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Vec<_Tp, cn> operator * (const Vec<_Tp, cn>& a, double alpha) +{ + return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Vec<_Tp, cn> operator * (double alpha, const Vec<_Tp, cn>& a) +{ + return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp()); +} + +template static inline +Vec<_Tp, cn> operator / (const Vec<_Tp, cn>& a, int alpha) +{ + return Vec<_Tp, cn>(a, 1./alpha, Matx_ScaleOp()); +} + +template static inline +Vec<_Tp, cn> operator / (const Vec<_Tp, cn>& a, float alpha) +{ + return Vec<_Tp, cn>(a, 1.f/alpha, Matx_ScaleOp()); +} + +template static inline +Vec<_Tp, cn> operator / (const Vec<_Tp, cn>& a, double alpha) +{ + return Vec<_Tp, cn>(a, 1./alpha, Matx_ScaleOp()); +} + +template static inline +Vec<_Tp, cn> operator - (const Vec<_Tp, cn>& a) +{ + Vec<_Tp,cn> t; + for( int i = 0; i < cn; i++ ) t.val[i] = saturate_cast<_Tp>(-a.val[i]); + return t; +} + +template inline Vec<_Tp, 4> operator * (const Vec<_Tp, 4>& v1, const Vec<_Tp, 4>& v2) +{ + return Vec<_Tp, 4>(saturate_cast<_Tp>(v1[0]*v2[0] - v1[1]*v2[1] - v1[2]*v2[2] - v1[3]*v2[3]), + saturate_cast<_Tp>(v1[0]*v2[1] + v1[1]*v2[0] + v1[2]*v2[3] - v1[3]*v2[2]), + saturate_cast<_Tp>(v1[0]*v2[2] - v1[1]*v2[3] + v1[2]*v2[0] + v1[3]*v2[1]), + saturate_cast<_Tp>(v1[0]*v2[3] + v1[1]*v2[2] - v1[2]*v2[1] + v1[3]*v2[0])); +} + +template inline Vec<_Tp, 4>& operator *= (Vec<_Tp, 4>& v1, const Vec<_Tp, 4>& v2) +{ + v1 = v1 * v2; + return v1; +} + +//! @} + +} // cv + +#endif // OPENCV_CORE_MATX_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/neon_utils.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/neon_utils.hpp new file mode 100644 index 0000000000000000000000000000000000000000..573ba99ec3569e5dc13b637daf1493b7c1ad6afb --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/neon_utils.hpp @@ -0,0 +1,128 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_HAL_NEON_UTILS_HPP +#define OPENCV_HAL_NEON_UTILS_HPP + +#include "opencv2/core/cvdef.h" + +//! @addtogroup core_utils_neon +//! @{ + +#if CV_NEON + +inline int32x2_t cv_vrnd_s32_f32(float32x2_t v) +{ + static int32x2_t v_sign = vdup_n_s32(1 << 31), + v_05 = vreinterpret_s32_f32(vdup_n_f32(0.5f)); + + int32x2_t v_addition = vorr_s32(v_05, vand_s32(v_sign, vreinterpret_s32_f32(v))); + return vcvt_s32_f32(vadd_f32(v, vreinterpret_f32_s32(v_addition))); +} + +inline int32x4_t cv_vrndq_s32_f32(float32x4_t v) +{ + static int32x4_t v_sign = vdupq_n_s32(1 << 31), + v_05 = vreinterpretq_s32_f32(vdupq_n_f32(0.5f)); + + int32x4_t v_addition = vorrq_s32(v_05, vandq_s32(v_sign, vreinterpretq_s32_f32(v))); + return vcvtq_s32_f32(vaddq_f32(v, vreinterpretq_f32_s32(v_addition))); +} + +inline uint32x2_t cv_vrnd_u32_f32(float32x2_t v) +{ + static float32x2_t v_05 = vdup_n_f32(0.5f); + return vcvt_u32_f32(vadd_f32(v, v_05)); +} + +inline uint32x4_t cv_vrndq_u32_f32(float32x4_t v) +{ + static float32x4_t v_05 = vdupq_n_f32(0.5f); + return vcvtq_u32_f32(vaddq_f32(v, v_05)); +} + +inline float32x4_t cv_vrecpq_f32(float32x4_t val) +{ + float32x4_t reciprocal = vrecpeq_f32(val); + reciprocal = vmulq_f32(vrecpsq_f32(val, reciprocal), reciprocal); + reciprocal = vmulq_f32(vrecpsq_f32(val, reciprocal), reciprocal); + return reciprocal; +} + +inline float32x2_t cv_vrecp_f32(float32x2_t val) +{ + float32x2_t reciprocal = vrecpe_f32(val); + reciprocal = vmul_f32(vrecps_f32(val, reciprocal), reciprocal); + reciprocal = vmul_f32(vrecps_f32(val, reciprocal), reciprocal); + return reciprocal; +} + +inline float32x4_t cv_vrsqrtq_f32(float32x4_t val) +{ + float32x4_t e = vrsqrteq_f32(val); + e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(e, e), val), e); + e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(e, e), val), e); + return e; +} + +inline float32x2_t cv_vrsqrt_f32(float32x2_t val) +{ + float32x2_t e = vrsqrte_f32(val); + e = vmul_f32(vrsqrts_f32(vmul_f32(e, e), val), e); + e = vmul_f32(vrsqrts_f32(vmul_f32(e, e), val), e); + return e; +} + +inline float32x4_t cv_vsqrtq_f32(float32x4_t val) +{ + return cv_vrecpq_f32(cv_vrsqrtq_f32(val)); +} + +inline float32x2_t cv_vsqrt_f32(float32x2_t val) +{ + return cv_vrecp_f32(cv_vrsqrt_f32(val)); +} + +#endif + +//! @} + +#endif // OPENCV_HAL_NEON_UTILS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/ocl.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/ocl.hpp new file mode 100644 index 0000000000000000000000000000000000000000..95f0fcdd66987fb97b75905c8a4f0716e338ac2b --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/ocl.hpp @@ -0,0 +1,843 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the OpenCV Foundation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OPENCL_HPP +#define OPENCV_OPENCL_HPP + +#include "opencv2/core.hpp" + +namespace cv { namespace ocl { + +//! @addtogroup core_opencl +//! @{ + +CV_EXPORTS_W bool haveOpenCL(); +CV_EXPORTS_W bool useOpenCL(); +CV_EXPORTS_W bool haveAmdBlas(); +CV_EXPORTS_W bool haveAmdFft(); +CV_EXPORTS_W void setUseOpenCL(bool flag); +CV_EXPORTS_W void finish(); + +CV_EXPORTS bool haveSVM(); + +class CV_EXPORTS Context; +class CV_EXPORTS_W_SIMPLE Device; +class CV_EXPORTS Kernel; +class CV_EXPORTS Program; +class CV_EXPORTS ProgramSource; +class CV_EXPORTS Queue; +class CV_EXPORTS PlatformInfo; +class CV_EXPORTS Image2D; + +class CV_EXPORTS_W_SIMPLE Device +{ +public: + CV_WRAP Device(); + explicit Device(void* d); + Device(const Device& d); + Device& operator = (const Device& d); + CV_WRAP ~Device(); + + void set(void* d); + + enum + { + TYPE_DEFAULT = (1 << 0), + TYPE_CPU = (1 << 1), + TYPE_GPU = (1 << 2), + TYPE_ACCELERATOR = (1 << 3), + TYPE_DGPU = TYPE_GPU + (1 << 16), + TYPE_IGPU = TYPE_GPU + (1 << 17), + TYPE_ALL = 0xFFFFFFFF + }; + + CV_WRAP String name() const; + CV_WRAP String extensions() const; + CV_WRAP bool isExtensionSupported(const String& extensionName) const; + CV_WRAP String version() const; + CV_WRAP String vendorName() const; + CV_WRAP String OpenCL_C_Version() const; + CV_WRAP String OpenCLVersion() const; + CV_WRAP int deviceVersionMajor() const; + CV_WRAP int deviceVersionMinor() const; + CV_WRAP String driverVersion() const; + void* ptr() const; + + CV_WRAP int type() const; + + CV_WRAP int addressBits() const; + CV_WRAP bool available() const; + CV_WRAP bool compilerAvailable() const; + CV_WRAP bool linkerAvailable() const; + + enum + { + FP_DENORM=(1 << 0), + FP_INF_NAN=(1 << 1), + FP_ROUND_TO_NEAREST=(1 << 2), + FP_ROUND_TO_ZERO=(1 << 3), + FP_ROUND_TO_INF=(1 << 4), + FP_FMA=(1 << 5), + FP_SOFT_FLOAT=(1 << 6), + FP_CORRECTLY_ROUNDED_DIVIDE_SQRT=(1 << 7) + }; + CV_WRAP int doubleFPConfig() const; + CV_WRAP int singleFPConfig() const; + CV_WRAP int halfFPConfig() const; + + CV_WRAP bool endianLittle() const; + CV_WRAP bool errorCorrectionSupport() const; + + enum + { + EXEC_KERNEL=(1 << 0), + EXEC_NATIVE_KERNEL=(1 << 1) + }; + CV_WRAP int executionCapabilities() const; + + CV_WRAP size_t globalMemCacheSize() const; + + enum + { + NO_CACHE=0, + READ_ONLY_CACHE=1, + READ_WRITE_CACHE=2 + }; + CV_WRAP int globalMemCacheType() const; + CV_WRAP int globalMemCacheLineSize() const; + CV_WRAP size_t globalMemSize() const; + + CV_WRAP size_t localMemSize() const; + enum + { + NO_LOCAL_MEM=0, + LOCAL_IS_LOCAL=1, + LOCAL_IS_GLOBAL=2 + }; + CV_WRAP int localMemType() const; + CV_WRAP bool hostUnifiedMemory() const; + + CV_WRAP bool imageSupport() const; + + CV_WRAP bool imageFromBufferSupport() const; + uint imagePitchAlignment() const; + uint imageBaseAddressAlignment() const; + + /// deprecated, use isExtensionSupported() method (probably with "cl_khr_subgroups" value) + CV_WRAP bool intelSubgroupsSupport() const; + + CV_WRAP size_t image2DMaxWidth() const; + CV_WRAP size_t image2DMaxHeight() const; + + CV_WRAP size_t image3DMaxWidth() const; + CV_WRAP size_t image3DMaxHeight() const; + CV_WRAP size_t image3DMaxDepth() const; + + CV_WRAP size_t imageMaxBufferSize() const; + CV_WRAP size_t imageMaxArraySize() const; + + enum + { + UNKNOWN_VENDOR=0, + VENDOR_AMD=1, + VENDOR_INTEL=2, + VENDOR_NVIDIA=3 + }; + CV_WRAP int vendorID() const; + // FIXIT + // dev.isAMD() doesn't work for OpenCL CPU devices from AMD OpenCL platform. + // This method should use platform name instead of vendor name. + // After fix restore code in arithm.cpp: ocl_compare() + CV_WRAP inline bool isAMD() const { return vendorID() == VENDOR_AMD; } + CV_WRAP inline bool isIntel() const { return vendorID() == VENDOR_INTEL; } + CV_WRAP inline bool isNVidia() const { return vendorID() == VENDOR_NVIDIA; } + + CV_WRAP int maxClockFrequency() const; + CV_WRAP int maxComputeUnits() const; + CV_WRAP int maxConstantArgs() const; + CV_WRAP size_t maxConstantBufferSize() const; + + CV_WRAP size_t maxMemAllocSize() const; + CV_WRAP size_t maxParameterSize() const; + + CV_WRAP int maxReadImageArgs() const; + CV_WRAP int maxWriteImageArgs() const; + CV_WRAP int maxSamplers() const; + + CV_WRAP size_t maxWorkGroupSize() const; + CV_WRAP int maxWorkItemDims() const; + void maxWorkItemSizes(size_t*) const; + + CV_WRAP int memBaseAddrAlign() const; + + CV_WRAP int nativeVectorWidthChar() const; + CV_WRAP int nativeVectorWidthShort() const; + CV_WRAP int nativeVectorWidthInt() const; + CV_WRAP int nativeVectorWidthLong() const; + CV_WRAP int nativeVectorWidthFloat() const; + CV_WRAP int nativeVectorWidthDouble() const; + CV_WRAP int nativeVectorWidthHalf() const; + + CV_WRAP int preferredVectorWidthChar() const; + CV_WRAP int preferredVectorWidthShort() const; + CV_WRAP int preferredVectorWidthInt() const; + CV_WRAP int preferredVectorWidthLong() const; + CV_WRAP int preferredVectorWidthFloat() const; + CV_WRAP int preferredVectorWidthDouble() const; + CV_WRAP int preferredVectorWidthHalf() const; + + CV_WRAP size_t printfBufferSize() const; + CV_WRAP size_t profilingTimerResolution() const; + + CV_WRAP static const Device& getDefault(); + +protected: + struct Impl; + Impl* p; +}; + + +class CV_EXPORTS Context +{ +public: + Context(); + explicit Context(int dtype); + ~Context(); + Context(const Context& c); + Context& operator = (const Context& c); + + bool create(); + bool create(int dtype); + size_t ndevices() const; + const Device& device(size_t idx) const; + Program getProg(const ProgramSource& prog, + const String& buildopt, String& errmsg); + void unloadProg(Program& prog); + + static Context& getDefault(bool initialize = true); + void* ptr() const; + + friend void initializeContextFromHandle(Context& ctx, void* platform, void* context, void* device); + + bool useSVM() const; + void setUseSVM(bool enabled); + + struct Impl; + inline Impl* getImpl() const { return (Impl*)p; } +//protected: + Impl* p; +}; + +class CV_EXPORTS Platform +{ +public: + Platform(); + ~Platform(); + Platform(const Platform& p); + Platform& operator = (const Platform& p); + + void* ptr() const; + static Platform& getDefault(); + + friend void initializeContextFromHandle(Context& ctx, void* platform, void* context, void* device); +protected: + struct Impl; + Impl* p; +}; + +/** @brief Attaches OpenCL context to OpenCV +@note + OpenCV will check if available OpenCL platform has platformName name, then assign context to + OpenCV and call `clRetainContext` function. The deviceID device will be used as target device and + new command queue will be created. +@param platformName name of OpenCL platform to attach, this string is used to check if platform is available to OpenCV at runtime +@param platformID ID of platform attached context was created for +@param context OpenCL context to be attached to OpenCV +@param deviceID ID of device, must be created from attached context +*/ +CV_EXPORTS void attachContext(const String& platformName, void* platformID, void* context, void* deviceID); + +/** @brief Convert OpenCL buffer to UMat +@note + OpenCL buffer (cl_mem_buffer) should contain 2D image data, compatible with OpenCV. Memory + content is not copied from `clBuffer` to UMat. Instead, buffer handle assigned to UMat and + `clRetainMemObject` is called. +@param cl_mem_buffer source clBuffer handle +@param step num of bytes in single row +@param rows number of rows +@param cols number of cols +@param type OpenCV type of image +@param dst destination UMat +*/ +CV_EXPORTS void convertFromBuffer(void* cl_mem_buffer, size_t step, int rows, int cols, int type, UMat& dst); + +/** @brief Convert OpenCL image2d_t to UMat +@note + OpenCL `image2d_t` (cl_mem_image), should be compatible with OpenCV UMat formats. Memory content + is copied from image to UMat with `clEnqueueCopyImageToBuffer` function. +@param cl_mem_image source image2d_t handle +@param dst destination UMat +*/ +CV_EXPORTS void convertFromImage(void* cl_mem_image, UMat& dst); + +// TODO Move to internal header +void initializeContextFromHandle(Context& ctx, void* platform, void* context, void* device); + +class CV_EXPORTS Queue +{ +public: + Queue(); + explicit Queue(const Context& c, const Device& d=Device()); + ~Queue(); + Queue(const Queue& q); + Queue& operator = (const Queue& q); + + bool create(const Context& c=Context(), const Device& d=Device()); + void finish(); + void* ptr() const; + static Queue& getDefault(); + + /// @brief Returns OpenCL command queue with enable profiling mode support + const Queue& getProfilingQueue() const; + + struct Impl; friend struct Impl; + inline Impl* getImpl() const { return p; } +protected: + Impl* p; +}; + + +class CV_EXPORTS KernelArg +{ +public: + enum { LOCAL=1, READ_ONLY=2, WRITE_ONLY=4, READ_WRITE=6, CONSTANT=8, PTR_ONLY = 16, NO_SIZE=256 }; + KernelArg(int _flags, UMat* _m, int wscale=1, int iwscale=1, const void* _obj=0, size_t _sz=0); + KernelArg(); + + static KernelArg Local(size_t localMemSize) + { return KernelArg(LOCAL, 0, 1, 1, 0, localMemSize); } + static KernelArg PtrWriteOnly(const UMat& m) + { return KernelArg(PTR_ONLY+WRITE_ONLY, (UMat*)&m); } + static KernelArg PtrReadOnly(const UMat& m) + { return KernelArg(PTR_ONLY+READ_ONLY, (UMat*)&m); } + static KernelArg PtrReadWrite(const UMat& m) + { return KernelArg(PTR_ONLY+READ_WRITE, (UMat*)&m); } + static KernelArg ReadWrite(const UMat& m, int wscale=1, int iwscale=1) + { return KernelArg(READ_WRITE, (UMat*)&m, wscale, iwscale); } + static KernelArg ReadWriteNoSize(const UMat& m, int wscale=1, int iwscale=1) + { return KernelArg(READ_WRITE+NO_SIZE, (UMat*)&m, wscale, iwscale); } + static KernelArg ReadOnly(const UMat& m, int wscale=1, int iwscale=1) + { return KernelArg(READ_ONLY, (UMat*)&m, wscale, iwscale); } + static KernelArg WriteOnly(const UMat& m, int wscale=1, int iwscale=1) + { return KernelArg(WRITE_ONLY, (UMat*)&m, wscale, iwscale); } + static KernelArg ReadOnlyNoSize(const UMat& m, int wscale=1, int iwscale=1) + { return KernelArg(READ_ONLY+NO_SIZE, (UMat*)&m, wscale, iwscale); } + static KernelArg WriteOnlyNoSize(const UMat& m, int wscale=1, int iwscale=1) + { return KernelArg(WRITE_ONLY+NO_SIZE, (UMat*)&m, wscale, iwscale); } + static KernelArg Constant(const Mat& m); + template static KernelArg Constant(const _Tp* arr, size_t n) + { return KernelArg(CONSTANT, 0, 1, 1, (void*)arr, n); } + + int flags; + UMat* m; + const void* obj; + size_t sz; + int wscale, iwscale; +}; + + +class CV_EXPORTS Kernel +{ +public: + Kernel(); + Kernel(const char* kname, const Program& prog); + Kernel(const char* kname, const ProgramSource& prog, + const String& buildopts = String(), String* errmsg=0); + ~Kernel(); + Kernel(const Kernel& k); + Kernel& operator = (const Kernel& k); + + bool empty() const; + bool create(const char* kname, const Program& prog); + bool create(const char* kname, const ProgramSource& prog, + const String& buildopts, String* errmsg=0); + + int set(int i, const void* value, size_t sz); + int set(int i, const Image2D& image2D); + int set(int i, const UMat& m); + int set(int i, const KernelArg& arg); + template int set(int i, const _Tp& value) + { return set(i, &value, sizeof(value)); } + + template + Kernel& args(const _Tp0& a0) + { + set(0, a0); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1) + { + int i = set(0, a0); set(i, a1); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2) + { + int i = set(0, a0); i = set(i, a1); set(i, a2); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, + const _Tp3& a3, const _Tp4& a4) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); + i = set(i, a3); set(i, a4); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, + const _Tp3& a3, const _Tp4& a4, const _Tp5& a5) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); + i = set(i, a3); i = set(i, a4); set(i, a5); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, + const _Tp4& a4, const _Tp5& a5, const _Tp6& a6) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); + i = set(i, a4); i = set(i, a5); set(i, a6); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, + const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); + i = set(i, a4); i = set(i, a5); i = set(i, a6); set(i, a7); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, + const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, + const _Tp8& a8) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); + i = set(i, a5); i = set(i, a6); i = set(i, a7); set(i, a8); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, + const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, + const _Tp8& a8, const _Tp9& a9) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5); + i = set(i, a6); i = set(i, a7); i = set(i, a8); set(i, a9); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, + const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, + const _Tp8& a8, const _Tp9& a9, const _Tp10& a10) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5); + i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); set(i, a10); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, + const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, + const _Tp8& a8, const _Tp9& a9, const _Tp10& a10, const _Tp11& a11) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5); + i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); i = set(i, a10); set(i, a11); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, + const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, + const _Tp8& a8, const _Tp9& a9, const _Tp10& a10, const _Tp11& a11, + const _Tp12& a12) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5); + i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); i = set(i, a10); i = set(i, a11); + set(i, a12); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, + const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, + const _Tp8& a8, const _Tp9& a9, const _Tp10& a10, const _Tp11& a11, + const _Tp12& a12, const _Tp13& a13) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5); + i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); i = set(i, a10); i = set(i, a11); + i = set(i, a12); set(i, a13); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, + const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, + const _Tp8& a8, const _Tp9& a9, const _Tp10& a10, const _Tp11& a11, + const _Tp12& a12, const _Tp13& a13, const _Tp14& a14) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5); + i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); i = set(i, a10); i = set(i, a11); + i = set(i, a12); i = set(i, a13); set(i, a14); return *this; + } + + template + Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, + const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, + const _Tp8& a8, const _Tp9& a9, const _Tp10& a10, const _Tp11& a11, + const _Tp12& a12, const _Tp13& a13, const _Tp14& a14, const _Tp15& a15) + { + int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5); + i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); i = set(i, a10); i = set(i, a11); + i = set(i, a12); i = set(i, a13); i = set(i, a14); set(i, a15); return *this; + } + /** @brief Run the OpenCL kernel. + @param dims the work problem dimensions. It is the length of globalsize and localsize. It can be either 1, 2 or 3. + @param globalsize work items for each dimension. It is not the final globalsize passed to + OpenCL. Each dimension will be adjusted to the nearest integer divisible by the corresponding + value in localsize. If localsize is NULL, it will still be adjusted depending on dims. The + adjusted values are greater than or equal to the original values. + @param localsize work-group size for each dimension. + @param sync specify whether to wait for OpenCL computation to finish before return. + @param q command queue + */ + bool run(int dims, size_t globalsize[], + size_t localsize[], bool sync, const Queue& q=Queue()); + bool runTask(bool sync, const Queue& q=Queue()); + + /** @brief Similar to synchronized run() call with returning of kernel execution time + * Separate OpenCL command queue may be used (with CL_QUEUE_PROFILING_ENABLE) + * @return Execution time in nanoseconds or negative number on error + */ + int64 runProfiling(int dims, size_t globalsize[], size_t localsize[], const Queue& q=Queue()); + + size_t workGroupSize() const; + size_t preferedWorkGroupSizeMultiple() const; + bool compileWorkGroupSize(size_t wsz[]) const; + size_t localMemSize() const; + + void* ptr() const; + struct Impl; + +protected: + Impl* p; +}; + +class CV_EXPORTS Program +{ +public: + Program(); + Program(const ProgramSource& src, + const String& buildflags, String& errmsg); + Program(const Program& prog); + + Program& operator = (const Program& prog); + ~Program(); + + bool create(const ProgramSource& src, + const String& buildflags, String& errmsg); + + void* ptr() const; + + /** + * @brief Query device-specific program binary. + * + * Returns RAW OpenCL executable binary without additional attachments. + * + * @sa ProgramSource::fromBinary + * + * @param[out] binary output buffer + */ + void getBinary(std::vector& binary) const; + + struct Impl; friend struct Impl; + inline Impl* getImpl() const { return (Impl*)p; } +protected: + Impl* p; +public: +#ifndef OPENCV_REMOVE_DEPRECATED_API + // TODO Remove this + CV_DEPRECATED bool read(const String& buf, const String& buildflags); // removed, use ProgramSource instead + CV_DEPRECATED bool write(String& buf) const; // removed, use getBinary() method instead (RAW OpenCL binary) + CV_DEPRECATED const ProgramSource& source() const; // implementation removed + CV_DEPRECATED String getPrefix() const; // deprecated, implementation replaced + CV_DEPRECATED static String getPrefix(const String& buildflags); // deprecated, implementation replaced +#endif +}; + + +class CV_EXPORTS ProgramSource +{ +public: + typedef uint64 hash_t; // deprecated + + ProgramSource(); + explicit ProgramSource(const String& module, const String& name, const String& codeStr, const String& codeHash); + explicit ProgramSource(const String& prog); // deprecated + explicit ProgramSource(const char* prog); // deprecated + ~ProgramSource(); + ProgramSource(const ProgramSource& prog); + ProgramSource& operator = (const ProgramSource& prog); + + const String& source() const; // deprecated + hash_t hash() const; // deprecated + + + /** @brief Describe OpenCL program binary. + * Do not call clCreateProgramWithBinary() and/or clBuildProgram(). + * + * Caller should guarantee binary buffer lifetime greater than ProgramSource object (and any of its copies). + * + * This kind of binary is not portable between platforms in general - it is specific to OpenCL vendor / device / driver version. + * + * @param module name of program owner module + * @param name unique name of program (module+name is used as key for OpenCL program caching) + * @param binary buffer address. See buffer lifetime requirement in description. + * @param size buffer size + * @param buildOptions additional program-related build options passed to clBuildProgram() + * @return created ProgramSource object + */ + static ProgramSource fromBinary(const String& module, const String& name, + const unsigned char* binary, const size_t size, + const cv::String& buildOptions = cv::String()); + + /** @brief Describe OpenCL program in SPIR format. + * Do not call clCreateProgramWithBinary() and/or clBuildProgram(). + * + * Supports SPIR 1.2 by default (pass '-spir-std=X.Y' in buildOptions to override this behavior) + * + * Caller should guarantee binary buffer lifetime greater than ProgramSource object (and any of its copies). + * + * Programs in this format are portable between OpenCL implementations with 'khr_spir' extension: + * https://www.khronos.org/registry/OpenCL/sdk/2.0/docs/man/xhtml/cl_khr_spir.html + * (but they are not portable between different platforms: 32-bit / 64-bit) + * + * Note: these programs can't support vendor specific extensions, like 'cl_intel_subgroups'. + * + * @param module name of program owner module + * @param name unique name of program (module+name is used as key for OpenCL program caching) + * @param binary buffer address. See buffer lifetime requirement in description. + * @param size buffer size + * @param buildOptions additional program-related build options passed to clBuildProgram() + * (these options are added automatically: '-x spir' and '-spir-std=1.2') + * @return created ProgramSource object. + */ + static ProgramSource fromSPIR(const String& module, const String& name, + const unsigned char* binary, const size_t size, + const cv::String& buildOptions = cv::String()); + + //OpenCL 2.1+ only + //static Program fromSPIRV(const String& module, const String& name, + // const unsigned char* binary, const size_t size, + // const cv::String& buildOptions = cv::String()); + + struct Impl; friend struct Impl; + inline Impl* getImpl() const { return (Impl*)p; } +protected: + Impl* p; +}; + +class CV_EXPORTS PlatformInfo +{ +public: + PlatformInfo(); + explicit PlatformInfo(void* id); + ~PlatformInfo(); + + PlatformInfo(const PlatformInfo& i); + PlatformInfo& operator =(const PlatformInfo& i); + + String name() const; + String vendor() const; + String version() const; + int deviceNumber() const; + void getDevice(Device& device, int d) const; + +protected: + struct Impl; + Impl* p; +}; + +CV_EXPORTS const char* convertTypeStr(int sdepth, int ddepth, int cn, char* buf); +CV_EXPORTS const char* typeToStr(int t); +CV_EXPORTS const char* memopTypeToStr(int t); +CV_EXPORTS const char* vecopTypeToStr(int t); +CV_EXPORTS const char* getOpenCLErrorString(int errorCode); +CV_EXPORTS String kernelToStr(InputArray _kernel, int ddepth = -1, const char * name = NULL); +CV_EXPORTS void getPlatfomsInfo(std::vector& platform_info); + + +enum OclVectorStrategy +{ + // all matrices have its own vector width + OCL_VECTOR_OWN = 0, + // all matrices have maximal vector width among all matrices + // (useful for cases when matrices have different data types) + OCL_VECTOR_MAX = 1, + + // default strategy + OCL_VECTOR_DEFAULT = OCL_VECTOR_OWN +}; + +CV_EXPORTS int predictOptimalVectorWidth(InputArray src1, InputArray src2 = noArray(), InputArray src3 = noArray(), + InputArray src4 = noArray(), InputArray src5 = noArray(), InputArray src6 = noArray(), + InputArray src7 = noArray(), InputArray src8 = noArray(), InputArray src9 = noArray(), + OclVectorStrategy strat = OCL_VECTOR_DEFAULT); + +CV_EXPORTS int checkOptimalVectorWidth(const int *vectorWidths, + InputArray src1, InputArray src2 = noArray(), InputArray src3 = noArray(), + InputArray src4 = noArray(), InputArray src5 = noArray(), InputArray src6 = noArray(), + InputArray src7 = noArray(), InputArray src8 = noArray(), InputArray src9 = noArray(), + OclVectorStrategy strat = OCL_VECTOR_DEFAULT); + +// with OCL_VECTOR_MAX strategy +CV_EXPORTS int predictOptimalVectorWidthMax(InputArray src1, InputArray src2 = noArray(), InputArray src3 = noArray(), + InputArray src4 = noArray(), InputArray src5 = noArray(), InputArray src6 = noArray(), + InputArray src7 = noArray(), InputArray src8 = noArray(), InputArray src9 = noArray()); + +CV_EXPORTS void buildOptionsAddMatrixDescription(String& buildOptions, const String& name, InputArray _m); + +class CV_EXPORTS Image2D +{ +public: + Image2D(); + + /** + @param src UMat object from which to get image properties and data + @param norm flag to enable the use of normalized channel data types + @param alias flag indicating that the image should alias the src UMat. If true, changes to the + image or src will be reflected in both objects. + */ + explicit Image2D(const UMat &src, bool norm = false, bool alias = false); + Image2D(const Image2D & i); + ~Image2D(); + + Image2D & operator = (const Image2D & i); + + /** Indicates if creating an aliased image should succeed. + Depends on the underlying platform and the dimensions of the UMat. + */ + static bool canCreateAlias(const UMat &u); + + /** Indicates if the image format is supported. + */ + static bool isFormatSupported(int depth, int cn, bool norm); + + void* ptr() const; +protected: + struct Impl; + Impl* p; +}; + +class CV_EXPORTS Timer +{ +public: + Timer(const Queue& q); + ~Timer(); + void start(); + void stop(); + + uint64 durationNS() const; //< duration in nanoseconds + +protected: + struct Impl; + Impl* const p; + +private: + Timer(const Timer&); // disabled + Timer& operator=(const Timer&); // disabled +}; + +CV_EXPORTS MatAllocator* getOpenCLAllocator(); + + +#ifdef __OPENCV_BUILD +namespace internal { + +CV_EXPORTS bool isOpenCLForced(); +#define OCL_FORCE_CHECK(condition) (cv::ocl::internal::isOpenCLForced() || (condition)) + +CV_EXPORTS bool isPerformanceCheckBypassed(); +#define OCL_PERFORMANCE_CHECK(condition) (cv::ocl::internal::isPerformanceCheckBypassed() || (condition)) + +CV_EXPORTS bool isCLBuffer(UMat& u); + +} // namespace internal +#endif + +//! @} + +}} + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/ocl_genbase.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/ocl_genbase.hpp new file mode 100644 index 0000000000000000000000000000000000000000..5334cf1f4f2db81ade82e43035396fb8ea9dc1e0 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/ocl_genbase.hpp @@ -0,0 +1,69 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the OpenCV Foundation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OPENCL_GENBASE_HPP +#define OPENCV_OPENCL_GENBASE_HPP + +//! @cond IGNORED + +namespace cv { +namespace ocl { + +class ProgramSource; + +namespace internal { + +struct CV_EXPORTS ProgramEntry +{ + const char* module; + const char* name; + const char* programCode; + const char* programHash; + ProgramSource* pProgramSource; + + operator ProgramSource& () const; +}; + +} } } // namespace + +//! @endcond + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/ocl_defs.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/ocl_defs.hpp new file mode 100644 index 0000000000000000000000000000000000000000..605a65f8d8fc42885eabc2e9e30cbb47c26f7bad --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/ocl_defs.hpp @@ -0,0 +1,75 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +// Copyright (C) 2014, Advanced Micro Devices, Inc., all rights reserved. +// Third party copyrights are property of their respective owners. + +#ifndef OPENCV_CORE_OPENCL_DEFS_HPP +#define OPENCV_CORE_OPENCL_DEFS_HPP + +#include "opencv2/core/utility.hpp" +#include "cvconfig.h" + +namespace cv { namespace ocl { +#ifdef HAVE_OPENCL +/// Call is similar to useOpenCL() but doesn't try to load OpenCL runtime or create OpenCL context +CV_EXPORTS bool isOpenCLActivated(); +#else +static inline bool isOpenCLActivated() { return false; } +#endif +}} // namespace + + +//#define CV_OPENCL_RUN_ASSERT + +#ifdef HAVE_OPENCL + +#ifdef CV_OPENCL_RUN_VERBOSE +#define CV_OCL_RUN_(condition, func, ...) \ + { \ + if (cv::ocl::isOpenCLActivated() && (condition) && func) \ + { \ + printf("%s: OpenCL implementation is running\n", CV_Func); \ + fflush(stdout); \ + CV_IMPL_ADD(CV_IMPL_OCL); \ + return __VA_ARGS__; \ + } \ + else \ + { \ + printf("%s: Plain implementation is running\n", CV_Func); \ + fflush(stdout); \ + } \ + } +#elif defined CV_OPENCL_RUN_ASSERT +#define CV_OCL_RUN_(condition, func, ...) \ + { \ + if (cv::ocl::isOpenCLActivated() && (condition)) \ + { \ + if(func) \ + { \ + CV_IMPL_ADD(CV_IMPL_OCL); \ + } \ + else \ + { \ + CV_Error(cv::Error::StsAssert, #func); \ + } \ + return __VA_ARGS__; \ + } \ + } +#else +#define CV_OCL_RUN_(condition, func, ...) \ + if (cv::ocl::isOpenCLActivated() && (condition) && func) \ + { \ + CV_IMPL_ADD(CV_IMPL_OCL); \ + return __VA_ARGS__; \ + } +#endif + +#else +#define CV_OCL_RUN_(condition, func, ...) +#endif + +#define CV_OCL_RUN(condition, func) CV_OCL_RUN_(condition, func) + +#endif // OPENCV_CORE_OPENCL_DEFS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/opencl_info.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/opencl_info.hpp new file mode 100644 index 0000000000000000000000000000000000000000..b5d37394a73670bc04d2edde1725072b9c51c523 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/opencl_info.hpp @@ -0,0 +1,198 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#include + +#include +#include + +#ifndef DUMP_CONFIG_PROPERTY +#define DUMP_CONFIG_PROPERTY(...) +#endif + +#ifndef DUMP_MESSAGE_STDOUT +#define DUMP_MESSAGE_STDOUT(...) do { std::cout << __VA_ARGS__ << std::endl; } while (false) +#endif + +namespace cv { + +namespace { +static std::string bytesToStringRepr(size_t value) +{ + size_t b = value % 1024; + value /= 1024; + + size_t kb = value % 1024; + value /= 1024; + + size_t mb = value % 1024; + value /= 1024; + + size_t gb = value; + + std::ostringstream stream; + + if (gb > 0) + stream << gb << " GB "; + if (mb > 0) + stream << mb << " MB "; + if (kb > 0) + stream << kb << " KB "; + if (b > 0) + stream << b << " B"; + + std::string s = stream.str(); + if (s[s.size() - 1] == ' ') + s = s.substr(0, s.size() - 1); + return s; +} +} // namespace + +static void dumpOpenCLInformation() +{ + using namespace cv::ocl; + + try + { + if (!haveOpenCL() || !useOpenCL()) + { + DUMP_MESSAGE_STDOUT("OpenCL is disabled"); + DUMP_CONFIG_PROPERTY("cv_ocl", "disabled"); + return; + } + + std::vector platforms; + cv::ocl::getPlatfomsInfo(platforms); + if (platforms.size() > 0) + { + DUMP_MESSAGE_STDOUT("OpenCL Platforms: "); + for (size_t i = 0; i < platforms.size(); i++) + { + const PlatformInfo* platform = &platforms[i]; + DUMP_MESSAGE_STDOUT(" " << platform->name().c_str()); + Device current_device; + for (int j = 0; j < platform->deviceNumber(); j++) + { + platform->getDevice(current_device, j); + const char* deviceTypeStr = current_device.type() == Device::TYPE_CPU + ? ("CPU") : (current_device.type() == Device::TYPE_GPU ? current_device.hostUnifiedMemory() ? "iGPU" : "dGPU" : "unknown"); + DUMP_MESSAGE_STDOUT( " " << deviceTypeStr << ": " << current_device.name().c_str() << " (" << current_device.version().c_str() << ")"); + DUMP_CONFIG_PROPERTY( cv::format("cv_ocl_platform_%d_device_%d", (int)i, (int)j ), + cv::format("(Platform=%s)(Type=%s)(Name=%s)(Version=%s)", + platform->name().c_str(), deviceTypeStr, current_device.name().c_str(), current_device.version().c_str()) ); + } + } + } + else + { + DUMP_MESSAGE_STDOUT("OpenCL is not available"); + DUMP_CONFIG_PROPERTY("cv_ocl", "not available"); + return; + } + + const Device& device = Device::getDefault(); + if (!device.available()) + CV_Error(Error::OpenCLInitError, "OpenCL device is not available"); + + DUMP_MESSAGE_STDOUT("Current OpenCL device: "); + +#if 0 + DUMP_MESSAGE_STDOUT(" Platform = " << device.getPlatform().name()); + DUMP_CONFIG_PROPERTY("cv_ocl_current_platformName", device.getPlatform().name()); +#endif + + const char* deviceTypeStr = device.type() == Device::TYPE_CPU + ? ("CPU") : (device.type() == Device::TYPE_GPU ? device.hostUnifiedMemory() ? "iGPU" : "dGPU" : "unknown"); + DUMP_MESSAGE_STDOUT(" Type = " << deviceTypeStr); + DUMP_CONFIG_PROPERTY("cv_ocl_current_deviceType", deviceTypeStr); + + DUMP_MESSAGE_STDOUT(" Name = " << device.name()); + DUMP_CONFIG_PROPERTY("cv_ocl_current_deviceName", device.name()); + + DUMP_MESSAGE_STDOUT(" Version = " << device.version()); + DUMP_CONFIG_PROPERTY("cv_ocl_current_deviceVersion", device.version()); + + DUMP_MESSAGE_STDOUT(" Driver version = " << device.driverVersion()); + DUMP_CONFIG_PROPERTY("cv_ocl_current_driverVersion", device.driverVersion()); + + DUMP_MESSAGE_STDOUT(" Address bits = " << device.addressBits()); + DUMP_CONFIG_PROPERTY("cv_ocl_current_addressBits", device.addressBits()); + + DUMP_MESSAGE_STDOUT(" Compute units = " << device.maxComputeUnits()); + DUMP_CONFIG_PROPERTY("cv_ocl_current_maxComputeUnits", device.maxComputeUnits()); + + DUMP_MESSAGE_STDOUT(" Max work group size = " << device.maxWorkGroupSize()); + DUMP_CONFIG_PROPERTY("cv_ocl_current_maxWorkGroupSize", device.maxWorkGroupSize()); + + std::string localMemorySizeStr = bytesToStringRepr(device.localMemSize()); + DUMP_MESSAGE_STDOUT(" Local memory size = " << localMemorySizeStr); + DUMP_CONFIG_PROPERTY("cv_ocl_current_localMemSize", device.localMemSize()); + + std::string maxMemAllocSizeStr = bytesToStringRepr(device.maxMemAllocSize()); + DUMP_MESSAGE_STDOUT(" Max memory allocation size = " << maxMemAllocSizeStr); + DUMP_CONFIG_PROPERTY("cv_ocl_current_maxMemAllocSize", device.maxMemAllocSize()); + + const char* doubleSupportStr = device.doubleFPConfig() > 0 ? "Yes" : "No"; + DUMP_MESSAGE_STDOUT(" Double support = " << doubleSupportStr); + DUMP_CONFIG_PROPERTY("cv_ocl_current_haveDoubleSupport", device.doubleFPConfig() > 0); + + const char* isUnifiedMemoryStr = device.hostUnifiedMemory() ? "Yes" : "No"; + DUMP_MESSAGE_STDOUT(" Host unified memory = " << isUnifiedMemoryStr); + DUMP_CONFIG_PROPERTY("cv_ocl_current_hostUnifiedMemory", device.hostUnifiedMemory()); + + DUMP_MESSAGE_STDOUT(" Device extensions:"); + String extensionsStr = device.extensions(); + size_t pos = 0; + while (pos < extensionsStr.size()) + { + size_t pos2 = extensionsStr.find(' ', pos); + if (pos2 == String::npos) + pos2 = extensionsStr.size(); + if (pos2 > pos) + { + String extensionName = extensionsStr.substr(pos, pos2 - pos); + DUMP_MESSAGE_STDOUT(" " << extensionName); + } + pos = pos2 + 1; + } + DUMP_CONFIG_PROPERTY("cv_ocl_current_extensions", extensionsStr.c_str()); + + const char* haveAmdBlasStr = haveAmdBlas() ? "Yes" : "No"; + DUMP_MESSAGE_STDOUT(" Has AMD Blas = " << haveAmdBlasStr); + DUMP_CONFIG_PROPERTY("cv_ocl_current_AmdBlas", haveAmdBlas()); + + const char* haveAmdFftStr = haveAmdFft() ? "Yes" : "No"; + DUMP_MESSAGE_STDOUT(" Has AMD Fft = " << haveAmdFftStr); + DUMP_CONFIG_PROPERTY("cv_ocl_current_AmdFft", haveAmdFft()); + + + DUMP_MESSAGE_STDOUT(" Preferred vector width char = " << device.preferredVectorWidthChar()); + DUMP_CONFIG_PROPERTY("cv_ocl_current_preferredVectorWidthChar", device.preferredVectorWidthChar()); + + DUMP_MESSAGE_STDOUT(" Preferred vector width short = " << device.preferredVectorWidthShort()); + DUMP_CONFIG_PROPERTY("cv_ocl_current_preferredVectorWidthShort", device.preferredVectorWidthShort()); + + DUMP_MESSAGE_STDOUT(" Preferred vector width int = " << device.preferredVectorWidthInt()); + DUMP_CONFIG_PROPERTY("cv_ocl_current_preferredVectorWidthInt", device.preferredVectorWidthInt()); + + DUMP_MESSAGE_STDOUT(" Preferred vector width long = " << device.preferredVectorWidthLong()); + DUMP_CONFIG_PROPERTY("cv_ocl_current_preferredVectorWidthLong", device.preferredVectorWidthLong()); + + DUMP_MESSAGE_STDOUT(" Preferred vector width float = " << device.preferredVectorWidthFloat()); + DUMP_CONFIG_PROPERTY("cv_ocl_current_preferredVectorWidthFloat", device.preferredVectorWidthFloat()); + + DUMP_MESSAGE_STDOUT(" Preferred vector width double = " << device.preferredVectorWidthDouble()); + DUMP_CONFIG_PROPERTY("cv_ocl_current_preferredVectorWidthDouble", device.preferredVectorWidthDouble()); + } + catch (...) + { + DUMP_MESSAGE_STDOUT("Exception. Can't dump OpenCL info"); + DUMP_MESSAGE_STDOUT("OpenCL device not available"); + DUMP_CONFIG_PROPERTY("cv_ocl", "not available"); + } +} +#undef DUMP_MESSAGE_STDOUT +#undef DUMP_CONFIG_PROPERTY + +} // namespace diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/opencl_svm.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/opencl_svm.hpp new file mode 100644 index 0000000000000000000000000000000000000000..7453082a67b374c01591e7480bbf62f595683fa5 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/opencl_svm.hpp @@ -0,0 +1,81 @@ +/* See LICENSE file in the root OpenCV directory */ + +#ifndef OPENCV_CORE_OPENCL_SVM_HPP +#define OPENCV_CORE_OPENCL_SVM_HPP + +// +// Internal usage only (binary compatibility is not guaranteed) +// +#ifndef __OPENCV_BUILD +#error Internal header file +#endif + +#if defined(HAVE_OPENCL) && defined(HAVE_OPENCL_SVM) +#include "runtime/opencl_core.hpp" +#include "runtime/opencl_svm_20.hpp" +#include "runtime/opencl_svm_hsa_extension.hpp" + +namespace cv { namespace ocl { namespace svm { + +struct SVMCapabilities +{ + enum Value + { + SVM_COARSE_GRAIN_BUFFER = (1 << 0), + SVM_FINE_GRAIN_BUFFER = (1 << 1), + SVM_FINE_GRAIN_SYSTEM = (1 << 2), + SVM_ATOMICS = (1 << 3), + }; + int value_; + + SVMCapabilities(int capabilities = 0) : value_(capabilities) { } + operator int() const { return value_; } + + inline bool isNoSVMSupport() const { return value_ == 0; } + inline bool isSupportCoarseGrainBuffer() const { return (value_ & SVM_COARSE_GRAIN_BUFFER) != 0; } + inline bool isSupportFineGrainBuffer() const { return (value_ & SVM_FINE_GRAIN_BUFFER) != 0; } + inline bool isSupportFineGrainSystem() const { return (value_ & SVM_FINE_GRAIN_SYSTEM) != 0; } + inline bool isSupportAtomics() const { return (value_ & SVM_ATOMICS) != 0; } +}; + +CV_EXPORTS const SVMCapabilities getSVMCapabilitites(const ocl::Context& context); + +struct SVMFunctions +{ + clSVMAllocAMD_fn fn_clSVMAlloc; + clSVMFreeAMD_fn fn_clSVMFree; + clSetKernelArgSVMPointerAMD_fn fn_clSetKernelArgSVMPointer; + //clSetKernelExecInfoAMD_fn fn_clSetKernelExecInfo; + //clEnqueueSVMFreeAMD_fn fn_clEnqueueSVMFree; + clEnqueueSVMMemcpyAMD_fn fn_clEnqueueSVMMemcpy; + clEnqueueSVMMemFillAMD_fn fn_clEnqueueSVMMemFill; + clEnqueueSVMMapAMD_fn fn_clEnqueueSVMMap; + clEnqueueSVMUnmapAMD_fn fn_clEnqueueSVMUnmap; + + inline SVMFunctions() + : fn_clSVMAlloc(NULL), fn_clSVMFree(NULL), + fn_clSetKernelArgSVMPointer(NULL), /*fn_clSetKernelExecInfo(NULL),*/ + /*fn_clEnqueueSVMFree(NULL),*/ fn_clEnqueueSVMMemcpy(NULL), fn_clEnqueueSVMMemFill(NULL), + fn_clEnqueueSVMMap(NULL), fn_clEnqueueSVMUnmap(NULL) + { + // nothing + } + + inline bool isValid() const + { + return fn_clSVMAlloc != NULL && fn_clSVMFree && fn_clSetKernelArgSVMPointer && + /*fn_clSetKernelExecInfo && fn_clEnqueueSVMFree &&*/ fn_clEnqueueSVMMemcpy && + fn_clEnqueueSVMMemFill && fn_clEnqueueSVMMap && fn_clEnqueueSVMUnmap; + } +}; + +// We should guarantee that SVMFunctions lifetime is not less than context's lifetime +CV_EXPORTS const SVMFunctions* getSVMFunctions(const ocl::Context& context); + +CV_EXPORTS bool useSVM(UMatUsageFlags usageFlags); + +}}} //namespace cv::ocl::svm +#endif + +#endif // OPENCV_CORE_OPENCL_SVM_HPP +/* End of file. */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/runtime/autogenerated/opencl_clamdblas.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/runtime/autogenerated/opencl_clamdblas.hpp new file mode 100644 index 0000000000000000000000000000000000000000..65c84935240d3618e0e984eff5102154e3b97a6c --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/runtime/autogenerated/opencl_clamdblas.hpp @@ -0,0 +1,714 @@ +// +// AUTOGENERATED, DO NOT EDIT +// +#ifndef OPENCV_CORE_OCL_RUNTIME_CLAMDBLAS_HPP +#error "Invalid usage" +#endif + +// generated by parser_clamdblas.py +#define clAmdBlasAddScratchImage clAmdBlasAddScratchImage_ +#define clAmdBlasCaxpy clAmdBlasCaxpy_ +#define clAmdBlasCcopy clAmdBlasCcopy_ +#define clAmdBlasCdotc clAmdBlasCdotc_ +#define clAmdBlasCdotu clAmdBlasCdotu_ +#define clAmdBlasCgbmv clAmdBlasCgbmv_ +#define clAmdBlasCgemm clAmdBlasCgemm_ +#define clAmdBlasCgemmEx clAmdBlasCgemmEx_ +#define clAmdBlasCgemv clAmdBlasCgemv_ +#define clAmdBlasCgemvEx clAmdBlasCgemvEx_ +#define clAmdBlasCgerc clAmdBlasCgerc_ +#define clAmdBlasCgeru clAmdBlasCgeru_ +#define clAmdBlasChbmv clAmdBlasChbmv_ +#define clAmdBlasChemm clAmdBlasChemm_ +#define clAmdBlasChemv clAmdBlasChemv_ +#define clAmdBlasCher clAmdBlasCher_ +#define clAmdBlasCher2 clAmdBlasCher2_ +#define clAmdBlasCher2k clAmdBlasCher2k_ +#define clAmdBlasCherk clAmdBlasCherk_ +#define clAmdBlasChpmv clAmdBlasChpmv_ +#define clAmdBlasChpr clAmdBlasChpr_ +#define clAmdBlasChpr2 clAmdBlasChpr2_ +#define clAmdBlasCrotg clAmdBlasCrotg_ +#define clAmdBlasCscal clAmdBlasCscal_ +#define clAmdBlasCsrot clAmdBlasCsrot_ +#define clAmdBlasCsscal clAmdBlasCsscal_ +#define clAmdBlasCswap clAmdBlasCswap_ +#define clAmdBlasCsymm clAmdBlasCsymm_ +#define clAmdBlasCsyr2k clAmdBlasCsyr2k_ +#define clAmdBlasCsyr2kEx clAmdBlasCsyr2kEx_ +#define clAmdBlasCsyrk clAmdBlasCsyrk_ +#define clAmdBlasCsyrkEx clAmdBlasCsyrkEx_ +#define clAmdBlasCtbmv clAmdBlasCtbmv_ +#define clAmdBlasCtbsv clAmdBlasCtbsv_ +#define clAmdBlasCtpmv clAmdBlasCtpmv_ +#define clAmdBlasCtpsv clAmdBlasCtpsv_ +#define clAmdBlasCtrmm clAmdBlasCtrmm_ +#define clAmdBlasCtrmmEx clAmdBlasCtrmmEx_ +#define clAmdBlasCtrmv clAmdBlasCtrmv_ +#define clAmdBlasCtrsm clAmdBlasCtrsm_ +#define clAmdBlasCtrsmEx clAmdBlasCtrsmEx_ +#define clAmdBlasCtrsv clAmdBlasCtrsv_ +#define clAmdBlasDasum clAmdBlasDasum_ +#define clAmdBlasDaxpy clAmdBlasDaxpy_ +#define clAmdBlasDcopy clAmdBlasDcopy_ +#define clAmdBlasDdot clAmdBlasDdot_ +#define clAmdBlasDgbmv clAmdBlasDgbmv_ +#define clAmdBlasDgemm clAmdBlasDgemm_ +#define clAmdBlasDgemmEx clAmdBlasDgemmEx_ +#define clAmdBlasDgemv clAmdBlasDgemv_ +#define clAmdBlasDgemvEx clAmdBlasDgemvEx_ +#define clAmdBlasDger clAmdBlasDger_ +#define clAmdBlasDnrm2 clAmdBlasDnrm2_ +#define clAmdBlasDrot clAmdBlasDrot_ +#define clAmdBlasDrotg clAmdBlasDrotg_ +#define clAmdBlasDrotm clAmdBlasDrotm_ +#define clAmdBlasDrotmg clAmdBlasDrotmg_ +#define clAmdBlasDsbmv clAmdBlasDsbmv_ +#define clAmdBlasDscal clAmdBlasDscal_ +#define clAmdBlasDspmv clAmdBlasDspmv_ +#define clAmdBlasDspr clAmdBlasDspr_ +#define clAmdBlasDspr2 clAmdBlasDspr2_ +#define clAmdBlasDswap clAmdBlasDswap_ +#define clAmdBlasDsymm clAmdBlasDsymm_ +#define clAmdBlasDsymv clAmdBlasDsymv_ +#define clAmdBlasDsymvEx clAmdBlasDsymvEx_ +#define clAmdBlasDsyr clAmdBlasDsyr_ +#define clAmdBlasDsyr2 clAmdBlasDsyr2_ +#define clAmdBlasDsyr2k clAmdBlasDsyr2k_ +#define clAmdBlasDsyr2kEx clAmdBlasDsyr2kEx_ +#define clAmdBlasDsyrk clAmdBlasDsyrk_ +#define clAmdBlasDsyrkEx clAmdBlasDsyrkEx_ +#define clAmdBlasDtbmv clAmdBlasDtbmv_ +#define clAmdBlasDtbsv clAmdBlasDtbsv_ +#define clAmdBlasDtpmv clAmdBlasDtpmv_ +#define clAmdBlasDtpsv clAmdBlasDtpsv_ +#define clAmdBlasDtrmm clAmdBlasDtrmm_ +#define clAmdBlasDtrmmEx clAmdBlasDtrmmEx_ +#define clAmdBlasDtrmv clAmdBlasDtrmv_ +#define clAmdBlasDtrsm clAmdBlasDtrsm_ +#define clAmdBlasDtrsmEx clAmdBlasDtrsmEx_ +#define clAmdBlasDtrsv clAmdBlasDtrsv_ +#define clAmdBlasDzasum clAmdBlasDzasum_ +#define clAmdBlasDznrm2 clAmdBlasDznrm2_ +#define clAmdBlasGetVersion clAmdBlasGetVersion_ +#define clAmdBlasRemoveScratchImage clAmdBlasRemoveScratchImage_ +#define clAmdBlasSasum clAmdBlasSasum_ +#define clAmdBlasSaxpy clAmdBlasSaxpy_ +#define clAmdBlasScasum clAmdBlasScasum_ +#define clAmdBlasScnrm2 clAmdBlasScnrm2_ +#define clAmdBlasScopy clAmdBlasScopy_ +#define clAmdBlasSdot clAmdBlasSdot_ +#define clAmdBlasSetup clAmdBlasSetup_ +#define clAmdBlasSgbmv clAmdBlasSgbmv_ +#define clAmdBlasSgemm clAmdBlasSgemm_ +#define clAmdBlasSgemmEx clAmdBlasSgemmEx_ +#define clAmdBlasSgemv clAmdBlasSgemv_ +#define clAmdBlasSgemvEx clAmdBlasSgemvEx_ +#define clAmdBlasSger clAmdBlasSger_ +#define clAmdBlasSnrm2 clAmdBlasSnrm2_ +#define clAmdBlasSrot clAmdBlasSrot_ +#define clAmdBlasSrotg clAmdBlasSrotg_ +#define clAmdBlasSrotm clAmdBlasSrotm_ +#define clAmdBlasSrotmg clAmdBlasSrotmg_ +#define clAmdBlasSsbmv clAmdBlasSsbmv_ +#define clAmdBlasSscal clAmdBlasSscal_ +#define clAmdBlasSspmv clAmdBlasSspmv_ +#define clAmdBlasSspr clAmdBlasSspr_ +#define clAmdBlasSspr2 clAmdBlasSspr2_ +#define clAmdBlasSswap clAmdBlasSswap_ +#define clAmdBlasSsymm clAmdBlasSsymm_ +#define clAmdBlasSsymv clAmdBlasSsymv_ +#define clAmdBlasSsymvEx clAmdBlasSsymvEx_ +#define clAmdBlasSsyr clAmdBlasSsyr_ +#define clAmdBlasSsyr2 clAmdBlasSsyr2_ +#define clAmdBlasSsyr2k clAmdBlasSsyr2k_ +#define clAmdBlasSsyr2kEx clAmdBlasSsyr2kEx_ +#define clAmdBlasSsyrk clAmdBlasSsyrk_ +#define clAmdBlasSsyrkEx clAmdBlasSsyrkEx_ +#define clAmdBlasStbmv clAmdBlasStbmv_ +#define clAmdBlasStbsv clAmdBlasStbsv_ +#define clAmdBlasStpmv clAmdBlasStpmv_ +#define clAmdBlasStpsv clAmdBlasStpsv_ +#define clAmdBlasStrmm clAmdBlasStrmm_ +#define clAmdBlasStrmmEx clAmdBlasStrmmEx_ +#define clAmdBlasStrmv clAmdBlasStrmv_ +#define clAmdBlasStrsm clAmdBlasStrsm_ +#define clAmdBlasStrsmEx clAmdBlasStrsmEx_ +#define clAmdBlasStrsv clAmdBlasStrsv_ +#define clAmdBlasTeardown clAmdBlasTeardown_ +#define clAmdBlasZaxpy clAmdBlasZaxpy_ +#define clAmdBlasZcopy clAmdBlasZcopy_ +#define clAmdBlasZdotc clAmdBlasZdotc_ +#define clAmdBlasZdotu clAmdBlasZdotu_ +#define clAmdBlasZdrot clAmdBlasZdrot_ +#define clAmdBlasZdscal clAmdBlasZdscal_ +#define clAmdBlasZgbmv clAmdBlasZgbmv_ +#define clAmdBlasZgemm clAmdBlasZgemm_ +#define clAmdBlasZgemmEx clAmdBlasZgemmEx_ +#define clAmdBlasZgemv clAmdBlasZgemv_ +#define clAmdBlasZgemvEx clAmdBlasZgemvEx_ +#define clAmdBlasZgerc clAmdBlasZgerc_ +#define clAmdBlasZgeru clAmdBlasZgeru_ +#define clAmdBlasZhbmv clAmdBlasZhbmv_ +#define clAmdBlasZhemm clAmdBlasZhemm_ +#define clAmdBlasZhemv clAmdBlasZhemv_ +#define clAmdBlasZher clAmdBlasZher_ +#define clAmdBlasZher2 clAmdBlasZher2_ +#define clAmdBlasZher2k clAmdBlasZher2k_ +#define clAmdBlasZherk clAmdBlasZherk_ +#define clAmdBlasZhpmv clAmdBlasZhpmv_ +#define clAmdBlasZhpr clAmdBlasZhpr_ +#define clAmdBlasZhpr2 clAmdBlasZhpr2_ +#define clAmdBlasZrotg clAmdBlasZrotg_ +#define clAmdBlasZscal clAmdBlasZscal_ +#define clAmdBlasZswap clAmdBlasZswap_ +#define clAmdBlasZsymm clAmdBlasZsymm_ +#define clAmdBlasZsyr2k clAmdBlasZsyr2k_ +#define clAmdBlasZsyr2kEx clAmdBlasZsyr2kEx_ +#define clAmdBlasZsyrk clAmdBlasZsyrk_ +#define clAmdBlasZsyrkEx clAmdBlasZsyrkEx_ +#define clAmdBlasZtbmv clAmdBlasZtbmv_ +#define clAmdBlasZtbsv clAmdBlasZtbsv_ +#define clAmdBlasZtpmv clAmdBlasZtpmv_ +#define clAmdBlasZtpsv clAmdBlasZtpsv_ +#define clAmdBlasZtrmm clAmdBlasZtrmm_ +#define clAmdBlasZtrmmEx clAmdBlasZtrmmEx_ +#define clAmdBlasZtrmv clAmdBlasZtrmv_ +#define clAmdBlasZtrsm clAmdBlasZtrsm_ +#define clAmdBlasZtrsmEx clAmdBlasZtrsmEx_ +#define clAmdBlasZtrsv clAmdBlasZtrsv_ +#define clAmdBlasiCamax clAmdBlasiCamax_ +#define clAmdBlasiDamax clAmdBlasiDamax_ +#define clAmdBlasiSamax clAmdBlasiSamax_ +#define clAmdBlasiZamax clAmdBlasiZamax_ + +#include + +// generated by parser_clamdblas.py +#undef clAmdBlasAddScratchImage +//#define clAmdBlasAddScratchImage clAmdBlasAddScratchImage_pfn +#undef clAmdBlasCaxpy +//#define clAmdBlasCaxpy clAmdBlasCaxpy_pfn +#undef clAmdBlasCcopy +//#define clAmdBlasCcopy clAmdBlasCcopy_pfn +#undef clAmdBlasCdotc +//#define clAmdBlasCdotc clAmdBlasCdotc_pfn +#undef clAmdBlasCdotu +//#define clAmdBlasCdotu clAmdBlasCdotu_pfn +#undef clAmdBlasCgbmv +//#define clAmdBlasCgbmv clAmdBlasCgbmv_pfn +#undef clAmdBlasCgemm +//#define clAmdBlasCgemm clAmdBlasCgemm_pfn +#undef clAmdBlasCgemmEx +#define clAmdBlasCgemmEx clAmdBlasCgemmEx_pfn +#undef clAmdBlasCgemv +//#define clAmdBlasCgemv clAmdBlasCgemv_pfn +#undef clAmdBlasCgemvEx +//#define clAmdBlasCgemvEx clAmdBlasCgemvEx_pfn +#undef clAmdBlasCgerc +//#define clAmdBlasCgerc clAmdBlasCgerc_pfn +#undef clAmdBlasCgeru +//#define clAmdBlasCgeru clAmdBlasCgeru_pfn +#undef clAmdBlasChbmv +//#define clAmdBlasChbmv clAmdBlasChbmv_pfn +#undef clAmdBlasChemm +//#define clAmdBlasChemm clAmdBlasChemm_pfn +#undef clAmdBlasChemv +//#define clAmdBlasChemv clAmdBlasChemv_pfn +#undef clAmdBlasCher +//#define clAmdBlasCher clAmdBlasCher_pfn +#undef clAmdBlasCher2 +//#define clAmdBlasCher2 clAmdBlasCher2_pfn +#undef clAmdBlasCher2k +//#define clAmdBlasCher2k clAmdBlasCher2k_pfn +#undef clAmdBlasCherk +//#define clAmdBlasCherk clAmdBlasCherk_pfn +#undef clAmdBlasChpmv +//#define clAmdBlasChpmv clAmdBlasChpmv_pfn +#undef clAmdBlasChpr +//#define clAmdBlasChpr clAmdBlasChpr_pfn +#undef clAmdBlasChpr2 +//#define clAmdBlasChpr2 clAmdBlasChpr2_pfn +#undef clAmdBlasCrotg +//#define clAmdBlasCrotg clAmdBlasCrotg_pfn +#undef clAmdBlasCscal +//#define clAmdBlasCscal clAmdBlasCscal_pfn +#undef clAmdBlasCsrot +//#define clAmdBlasCsrot clAmdBlasCsrot_pfn +#undef clAmdBlasCsscal +//#define clAmdBlasCsscal clAmdBlasCsscal_pfn +#undef clAmdBlasCswap +//#define clAmdBlasCswap clAmdBlasCswap_pfn +#undef clAmdBlasCsymm +//#define clAmdBlasCsymm clAmdBlasCsymm_pfn +#undef clAmdBlasCsyr2k +//#define clAmdBlasCsyr2k clAmdBlasCsyr2k_pfn +#undef clAmdBlasCsyr2kEx +//#define clAmdBlasCsyr2kEx clAmdBlasCsyr2kEx_pfn +#undef clAmdBlasCsyrk +//#define clAmdBlasCsyrk clAmdBlasCsyrk_pfn +#undef clAmdBlasCsyrkEx +//#define clAmdBlasCsyrkEx clAmdBlasCsyrkEx_pfn +#undef clAmdBlasCtbmv +//#define clAmdBlasCtbmv clAmdBlasCtbmv_pfn +#undef clAmdBlasCtbsv +//#define clAmdBlasCtbsv clAmdBlasCtbsv_pfn +#undef clAmdBlasCtpmv +//#define clAmdBlasCtpmv clAmdBlasCtpmv_pfn +#undef clAmdBlasCtpsv +//#define clAmdBlasCtpsv clAmdBlasCtpsv_pfn +#undef clAmdBlasCtrmm +//#define clAmdBlasCtrmm clAmdBlasCtrmm_pfn +#undef clAmdBlasCtrmmEx +//#define clAmdBlasCtrmmEx clAmdBlasCtrmmEx_pfn +#undef clAmdBlasCtrmv +//#define clAmdBlasCtrmv clAmdBlasCtrmv_pfn +#undef clAmdBlasCtrsm +//#define clAmdBlasCtrsm clAmdBlasCtrsm_pfn +#undef clAmdBlasCtrsmEx +//#define clAmdBlasCtrsmEx clAmdBlasCtrsmEx_pfn +#undef clAmdBlasCtrsv +//#define clAmdBlasCtrsv clAmdBlasCtrsv_pfn +#undef clAmdBlasDasum +//#define clAmdBlasDasum clAmdBlasDasum_pfn +#undef clAmdBlasDaxpy +//#define clAmdBlasDaxpy clAmdBlasDaxpy_pfn +#undef clAmdBlasDcopy +//#define clAmdBlasDcopy clAmdBlasDcopy_pfn +#undef clAmdBlasDdot +//#define clAmdBlasDdot clAmdBlasDdot_pfn +#undef clAmdBlasDgbmv +//#define clAmdBlasDgbmv clAmdBlasDgbmv_pfn +#undef clAmdBlasDgemm +//#define clAmdBlasDgemm clAmdBlasDgemm_pfn +#undef clAmdBlasDgemmEx +#define clAmdBlasDgemmEx clAmdBlasDgemmEx_pfn +#undef clAmdBlasDgemv +//#define clAmdBlasDgemv clAmdBlasDgemv_pfn +#undef clAmdBlasDgemvEx +//#define clAmdBlasDgemvEx clAmdBlasDgemvEx_pfn +#undef clAmdBlasDger +//#define clAmdBlasDger clAmdBlasDger_pfn +#undef clAmdBlasDnrm2 +//#define clAmdBlasDnrm2 clAmdBlasDnrm2_pfn +#undef clAmdBlasDrot +//#define clAmdBlasDrot clAmdBlasDrot_pfn +#undef clAmdBlasDrotg +//#define clAmdBlasDrotg clAmdBlasDrotg_pfn +#undef clAmdBlasDrotm +//#define clAmdBlasDrotm clAmdBlasDrotm_pfn +#undef clAmdBlasDrotmg +//#define clAmdBlasDrotmg clAmdBlasDrotmg_pfn +#undef clAmdBlasDsbmv +//#define clAmdBlasDsbmv clAmdBlasDsbmv_pfn +#undef clAmdBlasDscal +//#define clAmdBlasDscal clAmdBlasDscal_pfn +#undef clAmdBlasDspmv +//#define clAmdBlasDspmv clAmdBlasDspmv_pfn +#undef clAmdBlasDspr +//#define clAmdBlasDspr clAmdBlasDspr_pfn +#undef clAmdBlasDspr2 +//#define clAmdBlasDspr2 clAmdBlasDspr2_pfn +#undef clAmdBlasDswap +//#define clAmdBlasDswap clAmdBlasDswap_pfn +#undef clAmdBlasDsymm +//#define clAmdBlasDsymm clAmdBlasDsymm_pfn +#undef clAmdBlasDsymv +//#define clAmdBlasDsymv clAmdBlasDsymv_pfn +#undef clAmdBlasDsymvEx +//#define clAmdBlasDsymvEx clAmdBlasDsymvEx_pfn +#undef clAmdBlasDsyr +//#define clAmdBlasDsyr clAmdBlasDsyr_pfn +#undef clAmdBlasDsyr2 +//#define clAmdBlasDsyr2 clAmdBlasDsyr2_pfn +#undef clAmdBlasDsyr2k +//#define clAmdBlasDsyr2k clAmdBlasDsyr2k_pfn +#undef clAmdBlasDsyr2kEx +//#define clAmdBlasDsyr2kEx clAmdBlasDsyr2kEx_pfn +#undef clAmdBlasDsyrk +//#define clAmdBlasDsyrk clAmdBlasDsyrk_pfn +#undef clAmdBlasDsyrkEx +//#define clAmdBlasDsyrkEx clAmdBlasDsyrkEx_pfn +#undef clAmdBlasDtbmv +//#define clAmdBlasDtbmv clAmdBlasDtbmv_pfn +#undef clAmdBlasDtbsv +//#define clAmdBlasDtbsv clAmdBlasDtbsv_pfn +#undef clAmdBlasDtpmv +//#define clAmdBlasDtpmv clAmdBlasDtpmv_pfn +#undef clAmdBlasDtpsv +//#define clAmdBlasDtpsv clAmdBlasDtpsv_pfn +#undef clAmdBlasDtrmm +//#define clAmdBlasDtrmm clAmdBlasDtrmm_pfn +#undef clAmdBlasDtrmmEx +//#define clAmdBlasDtrmmEx clAmdBlasDtrmmEx_pfn +#undef clAmdBlasDtrmv +//#define clAmdBlasDtrmv clAmdBlasDtrmv_pfn +#undef clAmdBlasDtrsm +//#define clAmdBlasDtrsm clAmdBlasDtrsm_pfn +#undef clAmdBlasDtrsmEx +//#define clAmdBlasDtrsmEx clAmdBlasDtrsmEx_pfn +#undef clAmdBlasDtrsv +//#define clAmdBlasDtrsv clAmdBlasDtrsv_pfn +#undef clAmdBlasDzasum +//#define clAmdBlasDzasum clAmdBlasDzasum_pfn +#undef clAmdBlasDznrm2 +//#define clAmdBlasDznrm2 clAmdBlasDznrm2_pfn +#undef clAmdBlasGetVersion +//#define clAmdBlasGetVersion clAmdBlasGetVersion_pfn +#undef clAmdBlasRemoveScratchImage +//#define clAmdBlasRemoveScratchImage clAmdBlasRemoveScratchImage_pfn +#undef clAmdBlasSasum +//#define clAmdBlasSasum clAmdBlasSasum_pfn +#undef clAmdBlasSaxpy +//#define clAmdBlasSaxpy clAmdBlasSaxpy_pfn +#undef clAmdBlasScasum +//#define clAmdBlasScasum clAmdBlasScasum_pfn +#undef clAmdBlasScnrm2 +//#define clAmdBlasScnrm2 clAmdBlasScnrm2_pfn +#undef clAmdBlasScopy +//#define clAmdBlasScopy clAmdBlasScopy_pfn +#undef clAmdBlasSdot +//#define clAmdBlasSdot clAmdBlasSdot_pfn +#undef clAmdBlasSetup +#define clAmdBlasSetup clAmdBlasSetup_pfn +#undef clAmdBlasSgbmv +//#define clAmdBlasSgbmv clAmdBlasSgbmv_pfn +#undef clAmdBlasSgemm +//#define clAmdBlasSgemm clAmdBlasSgemm_pfn +#undef clAmdBlasSgemmEx +#define clAmdBlasSgemmEx clAmdBlasSgemmEx_pfn +#undef clAmdBlasSgemv +//#define clAmdBlasSgemv clAmdBlasSgemv_pfn +#undef clAmdBlasSgemvEx +//#define clAmdBlasSgemvEx clAmdBlasSgemvEx_pfn +#undef clAmdBlasSger +//#define clAmdBlasSger clAmdBlasSger_pfn +#undef clAmdBlasSnrm2 +//#define clAmdBlasSnrm2 clAmdBlasSnrm2_pfn +#undef clAmdBlasSrot +//#define clAmdBlasSrot clAmdBlasSrot_pfn +#undef clAmdBlasSrotg +//#define clAmdBlasSrotg clAmdBlasSrotg_pfn +#undef clAmdBlasSrotm +//#define clAmdBlasSrotm clAmdBlasSrotm_pfn +#undef clAmdBlasSrotmg +//#define clAmdBlasSrotmg clAmdBlasSrotmg_pfn +#undef clAmdBlasSsbmv +//#define clAmdBlasSsbmv clAmdBlasSsbmv_pfn +#undef clAmdBlasSscal +//#define clAmdBlasSscal clAmdBlasSscal_pfn +#undef clAmdBlasSspmv +//#define clAmdBlasSspmv clAmdBlasSspmv_pfn +#undef clAmdBlasSspr +//#define clAmdBlasSspr clAmdBlasSspr_pfn +#undef clAmdBlasSspr2 +//#define clAmdBlasSspr2 clAmdBlasSspr2_pfn +#undef clAmdBlasSswap +//#define clAmdBlasSswap clAmdBlasSswap_pfn +#undef clAmdBlasSsymm +//#define clAmdBlasSsymm clAmdBlasSsymm_pfn +#undef clAmdBlasSsymv +//#define clAmdBlasSsymv clAmdBlasSsymv_pfn +#undef clAmdBlasSsymvEx +//#define clAmdBlasSsymvEx clAmdBlasSsymvEx_pfn +#undef clAmdBlasSsyr +//#define clAmdBlasSsyr clAmdBlasSsyr_pfn +#undef clAmdBlasSsyr2 +//#define clAmdBlasSsyr2 clAmdBlasSsyr2_pfn +#undef clAmdBlasSsyr2k +//#define clAmdBlasSsyr2k clAmdBlasSsyr2k_pfn +#undef clAmdBlasSsyr2kEx +//#define clAmdBlasSsyr2kEx clAmdBlasSsyr2kEx_pfn +#undef clAmdBlasSsyrk +//#define clAmdBlasSsyrk clAmdBlasSsyrk_pfn +#undef clAmdBlasSsyrkEx +//#define clAmdBlasSsyrkEx clAmdBlasSsyrkEx_pfn +#undef clAmdBlasStbmv +//#define clAmdBlasStbmv clAmdBlasStbmv_pfn +#undef clAmdBlasStbsv +//#define clAmdBlasStbsv clAmdBlasStbsv_pfn +#undef clAmdBlasStpmv +//#define clAmdBlasStpmv clAmdBlasStpmv_pfn +#undef clAmdBlasStpsv +//#define clAmdBlasStpsv clAmdBlasStpsv_pfn +#undef clAmdBlasStrmm +//#define clAmdBlasStrmm clAmdBlasStrmm_pfn +#undef clAmdBlasStrmmEx +//#define clAmdBlasStrmmEx clAmdBlasStrmmEx_pfn +#undef clAmdBlasStrmv +//#define clAmdBlasStrmv clAmdBlasStrmv_pfn +#undef clAmdBlasStrsm +//#define clAmdBlasStrsm clAmdBlasStrsm_pfn +#undef clAmdBlasStrsmEx +//#define clAmdBlasStrsmEx clAmdBlasStrsmEx_pfn +#undef clAmdBlasStrsv +//#define clAmdBlasStrsv clAmdBlasStrsv_pfn +#undef clAmdBlasTeardown +#define clAmdBlasTeardown clAmdBlasTeardown_pfn +#undef clAmdBlasZaxpy +//#define clAmdBlasZaxpy clAmdBlasZaxpy_pfn +#undef clAmdBlasZcopy +//#define clAmdBlasZcopy clAmdBlasZcopy_pfn +#undef clAmdBlasZdotc +//#define clAmdBlasZdotc clAmdBlasZdotc_pfn +#undef clAmdBlasZdotu +//#define clAmdBlasZdotu clAmdBlasZdotu_pfn +#undef clAmdBlasZdrot +//#define clAmdBlasZdrot clAmdBlasZdrot_pfn +#undef clAmdBlasZdscal +//#define clAmdBlasZdscal clAmdBlasZdscal_pfn +#undef clAmdBlasZgbmv +//#define clAmdBlasZgbmv clAmdBlasZgbmv_pfn +#undef clAmdBlasZgemm +//#define clAmdBlasZgemm clAmdBlasZgemm_pfn +#undef clAmdBlasZgemmEx +#define clAmdBlasZgemmEx clAmdBlasZgemmEx_pfn +#undef clAmdBlasZgemv +//#define clAmdBlasZgemv clAmdBlasZgemv_pfn +#undef clAmdBlasZgemvEx +//#define clAmdBlasZgemvEx clAmdBlasZgemvEx_pfn +#undef clAmdBlasZgerc +//#define clAmdBlasZgerc clAmdBlasZgerc_pfn +#undef clAmdBlasZgeru +//#define clAmdBlasZgeru clAmdBlasZgeru_pfn +#undef clAmdBlasZhbmv +//#define clAmdBlasZhbmv clAmdBlasZhbmv_pfn +#undef clAmdBlasZhemm +//#define clAmdBlasZhemm clAmdBlasZhemm_pfn +#undef clAmdBlasZhemv +//#define clAmdBlasZhemv clAmdBlasZhemv_pfn +#undef clAmdBlasZher +//#define clAmdBlasZher clAmdBlasZher_pfn +#undef clAmdBlasZher2 +//#define clAmdBlasZher2 clAmdBlasZher2_pfn +#undef clAmdBlasZher2k +//#define clAmdBlasZher2k clAmdBlasZher2k_pfn +#undef clAmdBlasZherk +//#define clAmdBlasZherk clAmdBlasZherk_pfn +#undef clAmdBlasZhpmv +//#define clAmdBlasZhpmv clAmdBlasZhpmv_pfn +#undef clAmdBlasZhpr +//#define clAmdBlasZhpr clAmdBlasZhpr_pfn +#undef clAmdBlasZhpr2 +//#define clAmdBlasZhpr2 clAmdBlasZhpr2_pfn +#undef clAmdBlasZrotg +//#define clAmdBlasZrotg clAmdBlasZrotg_pfn +#undef clAmdBlasZscal +//#define clAmdBlasZscal clAmdBlasZscal_pfn +#undef clAmdBlasZswap +//#define clAmdBlasZswap clAmdBlasZswap_pfn +#undef clAmdBlasZsymm +//#define clAmdBlasZsymm clAmdBlasZsymm_pfn +#undef clAmdBlasZsyr2k +//#define clAmdBlasZsyr2k clAmdBlasZsyr2k_pfn +#undef clAmdBlasZsyr2kEx +//#define clAmdBlasZsyr2kEx clAmdBlasZsyr2kEx_pfn +#undef clAmdBlasZsyrk +//#define clAmdBlasZsyrk clAmdBlasZsyrk_pfn +#undef clAmdBlasZsyrkEx +//#define clAmdBlasZsyrkEx clAmdBlasZsyrkEx_pfn +#undef clAmdBlasZtbmv +//#define clAmdBlasZtbmv clAmdBlasZtbmv_pfn +#undef clAmdBlasZtbsv +//#define clAmdBlasZtbsv clAmdBlasZtbsv_pfn +#undef clAmdBlasZtpmv +//#define clAmdBlasZtpmv clAmdBlasZtpmv_pfn +#undef clAmdBlasZtpsv +//#define clAmdBlasZtpsv clAmdBlasZtpsv_pfn +#undef clAmdBlasZtrmm +//#define clAmdBlasZtrmm clAmdBlasZtrmm_pfn +#undef clAmdBlasZtrmmEx +//#define clAmdBlasZtrmmEx clAmdBlasZtrmmEx_pfn +#undef clAmdBlasZtrmv +//#define clAmdBlasZtrmv clAmdBlasZtrmv_pfn +#undef clAmdBlasZtrsm +//#define clAmdBlasZtrsm clAmdBlasZtrsm_pfn +#undef clAmdBlasZtrsmEx +//#define clAmdBlasZtrsmEx clAmdBlasZtrsmEx_pfn +#undef clAmdBlasZtrsv +//#define clAmdBlasZtrsv clAmdBlasZtrsv_pfn +#undef clAmdBlasiCamax +//#define clAmdBlasiCamax clAmdBlasiCamax_pfn +#undef clAmdBlasiDamax +//#define clAmdBlasiDamax clAmdBlasiDamax_pfn +#undef clAmdBlasiSamax +//#define clAmdBlasiSamax clAmdBlasiSamax_pfn +#undef clAmdBlasiZamax +//#define clAmdBlasiZamax clAmdBlasiZamax_pfn + +// generated by parser_clamdblas.py +//extern CL_RUNTIME_EXPORT cl_ulong (*clAmdBlasAddScratchImage)(cl_context context, size_t width, size_t height, clAmdBlasStatus* status); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCaxpy)(size_t N, cl_float2 alpha, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCcopy)(size_t N, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCdotc)(size_t N, cl_mem dotProduct, size_t offDP, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCdotu)(size_t N, cl_mem dotProduct, size_t offDP, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCgbmv)(clAmdBlasOrder order, clAmdBlasTranspose trans, size_t M, size_t N, size_t KL, size_t KU, cl_float2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_float2 beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCgemm)(clAmdBlasOrder order, clAmdBlasTranspose transA, clAmdBlasTranspose transB, size_t M, size_t N, size_t K, FloatComplex alpha, const cl_mem A, size_t lda, const cl_mem B, size_t ldb, FloatComplex beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCgemmEx)(clAmdBlasOrder order, clAmdBlasTranspose transA, clAmdBlasTranspose transB, size_t M, size_t N, size_t K, FloatComplex alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, FloatComplex beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCgemv)(clAmdBlasOrder order, clAmdBlasTranspose transA, size_t M, size_t N, FloatComplex alpha, const cl_mem A, size_t lda, const cl_mem x, size_t offx, int incx, FloatComplex beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCgemvEx)(clAmdBlasOrder order, clAmdBlasTranspose transA, size_t M, size_t N, FloatComplex alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem x, size_t offx, int incx, FloatComplex beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCgerc)(clAmdBlasOrder order, size_t M, size_t N, cl_float2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCgeru)(clAmdBlasOrder order, size_t M, size_t N, cl_float2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasChbmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, size_t K, cl_float2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_float2 beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasChemm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, size_t M, size_t N, cl_float2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_float2 beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasChemv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, FloatComplex alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, FloatComplex beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCher)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCher2)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCher2k)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, size_t N, size_t K, FloatComplex alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_float beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCherk)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, float alpha, const cl_mem A, size_t offa, size_t lda, float beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasChpmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float2 alpha, const cl_mem AP, size_t offa, const cl_mem X, size_t offx, int incx, cl_float2 beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasChpr)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasChpr2)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCrotg)(cl_mem CA, size_t offCA, cl_mem CB, size_t offCB, cl_mem C, size_t offC, cl_mem S, size_t offS, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCscal)(size_t N, cl_float2 alpha, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCsrot)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_float C, cl_float S, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCsscal)(size_t N, cl_float alpha, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCswap)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCsymm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, size_t M, size_t N, cl_float2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_float2 beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCsyr2k)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transAB, size_t N, size_t K, FloatComplex alpha, const cl_mem A, size_t lda, const cl_mem B, size_t ldb, FloatComplex beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCsyr2kEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transAB, size_t N, size_t K, FloatComplex alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, FloatComplex beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCsyrk)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, FloatComplex alpha, const cl_mem A, size_t lda, FloatComplex beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCsyrkEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, FloatComplex alpha, const cl_mem A, size_t offA, size_t lda, FloatComplex beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtbmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtbsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtpmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem AP, size_t offa, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtpsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtrmm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, FloatComplex alpha, const cl_mem A, size_t lda, cl_mem B, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtrmmEx)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, FloatComplex alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtrmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtrsm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, FloatComplex alpha, const cl_mem A, size_t lda, cl_mem B, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtrsmEx)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, FloatComplex alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtrsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDasum)(size_t N, cl_mem asum, size_t offAsum, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDaxpy)(size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDcopy)(size_t N, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDdot)(size_t N, cl_mem dotProduct, size_t offDP, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDgbmv)(clAmdBlasOrder order, clAmdBlasTranspose trans, size_t M, size_t N, size_t KL, size_t KU, cl_double alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_double beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDgemm)(clAmdBlasOrder order, clAmdBlasTranspose transA, clAmdBlasTranspose transB, size_t M, size_t N, size_t K, cl_double alpha, const cl_mem A, size_t lda, const cl_mem B, size_t ldb, cl_double beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDgemmEx)(clAmdBlasOrder order, clAmdBlasTranspose transA, clAmdBlasTranspose transB, size_t M, size_t N, size_t K, cl_double alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, cl_double beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDgemv)(clAmdBlasOrder order, clAmdBlasTranspose transA, size_t M, size_t N, cl_double alpha, const cl_mem A, size_t lda, const cl_mem x, size_t offx, int incx, cl_double beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDgemvEx)(clAmdBlasOrder order, clAmdBlasTranspose transA, size_t M, size_t N, cl_double alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem x, size_t offx, int incx, cl_double beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDger)(clAmdBlasOrder order, size_t M, size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDnrm2)(size_t N, cl_mem NRM2, size_t offNRM2, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDrot)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_double C, cl_double S, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDrotg)(cl_mem DA, size_t offDA, cl_mem DB, size_t offDB, cl_mem C, size_t offC, cl_mem S, size_t offS, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDrotm)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, const cl_mem DPARAM, size_t offDparam, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDrotmg)(cl_mem DD1, size_t offDD1, cl_mem DD2, size_t offDD2, cl_mem DX1, size_t offDX1, const cl_mem DY1, size_t offDY1, cl_mem DPARAM, size_t offDparam, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsbmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, size_t K, cl_double alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_double beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDscal)(size_t N, cl_double alpha, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDspmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double alpha, const cl_mem AP, size_t offa, const cl_mem X, size_t offx, int incx, cl_double beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDspr)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDspr2)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDswap)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsymm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, size_t M, size_t N, cl_double alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_double beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsymv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double alpha, const cl_mem A, size_t lda, const cl_mem x, size_t offx, int incx, cl_double beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsymvEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem x, size_t offx, int incx, cl_double beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsyr)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsyr2)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsyr2k)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transAB, size_t N, size_t K, cl_double alpha, const cl_mem A, size_t lda, const cl_mem B, size_t ldb, cl_double beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsyr2kEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transAB, size_t N, size_t K, cl_double alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, cl_double beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsyrk)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, cl_double alpha, const cl_mem A, size_t lda, cl_double beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsyrkEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, cl_double alpha, const cl_mem A, size_t offA, size_t lda, cl_double beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtbmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtbsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtpmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem AP, size_t offa, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtpsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtrmm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, cl_double alpha, const cl_mem A, size_t lda, cl_mem B, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtrmmEx)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, cl_double alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtrmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtrsm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, cl_double alpha, const cl_mem A, size_t lda, cl_mem B, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtrsmEx)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, cl_double alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtrsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDzasum)(size_t N, cl_mem asum, size_t offAsum, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDznrm2)(size_t N, cl_mem NRM2, size_t offNRM2, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasGetVersion)(cl_uint* major, cl_uint* minor, cl_uint* patch); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasRemoveScratchImage)(cl_ulong imageID); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSasum)(size_t N, cl_mem asum, size_t offAsum, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSaxpy)(size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasScasum)(size_t N, cl_mem asum, size_t offAsum, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasScnrm2)(size_t N, cl_mem NRM2, size_t offNRM2, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasScopy)(size_t N, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSdot)(size_t N, cl_mem dotProduct, size_t offDP, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSetup)(); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSgbmv)(clAmdBlasOrder order, clAmdBlasTranspose trans, size_t M, size_t N, size_t KL, size_t KU, cl_float alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_float beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSgemm)(clAmdBlasOrder order, clAmdBlasTranspose transA, clAmdBlasTranspose transB, size_t M, size_t N, size_t K, cl_float alpha, const cl_mem A, size_t lda, const cl_mem B, size_t ldb, cl_float beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSgemmEx)(clAmdBlasOrder order, clAmdBlasTranspose transA, clAmdBlasTranspose transB, size_t M, size_t N, size_t K, cl_float alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, cl_float beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSgemv)(clAmdBlasOrder order, clAmdBlasTranspose transA, size_t M, size_t N, cl_float alpha, const cl_mem A, size_t lda, const cl_mem x, size_t offx, int incx, cl_float beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSgemvEx)(clAmdBlasOrder order, clAmdBlasTranspose transA, size_t M, size_t N, cl_float alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem x, size_t offx, int incx, cl_float beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSger)(clAmdBlasOrder order, size_t M, size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSnrm2)(size_t N, cl_mem NRM2, size_t offNRM2, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSrot)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_float C, cl_float S, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSrotg)(cl_mem SA, size_t offSA, cl_mem SB, size_t offSB, cl_mem C, size_t offC, cl_mem S, size_t offS, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSrotm)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, const cl_mem SPARAM, size_t offSparam, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSrotmg)(cl_mem SD1, size_t offSD1, cl_mem SD2, size_t offSD2, cl_mem SX1, size_t offSX1, const cl_mem SY1, size_t offSY1, cl_mem SPARAM, size_t offSparam, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsbmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, size_t K, cl_float alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_float beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSscal)(size_t N, cl_float alpha, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSspmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float alpha, const cl_mem AP, size_t offa, const cl_mem X, size_t offx, int incx, cl_float beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSspr)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSspr2)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSswap)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsymm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, size_t M, size_t N, cl_float alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_float beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsymv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float alpha, const cl_mem A, size_t lda, const cl_mem x, size_t offx, int incx, cl_float beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsymvEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem x, size_t offx, int incx, cl_float beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsyr)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsyr2)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsyr2k)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transAB, size_t N, size_t K, cl_float alpha, const cl_mem A, size_t lda, const cl_mem B, size_t ldb, cl_float beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsyr2kEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transAB, size_t N, size_t K, cl_float alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, cl_float beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsyrk)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, cl_float alpha, const cl_mem A, size_t lda, cl_float beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsyrkEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, cl_float alpha, const cl_mem A, size_t offA, size_t lda, cl_float beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStbmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStbsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStpmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem AP, size_t offa, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStpsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStrmm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, cl_float alpha, const cl_mem A, size_t lda, cl_mem B, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStrmmEx)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, cl_float alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStrmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStrsm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, cl_float alpha, const cl_mem A, size_t lda, cl_mem B, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStrsmEx)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, cl_float alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStrsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +extern CL_RUNTIME_EXPORT void (*clAmdBlasTeardown)(); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZaxpy)(size_t N, cl_double2 alpha, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZcopy)(size_t N, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZdotc)(size_t N, cl_mem dotProduct, size_t offDP, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZdotu)(size_t N, cl_mem dotProduct, size_t offDP, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZdrot)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_double C, cl_double S, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZdscal)(size_t N, cl_double alpha, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZgbmv)(clAmdBlasOrder order, clAmdBlasTranspose trans, size_t M, size_t N, size_t KL, size_t KU, cl_double2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_double2 beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZgemm)(clAmdBlasOrder order, clAmdBlasTranspose transA, clAmdBlasTranspose transB, size_t M, size_t N, size_t K, DoubleComplex alpha, const cl_mem A, size_t lda, const cl_mem B, size_t ldb, DoubleComplex beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZgemmEx)(clAmdBlasOrder order, clAmdBlasTranspose transA, clAmdBlasTranspose transB, size_t M, size_t N, size_t K, DoubleComplex alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, DoubleComplex beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZgemv)(clAmdBlasOrder order, clAmdBlasTranspose transA, size_t M, size_t N, DoubleComplex alpha, const cl_mem A, size_t lda, const cl_mem x, size_t offx, int incx, DoubleComplex beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZgemvEx)(clAmdBlasOrder order, clAmdBlasTranspose transA, size_t M, size_t N, DoubleComplex alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem x, size_t offx, int incx, DoubleComplex beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZgerc)(clAmdBlasOrder order, size_t M, size_t N, cl_double2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZgeru)(clAmdBlasOrder order, size_t M, size_t N, cl_double2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZhbmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, size_t K, cl_double2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_double2 beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZhemm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, size_t M, size_t N, cl_double2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_double2 beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZhemv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, DoubleComplex alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, DoubleComplex beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZher)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZher2)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZher2k)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, size_t N, size_t K, DoubleComplex alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_double beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZherk)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, double alpha, const cl_mem A, size_t offa, size_t lda, double beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZhpmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double2 alpha, const cl_mem AP, size_t offa, const cl_mem X, size_t offx, int incx, cl_double2 beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZhpr)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZhpr2)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZrotg)(cl_mem CA, size_t offCA, cl_mem CB, size_t offCB, cl_mem C, size_t offC, cl_mem S, size_t offS, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZscal)(size_t N, cl_double2 alpha, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZswap)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZsymm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, size_t M, size_t N, cl_double2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_double2 beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZsyr2k)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transAB, size_t N, size_t K, DoubleComplex alpha, const cl_mem A, size_t lda, const cl_mem B, size_t ldb, DoubleComplex beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZsyr2kEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transAB, size_t N, size_t K, DoubleComplex alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, DoubleComplex beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZsyrk)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, DoubleComplex alpha, const cl_mem A, size_t lda, DoubleComplex beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZsyrkEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, DoubleComplex alpha, const cl_mem A, size_t offA, size_t lda, DoubleComplex beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtbmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtbsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtpmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem AP, size_t offa, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtpsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtrmm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, DoubleComplex alpha, const cl_mem A, size_t lda, cl_mem B, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtrmmEx)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, DoubleComplex alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtrmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtrsm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, DoubleComplex alpha, const cl_mem A, size_t lda, cl_mem B, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtrsmEx)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, DoubleComplex alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtrsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasiCamax)(size_t N, cl_mem iMax, size_t offiMax, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasiDamax)(size_t N, cl_mem iMax, size_t offiMax, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasiSamax)(size_t N, cl_mem iMax, size_t offiMax, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); +//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasiZamax)(size_t N, cl_mem iMax, size_t offiMax, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/runtime/autogenerated/opencl_clamdfft.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/runtime/autogenerated/opencl_clamdfft.hpp new file mode 100644 index 0000000000000000000000000000000000000000..1457d7eb8d69a56197b8e80430728d571282088f --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/runtime/autogenerated/opencl_clamdfft.hpp @@ -0,0 +1,142 @@ +// +// AUTOGENERATED, DO NOT EDIT +// +#ifndef OPENCV_CORE_OCL_RUNTIME_CLAMDFFT_HPP +#error "Invalid usage" +#endif + +// generated by parser_clamdfft.py +#define clAmdFftBakePlan clAmdFftBakePlan_ +#define clAmdFftCopyPlan clAmdFftCopyPlan_ +#define clAmdFftCreateDefaultPlan clAmdFftCreateDefaultPlan_ +#define clAmdFftDestroyPlan clAmdFftDestroyPlan_ +#define clAmdFftEnqueueTransform clAmdFftEnqueueTransform_ +#define clAmdFftGetLayout clAmdFftGetLayout_ +#define clAmdFftGetPlanBatchSize clAmdFftGetPlanBatchSize_ +#define clAmdFftGetPlanContext clAmdFftGetPlanContext_ +#define clAmdFftGetPlanDim clAmdFftGetPlanDim_ +#define clAmdFftGetPlanDistance clAmdFftGetPlanDistance_ +#define clAmdFftGetPlanInStride clAmdFftGetPlanInStride_ +#define clAmdFftGetPlanLength clAmdFftGetPlanLength_ +#define clAmdFftGetPlanOutStride clAmdFftGetPlanOutStride_ +#define clAmdFftGetPlanPrecision clAmdFftGetPlanPrecision_ +#define clAmdFftGetPlanScale clAmdFftGetPlanScale_ +#define clAmdFftGetPlanTransposeResult clAmdFftGetPlanTransposeResult_ +#define clAmdFftGetResultLocation clAmdFftGetResultLocation_ +#define clAmdFftGetTmpBufSize clAmdFftGetTmpBufSize_ +#define clAmdFftGetVersion clAmdFftGetVersion_ +#define clAmdFftSetLayout clAmdFftSetLayout_ +#define clAmdFftSetPlanBatchSize clAmdFftSetPlanBatchSize_ +#define clAmdFftSetPlanDim clAmdFftSetPlanDim_ +#define clAmdFftSetPlanDistance clAmdFftSetPlanDistance_ +#define clAmdFftSetPlanInStride clAmdFftSetPlanInStride_ +#define clAmdFftSetPlanLength clAmdFftSetPlanLength_ +#define clAmdFftSetPlanOutStride clAmdFftSetPlanOutStride_ +#define clAmdFftSetPlanPrecision clAmdFftSetPlanPrecision_ +#define clAmdFftSetPlanScale clAmdFftSetPlanScale_ +#define clAmdFftSetPlanTransposeResult clAmdFftSetPlanTransposeResult_ +#define clAmdFftSetResultLocation clAmdFftSetResultLocation_ +#define clAmdFftSetup clAmdFftSetup_ +#define clAmdFftTeardown clAmdFftTeardown_ + +#include + +// generated by parser_clamdfft.py +#undef clAmdFftBakePlan +#define clAmdFftBakePlan clAmdFftBakePlan_pfn +#undef clAmdFftCopyPlan +//#define clAmdFftCopyPlan clAmdFftCopyPlan_pfn +#undef clAmdFftCreateDefaultPlan +#define clAmdFftCreateDefaultPlan clAmdFftCreateDefaultPlan_pfn +#undef clAmdFftDestroyPlan +#define clAmdFftDestroyPlan clAmdFftDestroyPlan_pfn +#undef clAmdFftEnqueueTransform +#define clAmdFftEnqueueTransform clAmdFftEnqueueTransform_pfn +#undef clAmdFftGetLayout +//#define clAmdFftGetLayout clAmdFftGetLayout_pfn +#undef clAmdFftGetPlanBatchSize +//#define clAmdFftGetPlanBatchSize clAmdFftGetPlanBatchSize_pfn +#undef clAmdFftGetPlanContext +//#define clAmdFftGetPlanContext clAmdFftGetPlanContext_pfn +#undef clAmdFftGetPlanDim +//#define clAmdFftGetPlanDim clAmdFftGetPlanDim_pfn +#undef clAmdFftGetPlanDistance +//#define clAmdFftGetPlanDistance clAmdFftGetPlanDistance_pfn +#undef clAmdFftGetPlanInStride +//#define clAmdFftGetPlanInStride clAmdFftGetPlanInStride_pfn +#undef clAmdFftGetPlanLength +//#define clAmdFftGetPlanLength clAmdFftGetPlanLength_pfn +#undef clAmdFftGetPlanOutStride +//#define clAmdFftGetPlanOutStride clAmdFftGetPlanOutStride_pfn +#undef clAmdFftGetPlanPrecision +//#define clAmdFftGetPlanPrecision clAmdFftGetPlanPrecision_pfn +#undef clAmdFftGetPlanScale +//#define clAmdFftGetPlanScale clAmdFftGetPlanScale_pfn +#undef clAmdFftGetPlanTransposeResult +//#define clAmdFftGetPlanTransposeResult clAmdFftGetPlanTransposeResult_pfn +#undef clAmdFftGetResultLocation +//#define clAmdFftGetResultLocation clAmdFftGetResultLocation_pfn +#undef clAmdFftGetTmpBufSize +#define clAmdFftGetTmpBufSize clAmdFftGetTmpBufSize_pfn +#undef clAmdFftGetVersion +#define clAmdFftGetVersion clAmdFftGetVersion_pfn +#undef clAmdFftSetLayout +#define clAmdFftSetLayout clAmdFftSetLayout_pfn +#undef clAmdFftSetPlanBatchSize +#define clAmdFftSetPlanBatchSize clAmdFftSetPlanBatchSize_pfn +#undef clAmdFftSetPlanDim +//#define clAmdFftSetPlanDim clAmdFftSetPlanDim_pfn +#undef clAmdFftSetPlanDistance +#define clAmdFftSetPlanDistance clAmdFftSetPlanDistance_pfn +#undef clAmdFftSetPlanInStride +#define clAmdFftSetPlanInStride clAmdFftSetPlanInStride_pfn +#undef clAmdFftSetPlanLength +//#define clAmdFftSetPlanLength clAmdFftSetPlanLength_pfn +#undef clAmdFftSetPlanOutStride +#define clAmdFftSetPlanOutStride clAmdFftSetPlanOutStride_pfn +#undef clAmdFftSetPlanPrecision +#define clAmdFftSetPlanPrecision clAmdFftSetPlanPrecision_pfn +#undef clAmdFftSetPlanScale +#define clAmdFftSetPlanScale clAmdFftSetPlanScale_pfn +#undef clAmdFftSetPlanTransposeResult +//#define clAmdFftSetPlanTransposeResult clAmdFftSetPlanTransposeResult_pfn +#undef clAmdFftSetResultLocation +#define clAmdFftSetResultLocation clAmdFftSetResultLocation_pfn +#undef clAmdFftSetup +#define clAmdFftSetup clAmdFftSetup_pfn +#undef clAmdFftTeardown +#define clAmdFftTeardown clAmdFftTeardown_pfn + +// generated by parser_clamdfft.py +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftBakePlan)(clAmdFftPlanHandle plHandle, cl_uint numQueues, cl_command_queue* commQueueFFT, void (CL_CALLBACK* pfn_notify) (clAmdFftPlanHandle plHandle, void* user_data), void* user_data); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftCopyPlan)(clAmdFftPlanHandle* out_plHandle, cl_context new_context, clAmdFftPlanHandle in_plHandle); +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftCreateDefaultPlan)(clAmdFftPlanHandle* plHandle, cl_context context, const clAmdFftDim dim, const size_t* clLengths); +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftDestroyPlan)(clAmdFftPlanHandle* plHandle); +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftEnqueueTransform)(clAmdFftPlanHandle plHandle, clAmdFftDirection dir, cl_uint numQueuesAndEvents, cl_command_queue* commQueues, cl_uint numWaitEvents, const cl_event* waitEvents, cl_event* outEvents, cl_mem* inputBuffers, cl_mem* outputBuffers, cl_mem tmpBuffer); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetLayout)(const clAmdFftPlanHandle plHandle, clAmdFftLayout* iLayout, clAmdFftLayout* oLayout); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanBatchSize)(const clAmdFftPlanHandle plHandle, size_t* batchSize); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanContext)(const clAmdFftPlanHandle plHandle, cl_context* context); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanDim)(const clAmdFftPlanHandle plHandle, clAmdFftDim* dim, cl_uint* size); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanDistance)(const clAmdFftPlanHandle plHandle, size_t* iDist, size_t* oDist); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanInStride)(const clAmdFftPlanHandle plHandle, const clAmdFftDim dim, size_t* clStrides); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanLength)(const clAmdFftPlanHandle plHandle, const clAmdFftDim dim, size_t* clLengths); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanOutStride)(const clAmdFftPlanHandle plHandle, const clAmdFftDim dim, size_t* clStrides); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanPrecision)(const clAmdFftPlanHandle plHandle, clAmdFftPrecision* precision); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanScale)(const clAmdFftPlanHandle plHandle, clAmdFftDirection dir, cl_float* scale); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanTransposeResult)(const clAmdFftPlanHandle plHandle, clAmdFftResultTransposed* transposed); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetResultLocation)(const clAmdFftPlanHandle plHandle, clAmdFftResultLocation* placeness); +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetTmpBufSize)(const clAmdFftPlanHandle plHandle, size_t* buffersize); +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetVersion)(cl_uint* major, cl_uint* minor, cl_uint* patch); +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetLayout)(clAmdFftPlanHandle plHandle, clAmdFftLayout iLayout, clAmdFftLayout oLayout); +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetPlanBatchSize)(clAmdFftPlanHandle plHandle, size_t batchSize); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetPlanDim)(clAmdFftPlanHandle plHandle, const clAmdFftDim dim); +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetPlanDistance)(clAmdFftPlanHandle plHandle, size_t iDist, size_t oDist); +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetPlanInStride)(clAmdFftPlanHandle plHandle, const clAmdFftDim dim, size_t* clStrides); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetPlanLength)(clAmdFftPlanHandle plHandle, const clAmdFftDim dim, const size_t* clLengths); +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetPlanOutStride)(clAmdFftPlanHandle plHandle, const clAmdFftDim dim, size_t* clStrides); +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetPlanPrecision)(clAmdFftPlanHandle plHandle, clAmdFftPrecision precision); +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetPlanScale)(clAmdFftPlanHandle plHandle, clAmdFftDirection dir, cl_float scale); +//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetPlanTransposeResult)(clAmdFftPlanHandle plHandle, clAmdFftResultTransposed transposed); +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetResultLocation)(clAmdFftPlanHandle plHandle, clAmdFftResultLocation placeness); +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetup)(const clAmdFftSetupData* setupData); +extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftTeardown)(); diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/runtime/autogenerated/opencl_core.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/runtime/autogenerated/opencl_core.hpp new file mode 100644 index 0000000000000000000000000000000000000000..fdaf469ff1cee7dada0e45311533f14a4db89d71 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/runtime/autogenerated/opencl_core.hpp @@ -0,0 +1,370 @@ +// +// AUTOGENERATED, DO NOT EDIT +// +#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_CORE_HPP +#error "Invalid usage" +#endif + +// generated by parser_cl.py +#define clBuildProgram clBuildProgram_ +#define clCompileProgram clCompileProgram_ +#define clCreateBuffer clCreateBuffer_ +#define clCreateCommandQueue clCreateCommandQueue_ +#define clCreateContext clCreateContext_ +#define clCreateContextFromType clCreateContextFromType_ +#define clCreateImage clCreateImage_ +#define clCreateImage2D clCreateImage2D_ +#define clCreateImage3D clCreateImage3D_ +#define clCreateKernel clCreateKernel_ +#define clCreateKernelsInProgram clCreateKernelsInProgram_ +#define clCreateProgramWithBinary clCreateProgramWithBinary_ +#define clCreateProgramWithBuiltInKernels clCreateProgramWithBuiltInKernels_ +#define clCreateProgramWithSource clCreateProgramWithSource_ +#define clCreateSampler clCreateSampler_ +#define clCreateSubBuffer clCreateSubBuffer_ +#define clCreateSubDevices clCreateSubDevices_ +#define clCreateUserEvent clCreateUserEvent_ +#define clEnqueueBarrier clEnqueueBarrier_ +#define clEnqueueBarrierWithWaitList clEnqueueBarrierWithWaitList_ +#define clEnqueueCopyBuffer clEnqueueCopyBuffer_ +#define clEnqueueCopyBufferRect clEnqueueCopyBufferRect_ +#define clEnqueueCopyBufferToImage clEnqueueCopyBufferToImage_ +#define clEnqueueCopyImage clEnqueueCopyImage_ +#define clEnqueueCopyImageToBuffer clEnqueueCopyImageToBuffer_ +#define clEnqueueFillBuffer clEnqueueFillBuffer_ +#define clEnqueueFillImage clEnqueueFillImage_ +#define clEnqueueMapBuffer clEnqueueMapBuffer_ +#define clEnqueueMapImage clEnqueueMapImage_ +#define clEnqueueMarker clEnqueueMarker_ +#define clEnqueueMarkerWithWaitList clEnqueueMarkerWithWaitList_ +#define clEnqueueMigrateMemObjects clEnqueueMigrateMemObjects_ +#define clEnqueueNDRangeKernel clEnqueueNDRangeKernel_ +#define clEnqueueNativeKernel clEnqueueNativeKernel_ +#define clEnqueueReadBuffer clEnqueueReadBuffer_ +#define clEnqueueReadBufferRect clEnqueueReadBufferRect_ +#define clEnqueueReadImage clEnqueueReadImage_ +#define clEnqueueTask clEnqueueTask_ +#define clEnqueueUnmapMemObject clEnqueueUnmapMemObject_ +#define clEnqueueWaitForEvents clEnqueueWaitForEvents_ +#define clEnqueueWriteBuffer clEnqueueWriteBuffer_ +#define clEnqueueWriteBufferRect clEnqueueWriteBufferRect_ +#define clEnqueueWriteImage clEnqueueWriteImage_ +#define clFinish clFinish_ +#define clFlush clFlush_ +#define clGetCommandQueueInfo clGetCommandQueueInfo_ +#define clGetContextInfo clGetContextInfo_ +#define clGetDeviceIDs clGetDeviceIDs_ +#define clGetDeviceInfo clGetDeviceInfo_ +#define clGetEventInfo clGetEventInfo_ +#define clGetEventProfilingInfo clGetEventProfilingInfo_ +#define clGetExtensionFunctionAddress clGetExtensionFunctionAddress_ +#define clGetExtensionFunctionAddressForPlatform clGetExtensionFunctionAddressForPlatform_ +#define clGetImageInfo clGetImageInfo_ +#define clGetKernelArgInfo clGetKernelArgInfo_ +#define clGetKernelInfo clGetKernelInfo_ +#define clGetKernelWorkGroupInfo clGetKernelWorkGroupInfo_ +#define clGetMemObjectInfo clGetMemObjectInfo_ +#define clGetPlatformIDs clGetPlatformIDs_ +#define clGetPlatformInfo clGetPlatformInfo_ +#define clGetProgramBuildInfo clGetProgramBuildInfo_ +#define clGetProgramInfo clGetProgramInfo_ +#define clGetSamplerInfo clGetSamplerInfo_ +#define clGetSupportedImageFormats clGetSupportedImageFormats_ +#define clLinkProgram clLinkProgram_ +#define clReleaseCommandQueue clReleaseCommandQueue_ +#define clReleaseContext clReleaseContext_ +#define clReleaseDevice clReleaseDevice_ +#define clReleaseEvent clReleaseEvent_ +#define clReleaseKernel clReleaseKernel_ +#define clReleaseMemObject clReleaseMemObject_ +#define clReleaseProgram clReleaseProgram_ +#define clReleaseSampler clReleaseSampler_ +#define clRetainCommandQueue clRetainCommandQueue_ +#define clRetainContext clRetainContext_ +#define clRetainDevice clRetainDevice_ +#define clRetainEvent clRetainEvent_ +#define clRetainKernel clRetainKernel_ +#define clRetainMemObject clRetainMemObject_ +#define clRetainProgram clRetainProgram_ +#define clRetainSampler clRetainSampler_ +#define clSetEventCallback clSetEventCallback_ +#define clSetKernelArg clSetKernelArg_ +#define clSetMemObjectDestructorCallback clSetMemObjectDestructorCallback_ +#define clSetUserEventStatus clSetUserEventStatus_ +#define clUnloadCompiler clUnloadCompiler_ +#define clUnloadPlatformCompiler clUnloadPlatformCompiler_ +#define clWaitForEvents clWaitForEvents_ + +#if defined __APPLE__ +#include +#else +#include +#endif + +// generated by parser_cl.py +#undef clBuildProgram +#define clBuildProgram clBuildProgram_pfn +#undef clCompileProgram +#define clCompileProgram clCompileProgram_pfn +#undef clCreateBuffer +#define clCreateBuffer clCreateBuffer_pfn +#undef clCreateCommandQueue +#define clCreateCommandQueue clCreateCommandQueue_pfn +#undef clCreateContext +#define clCreateContext clCreateContext_pfn +#undef clCreateContextFromType +#define clCreateContextFromType clCreateContextFromType_pfn +#undef clCreateImage +#define clCreateImage clCreateImage_pfn +#undef clCreateImage2D +#define clCreateImage2D clCreateImage2D_pfn +#undef clCreateImage3D +#define clCreateImage3D clCreateImage3D_pfn +#undef clCreateKernel +#define clCreateKernel clCreateKernel_pfn +#undef clCreateKernelsInProgram +#define clCreateKernelsInProgram clCreateKernelsInProgram_pfn +#undef clCreateProgramWithBinary +#define clCreateProgramWithBinary clCreateProgramWithBinary_pfn +#undef clCreateProgramWithBuiltInKernels +#define clCreateProgramWithBuiltInKernels clCreateProgramWithBuiltInKernels_pfn +#undef clCreateProgramWithSource +#define clCreateProgramWithSource clCreateProgramWithSource_pfn +#undef clCreateSampler +#define clCreateSampler clCreateSampler_pfn +#undef clCreateSubBuffer +#define clCreateSubBuffer clCreateSubBuffer_pfn +#undef clCreateSubDevices +#define clCreateSubDevices clCreateSubDevices_pfn +#undef clCreateUserEvent +#define clCreateUserEvent clCreateUserEvent_pfn +#undef clEnqueueBarrier +#define clEnqueueBarrier clEnqueueBarrier_pfn +#undef clEnqueueBarrierWithWaitList +#define clEnqueueBarrierWithWaitList clEnqueueBarrierWithWaitList_pfn +#undef clEnqueueCopyBuffer +#define clEnqueueCopyBuffer clEnqueueCopyBuffer_pfn +#undef clEnqueueCopyBufferRect +#define clEnqueueCopyBufferRect clEnqueueCopyBufferRect_pfn +#undef clEnqueueCopyBufferToImage +#define clEnqueueCopyBufferToImage clEnqueueCopyBufferToImage_pfn +#undef clEnqueueCopyImage +#define clEnqueueCopyImage clEnqueueCopyImage_pfn +#undef clEnqueueCopyImageToBuffer +#define clEnqueueCopyImageToBuffer clEnqueueCopyImageToBuffer_pfn +#undef clEnqueueFillBuffer +#define clEnqueueFillBuffer clEnqueueFillBuffer_pfn +#undef clEnqueueFillImage +#define clEnqueueFillImage clEnqueueFillImage_pfn +#undef clEnqueueMapBuffer +#define clEnqueueMapBuffer clEnqueueMapBuffer_pfn +#undef clEnqueueMapImage +#define clEnqueueMapImage clEnqueueMapImage_pfn +#undef clEnqueueMarker +#define clEnqueueMarker clEnqueueMarker_pfn +#undef clEnqueueMarkerWithWaitList +#define clEnqueueMarkerWithWaitList clEnqueueMarkerWithWaitList_pfn +#undef clEnqueueMigrateMemObjects +#define clEnqueueMigrateMemObjects clEnqueueMigrateMemObjects_pfn +#undef clEnqueueNDRangeKernel +#define clEnqueueNDRangeKernel clEnqueueNDRangeKernel_pfn +#undef clEnqueueNativeKernel +#define clEnqueueNativeKernel clEnqueueNativeKernel_pfn +#undef clEnqueueReadBuffer +#define clEnqueueReadBuffer clEnqueueReadBuffer_pfn +#undef clEnqueueReadBufferRect +#define clEnqueueReadBufferRect clEnqueueReadBufferRect_pfn +#undef clEnqueueReadImage +#define clEnqueueReadImage clEnqueueReadImage_pfn +#undef clEnqueueTask +#define clEnqueueTask clEnqueueTask_pfn +#undef clEnqueueUnmapMemObject +#define clEnqueueUnmapMemObject clEnqueueUnmapMemObject_pfn +#undef clEnqueueWaitForEvents +#define clEnqueueWaitForEvents clEnqueueWaitForEvents_pfn +#undef clEnqueueWriteBuffer +#define clEnqueueWriteBuffer clEnqueueWriteBuffer_pfn +#undef clEnqueueWriteBufferRect +#define clEnqueueWriteBufferRect clEnqueueWriteBufferRect_pfn +#undef clEnqueueWriteImage +#define clEnqueueWriteImage clEnqueueWriteImage_pfn +#undef clFinish +#define clFinish clFinish_pfn +#undef clFlush +#define clFlush clFlush_pfn +#undef clGetCommandQueueInfo +#define clGetCommandQueueInfo clGetCommandQueueInfo_pfn +#undef clGetContextInfo +#define clGetContextInfo clGetContextInfo_pfn +#undef clGetDeviceIDs +#define clGetDeviceIDs clGetDeviceIDs_pfn +#undef clGetDeviceInfo +#define clGetDeviceInfo clGetDeviceInfo_pfn +#undef clGetEventInfo +#define clGetEventInfo clGetEventInfo_pfn +#undef clGetEventProfilingInfo +#define clGetEventProfilingInfo clGetEventProfilingInfo_pfn +#undef clGetExtensionFunctionAddress +#define clGetExtensionFunctionAddress clGetExtensionFunctionAddress_pfn +#undef clGetExtensionFunctionAddressForPlatform +#define clGetExtensionFunctionAddressForPlatform clGetExtensionFunctionAddressForPlatform_pfn +#undef clGetImageInfo +#define clGetImageInfo clGetImageInfo_pfn +#undef clGetKernelArgInfo +#define clGetKernelArgInfo clGetKernelArgInfo_pfn +#undef clGetKernelInfo +#define clGetKernelInfo clGetKernelInfo_pfn +#undef clGetKernelWorkGroupInfo +#define clGetKernelWorkGroupInfo clGetKernelWorkGroupInfo_pfn +#undef clGetMemObjectInfo +#define clGetMemObjectInfo clGetMemObjectInfo_pfn +#undef clGetPlatformIDs +#define clGetPlatformIDs clGetPlatformIDs_pfn +#undef clGetPlatformInfo +#define clGetPlatformInfo clGetPlatformInfo_pfn +#undef clGetProgramBuildInfo +#define clGetProgramBuildInfo clGetProgramBuildInfo_pfn +#undef clGetProgramInfo +#define clGetProgramInfo clGetProgramInfo_pfn +#undef clGetSamplerInfo +#define clGetSamplerInfo clGetSamplerInfo_pfn +#undef clGetSupportedImageFormats +#define clGetSupportedImageFormats clGetSupportedImageFormats_pfn +#undef clLinkProgram +#define clLinkProgram clLinkProgram_pfn +#undef clReleaseCommandQueue +#define clReleaseCommandQueue clReleaseCommandQueue_pfn +#undef clReleaseContext +#define clReleaseContext clReleaseContext_pfn +#undef clReleaseDevice +#define clReleaseDevice clReleaseDevice_pfn +#undef clReleaseEvent +#define clReleaseEvent clReleaseEvent_pfn +#undef clReleaseKernel +#define clReleaseKernel clReleaseKernel_pfn +#undef clReleaseMemObject +#define clReleaseMemObject clReleaseMemObject_pfn +#undef clReleaseProgram +#define clReleaseProgram clReleaseProgram_pfn +#undef clReleaseSampler +#define clReleaseSampler clReleaseSampler_pfn +#undef clRetainCommandQueue +#define clRetainCommandQueue clRetainCommandQueue_pfn +#undef clRetainContext +#define clRetainContext clRetainContext_pfn +#undef clRetainDevice +#define clRetainDevice clRetainDevice_pfn +#undef clRetainEvent +#define clRetainEvent clRetainEvent_pfn +#undef clRetainKernel +#define clRetainKernel clRetainKernel_pfn +#undef clRetainMemObject +#define clRetainMemObject clRetainMemObject_pfn +#undef clRetainProgram +#define clRetainProgram clRetainProgram_pfn +#undef clRetainSampler +#define clRetainSampler clRetainSampler_pfn +#undef clSetEventCallback +#define clSetEventCallback clSetEventCallback_pfn +#undef clSetKernelArg +#define clSetKernelArg clSetKernelArg_pfn +#undef clSetMemObjectDestructorCallback +#define clSetMemObjectDestructorCallback clSetMemObjectDestructorCallback_pfn +#undef clSetUserEventStatus +#define clSetUserEventStatus clSetUserEventStatus_pfn +#undef clUnloadCompiler +#define clUnloadCompiler clUnloadCompiler_pfn +#undef clUnloadPlatformCompiler +#define clUnloadPlatformCompiler clUnloadPlatformCompiler_pfn +#undef clWaitForEvents +#define clWaitForEvents clWaitForEvents_pfn + +// generated by parser_cl.py +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clBuildProgram)(cl_program, cl_uint, const cl_device_id*, const char*, void (CL_CALLBACK*) (cl_program, void*), void*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clCompileProgram)(cl_program, cl_uint, const cl_device_id*, const char*, cl_uint, const cl_program*, const char**, void (CL_CALLBACK*) (cl_program, void*), void*); +extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateBuffer)(cl_context, cl_mem_flags, size_t, void*, cl_int*); +extern CL_RUNTIME_EXPORT cl_command_queue (CL_API_CALL*clCreateCommandQueue)(cl_context, cl_device_id, cl_command_queue_properties, cl_int*); +extern CL_RUNTIME_EXPORT cl_context (CL_API_CALL*clCreateContext)(const cl_context_properties*, cl_uint, const cl_device_id*, void (CL_CALLBACK*) (const char*, const void*, size_t, void*), void*, cl_int*); +extern CL_RUNTIME_EXPORT cl_context (CL_API_CALL*clCreateContextFromType)(const cl_context_properties*, cl_device_type, void (CL_CALLBACK*) (const char*, const void*, size_t, void*), void*, cl_int*); +extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateImage)(cl_context, cl_mem_flags, const cl_image_format*, const cl_image_desc*, void*, cl_int*); +extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateImage2D)(cl_context, cl_mem_flags, const cl_image_format*, size_t, size_t, size_t, void*, cl_int*); +extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateImage3D)(cl_context, cl_mem_flags, const cl_image_format*, size_t, size_t, size_t, size_t, size_t, void*, cl_int*); +extern CL_RUNTIME_EXPORT cl_kernel (CL_API_CALL*clCreateKernel)(cl_program, const char*, cl_int*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clCreateKernelsInProgram)(cl_program, cl_uint, cl_kernel*, cl_uint*); +extern CL_RUNTIME_EXPORT cl_program (CL_API_CALL*clCreateProgramWithBinary)(cl_context, cl_uint, const cl_device_id*, const size_t*, const unsigned char**, cl_int*, cl_int*); +extern CL_RUNTIME_EXPORT cl_program (CL_API_CALL*clCreateProgramWithBuiltInKernels)(cl_context, cl_uint, const cl_device_id*, const char*, cl_int*); +extern CL_RUNTIME_EXPORT cl_program (CL_API_CALL*clCreateProgramWithSource)(cl_context, cl_uint, const char**, const size_t*, cl_int*); +extern CL_RUNTIME_EXPORT cl_sampler (CL_API_CALL*clCreateSampler)(cl_context, cl_bool, cl_addressing_mode, cl_filter_mode, cl_int*); +extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateSubBuffer)(cl_mem, cl_mem_flags, cl_buffer_create_type, const void*, cl_int*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clCreateSubDevices)(cl_device_id, const cl_device_partition_property*, cl_uint, cl_device_id*, cl_uint*); +extern CL_RUNTIME_EXPORT cl_event (CL_API_CALL*clCreateUserEvent)(cl_context, cl_int*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueBarrier)(cl_command_queue); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueBarrierWithWaitList)(cl_command_queue, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueCopyBuffer)(cl_command_queue, cl_mem, cl_mem, size_t, size_t, size_t, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueCopyBufferRect)(cl_command_queue, cl_mem, cl_mem, const size_t*, const size_t*, const size_t*, size_t, size_t, size_t, size_t, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueCopyBufferToImage)(cl_command_queue, cl_mem, cl_mem, size_t, const size_t*, const size_t*, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueCopyImage)(cl_command_queue, cl_mem, cl_mem, const size_t*, const size_t*, const size_t*, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueCopyImageToBuffer)(cl_command_queue, cl_mem, cl_mem, const size_t*, const size_t*, size_t, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueFillBuffer)(cl_command_queue, cl_mem, const void*, size_t, size_t, size_t, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueFillImage)(cl_command_queue, cl_mem, const void*, const size_t*, const size_t*, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT void* (CL_API_CALL*clEnqueueMapBuffer)(cl_command_queue, cl_mem, cl_bool, cl_map_flags, size_t, size_t, cl_uint, const cl_event*, cl_event*, cl_int*); +extern CL_RUNTIME_EXPORT void* (CL_API_CALL*clEnqueueMapImage)(cl_command_queue, cl_mem, cl_bool, cl_map_flags, const size_t*, const size_t*, size_t*, size_t*, cl_uint, const cl_event*, cl_event*, cl_int*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueMarker)(cl_command_queue, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueMarkerWithWaitList)(cl_command_queue, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueMigrateMemObjects)(cl_command_queue, cl_uint, const cl_mem*, cl_mem_migration_flags, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueNDRangeKernel)(cl_command_queue, cl_kernel, cl_uint, const size_t*, const size_t*, const size_t*, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueNativeKernel)(cl_command_queue, void (CL_CALLBACK*) (void*), void*, size_t, cl_uint, const cl_mem*, const void**, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueReadBuffer)(cl_command_queue, cl_mem, cl_bool, size_t, size_t, void*, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueReadBufferRect)(cl_command_queue, cl_mem, cl_bool, const size_t*, const size_t*, const size_t*, size_t, size_t, size_t, size_t, void*, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueReadImage)(cl_command_queue, cl_mem, cl_bool, const size_t*, const size_t*, size_t, size_t, void*, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueTask)(cl_command_queue, cl_kernel, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueUnmapMemObject)(cl_command_queue, cl_mem, void*, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueWaitForEvents)(cl_command_queue, cl_uint, const cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueWriteBuffer)(cl_command_queue, cl_mem, cl_bool, size_t, size_t, const void*, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueWriteBufferRect)(cl_command_queue, cl_mem, cl_bool, const size_t*, const size_t*, const size_t*, size_t, size_t, size_t, size_t, const void*, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueWriteImage)(cl_command_queue, cl_mem, cl_bool, const size_t*, const size_t*, size_t, size_t, const void*, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clFinish)(cl_command_queue); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clFlush)(cl_command_queue); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetCommandQueueInfo)(cl_command_queue, cl_command_queue_info, size_t, void*, size_t*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetContextInfo)(cl_context, cl_context_info, size_t, void*, size_t*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetDeviceIDs)(cl_platform_id, cl_device_type, cl_uint, cl_device_id*, cl_uint*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetDeviceInfo)(cl_device_id, cl_device_info, size_t, void*, size_t*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetEventInfo)(cl_event, cl_event_info, size_t, void*, size_t*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetEventProfilingInfo)(cl_event, cl_profiling_info, size_t, void*, size_t*); +extern CL_RUNTIME_EXPORT void* (CL_API_CALL*clGetExtensionFunctionAddress)(const char*); +extern CL_RUNTIME_EXPORT void* (CL_API_CALL*clGetExtensionFunctionAddressForPlatform)(cl_platform_id, const char*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetImageInfo)(cl_mem, cl_image_info, size_t, void*, size_t*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetKernelArgInfo)(cl_kernel, cl_uint, cl_kernel_arg_info, size_t, void*, size_t*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetKernelInfo)(cl_kernel, cl_kernel_info, size_t, void*, size_t*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetKernelWorkGroupInfo)(cl_kernel, cl_device_id, cl_kernel_work_group_info, size_t, void*, size_t*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetMemObjectInfo)(cl_mem, cl_mem_info, size_t, void*, size_t*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetPlatformIDs)(cl_uint, cl_platform_id*, cl_uint*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetPlatformInfo)(cl_platform_id, cl_platform_info, size_t, void*, size_t*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetProgramBuildInfo)(cl_program, cl_device_id, cl_program_build_info, size_t, void*, size_t*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetProgramInfo)(cl_program, cl_program_info, size_t, void*, size_t*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetSamplerInfo)(cl_sampler, cl_sampler_info, size_t, void*, size_t*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetSupportedImageFormats)(cl_context, cl_mem_flags, cl_mem_object_type, cl_uint, cl_image_format*, cl_uint*); +extern CL_RUNTIME_EXPORT cl_program (CL_API_CALL*clLinkProgram)(cl_context, cl_uint, const cl_device_id*, const char*, cl_uint, const cl_program*, void (CL_CALLBACK*) (cl_program, void*), void*, cl_int*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseCommandQueue)(cl_command_queue); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseContext)(cl_context); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseDevice)(cl_device_id); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseEvent)(cl_event); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseKernel)(cl_kernel); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseMemObject)(cl_mem); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseProgram)(cl_program); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseSampler)(cl_sampler); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainCommandQueue)(cl_command_queue); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainContext)(cl_context); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainDevice)(cl_device_id); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainEvent)(cl_event); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainKernel)(cl_kernel); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainMemObject)(cl_mem); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainProgram)(cl_program); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainSampler)(cl_sampler); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clSetEventCallback)(cl_event, cl_int, void (CL_CALLBACK*) (cl_event, cl_int, void*), void*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clSetKernelArg)(cl_kernel, cl_uint, size_t, const void*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clSetMemObjectDestructorCallback)(cl_mem, void (CL_CALLBACK*) (cl_mem, void*), void*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clSetUserEventStatus)(cl_event, cl_int); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clUnloadCompiler)(); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clUnloadPlatformCompiler)(cl_platform_id); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clWaitForEvents)(cl_uint, const cl_event*); diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/runtime/autogenerated/opencl_core_wrappers.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/runtime/autogenerated/opencl_core_wrappers.hpp new file mode 100644 index 0000000000000000000000000000000000000000..216b22b8a85c825db0645536bbfcfe392f6b9e3f --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/runtime/autogenerated/opencl_core_wrappers.hpp @@ -0,0 +1,272 @@ +// +// AUTOGENERATED, DO NOT EDIT +// +#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_WRAPPERS_HPP +#error "Invalid usage" +#endif + +// generated by parser_cl.py +#undef clBuildProgram +#define clBuildProgram clBuildProgram_fn +inline cl_int clBuildProgram(cl_program p0, cl_uint p1, const cl_device_id* p2, const char* p3, void (CL_CALLBACK*p4) (cl_program, void*), void* p5) { return clBuildProgram_pfn(p0, p1, p2, p3, p4, p5); } +#undef clCompileProgram +#define clCompileProgram clCompileProgram_fn +inline cl_int clCompileProgram(cl_program p0, cl_uint p1, const cl_device_id* p2, const char* p3, cl_uint p4, const cl_program* p5, const char** p6, void (CL_CALLBACK*p7) (cl_program, void*), void* p8) { return clCompileProgram_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); } +#undef clCreateBuffer +#define clCreateBuffer clCreateBuffer_fn +inline cl_mem clCreateBuffer(cl_context p0, cl_mem_flags p1, size_t p2, void* p3, cl_int* p4) { return clCreateBuffer_pfn(p0, p1, p2, p3, p4); } +#undef clCreateCommandQueue +#define clCreateCommandQueue clCreateCommandQueue_fn +inline cl_command_queue clCreateCommandQueue(cl_context p0, cl_device_id p1, cl_command_queue_properties p2, cl_int* p3) { return clCreateCommandQueue_pfn(p0, p1, p2, p3); } +#undef clCreateContext +#define clCreateContext clCreateContext_fn +inline cl_context clCreateContext(const cl_context_properties* p0, cl_uint p1, const cl_device_id* p2, void (CL_CALLBACK*p3) (const char*, const void*, size_t, void*), void* p4, cl_int* p5) { return clCreateContext_pfn(p0, p1, p2, p3, p4, p5); } +#undef clCreateContextFromType +#define clCreateContextFromType clCreateContextFromType_fn +inline cl_context clCreateContextFromType(const cl_context_properties* p0, cl_device_type p1, void (CL_CALLBACK*p2) (const char*, const void*, size_t, void*), void* p3, cl_int* p4) { return clCreateContextFromType_pfn(p0, p1, p2, p3, p4); } +#undef clCreateImage +#define clCreateImage clCreateImage_fn +inline cl_mem clCreateImage(cl_context p0, cl_mem_flags p1, const cl_image_format* p2, const cl_image_desc* p3, void* p4, cl_int* p5) { return clCreateImage_pfn(p0, p1, p2, p3, p4, p5); } +#undef clCreateImage2D +#define clCreateImage2D clCreateImage2D_fn +inline cl_mem clCreateImage2D(cl_context p0, cl_mem_flags p1, const cl_image_format* p2, size_t p3, size_t p4, size_t p5, void* p6, cl_int* p7) { return clCreateImage2D_pfn(p0, p1, p2, p3, p4, p5, p6, p7); } +#undef clCreateImage3D +#define clCreateImage3D clCreateImage3D_fn +inline cl_mem clCreateImage3D(cl_context p0, cl_mem_flags p1, const cl_image_format* p2, size_t p3, size_t p4, size_t p5, size_t p6, size_t p7, void* p8, cl_int* p9) { return clCreateImage3D_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9); } +#undef clCreateKernel +#define clCreateKernel clCreateKernel_fn +inline cl_kernel clCreateKernel(cl_program p0, const char* p1, cl_int* p2) { return clCreateKernel_pfn(p0, p1, p2); } +#undef clCreateKernelsInProgram +#define clCreateKernelsInProgram clCreateKernelsInProgram_fn +inline cl_int clCreateKernelsInProgram(cl_program p0, cl_uint p1, cl_kernel* p2, cl_uint* p3) { return clCreateKernelsInProgram_pfn(p0, p1, p2, p3); } +#undef clCreateProgramWithBinary +#define clCreateProgramWithBinary clCreateProgramWithBinary_fn +inline cl_program clCreateProgramWithBinary(cl_context p0, cl_uint p1, const cl_device_id* p2, const size_t* p3, const unsigned char** p4, cl_int* p5, cl_int* p6) { return clCreateProgramWithBinary_pfn(p0, p1, p2, p3, p4, p5, p6); } +#undef clCreateProgramWithBuiltInKernels +#define clCreateProgramWithBuiltInKernels clCreateProgramWithBuiltInKernels_fn +inline cl_program clCreateProgramWithBuiltInKernels(cl_context p0, cl_uint p1, const cl_device_id* p2, const char* p3, cl_int* p4) { return clCreateProgramWithBuiltInKernels_pfn(p0, p1, p2, p3, p4); } +#undef clCreateProgramWithSource +#define clCreateProgramWithSource clCreateProgramWithSource_fn +inline cl_program clCreateProgramWithSource(cl_context p0, cl_uint p1, const char** p2, const size_t* p3, cl_int* p4) { return clCreateProgramWithSource_pfn(p0, p1, p2, p3, p4); } +#undef clCreateSampler +#define clCreateSampler clCreateSampler_fn +inline cl_sampler clCreateSampler(cl_context p0, cl_bool p1, cl_addressing_mode p2, cl_filter_mode p3, cl_int* p4) { return clCreateSampler_pfn(p0, p1, p2, p3, p4); } +#undef clCreateSubBuffer +#define clCreateSubBuffer clCreateSubBuffer_fn +inline cl_mem clCreateSubBuffer(cl_mem p0, cl_mem_flags p1, cl_buffer_create_type p2, const void* p3, cl_int* p4) { return clCreateSubBuffer_pfn(p0, p1, p2, p3, p4); } +#undef clCreateSubDevices +#define clCreateSubDevices clCreateSubDevices_fn +inline cl_int clCreateSubDevices(cl_device_id p0, const cl_device_partition_property* p1, cl_uint p2, cl_device_id* p3, cl_uint* p4) { return clCreateSubDevices_pfn(p0, p1, p2, p3, p4); } +#undef clCreateUserEvent +#define clCreateUserEvent clCreateUserEvent_fn +inline cl_event clCreateUserEvent(cl_context p0, cl_int* p1) { return clCreateUserEvent_pfn(p0, p1); } +#undef clEnqueueBarrier +#define clEnqueueBarrier clEnqueueBarrier_fn +inline cl_int clEnqueueBarrier(cl_command_queue p0) { return clEnqueueBarrier_pfn(p0); } +#undef clEnqueueBarrierWithWaitList +#define clEnqueueBarrierWithWaitList clEnqueueBarrierWithWaitList_fn +inline cl_int clEnqueueBarrierWithWaitList(cl_command_queue p0, cl_uint p1, const cl_event* p2, cl_event* p3) { return clEnqueueBarrierWithWaitList_pfn(p0, p1, p2, p3); } +#undef clEnqueueCopyBuffer +#define clEnqueueCopyBuffer clEnqueueCopyBuffer_fn +inline cl_int clEnqueueCopyBuffer(cl_command_queue p0, cl_mem p1, cl_mem p2, size_t p3, size_t p4, size_t p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueCopyBuffer_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); } +#undef clEnqueueCopyBufferRect +#define clEnqueueCopyBufferRect clEnqueueCopyBufferRect_fn +inline cl_int clEnqueueCopyBufferRect(cl_command_queue p0, cl_mem p1, cl_mem p2, const size_t* p3, const size_t* p4, const size_t* p5, size_t p6, size_t p7, size_t p8, size_t p9, cl_uint p10, const cl_event* p11, cl_event* p12) { return clEnqueueCopyBufferRect_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12); } +#undef clEnqueueCopyBufferToImage +#define clEnqueueCopyBufferToImage clEnqueueCopyBufferToImage_fn +inline cl_int clEnqueueCopyBufferToImage(cl_command_queue p0, cl_mem p1, cl_mem p2, size_t p3, const size_t* p4, const size_t* p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueCopyBufferToImage_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); } +#undef clEnqueueCopyImage +#define clEnqueueCopyImage clEnqueueCopyImage_fn +inline cl_int clEnqueueCopyImage(cl_command_queue p0, cl_mem p1, cl_mem p2, const size_t* p3, const size_t* p4, const size_t* p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueCopyImage_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); } +#undef clEnqueueCopyImageToBuffer +#define clEnqueueCopyImageToBuffer clEnqueueCopyImageToBuffer_fn +inline cl_int clEnqueueCopyImageToBuffer(cl_command_queue p0, cl_mem p1, cl_mem p2, const size_t* p3, const size_t* p4, size_t p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueCopyImageToBuffer_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); } +#undef clEnqueueFillBuffer +#define clEnqueueFillBuffer clEnqueueFillBuffer_fn +inline cl_int clEnqueueFillBuffer(cl_command_queue p0, cl_mem p1, const void* p2, size_t p3, size_t p4, size_t p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueFillBuffer_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); } +#undef clEnqueueFillImage +#define clEnqueueFillImage clEnqueueFillImage_fn +inline cl_int clEnqueueFillImage(cl_command_queue p0, cl_mem p1, const void* p2, const size_t* p3, const size_t* p4, cl_uint p5, const cl_event* p6, cl_event* p7) { return clEnqueueFillImage_pfn(p0, p1, p2, p3, p4, p5, p6, p7); } +#undef clEnqueueMapBuffer +#define clEnqueueMapBuffer clEnqueueMapBuffer_fn +inline void* clEnqueueMapBuffer(cl_command_queue p0, cl_mem p1, cl_bool p2, cl_map_flags p3, size_t p4, size_t p5, cl_uint p6, const cl_event* p7, cl_event* p8, cl_int* p9) { return clEnqueueMapBuffer_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9); } +#undef clEnqueueMapImage +#define clEnqueueMapImage clEnqueueMapImage_fn +inline void* clEnqueueMapImage(cl_command_queue p0, cl_mem p1, cl_bool p2, cl_map_flags p3, const size_t* p4, const size_t* p5, size_t* p6, size_t* p7, cl_uint p8, const cl_event* p9, cl_event* p10, cl_int* p11) { return clEnqueueMapImage_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11); } +#undef clEnqueueMarker +#define clEnqueueMarker clEnqueueMarker_fn +inline cl_int clEnqueueMarker(cl_command_queue p0, cl_event* p1) { return clEnqueueMarker_pfn(p0, p1); } +#undef clEnqueueMarkerWithWaitList +#define clEnqueueMarkerWithWaitList clEnqueueMarkerWithWaitList_fn +inline cl_int clEnqueueMarkerWithWaitList(cl_command_queue p0, cl_uint p1, const cl_event* p2, cl_event* p3) { return clEnqueueMarkerWithWaitList_pfn(p0, p1, p2, p3); } +#undef clEnqueueMigrateMemObjects +#define clEnqueueMigrateMemObjects clEnqueueMigrateMemObjects_fn +inline cl_int clEnqueueMigrateMemObjects(cl_command_queue p0, cl_uint p1, const cl_mem* p2, cl_mem_migration_flags p3, cl_uint p4, const cl_event* p5, cl_event* p6) { return clEnqueueMigrateMemObjects_pfn(p0, p1, p2, p3, p4, p5, p6); } +#undef clEnqueueNDRangeKernel +#define clEnqueueNDRangeKernel clEnqueueNDRangeKernel_fn +inline cl_int clEnqueueNDRangeKernel(cl_command_queue p0, cl_kernel p1, cl_uint p2, const size_t* p3, const size_t* p4, const size_t* p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueNDRangeKernel_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); } +#undef clEnqueueNativeKernel +#define clEnqueueNativeKernel clEnqueueNativeKernel_fn +inline cl_int clEnqueueNativeKernel(cl_command_queue p0, void (CL_CALLBACK*p1) (void*), void* p2, size_t p3, cl_uint p4, const cl_mem* p5, const void** p6, cl_uint p7, const cl_event* p8, cl_event* p9) { return clEnqueueNativeKernel_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9); } +#undef clEnqueueReadBuffer +#define clEnqueueReadBuffer clEnqueueReadBuffer_fn +inline cl_int clEnqueueReadBuffer(cl_command_queue p0, cl_mem p1, cl_bool p2, size_t p3, size_t p4, void* p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueReadBuffer_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); } +#undef clEnqueueReadBufferRect +#define clEnqueueReadBufferRect clEnqueueReadBufferRect_fn +inline cl_int clEnqueueReadBufferRect(cl_command_queue p0, cl_mem p1, cl_bool p2, const size_t* p3, const size_t* p4, const size_t* p5, size_t p6, size_t p7, size_t p8, size_t p9, void* p10, cl_uint p11, const cl_event* p12, cl_event* p13) { return clEnqueueReadBufferRect_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, p13); } +#undef clEnqueueReadImage +#define clEnqueueReadImage clEnqueueReadImage_fn +inline cl_int clEnqueueReadImage(cl_command_queue p0, cl_mem p1, cl_bool p2, const size_t* p3, const size_t* p4, size_t p5, size_t p6, void* p7, cl_uint p8, const cl_event* p9, cl_event* p10) { return clEnqueueReadImage_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, p10); } +#undef clEnqueueTask +#define clEnqueueTask clEnqueueTask_fn +inline cl_int clEnqueueTask(cl_command_queue p0, cl_kernel p1, cl_uint p2, const cl_event* p3, cl_event* p4) { return clEnqueueTask_pfn(p0, p1, p2, p3, p4); } +#undef clEnqueueUnmapMemObject +#define clEnqueueUnmapMemObject clEnqueueUnmapMemObject_fn +inline cl_int clEnqueueUnmapMemObject(cl_command_queue p0, cl_mem p1, void* p2, cl_uint p3, const cl_event* p4, cl_event* p5) { return clEnqueueUnmapMemObject_pfn(p0, p1, p2, p3, p4, p5); } +#undef clEnqueueWaitForEvents +#define clEnqueueWaitForEvents clEnqueueWaitForEvents_fn +inline cl_int clEnqueueWaitForEvents(cl_command_queue p0, cl_uint p1, const cl_event* p2) { return clEnqueueWaitForEvents_pfn(p0, p1, p2); } +#undef clEnqueueWriteBuffer +#define clEnqueueWriteBuffer clEnqueueWriteBuffer_fn +inline cl_int clEnqueueWriteBuffer(cl_command_queue p0, cl_mem p1, cl_bool p2, size_t p3, size_t p4, const void* p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueWriteBuffer_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); } +#undef clEnqueueWriteBufferRect +#define clEnqueueWriteBufferRect clEnqueueWriteBufferRect_fn +inline cl_int clEnqueueWriteBufferRect(cl_command_queue p0, cl_mem p1, cl_bool p2, const size_t* p3, const size_t* p4, const size_t* p5, size_t p6, size_t p7, size_t p8, size_t p9, const void* p10, cl_uint p11, const cl_event* p12, cl_event* p13) { return clEnqueueWriteBufferRect_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, p13); } +#undef clEnqueueWriteImage +#define clEnqueueWriteImage clEnqueueWriteImage_fn +inline cl_int clEnqueueWriteImage(cl_command_queue p0, cl_mem p1, cl_bool p2, const size_t* p3, const size_t* p4, size_t p5, size_t p6, const void* p7, cl_uint p8, const cl_event* p9, cl_event* p10) { return clEnqueueWriteImage_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, p10); } +#undef clFinish +#define clFinish clFinish_fn +inline cl_int clFinish(cl_command_queue p0) { return clFinish_pfn(p0); } +#undef clFlush +#define clFlush clFlush_fn +inline cl_int clFlush(cl_command_queue p0) { return clFlush_pfn(p0); } +#undef clGetCommandQueueInfo +#define clGetCommandQueueInfo clGetCommandQueueInfo_fn +inline cl_int clGetCommandQueueInfo(cl_command_queue p0, cl_command_queue_info p1, size_t p2, void* p3, size_t* p4) { return clGetCommandQueueInfo_pfn(p0, p1, p2, p3, p4); } +#undef clGetContextInfo +#define clGetContextInfo clGetContextInfo_fn +inline cl_int clGetContextInfo(cl_context p0, cl_context_info p1, size_t p2, void* p3, size_t* p4) { return clGetContextInfo_pfn(p0, p1, p2, p3, p4); } +#undef clGetDeviceIDs +#define clGetDeviceIDs clGetDeviceIDs_fn +inline cl_int clGetDeviceIDs(cl_platform_id p0, cl_device_type p1, cl_uint p2, cl_device_id* p3, cl_uint* p4) { return clGetDeviceIDs_pfn(p0, p1, p2, p3, p4); } +#undef clGetDeviceInfo +#define clGetDeviceInfo clGetDeviceInfo_fn +inline cl_int clGetDeviceInfo(cl_device_id p0, cl_device_info p1, size_t p2, void* p3, size_t* p4) { return clGetDeviceInfo_pfn(p0, p1, p2, p3, p4); } +#undef clGetEventInfo +#define clGetEventInfo clGetEventInfo_fn +inline cl_int clGetEventInfo(cl_event p0, cl_event_info p1, size_t p2, void* p3, size_t* p4) { return clGetEventInfo_pfn(p0, p1, p2, p3, p4); } +#undef clGetEventProfilingInfo +#define clGetEventProfilingInfo clGetEventProfilingInfo_fn +inline cl_int clGetEventProfilingInfo(cl_event p0, cl_profiling_info p1, size_t p2, void* p3, size_t* p4) { return clGetEventProfilingInfo_pfn(p0, p1, p2, p3, p4); } +#undef clGetExtensionFunctionAddress +#define clGetExtensionFunctionAddress clGetExtensionFunctionAddress_fn +inline void* clGetExtensionFunctionAddress(const char* p0) { return clGetExtensionFunctionAddress_pfn(p0); } +#undef clGetExtensionFunctionAddressForPlatform +#define clGetExtensionFunctionAddressForPlatform clGetExtensionFunctionAddressForPlatform_fn +inline void* clGetExtensionFunctionAddressForPlatform(cl_platform_id p0, const char* p1) { return clGetExtensionFunctionAddressForPlatform_pfn(p0, p1); } +#undef clGetImageInfo +#define clGetImageInfo clGetImageInfo_fn +inline cl_int clGetImageInfo(cl_mem p0, cl_image_info p1, size_t p2, void* p3, size_t* p4) { return clGetImageInfo_pfn(p0, p1, p2, p3, p4); } +#undef clGetKernelArgInfo +#define clGetKernelArgInfo clGetKernelArgInfo_fn +inline cl_int clGetKernelArgInfo(cl_kernel p0, cl_uint p1, cl_kernel_arg_info p2, size_t p3, void* p4, size_t* p5) { return clGetKernelArgInfo_pfn(p0, p1, p2, p3, p4, p5); } +#undef clGetKernelInfo +#define clGetKernelInfo clGetKernelInfo_fn +inline cl_int clGetKernelInfo(cl_kernel p0, cl_kernel_info p1, size_t p2, void* p3, size_t* p4) { return clGetKernelInfo_pfn(p0, p1, p2, p3, p4); } +#undef clGetKernelWorkGroupInfo +#define clGetKernelWorkGroupInfo clGetKernelWorkGroupInfo_fn +inline cl_int clGetKernelWorkGroupInfo(cl_kernel p0, cl_device_id p1, cl_kernel_work_group_info p2, size_t p3, void* p4, size_t* p5) { return clGetKernelWorkGroupInfo_pfn(p0, p1, p2, p3, p4, p5); } +#undef clGetMemObjectInfo +#define clGetMemObjectInfo clGetMemObjectInfo_fn +inline cl_int clGetMemObjectInfo(cl_mem p0, cl_mem_info p1, size_t p2, void* p3, size_t* p4) { return clGetMemObjectInfo_pfn(p0, p1, p2, p3, p4); } +#undef clGetPlatformIDs +#define clGetPlatformIDs clGetPlatformIDs_fn +inline cl_int clGetPlatformIDs(cl_uint p0, cl_platform_id* p1, cl_uint* p2) { return clGetPlatformIDs_pfn(p0, p1, p2); } +#undef clGetPlatformInfo +#define clGetPlatformInfo clGetPlatformInfo_fn +inline cl_int clGetPlatformInfo(cl_platform_id p0, cl_platform_info p1, size_t p2, void* p3, size_t* p4) { return clGetPlatformInfo_pfn(p0, p1, p2, p3, p4); } +#undef clGetProgramBuildInfo +#define clGetProgramBuildInfo clGetProgramBuildInfo_fn +inline cl_int clGetProgramBuildInfo(cl_program p0, cl_device_id p1, cl_program_build_info p2, size_t p3, void* p4, size_t* p5) { return clGetProgramBuildInfo_pfn(p0, p1, p2, p3, p4, p5); } +#undef clGetProgramInfo +#define clGetProgramInfo clGetProgramInfo_fn +inline cl_int clGetProgramInfo(cl_program p0, cl_program_info p1, size_t p2, void* p3, size_t* p4) { return clGetProgramInfo_pfn(p0, p1, p2, p3, p4); } +#undef clGetSamplerInfo +#define clGetSamplerInfo clGetSamplerInfo_fn +inline cl_int clGetSamplerInfo(cl_sampler p0, cl_sampler_info p1, size_t p2, void* p3, size_t* p4) { return clGetSamplerInfo_pfn(p0, p1, p2, p3, p4); } +#undef clGetSupportedImageFormats +#define clGetSupportedImageFormats clGetSupportedImageFormats_fn +inline cl_int clGetSupportedImageFormats(cl_context p0, cl_mem_flags p1, cl_mem_object_type p2, cl_uint p3, cl_image_format* p4, cl_uint* p5) { return clGetSupportedImageFormats_pfn(p0, p1, p2, p3, p4, p5); } +#undef clLinkProgram +#define clLinkProgram clLinkProgram_fn +inline cl_program clLinkProgram(cl_context p0, cl_uint p1, const cl_device_id* p2, const char* p3, cl_uint p4, const cl_program* p5, void (CL_CALLBACK*p6) (cl_program, void*), void* p7, cl_int* p8) { return clLinkProgram_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); } +#undef clReleaseCommandQueue +#define clReleaseCommandQueue clReleaseCommandQueue_fn +inline cl_int clReleaseCommandQueue(cl_command_queue p0) { return clReleaseCommandQueue_pfn(p0); } +#undef clReleaseContext +#define clReleaseContext clReleaseContext_fn +inline cl_int clReleaseContext(cl_context p0) { return clReleaseContext_pfn(p0); } +#undef clReleaseDevice +#define clReleaseDevice clReleaseDevice_fn +inline cl_int clReleaseDevice(cl_device_id p0) { return clReleaseDevice_pfn(p0); } +#undef clReleaseEvent +#define clReleaseEvent clReleaseEvent_fn +inline cl_int clReleaseEvent(cl_event p0) { return clReleaseEvent_pfn(p0); } +#undef clReleaseKernel +#define clReleaseKernel clReleaseKernel_fn +inline cl_int clReleaseKernel(cl_kernel p0) { return clReleaseKernel_pfn(p0); } +#undef clReleaseMemObject +#define clReleaseMemObject clReleaseMemObject_fn +inline cl_int clReleaseMemObject(cl_mem p0) { return clReleaseMemObject_pfn(p0); } +#undef clReleaseProgram +#define clReleaseProgram clReleaseProgram_fn +inline cl_int clReleaseProgram(cl_program p0) { return clReleaseProgram_pfn(p0); } +#undef clReleaseSampler +#define clReleaseSampler clReleaseSampler_fn +inline cl_int clReleaseSampler(cl_sampler p0) { return clReleaseSampler_pfn(p0); } +#undef clRetainCommandQueue +#define clRetainCommandQueue clRetainCommandQueue_fn +inline cl_int clRetainCommandQueue(cl_command_queue p0) { return clRetainCommandQueue_pfn(p0); } +#undef clRetainContext +#define clRetainContext clRetainContext_fn +inline cl_int clRetainContext(cl_context p0) { return clRetainContext_pfn(p0); } +#undef clRetainDevice +#define clRetainDevice clRetainDevice_fn +inline cl_int clRetainDevice(cl_device_id p0) { return clRetainDevice_pfn(p0); } +#undef clRetainEvent +#define clRetainEvent clRetainEvent_fn +inline cl_int clRetainEvent(cl_event p0) { return clRetainEvent_pfn(p0); } +#undef clRetainKernel +#define clRetainKernel clRetainKernel_fn +inline cl_int clRetainKernel(cl_kernel p0) { return clRetainKernel_pfn(p0); } +#undef clRetainMemObject +#define clRetainMemObject clRetainMemObject_fn +inline cl_int clRetainMemObject(cl_mem p0) { return clRetainMemObject_pfn(p0); } +#undef clRetainProgram +#define clRetainProgram clRetainProgram_fn +inline cl_int clRetainProgram(cl_program p0) { return clRetainProgram_pfn(p0); } +#undef clRetainSampler +#define clRetainSampler clRetainSampler_fn +inline cl_int clRetainSampler(cl_sampler p0) { return clRetainSampler_pfn(p0); } +#undef clSetEventCallback +#define clSetEventCallback clSetEventCallback_fn +inline cl_int clSetEventCallback(cl_event p0, cl_int p1, void (CL_CALLBACK*p2) (cl_event, cl_int, void*), void* p3) { return clSetEventCallback_pfn(p0, p1, p2, p3); } +#undef clSetKernelArg +#define clSetKernelArg clSetKernelArg_fn +inline cl_int clSetKernelArg(cl_kernel p0, cl_uint p1, size_t p2, const void* p3) { return clSetKernelArg_pfn(p0, p1, p2, p3); } +#undef clSetMemObjectDestructorCallback +#define clSetMemObjectDestructorCallback clSetMemObjectDestructorCallback_fn +inline cl_int clSetMemObjectDestructorCallback(cl_mem p0, void (CL_CALLBACK*p1) (cl_mem, void*), void* p2) { return clSetMemObjectDestructorCallback_pfn(p0, p1, p2); } +#undef clSetUserEventStatus +#define clSetUserEventStatus clSetUserEventStatus_fn +inline cl_int clSetUserEventStatus(cl_event p0, cl_int p1) { return clSetUserEventStatus_pfn(p0, p1); } +#undef clUnloadCompiler +#define clUnloadCompiler clUnloadCompiler_fn +inline cl_int clUnloadCompiler() { return clUnloadCompiler_pfn(); } +#undef clUnloadPlatformCompiler +#define clUnloadPlatformCompiler clUnloadPlatformCompiler_fn +inline cl_int clUnloadPlatformCompiler(cl_platform_id p0) { return clUnloadPlatformCompiler_pfn(p0); } +#undef clWaitForEvents +#define clWaitForEvents clWaitForEvents_fn +inline cl_int clWaitForEvents(cl_uint p0, const cl_event* p1) { return clWaitForEvents_pfn(p0, p1); } diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/runtime/autogenerated/opencl_gl.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/runtime/autogenerated/opencl_gl.hpp new file mode 100644 index 0000000000000000000000000000000000000000..0b12aed6c6ce9def83b9fecc15f8be14b4790363 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/runtime/autogenerated/opencl_gl.hpp @@ -0,0 +1,62 @@ +// +// AUTOGENERATED, DO NOT EDIT +// +#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_HPP +#error "Invalid usage" +#endif + +// generated by parser_cl.py +#define clCreateFromGLBuffer clCreateFromGLBuffer_ +#define clCreateFromGLRenderbuffer clCreateFromGLRenderbuffer_ +#define clCreateFromGLTexture clCreateFromGLTexture_ +#define clCreateFromGLTexture2D clCreateFromGLTexture2D_ +#define clCreateFromGLTexture3D clCreateFromGLTexture3D_ +#define clEnqueueAcquireGLObjects clEnqueueAcquireGLObjects_ +#define clEnqueueReleaseGLObjects clEnqueueReleaseGLObjects_ +#define clGetGLContextInfoKHR clGetGLContextInfoKHR_ +#define clGetGLObjectInfo clGetGLObjectInfo_ +#define clGetGLTextureInfo clGetGLTextureInfo_ + +#if defined __APPLE__ +#include +#else +#include +#endif + +// generated by parser_cl.py +#undef clCreateFromGLBuffer +#define clCreateFromGLBuffer clCreateFromGLBuffer_pfn +#undef clCreateFromGLRenderbuffer +#define clCreateFromGLRenderbuffer clCreateFromGLRenderbuffer_pfn +#undef clCreateFromGLTexture +#define clCreateFromGLTexture clCreateFromGLTexture_pfn +#undef clCreateFromGLTexture2D +#define clCreateFromGLTexture2D clCreateFromGLTexture2D_pfn +#undef clCreateFromGLTexture3D +#define clCreateFromGLTexture3D clCreateFromGLTexture3D_pfn +#undef clEnqueueAcquireGLObjects +#define clEnqueueAcquireGLObjects clEnqueueAcquireGLObjects_pfn +#undef clEnqueueReleaseGLObjects +#define clEnqueueReleaseGLObjects clEnqueueReleaseGLObjects_pfn +#undef clGetGLContextInfoKHR +#define clGetGLContextInfoKHR clGetGLContextInfoKHR_pfn +#undef clGetGLObjectInfo +#define clGetGLObjectInfo clGetGLObjectInfo_pfn +#undef clGetGLTextureInfo +#define clGetGLTextureInfo clGetGLTextureInfo_pfn + +#ifdef cl_khr_gl_sharing + +// generated by parser_cl.py +extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateFromGLBuffer)(cl_context, cl_mem_flags, cl_GLuint, int*); +extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateFromGLRenderbuffer)(cl_context, cl_mem_flags, cl_GLuint, cl_int*); +extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateFromGLTexture)(cl_context, cl_mem_flags, cl_GLenum, cl_GLint, cl_GLuint, cl_int*); +extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateFromGLTexture2D)(cl_context, cl_mem_flags, cl_GLenum, cl_GLint, cl_GLuint, cl_int*); +extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateFromGLTexture3D)(cl_context, cl_mem_flags, cl_GLenum, cl_GLint, cl_GLuint, cl_int*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueAcquireGLObjects)(cl_command_queue, cl_uint, const cl_mem*, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueReleaseGLObjects)(cl_command_queue, cl_uint, const cl_mem*, cl_uint, const cl_event*, cl_event*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetGLContextInfoKHR)(const cl_context_properties*, cl_gl_context_info, size_t, void*, size_t*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetGLObjectInfo)(cl_mem, cl_gl_object_type*, cl_GLuint*); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetGLTextureInfo)(cl_mem, cl_gl_texture_info, size_t, void*, size_t*); + +#endif // cl_khr_gl_sharing diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/runtime/autogenerated/opencl_gl_wrappers.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/runtime/autogenerated/opencl_gl_wrappers.hpp new file mode 100644 index 0000000000000000000000000000000000000000..12f342b2e4be18b0bfb3a9fb661d47fdf8bac44c --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/runtime/autogenerated/opencl_gl_wrappers.hpp @@ -0,0 +1,42 @@ +// +// AUTOGENERATED, DO NOT EDIT +// +#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_WRAPPERS_HPP +#error "Invalid usage" +#endif + +#ifdef cl_khr_gl_sharing + +// generated by parser_cl.py +#undef clCreateFromGLBuffer +#define clCreateFromGLBuffer clCreateFromGLBuffer_fn +inline cl_mem clCreateFromGLBuffer(cl_context p0, cl_mem_flags p1, cl_GLuint p2, int* p3) { return clCreateFromGLBuffer_pfn(p0, p1, p2, p3); } +#undef clCreateFromGLRenderbuffer +#define clCreateFromGLRenderbuffer clCreateFromGLRenderbuffer_fn +inline cl_mem clCreateFromGLRenderbuffer(cl_context p0, cl_mem_flags p1, cl_GLuint p2, cl_int* p3) { return clCreateFromGLRenderbuffer_pfn(p0, p1, p2, p3); } +#undef clCreateFromGLTexture +#define clCreateFromGLTexture clCreateFromGLTexture_fn +inline cl_mem clCreateFromGLTexture(cl_context p0, cl_mem_flags p1, cl_GLenum p2, cl_GLint p3, cl_GLuint p4, cl_int* p5) { return clCreateFromGLTexture_pfn(p0, p1, p2, p3, p4, p5); } +#undef clCreateFromGLTexture2D +#define clCreateFromGLTexture2D clCreateFromGLTexture2D_fn +inline cl_mem clCreateFromGLTexture2D(cl_context p0, cl_mem_flags p1, cl_GLenum p2, cl_GLint p3, cl_GLuint p4, cl_int* p5) { return clCreateFromGLTexture2D_pfn(p0, p1, p2, p3, p4, p5); } +#undef clCreateFromGLTexture3D +#define clCreateFromGLTexture3D clCreateFromGLTexture3D_fn +inline cl_mem clCreateFromGLTexture3D(cl_context p0, cl_mem_flags p1, cl_GLenum p2, cl_GLint p3, cl_GLuint p4, cl_int* p5) { return clCreateFromGLTexture3D_pfn(p0, p1, p2, p3, p4, p5); } +#undef clEnqueueAcquireGLObjects +#define clEnqueueAcquireGLObjects clEnqueueAcquireGLObjects_fn +inline cl_int clEnqueueAcquireGLObjects(cl_command_queue p0, cl_uint p1, const cl_mem* p2, cl_uint p3, const cl_event* p4, cl_event* p5) { return clEnqueueAcquireGLObjects_pfn(p0, p1, p2, p3, p4, p5); } +#undef clEnqueueReleaseGLObjects +#define clEnqueueReleaseGLObjects clEnqueueReleaseGLObjects_fn +inline cl_int clEnqueueReleaseGLObjects(cl_command_queue p0, cl_uint p1, const cl_mem* p2, cl_uint p3, const cl_event* p4, cl_event* p5) { return clEnqueueReleaseGLObjects_pfn(p0, p1, p2, p3, p4, p5); } +#undef clGetGLContextInfoKHR +#define clGetGLContextInfoKHR clGetGLContextInfoKHR_fn +inline cl_int clGetGLContextInfoKHR(const cl_context_properties* p0, cl_gl_context_info p1, size_t p2, void* p3, size_t* p4) { return clGetGLContextInfoKHR_pfn(p0, p1, p2, p3, p4); } +#undef clGetGLObjectInfo +#define clGetGLObjectInfo clGetGLObjectInfo_fn +inline cl_int clGetGLObjectInfo(cl_mem p0, cl_gl_object_type* p1, cl_GLuint* p2) { return clGetGLObjectInfo_pfn(p0, p1, p2); } +#undef clGetGLTextureInfo +#define clGetGLTextureInfo clGetGLTextureInfo_fn +inline cl_int clGetGLTextureInfo(cl_mem p0, cl_gl_texture_info p1, size_t p2, void* p3, size_t* p4) { return clGetGLTextureInfo_pfn(p0, p1, p2, p3, p4); } + +#endif // cl_khr_gl_sharing diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/runtime/opencl_clamdblas.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/runtime/opencl_clamdblas.hpp new file mode 100644 index 0000000000000000000000000000000000000000..2ad8ac0b5db8bdd0587f60dd3ed3d2ecd28e7048 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/runtime/opencl_clamdblas.hpp @@ -0,0 +1,53 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the OpenCV Foundation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_OCL_RUNTIME_CLAMDBLAS_HPP +#define OPENCV_CORE_OCL_RUNTIME_CLAMDBLAS_HPP + +#ifdef HAVE_CLAMDBLAS + +#include "opencl_core.hpp" + +#include "autogenerated/opencl_clamdblas.hpp" + +#endif // HAVE_CLAMDBLAS + +#endif // OPENCV_CORE_OCL_RUNTIME_CLAMDBLAS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/runtime/opencl_clamdfft.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/runtime/opencl_clamdfft.hpp new file mode 100644 index 0000000000000000000000000000000000000000..a328f722fccacd8e7491abfd464b8b7c43b51074 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/runtime/opencl_clamdfft.hpp @@ -0,0 +1,53 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the OpenCV Foundation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_OCL_RUNTIME_CLAMDFFT_HPP +#define OPENCV_CORE_OCL_RUNTIME_CLAMDFFT_HPP + +#ifdef HAVE_CLAMDFFT + +#include "opencl_core.hpp" + +#include "autogenerated/opencl_clamdfft.hpp" + +#endif // HAVE_CLAMDFFT + +#endif // OPENCV_CORE_OCL_RUNTIME_CLAMDFFT_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/runtime/opencl_core.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/runtime/opencl_core.hpp new file mode 100644 index 0000000000000000000000000000000000000000..0404b3177a58f51a4137d92803198a17f8ca3ab4 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/runtime/opencl_core.hpp @@ -0,0 +1,84 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the OpenCV Foundation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_CORE_HPP +#define OPENCV_CORE_OCL_RUNTIME_OPENCL_CORE_HPP + +#ifdef HAVE_OPENCL + +#ifndef CL_RUNTIME_EXPORT +#if (defined(BUILD_SHARED_LIBS) || defined(OPENCV_CORE_SHARED)) && (defined _WIN32 || defined WINCE) && \ + !(defined(__OPENCV_BUILD) && defined(OPENCV_MODULE_IS_PART_OF_WORLD)) +#define CL_RUNTIME_EXPORT __declspec(dllimport) +#else +#define CL_RUNTIME_EXPORT +#endif +#endif + +#ifdef HAVE_OPENCL_SVM +#define clSVMAlloc clSVMAlloc_ +#define clSVMFree clSVMFree_ +#define clSetKernelArgSVMPointer clSetKernelArgSVMPointer_ +#define clSetKernelExecInfo clSetKernelExecInfo_ +#define clEnqueueSVMFree clEnqueueSVMFree_ +#define clEnqueueSVMMemcpy clEnqueueSVMMemcpy_ +#define clEnqueueSVMMemFill clEnqueueSVMMemFill_ +#define clEnqueueSVMMap clEnqueueSVMMap_ +#define clEnqueueSVMUnmap clEnqueueSVMUnmap_ +#endif + +#include "autogenerated/opencl_core.hpp" + +#ifndef CL_DEVICE_DOUBLE_FP_CONFIG +#define CL_DEVICE_DOUBLE_FP_CONFIG 0x1032 +#endif + +#ifndef CL_DEVICE_HALF_FP_CONFIG +#define CL_DEVICE_HALF_FP_CONFIG 0x1033 +#endif + +#ifndef CL_VERSION_1_2 +#define CV_REQUIRE_OPENCL_1_2_ERROR CV_Error(cv::Error::OpenCLApiCallError, "OpenCV compiled without OpenCL v1.2 support, so we can't use functionality from OpenCL v1.2") +#endif + +#endif // HAVE_OPENCL + +#endif // OPENCV_CORE_OCL_RUNTIME_OPENCL_CORE_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/runtime/opencl_core_wrappers.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/runtime/opencl_core_wrappers.hpp new file mode 100644 index 0000000000000000000000000000000000000000..38fcae9952f2838d15f7dbf79549aa9cd30bbe26 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/runtime/opencl_core_wrappers.hpp @@ -0,0 +1,47 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the OpenCV Foundation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_WRAPPERS_HPP +#define OPENCV_CORE_OCL_RUNTIME_OPENCL_WRAPPERS_HPP + +#include "autogenerated/opencl_core_wrappers.hpp" + +#endif // OPENCV_CORE_OCL_RUNTIME_OPENCL_WRAPPERS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/runtime/opencl_gl.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/runtime/opencl_gl.hpp new file mode 100644 index 0000000000000000000000000000000000000000..659c7d80581489d95c91c5ce844bc574b8c81598 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/runtime/opencl_gl.hpp @@ -0,0 +1,53 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the OpenCV Foundation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_HPP +#define OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_HPP + +#if defined HAVE_OPENCL && defined HAVE_OPENGL + +#include "opencl_core.hpp" + +#include "autogenerated/opencl_gl.hpp" + +#endif // defined HAVE_OPENCL && defined HAVE_OPENGL + +#endif // OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/runtime/opencl_gl_wrappers.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/runtime/opencl_gl_wrappers.hpp new file mode 100644 index 0000000000000000000000000000000000000000..9700004cae5ac6e11b76652ce8c78d4430e05688 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/runtime/opencl_gl_wrappers.hpp @@ -0,0 +1,47 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the OpenCV Foundation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_WRAPPERS_HPP +#define OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_WRAPPERS_HPP + +#include "autogenerated/opencl_gl_wrappers.hpp" + +#endif // OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_WRAPPERS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/runtime/opencl_svm_20.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/runtime/opencl_svm_20.hpp new file mode 100644 index 0000000000000000000000000000000000000000..9636b19b027fcc5ec2ff78c94f28b46919425fda --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/runtime/opencl_svm_20.hpp @@ -0,0 +1,48 @@ +/* See LICENSE file in the root OpenCV directory */ + +#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_2_0_HPP +#define OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_2_0_HPP + +#if defined(HAVE_OPENCL_SVM) +#include "opencl_core.hpp" + +#include "opencl_svm_definitions.hpp" + +#undef clSVMAlloc +#define clSVMAlloc clSVMAlloc_pfn +#undef clSVMFree +#define clSVMFree clSVMFree_pfn +#undef clSetKernelArgSVMPointer +#define clSetKernelArgSVMPointer clSetKernelArgSVMPointer_pfn +#undef clSetKernelExecInfo +//#define clSetKernelExecInfo clSetKernelExecInfo_pfn +#undef clEnqueueSVMFree +//#define clEnqueueSVMFree clEnqueueSVMFree_pfn +#undef clEnqueueSVMMemcpy +#define clEnqueueSVMMemcpy clEnqueueSVMMemcpy_pfn +#undef clEnqueueSVMMemFill +#define clEnqueueSVMMemFill clEnqueueSVMMemFill_pfn +#undef clEnqueueSVMMap +#define clEnqueueSVMMap clEnqueueSVMMap_pfn +#undef clEnqueueSVMUnmap +#define clEnqueueSVMUnmap clEnqueueSVMUnmap_pfn + +extern CL_RUNTIME_EXPORT void* (CL_API_CALL *clSVMAlloc)(cl_context context, cl_svm_mem_flags flags, size_t size, unsigned int alignment); +extern CL_RUNTIME_EXPORT void (CL_API_CALL *clSVMFree)(cl_context context, void* svm_pointer); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL *clSetKernelArgSVMPointer)(cl_kernel kernel, cl_uint arg_index, const void* arg_value); +//extern CL_RUNTIME_EXPORT void* (CL_API_CALL *clSetKernelExecInfo)(cl_kernel kernel, cl_kernel_exec_info param_name, size_t param_value_size, const void* param_value); +//extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL *clEnqueueSVMFree)(cl_command_queue command_queue, cl_uint num_svm_pointers, void* svm_pointers[], +// void (CL_CALLBACK *pfn_free_func)(cl_command_queue queue, cl_uint num_svm_pointers, void* svm_pointers[], void* user_data), void* user_data, +// cl_uint num_events_in_wait_list, const cl_event* event_wait_list, cl_event* event); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL *clEnqueueSVMMemcpy)(cl_command_queue command_queue, cl_bool blocking_copy, void* dst_ptr, const void* src_ptr, size_t size, + cl_uint num_events_in_wait_list, const cl_event* event_wait_list, cl_event* event); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL *clEnqueueSVMMemFill)(cl_command_queue command_queue, void* svm_ptr, const void* pattern, size_t pattern_size, size_t size, + cl_uint num_events_in_wait_list, const cl_event* event_wait_list, cl_event* event); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL *clEnqueueSVMMap)(cl_command_queue command_queue, cl_bool blocking_map, cl_map_flags map_flags, void* svm_ptr, size_t size, + cl_uint num_events_in_wait_list, const cl_event* event_wait_list, cl_event* event); +extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL *clEnqueueSVMUnmap)(cl_command_queue command_queue, void* svm_ptr, + cl_uint num_events_in_wait_list, const cl_event* event_wait_list, cl_event* event); + +#endif // HAVE_OPENCL_SVM + +#endif // OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_2_0_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/runtime/opencl_svm_definitions.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/runtime/opencl_svm_definitions.hpp new file mode 100644 index 0000000000000000000000000000000000000000..97c927b44d8cc6310623ad4156ff2aae7b7c193c --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/runtime/opencl_svm_definitions.hpp @@ -0,0 +1,42 @@ +/* See LICENSE file in the root OpenCV directory */ + +#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_DEFINITIONS_HPP +#define OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_DEFINITIONS_HPP + +#if defined(HAVE_OPENCL_SVM) +#if defined(CL_VERSION_2_0) + +// OpenCL 2.0 contains SVM definitions + +#else + +typedef cl_bitfield cl_device_svm_capabilities; +typedef cl_bitfield cl_svm_mem_flags; +typedef cl_uint cl_kernel_exec_info; + +// +// TODO Add real values after OpenCL 2.0 release +// + +#ifndef CL_DEVICE_SVM_CAPABILITIES +#define CL_DEVICE_SVM_CAPABILITIES 0x1053 + +#define CL_DEVICE_SVM_COARSE_GRAIN_BUFFER (1 << 0) +#define CL_DEVICE_SVM_FINE_GRAIN_BUFFER (1 << 1) +#define CL_DEVICE_SVM_FINE_GRAIN_SYSTEM (1 << 2) +#define CL_DEVICE_SVM_ATOMICS (1 << 3) +#endif + +#ifndef CL_MEM_SVM_FINE_GRAIN_BUFFER +#define CL_MEM_SVM_FINE_GRAIN_BUFFER (1 << 10) +#endif + +#ifndef CL_MEM_SVM_ATOMICS +#define CL_MEM_SVM_ATOMICS (1 << 11) +#endif + + +#endif // CL_VERSION_2_0 +#endif // HAVE_OPENCL_SVM + +#endif // OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_DEFINITIONS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/runtime/opencl_svm_hsa_extension.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/runtime/opencl_svm_hsa_extension.hpp new file mode 100644 index 0000000000000000000000000000000000000000..497bc3de7205d6bad28328772c3b20f48611d63c --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opencl/runtime/opencl_svm_hsa_extension.hpp @@ -0,0 +1,166 @@ +/* See LICENSE file in the root OpenCV directory */ + +#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_HSA_EXTENSION_HPP +#define OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_HSA_EXTENSION_HPP + +#if defined(HAVE_OPENCL_SVM) +#include "opencl_core.hpp" + +#ifndef CL_DEVICE_SVM_CAPABILITIES_AMD +// +// Part of the file is an extract from the cl_ext.h file from AMD APP SDK package. +// Below is the original copyright. +// +/******************************************************************************* + * Copyright (c) 2008-2013 The Khronos Group Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a + * copy of this software and/or associated documentation files (the + * "Materials"), to deal in the Materials without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, + * distribute, sublicense, and/or sell copies of the Materials, and to + * permit persons to whom the Materials are furnished to do so, subject to + * the following conditions: + * + * The above copyright notice and this permission notice shall be included + * in all copies or substantial portions of the Materials. + * + * THE MATERIALS ARE PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF + * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. + * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY + * CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, + * TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE + * MATERIALS OR THE USE OR OTHER DEALINGS IN THE MATERIALS. + ******************************************************************************/ + +/******************************************* + * Shared Virtual Memory (SVM) extension + *******************************************/ +typedef cl_bitfield cl_device_svm_capabilities_amd; +typedef cl_bitfield cl_svm_mem_flags_amd; +typedef cl_uint cl_kernel_exec_info_amd; + +/* cl_device_info */ +#define CL_DEVICE_SVM_CAPABILITIES_AMD 0x1053 +#define CL_DEVICE_PREFERRED_PLATFORM_ATOMIC_ALIGNMENT_AMD 0x1054 + +/* cl_device_svm_capabilities_amd */ +#define CL_DEVICE_SVM_COARSE_GRAIN_BUFFER_AMD (1 << 0) +#define CL_DEVICE_SVM_FINE_GRAIN_BUFFER_AMD (1 << 1) +#define CL_DEVICE_SVM_FINE_GRAIN_SYSTEM_AMD (1 << 2) +#define CL_DEVICE_SVM_ATOMICS_AMD (1 << 3) + +/* cl_svm_mem_flags_amd */ +#define CL_MEM_SVM_FINE_GRAIN_BUFFER_AMD (1 << 10) +#define CL_MEM_SVM_ATOMICS_AMD (1 << 11) + +/* cl_mem_info */ +#define CL_MEM_USES_SVM_POINTER_AMD 0x1109 + +/* cl_kernel_exec_info_amd */ +#define CL_KERNEL_EXEC_INFO_SVM_PTRS_AMD 0x11B6 +#define CL_KERNEL_EXEC_INFO_SVM_FINE_GRAIN_SYSTEM_AMD 0x11B7 + +/* cl_command_type */ +#define CL_COMMAND_SVM_FREE_AMD 0x1209 +#define CL_COMMAND_SVM_MEMCPY_AMD 0x120A +#define CL_COMMAND_SVM_MEMFILL_AMD 0x120B +#define CL_COMMAND_SVM_MAP_AMD 0x120C +#define CL_COMMAND_SVM_UNMAP_AMD 0x120D + +typedef CL_API_ENTRY void* +(CL_API_CALL * clSVMAllocAMD_fn)( + cl_context /* context */, + cl_svm_mem_flags_amd /* flags */, + size_t /* size */, + unsigned int /* alignment */ +) CL_EXT_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY void +(CL_API_CALL * clSVMFreeAMD_fn)( + cl_context /* context */, + void* /* svm_pointer */ +) CL_EXT_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_int +(CL_API_CALL * clEnqueueSVMFreeAMD_fn)( + cl_command_queue /* command_queue */, + cl_uint /* num_svm_pointers */, + void** /* svm_pointers */, + void (CL_CALLBACK *)( /*pfn_free_func*/ + cl_command_queue /* queue */, + cl_uint /* num_svm_pointers */, + void** /* svm_pointers */, + void* /* user_data */), + void* /* user_data */, + cl_uint /* num_events_in_wait_list */, + const cl_event* /* event_wait_list */, + cl_event* /* event */ +) CL_EXT_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_int +(CL_API_CALL * clEnqueueSVMMemcpyAMD_fn)( + cl_command_queue /* command_queue */, + cl_bool /* blocking_copy */, + void* /* dst_ptr */, + const void* /* src_ptr */, + size_t /* size */, + cl_uint /* num_events_in_wait_list */, + const cl_event* /* event_wait_list */, + cl_event* /* event */ +) CL_EXT_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_int +(CL_API_CALL * clEnqueueSVMMemFillAMD_fn)( + cl_command_queue /* command_queue */, + void* /* svm_ptr */, + const void* /* pattern */, + size_t /* pattern_size */, + size_t /* size */, + cl_uint /* num_events_in_wait_list */, + const cl_event* /* event_wait_list */, + cl_event* /* event */ +) CL_EXT_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_int +(CL_API_CALL * clEnqueueSVMMapAMD_fn)( + cl_command_queue /* command_queue */, + cl_bool /* blocking_map */, + cl_map_flags /* map_flags */, + void* /* svm_ptr */, + size_t /* size */, + cl_uint /* num_events_in_wait_list */, + const cl_event* /* event_wait_list */, + cl_event* /* event */ +) CL_EXT_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_int +(CL_API_CALL * clEnqueueSVMUnmapAMD_fn)( + cl_command_queue /* command_queue */, + void* /* svm_ptr */, + cl_uint /* num_events_in_wait_list */, + const cl_event* /* event_wait_list */, + cl_event* /* event */ +) CL_EXT_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_int +(CL_API_CALL * clSetKernelArgSVMPointerAMD_fn)( + cl_kernel /* kernel */, + cl_uint /* arg_index */, + const void * /* arg_value */ +) CL_EXT_SUFFIX__VERSION_1_2; + +typedef CL_API_ENTRY cl_int +(CL_API_CALL * clSetKernelExecInfoAMD_fn)( + cl_kernel /* kernel */, + cl_kernel_exec_info_amd /* param_name */, + size_t /* param_value_size */, + const void * /* param_value */ +) CL_EXT_SUFFIX__VERSION_1_2; + +#endif + +#endif // HAVE_OPENCL_SVM + +#endif // OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_HSA_EXTENSION_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opengl.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opengl.hpp new file mode 100644 index 0000000000000000000000000000000000000000..a6288bebe8f51cd64f2a089bc8830320845b22c5 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/opengl.hpp @@ -0,0 +1,725 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_OPENGL_HPP +#define OPENCV_CORE_OPENGL_HPP + +#ifndef __cplusplus +# error opengl.hpp header must be compiled as C++ +#endif + +#include "opencv2/core.hpp" +#include "ocl.hpp" + +namespace cv { namespace ogl { + +/** @addtogroup core_opengl +This section describes OpenGL interoperability. + +To enable OpenGL support, configure OpenCV using CMake with WITH_OPENGL=ON . Currently OpenGL is +supported only with WIN32, GTK and Qt backends on Windows and Linux (MacOS and Android are not +supported). For GTK backend gtkglext-1.0 library is required. + +To use OpenGL functionality you should first create OpenGL context (window or frame buffer). You can +do this with namedWindow function or with other OpenGL toolkit (GLUT, for example). +*/ +//! @{ + +/////////////////// OpenGL Objects /////////////////// + +/** @brief Smart pointer for OpenGL buffer object with reference counting. + +Buffer Objects are OpenGL objects that store an array of unformatted memory allocated by the OpenGL +context. These can be used to store vertex data, pixel data retrieved from images or the +framebuffer, and a variety of other things. + +ogl::Buffer has interface similar with Mat interface and represents 2D array memory. + +ogl::Buffer supports memory transfers between host and device and also can be mapped to CUDA memory. + */ +class CV_EXPORTS Buffer +{ +public: + /** @brief The target defines how you intend to use the buffer object. + */ + enum Target + { + ARRAY_BUFFER = 0x8892, //!< The buffer will be used as a source for vertex data + ELEMENT_ARRAY_BUFFER = 0x8893, //!< The buffer will be used for indices (in glDrawElements, for example) + PIXEL_PACK_BUFFER = 0x88EB, //!< The buffer will be used for reading from OpenGL textures + PIXEL_UNPACK_BUFFER = 0x88EC //!< The buffer will be used for writing to OpenGL textures + }; + + enum Access + { + READ_ONLY = 0x88B8, + WRITE_ONLY = 0x88B9, + READ_WRITE = 0x88BA + }; + + /** @brief The constructors. + + Creates empty ogl::Buffer object, creates ogl::Buffer object from existed buffer ( abufId + parameter), allocates memory for ogl::Buffer object or copies from host/device memory. + */ + Buffer(); + + /** @overload + @param arows Number of rows in a 2D array. + @param acols Number of columns in a 2D array. + @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details. + @param abufId Buffer object name. + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + Buffer(int arows, int acols, int atype, unsigned int abufId, bool autoRelease = false); + + /** @overload + @param asize 2D array size. + @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details. + @param abufId Buffer object name. + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + Buffer(Size asize, int atype, unsigned int abufId, bool autoRelease = false); + + /** @overload + @param arows Number of rows in a 2D array. + @param acols Number of columns in a 2D array. + @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details. + @param target Buffer usage. See cv::ogl::Buffer::Target . + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + Buffer(int arows, int acols, int atype, Target target = ARRAY_BUFFER, bool autoRelease = false); + + /** @overload + @param asize 2D array size. + @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details. + @param target Buffer usage. See cv::ogl::Buffer::Target . + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + Buffer(Size asize, int atype, Target target = ARRAY_BUFFER, bool autoRelease = false); + + /** @overload + @param arr Input array (host or device memory, it can be Mat , cuda::GpuMat or std::vector ). + @param target Buffer usage. See cv::ogl::Buffer::Target . + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + explicit Buffer(InputArray arr, Target target = ARRAY_BUFFER, bool autoRelease = false); + + /** @brief Allocates memory for ogl::Buffer object. + + @param arows Number of rows in a 2D array. + @param acols Number of columns in a 2D array. + @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details. + @param target Buffer usage. See cv::ogl::Buffer::Target . + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + void create(int arows, int acols, int atype, Target target = ARRAY_BUFFER, bool autoRelease = false); + + /** @overload + @param asize 2D array size. + @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details. + @param target Buffer usage. See cv::ogl::Buffer::Target . + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + void create(Size asize, int atype, Target target = ARRAY_BUFFER, bool autoRelease = false); + + /** @brief Decrements the reference counter and destroys the buffer object if needed. + + The function will call setAutoRelease(true) . + */ + void release(); + + /** @brief Sets auto release mode. + + The lifetime of the OpenGL object is tied to the lifetime of the context. If OpenGL context was + bound to a window it could be released at any time (user can close a window). If object's destructor + is called after destruction of the context it will cause an error. Thus ogl::Buffer doesn't destroy + OpenGL object in destructor by default (all OpenGL resources will be released with OpenGL context). + This function can force ogl::Buffer destructor to destroy OpenGL object. + @param flag Auto release mode (if true, release will be called in object's destructor). + */ + void setAutoRelease(bool flag); + + /** @brief Copies from host/device memory to OpenGL buffer. + @param arr Input array (host or device memory, it can be Mat , cuda::GpuMat or std::vector ). + @param target Buffer usage. See cv::ogl::Buffer::Target . + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + void copyFrom(InputArray arr, Target target = ARRAY_BUFFER, bool autoRelease = false); + + /** @overload */ + void copyFrom(InputArray arr, cuda::Stream& stream, Target target = ARRAY_BUFFER, bool autoRelease = false); + + /** @brief Copies from OpenGL buffer to host/device memory or another OpenGL buffer object. + + @param arr Destination array (host or device memory, can be Mat , cuda::GpuMat , std::vector or + ogl::Buffer ). + */ + void copyTo(OutputArray arr) const; + + /** @overload */ + void copyTo(OutputArray arr, cuda::Stream& stream) const; + + /** @brief Creates a full copy of the buffer object and the underlying data. + + @param target Buffer usage for destination buffer. + @param autoRelease Auto release mode for destination buffer. + */ + Buffer clone(Target target = ARRAY_BUFFER, bool autoRelease = false) const; + + /** @brief Binds OpenGL buffer to the specified buffer binding point. + + @param target Binding point. See cv::ogl::Buffer::Target . + */ + void bind(Target target) const; + + /** @brief Unbind any buffers from the specified binding point. + + @param target Binding point. See cv::ogl::Buffer::Target . + */ + static void unbind(Target target); + + /** @brief Maps OpenGL buffer to host memory. + + mapHost maps to the client's address space the entire data store of the buffer object. The data can + then be directly read and/or written relative to the returned pointer, depending on the specified + access policy. + + A mapped data store must be unmapped with ogl::Buffer::unmapHost before its buffer object is used. + + This operation can lead to memory transfers between host and device. + + Only one buffer object can be mapped at a time. + @param access Access policy, indicating whether it will be possible to read from, write to, or both + read from and write to the buffer object's mapped data store. The symbolic constant must be + ogl::Buffer::READ_ONLY , ogl::Buffer::WRITE_ONLY or ogl::Buffer::READ_WRITE . + */ + Mat mapHost(Access access); + + /** @brief Unmaps OpenGL buffer. + */ + void unmapHost(); + + //! map to device memory (blocking) + cuda::GpuMat mapDevice(); + void unmapDevice(); + + /** @brief Maps OpenGL buffer to CUDA device memory. + + This operation doesn't copy data. Several buffer objects can be mapped to CUDA memory at a time. + + A mapped data store must be unmapped with ogl::Buffer::unmapDevice before its buffer object is used. + */ + cuda::GpuMat mapDevice(cuda::Stream& stream); + + /** @brief Unmaps OpenGL buffer. + */ + void unmapDevice(cuda::Stream& stream); + + int rows() const; + int cols() const; + Size size() const; + bool empty() const; + + int type() const; + int depth() const; + int channels() const; + int elemSize() const; + int elemSize1() const; + + //! get OpenGL opject id + unsigned int bufId() const; + + class Impl; + +private: + Ptr impl_; + int rows_; + int cols_; + int type_; +}; + +/** @brief Smart pointer for OpenGL 2D texture memory with reference counting. + */ +class CV_EXPORTS Texture2D +{ +public: + /** @brief An Image Format describes the way that the images in Textures store their data. + */ + enum Format + { + NONE = 0, + DEPTH_COMPONENT = 0x1902, //!< Depth + RGB = 0x1907, //!< Red, Green, Blue + RGBA = 0x1908 //!< Red, Green, Blue, Alpha + }; + + /** @brief The constructors. + + Creates empty ogl::Texture2D object, allocates memory for ogl::Texture2D object or copies from + host/device memory. + */ + Texture2D(); + + /** @overload */ + Texture2D(int arows, int acols, Format aformat, unsigned int atexId, bool autoRelease = false); + + /** @overload */ + Texture2D(Size asize, Format aformat, unsigned int atexId, bool autoRelease = false); + + /** @overload + @param arows Number of rows. + @param acols Number of columns. + @param aformat Image format. See cv::ogl::Texture2D::Format . + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + Texture2D(int arows, int acols, Format aformat, bool autoRelease = false); + + /** @overload + @param asize 2D array size. + @param aformat Image format. See cv::ogl::Texture2D::Format . + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + Texture2D(Size asize, Format aformat, bool autoRelease = false); + + /** @overload + @param arr Input array (host or device memory, it can be Mat , cuda::GpuMat or ogl::Buffer ). + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + explicit Texture2D(InputArray arr, bool autoRelease = false); + + /** @brief Allocates memory for ogl::Texture2D object. + + @param arows Number of rows. + @param acols Number of columns. + @param aformat Image format. See cv::ogl::Texture2D::Format . + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + void create(int arows, int acols, Format aformat, bool autoRelease = false); + /** @overload + @param asize 2D array size. + @param aformat Image format. See cv::ogl::Texture2D::Format . + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + void create(Size asize, Format aformat, bool autoRelease = false); + + /** @brief Decrements the reference counter and destroys the texture object if needed. + + The function will call setAutoRelease(true) . + */ + void release(); + + /** @brief Sets auto release mode. + + @param flag Auto release mode (if true, release will be called in object's destructor). + + The lifetime of the OpenGL object is tied to the lifetime of the context. If OpenGL context was + bound to a window it could be released at any time (user can close a window). If object's destructor + is called after destruction of the context it will cause an error. Thus ogl::Texture2D doesn't + destroy OpenGL object in destructor by default (all OpenGL resources will be released with OpenGL + context). This function can force ogl::Texture2D destructor to destroy OpenGL object. + */ + void setAutoRelease(bool flag); + + /** @brief Copies from host/device memory to OpenGL texture. + + @param arr Input array (host or device memory, it can be Mat , cuda::GpuMat or ogl::Buffer ). + @param autoRelease Auto release mode (if true, release will be called in object's destructor). + */ + void copyFrom(InputArray arr, bool autoRelease = false); + + /** @brief Copies from OpenGL texture to host/device memory or another OpenGL texture object. + + @param arr Destination array (host or device memory, can be Mat , cuda::GpuMat , ogl::Buffer or + ogl::Texture2D ). + @param ddepth Destination depth. + @param autoRelease Auto release mode for destination buffer (if arr is OpenGL buffer or texture). + */ + void copyTo(OutputArray arr, int ddepth = CV_32F, bool autoRelease = false) const; + + /** @brief Binds texture to current active texture unit for GL_TEXTURE_2D target. + */ + void bind() const; + + int rows() const; + int cols() const; + Size size() const; + bool empty() const; + + Format format() const; + + //! get OpenGL opject id + unsigned int texId() const; + + class Impl; + +private: + Ptr impl_; + int rows_; + int cols_; + Format format_; +}; + +/** @brief Wrapper for OpenGL Client-Side Vertex arrays. + +ogl::Arrays stores vertex data in ogl::Buffer objects. + */ +class CV_EXPORTS Arrays +{ +public: + /** @brief Default constructor + */ + Arrays(); + + /** @brief Sets an array of vertex coordinates. + @param vertex array with vertex coordinates, can be both host and device memory. + */ + void setVertexArray(InputArray vertex); + + /** @brief Resets vertex coordinates. + */ + void resetVertexArray(); + + /** @brief Sets an array of vertex colors. + @param color array with vertex colors, can be both host and device memory. + */ + void setColorArray(InputArray color); + + /** @brief Resets vertex colors. + */ + void resetColorArray(); + + /** @brief Sets an array of vertex normals. + @param normal array with vertex normals, can be both host and device memory. + */ + void setNormalArray(InputArray normal); + + /** @brief Resets vertex normals. + */ + void resetNormalArray(); + + /** @brief Sets an array of vertex texture coordinates. + @param texCoord array with vertex texture coordinates, can be both host and device memory. + */ + void setTexCoordArray(InputArray texCoord); + + /** @brief Resets vertex texture coordinates. + */ + void resetTexCoordArray(); + + /** @brief Releases all inner buffers. + */ + void release(); + + /** @brief Sets auto release mode all inner buffers. + @param flag Auto release mode. + */ + void setAutoRelease(bool flag); + + /** @brief Binds all vertex arrays. + */ + void bind() const; + + /** @brief Returns the vertex count. + */ + int size() const; + bool empty() const; + +private: + int size_; + Buffer vertex_; + Buffer color_; + Buffer normal_; + Buffer texCoord_; +}; + +/////////////////// Render Functions /////////////////// + +//! render mode +enum RenderModes { + POINTS = 0x0000, + LINES = 0x0001, + LINE_LOOP = 0x0002, + LINE_STRIP = 0x0003, + TRIANGLES = 0x0004, + TRIANGLE_STRIP = 0x0005, + TRIANGLE_FAN = 0x0006, + QUADS = 0x0007, + QUAD_STRIP = 0x0008, + POLYGON = 0x0009 +}; + +/** @brief Render OpenGL texture or primitives. +@param tex Texture to draw. +@param wndRect Region of window, where to draw a texture (normalized coordinates). +@param texRect Region of texture to draw (normalized coordinates). + */ +CV_EXPORTS void render(const Texture2D& tex, + Rect_ wndRect = Rect_(0.0, 0.0, 1.0, 1.0), + Rect_ texRect = Rect_(0.0, 0.0, 1.0, 1.0)); + +/** @overload +@param arr Array of privitives vertices. +@param mode Render mode. One of cv::ogl::RenderModes +@param color Color for all vertices. Will be used if arr doesn't contain color array. +*/ +CV_EXPORTS void render(const Arrays& arr, int mode = POINTS, Scalar color = Scalar::all(255)); + +/** @overload +@param arr Array of privitives vertices. +@param indices Array of vertices indices (host or device memory). +@param mode Render mode. One of cv::ogl::RenderModes +@param color Color for all vertices. Will be used if arr doesn't contain color array. +*/ +CV_EXPORTS void render(const Arrays& arr, InputArray indices, int mode = POINTS, Scalar color = Scalar::all(255)); + +/////////////////// CL-GL Interoperability Functions /////////////////// + +namespace ocl { +using namespace cv::ocl; + +// TODO static functions in the Context class +/** @brief Creates OpenCL context from GL. +@return Returns reference to OpenCL Context + */ +CV_EXPORTS Context& initializeContextFromGL(); + +} // namespace cv::ogl::ocl + +/** @brief Converts InputArray to Texture2D object. +@param src - source InputArray. +@param texture - destination Texture2D object. + */ +CV_EXPORTS void convertToGLTexture2D(InputArray src, Texture2D& texture); + +/** @brief Converts Texture2D object to OutputArray. +@param texture - source Texture2D object. +@param dst - destination OutputArray. + */ +CV_EXPORTS void convertFromGLTexture2D(const Texture2D& texture, OutputArray dst); + +/** @brief Maps Buffer object to process on CL side (convert to UMat). + +Function creates CL buffer from GL one, and then constructs UMat that can be used +to process buffer data with OpenCV functions. Note that in current implementation +UMat constructed this way doesn't own corresponding GL buffer object, so it is +the user responsibility to close down CL/GL buffers relationships by explicitly +calling unmapGLBuffer() function. +@param buffer - source Buffer object. +@param accessFlags - data access flags (ACCESS_READ|ACCESS_WRITE). +@return Returns UMat object + */ +CV_EXPORTS UMat mapGLBuffer(const Buffer& buffer, int accessFlags = ACCESS_READ|ACCESS_WRITE); + +/** @brief Unmaps Buffer object (releases UMat, previously mapped from Buffer). + +Function must be called explicitly by the user for each UMat previously constructed +by the call to mapGLBuffer() function. +@param u - source UMat, created by mapGLBuffer(). + */ +CV_EXPORTS void unmapGLBuffer(UMat& u); + +//! @} +}} // namespace cv::ogl + +namespace cv { namespace cuda { + +/** @brief Sets a CUDA device and initializes it for the current thread with OpenGL interoperability. + +This function should be explicitly called after OpenGL context creation and before any CUDA calls. +@param device System index of a CUDA device starting with 0. +@ingroup core_opengl + */ +CV_EXPORTS void setGlDevice(int device = 0); + +}} + +//! @cond IGNORED + +//////////////////////////////////////////////////////////////////////// +//////////////////////////////////////////////////////////////////////// +//////////////////////////////////////////////////////////////////////// + +inline +cv::ogl::Buffer::Buffer(int arows, int acols, int atype, Target target, bool autoRelease) : rows_(0), cols_(0), type_(0) +{ + create(arows, acols, atype, target, autoRelease); +} + +inline +cv::ogl::Buffer::Buffer(Size asize, int atype, Target target, bool autoRelease) : rows_(0), cols_(0), type_(0) +{ + create(asize, atype, target, autoRelease); +} + +inline +void cv::ogl::Buffer::create(Size asize, int atype, Target target, bool autoRelease) +{ + create(asize.height, asize.width, atype, target, autoRelease); +} + +inline +int cv::ogl::Buffer::rows() const +{ + return rows_; +} + +inline +int cv::ogl::Buffer::cols() const +{ + return cols_; +} + +inline +cv::Size cv::ogl::Buffer::size() const +{ + return Size(cols_, rows_); +} + +inline +bool cv::ogl::Buffer::empty() const +{ + return rows_ == 0 || cols_ == 0; +} + +inline +int cv::ogl::Buffer::type() const +{ + return type_; +} + +inline +int cv::ogl::Buffer::depth() const +{ + return CV_MAT_DEPTH(type_); +} + +inline +int cv::ogl::Buffer::channels() const +{ + return CV_MAT_CN(type_); +} + +inline +int cv::ogl::Buffer::elemSize() const +{ + return CV_ELEM_SIZE(type_); +} + +inline +int cv::ogl::Buffer::elemSize1() const +{ + return CV_ELEM_SIZE1(type_); +} + +/////// + +inline +cv::ogl::Texture2D::Texture2D(int arows, int acols, Format aformat, bool autoRelease) : rows_(0), cols_(0), format_(NONE) +{ + create(arows, acols, aformat, autoRelease); +} + +inline +cv::ogl::Texture2D::Texture2D(Size asize, Format aformat, bool autoRelease) : rows_(0), cols_(0), format_(NONE) +{ + create(asize, aformat, autoRelease); +} + +inline +void cv::ogl::Texture2D::create(Size asize, Format aformat, bool autoRelease) +{ + create(asize.height, asize.width, aformat, autoRelease); +} + +inline +int cv::ogl::Texture2D::rows() const +{ + return rows_; +} + +inline +int cv::ogl::Texture2D::cols() const +{ + return cols_; +} + +inline +cv::Size cv::ogl::Texture2D::size() const +{ + return Size(cols_, rows_); +} + +inline +bool cv::ogl::Texture2D::empty() const +{ + return rows_ == 0 || cols_ == 0; +} + +inline +cv::ogl::Texture2D::Format cv::ogl::Texture2D::format() const +{ + return format_; +} + +/////// + +inline +cv::ogl::Arrays::Arrays() : size_(0) +{ +} + +inline +int cv::ogl::Arrays::size() const +{ + return size_; +} + +inline +bool cv::ogl::Arrays::empty() const +{ + return size_ == 0; +} + +//! @endcond + +#endif /* OPENCV_CORE_OPENGL_HPP */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/operations.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/operations.hpp new file mode 100644 index 0000000000000000000000000000000000000000..d706d9664e0b1882a6b2c77bb55823f9fc2f2568 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/operations.hpp @@ -0,0 +1,554 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_OPERATIONS_HPP +#define OPENCV_CORE_OPERATIONS_HPP + +#ifndef __cplusplus +# error operations.hpp header must be compiled as C++ +#endif + +#include + +//! @cond IGNORED + +namespace cv +{ + +////////////////////////////// Matx methods depending on core API ///////////////////////////// + +namespace internal +{ + +template struct Matx_FastInvOp +{ + bool operator()(const Matx<_Tp, m, n>& a, Matx<_Tp, n, m>& b, int method) const + { + return invert(a, b, method) != 0; + } +}; + +template struct Matx_FastInvOp<_Tp, m, m> +{ + bool operator()(const Matx<_Tp, m, m>& a, Matx<_Tp, m, m>& b, int method) const + { + if (method == DECOMP_LU || method == DECOMP_CHOLESKY) + { + Matx<_Tp, m, m> temp = a; + + // assume that b is all 0's on input => make it a unity matrix + for (int i = 0; i < m; i++) + b(i, i) = (_Tp)1; + + if (method == DECOMP_CHOLESKY) + return Cholesky(temp.val, m*sizeof(_Tp), m, b.val, m*sizeof(_Tp), m); + + return LU(temp.val, m*sizeof(_Tp), m, b.val, m*sizeof(_Tp), m) != 0; + } + else + { + return invert(a, b, method) != 0; + } + } +}; + +template struct Matx_FastInvOp<_Tp, 2, 2> +{ + bool operator()(const Matx<_Tp, 2, 2>& a, Matx<_Tp, 2, 2>& b, int /*method*/) const + { + _Tp d = (_Tp)determinant(a); + if (d == 0) + return false; + d = 1/d; + b(1,1) = a(0,0)*d; + b(0,0) = a(1,1)*d; + b(0,1) = -a(0,1)*d; + b(1,0) = -a(1,0)*d; + return true; + } +}; + +template struct Matx_FastInvOp<_Tp, 3, 3> +{ + bool operator()(const Matx<_Tp, 3, 3>& a, Matx<_Tp, 3, 3>& b, int /*method*/) const + { + _Tp d = (_Tp)determinant(a); + if (d == 0) + return false; + d = 1/d; + b(0,0) = (a(1,1) * a(2,2) - a(1,2) * a(2,1)) * d; + b(0,1) = (a(0,2) * a(2,1) - a(0,1) * a(2,2)) * d; + b(0,2) = (a(0,1) * a(1,2) - a(0,2) * a(1,1)) * d; + + b(1,0) = (a(1,2) * a(2,0) - a(1,0) * a(2,2)) * d; + b(1,1) = (a(0,0) * a(2,2) - a(0,2) * a(2,0)) * d; + b(1,2) = (a(0,2) * a(1,0) - a(0,0) * a(1,2)) * d; + + b(2,0) = (a(1,0) * a(2,1) - a(1,1) * a(2,0)) * d; + b(2,1) = (a(0,1) * a(2,0) - a(0,0) * a(2,1)) * d; + b(2,2) = (a(0,0) * a(1,1) - a(0,1) * a(1,0)) * d; + return true; + } +}; + + +template struct Matx_FastSolveOp +{ + bool operator()(const Matx<_Tp, m, l>& a, const Matx<_Tp, m, n>& b, + Matx<_Tp, l, n>& x, int method) const + { + return cv::solve(a, b, x, method); + } +}; + +template struct Matx_FastSolveOp<_Tp, m, m, n> +{ + bool operator()(const Matx<_Tp, m, m>& a, const Matx<_Tp, m, n>& b, + Matx<_Tp, m, n>& x, int method) const + { + if (method == DECOMP_LU || method == DECOMP_CHOLESKY) + { + Matx<_Tp, m, m> temp = a; + x = b; + if( method == DECOMP_CHOLESKY ) + return Cholesky(temp.val, m*sizeof(_Tp), m, x.val, n*sizeof(_Tp), n); + + return LU(temp.val, m*sizeof(_Tp), m, x.val, n*sizeof(_Tp), n) != 0; + } + else + { + return cv::solve(a, b, x, method); + } + } +}; + +template struct Matx_FastSolveOp<_Tp, 2, 2, 1> +{ + bool operator()(const Matx<_Tp, 2, 2>& a, const Matx<_Tp, 2, 1>& b, + Matx<_Tp, 2, 1>& x, int) const + { + _Tp d = (_Tp)determinant(a); + if (d == 0) + return false; + d = 1/d; + x(0) = (b(0)*a(1,1) - b(1)*a(0,1))*d; + x(1) = (b(1)*a(0,0) - b(0)*a(1,0))*d; + return true; + } +}; + +template struct Matx_FastSolveOp<_Tp, 3, 3, 1> +{ + bool operator()(const Matx<_Tp, 3, 3>& a, const Matx<_Tp, 3, 1>& b, + Matx<_Tp, 3, 1>& x, int) const + { + _Tp d = (_Tp)determinant(a); + if (d == 0) + return false; + d = 1/d; + x(0) = d*(b(0)*(a(1,1)*a(2,2) - a(1,2)*a(2,1)) - + a(0,1)*(b(1)*a(2,2) - a(1,2)*b(2)) + + a(0,2)*(b(1)*a(2,1) - a(1,1)*b(2))); + + x(1) = d*(a(0,0)*(b(1)*a(2,2) - a(1,2)*b(2)) - + b(0)*(a(1,0)*a(2,2) - a(1,2)*a(2,0)) + + a(0,2)*(a(1,0)*b(2) - b(1)*a(2,0))); + + x(2) = d*(a(0,0)*(a(1,1)*b(2) - b(1)*a(2,1)) - + a(0,1)*(a(1,0)*b(2) - b(1)*a(2,0)) + + b(0)*(a(1,0)*a(2,1) - a(1,1)*a(2,0))); + return true; + } +}; + +} // internal + +template inline +Matx<_Tp,m,n> Matx<_Tp,m,n>::randu(_Tp a, _Tp b) +{ + Matx<_Tp,m,n> M; + cv::randu(M, Scalar(a), Scalar(b)); + return M; +} + +template inline +Matx<_Tp,m,n> Matx<_Tp,m,n>::randn(_Tp a, _Tp b) +{ + Matx<_Tp,m,n> M; + cv::randn(M, Scalar(a), Scalar(b)); + return M; +} + +template inline +Matx<_Tp, n, m> Matx<_Tp, m, n>::inv(int method, bool *p_is_ok /*= NULL*/) const +{ + Matx<_Tp, n, m> b; + bool ok = cv::internal::Matx_FastInvOp<_Tp, m, n>()(*this, b, method); + if (p_is_ok) *p_is_ok = ok; + return ok ? b : Matx<_Tp, n, m>::zeros(); +} + +template template inline +Matx<_Tp, n, l> Matx<_Tp, m, n>::solve(const Matx<_Tp, m, l>& rhs, int method) const +{ + Matx<_Tp, n, l> x; + bool ok = cv::internal::Matx_FastSolveOp<_Tp, m, n, l>()(*this, rhs, x, method); + return ok ? x : Matx<_Tp, n, l>::zeros(); +} + + + +////////////////////////// Augmenting algebraic & logical operations ////////////////////////// + +#define CV_MAT_AUG_OPERATOR1(op, cvop, A, B) \ + static inline A& operator op (A& a, const B& b) { cvop; return a; } + +#define CV_MAT_AUG_OPERATOR(op, cvop, A, B) \ + CV_MAT_AUG_OPERATOR1(op, cvop, A, B) \ + CV_MAT_AUG_OPERATOR1(op, cvop, const A, B) + +#define CV_MAT_AUG_OPERATOR_T(op, cvop, A, B) \ + template CV_MAT_AUG_OPERATOR1(op, cvop, A, B) \ + template CV_MAT_AUG_OPERATOR1(op, cvop, const A, B) + +CV_MAT_AUG_OPERATOR (+=, cv::add(a,b,a), Mat, Mat) +CV_MAT_AUG_OPERATOR (+=, cv::add(a,b,a), Mat, Scalar) +CV_MAT_AUG_OPERATOR_T(+=, cv::add(a,b,a), Mat_<_Tp>, Mat) +CV_MAT_AUG_OPERATOR_T(+=, cv::add(a,b,a), Mat_<_Tp>, Scalar) +CV_MAT_AUG_OPERATOR_T(+=, cv::add(a,b,a), Mat_<_Tp>, Mat_<_Tp>) + +CV_MAT_AUG_OPERATOR (-=, cv::subtract(a,b,a), Mat, Mat) +CV_MAT_AUG_OPERATOR (-=, cv::subtract(a,b,a), Mat, Scalar) +CV_MAT_AUG_OPERATOR_T(-=, cv::subtract(a,b,a), Mat_<_Tp>, Mat) +CV_MAT_AUG_OPERATOR_T(-=, cv::subtract(a,b,a), Mat_<_Tp>, Scalar) +CV_MAT_AUG_OPERATOR_T(-=, cv::subtract(a,b,a), Mat_<_Tp>, Mat_<_Tp>) + +CV_MAT_AUG_OPERATOR (*=, cv::gemm(a, b, 1, Mat(), 0, a, 0), Mat, Mat) +CV_MAT_AUG_OPERATOR_T(*=, cv::gemm(a, b, 1, Mat(), 0, a, 0), Mat_<_Tp>, Mat) +CV_MAT_AUG_OPERATOR_T(*=, cv::gemm(a, b, 1, Mat(), 0, a, 0), Mat_<_Tp>, Mat_<_Tp>) +CV_MAT_AUG_OPERATOR (*=, a.convertTo(a, -1, b), Mat, double) +CV_MAT_AUG_OPERATOR_T(*=, a.convertTo(a, -1, b), Mat_<_Tp>, double) + +CV_MAT_AUG_OPERATOR (/=, cv::divide(a,b,a), Mat, Mat) +CV_MAT_AUG_OPERATOR_T(/=, cv::divide(a,b,a), Mat_<_Tp>, Mat) +CV_MAT_AUG_OPERATOR_T(/=, cv::divide(a,b,a), Mat_<_Tp>, Mat_<_Tp>) +CV_MAT_AUG_OPERATOR (/=, a.convertTo((Mat&)a, -1, 1./b), Mat, double) +CV_MAT_AUG_OPERATOR_T(/=, a.convertTo((Mat&)a, -1, 1./b), Mat_<_Tp>, double) + +CV_MAT_AUG_OPERATOR (&=, cv::bitwise_and(a,b,a), Mat, Mat) +CV_MAT_AUG_OPERATOR (&=, cv::bitwise_and(a,b,a), Mat, Scalar) +CV_MAT_AUG_OPERATOR_T(&=, cv::bitwise_and(a,b,a), Mat_<_Tp>, Mat) +CV_MAT_AUG_OPERATOR_T(&=, cv::bitwise_and(a,b,a), Mat_<_Tp>, Scalar) +CV_MAT_AUG_OPERATOR_T(&=, cv::bitwise_and(a,b,a), Mat_<_Tp>, Mat_<_Tp>) + +CV_MAT_AUG_OPERATOR (|=, cv::bitwise_or(a,b,a), Mat, Mat) +CV_MAT_AUG_OPERATOR (|=, cv::bitwise_or(a,b,a), Mat, Scalar) +CV_MAT_AUG_OPERATOR_T(|=, cv::bitwise_or(a,b,a), Mat_<_Tp>, Mat) +CV_MAT_AUG_OPERATOR_T(|=, cv::bitwise_or(a,b,a), Mat_<_Tp>, Scalar) +CV_MAT_AUG_OPERATOR_T(|=, cv::bitwise_or(a,b,a), Mat_<_Tp>, Mat_<_Tp>) + +CV_MAT_AUG_OPERATOR (^=, cv::bitwise_xor(a,b,a), Mat, Mat) +CV_MAT_AUG_OPERATOR (^=, cv::bitwise_xor(a,b,a), Mat, Scalar) +CV_MAT_AUG_OPERATOR_T(^=, cv::bitwise_xor(a,b,a), Mat_<_Tp>, Mat) +CV_MAT_AUG_OPERATOR_T(^=, cv::bitwise_xor(a,b,a), Mat_<_Tp>, Scalar) +CV_MAT_AUG_OPERATOR_T(^=, cv::bitwise_xor(a,b,a), Mat_<_Tp>, Mat_<_Tp>) + +#undef CV_MAT_AUG_OPERATOR_T +#undef CV_MAT_AUG_OPERATOR +#undef CV_MAT_AUG_OPERATOR1 + + + +///////////////////////////////////////////// SVD ///////////////////////////////////////////// + +inline SVD::SVD() {} +inline SVD::SVD( InputArray m, int flags ) { operator ()(m, flags); } +inline void SVD::solveZ( InputArray m, OutputArray _dst ) +{ + Mat mtx = m.getMat(); + SVD svd(mtx, (mtx.rows >= mtx.cols ? 0 : SVD::FULL_UV)); + _dst.create(svd.vt.cols, 1, svd.vt.type()); + Mat dst = _dst.getMat(); + svd.vt.row(svd.vt.rows-1).reshape(1,svd.vt.cols).copyTo(dst); +} + +template inline void + SVD::compute( const Matx<_Tp, m, n>& a, Matx<_Tp, nm, 1>& w, Matx<_Tp, m, nm>& u, Matx<_Tp, n, nm>& vt ) +{ + CV_StaticAssert( nm == MIN(m, n), "Invalid size of output vector."); + Mat _a(a, false), _u(u, false), _w(w, false), _vt(vt, false); + SVD::compute(_a, _w, _u, _vt); + CV_Assert(_w.data == (uchar*)&w.val[0] && _u.data == (uchar*)&u.val[0] && _vt.data == (uchar*)&vt.val[0]); +} + +template inline void +SVD::compute( const Matx<_Tp, m, n>& a, Matx<_Tp, nm, 1>& w ) +{ + CV_StaticAssert( nm == MIN(m, n), "Invalid size of output vector."); + Mat _a(a, false), _w(w, false); + SVD::compute(_a, _w); + CV_Assert(_w.data == (uchar*)&w.val[0]); +} + +template inline void +SVD::backSubst( const Matx<_Tp, nm, 1>& w, const Matx<_Tp, m, nm>& u, + const Matx<_Tp, n, nm>& vt, const Matx<_Tp, m, nb>& rhs, + Matx<_Tp, n, nb>& dst ) +{ + CV_StaticAssert( nm == MIN(m, n), "Invalid size of output vector."); + Mat _u(u, false), _w(w, false), _vt(vt, false), _rhs(rhs, false), _dst(dst, false); + SVD::backSubst(_w, _u, _vt, _rhs, _dst); + CV_Assert(_dst.data == (uchar*)&dst.val[0]); +} + + + +/////////////////////////////////// Multiply-with-Carry RNG /////////////////////////////////// + +inline RNG::RNG() { state = 0xffffffff; } +inline RNG::RNG(uint64 _state) { state = _state ? _state : 0xffffffff; } + +inline RNG::operator uchar() { return (uchar)next(); } +inline RNG::operator schar() { return (schar)next(); } +inline RNG::operator ushort() { return (ushort)next(); } +inline RNG::operator short() { return (short)next(); } +inline RNG::operator int() { return (int)next(); } +inline RNG::operator unsigned() { return next(); } +inline RNG::operator float() { return next()*2.3283064365386962890625e-10f; } +inline RNG::operator double() { unsigned t = next(); return (((uint64)t << 32) | next()) * 5.4210108624275221700372640043497e-20; } + +inline unsigned RNG::operator ()(unsigned N) { return (unsigned)uniform(0,N); } +inline unsigned RNG::operator ()() { return next(); } + +inline int RNG::uniform(int a, int b) { return a == b ? a : (int)(next() % (b - a) + a); } +inline float RNG::uniform(float a, float b) { return ((float)*this)*(b - a) + a; } +inline double RNG::uniform(double a, double b) { return ((double)*this)*(b - a) + a; } + +inline bool RNG::operator ==(const RNG& other) const { return state == other.state; } + +inline unsigned RNG::next() +{ + state = (uint64)(unsigned)state* /*CV_RNG_COEFF*/ 4164903690U + (unsigned)(state >> 32); + return (unsigned)state; +} + +//! returns the next unifomly-distributed random number of the specified type +template static inline _Tp randu() +{ + return (_Tp)theRNG(); +} + +///////////////////////////////// Formatted string generation ///////////////////////////////// + +/** @brief Returns a text string formatted using the printf-like expression. + +The function acts like sprintf but forms and returns an STL string. It can be used to form an error +message in the Exception constructor. +@param fmt printf-compatible formatting specifiers. + */ +CV_EXPORTS String format( const char* fmt, ... ); + +///////////////////////////////// Formatted output of cv::Mat ///////////////////////////////// + +static inline +Ptr format(InputArray mtx, int fmt) +{ + return Formatter::get(fmt)->format(mtx.getMat()); +} + +static inline +int print(Ptr fmtd, FILE* stream = stdout) +{ + int written = 0; + fmtd->reset(); + for(const char* str = fmtd->next(); str; str = fmtd->next()) + written += fputs(str, stream); + + return written; +} + +static inline +int print(const Mat& mtx, FILE* stream = stdout) +{ + return print(Formatter::get()->format(mtx), stream); +} + +static inline +int print(const UMat& mtx, FILE* stream = stdout) +{ + return print(Formatter::get()->format(mtx.getMat(ACCESS_READ)), stream); +} + +template static inline +int print(const std::vector >& vec, FILE* stream = stdout) +{ + return print(Formatter::get()->format(Mat(vec)), stream); +} + +template static inline +int print(const std::vector >& vec, FILE* stream = stdout) +{ + return print(Formatter::get()->format(Mat(vec)), stream); +} + +template static inline +int print(const Matx<_Tp, m, n>& matx, FILE* stream = stdout) +{ + return print(Formatter::get()->format(cv::Mat(matx)), stream); +} + +//! @endcond + +/****************************************************************************************\ +* Auxiliary algorithms * +\****************************************************************************************/ + +/** @brief Splits an element set into equivalency classes. + +The generic function partition implements an \f$O(N^2)\f$ algorithm for splitting a set of \f$N\f$ elements +into one or more equivalency classes, as described in + . The function returns the number of +equivalency classes. +@param _vec Set of elements stored as a vector. +@param labels Output vector of labels. It contains as many elements as vec. Each label labels[i] is +a 0-based cluster index of `vec[i]`. +@param predicate Equivalence predicate (pointer to a boolean function of two arguments or an +instance of the class that has the method bool operator()(const _Tp& a, const _Tp& b) ). The +predicate returns true when the elements are certainly in the same class, and returns false if they +may or may not be in the same class. +@ingroup core_cluster +*/ +template int +partition( const std::vector<_Tp>& _vec, std::vector& labels, + _EqPredicate predicate=_EqPredicate()) +{ + int i, j, N = (int)_vec.size(); + const _Tp* vec = &_vec[0]; + + const int PARENT=0; + const int RANK=1; + + std::vector _nodes(N*2); + int (*nodes)[2] = (int(*)[2])&_nodes[0]; + + // The first O(N) pass: create N single-vertex trees + for(i = 0; i < N; i++) + { + nodes[i][PARENT]=-1; + nodes[i][RANK] = 0; + } + + // The main O(N^2) pass: merge connected components + for( i = 0; i < N; i++ ) + { + int root = i; + + // find root + while( nodes[root][PARENT] >= 0 ) + root = nodes[root][PARENT]; + + for( j = 0; j < N; j++ ) + { + if( i == j || !predicate(vec[i], vec[j])) + continue; + int root2 = j; + + while( nodes[root2][PARENT] >= 0 ) + root2 = nodes[root2][PARENT]; + + if( root2 != root ) + { + // unite both trees + int rank = nodes[root][RANK], rank2 = nodes[root2][RANK]; + if( rank > rank2 ) + nodes[root2][PARENT] = root; + else + { + nodes[root][PARENT] = root2; + nodes[root2][RANK] += rank == rank2; + root = root2; + } + CV_Assert( nodes[root][PARENT] < 0 ); + + int k = j, parent; + + // compress the path from node2 to root + while( (parent = nodes[k][PARENT]) >= 0 ) + { + nodes[k][PARENT] = root; + k = parent; + } + + // compress the path from node to root + k = i; + while( (parent = nodes[k][PARENT]) >= 0 ) + { + nodes[k][PARENT] = root; + k = parent; + } + } + } + } + + // Final O(N) pass: enumerate classes + labels.resize(N); + int nclasses = 0; + + for( i = 0; i < N; i++ ) + { + int root = i; + while( nodes[root][PARENT] >= 0 ) + root = nodes[root][PARENT]; + // re-use the rank as the class label + if( nodes[root][RANK] >= 0 ) + nodes[root][RANK] = ~nclasses++; + labels[i] = ~nodes[root][RANK]; + } + + return nclasses; +} + +} // cv + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/optim.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/optim.hpp new file mode 100644 index 0000000000000000000000000000000000000000..c4729a9c105d61f3a43c278fd0f41660b12f4d59 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/optim.hpp @@ -0,0 +1,302 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the OpenCV Foundation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_OPTIM_HPP +#define OPENCV_OPTIM_HPP + +#include "opencv2/core.hpp" + +namespace cv +{ + +/** @addtogroup core_optim +The algorithms in this section minimize or maximize function value within specified constraints or +without any constraints. +@{ +*/ + +/** @brief Basic interface for all solvers + */ +class CV_EXPORTS MinProblemSolver : public Algorithm +{ +public: + /** @brief Represents function being optimized + */ + class CV_EXPORTS Function + { + public: + virtual ~Function() {} + virtual int getDims() const = 0; + virtual double getGradientEps() const; + virtual double calc(const double* x) const = 0; + virtual void getGradient(const double* x,double* grad); + }; + + /** @brief Getter for the optimized function. + + The optimized function is represented by Function interface, which requires derivatives to + implement the calc(double*) and getDim() methods to evaluate the function. + + @return Smart-pointer to an object that implements Function interface - it represents the + function that is being optimized. It can be empty, if no function was given so far. + */ + virtual Ptr getFunction() const = 0; + + /** @brief Setter for the optimized function. + + *It should be called at least once before the call to* minimize(), as default value is not usable. + + @param f The new function to optimize. + */ + virtual void setFunction(const Ptr& f) = 0; + + /** @brief Getter for the previously set terminal criteria for this algorithm. + + @return Deep copy of the terminal criteria used at the moment. + */ + virtual TermCriteria getTermCriteria() const = 0; + + /** @brief Set terminal criteria for solver. + + This method *is not necessary* to be called before the first call to minimize(), as the default + value is sensible. + + Algorithm stops when the number of function evaluations done exceeds termcrit.maxCount, when + the function values at the vertices of simplex are within termcrit.epsilon range or simplex + becomes so small that it can enclosed in a box with termcrit.epsilon sides, whatever comes + first. + @param termcrit Terminal criteria to be used, represented as cv::TermCriteria structure. + */ + virtual void setTermCriteria(const TermCriteria& termcrit) = 0; + + /** @brief actually runs the algorithm and performs the minimization. + + The sole input parameter determines the centroid of the starting simplex (roughly, it tells + where to start), all the others (terminal criteria, initial step, function to be minimized) are + supposed to be set via the setters before the call to this method or the default values (not + always sensible) will be used. + + @param x The initial point, that will become a centroid of an initial simplex. After the algorithm + will terminate, it will be set to the point where the algorithm stops, the point of possible + minimum. + @return The value of a function at the point found. + */ + virtual double minimize(InputOutputArray x) = 0; +}; + +/** @brief This class is used to perform the non-linear non-constrained minimization of a function, + +defined on an `n`-dimensional Euclidean space, using the **Nelder-Mead method**, also known as +**downhill simplex method**. The basic idea about the method can be obtained from +. + +It should be noted, that this method, although deterministic, is rather a heuristic and therefore +may converge to a local minima, not necessary a global one. It is iterative optimization technique, +which at each step uses an information about the values of a function evaluated only at `n+1` +points, arranged as a *simplex* in `n`-dimensional space (hence the second name of the method). At +each step new point is chosen to evaluate function at, obtained value is compared with previous +ones and based on this information simplex changes it's shape , slowly moving to the local minimum. +Thus this method is using *only* function values to make decision, on contrary to, say, Nonlinear +Conjugate Gradient method (which is also implemented in optim). + +Algorithm stops when the number of function evaluations done exceeds termcrit.maxCount, when the +function values at the vertices of simplex are within termcrit.epsilon range or simplex becomes so +small that it can enclosed in a box with termcrit.epsilon sides, whatever comes first, for some +defined by user positive integer termcrit.maxCount and positive non-integer termcrit.epsilon. + +@note DownhillSolver is a derivative of the abstract interface +cv::MinProblemSolver, which in turn is derived from the Algorithm interface and is used to +encapsulate the functionality, common to all non-linear optimization algorithms in the optim +module. + +@note term criteria should meet following condition: +@code + termcrit.type == (TermCriteria::MAX_ITER + TermCriteria::EPS) && termcrit.epsilon > 0 && termcrit.maxCount > 0 +@endcode + */ +class CV_EXPORTS DownhillSolver : public MinProblemSolver +{ +public: + /** @brief Returns the initial step that will be used in downhill simplex algorithm. + + @param step Initial step that will be used in algorithm. Note, that although corresponding setter + accepts column-vectors as well as row-vectors, this method will return a row-vector. + @see DownhillSolver::setInitStep + */ + virtual void getInitStep(OutputArray step) const=0; + + /** @brief Sets the initial step that will be used in downhill simplex algorithm. + + Step, together with initial point (givin in DownhillSolver::minimize) are two `n`-dimensional + vectors that are used to determine the shape of initial simplex. Roughly said, initial point + determines the position of a simplex (it will become simplex's centroid), while step determines the + spread (size in each dimension) of a simplex. To be more precise, if \f$s,x_0\in\mathbb{R}^n\f$ are + the initial step and initial point respectively, the vertices of a simplex will be: + \f$v_0:=x_0-\frac{1}{2} s\f$ and \f$v_i:=x_0+s_i\f$ for \f$i=1,2,\dots,n\f$ where \f$s_i\f$ denotes + projections of the initial step of *n*-th coordinate (the result of projection is treated to be + vector given by \f$s_i:=e_i\cdot\left\f$, where \f$e_i\f$ form canonical basis) + + @param step Initial step that will be used in algorithm. Roughly said, it determines the spread + (size in each dimension) of an initial simplex. + */ + virtual void setInitStep(InputArray step)=0; + + /** @brief This function returns the reference to the ready-to-use DownhillSolver object. + + All the parameters are optional, so this procedure can be called even without parameters at + all. In this case, the default values will be used. As default value for terminal criteria are + the only sensible ones, MinProblemSolver::setFunction() and DownhillSolver::setInitStep() + should be called upon the obtained object, if the respective parameters were not given to + create(). Otherwise, the two ways (give parameters to createDownhillSolver() or miss them out + and call the MinProblemSolver::setFunction() and DownhillSolver::setInitStep()) are absolutely + equivalent (and will drop the same errors in the same way, should invalid input be detected). + @param f Pointer to the function that will be minimized, similarly to the one you submit via + MinProblemSolver::setFunction. + @param initStep Initial step, that will be used to construct the initial simplex, similarly to the one + you submit via MinProblemSolver::setInitStep. + @param termcrit Terminal criteria to the algorithm, similarly to the one you submit via + MinProblemSolver::setTermCriteria. + */ + static Ptr create(const Ptr& f=Ptr(), + InputArray initStep=Mat_(1,1,0.0), + TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5000,0.000001)); +}; + +/** @brief This class is used to perform the non-linear non-constrained minimization of a function +with known gradient, + +defined on an *n*-dimensional Euclidean space, using the **Nonlinear Conjugate Gradient method**. +The implementation was done based on the beautifully clear explanatory article [An Introduction to +the Conjugate Gradient Method Without the Agonizing +Pain](http://www.cs.cmu.edu/~quake-papers/painless-conjugate-gradient.pdf) by Jonathan Richard +Shewchuk. The method can be seen as an adaptation of a standard Conjugate Gradient method (see, for +example ) for numerically solving the +systems of linear equations. + +It should be noted, that this method, although deterministic, is rather a heuristic method and +therefore may converge to a local minima, not necessary a global one. What is even more disastrous, +most of its behaviour is ruled by gradient, therefore it essentially cannot distinguish between +local minima and maxima. Therefore, if it starts sufficiently near to the local maximum, it may +converge to it. Another obvious restriction is that it should be possible to compute the gradient of +a function at any point, thus it is preferable to have analytic expression for gradient and +computational burden should be born by the user. + +The latter responsibility is accompilished via the getGradient method of a +MinProblemSolver::Function interface (which represents function being optimized). This method takes +point a point in *n*-dimensional space (first argument represents the array of coordinates of that +point) and comput its gradient (it should be stored in the second argument as an array). + +@note class ConjGradSolver thus does not add any new methods to the basic MinProblemSolver interface. + +@note term criteria should meet following condition: +@code + termcrit.type == (TermCriteria::MAX_ITER + TermCriteria::EPS) && termcrit.epsilon > 0 && termcrit.maxCount > 0 + // or + termcrit.type == TermCriteria::MAX_ITER) && termcrit.maxCount > 0 +@endcode + */ +class CV_EXPORTS ConjGradSolver : public MinProblemSolver +{ +public: + /** @brief This function returns the reference to the ready-to-use ConjGradSolver object. + + All the parameters are optional, so this procedure can be called even without parameters at + all. In this case, the default values will be used. As default value for terminal criteria are + the only sensible ones, MinProblemSolver::setFunction() should be called upon the obtained + object, if the function was not given to create(). Otherwise, the two ways (submit it to + create() or miss it out and call the MinProblemSolver::setFunction()) are absolutely equivalent + (and will drop the same errors in the same way, should invalid input be detected). + @param f Pointer to the function that will be minimized, similarly to the one you submit via + MinProblemSolver::setFunction. + @param termcrit Terminal criteria to the algorithm, similarly to the one you submit via + MinProblemSolver::setTermCriteria. + */ + static Ptr create(const Ptr& f=Ptr(), + TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5000,0.000001)); +}; + +//! return codes for cv::solveLP() function +enum SolveLPResult +{ + SOLVELP_UNBOUNDED = -2, //!< problem is unbounded (target function can achieve arbitrary high values) + SOLVELP_UNFEASIBLE = -1, //!< problem is unfeasible (there are no points that satisfy all the constraints imposed) + SOLVELP_SINGLE = 0, //!< there is only one maximum for target function + SOLVELP_MULTI = 1 //!< there are multiple maxima for target function - the arbitrary one is returned +}; + +/** @brief Solve given (non-integer) linear programming problem using the Simplex Algorithm (Simplex Method). + +What we mean here by "linear programming problem" (or LP problem, for short) can be formulated as: + +\f[\mbox{Maximize } c\cdot x\\ + \mbox{Subject to:}\\ + Ax\leq b\\ + x\geq 0\f] + +Where \f$c\f$ is fixed `1`-by-`n` row-vector, \f$A\f$ is fixed `m`-by-`n` matrix, \f$b\f$ is fixed `m`-by-`1` +column vector and \f$x\f$ is an arbitrary `n`-by-`1` column vector, which satisfies the constraints. + +Simplex algorithm is one of many algorithms that are designed to handle this sort of problems +efficiently. Although it is not optimal in theoretical sense (there exist algorithms that can solve +any problem written as above in polynomial time, while simplex method degenerates to exponential +time for some special cases), it is well-studied, easy to implement and is shown to work well for +real-life purposes. + +The particular implementation is taken almost verbatim from **Introduction to Algorithms, third +edition** by T. H. Cormen, C. E. Leiserson, R. L. Rivest and Clifford Stein. In particular, the +Bland's rule is used to prevent cycling. + +@param Func This row-vector corresponds to \f$c\f$ in the LP problem formulation (see above). It should +contain 32- or 64-bit floating point numbers. As a convenience, column-vector may be also submitted, +in the latter case it is understood to correspond to \f$c^T\f$. +@param Constr `m`-by-`n+1` matrix, whose rightmost column corresponds to \f$b\f$ in formulation above +and the remaining to \f$A\f$. It should contain 32- or 64-bit floating point numbers. +@param z The solution will be returned here as a column-vector - it corresponds to \f$c\f$ in the +formulation above. It will contain 64-bit floating point numbers. +@return One of cv::SolveLPResult + */ +CV_EXPORTS_W int solveLP(const Mat& Func, const Mat& Constr, Mat& z); + +//! @} + +}// cv + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/ovx.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/ovx.hpp new file mode 100644 index 0000000000000000000000000000000000000000..8bb7d54911722ba38212bc1e082aa9281d879af4 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/ovx.hpp @@ -0,0 +1,28 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +// Copyright (C) 2016, Intel Corporation, all rights reserved. +// Third party copyrights are property of their respective owners. + +// OpenVX related definitions and declarations + +#pragma once +#ifndef OPENCV_OVX_HPP +#define OPENCV_OVX_HPP + +#include "cvdef.h" + +namespace cv +{ +/// Check if use of OpenVX is possible +CV_EXPORTS_W bool haveOpenVX(); + +/// Check if use of OpenVX is enabled +CV_EXPORTS_W bool useOpenVX(); + +/// Enable/disable use of OpenVX +CV_EXPORTS_W void setUseOpenVX(bool flag); +} // namespace cv + +#endif // OPENCV_OVX_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/persistence.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/persistence.hpp new file mode 100644 index 0000000000000000000000000000000000000000..126393fae13272d17e2114c7574a38b9a7cca6a2 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/persistence.hpp @@ -0,0 +1,1366 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_PERSISTENCE_HPP +#define OPENCV_CORE_PERSISTENCE_HPP + +#ifndef CV_DOXYGEN +/// Define to support persistence legacy formats +#define CV__LEGACY_PERSISTENCE +#endif + +#ifndef __cplusplus +# error persistence.hpp header must be compiled as C++ +#endif + +//! @addtogroup core_c +//! @{ + +/** @brief "black box" representation of the file storage associated with a file on disk. + +Several functions that are described below take CvFileStorage\* as inputs and allow the user to +save or to load hierarchical collections that consist of scalar values, standard CXCore objects +(such as matrices, sequences, graphs), and user-defined objects. + +OpenCV can read and write data in XML (), YAML () or +JSON () formats. Below is an example of 3x3 floating-point identity matrix A, +stored in XML and YAML files +using CXCore functions: +XML: +@code{.xml} + + + + 3 + 3 +

f
+ 1. 0. 0. 0. 1. 0. 0. 0. 1. + + +@endcode +YAML: +@code{.yaml} + %YAML:1.0 + A: !!opencv-matrix + rows: 3 + cols: 3 + dt: f + data: [ 1., 0., 0., 0., 1., 0., 0., 0., 1.] +@endcode +As it can be seen from the examples, XML uses nested tags to represent hierarchy, while YAML uses +indentation for that purpose (similar to the Python programming language). + +The same functions can read and write data in both formats; the particular format is determined by +the extension of the opened file, ".xml" for XML files, ".yml" or ".yaml" for YAML and ".json" for +JSON. + */ +typedef struct CvFileStorage CvFileStorage; +typedef struct CvFileNode CvFileNode; +typedef struct CvMat CvMat; +typedef struct CvMatND CvMatND; + +//! @} core_c + +#include "opencv2/core/types.hpp" +#include "opencv2/core/mat.hpp" + +namespace cv { + +/** @addtogroup core_xml + +XML/YAML/JSON file storages. {#xml_storage} +======================= +Writing to a file storage. +-------------------------- +You can store and then restore various OpenCV data structures to/from XML (), +YAML () or JSON () formats. Also, it is possible to store +and load arbitrarily complex data structures, which include OpenCV data structures, as well as +primitive data types (integer and floating-point numbers and text strings) as their elements. + +Use the following procedure to write something to XML, YAML or JSON: +-# Create new FileStorage and open it for writing. It can be done with a single call to +FileStorage::FileStorage constructor that takes a filename, or you can use the default constructor +and then call FileStorage::open. Format of the file (XML, YAML or JSON) is determined from the filename +extension (".xml", ".yml"/".yaml" and ".json", respectively) +-# Write all the data you want using the streaming operator `<<`, just like in the case of STL +streams. +-# Close the file using FileStorage::release. FileStorage destructor also closes the file. + +Here is an example: +@code + #include "opencv2/opencv.hpp" + #include + + using namespace cv; + + int main(int, char** argv) + { + FileStorage fs("test.yml", FileStorage::WRITE); + + fs << "frameCount" << 5; + time_t rawtime; time(&rawtime); + fs << "calibrationDate" << asctime(localtime(&rawtime)); + Mat cameraMatrix = (Mat_(3,3) << 1000, 0, 320, 0, 1000, 240, 0, 0, 1); + Mat distCoeffs = (Mat_(5,1) << 0.1, 0.01, -0.001, 0, 0); + fs << "cameraMatrix" << cameraMatrix << "distCoeffs" << distCoeffs; + fs << "features" << "["; + for( int i = 0; i < 3; i++ ) + { + int x = rand() % 640; + int y = rand() % 480; + uchar lbp = rand() % 256; + + fs << "{:" << "x" << x << "y" << y << "lbp" << "[:"; + for( int j = 0; j < 8; j++ ) + fs << ((lbp >> j) & 1); + fs << "]" << "}"; + } + fs << "]"; + fs.release(); + return 0; + } +@endcode +The sample above stores to YML an integer, a text string (calibration date), 2 matrices, and a custom +structure "feature", which includes feature coordinates and LBP (local binary pattern) value. Here +is output of the sample: +@code{.yaml} +%YAML:1.0 +frameCount: 5 +calibrationDate: "Fri Jun 17 14:09:29 2011\n" +cameraMatrix: !!opencv-matrix + rows: 3 + cols: 3 + dt: d + data: [ 1000., 0., 320., 0., 1000., 240., 0., 0., 1. ] +distCoeffs: !!opencv-matrix + rows: 5 + cols: 1 + dt: d + data: [ 1.0000000000000001e-01, 1.0000000000000000e-02, + -1.0000000000000000e-03, 0., 0. ] +features: + - { x:167, y:49, lbp:[ 1, 0, 0, 1, 1, 0, 1, 1 ] } + - { x:298, y:130, lbp:[ 0, 0, 0, 1, 0, 0, 1, 1 ] } + - { x:344, y:158, lbp:[ 1, 1, 0, 0, 0, 0, 1, 0 ] } +@endcode + +As an exercise, you can replace ".yml" with ".xml" or ".json" in the sample above and see, how the +corresponding XML file will look like. + +Several things can be noted by looking at the sample code and the output: + +- The produced YAML (and XML/JSON) consists of heterogeneous collections that can be nested. There are + 2 types of collections: named collections (mappings) and unnamed collections (sequences). In mappings + each element has a name and is accessed by name. This is similar to structures and std::map in + C/C++ and dictionaries in Python. In sequences elements do not have names, they are accessed by + indices. This is similar to arrays and std::vector in C/C++ and lists, tuples in Python. + "Heterogeneous" means that elements of each single collection can have different types. + + Top-level collection in YAML/XML/JSON is a mapping. Each matrix is stored as a mapping, and the matrix + elements are stored as a sequence. Then, there is a sequence of features, where each feature is + represented a mapping, and lbp value in a nested sequence. + +- When you write to a mapping (a structure), you write element name followed by its value. When you + write to a sequence, you simply write the elements one by one. OpenCV data structures (such as + cv::Mat) are written in absolutely the same way as simple C data structures - using `<<` + operator. + +- To write a mapping, you first write the special string `{` to the storage, then write the + elements as pairs (`fs << << `) and then write the closing + `}`. + +- To write a sequence, you first write the special string `[`, then write the elements, then + write the closing `]`. + +- In YAML/JSON (but not XML), mappings and sequences can be written in a compact Python-like inline + form. In the sample above matrix elements, as well as each feature, including its lbp value, is + stored in such inline form. To store a mapping/sequence in a compact form, put `:` after the + opening character, e.g. use `{:` instead of `{` and `[:` instead of `[`. When the + data is written to XML, those extra `:` are ignored. + +Reading data from a file storage. +--------------------------------- +To read the previously written XML, YAML or JSON file, do the following: +-# Open the file storage using FileStorage::FileStorage constructor or FileStorage::open method. + In the current implementation the whole file is parsed and the whole representation of file + storage is built in memory as a hierarchy of file nodes (see FileNode) + +-# Read the data you are interested in. Use FileStorage::operator [], FileNode::operator [] + and/or FileNodeIterator. + +-# Close the storage using FileStorage::release. + +Here is how to read the file created by the code sample above: +@code + FileStorage fs2("test.yml", FileStorage::READ); + + // first method: use (type) operator on FileNode. + int frameCount = (int)fs2["frameCount"]; + + String date; + // second method: use FileNode::operator >> + fs2["calibrationDate"] >> date; + + Mat cameraMatrix2, distCoeffs2; + fs2["cameraMatrix"] >> cameraMatrix2; + fs2["distCoeffs"] >> distCoeffs2; + + cout << "frameCount: " << frameCount << endl + << "calibration date: " << date << endl + << "camera matrix: " << cameraMatrix2 << endl + << "distortion coeffs: " << distCoeffs2 << endl; + + FileNode features = fs2["features"]; + FileNodeIterator it = features.begin(), it_end = features.end(); + int idx = 0; + std::vector lbpval; + + // iterate through a sequence using FileNodeIterator + for( ; it != it_end; ++it, idx++ ) + { + cout << "feature #" << idx << ": "; + cout << "x=" << (int)(*it)["x"] << ", y=" << (int)(*it)["y"] << ", lbp: ("; + // you can also easily read numerical arrays using FileNode >> std::vector operator. + (*it)["lbp"] >> lbpval; + for( int i = 0; i < (int)lbpval.size(); i++ ) + cout << " " << (int)lbpval[i]; + cout << ")" << endl; + } + fs2.release(); +@endcode + +Format specification {#format_spec} +-------------------- +`([count]{u|c|w|s|i|f|d})`... where the characters correspond to fundamental C++ types: +- `u` 8-bit unsigned number +- `c` 8-bit signed number +- `w` 16-bit unsigned number +- `s` 16-bit signed number +- `i` 32-bit signed number +- `f` single precision floating-point number +- `d` double precision floating-point number +- `r` pointer, 32 lower bits of which are written as a signed integer. The type can be used to + store structures with links between the elements. + +`count` is the optional counter of values of a given type. For example, `2if` means that each array +element is a structure of 2 integers, followed by a single-precision floating-point number. The +equivalent notations of the above specification are `iif`, `2i1f` and so forth. Other examples: `u` +means that the array consists of bytes, and `2d` means the array consists of pairs of doubles. + +@see @ref samples/cpp/filestorage.cpp +*/ + +//! @{ + +/** @example samples/cpp/filestorage.cpp +A complete example using the FileStorage interface +*/ + +////////////////////////// XML & YAML I/O ////////////////////////// + +class CV_EXPORTS FileNode; +class CV_EXPORTS FileNodeIterator; + +/** @brief XML/YAML/JSON file storage class that encapsulates all the information necessary for writing or +reading data to/from a file. + */ +class CV_EXPORTS_W FileStorage +{ +public: + //! file storage mode + enum Mode + { + READ = 0, //!< value, open the file for reading + WRITE = 1, //!< value, open the file for writing + APPEND = 2, //!< value, open the file for appending + MEMORY = 4, //!< flag, read data from source or write data to the internal buffer (which is + //!< returned by FileStorage::release) + FORMAT_MASK = (7<<3), //!< mask for format flags + FORMAT_AUTO = 0, //!< flag, auto format + FORMAT_XML = (1<<3), //!< flag, XML format + FORMAT_YAML = (2<<3), //!< flag, YAML format + FORMAT_JSON = (3<<3), //!< flag, JSON format + + BASE64 = 64, //!< flag, write rawdata in Base64 by default. (consider using WRITE_BASE64) + WRITE_BASE64 = BASE64 | WRITE, //!< flag, enable both WRITE and BASE64 + }; + enum + { + UNDEFINED = 0, + VALUE_EXPECTED = 1, + NAME_EXPECTED = 2, + INSIDE_MAP = 4 + }; + + /** @brief The constructors. + + The full constructor opens the file. Alternatively you can use the default constructor and then + call FileStorage::open. + */ + CV_WRAP FileStorage(); + + /** @overload + @copydoc open() + */ + CV_WRAP FileStorage(const String& filename, int flags, const String& encoding=String()); + + /** @overload */ + FileStorage(CvFileStorage* fs, bool owning=true); + + //! the destructor. calls release() + virtual ~FileStorage(); + + /** @brief Opens a file. + + See description of parameters in FileStorage::FileStorage. The method calls FileStorage::release + before opening the file. + @param filename Name of the file to open or the text string to read the data from. + Extension of the file (.xml, .yml/.yaml or .json) determines its format (XML, YAML or JSON + respectively). Also you can append .gz to work with compressed files, for example myHugeMatrix.xml.gz. If both + FileStorage::WRITE and FileStorage::MEMORY flags are specified, source is used just to specify + the output file format (e.g. mydata.xml, .yml etc.). A file name can also contain parameters. + You can use this format, "*?base64" (e.g. "file.json?base64" (case sensitive)), as an alternative to + FileStorage::BASE64 flag. + @param flags Mode of operation. One of FileStorage::Mode + @param encoding Encoding of the file. Note that UTF-16 XML encoding is not supported currently and + you should use 8-bit encoding instead of it. + */ + CV_WRAP virtual bool open(const String& filename, int flags, const String& encoding=String()); + + /** @brief Checks whether the file is opened. + + @returns true if the object is associated with the current file and false otherwise. It is a + good practice to call this method after you tried to open a file. + */ + CV_WRAP virtual bool isOpened() const; + + /** @brief Closes the file and releases all the memory buffers. + + Call this method after all I/O operations with the storage are finished. + */ + CV_WRAP virtual void release(); + + /** @brief Closes the file and releases all the memory buffers. + + Call this method after all I/O operations with the storage are finished. If the storage was + opened for writing data and FileStorage::WRITE was specified + */ + CV_WRAP virtual String releaseAndGetString(); + + /** @brief Returns the first element of the top-level mapping. + @returns The first element of the top-level mapping. + */ + CV_WRAP FileNode getFirstTopLevelNode() const; + + /** @brief Returns the top-level mapping + @param streamidx Zero-based index of the stream. In most cases there is only one stream in the file. + However, YAML supports multiple streams and so there can be several. + @returns The top-level mapping. + */ + CV_WRAP FileNode root(int streamidx=0) const; + + /** @brief Returns the specified element of the top-level mapping. + @param nodename Name of the file node. + @returns Node with the given name. + */ + FileNode operator[](const String& nodename) const; + + /** @overload */ + CV_WRAP_AS(getNode) FileNode operator[](const char* nodename) const; + + /** @brief Returns the obsolete C FileStorage structure. + @returns Pointer to the underlying C FileStorage structure + */ + CvFileStorage* operator *() { return fs.get(); } + + /** @overload */ + const CvFileStorage* operator *() const { return fs.get(); } + + /** @brief Writes multiple numbers. + + Writes one or more numbers of the specified format to the currently written structure. Usually it is + more convenient to use operator `<<` instead of this method. + @param fmt Specification of each array element, see @ref format_spec "format specification" + @param vec Pointer to the written array. + @param len Number of the uchar elements to write. + */ + void writeRaw( const String& fmt, const uchar* vec, size_t len ); + + /** @brief Writes the registered C structure (CvMat, CvMatND, CvSeq). + @param name Name of the written object. + @param obj Pointer to the object. + @see ocvWrite for details. + */ + void writeObj( const String& name, const void* obj ); + + /** + * @brief Simplified writing API to use with bindings. + * @param name Name of the written object + * @param val Value of the written object + */ + CV_WRAP void write(const String& name, int val); + /// @overload + CV_WRAP void write(const String& name, double val); + /// @overload + CV_WRAP void write(const String& name, const String& val); + /// @overload + CV_WRAP void write(const String& name, InputArray val); + + /** @brief Writes a comment. + + The function writes a comment into file storage. The comments are skipped when the storage is read. + @param comment The written comment, single-line or multi-line + @param append If true, the function tries to put the comment at the end of current line. + Else if the comment is multi-line, or if it does not fit at the end of the current + line, the comment starts a new line. + */ + CV_WRAP void writeComment(const String& comment, bool append = false); + + /** @brief Returns the normalized object name for the specified name of a file. + @param filename Name of a file + @returns The normalized object name. + */ + static String getDefaultObjectName(const String& filename); + + /** @brief Returns the current format. + * @returns The current format, see FileStorage::Mode + */ + CV_WRAP int getFormat() const; + + Ptr fs; //!< the underlying C FileStorage structure + String elname; //!< the currently written element + std::vector structs; //!< the stack of written structures + int state; //!< the writer state +}; + +template<> CV_EXPORTS void DefaultDeleter::operator ()(CvFileStorage* obj) const; + +/** @brief File Storage Node class. + +The node is used to store each and every element of the file storage opened for reading. When +XML/YAML file is read, it is first parsed and stored in the memory as a hierarchical collection of +nodes. Each node can be a "leaf" that is contain a single number or a string, or be a collection of +other nodes. There can be named collections (mappings) where each element has a name and it is +accessed by a name, and ordered collections (sequences) where elements do not have names but rather +accessed by index. Type of the file node can be determined using FileNode::type method. + +Note that file nodes are only used for navigating file storages opened for reading. When a file +storage is opened for writing, no data is stored in memory after it is written. + */ +class CV_EXPORTS_W_SIMPLE FileNode +{ +public: + //! type of the file storage node + enum Type + { + NONE = 0, //!< empty node + INT = 1, //!< an integer + REAL = 2, //!< floating-point number + FLOAT = REAL, //!< synonym or REAL + STR = 3, //!< text string in UTF-8 encoding + STRING = STR, //!< synonym for STR + REF = 4, //!< integer of size size_t. Typically used for storing complex dynamic structures where some elements reference the others + SEQ = 5, //!< sequence + MAP = 6, //!< mapping + TYPE_MASK = 7, + FLOW = 8, //!< compact representation of a sequence or mapping. Used only by YAML writer + USER = 16, //!< a registered object (e.g. a matrix) + EMPTY = 32, //!< empty structure (sequence or mapping) + NAMED = 64 //!< the node has a name (i.e. it is element of a mapping) + }; + /** @brief The constructors. + + These constructors are used to create a default file node, construct it from obsolete structures or + from the another file node. + */ + CV_WRAP FileNode(); + + /** @overload + @param fs Pointer to the obsolete file storage structure. + @param node File node to be used as initialization for the created file node. + */ + FileNode(const CvFileStorage* fs, const CvFileNode* node); + + /** @overload + @param node File node to be used as initialization for the created file node. + */ + FileNode(const FileNode& node); + + /** @brief Returns element of a mapping node or a sequence node. + @param nodename Name of an element in the mapping node. + @returns Returns the element with the given identifier. + */ + FileNode operator[](const String& nodename) const; + + /** @overload + @param nodename Name of an element in the mapping node. + */ + CV_WRAP_AS(getNode) FileNode operator[](const char* nodename) const; + + /** @overload + @param i Index of an element in the sequence node. + */ + CV_WRAP_AS(at) FileNode operator[](int i) const; + + /** @brief Returns keys of a mapping node. + @returns Keys of a mapping node. + */ + CV_WRAP std::vector keys() const; + + /** @brief Returns type of the node. + @returns Type of the node. See FileNode::Type + */ + CV_WRAP int type() const; + + //! returns true if the node is empty + CV_WRAP bool empty() const; + //! returns true if the node is a "none" object + CV_WRAP bool isNone() const; + //! returns true if the node is a sequence + CV_WRAP bool isSeq() const; + //! returns true if the node is a mapping + CV_WRAP bool isMap() const; + //! returns true if the node is an integer + CV_WRAP bool isInt() const; + //! returns true if the node is a floating-point number + CV_WRAP bool isReal() const; + //! returns true if the node is a text string + CV_WRAP bool isString() const; + //! returns true if the node has a name + CV_WRAP bool isNamed() const; + //! returns the node name or an empty string if the node is nameless + CV_WRAP String name() const; + //! returns the number of elements in the node, if it is a sequence or mapping, or 1 otherwise. + CV_WRAP size_t size() const; + //! returns the node content as an integer. If the node stores floating-point number, it is rounded. + operator int() const; + //! returns the node content as float + operator float() const; + //! returns the node content as double + operator double() const; + //! returns the node content as text string + operator String() const; + operator std::string() const; + + //! returns pointer to the underlying file node + CvFileNode* operator *(); + //! returns pointer to the underlying file node + const CvFileNode* operator* () const; + + //! returns iterator pointing to the first node element + FileNodeIterator begin() const; + //! returns iterator pointing to the element following the last node element + FileNodeIterator end() const; + + /** @brief Reads node elements to the buffer with the specified format. + + Usually it is more convenient to use operator `>>` instead of this method. + @param fmt Specification of each array element. See @ref format_spec "format specification" + @param vec Pointer to the destination array. + @param len Number of elements to read. If it is greater than number of remaining elements then all + of them will be read. + */ + void readRaw( const String& fmt, uchar* vec, size_t len ) const; + + //! reads the registered object and returns pointer to it + void* readObj() const; + + //! Simplified reading API to use with bindings. + CV_WRAP double real() const; + //! Simplified reading API to use with bindings. + CV_WRAP String string() const; + //! Simplified reading API to use with bindings. + CV_WRAP Mat mat() const; + + // do not use wrapper pointer classes for better efficiency + const CvFileStorage* fs; + const CvFileNode* node; +}; + + +/** @brief used to iterate through sequences and mappings. + +A standard STL notation, with node.begin(), node.end() denoting the beginning and the end of a +sequence, stored in node. See the data reading sample in the beginning of the section. + */ +class CV_EXPORTS FileNodeIterator +{ +public: + /** @brief The constructors. + + These constructors are used to create a default iterator, set it to specific element in a file node + or construct it from another iterator. + */ + FileNodeIterator(); + + /** @overload + @param fs File storage for the iterator. + @param node File node for the iterator. + @param ofs Index of the element in the node. The created iterator will point to this element. + */ + FileNodeIterator(const CvFileStorage* fs, const CvFileNode* node, size_t ofs=0); + + /** @overload + @param it Iterator to be used as initialization for the created iterator. + */ + FileNodeIterator(const FileNodeIterator& it); + + //! returns the currently observed element + FileNode operator *() const; + //! accesses the currently observed element methods + FileNode operator ->() const; + + //! moves iterator to the next node + FileNodeIterator& operator ++ (); + //! moves iterator to the next node + FileNodeIterator operator ++ (int); + //! moves iterator to the previous node + FileNodeIterator& operator -- (); + //! moves iterator to the previous node + FileNodeIterator operator -- (int); + //! moves iterator forward by the specified offset (possibly negative) + FileNodeIterator& operator += (int ofs); + //! moves iterator backward by the specified offset (possibly negative) + FileNodeIterator& operator -= (int ofs); + + /** @brief Reads node elements to the buffer with the specified format. + + Usually it is more convenient to use operator `>>` instead of this method. + @param fmt Specification of each array element. See @ref format_spec "format specification" + @param vec Pointer to the destination array. + @param maxCount Number of elements to read. If it is greater than number of remaining elements then + all of them will be read. + */ + FileNodeIterator& readRaw( const String& fmt, uchar* vec, + size_t maxCount=(size_t)INT_MAX ); + + struct SeqReader + { + int header_size; + void* seq; /* sequence, beign read; CvSeq */ + void* block; /* current block; CvSeqBlock */ + schar* ptr; /* pointer to element be read next */ + schar* block_min; /* pointer to the beginning of block */ + schar* block_max; /* pointer to the end of block */ + int delta_index;/* = seq->first->start_index */ + schar* prev_elem; /* pointer to previous element */ + }; + + const CvFileStorage* fs; + const CvFileNode* container; + SeqReader reader; + size_t remaining; +}; + +//! @} core_xml + +/////////////////// XML & YAML I/O implementation ////////////////// + +//! @relates cv::FileStorage +//! @{ + +CV_EXPORTS void write( FileStorage& fs, const String& name, int value ); +CV_EXPORTS void write( FileStorage& fs, const String& name, float value ); +CV_EXPORTS void write( FileStorage& fs, const String& name, double value ); +CV_EXPORTS void write( FileStorage& fs, const String& name, const String& value ); +CV_EXPORTS void write( FileStorage& fs, const String& name, const Mat& value ); +CV_EXPORTS void write( FileStorage& fs, const String& name, const SparseMat& value ); +#ifdef CV__LEGACY_PERSISTENCE +CV_EXPORTS void write( FileStorage& fs, const String& name, const std::vector& value); +CV_EXPORTS void write( FileStorage& fs, const String& name, const std::vector& value); +#endif + +CV_EXPORTS void writeScalar( FileStorage& fs, int value ); +CV_EXPORTS void writeScalar( FileStorage& fs, float value ); +CV_EXPORTS void writeScalar( FileStorage& fs, double value ); +CV_EXPORTS void writeScalar( FileStorage& fs, const String& value ); + +//! @} + +//! @relates cv::FileNode +//! @{ + +CV_EXPORTS void read(const FileNode& node, int& value, int default_value); +CV_EXPORTS void read(const FileNode& node, float& value, float default_value); +CV_EXPORTS void read(const FileNode& node, double& value, double default_value); +CV_EXPORTS void read(const FileNode& node, String& value, const String& default_value); +CV_EXPORTS void read(const FileNode& node, std::string& value, const std::string& default_value); +CV_EXPORTS void read(const FileNode& node, Mat& mat, const Mat& default_mat = Mat() ); +CV_EXPORTS void read(const FileNode& node, SparseMat& mat, const SparseMat& default_mat = SparseMat() ); +#ifdef CV__LEGACY_PERSISTENCE +CV_EXPORTS void read(const FileNode& node, std::vector& keypoints); +CV_EXPORTS void read(const FileNode& node, std::vector& matches); +#endif +CV_EXPORTS void read(const FileNode& node, KeyPoint& value, const KeyPoint& default_value); +CV_EXPORTS void read(const FileNode& node, DMatch& value, const DMatch& default_value); + +template static inline void read(const FileNode& node, Point_<_Tp>& value, const Point_<_Tp>& default_value) +{ + std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp; + value = temp.size() != 2 ? default_value : Point_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1])); +} + +template static inline void read(const FileNode& node, Point3_<_Tp>& value, const Point3_<_Tp>& default_value) +{ + std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp; + value = temp.size() != 3 ? default_value : Point3_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1]), + saturate_cast<_Tp>(temp[2])); +} + +template static inline void read(const FileNode& node, Size_<_Tp>& value, const Size_<_Tp>& default_value) +{ + std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp; + value = temp.size() != 2 ? default_value : Size_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1])); +} + +template static inline void read(const FileNode& node, Complex<_Tp>& value, const Complex<_Tp>& default_value) +{ + std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp; + value = temp.size() != 2 ? default_value : Complex<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1])); +} + +template static inline void read(const FileNode& node, Rect_<_Tp>& value, const Rect_<_Tp>& default_value) +{ + std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp; + value = temp.size() != 4 ? default_value : Rect_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1]), + saturate_cast<_Tp>(temp[2]), saturate_cast<_Tp>(temp[3])); +} + +template static inline void read(const FileNode& node, Vec<_Tp, cn>& value, const Vec<_Tp, cn>& default_value) +{ + std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp; + value = temp.size() != cn ? default_value : Vec<_Tp, cn>(&temp[0]); +} + +template static inline void read(const FileNode& node, Scalar_<_Tp>& value, const Scalar_<_Tp>& default_value) +{ + std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp; + value = temp.size() != 4 ? default_value : Scalar_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1]), + saturate_cast<_Tp>(temp[2]), saturate_cast<_Tp>(temp[3])); +} + +static inline void read(const FileNode& node, Range& value, const Range& default_value) +{ + Point2i temp(value.start, value.end); const Point2i default_temp = Point2i(default_value.start, default_value.end); + read(node, temp, default_temp); + value.start = temp.x; value.end = temp.y; +} + +//! @} + +/** @brief Writes string to a file storage. +@relates cv::FileStorage + */ +CV_EXPORTS FileStorage& operator << (FileStorage& fs, const String& str); + +//! @cond IGNORED + +namespace internal +{ + class CV_EXPORTS WriteStructContext + { + public: + WriteStructContext(FileStorage& _fs, const String& name, int flags, const String& typeName = String()); + ~WriteStructContext(); + private: + FileStorage* fs; + }; + + template class VecWriterProxy + { + public: + VecWriterProxy( FileStorage* _fs ) : fs(_fs) {} + void operator()(const std::vector<_Tp>& vec) const + { + size_t count = vec.size(); + for (size_t i = 0; i < count; i++) + write(*fs, vec[i]); + } + private: + FileStorage* fs; + }; + + template class VecWriterProxy<_Tp, 1> + { + public: + VecWriterProxy( FileStorage* _fs ) : fs(_fs) {} + void operator()(const std::vector<_Tp>& vec) const + { + int _fmt = traits::SafeFmt<_Tp>::fmt; + char fmt[] = { (char)((_fmt >> 8) + '1'), (char)_fmt, '\0' }; + fs->writeRaw(fmt, !vec.empty() ? (uchar*)&vec[0] : 0, vec.size() * sizeof(_Tp)); + } + private: + FileStorage* fs; + }; + + template class VecReaderProxy + { + public: + VecReaderProxy( FileNodeIterator* _it ) : it(_it) {} + void operator()(std::vector<_Tp>& vec, size_t count) const + { + count = std::min(count, it->remaining); + vec.resize(count); + for (size_t i = 0; i < count; i++, ++(*it)) + read(**it, vec[i], _Tp()); + } + private: + FileNodeIterator* it; + }; + + template class VecReaderProxy<_Tp, 1> + { + public: + VecReaderProxy( FileNodeIterator* _it ) : it(_it) {} + void operator()(std::vector<_Tp>& vec, size_t count) const + { + size_t remaining = it->remaining; + size_t cn = DataType<_Tp>::channels; + int _fmt = traits::SafeFmt<_Tp>::fmt; + CV_Assert((_fmt >> 8) < 9); + char fmt[] = { (char)((_fmt >> 8)+'1'), (char)_fmt, '\0' }; + CV_Assert((remaining % cn) == 0); + size_t remaining1 = remaining / cn; + count = count < remaining1 ? count : remaining1; + vec.resize(count); + it->readRaw(fmt, !vec.empty() ? (uchar*)&vec[0] : 0, count*sizeof(_Tp)); + } + private: + FileNodeIterator* it; + }; + +} // internal + +//! @endcond + +//! @relates cv::FileStorage +//! @{ + +template static inline +void write(FileStorage& fs, const _Tp& value) +{ + write(fs, String(), value); +} + +template<> inline +void write( FileStorage& fs, const int& value ) +{ + writeScalar(fs, value); +} + +template<> inline +void write( FileStorage& fs, const float& value ) +{ + writeScalar(fs, value); +} + +template<> inline +void write( FileStorage& fs, const double& value ) +{ + writeScalar(fs, value); +} + +template<> inline +void write( FileStorage& fs, const String& value ) +{ + writeScalar(fs, value); +} + +template static inline +void write(FileStorage& fs, const Point_<_Tp>& pt ) +{ + write(fs, pt.x); + write(fs, pt.y); +} + +template static inline +void write(FileStorage& fs, const Point3_<_Tp>& pt ) +{ + write(fs, pt.x); + write(fs, pt.y); + write(fs, pt.z); +} + +template static inline +void write(FileStorage& fs, const Size_<_Tp>& sz ) +{ + write(fs, sz.width); + write(fs, sz.height); +} + +template static inline +void write(FileStorage& fs, const Complex<_Tp>& c ) +{ + write(fs, c.re); + write(fs, c.im); +} + +template static inline +void write(FileStorage& fs, const Rect_<_Tp>& r ) +{ + write(fs, r.x); + write(fs, r.y); + write(fs, r.width); + write(fs, r.height); +} + +template static inline +void write(FileStorage& fs, const Vec<_Tp, cn>& v ) +{ + for(int i = 0; i < cn; i++) + write(fs, v.val[i]); +} + +template static inline +void write(FileStorage& fs, const Scalar_<_Tp>& s ) +{ + write(fs, s.val[0]); + write(fs, s.val[1]); + write(fs, s.val[2]); + write(fs, s.val[3]); +} + +static inline +void write(FileStorage& fs, const Range& r ) +{ + write(fs, r.start); + write(fs, r.end); +} + +template static inline +void write( FileStorage& fs, const std::vector<_Tp>& vec ) +{ + cv::internal::VecWriterProxy<_Tp, traits::SafeFmt<_Tp>::fmt != 0> w(&fs); + w(vec); +} + +template static inline +void write(FileStorage& fs, const String& name, const Point_<_Tp>& pt ) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); + write(fs, pt); +} + +template static inline +void write(FileStorage& fs, const String& name, const Point3_<_Tp>& pt ) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); + write(fs, pt); +} + +template static inline +void write(FileStorage& fs, const String& name, const Size_<_Tp>& sz ) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); + write(fs, sz); +} + +template static inline +void write(FileStorage& fs, const String& name, const Complex<_Tp>& c ) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); + write(fs, c); +} + +template static inline +void write(FileStorage& fs, const String& name, const Rect_<_Tp>& r ) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); + write(fs, r); +} + +template static inline +void write(FileStorage& fs, const String& name, const Vec<_Tp, cn>& v ) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); + write(fs, v); +} + +template static inline +void write(FileStorage& fs, const String& name, const Scalar_<_Tp>& s ) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); + write(fs, s); +} + +static inline +void write(FileStorage& fs, const String& name, const Range& r ) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); + write(fs, r); +} + +static inline +void write(FileStorage& fs, const String& name, const KeyPoint& kpt) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); + write(fs, kpt.pt.x); + write(fs, kpt.pt.y); + write(fs, kpt.size); + write(fs, kpt.angle); + write(fs, kpt.response); + write(fs, kpt.octave); + write(fs, kpt.class_id); +} + +static inline +void write(FileStorage& fs, const String& name, const DMatch& m) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); + write(fs, m.queryIdx); + write(fs, m.trainIdx); + write(fs, m.imgIdx); + write(fs, m.distance); +} + +template static inline +void write( FileStorage& fs, const String& name, const std::vector<_Tp>& vec ) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+(traits::SafeFmt<_Tp>::fmt != 0 ? FileNode::FLOW : 0)); + write(fs, vec); +} + +template static inline +void write( FileStorage& fs, const String& name, const std::vector< std::vector<_Tp> >& vec ) +{ + cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ); + for(size_t i = 0; i < vec.size(); i++) + { + cv::internal::WriteStructContext ws_(fs, name, FileNode::SEQ+(traits::SafeFmt<_Tp>::fmt != 0 ? FileNode::FLOW : 0)); + write(fs, vec[i]); + } +} + +#ifdef CV__LEGACY_PERSISTENCE +// This code is not needed anymore, but it is preserved here to keep source compatibility +// Implementation is similar to templates instantiations +static inline void write(FileStorage& fs, const KeyPoint& kpt) { write(fs, String(), kpt); } +static inline void write(FileStorage& fs, const DMatch& m) { write(fs, String(), m); } +static inline void write(FileStorage& fs, const std::vector& vec) +{ + cv::internal::VecWriterProxy w(&fs); + w(vec); +} +static inline void write(FileStorage& fs, const std::vector& vec) +{ + cv::internal::VecWriterProxy w(&fs); + w(vec); + +} +#endif + +//! @} FileStorage + +//! @relates cv::FileNode +//! @{ + +static inline +void read(const FileNode& node, bool& value, bool default_value) +{ + int temp; + read(node, temp, (int)default_value); + value = temp != 0; +} + +static inline +void read(const FileNode& node, uchar& value, uchar default_value) +{ + int temp; + read(node, temp, (int)default_value); + value = saturate_cast(temp); +} + +static inline +void read(const FileNode& node, schar& value, schar default_value) +{ + int temp; + read(node, temp, (int)default_value); + value = saturate_cast(temp); +} + +static inline +void read(const FileNode& node, ushort& value, ushort default_value) +{ + int temp; + read(node, temp, (int)default_value); + value = saturate_cast(temp); +} + +static inline +void read(const FileNode& node, short& value, short default_value) +{ + int temp; + read(node, temp, (int)default_value); + value = saturate_cast(temp); +} + +template static inline +void read( FileNodeIterator& it, std::vector<_Tp>& vec, size_t maxCount = (size_t)INT_MAX ) +{ + cv::internal::VecReaderProxy<_Tp, traits::SafeFmt<_Tp>::fmt != 0> r(&it); + r(vec, maxCount); +} + +template static inline +void read( const FileNode& node, std::vector<_Tp>& vec, const std::vector<_Tp>& default_value = std::vector<_Tp>() ) +{ + if(!node.node) + vec = default_value; + else + { + FileNodeIterator it = node.begin(); + read( it, vec ); + } +} + +static inline +void read( const FileNode& node, std::vector& vec, const std::vector& default_value ) +{ + if(!node.node) + vec = default_value; + else + read(node, vec); +} + +static inline +void read( const FileNode& node, std::vector& vec, const std::vector& default_value ) +{ + if(!node.node) + vec = default_value; + else + read(node, vec); +} + +//! @} FileNode + +//! @relates cv::FileStorage +//! @{ + +/** @brief Writes data to a file storage. + */ +template static inline +FileStorage& operator << (FileStorage& fs, const _Tp& value) +{ + if( !fs.isOpened() ) + return fs; + if( fs.state == FileStorage::NAME_EXPECTED + FileStorage::INSIDE_MAP ) + CV_Error( Error::StsError, "No element name has been given" ); + write( fs, fs.elname, value ); + if( fs.state & FileStorage::INSIDE_MAP ) + fs.state = FileStorage::NAME_EXPECTED + FileStorage::INSIDE_MAP; + return fs; +} + +/** @brief Writes data to a file storage. + */ +static inline +FileStorage& operator << (FileStorage& fs, const char* str) +{ + return (fs << String(str)); +} + +/** @brief Writes data to a file storage. + */ +static inline +FileStorage& operator << (FileStorage& fs, char* value) +{ + return (fs << String(value)); +} + +//! @} FileStorage + +//! @relates cv::FileNodeIterator +//! @{ + +/** @brief Reads data from a file storage. + */ +template static inline +FileNodeIterator& operator >> (FileNodeIterator& it, _Tp& value) +{ + read( *it, value, _Tp()); + return ++it; +} + +/** @brief Reads data from a file storage. + */ +template static inline +FileNodeIterator& operator >> (FileNodeIterator& it, std::vector<_Tp>& vec) +{ + cv::internal::VecReaderProxy<_Tp, traits::SafeFmt<_Tp>::fmt != 0> r(&it); + r(vec, (size_t)INT_MAX); + return it; +} + +//! @} FileNodeIterator + +//! @relates cv::FileNode +//! @{ + +/** @brief Reads data from a file storage. + */ +template static inline +void operator >> (const FileNode& n, _Tp& value) +{ + read( n, value, _Tp()); +} + +/** @brief Reads data from a file storage. + */ +template static inline +void operator >> (const FileNode& n, std::vector<_Tp>& vec) +{ + FileNodeIterator it = n.begin(); + it >> vec; +} + +/** @brief Reads KeyPoint from a file storage. +*/ +//It needs special handling because it contains two types of fields, int & float. +static inline +void operator >> (const FileNode& n, KeyPoint& kpt) +{ + FileNodeIterator it = n.begin(); + it >> kpt.pt.x >> kpt.pt.y >> kpt.size >> kpt.angle >> kpt.response >> kpt.octave >> kpt.class_id; +} + +#ifdef CV__LEGACY_PERSISTENCE +static inline +void operator >> (const FileNode& n, std::vector& vec) +{ + read(n, vec); +} +static inline +void operator >> (const FileNode& n, std::vector& vec) +{ + read(n, vec); +} +#endif + +/** @brief Reads DMatch from a file storage. +*/ +//It needs special handling because it contains two types of fields, int & float. +static inline +void operator >> (const FileNode& n, DMatch& m) +{ + FileNodeIterator it = n.begin(); + it >> m.queryIdx >> m.trainIdx >> m.imgIdx >> m.distance; +} + +//! @} FileNode + +//! @relates cv::FileNodeIterator +//! @{ + +static inline +bool operator == (const FileNodeIterator& it1, const FileNodeIterator& it2) +{ + return it1.fs == it2.fs && it1.container == it2.container && + it1.reader.ptr == it2.reader.ptr && it1.remaining == it2.remaining; +} + +static inline +bool operator != (const FileNodeIterator& it1, const FileNodeIterator& it2) +{ + return !(it1 == it2); +} + +static inline +ptrdiff_t operator - (const FileNodeIterator& it1, const FileNodeIterator& it2) +{ + return it2.remaining - it1.remaining; +} + +static inline +bool operator < (const FileNodeIterator& it1, const FileNodeIterator& it2) +{ + return it1.remaining > it2.remaining; +} + +//! @} FileNodeIterator + +//! @cond IGNORED + +inline FileNode FileStorage::getFirstTopLevelNode() const { FileNode r = root(); FileNodeIterator it = r.begin(); return it != r.end() ? *it : FileNode(); } +inline FileNode::FileNode() : fs(0), node(0) {} +inline FileNode::FileNode(const CvFileStorage* _fs, const CvFileNode* _node) : fs(_fs), node(_node) {} +inline FileNode::FileNode(const FileNode& _node) : fs(_node.fs), node(_node.node) {} +inline bool FileNode::empty() const { return node == 0; } +inline bool FileNode::isNone() const { return type() == NONE; } +inline bool FileNode::isSeq() const { return type() == SEQ; } +inline bool FileNode::isMap() const { return type() == MAP; } +inline bool FileNode::isInt() const { return type() == INT; } +inline bool FileNode::isReal() const { return type() == REAL; } +inline bool FileNode::isString() const { return type() == STR; } +inline CvFileNode* FileNode::operator *() { return (CvFileNode*)node; } +inline const CvFileNode* FileNode::operator* () const { return node; } +inline FileNode::operator int() const { int value; read(*this, value, 0); return value; } +inline FileNode::operator float() const { float value; read(*this, value, 0.f); return value; } +inline FileNode::operator double() const { double value; read(*this, value, 0.); return value; } +inline FileNode::operator String() const { String value; read(*this, value, value); return value; } +inline double FileNode::real() const { return double(*this); } +inline String FileNode::string() const { return String(*this); } +inline Mat FileNode::mat() const { Mat value; read(*this, value, value); return value; } +inline FileNodeIterator FileNode::begin() const { return FileNodeIterator(fs, node); } +inline FileNodeIterator FileNode::end() const { return FileNodeIterator(fs, node, size()); } +inline void FileNode::readRaw( const String& fmt, uchar* vec, size_t len ) const { begin().readRaw( fmt, vec, len ); } +inline FileNode FileNodeIterator::operator *() const { return FileNode(fs, (const CvFileNode*)(const void*)reader.ptr); } +inline FileNode FileNodeIterator::operator ->() const { return FileNode(fs, (const CvFileNode*)(const void*)reader.ptr); } +inline String::String(const FileNode& fn): cstr_(0), len_(0) { read(fn, *this, *this); } + +//! @endcond + + +CV_EXPORTS void cvStartWriteRawData_Base64(::CvFileStorage * fs, const char* name, int len, const char* dt); + +CV_EXPORTS void cvWriteRawData_Base64(::CvFileStorage * fs, const void* _data, int len); + +CV_EXPORTS void cvEndWriteRawData_Base64(::CvFileStorage * fs); + +CV_EXPORTS void cvWriteMat_Base64(::CvFileStorage* fs, const char* name, const ::CvMat* mat); + +CV_EXPORTS void cvWriteMatND_Base64(::CvFileStorage* fs, const char* name, const ::CvMatND* mat); + +} // cv + +#endif // OPENCV_CORE_PERSISTENCE_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/ptr.inl.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/ptr.inl.hpp new file mode 100644 index 0000000000000000000000000000000000000000..466f6342ec523717bff6dbbba2a6f0754a335567 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/ptr.inl.hpp @@ -0,0 +1,379 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2013, NVIDIA Corporation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the copyright holders or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_PTR_INL_HPP +#define OPENCV_CORE_PTR_INL_HPP + +#include + +//! @cond IGNORED + +namespace cv { + +template +void DefaultDeleter::operator () (Y* p) const +{ + delete p; +} + +namespace detail +{ + +struct PtrOwner +{ + PtrOwner() : refCount(1) + {} + + void incRef() + { + CV_XADD(&refCount, 1); + } + + void decRef() + { + if (CV_XADD(&refCount, -1) == 1) deleteSelf(); + } + +protected: + /* This doesn't really need to be virtual, since PtrOwner is never deleted + directly, but it doesn't hurt and it helps avoid warnings. */ + virtual ~PtrOwner() + {} + + virtual void deleteSelf() = 0; + +private: + unsigned int refCount; + + // noncopyable + PtrOwner(const PtrOwner&); + PtrOwner& operator = (const PtrOwner&); +}; + +template +struct PtrOwnerImpl CV_FINAL : PtrOwner +{ + PtrOwnerImpl(Y* p, D d) : owned(p), deleter(d) + {} + + void deleteSelf() CV_OVERRIDE + { + deleter(owned); + delete this; + } + +private: + Y* owned; + D deleter; +}; + + +} + +template +Ptr::Ptr() : owner(NULL), stored(NULL) +{} + +template +template +Ptr::Ptr(Y* p) + : owner(p + ? new detail::PtrOwnerImpl >(p, DefaultDeleter()) + : NULL), + stored(p) +{} + +template +template +Ptr::Ptr(Y* p, D d) + : owner(p + ? new detail::PtrOwnerImpl(p, d) + : NULL), + stored(p) +{} + +template +Ptr::Ptr(const Ptr& o) : owner(o.owner), stored(o.stored) +{ + if (owner) owner->incRef(); +} + +template +template +Ptr::Ptr(const Ptr& o) : owner(o.owner), stored(o.stored) +{ + if (owner) owner->incRef(); +} + +template +template +Ptr::Ptr(const Ptr& o, T* p) : owner(o.owner), stored(p) +{ + if (owner) owner->incRef(); +} + +template +Ptr::~Ptr() +{ + release(); +} + +template +Ptr& Ptr::operator = (const Ptr& o) +{ + Ptr(o).swap(*this); + return *this; +} + +template +template +Ptr& Ptr::operator = (const Ptr& o) +{ + Ptr(o).swap(*this); + return *this; +} + +template +void Ptr::release() +{ + if (owner) owner->decRef(); + owner = NULL; + stored = NULL; +} + +template +template +void Ptr::reset(Y* p) +{ + Ptr(p).swap(*this); +} + +template +template +void Ptr::reset(Y* p, D d) +{ + Ptr(p, d).swap(*this); +} + +template +void Ptr::swap(Ptr& o) +{ + std::swap(owner, o.owner); + std::swap(stored, o.stored); +} + +template +T* Ptr::get() const +{ + return stored; +} + +template +typename detail::RefOrVoid::type Ptr::operator * () const +{ + return *stored; +} + +template +T* Ptr::operator -> () const +{ + return stored; +} + +template +Ptr::operator T* () const +{ + return stored; +} + + +template +bool Ptr::empty() const +{ + return !stored; +} + +template +template +Ptr Ptr::staticCast() const +{ + return Ptr(*this, static_cast(stored)); +} + +template +template +Ptr Ptr::constCast() const +{ + return Ptr(*this, const_cast(stored)); +} + +template +template +Ptr Ptr::dynamicCast() const +{ + return Ptr(*this, dynamic_cast(stored)); +} + +#ifdef CV_CXX_MOVE_SEMANTICS + +template +Ptr::Ptr(Ptr&& o) : owner(o.owner), stored(o.stored) +{ + o.owner = NULL; + o.stored = NULL; +} + +template +Ptr& Ptr::operator = (Ptr&& o) +{ + if (this == &o) + return *this; + + release(); + owner = o.owner; + stored = o.stored; + o.owner = NULL; + o.stored = NULL; + return *this; +} + +#endif + + +template +void swap(Ptr& ptr1, Ptr& ptr2){ + ptr1.swap(ptr2); +} + +template +bool operator == (const Ptr& ptr1, const Ptr& ptr2) +{ + return ptr1.get() == ptr2.get(); +} + +template +bool operator != (const Ptr& ptr1, const Ptr& ptr2) +{ + return ptr1.get() != ptr2.get(); +} + +template +Ptr makePtr() +{ + return Ptr(new T()); +} + +template +Ptr makePtr(const A1& a1) +{ + return Ptr(new T(a1)); +} + +template +Ptr makePtr(const A1& a1, const A2& a2) +{ + return Ptr(new T(a1, a2)); +} + +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3) +{ + return Ptr(new T(a1, a2, a3)); +} + +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4) +{ + return Ptr(new T(a1, a2, a3, a4)); +} + +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5) +{ + return Ptr(new T(a1, a2, a3, a4, a5)); +} + +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6) +{ + return Ptr(new T(a1, a2, a3, a4, a5, a6)); +} + +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7) +{ + return Ptr(new T(a1, a2, a3, a4, a5, a6, a7)); +} + +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8) +{ + return Ptr(new T(a1, a2, a3, a4, a5, a6, a7, a8)); +} + +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8, const A9& a9) +{ + return Ptr(new T(a1, a2, a3, a4, a5, a6, a7, a8, a9)); +} + +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8, const A9& a9, const A10& a10) +{ + return Ptr(new T(a1, a2, a3, a4, a5, a6, a7, a8, a9, a10)); +} + +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8, const A9& a9, const A10& a10, const A11& a11) +{ + return Ptr(new T(a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11)); +} + +template +Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8, const A9& a9, const A10& a10, const A11& a11, const A12& a12) +{ + return Ptr(new T(a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12)); +} +} // namespace cv + +//! @endcond + +#endif // OPENCV_CORE_PTR_INL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/saturate.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/saturate.hpp new file mode 100644 index 0000000000000000000000000000000000000000..118599f8f97e1e3e5049857f23e3593beaf3d4b0 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/saturate.hpp @@ -0,0 +1,165 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2014, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_SATURATE_HPP +#define OPENCV_CORE_SATURATE_HPP + +#include "opencv2/core/cvdef.h" +#include "opencv2/core/fast_math.hpp" + +namespace cv +{ + +//! @addtogroup core_utils +//! @{ + +/////////////// saturate_cast (used in image & signal processing) /////////////////// + +/** @brief Template function for accurate conversion from one primitive type to another. + + The function saturate_cast resembles the standard C++ cast operations, such as static_cast\() + and others. It perform an efficient and accurate conversion from one primitive type to another + (see the introduction chapter). saturate in the name means that when the input value v is out of the + range of the target type, the result is not formed just by taking low bits of the input, but instead + the value is clipped. For example: + @code + uchar a = saturate_cast(-100); // a = 0 (UCHAR_MIN) + short b = saturate_cast(33333.33333); // b = 32767 (SHRT_MAX) + @endcode + Such clipping is done when the target type is unsigned char , signed char , unsigned short or + signed short . For 32-bit integers, no clipping is done. + + When the parameter is a floating-point value and the target type is an integer (8-, 16- or 32-bit), + the floating-point value is first rounded to the nearest integer and then clipped if needed (when + the target type is 8- or 16-bit). + + This operation is used in the simplest or most complex image processing functions in OpenCV. + + @param v Function parameter. + @sa add, subtract, multiply, divide, Mat::convertTo + */ +template static inline _Tp saturate_cast(uchar v) { return _Tp(v); } +/** @overload */ +template static inline _Tp saturate_cast(schar v) { return _Tp(v); } +/** @overload */ +template static inline _Tp saturate_cast(ushort v) { return _Tp(v); } +/** @overload */ +template static inline _Tp saturate_cast(short v) { return _Tp(v); } +/** @overload */ +template static inline _Tp saturate_cast(unsigned v) { return _Tp(v); } +/** @overload */ +template static inline _Tp saturate_cast(int v) { return _Tp(v); } +/** @overload */ +template static inline _Tp saturate_cast(float v) { return _Tp(v); } +/** @overload */ +template static inline _Tp saturate_cast(double v) { return _Tp(v); } +/** @overload */ +template static inline _Tp saturate_cast(int64 v) { return _Tp(v); } +/** @overload */ +template static inline _Tp saturate_cast(uint64 v) { return _Tp(v); } + +template<> inline uchar saturate_cast(schar v) { return (uchar)std::max((int)v, 0); } +template<> inline uchar saturate_cast(ushort v) { return (uchar)std::min((unsigned)v, (unsigned)UCHAR_MAX); } +template<> inline uchar saturate_cast(int v) { return (uchar)((unsigned)v <= UCHAR_MAX ? v : v > 0 ? UCHAR_MAX : 0); } +template<> inline uchar saturate_cast(short v) { return saturate_cast((int)v); } +template<> inline uchar saturate_cast(unsigned v) { return (uchar)std::min(v, (unsigned)UCHAR_MAX); } +template<> inline uchar saturate_cast(float v) { int iv = cvRound(v); return saturate_cast(iv); } +template<> inline uchar saturate_cast(double v) { int iv = cvRound(v); return saturate_cast(iv); } +template<> inline uchar saturate_cast(int64 v) { return (uchar)((uint64)v <= (uint64)UCHAR_MAX ? v : v > 0 ? UCHAR_MAX : 0); } +template<> inline uchar saturate_cast(uint64 v) { return (uchar)std::min(v, (uint64)UCHAR_MAX); } + +template<> inline schar saturate_cast(uchar v) { return (schar)std::min((int)v, SCHAR_MAX); } +template<> inline schar saturate_cast(ushort v) { return (schar)std::min((unsigned)v, (unsigned)SCHAR_MAX); } +template<> inline schar saturate_cast(int v) { return (schar)((unsigned)(v-SCHAR_MIN) <= (unsigned)UCHAR_MAX ? v : v > 0 ? SCHAR_MAX : SCHAR_MIN); } +template<> inline schar saturate_cast(short v) { return saturate_cast((int)v); } +template<> inline schar saturate_cast(unsigned v) { return (schar)std::min(v, (unsigned)SCHAR_MAX); } +template<> inline schar saturate_cast(float v) { int iv = cvRound(v); return saturate_cast(iv); } +template<> inline schar saturate_cast(double v) { int iv = cvRound(v); return saturate_cast(iv); } +template<> inline schar saturate_cast(int64 v) { return (schar)((uint64)((int64)v-SCHAR_MIN) <= (uint64)UCHAR_MAX ? v : v > 0 ? SCHAR_MAX : SCHAR_MIN); } +template<> inline schar saturate_cast(uint64 v) { return (schar)std::min(v, (uint64)SCHAR_MAX); } + +template<> inline ushort saturate_cast(schar v) { return (ushort)std::max((int)v, 0); } +template<> inline ushort saturate_cast(short v) { return (ushort)std::max((int)v, 0); } +template<> inline ushort saturate_cast(int v) { return (ushort)((unsigned)v <= (unsigned)USHRT_MAX ? v : v > 0 ? USHRT_MAX : 0); } +template<> inline ushort saturate_cast(unsigned v) { return (ushort)std::min(v, (unsigned)USHRT_MAX); } +template<> inline ushort saturate_cast(float v) { int iv = cvRound(v); return saturate_cast(iv); } +template<> inline ushort saturate_cast(double v) { int iv = cvRound(v); return saturate_cast(iv); } +template<> inline ushort saturate_cast(int64 v) { return (ushort)((uint64)v <= (uint64)USHRT_MAX ? v : v > 0 ? USHRT_MAX : 0); } +template<> inline ushort saturate_cast(uint64 v) { return (ushort)std::min(v, (uint64)USHRT_MAX); } + +template<> inline short saturate_cast(ushort v) { return (short)std::min((int)v, SHRT_MAX); } +template<> inline short saturate_cast(int v) { return (short)((unsigned)(v - SHRT_MIN) <= (unsigned)USHRT_MAX ? v : v > 0 ? SHRT_MAX : SHRT_MIN); } +template<> inline short saturate_cast(unsigned v) { return (short)std::min(v, (unsigned)SHRT_MAX); } +template<> inline short saturate_cast(float v) { int iv = cvRound(v); return saturate_cast(iv); } +template<> inline short saturate_cast(double v) { int iv = cvRound(v); return saturate_cast(iv); } +template<> inline short saturate_cast(int64 v) { return (short)((uint64)((int64)v - SHRT_MIN) <= (uint64)USHRT_MAX ? v : v > 0 ? SHRT_MAX : SHRT_MIN); } +template<> inline short saturate_cast(uint64 v) { return (short)std::min(v, (uint64)SHRT_MAX); } + +template<> inline int saturate_cast(unsigned v) { return (int)std::min(v, (unsigned)INT_MAX); } +template<> inline int saturate_cast(int64 v) { return (int)((uint64)(v - INT_MIN) <= (uint64)UINT_MAX ? v : v > 0 ? INT_MAX : INT_MIN); } +template<> inline int saturate_cast(uint64 v) { return (int)std::min(v, (uint64)INT_MAX); } +template<> inline int saturate_cast(float v) { return cvRound(v); } +template<> inline int saturate_cast(double v) { return cvRound(v); } + +template<> inline unsigned saturate_cast(schar v) { return (unsigned)std::max(v, (schar)0); } +template<> inline unsigned saturate_cast(short v) { return (unsigned)std::max(v, (short)0); } +template<> inline unsigned saturate_cast(int v) { return (unsigned)std::max(v, (int)0); } +template<> inline unsigned saturate_cast(int64 v) { return (unsigned)((uint64)v <= (uint64)UINT_MAX ? v : v > 0 ? UINT_MAX : 0); } +template<> inline unsigned saturate_cast(uint64 v) { return (unsigned)std::min(v, (uint64)UINT_MAX); } +// we intentionally do not clip negative numbers, to make -1 become 0xffffffff etc. +template<> inline unsigned saturate_cast(float v) { return static_cast(cvRound(v)); } +template<> inline unsigned saturate_cast(double v) { return static_cast(cvRound(v)); } + +template<> inline uint64 saturate_cast(schar v) { return (uint64)std::max(v, (schar)0); } +template<> inline uint64 saturate_cast(short v) { return (uint64)std::max(v, (short)0); } +template<> inline uint64 saturate_cast(int v) { return (uint64)std::max(v, (int)0); } +template<> inline uint64 saturate_cast(int64 v) { return (uint64)std::max(v, (int64)0); } + +template<> inline int64 saturate_cast(uint64 v) { return (int64)std::min(v, (uint64)LLONG_MAX); } + +//! @} + +} // cv + +#endif // OPENCV_CORE_SATURATE_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/softfloat.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/softfloat.hpp new file mode 100644 index 0000000000000000000000000000000000000000..547098038d8349ce3a23f72829875030b44a02e6 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/softfloat.hpp @@ -0,0 +1,514 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html + +// This file is based on files from package issued with the following license: + +/*============================================================================ + +This C header file is part of the SoftFloat IEEE Floating-Point Arithmetic +Package, Release 3c, by John R. Hauser. + +Copyright 2011, 2012, 2013, 2014, 2015, 2016, 2017 The Regents of the +University of California. All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + + 1. Redistributions of source code must retain the above copyright notice, + this list of conditions, and the following disclaimer. + + 2. Redistributions in binary form must reproduce the above copyright notice, + this list of conditions, and the following disclaimer in the documentation + and/or other materials provided with the distribution. + + 3. Neither the name of the University nor the names of its contributors may + be used to endorse or promote products derived from this software without + specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE REGENTS AND CONTRIBUTORS "AS IS", AND ANY +EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE, ARE +DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR ANY +DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND +ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +=============================================================================*/ + +#pragma once +#ifndef softfloat_h +#define softfloat_h 1 + +#include "cvdef.h" + +namespace cv +{ + +/** @addtogroup core_utils_softfloat + + [SoftFloat](http://www.jhauser.us/arithmetic/SoftFloat.html) is a software implementation + of floating-point calculations according to IEEE 754 standard. + All calculations are done in integers, that's why they are machine-independent and bit-exact. + This library can be useful in accuracy-critical parts like look-up tables generation, tests, etc. + OpenCV contains a subset of SoftFloat partially rewritten to C++. + + ### Types + + There are two basic types: @ref softfloat and @ref softdouble. + These types are binary compatible with float and double types respectively + and support conversions to/from them. + Other types from original SoftFloat library like fp16 or fp128 were thrown away + as well as quiet/signaling NaN support, on-the-fly rounding mode switch + and exception flags (though exceptions can be implemented in the future). + + ### Operations + + Both types support the following: + - Construction from signed and unsigned 32-bit and 64 integers, + float/double or raw binary representation + - Conversions between each other, to float or double and to int + using @ref cvRound, @ref cvTrunc, @ref cvFloor, @ref cvCeil or a bunch of + saturate_cast functions + - Add, subtract, multiply, divide, remainder, square root, FMA with absolute precision + - Comparison operations + - Explicit sign, exponent and significand manipulation through get/set methods, + number state indicators (isInf, isNan, isSubnormal) + - Type-specific constants like eps, minimum/maximum value, best pi approximation, etc. + - min(), max(), abs(), exp(), log() and pow() functions + +*/ +//! @{ + +struct softfloat; +struct softdouble; + +struct CV_EXPORTS softfloat +{ +public: + /** @brief Default constructor */ + softfloat() { v = 0; } + /** @brief Copy constructor */ + softfloat( const softfloat& c) { v = c.v; } + /** @brief Assign constructor */ + softfloat& operator=( const softfloat& c ) + { + if(&c != this) v = c.v; + return *this; + } + /** @brief Construct from raw + + Builds new value from raw binary representation + */ + static const softfloat fromRaw( const uint32_t a ) { softfloat x; x.v = a; return x; } + + /** @brief Construct from integer */ + explicit softfloat( const uint32_t ); + explicit softfloat( const uint64_t ); + explicit softfloat( const int32_t ); + explicit softfloat( const int64_t ); + +#ifdef CV_INT32_T_IS_LONG_INT + // for platforms with int32_t = long int + explicit softfloat( const int a ) { *this = softfloat(static_cast(a)); } +#endif + + /** @brief Construct from float */ + explicit softfloat( const float a ) { Cv32suf s; s.f = a; v = s.u; } + + /** @brief Type casts */ + operator softdouble() const; + operator float() const { Cv32suf s; s.u = v; return s.f; } + + /** @brief Basic arithmetics */ + softfloat operator + (const softfloat&) const; + softfloat operator - (const softfloat&) const; + softfloat operator * (const softfloat&) const; + softfloat operator / (const softfloat&) const; + softfloat operator - () const { softfloat x; x.v = v ^ (1U << 31); return x; } + + /** @brief Remainder operator + + A quote from original SoftFloat manual: + + > The IEEE Standard remainder operation computes the value + > a - n * b, where n is the integer closest to a / b. + > If a / b is exactly halfway between two integers, n is the even integer + > closest to a / b. The IEEE Standard’s remainder operation is always exact and so requires no rounding. + > Depending on the relative magnitudes of the operands, the remainder functions + > can take considerably longer to execute than the other SoftFloat functions. + > This is an inherent characteristic of the remainder operation itself and is not a flaw + > in the SoftFloat implementation. + */ + softfloat operator % (const softfloat&) const; + + softfloat& operator += (const softfloat& a) { *this = *this + a; return *this; } + softfloat& operator -= (const softfloat& a) { *this = *this - a; return *this; } + softfloat& operator *= (const softfloat& a) { *this = *this * a; return *this; } + softfloat& operator /= (const softfloat& a) { *this = *this / a; return *this; } + softfloat& operator %= (const softfloat& a) { *this = *this % a; return *this; } + + /** @brief Comparison operations + + - Any operation with NaN produces false + + The only exception is when x is NaN: x != y for any y. + - Positive and negative zeros are equal + */ + bool operator == ( const softfloat& ) const; + bool operator != ( const softfloat& ) const; + bool operator > ( const softfloat& ) const; + bool operator >= ( const softfloat& ) const; + bool operator < ( const softfloat& ) const; + bool operator <= ( const softfloat& ) const; + + /** @brief NaN state indicator */ + inline bool isNaN() const { return (v & 0x7fffffff) > 0x7f800000; } + /** @brief Inf state indicator */ + inline bool isInf() const { return (v & 0x7fffffff) == 0x7f800000; } + /** @brief Subnormal number indicator */ + inline bool isSubnormal() const { return ((v >> 23) & 0xFF) == 0; } + + /** @brief Get sign bit */ + inline bool getSign() const { return (v >> 31) != 0; } + /** @brief Construct a copy with new sign bit */ + inline softfloat setSign(bool sign) const { softfloat x; x.v = (v & ((1U << 31) - 1)) | ((uint32_t)sign << 31); return x; } + /** @brief Get 0-based exponent */ + inline int getExp() const { return ((v >> 23) & 0xFF) - 127; } + /** @brief Construct a copy with new 0-based exponent */ + inline softfloat setExp(int e) const { softfloat x; x.v = (v & 0x807fffff) | (((e + 127) & 0xFF) << 23 ); return x; } + + /** @brief Get a fraction part + + Returns a number 1 <= x < 2 with the same significand + */ + inline softfloat getFrac() const + { + uint_fast32_t vv = (v & 0x007fffff) | (127 << 23); + return softfloat::fromRaw(vv); + } + /** @brief Construct a copy with provided significand + + Constructs a copy of a number with significand taken from parameter + */ + inline softfloat setFrac(const softfloat& s) const + { + softfloat x; + x.v = (v & 0xff800000) | (s.v & 0x007fffff); + return x; + } + + /** @brief Zero constant */ + static softfloat zero() { return softfloat::fromRaw( 0 ); } + /** @brief Positive infinity constant */ + static softfloat inf() { return softfloat::fromRaw( 0xFF << 23 ); } + /** @brief Default NaN constant */ + static softfloat nan() { return softfloat::fromRaw( 0x7fffffff ); } + /** @brief One constant */ + static softfloat one() { return softfloat::fromRaw( 127 << 23 ); } + /** @brief Smallest normalized value */ + static softfloat min() { return softfloat::fromRaw( 0x01 << 23 ); } + /** @brief Difference between 1 and next representable value */ + static softfloat eps() { return softfloat::fromRaw( (127 - 23) << 23 ); } + /** @brief Biggest finite value */ + static softfloat max() { return softfloat::fromRaw( (0xFF << 23) - 1 ); } + /** @brief Correct pi approximation */ + static softfloat pi() { return softfloat::fromRaw( 0x40490fdb ); } + + uint32_t v; +}; + +/*---------------------------------------------------------------------------- +*----------------------------------------------------------------------------*/ + +struct CV_EXPORTS softdouble +{ +public: + /** @brief Default constructor */ + softdouble() : v(0) { } + /** @brief Copy constructor */ + softdouble( const softdouble& c) { v = c.v; } + /** @brief Assign constructor */ + softdouble& operator=( const softdouble& c ) + { + if(&c != this) v = c.v; + return *this; + } + /** @brief Construct from raw + + Builds new value from raw binary representation + */ + static softdouble fromRaw( const uint64_t a ) { softdouble x; x.v = a; return x; } + + /** @brief Construct from integer */ + explicit softdouble( const uint32_t ); + explicit softdouble( const uint64_t ); + explicit softdouble( const int32_t ); + explicit softdouble( const int64_t ); + +#ifdef CV_INT32_T_IS_LONG_INT + // for platforms with int32_t = long int + explicit softdouble( const int a ) { *this = softdouble(static_cast(a)); } +#endif + + /** @brief Construct from double */ + explicit softdouble( const double a ) { Cv64suf s; s.f = a; v = s.u; } + + /** @brief Type casts */ + operator softfloat() const; + operator double() const { Cv64suf s; s.u = v; return s.f; } + + /** @brief Basic arithmetics */ + softdouble operator + (const softdouble&) const; + softdouble operator - (const softdouble&) const; + softdouble operator * (const softdouble&) const; + softdouble operator / (const softdouble&) const; + softdouble operator - () const { softdouble x; x.v = v ^ (1ULL << 63); return x; } + + /** @brief Remainder operator + + A quote from original SoftFloat manual: + + > The IEEE Standard remainder operation computes the value + > a - n * b, where n is the integer closest to a / b. + > If a / b is exactly halfway between two integers, n is the even integer + > closest to a / b. The IEEE Standard’s remainder operation is always exact and so requires no rounding. + > Depending on the relative magnitudes of the operands, the remainder functions + > can take considerably longer to execute than the other SoftFloat functions. + > This is an inherent characteristic of the remainder operation itself and is not a flaw + > in the SoftFloat implementation. + */ + softdouble operator % (const softdouble&) const; + + softdouble& operator += (const softdouble& a) { *this = *this + a; return *this; } + softdouble& operator -= (const softdouble& a) { *this = *this - a; return *this; } + softdouble& operator *= (const softdouble& a) { *this = *this * a; return *this; } + softdouble& operator /= (const softdouble& a) { *this = *this / a; return *this; } + softdouble& operator %= (const softdouble& a) { *this = *this % a; return *this; } + + /** @brief Comparison operations + + - Any operation with NaN produces false + + The only exception is when x is NaN: x != y for any y. + - Positive and negative zeros are equal + */ + bool operator == ( const softdouble& ) const; + bool operator != ( const softdouble& ) const; + bool operator > ( const softdouble& ) const; + bool operator >= ( const softdouble& ) const; + bool operator < ( const softdouble& ) const; + bool operator <= ( const softdouble& ) const; + + /** @brief NaN state indicator */ + inline bool isNaN() const { return (v & 0x7fffffffffffffff) > 0x7ff0000000000000; } + /** @brief Inf state indicator */ + inline bool isInf() const { return (v & 0x7fffffffffffffff) == 0x7ff0000000000000; } + /** @brief Subnormal number indicator */ + inline bool isSubnormal() const { return ((v >> 52) & 0x7FF) == 0; } + + /** @brief Get sign bit */ + inline bool getSign() const { return (v >> 63) != 0; } + /** @brief Construct a copy with new sign bit */ + softdouble setSign(bool sign) const { softdouble x; x.v = (v & ((1ULL << 63) - 1)) | ((uint_fast64_t)(sign) << 63); return x; } + /** @brief Get 0-based exponent */ + inline int getExp() const { return ((v >> 52) & 0x7FF) - 1023; } + /** @brief Construct a copy with new 0-based exponent */ + inline softdouble setExp(int e) const + { + softdouble x; + x.v = (v & 0x800FFFFFFFFFFFFF) | ((uint_fast64_t)((e + 1023) & 0x7FF) << 52); + return x; + } + + /** @brief Get a fraction part + + Returns a number 1 <= x < 2 with the same significand + */ + inline softdouble getFrac() const + { + uint_fast64_t vv = (v & 0x000FFFFFFFFFFFFF) | ((uint_fast64_t)(1023) << 52); + return softdouble::fromRaw(vv); + } + /** @brief Construct a copy with provided significand + + Constructs a copy of a number with significand taken from parameter + */ + inline softdouble setFrac(const softdouble& s) const + { + softdouble x; + x.v = (v & 0xFFF0000000000000) | (s.v & 0x000FFFFFFFFFFFFF); + return x; + } + + /** @brief Zero constant */ + static softdouble zero() { return softdouble::fromRaw( 0 ); } + /** @brief Positive infinity constant */ + static softdouble inf() { return softdouble::fromRaw( (uint_fast64_t)(0x7FF) << 52 ); } + /** @brief Default NaN constant */ + static softdouble nan() { return softdouble::fromRaw( CV_BIG_INT(0x7FFFFFFFFFFFFFFF) ); } + /** @brief One constant */ + static softdouble one() { return softdouble::fromRaw( (uint_fast64_t)( 1023) << 52 ); } + /** @brief Smallest normalized value */ + static softdouble min() { return softdouble::fromRaw( (uint_fast64_t)( 0x01) << 52 ); } + /** @brief Difference between 1 and next representable value */ + static softdouble eps() { return softdouble::fromRaw( (uint_fast64_t)( 1023 - 52 ) << 52 ); } + /** @brief Biggest finite value */ + static softdouble max() { return softdouble::fromRaw( ((uint_fast64_t)(0x7FF) << 52) - 1 ); } + /** @brief Correct pi approximation */ + static softdouble pi() { return softdouble::fromRaw( CV_BIG_INT(0x400921FB54442D18) ); } + + uint64_t v; +}; + +/*---------------------------------------------------------------------------- +*----------------------------------------------------------------------------*/ + +/** @brief Fused Multiplication and Addition + +Computes (a*b)+c with single rounding +*/ +CV_EXPORTS softfloat mulAdd( const softfloat& a, const softfloat& b, const softfloat & c); +CV_EXPORTS softdouble mulAdd( const softdouble& a, const softdouble& b, const softdouble& c); + +/** @brief Square root */ +CV_EXPORTS softfloat sqrt( const softfloat& a ); +CV_EXPORTS softdouble sqrt( const softdouble& a ); +} + +/*---------------------------------------------------------------------------- +| Ported from OpenCV and added for usability +*----------------------------------------------------------------------------*/ + +/** @brief Truncates number to integer with minimum magnitude */ +CV_EXPORTS int cvTrunc(const cv::softfloat& a); +CV_EXPORTS int cvTrunc(const cv::softdouble& a); + +/** @brief Rounds a number to nearest even integer */ +CV_EXPORTS int cvRound(const cv::softfloat& a); +CV_EXPORTS int cvRound(const cv::softdouble& a); + +/** @brief Rounds a number to nearest even long long integer */ +CV_EXPORTS int64_t cvRound64(const cv::softdouble& a); + +/** @brief Rounds a number down to integer */ +CV_EXPORTS int cvFloor(const cv::softfloat& a); +CV_EXPORTS int cvFloor(const cv::softdouble& a); + +/** @brief Rounds number up to integer */ +CV_EXPORTS int cvCeil(const cv::softfloat& a); +CV_EXPORTS int cvCeil(const cv::softdouble& a); + +namespace cv +{ +/** @brief Saturate casts */ +template static inline _Tp saturate_cast(softfloat a) { return _Tp(a); } +template static inline _Tp saturate_cast(softdouble a) { return _Tp(a); } + +template<> inline uchar saturate_cast(softfloat a) { return (uchar)std::max(std::min(cvRound(a), (int)UCHAR_MAX), 0); } +template<> inline uchar saturate_cast(softdouble a) { return (uchar)std::max(std::min(cvRound(a), (int)UCHAR_MAX), 0); } + +template<> inline schar saturate_cast(softfloat a) { return (schar)std::min(std::max(cvRound(a), (int)SCHAR_MIN), (int)SCHAR_MAX); } +template<> inline schar saturate_cast(softdouble a) { return (schar)std::min(std::max(cvRound(a), (int)SCHAR_MIN), (int)SCHAR_MAX); } + +template<> inline ushort saturate_cast(softfloat a) { return (ushort)std::max(std::min(cvRound(a), (int)USHRT_MAX), 0); } +template<> inline ushort saturate_cast(softdouble a) { return (ushort)std::max(std::min(cvRound(a), (int)USHRT_MAX), 0); } + +template<> inline short saturate_cast(softfloat a) { return (short)std::min(std::max(cvRound(a), (int)SHRT_MIN), (int)SHRT_MAX); } +template<> inline short saturate_cast(softdouble a) { return (short)std::min(std::max(cvRound(a), (int)SHRT_MIN), (int)SHRT_MAX); } + +template<> inline int saturate_cast(softfloat a) { return cvRound(a); } +template<> inline int saturate_cast(softdouble a) { return cvRound(a); } + +template<> inline int64_t saturate_cast(softfloat a) { return cvRound(a); } +template<> inline int64_t saturate_cast(softdouble a) { return cvRound64(a); } + +/** @brief Saturate cast to unsigned integer and unsigned long long integer +We intentionally do not clip negative numbers, to make -1 become 0xffffffff etc. +*/ +template<> inline unsigned saturate_cast(softfloat a) { return cvRound(a); } +template<> inline unsigned saturate_cast(softdouble a) { return cvRound(a); } + +template<> inline uint64_t saturate_cast(softfloat a) { return cvRound(a); } +template<> inline uint64_t saturate_cast(softdouble a) { return cvRound64(a); } + +/** @brief Min and Max functions */ +inline softfloat min(const softfloat& a, const softfloat& b) { return (a > b) ? b : a; } +inline softdouble min(const softdouble& a, const softdouble& b) { return (a > b) ? b : a; } + +inline softfloat max(const softfloat& a, const softfloat& b) { return (a > b) ? a : b; } +inline softdouble max(const softdouble& a, const softdouble& b) { return (a > b) ? a : b; } + +/** @brief Absolute value */ +inline softfloat abs( softfloat a) { softfloat x; x.v = a.v & ((1U << 31) - 1); return x; } +inline softdouble abs( softdouble a) { softdouble x; x.v = a.v & ((1ULL << 63) - 1); return x; } + +/** @brief Exponent + +Special cases: +- exp(NaN) is NaN +- exp(-Inf) == 0 +- exp(+Inf) == +Inf +*/ +CV_EXPORTS softfloat exp( const softfloat& a); +CV_EXPORTS softdouble exp( const softdouble& a); + +/** @brief Natural logarithm + +Special cases: +- log(NaN), log(x < 0) are NaN +- log(0) == -Inf +*/ +CV_EXPORTS softfloat log( const softfloat& a ); +CV_EXPORTS softdouble log( const softdouble& a ); + +/** @brief Raising to the power + +Special cases: +- x**NaN is NaN for any x +- ( |x| == 1 )**Inf is NaN +- ( |x| > 1 )**+Inf or ( |x| < 1 )**-Inf is +Inf +- ( |x| > 1 )**-Inf or ( |x| < 1 )**+Inf is 0 +- x ** 0 == 1 for any x +- x ** 1 == 1 for any x +- NaN ** y is NaN for any other y +- Inf**(y < 0) == 0 +- Inf ** y is +Inf for any other y +- (x < 0)**y is NaN for any other y if x can't be correctly rounded to integer +- 0 ** 0 == 1 +- 0 ** (y < 0) is +Inf +- 0 ** (y > 0) is 0 +*/ +CV_EXPORTS softfloat pow( const softfloat& a, const softfloat& b); +CV_EXPORTS softdouble pow( const softdouble& a, const softdouble& b); + +/** @brief Cube root + +Special cases: +- cbrt(NaN) is NaN +- cbrt(+/-Inf) is +/-Inf +*/ +CV_EXPORTS softfloat cbrt( const softfloat& a ); + +/** @brief Sine + +Special cases: +- sin(Inf) or sin(NaN) is NaN +- sin(x) == x when sin(x) is close to zero +*/ +CV_EXPORTS softdouble sin( const softdouble& a ); + +/** @brief Cosine + * +Special cases: +- cos(Inf) or cos(NaN) is NaN +- cos(x) == +/- 1 when cos(x) is close to +/- 1 +*/ +CV_EXPORTS softdouble cos( const softdouble& a ); + +} + +//! @} + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/sse_utils.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/sse_utils.hpp new file mode 100644 index 0000000000000000000000000000000000000000..0906583ea433e289e883270d81f8641985cc3ebb --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/sse_utils.hpp @@ -0,0 +1,652 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_SSE_UTILS_HPP +#define OPENCV_CORE_SSE_UTILS_HPP + +#ifndef __cplusplus +# error sse_utils.hpp header must be compiled as C++ +#endif + +#include "opencv2/core/cvdef.h" + +//! @addtogroup core_utils_sse +//! @{ + +#if CV_SSE2 + +inline void _mm_deinterleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1) +{ + __m128i layer1_chunk0 = _mm_unpacklo_epi8(v_r0, v_g0); + __m128i layer1_chunk1 = _mm_unpackhi_epi8(v_r0, v_g0); + __m128i layer1_chunk2 = _mm_unpacklo_epi8(v_r1, v_g1); + __m128i layer1_chunk3 = _mm_unpackhi_epi8(v_r1, v_g1); + + __m128i layer2_chunk0 = _mm_unpacklo_epi8(layer1_chunk0, layer1_chunk2); + __m128i layer2_chunk1 = _mm_unpackhi_epi8(layer1_chunk0, layer1_chunk2); + __m128i layer2_chunk2 = _mm_unpacklo_epi8(layer1_chunk1, layer1_chunk3); + __m128i layer2_chunk3 = _mm_unpackhi_epi8(layer1_chunk1, layer1_chunk3); + + __m128i layer3_chunk0 = _mm_unpacklo_epi8(layer2_chunk0, layer2_chunk2); + __m128i layer3_chunk1 = _mm_unpackhi_epi8(layer2_chunk0, layer2_chunk2); + __m128i layer3_chunk2 = _mm_unpacklo_epi8(layer2_chunk1, layer2_chunk3); + __m128i layer3_chunk3 = _mm_unpackhi_epi8(layer2_chunk1, layer2_chunk3); + + __m128i layer4_chunk0 = _mm_unpacklo_epi8(layer3_chunk0, layer3_chunk2); + __m128i layer4_chunk1 = _mm_unpackhi_epi8(layer3_chunk0, layer3_chunk2); + __m128i layer4_chunk2 = _mm_unpacklo_epi8(layer3_chunk1, layer3_chunk3); + __m128i layer4_chunk3 = _mm_unpackhi_epi8(layer3_chunk1, layer3_chunk3); + + v_r0 = _mm_unpacklo_epi8(layer4_chunk0, layer4_chunk2); + v_r1 = _mm_unpackhi_epi8(layer4_chunk0, layer4_chunk2); + v_g0 = _mm_unpacklo_epi8(layer4_chunk1, layer4_chunk3); + v_g1 = _mm_unpackhi_epi8(layer4_chunk1, layer4_chunk3); +} + +inline void _mm_deinterleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, + __m128i & v_g1, __m128i & v_b0, __m128i & v_b1) +{ + __m128i layer1_chunk0 = _mm_unpacklo_epi8(v_r0, v_g1); + __m128i layer1_chunk1 = _mm_unpackhi_epi8(v_r0, v_g1); + __m128i layer1_chunk2 = _mm_unpacklo_epi8(v_r1, v_b0); + __m128i layer1_chunk3 = _mm_unpackhi_epi8(v_r1, v_b0); + __m128i layer1_chunk4 = _mm_unpacklo_epi8(v_g0, v_b1); + __m128i layer1_chunk5 = _mm_unpackhi_epi8(v_g0, v_b1); + + __m128i layer2_chunk0 = _mm_unpacklo_epi8(layer1_chunk0, layer1_chunk3); + __m128i layer2_chunk1 = _mm_unpackhi_epi8(layer1_chunk0, layer1_chunk3); + __m128i layer2_chunk2 = _mm_unpacklo_epi8(layer1_chunk1, layer1_chunk4); + __m128i layer2_chunk3 = _mm_unpackhi_epi8(layer1_chunk1, layer1_chunk4); + __m128i layer2_chunk4 = _mm_unpacklo_epi8(layer1_chunk2, layer1_chunk5); + __m128i layer2_chunk5 = _mm_unpackhi_epi8(layer1_chunk2, layer1_chunk5); + + __m128i layer3_chunk0 = _mm_unpacklo_epi8(layer2_chunk0, layer2_chunk3); + __m128i layer3_chunk1 = _mm_unpackhi_epi8(layer2_chunk0, layer2_chunk3); + __m128i layer3_chunk2 = _mm_unpacklo_epi8(layer2_chunk1, layer2_chunk4); + __m128i layer3_chunk3 = _mm_unpackhi_epi8(layer2_chunk1, layer2_chunk4); + __m128i layer3_chunk4 = _mm_unpacklo_epi8(layer2_chunk2, layer2_chunk5); + __m128i layer3_chunk5 = _mm_unpackhi_epi8(layer2_chunk2, layer2_chunk5); + + __m128i layer4_chunk0 = _mm_unpacklo_epi8(layer3_chunk0, layer3_chunk3); + __m128i layer4_chunk1 = _mm_unpackhi_epi8(layer3_chunk0, layer3_chunk3); + __m128i layer4_chunk2 = _mm_unpacklo_epi8(layer3_chunk1, layer3_chunk4); + __m128i layer4_chunk3 = _mm_unpackhi_epi8(layer3_chunk1, layer3_chunk4); + __m128i layer4_chunk4 = _mm_unpacklo_epi8(layer3_chunk2, layer3_chunk5); + __m128i layer4_chunk5 = _mm_unpackhi_epi8(layer3_chunk2, layer3_chunk5); + + v_r0 = _mm_unpacklo_epi8(layer4_chunk0, layer4_chunk3); + v_r1 = _mm_unpackhi_epi8(layer4_chunk0, layer4_chunk3); + v_g0 = _mm_unpacklo_epi8(layer4_chunk1, layer4_chunk4); + v_g1 = _mm_unpackhi_epi8(layer4_chunk1, layer4_chunk4); + v_b0 = _mm_unpacklo_epi8(layer4_chunk2, layer4_chunk5); + v_b1 = _mm_unpackhi_epi8(layer4_chunk2, layer4_chunk5); +} + +inline void _mm_deinterleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1, + __m128i & v_b0, __m128i & v_b1, __m128i & v_a0, __m128i & v_a1) +{ + __m128i layer1_chunk0 = _mm_unpacklo_epi8(v_r0, v_b0); + __m128i layer1_chunk1 = _mm_unpackhi_epi8(v_r0, v_b0); + __m128i layer1_chunk2 = _mm_unpacklo_epi8(v_r1, v_b1); + __m128i layer1_chunk3 = _mm_unpackhi_epi8(v_r1, v_b1); + __m128i layer1_chunk4 = _mm_unpacklo_epi8(v_g0, v_a0); + __m128i layer1_chunk5 = _mm_unpackhi_epi8(v_g0, v_a0); + __m128i layer1_chunk6 = _mm_unpacklo_epi8(v_g1, v_a1); + __m128i layer1_chunk7 = _mm_unpackhi_epi8(v_g1, v_a1); + + __m128i layer2_chunk0 = _mm_unpacklo_epi8(layer1_chunk0, layer1_chunk4); + __m128i layer2_chunk1 = _mm_unpackhi_epi8(layer1_chunk0, layer1_chunk4); + __m128i layer2_chunk2 = _mm_unpacklo_epi8(layer1_chunk1, layer1_chunk5); + __m128i layer2_chunk3 = _mm_unpackhi_epi8(layer1_chunk1, layer1_chunk5); + __m128i layer2_chunk4 = _mm_unpacklo_epi8(layer1_chunk2, layer1_chunk6); + __m128i layer2_chunk5 = _mm_unpackhi_epi8(layer1_chunk2, layer1_chunk6); + __m128i layer2_chunk6 = _mm_unpacklo_epi8(layer1_chunk3, layer1_chunk7); + __m128i layer2_chunk7 = _mm_unpackhi_epi8(layer1_chunk3, layer1_chunk7); + + __m128i layer3_chunk0 = _mm_unpacklo_epi8(layer2_chunk0, layer2_chunk4); + __m128i layer3_chunk1 = _mm_unpackhi_epi8(layer2_chunk0, layer2_chunk4); + __m128i layer3_chunk2 = _mm_unpacklo_epi8(layer2_chunk1, layer2_chunk5); + __m128i layer3_chunk3 = _mm_unpackhi_epi8(layer2_chunk1, layer2_chunk5); + __m128i layer3_chunk4 = _mm_unpacklo_epi8(layer2_chunk2, layer2_chunk6); + __m128i layer3_chunk5 = _mm_unpackhi_epi8(layer2_chunk2, layer2_chunk6); + __m128i layer3_chunk6 = _mm_unpacklo_epi8(layer2_chunk3, layer2_chunk7); + __m128i layer3_chunk7 = _mm_unpackhi_epi8(layer2_chunk3, layer2_chunk7); + + __m128i layer4_chunk0 = _mm_unpacklo_epi8(layer3_chunk0, layer3_chunk4); + __m128i layer4_chunk1 = _mm_unpackhi_epi8(layer3_chunk0, layer3_chunk4); + __m128i layer4_chunk2 = _mm_unpacklo_epi8(layer3_chunk1, layer3_chunk5); + __m128i layer4_chunk3 = _mm_unpackhi_epi8(layer3_chunk1, layer3_chunk5); + __m128i layer4_chunk4 = _mm_unpacklo_epi8(layer3_chunk2, layer3_chunk6); + __m128i layer4_chunk5 = _mm_unpackhi_epi8(layer3_chunk2, layer3_chunk6); + __m128i layer4_chunk6 = _mm_unpacklo_epi8(layer3_chunk3, layer3_chunk7); + __m128i layer4_chunk7 = _mm_unpackhi_epi8(layer3_chunk3, layer3_chunk7); + + v_r0 = _mm_unpacklo_epi8(layer4_chunk0, layer4_chunk4); + v_r1 = _mm_unpackhi_epi8(layer4_chunk0, layer4_chunk4); + v_g0 = _mm_unpacklo_epi8(layer4_chunk1, layer4_chunk5); + v_g1 = _mm_unpackhi_epi8(layer4_chunk1, layer4_chunk5); + v_b0 = _mm_unpacklo_epi8(layer4_chunk2, layer4_chunk6); + v_b1 = _mm_unpackhi_epi8(layer4_chunk2, layer4_chunk6); + v_a0 = _mm_unpacklo_epi8(layer4_chunk3, layer4_chunk7); + v_a1 = _mm_unpackhi_epi8(layer4_chunk3, layer4_chunk7); +} + +inline void _mm_interleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1) +{ + __m128i v_mask = _mm_set1_epi16(0x00ff); + + __m128i layer4_chunk0 = _mm_packus_epi16(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask)); + __m128i layer4_chunk2 = _mm_packus_epi16(_mm_srli_epi16(v_r0, 8), _mm_srli_epi16(v_r1, 8)); + __m128i layer4_chunk1 = _mm_packus_epi16(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask)); + __m128i layer4_chunk3 = _mm_packus_epi16(_mm_srli_epi16(v_g0, 8), _mm_srli_epi16(v_g1, 8)); + + __m128i layer3_chunk0 = _mm_packus_epi16(_mm_and_si128(layer4_chunk0, v_mask), _mm_and_si128(layer4_chunk1, v_mask)); + __m128i layer3_chunk2 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk0, 8), _mm_srli_epi16(layer4_chunk1, 8)); + __m128i layer3_chunk1 = _mm_packus_epi16(_mm_and_si128(layer4_chunk2, v_mask), _mm_and_si128(layer4_chunk3, v_mask)); + __m128i layer3_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk2, 8), _mm_srli_epi16(layer4_chunk3, 8)); + + __m128i layer2_chunk0 = _mm_packus_epi16(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask)); + __m128i layer2_chunk2 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk0, 8), _mm_srli_epi16(layer3_chunk1, 8)); + __m128i layer2_chunk1 = _mm_packus_epi16(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask)); + __m128i layer2_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk2, 8), _mm_srli_epi16(layer3_chunk3, 8)); + + __m128i layer1_chunk0 = _mm_packus_epi16(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask)); + __m128i layer1_chunk2 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk0, 8), _mm_srli_epi16(layer2_chunk1, 8)); + __m128i layer1_chunk1 = _mm_packus_epi16(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask)); + __m128i layer1_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk2, 8), _mm_srli_epi16(layer2_chunk3, 8)); + + v_r0 = _mm_packus_epi16(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask)); + v_g0 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk0, 8), _mm_srli_epi16(layer1_chunk1, 8)); + v_r1 = _mm_packus_epi16(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask)); + v_g1 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk2, 8), _mm_srli_epi16(layer1_chunk3, 8)); +} + +inline void _mm_interleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, + __m128i & v_g1, __m128i & v_b0, __m128i & v_b1) +{ + __m128i v_mask = _mm_set1_epi16(0x00ff); + + __m128i layer4_chunk0 = _mm_packus_epi16(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask)); + __m128i layer4_chunk3 = _mm_packus_epi16(_mm_srli_epi16(v_r0, 8), _mm_srli_epi16(v_r1, 8)); + __m128i layer4_chunk1 = _mm_packus_epi16(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask)); + __m128i layer4_chunk4 = _mm_packus_epi16(_mm_srli_epi16(v_g0, 8), _mm_srli_epi16(v_g1, 8)); + __m128i layer4_chunk2 = _mm_packus_epi16(_mm_and_si128(v_b0, v_mask), _mm_and_si128(v_b1, v_mask)); + __m128i layer4_chunk5 = _mm_packus_epi16(_mm_srli_epi16(v_b0, 8), _mm_srli_epi16(v_b1, 8)); + + __m128i layer3_chunk0 = _mm_packus_epi16(_mm_and_si128(layer4_chunk0, v_mask), _mm_and_si128(layer4_chunk1, v_mask)); + __m128i layer3_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk0, 8), _mm_srli_epi16(layer4_chunk1, 8)); + __m128i layer3_chunk1 = _mm_packus_epi16(_mm_and_si128(layer4_chunk2, v_mask), _mm_and_si128(layer4_chunk3, v_mask)); + __m128i layer3_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk2, 8), _mm_srli_epi16(layer4_chunk3, 8)); + __m128i layer3_chunk2 = _mm_packus_epi16(_mm_and_si128(layer4_chunk4, v_mask), _mm_and_si128(layer4_chunk5, v_mask)); + __m128i layer3_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk4, 8), _mm_srli_epi16(layer4_chunk5, 8)); + + __m128i layer2_chunk0 = _mm_packus_epi16(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask)); + __m128i layer2_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk0, 8), _mm_srli_epi16(layer3_chunk1, 8)); + __m128i layer2_chunk1 = _mm_packus_epi16(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask)); + __m128i layer2_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk2, 8), _mm_srli_epi16(layer3_chunk3, 8)); + __m128i layer2_chunk2 = _mm_packus_epi16(_mm_and_si128(layer3_chunk4, v_mask), _mm_and_si128(layer3_chunk5, v_mask)); + __m128i layer2_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk4, 8), _mm_srli_epi16(layer3_chunk5, 8)); + + __m128i layer1_chunk0 = _mm_packus_epi16(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask)); + __m128i layer1_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk0, 8), _mm_srli_epi16(layer2_chunk1, 8)); + __m128i layer1_chunk1 = _mm_packus_epi16(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask)); + __m128i layer1_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk2, 8), _mm_srli_epi16(layer2_chunk3, 8)); + __m128i layer1_chunk2 = _mm_packus_epi16(_mm_and_si128(layer2_chunk4, v_mask), _mm_and_si128(layer2_chunk5, v_mask)); + __m128i layer1_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk4, 8), _mm_srli_epi16(layer2_chunk5, 8)); + + v_r0 = _mm_packus_epi16(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask)); + v_g1 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk0, 8), _mm_srli_epi16(layer1_chunk1, 8)); + v_r1 = _mm_packus_epi16(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask)); + v_b0 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk2, 8), _mm_srli_epi16(layer1_chunk3, 8)); + v_g0 = _mm_packus_epi16(_mm_and_si128(layer1_chunk4, v_mask), _mm_and_si128(layer1_chunk5, v_mask)); + v_b1 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk4, 8), _mm_srli_epi16(layer1_chunk5, 8)); +} + +inline void _mm_interleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1, + __m128i & v_b0, __m128i & v_b1, __m128i & v_a0, __m128i & v_a1) +{ + __m128i v_mask = _mm_set1_epi16(0x00ff); + + __m128i layer4_chunk0 = _mm_packus_epi16(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask)); + __m128i layer4_chunk4 = _mm_packus_epi16(_mm_srli_epi16(v_r0, 8), _mm_srli_epi16(v_r1, 8)); + __m128i layer4_chunk1 = _mm_packus_epi16(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask)); + __m128i layer4_chunk5 = _mm_packus_epi16(_mm_srli_epi16(v_g0, 8), _mm_srli_epi16(v_g1, 8)); + __m128i layer4_chunk2 = _mm_packus_epi16(_mm_and_si128(v_b0, v_mask), _mm_and_si128(v_b1, v_mask)); + __m128i layer4_chunk6 = _mm_packus_epi16(_mm_srli_epi16(v_b0, 8), _mm_srli_epi16(v_b1, 8)); + __m128i layer4_chunk3 = _mm_packus_epi16(_mm_and_si128(v_a0, v_mask), _mm_and_si128(v_a1, v_mask)); + __m128i layer4_chunk7 = _mm_packus_epi16(_mm_srli_epi16(v_a0, 8), _mm_srli_epi16(v_a1, 8)); + + __m128i layer3_chunk0 = _mm_packus_epi16(_mm_and_si128(layer4_chunk0, v_mask), _mm_and_si128(layer4_chunk1, v_mask)); + __m128i layer3_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk0, 8), _mm_srli_epi16(layer4_chunk1, 8)); + __m128i layer3_chunk1 = _mm_packus_epi16(_mm_and_si128(layer4_chunk2, v_mask), _mm_and_si128(layer4_chunk3, v_mask)); + __m128i layer3_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk2, 8), _mm_srli_epi16(layer4_chunk3, 8)); + __m128i layer3_chunk2 = _mm_packus_epi16(_mm_and_si128(layer4_chunk4, v_mask), _mm_and_si128(layer4_chunk5, v_mask)); + __m128i layer3_chunk6 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk4, 8), _mm_srli_epi16(layer4_chunk5, 8)); + __m128i layer3_chunk3 = _mm_packus_epi16(_mm_and_si128(layer4_chunk6, v_mask), _mm_and_si128(layer4_chunk7, v_mask)); + __m128i layer3_chunk7 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk6, 8), _mm_srli_epi16(layer4_chunk7, 8)); + + __m128i layer2_chunk0 = _mm_packus_epi16(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask)); + __m128i layer2_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk0, 8), _mm_srli_epi16(layer3_chunk1, 8)); + __m128i layer2_chunk1 = _mm_packus_epi16(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask)); + __m128i layer2_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk2, 8), _mm_srli_epi16(layer3_chunk3, 8)); + __m128i layer2_chunk2 = _mm_packus_epi16(_mm_and_si128(layer3_chunk4, v_mask), _mm_and_si128(layer3_chunk5, v_mask)); + __m128i layer2_chunk6 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk4, 8), _mm_srli_epi16(layer3_chunk5, 8)); + __m128i layer2_chunk3 = _mm_packus_epi16(_mm_and_si128(layer3_chunk6, v_mask), _mm_and_si128(layer3_chunk7, v_mask)); + __m128i layer2_chunk7 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk6, 8), _mm_srli_epi16(layer3_chunk7, 8)); + + __m128i layer1_chunk0 = _mm_packus_epi16(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask)); + __m128i layer1_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk0, 8), _mm_srli_epi16(layer2_chunk1, 8)); + __m128i layer1_chunk1 = _mm_packus_epi16(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask)); + __m128i layer1_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk2, 8), _mm_srli_epi16(layer2_chunk3, 8)); + __m128i layer1_chunk2 = _mm_packus_epi16(_mm_and_si128(layer2_chunk4, v_mask), _mm_and_si128(layer2_chunk5, v_mask)); + __m128i layer1_chunk6 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk4, 8), _mm_srli_epi16(layer2_chunk5, 8)); + __m128i layer1_chunk3 = _mm_packus_epi16(_mm_and_si128(layer2_chunk6, v_mask), _mm_and_si128(layer2_chunk7, v_mask)); + __m128i layer1_chunk7 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk6, 8), _mm_srli_epi16(layer2_chunk7, 8)); + + v_r0 = _mm_packus_epi16(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask)); + v_b0 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk0, 8), _mm_srli_epi16(layer1_chunk1, 8)); + v_r1 = _mm_packus_epi16(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask)); + v_b1 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk2, 8), _mm_srli_epi16(layer1_chunk3, 8)); + v_g0 = _mm_packus_epi16(_mm_and_si128(layer1_chunk4, v_mask), _mm_and_si128(layer1_chunk5, v_mask)); + v_a0 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk4, 8), _mm_srli_epi16(layer1_chunk5, 8)); + v_g1 = _mm_packus_epi16(_mm_and_si128(layer1_chunk6, v_mask), _mm_and_si128(layer1_chunk7, v_mask)); + v_a1 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk6, 8), _mm_srli_epi16(layer1_chunk7, 8)); +} + +inline void _mm_deinterleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1) +{ + __m128i layer1_chunk0 = _mm_unpacklo_epi16(v_r0, v_g0); + __m128i layer1_chunk1 = _mm_unpackhi_epi16(v_r0, v_g0); + __m128i layer1_chunk2 = _mm_unpacklo_epi16(v_r1, v_g1); + __m128i layer1_chunk3 = _mm_unpackhi_epi16(v_r1, v_g1); + + __m128i layer2_chunk0 = _mm_unpacklo_epi16(layer1_chunk0, layer1_chunk2); + __m128i layer2_chunk1 = _mm_unpackhi_epi16(layer1_chunk0, layer1_chunk2); + __m128i layer2_chunk2 = _mm_unpacklo_epi16(layer1_chunk1, layer1_chunk3); + __m128i layer2_chunk3 = _mm_unpackhi_epi16(layer1_chunk1, layer1_chunk3); + + __m128i layer3_chunk0 = _mm_unpacklo_epi16(layer2_chunk0, layer2_chunk2); + __m128i layer3_chunk1 = _mm_unpackhi_epi16(layer2_chunk0, layer2_chunk2); + __m128i layer3_chunk2 = _mm_unpacklo_epi16(layer2_chunk1, layer2_chunk3); + __m128i layer3_chunk3 = _mm_unpackhi_epi16(layer2_chunk1, layer2_chunk3); + + v_r0 = _mm_unpacklo_epi16(layer3_chunk0, layer3_chunk2); + v_r1 = _mm_unpackhi_epi16(layer3_chunk0, layer3_chunk2); + v_g0 = _mm_unpacklo_epi16(layer3_chunk1, layer3_chunk3); + v_g1 = _mm_unpackhi_epi16(layer3_chunk1, layer3_chunk3); +} + +inline void _mm_deinterleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, + __m128i & v_g1, __m128i & v_b0, __m128i & v_b1) +{ + __m128i layer1_chunk0 = _mm_unpacklo_epi16(v_r0, v_g1); + __m128i layer1_chunk1 = _mm_unpackhi_epi16(v_r0, v_g1); + __m128i layer1_chunk2 = _mm_unpacklo_epi16(v_r1, v_b0); + __m128i layer1_chunk3 = _mm_unpackhi_epi16(v_r1, v_b0); + __m128i layer1_chunk4 = _mm_unpacklo_epi16(v_g0, v_b1); + __m128i layer1_chunk5 = _mm_unpackhi_epi16(v_g0, v_b1); + + __m128i layer2_chunk0 = _mm_unpacklo_epi16(layer1_chunk0, layer1_chunk3); + __m128i layer2_chunk1 = _mm_unpackhi_epi16(layer1_chunk0, layer1_chunk3); + __m128i layer2_chunk2 = _mm_unpacklo_epi16(layer1_chunk1, layer1_chunk4); + __m128i layer2_chunk3 = _mm_unpackhi_epi16(layer1_chunk1, layer1_chunk4); + __m128i layer2_chunk4 = _mm_unpacklo_epi16(layer1_chunk2, layer1_chunk5); + __m128i layer2_chunk5 = _mm_unpackhi_epi16(layer1_chunk2, layer1_chunk5); + + __m128i layer3_chunk0 = _mm_unpacklo_epi16(layer2_chunk0, layer2_chunk3); + __m128i layer3_chunk1 = _mm_unpackhi_epi16(layer2_chunk0, layer2_chunk3); + __m128i layer3_chunk2 = _mm_unpacklo_epi16(layer2_chunk1, layer2_chunk4); + __m128i layer3_chunk3 = _mm_unpackhi_epi16(layer2_chunk1, layer2_chunk4); + __m128i layer3_chunk4 = _mm_unpacklo_epi16(layer2_chunk2, layer2_chunk5); + __m128i layer3_chunk5 = _mm_unpackhi_epi16(layer2_chunk2, layer2_chunk5); + + v_r0 = _mm_unpacklo_epi16(layer3_chunk0, layer3_chunk3); + v_r1 = _mm_unpackhi_epi16(layer3_chunk0, layer3_chunk3); + v_g0 = _mm_unpacklo_epi16(layer3_chunk1, layer3_chunk4); + v_g1 = _mm_unpackhi_epi16(layer3_chunk1, layer3_chunk4); + v_b0 = _mm_unpacklo_epi16(layer3_chunk2, layer3_chunk5); + v_b1 = _mm_unpackhi_epi16(layer3_chunk2, layer3_chunk5); +} + +inline void _mm_deinterleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1, + __m128i & v_b0, __m128i & v_b1, __m128i & v_a0, __m128i & v_a1) +{ + __m128i layer1_chunk0 = _mm_unpacklo_epi16(v_r0, v_b0); + __m128i layer1_chunk1 = _mm_unpackhi_epi16(v_r0, v_b0); + __m128i layer1_chunk2 = _mm_unpacklo_epi16(v_r1, v_b1); + __m128i layer1_chunk3 = _mm_unpackhi_epi16(v_r1, v_b1); + __m128i layer1_chunk4 = _mm_unpacklo_epi16(v_g0, v_a0); + __m128i layer1_chunk5 = _mm_unpackhi_epi16(v_g0, v_a0); + __m128i layer1_chunk6 = _mm_unpacklo_epi16(v_g1, v_a1); + __m128i layer1_chunk7 = _mm_unpackhi_epi16(v_g1, v_a1); + + __m128i layer2_chunk0 = _mm_unpacklo_epi16(layer1_chunk0, layer1_chunk4); + __m128i layer2_chunk1 = _mm_unpackhi_epi16(layer1_chunk0, layer1_chunk4); + __m128i layer2_chunk2 = _mm_unpacklo_epi16(layer1_chunk1, layer1_chunk5); + __m128i layer2_chunk3 = _mm_unpackhi_epi16(layer1_chunk1, layer1_chunk5); + __m128i layer2_chunk4 = _mm_unpacklo_epi16(layer1_chunk2, layer1_chunk6); + __m128i layer2_chunk5 = _mm_unpackhi_epi16(layer1_chunk2, layer1_chunk6); + __m128i layer2_chunk6 = _mm_unpacklo_epi16(layer1_chunk3, layer1_chunk7); + __m128i layer2_chunk7 = _mm_unpackhi_epi16(layer1_chunk3, layer1_chunk7); + + __m128i layer3_chunk0 = _mm_unpacklo_epi16(layer2_chunk0, layer2_chunk4); + __m128i layer3_chunk1 = _mm_unpackhi_epi16(layer2_chunk0, layer2_chunk4); + __m128i layer3_chunk2 = _mm_unpacklo_epi16(layer2_chunk1, layer2_chunk5); + __m128i layer3_chunk3 = _mm_unpackhi_epi16(layer2_chunk1, layer2_chunk5); + __m128i layer3_chunk4 = _mm_unpacklo_epi16(layer2_chunk2, layer2_chunk6); + __m128i layer3_chunk5 = _mm_unpackhi_epi16(layer2_chunk2, layer2_chunk6); + __m128i layer3_chunk6 = _mm_unpacklo_epi16(layer2_chunk3, layer2_chunk7); + __m128i layer3_chunk7 = _mm_unpackhi_epi16(layer2_chunk3, layer2_chunk7); + + v_r0 = _mm_unpacklo_epi16(layer3_chunk0, layer3_chunk4); + v_r1 = _mm_unpackhi_epi16(layer3_chunk0, layer3_chunk4); + v_g0 = _mm_unpacklo_epi16(layer3_chunk1, layer3_chunk5); + v_g1 = _mm_unpackhi_epi16(layer3_chunk1, layer3_chunk5); + v_b0 = _mm_unpacklo_epi16(layer3_chunk2, layer3_chunk6); + v_b1 = _mm_unpackhi_epi16(layer3_chunk2, layer3_chunk6); + v_a0 = _mm_unpacklo_epi16(layer3_chunk3, layer3_chunk7); + v_a1 = _mm_unpackhi_epi16(layer3_chunk3, layer3_chunk7); +} + +#if CV_SSE4_1 + +inline void _mm_interleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1) +{ + __m128i v_mask = _mm_set1_epi32(0x0000ffff); + + __m128i layer3_chunk0 = _mm_packus_epi32(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask)); + __m128i layer3_chunk2 = _mm_packus_epi32(_mm_srli_epi32(v_r0, 16), _mm_srli_epi32(v_r1, 16)); + __m128i layer3_chunk1 = _mm_packus_epi32(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask)); + __m128i layer3_chunk3 = _mm_packus_epi32(_mm_srli_epi32(v_g0, 16), _mm_srli_epi32(v_g1, 16)); + + __m128i layer2_chunk0 = _mm_packus_epi32(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask)); + __m128i layer2_chunk2 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk0, 16), _mm_srli_epi32(layer3_chunk1, 16)); + __m128i layer2_chunk1 = _mm_packus_epi32(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask)); + __m128i layer2_chunk3 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk2, 16), _mm_srli_epi32(layer3_chunk3, 16)); + + __m128i layer1_chunk0 = _mm_packus_epi32(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask)); + __m128i layer1_chunk2 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk0, 16), _mm_srli_epi32(layer2_chunk1, 16)); + __m128i layer1_chunk1 = _mm_packus_epi32(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask)); + __m128i layer1_chunk3 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk2, 16), _mm_srli_epi32(layer2_chunk3, 16)); + + v_r0 = _mm_packus_epi32(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask)); + v_g0 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk0, 16), _mm_srli_epi32(layer1_chunk1, 16)); + v_r1 = _mm_packus_epi32(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask)); + v_g1 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk2, 16), _mm_srli_epi32(layer1_chunk3, 16)); +} + +inline void _mm_interleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, + __m128i & v_g1, __m128i & v_b0, __m128i & v_b1) +{ + __m128i v_mask = _mm_set1_epi32(0x0000ffff); + + __m128i layer3_chunk0 = _mm_packus_epi32(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask)); + __m128i layer3_chunk3 = _mm_packus_epi32(_mm_srli_epi32(v_r0, 16), _mm_srli_epi32(v_r1, 16)); + __m128i layer3_chunk1 = _mm_packus_epi32(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask)); + __m128i layer3_chunk4 = _mm_packus_epi32(_mm_srli_epi32(v_g0, 16), _mm_srli_epi32(v_g1, 16)); + __m128i layer3_chunk2 = _mm_packus_epi32(_mm_and_si128(v_b0, v_mask), _mm_and_si128(v_b1, v_mask)); + __m128i layer3_chunk5 = _mm_packus_epi32(_mm_srli_epi32(v_b0, 16), _mm_srli_epi32(v_b1, 16)); + + __m128i layer2_chunk0 = _mm_packus_epi32(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask)); + __m128i layer2_chunk3 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk0, 16), _mm_srli_epi32(layer3_chunk1, 16)); + __m128i layer2_chunk1 = _mm_packus_epi32(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask)); + __m128i layer2_chunk4 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk2, 16), _mm_srli_epi32(layer3_chunk3, 16)); + __m128i layer2_chunk2 = _mm_packus_epi32(_mm_and_si128(layer3_chunk4, v_mask), _mm_and_si128(layer3_chunk5, v_mask)); + __m128i layer2_chunk5 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk4, 16), _mm_srli_epi32(layer3_chunk5, 16)); + + __m128i layer1_chunk0 = _mm_packus_epi32(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask)); + __m128i layer1_chunk3 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk0, 16), _mm_srli_epi32(layer2_chunk1, 16)); + __m128i layer1_chunk1 = _mm_packus_epi32(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask)); + __m128i layer1_chunk4 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk2, 16), _mm_srli_epi32(layer2_chunk3, 16)); + __m128i layer1_chunk2 = _mm_packus_epi32(_mm_and_si128(layer2_chunk4, v_mask), _mm_and_si128(layer2_chunk5, v_mask)); + __m128i layer1_chunk5 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk4, 16), _mm_srli_epi32(layer2_chunk5, 16)); + + v_r0 = _mm_packus_epi32(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask)); + v_g1 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk0, 16), _mm_srli_epi32(layer1_chunk1, 16)); + v_r1 = _mm_packus_epi32(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask)); + v_b0 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk2, 16), _mm_srli_epi32(layer1_chunk3, 16)); + v_g0 = _mm_packus_epi32(_mm_and_si128(layer1_chunk4, v_mask), _mm_and_si128(layer1_chunk5, v_mask)); + v_b1 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk4, 16), _mm_srli_epi32(layer1_chunk5, 16)); +} + +inline void _mm_interleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1, + __m128i & v_b0, __m128i & v_b1, __m128i & v_a0, __m128i & v_a1) +{ + __m128i v_mask = _mm_set1_epi32(0x0000ffff); + + __m128i layer3_chunk0 = _mm_packus_epi32(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask)); + __m128i layer3_chunk4 = _mm_packus_epi32(_mm_srli_epi32(v_r0, 16), _mm_srli_epi32(v_r1, 16)); + __m128i layer3_chunk1 = _mm_packus_epi32(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask)); + __m128i layer3_chunk5 = _mm_packus_epi32(_mm_srli_epi32(v_g0, 16), _mm_srli_epi32(v_g1, 16)); + __m128i layer3_chunk2 = _mm_packus_epi32(_mm_and_si128(v_b0, v_mask), _mm_and_si128(v_b1, v_mask)); + __m128i layer3_chunk6 = _mm_packus_epi32(_mm_srli_epi32(v_b0, 16), _mm_srli_epi32(v_b1, 16)); + __m128i layer3_chunk3 = _mm_packus_epi32(_mm_and_si128(v_a0, v_mask), _mm_and_si128(v_a1, v_mask)); + __m128i layer3_chunk7 = _mm_packus_epi32(_mm_srli_epi32(v_a0, 16), _mm_srli_epi32(v_a1, 16)); + + __m128i layer2_chunk0 = _mm_packus_epi32(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask)); + __m128i layer2_chunk4 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk0, 16), _mm_srli_epi32(layer3_chunk1, 16)); + __m128i layer2_chunk1 = _mm_packus_epi32(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask)); + __m128i layer2_chunk5 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk2, 16), _mm_srli_epi32(layer3_chunk3, 16)); + __m128i layer2_chunk2 = _mm_packus_epi32(_mm_and_si128(layer3_chunk4, v_mask), _mm_and_si128(layer3_chunk5, v_mask)); + __m128i layer2_chunk6 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk4, 16), _mm_srli_epi32(layer3_chunk5, 16)); + __m128i layer2_chunk3 = _mm_packus_epi32(_mm_and_si128(layer3_chunk6, v_mask), _mm_and_si128(layer3_chunk7, v_mask)); + __m128i layer2_chunk7 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk6, 16), _mm_srli_epi32(layer3_chunk7, 16)); + + __m128i layer1_chunk0 = _mm_packus_epi32(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask)); + __m128i layer1_chunk4 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk0, 16), _mm_srli_epi32(layer2_chunk1, 16)); + __m128i layer1_chunk1 = _mm_packus_epi32(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask)); + __m128i layer1_chunk5 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk2, 16), _mm_srli_epi32(layer2_chunk3, 16)); + __m128i layer1_chunk2 = _mm_packus_epi32(_mm_and_si128(layer2_chunk4, v_mask), _mm_and_si128(layer2_chunk5, v_mask)); + __m128i layer1_chunk6 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk4, 16), _mm_srli_epi32(layer2_chunk5, 16)); + __m128i layer1_chunk3 = _mm_packus_epi32(_mm_and_si128(layer2_chunk6, v_mask), _mm_and_si128(layer2_chunk7, v_mask)); + __m128i layer1_chunk7 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk6, 16), _mm_srli_epi32(layer2_chunk7, 16)); + + v_r0 = _mm_packus_epi32(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask)); + v_b0 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk0, 16), _mm_srli_epi32(layer1_chunk1, 16)); + v_r1 = _mm_packus_epi32(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask)); + v_b1 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk2, 16), _mm_srli_epi32(layer1_chunk3, 16)); + v_g0 = _mm_packus_epi32(_mm_and_si128(layer1_chunk4, v_mask), _mm_and_si128(layer1_chunk5, v_mask)); + v_a0 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk4, 16), _mm_srli_epi32(layer1_chunk5, 16)); + v_g1 = _mm_packus_epi32(_mm_and_si128(layer1_chunk6, v_mask), _mm_and_si128(layer1_chunk7, v_mask)); + v_a1 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk6, 16), _mm_srli_epi32(layer1_chunk7, 16)); +} + +#endif // CV_SSE4_1 + +inline void _mm_deinterleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0, __m128 & v_g1) +{ + __m128 layer1_chunk0 = _mm_unpacklo_ps(v_r0, v_g0); + __m128 layer1_chunk1 = _mm_unpackhi_ps(v_r0, v_g0); + __m128 layer1_chunk2 = _mm_unpacklo_ps(v_r1, v_g1); + __m128 layer1_chunk3 = _mm_unpackhi_ps(v_r1, v_g1); + + __m128 layer2_chunk0 = _mm_unpacklo_ps(layer1_chunk0, layer1_chunk2); + __m128 layer2_chunk1 = _mm_unpackhi_ps(layer1_chunk0, layer1_chunk2); + __m128 layer2_chunk2 = _mm_unpacklo_ps(layer1_chunk1, layer1_chunk3); + __m128 layer2_chunk3 = _mm_unpackhi_ps(layer1_chunk1, layer1_chunk3); + + v_r0 = _mm_unpacklo_ps(layer2_chunk0, layer2_chunk2); + v_r1 = _mm_unpackhi_ps(layer2_chunk0, layer2_chunk2); + v_g0 = _mm_unpacklo_ps(layer2_chunk1, layer2_chunk3); + v_g1 = _mm_unpackhi_ps(layer2_chunk1, layer2_chunk3); +} + +inline void _mm_deinterleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0, + __m128 & v_g1, __m128 & v_b0, __m128 & v_b1) +{ + __m128 layer1_chunk0 = _mm_unpacklo_ps(v_r0, v_g1); + __m128 layer1_chunk1 = _mm_unpackhi_ps(v_r0, v_g1); + __m128 layer1_chunk2 = _mm_unpacklo_ps(v_r1, v_b0); + __m128 layer1_chunk3 = _mm_unpackhi_ps(v_r1, v_b0); + __m128 layer1_chunk4 = _mm_unpacklo_ps(v_g0, v_b1); + __m128 layer1_chunk5 = _mm_unpackhi_ps(v_g0, v_b1); + + __m128 layer2_chunk0 = _mm_unpacklo_ps(layer1_chunk0, layer1_chunk3); + __m128 layer2_chunk1 = _mm_unpackhi_ps(layer1_chunk0, layer1_chunk3); + __m128 layer2_chunk2 = _mm_unpacklo_ps(layer1_chunk1, layer1_chunk4); + __m128 layer2_chunk3 = _mm_unpackhi_ps(layer1_chunk1, layer1_chunk4); + __m128 layer2_chunk4 = _mm_unpacklo_ps(layer1_chunk2, layer1_chunk5); + __m128 layer2_chunk5 = _mm_unpackhi_ps(layer1_chunk2, layer1_chunk5); + + v_r0 = _mm_unpacklo_ps(layer2_chunk0, layer2_chunk3); + v_r1 = _mm_unpackhi_ps(layer2_chunk0, layer2_chunk3); + v_g0 = _mm_unpacklo_ps(layer2_chunk1, layer2_chunk4); + v_g1 = _mm_unpackhi_ps(layer2_chunk1, layer2_chunk4); + v_b0 = _mm_unpacklo_ps(layer2_chunk2, layer2_chunk5); + v_b1 = _mm_unpackhi_ps(layer2_chunk2, layer2_chunk5); +} + +inline void _mm_deinterleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0, __m128 & v_g1, + __m128 & v_b0, __m128 & v_b1, __m128 & v_a0, __m128 & v_a1) +{ + __m128 layer1_chunk0 = _mm_unpacklo_ps(v_r0, v_b0); + __m128 layer1_chunk1 = _mm_unpackhi_ps(v_r0, v_b0); + __m128 layer1_chunk2 = _mm_unpacklo_ps(v_r1, v_b1); + __m128 layer1_chunk3 = _mm_unpackhi_ps(v_r1, v_b1); + __m128 layer1_chunk4 = _mm_unpacklo_ps(v_g0, v_a0); + __m128 layer1_chunk5 = _mm_unpackhi_ps(v_g0, v_a0); + __m128 layer1_chunk6 = _mm_unpacklo_ps(v_g1, v_a1); + __m128 layer1_chunk7 = _mm_unpackhi_ps(v_g1, v_a1); + + __m128 layer2_chunk0 = _mm_unpacklo_ps(layer1_chunk0, layer1_chunk4); + __m128 layer2_chunk1 = _mm_unpackhi_ps(layer1_chunk0, layer1_chunk4); + __m128 layer2_chunk2 = _mm_unpacklo_ps(layer1_chunk1, layer1_chunk5); + __m128 layer2_chunk3 = _mm_unpackhi_ps(layer1_chunk1, layer1_chunk5); + __m128 layer2_chunk4 = _mm_unpacklo_ps(layer1_chunk2, layer1_chunk6); + __m128 layer2_chunk5 = _mm_unpackhi_ps(layer1_chunk2, layer1_chunk6); + __m128 layer2_chunk6 = _mm_unpacklo_ps(layer1_chunk3, layer1_chunk7); + __m128 layer2_chunk7 = _mm_unpackhi_ps(layer1_chunk3, layer1_chunk7); + + v_r0 = _mm_unpacklo_ps(layer2_chunk0, layer2_chunk4); + v_r1 = _mm_unpackhi_ps(layer2_chunk0, layer2_chunk4); + v_g0 = _mm_unpacklo_ps(layer2_chunk1, layer2_chunk5); + v_g1 = _mm_unpackhi_ps(layer2_chunk1, layer2_chunk5); + v_b0 = _mm_unpacklo_ps(layer2_chunk2, layer2_chunk6); + v_b1 = _mm_unpackhi_ps(layer2_chunk2, layer2_chunk6); + v_a0 = _mm_unpacklo_ps(layer2_chunk3, layer2_chunk7); + v_a1 = _mm_unpackhi_ps(layer2_chunk3, layer2_chunk7); +} + +inline void _mm_interleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0, __m128 & v_g1) +{ + enum { mask_lo = _MM_SHUFFLE(2, 0, 2, 0), mask_hi = _MM_SHUFFLE(3, 1, 3, 1) }; + + __m128 layer2_chunk0 = _mm_shuffle_ps(v_r0, v_r1, mask_lo); + __m128 layer2_chunk2 = _mm_shuffle_ps(v_r0, v_r1, mask_hi); + __m128 layer2_chunk1 = _mm_shuffle_ps(v_g0, v_g1, mask_lo); + __m128 layer2_chunk3 = _mm_shuffle_ps(v_g0, v_g1, mask_hi); + + __m128 layer1_chunk0 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_lo); + __m128 layer1_chunk2 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_hi); + __m128 layer1_chunk1 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_lo); + __m128 layer1_chunk3 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_hi); + + v_r0 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_lo); + v_g0 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_hi); + v_r1 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_lo); + v_g1 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_hi); +} + +inline void _mm_interleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0, + __m128 & v_g1, __m128 & v_b0, __m128 & v_b1) +{ + enum { mask_lo = _MM_SHUFFLE(2, 0, 2, 0), mask_hi = _MM_SHUFFLE(3, 1, 3, 1) }; + + __m128 layer2_chunk0 = _mm_shuffle_ps(v_r0, v_r1, mask_lo); + __m128 layer2_chunk3 = _mm_shuffle_ps(v_r0, v_r1, mask_hi); + __m128 layer2_chunk1 = _mm_shuffle_ps(v_g0, v_g1, mask_lo); + __m128 layer2_chunk4 = _mm_shuffle_ps(v_g0, v_g1, mask_hi); + __m128 layer2_chunk2 = _mm_shuffle_ps(v_b0, v_b1, mask_lo); + __m128 layer2_chunk5 = _mm_shuffle_ps(v_b0, v_b1, mask_hi); + + __m128 layer1_chunk0 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_lo); + __m128 layer1_chunk3 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_hi); + __m128 layer1_chunk1 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_lo); + __m128 layer1_chunk4 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_hi); + __m128 layer1_chunk2 = _mm_shuffle_ps(layer2_chunk4, layer2_chunk5, mask_lo); + __m128 layer1_chunk5 = _mm_shuffle_ps(layer2_chunk4, layer2_chunk5, mask_hi); + + v_r0 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_lo); + v_g1 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_hi); + v_r1 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_lo); + v_b0 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_hi); + v_g0 = _mm_shuffle_ps(layer1_chunk4, layer1_chunk5, mask_lo); + v_b1 = _mm_shuffle_ps(layer1_chunk4, layer1_chunk5, mask_hi); +} + +inline void _mm_interleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0, __m128 & v_g1, + __m128 & v_b0, __m128 & v_b1, __m128 & v_a0, __m128 & v_a1) +{ + enum { mask_lo = _MM_SHUFFLE(2, 0, 2, 0), mask_hi = _MM_SHUFFLE(3, 1, 3, 1) }; + + __m128 layer2_chunk0 = _mm_shuffle_ps(v_r0, v_r1, mask_lo); + __m128 layer2_chunk4 = _mm_shuffle_ps(v_r0, v_r1, mask_hi); + __m128 layer2_chunk1 = _mm_shuffle_ps(v_g0, v_g1, mask_lo); + __m128 layer2_chunk5 = _mm_shuffle_ps(v_g0, v_g1, mask_hi); + __m128 layer2_chunk2 = _mm_shuffle_ps(v_b0, v_b1, mask_lo); + __m128 layer2_chunk6 = _mm_shuffle_ps(v_b0, v_b1, mask_hi); + __m128 layer2_chunk3 = _mm_shuffle_ps(v_a0, v_a1, mask_lo); + __m128 layer2_chunk7 = _mm_shuffle_ps(v_a0, v_a1, mask_hi); + + __m128 layer1_chunk0 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_lo); + __m128 layer1_chunk4 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_hi); + __m128 layer1_chunk1 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_lo); + __m128 layer1_chunk5 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_hi); + __m128 layer1_chunk2 = _mm_shuffle_ps(layer2_chunk4, layer2_chunk5, mask_lo); + __m128 layer1_chunk6 = _mm_shuffle_ps(layer2_chunk4, layer2_chunk5, mask_hi); + __m128 layer1_chunk3 = _mm_shuffle_ps(layer2_chunk6, layer2_chunk7, mask_lo); + __m128 layer1_chunk7 = _mm_shuffle_ps(layer2_chunk6, layer2_chunk7, mask_hi); + + v_r0 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_lo); + v_b0 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_hi); + v_r1 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_lo); + v_b1 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_hi); + v_g0 = _mm_shuffle_ps(layer1_chunk4, layer1_chunk5, mask_lo); + v_a0 = _mm_shuffle_ps(layer1_chunk4, layer1_chunk5, mask_hi); + v_g1 = _mm_shuffle_ps(layer1_chunk6, layer1_chunk7, mask_lo); + v_a1 = _mm_shuffle_ps(layer1_chunk6, layer1_chunk7, mask_hi); +} + +#endif // CV_SSE2 + +//! @} + +#endif //OPENCV_CORE_SSE_UTILS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/traits.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/traits.hpp new file mode 100644 index 0000000000000000000000000000000000000000..6cb10f44cf59fde0dccf6768281bb4bd86d28b3d --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/traits.hpp @@ -0,0 +1,397 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_TRAITS_HPP +#define OPENCV_CORE_TRAITS_HPP + +#include "opencv2/core/cvdef.h" + +namespace cv +{ + +//#define OPENCV_TRAITS_ENABLE_DEPRECATED + +//! @addtogroup core_basic +//! @{ + +/** @brief Template "trait" class for OpenCV primitive data types. + +@note Deprecated. This is replaced by "single purpose" traits: traits::Type and traits::Depth + +A primitive OpenCV data type is one of unsigned char, bool, signed char, unsigned short, signed +short, int, float, double, or a tuple of values of one of these types, where all the values in the +tuple have the same type. Any primitive type from the list can be defined by an identifier in the +form CV_\{U|S|F}C(\), for example: uchar \~ CV_8UC1, 3-element +floating-point tuple \~ CV_32FC3, and so on. A universal OpenCV structure that is able to store a +single instance of such a primitive data type is Vec. Multiple instances of such a type can be +stored in a std::vector, Mat, Mat_, SparseMat, SparseMat_, or any other container that is able to +store Vec instances. + +The DataType class is basically used to provide a description of such primitive data types without +adding any fields or methods to the corresponding classes (and it is actually impossible to add +anything to primitive C/C++ data types). This technique is known in C++ as class traits. It is not +DataType itself that is used but its specialized versions, such as: +@code + template<> class DataType + { + typedef uchar value_type; + typedef int work_type; + typedef uchar channel_type; + enum { channel_type = CV_8U, channels = 1, fmt='u', type = CV_8U }; + }; + ... + template DataType > + { + typedef std::complex<_Tp> value_type; + typedef std::complex<_Tp> work_type; + typedef _Tp channel_type; + // DataDepth is another helper trait class + enum { depth = DataDepth<_Tp>::value, channels=2, + fmt=(channels-1)*256+DataDepth<_Tp>::fmt, + type=CV_MAKETYPE(depth, channels) }; + }; + ... +@endcode +The main purpose of this class is to convert compilation-time type information to an +OpenCV-compatible data type identifier, for example: +@code + // allocates a 30x40 floating-point matrix + Mat A(30, 40, DataType::type); + + Mat B = Mat_ >(3, 3); + // the statement below will print 6, 2 , that is depth == CV_64F, channels == 2 + cout << B.depth() << ", " << B.channels() << endl; +@endcode +So, such traits are used to tell OpenCV which data type you are working with, even if such a type is +not native to OpenCV. For example, the matrix B initialization above is compiled because OpenCV +defines the proper specialized template class DataType\ \> . This mechanism is also +useful (and used in OpenCV this way) for generic algorithms implementations. + +@note Default values were dropped to stop confusing developers about using of unsupported types (see #7599) +*/ +template class DataType +{ +public: +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + typedef _Tp value_type; + typedef value_type work_type; + typedef value_type channel_type; + typedef value_type vec_type; + enum { generic_type = 1, + depth = -1, + channels = 1, + fmt = 0, + type = CV_MAKETYPE(depth, channels) + }; +#endif +}; + +template<> class DataType +{ +public: + typedef bool value_type; + typedef int work_type; + typedef value_type channel_type; + typedef value_type vec_type; + enum { generic_type = 0, + depth = CV_8U, + channels = 1, + fmt = (int)'u', + type = CV_MAKETYPE(depth, channels) + }; +}; + +template<> class DataType +{ +public: + typedef uchar value_type; + typedef int work_type; + typedef value_type channel_type; + typedef value_type vec_type; + enum { generic_type = 0, + depth = CV_8U, + channels = 1, + fmt = (int)'u', + type = CV_MAKETYPE(depth, channels) + }; +}; + +template<> class DataType +{ +public: + typedef schar value_type; + typedef int work_type; + typedef value_type channel_type; + typedef value_type vec_type; + enum { generic_type = 0, + depth = CV_8S, + channels = 1, + fmt = (int)'c', + type = CV_MAKETYPE(depth, channels) + }; +}; + +template<> class DataType +{ +public: + typedef schar value_type; + typedef int work_type; + typedef value_type channel_type; + typedef value_type vec_type; + enum { generic_type = 0, + depth = CV_8S, + channels = 1, + fmt = (int)'c', + type = CV_MAKETYPE(depth, channels) + }; +}; + +template<> class DataType +{ +public: + typedef ushort value_type; + typedef int work_type; + typedef value_type channel_type; + typedef value_type vec_type; + enum { generic_type = 0, + depth = CV_16U, + channels = 1, + fmt = (int)'w', + type = CV_MAKETYPE(depth, channels) + }; +}; + +template<> class DataType +{ +public: + typedef short value_type; + typedef int work_type; + typedef value_type channel_type; + typedef value_type vec_type; + enum { generic_type = 0, + depth = CV_16S, + channels = 1, + fmt = (int)'s', + type = CV_MAKETYPE(depth, channels) + }; +}; + +template<> class DataType +{ +public: + typedef int value_type; + typedef value_type work_type; + typedef value_type channel_type; + typedef value_type vec_type; + enum { generic_type = 0, + depth = CV_32S, + channels = 1, + fmt = (int)'i', + type = CV_MAKETYPE(depth, channels) + }; +}; + +template<> class DataType +{ +public: + typedef float value_type; + typedef value_type work_type; + typedef value_type channel_type; + typedef value_type vec_type; + enum { generic_type = 0, + depth = CV_32F, + channels = 1, + fmt = (int)'f', + type = CV_MAKETYPE(depth, channels) + }; +}; + +template<> class DataType +{ +public: + typedef double value_type; + typedef value_type work_type; + typedef value_type channel_type; + typedef value_type vec_type; + enum { generic_type = 0, + depth = CV_64F, + channels = 1, + fmt = (int)'d', + type = CV_MAKETYPE(depth, channels) + }; +}; + + +/** @brief A helper class for cv::DataType + +The class is specialized for each fundamental numerical data type supported by OpenCV. It provides +DataDepth::value constant. +*/ +template class DataDepth +{ +public: + enum + { + value = DataType<_Tp>::depth, + fmt = DataType<_Tp>::fmt + }; +}; + + +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + +template class TypeDepth +{ +#ifdef OPENCV_TRAITS_ENABLE_LEGACY_DEFAULTS + enum { depth = CV_USRTYPE1 }; + typedef void value_type; +#endif +}; + +template<> class TypeDepth +{ + enum { depth = CV_8U }; + typedef uchar value_type; +}; + +template<> class TypeDepth +{ + enum { depth = CV_8S }; + typedef schar value_type; +}; + +template<> class TypeDepth +{ + enum { depth = CV_16U }; + typedef ushort value_type; +}; + +template<> class TypeDepth +{ + enum { depth = CV_16S }; + typedef short value_type; +}; + +template<> class TypeDepth +{ + enum { depth = CV_32S }; + typedef int value_type; +}; + +template<> class TypeDepth +{ + enum { depth = CV_32F }; + typedef float value_type; +}; + +template<> class TypeDepth +{ + enum { depth = CV_64F }; + typedef double value_type; +}; + +#endif + +//! @} + +namespace traits { + +namespace internal { +#define CV_CREATE_MEMBER_CHECK(X) \ +template class CheckMember_##X { \ + struct Fallback { int X; }; \ + struct Derived : T, Fallback { }; \ + template struct Check; \ + typedef char CV_NO[1]; \ + typedef char CV_YES[2]; \ + template static CV_NO & func(Check *); \ + template static CV_YES & func(...); \ +public: \ + typedef CheckMember_##X type; \ + enum { value = sizeof(func(0)) == sizeof(CV_YES) }; \ +}; + +CV_CREATE_MEMBER_CHECK(fmt) +CV_CREATE_MEMBER_CHECK(type) + +} // namespace internal + + +template +struct Depth +{ enum { value = DataType::depth }; }; + +template +struct Type +{ enum { value = DataType::type }; }; + +/** Similar to traits::Type but has value = -1 in case of unknown type (instead of compiler error) */ +template >::value > +struct SafeType {}; + +template +struct SafeType +{ enum { value = -1 }; }; + +template +struct SafeType +{ enum { value = Type::value }; }; + + +template >::value > +struct SafeFmt {}; + +template +struct SafeFmt +{ enum { fmt = 0 }; }; + +template +struct SafeFmt +{ enum { fmt = DataType::fmt }; }; + + +} // namespace + +} // cv + +#endif // OPENCV_CORE_TRAITS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/types.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/types.hpp new file mode 100644 index 0000000000000000000000000000000000000000..ef9ab593830ace4e94f60f5c1d52419d48e43435 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/types.hpp @@ -0,0 +1,2391 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_TYPES_HPP +#define OPENCV_CORE_TYPES_HPP + +#ifndef __cplusplus +# error types.hpp header must be compiled as C++ +#endif + +#include +#include +#include +#include + +#include "opencv2/core/cvdef.h" +#include "opencv2/core/cvstd.hpp" +#include "opencv2/core/matx.hpp" + +namespace cv +{ + +//! @addtogroup core_basic +//! @{ + +//////////////////////////////// Complex ////////////////////////////// + +/** @brief A complex number class. + + The template class is similar and compatible with std::complex, however it provides slightly + more convenient access to the real and imaginary parts using through the simple field access, as opposite + to std::complex::real() and std::complex::imag(). +*/ +template class Complex +{ +public: + + //! default constructor + Complex(); + Complex( _Tp _re, _Tp _im = 0 ); + + //! conversion to another data type + template operator Complex() const; + //! conjugation + Complex conj() const; + + _Tp re, im; //< the real and the imaginary parts +}; + +typedef Complex Complexf; +typedef Complex Complexd; + +template class DataType< Complex<_Tp> > +{ +public: + typedef Complex<_Tp> value_type; + typedef value_type work_type; + typedef _Tp channel_type; + + enum { generic_type = 0, + channels = 2, + fmt = DataType::fmt + ((channels - 1) << 8) +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + ,depth = DataType::depth + ,type = CV_MAKETYPE(depth, channels) +#endif + }; + + typedef Vec vec_type; +}; + +namespace traits { +template +struct Depth< Complex<_Tp> > { enum { value = Depth<_Tp>::value }; }; +template +struct Type< Complex<_Tp> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, 2) }; }; +} // namespace + + +//////////////////////////////// Point_ //////////////////////////////// + +/** @brief Template class for 2D points specified by its coordinates `x` and `y`. + +An instance of the class is interchangeable with C structures, CvPoint and CvPoint2D32f . There is +also a cast operator to convert point coordinates to the specified type. The conversion from +floating-point coordinates to integer coordinates is done by rounding. Commonly, the conversion +uses this operation for each of the coordinates. Besides the class members listed in the +declaration above, the following operations on points are implemented: +@code + pt1 = pt2 + pt3; + pt1 = pt2 - pt3; + pt1 = pt2 * a; + pt1 = a * pt2; + pt1 = pt2 / a; + pt1 += pt2; + pt1 -= pt2; + pt1 *= a; + pt1 /= a; + double value = norm(pt); // L2 norm + pt1 == pt2; + pt1 != pt2; +@endcode +For your convenience, the following type aliases are defined: +@code + typedef Point_ Point2i; + typedef Point2i Point; + typedef Point_ Point2f; + typedef Point_ Point2d; +@endcode +Example: +@code + Point2f a(0.3f, 0.f), b(0.f, 0.4f); + Point pt = (a + b)*10.f; + cout << pt.x << ", " << pt.y << endl; +@endcode +*/ +template class Point_ +{ +public: + typedef _Tp value_type; + + //! default constructor + Point_(); + Point_(_Tp _x, _Tp _y); + Point_(const Point_& pt); + Point_(const Size_<_Tp>& sz); + Point_(const Vec<_Tp, 2>& v); + + Point_& operator = (const Point_& pt); + //! conversion to another data type + template operator Point_<_Tp2>() const; + + //! conversion to the old-style C structures + operator Vec<_Tp, 2>() const; + + //! dot product + _Tp dot(const Point_& pt) const; + //! dot product computed in double-precision arithmetics + double ddot(const Point_& pt) const; + //! cross-product + double cross(const Point_& pt) const; + //! checks whether the point is inside the specified rectangle + bool inside(const Rect_<_Tp>& r) const; + _Tp x; //!< x coordinate of the point + _Tp y; //!< y coordinate of the point +}; + +typedef Point_ Point2i; +typedef Point_ Point2l; +typedef Point_ Point2f; +typedef Point_ Point2d; +typedef Point2i Point; + +template class DataType< Point_<_Tp> > +{ +public: + typedef Point_<_Tp> value_type; + typedef Point_::work_type> work_type; + typedef _Tp channel_type; + + enum { generic_type = 0, + channels = 2, + fmt = traits::SafeFmt::fmt + ((channels - 1) << 8) +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + ,depth = DataType::depth + ,type = CV_MAKETYPE(depth, channels) +#endif + }; + + typedef Vec vec_type; +}; + +namespace traits { +template +struct Depth< Point_<_Tp> > { enum { value = Depth<_Tp>::value }; }; +template +struct Type< Point_<_Tp> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, 2) }; }; +} // namespace + + +//////////////////////////////// Point3_ //////////////////////////////// + +/** @brief Template class for 3D points specified by its coordinates `x`, `y` and `z`. + +An instance of the class is interchangeable with the C structure CvPoint2D32f . Similarly to +Point_ , the coordinates of 3D points can be converted to another type. The vector arithmetic and +comparison operations are also supported. + +The following Point3_\<\> aliases are available: +@code + typedef Point3_ Point3i; + typedef Point3_ Point3f; + typedef Point3_ Point3d; +@endcode +@see cv::Point3i, cv::Point3f and cv::Point3d +*/ +template class Point3_ +{ +public: + typedef _Tp value_type; + + //! default constructor + Point3_(); + Point3_(_Tp _x, _Tp _y, _Tp _z); + Point3_(const Point3_& pt); + explicit Point3_(const Point_<_Tp>& pt); + Point3_(const Vec<_Tp, 3>& v); + + Point3_& operator = (const Point3_& pt); + //! conversion to another data type + template operator Point3_<_Tp2>() const; + //! conversion to cv::Vec<> +#if OPENCV_ABI_COMPATIBILITY > 300 + template operator Vec<_Tp2, 3>() const; +#else + operator Vec<_Tp, 3>() const; +#endif + + //! dot product + _Tp dot(const Point3_& pt) const; + //! dot product computed in double-precision arithmetics + double ddot(const Point3_& pt) const; + //! cross product of the 2 3D points + Point3_ cross(const Point3_& pt) const; + _Tp x; //!< x coordinate of the 3D point + _Tp y; //!< y coordinate of the 3D point + _Tp z; //!< z coordinate of the 3D point +}; + +typedef Point3_ Point3i; +typedef Point3_ Point3f; +typedef Point3_ Point3d; + +template class DataType< Point3_<_Tp> > +{ +public: + typedef Point3_<_Tp> value_type; + typedef Point3_::work_type> work_type; + typedef _Tp channel_type; + + enum { generic_type = 0, + channels = 3, + fmt = traits::SafeFmt::fmt + ((channels - 1) << 8) +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + ,depth = DataType::depth + ,type = CV_MAKETYPE(depth, channels) +#endif + }; + + typedef Vec vec_type; +}; + +namespace traits { +template +struct Depth< Point3_<_Tp> > { enum { value = Depth<_Tp>::value }; }; +template +struct Type< Point3_<_Tp> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, 3) }; }; +} // namespace + +//////////////////////////////// Size_ //////////////////////////////// + +/** @brief Template class for specifying the size of an image or rectangle. + +The class includes two members called width and height. The structure can be converted to and from +the old OpenCV structures CvSize and CvSize2D32f . The same set of arithmetic and comparison +operations as for Point_ is available. + +OpenCV defines the following Size_\<\> aliases: +@code + typedef Size_ Size2i; + typedef Size2i Size; + typedef Size_ Size2f; +@endcode +*/ +template class Size_ +{ +public: + typedef _Tp value_type; + + //! default constructor + Size_(); + Size_(_Tp _width, _Tp _height); + Size_(const Size_& sz); + Size_(const Point_<_Tp>& pt); + + Size_& operator = (const Size_& sz); + //! the area (width*height) + _Tp area() const; + //! true if empty + bool empty() const; + + //! conversion of another data type. + template operator Size_<_Tp2>() const; + + _Tp width; //!< the width + _Tp height; //!< the height +}; + +typedef Size_ Size2i; +typedef Size_ Size2l; +typedef Size_ Size2f; +typedef Size_ Size2d; +typedef Size2i Size; + +template class DataType< Size_<_Tp> > +{ +public: + typedef Size_<_Tp> value_type; + typedef Size_::work_type> work_type; + typedef _Tp channel_type; + + enum { generic_type = 0, + channels = 2, + fmt = DataType::fmt + ((channels - 1) << 8) +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + ,depth = DataType::depth + ,type = CV_MAKETYPE(depth, channels) +#endif + }; + + typedef Vec vec_type; +}; + +namespace traits { +template +struct Depth< Size_<_Tp> > { enum { value = Depth<_Tp>::value }; }; +template +struct Type< Size_<_Tp> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, 2) }; }; +} // namespace + +//////////////////////////////// Rect_ //////////////////////////////// + +/** @brief Template class for 2D rectangles + +described by the following parameters: +- Coordinates of the top-left corner. This is a default interpretation of Rect_::x and Rect_::y + in OpenCV. Though, in your algorithms you may count x and y from the bottom-left corner. +- Rectangle width and height. + +OpenCV typically assumes that the top and left boundary of the rectangle are inclusive, while the +right and bottom boundaries are not. For example, the method Rect_::contains returns true if + +\f[x \leq pt.x < x+width, + y \leq pt.y < y+height\f] + +Virtually every loop over an image ROI in OpenCV (where ROI is specified by Rect_\ ) is +implemented as: +@code + for(int y = roi.y; y < roi.y + roi.height; y++) + for(int x = roi.x; x < roi.x + roi.width; x++) + { + // ... + } +@endcode +In addition to the class members, the following operations on rectangles are implemented: +- \f$\texttt{rect} = \texttt{rect} \pm \texttt{point}\f$ (shifting a rectangle by a certain offset) +- \f$\texttt{rect} = \texttt{rect} \pm \texttt{size}\f$ (expanding or shrinking a rectangle by a + certain amount) +- rect += point, rect -= point, rect += size, rect -= size (augmenting operations) +- rect = rect1 & rect2 (rectangle intersection) +- rect = rect1 | rect2 (minimum area rectangle containing rect1 and rect2 ) +- rect &= rect1, rect |= rect1 (and the corresponding augmenting operations) +- rect == rect1, rect != rect1 (rectangle comparison) + +This is an example how the partial ordering on rectangles can be established (rect1 \f$\subseteq\f$ +rect2): +@code + template inline bool + operator <= (const Rect_<_Tp>& r1, const Rect_<_Tp>& r2) + { + return (r1 & r2) == r1; + } +@endcode +For your convenience, the Rect_\<\> alias is available: cv::Rect +*/ +template class Rect_ +{ +public: + typedef _Tp value_type; + + //! default constructor + Rect_(); + Rect_(_Tp _x, _Tp _y, _Tp _width, _Tp _height); + Rect_(const Rect_& r); + Rect_(const Point_<_Tp>& org, const Size_<_Tp>& sz); + Rect_(const Point_<_Tp>& pt1, const Point_<_Tp>& pt2); + + Rect_& operator = ( const Rect_& r ); + //! the top-left corner + Point_<_Tp> tl() const; + //! the bottom-right corner + Point_<_Tp> br() const; + + //! size (width, height) of the rectangle + Size_<_Tp> size() const; + //! area (width*height) of the rectangle + _Tp area() const; + //! true if empty + bool empty() const; + + //! conversion to another data type + template operator Rect_<_Tp2>() const; + + //! checks whether the rectangle contains the point + bool contains(const Point_<_Tp>& pt) const; + + _Tp x; //!< x coordinate of the top-left corner + _Tp y; //!< y coordinate of the top-left corner + _Tp width; //!< width of the rectangle + _Tp height; //!< height of the rectangle +}; + +typedef Rect_ Rect2i; +typedef Rect_ Rect2f; +typedef Rect_ Rect2d; +typedef Rect2i Rect; + +template class DataType< Rect_<_Tp> > +{ +public: + typedef Rect_<_Tp> value_type; + typedef Rect_::work_type> work_type; + typedef _Tp channel_type; + + enum { generic_type = 0, + channels = 4, + fmt = traits::SafeFmt::fmt + ((channels - 1) << 8) +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + ,depth = DataType::depth + ,type = CV_MAKETYPE(depth, channels) +#endif + }; + + typedef Vec vec_type; +}; + +namespace traits { +template +struct Depth< Rect_<_Tp> > { enum { value = Depth<_Tp>::value }; }; +template +struct Type< Rect_<_Tp> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, 4) }; }; +} // namespace + +///////////////////////////// RotatedRect ///////////////////////////// + +/** @brief The class represents rotated (i.e. not up-right) rectangles on a plane. + +Each rectangle is specified by the center point (mass center), length of each side (represented by +#Size2f structure) and the rotation angle in degrees. + +The sample below demonstrates how to use RotatedRect: +@snippet snippets/core_various.cpp RotatedRect_demo +![image](pics/rotatedrect.png) + +@sa CamShift, fitEllipse, minAreaRect, CvBox2D +*/ +class CV_EXPORTS RotatedRect +{ +public: + //! default constructor + RotatedRect(); + /** full constructor + @param center The rectangle mass center. + @param size Width and height of the rectangle. + @param angle The rotation angle in a clockwise direction. When the angle is 0, 90, 180, 270 etc., + the rectangle becomes an up-right rectangle. + */ + RotatedRect(const Point2f& center, const Size2f& size, float angle); + /** + Any 3 end points of the RotatedRect. They must be given in order (either clockwise or + anticlockwise). + */ + RotatedRect(const Point2f& point1, const Point2f& point2, const Point2f& point3); + + /** returns 4 vertices of the rectangle + @param pts The points array for storing rectangle vertices. The order is bottomLeft, topLeft, topRight, bottomRight. + */ + void points(Point2f pts[]) const; + //! returns the minimal up-right integer rectangle containing the rotated rectangle + Rect boundingRect() const; + //! returns the minimal (exact) floating point rectangle containing the rotated rectangle, not intended for use with images + Rect_ boundingRect2f() const; + //! returns the rectangle mass center + Point2f center; + //! returns width and height of the rectangle + Size2f size; + //! returns the rotation angle. When the angle is 0, 90, 180, 270 etc., the rectangle becomes an up-right rectangle. + float angle; +}; + +template<> class DataType< RotatedRect > +{ +public: + typedef RotatedRect value_type; + typedef value_type work_type; + typedef float channel_type; + + enum { generic_type = 0, + channels = (int)sizeof(value_type)/sizeof(channel_type), // 5 + fmt = traits::SafeFmt::fmt + ((channels - 1) << 8) +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + ,depth = DataType::depth + ,type = CV_MAKETYPE(depth, channels) +#endif + }; + + typedef Vec vec_type; +}; + +namespace traits { +template<> +struct Depth< RotatedRect > { enum { value = Depth::value }; }; +template<> +struct Type< RotatedRect > { enum { value = CV_MAKETYPE(Depth::value, (int)sizeof(RotatedRect)/sizeof(float)) }; }; +} // namespace + + +//////////////////////////////// Range ///////////////////////////////// + +/** @brief Template class specifying a continuous subsequence (slice) of a sequence. + +The class is used to specify a row or a column span in a matrix ( Mat ) and for many other purposes. +Range(a,b) is basically the same as a:b in Matlab or a..b in Python. As in Python, start is an +inclusive left boundary of the range and end is an exclusive right boundary of the range. Such a +half-opened interval is usually denoted as \f$[start,end)\f$ . + +The static method Range::all() returns a special variable that means "the whole sequence" or "the +whole range", just like " : " in Matlab or " ... " in Python. All the methods and functions in +OpenCV that take Range support this special Range::all() value. But, of course, in case of your own +custom processing, you will probably have to check and handle it explicitly: +@code + void my_function(..., const Range& r, ....) + { + if(r == Range::all()) { + // process all the data + } + else { + // process [r.start, r.end) + } + } +@endcode +*/ +class CV_EXPORTS Range +{ +public: + Range(); + Range(int _start, int _end); + int size() const; + bool empty() const; + static Range all(); + + int start, end; +}; + +template<> class DataType +{ +public: + typedef Range value_type; + typedef value_type work_type; + typedef int channel_type; + + enum { generic_type = 0, + channels = 2, + fmt = traits::SafeFmt::fmt + ((channels - 1) << 8) +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + ,depth = DataType::depth + ,type = CV_MAKETYPE(depth, channels) +#endif + }; + + typedef Vec vec_type; +}; + +namespace traits { +template<> +struct Depth< Range > { enum { value = Depth::value }; }; +template<> +struct Type< Range > { enum { value = CV_MAKETYPE(Depth::value, 2) }; }; +} // namespace + + +//////////////////////////////// Scalar_ /////////////////////////////// + +/** @brief Template class for a 4-element vector derived from Vec. + +Being derived from Vec\<_Tp, 4\> , Scalar\_ and Scalar can be used just as typical 4-element +vectors. In addition, they can be converted to/from CvScalar . The type Scalar is widely used in +OpenCV to pass pixel values. +*/ +template class Scalar_ : public Vec<_Tp, 4> +{ +public: + //! default constructor + Scalar_(); + Scalar_(_Tp v0, _Tp v1, _Tp v2=0, _Tp v3=0); + Scalar_(_Tp v0); + + template + Scalar_(const Vec<_Tp2, cn>& v); + + //! returns a scalar with all elements set to v0 + static Scalar_<_Tp> all(_Tp v0); + + //! conversion to another data type + template operator Scalar_() const; + + //! per-element product + Scalar_<_Tp> mul(const Scalar_<_Tp>& a, double scale=1 ) const; + + //! returns (v0, -v1, -v2, -v3) + Scalar_<_Tp> conj() const; + + //! returns true iff v1 == v2 == v3 == 0 + bool isReal() const; +}; + +typedef Scalar_ Scalar; + +template class DataType< Scalar_<_Tp> > +{ +public: + typedef Scalar_<_Tp> value_type; + typedef Scalar_::work_type> work_type; + typedef _Tp channel_type; + + enum { generic_type = 0, + channels = 4, + fmt = traits::SafeFmt::fmt + ((channels - 1) << 8) +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + ,depth = DataType::depth + ,type = CV_MAKETYPE(depth, channels) +#endif + }; + + typedef Vec vec_type; +}; + +namespace traits { +template +struct Depth< Scalar_<_Tp> > { enum { value = Depth<_Tp>::value }; }; +template +struct Type< Scalar_<_Tp> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, 4) }; }; +} // namespace + + +/////////////////////////////// KeyPoint //////////////////////////////// + +/** @brief Data structure for salient point detectors. + +The class instance stores a keypoint, i.e. a point feature found by one of many available keypoint +detectors, such as Harris corner detector, #FAST, %StarDetector, %SURF, %SIFT etc. + +The keypoint is characterized by the 2D position, scale (proportional to the diameter of the +neighborhood that needs to be taken into account), orientation and some other parameters. The +keypoint neighborhood is then analyzed by another algorithm that builds a descriptor (usually +represented as a feature vector). The keypoints representing the same object in different images +can then be matched using %KDTree or another method. +*/ +class CV_EXPORTS_W_SIMPLE KeyPoint +{ +public: + //! the default constructor + CV_WRAP KeyPoint(); + /** + @param _pt x & y coordinates of the keypoint + @param _size keypoint diameter + @param _angle keypoint orientation + @param _response keypoint detector response on the keypoint (that is, strength of the keypoint) + @param _octave pyramid octave in which the keypoint has been detected + @param _class_id object id + */ + KeyPoint(Point2f _pt, float _size, float _angle=-1, float _response=0, int _octave=0, int _class_id=-1); + /** + @param x x-coordinate of the keypoint + @param y y-coordinate of the keypoint + @param _size keypoint diameter + @param _angle keypoint orientation + @param _response keypoint detector response on the keypoint (that is, strength of the keypoint) + @param _octave pyramid octave in which the keypoint has been detected + @param _class_id object id + */ + CV_WRAP KeyPoint(float x, float y, float _size, float _angle=-1, float _response=0, int _octave=0, int _class_id=-1); + + size_t hash() const; + + /** + This method converts vector of keypoints to vector of points or the reverse, where each keypoint is + assigned the same size and the same orientation. + + @param keypoints Keypoints obtained from any feature detection algorithm like SIFT/SURF/ORB + @param points2f Array of (x,y) coordinates of each keypoint + @param keypointIndexes Array of indexes of keypoints to be converted to points. (Acts like a mask to + convert only specified keypoints) + */ + CV_WRAP static void convert(const std::vector& keypoints, + CV_OUT std::vector& points2f, + const std::vector& keypointIndexes=std::vector()); + /** @overload + @param points2f Array of (x,y) coordinates of each keypoint + @param keypoints Keypoints obtained from any feature detection algorithm like SIFT/SURF/ORB + @param size keypoint diameter + @param response keypoint detector response on the keypoint (that is, strength of the keypoint) + @param octave pyramid octave in which the keypoint has been detected + @param class_id object id + */ + CV_WRAP static void convert(const std::vector& points2f, + CV_OUT std::vector& keypoints, + float size=1, float response=1, int octave=0, int class_id=-1); + + /** + This method computes overlap for pair of keypoints. Overlap is the ratio between area of keypoint + regions' intersection and area of keypoint regions' union (considering keypoint region as circle). + If they don't overlap, we get zero. If they coincide at same location with same size, we get 1. + @param kp1 First keypoint + @param kp2 Second keypoint + */ + CV_WRAP static float overlap(const KeyPoint& kp1, const KeyPoint& kp2); + + CV_PROP_RW Point2f pt; //!< coordinates of the keypoints + CV_PROP_RW float size; //!< diameter of the meaningful keypoint neighborhood + CV_PROP_RW float angle; //!< computed orientation of the keypoint (-1 if not applicable); + //!< it's in [0,360) degrees and measured relative to + //!< image coordinate system, ie in clockwise. + CV_PROP_RW float response; //!< the response by which the most strong keypoints have been selected. Can be used for the further sorting or subsampling + CV_PROP_RW int octave; //!< octave (pyramid layer) from which the keypoint has been extracted + CV_PROP_RW int class_id; //!< object class (if the keypoints need to be clustered by an object they belong to) +}; + +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED +template<> class DataType +{ +public: + typedef KeyPoint value_type; + typedef float work_type; + typedef float channel_type; + + enum { generic_type = 0, + depth = DataType::depth, + channels = (int)(sizeof(value_type)/sizeof(channel_type)), // 7 + fmt = DataType::fmt + ((channels - 1) << 8), + type = CV_MAKETYPE(depth, channels) + }; + + typedef Vec vec_type; +}; +#endif + + +//////////////////////////////// DMatch ///////////////////////////////// + +/** @brief Class for matching keypoint descriptors + +query descriptor index, train descriptor index, train image index, and distance between +descriptors. +*/ +class CV_EXPORTS_W_SIMPLE DMatch +{ +public: + CV_WRAP DMatch(); + CV_WRAP DMatch(int _queryIdx, int _trainIdx, float _distance); + CV_WRAP DMatch(int _queryIdx, int _trainIdx, int _imgIdx, float _distance); + + CV_PROP_RW int queryIdx; //!< query descriptor index + CV_PROP_RW int trainIdx; //!< train descriptor index + CV_PROP_RW int imgIdx; //!< train image index + + CV_PROP_RW float distance; + + // less is better + bool operator<(const DMatch &m) const; +}; + +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED +template<> class DataType +{ +public: + typedef DMatch value_type; + typedef int work_type; + typedef int channel_type; + + enum { generic_type = 0, + depth = DataType::depth, + channels = (int)(sizeof(value_type)/sizeof(channel_type)), // 4 + fmt = DataType::fmt + ((channels - 1) << 8), + type = CV_MAKETYPE(depth, channels) + }; + + typedef Vec vec_type; +}; +#endif + + +///////////////////////////// TermCriteria ////////////////////////////// + +/** @brief The class defining termination criteria for iterative algorithms. + +You can initialize it by default constructor and then override any parameters, or the structure may +be fully initialized using the advanced variant of the constructor. +*/ +class CV_EXPORTS TermCriteria +{ +public: + /** + Criteria type, can be one of: COUNT, EPS or COUNT + EPS + */ + enum Type + { + COUNT=1, //!< the maximum number of iterations or elements to compute + MAX_ITER=COUNT, //!< ditto + EPS=2 //!< the desired accuracy or change in parameters at which the iterative algorithm stops + }; + + //! default constructor + TermCriteria(); + /** + @param type The type of termination criteria, one of TermCriteria::Type + @param maxCount The maximum number of iterations or elements to compute. + @param epsilon The desired accuracy or change in parameters at which the iterative algorithm stops. + */ + TermCriteria(int type, int maxCount, double epsilon); + + inline bool isValid() const + { + const bool isCount = (type & COUNT) && maxCount > 0; + const bool isEps = (type & EPS) && !cvIsNaN(epsilon); + return isCount || isEps; + } + + int type; //!< the type of termination criteria: COUNT, EPS or COUNT + EPS + int maxCount; //!< the maximum number of iterations/elements + double epsilon; //!< the desired accuracy +}; + + +//! @} core_basic + +///////////////////////// raster image moments ////////////////////////// + +//! @addtogroup imgproc_shape +//! @{ + +/** @brief struct returned by cv::moments + +The spatial moments \f$\texttt{Moments::m}_{ji}\f$ are computed as: + +\f[\texttt{m} _{ji}= \sum _{x,y} \left ( \texttt{array} (x,y) \cdot x^j \cdot y^i \right )\f] + +The central moments \f$\texttt{Moments::mu}_{ji}\f$ are computed as: + +\f[\texttt{mu} _{ji}= \sum _{x,y} \left ( \texttt{array} (x,y) \cdot (x - \bar{x} )^j \cdot (y - \bar{y} )^i \right )\f] + +where \f$(\bar{x}, \bar{y})\f$ is the mass center: + +\f[\bar{x} = \frac{\texttt{m}_{10}}{\texttt{m}_{00}} , \; \bar{y} = \frac{\texttt{m}_{01}}{\texttt{m}_{00}}\f] + +The normalized central moments \f$\texttt{Moments::nu}_{ij}\f$ are computed as: + +\f[\texttt{nu} _{ji}= \frac{\texttt{mu}_{ji}}{\texttt{m}_{00}^{(i+j)/2+1}} .\f] + +@note +\f$\texttt{mu}_{00}=\texttt{m}_{00}\f$, \f$\texttt{nu}_{00}=1\f$ +\f$\texttt{nu}_{10}=\texttt{mu}_{10}=\texttt{mu}_{01}=\texttt{mu}_{10}=0\f$ , hence the values are not +stored. + +The moments of a contour are defined in the same way but computed using the Green's formula (see +). So, due to a limited raster resolution, the moments +computed for a contour are slightly different from the moments computed for the same rasterized +contour. + +@note +Since the contour moments are computed using Green formula, you may get seemingly odd results for +contours with self-intersections, e.g. a zero area (m00) for butterfly-shaped contours. + */ +class CV_EXPORTS_W_MAP Moments +{ +public: + //! the default constructor + Moments(); + //! the full constructor + Moments(double m00, double m10, double m01, double m20, double m11, + double m02, double m30, double m21, double m12, double m03 ); + ////! the conversion from CvMoments + //Moments( const CvMoments& moments ); + ////! the conversion to CvMoments + //operator CvMoments() const; + + //! @name spatial moments + //! @{ + CV_PROP_RW double m00, m10, m01, m20, m11, m02, m30, m21, m12, m03; + //! @} + + //! @name central moments + //! @{ + CV_PROP_RW double mu20, mu11, mu02, mu30, mu21, mu12, mu03; + //! @} + + //! @name central normalized moments + //! @{ + CV_PROP_RW double nu20, nu11, nu02, nu30, nu21, nu12, nu03; + //! @} +}; + +template<> class DataType +{ +public: + typedef Moments value_type; + typedef double work_type; + typedef double channel_type; + + enum { generic_type = 0, + channels = (int)(sizeof(value_type)/sizeof(channel_type)), // 24 + fmt = DataType::fmt + ((channels - 1) << 8) +#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED + ,depth = DataType::depth + ,type = CV_MAKETYPE(depth, channels) +#endif + }; + + typedef Vec vec_type; +}; + +namespace traits { +template<> +struct Depth< Moments > { enum { value = Depth::value }; }; +template<> +struct Type< Moments > { enum { value = CV_MAKETYPE(Depth::value, (int)(sizeof(Moments)/sizeof(double))) }; }; +} // namespace + +//! @} imgproc_shape + +//! @cond IGNORED + +///////////////////////////////////////////////////////////////////////// +///////////////////////////// Implementation //////////////////////////// +///////////////////////////////////////////////////////////////////////// + +//////////////////////////////// Complex //////////////////////////////// + +template inline +Complex<_Tp>::Complex() + : re(0), im(0) {} + +template inline +Complex<_Tp>::Complex( _Tp _re, _Tp _im ) + : re(_re), im(_im) {} + +template template inline +Complex<_Tp>::operator Complex() const +{ + return Complex(saturate_cast(re), saturate_cast(im)); +} + +template inline +Complex<_Tp> Complex<_Tp>::conj() const +{ + return Complex<_Tp>(re, -im); +} + + +template static inline +bool operator == (const Complex<_Tp>& a, const Complex<_Tp>& b) +{ + return a.re == b.re && a.im == b.im; +} + +template static inline +bool operator != (const Complex<_Tp>& a, const Complex<_Tp>& b) +{ + return a.re != b.re || a.im != b.im; +} + +template static inline +Complex<_Tp> operator + (const Complex<_Tp>& a, const Complex<_Tp>& b) +{ + return Complex<_Tp>( a.re + b.re, a.im + b.im ); +} + +template static inline +Complex<_Tp>& operator += (Complex<_Tp>& a, const Complex<_Tp>& b) +{ + a.re += b.re; a.im += b.im; + return a; +} + +template static inline +Complex<_Tp> operator - (const Complex<_Tp>& a, const Complex<_Tp>& b) +{ + return Complex<_Tp>( a.re - b.re, a.im - b.im ); +} + +template static inline +Complex<_Tp>& operator -= (Complex<_Tp>& a, const Complex<_Tp>& b) +{ + a.re -= b.re; a.im -= b.im; + return a; +} + +template static inline +Complex<_Tp> operator - (const Complex<_Tp>& a) +{ + return Complex<_Tp>(-a.re, -a.im); +} + +template static inline +Complex<_Tp> operator * (const Complex<_Tp>& a, const Complex<_Tp>& b) +{ + return Complex<_Tp>( a.re*b.re - a.im*b.im, a.re*b.im + a.im*b.re ); +} + +template static inline +Complex<_Tp> operator * (const Complex<_Tp>& a, _Tp b) +{ + return Complex<_Tp>( a.re*b, a.im*b ); +} + +template static inline +Complex<_Tp> operator * (_Tp b, const Complex<_Tp>& a) +{ + return Complex<_Tp>( a.re*b, a.im*b ); +} + +template static inline +Complex<_Tp> operator + (const Complex<_Tp>& a, _Tp b) +{ + return Complex<_Tp>( a.re + b, a.im ); +} + +template static inline +Complex<_Tp> operator - (const Complex<_Tp>& a, _Tp b) +{ return Complex<_Tp>( a.re - b, a.im ); } + +template static inline +Complex<_Tp> operator + (_Tp b, const Complex<_Tp>& a) +{ + return Complex<_Tp>( a.re + b, a.im ); +} + +template static inline +Complex<_Tp> operator - (_Tp b, const Complex<_Tp>& a) +{ + return Complex<_Tp>( b - a.re, -a.im ); +} + +template static inline +Complex<_Tp>& operator += (Complex<_Tp>& a, _Tp b) +{ + a.re += b; return a; +} + +template static inline +Complex<_Tp>& operator -= (Complex<_Tp>& a, _Tp b) +{ + a.re -= b; return a; +} + +template static inline +Complex<_Tp>& operator *= (Complex<_Tp>& a, _Tp b) +{ + a.re *= b; a.im *= b; return a; +} + +template static inline +double abs(const Complex<_Tp>& a) +{ + return std::sqrt( (double)a.re*a.re + (double)a.im*a.im); +} + +template static inline +Complex<_Tp> operator / (const Complex<_Tp>& a, const Complex<_Tp>& b) +{ + double t = 1./((double)b.re*b.re + (double)b.im*b.im); + return Complex<_Tp>( (_Tp)((a.re*b.re + a.im*b.im)*t), + (_Tp)((-a.re*b.im + a.im*b.re)*t) ); +} + +template static inline +Complex<_Tp>& operator /= (Complex<_Tp>& a, const Complex<_Tp>& b) +{ + a = a / b; + return a; +} + +template static inline +Complex<_Tp> operator / (const Complex<_Tp>& a, _Tp b) +{ + _Tp t = (_Tp)1/b; + return Complex<_Tp>( a.re*t, a.im*t ); +} + +template static inline +Complex<_Tp> operator / (_Tp b, const Complex<_Tp>& a) +{ + return Complex<_Tp>(b)/a; +} + +template static inline +Complex<_Tp> operator /= (const Complex<_Tp>& a, _Tp b) +{ + _Tp t = (_Tp)1/b; + a.re *= t; a.im *= t; return a; +} + + + +//////////////////////////////// 2D Point /////////////////////////////// + +template inline +Point_<_Tp>::Point_() + : x(0), y(0) {} + +template inline +Point_<_Tp>::Point_(_Tp _x, _Tp _y) + : x(_x), y(_y) {} + +template inline +Point_<_Tp>::Point_(const Point_& pt) + : x(pt.x), y(pt.y) {} + +template inline +Point_<_Tp>::Point_(const Size_<_Tp>& sz) + : x(sz.width), y(sz.height) {} + +template inline +Point_<_Tp>::Point_(const Vec<_Tp,2>& v) + : x(v[0]), y(v[1]) {} + +template inline +Point_<_Tp>& Point_<_Tp>::operator = (const Point_& pt) +{ + x = pt.x; y = pt.y; + return *this; +} + +template template inline +Point_<_Tp>::operator Point_<_Tp2>() const +{ + return Point_<_Tp2>(saturate_cast<_Tp2>(x), saturate_cast<_Tp2>(y)); +} + +template inline +Point_<_Tp>::operator Vec<_Tp, 2>() const +{ + return Vec<_Tp, 2>(x, y); +} + +template inline +_Tp Point_<_Tp>::dot(const Point_& pt) const +{ + return saturate_cast<_Tp>(x*pt.x + y*pt.y); +} + +template inline +double Point_<_Tp>::ddot(const Point_& pt) const +{ + return (double)x*pt.x + (double)y*pt.y; +} + +template inline +double Point_<_Tp>::cross(const Point_& pt) const +{ + return (double)x*pt.y - (double)y*pt.x; +} + +template inline bool +Point_<_Tp>::inside( const Rect_<_Tp>& r ) const +{ + return r.contains(*this); +} + + +template static inline +Point_<_Tp>& operator += (Point_<_Tp>& a, const Point_<_Tp>& b) +{ + a.x += b.x; + a.y += b.y; + return a; +} + +template static inline +Point_<_Tp>& operator -= (Point_<_Tp>& a, const Point_<_Tp>& b) +{ + a.x -= b.x; + a.y -= b.y; + return a; +} + +template static inline +Point_<_Tp>& operator *= (Point_<_Tp>& a, int b) +{ + a.x = saturate_cast<_Tp>(a.x * b); + a.y = saturate_cast<_Tp>(a.y * b); + return a; +} + +template static inline +Point_<_Tp>& operator *= (Point_<_Tp>& a, float b) +{ + a.x = saturate_cast<_Tp>(a.x * b); + a.y = saturate_cast<_Tp>(a.y * b); + return a; +} + +template static inline +Point_<_Tp>& operator *= (Point_<_Tp>& a, double b) +{ + a.x = saturate_cast<_Tp>(a.x * b); + a.y = saturate_cast<_Tp>(a.y * b); + return a; +} + +template static inline +Point_<_Tp>& operator /= (Point_<_Tp>& a, int b) +{ + a.x = saturate_cast<_Tp>(a.x / b); + a.y = saturate_cast<_Tp>(a.y / b); + return a; +} + +template static inline +Point_<_Tp>& operator /= (Point_<_Tp>& a, float b) +{ + a.x = saturate_cast<_Tp>(a.x / b); + a.y = saturate_cast<_Tp>(a.y / b); + return a; +} + +template static inline +Point_<_Tp>& operator /= (Point_<_Tp>& a, double b) +{ + a.x = saturate_cast<_Tp>(a.x / b); + a.y = saturate_cast<_Tp>(a.y / b); + return a; +} + +template static inline +double norm(const Point_<_Tp>& pt) +{ + return std::sqrt((double)pt.x*pt.x + (double)pt.y*pt.y); +} + +template static inline +bool operator == (const Point_<_Tp>& a, const Point_<_Tp>& b) +{ + return a.x == b.x && a.y == b.y; +} + +template static inline +bool operator != (const Point_<_Tp>& a, const Point_<_Tp>& b) +{ + return a.x != b.x || a.y != b.y; +} + +template static inline +Point_<_Tp> operator + (const Point_<_Tp>& a, const Point_<_Tp>& b) +{ + return Point_<_Tp>( saturate_cast<_Tp>(a.x + b.x), saturate_cast<_Tp>(a.y + b.y) ); +} + +template static inline +Point_<_Tp> operator - (const Point_<_Tp>& a, const Point_<_Tp>& b) +{ + return Point_<_Tp>( saturate_cast<_Tp>(a.x - b.x), saturate_cast<_Tp>(a.y - b.y) ); +} + +template static inline +Point_<_Tp> operator - (const Point_<_Tp>& a) +{ + return Point_<_Tp>( saturate_cast<_Tp>(-a.x), saturate_cast<_Tp>(-a.y) ); +} + +template static inline +Point_<_Tp> operator * (const Point_<_Tp>& a, int b) +{ + return Point_<_Tp>( saturate_cast<_Tp>(a.x*b), saturate_cast<_Tp>(a.y*b) ); +} + +template static inline +Point_<_Tp> operator * (int a, const Point_<_Tp>& b) +{ + return Point_<_Tp>( saturate_cast<_Tp>(b.x*a), saturate_cast<_Tp>(b.y*a) ); +} + +template static inline +Point_<_Tp> operator * (const Point_<_Tp>& a, float b) +{ + return Point_<_Tp>( saturate_cast<_Tp>(a.x*b), saturate_cast<_Tp>(a.y*b) ); +} + +template static inline +Point_<_Tp> operator * (float a, const Point_<_Tp>& b) +{ + return Point_<_Tp>( saturate_cast<_Tp>(b.x*a), saturate_cast<_Tp>(b.y*a) ); +} + +template static inline +Point_<_Tp> operator * (const Point_<_Tp>& a, double b) +{ + return Point_<_Tp>( saturate_cast<_Tp>(a.x*b), saturate_cast<_Tp>(a.y*b) ); +} + +template static inline +Point_<_Tp> operator * (double a, const Point_<_Tp>& b) +{ + return Point_<_Tp>( saturate_cast<_Tp>(b.x*a), saturate_cast<_Tp>(b.y*a) ); +} + +template static inline +Point_<_Tp> operator * (const Matx<_Tp, 2, 2>& a, const Point_<_Tp>& b) +{ + Matx<_Tp, 2, 1> tmp = a * Vec<_Tp,2>(b.x, b.y); + return Point_<_Tp>(tmp.val[0], tmp.val[1]); +} + +template static inline +Point3_<_Tp> operator * (const Matx<_Tp, 3, 3>& a, const Point_<_Tp>& b) +{ + Matx<_Tp, 3, 1> tmp = a * Vec<_Tp,3>(b.x, b.y, 1); + return Point3_<_Tp>(tmp.val[0], tmp.val[1], tmp.val[2]); +} + +template static inline +Point_<_Tp> operator / (const Point_<_Tp>& a, int b) +{ + Point_<_Tp> tmp(a); + tmp /= b; + return tmp; +} + +template static inline +Point_<_Tp> operator / (const Point_<_Tp>& a, float b) +{ + Point_<_Tp> tmp(a); + tmp /= b; + return tmp; +} + +template static inline +Point_<_Tp> operator / (const Point_<_Tp>& a, double b) +{ + Point_<_Tp> tmp(a); + tmp /= b; + return tmp; +} + + +template static inline _AccTp normL2Sqr(const Point_& pt); +template static inline _AccTp normL2Sqr(const Point_& pt); +template static inline _AccTp normL2Sqr(const Point_& pt); +template static inline _AccTp normL2Sqr(const Point_& pt); + +template<> inline int normL2Sqr(const Point_& pt) { return pt.dot(pt); } +template<> inline int64 normL2Sqr(const Point_& pt) { return pt.dot(pt); } +template<> inline float normL2Sqr(const Point_& pt) { return pt.dot(pt); } +template<> inline double normL2Sqr(const Point_& pt) { return pt.dot(pt); } + +template<> inline double normL2Sqr(const Point_& pt) { return pt.ddot(pt); } +template<> inline double normL2Sqr(const Point_& pt) { return pt.ddot(pt); } + + + +//////////////////////////////// 3D Point /////////////////////////////// + +template inline +Point3_<_Tp>::Point3_() + : x(0), y(0), z(0) {} + +template inline +Point3_<_Tp>::Point3_(_Tp _x, _Tp _y, _Tp _z) + : x(_x), y(_y), z(_z) {} + +template inline +Point3_<_Tp>::Point3_(const Point3_& pt) + : x(pt.x), y(pt.y), z(pt.z) {} + +template inline +Point3_<_Tp>::Point3_(const Point_<_Tp>& pt) + : x(pt.x), y(pt.y), z(_Tp()) {} + +template inline +Point3_<_Tp>::Point3_(const Vec<_Tp, 3>& v) + : x(v[0]), y(v[1]), z(v[2]) {} + +template template inline +Point3_<_Tp>::operator Point3_<_Tp2>() const +{ + return Point3_<_Tp2>(saturate_cast<_Tp2>(x), saturate_cast<_Tp2>(y), saturate_cast<_Tp2>(z)); +} + +#if OPENCV_ABI_COMPATIBILITY > 300 +template template inline +Point3_<_Tp>::operator Vec<_Tp2, 3>() const +{ + return Vec<_Tp2, 3>(x, y, z); +} +#else +template inline +Point3_<_Tp>::operator Vec<_Tp, 3>() const +{ + return Vec<_Tp, 3>(x, y, z); +} +#endif + +template inline +Point3_<_Tp>& Point3_<_Tp>::operator = (const Point3_& pt) +{ + x = pt.x; y = pt.y; z = pt.z; + return *this; +} + +template inline +_Tp Point3_<_Tp>::dot(const Point3_& pt) const +{ + return saturate_cast<_Tp>(x*pt.x + y*pt.y + z*pt.z); +} + +template inline +double Point3_<_Tp>::ddot(const Point3_& pt) const +{ + return (double)x*pt.x + (double)y*pt.y + (double)z*pt.z; +} + +template inline +Point3_<_Tp> Point3_<_Tp>::cross(const Point3_<_Tp>& pt) const +{ + return Point3_<_Tp>(y*pt.z - z*pt.y, z*pt.x - x*pt.z, x*pt.y - y*pt.x); +} + + +template static inline +Point3_<_Tp>& operator += (Point3_<_Tp>& a, const Point3_<_Tp>& b) +{ + a.x += b.x; + a.y += b.y; + a.z += b.z; + return a; +} + +template static inline +Point3_<_Tp>& operator -= (Point3_<_Tp>& a, const Point3_<_Tp>& b) +{ + a.x -= b.x; + a.y -= b.y; + a.z -= b.z; + return a; +} + +template static inline +Point3_<_Tp>& operator *= (Point3_<_Tp>& a, int b) +{ + a.x = saturate_cast<_Tp>(a.x * b); + a.y = saturate_cast<_Tp>(a.y * b); + a.z = saturate_cast<_Tp>(a.z * b); + return a; +} + +template static inline +Point3_<_Tp>& operator *= (Point3_<_Tp>& a, float b) +{ + a.x = saturate_cast<_Tp>(a.x * b); + a.y = saturate_cast<_Tp>(a.y * b); + a.z = saturate_cast<_Tp>(a.z * b); + return a; +} + +template static inline +Point3_<_Tp>& operator *= (Point3_<_Tp>& a, double b) +{ + a.x = saturate_cast<_Tp>(a.x * b); + a.y = saturate_cast<_Tp>(a.y * b); + a.z = saturate_cast<_Tp>(a.z * b); + return a; +} + +template static inline +Point3_<_Tp>& operator /= (Point3_<_Tp>& a, int b) +{ + a.x = saturate_cast<_Tp>(a.x / b); + a.y = saturate_cast<_Tp>(a.y / b); + a.z = saturate_cast<_Tp>(a.z / b); + return a; +} + +template static inline +Point3_<_Tp>& operator /= (Point3_<_Tp>& a, float b) +{ + a.x = saturate_cast<_Tp>(a.x / b); + a.y = saturate_cast<_Tp>(a.y / b); + a.z = saturate_cast<_Tp>(a.z / b); + return a; +} + +template static inline +Point3_<_Tp>& operator /= (Point3_<_Tp>& a, double b) +{ + a.x = saturate_cast<_Tp>(a.x / b); + a.y = saturate_cast<_Tp>(a.y / b); + a.z = saturate_cast<_Tp>(a.z / b); + return a; +} + +template static inline +double norm(const Point3_<_Tp>& pt) +{ + return std::sqrt((double)pt.x*pt.x + (double)pt.y*pt.y + (double)pt.z*pt.z); +} + +template static inline +bool operator == (const Point3_<_Tp>& a, const Point3_<_Tp>& b) +{ + return a.x == b.x && a.y == b.y && a.z == b.z; +} + +template static inline +bool operator != (const Point3_<_Tp>& a, const Point3_<_Tp>& b) +{ + return a.x != b.x || a.y != b.y || a.z != b.z; +} + +template static inline +Point3_<_Tp> operator + (const Point3_<_Tp>& a, const Point3_<_Tp>& b) +{ + return Point3_<_Tp>( saturate_cast<_Tp>(a.x + b.x), saturate_cast<_Tp>(a.y + b.y), saturate_cast<_Tp>(a.z + b.z)); +} + +template static inline +Point3_<_Tp> operator - (const Point3_<_Tp>& a, const Point3_<_Tp>& b) +{ + return Point3_<_Tp>( saturate_cast<_Tp>(a.x - b.x), saturate_cast<_Tp>(a.y - b.y), saturate_cast<_Tp>(a.z - b.z)); +} + +template static inline +Point3_<_Tp> operator - (const Point3_<_Tp>& a) +{ + return Point3_<_Tp>( saturate_cast<_Tp>(-a.x), saturate_cast<_Tp>(-a.y), saturate_cast<_Tp>(-a.z) ); +} + +template static inline +Point3_<_Tp> operator * (const Point3_<_Tp>& a, int b) +{ + return Point3_<_Tp>( saturate_cast<_Tp>(a.x*b), saturate_cast<_Tp>(a.y*b), saturate_cast<_Tp>(a.z*b) ); +} + +template static inline +Point3_<_Tp> operator * (int a, const Point3_<_Tp>& b) +{ + return Point3_<_Tp>( saturate_cast<_Tp>(b.x * a), saturate_cast<_Tp>(b.y * a), saturate_cast<_Tp>(b.z * a) ); +} + +template static inline +Point3_<_Tp> operator * (const Point3_<_Tp>& a, float b) +{ + return Point3_<_Tp>( saturate_cast<_Tp>(a.x * b), saturate_cast<_Tp>(a.y * b), saturate_cast<_Tp>(a.z * b) ); +} + +template static inline +Point3_<_Tp> operator * (float a, const Point3_<_Tp>& b) +{ + return Point3_<_Tp>( saturate_cast<_Tp>(b.x * a), saturate_cast<_Tp>(b.y * a), saturate_cast<_Tp>(b.z * a) ); +} + +template static inline +Point3_<_Tp> operator * (const Point3_<_Tp>& a, double b) +{ + return Point3_<_Tp>( saturate_cast<_Tp>(a.x * b), saturate_cast<_Tp>(a.y * b), saturate_cast<_Tp>(a.z * b) ); +} + +template static inline +Point3_<_Tp> operator * (double a, const Point3_<_Tp>& b) +{ + return Point3_<_Tp>( saturate_cast<_Tp>(b.x * a), saturate_cast<_Tp>(b.y * a), saturate_cast<_Tp>(b.z * a) ); +} + +template static inline +Point3_<_Tp> operator * (const Matx<_Tp, 3, 3>& a, const Point3_<_Tp>& b) +{ + Matx<_Tp, 3, 1> tmp = a * Vec<_Tp,3>(b.x, b.y, b.z); + return Point3_<_Tp>(tmp.val[0], tmp.val[1], tmp.val[2]); +} + +template static inline +Matx<_Tp, 4, 1> operator * (const Matx<_Tp, 4, 4>& a, const Point3_<_Tp>& b) +{ + return a * Matx<_Tp, 4, 1>(b.x, b.y, b.z, 1); +} + +template static inline +Point3_<_Tp> operator / (const Point3_<_Tp>& a, int b) +{ + Point3_<_Tp> tmp(a); + tmp /= b; + return tmp; +} + +template static inline +Point3_<_Tp> operator / (const Point3_<_Tp>& a, float b) +{ + Point3_<_Tp> tmp(a); + tmp /= b; + return tmp; +} + +template static inline +Point3_<_Tp> operator / (const Point3_<_Tp>& a, double b) +{ + Point3_<_Tp> tmp(a); + tmp /= b; + return tmp; +} + + + +////////////////////////////////// Size ///////////////////////////////// + +template inline +Size_<_Tp>::Size_() + : width(0), height(0) {} + +template inline +Size_<_Tp>::Size_(_Tp _width, _Tp _height) + : width(_width), height(_height) {} + +template inline +Size_<_Tp>::Size_(const Size_& sz) + : width(sz.width), height(sz.height) {} + +template inline +Size_<_Tp>::Size_(const Point_<_Tp>& pt) + : width(pt.x), height(pt.y) {} + +template template inline +Size_<_Tp>::operator Size_<_Tp2>() const +{ + return Size_<_Tp2>(saturate_cast<_Tp2>(width), saturate_cast<_Tp2>(height)); +} + +template inline +Size_<_Tp>& Size_<_Tp>::operator = (const Size_<_Tp>& sz) +{ + width = sz.width; height = sz.height; + return *this; +} + +template inline +_Tp Size_<_Tp>::area() const +{ + const _Tp result = width * height; + CV_DbgAssert(!std::numeric_limits<_Tp>::is_integer + || width == 0 || result / width == height); // make sure the result fits in the return value + return result; +} + +template inline +bool Size_<_Tp>::empty() const +{ + return width <= 0 || height <= 0; +} + + +template static inline +Size_<_Tp>& operator *= (Size_<_Tp>& a, _Tp b) +{ + a.width *= b; + a.height *= b; + return a; +} + +template static inline +Size_<_Tp> operator * (const Size_<_Tp>& a, _Tp b) +{ + Size_<_Tp> tmp(a); + tmp *= b; + return tmp; +} + +template static inline +Size_<_Tp>& operator /= (Size_<_Tp>& a, _Tp b) +{ + a.width /= b; + a.height /= b; + return a; +} + +template static inline +Size_<_Tp> operator / (const Size_<_Tp>& a, _Tp b) +{ + Size_<_Tp> tmp(a); + tmp /= b; + return tmp; +} + +template static inline +Size_<_Tp>& operator += (Size_<_Tp>& a, const Size_<_Tp>& b) +{ + a.width += b.width; + a.height += b.height; + return a; +} + +template static inline +Size_<_Tp> operator + (const Size_<_Tp>& a, const Size_<_Tp>& b) +{ + Size_<_Tp> tmp(a); + tmp += b; + return tmp; +} + +template static inline +Size_<_Tp>& operator -= (Size_<_Tp>& a, const Size_<_Tp>& b) +{ + a.width -= b.width; + a.height -= b.height; + return a; +} + +template static inline +Size_<_Tp> operator - (const Size_<_Tp>& a, const Size_<_Tp>& b) +{ + Size_<_Tp> tmp(a); + tmp -= b; + return tmp; +} + +template static inline +bool operator == (const Size_<_Tp>& a, const Size_<_Tp>& b) +{ + return a.width == b.width && a.height == b.height; +} + +template static inline +bool operator != (const Size_<_Tp>& a, const Size_<_Tp>& b) +{ + return !(a == b); +} + + + +////////////////////////////////// Rect ///////////////////////////////// + +template inline +Rect_<_Tp>::Rect_() + : x(0), y(0), width(0), height(0) {} + +template inline +Rect_<_Tp>::Rect_(_Tp _x, _Tp _y, _Tp _width, _Tp _height) + : x(_x), y(_y), width(_width), height(_height) {} + +template inline +Rect_<_Tp>::Rect_(const Rect_<_Tp>& r) + : x(r.x), y(r.y), width(r.width), height(r.height) {} + +template inline +Rect_<_Tp>::Rect_(const Point_<_Tp>& org, const Size_<_Tp>& sz) + : x(org.x), y(org.y), width(sz.width), height(sz.height) {} + +template inline +Rect_<_Tp>::Rect_(const Point_<_Tp>& pt1, const Point_<_Tp>& pt2) +{ + x = std::min(pt1.x, pt2.x); + y = std::min(pt1.y, pt2.y); + width = std::max(pt1.x, pt2.x) - x; + height = std::max(pt1.y, pt2.y) - y; +} + +template inline +Rect_<_Tp>& Rect_<_Tp>::operator = ( const Rect_<_Tp>& r ) +{ + x = r.x; + y = r.y; + width = r.width; + height = r.height; + return *this; +} + +template inline +Point_<_Tp> Rect_<_Tp>::tl() const +{ + return Point_<_Tp>(x,y); +} + +template inline +Point_<_Tp> Rect_<_Tp>::br() const +{ + return Point_<_Tp>(x + width, y + height); +} + +template inline +Size_<_Tp> Rect_<_Tp>::size() const +{ + return Size_<_Tp>(width, height); +} + +template inline +_Tp Rect_<_Tp>::area() const +{ + const _Tp result = width * height; + CV_DbgAssert(!std::numeric_limits<_Tp>::is_integer + || width == 0 || result / width == height); // make sure the result fits in the return value + return result; +} + +template inline +bool Rect_<_Tp>::empty() const +{ + return width <= 0 || height <= 0; +} + +template template inline +Rect_<_Tp>::operator Rect_<_Tp2>() const +{ + return Rect_<_Tp2>(saturate_cast<_Tp2>(x), saturate_cast<_Tp2>(y), saturate_cast<_Tp2>(width), saturate_cast<_Tp2>(height)); +} + +template inline +bool Rect_<_Tp>::contains(const Point_<_Tp>& pt) const +{ + return x <= pt.x && pt.x < x + width && y <= pt.y && pt.y < y + height; +} + + +template static inline +Rect_<_Tp>& operator += ( Rect_<_Tp>& a, const Point_<_Tp>& b ) +{ + a.x += b.x; + a.y += b.y; + return a; +} + +template static inline +Rect_<_Tp>& operator -= ( Rect_<_Tp>& a, const Point_<_Tp>& b ) +{ + a.x -= b.x; + a.y -= b.y; + return a; +} + +template static inline +Rect_<_Tp>& operator += ( Rect_<_Tp>& a, const Size_<_Tp>& b ) +{ + a.width += b.width; + a.height += b.height; + return a; +} + +template static inline +Rect_<_Tp>& operator -= ( Rect_<_Tp>& a, const Size_<_Tp>& b ) +{ + const _Tp width = a.width - b.width; + const _Tp height = a.height - b.height; + CV_DbgAssert(width >= 0 && height >= 0); + a.width = width; + a.height = height; + return a; +} + +template static inline +Rect_<_Tp>& operator &= ( Rect_<_Tp>& a, const Rect_<_Tp>& b ) +{ + _Tp x1 = std::max(a.x, b.x); + _Tp y1 = std::max(a.y, b.y); + a.width = std::min(a.x + a.width, b.x + b.width) - x1; + a.height = std::min(a.y + a.height, b.y + b.height) - y1; + a.x = x1; + a.y = y1; + if( a.width <= 0 || a.height <= 0 ) + a = Rect(); + return a; +} + +template static inline +Rect_<_Tp>& operator |= ( Rect_<_Tp>& a, const Rect_<_Tp>& b ) +{ + if (a.empty()) { + a = b; + } + else if (!b.empty()) { + _Tp x1 = std::min(a.x, b.x); + _Tp y1 = std::min(a.y, b.y); + a.width = std::max(a.x + a.width, b.x + b.width) - x1; + a.height = std::max(a.y + a.height, b.y + b.height) - y1; + a.x = x1; + a.y = y1; + } + return a; +} + +template static inline +bool operator == (const Rect_<_Tp>& a, const Rect_<_Tp>& b) +{ + return a.x == b.x && a.y == b.y && a.width == b.width && a.height == b.height; +} + +template static inline +bool operator != (const Rect_<_Tp>& a, const Rect_<_Tp>& b) +{ + return a.x != b.x || a.y != b.y || a.width != b.width || a.height != b.height; +} + +template static inline +Rect_<_Tp> operator + (const Rect_<_Tp>& a, const Point_<_Tp>& b) +{ + return Rect_<_Tp>( a.x + b.x, a.y + b.y, a.width, a.height ); +} + +template static inline +Rect_<_Tp> operator - (const Rect_<_Tp>& a, const Point_<_Tp>& b) +{ + return Rect_<_Tp>( a.x - b.x, a.y - b.y, a.width, a.height ); +} + +template static inline +Rect_<_Tp> operator + (const Rect_<_Tp>& a, const Size_<_Tp>& b) +{ + return Rect_<_Tp>( a.x, a.y, a.width + b.width, a.height + b.height ); +} + +template static inline +Rect_<_Tp> operator - (const Rect_<_Tp>& a, const Size_<_Tp>& b) +{ + const _Tp width = a.width - b.width; + const _Tp height = a.height - b.height; + CV_DbgAssert(width >= 0 && height >= 0); + return Rect_<_Tp>( a.x, a.y, width, height ); +} + +template static inline +Rect_<_Tp> operator & (const Rect_<_Tp>& a, const Rect_<_Tp>& b) +{ + Rect_<_Tp> c = a; + return c &= b; +} + +template static inline +Rect_<_Tp> operator | (const Rect_<_Tp>& a, const Rect_<_Tp>& b) +{ + Rect_<_Tp> c = a; + return c |= b; +} + +/** + * @brief measure dissimilarity between two sample sets + * + * computes the complement of the Jaccard Index as described in . + * For rectangles this reduces to computing the intersection over the union. + */ +template static inline +double jaccardDistance(const Rect_<_Tp>& a, const Rect_<_Tp>& b) { + _Tp Aa = a.area(); + _Tp Ab = b.area(); + + if ((Aa + Ab) <= std::numeric_limits<_Tp>::epsilon()) { + // jaccard_index = 1 -> distance = 0 + return 0.0; + } + + double Aab = (a & b).area(); + // distance = 1 - jaccard_index + return 1.0 - Aab / (Aa + Ab - Aab); +} + +////////////////////////////// RotatedRect ////////////////////////////// + +inline +RotatedRect::RotatedRect() + : center(), size(), angle(0) {} + +inline +RotatedRect::RotatedRect(const Point2f& _center, const Size2f& _size, float _angle) + : center(_center), size(_size), angle(_angle) {} + + + +///////////////////////////////// Range ///////////////////////////////// + +inline +Range::Range() + : start(0), end(0) {} + +inline +Range::Range(int _start, int _end) + : start(_start), end(_end) {} + +inline +int Range::size() const +{ + return end - start; +} + +inline +bool Range::empty() const +{ + return start == end; +} + +inline +Range Range::all() +{ + return Range(INT_MIN, INT_MAX); +} + + +static inline +bool operator == (const Range& r1, const Range& r2) +{ + return r1.start == r2.start && r1.end == r2.end; +} + +static inline +bool operator != (const Range& r1, const Range& r2) +{ + return !(r1 == r2); +} + +static inline +bool operator !(const Range& r) +{ + return r.start == r.end; +} + +static inline +Range operator & (const Range& r1, const Range& r2) +{ + Range r(std::max(r1.start, r2.start), std::min(r1.end, r2.end)); + r.end = std::max(r.end, r.start); + return r; +} + +static inline +Range& operator &= (Range& r1, const Range& r2) +{ + r1 = r1 & r2; + return r1; +} + +static inline +Range operator + (const Range& r1, int delta) +{ + return Range(r1.start + delta, r1.end + delta); +} + +static inline +Range operator + (int delta, const Range& r1) +{ + return Range(r1.start + delta, r1.end + delta); +} + +static inline +Range operator - (const Range& r1, int delta) +{ + return r1 + (-delta); +} + + + +///////////////////////////////// Scalar //////////////////////////////// + +template inline +Scalar_<_Tp>::Scalar_() +{ + this->val[0] = this->val[1] = this->val[2] = this->val[3] = 0; +} + +template inline +Scalar_<_Tp>::Scalar_(_Tp v0, _Tp v1, _Tp v2, _Tp v3) +{ + this->val[0] = v0; + this->val[1] = v1; + this->val[2] = v2; + this->val[3] = v3; +} + +template template inline +Scalar_<_Tp>::Scalar_(const Vec<_Tp2, cn>& v) +{ + int i; + for( i = 0; i < (cn < 4 ? cn : 4); i++ ) + this->val[i] = cv::saturate_cast<_Tp>(v.val[i]); + for( ; i < 4; i++ ) + this->val[i] = 0; +} + +template inline +Scalar_<_Tp>::Scalar_(_Tp v0) +{ + this->val[0] = v0; + this->val[1] = this->val[2] = this->val[3] = 0; +} + +template inline +Scalar_<_Tp> Scalar_<_Tp>::all(_Tp v0) +{ + return Scalar_<_Tp>(v0, v0, v0, v0); +} + + +template inline +Scalar_<_Tp> Scalar_<_Tp>::mul(const Scalar_<_Tp>& a, double scale ) const +{ + return Scalar_<_Tp>(saturate_cast<_Tp>(this->val[0] * a.val[0] * scale), + saturate_cast<_Tp>(this->val[1] * a.val[1] * scale), + saturate_cast<_Tp>(this->val[2] * a.val[2] * scale), + saturate_cast<_Tp>(this->val[3] * a.val[3] * scale)); +} + +template inline +Scalar_<_Tp> Scalar_<_Tp>::conj() const +{ + return Scalar_<_Tp>(saturate_cast<_Tp>( this->val[0]), + saturate_cast<_Tp>(-this->val[1]), + saturate_cast<_Tp>(-this->val[2]), + saturate_cast<_Tp>(-this->val[3])); +} + +template inline +bool Scalar_<_Tp>::isReal() const +{ + return this->val[1] == 0 && this->val[2] == 0 && this->val[3] == 0; +} + + +template template inline +Scalar_<_Tp>::operator Scalar_() const +{ + return Scalar_(saturate_cast(this->val[0]), + saturate_cast(this->val[1]), + saturate_cast(this->val[2]), + saturate_cast(this->val[3])); +} + + +template static inline +Scalar_<_Tp>& operator += (Scalar_<_Tp>& a, const Scalar_<_Tp>& b) +{ + a.val[0] += b.val[0]; + a.val[1] += b.val[1]; + a.val[2] += b.val[2]; + a.val[3] += b.val[3]; + return a; +} + +template static inline +Scalar_<_Tp>& operator -= (Scalar_<_Tp>& a, const Scalar_<_Tp>& b) +{ + a.val[0] -= b.val[0]; + a.val[1] -= b.val[1]; + a.val[2] -= b.val[2]; + a.val[3] -= b.val[3]; + return a; +} + +template static inline +Scalar_<_Tp>& operator *= ( Scalar_<_Tp>& a, _Tp v ) +{ + a.val[0] *= v; + a.val[1] *= v; + a.val[2] *= v; + a.val[3] *= v; + return a; +} + +template static inline +bool operator == ( const Scalar_<_Tp>& a, const Scalar_<_Tp>& b ) +{ + return a.val[0] == b.val[0] && a.val[1] == b.val[1] && + a.val[2] == b.val[2] && a.val[3] == b.val[3]; +} + +template static inline +bool operator != ( const Scalar_<_Tp>& a, const Scalar_<_Tp>& b ) +{ + return a.val[0] != b.val[0] || a.val[1] != b.val[1] || + a.val[2] != b.val[2] || a.val[3] != b.val[3]; +} + +template static inline +Scalar_<_Tp> operator + (const Scalar_<_Tp>& a, const Scalar_<_Tp>& b) +{ + return Scalar_<_Tp>(a.val[0] + b.val[0], + a.val[1] + b.val[1], + a.val[2] + b.val[2], + a.val[3] + b.val[3]); +} + +template static inline +Scalar_<_Tp> operator - (const Scalar_<_Tp>& a, const Scalar_<_Tp>& b) +{ + return Scalar_<_Tp>(saturate_cast<_Tp>(a.val[0] - b.val[0]), + saturate_cast<_Tp>(a.val[1] - b.val[1]), + saturate_cast<_Tp>(a.val[2] - b.val[2]), + saturate_cast<_Tp>(a.val[3] - b.val[3])); +} + +template static inline +Scalar_<_Tp> operator * (const Scalar_<_Tp>& a, _Tp alpha) +{ + return Scalar_<_Tp>(a.val[0] * alpha, + a.val[1] * alpha, + a.val[2] * alpha, + a.val[3] * alpha); +} + +template static inline +Scalar_<_Tp> operator * (_Tp alpha, const Scalar_<_Tp>& a) +{ + return a*alpha; +} + +template static inline +Scalar_<_Tp> operator - (const Scalar_<_Tp>& a) +{ + return Scalar_<_Tp>(saturate_cast<_Tp>(-a.val[0]), + saturate_cast<_Tp>(-a.val[1]), + saturate_cast<_Tp>(-a.val[2]), + saturate_cast<_Tp>(-a.val[3])); +} + + +template static inline +Scalar_<_Tp> operator * (const Scalar_<_Tp>& a, const Scalar_<_Tp>& b) +{ + return Scalar_<_Tp>(saturate_cast<_Tp>(a[0]*b[0] - a[1]*b[1] - a[2]*b[2] - a[3]*b[3]), + saturate_cast<_Tp>(a[0]*b[1] + a[1]*b[0] + a[2]*b[3] - a[3]*b[2]), + saturate_cast<_Tp>(a[0]*b[2] - a[1]*b[3] + a[2]*b[0] + a[3]*b[1]), + saturate_cast<_Tp>(a[0]*b[3] + a[1]*b[2] - a[2]*b[1] + a[3]*b[0])); +} + +template static inline +Scalar_<_Tp>& operator *= (Scalar_<_Tp>& a, const Scalar_<_Tp>& b) +{ + a = a * b; + return a; +} + +template static inline +Scalar_<_Tp> operator / (const Scalar_<_Tp>& a, _Tp alpha) +{ + return Scalar_<_Tp>(a.val[0] / alpha, + a.val[1] / alpha, + a.val[2] / alpha, + a.val[3] / alpha); +} + +template static inline +Scalar_ operator / (const Scalar_& a, float alpha) +{ + float s = 1 / alpha; + return Scalar_(a.val[0] * s, a.val[1] * s, a.val[2] * s, a.val[3] * s); +} + +template static inline +Scalar_ operator / (const Scalar_& a, double alpha) +{ + double s = 1 / alpha; + return Scalar_(a.val[0] * s, a.val[1] * s, a.val[2] * s, a.val[3] * s); +} + +template static inline +Scalar_<_Tp>& operator /= (Scalar_<_Tp>& a, _Tp alpha) +{ + a = a / alpha; + return a; +} + +template static inline +Scalar_<_Tp> operator / (_Tp a, const Scalar_<_Tp>& b) +{ + _Tp s = a / (b[0]*b[0] + b[1]*b[1] + b[2]*b[2] + b[3]*b[3]); + return b.conj() * s; +} + +template static inline +Scalar_<_Tp> operator / (const Scalar_<_Tp>& a, const Scalar_<_Tp>& b) +{ + return a * ((_Tp)1 / b); +} + +template static inline +Scalar_<_Tp>& operator /= (Scalar_<_Tp>& a, const Scalar_<_Tp>& b) +{ + a = a / b; + return a; +} + +template static inline +Scalar operator * (const Matx<_Tp, 4, 4>& a, const Scalar& b) +{ + Matx c((Matx)a, b, Matx_MatMulOp()); + return reinterpret_cast(c); +} + +template<> inline +Scalar operator * (const Matx& a, const Scalar& b) +{ + Matx c(a, b, Matx_MatMulOp()); + return reinterpret_cast(c); +} + + + +//////////////////////////////// KeyPoint /////////////////////////////// + +inline +KeyPoint::KeyPoint() + : pt(0,0), size(0), angle(-1), response(0), octave(0), class_id(-1) {} + +inline +KeyPoint::KeyPoint(Point2f _pt, float _size, float _angle, float _response, int _octave, int _class_id) + : pt(_pt), size(_size), angle(_angle), response(_response), octave(_octave), class_id(_class_id) {} + +inline +KeyPoint::KeyPoint(float x, float y, float _size, float _angle, float _response, int _octave, int _class_id) + : pt(x, y), size(_size), angle(_angle), response(_response), octave(_octave), class_id(_class_id) {} + + + +///////////////////////////////// DMatch //////////////////////////////// + +inline +DMatch::DMatch() + : queryIdx(-1), trainIdx(-1), imgIdx(-1), distance(FLT_MAX) {} + +inline +DMatch::DMatch(int _queryIdx, int _trainIdx, float _distance) + : queryIdx(_queryIdx), trainIdx(_trainIdx), imgIdx(-1), distance(_distance) {} + +inline +DMatch::DMatch(int _queryIdx, int _trainIdx, int _imgIdx, float _distance) + : queryIdx(_queryIdx), trainIdx(_trainIdx), imgIdx(_imgIdx), distance(_distance) {} + +inline +bool DMatch::operator < (const DMatch &m) const +{ + return distance < m.distance; +} + + + +////////////////////////////// TermCriteria ///////////////////////////// + +inline +TermCriteria::TermCriteria() + : type(0), maxCount(0), epsilon(0) {} + +inline +TermCriteria::TermCriteria(int _type, int _maxCount, double _epsilon) + : type(_type), maxCount(_maxCount), epsilon(_epsilon) {} + +//! @endcond + +} // cv + +#endif //OPENCV_CORE_TYPES_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/types_c.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/types_c.h new file mode 100644 index 0000000000000000000000000000000000000000..5f63eb8fb43aaed60b38b7ee3e7386c2b29bbd96 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/types_c.h @@ -0,0 +1,2139 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_TYPES_H +#define OPENCV_CORE_TYPES_H + +#if !defined(__OPENCV_BUILD) && !defined(CV__DISABLE_C_API_CTORS) +#define CV__ENABLE_C_API_CTORS // enable C API ctors (must be removed) +#endif + +//#define CV__VALIDATE_UNUNITIALIZED_VARS 1 // C++11 & GCC only + +#ifdef __cplusplus + +#ifdef CV__VALIDATE_UNUNITIALIZED_VARS +#pragma GCC diagnostic ignored "-Wmissing-field-initializers" +#define CV_STRUCT_INITIALIZER {0,} +#else +#if defined(__GNUC__) && __GNUC__ == 4 // GCC 4.x warns on "= {}" initialization, fixed in GCC 5.0 +#pragma GCC diagnostic ignored "-Wmissing-field-initializers" +#endif +#define CV_STRUCT_INITIALIZER {} +#endif + +#else +#define CV_STRUCT_INITIALIZER {0} +#endif + + +#ifdef HAVE_IPL +# ifndef __IPL_H__ +# if defined _WIN32 +# include +# else +# include +# endif +# endif +#elif defined __IPL_H__ +# define HAVE_IPL +#endif + +#include "opencv2/core/cvdef.h" + +#ifndef SKIP_INCLUDES +#include +#include +#include +#include +#endif // SKIP_INCLUDES + +#if defined _WIN32 +# define CV_CDECL __cdecl +# define CV_STDCALL __stdcall +#else +# define CV_CDECL +# define CV_STDCALL +#endif + +#ifndef CV_DEFAULT +# ifdef __cplusplus +# define CV_DEFAULT(val) = val +# else +# define CV_DEFAULT(val) +# endif +#endif + +#ifndef CV_EXTERN_C_FUNCPTR +# ifdef __cplusplus +# define CV_EXTERN_C_FUNCPTR(x) extern "C" { typedef x; } +# else +# define CV_EXTERN_C_FUNCPTR(x) typedef x +# endif +#endif + +#ifndef CVAPI +# define CVAPI(rettype) CV_EXTERN_C CV_EXPORTS rettype CV_CDECL +#endif + +#ifndef CV_IMPL +# define CV_IMPL CV_EXTERN_C +#endif + +#ifdef __cplusplus +# include "opencv2/core.hpp" +#endif + +/** @addtogroup core_c + @{ +*/ + +/** @brief This is the "metatype" used *only* as a function parameter. + +It denotes that the function accepts arrays of multiple types, such as IplImage*, CvMat* or even +CvSeq* sometimes. The particular array type is determined at runtime by analyzing the first 4 +bytes of the header. In C++ interface the role of CvArr is played by InputArray and OutputArray. + */ +typedef void CvArr; + +typedef int CVStatus; + +/** @see cv::Error::Code */ +enum { + CV_StsOk= 0, /**< everything is ok */ + CV_StsBackTrace= -1, /**< pseudo error for back trace */ + CV_StsError= -2, /**< unknown /unspecified error */ + CV_StsInternal= -3, /**< internal error (bad state) */ + CV_StsNoMem= -4, /**< insufficient memory */ + CV_StsBadArg= -5, /**< function arg/param is bad */ + CV_StsBadFunc= -6, /**< unsupported function */ + CV_StsNoConv= -7, /**< iter. didn't converge */ + CV_StsAutoTrace= -8, /**< tracing */ + CV_HeaderIsNull= -9, /**< image header is NULL */ + CV_BadImageSize= -10, /**< image size is invalid */ + CV_BadOffset= -11, /**< offset is invalid */ + CV_BadDataPtr= -12, /**/ + CV_BadStep= -13, /**< image step is wrong, this may happen for a non-continuous matrix */ + CV_BadModelOrChSeq= -14, /**/ + CV_BadNumChannels= -15, /**< bad number of channels, for example, some functions accept only single channel matrices */ + CV_BadNumChannel1U= -16, /**/ + CV_BadDepth= -17, /**< input image depth is not supported by the function */ + CV_BadAlphaChannel= -18, /**/ + CV_BadOrder= -19, /**< number of dimensions is out of range */ + CV_BadOrigin= -20, /**< incorrect input origin */ + CV_BadAlign= -21, /**< incorrect input align */ + CV_BadCallBack= -22, /**/ + CV_BadTileSize= -23, /**/ + CV_BadCOI= -24, /**< input COI is not supported */ + CV_BadROISize= -25, /**< incorrect input roi */ + CV_MaskIsTiled= -26, /**/ + CV_StsNullPtr= -27, /**< null pointer */ + CV_StsVecLengthErr= -28, /**< incorrect vector length */ + CV_StsFilterStructContentErr= -29, /**< incorrect filter structure content */ + CV_StsKernelStructContentErr= -30, /**< incorrect transform kernel content */ + CV_StsFilterOffsetErr= -31, /**< incorrect filter offset value */ + CV_StsBadSize= -201, /**< the input/output structure size is incorrect */ + CV_StsDivByZero= -202, /**< division by zero */ + CV_StsInplaceNotSupported= -203, /**< in-place operation is not supported */ + CV_StsObjectNotFound= -204, /**< request can't be completed */ + CV_StsUnmatchedFormats= -205, /**< formats of input/output arrays differ */ + CV_StsBadFlag= -206, /**< flag is wrong or not supported */ + CV_StsBadPoint= -207, /**< bad CvPoint */ + CV_StsBadMask= -208, /**< bad format of mask (neither 8uC1 nor 8sC1)*/ + CV_StsUnmatchedSizes= -209, /**< sizes of input/output structures do not match */ + CV_StsUnsupportedFormat= -210, /**< the data format/type is not supported by the function*/ + CV_StsOutOfRange= -211, /**< some of parameters are out of range */ + CV_StsParseError= -212, /**< invalid syntax/structure of the parsed file */ + CV_StsNotImplemented= -213, /**< the requested function/feature is not implemented */ + CV_StsBadMemBlock= -214, /**< an allocated block has been corrupted */ + CV_StsAssert= -215, /**< assertion failed */ + CV_GpuNotSupported= -216, /**< no CUDA support */ + CV_GpuApiCallError= -217, /**< GPU API call error */ + CV_OpenGlNotSupported= -218, /**< no OpenGL support */ + CV_OpenGlApiCallError= -219, /**< OpenGL API call error */ + CV_OpenCLApiCallError= -220, /**< OpenCL API call error */ + CV_OpenCLDoubleNotSupported= -221, + CV_OpenCLInitError= -222, /**< OpenCL initialization error */ + CV_OpenCLNoAMDBlasFft= -223 +}; + +/****************************************************************************************\ +* Common macros and inline functions * +\****************************************************************************************/ + +#define CV_SWAP(a,b,t) ((t) = (a), (a) = (b), (b) = (t)) + +/** min & max without jumps */ +#define CV_IMIN(a, b) ((a) ^ (((a)^(b)) & (((a) < (b)) - 1))) + +#define CV_IMAX(a, b) ((a) ^ (((a)^(b)) & (((a) > (b)) - 1))) + +/** absolute value without jumps */ +#ifndef __cplusplus +# define CV_IABS(a) (((a) ^ ((a) < 0 ? -1 : 0)) - ((a) < 0 ? -1 : 0)) +#else +# define CV_IABS(a) abs(a) +#endif +#define CV_CMP(a,b) (((a) > (b)) - ((a) < (b))) +#define CV_SIGN(a) CV_CMP((a),0) + +#define cvInvSqrt(value) ((float)(1./sqrt(value))) +#define cvSqrt(value) ((float)sqrt(value)) + + +/*************** Random number generation *******************/ + +typedef uint64 CvRNG; + +#define CV_RNG_COEFF 4164903690U + +/** @brief Initializes a random number generator state. + +The function initializes a random number generator and returns the state. The pointer to the state +can be then passed to the cvRandInt, cvRandReal and cvRandArr functions. In the current +implementation a multiply-with-carry generator is used. +@param seed 64-bit value used to initiate a random sequence +@sa the C++ class RNG replaced CvRNG. + */ +CV_INLINE CvRNG cvRNG( int64 seed CV_DEFAULT(-1)) +{ + CvRNG rng = seed ? (uint64)seed : (uint64)(int64)-1; + return rng; +} + +/** @brief Returns a 32-bit unsigned integer and updates RNG. + +The function returns a uniformly-distributed random 32-bit unsigned integer and updates the RNG +state. It is similar to the rand() function from the C runtime library, except that OpenCV functions +always generates a 32-bit random number, regardless of the platform. +@param rng CvRNG state initialized by cvRNG. + */ +CV_INLINE unsigned cvRandInt( CvRNG* rng ) +{ + uint64 temp = *rng; + temp = (uint64)(unsigned)temp*CV_RNG_COEFF + (temp >> 32); + *rng = temp; + return (unsigned)temp; +} + +/** @brief Returns a floating-point random number and updates RNG. + +The function returns a uniformly-distributed random floating-point number between 0 and 1 (1 is not +included). +@param rng RNG state initialized by cvRNG + */ +CV_INLINE double cvRandReal( CvRNG* rng ) +{ + return cvRandInt(rng)*2.3283064365386962890625e-10 /* 2^-32 */; +} + +/****************************************************************************************\ +* Image type (IplImage) * +\****************************************************************************************/ + +#ifndef HAVE_IPL + +/* + * The following definitions (until #endif) + * is an extract from IPL headers. + * Copyright (c) 1995 Intel Corporation. + */ +#define IPL_DEPTH_SIGN 0x80000000 + +#define IPL_DEPTH_1U 1 +#define IPL_DEPTH_8U 8 +#define IPL_DEPTH_16U 16 +#define IPL_DEPTH_32F 32 + +#define IPL_DEPTH_8S (IPL_DEPTH_SIGN| 8) +#define IPL_DEPTH_16S (IPL_DEPTH_SIGN|16) +#define IPL_DEPTH_32S (IPL_DEPTH_SIGN|32) + +#define IPL_DATA_ORDER_PIXEL 0 +#define IPL_DATA_ORDER_PLANE 1 + +#define IPL_ORIGIN_TL 0 +#define IPL_ORIGIN_BL 1 + +#define IPL_ALIGN_4BYTES 4 +#define IPL_ALIGN_8BYTES 8 +#define IPL_ALIGN_16BYTES 16 +#define IPL_ALIGN_32BYTES 32 + +#define IPL_ALIGN_DWORD IPL_ALIGN_4BYTES +#define IPL_ALIGN_QWORD IPL_ALIGN_8BYTES + +#define IPL_BORDER_CONSTANT 0 +#define IPL_BORDER_REPLICATE 1 +#define IPL_BORDER_REFLECT 2 +#define IPL_BORDER_WRAP 3 + +#ifdef __cplusplus +typedef struct _IplImage IplImage; +CV_EXPORTS _IplImage cvIplImage(const cv::Mat& m); +#endif + +/** The IplImage is taken from the Intel Image Processing Library, in which the format is native. OpenCV +only supports a subset of possible IplImage formats, as outlined in the parameter list above. + +In addition to the above restrictions, OpenCV handles ROIs differently. OpenCV functions require +that the image size or ROI size of all source and destination images match exactly. On the other +hand, the Intel Image Processing Library processes the area of intersection between the source and +destination images (or ROIs), allowing them to vary independently. +*/ +typedef struct +_IplImage +{ + int nSize; /**< sizeof(IplImage) */ + int ID; /**< version (=0)*/ + int nChannels; /**< Most of OpenCV functions support 1,2,3 or 4 channels */ + int alphaChannel; /**< Ignored by OpenCV */ + int depth; /**< Pixel depth in bits: IPL_DEPTH_8U, IPL_DEPTH_8S, IPL_DEPTH_16S, + IPL_DEPTH_32S, IPL_DEPTH_32F and IPL_DEPTH_64F are supported. */ + char colorModel[4]; /**< Ignored by OpenCV */ + char channelSeq[4]; /**< ditto */ + int dataOrder; /**< 0 - interleaved color channels, 1 - separate color channels. + cvCreateImage can only create interleaved images */ + int origin; /**< 0 - top-left origin, + 1 - bottom-left origin (Windows bitmaps style). */ + int align; /**< Alignment of image rows (4 or 8). + OpenCV ignores it and uses widthStep instead. */ + int width; /**< Image width in pixels. */ + int height; /**< Image height in pixels. */ + struct _IplROI *roi; /**< Image ROI. If NULL, the whole image is selected. */ + struct _IplImage *maskROI; /**< Must be NULL. */ + void *imageId; /**< " " */ + struct _IplTileInfo *tileInfo; /**< " " */ + int imageSize; /**< Image data size in bytes + (==image->height*image->widthStep + in case of interleaved data)*/ + char *imageData; /**< Pointer to aligned image data. */ + int widthStep; /**< Size of aligned image row in bytes. */ + int BorderMode[4]; /**< Ignored by OpenCV. */ + int BorderConst[4]; /**< Ditto. */ + char *imageDataOrigin; /**< Pointer to very origin of image data + (not necessarily aligned) - + needed for correct deallocation */ + +#if defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) + _IplImage() {} + _IplImage(const cv::Mat& m) { *this = cvIplImage(m); } +#endif +} +IplImage; + +CV_INLINE IplImage cvIplImage() +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + IplImage self = CV_STRUCT_INITIALIZER; self.nSize = sizeof(IplImage); return self; +#else + return _IplImage(); +#endif +} + +typedef struct _IplTileInfo IplTileInfo; + +typedef struct _IplROI +{ + int coi; /**< 0 - no COI (all channels are selected), 1 - 0th channel is selected ...*/ + int xOffset; + int yOffset; + int width; + int height; +} +IplROI; + +typedef struct _IplConvKernel +{ + int nCols; + int nRows; + int anchorX; + int anchorY; + int *values; + int nShiftR; +} +IplConvKernel; + +typedef struct _IplConvKernelFP +{ + int nCols; + int nRows; + int anchorX; + int anchorY; + float *values; +} +IplConvKernelFP; + +#define IPL_IMAGE_HEADER 1 +#define IPL_IMAGE_DATA 2 +#define IPL_IMAGE_ROI 4 + +#endif/*HAVE_IPL*/ + +/** extra border mode */ +#define IPL_BORDER_REFLECT_101 4 +#define IPL_BORDER_TRANSPARENT 5 + +#define IPL_IMAGE_MAGIC_VAL ((int)sizeof(IplImage)) +#define CV_TYPE_NAME_IMAGE "opencv-image" + +#define CV_IS_IMAGE_HDR(img) \ + ((img) != NULL && ((const IplImage*)(img))->nSize == sizeof(IplImage)) + +#define CV_IS_IMAGE(img) \ + (CV_IS_IMAGE_HDR(img) && ((IplImage*)img)->imageData != NULL) + +/** for storing double-precision + floating point data in IplImage's */ +#define IPL_DEPTH_64F 64 + +/** get reference to pixel at (col,row), + for multi-channel images (col) should be multiplied by number of channels */ +#define CV_IMAGE_ELEM( image, elemtype, row, col ) \ + (((elemtype*)((image)->imageData + (image)->widthStep*(row)))[(col)]) + +/****************************************************************************************\ +* Matrix type (CvMat) * +\****************************************************************************************/ + +#define CV_AUTO_STEP 0x7fffffff +#define CV_WHOLE_ARR cvSlice( 0, 0x3fffffff ) + +#define CV_MAGIC_MASK 0xFFFF0000 +#define CV_MAT_MAGIC_VAL 0x42420000 +#define CV_TYPE_NAME_MAT "opencv-matrix" + +#ifdef __cplusplus +typedef struct CvMat CvMat; +CV_INLINE CvMat cvMat(const cv::Mat& m); +#endif + +/** Matrix elements are stored row by row. Element (i, j) (i - 0-based row index, j - 0-based column +index) of a matrix can be retrieved or modified using CV_MAT_ELEM macro: + + uchar pixval = CV_MAT_ELEM(grayimg, uchar, i, j) + CV_MAT_ELEM(cameraMatrix, float, 0, 2) = image.width*0.5f; + +To access multiple-channel matrices, you can use +CV_MAT_ELEM(matrix, type, i, j\*nchannels + channel_idx). + +@deprecated CvMat is now obsolete; consider using Mat instead. + */ +typedef struct CvMat +{ + int type; + int step; + + /* for internal use only */ + int* refcount; + int hdr_refcount; + + union + { + uchar* ptr; + short* s; + int* i; + float* fl; + double* db; + } data; + +#ifdef __cplusplus + union + { + int rows; + int height; + }; + + union + { + int cols; + int width; + }; +#else + int rows; + int cols; +#endif + +#if defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) + CvMat() {} + CvMat(const cv::Mat& m) { *this = cvMat(m); } +#endif +} +CvMat; + + +#define CV_IS_MAT_HDR(mat) \ + ((mat) != NULL && \ + (((const CvMat*)(mat))->type & CV_MAGIC_MASK) == CV_MAT_MAGIC_VAL && \ + ((const CvMat*)(mat))->cols > 0 && ((const CvMat*)(mat))->rows > 0) + +#define CV_IS_MAT_HDR_Z(mat) \ + ((mat) != NULL && \ + (((const CvMat*)(mat))->type & CV_MAGIC_MASK) == CV_MAT_MAGIC_VAL && \ + ((const CvMat*)(mat))->cols >= 0 && ((const CvMat*)(mat))->rows >= 0) + +#define CV_IS_MAT(mat) \ + (CV_IS_MAT_HDR(mat) && ((const CvMat*)(mat))->data.ptr != NULL) + +#define CV_IS_MASK_ARR(mat) \ + (((mat)->type & (CV_MAT_TYPE_MASK & ~CV_8SC1)) == 0) + +#define CV_ARE_TYPES_EQ(mat1, mat2) \ + ((((mat1)->type ^ (mat2)->type) & CV_MAT_TYPE_MASK) == 0) + +#define CV_ARE_CNS_EQ(mat1, mat2) \ + ((((mat1)->type ^ (mat2)->type) & CV_MAT_CN_MASK) == 0) + +#define CV_ARE_DEPTHS_EQ(mat1, mat2) \ + ((((mat1)->type ^ (mat2)->type) & CV_MAT_DEPTH_MASK) == 0) + +#define CV_ARE_SIZES_EQ(mat1, mat2) \ + ((mat1)->rows == (mat2)->rows && (mat1)->cols == (mat2)->cols) + +#define CV_IS_MAT_CONST(mat) \ + (((mat)->rows|(mat)->cols) == 1) + +#define IPL2CV_DEPTH(depth) \ + ((((CV_8U)+(CV_16U<<4)+(CV_32F<<8)+(CV_64F<<16)+(CV_8S<<20)+ \ + (CV_16S<<24)+(CV_32S<<28)) >> ((((depth) & 0xF0) >> 2) + \ + (((depth) & IPL_DEPTH_SIGN) ? 20 : 0))) & 15) + +/** Inline constructor. No data is allocated internally!!! + * (Use together with cvCreateData, or use cvCreateMat instead to + * get a matrix with allocated data): + */ +CV_INLINE CvMat cvMat( int rows, int cols, int type, void* data CV_DEFAULT(NULL)) +{ + CvMat m; + + assert( (unsigned)CV_MAT_DEPTH(type) <= CV_64F ); + type = CV_MAT_TYPE(type); + m.type = CV_MAT_MAGIC_VAL | CV_MAT_CONT_FLAG | type; + m.cols = cols; + m.rows = rows; + m.step = m.cols*CV_ELEM_SIZE(type); + m.data.ptr = (uchar*)data; + m.refcount = NULL; + m.hdr_refcount = 0; + + return m; +} + +#ifdef __cplusplus + +CV_INLINE CvMat cvMat(const cv::Mat& m) +{ + CvMat self; + CV_DbgAssert(m.dims <= 2); + self = cvMat(m.rows, m.dims == 1 ? 1 : m.cols, m.type(), m.data); + self.step = (int)m.step[0]; + self.type = (self.type & ~cv::Mat::CONTINUOUS_FLAG) | (m.flags & cv::Mat::CONTINUOUS_FLAG); + return self; +} +CV_INLINE CvMat cvMat() +{ +#if !defined(CV__ENABLE_C_API_CTORS) + CvMat self = CV_STRUCT_INITIALIZER; return self; +#else + return CvMat(); +#endif +} +CV_INLINE CvMat cvMat(const CvMat& m) +{ +#if !defined(CV__ENABLE_C_API_CTORS) + CvMat self = CV_STRUCT_INITIALIZER; memcpy(&self, &m, sizeof(self)); return self; +#else + return CvMat(m); +#endif +} + +#endif // __cplusplus + + +#define CV_MAT_ELEM_PTR_FAST( mat, row, col, pix_size ) \ + (assert( (unsigned)(row) < (unsigned)(mat).rows && \ + (unsigned)(col) < (unsigned)(mat).cols ), \ + (mat).data.ptr + (size_t)(mat).step*(row) + (pix_size)*(col)) + +#define CV_MAT_ELEM_PTR( mat, row, col ) \ + CV_MAT_ELEM_PTR_FAST( mat, row, col, CV_ELEM_SIZE((mat).type) ) + +#define CV_MAT_ELEM( mat, elemtype, row, col ) \ + (*(elemtype*)CV_MAT_ELEM_PTR_FAST( mat, row, col, sizeof(elemtype))) + +/** @brief Returns the particular element of single-channel floating-point matrix. + +The function is a fast replacement for cvGetReal2D in the case of single-channel floating-point +matrices. It is faster because it is inline, it does fewer checks for array type and array element +type, and it checks for the row and column ranges only in debug mode. +@param mat Input matrix +@param row The zero-based index of row +@param col The zero-based index of column + */ +CV_INLINE double cvmGet( const CvMat* mat, int row, int col ) +{ + int type; + + type = CV_MAT_TYPE(mat->type); + assert( (unsigned)row < (unsigned)mat->rows && + (unsigned)col < (unsigned)mat->cols ); + + if( type == CV_32FC1 ) + return ((float*)(void*)(mat->data.ptr + (size_t)mat->step*row))[col]; + else + { + assert( type == CV_64FC1 ); + return ((double*)(void*)(mat->data.ptr + (size_t)mat->step*row))[col]; + } +} + +/** @brief Sets a specific element of a single-channel floating-point matrix. + +The function is a fast replacement for cvSetReal2D in the case of single-channel floating-point +matrices. It is faster because it is inline, it does fewer checks for array type and array element +type, and it checks for the row and column ranges only in debug mode. +@param mat The matrix +@param row The zero-based index of row +@param col The zero-based index of column +@param value The new value of the matrix element + */ +CV_INLINE void cvmSet( CvMat* mat, int row, int col, double value ) +{ + int type; + type = CV_MAT_TYPE(mat->type); + assert( (unsigned)row < (unsigned)mat->rows && + (unsigned)col < (unsigned)mat->cols ); + + if( type == CV_32FC1 ) + ((float*)(void*)(mat->data.ptr + (size_t)mat->step*row))[col] = (float)value; + else + { + assert( type == CV_64FC1 ); + ((double*)(void*)(mat->data.ptr + (size_t)mat->step*row))[col] = value; + } +} + + +CV_INLINE int cvIplDepth( int type ) +{ + int depth = CV_MAT_DEPTH(type); + return CV_ELEM_SIZE1(depth)*8 | (depth == CV_8S || depth == CV_16S || + depth == CV_32S ? IPL_DEPTH_SIGN : 0); +} + + +/****************************************************************************************\ +* Multi-dimensional dense array (CvMatND) * +\****************************************************************************************/ + +#define CV_MATND_MAGIC_VAL 0x42430000 +#define CV_TYPE_NAME_MATND "opencv-nd-matrix" + +#define CV_MAX_DIM 32 + +#ifdef __cplusplus +typedef struct CvMatND CvMatND; +CV_EXPORTS CvMatND cvMatND(const cv::Mat& m); +#endif + +/** + @deprecated consider using cv::Mat instead + */ +typedef struct +CvMatND +{ + int type; + int dims; + + int* refcount; + int hdr_refcount; + + union + { + uchar* ptr; + float* fl; + double* db; + int* i; + short* s; + } data; + + struct + { + int size; + int step; + } + dim[CV_MAX_DIM]; + +#if defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) + CvMatND() {} + CvMatND(const cv::Mat& m) { *this = cvMatND(m); } +#endif +} +CvMatND; + + +CV_INLINE CvMatND cvMatND() +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvMatND self = CV_STRUCT_INITIALIZER; return self; +#else + return CvMatND(); +#endif +} + +#define CV_IS_MATND_HDR(mat) \ + ((mat) != NULL && (((const CvMatND*)(mat))->type & CV_MAGIC_MASK) == CV_MATND_MAGIC_VAL) + +#define CV_IS_MATND(mat) \ + (CV_IS_MATND_HDR(mat) && ((const CvMatND*)(mat))->data.ptr != NULL) + + +/****************************************************************************************\ +* Multi-dimensional sparse array (CvSparseMat) * +\****************************************************************************************/ + +#define CV_SPARSE_MAT_MAGIC_VAL 0x42440000 +#define CV_TYPE_NAME_SPARSE_MAT "opencv-sparse-matrix" + +struct CvSet; + +typedef struct CvSparseMat +{ + int type; + int dims; + int* refcount; + int hdr_refcount; + + struct CvSet* heap; + void** hashtable; + int hashsize; + int valoffset; + int idxoffset; + int size[CV_MAX_DIM]; + +#ifdef __cplusplus + CV_EXPORTS void copyToSparseMat(cv::SparseMat& m) const; +#endif +} +CvSparseMat; + +#ifdef __cplusplus +CV_EXPORTS CvSparseMat* cvCreateSparseMat(const cv::SparseMat& m); +#endif + +#define CV_IS_SPARSE_MAT_HDR(mat) \ + ((mat) != NULL && \ + (((const CvSparseMat*)(mat))->type & CV_MAGIC_MASK) == CV_SPARSE_MAT_MAGIC_VAL) + +#define CV_IS_SPARSE_MAT(mat) \ + CV_IS_SPARSE_MAT_HDR(mat) + +/**************** iteration through a sparse array *****************/ + +typedef struct CvSparseNode +{ + unsigned hashval; + struct CvSparseNode* next; +} +CvSparseNode; + +typedef struct CvSparseMatIterator +{ + CvSparseMat* mat; + CvSparseNode* node; + int curidx; +} +CvSparseMatIterator; + +#define CV_NODE_VAL(mat,node) ((void*)((uchar*)(node) + (mat)->valoffset)) +#define CV_NODE_IDX(mat,node) ((int*)((uchar*)(node) + (mat)->idxoffset)) + +/****************************************************************************************\ +* Histogram * +\****************************************************************************************/ + +typedef int CvHistType; + +#define CV_HIST_MAGIC_VAL 0x42450000 +#define CV_HIST_UNIFORM_FLAG (1 << 10) + +/** indicates whether bin ranges are set already or not */ +#define CV_HIST_RANGES_FLAG (1 << 11) + +#define CV_HIST_ARRAY 0 +#define CV_HIST_SPARSE 1 +#define CV_HIST_TREE CV_HIST_SPARSE + +/** should be used as a parameter only, + it turns to CV_HIST_UNIFORM_FLAG of hist->type */ +#define CV_HIST_UNIFORM 1 + +typedef struct CvHistogram +{ + int type; + CvArr* bins; + float thresh[CV_MAX_DIM][2]; /**< For uniform histograms. */ + float** thresh2; /**< For non-uniform histograms. */ + CvMatND mat; /**< Embedded matrix header for array histograms. */ +} +CvHistogram; + +#define CV_IS_HIST( hist ) \ + ((hist) != NULL && \ + (((CvHistogram*)(hist))->type & CV_MAGIC_MASK) == CV_HIST_MAGIC_VAL && \ + (hist)->bins != NULL) + +#define CV_IS_UNIFORM_HIST( hist ) \ + (((hist)->type & CV_HIST_UNIFORM_FLAG) != 0) + +#define CV_IS_SPARSE_HIST( hist ) \ + CV_IS_SPARSE_MAT((hist)->bins) + +#define CV_HIST_HAS_RANGES( hist ) \ + (((hist)->type & CV_HIST_RANGES_FLAG) != 0) + +/****************************************************************************************\ +* Other supplementary data type definitions * +\****************************************************************************************/ + +/*************************************** CvRect *****************************************/ +/** @sa Rect_ */ +typedef struct CvRect +{ + int x; + int y; + int width; + int height; + +#ifdef CV__VALIDATE_UNUNITIALIZED_VARS + CvRect() __attribute__(( warning("Non-initialized variable") )) {}; + template CvRect(const std::initializer_list<_Tp> list) + { + CV_Assert(list.size() == 0 || list.size() == 4); + x = y = width = height = 0; + if (list.size() == 4) + { + x = list.begin()[0]; y = list.begin()[1]; width = list.begin()[2]; height = list.begin()[3]; + } + }; +#elif defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) + CvRect(int _x = 0, int _y = 0, int w = 0, int h = 0): x(_x), y(_y), width(w), height(h) {} + template + CvRect(const cv::Rect_<_Tp>& r): x(cv::saturate_cast(r.x)), y(cv::saturate_cast(r.y)), width(cv::saturate_cast(r.width)), height(cv::saturate_cast(r.height)) {} +#endif +#ifdef __cplusplus + template + operator cv::Rect_<_Tp>() const { return cv::Rect_<_Tp>((_Tp)x, (_Tp)y, (_Tp)width, (_Tp)height); } +#endif +} +CvRect; + +/** constructs CvRect structure. */ +CV_INLINE CvRect cvRect( int x, int y, int width, int height ) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvRect r = {x, y, width, height}; +#else + CvRect r(x, y , width, height); +#endif + return r; +} +#ifdef __cplusplus +CV_INLINE CvRect cvRect(const cv::Rect& rc) { return cvRect(rc.x, rc.y, rc.width, rc.height); } +#endif + +CV_INLINE IplROI cvRectToROI( CvRect rect, int coi ) +{ + IplROI roi; + roi.xOffset = rect.x; + roi.yOffset = rect.y; + roi.width = rect.width; + roi.height = rect.height; + roi.coi = coi; + + return roi; +} + + +CV_INLINE CvRect cvROIToRect( IplROI roi ) +{ + return cvRect( roi.xOffset, roi.yOffset, roi.width, roi.height ); +} + +/*********************************** CvTermCriteria *************************************/ + +#define CV_TERMCRIT_ITER 1 +#define CV_TERMCRIT_NUMBER CV_TERMCRIT_ITER +#define CV_TERMCRIT_EPS 2 + +/** @sa TermCriteria + */ +typedef struct CvTermCriteria +{ + int type; /**< may be combination of + CV_TERMCRIT_ITER + CV_TERMCRIT_EPS */ + int max_iter; + double epsilon; +#if defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) + CvTermCriteria(int _type = 0, int _iter = 0, double _eps = 0) : type(_type), max_iter(_iter), epsilon(_eps) {} + CvTermCriteria(const cv::TermCriteria& t) : type(t.type), max_iter(t.maxCount), epsilon(t.epsilon) {} +#endif +#ifdef __cplusplus + operator cv::TermCriteria() const { return cv::TermCriteria(type, max_iter, epsilon); } +#endif +} +CvTermCriteria; + +CV_INLINE CvTermCriteria cvTermCriteria( int type, int max_iter, double epsilon ) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvTermCriteria t = { type, max_iter, (float)epsilon}; +#else + CvTermCriteria t(type, max_iter, epsilon); +#endif + return t; +} +#ifdef __cplusplus +CV_INLINE CvTermCriteria cvTermCriteria(const cv::TermCriteria& t) { return cvTermCriteria(t.type, t.maxCount, t.epsilon); } +#endif + + +/******************************* CvPoint and variants ***********************************/ + +typedef struct CvPoint +{ + int x; + int y; + +#ifdef CV__VALIDATE_UNUNITIALIZED_VARS + CvPoint() __attribute__(( warning("Non-initialized variable") )) {} + template CvPoint(const std::initializer_list<_Tp> list) + { + CV_Assert(list.size() == 0 || list.size() == 2); + x = y = 0; + if (list.size() == 2) + { + x = list.begin()[0]; y = list.begin()[1]; + } + }; +#elif defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) + CvPoint(int _x = 0, int _y = 0): x(_x), y(_y) {} + template + CvPoint(const cv::Point_<_Tp>& pt): x((int)pt.x), y((int)pt.y) {} +#endif +#ifdef __cplusplus + template + operator cv::Point_<_Tp>() const { return cv::Point_<_Tp>(cv::saturate_cast<_Tp>(x), cv::saturate_cast<_Tp>(y)); } +#endif +} +CvPoint; + +/** constructs CvPoint structure. */ +CV_INLINE CvPoint cvPoint( int x, int y ) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvPoint p = {x, y}; +#else + CvPoint p(x, y); +#endif + return p; +} +#ifdef __cplusplus +CV_INLINE CvPoint cvPoint(const cv::Point& pt) { return cvPoint(pt.x, pt.y); } +#endif + +typedef struct CvPoint2D32f +{ + float x; + float y; + +#ifdef CV__VALIDATE_UNUNITIALIZED_VARS + CvPoint2D32f() __attribute__(( warning("Non-initialized variable") )) {} + template CvPoint2D32f(const std::initializer_list<_Tp> list) + { + CV_Assert(list.size() == 0 || list.size() == 2); + x = y = 0; + if (list.size() == 2) + { + x = list.begin()[0]; y = list.begin()[1]; + } + }; +#elif defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) + CvPoint2D32f(float _x = 0, float _y = 0): x(_x), y(_y) {} + template + CvPoint2D32f(const cv::Point_<_Tp>& pt): x((float)pt.x), y((float)pt.y) {} +#endif +#ifdef __cplusplus + template + operator cv::Point_<_Tp>() const { return cv::Point_<_Tp>(cv::saturate_cast<_Tp>(x), cv::saturate_cast<_Tp>(y)); } +#endif +} +CvPoint2D32f; + +/** constructs CvPoint2D32f structure. */ +CV_INLINE CvPoint2D32f cvPoint2D32f( double x, double y ) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvPoint2D32f p = { (float)x, (float)y }; +#else + CvPoint2D32f p((float)x, (float)y); +#endif + return p; +} + +#ifdef __cplusplus +template +CvPoint2D32f cvPoint2D32f(const cv::Point_<_Tp>& pt) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvPoint2D32f p = { (float)pt.x, (float)pt.y }; +#else + CvPoint2D32f p((float)pt.x, (float)pt.y); +#endif + return p; +} +#endif + +/** converts CvPoint to CvPoint2D32f. */ +CV_INLINE CvPoint2D32f cvPointTo32f( CvPoint point ) +{ + return cvPoint2D32f( (float)point.x, (float)point.y ); +} + +/** converts CvPoint2D32f to CvPoint. */ +CV_INLINE CvPoint cvPointFrom32f( CvPoint2D32f point ) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvPoint ipt = { cvRound(point.x), cvRound(point.y) }; +#else + CvPoint ipt(cvRound(point.x), cvRound(point.y)); +#endif + return ipt; +} + + +typedef struct CvPoint3D32f +{ + float x; + float y; + float z; + +#ifdef CV__VALIDATE_UNUNITIALIZED_VARS + CvPoint3D32f() __attribute__(( warning("Non-initialized variable") )) {} + template CvPoint3D32f(const std::initializer_list<_Tp> list) + { + CV_Assert(list.size() == 0 || list.size() == 3); + x = y = z = 0; + if (list.size() == 3) + { + x = list.begin()[0]; y = list.begin()[1]; z = list.begin()[2]; + } + }; +#elif defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) + CvPoint3D32f(float _x = 0, float _y = 0, float _z = 0): x(_x), y(_y), z(_z) {} + template + CvPoint3D32f(const cv::Point3_<_Tp>& pt): x((float)pt.x), y((float)pt.y), z((float)pt.z) {} +#endif +#ifdef __cplusplus + template + operator cv::Point3_<_Tp>() const { return cv::Point3_<_Tp>(cv::saturate_cast<_Tp>(x), cv::saturate_cast<_Tp>(y), cv::saturate_cast<_Tp>(z)); } +#endif +} +CvPoint3D32f; + +/** constructs CvPoint3D32f structure. */ +CV_INLINE CvPoint3D32f cvPoint3D32f( double x, double y, double z ) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvPoint3D32f p = { (float)x, (float)y, (float)z }; +#else + CvPoint3D32f p((float)x, (float)y, (float)z); +#endif + return p; +} + +#ifdef __cplusplus +template +CvPoint3D32f cvPoint3D32f(const cv::Point3_<_Tp>& pt) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvPoint3D32f p = { (float)pt.x, (float)pt.y, (float)pt.z }; +#else + CvPoint3D32f p((float)pt.x, (float)pt.y, (float)pt.z); +#endif + return p; +} +#endif + + +typedef struct CvPoint2D64f +{ + double x; + double y; +#ifdef CV__VALIDATE_UNUNITIALIZED_VARS + CvPoint2D64f() __attribute__(( warning("Non-initialized variable") )) {} + template CvPoint2D64f(const std::initializer_list<_Tp> list) + { + CV_Assert(list.size() == 0 || list.size() == 2); + x = y = 0; + if (list.size() == 2) + { + x = list.begin()[0]; y = list.begin()[1]; + } + }; +#endif +} +CvPoint2D64f; + +/** constructs CvPoint2D64f structure.*/ +CV_INLINE CvPoint2D64f cvPoint2D64f( double x, double y ) +{ + CvPoint2D64f p = { x, y }; + return p; +} + + +typedef struct CvPoint3D64f +{ + double x; + double y; + double z; +#ifdef CV__VALIDATE_UNUNITIALIZED_VARS + CvPoint3D64f() __attribute__(( warning("Non-initialized variable") )) {} + template CvPoint3D64f(const std::initializer_list<_Tp> list) + { + CV_Assert(list.size() == 0 || list.size() == 3); + x = y = z = 0; + if (list.size() == 3) + { + x = list.begin()[0]; y = list.begin()[1]; z = list.begin()[2]; + } + }; +#endif +} +CvPoint3D64f; + +/** constructs CvPoint3D64f structure. */ +CV_INLINE CvPoint3D64f cvPoint3D64f( double x, double y, double z ) +{ + CvPoint3D64f p = { x, y, z }; + return p; +} + + +/******************************** CvSize's & CvBox **************************************/ + +typedef struct CvSize +{ + int width; + int height; + +#ifdef CV__VALIDATE_UNUNITIALIZED_VARS + CvSize() __attribute__(( warning("Non-initialized variable") )) {} + template CvSize(const std::initializer_list<_Tp> list) + { + CV_Assert(list.size() == 0 || list.size() == 2); + width = 0; height = 0; + if (list.size() == 2) + { + width = list.begin()[0]; height = list.begin()[1]; + } + }; +#elif defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) + CvSize(int w = 0, int h = 0): width(w), height(h) {} + template + CvSize(const cv::Size_<_Tp>& sz): width(cv::saturate_cast(sz.width)), height(cv::saturate_cast(sz.height)) {} +#endif +#ifdef __cplusplus + template + operator cv::Size_<_Tp>() const { return cv::Size_<_Tp>(cv::saturate_cast<_Tp>(width), cv::saturate_cast<_Tp>(height)); } +#endif +} +CvSize; + +/** constructs CvSize structure. */ +CV_INLINE CvSize cvSize( int width, int height ) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvSize s = { width, height }; +#else + CvSize s(width, height); +#endif + return s; +} + +#ifdef __cplusplus +CV_INLINE CvSize cvSize(const cv::Size& sz) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvSize s = { sz.width, sz.height }; +#else + CvSize s(sz.width, sz.height); +#endif + return s; +} +#endif + +typedef struct CvSize2D32f +{ + float width; + float height; + +#ifdef CV__VALIDATE_UNUNITIALIZED_VARS + CvSize2D32f() __attribute__(( warning("Non-initialized variable") )) {} + template CvSize2D32f(const std::initializer_list<_Tp> list) + { + CV_Assert(list.size() == 0 || list.size() == 2); + width = 0; height = 0; + if (list.size() == 2) + { + width = list.begin()[0]; height = list.begin()[1]; + } + }; +#elif defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) + CvSize2D32f(float w = 0, float h = 0): width(w), height(h) {} + template + CvSize2D32f(const cv::Size_<_Tp>& sz): width(cv::saturate_cast(sz.width)), height(cv::saturate_cast(sz.height)) {} +#endif +#ifdef __cplusplus + template + operator cv::Size_<_Tp>() const { return cv::Size_<_Tp>(cv::saturate_cast<_Tp>(width), cv::saturate_cast<_Tp>(height)); } +#endif +} +CvSize2D32f; + +/** constructs CvSize2D32f structure. */ +CV_INLINE CvSize2D32f cvSize2D32f( double width, double height ) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvSize2D32f s = { (float)width, (float)height }; +#else + CvSize2D32f s((float)width, (float)height); +#endif + return s; +} +#ifdef __cplusplus +template +CvSize2D32f cvSize2D32f(const cv::Size_<_Tp>& sz) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvSize2D32f s = { (float)sz.width, (float)sz.height }; +#else + CvSize2D32f s((float)sz.width, (float)sz.height); +#endif + return s; +} +#endif + +/** @sa RotatedRect + */ +typedef struct CvBox2D +{ + CvPoint2D32f center; /**< Center of the box. */ + CvSize2D32f size; /**< Box width and length. */ + float angle; /**< Angle between the horizontal axis */ + /**< and the first side (i.e. length) in degrees */ + +#if defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) + CvBox2D(CvPoint2D32f c = CvPoint2D32f(), CvSize2D32f s = CvSize2D32f(), float a = 0) : center(c), size(s), angle(a) {} + CvBox2D(const cv::RotatedRect& rr) : center(rr.center), size(rr.size), angle(rr.angle) {} +#endif +#ifdef __cplusplus + operator cv::RotatedRect() const { return cv::RotatedRect(center, size, angle); } +#endif +} +CvBox2D; + + +#ifdef __cplusplus +CV_INLINE CvBox2D cvBox2D(CvPoint2D32f c = CvPoint2D32f(), CvSize2D32f s = CvSize2D32f(), float a = 0) +{ + CvBox2D self; + self.center = c; + self.size = s; + self.angle = a; + return self; +} +CV_INLINE CvBox2D cvBox2D(const cv::RotatedRect& rr) +{ + CvBox2D self; + self.center = cvPoint2D32f(rr.center); + self.size = cvSize2D32f(rr.size); + self.angle = rr.angle; + return self; +} +#endif + + +/** Line iterator state: */ +typedef struct CvLineIterator +{ + /** Pointer to the current point: */ + uchar* ptr; + + /* Bresenham algorithm state: */ + int err; + int plus_delta; + int minus_delta; + int plus_step; + int minus_step; +} +CvLineIterator; + + + +/************************************* CvSlice ******************************************/ +#define CV_WHOLE_SEQ_END_INDEX 0x3fffffff +#define CV_WHOLE_SEQ cvSlice(0, CV_WHOLE_SEQ_END_INDEX) + +typedef struct CvSlice +{ + int start_index, end_index; + +#ifdef CV__VALIDATE_UNUNITIALIZED_VARS + CvSlice() __attribute__(( warning("Non-initialized variable") )) {} + template CvSlice(const std::initializer_list<_Tp> list) + { + CV_Assert(list.size() == 0 || list.size() == 2); + start_index = end_index = 0; + if (list.size() == 2) + { + start_index = list.begin()[0]; end_index = list.begin()[1]; + } + }; +#endif +#if defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) && !defined(__CUDACC__) + CvSlice(int start = 0, int end = 0) : start_index(start), end_index(end) {} + CvSlice(const cv::Range& r) { *this = (r.start != INT_MIN && r.end != INT_MAX) ? CvSlice(r.start, r.end) : CvSlice(0, CV_WHOLE_SEQ_END_INDEX); } + operator cv::Range() const { return (start_index == 0 && end_index == CV_WHOLE_SEQ_END_INDEX ) ? cv::Range::all() : cv::Range(start_index, end_index); } +#endif +} +CvSlice; + +CV_INLINE CvSlice cvSlice( int start, int end ) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) && !defined(__CUDACC__)) + CvSlice slice = { start, end }; +#else + CvSlice slice(start, end); +#endif + return slice; +} + +#if defined(__cplusplus) +CV_INLINE CvSlice cvSlice(const cv::Range& r) +{ + CvSlice slice = (r.start != INT_MIN && r.end != INT_MAX) ? cvSlice(r.start, r.end) : cvSlice(0, CV_WHOLE_SEQ_END_INDEX); + return slice; +} +#endif + + +/************************************* CvScalar *****************************************/ +/** @sa Scalar_ + */ +typedef struct CvScalar +{ + double val[4]; + +#ifdef CV__VALIDATE_UNUNITIALIZED_VARS + CvScalar() __attribute__(( warning("Non-initialized variable") )) {} + CvScalar(const std::initializer_list list) + { + CV_Assert(list.size() == 0 || list.size() == 4); + val[0] = val[1] = val[2] = val[3] = 0; + if (list.size() == 4) + { + val[0] = list.begin()[0]; val[1] = list.begin()[1]; val[2] = list.begin()[2]; val[3] = list.begin()[3]; + } + }; +#elif defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) + CvScalar() {} + CvScalar(double d0, double d1 = 0, double d2 = 0, double d3 = 0) { val[0] = d0; val[1] = d1; val[2] = d2; val[3] = d3; } + template + CvScalar(const cv::Scalar_<_Tp>& s) { val[0] = s.val[0]; val[1] = s.val[1]; val[2] = s.val[2]; val[3] = s.val[3]; } + template + CvScalar(const cv::Vec<_Tp, cn>& v) + { + int i; + for( i = 0; i < (cn < 4 ? cn : 4); i++ ) val[i] = v.val[i]; + for( ; i < 4; i++ ) val[i] = 0; + } +#endif +#ifdef __cplusplus + template + operator cv::Scalar_<_Tp>() const { return cv::Scalar_<_Tp>(cv::saturate_cast<_Tp>(val[0]), cv::saturate_cast<_Tp>(val[1]), cv::saturate_cast<_Tp>(val[2]), cv::saturate_cast<_Tp>(val[3])); } +#endif +} +CvScalar; + +CV_INLINE CvScalar cvScalar( double val0, double val1 CV_DEFAULT(0), + double val2 CV_DEFAULT(0), double val3 CV_DEFAULT(0)) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvScalar scalar = CV_STRUCT_INITIALIZER; +#else + CvScalar scalar; +#endif + scalar.val[0] = val0; scalar.val[1] = val1; + scalar.val[2] = val2; scalar.val[3] = val3; + return scalar; +} + +#ifdef __cplusplus +CV_INLINE CvScalar cvScalar() +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvScalar scalar = CV_STRUCT_INITIALIZER; +#else + CvScalar scalar; +#endif + scalar.val[0] = scalar.val[1] = scalar.val[2] = scalar.val[3] = 0; + return scalar; +} +CV_INLINE CvScalar cvScalar(const cv::Scalar& s) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvScalar scalar = CV_STRUCT_INITIALIZER; +#else + CvScalar scalar; +#endif + scalar.val[0] = s.val[0]; + scalar.val[1] = s.val[1]; + scalar.val[2] = s.val[2]; + scalar.val[3] = s.val[3]; + return scalar; +} +#endif + +CV_INLINE CvScalar cvRealScalar( double val0 ) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvScalar scalar = CV_STRUCT_INITIALIZER; +#else + CvScalar scalar; +#endif + scalar.val[0] = val0; + scalar.val[1] = scalar.val[2] = scalar.val[3] = 0; + return scalar; +} + +CV_INLINE CvScalar cvScalarAll( double val0123 ) +{ +#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) + CvScalar scalar = CV_STRUCT_INITIALIZER; +#else + CvScalar scalar; +#endif + scalar.val[0] = val0123; + scalar.val[1] = val0123; + scalar.val[2] = val0123; + scalar.val[3] = val0123; + return scalar; +} + +/****************************************************************************************\ +* Dynamic Data structures * +\****************************************************************************************/ + +/******************************** Memory storage ****************************************/ + +typedef struct CvMemBlock +{ + struct CvMemBlock* prev; + struct CvMemBlock* next; +} +CvMemBlock; + +#define CV_STORAGE_MAGIC_VAL 0x42890000 + +typedef struct CvMemStorage +{ + int signature; + CvMemBlock* bottom; /**< First allocated block. */ + CvMemBlock* top; /**< Current memory block - top of the stack. */ + struct CvMemStorage* parent; /**< We get new blocks from parent as needed. */ + int block_size; /**< Block size. */ + int free_space; /**< Remaining free space in current block. */ +} +CvMemStorage; + +#define CV_IS_STORAGE(storage) \ + ((storage) != NULL && \ + (((CvMemStorage*)(storage))->signature & CV_MAGIC_MASK) == CV_STORAGE_MAGIC_VAL) + + +typedef struct CvMemStoragePos +{ + CvMemBlock* top; + int free_space; +} +CvMemStoragePos; + + +/*********************************** Sequence *******************************************/ + +typedef struct CvSeqBlock +{ + struct CvSeqBlock* prev; /**< Previous sequence block. */ + struct CvSeqBlock* next; /**< Next sequence block. */ + int start_index; /**< Index of the first element in the block + */ + /**< sequence->first->start_index. */ + int count; /**< Number of elements in the block. */ + schar* data; /**< Pointer to the first element of the block. */ +} +CvSeqBlock; + + +#define CV_TREE_NODE_FIELDS(node_type) \ + int flags; /**< Miscellaneous flags. */ \ + int header_size; /**< Size of sequence header. */ \ + struct node_type* h_prev; /**< Previous sequence. */ \ + struct node_type* h_next; /**< Next sequence. */ \ + struct node_type* v_prev; /**< 2nd previous sequence. */ \ + struct node_type* v_next /**< 2nd next sequence. */ + +/** + Read/Write sequence. + Elements can be dynamically inserted to or deleted from the sequence. +*/ +#define CV_SEQUENCE_FIELDS() \ + CV_TREE_NODE_FIELDS(CvSeq); \ + int total; /**< Total number of elements. */ \ + int elem_size; /**< Size of sequence element in bytes. */ \ + schar* block_max; /**< Maximal bound of the last block. */ \ + schar* ptr; /**< Current write pointer. */ \ + int delta_elems; /**< Grow seq this many at a time. */ \ + CvMemStorage* storage; /**< Where the seq is stored. */ \ + CvSeqBlock* free_blocks; /**< Free blocks list. */ \ + CvSeqBlock* first; /**< Pointer to the first sequence block. */ + +typedef struct CvSeq +{ + CV_SEQUENCE_FIELDS() +} +CvSeq; + +#define CV_TYPE_NAME_SEQ "opencv-sequence" +#define CV_TYPE_NAME_SEQ_TREE "opencv-sequence-tree" + +/*************************************** Set ********************************************/ +/** @brief Set + Order is not preserved. There can be gaps between sequence elements. + After the element has been inserted it stays in the same place all the time. + The MSB(most-significant or sign bit) of the first field (flags) is 0 iff the element exists. +*/ +#define CV_SET_ELEM_FIELDS(elem_type) \ + int flags; \ + struct elem_type* next_free; + +typedef struct CvSetElem +{ + CV_SET_ELEM_FIELDS(CvSetElem) +} +CvSetElem; + +#define CV_SET_FIELDS() \ + CV_SEQUENCE_FIELDS() \ + CvSetElem* free_elems; \ + int active_count; + +typedef struct CvSet +{ + CV_SET_FIELDS() +} +CvSet; + + +#define CV_SET_ELEM_IDX_MASK ((1 << 26) - 1) +#define CV_SET_ELEM_FREE_FLAG (1 << (sizeof(int)*8-1)) + +/** Checks whether the element pointed by ptr belongs to a set or not */ +#define CV_IS_SET_ELEM( ptr ) (((CvSetElem*)(ptr))->flags >= 0) + +/************************************* Graph ********************************************/ + +/** @name Graph + +We represent a graph as a set of vertices. Vertices contain their adjacency lists (more exactly, +pointers to first incoming or outcoming edge (or 0 if isolated vertex)). Edges are stored in +another set. There is a singly-linked list of incoming/outcoming edges for each vertex. + +Each edge consists of: + +- Two pointers to the starting and ending vertices (vtx[0] and vtx[1] respectively). + + A graph may be oriented or not. In the latter case, edges between vertex i to vertex j are not +distinguished during search operations. + +- Two pointers to next edges for the starting and ending vertices, where next[0] points to the +next edge in the vtx[0] adjacency list and next[1] points to the next edge in the vtx[1] +adjacency list. + +@see CvGraphEdge, CvGraphVtx, CvGraphVtx2D, CvGraph +@{ +*/ +#define CV_GRAPH_EDGE_FIELDS() \ + int flags; \ + float weight; \ + struct CvGraphEdge* next[2]; \ + struct CvGraphVtx* vtx[2]; + + +#define CV_GRAPH_VERTEX_FIELDS() \ + int flags; \ + struct CvGraphEdge* first; + + +typedef struct CvGraphEdge +{ + CV_GRAPH_EDGE_FIELDS() +} +CvGraphEdge; + +typedef struct CvGraphVtx +{ + CV_GRAPH_VERTEX_FIELDS() +} +CvGraphVtx; + +typedef struct CvGraphVtx2D +{ + CV_GRAPH_VERTEX_FIELDS() + CvPoint2D32f* ptr; +} +CvGraphVtx2D; + +/** + Graph is "derived" from the set (this is set a of vertices) + and includes another set (edges) +*/ +#define CV_GRAPH_FIELDS() \ + CV_SET_FIELDS() \ + CvSet* edges; + +typedef struct CvGraph +{ + CV_GRAPH_FIELDS() +} +CvGraph; + +#define CV_TYPE_NAME_GRAPH "opencv-graph" + +/** @} */ + +/*********************************** Chain/Contour *************************************/ + +typedef struct CvChain +{ + CV_SEQUENCE_FIELDS() + CvPoint origin; +} +CvChain; + +#define CV_CONTOUR_FIELDS() \ + CV_SEQUENCE_FIELDS() \ + CvRect rect; \ + int color; \ + int reserved[3]; + +typedef struct CvContour +{ + CV_CONTOUR_FIELDS() +} +CvContour; + +typedef CvContour CvPoint2DSeq; + +/****************************************************************************************\ +* Sequence types * +\****************************************************************************************/ + +#define CV_SEQ_MAGIC_VAL 0x42990000 + +#define CV_IS_SEQ(seq) \ + ((seq) != NULL && (((CvSeq*)(seq))->flags & CV_MAGIC_MASK) == CV_SEQ_MAGIC_VAL) + +#define CV_SET_MAGIC_VAL 0x42980000 +#define CV_IS_SET(set) \ + ((set) != NULL && (((CvSeq*)(set))->flags & CV_MAGIC_MASK) == CV_SET_MAGIC_VAL) + +#define CV_SEQ_ELTYPE_BITS 12 +#define CV_SEQ_ELTYPE_MASK ((1 << CV_SEQ_ELTYPE_BITS) - 1) + +#define CV_SEQ_ELTYPE_POINT CV_32SC2 /**< (x,y) */ +#define CV_SEQ_ELTYPE_CODE CV_8UC1 /**< freeman code: 0..7 */ +#define CV_SEQ_ELTYPE_GENERIC 0 +#define CV_SEQ_ELTYPE_PTR CV_USRTYPE1 +#define CV_SEQ_ELTYPE_PPOINT CV_SEQ_ELTYPE_PTR /**< &(x,y) */ +#define CV_SEQ_ELTYPE_INDEX CV_32SC1 /**< #(x,y) */ +#define CV_SEQ_ELTYPE_GRAPH_EDGE 0 /**< &next_o, &next_d, &vtx_o, &vtx_d */ +#define CV_SEQ_ELTYPE_GRAPH_VERTEX 0 /**< first_edge, &(x,y) */ +#define CV_SEQ_ELTYPE_TRIAN_ATR 0 /**< vertex of the binary tree */ +#define CV_SEQ_ELTYPE_CONNECTED_COMP 0 /**< connected component */ +#define CV_SEQ_ELTYPE_POINT3D CV_32FC3 /**< (x,y,z) */ + +#define CV_SEQ_KIND_BITS 2 +#define CV_SEQ_KIND_MASK (((1 << CV_SEQ_KIND_BITS) - 1)<flags & CV_SEQ_ELTYPE_MASK) +#define CV_SEQ_KIND( seq ) ((seq)->flags & CV_SEQ_KIND_MASK ) + +/** flag checking */ +#define CV_IS_SEQ_INDEX( seq ) ((CV_SEQ_ELTYPE(seq) == CV_SEQ_ELTYPE_INDEX) && \ + (CV_SEQ_KIND(seq) == CV_SEQ_KIND_GENERIC)) + +#define CV_IS_SEQ_CURVE( seq ) (CV_SEQ_KIND(seq) == CV_SEQ_KIND_CURVE) +#define CV_IS_SEQ_CLOSED( seq ) (((seq)->flags & CV_SEQ_FLAG_CLOSED) != 0) +#define CV_IS_SEQ_CONVEX( seq ) 0 +#define CV_IS_SEQ_HOLE( seq ) (((seq)->flags & CV_SEQ_FLAG_HOLE) != 0) +#define CV_IS_SEQ_SIMPLE( seq ) 1 + +/** type checking macros */ +#define CV_IS_SEQ_POINT_SET( seq ) \ + ((CV_SEQ_ELTYPE(seq) == CV_32SC2 || CV_SEQ_ELTYPE(seq) == CV_32FC2)) + +#define CV_IS_SEQ_POINT_SUBSET( seq ) \ + (CV_IS_SEQ_INDEX( seq ) || CV_SEQ_ELTYPE(seq) == CV_SEQ_ELTYPE_PPOINT) + +#define CV_IS_SEQ_POLYLINE( seq ) \ + (CV_SEQ_KIND(seq) == CV_SEQ_KIND_CURVE && CV_IS_SEQ_POINT_SET(seq)) + +#define CV_IS_SEQ_POLYGON( seq ) \ + (CV_IS_SEQ_POLYLINE(seq) && CV_IS_SEQ_CLOSED(seq)) + +#define CV_IS_SEQ_CHAIN( seq ) \ + (CV_SEQ_KIND(seq) == CV_SEQ_KIND_CURVE && (seq)->elem_size == 1) + +#define CV_IS_SEQ_CONTOUR( seq ) \ + (CV_IS_SEQ_CLOSED(seq) && (CV_IS_SEQ_POLYLINE(seq) || CV_IS_SEQ_CHAIN(seq))) + +#define CV_IS_SEQ_CHAIN_CONTOUR( seq ) \ + (CV_IS_SEQ_CHAIN( seq ) && CV_IS_SEQ_CLOSED( seq )) + +#define CV_IS_SEQ_POLYGON_TREE( seq ) \ + (CV_SEQ_ELTYPE (seq) == CV_SEQ_ELTYPE_TRIAN_ATR && \ + CV_SEQ_KIND( seq ) == CV_SEQ_KIND_BIN_TREE ) + +#define CV_IS_GRAPH( seq ) \ + (CV_IS_SET(seq) && CV_SEQ_KIND((CvSet*)(seq)) == CV_SEQ_KIND_GRAPH) + +#define CV_IS_GRAPH_ORIENTED( seq ) \ + (((seq)->flags & CV_GRAPH_FLAG_ORIENTED) != 0) + +#define CV_IS_SUBDIV2D( seq ) \ + (CV_IS_SET(seq) && CV_SEQ_KIND((CvSet*)(seq)) == CV_SEQ_KIND_SUBDIV2D) + +/****************************************************************************************/ +/* Sequence writer & reader */ +/****************************************************************************************/ + +#define CV_SEQ_WRITER_FIELDS() \ + int header_size; \ + CvSeq* seq; /**< the sequence written */ \ + CvSeqBlock* block; /**< current block */ \ + schar* ptr; /**< pointer to free space */ \ + schar* block_min; /**< pointer to the beginning of block*/\ + schar* block_max; /**< pointer to the end of block */ + +typedef struct CvSeqWriter +{ + CV_SEQ_WRITER_FIELDS() +} +CvSeqWriter; + + +#define CV_SEQ_READER_FIELDS() \ + int header_size; \ + CvSeq* seq; /**< sequence, beign read */ \ + CvSeqBlock* block; /**< current block */ \ + schar* ptr; /**< pointer to element be read next */ \ + schar* block_min; /**< pointer to the beginning of block */\ + schar* block_max; /**< pointer to the end of block */ \ + int delta_index;/**< = seq->first->start_index */ \ + schar* prev_elem; /**< pointer to previous element */ + +typedef struct CvSeqReader +{ + CV_SEQ_READER_FIELDS() +} +CvSeqReader; + +/****************************************************************************************/ +/* Operations on sequences */ +/****************************************************************************************/ + +#define CV_SEQ_ELEM( seq, elem_type, index ) \ +/** assert gives some guarantee that parameter is valid */ \ +( assert(sizeof((seq)->first[0]) == sizeof(CvSeqBlock) && \ + (seq)->elem_size == sizeof(elem_type)), \ + (elem_type*)((seq)->first && (unsigned)index < \ + (unsigned)((seq)->first->count) ? \ + (seq)->first->data + (index) * sizeof(elem_type) : \ + cvGetSeqElem( (CvSeq*)(seq), (index) ))) +#define CV_GET_SEQ_ELEM( elem_type, seq, index ) CV_SEQ_ELEM( (seq), elem_type, (index) ) + +/** Add element to sequence: */ +#define CV_WRITE_SEQ_ELEM_VAR( elem_ptr, writer ) \ +{ \ + if( (writer).ptr >= (writer).block_max ) \ + { \ + cvCreateSeqBlock( &writer); \ + } \ + memcpy((writer).ptr, elem_ptr, (writer).seq->elem_size);\ + (writer).ptr += (writer).seq->elem_size; \ +} + +#define CV_WRITE_SEQ_ELEM( elem, writer ) \ +{ \ + assert( (writer).seq->elem_size == sizeof(elem)); \ + if( (writer).ptr >= (writer).block_max ) \ + { \ + cvCreateSeqBlock( &writer); \ + } \ + assert( (writer).ptr <= (writer).block_max - sizeof(elem));\ + memcpy((writer).ptr, &(elem), sizeof(elem)); \ + (writer).ptr += sizeof(elem); \ +} + + +/** Move reader position forward: */ +#define CV_NEXT_SEQ_ELEM( elem_size, reader ) \ +{ \ + if( ((reader).ptr += (elem_size)) >= (reader).block_max ) \ + { \ + cvChangeSeqBlock( &(reader), 1 ); \ + } \ +} + + +/** Move reader position backward: */ +#define CV_PREV_SEQ_ELEM( elem_size, reader ) \ +{ \ + if( ((reader).ptr -= (elem_size)) < (reader).block_min ) \ + { \ + cvChangeSeqBlock( &(reader), -1 ); \ + } \ +} + +/** Read element and move read position forward: */ +#define CV_READ_SEQ_ELEM( elem, reader ) \ +{ \ + assert( (reader).seq->elem_size == sizeof(elem)); \ + memcpy( &(elem), (reader).ptr, sizeof((elem))); \ + CV_NEXT_SEQ_ELEM( sizeof(elem), reader ) \ +} + +/** Read element and move read position backward: */ +#define CV_REV_READ_SEQ_ELEM( elem, reader ) \ +{ \ + assert( (reader).seq->elem_size == sizeof(elem)); \ + memcpy(&(elem), (reader).ptr, sizeof((elem))); \ + CV_PREV_SEQ_ELEM( sizeof(elem), reader ) \ +} + + +#define CV_READ_CHAIN_POINT( _pt, reader ) \ +{ \ + (_pt) = (reader).pt; \ + if( (reader).ptr ) \ + { \ + CV_READ_SEQ_ELEM( (reader).code, (reader)); \ + assert( ((reader).code & ~7) == 0 ); \ + (reader).pt.x += (reader).deltas[(int)(reader).code][0]; \ + (reader).pt.y += (reader).deltas[(int)(reader).code][1]; \ + } \ +} + +#define CV_CURRENT_POINT( reader ) (*((CvPoint*)((reader).ptr))) +#define CV_PREV_POINT( reader ) (*((CvPoint*)((reader).prev_elem))) + +#define CV_READ_EDGE( pt1, pt2, reader ) \ +{ \ + assert( sizeof(pt1) == sizeof(CvPoint) && \ + sizeof(pt2) == sizeof(CvPoint) && \ + reader.seq->elem_size == sizeof(CvPoint)); \ + (pt1) = CV_PREV_POINT( reader ); \ + (pt2) = CV_CURRENT_POINT( reader ); \ + (reader).prev_elem = (reader).ptr; \ + CV_NEXT_SEQ_ELEM( sizeof(CvPoint), (reader)); \ +} + +/************ Graph macros ************/ + +/** Return next graph edge for given vertex: */ +#define CV_NEXT_GRAPH_EDGE( edge, vertex ) \ + (assert((edge)->vtx[0] == (vertex) || (edge)->vtx[1] == (vertex)), \ + (edge)->next[(edge)->vtx[1] == (vertex)]) + + + +/****************************************************************************************\ +* Data structures for persistence (a.k.a serialization) functionality * +\****************************************************************************************/ + +/** "black box" file storage */ +typedef struct CvFileStorage CvFileStorage; + +/** Storage flags: */ +#define CV_STORAGE_READ 0 +#define CV_STORAGE_WRITE 1 +#define CV_STORAGE_WRITE_TEXT CV_STORAGE_WRITE +#define CV_STORAGE_WRITE_BINARY CV_STORAGE_WRITE +#define CV_STORAGE_APPEND 2 +#define CV_STORAGE_MEMORY 4 +#define CV_STORAGE_FORMAT_MASK (7<<3) +#define CV_STORAGE_FORMAT_AUTO 0 +#define CV_STORAGE_FORMAT_XML 8 +#define CV_STORAGE_FORMAT_YAML 16 +#define CV_STORAGE_FORMAT_JSON 24 +#define CV_STORAGE_BASE64 64 +#define CV_STORAGE_WRITE_BASE64 (CV_STORAGE_BASE64 | CV_STORAGE_WRITE) + +/** @brief List of attributes. : + +In the current implementation, attributes are used to pass extra parameters when writing user +objects (see cvWrite). XML attributes inside tags are not supported, aside from the object type +specification (type_id attribute). +@see cvAttrList, cvAttrValue + */ +typedef struct CvAttrList +{ + const char** attr; /**< NULL-terminated array of (attribute_name,attribute_value) pairs. */ + struct CvAttrList* next; /**< Pointer to next chunk of the attributes list. */ +} +CvAttrList; + +/** initializes CvAttrList structure */ +CV_INLINE CvAttrList cvAttrList( const char** attr CV_DEFAULT(NULL), + CvAttrList* next CV_DEFAULT(NULL) ) +{ + CvAttrList l; + l.attr = attr; + l.next = next; + + return l; +} + +struct CvTypeInfo; + +#define CV_NODE_NONE 0 +#define CV_NODE_INT 1 +#define CV_NODE_INTEGER CV_NODE_INT +#define CV_NODE_REAL 2 +#define CV_NODE_FLOAT CV_NODE_REAL +#define CV_NODE_STR 3 +#define CV_NODE_STRING CV_NODE_STR +#define CV_NODE_REF 4 /**< not used */ +#define CV_NODE_SEQ 5 +#define CV_NODE_MAP 6 +#define CV_NODE_TYPE_MASK 7 + +#define CV_NODE_TYPE(flags) ((flags) & CV_NODE_TYPE_MASK) + +/** file node flags */ +#define CV_NODE_FLOW 8 /**= CV_NODE_SEQ) +#define CV_NODE_IS_FLOW(flags) (((flags) & CV_NODE_FLOW) != 0) +#define CV_NODE_IS_EMPTY(flags) (((flags) & CV_NODE_EMPTY) != 0) +#define CV_NODE_IS_USER(flags) (((flags) & CV_NODE_USER) != 0) +#define CV_NODE_HAS_NAME(flags) (((flags) & CV_NODE_NAMED) != 0) + +#define CV_NODE_SEQ_SIMPLE 256 +#define CV_NODE_SEQ_IS_SIMPLE(seq) (((seq)->flags & CV_NODE_SEQ_SIMPLE) != 0) + +typedef struct CvString +{ + int len; + char* ptr; +} +CvString; + +/** All the keys (names) of elements in the read file storage + are stored in the hash to speed up the lookup operations: */ +typedef struct CvStringHashNode +{ + unsigned hashval; + CvString str; + struct CvStringHashNode* next; +} +CvStringHashNode; + +typedef struct CvGenericHash CvFileNodeHash; + +/** Basic element of the file storage - scalar or collection: */ +typedef struct CvFileNode +{ + int tag; + struct CvTypeInfo* info; /**< type information + (only for user-defined object, for others it is 0) */ + union + { + double f; /**< scalar floating-point number */ + int i; /**< scalar integer number */ + CvString str; /**< text string */ + CvSeq* seq; /**< sequence (ordered collection of file nodes) */ + CvFileNodeHash* map; /**< map (collection of named file nodes) */ + } data; +} +CvFileNode; + +#ifdef __cplusplus +extern "C" { +#endif +typedef int (CV_CDECL *CvIsInstanceFunc)( const void* struct_ptr ); +typedef void (CV_CDECL *CvReleaseFunc)( void** struct_dblptr ); +typedef void* (CV_CDECL *CvReadFunc)( CvFileStorage* storage, CvFileNode* node ); +typedef void (CV_CDECL *CvWriteFunc)( CvFileStorage* storage, const char* name, + const void* struct_ptr, CvAttrList attributes ); +typedef void* (CV_CDECL *CvCloneFunc)( const void* struct_ptr ); +#ifdef __cplusplus +} +#endif + +/** @brief Type information + +The structure contains information about one of the standard or user-defined types. Instances of the +type may or may not contain a pointer to the corresponding CvTypeInfo structure. In any case, there +is a way to find the type info structure for a given object using the cvTypeOf function. +Alternatively, type info can be found by type name using cvFindType, which is used when an object +is read from file storage. The user can register a new type with cvRegisterType that adds the type +information structure into the beginning of the type list. Thus, it is possible to create +specialized types from generic standard types and override the basic methods. + */ +typedef struct CvTypeInfo +{ + int flags; /**< not used */ + int header_size; /**< sizeof(CvTypeInfo) */ + struct CvTypeInfo* prev; /**< previous registered type in the list */ + struct CvTypeInfo* next; /**< next registered type in the list */ + const char* type_name; /**< type name, written to file storage */ + CvIsInstanceFunc is_instance; /**< checks if the passed object belongs to the type */ + CvReleaseFunc release; /**< releases object (memory etc.) */ + CvReadFunc read; /**< reads object from file storage */ + CvWriteFunc write; /**< writes object to file storage */ + CvCloneFunc clone; /**< creates a copy of the object */ +} +CvTypeInfo; + + +/**** System data types ******/ + +typedef struct CvPluginFuncInfo +{ + void** func_addr; + void* default_func_addr; + const char* func_names; + int search_modules; + int loaded_from; +} +CvPluginFuncInfo; + +typedef struct CvModuleInfo +{ + struct CvModuleInfo* next; + const char* name; + const char* version; + CvPluginFuncInfo* func_tab; +} +CvModuleInfo; + +/** @} */ + +#endif /*OPENCV_CORE_TYPES_H*/ + +/* End of file. */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/utility.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/utility.hpp new file mode 100644 index 0000000000000000000000000000000000000000..7a7158f5f9d5173c740c46f623b9cb723eabdcfd --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/utility.hpp @@ -0,0 +1,1358 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Copyright (C) 2015, Itseez Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_CORE_UTILITY_H +#define OPENCV_CORE_UTILITY_H + +#ifndef __cplusplus +# error utility.hpp header must be compiled as C++ +#endif + +#if defined(check) +# warning Detected Apple 'check' macro definition, it can cause build conflicts. Please, include this header before any Apple headers. +#endif + +#include "opencv2/core.hpp" +#include + +#ifdef CV_CXX11 +#include +#endif + +namespace cv +{ + +#ifdef CV_COLLECT_IMPL_DATA +CV_EXPORTS void setImpl(int flags); // set implementation flags and reset storage arrays +CV_EXPORTS void addImpl(int flag, const char* func = 0); // add implementation and function name to storage arrays +// Get stored implementation flags and functions names arrays +// Each implementation entry correspond to function name entry, so you can find which implementation was executed in which function +CV_EXPORTS int getImpl(std::vector &impl, std::vector &funName); + +CV_EXPORTS bool useCollection(); // return implementation collection state +CV_EXPORTS void setUseCollection(bool flag); // set implementation collection state + +#define CV_IMPL_PLAIN 0x01 // native CPU OpenCV implementation +#define CV_IMPL_OCL 0x02 // OpenCL implementation +#define CV_IMPL_IPP 0x04 // IPP implementation +#define CV_IMPL_MT 0x10 // multithreaded implementation + +#define CV_IMPL_ADD(impl) \ + if(cv::useCollection()) \ + { \ + cv::addImpl(impl, CV_Func); \ + } +#else +#define CV_IMPL_ADD(impl) +#endif + +//! @addtogroup core_utils +//! @{ + +/** @brief Automatically Allocated Buffer Class + + The class is used for temporary buffers in functions and methods. + If a temporary buffer is usually small (a few K's of memory), + but its size depends on the parameters, it makes sense to create a small + fixed-size array on stack and use it if it's large enough. If the required buffer size + is larger than the fixed size, another buffer of sufficient size is allocated dynamically + and released after the processing. Therefore, in typical cases, when the buffer size is small, + there is no overhead associated with malloc()/free(). + At the same time, there is no limit on the size of processed data. + + This is what AutoBuffer does. The template takes 2 parameters - type of the buffer elements and + the number of stack-allocated elements. Here is how the class is used: + + \code + void my_func(const cv::Mat& m) + { + cv::AutoBuffer buf(1000); // create automatic buffer containing 1000 floats + + buf.allocate(m.rows); // if m.rows <= 1000, the pre-allocated buffer is used, + // otherwise the buffer of "m.rows" floats will be allocated + // dynamically and deallocated in cv::AutoBuffer destructor + ... + } + \endcode +*/ +template class AutoBuffer +{ +public: + typedef _Tp value_type; + + //! the default constructor + AutoBuffer(); + //! constructor taking the real buffer size + explicit AutoBuffer(size_t _size); + + //! the copy constructor + AutoBuffer(const AutoBuffer<_Tp, fixed_size>& buf); + //! the assignment operator + AutoBuffer<_Tp, fixed_size>& operator = (const AutoBuffer<_Tp, fixed_size>& buf); + + //! destructor. calls deallocate() + ~AutoBuffer(); + + //! allocates the new buffer of size _size. if the _size is small enough, stack-allocated buffer is used + void allocate(size_t _size); + //! deallocates the buffer if it was dynamically allocated + void deallocate(); + //! resizes the buffer and preserves the content + void resize(size_t _size); + //! returns the current buffer size + size_t size() const; + //! returns pointer to the real buffer, stack-allocated or heap-allocated + inline _Tp* data() { return ptr; } + //! returns read-only pointer to the real buffer, stack-allocated or heap-allocated + inline const _Tp* data() const { return ptr; } + +#if !defined(OPENCV_DISABLE_DEPRECATED_COMPATIBILITY) // use to .data() calls instead + //! returns pointer to the real buffer, stack-allocated or heap-allocated + operator _Tp* () { return ptr; } + //! returns read-only pointer to the real buffer, stack-allocated or heap-allocated + operator const _Tp* () const { return ptr; } +#else + //! returns a reference to the element at specified location. No bounds checking is performed in Release builds. + inline _Tp& operator[] (size_t i) { CV_DbgCheckLT(i, sz, "out of range"); return ptr[i]; } + //! returns a reference to the element at specified location. No bounds checking is performed in Release builds. + inline const _Tp& operator[] (size_t i) const { CV_DbgCheckLT(i, sz, "out of range"); return ptr[i]; } +#endif + +protected: + //! pointer to the real buffer, can point to buf if the buffer is small enough + _Tp* ptr; + //! size of the real buffer + size_t sz; + //! pre-allocated buffer. At least 1 element to confirm C++ standard requirements + _Tp buf[(fixed_size > 0) ? fixed_size : 1]; +}; + +/** @brief Sets/resets the break-on-error mode. + +When the break-on-error mode is set, the default error handler issues a hardware exception, which +can make debugging more convenient. + +\return the previous state + */ +CV_EXPORTS bool setBreakOnError(bool flag); + +extern "C" typedef int (*ErrorCallback)( int status, const char* func_name, + const char* err_msg, const char* file_name, + int line, void* userdata ); + + +/** @brief Sets the new error handler and the optional user data. + + The function sets the new error handler, called from cv::error(). + + \param errCallback the new error handler. If NULL, the default error handler is used. + \param userdata the optional user data pointer, passed to the callback. + \param prevUserdata the optional output parameter where the previous user data pointer is stored + + \return the previous error handler +*/ +CV_EXPORTS ErrorCallback redirectError( ErrorCallback errCallback, void* userdata=0, void** prevUserdata=0); + +CV_EXPORTS String tempfile( const char* suffix = 0); +CV_EXPORTS void glob(String pattern, std::vector& result, bool recursive = false); + +/** @brief OpenCV will try to set the number of threads for the next parallel region. + +If threads == 0, OpenCV will disable threading optimizations and run all it's functions +sequentially. Passing threads \< 0 will reset threads number to system default. This function must +be called outside of parallel region. + +OpenCV will try to run its functions with specified threads number, but some behaviour differs from +framework: +- `TBB` - User-defined parallel constructions will run with the same threads number, if + another is not specified. If later on user creates his own scheduler, OpenCV will use it. +- `OpenMP` - No special defined behaviour. +- `Concurrency` - If threads == 1, OpenCV will disable threading optimizations and run its + functions sequentially. +- `GCD` - Supports only values \<= 0. +- `C=` - No special defined behaviour. +@param nthreads Number of threads used by OpenCV. +@sa getNumThreads, getThreadNum + */ +CV_EXPORTS_W void setNumThreads(int nthreads); + +/** @brief Returns the number of threads used by OpenCV for parallel regions. + +Always returns 1 if OpenCV is built without threading support. + +The exact meaning of return value depends on the threading framework used by OpenCV library: +- `TBB` - The number of threads, that OpenCV will try to use for parallel regions. If there is + any tbb::thread_scheduler_init in user code conflicting with OpenCV, then function returns + default number of threads used by TBB library. +- `OpenMP` - An upper bound on the number of threads that could be used to form a new team. +- `Concurrency` - The number of threads, that OpenCV will try to use for parallel regions. +- `GCD` - Unsupported; returns the GCD thread pool limit (512) for compatibility. +- `C=` - The number of threads, that OpenCV will try to use for parallel regions, if before + called setNumThreads with threads \> 0, otherwise returns the number of logical CPUs, + available for the process. +@sa setNumThreads, getThreadNum + */ +CV_EXPORTS_W int getNumThreads(); + +/** @brief Returns the index of the currently executed thread within the current parallel region. Always +returns 0 if called outside of parallel region. + +@deprecated Current implementation doesn't corresponding to this documentation. + +The exact meaning of the return value depends on the threading framework used by OpenCV library: +- `TBB` - Unsupported with current 4.1 TBB release. Maybe will be supported in future. +- `OpenMP` - The thread number, within the current team, of the calling thread. +- `Concurrency` - An ID for the virtual processor that the current context is executing on (0 + for master thread and unique number for others, but not necessary 1,2,3,...). +- `GCD` - System calling thread's ID. Never returns 0 inside parallel region. +- `C=` - The index of the current parallel task. +@sa setNumThreads, getNumThreads + */ +CV_EXPORTS_W int getThreadNum(); + +/** @brief Returns full configuration time cmake output. + +Returned value is raw cmake output including version control system revision, compiler version, +compiler flags, enabled modules and third party libraries, etc. Output format depends on target +architecture. + */ +CV_EXPORTS_W const String& getBuildInformation(); + +/** @brief Returns library version string + +For example "3.4.1-dev". + +@sa getMajorVersion, getMinorVersion, getRevisionVersion +*/ +CV_EXPORTS_W String getVersionString(); + +/** @brief Returns major library version */ +CV_EXPORTS_W int getVersionMajor(); + +/** @brief Returns minor library version */ +CV_EXPORTS_W int getVersionMinor(); + +/** @brief Returns revision field of the library version */ +CV_EXPORTS_W int getVersionRevision(); + +/** @brief Returns the number of ticks. + +The function returns the number of ticks after the certain event (for example, when the machine was +turned on). It can be used to initialize RNG or to measure a function execution time by reading the +tick count before and after the function call. +@sa getTickFrequency, TickMeter + */ +CV_EXPORTS_W int64 getTickCount(); + +/** @brief Returns the number of ticks per second. + +The function returns the number of ticks per second. That is, the following code computes the +execution time in seconds: +@code + double t = (double)getTickCount(); + // do something ... + t = ((double)getTickCount() - t)/getTickFrequency(); +@endcode +@sa getTickCount, TickMeter + */ +CV_EXPORTS_W double getTickFrequency(); + +/** @brief a Class to measure passing time. + +The class computes passing time by counting the number of ticks per second. That is, the following code computes the +execution time in seconds: +@code +TickMeter tm; +tm.start(); +// do something ... +tm.stop(); +std::cout << tm.getTimeSec(); +@endcode + +It is also possible to compute the average time over multiple runs: +@code +TickMeter tm; +for (int i = 0; i < 100; i++) +{ + tm.start(); + // do something ... + tm.stop(); +} +double average_time = tm.getTimeSec() / tm.getCounter(); +std::cout << "Average time in second per iteration is: " << average_time << std::endl; +@endcode +@sa getTickCount, getTickFrequency +*/ + +class CV_EXPORTS_W TickMeter +{ +public: + //! the default constructor + CV_WRAP TickMeter() + { + reset(); + } + + /** + starts counting ticks. + */ + CV_WRAP void start() + { + startTime = cv::getTickCount(); + } + + /** + stops counting ticks. + */ + CV_WRAP void stop() + { + int64 time = cv::getTickCount(); + if (startTime == 0) + return; + ++counter; + sumTime += (time - startTime); + startTime = 0; + } + + /** + returns counted ticks. + */ + CV_WRAP int64 getTimeTicks() const + { + return sumTime; + } + + /** + returns passed time in microseconds. + */ + CV_WRAP double getTimeMicro() const + { + return getTimeMilli()*1e3; + } + + /** + returns passed time in milliseconds. + */ + CV_WRAP double getTimeMilli() const + { + return getTimeSec()*1e3; + } + + /** + returns passed time in seconds. + */ + CV_WRAP double getTimeSec() const + { + return (double)getTimeTicks() / getTickFrequency(); + } + + /** + returns internal counter value. + */ + CV_WRAP int64 getCounter() const + { + return counter; + } + + /** + resets internal values. + */ + CV_WRAP void reset() + { + startTime = 0; + sumTime = 0; + counter = 0; + } + +private: + int64 counter; + int64 sumTime; + int64 startTime; +}; + +/** @brief output operator +@code +TickMeter tm; +tm.start(); +// do something ... +tm.stop(); +std::cout << tm; +@endcode +*/ + +static inline +std::ostream& operator << (std::ostream& out, const TickMeter& tm) +{ + return out << tm.getTimeSec() << "sec"; +} + +/** @brief Returns the number of CPU ticks. + +The function returns the current number of CPU ticks on some architectures (such as x86, x64, +PowerPC). On other platforms the function is equivalent to getTickCount. It can also be used for +very accurate time measurements, as well as for RNG initialization. Note that in case of multi-CPU +systems a thread, from which getCPUTickCount is called, can be suspended and resumed at another CPU +with its own counter. So, theoretically (and practically) the subsequent calls to the function do +not necessary return the monotonously increasing values. Also, since a modern CPU varies the CPU +frequency depending on the load, the number of CPU clocks spent in some code cannot be directly +converted to time units. Therefore, getTickCount is generally a preferable solution for measuring +execution time. + */ +CV_EXPORTS_W int64 getCPUTickCount(); + +/** @brief Returns true if the specified feature is supported by the host hardware. + +The function returns true if the host hardware supports the specified feature. When user calls +setUseOptimized(false), the subsequent calls to checkHardwareSupport() will return false until +setUseOptimized(true) is called. This way user can dynamically switch on and off the optimized code +in OpenCV. +@param feature The feature of interest, one of cv::CpuFeatures + */ +CV_EXPORTS_W bool checkHardwareSupport(int feature); + +/** @brief Returns feature name by ID + +Returns empty string if feature is not defined +*/ +CV_EXPORTS_W String getHardwareFeatureName(int feature); + +/** @brief Returns list of CPU features enabled during compilation. + +Returned value is a string containing space separated list of CPU features with following markers: + +- no markers - baseline features +- prefix `*` - features enabled in dispatcher +- suffix `?` - features enabled but not available in HW + +Example: `SSE SSE2 SSE3 *SSE4.1 *SSE4.2 *FP16 *AVX *AVX2 *AVX512-SKX?` +*/ +CV_EXPORTS std::string getCPUFeaturesLine(); + +/** @brief Returns the number of logical CPUs available for the process. + */ +CV_EXPORTS_W int getNumberOfCPUs(); + + +/** @brief Aligns a pointer to the specified number of bytes. + +The function returns the aligned pointer of the same type as the input pointer: +\f[\texttt{(_Tp*)(((size_t)ptr + n-1) & -n)}\f] +@param ptr Aligned pointer. +@param n Alignment size that must be a power of two. + */ +template static inline _Tp* alignPtr(_Tp* ptr, int n=(int)sizeof(_Tp)) +{ + CV_DbgAssert((n & (n - 1)) == 0); // n is a power of 2 + return (_Tp*)(((size_t)ptr + n-1) & -n); +} + +/** @brief Aligns a buffer size to the specified number of bytes. + +The function returns the minimum number that is greater than or equal to sz and is divisible by n : +\f[\texttt{(sz + n-1) & -n}\f] +@param sz Buffer size to align. +@param n Alignment size that must be a power of two. + */ +static inline size_t alignSize(size_t sz, int n) +{ + CV_DbgAssert((n & (n - 1)) == 0); // n is a power of 2 + return (sz + n-1) & -n; +} + +/** @brief Integer division with result round up. + +Use this function instead of `ceil((float)a / b)` expressions. + +@sa alignSize +*/ +static inline int divUp(int a, unsigned int b) +{ + CV_DbgAssert(a >= 0); + return (a + b - 1) / b; +} +/** @overload */ +static inline size_t divUp(size_t a, unsigned int b) +{ + return (a + b - 1) / b; +} + +/** @brief Round first value up to the nearest multiple of second value. + +Use this function instead of `ceil((float)a / b) * b` expressions. + +@sa divUp +*/ +static inline int roundUp(int a, unsigned int b) +{ + CV_DbgAssert(a >= 0); + return a + b - 1 - (a + b -1) % b; +} +/** @overload */ +static inline size_t roundUp(size_t a, unsigned int b) +{ + return a + b - 1 - (a + b - 1) % b; +} + +/** @brief Enables or disables the optimized code. + +The function can be used to dynamically turn on and off optimized dispatched code (code that uses SSE4.2, AVX/AVX2, +and other instructions on the platforms that support it). It sets a global flag that is further +checked by OpenCV functions. Since the flag is not checked in the inner OpenCV loops, it is only +safe to call the function on the very top level in your application where you can be sure that no +other OpenCV function is currently executed. + +By default, the optimized code is enabled unless you disable it in CMake. The current status can be +retrieved using useOptimized. +@param onoff The boolean flag specifying whether the optimized code should be used (onoff=true) +or not (onoff=false). + */ +CV_EXPORTS_W void setUseOptimized(bool onoff); + +/** @brief Returns the status of optimized code usage. + +The function returns true if the optimized code is enabled. Otherwise, it returns false. + */ +CV_EXPORTS_W bool useOptimized(); + +static inline size_t getElemSize(int type) { return (size_t)CV_ELEM_SIZE(type); } + +/////////////////////////////// Parallel Primitives ////////////////////////////////// + +/** @brief Base class for parallel data processors +*/ +class CV_EXPORTS ParallelLoopBody +{ +public: + virtual ~ParallelLoopBody(); + virtual void operator() (const Range& range) const = 0; +}; + +/** @brief Parallel data processor +*/ +CV_EXPORTS void parallel_for_(const Range& range, const ParallelLoopBody& body, double nstripes=-1.); + +#ifdef CV_CXX11 +class ParallelLoopBodyLambdaWrapper : public ParallelLoopBody +{ +private: + std::function m_functor; +public: + ParallelLoopBodyLambdaWrapper(std::function functor) : + m_functor(functor) + { } + + virtual void operator() (const cv::Range& range) const CV_OVERRIDE + { + m_functor(range); + } +}; + +inline void parallel_for_(const Range& range, std::function functor, double nstripes=-1.) +{ + parallel_for_(range, ParallelLoopBodyLambdaWrapper(functor), nstripes); +} +#endif + +/////////////////////////////// forEach method of cv::Mat //////////////////////////// +template inline +void Mat::forEach_impl(const Functor& operation) { + if (false) { + operation(*reinterpret_cast<_Tp*>(0), reinterpret_cast(0)); + // If your compiler fails in this line. + // Please check that your functor signature is + // (_Tp&, const int*) <- multi-dimensional + // or (_Tp&, void*) <- in case you don't need current idx. + } + + CV_Assert(this->total() / this->size[this->dims - 1] <= INT_MAX); + const int LINES = static_cast(this->total() / this->size[this->dims - 1]); + + class PixelOperationWrapper :public ParallelLoopBody + { + public: + PixelOperationWrapper(Mat_<_Tp>* const frame, const Functor& _operation) + : mat(frame), op(_operation) {} + virtual ~PixelOperationWrapper(){} + // ! Overloaded virtual operator + // convert range call to row call. + virtual void operator()(const Range &range) const CV_OVERRIDE + { + const int DIMS = mat->dims; + const int COLS = mat->size[DIMS - 1]; + if (DIMS <= 2) { + for (int row = range.start; row < range.end; ++row) { + this->rowCall2(row, COLS); + } + } else { + std::vector idx(DIMS); /// idx is modified in this->rowCall + idx[DIMS - 2] = range.start - 1; + + for (int line_num = range.start; line_num < range.end; ++line_num) { + idx[DIMS - 2]++; + for (int i = DIMS - 2; i >= 0; --i) { + if (idx[i] >= mat->size[i]) { + idx[i - 1] += idx[i] / mat->size[i]; + idx[i] %= mat->size[i]; + continue; // carry-over; + } + else { + break; + } + } + this->rowCall(&idx[0], COLS, DIMS); + } + } + } + private: + Mat_<_Tp>* const mat; + const Functor op; + // ! Call operator for each elements in this row. + inline void rowCall(int* const idx, const int COLS, const int DIMS) const { + int &col = idx[DIMS - 1]; + col = 0; + _Tp* pixel = &(mat->template at<_Tp>(idx)); + + while (col < COLS) { + op(*pixel, const_cast(idx)); + pixel++; col++; + } + col = 0; + } + // ! Call operator for each elements in this row. 2d mat special version. + inline void rowCall2(const int row, const int COLS) const { + union Index{ + int body[2]; + operator const int*() const { + return reinterpret_cast(this); + } + int& operator[](const int i) { + return body[i]; + } + } idx = {{row, 0}}; + // Special union is needed to avoid + // "error: array subscript is above array bounds [-Werror=array-bounds]" + // when call the functor `op` such that access idx[3]. + + _Tp* pixel = &(mat->template at<_Tp>(idx)); + const _Tp* const pixel_end = pixel + COLS; + while(pixel < pixel_end) { + op(*pixel++, static_cast(idx)); + idx[1]++; + } + } + PixelOperationWrapper& operator=(const PixelOperationWrapper &) { + CV_Assert(false); + // We can not remove this implementation because Visual Studio warning C4822. + return *this; + } + }; + + parallel_for_(cv::Range(0, LINES), PixelOperationWrapper(reinterpret_cast*>(this), operation)); +} + +/////////////////////////// Synchronization Primitives /////////////////////////////// + +class CV_EXPORTS Mutex +{ +public: + Mutex(); + ~Mutex(); + Mutex(const Mutex& m); + Mutex& operator = (const Mutex& m); + + void lock(); + bool trylock(); + void unlock(); + + struct Impl; +protected: + Impl* impl; +}; + +class CV_EXPORTS AutoLock +{ +public: + AutoLock(Mutex& m) : mutex(&m) { mutex->lock(); } + ~AutoLock() { mutex->unlock(); } +protected: + Mutex* mutex; +private: + AutoLock(const AutoLock&); + AutoLock& operator = (const AutoLock&); +}; + +// TLS interface +class CV_EXPORTS TLSDataContainer +{ +protected: + TLSDataContainer(); + virtual ~TLSDataContainer(); + + void gatherData(std::vector &data) const; +#if OPENCV_ABI_COMPATIBILITY > 300 + void* getData() const; + void release(); + +private: +#else + void release(); + +public: + void* getData() const; +#endif + virtual void* createDataInstance() const = 0; + virtual void deleteDataInstance(void* pData) const = 0; + + int key_; + +public: + void cleanup(); //! Release created TLS data container objects. It is similar to release() call, but it keeps TLS container valid. +}; + +// Main TLS data class +template +class TLSData : protected TLSDataContainer +{ +public: + inline TLSData() {} + inline ~TLSData() { release(); } // Release key and delete associated data + inline T* get() const { return (T*)getData(); } // Get data associated with key + inline T& getRef() const { T* ptr = (T*)getData(); CV_Assert(ptr); return *ptr; } // Get data associated with key + + // Get data from all threads + inline void gather(std::vector &data) const + { + std::vector &dataVoid = reinterpret_cast&>(data); + gatherData(dataVoid); + } + + inline void cleanup() { TLSDataContainer::cleanup(); } + +private: + virtual void* createDataInstance() const CV_OVERRIDE {return new T;} // Wrapper to allocate data by template + virtual void deleteDataInstance(void* pData) const CV_OVERRIDE {delete (T*)pData;} // Wrapper to release data by template + + // Disable TLS copy operations + TLSData(TLSData &) {} + TLSData& operator =(const TLSData &) {return *this;} +}; + +/** @brief Designed for command line parsing + +The sample below demonstrates how to use CommandLineParser: +@code + CommandLineParser parser(argc, argv, keys); + parser.about("Application name v1.0.0"); + + if (parser.has("help")) + { + parser.printMessage(); + return 0; + } + + int N = parser.get("N"); + double fps = parser.get("fps"); + String path = parser.get("path"); + + use_time_stamp = parser.has("timestamp"); + + String img1 = parser.get(0); + String img2 = parser.get(1); + + int repeat = parser.get(2); + + if (!parser.check()) + { + parser.printErrors(); + return 0; + } +@endcode + +### Keys syntax + +The keys parameter is a string containing several blocks, each one is enclosed in curly braces and +describes one argument. Each argument contains three parts separated by the `|` symbol: + +-# argument names is a space-separated list of option synonyms (to mark argument as positional, prefix it with the `@` symbol) +-# default value will be used if the argument was not provided (can be empty) +-# help message (can be empty) + +For example: + +@code{.cpp} + const String keys = + "{help h usage ? | | print this message }" + "{@image1 | | image1 for compare }" + "{@image2 || image2 for compare }" + "{@repeat |1 | number }" + "{path |. | path to file }" + "{fps | -1.0 | fps for output video }" + "{N count |100 | count of objects }" + "{ts timestamp | | use time stamp }" + ; +} +@endcode + +Note that there are no default values for `help` and `timestamp` so we can check their presence using the `has()` method. +Arguments with default values are considered to be always present. Use the `get()` method in these cases to check their +actual value instead. + +String keys like `get("@image1")` return the empty string `""` by default - even with an empty default value. +Use the special `` default value to enforce that the returned string must not be empty. (like in `get("@image2")`) + +### Usage + +For the described keys: + +@code{.sh} + # Good call (3 positional parameters: image1, image2 and repeat; N is 200, ts is true) + $ ./app -N=200 1.png 2.jpg 19 -ts + + # Bad call + $ ./app -fps=aaa + ERRORS: + Parameter 'fps': can not convert: [aaa] to [double] +@endcode + */ +class CV_EXPORTS CommandLineParser +{ +public: + + /** @brief Constructor + + Initializes command line parser object + + @param argc number of command line arguments (from main()) + @param argv array of command line arguments (from main()) + @param keys string describing acceptable command line parameters (see class description for syntax) + */ + CommandLineParser(int argc, const char* const argv[], const String& keys); + + /** @brief Copy constructor */ + CommandLineParser(const CommandLineParser& parser); + + /** @brief Assignment operator */ + CommandLineParser& operator = (const CommandLineParser& parser); + + /** @brief Destructor */ + ~CommandLineParser(); + + /** @brief Returns application path + + This method returns the path to the executable from the command line (`argv[0]`). + + For example, if the application has been started with such a command: + @code{.sh} + $ ./bin/my-executable + @endcode + this method will return `./bin`. + */ + String getPathToApplication() const; + + /** @brief Access arguments by name + + Returns argument converted to selected type. If the argument is not known or can not be + converted to selected type, the error flag is set (can be checked with @ref check). + + For example, define: + @code{.cpp} + String keys = "{N count||}"; + @endcode + + Call: + @code{.sh} + $ ./my-app -N=20 + # or + $ ./my-app --count=20 + @endcode + + Access: + @code{.cpp} + int N = parser.get("N"); + @endcode + + @param name name of the argument + @param space_delete remove spaces from the left and right of the string + @tparam T the argument will be converted to this type if possible + + @note You can access positional arguments by their `@`-prefixed name: + @code{.cpp} + parser.get("@image"); + @endcode + */ + template + T get(const String& name, bool space_delete = true) const + { + T val = T(); + getByName(name, space_delete, ParamType::type, (void*)&val); + return val; + } + + /** @brief Access positional arguments by index + + Returns argument converted to selected type. Indexes are counted from zero. + + For example, define: + @code{.cpp} + String keys = "{@arg1||}{@arg2||}" + @endcode + + Call: + @code{.sh} + ./my-app abc qwe + @endcode + + Access arguments: + @code{.cpp} + String val_1 = parser.get(0); // returns "abc", arg1 + String val_2 = parser.get(1); // returns "qwe", arg2 + @endcode + + @param index index of the argument + @param space_delete remove spaces from the left and right of the string + @tparam T the argument will be converted to this type if possible + */ + template + T get(int index, bool space_delete = true) const + { + T val = T(); + getByIndex(index, space_delete, ParamType::type, (void*)&val); + return val; + } + + /** @brief Check if field was provided in the command line + + @param name argument name to check + */ + bool has(const String& name) const; + + /** @brief Check for parsing errors + + Returns false if error occurred while accessing the parameters (bad conversion, missing arguments, + etc.). Call @ref printErrors to print error messages list. + */ + bool check() const; + + /** @brief Set the about message + + The about message will be shown when @ref printMessage is called, right before arguments table. + */ + void about(const String& message); + + /** @brief Print help message + + This method will print standard help message containing the about message and arguments description. + + @sa about + */ + void printMessage() const; + + /** @brief Print list of errors occurred + + @sa check + */ + void printErrors() const; + +protected: + void getByName(const String& name, bool space_delete, int type, void* dst) const; + void getByIndex(int index, bool space_delete, int type, void* dst) const; + + struct Impl; + Impl* impl; +}; + +//! @} core_utils + +//! @cond IGNORED + +/////////////////////////////// AutoBuffer implementation //////////////////////////////////////// + +template inline +AutoBuffer<_Tp, fixed_size>::AutoBuffer() +{ + ptr = buf; + sz = fixed_size; +} + +template inline +AutoBuffer<_Tp, fixed_size>::AutoBuffer(size_t _size) +{ + ptr = buf; + sz = fixed_size; + allocate(_size); +} + +template inline +AutoBuffer<_Tp, fixed_size>::AutoBuffer(const AutoBuffer<_Tp, fixed_size>& abuf ) +{ + ptr = buf; + sz = fixed_size; + allocate(abuf.size()); + for( size_t i = 0; i < sz; i++ ) + ptr[i] = abuf.ptr[i]; +} + +template inline AutoBuffer<_Tp, fixed_size>& +AutoBuffer<_Tp, fixed_size>::operator = (const AutoBuffer<_Tp, fixed_size>& abuf) +{ + if( this != &abuf ) + { + deallocate(); + allocate(abuf.size()); + for( size_t i = 0; i < sz; i++ ) + ptr[i] = abuf.ptr[i]; + } + return *this; +} + +template inline +AutoBuffer<_Tp, fixed_size>::~AutoBuffer() +{ deallocate(); } + +template inline void +AutoBuffer<_Tp, fixed_size>::allocate(size_t _size) +{ + if(_size <= sz) + { + sz = _size; + return; + } + deallocate(); + sz = _size; + if(_size > fixed_size) + { + ptr = new _Tp[_size]; + } +} + +template inline void +AutoBuffer<_Tp, fixed_size>::deallocate() +{ + if( ptr != buf ) + { + delete[] ptr; + ptr = buf; + sz = fixed_size; + } +} + +template inline void +AutoBuffer<_Tp, fixed_size>::resize(size_t _size) +{ + if(_size <= sz) + { + sz = _size; + return; + } + size_t i, prevsize = sz, minsize = MIN(prevsize, _size); + _Tp* prevptr = ptr; + + ptr = _size > fixed_size ? new _Tp[_size] : buf; + sz = _size; + + if( ptr != prevptr ) + for( i = 0; i < minsize; i++ ) + ptr[i] = prevptr[i]; + for( i = prevsize; i < _size; i++ ) + ptr[i] = _Tp(); + + if( prevptr != buf ) + delete[] prevptr; +} + +template inline size_t +AutoBuffer<_Tp, fixed_size>::size() const +{ return sz; } + +template<> inline std::string CommandLineParser::get(int index, bool space_delete) const +{ + return get(index, space_delete); +} +template<> inline std::string CommandLineParser::get(const String& name, bool space_delete) const +{ + return get(name, space_delete); +} + +//! @endcond + + +// Basic Node class for tree building +template +class CV_EXPORTS Node +{ +public: + Node() + { + m_pParent = 0; + } + Node(OBJECT& payload) : m_payload(payload) + { + m_pParent = 0; + } + ~Node() + { + removeChilds(); + if (m_pParent) + { + int idx = m_pParent->findChild(this); + if (idx >= 0) + m_pParent->m_childs.erase(m_pParent->m_childs.begin() + idx); + } + } + + Node* findChild(OBJECT& payload) const + { + for(size_t i = 0; i < this->m_childs.size(); i++) + { + if(this->m_childs[i]->m_payload == payload) + return this->m_childs[i]; + } + return NULL; + } + + int findChild(Node *pNode) const + { + for (size_t i = 0; i < this->m_childs.size(); i++) + { + if(this->m_childs[i] == pNode) + return (int)i; + } + return -1; + } + + void addChild(Node *pNode) + { + if(!pNode) + return; + + CV_Assert(pNode->m_pParent == 0); + pNode->m_pParent = this; + this->m_childs.push_back(pNode); + } + + void removeChilds() + { + for(size_t i = 0; i < m_childs.size(); i++) + { + m_childs[i]->m_pParent = 0; // avoid excessive parent vector trimming + delete m_childs[i]; + } + m_childs.clear(); + } + + int getDepth() + { + int count = 0; + Node *pParent = m_pParent; + while(pParent) count++, pParent = pParent->m_pParent; + return count; + } + +public: + OBJECT m_payload; + Node* m_pParent; + std::vector*> m_childs; +}; + +// Instrumentation external interface +namespace instr +{ + +#if !defined OPENCV_ABI_CHECK + +enum TYPE +{ + TYPE_GENERAL = 0, // OpenCV API function, e.g. exported function + TYPE_MARKER, // Information marker + TYPE_WRAPPER, // Wrapper function for implementation + TYPE_FUN, // Simple function call +}; + +enum IMPL +{ + IMPL_PLAIN = 0, + IMPL_IPP, + IMPL_OPENCL, +}; + +struct NodeDataTls +{ + NodeDataTls() + { + m_ticksTotal = 0; + } + uint64 m_ticksTotal; +}; + +class CV_EXPORTS NodeData +{ +public: + NodeData(const char* funName = 0, const char* fileName = NULL, int lineNum = 0, void* retAddress = NULL, bool alwaysExpand = false, cv::instr::TYPE instrType = TYPE_GENERAL, cv::instr::IMPL implType = IMPL_PLAIN); + NodeData(NodeData &ref); + ~NodeData(); + NodeData& operator=(const NodeData&); + + cv::String m_funName; + cv::instr::TYPE m_instrType; + cv::instr::IMPL m_implType; + const char* m_fileName; + int m_lineNum; + void* m_retAddress; + bool m_alwaysExpand; + bool m_funError; + + volatile int m_counter; + volatile uint64 m_ticksTotal; + TLSData m_tls; + int m_threads; + + // No synchronization + double getTotalMs() const { return ((double)m_ticksTotal / cv::getTickFrequency()) * 1000; } + double getMeanMs() const { return (((double)m_ticksTotal/m_counter) / cv::getTickFrequency()) * 1000; } +}; +bool operator==(const NodeData& lhs, const NodeData& rhs); + +typedef Node InstrNode; + +CV_EXPORTS InstrNode* getTrace(); + +#endif // !defined OPENCV_ABI_CHECK + + +CV_EXPORTS bool useInstrumentation(); +CV_EXPORTS void setUseInstrumentation(bool flag); +CV_EXPORTS void resetTrace(); + +enum FLAGS +{ + FLAGS_NONE = 0, + FLAGS_MAPPING = 0x01, + FLAGS_EXPAND_SAME_NAMES = 0x02, +}; + +CV_EXPORTS void setFlags(FLAGS modeFlags); +static inline void setFlags(int modeFlags) { setFlags((FLAGS)modeFlags); } +CV_EXPORTS FLAGS getFlags(); + +} // namespace instr + + +namespace samples { + +//! @addtogroup core_utils_samples +// This section describes utility functions for OpenCV samples. +// +// @note Implementation of these utilities is not thread-safe. +// +//! @{ + +/** @brief Try to find requested data file + +Search directories: + +1. Directories passed via `addSamplesDataSearchPath()` +2. OPENCV_SAMPLES_DATA_PATH_HINT environment variable +3. OPENCV_SAMPLES_DATA_PATH environment variable + If parameter value is not empty and nothing is found then stop searching. +4. Detects build/install path based on: + a. current working directory (CWD) + b. and/or binary module location (opencv_core/opencv_world, doesn't work with static linkage) +5. Scan `/{,data,samples/data}` directories if build directory is detected or the current directory is in source tree. +6. Scan `/share/OpenCV` directory if install directory is detected. + +@see cv::utils::findDataFile + +@param relative_path Relative path to data file +@param required Specify "file not found" handling. + If true, function prints information message and raises cv::Exception. + If false, function returns empty result +@param silentMode Disables messages +@return Returns path (absolute or relative to the current directory) or empty string if file is not found +*/ +CV_EXPORTS_W cv::String findFile(const cv::String& relative_path, bool required = true, bool silentMode = false); + +CV_EXPORTS_W cv::String findFileOrKeep(const cv::String& relative_path, bool silentMode = false); + +inline cv::String findFileOrKeep(const cv::String& relative_path, bool silentMode) +{ + cv::String res = findFile(relative_path, false, silentMode); + if (res.empty()) + return relative_path; + return res; +} + +/** @brief Override search data path by adding new search location + +Use this only to override default behavior +Passed paths are used in LIFO order. + +@param path Path to used samples data +*/ +CV_EXPORTS_W void addSamplesDataSearchPath(const cv::String& path); + +/** @brief Append samples search data sub directory + +General usage is to add OpenCV modules name (`/modules//samples/data` -> `/samples/data` + `modules//samples/data`). +Passed subdirectories are used in LIFO order. + +@param subdir samples data sub directory +*/ +CV_EXPORTS_W void addSamplesDataSearchSubDirectory(const cv::String& subdir); + +//! @} +} // namespace samples + +namespace utils { + +CV_EXPORTS int getThreadID(); + +} // namespace + +} //namespace cv + +#ifndef DISABLE_OPENCV_24_COMPATIBILITY +#include "opencv2/core/core_c.h" +#endif + +#endif //OPENCV_CORE_UTILITY_H diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/utils/filesystem.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/utils/filesystem.hpp new file mode 100644 index 0000000000000000000000000000000000000000..00b0dd1c123e2e3bfa80f9372ecd47502eb19875 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/utils/filesystem.hpp @@ -0,0 +1,78 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#ifndef OPENCV_UTILS_FILESYSTEM_HPP +#define OPENCV_UTILS_FILESYSTEM_HPP + +namespace cv { namespace utils { namespace fs { + + +CV_EXPORTS bool exists(const cv::String& path); +CV_EXPORTS bool isDirectory(const cv::String& path); + +CV_EXPORTS void remove_all(const cv::String& path); + + +CV_EXPORTS cv::String getcwd(); + +/** @brief Converts path p to a canonical absolute path + * Symlinks are processed if there is support for them on running platform. + * + * @param path input path. Target file/directory should exist. + */ +CV_EXPORTS cv::String canonical(const cv::String& path); + +/** Join path components */ +CV_EXPORTS cv::String join(const cv::String& base, const cv::String& path); + +/** + * Generate a list of all files that match the globbing pattern. + * + * Result entries are prefixed by base directory path. + * + * @param directory base directory + * @param pattern filter pattern (based on '*'/'?' symbols). Use empty string to disable filtering and return all results + * @param[out] result result of globing. + * @param recursive scan nested directories too + * @param includeDirectories include directories into results list + */ +CV_EXPORTS void glob(const cv::String& directory, const cv::String& pattern, + CV_OUT std::vector& result, + bool recursive = false, bool includeDirectories = false); + +/** + * Generate a list of all files that match the globbing pattern. + * + * @param directory base directory + * @param pattern filter pattern (based on '*'/'?' symbols). Use empty string to disable filtering and return all results + * @param[out] result globbing result with relative paths from base directory + * @param recursive scan nested directories too + * @param includeDirectories include directories into results list + */ +CV_EXPORTS void glob_relative(const cv::String& directory, const cv::String& pattern, + CV_OUT std::vector& result, + bool recursive = false, bool includeDirectories = false); + + +CV_EXPORTS bool createDirectory(const cv::String& path); +CV_EXPORTS bool createDirectories(const cv::String& path); + +#ifdef __OPENCV_BUILD +// TODO +//CV_EXPORTS cv::String getTempDirectory(); + +/** + * @brief Returns directory to store OpenCV cache files + * Create sub-directory in common OpenCV cache directory if it doesn't exist. + * @param sub_directory_name name of sub-directory. NULL or "" value asks to return root cache directory. + * @param configuration_name optional name of configuration parameter name which overrides default behavior. + * @return Path to cache directory. Returns empty string if cache directories support is not available. Returns "disabled" if cache disabled by user. + */ +CV_EXPORTS cv::String getCacheDirectory(const char* sub_directory_name, const char* configuration_name = NULL); + +#endif + +}}} // namespace + +#endif // OPENCV_UTILS_FILESYSTEM_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/utils/logger.defines.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/utils/logger.defines.hpp new file mode 100644 index 0000000000000000000000000000000000000000..b2dfc417045f69e7586b212d3280655d11b29f57 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/utils/logger.defines.hpp @@ -0,0 +1,22 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#ifndef OPENCV_LOGGER_DEFINES_HPP +#define OPENCV_LOGGER_DEFINES_HPP + +//! @addtogroup core_logging +//! @{ + +// Supported logging levels and their semantic +#define CV_LOG_LEVEL_SILENT 0 //!< for using in setLogLevel() call +#define CV_LOG_LEVEL_FATAL 1 //!< Fatal (critical) error (unrecoverable internal error) +#define CV_LOG_LEVEL_ERROR 2 //!< Error message +#define CV_LOG_LEVEL_WARN 3 //!< Warning message +#define CV_LOG_LEVEL_INFO 4 //!< Info message +#define CV_LOG_LEVEL_DEBUG 5 //!< Debug message. Disabled in the "Release" build. +#define CV_LOG_LEVEL_VERBOSE 6 //!< Verbose (trace) messages. Requires verbosity level. Disabled in the "Release" build. + +//! @} + +#endif // OPENCV_LOGGER_DEFINES_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/utils/logger.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/utils/logger.hpp new file mode 100644 index 0000000000000000000000000000000000000000..47094f994d09e21c50611f6629f45a7ced042082 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/utils/logger.hpp @@ -0,0 +1,87 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#ifndef OPENCV_LOGGER_HPP +#define OPENCV_LOGGER_HPP + +#include +#include +#include // INT_MAX + +#include "logger.defines.hpp" + +//! @addtogroup core_logging +// This section describes OpenCV logging utilities. +// +//! @{ + +namespace cv { +namespace utils { +namespace logging { + +//! Supported logging levels and their semantic +enum LogLevel { + LOG_LEVEL_SILENT = 0, //!< for using in setLogVevel() call + LOG_LEVEL_FATAL = 1, //!< Fatal (critical) error (unrecoverable internal error) + LOG_LEVEL_ERROR = 2, //!< Error message + LOG_LEVEL_WARNING = 3, //!< Warning message + LOG_LEVEL_INFO = 4, //!< Info message + LOG_LEVEL_DEBUG = 5, //!< Debug message. Disabled in the "Release" build. + LOG_LEVEL_VERBOSE = 6, //!< Verbose (trace) messages. Requires verbosity level. Disabled in the "Release" build. +#ifndef CV_DOXYGEN + ENUM_LOG_LEVEL_FORCE_INT = INT_MAX +#endif +}; + +/** Set global logging level +@return previous logging level +*/ +CV_EXPORTS LogLevel setLogLevel(LogLevel logLevel); +/** Get global logging level */ +CV_EXPORTS LogLevel getLogLevel(); + +namespace internal { +/** Write log message */ +CV_EXPORTS void writeLogMessage(LogLevel logLevel, const char* message); +} // namespace + +/** + * \def CV_LOG_STRIP_LEVEL + * + * Define CV_LOG_STRIP_LEVEL=CV_LOG_LEVEL_[DEBUG|INFO|WARN|ERROR|FATAL|DISABLED] to compile out anything at that and before that logging level + */ +#ifndef CV_LOG_STRIP_LEVEL +# if defined NDEBUG +# define CV_LOG_STRIP_LEVEL CV_LOG_LEVEL_DEBUG +# else +# define CV_LOG_STRIP_LEVEL CV_LOG_LEVEL_VERBOSE +# endif +#endif + + +#define CV_LOG_FATAL(tag, ...) for(;;) { if (cv::utils::logging::getLogLevel() < cv::utils::logging::LOG_LEVEL_FATAL) break; std::stringstream ss; ss << __VA_ARGS__; cv::utils::logging::internal::writeLogMessage(cv::utils::logging::LOG_LEVEL_FATAL, ss.str().c_str()); break; } +#define CV_LOG_ERROR(tag, ...) for(;;) { if (cv::utils::logging::getLogLevel() < cv::utils::logging::LOG_LEVEL_ERROR) break; std::stringstream ss; ss << __VA_ARGS__; cv::utils::logging::internal::writeLogMessage(cv::utils::logging::LOG_LEVEL_ERROR, ss.str().c_str()); break; } +#define CV_LOG_WARNING(tag, ...) for(;;) { if (cv::utils::logging::getLogLevel() < cv::utils::logging::LOG_LEVEL_WARNING) break; std::stringstream ss; ss << __VA_ARGS__; cv::utils::logging::internal::writeLogMessage(cv::utils::logging::LOG_LEVEL_WARNING, ss.str().c_str()); break; } +#if CV_LOG_STRIP_LEVEL <= CV_LOG_LEVEL_INFO +#define CV_LOG_INFO(tag, ...) +#else +#define CV_LOG_INFO(tag, ...) for(;;) { if (cv::utils::logging::getLogLevel() < cv::utils::logging::LOG_LEVEL_INFO) break; std::stringstream ss; ss << __VA_ARGS__; cv::utils::logging::internal::writeLogMessage(cv::utils::logging::LOG_LEVEL_INFO, ss.str().c_str()); break; } +#endif +#if CV_LOG_STRIP_LEVEL <= CV_LOG_LEVEL_DEBUG +#define CV_LOG_DEBUG(tag, ...) +#else +#define CV_LOG_DEBUG(tag, ...) for(;;) { if (cv::utils::logging::getLogLevel() < cv::utils::logging::LOG_LEVEL_DEBUG) break; std::stringstream ss; ss << __VA_ARGS__; cv::utils::logging::internal::writeLogMessage(cv::utils::logging::LOG_LEVEL_DEBUG, ss.str().c_str()); break; } +#endif +#if CV_LOG_STRIP_LEVEL <= CV_LOG_LEVEL_VERBOSE +#define CV_LOG_VERBOSE(tag, v, ...) +#else +#define CV_LOG_VERBOSE(tag, v, ...) for(;;) { if (cv::utils::logging::getLogLevel() < cv::utils::logging::LOG_LEVEL_VERBOSE) break; std::stringstream ss; ss << "[VERB" << v << ":" << cv::utils::getThreadID() << "] " << __VA_ARGS__; cv::utils::logging::internal::writeLogMessage(cv::utils::logging::LOG_LEVEL_VERBOSE, ss.str().c_str()); break; } +#endif + + +}}} // namespace + +//! @} + +#endif // OPENCV_LOGGER_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/utils/trace.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/utils/trace.hpp new file mode 100644 index 0000000000000000000000000000000000000000..858e973a831370c17b87e3b9550b3b9a6c4873c3 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/utils/trace.hpp @@ -0,0 +1,254 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#ifndef OPENCV_TRACE_HPP +#define OPENCV_TRACE_HPP + +#include + +//! @addtogroup core_logging +// This section describes OpenCV tracing utilities. +// +//! @{ + +namespace cv { +namespace utils { +namespace trace { + +//! Macro to trace function +#define CV_TRACE_FUNCTION() + +#define CV_TRACE_FUNCTION_SKIP_NESTED() + +//! Trace code scope. +//! @note Dynamic names are not supported in this macro (on stack or heap). Use string literals here only, like "initialize". +#define CV_TRACE_REGION(name_as_static_string_literal) +//! mark completed of the current opened region and create new one +//! @note Dynamic names are not supported in this macro (on stack or heap). Use string literals here only, like "step1". +#define CV_TRACE_REGION_NEXT(name_as_static_string_literal) + +//! Macro to trace argument value +#define CV_TRACE_ARG(arg_id) + +//! Macro to trace argument value (expanded version) +#define CV_TRACE_ARG_VALUE(arg_id, arg_name, value) + +//! @cond IGNORED +#define CV_TRACE_NS cv::utils::trace + +#if !defined(OPENCV_DISABLE_TRACE) && defined(__EMSCRIPTEN__) +#define OPENCV_DISABLE_TRACE 1 +#endif + +namespace details { + +#ifndef __OPENCV_TRACE +# if defined __OPENCV_BUILD && !defined __OPENCV_TESTS && !defined __OPENCV_APPS +# define __OPENCV_TRACE 1 +# else +# define __OPENCV_TRACE 0 +# endif +#endif + +#ifndef CV_TRACE_FILENAME +# define CV_TRACE_FILENAME __FILE__ +#endif + +#ifndef CV__TRACE_FUNCTION +# if defined _MSC_VER +# define CV__TRACE_FUNCTION __FUNCSIG__ +# elif defined __GNUC__ +# define CV__TRACE_FUNCTION __PRETTY_FUNCTION__ +# else +# define CV__TRACE_FUNCTION "" +# endif +#endif + +//! Thread-local instance (usually allocated on stack) +class CV_EXPORTS Region +{ +public: + struct LocationExtraData; + struct LocationStaticStorage + { + LocationExtraData** ppExtra; //< implementation specific data + const char* name; //< region name (function name or other custom name) + const char* filename; //< source code filename + int line; //< source code line + int flags; //< flags (implementation code path: Plain, IPP, OpenCL) + }; + + Region(const LocationStaticStorage& location); + inline ~Region() + { + if (implFlags != 0) + destroy(); + CV_DbgAssert(implFlags == 0); + CV_DbgAssert(pImpl == NULL); + } + + class Impl; + Impl* pImpl; // NULL if current region is not active + int implFlags; // see RegionFlag, 0 if region is ignored + + bool isActive() const { return pImpl != NULL; } + + void destroy(); +private: + Region(const Region&); // disabled + Region& operator= (const Region&); // disabled +}; + +//! Specify region flags +enum RegionLocationFlag { + REGION_FLAG_FUNCTION = (1 << 0), //< region is function (=1) / nested named region (=0) + REGION_FLAG_APP_CODE = (1 << 1), //< region is Application code (=1) / OpenCV library code (=0) + REGION_FLAG_SKIP_NESTED = (1 << 2), //< avoid processing of nested regions + + REGION_FLAG_IMPL_IPP = (1 << 16), //< region is part of IPP code path + REGION_FLAG_IMPL_OPENCL = (2 << 16), //< region is part of OpenCL code path + REGION_FLAG_IMPL_OPENVX = (3 << 16), //< region is part of OpenVX code path + + REGION_FLAG_IMPL_MASK = (15 << 16), + + REGION_FLAG_REGION_FORCE = (1 << 30), + REGION_FLAG_REGION_NEXT = (1 << 31), //< close previous region (see #CV_TRACE_REGION_NEXT macro) + + ENUM_REGION_FLAG_FORCE_INT = INT_MAX +}; + +struct CV_EXPORTS TraceArg { +public: + struct ExtraData; + ExtraData** ppExtra; + const char* name; + int flags; +}; +/** @brief Add meta information to current region (function) + * See CV_TRACE_ARG macro + * @param arg argument information structure (global static cache) + * @param value argument value (can by dynamic string literal in case of string, static allocation is not required) + */ +CV_EXPORTS void traceArg(const TraceArg& arg, const char* value); +//! @overload +CV_EXPORTS void traceArg(const TraceArg& arg, int value); +//! @overload +CV_EXPORTS void traceArg(const TraceArg& arg, int64 value); +//! @overload +CV_EXPORTS void traceArg(const TraceArg& arg, double value); + +#define CV__TRACE_LOCATION_VARNAME(loc_id) CVAUX_CONCAT(CVAUX_CONCAT(__cv_trace_location_, loc_id), __LINE__) +#define CV__TRACE_LOCATION_EXTRA_VARNAME(loc_id) CVAUX_CONCAT(CVAUX_CONCAT(__cv_trace_location_extra_, loc_id) , __LINE__) + +#define CV__TRACE_DEFINE_LOCATION_(loc_id, name, flags) \ + static CV_TRACE_NS::details::Region::LocationExtraData* CV__TRACE_LOCATION_EXTRA_VARNAME(loc_id) = 0; \ + static const CV_TRACE_NS::details::Region::LocationStaticStorage \ + CV__TRACE_LOCATION_VARNAME(loc_id) = { &(CV__TRACE_LOCATION_EXTRA_VARNAME(loc_id)), name, CV_TRACE_FILENAME, __LINE__, flags}; + +#define CV__TRACE_DEFINE_LOCATION_FN(name, flags) CV__TRACE_DEFINE_LOCATION_(fn, name, ((flags) | CV_TRACE_NS::details::REGION_FLAG_FUNCTION)) + + +#define CV__TRACE_OPENCV_FUNCTION() \ + CV__TRACE_DEFINE_LOCATION_FN(CV__TRACE_FUNCTION, 0); \ + const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn)); + +#define CV__TRACE_OPENCV_FUNCTION_NAME(name) \ + CV__TRACE_DEFINE_LOCATION_FN(name, 0); \ + const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn)); + +#define CV__TRACE_APP_FUNCTION() \ + CV__TRACE_DEFINE_LOCATION_FN(CV__TRACE_FUNCTION, CV_TRACE_NS::details::REGION_FLAG_APP_CODE); \ + const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn)); + +#define CV__TRACE_APP_FUNCTION_NAME(name) \ + CV__TRACE_DEFINE_LOCATION_FN(name, CV_TRACE_NS::details::REGION_FLAG_APP_CODE); \ + const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn)); + + +#define CV__TRACE_OPENCV_FUNCTION_SKIP_NESTED() \ + CV__TRACE_DEFINE_LOCATION_FN(CV__TRACE_FUNCTION, CV_TRACE_NS::details::REGION_FLAG_SKIP_NESTED); \ + const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn)); + +#define CV__TRACE_OPENCV_FUNCTION_NAME_SKIP_NESTED(name) \ + CV__TRACE_DEFINE_LOCATION_FN(name, CV_TRACE_NS::details::REGION_FLAG_SKIP_NESTED); \ + const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn)); + +#define CV__TRACE_APP_FUNCTION_SKIP_NESTED() \ + CV__TRACE_DEFINE_LOCATION_FN(CV__TRACE_FUNCTION, CV_TRACE_NS::details::REGION_FLAG_SKIP_NESTED | CV_TRACE_NS::details::REGION_FLAG_APP_CODE); \ + const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn)); + + +#define CV__TRACE_REGION_(name_as_static_string_literal, flags) \ + CV__TRACE_DEFINE_LOCATION_(region, name_as_static_string_literal, flags); \ + CV_TRACE_NS::details::Region CVAUX_CONCAT(__region_, __LINE__)(CV__TRACE_LOCATION_VARNAME(region)); + +#define CV__TRACE_REGION(name_as_static_string_literal) CV__TRACE_REGION_(name_as_static_string_literal, 0) +#define CV__TRACE_REGION_NEXT(name_as_static_string_literal) CV__TRACE_REGION_(name_as_static_string_literal, CV_TRACE_NS::details::REGION_FLAG_REGION_NEXT) + +#define CV__TRACE_ARG_VARNAME(arg_id) CVAUX_CONCAT(__cv_trace_arg_ ## arg_id, __LINE__) +#define CV__TRACE_ARG_EXTRA_VARNAME(arg_id) CVAUX_CONCAT(__cv_trace_arg_extra_ ## arg_id, __LINE__) + +#define CV__TRACE_DEFINE_ARG_(arg_id, name, flags) \ + static CV_TRACE_NS::details::TraceArg::ExtraData* CV__TRACE_ARG_EXTRA_VARNAME(arg_id) = 0; \ + static const CV_TRACE_NS::details::TraceArg \ + CV__TRACE_ARG_VARNAME(arg_id) = { &(CV__TRACE_ARG_EXTRA_VARNAME(arg_id)), name, flags }; + +#define CV__TRACE_ARG_VALUE(arg_id, arg_name, value) \ + CV__TRACE_DEFINE_ARG_(arg_id, arg_name, 0); \ + CV_TRACE_NS::details::traceArg((CV__TRACE_ARG_VARNAME(arg_id)), value); + +#define CV__TRACE_ARG(arg_id) CV_TRACE_ARG_VALUE(arg_id, #arg_id, (arg_id)) + +} // namespace + +#ifndef OPENCV_DISABLE_TRACE +#undef CV_TRACE_FUNCTION +#undef CV_TRACE_FUNCTION_SKIP_NESTED +#if __OPENCV_TRACE +#define CV_TRACE_FUNCTION CV__TRACE_OPENCV_FUNCTION +#define CV_TRACE_FUNCTION_SKIP_NESTED CV__TRACE_OPENCV_FUNCTION_SKIP_NESTED +#else +#define CV_TRACE_FUNCTION CV__TRACE_APP_FUNCTION +#define CV_TRACE_FUNCTION_SKIP_NESTED CV__TRACE_APP_FUNCTION_SKIP_NESTED +#endif + +#undef CV_TRACE_REGION +#define CV_TRACE_REGION CV__TRACE_REGION + +#undef CV_TRACE_REGION_NEXT +#define CV_TRACE_REGION_NEXT CV__TRACE_REGION_NEXT + +#undef CV_TRACE_ARG_VALUE +#define CV_TRACE_ARG_VALUE(arg_id, arg_name, value) \ + if (__region_fn.isActive()) \ + { \ + CV__TRACE_ARG_VALUE(arg_id, arg_name, value); \ + } + +#undef CV_TRACE_ARG +#define CV_TRACE_ARG CV__TRACE_ARG + +#endif // OPENCV_DISABLE_TRACE + +#ifdef OPENCV_TRACE_VERBOSE +#define CV_TRACE_FUNCTION_VERBOSE CV_TRACE_FUNCTION +#define CV_TRACE_REGION_VERBOSE CV_TRACE_REGION +#define CV_TRACE_REGION_NEXT_VERBOSE CV_TRACE_REGION_NEXT +#define CV_TRACE_ARG_VALUE_VERBOSE CV_TRACE_ARG_VALUE +#define CV_TRACE_ARG_VERBOSE CV_TRACE_ARG +#else +#define CV_TRACE_FUNCTION_VERBOSE(...) +#define CV_TRACE_REGION_VERBOSE(...) +#define CV_TRACE_REGION_NEXT_VERBOSE(...) +#define CV_TRACE_ARG_VALUE_VERBOSE(...) +#define CV_TRACE_ARG_VERBOSE(...) +#endif + +//! @endcond + +}}} // namespace + +//! @} + +#endif // OPENCV_TRACE_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/va_intel.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/va_intel.hpp new file mode 100644 index 0000000000000000000000000000000000000000..f66547093e0ad69533af4cceb02bc796448f8db9 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/va_intel.hpp @@ -0,0 +1,78 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +// Copyright (C) 2015, Itseez, Inc., all rights reserved. +// Third party copyrights are property of their respective owners. + +#ifndef OPENCV_CORE_VA_INTEL_HPP +#define OPENCV_CORE_VA_INTEL_HPP + +#ifndef __cplusplus +# error va_intel.hpp header must be compiled as C++ +#endif + +#include "opencv2/core.hpp" +#include "ocl.hpp" + +#if defined(HAVE_VA) +# include "va/va.h" +#else // HAVE_VA +# if !defined(_VA_H_) + typedef void* VADisplay; + typedef unsigned int VASurfaceID; +# endif // !_VA_H_ +#endif // HAVE_VA + +namespace cv { namespace va_intel { + +/** @addtogroup core_va_intel +This section describes Intel VA-API/OpenCL (CL-VA) interoperability. + +To enable CL-VA interoperability support, configure OpenCV using CMake with WITH_VA_INTEL=ON . Currently VA-API is +supported on Linux only. You should also install Intel Media Server Studio (MSS) to use this feature. You may +have to specify the path(s) to MSS components for cmake in environment variables: + +- VA_INTEL_IOCL_ROOT for Intel OpenCL (default is "/opt/intel/opencl"). + +To use CL-VA interoperability you should first create VADisplay (libva), and then call initializeContextFromVA() +function to create OpenCL context and set up interoperability. +*/ +//! @{ + +/////////////////// CL-VA Interoperability Functions /////////////////// + +namespace ocl { +using namespace cv::ocl; + +// TODO static functions in the Context class +/** @brief Creates OpenCL context from VA. +@param display - VADisplay for which CL interop should be established. +@param tryInterop - try to set up for interoperability, if true; set up for use slow copy if false. +@return Returns reference to OpenCL Context + */ +CV_EXPORTS Context& initializeContextFromVA(VADisplay display, bool tryInterop = true); + +} // namespace cv::va_intel::ocl + +/** @brief Converts InputArray to VASurfaceID object. +@param display - VADisplay object. +@param src - source InputArray. +@param surface - destination VASurfaceID object. +@param size - size of image represented by VASurfaceID object. + */ +CV_EXPORTS void convertToVASurface(VADisplay display, InputArray src, VASurfaceID surface, Size size); + +/** @brief Converts VASurfaceID object to OutputArray. +@param display - VADisplay object. +@param surface - source VASurfaceID object. +@param size - size of image represented by VASurfaceID object. +@param dst - destination OutputArray. + */ +CV_EXPORTS void convertFromVASurface(VADisplay display, VASurfaceID surface, Size size, OutputArray dst); + +//! @} + +}} // namespace cv::va_intel + +#endif /* OPENCV_CORE_VA_INTEL_HPP */ diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/version.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/version.hpp new file mode 100644 index 0000000000000000000000000000000000000000..91f95eeec4439ae3941fe4dc0c4e13b55cf3f974 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/version.hpp @@ -0,0 +1,26 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#ifndef OPENCV_VERSION_HPP +#define OPENCV_VERSION_HPP + +#define CV_VERSION_MAJOR 3 +#define CV_VERSION_MINOR 4 +#define CV_VERSION_REVISION 5 +#define CV_VERSION_STATUS "" + +#define CVAUX_STR_EXP(__A) #__A +#define CVAUX_STR(__A) CVAUX_STR_EXP(__A) + +#define CVAUX_STRW_EXP(__A) L ## #__A +#define CVAUX_STRW(__A) CVAUX_STRW_EXP(__A) + +#define CV_VERSION CVAUX_STR(CV_VERSION_MAJOR) "." CVAUX_STR(CV_VERSION_MINOR) "." CVAUX_STR(CV_VERSION_REVISION) CV_VERSION_STATUS + +/* old style version constants*/ +#define CV_MAJOR_VERSION CV_VERSION_MAJOR +#define CV_MINOR_VERSION CV_VERSION_MINOR +#define CV_SUBMINOR_VERSION CV_VERSION_REVISION + +#endif // OPENCV_VERSION_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/vsx_utils.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/vsx_utils.hpp new file mode 100644 index 0000000000000000000000000000000000000000..b4e3f30562b7c4e8367d32c5eea8b8fb9e1685c6 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/vsx_utils.hpp @@ -0,0 +1,1011 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html + +#ifndef OPENCV_HAL_VSX_UTILS_HPP +#define OPENCV_HAL_VSX_UTILS_HPP + +#include "opencv2/core/cvdef.h" + +#ifndef SKIP_INCLUDES +# include +#endif + +//! @addtogroup core_utils_vsx +//! @{ +#if CV_VSX + +#define __VSX_S16__(c, v) (c){v, v, v, v, v, v, v, v, v, v, v, v, v, v, v, v} +#define __VSX_S8__(c, v) (c){v, v, v, v, v, v, v, v} +#define __VSX_S4__(c, v) (c){v, v, v, v} +#define __VSX_S2__(c, v) (c){v, v} + +typedef __vector unsigned char vec_uchar16; +#define vec_uchar16_set(...) (vec_uchar16){__VA_ARGS__} +#define vec_uchar16_sp(c) (__VSX_S16__(vec_uchar16, c)) +#define vec_uchar16_c(v) ((vec_uchar16)(v)) +#define vec_uchar16_z vec_uchar16_sp(0) + +typedef __vector signed char vec_char16; +#define vec_char16_set(...) (vec_char16){__VA_ARGS__} +#define vec_char16_sp(c) (__VSX_S16__(vec_char16, c)) +#define vec_char16_c(v) ((vec_char16)(v)) +#define vec_char16_z vec_char16_sp(0) + +typedef __vector unsigned short vec_ushort8; +#define vec_ushort8_set(...) (vec_ushort8){__VA_ARGS__} +#define vec_ushort8_sp(c) (__VSX_S8__(vec_ushort8, c)) +#define vec_ushort8_c(v) ((vec_ushort8)(v)) +#define vec_ushort8_z vec_ushort8_sp(0) + +typedef __vector signed short vec_short8; +#define vec_short8_set(...) (vec_short8){__VA_ARGS__} +#define vec_short8_sp(c) (__VSX_S8__(vec_short8, c)) +#define vec_short8_c(v) ((vec_short8)(v)) +#define vec_short8_z vec_short8_sp(0) + +typedef __vector unsigned int vec_uint4; +#define vec_uint4_set(...) (vec_uint4){__VA_ARGS__} +#define vec_uint4_sp(c) (__VSX_S4__(vec_uint4, c)) +#define vec_uint4_c(v) ((vec_uint4)(v)) +#define vec_uint4_z vec_uint4_sp(0) + +typedef __vector signed int vec_int4; +#define vec_int4_set(...) (vec_int4){__VA_ARGS__} +#define vec_int4_sp(c) (__VSX_S4__(vec_int4, c)) +#define vec_int4_c(v) ((vec_int4)(v)) +#define vec_int4_z vec_int4_sp(0) + +typedef __vector float vec_float4; +#define vec_float4_set(...) (vec_float4){__VA_ARGS__} +#define vec_float4_sp(c) (__VSX_S4__(vec_float4, c)) +#define vec_float4_c(v) ((vec_float4)(v)) +#define vec_float4_z vec_float4_sp(0) + +typedef __vector unsigned long long vec_udword2; +#define vec_udword2_set(...) (vec_udword2){__VA_ARGS__} +#define vec_udword2_sp(c) (__VSX_S2__(vec_udword2, c)) +#define vec_udword2_c(v) ((vec_udword2)(v)) +#define vec_udword2_z vec_udword2_sp(0) + +typedef __vector signed long long vec_dword2; +#define vec_dword2_set(...) (vec_dword2){__VA_ARGS__} +#define vec_dword2_sp(c) (__VSX_S2__(vec_dword2, c)) +#define vec_dword2_c(v) ((vec_dword2)(v)) +#define vec_dword2_z vec_dword2_sp(0) + +typedef __vector double vec_double2; +#define vec_double2_set(...) (vec_double2){__VA_ARGS__} +#define vec_double2_c(v) ((vec_double2)(v)) +#define vec_double2_sp(c) (__VSX_S2__(vec_double2, c)) +#define vec_double2_z vec_double2_sp(0) + +#define vec_bchar16 __vector __bool char +#define vec_bchar16_set(...) (vec_bchar16){__VA_ARGS__} +#define vec_bchar16_c(v) ((vec_bchar16)(v)) + +#define vec_bshort8 __vector __bool short +#define vec_bshort8_set(...) (vec_bshort8){__VA_ARGS__} +#define vec_bshort8_c(v) ((vec_bshort8)(v)) + +#define vec_bint4 __vector __bool int +#define vec_bint4_set(...) (vec_bint4){__VA_ARGS__} +#define vec_bint4_c(v) ((vec_bint4)(v)) + +#define vec_bdword2 __vector __bool long long +#define vec_bdword2_set(...) (vec_bdword2){__VA_ARGS__} +#define vec_bdword2_c(v) ((vec_bdword2)(v)) + +#define VSX_FINLINE(tp) extern inline tp __attribute__((always_inline)) + +#define VSX_REDIRECT_1RG(rt, rg, fnm, fn2) \ +VSX_FINLINE(rt) fnm(const rg& a) { return fn2(a); } + +#define VSX_REDIRECT_2RG(rt, rg, fnm, fn2) \ +VSX_FINLINE(rt) fnm(const rg& a, const rg& b) { return fn2(a, b); } + +/* + * GCC VSX compatibility +**/ +#if defined(__GNUG__) && !defined(__clang__) + +// inline asm helper +#define VSX_IMPL_1RG(rt, rto, rg, rgo, opc, fnm) \ +VSX_FINLINE(rt) fnm(const rg& a) \ +{ rt rs; __asm__ __volatile__(#opc" %x0,%x1" : "="#rto (rs) : #rgo (a)); return rs; } + +#define VSX_IMPL_1VRG(rt, rg, opc, fnm) \ +VSX_FINLINE(rt) fnm(const rg& a) \ +{ rt rs; __asm__ __volatile__(#opc" %0,%1" : "=v" (rs) : "v" (a)); return rs; } + +#define VSX_IMPL_2VRG_F(rt, rg, fopc, fnm) \ +VSX_FINLINE(rt) fnm(const rg& a, const rg& b) \ +{ rt rs; __asm__ __volatile__(fopc : "=v" (rs) : "v" (a), "v" (b)); return rs; } + +#define VSX_IMPL_2VRG(rt, rg, opc, fnm) VSX_IMPL_2VRG_F(rt, rg, #opc" %0,%1,%2", fnm) + +#if __GNUG__ < 7 +// up to GCC 6 vec_mul only supports precisions and llong +# ifdef vec_mul +# undef vec_mul +# endif +/* + * there's no a direct instruction for supporting 8-bit, 16-bit multiplication in ISA 2.07, + * XLC Implement it by using instruction "multiply even", "multiply odd" and "permute" +**/ +# define VSX_IMPL_MULH(Tvec, cperm) \ + VSX_FINLINE(Tvec) vec_mul(const Tvec& a, const Tvec& b) \ + { \ + static const vec_uchar16 ev_od = {cperm}; \ + return vec_perm((Tvec)vec_mule(a, b), (Tvec)vec_mulo(a, b), ev_od); \ + } + #define VSX_IMPL_MULH_P16 0, 16, 2, 18, 4, 20, 6, 22, 8, 24, 10, 26, 12, 28, 14, 30 + VSX_IMPL_MULH(vec_char16, VSX_IMPL_MULH_P16) + VSX_IMPL_MULH(vec_uchar16, VSX_IMPL_MULH_P16) + #define VSX_IMPL_MULH_P8 0, 1, 16, 17, 4, 5, 20, 21, 8, 9, 24, 25, 12, 13, 28, 29 + VSX_IMPL_MULH(vec_short8, VSX_IMPL_MULH_P8) + VSX_IMPL_MULH(vec_ushort8, VSX_IMPL_MULH_P8) + // vmuluwm can be used for unsigned or signed integers, that's what they said + VSX_IMPL_2VRG(vec_int4, vec_int4, vmuluwm, vec_mul) + VSX_IMPL_2VRG(vec_uint4, vec_uint4, vmuluwm, vec_mul) + // redirect to GCC builtin vec_mul, since it already supports precisions and llong + VSX_REDIRECT_2RG(vec_float4, vec_float4, vec_mul, __builtin_vec_mul) + VSX_REDIRECT_2RG(vec_double2, vec_double2, vec_mul, __builtin_vec_mul) + VSX_REDIRECT_2RG(vec_dword2, vec_dword2, vec_mul, __builtin_vec_mul) + VSX_REDIRECT_2RG(vec_udword2, vec_udword2, vec_mul, __builtin_vec_mul) +#endif // __GNUG__ < 7 + +#if __GNUG__ < 6 +/* + * Instruction "compare greater than or equal" in ISA 2.07 only supports single + * and double precision. + * In XLC and new versions of GCC implement integers by using instruction "greater than" and NOR. +**/ +# ifdef vec_cmpge +# undef vec_cmpge +# endif +# ifdef vec_cmple +# undef vec_cmple +# endif +# define vec_cmple(a, b) vec_cmpge(b, a) +# define VSX_IMPL_CMPGE(rt, rg, opc, fnm) \ + VSX_IMPL_2VRG_F(rt, rg, #opc" %0,%2,%1\n\t xxlnor %x0,%x0,%x0", fnm) + + VSX_IMPL_CMPGE(vec_bchar16, vec_char16, vcmpgtsb, vec_cmpge) + VSX_IMPL_CMPGE(vec_bchar16, vec_uchar16, vcmpgtub, vec_cmpge) + VSX_IMPL_CMPGE(vec_bshort8, vec_short8, vcmpgtsh, vec_cmpge) + VSX_IMPL_CMPGE(vec_bshort8, vec_ushort8, vcmpgtuh, vec_cmpge) + VSX_IMPL_CMPGE(vec_bint4, vec_int4, vcmpgtsw, vec_cmpge) + VSX_IMPL_CMPGE(vec_bint4, vec_uint4, vcmpgtuw, vec_cmpge) + VSX_IMPL_CMPGE(vec_bdword2, vec_dword2, vcmpgtsd, vec_cmpge) + VSX_IMPL_CMPGE(vec_bdword2, vec_udword2, vcmpgtud, vec_cmpge) + +// redirect to GCC builtin cmpge, since it already supports precisions + VSX_REDIRECT_2RG(vec_bint4, vec_float4, vec_cmpge, __builtin_vec_cmpge) + VSX_REDIRECT_2RG(vec_bdword2, vec_double2, vec_cmpge, __builtin_vec_cmpge) + +// up to gcc5 vec_nor doesn't support bool long long +# undef vec_nor + template + VSX_REDIRECT_2RG(T, T, vec_nor, __builtin_vec_nor) + + VSX_FINLINE(vec_bdword2) vec_nor(const vec_bdword2& a, const vec_bdword2& b) + { return vec_bdword2_c(__builtin_vec_nor(vec_dword2_c(a), vec_dword2_c(b))); } + +// vec_packs doesn't support double words in gcc4 and old versions of gcc5 +# undef vec_packs + VSX_REDIRECT_2RG(vec_char16, vec_short8, vec_packs, __builtin_vec_packs) + VSX_REDIRECT_2RG(vec_uchar16, vec_ushort8, vec_packs, __builtin_vec_packs) + VSX_REDIRECT_2RG(vec_short8, vec_int4, vec_packs, __builtin_vec_packs) + VSX_REDIRECT_2RG(vec_ushort8, vec_uint4, vec_packs, __builtin_vec_packs) + + VSX_IMPL_2VRG_F(vec_int4, vec_dword2, "vpksdss %0,%2,%1", vec_packs) + VSX_IMPL_2VRG_F(vec_uint4, vec_udword2, "vpkudus %0,%2,%1", vec_packs) +#endif // __GNUG__ < 6 + +#if __GNUG__ < 5 +// vec_xxpermdi in gcc4 missing little-endian supports just like clang +# define vec_permi(a, b, c) vec_xxpermdi(b, a, (3 ^ (((c) & 1) << 1 | (c) >> 1))) +#else +# define vec_permi vec_xxpermdi +#endif // __GNUG__ < 5 + +// shift left double by word immediate +#ifndef vec_sldw +# define vec_sldw __builtin_vsx_xxsldwi +#endif + +// vector population count +VSX_IMPL_1VRG(vec_uchar16, vec_uchar16, vpopcntb, vec_popcntu) +VSX_IMPL_1VRG(vec_uchar16, vec_char16, vpopcntb, vec_popcntu) +VSX_IMPL_1VRG(vec_ushort8, vec_ushort8, vpopcnth, vec_popcntu) +VSX_IMPL_1VRG(vec_ushort8, vec_short8, vpopcnth, vec_popcntu) +VSX_IMPL_1VRG(vec_uint4, vec_uint4, vpopcntw, vec_popcntu) +VSX_IMPL_1VRG(vec_uint4, vec_int4, vpopcntw, vec_popcntu) +VSX_IMPL_1VRG(vec_udword2, vec_udword2, vpopcntd, vec_popcntu) +VSX_IMPL_1VRG(vec_udword2, vec_dword2, vpopcntd, vec_popcntu) + +// converts between single and double-precision +VSX_REDIRECT_1RG(vec_float4, vec_double2, vec_cvfo, __builtin_vsx_xvcvdpsp) +VSX_REDIRECT_1RG(vec_double2, vec_float4, vec_cvfo, __builtin_vsx_xvcvspdp) + +// converts word and doubleword to double-precision +#ifdef vec_ctd +# undef vec_ctd +#endif +VSX_IMPL_1RG(vec_double2, wd, vec_int4, wa, xvcvsxwdp, vec_ctdo) +VSX_IMPL_1RG(vec_double2, wd, vec_uint4, wa, xvcvuxwdp, vec_ctdo) +VSX_IMPL_1RG(vec_double2, wd, vec_dword2, wi, xvcvsxddp, vec_ctd) +VSX_IMPL_1RG(vec_double2, wd, vec_udword2, wi, xvcvuxddp, vec_ctd) + +// converts word and doubleword to single-precision +#undef vec_ctf +VSX_IMPL_1RG(vec_float4, wf, vec_int4, wa, xvcvsxwsp, vec_ctf) +VSX_IMPL_1RG(vec_float4, wf, vec_uint4, wa, xvcvuxwsp, vec_ctf) +VSX_IMPL_1RG(vec_float4, wf, vec_dword2, wi, xvcvsxdsp, vec_ctfo) +VSX_IMPL_1RG(vec_float4, wf, vec_udword2, wi, xvcvuxdsp, vec_ctfo) + +// converts single and double precision to signed word +#undef vec_cts +VSX_IMPL_1RG(vec_int4, wa, vec_double2, wd, xvcvdpsxws, vec_ctso) +VSX_IMPL_1RG(vec_int4, wa, vec_float4, wf, xvcvspsxws, vec_cts) + +// converts single and double precision to unsigned word +#undef vec_ctu +VSX_IMPL_1RG(vec_uint4, wa, vec_double2, wd, xvcvdpuxws, vec_ctuo) +VSX_IMPL_1RG(vec_uint4, wa, vec_float4, wf, xvcvspuxws, vec_ctu) + +// converts single and double precision to signed doubleword +#ifdef vec_ctsl +# undef vec_ctsl +#endif +VSX_IMPL_1RG(vec_dword2, wi, vec_double2, wd, xvcvdpsxds, vec_ctsl) +VSX_IMPL_1RG(vec_dword2, wi, vec_float4, wf, xvcvspsxds, vec_ctslo) + +// converts single and double precision to unsigned doubleword +#ifdef vec_ctul +# undef vec_ctul +#endif +VSX_IMPL_1RG(vec_udword2, wi, vec_double2, wd, xvcvdpuxds, vec_ctul) +VSX_IMPL_1RG(vec_udword2, wi, vec_float4, wf, xvcvspuxds, vec_ctulo) + +// just in case if GCC doesn't define it +#ifndef vec_xl +# define vec_xl vec_vsx_ld +# define vec_xst vec_vsx_st +#endif + +#endif // GCC VSX compatibility + +/* + * CLANG VSX compatibility +**/ +#if defined(__clang__) && !defined(__IBMCPP__) + +/* + * CLANG doesn't support %x in the inline asm template which fixes register number + * when using any of the register constraints wa, wd, wf + * + * For more explanation checkout PowerPC and IBM RS6000 in https://gcc.gnu.org/onlinedocs/gcc/Machine-Constraints.html + * Also there's already an open bug https://bugs.llvm.org/show_bug.cgi?id=31837 + * + * So we're not able to use inline asm and only use built-in functions that CLANG supports + * and use __builtin_convertvector if clang missng any of vector conversions built-in functions +*/ + +// convert vector helper +#define VSX_IMPL_CONVERT(rt, rg, fnm) \ +VSX_FINLINE(rt) fnm(const rg& a) { return __builtin_convertvector(a, rt); } + +#if __clang_major__ < 5 +// implement vec_permi in a dirty way +# define VSX_IMPL_CLANG_4_PERMI(Tvec) \ + VSX_FINLINE(Tvec) vec_permi(const Tvec& a, const Tvec& b, unsigned const char c) \ + { \ + switch (c) \ + { \ + case 0: \ + return vec_mergeh(a, b); \ + case 1: \ + return vec_mergel(vec_mergeh(a, a), b); \ + case 2: \ + return vec_mergeh(vec_mergel(a, a), b); \ + default: \ + return vec_mergel(a, b); \ + } \ + } + VSX_IMPL_CLANG_4_PERMI(vec_udword2) + VSX_IMPL_CLANG_4_PERMI(vec_dword2) + VSX_IMPL_CLANG_4_PERMI(vec_double2) + +// vec_xxsldwi is missing in clang 4 +# define vec_xxsldwi(a, b, c) vec_sld(a, b, (c) * 4) +#else +// vec_xxpermdi is missing little-endian supports in clang 4 just like gcc4 +# define vec_permi(a, b, c) vec_xxpermdi(b, a, (3 ^ (((c) & 1) << 1 | (c) >> 1))) +#endif // __clang_major__ < 5 + +// shift left double by word immediate +#ifndef vec_sldw +# define vec_sldw vec_xxsldwi +#endif + +// Implement vec_rsqrt since clang only supports vec_rsqrte +#ifndef vec_rsqrt + VSX_FINLINE(vec_float4) vec_rsqrt(const vec_float4& a) + { return vec_div(vec_float4_sp(1), vec_sqrt(a)); } + + VSX_FINLINE(vec_double2) vec_rsqrt(const vec_double2& a) + { return vec_div(vec_double2_sp(1), vec_sqrt(a)); } +#endif + +// vec_promote missing support for doubleword +VSX_FINLINE(vec_dword2) vec_promote(long long a, int b) +{ + vec_dword2 ret = vec_dword2_z; + ret[b & 1] = a; + return ret; +} + +VSX_FINLINE(vec_udword2) vec_promote(unsigned long long a, int b) +{ + vec_udword2 ret = vec_udword2_z; + ret[b & 1] = a; + return ret; +} + +// vec_popcnt should return unsigned but clang has different thought just like gcc in vec_vpopcnt +#define VSX_IMPL_POPCNTU(Tvec, Tvec2, ucast) \ +VSX_FINLINE(Tvec) vec_popcntu(const Tvec2& a) \ +{ return ucast(vec_popcnt(a)); } +VSX_IMPL_POPCNTU(vec_uchar16, vec_char16, vec_uchar16_c); +VSX_IMPL_POPCNTU(vec_ushort8, vec_short8, vec_ushort8_c); +VSX_IMPL_POPCNTU(vec_uint4, vec_int4, vec_uint4_c); +// redirect unsigned types +VSX_REDIRECT_1RG(vec_uchar16, vec_uchar16, vec_popcntu, vec_popcnt) +VSX_REDIRECT_1RG(vec_ushort8, vec_ushort8, vec_popcntu, vec_popcnt) +VSX_REDIRECT_1RG(vec_uint4, vec_uint4, vec_popcntu, vec_popcnt) + +// converts between single and double precision +VSX_REDIRECT_1RG(vec_float4, vec_double2, vec_cvfo, __builtin_vsx_xvcvdpsp) +VSX_REDIRECT_1RG(vec_double2, vec_float4, vec_cvfo, __builtin_vsx_xvcvspdp) + +// converts word and doubleword to double-precision +#ifdef vec_ctd +# undef vec_ctd +#endif +VSX_REDIRECT_1RG(vec_double2, vec_int4, vec_ctdo, __builtin_vsx_xvcvsxwdp) +VSX_REDIRECT_1RG(vec_double2, vec_uint4, vec_ctdo, __builtin_vsx_xvcvuxwdp) + +VSX_IMPL_CONVERT(vec_double2, vec_dword2, vec_ctd) +VSX_IMPL_CONVERT(vec_double2, vec_udword2, vec_ctd) + +// converts word and doubleword to single-precision +#if __clang_major__ > 4 +# undef vec_ctf +#endif +VSX_IMPL_CONVERT(vec_float4, vec_int4, vec_ctf) +VSX_IMPL_CONVERT(vec_float4, vec_uint4, vec_ctf) +VSX_REDIRECT_1RG(vec_float4, vec_dword2, vec_ctfo, __builtin_vsx_xvcvsxdsp) +VSX_REDIRECT_1RG(vec_float4, vec_udword2, vec_ctfo, __builtin_vsx_xvcvuxdsp) + +// converts single and double precision to signed word +#if __clang_major__ > 4 +# undef vec_cts +#endif +VSX_REDIRECT_1RG(vec_int4, vec_double2, vec_ctso, __builtin_vsx_xvcvdpsxws) +VSX_IMPL_CONVERT(vec_int4, vec_float4, vec_cts) + +// converts single and double precision to unsigned word +#if __clang_major__ > 4 +# undef vec_ctu +#endif +VSX_REDIRECT_1RG(vec_uint4, vec_double2, vec_ctuo, __builtin_vsx_xvcvdpuxws) +VSX_IMPL_CONVERT(vec_uint4, vec_float4, vec_ctu) + +// converts single and double precision to signed doubleword +#ifdef vec_ctsl +# undef vec_ctsl +#endif +VSX_IMPL_CONVERT(vec_dword2, vec_double2, vec_ctsl) +// __builtin_convertvector unable to convert, xvcvspsxds is missing on it +VSX_FINLINE(vec_dword2) vec_ctslo(const vec_float4& a) +{ return vec_ctsl(vec_cvfo(a)); } + +// converts single and double precision to unsigned doubleword +#ifdef vec_ctul +# undef vec_ctul +#endif +VSX_IMPL_CONVERT(vec_udword2, vec_double2, vec_ctul) +// __builtin_convertvector unable to convert, xvcvspuxds is missing on it +VSX_FINLINE(vec_udword2) vec_ctulo(const vec_float4& a) +{ return vec_ctul(vec_cvfo(a)); } + +#endif // CLANG VSX compatibility + +/* + * Common GCC, CLANG compatibility +**/ +#if defined(__GNUG__) && !defined(__IBMCPP__) + +#ifdef vec_cvf +# undef vec_cvf +#endif + +#define VSX_IMPL_CONV_EVEN_4_2(rt, rg, fnm, fn2) \ +VSX_FINLINE(rt) fnm(const rg& a) \ +{ return fn2(vec_sldw(a, a, 1)); } + +VSX_IMPL_CONV_EVEN_4_2(vec_double2, vec_float4, vec_cvf, vec_cvfo) +VSX_IMPL_CONV_EVEN_4_2(vec_double2, vec_int4, vec_ctd, vec_ctdo) +VSX_IMPL_CONV_EVEN_4_2(vec_double2, vec_uint4, vec_ctd, vec_ctdo) + +VSX_IMPL_CONV_EVEN_4_2(vec_dword2, vec_float4, vec_ctsl, vec_ctslo) +VSX_IMPL_CONV_EVEN_4_2(vec_udword2, vec_float4, vec_ctul, vec_ctulo) + +#define VSX_IMPL_CONV_EVEN_2_4(rt, rg, fnm, fn2) \ +VSX_FINLINE(rt) fnm(const rg& a) \ +{ \ + rt v4 = fn2(a); \ + return vec_sldw(v4, v4, 3); \ +} + +VSX_IMPL_CONV_EVEN_2_4(vec_float4, vec_double2, vec_cvf, vec_cvfo) +VSX_IMPL_CONV_EVEN_2_4(vec_float4, vec_dword2, vec_ctf, vec_ctfo) +VSX_IMPL_CONV_EVEN_2_4(vec_float4, vec_udword2, vec_ctf, vec_ctfo) + +VSX_IMPL_CONV_EVEN_2_4(vec_int4, vec_double2, vec_cts, vec_ctso) +VSX_IMPL_CONV_EVEN_2_4(vec_uint4, vec_double2, vec_ctu, vec_ctuo) + +// Only for Eigen! +/* + * changing behavior of conversion intrinsics for gcc has effect on Eigen + * so we redfine old behavior again only on gcc, clang +*/ +#if !defined(__clang__) || __clang_major__ > 4 + // ignoring second arg since Eigen only truncates toward zero +# define VSX_IMPL_CONV_2VARIANT(rt, rg, fnm, fn2) \ + VSX_FINLINE(rt) fnm(const rg& a, int only_truncate) \ + { \ + assert(only_truncate == 0); \ + CV_UNUSED(only_truncate); \ + return fn2(a); \ + } + VSX_IMPL_CONV_2VARIANT(vec_int4, vec_float4, vec_cts, vec_cts) + VSX_IMPL_CONV_2VARIANT(vec_float4, vec_int4, vec_ctf, vec_ctf) + // define vec_cts for converting double precision to signed doubleword + // which isn't combitable with xlc but its okay since Eigen only use it for gcc + VSX_IMPL_CONV_2VARIANT(vec_dword2, vec_double2, vec_cts, vec_ctsl) +#endif // Eigen + +#endif // Common GCC, CLANG compatibility + +/* + * XLC VSX compatibility +**/ +#if defined(__IBMCPP__) + +// vector population count +#define vec_popcntu vec_popcnt + +// overload and redirect with setting second arg to zero +// since we only support conversions without the second arg +#define VSX_IMPL_OVERLOAD_Z2(rt, rg, fnm) \ +VSX_FINLINE(rt) fnm(const rg& a) { return fnm(a, 0); } + +VSX_IMPL_OVERLOAD_Z2(vec_double2, vec_int4, vec_ctd) +VSX_IMPL_OVERLOAD_Z2(vec_double2, vec_uint4, vec_ctd) +VSX_IMPL_OVERLOAD_Z2(vec_double2, vec_dword2, vec_ctd) +VSX_IMPL_OVERLOAD_Z2(vec_double2, vec_udword2, vec_ctd) + +VSX_IMPL_OVERLOAD_Z2(vec_float4, vec_int4, vec_ctf) +VSX_IMPL_OVERLOAD_Z2(vec_float4, vec_uint4, vec_ctf) +VSX_IMPL_OVERLOAD_Z2(vec_float4, vec_dword2, vec_ctf) +VSX_IMPL_OVERLOAD_Z2(vec_float4, vec_udword2, vec_ctf) + +VSX_IMPL_OVERLOAD_Z2(vec_int4, vec_double2, vec_cts) +VSX_IMPL_OVERLOAD_Z2(vec_int4, vec_float4, vec_cts) + +VSX_IMPL_OVERLOAD_Z2(vec_uint4, vec_double2, vec_ctu) +VSX_IMPL_OVERLOAD_Z2(vec_uint4, vec_float4, vec_ctu) + +VSX_IMPL_OVERLOAD_Z2(vec_dword2, vec_double2, vec_ctsl) +VSX_IMPL_OVERLOAD_Z2(vec_dword2, vec_float4, vec_ctsl) + +VSX_IMPL_OVERLOAD_Z2(vec_udword2, vec_double2, vec_ctul) +VSX_IMPL_OVERLOAD_Z2(vec_udword2, vec_float4, vec_ctul) + +// fixme: implement conversions of odd-numbered elements in a dirty way +// since xlc doesn't support VSX registers operand in inline asm. +#define VSX_IMPL_CONV_ODD_4_2(rt, rg, fnm, fn2) \ +VSX_FINLINE(rt) fnm(const rg& a) { return fn2(vec_sldw(a, a, 3)); } + +VSX_IMPL_CONV_ODD_4_2(vec_double2, vec_float4, vec_cvfo, vec_cvf) +VSX_IMPL_CONV_ODD_4_2(vec_double2, vec_int4, vec_ctdo, vec_ctd) +VSX_IMPL_CONV_ODD_4_2(vec_double2, vec_uint4, vec_ctdo, vec_ctd) + +VSX_IMPL_CONV_ODD_4_2(vec_dword2, vec_float4, vec_ctslo, vec_ctsl) +VSX_IMPL_CONV_ODD_4_2(vec_udword2, vec_float4, vec_ctulo, vec_ctul) + +#define VSX_IMPL_CONV_ODD_2_4(rt, rg, fnm, fn2) \ +VSX_FINLINE(rt) fnm(const rg& a) \ +{ \ + rt v4 = fn2(a); \ + return vec_sldw(v4, v4, 1); \ +} + +VSX_IMPL_CONV_ODD_2_4(vec_float4, vec_double2, vec_cvfo, vec_cvf) +VSX_IMPL_CONV_ODD_2_4(vec_float4, vec_dword2, vec_ctfo, vec_ctf) +VSX_IMPL_CONV_ODD_2_4(vec_float4, vec_udword2, vec_ctfo, vec_ctf) + +VSX_IMPL_CONV_ODD_2_4(vec_int4, vec_double2, vec_ctso, vec_cts) +VSX_IMPL_CONV_ODD_2_4(vec_uint4, vec_double2, vec_ctuo, vec_ctu) + +#endif // XLC VSX compatibility + +// ignore GCC warning that caused by -Wunused-but-set-variable in rare cases +#if defined(__GNUG__) && !defined(__clang__) +# define VSX_UNUSED(Tvec) Tvec __attribute__((__unused__)) +#else // CLANG, XLC +# define VSX_UNUSED(Tvec) Tvec +#endif + +// gcc can find his way in casting log int and XLC, CLANG ambiguous +#if defined(__clang__) || defined(__IBMCPP__) + VSX_FINLINE(vec_udword2) vec_splats(uint64 v) + { return vec_splats((unsigned long long) v); } + + VSX_FINLINE(vec_dword2) vec_splats(int64 v) + { return vec_splats((long long) v); } + + VSX_FINLINE(vec_udword2) vec_promote(uint64 a, int b) + { return vec_promote((unsigned long long) a, b); } + + VSX_FINLINE(vec_dword2) vec_promote(int64 a, int b) + { return vec_promote((long long) a, b); } +#endif + +/* + * implement vsx_ld(offset, pointer), vsx_st(vector, offset, pointer) + * load and set using offset depend on the pointer type + * + * implement vsx_ldf(offset, pointer), vsx_stf(vector, offset, pointer) + * load and set using offset depend on fixed bytes size + * + * Note: In clang vec_xl and vec_xst fails to load unaligned addresses + * so we are using vec_vsx_ld, vec_vsx_st instead +*/ + +#if defined(__clang__) && !defined(__IBMCPP__) +# define vsx_ldf vec_vsx_ld +# define vsx_stf vec_vsx_st +#else // GCC , XLC +# define vsx_ldf vec_xl +# define vsx_stf vec_xst +#endif + +#define VSX_OFFSET(o, p) ((o) * sizeof(*(p))) +#define vsx_ld(o, p) vsx_ldf(VSX_OFFSET(o, p), p) +#define vsx_st(v, o, p) vsx_stf(v, VSX_OFFSET(o, p), p) + +/* + * implement vsx_ld2(offset, pointer), vsx_st2(vector, offset, pointer) to load and store double words + * In GCC vec_xl and vec_xst it maps to vec_vsx_ld, vec_vsx_st which doesn't support long long + * and in CLANG we are using vec_vsx_ld, vec_vsx_st because vec_xl, vec_xst fails to load unaligned addresses + * + * In XLC vec_xl and vec_xst fail to cast int64(long int) to long long +*/ +#if (defined(__GNUG__) || defined(__clang__)) && !defined(__IBMCPP__) + VSX_FINLINE(vec_udword2) vsx_ld2(long o, const uint64* p) + { return vec_udword2_c(vsx_ldf(VSX_OFFSET(o, p), (unsigned int*)p)); } + + VSX_FINLINE(vec_dword2) vsx_ld2(long o, const int64* p) + { return vec_dword2_c(vsx_ldf(VSX_OFFSET(o, p), (int*)p)); } + + VSX_FINLINE(void) vsx_st2(const vec_udword2& vec, long o, uint64* p) + { vsx_stf(vec_uint4_c(vec), VSX_OFFSET(o, p), (unsigned int*)p); } + + VSX_FINLINE(void) vsx_st2(const vec_dword2& vec, long o, int64* p) + { vsx_stf(vec_int4_c(vec), VSX_OFFSET(o, p), (int*)p); } +#else // XLC + VSX_FINLINE(vec_udword2) vsx_ld2(long o, const uint64* p) + { return vsx_ldf(VSX_OFFSET(o, p), (unsigned long long*)p); } + + VSX_FINLINE(vec_dword2) vsx_ld2(long o, const int64* p) + { return vsx_ldf(VSX_OFFSET(o, p), (long long*)p); } + + VSX_FINLINE(void) vsx_st2(const vec_udword2& vec, long o, uint64* p) + { vsx_stf(vec, VSX_OFFSET(o, p), (unsigned long long*)p); } + + VSX_FINLINE(void) vsx_st2(const vec_dword2& vec, long o, int64* p) + { vsx_stf(vec, VSX_OFFSET(o, p), (long long*)p); } +#endif + +// Store lower 8 byte +#define vec_st_l8(v, p) *((uint64*)(p)) = vec_extract(vec_udword2_c(v), 0) + +// Store higher 8 byte +#define vec_st_h8(v, p) *((uint64*)(p)) = vec_extract(vec_udword2_c(v), 1) + +// Load 64-bits of integer data to lower part +#define VSX_IMPL_LOAD_L8(Tvec, Tp) \ +VSX_FINLINE(Tvec) vec_ld_l8(const Tp *p) \ +{ return ((Tvec)vec_promote(*((uint64*)p), 0)); } + +VSX_IMPL_LOAD_L8(vec_uchar16, uchar) +VSX_IMPL_LOAD_L8(vec_char16, schar) +VSX_IMPL_LOAD_L8(vec_ushort8, ushort) +VSX_IMPL_LOAD_L8(vec_short8, short) +VSX_IMPL_LOAD_L8(vec_uint4, uint) +VSX_IMPL_LOAD_L8(vec_int4, int) +VSX_IMPL_LOAD_L8(vec_float4, float) +VSX_IMPL_LOAD_L8(vec_udword2, uint64) +VSX_IMPL_LOAD_L8(vec_dword2, int64) +VSX_IMPL_LOAD_L8(vec_double2, double) + +// logical not +#define vec_not(a) vec_nor(a, a) + +// power9 yaya +// not equal +#ifndef vec_cmpne +# define vec_cmpne(a, b) vec_not(vec_cmpeq(a, b)) +#endif + +// absolute difference +#ifndef vec_absd +# define vec_absd(a, b) vec_sub(vec_max(a, b), vec_min(a, b)) +#endif + +/* + * Implement vec_unpacklu and vec_unpackhu + * since vec_unpackl, vec_unpackh only support signed integers +**/ +#define VSX_IMPL_UNPACKU(rt, rg, zero) \ +VSX_FINLINE(rt) vec_unpacklu(const rg& a) \ +{ return (rt)(vec_mergel(a, zero)); } \ +VSX_FINLINE(rt) vec_unpackhu(const rg& a) \ +{ return (rt)(vec_mergeh(a, zero)); } + +VSX_IMPL_UNPACKU(vec_ushort8, vec_uchar16, vec_uchar16_z) +VSX_IMPL_UNPACKU(vec_uint4, vec_ushort8, vec_ushort8_z) +VSX_IMPL_UNPACKU(vec_udword2, vec_uint4, vec_uint4_z) + +/* + * Implement vec_mergesqe and vec_mergesqo + * Merges the sequence values of even and odd elements of two vectors +*/ +#define VSX_IMPL_PERM(rt, fnm, ...) \ +VSX_FINLINE(rt) fnm(const rt& a, const rt& b) \ +{ static const vec_uchar16 perm = {__VA_ARGS__}; return vec_perm(a, b, perm); } + +// 16 +#define perm16_mergesqe 0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30 +#define perm16_mergesqo 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31 +VSX_IMPL_PERM(vec_uchar16, vec_mergesqe, perm16_mergesqe) +VSX_IMPL_PERM(vec_uchar16, vec_mergesqo, perm16_mergesqo) +VSX_IMPL_PERM(vec_char16, vec_mergesqe, perm16_mergesqe) +VSX_IMPL_PERM(vec_char16, vec_mergesqo, perm16_mergesqo) +// 8 +#define perm8_mergesqe 0, 1, 4, 5, 8, 9, 12, 13, 16, 17, 20, 21, 24, 25, 28, 29 +#define perm8_mergesqo 2, 3, 6, 7, 10, 11, 14, 15, 18, 19, 22, 23, 26, 27, 30, 31 +VSX_IMPL_PERM(vec_ushort8, vec_mergesqe, perm8_mergesqe) +VSX_IMPL_PERM(vec_ushort8, vec_mergesqo, perm8_mergesqo) +VSX_IMPL_PERM(vec_short8, vec_mergesqe, perm8_mergesqe) +VSX_IMPL_PERM(vec_short8, vec_mergesqo, perm8_mergesqo) +// 4 +#define perm4_mergesqe 0, 1, 2, 3, 8, 9, 10, 11, 16, 17, 18, 19, 24, 25, 26, 27 +#define perm4_mergesqo 4, 5, 6, 7, 12, 13, 14, 15, 20, 21, 22, 23, 28, 29, 30, 31 +VSX_IMPL_PERM(vec_uint4, vec_mergesqe, perm4_mergesqe) +VSX_IMPL_PERM(vec_uint4, vec_mergesqo, perm4_mergesqo) +VSX_IMPL_PERM(vec_int4, vec_mergesqe, perm4_mergesqe) +VSX_IMPL_PERM(vec_int4, vec_mergesqo, perm4_mergesqo) +VSX_IMPL_PERM(vec_float4, vec_mergesqe, perm4_mergesqe) +VSX_IMPL_PERM(vec_float4, vec_mergesqo, perm4_mergesqo) +// 2 +VSX_REDIRECT_2RG(vec_double2, vec_double2, vec_mergesqe, vec_mergeh) +VSX_REDIRECT_2RG(vec_double2, vec_double2, vec_mergesqo, vec_mergel) +VSX_REDIRECT_2RG(vec_dword2, vec_dword2, vec_mergesqe, vec_mergeh) +VSX_REDIRECT_2RG(vec_dword2, vec_dword2, vec_mergesqo, vec_mergel) +VSX_REDIRECT_2RG(vec_udword2, vec_udword2, vec_mergesqe, vec_mergeh) +VSX_REDIRECT_2RG(vec_udword2, vec_udword2, vec_mergesqo, vec_mergel) + +/* + * Implement vec_mergesqh and vec_mergesql + * Merges the sequence most and least significant halves of two vectors +*/ +#define VSX_IMPL_MERGESQHL(Tvec) \ +VSX_FINLINE(Tvec) vec_mergesqh(const Tvec& a, const Tvec& b) \ +{ return (Tvec)vec_mergeh(vec_udword2_c(a), vec_udword2_c(b)); } \ +VSX_FINLINE(Tvec) vec_mergesql(const Tvec& a, const Tvec& b) \ +{ return (Tvec)vec_mergel(vec_udword2_c(a), vec_udword2_c(b)); } +VSX_IMPL_MERGESQHL(vec_uchar16) +VSX_IMPL_MERGESQHL(vec_char16) +VSX_IMPL_MERGESQHL(vec_ushort8) +VSX_IMPL_MERGESQHL(vec_short8) +VSX_IMPL_MERGESQHL(vec_uint4) +VSX_IMPL_MERGESQHL(vec_int4) +VSX_IMPL_MERGESQHL(vec_float4) +VSX_REDIRECT_2RG(vec_udword2, vec_udword2, vec_mergesqh, vec_mergeh) +VSX_REDIRECT_2RG(vec_udword2, vec_udword2, vec_mergesql, vec_mergel) +VSX_REDIRECT_2RG(vec_dword2, vec_dword2, vec_mergesqh, vec_mergeh) +VSX_REDIRECT_2RG(vec_dword2, vec_dword2, vec_mergesql, vec_mergel) +VSX_REDIRECT_2RG(vec_double2, vec_double2, vec_mergesqh, vec_mergeh) +VSX_REDIRECT_2RG(vec_double2, vec_double2, vec_mergesql, vec_mergel) + + +// 2 and 4 channels interleave for all types except 2 lanes +#define VSX_IMPL_ST_INTERLEAVE(Tp, Tvec) \ +VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b, Tp* ptr) \ +{ \ + vsx_stf(vec_mergeh(a, b), 0, ptr); \ + vsx_stf(vec_mergel(a, b), 16, ptr); \ +} \ +VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b, \ + const Tvec& c, const Tvec& d, Tp* ptr) \ +{ \ + Tvec ac = vec_mergeh(a, c); \ + Tvec bd = vec_mergeh(b, d); \ + vsx_stf(vec_mergeh(ac, bd), 0, ptr); \ + vsx_stf(vec_mergel(ac, bd), 16, ptr); \ + ac = vec_mergel(a, c); \ + bd = vec_mergel(b, d); \ + vsx_stf(vec_mergeh(ac, bd), 32, ptr); \ + vsx_stf(vec_mergel(ac, bd), 48, ptr); \ +} +VSX_IMPL_ST_INTERLEAVE(uchar, vec_uchar16) +VSX_IMPL_ST_INTERLEAVE(schar, vec_char16) +VSX_IMPL_ST_INTERLEAVE(ushort, vec_ushort8) +VSX_IMPL_ST_INTERLEAVE(short, vec_short8) +VSX_IMPL_ST_INTERLEAVE(uint, vec_uint4) +VSX_IMPL_ST_INTERLEAVE(int, vec_int4) +VSX_IMPL_ST_INTERLEAVE(float, vec_float4) + +// 2 and 4 channels deinterleave for 16 lanes +#define VSX_IMPL_ST_DINTERLEAVE_8(Tp, Tvec) \ +VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b) \ +{ \ + Tvec v0 = vsx_ld(0, ptr); \ + Tvec v1 = vsx_ld(16, ptr); \ + a = vec_mergesqe(v0, v1); \ + b = vec_mergesqo(v0, v1); \ +} \ +VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b, \ + Tvec& c, Tvec& d) \ +{ \ + Tvec v0 = vsx_ld(0, ptr); \ + Tvec v1 = vsx_ld(16, ptr); \ + Tvec v2 = vsx_ld(32, ptr); \ + Tvec v3 = vsx_ld(48, ptr); \ + Tvec m0 = vec_mergesqe(v0, v1); \ + Tvec m1 = vec_mergesqe(v2, v3); \ + a = vec_mergesqe(m0, m1); \ + c = vec_mergesqo(m0, m1); \ + m0 = vec_mergesqo(v0, v1); \ + m1 = vec_mergesqo(v2, v3); \ + b = vec_mergesqe(m0, m1); \ + d = vec_mergesqo(m0, m1); \ +} +VSX_IMPL_ST_DINTERLEAVE_8(uchar, vec_uchar16) +VSX_IMPL_ST_DINTERLEAVE_8(schar, vec_char16) + +// 2 and 4 channels deinterleave for 8 lanes +#define VSX_IMPL_ST_DINTERLEAVE_16(Tp, Tvec) \ +VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b) \ +{ \ + Tvec v0 = vsx_ld(0, ptr); \ + Tvec v1 = vsx_ld(8, ptr); \ + a = vec_mergesqe(v0, v1); \ + b = vec_mergesqo(v0, v1); \ +} \ +VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b, \ + Tvec& c, Tvec& d) \ +{ \ + Tvec v0 = vsx_ld(0, ptr); \ + Tvec v1 = vsx_ld(8, ptr); \ + Tvec m0 = vec_mergeh(v0, v1); \ + Tvec m1 = vec_mergel(v0, v1); \ + Tvec ab0 = vec_mergeh(m0, m1); \ + Tvec cd0 = vec_mergel(m0, m1); \ + v0 = vsx_ld(16, ptr); \ + v1 = vsx_ld(24, ptr); \ + m0 = vec_mergeh(v0, v1); \ + m1 = vec_mergel(v0, v1); \ + Tvec ab1 = vec_mergeh(m0, m1); \ + Tvec cd1 = vec_mergel(m0, m1); \ + a = vec_mergesqh(ab0, ab1); \ + b = vec_mergesql(ab0, ab1); \ + c = vec_mergesqh(cd0, cd1); \ + d = vec_mergesql(cd0, cd1); \ +} +VSX_IMPL_ST_DINTERLEAVE_16(ushort, vec_ushort8) +VSX_IMPL_ST_DINTERLEAVE_16(short, vec_short8) + +// 2 and 4 channels deinterleave for 4 lanes +#define VSX_IMPL_ST_DINTERLEAVE_32(Tp, Tvec) \ +VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b) \ +{ \ + a = vsx_ld(0, ptr); \ + b = vsx_ld(4, ptr); \ + Tvec m0 = vec_mergeh(a, b); \ + Tvec m1 = vec_mergel(a, b); \ + a = vec_mergeh(m0, m1); \ + b = vec_mergel(m0, m1); \ +} \ +VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b, \ + Tvec& c, Tvec& d) \ +{ \ + Tvec v0 = vsx_ld(0, ptr); \ + Tvec v1 = vsx_ld(4, ptr); \ + Tvec v2 = vsx_ld(8, ptr); \ + Tvec v3 = vsx_ld(12, ptr); \ + Tvec m0 = vec_mergeh(v0, v2); \ + Tvec m1 = vec_mergeh(v1, v3); \ + a = vec_mergeh(m0, m1); \ + b = vec_mergel(m0, m1); \ + m0 = vec_mergel(v0, v2); \ + m1 = vec_mergel(v1, v3); \ + c = vec_mergeh(m0, m1); \ + d = vec_mergel(m0, m1); \ +} +VSX_IMPL_ST_DINTERLEAVE_32(uint, vec_uint4) +VSX_IMPL_ST_DINTERLEAVE_32(int, vec_int4) +VSX_IMPL_ST_DINTERLEAVE_32(float, vec_float4) + +// 2 and 4 channels interleave and deinterleave for 2 lanes +#define VSX_IMPL_ST_D_INTERLEAVE_64(Tp, Tvec, ld_func, st_func) \ +VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b, Tp* ptr) \ +{ \ + st_func(vec_mergeh(a, b), 0, ptr); \ + st_func(vec_mergel(a, b), 2, ptr); \ +} \ +VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b, \ + const Tvec& c, const Tvec& d, Tp* ptr) \ +{ \ + st_func(vec_mergeh(a, b), 0, ptr); \ + st_func(vec_mergeh(c, d), 2, ptr); \ + st_func(vec_mergel(a, b), 4, ptr); \ + st_func(vec_mergel(c, d), 6, ptr); \ +} \ +VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b) \ +{ \ + Tvec m0 = ld_func(0, ptr); \ + Tvec m1 = ld_func(2, ptr); \ + a = vec_mergeh(m0, m1); \ + b = vec_mergel(m0, m1); \ +} \ +VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b, \ + Tvec& c, Tvec& d) \ +{ \ + Tvec v0 = ld_func(0, ptr); \ + Tvec v1 = ld_func(2, ptr); \ + Tvec v2 = ld_func(4, ptr); \ + Tvec v3 = ld_func(6, ptr); \ + a = vec_mergeh(v0, v2); \ + b = vec_mergel(v0, v2); \ + c = vec_mergeh(v1, v3); \ + d = vec_mergel(v1, v3); \ +} +VSX_IMPL_ST_D_INTERLEAVE_64(int64, vec_dword2, vsx_ld2, vsx_st2) +VSX_IMPL_ST_D_INTERLEAVE_64(uint64, vec_udword2, vsx_ld2, vsx_st2) +VSX_IMPL_ST_D_INTERLEAVE_64(double, vec_double2, vsx_ld, vsx_st) + +/* 3 channels */ +#define VSX_IMPL_ST_INTERLEAVE_3CH_16(Tp, Tvec) \ +VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b, \ + const Tvec& c, Tp* ptr) \ +{ \ + static const vec_uchar16 a12 = {0, 16, 0, 1, 17, 0, 2, 18, 0, 3, 19, 0, 4, 20, 0, 5}; \ + static const vec_uchar16 a123 = {0, 1, 16, 3, 4, 17, 6, 7, 18, 9, 10, 19, 12, 13, 20, 15}; \ + vsx_st(vec_perm(vec_perm(a, b, a12), c, a123), 0, ptr); \ + static const vec_uchar16 b12 = {21, 0, 6, 22, 0, 7, 23, 0, 8, 24, 0, 9, 25, 0, 10, 26}; \ + static const vec_uchar16 b123 = {0, 21, 2, 3, 22, 5, 6, 23, 8, 9, 24, 11, 12, 25, 14, 15}; \ + vsx_st(vec_perm(vec_perm(a, b, b12), c, b123), 16, ptr); \ + static const vec_uchar16 c12 = {0, 11, 27, 0, 12, 28, 0, 13, 29, 0, 14, 30, 0, 15, 31, 0}; \ + static const vec_uchar16 c123 = {26, 1, 2, 27, 4, 5, 28, 7, 8, 29, 10, 11, 30, 13, 14, 31}; \ + vsx_st(vec_perm(vec_perm(a, b, c12), c, c123), 32, ptr); \ +} \ +VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b, Tvec& c) \ +{ \ + Tvec v1 = vsx_ld(0, ptr); \ + Tvec v2 = vsx_ld(16, ptr); \ + Tvec v3 = vsx_ld(32, ptr); \ + static const vec_uchar16 a12_perm = {0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 0, 0, 0, 0, 0}; \ + static const vec_uchar16 a123_perm = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 17, 20, 23, 26, 29}; \ + a = vec_perm(vec_perm(v1, v2, a12_perm), v3, a123_perm); \ + static const vec_uchar16 b12_perm = {1, 4, 7, 10, 13, 16, 19, 22, 25, 28, 31, 0, 0, 0, 0, 0}; \ + static const vec_uchar16 b123_perm = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 18, 21, 24, 27, 30}; \ + b = vec_perm(vec_perm(v1, v2, b12_perm), v3, b123_perm); \ + static const vec_uchar16 c12_perm = {2, 5, 8, 11, 14, 17, 20, 23, 26, 29, 0, 0, 0, 0, 0, 0}; \ + static const vec_uchar16 c123_perm = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 16, 19, 22, 25, 28, 31}; \ + c = vec_perm(vec_perm(v1, v2, c12_perm), v3, c123_perm); \ +} +VSX_IMPL_ST_INTERLEAVE_3CH_16(uchar, vec_uchar16) +VSX_IMPL_ST_INTERLEAVE_3CH_16(schar, vec_char16) + +#define VSX_IMPL_ST_INTERLEAVE_3CH_8(Tp, Tvec) \ +VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b, \ + const Tvec& c, Tp* ptr) \ +{ \ + static const vec_uchar16 a12 = {0, 1, 16, 17, 0, 0, 2, 3, 18, 19, 0, 0, 4, 5, 20, 21}; \ + static const vec_uchar16 a123 = {0, 1, 2, 3, 16, 17, 6, 7, 8, 9, 18, 19, 12, 13, 14, 15}; \ + vsx_st(vec_perm(vec_perm(a, b, a12), c, a123), 0, ptr); \ + static const vec_uchar16 b12 = {0, 0, 6, 7, 22, 23, 0, 0, 8, 9, 24, 25, 0, 0, 10, 11}; \ + static const vec_uchar16 b123 = {20, 21, 2, 3, 4, 5, 22, 23, 8, 9, 10, 11, 24, 25, 14, 15}; \ + vsx_st(vec_perm(vec_perm(a, b, b12), c, b123), 8, ptr); \ + static const vec_uchar16 c12 = {26, 27, 0, 0, 12, 13, 28, 29, 0, 0, 14, 15, 30, 31, 0, 0}; \ + static const vec_uchar16 c123 = {0, 1, 26, 27, 4, 5, 6, 7, 28, 29, 10, 11, 12, 13, 30, 31}; \ + vsx_st(vec_perm(vec_perm(a, b, c12), c, c123), 16, ptr); \ +} \ +VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b, Tvec& c) \ +{ \ + Tvec v1 = vsx_ld(0, ptr); \ + Tvec v2 = vsx_ld(8, ptr); \ + Tvec v3 = vsx_ld(16, ptr); \ + static const vec_uchar16 a12_perm = {0, 1, 6, 7, 12, 13, 18, 19, 24, 25, 30, 31, 0, 0, 0, 0}; \ + static const vec_uchar16 a123_perm = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 20, 21, 26, 27}; \ + a = vec_perm(vec_perm(v1, v2, a12_perm), v3, a123_perm); \ + static const vec_uchar16 b12_perm = {2, 3, 8, 9, 14, 15, 20, 21, 26, 27, 0, 0, 0, 0, 0, 0}; \ + static const vec_uchar16 b123_perm = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 16, 17, 22, 23, 28, 29}; \ + b = vec_perm(vec_perm(v1, v2, b12_perm), v3, b123_perm); \ + static const vec_uchar16 c12_perm = {4, 5, 10, 11, 16, 17, 22, 23, 28, 29, 0, 0, 0, 0, 0, 0}; \ + static const vec_uchar16 c123_perm = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 18, 19, 24, 25, 30, 31}; \ + c = vec_perm(vec_perm(v1, v2, c12_perm), v3, c123_perm); \ +} +VSX_IMPL_ST_INTERLEAVE_3CH_8(ushort, vec_ushort8) +VSX_IMPL_ST_INTERLEAVE_3CH_8(short, vec_short8) + +#define VSX_IMPL_ST_INTERLEAVE_3CH_4(Tp, Tvec) \ +VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b, \ + const Tvec& c, Tp* ptr) \ +{ \ + Tvec hbc = vec_mergeh(b, c); \ + static const vec_uchar16 ahbc = {0, 1, 2, 3, 16, 17, 18, 19, 20, 21, 22, 23, 4, 5, 6, 7}; \ + vsx_st(vec_perm(a, hbc, ahbc), 0, ptr); \ + Tvec lab = vec_mergel(a, b); \ + vsx_st(vec_sld(lab, hbc, 8), 4, ptr); \ + static const vec_uchar16 clab = {8, 9, 10, 11, 24, 25, 26, 27, 28, 29, 30, 31, 12, 13, 14, 15};\ + vsx_st(vec_perm(c, lab, clab), 8, ptr); \ +} \ +VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b, Tvec& c) \ +{ \ + Tvec v1 = vsx_ld(0, ptr); \ + Tvec v2 = vsx_ld(4, ptr); \ + Tvec v3 = vsx_ld(8, ptr); \ + static const vec_uchar16 flp = {0, 1, 2, 3, 12, 13, 14, 15, 16, 17, 18, 19, 28, 29, 30, 31}; \ + a = vec_perm(v1, vec_sld(v3, v2, 8), flp); \ + static const vec_uchar16 flp2 = {28, 29, 30, 31, 0, 1, 2, 3, 12, 13, 14, 15, 16, 17, 18, 19}; \ + b = vec_perm(v2, vec_sld(v1, v3, 8), flp2); \ + c = vec_perm(vec_sld(v2, v1, 8), v3, flp); \ +} +VSX_IMPL_ST_INTERLEAVE_3CH_4(uint, vec_uint4) +VSX_IMPL_ST_INTERLEAVE_3CH_4(int, vec_int4) +VSX_IMPL_ST_INTERLEAVE_3CH_4(float, vec_float4) + +#define VSX_IMPL_ST_INTERLEAVE_3CH_2(Tp, Tvec, ld_func, st_func) \ +VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b, \ + const Tvec& c, Tp* ptr) \ +{ \ + st_func(vec_mergeh(a, b), 0, ptr); \ + st_func(vec_permi(c, a, 1), 2, ptr); \ + st_func(vec_mergel(b, c), 4, ptr); \ +} \ +VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, \ + Tvec& b, Tvec& c) \ +{ \ + Tvec v1 = ld_func(0, ptr); \ + Tvec v2 = ld_func(2, ptr); \ + Tvec v3 = ld_func(4, ptr); \ + a = vec_permi(v1, v2, 1); \ + b = vec_permi(v1, v3, 2); \ + c = vec_permi(v2, v3, 1); \ +} +VSX_IMPL_ST_INTERLEAVE_3CH_2(int64, vec_dword2, vsx_ld2, vsx_st2) +VSX_IMPL_ST_INTERLEAVE_3CH_2(uint64, vec_udword2, vsx_ld2, vsx_st2) +VSX_IMPL_ST_INTERLEAVE_3CH_2(double, vec_double2, vsx_ld, vsx_st) + +#endif // CV_VSX + +//! @} + +#endif // OPENCV_HAL_VSX_UTILS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/wimage.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/wimage.hpp new file mode 100644 index 0000000000000000000000000000000000000000..c7b6efa06a70e984035cb2bc7ad050ded3c3e985 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/core/wimage.hpp @@ -0,0 +1,603 @@ +/*M////////////////////////////////////////////////////////////////////////////// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to +// this license. If you do not agree to this license, do not download, +// install, copy or use the software. +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2008, Google, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of Intel Corporation or contributors may not be used to endorse +// or promote products derived from this software without specific +// prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" +// and any express or implied warranties, including, but not limited to, the +// implied warranties of merchantability and fitness for a particular purpose +// are disclaimed. In no event shall the Intel Corporation or contributors be +// liable for any direct, indirect, incidental, special, exemplary, or +// consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +///////////////////////////////////////////////////////////////////////////////// +//M*/ + +#ifndef OPENCV_CORE_WIMAGE_HPP +#define OPENCV_CORE_WIMAGE_HPP + +#include "opencv2/core/core_c.h" + +#ifdef __cplusplus + +namespace cv { + +//! @addtogroup core +//! @{ + +template class WImage; +template class WImageBuffer; +template class WImageView; + +template class WImageC; +template class WImageBufferC; +template class WImageViewC; + +// Commonly used typedefs. +typedef WImage WImage_b; +typedef WImageView WImageView_b; +typedef WImageBuffer WImageBuffer_b; + +typedef WImageC WImage1_b; +typedef WImageViewC WImageView1_b; +typedef WImageBufferC WImageBuffer1_b; + +typedef WImageC WImage3_b; +typedef WImageViewC WImageView3_b; +typedef WImageBufferC WImageBuffer3_b; + +typedef WImage WImage_f; +typedef WImageView WImageView_f; +typedef WImageBuffer WImageBuffer_f; + +typedef WImageC WImage1_f; +typedef WImageViewC WImageView1_f; +typedef WImageBufferC WImageBuffer1_f; + +typedef WImageC WImage3_f; +typedef WImageViewC WImageView3_f; +typedef WImageBufferC WImageBuffer3_f; + +// There isn't a standard for signed and unsigned short so be more +// explicit in the typename for these cases. +typedef WImage WImage_16s; +typedef WImageView WImageView_16s; +typedef WImageBuffer WImageBuffer_16s; + +typedef WImageC WImage1_16s; +typedef WImageViewC WImageView1_16s; +typedef WImageBufferC WImageBuffer1_16s; + +typedef WImageC WImage3_16s; +typedef WImageViewC WImageView3_16s; +typedef WImageBufferC WImageBuffer3_16s; + +typedef WImage WImage_16u; +typedef WImageView WImageView_16u; +typedef WImageBuffer WImageBuffer_16u; + +typedef WImageC WImage1_16u; +typedef WImageViewC WImageView1_16u; +typedef WImageBufferC WImageBuffer1_16u; + +typedef WImageC WImage3_16u; +typedef WImageViewC WImageView3_16u; +typedef WImageBufferC WImageBuffer3_16u; + +/** @brief Image class which provides a thin layer around an IplImage. + +The goals of the class design are: + + -# All the data has explicit ownership to avoid memory leaks + -# No hidden allocations or copies for performance. + -# Easy access to OpenCV methods (which will access IPP if available) + -# Can easily treat external data as an image + -# Easy to create images which are subsets of other images + -# Fast pixel access which can take advantage of number of channels if known at compile time. + +The WImage class is the image class which provides the data accessors. The 'W' comes from the fact +that it is also a wrapper around the popular but inconvenient IplImage class. A WImage can be +constructed either using a WImageBuffer class which allocates and frees the data, or using a +WImageView class which constructs a subimage or a view into external data. The view class does no +memory management. Each class actually has two versions, one when the number of channels is known +at compile time and one when it isn't. Using the one with the number of channels specified can +provide some compile time optimizations by using the fact that the number of channels is a +constant. + +We use the convention (c,r) to refer to column c and row r with (0,0) being the upper left corner. +This is similar to standard Euclidean coordinates with the first coordinate varying in the +horizontal direction and the second coordinate varying in the vertical direction. Thus (c,r) is +usually in the domain [0, width) X [0, height) + +Example usage: +@code +WImageBuffer3_b im(5,7); // Make a 5X7 3 channel image of type uchar +WImageView3_b sub_im(im, 2,2, 3,3); // 3X3 submatrix +vector vec(10, 3.0f); +WImageView1_f user_im(&vec[0], 2, 5); // 2X5 image w/ supplied data + +im.SetZero(); // same as cvSetZero(im.Ipl()) +*im(2, 3) = 15; // Modify the element at column 2, row 3 +MySetRand(&sub_im); + +// Copy the second row into the first. This can be done with no memory +// allocation and will use SSE if IPP is available. +int w = im.Width(); +im.View(0,0, w,1).CopyFrom(im.View(0,1, w,1)); + +// Doesn't care about source of data since using WImage +void MySetRand(WImage_b* im) { // Works with any number of channels +for (int r = 0; r < im->Height(); ++r) { + float* row = im->Row(r); + for (int c = 0; c < im->Width(); ++c) { + for (int ch = 0; ch < im->Channels(); ++ch, ++row) { + *row = uchar(rand() & 255); + } + } +} +} +@endcode + +Functions that are not part of the basic image allocation, viewing, and access should come from +OpenCV, except some useful functions that are not part of OpenCV can be found in wimage_util.h +*/ +template +class WImage +{ +public: + typedef T BaseType; + + // WImage is an abstract class with no other virtual methods so make the + // destructor virtual. + virtual ~WImage() = 0; + + // Accessors + IplImage* Ipl() {return image_; } + const IplImage* Ipl() const {return image_; } + T* ImageData() { return reinterpret_cast(image_->imageData); } + const T* ImageData() const { + return reinterpret_cast(image_->imageData); + } + + int Width() const {return image_->width; } + int Height() const {return image_->height; } + + // WidthStep is the number of bytes to go to the pixel with the next y coord + int WidthStep() const {return image_->widthStep; } + + int Channels() const {return image_->nChannels; } + int ChannelSize() const {return sizeof(T); } // number of bytes per channel + + // Number of bytes per pixel + int PixelSize() const {return Channels() * ChannelSize(); } + + // Return depth type (e.g. IPL_DEPTH_8U, IPL_DEPTH_32F) which is the number + // of bits per channel and with the signed bit set. + // This is known at compile time using specializations. + int Depth() const; + + inline const T* Row(int r) const { + return reinterpret_cast(image_->imageData + r*image_->widthStep); + } + + inline T* Row(int r) { + return reinterpret_cast(image_->imageData + r*image_->widthStep); + } + + // Pixel accessors which returns a pointer to the start of the channel + inline T* operator() (int c, int r) { + return reinterpret_cast(image_->imageData + r*image_->widthStep) + + c*Channels(); + } + + inline const T* operator() (int c, int r) const { + return reinterpret_cast(image_->imageData + r*image_->widthStep) + + c*Channels(); + } + + // Copy the contents from another image which is just a convenience to cvCopy + void CopyFrom(const WImage& src) { cvCopy(src.Ipl(), image_); } + + // Set contents to zero which is just a convenient to cvSetZero + void SetZero() { cvSetZero(image_); } + + // Construct a view into a region of this image + WImageView View(int c, int r, int width, int height); + +protected: + // Disallow copy and assignment + WImage(const WImage&); + void operator=(const WImage&); + + explicit WImage(IplImage* img) : image_(img) { + assert(!img || img->depth == Depth()); + } + + void SetIpl(IplImage* image) { + assert(!image || image->depth == Depth()); + image_ = image; + } + + IplImage* image_; +}; + + +/** Image class when both the pixel type and number of channels +are known at compile time. This wrapper will speed up some of the operations +like accessing individual pixels using the () operator. +*/ +template +class WImageC : public WImage +{ +public: + typedef typename WImage::BaseType BaseType; + enum { kChannels = C }; + + explicit WImageC(IplImage* img) : WImage(img) { + assert(!img || img->nChannels == Channels()); + } + + // Construct a view into a region of this image + WImageViewC View(int c, int r, int width, int height); + + // Copy the contents from another image which is just a convenience to cvCopy + void CopyFrom(const WImageC& src) { + cvCopy(src.Ipl(), WImage::image_); + } + + // WImageC is an abstract class with no other virtual methods so make the + // destructor virtual. + virtual ~WImageC() = 0; + + int Channels() const {return C; } + +protected: + // Disallow copy and assignment + WImageC(const WImageC&); + void operator=(const WImageC&); + + void SetIpl(IplImage* image) { + assert(!image || image->depth == WImage::Depth()); + WImage::SetIpl(image); + } +}; + +/** Image class which owns the data, so it can be allocated and is always +freed. It cannot be copied but can be explicitly cloned. +*/ +template +class WImageBuffer : public WImage +{ +public: + typedef typename WImage::BaseType BaseType; + + // Default constructor which creates an object that can be + WImageBuffer() : WImage(0) {} + + WImageBuffer(int width, int height, int nchannels) : WImage(0) { + Allocate(width, height, nchannels); + } + + // Constructor which takes ownership of a given IplImage so releases + // the image on destruction. + explicit WImageBuffer(IplImage* img) : WImage(img) {} + + // Allocate an image. Does nothing if current size is the same as + // the new size. + void Allocate(int width, int height, int nchannels); + + // Set the data to point to an image, releasing the old data + void SetIpl(IplImage* img) { + ReleaseImage(); + WImage::SetIpl(img); + } + + // Clone an image which reallocates the image if of a different dimension. + void CloneFrom(const WImage& src) { + Allocate(src.Width(), src.Height(), src.Channels()); + CopyFrom(src); + } + + ~WImageBuffer() { + ReleaseImage(); + } + + // Release the image if it isn't null. + void ReleaseImage() { + if (WImage::image_) { + IplImage* image = WImage::image_; + cvReleaseImage(&image); + WImage::SetIpl(0); + } + } + + bool IsNull() const {return WImage::image_ == NULL; } + +private: + // Disallow copy and assignment + WImageBuffer(const WImageBuffer&); + void operator=(const WImageBuffer&); +}; + +/** Like a WImageBuffer class but when the number of channels is known at compile time. +*/ +template +class WImageBufferC : public WImageC +{ +public: + typedef typename WImage::BaseType BaseType; + enum { kChannels = C }; + + // Default constructor which creates an object that can be + WImageBufferC() : WImageC(0) {} + + WImageBufferC(int width, int height) : WImageC(0) { + Allocate(width, height); + } + + // Constructor which takes ownership of a given IplImage so releases + // the image on destruction. + explicit WImageBufferC(IplImage* img) : WImageC(img) {} + + // Allocate an image. Does nothing if current size is the same as + // the new size. + void Allocate(int width, int height); + + // Set the data to point to an image, releasing the old data + void SetIpl(IplImage* img) { + ReleaseImage(); + WImageC::SetIpl(img); + } + + // Clone an image which reallocates the image if of a different dimension. + void CloneFrom(const WImageC& src) { + Allocate(src.Width(), src.Height()); + CopyFrom(src); + } + + ~WImageBufferC() { + ReleaseImage(); + } + + // Release the image if it isn't null. + void ReleaseImage() { + if (WImage::image_) { + IplImage* image = WImage::image_; + cvReleaseImage(&image); + WImageC::SetIpl(0); + } + } + + bool IsNull() const {return WImage::image_ == NULL; } + +private: + // Disallow copy and assignment + WImageBufferC(const WImageBufferC&); + void operator=(const WImageBufferC&); +}; + +/** View into an image class which allows treating a subimage as an image or treating external data +as an image +*/ +template class WImageView : public WImage +{ +public: + typedef typename WImage::BaseType BaseType; + + // Construct a subimage. No checks are done that the subimage lies + // completely inside the original image. + WImageView(WImage* img, int c, int r, int width, int height); + + // Refer to external data. + // If not given width_step assumed to be same as width. + WImageView(T* data, int width, int height, int channels, int width_step = -1); + + // Refer to external data. This does NOT take ownership + // of the supplied IplImage. + WImageView(IplImage* img) : WImage(img) {} + + // Copy constructor + WImageView(const WImage& img) : WImage(0) { + header_ = *(img.Ipl()); + WImage::SetIpl(&header_); + } + + WImageView& operator=(const WImage& img) { + header_ = *(img.Ipl()); + WImage::SetIpl(&header_); + return *this; + } + +protected: + IplImage header_; +}; + + +template +class WImageViewC : public WImageC +{ +public: + typedef typename WImage::BaseType BaseType; + enum { kChannels = C }; + + // Default constructor needed for vectors of views. + WImageViewC(); + + virtual ~WImageViewC() {} + + // Construct a subimage. No checks are done that the subimage lies + // completely inside the original image. + WImageViewC(WImageC* img, + int c, int r, int width, int height); + + // Refer to external data + WImageViewC(T* data, int width, int height, int width_step = -1); + + // Refer to external data. This does NOT take ownership + // of the supplied IplImage. + WImageViewC(IplImage* img) : WImageC(img) {} + + // Copy constructor which does a shallow copy to allow multiple views + // of same data. gcc-4.1.1 gets confused if both versions of + // the constructor and assignment operator are not provided. + WImageViewC(const WImageC& img) : WImageC(0) { + header_ = *(img.Ipl()); + WImageC::SetIpl(&header_); + } + WImageViewC(const WImageViewC& img) : WImageC(0) { + header_ = *(img.Ipl()); + WImageC::SetIpl(&header_); + } + + WImageViewC& operator=(const WImageC& img) { + header_ = *(img.Ipl()); + WImageC::SetIpl(&header_); + return *this; + } + WImageViewC& operator=(const WImageViewC& img) { + header_ = *(img.Ipl()); + WImageC::SetIpl(&header_); + return *this; + } + +protected: + IplImage header_; +}; + + +// Specializations for depth +template<> +inline int WImage::Depth() const {return IPL_DEPTH_8U; } +template<> +inline int WImage::Depth() const {return IPL_DEPTH_8S; } +template<> +inline int WImage::Depth() const {return IPL_DEPTH_16S; } +template<> +inline int WImage::Depth() const {return IPL_DEPTH_16U; } +template<> +inline int WImage::Depth() const {return IPL_DEPTH_32S; } +template<> +inline int WImage::Depth() const {return IPL_DEPTH_32F; } +template<> +inline int WImage::Depth() const {return IPL_DEPTH_64F; } + +template inline WImage::~WImage() {} +template inline WImageC::~WImageC() {} + +template +inline void WImageBuffer::Allocate(int width, int height, int nchannels) +{ + if (IsNull() || WImage::Width() != width || + WImage::Height() != height || WImage::Channels() != nchannels) { + ReleaseImage(); + WImage::image_ = cvCreateImage(cvSize(width, height), + WImage::Depth(), nchannels); + } +} + +template +inline void WImageBufferC::Allocate(int width, int height) +{ + if (IsNull() || WImage::Width() != width || WImage::Height() != height) { + ReleaseImage(); + WImageC::SetIpl(cvCreateImage(cvSize(width, height),WImage::Depth(), C)); + } +} + +template +WImageView::WImageView(WImage* img, int c, int r, int width, int height) + : WImage(0) +{ + header_ = *(img->Ipl()); + header_.imageData = reinterpret_cast((*img)(c, r)); + header_.width = width; + header_.height = height; + WImage::SetIpl(&header_); +} + +template +WImageView::WImageView(T* data, int width, int height, int nchannels, int width_step) + : WImage(0) +{ + cvInitImageHeader(&header_, cvSize(width, height), WImage::Depth(), nchannels); + header_.imageData = reinterpret_cast(data); + if (width_step > 0) { + header_.widthStep = width_step; + } + WImage::SetIpl(&header_); +} + +template +WImageViewC::WImageViewC(WImageC* img, int c, int r, int width, int height) + : WImageC(0) +{ + header_ = *(img->Ipl()); + header_.imageData = reinterpret_cast((*img)(c, r)); + header_.width = width; + header_.height = height; + WImageC::SetIpl(&header_); +} + +template +WImageViewC::WImageViewC() : WImageC(0) { + cvInitImageHeader(&header_, cvSize(0, 0), WImage::Depth(), C); + header_.imageData = reinterpret_cast(0); + WImageC::SetIpl(&header_); +} + +template +WImageViewC::WImageViewC(T* data, int width, int height, int width_step) + : WImageC(0) +{ + cvInitImageHeader(&header_, cvSize(width, height), WImage::Depth(), C); + header_.imageData = reinterpret_cast(data); + if (width_step > 0) { + header_.widthStep = width_step; + } + WImageC::SetIpl(&header_); +} + +// Construct a view into a region of an image +template +WImageView WImage::View(int c, int r, int width, int height) { + return WImageView(this, c, r, width, height); +} + +template +WImageViewC WImageC::View(int c, int r, int width, int height) { + return WImageViewC(this, c, r, width, height); +} + +//! @} core + +} // end of namespace + +#endif // __cplusplus + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/cvconfig.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/cvconfig.h new file mode 100644 index 0000000000000000000000000000000000000000..b5192fb1f9619254d5c35f47c1fd1a271b00bd87 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/cvconfig.h @@ -0,0 +1,248 @@ +#ifndef OPENCV_CVCONFIG_H_INCLUDED +#define OPENCV_CVCONFIG_H_INCLUDED + +/* OpenCV compiled as static or dynamic libs */ +/* #undef BUILD_SHARED_LIBS */ + +/* OpenCV intrinsics optimized code */ +#define CV_ENABLE_INTRINSICS + +/* OpenCV additional optimized code */ +/* #undef CV_DISABLE_OPTIMIZATION */ + +/* Compile for 'real' NVIDIA GPU architectures */ +#define CUDA_ARCH_BIN "" + +/* Create PTX or BIN for 1.0 compute capability */ +/* #undef CUDA_ARCH_BIN_OR_PTX_10 */ + +/* NVIDIA GPU features are used */ +#define CUDA_ARCH_FEATURES "" + +/* Compile for 'virtual' NVIDIA PTX architectures */ +#define CUDA_ARCH_PTX "" + +/* AVFoundation video libraries */ +/* #undef HAVE_AVFOUNDATION */ + +/* V4L capturing support */ +/* #undef HAVE_CAMV4L */ + +/* V4L2 capturing support */ +#define HAVE_CAMV4L2 + +/* Carbon windowing environment */ +/* #undef HAVE_CARBON */ + +/* AMD's Basic Linear Algebra Subprograms Library*/ +/* #undef HAVE_CLAMDBLAS */ + +/* AMD's OpenCL Fast Fourier Transform Library*/ +/* #undef HAVE_CLAMDFFT */ + +/* Clp support */ +/* #undef HAVE_CLP */ + +/* Cocoa API */ +/* #undef HAVE_COCOA */ + +/* C= */ +/* #undef HAVE_CSTRIPES */ + +/* NVIDIA CUDA Basic Linear Algebra Subprograms (BLAS) API*/ +/* #undef HAVE_CUBLAS */ + +/* NVIDIA CUDA Runtime API*/ +/* #undef HAVE_CUDA */ + +/* NVIDIA CUDA Fast Fourier Transform (FFT) API*/ +/* #undef HAVE_CUFFT */ + +/* IEEE1394 capturing support */ +/* #undef HAVE_DC1394 */ + +/* IEEE1394 capturing support - libdc1394 v2.x */ +/* #undef HAVE_DC1394_2 */ + +/* DirectX */ +/* #undef HAVE_DIRECTX */ +/* #undef HAVE_DIRECTX_NV12 */ +/* #undef HAVE_D3D11 */ +/* #undef HAVE_D3D10 */ +/* #undef HAVE_D3D9 */ + +/* DirectShow Video Capture library */ +/* #undef HAVE_DSHOW */ + +/* Eigen Matrix & Linear Algebra Library */ +/* #undef HAVE_EIGEN */ + +/* FFMpeg video library */ +/* #undef HAVE_FFMPEG */ + +/* Geospatial Data Abstraction Library */ +/* #undef HAVE_GDAL */ + +/* GStreamer multimedia framework */ +/* #undef HAVE_GSTREAMER */ + +/* GTK+ 2.0 Thread support */ +/* #undef HAVE_GTHREAD */ + +/* GTK+ 2.x toolkit */ +/* #undef HAVE_GTK */ + +/* Halide support */ +/* #undef HAVE_HALIDE */ + +/* Define to 1 if you have the header file. */ +#define HAVE_INTTYPES_H 1 + +/* Intel Perceptual Computing SDK library */ +/* #undef HAVE_INTELPERC */ + +/* Intel Integrated Performance Primitives */ +/* #undef HAVE_IPP */ +/* #undef HAVE_IPP_ICV */ +/* #undef HAVE_IPP_IW */ +/* #undef HAVE_IPP_IW_LL */ + +/* JPEG-2000 codec */ +#define HAVE_JASPER + +/* IJG JPEG codec */ +#define HAVE_JPEG + +/* libpng/png.h needs to be included */ +/* #undef HAVE_LIBPNG_PNG_H */ + +/* GDCM DICOM codec */ +/* #undef HAVE_GDCM */ + +/* V4L/V4L2 capturing support via libv4l */ +/* #undef HAVE_LIBV4L */ + +/* Microsoft Media Foundation Capture library */ +/* #undef HAVE_MSMF */ + +/* NVIDIA Video Decoding API*/ +/* #undef HAVE_NVCUVID */ + +/* NVIDIA Video Encoding API*/ +/* #undef HAVE_NVCUVENC */ + +/* OpenCL Support */ +/* #undef HAVE_OPENCL */ +/* #undef HAVE_OPENCL_STATIC */ +/* #undef HAVE_OPENCL_SVM */ + +/* OpenEXR codec */ +#define HAVE_OPENEXR + +/* OpenGL support*/ +/* #undef HAVE_OPENGL */ + +/* OpenNI library */ +/* #undef HAVE_OPENNI */ + +/* OpenNI library */ +/* #undef HAVE_OPENNI2 */ + +/* PNG codec */ +#define HAVE_PNG + +/* Posix threads (pthreads) */ +#define HAVE_PTHREAD + +/* parallel_for with pthreads */ +#define HAVE_PTHREADS_PF + +/* Qt support */ +/* #undef HAVE_QT */ + +/* Qt OpenGL support */ +/* #undef HAVE_QT_OPENGL */ + +/* QuickTime video libraries */ +/* #undef HAVE_QUICKTIME */ + +/* QTKit video libraries */ +/* #undef HAVE_QTKIT */ + +/* Intel Threading Building Blocks */ +/* #undef HAVE_TBB */ + +/* TIFF codec */ +#define HAVE_TIFF + +/* Unicap video capture library */ +/* #undef HAVE_UNICAP */ + +/* Video for Windows support */ +/* #undef HAVE_VFW */ + +/* V4L2 capturing support in videoio.h */ +/* #undef HAVE_VIDEOIO */ + +/* Win32 UI */ +/* #undef HAVE_WIN32UI */ + +/* XIMEA camera support */ +/* #undef HAVE_XIMEA */ + +/* Xine video library */ +/* #undef HAVE_XINE */ + +/* Define if your processor stores words with the most significant byte + first (like Motorola and SPARC, unlike Intel and VAX). */ +/* #undef WORDS_BIGENDIAN */ + +/* gPhoto2 library */ +/* #undef HAVE_GPHOTO2 */ + +/* VA library (libva) */ +/* #undef HAVE_VA */ + +/* Intel VA-API/OpenCL */ +/* #undef HAVE_VA_INTEL */ + +/* Intel Media SDK */ +/* #undef HAVE_MFX */ + +/* Lapack */ +/* #undef HAVE_LAPACK */ + +/* Library was compiled with functions instrumentation */ +/* #undef ENABLE_INSTRUMENTATION */ + +/* OpenVX */ +/* #undef HAVE_OPENVX */ + +#if defined(HAVE_XINE) || \ + defined(HAVE_GSTREAMER) || \ + defined(HAVE_QUICKTIME) || \ + defined(HAVE_QTKIT) || \ + defined(HAVE_AVFOUNDATION) || \ + /*defined(HAVE_OPENNI) || too specialized */ \ + defined(HAVE_FFMPEG) || \ + defined(HAVE_MSMF) +#define HAVE_VIDEO_INPUT +#endif + +#if /*defined(HAVE_XINE) || */\ + defined(HAVE_GSTREAMER) || \ + defined(HAVE_QUICKTIME) || \ + defined(HAVE_QTKIT) || \ + defined(HAVE_AVFOUNDATION) || \ + defined(HAVE_FFMPEG) || \ + defined(HAVE_MSMF) +#define HAVE_VIDEO_OUTPUT +#endif + +/* OpenCV trace utilities */ +#define OPENCV_TRACE + +/* Library QR-code decoding */ +#define HAVE_QUIRC + +#endif // OPENCV_CVCONFIG_H_INCLUDED diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/features2d.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/features2d.hpp new file mode 100644 index 0000000000000000000000000000000000000000..ee81ebe38562da7be600a5101e0d8aa1f9b60318 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/features2d.hpp @@ -0,0 +1,1428 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_FEATURES_2D_HPP +#define OPENCV_FEATURES_2D_HPP + +#include "opencv2/opencv_modules.hpp" +#include "opencv2/core.hpp" + +#ifdef HAVE_OPENCV_FLANN +#include "opencv2/flann/miniflann.hpp" +#endif + +/** + @defgroup features2d 2D Features Framework + @{ + @defgroup features2d_main Feature Detection and Description + @defgroup features2d_match Descriptor Matchers + +Matchers of keypoint descriptors in OpenCV have wrappers with a common interface that enables you to +easily switch between different algorithms solving the same problem. This section is devoted to +matching descriptors that are represented as vectors in a multidimensional space. All objects that +implement vector descriptor matchers inherit the DescriptorMatcher interface. + +@note + - An example explaining keypoint matching can be found at + opencv_source_code/samples/cpp/descriptor_extractor_matcher.cpp + - An example on descriptor matching evaluation can be found at + opencv_source_code/samples/cpp/detector_descriptor_matcher_evaluation.cpp + - An example on one to many image matching can be found at + opencv_source_code/samples/cpp/matching_to_many_images.cpp + + @defgroup features2d_draw Drawing Function of Keypoints and Matches + @defgroup features2d_category Object Categorization + +This section describes approaches based on local 2D features and used to categorize objects. + +@note + - A complete Bag-Of-Words sample can be found at + opencv_source_code/samples/cpp/bagofwords_classification.cpp + - (Python) An example using the features2D framework to perform object categorization can be + found at opencv_source_code/samples/python/find_obj.py + + @} + */ + +namespace cv +{ + +//! @addtogroup features2d +//! @{ + +// //! writes vector of keypoints to the file storage +// CV_EXPORTS void write(FileStorage& fs, const String& name, const std::vector& keypoints); +// //! reads vector of keypoints from the specified file storage node +// CV_EXPORTS void read(const FileNode& node, CV_OUT std::vector& keypoints); + +/** @brief A class filters a vector of keypoints. + + Because now it is difficult to provide a convenient interface for all usage scenarios of the + keypoints filter class, it has only several needed by now static methods. + */ +class CV_EXPORTS KeyPointsFilter +{ +public: + KeyPointsFilter(){} + + /* + * Remove keypoints within borderPixels of an image edge. + */ + static void runByImageBorder( std::vector& keypoints, Size imageSize, int borderSize ); + /* + * Remove keypoints of sizes out of range. + */ + static void runByKeypointSize( std::vector& keypoints, float minSize, + float maxSize=FLT_MAX ); + /* + * Remove keypoints from some image by mask for pixels of this image. + */ + static void runByPixelsMask( std::vector& keypoints, const Mat& mask ); + /* + * Remove duplicated keypoints. + */ + static void removeDuplicated( std::vector& keypoints ); + /* + * Remove duplicated keypoints and sort the remaining keypoints + */ + static void removeDuplicatedSorted( std::vector& keypoints ); + + /* + * Retain the specified number of the best keypoints (according to the response) + */ + static void retainBest( std::vector& keypoints, int npoints ); +}; + + +/************************************ Base Classes ************************************/ + +/** @brief Abstract base class for 2D image feature detectors and descriptor extractors +*/ +#ifdef __EMSCRIPTEN__ +class CV_EXPORTS_W Feature2D : public Algorithm +#else +class CV_EXPORTS_W Feature2D : public virtual Algorithm +#endif +{ +public: + virtual ~Feature2D(); + + /** @brief Detects keypoints in an image (first variant) or image set (second variant). + + @param image Image. + @param keypoints The detected keypoints. In the second variant of the method keypoints[i] is a set + of keypoints detected in images[i] . + @param mask Mask specifying where to look for keypoints (optional). It must be a 8-bit integer + matrix with non-zero values in the region of interest. + */ + CV_WRAP virtual void detect( InputArray image, + CV_OUT std::vector& keypoints, + InputArray mask=noArray() ); + + /** @overload + @param images Image set. + @param keypoints The detected keypoints. In the second variant of the method keypoints[i] is a set + of keypoints detected in images[i] . + @param masks Masks for each input image specifying where to look for keypoints (optional). + masks[i] is a mask for images[i]. + */ + CV_WRAP virtual void detect( InputArrayOfArrays images, + CV_OUT std::vector >& keypoints, + InputArrayOfArrays masks=noArray() ); + + /** @brief Computes the descriptors for a set of keypoints detected in an image (first variant) or image set + (second variant). + + @param image Image. + @param keypoints Input collection of keypoints. Keypoints for which a descriptor cannot be + computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint + with several dominant orientations (for each orientation). + @param descriptors Computed descriptors. In the second variant of the method descriptors[i] are + descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the + descriptor for keypoint j-th keypoint. + */ + CV_WRAP virtual void compute( InputArray image, + CV_OUT CV_IN_OUT std::vector& keypoints, + OutputArray descriptors ); + + /** @overload + + @param images Image set. + @param keypoints Input collection of keypoints. Keypoints for which a descriptor cannot be + computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint + with several dominant orientations (for each orientation). + @param descriptors Computed descriptors. In the second variant of the method descriptors[i] are + descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the + descriptor for keypoint j-th keypoint. + */ + CV_WRAP virtual void compute( InputArrayOfArrays images, + CV_OUT CV_IN_OUT std::vector >& keypoints, + OutputArrayOfArrays descriptors ); + + /** Detects keypoints and computes the descriptors */ + CV_WRAP virtual void detectAndCompute( InputArray image, InputArray mask, + CV_OUT std::vector& keypoints, + OutputArray descriptors, + bool useProvidedKeypoints=false ); + + CV_WRAP virtual int descriptorSize() const; + CV_WRAP virtual int descriptorType() const; + CV_WRAP virtual int defaultNorm() const; + + CV_WRAP void write( const String& fileName ) const; + + CV_WRAP void read( const String& fileName ); + + virtual void write( FileStorage&) const CV_OVERRIDE; + + // see corresponding cv::Algorithm method + CV_WRAP virtual void read( const FileNode&) CV_OVERRIDE; + + //! Return true if detector object is empty + CV_WRAP virtual bool empty() const CV_OVERRIDE; + CV_WRAP virtual String getDefaultName() const CV_OVERRIDE; + + // see corresponding cv::Algorithm method + CV_WRAP inline void write(const Ptr& fs, const String& name = String()) const { Algorithm::write(fs, name); } +}; + +/** Feature detectors in OpenCV have wrappers with a common interface that enables you to easily switch +between different algorithms solving the same problem. All objects that implement keypoint detectors +inherit the FeatureDetector interface. */ +typedef Feature2D FeatureDetector; + +/** Extractors of keypoint descriptors in OpenCV have wrappers with a common interface that enables you +to easily switch between different algorithms solving the same problem. This section is devoted to +computing descriptors represented as vectors in a multidimensional space. All objects that implement +the vector descriptor extractors inherit the DescriptorExtractor interface. + */ +typedef Feature2D DescriptorExtractor; + +//! @addtogroup features2d_main +//! @{ + +/** @brief Class implementing the BRISK keypoint detector and descriptor extractor, described in @cite LCS11 . + */ +class CV_EXPORTS_W BRISK : public Feature2D +{ +public: + /** @brief The BRISK constructor + + @param thresh AGAST detection threshold score. + @param octaves detection octaves. Use 0 to do single scale. + @param patternScale apply this scale to the pattern used for sampling the neighbourhood of a + keypoint. + */ + CV_WRAP static Ptr create(int thresh=30, int octaves=3, float patternScale=1.0f); + + /** @brief The BRISK constructor for a custom pattern + + @param radiusList defines the radii (in pixels) where the samples around a keypoint are taken (for + keypoint scale 1). + @param numberList defines the number of sampling points on the sampling circle. Must be the same + size as radiusList.. + @param dMax threshold for the short pairings used for descriptor formation (in pixels for keypoint + scale 1). + @param dMin threshold for the long pairings used for orientation determination (in pixels for + keypoint scale 1). + @param indexChange index remapping of the bits. */ + CV_WRAP static Ptr create(const std::vector &radiusList, const std::vector &numberList, + float dMax=5.85f, float dMin=8.2f, const std::vector& indexChange=std::vector()); + + /** @brief The BRISK constructor for a custom pattern, detection threshold and octaves + + @param thresh AGAST detection threshold score. + @param octaves detection octaves. Use 0 to do single scale. + @param radiusList defines the radii (in pixels) where the samples around a keypoint are taken (for + keypoint scale 1). + @param numberList defines the number of sampling points on the sampling circle. Must be the same + size as radiusList.. + @param dMax threshold for the short pairings used for descriptor formation (in pixels for keypoint + scale 1). + @param dMin threshold for the long pairings used for orientation determination (in pixels for + keypoint scale 1). + @param indexChange index remapping of the bits. */ + CV_WRAP static Ptr create(int thresh, int octaves, const std::vector &radiusList, + const std::vector &numberList, float dMax=5.85f, float dMin=8.2f, + const std::vector& indexChange=std::vector()); + CV_WRAP virtual String getDefaultName() const CV_OVERRIDE; +}; + +/** @brief Class implementing the ORB (*oriented BRIEF*) keypoint detector and descriptor extractor + +described in @cite RRKB11 . The algorithm uses FAST in pyramids to detect stable keypoints, selects +the strongest features using FAST or Harris response, finds their orientation using first-order +moments and computes the descriptors using BRIEF (where the coordinates of random point pairs (or +k-tuples) are rotated according to the measured orientation). + */ +class CV_EXPORTS_W ORB : public Feature2D +{ +public: + enum { kBytes = 32, HARRIS_SCORE=0, FAST_SCORE=1 }; + + /** @brief The ORB constructor + + @param nfeatures The maximum number of features to retain. + @param scaleFactor Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical + pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor + will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor + will mean that to cover certain scale range you will need more pyramid levels and so the speed + will suffer. + @param nlevels The number of pyramid levels. The smallest level will have linear size equal to + input_image_linear_size/pow(scaleFactor, nlevels - firstLevel). + @param edgeThreshold This is size of the border where the features are not detected. It should + roughly match the patchSize parameter. + @param firstLevel The level of pyramid to put source image to. Previous layers are filled + with upscaled source image. + @param WTA_K The number of points that produce each element of the oriented BRIEF descriptor. The + default value 2 means the BRIEF where we take a random point pair and compare their brightnesses, + so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3 + random points (of course, those point coordinates are random, but they are generated from the + pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel + rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such + output will occupy 2 bits, and therefore it will need a special variant of Hamming distance, + denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each + bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3). + @param scoreType The default HARRIS_SCORE means that Harris algorithm is used to rank features + (the score is written to KeyPoint::score and is used to retain best nfeatures features); + FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints, + but it is a little faster to compute. + @param patchSize size of the patch used by the oriented BRIEF descriptor. Of course, on smaller + pyramid layers the perceived image area covered by a feature will be larger. + @param fastThreshold + */ + CV_WRAP static Ptr create(int nfeatures=500, float scaleFactor=1.2f, int nlevels=8, int edgeThreshold=31, + int firstLevel=0, int WTA_K=2, int scoreType=ORB::HARRIS_SCORE, int patchSize=31, int fastThreshold=20); + + CV_WRAP virtual void setMaxFeatures(int maxFeatures) = 0; + CV_WRAP virtual int getMaxFeatures() const = 0; + + CV_WRAP virtual void setScaleFactor(double scaleFactor) = 0; + CV_WRAP virtual double getScaleFactor() const = 0; + + CV_WRAP virtual void setNLevels(int nlevels) = 0; + CV_WRAP virtual int getNLevels() const = 0; + + CV_WRAP virtual void setEdgeThreshold(int edgeThreshold) = 0; + CV_WRAP virtual int getEdgeThreshold() const = 0; + + CV_WRAP virtual void setFirstLevel(int firstLevel) = 0; + CV_WRAP virtual int getFirstLevel() const = 0; + + CV_WRAP virtual void setWTA_K(int wta_k) = 0; + CV_WRAP virtual int getWTA_K() const = 0; + + CV_WRAP virtual void setScoreType(int scoreType) = 0; + CV_WRAP virtual int getScoreType() const = 0; + + CV_WRAP virtual void setPatchSize(int patchSize) = 0; + CV_WRAP virtual int getPatchSize() const = 0; + + CV_WRAP virtual void setFastThreshold(int fastThreshold) = 0; + CV_WRAP virtual int getFastThreshold() const = 0; + CV_WRAP virtual String getDefaultName() const CV_OVERRIDE; +}; + +/** @brief Maximally stable extremal region extractor + +The class encapsulates all the parameters of the %MSER extraction algorithm (see [wiki +article](http://en.wikipedia.org/wiki/Maximally_stable_extremal_regions)). + +- there are two different implementation of %MSER: one for grey image, one for color image + +- the grey image algorithm is taken from: @cite nister2008linear ; the paper claims to be faster +than union-find method; it actually get 1.5~2m/s on my centrino L7200 1.2GHz laptop. + +- the color image algorithm is taken from: @cite forssen2007maximally ; it should be much slower +than grey image method ( 3~4 times ); the chi_table.h file is taken directly from paper's source +code which is distributed under GPL. + +- (Python) A complete example showing the use of the %MSER detector can be found at samples/python/mser.py +*/ +class CV_EXPORTS_W MSER : public Feature2D +{ +public: + /** @brief Full consturctor for %MSER detector + + @param _delta it compares \f$(size_{i}-size_{i-delta})/size_{i-delta}\f$ + @param _min_area prune the area which smaller than minArea + @param _max_area prune the area which bigger than maxArea + @param _max_variation prune the area have similar size to its children + @param _min_diversity for color image, trace back to cut off mser with diversity less than min_diversity + @param _max_evolution for color image, the evolution steps + @param _area_threshold for color image, the area threshold to cause re-initialize + @param _min_margin for color image, ignore too small margin + @param _edge_blur_size for color image, the aperture size for edge blur + */ + CV_WRAP static Ptr create( int _delta=5, int _min_area=60, int _max_area=14400, + double _max_variation=0.25, double _min_diversity=.2, + int _max_evolution=200, double _area_threshold=1.01, + double _min_margin=0.003, int _edge_blur_size=5 ); + + /** @brief Detect %MSER regions + + @param image input image (8UC1, 8UC3 or 8UC4, must be greater or equal than 3x3) + @param msers resulting list of point sets + @param bboxes resulting bounding boxes + */ + CV_WRAP virtual void detectRegions( InputArray image, + CV_OUT std::vector >& msers, + CV_OUT std::vector& bboxes ) = 0; + + CV_WRAP virtual void setDelta(int delta) = 0; + CV_WRAP virtual int getDelta() const = 0; + + CV_WRAP virtual void setMinArea(int minArea) = 0; + CV_WRAP virtual int getMinArea() const = 0; + + CV_WRAP virtual void setMaxArea(int maxArea) = 0; + CV_WRAP virtual int getMaxArea() const = 0; + + CV_WRAP virtual void setPass2Only(bool f) = 0; + CV_WRAP virtual bool getPass2Only() const = 0; + CV_WRAP virtual String getDefaultName() const CV_OVERRIDE; +}; + +/** @overload */ +CV_EXPORTS void FAST( InputArray image, CV_OUT std::vector& keypoints, + int threshold, bool nonmaxSuppression=true ); + +/** @brief Detects corners using the FAST algorithm + +@param image grayscale image where keypoints (corners) are detected. +@param keypoints keypoints detected on the image. +@param threshold threshold on difference between intensity of the central pixel and pixels of a +circle around this pixel. +@param nonmaxSuppression if true, non-maximum suppression is applied to detected corners +(keypoints). +@param type one of the three neighborhoods as defined in the paper: +FastFeatureDetector::TYPE_9_16, FastFeatureDetector::TYPE_7_12, +FastFeatureDetector::TYPE_5_8 + +Detects corners using the FAST algorithm by @cite Rosten06 . + +@note In Python API, types are given as cv2.FAST_FEATURE_DETECTOR_TYPE_5_8, +cv2.FAST_FEATURE_DETECTOR_TYPE_7_12 and cv2.FAST_FEATURE_DETECTOR_TYPE_9_16. For corner +detection, use cv2.FAST.detect() method. + */ +CV_EXPORTS void FAST( InputArray image, CV_OUT std::vector& keypoints, + int threshold, bool nonmaxSuppression, int type ); + +//! @} features2d_main + +//! @addtogroup features2d_main +//! @{ + +/** @brief Wrapping class for feature detection using the FAST method. : + */ +class CV_EXPORTS_W FastFeatureDetector : public Feature2D +{ +public: + enum + { + TYPE_5_8 = 0, TYPE_7_12 = 1, TYPE_9_16 = 2, + THRESHOLD = 10000, NONMAX_SUPPRESSION=10001, FAST_N=10002, + }; + + CV_WRAP static Ptr create( int threshold=10, + bool nonmaxSuppression=true, + int type=FastFeatureDetector::TYPE_9_16 ); + + CV_WRAP virtual void setThreshold(int threshold) = 0; + CV_WRAP virtual int getThreshold() const = 0; + + CV_WRAP virtual void setNonmaxSuppression(bool f) = 0; + CV_WRAP virtual bool getNonmaxSuppression() const = 0; + + CV_WRAP virtual void setType(int type) = 0; + CV_WRAP virtual int getType() const = 0; + CV_WRAP virtual String getDefaultName() const CV_OVERRIDE; +}; + +/** @overload */ +CV_EXPORTS void AGAST( InputArray image, CV_OUT std::vector& keypoints, + int threshold, bool nonmaxSuppression=true ); + +/** @brief Detects corners using the AGAST algorithm + +@param image grayscale image where keypoints (corners) are detected. +@param keypoints keypoints detected on the image. +@param threshold threshold on difference between intensity of the central pixel and pixels of a +circle around this pixel. +@param nonmaxSuppression if true, non-maximum suppression is applied to detected corners +(keypoints). +@param type one of the four neighborhoods as defined in the paper: +AgastFeatureDetector::AGAST_5_8, AgastFeatureDetector::AGAST_7_12d, +AgastFeatureDetector::AGAST_7_12s, AgastFeatureDetector::OAST_9_16 + +For non-Intel platforms, there is a tree optimised variant of AGAST with same numerical results. +The 32-bit binary tree tables were generated automatically from original code using perl script. +The perl script and examples of tree generation are placed in features2d/doc folder. +Detects corners using the AGAST algorithm by @cite mair2010_agast . + + */ +CV_EXPORTS void AGAST( InputArray image, CV_OUT std::vector& keypoints, + int threshold, bool nonmaxSuppression, int type ); +//! @} features2d_main + +//! @addtogroup features2d_main +//! @{ + +/** @brief Wrapping class for feature detection using the AGAST method. : + */ +class CV_EXPORTS_W AgastFeatureDetector : public Feature2D +{ +public: + enum + { + AGAST_5_8 = 0, AGAST_7_12d = 1, AGAST_7_12s = 2, OAST_9_16 = 3, + THRESHOLD = 10000, NONMAX_SUPPRESSION = 10001, + }; + + CV_WRAP static Ptr create( int threshold=10, + bool nonmaxSuppression=true, + int type=AgastFeatureDetector::OAST_9_16 ); + + CV_WRAP virtual void setThreshold(int threshold) = 0; + CV_WRAP virtual int getThreshold() const = 0; + + CV_WRAP virtual void setNonmaxSuppression(bool f) = 0; + CV_WRAP virtual bool getNonmaxSuppression() const = 0; + + CV_WRAP virtual void setType(int type) = 0; + CV_WRAP virtual int getType() const = 0; + CV_WRAP virtual String getDefaultName() const CV_OVERRIDE; +}; + +/** @brief Wrapping class for feature detection using the goodFeaturesToTrack function. : + */ +class CV_EXPORTS_W GFTTDetector : public Feature2D +{ +public: + CV_WRAP static Ptr create( int maxCorners=1000, double qualityLevel=0.01, double minDistance=1, + int blockSize=3, bool useHarrisDetector=false, double k=0.04 ); + CV_WRAP static Ptr create( int maxCorners, double qualityLevel, double minDistance, + int blockSize, int gradiantSize, bool useHarrisDetector=false, double k=0.04 ); + CV_WRAP virtual void setMaxFeatures(int maxFeatures) = 0; + CV_WRAP virtual int getMaxFeatures() const = 0; + + CV_WRAP virtual void setQualityLevel(double qlevel) = 0; + CV_WRAP virtual double getQualityLevel() const = 0; + + CV_WRAP virtual void setMinDistance(double minDistance) = 0; + CV_WRAP virtual double getMinDistance() const = 0; + + CV_WRAP virtual void setBlockSize(int blockSize) = 0; + CV_WRAP virtual int getBlockSize() const = 0; + + CV_WRAP virtual void setHarrisDetector(bool val) = 0; + CV_WRAP virtual bool getHarrisDetector() const = 0; + + CV_WRAP virtual void setK(double k) = 0; + CV_WRAP virtual double getK() const = 0; + CV_WRAP virtual String getDefaultName() const CV_OVERRIDE; +}; + +/** @brief Class for extracting blobs from an image. : + +The class implements a simple algorithm for extracting blobs from an image: + +1. Convert the source image to binary images by applying thresholding with several thresholds from + minThreshold (inclusive) to maxThreshold (exclusive) with distance thresholdStep between + neighboring thresholds. +2. Extract connected components from every binary image by findContours and calculate their + centers. +3. Group centers from several binary images by their coordinates. Close centers form one group that + corresponds to one blob, which is controlled by the minDistBetweenBlobs parameter. +4. From the groups, estimate final centers of blobs and their radiuses and return as locations and + sizes of keypoints. + +This class performs several filtrations of returned blobs. You should set filterBy\* to true/false +to turn on/off corresponding filtration. Available filtrations: + +- **By color**. This filter compares the intensity of a binary image at the center of a blob to +blobColor. If they differ, the blob is filtered out. Use blobColor = 0 to extract dark blobs +and blobColor = 255 to extract light blobs. +- **By area**. Extracted blobs have an area between minArea (inclusive) and maxArea (exclusive). +- **By circularity**. Extracted blobs have circularity +(\f$\frac{4*\pi*Area}{perimeter * perimeter}\f$) between minCircularity (inclusive) and +maxCircularity (exclusive). +- **By ratio of the minimum inertia to maximum inertia**. Extracted blobs have this ratio +between minInertiaRatio (inclusive) and maxInertiaRatio (exclusive). +- **By convexity**. Extracted blobs have convexity (area / area of blob convex hull) between +minConvexity (inclusive) and maxConvexity (exclusive). + +Default values of parameters are tuned to extract dark circular blobs. + */ +class CV_EXPORTS_W SimpleBlobDetector : public Feature2D +{ +public: + struct CV_EXPORTS_W_SIMPLE Params + { + CV_WRAP Params(); + CV_PROP_RW float thresholdStep; + CV_PROP_RW float minThreshold; + CV_PROP_RW float maxThreshold; + CV_PROP_RW size_t minRepeatability; + CV_PROP_RW float minDistBetweenBlobs; + + CV_PROP_RW bool filterByColor; + CV_PROP_RW uchar blobColor; + + CV_PROP_RW bool filterByArea; + CV_PROP_RW float minArea, maxArea; + + CV_PROP_RW bool filterByCircularity; + CV_PROP_RW float minCircularity, maxCircularity; + + CV_PROP_RW bool filterByInertia; + CV_PROP_RW float minInertiaRatio, maxInertiaRatio; + + CV_PROP_RW bool filterByConvexity; + CV_PROP_RW float minConvexity, maxConvexity; + + void read( const FileNode& fn ); + void write( FileStorage& fs ) const; + }; + + CV_WRAP static Ptr + create(const SimpleBlobDetector::Params ¶meters = SimpleBlobDetector::Params()); + CV_WRAP virtual String getDefaultName() const CV_OVERRIDE; +}; + +//! @} features2d_main + +//! @addtogroup features2d_main +//! @{ + +/** @brief Class implementing the KAZE keypoint detector and descriptor extractor, described in @cite ABD12 . + +@note AKAZE descriptor can only be used with KAZE or AKAZE keypoints .. [ABD12] KAZE Features. Pablo +F. Alcantarilla, Adrien Bartoli and Andrew J. Davison. In European Conference on Computer Vision +(ECCV), Fiorenze, Italy, October 2012. +*/ +class CV_EXPORTS_W KAZE : public Feature2D +{ +public: + enum + { + DIFF_PM_G1 = 0, + DIFF_PM_G2 = 1, + DIFF_WEICKERT = 2, + DIFF_CHARBONNIER = 3 + }; + + /** @brief The KAZE constructor + + @param extended Set to enable extraction of extended (128-byte) descriptor. + @param upright Set to enable use of upright descriptors (non rotation-invariant). + @param threshold Detector response threshold to accept point + @param nOctaves Maximum octave evolution of the image + @param nOctaveLayers Default number of sublevels per scale level + @param diffusivity Diffusivity type. DIFF_PM_G1, DIFF_PM_G2, DIFF_WEICKERT or + DIFF_CHARBONNIER + */ + CV_WRAP static Ptr create(bool extended=false, bool upright=false, + float threshold = 0.001f, + int nOctaves = 4, int nOctaveLayers = 4, + int diffusivity = KAZE::DIFF_PM_G2); + + CV_WRAP virtual void setExtended(bool extended) = 0; + CV_WRAP virtual bool getExtended() const = 0; + + CV_WRAP virtual void setUpright(bool upright) = 0; + CV_WRAP virtual bool getUpright() const = 0; + + CV_WRAP virtual void setThreshold(double threshold) = 0; + CV_WRAP virtual double getThreshold() const = 0; + + CV_WRAP virtual void setNOctaves(int octaves) = 0; + CV_WRAP virtual int getNOctaves() const = 0; + + CV_WRAP virtual void setNOctaveLayers(int octaveLayers) = 0; + CV_WRAP virtual int getNOctaveLayers() const = 0; + + CV_WRAP virtual void setDiffusivity(int diff) = 0; + CV_WRAP virtual int getDiffusivity() const = 0; + CV_WRAP virtual String getDefaultName() const CV_OVERRIDE; +}; + +/** @brief Class implementing the AKAZE keypoint detector and descriptor extractor, described in @cite ANB13. + +@details AKAZE descriptors can only be used with KAZE or AKAZE keypoints. This class is thread-safe. + +@note When you need descriptors use Feature2D::detectAndCompute, which +provides better performance. When using Feature2D::detect followed by +Feature2D::compute scale space pyramid is computed twice. + +@note AKAZE implements T-API. When image is passed as UMat some parts of the algorithm +will use OpenCL. + +@note [ANB13] Fast Explicit Diffusion for Accelerated Features in Nonlinear +Scale Spaces. Pablo F. Alcantarilla, Jesús Nuevo and Adrien Bartoli. In +British Machine Vision Conference (BMVC), Bristol, UK, September 2013. + +*/ +class CV_EXPORTS_W AKAZE : public Feature2D +{ +public: + // AKAZE descriptor type + enum + { + DESCRIPTOR_KAZE_UPRIGHT = 2, ///< Upright descriptors, not invariant to rotation + DESCRIPTOR_KAZE = 3, + DESCRIPTOR_MLDB_UPRIGHT = 4, ///< Upright descriptors, not invariant to rotation + DESCRIPTOR_MLDB = 5 + }; + + /** @brief The AKAZE constructor + + @param descriptor_type Type of the extracted descriptor: DESCRIPTOR_KAZE, + DESCRIPTOR_KAZE_UPRIGHT, DESCRIPTOR_MLDB or DESCRIPTOR_MLDB_UPRIGHT. + @param descriptor_size Size of the descriptor in bits. 0 -\> Full size + @param descriptor_channels Number of channels in the descriptor (1, 2, 3) + @param threshold Detector response threshold to accept point + @param nOctaves Maximum octave evolution of the image + @param nOctaveLayers Default number of sublevels per scale level + @param diffusivity Diffusivity type. DIFF_PM_G1, DIFF_PM_G2, DIFF_WEICKERT or + DIFF_CHARBONNIER + */ + CV_WRAP static Ptr create(int descriptor_type=AKAZE::DESCRIPTOR_MLDB, + int descriptor_size = 0, int descriptor_channels = 3, + float threshold = 0.001f, int nOctaves = 4, + int nOctaveLayers = 4, int diffusivity = KAZE::DIFF_PM_G2); + + CV_WRAP virtual void setDescriptorType(int dtype) = 0; + CV_WRAP virtual int getDescriptorType() const = 0; + + CV_WRAP virtual void setDescriptorSize(int dsize) = 0; + CV_WRAP virtual int getDescriptorSize() const = 0; + + CV_WRAP virtual void setDescriptorChannels(int dch) = 0; + CV_WRAP virtual int getDescriptorChannels() const = 0; + + CV_WRAP virtual void setThreshold(double threshold) = 0; + CV_WRAP virtual double getThreshold() const = 0; + + CV_WRAP virtual void setNOctaves(int octaves) = 0; + CV_WRAP virtual int getNOctaves() const = 0; + + CV_WRAP virtual void setNOctaveLayers(int octaveLayers) = 0; + CV_WRAP virtual int getNOctaveLayers() const = 0; + + CV_WRAP virtual void setDiffusivity(int diff) = 0; + CV_WRAP virtual int getDiffusivity() const = 0; + CV_WRAP virtual String getDefaultName() const CV_OVERRIDE; +}; + +//! @} features2d_main + +/****************************************************************************************\ +* Distance * +\****************************************************************************************/ + +template +struct CV_EXPORTS Accumulator +{ + typedef T Type; +}; + +template<> struct Accumulator { typedef float Type; }; +template<> struct Accumulator { typedef float Type; }; +template<> struct Accumulator { typedef float Type; }; +template<> struct Accumulator { typedef float Type; }; + +/* + * Squared Euclidean distance functor + */ +template +struct CV_EXPORTS SL2 +{ + enum { normType = NORM_L2SQR }; + typedef T ValueType; + typedef typename Accumulator::Type ResultType; + + ResultType operator()( const T* a, const T* b, int size ) const + { + return normL2Sqr(a, b, size); + } +}; + +/* + * Euclidean distance functor + */ +template +struct L2 +{ + enum { normType = NORM_L2 }; + typedef T ValueType; + typedef typename Accumulator::Type ResultType; + + ResultType operator()( const T* a, const T* b, int size ) const + { + return (ResultType)std::sqrt((double)normL2Sqr(a, b, size)); + } +}; + +/* + * Manhattan distance (city block distance) functor + */ +template +struct L1 +{ + enum { normType = NORM_L1 }; + typedef T ValueType; + typedef typename Accumulator::Type ResultType; + + ResultType operator()( const T* a, const T* b, int size ) const + { + return normL1(a, b, size); + } +}; + +/****************************************************************************************\ +* DescriptorMatcher * +\****************************************************************************************/ + +//! @addtogroup features2d_match +//! @{ + +/** @brief Abstract base class for matching keypoint descriptors. + +It has two groups of match methods: for matching descriptors of an image with another image or with +an image set. + */ +class CV_EXPORTS_W DescriptorMatcher : public Algorithm +{ +public: + enum + { + FLANNBASED = 1, + BRUTEFORCE = 2, + BRUTEFORCE_L1 = 3, + BRUTEFORCE_HAMMING = 4, + BRUTEFORCE_HAMMINGLUT = 5, + BRUTEFORCE_SL2 = 6 + }; + virtual ~DescriptorMatcher(); + + /** @brief Adds descriptors to train a CPU(trainDescCollectionis) or GPU(utrainDescCollectionis) descriptor + collection. + + If the collection is not empty, the new descriptors are added to existing train descriptors. + + @param descriptors Descriptors to add. Each descriptors[i] is a set of descriptors from the same + train image. + */ + CV_WRAP virtual void add( InputArrayOfArrays descriptors ); + + /** @brief Returns a constant link to the train descriptor collection trainDescCollection . + */ + CV_WRAP const std::vector& getTrainDescriptors() const; + + /** @brief Clears the train descriptor collections. + */ + CV_WRAP virtual void clear() CV_OVERRIDE; + + /** @brief Returns true if there are no train descriptors in the both collections. + */ + CV_WRAP virtual bool empty() const CV_OVERRIDE; + + /** @brief Returns true if the descriptor matcher supports masking permissible matches. + */ + CV_WRAP virtual bool isMaskSupported() const = 0; + + /** @brief Trains a descriptor matcher + + Trains a descriptor matcher (for example, the flann index). In all methods to match, the method + train() is run every time before matching. Some descriptor matchers (for example, BruteForceMatcher) + have an empty implementation of this method. Other matchers really train their inner structures (for + example, FlannBasedMatcher trains flann::Index ). + */ + CV_WRAP virtual void train(); + + /** @brief Finds the best match for each descriptor from a query set. + + @param queryDescriptors Query set of descriptors. + @param trainDescriptors Train set of descriptors. This set is not added to the train descriptors + collection stored in the class object. + @param matches Matches. If a query descriptor is masked out in mask , no match is added for this + descriptor. So, matches size may be smaller than the query descriptors count. + @param mask Mask specifying permissible matches between an input query and train matrices of + descriptors. + + In the first variant of this method, the train descriptors are passed as an input argument. In the + second variant of the method, train descriptors collection that was set by DescriptorMatcher::add is + used. Optional mask (or masks) can be passed to specify which query and training descriptors can be + matched. Namely, queryDescriptors[i] can be matched with trainDescriptors[j] only if + mask.at\(i,j) is non-zero. + */ + CV_WRAP void match( InputArray queryDescriptors, InputArray trainDescriptors, + CV_OUT std::vector& matches, InputArray mask=noArray() ) const; + + /** @brief Finds the k best matches for each descriptor from a query set. + + @param queryDescriptors Query set of descriptors. + @param trainDescriptors Train set of descriptors. This set is not added to the train descriptors + collection stored in the class object. + @param mask Mask specifying permissible matches between an input query and train matrices of + descriptors. + @param matches Matches. Each matches[i] is k or less matches for the same query descriptor. + @param k Count of best matches found per each query descriptor or less if a query descriptor has + less than k possible matches in total. + @param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is + false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, + the matches vector does not contain matches for fully masked-out query descriptors. + + These extended variants of DescriptorMatcher::match methods find several best matches for each query + descriptor. The matches are returned in the distance increasing order. See DescriptorMatcher::match + for the details about query and train descriptors. + */ + CV_WRAP void knnMatch( InputArray queryDescriptors, InputArray trainDescriptors, + CV_OUT std::vector >& matches, int k, + InputArray mask=noArray(), bool compactResult=false ) const; + + /** @brief For each query descriptor, finds the training descriptors not farther than the specified distance. + + @param queryDescriptors Query set of descriptors. + @param trainDescriptors Train set of descriptors. This set is not added to the train descriptors + collection stored in the class object. + @param matches Found matches. + @param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is + false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, + the matches vector does not contain matches for fully masked-out query descriptors. + @param maxDistance Threshold for the distance between matched descriptors. Distance means here + metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured + in Pixels)! + @param mask Mask specifying permissible matches between an input query and train matrices of + descriptors. + + For each query descriptor, the methods find such training descriptors that the distance between the + query descriptor and the training descriptor is equal or smaller than maxDistance. Found matches are + returned in the distance increasing order. + */ + CV_WRAP void radiusMatch( InputArray queryDescriptors, InputArray trainDescriptors, + CV_OUT std::vector >& matches, float maxDistance, + InputArray mask=noArray(), bool compactResult=false ) const; + + /** @overload + @param queryDescriptors Query set of descriptors. + @param matches Matches. If a query descriptor is masked out in mask , no match is added for this + descriptor. So, matches size may be smaller than the query descriptors count. + @param masks Set of masks. Each masks[i] specifies permissible matches between the input query + descriptors and stored train descriptors from the i-th image trainDescCollection[i]. + */ + CV_WRAP void match( InputArray queryDescriptors, CV_OUT std::vector& matches, + InputArrayOfArrays masks=noArray() ); + /** @overload + @param queryDescriptors Query set of descriptors. + @param matches Matches. Each matches[i] is k or less matches for the same query descriptor. + @param k Count of best matches found per each query descriptor or less if a query descriptor has + less than k possible matches in total. + @param masks Set of masks. Each masks[i] specifies permissible matches between the input query + descriptors and stored train descriptors from the i-th image trainDescCollection[i]. + @param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is + false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, + the matches vector does not contain matches for fully masked-out query descriptors. + */ + CV_WRAP void knnMatch( InputArray queryDescriptors, CV_OUT std::vector >& matches, int k, + InputArrayOfArrays masks=noArray(), bool compactResult=false ); + /** @overload + @param queryDescriptors Query set of descriptors. + @param matches Found matches. + @param maxDistance Threshold for the distance between matched descriptors. Distance means here + metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured + in Pixels)! + @param masks Set of masks. Each masks[i] specifies permissible matches between the input query + descriptors and stored train descriptors from the i-th image trainDescCollection[i]. + @param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is + false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, + the matches vector does not contain matches for fully masked-out query descriptors. + */ + CV_WRAP void radiusMatch( InputArray queryDescriptors, CV_OUT std::vector >& matches, float maxDistance, + InputArrayOfArrays masks=noArray(), bool compactResult=false ); + + + CV_WRAP void write( const String& fileName ) const + { + FileStorage fs(fileName, FileStorage::WRITE); + write(fs); + } + + CV_WRAP void read( const String& fileName ) + { + FileStorage fs(fileName, FileStorage::READ); + read(fs.root()); + } + // Reads matcher object from a file node + // see corresponding cv::Algorithm method + CV_WRAP virtual void read( const FileNode& ) CV_OVERRIDE; + // Writes matcher object to a file storage + virtual void write( FileStorage& ) const CV_OVERRIDE; + + /** @brief Clones the matcher. + + @param emptyTrainData If emptyTrainData is false, the method creates a deep copy of the object, + that is, copies both parameters and train data. If emptyTrainData is true, the method creates an + object copy with the current parameters but with empty train data. + */ + CV_WRAP virtual Ptr clone( bool emptyTrainData=false ) const = 0; + + /** @brief Creates a descriptor matcher of a given type with the default parameters (using default + constructor). + + @param descriptorMatcherType Descriptor matcher type. Now the following matcher types are + supported: + - `BruteForce` (it uses L2 ) + - `BruteForce-L1` + - `BruteForce-Hamming` + - `BruteForce-Hamming(2)` + - `FlannBased` + */ + CV_WRAP static Ptr create( const String& descriptorMatcherType ); + + CV_WRAP static Ptr create( int matcherType ); + + + // see corresponding cv::Algorithm method + CV_WRAP inline void write(const Ptr& fs, const String& name = String()) const { Algorithm::write(fs, name); } + +protected: + /** + * Class to work with descriptors from several images as with one merged matrix. + * It is used e.g. in FlannBasedMatcher. + */ + class CV_EXPORTS DescriptorCollection + { + public: + DescriptorCollection(); + DescriptorCollection( const DescriptorCollection& collection ); + virtual ~DescriptorCollection(); + + // Vector of matrices "descriptors" will be merged to one matrix "mergedDescriptors" here. + void set( const std::vector& descriptors ); + virtual void clear(); + + const Mat& getDescriptors() const; + const Mat getDescriptor( int imgIdx, int localDescIdx ) const; + const Mat getDescriptor( int globalDescIdx ) const; + void getLocalIdx( int globalDescIdx, int& imgIdx, int& localDescIdx ) const; + + int size() const; + + protected: + Mat mergedDescriptors; + std::vector startIdxs; + }; + + //! In fact the matching is implemented only by the following two methods. These methods suppose + //! that the class object has been trained already. Public match methods call these methods + //! after calling train(). + virtual void knnMatchImpl( InputArray queryDescriptors, std::vector >& matches, int k, + InputArrayOfArrays masks=noArray(), bool compactResult=false ) = 0; + virtual void radiusMatchImpl( InputArray queryDescriptors, std::vector >& matches, float maxDistance, + InputArrayOfArrays masks=noArray(), bool compactResult=false ) = 0; + + static bool isPossibleMatch( InputArray mask, int queryIdx, int trainIdx ); + static bool isMaskedOut( InputArrayOfArrays masks, int queryIdx ); + + static Mat clone_op( Mat m ) { return m.clone(); } + void checkMasks( InputArrayOfArrays masks, int queryDescriptorsCount ) const; + + //! Collection of descriptors from train images. + std::vector trainDescCollection; + std::vector utrainDescCollection; +}; + +/** @brief Brute-force descriptor matcher. + +For each descriptor in the first set, this matcher finds the closest descriptor in the second set +by trying each one. This descriptor matcher supports masking permissible matches of descriptor +sets. + */ +class CV_EXPORTS_W BFMatcher : public DescriptorMatcher +{ +public: + /** @brief Brute-force matcher constructor (obsolete). Please use BFMatcher.create() + * + * + */ + CV_WRAP BFMatcher( int normType=NORM_L2, bool crossCheck=false ); + + virtual ~BFMatcher() {} + + virtual bool isMaskSupported() const CV_OVERRIDE { return true; } + + /** @brief Brute-force matcher create method. + @param normType One of NORM_L1, NORM_L2, NORM_HAMMING, NORM_HAMMING2. L1 and L2 norms are + preferable choices for SIFT and SURF descriptors, NORM_HAMMING should be used with ORB, BRISK and + BRIEF, NORM_HAMMING2 should be used with ORB when WTA_K==3 or 4 (see ORB::ORB constructor + description). + @param crossCheck If it is false, this is will be default BFMatcher behaviour when it finds the k + nearest neighbors for each query descriptor. If crossCheck==true, then the knnMatch() method with + k=1 will only return pairs (i,j) such that for i-th query descriptor the j-th descriptor in the + matcher's collection is the nearest and vice versa, i.e. the BFMatcher will only return consistent + pairs. Such technique usually produces best results with minimal number of outliers when there are + enough matches. This is alternative to the ratio test, used by D. Lowe in SIFT paper. + */ + CV_WRAP static Ptr create( int normType=NORM_L2, bool crossCheck=false ) ; + + virtual Ptr clone( bool emptyTrainData=false ) const CV_OVERRIDE; +protected: + virtual void knnMatchImpl( InputArray queryDescriptors, std::vector >& matches, int k, + InputArrayOfArrays masks=noArray(), bool compactResult=false ) CV_OVERRIDE; + virtual void radiusMatchImpl( InputArray queryDescriptors, std::vector >& matches, float maxDistance, + InputArrayOfArrays masks=noArray(), bool compactResult=false ) CV_OVERRIDE; + + int normType; + bool crossCheck; +}; + +#if defined(HAVE_OPENCV_FLANN) || defined(CV_DOXYGEN) + +/** @brief Flann-based descriptor matcher. + +This matcher trains cv::flann::Index on a train descriptor collection and calls its nearest search +methods to find the best matches. So, this matcher may be faster when matching a large train +collection than the brute force matcher. FlannBasedMatcher does not support masking permissible +matches of descriptor sets because flann::Index does not support this. : + */ +class CV_EXPORTS_W FlannBasedMatcher : public DescriptorMatcher +{ +public: + CV_WRAP FlannBasedMatcher( const Ptr& indexParams=makePtr(), + const Ptr& searchParams=makePtr() ); + + virtual void add( InputArrayOfArrays descriptors ) CV_OVERRIDE; + virtual void clear() CV_OVERRIDE; + + // Reads matcher object from a file node + virtual void read( const FileNode& ) CV_OVERRIDE; + // Writes matcher object to a file storage + virtual void write( FileStorage& ) const CV_OVERRIDE; + + virtual void train() CV_OVERRIDE; + virtual bool isMaskSupported() const CV_OVERRIDE; + + CV_WRAP static Ptr create(); + + virtual Ptr clone( bool emptyTrainData=false ) const CV_OVERRIDE; +protected: + static void convertToDMatches( const DescriptorCollection& descriptors, + const Mat& indices, const Mat& distances, + std::vector >& matches ); + + virtual void knnMatchImpl( InputArray queryDescriptors, std::vector >& matches, int k, + InputArrayOfArrays masks=noArray(), bool compactResult=false ) CV_OVERRIDE; + virtual void radiusMatchImpl( InputArray queryDescriptors, std::vector >& matches, float maxDistance, + InputArrayOfArrays masks=noArray(), bool compactResult=false ) CV_OVERRIDE; + + Ptr indexParams; + Ptr searchParams; + Ptr flannIndex; + + DescriptorCollection mergedDescriptors; + int addedDescCount; +}; + +#endif + +//! @} features2d_match + +/****************************************************************************************\ +* Drawing functions * +\****************************************************************************************/ + +//! @addtogroup features2d_draw +//! @{ + +struct CV_EXPORTS DrawMatchesFlags +{ + enum{ DEFAULT = 0, //!< Output image matrix will be created (Mat::create), + //!< i.e. existing memory of output image may be reused. + //!< Two source image, matches and single keypoints will be drawn. + //!< For each keypoint only the center point will be drawn (without + //!< the circle around keypoint with keypoint size and orientation). + DRAW_OVER_OUTIMG = 1, //!< Output image matrix will not be created (Mat::create). + //!< Matches will be drawn on existing content of output image. + NOT_DRAW_SINGLE_POINTS = 2, //!< Single keypoints will not be drawn. + DRAW_RICH_KEYPOINTS = 4 //!< For each keypoint the circle around keypoint with keypoint size and + //!< orientation will be drawn. + }; +}; + +/** @brief Draws keypoints. + +@param image Source image. +@param keypoints Keypoints from the source image. +@param outImage Output image. Its content depends on the flags value defining what is drawn in the +output image. See possible flags bit values below. +@param color Color of keypoints. +@param flags Flags setting drawing features. Possible flags bit values are defined by +DrawMatchesFlags. See details above in drawMatches . + +@note +For Python API, flags are modified as cv2.DRAW_MATCHES_FLAGS_DEFAULT, +cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS, cv2.DRAW_MATCHES_FLAGS_DRAW_OVER_OUTIMG, +cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS + */ +CV_EXPORTS_W void drawKeypoints( InputArray image, const std::vector& keypoints, InputOutputArray outImage, + const Scalar& color=Scalar::all(-1), int flags=DrawMatchesFlags::DEFAULT ); + +/** @brief Draws the found matches of keypoints from two images. + +@param img1 First source image. +@param keypoints1 Keypoints from the first source image. +@param img2 Second source image. +@param keypoints2 Keypoints from the second source image. +@param matches1to2 Matches from the first image to the second one, which means that keypoints1[i] +has a corresponding point in keypoints2[matches[i]] . +@param outImg Output image. Its content depends on the flags value defining what is drawn in the +output image. See possible flags bit values below. +@param matchColor Color of matches (lines and connected keypoints). If matchColor==Scalar::all(-1) +, the color is generated randomly. +@param singlePointColor Color of single keypoints (circles), which means that keypoints do not +have the matches. If singlePointColor==Scalar::all(-1) , the color is generated randomly. +@param matchesMask Mask determining which matches are drawn. If the mask is empty, all matches are +drawn. +@param flags Flags setting drawing features. Possible flags bit values are defined by +DrawMatchesFlags. + +This function draws matches of keypoints from two images in the output image. Match is a line +connecting two keypoints (circles). See cv::DrawMatchesFlags. + */ +CV_EXPORTS_W void drawMatches( InputArray img1, const std::vector& keypoints1, + InputArray img2, const std::vector& keypoints2, + const std::vector& matches1to2, InputOutputArray outImg, + const Scalar& matchColor=Scalar::all(-1), const Scalar& singlePointColor=Scalar::all(-1), + const std::vector& matchesMask=std::vector(), int flags=DrawMatchesFlags::DEFAULT ); + +/** @overload */ +CV_EXPORTS_AS(drawMatchesKnn) void drawMatches( InputArray img1, const std::vector& keypoints1, + InputArray img2, const std::vector& keypoints2, + const std::vector >& matches1to2, InputOutputArray outImg, + const Scalar& matchColor=Scalar::all(-1), const Scalar& singlePointColor=Scalar::all(-1), + const std::vector >& matchesMask=std::vector >(), int flags=DrawMatchesFlags::DEFAULT ); + +//! @} features2d_draw + +/****************************************************************************************\ +* Functions to evaluate the feature detectors and [generic] descriptor extractors * +\****************************************************************************************/ + +CV_EXPORTS void evaluateFeatureDetector( const Mat& img1, const Mat& img2, const Mat& H1to2, + std::vector* keypoints1, std::vector* keypoints2, + float& repeatability, int& correspCount, + const Ptr& fdetector=Ptr() ); + +CV_EXPORTS void computeRecallPrecisionCurve( const std::vector >& matches1to2, + const std::vector >& correctMatches1to2Mask, + std::vector& recallPrecisionCurve ); + +CV_EXPORTS float getRecall( const std::vector& recallPrecisionCurve, float l_precision ); +CV_EXPORTS int getNearestPoint( const std::vector& recallPrecisionCurve, float l_precision ); + +/****************************************************************************************\ +* Bag of visual words * +\****************************************************************************************/ + +//! @addtogroup features2d_category +//! @{ + +/** @brief Abstract base class for training the *bag of visual words* vocabulary from a set of descriptors. + +For details, see, for example, *Visual Categorization with Bags of Keypoints* by Gabriella Csurka, +Christopher R. Dance, Lixin Fan, Jutta Willamowski, Cedric Bray, 2004. : + */ +class CV_EXPORTS_W BOWTrainer +{ +public: + BOWTrainer(); + virtual ~BOWTrainer(); + + /** @brief Adds descriptors to a training set. + + @param descriptors Descriptors to add to a training set. Each row of the descriptors matrix is a + descriptor. + + The training set is clustered using clustermethod to construct the vocabulary. + */ + CV_WRAP void add( const Mat& descriptors ); + + /** @brief Returns a training set of descriptors. + */ + CV_WRAP const std::vector& getDescriptors() const; + + /** @brief Returns the count of all descriptors stored in the training set. + */ + CV_WRAP int descriptorsCount() const; + + CV_WRAP virtual void clear(); + + /** @overload */ + CV_WRAP virtual Mat cluster() const = 0; + + /** @brief Clusters train descriptors. + + @param descriptors Descriptors to cluster. Each row of the descriptors matrix is a descriptor. + Descriptors are not added to the inner train descriptor set. + + The vocabulary consists of cluster centers. So, this method returns the vocabulary. In the first + variant of the method, train descriptors stored in the object are clustered. In the second variant, + input descriptors are clustered. + */ + CV_WRAP virtual Mat cluster( const Mat& descriptors ) const = 0; + +protected: + std::vector descriptors; + int size; +}; + +/** @brief kmeans -based class to train visual vocabulary using the *bag of visual words* approach. : + */ +class CV_EXPORTS_W BOWKMeansTrainer : public BOWTrainer +{ +public: + /** @brief The constructor. + + @see cv::kmeans + */ + CV_WRAP BOWKMeansTrainer( int clusterCount, const TermCriteria& termcrit=TermCriteria(), + int attempts=3, int flags=KMEANS_PP_CENTERS ); + virtual ~BOWKMeansTrainer(); + + // Returns trained vocabulary (i.e. cluster centers). + CV_WRAP virtual Mat cluster() const CV_OVERRIDE; + CV_WRAP virtual Mat cluster( const Mat& descriptors ) const CV_OVERRIDE; + +protected: + + int clusterCount; + TermCriteria termcrit; + int attempts; + int flags; +}; + +/** @brief Class to compute an image descriptor using the *bag of visual words*. + +Such a computation consists of the following steps: + +1. Compute descriptors for a given image and its keypoints set. +2. Find the nearest visual words from the vocabulary for each keypoint descriptor. +3. Compute the bag-of-words image descriptor as is a normalized histogram of vocabulary words +encountered in the image. The i-th bin of the histogram is a frequency of i-th word of the +vocabulary in the given image. + */ +class CV_EXPORTS_W BOWImgDescriptorExtractor +{ +public: + /** @brief The constructor. + + @param dextractor Descriptor extractor that is used to compute descriptors for an input image and + its keypoints. + @param dmatcher Descriptor matcher that is used to find the nearest word of the trained vocabulary + for each keypoint descriptor of the image. + */ + CV_WRAP BOWImgDescriptorExtractor( const Ptr& dextractor, + const Ptr& dmatcher ); + /** @overload */ + BOWImgDescriptorExtractor( const Ptr& dmatcher ); + virtual ~BOWImgDescriptorExtractor(); + + /** @brief Sets a visual vocabulary. + + @param vocabulary Vocabulary (can be trained using the inheritor of BOWTrainer ). Each row of the + vocabulary is a visual word (cluster center). + */ + CV_WRAP void setVocabulary( const Mat& vocabulary ); + + /** @brief Returns the set vocabulary. + */ + CV_WRAP const Mat& getVocabulary() const; + + /** @brief Computes an image descriptor using the set visual vocabulary. + + @param image Image, for which the descriptor is computed. + @param keypoints Keypoints detected in the input image. + @param imgDescriptor Computed output image descriptor. + @param pointIdxsOfClusters Indices of keypoints that belong to the cluster. This means that + pointIdxsOfClusters[i] are keypoint indices that belong to the i -th cluster (word of vocabulary) + returned if it is non-zero. + @param descriptors Descriptors of the image keypoints that are returned if they are non-zero. + */ + void compute( InputArray image, std::vector& keypoints, OutputArray imgDescriptor, + std::vector >* pointIdxsOfClusters=0, Mat* descriptors=0 ); + /** @overload + @param keypointDescriptors Computed descriptors to match with vocabulary. + @param imgDescriptor Computed output image descriptor. + @param pointIdxsOfClusters Indices of keypoints that belong to the cluster. This means that + pointIdxsOfClusters[i] are keypoint indices that belong to the i -th cluster (word of vocabulary) + returned if it is non-zero. + */ + void compute( InputArray keypointDescriptors, OutputArray imgDescriptor, + std::vector >* pointIdxsOfClusters=0 ); + // compute() is not constant because DescriptorMatcher::match is not constant + + CV_WRAP_AS(compute) void compute2( const Mat& image, std::vector& keypoints, CV_OUT Mat& imgDescriptor ) + { compute(image,keypoints,imgDescriptor); } + + /** @brief Returns an image descriptor size if the vocabulary is set. Otherwise, it returns 0. + */ + CV_WRAP int descriptorSize() const; + + /** @brief Returns an image descriptor type. + */ + CV_WRAP int descriptorType() const; + +protected: + Mat vocabulary; + Ptr dextractor; + Ptr dmatcher; +}; + +//! @} features2d_category + +//! @} features2d + +} /* namespace cv */ + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/features2d/features2d.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/features2d/features2d.hpp new file mode 100644 index 0000000000000000000000000000000000000000..e81df0ad08c1757800a26376e047b0746b67a2ae --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/features2d/features2d.hpp @@ -0,0 +1,48 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifdef __OPENCV_BUILD +#error this is a compatibility header which should not be used inside the OpenCV library +#endif + +#include "opencv2/features2d.hpp" diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/features2d/hal/interface.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/features2d/hal/interface.h new file mode 100644 index 0000000000000000000000000000000000000000..bcc6577c19f2241af17994f149c29a3fba5c5951 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/features2d/hal/interface.h @@ -0,0 +1,33 @@ +#ifndef OPENCV_FEATURE2D_HAL_INTERFACE_H +#define OPENCV_FEATURE2D_HAL_INTERFACE_H + +#include "opencv2/core/cvdef.h" +//! @addtogroup featrure2d_hal_interface +//! @{ + +//! @name Fast feature detector types +//! @sa cv::FastFeatureDetector +//! @{ +#define CV_HAL_TYPE_5_8 0 +#define CV_HAL_TYPE_7_12 1 +#define CV_HAL_TYPE_9_16 2 +//! @} + +//! @name Key point +//! @sa cv::KeyPoint +//! @{ +struct CV_EXPORTS cvhalKeyPoint +{ + float x; + float y; + float size; + float angle; + float response; + int octave; + int class_id; +}; +//! @} + +//! @} + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/imgcodecs.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/imgcodecs.hpp new file mode 100644 index 0000000000000000000000000000000000000000..4e79518ae16fc03fd1db0f9c280e9290ab500bbb --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/imgcodecs.hpp @@ -0,0 +1,259 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_IMGCODECS_HPP +#define OPENCV_IMGCODECS_HPP + +#include "opencv2/core.hpp" + +/** + @defgroup imgcodecs Image file reading and writing + @{ + @defgroup imgcodecs_c C API + @defgroup imgcodecs_ios iOS glue + @} +*/ + +//////////////////////////////// image codec //////////////////////////////// +namespace cv +{ + +//! @addtogroup imgcodecs +//! @{ + +//! Imread flags +enum ImreadModes { + IMREAD_UNCHANGED = -1, //!< If set, return the loaded image as is (with alpha channel, otherwise it gets cropped). + IMREAD_GRAYSCALE = 0, //!< If set, always convert image to the single channel grayscale image (codec internal conversion). + IMREAD_COLOR = 1, //!< If set, always convert image to the 3 channel BGR color image. + IMREAD_ANYDEPTH = 2, //!< If set, return 16-bit/32-bit image when the input has the corresponding depth, otherwise convert it to 8-bit. + IMREAD_ANYCOLOR = 4, //!< If set, the image is read in any possible color format. + IMREAD_LOAD_GDAL = 8, //!< If set, use the gdal driver for loading the image. + IMREAD_REDUCED_GRAYSCALE_2 = 16, //!< If set, always convert image to the single channel grayscale image and the image size reduced 1/2. + IMREAD_REDUCED_COLOR_2 = 17, //!< If set, always convert image to the 3 channel BGR color image and the image size reduced 1/2. + IMREAD_REDUCED_GRAYSCALE_4 = 32, //!< If set, always convert image to the single channel grayscale image and the image size reduced 1/4. + IMREAD_REDUCED_COLOR_4 = 33, //!< If set, always convert image to the 3 channel BGR color image and the image size reduced 1/4. + IMREAD_REDUCED_GRAYSCALE_8 = 64, //!< If set, always convert image to the single channel grayscale image and the image size reduced 1/8. + IMREAD_REDUCED_COLOR_8 = 65, //!< If set, always convert image to the 3 channel BGR color image and the image size reduced 1/8. + IMREAD_IGNORE_ORIENTATION = 128 //!< If set, do not rotate the image according to EXIF's orientation flag. + }; + +//! Imwrite flags +enum ImwriteFlags { + IMWRITE_JPEG_QUALITY = 1, //!< For JPEG, it can be a quality from 0 to 100 (the higher is the better). Default value is 95. + IMWRITE_JPEG_PROGRESSIVE = 2, //!< Enable JPEG features, 0 or 1, default is False. + IMWRITE_JPEG_OPTIMIZE = 3, //!< Enable JPEG features, 0 or 1, default is False. + IMWRITE_JPEG_RST_INTERVAL = 4, //!< JPEG restart interval, 0 - 65535, default is 0 - no restart. + IMWRITE_JPEG_LUMA_QUALITY = 5, //!< Separate luma quality level, 0 - 100, default is 0 - don't use. + IMWRITE_JPEG_CHROMA_QUALITY = 6, //!< Separate chroma quality level, 0 - 100, default is 0 - don't use. + IMWRITE_PNG_COMPRESSION = 16, //!< For PNG, it can be the compression level from 0 to 9. A higher value means a smaller size and longer compression time. If specified, strategy is changed to IMWRITE_PNG_STRATEGY_DEFAULT (Z_DEFAULT_STRATEGY). Default value is 1 (best speed setting). + IMWRITE_PNG_STRATEGY = 17, //!< One of cv::ImwritePNGFlags, default is IMWRITE_PNG_STRATEGY_RLE. + IMWRITE_PNG_BILEVEL = 18, //!< Binary level PNG, 0 or 1, default is 0. + IMWRITE_PXM_BINARY = 32, //!< For PPM, PGM, or PBM, it can be a binary format flag, 0 or 1. Default value is 1. + IMWRITE_EXR_TYPE = (3 << 4) + 0, /* 48 */ //!< override EXR storage type (FLOAT (FP32) is default) + IMWRITE_WEBP_QUALITY = 64, //!< For WEBP, it can be a quality from 1 to 100 (the higher is the better). By default (without any parameter) and for quality above 100 the lossless compression is used. + IMWRITE_PAM_TUPLETYPE = 128,//!< For PAM, sets the TUPLETYPE field to the corresponding string value that is defined for the format + IMWRITE_TIFF_RESUNIT = 256,//!< For TIFF, use to specify which DPI resolution unit to set; see libtiff documentation for valid values + IMWRITE_TIFF_XDPI = 257,//!< For TIFF, use to specify the X direction DPI + IMWRITE_TIFF_YDPI = 258 //!< For TIFF, use to specify the Y direction DPI + }; + +enum ImwriteEXRTypeFlags { + /*IMWRITE_EXR_TYPE_UNIT = 0, //!< not supported */ + IMWRITE_EXR_TYPE_HALF = 1, //!< store as HALF (FP16) + IMWRITE_EXR_TYPE_FLOAT = 2 //!< store as FP32 (default) + }; + +//! Imwrite PNG specific flags used to tune the compression algorithm. +/** These flags will be modify the way of PNG image compression and will be passed to the underlying zlib processing stage. + +- The effect of IMWRITE_PNG_STRATEGY_FILTERED is to force more Huffman coding and less string matching; it is somewhat intermediate between IMWRITE_PNG_STRATEGY_DEFAULT and IMWRITE_PNG_STRATEGY_HUFFMAN_ONLY. +- IMWRITE_PNG_STRATEGY_RLE is designed to be almost as fast as IMWRITE_PNG_STRATEGY_HUFFMAN_ONLY, but give better compression for PNG image data. +- The strategy parameter only affects the compression ratio but not the correctness of the compressed output even if it is not set appropriately. +- IMWRITE_PNG_STRATEGY_FIXED prevents the use of dynamic Huffman codes, allowing for a simpler decoder for special applications. +*/ +enum ImwritePNGFlags { + IMWRITE_PNG_STRATEGY_DEFAULT = 0, //!< Use this value for normal data. + IMWRITE_PNG_STRATEGY_FILTERED = 1, //!< Use this value for data produced by a filter (or predictor).Filtered data consists mostly of small values with a somewhat random distribution. In this case, the compression algorithm is tuned to compress them better. + IMWRITE_PNG_STRATEGY_HUFFMAN_ONLY = 2, //!< Use this value to force Huffman encoding only (no string match). + IMWRITE_PNG_STRATEGY_RLE = 3, //!< Use this value to limit match distances to one (run-length encoding). + IMWRITE_PNG_STRATEGY_FIXED = 4 //!< Using this value prevents the use of dynamic Huffman codes, allowing for a simpler decoder for special applications. + }; + +//! Imwrite PAM specific tupletype flags used to define the 'TUPETYPE' field of a PAM file. +enum ImwritePAMFlags { + IMWRITE_PAM_FORMAT_NULL = 0, + IMWRITE_PAM_FORMAT_BLACKANDWHITE = 1, + IMWRITE_PAM_FORMAT_GRAYSCALE = 2, + IMWRITE_PAM_FORMAT_GRAYSCALE_ALPHA = 3, + IMWRITE_PAM_FORMAT_RGB = 4, + IMWRITE_PAM_FORMAT_RGB_ALPHA = 5, + }; + +/** @brief Loads an image from a file. + +@anchor imread + +The function imread loads an image from the specified file and returns it. If the image cannot be +read (because of missing file, improper permissions, unsupported or invalid format), the function +returns an empty matrix ( Mat::data==NULL ). + +Currently, the following file formats are supported: + +- Windows bitmaps - \*.bmp, \*.dib (always supported) +- JPEG files - \*.jpeg, \*.jpg, \*.jpe (see the *Note* section) +- JPEG 2000 files - \*.jp2 (see the *Note* section) +- Portable Network Graphics - \*.png (see the *Note* section) +- WebP - \*.webp (see the *Note* section) +- Portable image format - \*.pbm, \*.pgm, \*.ppm \*.pxm, \*.pnm (always supported) +- Sun rasters - \*.sr, \*.ras (always supported) +- TIFF files - \*.tiff, \*.tif (see the *Note* section) +- OpenEXR Image files - \*.exr (see the *Note* section) +- Radiance HDR - \*.hdr, \*.pic (always supported) +- Raster and Vector geospatial data supported by GDAL (see the *Note* section) + +@note +- The function determines the type of an image by the content, not by the file extension. +- In the case of color images, the decoded images will have the channels stored in **B G R** order. +- When using IMREAD_GRAYSCALE, the codec's internal grayscale conversion will be used, if available. + Results may differ to the output of cvtColor() +- On Microsoft Windows\* OS and MacOSX\*, the codecs shipped with an OpenCV image (libjpeg, + libpng, libtiff, and libjasper) are used by default. So, OpenCV can always read JPEGs, PNGs, + and TIFFs. On MacOSX, there is also an option to use native MacOSX image readers. But beware + that currently these native image loaders give images with different pixel values because of + the color management embedded into MacOSX. +- On Linux\*, BSD flavors and other Unix-like open-source operating systems, OpenCV looks for + codecs supplied with an OS image. Install the relevant packages (do not forget the development + files, for example, "libjpeg-dev", in Debian\* and Ubuntu\*) to get the codec support or turn + on the OPENCV_BUILD_3RDPARTY_LIBS flag in CMake. +- In the case you set *WITH_GDAL* flag to true in CMake and @ref IMREAD_LOAD_GDAL to load the image, + then the [GDAL](http://www.gdal.org) driver will be used in order to decode the image, supporting + the following formats: [Raster](http://www.gdal.org/formats_list.html), + [Vector](http://www.gdal.org/ogr_formats.html). +- If EXIF information are embedded in the image file, the EXIF orientation will be taken into account + and thus the image will be rotated accordingly except if the flag @ref IMREAD_IGNORE_ORIENTATION is passed. +- By default number of pixels must be less than 2^30. Limit can be set using system + variable OPENCV_IO_MAX_IMAGE_PIXELS + +@param filename Name of file to be loaded. +@param flags Flag that can take values of cv::ImreadModes +*/ +CV_EXPORTS_W Mat imread( const String& filename, int flags = IMREAD_COLOR ); + +/** @brief Loads a multi-page image from a file. + +The function imreadmulti loads a multi-page image from the specified file into a vector of Mat objects. +@param filename Name of file to be loaded. +@param flags Flag that can take values of cv::ImreadModes, default with cv::IMREAD_ANYCOLOR. +@param mats A vector of Mat objects holding each page, if more than one. +@sa cv::imread +*/ +CV_EXPORTS_W bool imreadmulti(const String& filename, CV_OUT std::vector& mats, int flags = IMREAD_ANYCOLOR); + +/** @brief Saves an image to a specified file. + +The function imwrite saves the image to the specified file. The image format is chosen based on the +filename extension (see cv::imread for the list of extensions). In general, only 8-bit +single-channel or 3-channel (with 'BGR' channel order) images +can be saved using this function, with these exceptions: + +- 16-bit unsigned (CV_16U) images can be saved in the case of PNG, JPEG 2000, and TIFF formats +- 32-bit float (CV_32F) images can be saved in TIFF, OpenEXR, and Radiance HDR formats; 3-channel +(CV_32FC3) TIFF images will be saved using the LogLuv high dynamic range encoding (4 bytes per pixel) +- PNG images with an alpha channel can be saved using this function. To do this, create +8-bit (or 16-bit) 4-channel image BGRA, where the alpha channel goes last. Fully transparent pixels +should have alpha set to 0, fully opaque pixels should have alpha set to 255/65535 (see the code sample below). + +If the format, depth or channel order is different, use +Mat::convertTo and cv::cvtColor to convert it before saving. Or, use the universal FileStorage I/O +functions to save the image to XML or YAML format. + +The sample below shows how to create a BGRA image and save it to a PNG file. It also demonstrates how to set custom +compression parameters: +@include snippets/imgcodecs_imwrite.cpp +@param filename Name of the file. +@param img Image to be saved. +@param params Format-specific parameters encoded as pairs (paramId_1, paramValue_1, paramId_2, paramValue_2, ... .) see cv::ImwriteFlags +*/ +CV_EXPORTS_W bool imwrite( const String& filename, InputArray img, + const std::vector& params = std::vector()); + +/** @brief Reads an image from a buffer in memory. + +The function imdecode reads an image from the specified buffer in the memory. If the buffer is too short or +contains invalid data, the function returns an empty matrix ( Mat::data==NULL ). + +See cv::imread for the list of supported formats and flags description. + +@note In the case of color images, the decoded images will have the channels stored in **B G R** order. +@param buf Input array or vector of bytes. +@param flags The same flags as in cv::imread, see cv::ImreadModes. +*/ +CV_EXPORTS_W Mat imdecode( InputArray buf, int flags ); + +/** @overload +@param buf +@param flags +@param dst The optional output placeholder for the decoded matrix. It can save the image +reallocations when the function is called repeatedly for images of the same size. +*/ +CV_EXPORTS Mat imdecode( InputArray buf, int flags, Mat* dst); + +/** @brief Encodes an image into a memory buffer. + +The function imencode compresses the image and stores it in the memory buffer that is resized to fit the +result. See cv::imwrite for the list of supported formats and flags description. + +@param ext File extension that defines the output format. +@param img Image to be written. +@param buf Output buffer resized to fit the compressed image. +@param params Format-specific parameters. See cv::imwrite and cv::ImwriteFlags. +*/ +CV_EXPORTS_W bool imencode( const String& ext, InputArray img, + CV_OUT std::vector& buf, + const std::vector& params = std::vector()); + +//! @} imgcodecs + +} // cv + +#endif //OPENCV_IMGCODECS_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/imgcodecs/imgcodecs.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/imgcodecs/imgcodecs.hpp new file mode 100644 index 0000000000000000000000000000000000000000..a3cd232645465f4c8148d1fcd3d9fde6eddddffb --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/imgcodecs/imgcodecs.hpp @@ -0,0 +1,48 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifdef __OPENCV_BUILD +#error this is a compatibility header which should not be used inside the OpenCV library +#endif + +#include "opencv2/imgcodecs.hpp" diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/imgcodecs/imgcodecs_c.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/imgcodecs/imgcodecs_c.h new file mode 100644 index 0000000000000000000000000000000000000000..c36dac33435605d126b3dd683b7edb72110e3ae8 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/imgcodecs/imgcodecs_c.h @@ -0,0 +1,149 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// Intel License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000, Intel Corporation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of Intel Corporation may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_IMGCODECS_H +#define OPENCV_IMGCODECS_H + +#include "opencv2/core/core_c.h" + +#ifdef __cplusplus +extern "C" { +#endif /* __cplusplus */ + +/** @addtogroup imgcodecs_c + @{ + */ + +enum +{ +/* 8bit, color or not */ + CV_LOAD_IMAGE_UNCHANGED =-1, +/* 8bit, gray */ + CV_LOAD_IMAGE_GRAYSCALE =0, +/* ?, color */ + CV_LOAD_IMAGE_COLOR =1, +/* any depth, ? */ + CV_LOAD_IMAGE_ANYDEPTH =2, +/* ?, any color */ + CV_LOAD_IMAGE_ANYCOLOR =4, +/* ?, no rotate */ + CV_LOAD_IMAGE_IGNORE_ORIENTATION =128 +}; + +/* load image from file + iscolor can be a combination of above flags where CV_LOAD_IMAGE_UNCHANGED + overrides the other flags + using CV_LOAD_IMAGE_ANYCOLOR alone is equivalent to CV_LOAD_IMAGE_UNCHANGED + unless CV_LOAD_IMAGE_ANYDEPTH is specified images are converted to 8bit +*/ +CVAPI(IplImage*) cvLoadImage( const char* filename, int iscolor CV_DEFAULT(CV_LOAD_IMAGE_COLOR)); +CVAPI(CvMat*) cvLoadImageM( const char* filename, int iscolor CV_DEFAULT(CV_LOAD_IMAGE_COLOR)); + +enum +{ + CV_IMWRITE_JPEG_QUALITY =1, + CV_IMWRITE_JPEG_PROGRESSIVE =2, + CV_IMWRITE_JPEG_OPTIMIZE =3, + CV_IMWRITE_JPEG_RST_INTERVAL =4, + CV_IMWRITE_JPEG_LUMA_QUALITY =5, + CV_IMWRITE_JPEG_CHROMA_QUALITY =6, + CV_IMWRITE_PNG_COMPRESSION =16, + CV_IMWRITE_PNG_STRATEGY =17, + CV_IMWRITE_PNG_BILEVEL =18, + CV_IMWRITE_PNG_STRATEGY_DEFAULT =0, + CV_IMWRITE_PNG_STRATEGY_FILTERED =1, + CV_IMWRITE_PNG_STRATEGY_HUFFMAN_ONLY =2, + CV_IMWRITE_PNG_STRATEGY_RLE =3, + CV_IMWRITE_PNG_STRATEGY_FIXED =4, + CV_IMWRITE_PXM_BINARY =32, + CV_IMWRITE_EXR_TYPE = 48, + CV_IMWRITE_WEBP_QUALITY =64, + CV_IMWRITE_PAM_TUPLETYPE = 128, + CV_IMWRITE_PAM_FORMAT_NULL = 0, + CV_IMWRITE_PAM_FORMAT_BLACKANDWHITE = 1, + CV_IMWRITE_PAM_FORMAT_GRAYSCALE = 2, + CV_IMWRITE_PAM_FORMAT_GRAYSCALE_ALPHA = 3, + CV_IMWRITE_PAM_FORMAT_RGB = 4, + CV_IMWRITE_PAM_FORMAT_RGB_ALPHA = 5, +}; + + + +/* save image to file */ +CVAPI(int) cvSaveImage( const char* filename, const CvArr* image, + const int* params CV_DEFAULT(0) ); + +/* decode image stored in the buffer */ +CVAPI(IplImage*) cvDecodeImage( const CvMat* buf, int iscolor CV_DEFAULT(CV_LOAD_IMAGE_COLOR)); +CVAPI(CvMat*) cvDecodeImageM( const CvMat* buf, int iscolor CV_DEFAULT(CV_LOAD_IMAGE_COLOR)); + +/* encode image and store the result as a byte vector (single-row 8uC1 matrix) */ +CVAPI(CvMat*) cvEncodeImage( const char* ext, const CvArr* image, + const int* params CV_DEFAULT(0) ); + +enum +{ + CV_CVTIMG_FLIP =1, + CV_CVTIMG_SWAP_RB =2 +}; + +/* utility function: convert one image to another with optional vertical flip */ +CVAPI(void) cvConvertImage( const CvArr* src, CvArr* dst, int flags CV_DEFAULT(0)); + +CVAPI(int) cvHaveImageReader(const char* filename); +CVAPI(int) cvHaveImageWriter(const char* filename); + + +/****************************************************************************************\ +* Obsolete functions/synonyms * +\****************************************************************************************/ + +#define cvvLoadImage(name) cvLoadImage((name),1) +#define cvvSaveImage cvSaveImage +#define cvvConvertImage cvConvertImage + +/** @} imgcodecs_c */ + +#ifdef __cplusplus +} +#endif + +#endif // OPENCV_IMGCODECS_H diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/imgcodecs/ios.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/imgcodecs/ios.h new file mode 100644 index 0000000000000000000000000000000000000000..a90c6d37a86c8bd6fd3c93d7b89f3c52b5ffbd96 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/imgcodecs/ios.h @@ -0,0 +1,57 @@ + +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#import +#import +#import +#import +#include "opencv2/core/core.hpp" + +//! @addtogroup imgcodecs_ios +//! @{ + +CV_EXPORTS UIImage* MatToUIImage(const cv::Mat& image); +CV_EXPORTS void UIImageToMat(const UIImage* image, + cv::Mat& m, bool alphaExist = false); + +//! @} diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/imgproc.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/imgproc.hpp new file mode 100644 index 0000000000000000000000000000000000000000..14a63f3671cc8c91516ffa36219dcc26f559c02c --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/imgproc.hpp @@ -0,0 +1,4915 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_IMGPROC_HPP +#define OPENCV_IMGPROC_HPP + +#include "opencv2/core.hpp" + +/** + @defgroup imgproc Image Processing + +This module includes image-processing functions. + + @{ + @defgroup imgproc_filter Image Filtering + +Functions and classes described in this section are used to perform various linear or non-linear +filtering operations on 2D images (represented as Mat's). It means that for each pixel location +\f$(x,y)\f$ in the source image (normally, rectangular), its neighborhood is considered and used to +compute the response. In case of a linear filter, it is a weighted sum of pixel values. In case of +morphological operations, it is the minimum or maximum values, and so on. The computed response is +stored in the destination image at the same location \f$(x,y)\f$. It means that the output image +will be of the same size as the input image. Normally, the functions support multi-channel arrays, +in which case every channel is processed independently. Therefore, the output image will also have +the same number of channels as the input one. + +Another common feature of the functions and classes described in this section is that, unlike +simple arithmetic functions, they need to extrapolate values of some non-existing pixels. For +example, if you want to smooth an image using a Gaussian \f$3 \times 3\f$ filter, then, when +processing the left-most pixels in each row, you need pixels to the left of them, that is, outside +of the image. You can let these pixels be the same as the left-most image pixels ("replicated +border" extrapolation method), or assume that all the non-existing pixels are zeros ("constant +border" extrapolation method), and so on. OpenCV enables you to specify the extrapolation method. +For details, see #BorderTypes + +@anchor filter_depths +### Depth combinations +Input depth (src.depth()) | Output depth (ddepth) +--------------------------|---------------------- +CV_8U | -1/CV_16S/CV_32F/CV_64F +CV_16U/CV_16S | -1/CV_32F/CV_64F +CV_32F | -1/CV_32F/CV_64F +CV_64F | -1/CV_64F + +@note when ddepth=-1, the output image will have the same depth as the source. + + @defgroup imgproc_transform Geometric Image Transformations + +The functions in this section perform various geometrical transformations of 2D images. They do not +change the image content but deform the pixel grid and map this deformed grid to the destination +image. In fact, to avoid sampling artifacts, the mapping is done in the reverse order, from +destination to the source. That is, for each pixel \f$(x, y)\f$ of the destination image, the +functions compute coordinates of the corresponding "donor" pixel in the source image and copy the +pixel value: + +\f[\texttt{dst} (x,y)= \texttt{src} (f_x(x,y), f_y(x,y))\f] + +In case when you specify the forward mapping \f$\left: \texttt{src} \rightarrow +\texttt{dst}\f$, the OpenCV functions first compute the corresponding inverse mapping +\f$\left: \texttt{dst} \rightarrow \texttt{src}\f$ and then use the above formula. + +The actual implementations of the geometrical transformations, from the most generic remap and to +the simplest and the fastest resize, need to solve two main problems with the above formula: + +- Extrapolation of non-existing pixels. Similarly to the filtering functions described in the +previous section, for some \f$(x,y)\f$, either one of \f$f_x(x,y)\f$, or \f$f_y(x,y)\f$, or both +of them may fall outside of the image. In this case, an extrapolation method needs to be used. +OpenCV provides the same selection of extrapolation methods as in the filtering functions. In +addition, it provides the method #BORDER_TRANSPARENT. This means that the corresponding pixels in +the destination image will not be modified at all. + +- Interpolation of pixel values. Usually \f$f_x(x,y)\f$ and \f$f_y(x,y)\f$ are floating-point +numbers. This means that \f$\left\f$ can be either an affine or perspective +transformation, or radial lens distortion correction, and so on. So, a pixel value at fractional +coordinates needs to be retrieved. In the simplest case, the coordinates can be just rounded to the +nearest integer coordinates and the corresponding pixel can be used. This is called a +nearest-neighbor interpolation. However, a better result can be achieved by using more +sophisticated [interpolation methods](http://en.wikipedia.org/wiki/Multivariate_interpolation) , +where a polynomial function is fit into some neighborhood of the computed pixel \f$(f_x(x,y), +f_y(x,y))\f$, and then the value of the polynomial at \f$(f_x(x,y), f_y(x,y))\f$ is taken as the +interpolated pixel value. In OpenCV, you can choose between several interpolation methods. See +resize for details. + +@note The geometrical transformations do not work with `CV_8S` or `CV_32S` images. + + @defgroup imgproc_misc Miscellaneous Image Transformations + @defgroup imgproc_draw Drawing Functions + +Drawing functions work with matrices/images of arbitrary depth. The boundaries of the shapes can be +rendered with antialiasing (implemented only for 8-bit images for now). All the functions include +the parameter color that uses an RGB value (that may be constructed with the Scalar constructor ) +for color images and brightness for grayscale images. For color images, the channel ordering is +normally *Blue, Green, Red*. This is what imshow, imread, and imwrite expect. So, if you form a +color using the Scalar constructor, it should look like: + +\f[\texttt{Scalar} (blue \_ component, green \_ component, red \_ component[, alpha \_ component])\f] + +If you are using your own image rendering and I/O functions, you can use any channel ordering. The +drawing functions process each channel independently and do not depend on the channel order or even +on the used color space. The whole image can be converted from BGR to RGB or to a different color +space using cvtColor . + +If a drawn figure is partially or completely outside the image, the drawing functions clip it. Also, +many drawing functions can handle pixel coordinates specified with sub-pixel accuracy. This means +that the coordinates can be passed as fixed-point numbers encoded as integers. The number of +fractional bits is specified by the shift parameter and the real point coordinates are calculated as +\f$\texttt{Point}(x,y)\rightarrow\texttt{Point2f}(x*2^{-shift},y*2^{-shift})\f$ . This feature is +especially effective when rendering antialiased shapes. + +@note The functions do not support alpha-transparency when the target image is 4-channel. In this +case, the color[3] is simply copied to the repainted pixels. Thus, if you want to paint +semi-transparent shapes, you can paint them in a separate buffer and then blend it with the main +image. + + @defgroup imgproc_color_conversions Color Space Conversions + @defgroup imgproc_colormap ColorMaps in OpenCV + +The human perception isn't built for observing fine changes in grayscale images. Human eyes are more +sensitive to observing changes between colors, so you often need to recolor your grayscale images to +get a clue about them. OpenCV now comes with various colormaps to enhance the visualization in your +computer vision application. + +In OpenCV you only need applyColorMap to apply a colormap on a given image. The following sample +code reads the path to an image from command line, applies a Jet colormap on it and shows the +result: + +@include snippets/imgproc_applyColorMap.cpp + +@see #ColormapTypes + + @defgroup imgproc_subdiv2d Planar Subdivision + +The Subdiv2D class described in this section is used to perform various planar subdivision on +a set of 2D points (represented as vector of Point2f). OpenCV subdivides a plane into triangles +using the Delaunay's algorithm, which corresponds to the dual graph of the Voronoi diagram. +In the figure below, the Delaunay's triangulation is marked with black lines and the Voronoi +diagram with red lines. + +![Delaunay triangulation (black) and Voronoi (red)](pics/delaunay_voronoi.png) + +The subdivisions can be used for the 3D piece-wise transformation of a plane, morphing, fast +location of points on the plane, building special graphs (such as NNG,RNG), and so forth. + + @defgroup imgproc_hist Histograms + @defgroup imgproc_shape Structural Analysis and Shape Descriptors + @defgroup imgproc_motion Motion Analysis and Object Tracking + @defgroup imgproc_feature Feature Detection + @defgroup imgproc_object Object Detection + @defgroup imgproc_c C API + @defgroup imgproc_hal Hardware Acceleration Layer + @{ + @defgroup imgproc_hal_functions Functions + @defgroup imgproc_hal_interface Interface + @} + @} +*/ + +namespace cv +{ + +/** @addtogroup imgproc +@{ +*/ + +//! @addtogroup imgproc_filter +//! @{ + +//! type of morphological operation +enum MorphTypes{ + MORPH_ERODE = 0, //!< see #erode + MORPH_DILATE = 1, //!< see #dilate + MORPH_OPEN = 2, //!< an opening operation + //!< \f[\texttt{dst} = \mathrm{open} ( \texttt{src} , \texttt{element} )= \mathrm{dilate} ( \mathrm{erode} ( \texttt{src} , \texttt{element} ))\f] + MORPH_CLOSE = 3, //!< a closing operation + //!< \f[\texttt{dst} = \mathrm{close} ( \texttt{src} , \texttt{element} )= \mathrm{erode} ( \mathrm{dilate} ( \texttt{src} , \texttt{element} ))\f] + MORPH_GRADIENT = 4, //!< a morphological gradient + //!< \f[\texttt{dst} = \mathrm{morph\_grad} ( \texttt{src} , \texttt{element} )= \mathrm{dilate} ( \texttt{src} , \texttt{element} )- \mathrm{erode} ( \texttt{src} , \texttt{element} )\f] + MORPH_TOPHAT = 5, //!< "top hat" + //!< \f[\texttt{dst} = \mathrm{tophat} ( \texttt{src} , \texttt{element} )= \texttt{src} - \mathrm{open} ( \texttt{src} , \texttt{element} )\f] + MORPH_BLACKHAT = 6, //!< "black hat" + //!< \f[\texttt{dst} = \mathrm{blackhat} ( \texttt{src} , \texttt{element} )= \mathrm{close} ( \texttt{src} , \texttt{element} )- \texttt{src}\f] + MORPH_HITMISS = 7 //!< "hit or miss" + //!< .- Only supported for CV_8UC1 binary images. A tutorial can be found in the documentation +}; + +//! shape of the structuring element +enum MorphShapes { + MORPH_RECT = 0, //!< a rectangular structuring element: \f[E_{ij}=1\f] + MORPH_CROSS = 1, //!< a cross-shaped structuring element: + //!< \f[E_{ij} = \fork{1}{if i=\texttt{anchor.y} or j=\texttt{anchor.x}}{0}{otherwise}\f] + MORPH_ELLIPSE = 2 //!< an elliptic structuring element, that is, a filled ellipse inscribed + //!< into the rectangle Rect(0, 0, esize.width, 0.esize.height) +}; + +//! @} imgproc_filter + +//! @addtogroup imgproc_transform +//! @{ + +//! interpolation algorithm +enum InterpolationFlags{ + /** nearest neighbor interpolation */ + INTER_NEAREST = 0, + /** bilinear interpolation */ + INTER_LINEAR = 1, + /** bicubic interpolation */ + INTER_CUBIC = 2, + /** resampling using pixel area relation. It may be a preferred method for image decimation, as + it gives moire'-free results. But when the image is zoomed, it is similar to the INTER_NEAREST + method. */ + INTER_AREA = 3, + /** Lanczos interpolation over 8x8 neighborhood */ + INTER_LANCZOS4 = 4, + /** Bit exact bilinear interpolation */ + INTER_LINEAR_EXACT = 5, + /** mask for interpolation codes */ + INTER_MAX = 7, + /** flag, fills all of the destination image pixels. If some of them correspond to outliers in the + source image, they are set to zero */ + WARP_FILL_OUTLIERS = 8, + /** flag, inverse transformation + + For example, #linearPolar or #logPolar transforms: + - flag is __not__ set: \f$dst( \rho , \phi ) = src(x,y)\f$ + - flag is set: \f$dst(x,y) = src( \rho , \phi )\f$ + */ + WARP_INVERSE_MAP = 16 +}; + +/** \brief Specify the polar mapping mode +@sa warpPolar +*/ +enum WarpPolarMode +{ + WARP_POLAR_LINEAR = 0, ///< Remaps an image to/from polar space. + WARP_POLAR_LOG = 256 ///< Remaps an image to/from semilog-polar space. +}; + +enum InterpolationMasks { + INTER_BITS = 5, + INTER_BITS2 = INTER_BITS * 2, + INTER_TAB_SIZE = 1 << INTER_BITS, + INTER_TAB_SIZE2 = INTER_TAB_SIZE * INTER_TAB_SIZE + }; + +//! @} imgproc_transform + +//! @addtogroup imgproc_misc +//! @{ + +//! Distance types for Distance Transform and M-estimators +//! @see distanceTransform, fitLine +enum DistanceTypes { + DIST_USER = -1, //!< User defined distance + DIST_L1 = 1, //!< distance = |x1-x2| + |y1-y2| + DIST_L2 = 2, //!< the simple euclidean distance + DIST_C = 3, //!< distance = max(|x1-x2|,|y1-y2|) + DIST_L12 = 4, //!< L1-L2 metric: distance = 2(sqrt(1+x*x/2) - 1)) + DIST_FAIR = 5, //!< distance = c^2(|x|/c-log(1+|x|/c)), c = 1.3998 + DIST_WELSCH = 6, //!< distance = c^2/2(1-exp(-(x/c)^2)), c = 2.9846 + DIST_HUBER = 7 //!< distance = |x| \texttt{thresh}\)}{0}{otherwise}\f] + THRESH_BINARY_INV = 1, //!< \f[\texttt{dst} (x,y) = \fork{0}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{\texttt{maxval}}{otherwise}\f] + THRESH_TRUNC = 2, //!< \f[\texttt{dst} (x,y) = \fork{\texttt{threshold}}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{\texttt{src}(x,y)}{otherwise}\f] + THRESH_TOZERO = 3, //!< \f[\texttt{dst} (x,y) = \fork{\texttt{src}(x,y)}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{0}{otherwise}\f] + THRESH_TOZERO_INV = 4, //!< \f[\texttt{dst} (x,y) = \fork{0}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{\texttt{src}(x,y)}{otherwise}\f] + THRESH_MASK = 7, + THRESH_OTSU = 8, //!< flag, use Otsu algorithm to choose the optimal threshold value + THRESH_TRIANGLE = 16 //!< flag, use Triangle algorithm to choose the optimal threshold value +}; + +//! adaptive threshold algorithm +//! @see adaptiveThreshold +enum AdaptiveThresholdTypes { + /** the threshold value \f$T(x,y)\f$ is a mean of the \f$\texttt{blockSize} \times + \texttt{blockSize}\f$ neighborhood of \f$(x, y)\f$ minus C */ + ADAPTIVE_THRESH_MEAN_C = 0, + /** the threshold value \f$T(x, y)\f$ is a weighted sum (cross-correlation with a Gaussian + window) of the \f$\texttt{blockSize} \times \texttt{blockSize}\f$ neighborhood of \f$(x, y)\f$ + minus C . The default sigma (standard deviation) is used for the specified blockSize . See + #getGaussianKernel*/ + ADAPTIVE_THRESH_GAUSSIAN_C = 1 +}; + +//! cv::undistort mode +enum UndistortTypes { + PROJ_SPHERICAL_ORTHO = 0, + PROJ_SPHERICAL_EQRECT = 1 + }; + +//! class of the pixel in GrabCut algorithm +enum GrabCutClasses { + GC_BGD = 0, //!< an obvious background pixels + GC_FGD = 1, //!< an obvious foreground (object) pixel + GC_PR_BGD = 2, //!< a possible background pixel + GC_PR_FGD = 3 //!< a possible foreground pixel +}; + +//! GrabCut algorithm flags +enum GrabCutModes { + /** The function initializes the state and the mask using the provided rectangle. After that it + runs iterCount iterations of the algorithm. */ + GC_INIT_WITH_RECT = 0, + /** The function initializes the state using the provided mask. Note that GC_INIT_WITH_RECT + and GC_INIT_WITH_MASK can be combined. Then, all the pixels outside of the ROI are + automatically initialized with GC_BGD .*/ + GC_INIT_WITH_MASK = 1, + /** The value means that the algorithm should just resume. */ + GC_EVAL = 2, + /** The value means that the algorithm should just run the grabCut algorithm (a single iteration) with the fixed model */ + GC_EVAL_FREEZE_MODEL = 3 +}; + +//! distanceTransform algorithm flags +enum DistanceTransformLabelTypes { + /** each connected component of zeros in src (as well as all the non-zero pixels closest to the + connected component) will be assigned the same label */ + DIST_LABEL_CCOMP = 0, + /** each zero pixel (and all the non-zero pixels closest to it) gets its own label. */ + DIST_LABEL_PIXEL = 1 +}; + +//! floodfill algorithm flags +enum FloodFillFlags { + /** If set, the difference between the current pixel and seed pixel is considered. Otherwise, + the difference between neighbor pixels is considered (that is, the range is floating). */ + FLOODFILL_FIXED_RANGE = 1 << 16, + /** If set, the function does not change the image ( newVal is ignored), and only fills the + mask with the value specified in bits 8-16 of flags as described above. This option only make + sense in function variants that have the mask parameter. */ + FLOODFILL_MASK_ONLY = 1 << 17 +}; + +//! @} imgproc_misc + +//! @addtogroup imgproc_shape +//! @{ + +//! connected components algorithm output formats +enum ConnectedComponentsTypes { + CC_STAT_LEFT = 0, //!< The leftmost (x) coordinate which is the inclusive start of the bounding + //!< box in the horizontal direction. + CC_STAT_TOP = 1, //!< The topmost (y) coordinate which is the inclusive start of the bounding + //!< box in the vertical direction. + CC_STAT_WIDTH = 2, //!< The horizontal size of the bounding box + CC_STAT_HEIGHT = 3, //!< The vertical size of the bounding box + CC_STAT_AREA = 4, //!< The total area (in pixels) of the connected component + CC_STAT_MAX = 5 +}; + +//! connected components algorithm +enum ConnectedComponentsAlgorithmsTypes { + CCL_WU = 0, //!< SAUF algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity + CCL_DEFAULT = -1, //!< BBDT algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity + CCL_GRANA = 1 //!< BBDT algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity +}; + +//! mode of the contour retrieval algorithm +enum RetrievalModes { + /** retrieves only the extreme outer contours. It sets `hierarchy[i][2]=hierarchy[i][3]=-1` for + all the contours. */ + RETR_EXTERNAL = 0, + /** retrieves all of the contours without establishing any hierarchical relationships. */ + RETR_LIST = 1, + /** retrieves all of the contours and organizes them into a two-level hierarchy. At the top + level, there are external boundaries of the components. At the second level, there are + boundaries of the holes. If there is another contour inside a hole of a connected component, it + is still put at the top level. */ + RETR_CCOMP = 2, + /** retrieves all of the contours and reconstructs a full hierarchy of nested contours.*/ + RETR_TREE = 3, + RETR_FLOODFILL = 4 //!< +}; + +//! the contour approximation algorithm +enum ContourApproximationModes { + /** stores absolutely all the contour points. That is, any 2 subsequent points (x1,y1) and + (x2,y2) of the contour will be either horizontal, vertical or diagonal neighbors, that is, + max(abs(x1-x2),abs(y2-y1))==1. */ + CHAIN_APPROX_NONE = 1, + /** compresses horizontal, vertical, and diagonal segments and leaves only their end points. + For example, an up-right rectangular contour is encoded with 4 points. */ + CHAIN_APPROX_SIMPLE = 2, + /** applies one of the flavors of the Teh-Chin chain approximation algorithm @cite TehChin89 */ + CHAIN_APPROX_TC89_L1 = 3, + /** applies one of the flavors of the Teh-Chin chain approximation algorithm @cite TehChin89 */ + CHAIN_APPROX_TC89_KCOS = 4 +}; + +/** @brief Shape matching methods + +\f$A\f$ denotes object1,\f$B\f$ denotes object2 + +\f$\begin{array}{l} m^A_i = \mathrm{sign} (h^A_i) \cdot \log{h^A_i} \\ m^B_i = \mathrm{sign} (h^B_i) \cdot \log{h^B_i} \end{array}\f$ + +and \f$h^A_i, h^B_i\f$ are the Hu moments of \f$A\f$ and \f$B\f$ , respectively. +*/ +enum ShapeMatchModes { + CONTOURS_MATCH_I1 =1, //!< \f[I_1(A,B) = \sum _{i=1...7} \left | \frac{1}{m^A_i} - \frac{1}{m^B_i} \right |\f] + CONTOURS_MATCH_I2 =2, //!< \f[I_2(A,B) = \sum _{i=1...7} \left | m^A_i - m^B_i \right |\f] + CONTOURS_MATCH_I3 =3 //!< \f[I_3(A,B) = \max _{i=1...7} \frac{ \left| m^A_i - m^B_i \right| }{ \left| m^A_i \right| }\f] +}; + +//! @} imgproc_shape + +//! @addtogroup imgproc_feature +//! @{ + +//! Variants of a Hough transform +enum HoughModes { + + /** classical or standard Hough transform. Every line is represented by two floating-point + numbers \f$(\rho, \theta)\f$ , where \f$\rho\f$ is a distance between (0,0) point and the line, + and \f$\theta\f$ is the angle between x-axis and the normal to the line. Thus, the matrix must + be (the created sequence will be) of CV_32FC2 type */ + HOUGH_STANDARD = 0, + /** probabilistic Hough transform (more efficient in case if the picture contains a few long + linear segments). It returns line segments rather than the whole line. Each segment is + represented by starting and ending points, and the matrix must be (the created sequence will + be) of the CV_32SC4 type. */ + HOUGH_PROBABILISTIC = 1, + /** multi-scale variant of the classical Hough transform. The lines are encoded the same way as + HOUGH_STANDARD. */ + HOUGH_MULTI_SCALE = 2, + HOUGH_GRADIENT = 3 //!< basically *21HT*, described in @cite Yuen90 +}; + +//! Variants of Line Segment %Detector +enum LineSegmentDetectorModes { + LSD_REFINE_NONE = 0, //!< No refinement applied + LSD_REFINE_STD = 1, //!< Standard refinement is applied. E.g. breaking arches into smaller straighter line approximations. + LSD_REFINE_ADV = 2 //!< Advanced refinement. Number of false alarms is calculated, lines are + //!< refined through increase of precision, decrement in size, etc. +}; + +//! @} imgproc_feature + +/** Histogram comparison methods + @ingroup imgproc_hist +*/ +enum HistCompMethods { + /** Correlation + \f[d(H_1,H_2) = \frac{\sum_I (H_1(I) - \bar{H_1}) (H_2(I) - \bar{H_2})}{\sqrt{\sum_I(H_1(I) - \bar{H_1})^2 \sum_I(H_2(I) - \bar{H_2})^2}}\f] + where + \f[\bar{H_k} = \frac{1}{N} \sum _J H_k(J)\f] + and \f$N\f$ is a total number of histogram bins. */ + HISTCMP_CORREL = 0, + /** Chi-Square + \f[d(H_1,H_2) = \sum _I \frac{\left(H_1(I)-H_2(I)\right)^2}{H_1(I)}\f] */ + HISTCMP_CHISQR = 1, + /** Intersection + \f[d(H_1,H_2) = \sum _I \min (H_1(I), H_2(I))\f] */ + HISTCMP_INTERSECT = 2, + /** Bhattacharyya distance + (In fact, OpenCV computes Hellinger distance, which is related to Bhattacharyya coefficient.) + \f[d(H_1,H_2) = \sqrt{1 - \frac{1}{\sqrt{\bar{H_1} \bar{H_2} N^2}} \sum_I \sqrt{H_1(I) \cdot H_2(I)}}\f] */ + HISTCMP_BHATTACHARYYA = 3, + HISTCMP_HELLINGER = HISTCMP_BHATTACHARYYA, //!< Synonym for HISTCMP_BHATTACHARYYA + /** Alternative Chi-Square + \f[d(H_1,H_2) = 2 * \sum _I \frac{\left(H_1(I)-H_2(I)\right)^2}{H_1(I)+H_2(I)}\f] + This alternative formula is regularly used for texture comparison. See e.g. @cite Puzicha1997 */ + HISTCMP_CHISQR_ALT = 4, + /** Kullback-Leibler divergence + \f[d(H_1,H_2) = \sum _I H_1(I) \log \left(\frac{H_1(I)}{H_2(I)}\right)\f] */ + HISTCMP_KL_DIV = 5 +}; + +/** the color conversion codes +@see @ref imgproc_color_conversions +@ingroup imgproc_color_conversions + */ +enum ColorConversionCodes { + COLOR_BGR2BGRA = 0, //!< add alpha channel to RGB or BGR image + COLOR_RGB2RGBA = COLOR_BGR2BGRA, + + COLOR_BGRA2BGR = 1, //!< remove alpha channel from RGB or BGR image + COLOR_RGBA2RGB = COLOR_BGRA2BGR, + + COLOR_BGR2RGBA = 2, //!< convert between RGB and BGR color spaces (with or without alpha channel) + COLOR_RGB2BGRA = COLOR_BGR2RGBA, + + COLOR_RGBA2BGR = 3, + COLOR_BGRA2RGB = COLOR_RGBA2BGR, + + COLOR_BGR2RGB = 4, + COLOR_RGB2BGR = COLOR_BGR2RGB, + + COLOR_BGRA2RGBA = 5, + COLOR_RGBA2BGRA = COLOR_BGRA2RGBA, + + COLOR_BGR2GRAY = 6, //!< convert between RGB/BGR and grayscale, @ref color_convert_rgb_gray "color conversions" + COLOR_RGB2GRAY = 7, + COLOR_GRAY2BGR = 8, + COLOR_GRAY2RGB = COLOR_GRAY2BGR, + COLOR_GRAY2BGRA = 9, + COLOR_GRAY2RGBA = COLOR_GRAY2BGRA, + COLOR_BGRA2GRAY = 10, + COLOR_RGBA2GRAY = 11, + + COLOR_BGR2BGR565 = 12, //!< convert between RGB/BGR and BGR565 (16-bit images) + COLOR_RGB2BGR565 = 13, + COLOR_BGR5652BGR = 14, + COLOR_BGR5652RGB = 15, + COLOR_BGRA2BGR565 = 16, + COLOR_RGBA2BGR565 = 17, + COLOR_BGR5652BGRA = 18, + COLOR_BGR5652RGBA = 19, + + COLOR_GRAY2BGR565 = 20, //!< convert between grayscale to BGR565 (16-bit images) + COLOR_BGR5652GRAY = 21, + + COLOR_BGR2BGR555 = 22, //!< convert between RGB/BGR and BGR555 (16-bit images) + COLOR_RGB2BGR555 = 23, + COLOR_BGR5552BGR = 24, + COLOR_BGR5552RGB = 25, + COLOR_BGRA2BGR555 = 26, + COLOR_RGBA2BGR555 = 27, + COLOR_BGR5552BGRA = 28, + COLOR_BGR5552RGBA = 29, + + COLOR_GRAY2BGR555 = 30, //!< convert between grayscale and BGR555 (16-bit images) + COLOR_BGR5552GRAY = 31, + + COLOR_BGR2XYZ = 32, //!< convert RGB/BGR to CIE XYZ, @ref color_convert_rgb_xyz "color conversions" + COLOR_RGB2XYZ = 33, + COLOR_XYZ2BGR = 34, + COLOR_XYZ2RGB = 35, + + COLOR_BGR2YCrCb = 36, //!< convert RGB/BGR to luma-chroma (aka YCC), @ref color_convert_rgb_ycrcb "color conversions" + COLOR_RGB2YCrCb = 37, + COLOR_YCrCb2BGR = 38, + COLOR_YCrCb2RGB = 39, + + COLOR_BGR2HSV = 40, //!< convert RGB/BGR to HSV (hue saturation value), @ref color_convert_rgb_hsv "color conversions" + COLOR_RGB2HSV = 41, + + COLOR_BGR2Lab = 44, //!< convert RGB/BGR to CIE Lab, @ref color_convert_rgb_lab "color conversions" + COLOR_RGB2Lab = 45, + + COLOR_BGR2Luv = 50, //!< convert RGB/BGR to CIE Luv, @ref color_convert_rgb_luv "color conversions" + COLOR_RGB2Luv = 51, + COLOR_BGR2HLS = 52, //!< convert RGB/BGR to HLS (hue lightness saturation), @ref color_convert_rgb_hls "color conversions" + COLOR_RGB2HLS = 53, + + COLOR_HSV2BGR = 54, //!< backward conversions to RGB/BGR + COLOR_HSV2RGB = 55, + + COLOR_Lab2BGR = 56, + COLOR_Lab2RGB = 57, + COLOR_Luv2BGR = 58, + COLOR_Luv2RGB = 59, + COLOR_HLS2BGR = 60, + COLOR_HLS2RGB = 61, + + COLOR_BGR2HSV_FULL = 66, + COLOR_RGB2HSV_FULL = 67, + COLOR_BGR2HLS_FULL = 68, + COLOR_RGB2HLS_FULL = 69, + + COLOR_HSV2BGR_FULL = 70, + COLOR_HSV2RGB_FULL = 71, + COLOR_HLS2BGR_FULL = 72, + COLOR_HLS2RGB_FULL = 73, + + COLOR_LBGR2Lab = 74, + COLOR_LRGB2Lab = 75, + COLOR_LBGR2Luv = 76, + COLOR_LRGB2Luv = 77, + + COLOR_Lab2LBGR = 78, + COLOR_Lab2LRGB = 79, + COLOR_Luv2LBGR = 80, + COLOR_Luv2LRGB = 81, + + COLOR_BGR2YUV = 82, //!< convert between RGB/BGR and YUV + COLOR_RGB2YUV = 83, + COLOR_YUV2BGR = 84, + COLOR_YUV2RGB = 85, + + //! YUV 4:2:0 family to RGB + COLOR_YUV2RGB_NV12 = 90, + COLOR_YUV2BGR_NV12 = 91, + COLOR_YUV2RGB_NV21 = 92, + COLOR_YUV2BGR_NV21 = 93, + COLOR_YUV420sp2RGB = COLOR_YUV2RGB_NV21, + COLOR_YUV420sp2BGR = COLOR_YUV2BGR_NV21, + + COLOR_YUV2RGBA_NV12 = 94, + COLOR_YUV2BGRA_NV12 = 95, + COLOR_YUV2RGBA_NV21 = 96, + COLOR_YUV2BGRA_NV21 = 97, + COLOR_YUV420sp2RGBA = COLOR_YUV2RGBA_NV21, + COLOR_YUV420sp2BGRA = COLOR_YUV2BGRA_NV21, + + COLOR_YUV2RGB_YV12 = 98, + COLOR_YUV2BGR_YV12 = 99, + COLOR_YUV2RGB_IYUV = 100, + COLOR_YUV2BGR_IYUV = 101, + COLOR_YUV2RGB_I420 = COLOR_YUV2RGB_IYUV, + COLOR_YUV2BGR_I420 = COLOR_YUV2BGR_IYUV, + COLOR_YUV420p2RGB = COLOR_YUV2RGB_YV12, + COLOR_YUV420p2BGR = COLOR_YUV2BGR_YV12, + + COLOR_YUV2RGBA_YV12 = 102, + COLOR_YUV2BGRA_YV12 = 103, + COLOR_YUV2RGBA_IYUV = 104, + COLOR_YUV2BGRA_IYUV = 105, + COLOR_YUV2RGBA_I420 = COLOR_YUV2RGBA_IYUV, + COLOR_YUV2BGRA_I420 = COLOR_YUV2BGRA_IYUV, + COLOR_YUV420p2RGBA = COLOR_YUV2RGBA_YV12, + COLOR_YUV420p2BGRA = COLOR_YUV2BGRA_YV12, + + COLOR_YUV2GRAY_420 = 106, + COLOR_YUV2GRAY_NV21 = COLOR_YUV2GRAY_420, + COLOR_YUV2GRAY_NV12 = COLOR_YUV2GRAY_420, + COLOR_YUV2GRAY_YV12 = COLOR_YUV2GRAY_420, + COLOR_YUV2GRAY_IYUV = COLOR_YUV2GRAY_420, + COLOR_YUV2GRAY_I420 = COLOR_YUV2GRAY_420, + COLOR_YUV420sp2GRAY = COLOR_YUV2GRAY_420, + COLOR_YUV420p2GRAY = COLOR_YUV2GRAY_420, + + //! YUV 4:2:2 family to RGB + COLOR_YUV2RGB_UYVY = 107, + COLOR_YUV2BGR_UYVY = 108, + //COLOR_YUV2RGB_VYUY = 109, + //COLOR_YUV2BGR_VYUY = 110, + COLOR_YUV2RGB_Y422 = COLOR_YUV2RGB_UYVY, + COLOR_YUV2BGR_Y422 = COLOR_YUV2BGR_UYVY, + COLOR_YUV2RGB_UYNV = COLOR_YUV2RGB_UYVY, + COLOR_YUV2BGR_UYNV = COLOR_YUV2BGR_UYVY, + + COLOR_YUV2RGBA_UYVY = 111, + COLOR_YUV2BGRA_UYVY = 112, + //COLOR_YUV2RGBA_VYUY = 113, + //COLOR_YUV2BGRA_VYUY = 114, + COLOR_YUV2RGBA_Y422 = COLOR_YUV2RGBA_UYVY, + COLOR_YUV2BGRA_Y422 = COLOR_YUV2BGRA_UYVY, + COLOR_YUV2RGBA_UYNV = COLOR_YUV2RGBA_UYVY, + COLOR_YUV2BGRA_UYNV = COLOR_YUV2BGRA_UYVY, + + COLOR_YUV2RGB_YUY2 = 115, + COLOR_YUV2BGR_YUY2 = 116, + COLOR_YUV2RGB_YVYU = 117, + COLOR_YUV2BGR_YVYU = 118, + COLOR_YUV2RGB_YUYV = COLOR_YUV2RGB_YUY2, + COLOR_YUV2BGR_YUYV = COLOR_YUV2BGR_YUY2, + COLOR_YUV2RGB_YUNV = COLOR_YUV2RGB_YUY2, + COLOR_YUV2BGR_YUNV = COLOR_YUV2BGR_YUY2, + + COLOR_YUV2RGBA_YUY2 = 119, + COLOR_YUV2BGRA_YUY2 = 120, + COLOR_YUV2RGBA_YVYU = 121, + COLOR_YUV2BGRA_YVYU = 122, + COLOR_YUV2RGBA_YUYV = COLOR_YUV2RGBA_YUY2, + COLOR_YUV2BGRA_YUYV = COLOR_YUV2BGRA_YUY2, + COLOR_YUV2RGBA_YUNV = COLOR_YUV2RGBA_YUY2, + COLOR_YUV2BGRA_YUNV = COLOR_YUV2BGRA_YUY2, + + COLOR_YUV2GRAY_UYVY = 123, + COLOR_YUV2GRAY_YUY2 = 124, + //CV_YUV2GRAY_VYUY = CV_YUV2GRAY_UYVY, + COLOR_YUV2GRAY_Y422 = COLOR_YUV2GRAY_UYVY, + COLOR_YUV2GRAY_UYNV = COLOR_YUV2GRAY_UYVY, + COLOR_YUV2GRAY_YVYU = COLOR_YUV2GRAY_YUY2, + COLOR_YUV2GRAY_YUYV = COLOR_YUV2GRAY_YUY2, + COLOR_YUV2GRAY_YUNV = COLOR_YUV2GRAY_YUY2, + + //! alpha premultiplication + COLOR_RGBA2mRGBA = 125, + COLOR_mRGBA2RGBA = 126, + + //! RGB to YUV 4:2:0 family + COLOR_RGB2YUV_I420 = 127, + COLOR_BGR2YUV_I420 = 128, + COLOR_RGB2YUV_IYUV = COLOR_RGB2YUV_I420, + COLOR_BGR2YUV_IYUV = COLOR_BGR2YUV_I420, + + COLOR_RGBA2YUV_I420 = 129, + COLOR_BGRA2YUV_I420 = 130, + COLOR_RGBA2YUV_IYUV = COLOR_RGBA2YUV_I420, + COLOR_BGRA2YUV_IYUV = COLOR_BGRA2YUV_I420, + COLOR_RGB2YUV_YV12 = 131, + COLOR_BGR2YUV_YV12 = 132, + COLOR_RGBA2YUV_YV12 = 133, + COLOR_BGRA2YUV_YV12 = 134, + + //! Demosaicing + COLOR_BayerBG2BGR = 46, + COLOR_BayerGB2BGR = 47, + COLOR_BayerRG2BGR = 48, + COLOR_BayerGR2BGR = 49, + + COLOR_BayerBG2RGB = COLOR_BayerRG2BGR, + COLOR_BayerGB2RGB = COLOR_BayerGR2BGR, + COLOR_BayerRG2RGB = COLOR_BayerBG2BGR, + COLOR_BayerGR2RGB = COLOR_BayerGB2BGR, + + COLOR_BayerBG2GRAY = 86, + COLOR_BayerGB2GRAY = 87, + COLOR_BayerRG2GRAY = 88, + COLOR_BayerGR2GRAY = 89, + + //! Demosaicing using Variable Number of Gradients + COLOR_BayerBG2BGR_VNG = 62, + COLOR_BayerGB2BGR_VNG = 63, + COLOR_BayerRG2BGR_VNG = 64, + COLOR_BayerGR2BGR_VNG = 65, + + COLOR_BayerBG2RGB_VNG = COLOR_BayerRG2BGR_VNG, + COLOR_BayerGB2RGB_VNG = COLOR_BayerGR2BGR_VNG, + COLOR_BayerRG2RGB_VNG = COLOR_BayerBG2BGR_VNG, + COLOR_BayerGR2RGB_VNG = COLOR_BayerGB2BGR_VNG, + + //! Edge-Aware Demosaicing + COLOR_BayerBG2BGR_EA = 135, + COLOR_BayerGB2BGR_EA = 136, + COLOR_BayerRG2BGR_EA = 137, + COLOR_BayerGR2BGR_EA = 138, + + COLOR_BayerBG2RGB_EA = COLOR_BayerRG2BGR_EA, + COLOR_BayerGB2RGB_EA = COLOR_BayerGR2BGR_EA, + COLOR_BayerRG2RGB_EA = COLOR_BayerBG2BGR_EA, + COLOR_BayerGR2RGB_EA = COLOR_BayerGB2BGR_EA, + + //! Demosaicing with alpha channel + COLOR_BayerBG2BGRA = 139, + COLOR_BayerGB2BGRA = 140, + COLOR_BayerRG2BGRA = 141, + COLOR_BayerGR2BGRA = 142, + + COLOR_BayerBG2RGBA = COLOR_BayerRG2BGRA, + COLOR_BayerGB2RGBA = COLOR_BayerGR2BGRA, + COLOR_BayerRG2RGBA = COLOR_BayerBG2BGRA, + COLOR_BayerGR2RGBA = COLOR_BayerGB2BGRA, + + COLOR_COLORCVT_MAX = 143 +}; + +//! @addtogroup imgproc_shape +//! @{ + +//! types of intersection between rectangles +enum RectanglesIntersectTypes { + INTERSECT_NONE = 0, //!< No intersection + INTERSECT_PARTIAL = 1, //!< There is a partial intersection + INTERSECT_FULL = 2 //!< One of the rectangle is fully enclosed in the other +}; + +/** @brief finds arbitrary template in the grayscale image using Generalized Hough Transform +*/ +class CV_EXPORTS GeneralizedHough : public Algorithm +{ +public: + //! set template to search + virtual void setTemplate(InputArray templ, Point templCenter = Point(-1, -1)) = 0; + virtual void setTemplate(InputArray edges, InputArray dx, InputArray dy, Point templCenter = Point(-1, -1)) = 0; + + //! find template on image + virtual void detect(InputArray image, OutputArray positions, OutputArray votes = noArray()) = 0; + virtual void detect(InputArray edges, InputArray dx, InputArray dy, OutputArray positions, OutputArray votes = noArray()) = 0; + + //! Canny low threshold. + virtual void setCannyLowThresh(int cannyLowThresh) = 0; + virtual int getCannyLowThresh() const = 0; + + //! Canny high threshold. + virtual void setCannyHighThresh(int cannyHighThresh) = 0; + virtual int getCannyHighThresh() const = 0; + + //! Minimum distance between the centers of the detected objects. + virtual void setMinDist(double minDist) = 0; + virtual double getMinDist() const = 0; + + //! Inverse ratio of the accumulator resolution to the image resolution. + virtual void setDp(double dp) = 0; + virtual double getDp() const = 0; + + //! Maximal size of inner buffers. + virtual void setMaxBufferSize(int maxBufferSize) = 0; + virtual int getMaxBufferSize() const = 0; +}; + +/** @brief finds arbitrary template in the grayscale image using Generalized Hough Transform + +Detects position only without translation and rotation @cite Ballard1981 . +*/ +class CV_EXPORTS GeneralizedHoughBallard : public GeneralizedHough +{ +public: + //! R-Table levels. + virtual void setLevels(int levels) = 0; + virtual int getLevels() const = 0; + + //! The accumulator threshold for the template centers at the detection stage. The smaller it is, the more false positions may be detected. + virtual void setVotesThreshold(int votesThreshold) = 0; + virtual int getVotesThreshold() const = 0; +}; + +/** @brief finds arbitrary template in the grayscale image using Generalized Hough Transform + +Detects position, translation and rotation @cite Guil1999 . +*/ +class CV_EXPORTS GeneralizedHoughGuil : public GeneralizedHough +{ +public: + //! Angle difference in degrees between two points in feature. + virtual void setXi(double xi) = 0; + virtual double getXi() const = 0; + + //! Feature table levels. + virtual void setLevels(int levels) = 0; + virtual int getLevels() const = 0; + + //! Maximal difference between angles that treated as equal. + virtual void setAngleEpsilon(double angleEpsilon) = 0; + virtual double getAngleEpsilon() const = 0; + + //! Minimal rotation angle to detect in degrees. + virtual void setMinAngle(double minAngle) = 0; + virtual double getMinAngle() const = 0; + + //! Maximal rotation angle to detect in degrees. + virtual void setMaxAngle(double maxAngle) = 0; + virtual double getMaxAngle() const = 0; + + //! Angle step in degrees. + virtual void setAngleStep(double angleStep) = 0; + virtual double getAngleStep() const = 0; + + //! Angle votes threshold. + virtual void setAngleThresh(int angleThresh) = 0; + virtual int getAngleThresh() const = 0; + + //! Minimal scale to detect. + virtual void setMinScale(double minScale) = 0; + virtual double getMinScale() const = 0; + + //! Maximal scale to detect. + virtual void setMaxScale(double maxScale) = 0; + virtual double getMaxScale() const = 0; + + //! Scale step. + virtual void setScaleStep(double scaleStep) = 0; + virtual double getScaleStep() const = 0; + + //! Scale votes threshold. + virtual void setScaleThresh(int scaleThresh) = 0; + virtual int getScaleThresh() const = 0; + + //! Position votes threshold. + virtual void setPosThresh(int posThresh) = 0; + virtual int getPosThresh() const = 0; +}; + +//! @} imgproc_shape + +//! @addtogroup imgproc_hist +//! @{ + +/** @brief Base class for Contrast Limited Adaptive Histogram Equalization. +*/ +class CV_EXPORTS_W CLAHE : public Algorithm +{ +public: + /** @brief Equalizes the histogram of a grayscale image using Contrast Limited Adaptive Histogram Equalization. + + @param src Source image of type CV_8UC1 or CV_16UC1. + @param dst Destination image. + */ + CV_WRAP virtual void apply(InputArray src, OutputArray dst) = 0; + + /** @brief Sets threshold for contrast limiting. + + @param clipLimit threshold value. + */ + CV_WRAP virtual void setClipLimit(double clipLimit) = 0; + + //! Returns threshold value for contrast limiting. + CV_WRAP virtual double getClipLimit() const = 0; + + /** @brief Sets size of grid for histogram equalization. Input image will be divided into + equally sized rectangular tiles. + + @param tileGridSize defines the number of tiles in row and column. + */ + CV_WRAP virtual void setTilesGridSize(Size tileGridSize) = 0; + + //!@brief Returns Size defines the number of tiles in row and column. + CV_WRAP virtual Size getTilesGridSize() const = 0; + + CV_WRAP virtual void collectGarbage() = 0; +}; + +//! @} imgproc_hist + +//! @addtogroup imgproc_subdiv2d +//! @{ + +class CV_EXPORTS_W Subdiv2D +{ +public: + /** Subdiv2D point location cases */ + enum { PTLOC_ERROR = -2, //!< Point location error + PTLOC_OUTSIDE_RECT = -1, //!< Point outside the subdivision bounding rect + PTLOC_INSIDE = 0, //!< Point inside some facet + PTLOC_VERTEX = 1, //!< Point coincides with one of the subdivision vertices + PTLOC_ON_EDGE = 2 //!< Point on some edge + }; + + /** Subdiv2D edge type navigation (see: getEdge()) */ + enum { NEXT_AROUND_ORG = 0x00, + NEXT_AROUND_DST = 0x22, + PREV_AROUND_ORG = 0x11, + PREV_AROUND_DST = 0x33, + NEXT_AROUND_LEFT = 0x13, + NEXT_AROUND_RIGHT = 0x31, + PREV_AROUND_LEFT = 0x20, + PREV_AROUND_RIGHT = 0x02 + }; + + /** creates an empty Subdiv2D object. + To create a new empty Delaunay subdivision you need to use the #initDelaunay function. + */ + CV_WRAP Subdiv2D(); + + /** @overload + + @param rect Rectangle that includes all of the 2D points that are to be added to the subdivision. + + The function creates an empty Delaunay subdivision where 2D points can be added using the function + insert() . All of the points to be added must be within the specified rectangle, otherwise a runtime + error is raised. + */ + CV_WRAP Subdiv2D(Rect rect); + + /** @brief Creates a new empty Delaunay subdivision + + @param rect Rectangle that includes all of the 2D points that are to be added to the subdivision. + + */ + CV_WRAP void initDelaunay(Rect rect); + + /** @brief Insert a single point into a Delaunay triangulation. + + @param pt Point to insert. + + The function inserts a single point into a subdivision and modifies the subdivision topology + appropriately. If a point with the same coordinates exists already, no new point is added. + @returns the ID of the point. + + @note If the point is outside of the triangulation specified rect a runtime error is raised. + */ + CV_WRAP int insert(Point2f pt); + + /** @brief Insert multiple points into a Delaunay triangulation. + + @param ptvec Points to insert. + + The function inserts a vector of points into a subdivision and modifies the subdivision topology + appropriately. + */ + CV_WRAP void insert(const std::vector& ptvec); + + /** @brief Returns the location of a point within a Delaunay triangulation. + + @param pt Point to locate. + @param edge Output edge that the point belongs to or is located to the right of it. + @param vertex Optional output vertex the input point coincides with. + + The function locates the input point within the subdivision and gives one of the triangle edges + or vertices. + + @returns an integer which specify one of the following five cases for point location: + - The point falls into some facet. The function returns #PTLOC_INSIDE and edge will contain one of + edges of the facet. + - The point falls onto the edge. The function returns #PTLOC_ON_EDGE and edge will contain this edge. + - The point coincides with one of the subdivision vertices. The function returns #PTLOC_VERTEX and + vertex will contain a pointer to the vertex. + - The point is outside the subdivision reference rectangle. The function returns #PTLOC_OUTSIDE_RECT + and no pointers are filled. + - One of input arguments is invalid. A runtime error is raised or, if silent or "parent" error + processing mode is selected, #PTLOC_ERROR is returned. + */ + CV_WRAP int locate(Point2f pt, CV_OUT int& edge, CV_OUT int& vertex); + + /** @brief Finds the subdivision vertex closest to the given point. + + @param pt Input point. + @param nearestPt Output subdivision vertex point. + + The function is another function that locates the input point within the subdivision. It finds the + subdivision vertex that is the closest to the input point. It is not necessarily one of vertices + of the facet containing the input point, though the facet (located using locate() ) is used as a + starting point. + + @returns vertex ID. + */ + CV_WRAP int findNearest(Point2f pt, CV_OUT Point2f* nearestPt = 0); + + /** @brief Returns a list of all edges. + + @param edgeList Output vector. + + The function gives each edge as a 4 numbers vector, where each two are one of the edge + vertices. i.e. org_x = v[0], org_y = v[1], dst_x = v[2], dst_y = v[3]. + */ + CV_WRAP void getEdgeList(CV_OUT std::vector& edgeList) const; + + /** @brief Returns a list of the leading edge ID connected to each triangle. + + @param leadingEdgeList Output vector. + + The function gives one edge ID for each triangle. + */ + CV_WRAP void getLeadingEdgeList(CV_OUT std::vector& leadingEdgeList) const; + + /** @brief Returns a list of all triangles. + + @param triangleList Output vector. + + The function gives each triangle as a 6 numbers vector, where each two are one of the triangle + vertices. i.e. p1_x = v[0], p1_y = v[1], p2_x = v[2], p2_y = v[3], p3_x = v[4], p3_y = v[5]. + */ + CV_WRAP void getTriangleList(CV_OUT std::vector& triangleList) const; + + /** @brief Returns a list of all Voroni facets. + + @param idx Vector of vertices IDs to consider. For all vertices you can pass empty vector. + @param facetList Output vector of the Voroni facets. + @param facetCenters Output vector of the Voroni facets center points. + + */ + CV_WRAP void getVoronoiFacetList(const std::vector& idx, CV_OUT std::vector >& facetList, + CV_OUT std::vector& facetCenters); + + /** @brief Returns vertex location from vertex ID. + + @param vertex vertex ID. + @param firstEdge Optional. The first edge ID which is connected to the vertex. + @returns vertex (x,y) + + */ + CV_WRAP Point2f getVertex(int vertex, CV_OUT int* firstEdge = 0) const; + + /** @brief Returns one of the edges related to the given edge. + + @param edge Subdivision edge ID. + @param nextEdgeType Parameter specifying which of the related edges to return. + The following values are possible: + - NEXT_AROUND_ORG next around the edge origin ( eOnext on the picture below if e is the input edge) + - NEXT_AROUND_DST next around the edge vertex ( eDnext ) + - PREV_AROUND_ORG previous around the edge origin (reversed eRnext ) + - PREV_AROUND_DST previous around the edge destination (reversed eLnext ) + - NEXT_AROUND_LEFT next around the left facet ( eLnext ) + - NEXT_AROUND_RIGHT next around the right facet ( eRnext ) + - PREV_AROUND_LEFT previous around the left facet (reversed eOnext ) + - PREV_AROUND_RIGHT previous around the right facet (reversed eDnext ) + + ![sample output](pics/quadedge.png) + + @returns edge ID related to the input edge. + */ + CV_WRAP int getEdge( int edge, int nextEdgeType ) const; + + /** @brief Returns next edge around the edge origin. + + @param edge Subdivision edge ID. + + @returns an integer which is next edge ID around the edge origin: eOnext on the + picture above if e is the input edge). + */ + CV_WRAP int nextEdge(int edge) const; + + /** @brief Returns another edge of the same quad-edge. + + @param edge Subdivision edge ID. + @param rotate Parameter specifying which of the edges of the same quad-edge as the input + one to return. The following values are possible: + - 0 - the input edge ( e on the picture below if e is the input edge) + - 1 - the rotated edge ( eRot ) + - 2 - the reversed edge (reversed e (in green)) + - 3 - the reversed rotated edge (reversed eRot (in green)) + + @returns one of the edges ID of the same quad-edge as the input edge. + */ + CV_WRAP int rotateEdge(int edge, int rotate) const; + CV_WRAP int symEdge(int edge) const; + + /** @brief Returns the edge origin. + + @param edge Subdivision edge ID. + @param orgpt Output vertex location. + + @returns vertex ID. + */ + CV_WRAP int edgeOrg(int edge, CV_OUT Point2f* orgpt = 0) const; + + /** @brief Returns the edge destination. + + @param edge Subdivision edge ID. + @param dstpt Output vertex location. + + @returns vertex ID. + */ + CV_WRAP int edgeDst(int edge, CV_OUT Point2f* dstpt = 0) const; + +protected: + int newEdge(); + void deleteEdge(int edge); + int newPoint(Point2f pt, bool isvirtual, int firstEdge = 0); + void deletePoint(int vtx); + void setEdgePoints( int edge, int orgPt, int dstPt ); + void splice( int edgeA, int edgeB ); + int connectEdges( int edgeA, int edgeB ); + void swapEdges( int edge ); + int isRightOf(Point2f pt, int edge) const; + void calcVoronoi(); + void clearVoronoi(); + void checkSubdiv() const; + + struct CV_EXPORTS Vertex + { + Vertex(); + Vertex(Point2f pt, bool _isvirtual, int _firstEdge=0); + bool isvirtual() const; + bool isfree() const; + + int firstEdge; + int type; + Point2f pt; + }; + + struct CV_EXPORTS QuadEdge + { + QuadEdge(); + QuadEdge(int edgeidx); + bool isfree() const; + + int next[4]; + int pt[4]; + }; + + //! All of the vertices + std::vector vtx; + //! All of the edges + std::vector qedges; + int freeQEdge; + int freePoint; + bool validGeometry; + + int recentEdge; + //! Top left corner of the bounding rect + Point2f topLeft; + //! Bottom right corner of the bounding rect + Point2f bottomRight; +}; + +//! @} imgproc_subdiv2d + +//! @addtogroup imgproc_feature +//! @{ + +/** @example samples/cpp/lsd_lines.cpp +An example using the LineSegmentDetector +\image html building_lsd.png "Sample output image" width=434 height=300 +*/ + +/** @brief Line segment detector class + +following the algorithm described at @cite Rafael12 . +*/ +class CV_EXPORTS_W LineSegmentDetector : public Algorithm +{ +public: + + /** @brief Finds lines in the input image. + + This is the output of the default parameters of the algorithm on the above shown image. + + ![image](pics/building_lsd.png) + + @param _image A grayscale (CV_8UC1) input image. If only a roi needs to be selected, use: + `lsd_ptr-\>detect(image(roi), lines, ...); lines += Scalar(roi.x, roi.y, roi.x, roi.y);` + @param _lines A vector of Vec4i or Vec4f elements specifying the beginning and ending point of a line. Where + Vec4i/Vec4f is (x1, y1, x2, y2), point 1 is the start, point 2 - end. Returned lines are strictly + oriented depending on the gradient. + @param width Vector of widths of the regions, where the lines are found. E.g. Width of line. + @param prec Vector of precisions with which the lines are found. + @param nfa Vector containing number of false alarms in the line region, with precision of 10%. The + bigger the value, logarithmically better the detection. + - -1 corresponds to 10 mean false alarms + - 0 corresponds to 1 mean false alarm + - 1 corresponds to 0.1 mean false alarms + This vector will be calculated only when the objects type is #LSD_REFINE_ADV. + */ + CV_WRAP virtual void detect(InputArray _image, OutputArray _lines, + OutputArray width = noArray(), OutputArray prec = noArray(), + OutputArray nfa = noArray()) = 0; + + /** @brief Draws the line segments on a given image. + @param _image The image, where the lines will be drawn. Should be bigger or equal to the image, + where the lines were found. + @param lines A vector of the lines that needed to be drawn. + */ + CV_WRAP virtual void drawSegments(InputOutputArray _image, InputArray lines) = 0; + + /** @brief Draws two groups of lines in blue and red, counting the non overlapping (mismatching) pixels. + + @param size The size of the image, where lines1 and lines2 were found. + @param lines1 The first group of lines that needs to be drawn. It is visualized in blue color. + @param lines2 The second group of lines. They visualized in red color. + @param _image Optional image, where the lines will be drawn. The image should be color(3-channel) + in order for lines1 and lines2 to be drawn in the above mentioned colors. + */ + CV_WRAP virtual int compareSegments(const Size& size, InputArray lines1, InputArray lines2, InputOutputArray _image = noArray()) = 0; + + virtual ~LineSegmentDetector() { } +}; + +/** @brief Creates a smart pointer to a LineSegmentDetector object and initializes it. + +The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want +to edit those, as to tailor it for their own application. + +@param _refine The way found lines will be refined, see #LineSegmentDetectorModes +@param _scale The scale of the image that will be used to find the lines. Range (0..1]. +@param _sigma_scale Sigma for Gaussian filter. It is computed as sigma = _sigma_scale/_scale. +@param _quant Bound to the quantization error on the gradient norm. +@param _ang_th Gradient angle tolerance in degrees. +@param _log_eps Detection threshold: -log10(NFA) \> log_eps. Used only when advance refinement +is chosen. +@param _density_th Minimal density of aligned region points in the enclosing rectangle. +@param _n_bins Number of bins in pseudo-ordering of gradient modulus. + */ +CV_EXPORTS_W Ptr createLineSegmentDetector( + int _refine = LSD_REFINE_STD, double _scale = 0.8, + double _sigma_scale = 0.6, double _quant = 2.0, double _ang_th = 22.5, + double _log_eps = 0, double _density_th = 0.7, int _n_bins = 1024); + +//! @} imgproc_feature + +//! @addtogroup imgproc_filter +//! @{ + +/** @brief Returns Gaussian filter coefficients. + +The function computes and returns the \f$\texttt{ksize} \times 1\f$ matrix of Gaussian filter +coefficients: + +\f[G_i= \alpha *e^{-(i-( \texttt{ksize} -1)/2)^2/(2* \texttt{sigma}^2)},\f] + +where \f$i=0..\texttt{ksize}-1\f$ and \f$\alpha\f$ is the scale factor chosen so that \f$\sum_i G_i=1\f$. + +Two of such generated kernels can be passed to sepFilter2D. Those functions automatically recognize +smoothing kernels (a symmetrical kernel with sum of weights equal to 1) and handle them accordingly. +You may also use the higher-level GaussianBlur. +@param ksize Aperture size. It should be odd ( \f$\texttt{ksize} \mod 2 = 1\f$ ) and positive. +@param sigma Gaussian standard deviation. If it is non-positive, it is computed from ksize as +`sigma = 0.3*((ksize-1)*0.5 - 1) + 0.8`. +@param ktype Type of filter coefficients. It can be CV_32F or CV_64F . +@sa sepFilter2D, getDerivKernels, getStructuringElement, GaussianBlur + */ +CV_EXPORTS_W Mat getGaussianKernel( int ksize, double sigma, int ktype = CV_64F ); + +/** @brief Returns filter coefficients for computing spatial image derivatives. + +The function computes and returns the filter coefficients for spatial image derivatives. When +`ksize=CV_SCHARR`, the Scharr \f$3 \times 3\f$ kernels are generated (see #Scharr). Otherwise, Sobel +kernels are generated (see #Sobel). The filters are normally passed to #sepFilter2D or to + +@param kx Output matrix of row filter coefficients. It has the type ktype . +@param ky Output matrix of column filter coefficients. It has the type ktype . +@param dx Derivative order in respect of x. +@param dy Derivative order in respect of y. +@param ksize Aperture size. It can be CV_SCHARR, 1, 3, 5, or 7. +@param normalize Flag indicating whether to normalize (scale down) the filter coefficients or not. +Theoretically, the coefficients should have the denominator \f$=2^{ksize*2-dx-dy-2}\f$. If you are +going to filter floating-point images, you are likely to use the normalized kernels. But if you +compute derivatives of an 8-bit image, store the results in a 16-bit image, and wish to preserve +all the fractional bits, you may want to set normalize=false . +@param ktype Type of filter coefficients. It can be CV_32f or CV_64F . + */ +CV_EXPORTS_W void getDerivKernels( OutputArray kx, OutputArray ky, + int dx, int dy, int ksize, + bool normalize = false, int ktype = CV_32F ); + +/** @brief Returns Gabor filter coefficients. + +For more details about gabor filter equations and parameters, see: [Gabor +Filter](http://en.wikipedia.org/wiki/Gabor_filter). + +@param ksize Size of the filter returned. +@param sigma Standard deviation of the gaussian envelope. +@param theta Orientation of the normal to the parallel stripes of a Gabor function. +@param lambd Wavelength of the sinusoidal factor. +@param gamma Spatial aspect ratio. +@param psi Phase offset. +@param ktype Type of filter coefficients. It can be CV_32F or CV_64F . + */ +CV_EXPORTS_W Mat getGaborKernel( Size ksize, double sigma, double theta, double lambd, + double gamma, double psi = CV_PI*0.5, int ktype = CV_64F ); + +//! returns "magic" border value for erosion and dilation. It is automatically transformed to Scalar::all(-DBL_MAX) for dilation. +static inline Scalar morphologyDefaultBorderValue() { return Scalar::all(DBL_MAX); } + +/** @brief Returns a structuring element of the specified size and shape for morphological operations. + +The function constructs and returns the structuring element that can be further passed to #erode, +#dilate or #morphologyEx. But you can also construct an arbitrary binary mask yourself and use it as +the structuring element. + +@param shape Element shape that could be one of #MorphShapes +@param ksize Size of the structuring element. +@param anchor Anchor position within the element. The default value \f$(-1, -1)\f$ means that the +anchor is at the center. Note that only the shape of a cross-shaped element depends on the anchor +position. In other cases the anchor just regulates how much the result of the morphological +operation is shifted. + */ +CV_EXPORTS_W Mat getStructuringElement(int shape, Size ksize, Point anchor = Point(-1,-1)); + +/** @example samples/cpp/tutorial_code/ImgProc/Smoothing/Smoothing.cpp +Sample code for simple filters +![Sample screenshot](Smoothing_Tutorial_Result_Median_Filter.jpg) +Check @ref tutorial_gausian_median_blur_bilateral_filter "the corresponding tutorial" for more details + */ + +/** @brief Blurs an image using the median filter. + +The function smoothes an image using the median filter with the \f$\texttt{ksize} \times +\texttt{ksize}\f$ aperture. Each channel of a multi-channel image is processed independently. +In-place operation is supported. + +@note The median filter uses #BORDER_REPLICATE internally to cope with border pixels, see #BorderTypes + +@param src input 1-, 3-, or 4-channel image; when ksize is 3 or 5, the image depth should be +CV_8U, CV_16U, or CV_32F, for larger aperture sizes, it can only be CV_8U. +@param dst destination array of the same size and type as src. +@param ksize aperture linear size; it must be odd and greater than 1, for example: 3, 5, 7 ... +@sa bilateralFilter, blur, boxFilter, GaussianBlur + */ +CV_EXPORTS_W void medianBlur( InputArray src, OutputArray dst, int ksize ); + +/** @brief Blurs an image using a Gaussian filter. + +The function convolves the source image with the specified Gaussian kernel. In-place filtering is +supported. + +@param src input image; the image can have any number of channels, which are processed +independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. +@param dst output image of the same size and type as src. +@param ksize Gaussian kernel size. ksize.width and ksize.height can differ but they both must be +positive and odd. Or, they can be zero's and then they are computed from sigma. +@param sigmaX Gaussian kernel standard deviation in X direction. +@param sigmaY Gaussian kernel standard deviation in Y direction; if sigmaY is zero, it is set to be +equal to sigmaX, if both sigmas are zeros, they are computed from ksize.width and ksize.height, +respectively (see #getGaussianKernel for details); to fully control the result regardless of +possible future modifications of all this semantics, it is recommended to specify all of ksize, +sigmaX, and sigmaY. +@param borderType pixel extrapolation method, see #BorderTypes + +@sa sepFilter2D, filter2D, blur, boxFilter, bilateralFilter, medianBlur + */ +CV_EXPORTS_W void GaussianBlur( InputArray src, OutputArray dst, Size ksize, + double sigmaX, double sigmaY = 0, + int borderType = BORDER_DEFAULT ); + +/** @brief Applies the bilateral filter to an image. + +The function applies bilateral filtering to the input image, as described in +http://www.dai.ed.ac.uk/CVonline/LOCAL_COPIES/MANDUCHI1/Bilateral_Filtering.html +bilateralFilter can reduce unwanted noise very well while keeping edges fairly sharp. However, it is +very slow compared to most filters. + +_Sigma values_: For simplicity, you can set the 2 sigma values to be the same. If they are small (\< +10), the filter will not have much effect, whereas if they are large (\> 150), they will have a very +strong effect, making the image look "cartoonish". + +_Filter size_: Large filters (d \> 5) are very slow, so it is recommended to use d=5 for real-time +applications, and perhaps d=9 for offline applications that need heavy noise filtering. + +This filter does not work inplace. +@param src Source 8-bit or floating-point, 1-channel or 3-channel image. +@param dst Destination image of the same size and type as src . +@param d Diameter of each pixel neighborhood that is used during filtering. If it is non-positive, +it is computed from sigmaSpace. +@param sigmaColor Filter sigma in the color space. A larger value of the parameter means that +farther colors within the pixel neighborhood (see sigmaSpace) will be mixed together, resulting +in larger areas of semi-equal color. +@param sigmaSpace Filter sigma in the coordinate space. A larger value of the parameter means that +farther pixels will influence each other as long as their colors are close enough (see sigmaColor +). When d\>0, it specifies the neighborhood size regardless of sigmaSpace. Otherwise, d is +proportional to sigmaSpace. +@param borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes + */ +CV_EXPORTS_W void bilateralFilter( InputArray src, OutputArray dst, int d, + double sigmaColor, double sigmaSpace, + int borderType = BORDER_DEFAULT ); + +/** @brief Blurs an image using the box filter. + +The function smooths an image using the kernel: + +\f[\texttt{K} = \alpha \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \end{bmatrix}\f] + +where + +\f[\alpha = \fork{\frac{1}{\texttt{ksize.width*ksize.height}}}{when \texttt{normalize=true}}{1}{otherwise}\f] + +Unnormalized box filter is useful for computing various integral characteristics over each pixel +neighborhood, such as covariance matrices of image derivatives (used in dense optical flow +algorithms, and so on). If you need to compute pixel sums over variable-size windows, use #integral. + +@param src input image. +@param dst output image of the same size and type as src. +@param ddepth the output image depth (-1 to use src.depth()). +@param ksize blurring kernel size. +@param anchor anchor point; default value Point(-1,-1) means that the anchor is at the kernel +center. +@param normalize flag, specifying whether the kernel is normalized by its area or not. +@param borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes +@sa blur, bilateralFilter, GaussianBlur, medianBlur, integral + */ +CV_EXPORTS_W void boxFilter( InputArray src, OutputArray dst, int ddepth, + Size ksize, Point anchor = Point(-1,-1), + bool normalize = true, + int borderType = BORDER_DEFAULT ); + +/** @brief Calculates the normalized sum of squares of the pixel values overlapping the filter. + +For every pixel \f$ (x, y) \f$ in the source image, the function calculates the sum of squares of those neighboring +pixel values which overlap the filter placed over the pixel \f$ (x, y) \f$. + +The unnormalized square box filter can be useful in computing local image statistics such as the the local +variance and standard deviation around the neighborhood of a pixel. + +@param src input image +@param dst output image of the same size and type as _src +@param ddepth the output image depth (-1 to use src.depth()) +@param ksize kernel size +@param anchor kernel anchor point. The default value of Point(-1, -1) denotes that the anchor is at the kernel +center. +@param normalize flag, specifying whether the kernel is to be normalized by it's area or not. +@param borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes +@sa boxFilter +*/ +CV_EXPORTS_W void sqrBoxFilter( InputArray src, OutputArray dst, int ddepth, + Size ksize, Point anchor = Point(-1, -1), + bool normalize = true, + int borderType = BORDER_DEFAULT ); + +/** @brief Blurs an image using the normalized box filter. + +The function smooths an image using the kernel: + +\f[\texttt{K} = \frac{1}{\texttt{ksize.width*ksize.height}} \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \end{bmatrix}\f] + +The call `blur(src, dst, ksize, anchor, borderType)` is equivalent to `boxFilter(src, dst, src.type(), +anchor, true, borderType)`. + +@param src input image; it can have any number of channels, which are processed independently, but +the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. +@param dst output image of the same size and type as src. +@param ksize blurring kernel size. +@param anchor anchor point; default value Point(-1,-1) means that the anchor is at the kernel +center. +@param borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes +@sa boxFilter, bilateralFilter, GaussianBlur, medianBlur + */ +CV_EXPORTS_W void blur( InputArray src, OutputArray dst, + Size ksize, Point anchor = Point(-1,-1), + int borderType = BORDER_DEFAULT ); + +/** @brief Convolves an image with the kernel. + +The function applies an arbitrary linear filter to an image. In-place operation is supported. When +the aperture is partially outside the image, the function interpolates outlier pixel values +according to the specified border mode. + +The function does actually compute correlation, not the convolution: + +\f[\texttt{dst} (x,y) = \sum _{ \stackrel{0\leq x' < \texttt{kernel.cols},}{0\leq y' < \texttt{kernel.rows}} } \texttt{kernel} (x',y')* \texttt{src} (x+x'- \texttt{anchor.x} ,y+y'- \texttt{anchor.y} )\f] + +That is, the kernel is not mirrored around the anchor point. If you need a real convolution, flip +the kernel using #flip and set the new anchor to `(kernel.cols - anchor.x - 1, kernel.rows - +anchor.y - 1)`. + +The function uses the DFT-based algorithm in case of sufficiently large kernels (~`11 x 11` or +larger) and the direct algorithm for small kernels. + +@param src input image. +@param dst output image of the same size and the same number of channels as src. +@param ddepth desired depth of the destination image, see @ref filter_depths "combinations" +@param kernel convolution kernel (or rather a correlation kernel), a single-channel floating point +matrix; if you want to apply different kernels to different channels, split the image into +separate color planes using split and process them individually. +@param anchor anchor of the kernel that indicates the relative position of a filtered point within +the kernel; the anchor should lie within the kernel; default value (-1,-1) means that the anchor +is at the kernel center. +@param delta optional value added to the filtered pixels before storing them in dst. +@param borderType pixel extrapolation method, see #BorderTypes +@sa sepFilter2D, dft, matchTemplate + */ +CV_EXPORTS_W void filter2D( InputArray src, OutputArray dst, int ddepth, + InputArray kernel, Point anchor = Point(-1,-1), + double delta = 0, int borderType = BORDER_DEFAULT ); + +/** @brief Applies a separable linear filter to an image. + +The function applies a separable linear filter to the image. That is, first, every row of src is +filtered with the 1D kernel kernelX. Then, every column of the result is filtered with the 1D +kernel kernelY. The final result shifted by delta is stored in dst . + +@param src Source image. +@param dst Destination image of the same size and the same number of channels as src . +@param ddepth Destination image depth, see @ref filter_depths "combinations" +@param kernelX Coefficients for filtering each row. +@param kernelY Coefficients for filtering each column. +@param anchor Anchor position within the kernel. The default value \f$(-1,-1)\f$ means that the anchor +is at the kernel center. +@param delta Value added to the filtered results before storing them. +@param borderType Pixel extrapolation method, see #BorderTypes +@sa filter2D, Sobel, GaussianBlur, boxFilter, blur + */ +CV_EXPORTS_W void sepFilter2D( InputArray src, OutputArray dst, int ddepth, + InputArray kernelX, InputArray kernelY, + Point anchor = Point(-1,-1), + double delta = 0, int borderType = BORDER_DEFAULT ); + +/** @example samples/cpp/tutorial_code/ImgTrans/Sobel_Demo.cpp +Sample code using Sobel and/or Scharr OpenCV functions to make a simple Edge Detector +![Sample screenshot](Sobel_Derivatives_Tutorial_Result.jpg) +Check @ref tutorial_sobel_derivatives "the corresponding tutorial" for more details +*/ + +/** @brief Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator. + +In all cases except one, the \f$\texttt{ksize} \times \texttt{ksize}\f$ separable kernel is used to +calculate the derivative. When \f$\texttt{ksize = 1}\f$, the \f$3 \times 1\f$ or \f$1 \times 3\f$ +kernel is used (that is, no Gaussian smoothing is done). `ksize = 1` can only be used for the first +or the second x- or y- derivatives. + +There is also the special value `ksize = #CV_SCHARR (-1)` that corresponds to the \f$3\times3\f$ Scharr +filter that may give more accurate results than the \f$3\times3\f$ Sobel. The Scharr aperture is + +\f[\vecthreethree{-3}{0}{3}{-10}{0}{10}{-3}{0}{3}\f] + +for the x-derivative, or transposed for the y-derivative. + +The function calculates an image derivative by convolving the image with the appropriate kernel: + +\f[\texttt{dst} = \frac{\partial^{xorder+yorder} \texttt{src}}{\partial x^{xorder} \partial y^{yorder}}\f] + +The Sobel operators combine Gaussian smoothing and differentiation, so the result is more or less +resistant to the noise. Most often, the function is called with ( xorder = 1, yorder = 0, ksize = 3) +or ( xorder = 0, yorder = 1, ksize = 3) to calculate the first x- or y- image derivative. The first +case corresponds to a kernel of: + +\f[\vecthreethree{-1}{0}{1}{-2}{0}{2}{-1}{0}{1}\f] + +The second case corresponds to a kernel of: + +\f[\vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}\f] + +@param src input image. +@param dst output image of the same size and the same number of channels as src . +@param ddepth output image depth, see @ref filter_depths "combinations"; in the case of + 8-bit input images it will result in truncated derivatives. +@param dx order of the derivative x. +@param dy order of the derivative y. +@param ksize size of the extended Sobel kernel; it must be 1, 3, 5, or 7. +@param scale optional scale factor for the computed derivative values; by default, no scaling is +applied (see #getDerivKernels for details). +@param delta optional delta value that is added to the results prior to storing them in dst. +@param borderType pixel extrapolation method, see #BorderTypes +@sa Scharr, Laplacian, sepFilter2D, filter2D, GaussianBlur, cartToPolar + */ +CV_EXPORTS_W void Sobel( InputArray src, OutputArray dst, int ddepth, + int dx, int dy, int ksize = 3, + double scale = 1, double delta = 0, + int borderType = BORDER_DEFAULT ); + +/** @brief Calculates the first order image derivative in both x and y using a Sobel operator + +Equivalent to calling: + +@code +Sobel( src, dx, CV_16SC1, 1, 0, 3 ); +Sobel( src, dy, CV_16SC1, 0, 1, 3 ); +@endcode + +@param src input image. +@param dx output image with first-order derivative in x. +@param dy output image with first-order derivative in y. +@param ksize size of Sobel kernel. It must be 3. +@param borderType pixel extrapolation method, see #BorderTypes + +@sa Sobel + */ + +CV_EXPORTS_W void spatialGradient( InputArray src, OutputArray dx, + OutputArray dy, int ksize = 3, + int borderType = BORDER_DEFAULT ); + +/** @brief Calculates the first x- or y- image derivative using Scharr operator. + +The function computes the first x- or y- spatial image derivative using the Scharr operator. The +call + +\f[\texttt{Scharr(src, dst, ddepth, dx, dy, scale, delta, borderType)}\f] + +is equivalent to + +\f[\texttt{Sobel(src, dst, ddepth, dx, dy, CV_SCHARR, scale, delta, borderType)} .\f] + +@param src input image. +@param dst output image of the same size and the same number of channels as src. +@param ddepth output image depth, see @ref filter_depths "combinations" +@param dx order of the derivative x. +@param dy order of the derivative y. +@param scale optional scale factor for the computed derivative values; by default, no scaling is +applied (see #getDerivKernels for details). +@param delta optional delta value that is added to the results prior to storing them in dst. +@param borderType pixel extrapolation method, see #BorderTypes +@sa cartToPolar + */ +CV_EXPORTS_W void Scharr( InputArray src, OutputArray dst, int ddepth, + int dx, int dy, double scale = 1, double delta = 0, + int borderType = BORDER_DEFAULT ); + +/** @example samples/cpp/laplace.cpp +An example using Laplace transformations for edge detection +*/ + +/** @brief Calculates the Laplacian of an image. + +The function calculates the Laplacian of the source image by adding up the second x and y +derivatives calculated using the Sobel operator: + +\f[\texttt{dst} = \Delta \texttt{src} = \frac{\partial^2 \texttt{src}}{\partial x^2} + \frac{\partial^2 \texttt{src}}{\partial y^2}\f] + +This is done when `ksize > 1`. When `ksize == 1`, the Laplacian is computed by filtering the image +with the following \f$3 \times 3\f$ aperture: + +\f[\vecthreethree {0}{1}{0}{1}{-4}{1}{0}{1}{0}\f] + +@param src Source image. +@param dst Destination image of the same size and the same number of channels as src . +@param ddepth Desired depth of the destination image. +@param ksize Aperture size used to compute the second-derivative filters. See #getDerivKernels for +details. The size must be positive and odd. +@param scale Optional scale factor for the computed Laplacian values. By default, no scaling is +applied. See #getDerivKernels for details. +@param delta Optional delta value that is added to the results prior to storing them in dst . +@param borderType Pixel extrapolation method, see #BorderTypes +@sa Sobel, Scharr + */ +CV_EXPORTS_W void Laplacian( InputArray src, OutputArray dst, int ddepth, + int ksize = 1, double scale = 1, double delta = 0, + int borderType = BORDER_DEFAULT ); + +//! @} imgproc_filter + +//! @addtogroup imgproc_feature +//! @{ + +/** @example samples/cpp/edge.cpp +This program demonstrates usage of the Canny edge detector + +Check @ref tutorial_canny_detector "the corresponding tutorial" for more details +*/ + +/** @brief Finds edges in an image using the Canny algorithm @cite Canny86 . + +The function finds edges in the input image and marks them in the output map edges using the +Canny algorithm. The smallest value between threshold1 and threshold2 is used for edge linking. The +largest value is used to find initial segments of strong edges. See + + +@param image 8-bit input image. +@param edges output edge map; single channels 8-bit image, which has the same size as image . +@param threshold1 first threshold for the hysteresis procedure. +@param threshold2 second threshold for the hysteresis procedure. +@param apertureSize aperture size for the Sobel operator. +@param L2gradient a flag, indicating whether a more accurate \f$L_2\f$ norm +\f$=\sqrt{(dI/dx)^2 + (dI/dy)^2}\f$ should be used to calculate the image gradient magnitude ( +L2gradient=true ), or whether the default \f$L_1\f$ norm \f$=|dI/dx|+|dI/dy|\f$ is enough ( +L2gradient=false ). + */ +CV_EXPORTS_W void Canny( InputArray image, OutputArray edges, + double threshold1, double threshold2, + int apertureSize = 3, bool L2gradient = false ); + +/** \overload + +Finds edges in an image using the Canny algorithm with custom image gradient. + +@param dx 16-bit x derivative of input image (CV_16SC1 or CV_16SC3). +@param dy 16-bit y derivative of input image (same type as dx). +@param edges output edge map; single channels 8-bit image, which has the same size as image . +@param threshold1 first threshold for the hysteresis procedure. +@param threshold2 second threshold for the hysteresis procedure. +@param L2gradient a flag, indicating whether a more accurate \f$L_2\f$ norm +\f$=\sqrt{(dI/dx)^2 + (dI/dy)^2}\f$ should be used to calculate the image gradient magnitude ( +L2gradient=true ), or whether the default \f$L_1\f$ norm \f$=|dI/dx|+|dI/dy|\f$ is enough ( +L2gradient=false ). + */ +CV_EXPORTS_W void Canny( InputArray dx, InputArray dy, + OutputArray edges, + double threshold1, double threshold2, + bool L2gradient = false ); + +/** @brief Calculates the minimal eigenvalue of gradient matrices for corner detection. + +The function is similar to cornerEigenValsAndVecs but it calculates and stores only the minimal +eigenvalue of the covariance matrix of derivatives, that is, \f$\min(\lambda_1, \lambda_2)\f$ in terms +of the formulae in the cornerEigenValsAndVecs description. + +@param src Input single-channel 8-bit or floating-point image. +@param dst Image to store the minimal eigenvalues. It has the type CV_32FC1 and the same size as +src . +@param blockSize Neighborhood size (see the details on #cornerEigenValsAndVecs ). +@param ksize Aperture parameter for the Sobel operator. +@param borderType Pixel extrapolation method. See #BorderTypes. + */ +CV_EXPORTS_W void cornerMinEigenVal( InputArray src, OutputArray dst, + int blockSize, int ksize = 3, + int borderType = BORDER_DEFAULT ); + +/** @brief Harris corner detector. + +The function runs the Harris corner detector on the image. Similarly to cornerMinEigenVal and +cornerEigenValsAndVecs , for each pixel \f$(x, y)\f$ it calculates a \f$2\times2\f$ gradient covariance +matrix \f$M^{(x,y)}\f$ over a \f$\texttt{blockSize} \times \texttt{blockSize}\f$ neighborhood. Then, it +computes the following characteristic: + +\f[\texttt{dst} (x,y) = \mathrm{det} M^{(x,y)} - k \cdot \left ( \mathrm{tr} M^{(x,y)} \right )^2\f] + +Corners in the image can be found as the local maxima of this response map. + +@param src Input single-channel 8-bit or floating-point image. +@param dst Image to store the Harris detector responses. It has the type CV_32FC1 and the same +size as src . +@param blockSize Neighborhood size (see the details on #cornerEigenValsAndVecs ). +@param ksize Aperture parameter for the Sobel operator. +@param k Harris detector free parameter. See the formula above. +@param borderType Pixel extrapolation method. See #BorderTypes. + */ +CV_EXPORTS_W void cornerHarris( InputArray src, OutputArray dst, int blockSize, + int ksize, double k, + int borderType = BORDER_DEFAULT ); + +/** @brief Calculates eigenvalues and eigenvectors of image blocks for corner detection. + +For every pixel \f$p\f$ , the function cornerEigenValsAndVecs considers a blockSize \f$\times\f$ blockSize +neighborhood \f$S(p)\f$ . It calculates the covariation matrix of derivatives over the neighborhood as: + +\f[M = \begin{bmatrix} \sum _{S(p)}(dI/dx)^2 & \sum _{S(p)}dI/dx dI/dy \\ \sum _{S(p)}dI/dx dI/dy & \sum _{S(p)}(dI/dy)^2 \end{bmatrix}\f] + +where the derivatives are computed using the Sobel operator. + +After that, it finds eigenvectors and eigenvalues of \f$M\f$ and stores them in the destination image as +\f$(\lambda_1, \lambda_2, x_1, y_1, x_2, y_2)\f$ where + +- \f$\lambda_1, \lambda_2\f$ are the non-sorted eigenvalues of \f$M\f$ +- \f$x_1, y_1\f$ are the eigenvectors corresponding to \f$\lambda_1\f$ +- \f$x_2, y_2\f$ are the eigenvectors corresponding to \f$\lambda_2\f$ + +The output of the function can be used for robust edge or corner detection. + +@param src Input single-channel 8-bit or floating-point image. +@param dst Image to store the results. It has the same size as src and the type CV_32FC(6) . +@param blockSize Neighborhood size (see details below). +@param ksize Aperture parameter for the Sobel operator. +@param borderType Pixel extrapolation method. See #BorderTypes. + +@sa cornerMinEigenVal, cornerHarris, preCornerDetect + */ +CV_EXPORTS_W void cornerEigenValsAndVecs( InputArray src, OutputArray dst, + int blockSize, int ksize, + int borderType = BORDER_DEFAULT ); + +/** @brief Calculates a feature map for corner detection. + +The function calculates the complex spatial derivative-based function of the source image + +\f[\texttt{dst} = (D_x \texttt{src} )^2 \cdot D_{yy} \texttt{src} + (D_y \texttt{src} )^2 \cdot D_{xx} \texttt{src} - 2 D_x \texttt{src} \cdot D_y \texttt{src} \cdot D_{xy} \texttt{src}\f] + +where \f$D_x\f$,\f$D_y\f$ are the first image derivatives, \f$D_{xx}\f$,\f$D_{yy}\f$ are the second image +derivatives, and \f$D_{xy}\f$ is the mixed derivative. + +The corners can be found as local maximums of the functions, as shown below: +@code + Mat corners, dilated_corners; + preCornerDetect(image, corners, 3); + // dilation with 3x3 rectangular structuring element + dilate(corners, dilated_corners, Mat(), 1); + Mat corner_mask = corners == dilated_corners; +@endcode + +@param src Source single-channel 8-bit of floating-point image. +@param dst Output image that has the type CV_32F and the same size as src . +@param ksize %Aperture size of the Sobel . +@param borderType Pixel extrapolation method. See #BorderTypes. + */ +CV_EXPORTS_W void preCornerDetect( InputArray src, OutputArray dst, int ksize, + int borderType = BORDER_DEFAULT ); + +/** @brief Refines the corner locations. + +The function iterates to find the sub-pixel accurate location of corners or radial saddle points, as +shown on the figure below. + +![image](pics/cornersubpix.png) + +Sub-pixel accurate corner locator is based on the observation that every vector from the center \f$q\f$ +to a point \f$p\f$ located within a neighborhood of \f$q\f$ is orthogonal to the image gradient at \f$p\f$ +subject to image and measurement noise. Consider the expression: + +\f[\epsilon _i = {DI_{p_i}}^T \cdot (q - p_i)\f] + +where \f${DI_{p_i}}\f$ is an image gradient at one of the points \f$p_i\f$ in a neighborhood of \f$q\f$ . The +value of \f$q\f$ is to be found so that \f$\epsilon_i\f$ is minimized. A system of equations may be set up +with \f$\epsilon_i\f$ set to zero: + +\f[\sum _i(DI_{p_i} \cdot {DI_{p_i}}^T) \cdot q - \sum _i(DI_{p_i} \cdot {DI_{p_i}}^T \cdot p_i)\f] + +where the gradients are summed within a neighborhood ("search window") of \f$q\f$ . Calling the first +gradient term \f$G\f$ and the second gradient term \f$b\f$ gives: + +\f[q = G^{-1} \cdot b\f] + +The algorithm sets the center of the neighborhood window at this new center \f$q\f$ and then iterates +until the center stays within a set threshold. + +@param image Input single-channel, 8-bit or float image. +@param corners Initial coordinates of the input corners and refined coordinates provided for +output. +@param winSize Half of the side length of the search window. For example, if winSize=Size(5,5) , +then a \f$(5*2+1) \times (5*2+1) = 11 \times 11\f$ search window is used. +@param zeroZone Half of the size of the dead region in the middle of the search zone over which +the summation in the formula below is not done. It is used sometimes to avoid possible +singularities of the autocorrelation matrix. The value of (-1,-1) indicates that there is no such +a size. +@param criteria Criteria for termination of the iterative process of corner refinement. That is, +the process of corner position refinement stops either after criteria.maxCount iterations or when +the corner position moves by less than criteria.epsilon on some iteration. + */ +CV_EXPORTS_W void cornerSubPix( InputArray image, InputOutputArray corners, + Size winSize, Size zeroZone, + TermCriteria criteria ); + +/** @brief Determines strong corners on an image. + +The function finds the most prominent corners in the image or in the specified image region, as +described in @cite Shi94 + +- Function calculates the corner quality measure at every source image pixel using the + #cornerMinEigenVal or #cornerHarris . +- Function performs a non-maximum suppression (the local maximums in *3 x 3* neighborhood are + retained). +- The corners with the minimal eigenvalue less than + \f$\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)\f$ are rejected. +- The remaining corners are sorted by the quality measure in the descending order. +- Function throws away each corner for which there is a stronger corner at a distance less than + maxDistance. + +The function can be used to initialize a point-based tracker of an object. + +@note If the function is called with different values A and B of the parameter qualityLevel , and +A \> B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector +with qualityLevel=B . + +@param image Input 8-bit or floating-point 32-bit, single-channel image. +@param corners Output vector of detected corners. +@param maxCorners Maximum number of corners to return. If there are more corners than are found, +the strongest of them is returned. `maxCorners <= 0` implies that no limit on the maximum is set +and all detected corners are returned. +@param qualityLevel Parameter characterizing the minimal accepted quality of image corners. The +parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue +(see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the +quality measure less than the product are rejected. For example, if the best corner has the +quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure +less than 15 are rejected. +@param minDistance Minimum possible Euclidean distance between the returned corners. +@param mask Optional region of interest. If the image is not empty (it needs to have the type +CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected. +@param blockSize Size of an average block for computing a derivative covariation matrix over each +pixel neighborhood. See cornerEigenValsAndVecs . +@param useHarrisDetector Parameter indicating whether to use a Harris detector (see #cornerHarris) +or #cornerMinEigenVal. +@param k Free parameter of the Harris detector. + +@sa cornerMinEigenVal, cornerHarris, calcOpticalFlowPyrLK, estimateRigidTransform, + */ + +CV_EXPORTS_W void goodFeaturesToTrack( InputArray image, OutputArray corners, + int maxCorners, double qualityLevel, double minDistance, + InputArray mask = noArray(), int blockSize = 3, + bool useHarrisDetector = false, double k = 0.04 ); + +CV_EXPORTS_W void goodFeaturesToTrack( InputArray image, OutputArray corners, + int maxCorners, double qualityLevel, double minDistance, + InputArray mask, int blockSize, + int gradientSize, bool useHarrisDetector = false, + double k = 0.04 ); +/** @example samples/cpp/tutorial_code/ImgTrans/houghlines.cpp +An example using the Hough line detector +![Sample input image](Hough_Lines_Tutorial_Original_Image.jpg) ![Output image](Hough_Lines_Tutorial_Result.jpg) +*/ + +/** @brief Finds lines in a binary image using the standard Hough transform. + +The function implements the standard or standard multi-scale Hough transform algorithm for line +detection. See for a good explanation of Hough +transform. + +@param image 8-bit, single-channel binary source image. The image may be modified by the function. +@param lines Output vector of lines. Each line is represented by a 2 or 3 element vector +\f$(\rho, \theta)\f$ or \f$(\rho, \theta, \textrm{votes})\f$ . \f$\rho\f$ is the distance from the coordinate origin \f$(0,0)\f$ (top-left corner of +the image). \f$\theta\f$ is the line rotation angle in radians ( +\f$0 \sim \textrm{vertical line}, \pi/2 \sim \textrm{horizontal line}\f$ ). +\f$\textrm{votes}\f$ is the value of accumulator. +@param rho Distance resolution of the accumulator in pixels. +@param theta Angle resolution of the accumulator in radians. +@param threshold Accumulator threshold parameter. Only those lines are returned that get enough +votes ( \f$>\texttt{threshold}\f$ ). +@param srn For the multi-scale Hough transform, it is a divisor for the distance resolution rho . +The coarse accumulator distance resolution is rho and the accurate accumulator resolution is +rho/srn . If both srn=0 and stn=0 , the classical Hough transform is used. Otherwise, both these +parameters should be positive. +@param stn For the multi-scale Hough transform, it is a divisor for the distance resolution theta. +@param min_theta For standard and multi-scale Hough transform, minimum angle to check for lines. +Must fall between 0 and max_theta. +@param max_theta For standard and multi-scale Hough transform, maximum angle to check for lines. +Must fall between min_theta and CV_PI. + */ +CV_EXPORTS_W void HoughLines( InputArray image, OutputArray lines, + double rho, double theta, int threshold, + double srn = 0, double stn = 0, + double min_theta = 0, double max_theta = CV_PI ); + +/** @brief Finds line segments in a binary image using the probabilistic Hough transform. + +The function implements the probabilistic Hough transform algorithm for line detection, described +in @cite Matas00 + +See the line detection example below: +@include snippets/imgproc_HoughLinesP.cpp +This is a sample picture the function parameters have been tuned for: + +![image](pics/building.jpg) + +And this is the output of the above program in case of the probabilistic Hough transform: + +![image](pics/houghp.png) + +@param image 8-bit, single-channel binary source image. The image may be modified by the function. +@param lines Output vector of lines. Each line is represented by a 4-element vector +\f$(x_1, y_1, x_2, y_2)\f$ , where \f$(x_1,y_1)\f$ and \f$(x_2, y_2)\f$ are the ending points of each detected +line segment. +@param rho Distance resolution of the accumulator in pixels. +@param theta Angle resolution of the accumulator in radians. +@param threshold Accumulator threshold parameter. Only those lines are returned that get enough +votes ( \f$>\texttt{threshold}\f$ ). +@param minLineLength Minimum line length. Line segments shorter than that are rejected. +@param maxLineGap Maximum allowed gap between points on the same line to link them. + +@sa LineSegmentDetector + */ +CV_EXPORTS_W void HoughLinesP( InputArray image, OutputArray lines, + double rho, double theta, int threshold, + double minLineLength = 0, double maxLineGap = 0 ); + +/** @brief Finds lines in a set of points using the standard Hough transform. + +The function finds lines in a set of points using a modification of the Hough transform. +@include snippets/imgproc_HoughLinesPointSet.cpp +@param _point Input vector of points. Each vector must be encoded as a Point vector \f$(x,y)\f$. Type must be CV_32FC2 or CV_32SC2. +@param _lines Output vector of found lines. Each vector is encoded as a vector \f$(votes, rho, theta)\f$. +The larger the value of 'votes', the higher the reliability of the Hough line. +@param lines_max Max count of hough lines. +@param threshold Accumulator threshold parameter. Only those lines are returned that get enough +votes ( \f$>\texttt{threshold}\f$ ) +@param min_rho Minimum Distance value of the accumulator in pixels. +@param max_rho Maximum Distance value of the accumulator in pixels. +@param rho_step Distance resolution of the accumulator in pixels. +@param min_theta Minimum angle value of the accumulator in radians. +@param max_theta Maximum angle value of the accumulator in radians. +@param theta_step Angle resolution of the accumulator in radians. + */ +CV_EXPORTS_W void HoughLinesPointSet( InputArray _point, OutputArray _lines, int lines_max, int threshold, + double min_rho, double max_rho, double rho_step, + double min_theta, double max_theta, double theta_step ); + +/** @example samples/cpp/tutorial_code/ImgTrans/houghcircles.cpp +An example using the Hough circle detector +*/ + +/** @brief Finds circles in a grayscale image using the Hough transform. + +The function finds circles in a grayscale image using a modification of the Hough transform. + +Example: : +@include snippets/imgproc_HoughLinesCircles.cpp + +@note Usually the function detects the centers of circles well. However, it may fail to find correct +radii. You can assist to the function by specifying the radius range ( minRadius and maxRadius ) if +you know it. Or, you may set maxRadius to a negative number to return centers only without radius +search, and find the correct radius using an additional procedure. + +@param image 8-bit, single-channel, grayscale input image. +@param circles Output vector of found circles. Each vector is encoded as 3 or 4 element +floating-point vector \f$(x, y, radius)\f$ or \f$(x, y, radius, votes)\f$ . +@param method Detection method, see #HoughModes. Currently, the only implemented method is #HOUGH_GRADIENT +@param dp Inverse ratio of the accumulator resolution to the image resolution. For example, if +dp=1 , the accumulator has the same resolution as the input image. If dp=2 , the accumulator has +half as big width and height. +@param minDist Minimum distance between the centers of the detected circles. If the parameter is +too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is +too large, some circles may be missed. +@param param1 First method-specific parameter. In case of #HOUGH_GRADIENT , it is the higher +threshold of the two passed to the Canny edge detector (the lower one is twice smaller). +@param param2 Second method-specific parameter. In case of #HOUGH_GRADIENT , it is the +accumulator threshold for the circle centers at the detection stage. The smaller it is, the more +false circles may be detected. Circles, corresponding to the larger accumulator values, will be +returned first. +@param minRadius Minimum circle radius. +@param maxRadius Maximum circle radius. If <= 0, uses the maximum image dimension. If < 0, returns +centers without finding the radius. + +@sa fitEllipse, minEnclosingCircle + */ +CV_EXPORTS_W void HoughCircles( InputArray image, OutputArray circles, + int method, double dp, double minDist, + double param1 = 100, double param2 = 100, + int minRadius = 0, int maxRadius = 0 ); + +//! @} imgproc_feature + +//! @addtogroup imgproc_filter +//! @{ + +/** @example samples/cpp/tutorial_code/ImgProc/Morphology_2.cpp +Advanced morphology Transformations sample code +![Sample screenshot](Morphology_2_Tutorial_Result.jpg) +Check @ref tutorial_opening_closing_hats "the corresponding tutorial" for more details +*/ + +/** @brief Erodes an image by using a specific structuring element. + +The function erodes the source image using the specified structuring element that determines the +shape of a pixel neighborhood over which the minimum is taken: + +\f[\texttt{dst} (x,y) = \min _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\f] + +The function supports the in-place mode. Erosion can be applied several ( iterations ) times. In +case of multi-channel images, each channel is processed independently. + +@param src input image; the number of channels can be arbitrary, but the depth should be one of +CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. +@param dst output image of the same size and type as src. +@param kernel structuring element used for erosion; if `element=Mat()`, a `3 x 3` rectangular +structuring element is used. Kernel can be created using #getStructuringElement. +@param anchor position of the anchor within the element; default value (-1, -1) means that the +anchor is at the element center. +@param iterations number of times erosion is applied. +@param borderType pixel extrapolation method, see #BorderTypes +@param borderValue border value in case of a constant border +@sa dilate, morphologyEx, getStructuringElement + */ +CV_EXPORTS_W void erode( InputArray src, OutputArray dst, InputArray kernel, + Point anchor = Point(-1,-1), int iterations = 1, + int borderType = BORDER_CONSTANT, + const Scalar& borderValue = morphologyDefaultBorderValue() ); + +/** @example samples/cpp/tutorial_code/ImgProc/Morphology_1.cpp +Erosion and Dilation sample code +![Sample Screenshot-Erosion](Morphology_1_Tutorial_Erosion_Result.jpg)![Sample Screenshot-Dilation](Morphology_1_Tutorial_Dilation_Result.jpg) +Check @ref tutorial_erosion_dilatation "the corresponding tutorial" for more details +*/ + +/** @brief Dilates an image by using a specific structuring element. + +The function dilates the source image using the specified structuring element that determines the +shape of a pixel neighborhood over which the maximum is taken: +\f[\texttt{dst} (x,y) = \max _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\f] + +The function supports the in-place mode. Dilation can be applied several ( iterations ) times. In +case of multi-channel images, each channel is processed independently. + +@param src input image; the number of channels can be arbitrary, but the depth should be one of +CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. +@param dst output image of the same size and type as src. +@param kernel structuring element used for dilation; if elemenat=Mat(), a 3 x 3 rectangular +structuring element is used. Kernel can be created using #getStructuringElement +@param anchor position of the anchor within the element; default value (-1, -1) means that the +anchor is at the element center. +@param iterations number of times dilation is applied. +@param borderType pixel extrapolation method, see #BorderTypes +@param borderValue border value in case of a constant border +@sa erode, morphologyEx, getStructuringElement + */ +CV_EXPORTS_W void dilate( InputArray src, OutputArray dst, InputArray kernel, + Point anchor = Point(-1,-1), int iterations = 1, + int borderType = BORDER_CONSTANT, + const Scalar& borderValue = morphologyDefaultBorderValue() ); + +/** @brief Performs advanced morphological transformations. + +The function cv::morphologyEx can perform advanced morphological transformations using an erosion and dilation as +basic operations. + +Any of the operations can be done in-place. In case of multi-channel images, each channel is +processed independently. + +@param src Source image. The number of channels can be arbitrary. The depth should be one of +CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. +@param dst Destination image of the same size and type as source image. +@param op Type of a morphological operation, see #MorphTypes +@param kernel Structuring element. It can be created using #getStructuringElement. +@param anchor Anchor position with the kernel. Negative values mean that the anchor is at the +kernel center. +@param iterations Number of times erosion and dilation are applied. +@param borderType Pixel extrapolation method, see #BorderTypes +@param borderValue Border value in case of a constant border. The default value has a special +meaning. +@sa dilate, erode, getStructuringElement +@note The number of iterations is the number of times erosion or dilatation operation will be applied. +For instance, an opening operation (#MORPH_OPEN) with two iterations is equivalent to apply +successively: erode -> erode -> dilate -> dilate (and not erode -> dilate -> erode -> dilate). + */ +CV_EXPORTS_W void morphologyEx( InputArray src, OutputArray dst, + int op, InputArray kernel, + Point anchor = Point(-1,-1), int iterations = 1, + int borderType = BORDER_CONSTANT, + const Scalar& borderValue = morphologyDefaultBorderValue() ); + +//! @} imgproc_filter + +//! @addtogroup imgproc_transform +//! @{ + +/** @brief Resizes an image. + +The function resize resizes the image src down to or up to the specified size. Note that the +initial dst type or size are not taken into account. Instead, the size and type are derived from +the `src`,`dsize`,`fx`, and `fy`. If you want to resize src so that it fits the pre-created dst, +you may call the function as follows: +@code + // explicitly specify dsize=dst.size(); fx and fy will be computed from that. + resize(src, dst, dst.size(), 0, 0, interpolation); +@endcode +If you want to decimate the image by factor of 2 in each direction, you can call the function this +way: +@code + // specify fx and fy and let the function compute the destination image size. + resize(src, dst, Size(), 0.5, 0.5, interpolation); +@endcode +To shrink an image, it will generally look best with #INTER_AREA interpolation, whereas to +enlarge an image, it will generally look best with c#INTER_CUBIC (slow) or #INTER_LINEAR +(faster but still looks OK). + +@param src input image. +@param dst output image; it has the size dsize (when it is non-zero) or the size computed from +src.size(), fx, and fy; the type of dst is the same as of src. +@param dsize output image size; if it equals zero, it is computed as: + \f[\texttt{dsize = Size(round(fx*src.cols), round(fy*src.rows))}\f] + Either dsize or both fx and fy must be non-zero. +@param fx scale factor along the horizontal axis; when it equals 0, it is computed as +\f[\texttt{(double)dsize.width/src.cols}\f] +@param fy scale factor along the vertical axis; when it equals 0, it is computed as +\f[\texttt{(double)dsize.height/src.rows}\f] +@param interpolation interpolation method, see #InterpolationFlags + +@sa warpAffine, warpPerspective, remap + */ +CV_EXPORTS_W void resize( InputArray src, OutputArray dst, + Size dsize, double fx = 0, double fy = 0, + int interpolation = INTER_LINEAR ); + +/** @brief Applies an affine transformation to an image. + +The function warpAffine transforms the source image using the specified matrix: + +\f[\texttt{dst} (x,y) = \texttt{src} ( \texttt{M} _{11} x + \texttt{M} _{12} y + \texttt{M} _{13}, \texttt{M} _{21} x + \texttt{M} _{22} y + \texttt{M} _{23})\f] + +when the flag #WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted +with #invertAffineTransform and then put in the formula above instead of M. The function cannot +operate in-place. + +@param src input image. +@param dst output image that has the size dsize and the same type as src . +@param M \f$2\times 3\f$ transformation matrix. +@param dsize size of the output image. +@param flags combination of interpolation methods (see #InterpolationFlags) and the optional +flag #WARP_INVERSE_MAP that means that M is the inverse transformation ( +\f$\texttt{dst}\rightarrow\texttt{src}\f$ ). +@param borderMode pixel extrapolation method (see #BorderTypes); when +borderMode=#BORDER_TRANSPARENT, it means that the pixels in the destination image corresponding to +the "outliers" in the source image are not modified by the function. +@param borderValue value used in case of a constant border; by default, it is 0. + +@sa warpPerspective, resize, remap, getRectSubPix, transform + */ +CV_EXPORTS_W void warpAffine( InputArray src, OutputArray dst, + InputArray M, Size dsize, + int flags = INTER_LINEAR, + int borderMode = BORDER_CONSTANT, + const Scalar& borderValue = Scalar()); + +/** @example samples/cpp/warpPerspective_demo.cpp +An example program shows using cv::findHomography and cv::warpPerspective for image warping +*/ + +/** @brief Applies a perspective transformation to an image. + +The function warpPerspective transforms the source image using the specified matrix: + +\f[\texttt{dst} (x,y) = \texttt{src} \left ( \frac{M_{11} x + M_{12} y + M_{13}}{M_{31} x + M_{32} y + M_{33}} , + \frac{M_{21} x + M_{22} y + M_{23}}{M_{31} x + M_{32} y + M_{33}} \right )\f] + +when the flag #WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted with invert +and then put in the formula above instead of M. The function cannot operate in-place. + +@param src input image. +@param dst output image that has the size dsize and the same type as src . +@param M \f$3\times 3\f$ transformation matrix. +@param dsize size of the output image. +@param flags combination of interpolation methods (#INTER_LINEAR or #INTER_NEAREST) and the +optional flag #WARP_INVERSE_MAP, that sets M as the inverse transformation ( +\f$\texttt{dst}\rightarrow\texttt{src}\f$ ). +@param borderMode pixel extrapolation method (#BORDER_CONSTANT or #BORDER_REPLICATE). +@param borderValue value used in case of a constant border; by default, it equals 0. + +@sa warpAffine, resize, remap, getRectSubPix, perspectiveTransform + */ +CV_EXPORTS_W void warpPerspective( InputArray src, OutputArray dst, + InputArray M, Size dsize, + int flags = INTER_LINEAR, + int borderMode = BORDER_CONSTANT, + const Scalar& borderValue = Scalar()); + +/** @brief Applies a generic geometrical transformation to an image. + +The function remap transforms the source image using the specified map: + +\f[\texttt{dst} (x,y) = \texttt{src} (map_x(x,y),map_y(x,y))\f] + +where values of pixels with non-integer coordinates are computed using one of available +interpolation methods. \f$map_x\f$ and \f$map_y\f$ can be encoded as separate floating-point maps +in \f$map_1\f$ and \f$map_2\f$ respectively, or interleaved floating-point maps of \f$(x,y)\f$ in +\f$map_1\f$, or fixed-point maps created by using convertMaps. The reason you might want to +convert from floating to fixed-point representations of a map is that they can yield much faster +(\~2x) remapping operations. In the converted case, \f$map_1\f$ contains pairs (cvFloor(x), +cvFloor(y)) and \f$map_2\f$ contains indices in a table of interpolation coefficients. + +This function cannot operate in-place. + +@param src Source image. +@param dst Destination image. It has the same size as map1 and the same type as src . +@param map1 The first map of either (x,y) points or just x values having the type CV_16SC2 , +CV_32FC1, or CV_32FC2. See convertMaps for details on converting a floating point +representation to fixed-point for speed. +@param map2 The second map of y values having the type CV_16UC1, CV_32FC1, or none (empty map +if map1 is (x,y) points), respectively. +@param interpolation Interpolation method (see #InterpolationFlags). The method #INTER_AREA is +not supported by this function. +@param borderMode Pixel extrapolation method (see #BorderTypes). When +borderMode=#BORDER_TRANSPARENT, it means that the pixels in the destination image that +corresponds to the "outliers" in the source image are not modified by the function. +@param borderValue Value used in case of a constant border. By default, it is 0. +@note +Due to current implementation limitations the size of an input and output images should be less than 32767x32767. + */ +CV_EXPORTS_W void remap( InputArray src, OutputArray dst, + InputArray map1, InputArray map2, + int interpolation, int borderMode = BORDER_CONSTANT, + const Scalar& borderValue = Scalar()); + +/** @brief Converts image transformation maps from one representation to another. + +The function converts a pair of maps for remap from one representation to another. The following +options ( (map1.type(), map2.type()) \f$\rightarrow\f$ (dstmap1.type(), dstmap2.type()) ) are +supported: + +- \f$\texttt{(CV_32FC1, CV_32FC1)} \rightarrow \texttt{(CV_16SC2, CV_16UC1)}\f$. This is the +most frequently used conversion operation, in which the original floating-point maps (see remap ) +are converted to a more compact and much faster fixed-point representation. The first output array +contains the rounded coordinates and the second array (created only when nninterpolation=false ) +contains indices in the interpolation tables. + +- \f$\texttt{(CV_32FC2)} \rightarrow \texttt{(CV_16SC2, CV_16UC1)}\f$. The same as above but +the original maps are stored in one 2-channel matrix. + +- Reverse conversion. Obviously, the reconstructed floating-point maps will not be exactly the same +as the originals. + +@param map1 The first input map of type CV_16SC2, CV_32FC1, or CV_32FC2 . +@param map2 The second input map of type CV_16UC1, CV_32FC1, or none (empty matrix), +respectively. +@param dstmap1 The first output map that has the type dstmap1type and the same size as src . +@param dstmap2 The second output map. +@param dstmap1type Type of the first output map that should be CV_16SC2, CV_32FC1, or +CV_32FC2 . +@param nninterpolation Flag indicating whether the fixed-point maps are used for the +nearest-neighbor or for a more complex interpolation. + +@sa remap, undistort, initUndistortRectifyMap + */ +CV_EXPORTS_W void convertMaps( InputArray map1, InputArray map2, + OutputArray dstmap1, OutputArray dstmap2, + int dstmap1type, bool nninterpolation = false ); + +/** @brief Calculates an affine matrix of 2D rotation. + +The function calculates the following matrix: + +\f[\begin{bmatrix} \alpha & \beta & (1- \alpha ) \cdot \texttt{center.x} - \beta \cdot \texttt{center.y} \\ - \beta & \alpha & \beta \cdot \texttt{center.x} + (1- \alpha ) \cdot \texttt{center.y} \end{bmatrix}\f] + +where + +\f[\begin{array}{l} \alpha = \texttt{scale} \cdot \cos \texttt{angle} , \\ \beta = \texttt{scale} \cdot \sin \texttt{angle} \end{array}\f] + +The transformation maps the rotation center to itself. If this is not the target, adjust the shift. + +@param center Center of the rotation in the source image. +@param angle Rotation angle in degrees. Positive values mean counter-clockwise rotation (the +coordinate origin is assumed to be the top-left corner). +@param scale Isotropic scale factor. + +@sa getAffineTransform, warpAffine, transform + */ +CV_EXPORTS_W Mat getRotationMatrix2D( Point2f center, double angle, double scale ); + +//! returns 3x3 perspective transformation for the corresponding 4 point pairs. +CV_EXPORTS Mat getPerspectiveTransform( const Point2f src[], const Point2f dst[] ); + +/** @brief Calculates an affine transform from three pairs of the corresponding points. + +The function calculates the \f$2 \times 3\f$ matrix of an affine transform so that: + +\f[\begin{bmatrix} x'_i \\ y'_i \end{bmatrix} = \texttt{map_matrix} \cdot \begin{bmatrix} x_i \\ y_i \\ 1 \end{bmatrix}\f] + +where + +\f[dst(i)=(x'_i,y'_i), src(i)=(x_i, y_i), i=0,1,2\f] + +@param src Coordinates of triangle vertices in the source image. +@param dst Coordinates of the corresponding triangle vertices in the destination image. + +@sa warpAffine, transform + */ +CV_EXPORTS Mat getAffineTransform( const Point2f src[], const Point2f dst[] ); + +/** @brief Inverts an affine transformation. + +The function computes an inverse affine transformation represented by \f$2 \times 3\f$ matrix M: + +\f[\begin{bmatrix} a_{11} & a_{12} & b_1 \\ a_{21} & a_{22} & b_2 \end{bmatrix}\f] + +The result is also a \f$2 \times 3\f$ matrix of the same type as M. + +@param M Original affine transformation. +@param iM Output reverse affine transformation. + */ +CV_EXPORTS_W void invertAffineTransform( InputArray M, OutputArray iM ); + +/** @brief Calculates a perspective transform from four pairs of the corresponding points. + +The function calculates the \f$3 \times 3\f$ matrix of a perspective transform so that: + +\f[\begin{bmatrix} t_i x'_i \\ t_i y'_i \\ t_i \end{bmatrix} = \texttt{map_matrix} \cdot \begin{bmatrix} x_i \\ y_i \\ 1 \end{bmatrix}\f] + +where + +\f[dst(i)=(x'_i,y'_i), src(i)=(x_i, y_i), i=0,1,2,3\f] + +@param src Coordinates of quadrangle vertices in the source image. +@param dst Coordinates of the corresponding quadrangle vertices in the destination image. + +@sa findHomography, warpPerspective, perspectiveTransform + */ +CV_EXPORTS_W Mat getPerspectiveTransform( InputArray src, InputArray dst ); + +CV_EXPORTS_W Mat getAffineTransform( InputArray src, InputArray dst ); + +/** @brief Retrieves a pixel rectangle from an image with sub-pixel accuracy. + +The function getRectSubPix extracts pixels from src: + +\f[patch(x, y) = src(x + \texttt{center.x} - ( \texttt{dst.cols} -1)*0.5, y + \texttt{center.y} - ( \texttt{dst.rows} -1)*0.5)\f] + +where the values of the pixels at non-integer coordinates are retrieved using bilinear +interpolation. Every channel of multi-channel images is processed independently. Also +the image should be a single channel or three channel image. While the center of the +rectangle must be inside the image, parts of the rectangle may be outside. + +@param image Source image. +@param patchSize Size of the extracted patch. +@param center Floating point coordinates of the center of the extracted rectangle within the +source image. The center must be inside the image. +@param patch Extracted patch that has the size patchSize and the same number of channels as src . +@param patchType Depth of the extracted pixels. By default, they have the same depth as src . + +@sa warpAffine, warpPerspective + */ +CV_EXPORTS_W void getRectSubPix( InputArray image, Size patchSize, + Point2f center, OutputArray patch, int patchType = -1 ); + +/** @example samples/cpp/polar_transforms.cpp +An example using the cv::linearPolar and cv::logPolar operations +*/ + +/** @brief Remaps an image to semilog-polar coordinates space. + +@deprecated This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags+WARP_POLAR_LOG); + +@internal +Transform the source image using the following transformation (See @ref polar_remaps_reference_image "Polar remaps reference image d)"): +\f[\begin{array}{l} + dst( \rho , \phi ) = src(x,y) \\ + dst.size() \leftarrow src.size() +\end{array}\f] + +where +\f[\begin{array}{l} + I = (dx,dy) = (x - center.x,y - center.y) \\ + \rho = M \cdot log_e(\texttt{magnitude} (I)) ,\\ + \phi = Kangle \cdot \texttt{angle} (I) \\ +\end{array}\f] + +and +\f[\begin{array}{l} + M = src.cols / log_e(maxRadius) \\ + Kangle = src.rows / 2\Pi \\ +\end{array}\f] + +The function emulates the human "foveal" vision and can be used for fast scale and +rotation-invariant template matching, for object tracking and so forth. +@param src Source image +@param dst Destination image. It will have same size and type as src. +@param center The transformation center; where the output precision is maximal +@param M Magnitude scale parameter. It determines the radius of the bounding circle to transform too. +@param flags A combination of interpolation methods, see #InterpolationFlags + +@note +- The function can not operate in-place. +- To calculate magnitude and angle in degrees #cartToPolar is used internally thus angles are measured from 0 to 360 with accuracy about 0.3 degrees. + +@sa cv::linearPolar +@endinternal +*/ +CV_EXPORTS_W void logPolar( InputArray src, OutputArray dst, + Point2f center, double M, int flags ); + +/** @brief Remaps an image to polar coordinates space. + +@deprecated This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags) + +@internal +Transform the source image using the following transformation (See @ref polar_remaps_reference_image "Polar remaps reference image c)"): +\f[\begin{array}{l} + dst( \rho , \phi ) = src(x,y) \\ + dst.size() \leftarrow src.size() +\end{array}\f] + +where +\f[\begin{array}{l} + I = (dx,dy) = (x - center.x,y - center.y) \\ + \rho = Kmag \cdot \texttt{magnitude} (I) ,\\ + \phi = angle \cdot \texttt{angle} (I) +\end{array}\f] + +and +\f[\begin{array}{l} + Kx = src.cols / maxRadius \\ + Ky = src.rows / 2\Pi +\end{array}\f] + + +@param src Source image +@param dst Destination image. It will have same size and type as src. +@param center The transformation center; +@param maxRadius The radius of the bounding circle to transform. It determines the inverse magnitude scale parameter too. +@param flags A combination of interpolation methods, see #InterpolationFlags + +@note +- The function can not operate in-place. +- To calculate magnitude and angle in degrees #cartToPolar is used internally thus angles are measured from 0 to 360 with accuracy about 0.3 degrees. + +@sa cv::logPolar +@endinternal +*/ +CV_EXPORTS_W void linearPolar( InputArray src, OutputArray dst, + Point2f center, double maxRadius, int flags ); + + +/** \brief Remaps an image to polar or semilog-polar coordinates space + +@anchor polar_remaps_reference_image +![Polar remaps reference](pics/polar_remap_doc.png) + +Transform the source image using the following transformation: +\f[ +dst(\rho , \phi ) = src(x,y) +\f] + +where +\f[ +\begin{array}{l} +\vec{I} = (x - center.x, \;y - center.y) \\ +\phi = Kangle \cdot \texttt{angle} (\vec{I}) \\ +\rho = \left\{\begin{matrix} +Klin \cdot \texttt{magnitude} (\vec{I}) & default \\ +Klog \cdot log_e(\texttt{magnitude} (\vec{I})) & if \; semilog \\ +\end{matrix}\right. +\end{array} +\f] + +and +\f[ +\begin{array}{l} +Kangle = dsize.height / 2\Pi \\ +Klin = dsize.width / maxRadius \\ +Klog = dsize.width / log_e(maxRadius) \\ +\end{array} +\f] + + +\par Linear vs semilog mapping + +Polar mapping can be linear or semi-log. Add one of #WarpPolarMode to `flags` to specify the polar mapping mode. + +Linear is the default mode. + +The semilog mapping emulates the human "foveal" vision that permit very high acuity on the line of sight (central vision) +in contrast to peripheral vision where acuity is minor. + +\par Option on `dsize`: + +- if both values in `dsize <=0 ` (default), +the destination image will have (almost) same area of source bounding circle: +\f[\begin{array}{l} +dsize.area \leftarrow (maxRadius^2 \cdot \Pi) \\ +dsize.width = \texttt{cvRound}(maxRadius) \\ +dsize.height = \texttt{cvRound}(maxRadius \cdot \Pi) \\ +\end{array}\f] + + +- if only `dsize.height <= 0`, +the destination image area will be proportional to the bounding circle area but scaled by `Kx * Kx`: +\f[\begin{array}{l} +dsize.height = \texttt{cvRound}(dsize.width \cdot \Pi) \\ +\end{array} +\f] + +- if both values in `dsize > 0 `, +the destination image will have the given size therefore the area of the bounding circle will be scaled to `dsize`. + + +\par Reverse mapping + +You can get reverse mapping adding #WARP_INVERSE_MAP to `flags` +\snippet polar_transforms.cpp InverseMap + +In addiction, to calculate the original coordinate from a polar mapped coordinate \f$(rho, phi)->(x, y)\f$: +\snippet polar_transforms.cpp InverseCoordinate + +@param src Source image. +@param dst Destination image. It will have same type as src. +@param dsize The destination image size (see description for valid options). +@param center The transformation center. +@param maxRadius The radius of the bounding circle to transform. It determines the inverse magnitude scale parameter too. +@param flags A combination of interpolation methods, #InterpolationFlags + #WarpPolarMode. + - Add #WARP_POLAR_LINEAR to select linear polar mapping (default) + - Add #WARP_POLAR_LOG to select semilog polar mapping + - Add #WARP_INVERSE_MAP for reverse mapping. +@note +- The function can not operate in-place. +- To calculate magnitude and angle in degrees #cartToPolar is used internally thus angles are measured from 0 to 360 with accuracy about 0.3 degrees. +- This function uses #remap. Due to current implementation limitations the size of an input and output images should be less than 32767x32767. + +@sa cv::remap +*/ +CV_EXPORTS_W void warpPolar(InputArray src, OutputArray dst, Size dsize, + Point2f center, double maxRadius, int flags); + + +//! @} imgproc_transform + +//! @addtogroup imgproc_misc +//! @{ + +/** @overload */ +CV_EXPORTS_W void integral( InputArray src, OutputArray sum, int sdepth = -1 ); + +/** @overload */ +CV_EXPORTS_AS(integral2) void integral( InputArray src, OutputArray sum, + OutputArray sqsum, int sdepth = -1, int sqdepth = -1 ); + +/** @brief Calculates the integral of an image. + +The function calculates one or more integral images for the source image as follows: + +\f[\texttt{sum} (X,Y) = \sum _{x + +Calculates the cross-power spectrum of two supplied source arrays. The arrays are padded if needed +with getOptimalDFTSize. + +The function performs the following equations: +- First it applies a Hanning window (see ) to each +image to remove possible edge effects. This window is cached until the array size changes to speed +up processing time. +- Next it computes the forward DFTs of each source array: +\f[\mathbf{G}_a = \mathcal{F}\{src_1\}, \; \mathbf{G}_b = \mathcal{F}\{src_2\}\f] +where \f$\mathcal{F}\f$ is the forward DFT. +- It then computes the cross-power spectrum of each frequency domain array: +\f[R = \frac{ \mathbf{G}_a \mathbf{G}_b^*}{|\mathbf{G}_a \mathbf{G}_b^*|}\f] +- Next the cross-correlation is converted back into the time domain via the inverse DFT: +\f[r = \mathcal{F}^{-1}\{R\}\f] +- Finally, it computes the peak location and computes a 5x5 weighted centroid around the peak to +achieve sub-pixel accuracy. +\f[(\Delta x, \Delta y) = \texttt{weightedCentroid} \{\arg \max_{(x, y)}\{r\}\}\f] +- If non-zero, the response parameter is computed as the sum of the elements of r within the 5x5 +centroid around the peak location. It is normalized to a maximum of 1 (meaning there is a single +peak) and will be smaller when there are multiple peaks. + +@param src1 Source floating point array (CV_32FC1 or CV_64FC1) +@param src2 Source floating point array (CV_32FC1 or CV_64FC1) +@param window Floating point array with windowing coefficients to reduce edge effects (optional). +@param response Signal power within the 5x5 centroid around the peak, between 0 and 1 (optional). +@returns detected phase shift (sub-pixel) between the two arrays. + +@sa dft, getOptimalDFTSize, idft, mulSpectrums createHanningWindow + */ +CV_EXPORTS_W Point2d phaseCorrelate(InputArray src1, InputArray src2, + InputArray window = noArray(), CV_OUT double* response = 0); + +/** @brief This function computes a Hanning window coefficients in two dimensions. + +See (http://en.wikipedia.org/wiki/Hann_function) and (http://en.wikipedia.org/wiki/Window_function) +for more information. + +An example is shown below: +@code + // create hanning window of size 100x100 and type CV_32F + Mat hann; + createHanningWindow(hann, Size(100, 100), CV_32F); +@endcode +@param dst Destination array to place Hann coefficients in +@param winSize The window size specifications (both width and height must be > 1) +@param type Created array type + */ +CV_EXPORTS_W void createHanningWindow(OutputArray dst, Size winSize, int type); + +//! @} imgproc_motion + +//! @addtogroup imgproc_misc +//! @{ + +/** @brief Applies a fixed-level threshold to each array element. + +The function applies fixed-level thresholding to a multiple-channel array. The function is typically +used to get a bi-level (binary) image out of a grayscale image ( #compare could be also used for +this purpose) or for removing a noise, that is, filtering out pixels with too small or too large +values. There are several types of thresholding supported by the function. They are determined by +type parameter. + +Also, the special values #THRESH_OTSU or #THRESH_TRIANGLE may be combined with one of the +above values. In these cases, the function determines the optimal threshold value using the Otsu's +or Triangle algorithm and uses it instead of the specified thresh. + +@note Currently, the Otsu's and Triangle methods are implemented only for 8-bit single-channel images. + +@param src input array (multiple-channel, 8-bit or 32-bit floating point). +@param dst output array of the same size and type and the same number of channels as src. +@param thresh threshold value. +@param maxval maximum value to use with the #THRESH_BINARY and #THRESH_BINARY_INV thresholding +types. +@param type thresholding type (see #ThresholdTypes). +@return the computed threshold value if Otsu's or Triangle methods used. + +@sa adaptiveThreshold, findContours, compare, min, max + */ +CV_EXPORTS_W double threshold( InputArray src, OutputArray dst, + double thresh, double maxval, int type ); + + +/** @brief Applies an adaptive threshold to an array. + +The function transforms a grayscale image to a binary image according to the formulae: +- **THRESH_BINARY** + \f[dst(x,y) = \fork{\texttt{maxValue}}{if \(src(x,y) > T(x,y)\)}{0}{otherwise}\f] +- **THRESH_BINARY_INV** + \f[dst(x,y) = \fork{0}{if \(src(x,y) > T(x,y)\)}{\texttt{maxValue}}{otherwise}\f] +where \f$T(x,y)\f$ is a threshold calculated individually for each pixel (see adaptiveMethod parameter). + +The function can process the image in-place. + +@param src Source 8-bit single-channel image. +@param dst Destination image of the same size and the same type as src. +@param maxValue Non-zero value assigned to the pixels for which the condition is satisfied +@param adaptiveMethod Adaptive thresholding algorithm to use, see #AdaptiveThresholdTypes. +The #BORDER_REPLICATE | #BORDER_ISOLATED is used to process boundaries. +@param thresholdType Thresholding type that must be either #THRESH_BINARY or #THRESH_BINARY_INV, +see #ThresholdTypes. +@param blockSize Size of a pixel neighborhood that is used to calculate a threshold value for the +pixel: 3, 5, 7, and so on. +@param C Constant subtracted from the mean or weighted mean (see the details below). Normally, it +is positive but may be zero or negative as well. + +@sa threshold, blur, GaussianBlur + */ +CV_EXPORTS_W void adaptiveThreshold( InputArray src, OutputArray dst, + double maxValue, int adaptiveMethod, + int thresholdType, int blockSize, double C ); + +//! @} imgproc_misc + +//! @addtogroup imgproc_filter +//! @{ + +/** @example samples/cpp/tutorial_code/ImgProc/Pyramids/Pyramids.cpp +An example using pyrDown and pyrUp functions +*/ + +/** @brief Blurs an image and downsamples it. + +By default, size of the output image is computed as `Size((src.cols+1)/2, (src.rows+1)/2)`, but in +any case, the following conditions should be satisfied: + +\f[\begin{array}{l} | \texttt{dstsize.width} *2-src.cols| \leq 2 \\ | \texttt{dstsize.height} *2-src.rows| \leq 2 \end{array}\f] + +The function performs the downsampling step of the Gaussian pyramid construction. First, it +convolves the source image with the kernel: + +\f[\frac{1}{256} \begin{bmatrix} 1 & 4 & 6 & 4 & 1 \\ 4 & 16 & 24 & 16 & 4 \\ 6 & 24 & 36 & 24 & 6 \\ 4 & 16 & 24 & 16 & 4 \\ 1 & 4 & 6 & 4 & 1 \end{bmatrix}\f] + +Then, it downsamples the image by rejecting even rows and columns. + +@param src input image. +@param dst output image; it has the specified size and the same type as src. +@param dstsize size of the output image. +@param borderType Pixel extrapolation method, see #BorderTypes (#BORDER_CONSTANT isn't supported) + */ +CV_EXPORTS_W void pyrDown( InputArray src, OutputArray dst, + const Size& dstsize = Size(), int borderType = BORDER_DEFAULT ); + +/** @brief Upsamples an image and then blurs it. + +By default, size of the output image is computed as `Size(src.cols\*2, (src.rows\*2)`, but in any +case, the following conditions should be satisfied: + +\f[\begin{array}{l} | \texttt{dstsize.width} -src.cols*2| \leq ( \texttt{dstsize.width} \mod 2) \\ | \texttt{dstsize.height} -src.rows*2| \leq ( \texttt{dstsize.height} \mod 2) \end{array}\f] + +The function performs the upsampling step of the Gaussian pyramid construction, though it can +actually be used to construct the Laplacian pyramid. First, it upsamples the source image by +injecting even zero rows and columns and then convolves the result with the same kernel as in +pyrDown multiplied by 4. + +@param src input image. +@param dst output image. It has the specified size and the same type as src . +@param dstsize size of the output image. +@param borderType Pixel extrapolation method, see #BorderTypes (only #BORDER_DEFAULT is supported) + */ +CV_EXPORTS_W void pyrUp( InputArray src, OutputArray dst, + const Size& dstsize = Size(), int borderType = BORDER_DEFAULT ); + +/** @brief Constructs the Gaussian pyramid for an image. + +The function constructs a vector of images and builds the Gaussian pyramid by recursively applying +pyrDown to the previously built pyramid layers, starting from `dst[0]==src`. + +@param src Source image. Check pyrDown for the list of supported types. +@param dst Destination vector of maxlevel+1 images of the same type as src. dst[0] will be the +same as src. dst[1] is the next pyramid layer, a smoothed and down-sized src, and so on. +@param maxlevel 0-based index of the last (the smallest) pyramid layer. It must be non-negative. +@param borderType Pixel extrapolation method, see #BorderTypes (#BORDER_CONSTANT isn't supported) + */ +CV_EXPORTS void buildPyramid( InputArray src, OutputArrayOfArrays dst, + int maxlevel, int borderType = BORDER_DEFAULT ); + +//! @} imgproc_filter + +//! @addtogroup imgproc_transform +//! @{ + +/** @brief Transforms an image to compensate for lens distortion. + +The function transforms an image to compensate radial and tangential lens distortion. + +The function is simply a combination of #initUndistortRectifyMap (with unity R ) and #remap +(with bilinear interpolation). See the former function for details of the transformation being +performed. + +Those pixels in the destination image, for which there is no correspondent pixels in the source +image, are filled with zeros (black color). + +A particular subset of the source image that will be visible in the corrected image can be regulated +by newCameraMatrix. You can use #getOptimalNewCameraMatrix to compute the appropriate +newCameraMatrix depending on your requirements. + +The camera matrix and the distortion parameters can be determined using #calibrateCamera. If +the resolution of images is different from the resolution used at the calibration stage, \f$f_x, +f_y, c_x\f$ and \f$c_y\f$ need to be scaled accordingly, while the distortion coefficients remain +the same. + +@param src Input (distorted) image. +@param dst Output (corrected) image that has the same size and type as src . +@param cameraMatrix Input camera matrix \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . +@param distCoeffs Input vector of distortion coefficients +\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ +of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed. +@param newCameraMatrix Camera matrix of the distorted image. By default, it is the same as +cameraMatrix but you may additionally scale and shift the result by using a different matrix. + */ +CV_EXPORTS_W void undistort( InputArray src, OutputArray dst, + InputArray cameraMatrix, + InputArray distCoeffs, + InputArray newCameraMatrix = noArray() ); + +/** @brief Computes the undistortion and rectification transformation map. + +The function computes the joint undistortion and rectification transformation and represents the +result in the form of maps for remap. The undistorted image looks like original, as if it is +captured with a camera using the camera matrix =newCameraMatrix and zero distortion. In case of a +monocular camera, newCameraMatrix is usually equal to cameraMatrix, or it can be computed by +#getOptimalNewCameraMatrix for a better control over scaling. In case of a stereo camera, +newCameraMatrix is normally set to P1 or P2 computed by #stereoRectify . + +Also, this new camera is oriented differently in the coordinate space, according to R. That, for +example, helps to align two heads of a stereo camera so that the epipolar lines on both images +become horizontal and have the same y- coordinate (in case of a horizontally aligned stereo camera). + +The function actually builds the maps for the inverse mapping algorithm that is used by remap. That +is, for each pixel \f$(u, v)\f$ in the destination (corrected and rectified) image, the function +computes the corresponding coordinates in the source image (that is, in the original image from +camera). The following process is applied: +\f[ +\begin{array}{l} +x \leftarrow (u - {c'}_x)/{f'}_x \\ +y \leftarrow (v - {c'}_y)/{f'}_y \\ +{[X\,Y\,W]} ^T \leftarrow R^{-1}*[x \, y \, 1]^T \\ +x' \leftarrow X/W \\ +y' \leftarrow Y/W \\ +r^2 \leftarrow x'^2 + y'^2 \\ +x'' \leftarrow x' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} ++ 2p_1 x' y' + p_2(r^2 + 2 x'^2) + s_1 r^2 + s_2 r^4\\ +y'' \leftarrow y' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} ++ p_1 (r^2 + 2 y'^2) + 2 p_2 x' y' + s_3 r^2 + s_4 r^4 \\ +s\vecthree{x'''}{y'''}{1} = +\vecthreethree{R_{33}(\tau_x, \tau_y)}{0}{-R_{13}((\tau_x, \tau_y)} +{0}{R_{33}(\tau_x, \tau_y)}{-R_{23}(\tau_x, \tau_y)} +{0}{0}{1} R(\tau_x, \tau_y) \vecthree{x''}{y''}{1}\\ +map_x(u,v) \leftarrow x''' f_x + c_x \\ +map_y(u,v) \leftarrow y''' f_y + c_y +\end{array} +\f] +where \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ +are the distortion coefficients. + +In case of a stereo camera, this function is called twice: once for each camera head, after +stereoRectify, which in its turn is called after #stereoCalibrate. But if the stereo camera +was not calibrated, it is still possible to compute the rectification transformations directly from +the fundamental matrix using #stereoRectifyUncalibrated. For each camera, the function computes +homography H as the rectification transformation in a pixel domain, not a rotation matrix R in 3D +space. R can be computed from H as +\f[\texttt{R} = \texttt{cameraMatrix} ^{-1} \cdot \texttt{H} \cdot \texttt{cameraMatrix}\f] +where cameraMatrix can be chosen arbitrarily. + +@param cameraMatrix Input camera matrix \f$A=\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . +@param distCoeffs Input vector of distortion coefficients +\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ +of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed. +@param R Optional rectification transformation in the object space (3x3 matrix). R1 or R2 , +computed by #stereoRectify can be passed here. If the matrix is empty, the identity transformation +is assumed. In cvInitUndistortMap R assumed to be an identity matrix. +@param newCameraMatrix New camera matrix \f$A'=\vecthreethree{f_x'}{0}{c_x'}{0}{f_y'}{c_y'}{0}{0}{1}\f$. +@param size Undistorted image size. +@param m1type Type of the first output map that can be CV_32FC1, CV_32FC2 or CV_16SC2, see #convertMaps +@param map1 The first output map. +@param map2 The second output map. + */ +CV_EXPORTS_W void initUndistortRectifyMap( InputArray cameraMatrix, InputArray distCoeffs, + InputArray R, InputArray newCameraMatrix, + Size size, int m1type, OutputArray map1, OutputArray map2 ); + +//! initializes maps for #remap for wide-angle +CV_EXPORTS_W float initWideAngleProjMap( InputArray cameraMatrix, InputArray distCoeffs, + Size imageSize, int destImageWidth, + int m1type, OutputArray map1, OutputArray map2, + int projType = PROJ_SPHERICAL_EQRECT, double alpha = 0); + +/** @brief Returns the default new camera matrix. + +The function returns the camera matrix that is either an exact copy of the input cameraMatrix (when +centerPrinicipalPoint=false ), or the modified one (when centerPrincipalPoint=true). + +In the latter case, the new camera matrix will be: + +\f[\begin{bmatrix} f_x && 0 && ( \texttt{imgSize.width} -1)*0.5 \\ 0 && f_y && ( \texttt{imgSize.height} -1)*0.5 \\ 0 && 0 && 1 \end{bmatrix} ,\f] + +where \f$f_x\f$ and \f$f_y\f$ are \f$(0,0)\f$ and \f$(1,1)\f$ elements of cameraMatrix, respectively. + +By default, the undistortion functions in OpenCV (see #initUndistortRectifyMap, #undistort) do not +move the principal point. However, when you work with stereo, it is important to move the principal +points in both views to the same y-coordinate (which is required by most of stereo correspondence +algorithms), and may be to the same x-coordinate too. So, you can form the new camera matrix for +each view where the principal points are located at the center. + +@param cameraMatrix Input camera matrix. +@param imgsize Camera view image size in pixels. +@param centerPrincipalPoint Location of the principal point in the new camera matrix. The +parameter indicates whether this location should be at the image center or not. + */ +CV_EXPORTS_W Mat getDefaultNewCameraMatrix( InputArray cameraMatrix, Size imgsize = Size(), + bool centerPrincipalPoint = false ); + +/** @brief Computes the ideal point coordinates from the observed point coordinates. + +The function is similar to #undistort and #initUndistortRectifyMap but it operates on a +sparse set of points instead of a raster image. Also the function performs a reverse transformation +to projectPoints. In case of a 3D object, it does not reconstruct its 3D coordinates, but for a +planar object, it does, up to a translation vector, if the proper R is specified. + +For each observed point coordinate \f$(u, v)\f$ the function computes: +\f[ +\begin{array}{l} +x^{"} \leftarrow (u - c_x)/f_x \\ +y^{"} \leftarrow (v - c_y)/f_y \\ +(x',y') = undistort(x^{"},y^{"}, \texttt{distCoeffs}) \\ +{[X\,Y\,W]} ^T \leftarrow R*[x' \, y' \, 1]^T \\ +x \leftarrow X/W \\ +y \leftarrow Y/W \\ +\text{only performed if P is specified:} \\ +u' \leftarrow x {f'}_x + {c'}_x \\ +v' \leftarrow y {f'}_y + {c'}_y +\end{array} +\f] + +where *undistort* is an approximate iterative algorithm that estimates the normalized original +point coordinates out of the normalized distorted point coordinates ("normalized" means that the +coordinates do not depend on the camera matrix). + +The function can be used for both a stereo camera head or a monocular camera (when R is empty). + +@param src Observed point coordinates, 1xN or Nx1 2-channel (CV_32FC2 or CV_64FC2). +@param dst Output ideal point coordinates after undistortion and reverse perspective +transformation. If matrix P is identity or omitted, dst will contain normalized point coordinates. +@param cameraMatrix Camera matrix \f$\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . +@param distCoeffs Input vector of distortion coefficients +\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ +of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed. +@param R Rectification transformation in the object space (3x3 matrix). R1 or R2 computed by +#stereoRectify can be passed here. If the matrix is empty, the identity transformation is used. +@param P New camera matrix (3x3) or new projection matrix (3x4) \f$\begin{bmatrix} {f'}_x & 0 & {c'}_x & t_x \\ 0 & {f'}_y & {c'}_y & t_y \\ 0 & 0 & 1 & t_z \end{bmatrix}\f$. P1 or P2 computed by +#stereoRectify can be passed here. If the matrix is empty, the identity new camera matrix is used. + */ +CV_EXPORTS_W void undistortPoints( InputArray src, OutputArray dst, + InputArray cameraMatrix, InputArray distCoeffs, + InputArray R = noArray(), InputArray P = noArray()); +/** @overload + @note Default version of #undistortPoints does 5 iterations to compute undistorted points. + + */ +CV_EXPORTS_AS(undistortPointsIter) void undistortPoints( InputArray src, OutputArray dst, + InputArray cameraMatrix, InputArray distCoeffs, + InputArray R, InputArray P, TermCriteria criteria); + +//! @} imgproc_transform + +//! @addtogroup imgproc_hist +//! @{ + +/** @example samples/cpp/demhist.cpp +An example for creating histograms of an image +*/ + +/** @brief Calculates a histogram of a set of arrays. + +The function cv::calcHist calculates the histogram of one or more arrays. The elements of a tuple used +to increment a histogram bin are taken from the corresponding input arrays at the same location. The +sample below shows how to compute a 2D Hue-Saturation histogram for a color image. : +@include snippets/imgproc_calcHist.cpp + +@param images Source arrays. They all should have the same depth, CV_8U, CV_16U or CV_32F , and the same +size. Each of them can have an arbitrary number of channels. +@param nimages Number of source images. +@param channels List of the dims channels used to compute the histogram. The first array channels +are numerated from 0 to images[0].channels()-1 , the second array channels are counted from +images[0].channels() to images[0].channels() + images[1].channels()-1, and so on. +@param mask Optional mask. If the matrix is not empty, it must be an 8-bit array of the same size +as images[i] . The non-zero mask elements mark the array elements counted in the histogram. +@param hist Output histogram, which is a dense or sparse dims -dimensional array. +@param dims Histogram dimensionality that must be positive and not greater than CV_MAX_DIMS +(equal to 32 in the current OpenCV version). +@param histSize Array of histogram sizes in each dimension. +@param ranges Array of the dims arrays of the histogram bin boundaries in each dimension. When the +histogram is uniform ( uniform =true), then for each dimension i it is enough to specify the lower +(inclusive) boundary \f$L_0\f$ of the 0-th histogram bin and the upper (exclusive) boundary +\f$U_{\texttt{histSize}[i]-1}\f$ for the last histogram bin histSize[i]-1 . That is, in case of a +uniform histogram each of ranges[i] is an array of 2 elements. When the histogram is not uniform ( +uniform=false ), then each of ranges[i] contains histSize[i]+1 elements: +\f$L_0, U_0=L_1, U_1=L_2, ..., U_{\texttt{histSize[i]}-2}=L_{\texttt{histSize[i]}-1}, U_{\texttt{histSize[i]}-1}\f$ +. The array elements, that are not between \f$L_0\f$ and \f$U_{\texttt{histSize[i]}-1}\f$ , are not +counted in the histogram. +@param uniform Flag indicating whether the histogram is uniform or not (see above). +@param accumulate Accumulation flag. If it is set, the histogram is not cleared in the beginning +when it is allocated. This feature enables you to compute a single histogram from several sets of +arrays, or to update the histogram in time. +*/ +CV_EXPORTS void calcHist( const Mat* images, int nimages, + const int* channels, InputArray mask, + OutputArray hist, int dims, const int* histSize, + const float** ranges, bool uniform = true, bool accumulate = false ); + +/** @overload + +this variant uses %SparseMat for output +*/ +CV_EXPORTS void calcHist( const Mat* images, int nimages, + const int* channels, InputArray mask, + SparseMat& hist, int dims, + const int* histSize, const float** ranges, + bool uniform = true, bool accumulate = false ); + +/** @overload */ +CV_EXPORTS_W void calcHist( InputArrayOfArrays images, + const std::vector& channels, + InputArray mask, OutputArray hist, + const std::vector& histSize, + const std::vector& ranges, + bool accumulate = false ); + +/** @brief Calculates the back projection of a histogram. + +The function cv::calcBackProject calculates the back project of the histogram. That is, similarly to +#calcHist , at each location (x, y) the function collects the values from the selected channels +in the input images and finds the corresponding histogram bin. But instead of incrementing it, the +function reads the bin value, scales it by scale , and stores in backProject(x,y) . In terms of +statistics, the function computes probability of each element value in respect with the empirical +probability distribution represented by the histogram. See how, for example, you can find and track +a bright-colored object in a scene: + +- Before tracking, show the object to the camera so that it covers almost the whole frame. +Calculate a hue histogram. The histogram may have strong maximums, corresponding to the dominant +colors in the object. + +- When tracking, calculate a back projection of a hue plane of each input video frame using that +pre-computed histogram. Threshold the back projection to suppress weak colors. It may also make +sense to suppress pixels with non-sufficient color saturation and too dark or too bright pixels. + +- Find connected components in the resulting picture and choose, for example, the largest +component. + +This is an approximate algorithm of the CamShift color object tracker. + +@param images Source arrays. They all should have the same depth, CV_8U, CV_16U or CV_32F , and the same +size. Each of them can have an arbitrary number of channels. +@param nimages Number of source images. +@param channels The list of channels used to compute the back projection. The number of channels +must match the histogram dimensionality. The first array channels are numerated from 0 to +images[0].channels()-1 , the second array channels are counted from images[0].channels() to +images[0].channels() + images[1].channels()-1, and so on. +@param hist Input histogram that can be dense or sparse. +@param backProject Destination back projection array that is a single-channel array of the same +size and depth as images[0] . +@param ranges Array of arrays of the histogram bin boundaries in each dimension. See #calcHist . +@param scale Optional scale factor for the output back projection. +@param uniform Flag indicating whether the histogram is uniform or not (see above). + +@sa calcHist, compareHist + */ +CV_EXPORTS void calcBackProject( const Mat* images, int nimages, + const int* channels, InputArray hist, + OutputArray backProject, const float** ranges, + double scale = 1, bool uniform = true ); + +/** @overload */ +CV_EXPORTS void calcBackProject( const Mat* images, int nimages, + const int* channels, const SparseMat& hist, + OutputArray backProject, const float** ranges, + double scale = 1, bool uniform = true ); + +/** @overload */ +CV_EXPORTS_W void calcBackProject( InputArrayOfArrays images, const std::vector& channels, + InputArray hist, OutputArray dst, + const std::vector& ranges, + double scale ); + +/** @brief Compares two histograms. + +The function cv::compareHist compares two dense or two sparse histograms using the specified method. + +The function returns \f$d(H_1, H_2)\f$ . + +While the function works well with 1-, 2-, 3-dimensional dense histograms, it may not be suitable +for high-dimensional sparse histograms. In such histograms, because of aliasing and sampling +problems, the coordinates of non-zero histogram bins can slightly shift. To compare such histograms +or more general sparse configurations of weighted points, consider using the #EMD function. + +@param H1 First compared histogram. +@param H2 Second compared histogram of the same size as H1 . +@param method Comparison method, see #HistCompMethods + */ +CV_EXPORTS_W double compareHist( InputArray H1, InputArray H2, int method ); + +/** @overload */ +CV_EXPORTS double compareHist( const SparseMat& H1, const SparseMat& H2, int method ); + +/** @brief Equalizes the histogram of a grayscale image. + +The function equalizes the histogram of the input image using the following algorithm: + +- Calculate the histogram \f$H\f$ for src . +- Normalize the histogram so that the sum of histogram bins is 255. +- Compute the integral of the histogram: +\f[H'_i = \sum _{0 \le j < i} H(j)\f] +- Transform the image using \f$H'\f$ as a look-up table: \f$\texttt{dst}(x,y) = H'(\texttt{src}(x,y))\f$ + +The algorithm normalizes the brightness and increases the contrast of the image. + +@param src Source 8-bit single channel image. +@param dst Destination image of the same size and type as src . + */ +CV_EXPORTS_W void equalizeHist( InputArray src, OutputArray dst ); + +/** @brief Creates a smart pointer to a cv::CLAHE class and initializes it. + +@param clipLimit Threshold for contrast limiting. +@param tileGridSize Size of grid for histogram equalization. Input image will be divided into +equally sized rectangular tiles. tileGridSize defines the number of tiles in row and column. + */ +CV_EXPORTS_W Ptr createCLAHE(double clipLimit = 40.0, Size tileGridSize = Size(8, 8)); + +/** @brief Computes the "minimal work" distance between two weighted point configurations. + +The function computes the earth mover distance and/or a lower boundary of the distance between the +two weighted point configurations. One of the applications described in @cite RubnerSept98, +@cite Rubner2000 is multi-dimensional histogram comparison for image retrieval. EMD is a transportation +problem that is solved using some modification of a simplex algorithm, thus the complexity is +exponential in the worst case, though, on average it is much faster. In the case of a real metric +the lower boundary can be calculated even faster (using linear-time algorithm) and it can be used +to determine roughly whether the two signatures are far enough so that they cannot relate to the +same object. + +@param signature1 First signature, a \f$\texttt{size1}\times \texttt{dims}+1\f$ floating-point matrix. +Each row stores the point weight followed by the point coordinates. The matrix is allowed to have +a single column (weights only) if the user-defined cost matrix is used. The weights must be +non-negative and have at least one non-zero value. +@param signature2 Second signature of the same format as signature1 , though the number of rows +may be different. The total weights may be different. In this case an extra "dummy" point is added +to either signature1 or signature2. The weights must be non-negative and have at least one non-zero +value. +@param distType Used metric. See #DistanceTypes. +@param cost User-defined \f$\texttt{size1}\times \texttt{size2}\f$ cost matrix. Also, if a cost matrix +is used, lower boundary lowerBound cannot be calculated because it needs a metric function. +@param lowerBound Optional input/output parameter: lower boundary of a distance between the two +signatures that is a distance between mass centers. The lower boundary may not be calculated if +the user-defined cost matrix is used, the total weights of point configurations are not equal, or +if the signatures consist of weights only (the signature matrices have a single column). You +**must** initialize \*lowerBound . If the calculated distance between mass centers is greater or +equal to \*lowerBound (it means that the signatures are far enough), the function does not +calculate EMD. In any case \*lowerBound is set to the calculated distance between mass centers on +return. Thus, if you want to calculate both distance between mass centers and EMD, \*lowerBound +should be set to 0. +@param flow Resultant \f$\texttt{size1} \times \texttt{size2}\f$ flow matrix: \f$\texttt{flow}_{i,j}\f$ is +a flow from \f$i\f$ -th point of signature1 to \f$j\f$ -th point of signature2 . + */ +CV_EXPORTS float EMD( InputArray signature1, InputArray signature2, + int distType, InputArray cost=noArray(), + float* lowerBound = 0, OutputArray flow = noArray() ); + +CV_EXPORTS_AS(EMD) float wrapperEMD( InputArray signature1, InputArray signature2, + int distType, InputArray cost=noArray(), + CV_IN_OUT Ptr lowerBound = Ptr(), OutputArray flow = noArray() ); + +//! @} imgproc_hist + +/** @example samples/cpp/watershed.cpp +An example using the watershed algorithm +*/ + +/** @brief Performs a marker-based image segmentation using the watershed algorithm. + +The function implements one of the variants of watershed, non-parametric marker-based segmentation +algorithm, described in @cite Meyer92 . + +Before passing the image to the function, you have to roughly outline the desired regions in the +image markers with positive (\>0) indices. So, every region is represented as one or more connected +components with the pixel values 1, 2, 3, and so on. Such markers can be retrieved from a binary +mask using #findContours and #drawContours (see the watershed.cpp demo). The markers are "seeds" of +the future image regions. All the other pixels in markers , whose relation to the outlined regions +is not known and should be defined by the algorithm, should be set to 0's. In the function output, +each pixel in markers is set to a value of the "seed" components or to -1 at boundaries between the +regions. + +@note Any two neighbor connected components are not necessarily separated by a watershed boundary +(-1's pixels); for example, they can touch each other in the initial marker image passed to the +function. + +@param image Input 8-bit 3-channel image. +@param markers Input/output 32-bit single-channel image (map) of markers. It should have the same +size as image . + +@sa findContours + +@ingroup imgproc_misc + */ +CV_EXPORTS_W void watershed( InputArray image, InputOutputArray markers ); + +//! @addtogroup imgproc_filter +//! @{ + +/** @brief Performs initial step of meanshift segmentation of an image. + +The function implements the filtering stage of meanshift segmentation, that is, the output of the +function is the filtered "posterized" image with color gradients and fine-grain texture flattened. +At every pixel (X,Y) of the input image (or down-sized input image, see below) the function executes +meanshift iterations, that is, the pixel (X,Y) neighborhood in the joint space-color hyperspace is +considered: + +\f[(x,y): X- \texttt{sp} \le x \le X+ \texttt{sp} , Y- \texttt{sp} \le y \le Y+ \texttt{sp} , ||(R,G,B)-(r,g,b)|| \le \texttt{sr}\f] + +where (R,G,B) and (r,g,b) are the vectors of color components at (X,Y) and (x,y), respectively +(though, the algorithm does not depend on the color space used, so any 3-component color space can +be used instead). Over the neighborhood the average spatial value (X',Y') and average color vector +(R',G',B') are found and they act as the neighborhood center on the next iteration: + +\f[(X,Y)~(X',Y'), (R,G,B)~(R',G',B').\f] + +After the iterations over, the color components of the initial pixel (that is, the pixel from where +the iterations started) are set to the final value (average color at the last iteration): + +\f[I(X,Y) <- (R*,G*,B*)\f] + +When maxLevel \> 0, the gaussian pyramid of maxLevel+1 levels is built, and the above procedure is +run on the smallest layer first. After that, the results are propagated to the larger layer and the +iterations are run again only on those pixels where the layer colors differ by more than sr from the +lower-resolution layer of the pyramid. That makes boundaries of color regions sharper. Note that the +results will be actually different from the ones obtained by running the meanshift procedure on the +whole original image (i.e. when maxLevel==0). + +@param src The source 8-bit, 3-channel image. +@param dst The destination image of the same format and the same size as the source. +@param sp The spatial window radius. +@param sr The color window radius. +@param maxLevel Maximum level of the pyramid for the segmentation. +@param termcrit Termination criteria: when to stop meanshift iterations. + */ +CV_EXPORTS_W void pyrMeanShiftFiltering( InputArray src, OutputArray dst, + double sp, double sr, int maxLevel = 1, + TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5,1) ); + +//! @} + +//! @addtogroup imgproc_misc +//! @{ + +/** @example samples/cpp/grabcut.cpp +An example using the GrabCut algorithm +![Sample Screenshot](grabcut_output1.jpg) +*/ + +/** @brief Runs the GrabCut algorithm. + +The function implements the [GrabCut image segmentation algorithm](http://en.wikipedia.org/wiki/GrabCut). + +@param img Input 8-bit 3-channel image. +@param mask Input/output 8-bit single-channel mask. The mask is initialized by the function when +mode is set to #GC_INIT_WITH_RECT. Its elements may have one of the #GrabCutClasses. +@param rect ROI containing a segmented object. The pixels outside of the ROI are marked as +"obvious background". The parameter is only used when mode==#GC_INIT_WITH_RECT . +@param bgdModel Temporary array for the background model. Do not modify it while you are +processing the same image. +@param fgdModel Temporary arrays for the foreground model. Do not modify it while you are +processing the same image. +@param iterCount Number of iterations the algorithm should make before returning the result. Note +that the result can be refined with further calls with mode==#GC_INIT_WITH_MASK or +mode==GC_EVAL . +@param mode Operation mode that could be one of the #GrabCutModes + */ +CV_EXPORTS_W void grabCut( InputArray img, InputOutputArray mask, Rect rect, + InputOutputArray bgdModel, InputOutputArray fgdModel, + int iterCount, int mode = GC_EVAL ); + +/** @example samples/cpp/distrans.cpp +An example on using the distance transform +*/ + +/** @brief Calculates the distance to the closest zero pixel for each pixel of the source image. + +The function cv::distanceTransform calculates the approximate or precise distance from every binary +image pixel to the nearest zero pixel. For zero image pixels, the distance will obviously be zero. + +When maskSize == #DIST_MASK_PRECISE and distanceType == #DIST_L2 , the function runs the +algorithm described in @cite Felzenszwalb04 . This algorithm is parallelized with the TBB library. + +In other cases, the algorithm @cite Borgefors86 is used. This means that for a pixel the function +finds the shortest path to the nearest zero pixel consisting of basic shifts: horizontal, vertical, +diagonal, or knight's move (the latest is available for a \f$5\times 5\f$ mask). The overall +distance is calculated as a sum of these basic distances. Since the distance function should be +symmetric, all of the horizontal and vertical shifts must have the same cost (denoted as a ), all +the diagonal shifts must have the same cost (denoted as `b`), and all knight's moves must have the +same cost (denoted as `c`). For the #DIST_C and #DIST_L1 types, the distance is calculated +precisely, whereas for #DIST_L2 (Euclidean distance) the distance can be calculated only with a +relative error (a \f$5\times 5\f$ mask gives more accurate results). For `a`,`b`, and `c`, OpenCV +uses the values suggested in the original paper: +- DIST_L1: `a = 1, b = 2` +- DIST_L2: + - `3 x 3`: `a=0.955, b=1.3693` + - `5 x 5`: `a=1, b=1.4, c=2.1969` +- DIST_C: `a = 1, b = 1` + +Typically, for a fast, coarse distance estimation #DIST_L2, a \f$3\times 3\f$ mask is used. For a +more accurate distance estimation #DIST_L2, a \f$5\times 5\f$ mask or the precise algorithm is used. +Note that both the precise and the approximate algorithms are linear on the number of pixels. + +This variant of the function does not only compute the minimum distance for each pixel \f$(x, y)\f$ +but also identifies the nearest connected component consisting of zero pixels +(labelType==#DIST_LABEL_CCOMP) or the nearest zero pixel (labelType==#DIST_LABEL_PIXEL). Index of the +component/pixel is stored in `labels(x, y)`. When labelType==#DIST_LABEL_CCOMP, the function +automatically finds connected components of zero pixels in the input image and marks them with +distinct labels. When labelType==#DIST_LABEL_CCOMP, the function scans through the input image and +marks all the zero pixels with distinct labels. + +In this mode, the complexity is still linear. That is, the function provides a very fast way to +compute the Voronoi diagram for a binary image. Currently, the second variant can use only the +approximate distance transform algorithm, i.e. maskSize=#DIST_MASK_PRECISE is not supported +yet. + +@param src 8-bit, single-channel (binary) source image. +@param dst Output image with calculated distances. It is a 8-bit or 32-bit floating-point, +single-channel image of the same size as src. +@param labels Output 2D array of labels (the discrete Voronoi diagram). It has the type +CV_32SC1 and the same size as src. +@param distanceType Type of distance, see #DistanceTypes +@param maskSize Size of the distance transform mask, see #DistanceTransformMasks. +#DIST_MASK_PRECISE is not supported by this variant. In case of the #DIST_L1 or #DIST_C distance type, +the parameter is forced to 3 because a \f$3\times 3\f$ mask gives the same result as \f$5\times +5\f$ or any larger aperture. +@param labelType Type of the label array to build, see #DistanceTransformLabelTypes. + */ +CV_EXPORTS_AS(distanceTransformWithLabels) void distanceTransform( InputArray src, OutputArray dst, + OutputArray labels, int distanceType, int maskSize, + int labelType = DIST_LABEL_CCOMP ); + +/** @overload +@param src 8-bit, single-channel (binary) source image. +@param dst Output image with calculated distances. It is a 8-bit or 32-bit floating-point, +single-channel image of the same size as src . +@param distanceType Type of distance, see #DistanceTypes +@param maskSize Size of the distance transform mask, see #DistanceTransformMasks. In case of the +#DIST_L1 or #DIST_C distance type, the parameter is forced to 3 because a \f$3\times 3\f$ mask gives +the same result as \f$5\times 5\f$ or any larger aperture. +@param dstType Type of output image. It can be CV_8U or CV_32F. Type CV_8U can be used only for +the first variant of the function and distanceType == #DIST_L1. +*/ +CV_EXPORTS_W void distanceTransform( InputArray src, OutputArray dst, + int distanceType, int maskSize, int dstType=CV_32F); + +/** @example samples/cpp/ffilldemo.cpp +An example using the FloodFill technique +*/ + +/** @overload + +variant without `mask` parameter +*/ +CV_EXPORTS int floodFill( InputOutputArray image, + Point seedPoint, Scalar newVal, CV_OUT Rect* rect = 0, + Scalar loDiff = Scalar(), Scalar upDiff = Scalar(), + int flags = 4 ); + +/** @brief Fills a connected component with the given color. + +The function cv::floodFill fills a connected component starting from the seed point with the specified +color. The connectivity is determined by the color/brightness closeness of the neighbor pixels. The +pixel at \f$(x,y)\f$ is considered to belong to the repainted domain if: + +- in case of a grayscale image and floating range +\f[\texttt{src} (x',y')- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} (x',y')+ \texttt{upDiff}\f] + + +- in case of a grayscale image and fixed range +\f[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)+ \texttt{upDiff}\f] + + +- in case of a color image and floating range +\f[\texttt{src} (x',y')_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} (x',y')_r+ \texttt{upDiff} _r,\f] +\f[\texttt{src} (x',y')_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} (x',y')_g+ \texttt{upDiff} _g\f] +and +\f[\texttt{src} (x',y')_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} (x',y')_b+ \texttt{upDiff} _b\f] + + +- in case of a color image and fixed range +\f[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r+ \texttt{upDiff} _r,\f] +\f[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g+ \texttt{upDiff} _g\f] +and +\f[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b\f] + + +where \f$src(x',y')\f$ is the value of one of pixel neighbors that is already known to belong to the +component. That is, to be added to the connected component, a color/brightness of the pixel should +be close enough to: +- Color/brightness of one of its neighbors that already belong to the connected component in case +of a floating range. +- Color/brightness of the seed point in case of a fixed range. + +Use these functions to either mark a connected component with the specified color in-place, or build +a mask and then extract the contour, or copy the region to another image, and so on. + +@param image Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the +function unless the #FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See +the details below. +@param mask Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixels +taller than image. Since this is both an input and output parameter, you must take responsibility +of initializing it. Flood-filling cannot go across non-zero pixels in the input mask. For example, +an edge detector output can be used as a mask to stop filling at edges. On output, pixels in the +mask corresponding to filled pixels in the image are set to 1 or to the a value specified in flags +as described below. Additionally, the function fills the border of the mask with ones to simplify +internal processing. It is therefore possible to use the same mask in multiple calls to the function +to make sure the filled areas do not overlap. +@param seedPoint Starting point. +@param newVal New value of the repainted domain pixels. +@param loDiff Maximal lower brightness/color difference between the currently observed pixel and +one of its neighbors belonging to the component, or a seed pixel being added to the component. +@param upDiff Maximal upper brightness/color difference between the currently observed pixel and +one of its neighbors belonging to the component, or a seed pixel being added to the component. +@param rect Optional output parameter set by the function to the minimum bounding rectangle of the +repainted domain. +@param flags Operation flags. The first 8 bits contain a connectivity value. The default value of +4 means that only the four nearest neighbor pixels (those that share an edge) are considered. A +connectivity value of 8 means that the eight nearest neighbor pixels (those that share a corner) +will be considered. The next 8 bits (8-16) contain a value between 1 and 255 with which to fill +the mask (the default value is 1). For example, 4 | ( 255 \<\< 8 ) will consider 4 nearest +neighbours and fill the mask with a value of 255. The following additional options occupy higher +bits and therefore may be further combined with the connectivity and mask fill values using +bit-wise or (|), see #FloodFillFlags. + +@note Since the mask is larger than the filled image, a pixel \f$(x, y)\f$ in image corresponds to the +pixel \f$(x+1, y+1)\f$ in the mask . + +@sa findContours + */ +CV_EXPORTS_W int floodFill( InputOutputArray image, InputOutputArray mask, + Point seedPoint, Scalar newVal, CV_OUT Rect* rect=0, + Scalar loDiff = Scalar(), Scalar upDiff = Scalar(), + int flags = 4 ); + +//! Performs linear blending of two images: +//! \f[ \texttt{dst}(i,j) = \texttt{weights1}(i,j)*\texttt{src1}(i,j) + \texttt{weights2}(i,j)*\texttt{src2}(i,j) \f] +//! @param src1 It has a type of CV_8UC(n) or CV_32FC(n), where n is a positive integer. +//! @param src2 It has the same type and size as src1. +//! @param weights1 It has a type of CV_32FC1 and the same size with src1. +//! @param weights2 It has a type of CV_32FC1 and the same size with src1. +//! @param dst It is created if it does not have the same size and type with src1. +CV_EXPORTS void blendLinear(InputArray src1, InputArray src2, InputArray weights1, InputArray weights2, OutputArray dst); + +//! @} imgproc_misc + +//! @addtogroup imgproc_color_conversions +//! @{ + +/** @brief Converts an image from one color space to another. + +The function converts an input image from one color space to another. In case of a transformation +to-from RGB color space, the order of the channels should be specified explicitly (RGB or BGR). Note +that the default color format in OpenCV is often referred to as RGB but it is actually BGR (the +bytes are reversed). So the first byte in a standard (24-bit) color image will be an 8-bit Blue +component, the second byte will be Green, and the third byte will be Red. The fourth, fifth, and +sixth bytes would then be the second pixel (Blue, then Green, then Red), and so on. + +The conventional ranges for R, G, and B channel values are: +- 0 to 255 for CV_8U images +- 0 to 65535 for CV_16U images +- 0 to 1 for CV_32F images + +In case of linear transformations, the range does not matter. But in case of a non-linear +transformation, an input RGB image should be normalized to the proper value range to get the correct +results, for example, for RGB \f$\rightarrow\f$ L\*u\*v\* transformation. For example, if you have a +32-bit floating-point image directly converted from an 8-bit image without any scaling, then it will +have the 0..255 value range instead of 0..1 assumed by the function. So, before calling #cvtColor , +you need first to scale the image down: +@code + img *= 1./255; + cvtColor(img, img, COLOR_BGR2Luv); +@endcode +If you use #cvtColor with 8-bit images, the conversion will have some information lost. For many +applications, this will not be noticeable but it is recommended to use 32-bit images in applications +that need the full range of colors or that convert an image before an operation and then convert +back. + +If conversion adds the alpha channel, its value will set to the maximum of corresponding channel +range: 255 for CV_8U, 65535 for CV_16U, 1 for CV_32F. + +@param src input image: 8-bit unsigned, 16-bit unsigned ( CV_16UC... ), or single-precision +floating-point. +@param dst output image of the same size and depth as src. +@param code color space conversion code (see #ColorConversionCodes). +@param dstCn number of channels in the destination image; if the parameter is 0, the number of the +channels is derived automatically from src and code. + +@see @ref imgproc_color_conversions + */ +CV_EXPORTS_W void cvtColor( InputArray src, OutputArray dst, int code, int dstCn = 0 ); + +/** @brief Converts an image from one color space to another where the source image is +stored in two planes. + +This function only supports YUV420 to RGB conversion as of now. + +@param src1: 8-bit image (#CV_8U) of the Y plane. +@param src2: image containing interleaved U/V plane. +@param dst: output image. +@param code: Specifies the type of conversion. It can take any of the following values: +- #COLOR_YUV2BGR_NV12 +- #COLOR_YUV2RGB_NV12 +- #COLOR_YUV2BGRA_NV12 +- #COLOR_YUV2RGBA_NV12 +- #COLOR_YUV2BGR_NV21 +- #COLOR_YUV2RGB_NV21 +- #COLOR_YUV2BGRA_NV21 +- #COLOR_YUV2RGBA_NV21 +*/ +CV_EXPORTS_W void cvtColorTwoPlane( InputArray src1, InputArray src2, OutputArray dst, int code ); + +/** @brief main function for all demosaicing processes + +@param src input image: 8-bit unsigned or 16-bit unsigned. +@param dst output image of the same size and depth as src. +@param code Color space conversion code (see the description below). +@param dstCn number of channels in the destination image; if the parameter is 0, the number of the +channels is derived automatically from src and code. + +The function can do the following transformations: + +- Demosaicing using bilinear interpolation + + #COLOR_BayerBG2BGR , #COLOR_BayerGB2BGR , #COLOR_BayerRG2BGR , #COLOR_BayerGR2BGR + + #COLOR_BayerBG2GRAY , #COLOR_BayerGB2GRAY , #COLOR_BayerRG2GRAY , #COLOR_BayerGR2GRAY + +- Demosaicing using Variable Number of Gradients. + + #COLOR_BayerBG2BGR_VNG , #COLOR_BayerGB2BGR_VNG , #COLOR_BayerRG2BGR_VNG , #COLOR_BayerGR2BGR_VNG + +- Edge-Aware Demosaicing. + + #COLOR_BayerBG2BGR_EA , #COLOR_BayerGB2BGR_EA , #COLOR_BayerRG2BGR_EA , #COLOR_BayerGR2BGR_EA + +- Demosaicing with alpha channel + + #COLOR_BayerBG2BGRA , #COLOR_BayerGB2BGRA , #COLOR_BayerRG2BGRA , #COLOR_BayerGR2BGRA + +@sa cvtColor +*/ +CV_EXPORTS_W void demosaicing(InputArray src, OutputArray dst, int code, int dstCn = 0); + +//! @} imgproc_color_conversions + +//! @addtogroup imgproc_shape +//! @{ + +/** @brief Calculates all of the moments up to the third order of a polygon or rasterized shape. + +The function computes moments, up to the 3rd order, of a vector shape or a rasterized shape. The +results are returned in the structure cv::Moments. + +@param array Raster image (single-channel, 8-bit or floating-point 2D array) or an array ( +\f$1 \times N\f$ or \f$N \times 1\f$ ) of 2D points (Point or Point2f ). +@param binaryImage If it is true, all non-zero image pixels are treated as 1's. The parameter is +used for images only. +@returns moments. + +@note Only applicable to contour moments calculations from Python bindings: Note that the numpy +type for the input array should be either np.int32 or np.float32. + +@sa contourArea, arcLength + */ +CV_EXPORTS_W Moments moments( InputArray array, bool binaryImage = false ); + +/** @brief Calculates seven Hu invariants. + +The function calculates seven Hu invariants (introduced in @cite Hu62; see also +) defined as: + +\f[\begin{array}{l} hu[0]= \eta _{20}+ \eta _{02} \\ hu[1]=( \eta _{20}- \eta _{02})^{2}+4 \eta _{11}^{2} \\ hu[2]=( \eta _{30}-3 \eta _{12})^{2}+ (3 \eta _{21}- \eta _{03})^{2} \\ hu[3]=( \eta _{30}+ \eta _{12})^{2}+ ( \eta _{21}+ \eta _{03})^{2} \\ hu[4]=( \eta _{30}-3 \eta _{12})( \eta _{30}+ \eta _{12})[( \eta _{30}+ \eta _{12})^{2}-3( \eta _{21}+ \eta _{03})^{2}]+(3 \eta _{21}- \eta _{03})( \eta _{21}+ \eta _{03})[3( \eta _{30}+ \eta _{12})^{2}-( \eta _{21}+ \eta _{03})^{2}] \\ hu[5]=( \eta _{20}- \eta _{02})[( \eta _{30}+ \eta _{12})^{2}- ( \eta _{21}+ \eta _{03})^{2}]+4 \eta _{11}( \eta _{30}+ \eta _{12})( \eta _{21}+ \eta _{03}) \\ hu[6]=(3 \eta _{21}- \eta _{03})( \eta _{21}+ \eta _{03})[3( \eta _{30}+ \eta _{12})^{2}-( \eta _{21}+ \eta _{03})^{2}]-( \eta _{30}-3 \eta _{12})( \eta _{21}+ \eta _{03})[3( \eta _{30}+ \eta _{12})^{2}-( \eta _{21}+ \eta _{03})^{2}] \\ \end{array}\f] + +where \f$\eta_{ji}\f$ stands for \f$\texttt{Moments::nu}_{ji}\f$ . + +These values are proved to be invariants to the image scale, rotation, and reflection except the +seventh one, whose sign is changed by reflection. This invariance is proved with the assumption of +infinite image resolution. In case of raster images, the computed Hu invariants for the original and +transformed images are a bit different. + +@param moments Input moments computed with moments . +@param hu Output Hu invariants. + +@sa matchShapes + */ +CV_EXPORTS void HuMoments( const Moments& moments, double hu[7] ); + +/** @overload */ +CV_EXPORTS_W void HuMoments( const Moments& m, OutputArray hu ); + +//! @} imgproc_shape + +//! @addtogroup imgproc_object +//! @{ + +//! type of the template matching operation +enum TemplateMatchModes { + TM_SQDIFF = 0, //!< \f[R(x,y)= \sum _{x',y'} (T(x',y')-I(x+x',y+y'))^2\f] + TM_SQDIFF_NORMED = 1, //!< \f[R(x,y)= \frac{\sum_{x',y'} (T(x',y')-I(x+x',y+y'))^2}{\sqrt{\sum_{x',y'}T(x',y')^2 \cdot \sum_{x',y'} I(x+x',y+y')^2}}\f] + TM_CCORR = 2, //!< \f[R(x,y)= \sum _{x',y'} (T(x',y') \cdot I(x+x',y+y'))\f] + TM_CCORR_NORMED = 3, //!< \f[R(x,y)= \frac{\sum_{x',y'} (T(x',y') \cdot I(x+x',y+y'))}{\sqrt{\sum_{x',y'}T(x',y')^2 \cdot \sum_{x',y'} I(x+x',y+y')^2}}\f] + TM_CCOEFF = 4, //!< \f[R(x,y)= \sum _{x',y'} (T'(x',y') \cdot I'(x+x',y+y'))\f] + //!< where + //!< \f[\begin{array}{l} T'(x',y')=T(x',y') - 1/(w \cdot h) \cdot \sum _{x'',y''} T(x'',y'') \\ I'(x+x',y+y')=I(x+x',y+y') - 1/(w \cdot h) \cdot \sum _{x'',y''} I(x+x'',y+y'') \end{array}\f] + TM_CCOEFF_NORMED = 5 //!< \f[R(x,y)= \frac{ \sum_{x',y'} (T'(x',y') \cdot I'(x+x',y+y')) }{ \sqrt{\sum_{x',y'}T'(x',y')^2 \cdot \sum_{x',y'} I'(x+x',y+y')^2} }\f] +}; + +/** @example samples/cpp/tutorial_code/Histograms_Matching/MatchTemplate_Demo.cpp +An example using Template Matching algorithm +*/ + +/** @brief Compares a template against overlapped image regions. + +The function slides through image , compares the overlapped patches of size \f$w \times h\f$ against +templ using the specified method and stores the comparison results in result . Here are the formulae +for the available comparison methods ( \f$I\f$ denotes image, \f$T\f$ template, \f$R\f$ result ). The summation +is done over template and/or the image patch: \f$x' = 0...w-1, y' = 0...h-1\f$ + +After the function finishes the comparison, the best matches can be found as global minimums (when +#TM_SQDIFF was used) or maximums (when #TM_CCORR or #TM_CCOEFF was used) using the +#minMaxLoc function. In case of a color image, template summation in the numerator and each sum in +the denominator is done over all of the channels and separate mean values are used for each channel. +That is, the function can take a color template and a color image. The result will still be a +single-channel image, which is easier to analyze. + +@param image Image where the search is running. It must be 8-bit or 32-bit floating-point. +@param templ Searched template. It must be not greater than the source image and have the same +data type. +@param result Map of comparison results. It must be single-channel 32-bit floating-point. If image +is \f$W \times H\f$ and templ is \f$w \times h\f$ , then result is \f$(W-w+1) \times (H-h+1)\f$ . +@param method Parameter specifying the comparison method, see #TemplateMatchModes +@param mask Mask of searched template. It must have the same datatype and size with templ. It is +not set by default. Currently, only the #TM_SQDIFF and #TM_CCORR_NORMED methods are supported. + */ +CV_EXPORTS_W void matchTemplate( InputArray image, InputArray templ, + OutputArray result, int method, InputArray mask = noArray() ); + +//! @} + +//! @addtogroup imgproc_shape +//! @{ + +/** @example samples/cpp/connected_components.cpp +This program demonstrates connected components and use of the trackbar +*/ + +/** @brief computes the connected components labeled image of boolean image + +image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0 +represents the background label. ltype specifies the output label image type, an important +consideration based on the total number of labels or alternatively the total number of pixels in +the source image. ccltype specifies the connected components labeling algorithm to use, currently +Grana (BBDT) and Wu's (SAUF) algorithms are supported, see the #ConnectedComponentsAlgorithmsTypes +for details. Note that SAUF algorithm forces a row major ordering of labels while BBDT does not. +This function uses parallel version of both Grana and Wu's algorithms if at least one allowed +parallel framework is enabled and if the rows of the image are at least twice the number returned by #getNumberOfCPUs. + +@param image the 8-bit single-channel image to be labeled +@param labels destination labeled image +@param connectivity 8 or 4 for 8-way or 4-way connectivity respectively +@param ltype output image label type. Currently CV_32S and CV_16U are supported. +@param ccltype connected components algorithm type (see the #ConnectedComponentsAlgorithmsTypes). +*/ +CV_EXPORTS_AS(connectedComponentsWithAlgorithm) int connectedComponents(InputArray image, OutputArray labels, + int connectivity, int ltype, int ccltype); + + +/** @overload + +@param image the 8-bit single-channel image to be labeled +@param labels destination labeled image +@param connectivity 8 or 4 for 8-way or 4-way connectivity respectively +@param ltype output image label type. Currently CV_32S and CV_16U are supported. +*/ +CV_EXPORTS_W int connectedComponents(InputArray image, OutputArray labels, + int connectivity = 8, int ltype = CV_32S); + + +/** @brief computes the connected components labeled image of boolean image and also produces a statistics output for each label + +image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0 +represents the background label. ltype specifies the output label image type, an important +consideration based on the total number of labels or alternatively the total number of pixels in +the source image. ccltype specifies the connected components labeling algorithm to use, currently +Grana's (BBDT) and Wu's (SAUF) algorithms are supported, see the #ConnectedComponentsAlgorithmsTypes +for details. Note that SAUF algorithm forces a row major ordering of labels while BBDT does not. +This function uses parallel version of both Grana and Wu's algorithms (statistics included) if at least one allowed +parallel framework is enabled and if the rows of the image are at least twice the number returned by #getNumberOfCPUs. + +@param image the 8-bit single-channel image to be labeled +@param labels destination labeled image +@param stats statistics output for each label, including the background label, see below for +available statistics. Statistics are accessed via stats(label, COLUMN) where COLUMN is one of +#ConnectedComponentsTypes. The data type is CV_32S. +@param centroids centroid output for each label, including the background label. Centroids are +accessed via centroids(label, 0) for x and centroids(label, 1) for y. The data type CV_64F. +@param connectivity 8 or 4 for 8-way or 4-way connectivity respectively +@param ltype output image label type. Currently CV_32S and CV_16U are supported. +@param ccltype connected components algorithm type (see #ConnectedComponentsAlgorithmsTypes). +*/ +CV_EXPORTS_AS(connectedComponentsWithStatsWithAlgorithm) int connectedComponentsWithStats(InputArray image, OutputArray labels, + OutputArray stats, OutputArray centroids, + int connectivity, int ltype, int ccltype); + +/** @overload +@param image the 8-bit single-channel image to be labeled +@param labels destination labeled image +@param stats statistics output for each label, including the background label, see below for +available statistics. Statistics are accessed via stats(label, COLUMN) where COLUMN is one of +#ConnectedComponentsTypes. The data type is CV_32S. +@param centroids centroid output for each label, including the background label. Centroids are +accessed via centroids(label, 0) for x and centroids(label, 1) for y. The data type CV_64F. +@param connectivity 8 or 4 for 8-way or 4-way connectivity respectively +@param ltype output image label type. Currently CV_32S and CV_16U are supported. +*/ +CV_EXPORTS_W int connectedComponentsWithStats(InputArray image, OutputArray labels, + OutputArray stats, OutputArray centroids, + int connectivity = 8, int ltype = CV_32S); + + +/** @brief Finds contours in a binary image. + +The function retrieves contours from the binary image using the algorithm @cite Suzuki85 . The contours +are a useful tool for shape analysis and object detection and recognition. See squares.cpp in the +OpenCV sample directory. +@note Since opencv 3.2 source image is not modified by this function. + +@param image Source, an 8-bit single-channel image. Non-zero pixels are treated as 1's. Zero +pixels remain 0's, so the image is treated as binary . You can use #compare, #inRange, #threshold , +#adaptiveThreshold, #Canny, and others to create a binary image out of a grayscale or color one. +If mode equals to #RETR_CCOMP or #RETR_FLOODFILL, the input can also be a 32-bit integer image of labels (CV_32SC1). +@param contours Detected contours. Each contour is stored as a vector of points (e.g. +std::vector >). +@param hierarchy Optional output vector (e.g. std::vector), containing information about the image topology. It has +as many elements as the number of contours. For each i-th contour contours[i], the elements +hierarchy[i][0] , hierarchy[i][1] , hierarchy[i][2] , and hierarchy[i][3] are set to 0-based indices +in contours of the next and previous contours at the same hierarchical level, the first child +contour and the parent contour, respectively. If for the contour i there are no next, previous, +parent, or nested contours, the corresponding elements of hierarchy[i] will be negative. +@param mode Contour retrieval mode, see #RetrievalModes +@param method Contour approximation method, see #ContourApproximationModes +@param offset Optional offset by which every contour point is shifted. This is useful if the +contours are extracted from the image ROI and then they should be analyzed in the whole image +context. + */ +CV_EXPORTS_W void findContours( InputOutputArray image, OutputArrayOfArrays contours, + OutputArray hierarchy, int mode, + int method, Point offset = Point()); + +/** @overload */ +CV_EXPORTS void findContours( InputOutputArray image, OutputArrayOfArrays contours, + int mode, int method, Point offset = Point()); + +/** @example samples/cpp/squares.cpp +A program using pyramid scaling, Canny, contours and contour simplification to find +squares in a list of images (pic1-6.png). Returns sequence of squares detected on the image. +*/ + +/** @example samples/tapi/squares.cpp +A program using pyramid scaling, Canny, contours and contour simplification to find +squares in the input image. +*/ + +/** @brief Approximates a polygonal curve(s) with the specified precision. + +The function cv::approxPolyDP approximates a curve or a polygon with another curve/polygon with less +vertices so that the distance between them is less or equal to the specified precision. It uses the +Douglas-Peucker algorithm + +@param curve Input vector of a 2D point stored in std::vector or Mat +@param approxCurve Result of the approximation. The type should match the type of the input curve. +@param epsilon Parameter specifying the approximation accuracy. This is the maximum distance +between the original curve and its approximation. +@param closed If true, the approximated curve is closed (its first and last vertices are +connected). Otherwise, it is not closed. + */ +CV_EXPORTS_W void approxPolyDP( InputArray curve, + OutputArray approxCurve, + double epsilon, bool closed ); + +/** @brief Calculates a contour perimeter or a curve length. + +The function computes a curve length or a closed contour perimeter. + +@param curve Input vector of 2D points, stored in std::vector or Mat. +@param closed Flag indicating whether the curve is closed or not. + */ +CV_EXPORTS_W double arcLength( InputArray curve, bool closed ); + +/** @brief Calculates the up-right bounding rectangle of a point set or non-zero pixels of gray-scale image. + +The function calculates and returns the minimal up-right bounding rectangle for the specified point set or +non-zero pixels of gray-scale image. + +@param array Input gray-scale image or 2D point set, stored in std::vector or Mat. + */ +CV_EXPORTS_W Rect boundingRect( InputArray array ); + +/** @brief Calculates a contour area. + +The function computes a contour area. Similarly to moments , the area is computed using the Green +formula. Thus, the returned area and the number of non-zero pixels, if you draw the contour using +#drawContours or #fillPoly , can be different. Also, the function will most certainly give a wrong +results for contours with self-intersections. + +Example: +@code + vector contour; + contour.push_back(Point2f(0, 0)); + contour.push_back(Point2f(10, 0)); + contour.push_back(Point2f(10, 10)); + contour.push_back(Point2f(5, 4)); + + double area0 = contourArea(contour); + vector approx; + approxPolyDP(contour, approx, 5, true); + double area1 = contourArea(approx); + + cout << "area0 =" << area0 << endl << + "area1 =" << area1 << endl << + "approx poly vertices" << approx.size() << endl; +@endcode +@param contour Input vector of 2D points (contour vertices), stored in std::vector or Mat. +@param oriented Oriented area flag. If it is true, the function returns a signed area value, +depending on the contour orientation (clockwise or counter-clockwise). Using this feature you can +determine orientation of a contour by taking the sign of an area. By default, the parameter is +false, which means that the absolute value is returned. + */ +CV_EXPORTS_W double contourArea( InputArray contour, bool oriented = false ); + +/** @brief Finds a rotated rectangle of the minimum area enclosing the input 2D point set. + +The function calculates and returns the minimum-area bounding rectangle (possibly rotated) for a +specified point set. Developer should keep in mind that the returned RotatedRect can contain negative +indices when data is close to the containing Mat element boundary. + +@param points Input vector of 2D points, stored in std::vector\<\> or Mat + */ +CV_EXPORTS_W RotatedRect minAreaRect( InputArray points ); + +/** @brief Finds the four vertices of a rotated rect. Useful to draw the rotated rectangle. + +The function finds the four vertices of a rotated rectangle. This function is useful to draw the +rectangle. In C++, instead of using this function, you can directly use RotatedRect::points method. Please +visit the @ref tutorial_bounding_rotated_ellipses "tutorial on Creating Bounding rotated boxes and ellipses for contours" for more information. + +@param box The input rotated rectangle. It may be the output of +@param points The output array of four vertices of rectangles. + */ +CV_EXPORTS_W void boxPoints(RotatedRect box, OutputArray points); + +/** @brief Finds a circle of the minimum area enclosing a 2D point set. + +The function finds the minimal enclosing circle of a 2D point set using an iterative algorithm. + +@param points Input vector of 2D points, stored in std::vector\<\> or Mat +@param center Output center of the circle. +@param radius Output radius of the circle. + */ +CV_EXPORTS_W void minEnclosingCircle( InputArray points, + CV_OUT Point2f& center, CV_OUT float& radius ); + +/** @example samples/cpp/minarea.cpp +*/ + +/** @brief Finds a triangle of minimum area enclosing a 2D point set and returns its area. + +The function finds a triangle of minimum area enclosing the given set of 2D points and returns its +area. The output for a given 2D point set is shown in the image below. 2D points are depicted in +*red* and the enclosing triangle in *yellow*. + +![Sample output of the minimum enclosing triangle function](pics/minenclosingtriangle.png) + +The implementation of the algorithm is based on O'Rourke's @cite ORourke86 and Klee and Laskowski's +@cite KleeLaskowski85 papers. O'Rourke provides a \f$\theta(n)\f$ algorithm for finding the minimal +enclosing triangle of a 2D convex polygon with n vertices. Since the #minEnclosingTriangle function +takes a 2D point set as input an additional preprocessing step of computing the convex hull of the +2D point set is required. The complexity of the #convexHull function is \f$O(n log(n))\f$ which is higher +than \f$\theta(n)\f$. Thus the overall complexity of the function is \f$O(n log(n))\f$. + +@param points Input vector of 2D points with depth CV_32S or CV_32F, stored in std::vector\<\> or Mat +@param triangle Output vector of three 2D points defining the vertices of the triangle. The depth +of the OutputArray must be CV_32F. + */ +CV_EXPORTS_W double minEnclosingTriangle( InputArray points, CV_OUT OutputArray triangle ); + +/** @brief Compares two shapes. + +The function compares two shapes. All three implemented methods use the Hu invariants (see #HuMoments) + +@param contour1 First contour or grayscale image. +@param contour2 Second contour or grayscale image. +@param method Comparison method, see #ShapeMatchModes +@param parameter Method-specific parameter (not supported now). + */ +CV_EXPORTS_W double matchShapes( InputArray contour1, InputArray contour2, + int method, double parameter ); + +/** @example samples/cpp/convexhull.cpp +An example using the convexHull functionality +*/ + +/** @brief Finds the convex hull of a point set. + +The function cv::convexHull finds the convex hull of a 2D point set using the Sklansky's algorithm @cite Sklansky82 +that has *O(N logN)* complexity in the current implementation. + +@param points Input 2D point set, stored in std::vector or Mat. +@param hull Output convex hull. It is either an integer vector of indices or vector of points. In +the first case, the hull elements are 0-based indices of the convex hull points in the original +array (since the set of convex hull points is a subset of the original point set). In the second +case, hull elements are the convex hull points themselves. +@param clockwise Orientation flag. If it is true, the output convex hull is oriented clockwise. +Otherwise, it is oriented counter-clockwise. The assumed coordinate system has its X axis pointing +to the right, and its Y axis pointing upwards. +@param returnPoints Operation flag. In case of a matrix, when the flag is true, the function +returns convex hull points. Otherwise, it returns indices of the convex hull points. When the +output array is std::vector, the flag is ignored, and the output depends on the type of the +vector: std::vector\ implies returnPoints=false, std::vector\ implies +returnPoints=true. + +@note `points` and `hull` should be different arrays, inplace processing isn't supported. + +Check @ref tutorial_hull "the corresponding tutorial" for more details. + +useful links: + +https://www.learnopencv.com/convex-hull-using-opencv-in-python-and-c/ + */ +CV_EXPORTS_W void convexHull( InputArray points, OutputArray hull, + bool clockwise = false, bool returnPoints = true ); + +/** @brief Finds the convexity defects of a contour. + +The figure below displays convexity defects of a hand contour: + +![image](pics/defects.png) + +@param contour Input contour. +@param convexhull Convex hull obtained using convexHull that should contain indices of the contour +points that make the hull. +@param convexityDefects The output vector of convexity defects. In C++ and the new Python/Java +interface each convexity defect is represented as 4-element integer vector (a.k.a. #Vec4i): +(start_index, end_index, farthest_pt_index, fixpt_depth), where indices are 0-based indices +in the original contour of the convexity defect beginning, end and the farthest point, and +fixpt_depth is fixed-point approximation (with 8 fractional bits) of the distance between the +farthest contour point and the hull. That is, to get the floating-point value of the depth will be +fixpt_depth/256.0. + */ +CV_EXPORTS_W void convexityDefects( InputArray contour, InputArray convexhull, OutputArray convexityDefects ); + +/** @brief Tests a contour convexity. + +The function tests whether the input contour is convex or not. The contour must be simple, that is, +without self-intersections. Otherwise, the function output is undefined. + +@param contour Input vector of 2D points, stored in std::vector\<\> or Mat + */ +CV_EXPORTS_W bool isContourConvex( InputArray contour ); + +//! finds intersection of two convex polygons +CV_EXPORTS_W float intersectConvexConvex( InputArray _p1, InputArray _p2, + OutputArray _p12, bool handleNested = true ); + +/** @example samples/cpp/fitellipse.cpp +An example using the fitEllipse technique +*/ + +/** @brief Fits an ellipse around a set of 2D points. + +The function calculates the ellipse that fits (in a least-squares sense) a set of 2D points best of +all. It returns the rotated rectangle in which the ellipse is inscribed. The first algorithm described by @cite Fitzgibbon95 +is used. Developer should keep in mind that it is possible that the returned +ellipse/rotatedRect data contains negative indices, due to the data points being close to the +border of the containing Mat element. + +@param points Input 2D point set, stored in std::vector\<\> or Mat + */ +CV_EXPORTS_W RotatedRect fitEllipse( InputArray points ); + +/** @brief Fits an ellipse around a set of 2D points. + + The function calculates the ellipse that fits a set of 2D points. + It returns the rotated rectangle in which the ellipse is inscribed. + The Approximate Mean Square (AMS) proposed by @cite Taubin1991 is used. + + For an ellipse, this basis set is \f$ \chi= \left(x^2, x y, y^2, x, y, 1\right) \f$, + which is a set of six free coefficients \f$ A^T=\left\{A_{\text{xx}},A_{\text{xy}},A_{\text{yy}},A_x,A_y,A_0\right\} \f$. + However, to specify an ellipse, all that is needed is five numbers; the major and minor axes lengths \f$ (a,b) \f$, + the position \f$ (x_0,y_0) \f$, and the orientation \f$ \theta \f$. This is because the basis set includes lines, + quadratics, parabolic and hyperbolic functions as well as elliptical functions as possible fits. + If the fit is found to be a parabolic or hyperbolic function then the standard #fitEllipse method is used. + The AMS method restricts the fit to parabolic, hyperbolic and elliptical curves + by imposing the condition that \f$ A^T ( D_x^T D_x + D_y^T D_y) A = 1 \f$ where + the matrices \f$ Dx \f$ and \f$ Dy \f$ are the partial derivatives of the design matrix \f$ D \f$ with + respect to x and y. The matrices are formed row by row applying the following to + each of the points in the set: + \f{align*}{ + D(i,:)&=\left\{x_i^2, x_i y_i, y_i^2, x_i, y_i, 1\right\} & + D_x(i,:)&=\left\{2 x_i,y_i,0,1,0,0\right\} & + D_y(i,:)&=\left\{0,x_i,2 y_i,0,1,0\right\} + \f} + The AMS method minimizes the cost function + \f{equation*}{ + \epsilon ^2=\frac{ A^T D^T D A }{ A^T (D_x^T D_x + D_y^T D_y) A^T } + \f} + + The minimum cost is found by solving the generalized eigenvalue problem. + + \f{equation*}{ + D^T D A = \lambda \left( D_x^T D_x + D_y^T D_y\right) A + \f} + + @param points Input 2D point set, stored in std::vector\<\> or Mat + */ +CV_EXPORTS_W RotatedRect fitEllipseAMS( InputArray points ); + + +/** @brief Fits an ellipse around a set of 2D points. + + The function calculates the ellipse that fits a set of 2D points. + It returns the rotated rectangle in which the ellipse is inscribed. + The Direct least square (Direct) method by @cite Fitzgibbon1999 is used. + + For an ellipse, this basis set is \f$ \chi= \left(x^2, x y, y^2, x, y, 1\right) \f$, + which is a set of six free coefficients \f$ A^T=\left\{A_{\text{xx}},A_{\text{xy}},A_{\text{yy}},A_x,A_y,A_0\right\} \f$. + However, to specify an ellipse, all that is needed is five numbers; the major and minor axes lengths \f$ (a,b) \f$, + the position \f$ (x_0,y_0) \f$, and the orientation \f$ \theta \f$. This is because the basis set includes lines, + quadratics, parabolic and hyperbolic functions as well as elliptical functions as possible fits. + The Direct method confines the fit to ellipses by ensuring that \f$ 4 A_{xx} A_{yy}- A_{xy}^2 > 0 \f$. + The condition imposed is that \f$ 4 A_{xx} A_{yy}- A_{xy}^2=1 \f$ which satisfies the inequality + and as the coefficients can be arbitrarily scaled is not overly restrictive. + + \f{equation*}{ + \epsilon ^2= A^T D^T D A \quad \text{with} \quad A^T C A =1 \quad \text{and} \quad C=\left(\begin{matrix} + 0 & 0 & 2 & 0 & 0 & 0 \\ + 0 & -1 & 0 & 0 & 0 & 0 \\ + 2 & 0 & 0 & 0 & 0 & 0 \\ + 0 & 0 & 0 & 0 & 0 & 0 \\ + 0 & 0 & 0 & 0 & 0 & 0 \\ + 0 & 0 & 0 & 0 & 0 & 0 + \end{matrix} \right) + \f} + + The minimum cost is found by solving the generalized eigenvalue problem. + + \f{equation*}{ + D^T D A = \lambda \left( C\right) A + \f} + + The system produces only one positive eigenvalue \f$ \lambda\f$ which is chosen as the solution + with its eigenvector \f$\mathbf{u}\f$. These are used to find the coefficients + + \f{equation*}{ + A = \sqrt{\frac{1}{\mathbf{u}^T C \mathbf{u}}} \mathbf{u} + \f} + The scaling factor guarantees that \f$A^T C A =1\f$. + + @param points Input 2D point set, stored in std::vector\<\> or Mat + */ +CV_EXPORTS_W RotatedRect fitEllipseDirect( InputArray points ); + +/** @brief Fits a line to a 2D or 3D point set. + +The function fitLine fits a line to a 2D or 3D point set by minimizing \f$\sum_i \rho(r_i)\f$ where +\f$r_i\f$ is a distance between the \f$i^{th}\f$ point, the line and \f$\rho(r)\f$ is a distance function, one +of the following: +- DIST_L2 +\f[\rho (r) = r^2/2 \quad \text{(the simplest and the fastest least-squares method)}\f] +- DIST_L1 +\f[\rho (r) = r\f] +- DIST_L12 +\f[\rho (r) = 2 \cdot ( \sqrt{1 + \frac{r^2}{2}} - 1)\f] +- DIST_FAIR +\f[\rho \left (r \right ) = C^2 \cdot \left ( \frac{r}{C} - \log{\left(1 + \frac{r}{C}\right)} \right ) \quad \text{where} \quad C=1.3998\f] +- DIST_WELSCH +\f[\rho \left (r \right ) = \frac{C^2}{2} \cdot \left ( 1 - \exp{\left(-\left(\frac{r}{C}\right)^2\right)} \right ) \quad \text{where} \quad C=2.9846\f] +- DIST_HUBER +\f[\rho (r) = \fork{r^2/2}{if \(r < C\)}{C \cdot (r-C/2)}{otherwise} \quad \text{where} \quad C=1.345\f] + +The algorithm is based on the M-estimator ( ) technique +that iteratively fits the line using the weighted least-squares algorithm. After each iteration the +weights \f$w_i\f$ are adjusted to be inversely proportional to \f$\rho(r_i)\f$ . + +@param points Input vector of 2D or 3D points, stored in std::vector\<\> or Mat. +@param line Output line parameters. In case of 2D fitting, it should be a vector of 4 elements +(like Vec4f) - (vx, vy, x0, y0), where (vx, vy) is a normalized vector collinear to the line and +(x0, y0) is a point on the line. In case of 3D fitting, it should be a vector of 6 elements (like +Vec6f) - (vx, vy, vz, x0, y0, z0), where (vx, vy, vz) is a normalized vector collinear to the line +and (x0, y0, z0) is a point on the line. +@param distType Distance used by the M-estimator, see #DistanceTypes +@param param Numerical parameter ( C ) for some types of distances. If it is 0, an optimal value +is chosen. +@param reps Sufficient accuracy for the radius (distance between the coordinate origin and the line). +@param aeps Sufficient accuracy for the angle. 0.01 would be a good default value for reps and aeps. + */ +CV_EXPORTS_W void fitLine( InputArray points, OutputArray line, int distType, + double param, double reps, double aeps ); + +/** @brief Performs a point-in-contour test. + +The function determines whether the point is inside a contour, outside, or lies on an edge (or +coincides with a vertex). It returns positive (inside), negative (outside), or zero (on an edge) +value, correspondingly. When measureDist=false , the return value is +1, -1, and 0, respectively. +Otherwise, the return value is a signed distance between the point and the nearest contour edge. + +See below a sample output of the function where each image pixel is tested against the contour: + +![sample output](pics/pointpolygon.png) + +@param contour Input contour. +@param pt Point tested against the contour. +@param measureDist If true, the function estimates the signed distance from the point to the +nearest contour edge. Otherwise, the function only checks if the point is inside a contour or not. + */ +CV_EXPORTS_W double pointPolygonTest( InputArray contour, Point2f pt, bool measureDist ); + +/** @brief Finds out if there is any intersection between two rotated rectangles. + +If there is then the vertices of the intersecting region are returned as well. + +Below are some examples of intersection configurations. The hatched pattern indicates the +intersecting region and the red vertices are returned by the function. + +![intersection examples](pics/intersection.png) + +@param rect1 First rectangle +@param rect2 Second rectangle +@param intersectingRegion The output array of the vertices of the intersecting region. It returns +at most 8 vertices. Stored as std::vector\ or cv::Mat as Mx1 of type CV_32FC2. +@returns One of #RectanglesIntersectTypes + */ +CV_EXPORTS_W int rotatedRectangleIntersection( const RotatedRect& rect1, const RotatedRect& rect2, OutputArray intersectingRegion ); + +/** @brief Creates a smart pointer to a cv::GeneralizedHoughBallard class and initializes it. +*/ +CV_EXPORTS Ptr createGeneralizedHoughBallard(); + +/** @brief Creates a smart pointer to a cv::GeneralizedHoughGuil class and initializes it. +*/ +CV_EXPORTS Ptr createGeneralizedHoughGuil(); + +//! @} imgproc_shape + +//! @addtogroup imgproc_colormap +//! @{ + +//! GNU Octave/MATLAB equivalent colormaps +enum ColormapTypes +{ + COLORMAP_AUTUMN = 0, //!< ![autumn](pics/colormaps/colorscale_autumn.jpg) + COLORMAP_BONE = 1, //!< ![bone](pics/colormaps/colorscale_bone.jpg) + COLORMAP_JET = 2, //!< ![jet](pics/colormaps/colorscale_jet.jpg) + COLORMAP_WINTER = 3, //!< ![winter](pics/colormaps/colorscale_winter.jpg) + COLORMAP_RAINBOW = 4, //!< ![rainbow](pics/colormaps/colorscale_rainbow.jpg) + COLORMAP_OCEAN = 5, //!< ![ocean](pics/colormaps/colorscale_ocean.jpg) + COLORMAP_SUMMER = 6, //!< ![summer](pics/colormaps/colorscale_summer.jpg) + COLORMAP_SPRING = 7, //!< ![spring](pics/colormaps/colorscale_spring.jpg) + COLORMAP_COOL = 8, //!< ![cool](pics/colormaps/colorscale_cool.jpg) + COLORMAP_HSV = 9, //!< ![HSV](pics/colormaps/colorscale_hsv.jpg) + COLORMAP_PINK = 10, //!< ![pink](pics/colormaps/colorscale_pink.jpg) + COLORMAP_HOT = 11, //!< ![hot](pics/colormaps/colorscale_hot.jpg) + COLORMAP_PARULA = 12 //!< ![parula](pics/colormaps/colorscale_parula.jpg) +}; + +/** @example samples/cpp/falsecolor.cpp +An example using applyColorMap function +*/ + +/** @brief Applies a GNU Octave/MATLAB equivalent colormap on a given image. + +@param src The source image, grayscale or colored of type CV_8UC1 or CV_8UC3. +@param dst The result is the colormapped source image. Note: Mat::create is called on dst. +@param colormap The colormap to apply, see #ColormapTypes +*/ +CV_EXPORTS_W void applyColorMap(InputArray src, OutputArray dst, int colormap); + +/** @brief Applies a user colormap on a given image. + +@param src The source image, grayscale or colored of type CV_8UC1 or CV_8UC3. +@param dst The result is the colormapped source image. Note: Mat::create is called on dst. +@param userColor The colormap to apply of type CV_8UC1 or CV_8UC3 and size 256 +*/ +CV_EXPORTS_W void applyColorMap(InputArray src, OutputArray dst, InputArray userColor); + +//! @} imgproc_colormap + +//! @addtogroup imgproc_draw +//! @{ + + +/** OpenCV color channel order is BGR[A] */ +#define CV_RGB(r, g, b) cv::Scalar((b), (g), (r), 0) + +/** @brief Draws a line segment connecting two points. + +The function line draws the line segment between pt1 and pt2 points in the image. The line is +clipped by the image boundaries. For non-antialiased lines with integer coordinates, the 8-connected +or 4-connected Bresenham algorithm is used. Thick lines are drawn with rounding endings. Antialiased +lines are drawn using Gaussian filtering. + +@param img Image. +@param pt1 First point of the line segment. +@param pt2 Second point of the line segment. +@param color Line color. +@param thickness Line thickness. +@param lineType Type of the line. See #LineTypes. +@param shift Number of fractional bits in the point coordinates. + */ +CV_EXPORTS_W void line(InputOutputArray img, Point pt1, Point pt2, const Scalar& color, + int thickness = 1, int lineType = LINE_8, int shift = 0); + +/** @brief Draws a arrow segment pointing from the first point to the second one. + +The function cv::arrowedLine draws an arrow between pt1 and pt2 points in the image. See also #line. + +@param img Image. +@param pt1 The point the arrow starts from. +@param pt2 The point the arrow points to. +@param color Line color. +@param thickness Line thickness. +@param line_type Type of the line. See #LineTypes +@param shift Number of fractional bits in the point coordinates. +@param tipLength The length of the arrow tip in relation to the arrow length + */ +CV_EXPORTS_W void arrowedLine(InputOutputArray img, Point pt1, Point pt2, const Scalar& color, + int thickness=1, int line_type=8, int shift=0, double tipLength=0.1); + +/** @brief Draws a simple, thick, or filled up-right rectangle. + +The function cv::rectangle draws a rectangle outline or a filled rectangle whose two opposite corners +are pt1 and pt2. + +@param img Image. +@param pt1 Vertex of the rectangle. +@param pt2 Vertex of the rectangle opposite to pt1 . +@param color Rectangle color or brightness (grayscale image). +@param thickness Thickness of lines that make up the rectangle. Negative values, like #FILLED, +mean that the function has to draw a filled rectangle. +@param lineType Type of the line. See #LineTypes +@param shift Number of fractional bits in the point coordinates. + */ +CV_EXPORTS_W void rectangle(InputOutputArray img, Point pt1, Point pt2, + const Scalar& color, int thickness = 1, + int lineType = LINE_8, int shift = 0); + +/** @overload + +use `rec` parameter as alternative specification of the drawn rectangle: `r.tl() and +r.br()-Point(1,1)` are opposite corners +*/ +CV_EXPORTS void rectangle(CV_IN_OUT Mat& img, Rect rec, + const Scalar& color, int thickness = 1, + int lineType = LINE_8, int shift = 0); + +/** @example samples/cpp/tutorial_code/ImgProc/basic_drawing/Drawing_2.cpp +An example using drawing functions +*/ + +/** @brief Draws a circle. + +The function cv::circle draws a simple or filled circle with a given center and radius. +@param img Image where the circle is drawn. +@param center Center of the circle. +@param radius Radius of the circle. +@param color Circle color. +@param thickness Thickness of the circle outline, if positive. Negative values, like #FILLED, +mean that a filled circle is to be drawn. +@param lineType Type of the circle boundary. See #LineTypes +@param shift Number of fractional bits in the coordinates of the center and in the radius value. + */ +CV_EXPORTS_W void circle(InputOutputArray img, Point center, int radius, + const Scalar& color, int thickness = 1, + int lineType = LINE_8, int shift = 0); + +/** @brief Draws a simple or thick elliptic arc or fills an ellipse sector. + +The function cv::ellipse with more parameters draws an ellipse outline, a filled ellipse, an elliptic +arc, or a filled ellipse sector. The drawing code uses general parametric form. +A piecewise-linear curve is used to approximate the elliptic arc +boundary. If you need more control of the ellipse rendering, you can retrieve the curve using +#ellipse2Poly and then render it with #polylines or fill it with #fillPoly. If you use the first +variant of the function and want to draw the whole ellipse, not an arc, pass `startAngle=0` and +`endAngle=360`. If `startAngle` is greater than `endAngle`, they are swapped. The figure below explains +the meaning of the parameters to draw the blue arc. + +![Parameters of Elliptic Arc](pics/ellipse.svg) + +@param img Image. +@param center Center of the ellipse. +@param axes Half of the size of the ellipse main axes. +@param angle Ellipse rotation angle in degrees. +@param startAngle Starting angle of the elliptic arc in degrees. +@param endAngle Ending angle of the elliptic arc in degrees. +@param color Ellipse color. +@param thickness Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that +a filled ellipse sector is to be drawn. +@param lineType Type of the ellipse boundary. See #LineTypes +@param shift Number of fractional bits in the coordinates of the center and values of axes. + */ +CV_EXPORTS_W void ellipse(InputOutputArray img, Point center, Size axes, + double angle, double startAngle, double endAngle, + const Scalar& color, int thickness = 1, + int lineType = LINE_8, int shift = 0); + +/** @overload +@param img Image. +@param box Alternative ellipse representation via RotatedRect. This means that the function draws +an ellipse inscribed in the rotated rectangle. +@param color Ellipse color. +@param thickness Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that +a filled ellipse sector is to be drawn. +@param lineType Type of the ellipse boundary. See #LineTypes +*/ +CV_EXPORTS_W void ellipse(InputOutputArray img, const RotatedRect& box, const Scalar& color, + int thickness = 1, int lineType = LINE_8); + +/* ----------------------------------------------------------------------------------------- */ +/* ADDING A SET OF PREDEFINED MARKERS WHICH COULD BE USED TO HIGHLIGHT POSITIONS IN AN IMAGE */ +/* ----------------------------------------------------------------------------------------- */ + +//! Possible set of marker types used for the cv::drawMarker function +enum MarkerTypes +{ + MARKER_CROSS = 0, //!< A crosshair marker shape + MARKER_TILTED_CROSS = 1, //!< A 45 degree tilted crosshair marker shape + MARKER_STAR = 2, //!< A star marker shape, combination of cross and tilted cross + MARKER_DIAMOND = 3, //!< A diamond marker shape + MARKER_SQUARE = 4, //!< A square marker shape + MARKER_TRIANGLE_UP = 5, //!< An upwards pointing triangle marker shape + MARKER_TRIANGLE_DOWN = 6 //!< A downwards pointing triangle marker shape +}; + +/** @brief Draws a marker on a predefined position in an image. + +The function cv::drawMarker draws a marker on a given position in the image. For the moment several +marker types are supported, see #MarkerTypes for more information. + +@param img Image. +@param position The point where the crosshair is positioned. +@param color Line color. +@param markerType The specific type of marker you want to use, see #MarkerTypes +@param thickness Line thickness. +@param line_type Type of the line, See #LineTypes +@param markerSize The length of the marker axis [default = 20 pixels] + */ +CV_EXPORTS_W void drawMarker(CV_IN_OUT Mat& img, Point position, const Scalar& color, + int markerType = MARKER_CROSS, int markerSize=20, int thickness=1, + int line_type=8); + +/* ----------------------------------------------------------------------------------------- */ +/* END OF MARKER SECTION */ +/* ----------------------------------------------------------------------------------------- */ + +/** @overload */ +CV_EXPORTS void fillConvexPoly(Mat& img, const Point* pts, int npts, + const Scalar& color, int lineType = LINE_8, + int shift = 0); + +/** @brief Fills a convex polygon. + +The function cv::fillConvexPoly draws a filled convex polygon. This function is much faster than the +function #fillPoly . It can fill not only convex polygons but any monotonic polygon without +self-intersections, that is, a polygon whose contour intersects every horizontal line (scan line) +twice at the most (though, its top-most and/or the bottom edge could be horizontal). + +@param img Image. +@param points Polygon vertices. +@param color Polygon color. +@param lineType Type of the polygon boundaries. See #LineTypes +@param shift Number of fractional bits in the vertex coordinates. + */ +CV_EXPORTS_W void fillConvexPoly(InputOutputArray img, InputArray points, + const Scalar& color, int lineType = LINE_8, + int shift = 0); + +/** @overload */ +CV_EXPORTS void fillPoly(Mat& img, const Point** pts, + const int* npts, int ncontours, + const Scalar& color, int lineType = LINE_8, int shift = 0, + Point offset = Point() ); + +/** @example samples/cpp/tutorial_code/ImgProc/basic_drawing/Drawing_1.cpp +An example using drawing functions +Check @ref tutorial_random_generator_and_text "the corresponding tutorial" for more details +*/ + +/** @brief Fills the area bounded by one or more polygons. + +The function cv::fillPoly fills an area bounded by several polygonal contours. The function can fill +complex areas, for example, areas with holes, contours with self-intersections (some of their +parts), and so forth. + +@param img Image. +@param pts Array of polygons where each polygon is represented as an array of points. +@param color Polygon color. +@param lineType Type of the polygon boundaries. See #LineTypes +@param shift Number of fractional bits in the vertex coordinates. +@param offset Optional offset of all points of the contours. + */ +CV_EXPORTS_W void fillPoly(InputOutputArray img, InputArrayOfArrays pts, + const Scalar& color, int lineType = LINE_8, int shift = 0, + Point offset = Point() ); + +/** @overload */ +CV_EXPORTS void polylines(Mat& img, const Point* const* pts, const int* npts, + int ncontours, bool isClosed, const Scalar& color, + int thickness = 1, int lineType = LINE_8, int shift = 0 ); + +/** @brief Draws several polygonal curves. + +@param img Image. +@param pts Array of polygonal curves. +@param isClosed Flag indicating whether the drawn polylines are closed or not. If they are closed, +the function draws a line from the last vertex of each curve to its first vertex. +@param color Polyline color. +@param thickness Thickness of the polyline edges. +@param lineType Type of the line segments. See #LineTypes +@param shift Number of fractional bits in the vertex coordinates. + +The function cv::polylines draws one or more polygonal curves. + */ +CV_EXPORTS_W void polylines(InputOutputArray img, InputArrayOfArrays pts, + bool isClosed, const Scalar& color, + int thickness = 1, int lineType = LINE_8, int shift = 0 ); + +/** @example samples/cpp/contours2.cpp +An example program illustrates the use of cv::findContours and cv::drawContours +\image html WindowsQtContoursOutput.png "Screenshot of the program" +*/ + +/** @example samples/cpp/segment_objects.cpp +An example using drawContours to clean up a background segmentation result +*/ + +/** @brief Draws contours outlines or filled contours. + +The function draws contour outlines in the image if \f$\texttt{thickness} \ge 0\f$ or fills the area +bounded by the contours if \f$\texttt{thickness}<0\f$ . The example below shows how to retrieve +connected components from the binary image and label them: : +@include snippets/imgproc_drawContours.cpp + +@param image Destination image. +@param contours All the input contours. Each contour is stored as a point vector. +@param contourIdx Parameter indicating a contour to draw. If it is negative, all the contours are drawn. +@param color Color of the contours. +@param thickness Thickness of lines the contours are drawn with. If it is negative (for example, +thickness=#FILLED ), the contour interiors are drawn. +@param lineType Line connectivity. See #LineTypes +@param hierarchy Optional information about hierarchy. It is only needed if you want to draw only +some of the contours (see maxLevel ). +@param maxLevel Maximal level for drawn contours. If it is 0, only the specified contour is drawn. +If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function +draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This +parameter is only taken into account when there is hierarchy available. +@param offset Optional contour shift parameter. Shift all the drawn contours by the specified +\f$\texttt{offset}=(dx,dy)\f$ . +@note When thickness=#FILLED, the function is designed to handle connected components with holes correctly +even when no hierarchy date is provided. This is done by analyzing all the outlines together +using even-odd rule. This may give incorrect results if you have a joint collection of separately retrieved +contours. In order to solve this problem, you need to call #drawContours separately for each sub-group +of contours, or iterate over the collection using contourIdx parameter. + */ +CV_EXPORTS_W void drawContours( InputOutputArray image, InputArrayOfArrays contours, + int contourIdx, const Scalar& color, + int thickness = 1, int lineType = LINE_8, + InputArray hierarchy = noArray(), + int maxLevel = INT_MAX, Point offset = Point() ); + +/** @brief Clips the line against the image rectangle. + +The function cv::clipLine calculates a part of the line segment that is entirely within the specified +rectangle. it returns false if the line segment is completely outside the rectangle. Otherwise, +it returns true . +@param imgSize Image size. The image rectangle is Rect(0, 0, imgSize.width, imgSize.height) . +@param pt1 First line point. +@param pt2 Second line point. + */ +CV_EXPORTS bool clipLine(Size imgSize, CV_IN_OUT Point& pt1, CV_IN_OUT Point& pt2); + +/** @overload +@param imgSize Image size. The image rectangle is Rect(0, 0, imgSize.width, imgSize.height) . +@param pt1 First line point. +@param pt2 Second line point. +*/ +CV_EXPORTS bool clipLine(Size2l imgSize, CV_IN_OUT Point2l& pt1, CV_IN_OUT Point2l& pt2); + +/** @overload +@param imgRect Image rectangle. +@param pt1 First line point. +@param pt2 Second line point. +*/ +CV_EXPORTS_W bool clipLine(Rect imgRect, CV_OUT CV_IN_OUT Point& pt1, CV_OUT CV_IN_OUT Point& pt2); + +/** @brief Approximates an elliptic arc with a polyline. + +The function ellipse2Poly computes the vertices of a polyline that approximates the specified +elliptic arc. It is used by #ellipse. If `arcStart` is greater than `arcEnd`, they are swapped. + +@param center Center of the arc. +@param axes Half of the size of the ellipse main axes. See #ellipse for details. +@param angle Rotation angle of the ellipse in degrees. See #ellipse for details. +@param arcStart Starting angle of the elliptic arc in degrees. +@param arcEnd Ending angle of the elliptic arc in degrees. +@param delta Angle between the subsequent polyline vertices. It defines the approximation +accuracy. +@param pts Output vector of polyline vertices. + */ +CV_EXPORTS_W void ellipse2Poly( Point center, Size axes, int angle, + int arcStart, int arcEnd, int delta, + CV_OUT std::vector& pts ); + +/** @overload +@param center Center of the arc. +@param axes Half of the size of the ellipse main axes. See #ellipse for details. +@param angle Rotation angle of the ellipse in degrees. See #ellipse for details. +@param arcStart Starting angle of the elliptic arc in degrees. +@param arcEnd Ending angle of the elliptic arc in degrees. +@param delta Angle between the subsequent polyline vertices. It defines the approximation accuracy. +@param pts Output vector of polyline vertices. +*/ +CV_EXPORTS void ellipse2Poly(Point2d center, Size2d axes, int angle, + int arcStart, int arcEnd, int delta, + CV_OUT std::vector& pts); + +/** @brief Draws a text string. + +The function cv::putText renders the specified text string in the image. Symbols that cannot be rendered +using the specified font are replaced by question marks. See #getTextSize for a text rendering code +example. + +@param img Image. +@param text Text string to be drawn. +@param org Bottom-left corner of the text string in the image. +@param fontFace Font type, see #HersheyFonts. +@param fontScale Font scale factor that is multiplied by the font-specific base size. +@param color Text color. +@param thickness Thickness of the lines used to draw a text. +@param lineType Line type. See #LineTypes +@param bottomLeftOrigin When true, the image data origin is at the bottom-left corner. Otherwise, +it is at the top-left corner. + */ +CV_EXPORTS_W void putText( InputOutputArray img, const String& text, Point org, + int fontFace, double fontScale, Scalar color, + int thickness = 1, int lineType = LINE_8, + bool bottomLeftOrigin = false ); + +/** @brief Calculates the width and height of a text string. + +The function cv::getTextSize calculates and returns the size of a box that contains the specified text. +That is, the following code renders some text, the tight box surrounding it, and the baseline: : +@code + String text = "Funny text inside the box"; + int fontFace = FONT_HERSHEY_SCRIPT_SIMPLEX; + double fontScale = 2; + int thickness = 3; + + Mat img(600, 800, CV_8UC3, Scalar::all(0)); + + int baseline=0; + Size textSize = getTextSize(text, fontFace, + fontScale, thickness, &baseline); + baseline += thickness; + + // center the text + Point textOrg((img.cols - textSize.width)/2, + (img.rows + textSize.height)/2); + + // draw the box + rectangle(img, textOrg + Point(0, baseline), + textOrg + Point(textSize.width, -textSize.height), + Scalar(0,0,255)); + // ... and the baseline first + line(img, textOrg + Point(0, thickness), + textOrg + Point(textSize.width, thickness), + Scalar(0, 0, 255)); + + // then put the text itself + putText(img, text, textOrg, fontFace, fontScale, + Scalar::all(255), thickness, 8); +@endcode + +@param text Input text string. +@param fontFace Font to use, see #HersheyFonts. +@param fontScale Font scale factor that is multiplied by the font-specific base size. +@param thickness Thickness of lines used to render the text. See #putText for details. +@param[out] baseLine y-coordinate of the baseline relative to the bottom-most text +point. +@return The size of a box that contains the specified text. + +@see putText + */ +CV_EXPORTS_W Size getTextSize(const String& text, int fontFace, + double fontScale, int thickness, + CV_OUT int* baseLine); + + +/** @brief Calculates the font-specific size to use to achieve a given height in pixels. + +@param fontFace Font to use, see cv::HersheyFonts. +@param pixelHeight Pixel height to compute the fontScale for +@param thickness Thickness of lines used to render the text.See putText for details. +@return The fontSize to use for cv::putText + +@see cv::putText +*/ +CV_EXPORTS_W double getFontScaleFromHeight(const int fontFace, + const int pixelHeight, + const int thickness = 1); + +/** @brief Line iterator + +The class is used to iterate over all the pixels on the raster line +segment connecting two specified points. + +The class LineIterator is used to get each pixel of a raster line. It +can be treated as versatile implementation of the Bresenham algorithm +where you can stop at each pixel and do some extra processing, for +example, grab pixel values along the line or draw a line with an effect +(for example, with XOR operation). + +The number of pixels along the line is stored in LineIterator::count. +The method LineIterator::pos returns the current position in the image: + +@code{.cpp} +// grabs pixels along the line (pt1, pt2) +// from 8-bit 3-channel image to the buffer +LineIterator it(img, pt1, pt2, 8); +LineIterator it2 = it; +vector buf(it.count); + +for(int i = 0; i < it.count; i++, ++it) + buf[i] = *(const Vec3b*)*it; + +// alternative way of iterating through the line +for(int i = 0; i < it2.count; i++, ++it2) +{ + Vec3b val = img.at(it2.pos()); + CV_Assert(buf[i] == val); +} +@endcode +*/ +class CV_EXPORTS LineIterator +{ +public: + /** @brief initializes the iterator + + creates iterators for the line connecting pt1 and pt2 + the line will be clipped on the image boundaries + the line is 8-connected or 4-connected + If leftToRight=true, then the iteration is always done + from the left-most point to the right most, + not to depend on the ordering of pt1 and pt2 parameters + */ + LineIterator( const Mat& img, Point pt1, Point pt2, + int connectivity = 8, bool leftToRight = false ); + /** @brief returns pointer to the current pixel + */ + uchar* operator *(); + /** @brief prefix increment operator (++it). shifts iterator to the next pixel + */ + LineIterator& operator ++(); + /** @brief postfix increment operator (it++). shifts iterator to the next pixel + */ + LineIterator operator ++(int); + /** @brief returns coordinates of the current pixel + */ + Point pos() const; + + uchar* ptr; + const uchar* ptr0; + int step, elemSize; + int err, count; + int minusDelta, plusDelta; + int minusStep, plusStep; +}; + +//! @cond IGNORED + +// === LineIterator implementation === + +inline +uchar* LineIterator::operator *() +{ + return ptr; +} + +inline +LineIterator& LineIterator::operator ++() +{ + int mask = err < 0 ? -1 : 0; + err += minusDelta + (plusDelta & mask); + ptr += minusStep + (plusStep & mask); + return *this; +} + +inline +LineIterator LineIterator::operator ++(int) +{ + LineIterator it = *this; + ++(*this); + return it; +} + +inline +Point LineIterator::pos() const +{ + Point p; + p.y = (int)((ptr - ptr0)/step); + p.x = (int)(((ptr - ptr0) - p.y*step)/elemSize); + return p; +} + +//! @endcond + +//! @} imgproc_draw + +//! @} imgproc + +} // cv + +#ifndef DISABLE_OPENCV_24_COMPATIBILITY +#include "opencv2/imgproc/imgproc_c.h" +#endif + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/imgproc/detail/distortion_model.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/imgproc/detail/distortion_model.hpp new file mode 100644 index 0000000000000000000000000000000000000000..a9c3ddec8a7febbff1adbf78a9e2d4d403819bfb --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/imgproc/detail/distortion_model.hpp @@ -0,0 +1,123 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_IMGPROC_DETAIL_DISTORTION_MODEL_HPP +#define OPENCV_IMGPROC_DETAIL_DISTORTION_MODEL_HPP + +//! @cond IGNORED + +namespace cv { namespace detail { +/** +Computes the matrix for the projection onto a tilted image sensor +\param tauX angular parameter rotation around x-axis +\param tauY angular parameter rotation around y-axis +\param matTilt if not NULL returns the matrix +\f[ +\vecthreethree{R_{33}(\tau_x, \tau_y)}{0}{-R_{13}((\tau_x, \tau_y)} +{0}{R_{33}(\tau_x, \tau_y)}{-R_{23}(\tau_x, \tau_y)} +{0}{0}{1} R(\tau_x, \tau_y) +\f] +where +\f[ +R(\tau_x, \tau_y) = +\vecthreethree{\cos(\tau_y)}{0}{-\sin(\tau_y)}{0}{1}{0}{\sin(\tau_y)}{0}{\cos(\tau_y)} +\vecthreethree{1}{0}{0}{0}{\cos(\tau_x)}{\sin(\tau_x)}{0}{-\sin(\tau_x)}{\cos(\tau_x)} = +\vecthreethree{\cos(\tau_y)}{\sin(\tau_y)\sin(\tau_x)}{-\sin(\tau_y)\cos(\tau_x)} +{0}{\cos(\tau_x)}{\sin(\tau_x)} +{\sin(\tau_y)}{-\cos(\tau_y)\sin(\tau_x)}{\cos(\tau_y)\cos(\tau_x)}. +\f] +\param dMatTiltdTauX if not NULL it returns the derivative of matTilt with +respect to \f$\tau_x\f$. +\param dMatTiltdTauY if not NULL it returns the derivative of matTilt with +respect to \f$\tau_y\f$. +\param invMatTilt if not NULL it returns the inverse of matTilt +**/ +template +void computeTiltProjectionMatrix(FLOAT tauX, + FLOAT tauY, + Matx* matTilt = 0, + Matx* dMatTiltdTauX = 0, + Matx* dMatTiltdTauY = 0, + Matx* invMatTilt = 0) +{ + FLOAT cTauX = cos(tauX); + FLOAT sTauX = sin(tauX); + FLOAT cTauY = cos(tauY); + FLOAT sTauY = sin(tauY); + Matx matRotX = Matx(1,0,0,0,cTauX,sTauX,0,-sTauX,cTauX); + Matx matRotY = Matx(cTauY,0,-sTauY,0,1,0,sTauY,0,cTauY); + Matx matRotXY = matRotY * matRotX; + Matx matProjZ = Matx(matRotXY(2,2),0,-matRotXY(0,2),0,matRotXY(2,2),-matRotXY(1,2),0,0,1); + if (matTilt) + { + // Matrix for trapezoidal distortion of tilted image sensor + *matTilt = matProjZ * matRotXY; + } + if (dMatTiltdTauX) + { + // Derivative with respect to tauX + Matx dMatRotXYdTauX = matRotY * Matx(0,0,0,0,-sTauX,cTauX,0,-cTauX,-sTauX); + Matx dMatProjZdTauX = Matx(dMatRotXYdTauX(2,2),0,-dMatRotXYdTauX(0,2), + 0,dMatRotXYdTauX(2,2),-dMatRotXYdTauX(1,2),0,0,0); + *dMatTiltdTauX = (matProjZ * dMatRotXYdTauX) + (dMatProjZdTauX * matRotXY); + } + if (dMatTiltdTauY) + { + // Derivative with respect to tauY + Matx dMatRotXYdTauY = Matx(-sTauY,0,-cTauY,0,0,0,cTauY,0,-sTauY) * matRotX; + Matx dMatProjZdTauY = Matx(dMatRotXYdTauY(2,2),0,-dMatRotXYdTauY(0,2), + 0,dMatRotXYdTauY(2,2),-dMatRotXYdTauY(1,2),0,0,0); + *dMatTiltdTauY = (matProjZ * dMatRotXYdTauY) + (dMatProjZdTauY * matRotXY); + } + if (invMatTilt) + { + FLOAT inv = 1./matRotXY(2,2); + Matx invMatProjZ = Matx(inv,0,inv*matRotXY(0,2),0,inv,inv*matRotXY(1,2),0,0,1); + *invMatTilt = matRotXY.t()*invMatProjZ; + } +} +}} // namespace detail, cv + + +//! @endcond + +#endif // OPENCV_IMGPROC_DETAIL_DISTORTION_MODEL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/imgproc/hal/hal.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/imgproc/hal/hal.hpp new file mode 100644 index 0000000000000000000000000000000000000000..a435fd6b8558ec7460cada0656c894ea26429989 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/imgproc/hal/hal.hpp @@ -0,0 +1,241 @@ +#ifndef CV_IMGPROC_HAL_HPP +#define CV_IMGPROC_HAL_HPP + +#include "opencv2/core/cvdef.h" +#include "opencv2/core/cvstd.hpp" +#include "opencv2/core/hal/interface.h" + +namespace cv { namespace hal { + +//! @addtogroup imgproc_hal_functions +//! @{ + +//--------------------------- +//! @cond IGNORED + +struct CV_EXPORTS Filter2D +{ + CV_DEPRECATED static Ptr create(uchar * , size_t , int , + int , int , + int , int , + int , int , + int , double , + int , int , + bool , bool ); + virtual void apply(uchar * , size_t , + uchar * , size_t , + int , int , + int , int , + int , int ) = 0; + virtual ~Filter2D() {} +}; + +struct CV_EXPORTS SepFilter2D +{ + CV_DEPRECATED static Ptr create(int , int , int , + uchar * , int , + uchar * , int , + int , int , + double , int ); + virtual void apply(uchar * , size_t , + uchar * , size_t , + int , int , + int , int , + int , int ) = 0; + virtual ~SepFilter2D() {} +}; + + +struct CV_EXPORTS Morph +{ + CV_DEPRECATED static Ptr create(int , int , int , int , int , + int , uchar * , size_t , + int , int , + int , int , + int , const double *, + int , bool , bool ); + virtual void apply(uchar * , size_t , uchar * , size_t , int , int , + int , int , int , int , + int , int , int , int ) = 0; + virtual ~Morph() {} +}; + +//! @endcond +//--------------------------- + +CV_EXPORTS void filter2D(int stype, int dtype, int kernel_type, + uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int full_width, int full_height, + int offset_x, int offset_y, + uchar * kernel_data, size_t kernel_step, + int kernel_width, int kernel_height, + int anchor_x, int anchor_y, + double delta, int borderType, + bool isSubmatrix); + +CV_EXPORTS void sepFilter2D(int stype, int dtype, int ktype, + uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int full_width, int full_height, + int offset_x, int offset_y, + uchar * kernelx_data, int kernelx_len, + uchar * kernely_data, int kernely_len, + int anchor_x, int anchor_y, + double delta, int borderType); + +CV_EXPORTS void morph(int op, int src_type, int dst_type, + uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int roi_width, int roi_height, int roi_x, int roi_y, + int roi_width2, int roi_height2, int roi_x2, int roi_y2, + int kernel_type, uchar * kernel_data, size_t kernel_step, + int kernel_width, int kernel_height, int anchor_x, int anchor_y, + int borderType, const double borderValue[4], + int iterations, bool isSubmatrix); + + +CV_EXPORTS void resize(int src_type, + const uchar * src_data, size_t src_step, int src_width, int src_height, + uchar * dst_data, size_t dst_step, int dst_width, int dst_height, + double inv_scale_x, double inv_scale_y, int interpolation); + +CV_EXPORTS void warpAffine(int src_type, + const uchar * src_data, size_t src_step, int src_width, int src_height, + uchar * dst_data, size_t dst_step, int dst_width, int dst_height, + const double M[6], int interpolation, int borderType, const double borderValue[4]); + +CV_EXPORTS void warpPerspectve(int src_type, + const uchar * src_data, size_t src_step, int src_width, int src_height, + uchar * dst_data, size_t dst_step, int dst_width, int dst_height, + const double M[9], int interpolation, int borderType, const double borderValue[4]); + +CV_EXPORTS void cvtBGRtoBGR(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int depth, int scn, int dcn, bool swapBlue); + +CV_EXPORTS void cvtBGRtoBGR5x5(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int scn, bool swapBlue, int greenBits); + +CV_EXPORTS void cvtBGR5x5toBGR(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int dcn, bool swapBlue, int greenBits); + +CV_EXPORTS void cvtBGRtoGray(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int depth, int scn, bool swapBlue); + +CV_EXPORTS void cvtGraytoBGR(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int depth, int dcn); + +CV_EXPORTS void cvtBGR5x5toGray(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int greenBits); + +CV_EXPORTS void cvtGraytoBGR5x5(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int greenBits); +CV_EXPORTS void cvtBGRtoYUV(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int depth, int scn, bool swapBlue, bool isCbCr); + +CV_EXPORTS void cvtYUVtoBGR(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int depth, int dcn, bool swapBlue, bool isCbCr); + +CV_EXPORTS void cvtBGRtoXYZ(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int depth, int scn, bool swapBlue); + +CV_EXPORTS void cvtXYZtoBGR(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int depth, int dcn, bool swapBlue); + +CV_EXPORTS void cvtBGRtoHSV(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int depth, int scn, bool swapBlue, bool isFullRange, bool isHSV); + +CV_EXPORTS void cvtHSVtoBGR(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int depth, int dcn, bool swapBlue, bool isFullRange, bool isHSV); + +CV_EXPORTS void cvtBGRtoLab(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int depth, int scn, bool swapBlue, bool isLab, bool srgb); + +CV_EXPORTS void cvtLabtoBGR(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int depth, int dcn, bool swapBlue, bool isLab, bool srgb); + +CV_EXPORTS void cvtTwoPlaneYUVtoBGR(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int dst_width, int dst_height, + int dcn, bool swapBlue, int uIdx); + +//! Separate Y and UV planes +CV_EXPORTS void cvtTwoPlaneYUVtoBGR(const uchar * y_data, const uchar * uv_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int dst_width, int dst_height, + int dcn, bool swapBlue, int uIdx); + +CV_EXPORTS void cvtThreePlaneYUVtoBGR(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int dst_width, int dst_height, + int dcn, bool swapBlue, int uIdx); + +CV_EXPORTS void cvtBGRtoThreePlaneYUV(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int scn, bool swapBlue, int uIdx); + +//! Separate Y and UV planes +CV_EXPORTS void cvtBGRtoTwoPlaneYUV(const uchar * src_data, size_t src_step, + uchar * y_data, uchar * uv_data, size_t dst_step, + int width, int height, + int scn, bool swapBlue, int uIdx); + +CV_EXPORTS void cvtOnePlaneYUVtoBGR(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height, + int dcn, bool swapBlue, int uIdx, int ycn); + +CV_EXPORTS void cvtRGBAtoMultipliedRGBA(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height); + +CV_EXPORTS void cvtMultipliedRGBAtoRGBA(const uchar * src_data, size_t src_step, + uchar * dst_data, size_t dst_step, + int width, int height); + +CV_EXPORTS void integral(int depth, int sdepth, int sqdepth, + const uchar* src, size_t srcstep, + uchar* sum, size_t sumstep, + uchar* sqsum, size_t sqsumstep, + uchar* tilted, size_t tstep, + int width, int height, int cn); + +//! @} + +}} + +#endif // CV_IMGPROC_HAL_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/imgproc/hal/interface.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/imgproc/hal/interface.h new file mode 100644 index 0000000000000000000000000000000000000000..f8dbcfe791d90fd957cace7b7700d737864990e9 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/imgproc/hal/interface.h @@ -0,0 +1,46 @@ +#ifndef OPENCV_IMGPROC_HAL_INTERFACE_H +#define OPENCV_IMGPROC_HAL_INTERFACE_H + +//! @addtogroup imgproc_hal_interface +//! @{ + +//! @name Interpolation modes +//! @sa cv::InterpolationFlags +//! @{ +#define CV_HAL_INTER_NEAREST 0 +#define CV_HAL_INTER_LINEAR 1 +#define CV_HAL_INTER_CUBIC 2 +#define CV_HAL_INTER_AREA 3 +#define CV_HAL_INTER_LANCZOS4 4 +//! @} + +//! @name Morphology operations +//! @sa cv::MorphTypes +//! @{ +#define CV_HAL_MORPH_ERODE 0 +#define CV_HAL_MORPH_DILATE 1 +//! @} + +//! @name Threshold types +//! @sa cv::ThresholdTypes +//! @{ +#define CV_HAL_THRESH_BINARY 0 +#define CV_HAL_THRESH_BINARY_INV 1 +#define CV_HAL_THRESH_TRUNC 2 +#define CV_HAL_THRESH_TOZERO 3 +#define CV_HAL_THRESH_TOZERO_INV 4 +#define CV_HAL_THRESH_MASK 7 +#define CV_HAL_THRESH_OTSU 8 +#define CV_HAL_THRESH_TRIANGLE 16 +//! @} + +//! @name Adaptive threshold algorithm +//! @sa cv::AdaptiveThresholdTypes +//! @{ +#define CV_HAL_ADAPTIVE_THRESH_MEAN_C 0 +#define CV_HAL_ADAPTIVE_THRESH_GAUSSIAN_C 1 +//! @} + +//! @} + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/imgproc/imgproc.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/imgproc/imgproc.hpp new file mode 100644 index 0000000000000000000000000000000000000000..4175bd0bc01e0189abc4c698053c00e28277ef33 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/imgproc/imgproc.hpp @@ -0,0 +1,48 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifdef __OPENCV_BUILD +#error this is a compatibility header which should not be used inside the OpenCV library +#endif + +#include "opencv2/imgproc.hpp" diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/imgproc/imgproc_c.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/imgproc/imgproc_c.h new file mode 100644 index 0000000000000000000000000000000000000000..cec0f3653ab425b86cc6840edee9d6ced8b31854 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/imgproc/imgproc_c.h @@ -0,0 +1,1210 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_IMGPROC_IMGPROC_C_H +#define OPENCV_IMGPROC_IMGPROC_C_H + +#include "opencv2/imgproc/types_c.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/** @addtogroup imgproc_c +@{ +*/ + +/*********************** Background statistics accumulation *****************************/ + +/** @brief Adds image to accumulator +@see cv::accumulate +*/ +CVAPI(void) cvAcc( const CvArr* image, CvArr* sum, + const CvArr* mask CV_DEFAULT(NULL) ); + +/** @brief Adds squared image to accumulator +@see cv::accumulateSquare +*/ +CVAPI(void) cvSquareAcc( const CvArr* image, CvArr* sqsum, + const CvArr* mask CV_DEFAULT(NULL) ); + +/** @brief Adds a product of two images to accumulator +@see cv::accumulateProduct +*/ +CVAPI(void) cvMultiplyAcc( const CvArr* image1, const CvArr* image2, CvArr* acc, + const CvArr* mask CV_DEFAULT(NULL) ); + +/** @brief Adds image to accumulator with weights: acc = acc*(1-alpha) + image*alpha +@see cv::accumulateWeighted +*/ +CVAPI(void) cvRunningAvg( const CvArr* image, CvArr* acc, double alpha, + const CvArr* mask CV_DEFAULT(NULL) ); + +/****************************************************************************************\ +* Image Processing * +\****************************************************************************************/ + +/** Copies source 2D array inside of the larger destination array and + makes a border of the specified type (IPL_BORDER_*) around the copied area. */ +CVAPI(void) cvCopyMakeBorder( const CvArr* src, CvArr* dst, CvPoint offset, + int bordertype, CvScalar value CV_DEFAULT(cvScalarAll(0))); + +/** @brief Smooths the image in one of several ways. + +@param src The source image +@param dst The destination image +@param smoothtype Type of the smoothing, see SmoothMethod_c +@param size1 The first parameter of the smoothing operation, the aperture width. Must be a +positive odd number (1, 3, 5, ...) +@param size2 The second parameter of the smoothing operation, the aperture height. Ignored by +CV_MEDIAN and CV_BILATERAL methods. In the case of simple scaled/non-scaled and Gaussian blur if +size2 is zero, it is set to size1. Otherwise it must be a positive odd number. +@param sigma1 In the case of a Gaussian parameter this parameter may specify Gaussian \f$\sigma\f$ +(standard deviation). If it is zero, it is calculated from the kernel size: +\f[\sigma = 0.3 (n/2 - 1) + 0.8 \quad \text{where} \quad n= \begin{array}{l l} \mbox{\texttt{size1} for horizontal kernel} \\ \mbox{\texttt{size2} for vertical kernel} \end{array}\f] +Using standard sigma for small kernels ( \f$3\times 3\f$ to \f$7\times 7\f$ ) gives better speed. If +sigma1 is not zero, while size1 and size2 are zeros, the kernel size is calculated from the +sigma (to provide accurate enough operation). +@param sigma2 additional parameter for bilateral filtering + +@see cv::GaussianBlur, cv::blur, cv::medianBlur, cv::bilateralFilter. + */ +CVAPI(void) cvSmooth( const CvArr* src, CvArr* dst, + int smoothtype CV_DEFAULT(CV_GAUSSIAN), + int size1 CV_DEFAULT(3), + int size2 CV_DEFAULT(0), + double sigma1 CV_DEFAULT(0), + double sigma2 CV_DEFAULT(0)); + +/** @brief Convolves an image with the kernel. + +@param src input image. +@param dst output image of the same size and the same number of channels as src. +@param kernel convolution kernel (or rather a correlation kernel), a single-channel floating point +matrix; if you want to apply different kernels to different channels, split the image into +separate color planes using split and process them individually. +@param anchor anchor of the kernel that indicates the relative position of a filtered point within +the kernel; the anchor should lie within the kernel; default value (-1,-1) means that the anchor +is at the kernel center. + +@see cv::filter2D + */ +CVAPI(void) cvFilter2D( const CvArr* src, CvArr* dst, const CvMat* kernel, + CvPoint anchor CV_DEFAULT(cvPoint(-1,-1))); + +/** @brief Finds integral image: SUM(X,Y) = sum(x \texttt{hist1}(I)\)}{\frac{\texttt{hist2}(I) \cdot \texttt{scale}}{\texttt{hist1}(I)}}{if \(\texttt{hist1}(I) \ne 0\) and \(\texttt{hist2}(I) \le \texttt{hist1}(I)\)}\f] + +@param hist1 First histogram (the divisor). +@param hist2 Second histogram. +@param dst_hist Destination histogram. +@param scale Scale factor for the destination histogram. + */ +CVAPI(void) cvCalcProbDensity( const CvHistogram* hist1, const CvHistogram* hist2, + CvHistogram* dst_hist, double scale CV_DEFAULT(255) ); + +/** @brief equalizes histogram of 8-bit single-channel image +@see cv::equalizeHist +*/ +CVAPI(void) cvEqualizeHist( const CvArr* src, CvArr* dst ); + + +/** @brief Applies distance transform to binary image +@see cv::distanceTransform +*/ +CVAPI(void) cvDistTransform( const CvArr* src, CvArr* dst, + int distance_type CV_DEFAULT(CV_DIST_L2), + int mask_size CV_DEFAULT(3), + const float* mask CV_DEFAULT(NULL), + CvArr* labels CV_DEFAULT(NULL), + int labelType CV_DEFAULT(CV_DIST_LABEL_CCOMP)); + + +/** @brief Applies fixed-level threshold to grayscale image. + + This is a basic operation applied before retrieving contours +@see cv::threshold +*/ +CVAPI(double) cvThreshold( const CvArr* src, CvArr* dst, + double threshold, double max_value, + int threshold_type ); + +/** @brief Applies adaptive threshold to grayscale image. + + The two parameters for methods CV_ADAPTIVE_THRESH_MEAN_C and + CV_ADAPTIVE_THRESH_GAUSSIAN_C are: + neighborhood size (3, 5, 7 etc.), + and a constant subtracted from mean (...,-3,-2,-1,0,1,2,3,...) +@see cv::adaptiveThreshold +*/ +CVAPI(void) cvAdaptiveThreshold( const CvArr* src, CvArr* dst, double max_value, + int adaptive_method CV_DEFAULT(CV_ADAPTIVE_THRESH_MEAN_C), + int threshold_type CV_DEFAULT(CV_THRESH_BINARY), + int block_size CV_DEFAULT(3), + double param1 CV_DEFAULT(5)); + +/** @brief Fills the connected component until the color difference gets large enough +@see cv::floodFill +*/ +CVAPI(void) cvFloodFill( CvArr* image, CvPoint seed_point, + CvScalar new_val, CvScalar lo_diff CV_DEFAULT(cvScalarAll(0)), + CvScalar up_diff CV_DEFAULT(cvScalarAll(0)), + CvConnectedComp* comp CV_DEFAULT(NULL), + int flags CV_DEFAULT(4), + CvArr* mask CV_DEFAULT(NULL)); + +/****************************************************************************************\ +* Feature detection * +\****************************************************************************************/ + +/** @brief Runs canny edge detector +@see cv::Canny +*/ +CVAPI(void) cvCanny( const CvArr* image, CvArr* edges, double threshold1, + double threshold2, int aperture_size CV_DEFAULT(3) ); + +/** @brief Calculates constraint image for corner detection + + Dx^2 * Dyy + Dxx * Dy^2 - 2 * Dx * Dy * Dxy. + Applying threshold to the result gives coordinates of corners +@see cv::preCornerDetect +*/ +CVAPI(void) cvPreCornerDetect( const CvArr* image, CvArr* corners, + int aperture_size CV_DEFAULT(3) ); + +/** @brief Calculates eigen values and vectors of 2x2 + gradient covariation matrix at every image pixel +@see cv::cornerEigenValsAndVecs +*/ +CVAPI(void) cvCornerEigenValsAndVecs( const CvArr* image, CvArr* eigenvv, + int block_size, int aperture_size CV_DEFAULT(3) ); + +/** @brief Calculates minimal eigenvalue for 2x2 gradient covariation matrix at + every image pixel +@see cv::cornerMinEigenVal +*/ +CVAPI(void) cvCornerMinEigenVal( const CvArr* image, CvArr* eigenval, + int block_size, int aperture_size CV_DEFAULT(3) ); + +/** @brief Harris corner detector: + + Calculates det(M) - k*(trace(M)^2), where M is 2x2 gradient covariation matrix for each pixel +@see cv::cornerHarris +*/ +CVAPI(void) cvCornerHarris( const CvArr* image, CvArr* harris_response, + int block_size, int aperture_size CV_DEFAULT(3), + double k CV_DEFAULT(0.04) ); + +/** @brief Adjust corner position using some sort of gradient search +@see cv::cornerSubPix +*/ +CVAPI(void) cvFindCornerSubPix( const CvArr* image, CvPoint2D32f* corners, + int count, CvSize win, CvSize zero_zone, + CvTermCriteria criteria ); + +/** @brief Finds a sparse set of points within the selected region + that seem to be easy to track +@see cv::goodFeaturesToTrack +*/ +CVAPI(void) cvGoodFeaturesToTrack( const CvArr* image, CvArr* eig_image, + CvArr* temp_image, CvPoint2D32f* corners, + int* corner_count, double quality_level, + double min_distance, + const CvArr* mask CV_DEFAULT(NULL), + int block_size CV_DEFAULT(3), + int use_harris CV_DEFAULT(0), + double k CV_DEFAULT(0.04) ); + +/** @brief Finds lines on binary image using one of several methods. + + line_storage is either memory storage or 1 x _max number of lines_ CvMat, its + number of columns is changed by the function. + method is one of CV_HOUGH_*; + rho, theta and threshold are used for each of those methods; + param1 ~ line length, param2 ~ line gap - for probabilistic, + param1 ~ srn, param2 ~ stn - for multi-scale +@see cv::HoughLines +*/ +CVAPI(CvSeq*) cvHoughLines2( CvArr* image, void* line_storage, int method, + double rho, double theta, int threshold, + double param1 CV_DEFAULT(0), double param2 CV_DEFAULT(0), + double min_theta CV_DEFAULT(0), double max_theta CV_DEFAULT(CV_PI)); + +/** @brief Finds circles in the image +@see cv::HoughCircles +*/ +CVAPI(CvSeq*) cvHoughCircles( CvArr* image, void* circle_storage, + int method, double dp, double min_dist, + double param1 CV_DEFAULT(100), + double param2 CV_DEFAULT(100), + int min_radius CV_DEFAULT(0), + int max_radius CV_DEFAULT(0)); + +/** @brief Fits a line into set of 2d or 3d points in a robust way (M-estimator technique) +@see cv::fitLine +*/ +CVAPI(void) cvFitLine( const CvArr* points, int dist_type, double param, + double reps, double aeps, float* line ); + +/****************************************************************************************\ +* Drawing * +\****************************************************************************************/ + +/****************************************************************************************\ +* Drawing functions work with images/matrices of arbitrary type. * +* For color images the channel order is BGR[A] * +* Antialiasing is supported only for 8-bit image now. * +* All the functions include parameter color that means rgb value (that may be * +* constructed with CV_RGB macro) for color images and brightness * +* for grayscale images. * +* If a drawn figure is partially or completely outside of the image, it is clipped.* +\****************************************************************************************/ + +#define CV_FILLED -1 + +#define CV_AA 16 + +/** @brief Draws 4-connected, 8-connected or antialiased line segment connecting two points +@see cv::line +*/ +CVAPI(void) cvLine( CvArr* img, CvPoint pt1, CvPoint pt2, + CvScalar color, int thickness CV_DEFAULT(1), + int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0) ); + +/** @brief Draws a rectangle given two opposite corners of the rectangle (pt1 & pt2) + + if thickness<0 (e.g. thickness == CV_FILLED), the filled box is drawn +@see cv::rectangle +*/ +CVAPI(void) cvRectangle( CvArr* img, CvPoint pt1, CvPoint pt2, + CvScalar color, int thickness CV_DEFAULT(1), + int line_type CV_DEFAULT(8), + int shift CV_DEFAULT(0)); + +/** @brief Draws a rectangle specified by a CvRect structure +@see cv::rectangle +*/ +CVAPI(void) cvRectangleR( CvArr* img, CvRect r, + CvScalar color, int thickness CV_DEFAULT(1), + int line_type CV_DEFAULT(8), + int shift CV_DEFAULT(0)); + + +/** @brief Draws a circle with specified center and radius. + + Thickness works in the same way as with cvRectangle +@see cv::circle +*/ +CVAPI(void) cvCircle( CvArr* img, CvPoint center, int radius, + CvScalar color, int thickness CV_DEFAULT(1), + int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0)); + +/** @brief Draws ellipse outline, filled ellipse, elliptic arc or filled elliptic sector + + depending on _thickness_, _start_angle_ and _end_angle_ parameters. The resultant figure + is rotated by _angle_. All the angles are in degrees +@see cv::ellipse +*/ +CVAPI(void) cvEllipse( CvArr* img, CvPoint center, CvSize axes, + double angle, double start_angle, double end_angle, + CvScalar color, int thickness CV_DEFAULT(1), + int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0)); + +CV_INLINE void cvEllipseBox( CvArr* img, CvBox2D box, CvScalar color, + int thickness CV_DEFAULT(1), + int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0) ) +{ + CvSize axes = cvSize( + cvRound(box.size.width*0.5), + cvRound(box.size.height*0.5) + ); + + cvEllipse( img, cvPointFrom32f( box.center ), axes, box.angle, + 0, 360, color, thickness, line_type, shift ); +} + +/** @brief Fills convex or monotonous polygon. +@see cv::fillConvexPoly +*/ +CVAPI(void) cvFillConvexPoly( CvArr* img, const CvPoint* pts, int npts, CvScalar color, + int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0)); + +/** @brief Fills an area bounded by one or more arbitrary polygons +@see cv::fillPoly +*/ +CVAPI(void) cvFillPoly( CvArr* img, CvPoint** pts, const int* npts, + int contours, CvScalar color, + int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0) ); + +/** @brief Draws one or more polygonal curves +@see cv::polylines +*/ +CVAPI(void) cvPolyLine( CvArr* img, CvPoint** pts, const int* npts, int contours, + int is_closed, CvScalar color, int thickness CV_DEFAULT(1), + int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0) ); + +#define cvDrawRect cvRectangle +#define cvDrawLine cvLine +#define cvDrawCircle cvCircle +#define cvDrawEllipse cvEllipse +#define cvDrawPolyLine cvPolyLine + +/** @brief Clips the line segment connecting *pt1 and *pt2 + by the rectangular window + + (0<=xptr will point to pt1 (or pt2, see left_to_right description) location in +the image. Returns the number of pixels on the line between the ending points. +@see cv::LineIterator +*/ +CVAPI(int) cvInitLineIterator( const CvArr* image, CvPoint pt1, CvPoint pt2, + CvLineIterator* line_iterator, + int connectivity CV_DEFAULT(8), + int left_to_right CV_DEFAULT(0)); + +#define CV_NEXT_LINE_POINT( line_iterator ) \ +{ \ + int _line_iterator_mask = (line_iterator).err < 0 ? -1 : 0; \ + (line_iterator).err += (line_iterator).minus_delta + \ + ((line_iterator).plus_delta & _line_iterator_mask); \ + (line_iterator).ptr += (line_iterator).minus_step + \ + ((line_iterator).plus_step & _line_iterator_mask); \ +} + + +#define CV_FONT_HERSHEY_SIMPLEX 0 +#define CV_FONT_HERSHEY_PLAIN 1 +#define CV_FONT_HERSHEY_DUPLEX 2 +#define CV_FONT_HERSHEY_COMPLEX 3 +#define CV_FONT_HERSHEY_TRIPLEX 4 +#define CV_FONT_HERSHEY_COMPLEX_SMALL 5 +#define CV_FONT_HERSHEY_SCRIPT_SIMPLEX 6 +#define CV_FONT_HERSHEY_SCRIPT_COMPLEX 7 + +#define CV_FONT_ITALIC 16 + +#define CV_FONT_VECTOR0 CV_FONT_HERSHEY_SIMPLEX + + +/** Font structure */ +typedef struct CvFont +{ + const char* nameFont; //Qt:nameFont + CvScalar color; //Qt:ColorFont -> cvScalar(blue_component, green_component, red_component[, alpha_component]) + int font_face; //Qt: bool italic /** =CV_FONT_* */ + const int* ascii; //!< font data and metrics + const int* greek; + const int* cyrillic; + float hscale, vscale; + float shear; //!< slope coefficient: 0 - normal, >0 - italic + int thickness; //!< Qt: weight /** letters thickness */ + float dx; //!< horizontal interval between letters + int line_type; //!< Qt: PointSize +} +CvFont; + +/** @brief Initializes font structure (OpenCV 1.x API). + +The function initializes the font structure that can be passed to text rendering functions. + +@param font Pointer to the font structure initialized by the function +@param font_face Font name identifier. See cv::HersheyFonts and corresponding old CV_* identifiers. +@param hscale Horizontal scale. If equal to 1.0f , the characters have the original width +depending on the font type. If equal to 0.5f , the characters are of half the original width. +@param vscale Vertical scale. If equal to 1.0f , the characters have the original height depending +on the font type. If equal to 0.5f , the characters are of half the original height. +@param shear Approximate tangent of the character slope relative to the vertical line. A zero +value means a non-italic font, 1.0f means about a 45 degree slope, etc. +@param thickness Thickness of the text strokes +@param line_type Type of the strokes, see line description + +@sa cvPutText + */ +CVAPI(void) cvInitFont( CvFont* font, int font_face, + double hscale, double vscale, + double shear CV_DEFAULT(0), + int thickness CV_DEFAULT(1), + int line_type CV_DEFAULT(8)); + +CV_INLINE CvFont cvFont( double scale, int thickness CV_DEFAULT(1) ) +{ + CvFont font; + cvInitFont( &font, CV_FONT_HERSHEY_PLAIN, scale, scale, 0, thickness, CV_AA ); + return font; +} + +/** @brief Renders text stroke with specified font and color at specified location. + CvFont should be initialized with cvInitFont +@see cvInitFont, cvGetTextSize, cvFont, cv::putText +*/ +CVAPI(void) cvPutText( CvArr* img, const char* text, CvPoint org, + const CvFont* font, CvScalar color ); + +/** @brief Calculates bounding box of text stroke (useful for alignment) +@see cv::getTextSize +*/ +CVAPI(void) cvGetTextSize( const char* text_string, const CvFont* font, + CvSize* text_size, int* baseline ); + +/** @brief Unpacks color value + +if arrtype is CV_8UC?, _color_ is treated as packed color value, otherwise the first channels +(depending on arrtype) of destination scalar are set to the same value = _color_ +*/ +CVAPI(CvScalar) cvColorToScalar( double packed_color, int arrtype ); + +/** @brief Returns the polygon points which make up the given ellipse. + +The ellipse is define by the box of size 'axes' rotated 'angle' around the 'center'. A partial +sweep of the ellipse arc can be done by spcifying arc_start and arc_end to be something other than +0 and 360, respectively. The input array 'pts' must be large enough to hold the result. The total +number of points stored into 'pts' is returned by this function. +@see cv::ellipse2Poly +*/ +CVAPI(int) cvEllipse2Poly( CvPoint center, CvSize axes, + int angle, int arc_start, int arc_end, CvPoint * pts, int delta ); + +/** @brief Draws contour outlines or filled interiors on the image +@see cv::drawContours +*/ +CVAPI(void) cvDrawContours( CvArr *img, CvSeq* contour, + CvScalar external_color, CvScalar hole_color, + int max_level, int thickness CV_DEFAULT(1), + int line_type CV_DEFAULT(8), + CvPoint offset CV_DEFAULT(cvPoint(0,0))); + +/** @} */ + +#ifdef __cplusplus +} +#endif + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/imgproc/types_c.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/imgproc/types_c.h new file mode 100644 index 0000000000000000000000000000000000000000..d3e55f576f6e93136d030285f7e753865a34ed41 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/imgproc/types_c.h @@ -0,0 +1,659 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_IMGPROC_TYPES_C_H +#define OPENCV_IMGPROC_TYPES_C_H + +#include "opencv2/core/core_c.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/** @addtogroup imgproc_c + @{ +*/ + +/** Connected component structure */ +typedef struct CvConnectedComp +{ + double area; /** DBL_EPSILON ? 1./std::sqrt(am00) : 0; + } + operator cv::Moments() const + { + return cv::Moments(m00, m10, m01, m20, m11, m02, m30, m21, m12, m03); + } +#endif +} +CvMoments; + +#ifdef __cplusplus +} // extern "C" + +CV_INLINE CvMoments cvMoments() +{ +#if !defined(CV__ENABLE_C_API_CTORS) + CvMoments self = CV_STRUCT_INITIALIZER; return self; +#else + return CvMoments(); +#endif +} + +CV_INLINE CvMoments cvMoments(const cv::Moments& m) +{ +#if !defined(CV__ENABLE_C_API_CTORS) + double am00 = std::abs(m.m00); + CvMoments self = { + m.m00, m.m10, m.m01, m.m20, m.m11, m.m02, m.m30, m.m21, m.m12, m.m03, + m.mu20, m.mu11, m.mu02, m.mu30, m.mu21, m.mu12, m.mu03, + am00 > DBL_EPSILON ? 1./std::sqrt(am00) : 0 + }; + return self; +#else + return CvMoments(m); +#endif +} + +extern "C" { +#endif // __cplusplus + +/** Hu invariants */ +typedef struct CvHuMoments +{ + double hu1, hu2, hu3, hu4, hu5, hu6, hu7; /**< Hu invariants */ +} +CvHuMoments; + +/** Template matching methods */ +enum +{ + CV_TM_SQDIFF =0, + CV_TM_SQDIFF_NORMED =1, + CV_TM_CCORR =2, + CV_TM_CCORR_NORMED =3, + CV_TM_CCOEFF =4, + CV_TM_CCOEFF_NORMED =5 +}; + +typedef float (CV_CDECL * CvDistanceFunction)( const float* a, const float* b, void* user_param ); + +/** Contour retrieval modes */ +enum +{ + CV_RETR_EXTERNAL=0, + CV_RETR_LIST=1, + CV_RETR_CCOMP=2, + CV_RETR_TREE=3, + CV_RETR_FLOODFILL=4 +}; + +/** Contour approximation methods */ +enum +{ + CV_CHAIN_CODE=0, + CV_CHAIN_APPROX_NONE=1, + CV_CHAIN_APPROX_SIMPLE=2, + CV_CHAIN_APPROX_TC89_L1=3, + CV_CHAIN_APPROX_TC89_KCOS=4, + CV_LINK_RUNS=5 +}; + +/* +Internal structure that is used for sequential retrieving contours from the image. +It supports both hierarchical and plane variants of Suzuki algorithm. +*/ +typedef struct _CvContourScanner* CvContourScanner; + +/** Freeman chain reader state */ +typedef struct CvChainPtReader +{ + CV_SEQ_READER_FIELDS() + char code; + CvPoint pt; + schar deltas[8][2]; +} +CvChainPtReader; + +/** initializes 8-element array for fast access to 3x3 neighborhood of a pixel */ +#define CV_INIT_3X3_DELTAS( deltas, step, nch ) \ + ((deltas)[0] = (nch), (deltas)[1] = -(step) + (nch), \ + (deltas)[2] = -(step), (deltas)[3] = -(step) - (nch), \ + (deltas)[4] = -(nch), (deltas)[5] = (step) - (nch), \ + (deltas)[6] = (step), (deltas)[7] = (step) + (nch)) + + +/** Contour approximation algorithms */ +enum +{ + CV_POLY_APPROX_DP = 0 +}; + +/** Shape matching methods */ +enum +{ + CV_CONTOURS_MATCH_I1 =1, //!< \f[I_1(A,B) = \sum _{i=1...7} \left | \frac{1}{m^A_i} - \frac{1}{m^B_i} \right |\f] + CV_CONTOURS_MATCH_I2 =2, //!< \f[I_2(A,B) = \sum _{i=1...7} \left | m^A_i - m^B_i \right |\f] + CV_CONTOURS_MATCH_I3 =3 //!< \f[I_3(A,B) = \max _{i=1...7} \frac{ \left| m^A_i - m^B_i \right| }{ \left| m^A_i \right| }\f] +}; + +/** Shape orientation */ +enum +{ + CV_CLOCKWISE =1, + CV_COUNTER_CLOCKWISE =2 +}; + + +/** Convexity defect */ +typedef struct CvConvexityDefect +{ + CvPoint* start; /**< point of the contour where the defect begins */ + CvPoint* end; /**< point of the contour where the defect ends */ + CvPoint* depth_point; /**< the farthest from the convex hull point within the defect */ + float depth; /**< distance between the farthest point and the convex hull */ +} CvConvexityDefect; + + +/** Histogram comparison methods */ +enum +{ + CV_COMP_CORREL =0, + CV_COMP_CHISQR =1, + CV_COMP_INTERSECT =2, + CV_COMP_BHATTACHARYYA =3, + CV_COMP_HELLINGER =CV_COMP_BHATTACHARYYA, + CV_COMP_CHISQR_ALT =4, + CV_COMP_KL_DIV =5 +}; + +/** Mask size for distance transform */ +enum +{ + CV_DIST_MASK_3 =3, + CV_DIST_MASK_5 =5, + CV_DIST_MASK_PRECISE =0 +}; + +/** Content of output label array: connected components or pixels */ +enum +{ + CV_DIST_LABEL_CCOMP = 0, + CV_DIST_LABEL_PIXEL = 1 +}; + +/** Distance types for Distance Transform and M-estimators */ +enum +{ + CV_DIST_USER =-1, /**< User defined distance */ + CV_DIST_L1 =1, /**< distance = |x1-x2| + |y1-y2| */ + CV_DIST_L2 =2, /**< the simple euclidean distance */ + CV_DIST_C =3, /**< distance = max(|x1-x2|,|y1-y2|) */ + CV_DIST_L12 =4, /**< L1-L2 metric: distance = 2(sqrt(1+x*x/2) - 1)) */ + CV_DIST_FAIR =5, /**< distance = c^2(|x|/c-log(1+|x|/c)), c = 1.3998 */ + CV_DIST_WELSCH =6, /**< distance = c^2/2(1-exp(-(x/c)^2)), c = 2.9846 */ + CV_DIST_HUBER =7 /**< distance = |x| threshold ? max_value : 0 */ + CV_THRESH_BINARY_INV =1, /**< value = value > threshold ? 0 : max_value */ + CV_THRESH_TRUNC =2, /**< value = value > threshold ? threshold : value */ + CV_THRESH_TOZERO =3, /**< value = value > threshold ? value : 0 */ + CV_THRESH_TOZERO_INV =4, /**< value = value > threshold ? 0 : value */ + CV_THRESH_MASK =7, + CV_THRESH_OTSU =8, /**< use Otsu algorithm to choose the optimal threshold value; + combine the flag with one of the above CV_THRESH_* values */ + CV_THRESH_TRIANGLE =16 /**< use Triangle algorithm to choose the optimal threshold value; + combine the flag with one of the above CV_THRESH_* values, but not + with CV_THRESH_OTSU */ +}; + +/** Adaptive threshold methods */ +enum +{ + CV_ADAPTIVE_THRESH_MEAN_C =0, + CV_ADAPTIVE_THRESH_GAUSSIAN_C =1 +}; + +/** FloodFill flags */ +enum +{ + CV_FLOODFILL_FIXED_RANGE =(1 << 16), + CV_FLOODFILL_MASK_ONLY =(1 << 17) +}; + + +/** Canny edge detector flags */ +enum +{ + CV_CANNY_L2_GRADIENT =(1 << 31) +}; + +/** Variants of a Hough transform */ +enum +{ + CV_HOUGH_STANDARD =0, + CV_HOUGH_PROBABILISTIC =1, + CV_HOUGH_MULTI_SCALE =2, + CV_HOUGH_GRADIENT =3 +}; + + +/* Fast search data structures */ +struct CvFeatureTree; +struct CvLSH; +struct CvLSHOperations; + +/** @} */ + +#ifdef __cplusplus +} +#endif + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/opencv.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/opencv.hpp new file mode 100644 index 0000000000000000000000000000000000000000..40481587f05782ae97989657dec822276096a48c --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/opencv.hpp @@ -0,0 +1,139 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009-2010, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_ALL_HPP +#define OPENCV_ALL_HPP + +// File that defines what modules where included during the build of OpenCV +// These are purely the defines of the correct HAVE_OPENCV_modulename values +#include "opencv2/opencv_modules.hpp" + +// Then the list of defines is checked to include the correct headers +// Core library is always included --> without no OpenCV functionality available +#include "opencv2/core.hpp" + +// Then the optional modules are checked +#ifdef HAVE_OPENCV_CALIB3D +#include "opencv2/calib3d.hpp" +#endif +#ifdef HAVE_OPENCV_FEATURES2D +#include "opencv2/features2d.hpp" +#endif +#ifdef HAVE_OPENCV_DNN +#include "opencv2/dnn.hpp" +#endif +#ifdef HAVE_OPENCV_FLANN +#include "opencv2/flann.hpp" +#endif +#ifdef HAVE_OPENCV_HIGHGUI +#include "opencv2/highgui.hpp" +#endif +#ifdef HAVE_OPENCV_IMGCODECS +#include "opencv2/imgcodecs.hpp" +#endif +#ifdef HAVE_OPENCV_IMGPROC +#include "opencv2/imgproc.hpp" +#endif +#ifdef HAVE_OPENCV_ML +#include "opencv2/ml.hpp" +#endif +#ifdef HAVE_OPENCV_OBJDETECT +#include "opencv2/objdetect.hpp" +#endif +#ifdef HAVE_OPENCV_PHOTO +#include "opencv2/photo.hpp" +#endif +#ifdef HAVE_OPENCV_SHAPE +#include "opencv2/shape.hpp" +#endif +#ifdef HAVE_OPENCV_STITCHING +#include "opencv2/stitching.hpp" +#endif +#ifdef HAVE_OPENCV_SUPERRES +#include "opencv2/superres.hpp" +#endif +#ifdef HAVE_OPENCV_VIDEO +#include "opencv2/video.hpp" +#endif +#ifdef HAVE_OPENCV_VIDEOIO +#include "opencv2/videoio.hpp" +#endif +#ifdef HAVE_OPENCV_VIDEOSTAB +#include "opencv2/videostab.hpp" +#endif +#ifdef HAVE_OPENCV_VIZ +#include "opencv2/viz.hpp" +#endif + +// Finally CUDA specific entries are checked and added +#ifdef HAVE_OPENCV_CUDAARITHM +#include "opencv2/cudaarithm.hpp" +#endif +#ifdef HAVE_OPENCV_CUDABGSEGM +#include "opencv2/cudabgsegm.hpp" +#endif +#ifdef HAVE_OPENCV_CUDACODEC +#include "opencv2/cudacodec.hpp" +#endif +#ifdef HAVE_OPENCV_CUDAFEATURES2D +#include "opencv2/cudafeatures2d.hpp" +#endif +#ifdef HAVE_OPENCV_CUDAFILTERS +#include "opencv2/cudafilters.hpp" +#endif +#ifdef HAVE_OPENCV_CUDAIMGPROC +#include "opencv2/cudaimgproc.hpp" +#endif +#ifdef HAVE_OPENCV_CUDAOBJDETECT +#include "opencv2/cudaobjdetect.hpp" +#endif +#ifdef HAVE_OPENCV_CUDAOPTFLOW +#include "opencv2/cudaoptflow.hpp" +#endif +#ifdef HAVE_OPENCV_CUDASTEREO +#include "opencv2/cudastereo.hpp" +#endif +#ifdef HAVE_OPENCV_CUDAWARPING +#include "opencv2/cudawarping.hpp" +#endif + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/opencv_modules.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/opencv_modules.hpp new file mode 100644 index 0000000000000000000000000000000000000000..e142b25e0ba7c3879831719f8b362f7af176d274 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/opencv_modules.hpp @@ -0,0 +1,20 @@ +/* + * ** File generated automatically, do not modify ** + * + * This file defines the list of modules available in current build configuration + * + * +*/ + +// This definition means that OpenCV is built with enabled non-free code. +// For example, patented algorithms for non-profit/non-commercial use only. +/* #undef OPENCV_ENABLE_NONFREE */ + +#define HAVE_OPENCV_CALIB3D +#define HAVE_OPENCV_CORE +#define HAVE_OPENCV_FEATURES2D +#define HAVE_OPENCV_IMGCODECS +#define HAVE_OPENCV_IMGPROC +#define HAVE_OPENCV_VIDEO + + diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/video.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/video.hpp new file mode 100644 index 0000000000000000000000000000000000000000..aa644a937a7578f562a926c836edfd2d00894e86 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/video.hpp @@ -0,0 +1,63 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_VIDEO_HPP +#define OPENCV_VIDEO_HPP + +/** + @defgroup video Video Analysis + @{ + @defgroup video_motion Motion Analysis + @defgroup video_track Object Tracking + @defgroup video_c C API + @} +*/ + +#include "opencv2/video/tracking.hpp" +#include "opencv2/video/background_segm.hpp" + +#ifndef DISABLE_OPENCV_24_COMPATIBILITY +#include "opencv2/video/tracking_c.h" +#endif + +#endif //OPENCV_VIDEO_HPP diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/video/background_segm.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/video/background_segm.hpp new file mode 100644 index 0000000000000000000000000000000000000000..e1dfa15a9a554aceee71d3a935b11201b9326b9f --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/video/background_segm.hpp @@ -0,0 +1,317 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_BACKGROUND_SEGM_HPP +#define OPENCV_BACKGROUND_SEGM_HPP + +#include "opencv2/core.hpp" + +namespace cv +{ + +//! @addtogroup video_motion +//! @{ + +/** @brief Base class for background/foreground segmentation. : + +The class is only used to define the common interface for the whole family of background/foreground +segmentation algorithms. + */ +class CV_EXPORTS_W BackgroundSubtractor : public Algorithm +{ +public: + /** @brief Computes a foreground mask. + + @param image Next video frame. + @param fgmask The output foreground mask as an 8-bit binary image. + @param learningRate The value between 0 and 1 that indicates how fast the background model is + learnt. Negative parameter value makes the algorithm to use some automatically chosen learning + rate. 0 means that the background model is not updated at all, 1 means that the background model + is completely reinitialized from the last frame. + */ + CV_WRAP virtual void apply(InputArray image, OutputArray fgmask, double learningRate=-1) = 0; + + /** @brief Computes a background image. + + @param backgroundImage The output background image. + + @note Sometimes the background image can be very blurry, as it contain the average background + statistics. + */ + CV_WRAP virtual void getBackgroundImage(OutputArray backgroundImage) const = 0; +}; + + +/** @brief Gaussian Mixture-based Background/Foreground Segmentation Algorithm. + +The class implements the Gaussian mixture model background subtraction described in @cite Zivkovic2004 +and @cite Zivkovic2006 . + */ +class CV_EXPORTS_W BackgroundSubtractorMOG2 : public BackgroundSubtractor +{ +public: + /** @brief Returns the number of last frames that affect the background model + */ + CV_WRAP virtual int getHistory() const = 0; + /** @brief Sets the number of last frames that affect the background model + */ + CV_WRAP virtual void setHistory(int history) = 0; + + /** @brief Returns the number of gaussian components in the background model + */ + CV_WRAP virtual int getNMixtures() const = 0; + /** @brief Sets the number of gaussian components in the background model. + + The model needs to be reinitalized to reserve memory. + */ + CV_WRAP virtual void setNMixtures(int nmixtures) = 0;//needs reinitialization! + + /** @brief Returns the "background ratio" parameter of the algorithm + + If a foreground pixel keeps semi-constant value for about backgroundRatio\*history frames, it's + considered background and added to the model as a center of a new component. It corresponds to TB + parameter in the paper. + */ + CV_WRAP virtual double getBackgroundRatio() const = 0; + /** @brief Sets the "background ratio" parameter of the algorithm + */ + CV_WRAP virtual void setBackgroundRatio(double ratio) = 0; + + /** @brief Returns the variance threshold for the pixel-model match + + The main threshold on the squared Mahalanobis distance to decide if the sample is well described by + the background model or not. Related to Cthr from the paper. + */ + CV_WRAP virtual double getVarThreshold() const = 0; + /** @brief Sets the variance threshold for the pixel-model match + */ + CV_WRAP virtual void setVarThreshold(double varThreshold) = 0; + + /** @brief Returns the variance threshold for the pixel-model match used for new mixture component generation + + Threshold for the squared Mahalanobis distance that helps decide when a sample is close to the + existing components (corresponds to Tg in the paper). If a pixel is not close to any component, it + is considered foreground or added as a new component. 3 sigma =\> Tg=3\*3=9 is default. A smaller Tg + value generates more components. A higher Tg value may result in a small number of components but + they can grow too large. + */ + CV_WRAP virtual double getVarThresholdGen() const = 0; + /** @brief Sets the variance threshold for the pixel-model match used for new mixture component generation + */ + CV_WRAP virtual void setVarThresholdGen(double varThresholdGen) = 0; + + /** @brief Returns the initial variance of each gaussian component + */ + CV_WRAP virtual double getVarInit() const = 0; + /** @brief Sets the initial variance of each gaussian component + */ + CV_WRAP virtual void setVarInit(double varInit) = 0; + + CV_WRAP virtual double getVarMin() const = 0; + CV_WRAP virtual void setVarMin(double varMin) = 0; + + CV_WRAP virtual double getVarMax() const = 0; + CV_WRAP virtual void setVarMax(double varMax) = 0; + + /** @brief Returns the complexity reduction threshold + + This parameter defines the number of samples needed to accept to prove the component exists. CT=0.05 + is a default value for all the samples. By setting CT=0 you get an algorithm very similar to the + standard Stauffer&Grimson algorithm. + */ + CV_WRAP virtual double getComplexityReductionThreshold() const = 0; + /** @brief Sets the complexity reduction threshold + */ + CV_WRAP virtual void setComplexityReductionThreshold(double ct) = 0; + + /** @brief Returns the shadow detection flag + + If true, the algorithm detects shadows and marks them. See createBackgroundSubtractorMOG2 for + details. + */ + CV_WRAP virtual bool getDetectShadows() const = 0; + /** @brief Enables or disables shadow detection + */ + CV_WRAP virtual void setDetectShadows(bool detectShadows) = 0; + + /** @brief Returns the shadow value + + Shadow value is the value used to mark shadows in the foreground mask. Default value is 127. Value 0 + in the mask always means background, 255 means foreground. + */ + CV_WRAP virtual int getShadowValue() const = 0; + /** @brief Sets the shadow value + */ + CV_WRAP virtual void setShadowValue(int value) = 0; + + /** @brief Returns the shadow threshold + + A shadow is detected if pixel is a darker version of the background. The shadow threshold (Tau in + the paper) is a threshold defining how much darker the shadow can be. Tau= 0.5 means that if a pixel + is more than twice darker then it is not shadow. See Prati, Mikic, Trivedi and Cucchiara, + *Detecting Moving Shadows...*, IEEE PAMI,2003. + */ + CV_WRAP virtual double getShadowThreshold() const = 0; + /** @brief Sets the shadow threshold + */ + CV_WRAP virtual void setShadowThreshold(double threshold) = 0; + + /** @brief Computes a foreground mask. + + @param image Next video frame. Floating point frame will be used without scaling and should be in range \f$[0,255]\f$. + @param fgmask The output foreground mask as an 8-bit binary image. + @param learningRate The value between 0 and 1 that indicates how fast the background model is + learnt. Negative parameter value makes the algorithm to use some automatically chosen learning + rate. 0 means that the background model is not updated at all, 1 means that the background model + is completely reinitialized from the last frame. + */ + CV_WRAP virtual void apply(InputArray image, OutputArray fgmask, double learningRate=-1) CV_OVERRIDE = 0; +}; + +/** @brief Creates MOG2 Background Subtractor + +@param history Length of the history. +@param varThreshold Threshold on the squared Mahalanobis distance between the pixel and the model +to decide whether a pixel is well described by the background model. This parameter does not +affect the background update. +@param detectShadows If true, the algorithm will detect shadows and mark them. It decreases the +speed a bit, so if you do not need this feature, set the parameter to false. + */ +CV_EXPORTS_W Ptr + createBackgroundSubtractorMOG2(int history=500, double varThreshold=16, + bool detectShadows=true); + +/** @brief K-nearest neighbours - based Background/Foreground Segmentation Algorithm. + +The class implements the K-nearest neighbours background subtraction described in @cite Zivkovic2006 . +Very efficient if number of foreground pixels is low. + */ +class CV_EXPORTS_W BackgroundSubtractorKNN : public BackgroundSubtractor +{ +public: + /** @brief Returns the number of last frames that affect the background model + */ + CV_WRAP virtual int getHistory() const = 0; + /** @brief Sets the number of last frames that affect the background model + */ + CV_WRAP virtual void setHistory(int history) = 0; + + /** @brief Returns the number of data samples in the background model + */ + CV_WRAP virtual int getNSamples() const = 0; + /** @brief Sets the number of data samples in the background model. + + The model needs to be reinitalized to reserve memory. + */ + CV_WRAP virtual void setNSamples(int _nN) = 0;//needs reinitialization! + + /** @brief Returns the threshold on the squared distance between the pixel and the sample + + The threshold on the squared distance between the pixel and the sample to decide whether a pixel is + close to a data sample. + */ + CV_WRAP virtual double getDist2Threshold() const = 0; + /** @brief Sets the threshold on the squared distance + */ + CV_WRAP virtual void setDist2Threshold(double _dist2Threshold) = 0; + + /** @brief Returns the number of neighbours, the k in the kNN. + + K is the number of samples that need to be within dist2Threshold in order to decide that that + pixel is matching the kNN background model. + */ + CV_WRAP virtual int getkNNSamples() const = 0; + /** @brief Sets the k in the kNN. How many nearest neighbours need to match. + */ + CV_WRAP virtual void setkNNSamples(int _nkNN) = 0; + + /** @brief Returns the shadow detection flag + + If true, the algorithm detects shadows and marks them. See createBackgroundSubtractorKNN for + details. + */ + CV_WRAP virtual bool getDetectShadows() const = 0; + /** @brief Enables or disables shadow detection + */ + CV_WRAP virtual void setDetectShadows(bool detectShadows) = 0; + + /** @brief Returns the shadow value + + Shadow value is the value used to mark shadows in the foreground mask. Default value is 127. Value 0 + in the mask always means background, 255 means foreground. + */ + CV_WRAP virtual int getShadowValue() const = 0; + /** @brief Sets the shadow value + */ + CV_WRAP virtual void setShadowValue(int value) = 0; + + /** @brief Returns the shadow threshold + + A shadow is detected if pixel is a darker version of the background. The shadow threshold (Tau in + the paper) is a threshold defining how much darker the shadow can be. Tau= 0.5 means that if a pixel + is more than twice darker then it is not shadow. See Prati, Mikic, Trivedi and Cucchiara, + *Detecting Moving Shadows...*, IEEE PAMI,2003. + */ + CV_WRAP virtual double getShadowThreshold() const = 0; + /** @brief Sets the shadow threshold + */ + CV_WRAP virtual void setShadowThreshold(double threshold) = 0; +}; + +/** @brief Creates KNN Background Subtractor + +@param history Length of the history. +@param dist2Threshold Threshold on the squared distance between the pixel and the sample to decide +whether a pixel is close to that sample. This parameter does not affect the background update. +@param detectShadows If true, the algorithm will detect shadows and mark them. It decreases the +speed a bit, so if you do not need this feature, set the parameter to false. + */ +CV_EXPORTS_W Ptr + createBackgroundSubtractorKNN(int history=500, double dist2Threshold=400.0, + bool detectShadows=true); + +//! @} video_motion + +} // cv + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/video/tracking.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/video/tracking.hpp new file mode 100644 index 0000000000000000000000000000000000000000..e757b0fa91025411e8e7a6fe2ad20c0d63bede2f --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/video/tracking.hpp @@ -0,0 +1,633 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_TRACKING_HPP +#define OPENCV_TRACKING_HPP + +#include "opencv2/core.hpp" +#include "opencv2/imgproc.hpp" + +namespace cv +{ + +//! @addtogroup video_track +//! @{ + +enum { OPTFLOW_USE_INITIAL_FLOW = 4, + OPTFLOW_LK_GET_MIN_EIGENVALS = 8, + OPTFLOW_FARNEBACK_GAUSSIAN = 256 + }; + +/** @brief Finds an object center, size, and orientation. + +@param probImage Back projection of the object histogram. See calcBackProject. +@param window Initial search window. +@param criteria Stop criteria for the underlying meanShift. +returns +(in old interfaces) Number of iterations CAMSHIFT took to converge +The function implements the CAMSHIFT object tracking algorithm @cite Bradski98 . First, it finds an +object center using meanShift and then adjusts the window size and finds the optimal rotation. The +function returns the rotated rectangle structure that includes the object position, size, and +orientation. The next position of the search window can be obtained with RotatedRect::boundingRect() + +See the OpenCV sample camshiftdemo.c that tracks colored objects. + +@note +- (Python) A sample explaining the camshift tracking algorithm can be found at + opencv_source_code/samples/python/camshift.py + */ +CV_EXPORTS_W RotatedRect CamShift( InputArray probImage, CV_IN_OUT Rect& window, + TermCriteria criteria ); +/** @example samples/cpp/camshiftdemo.cpp +An example using the mean-shift tracking algorithm +*/ + +/** @brief Finds an object on a back projection image. + +@param probImage Back projection of the object histogram. See calcBackProject for details. +@param window Initial search window. +@param criteria Stop criteria for the iterative search algorithm. +returns +: Number of iterations CAMSHIFT took to converge. +The function implements the iterative object search algorithm. It takes the input back projection of +an object and the initial position. The mass center in window of the back projection image is +computed and the search window center shifts to the mass center. The procedure is repeated until the +specified number of iterations criteria.maxCount is done or until the window center shifts by less +than criteria.epsilon. The algorithm is used inside CamShift and, unlike CamShift , the search +window size or orientation do not change during the search. You can simply pass the output of +calcBackProject to this function. But better results can be obtained if you pre-filter the back +projection and remove the noise. For example, you can do this by retrieving connected components +with findContours , throwing away contours with small area ( contourArea ), and rendering the +remaining contours with drawContours. + + */ +CV_EXPORTS_W int meanShift( InputArray probImage, CV_IN_OUT Rect& window, TermCriteria criteria ); + +/** @brief Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK. + +@param img 8-bit input image. +@param pyramid output pyramid. +@param winSize window size of optical flow algorithm. Must be not less than winSize argument of +calcOpticalFlowPyrLK. It is needed to calculate required padding for pyramid levels. +@param maxLevel 0-based maximal pyramid level number. +@param withDerivatives set to precompute gradients for the every pyramid level. If pyramid is +constructed without the gradients then calcOpticalFlowPyrLK will calculate them internally. +@param pyrBorder the border mode for pyramid layers. +@param derivBorder the border mode for gradients. +@param tryReuseInputImage put ROI of input image into the pyramid if possible. You can pass false +to force data copying. +@return number of levels in constructed pyramid. Can be less than maxLevel. + */ +CV_EXPORTS_W int buildOpticalFlowPyramid( InputArray img, OutputArrayOfArrays pyramid, + Size winSize, int maxLevel, bool withDerivatives = true, + int pyrBorder = BORDER_REFLECT_101, + int derivBorder = BORDER_CONSTANT, + bool tryReuseInputImage = true ); + +/** @example samples/cpp/lkdemo.cpp +An example using the Lucas-Kanade optical flow algorithm +*/ + +/** @brief Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with +pyramids. + +@param prevImg first 8-bit input image or pyramid constructed by buildOpticalFlowPyramid. +@param nextImg second input image or pyramid of the same size and the same type as prevImg. +@param prevPts vector of 2D points for which the flow needs to be found; point coordinates must be +single-precision floating-point numbers. +@param nextPts output vector of 2D points (with single-precision floating-point coordinates) +containing the calculated new positions of input features in the second image; when +OPTFLOW_USE_INITIAL_FLOW flag is passed, the vector must have the same size as in the input. +@param status output status vector (of unsigned chars); each element of the vector is set to 1 if +the flow for the corresponding features has been found, otherwise, it is set to 0. +@param err output vector of errors; each element of the vector is set to an error for the +corresponding feature, type of the error measure can be set in flags parameter; if the flow wasn't +found then the error is not defined (use the status parameter to find such cases). +@param winSize size of the search window at each pyramid level. +@param maxLevel 0-based maximal pyramid level number; if set to 0, pyramids are not used (single +level), if set to 1, two levels are used, and so on; if pyramids are passed to input then +algorithm will use as many levels as pyramids have but no more than maxLevel. +@param criteria parameter, specifying the termination criteria of the iterative search algorithm +(after the specified maximum number of iterations criteria.maxCount or when the search window +moves by less than criteria.epsilon. +@param flags operation flags: + - **OPTFLOW_USE_INITIAL_FLOW** uses initial estimations, stored in nextPts; if the flag is + not set, then prevPts is copied to nextPts and is considered the initial estimate. + - **OPTFLOW_LK_GET_MIN_EIGENVALS** use minimum eigen values as an error measure (see + minEigThreshold description); if the flag is not set, then L1 distance between patches + around the original and a moved point, divided by number of pixels in a window, is used as a + error measure. +@param minEigThreshold the algorithm calculates the minimum eigen value of a 2x2 normal matrix of +optical flow equations (this matrix is called a spatial gradient matrix in @cite Bouguet00), divided +by number of pixels in a window; if this value is less than minEigThreshold, then a corresponding +feature is filtered out and its flow is not processed, so it allows to remove bad points and get a +performance boost. + +The function implements a sparse iterative version of the Lucas-Kanade optical flow in pyramids. See +@cite Bouguet00 . The function is parallelized with the TBB library. + +@note + +- An example using the Lucas-Kanade optical flow algorithm can be found at + opencv_source_code/samples/cpp/lkdemo.cpp +- (Python) An example using the Lucas-Kanade optical flow algorithm can be found at + opencv_source_code/samples/python/lk_track.py +- (Python) An example using the Lucas-Kanade tracker for homography matching can be found at + opencv_source_code/samples/python/lk_homography.py + */ +CV_EXPORTS_W void calcOpticalFlowPyrLK( InputArray prevImg, InputArray nextImg, + InputArray prevPts, InputOutputArray nextPts, + OutputArray status, OutputArray err, + Size winSize = Size(21,21), int maxLevel = 3, + TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 0.01), + int flags = 0, double minEigThreshold = 1e-4 ); + +/** @brief Computes a dense optical flow using the Gunnar Farneback's algorithm. + +@param prev first 8-bit single-channel input image. +@param next second input image of the same size and the same type as prev. +@param flow computed flow image that has the same size as prev and type CV_32FC2. +@param pyr_scale parameter, specifying the image scale (\<1) to build pyramids for each image; +pyr_scale=0.5 means a classical pyramid, where each next layer is twice smaller than the previous +one. +@param levels number of pyramid layers including the initial image; levels=1 means that no extra +layers are created and only the original images are used. +@param winsize averaging window size; larger values increase the algorithm robustness to image +noise and give more chances for fast motion detection, but yield more blurred motion field. +@param iterations number of iterations the algorithm does at each pyramid level. +@param poly_n size of the pixel neighborhood used to find polynomial expansion in each pixel; +larger values mean that the image will be approximated with smoother surfaces, yielding more +robust algorithm and more blurred motion field, typically poly_n =5 or 7. +@param poly_sigma standard deviation of the Gaussian that is used to smooth derivatives used as a +basis for the polynomial expansion; for poly_n=5, you can set poly_sigma=1.1, for poly_n=7, a +good value would be poly_sigma=1.5. +@param flags operation flags that can be a combination of the following: + - **OPTFLOW_USE_INITIAL_FLOW** uses the input flow as an initial flow approximation. + - **OPTFLOW_FARNEBACK_GAUSSIAN** uses the Gaussian \f$\texttt{winsize}\times\texttt{winsize}\f$ + filter instead of a box filter of the same size for optical flow estimation; usually, this + option gives z more accurate flow than with a box filter, at the cost of lower speed; + normally, winsize for a Gaussian window should be set to a larger value to achieve the same + level of robustness. + +The function finds an optical flow for each prev pixel using the @cite Farneback2003 algorithm so that + +\f[\texttt{prev} (y,x) \sim \texttt{next} ( y + \texttt{flow} (y,x)[1], x + \texttt{flow} (y,x)[0])\f] + +@note + +- An example using the optical flow algorithm described by Gunnar Farneback can be found at + opencv_source_code/samples/cpp/fback.cpp +- (Python) An example using the optical flow algorithm described by Gunnar Farneback can be + found at opencv_source_code/samples/python/opt_flow.py + */ +CV_EXPORTS_W void calcOpticalFlowFarneback( InputArray prev, InputArray next, InputOutputArray flow, + double pyr_scale, int levels, int winsize, + int iterations, int poly_n, double poly_sigma, + int flags ); + +/** @brief Computes an optimal affine transformation between two 2D point sets. + +@param src First input 2D point set stored in std::vector or Mat, or an image stored in Mat. +@param dst Second input 2D point set of the same size and the same type as A, or another image. +@param fullAffine If true, the function finds an optimal affine transformation with no additional +restrictions (6 degrees of freedom). Otherwise, the class of transformations to choose from is +limited to combinations of translation, rotation, and uniform scaling (4 degrees of freedom). + +The function finds an optimal affine transform *[A|b]* (a 2 x 3 floating-point matrix) that +approximates best the affine transformation between: + +* Two point sets +* Two raster images. In this case, the function first finds some features in the src image and + finds the corresponding features in dst image. After that, the problem is reduced to the first + case. +In case of point sets, the problem is formulated as follows: you need to find a 2x2 matrix *A* and +2x1 vector *b* so that: + +\f[[A^*|b^*] = arg \min _{[A|b]} \sum _i \| \texttt{dst}[i] - A { \texttt{src}[i]}^T - b \| ^2\f] +where src[i] and dst[i] are the i-th points in src and dst, respectively +\f$[A|b]\f$ can be either arbitrary (when fullAffine=true ) or have a form of +\f[\begin{bmatrix} a_{11} & a_{12} & b_1 \\ -a_{12} & a_{11} & b_2 \end{bmatrix}\f] +when fullAffine=false. + +@sa +estimateAffine2D, estimateAffinePartial2D, getAffineTransform, getPerspectiveTransform, findHomography + */ +CV_EXPORTS_W Mat estimateRigidTransform( InputArray src, InputArray dst, bool fullAffine); +CV_EXPORTS_W Mat estimateRigidTransform( InputArray src, InputArray dst, bool fullAffine, int ransacMaxIters, double ransacGoodRatio, + int ransacSize0); + + +enum +{ + MOTION_TRANSLATION = 0, + MOTION_EUCLIDEAN = 1, + MOTION_AFFINE = 2, + MOTION_HOMOGRAPHY = 3 +}; + +/** @example samples/cpp/image_alignment.cpp +An example using the image alignment ECC algorithm +*/ + +/** @brief Finds the geometric transform (warp) between two images in terms of the ECC criterion @cite EP08 . + +@param templateImage single-channel template image; CV_8U or CV_32F array. +@param inputImage single-channel input image which should be warped with the final warpMatrix in +order to provide an image similar to templateImage, same type as temlateImage. +@param warpMatrix floating-point \f$2\times 3\f$ or \f$3\times 3\f$ mapping matrix (warp). +@param motionType parameter, specifying the type of motion: + - **MOTION_TRANSLATION** sets a translational motion model; warpMatrix is \f$2\times 3\f$ with + the first \f$2\times 2\f$ part being the unity matrix and the rest two parameters being + estimated. + - **MOTION_EUCLIDEAN** sets a Euclidean (rigid) transformation as motion model; three + parameters are estimated; warpMatrix is \f$2\times 3\f$. + - **MOTION_AFFINE** sets an affine motion model (DEFAULT); six parameters are estimated; + warpMatrix is \f$2\times 3\f$. + - **MOTION_HOMOGRAPHY** sets a homography as a motion model; eight parameters are + estimated;\`warpMatrix\` is \f$3\times 3\f$. +@param criteria parameter, specifying the termination criteria of the ECC algorithm; +criteria.epsilon defines the threshold of the increment in the correlation coefficient between two +iterations (a negative criteria.epsilon makes criteria.maxcount the only termination criterion). +Default values are shown in the declaration above. +@param inputMask An optional mask to indicate valid values of inputImage. + +The function estimates the optimum transformation (warpMatrix) with respect to ECC criterion +(@cite EP08), that is + +\f[\texttt{warpMatrix} = \texttt{warpMatrix} = \arg\max_{W} \texttt{ECC}(\texttt{templateImage}(x,y),\texttt{inputImage}(x',y'))\f] + +where + +\f[\begin{bmatrix} x' \\ y' \end{bmatrix} = W \cdot \begin{bmatrix} x \\ y \\ 1 \end{bmatrix}\f] + +(the equation holds with homogeneous coordinates for homography). It returns the final enhanced +correlation coefficient, that is the correlation coefficient between the template image and the +final warped input image. When a \f$3\times 3\f$ matrix is given with motionType =0, 1 or 2, the third +row is ignored. + +Unlike findHomography and estimateRigidTransform, the function findTransformECC implements an +area-based alignment that builds on intensity similarities. In essence, the function updates the +initial transformation that roughly aligns the images. If this information is missing, the identity +warp (unity matrix) is used as an initialization. Note that if images undergo strong +displacements/rotations, an initial transformation that roughly aligns the images is necessary +(e.g., a simple euclidean/similarity transform that allows for the images showing the same image +content approximately). Use inverse warping in the second image to take an image close to the first +one, i.e. use the flag WARP_INVERSE_MAP with warpAffine or warpPerspective. See also the OpenCV +sample image_alignment.cpp that demonstrates the use of the function. Note that the function throws +an exception if algorithm does not converges. + +@sa +estimateAffine2D, estimateAffinePartial2D, findHomography + */ +CV_EXPORTS_W double findTransformECC( InputArray templateImage, InputArray inputImage, + InputOutputArray warpMatrix, int motionType = MOTION_AFFINE, + TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 50, 0.001), + InputArray inputMask = noArray()); + +/** @example samples/cpp/kalman.cpp +An example using the standard Kalman filter +*/ + +/** @brief Kalman filter class. + +The class implements a standard Kalman filter , +@cite Welch95 . However, you can modify transitionMatrix, controlMatrix, and measurementMatrix to get +an extended Kalman filter functionality. +@note In C API when CvKalman\* kalmanFilter structure is not needed anymore, it should be released +with cvReleaseKalman(&kalmanFilter) + */ +class CV_EXPORTS_W KalmanFilter +{ +public: + CV_WRAP KalmanFilter(); + /** @overload + @param dynamParams Dimensionality of the state. + @param measureParams Dimensionality of the measurement. + @param controlParams Dimensionality of the control vector. + @param type Type of the created matrices that should be CV_32F or CV_64F. + */ + CV_WRAP KalmanFilter( int dynamParams, int measureParams, int controlParams = 0, int type = CV_32F ); + + /** @brief Re-initializes Kalman filter. The previous content is destroyed. + + @param dynamParams Dimensionality of the state. + @param measureParams Dimensionality of the measurement. + @param controlParams Dimensionality of the control vector. + @param type Type of the created matrices that should be CV_32F or CV_64F. + */ + void init( int dynamParams, int measureParams, int controlParams = 0, int type = CV_32F ); + + /** @brief Computes a predicted state. + + @param control The optional input control + */ + CV_WRAP const Mat& predict( const Mat& control = Mat() ); + + /** @brief Updates the predicted state from the measurement. + + @param measurement The measured system parameters + */ + CV_WRAP const Mat& correct( const Mat& measurement ); + + CV_PROP_RW Mat statePre; //!< predicted state (x'(k)): x(k)=A*x(k-1)+B*u(k) + CV_PROP_RW Mat statePost; //!< corrected state (x(k)): x(k)=x'(k)+K(k)*(z(k)-H*x'(k)) + CV_PROP_RW Mat transitionMatrix; //!< state transition matrix (A) + CV_PROP_RW Mat controlMatrix; //!< control matrix (B) (not used if there is no control) + CV_PROP_RW Mat measurementMatrix; //!< measurement matrix (H) + CV_PROP_RW Mat processNoiseCov; //!< process noise covariance matrix (Q) + CV_PROP_RW Mat measurementNoiseCov;//!< measurement noise covariance matrix (R) + CV_PROP_RW Mat errorCovPre; //!< priori error estimate covariance matrix (P'(k)): P'(k)=A*P(k-1)*At + Q)*/ + CV_PROP_RW Mat gain; //!< Kalman gain matrix (K(k)): K(k)=P'(k)*Ht*inv(H*P'(k)*Ht+R) + CV_PROP_RW Mat errorCovPost; //!< posteriori error estimate covariance matrix (P(k)): P(k)=(I-K(k)*H)*P'(k) + + // temporary matrices + Mat temp1; + Mat temp2; + Mat temp3; + Mat temp4; + Mat temp5; +}; + + +class CV_EXPORTS_W DenseOpticalFlow : public Algorithm +{ +public: + /** @brief Calculates an optical flow. + + @param I0 first 8-bit single-channel input image. + @param I1 second input image of the same size and the same type as prev. + @param flow computed flow image that has the same size as prev and type CV_32FC2. + */ + CV_WRAP virtual void calc( InputArray I0, InputArray I1, InputOutputArray flow ) = 0; + /** @brief Releases all inner buffers. + */ + CV_WRAP virtual void collectGarbage() = 0; +}; + +/** @brief Base interface for sparse optical flow algorithms. + */ +class CV_EXPORTS_W SparseOpticalFlow : public Algorithm +{ +public: + /** @brief Calculates a sparse optical flow. + + @param prevImg First input image. + @param nextImg Second input image of the same size and the same type as prevImg. + @param prevPts Vector of 2D points for which the flow needs to be found. + @param nextPts Output vector of 2D points containing the calculated new positions of input features in the second image. + @param status Output status vector. Each element of the vector is set to 1 if the + flow for the corresponding features has been found. Otherwise, it is set to 0. + @param err Optional output vector that contains error response for each point (inverse confidence). + */ + CV_WRAP virtual void calc(InputArray prevImg, InputArray nextImg, + InputArray prevPts, InputOutputArray nextPts, + OutputArray status, + OutputArray err = cv::noArray()) = 0; +}; + +/** @brief "Dual TV L1" Optical Flow Algorithm. + +The class implements the "Dual TV L1" optical flow algorithm described in @cite Zach2007 and +@cite Javier2012 . +Here are important members of the class that control the algorithm, which you can set after +constructing the class instance: + +- member double tau + Time step of the numerical scheme. + +- member double lambda + Weight parameter for the data term, attachment parameter. This is the most relevant + parameter, which determines the smoothness of the output. The smaller this parameter is, + the smoother the solutions we obtain. It depends on the range of motions of the images, so + its value should be adapted to each image sequence. + +- member double theta + Weight parameter for (u - v)\^2, tightness parameter. It serves as a link between the + attachment and the regularization terms. In theory, it should have a small value in order + to maintain both parts in correspondence. The method is stable for a large range of values + of this parameter. + +- member int nscales + Number of scales used to create the pyramid of images. + +- member int warps + Number of warpings per scale. Represents the number of times that I1(x+u0) and grad( + I1(x+u0) ) are computed per scale. This is a parameter that assures the stability of the + method. It also affects the running time, so it is a compromise between speed and + accuracy. + +- member double epsilon + Stopping criterion threshold used in the numerical scheme, which is a trade-off between + precision and running time. A small value will yield more accurate solutions at the + expense of a slower convergence. + +- member int iterations + Stopping criterion iterations number used in the numerical scheme. + +C. Zach, T. Pock and H. Bischof, "A Duality Based Approach for Realtime TV-L1 Optical Flow". +Javier Sanchez, Enric Meinhardt-Llopis and Gabriele Facciolo. "TV-L1 Optical Flow Estimation". +*/ +class CV_EXPORTS_W DualTVL1OpticalFlow : public DenseOpticalFlow +{ +public: + //! @brief Time step of the numerical scheme + /** @see setTau */ + CV_WRAP virtual double getTau() const = 0; + /** @copybrief getTau @see getTau */ + CV_WRAP virtual void setTau(double val) = 0; + //! @brief Weight parameter for the data term, attachment parameter + /** @see setLambda */ + CV_WRAP virtual double getLambda() const = 0; + /** @copybrief getLambda @see getLambda */ + CV_WRAP virtual void setLambda(double val) = 0; + //! @brief Weight parameter for (u - v)^2, tightness parameter + /** @see setTheta */ + CV_WRAP virtual double getTheta() const = 0; + /** @copybrief getTheta @see getTheta */ + CV_WRAP virtual void setTheta(double val) = 0; + //! @brief coefficient for additional illumination variation term + /** @see setGamma */ + CV_WRAP virtual double getGamma() const = 0; + /** @copybrief getGamma @see getGamma */ + CV_WRAP virtual void setGamma(double val) = 0; + //! @brief Number of scales used to create the pyramid of images + /** @see setScalesNumber */ + CV_WRAP virtual int getScalesNumber() const = 0; + /** @copybrief getScalesNumber @see getScalesNumber */ + CV_WRAP virtual void setScalesNumber(int val) = 0; + //! @brief Number of warpings per scale + /** @see setWarpingsNumber */ + CV_WRAP virtual int getWarpingsNumber() const = 0; + /** @copybrief getWarpingsNumber @see getWarpingsNumber */ + CV_WRAP virtual void setWarpingsNumber(int val) = 0; + //! @brief Stopping criterion threshold used in the numerical scheme, which is a trade-off between precision and running time + /** @see setEpsilon */ + CV_WRAP virtual double getEpsilon() const = 0; + /** @copybrief getEpsilon @see getEpsilon */ + CV_WRAP virtual void setEpsilon(double val) = 0; + //! @brief Inner iterations (between outlier filtering) used in the numerical scheme + /** @see setInnerIterations */ + CV_WRAP virtual int getInnerIterations() const = 0; + /** @copybrief getInnerIterations @see getInnerIterations */ + CV_WRAP virtual void setInnerIterations(int val) = 0; + //! @brief Outer iterations (number of inner loops) used in the numerical scheme + /** @see setOuterIterations */ + CV_WRAP virtual int getOuterIterations() const = 0; + /** @copybrief getOuterIterations @see getOuterIterations */ + CV_WRAP virtual void setOuterIterations(int val) = 0; + //! @brief Use initial flow + /** @see setUseInitialFlow */ + CV_WRAP virtual bool getUseInitialFlow() const = 0; + /** @copybrief getUseInitialFlow @see getUseInitialFlow */ + CV_WRAP virtual void setUseInitialFlow(bool val) = 0; + //! @brief Step between scales (<1) + /** @see setScaleStep */ + CV_WRAP virtual double getScaleStep() const = 0; + /** @copybrief getScaleStep @see getScaleStep */ + CV_WRAP virtual void setScaleStep(double val) = 0; + //! @brief Median filter kernel size (1 = no filter) (3 or 5) + /** @see setMedianFiltering */ + CV_WRAP virtual int getMedianFiltering() const = 0; + /** @copybrief getMedianFiltering @see getMedianFiltering */ + CV_WRAP virtual void setMedianFiltering(int val) = 0; + + /** @brief Creates instance of cv::DualTVL1OpticalFlow*/ + CV_WRAP static Ptr create( + double tau = 0.25, + double lambda = 0.15, + double theta = 0.3, + int nscales = 5, + int warps = 5, + double epsilon = 0.01, + int innnerIterations = 30, + int outerIterations = 10, + double scaleStep = 0.8, + double gamma = 0.0, + int medianFiltering = 5, + bool useInitialFlow = false); +}; + +/** @brief Creates instance of cv::DenseOpticalFlow +*/ +CV_EXPORTS_W Ptr createOptFlow_DualTVL1(); + +/** @brief Class computing a dense optical flow using the Gunnar Farneback's algorithm. + */ +class CV_EXPORTS_W FarnebackOpticalFlow : public DenseOpticalFlow +{ +public: + CV_WRAP virtual int getNumLevels() const = 0; + CV_WRAP virtual void setNumLevels(int numLevels) = 0; + + CV_WRAP virtual double getPyrScale() const = 0; + CV_WRAP virtual void setPyrScale(double pyrScale) = 0; + + CV_WRAP virtual bool getFastPyramids() const = 0; + CV_WRAP virtual void setFastPyramids(bool fastPyramids) = 0; + + CV_WRAP virtual int getWinSize() const = 0; + CV_WRAP virtual void setWinSize(int winSize) = 0; + + CV_WRAP virtual int getNumIters() const = 0; + CV_WRAP virtual void setNumIters(int numIters) = 0; + + CV_WRAP virtual int getPolyN() const = 0; + CV_WRAP virtual void setPolyN(int polyN) = 0; + + CV_WRAP virtual double getPolySigma() const = 0; + CV_WRAP virtual void setPolySigma(double polySigma) = 0; + + CV_WRAP virtual int getFlags() const = 0; + CV_WRAP virtual void setFlags(int flags) = 0; + + CV_WRAP static Ptr create( + int numLevels = 5, + double pyrScale = 0.5, + bool fastPyramids = false, + int winSize = 13, + int numIters = 10, + int polyN = 5, + double polySigma = 1.1, + int flags = 0); +}; + + +/** @brief Class used for calculating a sparse optical flow. + +The class can calculate an optical flow for a sparse feature set using the +iterative Lucas-Kanade method with pyramids. + +@sa calcOpticalFlowPyrLK + +*/ +class CV_EXPORTS_W SparsePyrLKOpticalFlow : public SparseOpticalFlow +{ +public: + CV_WRAP virtual Size getWinSize() const = 0; + CV_WRAP virtual void setWinSize(Size winSize) = 0; + + CV_WRAP virtual int getMaxLevel() const = 0; + CV_WRAP virtual void setMaxLevel(int maxLevel) = 0; + + CV_WRAP virtual TermCriteria getTermCriteria() const = 0; + CV_WRAP virtual void setTermCriteria(TermCriteria& crit) = 0; + + CV_WRAP virtual int getFlags() const = 0; + CV_WRAP virtual void setFlags(int flags) = 0; + + CV_WRAP virtual double getMinEigThreshold() const = 0; + CV_WRAP virtual void setMinEigThreshold(double minEigThreshold) = 0; + + CV_WRAP static Ptr create( + Size winSize = Size(21, 21), + int maxLevel = 3, TermCriteria crit = + TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 0.01), + int flags = 0, + double minEigThreshold = 1e-4); +}; + +//! @} video_track + +} // cv + +#endif diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/video/tracking_c.h b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/video/tracking_c.h new file mode 100644 index 0000000000000000000000000000000000000000..3e32fbd0c0bb5693f7e8b4ad3a11f97be01b2e48 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/video/tracking_c.h @@ -0,0 +1,232 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef OPENCV_TRACKING_C_H +#define OPENCV_TRACKING_C_H + +#include "opencv2/imgproc/types_c.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/** @addtogroup video_c + @{ +*/ + +/****************************************************************************************\ +* Motion Analysis * +\****************************************************************************************/ + +/************************************ optical flow ***************************************/ + +#define CV_LKFLOW_PYR_A_READY 1 +#define CV_LKFLOW_PYR_B_READY 2 +#define CV_LKFLOW_INITIAL_GUESSES 4 +#define CV_LKFLOW_GET_MIN_EIGENVALS 8 + +/* It is Lucas & Kanade method, modified to use pyramids. + Also it does several iterations to get optical flow for + every point at every pyramid level. + Calculates optical flow between two images for certain set of points (i.e. + it is a "sparse" optical flow, which is opposite to the previous 3 methods) */ +CVAPI(void) cvCalcOpticalFlowPyrLK( const CvArr* prev, const CvArr* curr, + CvArr* prev_pyr, CvArr* curr_pyr, + const CvPoint2D32f* prev_features, + CvPoint2D32f* curr_features, + int count, + CvSize win_size, + int level, + char* status, + float* track_error, + CvTermCriteria criteria, + int flags ); + + +/* Modification of a previous sparse optical flow algorithm to calculate + affine flow */ +CVAPI(void) cvCalcAffineFlowPyrLK( const CvArr* prev, const CvArr* curr, + CvArr* prev_pyr, CvArr* curr_pyr, + const CvPoint2D32f* prev_features, + CvPoint2D32f* curr_features, + float* matrices, int count, + CvSize win_size, int level, + char* status, float* track_error, + CvTermCriteria criteria, int flags ); + +/* Estimate rigid transformation between 2 images or 2 point sets */ +CVAPI(int) cvEstimateRigidTransform( const CvArr* A, const CvArr* B, + CvMat* M, int full_affine ); + +/* Estimate optical flow for each pixel using the two-frame G. Farneback algorithm */ +CVAPI(void) cvCalcOpticalFlowFarneback( const CvArr* prev, const CvArr* next, + CvArr* flow, double pyr_scale, int levels, + int winsize, int iterations, int poly_n, + double poly_sigma, int flags ); + +/********************************* motion templates *************************************/ + +/****************************************************************************************\ +* All the motion template functions work only with single channel images. * +* Silhouette image must have depth IPL_DEPTH_8U or IPL_DEPTH_8S * +* Motion history image must have depth IPL_DEPTH_32F, * +* Gradient mask - IPL_DEPTH_8U or IPL_DEPTH_8S, * +* Motion orientation image - IPL_DEPTH_32F * +* Segmentation mask - IPL_DEPTH_32F * +* All the angles are in degrees, all the times are in milliseconds * +\****************************************************************************************/ + +/* Updates motion history image given motion silhouette */ +CVAPI(void) cvUpdateMotionHistory( const CvArr* silhouette, CvArr* mhi, + double timestamp, double duration ); + +/* Calculates gradient of the motion history image and fills + a mask indicating where the gradient is valid */ +CVAPI(void) cvCalcMotionGradient( const CvArr* mhi, CvArr* mask, CvArr* orientation, + double delta1, double delta2, + int aperture_size CV_DEFAULT(3)); + +/* Calculates average motion direction within a selected motion region + (region can be selected by setting ROIs and/or by composing a valid gradient mask + with the region mask) */ +CVAPI(double) cvCalcGlobalOrientation( const CvArr* orientation, const CvArr* mask, + const CvArr* mhi, double timestamp, + double duration ); + +/* Splits a motion history image into a few parts corresponding to separate independent motions + (e.g. left hand, right hand) */ +CVAPI(CvSeq*) cvSegmentMotion( const CvArr* mhi, CvArr* seg_mask, + CvMemStorage* storage, + double timestamp, double seg_thresh ); + +/****************************************************************************************\ +* Tracking * +\****************************************************************************************/ + +/* Implements CAMSHIFT algorithm - determines object position, size and orientation + from the object histogram back project (extension of meanshift) */ +CVAPI(int) cvCamShift( const CvArr* prob_image, CvRect window, + CvTermCriteria criteria, CvConnectedComp* comp, + CvBox2D* box CV_DEFAULT(NULL) ); + +/* Implements MeanShift algorithm - determines object position + from the object histogram back project */ +CVAPI(int) cvMeanShift( const CvArr* prob_image, CvRect window, + CvTermCriteria criteria, CvConnectedComp* comp ); + +/* +standard Kalman filter (in G. Welch' and G. Bishop's notation): + + x(k)=A*x(k-1)+B*u(k)+w(k) p(w)~N(0,Q) + z(k)=H*x(k)+v(k), p(v)~N(0,R) +*/ +typedef struct CvKalman +{ + int MP; /* number of measurement vector dimensions */ + int DP; /* number of state vector dimensions */ + int CP; /* number of control vector dimensions */ + + /* backward compatibility fields */ +#if 1 + float* PosterState; /* =state_pre->data.fl */ + float* PriorState; /* =state_post->data.fl */ + float* DynamMatr; /* =transition_matrix->data.fl */ + float* MeasurementMatr; /* =measurement_matrix->data.fl */ + float* MNCovariance; /* =measurement_noise_cov->data.fl */ + float* PNCovariance; /* =process_noise_cov->data.fl */ + float* KalmGainMatr; /* =gain->data.fl */ + float* PriorErrorCovariance;/* =error_cov_pre->data.fl */ + float* PosterErrorCovariance;/* =error_cov_post->data.fl */ + float* Temp1; /* temp1->data.fl */ + float* Temp2; /* temp2->data.fl */ +#endif + + CvMat* state_pre; /* predicted state (x'(k)): + x(k)=A*x(k-1)+B*u(k) */ + CvMat* state_post; /* corrected state (x(k)): + x(k)=x'(k)+K(k)*(z(k)-H*x'(k)) */ + CvMat* transition_matrix; /* state transition matrix (A) */ + CvMat* control_matrix; /* control matrix (B) + (it is not used if there is no control)*/ + CvMat* measurement_matrix; /* measurement matrix (H) */ + CvMat* process_noise_cov; /* process noise covariance matrix (Q) */ + CvMat* measurement_noise_cov; /* measurement noise covariance matrix (R) */ + CvMat* error_cov_pre; /* priori error estimate covariance matrix (P'(k)): + P'(k)=A*P(k-1)*At + Q)*/ + CvMat* gain; /* Kalman gain matrix (K(k)): + K(k)=P'(k)*Ht*inv(H*P'(k)*Ht+R)*/ + CvMat* error_cov_post; /* posteriori error estimate covariance matrix (P(k)): + P(k)=(I-K(k)*H)*P'(k) */ + CvMat* temp1; /* temporary matrices */ + CvMat* temp2; + CvMat* temp3; + CvMat* temp4; + CvMat* temp5; +} CvKalman; + +/* Creates Kalman filter and sets A, B, Q, R and state to some initial values */ +CVAPI(CvKalman*) cvCreateKalman( int dynam_params, int measure_params, + int control_params CV_DEFAULT(0)); + +/* Releases Kalman filter state */ +CVAPI(void) cvReleaseKalman( CvKalman** kalman); + +/* Updates Kalman filter by time (predicts future state of the system) */ +CVAPI(const CvMat*) cvKalmanPredict( CvKalman* kalman, + const CvMat* control CV_DEFAULT(NULL)); + +/* Updates Kalman filter by measurement + (corrects state of the system and internal matrices) */ +CVAPI(const CvMat*) cvKalmanCorrect( CvKalman* kalman, const CvMat* measurement ); + +#define cvKalmanUpdateByTime cvKalmanPredict +#define cvKalmanUpdateByMeasurement cvKalmanCorrect + +/** @} video_c */ + +#ifdef __cplusplus +} // extern "C" +#endif + + +#endif // OPENCV_TRACKING_C_H diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/video/video.hpp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/video/video.hpp new file mode 100644 index 0000000000000000000000000000000000000000..8267b85d59585b2bfc4ee6c9d67a5319614a8e58 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/include/opencv2/video/video.hpp @@ -0,0 +1,48 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifdef __OPENCV_BUILD +#error this is a compatibility header which should not be used inside the OpenCV library +#endif + +#include "opencv2/video.hpp" diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/lib/libopencv_calib3d.a b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/lib/libopencv_calib3d.a new file mode 100644 index 0000000000000000000000000000000000000000..a4195431d9637490f8c1963effefbd5dc407f660 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/lib/libopencv_calib3d.a @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:25a02683328146d493034657613af78c9d62bfe711dda53db5e89839fd45719d +size 1942182 diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/lib/libopencv_core.a b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/lib/libopencv_core.a new file mode 100644 index 0000000000000000000000000000000000000000..5af4e5a8242a9aa7db9ab63e66a4b7fa2c6cac17 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/lib/libopencv_core.a @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1167e6e11a4a91fa2082c9170c3ba61889a53f47c58170220aea01b057f364bf +size 5360780 diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/lib/libopencv_features2d.a b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/lib/libopencv_features2d.a new file mode 100644 index 0000000000000000000000000000000000000000..392e8851d6417f4de642b718de734cd09f3732a2 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/lib/libopencv_features2d.a @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:50e3eecf061209159832a2dcd151388467f4715265157e7f8c925cdc0347c6f6 +size 1109632 diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/lib/libopencv_imgcodecs.a b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/lib/libopencv_imgcodecs.a new file mode 100644 index 0000000000000000000000000000000000000000..c9df0217169f0fd7ff2b469364f5fa1160bcdc1f --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/lib/libopencv_imgcodecs.a @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e6d8662a9d7c97de621bba53e9948812d592730ad527020e3acfda99d75e6dd6 +size 828948 diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/lib/libopencv_imgproc.a b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/lib/libopencv_imgproc.a new file mode 100644 index 0000000000000000000000000000000000000000..306efaf45649e844849c3fe4c84517b72c787622 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/lib/libopencv_imgproc.a @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:35cc15edb756aceb12df9ae0f1df17489255f84a6d409c5005ceb1d05ae23d77 +size 5403866 diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/lib/libopencv_video.a b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/lib/libopencv_video.a new file mode 100644 index 0000000000000000000000000000000000000000..27da40bcb574efbeef908784bd05fca357f3fc48 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/lib/libopencv_video.a @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:21c02ed274acc38e1b04bda48a7c1aa0e16f9b54682182b89893029b2a22971d +size 464504 diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/lib/pkgconfig/opencv.pc b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/lib/pkgconfig/opencv.pc new file mode 100644 index 0000000000000000000000000000000000000000..12389892961b9e9f84a0b3b78e5bfb93e8a2a6ad --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/lib/pkgconfig/opencv.pc @@ -0,0 +1,14 @@ +# Package Information for pkg-config + +prefix=/data/chifred/git/UtilsLib/opencv/armhf_install +exec_prefix=${prefix} +libdir=${exec_prefix}/lib +includedir_old=${prefix}/include/opencv +includedir_new=${prefix}/include + +Name: OpenCV +Description: Open Source Computer Vision Library +Version: 3.4.5 +Libs: -L${exec_prefix}/lib -lopencv_calib3d -lopencv_features2d -lopencv_imgcodecs -lopencv_video -lopencv_imgproc -lopencv_core +Libs.private: -L${exec_prefix}/share/OpenCV/3rdparty/lib -lzlib -llibjpeg-turbo -llibwebp -llibpng -llibtiff -llibjasper -lIlmImf -ltegra_hal -ldl -lm -lpthread -lrt +Cflags: -I${includedir_old} -I${includedir_new} diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/3rdparty/lib/libIlmImf.a b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/3rdparty/lib/libIlmImf.a new file mode 100644 index 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+version https://git-lfs.github.com/spec/v1 +oid sha256:1b13e7c08b20f38e238d1ec2483e99277ccfd20bb35627bc78b51debbaa75e98 +size 371018 diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/3rdparty/lib/liblibprotobuf.a b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/3rdparty/lib/liblibprotobuf.a new file mode 100644 index 0000000000000000000000000000000000000000..632dac239395888811666c6acc84a1ad07520a58 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/3rdparty/lib/liblibprotobuf.a @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5847d082d08f578bdfb4c00f1362ea2102c6b5a2a5662a17e859710dabf97f35 +size 6383744 diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/3rdparty/lib/liblibtiff.a b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/3rdparty/lib/liblibtiff.a new file mode 100644 index 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a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/3rdparty/lib/libquirc.a b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/3rdparty/lib/libquirc.a new file mode 100644 index 0000000000000000000000000000000000000000..10c96b620bbad889a823a793c02562f0078e6697 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/3rdparty/lib/libquirc.a @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:88d66423a2d16277f6a9037e68c94ac07aec93ca44737868b4f4fcb22452b229 +size 22668 diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/3rdparty/lib/libtegra_hal.a b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/3rdparty/lib/libtegra_hal.a new file mode 100644 index 0000000000000000000000000000000000000000..a67f07456e287bff07b9d6d85bc0405cc116d104 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/3rdparty/lib/libtegra_hal.a @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b2b45a5f488f464e6650d59f1316f40dc32d0d16c1ded81d11df4457784470be +size 1172948 diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/3rdparty/lib/libzlib.a b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/3rdparty/lib/libzlib.a new file mode 100644 index 0000000000000000000000000000000000000000..465b3d9e443b51b21b8b0b7b757c1f380c1f40aa --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/3rdparty/lib/libzlib.a @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4ec81b76b8d27c5f77fbfa2207911621c638093da9978822fa7b50e5b000f52a +size 120074 diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/OpenCVConfig-version.cmake b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/OpenCVConfig-version.cmake new file mode 100644 index 0000000000000000000000000000000000000000..f8be90d0b64ad0ff98b823e89d10f5399908a262 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/OpenCVConfig-version.cmake @@ -0,0 +1,15 @@ +set(OpenCV_VERSION 3.4.5) +set(PACKAGE_VERSION ${OpenCV_VERSION}) + +set(PACKAGE_VERSION_EXACT False) +set(PACKAGE_VERSION_COMPATIBLE False) + +if(PACKAGE_FIND_VERSION VERSION_EQUAL PACKAGE_VERSION) + set(PACKAGE_VERSION_EXACT True) + set(PACKAGE_VERSION_COMPATIBLE True) +endif() + +if(PACKAGE_FIND_VERSION_MAJOR EQUAL 3 + AND PACKAGE_FIND_VERSION VERSION_LESS PACKAGE_VERSION) + set(PACKAGE_VERSION_COMPATIBLE True) +endif() diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/OpenCVConfig.cmake b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/OpenCVConfig.cmake new file mode 100644 index 0000000000000000000000000000000000000000..efc56ff317c571db635429a861d9fff82af93c6f --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/OpenCVConfig.cmake @@ -0,0 +1,315 @@ +# =================================================================================== +# The OpenCV CMake configuration file +# +# ** File generated automatically, do not modify ** +# +# Usage from an external project: +# In your CMakeLists.txt, add these lines: +# +# find_package(OpenCV REQUIRED) +# include_directories(${OpenCV_INCLUDE_DIRS}) # Not needed for CMake >= 2.8.11 +# target_link_libraries(MY_TARGET_NAME ${OpenCV_LIBS}) +# +# Or you can search for specific OpenCV modules: +# +# find_package(OpenCV REQUIRED core videoio) +# +# You can also mark OpenCV components as optional: + +# find_package(OpenCV REQUIRED core OPTIONAL_COMPONENTS viz) +# +# If the module is found then OPENCV__FOUND is set to TRUE. +# +# This file will define the following variables: +# - OpenCV_LIBS : The list of all imported targets for OpenCV modules. +# - OpenCV_INCLUDE_DIRS : The OpenCV include directories. +# - OpenCV_COMPUTE_CAPABILITIES : The version of compute capability. +# - OpenCV_ANDROID_NATIVE_API_LEVEL : Minimum required level of Android API. +# - OpenCV_VERSION : The version of this OpenCV build: "3.4.5" +# - OpenCV_VERSION_MAJOR : Major version part of OpenCV_VERSION: "3" +# - OpenCV_VERSION_MINOR : Minor version part of OpenCV_VERSION: "4" +# - OpenCV_VERSION_PATCH : Patch version part of OpenCV_VERSION: "5" +# - OpenCV_VERSION_STATUS : Development status of this build: "" +# +# Advanced variables: +# - OpenCV_SHARED : Use OpenCV as shared library +# - OpenCV_INSTALL_PATH : OpenCV location +# - OpenCV_LIB_COMPONENTS : Present OpenCV modules list +# - OpenCV_USE_MANGLED_PATHS : Mangled OpenCV path flag +# +# Deprecated variables: +# - OpenCV_VERSION_TWEAK : Always "0" +# +# =================================================================================== + +# ====================================================== +# Version variables: +# ====================================================== +SET(OpenCV_VERSION 3.4.5) +SET(OpenCV_VERSION_MAJOR 3) +SET(OpenCV_VERSION_MINOR 4) +SET(OpenCV_VERSION_PATCH 5) +SET(OpenCV_VERSION_TWEAK 0) +SET(OpenCV_VERSION_STATUS "") + +include(FindPackageHandleStandardArgs) + +if(NOT CMAKE_VERSION VERSION_LESS 2.8.8 + AND OpenCV_FIND_COMPONENTS # prevent excessive output +) + # HANDLE_COMPONENTS was introduced in CMake 2.8.8 + list(APPEND _OpenCV_FPHSA_ARGS HANDLE_COMPONENTS) + # The missing components will be handled by the FindPackageHandleStandardArgs + # module. + set(_OpenCV_HANDLE_COMPONENTS_MANUALLY FALSE) +else() + # The missing components will be handled by this config. + set(_OpenCV_HANDLE_COMPONENTS_MANUALLY TRUE) +endif() + +# Extract directory name from full path of the file currently being processed. +# Note that CMake 2.8.3 introduced CMAKE_CURRENT_LIST_DIR. We reimplement it +# for older versions of CMake to support these as well. +if(CMAKE_VERSION VERSION_LESS "2.8.3") + get_filename_component(CMAKE_CURRENT_LIST_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH) +endif() + +# Extract the directory where *this* file has been installed (determined at cmake run-time) +# Get the absolute path with no ../.. relative marks, to eliminate implicit linker warnings +set(OpenCV_CONFIG_PATH "${CMAKE_CURRENT_LIST_DIR}") +get_filename_component(OpenCV_INSTALL_PATH "${OpenCV_CONFIG_PATH}/../../" REALPATH) + +# Search packages for host system instead of packages for target system. +# in case of cross compilation this macro should be defined by toolchain file +if(NOT COMMAND find_host_package) + macro(find_host_package) + find_package(${ARGN}) + endmacro() +endif() +if(NOT COMMAND find_host_program) + macro(find_host_program) + find_program(${ARGN}) + endmacro() +endif() + + + + + + + + +# Some additional settings are required if OpenCV is built as static libs +set(OpenCV_SHARED OFF) + +# Enables mangled install paths, that help with side by side installs +set(OpenCV_USE_MANGLED_PATHS FALSE) + +set(OpenCV_LIB_COMPONENTS opencv_calib3d;opencv_core;opencv_features2d;opencv_imgcodecs;opencv_imgproc;opencv_video) +set(OpenCV_INCLUDE_DIRS "${OpenCV_INSTALL_PATH}/include" "${OpenCV_INSTALL_PATH}/include/opencv") + +if(NOT TARGET opencv_core) + include(${CMAKE_CURRENT_LIST_DIR}/OpenCVModules${OpenCV_MODULES_SUFFIX}.cmake) +endif() + +if(NOT CMAKE_VERSION VERSION_LESS "2.8.11") + # Target property INTERFACE_INCLUDE_DIRECTORIES available since 2.8.11: + # * http://www.cmake.org/cmake/help/v2.8.11/cmake.html#prop_tgt:INTERFACE_INCLUDE_DIRECTORIES + foreach(__component ${OpenCV_LIB_COMPONENTS}) + if(TARGET ${__component}) + set_target_properties( + ${__component} + PROPERTIES + INTERFACE_INCLUDE_DIRECTORIES "${OpenCV_INCLUDE_DIRS}" + ) + endif() + endforeach() +endif() + + +if(NOT DEFINED OPENCV_MAP_IMPORTED_CONFIG) + if(CMAKE_GENERATOR MATCHES "Visual Studio" OR MSVC) + # OpenCV supports Debug and Release builds only. + # But MSVS has 'RelWithDebInfo' 'MinSizeRel' configurations for applications. + # By default CMake maps these configuration on the first available (Debug) which is wrong. + # Non-Debug build of Application can't be used with OpenCV Debug build (ABI mismatch problem) + # Add mapping of RelWithDebInfo and MinSizeRel to Release here + set(OPENCV_MAP_IMPORTED_CONFIG "RELWITHDEBINFO=!Release;MINSIZEREL=!Release") + endif() +endif() +set(__remap_warnings "") +macro(ocv_map_imported_config target) + if(DEFINED OPENCV_MAP_IMPORTED_CONFIG) # list, "RELWITHDEBINFO=Release;MINSIZEREL=Release" + get_target_property(__available_configurations ${target} IMPORTED_CONFIGURATIONS) + foreach(remap ${OPENCV_MAP_IMPORTED_CONFIG}) + if(remap MATCHES "^(.+)=(!?)([^!]+)$") + set(__remap_config "${CMAKE_MATCH_1}") + set(__final_config "${CMAKE_MATCH_3}") + set(__force_flag "${CMAKE_MATCH_2}") + string(TOUPPER "${__final_config}" __final_config_upper) + string(TOUPPER "${__remap_config}" __remap_config_upper) + if(";${__available_configurations};" MATCHES ";${__remap_config_upper};" AND NOT "${__force_flag}" STREQUAL "!") + # configuration already exists, skip remap + set(__remap_warnings "${__remap_warnings}... Configuration already exists ${__remap_config} (skip mapping ${__remap_config} => ${__final_config}) (available configurations: ${__available_configurations})\n") + continue() + endif() + if(__available_configurations AND NOT ";${__available_configurations};" MATCHES ";${__final_config_upper};") + # skip, configuration is not available + if(NOT "${__force_flag}" STREQUAL "!") + set(__remap_warnings "${__remap_warnings}... Configuration is not available '${__final_config}' for ${target}, build may fail (available configurations: ${__available_configurations})\n") + endif() + endif() + set_target_properties(${target} PROPERTIES + MAP_IMPORTED_CONFIG_${__remap_config} "${__final_config}" + ) + else() + message(WARNING "Invalid entry of OPENCV_MAP_IMPORTED_CONFIG: '${remap}' (${OPENCV_MAP_IMPORTED_CONFIG})") + endif() + endforeach() + endif() +endmacro() + + +# ============================================================== +# Form list of modules (components) to find +# ============================================================== +if(NOT OpenCV_FIND_COMPONENTS) + set(OpenCV_FIND_COMPONENTS ${OpenCV_LIB_COMPONENTS}) + list(REMOVE_ITEM OpenCV_FIND_COMPONENTS opencv_java) + if(GTest_FOUND OR GTEST_FOUND) + list(REMOVE_ITEM OpenCV_FIND_COMPONENTS opencv_ts) + endif() +endif() + +set(OpenCV_WORLD_COMPONENTS ) + +# expand short module names and see if requested components exist +foreach(__cvcomponent ${OpenCV_FIND_COMPONENTS}) + # Store the name of the original component so we can set the + # OpenCV__FOUND variable which can be checked by the user. + set (__original_cvcomponent ${__cvcomponent}) + if(NOT __cvcomponent MATCHES "^opencv_") + set(__cvcomponent opencv_${__cvcomponent}) + endif() + list(FIND OpenCV_LIB_COMPONENTS ${__cvcomponent} __cvcomponentIdx) + if(__cvcomponentIdx LESS 0) + if(_OpenCV_HANDLE_COMPONENTS_MANUALLY) + # Either the component is required or the user did not set any components at + # all. In the latter case, the OpenCV_FIND_REQUIRED_ variable + # will not be defined since it is not set by this config. So let's assume + # the implicitly set components are always required. + if(NOT DEFINED OpenCV_FIND_REQUIRED_${__original_cvcomponent} OR + OpenCV_FIND_REQUIRED_${__original_cvcomponent}) + message(FATAL_ERROR "${__cvcomponent} is required but was not found") + elseif(NOT OpenCV_FIND_QUIETLY) + # The component was marked as optional using OPTIONAL_COMPONENTS + message(WARNING "Optional component ${__cvcomponent} was not found") + endif() + endif(_OpenCV_HANDLE_COMPONENTS_MANUALLY) + #indicate that module is NOT found + string(TOUPPER "${__cvcomponent}" __cvcomponentUP) + set(${__cvcomponentUP}_FOUND "${__cvcomponentUP}_FOUND-NOTFOUND") + set(OpenCV_${__original_cvcomponent}_FOUND FALSE) + else() + # Not using list(APPEND) here, because OpenCV_LIBS may not exist yet. + # Also not clearing OpenCV_LIBS anywhere, so that multiple calls + # to find_package(OpenCV) with different component lists add up. + set(OpenCV_LIBS ${OpenCV_LIBS} "${__cvcomponent}") + #indicate that module is found + string(TOUPPER "${__cvcomponent}" __cvcomponentUP) + set(${__cvcomponentUP}_FOUND 1) + set(OpenCV_${__original_cvcomponent}_FOUND TRUE) + endif() + if(OpenCV_SHARED AND ";${OpenCV_WORLD_COMPONENTS};" MATCHES ";${__cvcomponent};" AND NOT TARGET ${__cvcomponent}) + get_target_property(__implib_dbg opencv_world IMPORTED_IMPLIB_DEBUG) + get_target_property(__implib_release opencv_world IMPORTED_IMPLIB_RELEASE) + get_target_property(__location_dbg opencv_world IMPORTED_LOCATION_DEBUG) + get_target_property(__location_release opencv_world IMPORTED_LOCATION_RELEASE) + get_target_property(__include_dir opencv_world INTERFACE_INCLUDE_DIRECTORIES) + add_library(${__cvcomponent} SHARED IMPORTED) + set_target_properties(${__cvcomponent} PROPERTIES INTERFACE_INCLUDE_DIRECTORIES "${__include_dir}") + if(__location_dbg) + set_property(TARGET ${__cvcomponent} APPEND PROPERTY IMPORTED_CONFIGURATIONS DEBUG) + set_target_properties(${__cvcomponent} PROPERTIES + IMPORTED_IMPLIB_DEBUG "${__implib_dbg}" + IMPORTED_LINK_INTERFACE_LIBRARIES_DEBUG "" + IMPORTED_LOCATION_DEBUG "${__location_dbg}" + ) + endif() + if(__location_release) + set_property(TARGET ${__cvcomponent} APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) + set_target_properties(${__cvcomponent} PROPERTIES + IMPORTED_IMPLIB_RELEASE "${__implib_release}" + IMPORTED_LINK_INTERFACE_LIBRARIES_RELEASE "" + IMPORTED_LOCATION_RELEASE "${__location_release}" + ) + endif() + endif() + if(TARGET ${__cvcomponent}) + ocv_map_imported_config(${__cvcomponent}) + endif() +endforeach() + +if(__remap_warnings AND NOT OpenCV_FIND_QUIETLY) + message("OpenCV: configurations remap warnings:\n${__remap_warnings}OpenCV: Check variable OPENCV_MAP_IMPORTED_CONFIG=${OPENCV_MAP_IMPORTED_CONFIG}") +endif() + +# ============================================================== +# Compatibility stuff +# ============================================================== +set(OpenCV_LIBRARIES ${OpenCV_LIBS}) + +# +# Some macros for samples +# +macro(ocv_check_dependencies) + set(OCV_DEPENDENCIES_FOUND TRUE) + foreach(d ${ARGN}) + if(NOT TARGET ${d}) + message(WARNING "OpenCV: Can't resolve dependency: ${d}") + set(OCV_DEPENDENCIES_FOUND FALSE) + break() + endif() + endforeach() +endmacro() + +# adds include directories in such way that directories from the OpenCV source tree go first +function(ocv_include_directories) + set(__add_before "") + file(TO_CMAKE_PATH "${OpenCV_INSTALL_PATH}" __baseDir) + foreach(dir ${ARGN}) + get_filename_component(__abs_dir "${dir}" ABSOLUTE) + if("${__abs_dir}" MATCHES "^${__baseDir}") + list(APPEND __add_before "${dir}") + else() + include_directories(AFTER SYSTEM "${dir}") + endif() + endforeach() + include_directories(BEFORE ${__add_before}) +endfunction() + +macro(ocv_include_modules) + include_directories(BEFORE "${OpenCV_INCLUDE_DIRS}") +endmacro() + +macro(ocv_include_modules_recurse) + include_directories(BEFORE "${OpenCV_INCLUDE_DIRS}") +endmacro() + +macro(ocv_target_link_libraries) + target_link_libraries(${ARGN}) +endmacro() + +# remove all matching elements from the list +macro(ocv_list_filterout lst regex) + foreach(item ${${lst}}) + if(item MATCHES "${regex}") + list(REMOVE_ITEM ${lst} "${item}") + endif() + endforeach() +endmacro() + +# We do not actually need REQUIRED_VARS to be checked for. Just use the +# installation directory for the status. +find_package_handle_standard_args(OpenCV REQUIRED_VARS OpenCV_INSTALL_PATH + VERSION_VAR OpenCV_VERSION ${_OpenCV_FPHSA_ARGS}) diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/OpenCVModules-release.cmake b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/OpenCVModules-release.cmake new file mode 100644 index 0000000000000000000000000000000000000000..f9e275c462183a6e0c0c94433b5ea2654bb5eb84 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/OpenCVModules-release.cmake @@ -0,0 +1,169 @@ +#---------------------------------------------------------------- +# Generated CMake target import file for configuration "Release". +#---------------------------------------------------------------- + +# Commands may need to know the format version. +set(CMAKE_IMPORT_FILE_VERSION 1) + +# Import target "zlib" for configuration "Release" +set_property(TARGET zlib APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(zlib PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "C" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/share/OpenCV/3rdparty/lib/libzlib.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS zlib ) +list(APPEND _IMPORT_CHECK_FILES_FOR_zlib "${_IMPORT_PREFIX}/share/OpenCV/3rdparty/lib/libzlib.a" ) + +# Import target "libjpeg-turbo" for configuration "Release" +set_property(TARGET libjpeg-turbo APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(libjpeg-turbo PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "C" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/share/OpenCV/3rdparty/lib/liblibjpeg-turbo.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS libjpeg-turbo ) +list(APPEND _IMPORT_CHECK_FILES_FOR_libjpeg-turbo "${_IMPORT_PREFIX}/share/OpenCV/3rdparty/lib/liblibjpeg-turbo.a" ) + +# Import target "libtiff" for configuration "Release" +set_property(TARGET libtiff APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(libtiff PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "C;CXX" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/share/OpenCV/3rdparty/lib/liblibtiff.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS libtiff ) +list(APPEND _IMPORT_CHECK_FILES_FOR_libtiff "${_IMPORT_PREFIX}/share/OpenCV/3rdparty/lib/liblibtiff.a" ) + +# Import target "libwebp" for configuration "Release" +set_property(TARGET libwebp APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(libwebp PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "C" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/share/OpenCV/3rdparty/lib/liblibwebp.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS libwebp ) +list(APPEND _IMPORT_CHECK_FILES_FOR_libwebp "${_IMPORT_PREFIX}/share/OpenCV/3rdparty/lib/liblibwebp.a" ) + +# Import target "libjasper" for configuration "Release" +set_property(TARGET libjasper APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(libjasper PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "C" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/share/OpenCV/3rdparty/lib/liblibjasper.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS libjasper ) +list(APPEND _IMPORT_CHECK_FILES_FOR_libjasper "${_IMPORT_PREFIX}/share/OpenCV/3rdparty/lib/liblibjasper.a" ) + +# Import target "libpng" for configuration "Release" +set_property(TARGET libpng APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(libpng PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "ASM;C" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/share/OpenCV/3rdparty/lib/liblibpng.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS libpng ) +list(APPEND _IMPORT_CHECK_FILES_FOR_libpng "${_IMPORT_PREFIX}/share/OpenCV/3rdparty/lib/liblibpng.a" ) + +# Import target "IlmImf" for configuration "Release" +set_property(TARGET IlmImf APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(IlmImf PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/share/OpenCV/3rdparty/lib/libIlmImf.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS IlmImf ) +list(APPEND _IMPORT_CHECK_FILES_FOR_IlmImf "${_IMPORT_PREFIX}/share/OpenCV/3rdparty/lib/libIlmImf.a" ) + +# Import target "libprotobuf" for configuration "Release" +set_property(TARGET libprotobuf APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(libprotobuf PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/share/OpenCV/3rdparty/lib/liblibprotobuf.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS libprotobuf ) +list(APPEND _IMPORT_CHECK_FILES_FOR_libprotobuf "${_IMPORT_PREFIX}/share/OpenCV/3rdparty/lib/liblibprotobuf.a" ) + +# Import target "quirc" for configuration "Release" +set_property(TARGET quirc APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(quirc PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "C" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/share/OpenCV/3rdparty/lib/libquirc.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS quirc ) +list(APPEND _IMPORT_CHECK_FILES_FOR_quirc "${_IMPORT_PREFIX}/share/OpenCV/3rdparty/lib/libquirc.a" ) + +# Import target "tegra_hal" for configuration "Release" +set_property(TARGET tegra_hal APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(tegra_hal PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/share/OpenCV/3rdparty/lib/libtegra_hal.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS tegra_hal ) +list(APPEND _IMPORT_CHECK_FILES_FOR_tegra_hal "${_IMPORT_PREFIX}/share/OpenCV/3rdparty/lib/libtegra_hal.a" ) + +# Import target "opencv_core" for configuration "Release" +set_property(TARGET opencv_core APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(opencv_core PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/lib/libopencv_core.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS opencv_core ) +list(APPEND _IMPORT_CHECK_FILES_FOR_opencv_core "${_IMPORT_PREFIX}/lib/libopencv_core.a" ) + +# Import target "opencv_imgproc" for configuration "Release" +set_property(TARGET opencv_imgproc APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(opencv_imgproc PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/lib/libopencv_imgproc.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS opencv_imgproc ) +list(APPEND _IMPORT_CHECK_FILES_FOR_opencv_imgproc "${_IMPORT_PREFIX}/lib/libopencv_imgproc.a" ) + +# Import target "opencv_video" for configuration "Release" +set_property(TARGET opencv_video APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(opencv_video PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/lib/libopencv_video.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS opencv_video ) +list(APPEND _IMPORT_CHECK_FILES_FOR_opencv_video "${_IMPORT_PREFIX}/lib/libopencv_video.a" ) + +# Import target "opencv_features2d" for configuration "Release" +set_property(TARGET opencv_features2d APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(opencv_features2d PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/lib/libopencv_features2d.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS opencv_features2d ) +list(APPEND _IMPORT_CHECK_FILES_FOR_opencv_features2d "${_IMPORT_PREFIX}/lib/libopencv_features2d.a" ) + +# Import target "opencv_imgcodecs" for configuration "Release" +set_property(TARGET opencv_imgcodecs APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(opencv_imgcodecs PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/lib/libopencv_imgcodecs.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS opencv_imgcodecs ) +list(APPEND _IMPORT_CHECK_FILES_FOR_opencv_imgcodecs "${_IMPORT_PREFIX}/lib/libopencv_imgcodecs.a" ) + +# Import target "opencv_calib3d" for configuration "Release" +set_property(TARGET opencv_calib3d APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(opencv_calib3d PROPERTIES + IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/lib/libopencv_calib3d.a" + ) + +list(APPEND _IMPORT_CHECK_TARGETS opencv_calib3d ) +list(APPEND _IMPORT_CHECK_FILES_FOR_opencv_calib3d "${_IMPORT_PREFIX}/lib/libopencv_calib3d.a" ) + +# Commands beyond this point should not need to know the version. +set(CMAKE_IMPORT_FILE_VERSION) diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/OpenCVModules.cmake b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/OpenCVModules.cmake new file mode 100644 index 0000000000000000000000000000000000000000..55aa2db299500b55ca6484422ab75943ec9768bd --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/OpenCVModules.cmake @@ -0,0 +1,174 @@ +# Generated by CMake + +if("${CMAKE_MAJOR_VERSION}.${CMAKE_MINOR_VERSION}" LESS 2.5) + message(FATAL_ERROR "CMake >= 2.6.0 required") +endif() +cmake_policy(PUSH) +cmake_policy(VERSION 2.6) +#---------------------------------------------------------------- +# Generated CMake target import file. +#---------------------------------------------------------------- + +# Commands may need to know the format version. +set(CMAKE_IMPORT_FILE_VERSION 1) + +# Protect against multiple inclusion, which would fail when already imported targets are added once more. +set(_targetsDefined) +set(_targetsNotDefined) +set(_expectedTargets) +foreach(_expectedTarget zlib libjpeg-turbo libtiff libwebp libjasper libpng IlmImf libprotobuf quirc tegra_hal opencv_core opencv_imgproc opencv_video opencv_features2d opencv_imgcodecs opencv_calib3d) + list(APPEND _expectedTargets ${_expectedTarget}) + if(NOT TARGET ${_expectedTarget}) + list(APPEND _targetsNotDefined ${_expectedTarget}) + endif() + if(TARGET ${_expectedTarget}) + list(APPEND _targetsDefined ${_expectedTarget}) + endif() +endforeach() +if("${_targetsDefined}" STREQUAL "${_expectedTargets}") + unset(_targetsDefined) + unset(_targetsNotDefined) + unset(_expectedTargets) + set(CMAKE_IMPORT_FILE_VERSION) + cmake_policy(POP) + return() +endif() +if(NOT "${_targetsDefined}" STREQUAL "") + message(FATAL_ERROR "Some (but not all) targets in this export set were already defined.\nTargets Defined: ${_targetsDefined}\nTargets not yet defined: ${_targetsNotDefined}\n") +endif() +unset(_targetsDefined) +unset(_targetsNotDefined) +unset(_expectedTargets) + + +# Compute the installation prefix relative to this file. +get_filename_component(_IMPORT_PREFIX "${CMAKE_CURRENT_LIST_FILE}" PATH) +get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH) +get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH) +if(_IMPORT_PREFIX STREQUAL "/") + set(_IMPORT_PREFIX "") +endif() + +# Create imported target zlib +add_library(zlib STATIC IMPORTED) + +# Create imported target libjpeg-turbo +add_library(libjpeg-turbo STATIC IMPORTED) + +# Create imported target libtiff +add_library(libtiff STATIC IMPORTED) + +set_target_properties(libtiff PROPERTIES + INTERFACE_LINK_LIBRARIES "zlib" +) + +# Create imported target libwebp +add_library(libwebp STATIC IMPORTED) + +# Create imported target libjasper +add_library(libjasper STATIC IMPORTED) + +# Create imported target libpng +add_library(libpng STATIC IMPORTED) + +set_target_properties(libpng PROPERTIES + INTERFACE_LINK_LIBRARIES "zlib" +) + +# Create imported target IlmImf +add_library(IlmImf STATIC IMPORTED) + +set_target_properties(IlmImf PROPERTIES + INTERFACE_LINK_LIBRARIES "zlib" +) + +# Create imported target libprotobuf +add_library(libprotobuf STATIC IMPORTED) + +# Create imported target quirc +add_library(quirc STATIC IMPORTED) + +# Create imported target tegra_hal +add_library(tegra_hal STATIC IMPORTED) + +# Create imported target opencv_core +add_library(opencv_core STATIC IMPORTED) + +set_target_properties(opencv_core PROPERTIES + INTERFACE_LINK_LIBRARIES "\$;\$;\$;\$;\$;\$;\$" +) + +# Create imported target opencv_imgproc +add_library(opencv_imgproc STATIC IMPORTED) + +set_target_properties(opencv_imgproc PROPERTIES + INTERFACE_LINK_LIBRARIES "opencv_core;\$;\$;\$;\$;\$" +) + +# Create imported target opencv_video +add_library(opencv_video STATIC IMPORTED) + +set_target_properties(opencv_video PROPERTIES + INTERFACE_LINK_LIBRARIES "opencv_core;opencv_imgproc;\$;\$;\$;\$;\$" +) + +# Create imported target opencv_features2d +add_library(opencv_features2d STATIC IMPORTED) + +set_target_properties(opencv_features2d PROPERTIES + INTERFACE_LINK_LIBRARIES "opencv_core;opencv_imgproc;\$;\$;\$;\$;\$" +) + +# Create imported target opencv_imgcodecs +add_library(opencv_imgcodecs STATIC IMPORTED) + +set_target_properties(opencv_imgcodecs PROPERTIES + INTERFACE_LINK_LIBRARIES "opencv_core;opencv_imgproc;\$;\$;\$;\$;\$;\$;\$;\$;\$;\$;\$;\$" +) + +# Create imported target opencv_calib3d +add_library(opencv_calib3d STATIC IMPORTED) + +set_target_properties(opencv_calib3d PROPERTIES + INTERFACE_LINK_LIBRARIES "opencv_core;opencv_imgproc;opencv_features2d;\$;\$;\$;\$;\$" +) + +if(CMAKE_VERSION VERSION_LESS 2.8.12) + message(FATAL_ERROR "This file relies on consumers using CMake 2.8.12 or greater.") +endif() + +# Load information for each installed configuration. +get_filename_component(_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH) +file(GLOB CONFIG_FILES "${_DIR}/OpenCVModules-*.cmake") +foreach(f ${CONFIG_FILES}) + include(${f}) +endforeach() + +# Cleanup temporary variables. +set(_IMPORT_PREFIX) + +# Loop over all imported files and verify that they actually exist +foreach(target ${_IMPORT_CHECK_TARGETS} ) + foreach(file ${_IMPORT_CHECK_FILES_FOR_${target}} ) + if(NOT EXISTS "${file}" ) + message(FATAL_ERROR "The imported target \"${target}\" references the file + \"${file}\" +but this file does not exist. Possible reasons include: +* The file was deleted, renamed, or moved to another location. +* An install or uninstall procedure did not complete successfully. +* The installation package was faulty and contained + \"${CMAKE_CURRENT_LIST_FILE}\" +but not all the files it references. +") + endif() + endforeach() + unset(_IMPORT_CHECK_FILES_FOR_${target}) +endforeach() +unset(_IMPORT_CHECK_TARGETS) + +# This file does not depend on other imported targets which have +# been exported from the same project but in a separate export set. + +# Commands beyond this point should not need to know the version. +set(CMAKE_IMPORT_FILE_VERSION) +cmake_policy(POP) diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/haarcascades/haarcascade_eye.xml b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/haarcascades/haarcascade_eye.xml new file mode 100644 index 0000000000000000000000000000000000000000..b21e3b93d74b5130b5a1323be9fc46017ab0e8c7 --- /dev/null +++ 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a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/SoftFloat-COPYING.txt b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/SoftFloat-COPYING.txt new file mode 100644 index 0000000000000000000000000000000000000000..e16aa0fd58c12d2da71a95650be9a23b87ef46e0 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/SoftFloat-COPYING.txt @@ -0,0 +1,36 @@ + +License for Berkeley SoftFloat Release 3c + +John R. Hauser +2017 February 10 + +The following applies to the whole of SoftFloat Release 3c as well as to +each source file individually. + +Copyright 2011, 2012, 2013, 2014, 2015, 2016, 2017 The Regents of the +University of California. All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + + 1. Redistributions of source code must retain the above copyright notice, + this list of conditions, and the following disclaimer. + + 2. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions, and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + 3. Neither the name of the University nor the names of its contributors + may be used to endorse or promote products derived from this software + without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE REGENTS AND CONTRIBUTORS "AS IS", AND ANY +EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE, ARE +DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR ANY +DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND +ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF +THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/jasper-LICENSE b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/jasper-LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..f817ef43a55a70eb1646b07bf254218fee97ee20 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/jasper-LICENSE @@ -0,0 +1,51 @@ +JasPer License Version 2.0 + +Copyright (c) 2001-2006 Michael David Adams +Copyright (c) 1999-2000 Image Power, Inc. +Copyright (c) 1999-2000 The University of British Columbia + +All rights reserved. + +Permission is hereby granted, free of charge, to any person (the +"User") obtaining a copy of this software and associated documentation +files (the "Software"), to deal in the Software without restriction, +including without limitation the rights to use, copy, modify, merge, +publish, distribute, and/or sell copies of the Software, and to permit +persons to whom the Software is furnished to do so, subject to the +following conditions: + +1. The above copyright notices and this permission notice (which +includes the disclaimer below) shall be included in all copies or +substantial portions of the Software. + +2. The name of a copyright holder shall not be used to endorse or +promote products derived from the Software without specific prior +written permission. + +THIS DISCLAIMER OF WARRANTY CONSTITUTES AN ESSENTIAL PART OF THIS +LICENSE. NO USE OF THE SOFTWARE IS AUTHORIZED HEREUNDER EXCEPT UNDER +THIS DISCLAIMER. THE SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS +"AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING +BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A +PARTICULAR PURPOSE AND NONINFRINGEMENT OF THIRD PARTY RIGHTS. IN NO +EVENT SHALL THE COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, OR ANY SPECIAL +INDIRECT OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING +FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, +NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION +WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. NO ASSURANCES ARE +PROVIDED BY THE COPYRIGHT HOLDERS THAT THE SOFTWARE DOES NOT INFRINGE +THE PATENT OR OTHER INTELLECTUAL PROPERTY RIGHTS OF ANY OTHER ENTITY. +EACH COPYRIGHT HOLDER DISCLAIMS ANY LIABILITY TO THE USER FOR CLAIMS +BROUGHT BY ANY OTHER ENTITY BASED ON INFRINGEMENT OF INTELLECTUAL +PROPERTY RIGHTS OR OTHERWISE. AS A CONDITION TO EXERCISING THE RIGHTS +GRANTED HEREUNDER, EACH USER HEREBY ASSUMES SOLE RESPONSIBILITY TO SECURE +ANY OTHER INTELLECTUAL PROPERTY RIGHTS NEEDED, IF ANY. THE SOFTWARE +IS NOT FAULT-TOLERANT AND IS NOT INTENDED FOR USE IN MISSION-CRITICAL +SYSTEMS, SUCH AS THOSE USED IN THE OPERATION OF NUCLEAR FACILITIES, +AIRCRAFT NAVIGATION OR COMMUNICATION SYSTEMS, AIR TRAFFIC CONTROL +SYSTEMS, DIRECT LIFE SUPPORT MACHINES, OR WEAPONS SYSTEMS, IN WHICH +THE FAILURE OF THE SOFTWARE OR SYSTEM COULD LEAD DIRECTLY TO DEATH, +PERSONAL INJURY, OR SEVERE PHYSICAL OR ENVIRONMENTAL DAMAGE ("HIGH +RISK ACTIVITIES"). THE COPYRIGHT HOLDERS SPECIFICALLY DISCLAIM ANY +EXPRESS OR IMPLIED WARRANTY OF FITNESS FOR HIGH RISK ACTIVITIES. + diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/jasper-README b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/jasper-README new file mode 100644 index 0000000000000000000000000000000000000000..88fd2f10bd4d4809a53cc8076668d71829c249ad --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/jasper-README @@ -0,0 +1,22 @@ +JasPer Readme +************* + +This is the source code distribution for JasPer. JasPer is a collection +of software (i.e., a library and application programs) for the coding +and manipulation of images. This software can handle image data in a +variety of formats. One such format supported by JasPer is the JPEG-2000 +format defined in ISO/IEC 15444-1. + +The complete licensing terms for the JasPer software can be found in +the file named "LICENSE" in the top level directory of this software +distribution. Any use of this software contrary to the terms of the +license is strictly prohibited. The changes made to the software +since the last release are described in the file "NEWS". Detailed +documentation on the JasPer software can be found in the JasPer Software +Reference Manual. This manual is located in the "doc" directory, and +includes useful information such as: 1) how to build, install, and use +the software, 2) how to submit report bugs, and 3) where to find +additional information about the software. + +Enjoy! :) + diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/jasper-copyright b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/jasper-copyright new file mode 100644 index 0000000000000000000000000000000000000000..88a55abcad68c9a21328907ca6b64f816f2d7265 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/jasper-copyright @@ -0,0 +1,62 @@ +This package was debianized by Christopher L Cheney on +Fri, 22 Aug 2003 01:33:34 -0500. + +The current maintainer is Roland Stigge + +It was downloaded from http://www.ece.uvic.ca/~mdadams/jasper/ + +Upstream Author: Michael Adams + +License: + +JasPer License Version 2.0 + +Copyright (c) 1999-2000 Image Power, Inc. +Copyright (c) 1999-2000 The University of British Columbia +Copyright (c) 2001-2003 Michael David Adams + +All rights reserved. + +Permission is hereby granted, free of charge, to any person (the +"User") obtaining a copy of this software and associated documentation +files (the "Software"), to deal in the Software without restriction, +including without limitation the rights to use, copy, modify, merge, +publish, distribute, and/or sell copies of the Software, and to permit +persons to whom the Software is furnished to do so, subject to the +following conditions: + +1. The above copyright notices and this permission notice (which +includes the disclaimer below) shall be included in all copies or +substantial portions of the Software. + +2. The name of a copyright holder shall not be used to endorse or +promote products derived from the Software without specific prior +written permission. + +THIS DISCLAIMER OF WARRANTY CONSTITUTES AN ESSENTIAL PART OF THIS +LICENSE. NO USE OF THE SOFTWARE IS AUTHORIZED HEREUNDER EXCEPT UNDER +THIS DISCLAIMER. THE SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS +"AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING +BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A +PARTICULAR PURPOSE AND NONINFRINGEMENT OF THIRD PARTY RIGHTS. IN NO +EVENT SHALL THE COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, OR ANY SPECIAL +INDIRECT OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING +FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, +NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION +WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. NO ASSURANCES ARE +PROVIDED BY THE COPYRIGHT HOLDERS THAT THE SOFTWARE DOES NOT INFRINGE +THE PATENT OR OTHER INTELLECTUAL PROPERTY RIGHTS OF ANY OTHER ENTITY. +EACH COPYRIGHT HOLDER DISCLAIMS ANY LIABILITY TO THE USER FOR CLAIMS +BROUGHT BY ANY OTHER ENTITY BASED ON INFRINGEMENT OF INTELLECTUAL +PROPERTY RIGHTS OR OTHERWISE. AS A CONDITION TO EXERCISING THE RIGHTS +GRANTED HEREUNDER, EACH USER HEREBY ASSUMES SOLE RESPONSIBILITY TO SECURE +ANY OTHER INTELLECTUAL PROPERTY RIGHTS NEEDED, IF ANY. THE SOFTWARE +IS NOT FAULT-TOLERANT AND IS NOT INTENDED FOR USE IN MISSION-CRITICAL +SYSTEMS, SUCH AS THOSE USED IN THE OPERATION OF NUCLEAR FACILITIES, +AIRCRAFT NAVIGATION OR COMMUNICATION SYSTEMS, AIR TRAFFIC CONTROL +SYSTEMS, DIRECT LIFE SUPPORT MACHINES, OR WEAPONS SYSTEMS, IN WHICH +THE FAILURE OF THE SOFTWARE OR SYSTEM COULD LEAD DIRECTLY TO DEATH, +PERSONAL INJURY, OR SEVERE PHYSICAL OR ENVIRONMENTAL DAMAGE ("HIGH +RISK ACTIVITIES"). THE COPYRIGHT HOLDERS SPECIFICALLY DISCLAIM ANY +EXPRESS OR IMPLIED WARRANTY OF FITNESS FOR HIGH RISK ACTIVITIES. + diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/libjpeg-turbo-LICENSE.md b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/libjpeg-turbo-LICENSE.md new file mode 100644 index 0000000000000000000000000000000000000000..0572390635d944af818ebe132019722650e3ac0b --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/libjpeg-turbo-LICENSE.md @@ -0,0 +1,139 @@ +libjpeg-turbo Licenses +====================== + +libjpeg-turbo is covered by three compatible BSD-style open source licenses: + +- The IJG (Independent JPEG Group) License, which is listed in + [README.ijg](README.ijg) + + This license applies to the libjpeg API library and associated programs + (any code inherited from libjpeg, and any modifications to that code.) + +- The Modified (3-clause) BSD License, which is listed below + + This license covers the TurboJPEG API library and associated programs. + +- The zlib License, which is listed below + + This license is a subset of the other two, and it covers the libjpeg-turbo + SIMD extensions. + + +Complying with the libjpeg-turbo Licenses +========================================= + +This section provides a roll-up of the libjpeg-turbo licensing terms, to the +best of our understanding. + +1. If you are distributing a modified version of the libjpeg-turbo source, + then: + + 1. You cannot alter or remove any existing copyright or license notices + from the source. + + **Origin** + - Clause 1 of the IJG License + - Clause 1 of the Modified BSD License + - Clauses 1 and 3 of the zlib License + + 2. You must add your own copyright notice to the header of each source + file you modified, so others can tell that you modified that file (if + there is not an existing copyright header in that file, then you can + simply add a notice stating that you modified the file.) + + **Origin** + - Clause 1 of the IJG License + - Clause 2 of the zlib License + + 3. You must include the IJG README file, and you must not alter any of the + copyright or license text in that file. + + **Origin** + - Clause 1 of the IJG License + +2. If you are distributing only libjpeg-turbo binaries without the source, or + if you are distributing an application that statically links with + libjpeg-turbo, then: + + 1. Your product documentation must include a message stating: + + This software is based in part on the work of the Independent JPEG + Group. + + **Origin** + - Clause 2 of the IJG license + + 2. If your binary distribution includes or uses the TurboJPEG API, then + your product documentation must include the text of the Modified BSD + License. + + **Origin** + - Clause 2 of the Modified BSD License + +3. You cannot use the name of the IJG or The libjpeg-turbo Project or the + contributors thereof in advertising, publicity, etc. + + **Origin** + - IJG License + - Clause 3 of the Modified BSD License + +4. The IJG and The libjpeg-turbo Project do not warrant libjpeg-turbo to be + free of defects, nor do we accept any liability for undesirable + consequences resulting from your use of the software. + + **Origin** + - IJG License + - Modified BSD License + - zlib License + + +The Modified (3-clause) BSD License +=================================== + +Copyright (C)\ \. All Rights Reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +- Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. +- Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. +- Neither the name of the libjpeg-turbo Project nor the names of its + contributors may be used to endorse or promote products derived from this + software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS", +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE +LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +POSSIBILITY OF SUCH DAMAGE. + + +The zlib License +================ + +Copyright (C) \, \. + +This software is provided 'as-is', without any express or implied +warranty. In no event will the authors be held liable for any damages +arising from the use of this software. + +Permission is granted to anyone to use this software for any purpose, +including commercial applications, and to alter it and redistribute it +freely, subject to the following restrictions: + +1. The origin of this software must not be misrepresented; you must not + claim that you wrote the original software. If you use this software + in a product, an acknowledgment in the product documentation would be + appreciated but is not required. +2. Altered source versions must be plainly marked as such, and must not be + misrepresented as being the original software. +3. This notice may not be removed or altered from any source distribution. diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/libjpeg-turbo-README.ijg b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/libjpeg-turbo-README.ijg new file mode 100644 index 0000000000000000000000000000000000000000..9c450ceb07ce9a43325e6524b0b2da017fea8db3 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/libjpeg-turbo-README.ijg @@ -0,0 +1,279 @@ +libjpeg-turbo note: This file has been modified by The libjpeg-turbo Project +to include only information relevant to libjpeg-turbo, to wordsmith certain +sections, and to remove impolitic language that existed in the libjpeg v8 +README. It is included only for reference. Please see README.md for +information specific to libjpeg-turbo. + + +The Independent JPEG Group's JPEG software +========================================== + +This distribution contains a release of the Independent JPEG Group's free JPEG +software. You are welcome to redistribute this software and to use it for any +purpose, subject to the conditions under LEGAL ISSUES, below. + +This software is the work of Tom Lane, Guido Vollbeding, Philip Gladstone, +Bill Allombert, Jim Boucher, Lee Crocker, Bob Friesenhahn, Ben Jackson, +Julian Minguillon, Luis Ortiz, George Phillips, Davide Rossi, Ge' Weijers, +and other members of the Independent JPEG Group. + +IJG is not affiliated with the ISO/IEC JTC1/SC29/WG1 standards committee +(also known as JPEG, together with ITU-T SG16). + + +DOCUMENTATION ROADMAP +===================== + +This file contains the following sections: + +OVERVIEW General description of JPEG and the IJG software. +LEGAL ISSUES Copyright, lack of warranty, terms of distribution. +REFERENCES Where to learn more about JPEG. +ARCHIVE LOCATIONS Where to find newer versions of this software. +FILE FORMAT WARS Software *not* to get. +TO DO Plans for future IJG releases. + +Other documentation files in the distribution are: + +User documentation: + usage.txt Usage instructions for cjpeg, djpeg, jpegtran, + rdjpgcom, and wrjpgcom. + *.1 Unix-style man pages for programs (same info as usage.txt). + wizard.txt Advanced usage instructions for JPEG wizards only. + change.log Version-to-version change highlights. +Programmer and internal documentation: + libjpeg.txt How to use the JPEG library in your own programs. + example.c Sample code for calling the JPEG library. + structure.txt Overview of the JPEG library's internal structure. + coderules.txt Coding style rules --- please read if you contribute code. + +Please read at least usage.txt. Some information can also be found in the JPEG +FAQ (Frequently Asked Questions) article. See ARCHIVE LOCATIONS below to find +out where to obtain the FAQ article. + +If you want to understand how the JPEG code works, we suggest reading one or +more of the REFERENCES, then looking at the documentation files (in roughly +the order listed) before diving into the code. + + +OVERVIEW +======== + +This package contains C software to implement JPEG image encoding, decoding, +and transcoding. JPEG (pronounced "jay-peg") is a standardized compression +method for full-color and grayscale images. JPEG's strong suit is compressing +photographic images or other types of images that have smooth color and +brightness transitions between neighboring pixels. Images with sharp lines or +other abrupt features may not compress well with JPEG, and a higher JPEG +quality may have to be used to avoid visible compression artifacts with such +images. + +JPEG is lossy, meaning that the output pixels are not necessarily identical to +the input pixels. However, on photographic content and other "smooth" images, +very good compression ratios can be obtained with no visible compression +artifacts, and extremely high compression ratios are possible if you are +willing to sacrifice image quality (by reducing the "quality" setting in the +compressor.) + +This software implements JPEG baseline, extended-sequential, and progressive +compression processes. Provision is made for supporting all variants of these +processes, although some uncommon parameter settings aren't implemented yet. +We have made no provision for supporting the hierarchical or lossless +processes defined in the standard. + +We provide a set of library routines for reading and writing JPEG image files, +plus two sample applications "cjpeg" and "djpeg", which use the library to +perform conversion between JPEG and some other popular image file formats. +The library is intended to be reused in other applications. + +In order to support file conversion and viewing software, we have included +considerable functionality beyond the bare JPEG coding/decoding capability; +for example, the color quantization modules are not strictly part of JPEG +decoding, but they are essential for output to colormapped file formats or +colormapped displays. These extra functions can be compiled out of the +library if not required for a particular application. + +We have also included "jpegtran", a utility for lossless transcoding between +different JPEG processes, and "rdjpgcom" and "wrjpgcom", two simple +applications for inserting and extracting textual comments in JFIF files. + +The emphasis in designing this software has been on achieving portability and +flexibility, while also making it fast enough to be useful. In particular, +the software is not intended to be read as a tutorial on JPEG. (See the +REFERENCES section for introductory material.) Rather, it is intended to +be reliable, portable, industrial-strength code. We do not claim to have +achieved that goal in every aspect of the software, but we strive for it. + +We welcome the use of this software as a component of commercial products. +No royalty is required, but we do ask for an acknowledgement in product +documentation, as described under LEGAL ISSUES. + + +LEGAL ISSUES +============ + +In plain English: + +1. We don't promise that this software works. (But if you find any bugs, + please let us know!) +2. You can use this software for whatever you want. You don't have to pay us. +3. You may not pretend that you wrote this software. If you use it in a + program, you must acknowledge somewhere in your documentation that + you've used the IJG code. + +In legalese: + +The authors make NO WARRANTY or representation, either express or implied, +with respect to this software, its quality, accuracy, merchantability, or +fitness for a particular purpose. This software is provided "AS IS", and you, +its user, assume the entire risk as to its quality and accuracy. + +This software is copyright (C) 1991-2016, Thomas G. Lane, Guido Vollbeding. +All Rights Reserved except as specified below. + +Permission is hereby granted to use, copy, modify, and distribute this +software (or portions thereof) for any purpose, without fee, subject to these +conditions: +(1) If any part of the source code for this software is distributed, then this +README file must be included, with this copyright and no-warranty notice +unaltered; and any additions, deletions, or changes to the original files +must be clearly indicated in accompanying documentation. +(2) If only executable code is distributed, then the accompanying +documentation must state that "this software is based in part on the work of +the Independent JPEG Group". +(3) Permission for use of this software is granted only if the user accepts +full responsibility for any undesirable consequences; the authors accept +NO LIABILITY for damages of any kind. + +These conditions apply to any software derived from or based on the IJG code, +not just to the unmodified library. If you use our work, you ought to +acknowledge us. + +Permission is NOT granted for the use of any IJG author's name or company name +in advertising or publicity relating to this software or products derived from +it. This software may be referred to only as "the Independent JPEG Group's +software". + +We specifically permit and encourage the use of this software as the basis of +commercial products, provided that all warranty or liability claims are +assumed by the product vendor. + + +The Unix configuration script "configure" was produced with GNU Autoconf. +It is copyright by the Free Software Foundation but is freely distributable. +The same holds for its supporting scripts (config.guess, config.sub, +ltmain.sh). Another support script, install-sh, is copyright by X Consortium +but is also freely distributable. + +The IJG distribution formerly included code to read and write GIF files. +To avoid entanglement with the Unisys LZW patent (now expired), GIF reading +support has been removed altogether, and the GIF writer has been simplified +to produce "uncompressed GIFs". This technique does not use the LZW +algorithm; the resulting GIF files are larger than usual, but are readable +by all standard GIF decoders. + +We are required to state that + "The Graphics Interchange Format(c) is the Copyright property of + CompuServe Incorporated. GIF(sm) is a Service Mark property of + CompuServe Incorporated." + + +REFERENCES +========== + +We recommend reading one or more of these references before trying to +understand the innards of the JPEG software. + +The best short technical introduction to the JPEG compression algorithm is + Wallace, Gregory K. "The JPEG Still Picture Compression Standard", + Communications of the ACM, April 1991 (vol. 34 no. 4), pp. 30-44. +(Adjacent articles in that issue discuss MPEG motion picture compression, +applications of JPEG, and related topics.) If you don't have the CACM issue +handy, a PDF file containing a revised version of Wallace's article is +available at http://www.ijg.org/files/Wallace.JPEG.pdf. The file (actually +a preprint for an article that appeared in IEEE Trans. Consumer Electronics) +omits the sample images that appeared in CACM, but it includes corrections +and some added material. Note: the Wallace article is copyright ACM and IEEE, +and it may not be used for commercial purposes. + +A somewhat less technical, more leisurely introduction to JPEG can be found in +"The Data Compression Book" by Mark Nelson and Jean-loup Gailly, published by +M&T Books (New York), 2nd ed. 1996, ISBN 1-55851-434-1. This book provides +good explanations and example C code for a multitude of compression methods +including JPEG. It is an excellent source if you are comfortable reading C +code but don't know much about data compression in general. The book's JPEG +sample code is far from industrial-strength, but when you are ready to look +at a full implementation, you've got one here... + +The best currently available description of JPEG is the textbook "JPEG Still +Image Data Compression Standard" by William B. Pennebaker and Joan L. +Mitchell, published by Van Nostrand Reinhold, 1993, ISBN 0-442-01272-1. +Price US$59.95, 638 pp. The book includes the complete text of the ISO JPEG +standards (DIS 10918-1 and draft DIS 10918-2). + +The original JPEG standard is divided into two parts, Part 1 being the actual +specification, while Part 2 covers compliance testing methods. Part 1 is +titled "Digital Compression and Coding of Continuous-tone Still Images, +Part 1: Requirements and guidelines" and has document numbers ISO/IEC IS +10918-1, ITU-T T.81. Part 2 is titled "Digital Compression and Coding of +Continuous-tone Still Images, Part 2: Compliance testing" and has document +numbers ISO/IEC IS 10918-2, ITU-T T.83. + +The JPEG standard does not specify all details of an interchangeable file +format. For the omitted details we follow the "JFIF" conventions, revision +1.02. JFIF 1.02 has been adopted as an Ecma International Technical Report +and thus received a formal publication status. It is available as a free +download in PDF format from +http://www.ecma-international.org/publications/techreports/E-TR-098.htm. +A PostScript version of the JFIF document is available at +http://www.ijg.org/files/jfif.ps.gz. There is also a plain text version at +http://www.ijg.org/files/jfif.txt.gz, but it is missing the figures. + +The TIFF 6.0 file format specification can be obtained by FTP from +ftp://ftp.sgi.com/graphics/tiff/TIFF6.ps.gz. The JPEG incorporation scheme +found in the TIFF 6.0 spec of 3-June-92 has a number of serious problems. +IJG does not recommend use of the TIFF 6.0 design (TIFF Compression tag 6). +Instead, we recommend the JPEG design proposed by TIFF Technical Note #2 +(Compression tag 7). Copies of this Note can be obtained from +http://www.ijg.org/files/. It is expected that the next revision +of the TIFF spec will replace the 6.0 JPEG design with the Note's design. +Although IJG's own code does not support TIFF/JPEG, the free libtiff library +uses our library to implement TIFF/JPEG per the Note. + + +ARCHIVE LOCATIONS +================= + +The "official" archive site for this software is www.ijg.org. +The most recent released version can always be found there in +directory "files". + +The JPEG FAQ (Frequently Asked Questions) article is a source of some +general information about JPEG. +It is available on the World Wide Web at http://www.faqs.org/faqs/jpeg-faq/ +and other news.answers archive sites, including the official news.answers +archive at rtfm.mit.edu: ftp://rtfm.mit.edu/pub/usenet/news.answers/jpeg-faq/. +If you don't have Web or FTP access, send e-mail to mail-server@rtfm.mit.edu +with body + send usenet/news.answers/jpeg-faq/part1 + send usenet/news.answers/jpeg-faq/part2 + + +FILE FORMAT WARS +================ + +The ISO/IEC JTC1/SC29/WG1 standards committee (also known as JPEG, together +with ITU-T SG16) currently promotes different formats containing the name +"JPEG" which are incompatible with original DCT-based JPEG. IJG therefore does +not support these formats (see REFERENCES). Indeed, one of the original +reasons for developing this free software was to help force convergence on +common, interoperable format standards for JPEG files. +Don't use an incompatible file format! +(In any case, our decoder will remain capable of reading existing JPEG +image files indefinitely.) + + +TO DO +===== + +Please send bug reports, offers of help, etc. to jpeg-info@jpegclub.org. diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/libjpeg-turbo-README.md b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/libjpeg-turbo-README.md new file mode 100644 index 0000000000000000000000000000000000000000..74e6eac45a78b7c4817b2c85373c1673dfb3d5a2 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/libjpeg-turbo-README.md @@ -0,0 +1,341 @@ +Background +========== + +libjpeg-turbo is a JPEG image codec that uses SIMD instructions (MMX, SSE2, +NEON, AltiVec) to accelerate baseline JPEG compression and decompression on +x86, x86-64, ARM, and PowerPC systems. On such systems, libjpeg-turbo is +generally 2-6x as fast as libjpeg, all else being equal. On other types of +systems, libjpeg-turbo can still outperform libjpeg by a significant amount, by +virtue of its highly-optimized Huffman coding routines. In many cases, the +performance of libjpeg-turbo rivals that of proprietary high-speed JPEG codecs. + +libjpeg-turbo implements both the traditional libjpeg API as well as the less +powerful but more straightforward TurboJPEG API. libjpeg-turbo also features +colorspace extensions that allow it to compress from/decompress to 32-bit and +big-endian pixel buffers (RGBX, XBGR, etc.), as well as a full-featured Java +interface. + +libjpeg-turbo was originally based on libjpeg/SIMD, an MMX-accelerated +derivative of libjpeg v6b developed by Miyasaka Masaru. The TigerVNC and +VirtualGL projects made numerous enhancements to the codec in 2009, and in +early 2010, libjpeg-turbo spun off into an independent project, with the goal +of making high-speed JPEG compression/decompression technology available to a +broader range of users and developers. + + +License +======= + +libjpeg-turbo is covered by three compatible BSD-style open source licenses. +Refer to [LICENSE.md](LICENSE.md) for a roll-up of license terms. + + +Building libjpeg-turbo +====================== + +Refer to [BUILDING.md](BUILDING.md) for complete instructions. + + +Using libjpeg-turbo +=================== + +libjpeg-turbo includes two APIs that can be used to compress and decompress +JPEG images: + +- **TurboJPEG API**
+ This API provides an easy-to-use interface for compressing and decompressing + JPEG images in memory. It also provides some functionality that would not be + straightforward to achieve using the underlying libjpeg API, such as + generating planar YUV images and performing multiple simultaneous lossless + transforms on an image. The Java interface for libjpeg-turbo is written on + top of the TurboJPEG API. + +- **libjpeg API**
+ This is the de facto industry-standard API for compressing and decompressing + JPEG images. It is more difficult to use than the TurboJPEG API but also + more powerful. The libjpeg API implementation in libjpeg-turbo is both + API/ABI-compatible and mathematically compatible with libjpeg v6b. It can + also optionally be configured to be API/ABI-compatible with libjpeg v7 and v8 + (see below.) + +There is no significant performance advantage to either API when both are used +to perform similar operations. + +Colorspace Extensions +--------------------- + +libjpeg-turbo includes extensions that allow JPEG images to be compressed +directly from (and decompressed directly to) buffers that use BGR, BGRX, +RGBX, XBGR, and XRGB pixel ordering. This is implemented with ten new +colorspace constants: + + JCS_EXT_RGB /* red/green/blue */ + JCS_EXT_RGBX /* red/green/blue/x */ + JCS_EXT_BGR /* blue/green/red */ + JCS_EXT_BGRX /* blue/green/red/x */ + JCS_EXT_XBGR /* x/blue/green/red */ + JCS_EXT_XRGB /* x/red/green/blue */ + JCS_EXT_RGBA /* red/green/blue/alpha */ + JCS_EXT_BGRA /* blue/green/red/alpha */ + JCS_EXT_ABGR /* alpha/blue/green/red */ + JCS_EXT_ARGB /* alpha/red/green/blue */ + +Setting `cinfo.in_color_space` (compression) or `cinfo.out_color_space` +(decompression) to one of these values will cause libjpeg-turbo to read the +red, green, and blue values from (or write them to) the appropriate position in +the pixel when compressing from/decompressing to an RGB buffer. + +Your application can check for the existence of these extensions at compile +time with: + + #ifdef JCS_EXTENSIONS + +At run time, attempting to use these extensions with a libjpeg implementation +that does not support them will result in a "Bogus input colorspace" error. +Applications can trap this error in order to test whether run-time support is +available for the colorspace extensions. + +When using the RGBX, BGRX, XBGR, and XRGB colorspaces during decompression, the +X byte is undefined, and in order to ensure the best performance, libjpeg-turbo +can set that byte to whatever value it wishes. If an application expects the X +byte to be used as an alpha channel, then it should specify `JCS_EXT_RGBA`, +`JCS_EXT_BGRA`, `JCS_EXT_ABGR`, or `JCS_EXT_ARGB`. When these colorspace +constants are used, the X byte is guaranteed to be 0xFF, which is interpreted +as opaque. + +Your application can check for the existence of the alpha channel colorspace +extensions at compile time with: + + #ifdef JCS_ALPHA_EXTENSIONS + +[jcstest.c](jcstest.c), located in the libjpeg-turbo source tree, demonstrates +how to check for the existence of the colorspace extensions at compile time and +run time. + +libjpeg v7 and v8 API/ABI Emulation +----------------------------------- + +With libjpeg v7 and v8, new features were added that necessitated extending the +compression and decompression structures. Unfortunately, due to the exposed +nature of those structures, extending them also necessitated breaking backward +ABI compatibility with previous libjpeg releases. Thus, programs that were +built to use libjpeg v7 or v8 did not work with libjpeg-turbo, since it is +based on the libjpeg v6b code base. Although libjpeg v7 and v8 are not +as widely used as v6b, enough programs (including a few Linux distros) made +the switch that there was a demand to emulate the libjpeg v7 and v8 ABIs +in libjpeg-turbo. It should be noted, however, that this feature was added +primarily so that applications that had already been compiled to use libjpeg +v7+ could take advantage of accelerated baseline JPEG encoding/decoding +without recompiling. libjpeg-turbo does not claim to support all of the +libjpeg v7+ features, nor to produce identical output to libjpeg v7+ in all +cases (see below.) + +By passing an argument of `--with-jpeg7` or `--with-jpeg8` to `configure`, or +an argument of `-DWITH_JPEG7=1` or `-DWITH_JPEG8=1` to `cmake`, you can build a +version of libjpeg-turbo that emulates the libjpeg v7 or v8 ABI, so that +programs that are built against libjpeg v7 or v8 can be run with libjpeg-turbo. +The following section describes which libjpeg v7+ features are supported and +which aren't. + +### Support for libjpeg v7 and v8 Features + +#### Fully supported + +- **libjpeg: IDCT scaling extensions in decompressor**
+ libjpeg-turbo supports IDCT scaling with scaling factors of 1/8, 1/4, 3/8, + 1/2, 5/8, 3/4, 7/8, 9/8, 5/4, 11/8, 3/2, 13/8, 7/4, 15/8, and 2/1 (only 1/4 + and 1/2 are SIMD-accelerated.) + +- **libjpeg: Arithmetic coding** + +- **libjpeg: In-memory source and destination managers**
+ See notes below. + +- **cjpeg: Separate quality settings for luminance and chrominance**
+ Note that the libpjeg v7+ API was extended to accommodate this feature only + for convenience purposes. It has always been possible to implement this + feature with libjpeg v6b (see rdswitch.c for an example.) + +- **cjpeg: 32-bit BMP support** + +- **cjpeg: `-rgb` option** + +- **jpegtran: Lossless cropping** + +- **jpegtran: `-perfect` option** + +- **jpegtran: Forcing width/height when performing lossless crop** + +- **rdjpgcom: `-raw` option** + +- **rdjpgcom: Locale awareness** + + +#### Not supported + +NOTE: As of this writing, extensive research has been conducted into the +usefulness of DCT scaling as a means of data reduction and SmartScale as a +means of quality improvement. The reader is invited to peruse the research at + and draw his/her own conclusions, +but it is the general belief of our project that these features have not +demonstrated sufficient usefulness to justify inclusion in libjpeg-turbo. + +- **libjpeg: DCT scaling in compressor**
+ `cinfo.scale_num` and `cinfo.scale_denom` are silently ignored. + There is no technical reason why DCT scaling could not be supported when + emulating the libjpeg v7+ API/ABI, but without the SmartScale extension (see + below), only scaling factors of 1/2, 8/15, 4/7, 8/13, 2/3, 8/11, 4/5, and + 8/9 would be available, which is of limited usefulness. + +- **libjpeg: SmartScale**
+ `cinfo.block_size` is silently ignored. + SmartScale is an extension to the JPEG format that allows for DCT block + sizes other than 8x8. Providing support for this new format would be + feasible (particularly without full acceleration.) However, until/unless + the format becomes either an official industry standard or, at minimum, an + accepted solution in the community, we are hesitant to implement it, as + there is no sense of whether or how it might change in the future. It is + our belief that SmartScale has not demonstrated sufficient usefulness as a + lossless format nor as a means of quality enhancement, and thus our primary + interest in providing this feature would be as a means of supporting + additional DCT scaling factors. + +- **libjpeg: Fancy downsampling in compressor**
+ `cinfo.do_fancy_downsampling` is silently ignored. + This requires the DCT scaling feature, which is not supported. + +- **jpegtran: Scaling**
+ This requires both the DCT scaling and SmartScale features, which are not + supported. + +- **Lossless RGB JPEG files**
+ This requires the SmartScale feature, which is not supported. + +### What About libjpeg v9? + +libjpeg v9 introduced yet another field to the JPEG compression structure +(`color_transform`), thus making the ABI backward incompatible with that of +libjpeg v8. This new field was introduced solely for the purpose of supporting +lossless SmartScale encoding. Furthermore, there was actually no reason to +extend the API in this manner, as the color transform could have just as easily +been activated by way of a new JPEG colorspace constant, thus preserving +backward ABI compatibility. + +Our research (see link above) has shown that lossless SmartScale does not +generally accomplish anything that can't already be accomplished better with +existing, standard lossless formats. Therefore, at this time it is our belief +that there is not sufficient technical justification for software projects to +upgrade from libjpeg v8 to libjpeg v9, and thus there is not sufficient +technical justification for us to emulate the libjpeg v9 ABI. + +In-Memory Source/Destination Managers +------------------------------------- + +By default, libjpeg-turbo 1.3 and later includes the `jpeg_mem_src()` and +`jpeg_mem_dest()` functions, even when not emulating the libjpeg v8 API/ABI. +Previously, it was necessary to build libjpeg-turbo from source with libjpeg v8 +API/ABI emulation in order to use the in-memory source/destination managers, +but several projects requested that those functions be included when emulating +the libjpeg v6b API/ABI as well. This allows the use of those functions by +programs that need them, without breaking ABI compatibility for programs that +don't, and it allows those functions to be provided in the "official" +libjpeg-turbo binaries. + +Those who are concerned about maintaining strict conformance with the libjpeg +v6b or v7 API can pass an argument of `--without-mem-srcdst` to `configure` or +an argument of `-DWITH_MEM_SRCDST=0` to `cmake` prior to building +libjpeg-turbo. This will restore the pre-1.3 behavior, in which +`jpeg_mem_src()` and `jpeg_mem_dest()` are only included when emulating the +libjpeg v8 API/ABI. + +On Un*x systems, including the in-memory source/destination managers changes +the dynamic library version from 62.1.0 to 62.2.0 if using libjpeg v6b API/ABI +emulation and from 7.1.0 to 7.2.0 if using libjpeg v7 API/ABI emulation. + +Note that, on most Un*x systems, the dynamic linker will not look for a +function in a library until that function is actually used. Thus, if a program +is built against libjpeg-turbo 1.3+ and uses `jpeg_mem_src()` or +`jpeg_mem_dest()`, that program will not fail if run against an older version +of libjpeg-turbo or against libjpeg v7- until the program actually tries to +call `jpeg_mem_src()` or `jpeg_mem_dest()`. Such is not the case on Windows. +If a program is built against the libjpeg-turbo 1.3+ DLL and uses +`jpeg_mem_src()` or `jpeg_mem_dest()`, then it must use the libjpeg-turbo 1.3+ +DLL at run time. + +Both cjpeg and djpeg have been extended to allow testing the in-memory +source/destination manager functions. See their respective man pages for more +details. + + +Mathematical Compatibility +========================== + +For the most part, libjpeg-turbo should produce identical output to libjpeg +v6b. The one exception to this is when using the floating point DCT/IDCT, in +which case the outputs of libjpeg v6b and libjpeg-turbo can differ for the +following reasons: + +- The SSE/SSE2 floating point DCT implementation in libjpeg-turbo is ever so + slightly more accurate than the implementation in libjpeg v6b, but not by + any amount perceptible to human vision (generally in the range of 0.01 to + 0.08 dB gain in PNSR.) + +- When not using the SIMD extensions, libjpeg-turbo uses the more accurate + (and slightly faster) floating point IDCT algorithm introduced in libjpeg + v8a as opposed to the algorithm used in libjpeg v6b. It should be noted, + however, that this algorithm basically brings the accuracy of the floating + point IDCT in line with the accuracy of the slow integer IDCT. The floating + point DCT/IDCT algorithms are mainly a legacy feature, and they do not + produce significantly more accuracy than the slow integer algorithms (to put + numbers on this, the typical difference in PNSR between the two algorithms + is less than 0.10 dB, whereas changing the quality level by 1 in the upper + range of the quality scale is typically more like a 1.0 dB difference.) + +- If the floating point algorithms in libjpeg-turbo are not implemented using + SIMD instructions on a particular platform, then the accuracy of the + floating point DCT/IDCT can depend on the compiler settings. + +While libjpeg-turbo does emulate the libjpeg v8 API/ABI, under the hood it is +still using the same algorithms as libjpeg v6b, so there are several specific +cases in which libjpeg-turbo cannot be expected to produce the same output as +libjpeg v8: + +- When decompressing using scaling factors of 1/2 and 1/4, because libjpeg v8 + implements those scaling algorithms differently than libjpeg v6b does, and + libjpeg-turbo's SIMD extensions are based on the libjpeg v6b behavior. + +- When using chrominance subsampling, because libjpeg v8 implements this + with its DCT/IDCT scaling algorithms rather than with a separate + downsampling/upsampling algorithm. In our testing, the subsampled/upsampled + output of libjpeg v8 is less accurate than that of libjpeg v6b for this + reason. + +- When decompressing using a scaling factor > 1 and merged (AKA "non-fancy" or + "non-smooth") chrominance upsampling, because libjpeg v8 does not support + merged upsampling with scaling factors > 1. + + +Performance Pitfalls +==================== + +Restart Markers +--------------- + +The optimized Huffman decoder in libjpeg-turbo does not handle restart markers +in a way that makes the rest of the libjpeg infrastructure happy, so it is +necessary to use the slow Huffman decoder when decompressing a JPEG image that +has restart markers. This can cause the decompression performance to drop by +as much as 20%, but the performance will still be much greater than that of +libjpeg. Many consumer packages, such as PhotoShop, use restart markers when +generating JPEG images, so images generated by those programs will experience +this issue. + +Fast Integer Forward DCT at High Quality Levels +----------------------------------------------- + +The algorithm used by the SIMD-accelerated quantization function cannot produce +correct results whenever the fast integer forward DCT is used along with a JPEG +quality of 98-100. Thus, libjpeg-turbo must use the non-SIMD quantization +function in those cases. This causes performance to drop by as much as 40%. +It is therefore strongly advised that you use the slow integer forward DCT +whenever encoding images with a JPEG quality of 98 or higher. diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/libpng-LICENSE b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/libpng-LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..6ee9c8f5548223ab91d4600840d2597bd5cdbe9f --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/libpng-LICENSE @@ -0,0 +1,133 @@ + +This copy of the libpng notices is provided for your convenience. In case of +any discrepancy between this copy and the notices in the file png.h that is +included in the libpng distribution, the latter shall prevail. + +COPYRIGHT NOTICE, DISCLAIMER, and LICENSE: + +If you modify libpng you may insert additional notices immediately following +this sentence. + +This code is released under the libpng license. + +libpng versions 1.0.7, July 1, 2000 through 1.6.35, July 15, 2018 are +Copyright (c) 2000-2002, 2004, 2006-2018 Glenn Randers-Pehrson, are +derived from libpng-1.0.6, and are distributed according to the same +disclaimer and license as libpng-1.0.6 with the following individuals +added to the list of Contributing Authors: + + Simon-Pierre Cadieux + Eric S. Raymond + Mans Rullgard + Cosmin Truta + Gilles Vollant + James Yu + Mandar Sahastrabuddhe + Google Inc. + Vadim Barkov + +and with the following additions to the disclaimer: + + There is no warranty against interference with your enjoyment of the + library or against infringement. There is no warranty that our + efforts or the library will fulfill any of your particular purposes + or needs. This library is provided with all faults, and the entire + risk of satisfactory quality, performance, accuracy, and effort is with + the user. + +Some files in the "contrib" directory and some configure-generated +files that are distributed with libpng have other copyright owners and +are released under other open source licenses. + +libpng versions 0.97, January 1998, through 1.0.6, March 20, 2000, are +Copyright (c) 1998-2000 Glenn Randers-Pehrson, are derived from +libpng-0.96, and are distributed according to the same disclaimer and +license as libpng-0.96, with the following individuals added to the list +of Contributing Authors: + + Tom Lane + Glenn Randers-Pehrson + Willem van Schaik + +libpng versions 0.89, June 1996, through 0.96, May 1997, are +Copyright (c) 1996-1997 Andreas Dilger, are derived from libpng-0.88, +and are distributed according to the same disclaimer and license as +libpng-0.88, with the following individuals added to the list of +Contributing Authors: + + John Bowler + Kevin Bracey + Sam Bushell + Magnus Holmgren + Greg Roelofs + Tom Tanner + +Some files in the "scripts" directory have other copyright owners +but are released under this license. + +libpng versions 0.5, May 1995, through 0.88, January 1996, are +Copyright (c) 1995-1996 Guy Eric Schalnat, Group 42, Inc. + +For the purposes of this copyright and license, "Contributing Authors" +is defined as the following set of individuals: + + Andreas Dilger + Dave Martindale + Guy Eric Schalnat + Paul Schmidt + Tim Wegner + +The PNG Reference Library is supplied "AS IS". The Contributing Authors +and Group 42, Inc. disclaim all warranties, expressed or implied, +including, without limitation, the warranties of merchantability and of +fitness for any purpose. The Contributing Authors and Group 42, Inc. +assume no liability for direct, indirect, incidental, special, exemplary, +or consequential damages, which may result from the use of the PNG +Reference Library, even if advised of the possibility of such damage. + +Permission is hereby granted to use, copy, modify, and distribute this +source code, or portions hereof, for any purpose, without fee, subject +to the following restrictions: + + 1. The origin of this source code must not be misrepresented. + + 2. Altered versions must be plainly marked as such and must not + be misrepresented as being the original source. + + 3. This Copyright notice may not be removed or altered from any + source or altered source distribution. + +The Contributing Authors and Group 42, Inc. specifically permit, without +fee, and encourage the use of this source code as a component to +supporting the PNG file format in commercial products. If you use this +source code in a product, acknowledgment is not required but would be +appreciated. + +END OF COPYRIGHT NOTICE, DISCLAIMER, and LICENSE. + +TRADEMARK: + +The name "libpng" has not been registered by the Copyright owner +as a trademark in any jurisdiction. However, because libpng has +been distributed and maintained world-wide, continually since 1995, +the Copyright owner claims "common-law trademark protection" in any +jurisdiction where common-law trademark is recognized. + +OSI CERTIFICATION: + +Libpng is OSI Certified Open Source Software. OSI Certified Open Source is +a certification mark of the Open Source Initiative. OSI has not addressed +the additional disclaimers inserted at version 1.0.7. + +EXPORT CONTROL: + +The Copyright owner believes that the Export Control Classification +Number (ECCN) for libpng is EAR99, which means not subject to export +controls or International Traffic in Arms Regulations (ITAR) because +it is open source, publicly available software, that does not contain +any encryption software. See the EAR, paragraphs 734.3(b)(3) and +734.7(b). + +Glenn Randers-Pehrson +glennrp at users.sourceforge.net +July 15, 2018 diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/libpng-README b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/libpng-README new file mode 100644 index 0000000000000000000000000000000000000000..f098b27f3971aec2dd120678d09c5aac707704d1 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/libpng-README @@ -0,0 +1,222 @@ +README for libpng version 1.6.35 - July 15, 2018 (shared library 16.0) +See the note about version numbers near the top of png.h + +See INSTALL for instructions on how to install libpng. + +Libpng comes in several distribution formats. Get libpng-*.tar.gz or +libpng-*.tar.xz or if you want UNIX-style line endings in the text files, +or lpng*.7z or lpng*.zip if you want DOS-style line endings. + +Version 0.89 was the first official release of libpng. Don't let the +fact that it's the first release fool you. The libpng library has been in +extensive use and testing since mid-1995. By late 1997 it had +finally gotten to the stage where there hadn't been significant +changes to the API in some time, and people have a bad feeling about +libraries with versions < 1.0. Version 1.0.0 was released in +March 1998. + +**** +Note that some of the changes to the png_info structure render this +version of the library binary incompatible with libpng-0.89 or +earlier versions if you are using a shared library. The type of the +"filler" parameter for png_set_filler() has changed from png_byte to +png_uint_32, which will affect shared-library applications that use +this function. + +To avoid problems with changes to the internals of the png info_struct, +new APIs have been made available in 0.95 to avoid direct application +access to info_ptr. These functions are the png_set_ and +png_get_ functions. These functions should be used when +accessing/storing the info_struct data, rather than manipulating it +directly, to avoid such problems in the future. + +It is important to note that the APIs did not make current programs +that access the info struct directly incompatible with the new +library, through libpng-1.2.x. In libpng-1.4.x, which was meant to +be a transitional release, members of the png_struct and the +info_struct can still be accessed, but the compiler will issue a +warning about deprecated usage. Since libpng-1.5.0, direct access +to these structs is not allowed, and the definitions of the structs +reside in private pngstruct.h and pnginfo.h header files that are not +accessible to applications. It is strongly suggested that new +programs use the new APIs (as shown in example.c and pngtest.c), and +older programs be converted to the new format, to facilitate upgrades +in the future. +**** + +Additions since 0.90 include the ability to compile libpng as a +Windows DLL, and new APIs for accessing data in the info struct. +Experimental functions include the ability to set weighting and cost +factors for row filter selection, direct reads of integers from buffers +on big-endian processors that support misaligned data access, faster +methods of doing alpha composition, and more accurate 16->8 bit color +conversion. + +The additions since 0.89 include the ability to read from a PNG stream +which has had some (or all) of the signature bytes read by the calling +application. This also allows the reading of embedded PNG streams that +do not have the PNG file signature. As well, it is now possible to set +the library action on the detection of chunk CRC errors. It is possible +to set different actions based on whether the CRC error occurred in a +critical or an ancillary chunk. + +The changes made to the library, and bugs fixed are based on discussions +on the PNG-implement mailing list and not on material submitted +privately to Guy, Andreas, or Glenn. They will forward any good +suggestions to the list. + +For a detailed description on using libpng, read libpng-manual.txt. For +examples of libpng in a program, see example.c and pngtest.c. For usage +information and restrictions (what little they are) on libpng, see +png.h. For a description on using zlib (the compression library used by +libpng) and zlib's restrictions, see zlib.h + +I have included a general makefile, as well as several machine and +compiler specific ones, but you may have to modify one for your own needs. + +You should use zlib 1.0.4 or later to run this, but it MAY work with +versions as old as zlib 0.95. Even so, there are bugs in older zlib +versions which can cause the output of invalid compression streams for +some images. You will definitely need zlib 1.0.4 or later if you are +taking advantage of the MS-DOS "far" structure allocation for the small +and medium memory models. You should also note that zlib is a +compression library that is useful for more things than just PNG files. +You can use zlib as a drop-in replacement for fread() and fwrite() if +you are so inclined. + +zlib should be available at the same place that libpng is, or at zlib.net. + +You may also want a copy of the PNG specification. It is available +as an RFC, a W3C Recommendation, and an ISO/IEC Standard. You can find +these at http://www.libpng.org/pub/png/pngdocs.html . + +This code is currently being archived at libpng.sourceforge.io in the +[DOWNLOAD] area, and at http://libpng.download/src . If you +can't find it in any of those places, e-mail me, and I'll help you find it. + +I am not a lawyer, but I believe that the Export Control Classification +Number (ECCN) for libpng is EAR99, which means not subject to export +controls or International Traffic in Arms Regulations (ITAR) because it +is open source, publicly available software, that does not contain any +encryption software. See the EAR, paragraphs 734.3(b)(3) and 734.7(b). + +If you have any code changes, requests, problems, etc., please e-mail +them to me. Also, I'd appreciate any make files or project files, +and any modifications you needed to make to get libpng to compile, +along with a #define variable to tell what compiler/system you are on. +If you needed to add transformations to libpng, or wish libpng would +provide the image in a different way, drop me a note (and code, if +possible), so I can consider supporting the transformation. +Finally, if you get any warning messages when compiling libpng +(note: not zlib), and they are easy to fix, I'd appreciate the +fix. Please mention "libpng" somewhere in the subject line. Thanks. + +This release was created and will be supported by myself (of course +based in a large way on Guy's and Andreas' earlier work), and the PNG +development group. + +Send comments/corrections/commendations to png-mng-implement at +lists.sourceforge.net (subscription required; visit +https://lists.sourceforge.net/lists/listinfo/png-mng-implement +to subscribe). + +You can't reach Guy, the original libpng author, at the addresses +given in previous versions of this document. He and Andreas will +read mail addressed to the png-implement list, however. + +Please do not send general questions about PNG. Send them to +png-mng-misc at lists.sf.net (subscription required; visit +https://lists.sourceforge.net/lists/listinfo/png-mng-misc to +subscribe). If you have a question about something +in the PNG specification that is related to using libpng, send it +to me. Send me any questions that start with "I was using libpng, +and ...". If in doubt, send questions to me. I'll bounce them +to others, if necessary. + +Please do not send suggestions on how to change PNG. We have +been discussing PNG for twenty years now, and it is official and +finished. If you have suggestions for libpng, however, I'll +gladly listen. Even if your suggestion is not used immediately, +it may be used later. + +Files in this distribution: + + ANNOUNCE => Announcement of this version, with recent changes + CHANGES => Description of changes between libpng versions + KNOWNBUG => List of known bugs and deficiencies + LICENSE => License to use and redistribute libpng + README => This file + TODO => Things not implemented in the current library + Y2KINFO => Statement of Y2K compliance + example.c => Example code for using libpng functions + libpng.3 => manual page for libpng (includes libpng-manual.txt) + libpng-manual.txt => Description of libpng and its functions + libpngpf.3 => manual page for libpng's private functions + png.5 => manual page for the PNG format + png.c => Basic interface functions common to library + png.h => Library function and interface declarations (public) + pngpriv.h => Library function and interface declarations (private) + pngconf.h => System specific library configuration (public) + pngstruct.h => png_struct declaration (private) + pnginfo.h => png_info struct declaration (private) + pngdebug.h => debugging macros (private) + pngerror.c => Error/warning message I/O functions + pngget.c => Functions for retrieving info from struct + pngmem.c => Memory handling functions + pngbar.png => PNG logo, 88x31 + pngnow.png => PNG logo, 98x31 + pngpread.c => Progressive reading functions + pngread.c => Read data/helper high-level functions + pngrio.c => Lowest-level data read I/O functions + pngrtran.c => Read data transformation functions + pngrutil.c => Read data utility functions + pngset.c => Functions for storing data into the info_struct + pngtest.c => Library test program + pngtest.png => Library test sample image + pngtrans.c => Common data transformation functions + pngwio.c => Lowest-level write I/O functions + pngwrite.c => High-level write functions + pngwtran.c => Write data transformations + pngwutil.c => Write utility functions + arm => Contains optimized code for the ARM platform + powerpc => Contains optimized code for the PowerPC platform + contrib => Contributions + arm-neon => Optimized code for ARM-NEON platform + powerpc-vsx => Optimized code for POWERPC-VSX platform + examples => Example programs + gregbook => source code for PNG reading and writing, from + Greg Roelofs' "PNG: The Definitive Guide", + O'Reilly, 1999 + libtests => Test programs + mips-msa => Optimized code for MIPS-MSA platform + pngminim => Minimal decoder, encoder, and progressive decoder + programs demonstrating use of pngusr.dfa + pngminus => Simple pnm2png and png2pnm programs + pngsuite => Test images + testpngs + tools => Various tools + visupng => Contains a MSVC workspace for VisualPng + intel => Optimized code for INTEL-SSE2 platform + mips => Optimized code for MIPS platform + projects => Contains project files and workspaces for + building a DLL + owatcom => Contains a WATCOM project for building libpng + visualc71 => Contains a Microsoft Visual C++ (MSVC) + workspace for building libpng and zlib + vstudio => Contains a Microsoft Visual C++ (MSVC) + workspace for building libpng and zlib + scripts => Directory containing scripts for building libpng: + (see scripts/README.txt for the list of scripts) + +Good luck, and happy coding. + +-Glenn Randers-Pehrson (current maintainer, since 1998) + Internet: glennrp at users.sourceforge.net + +-Andreas Eric Dilger (former maintainer, 1996-1997) + Internet: adilger at enel.ucalgary.ca + Web: http://www-mddsp.enel.ucalgary.ca/People/adilger/ + +-Guy Eric Schalnat (original author and former maintainer, 1995-1996) + (formerly of Group 42, Inc) + Internet: gschal at infinet.com diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/libtiff-COPYRIGHT b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/libtiff-COPYRIGHT new file mode 100644 index 0000000000000000000000000000000000000000..8282186151e224ac83d9990805062ff7b8effa00 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/libtiff-COPYRIGHT @@ -0,0 +1,21 @@ +Copyright (c) 1988-1997 Sam Leffler +Copyright (c) 1991-1997 Silicon Graphics, Inc. + +Permission to use, copy, modify, distribute, and sell this software and +its documentation for any purpose is hereby granted without fee, provided +that (i) the above copyright notices and this permission notice appear in +all copies of the software and related documentation, and (ii) the names of +Sam Leffler and Silicon Graphics may not be used in any advertising or +publicity relating to the software without the specific, prior written +permission of Sam Leffler and Silicon Graphics. + +THE SOFTWARE IS PROVIDED "AS-IS" AND WITHOUT WARRANTY OF ANY KIND, +EXPRESS, IMPLIED OR OTHERWISE, INCLUDING WITHOUT LIMITATION, ANY +WARRANTY OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. + +IN NO EVENT SHALL SAM LEFFLER OR SILICON GRAPHICS BE LIABLE FOR +ANY SPECIAL, INCIDENTAL, INDIRECT OR CONSEQUENTIAL DAMAGES OF ANY KIND, +OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, +WHETHER OR NOT ADVISED OF THE POSSIBILITY OF DAMAGE, AND ON ANY THEORY OF +LIABILITY, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE +OF THIS SOFTWARE. diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/openexr-AUTHORS.ilmbase b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/openexr-AUTHORS.ilmbase new file mode 100644 index 0000000000000000000000000000000000000000..51c1f9cea5329ed8e8cd4ce118a1a6a16db49b7a --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/openexr-AUTHORS.ilmbase @@ -0,0 +1,21 @@ +Developers: +----------- + +Florian Kainz +Rod Bogart +Drew Hess +Bill Anderson +Wojciech Jarosz + +Contributors: +------------- + +Rito Trevino +Josh Pines +Christian Rouet + +Win32 build system: +------------------- + +Nick Porcino +Kimball Thurston diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/openexr-AUTHORS.openexr b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/openexr-AUTHORS.openexr new file mode 100644 index 0000000000000000000000000000000000000000..2926b13e837b2a84dfa827dcc222d495185e2ae0 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/openexr-AUTHORS.openexr @@ -0,0 +1,41 @@ +Developers: +----------- + +Florian Kainz +Rod Bogart +Drew Hess +Paul Schneider +Bill Anderson +Wojciech Jarosz +Andrew Kunz + +Contributors: +------------- + +Simon Green +Rito Trevino +Josh Pines +Christian Rouet +Rodrigo Damazio +Greg Ward +Joseph Goldstone +Loren Carpenter, Pixar Animation Studios + +Win32 build system: +------------------- + +Nick Porcino +Kimball Thurston + +Win32 port contributors: +------------------------ + +Dustin Graves +Jukka Liimatta +Baumann Konstantin +Daniel Koch +E. Scott Larsen +stephan mantler +Andreas Kahler +Frank Jargstorff +Lutz Latta diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/openexr-LICENSE b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/openexr-LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..6372750d3cc40ba4dd12c57b2ac9026a2d7d54b9 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/openexr-LICENSE @@ -0,0 +1,34 @@ +Copyright (c) 2006, Industrial Light & Magic, a division of Lucasfilm +Entertainment Company Ltd. Portions contributed and copyright held by +others as indicated. All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are +met: + + * Redistributions of source code must retain the above + copyright notice, this list of conditions and the following + disclaimer. + + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided with + the distribution. + + * Neither the name of Industrial Light & Magic nor the names of + any other contributors to this software may be used to endorse or + promote products derived from this software without specific prior + written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS +IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, +THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR +PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR +CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, +EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, +PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR +PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF +LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING +NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/openexr-fix_msvc2013_errors.patch b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/openexr-fix_msvc2013_errors.patch new file mode 100644 index 0000000000000000000000000000000000000000..0ce106f13b5778d8cdb364074309c6d18ff61e29 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/openexr-fix_msvc2013_errors.patch @@ -0,0 +1,72 @@ +diff --git a/3rdparty/openexr/IlmImf/ImfAcesFile.cpp b/3rdparty/openexr/IlmImf/ImfAcesFile.cpp +index de4bf83..9418b9d 100644 +--- a/3rdparty/openexr/IlmImf/ImfAcesFile.cpp ++++ b/3rdparty/openexr/IlmImf/ImfAcesFile.cpp +@@ -42,6 +42,7 @@ + #include + #include + #include ++#include // for std::max() + + using namespace std; + using namespace Imath; +diff --git a/3rdparty/openexr/IlmImf/ImfOutputFile.cpp b/3rdparty/openexr/IlmImf/ImfOutputFile.cpp +index 8831ec9..e69b92b 100644 +--- a/3rdparty/openexr/IlmImf/ImfOutputFile.cpp ++++ b/3rdparty/openexr/IlmImf/ImfOutputFile.cpp +@@ -58,6 +58,7 @@ + #include + #include + #include ++#include // for std::max() + + + namespace Imf { +diff --git a/3rdparty/openexr/IlmImf/ImfScanLineInputFile.cpp b/3rdparty/openexr/IlmImf/ImfScanLineInputFile.cpp +index f7a12a3..5d8b522 100644 +--- a/3rdparty/openexr/IlmImf/ImfScanLineInputFile.cpp ++++ b/3rdparty/openexr/IlmImf/ImfScanLineInputFile.cpp +@@ -56,6 +56,7 @@ + #include + #include + #include ++#include // for std::max() + + + namespace Imf { +diff --git a/3rdparty/openexr/IlmImf/ImfTiledMisc.cpp b/3rdparty/openexr/IlmImf/ImfTiledMisc.cpp +index 57f52f1..9588e78 100644 +--- a/3rdparty/openexr/IlmImf/ImfTiledMisc.cpp ++++ b/3rdparty/openexr/IlmImf/ImfTiledMisc.cpp +@@ -43,6 +43,7 @@ + #include "Iex.h" + #include + #include ++#include // for std::max() + + + namespace Imf { +diff --git a/3rdparty/openexr/IlmImf/ImfTiledOutputFile.cpp b/3rdparty/openexr/IlmImf/ImfTiledOutputFile.cpp +index 0882106..0bc3cb3 100644 +--- a/3rdparty/openexr/IlmImf/ImfTiledOutputFile.cpp ++++ b/3rdparty/openexr/IlmImf/ImfTiledOutputFile.cpp +@@ -63,6 +63,7 @@ + #include + #include + #include ++#include // for std::max() + + + namespace Imf { +diff --git a/3rdparty/openexr/Imath/ImathMatrixAlgo.cpp b/3rdparty/openexr/Imath/ImathMatrixAlgo.cpp +index f0d2ed6..7ddc649 100644 +--- a/3rdparty/openexr/Imath/ImathMatrixAlgo.cpp ++++ b/3rdparty/openexr/Imath/ImathMatrixAlgo.cpp +@@ -44,6 +44,7 @@ + + #include "ImathMatrixAlgo.h" + #include ++#include // for std::max() + + #if defined(OPENEXR_DLL) + #define EXPORT_CONST __declspec(dllexport) diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/protobuf-LICENSE b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/protobuf-LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..f028c82324202241b99522ed311d1c369c1eee66 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/protobuf-LICENSE @@ -0,0 +1,42 @@ +This license applies to all parts of Protocol Buffers except the following: + + - Atomicops support for generic gcc, located in + src/google/protobuf/stubs/atomicops_internals_generic_gcc.h. + This file is copyrighted by Red Hat Inc. + + - Atomicops support for AIX/POWER, located in + src/google/protobuf/stubs/atomicops_internals_power.h. + This file is copyrighted by Bloomberg Finance LP. + +Copyright 2014, Google Inc. All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are +met: + + * Redistributions of source code must retain the above copyright +notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above +copyright notice, this list of conditions and the following disclaimer +in the documentation and/or other materials provided with the +distribution. + * Neither the name of Google Inc. nor the names of its +contributors may be used to endorse or promote products derived from +this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +Code generated by the Protocol Buffer compiler is owned by the owner +of the input file used when generating it. This code is not +standalone and requires a support library to be linked with it. This +support library is itself covered by the above license. diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/protobuf-README.md b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/protobuf-README.md new file mode 100644 index 0000000000000000000000000000000000000000..e0edb75f580ec43c1e7abf2aa99aeb6a8d01001f --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/protobuf-README.md @@ -0,0 +1,3 @@ +Project: Protocol Buffers - Google's data interchange format +Source code: https://github.com/google/protobuf +Version: 3.5.1 diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/quirc-LICENSE b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/quirc-LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..d47c0262c2f2a785b6b0f5a87f940251352decc9 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/quirc-LICENSE @@ -0,0 +1,16 @@ +quirc -- QR-code recognition library +Copyright (C) 2010-2012 Daniel Beer + +Permission to use, copy, modify, and/or distribute this software for +any purpose with or without fee is hereby granted, provided that the +above copyright notice and this permission notice appear in all +copies. + +THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL +WARRANTIES WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE +AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL +DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR +PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER +TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR +PERFORMANCE OF THIS SOFTWARE. diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/zlib-README b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/zlib-README new file mode 100644 index 0000000000000000000000000000000000000000..51106de4753292ad59de03de9e634e6814eeb7a2 --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/licenses/zlib-README @@ -0,0 +1,115 @@ +ZLIB DATA COMPRESSION LIBRARY + +zlib 1.2.11 is a general purpose data compression library. All the code is +thread safe. The data format used by the zlib library is described by RFCs +(Request for Comments) 1950 to 1952 in the files +http://tools.ietf.org/html/rfc1950 (zlib format), rfc1951 (deflate format) and +rfc1952 (gzip format). + +All functions of the compression library are documented in the file zlib.h +(volunteer to write man pages welcome, contact zlib@gzip.org). A usage example +of the library is given in the file test/example.c which also tests that +the library is working correctly. Another example is given in the file +test/minigzip.c. The compression library itself is composed of all source +files in the root directory. + +To compile all files and run the test program, follow the instructions given at +the top of Makefile.in. In short "./configure; make test", and if that goes +well, "make install" should work for most flavors of Unix. For Windows, use +one of the special makefiles in win32/ or contrib/vstudio/ . For VMS, use +make_vms.com. + +Questions about zlib should be sent to , or to Gilles Vollant + for the Windows DLL version. The zlib home page is +http://zlib.net/ . Before reporting a problem, please check this site to +verify that you have the latest version of zlib; otherwise get the latest +version and check whether the problem still exists or not. + +PLEASE read the zlib FAQ http://zlib.net/zlib_faq.html before asking for help. + +Mark Nelson wrote an article about zlib for the Jan. 1997 +issue of Dr. Dobb's Journal; a copy of the article is available at +http://marknelson.us/1997/01/01/zlib-engine/ . + +The changes made in version 1.2.11 are documented in the file ChangeLog. + +Unsupported third party contributions are provided in directory contrib/ . + +zlib is available in Java using the java.util.zip package, documented at +http://java.sun.com/developer/technicalArticles/Programming/compression/ . + +A Perl interface to zlib written by Paul Marquess is available +at CPAN (Comprehensive Perl Archive Network) sites, including +http://search.cpan.org/~pmqs/IO-Compress-Zlib/ . + +A Python interface to zlib written by A.M. Kuchling is +available in Python 1.5 and later versions, see +http://docs.python.org/library/zlib.html . + +zlib is built into tcl: http://wiki.tcl.tk/4610 . + +An experimental package to read and write files in .zip format, written on top +of zlib by Gilles Vollant , is available in the +contrib/minizip directory of zlib. + + +Notes for some targets: + +- For Windows DLL versions, please see win32/DLL_FAQ.txt + +- For 64-bit Irix, deflate.c must be compiled without any optimization. With + -O, one libpng test fails. The test works in 32 bit mode (with the -n32 + compiler flag). The compiler bug has been reported to SGI. + +- zlib doesn't work with gcc 2.6.3 on a DEC 3000/300LX under OSF/1 2.1 it works + when compiled with cc. + +- On Digital Unix 4.0D (formely OSF/1) on AlphaServer, the cc option -std1 is + necessary to get gzprintf working correctly. This is done by configure. + +- zlib doesn't work on HP-UX 9.05 with some versions of /bin/cc. It works with + other compilers. Use "make test" to check your compiler. + +- gzdopen is not supported on RISCOS or BEOS. + +- For PalmOs, see http://palmzlib.sourceforge.net/ + + +Acknowledgments: + + The deflate format used by zlib was defined by Phil Katz. The deflate and + zlib specifications were written by L. Peter Deutsch. Thanks to all the + people who reported problems and suggested various improvements in zlib; they + are too numerous to cite here. + +Copyright notice: + + (C) 1995-2017 Jean-loup Gailly and Mark Adler + + This software is provided 'as-is', without any express or implied + warranty. In no event will the authors be held liable for any damages + arising from the use of this software. + + Permission is granted to anyone to use this software for any purpose, + including commercial applications, and to alter it and redistribute it + freely, subject to the following restrictions: + + 1. The origin of this software must not be misrepresented; you must not + claim that you wrote the original software. If you use this software + in a product, an acknowledgment in the product documentation would be + appreciated but is not required. + 2. Altered source versions must be plainly marked as such, and must not be + misrepresented as being the original software. + 3. This notice may not be removed or altered from any source distribution. + + Jean-loup Gailly Mark Adler + jloup@gzip.org madler@alumni.caltech.edu + +If you use the zlib library in a product, we would appreciate *not* receiving +lengthy legal documents to sign. The sources are provided for free but without +warranty of any kind. The library has been entirely written by Jean-loup +Gailly and Mark Adler; it does not include third-party code. + +If you redistribute modified sources, we would appreciate that you include in +the file ChangeLog history information documenting your changes. Please read +the FAQ for more information on the distribution of modified source versions. diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/valgrind.supp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/valgrind.supp new file mode 100644 index 0000000000000000000000000000000000000000..7a4341467f2fd9beda7f5ac006ba5ff2d126c75f --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/valgrind.supp @@ -0,0 +1,221 @@ +{ + OpenCV-IPP static init + Memcheck:Cond + fun:ippicvGetCpuFeatures + fun:ippicvStaticInit +} + +{ + OpenCV-getInitializationMutex + Memcheck:Leak + ... + fun:_ZN2cv22getInitializationMutexEv +} + +{ + OpenCV-SingletonBuffer + Memcheck:Leak + ... + fun:_ZN2cv20allocSingletonBufferEm +} + +{ + OpenCV-getStdAllocator + Memcheck:Leak + ... + fun:_ZN2cv3Mat15getStdAllocatorEv +} + +{ + OpenCV-getOpenCLAllocator + Memcheck:Leak + ... + fun:_ZN2cv3ocl18getOpenCLAllocatorEv +} + +{ + OpenCV-getCoreTlsData + Memcheck:Leak + fun:_Znwm + fun:_ZN2cv14getCoreTlsDataEv +} + +{ + OpenCV-TLS-getTlsStorage + Memcheck:Leak + ... + fun:_ZN2cvL13getTlsStorageEv +} + +{ + OpenCV-TLS-getData() + Memcheck:Leak + ... + fun:*setData* + fun:_ZNK2cv16TLSDataContainer7getDataEv +} + +{ + OpenCV-parallel_for-reconfigure + Memcheck:Leak + ... + fun:_ZN2cv10ThreadPool12reconfigure_Ej +} + +{ + OpenCV-parallel_for-instance + Memcheck:Leak + fun:_Znwm + fun:*instance* + ... + fun:_ZN2cv13parallel_for_ERKNS_5RangeERKNS_16ParallelLoopBodyEd +} + +{ + OpenCV-parallel_for-setNumThreads() + Memcheck:Leak + ... + fun:_ZN2cv13setNumThreadsEi +} + +{ + OpenCV-parallel_for-getNumThreads() + Memcheck:Leak + ... + fun:_ZN2cv13getNumThreadsEv +} + +{ + OpenCV-getIPPSingelton + Memcheck:Leak + ... + fun:_ZN2cv3ippL15getIPPSingeltonEv +} + +{ + OpenCV-getGlobalMatOpInitializer + Memcheck:Leak + fun:_Znwm + fun:_ZN2cvL25getGlobalMatOpInitializerEv +} + +{ + OpenCV-CoreTLSData + Memcheck:Leak + ... + fun:_ZNK2cv7TLSDataINS_11CoreTLSDataEE3getEv +} + +{ + OpenCV-getThreadID() + Memcheck:Leak + ... + fun:_ZN2cv5utils11getThreadIDEv +} + +{ + OpenCV-ThreadID + Memcheck:Leak + fun:_Znwm + fun:_ZNK2cv7TLSDataINS_12_GLOBAL__N_18ThreadIDEE18createDataInstanceEv +} + +{ + OpenCV-ThreadID-TLS + Memcheck:Leak + fun:_Znwm + fun:getThreadIDTLS +} + +{ + OpenCV-CoreTLS + Memcheck:Leak + fun:_Znwm + fun:_ZNK2cv7TLSDataINS_11CoreTLSDataEE18createDataInstanceEv +} + +{ + OpenCV-UMatDataAutoLockerTLS + Memcheck:Leak + ... + fun:_ZN2cvL21getUMatDataAutoLockerEv +} + +{ + OpenCV-haveOpenCL + Memcheck:Leak + ... + fun:_ZN2cv3ocl10haveOpenCLEv +} + +{ + OpenCV-DNN-getLayerFactoryMutex + Memcheck:Leak + ... + fun:_ZN2cv3dnn*L20getLayerFactoryMutexEv +} + +{ + OpenCV-ocl::Context + Memcheck:Leak + ... + fun:_ZN2cv3ocl7Context10getDefaultEb +} + +{ + OpenCV-ocl::Device + Memcheck:Leak + ... + fun:_ZN2cv3ocl6Device10getDefaultEv +} + +{ + OpenCV-ocl::Queue + Memcheck:Leak + ... + fun:_ZN2cv3ocl5Queue6createERKNS0_7ContextERKNS0_6DeviceE +} + +{ + OpenCV-ocl::Program + Memcheck:Leak + ... + fun:_ZN2cv3ocl6Kernel6createEPKcRKNS0_7ProgramE +} + +{ + OpenCV-ocl::ProgramEntry + Memcheck:Leak + ... + fun:_ZNK2cv3ocl8internal12ProgramEntrycvRNS0_13ProgramSourceEEv +} + +{ + OpenCV-ocl::Context::getProg + Memcheck:Leak + ... + fun:_ZN2cv3ocl7Context7getProgERKNS0_13ProgramSourceERKNS_6StringERS5_ +} + +{ + OpenCV-getTraceManager() + Memcheck:Leak + ... + fun:getTraceManagerCallOnce +} + +{ + OpenCV-ITT + Memcheck:Leak + ... + fun:__itt_*create* +} + +{ + OpenCV-FFmpeg-swsscale + Memcheck:Addr16 + ... + fun:sws_scale + fun:_ZN20CvVideoWriter_FFMPEG10writeFrameEPKhiiiii + fun:cvWriteFrame_FFMPEG +} diff --git a/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/valgrind_3rdparty.supp b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/valgrind_3rdparty.supp new file mode 100644 index 0000000000000000000000000000000000000000..50811d112d37a8ae6007bfc81a7b16fb9a980f9b --- /dev/null +++ b/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/valgrind_3rdparty.supp @@ -0,0 +1,122 @@ +{ + IPP static init + Memcheck:Cond + fun:ippicvGetCpuFeatures + fun:ippicvStaticInit +} + +{ + TBB - allocate_via_handler_v3 issue + Memcheck:Leak + fun:malloc + fun:_ZN3tbb8internal23allocate_via_handler_v3Em +} + +{ + GTest + Memcheck:Cond + fun:_ZN7testing8internal11CmpHelperLEIddEENS_15AssertionResultEPKcS4_RKT_RKT0_ +} + +{ + OpenCL + Memcheck:Cond + ... + obj:**/libOpenCL.so* +} + +{ + OpenCL-Intel + Memcheck:Cond + ... + obj:**/libigdrcl.so +} + +{ + OpenCL-Intel + Memcheck:Leak + ... + obj:*/libigdrcl.so* +} + +{ + OpenCL + Memcheck:Param + ioctl(generic) + ... + fun:clGetPlatformIDs +} + +{ + OpenCL-Init + Memcheck:Leak + ... + fun:clGetPlatformIDs +} + +{ + glib + Memcheck:Leak + fun:*alloc + obj:*/libglib* +} + +{ + gcrypt + Memcheck:Leak + ... + obj:*/libgcrypt* +} + +{ + p11-kit + Memcheck:Leak + fun:*alloc + obj:*/libp11-kit* +} + +{ + gobject + Memcheck:Leak + fun:*alloc + ... + obj:*/libgobject* +} + +{ + tasn + Memcheck:Leak + fun:*alloc + obj:*/libtasn*.so* +} + +{ + dl_init + Memcheck:Leak + ... + fun:_dl_init +} + +{ + dl_open + Memcheck:Leak + ... + fun:_dl_open +} + +{ + GDAL + Memcheck:Leak + fun:*alloc + ... + obj:/usr/lib/libgdal.so.1.17.1 +} + +{ + FFMPEG-sws_scale + Memcheck:Addr16 + ... + fun:sws_scale + ... + fun:cvWriteFrame_FFMPEG +} diff --git a/rknpu2/examples/3rdparty/rga/README.md b/rknpu2/examples/3rdparty/rga/README.md new file mode 100644 index 0000000000000000000000000000000000000000..a20afea2d5c3e4a5cabfc4257f2bf9fb0737ea9f --- /dev/null +++ b/rknpu2/examples/3rdparty/rga/README.md @@ -0,0 +1,2 @@ +# RGA +The RGA libraries and docs are obtained from https://github.com/airockchip/librga \ No newline at end of file diff --git a/rknpu2/examples/3rdparty/rga/include/GrallocOps.h b/rknpu2/examples/3rdparty/rga/include/GrallocOps.h new file mode 100644 index 0000000000000000000000000000000000000000..97e92d427b37e01990fc6a5f33f15461147bf7ca --- /dev/null +++ b/rknpu2/examples/3rdparty/rga/include/GrallocOps.h @@ -0,0 +1,59 @@ +/* + * Copyright (C) 2016 Rockchip Electronics Co., Ltd. + * Authors: + * Zhiqin Wei + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef _rk_graphic_buffer_h_ +#define _rk_graphic_buffer_h_ + +#ifdef ANDROID + +#include +#include +#include + +#include + +#include + +#include +#include +#include +#include +#include +#include + +#include +#include + +#include +#include +#include +#include +#include + +#include "drmrga.h" +#include "rga.h" + +// ------------------------------------------------------------------------------- +int RkRgaGetHandleFd(buffer_handle_t handle, int *fd); +int RkRgaGetHandleAttributes(buffer_handle_t handle, + std::vector *attrs); +int RkRgaGetHandleMapAddress(buffer_handle_t handle, + void **buf); +#endif //Android + +#endif //_rk_graphic_buffer_h_ diff --git a/rknpu2/examples/3rdparty/rga/include/RgaApi.h b/rknpu2/examples/3rdparty/rga/include/RgaApi.h new file mode 100644 index 0000000000000000000000000000000000000000..b0049f72b71f2bbfea749f3e921db26dca388919 --- /dev/null +++ b/rknpu2/examples/3rdparty/rga/include/RgaApi.h @@ -0,0 +1,81 @@ +/* + * Copyright (C) 2016 Rockchip Electronics Co., Ltd. + * Authors: + * Zhiqin Wei + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef _rockchip_rga_c_h_ +#define _rockchip_rga_c_h_ + +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include + +#include "drmrga.h" +#include "rga.h" + +#ifdef __cplusplus +extern "C"{ +#endif + +/* + * Compatible with the old version of C interface.The new + * version of the C interface no longer requires users to + * initialize rga, so RgaInit and RgaDeInit are just for + * compatibility with the old C interface, so please do + * not use ctx, because it is usually a NULL. + */ +#define RgaInit(ctx) ({ \ + int ret = 0; \ + ret = c_RkRgaInit(); \ + c_RkRgaGetContext(ctx); \ + ret;\ +}) +#define RgaDeInit(ctx) { \ + (void)ctx; /* unused */ \ + c_RkRgaDeInit(); \ +} +#define RgaBlit(...) c_RkRgaBlit(__VA_ARGS__) +#define RgaCollorFill(...) c_RkRgaColorFill(__VA_ARGS__) +#define RgaFlush() c_RkRgaFlush() + +int c_RkRgaInit(); +void c_RkRgaDeInit(); +void c_RkRgaGetContext(void **ctx); +int c_RkRgaBlit(rga_info_t *src, rga_info_t *dst, rga_info_t *src1); +int c_RkRgaColorFill(rga_info_t *dst); +int c_RkRgaFlush(); + +#ifndef ANDROID /* linux */ +int c_RkRgaGetAllocBuffer(bo_t *bo_info, int width, int height, int bpp); +int c_RkRgaGetAllocBufferCache(bo_t *bo_info, int width, int height, int bpp); +int c_RkRgaGetMmap(bo_t *bo_info); +int c_RkRgaUnmap(bo_t *bo_info); +int c_RkRgaFree(bo_t *bo_info); +int c_RkRgaGetBufferFd(bo_t *bo_info, int *fd); +#endif /* #ifndef ANDROID */ + +#ifdef __cplusplus +} +#endif + +#endif /* #ifndef _rockchip_rga_c_h_ */ diff --git a/rknpu2/examples/3rdparty/rga/include/RgaMutex.h b/rknpu2/examples/3rdparty/rga/include/RgaMutex.h new file mode 100644 index 0000000000000000000000000000000000000000..61909e2c809e3b33cda6054ebfc9b397c0cd88cb --- /dev/null +++ b/rknpu2/examples/3rdparty/rga/include/RgaMutex.h @@ -0,0 +1,193 @@ +/* + * Copyright (C) 2020 Rockchip Electronics Co., Ltd. + * Authors: + * PutinLee + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + + +#ifndef _LIBS_RGA_MUTEX_H +#define _LIBS_RGA_MUTEX_H + +#ifndef ANDROID +#include +#include +#include + +#include + + +// Enable thread safety attributes only with clang. +// The attributes can be safely erased when compiling with other compilers. +#if defined(__clang__) && (!defined(SWIG)) +#define THREAD_ANNOTATION_ATTRIBUTE__(x) __attribute__((x)) +#else +#define THREAD_ANNOTATION_ATTRIBUTE__(x) // no-op +#endif + +#define CAPABILITY(x) THREAD_ANNOTATION_ATTRIBUTE__(capability(x)) + +#define SCOPED_CAPABILITY THREAD_ANNOTATION_ATTRIBUTE__(scoped_lockable) + +#define GUARDED_BY(x) THREAD_ANNOTATION_ATTRIBUTE__(guarded_by(x)) + +#define PT_GUARDED_BY(x) THREAD_ANNOTATION_ATTRIBUTE__(pt_guarded_by(x)) + +#define ACQUIRED_BEFORE(...) THREAD_ANNOTATION_ATTRIBUTE__(acquired_before(__VA_ARGS__)) + +#define ACQUIRED_AFTER(...) THREAD_ANNOTATION_ATTRIBUTE__(acquired_after(__VA_ARGS__)) + +#define REQUIRES(...) THREAD_ANNOTATION_ATTRIBUTE__(requires_capability(__VA_ARGS__)) + +#define REQUIRES_SHARED(...) THREAD_ANNOTATION_ATTRIBUTE__(requires_shared_capability(__VA_ARGS__)) + +#define ACQUIRE(...) THREAD_ANNOTATION_ATTRIBUTE__(acquire_capability(__VA_ARGS__)) + +#define ACQUIRE_SHARED(...) THREAD_ANNOTATION_ATTRIBUTE__(acquire_shared_capability(__VA_ARGS__)) + +#define RELEASE(...) THREAD_ANNOTATION_ATTRIBUTE__(release_capability(__VA_ARGS__)) + +#define RELEASE_SHARED(...) THREAD_ANNOTATION_ATTRIBUTE__(release_shared_capability(__VA_ARGS__)) + +#define TRY_ACQUIRE(...) THREAD_ANNOTATION_ATTRIBUTE__(try_acquire_capability(__VA_ARGS__)) + +#define TRY_ACQUIRE_SHARED(...) \ + THREAD_ANNOTATION_ATTRIBUTE__(try_acquire_shared_capability(__VA_ARGS__)) + +#define EXCLUDES(...) THREAD_ANNOTATION_ATTRIBUTE__(locks_excluded(__VA_ARGS__)) + +#define ASSERT_CAPABILITY(x) THREAD_ANNOTATION_ATTRIBUTE__(assert_capability(x)) + +#define ASSERT_SHARED_CAPABILITY(x) THREAD_ANNOTATION_ATTRIBUTE__(assert_shared_capability(x)) + +#define RETURN_CAPABILITY(x) THREAD_ANNOTATION_ATTRIBUTE__(lock_returned(x)) + +#define NO_THREAD_SAFETY_ANALYSIS THREAD_ANNOTATION_ATTRIBUTE__(no_thread_safety_analysis) + +class Condition; + +/* + * NOTE: This class is for code that builds on Win32. Its usage is + * deprecated for code which doesn't build for Win32. New code which + * doesn't build for Win32 should use std::mutex and std::lock_guard instead. + * + * Simple mutex class. The implementation is system-dependent. + * + * The mutex must be unlocked by the thread that locked it. They are not + * recursive, i.e. the same thread can't lock it multiple times. + */ +class CAPABILITY("mutex") Mutex { + public: + enum { + PRIVATE = 0, + SHARED = 1 + }; + + Mutex(); + explicit Mutex(const char* name); + explicit Mutex(int type, const char* name = nullptr); + ~Mutex(); + + // lock or unlock the mutex + int32_t lock() ACQUIRE(); + void unlock() RELEASE(); + + // lock if possible; returns 0 on success, error otherwise + int32_t tryLock() TRY_ACQUIRE(0); + + int32_t timedLock(int64_t timeoutNs) TRY_ACQUIRE(0); + + // Manages the mutex automatically. It'll be locked when Autolock is + // constructed and released when Autolock goes out of scope. + class SCOPED_CAPABILITY Autolock { + public: + inline explicit Autolock(Mutex& mutex) ACQUIRE(mutex) : mLock(mutex) { + mLock.lock(); + } + inline explicit Autolock(Mutex* mutex) ACQUIRE(mutex) : mLock(*mutex) { + mLock.lock(); + } + inline ~Autolock() RELEASE() { + mLock.unlock(); + } + + private: + Mutex& mLock; + // Cannot be copied or moved - declarations only + Autolock(const Autolock&); + Autolock& operator=(const Autolock&); + }; + + private: + friend class Condition; + + // A mutex cannot be copied + Mutex(const Mutex&); + Mutex& operator=(const Mutex&); + + pthread_mutex_t mMutex; +}; + +// --------------------------------------------------------------------------- +inline Mutex::Mutex() { + pthread_mutex_init(&mMutex, nullptr); +} +inline Mutex::Mutex(__attribute__((unused)) const char* name) { + pthread_mutex_init(&mMutex, nullptr); +} +inline Mutex::Mutex(int type, __attribute__((unused)) const char* name) { + if (type == SHARED) { + pthread_mutexattr_t attr; + pthread_mutexattr_init(&attr); + pthread_mutexattr_setpshared(&attr, PTHREAD_PROCESS_SHARED); + pthread_mutex_init(&mMutex, &attr); + pthread_mutexattr_destroy(&attr); + } else { + pthread_mutex_init(&mMutex, nullptr); + } +} +inline Mutex::~Mutex() { + pthread_mutex_destroy(&mMutex); +} +inline int32_t Mutex::lock() { + return -pthread_mutex_lock(&mMutex); +} +inline void Mutex::unlock() { + pthread_mutex_unlock(&mMutex); +} +inline int32_t Mutex::tryLock() { + return -pthread_mutex_trylock(&mMutex); +} +inline int32_t Mutex::timedLock(int64_t timeoutNs) { + timespec now; + clock_gettime(CLOCK_REALTIME, &now); + timeoutNs += now.tv_sec*1000000000 + now.tv_nsec; + const struct timespec ts = { + /* .tv_sec = */ static_cast(timeoutNs / 1000000000), + /* .tv_nsec = */ static_cast(timeoutNs % 1000000000), + }; + return -pthread_mutex_timedlock(&mMutex, &ts); +} + +// --------------------------------------------------------------------------- + +/* + * Automatic mutex. Declare one of these at the top of a function. + * When the function returns, it will go out of scope, and release the + * mutex. + */ + +typedef Mutex::Autolock AutoMutex; +#endif // __ANDROID_VNDK__ +#endif // _LIBS_RGA_MUTEX_H diff --git a/rknpu2/examples/3rdparty/rga/include/RgaSingleton.h b/rknpu2/examples/3rdparty/rga/include/RgaSingleton.h new file mode 100644 index 0000000000000000000000000000000000000000..e87cf1c385533d009360f66e95d3badba0a66be7 --- /dev/null +++ b/rknpu2/examples/3rdparty/rga/include/RgaSingleton.h @@ -0,0 +1,70 @@ +/* + * Copyright (C) 2016 Rockchip Electronics Co., Ltd. + * Authors: + * Zhiqin Wei + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef _LIBS_RGA_SINGLETON_H +#define _LIBS_RGA_SINGLETON_H + +#ifndef ANDROID +#include "RgaMutex.h" + +#if defined(__clang__) +#pragma clang diagnostic push +#pragma clang diagnostic ignored "-Wundefined-var-template" +#endif + +template +class Singleton { + public: + static TYPE& getInstance() { + Mutex::Autolock _l(sLock); + TYPE* instance = sInstance; + if (instance == nullptr) { + instance = new TYPE(); + sInstance = instance; + } + return *instance; + } + + static bool hasInstance() { + Mutex::Autolock _l(sLock); + return sInstance != nullptr; + } + + protected: + ~Singleton() { } + Singleton() { } + + private: + Singleton(const Singleton&); + Singleton& operator = (const Singleton&); + static Mutex sLock; + static TYPE* sInstance; +}; + +#if defined(__clang__) +#pragma clang diagnostic pop +#endif + +#define RGA_SINGLETON_STATIC_INSTANCE(TYPE) \ + template<> ::Mutex \ + (::Singleton< TYPE >::sLock)(::Mutex::PRIVATE); \ + template<> TYPE* ::Singleton< TYPE >::sInstance(nullptr); /* NOLINT */ \ + template class ::Singleton< TYPE >; + +#endif //ANDROID +#endif //_LIBS_RGA_SINGLETON_H diff --git a/rknpu2/examples/3rdparty/rga/include/RgaUtils.h b/rknpu2/examples/3rdparty/rga/include/RgaUtils.h new file mode 100644 index 0000000000000000000000000000000000000000..217ecea0ae4db29b7afaa179351a19b4e8ac1e62 --- /dev/null +++ b/rknpu2/examples/3rdparty/rga/include/RgaUtils.h @@ -0,0 +1,31 @@ +/* + * Copyright (C) 2016 Rockchip Electronics Co., Ltd. + * Authors: + * Zhiqin Wei + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef _rga_utils_h_ +#define _rga_utils_h_ + +// ------------------------------------------------------------------------------- +float get_bpp_from_format(int format); +int get_perPixel_stride_from_format(int format); +int get_buf_from_file(void *buf, int f, int sw, int sh, int index); +int output_buf_data_to_file(void *buf, int f, int sw, int sh, int index); +const char *translate_format_str(int format); +int get_buf_from_file_FBC(void *buf, int f, int sw, int sh, int index); +int output_buf_data_to_file_FBC(void *buf, int f, int sw, int sh, int index); +#endif + diff --git a/rknpu2/examples/3rdparty/rga/include/RockchipRga.h b/rknpu2/examples/3rdparty/rga/include/RockchipRga.h new file mode 100644 index 0000000000000000000000000000000000000000..082f9e1e874d61d8063d971e276acca9e989a157 --- /dev/null +++ b/rknpu2/examples/3rdparty/rga/include/RockchipRga.h @@ -0,0 +1,110 @@ +/* + * Copyright (C) 2016 Rockchip Electronics Co., Ltd. + * Authors: + * Zhiqin Wei + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef _rockchip_rga_h_ +#define _rockchip_rga_h_ + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include "drmrga.h" +#include "GrallocOps.h" +#include "RgaUtils.h" +#include "rga.h" + +////////////////////////////////////////////////////////////////////////////////// +#ifndef ANDROID +#include "RgaSingleton.h" +#endif + +#ifdef ANDROID +#include +#include +#include + +namespace android { +#endif + + class RockchipRga :public Singleton { + public: + + static inline RockchipRga& get() { + return getInstance(); + } + + int RkRgaInit(); + void RkRgaDeInit(); + void RkRgaGetContext(void **ctx); +#ifndef ANDROID /* LINUX */ + int RkRgaAllocBuffer(int drm_fd /* input */, bo_t *bo_info, + int width, int height, int bpp, int flags); + int RkRgaFreeBuffer(int drm_fd /* input */, bo_t *bo_info); + int RkRgaGetAllocBuffer(bo_t *bo_info, int width, int height, int bpp); + int RkRgaGetAllocBufferExt(bo_t *bo_info, int width, int height, int bpp, int flags); + int RkRgaGetAllocBufferCache(bo_t *bo_info, int width, int height, int bpp); + int RkRgaGetMmap(bo_t *bo_info); + int RkRgaUnmap(bo_t *bo_info); + int RkRgaFree(bo_t *bo_info); + int RkRgaGetBufferFd(bo_t *bo_info, int *fd); +#else + int RkRgaGetBufferFd(buffer_handle_t handle, int *fd); + int RkRgaGetHandleMapCpuAddress(buffer_handle_t handle, void **buf); +#endif + int RkRgaBlit(rga_info *src, rga_info *dst, rga_info *src1); + int RkRgaCollorFill(rga_info *dst); + int RkRgaCollorPalette(rga_info *src, rga_info *dst, rga_info *lut); + int RkRgaFlush(); + + + void RkRgaSetLogOnceFlag(int log) { + mLogOnce = log; + } + void RkRgaSetAlwaysLogFlag(bool log) { + mLogAlways = log; + } + void RkRgaLogOutRgaReq(struct rga_req rgaReg); + int RkRgaLogOutUserPara(rga_info *rgaInfo); + inline bool RkRgaIsReady() { + return mSupportRga; + } + + RockchipRga(); + ~RockchipRga(); + private: + bool mSupportRga; + int mLogOnce; + int mLogAlways; + void * mContext; + + friend class Singleton; + }; + +#ifdef ANDROID +}; // namespace android +#endif + +#endif + diff --git a/rknpu2/examples/3rdparty/rga/include/drmrga.h b/rknpu2/examples/3rdparty/rga/include/drmrga.h new file mode 100644 index 0000000000000000000000000000000000000000..71c50feceb66b6d134630d217fff0efa828aae3f --- /dev/null +++ b/rknpu2/examples/3rdparty/rga/include/drmrga.h @@ -0,0 +1,362 @@ +/* + * Copyright (C) 2016 Rockchip Electronics Co., Ltd. + * Authors: + * Zhiqin Wei + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef _rk_drm_rga_ +#define _rk_drm_rga_ + +#include +#include +#include + +#include "rga.h" + +#ifdef ANDROID +#define DRMRGA_HARDWARE_MODULE_ID "librga" + +#include +#include +#include +#include + +#if defined(ANDROID_12) || defined(USE_HARDWARE_ROCKCHIP) +#include +#endif + +#endif + +#define RGA_BLIT_SYNC 0x5017 +#define RGA_BLIT_ASYNC 0x5018 + +#ifndef ANDROID /* LINUX */ +/* flip source image horizontally (around the vertical axis) */ +#define HAL_TRANSFORM_FLIP_H 0x01 +/* flip source image vertically (around the horizontal axis)*/ +#define HAL_TRANSFORM_FLIP_V 0x02 +/* rotate source image 90 degrees clockwise */ +#define HAL_TRANSFORM_ROT_90 0x04 +/* rotate source image 180 degrees */ +#define HAL_TRANSFORM_ROT_180 0x03 +/* rotate source image 270 degrees clockwise */ +#define HAL_TRANSFORM_ROT_270 0x07 +#endif + +#define HAL_TRANSFORM_FLIP_H_V 0x08 + +/*****************************************************************************/ + +/* for compatibility */ +#define DRM_RGA_MODULE_API_VERSION HWC_MODULE_API_VERSION_0_1 +#define DRM_RGA_DEVICE_API_VERSION HWC_DEVICE_API_VERSION_0_1 +#define DRM_RGA_API_VERSION HWC_DEVICE_API_VERSION + +#define DRM_RGA_TRANSFORM_ROT_MASK 0x0000000F +#define DRM_RGA_TRANSFORM_ROT_0 0x00000000 +#define DRM_RGA_TRANSFORM_ROT_90 HAL_TRANSFORM_ROT_90 +#define DRM_RGA_TRANSFORM_ROT_180 HAL_TRANSFORM_ROT_180 +#define DRM_RGA_TRANSFORM_ROT_270 HAL_TRANSFORM_ROT_270 + +#define DRM_RGA_TRANSFORM_FLIP_MASK 0x00000003 +#define DRM_RGA_TRANSFORM_FLIP_H HAL_TRANSFORM_FLIP_H +#define DRM_RGA_TRANSFORM_FLIP_V HAL_TRANSFORM_FLIP_V + +enum { + AWIDTH = 0, + AHEIGHT, + ASTRIDE, + AFORMAT, + ASIZE, + ATYPE, +}; +/*****************************************************************************/ + +#ifndef ANDROID /* LINUX */ +/* memory type definitions. */ +enum drm_rockchip_gem_mem_type { + /* Physically Continuous memory and used as default. */ + ROCKCHIP_BO_CONTIG = 1 << 0, + /* cachable mapping. */ + ROCKCHIP_BO_CACHABLE = 1 << 1, + /* write-combine mapping. */ + ROCKCHIP_BO_WC = 1 << 2, + ROCKCHIP_BO_SECURE = 1 << 3, + ROCKCHIP_BO_MASK = ROCKCHIP_BO_CONTIG | ROCKCHIP_BO_CACHABLE | + ROCKCHIP_BO_WC | ROCKCHIP_BO_SECURE +}; + +typedef struct bo { + int fd; + void *ptr; + size_t size; + size_t offset; + size_t pitch; + unsigned handle; +} bo_t; +#endif + +/* + @value size: user not need care about.For avoid read/write out of memory + */ +typedef struct rga_rect { + int xoffset; + int yoffset; + int width; + int height; + int wstride; + int hstride; + int format; + int size; +} rga_rect_t; + +typedef struct rga_nn { + int nn_flag; + int scale_r; + int scale_g; + int scale_b; + int offset_r; + int offset_g; + int offset_b; +} rga_nn_t; + +typedef struct rga_dither { + int enable; + int mode; + int lut0_l; + int lut0_h; + int lut1_l; + int lut1_h; +} rga_dither_t; + +struct rga_mosaic_info { + uint8_t enable; + uint8_t mode; +}; + +struct rga_pre_intr_info { + uint8_t enable; + + uint8_t read_intr_en; + uint8_t write_intr_en; + uint8_t read_hold_en; + uint32_t read_threshold; + uint32_t write_start; + uint32_t write_step; +}; + +/* MAX(min, (max - channel_value)) */ +struct rga_osd_invert_factor { + uint8_t alpha_max; + uint8_t alpha_min; + uint8_t yg_max; + uint8_t yg_min; + uint8_t crb_max; + uint8_t crb_min; +}; + +struct rga_color { + union { + struct { + uint8_t red; + uint8_t green; + uint8_t blue; + uint8_t alpha; + }; + uint32_t value; + }; +}; + +struct rga_osd_bpp2 { + uint8_t ac_swap; // ac swap flag + // 0: CA + // 1: AC + uint8_t endian_swap; // rgba2bpp endian swap + // 0: Big endian + // 1: Little endian + struct rga_color color0; + struct rga_color color1; +}; + +struct rga_osd_mode_ctrl { + uint8_t mode; // OSD cal mode: + // 0b'1: statistics mode + // 1b'1: auto inversion overlap mode + uint8_t direction_mode; // horizontal or vertical + // 0: horizontal + // 1: vertical + uint8_t width_mode; // using @fix_width or LUT width + // 0: fix width + // 1: LUT width + uint16_t block_fix_width; // OSD block fixed width + // real width = (fix_width + 1) * 2 + uint8_t block_num; // OSD block num + uint16_t flags_index; // auto invert flags index + + /* invertion config */ + uint8_t color_mode; // selete color + // 0: src1 color + // 1: config data color + uint8_t invert_flags_mode; // invert flag selete + // 0: use RAM flag + // 1: usr last result + uint8_t default_color_sel; // default color mode + // 0: default is bright + // 1: default is dark + uint8_t invert_enable; // invert channel enable + // 1 << 0: aplha enable + // 1 << 1: Y/G disable + // 1 << 2: C/RB disable + uint8_t invert_mode; // invert cal mode + // 0: normal(max-data) + // 1: swap + uint8_t invert_thresh; // if luma > thresh, osd_flag to be 1 + uint8_t unfix_index; // OSD width config index +}; + +struct rga_osd_info { + uint8_t enable; + + struct rga_osd_mode_ctrl mode_ctrl; + struct rga_osd_invert_factor cal_factor; + struct rga_osd_bpp2 bpp2_info; + + union { + struct { + uint32_t last_flags1; + uint32_t last_flags0; + }; + uint64_t last_flags; + }; + + union { + struct { + uint32_t cur_flags1; + uint32_t cur_flags0; + }; + uint64_t cur_flags; + }; +}; + +/* + @value fd: use fd to share memory, it can be ion shard fd,and dma fd. + @value virAddr:userspace address + @value phyAddr:use phy address + @value hnd: use buffer_handle_t + */ +typedef struct rga_info { + int fd; + void *virAddr; + void *phyAddr; +#ifndef ANDROID /* LINUX */ + unsigned hnd; +#else /* Android */ + buffer_handle_t hnd; +#endif + int format; + rga_rect_t rect; + unsigned int blend; + int bufferSize; + int rotation; + int color; + int testLog; + int mmuFlag; + int colorkey_en; + int colorkey_mode; + int colorkey_max; + int colorkey_min; + int scale_mode; + int color_space_mode; + int sync_mode; + rga_nn_t nn; + rga_dither_t dither; + int rop_code; + int rd_mode; + unsigned short is_10b_compact; + unsigned short is_10b_endian; + + int in_fence_fd; + int out_fence_fd; + + int core; + int priority; + + unsigned short enable; + + int handle; + + struct rga_mosaic_info mosaic_info; + + struct rga_osd_info osd_info; + + struct rga_pre_intr_info pre_intr; + + int mpi_mode; + + union { + int ctx_id; + int job_handle; + }; + + uint16_t rgba5551_flags; + uint8_t rgba5551_alpha0; + uint8_t rgba5551_alpha1; + + char reserve[398]; +} rga_info_t; + + +typedef struct drm_rga { + rga_rect_t src; + rga_rect_t dst; +} drm_rga_t; + +/* + @fun rga_set_rect:For use to set the rects esayly + + @param rect:The rect user want to set,like setting the src rect: + drm_rga_t rects; + rga_set_rect(rects.src,0,0,1920,1080,1920,NV12); + mean to set the src rect to the value. + */ +static inline int rga_set_rect(rga_rect_t *rect, + int x, int y, int w, int h, int sw, int sh, int f) { + if (!rect) + return -EINVAL; + + rect->xoffset = x; + rect->yoffset = y; + rect->width = w; + rect->height = h; + rect->wstride = sw; + rect->hstride = sh; + rect->format = f; + + return 0; +} + +#ifndef ANDROID /* LINUX */ +static inline void rga_set_rotation(rga_info_t *info, int angle) { + if (angle == 90) + info->rotation = HAL_TRANSFORM_ROT_90; + else if (angle == 180) + info->rotation = HAL_TRANSFORM_ROT_180; + else if (angle == 270) + info->rotation = HAL_TRANSFORM_ROT_270; +} +#endif +/*****************************************************************************/ + +#endif diff --git a/rknpu2/examples/3rdparty/rga/include/im2d.h b/rknpu2/examples/3rdparty/rga/include/im2d.h new file mode 100644 index 0000000000000000000000000000000000000000..187867035ff2048a679d95a26d0890b8783f1dee --- /dev/null +++ b/rknpu2/examples/3rdparty/rga/include/im2d.h @@ -0,0 +1,32 @@ +/* + * Copyright (C) 2020 Rockchip Electronics Co., Ltd. + * Authors: + * PutinLee + * Cerf Yu + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef _im2d_h_ +#define _im2d_h_ + +#include "im2d_version.h" +#include "im2d_type.h" + +#include "im2d_common.h" +#include "im2d_buffer.h" +#include "im2d_single.h" +#include "im2d_task.h" +#include "im2d_mpi.h" + +#endif /* #ifndef _im2d_h_ */ diff --git a/rknpu2/examples/3rdparty/rga/include/im2d.hpp b/rknpu2/examples/3rdparty/rga/include/im2d.hpp new file mode 100644 index 0000000000000000000000000000000000000000..2d5ac83d33bb7cdb6fcc1f1119979fc881624a44 --- /dev/null +++ b/rknpu2/examples/3rdparty/rga/include/im2d.hpp @@ -0,0 +1,27 @@ +/* + * Copyright (C) 2020 Rockchip Electronics Co., Ltd. + * Authors: + * PutinLee + * Cerf Yu + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef _im2d_hpp_ +#define _im2d_hpp_ + +#include "im2d.h" +#include "im2d_expand.h" + +#endif /* #ifndef _im2d_hpp_ */ + + diff --git a/rknpu2/examples/3rdparty/rga/include/im2d_buffer.h b/rknpu2/examples/3rdparty/rga/include/im2d_buffer.h new file mode 100644 index 0000000000000000000000000000000000000000..023aa2f9a1261fe8860fe3c49a992b3c65794918 --- /dev/null +++ b/rknpu2/examples/3rdparty/rga/include/im2d_buffer.h @@ -0,0 +1,183 @@ +/* + * Copyright (C) 2022 Rockchip Electronics Co., Ltd. + * Authors: + * Cerf Yu + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef _im2d_buffer_h_ +#define _im2d_buffer_h_ + +#include "im2d_type.h" + +/** + * Import external buffers into RGA driver. + * + * @param fd/va/pa + * Select dma_fd/virtual_address/physical_address by buffer type + * @param size + * Describes the size of the image buffer + * + * @return rga_buffer_handle_t + */ +#ifdef __cplusplus +IM_API rga_buffer_handle_t importbuffer_fd(int fd, int size); +IM_API rga_buffer_handle_t importbuffer_virtualaddr(void *va, int size); +IM_API rga_buffer_handle_t importbuffer_physicaladdr(uint64_t pa, int size); +#endif + +/** + * Import external buffers into RGA driver. + * + * @param fd/va/pa + * Select dma_fd/virtual_address/physical_address by buffer type + * @param width + * Describes the pixel width stride of the image buffer + * @param height + * Describes the pixel height stride of the image buffer + * @param format + * Describes the pixel format of the image buffer + * + * @return rga_buffer_handle_t + */ +#ifdef __cplusplus +IM_API rga_buffer_handle_t importbuffer_fd(int fd, int width, int height, int format); +IM_API rga_buffer_handle_t importbuffer_virtualaddr(void *va, int width, int height, int format); +IM_API rga_buffer_handle_t importbuffer_physicaladdr(uint64_t pa, int width, int height, int format); +#endif + +/** + * Import external buffers into RGA driver. + * + * @param fd/va/pa + * Select dma_fd/virtual_address/physical_address by buffer type + * @param param + * Configure buffer parameters + * + * @return rga_buffer_handle_t + */ +IM_EXPORT_API rga_buffer_handle_t importbuffer_fd(int fd, im_handle_param_t *param); +IM_EXPORT_API rga_buffer_handle_t importbuffer_virtualaddr(void *va, im_handle_param_t *param); +IM_EXPORT_API rga_buffer_handle_t importbuffer_physicaladdr(uint64_t pa, im_handle_param_t *param); + +/** + * Import external buffers into RGA driver. + * + * @param handle + * rga buffer handle + * + * @return success or else negative error code. + */ +IM_EXPORT_API IM_STATUS releasebuffer_handle(rga_buffer_handle_t handle); + +/** + * Wrap image Parameters. + * + * @param handle/virtualaddr/physicaladdr/fd + * RGA buffer handle/virtualaddr/physicaladdr/fd. + * @param width + * Width of image manipulation area. + * @param height + * Height of image manipulation area. + * @param wstride + * Width pixel stride, default (width = wstride). + * @param hstride + * Height pixel stride, default (height = hstride). + * @param format + * Image format. + * + * @return rga_buffer_t + */ +#define wrapbuffer_handle(handle, width, height, format, ...) \ + ({ \ + rga_buffer_t im2d_api_buffer; \ + int __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(int); \ + if (__argc == 0) { \ + im2d_api_buffer = wrapbuffer_handle_t(handle, width, height, width, height, format); \ + } else if (__argc == 2){ \ + im2d_api_buffer = wrapbuffer_handle_t(handle, width, height, __args[0], __args[1], format); \ + } else { \ + memset(&im2d_api_buffer, 0x0, sizeof(im2d_api_buffer)); \ + printf("invalid parameter\n"); \ + } \ + im2d_api_buffer; \ + }) + +#define wrapbuffer_virtualaddr(vir_addr, width, height, format, ...) \ + ({ \ + rga_buffer_t im2d_api_buffer; \ + int __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(int); \ + if (__argc == 0) { \ + im2d_api_buffer = wrapbuffer_virtualaddr_t(vir_addr, width, height, width, height, format); \ + } else if (__argc == 2){ \ + im2d_api_buffer = wrapbuffer_virtualaddr_t(vir_addr, width, height, __args[0], __args[1], format); \ + } else { \ + memset(&im2d_api_buffer, 0x0, sizeof(im2d_api_buffer)); \ + printf("invalid parameter\n"); \ + } \ + im2d_api_buffer; \ + }) + +#define wrapbuffer_physicaladdr(phy_addr, width, height, format, ...) \ + ({ \ + rga_buffer_t im2d_api_buffer; \ + int __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(int); \ + if (__argc == 0) { \ + im2d_api_buffer = wrapbuffer_physicaladdr_t(phy_addr, width, height, width, height, format); \ + } else if (__argc == 2){ \ + im2d_api_buffer = wrapbuffer_physicaladdr_t(phy_addr, width, height, __args[0], __args[1], format); \ + } else { \ + memset(&im2d_api_buffer, 0x0, sizeof(im2d_api_buffer)); \ + printf("invalid parameter\n"); \ + } \ + im2d_api_buffer; \ + }) + +#define wrapbuffer_fd(fd, width, height, format, ...) \ + ({ \ + rga_buffer_t im2d_api_buffer; \ + int __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(int); \ + if (__argc == 0) { \ + im2d_api_buffer = wrapbuffer_fd_t(fd, width, height, width, height, format); \ + } else if (__argc == 2){ \ + im2d_api_buffer = wrapbuffer_fd_t(fd, width, height, __args[0], __args[1], format); \ + } else { \ + memset(&im2d_api_buffer, 0x0, sizeof(im2d_api_buffer)); \ + printf("invalid parameter\n"); \ + } \ + im2d_api_buffer; \ + }) +/* Symbols for define *_t functions */ +IM_C_API rga_buffer_t wrapbuffer_handle_t(rga_buffer_handle_t handle, int width, int height, int wstride, int hstride, int format); +IM_C_API rga_buffer_t wrapbuffer_virtualaddr_t(void* vir_addr, int width, int height, int wstride, int hstride, int format); +IM_C_API rga_buffer_t wrapbuffer_physicaladdr_t(void* phy_addr, int width, int height, int wstride, int hstride, int format); +IM_C_API rga_buffer_t wrapbuffer_fd_t(int fd, int width, int height, int wstride, int hstride, int format); + +#ifdef __cplusplus +#undef wrapbuffer_handle +IM_API rga_buffer_t wrapbuffer_handle(rga_buffer_handle_t handle, + int width, int height, int format); +IM_API rga_buffer_t wrapbuffer_handle(rga_buffer_handle_t handle, + int width, int height, int format, + int wstride, int hstride); +#endif + +void imsetAlphaBit(rga_buffer_t *buf, uint8_t alpha0, uint8_t alpha1); +void imsetOpacity(rga_buffer_t *buf, uint8_t alpha); +void imsetColorSpace(rga_buffer_t *buf, IM_COLOR_SPACE_MODE mode); + +#endif /* #ifndef _im2d_buffer_h_ */ diff --git a/rknpu2/examples/3rdparty/rga/include/im2d_common.h b/rknpu2/examples/3rdparty/rga/include/im2d_common.h new file mode 100644 index 0000000000000000000000000000000000000000..9030efe29ff3ac5a2dc3ab2a8c73fb9bf40dd5dc --- /dev/null +++ b/rknpu2/examples/3rdparty/rga/include/im2d_common.h @@ -0,0 +1,151 @@ +/* + * Copyright (C) 2022 Rockchip Electronics Co., Ltd. + * Authors: + * Cerf Yu + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef _im2d_common_h_ +#define _im2d_common_h_ + +#include "im2d_type.h" + +/** + * Query RGA basic information, supported resolution, supported format, etc. + * + * @param name + * RGA_VENDOR + * RGA_VERSION + * RGA_MAX_INPUT + * RGA_MAX_OUTPUT + * RGA_INPUT_FORMAT + * RGA_OUTPUT_FORMAT + * RGA_EXPECTED + * RGA_ALL + * + * @returns a string describing properties of RGA. + */ +IM_EXPORT_API const char* querystring(int name); + +/** + * String to output the error message + * + * @param status + * process result value. + * + * @returns error message. + */ +#define imStrError(...) \ + ({ \ + const char* im2d_api_err; \ + int __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(int); \ + if (__argc == 0) { \ + im2d_api_err = imStrError_t(IM_STATUS_INVALID_PARAM); \ + } else if (__argc == 1){ \ + im2d_api_err = imStrError_t((IM_STATUS)__args[0]); \ + } else { \ + im2d_api_err = ("Fatal error, imStrError() too many parameters\n"); \ + printf("Fatal error, imStrError() too many parameters\n"); \ + } \ + im2d_api_err; \ + }) +IM_C_API const char* imStrError_t(IM_STATUS status); + +/** + * check im2d api header file + * + * @param header_version + * Default is RGA_CURRENT_API_HEADER_VERSION, no need to change if there are no special cases. + * + * @returns no error or else negative error code. + */ +#ifdef __cplusplus +IM_API IM_STATUS imcheckHeader(im_api_version_t header_version = RGA_CURRENT_API_HEADER_VERSION); +#endif + +/** + * check RGA basic information, supported resolution, supported format, etc. + * + * @param src + * @param dst + * @param pat + * @param src_rect + * @param dst_rect + * @param pat_rect + * @param mode_usage + * + * @returns no error or else negative error code. + */ +#define imcheck(src, dst, src_rect, dst_rect, ...) \ + ({ \ + IM_STATUS __ret = IM_STATUS_NOERROR; \ + rga_buffer_t __pat; \ + im_rect __pat_rect; \ + memset(&__pat, 0, sizeof(rga_buffer_t)); \ + memset(&__pat_rect, 0, sizeof(im_rect)); \ + int __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(int); \ + if (__argc == 0) { \ + __ret = imcheck_t(src, dst, __pat, src_rect, dst_rect, __pat_rect, 0); \ + } else if (__argc == 1){ \ + __ret = imcheck_t(src, dst, __pat, src_rect, dst_rect, __pat_rect, __args[0]); \ + } else { \ + __ret = IM_STATUS_FAILED; \ + printf("check failed\n"); \ + } \ + __ret; \ + }) +#define imcheck_composite(src, dst, pat, src_rect, dst_rect, pat_rect, ...) \ + ({ \ + IM_STATUS __ret = IM_STATUS_NOERROR; \ + int __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(int); \ + if (__argc == 0) { \ + __ret = imcheck_t(src, dst, pat, src_rect, dst_rect, pat_rect, 0); \ + } else if (__argc == 1){ \ + __ret = imcheck_t(src, dst, pat, src_rect, dst_rect, pat_rect, __args[0]); \ + } else { \ + __ret = IM_STATUS_FAILED; \ + printf("check failed\n"); \ + } \ + __ret; \ + }) +IM_C_API IM_STATUS imcheck_t(const rga_buffer_t src, const rga_buffer_t dst, const rga_buffer_t pat, + const im_rect src_rect, const im_rect dst_rect, const im_rect pat_rect, const int mode_usage); +/* Compatible with the legacy symbol */ +IM_C_API void rga_check_perpare(rga_buffer_t *src, rga_buffer_t *dst, rga_buffer_t *pat, + im_rect *src_rect, im_rect *dst_rect, im_rect *pat_rect, int mode_usage); + +/** + * block until all execution is complete + * + * @param release_fence_fd + * RGA job release fence fd + * + * @returns success or else negative error code. + */ +IM_EXPORT_API IM_STATUS imsync(int release_fence_fd); + +/** + * config + * + * @param name + * enum IM_CONFIG_NAME + * @param value + * + * @returns success or else negative error code. + */ +IM_EXPORT_API IM_STATUS imconfig(IM_CONFIG_NAME name, uint64_t value); + +#endif /* #ifndef _im2d_common_h_ */ diff --git a/rknpu2/examples/3rdparty/rga/include/im2d_expand.h b/rknpu2/examples/3rdparty/rga/include/im2d_expand.h new file mode 100644 index 0000000000000000000000000000000000000000..c213bda0808ea5e76a4e01c369bb99ed8c59ca96 --- /dev/null +++ b/rknpu2/examples/3rdparty/rga/include/im2d_expand.h @@ -0,0 +1,47 @@ +/* + * Copyright (C) 2022 Rockchip Electronics Co., Ltd. + * Authors: + * Cerf Yu + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef _im2d_expand_h_ +#define _im2d_expand_h_ + +#ifdef __cplusplus + +#include "im2d_type.h" + +#if ANDROID + +#include + +using namespace android; + +IM_API rga_buffer_handle_t importbuffer_GraphicBuffer_handle(buffer_handle_t hnd); +IM_API rga_buffer_handle_t importbuffer_GraphicBuffer(sp buf); + +IM_API rga_buffer_t wrapbuffer_handle(buffer_handle_t hnd); +IM_API rga_buffer_t wrapbuffer_GraphicBuffer(sp buf); + +#if USE_AHARDWAREBUFFER +#include +IM_API rga_buffer_handle_t importbuffer_AHardwareBuffer(AHardwareBuffer *buf); +IM_API rga_buffer_t wrapbuffer_AHardwareBuffer(AHardwareBuffer *buf); + +#endif /* #if USE_AHARDWAREBUFFER */ +#endif /* #if ANDROID */ + +#endif /* #ifdef __cplusplus */ + +#endif diff --git a/rknpu2/examples/3rdparty/rga/include/im2d_mpi.h b/rknpu2/examples/3rdparty/rga/include/im2d_mpi.h new file mode 100644 index 0000000000000000000000000000000000000000..f984d15daf9ec173be781aaf13f40ea73c57ecb1 --- /dev/null +++ b/rknpu2/examples/3rdparty/rga/include/im2d_mpi.h @@ -0,0 +1,80 @@ +/* + * Copyright (C) 2022 Rockchip Electronics Co., Ltd. + * Authors: + * Cerf Yu + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef _im2d_mpi_hpp_ +#define _im2d_mpi_hpp_ + +#include "im2d_type.h" + +/** + * Create and config an rga ctx for rockit-ko + * + * @param flags + * Some configuration flags for this job + * + * @returns job id. + */ +IM_EXPORT_API im_ctx_id_t imbegin(uint32_t flags); + +/** + * Cancel and delete an rga ctx for rockit-ko + * + * @param flags + * Some configuration flags for this job + * + * @returns success or else negative error code. + */ +IM_EXPORT_API IM_STATUS imcancel(im_ctx_id_t id); + +/** + * process for rockit-ko + * + * @param src + * The input source image and is also the foreground image in blend. + * @param dst + * The output destination image and is also the foreground image in blend. + * @param pat + * The foreground image, or a LUT table. + * @param srect + * The rectangle on the src channel image that needs to be processed. + * @param drect + * The rectangle on the dst channel image that needs to be processed. + * @param prect + * The rectangle on the pat channel image that needs to be processed. + * @param acquire_fence_fd + * @param release_fence_fd + * @param opt + * The image processing options configuration. + * @param usage + * The image processing usage. + * @param ctx_id + * ctx id + * + * @returns success or else negative error code. + */ +#ifdef __cplusplus +IM_API IM_STATUS improcess(rga_buffer_t src, rga_buffer_t dst, rga_buffer_t pat, + im_rect srect, im_rect drect, im_rect prect, + int acquire_fence_fd, int *release_fence_fd, + im_opt_t *opt, int usage, im_ctx_id_t ctx_id); +#endif +IM_EXPORT_API IM_STATUS improcess_ctx(rga_buffer_t src, rga_buffer_t dst, rga_buffer_t pat, + im_rect srect, im_rect drect, im_rect prect, + int acquire_fence_fd, int *release_fence_fd, + im_opt_t *opt, int usage, im_ctx_id_t ctx_id); + +#endif /* #ifndef _im2d_mpi_hpp_ */ \ No newline at end of file diff --git a/rknpu2/examples/3rdparty/rga/include/im2d_single.h b/rknpu2/examples/3rdparty/rga/include/im2d_single.h new file mode 100644 index 0000000000000000000000000000000000000000..fb96a9afadd2564550895b594140e9ec926500dd --- /dev/null +++ b/rknpu2/examples/3rdparty/rga/include/im2d_single.h @@ -0,0 +1,940 @@ +/* + * Copyright (C) 2022 Rockchip Electronics Co., Ltd. + * Authors: + * Cerf Yu + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef _im2d_single_h_ +#define _im2d_single_h_ + +#include "im2d_type.h" + +#ifdef __cplusplus + +/** + * copy + * + * @param src + * The input source image. + * @param dst + * The output destination image. + * @param sync + * When 'sync == 1', wait for the operation to complete and return, otherwise return directly. + * @param release_fence_fd + * When 'sync == 0', the fence_fd used to identify the current job state + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imcopy(const rga_buffer_t src, rga_buffer_t dst, int sync = 1, int *release_fence_fd = NULL); + +/** + * Resize + * + * @param src + * The input source image. + * @param dst + * The output destination image. + * @param fx + * X-direction resize factor. + * @param fy + * X-direction resize factor. + * @param interpolation + * Interpolation formula(Only RGA1 support). + * @param sync + * When 'sync == 1', wait for the operation to complete and return, otherwise return directly. + * @param release_fence_fd + * When 'sync == 0', the fence_fd used to identify the current job state + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imresize(const rga_buffer_t src, rga_buffer_t dst, double fx = 0, double fy = 0, int interpolation = 0, int sync = 1, int *release_fence_fd = NULL); + +/** + * Crop + * + * @param src + * The input source image. + * @param dst + * The output destination image. + * @param rect + * The rectangle on the source image that needs to be cropped. + * @param sync + * When 'sync == 1', wait for the operation to complete and return, otherwise return directly. + * @param release_fence_fd + * When 'sync == 0', the fence_fd used to identify the current job state + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imcrop(const rga_buffer_t src, rga_buffer_t dst, im_rect rect, int sync = 1, int *release_fence_fd = NULL); + +/** + * translate + * + * @param src + * The input source image. + * @param dst + * The output destination image. + * @param x + * Output the coordinates of the starting point in the X-direction of the destination image. + * @param y + * Output the coordinates of the starting point in the Y-direction of the destination image. + * @param sync + * When 'sync == 1', wait for the operation to complete and return, otherwise return directly. + * @param release_fence_fd + * When 'sync == 0', the fence_fd used to identify the current job state + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imtranslate(const rga_buffer_t src, rga_buffer_t dst, int x, int y, int sync = 1, int *release_fence_fd = NULL); + +/** + * format convert + * + * @param src + * The input source image. + * @param dst + * The output destination image. + * @param sfmt + * The source image format. + * @param dfmt + * The destination image format. + * @param mode + * color space mode: + * IM_YUV_TO_RGB_BT601_LIMIT + * IM_YUV_TO_RGB_BT601_FULL + * IM_YUV_TO_RGB_BT709_LIMIT + * IM_RGB_TO_YUV_BT601_FULL + * IM_RGB_TO_YUV_BT601_LIMIT + * IM_RGB_TO_YUV_BT709_LIMIT + * @param sync + * When 'sync == 1', wait for the operation to complete and return, otherwise return directly. + * @param release_fence_fd + * When 'sync == 0', the fence_fd used to identify the current job state + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imcvtcolor(rga_buffer_t src, rga_buffer_t dst, int sfmt, int dfmt, int mode = IM_COLOR_SPACE_DEFAULT, int sync = 1, int *release_fence_fd = NULL); + +/** + * rotation + * + * @param src + * The input source image. + * @param dst + * The output destination image. + * @param rotation + * IM_HAL_TRANSFORM_ROT_90 + * IM_HAL_TRANSFORM_ROT_180 + * IM_HAL_TRANSFORM_ROT_270 + * @param sync + * When 'sync == 1', wait for the operation to complete and return, otherwise return directly. + * @param release_fence_fd + * When 'sync == 0', the fence_fd used to identify the current job state + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imrotate(const rga_buffer_t src, rga_buffer_t dst, int rotation, int sync = 1, int *release_fence_fd = NULL); + +/** + * flip + * + * @param src + * The input source image. + * @param dst + * The output destination image. + * @param mode + * IM_HAL_TRANSFORM_FLIP_H + * IM_HAL_TRANSFORM_FLIP_V + * @param sync + * When 'sync == 1', wait for the operation to complete and return, otherwise return directly. + * @param release_fence_fd + * When 'sync == 0', the fence_fd used to identify the current job state + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imflip(const rga_buffer_t src, rga_buffer_t dst, int mode, int sync = 1, int *release_fence_fd = NULL); + +/** + * 2-channel blend (SRC + DST -> DST or SRCA + SRCB -> DST) + * + * @param fg_image + * The foreground image. + * @param bg_image + * The background image, which is also the output destination image. + * @param mode + * Port-Duff mode: + * IM_ALPHA_BLEND_SRC + * IM_ALPHA_BLEND_DST + * IM_ALPHA_BLEND_SRC_OVER + * IM_ALPHA_BLEND_DST_OVER + * @param sync + * When 'sync == 1', wait for the operation to complete and return, otherwise return directly. + * @param release_fence_fd + * When 'sync == 0', the fence_fd used to identify the current job state + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imblend(const rga_buffer_t fd_image, rga_buffer_t bg_image, int mode = IM_ALPHA_BLEND_SRC_OVER, int sync = 1, int *release_fence_fd = NULL); + +/** + * 3-channel blend (SRC + DST -> DST or SRCA + SRCB -> DST) + * + * @param fg_image + * The foreground image. + * @param bg_image + * The background image. + * @param output_image + * The output destination image. + * @param mode + * Port-Duff mode: + * IM_ALPHA_BLEND_SRC + * IM_ALPHA_BLEND_DST + * IM_ALPHA_BLEND_SRC_OVER + * IM_ALPHA_BLEND_DST_OVER + * @param sync + * When 'sync == 1', wait for the operation to complete and return, otherwise return directly. + * @param release_fence_fd + * When 'sync == 0', the fence_fd used to identify the current job state + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imcomposite(const rga_buffer_t srcA, const rga_buffer_t srcB, rga_buffer_t dst, int mode = IM_ALPHA_BLEND_SRC_OVER, int sync = 1, int *release_fence_fd = NULL); + +/** + * color key + * + * @param fg_image + * The foreground image. + * @param bg_image + * The background image, which is also the output destination image. + * @param colorkey_range + * The range of color key. + * @param sync + * When 'sync == 1', wait for the operation to complete and return, otherwise return directly. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imcolorkey(const rga_buffer_t src, rga_buffer_t dst, im_colorkey_range range, int mode = IM_ALPHA_COLORKEY_NORMAL, int sync = 1, int *release_fence_fd = NULL); + +/** + * OSD + * + * @param osd + * The osd text block. + * @param dst + * The background image. + * @param osd_rect + * The rectangle on the source image that needs to be OSD. + * @param osd_config + * osd mode configuration. + * @param sync + * When 'sync == 1', wait for the operation to complete and return, otherwise return directly. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imosd(const rga_buffer_t osd,const rga_buffer_t dst, + const im_rect osd_rect, im_osd_t *osd_config, + int sync = 1, int *release_fence_fd = NULL); + +/** + * nn quantize + * + * @param src + * The input source image. + * @param dst + * The output destination image. + * @param nninfo + * nn configuration + * @param sync + * When 'sync == 1', wait for the operation to complete and return, otherwise return directly. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imquantize(const rga_buffer_t src, rga_buffer_t dst, im_nn_t nn_info, int sync = 1, int *release_fence_fd = NULL); + +/** + * ROP + * + * @param src + * The input source image. + * @param dst + * The output destination image. + * @param rop_code + * The ROP opcode. + * @param sync + * When 'sync == 1', wait for the operation to complete and return, otherwise return directly. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imrop(const rga_buffer_t src, rga_buffer_t dst, int rop_code, int sync = 1, int *release_fence_fd = NULL); + +/** + * fill/reset/draw + * + * @param dst + * The output destination image. + * @param rect + * The rectangle on the source image that needs to be filled with color. + * @param color + * The fill color value. + * @param sync + * When 'sync == 1', wait for the operation to complete and return, otherwise return directly. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imfill(rga_buffer_t dst, im_rect rect, int color, int sync = 1, int *release_fence_fd = NULL); + +/** + * fill array + * + * @param dst + * The output destination image. + * @param rect_array + * The rectangle arrays on the source image that needs to be filled with color. + * @param array_size + * The size of rectangular area arrays. + * @param color + * The fill color value. + * @param sync + * When 'sync == 1', wait for the operation to complete and return, otherwise return directly. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imfillArray(rga_buffer_t dst, im_rect *rect_array, int array_size, uint32_t color, int sync = 1, int *release_fence_fd = NULL); + +/** + * fill rectangle + * + * @param dst + * The output destination image. + * @param rect + * The rectangle on the source image that needs to be filled with color. + * @param color + * The fill color value. + * @param thickness + * Thickness of lines that make up the rectangle. Negative values, like -1, + * mean that the function has to draw a filled rectangle. + * @param sync + * When 'sync == 1', wait for the operation to complete and return, otherwise return directly. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imrectangle(rga_buffer_t dst, im_rect rect, + uint32_t color, int thickness, + int sync = 1, int *release_fence_fd = NULL); + +/** + * fill rectangle array + * + * @param dst + * The output destination image. + * @param rect_array + * The rectangle arrays on the source image that needs to be filled with color. + * @param array_size + * The size of rectangular area arrays. + * @param color + * The fill color value. + * @param thickness + * Thickness of lines that make up the rectangle. Negative values, like -1, + * mean that the function has to draw a filled rectangle. + * @param sync + * When 'sync == 1', wait for the operation to complete and return, otherwise return directly. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imrectangleArray(rga_buffer_t dst, im_rect *rect_array, int array_size, + uint32_t color, int thickness, + int sync = 1, int *release_fence_fd = NULL); + +/** + * MOSAIC + * + * @param image + * The output destination image. + * @param rect + * The rectangle on the source image that needs to be mosaicked. + * @param mosaic_mode + * mosaic block width configuration: + * IM_MOSAIC_8 + * IM_MOSAIC_16 + * IM_MOSAIC_32 + * IM_MOSAIC_64 + * IM_MOSAIC_128 + * @param sync + * When 'sync == 1', wait for the operation to complete and return, otherwise return directly. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS immosaic(const rga_buffer_t image, im_rect rect, int mosaic_mode, int sync = 1, int *release_fence_fd = NULL); + +/** + * MOSAIC array + * + * @param image + * The output destination image. + * @param rect_array + * The rectangle arrays on the source image that needs to be filled with color. + * @param array_size + * The size of rectangular area arrays. + * @param mosaic_mode + * mosaic block width configuration: + * IM_MOSAIC_8 + * IM_MOSAIC_16 + * IM_MOSAIC_32 + * IM_MOSAIC_64 + * IM_MOSAIC_128 + * @param sync + * When 'sync == 1', wait for the operation to complete and return, otherwise return directly. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS immosaicArray(const rga_buffer_t image, im_rect *rect_array, int array_size, int mosaic_mode, int sync = 1, int *release_fence_fd = NULL); + +/** + * palette + * + * @param src + * The input source image. + * @param dst + * The output destination image. + * @param lut + * The LUT table. + * @param sync + * When 'sync == 1', wait for the operation to complete and return, otherwise return directly. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS impalette(rga_buffer_t src, rga_buffer_t dst, rga_buffer_t lut, int sync = 1, int *release_fence_fd = NULL); + +/** + * process for single task mode + * + * @param src + * The input source image and is also the foreground image in blend. + * @param dst + * The output destination image and is also the foreground image in blend. + * @param pat + * The foreground image, or a LUT table. + * @param srect + * The rectangle on the src channel image that needs to be processed. + * @param drect + * The rectangle on the dst channel image that needs to be processed. + * @param prect + * The rectangle on the pat channel image that needs to be processed. + * @param opt + * The image processing options configuration. + * @param usage + * The image processing usage. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS improcess(rga_buffer_t src, rga_buffer_t dst, rga_buffer_t pat, + im_rect srect, im_rect drect, im_rect prect, + int acquire_fence_fd, int *release_fence_fd, + im_opt_t *opt_ptr, int usage); + +/** + * make border + * + * @param src + * The input source image. + * @param dst + * The output destination image. + * @param top + * the top pixels + * @param bottom + * the bottom pixels + * @param left + * the left pixels + * @param right + * the right pixels + * @param border_type + * Border type. + * @param value + * The pixel value at which the border is filled. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS immakeBorder(rga_buffer_t src, rga_buffer_t dst, + int top, int bottom, int left, int right, + int border_type, int value = 0, + int sync = 1, int acquir_fence_fd = -1, int *release_fence_fd = NULL); + +#endif /* #ifdef __cplusplus */ + +IM_C_API IM_STATUS immosaic(const rga_buffer_t image, im_rect rect, int mosaic_mode, int sync); +IM_C_API IM_STATUS imosd(const rga_buffer_t osd,const rga_buffer_t dst, + const im_rect osd_rect, im_osd_t *osd_config, int sync); +IM_C_API IM_STATUS improcess(rga_buffer_t src, rga_buffer_t dst, rga_buffer_t pat, + im_rect srect, im_rect drect, im_rect prect, int usage); + +/* Start: Symbols reserved for compatibility with macro functions */ +IM_C_API IM_STATUS imcopy_t(const rga_buffer_t src, rga_buffer_t dst, int sync); +IM_C_API IM_STATUS imresize_t(const rga_buffer_t src, rga_buffer_t dst, double fx, double fy, int interpolation, int sync); +IM_C_API IM_STATUS imcrop_t(const rga_buffer_t src, rga_buffer_t dst, im_rect rect, int sync); +IM_C_API IM_STATUS imtranslate_t(const rga_buffer_t src, rga_buffer_t dst, int x, int y, int sync); +IM_C_API IM_STATUS imcvtcolor_t(rga_buffer_t src, rga_buffer_t dst, int sfmt, int dfmt, int mode, int sync); +IM_C_API IM_STATUS imrotate_t(const rga_buffer_t src, rga_buffer_t dst, int rotation, int sync); +IM_C_API IM_STATUS imflip_t (const rga_buffer_t src, rga_buffer_t dst, int mode, int sync); +IM_C_API IM_STATUS imblend_t(const rga_buffer_t srcA, const rga_buffer_t srcB, rga_buffer_t dst, int mode, int sync); +IM_C_API IM_STATUS imcolorkey_t(const rga_buffer_t src, rga_buffer_t dst, im_colorkey_range range, int mode, int sync); +IM_C_API IM_STATUS imquantize_t(const rga_buffer_t src, rga_buffer_t dst, im_nn_t nn_info, int sync); +IM_C_API IM_STATUS imrop_t(const rga_buffer_t src, rga_buffer_t dst, int rop_code, int sync); +IM_C_API IM_STATUS imfill_t(rga_buffer_t dst, im_rect rect, int color, int sync); +IM_C_API IM_STATUS impalette_t(rga_buffer_t src, rga_buffer_t dst, rga_buffer_t lut, int sync); +/* End: Symbols reserved for compatibility with macro functions */ + +#ifndef __cplusplus + +#define RGA_GET_MIN(n1, n2) ((n1) < (n2) ? (n1) : (n2)) + +/** + * copy + * + * @param src + * @param dst + * @param sync + * wait until operation complete + * + * @returns success or else negative error code. + */ +#define imcopy(src, dst, ...) \ + ({ \ + IM_STATUS __ret = IM_STATUS_SUCCESS; \ + int __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(int); \ + if (__argc == 0) { \ + __ret = imcopy_t(src, dst, 1); \ + } else if (__argc == 1){ \ + __ret = imcopy_t(src, dst, (int)__args[RGA_GET_MIN(__argc, 0)]); \ + } else { \ + __ret = IM_STATUS_INVALID_PARAM; \ + printf("invalid parameter\n"); \ + } \ + __ret; \ + }) + +/** + * Resize + * + * @param src + * @param dst + * @param fx + * @param fy + * @param interpolation + * @param sync + * wait until operation complete + * + * @returns success or else negative error code. + */ +#define imresize(src, dst, ...) \ + ({ \ + IM_STATUS __ret = IM_STATUS_SUCCESS; \ + double __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(double); \ + if (__argc == 0) { \ + __ret = imresize_t(src, dst, 0, 0, INTER_LINEAR, 1); \ + } else if (__argc == 2){ \ + __ret = imresize_t(src, dst, __args[RGA_GET_MIN(__argc, 0)], __args[RGA_GET_MIN(__argc, 1)], INTER_LINEAR, 1); \ + } else if (__argc == 3){ \ + __ret = imresize_t(src, dst, __args[RGA_GET_MIN(__argc, 0)], __args[RGA_GET_MIN(__argc, 1)], (int)__args[RGA_GET_MIN(__argc, 2)], 1); \ + } else if (__argc == 4){ \ + __ret = imresize_t(src, dst, __args[RGA_GET_MIN(__argc, 0)], __args[RGA_GET_MIN(__argc, 1)], (int)__args[RGA_GET_MIN(__argc, 2)], (int)__args[RGA_GET_MIN(__argc, 3)]); \ + } else { \ + __ret = IM_STATUS_INVALID_PARAM; \ + printf("invalid parameter\n"); \ + } \ + __ret; \ + }) + +#define impyramid(src, dst, direction) \ + imresize_t(src, \ + dst, \ + direction == IM_UP_SCALE ? 0.5 : 2, \ + direction == IM_UP_SCALE ? 0.5 : 2, \ + INTER_LINEAR, 1) + +/** + * format convert + * + * @param src + * @param dst + * @param sfmt + * @param dfmt + * @param mode + * color space mode: IM_COLOR_SPACE_MODE + * @param sync + * wait until operation complete + * + * @returns success or else negative error code. + */ +#define imcvtcolor(src, dst, sfmt, dfmt, ...) \ + ({ \ + IM_STATUS __ret = IM_STATUS_SUCCESS; \ + int __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(int); \ + if (__argc == 0) { \ + __ret = imcvtcolor_t(src, dst, sfmt, dfmt, IM_COLOR_SPACE_DEFAULT, 1); \ + } else if (__argc == 1){ \ + __ret = imcvtcolor_t(src, dst, sfmt, dfmt, (int)__args[RGA_GET_MIN(__argc, 0)], 1); \ + } else if (__argc == 2){ \ + __ret = imcvtcolor_t(src, dst, sfmt, dfmt, (int)__args[RGA_GET_MIN(__argc, 0)], (int)__args[RGA_GET_MIN(__argc, 1)]); \ + } else { \ + __ret = IM_STATUS_INVALID_PARAM; \ + printf("invalid parameter\n"); \ + } \ + __ret; \ + }) + +/** + * Crop + * + * @param src + * @param dst + * @param rect + * @param sync + * wait until operation complete + * + * @returns success or else negative error code. + */ +#define imcrop(src, dst, rect, ...) \ + ({ \ + IM_STATUS __ret = IM_STATUS_SUCCESS; \ + int __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(int); \ + if (__argc == 0) { \ + __ret = imcrop_t(src, dst, rect, 1); \ + } else if (__argc == 1){ \ + __ret = imcrop_t(src, dst, rect, (int)__args[RGA_GET_MIN(__argc, 0)]); \ + } else { \ + __ret = IM_STATUS_INVALID_PARAM; \ + printf("invalid parameter\n"); \ + } \ + __ret; \ + }) + +/** + * translate + * + * @param src + * @param dst + * @param x + * @param y + * @param sync + * wait until operation complete + * + * @returns success or else negative error code. + */ +#define imtranslate(src, dst, x, y, ...) \ + ({ \ + IM_STATUS __ret = IM_STATUS_SUCCESS; \ + int __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(int); \ + if (__argc == 0) { \ + __ret = imtranslate_t(src, dst, x, y, 1); \ + } else if (__argc == 1){ \ + __ret = imtranslate_t(src, dst, x, y, (int)__args[RGA_GET_MIN(__argc, 0)]); \ + } else { \ + __ret = IM_STATUS_INVALID_PARAM; \ + printf("invalid parameter\n"); \ + } \ + __ret; \ + }) + +/** + * rotation + * + * @param src + * @param dst + * @param rotation + * IM_HAL_TRANSFORM_ROT_90 + * IM_HAL_TRANSFORM_ROT_180 + * IM_HAL_TRANSFORM_ROT_270 + * @param sync + * wait until operation complete + * + * @returns success or else negative error code. + */ +#define imrotate(src, dst, rotation, ...) \ + ({ \ + IM_STATUS __ret = IM_STATUS_SUCCESS; \ + int __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(int); \ + if (__argc == 0) { \ + __ret = imrotate_t(src, dst, rotation, 1); \ + } else if (__argc == 1){ \ + __ret = imrotate_t(src, dst, rotation, (int)__args[RGA_GET_MIN(__argc, 0)]); \ + } else { \ + __ret = IM_STATUS_INVALID_PARAM; \ + printf("invalid parameter\n"); \ + } \ + __ret; \ + }) + + +/** + * flip + * + * @param src + * @param dst + * @param mode + * IM_HAL_TRANSFORM_FLIP_H + * IM_HAL_TRANSFORM_FLIP_V + * @param sync + * wait until operation complete + * + * @returns success or else negative error code. + */ +#define imflip(src, dst, mode, ...) \ + ({ \ + IM_STATUS __ret = IM_STATUS_SUCCESS; \ + int __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(int); \ + if (__argc == 0) { \ + __ret = imflip_t(src, dst, mode, 1); \ + } else if (__argc == 1){ \ + __ret = imflip_t(src, dst, mode, (int)__args[RGA_GET_MIN(__argc, 0)]); \ + } else { \ + __ret = IM_STATUS_INVALID_PARAM; \ + printf("invalid parameter\n"); \ + } \ + __ret; \ + }) + +/** + * blend (SRC + DST -> DST or SRCA + SRCB -> DST) + * + * @param srcA + * @param srcB can be NULL. + * @param dst + * @param mode + * IM_ALPHA_BLEND_MODE + * @param sync + * wait until operation complete + * + * @returns success or else negative error code. + */ +#define imblend(srcA, dst, ...) \ + ({ \ + IM_STATUS __ret = IM_STATUS_SUCCESS; \ + rga_buffer_t srcB; \ + memset(&srcB, 0x00, sizeof(rga_buffer_t)); \ + int __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(int); \ + if (__argc == 0) { \ + __ret = imblend_t(srcA, srcB, dst, IM_ALPHA_BLEND_SRC_OVER, 1); \ + } else if (__argc == 1){ \ + __ret = imblend_t(srcA, srcB, dst, (int)__args[RGA_GET_MIN(__argc, 0)], 1); \ + } else if (__argc == 2){ \ + __ret = imblend_t(srcA, srcB, dst, (int)__args[RGA_GET_MIN(__argc, 0)], (int)__args[RGA_GET_MIN(__argc, 1)]); \ + } else { \ + __ret = IM_STATUS_INVALID_PARAM; \ + printf("invalid parameter\n"); \ + } \ + __ret; \ + }) +#define imcomposite(srcA, srcB, dst, ...) \ + ({ \ + IM_STATUS __ret = IM_STATUS_SUCCESS; \ + int __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(int); \ + if (__argc == 0) { \ + __ret = imblend_t(srcA, srcB, dst, IM_ALPHA_BLEND_SRC_OVER, 1); \ + } else if (__argc == 1){ \ + __ret = imblend_t(srcA, srcB, dst, (int)__args[RGA_GET_MIN(__argc, 0)], 1); \ + } else if (__argc == 2){ \ + __ret = imblend_t(srcA, srcB, dst, (int)__args[RGA_GET_MIN(__argc, 0)], (int)__args[RGA_GET_MIN(__argc, 1)]); \ + } else { \ + __ret = IM_STATUS_INVALID_PARAM; \ + printf("invalid parameter\n"); \ + } \ + __ret; \ + }) + +/** + * color key + * + * @param src + * @param dst + * @param colorkey_range + * max color + * min color + * @param sync + * wait until operation complete + * + * @returns success or else negative error code. + */ +#define imcolorkey(src, dst, range, ...) \ + ({ \ + IM_STATUS __ret = IM_STATUS_SUCCESS; \ + int __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(int); \ + if (__argc == 0) { \ + __ret = imcolorkey_t(src, dst, range, IM_ALPHA_COLORKEY_NORMAL, 1); \ + } else if (__argc == 1){ \ + __ret = imcolorkey_t(src, dst, range, (int)__args[RGA_GET_MIN(__argc, 0)], 1); \ + } else if (__argc == 2){ \ + __ret = imcolorkey_t(src, dst, range, (int)__args[RGA_GET_MIN(__argc, 0)], (int)__args[RGA_GET_MIN(__argc, 1)]); \ + } else { \ + __ret = IM_STATUS_INVALID_PARAM; \ + printf("invalid parameter\n"); \ + } \ + __ret; \ + }) + +/** + * nn quantize + * + * @param src + * @param dst + * @param nninfo + * @param sync + * wait until operation complete + * + * @returns success or else negative error code. + */ +#define imquantize(src, dst, nn_info, ...) \ + ({ \ + IM_STATUS __ret = IM_STATUS_SUCCESS; \ + int __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(int); \ + if (__argc == 0) { \ + __ret = imquantize_t(src, dst, nn_info, 1); \ + } else if (__argc == 1){ \ + __ret = imquantize_t(src, dst, nn_info, (int)__args[RGA_GET_MIN(__argc, 0)]); \ + } else { \ + __ret = IM_STATUS_INVALID_PARAM; \ + printf("invalid parameter\n"); \ + } \ + __ret; \ + }) + + +/** + * ROP + * + * @param src + * @param dst + * @param rop_code + * @param sync + * wait until operation complete + * + * @returns success or else negative error code. + */ +#define imrop(src, dst, rop_code, ...) \ + ({ \ + IM_STATUS __ret = IM_STATUS_SUCCESS; \ + int __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(int); \ + if (__argc == 0) { \ + __ret = imrop_t(src, dst, rop_code, 1); \ + } else if (__argc == 1){ \ + __ret = imrop_t(src, dst, rop_code, (int)__args[RGA_GET_MIN(__argc, 0)]); \ + } else { \ + __ret = IM_STATUS_INVALID_PARAM; \ + printf("invalid parameter\n"); \ + } \ + __ret; \ + }) + +/** + * fill/reset/draw + * + * @param src + * @param dst + * @param rect + * @param color + * @param sync + * wait until operation complete + * + * @returns success or else negative error code. + */ +#define imfill(buf, rect, color, ...) \ + ({ \ + IM_STATUS __ret = IM_STATUS_SUCCESS; \ + int __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(int); \ + if (__argc == 0) { \ + __ret = imfill_t(buf, rect, color, 1); \ + } else if (__argc == 1){ \ + __ret = imfill_t(buf, rect, color, (int)__args[RGA_GET_MIN(__argc, 0)]); \ + } else { \ + __ret = IM_STATUS_INVALID_PARAM; \ + printf("invalid parameter\n"); \ + } \ + __ret; \ + }) + +#define imreset(buf, rect, color, ...) \ + ({ \ + IM_STATUS __ret = IM_STATUS_SUCCESS; \ + int __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(int); \ + if (__argc == 0) { \ + __ret = imfill_t(buf, rect, color, 1); \ + } else if (__argc == 1){ \ + __ret = imfill_t(buf, rect, color, (int)__args[RGA_GET_MIN(__argc, 0)]); \ + } else { \ + __ret = IM_STATUS_INVALID_PARAM; \ + printf("invalid parameter\n"); \ + } \ + __ret; \ + }) + +#define imdraw(buf, rect, color, ...) \ + ({ \ + IM_STATUS __ret = IM_STATUS_SUCCESS; \ + int __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(int); \ + if (__argc == 0) { \ + __ret = imfill_t(buf, rect, color, 1); \ + } else if (__argc == 1){ \ + __ret = imfill_t(buf, rect, color, (int)__args[RGA_GET_MIN(__argc, 0)]); \ + } else { \ + __ret = IM_STATUS_INVALID_PARAM; \ + printf("invalid parameter\n"); \ + } \ + __ret; \ + }) + +/** + * palette + * + * @param src + * @param dst + * @param lut + * @param sync + * wait until operation complete + * + * @returns success or else negative error code. + */ +#define impalette(src, dst, lut, ...) \ + ({ \ + IM_STATUS __ret = IM_STATUS_SUCCESS; \ + int __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(int); \ + if (__argc == 0) { \ + __ret = impalette_t(src, dst, lut, 1); \ + } else if (__argc == 1){ \ + __ret = impalette_t(src, dst, lut, (int)__args[RGA_GET_MIN(__argc, 0)]); \ + } else { \ + __ret = IM_STATUS_INVALID_PARAM; \ + printf("invalid parameter\n"); \ + } \ + __ret; \ + }) +/* End define IM2D macro API */ +#endif + +#endif /* #ifndef _im2d_single_h_ */ \ No newline at end of file diff --git a/rknpu2/examples/3rdparty/rga/include/im2d_task.h b/rknpu2/examples/3rdparty/rga/include/im2d_task.h new file mode 100644 index 0000000000000000000000000000000000000000..9f8db59a3b0fbb1d3b77cd13db8a23f3beae9a26 --- /dev/null +++ b/rknpu2/examples/3rdparty/rga/include/im2d_task.h @@ -0,0 +1,497 @@ +/* + * Copyright (C) 2022 Rockchip Electronics Co., Ltd. + * Authors: + * Cerf Yu + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef _im2d_task_h_ +#define _im2d_task_h_ + +#include "im2d_type.h" + +#ifdef __cplusplus + +/** + * Create an rga job + * + * @param flags + * Some configuration flags for this job + * + * @returns job handle. + */ +IM_API im_job_handle_t imbeginJob(uint64_t flags = 0); + +/** + * Submit and run an rga job + * + * @param job_handle + * This is the job handle that will be submitted. + * @param sync_mode + * run mode: + * IM_SYNC + * IM_ASYNC + * @param acquire_fence_fd + * @param release_fence_fd + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imendJob(im_job_handle_t job_handle, + int sync_mode = IM_SYNC, + int acquire_fence_fd = 0, int *release_fence_fd = NULL); + +/** + * Cancel and delete an rga job + * + * @param job_handle + * This is the job handle that will be cancelled. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imcancelJob(im_job_handle_t job_handle); + +/** + * Add copy task + * + * @param job_handle + * Insert the task into the job handle. + * @param src + * The input source image. + * @param dst + * The output destination image. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imcopyTask(im_job_handle_t job_handle, const rga_buffer_t src, rga_buffer_t dst); + +/** + * Add resize task + * + * @param job_handle + * Insert the task into the job handle. + * @param src + * The input source image. + * @param dst + * The output destination image. + * @param fx + * X-direction resize factor. + * @param fy + * X-direction resize factor. + * @param interpolation + * Interpolation formula(Only RGA1 support). + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imresizeTask(im_job_handle_t job_handle, + const rga_buffer_t src, rga_buffer_t dst, + double fx = 0, double fy = 0, + int interpolation = 0); + +/** + * Add crop task + * + * @param job_handle + * Insert the task into the job handle. + * @param src + * The input source image. + * @param dst + * The output destination image. + * @param rect + * The rectangle on the source image that needs to be cropped. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imcropTask(im_job_handle_t job_handle, + const rga_buffer_t src, rga_buffer_t dst, im_rect rect); + +/** + * Add translate task + * + * @param job_handle + * Insert the task into the job handle. + * @param src + * The input source image. + * @param dst + * The output destination image. + * @param x + * Output the coordinates of the starting point in the X-direction of the destination image. + * @param y + * Output the coordinates of the starting point in the Y-direction of the destination image. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imtranslateTask(im_job_handle_t job_handle, + const rga_buffer_t src, rga_buffer_t dst, int x, int y); + +/** + * Add format convert task + * + * @param job_handle + * Insert the task into the job handle. + * @param src + * The input source image. + * @param dst + * The output destination image. + * @param sfmt + * The source image format. + * @param dfmt + * The destination image format. + * @param mode + * color space mode: + * IM_YUV_TO_RGB_BT601_LIMIT + * IM_YUV_TO_RGB_BT601_FULL + * IM_YUV_TO_RGB_BT709_LIMIT + * IM_RGB_TO_YUV_BT601_FULL + * IM_RGB_TO_YUV_BT601_LIMIT + * IM_RGB_TO_YUV_BT709_LIMIT + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imcvtcolorTask(im_job_handle_t job_handle, + rga_buffer_t src, rga_buffer_t dst, + int sfmt, int dfmt, int mode = IM_COLOR_SPACE_DEFAULT); + +/** + * Add rotation task + * + * @param job_handle + * Insert the task into the job handle. + * @param src + * The input source image. + * @param dst + * The output destination image. + * @param rotation + * IM_HAL_TRANSFORM_ROT_90 + * IM_HAL_TRANSFORM_ROT_180 + * IM_HAL_TRANSFORM_ROT_270 + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imrotateTask(im_job_handle_t job_handle, + const rga_buffer_t src, rga_buffer_t dst, int rotation); + +/** + * Add flip task + * + * @param job_handle + * Insert the task into the job handle. + * @param src + * The input source image. + * @param dst + * The output destination image. + * @param mode + * IM_HAL_TRANSFORM_FLIP_H + * IM_HAL_TRANSFORM_FLIP_V + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imflipTask(im_job_handle_t job_handle, + const rga_buffer_t src, rga_buffer_t dst, int mode); + +/** + * Add blend(SRC + DST -> DST) task + * + * @param job_handle + * Insert the task into the job handle. + * @param fg_image + * The foreground image. + * @param bg_image + * The background image, which is also the output destination image. + * @param mode + * Port-Duff mode: + * IM_ALPHA_BLEND_SRC + * IM_ALPHA_BLEND_DST + * IM_ALPHA_BLEND_SRC_OVER + * IM_ALPHA_BLEND_DST_OVER + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imblendTask(im_job_handle_t job_handle, + const rga_buffer_t fg_image, rga_buffer_t bg_image, + int mode = IM_ALPHA_BLEND_SRC_OVER); + +/** + * Add composite(SRCA + SRCB -> DST) task + * + * @param job_handle + * Insert the task into the job handle. + * @param fg_image + * The foreground image. + * @param bg_image + * The background image. + * @param output_image + * The output destination image. + * @param mode + * Port-Duff mode: + * IM_ALPHA_BLEND_SRC + * IM_ALPHA_BLEND_DST + * IM_ALPHA_BLEND_SRC_OVER + * IM_ALPHA_BLEND_DST_OVER + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imcompositeTask(im_job_handle_t job_handle, + const rga_buffer_t fg_image, const rga_buffer_t bg_image, + rga_buffer_t output_image, + int mode = IM_ALPHA_BLEND_SRC_OVER); + +/** + * Add color key task + * + * @param job_handle + * Insert the task into the job handle. + * @param fg_image + * The foreground image. + * @param bg_image + * The background image, which is also the output destination image. + * @param colorkey_range + * The range of color key. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imcolorkeyTask(im_job_handle_t job_handle, + const rga_buffer_t fg_image, rga_buffer_t bg_image, + im_colorkey_range range, int mode = IM_ALPHA_COLORKEY_NORMAL); + +/** + * Add OSD task + * + * @param job_handle + * Insert the task into the job handle. + * @param osd + * The osd text block. + * @param dst + * The background image. + * @param osd_rect + * The rectangle on the source image that needs to be OSD. + * @param osd_config + * osd mode configuration. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imosdTask(im_job_handle_t job_handle, + const rga_buffer_t osd,const rga_buffer_t bg_image, + const im_rect osd_rect, im_osd_t *osd_config); + +/** + * Add nn quantize task + * + * @param job_handle + * Insert the task into the job handle. + * @param src + * The input source image. + * @param dst + * The output destination image. + * @param nninfo + * nn configuration + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imquantizeTask(im_job_handle_t job_handle, + const rga_buffer_t src, rga_buffer_t dst, im_nn_t nn_info); + +/** + * Add ROP task + * + * @param job_handle + * Insert the task into the job handle. + * @param src + * The input source image. + * @param dst + * The output destination image. + * @param rop_code + * The ROP opcode. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imropTask(im_job_handle_t job_handle, + const rga_buffer_t src, rga_buffer_t dst, int rop_code); + +/** + * Add color fill task + * + * @param job_handle + * Insert the task into the job handle. + * @param dst + * The output destination image. + * @param rect + * The rectangle on the source image that needs to be filled with color. + * @param color + * The fill color value. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imfillTask(im_job_handle_t job_handle, rga_buffer_t dst, im_rect rect, uint32_t color); + +/** + * Add color fill task array + * + * @param job_handle + * Insert the task into the job handle. + * @param dst + * The output destination image. + * @param rect_array + * The rectangle arrays on the source image that needs to be filled with color. + * @param array_size + * The size of rectangular area arrays. + * @param color + * The fill color value. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imfillTaskArray(im_job_handle_t job_handle, + rga_buffer_t dst, + im_rect *rect_array, int array_size, uint32_t color); + +/** + * Add fill rectangle task + * + * @param job_handle + * Insert the task into the job handle. + * @param dst + * The output destination image. + * @param rect + * The rectangle on the source image that needs to be filled with color. + * @param color + * The fill color value. + * @param thickness + * Thickness of lines that make up the rectangle. Negative values, like -1, + * mean that the function has to draw a filled rectangle. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imrectangleTask(im_job_handle_t job_handle, + rga_buffer_t dst, + im_rect rect, + uint32_t color, int thickness); + +/** + * Add fill rectangle task array + * + * @param job_handle + * Insert the task into the job handle. + * @param dst + * The output destination image. + * @param rect_array + * The rectangle arrays on the source image that needs to be filled with color. + * @param array_size + * The size of rectangular area arrays. + * @param color + * The fill color value. + * @param thickness + * Thickness of lines that make up the rectangle. Negative values, like -1, + * mean that the function has to draw a filled rectangle. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imrectangleTaskArray(im_job_handle_t job_handle, + rga_buffer_t dst, + im_rect *rect_array, int array_size, + uint32_t color, int thickness); + +/** + * Add mosaic task + * + * @param job_handle + * Insert the task into the job handle. + * @param image + * The output destination image. + * @param rect + * The rectangle on the source image that needs to be mosaicked. + * @param mosaic_mode + * mosaic block width configuration: + * IM_MOSAIC_8 + * IM_MOSAIC_16 + * IM_MOSAIC_32 + * IM_MOSAIC_64 + * IM_MOSAIC_128 + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS immosaicTask(im_job_handle_t job_handle, + const rga_buffer_t image, im_rect rect, int mosaic_mode); + +/** + * Add mosaic task + * + * @param job_handle + * Insert the task into the job handle. + * @param image + * The output destination image. + * @param rect_array + * The rectangle arrays on the source image that needs to be filled with color. + * @param array_size + * The size of rectangular area arrays. + * @param mosaic_mode + * mosaic block width configuration: + * IM_MOSAIC_8 + * IM_MOSAIC_16 + * IM_MOSAIC_32 + * IM_MOSAIC_64 + * IM_MOSAIC_128 + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS immosaicTaskArray(im_job_handle_t job_handle, + const rga_buffer_t image, + im_rect *rect_array, int array_size, int mosaic_mode); + +/** + * Add palette task + * + * @param job_handle + * Insert the task into the job handle. + * @param src + * The input source image. + * @param dst + * The output destination image. + * @param lut + * The LUT table. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS impaletteTask(im_job_handle_t job_handle, + rga_buffer_t src, rga_buffer_t dst, rga_buffer_t lut); + +/** + * Add process task + * + * @param job_handle + * Insert the task into the job handle. + * @param src + * The input source image and is also the foreground image in blend. + * @param dst + * The output destination image and is also the foreground image in blend. + * @param pat + * The foreground image, or a LUT table. + * @param srect + * The rectangle on the src channel image that needs to be processed. + * @param drect + * The rectangle on the dst channel image that needs to be processed. + * @param prect + * The rectangle on the pat channel image that needs to be processed. + * @param opt + * The image processing options configuration. + * @param usage + * The image processing usage. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS improcessTask(im_job_handle_t job_handle, + rga_buffer_t src, rga_buffer_t dst, rga_buffer_t pat, + im_rect srect, im_rect drect, im_rect prect, + im_opt_t *opt_ptr, int usage); + +#endif /* #ifdef __cplusplus */ + +#endif /* #ifndef _im2d_task_h_ */ diff --git a/rknpu2/examples/3rdparty/rga/include/im2d_type.h b/rknpu2/examples/3rdparty/rga/include/im2d_type.h new file mode 100644 index 0000000000000000000000000000000000000000..7ca8a2192ea119da9dfdf0f27844f81f273c127a --- /dev/null +++ b/rknpu2/examples/3rdparty/rga/include/im2d_type.h @@ -0,0 +1,470 @@ +/* + * Copyright (C) 2022 Rockchip Electronics Co., Ltd. + * Authors: + * Cerf Yu + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef _RGA_IM2D_TYPE_H_ +#define _RGA_IM2D_TYPE_H_ + +#include + +#include "rga.h" + +#define IM_API /* define API export as needed */ + +#ifdef __cplusplus +#define IM_C_API extern "C" +#define IM_EXPORT_API extern "C" +#else +#define IM_C_API +#define IM_EXPORT_API +#endif + +#ifdef __cplusplus +#define DEFAULT_INITIALIZER(x) = x +#else +#define DEFAULT_INITIALIZER(x) +#endif + +typedef uint32_t im_api_version_t; +typedef uint32_t im_job_handle_t; +typedef uint32_t im_ctx_id_t; +typedef uint32_t rga_buffer_handle_t; + +typedef enum { + /* Rotation */ + IM_HAL_TRANSFORM_ROT_90 = 1 << 0, + IM_HAL_TRANSFORM_ROT_180 = 1 << 1, + IM_HAL_TRANSFORM_ROT_270 = 1 << 2, + IM_HAL_TRANSFORM_FLIP_H = 1 << 3, + IM_HAL_TRANSFORM_FLIP_V = 1 << 4, + IM_HAL_TRANSFORM_FLIP_H_V = 1 << 5, + IM_HAL_TRANSFORM_MASK = 0x3f, + + /* + * Blend + * Additional blend usage, can be used with both source and target configs. + * If none of the below is set, the default "SRC over DST" is applied. + */ + IM_ALPHA_BLEND_SRC_OVER = 1 << 6, /* Default, Porter-Duff "SRC over DST" */ + IM_ALPHA_BLEND_SRC = 1 << 7, /* Porter-Duff "SRC" */ + IM_ALPHA_BLEND_DST = 1 << 8, /* Porter-Duff "DST" */ + IM_ALPHA_BLEND_SRC_IN = 1 << 9, /* Porter-Duff "SRC in DST" */ + IM_ALPHA_BLEND_DST_IN = 1 << 10, /* Porter-Duff "DST in SRC" */ + IM_ALPHA_BLEND_SRC_OUT = 1 << 11, /* Porter-Duff "SRC out DST" */ + IM_ALPHA_BLEND_DST_OUT = 1 << 12, /* Porter-Duff "DST out SRC" */ + IM_ALPHA_BLEND_DST_OVER = 1 << 13, /* Porter-Duff "DST over SRC" */ + IM_ALPHA_BLEND_SRC_ATOP = 1 << 14, /* Porter-Duff "SRC ATOP" */ + IM_ALPHA_BLEND_DST_ATOP = 1 << 15, /* Porter-Duff "DST ATOP" */ + IM_ALPHA_BLEND_XOR = 1 << 16, /* Xor */ + IM_ALPHA_BLEND_MASK = 0x1ffc0, + + IM_ALPHA_COLORKEY_NORMAL = 1 << 17, + IM_ALPHA_COLORKEY_INVERTED = 1 << 18, + IM_ALPHA_COLORKEY_MASK = 0x60000, + + IM_SYNC = 1 << 19, + IM_CROP = 1 << 20, /* Unused */ + IM_COLOR_FILL = 1 << 21, + IM_COLOR_PALETTE = 1 << 22, + IM_NN_QUANTIZE = 1 << 23, + IM_ROP = 1 << 24, + IM_ALPHA_BLEND_PRE_MUL = 1 << 25, + IM_ASYNC = 1 << 26, + IM_MOSAIC = 1 << 27, + IM_OSD = 1 << 28, + IM_PRE_INTR = 1 << 29, + IM_ALPHA_BIT_MAP = 1 << 30, +} IM_USAGE; + +typedef enum { + IM_RASTER_MODE = 1 << 0, + IM_AFBC16x16_MODE = 1 << 1, + IM_TILE8x8_MODE = 1 << 2, + IM_TILE4x4_MODE = 1 << 3, + IM_RKFBC64x4_MODE = 1 << 4, + IM_AFBC32x8_MODE = 1 << 5, + /* legacy */ + IM_FBC_MODE = IM_AFBC16x16_MODE, + IM_TILE_MODE = IM_TILE8x8_MODE, +} IM_RD_MODE; + +typedef enum { + IM_SCHEDULER_RGA3_CORE0 = 1 << 0, + IM_SCHEDULER_RGA3_CORE1 = 1 << 1, + IM_SCHEDULER_RGA2_CORE0 = 1 << 2, + IM_SCHEDULER_RGA2_CORE1 = 1 << 3, + IM_SCHEDULER_RGA3_DEFAULT = IM_SCHEDULER_RGA3_CORE0, + IM_SCHEDULER_RGA2_DEFAULT = IM_SCHEDULER_RGA2_CORE0, + IM_SCHEDULER_MASK = 0xf, + IM_SCHEDULER_DEFAULT = 0, +} IM_SCHEDULER_CORE; + +typedef enum { + IM_ROP_AND = 0x88, + IM_ROP_OR = 0xee, + IM_ROP_NOT_DST = 0x55, + IM_ROP_NOT_SRC = 0x33, + IM_ROP_XOR = 0xf6, + IM_ROP_NOT_XOR = 0xf9, +} IM_ROP_CODE; + +typedef enum { + IM_MOSAIC_8 = 0x0, + IM_MOSAIC_16 = 0x1, + IM_MOSAIC_32 = 0x2, + IM_MOSAIC_64 = 0x3, + IM_MOSAIC_128 = 0x4, +} IM_MOSAIC_MODE; + +typedef enum { + IM_BORDER_CONSTANT = 0, /* iiiiii|abcdefgh|iiiiiii with some specified value 'i' */ + IM_BORDER_REFLECT = 2, /* fedcba|abcdefgh|hgfedcb */ + IM_BORDER_WRAP = 3, /* cdefgh|abcdefgh|abcdefg */ +} IM_BORDER_TYPE; + +/* Status codes, returned by any blit function */ +typedef enum { + IM_YUV_TO_RGB_BT601_LIMIT = 1 << 0, + IM_YUV_TO_RGB_BT601_FULL = 2 << 0, + IM_YUV_TO_RGB_BT709_LIMIT = 3 << 0, + IM_YUV_TO_RGB_MASK = 3 << 0, + IM_RGB_TO_YUV_BT601_FULL = 1 << 2, + IM_RGB_TO_YUV_BT601_LIMIT = 2 << 2, + IM_RGB_TO_YUV_BT709_LIMIT = 3 << 2, + IM_RGB_TO_YUV_MASK = 3 << 2, + IM_RGB_TO_Y4 = 1 << 4, + IM_RGB_TO_Y4_DITHER = 2 << 4, + IM_RGB_TO_Y1_DITHER = 3 << 4, + IM_Y4_MASK = 3 << 4, + IM_RGB_FULL = 1 << 8, + IM_RGB_CLIP = 2 << 8, + IM_YUV_BT601_LIMIT_RANGE = 3 << 8, + IM_YUV_BT601_FULL_RANGE = 4 << 8, + IM_YUV_BT709_LIMIT_RANGE = 5 << 8, + IM_YUV_BT709_FULL_RANGE = 6 << 8, + IM_FULL_CSC_MASK = 0xf << 8, + IM_COLOR_SPACE_DEFAULT = 0, +} IM_COLOR_SPACE_MODE; + +typedef enum { + IM_UP_SCALE, + IM_DOWN_SCALE, +} IM_SCALE; + +/* legacy */ +typedef enum { + INTER_NEAREST, + INTER_LINEAR, + INTER_CUBIC, +} IM_SCALE_MODE; + +typedef enum { + IM_INTERP_DEFAULT = 0, + IM_INTERP_LINEAR = 1, + IM_INTERP_CUBIC = 2, + IM_INTERP_AVERAGE = 3, + + IM_INTERP_MASK = 0xf, + IM_INTERP_HORIZ_SHIFT = 0, + IM_INTERP_VERTI_SHIFT = 4, + IM_INTERP_HORIZ_FLAG = 0x1 << 8, + IM_INTERP_VERTI_FLAG = 0x1 << 9, +} IM_INTER_MODE; + +typedef enum { + IM_CONFIG_SCHEDULER_CORE, + IM_CONFIG_PRIORITY, + IM_CONFIG_CHECK, +} IM_CONFIG_NAME; + +typedef enum { + IM_OSD_MODE_STATISTICS = 0x1 << 0, + IM_OSD_MODE_AUTO_INVERT = 0x1 << 1, +} IM_OSD_MODE; + +typedef enum { + IM_OSD_INVERT_CHANNEL_NONE = 0x0, + IM_OSD_INVERT_CHANNEL_Y_G = 0x1 << 0, + IM_OSD_INVERT_CHANNEL_C_RB = 0x1 << 1, + IM_OSD_INVERT_CHANNEL_ALPHA = 0x1 << 2, + IM_OSD_INVERT_CHANNEL_COLOR = IM_OSD_INVERT_CHANNEL_Y_G | + IM_OSD_INVERT_CHANNEL_C_RB, + IM_OSD_INVERT_CHANNEL_BOTH = IM_OSD_INVERT_CHANNEL_COLOR | + IM_OSD_INVERT_CHANNEL_ALPHA, +} IM_OSD_INVERT_CHANNEL; + +typedef enum { + IM_OSD_FLAGS_INTERNAL = 0, + IM_OSD_FLAGS_EXTERNAL, +} IM_OSD_FLAGS_MODE; + +typedef enum { + IM_OSD_INVERT_USE_FACTOR, + IM_OSD_INVERT_USE_SWAP, +} IM_OSD_INVERT_MODE; + +typedef enum { + IM_OSD_BACKGROUND_DEFAULT_BRIGHT = 0, + IM_OSD_BACKGROUND_DEFAULT_DARK, +} IM_OSD_BACKGROUND_DEFAULT; + +typedef enum { + IM_OSD_BLOCK_MODE_NORMAL = 0, + IM_OSD_BLOCK_MODE_DIFFERENT, +} IM_OSD_BLOCK_WIDTH_MODE; + +typedef enum { + IM_OSD_MODE_HORIZONTAL, + IM_OSD_MODE_VERTICAL, +} IM_OSD_DIRECTION; + +typedef enum { + IM_OSD_COLOR_PIXEL, + IM_OSD_COLOR_EXTERNAL, +} IM_OSD_COLOR_MODE; + +typedef enum { + IM_INTR_READ_INTR = 1 << 0, + IM_INTR_READ_HOLD = 1 << 1, + IM_INTR_WRITE_INTR = 1 << 2, +} IM_PRE_INTR_FLAGS; + +typedef enum { + IM_CONTEXT_NONE = 0x0, + IM_CONTEXT_SRC_FIX_ENABLE = 0x1 << 0, // Enable kernel to modify the image parameters of the channel. + IM_CONTEXT_SRC_CACHE_INFO = 0x1 << 1, // It will replace the parameters in ctx with the modified parameters. + IM_CONTEXT_SRC1_FIX_ENABLE = 0x1 << 2, + IM_CONTEXT_SRC1_CACHE_INFO = 0x1 << 3, + IM_CONTEXT_DST_FIX_ENABLE = 0x1 << 4, + IM_CONTEXT_DST_CACHE_INFO = 0x1 << 5, +} IM_CONTEXT_FLAGS; + +/* Get RGA basic information index */ +typedef enum { + RGA_VENDOR = 0, + RGA_VERSION, + RGA_MAX_INPUT, + RGA_MAX_OUTPUT, + RGA_BYTE_STRIDE, + RGA_SCALE_LIMIT, + RGA_INPUT_FORMAT, + RGA_OUTPUT_FORMAT, + RGA_FEATURE, + RGA_EXPECTED, + RGA_ALL, +} IM_INFORMATION; + +/* Status codes, returned by any blit function */ +typedef enum { + IM_STATUS_NOERROR = 2, + IM_STATUS_SUCCESS = 1, + IM_STATUS_NOT_SUPPORTED = -1, + IM_STATUS_OUT_OF_MEMORY = -2, + IM_STATUS_INVALID_PARAM = -3, + IM_STATUS_ILLEGAL_PARAM = -4, + IM_STATUS_ERROR_VERSION = -5, + IM_STATUS_FAILED = 0, +} IM_STATUS; + +/* Rectangle definition */ +typedef struct { + int x; /* upper-left x */ + int y; /* upper-left y */ + int width; /* width */ + int height; /* height */ +} im_rect; + +typedef struct { + int max; /* The Maximum value of the color key */ + int min; /* The minimum value of the color key */ +} im_colorkey_range; + + +typedef struct im_nn { + int scale_r; /* scaling factor on R channal */ + int scale_g; /* scaling factor on G channal */ + int scale_b; /* scaling factor on B channal */ + int offset_r; /* offset on R channal */ + int offset_g; /* offset on G channal */ + int offset_b; /* offset on B channal */ +} im_nn_t; + +/* im_info definition */ +typedef struct { + void* vir_addr; /* virtual address */ + void* phy_addr; /* physical address */ + int fd; /* shared fd */ + + int width; /* width */ + int height; /* height */ + int wstride; /* wstride */ + int hstride; /* hstride */ + int format; /* format */ + + int color_space_mode; /* color_space_mode */ + union { + int global_alpha; /* global_alpha, the default should be 0xff */ + struct { + uint16_t alpha0; + uint16_t alpha1; + } alpha_bit; /* alpha bit(e.g. RGBA5551), 0: alpha0, 1: alpha1 */ + }; + int rd_mode; + + /* legacy */ + int color; /* color, used by color fill */ + im_colorkey_range colorkey_range; /* range value of color key */ + im_nn_t nn; + int rop_code; + + rga_buffer_handle_t handle; /* buffer handle */ +} rga_buffer_t; + +typedef struct im_color { + union { + struct { + uint8_t red; + uint8_t green; + uint8_t blue; + uint8_t alpha; + }; + uint32_t value; + }; +} im_color_t; + +typedef struct im_osd_invert_factor { + uint8_t alpha_max; + uint8_t alpha_min; + uint8_t yg_max; + uint8_t yg_min; + uint8_t crb_max; + uint8_t crb_min; +} im_osd_invert_factor_t; + +typedef struct im_osd_bpp2 { + uint8_t ac_swap; // ac swap flag + // 0: CA + // 1: AC + uint8_t endian_swap; // rgba2bpp endian swap + // 0: Big endian + // 1: Little endian + im_color_t color0; + im_color_t color1; +} im_osd_bpp2_t; + +typedef struct im_osd_block { + int width_mode; // normal or different + // IM_OSD_BLOCK_MODE_NORMAL + // IM_OSD_BLOCK_MODE_DIFFERENT + union { + int width; // normal_mode block width + int width_index; // different_mode block width index in RAM + }; + + int block_count; // block count + + int background_config; // background config is bright or dark + // IM_OSD_BACKGROUND_DEFAULT_BRIGHT + // IM_OSD_BACKGROUND_DEFAULT_DARK + + int direction; // osd block direction + // IM_OSD_MODE_HORIZONTAL + // IM_OSD_MODE_VERTICAL + + int color_mode; // using src1 color or config color + // IM_OSD_COLOR_PIXEL + // IM_OSD_COLOR_EXTERNAL + im_color_t normal_color; // config color: normal + im_color_t invert_color; // config color: invert +} im_osd_block_t; + +typedef struct im_osd_invert { + int invert_channel; // invert channel config: + // IM_OSD_INVERT_CHANNEL_NONE + // IM_OSD_INVERT_CHANNEL_Y_G + // IM_OSD_INVERT_CHANNEL_C_RB + // IM_OSD_INVERT_CHANNEL_ALPHA + // IM_OSD_INVERT_CHANNEL_COLOR + // IM_OSD_INVERT_CHANNEL_BOTH + int flags_mode; // use external or inertnal RAM invert flags + // IM_OSD_FLAGS_EXTERNAL + // IM_OSD_FLAGS_INTERNAL + int flags_index; // flags index when using internal RAM invert flags + + uint64_t invert_flags; // external invert flags + uint64_t current_flags; // current flags + + int invert_mode; // invert use swap or factor + // IM_OSD_INVERT_USE_FACTOR + // IM_OSD_INVERT_USE_SWAP + im_osd_invert_factor_t factor; + + int threash; +} im_osd_invert_t; + +typedef struct im_osd { + int osd_mode; // osd mode: statistics or auto_invert + // IM_OSD_MODE_STATISTICS + // IM_OSD_MODE_AUTO_INVERT + im_osd_block_t block_parm; // osd block info config + + im_osd_invert_t invert_config; + + im_osd_bpp2_t bpp2_info; +} im_osd_t; + +typedef struct im_intr_config { + uint32_t flags; + + int read_threshold; + int write_start; + int write_step; +} im_intr_config_t; + +typedef struct im_opt { + im_api_version_t version DEFAULT_INITIALIZER(RGA_CURRENT_API_HEADER_VERSION); + + int color; /* color, used by color fill */ + im_colorkey_range colorkey_range; /* range value of color key */ + im_nn_t nn; + int rop_code; + + int priority; + int core; + + int mosaic_mode; + + im_osd_t osd_config; + + im_intr_config_t intr_config; + + int interp; + + char reserve[124]; +} im_opt_t; + +typedef struct im_handle_param { + uint32_t width; + uint32_t height; + uint32_t format; +} im_handle_param_t; + +#define IM_INTERP_HORIZ(x) ( ((x) & IM_INTERP_MASK) << IM_INTERP_HORIZ_SHIFT | IM_INTERP_HORIZ_FLAG ) +#define IM_INTERP_VERTI(x) ( ((x) & IM_INTERP_MASK) << IM_INTERP_VERTI_SHIFT | IM_INTERP_VERTI_FLAG ) +#define IM_INTERP(h,v) ( IM_INTERP_HORIZ(h) | IM_INTERP_VERTI(v) ) + +#endif /* _RGA_IM2D_TYPE_H_ */ diff --git a/rknpu2/examples/3rdparty/rga/include/im2d_version.h b/rknpu2/examples/3rdparty/rga/include/im2d_version.h new file mode 100644 index 0000000000000000000000000000000000000000..370cb0d9c4ce1d09bfa74cd5a5d040e7006a7877 --- /dev/null +++ b/rknpu2/examples/3rdparty/rga/include/im2d_version.h @@ -0,0 +1,49 @@ +/* + * Copyright (C) 2022 Rockchip Electronics Co., Ltd. + * Authors: + * Cerf Yu + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef _RGA_IM2D_VERSION_H_ +#define _RGA_IM2D_VERSION_H_ + +#define RGA_VERSION_STR_HELPER(x) #x +#define RGA_VERSION_STR(x) RGA_VERSION_STR_HELPER(x) + +/* RGA im2d api verison */ +#define RGA_API_MAJOR_VERSION 1 +#define RGA_API_MINOR_VERSION 10 +#define RGA_API_REVISION_VERSION 1 +#define RGA_API_BUILD_VERSION 0 + +#define RGA_API_SUFFIX + +#define RGA_API_VERSION \ + RGA_VERSION_STR(RGA_API_MAJOR_VERSION) "." \ + RGA_VERSION_STR(RGA_API_MINOR_VERSION) "." \ + RGA_VERSION_STR(RGA_API_REVISION_VERSION) "_[" \ + RGA_VERSION_STR(RGA_API_BUILD_VERSION) "]" +#define RGA_API_FULL_VERSION "rga_api version " RGA_API_VERSION RGA_API_SUFFIX + +/* For header file version verification */ +#define RGA_CURRENT_API_VERSION (\ + (RGA_API_MAJOR_VERSION & 0xff) << 24 | \ + (RGA_API_MINOR_VERSION & 0xff) << 16 | \ + (RGA_API_REVISION_VERSION & 0xff) << 8 | \ + (RGA_API_BUILD_VERSION & 0xff)\ + ) +#define RGA_CURRENT_API_HEADER_VERSION RGA_CURRENT_API_VERSION + +#endif /* _RGA_IM2D_VERSION_H_ */ diff --git a/rknpu2/examples/3rdparty/rga/include/rga.h b/rknpu2/examples/3rdparty/rga/include/rga.h new file mode 100644 index 0000000000000000000000000000000000000000..efba2b0c6880d6998a41b76a03b82502c56674c8 --- /dev/null +++ b/rknpu2/examples/3rdparty/rga/include/rga.h @@ -0,0 +1,197 @@ +/* + * Copyright (C) 2016 Rockchip Electronics Co., Ltd. + * Authors: + * Zhiqin Wei + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef _RGA_DRIVER_H_ +#define _RGA_DRIVER_H_ + + +#ifndef ENABLE +#define ENABLE 1 +#endif + +#ifndef DISABLE +#define DISABLE 0 +#endif + +#ifdef __cplusplus +extern "C" +{ +#endif + +/* In order to be compatible with RK_FORMAT_XX and HAL_PIXEL_FORMAT_XX, + * RK_FORMAT_XX is shifted to the left by 8 bits to distinguish. */ +typedef enum _Rga_SURF_FORMAT { + RK_FORMAT_RGBA_8888 = 0x0 << 8, /* [0:31] R:G:B:A 8:8:8:8 little endian */ + RK_FORMAT_RGBX_8888 = 0x1 << 8, /* [0:31] R:G:B:X 8:8:8:8 little endian */ + RK_FORMAT_RGB_888 = 0x2 << 8, /* [0:23] R:G:B 8:8:8 little endian */ + RK_FORMAT_BGRA_8888 = 0x3 << 8, /* [0:31] B:G:R:A 8:8:8:8 little endian */ + RK_FORMAT_RGB_565 = 0x4 << 8, /* [0:15] R:G:B 5:6:5 little endian */ + RK_FORMAT_RGBA_5551 = 0x5 << 8, /* [0:15] R:G:B:A 5:5:5:1 little endian */ + RK_FORMAT_RGBA_4444 = 0x6 << 8, /* [0:15] R:G:B:A 4:4:4:4 little endian */ + RK_FORMAT_BGR_888 = 0x7 << 8, /* [0:23] B:G:R 8:8:8 little endian */ + + RK_FORMAT_YCbCr_422_SP = 0x8 << 8, /* 2 plane YCbCr little endian + * plane 0: [0:7] Y + * plane 1: 2x1 subsampled [0:15] Cb:Cr 8:8 */ + RK_FORMAT_YCbCr_422_P = 0x9 << 8, /* 3 plane YCbCr little endian + * plane 0: [0:7] Y + * plane 1: 2x1 subsampled [0:7] Cb + * plane 2: 2x1 subsampled [0:7] Cr */ + RK_FORMAT_YCbCr_420_SP = 0xa << 8, /* 2 plane YCbCr little endian + * plane 0: [0:7] Y + * plane 1: 2x2 subsampled [0:15] Cr:Cb 8:8 */ + RK_FORMAT_YCbCr_420_P = 0xb << 8, /* 3 plane YCbCr little endian + * plane 0: [0:7] Y + * plane 1: 2x2 subsampled [0:7] Cb + * plane 2: 2x2 subsampled [0:7] Cr */ + + RK_FORMAT_YCrCb_422_SP = 0xc << 8, /* 2 plane YCbCr little endian + * plane 0: [0:7] Y + * plane 1: 2x1 subsampled [0:15] Cb:Cr 8:8 */ + RK_FORMAT_YCrCb_422_P = 0xd << 8, /* 3 plane YCbCr little endian + * plane 0: [0:7] Y + * plane 1: 2x1 subsampled [0:7] Cr + * plane 2: 2x1 subsampled [0:7] Cb */ + RK_FORMAT_YCrCb_420_SP = 0xe << 8, /* 2 plane YCbCr little endian + * plane 0: [0:7] Y + * plane 1: 2x2 subsampled [0:15] Cb:Cr 8:8 */ + RK_FORMAT_YCrCb_420_P = 0xf << 8, /* 3 plane YCbCr little endian + * plane 0: [0:7] Y + * plane 1: 2x2 subsampled [0:7] Cr + * plane 2: 2x2 subsampled [0:7] Cb */ + + RK_FORMAT_BPP1 = 0x10 << 8, /* [0] little endian */ + RK_FORMAT_BPP2 = 0x11 << 8, /* [0:1] little endian */ + RK_FORMAT_BPP4 = 0x12 << 8, /* [0:3] little endian */ + RK_FORMAT_BPP8 = 0x13 << 8, /* [0:7] little endian */ + + RK_FORMAT_Y4 = 0x14 << 8, /* [0:3] Y little endian */ + RK_FORMAT_YCbCr_400 = 0x15 << 8, /* [0:7] Y little endian */ + + RK_FORMAT_BGRX_8888 = 0x16 << 8, /* [0:31] B:G:R:X 8:8:8:8 little endian */ + + RK_FORMAT_YVYU_422 = 0x18 << 8, /* [0:31] Y0:Cr0:Y1:cb0 8:8:8:8 little endian */ + RK_FORMAT_YVYU_420 = 0x19 << 8, /* ODD : [0:31] Y0:Cr0:Y1:cb0 8:8:8:8 little endian + * EVEN: [0:31] Y2:Y3:X:X 8:8:8:8 little endian */ + RK_FORMAT_VYUY_422 = 0x1a << 8, /* [0:31] Cr0:Y0:Cb0:Y1 8:8:8:8 little endian */ + RK_FORMAT_VYUY_420 = 0x1b << 8, /* ODD : [0:31] Cr0:Y0:Cb0:Y1 8:8:8:8 little endian + * EVEN: [0:31] Y2:Y3:X:X 8:8:8:8 little endian */ + RK_FORMAT_YUYV_422 = 0x1c << 8, /* [0:31] Y0:Cb0:Y1:cr0 8:8:8:8 little endian */ + RK_FORMAT_YUYV_420 = 0x1d << 8, /* ODD : [0:31] Y0:Cb0:Y1:cr0 8:8:8:8 little endian + * EVEN: [0:31] Y2:Y3:X:X 8:8:8:8 little endian */ + RK_FORMAT_UYVY_422 = 0x1e << 8, /* [0:31] Cb0:Y0:Cr0:Y1 8:8:8:8 little endian */ + RK_FORMAT_UYVY_420 = 0x1f << 8, /* ODD : [0:31] Cb0:Y0:Cr0:Y1 8:8:8:8 little endian + * EVEN: [0:31] Y2:Y3:X:X 8:8:8:8 little endian */ + + RK_FORMAT_YCbCr_420_SP_10B = 0x20 << 8, /* 2 plane YCbCr little endian + * plane 0: [0:9] Y + * plane 1: 2x2 subsampled [0:19] Cb:Cr 10: 10 (default) + * or + * plane 1: 2x2 subsampled [0:23] Cb:Cr 16: 16 */ + RK_FORMAT_YCrCb_420_SP_10B = 0x21 << 8, /* 2 plane YCbCr little endian + * plane 0: [0:9] Y + * plane 1: 2x2 subsampled [0:19] Cr:Cb 10: 10 (default) + * or + * plane 1: 2x2 subsampled [0:23] Cr:Cb 16: 16 */ + RK_FORMAT_YCbCr_422_SP_10B = 0x22 << 8, /* 2 plane YCbCr little endian + * plane 0: [0:9] Y + * plane 1: 2x1 subsampled [0:19] Cb:Cr 10:10 (default) + * or + * plane 1: 2x1 subsampled [0:23] Cb:Cr 16: 16 */ + RK_FORMAT_YCrCb_422_SP_10B = 0x23 << 8, /* 2 plane YCbCr little endian + * plane 0: [0:9] Y + * plane 1: 2x1 subsampled [0:19] Cr:Cb 10:10 (default) + * or + * plane 1: 2x1 subsampled [0:23] Cr:Cb 16: 16 */ + /* For compatibility with misspellings */ + RK_FORMAT_YCbCr_422_10b_SP = RK_FORMAT_YCbCr_422_SP_10B << 8, + RK_FORMAT_YCrCb_422_10b_SP = RK_FORMAT_YCrCb_422_SP_10B << 8, + + RK_FORMAT_BGR_565 = 0x24 << 8, /* [0:16] B:G:R 5:6:5 little endian */ + RK_FORMAT_BGRA_5551 = 0x25 << 8, /* [0:16] B:G:R:A 5:5:5:1 little endian */ + RK_FORMAT_BGRA_4444 = 0x26 << 8, /* [0:16] B:G:R:A 4:4:4:4 little endian */ + + RK_FORMAT_ARGB_8888 = 0x28 << 8, /* [0:31] A:R:G:B 8:8:8:8 little endian */ + RK_FORMAT_XRGB_8888 = 0x29 << 8, /* [0:31] X:R:G:B 8:8:8:8 little endian */ + RK_FORMAT_ARGB_5551 = 0x2a << 8, /* [0:16] A:R:G:B 5:5:5:1 little endian */ + RK_FORMAT_ARGB_4444 = 0x2b << 8, /* [0:16] A:R:G:B 4:4:4:4 little endian */ + RK_FORMAT_ABGR_8888 = 0x2c << 8, /* [0:31] A:B:G:R 8:8:8:8 little endian */ + RK_FORMAT_XBGR_8888 = 0x2d << 8, /* [0:31] X:B:G:R 8:8:8:8 little endian */ + RK_FORMAT_ABGR_5551 = 0x2e << 8, /* [0:16] A:B:G:R 5:5:5:1 little endian */ + RK_FORMAT_ABGR_4444 = 0x2f << 8, /* [0:16] A:B:G:R 4:4:4:4 little endian */ + + RK_FORMAT_RGBA2BPP = 0x30 << 8, /* [0:1] Color:Alpha 1:1 little endian */ + RK_FORMAT_A8 = 0x31 << 8, /* [0:7] Alpha */ + + RK_FORMAT_YCbCr_444_SP = 0x32 << 8, /* 2 plane YCbCr little endian + * plane 0: [0:7] Y + * plane 1: non-subsampled [0:15] Cb:Cr 8:8 */ + RK_FORMAT_YCrCb_444_SP = 0x33 << 8, /* 2 plane YCrCb little endian + * plane 0: [0:7] Y + * plane 1: non-subsampled [0:15] Cr:Cb 8:8 */ + RK_FORMAT_Y8 = 0x34 << 8, /* [0:7] zero:Y 4:4 little endian */ + + RK_FORMAT_UNKNOWN = 0x100 << 8, +} RgaSURF_FORMAT; + +enum { + yuv2rgb_mode0 = 0x0, /* BT.601 MPEG */ + yuv2rgb_mode1 = 0x1, /* BT.601 JPEG */ + yuv2rgb_mode2 = 0x2, /* BT.709 */ + + rgb2yuv_601_full = 0x1 << 8, + rgb2yuv_709_full = 0x2 << 8, + yuv2yuv_601_limit_2_709_limit = 0x3 << 8, + yuv2yuv_601_limit_2_709_full = 0x4 << 8, + yuv2yuv_709_limit_2_601_limit = 0x5 << 8, + yuv2yuv_709_limit_2_601_full = 0x6 << 8, //not support + yuv2yuv_601_full_2_709_limit = 0x7 << 8, + yuv2yuv_601_full_2_709_full = 0x8 << 8, //not support + yuv2yuv_709_full_2_601_limit = 0x9 << 8, //not support + yuv2yuv_709_full_2_601_full = 0xa << 8, //not support + rgb2yuv_709_limit = 0xb << 8, + yuv2rgb_709_full = 0xc << 8, //not support + yuv2yuv_601_limit_2_601_full = 0xd << 8, //not support + yuv2yuv_601_full_2_601_limit = 0xe << 8, //not support + yuv2yuv_709_limit_2_709_full = 0xf << 8, //not support + yuv2yuv_709_full_2_709_limit = 0x10 << 8, //not support + full_csc_mask = 0xf00, +}; + +enum { + RGA3_SCHEDULER_CORE0 = 1 << 0, + RGA3_SCHEDULER_CORE1 = 1 << 1, + RGA2_SCHEDULER_CORE0 = 1 << 2, + RGA2_SCHEDULER_CORE1 = 1 << 3, + RGA_CORE_MASK = 0xf, + RGA_NONE_CORE = 0x0, +}; + +/* RGA3 rd_mode */ +enum +{ + raster_mode = 0x1 << 0, + fbc_mode = 0x1 << 1, + tile_mode = 0x1 << 2, +}; + +#ifdef __cplusplus +} +#endif + +#endif /*_RK29_IPP_DRIVER_H_*/ diff --git a/rknpu2/examples/3rdparty/rga/libs/AndroidNdk/arm64-v8a/librga.a b/rknpu2/examples/3rdparty/rga/libs/AndroidNdk/arm64-v8a/librga.a new file mode 100644 index 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0000000000000000000000000000000000000000..f165a369894f0c7071e3c1d3485f212ecc47c1c0 --- /dev/null +++ b/rknpu2/examples/3rdparty/rk_mpi_mmz/include/rk_mpi_mmz.h @@ -0,0 +1,168 @@ +/* + * Copyright 2020 Rockchip Electronics Co. LTD + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * + */ + +#ifndef __RK_MPI_MMZ_H__ +#define __RK_MPI_MMZ_H__ + +#include + +#ifdef __cplusplus +#if __cplusplus +extern "C" { +#endif +#endif /* End of #ifdef __cplusplus */ + +typedef void* MB_BLK; +typedef int RK_S32; +typedef uint32_t RK_U32; +typedef uint64_t RK_U64; +typedef void RK_VOID; + +#define RK_MMZ_ALLOC_TYPE_IOMMU 0x00000000 +#define RK_MMZ_ALLOC_TYPE_CMA 0x00000001 + +#define RK_MMZ_ALLOC_CACHEABLE 0x00000000 +#define RK_MMZ_ALLOC_UNCACHEABLE 0x00000010 + +#define RK_MMZ_SYNC_READONLY 0x00000000 +#define RK_MMZ_SYNC_WRITEONLY 0x00000001 +#define RK_MMZ_SYNC_RW 0x00000002 + +/* + 申请buffer + pBlk 返回分配的buffer信息 + u32Len 申请buffer的大小 + u32Flags 申请buffer类型 + 成功 返回0 + 失败 返回负值 + */ +RK_S32 RK_MPI_MMZ_Alloc(MB_BLK *pBlk, RK_U32 u32Len, RK_U32 u32Flags); + +/* + 释放buffer + */ +RK_S32 RK_MPI_MMZ_Free(MB_BLK mb); + +/* + 获取物理地址 + 对于物理连续内存，返回其物理地址 + 对于非物理连续内存，返回-1 + */ +RK_U64 RK_MPI_MMZ_Handle2PhysAddr(MB_BLK mb); + +/* + 获取用户空间虚拟地址 + 失败返回NULL + */ +RK_VOID *RK_MPI_MMZ_Handle2VirAddr(MB_BLK mb); + +/* + 获取buffer的fd + 失败返回-1 + */ +RK_S32 RK_MPI_MMZ_Handle2Fd(MB_BLK mb); + +/* + 获取buffer大小 + 失败返回 (RK_U64)-1 + */ +RK_U64 RK_MPI_MMZ_GetSize(MB_BLK mb); + +/* + 通过fd查找到对应的buffer + 成功 返回mb + 失败 返回NULL + */ +MB_BLK RK_MPI_MMZ_Fd2Handle(RK_S32 fd); + +/* + 通过vaddr查找到对应的buffer + 成功 返回mb + 失败 返回NULL + */ +MB_BLK RK_MPI_MMZ_VirAddr2Handle(RK_VOID *pstVirAddr); + +/* + 通过paddr查找到对应的buffer + 成功 返回mb + 失败 返回NULL + */ +MB_BLK RK_MPI_MMZ_PhyAddr2Handle(RK_U64 paddr); + +/* + 查询buffer是否cacheable + 是 返回1 + 否 返回0 + 不确定 返回-1 + */ +RK_S32 RK_MPI_MMZ_IsCacheable(MB_BLK mb); + +/* + flush cache, 在cpu访问前调用 + 当offset和length都等于0时候，执行full sync，否则执行partial sync + 成功 返回0 + 失败 返回负值 + */ +RK_S32 RK_MPI_MMZ_FlushCacheStart(MB_BLK mb, RK_U32 offset, RK_U32 length, RK_U32 flags); + +/* + flush cache, 在cpu访问结束后调用 + 当offset和length都等于0时候，执行full sync，否则执行partial sync + 成功 返回0 + 失败 返回负值 + */ +RK_S32 RK_MPI_MMZ_FlushCacheEnd(MB_BLK mb, RK_U32 offset, RK_U32 length, RK_U32 flags); + +/* + flush cache, 在cpu访问前调用 + 指定待刷新内存的虚拟地址及其长度，只支持partial sync + 成功 返回0 + 失败 返回负值 + */ +RK_S32 RK_MPI_MMZ_FlushCacheVaddrStart(RK_VOID* vaddr, RK_U32 length, RK_U32 flags); + +/* + flush cache, 在cpu访问结束后调用 + 指定待刷新内存的虚拟地址及其长度，只支持partial sync + 成功 返回0 + 失败 返回负值 + */ +RK_S32 RK_MPI_MMZ_FlushCacheVaddrEnd(RK_VOID* vaddr, RK_U32 length, RK_U32 flags); + +/* + flush cache, 在cpu访问前调用 + 指定待刷新内存的物理地址及其长度，只支持partial sync + 成功 返回0 + 失败 返回负值 + */ +RK_S32 RK_MPI_MMZ_FlushCachePaddrStart(RK_U64 vaddr, RK_U32 length, RK_U32 flags); + +/* + flush cache, 在cpu访问结束后调用 + 指定待刷新内存的物理地址及其长度，只支持partial sync + 成功 返回0 + 失败 返回负值 + */ +RK_S32 RK_MPI_MMZ_FlushCachePaddrEnd(RK_U64 vaddr, RK_U32 length, RK_U32 flags); + +#ifdef __cplusplus +#if __cplusplus +} +#endif +#endif /* End of #ifdef __cplusplus */ + +#endif /* __RK_MPI_MMZ_H__ */ \ No newline at end of file diff --git a/rknpu2/examples/3rdparty/rk_mpi_mmz/lib/Android/arm64-v8a/libmpimmz.so b/rknpu2/examples/3rdparty/rk_mpi_mmz/lib/Android/arm64-v8a/libmpimmz.so new file mode 100644 index 0000000000000000000000000000000000000000..bd28c3052c277ddade10bb21cb1b0da6e647d2d4 --- /dev/null +++ b/rknpu2/examples/3rdparty/rk_mpi_mmz/lib/Android/arm64-v8a/libmpimmz.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:90d016b7b94391912ba505f120cb03611e5defc518eed1447e3407e43c64c323 +size 32136 diff --git a/rknpu2/examples/3rdparty/rk_mpi_mmz/lib/Android/armeabi-v7a/libmpimmz.so b/rknpu2/examples/3rdparty/rk_mpi_mmz/lib/Android/armeabi-v7a/libmpimmz.so new file mode 100644 index 0000000000000000000000000000000000000000..be73618b880b0bfa4d933329c58047d00e17dc9a --- /dev/null +++ b/rknpu2/examples/3rdparty/rk_mpi_mmz/lib/Android/armeabi-v7a/libmpimmz.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8d8b1aa6b92eecd568dadbec56b3f50bcb8c2c845bf54c74de971322a499013d +size 238956 diff --git a/rknpu2/examples/3rdparty/rk_mpi_mmz/lib/Linux/aarch64/libmpimmz.so b/rknpu2/examples/3rdparty/rk_mpi_mmz/lib/Linux/aarch64/libmpimmz.so new file mode 100644 index 0000000000000000000000000000000000000000..4348b8abe02abbbcc94e5b3a8a4fb63d79a307eb --- /dev/null +++ b/rknpu2/examples/3rdparty/rk_mpi_mmz/lib/Linux/aarch64/libmpimmz.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9b397ddec65e3448397e4b05b8ff7fcd0d6c3f577292f6307e95f6b84c089c34 +size 45192 diff --git a/rknpu2/examples/3rdparty/rk_mpi_mmz/lib/Linux/armhf/libmpimmz.so b/rknpu2/examples/3rdparty/rk_mpi_mmz/lib/Linux/armhf/libmpimmz.so new file mode 100644 index 0000000000000000000000000000000000000000..f83dd532b8300c0492a9112a1960d5d4541ca82b --- /dev/null +++ b/rknpu2/examples/3rdparty/rk_mpi_mmz/lib/Linux/armhf/libmpimmz.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2be3642d310ef458890551369c42f8a802b196e9f3b1bc7996c54066da91a014 +size 35116 diff --git a/rknpu2/examples/3rdparty/rk_mpi_mmz/readme.txt b/rknpu2/examples/3rdparty/rk_mpi_mmz/readme.txt new file mode 100644 index 0000000000000000000000000000000000000000..e38c6712f911a99a631b2a310d01e148928c4e12 --- /dev/null +++ b/rknpu2/examples/3rdparty/rk_mpi_mmz/readme.txt @@ -0,0 +1 @@ +version:1.6 diff --git a/rknpu2/examples/3rdparty/stb/stb_image.h b/rknpu2/examples/3rdparty/stb/stb_image.h new file mode 100644 index 0000000000000000000000000000000000000000..196dfd5cc6ee3be167ce6280fb243327e77f4245 --- /dev/null +++ b/rknpu2/examples/3rdparty/stb/stb_image.h @@ -0,0 +1,7559 @@ +/* stb_image - v2.23 - public domain image loader - http://nothings.org/stb + no warranty implied; use at your own risk + + Do this: + #define STB_IMAGE_IMPLEMENTATION + before you include this file in *one* C or C++ file to create the implementation. + + // i.e. it should look like this: + #include ... + #include ... + #include ... + #define STB_IMAGE_IMPLEMENTATION + #include "stb_image.h" + + You can #define STBI_ASSERT(x) before the #include to avoid using assert.h. + And #define STBI_MALLOC, STBI_REALLOC, and STBI_FREE to avoid using malloc,realloc,free + + + QUICK NOTES: + Primarily of interest to game developers and other people who can + avoid problematic images and only need the trivial interface + + JPEG baseline & progressive (12 bpc/arithmetic not supported, same as stock IJG lib) + PNG 1/2/4/8/16-bit-per-channel + + TGA (not sure what subset, if a subset) + BMP non-1bpp, non-RLE + PSD (composited view only, no extra channels, 8/16 bit-per-channel) + + GIF (*comp always reports as 4-channel) + HDR (radiance rgbE format) + PIC (Softimage PIC) + PNM (PPM and PGM binary only) + + Animated GIF still needs a proper API, but here's one way to do it: + http://gist.github.com/urraka/685d9a6340b26b830d49 + + - decode from memory or through FILE (define STBI_NO_STDIO to remove code) + - decode from arbitrary I/O callbacks + - SIMD acceleration on x86/x64 (SSE2) and ARM (NEON) + + Full documentation under "DOCUMENTATION" below. + + +LICENSE + + See end of file for license information. + +RECENT REVISION HISTORY: + + 2.23 (2019-08-11) fix clang static analysis warning + 2.22 (2019-03-04) gif fixes, fix warnings + 2.21 (2019-02-25) fix typo in comment + 2.20 (2019-02-07) support utf8 filenames in Windows; fix warnings and platform ifdefs + 2.19 (2018-02-11) fix warning + 2.18 (2018-01-30) fix warnings + 2.17 (2018-01-29) bugfix, 1-bit BMP, 16-bitness query, fix warnings + 2.16 (2017-07-23) all functions have 16-bit variants; optimizations; bugfixes + 2.15 (2017-03-18) fix png-1,2,4; all Imagenet JPGs; no runtime SSE detection on GCC + 2.14 (2017-03-03) remove deprecated STBI_JPEG_OLD; fixes for Imagenet JPGs + 2.13 (2016-12-04) experimental 16-bit API, only for PNG so far; fixes + 2.12 (2016-04-02) fix typo in 2.11 PSD fix that caused crashes + 2.11 (2016-04-02) 16-bit PNGS; enable SSE2 in non-gcc x64 + RGB-format JPEG; remove white matting in PSD; + allocate large structures on the stack; + correct channel count for PNG & BMP + 2.10 (2016-01-22) avoid warning introduced in 2.09 + 2.09 (2016-01-16) 16-bit TGA; comments in PNM files; STBI_REALLOC_SIZED + + See end of file for full revision history. + + + ============================ Contributors ========================= + + Image formats Extensions, features + Sean Barrett (jpeg, png, bmp) Jetro Lauha (stbi_info) + Nicolas Schulz (hdr, psd) Martin "SpartanJ" Golini (stbi_info) + Jonathan Dummer (tga) James "moose2000" Brown (iPhone PNG) + Jean-Marc Lienher (gif) Ben "Disch" Wenger (io callbacks) + Tom Seddon (pic) Omar Cornut (1/2/4-bit PNG) + Thatcher Ulrich (psd) Nicolas Guillemot (vertical flip) + Ken Miller (pgm, ppm) Richard Mitton (16-bit PSD) + github:urraka (animated gif) Junggon Kim (PNM comments) + Christopher Forseth (animated gif) Daniel Gibson (16-bit TGA) + socks-the-fox (16-bit PNG) + Jeremy Sawicki (handle all ImageNet JPGs) + Optimizations & bugfixes Mikhail Morozov (1-bit BMP) + Fabian "ryg" Giesen Anael Seghezzi (is-16-bit query) + Arseny Kapoulkine + John-Mark Allen + Carmelo J Fdez-Aguera + + Bug & warning fixes + Marc LeBlanc David Woo Guillaume George Martins Mozeiko + Christpher Lloyd Jerry Jansson Joseph Thomson Phil Jordan + Dave Moore Roy Eltham Hayaki Saito Nathan Reed + Won Chun Luke Graham Johan Duparc Nick Verigakis + the Horde3D community Thomas Ruf Ronny Chevalier github:rlyeh + Janez Zemva John Bartholomew Michal Cichon github:romigrou + Jonathan Blow Ken Hamada Tero Hanninen github:svdijk + Laurent Gomila Cort Stratton Sergio Gonzalez github:snagar + Aruelien Pocheville Thibault Reuille Cass Everitt github:Zelex + Ryamond Barbiero Paul Du Bois Engin Manap github:grim210 + Aldo Culquicondor Philipp Wiesemann Dale Weiler github:sammyhw + Oriol Ferrer Mesia Josh Tobin Matthew Gregan github:phprus + Julian Raschke Gregory Mullen Baldur Karlsson github:poppolopoppo + Christian Floisand Kevin Schmidt JR Smith github:darealshinji + Blazej Dariusz Roszkowski github:Michaelangel007 +*/ + +#ifndef STBI_INCLUDE_STB_IMAGE_H +#define STBI_INCLUDE_STB_IMAGE_H + +// DOCUMENTATION +// +// Limitations: +// - no 12-bit-per-channel JPEG +// - no JPEGs with arithmetic coding +// - GIF always returns *comp=4 +// +// Basic usage (see HDR discussion below for HDR usage): +// int x,y,n; +// unsigned char *data = stbi_load(filename, &x, &y, &n, 0); +// // ... process data if not NULL ... +// // ... x = width, y = height, n = # 8-bit components per pixel ... +// // ... replace '0' with '1'..'4' to force that many components per pixel +// // ... but 'n' will always be the number that it would have been if you said 0 +// stbi_image_free(data) +// +// Standard parameters: +// int *x -- outputs image width in pixels +// int *y -- outputs image height in pixels +// int *channels_in_file -- outputs # of image components in image file +// int desired_channels -- if non-zero, # of image components requested in result +// +// The return value from an image loader is an 'unsigned char *' which points +// to the pixel data, or NULL on an allocation failure or if the image is +// corrupt or invalid. The pixel data consists of *y scanlines of *x pixels, +// with each pixel consisting of N interleaved 8-bit components; the first +// pixel pointed to is top-left-most in the image. There is no padding between +// image scanlines or between pixels, regardless of format. The number of +// components N is 'desired_channels' if desired_channels is non-zero, or +// *channels_in_file otherwise. If desired_channels is non-zero, +// *channels_in_file has the number of components that _would_ have been +// output otherwise. E.g. if you set desired_channels to 4, you will always +// get RGBA output, but you can check *channels_in_file to see if it's trivially +// opaque because e.g. there were only 3 channels in the source image. +// +// An output image with N components has the following components interleaved +// in this order in each pixel: +// +// N=#comp components +// 1 grey +// 2 grey, alpha +// 3 red, green, blue +// 4 red, green, blue, alpha +// +// If image loading fails for any reason, the return value will be NULL, +// and *x, *y, *channels_in_file will be unchanged. The function +// stbi_failure_reason() can be queried for an extremely brief, end-user +// unfriendly explanation of why the load failed. Define STBI_NO_FAILURE_STRINGS +// to avoid compiling these strings at all, and STBI_FAILURE_USERMSG to get slightly +// more user-friendly ones. +// +// Paletted PNG, BMP, GIF, and PIC images are automatically depalettized. +// +// =========================================================================== +// +// UNICODE: +// +// If compiling for Windows and you wish to use Unicode filenames, compile +// with +// #define STBI_WINDOWS_UTF8 +// and pass utf8-encoded filenames. Call stbi_convert_wchar_to_utf8 to convert +// Windows wchar_t filenames to utf8. +// +// =========================================================================== +// +// Philosophy +// +// stb libraries are designed with the following priorities: +// +// 1. easy to use +// 2. easy to maintain +// 3. good performance +// +// Sometimes I let "good performance" creep up in priority over "easy to maintain", +// and for best performance I may provide less-easy-to-use APIs that give higher +// performance, in addition to the easy-to-use ones. Nevertheless, it's important +// to keep in mind that from the standpoint of you, a client of this library, +// all you care about is #1 and #3, and stb libraries DO NOT emphasize #3 above all. +// +// Some secondary priorities arise directly from the first two, some of which +// provide more explicit reasons why performance can't be emphasized. +// +// - Portable ("ease of use") +// - Small source code footprint ("easy to maintain") +// - No dependencies ("ease of use") +// +// =========================================================================== +// +// I/O callbacks +// +// I/O callbacks allow you to read from arbitrary sources, like packaged +// files or some other source. Data read from callbacks are processed +// through a small internal buffer (currently 128 bytes) to try to reduce +// overhead. +// +// The three functions you must define are "read" (reads some bytes of data), +// "skip" (skips some bytes of data), "eof" (reports if the stream is at the end). +// +// =========================================================================== +// +// SIMD support +// +// The JPEG decoder will try to automatically use SIMD kernels on x86 when +// supported by the compiler. For ARM Neon support, you must explicitly +// request it. +// +// (The old do-it-yourself SIMD API is no longer supported in the current +// code.) +// +// On x86, SSE2 will automatically be used when available based on a run-time +// test; if not, the generic C versions are used as a fall-back. On ARM targets, +// the typical path is to have separate builds for NEON and non-NEON devices +// (at least this is true for iOS and Android). Therefore, the NEON support is +// toggled by a build flag: define STBI_NEON to get NEON loops. +// +// If for some reason you do not want to use any of SIMD code, or if +// you have issues compiling it, you can disable it entirely by +// defining STBI_NO_SIMD. +// +// =========================================================================== +// +// HDR image support (disable by defining STBI_NO_HDR) +// +// stb_image supports loading HDR images in general, and currently the Radiance +// .HDR file format specifically. You can still load any file through the existing +// interface; if you attempt to load an HDR file, it will be automatically remapped +// to LDR, assuming gamma 2.2 and an arbitrary scale factor defaulting to 1; +// both of these constants can be reconfigured through this interface: +// +// stbi_hdr_to_ldr_gamma(2.2f); +// stbi_hdr_to_ldr_scale(1.0f); +// +// (note, do not use _inverse_ constants; stbi_image will invert them +// appropriately). +// +// Additionally, there is a new, parallel interface for loading files as +// (linear) floats to preserve the full dynamic range: +// +// float *data = stbi_loadf(filename, &x, &y, &n, 0); +// +// If you load LDR images through this interface, those images will +// be promoted to floating point values, run through the inverse of +// constants corresponding to the above: +// +// stbi_ldr_to_hdr_scale(1.0f); +// stbi_ldr_to_hdr_gamma(2.2f); +// +// Finally, given a filename (or an open file or memory block--see header +// file for details) containing image data, you can query for the "most +// appropriate" interface to use (that is, whether the image is HDR or +// not), using: +// +// stbi_is_hdr(char *filename); +// +// =========================================================================== +// +// iPhone PNG support: +// +// By default we convert iphone-formatted PNGs back to RGB, even though +// they are internally encoded differently. You can disable this conversion +// by calling stbi_convert_iphone_png_to_rgb(0), in which case +// you will always just get the native iphone "format" through (which +// is BGR stored in RGB). +// +// Call stbi_set_unpremultiply_on_load(1) as well to force a divide per +// pixel to remove any premultiplied alpha *only* if the image file explicitly +// says there's premultiplied data (currently only happens in iPhone images, +// and only if iPhone convert-to-rgb processing is on). +// +// =========================================================================== +// +// ADDITIONAL CONFIGURATION +// +// - You can suppress implementation of any of the decoders to reduce +// your code footprint by #defining one or more of the following +// symbols before creating the implementation. +// +// STBI_NO_JPEG +// STBI_NO_PNG +// STBI_NO_BMP +// STBI_NO_PSD +// STBI_NO_TGA +// STBI_NO_GIF +// STBI_NO_HDR +// STBI_NO_PIC +// STBI_NO_PNM (.ppm and .pgm) +// +// - You can request *only* certain decoders and suppress all other ones +// (this will be more forward-compatible, as addition of new decoders +// doesn't require you to disable them explicitly): +// +// STBI_ONLY_JPEG +// STBI_ONLY_PNG +// STBI_ONLY_BMP +// STBI_ONLY_PSD +// STBI_ONLY_TGA +// STBI_ONLY_GIF +// STBI_ONLY_HDR +// STBI_ONLY_PIC +// STBI_ONLY_PNM (.ppm and .pgm) +// +// - If you use STBI_NO_PNG (or _ONLY_ without PNG), and you still +// want the zlib decoder to be available, #define STBI_SUPPORT_ZLIB +// + + +#ifndef STBI_NO_STDIO +#include +#endif // STBI_NO_STDIO + +#define STBI_VERSION 1 + +enum +{ + STBI_default = 0, // only used for desired_channels + + STBI_grey = 1, + STBI_grey_alpha = 2, + STBI_rgb = 3, + STBI_rgb_alpha = 4 +}; + +#include +typedef unsigned char stbi_uc; +typedef unsigned short stbi_us; + +#ifdef __cplusplus +extern "C" { +#endif + +#ifndef STBIDEF +#ifdef STB_IMAGE_STATIC +#define STBIDEF static +#else +#define STBIDEF extern +#endif +#endif + +////////////////////////////////////////////////////////////////////////////// +// +// PRIMARY API - works on images of any type +// + +// +// load image by filename, open file, or memory buffer +// + +typedef struct +{ + int (*read) (void *user,char *data,int size); // fill 'data' with 'size' bytes. return number of bytes actually read + void (*skip) (void *user,int n); // skip the next 'n' bytes, or 'unget' the last -n bytes if negative + int (*eof) (void *user); // returns nonzero if we are at end of file/data +} stbi_io_callbacks; + +//////////////////////////////////// +// +// 8-bits-per-channel interface +// + +STBIDEF stbi_uc *stbi_load_from_memory (stbi_uc const *buffer, int len , int *x, int *y, int *channels_in_file, int desired_channels); +STBIDEF stbi_uc *stbi_load_from_callbacks(stbi_io_callbacks const *clbk , void *user, int *x, int *y, int *channels_in_file, int desired_channels); + +#ifndef STBI_NO_STDIO +STBIDEF stbi_uc *stbi_load (char const *filename, int *x, int *y, int *channels_in_file, int desired_channels); +STBIDEF stbi_uc *stbi_load_from_file (FILE *f, int *x, int *y, int *channels_in_file, int desired_channels); +// for stbi_load_from_file, file pointer is left pointing immediately after image +#endif + +#ifndef STBI_NO_GIF +STBIDEF stbi_uc *stbi_load_gif_from_memory(stbi_uc const *buffer, int len, int **delays, int *x, int *y, int *z, int *comp, int req_comp); +#endif + +#ifdef STBI_WINDOWS_UTF8 +STBIDEF int stbi_convert_wchar_to_utf8(char *buffer, size_t bufferlen, const wchar_t* input); +#endif + +//////////////////////////////////// +// +// 16-bits-per-channel interface +// + +STBIDEF stbi_us *stbi_load_16_from_memory (stbi_uc const *buffer, int len, int *x, int *y, int *channels_in_file, int desired_channels); +STBIDEF stbi_us *stbi_load_16_from_callbacks(stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *channels_in_file, int desired_channels); + +#ifndef STBI_NO_STDIO +STBIDEF stbi_us *stbi_load_16 (char const *filename, int *x, int *y, int *channels_in_file, int desired_channels); +STBIDEF stbi_us *stbi_load_from_file_16(FILE *f, int *x, int *y, int *channels_in_file, int desired_channels); +#endif + +//////////////////////////////////// +// +// float-per-channel interface +// +#ifndef STBI_NO_LINEAR + STBIDEF float *stbi_loadf_from_memory (stbi_uc const *buffer, int len, int *x, int *y, int *channels_in_file, int desired_channels); + STBIDEF float *stbi_loadf_from_callbacks (stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *channels_in_file, int desired_channels); + + #ifndef STBI_NO_STDIO + STBIDEF float *stbi_loadf (char const *filename, int *x, int *y, int *channels_in_file, int desired_channels); + STBIDEF float *stbi_loadf_from_file (FILE *f, int *x, int *y, int *channels_in_file, int desired_channels); + #endif +#endif + +#ifndef STBI_NO_HDR + STBIDEF void stbi_hdr_to_ldr_gamma(float gamma); + STBIDEF void stbi_hdr_to_ldr_scale(float scale); +#endif // STBI_NO_HDR + +#ifndef STBI_NO_LINEAR + STBIDEF void stbi_ldr_to_hdr_gamma(float gamma); + STBIDEF void stbi_ldr_to_hdr_scale(float scale); +#endif // STBI_NO_LINEAR + +// stbi_is_hdr is always defined, but always returns false if STBI_NO_HDR +STBIDEF int stbi_is_hdr_from_callbacks(stbi_io_callbacks const *clbk, void *user); +STBIDEF int stbi_is_hdr_from_memory(stbi_uc const *buffer, int len); +#ifndef STBI_NO_STDIO +STBIDEF int stbi_is_hdr (char const *filename); +STBIDEF int stbi_is_hdr_from_file(FILE *f); +#endif // STBI_NO_STDIO + + +// get a VERY brief reason for failure +// NOT THREADSAFE +STBIDEF const char *stbi_failure_reason (void); + +// free the loaded image -- this is just free() +STBIDEF void stbi_image_free (void *retval_from_stbi_load); + +// get image dimensions & components without fully decoding +STBIDEF int stbi_info_from_memory(stbi_uc const *buffer, int len, int *x, int *y, int *comp); +STBIDEF int stbi_info_from_callbacks(stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *comp); +STBIDEF int stbi_is_16_bit_from_memory(stbi_uc const *buffer, int len); +STBIDEF int stbi_is_16_bit_from_callbacks(stbi_io_callbacks const *clbk, void *user); + +#ifndef STBI_NO_STDIO +STBIDEF int stbi_info (char const *filename, int *x, int *y, int *comp); +STBIDEF int stbi_info_from_file (FILE *f, int *x, int *y, int *comp); +STBIDEF int stbi_is_16_bit (char const *filename); +STBIDEF int stbi_is_16_bit_from_file(FILE *f); +#endif + + + +// for image formats that explicitly notate that they have premultiplied alpha, +// we just return the colors as stored in the file. set this flag to force +// unpremultiplication. results are undefined if the unpremultiply overflow. +STBIDEF void stbi_set_unpremultiply_on_load(int flag_true_if_should_unpremultiply); + +// indicate whether we should process iphone images back to canonical format, +// or just pass them through "as-is" +STBIDEF void stbi_convert_iphone_png_to_rgb(int flag_true_if_should_convert); + +// flip the image vertically, so the first pixel in the output array is the bottom left +STBIDEF void stbi_set_flip_vertically_on_load(int flag_true_if_should_flip); + +// ZLIB client - used by PNG, available for other purposes + +STBIDEF char *stbi_zlib_decode_malloc_guesssize(const char *buffer, int len, int initial_size, int *outlen); +STBIDEF char *stbi_zlib_decode_malloc_guesssize_headerflag(const char *buffer, int len, int initial_size, int *outlen, int parse_header); +STBIDEF char *stbi_zlib_decode_malloc(const char *buffer, int len, int *outlen); +STBIDEF int stbi_zlib_decode_buffer(char *obuffer, int olen, const char *ibuffer, int ilen); + +STBIDEF char *stbi_zlib_decode_noheader_malloc(const char *buffer, int len, int *outlen); +STBIDEF int stbi_zlib_decode_noheader_buffer(char *obuffer, int olen, const char *ibuffer, int ilen); + + +#ifdef __cplusplus +} +#endif + +// +// +//// end header file ///////////////////////////////////////////////////// +#endif // STBI_INCLUDE_STB_IMAGE_H + +#ifdef STB_IMAGE_IMPLEMENTATION + +#if defined(STBI_ONLY_JPEG) || defined(STBI_ONLY_PNG) || defined(STBI_ONLY_BMP) \ + || defined(STBI_ONLY_TGA) || defined(STBI_ONLY_GIF) || defined(STBI_ONLY_PSD) \ + || defined(STBI_ONLY_HDR) || defined(STBI_ONLY_PIC) || defined(STBI_ONLY_PNM) \ + || defined(STBI_ONLY_ZLIB) + #ifndef STBI_ONLY_JPEG + #define STBI_NO_JPEG + #endif + #ifndef STBI_ONLY_PNG + #define STBI_NO_PNG + #endif + #ifndef STBI_ONLY_BMP + #define STBI_NO_BMP + #endif + #ifndef STBI_ONLY_PSD + #define STBI_NO_PSD + #endif + #ifndef STBI_ONLY_TGA + #define STBI_NO_TGA + #endif + #ifndef STBI_ONLY_GIF + #define STBI_NO_GIF + #endif + #ifndef STBI_ONLY_HDR + #define STBI_NO_HDR + #endif + #ifndef STBI_ONLY_PIC + #define STBI_NO_PIC + #endif + #ifndef STBI_ONLY_PNM + #define STBI_NO_PNM + #endif +#endif + +#if defined(STBI_NO_PNG) && !defined(STBI_SUPPORT_ZLIB) && !defined(STBI_NO_ZLIB) +#define STBI_NO_ZLIB +#endif + + +#include +#include // ptrdiff_t on osx +#include +#include +#include + +#if !defined(STBI_NO_LINEAR) || !defined(STBI_NO_HDR) +#include // ldexp, pow +#endif + +#ifndef STBI_NO_STDIO +#include +#endif + +#ifndef STBI_ASSERT +#include +#define STBI_ASSERT(x) assert(x) +#endif + +#ifdef __cplusplus +#define STBI_EXTERN extern "C" +#else +#define STBI_EXTERN extern +#endif + + +#ifndef _MSC_VER + #ifdef __cplusplus + #define stbi_inline inline + #else + #define stbi_inline + #endif +#else + #define stbi_inline __forceinline +#endif + + +#ifdef _MSC_VER +typedef unsigned short stbi__uint16; +typedef signed short stbi__int16; +typedef unsigned int stbi__uint32; +typedef signed int stbi__int32; +#else +#include +typedef uint16_t stbi__uint16; +typedef int16_t stbi__int16; +typedef uint32_t stbi__uint32; +typedef int32_t stbi__int32; +#endif + +// should produce compiler error if size is wrong +typedef unsigned char validate_uint32[sizeof(stbi__uint32)==4 ? 1 : -1]; + +#ifdef _MSC_VER +#define STBI_NOTUSED(v) (void)(v) +#else +#define STBI_NOTUSED(v) (void)sizeof(v) +#endif + +#ifdef _MSC_VER +#define STBI_HAS_LROTL +#endif + +#ifdef STBI_HAS_LROTL + #define stbi_lrot(x,y) _lrotl(x,y) +#else + #define stbi_lrot(x,y) (((x) << (y)) | ((x) >> (32 - (y)))) +#endif + +#if defined(STBI_MALLOC) && defined(STBI_FREE) && (defined(STBI_REALLOC) || defined(STBI_REALLOC_SIZED)) +// ok +#elif !defined(STBI_MALLOC) && !defined(STBI_FREE) && !defined(STBI_REALLOC) && !defined(STBI_REALLOC_SIZED) +// ok +#else +#error "Must define all or none of STBI_MALLOC, STBI_FREE, and STBI_REALLOC (or STBI_REALLOC_SIZED)." +#endif + +#ifndef STBI_MALLOC +#define STBI_MALLOC(sz) malloc(sz) +#define STBI_REALLOC(p,newsz) realloc(p,newsz) +#define STBI_FREE(p) free(p) +#endif + +#ifndef STBI_REALLOC_SIZED +#define STBI_REALLOC_SIZED(p,oldsz,newsz) STBI_REALLOC(p,newsz) +#endif + +// x86/x64 detection +#if defined(__x86_64__) || defined(_M_X64) +#define STBI__X64_TARGET +#elif defined(__i386) || defined(_M_IX86) +#define STBI__X86_TARGET +#endif + +#if defined(__GNUC__) && defined(STBI__X86_TARGET) && !defined(__SSE2__) && !defined(STBI_NO_SIMD) +// gcc doesn't support sse2 intrinsics unless you compile with -msse2, +// which in turn means it gets to use SSE2 everywhere. This is unfortunate, +// but previous attempts to provide the SSE2 functions with runtime +// detection caused numerous issues. The way architecture extensions are +// exposed in GCC/Clang is, sadly, not really suited for one-file libs. +// New behavior: if compiled with -msse2, we use SSE2 without any +// detection; if not, we don't use it at all. +#define STBI_NO_SIMD +#endif + +#if defined(__MINGW32__) && defined(STBI__X86_TARGET) && !defined(STBI_MINGW_ENABLE_SSE2) && !defined(STBI_NO_SIMD) +// Note that __MINGW32__ doesn't actually mean 32-bit, so we have to avoid STBI__X64_TARGET +// +// 32-bit MinGW wants ESP to be 16-byte aligned, but this is not in the +// Windows ABI and VC++ as well as Windows DLLs don't maintain that invariant. +// As a result, enabling SSE2 on 32-bit MinGW is dangerous when not +// simultaneously enabling "-mstackrealign". +// +// See https://github.com/nothings/stb/issues/81 for more information. +// +// So default to no SSE2 on 32-bit MinGW. If you've read this far and added +// -mstackrealign to your build settings, feel free to #define STBI_MINGW_ENABLE_SSE2. +#define STBI_NO_SIMD +#endif + +#if !defined(STBI_NO_SIMD) && (defined(STBI__X86_TARGET) || defined(STBI__X64_TARGET)) +#define STBI_SSE2 +#include + +#ifdef _MSC_VER + +#if _MSC_VER >= 1400 // not VC6 +#include // __cpuid +static int stbi__cpuid3(void) +{ + int info[4]; + __cpuid(info,1); + return info[3]; +} +#else +static int stbi__cpuid3(void) +{ + int res; + __asm { + mov eax,1 + cpuid + mov res,edx + } + return res; +} +#endif + +#define STBI_SIMD_ALIGN(type, name) __declspec(align(16)) type name + +#if !defined(STBI_NO_JPEG) && defined(STBI_SSE2) +static int stbi__sse2_available(void) +{ + int info3 = stbi__cpuid3(); + return ((info3 >> 26) & 1) != 0; +} +#endif + +#else // assume GCC-style if not VC++ +#define STBI_SIMD_ALIGN(type, name) type name __attribute__((aligned(16))) + +#if !defined(STBI_NO_JPEG) && defined(STBI_SSE2) +static int stbi__sse2_available(void) +{ + // If we're even attempting to compile this on GCC/Clang, that means + // -msse2 is on, which means the compiler is allowed to use SSE2 + // instructions at will, and so are we. + return 1; +} +#endif + +#endif +#endif + +// ARM NEON +#if defined(STBI_NO_SIMD) && defined(STBI_NEON) +#undef STBI_NEON +#endif + +#ifdef STBI_NEON +#include +// assume GCC or Clang on ARM targets +#define STBI_SIMD_ALIGN(type, name) type name __attribute__((aligned(16))) +#endif + +#ifndef STBI_SIMD_ALIGN +#define STBI_SIMD_ALIGN(type, name) type name +#endif + +/////////////////////////////////////////////// +// +// stbi__context struct and start_xxx functions + +// stbi__context structure is our basic context used by all images, so it +// contains all the IO context, plus some basic image information +typedef struct +{ + stbi__uint32 img_x, img_y; + int img_n, img_out_n; + + stbi_io_callbacks io; + void *io_user_data; + + int read_from_callbacks; + int buflen; + stbi_uc buffer_start[128]; + + stbi_uc *img_buffer, *img_buffer_end; + stbi_uc *img_buffer_original, *img_buffer_original_end; +} stbi__context; + + +static void stbi__refill_buffer(stbi__context *s); + +// initialize a memory-decode context +static void stbi__start_mem(stbi__context *s, stbi_uc const *buffer, int len) +{ + s->io.read = NULL; + s->read_from_callbacks = 0; + s->img_buffer = s->img_buffer_original = (stbi_uc *) buffer; + s->img_buffer_end = s->img_buffer_original_end = (stbi_uc *) buffer+len; +} + +// initialize a callback-based context +static void stbi__start_callbacks(stbi__context *s, stbi_io_callbacks *c, void *user) +{ + s->io = *c; + s->io_user_data = user; + s->buflen = sizeof(s->buffer_start); + s->read_from_callbacks = 1; + s->img_buffer_original = s->buffer_start; + stbi__refill_buffer(s); + s->img_buffer_original_end = s->img_buffer_end; +} + +#ifndef STBI_NO_STDIO + +static int stbi__stdio_read(void *user, char *data, int size) +{ + return (int) fread(data,1,size,(FILE*) user); +} + +static void stbi__stdio_skip(void *user, int n) +{ + fseek((FILE*) user, n, SEEK_CUR); +} + +static int stbi__stdio_eof(void *user) +{ + return feof((FILE*) user); +} + +static stbi_io_callbacks stbi__stdio_callbacks = +{ + stbi__stdio_read, + stbi__stdio_skip, + stbi__stdio_eof, +}; + +static void stbi__start_file(stbi__context *s, FILE *f) +{ + stbi__start_callbacks(s, &stbi__stdio_callbacks, (void *) f); +} + +//static void stop_file(stbi__context *s) { } + +#endif // !STBI_NO_STDIO + +static void stbi__rewind(stbi__context *s) +{ + // conceptually rewind SHOULD rewind to the beginning of the stream, + // but we just rewind to the beginning of the initial buffer, because + // we only use it after doing 'test', which only ever looks at at most 92 bytes + s->img_buffer = s->img_buffer_original; + s->img_buffer_end = s->img_buffer_original_end; +} + +enum +{ + STBI_ORDER_RGB, + STBI_ORDER_BGR +}; + +typedef struct +{ + int bits_per_channel; + int num_channels; + int channel_order; +} stbi__result_info; + +#ifndef STBI_NO_JPEG +static int stbi__jpeg_test(stbi__context *s); +static void *stbi__jpeg_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); +static int stbi__jpeg_info(stbi__context *s, int *x, int *y, int *comp); +#endif + +#ifndef STBI_NO_PNG +static int stbi__png_test(stbi__context *s); +static void *stbi__png_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); +static int stbi__png_info(stbi__context *s, int *x, int *y, int *comp); +static int stbi__png_is16(stbi__context *s); +#endif + +#ifndef STBI_NO_BMP +static int stbi__bmp_test(stbi__context *s); +static void *stbi__bmp_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); +static int stbi__bmp_info(stbi__context *s, int *x, int *y, int *comp); +#endif + +#ifndef STBI_NO_TGA +static int stbi__tga_test(stbi__context *s); +static void *stbi__tga_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); +static int stbi__tga_info(stbi__context *s, int *x, int *y, int *comp); +#endif + +#ifndef STBI_NO_PSD +static int stbi__psd_test(stbi__context *s); +static void *stbi__psd_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri, int bpc); +static int stbi__psd_info(stbi__context *s, int *x, int *y, int *comp); +static int stbi__psd_is16(stbi__context *s); +#endif + +#ifndef STBI_NO_HDR +static int stbi__hdr_test(stbi__context *s); +static float *stbi__hdr_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); +static int stbi__hdr_info(stbi__context *s, int *x, int *y, int *comp); +#endif + +#ifndef STBI_NO_PIC +static int stbi__pic_test(stbi__context *s); +static void *stbi__pic_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); +static int stbi__pic_info(stbi__context *s, int *x, int *y, int *comp); +#endif + +#ifndef STBI_NO_GIF +static int stbi__gif_test(stbi__context *s); +static void *stbi__gif_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); +static void *stbi__load_gif_main(stbi__context *s, int **delays, int *x, int *y, int *z, int *comp, int req_comp); +static int stbi__gif_info(stbi__context *s, int *x, int *y, int *comp); +#endif + +#ifndef STBI_NO_PNM +static int stbi__pnm_test(stbi__context *s); +static void *stbi__pnm_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); +static int stbi__pnm_info(stbi__context *s, int *x, int *y, int *comp); +#endif + +// this is not threadsafe +static const char *stbi__g_failure_reason; + +STBIDEF const char *stbi_failure_reason(void) +{ + return stbi__g_failure_reason; +} + +static int stbi__err(const char *str) +{ + stbi__g_failure_reason = str; + return 0; +} + +static void *stbi__malloc(size_t size) +{ + return STBI_MALLOC(size); +} + +// stb_image uses ints pervasively, including for offset calculations. +// therefore the largest decoded image size we can support with the +// current code, even on 64-bit targets, is INT_MAX. this is not a +// significant limitation for the intended use case. +// +// we do, however, need to make sure our size calculations don't +// overflow. hence a few helper functions for size calculations that +// multiply integers together, making sure that they're non-negative +// and no overflow occurs. + +// return 1 if the sum is valid, 0 on overflow. +// negative terms are considered invalid. +static int stbi__addsizes_valid(int a, int b) +{ + if (b < 0) return 0; + // now 0 <= b <= INT_MAX, hence also + // 0 <= INT_MAX - b <= INTMAX. + // And "a + b <= INT_MAX" (which might overflow) is the + // same as a <= INT_MAX - b (no overflow) + return a <= INT_MAX - b; +} + +// returns 1 if the product is valid, 0 on overflow. +// negative factors are considered invalid. +static int stbi__mul2sizes_valid(int a, int b) +{ + if (a < 0 || b < 0) return 0; + if (b == 0) return 1; // mul-by-0 is always safe + // portable way to check for no overflows in a*b + return a <= INT_MAX/b; +} + +// returns 1 if "a*b + add" has no negative terms/factors and doesn't overflow +static int stbi__mad2sizes_valid(int a, int b, int add) +{ + return stbi__mul2sizes_valid(a, b) && stbi__addsizes_valid(a*b, add); +} + +// returns 1 if "a*b*c + add" has no negative terms/factors and doesn't overflow +static int stbi__mad3sizes_valid(int a, int b, int c, int add) +{ + return stbi__mul2sizes_valid(a, b) && stbi__mul2sizes_valid(a*b, c) && + stbi__addsizes_valid(a*b*c, add); +} + +// returns 1 if "a*b*c*d + add" has no negative terms/factors and doesn't overflow +#if !defined(STBI_NO_LINEAR) || !defined(STBI_NO_HDR) +static int stbi__mad4sizes_valid(int a, int b, int c, int d, int add) +{ + return stbi__mul2sizes_valid(a, b) && stbi__mul2sizes_valid(a*b, c) && + stbi__mul2sizes_valid(a*b*c, d) && stbi__addsizes_valid(a*b*c*d, add); +} +#endif + +// mallocs with size overflow checking +static void *stbi__malloc_mad2(int a, int b, int add) +{ + if (!stbi__mad2sizes_valid(a, b, add)) return NULL; + return stbi__malloc(a*b + add); +} + +static void *stbi__malloc_mad3(int a, int b, int c, int add) +{ + if (!stbi__mad3sizes_valid(a, b, c, add)) return NULL; + return stbi__malloc(a*b*c + add); +} + +#if !defined(STBI_NO_LINEAR) || !defined(STBI_NO_HDR) +static void *stbi__malloc_mad4(int a, int b, int c, int d, int add) +{ + if (!stbi__mad4sizes_valid(a, b, c, d, add)) return NULL; + return stbi__malloc(a*b*c*d + add); +} +#endif + +// stbi__err - error +// stbi__errpf - error returning pointer to float +// stbi__errpuc - error returning pointer to unsigned char + +#ifdef STBI_NO_FAILURE_STRINGS + #define stbi__err(x,y) 0 +#elif defined(STBI_FAILURE_USERMSG) + #define stbi__err(x,y) stbi__err(y) +#else + #define stbi__err(x,y) stbi__err(x) +#endif + +#define stbi__errpf(x,y) ((float *)(size_t) (stbi__err(x,y)?NULL:NULL)) +#define stbi__errpuc(x,y) ((unsigned char *)(size_t) (stbi__err(x,y)?NULL:NULL)) + +STBIDEF void stbi_image_free(void *retval_from_stbi_load) +{ + STBI_FREE(retval_from_stbi_load); +} + +#ifndef STBI_NO_LINEAR +static float *stbi__ldr_to_hdr(stbi_uc *data, int x, int y, int comp); +#endif + +#ifndef STBI_NO_HDR +static stbi_uc *stbi__hdr_to_ldr(float *data, int x, int y, int comp); +#endif + +static int stbi__vertically_flip_on_load = 0; + +STBIDEF void stbi_set_flip_vertically_on_load(int flag_true_if_should_flip) +{ + stbi__vertically_flip_on_load = flag_true_if_should_flip; +} + +static void *stbi__load_main(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri, int bpc) +{ + memset(ri, 0, sizeof(*ri)); // make sure it's initialized if we add new fields + ri->bits_per_channel = 8; // default is 8 so most paths don't have to be changed + ri->channel_order = STBI_ORDER_RGB; // all current input & output are this, but this is here so we can add BGR order + ri->num_channels = 0; + + #ifndef STBI_NO_JPEG + if (stbi__jpeg_test(s)) return stbi__jpeg_load(s,x,y,comp,req_comp, ri); + #endif + #ifndef STBI_NO_PNG + if (stbi__png_test(s)) return stbi__png_load(s,x,y,comp,req_comp, ri); + #endif + #ifndef STBI_NO_BMP + if (stbi__bmp_test(s)) return stbi__bmp_load(s,x,y,comp,req_comp, ri); + #endif + #ifndef STBI_NO_GIF + if (stbi__gif_test(s)) return stbi__gif_load(s,x,y,comp,req_comp, ri); + #endif + #ifndef STBI_NO_PSD + if (stbi__psd_test(s)) return stbi__psd_load(s,x,y,comp,req_comp, ri, bpc); + #endif + #ifndef STBI_NO_PIC + if (stbi__pic_test(s)) return stbi__pic_load(s,x,y,comp,req_comp, ri); + #endif + #ifndef STBI_NO_PNM + if (stbi__pnm_test(s)) return stbi__pnm_load(s,x,y,comp,req_comp, ri); + #endif + + #ifndef STBI_NO_HDR + if (stbi__hdr_test(s)) { + float *hdr = stbi__hdr_load(s, x,y,comp,req_comp, ri); + return stbi__hdr_to_ldr(hdr, *x, *y, req_comp ? req_comp : *comp); + } + #endif + + #ifndef STBI_NO_TGA + // test tga last because it's a crappy test! + if (stbi__tga_test(s)) + return stbi__tga_load(s,x,y,comp,req_comp, ri); + #endif + + return stbi__errpuc("unknown image type", "Image not of any known type, or corrupt"); +} + +static stbi_uc *stbi__convert_16_to_8(stbi__uint16 *orig, int w, int h, int channels) +{ + int i; + int img_len = w * h * channels; + stbi_uc *reduced; + + reduced = (stbi_uc *) stbi__malloc(img_len); + if (reduced == NULL) return stbi__errpuc("outofmem", "Out of memory"); + + for (i = 0; i < img_len; ++i) + reduced[i] = (stbi_uc)((orig[i] >> 8) & 0xFF); // top half of each byte is sufficient approx of 16->8 bit scaling + + STBI_FREE(orig); + return reduced; +} + +static stbi__uint16 *stbi__convert_8_to_16(stbi_uc *orig, int w, int h, int channels) +{ + int i; + int img_len = w * h * channels; + stbi__uint16 *enlarged; + + enlarged = (stbi__uint16 *) stbi__malloc(img_len*2); + if (enlarged == NULL) return (stbi__uint16 *) stbi__errpuc("outofmem", "Out of memory"); + + for (i = 0; i < img_len; ++i) + enlarged[i] = (stbi__uint16)((orig[i] << 8) + orig[i]); // replicate to high and low byte, maps 0->0, 255->0xffff + + STBI_FREE(orig); + return enlarged; +} + +static void stbi__vertical_flip(void *image, int w, int h, int bytes_per_pixel) +{ + int row; + size_t bytes_per_row = (size_t)w * bytes_per_pixel; + stbi_uc temp[2048]; + stbi_uc *bytes = (stbi_uc *)image; + + for (row = 0; row < (h>>1); row++) { + stbi_uc *row0 = bytes + row*bytes_per_row; + stbi_uc *row1 = bytes + (h - row - 1)*bytes_per_row; + // swap row0 with row1 + size_t bytes_left = bytes_per_row; + while (bytes_left) { + size_t bytes_copy = (bytes_left < sizeof(temp)) ? bytes_left : sizeof(temp); + memcpy(temp, row0, bytes_copy); + memcpy(row0, row1, bytes_copy); + memcpy(row1, temp, bytes_copy); + row0 += bytes_copy; + row1 += bytes_copy; + bytes_left -= bytes_copy; + } + } +} + +#ifndef STBI_NO_GIF +static void stbi__vertical_flip_slices(void *image, int w, int h, int z, int bytes_per_pixel) +{ + int slice; + int slice_size = w * h * bytes_per_pixel; + + stbi_uc *bytes = (stbi_uc *)image; + for (slice = 0; slice < z; ++slice) { + stbi__vertical_flip(bytes, w, h, bytes_per_pixel); + bytes += slice_size; + } +} +#endif + +static unsigned char *stbi__load_and_postprocess_8bit(stbi__context *s, int *x, int *y, int *comp, int req_comp) +{ + stbi__result_info ri; + void *result = stbi__load_main(s, x, y, comp, req_comp, &ri, 8); + + if (result == NULL) + return NULL; + + if (ri.bits_per_channel != 8) { + STBI_ASSERT(ri.bits_per_channel == 16); + result = stbi__convert_16_to_8((stbi__uint16 *) result, *x, *y, req_comp == 0 ? *comp : req_comp); + ri.bits_per_channel = 8; + } + + // @TODO: move stbi__convert_format to here + + if (stbi__vertically_flip_on_load) { + int channels = req_comp ? req_comp : *comp; + stbi__vertical_flip(result, *x, *y, channels * sizeof(stbi_uc)); + } + + return (unsigned char *) result; +} + +static stbi__uint16 *stbi__load_and_postprocess_16bit(stbi__context *s, int *x, int *y, int *comp, int req_comp) +{ + stbi__result_info ri; + void *result = stbi__load_main(s, x, y, comp, req_comp, &ri, 16); + + if (result == NULL) + return NULL; + + if (ri.bits_per_channel != 16) { + STBI_ASSERT(ri.bits_per_channel == 8); + result = stbi__convert_8_to_16((stbi_uc *) result, *x, *y, req_comp == 0 ? *comp : req_comp); + ri.bits_per_channel = 16; + } + + // @TODO: move stbi__convert_format16 to here + // @TODO: special case RGB-to-Y (and RGBA-to-YA) for 8-bit-to-16-bit case to keep more precision + + if (stbi__vertically_flip_on_load) { + int channels = req_comp ? req_comp : *comp; + stbi__vertical_flip(result, *x, *y, channels * sizeof(stbi__uint16)); + } + + return (stbi__uint16 *) result; +} + +#if !defined(STBI_NO_HDR) && !defined(STBI_NO_LINEAR) +static void stbi__float_postprocess(float *result, int *x, int *y, int *comp, int req_comp) +{ + if (stbi__vertically_flip_on_load && result != NULL) { + int channels = req_comp ? req_comp : *comp; + stbi__vertical_flip(result, *x, *y, channels * sizeof(float)); + } +} +#endif + +#ifndef STBI_NO_STDIO + +#if defined(_MSC_VER) && defined(STBI_WINDOWS_UTF8) +STBI_EXTERN __declspec(dllimport) int __stdcall MultiByteToWideChar(unsigned int cp, unsigned long flags, const char *str, int cbmb, wchar_t *widestr, int cchwide); +STBI_EXTERN __declspec(dllimport) int __stdcall WideCharToMultiByte(unsigned int cp, unsigned long flags, const wchar_t *widestr, int cchwide, char *str, int cbmb, const char *defchar, int *used_default); +#endif + +#if defined(_MSC_VER) && defined(STBI_WINDOWS_UTF8) +STBIDEF int stbi_convert_wchar_to_utf8(char *buffer, size_t bufferlen, const wchar_t* input) +{ + return WideCharToMultiByte(65001 /* UTF8 */, 0, input, -1, buffer, (int) bufferlen, NULL, NULL); +} +#endif + +static FILE *stbi__fopen(char const *filename, char const *mode) +{ + FILE *f; +#if defined(_MSC_VER) && defined(STBI_WINDOWS_UTF8) + wchar_t wMode[64]; + wchar_t wFilename[1024]; + if (0 == MultiByteToWideChar(65001 /* UTF8 */, 0, filename, -1, wFilename, sizeof(wFilename))) + return 0; + + if (0 == MultiByteToWideChar(65001 /* UTF8 */, 0, mode, -1, wMode, sizeof(wMode))) + return 0; + +#if _MSC_VER >= 1400 + if (0 != _wfopen_s(&f, wFilename, wMode)) + f = 0; +#else + f = _wfopen(wFilename, wMode); +#endif + +#elif defined(_MSC_VER) && _MSC_VER >= 1400 + if (0 != fopen_s(&f, filename, mode)) + f=0; +#else + f = fopen(filename, mode); +#endif + return f; +} + + +STBIDEF stbi_uc *stbi_load(char const *filename, int *x, int *y, int *comp, int req_comp) +{ + FILE *f = stbi__fopen(filename, "rb"); + unsigned char *result; + if (!f) return stbi__errpuc("can't fopen", "Unable to open file"); + result = stbi_load_from_file(f,x,y,comp,req_comp); + fclose(f); + return result; +} + +STBIDEF stbi_uc *stbi_load_from_file(FILE *f, int *x, int *y, int *comp, int req_comp) +{ + unsigned char *result; + stbi__context s; + stbi__start_file(&s,f); + result = stbi__load_and_postprocess_8bit(&s,x,y,comp,req_comp); + if (result) { + // need to 'unget' all the characters in the IO buffer + fseek(f, - (int) (s.img_buffer_end - s.img_buffer), SEEK_CUR); + } + return result; +} + +STBIDEF stbi__uint16 *stbi_load_from_file_16(FILE *f, int *x, int *y, int *comp, int req_comp) +{ + stbi__uint16 *result; + stbi__context s; + stbi__start_file(&s,f); + result = stbi__load_and_postprocess_16bit(&s,x,y,comp,req_comp); + if (result) { + // need to 'unget' all the characters in the IO buffer + fseek(f, - (int) (s.img_buffer_end - s.img_buffer), SEEK_CUR); + } + return result; +} + +STBIDEF stbi_us *stbi_load_16(char const *filename, int *x, int *y, int *comp, int req_comp) +{ + FILE *f = stbi__fopen(filename, "rb"); + stbi__uint16 *result; + if (!f) return (stbi_us *) stbi__errpuc("can't fopen", "Unable to open file"); + result = stbi_load_from_file_16(f,x,y,comp,req_comp); + fclose(f); + return result; +} + + +#endif //!STBI_NO_STDIO + +STBIDEF stbi_us *stbi_load_16_from_memory(stbi_uc const *buffer, int len, int *x, int *y, int *channels_in_file, int desired_channels) +{ + stbi__context s; + stbi__start_mem(&s,buffer,len); + return stbi__load_and_postprocess_16bit(&s,x,y,channels_in_file,desired_channels); +} + +STBIDEF stbi_us *stbi_load_16_from_callbacks(stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *channels_in_file, int desired_channels) +{ + stbi__context s; + stbi__start_callbacks(&s, (stbi_io_callbacks *)clbk, user); + return stbi__load_and_postprocess_16bit(&s,x,y,channels_in_file,desired_channels); +} + +STBIDEF stbi_uc *stbi_load_from_memory(stbi_uc const *buffer, int len, int *x, int *y, int *comp, int req_comp) +{ + stbi__context s; + stbi__start_mem(&s,buffer,len); + return stbi__load_and_postprocess_8bit(&s,x,y,comp,req_comp); +} + +STBIDEF stbi_uc *stbi_load_from_callbacks(stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *comp, int req_comp) +{ + stbi__context s; + stbi__start_callbacks(&s, (stbi_io_callbacks *) clbk, user); + return stbi__load_and_postprocess_8bit(&s,x,y,comp,req_comp); +} + +#ifndef STBI_NO_GIF +STBIDEF stbi_uc *stbi_load_gif_from_memory(stbi_uc const *buffer, int len, int **delays, int *x, int *y, int *z, int *comp, int req_comp) +{ + unsigned char *result; + stbi__context s; + stbi__start_mem(&s,buffer,len); + + result = (unsigned char*) stbi__load_gif_main(&s, delays, x, y, z, comp, req_comp); + if (stbi__vertically_flip_on_load) { + stbi__vertical_flip_slices( result, *x, *y, *z, *comp ); + } + + return result; +} +#endif + +#ifndef STBI_NO_LINEAR +static float *stbi__loadf_main(stbi__context *s, int *x, int *y, int *comp, int req_comp) +{ + unsigned char *data; + #ifndef STBI_NO_HDR + if (stbi__hdr_test(s)) { + stbi__result_info ri; + float *hdr_data = stbi__hdr_load(s,x,y,comp,req_comp, &ri); + if (hdr_data) + stbi__float_postprocess(hdr_data,x,y,comp,req_comp); + return hdr_data; + } + #endif + data = stbi__load_and_postprocess_8bit(s, x, y, comp, req_comp); + if (data) + return stbi__ldr_to_hdr(data, *x, *y, req_comp ? req_comp : *comp); + return stbi__errpf("unknown image type", "Image not of any known type, or corrupt"); +} + +STBIDEF float *stbi_loadf_from_memory(stbi_uc const *buffer, int len, int *x, int *y, int *comp, int req_comp) +{ + stbi__context s; + stbi__start_mem(&s,buffer,len); + return stbi__loadf_main(&s,x,y,comp,req_comp); +} + +STBIDEF float *stbi_loadf_from_callbacks(stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *comp, int req_comp) +{ + stbi__context s; + stbi__start_callbacks(&s, (stbi_io_callbacks *) clbk, user); + return stbi__loadf_main(&s,x,y,comp,req_comp); +} + +#ifndef STBI_NO_STDIO +STBIDEF float *stbi_loadf(char const *filename, int *x, int *y, int *comp, int req_comp) +{ + float *result; + FILE *f = stbi__fopen(filename, "rb"); + if (!f) return stbi__errpf("can't fopen", "Unable to open file"); + result = stbi_loadf_from_file(f,x,y,comp,req_comp); + fclose(f); + return result; +} + +STBIDEF float *stbi_loadf_from_file(FILE *f, int *x, int *y, int *comp, int req_comp) +{ + stbi__context s; + stbi__start_file(&s,f); + return stbi__loadf_main(&s,x,y,comp,req_comp); +} +#endif // !STBI_NO_STDIO + +#endif // !STBI_NO_LINEAR + +// these is-hdr-or-not is defined independent of whether STBI_NO_LINEAR is +// defined, for API simplicity; if STBI_NO_LINEAR is defined, it always +// reports false! + +STBIDEF int stbi_is_hdr_from_memory(stbi_uc const *buffer, int len) +{ + #ifndef STBI_NO_HDR + stbi__context s; + stbi__start_mem(&s,buffer,len); + return stbi__hdr_test(&s); + #else + STBI_NOTUSED(buffer); + STBI_NOTUSED(len); + return 0; + #endif +} + +#ifndef STBI_NO_STDIO +STBIDEF int stbi_is_hdr (char const *filename) +{ + FILE *f = stbi__fopen(filename, "rb"); + int result=0; + if (f) { + result = stbi_is_hdr_from_file(f); + fclose(f); + } + return result; +} + +STBIDEF int stbi_is_hdr_from_file(FILE *f) +{ + #ifndef STBI_NO_HDR + long pos = ftell(f); + int res; + stbi__context s; + stbi__start_file(&s,f); + res = stbi__hdr_test(&s); + fseek(f, pos, SEEK_SET); + return res; + #else + STBI_NOTUSED(f); + return 0; + #endif +} +#endif // !STBI_NO_STDIO + +STBIDEF int stbi_is_hdr_from_callbacks(stbi_io_callbacks const *clbk, void *user) +{ + #ifndef STBI_NO_HDR + stbi__context s; + stbi__start_callbacks(&s, (stbi_io_callbacks *) clbk, user); + return stbi__hdr_test(&s); + #else + STBI_NOTUSED(clbk); + STBI_NOTUSED(user); + return 0; + #endif +} + +#ifndef STBI_NO_LINEAR +static float stbi__l2h_gamma=2.2f, stbi__l2h_scale=1.0f; + +STBIDEF void stbi_ldr_to_hdr_gamma(float gamma) { stbi__l2h_gamma = gamma; } +STBIDEF void stbi_ldr_to_hdr_scale(float scale) { stbi__l2h_scale = scale; } +#endif + +static float stbi__h2l_gamma_i=1.0f/2.2f, stbi__h2l_scale_i=1.0f; + +STBIDEF void stbi_hdr_to_ldr_gamma(float gamma) { stbi__h2l_gamma_i = 1/gamma; } +STBIDEF void stbi_hdr_to_ldr_scale(float scale) { stbi__h2l_scale_i = 1/scale; } + + +////////////////////////////////////////////////////////////////////////////// +// +// Common code used by all image loaders +// + +enum +{ + STBI__SCAN_load=0, + STBI__SCAN_type, + STBI__SCAN_header +}; + +static void stbi__refill_buffer(stbi__context *s) +{ + int n = (s->io.read)(s->io_user_data,(char*)s->buffer_start,s->buflen); + if (n == 0) { + // at end of file, treat same as if from memory, but need to handle case + // where s->img_buffer isn't pointing to safe memory, e.g. 0-byte file + s->read_from_callbacks = 0; + s->img_buffer = s->buffer_start; + s->img_buffer_end = s->buffer_start+1; + *s->img_buffer = 0; + } else { + s->img_buffer = s->buffer_start; + s->img_buffer_end = s->buffer_start + n; + } +} + +stbi_inline static stbi_uc stbi__get8(stbi__context *s) +{ + if (s->img_buffer < s->img_buffer_end) + return *s->img_buffer++; + if (s->read_from_callbacks) { + stbi__refill_buffer(s); + return *s->img_buffer++; + } + return 0; +} + +stbi_inline static int stbi__at_eof(stbi__context *s) +{ + if (s->io.read) { + if (!(s->io.eof)(s->io_user_data)) return 0; + // if feof() is true, check if buffer = end + // special case: we've only got the special 0 character at the end + if (s->read_from_callbacks == 0) return 1; + } + + return s->img_buffer >= s->img_buffer_end; +} + +static void stbi__skip(stbi__context *s, int n) +{ + if (n < 0) { + s->img_buffer = s->img_buffer_end; + return; + } + if (s->io.read) { + int blen = (int) (s->img_buffer_end - s->img_buffer); + if (blen < n) { + s->img_buffer = s->img_buffer_end; + (s->io.skip)(s->io_user_data, n - blen); + return; + } + } + s->img_buffer += n; +} + +static int stbi__getn(stbi__context *s, stbi_uc *buffer, int n) +{ + if (s->io.read) { + int blen = (int) (s->img_buffer_end - s->img_buffer); + if (blen < n) { + int res, count; + + memcpy(buffer, s->img_buffer, blen); + + count = (s->io.read)(s->io_user_data, (char*) buffer + blen, n - blen); + res = (count == (n-blen)); + s->img_buffer = s->img_buffer_end; + return res; + } + } + + if (s->img_buffer+n <= s->img_buffer_end) { + memcpy(buffer, s->img_buffer, n); + s->img_buffer += n; + return 1; + } else + return 0; +} + +static int stbi__get16be(stbi__context *s) +{ + int z = stbi__get8(s); + return (z << 8) + stbi__get8(s); +} + +static stbi__uint32 stbi__get32be(stbi__context *s) +{ + stbi__uint32 z = stbi__get16be(s); + return (z << 16) + stbi__get16be(s); +} + +#if defined(STBI_NO_BMP) && defined(STBI_NO_TGA) && defined(STBI_NO_GIF) +// nothing +#else +static int stbi__get16le(stbi__context *s) +{ + int z = stbi__get8(s); + return z + (stbi__get8(s) << 8); +} +#endif + +#ifndef STBI_NO_BMP +static stbi__uint32 stbi__get32le(stbi__context *s) +{ + stbi__uint32 z = stbi__get16le(s); + return z + (stbi__get16le(s) << 16); +} +#endif + +#define STBI__BYTECAST(x) ((stbi_uc) ((x) & 255)) // truncate int to byte without warnings + + +////////////////////////////////////////////////////////////////////////////// +// +// generic converter from built-in img_n to req_comp +// individual types do this automatically as much as possible (e.g. jpeg +// does all cases internally since it needs to colorspace convert anyway, +// and it never has alpha, so very few cases ). png can automatically +// interleave an alpha=255 channel, but falls back to this for other cases +// +// assume data buffer is malloced, so malloc a new one and free that one +// only failure mode is malloc failing + +static stbi_uc stbi__compute_y(int r, int g, int b) +{ + return (stbi_uc) (((r*77) + (g*150) + (29*b)) >> 8); +} + +static unsigned char *stbi__convert_format(unsigned char *data, int img_n, int req_comp, unsigned int x, unsigned int y) +{ + int i,j; + unsigned char *good; + + if (req_comp == img_n) return data; + STBI_ASSERT(req_comp >= 1 && req_comp <= 4); + + good = (unsigned char *) stbi__malloc_mad3(req_comp, x, y, 0); + if (good == NULL) { + STBI_FREE(data); + return stbi__errpuc("outofmem", "Out of memory"); + } + + for (j=0; j < (int) y; ++j) { + unsigned char *src = data + j * x * img_n ; + unsigned char *dest = good + j * x * req_comp; + + #define STBI__COMBO(a,b) ((a)*8+(b)) + #define STBI__CASE(a,b) case STBI__COMBO(a,b): for(i=x-1; i >= 0; --i, src += a, dest += b) + // convert source image with img_n components to one with req_comp components; + // avoid switch per pixel, so use switch per scanline and massive macros + switch (STBI__COMBO(img_n, req_comp)) { + STBI__CASE(1,2) { dest[0]=src[0]; dest[1]=255; } break; + STBI__CASE(1,3) { dest[0]=dest[1]=dest[2]=src[0]; } break; + STBI__CASE(1,4) { dest[0]=dest[1]=dest[2]=src[0]; dest[3]=255; } break; + STBI__CASE(2,1) { dest[0]=src[0]; } break; + STBI__CASE(2,3) { dest[0]=dest[1]=dest[2]=src[0]; } break; + STBI__CASE(2,4) { dest[0]=dest[1]=dest[2]=src[0]; dest[3]=src[1]; } break; + STBI__CASE(3,4) { dest[0]=src[0];dest[1]=src[1];dest[2]=src[2];dest[3]=255; } break; + STBI__CASE(3,1) { dest[0]=stbi__compute_y(src[0],src[1],src[2]); } break; + STBI__CASE(3,2) { dest[0]=stbi__compute_y(src[0],src[1],src[2]); dest[1] = 255; } break; + STBI__CASE(4,1) { dest[0]=stbi__compute_y(src[0],src[1],src[2]); } break; + STBI__CASE(4,2) { dest[0]=stbi__compute_y(src[0],src[1],src[2]); dest[1] = src[3]; } break; + STBI__CASE(4,3) { dest[0]=src[0];dest[1]=src[1];dest[2]=src[2]; } break; + default: STBI_ASSERT(0); + } + #undef STBI__CASE + } + + STBI_FREE(data); + return good; +} + +static stbi__uint16 stbi__compute_y_16(int r, int g, int b) +{ + return (stbi__uint16) (((r*77) + (g*150) + (29*b)) >> 8); +} + +static stbi__uint16 *stbi__convert_format16(stbi__uint16 *data, int img_n, int req_comp, unsigned int x, unsigned int y) +{ + int i,j; + stbi__uint16 *good; + + if (req_comp == img_n) return data; + STBI_ASSERT(req_comp >= 1 && req_comp <= 4); + + good = (stbi__uint16 *) stbi__malloc(req_comp * x * y * 2); + if (good == NULL) { + STBI_FREE(data); + return (stbi__uint16 *) stbi__errpuc("outofmem", "Out of memory"); + } + + for (j=0; j < (int) y; ++j) { + stbi__uint16 *src = data + j * x * img_n ; + stbi__uint16 *dest = good + j * x * req_comp; + + #define STBI__COMBO(a,b) ((a)*8+(b)) + #define STBI__CASE(a,b) case STBI__COMBO(a,b): for(i=x-1; i >= 0; --i, src += a, dest += b) + // convert source image with img_n components to one with req_comp components; + // avoid switch per pixel, so use switch per scanline and massive macros + switch (STBI__COMBO(img_n, req_comp)) { + STBI__CASE(1,2) { dest[0]=src[0]; dest[1]=0xffff; } break; + STBI__CASE(1,3) { dest[0]=dest[1]=dest[2]=src[0]; } break; + STBI__CASE(1,4) { dest[0]=dest[1]=dest[2]=src[0]; dest[3]=0xffff; } break; + STBI__CASE(2,1) { dest[0]=src[0]; } break; + STBI__CASE(2,3) { dest[0]=dest[1]=dest[2]=src[0]; } break; + STBI__CASE(2,4) { dest[0]=dest[1]=dest[2]=src[0]; dest[3]=src[1]; } break; + STBI__CASE(3,4) { dest[0]=src[0];dest[1]=src[1];dest[2]=src[2];dest[3]=0xffff; } break; + STBI__CASE(3,1) { dest[0]=stbi__compute_y_16(src[0],src[1],src[2]); } break; + STBI__CASE(3,2) { dest[0]=stbi__compute_y_16(src[0],src[1],src[2]); dest[1] = 0xffff; } break; + STBI__CASE(4,1) { dest[0]=stbi__compute_y_16(src[0],src[1],src[2]); } break; + STBI__CASE(4,2) { dest[0]=stbi__compute_y_16(src[0],src[1],src[2]); dest[1] = src[3]; } break; + STBI__CASE(4,3) { dest[0]=src[0];dest[1]=src[1];dest[2]=src[2]; } break; + default: STBI_ASSERT(0); + } + #undef STBI__CASE + } + + STBI_FREE(data); + return good; +} + +#ifndef STBI_NO_LINEAR +static float *stbi__ldr_to_hdr(stbi_uc *data, int x, int y, int comp) +{ + int i,k,n; + float *output; + if (!data) return NULL; + output = (float *) stbi__malloc_mad4(x, y, comp, sizeof(float), 0); + if (output == NULL) { STBI_FREE(data); return stbi__errpf("outofmem", "Out of memory"); } + // compute number of non-alpha components + if (comp & 1) n = comp; else n = comp-1; + for (i=0; i < x*y; ++i) { + for (k=0; k < n; ++k) { + output[i*comp + k] = (float) (pow(data[i*comp+k]/255.0f, stbi__l2h_gamma) * stbi__l2h_scale); + } + } + if (n < comp) { + for (i=0; i < x*y; ++i) { + output[i*comp + n] = data[i*comp + n]/255.0f; + } + } + STBI_FREE(data); + return output; +} +#endif + +#ifndef STBI_NO_HDR +#define stbi__float2int(x) ((int) (x)) +static stbi_uc *stbi__hdr_to_ldr(float *data, int x, int y, int comp) +{ + int i,k,n; + stbi_uc *output; + if (!data) return NULL; + output = (stbi_uc *) stbi__malloc_mad3(x, y, comp, 0); + if (output == NULL) { STBI_FREE(data); return stbi__errpuc("outofmem", "Out of memory"); } + // compute number of non-alpha components + if (comp & 1) n = comp; else n = comp-1; + for (i=0; i < x*y; ++i) { + for (k=0; k < n; ++k) { + float z = (float) pow(data[i*comp+k]*stbi__h2l_scale_i, stbi__h2l_gamma_i) * 255 + 0.5f; + if (z < 0) z = 0; + if (z > 255) z = 255; + output[i*comp + k] = (stbi_uc) stbi__float2int(z); + } + if (k < comp) { + float z = data[i*comp+k] * 255 + 0.5f; + if (z < 0) z = 0; + if (z > 255) z = 255; + output[i*comp + k] = (stbi_uc) stbi__float2int(z); + } + } + STBI_FREE(data); + return output; +} +#endif + +////////////////////////////////////////////////////////////////////////////// +// +// "baseline" JPEG/JFIF decoder +// +// simple implementation +// - doesn't support delayed output of y-dimension +// - simple interface (only one output format: 8-bit interleaved RGB) +// - doesn't try to recover corrupt jpegs +// - doesn't allow partial loading, loading multiple at once +// - still fast on x86 (copying globals into locals doesn't help x86) +// - allocates lots of intermediate memory (full size of all components) +// - non-interleaved case requires this anyway +// - allows good upsampling (see next) +// high-quality +// - upsampled channels are bilinearly interpolated, even across blocks +// - quality integer IDCT derived from IJG's 'slow' +// performance +// - fast huffman; reasonable integer IDCT +// - some SIMD kernels for common paths on targets with SSE2/NEON +// - uses a lot of intermediate memory, could cache poorly + +#ifndef STBI_NO_JPEG + +// huffman decoding acceleration +#define FAST_BITS 9 // larger handles more cases; smaller stomps less cache + +typedef struct +{ + stbi_uc fast[1 << FAST_BITS]; + // weirdly, repacking this into AoS is a 10% speed loss, instead of a win + stbi__uint16 code[256]; + stbi_uc values[256]; + stbi_uc size[257]; + unsigned int maxcode[18]; + int delta[17]; // old 'firstsymbol' - old 'firstcode' +} stbi__huffman; + +typedef struct +{ + stbi__context *s; + stbi__huffman huff_dc[4]; + stbi__huffman huff_ac[4]; + stbi__uint16 dequant[4][64]; + stbi__int16 fast_ac[4][1 << FAST_BITS]; + +// sizes for components, interleaved MCUs + int img_h_max, img_v_max; + int img_mcu_x, img_mcu_y; + int img_mcu_w, img_mcu_h; + +// definition of jpeg image component + struct + { + int id; + int h,v; + int tq; + int hd,ha; + int dc_pred; + + int x,y,w2,h2; + stbi_uc *data; + void *raw_data, *raw_coeff; + stbi_uc *linebuf; + short *coeff; // progressive only + int coeff_w, coeff_h; // number of 8x8 coefficient blocks + } img_comp[4]; + + stbi__uint32 code_buffer; // jpeg entropy-coded buffer + int code_bits; // number of valid bits + unsigned char marker; // marker seen while filling entropy buffer + int nomore; // flag if we saw a marker so must stop + + int progressive; + int spec_start; + int spec_end; + int succ_high; + int succ_low; + int eob_run; + int jfif; + int app14_color_transform; // Adobe APP14 tag + int rgb; + + int scan_n, order[4]; + int restart_interval, todo; + +// kernels + void (*idct_block_kernel)(stbi_uc *out, int out_stride, short data[64]); + void (*YCbCr_to_RGB_kernel)(stbi_uc *out, const stbi_uc *y, const stbi_uc *pcb, const stbi_uc *pcr, int count, int step); + stbi_uc *(*resample_row_hv_2_kernel)(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs); +} stbi__jpeg; + +static int stbi__build_huffman(stbi__huffman *h, int *count) +{ + int i,j,k=0; + unsigned int code; + // build size list for each symbol (from JPEG spec) + for (i=0; i < 16; ++i) + for (j=0; j < count[i]; ++j) + h->size[k++] = (stbi_uc) (i+1); + h->size[k] = 0; + + // compute actual symbols (from jpeg spec) + code = 0; + k = 0; + for(j=1; j <= 16; ++j) { + // compute delta to add to code to compute symbol id + h->delta[j] = k - code; + if (h->size[k] == j) { + while (h->size[k] == j) + h->code[k++] = (stbi__uint16) (code++); + if (code-1 >= (1u << j)) return stbi__err("bad code lengths","Corrupt JPEG"); + } + // compute largest code + 1 for this size, preshifted as needed later + h->maxcode[j] = code << (16-j); + code <<= 1; + } + h->maxcode[j] = 0xffffffff; + + // build non-spec acceleration table; 255 is flag for not-accelerated + memset(h->fast, 255, 1 << FAST_BITS); + for (i=0; i < k; ++i) { + int s = h->size[i]; + if (s <= FAST_BITS) { + int c = h->code[i] << (FAST_BITS-s); + int m = 1 << (FAST_BITS-s); + for (j=0; j < m; ++j) { + h->fast[c+j] = (stbi_uc) i; + } + } + } + return 1; +} + +// build a table that decodes both magnitude and value of small ACs in +// one go. +static void stbi__build_fast_ac(stbi__int16 *fast_ac, stbi__huffman *h) +{ + int i; + for (i=0; i < (1 << FAST_BITS); ++i) { + stbi_uc fast = h->fast[i]; + fast_ac[i] = 0; + if (fast < 255) { + int rs = h->values[fast]; + int run = (rs >> 4) & 15; + int magbits = rs & 15; + int len = h->size[fast]; + + if (magbits && len + magbits <= FAST_BITS) { + // magnitude code followed by receive_extend code + int k = ((i << len) & ((1 << FAST_BITS) - 1)) >> (FAST_BITS - magbits); + int m = 1 << (magbits - 1); + if (k < m) k += (~0U << magbits) + 1; + // if the result is small enough, we can fit it in fast_ac table + if (k >= -128 && k <= 127) + fast_ac[i] = (stbi__int16) ((k * 256) + (run * 16) + (len + magbits)); + } + } + } +} + +static void stbi__grow_buffer_unsafe(stbi__jpeg *j) +{ + do { + unsigned int b = j->nomore ? 0 : stbi__get8(j->s); + if (b == 0xff) { + int c = stbi__get8(j->s); + while (c == 0xff) c = stbi__get8(j->s); // consume fill bytes + if (c != 0) { + j->marker = (unsigned char) c; + j->nomore = 1; + return; + } + } + j->code_buffer |= b << (24 - j->code_bits); + j->code_bits += 8; + } while (j->code_bits <= 24); +} + +// (1 << n) - 1 +static const stbi__uint32 stbi__bmask[17]={0,1,3,7,15,31,63,127,255,511,1023,2047,4095,8191,16383,32767,65535}; + +// decode a jpeg huffman value from the bitstream +stbi_inline static int stbi__jpeg_huff_decode(stbi__jpeg *j, stbi__huffman *h) +{ + unsigned int temp; + int c,k; + + if (j->code_bits < 16) stbi__grow_buffer_unsafe(j); + + // look at the top FAST_BITS and determine what symbol ID it is, + // if the code is <= FAST_BITS + c = (j->code_buffer >> (32 - FAST_BITS)) & ((1 << FAST_BITS)-1); + k = h->fast[c]; + if (k < 255) { + int s = h->size[k]; + if (s > j->code_bits) + return -1; + j->code_buffer <<= s; + j->code_bits -= s; + return h->values[k]; + } + + // naive test is to shift the code_buffer down so k bits are + // valid, then test against maxcode. To speed this up, we've + // preshifted maxcode left so that it has (16-k) 0s at the + // end; in other words, regardless of the number of bits, it + // wants to be compared against something shifted to have 16; + // that way we don't need to shift inside the loop. + temp = j->code_buffer >> 16; + for (k=FAST_BITS+1 ; ; ++k) + if (temp < h->maxcode[k]) + break; + if (k == 17) { + // error! code not found + j->code_bits -= 16; + return -1; + } + + if (k > j->code_bits) + return -1; + + // convert the huffman code to the symbol id + c = ((j->code_buffer >> (32 - k)) & stbi__bmask[k]) + h->delta[k]; + STBI_ASSERT((((j->code_buffer) >> (32 - h->size[c])) & stbi__bmask[h->size[c]]) == h->code[c]); + + // convert the id to a symbol + j->code_bits -= k; + j->code_buffer <<= k; + return h->values[c]; +} + +// bias[n] = (-1<code_bits < n) stbi__grow_buffer_unsafe(j); + + sgn = (stbi__int32)j->code_buffer >> 31; // sign bit is always in MSB + k = stbi_lrot(j->code_buffer, n); + STBI_ASSERT(n >= 0 && n < (int) (sizeof(stbi__bmask)/sizeof(*stbi__bmask))); + j->code_buffer = k & ~stbi__bmask[n]; + k &= stbi__bmask[n]; + j->code_bits -= n; + return k + (stbi__jbias[n] & ~sgn); +} + +// get some unsigned bits +stbi_inline static int stbi__jpeg_get_bits(stbi__jpeg *j, int n) +{ + unsigned int k; + if (j->code_bits < n) stbi__grow_buffer_unsafe(j); + k = stbi_lrot(j->code_buffer, n); + j->code_buffer = k & ~stbi__bmask[n]; + k &= stbi__bmask[n]; + j->code_bits -= n; + return k; +} + +stbi_inline static int stbi__jpeg_get_bit(stbi__jpeg *j) +{ + unsigned int k; + if (j->code_bits < 1) stbi__grow_buffer_unsafe(j); + k = j->code_buffer; + j->code_buffer <<= 1; + --j->code_bits; + return k & 0x80000000; +} + +// given a value that's at position X in the zigzag stream, +// where does it appear in the 8x8 matrix coded as row-major? +static const stbi_uc stbi__jpeg_dezigzag[64+15] = +{ + 0, 1, 8, 16, 9, 2, 3, 10, + 17, 24, 32, 25, 18, 11, 4, 5, + 12, 19, 26, 33, 40, 48, 41, 34, + 27, 20, 13, 6, 7, 14, 21, 28, + 35, 42, 49, 56, 57, 50, 43, 36, + 29, 22, 15, 23, 30, 37, 44, 51, + 58, 59, 52, 45, 38, 31, 39, 46, + 53, 60, 61, 54, 47, 55, 62, 63, + // let corrupt input sample past end + 63, 63, 63, 63, 63, 63, 63, 63, + 63, 63, 63, 63, 63, 63, 63 +}; + +// decode one 64-entry block-- +static int stbi__jpeg_decode_block(stbi__jpeg *j, short data[64], stbi__huffman *hdc, stbi__huffman *hac, stbi__int16 *fac, int b, stbi__uint16 *dequant) +{ + int diff,dc,k; + int t; + + if (j->code_bits < 16) stbi__grow_buffer_unsafe(j); + t = stbi__jpeg_huff_decode(j, hdc); + if (t < 0) return stbi__err("bad huffman code","Corrupt JPEG"); + + // 0 all the ac values now so we can do it 32-bits at a time + memset(data,0,64*sizeof(data[0])); + + diff = t ? stbi__extend_receive(j, t) : 0; + dc = j->img_comp[b].dc_pred + diff; + j->img_comp[b].dc_pred = dc; + data[0] = (short) (dc * dequant[0]); + + // decode AC components, see JPEG spec + k = 1; + do { + unsigned int zig; + int c,r,s; + if (j->code_bits < 16) stbi__grow_buffer_unsafe(j); + c = (j->code_buffer >> (32 - FAST_BITS)) & ((1 << FAST_BITS)-1); + r = fac[c]; + if (r) { // fast-AC path + k += (r >> 4) & 15; // run + s = r & 15; // combined length + j->code_buffer <<= s; + j->code_bits -= s; + // decode into unzigzag'd location + zig = stbi__jpeg_dezigzag[k++]; + data[zig] = (short) ((r >> 8) * dequant[zig]); + } else { + int rs = stbi__jpeg_huff_decode(j, hac); + if (rs < 0) return stbi__err("bad huffman code","Corrupt JPEG"); + s = rs & 15; + r = rs >> 4; + if (s == 0) { + if (rs != 0xf0) break; // end block + k += 16; + } else { + k += r; + // decode into unzigzag'd location + zig = stbi__jpeg_dezigzag[k++]; + data[zig] = (short) (stbi__extend_receive(j,s) * dequant[zig]); + } + } + } while (k < 64); + return 1; +} + +static int stbi__jpeg_decode_block_prog_dc(stbi__jpeg *j, short data[64], stbi__huffman *hdc, int b) +{ + int diff,dc; + int t; + if (j->spec_end != 0) return stbi__err("can't merge dc and ac", "Corrupt JPEG"); + + if (j->code_bits < 16) stbi__grow_buffer_unsafe(j); + + if (j->succ_high == 0) { + // first scan for DC coefficient, must be first + memset(data,0,64*sizeof(data[0])); // 0 all the ac values now + t = stbi__jpeg_huff_decode(j, hdc); + diff = t ? stbi__extend_receive(j, t) : 0; + + dc = j->img_comp[b].dc_pred + diff; + j->img_comp[b].dc_pred = dc; + data[0] = (short) (dc << j->succ_low); + } else { + // refinement scan for DC coefficient + if (stbi__jpeg_get_bit(j)) + data[0] += (short) (1 << j->succ_low); + } + return 1; +} + +// @OPTIMIZE: store non-zigzagged during the decode passes, +// and only de-zigzag when dequantizing +static int stbi__jpeg_decode_block_prog_ac(stbi__jpeg *j, short data[64], stbi__huffman *hac, stbi__int16 *fac) +{ + int k; + if (j->spec_start == 0) return stbi__err("can't merge dc and ac", "Corrupt JPEG"); + + if (j->succ_high == 0) { + int shift = j->succ_low; + + if (j->eob_run) { + --j->eob_run; + return 1; + } + + k = j->spec_start; + do { + unsigned int zig; + int c,r,s; + if (j->code_bits < 16) stbi__grow_buffer_unsafe(j); + c = (j->code_buffer >> (32 - FAST_BITS)) & ((1 << FAST_BITS)-1); + r = fac[c]; + if (r) { // fast-AC path + k += (r >> 4) & 15; // run + s = r & 15; // combined length + j->code_buffer <<= s; + j->code_bits -= s; + zig = stbi__jpeg_dezigzag[k++]; + data[zig] = (short) ((r >> 8) << shift); + } else { + int rs = stbi__jpeg_huff_decode(j, hac); + if (rs < 0) return stbi__err("bad huffman code","Corrupt JPEG"); + s = rs & 15; + r = rs >> 4; + if (s == 0) { + if (r < 15) { + j->eob_run = (1 << r); + if (r) + j->eob_run += stbi__jpeg_get_bits(j, r); + --j->eob_run; + break; + } + k += 16; + } else { + k += r; + zig = stbi__jpeg_dezigzag[k++]; + data[zig] = (short) (stbi__extend_receive(j,s) << shift); + } + } + } while (k <= j->spec_end); + } else { + // refinement scan for these AC coefficients + + short bit = (short) (1 << j->succ_low); + + if (j->eob_run) { + --j->eob_run; + for (k = j->spec_start; k <= j->spec_end; ++k) { + short *p = &data[stbi__jpeg_dezigzag[k]]; + if (*p != 0) + if (stbi__jpeg_get_bit(j)) + if ((*p & bit)==0) { + if (*p > 0) + *p += bit; + else + *p -= bit; + } + } + } else { + k = j->spec_start; + do { + int r,s; + int rs = stbi__jpeg_huff_decode(j, hac); // @OPTIMIZE see if we can use the fast path here, advance-by-r is so slow, eh + if (rs < 0) return stbi__err("bad huffman code","Corrupt JPEG"); + s = rs & 15; + r = rs >> 4; + if (s == 0) { + if (r < 15) { + j->eob_run = (1 << r) - 1; + if (r) + j->eob_run += stbi__jpeg_get_bits(j, r); + r = 64; // force end of block + } else { + // r=15 s=0 should write 16 0s, so we just do + // a run of 15 0s and then write s (which is 0), + // so we don't have to do anything special here + } + } else { + if (s != 1) return stbi__err("bad huffman code", "Corrupt JPEG"); + // sign bit + if (stbi__jpeg_get_bit(j)) + s = bit; + else + s = -bit; + } + + // advance by r + while (k <= j->spec_end) { + short *p = &data[stbi__jpeg_dezigzag[k++]]; + if (*p != 0) { + if (stbi__jpeg_get_bit(j)) + if ((*p & bit)==0) { + if (*p > 0) + *p += bit; + else + *p -= bit; + } + } else { + if (r == 0) { + *p = (short) s; + break; + } + --r; + } + } + } while (k <= j->spec_end); + } + } + return 1; +} + +// take a -128..127 value and stbi__clamp it and convert to 0..255 +stbi_inline static stbi_uc stbi__clamp(int x) +{ + // trick to use a single test to catch both cases + if ((unsigned int) x > 255) { + if (x < 0) return 0; + if (x > 255) return 255; + } + return (stbi_uc) x; +} + +#define stbi__f2f(x) ((int) (((x) * 4096 + 0.5))) +#define stbi__fsh(x) ((x) * 4096) + +// derived from jidctint -- DCT_ISLOW +#define STBI__IDCT_1D(s0,s1,s2,s3,s4,s5,s6,s7) \ + int t0,t1,t2,t3,p1,p2,p3,p4,p5,x0,x1,x2,x3; \ + p2 = s2; \ + p3 = s6; \ + p1 = (p2+p3) * stbi__f2f(0.5411961f); \ + t2 = p1 + p3*stbi__f2f(-1.847759065f); \ + t3 = p1 + p2*stbi__f2f( 0.765366865f); \ + p2 = s0; \ + p3 = s4; \ + t0 = stbi__fsh(p2+p3); \ + t1 = stbi__fsh(p2-p3); \ + x0 = t0+t3; \ + x3 = t0-t3; \ + x1 = t1+t2; \ + x2 = t1-t2; \ + t0 = s7; \ + t1 = s5; \ + t2 = s3; \ + t3 = s1; \ + p3 = t0+t2; \ + p4 = t1+t3; \ + p1 = t0+t3; \ + p2 = t1+t2; \ + p5 = (p3+p4)*stbi__f2f( 1.175875602f); \ + t0 = t0*stbi__f2f( 0.298631336f); \ + t1 = t1*stbi__f2f( 2.053119869f); \ + t2 = t2*stbi__f2f( 3.072711026f); \ + t3 = t3*stbi__f2f( 1.501321110f); \ + p1 = p5 + p1*stbi__f2f(-0.899976223f); \ + p2 = p5 + p2*stbi__f2f(-2.562915447f); \ + p3 = p3*stbi__f2f(-1.961570560f); \ + p4 = p4*stbi__f2f(-0.390180644f); \ + t3 += p1+p4; \ + t2 += p2+p3; \ + t1 += p2+p4; \ + t0 += p1+p3; + +static void stbi__idct_block(stbi_uc *out, int out_stride, short data[64]) +{ + int i,val[64],*v=val; + stbi_uc *o; + short *d = data; + + // columns + for (i=0; i < 8; ++i,++d, ++v) { + // if all zeroes, shortcut -- this avoids dequantizing 0s and IDCTing + if (d[ 8]==0 && d[16]==0 && d[24]==0 && d[32]==0 + && d[40]==0 && d[48]==0 && d[56]==0) { + // no shortcut 0 seconds + // (1|2|3|4|5|6|7)==0 0 seconds + // all separate -0.047 seconds + // 1 && 2|3 && 4|5 && 6|7: -0.047 seconds + int dcterm = d[0]*4; + v[0] = v[8] = v[16] = v[24] = v[32] = v[40] = v[48] = v[56] = dcterm; + } else { + STBI__IDCT_1D(d[ 0],d[ 8],d[16],d[24],d[32],d[40],d[48],d[56]) + // constants scaled things up by 1<<12; let's bring them back + // down, but keep 2 extra bits of precision + x0 += 512; x1 += 512; x2 += 512; x3 += 512; + v[ 0] = (x0+t3) >> 10; + v[56] = (x0-t3) >> 10; + v[ 8] = (x1+t2) >> 10; + v[48] = (x1-t2) >> 10; + v[16] = (x2+t1) >> 10; + v[40] = (x2-t1) >> 10; + v[24] = (x3+t0) >> 10; + v[32] = (x3-t0) >> 10; + } + } + + for (i=0, v=val, o=out; i < 8; ++i,v+=8,o+=out_stride) { + // no fast case since the first 1D IDCT spread components out + STBI__IDCT_1D(v[0],v[1],v[2],v[3],v[4],v[5],v[6],v[7]) + // constants scaled things up by 1<<12, plus we had 1<<2 from first + // loop, plus horizontal and vertical each scale by sqrt(8) so together + // we've got an extra 1<<3, so 1<<17 total we need to remove. + // so we want to round that, which means adding 0.5 * 1<<17, + // aka 65536. Also, we'll end up with -128 to 127 that we want + // to encode as 0..255 by adding 128, so we'll add that before the shift + x0 += 65536 + (128<<17); + x1 += 65536 + (128<<17); + x2 += 65536 + (128<<17); + x3 += 65536 + (128<<17); + // tried computing the shifts into temps, or'ing the temps to see + // if any were out of range, but that was slower + o[0] = stbi__clamp((x0+t3) >> 17); + o[7] = stbi__clamp((x0-t3) >> 17); + o[1] = stbi__clamp((x1+t2) >> 17); + o[6] = stbi__clamp((x1-t2) >> 17); + o[2] = stbi__clamp((x2+t1) >> 17); + o[5] = stbi__clamp((x2-t1) >> 17); + o[3] = stbi__clamp((x3+t0) >> 17); + o[4] = stbi__clamp((x3-t0) >> 17); + } +} + +#ifdef STBI_SSE2 +// sse2 integer IDCT. not the fastest possible implementation but it +// produces bit-identical results to the generic C version so it's +// fully "transparent". +static void stbi__idct_simd(stbi_uc *out, int out_stride, short data[64]) +{ + // This is constructed to match our regular (generic) integer IDCT exactly. + __m128i row0, row1, row2, row3, row4, row5, row6, row7; + __m128i tmp; + + // dot product constant: even elems=x, odd elems=y + #define dct_const(x,y) _mm_setr_epi16((x),(y),(x),(y),(x),(y),(x),(y)) + + // out(0) = c0[even]*x + c0[odd]*y (c0, x, y 16-bit, out 32-bit) + // out(1) = c1[even]*x + c1[odd]*y + #define dct_rot(out0,out1, x,y,c0,c1) \ + __m128i c0##lo = _mm_unpacklo_epi16((x),(y)); \ + __m128i c0##hi = _mm_unpackhi_epi16((x),(y)); \ + __m128i out0##_l = _mm_madd_epi16(c0##lo, c0); \ + __m128i out0##_h = _mm_madd_epi16(c0##hi, c0); \ + __m128i out1##_l = _mm_madd_epi16(c0##lo, c1); \ + __m128i out1##_h = _mm_madd_epi16(c0##hi, c1) + + // out = in << 12 (in 16-bit, out 32-bit) + #define dct_widen(out, in) \ + __m128i out##_l = _mm_srai_epi32(_mm_unpacklo_epi16(_mm_setzero_si128(), (in)), 4); \ + __m128i out##_h = _mm_srai_epi32(_mm_unpackhi_epi16(_mm_setzero_si128(), (in)), 4) + + // wide add + #define dct_wadd(out, a, b) \ + __m128i out##_l = _mm_add_epi32(a##_l, b##_l); \ + __m128i out##_h = _mm_add_epi32(a##_h, b##_h) + + // wide sub + #define dct_wsub(out, a, b) \ + __m128i out##_l = _mm_sub_epi32(a##_l, b##_l); \ + __m128i out##_h = _mm_sub_epi32(a##_h, b##_h) + + // butterfly a/b, add bias, then shift by "s" and pack + #define dct_bfly32o(out0, out1, a,b,bias,s) \ + { \ + __m128i abiased_l = _mm_add_epi32(a##_l, bias); \ + __m128i abiased_h = _mm_add_epi32(a##_h, bias); \ + dct_wadd(sum, abiased, b); \ + dct_wsub(dif, abiased, b); \ + out0 = _mm_packs_epi32(_mm_srai_epi32(sum_l, s), _mm_srai_epi32(sum_h, s)); \ + out1 = _mm_packs_epi32(_mm_srai_epi32(dif_l, s), _mm_srai_epi32(dif_h, s)); \ + } + + // 8-bit interleave step (for transposes) + #define dct_interleave8(a, b) \ + tmp = a; \ + a = _mm_unpacklo_epi8(a, b); \ + b = _mm_unpackhi_epi8(tmp, b) + + // 16-bit interleave step (for transposes) + #define dct_interleave16(a, b) \ + tmp = a; \ + a = _mm_unpacklo_epi16(a, b); \ + b = _mm_unpackhi_epi16(tmp, b) + + #define dct_pass(bias,shift) \ + { \ + /* even part */ \ + dct_rot(t2e,t3e, row2,row6, rot0_0,rot0_1); \ + __m128i sum04 = _mm_add_epi16(row0, row4); \ + __m128i dif04 = _mm_sub_epi16(row0, row4); \ + dct_widen(t0e, sum04); \ + dct_widen(t1e, dif04); \ + dct_wadd(x0, t0e, t3e); \ + dct_wsub(x3, t0e, t3e); \ + dct_wadd(x1, t1e, t2e); \ + dct_wsub(x2, t1e, t2e); \ + /* odd part */ \ + dct_rot(y0o,y2o, row7,row3, rot2_0,rot2_1); \ + dct_rot(y1o,y3o, row5,row1, rot3_0,rot3_1); \ + __m128i sum17 = _mm_add_epi16(row1, row7); \ + __m128i sum35 = _mm_add_epi16(row3, row5); \ + dct_rot(y4o,y5o, sum17,sum35, rot1_0,rot1_1); \ + dct_wadd(x4, y0o, y4o); \ + dct_wadd(x5, y1o, y5o); \ + dct_wadd(x6, y2o, y5o); \ + dct_wadd(x7, y3o, y4o); \ + dct_bfly32o(row0,row7, x0,x7,bias,shift); \ + dct_bfly32o(row1,row6, x1,x6,bias,shift); \ + dct_bfly32o(row2,row5, x2,x5,bias,shift); \ + dct_bfly32o(row3,row4, x3,x4,bias,shift); \ + } + + __m128i rot0_0 = dct_const(stbi__f2f(0.5411961f), stbi__f2f(0.5411961f) + stbi__f2f(-1.847759065f)); + __m128i rot0_1 = dct_const(stbi__f2f(0.5411961f) + stbi__f2f( 0.765366865f), stbi__f2f(0.5411961f)); + __m128i rot1_0 = dct_const(stbi__f2f(1.175875602f) + stbi__f2f(-0.899976223f), stbi__f2f(1.175875602f)); + __m128i rot1_1 = dct_const(stbi__f2f(1.175875602f), stbi__f2f(1.175875602f) + stbi__f2f(-2.562915447f)); + __m128i rot2_0 = dct_const(stbi__f2f(-1.961570560f) + stbi__f2f( 0.298631336f), stbi__f2f(-1.961570560f)); + __m128i rot2_1 = dct_const(stbi__f2f(-1.961570560f), stbi__f2f(-1.961570560f) + stbi__f2f( 3.072711026f)); + __m128i rot3_0 = dct_const(stbi__f2f(-0.390180644f) + stbi__f2f( 2.053119869f), stbi__f2f(-0.390180644f)); + __m128i rot3_1 = dct_const(stbi__f2f(-0.390180644f), stbi__f2f(-0.390180644f) + stbi__f2f( 1.501321110f)); + + // rounding biases in column/row passes, see stbi__idct_block for explanation. + __m128i bias_0 = _mm_set1_epi32(512); + __m128i bias_1 = _mm_set1_epi32(65536 + (128<<17)); + + // load + row0 = _mm_load_si128((const __m128i *) (data + 0*8)); + row1 = _mm_load_si128((const __m128i *) (data + 1*8)); + row2 = _mm_load_si128((const __m128i *) (data + 2*8)); + row3 = _mm_load_si128((const __m128i *) (data + 3*8)); + row4 = _mm_load_si128((const __m128i *) (data + 4*8)); + row5 = _mm_load_si128((const __m128i *) (data + 5*8)); + row6 = _mm_load_si128((const __m128i *) (data + 6*8)); + row7 = _mm_load_si128((const __m128i *) (data + 7*8)); + + // column pass + dct_pass(bias_0, 10); + + { + // 16bit 8x8 transpose pass 1 + dct_interleave16(row0, row4); + dct_interleave16(row1, row5); + dct_interleave16(row2, row6); + dct_interleave16(row3, row7); + + // transpose pass 2 + dct_interleave16(row0, row2); + dct_interleave16(row1, row3); + dct_interleave16(row4, row6); + dct_interleave16(row5, row7); + + // transpose pass 3 + dct_interleave16(row0, row1); + dct_interleave16(row2, row3); + dct_interleave16(row4, row5); + dct_interleave16(row6, row7); + } + + // row pass + dct_pass(bias_1, 17); + + { + // pack + __m128i p0 = _mm_packus_epi16(row0, row1); // a0a1a2a3...a7b0b1b2b3...b7 + __m128i p1 = _mm_packus_epi16(row2, row3); + __m128i p2 = _mm_packus_epi16(row4, row5); + __m128i p3 = _mm_packus_epi16(row6, row7); + + // 8bit 8x8 transpose pass 1 + dct_interleave8(p0, p2); // a0e0a1e1... + dct_interleave8(p1, p3); // c0g0c1g1... + + // transpose pass 2 + dct_interleave8(p0, p1); // a0c0e0g0... + dct_interleave8(p2, p3); // b0d0f0h0... + + // transpose pass 3 + dct_interleave8(p0, p2); // a0b0c0d0... + dct_interleave8(p1, p3); // a4b4c4d4... + + // store + _mm_storel_epi64((__m128i *) out, p0); out += out_stride; + _mm_storel_epi64((__m128i *) out, _mm_shuffle_epi32(p0, 0x4e)); out += out_stride; + _mm_storel_epi64((__m128i *) out, p2); out += out_stride; + _mm_storel_epi64((__m128i *) out, _mm_shuffle_epi32(p2, 0x4e)); out += out_stride; + _mm_storel_epi64((__m128i *) out, p1); out += out_stride; + _mm_storel_epi64((__m128i *) out, _mm_shuffle_epi32(p1, 0x4e)); out += out_stride; + _mm_storel_epi64((__m128i *) out, p3); out += out_stride; + _mm_storel_epi64((__m128i *) out, _mm_shuffle_epi32(p3, 0x4e)); + } + +#undef dct_const +#undef dct_rot +#undef dct_widen +#undef dct_wadd +#undef dct_wsub +#undef dct_bfly32o +#undef dct_interleave8 +#undef dct_interleave16 +#undef dct_pass +} + +#endif // STBI_SSE2 + +#ifdef STBI_NEON + +// NEON integer IDCT. should produce bit-identical +// results to the generic C version. +static void stbi__idct_simd(stbi_uc *out, int out_stride, short data[64]) +{ + int16x8_t row0, row1, row2, row3, row4, row5, row6, row7; + + int16x4_t rot0_0 = vdup_n_s16(stbi__f2f(0.5411961f)); + int16x4_t rot0_1 = vdup_n_s16(stbi__f2f(-1.847759065f)); + int16x4_t rot0_2 = vdup_n_s16(stbi__f2f( 0.765366865f)); + int16x4_t rot1_0 = vdup_n_s16(stbi__f2f( 1.175875602f)); + int16x4_t rot1_1 = vdup_n_s16(stbi__f2f(-0.899976223f)); + int16x4_t rot1_2 = vdup_n_s16(stbi__f2f(-2.562915447f)); + int16x4_t rot2_0 = vdup_n_s16(stbi__f2f(-1.961570560f)); + int16x4_t rot2_1 = vdup_n_s16(stbi__f2f(-0.390180644f)); + int16x4_t rot3_0 = vdup_n_s16(stbi__f2f( 0.298631336f)); + int16x4_t rot3_1 = vdup_n_s16(stbi__f2f( 2.053119869f)); + int16x4_t rot3_2 = vdup_n_s16(stbi__f2f( 3.072711026f)); + int16x4_t rot3_3 = vdup_n_s16(stbi__f2f( 1.501321110f)); + +#define dct_long_mul(out, inq, coeff) \ + int32x4_t out##_l = vmull_s16(vget_low_s16(inq), coeff); \ + int32x4_t out##_h = vmull_s16(vget_high_s16(inq), coeff) + +#define dct_long_mac(out, acc, inq, coeff) \ + int32x4_t out##_l = vmlal_s16(acc##_l, vget_low_s16(inq), coeff); \ + int32x4_t out##_h = vmlal_s16(acc##_h, vget_high_s16(inq), coeff) + +#define dct_widen(out, inq) \ + int32x4_t out##_l = vshll_n_s16(vget_low_s16(inq), 12); \ + int32x4_t out##_h = vshll_n_s16(vget_high_s16(inq), 12) + +// wide add +#define dct_wadd(out, a, b) \ + int32x4_t out##_l = vaddq_s32(a##_l, b##_l); \ + int32x4_t out##_h = vaddq_s32(a##_h, b##_h) + +// wide sub +#define dct_wsub(out, a, b) \ + int32x4_t out##_l = vsubq_s32(a##_l, b##_l); \ + int32x4_t out##_h = vsubq_s32(a##_h, b##_h) + +// butterfly a/b, then shift using "shiftop" by "s" and pack +#define dct_bfly32o(out0,out1, a,b,shiftop,s) \ + { \ + dct_wadd(sum, a, b); \ + dct_wsub(dif, a, b); \ + out0 = vcombine_s16(shiftop(sum_l, s), shiftop(sum_h, s)); \ + out1 = vcombine_s16(shiftop(dif_l, s), shiftop(dif_h, s)); \ + } + +#define dct_pass(shiftop, shift) \ + { \ + /* even part */ \ + int16x8_t sum26 = vaddq_s16(row2, row6); \ + dct_long_mul(p1e, sum26, rot0_0); \ + dct_long_mac(t2e, p1e, row6, rot0_1); \ + dct_long_mac(t3e, p1e, row2, rot0_2); \ + int16x8_t sum04 = vaddq_s16(row0, row4); \ + int16x8_t dif04 = vsubq_s16(row0, row4); \ + dct_widen(t0e, sum04); \ + dct_widen(t1e, dif04); \ + dct_wadd(x0, t0e, t3e); \ + dct_wsub(x3, t0e, t3e); \ + dct_wadd(x1, t1e, t2e); \ + dct_wsub(x2, t1e, t2e); \ + /* odd part */ \ + int16x8_t sum15 = vaddq_s16(row1, row5); \ + int16x8_t sum17 = vaddq_s16(row1, row7); \ + int16x8_t sum35 = vaddq_s16(row3, row5); \ + int16x8_t sum37 = vaddq_s16(row3, row7); \ + int16x8_t sumodd = vaddq_s16(sum17, sum35); \ + dct_long_mul(p5o, sumodd, rot1_0); \ + dct_long_mac(p1o, p5o, sum17, rot1_1); \ + dct_long_mac(p2o, p5o, sum35, rot1_2); \ + dct_long_mul(p3o, sum37, rot2_0); \ + dct_long_mul(p4o, sum15, rot2_1); \ + dct_wadd(sump13o, p1o, p3o); \ + dct_wadd(sump24o, p2o, p4o); \ + dct_wadd(sump23o, p2o, p3o); \ + dct_wadd(sump14o, p1o, p4o); \ + dct_long_mac(x4, sump13o, row7, rot3_0); \ + dct_long_mac(x5, sump24o, row5, rot3_1); \ + dct_long_mac(x6, sump23o, row3, rot3_2); \ + dct_long_mac(x7, sump14o, row1, rot3_3); \ + dct_bfly32o(row0,row7, x0,x7,shiftop,shift); \ + dct_bfly32o(row1,row6, x1,x6,shiftop,shift); \ + dct_bfly32o(row2,row5, x2,x5,shiftop,shift); \ + dct_bfly32o(row3,row4, x3,x4,shiftop,shift); \ + } + + // load + row0 = vld1q_s16(data + 0*8); + row1 = vld1q_s16(data + 1*8); + row2 = vld1q_s16(data + 2*8); + row3 = vld1q_s16(data + 3*8); + row4 = vld1q_s16(data + 4*8); + row5 = vld1q_s16(data + 5*8); + row6 = vld1q_s16(data + 6*8); + row7 = vld1q_s16(data + 7*8); + + // add DC bias + row0 = vaddq_s16(row0, vsetq_lane_s16(1024, vdupq_n_s16(0), 0)); + + // column pass + dct_pass(vrshrn_n_s32, 10); + + // 16bit 8x8 transpose + { +// these three map to a single VTRN.16, VTRN.32, and VSWP, respectively. +// whether compilers actually get this is another story, sadly. +#define dct_trn16(x, y) { int16x8x2_t t = vtrnq_s16(x, y); x = t.val[0]; y = t.val[1]; } +#define dct_trn32(x, y) { int32x4x2_t t = vtrnq_s32(vreinterpretq_s32_s16(x), vreinterpretq_s32_s16(y)); x = vreinterpretq_s16_s32(t.val[0]); y = vreinterpretq_s16_s32(t.val[1]); } +#define dct_trn64(x, y) { int16x8_t x0 = x; int16x8_t y0 = y; x = vcombine_s16(vget_low_s16(x0), vget_low_s16(y0)); y = vcombine_s16(vget_high_s16(x0), vget_high_s16(y0)); } + + // pass 1 + dct_trn16(row0, row1); // a0b0a2b2a4b4a6b6 + dct_trn16(row2, row3); + dct_trn16(row4, row5); + dct_trn16(row6, row7); + + // pass 2 + dct_trn32(row0, row2); // a0b0c0d0a4b4c4d4 + dct_trn32(row1, row3); + dct_trn32(row4, row6); + dct_trn32(row5, row7); + + // pass 3 + dct_trn64(row0, row4); // a0b0c0d0e0f0g0h0 + dct_trn64(row1, row5); + dct_trn64(row2, row6); + dct_trn64(row3, row7); + +#undef dct_trn16 +#undef dct_trn32 +#undef dct_trn64 + } + + // row pass + // vrshrn_n_s32 only supports shifts up to 16, we need + // 17. so do a non-rounding shift of 16 first then follow + // up with a rounding shift by 1. + dct_pass(vshrn_n_s32, 16); + + { + // pack and round + uint8x8_t p0 = vqrshrun_n_s16(row0, 1); + uint8x8_t p1 = vqrshrun_n_s16(row1, 1); + uint8x8_t p2 = vqrshrun_n_s16(row2, 1); + uint8x8_t p3 = vqrshrun_n_s16(row3, 1); + uint8x8_t p4 = vqrshrun_n_s16(row4, 1); + uint8x8_t p5 = vqrshrun_n_s16(row5, 1); + uint8x8_t p6 = vqrshrun_n_s16(row6, 1); + uint8x8_t p7 = vqrshrun_n_s16(row7, 1); + + // again, these can translate into one instruction, but often don't. +#define dct_trn8_8(x, y) { uint8x8x2_t t = vtrn_u8(x, y); x = t.val[0]; y = t.val[1]; } +#define dct_trn8_16(x, y) { uint16x4x2_t t = vtrn_u16(vreinterpret_u16_u8(x), vreinterpret_u16_u8(y)); x = vreinterpret_u8_u16(t.val[0]); y = vreinterpret_u8_u16(t.val[1]); } +#define dct_trn8_32(x, y) { uint32x2x2_t t = vtrn_u32(vreinterpret_u32_u8(x), vreinterpret_u32_u8(y)); x = vreinterpret_u8_u32(t.val[0]); y = vreinterpret_u8_u32(t.val[1]); } + + // sadly can't use interleaved stores here since we only write + // 8 bytes to each scan line! + + // 8x8 8-bit transpose pass 1 + dct_trn8_8(p0, p1); + dct_trn8_8(p2, p3); + dct_trn8_8(p4, p5); + dct_trn8_8(p6, p7); + + // pass 2 + dct_trn8_16(p0, p2); + dct_trn8_16(p1, p3); + dct_trn8_16(p4, p6); + dct_trn8_16(p5, p7); + + // pass 3 + dct_trn8_32(p0, p4); + dct_trn8_32(p1, p5); + dct_trn8_32(p2, p6); + dct_trn8_32(p3, p7); + + // store + vst1_u8(out, p0); out += out_stride; + vst1_u8(out, p1); out += out_stride; + vst1_u8(out, p2); out += out_stride; + vst1_u8(out, p3); out += out_stride; + vst1_u8(out, p4); out += out_stride; + vst1_u8(out, p5); out += out_stride; + vst1_u8(out, p6); out += out_stride; + vst1_u8(out, p7); + +#undef dct_trn8_8 +#undef dct_trn8_16 +#undef dct_trn8_32 + } + +#undef dct_long_mul +#undef dct_long_mac +#undef dct_widen +#undef dct_wadd +#undef dct_wsub +#undef dct_bfly32o +#undef dct_pass +} + +#endif // STBI_NEON + +#define STBI__MARKER_none 0xff +// if there's a pending marker from the entropy stream, return that +// otherwise, fetch from the stream and get a marker. if there's no +// marker, return 0xff, which is never a valid marker value +static stbi_uc stbi__get_marker(stbi__jpeg *j) +{ + stbi_uc x; + if (j->marker != STBI__MARKER_none) { x = j->marker; j->marker = STBI__MARKER_none; return x; } + x = stbi__get8(j->s); + if (x != 0xff) return STBI__MARKER_none; + while (x == 0xff) + x = stbi__get8(j->s); // consume repeated 0xff fill bytes + return x; +} + +// in each scan, we'll have scan_n components, and the order +// of the components is specified by order[] +#define STBI__RESTART(x) ((x) >= 0xd0 && (x) <= 0xd7) + +// after a restart interval, stbi__jpeg_reset the entropy decoder and +// the dc prediction +static void stbi__jpeg_reset(stbi__jpeg *j) +{ + j->code_bits = 0; + j->code_buffer = 0; + j->nomore = 0; + j->img_comp[0].dc_pred = j->img_comp[1].dc_pred = j->img_comp[2].dc_pred = j->img_comp[3].dc_pred = 0; + j->marker = STBI__MARKER_none; + j->todo = j->restart_interval ? j->restart_interval : 0x7fffffff; + j->eob_run = 0; + // no more than 1<<31 MCUs if no restart_interal? that's plenty safe, + // since we don't even allow 1<<30 pixels +} + +static int stbi__parse_entropy_coded_data(stbi__jpeg *z) +{ + stbi__jpeg_reset(z); + if (!z->progressive) { + if (z->scan_n == 1) { + int i,j; + STBI_SIMD_ALIGN(short, data[64]); + int n = z->order[0]; + // non-interleaved data, we just need to process one block at a time, + // in trivial scanline order + // number of blocks to do just depends on how many actual "pixels" this + // component has, independent of interleaved MCU blocking and such + int w = (z->img_comp[n].x+7) >> 3; + int h = (z->img_comp[n].y+7) >> 3; + for (j=0; j < h; ++j) { + for (i=0; i < w; ++i) { + int ha = z->img_comp[n].ha; + if (!stbi__jpeg_decode_block(z, data, z->huff_dc+z->img_comp[n].hd, z->huff_ac+ha, z->fast_ac[ha], n, z->dequant[z->img_comp[n].tq])) return 0; + z->idct_block_kernel(z->img_comp[n].data+z->img_comp[n].w2*j*8+i*8, z->img_comp[n].w2, data); + // every data block is an MCU, so countdown the restart interval + if (--z->todo <= 0) { + if (z->code_bits < 24) stbi__grow_buffer_unsafe(z); + // if it's NOT a restart, then just bail, so we get corrupt data + // rather than no data + if (!STBI__RESTART(z->marker)) return 1; + stbi__jpeg_reset(z); + } + } + } + return 1; + } else { // interleaved + int i,j,k,x,y; + STBI_SIMD_ALIGN(short, data[64]); + for (j=0; j < z->img_mcu_y; ++j) { + for (i=0; i < z->img_mcu_x; ++i) { + // scan an interleaved mcu... process scan_n components in order + for (k=0; k < z->scan_n; ++k) { + int n = z->order[k]; + // scan out an mcu's worth of this component; that's just determined + // by the basic H and V specified for the component + for (y=0; y < z->img_comp[n].v; ++y) { + for (x=0; x < z->img_comp[n].h; ++x) { + int x2 = (i*z->img_comp[n].h + x)*8; + int y2 = (j*z->img_comp[n].v + y)*8; + int ha = z->img_comp[n].ha; + if (!stbi__jpeg_decode_block(z, data, z->huff_dc+z->img_comp[n].hd, z->huff_ac+ha, z->fast_ac[ha], n, z->dequant[z->img_comp[n].tq])) return 0; + z->idct_block_kernel(z->img_comp[n].data+z->img_comp[n].w2*y2+x2, z->img_comp[n].w2, data); + } + } + } + // after all interleaved components, that's an interleaved MCU, + // so now count down the restart interval + if (--z->todo <= 0) { + if (z->code_bits < 24) stbi__grow_buffer_unsafe(z); + if (!STBI__RESTART(z->marker)) return 1; + stbi__jpeg_reset(z); + } + } + } + return 1; + } + } else { + if (z->scan_n == 1) { + int i,j; + int n = z->order[0]; + // non-interleaved data, we just need to process one block at a time, + // in trivial scanline order + // number of blocks to do just depends on how many actual "pixels" this + // component has, independent of interleaved MCU blocking and such + int w = (z->img_comp[n].x+7) >> 3; + int h = (z->img_comp[n].y+7) >> 3; + for (j=0; j < h; ++j) { + for (i=0; i < w; ++i) { + short *data = z->img_comp[n].coeff + 64 * (i + j * z->img_comp[n].coeff_w); + if (z->spec_start == 0) { + if (!stbi__jpeg_decode_block_prog_dc(z, data, &z->huff_dc[z->img_comp[n].hd], n)) + return 0; + } else { + int ha = z->img_comp[n].ha; + if (!stbi__jpeg_decode_block_prog_ac(z, data, &z->huff_ac[ha], z->fast_ac[ha])) + return 0; + } + // every data block is an MCU, so countdown the restart interval + if (--z->todo <= 0) { + if (z->code_bits < 24) stbi__grow_buffer_unsafe(z); + if (!STBI__RESTART(z->marker)) return 1; + stbi__jpeg_reset(z); + } + } + } + return 1; + } else { // interleaved + int i,j,k,x,y; + for (j=0; j < z->img_mcu_y; ++j) { + for (i=0; i < z->img_mcu_x; ++i) { + // scan an interleaved mcu... process scan_n components in order + for (k=0; k < z->scan_n; ++k) { + int n = z->order[k]; + // scan out an mcu's worth of this component; that's just determined + // by the basic H and V specified for the component + for (y=0; y < z->img_comp[n].v; ++y) { + for (x=0; x < z->img_comp[n].h; ++x) { + int x2 = (i*z->img_comp[n].h + x); + int y2 = (j*z->img_comp[n].v + y); + short *data = z->img_comp[n].coeff + 64 * (x2 + y2 * z->img_comp[n].coeff_w); + if (!stbi__jpeg_decode_block_prog_dc(z, data, &z->huff_dc[z->img_comp[n].hd], n)) + return 0; + } + } + } + // after all interleaved components, that's an interleaved MCU, + // so now count down the restart interval + if (--z->todo <= 0) { + if (z->code_bits < 24) stbi__grow_buffer_unsafe(z); + if (!STBI__RESTART(z->marker)) return 1; + stbi__jpeg_reset(z); + } + } + } + return 1; + } + } +} + +static void stbi__jpeg_dequantize(short *data, stbi__uint16 *dequant) +{ + int i; + for (i=0; i < 64; ++i) + data[i] *= dequant[i]; +} + +static void stbi__jpeg_finish(stbi__jpeg *z) +{ + if (z->progressive) { + // dequantize and idct the data + int i,j,n; + for (n=0; n < z->s->img_n; ++n) { + int w = (z->img_comp[n].x+7) >> 3; + int h = (z->img_comp[n].y+7) >> 3; + for (j=0; j < h; ++j) { + for (i=0; i < w; ++i) { + short *data = z->img_comp[n].coeff + 64 * (i + j * z->img_comp[n].coeff_w); + stbi__jpeg_dequantize(data, z->dequant[z->img_comp[n].tq]); + z->idct_block_kernel(z->img_comp[n].data+z->img_comp[n].w2*j*8+i*8, z->img_comp[n].w2, data); + } + } + } + } +} + +static int stbi__process_marker(stbi__jpeg *z, int m) +{ + int L; + switch (m) { + case STBI__MARKER_none: // no marker found + return stbi__err("expected marker","Corrupt JPEG"); + + case 0xDD: // DRI - specify restart interval + if (stbi__get16be(z->s) != 4) return stbi__err("bad DRI len","Corrupt JPEG"); + z->restart_interval = stbi__get16be(z->s); + return 1; + + case 0xDB: // DQT - define quantization table + L = stbi__get16be(z->s)-2; + while (L > 0) { + int q = stbi__get8(z->s); + int p = q >> 4, sixteen = (p != 0); + int t = q & 15,i; + if (p != 0 && p != 1) return stbi__err("bad DQT type","Corrupt JPEG"); + if (t > 3) return stbi__err("bad DQT table","Corrupt JPEG"); + + for (i=0; i < 64; ++i) + z->dequant[t][stbi__jpeg_dezigzag[i]] = (stbi__uint16)(sixteen ? stbi__get16be(z->s) : stbi__get8(z->s)); + L -= (sixteen ? 129 : 65); + } + return L==0; + + case 0xC4: // DHT - define huffman table + L = stbi__get16be(z->s)-2; + while (L > 0) { + stbi_uc *v; + int sizes[16],i,n=0; + int q = stbi__get8(z->s); + int tc = q >> 4; + int th = q & 15; + if (tc > 1 || th > 3) return stbi__err("bad DHT header","Corrupt JPEG"); + for (i=0; i < 16; ++i) { + sizes[i] = stbi__get8(z->s); + n += sizes[i]; + } + L -= 17; + if (tc == 0) { + if (!stbi__build_huffman(z->huff_dc+th, sizes)) return 0; + v = z->huff_dc[th].values; + } else { + if (!stbi__build_huffman(z->huff_ac+th, sizes)) return 0; + v = z->huff_ac[th].values; + } + for (i=0; i < n; ++i) + v[i] = stbi__get8(z->s); + if (tc != 0) + stbi__build_fast_ac(z->fast_ac[th], z->huff_ac + th); + L -= n; + } + return L==0; + } + + // check for comment block or APP blocks + if ((m >= 0xE0 && m <= 0xEF) || m == 0xFE) { + L = stbi__get16be(z->s); + if (L < 2) { + if (m == 0xFE) + return stbi__err("bad COM len","Corrupt JPEG"); + else + return stbi__err("bad APP len","Corrupt JPEG"); + } + L -= 2; + + if (m == 0xE0 && L >= 5) { // JFIF APP0 segment + static const unsigned char tag[5] = {'J','F','I','F','\0'}; + int ok = 1; + int i; + for (i=0; i < 5; ++i) + if (stbi__get8(z->s) != tag[i]) + ok = 0; + L -= 5; + if (ok) + z->jfif = 1; + } else if (m == 0xEE && L >= 12) { // Adobe APP14 segment + static const unsigned char tag[6] = {'A','d','o','b','e','\0'}; + int ok = 1; + int i; + for (i=0; i < 6; ++i) + if (stbi__get8(z->s) != tag[i]) + ok = 0; + L -= 6; + if (ok) { + stbi__get8(z->s); // version + stbi__get16be(z->s); // flags0 + stbi__get16be(z->s); // flags1 + z->app14_color_transform = stbi__get8(z->s); // color transform + L -= 6; + } + } + + stbi__skip(z->s, L); + return 1; + } + + return stbi__err("unknown marker","Corrupt JPEG"); +} + +// after we see SOS +static int stbi__process_scan_header(stbi__jpeg *z) +{ + int i; + int Ls = stbi__get16be(z->s); + z->scan_n = stbi__get8(z->s); + if (z->scan_n < 1 || z->scan_n > 4 || z->scan_n > (int) z->s->img_n) return stbi__err("bad SOS component count","Corrupt JPEG"); + if (Ls != 6+2*z->scan_n) return stbi__err("bad SOS len","Corrupt JPEG"); + for (i=0; i < z->scan_n; ++i) { + int id = stbi__get8(z->s), which; + int q = stbi__get8(z->s); + for (which = 0; which < z->s->img_n; ++which) + if (z->img_comp[which].id == id) + break; + if (which == z->s->img_n) return 0; // no match + z->img_comp[which].hd = q >> 4; if (z->img_comp[which].hd > 3) return stbi__err("bad DC huff","Corrupt JPEG"); + z->img_comp[which].ha = q & 15; if (z->img_comp[which].ha > 3) return stbi__err("bad AC huff","Corrupt JPEG"); + z->order[i] = which; + } + + { + int aa; + z->spec_start = stbi__get8(z->s); + z->spec_end = stbi__get8(z->s); // should be 63, but might be 0 + aa = stbi__get8(z->s); + z->succ_high = (aa >> 4); + z->succ_low = (aa & 15); + if (z->progressive) { + if (z->spec_start > 63 || z->spec_end > 63 || z->spec_start > z->spec_end || z->succ_high > 13 || z->succ_low > 13) + return stbi__err("bad SOS", "Corrupt JPEG"); + } else { + if (z->spec_start != 0) return stbi__err("bad SOS","Corrupt JPEG"); + if (z->succ_high != 0 || z->succ_low != 0) return stbi__err("bad SOS","Corrupt JPEG"); + z->spec_end = 63; + } + } + + return 1; +} + +static int stbi__free_jpeg_components(stbi__jpeg *z, int ncomp, int why) +{ + int i; + for (i=0; i < ncomp; ++i) { + if (z->img_comp[i].raw_data) { + STBI_FREE(z->img_comp[i].raw_data); + z->img_comp[i].raw_data = NULL; + z->img_comp[i].data = NULL; + } + if (z->img_comp[i].raw_coeff) { + STBI_FREE(z->img_comp[i].raw_coeff); + z->img_comp[i].raw_coeff = 0; + z->img_comp[i].coeff = 0; + } + if (z->img_comp[i].linebuf) { + STBI_FREE(z->img_comp[i].linebuf); + z->img_comp[i].linebuf = NULL; + } + } + return why; +} + +static int stbi__process_frame_header(stbi__jpeg *z, int scan) +{ + stbi__context *s = z->s; + int Lf,p,i,q, h_max=1,v_max=1,c; + Lf = stbi__get16be(s); if (Lf < 11) return stbi__err("bad SOF len","Corrupt JPEG"); // JPEG + p = stbi__get8(s); if (p != 8) return stbi__err("only 8-bit","JPEG format not supported: 8-bit only"); // JPEG baseline + s->img_y = stbi__get16be(s); if (s->img_y == 0) return stbi__err("no header height", "JPEG format not supported: delayed height"); // Legal, but we don't handle it--but neither does IJG + s->img_x = stbi__get16be(s); if (s->img_x == 0) return stbi__err("0 width","Corrupt JPEG"); // JPEG requires + c = stbi__get8(s); + if (c != 3 && c != 1 && c != 4) return stbi__err("bad component count","Corrupt JPEG"); + s->img_n = c; + for (i=0; i < c; ++i) { + z->img_comp[i].data = NULL; + z->img_comp[i].linebuf = NULL; + } + + if (Lf != 8+3*s->img_n) return stbi__err("bad SOF len","Corrupt JPEG"); + + z->rgb = 0; + for (i=0; i < s->img_n; ++i) { + static const unsigned char rgb[3] = { 'R', 'G', 'B' }; + z->img_comp[i].id = stbi__get8(s); + if (s->img_n == 3 && z->img_comp[i].id == rgb[i]) + ++z->rgb; + q = stbi__get8(s); + z->img_comp[i].h = (q >> 4); if (!z->img_comp[i].h || z->img_comp[i].h > 4) return stbi__err("bad H","Corrupt JPEG"); + z->img_comp[i].v = q & 15; if (!z->img_comp[i].v || z->img_comp[i].v > 4) return stbi__err("bad V","Corrupt JPEG"); + z->img_comp[i].tq = stbi__get8(s); if (z->img_comp[i].tq > 3) return stbi__err("bad TQ","Corrupt JPEG"); + } + + if (scan != STBI__SCAN_load) return 1; + + if (!stbi__mad3sizes_valid(s->img_x, s->img_y, s->img_n, 0)) return stbi__err("too large", "Image too large to decode"); + + for (i=0; i < s->img_n; ++i) { + if (z->img_comp[i].h > h_max) h_max = z->img_comp[i].h; + if (z->img_comp[i].v > v_max) v_max = z->img_comp[i].v; + } + + // compute interleaved mcu info + z->img_h_max = h_max; + z->img_v_max = v_max; + z->img_mcu_w = h_max * 8; + z->img_mcu_h = v_max * 8; + // these sizes can't be more than 17 bits + z->img_mcu_x = (s->img_x + z->img_mcu_w-1) / z->img_mcu_w; + z->img_mcu_y = (s->img_y + z->img_mcu_h-1) / z->img_mcu_h; + + for (i=0; i < s->img_n; ++i) { + // number of effective pixels (e.g. for non-interleaved MCU) + z->img_comp[i].x = (s->img_x * z->img_comp[i].h + h_max-1) / h_max; + z->img_comp[i].y = (s->img_y * z->img_comp[i].v + v_max-1) / v_max; + // to simplify generation, we'll allocate enough memory to decode + // the bogus oversized data from using interleaved MCUs and their + // big blocks (e.g. a 16x16 iMCU on an image of width 33); we won't + // discard the extra data until colorspace conversion + // + // img_mcu_x, img_mcu_y: <=17 bits; comp[i].h and .v are <=4 (checked earlier) + // so these muls can't overflow with 32-bit ints (which we require) + z->img_comp[i].w2 = z->img_mcu_x * z->img_comp[i].h * 8; + z->img_comp[i].h2 = z->img_mcu_y * z->img_comp[i].v * 8; + z->img_comp[i].coeff = 0; + z->img_comp[i].raw_coeff = 0; + z->img_comp[i].linebuf = NULL; + z->img_comp[i].raw_data = stbi__malloc_mad2(z->img_comp[i].w2, z->img_comp[i].h2, 15); + if (z->img_comp[i].raw_data == NULL) + return stbi__free_jpeg_components(z, i+1, stbi__err("outofmem", "Out of memory")); + // align blocks for idct using mmx/sse + z->img_comp[i].data = (stbi_uc*) (((size_t) z->img_comp[i].raw_data + 15) & ~15); + if (z->progressive) { + // w2, h2 are multiples of 8 (see above) + z->img_comp[i].coeff_w = z->img_comp[i].w2 / 8; + z->img_comp[i].coeff_h = z->img_comp[i].h2 / 8; + z->img_comp[i].raw_coeff = stbi__malloc_mad3(z->img_comp[i].w2, z->img_comp[i].h2, sizeof(short), 15); + if (z->img_comp[i].raw_coeff == NULL) + return stbi__free_jpeg_components(z, i+1, stbi__err("outofmem", "Out of memory")); + z->img_comp[i].coeff = (short*) (((size_t) z->img_comp[i].raw_coeff + 15) & ~15); + } + } + + return 1; +} + +// use comparisons since in some cases we handle more than one case (e.g. SOF) +#define stbi__DNL(x) ((x) == 0xdc) +#define stbi__SOI(x) ((x) == 0xd8) +#define stbi__EOI(x) ((x) == 0xd9) +#define stbi__SOF(x) ((x) == 0xc0 || (x) == 0xc1 || (x) == 0xc2) +#define stbi__SOS(x) ((x) == 0xda) + +#define stbi__SOF_progressive(x) ((x) == 0xc2) + +static int stbi__decode_jpeg_header(stbi__jpeg *z, int scan) +{ + int m; + z->jfif = 0; + z->app14_color_transform = -1; // valid values are 0,1,2 + z->marker = STBI__MARKER_none; // initialize cached marker to empty + m = stbi__get_marker(z); + if (!stbi__SOI(m)) return stbi__err("no SOI","Corrupt JPEG"); + if (scan == STBI__SCAN_type) return 1; + m = stbi__get_marker(z); + while (!stbi__SOF(m)) { + if (!stbi__process_marker(z,m)) return 0; + m = stbi__get_marker(z); + while (m == STBI__MARKER_none) { + // some files have extra padding after their blocks, so ok, we'll scan + if (stbi__at_eof(z->s)) return stbi__err("no SOF", "Corrupt JPEG"); + m = stbi__get_marker(z); + } + } + z->progressive = stbi__SOF_progressive(m); + if (!stbi__process_frame_header(z, scan)) return 0; + return 1; +} + +// decode image to YCbCr format +static int stbi__decode_jpeg_image(stbi__jpeg *j) +{ + int m; + for (m = 0; m < 4; m++) { + j->img_comp[m].raw_data = NULL; + j->img_comp[m].raw_coeff = NULL; + } + j->restart_interval = 0; + if (!stbi__decode_jpeg_header(j, STBI__SCAN_load)) return 0; + m = stbi__get_marker(j); + while (!stbi__EOI(m)) { + if (stbi__SOS(m)) { + if (!stbi__process_scan_header(j)) return 0; + if (!stbi__parse_entropy_coded_data(j)) return 0; + if (j->marker == STBI__MARKER_none ) { + // handle 0s at the end of image data from IP Kamera 9060 + while (!stbi__at_eof(j->s)) { + int x = stbi__get8(j->s); + if (x == 255) { + j->marker = stbi__get8(j->s); + break; + } + } + // if we reach eof without hitting a marker, stbi__get_marker() below will fail and we'll eventually return 0 + } + } else if (stbi__DNL(m)) { + int Ld = stbi__get16be(j->s); + stbi__uint32 NL = stbi__get16be(j->s); + if (Ld != 4) return stbi__err("bad DNL len", "Corrupt JPEG"); + if (NL != j->s->img_y) return stbi__err("bad DNL height", "Corrupt JPEG"); + } else { + if (!stbi__process_marker(j, m)) return 0; + } + m = stbi__get_marker(j); + } + if (j->progressive) + stbi__jpeg_finish(j); + return 1; +} + +// static jfif-centered resampling (across block boundaries) + +typedef stbi_uc *(*resample_row_func)(stbi_uc *out, stbi_uc *in0, stbi_uc *in1, + int w, int hs); + +#define stbi__div4(x) ((stbi_uc) ((x) >> 2)) + +static stbi_uc *resample_row_1(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs) +{ + STBI_NOTUSED(out); + STBI_NOTUSED(in_far); + STBI_NOTUSED(w); + STBI_NOTUSED(hs); + return in_near; +} + +static stbi_uc* stbi__resample_row_v_2(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs) +{ + // need to generate two samples vertically for every one in input + int i; + STBI_NOTUSED(hs); + for (i=0; i < w; ++i) + out[i] = stbi__div4(3*in_near[i] + in_far[i] + 2); + return out; +} + +static stbi_uc* stbi__resample_row_h_2(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs) +{ + // need to generate two samples horizontally for every one in input + int i; + stbi_uc *input = in_near; + + if (w == 1) { + // if only one sample, can't do any interpolation + out[0] = out[1] = input[0]; + return out; + } + + out[0] = input[0]; + out[1] = stbi__div4(input[0]*3 + input[1] + 2); + for (i=1; i < w-1; ++i) { + int n = 3*input[i]+2; + out[i*2+0] = stbi__div4(n+input[i-1]); + out[i*2+1] = stbi__div4(n+input[i+1]); + } + out[i*2+0] = stbi__div4(input[w-2]*3 + input[w-1] + 2); + out[i*2+1] = input[w-1]; + + STBI_NOTUSED(in_far); + STBI_NOTUSED(hs); + + return out; +} + +#define stbi__div16(x) ((stbi_uc) ((x) >> 4)) + +static stbi_uc *stbi__resample_row_hv_2(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs) +{ + // need to generate 2x2 samples for every one in input + int i,t0,t1; + if (w == 1) { + out[0] = out[1] = stbi__div4(3*in_near[0] + in_far[0] + 2); + return out; + } + + t1 = 3*in_near[0] + in_far[0]; + out[0] = stbi__div4(t1+2); + for (i=1; i < w; ++i) { + t0 = t1; + t1 = 3*in_near[i]+in_far[i]; + out[i*2-1] = stbi__div16(3*t0 + t1 + 8); + out[i*2 ] = stbi__div16(3*t1 + t0 + 8); + } + out[w*2-1] = stbi__div4(t1+2); + + STBI_NOTUSED(hs); + + return out; +} + +#if defined(STBI_SSE2) || defined(STBI_NEON) +static stbi_uc *stbi__resample_row_hv_2_simd(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs) +{ + // need to generate 2x2 samples for every one in input + int i=0,t0,t1; + + if (w == 1) { + out[0] = out[1] = stbi__div4(3*in_near[0] + in_far[0] + 2); + return out; + } + + t1 = 3*in_near[0] + in_far[0]; + // process groups of 8 pixels for as long as we can. + // note we can't handle the last pixel in a row in this loop + // because we need to handle the filter boundary conditions. + for (; i < ((w-1) & ~7); i += 8) { +#if defined(STBI_SSE2) + // load and perform the vertical filtering pass + // this uses 3*x + y = 4*x + (y - x) + __m128i zero = _mm_setzero_si128(); + __m128i farb = _mm_loadl_epi64((__m128i *) (in_far + i)); + __m128i nearb = _mm_loadl_epi64((__m128i *) (in_near + i)); + __m128i farw = _mm_unpacklo_epi8(farb, zero); + __m128i nearw = _mm_unpacklo_epi8(nearb, zero); + __m128i diff = _mm_sub_epi16(farw, nearw); + __m128i nears = _mm_slli_epi16(nearw, 2); + __m128i curr = _mm_add_epi16(nears, diff); // current row + + // horizontal filter works the same based on shifted vers of current + // row. "prev" is current row shifted right by 1 pixel; we need to + // insert the previous pixel value (from t1). + // "next" is current row shifted left by 1 pixel, with first pixel + // of next block of 8 pixels added in. + __m128i prv0 = _mm_slli_si128(curr, 2); + __m128i nxt0 = _mm_srli_si128(curr, 2); + __m128i prev = _mm_insert_epi16(prv0, t1, 0); + __m128i next = _mm_insert_epi16(nxt0, 3*in_near[i+8] + in_far[i+8], 7); + + // horizontal filter, polyphase implementation since it's convenient: + // even pixels = 3*cur + prev = cur*4 + (prev - cur) + // odd pixels = 3*cur + next = cur*4 + (next - cur) + // note the shared term. + __m128i bias = _mm_set1_epi16(8); + __m128i curs = _mm_slli_epi16(curr, 2); + __m128i prvd = _mm_sub_epi16(prev, curr); + __m128i nxtd = _mm_sub_epi16(next, curr); + __m128i curb = _mm_add_epi16(curs, bias); + __m128i even = _mm_add_epi16(prvd, curb); + __m128i odd = _mm_add_epi16(nxtd, curb); + + // interleave even and odd pixels, then undo scaling. + __m128i int0 = _mm_unpacklo_epi16(even, odd); + __m128i int1 = _mm_unpackhi_epi16(even, odd); + __m128i de0 = _mm_srli_epi16(int0, 4); + __m128i de1 = _mm_srli_epi16(int1, 4); + + // pack and write output + __m128i outv = _mm_packus_epi16(de0, de1); + _mm_storeu_si128((__m128i *) (out + i*2), outv); +#elif defined(STBI_NEON) + // load and perform the vertical filtering pass + // this uses 3*x + y = 4*x + (y - x) + uint8x8_t farb = vld1_u8(in_far + i); + uint8x8_t nearb = vld1_u8(in_near + i); + int16x8_t diff = vreinterpretq_s16_u16(vsubl_u8(farb, nearb)); + int16x8_t nears = vreinterpretq_s16_u16(vshll_n_u8(nearb, 2)); + int16x8_t curr = vaddq_s16(nears, diff); // current row + + // horizontal filter works the same based on shifted vers of current + // row. "prev" is current row shifted right by 1 pixel; we need to + // insert the previous pixel value (from t1). + // "next" is current row shifted left by 1 pixel, with first pixel + // of next block of 8 pixels added in. + int16x8_t prv0 = vextq_s16(curr, curr, 7); + int16x8_t nxt0 = vextq_s16(curr, curr, 1); + int16x8_t prev = vsetq_lane_s16(t1, prv0, 0); + int16x8_t next = vsetq_lane_s16(3*in_near[i+8] + in_far[i+8], nxt0, 7); + + // horizontal filter, polyphase implementation since it's convenient: + // even pixels = 3*cur + prev = cur*4 + (prev - cur) + // odd pixels = 3*cur + next = cur*4 + (next - cur) + // note the shared term. + int16x8_t curs = vshlq_n_s16(curr, 2); + int16x8_t prvd = vsubq_s16(prev, curr); + int16x8_t nxtd = vsubq_s16(next, curr); + int16x8_t even = vaddq_s16(curs, prvd); + int16x8_t odd = vaddq_s16(curs, nxtd); + + // undo scaling and round, then store with even/odd phases interleaved + uint8x8x2_t o; + o.val[0] = vqrshrun_n_s16(even, 4); + o.val[1] = vqrshrun_n_s16(odd, 4); + vst2_u8(out + i*2, o); +#endif + + // "previous" value for next iter + t1 = 3*in_near[i+7] + in_far[i+7]; + } + + t0 = t1; + t1 = 3*in_near[i] + in_far[i]; + out[i*2] = stbi__div16(3*t1 + t0 + 8); + + for (++i; i < w; ++i) { + t0 = t1; + t1 = 3*in_near[i]+in_far[i]; + out[i*2-1] = stbi__div16(3*t0 + t1 + 8); + out[i*2 ] = stbi__div16(3*t1 + t0 + 8); + } + out[w*2-1] = stbi__div4(t1+2); + + STBI_NOTUSED(hs); + + return out; +} +#endif + +static stbi_uc *stbi__resample_row_generic(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs) +{ + // resample with nearest-neighbor + int i,j; + STBI_NOTUSED(in_far); + for (i=0; i < w; ++i) + for (j=0; j < hs; ++j) + out[i*hs+j] = in_near[i]; + return out; +} + +// this is a reduced-precision calculation of YCbCr-to-RGB introduced +// to make sure the code produces the same results in both SIMD and scalar +#define stbi__float2fixed(x) (((int) ((x) * 4096.0f + 0.5f)) << 8) +static void stbi__YCbCr_to_RGB_row(stbi_uc *out, const stbi_uc *y, const stbi_uc *pcb, const stbi_uc *pcr, int count, int step) +{ + int i; + for (i=0; i < count; ++i) { + int y_fixed = (y[i] << 20) + (1<<19); // rounding + int r,g,b; + int cr = pcr[i] - 128; + int cb = pcb[i] - 128; + r = y_fixed + cr* stbi__float2fixed(1.40200f); + g = y_fixed + (cr*-stbi__float2fixed(0.71414f)) + ((cb*-stbi__float2fixed(0.34414f)) & 0xffff0000); + b = y_fixed + cb* stbi__float2fixed(1.77200f); + r >>= 20; + g >>= 20; + b >>= 20; + if ((unsigned) r > 255) { if (r < 0) r = 0; else r = 255; } + if ((unsigned) g > 255) { if (g < 0) g = 0; else g = 255; } + if ((unsigned) b > 255) { if (b < 0) b = 0; else b = 255; } + out[0] = (stbi_uc)r; + out[1] = (stbi_uc)g; + out[2] = (stbi_uc)b; + out[3] = 255; + out += step; + } +} + +#if defined(STBI_SSE2) || defined(STBI_NEON) +static void stbi__YCbCr_to_RGB_simd(stbi_uc *out, stbi_uc const *y, stbi_uc const *pcb, stbi_uc const *pcr, int count, int step) +{ + int i = 0; + +#ifdef STBI_SSE2 + // step == 3 is pretty ugly on the final interleave, and i'm not convinced + // it's useful in practice (you wouldn't use it for textures, for example). + // so just accelerate step == 4 case. + if (step == 4) { + // this is a fairly straightforward implementation and not super-optimized. + __m128i signflip = _mm_set1_epi8(-0x80); + __m128i cr_const0 = _mm_set1_epi16( (short) ( 1.40200f*4096.0f+0.5f)); + __m128i cr_const1 = _mm_set1_epi16( - (short) ( 0.71414f*4096.0f+0.5f)); + __m128i cb_const0 = _mm_set1_epi16( - (short) ( 0.34414f*4096.0f+0.5f)); + __m128i cb_const1 = _mm_set1_epi16( (short) ( 1.77200f*4096.0f+0.5f)); + __m128i y_bias = _mm_set1_epi8((char) (unsigned char) 128); + __m128i xw = _mm_set1_epi16(255); // alpha channel + + for (; i+7 < count; i += 8) { + // load + __m128i y_bytes = _mm_loadl_epi64((__m128i *) (y+i)); + __m128i cr_bytes = _mm_loadl_epi64((__m128i *) (pcr+i)); + __m128i cb_bytes = _mm_loadl_epi64((__m128i *) (pcb+i)); + __m128i cr_biased = _mm_xor_si128(cr_bytes, signflip); // -128 + __m128i cb_biased = _mm_xor_si128(cb_bytes, signflip); // -128 + + // unpack to short (and left-shift cr, cb by 8) + __m128i yw = _mm_unpacklo_epi8(y_bias, y_bytes); + __m128i crw = _mm_unpacklo_epi8(_mm_setzero_si128(), cr_biased); + __m128i cbw = _mm_unpacklo_epi8(_mm_setzero_si128(), cb_biased); + + // color transform + __m128i yws = _mm_srli_epi16(yw, 4); + __m128i cr0 = _mm_mulhi_epi16(cr_const0, crw); + __m128i cb0 = _mm_mulhi_epi16(cb_const0, cbw); + __m128i cb1 = _mm_mulhi_epi16(cbw, cb_const1); + __m128i cr1 = _mm_mulhi_epi16(crw, cr_const1); + __m128i rws = _mm_add_epi16(cr0, yws); + __m128i gwt = _mm_add_epi16(cb0, yws); + __m128i bws = _mm_add_epi16(yws, cb1); + __m128i gws = _mm_add_epi16(gwt, cr1); + + // descale + __m128i rw = _mm_srai_epi16(rws, 4); + __m128i bw = _mm_srai_epi16(bws, 4); + __m128i gw = _mm_srai_epi16(gws, 4); + + // back to byte, set up for transpose + __m128i brb = _mm_packus_epi16(rw, bw); + __m128i gxb = _mm_packus_epi16(gw, xw); + + // transpose to interleave channels + __m128i t0 = _mm_unpacklo_epi8(brb, gxb); + __m128i t1 = _mm_unpackhi_epi8(brb, gxb); + __m128i o0 = _mm_unpacklo_epi16(t0, t1); + __m128i o1 = _mm_unpackhi_epi16(t0, t1); + + // store + _mm_storeu_si128((__m128i *) (out + 0), o0); + _mm_storeu_si128((__m128i *) (out + 16), o1); + out += 32; + } + } +#endif + +#ifdef STBI_NEON + // in this version, step=3 support would be easy to add. but is there demand? + if (step == 4) { + // this is a fairly straightforward implementation and not super-optimized. + uint8x8_t signflip = vdup_n_u8(0x80); + int16x8_t cr_const0 = vdupq_n_s16( (short) ( 1.40200f*4096.0f+0.5f)); + int16x8_t cr_const1 = vdupq_n_s16( - (short) ( 0.71414f*4096.0f+0.5f)); + int16x8_t cb_const0 = vdupq_n_s16( - (short) ( 0.34414f*4096.0f+0.5f)); + int16x8_t cb_const1 = vdupq_n_s16( (short) ( 1.77200f*4096.0f+0.5f)); + + for (; i+7 < count; i += 8) { + // load + uint8x8_t y_bytes = vld1_u8(y + i); + uint8x8_t cr_bytes = vld1_u8(pcr + i); + uint8x8_t cb_bytes = vld1_u8(pcb + i); + int8x8_t cr_biased = vreinterpret_s8_u8(vsub_u8(cr_bytes, signflip)); + int8x8_t cb_biased = vreinterpret_s8_u8(vsub_u8(cb_bytes, signflip)); + + // expand to s16 + int16x8_t yws = vreinterpretq_s16_u16(vshll_n_u8(y_bytes, 4)); + int16x8_t crw = vshll_n_s8(cr_biased, 7); + int16x8_t cbw = vshll_n_s8(cb_biased, 7); + + // color transform + int16x8_t cr0 = vqdmulhq_s16(crw, cr_const0); + int16x8_t cb0 = vqdmulhq_s16(cbw, cb_const0); + int16x8_t cr1 = vqdmulhq_s16(crw, cr_const1); + int16x8_t cb1 = vqdmulhq_s16(cbw, cb_const1); + int16x8_t rws = vaddq_s16(yws, cr0); + int16x8_t gws = vaddq_s16(vaddq_s16(yws, cb0), cr1); + int16x8_t bws = vaddq_s16(yws, cb1); + + // undo scaling, round, convert to byte + uint8x8x4_t o; + o.val[0] = vqrshrun_n_s16(rws, 4); + o.val[1] = vqrshrun_n_s16(gws, 4); + o.val[2] = vqrshrun_n_s16(bws, 4); + o.val[3] = vdup_n_u8(255); + + // store, interleaving r/g/b/a + vst4_u8(out, o); + out += 8*4; + } + } +#endif + + for (; i < count; ++i) { + int y_fixed = (y[i] << 20) + (1<<19); // rounding + int r,g,b; + int cr = pcr[i] - 128; + int cb = pcb[i] - 128; + r = y_fixed + cr* stbi__float2fixed(1.40200f); + g = y_fixed + cr*-stbi__float2fixed(0.71414f) + ((cb*-stbi__float2fixed(0.34414f)) & 0xffff0000); + b = y_fixed + cb* stbi__float2fixed(1.77200f); + r >>= 20; + g >>= 20; + b >>= 20; + if ((unsigned) r > 255) { if (r < 0) r = 0; else r = 255; } + if ((unsigned) g > 255) { if (g < 0) g = 0; else g = 255; } + if ((unsigned) b > 255) { if (b < 0) b = 0; else b = 255; } + out[0] = (stbi_uc)r; + out[1] = (stbi_uc)g; + out[2] = (stbi_uc)b; + out[3] = 255; + out += step; + } +} +#endif + +// set up the kernels +static void stbi__setup_jpeg(stbi__jpeg *j) +{ + j->idct_block_kernel = stbi__idct_block; + j->YCbCr_to_RGB_kernel = stbi__YCbCr_to_RGB_row; + j->resample_row_hv_2_kernel = stbi__resample_row_hv_2; + +#ifdef STBI_SSE2 + if (stbi__sse2_available()) { + j->idct_block_kernel = stbi__idct_simd; + j->YCbCr_to_RGB_kernel = stbi__YCbCr_to_RGB_simd; + j->resample_row_hv_2_kernel = stbi__resample_row_hv_2_simd; + } +#endif + +#ifdef STBI_NEON + j->idct_block_kernel = stbi__idct_simd; + j->YCbCr_to_RGB_kernel = stbi__YCbCr_to_RGB_simd; + j->resample_row_hv_2_kernel = stbi__resample_row_hv_2_simd; +#endif +} + +// clean up the temporary component buffers +static void stbi__cleanup_jpeg(stbi__jpeg *j) +{ + stbi__free_jpeg_components(j, j->s->img_n, 0); +} + +typedef struct +{ + resample_row_func resample; + stbi_uc *line0,*line1; + int hs,vs; // expansion factor in each axis + int w_lores; // horizontal pixels pre-expansion + int ystep; // how far through vertical expansion we are + int ypos; // which pre-expansion row we're on +} stbi__resample; + +// fast 0..255 * 0..255 => 0..255 rounded multiplication +static stbi_uc stbi__blinn_8x8(stbi_uc x, stbi_uc y) +{ + unsigned int t = x*y + 128; + return (stbi_uc) ((t + (t >>8)) >> 8); +} + +static stbi_uc *load_jpeg_image(stbi__jpeg *z, int *out_x, int *out_y, int *comp, int req_comp) +{ + int n, decode_n, is_rgb; + z->s->img_n = 0; // make stbi__cleanup_jpeg safe + + // validate req_comp + if (req_comp < 0 || req_comp > 4) return stbi__errpuc("bad req_comp", "Internal error"); + + // load a jpeg image from whichever source, but leave in YCbCr format + if (!stbi__decode_jpeg_image(z)) { stbi__cleanup_jpeg(z); return NULL; } + + // determine actual number of components to generate + n = req_comp ? req_comp : z->s->img_n >= 3 ? 3 : 1; + + is_rgb = z->s->img_n == 3 && (z->rgb == 3 || (z->app14_color_transform == 0 && !z->jfif)); + + if (z->s->img_n == 3 && n < 3 && !is_rgb) + decode_n = 1; + else + decode_n = z->s->img_n; + + // resample and color-convert + { + int k; + unsigned int i,j; + stbi_uc *output; + stbi_uc *coutput[4] = { NULL, NULL, NULL, NULL }; + + stbi__resample res_comp[4]; + + for (k=0; k < decode_n; ++k) { + stbi__resample *r = &res_comp[k]; + + // allocate line buffer big enough for upsampling off the edges + // with upsample factor of 4 + z->img_comp[k].linebuf = (stbi_uc *) stbi__malloc(z->s->img_x + 3); + if (!z->img_comp[k].linebuf) { stbi__cleanup_jpeg(z); return stbi__errpuc("outofmem", "Out of memory"); } + + r->hs = z->img_h_max / z->img_comp[k].h; + r->vs = z->img_v_max / z->img_comp[k].v; + r->ystep = r->vs >> 1; + r->w_lores = (z->s->img_x + r->hs-1) / r->hs; + r->ypos = 0; + r->line0 = r->line1 = z->img_comp[k].data; + + if (r->hs == 1 && r->vs == 1) r->resample = resample_row_1; + else if (r->hs == 1 && r->vs == 2) r->resample = stbi__resample_row_v_2; + else if (r->hs == 2 && r->vs == 1) r->resample = stbi__resample_row_h_2; + else if (r->hs == 2 && r->vs == 2) r->resample = z->resample_row_hv_2_kernel; + else r->resample = stbi__resample_row_generic; + } + + // can't error after this so, this is safe + output = (stbi_uc *) stbi__malloc_mad3(n, z->s->img_x, z->s->img_y, 1); + if (!output) { stbi__cleanup_jpeg(z); return stbi__errpuc("outofmem", "Out of memory"); } + + // now go ahead and resample + for (j=0; j < z->s->img_y; ++j) { + stbi_uc *out = output + n * z->s->img_x * j; + for (k=0; k < decode_n; ++k) { + stbi__resample *r = &res_comp[k]; + int y_bot = r->ystep >= (r->vs >> 1); + coutput[k] = r->resample(z->img_comp[k].linebuf, + y_bot ? r->line1 : r->line0, + y_bot ? r->line0 : r->line1, + r->w_lores, r->hs); + if (++r->ystep >= r->vs) { + r->ystep = 0; + r->line0 = r->line1; + if (++r->ypos < z->img_comp[k].y) + r->line1 += z->img_comp[k].w2; + } + } + if (n >= 3) { + stbi_uc *y = coutput[0]; + if (z->s->img_n == 3) { + if (is_rgb) { + for (i=0; i < z->s->img_x; ++i) { + out[0] = y[i]; + out[1] = coutput[1][i]; + out[2] = coutput[2][i]; + out[3] = 255; + out += n; + } + } else { + z->YCbCr_to_RGB_kernel(out, y, coutput[1], coutput[2], z->s->img_x, n); + } + } else if (z->s->img_n == 4) { + if (z->app14_color_transform == 0) { // CMYK + for (i=0; i < z->s->img_x; ++i) { + stbi_uc m = coutput[3][i]; + out[0] = stbi__blinn_8x8(coutput[0][i], m); + out[1] = stbi__blinn_8x8(coutput[1][i], m); + out[2] = stbi__blinn_8x8(coutput[2][i], m); + out[3] = 255; + out += n; + } + } else if (z->app14_color_transform == 2) { // YCCK + z->YCbCr_to_RGB_kernel(out, y, coutput[1], coutput[2], z->s->img_x, n); + for (i=0; i < z->s->img_x; ++i) { + stbi_uc m = coutput[3][i]; + out[0] = stbi__blinn_8x8(255 - out[0], m); + out[1] = stbi__blinn_8x8(255 - out[1], m); + out[2] = stbi__blinn_8x8(255 - out[2], m); + out += n; + } + } else { // YCbCr + alpha? Ignore the fourth channel for now + z->YCbCr_to_RGB_kernel(out, y, coutput[1], coutput[2], z->s->img_x, n); + } + } else + for (i=0; i < z->s->img_x; ++i) { + out[0] = out[1] = out[2] = y[i]; + out[3] = 255; // not used if n==3 + out += n; + } + } else { + if (is_rgb) { + if (n == 1) + for (i=0; i < z->s->img_x; ++i) + *out++ = stbi__compute_y(coutput[0][i], coutput[1][i], coutput[2][i]); + else { + for (i=0; i < z->s->img_x; ++i, out += 2) { + out[0] = stbi__compute_y(coutput[0][i], coutput[1][i], coutput[2][i]); + out[1] = 255; + } + } + } else if (z->s->img_n == 4 && z->app14_color_transform == 0) { + for (i=0; i < z->s->img_x; ++i) { + stbi_uc m = coutput[3][i]; + stbi_uc r = stbi__blinn_8x8(coutput[0][i], m); + stbi_uc g = stbi__blinn_8x8(coutput[1][i], m); + stbi_uc b = stbi__blinn_8x8(coutput[2][i], m); + out[0] = stbi__compute_y(r, g, b); + out[1] = 255; + out += n; + } + } else if (z->s->img_n == 4 && z->app14_color_transform == 2) { + for (i=0; i < z->s->img_x; ++i) { + out[0] = stbi__blinn_8x8(255 - coutput[0][i], coutput[3][i]); + out[1] = 255; + out += n; + } + } else { + stbi_uc *y = coutput[0]; + if (n == 1) + for (i=0; i < z->s->img_x; ++i) out[i] = y[i]; + else + for (i=0; i < z->s->img_x; ++i) { *out++ = y[i]; *out++ = 255; } + } + } + } + stbi__cleanup_jpeg(z); + *out_x = z->s->img_x; + *out_y = z->s->img_y; + if (comp) *comp = z->s->img_n >= 3 ? 3 : 1; // report original components, not output + return output; + } +} + +static void *stbi__jpeg_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) +{ + unsigned char* result; + stbi__jpeg* j = (stbi__jpeg*) stbi__malloc(sizeof(stbi__jpeg)); + STBI_NOTUSED(ri); + j->s = s; + stbi__setup_jpeg(j); + result = load_jpeg_image(j, x,y,comp,req_comp); + STBI_FREE(j); + return result; +} + +static int stbi__jpeg_test(stbi__context *s) +{ + int r; + stbi__jpeg* j = (stbi__jpeg*)stbi__malloc(sizeof(stbi__jpeg)); + j->s = s; + stbi__setup_jpeg(j); + r = stbi__decode_jpeg_header(j, STBI__SCAN_type); + stbi__rewind(s); + STBI_FREE(j); + return r; +} + +static int stbi__jpeg_info_raw(stbi__jpeg *j, int *x, int *y, int *comp) +{ + if (!stbi__decode_jpeg_header(j, STBI__SCAN_header)) { + stbi__rewind( j->s ); + return 0; + } + if (x) *x = j->s->img_x; + if (y) *y = j->s->img_y; + if (comp) *comp = j->s->img_n >= 3 ? 3 : 1; + return 1; +} + +static int stbi__jpeg_info(stbi__context *s, int *x, int *y, int *comp) +{ + int result; + stbi__jpeg* j = (stbi__jpeg*) (stbi__malloc(sizeof(stbi__jpeg))); + j->s = s; + result = stbi__jpeg_info_raw(j, x, y, comp); + STBI_FREE(j); + return result; +} +#endif + +// public domain zlib decode v0.2 Sean Barrett 2006-11-18 +// simple implementation +// - all input must be provided in an upfront buffer +// - all output is written to a single output buffer (can malloc/realloc) +// performance +// - fast huffman + +#ifndef STBI_NO_ZLIB + +// fast-way is faster to check than jpeg huffman, but slow way is slower +#define STBI__ZFAST_BITS 9 // accelerate all cases in default tables +#define STBI__ZFAST_MASK ((1 << STBI__ZFAST_BITS) - 1) + +// zlib-style huffman encoding +// (jpegs packs from left, zlib from right, so can't share code) +typedef struct +{ + stbi__uint16 fast[1 << STBI__ZFAST_BITS]; + stbi__uint16 firstcode[16]; + int maxcode[17]; + stbi__uint16 firstsymbol[16]; + stbi_uc size[288]; + stbi__uint16 value[288]; +} stbi__zhuffman; + +stbi_inline static int stbi__bitreverse16(int n) +{ + n = ((n & 0xAAAA) >> 1) | ((n & 0x5555) << 1); + n = ((n & 0xCCCC) >> 2) | ((n & 0x3333) << 2); + n = ((n & 0xF0F0) >> 4) | ((n & 0x0F0F) << 4); + n = ((n & 0xFF00) >> 8) | ((n & 0x00FF) << 8); + return n; +} + +stbi_inline static int stbi__bit_reverse(int v, int bits) +{ + STBI_ASSERT(bits <= 16); + // to bit reverse n bits, reverse 16 and shift + // e.g. 11 bits, bit reverse and shift away 5 + return stbi__bitreverse16(v) >> (16-bits); +} + +static int stbi__zbuild_huffman(stbi__zhuffman *z, const stbi_uc *sizelist, int num) +{ + int i,k=0; + int code, next_code[16], sizes[17]; + + // DEFLATE spec for generating codes + memset(sizes, 0, sizeof(sizes)); + memset(z->fast, 0, sizeof(z->fast)); + for (i=0; i < num; ++i) + ++sizes[sizelist[i]]; + sizes[0] = 0; + for (i=1; i < 16; ++i) + if (sizes[i] > (1 << i)) + return stbi__err("bad sizes", "Corrupt PNG"); + code = 0; + for (i=1; i < 16; ++i) { + next_code[i] = code; + z->firstcode[i] = (stbi__uint16) code; + z->firstsymbol[i] = (stbi__uint16) k; + code = (code + sizes[i]); + if (sizes[i]) + if (code-1 >= (1 << i)) return stbi__err("bad codelengths","Corrupt PNG"); + z->maxcode[i] = code << (16-i); // preshift for inner loop + code <<= 1; + k += sizes[i]; + } + z->maxcode[16] = 0x10000; // sentinel + for (i=0; i < num; ++i) { + int s = sizelist[i]; + if (s) { + int c = next_code[s] - z->firstcode[s] + z->firstsymbol[s]; + stbi__uint16 fastv = (stbi__uint16) ((s << 9) | i); + z->size [c] = (stbi_uc ) s; + z->value[c] = (stbi__uint16) i; + if (s <= STBI__ZFAST_BITS) { + int j = stbi__bit_reverse(next_code[s],s); + while (j < (1 << STBI__ZFAST_BITS)) { + z->fast[j] = fastv; + j += (1 << s); + } + } + ++next_code[s]; + } + } + return 1; +} + +// zlib-from-memory implementation for PNG reading +// because PNG allows splitting the zlib stream arbitrarily, +// and it's annoying structurally to have PNG call ZLIB call PNG, +// we require PNG read all the IDATs and combine them into a single +// memory buffer + +typedef struct +{ + stbi_uc *zbuffer, *zbuffer_end; + int num_bits; + stbi__uint32 code_buffer; + + char *zout; + char *zout_start; + char *zout_end; + int z_expandable; + + stbi__zhuffman z_length, z_distance; +} stbi__zbuf; + +stbi_inline static stbi_uc stbi__zget8(stbi__zbuf *z) +{ + if (z->zbuffer >= z->zbuffer_end) return 0; + return *z->zbuffer++; +} + +static void stbi__fill_bits(stbi__zbuf *z) +{ + do { + STBI_ASSERT(z->code_buffer < (1U << z->num_bits)); + z->code_buffer |= (unsigned int) stbi__zget8(z) << z->num_bits; + z->num_bits += 8; + } while (z->num_bits <= 24); +} + +stbi_inline static unsigned int stbi__zreceive(stbi__zbuf *z, int n) +{ + unsigned int k; + if (z->num_bits < n) stbi__fill_bits(z); + k = z->code_buffer & ((1 << n) - 1); + z->code_buffer >>= n; + z->num_bits -= n; + return k; +} + +static int stbi__zhuffman_decode_slowpath(stbi__zbuf *a, stbi__zhuffman *z) +{ + int b,s,k; + // not resolved by fast table, so compute it the slow way + // use jpeg approach, which requires MSbits at top + k = stbi__bit_reverse(a->code_buffer, 16); + for (s=STBI__ZFAST_BITS+1; ; ++s) + if (k < z->maxcode[s]) + break; + if (s == 16) return -1; // invalid code! + // code size is s, so: + b = (k >> (16-s)) - z->firstcode[s] + z->firstsymbol[s]; + STBI_ASSERT(z->size[b] == s); + a->code_buffer >>= s; + a->num_bits -= s; + return z->value[b]; +} + +stbi_inline static int stbi__zhuffman_decode(stbi__zbuf *a, stbi__zhuffman *z) +{ + int b,s; + if (a->num_bits < 16) stbi__fill_bits(a); + b = z->fast[a->code_buffer & STBI__ZFAST_MASK]; + if (b) { + s = b >> 9; + a->code_buffer >>= s; + a->num_bits -= s; + return b & 511; + } + return stbi__zhuffman_decode_slowpath(a, z); +} + +static int stbi__zexpand(stbi__zbuf *z, char *zout, int n) // need to make room for n bytes +{ + char *q; + int cur, limit, old_limit; + z->zout = zout; + if (!z->z_expandable) return stbi__err("output buffer limit","Corrupt PNG"); + cur = (int) (z->zout - z->zout_start); + limit = old_limit = (int) (z->zout_end - z->zout_start); + while (cur + n > limit) + limit *= 2; + q = (char *) STBI_REALLOC_SIZED(z->zout_start, old_limit, limit); + STBI_NOTUSED(old_limit); + if (q == NULL) return stbi__err("outofmem", "Out of memory"); + z->zout_start = q; + z->zout = q + cur; + z->zout_end = q + limit; + return 1; +} + +static const int stbi__zlength_base[31] = { + 3,4,5,6,7,8,9,10,11,13, + 15,17,19,23,27,31,35,43,51,59, + 67,83,99,115,131,163,195,227,258,0,0 }; + +static const int stbi__zlength_extra[31]= +{ 0,0,0,0,0,0,0,0,1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4,5,5,5,5,0,0,0 }; + +static const int stbi__zdist_base[32] = { 1,2,3,4,5,7,9,13,17,25,33,49,65,97,129,193, +257,385,513,769,1025,1537,2049,3073,4097,6145,8193,12289,16385,24577,0,0}; + +static const int stbi__zdist_extra[32] = +{ 0,0,0,0,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12,13,13}; + +static int stbi__parse_huffman_block(stbi__zbuf *a) +{ + char *zout = a->zout; + for(;;) { + int z = stbi__zhuffman_decode(a, &a->z_length); + if (z < 256) { + if (z < 0) return stbi__err("bad huffman code","Corrupt PNG"); // error in huffman codes + if (zout >= a->zout_end) { + if (!stbi__zexpand(a, zout, 1)) return 0; + zout = a->zout; + } + *zout++ = (char) z; + } else { + stbi_uc *p; + int len,dist; + if (z == 256) { + a->zout = zout; + return 1; + } + z -= 257; + len = stbi__zlength_base[z]; + if (stbi__zlength_extra[z]) len += stbi__zreceive(a, stbi__zlength_extra[z]); + z = stbi__zhuffman_decode(a, &a->z_distance); + if (z < 0) return stbi__err("bad huffman code","Corrupt PNG"); + dist = stbi__zdist_base[z]; + if (stbi__zdist_extra[z]) dist += stbi__zreceive(a, stbi__zdist_extra[z]); + if (zout - a->zout_start < dist) return stbi__err("bad dist","Corrupt PNG"); + if (zout + len > a->zout_end) { + if (!stbi__zexpand(a, zout, len)) return 0; + zout = a->zout; + } + p = (stbi_uc *) (zout - dist); + if (dist == 1) { // run of one byte; common in images. + stbi_uc v = *p; + if (len) { do *zout++ = v; while (--len); } + } else { + if (len) { do *zout++ = *p++; while (--len); } + } + } + } +} + +static int stbi__compute_huffman_codes(stbi__zbuf *a) +{ + static const stbi_uc length_dezigzag[19] = { 16,17,18,0,8,7,9,6,10,5,11,4,12,3,13,2,14,1,15 }; + stbi__zhuffman z_codelength; + stbi_uc lencodes[286+32+137];//padding for maximum single op + stbi_uc codelength_sizes[19]; + int i,n; + + int hlit = stbi__zreceive(a,5) + 257; + int hdist = stbi__zreceive(a,5) + 1; + int hclen = stbi__zreceive(a,4) + 4; + int ntot = hlit + hdist; + + memset(codelength_sizes, 0, sizeof(codelength_sizes)); + for (i=0; i < hclen; ++i) { + int s = stbi__zreceive(a,3); + codelength_sizes[length_dezigzag[i]] = (stbi_uc) s; + } + if (!stbi__zbuild_huffman(&z_codelength, codelength_sizes, 19)) return 0; + + n = 0; + while (n < ntot) { + int c = stbi__zhuffman_decode(a, &z_codelength); + if (c < 0 || c >= 19) return stbi__err("bad codelengths", "Corrupt PNG"); + if (c < 16) + lencodes[n++] = (stbi_uc) c; + else { + stbi_uc fill = 0; + if (c == 16) { + c = stbi__zreceive(a,2)+3; + if (n == 0) return stbi__err("bad codelengths", "Corrupt PNG"); + fill = lencodes[n-1]; + } else if (c == 17) + c = stbi__zreceive(a,3)+3; + else { + STBI_ASSERT(c == 18); + c = stbi__zreceive(a,7)+11; + } + if (ntot - n < c) return stbi__err("bad codelengths", "Corrupt PNG"); + memset(lencodes+n, fill, c); + n += c; + } + } + if (n != ntot) return stbi__err("bad codelengths","Corrupt PNG"); + if (!stbi__zbuild_huffman(&a->z_length, lencodes, hlit)) return 0; + if (!stbi__zbuild_huffman(&a->z_distance, lencodes+hlit, hdist)) return 0; + return 1; +} + +static int stbi__parse_uncompressed_block(stbi__zbuf *a) +{ + stbi_uc header[4]; + int len,nlen,k; + if (a->num_bits & 7) + stbi__zreceive(a, a->num_bits & 7); // discard + // drain the bit-packed data into header + k = 0; + while (a->num_bits > 0) { + header[k++] = (stbi_uc) (a->code_buffer & 255); // suppress MSVC run-time check + a->code_buffer >>= 8; + a->num_bits -= 8; + } + STBI_ASSERT(a->num_bits == 0); + // now fill header the normal way + while (k < 4) + header[k++] = stbi__zget8(a); + len = header[1] * 256 + header[0]; + nlen = header[3] * 256 + header[2]; + if (nlen != (len ^ 0xffff)) return stbi__err("zlib corrupt","Corrupt PNG"); + if (a->zbuffer + len > a->zbuffer_end) return stbi__err("read past buffer","Corrupt PNG"); + if (a->zout + len > a->zout_end) + if (!stbi__zexpand(a, a->zout, len)) return 0; + memcpy(a->zout, a->zbuffer, len); + a->zbuffer += len; + a->zout += len; + return 1; +} + +static int stbi__parse_zlib_header(stbi__zbuf *a) +{ + int cmf = stbi__zget8(a); + int cm = cmf & 15; + /* int cinfo = cmf >> 4; */ + int flg = stbi__zget8(a); + if ((cmf*256+flg) % 31 != 0) return stbi__err("bad zlib header","Corrupt PNG"); // zlib spec + if (flg & 32) return stbi__err("no preset dict","Corrupt PNG"); // preset dictionary not allowed in png + if (cm != 8) return stbi__err("bad compression","Corrupt PNG"); // DEFLATE required for png + // window = 1 << (8 + cinfo)... but who cares, we fully buffer output + return 1; +} + +static const stbi_uc stbi__zdefault_length[288] = +{ + 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, + 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, + 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, + 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, + 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, + 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, + 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, + 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, + 7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7, 7,7,7,7,7,7,7,7,8,8,8,8,8,8,8,8 +}; +static const stbi_uc stbi__zdefault_distance[32] = +{ + 5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5 +}; +/* +Init algorithm: +{ + int i; // use <= to match clearly with spec + for (i=0; i <= 143; ++i) stbi__zdefault_length[i] = 8; + for ( ; i <= 255; ++i) stbi__zdefault_length[i] = 9; + for ( ; i <= 279; ++i) stbi__zdefault_length[i] = 7; + for ( ; i <= 287; ++i) stbi__zdefault_length[i] = 8; + + for (i=0; i <= 31; ++i) stbi__zdefault_distance[i] = 5; +} +*/ + +static int stbi__parse_zlib(stbi__zbuf *a, int parse_header) +{ + int final, type; + if (parse_header) + if (!stbi__parse_zlib_header(a)) return 0; + a->num_bits = 0; + a->code_buffer = 0; + do { + final = stbi__zreceive(a,1); + type = stbi__zreceive(a,2); + if (type == 0) { + if (!stbi__parse_uncompressed_block(a)) return 0; + } else if (type == 3) { + return 0; + } else { + if (type == 1) { + // use fixed code lengths + if (!stbi__zbuild_huffman(&a->z_length , stbi__zdefault_length , 288)) return 0; + if (!stbi__zbuild_huffman(&a->z_distance, stbi__zdefault_distance, 32)) return 0; + } else { + if (!stbi__compute_huffman_codes(a)) return 0; + } + if (!stbi__parse_huffman_block(a)) return 0; + } + } while (!final); + return 1; +} + +static int stbi__do_zlib(stbi__zbuf *a, char *obuf, int olen, int exp, int parse_header) +{ + a->zout_start = obuf; + a->zout = obuf; + a->zout_end = obuf + olen; + a->z_expandable = exp; + + return stbi__parse_zlib(a, parse_header); +} + +STBIDEF char *stbi_zlib_decode_malloc_guesssize(const char *buffer, int len, int initial_size, int *outlen) +{ + stbi__zbuf a; + char *p = (char *) stbi__malloc(initial_size); + if (p == NULL) return NULL; + a.zbuffer = (stbi_uc *) buffer; + a.zbuffer_end = (stbi_uc *) buffer + len; + if (stbi__do_zlib(&a, p, initial_size, 1, 1)) { + if (outlen) *outlen = (int) (a.zout - a.zout_start); + return a.zout_start; + } else { + STBI_FREE(a.zout_start); + return NULL; + } +} + +STBIDEF char *stbi_zlib_decode_malloc(char const *buffer, int len, int *outlen) +{ + return stbi_zlib_decode_malloc_guesssize(buffer, len, 16384, outlen); +} + +STBIDEF char *stbi_zlib_decode_malloc_guesssize_headerflag(const char *buffer, int len, int initial_size, int *outlen, int parse_header) +{ + stbi__zbuf a; + char *p = (char *) stbi__malloc(initial_size); + if (p == NULL) return NULL; + a.zbuffer = (stbi_uc *) buffer; + a.zbuffer_end = (stbi_uc *) buffer + len; + if (stbi__do_zlib(&a, p, initial_size, 1, parse_header)) { + if (outlen) *outlen = (int) (a.zout - a.zout_start); + return a.zout_start; + } else { + STBI_FREE(a.zout_start); + return NULL; + } +} + +STBIDEF int stbi_zlib_decode_buffer(char *obuffer, int olen, char const *ibuffer, int ilen) +{ + stbi__zbuf a; + a.zbuffer = (stbi_uc *) ibuffer; + a.zbuffer_end = (stbi_uc *) ibuffer + ilen; + if (stbi__do_zlib(&a, obuffer, olen, 0, 1)) + return (int) (a.zout - a.zout_start); + else + return -1; +} + +STBIDEF char *stbi_zlib_decode_noheader_malloc(char const *buffer, int len, int *outlen) +{ + stbi__zbuf a; + char *p = (char *) stbi__malloc(16384); + if (p == NULL) return NULL; + a.zbuffer = (stbi_uc *) buffer; + a.zbuffer_end = (stbi_uc *) buffer+len; + if (stbi__do_zlib(&a, p, 16384, 1, 0)) { + if (outlen) *outlen = (int) (a.zout - a.zout_start); + return a.zout_start; + } else { + STBI_FREE(a.zout_start); + return NULL; + } +} + +STBIDEF int stbi_zlib_decode_noheader_buffer(char *obuffer, int olen, const char *ibuffer, int ilen) +{ + stbi__zbuf a; + a.zbuffer = (stbi_uc *) ibuffer; + a.zbuffer_end = (stbi_uc *) ibuffer + ilen; + if (stbi__do_zlib(&a, obuffer, olen, 0, 0)) + return (int) (a.zout - a.zout_start); + else + return -1; +} +#endif + +// public domain "baseline" PNG decoder v0.10 Sean Barrett 2006-11-18 +// simple implementation +// - only 8-bit samples +// - no CRC checking +// - allocates lots of intermediate memory +// - avoids problem of streaming data between subsystems +// - avoids explicit window management +// performance +// - uses stb_zlib, a PD zlib implementation with fast huffman decoding + +#ifndef STBI_NO_PNG +typedef struct +{ + stbi__uint32 length; + stbi__uint32 type; +} stbi__pngchunk; + +static stbi__pngchunk stbi__get_chunk_header(stbi__context *s) +{ + stbi__pngchunk c; + c.length = stbi__get32be(s); + c.type = stbi__get32be(s); + return c; +} + +static int stbi__check_png_header(stbi__context *s) +{ + static const stbi_uc png_sig[8] = { 137,80,78,71,13,10,26,10 }; + int i; + for (i=0; i < 8; ++i) + if (stbi__get8(s) != png_sig[i]) return stbi__err("bad png sig","Not a PNG"); + return 1; +} + +typedef struct +{ + stbi__context *s; + stbi_uc *idata, *expanded, *out; + int depth; +} stbi__png; + + +enum { + STBI__F_none=0, + STBI__F_sub=1, + STBI__F_up=2, + STBI__F_avg=3, + STBI__F_paeth=4, + // synthetic filters used for first scanline to avoid needing a dummy row of 0s + STBI__F_avg_first, + STBI__F_paeth_first +}; + +static stbi_uc first_row_filter[5] = +{ + STBI__F_none, + STBI__F_sub, + STBI__F_none, + STBI__F_avg_first, + STBI__F_paeth_first +}; + +static int stbi__paeth(int a, int b, int c) +{ + int p = a + b - c; + int pa = abs(p-a); + int pb = abs(p-b); + int pc = abs(p-c); + if (pa <= pb && pa <= pc) return a; + if (pb <= pc) return b; + return c; +} + +static const stbi_uc stbi__depth_scale_table[9] = { 0, 0xff, 0x55, 0, 0x11, 0,0,0, 0x01 }; + +// create the png data from post-deflated data +static int stbi__create_png_image_raw(stbi__png *a, stbi_uc *raw, stbi__uint32 raw_len, int out_n, stbi__uint32 x, stbi__uint32 y, int depth, int color) +{ + int bytes = (depth == 16? 2 : 1); + stbi__context *s = a->s; + stbi__uint32 i,j,stride = x*out_n*bytes; + stbi__uint32 img_len, img_width_bytes; + int k; + int img_n = s->img_n; // copy it into a local for later + + int output_bytes = out_n*bytes; + int filter_bytes = img_n*bytes; + int width = x; + + STBI_ASSERT(out_n == s->img_n || out_n == s->img_n+1); + a->out = (stbi_uc *) stbi__malloc_mad3(x, y, output_bytes, 0); // extra bytes to write off the end into + if (!a->out) return stbi__err("outofmem", "Out of memory"); + + if (!stbi__mad3sizes_valid(img_n, x, depth, 7)) return stbi__err("too large", "Corrupt PNG"); + img_width_bytes = (((img_n * x * depth) + 7) >> 3); + img_len = (img_width_bytes + 1) * y; + + // we used to check for exact match between raw_len and img_len on non-interlaced PNGs, + // but issue #276 reported a PNG in the wild that had extra data at the end (all zeros), + // so just check for raw_len < img_len always. + if (raw_len < img_len) return stbi__err("not enough pixels","Corrupt PNG"); + + for (j=0; j < y; ++j) { + stbi_uc *cur = a->out + stride*j; + stbi_uc *prior; + int filter = *raw++; + + if (filter > 4) + return stbi__err("invalid filter","Corrupt PNG"); + + if (depth < 8) { + STBI_ASSERT(img_width_bytes <= x); + cur += x*out_n - img_width_bytes; // store output to the rightmost img_len bytes, so we can decode in place + filter_bytes = 1; + width = img_width_bytes; + } + prior = cur - stride; // bugfix: need to compute this after 'cur +=' computation above + + // if first row, use special filter that doesn't sample previous row + if (j == 0) filter = first_row_filter[filter]; + + // handle first byte explicitly + for (k=0; k < filter_bytes; ++k) { + switch (filter) { + case STBI__F_none : cur[k] = raw[k]; break; + case STBI__F_sub : cur[k] = raw[k]; break; + case STBI__F_up : cur[k] = STBI__BYTECAST(raw[k] + prior[k]); break; + case STBI__F_avg : cur[k] = STBI__BYTECAST(raw[k] + (prior[k]>>1)); break; + case STBI__F_paeth : cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(0,prior[k],0)); break; + case STBI__F_avg_first : cur[k] = raw[k]; break; + case STBI__F_paeth_first: cur[k] = raw[k]; break; + } + } + + if (depth == 8) { + if (img_n != out_n) + cur[img_n] = 255; // first pixel + raw += img_n; + cur += out_n; + prior += out_n; + } else if (depth == 16) { + if (img_n != out_n) { + cur[filter_bytes] = 255; // first pixel top byte + cur[filter_bytes+1] = 255; // first pixel bottom byte + } + raw += filter_bytes; + cur += output_bytes; + prior += output_bytes; + } else { + raw += 1; + cur += 1; + prior += 1; + } + + // this is a little gross, so that we don't switch per-pixel or per-component + if (depth < 8 || img_n == out_n) { + int nk = (width - 1)*filter_bytes; + #define STBI__CASE(f) \ + case f: \ + for (k=0; k < nk; ++k) + switch (filter) { + // "none" filter turns into a memcpy here; make that explicit. + case STBI__F_none: memcpy(cur, raw, nk); break; + STBI__CASE(STBI__F_sub) { cur[k] = STBI__BYTECAST(raw[k] + cur[k-filter_bytes]); } break; + STBI__CASE(STBI__F_up) { cur[k] = STBI__BYTECAST(raw[k] + prior[k]); } break; + STBI__CASE(STBI__F_avg) { cur[k] = STBI__BYTECAST(raw[k] + ((prior[k] + cur[k-filter_bytes])>>1)); } break; + STBI__CASE(STBI__F_paeth) { cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k-filter_bytes],prior[k],prior[k-filter_bytes])); } break; + STBI__CASE(STBI__F_avg_first) { cur[k] = STBI__BYTECAST(raw[k] + (cur[k-filter_bytes] >> 1)); } break; + STBI__CASE(STBI__F_paeth_first) { cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k-filter_bytes],0,0)); } break; + } + #undef STBI__CASE + raw += nk; + } else { + STBI_ASSERT(img_n+1 == out_n); + #define STBI__CASE(f) \ + case f: \ + for (i=x-1; i >= 1; --i, cur[filter_bytes]=255,raw+=filter_bytes,cur+=output_bytes,prior+=output_bytes) \ + for (k=0; k < filter_bytes; ++k) + switch (filter) { + STBI__CASE(STBI__F_none) { cur[k] = raw[k]; } break; + STBI__CASE(STBI__F_sub) { cur[k] = STBI__BYTECAST(raw[k] + cur[k- output_bytes]); } break; + STBI__CASE(STBI__F_up) { cur[k] = STBI__BYTECAST(raw[k] + prior[k]); } break; + STBI__CASE(STBI__F_avg) { cur[k] = STBI__BYTECAST(raw[k] + ((prior[k] + cur[k- output_bytes])>>1)); } break; + STBI__CASE(STBI__F_paeth) { cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k- output_bytes],prior[k],prior[k- output_bytes])); } break; + STBI__CASE(STBI__F_avg_first) { cur[k] = STBI__BYTECAST(raw[k] + (cur[k- output_bytes] >> 1)); } break; + STBI__CASE(STBI__F_paeth_first) { cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k- output_bytes],0,0)); } break; + } + #undef STBI__CASE + + // the loop above sets the high byte of the pixels' alpha, but for + // 16 bit png files we also need the low byte set. we'll do that here. + if (depth == 16) { + cur = a->out + stride*j; // start at the beginning of the row again + for (i=0; i < x; ++i,cur+=output_bytes) { + cur[filter_bytes+1] = 255; + } + } + } + } + + // we make a separate pass to expand bits to pixels; for performance, + // this could run two scanlines behind the above code, so it won't + // intefere with filtering but will still be in the cache. + if (depth < 8) { + for (j=0; j < y; ++j) { + stbi_uc *cur = a->out + stride*j; + stbi_uc *in = a->out + stride*j + x*out_n - img_width_bytes; + // unpack 1/2/4-bit into a 8-bit buffer. allows us to keep the common 8-bit path optimal at minimal cost for 1/2/4-bit + // png guarante byte alignment, if width is not multiple of 8/4/2 we'll decode dummy trailing data that will be skipped in the later loop + stbi_uc scale = (color == 0) ? stbi__depth_scale_table[depth] : 1; // scale grayscale values to 0..255 range + + // note that the final byte might overshoot and write more data than desired. + // we can allocate enough data that this never writes out of memory, but it + // could also overwrite the next scanline. can it overwrite non-empty data + // on the next scanline? yes, consider 1-pixel-wide scanlines with 1-bit-per-pixel. + // so we need to explicitly clamp the final ones + + if (depth == 4) { + for (k=x*img_n; k >= 2; k-=2, ++in) { + *cur++ = scale * ((*in >> 4) ); + *cur++ = scale * ((*in ) & 0x0f); + } + if (k > 0) *cur++ = scale * ((*in >> 4) ); + } else if (depth == 2) { + for (k=x*img_n; k >= 4; k-=4, ++in) { + *cur++ = scale * ((*in >> 6) ); + *cur++ = scale * ((*in >> 4) & 0x03); + *cur++ = scale * ((*in >> 2) & 0x03); + *cur++ = scale * ((*in ) & 0x03); + } + if (k > 0) *cur++ = scale * ((*in >> 6) ); + if (k > 1) *cur++ = scale * ((*in >> 4) & 0x03); + if (k > 2) *cur++ = scale * ((*in >> 2) & 0x03); + } else if (depth == 1) { + for (k=x*img_n; k >= 8; k-=8, ++in) { + *cur++ = scale * ((*in >> 7) ); + *cur++ = scale * ((*in >> 6) & 0x01); + *cur++ = scale * ((*in >> 5) & 0x01); + *cur++ = scale * ((*in >> 4) & 0x01); + *cur++ = scale * ((*in >> 3) & 0x01); + *cur++ = scale * ((*in >> 2) & 0x01); + *cur++ = scale * ((*in >> 1) & 0x01); + *cur++ = scale * ((*in ) & 0x01); + } + if (k > 0) *cur++ = scale * ((*in >> 7) ); + if (k > 1) *cur++ = scale * ((*in >> 6) & 0x01); + if (k > 2) *cur++ = scale * ((*in >> 5) & 0x01); + if (k > 3) *cur++ = scale * ((*in >> 4) & 0x01); + if (k > 4) *cur++ = scale * ((*in >> 3) & 0x01); + if (k > 5) *cur++ = scale * ((*in >> 2) & 0x01); + if (k > 6) *cur++ = scale * ((*in >> 1) & 0x01); + } + if (img_n != out_n) { + int q; + // insert alpha = 255 + cur = a->out + stride*j; + if (img_n == 1) { + for (q=x-1; q >= 0; --q) { + cur[q*2+1] = 255; + cur[q*2+0] = cur[q]; + } + } else { + STBI_ASSERT(img_n == 3); + for (q=x-1; q >= 0; --q) { + cur[q*4+3] = 255; + cur[q*4+2] = cur[q*3+2]; + cur[q*4+1] = cur[q*3+1]; + cur[q*4+0] = cur[q*3+0]; + } + } + } + } + } else if (depth == 16) { + // force the image data from big-endian to platform-native. + // this is done in a separate pass due to the decoding relying + // on the data being untouched, but could probably be done + // per-line during decode if care is taken. + stbi_uc *cur = a->out; + stbi__uint16 *cur16 = (stbi__uint16*)cur; + + for(i=0; i < x*y*out_n; ++i,cur16++,cur+=2) { + *cur16 = (cur[0] << 8) | cur[1]; + } + } + + return 1; +} + +static int stbi__create_png_image(stbi__png *a, stbi_uc *image_data, stbi__uint32 image_data_len, int out_n, int depth, int color, int interlaced) +{ + int bytes = (depth == 16 ? 2 : 1); + int out_bytes = out_n * bytes; + stbi_uc *final; + int p; + if (!interlaced) + return stbi__create_png_image_raw(a, image_data, image_data_len, out_n, a->s->img_x, a->s->img_y, depth, color); + + // de-interlacing + final = (stbi_uc *) stbi__malloc_mad3(a->s->img_x, a->s->img_y, out_bytes, 0); + for (p=0; p < 7; ++p) { + int xorig[] = { 0,4,0,2,0,1,0 }; + int yorig[] = { 0,0,4,0,2,0,1 }; + int xspc[] = { 8,8,4,4,2,2,1 }; + int yspc[] = { 8,8,8,4,4,2,2 }; + int i,j,x,y; + // pass1_x[4] = 0, pass1_x[5] = 1, pass1_x[12] = 1 + x = (a->s->img_x - xorig[p] + xspc[p]-1) / xspc[p]; + y = (a->s->img_y - yorig[p] + yspc[p]-1) / yspc[p]; + if (x && y) { + stbi__uint32 img_len = ((((a->s->img_n * x * depth) + 7) >> 3) + 1) * y; + if (!stbi__create_png_image_raw(a, image_data, image_data_len, out_n, x, y, depth, color)) { + STBI_FREE(final); + return 0; + } + for (j=0; j < y; ++j) { + for (i=0; i < x; ++i) { + int out_y = j*yspc[p]+yorig[p]; + int out_x = i*xspc[p]+xorig[p]; + memcpy(final + out_y*a->s->img_x*out_bytes + out_x*out_bytes, + a->out + (j*x+i)*out_bytes, out_bytes); + } + } + STBI_FREE(a->out); + image_data += img_len; + image_data_len -= img_len; + } + } + a->out = final; + + return 1; +} + +static int stbi__compute_transparency(stbi__png *z, stbi_uc tc[3], int out_n) +{ + stbi__context *s = z->s; + stbi__uint32 i, pixel_count = s->img_x * s->img_y; + stbi_uc *p = z->out; + + // compute color-based transparency, assuming we've + // already got 255 as the alpha value in the output + STBI_ASSERT(out_n == 2 || out_n == 4); + + if (out_n == 2) { + for (i=0; i < pixel_count; ++i) { + p[1] = (p[0] == tc[0] ? 0 : 255); + p += 2; + } + } else { + for (i=0; i < pixel_count; ++i) { + if (p[0] == tc[0] && p[1] == tc[1] && p[2] == tc[2]) + p[3] = 0; + p += 4; + } + } + return 1; +} + +static int stbi__compute_transparency16(stbi__png *z, stbi__uint16 tc[3], int out_n) +{ + stbi__context *s = z->s; + stbi__uint32 i, pixel_count = s->img_x * s->img_y; + stbi__uint16 *p = (stbi__uint16*) z->out; + + // compute color-based transparency, assuming we've + // already got 65535 as the alpha value in the output + STBI_ASSERT(out_n == 2 || out_n == 4); + + if (out_n == 2) { + for (i = 0; i < pixel_count; ++i) { + p[1] = (p[0] == tc[0] ? 0 : 65535); + p += 2; + } + } else { + for (i = 0; i < pixel_count; ++i) { + if (p[0] == tc[0] && p[1] == tc[1] && p[2] == tc[2]) + p[3] = 0; + p += 4; + } + } + return 1; +} + +static int stbi__expand_png_palette(stbi__png *a, stbi_uc *palette, int len, int pal_img_n) +{ + stbi__uint32 i, pixel_count = a->s->img_x * a->s->img_y; + stbi_uc *p, *temp_out, *orig = a->out; + + p = (stbi_uc *) stbi__malloc_mad2(pixel_count, pal_img_n, 0); + if (p == NULL) return stbi__err("outofmem", "Out of memory"); + + // between here and free(out) below, exitting would leak + temp_out = p; + + if (pal_img_n == 3) { + for (i=0; i < pixel_count; ++i) { + int n = orig[i]*4; + p[0] = palette[n ]; + p[1] = palette[n+1]; + p[2] = palette[n+2]; + p += 3; + } + } else { + for (i=0; i < pixel_count; ++i) { + int n = orig[i]*4; + p[0] = palette[n ]; + p[1] = palette[n+1]; + p[2] = palette[n+2]; + p[3] = palette[n+3]; + p += 4; + } + } + STBI_FREE(a->out); + a->out = temp_out; + + STBI_NOTUSED(len); + + return 1; +} + +static int stbi__unpremultiply_on_load = 0; +static int stbi__de_iphone_flag = 0; + +STBIDEF void stbi_set_unpremultiply_on_load(int flag_true_if_should_unpremultiply) +{ + stbi__unpremultiply_on_load = flag_true_if_should_unpremultiply; +} + +STBIDEF void stbi_convert_iphone_png_to_rgb(int flag_true_if_should_convert) +{ + stbi__de_iphone_flag = flag_true_if_should_convert; +} + +static void stbi__de_iphone(stbi__png *z) +{ + stbi__context *s = z->s; + stbi__uint32 i, pixel_count = s->img_x * s->img_y; + stbi_uc *p = z->out; + + if (s->img_out_n == 3) { // convert bgr to rgb + for (i=0; i < pixel_count; ++i) { + stbi_uc t = p[0]; + p[0] = p[2]; + p[2] = t; + p += 3; + } + } else { + STBI_ASSERT(s->img_out_n == 4); + if (stbi__unpremultiply_on_load) { + // convert bgr to rgb and unpremultiply + for (i=0; i < pixel_count; ++i) { + stbi_uc a = p[3]; + stbi_uc t = p[0]; + if (a) { + stbi_uc half = a / 2; + p[0] = (p[2] * 255 + half) / a; + p[1] = (p[1] * 255 + half) / a; + p[2] = ( t * 255 + half) / a; + } else { + p[0] = p[2]; + p[2] = t; + } + p += 4; + } + } else { + // convert bgr to rgb + for (i=0; i < pixel_count; ++i) { + stbi_uc t = p[0]; + p[0] = p[2]; + p[2] = t; + p += 4; + } + } + } +} + +#define STBI__PNG_TYPE(a,b,c,d) (((unsigned) (a) << 24) + ((unsigned) (b) << 16) + ((unsigned) (c) << 8) + (unsigned) (d)) + +static int stbi__parse_png_file(stbi__png *z, int scan, int req_comp) +{ + stbi_uc palette[1024], pal_img_n=0; + stbi_uc has_trans=0, tc[3]={0}; + stbi__uint16 tc16[3]; + stbi__uint32 ioff=0, idata_limit=0, i, pal_len=0; + int first=1,k,interlace=0, color=0, is_iphone=0; + stbi__context *s = z->s; + + z->expanded = NULL; + z->idata = NULL; + z->out = NULL; + + if (!stbi__check_png_header(s)) return 0; + + if (scan == STBI__SCAN_type) return 1; + + for (;;) { + stbi__pngchunk c = stbi__get_chunk_header(s); + switch (c.type) { + case STBI__PNG_TYPE('C','g','B','I'): + is_iphone = 1; + stbi__skip(s, c.length); + break; + case STBI__PNG_TYPE('I','H','D','R'): { + int comp,filter; + if (!first) return stbi__err("multiple IHDR","Corrupt PNG"); + first = 0; + if (c.length != 13) return stbi__err("bad IHDR len","Corrupt PNG"); + s->img_x = stbi__get32be(s); if (s->img_x > (1 << 24)) return stbi__err("too large","Very large image (corrupt?)"); + s->img_y = stbi__get32be(s); if (s->img_y > (1 << 24)) return stbi__err("too large","Very large image (corrupt?)"); + z->depth = stbi__get8(s); if (z->depth != 1 && z->depth != 2 && z->depth != 4 && z->depth != 8 && z->depth != 16) return stbi__err("1/2/4/8/16-bit only","PNG not supported: 1/2/4/8/16-bit only"); + color = stbi__get8(s); if (color > 6) return stbi__err("bad ctype","Corrupt PNG"); + if (color == 3 && z->depth == 16) return stbi__err("bad ctype","Corrupt PNG"); + if (color == 3) pal_img_n = 3; else if (color & 1) return stbi__err("bad ctype","Corrupt PNG"); + comp = stbi__get8(s); if (comp) return stbi__err("bad comp method","Corrupt PNG"); + filter= stbi__get8(s); if (filter) return stbi__err("bad filter method","Corrupt PNG"); + interlace = stbi__get8(s); if (interlace>1) return stbi__err("bad interlace method","Corrupt PNG"); + if (!s->img_x || !s->img_y) return stbi__err("0-pixel image","Corrupt PNG"); + if (!pal_img_n) { + s->img_n = (color & 2 ? 3 : 1) + (color & 4 ? 1 : 0); + if ((1 << 30) / s->img_x / s->img_n < s->img_y) return stbi__err("too large", "Image too large to decode"); + if (scan == STBI__SCAN_header) return 1; + } else { + // if paletted, then pal_n is our final components, and + // img_n is # components to decompress/filter. + s->img_n = 1; + if ((1 << 30) / s->img_x / 4 < s->img_y) return stbi__err("too large","Corrupt PNG"); + // if SCAN_header, have to scan to see if we have a tRNS + } + break; + } + + case STBI__PNG_TYPE('P','L','T','E'): { + if (first) return stbi__err("first not IHDR", "Corrupt PNG"); + if (c.length > 256*3) return stbi__err("invalid PLTE","Corrupt PNG"); + pal_len = c.length / 3; + if (pal_len * 3 != c.length) return stbi__err("invalid PLTE","Corrupt PNG"); + for (i=0; i < pal_len; ++i) { + palette[i*4+0] = stbi__get8(s); + palette[i*4+1] = stbi__get8(s); + palette[i*4+2] = stbi__get8(s); + palette[i*4+3] = 255; + } + break; + } + + case STBI__PNG_TYPE('t','R','N','S'): { + if (first) return stbi__err("first not IHDR", "Corrupt PNG"); + if (z->idata) return stbi__err("tRNS after IDAT","Corrupt PNG"); + if (pal_img_n) { + if (scan == STBI__SCAN_header) { s->img_n = 4; return 1; } + if (pal_len == 0) return stbi__err("tRNS before PLTE","Corrupt PNG"); + if (c.length > pal_len) return stbi__err("bad tRNS len","Corrupt PNG"); + pal_img_n = 4; + for (i=0; i < c.length; ++i) + palette[i*4+3] = stbi__get8(s); + } else { + if (!(s->img_n & 1)) return stbi__err("tRNS with alpha","Corrupt PNG"); + if (c.length != (stbi__uint32) s->img_n*2) return stbi__err("bad tRNS len","Corrupt PNG"); + has_trans = 1; + if (z->depth == 16) { + for (k = 0; k < s->img_n; ++k) tc16[k] = (stbi__uint16)stbi__get16be(s); // copy the values as-is + } else { + for (k = 0; k < s->img_n; ++k) tc[k] = (stbi_uc)(stbi__get16be(s) & 255) * stbi__depth_scale_table[z->depth]; // non 8-bit images will be larger + } + } + break; + } + + case STBI__PNG_TYPE('I','D','A','T'): { + if (first) return stbi__err("first not IHDR", "Corrupt PNG"); + if (pal_img_n && !pal_len) return stbi__err("no PLTE","Corrupt PNG"); + if (scan == STBI__SCAN_header) { s->img_n = pal_img_n; return 1; } + if ((int)(ioff + c.length) < (int)ioff) return 0; + if (ioff + c.length > idata_limit) { + stbi__uint32 idata_limit_old = idata_limit; + stbi_uc *p; + if (idata_limit == 0) idata_limit = c.length > 4096 ? c.length : 4096; + while (ioff + c.length > idata_limit) + idata_limit *= 2; + STBI_NOTUSED(idata_limit_old); + p = (stbi_uc *) STBI_REALLOC_SIZED(z->idata, idata_limit_old, idata_limit); if (p == NULL) return stbi__err("outofmem", "Out of memory"); + z->idata = p; + } + if (!stbi__getn(s, z->idata+ioff,c.length)) return stbi__err("outofdata","Corrupt PNG"); + ioff += c.length; + break; + } + + case STBI__PNG_TYPE('I','E','N','D'): { + stbi__uint32 raw_len, bpl; + if (first) return stbi__err("first not IHDR", "Corrupt PNG"); + if (scan != STBI__SCAN_load) return 1; + if (z->idata == NULL) return stbi__err("no IDAT","Corrupt PNG"); + // initial guess for decoded data size to avoid unnecessary reallocs + bpl = (s->img_x * z->depth + 7) / 8; // bytes per line, per component + raw_len = bpl * s->img_y * s->img_n /* pixels */ + s->img_y /* filter mode per row */; + z->expanded = (stbi_uc *) stbi_zlib_decode_malloc_guesssize_headerflag((char *) z->idata, ioff, raw_len, (int *) &raw_len, !is_iphone); + if (z->expanded == NULL) return 0; // zlib should set error + STBI_FREE(z->idata); z->idata = NULL; + if ((req_comp == s->img_n+1 && req_comp != 3 && !pal_img_n) || has_trans) + s->img_out_n = s->img_n+1; + else + s->img_out_n = s->img_n; + if (!stbi__create_png_image(z, z->expanded, raw_len, s->img_out_n, z->depth, color, interlace)) return 0; + if (has_trans) { + if (z->depth == 16) { + if (!stbi__compute_transparency16(z, tc16, s->img_out_n)) return 0; + } else { + if (!stbi__compute_transparency(z, tc, s->img_out_n)) return 0; + } + } + if (is_iphone && stbi__de_iphone_flag && s->img_out_n > 2) + stbi__de_iphone(z); + if (pal_img_n) { + // pal_img_n == 3 or 4 + s->img_n = pal_img_n; // record the actual colors we had + s->img_out_n = pal_img_n; + if (req_comp >= 3) s->img_out_n = req_comp; + if (!stbi__expand_png_palette(z, palette, pal_len, s->img_out_n)) + return 0; + } else if (has_trans) { + // non-paletted image with tRNS -> source image has (constant) alpha + ++s->img_n; + } + STBI_FREE(z->expanded); z->expanded = NULL; + return 1; + } + + default: + // if critical, fail + if (first) return stbi__err("first not IHDR", "Corrupt PNG"); + if ((c.type & (1 << 29)) == 0) { + #ifndef STBI_NO_FAILURE_STRINGS + // not threadsafe + static char invalid_chunk[] = "XXXX PNG chunk not known"; + invalid_chunk[0] = STBI__BYTECAST(c.type >> 24); + invalid_chunk[1] = STBI__BYTECAST(c.type >> 16); + invalid_chunk[2] = STBI__BYTECAST(c.type >> 8); + invalid_chunk[3] = STBI__BYTECAST(c.type >> 0); + #endif + return stbi__err(invalid_chunk, "PNG not supported: unknown PNG chunk type"); + } + stbi__skip(s, c.length); + break; + } + // end of PNG chunk, read and skip CRC + stbi__get32be(s); + } +} + +static void *stbi__do_png(stbi__png *p, int *x, int *y, int *n, int req_comp, stbi__result_info *ri) +{ + void *result=NULL; + if (req_comp < 0 || req_comp > 4) return stbi__errpuc("bad req_comp", "Internal error"); + if (stbi__parse_png_file(p, STBI__SCAN_load, req_comp)) { + if (p->depth < 8) + ri->bits_per_channel = 8; + else + ri->bits_per_channel = p->depth; + result = p->out; + p->out = NULL; + if (req_comp && req_comp != p->s->img_out_n) { + if (ri->bits_per_channel == 8) + result = stbi__convert_format((unsigned char *) result, p->s->img_out_n, req_comp, p->s->img_x, p->s->img_y); + else + result = stbi__convert_format16((stbi__uint16 *) result, p->s->img_out_n, req_comp, p->s->img_x, p->s->img_y); + p->s->img_out_n = req_comp; + if (result == NULL) return result; + } + *x = p->s->img_x; + *y = p->s->img_y; + if (n) *n = p->s->img_n; + } + STBI_FREE(p->out); p->out = NULL; + STBI_FREE(p->expanded); p->expanded = NULL; + STBI_FREE(p->idata); p->idata = NULL; + + return result; +} + +static void *stbi__png_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) +{ + stbi__png p; + p.s = s; + return stbi__do_png(&p, x,y,comp,req_comp, ri); +} + +static int stbi__png_test(stbi__context *s) +{ + int r; + r = stbi__check_png_header(s); + stbi__rewind(s); + return r; +} + +static int stbi__png_info_raw(stbi__png *p, int *x, int *y, int *comp) +{ + if (!stbi__parse_png_file(p, STBI__SCAN_header, 0)) { + stbi__rewind( p->s ); + return 0; + } + if (x) *x = p->s->img_x; + if (y) *y = p->s->img_y; + if (comp) *comp = p->s->img_n; + return 1; +} + +static int stbi__png_info(stbi__context *s, int *x, int *y, int *comp) +{ + stbi__png p; + p.s = s; + return stbi__png_info_raw(&p, x, y, comp); +} + +static int stbi__png_is16(stbi__context *s) +{ + stbi__png p; + p.s = s; + if (!stbi__png_info_raw(&p, NULL, NULL, NULL)) + return 0; + if (p.depth != 16) { + stbi__rewind(p.s); + return 0; + } + return 1; +} +#endif + +// Microsoft/Windows BMP image + +#ifndef STBI_NO_BMP +static int stbi__bmp_test_raw(stbi__context *s) +{ + int r; + int sz; + if (stbi__get8(s) != 'B') return 0; + if (stbi__get8(s) != 'M') return 0; + stbi__get32le(s); // discard filesize + stbi__get16le(s); // discard reserved + stbi__get16le(s); // discard reserved + stbi__get32le(s); // discard data offset + sz = stbi__get32le(s); + r = (sz == 12 || sz == 40 || sz == 56 || sz == 108 || sz == 124); + return r; +} + +static int stbi__bmp_test(stbi__context *s) +{ + int r = stbi__bmp_test_raw(s); + stbi__rewind(s); + return r; +} + + +// returns 0..31 for the highest set bit +static int stbi__high_bit(unsigned int z) +{ + int n=0; + if (z == 0) return -1; + if (z >= 0x10000) { n += 16; z >>= 16; } + if (z >= 0x00100) { n += 8; z >>= 8; } + if (z >= 0x00010) { n += 4; z >>= 4; } + if (z >= 0x00004) { n += 2; z >>= 2; } + if (z >= 0x00002) { n += 1;/* >>= 1;*/ } + return n; +} + +static int stbi__bitcount(unsigned int a) +{ + a = (a & 0x55555555) + ((a >> 1) & 0x55555555); // max 2 + a = (a & 0x33333333) + ((a >> 2) & 0x33333333); // max 4 + a = (a + (a >> 4)) & 0x0f0f0f0f; // max 8 per 4, now 8 bits + a = (a + (a >> 8)); // max 16 per 8 bits + a = (a + (a >> 16)); // max 32 per 8 bits + return a & 0xff; +} + +// extract an arbitrarily-aligned N-bit value (N=bits) +// from v, and then make it 8-bits long and fractionally +// extend it to full full range. +static int stbi__shiftsigned(unsigned int v, int shift, int bits) +{ + static unsigned int mul_table[9] = { + 0, + 0xff/*0b11111111*/, 0x55/*0b01010101*/, 0x49/*0b01001001*/, 0x11/*0b00010001*/, + 0x21/*0b00100001*/, 0x41/*0b01000001*/, 0x81/*0b10000001*/, 0x01/*0b00000001*/, + }; + static unsigned int shift_table[9] = { + 0, 0,0,1,0,2,4,6,0, + }; + if (shift < 0) + v <<= -shift; + else + v >>= shift; + STBI_ASSERT(v >= 0 && v < 256); + v >>= (8-bits); + STBI_ASSERT(bits >= 0 && bits <= 8); + return (int) ((unsigned) v * mul_table[bits]) >> shift_table[bits]; +} + +typedef struct +{ + int bpp, offset, hsz; + unsigned int mr,mg,mb,ma, all_a; +} stbi__bmp_data; + +static void *stbi__bmp_parse_header(stbi__context *s, stbi__bmp_data *info) +{ + int hsz; + if (stbi__get8(s) != 'B' || stbi__get8(s) != 'M') return stbi__errpuc("not BMP", "Corrupt BMP"); + stbi__get32le(s); // discard filesize + stbi__get16le(s); // discard reserved + stbi__get16le(s); // discard reserved + info->offset = stbi__get32le(s); + info->hsz = hsz = stbi__get32le(s); + info->mr = info->mg = info->mb = info->ma = 0; + + if (hsz != 12 && hsz != 40 && hsz != 56 && hsz != 108 && hsz != 124) return stbi__errpuc("unknown BMP", "BMP type not supported: unknown"); + if (hsz == 12) { + s->img_x = stbi__get16le(s); + s->img_y = stbi__get16le(s); + } else { + s->img_x = stbi__get32le(s); + s->img_y = stbi__get32le(s); + } + if (stbi__get16le(s) != 1) return stbi__errpuc("bad BMP", "bad BMP"); + info->bpp = stbi__get16le(s); + if (hsz != 12) { + int compress = stbi__get32le(s); + if (compress == 1 || compress == 2) return stbi__errpuc("BMP RLE", "BMP type not supported: RLE"); + stbi__get32le(s); // discard sizeof + stbi__get32le(s); // discard hres + stbi__get32le(s); // discard vres + stbi__get32le(s); // discard colorsused + stbi__get32le(s); // discard max important + if (hsz == 40 || hsz == 56) { + if (hsz == 56) { + stbi__get32le(s); + stbi__get32le(s); + stbi__get32le(s); + stbi__get32le(s); + } + if (info->bpp == 16 || info->bpp == 32) { + if (compress == 0) { + if (info->bpp == 32) { + info->mr = 0xffu << 16; + info->mg = 0xffu << 8; + info->mb = 0xffu << 0; + info->ma = 0xffu << 24; + info->all_a = 0; // if all_a is 0 at end, then we loaded alpha channel but it was all 0 + } else { + info->mr = 31u << 10; + info->mg = 31u << 5; + info->mb = 31u << 0; + } + } else if (compress == 3) { + info->mr = stbi__get32le(s); + info->mg = stbi__get32le(s); + info->mb = stbi__get32le(s); + // not documented, but generated by photoshop and handled by mspaint + if (info->mr == info->mg && info->mg == info->mb) { + // ?!?!? + return stbi__errpuc("bad BMP", "bad BMP"); + } + } else + return stbi__errpuc("bad BMP", "bad BMP"); + } + } else { + int i; + if (hsz != 108 && hsz != 124) + return stbi__errpuc("bad BMP", "bad BMP"); + info->mr = stbi__get32le(s); + info->mg = stbi__get32le(s); + info->mb = stbi__get32le(s); + info->ma = stbi__get32le(s); + stbi__get32le(s); // discard color space + for (i=0; i < 12; ++i) + stbi__get32le(s); // discard color space parameters + if (hsz == 124) { + stbi__get32le(s); // discard rendering intent + stbi__get32le(s); // discard offset of profile data + stbi__get32le(s); // discard size of profile data + stbi__get32le(s); // discard reserved + } + } + } + return (void *) 1; +} + + +static void *stbi__bmp_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) +{ + stbi_uc *out; + unsigned int mr=0,mg=0,mb=0,ma=0, all_a; + stbi_uc pal[256][4]; + int psize=0,i,j,width; + int flip_vertically, pad, target; + stbi__bmp_data info; + STBI_NOTUSED(ri); + + info.all_a = 255; + if (stbi__bmp_parse_header(s, &info) == NULL) + return NULL; // error code already set + + flip_vertically = ((int) s->img_y) > 0; + s->img_y = abs((int) s->img_y); + + mr = info.mr; + mg = info.mg; + mb = info.mb; + ma = info.ma; + all_a = info.all_a; + + if (info.hsz == 12) { + if (info.bpp < 24) + psize = (info.offset - 14 - 24) / 3; + } else { + if (info.bpp < 16) + psize = (info.offset - 14 - info.hsz) >> 2; + } + + if (info.bpp == 24 && ma == 0xff000000) + s->img_n = 3; + else + s->img_n = ma ? 4 : 3; + if (req_comp && req_comp >= 3) // we can directly decode 3 or 4 + target = req_comp; + else + target = s->img_n; // if they want monochrome, we'll post-convert + + // sanity-check size + if (!stbi__mad3sizes_valid(target, s->img_x, s->img_y, 0)) + return stbi__errpuc("too large", "Corrupt BMP"); + + out = (stbi_uc *) stbi__malloc_mad3(target, s->img_x, s->img_y, 0); + if (!out) return stbi__errpuc("outofmem", "Out of memory"); + if (info.bpp < 16) { + int z=0; + if (psize == 0 || psize > 256) { STBI_FREE(out); return stbi__errpuc("invalid", "Corrupt BMP"); } + for (i=0; i < psize; ++i) { + pal[i][2] = stbi__get8(s); + pal[i][1] = stbi__get8(s); + pal[i][0] = stbi__get8(s); + if (info.hsz != 12) stbi__get8(s); + pal[i][3] = 255; + } + stbi__skip(s, info.offset - 14 - info.hsz - psize * (info.hsz == 12 ? 3 : 4)); + if (info.bpp == 1) width = (s->img_x + 7) >> 3; + else if (info.bpp == 4) width = (s->img_x + 1) >> 1; + else if (info.bpp == 8) width = s->img_x; + else { STBI_FREE(out); return stbi__errpuc("bad bpp", "Corrupt BMP"); } + pad = (-width)&3; + if (info.bpp == 1) { + for (j=0; j < (int) s->img_y; ++j) { + int bit_offset = 7, v = stbi__get8(s); + for (i=0; i < (int) s->img_x; ++i) { + int color = (v>>bit_offset)&0x1; + out[z++] = pal[color][0]; + out[z++] = pal[color][1]; + out[z++] = pal[color][2]; + if (target == 4) out[z++] = 255; + if (i+1 == (int) s->img_x) break; + if((--bit_offset) < 0) { + bit_offset = 7; + v = stbi__get8(s); + } + } + stbi__skip(s, pad); + } + } else { + for (j=0; j < (int) s->img_y; ++j) { + for (i=0; i < (int) s->img_x; i += 2) { + int v=stbi__get8(s),v2=0; + if (info.bpp == 4) { + v2 = v & 15; + v >>= 4; + } + out[z++] = pal[v][0]; + out[z++] = pal[v][1]; + out[z++] = pal[v][2]; + if (target == 4) out[z++] = 255; + if (i+1 == (int) s->img_x) break; + v = (info.bpp == 8) ? stbi__get8(s) : v2; + out[z++] = pal[v][0]; + out[z++] = pal[v][1]; + out[z++] = pal[v][2]; + if (target == 4) out[z++] = 255; + } + stbi__skip(s, pad); + } + } + } else { + int rshift=0,gshift=0,bshift=0,ashift=0,rcount=0,gcount=0,bcount=0,acount=0; + int z = 0; + int easy=0; + stbi__skip(s, info.offset - 14 - info.hsz); + if (info.bpp == 24) width = 3 * s->img_x; + else if (info.bpp == 16) width = 2*s->img_x; + else /* bpp = 32 and pad = 0 */ width=0; + pad = (-width) & 3; + if (info.bpp == 24) { + easy = 1; + } else if (info.bpp == 32) { + if (mb == 0xff && mg == 0xff00 && mr == 0x00ff0000 && ma == 0xff000000) + easy = 2; + } + if (!easy) { + if (!mr || !mg || !mb) { STBI_FREE(out); return stbi__errpuc("bad masks", "Corrupt BMP"); } + // right shift amt to put high bit in position #7 + rshift = stbi__high_bit(mr)-7; rcount = stbi__bitcount(mr); + gshift = stbi__high_bit(mg)-7; gcount = stbi__bitcount(mg); + bshift = stbi__high_bit(mb)-7; bcount = stbi__bitcount(mb); + ashift = stbi__high_bit(ma)-7; acount = stbi__bitcount(ma); + } + for (j=0; j < (int) s->img_y; ++j) { + if (easy) { + for (i=0; i < (int) s->img_x; ++i) { + unsigned char a; + out[z+2] = stbi__get8(s); + out[z+1] = stbi__get8(s); + out[z+0] = stbi__get8(s); + z += 3; + a = (easy == 2 ? stbi__get8(s) : 255); + all_a |= a; + if (target == 4) out[z++] = a; + } + } else { + int bpp = info.bpp; + for (i=0; i < (int) s->img_x; ++i) { + stbi__uint32 v = (bpp == 16 ? (stbi__uint32) stbi__get16le(s) : stbi__get32le(s)); + unsigned int a; + out[z++] = STBI__BYTECAST(stbi__shiftsigned(v & mr, rshift, rcount)); + out[z++] = STBI__BYTECAST(stbi__shiftsigned(v & mg, gshift, gcount)); + out[z++] = STBI__BYTECAST(stbi__shiftsigned(v & mb, bshift, bcount)); + a = (ma ? stbi__shiftsigned(v & ma, ashift, acount) : 255); + all_a |= a; + if (target == 4) out[z++] = STBI__BYTECAST(a); + } + } + stbi__skip(s, pad); + } + } + + // if alpha channel is all 0s, replace with all 255s + if (target == 4 && all_a == 0) + for (i=4*s->img_x*s->img_y-1; i >= 0; i -= 4) + out[i] = 255; + + if (flip_vertically) { + stbi_uc t; + for (j=0; j < (int) s->img_y>>1; ++j) { + stbi_uc *p1 = out + j *s->img_x*target; + stbi_uc *p2 = out + (s->img_y-1-j)*s->img_x*target; + for (i=0; i < (int) s->img_x*target; ++i) { + t = p1[i]; p1[i] = p2[i]; p2[i] = t; + } + } + } + + if (req_comp && req_comp != target) { + out = stbi__convert_format(out, target, req_comp, s->img_x, s->img_y); + if (out == NULL) return out; // stbi__convert_format frees input on failure + } + + *x = s->img_x; + *y = s->img_y; + if (comp) *comp = s->img_n; + return out; +} +#endif + +// Targa Truevision - TGA +// by Jonathan Dummer +#ifndef STBI_NO_TGA +// returns STBI_rgb or whatever, 0 on error +static int stbi__tga_get_comp(int bits_per_pixel, int is_grey, int* is_rgb16) +{ + // only RGB or RGBA (incl. 16bit) or grey allowed + if (is_rgb16) *is_rgb16 = 0; + switch(bits_per_pixel) { + case 8: return STBI_grey; + case 16: if(is_grey) return STBI_grey_alpha; + // fallthrough + case 15: if(is_rgb16) *is_rgb16 = 1; + return STBI_rgb; + case 24: // fallthrough + case 32: return bits_per_pixel/8; + default: return 0; + } +} + +static int stbi__tga_info(stbi__context *s, int *x, int *y, int *comp) +{ + int tga_w, tga_h, tga_comp, tga_image_type, tga_bits_per_pixel, tga_colormap_bpp; + int sz, tga_colormap_type; + stbi__get8(s); // discard Offset + tga_colormap_type = stbi__get8(s); // colormap type + if( tga_colormap_type > 1 ) { + stbi__rewind(s); + return 0; // only RGB or indexed allowed + } + tga_image_type = stbi__get8(s); // image type + if ( tga_colormap_type == 1 ) { // colormapped (paletted) image + if (tga_image_type != 1 && tga_image_type != 9) { + stbi__rewind(s); + return 0; + } + stbi__skip(s,4); // skip index of first colormap entry and number of entries + sz = stbi__get8(s); // check bits per palette color entry + if ( (sz != 8) && (sz != 15) && (sz != 16) && (sz != 24) && (sz != 32) ) { + stbi__rewind(s); + return 0; + } + stbi__skip(s,4); // skip image x and y origin + tga_colormap_bpp = sz; + } else { // "normal" image w/o colormap - only RGB or grey allowed, +/- RLE + if ( (tga_image_type != 2) && (tga_image_type != 3) && (tga_image_type != 10) && (tga_image_type != 11) ) { + stbi__rewind(s); + return 0; // only RGB or grey allowed, +/- RLE + } + stbi__skip(s,9); // skip colormap specification and image x/y origin + tga_colormap_bpp = 0; + } + tga_w = stbi__get16le(s); + if( tga_w < 1 ) { + stbi__rewind(s); + return 0; // test width + } + tga_h = stbi__get16le(s); + if( tga_h < 1 ) { + stbi__rewind(s); + return 0; // test height + } + tga_bits_per_pixel = stbi__get8(s); // bits per pixel + stbi__get8(s); // ignore alpha bits + if (tga_colormap_bpp != 0) { + if((tga_bits_per_pixel != 8) && (tga_bits_per_pixel != 16)) { + // when using a colormap, tga_bits_per_pixel is the size of the indexes + // I don't think anything but 8 or 16bit indexes makes sense + stbi__rewind(s); + return 0; + } + tga_comp = stbi__tga_get_comp(tga_colormap_bpp, 0, NULL); + } else { + tga_comp = stbi__tga_get_comp(tga_bits_per_pixel, (tga_image_type == 3) || (tga_image_type == 11), NULL); + } + if(!tga_comp) { + stbi__rewind(s); + return 0; + } + if (x) *x = tga_w; + if (y) *y = tga_h; + if (comp) *comp = tga_comp; + return 1; // seems to have passed everything +} + +static int stbi__tga_test(stbi__context *s) +{ + int res = 0; + int sz, tga_color_type; + stbi__get8(s); // discard Offset + tga_color_type = stbi__get8(s); // color type + if ( tga_color_type > 1 ) goto errorEnd; // only RGB or indexed allowed + sz = stbi__get8(s); // image type + if ( tga_color_type == 1 ) { // colormapped (paletted) image + if (sz != 1 && sz != 9) goto errorEnd; // colortype 1 demands image type 1 or 9 + stbi__skip(s,4); // skip index of first colormap entry and number of entries + sz = stbi__get8(s); // check bits per palette color entry + if ( (sz != 8) && (sz != 15) && (sz != 16) && (sz != 24) && (sz != 32) ) goto errorEnd; + stbi__skip(s,4); // skip image x and y origin + } else { // "normal" image w/o colormap + if ( (sz != 2) && (sz != 3) && (sz != 10) && (sz != 11) ) goto errorEnd; // only RGB or grey allowed, +/- RLE + stbi__skip(s,9); // skip colormap specification and image x/y origin + } + if ( stbi__get16le(s) < 1 ) goto errorEnd; // test width + if ( stbi__get16le(s) < 1 ) goto errorEnd; // test height + sz = stbi__get8(s); // bits per pixel + if ( (tga_color_type == 1) && (sz != 8) && (sz != 16) ) goto errorEnd; // for colormapped images, bpp is size of an index + if ( (sz != 8) && (sz != 15) && (sz != 16) && (sz != 24) && (sz != 32) ) goto errorEnd; + + res = 1; // if we got this far, everything's good and we can return 1 instead of 0 + +errorEnd: + stbi__rewind(s); + return res; +} + +// read 16bit value and convert to 24bit RGB +static void stbi__tga_read_rgb16(stbi__context *s, stbi_uc* out) +{ + stbi__uint16 px = (stbi__uint16)stbi__get16le(s); + stbi__uint16 fiveBitMask = 31; + // we have 3 channels with 5bits each + int r = (px >> 10) & fiveBitMask; + int g = (px >> 5) & fiveBitMask; + int b = px & fiveBitMask; + // Note that this saves the data in RGB(A) order, so it doesn't need to be swapped later + out[0] = (stbi_uc)((r * 255)/31); + out[1] = (stbi_uc)((g * 255)/31); + out[2] = (stbi_uc)((b * 255)/31); + + // some people claim that the most significant bit might be used for alpha + // (possibly if an alpha-bit is set in the "image descriptor byte") + // but that only made 16bit test images completely translucent.. + // so let's treat all 15 and 16bit TGAs as RGB with no alpha. +} + +static void *stbi__tga_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) +{ + // read in the TGA header stuff + int tga_offset = stbi__get8(s); + int tga_indexed = stbi__get8(s); + int tga_image_type = stbi__get8(s); + int tga_is_RLE = 0; + int tga_palette_start = stbi__get16le(s); + int tga_palette_len = stbi__get16le(s); + int tga_palette_bits = stbi__get8(s); + int tga_x_origin = stbi__get16le(s); + int tga_y_origin = stbi__get16le(s); + int tga_width = stbi__get16le(s); + int tga_height = stbi__get16le(s); + int tga_bits_per_pixel = stbi__get8(s); + int tga_comp, tga_rgb16=0; + int tga_inverted = stbi__get8(s); + // int tga_alpha_bits = tga_inverted & 15; // the 4 lowest bits - unused (useless?) + // image data + unsigned char *tga_data; + unsigned char *tga_palette = NULL; + int i, j; + unsigned char raw_data[4] = {0}; + int RLE_count = 0; + int RLE_repeating = 0; + int read_next_pixel = 1; + STBI_NOTUSED(ri); + STBI_NOTUSED(tga_x_origin); // @TODO + STBI_NOTUSED(tga_y_origin); // @TODO + + // do a tiny bit of precessing + if ( tga_image_type >= 8 ) + { + tga_image_type -= 8; + tga_is_RLE = 1; + } + tga_inverted = 1 - ((tga_inverted >> 5) & 1); + + // If I'm paletted, then I'll use the number of bits from the palette + if ( tga_indexed ) tga_comp = stbi__tga_get_comp(tga_palette_bits, 0, &tga_rgb16); + else tga_comp = stbi__tga_get_comp(tga_bits_per_pixel, (tga_image_type == 3), &tga_rgb16); + + if(!tga_comp) // shouldn't really happen, stbi__tga_test() should have ensured basic consistency + return stbi__errpuc("bad format", "Can't find out TGA pixelformat"); + + // tga info + *x = tga_width; + *y = tga_height; + if (comp) *comp = tga_comp; + + if (!stbi__mad3sizes_valid(tga_width, tga_height, tga_comp, 0)) + return stbi__errpuc("too large", "Corrupt TGA"); + + tga_data = (unsigned char*)stbi__malloc_mad3(tga_width, tga_height, tga_comp, 0); + if (!tga_data) return stbi__errpuc("outofmem", "Out of memory"); + + // skip to the data's starting position (offset usually = 0) + stbi__skip(s, tga_offset ); + + if ( !tga_indexed && !tga_is_RLE && !tga_rgb16 ) { + for (i=0; i < tga_height; ++i) { + int row = tga_inverted ? tga_height -i - 1 : i; + stbi_uc *tga_row = tga_data + row*tga_width*tga_comp; + stbi__getn(s, tga_row, tga_width * tga_comp); + } + } else { + // do I need to load a palette? + if ( tga_indexed) + { + // any data to skip? (offset usually = 0) + stbi__skip(s, tga_palette_start ); + // load the palette + tga_palette = (unsigned char*)stbi__malloc_mad2(tga_palette_len, tga_comp, 0); + if (!tga_palette) { + STBI_FREE(tga_data); + return stbi__errpuc("outofmem", "Out of memory"); + } + if (tga_rgb16) { + stbi_uc *pal_entry = tga_palette; + STBI_ASSERT(tga_comp == STBI_rgb); + for (i=0; i < tga_palette_len; ++i) { + stbi__tga_read_rgb16(s, pal_entry); + pal_entry += tga_comp; + } + } else if (!stbi__getn(s, tga_palette, tga_palette_len * tga_comp)) { + STBI_FREE(tga_data); + STBI_FREE(tga_palette); + return stbi__errpuc("bad palette", "Corrupt TGA"); + } + } + // load the data + for (i=0; i < tga_width * tga_height; ++i) + { + // if I'm in RLE mode, do I need to get a RLE stbi__pngchunk? + if ( tga_is_RLE ) + { + if ( RLE_count == 0 ) + { + // yep, get the next byte as a RLE command + int RLE_cmd = stbi__get8(s); + RLE_count = 1 + (RLE_cmd & 127); + RLE_repeating = RLE_cmd >> 7; + read_next_pixel = 1; + } else if ( !RLE_repeating ) + { + read_next_pixel = 1; + } + } else + { + read_next_pixel = 1; + } + // OK, if I need to read a pixel, do it now + if ( read_next_pixel ) + { + // load however much data we did have + if ( tga_indexed ) + { + // read in index, then perform the lookup + int pal_idx = (tga_bits_per_pixel == 8) ? stbi__get8(s) : stbi__get16le(s); + if ( pal_idx >= tga_palette_len ) { + // invalid index + pal_idx = 0; + } + pal_idx *= tga_comp; + for (j = 0; j < tga_comp; ++j) { + raw_data[j] = tga_palette[pal_idx+j]; + } + } else if(tga_rgb16) { + STBI_ASSERT(tga_comp == STBI_rgb); + stbi__tga_read_rgb16(s, raw_data); + } else { + // read in the data raw + for (j = 0; j < tga_comp; ++j) { + raw_data[j] = stbi__get8(s); + } + } + // clear the reading flag for the next pixel + read_next_pixel = 0; + } // end of reading a pixel + + // copy data + for (j = 0; j < tga_comp; ++j) + tga_data[i*tga_comp+j] = raw_data[j]; + + // in case we're in RLE mode, keep counting down + --RLE_count; + } + // do I need to invert the image? + if ( tga_inverted ) + { + for (j = 0; j*2 < tga_height; ++j) + { + int index1 = j * tga_width * tga_comp; + int index2 = (tga_height - 1 - j) * tga_width * tga_comp; + for (i = tga_width * tga_comp; i > 0; --i) + { + unsigned char temp = tga_data[index1]; + tga_data[index1] = tga_data[index2]; + tga_data[index2] = temp; + ++index1; + ++index2; + } + } + } + // clear my palette, if I had one + if ( tga_palette != NULL ) + { + STBI_FREE( tga_palette ); + } + } + + // swap RGB - if the source data was RGB16, it already is in the right order + if (tga_comp >= 3 && !tga_rgb16) + { + unsigned char* tga_pixel = tga_data; + for (i=0; i < tga_width * tga_height; ++i) + { + unsigned char temp = tga_pixel[0]; + tga_pixel[0] = tga_pixel[2]; + tga_pixel[2] = temp; + tga_pixel += tga_comp; + } + } + + // convert to target component count + if (req_comp && req_comp != tga_comp) + tga_data = stbi__convert_format(tga_data, tga_comp, req_comp, tga_width, tga_height); + + // the things I do to get rid of an error message, and yet keep + // Microsoft's C compilers happy... [8^( + tga_palette_start = tga_palette_len = tga_palette_bits = + tga_x_origin = tga_y_origin = 0; + STBI_NOTUSED(tga_palette_start); + // OK, done + return tga_data; +} +#endif + +// ************************************************************************************************* +// Photoshop PSD loader -- PD by Thatcher Ulrich, integration by Nicolas Schulz, tweaked by STB + +#ifndef STBI_NO_PSD +static int stbi__psd_test(stbi__context *s) +{ + int r = (stbi__get32be(s) == 0x38425053); + stbi__rewind(s); + return r; +} + +static int stbi__psd_decode_rle(stbi__context *s, stbi_uc *p, int pixelCount) +{ + int count, nleft, len; + + count = 0; + while ((nleft = pixelCount - count) > 0) { + len = stbi__get8(s); + if (len == 128) { + // No-op. + } else if (len < 128) { + // Copy next len+1 bytes literally. + len++; + if (len > nleft) return 0; // corrupt data + count += len; + while (len) { + *p = stbi__get8(s); + p += 4; + len--; + } + } else if (len > 128) { + stbi_uc val; + // Next -len+1 bytes in the dest are replicated from next source byte. + // (Interpret len as a negative 8-bit int.) + len = 257 - len; + if (len > nleft) return 0; // corrupt data + val = stbi__get8(s); + count += len; + while (len) { + *p = val; + p += 4; + len--; + } + } + } + + return 1; +} + +static void *stbi__psd_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri, int bpc) +{ + int pixelCount; + int channelCount, compression; + int channel, i; + int bitdepth; + int w,h; + stbi_uc *out; + STBI_NOTUSED(ri); + + // Check identifier + if (stbi__get32be(s) != 0x38425053) // "8BPS" + return stbi__errpuc("not PSD", "Corrupt PSD image"); + + // Check file type version. + if (stbi__get16be(s) != 1) + return stbi__errpuc("wrong version", "Unsupported version of PSD image"); + + // Skip 6 reserved bytes. + stbi__skip(s, 6 ); + + // Read the number of channels (R, G, B, A, etc). + channelCount = stbi__get16be(s); + if (channelCount < 0 || channelCount > 16) + return stbi__errpuc("wrong channel count", "Unsupported number of channels in PSD image"); + + // Read the rows and columns of the image. + h = stbi__get32be(s); + w = stbi__get32be(s); + + // Make sure the depth is 8 bits. + bitdepth = stbi__get16be(s); + if (bitdepth != 8 && bitdepth != 16) + return stbi__errpuc("unsupported bit depth", "PSD bit depth is not 8 or 16 bit"); + + // Make sure the color mode is RGB. + // Valid options are: + // 0: Bitmap + // 1: Grayscale + // 2: Indexed color + // 3: RGB color + // 4: CMYK color + // 7: Multichannel + // 8: Duotone + // 9: Lab color + if (stbi__get16be(s) != 3) + return stbi__errpuc("wrong color format", "PSD is not in RGB color format"); + + // Skip the Mode Data. (It's the palette for indexed color; other info for other modes.) + stbi__skip(s,stbi__get32be(s) ); + + // Skip the image resources. (resolution, pen tool paths, etc) + stbi__skip(s, stbi__get32be(s) ); + + // Skip the reserved data. + stbi__skip(s, stbi__get32be(s) ); + + // Find out if the data is compressed. + // Known values: + // 0: no compression + // 1: RLE compressed + compression = stbi__get16be(s); + if (compression > 1) + return stbi__errpuc("bad compression", "PSD has an unknown compression format"); + + // Check size + if (!stbi__mad3sizes_valid(4, w, h, 0)) + return stbi__errpuc("too large", "Corrupt PSD"); + + // Create the destination image. + + if (!compression && bitdepth == 16 && bpc == 16) { + out = (stbi_uc *) stbi__malloc_mad3(8, w, h, 0); + ri->bits_per_channel = 16; + } else + out = (stbi_uc *) stbi__malloc(4 * w*h); + + if (!out) return stbi__errpuc("outofmem", "Out of memory"); + pixelCount = w*h; + + // Initialize the data to zero. + //memset( out, 0, pixelCount * 4 ); + + // Finally, the image data. + if (compression) { + // RLE as used by .PSD and .TIFF + // Loop until you get the number of unpacked bytes you are expecting: + // Read the next source byte into n. + // If n is between 0 and 127 inclusive, copy the next n+1 bytes literally. + // Else if n is between -127 and -1 inclusive, copy the next byte -n+1 times. + // Else if n is 128, noop. + // Endloop + + // The RLE-compressed data is preceded by a 2-byte data count for each row in the data, + // which we're going to just skip. + stbi__skip(s, h * channelCount * 2 ); + + // Read the RLE data by channel. + for (channel = 0; channel < 4; channel++) { + stbi_uc *p; + + p = out+channel; + if (channel >= channelCount) { + // Fill this channel with default data. + for (i = 0; i < pixelCount; i++, p += 4) + *p = (channel == 3 ? 255 : 0); + } else { + // Read the RLE data. + if (!stbi__psd_decode_rle(s, p, pixelCount)) { + STBI_FREE(out); + return stbi__errpuc("corrupt", "bad RLE data"); + } + } + } + + } else { + // We're at the raw image data. It's each channel in order (Red, Green, Blue, Alpha, ...) + // where each channel consists of an 8-bit (or 16-bit) value for each pixel in the image. + + // Read the data by channel. + for (channel = 0; channel < 4; channel++) { + if (channel >= channelCount) { + // Fill this channel with default data. + if (bitdepth == 16 && bpc == 16) { + stbi__uint16 *q = ((stbi__uint16 *) out) + channel; + stbi__uint16 val = channel == 3 ? 65535 : 0; + for (i = 0; i < pixelCount; i++, q += 4) + *q = val; + } else { + stbi_uc *p = out+channel; + stbi_uc val = channel == 3 ? 255 : 0; + for (i = 0; i < pixelCount; i++, p += 4) + *p = val; + } + } else { + if (ri->bits_per_channel == 16) { // output bpc + stbi__uint16 *q = ((stbi__uint16 *) out) + channel; + for (i = 0; i < pixelCount; i++, q += 4) + *q = (stbi__uint16) stbi__get16be(s); + } else { + stbi_uc *p = out+channel; + if (bitdepth == 16) { // input bpc + for (i = 0; i < pixelCount; i++, p += 4) + *p = (stbi_uc) (stbi__get16be(s) >> 8); + } else { + for (i = 0; i < pixelCount; i++, p += 4) + *p = stbi__get8(s); + } + } + } + } + } + + // remove weird white matte from PSD + if (channelCount >= 4) { + if (ri->bits_per_channel == 16) { + for (i=0; i < w*h; ++i) { + stbi__uint16 *pixel = (stbi__uint16 *) out + 4*i; + if (pixel[3] != 0 && pixel[3] != 65535) { + float a = pixel[3] / 65535.0f; + float ra = 1.0f / a; + float inv_a = 65535.0f * (1 - ra); + pixel[0] = (stbi__uint16) (pixel[0]*ra + inv_a); + pixel[1] = (stbi__uint16) (pixel[1]*ra + inv_a); + pixel[2] = (stbi__uint16) (pixel[2]*ra + inv_a); + } + } + } else { + for (i=0; i < w*h; ++i) { + unsigned char *pixel = out + 4*i; + if (pixel[3] != 0 && pixel[3] != 255) { + float a = pixel[3] / 255.0f; + float ra = 1.0f / a; + float inv_a = 255.0f * (1 - ra); + pixel[0] = (unsigned char) (pixel[0]*ra + inv_a); + pixel[1] = (unsigned char) (pixel[1]*ra + inv_a); + pixel[2] = (unsigned char) (pixel[2]*ra + inv_a); + } + } + } + } + + // convert to desired output format + if (req_comp && req_comp != 4) { + if (ri->bits_per_channel == 16) + out = (stbi_uc *) stbi__convert_format16((stbi__uint16 *) out, 4, req_comp, w, h); + else + out = stbi__convert_format(out, 4, req_comp, w, h); + if (out == NULL) return out; // stbi__convert_format frees input on failure + } + + if (comp) *comp = 4; + *y = h; + *x = w; + + return out; +} +#endif + +// ************************************************************************************************* +// Softimage PIC loader +// by Tom Seddon +// +// See http://softimage.wiki.softimage.com/index.php/INFO:_PIC_file_format +// See http://ozviz.wasp.uwa.edu.au/~pbourke/dataformats/softimagepic/ + +#ifndef STBI_NO_PIC +static int stbi__pic_is4(stbi__context *s,const char *str) +{ + int i; + for (i=0; i<4; ++i) + if (stbi__get8(s) != (stbi_uc)str[i]) + return 0; + + return 1; +} + +static int stbi__pic_test_core(stbi__context *s) +{ + int i; + + if (!stbi__pic_is4(s,"\x53\x80\xF6\x34")) + return 0; + + for(i=0;i<84;++i) + stbi__get8(s); + + if (!stbi__pic_is4(s,"PICT")) + return 0; + + return 1; +} + +typedef struct +{ + stbi_uc size,type,channel; +} stbi__pic_packet; + +static stbi_uc *stbi__readval(stbi__context *s, int channel, stbi_uc *dest) +{ + int mask=0x80, i; + + for (i=0; i<4; ++i, mask>>=1) { + if (channel & mask) { + if (stbi__at_eof(s)) return stbi__errpuc("bad file","PIC file too short"); + dest[i]=stbi__get8(s); + } + } + + return dest; +} + +static void stbi__copyval(int channel,stbi_uc *dest,const stbi_uc *src) +{ + int mask=0x80,i; + + for (i=0;i<4; ++i, mask>>=1) + if (channel&mask) + dest[i]=src[i]; +} + +static stbi_uc *stbi__pic_load_core(stbi__context *s,int width,int height,int *comp, stbi_uc *result) +{ + int act_comp=0,num_packets=0,y,chained; + stbi__pic_packet packets[10]; + + // this will (should...) cater for even some bizarre stuff like having data + // for the same channel in multiple packets. + do { + stbi__pic_packet *packet; + + if (num_packets==sizeof(packets)/sizeof(packets[0])) + return stbi__errpuc("bad format","too many packets"); + + packet = &packets[num_packets++]; + + chained = stbi__get8(s); + packet->size = stbi__get8(s); + packet->type = stbi__get8(s); + packet->channel = stbi__get8(s); + + act_comp |= packet->channel; + + if (stbi__at_eof(s)) return stbi__errpuc("bad file","file too short (reading packets)"); + if (packet->size != 8) return stbi__errpuc("bad format","packet isn't 8bpp"); + } while (chained); + + *comp = (act_comp & 0x10 ? 4 : 3); // has alpha channel? + + for(y=0; ytype) { + default: + return stbi__errpuc("bad format","packet has bad compression type"); + + case 0: {//uncompressed + int x; + + for(x=0;xchannel,dest)) + return 0; + break; + } + + case 1://Pure RLE + { + int left=width, i; + + while (left>0) { + stbi_uc count,value[4]; + + count=stbi__get8(s); + if (stbi__at_eof(s)) return stbi__errpuc("bad file","file too short (pure read count)"); + + if (count > left) + count = (stbi_uc) left; + + if (!stbi__readval(s,packet->channel,value)) return 0; + + for(i=0; ichannel,dest,value); + left -= count; + } + } + break; + + case 2: {//Mixed RLE + int left=width; + while (left>0) { + int count = stbi__get8(s), i; + if (stbi__at_eof(s)) return stbi__errpuc("bad file","file too short (mixed read count)"); + + if (count >= 128) { // Repeated + stbi_uc value[4]; + + if (count==128) + count = stbi__get16be(s); + else + count -= 127; + if (count > left) + return stbi__errpuc("bad file","scanline overrun"); + + if (!stbi__readval(s,packet->channel,value)) + return 0; + + for(i=0;ichannel,dest,value); + } else { // Raw + ++count; + if (count>left) return stbi__errpuc("bad file","scanline overrun"); + + for(i=0;ichannel,dest)) + return 0; + } + left-=count; + } + break; + } + } + } + } + + return result; +} + +static void *stbi__pic_load(stbi__context *s,int *px,int *py,int *comp,int req_comp, stbi__result_info *ri) +{ + stbi_uc *result; + int i, x,y, internal_comp; + STBI_NOTUSED(ri); + + if (!comp) comp = &internal_comp; + + for (i=0; i<92; ++i) + stbi__get8(s); + + x = stbi__get16be(s); + y = stbi__get16be(s); + if (stbi__at_eof(s)) return stbi__errpuc("bad file","file too short (pic header)"); + if (!stbi__mad3sizes_valid(x, y, 4, 0)) return stbi__errpuc("too large", "PIC image too large to decode"); + + stbi__get32be(s); //skip `ratio' + stbi__get16be(s); //skip `fields' + stbi__get16be(s); //skip `pad' + + // intermediate buffer is RGBA + result = (stbi_uc *) stbi__malloc_mad3(x, y, 4, 0); + memset(result, 0xff, x*y*4); + + if (!stbi__pic_load_core(s,x,y,comp, result)) { + STBI_FREE(result); + result=0; + } + *px = x; + *py = y; + if (req_comp == 0) req_comp = *comp; + result=stbi__convert_format(result,4,req_comp,x,y); + + return result; +} + +static int stbi__pic_test(stbi__context *s) +{ + int r = stbi__pic_test_core(s); + stbi__rewind(s); + return r; +} +#endif + +// ************************************************************************************************* +// GIF loader -- public domain by Jean-Marc Lienher -- simplified/shrunk by stb + +#ifndef STBI_NO_GIF +typedef struct +{ + stbi__int16 prefix; + stbi_uc first; + stbi_uc suffix; +} stbi__gif_lzw; + +typedef struct +{ + int w,h; + stbi_uc *out; // output buffer (always 4 components) + stbi_uc *background; // The current "background" as far as a gif is concerned + stbi_uc *history; + int flags, bgindex, ratio, transparent, eflags; + stbi_uc pal[256][4]; + stbi_uc lpal[256][4]; + stbi__gif_lzw codes[8192]; + stbi_uc *color_table; + int parse, step; + int lflags; + int start_x, start_y; + int max_x, max_y; + int cur_x, cur_y; + int line_size; + int delay; +} stbi__gif; + +static int stbi__gif_test_raw(stbi__context *s) +{ + int sz; + if (stbi__get8(s) != 'G' || stbi__get8(s) != 'I' || stbi__get8(s) != 'F' || stbi__get8(s) != '8') return 0; + sz = stbi__get8(s); + if (sz != '9' && sz != '7') return 0; + if (stbi__get8(s) != 'a') return 0; + return 1; +} + +static int stbi__gif_test(stbi__context *s) +{ + int r = stbi__gif_test_raw(s); + stbi__rewind(s); + return r; +} + +static void stbi__gif_parse_colortable(stbi__context *s, stbi_uc pal[256][4], int num_entries, int transp) +{ + int i; + for (i=0; i < num_entries; ++i) { + pal[i][2] = stbi__get8(s); + pal[i][1] = stbi__get8(s); + pal[i][0] = stbi__get8(s); + pal[i][3] = transp == i ? 0 : 255; + } +} + +static int stbi__gif_header(stbi__context *s, stbi__gif *g, int *comp, int is_info) +{ + stbi_uc version; + if (stbi__get8(s) != 'G' || stbi__get8(s) != 'I' || stbi__get8(s) != 'F' || stbi__get8(s) != '8') + return stbi__err("not GIF", "Corrupt GIF"); + + version = stbi__get8(s); + if (version != '7' && version != '9') return stbi__err("not GIF", "Corrupt GIF"); + if (stbi__get8(s) != 'a') return stbi__err("not GIF", "Corrupt GIF"); + + stbi__g_failure_reason = ""; + g->w = stbi__get16le(s); + g->h = stbi__get16le(s); + g->flags = stbi__get8(s); + g->bgindex = stbi__get8(s); + g->ratio = stbi__get8(s); + g->transparent = -1; + + if (comp != 0) *comp = 4; // can't actually tell whether it's 3 or 4 until we parse the comments + + if (is_info) return 1; + + if (g->flags & 0x80) + stbi__gif_parse_colortable(s,g->pal, 2 << (g->flags & 7), -1); + + return 1; +} + +static int stbi__gif_info_raw(stbi__context *s, int *x, int *y, int *comp) +{ + stbi__gif* g = (stbi__gif*) stbi__malloc(sizeof(stbi__gif)); + if (!stbi__gif_header(s, g, comp, 1)) { + STBI_FREE(g); + stbi__rewind( s ); + return 0; + } + if (x) *x = g->w; + if (y) *y = g->h; + STBI_FREE(g); + return 1; +} + +static void stbi__out_gif_code(stbi__gif *g, stbi__uint16 code) +{ + stbi_uc *p, *c; + int idx; + + // recurse to decode the prefixes, since the linked-list is backwards, + // and working backwards through an interleaved image would be nasty + if (g->codes[code].prefix >= 0) + stbi__out_gif_code(g, g->codes[code].prefix); + + if (g->cur_y >= g->max_y) return; + + idx = g->cur_x + g->cur_y; + p = &g->out[idx]; + g->history[idx / 4] = 1; + + c = &g->color_table[g->codes[code].suffix * 4]; + if (c[3] > 128) { // don't render transparent pixels; + p[0] = c[2]; + p[1] = c[1]; + p[2] = c[0]; + p[3] = c[3]; + } + g->cur_x += 4; + + if (g->cur_x >= g->max_x) { + g->cur_x = g->start_x; + g->cur_y += g->step; + + while (g->cur_y >= g->max_y && g->parse > 0) { + g->step = (1 << g->parse) * g->line_size; + g->cur_y = g->start_y + (g->step >> 1); + --g->parse; + } + } +} + +static stbi_uc *stbi__process_gif_raster(stbi__context *s, stbi__gif *g) +{ + stbi_uc lzw_cs; + stbi__int32 len, init_code; + stbi__uint32 first; + stbi__int32 codesize, codemask, avail, oldcode, bits, valid_bits, clear; + stbi__gif_lzw *p; + + lzw_cs = stbi__get8(s); + if (lzw_cs > 12) return NULL; + clear = 1 << lzw_cs; + first = 1; + codesize = lzw_cs + 1; + codemask = (1 << codesize) - 1; + bits = 0; + valid_bits = 0; + for (init_code = 0; init_code < clear; init_code++) { + g->codes[init_code].prefix = -1; + g->codes[init_code].first = (stbi_uc) init_code; + g->codes[init_code].suffix = (stbi_uc) init_code; + } + + // support no starting clear code + avail = clear+2; + oldcode = -1; + + len = 0; + for(;;) { + if (valid_bits < codesize) { + if (len == 0) { + len = stbi__get8(s); // start new block + if (len == 0) + return g->out; + } + --len; + bits |= (stbi__int32) stbi__get8(s) << valid_bits; + valid_bits += 8; + } else { + stbi__int32 code = bits & codemask; + bits >>= codesize; + valid_bits -= codesize; + // @OPTIMIZE: is there some way we can accelerate the non-clear path? + if (code == clear) { // clear code + codesize = lzw_cs + 1; + codemask = (1 << codesize) - 1; + avail = clear + 2; + oldcode = -1; + first = 0; + } else if (code == clear + 1) { // end of stream code + stbi__skip(s, len); + while ((len = stbi__get8(s)) > 0) + stbi__skip(s,len); + return g->out; + } else if (code <= avail) { + if (first) { + return stbi__errpuc("no clear code", "Corrupt GIF"); + } + + if (oldcode >= 0) { + p = &g->codes[avail++]; + if (avail > 8192) { + return stbi__errpuc("too many codes", "Corrupt GIF"); + } + + p->prefix = (stbi__int16) oldcode; + p->first = g->codes[oldcode].first; + p->suffix = (code == avail) ? p->first : g->codes[code].first; + } else if (code == avail) + return stbi__errpuc("illegal code in raster", "Corrupt GIF"); + + stbi__out_gif_code(g, (stbi__uint16) code); + + if ((avail & codemask) == 0 && avail <= 0x0FFF) { + codesize++; + codemask = (1 << codesize) - 1; + } + + oldcode = code; + } else { + return stbi__errpuc("illegal code in raster", "Corrupt GIF"); + } + } + } +} + +// this function is designed to support animated gifs, although stb_image doesn't support it +// two back is the image from two frames ago, used for a very specific disposal format +static stbi_uc *stbi__gif_load_next(stbi__context *s, stbi__gif *g, int *comp, int req_comp, stbi_uc *two_back) +{ + int dispose; + int first_frame; + int pi; + int pcount; + STBI_NOTUSED(req_comp); + + // on first frame, any non-written pixels get the background colour (non-transparent) + first_frame = 0; + if (g->out == 0) { + if (!stbi__gif_header(s, g, comp,0)) return 0; // stbi__g_failure_reason set by stbi__gif_header + if (!stbi__mad3sizes_valid(4, g->w, g->h, 0)) + return stbi__errpuc("too large", "GIF image is too large"); + pcount = g->w * g->h; + g->out = (stbi_uc *) stbi__malloc(4 * pcount); + g->background = (stbi_uc *) stbi__malloc(4 * pcount); + g->history = (stbi_uc *) stbi__malloc(pcount); + if (!g->out || !g->background || !g->history) + return stbi__errpuc("outofmem", "Out of memory"); + + // image is treated as "transparent" at the start - ie, nothing overwrites the current background; + // background colour is only used for pixels that are not rendered first frame, after that "background" + // color refers to the color that was there the previous frame. + memset(g->out, 0x00, 4 * pcount); + memset(g->background, 0x00, 4 * pcount); // state of the background (starts transparent) + memset(g->history, 0x00, pcount); // pixels that were affected previous frame + first_frame = 1; + } else { + // second frame - how do we dispoase of the previous one? + dispose = (g->eflags & 0x1C) >> 2; + pcount = g->w * g->h; + + if ((dispose == 3) && (two_back == 0)) { + dispose = 2; // if I don't have an image to revert back to, default to the old background + } + + if (dispose == 3) { // use previous graphic + for (pi = 0; pi < pcount; ++pi) { + if (g->history[pi]) { + memcpy( &g->out[pi * 4], &two_back[pi * 4], 4 ); + } + } + } else if (dispose == 2) { + // restore what was changed last frame to background before that frame; + for (pi = 0; pi < pcount; ++pi) { + if (g->history[pi]) { + memcpy( &g->out[pi * 4], &g->background[pi * 4], 4 ); + } + } + } else { + // This is a non-disposal case eithe way, so just + // leave the pixels as is, and they will become the new background + // 1: do not dispose + // 0: not specified. + } + + // background is what out is after the undoing of the previou frame; + memcpy( g->background, g->out, 4 * g->w * g->h ); + } + + // clear my history; + memset( g->history, 0x00, g->w * g->h ); // pixels that were affected previous frame + + for (;;) { + int tag = stbi__get8(s); + switch (tag) { + case 0x2C: /* Image Descriptor */ + { + stbi__int32 x, y, w, h; + stbi_uc *o; + + x = stbi__get16le(s); + y = stbi__get16le(s); + w = stbi__get16le(s); + h = stbi__get16le(s); + if (((x + w) > (g->w)) || ((y + h) > (g->h))) + return stbi__errpuc("bad Image Descriptor", "Corrupt GIF"); + + g->line_size = g->w * 4; + g->start_x = x * 4; + g->start_y = y * g->line_size; + g->max_x = g->start_x + w * 4; + g->max_y = g->start_y + h * g->line_size; + g->cur_x = g->start_x; + g->cur_y = g->start_y; + + // if the width of the specified rectangle is 0, that means + // we may not see *any* pixels or the image is malformed; + // to make sure this is caught, move the current y down to + // max_y (which is what out_gif_code checks). + if (w == 0) + g->cur_y = g->max_y; + + g->lflags = stbi__get8(s); + + if (g->lflags & 0x40) { + g->step = 8 * g->line_size; // first interlaced spacing + g->parse = 3; + } else { + g->step = g->line_size; + g->parse = 0; + } + + if (g->lflags & 0x80) { + stbi__gif_parse_colortable(s,g->lpal, 2 << (g->lflags & 7), g->eflags & 0x01 ? g->transparent : -1); + g->color_table = (stbi_uc *) g->lpal; + } else if (g->flags & 0x80) { + g->color_table = (stbi_uc *) g->pal; + } else + return stbi__errpuc("missing color table", "Corrupt GIF"); + + o = stbi__process_gif_raster(s, g); + if (!o) return NULL; + + // if this was the first frame, + pcount = g->w * g->h; + if (first_frame && (g->bgindex > 0)) { + // if first frame, any pixel not drawn to gets the background color + for (pi = 0; pi < pcount; ++pi) { + if (g->history[pi] == 0) { + g->pal[g->bgindex][3] = 255; // just in case it was made transparent, undo that; It will be reset next frame if need be; + memcpy( &g->out[pi * 4], &g->pal[g->bgindex], 4 ); + } + } + } + + return o; + } + + case 0x21: // Comment Extension. + { + int len; + int ext = stbi__get8(s); + if (ext == 0xF9) { // Graphic Control Extension. + len = stbi__get8(s); + if (len == 4) { + g->eflags = stbi__get8(s); + g->delay = 10 * stbi__get16le(s); // delay - 1/100th of a second, saving as 1/1000ths. + + // unset old transparent + if (g->transparent >= 0) { + g->pal[g->transparent][3] = 255; + } + if (g->eflags & 0x01) { + g->transparent = stbi__get8(s); + if (g->transparent >= 0) { + g->pal[g->transparent][3] = 0; + } + } else { + // don't need transparent + stbi__skip(s, 1); + g->transparent = -1; + } + } else { + stbi__skip(s, len); + break; + } + } + while ((len = stbi__get8(s)) != 0) { + stbi__skip(s, len); + } + break; + } + + case 0x3B: // gif stream termination code + return (stbi_uc *) s; // using '1' causes warning on some compilers + + default: + return stbi__errpuc("unknown code", "Corrupt GIF"); + } + } +} + +static void *stbi__load_gif_main(stbi__context *s, int **delays, int *x, int *y, int *z, int *comp, int req_comp) +{ + if (stbi__gif_test(s)) { + int layers = 0; + stbi_uc *u = 0; + stbi_uc *out = 0; + stbi_uc *two_back = 0; + stbi__gif g; + int stride; + memset(&g, 0, sizeof(g)); + if (delays) { + *delays = 0; + } + + do { + u = stbi__gif_load_next(s, &g, comp, req_comp, two_back); + if (u == (stbi_uc *) s) u = 0; // end of animated gif marker + + if (u) { + *x = g.w; + *y = g.h; + ++layers; + stride = g.w * g.h * 4; + + if (out) { + out = (stbi_uc*) STBI_REALLOC( out, layers * stride ); + if (delays) { + *delays = (int*) STBI_REALLOC( *delays, sizeof(int) * layers ); + } + } else { + out = (stbi_uc*)stbi__malloc( layers * stride ); + if (delays) { + *delays = (int*) stbi__malloc( layers * sizeof(int) ); + } + } + memcpy( out + ((layers - 1) * stride), u, stride ); + if (layers >= 2) { + two_back = out - 2 * stride; + } + + if (delays) { + (*delays)[layers - 1U] = g.delay; + } + } + } while (u != 0); + + // free temp buffer; + STBI_FREE(g.out); + STBI_FREE(g.history); + STBI_FREE(g.background); + + // do the final conversion after loading everything; + if (req_comp && req_comp != 4) + out = stbi__convert_format(out, 4, req_comp, layers * g.w, g.h); + + *z = layers; + return out; + } else { + return stbi__errpuc("not GIF", "Image was not as a gif type."); + } +} + +static void *stbi__gif_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) +{ + stbi_uc *u = 0; + stbi__gif g; + memset(&g, 0, sizeof(g)); + STBI_NOTUSED(ri); + + u = stbi__gif_load_next(s, &g, comp, req_comp, 0); + if (u == (stbi_uc *) s) u = 0; // end of animated gif marker + if (u) { + *x = g.w; + *y = g.h; + + // moved conversion to after successful load so that the same + // can be done for multiple frames. + if (req_comp && req_comp != 4) + u = stbi__convert_format(u, 4, req_comp, g.w, g.h); + } else if (g.out) { + // if there was an error and we allocated an image buffer, free it! + STBI_FREE(g.out); + } + + // free buffers needed for multiple frame loading; + STBI_FREE(g.history); + STBI_FREE(g.background); + + return u; +} + +static int stbi__gif_info(stbi__context *s, int *x, int *y, int *comp) +{ + return stbi__gif_info_raw(s,x,y,comp); +} +#endif + +// ************************************************************************************************* +// Radiance RGBE HDR loader +// originally by Nicolas Schulz +#ifndef STBI_NO_HDR +static int stbi__hdr_test_core(stbi__context *s, const char *signature) +{ + int i; + for (i=0; signature[i]; ++i) + if (stbi__get8(s) != signature[i]) + return 0; + stbi__rewind(s); + return 1; +} + +static int stbi__hdr_test(stbi__context* s) +{ + int r = stbi__hdr_test_core(s, "#?RADIANCE\n"); + stbi__rewind(s); + if(!r) { + r = stbi__hdr_test_core(s, "#?RGBE\n"); + stbi__rewind(s); + } + return r; +} + +#define STBI__HDR_BUFLEN 1024 +static char *stbi__hdr_gettoken(stbi__context *z, char *buffer) +{ + int len=0; + char c = '\0'; + + c = (char) stbi__get8(z); + + while (!stbi__at_eof(z) && c != '\n') { + buffer[len++] = c; + if (len == STBI__HDR_BUFLEN-1) { + // flush to end of line + while (!stbi__at_eof(z) && stbi__get8(z) != '\n') + ; + break; + } + c = (char) stbi__get8(z); + } + + buffer[len] = 0; + return buffer; +} + +static void stbi__hdr_convert(float *output, stbi_uc *input, int req_comp) +{ + if ( input[3] != 0 ) { + float f1; + // Exponent + f1 = (float) ldexp(1.0f, input[3] - (int)(128 + 8)); + if (req_comp <= 2) + output[0] = (input[0] + input[1] + input[2]) * f1 / 3; + else { + output[0] = input[0] * f1; + output[1] = input[1] * f1; + output[2] = input[2] * f1; + } + if (req_comp == 2) output[1] = 1; + if (req_comp == 4) output[3] = 1; + } else { + switch (req_comp) { + case 4: output[3] = 1; /* fallthrough */ + case 3: output[0] = output[1] = output[2] = 0; + break; + case 2: output[1] = 1; /* fallthrough */ + case 1: output[0] = 0; + break; + } + } +} + +static float *stbi__hdr_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) +{ + char buffer[STBI__HDR_BUFLEN]; + char *token; + int valid = 0; + int width, height; + stbi_uc *scanline; + float *hdr_data; + int len; + unsigned char count, value; + int i, j, k, c1,c2, z; + const char *headerToken; + STBI_NOTUSED(ri); + + // Check identifier + headerToken = stbi__hdr_gettoken(s,buffer); + if (strcmp(headerToken, "#?RADIANCE") != 0 && strcmp(headerToken, "#?RGBE") != 0) + return stbi__errpf("not HDR", "Corrupt HDR image"); + + // Parse header + for(;;) { + token = stbi__hdr_gettoken(s,buffer); + if (token[0] == 0) break; + if (strcmp(token, "FORMAT=32-bit_rle_rgbe") == 0) valid = 1; + } + + if (!valid) return stbi__errpf("unsupported format", "Unsupported HDR format"); + + // Parse width and height + // can't use sscanf() if we're not using stdio! + token = stbi__hdr_gettoken(s,buffer); + if (strncmp(token, "-Y ", 3)) return stbi__errpf("unsupported data layout", "Unsupported HDR format"); + token += 3; + height = (int) strtol(token, &token, 10); + while (*token == ' ') ++token; + if (strncmp(token, "+X ", 3)) return stbi__errpf("unsupported data layout", "Unsupported HDR format"); + token += 3; + width = (int) strtol(token, NULL, 10); + + *x = width; + *y = height; + + if (comp) *comp = 3; + if (req_comp == 0) req_comp = 3; + + if (!stbi__mad4sizes_valid(width, height, req_comp, sizeof(float), 0)) + return stbi__errpf("too large", "HDR image is too large"); + + // Read data + hdr_data = (float *) stbi__malloc_mad4(width, height, req_comp, sizeof(float), 0); + if (!hdr_data) + return stbi__errpf("outofmem", "Out of memory"); + + // Load image data + // image data is stored as some number of sca + if ( width < 8 || width >= 32768) { + // Read flat data + for (j=0; j < height; ++j) { + for (i=0; i < width; ++i) { + stbi_uc rgbe[4]; + main_decode_loop: + stbi__getn(s, rgbe, 4); + stbi__hdr_convert(hdr_data + j * width * req_comp + i * req_comp, rgbe, req_comp); + } + } + } else { + // Read RLE-encoded data + scanline = NULL; + + for (j = 0; j < height; ++j) { + c1 = stbi__get8(s); + c2 = stbi__get8(s); + len = stbi__get8(s); + if (c1 != 2 || c2 != 2 || (len & 0x80)) { + // not run-length encoded, so we have to actually use THIS data as a decoded + // pixel (note this can't be a valid pixel--one of RGB must be >= 128) + stbi_uc rgbe[4]; + rgbe[0] = (stbi_uc) c1; + rgbe[1] = (stbi_uc) c2; + rgbe[2] = (stbi_uc) len; + rgbe[3] = (stbi_uc) stbi__get8(s); + stbi__hdr_convert(hdr_data, rgbe, req_comp); + i = 1; + j = 0; + STBI_FREE(scanline); + goto main_decode_loop; // yes, this makes no sense + } + len <<= 8; + len |= stbi__get8(s); + if (len != width) { STBI_FREE(hdr_data); STBI_FREE(scanline); return stbi__errpf("invalid decoded scanline length", "corrupt HDR"); } + if (scanline == NULL) { + scanline = (stbi_uc *) stbi__malloc_mad2(width, 4, 0); + if (!scanline) { + STBI_FREE(hdr_data); + return stbi__errpf("outofmem", "Out of memory"); + } + } + + for (k = 0; k < 4; ++k) { + int nleft; + i = 0; + while ((nleft = width - i) > 0) { + count = stbi__get8(s); + if (count > 128) { + // Run + value = stbi__get8(s); + count -= 128; + if (count > nleft) { STBI_FREE(hdr_data); STBI_FREE(scanline); return stbi__errpf("corrupt", "bad RLE data in HDR"); } + for (z = 0; z < count; ++z) + scanline[i++ * 4 + k] = value; + } else { + // Dump + if (count > nleft) { STBI_FREE(hdr_data); STBI_FREE(scanline); return stbi__errpf("corrupt", "bad RLE data in HDR"); } + for (z = 0; z < count; ++z) + scanline[i++ * 4 + k] = stbi__get8(s); + } + } + } + for (i=0; i < width; ++i) + stbi__hdr_convert(hdr_data+(j*width + i)*req_comp, scanline + i*4, req_comp); + } + if (scanline) + STBI_FREE(scanline); + } + + return hdr_data; +} + +static int stbi__hdr_info(stbi__context *s, int *x, int *y, int *comp) +{ + char buffer[STBI__HDR_BUFLEN]; + char *token; + int valid = 0; + int dummy; + + if (!x) x = &dummy; + if (!y) y = &dummy; + if (!comp) comp = &dummy; + + if (stbi__hdr_test(s) == 0) { + stbi__rewind( s ); + return 0; + } + + for(;;) { + token = stbi__hdr_gettoken(s,buffer); + if (token[0] == 0) break; + if (strcmp(token, "FORMAT=32-bit_rle_rgbe") == 0) valid = 1; + } + + if (!valid) { + stbi__rewind( s ); + return 0; + } + token = stbi__hdr_gettoken(s,buffer); + if (strncmp(token, "-Y ", 3)) { + stbi__rewind( s ); + return 0; + } + token += 3; + *y = (int) strtol(token, &token, 10); + while (*token == ' ') ++token; + if (strncmp(token, "+X ", 3)) { + stbi__rewind( s ); + return 0; + } + token += 3; + *x = (int) strtol(token, NULL, 10); + *comp = 3; + return 1; +} +#endif // STBI_NO_HDR + +#ifndef STBI_NO_BMP +static int stbi__bmp_info(stbi__context *s, int *x, int *y, int *comp) +{ + void *p; + stbi__bmp_data info; + + info.all_a = 255; + p = stbi__bmp_parse_header(s, &info); + stbi__rewind( s ); + if (p == NULL) + return 0; + if (x) *x = s->img_x; + if (y) *y = s->img_y; + if (comp) { + if (info.bpp == 24 && info.ma == 0xff000000) + *comp = 3; + else + *comp = info.ma ? 4 : 3; + } + return 1; +} +#endif + +#ifndef STBI_NO_PSD +static int stbi__psd_info(stbi__context *s, int *x, int *y, int *comp) +{ + int channelCount, dummy, depth; + if (!x) x = &dummy; + if (!y) y = &dummy; + if (!comp) comp = &dummy; + if (stbi__get32be(s) != 0x38425053) { + stbi__rewind( s ); + return 0; + } + if (stbi__get16be(s) != 1) { + stbi__rewind( s ); + return 0; + } + stbi__skip(s, 6); + channelCount = stbi__get16be(s); + if (channelCount < 0 || channelCount > 16) { + stbi__rewind( s ); + return 0; + } + *y = stbi__get32be(s); + *x = stbi__get32be(s); + depth = stbi__get16be(s); + if (depth != 8 && depth != 16) { + stbi__rewind( s ); + return 0; + } + if (stbi__get16be(s) != 3) { + stbi__rewind( s ); + return 0; + } + *comp = 4; + return 1; +} + +static int stbi__psd_is16(stbi__context *s) +{ + int channelCount, depth; + if (stbi__get32be(s) != 0x38425053) { + stbi__rewind( s ); + return 0; + } + if (stbi__get16be(s) != 1) { + stbi__rewind( s ); + return 0; + } + stbi__skip(s, 6); + channelCount = stbi__get16be(s); + if (channelCount < 0 || channelCount > 16) { + stbi__rewind( s ); + return 0; + } + (void) stbi__get32be(s); + (void) stbi__get32be(s); + depth = stbi__get16be(s); + if (depth != 16) { + stbi__rewind( s ); + return 0; + } + return 1; +} +#endif + +#ifndef STBI_NO_PIC +static int stbi__pic_info(stbi__context *s, int *x, int *y, int *comp) +{ + int act_comp=0,num_packets=0,chained,dummy; + stbi__pic_packet packets[10]; + + if (!x) x = &dummy; + if (!y) y = &dummy; + if (!comp) comp = &dummy; + + if (!stbi__pic_is4(s,"\x53\x80\xF6\x34")) { + stbi__rewind(s); + return 0; + } + + stbi__skip(s, 88); + + *x = stbi__get16be(s); + *y = stbi__get16be(s); + if (stbi__at_eof(s)) { + stbi__rewind( s); + return 0; + } + if ( (*x) != 0 && (1 << 28) / (*x) < (*y)) { + stbi__rewind( s ); + return 0; + } + + stbi__skip(s, 8); + + do { + stbi__pic_packet *packet; + + if (num_packets==sizeof(packets)/sizeof(packets[0])) + return 0; + + packet = &packets[num_packets++]; + chained = stbi__get8(s); + packet->size = stbi__get8(s); + packet->type = stbi__get8(s); + packet->channel = stbi__get8(s); + act_comp |= packet->channel; + + if (stbi__at_eof(s)) { + stbi__rewind( s ); + return 0; + } + if (packet->size != 8) { + stbi__rewind( s ); + return 0; + } + } while (chained); + + *comp = (act_comp & 0x10 ? 4 : 3); + + return 1; +} +#endif + +// ************************************************************************************************* +// Portable Gray Map and Portable Pixel Map loader +// by Ken Miller +// +// PGM: http://netpbm.sourceforge.net/doc/pgm.html +// PPM: http://netpbm.sourceforge.net/doc/ppm.html +// +// Known limitations: +// Does not support comments in the header section +// Does not support ASCII image data (formats P2 and P3) +// Does not support 16-bit-per-channel + +#ifndef STBI_NO_PNM + +static int stbi__pnm_test(stbi__context *s) +{ + char p, t; + p = (char) stbi__get8(s); + t = (char) stbi__get8(s); + if (p != 'P' || (t != '5' && t != '6')) { + stbi__rewind( s ); + return 0; + } + return 1; +} + +static void *stbi__pnm_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) +{ + stbi_uc *out; + STBI_NOTUSED(ri); + + if (!stbi__pnm_info(s, (int *)&s->img_x, (int *)&s->img_y, (int *)&s->img_n)) + return 0; + + *x = s->img_x; + *y = s->img_y; + if (comp) *comp = s->img_n; + + if (!stbi__mad3sizes_valid(s->img_n, s->img_x, s->img_y, 0)) + return stbi__errpuc("too large", "PNM too large"); + + out = (stbi_uc *) stbi__malloc_mad3(s->img_n, s->img_x, s->img_y, 0); + if (!out) return stbi__errpuc("outofmem", "Out of memory"); + stbi__getn(s, out, s->img_n * s->img_x * s->img_y); + + if (req_comp && req_comp != s->img_n) { + out = stbi__convert_format(out, s->img_n, req_comp, s->img_x, s->img_y); + if (out == NULL) return out; // stbi__convert_format frees input on failure + } + return out; +} + +static int stbi__pnm_isspace(char c) +{ + return c == ' ' || c == '\t' || c == '\n' || c == '\v' || c == '\f' || c == '\r'; +} + +static void stbi__pnm_skip_whitespace(stbi__context *s, char *c) +{ + for (;;) { + while (!stbi__at_eof(s) && stbi__pnm_isspace(*c)) + *c = (char) stbi__get8(s); + + if (stbi__at_eof(s) || *c != '#') + break; + + while (!stbi__at_eof(s) && *c != '\n' && *c != '\r' ) + *c = (char) stbi__get8(s); + } +} + +static int stbi__pnm_isdigit(char c) +{ + return c >= '0' && c <= '9'; +} + +static int stbi__pnm_getinteger(stbi__context *s, char *c) +{ + int value = 0; + + while (!stbi__at_eof(s) && stbi__pnm_isdigit(*c)) { + value = value*10 + (*c - '0'); + *c = (char) stbi__get8(s); + } + + return value; +} + +static int stbi__pnm_info(stbi__context *s, int *x, int *y, int *comp) +{ + int maxv, dummy; + char c, p, t; + + if (!x) x = &dummy; + if (!y) y = &dummy; + if (!comp) comp = &dummy; + + stbi__rewind(s); + + // Get identifier + p = (char) stbi__get8(s); + t = (char) stbi__get8(s); + if (p != 'P' || (t != '5' && t != '6')) { + stbi__rewind(s); + return 0; + } + + *comp = (t == '6') ? 3 : 1; // '5' is 1-component .pgm; '6' is 3-component .ppm + + c = (char) stbi__get8(s); + stbi__pnm_skip_whitespace(s, &c); + + *x = stbi__pnm_getinteger(s, &c); // read width + stbi__pnm_skip_whitespace(s, &c); + + *y = stbi__pnm_getinteger(s, &c); // read height + stbi__pnm_skip_whitespace(s, &c); + + maxv = stbi__pnm_getinteger(s, &c); // read max value + + if (maxv > 255) + return stbi__err("max value > 255", "PPM image not 8-bit"); + else + return 1; +} +#endif + +static int stbi__info_main(stbi__context *s, int *x, int *y, int *comp) +{ + #ifndef STBI_NO_JPEG + if (stbi__jpeg_info(s, x, y, comp)) return 1; + #endif + + #ifndef STBI_NO_PNG + if (stbi__png_info(s, x, y, comp)) return 1; + #endif + + #ifndef STBI_NO_GIF + if (stbi__gif_info(s, x, y, comp)) return 1; + #endif + + #ifndef STBI_NO_BMP + if (stbi__bmp_info(s, x, y, comp)) return 1; + #endif + + #ifndef STBI_NO_PSD + if (stbi__psd_info(s, x, y, comp)) return 1; + #endif + + #ifndef STBI_NO_PIC + if (stbi__pic_info(s, x, y, comp)) return 1; + #endif + + #ifndef STBI_NO_PNM + if (stbi__pnm_info(s, x, y, comp)) return 1; + #endif + + #ifndef STBI_NO_HDR + if (stbi__hdr_info(s, x, y, comp)) return 1; + #endif + + // test tga last because it's a crappy test! + #ifndef STBI_NO_TGA + if (stbi__tga_info(s, x, y, comp)) + return 1; + #endif + return stbi__err("unknown image type", "Image not of any known type, or corrupt"); +} + +static int stbi__is_16_main(stbi__context *s) +{ + #ifndef STBI_NO_PNG + if (stbi__png_is16(s)) return 1; + #endif + + #ifndef STBI_NO_PSD + if (stbi__psd_is16(s)) return 1; + #endif + + return 0; +} + +#ifndef STBI_NO_STDIO +STBIDEF int stbi_info(char const *filename, int *x, int *y, int *comp) +{ + FILE *f = stbi__fopen(filename, "rb"); + int result; + if (!f) return stbi__err("can't fopen", "Unable to open file"); + result = stbi_info_from_file(f, x, y, comp); + fclose(f); + return result; +} + +STBIDEF int stbi_info_from_file(FILE *f, int *x, int *y, int *comp) +{ + int r; + stbi__context s; + long pos = ftell(f); + stbi__start_file(&s, f); + r = stbi__info_main(&s,x,y,comp); + fseek(f,pos,SEEK_SET); + return r; +} + +STBIDEF int stbi_is_16_bit(char const *filename) +{ + FILE *f = stbi__fopen(filename, "rb"); + int result; + if (!f) return stbi__err("can't fopen", "Unable to open file"); + result = stbi_is_16_bit_from_file(f); + fclose(f); + return result; +} + +STBIDEF int stbi_is_16_bit_from_file(FILE *f) +{ + int r; + stbi__context s; + long pos = ftell(f); + stbi__start_file(&s, f); + r = stbi__is_16_main(&s); + fseek(f,pos,SEEK_SET); + return r; +} +#endif // !STBI_NO_STDIO + +STBIDEF int stbi_info_from_memory(stbi_uc const *buffer, int len, int *x, int *y, int *comp) +{ + stbi__context s; + stbi__start_mem(&s,buffer,len); + return stbi__info_main(&s,x,y,comp); +} + +STBIDEF int stbi_info_from_callbacks(stbi_io_callbacks const *c, void *user, int *x, int *y, int *comp) +{ + stbi__context s; + stbi__start_callbacks(&s, (stbi_io_callbacks *) c, user); + return stbi__info_main(&s,x,y,comp); +} + +STBIDEF int stbi_is_16_bit_from_memory(stbi_uc const *buffer, int len) +{ + stbi__context s; + stbi__start_mem(&s,buffer,len); + return stbi__is_16_main(&s); +} + +STBIDEF int stbi_is_16_bit_from_callbacks(stbi_io_callbacks const *c, void *user) +{ + stbi__context s; + stbi__start_callbacks(&s, (stbi_io_callbacks *) c, user); + return stbi__is_16_main(&s); +} + +#endif // STB_IMAGE_IMPLEMENTATION + +/* + revision history: + 2.20 (2019-02-07) support utf8 filenames in Windows; fix warnings and platform ifdefs + 2.19 (2018-02-11) fix warning + 2.18 (2018-01-30) fix warnings + 2.17 (2018-01-29) change sbti__shiftsigned to avoid clang -O2 bug + 1-bit BMP + *_is_16_bit api + avoid warnings + 2.16 (2017-07-23) all functions have 16-bit variants; + STBI_NO_STDIO works again; + compilation fixes; + fix rounding in unpremultiply; + optimize vertical flip; + disable raw_len validation; + documentation fixes + 2.15 (2017-03-18) fix png-1,2,4 bug; now all Imagenet JPGs decode; + warning fixes; disable run-time SSE detection on gcc; + uniform handling of optional "return" values; + thread-safe initialization of zlib tables + 2.14 (2017-03-03) remove deprecated STBI_JPEG_OLD; fixes for Imagenet JPGs + 2.13 (2016-11-29) add 16-bit API, only supported for PNG right now + 2.12 (2016-04-02) fix typo in 2.11 PSD fix that caused crashes + 2.11 (2016-04-02) allocate large structures on the stack + remove white matting for transparent PSD + fix reported channel count for PNG & BMP + re-enable SSE2 in non-gcc 64-bit + support RGB-formatted JPEG + read 16-bit PNGs (only as 8-bit) + 2.10 (2016-01-22) avoid warning introduced in 2.09 by STBI_REALLOC_SIZED + 2.09 (2016-01-16) allow comments in PNM files + 16-bit-per-pixel TGA (not bit-per-component) + info() for TGA could break due to .hdr handling + info() for BMP to shares code instead of sloppy parse + can use STBI_REALLOC_SIZED if allocator doesn't support realloc + code cleanup + 2.08 (2015-09-13) fix to 2.07 cleanup, reading RGB PSD as RGBA + 2.07 (2015-09-13) fix compiler warnings + partial animated GIF support + limited 16-bpc PSD support + #ifdef unused functions + bug with < 92 byte PIC,PNM,HDR,TGA + 2.06 (2015-04-19) fix bug where PSD returns wrong '*comp' value + 2.05 (2015-04-19) fix bug in progressive JPEG handling, fix warning + 2.04 (2015-04-15) try to re-enable SIMD on MinGW 64-bit + 2.03 (2015-04-12) extra corruption checking (mmozeiko) + stbi_set_flip_vertically_on_load (nguillemot) + fix NEON support; fix mingw support + 2.02 (2015-01-19) fix incorrect assert, fix warning + 2.01 (2015-01-17) fix various warnings; suppress SIMD on gcc 32-bit without -msse2 + 2.00b (2014-12-25) fix STBI_MALLOC in progressive JPEG + 2.00 (2014-12-25) optimize JPG, including x86 SSE2 & NEON SIMD (ryg) + progressive JPEG (stb) + PGM/PPM support (Ken Miller) + STBI_MALLOC,STBI_REALLOC,STBI_FREE + GIF bugfix -- seemingly never worked + STBI_NO_*, STBI_ONLY_* + 1.48 (2014-12-14) fix incorrectly-named assert() + 1.47 (2014-12-14) 1/2/4-bit PNG support, both direct and paletted (Omar Cornut & stb) + optimize PNG (ryg) + fix bug in interlaced PNG with user-specified channel count (stb) + 1.46 (2014-08-26) + fix broken tRNS chunk (colorkey-style transparency) in non-paletted PNG + 1.45 (2014-08-16) + fix MSVC-ARM internal compiler error by wrapping malloc + 1.44 (2014-08-07) + various warning fixes from Ronny Chevalier + 1.43 (2014-07-15) + fix MSVC-only compiler problem in code changed in 1.42 + 1.42 (2014-07-09) + don't define _CRT_SECURE_NO_WARNINGS (affects user code) + fixes to stbi__cleanup_jpeg path + added STBI_ASSERT to avoid requiring assert.h + 1.41 (2014-06-25) + fix search&replace from 1.36 that messed up comments/error messages + 1.40 (2014-06-22) + fix gcc struct-initialization warning + 1.39 (2014-06-15) + fix to TGA optimization when req_comp != number of components in TGA; + fix to GIF loading because BMP wasn't rewinding (whoops, no GIFs in my test suite) + add support for BMP version 5 (more ignored fields) + 1.38 (2014-06-06) + suppress MSVC warnings on integer casts truncating values + fix accidental rename of 'skip' field of I/O + 1.37 (2014-06-04) + remove duplicate typedef + 1.36 (2014-06-03) + convert to header file single-file library + if de-iphone isn't set, load iphone images color-swapped instead of returning NULL + 1.35 (2014-05-27) + various warnings + fix broken STBI_SIMD path + fix bug where stbi_load_from_file no longer left file pointer in correct place + fix broken non-easy path for 32-bit BMP (possibly never used) + TGA optimization by Arseny Kapoulkine + 1.34 (unknown) + use STBI_NOTUSED in stbi__resample_row_generic(), fix one more leak in tga failure case + 1.33 (2011-07-14) + make stbi_is_hdr work in STBI_NO_HDR (as specified), minor compiler-friendly improvements + 1.32 (2011-07-13) + support for "info" function for all supported filetypes (SpartanJ) + 1.31 (2011-06-20) + a few more leak fixes, bug in PNG handling (SpartanJ) + 1.30 (2011-06-11) + added ability to load files via callbacks to accomidate custom input streams (Ben Wenger) + removed deprecated format-specific test/load functions + removed support for installable file formats (stbi_loader) -- would have been broken for IO callbacks anyway + error cases in bmp and tga give messages and don't leak (Raymond Barbiero, grisha) + fix inefficiency in decoding 32-bit BMP (David Woo) + 1.29 (2010-08-16) + various warning fixes from Aurelien Pocheville + 1.28 (2010-08-01) + fix bug in GIF palette transparency (SpartanJ) + 1.27 (2010-08-01) + cast-to-stbi_uc to fix warnings + 1.26 (2010-07-24) + fix bug in file buffering for PNG reported by SpartanJ + 1.25 (2010-07-17) + refix trans_data warning (Won Chun) + 1.24 (2010-07-12) + perf improvements reading from files on platforms with lock-heavy fgetc() + minor perf improvements for jpeg + deprecated type-specific functions so we'll get feedback if they're needed + attempt to fix trans_data warning (Won Chun) + 1.23 fixed bug in iPhone support + 1.22 (2010-07-10) + removed image *writing* support + stbi_info support from Jetro Lauha + GIF support from Jean-Marc Lienher + iPhone PNG-extensions from James Brown + warning-fixes from Nicolas Schulz and Janez Zemva (i.stbi__err. Janez (U+017D)emva) + 1.21 fix use of 'stbi_uc' in header (reported by jon blow) + 1.20 added support for Softimage PIC, by Tom Seddon + 1.19 bug in interlaced PNG corruption check (found by ryg) + 1.18 (2008-08-02) + fix a threading bug (local mutable static) + 1.17 support interlaced PNG + 1.16 major bugfix - stbi__convert_format converted one too many pixels + 1.15 initialize some fields for thread safety + 1.14 fix threadsafe conversion bug + header-file-only version (#define STBI_HEADER_FILE_ONLY before including) + 1.13 threadsafe + 1.12 const qualifiers in the API + 1.11 Support installable IDCT, colorspace conversion routines + 1.10 Fixes for 64-bit (don't use "unsigned long") + optimized upsampling by Fabian "ryg" Giesen + 1.09 Fix format-conversion for PSD code (bad global variables!) + 1.08 Thatcher Ulrich's PSD code integrated by Nicolas Schulz + 1.07 attempt to fix C++ warning/errors again + 1.06 attempt to fix C++ warning/errors again + 1.05 fix TGA loading to return correct *comp and use good luminance calc + 1.04 default float alpha is 1, not 255; use 'void *' for stbi_image_free + 1.03 bugfixes to STBI_NO_STDIO, STBI_NO_HDR + 1.02 support for (subset of) HDR files, float interface for preferred access to them + 1.01 fix bug: possible bug in handling right-side up bmps... not sure + fix bug: the stbi__bmp_load() and stbi__tga_load() functions didn't work at all + 1.00 interface to zlib that skips zlib header + 0.99 correct handling of alpha in palette + 0.98 TGA loader by lonesock; dynamically add loaders (untested) + 0.97 jpeg errors on too large a file; also catch another malloc failure + 0.96 fix detection of invalid v value - particleman@mollyrocket forum + 0.95 during header scan, seek to markers in case of padding + 0.94 STBI_NO_STDIO to disable stdio usage; rename all #defines the same + 0.93 handle jpegtran output; verbose errors + 0.92 read 4,8,16,24,32-bit BMP files of several formats + 0.91 output 24-bit Windows 3.0 BMP files + 0.90 fix a few more warnings; bump version number to approach 1.0 + 0.61 bugfixes due to Marc LeBlanc, Christopher Lloyd + 0.60 fix compiling as c++ + 0.59 fix warnings: merge Dave Moore's -Wall fixes + 0.58 fix bug: zlib uncompressed mode len/nlen was wrong endian + 0.57 fix bug: jpg last huffman symbol before marker was >9 bits but less than 16 available + 0.56 fix bug: zlib uncompressed mode len vs. nlen + 0.55 fix bug: restart_interval not initialized to 0 + 0.54 allow NULL for 'int *comp' + 0.53 fix bug in png 3->4; speedup png decoding + 0.52 png handles req_comp=3,4 directly; minor cleanup; jpeg comments + 0.51 obey req_comp requests, 1-component jpegs return as 1-component, + on 'test' only check type, not whether we support this variant + 0.50 (2006-11-19) + first released version +*/ + + +/* +------------------------------------------------------------------------------ +This software is available under 2 licenses -- choose whichever you prefer. +------------------------------------------------------------------------------ +ALTERNATIVE A - MIT License +Copyright (c) 2017 Sean Barrett +Permission is hereby granted, free of charge, to any person obtaining a copy of +this software and associated documentation files (the "Software"), to deal in +the Software without restriction, including without limitation the rights to +use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies +of the Software, and to permit persons to whom the Software is furnished to do +so, subject to the following conditions: +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +------------------------------------------------------------------------------ +ALTERNATIVE B - Public Domain (www.unlicense.org) +This is free and unencumbered software released into the public domain. +Anyone is free to copy, modify, publish, use, compile, sell, or distribute this +software, either in source code form or as a compiled binary, for any purpose, +commercial or non-commercial, and by any means. +In jurisdictions that recognize copyright laws, the author or authors of this +software dedicate any and all copyright interest in the software to the public +domain. We make this dedication for the benefit of the public at large and to +the detriment of our heirs and successors. We intend this dedication to be an +overt act of relinquishment in perpetuity of all present and future rights to +this software under copyright law. +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN +ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION +WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +------------------------------------------------------------------------------ +*/ diff --git a/rknpu2/examples/3rdparty/stb/stb_image_resize.h b/rknpu2/examples/3rdparty/stb/stb_image_resize.h new file mode 100644 index 0000000000000000000000000000000000000000..4f6ad35e1cb51d5e48d79aa9eb73284a3fba58b7 --- /dev/null +++ b/rknpu2/examples/3rdparty/stb/stb_image_resize.h @@ -0,0 +1,2630 @@ +/* stb_image_resize - v0.96 - public domain image resizing + by Jorge L Rodriguez (@VinoBS) - 2014 + http://github.com/nothings/stb + + Written with emphasis on usability, portability, and efficiency. (No + SIMD or threads, so it be easily outperformed by libs that use those.) + Only scaling and translation is supported, no rotations or shears. + Easy API downsamples w/Mitchell filter, upsamples w/cubic interpolation. + + COMPILING & LINKING + In one C/C++ file that #includes this file, do this: + #define STB_IMAGE_RESIZE_IMPLEMENTATION + before the #include. That will create the implementation in that file. + + QUICKSTART + stbir_resize_uint8( input_pixels , in_w , in_h , 0, + output_pixels, out_w, out_h, 0, num_channels) + stbir_resize_float(...) + stbir_resize_uint8_srgb( input_pixels , in_w , in_h , 0, + output_pixels, out_w, out_h, 0, + num_channels , alpha_chan , 0) + stbir_resize_uint8_srgb_edgemode( + input_pixels , in_w , in_h , 0, + output_pixels, out_w, out_h, 0, + num_channels , alpha_chan , 0, STBIR_EDGE_CLAMP) + // WRAP/REFLECT/ZERO + + FULL API + See the "header file" section of the source for API documentation. + + ADDITIONAL DOCUMENTATION + + SRGB & FLOATING POINT REPRESENTATION + The sRGB functions presume IEEE floating point. If you do not have + IEEE floating point, define STBIR_NON_IEEE_FLOAT. This will use + a slower implementation. + + MEMORY ALLOCATION + The resize functions here perform a single memory allocation using + malloc. To control the memory allocation, before the #include that + triggers the implementation, do: + + #define STBIR_MALLOC(size,context) ... + #define STBIR_FREE(ptr,context) ... + + Each resize function makes exactly one call to malloc/free, so to use + temp memory, store the temp memory in the context and return that. + + ASSERT + Define STBIR_ASSERT(boolval) to override assert() and not use assert.h + + OPTIMIZATION + Define STBIR_SATURATE_INT to compute clamp values in-range using + integer operations instead of float operations. This may be faster + on some platforms. + + DEFAULT FILTERS + For functions which don't provide explicit control over what filters + to use, you can change the compile-time defaults with + + #define STBIR_DEFAULT_FILTER_UPSAMPLE STBIR_FILTER_something + #define STBIR_DEFAULT_FILTER_DOWNSAMPLE STBIR_FILTER_something + + See stbir_filter in the header-file section for the list of filters. + + NEW FILTERS + A number of 1D filter kernels are used. For a list of + supported filters see the stbir_filter enum. To add a new filter, + write a filter function and add it to stbir__filter_info_table. + + PROGRESS + For interactive use with slow resize operations, you can install + a progress-report callback: + + #define STBIR_PROGRESS_REPORT(val) some_func(val) + + The parameter val is a float which goes from 0 to 1 as progress is made. + + For example: + + static void my_progress_report(float progress); + #define STBIR_PROGRESS_REPORT(val) my_progress_report(val) + + #define STB_IMAGE_RESIZE_IMPLEMENTATION + #include "stb_image_resize.h" + + static void my_progress_report(float progress) + { + printf("Progress: %f%%\n", progress*100); + } + + MAX CHANNELS + If your image has more than 64 channels, define STBIR_MAX_CHANNELS + to the max you'll have. + + ALPHA CHANNEL + Most of the resizing functions provide the ability to control how + the alpha channel of an image is processed. The important things + to know about this: + + 1. The best mathematically-behaved version of alpha to use is + called "premultiplied alpha", in which the other color channels + have had the alpha value multiplied in. If you use premultiplied + alpha, linear filtering (such as image resampling done by this + library, or performed in texture units on GPUs) does the "right + thing". While premultiplied alpha is standard in the movie CGI + industry, it is still uncommon in the videogame/real-time world. + + If you linearly filter non-premultiplied alpha, strange effects + occur. (For example, the 50/50 average of 99% transparent bright green + and 1% transparent black produces 50% transparent dark green when + non-premultiplied, whereas premultiplied it produces 50% + transparent near-black. The former introduces green energy + that doesn't exist in the source image.) + + 2. Artists should not edit premultiplied-alpha images; artists + want non-premultiplied alpha images. Thus, art tools generally output + non-premultiplied alpha images. + + 3. You will get best results in most cases by converting images + to premultiplied alpha before processing them mathematically. + + 4. If you pass the flag STBIR_FLAG_ALPHA_PREMULTIPLIED, the + resizer does not do anything special for the alpha channel; + it is resampled identically to other channels. This produces + the correct results for premultiplied-alpha images, but produces + less-than-ideal results for non-premultiplied-alpha images. + + 5. If you do not pass the flag STBIR_FLAG_ALPHA_PREMULTIPLIED, + then the resizer weights the contribution of input pixels + based on their alpha values, or, equivalently, it multiplies + the alpha value into the color channels, resamples, then divides + by the resultant alpha value. Input pixels which have alpha=0 do + not contribute at all to output pixels unless _all_ of the input + pixels affecting that output pixel have alpha=0, in which case + the result for that pixel is the same as it would be without + STBIR_FLAG_ALPHA_PREMULTIPLIED. However, this is only true for + input images in integer formats. For input images in float format, + input pixels with alpha=0 have no effect, and output pixels + which have alpha=0 will be 0 in all channels. (For float images, + you can manually achieve the same result by adding a tiny epsilon + value to the alpha channel of every image, and then subtracting + or clamping it at the end.) + + 6. You can suppress the behavior described in #5 and make + all-0-alpha pixels have 0 in all channels by #defining + STBIR_NO_ALPHA_EPSILON. + + 7. You can separately control whether the alpha channel is + interpreted as linear or affected by the colorspace. By default + it is linear; you almost never want to apply the colorspace. + (For example, graphics hardware does not apply sRGB conversion + to the alpha channel.) + + CONTRIBUTORS + Jorge L Rodriguez: Implementation + Sean Barrett: API design, optimizations + Aras Pranckevicius: bugfix + Nathan Reed: warning fixes + + REVISIONS + 0.96 (2019-03-04) fixed warnings + 0.95 (2017-07-23) fixed warnings + 0.94 (2017-03-18) fixed warnings + 0.93 (2017-03-03) fixed bug with certain combinations of heights + 0.92 (2017-01-02) fix integer overflow on large (>2GB) images + 0.91 (2016-04-02) fix warnings; fix handling of subpixel regions + 0.90 (2014-09-17) first released version + + LICENSE + See end of file for license information. + + TODO + Don't decode all of the image data when only processing a partial tile + Don't use full-width decode buffers when only processing a partial tile + When processing wide images, break processing into tiles so data fits in L1 cache + Installable filters? + Resize that respects alpha test coverage + (Reference code: FloatImage::alphaTestCoverage and FloatImage::scaleAlphaToCoverage: + https://code.google.com/p/nvidia-texture-tools/source/browse/trunk/src/nvimage/FloatImage.cpp ) +*/ + +#ifndef STBIR_INCLUDE_STB_IMAGE_RESIZE_H +#define STBIR_INCLUDE_STB_IMAGE_RESIZE_H + +#ifdef _MSC_VER +typedef unsigned char stbir_uint8; +typedef unsigned short stbir_uint16; +typedef unsigned int stbir_uint32; +#else +#include +typedef uint8_t stbir_uint8; +typedef uint16_t stbir_uint16; +typedef uint32_t stbir_uint32; +#endif + +#ifndef STBIRDEF +#ifdef STB_IMAGE_RESIZE_STATIC +#define STBIRDEF static +#else +#ifdef __cplusplus +#define STBIRDEF extern "C" +#else +#define STBIRDEF extern +#endif +#endif +#endif + +////////////////////////////////////////////////////////////////////////////// +// +// Easy-to-use API: +// +// * "input pixels" points to an array of image data with 'num_channels' channels (e.g. RGB=3, RGBA=4) +// * input_w is input image width (x-axis), input_h is input image height (y-axis) +// * stride is the offset between successive rows of image data in memory, in bytes. you can +// specify 0 to mean packed continuously in memory +// * alpha channel is treated identically to other channels. +// * colorspace is linear or sRGB as specified by function name +// * returned result is 1 for success or 0 in case of an error. +// #define STBIR_ASSERT() to trigger an assert on parameter validation errors. +// * Memory required grows approximately linearly with input and output size, but with +// discontinuities at input_w == output_w and input_h == output_h. +// * These functions use a "default" resampling filter defined at compile time. To change the filter, +// you can change the compile-time defaults by #defining STBIR_DEFAULT_FILTER_UPSAMPLE +// and STBIR_DEFAULT_FILTER_DOWNSAMPLE, or you can use the medium-complexity API. + +STBIRDEF int stbir_resize_uint8( const unsigned char *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + unsigned char *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + int num_channels); + +STBIRDEF int stbir_resize_float( const float *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + float *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + int num_channels); + + +// The following functions interpret image data as gamma-corrected sRGB. +// Specify STBIR_ALPHA_CHANNEL_NONE if you have no alpha channel, +// or otherwise provide the index of the alpha channel. Flags value +// of 0 will probably do the right thing if you're not sure what +// the flags mean. + +#define STBIR_ALPHA_CHANNEL_NONE -1 + +// Set this flag if your texture has premultiplied alpha. Otherwise, stbir will +// use alpha-weighted resampling (effectively premultiplying, resampling, +// then unpremultiplying). +#define STBIR_FLAG_ALPHA_PREMULTIPLIED (1 << 0) +// The specified alpha channel should be handled as gamma-corrected value even +// when doing sRGB operations. +#define STBIR_FLAG_ALPHA_USES_COLORSPACE (1 << 1) + +STBIRDEF int stbir_resize_uint8_srgb(const unsigned char *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + unsigned char *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + int num_channels, int alpha_channel, int flags); + + +typedef enum +{ + STBIR_EDGE_CLAMP = 1, + STBIR_EDGE_REFLECT = 2, + STBIR_EDGE_WRAP = 3, + STBIR_EDGE_ZERO = 4, +} stbir_edge; + +// This function adds the ability to specify how requests to sample off the edge of the image are handled. +STBIRDEF int stbir_resize_uint8_srgb_edgemode(const unsigned char *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + unsigned char *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + int num_channels, int alpha_channel, int flags, + stbir_edge edge_wrap_mode); + +////////////////////////////////////////////////////////////////////////////// +// +// Medium-complexity API +// +// This extends the easy-to-use API as follows: +// +// * Alpha-channel can be processed separately +// * If alpha_channel is not STBIR_ALPHA_CHANNEL_NONE +// * Alpha channel will not be gamma corrected (unless flags&STBIR_FLAG_GAMMA_CORRECT) +// * Filters will be weighted by alpha channel (unless flags&STBIR_FLAG_ALPHA_PREMULTIPLIED) +// * Filter can be selected explicitly +// * uint16 image type +// * sRGB colorspace available for all types +// * context parameter for passing to STBIR_MALLOC + +typedef enum +{ + STBIR_FILTER_DEFAULT = 0, // use same filter type that easy-to-use API chooses + STBIR_FILTER_BOX = 1, // A trapezoid w/1-pixel wide ramps, same result as box for integer scale ratios + STBIR_FILTER_TRIANGLE = 2, // On upsampling, produces same results as bilinear texture filtering + STBIR_FILTER_CUBICBSPLINE = 3, // The cubic b-spline (aka Mitchell-Netrevalli with B=1,C=0), gaussian-esque + STBIR_FILTER_CATMULLROM = 4, // An interpolating cubic spline + STBIR_FILTER_MITCHELL = 5, // Mitchell-Netrevalli filter with B=1/3, C=1/3 +} stbir_filter; + +typedef enum +{ + STBIR_COLORSPACE_LINEAR, + STBIR_COLORSPACE_SRGB, + + STBIR_MAX_COLORSPACES, +} stbir_colorspace; + +// The following functions are all identical except for the type of the image data + +STBIRDEF int stbir_resize_uint8_generic( const unsigned char *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + unsigned char *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + int num_channels, int alpha_channel, int flags, + stbir_edge edge_wrap_mode, stbir_filter filter, stbir_colorspace space, + void *alloc_context); + +STBIRDEF int stbir_resize_uint16_generic(const stbir_uint16 *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + stbir_uint16 *output_pixels , int output_w, int output_h, int output_stride_in_bytes, + int num_channels, int alpha_channel, int flags, + stbir_edge edge_wrap_mode, stbir_filter filter, stbir_colorspace space, + void *alloc_context); + +STBIRDEF int stbir_resize_float_generic( const float *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + float *output_pixels , int output_w, int output_h, int output_stride_in_bytes, + int num_channels, int alpha_channel, int flags, + stbir_edge edge_wrap_mode, stbir_filter filter, stbir_colorspace space, + void *alloc_context); + + + +////////////////////////////////////////////////////////////////////////////// +// +// Full-complexity API +// +// This extends the medium API as follows: +// +// * uint32 image type +// * not typesafe +// * separate filter types for each axis +// * separate edge modes for each axis +// * can specify scale explicitly for subpixel correctness +// * can specify image source tile using texture coordinates + +typedef enum +{ + STBIR_TYPE_UINT8 , + STBIR_TYPE_UINT16, + STBIR_TYPE_UINT32, + STBIR_TYPE_FLOAT , + + STBIR_MAX_TYPES +} stbir_datatype; + +STBIRDEF int stbir_resize( const void *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + void *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + stbir_datatype datatype, + int num_channels, int alpha_channel, int flags, + stbir_edge edge_mode_horizontal, stbir_edge edge_mode_vertical, + stbir_filter filter_horizontal, stbir_filter filter_vertical, + stbir_colorspace space, void *alloc_context); + +STBIRDEF int stbir_resize_subpixel(const void *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + void *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + stbir_datatype datatype, + int num_channels, int alpha_channel, int flags, + stbir_edge edge_mode_horizontal, stbir_edge edge_mode_vertical, + stbir_filter filter_horizontal, stbir_filter filter_vertical, + stbir_colorspace space, void *alloc_context, + float x_scale, float y_scale, + float x_offset, float y_offset); + +STBIRDEF int stbir_resize_region( const void *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + void *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + stbir_datatype datatype, + int num_channels, int alpha_channel, int flags, + stbir_edge edge_mode_horizontal, stbir_edge edge_mode_vertical, + stbir_filter filter_horizontal, stbir_filter filter_vertical, + stbir_colorspace space, void *alloc_context, + float s0, float t0, float s1, float t1); +// (s0, t0) & (s1, t1) are the top-left and bottom right corner (uv addressing style: [0, 1]x[0, 1]) of a region of the input image to use. + +// +// +//// end header file ///////////////////////////////////////////////////// +#endif // STBIR_INCLUDE_STB_IMAGE_RESIZE_H + + + + + +#ifdef STB_IMAGE_RESIZE_IMPLEMENTATION + +#ifndef STBIR_ASSERT +#include +#define STBIR_ASSERT(x) assert(x) +#endif + +// For memset +#include + +#include + +#ifndef STBIR_MALLOC +#include +// use comma operator to evaluate c, to avoid "unused parameter" warnings +#define STBIR_MALLOC(size,c) ((void)(c), malloc(size)) +#define STBIR_FREE(ptr,c) ((void)(c), free(ptr)) +#endif + +#ifndef _MSC_VER +#ifdef __cplusplus +#define stbir__inline inline +#else +#define stbir__inline +#endif +#else +#define stbir__inline __forceinline +#endif + + +// should produce compiler error if size is wrong +typedef unsigned char stbir__validate_uint32[sizeof(stbir_uint32) == 4 ? 1 : -1]; + +#ifdef _MSC_VER +#define STBIR__NOTUSED(v) (void)(v) +#else +#define STBIR__NOTUSED(v) (void)sizeof(v) +#endif + +#define STBIR__ARRAY_SIZE(a) (sizeof((a))/sizeof((a)[0])) + +#ifndef STBIR_DEFAULT_FILTER_UPSAMPLE +#define STBIR_DEFAULT_FILTER_UPSAMPLE STBIR_FILTER_CATMULLROM +#endif + +#ifndef STBIR_DEFAULT_FILTER_DOWNSAMPLE +#define STBIR_DEFAULT_FILTER_DOWNSAMPLE STBIR_FILTER_MITCHELL +#endif + +#ifndef STBIR_PROGRESS_REPORT +#define STBIR_PROGRESS_REPORT(float_0_to_1) +#endif + +#ifndef STBIR_MAX_CHANNELS +#define STBIR_MAX_CHANNELS 64 +#endif + +#if STBIR_MAX_CHANNELS > 65536 +#error "Too many channels; STBIR_MAX_CHANNELS must be no more than 65536." +// because we store the indices in 16-bit variables +#endif + +// This value is added to alpha just before premultiplication to avoid +// zeroing out color values. It is equivalent to 2^-80. If you don't want +// that behavior (it may interfere if you have floating point images with +// very small alpha values) then you can define STBIR_NO_ALPHA_EPSILON to +// disable it. +#ifndef STBIR_ALPHA_EPSILON +#define STBIR_ALPHA_EPSILON ((float)1 / (1 << 20) / (1 << 20) / (1 << 20) / (1 << 20)) +#endif + + + +#ifdef _MSC_VER +#define STBIR__UNUSED_PARAM(v) (void)(v) +#else +#define STBIR__UNUSED_PARAM(v) (void)sizeof(v) +#endif + +// must match stbir_datatype +static unsigned char stbir__type_size[] = { + 1, // STBIR_TYPE_UINT8 + 2, // STBIR_TYPE_UINT16 + 4, // STBIR_TYPE_UINT32 + 4, // STBIR_TYPE_FLOAT +}; + +// Kernel function centered at 0 +typedef float (stbir__kernel_fn)(float x, float scale); +typedef float (stbir__support_fn)(float scale); + +typedef struct +{ + stbir__kernel_fn* kernel; + stbir__support_fn* support; +} stbir__filter_info; + +// When upsampling, the contributors are which source pixels contribute. +// When downsampling, the contributors are which destination pixels are contributed to. +typedef struct +{ + int n0; // First contributing pixel + int n1; // Last contributing pixel +} stbir__contributors; + +typedef struct +{ + const void* input_data; + int input_w; + int input_h; + int input_stride_bytes; + + void* output_data; + int output_w; + int output_h; + int output_stride_bytes; + + float s0, t0, s1, t1; + + float horizontal_shift; // Units: output pixels + float vertical_shift; // Units: output pixels + float horizontal_scale; + float vertical_scale; + + int channels; + int alpha_channel; + stbir_uint32 flags; + stbir_datatype type; + stbir_filter horizontal_filter; + stbir_filter vertical_filter; + stbir_edge edge_horizontal; + stbir_edge edge_vertical; + stbir_colorspace colorspace; + + stbir__contributors* horizontal_contributors; + float* horizontal_coefficients; + + stbir__contributors* vertical_contributors; + float* vertical_coefficients; + + int decode_buffer_pixels; + float* decode_buffer; + + float* horizontal_buffer; + + // cache these because ceil/floor are inexplicably showing up in profile + int horizontal_coefficient_width; + int vertical_coefficient_width; + int horizontal_filter_pixel_width; + int vertical_filter_pixel_width; + int horizontal_filter_pixel_margin; + int vertical_filter_pixel_margin; + int horizontal_num_contributors; + int vertical_num_contributors; + + int ring_buffer_length_bytes; // The length of an individual entry in the ring buffer. The total number of ring buffers is stbir__get_filter_pixel_width(filter) + int ring_buffer_num_entries; // Total number of entries in the ring buffer. + int ring_buffer_first_scanline; + int ring_buffer_last_scanline; + int ring_buffer_begin_index; // first_scanline is at this index in the ring buffer + float* ring_buffer; + + float* encode_buffer; // A temporary buffer to store floats so we don't lose precision while we do multiply-adds. + + int horizontal_contributors_size; + int horizontal_coefficients_size; + int vertical_contributors_size; + int vertical_coefficients_size; + int decode_buffer_size; + int horizontal_buffer_size; + int ring_buffer_size; + int encode_buffer_size; +} stbir__info; + + +static const float stbir__max_uint8_as_float = 255.0f; +static const float stbir__max_uint16_as_float = 65535.0f; +static const double stbir__max_uint32_as_float = 4294967295.0; + + +static stbir__inline int stbir__min(int a, int b) +{ + return a < b ? a : b; +} + +static stbir__inline float stbir__saturate(float x) +{ + if (x < 0) + return 0; + + if (x > 1) + return 1; + + return x; +} + +#ifdef STBIR_SATURATE_INT +static stbir__inline stbir_uint8 stbir__saturate8(int x) +{ + if ((unsigned int) x <= 255) + return x; + + if (x < 0) + return 0; + + return 255; +} + +static stbir__inline stbir_uint16 stbir__saturate16(int x) +{ + if ((unsigned int) x <= 65535) + return x; + + if (x < 0) + return 0; + + return 65535; +} +#endif + +static float stbir__srgb_uchar_to_linear_float[256] = { + 0.000000f, 0.000304f, 0.000607f, 0.000911f, 0.001214f, 0.001518f, 0.001821f, 0.002125f, 0.002428f, 0.002732f, 0.003035f, + 0.003347f, 0.003677f, 0.004025f, 0.004391f, 0.004777f, 0.005182f, 0.005605f, 0.006049f, 0.006512f, 0.006995f, 0.007499f, + 0.008023f, 0.008568f, 0.009134f, 0.009721f, 0.010330f, 0.010960f, 0.011612f, 0.012286f, 0.012983f, 0.013702f, 0.014444f, + 0.015209f, 0.015996f, 0.016807f, 0.017642f, 0.018500f, 0.019382f, 0.020289f, 0.021219f, 0.022174f, 0.023153f, 0.024158f, + 0.025187f, 0.026241f, 0.027321f, 0.028426f, 0.029557f, 0.030713f, 0.031896f, 0.033105f, 0.034340f, 0.035601f, 0.036889f, + 0.038204f, 0.039546f, 0.040915f, 0.042311f, 0.043735f, 0.045186f, 0.046665f, 0.048172f, 0.049707f, 0.051269f, 0.052861f, + 0.054480f, 0.056128f, 0.057805f, 0.059511f, 0.061246f, 0.063010f, 0.064803f, 0.066626f, 0.068478f, 0.070360f, 0.072272f, + 0.074214f, 0.076185f, 0.078187f, 0.080220f, 0.082283f, 0.084376f, 0.086500f, 0.088656f, 0.090842f, 0.093059f, 0.095307f, + 0.097587f, 0.099899f, 0.102242f, 0.104616f, 0.107023f, 0.109462f, 0.111932f, 0.114435f, 0.116971f, 0.119538f, 0.122139f, + 0.124772f, 0.127438f, 0.130136f, 0.132868f, 0.135633f, 0.138432f, 0.141263f, 0.144128f, 0.147027f, 0.149960f, 0.152926f, + 0.155926f, 0.158961f, 0.162029f, 0.165132f, 0.168269f, 0.171441f, 0.174647f, 0.177888f, 0.181164f, 0.184475f, 0.187821f, + 0.191202f, 0.194618f, 0.198069f, 0.201556f, 0.205079f, 0.208637f, 0.212231f, 0.215861f, 0.219526f, 0.223228f, 0.226966f, + 0.230740f, 0.234551f, 0.238398f, 0.242281f, 0.246201f, 0.250158f, 0.254152f, 0.258183f, 0.262251f, 0.266356f, 0.270498f, + 0.274677f, 0.278894f, 0.283149f, 0.287441f, 0.291771f, 0.296138f, 0.300544f, 0.304987f, 0.309469f, 0.313989f, 0.318547f, + 0.323143f, 0.327778f, 0.332452f, 0.337164f, 0.341914f, 0.346704f, 0.351533f, 0.356400f, 0.361307f, 0.366253f, 0.371238f, + 0.376262f, 0.381326f, 0.386430f, 0.391573f, 0.396755f, 0.401978f, 0.407240f, 0.412543f, 0.417885f, 0.423268f, 0.428691f, + 0.434154f, 0.439657f, 0.445201f, 0.450786f, 0.456411f, 0.462077f, 0.467784f, 0.473532f, 0.479320f, 0.485150f, 0.491021f, + 0.496933f, 0.502887f, 0.508881f, 0.514918f, 0.520996f, 0.527115f, 0.533276f, 0.539480f, 0.545725f, 0.552011f, 0.558340f, + 0.564712f, 0.571125f, 0.577581f, 0.584078f, 0.590619f, 0.597202f, 0.603827f, 0.610496f, 0.617207f, 0.623960f, 0.630757f, + 0.637597f, 0.644480f, 0.651406f, 0.658375f, 0.665387f, 0.672443f, 0.679543f, 0.686685f, 0.693872f, 0.701102f, 0.708376f, + 0.715694f, 0.723055f, 0.730461f, 0.737911f, 0.745404f, 0.752942f, 0.760525f, 0.768151f, 0.775822f, 0.783538f, 0.791298f, + 0.799103f, 0.806952f, 0.814847f, 0.822786f, 0.830770f, 0.838799f, 0.846873f, 0.854993f, 0.863157f, 0.871367f, 0.879622f, + 0.887923f, 0.896269f, 0.904661f, 0.913099f, 0.921582f, 0.930111f, 0.938686f, 0.947307f, 0.955974f, 0.964686f, 0.973445f, + 0.982251f, 0.991102f, 1.0f +}; + +static float stbir__srgb_to_linear(float f) +{ + if (f <= 0.04045f) + return f / 12.92f; + else + return (float)pow((f + 0.055f) / 1.055f, 2.4f); +} + +static float stbir__linear_to_srgb(float f) +{ + if (f <= 0.0031308f) + return f * 12.92f; + else + return 1.055f * (float)pow(f, 1 / 2.4f) - 0.055f; +} + +#ifndef STBIR_NON_IEEE_FLOAT +// From https://gist.github.com/rygorous/2203834 + +typedef union +{ + stbir_uint32 u; + float f; +} stbir__FP32; + +static const stbir_uint32 fp32_to_srgb8_tab4[104] = { + 0x0073000d, 0x007a000d, 0x0080000d, 0x0087000d, 0x008d000d, 0x0094000d, 0x009a000d, 0x00a1000d, + 0x00a7001a, 0x00b4001a, 0x00c1001a, 0x00ce001a, 0x00da001a, 0x00e7001a, 0x00f4001a, 0x0101001a, + 0x010e0033, 0x01280033, 0x01410033, 0x015b0033, 0x01750033, 0x018f0033, 0x01a80033, 0x01c20033, + 0x01dc0067, 0x020f0067, 0x02430067, 0x02760067, 0x02aa0067, 0x02dd0067, 0x03110067, 0x03440067, + 0x037800ce, 0x03df00ce, 0x044600ce, 0x04ad00ce, 0x051400ce, 0x057b00c5, 0x05dd00bc, 0x063b00b5, + 0x06970158, 0x07420142, 0x07e30130, 0x087b0120, 0x090b0112, 0x09940106, 0x0a1700fc, 0x0a9500f2, + 0x0b0f01cb, 0x0bf401ae, 0x0ccb0195, 0x0d950180, 0x0e56016e, 0x0f0d015e, 0x0fbc0150, 0x10630143, + 0x11070264, 0x1238023e, 0x1357021d, 0x14660201, 0x156601e9, 0x165a01d3, 0x174401c0, 0x182401af, + 0x18fe0331, 0x1a9602fe, 0x1c1502d2, 0x1d7e02ad, 0x1ed4028d, 0x201a0270, 0x21520256, 0x227d0240, + 0x239f0443, 0x25c003fe, 0x27bf03c4, 0x29a10392, 0x2b6a0367, 0x2d1d0341, 0x2ebe031f, 0x304d0300, + 0x31d105b0, 0x34a80555, 0x37520507, 0x39d504c5, 0x3c37048b, 0x3e7c0458, 0x40a8042a, 0x42bd0401, + 0x44c20798, 0x488e071e, 0x4c1c06b6, 0x4f76065d, 0x52a50610, 0x55ac05cc, 0x5892058f, 0x5b590559, + 0x5e0c0a23, 0x631c0980, 0x67db08f6, 0x6c55087f, 0x70940818, 0x74a007bd, 0x787d076c, 0x7c330723, +}; + +static stbir_uint8 stbir__linear_to_srgb_uchar(float in) +{ + static const stbir__FP32 almostone = { 0x3f7fffff }; // 1-eps + static const stbir__FP32 minval = { (127-13) << 23 }; + stbir_uint32 tab,bias,scale,t; + stbir__FP32 f; + + // Clamp to [2^(-13), 1-eps]; these two values map to 0 and 1, respectively. + // The tests are carefully written so that NaNs map to 0, same as in the reference + // implementation. + if (!(in > minval.f)) // written this way to catch NaNs + in = minval.f; + if (in > almostone.f) + in = almostone.f; + + // Do the table lookup and unpack bias, scale + f.f = in; + tab = fp32_to_srgb8_tab4[(f.u - minval.u) >> 20]; + bias = (tab >> 16) << 9; + scale = tab & 0xffff; + + // Grab next-highest mantissa bits and perform linear interpolation + t = (f.u >> 12) & 0xff; + return (unsigned char) ((bias + scale*t) >> 16); +} + +#else +// sRGB transition values, scaled by 1<<28 +static int stbir__srgb_offset_to_linear_scaled[256] = +{ + 0, 40738, 122216, 203693, 285170, 366648, 448125, 529603, + 611080, 692557, 774035, 855852, 942009, 1033024, 1128971, 1229926, + 1335959, 1447142, 1563542, 1685229, 1812268, 1944725, 2082664, 2226148, + 2375238, 2529996, 2690481, 2856753, 3028870, 3206888, 3390865, 3580856, + 3776916, 3979100, 4187460, 4402049, 4622919, 4850123, 5083710, 5323731, + 5570236, 5823273, 6082892, 6349140, 6622065, 6901714, 7188133, 7481369, + 7781466, 8088471, 8402427, 8723380, 9051372, 9386448, 9728650, 10078021, + 10434603, 10798439, 11169569, 11548036, 11933879, 12327139, 12727857, 13136073, + 13551826, 13975156, 14406100, 14844697, 15290987, 15745007, 16206795, 16676389, + 17153826, 17639142, 18132374, 18633560, 19142734, 19659934, 20185196, 20718552, + 21260042, 21809696, 22367554, 22933648, 23508010, 24090680, 24681686, 25281066, + 25888850, 26505076, 27129772, 27762974, 28404716, 29055026, 29713942, 30381490, + 31057708, 31742624, 32436272, 33138682, 33849884, 34569912, 35298800, 36036568, + 36783260, 37538896, 38303512, 39077136, 39859796, 40651528, 41452360, 42262316, + 43081432, 43909732, 44747252, 45594016, 46450052, 47315392, 48190064, 49074096, + 49967516, 50870356, 51782636, 52704392, 53635648, 54576432, 55526772, 56486700, + 57456236, 58435408, 59424248, 60422780, 61431036, 62449032, 63476804, 64514376, + 65561776, 66619028, 67686160, 68763192, 69850160, 70947088, 72053992, 73170912, + 74297864, 75434880, 76581976, 77739184, 78906536, 80084040, 81271736, 82469648, + 83677792, 84896192, 86124888, 87363888, 88613232, 89872928, 91143016, 92423512, + 93714432, 95015816, 96327688, 97650056, 98982952, 100326408, 101680440, 103045072, + 104420320, 105806224, 107202800, 108610064, 110028048, 111456776, 112896264, 114346544, + 115807632, 117279552, 118762328, 120255976, 121760536, 123276016, 124802440, 126339832, + 127888216, 129447616, 131018048, 132599544, 134192112, 135795792, 137410592, 139036528, + 140673648, 142321952, 143981456, 145652208, 147334208, 149027488, 150732064, 152447968, + 154175200, 155913792, 157663776, 159425168, 161197984, 162982240, 164777968, 166585184, + 168403904, 170234160, 172075968, 173929344, 175794320, 177670896, 179559120, 181458992, + 183370528, 185293776, 187228736, 189175424, 191133888, 193104112, 195086128, 197079968, + 199085648, 201103184, 203132592, 205173888, 207227120, 209292272, 211369392, 213458480, + 215559568, 217672656, 219797792, 221934976, 224084240, 226245600, 228419056, 230604656, + 232802400, 235012320, 237234432, 239468736, 241715280, 243974080, 246245120, 248528464, + 250824112, 253132064, 255452368, 257785040, 260130080, 262487520, 264857376, 267239664, +}; + +static stbir_uint8 stbir__linear_to_srgb_uchar(float f) +{ + int x = (int) (f * (1 << 28)); // has headroom so you don't need to clamp + int v = 0; + int i; + + // Refine the guess with a short binary search. + i = v + 128; if (x >= stbir__srgb_offset_to_linear_scaled[i]) v = i; + i = v + 64; if (x >= stbir__srgb_offset_to_linear_scaled[i]) v = i; + i = v + 32; if (x >= stbir__srgb_offset_to_linear_scaled[i]) v = i; + i = v + 16; if (x >= stbir__srgb_offset_to_linear_scaled[i]) v = i; + i = v + 8; if (x >= stbir__srgb_offset_to_linear_scaled[i]) v = i; + i = v + 4; if (x >= stbir__srgb_offset_to_linear_scaled[i]) v = i; + i = v + 2; if (x >= stbir__srgb_offset_to_linear_scaled[i]) v = i; + i = v + 1; if (x >= stbir__srgb_offset_to_linear_scaled[i]) v = i; + + return (stbir_uint8) v; +} +#endif + +static float stbir__filter_trapezoid(float x, float scale) +{ + float halfscale = scale / 2; + float t = 0.5f + halfscale; + STBIR_ASSERT(scale <= 1); + + x = (float)fabs(x); + + if (x >= t) + return 0; + else + { + float r = 0.5f - halfscale; + if (x <= r) + return 1; + else + return (t - x) / scale; + } +} + +static float stbir__support_trapezoid(float scale) +{ + STBIR_ASSERT(scale <= 1); + return 0.5f + scale / 2; +} + +static float stbir__filter_triangle(float x, float s) +{ + STBIR__UNUSED_PARAM(s); + + x = (float)fabs(x); + + if (x <= 1.0f) + return 1 - x; + else + return 0; +} + +static float stbir__filter_cubic(float x, float s) +{ + STBIR__UNUSED_PARAM(s); + + x = (float)fabs(x); + + if (x < 1.0f) + return (4 + x*x*(3*x - 6))/6; + else if (x < 2.0f) + return (8 + x*(-12 + x*(6 - x)))/6; + + return (0.0f); +} + +static float stbir__filter_catmullrom(float x, float s) +{ + STBIR__UNUSED_PARAM(s); + + x = (float)fabs(x); + + if (x < 1.0f) + return 1 - x*x*(2.5f - 1.5f*x); + else if (x < 2.0f) + return 2 - x*(4 + x*(0.5f*x - 2.5f)); + + return (0.0f); +} + +static float stbir__filter_mitchell(float x, float s) +{ + STBIR__UNUSED_PARAM(s); + + x = (float)fabs(x); + + if (x < 1.0f) + return (16 + x*x*(21 * x - 36))/18; + else if (x < 2.0f) + return (32 + x*(-60 + x*(36 - 7*x)))/18; + + return (0.0f); +} + +static float stbir__support_zero(float s) +{ + STBIR__UNUSED_PARAM(s); + return 0; +} + +static float stbir__support_one(float s) +{ + STBIR__UNUSED_PARAM(s); + return 1; +} + +static float stbir__support_two(float s) +{ + STBIR__UNUSED_PARAM(s); + return 2; +} + +static stbir__filter_info stbir__filter_info_table[] = { + { NULL, stbir__support_zero }, + { stbir__filter_trapezoid, stbir__support_trapezoid }, + { stbir__filter_triangle, stbir__support_one }, + { stbir__filter_cubic, stbir__support_two }, + { stbir__filter_catmullrom, stbir__support_two }, + { stbir__filter_mitchell, stbir__support_two }, +}; + +stbir__inline static int stbir__use_upsampling(float ratio) +{ + return ratio > 1; +} + +stbir__inline static int stbir__use_width_upsampling(stbir__info* stbir_info) +{ + return stbir__use_upsampling(stbir_info->horizontal_scale); +} + +stbir__inline static int stbir__use_height_upsampling(stbir__info* stbir_info) +{ + return stbir__use_upsampling(stbir_info->vertical_scale); +} + +// This is the maximum number of input samples that can affect an output sample +// with the given filter +static int stbir__get_filter_pixel_width(stbir_filter filter, float scale) +{ + STBIR_ASSERT(filter != 0); + STBIR_ASSERT(filter < STBIR__ARRAY_SIZE(stbir__filter_info_table)); + + if (stbir__use_upsampling(scale)) + return (int)ceil(stbir__filter_info_table[filter].support(1/scale) * 2); + else + return (int)ceil(stbir__filter_info_table[filter].support(scale) * 2 / scale); +} + +// This is how much to expand buffers to account for filters seeking outside +// the image boundaries. +static int stbir__get_filter_pixel_margin(stbir_filter filter, float scale) +{ + return stbir__get_filter_pixel_width(filter, scale) / 2; +} + +static int stbir__get_coefficient_width(stbir_filter filter, float scale) +{ + if (stbir__use_upsampling(scale)) + return (int)ceil(stbir__filter_info_table[filter].support(1 / scale) * 2); + else + return (int)ceil(stbir__filter_info_table[filter].support(scale) * 2); +} + +static int stbir__get_contributors(float scale, stbir_filter filter, int input_size, int output_size) +{ + if (stbir__use_upsampling(scale)) + return output_size; + else + return (input_size + stbir__get_filter_pixel_margin(filter, scale) * 2); +} + +static int stbir__get_total_horizontal_coefficients(stbir__info* info) +{ + return info->horizontal_num_contributors + * stbir__get_coefficient_width (info->horizontal_filter, info->horizontal_scale); +} + +static int stbir__get_total_vertical_coefficients(stbir__info* info) +{ + return info->vertical_num_contributors + * stbir__get_coefficient_width (info->vertical_filter, info->vertical_scale); +} + +static stbir__contributors* stbir__get_contributor(stbir__contributors* contributors, int n) +{ + return &contributors[n]; +} + +// For perf reasons this code is duplicated in stbir__resample_horizontal_upsample/downsample, +// if you change it here change it there too. +static float* stbir__get_coefficient(float* coefficients, stbir_filter filter, float scale, int n, int c) +{ + int width = stbir__get_coefficient_width(filter, scale); + return &coefficients[width*n + c]; +} + +static int stbir__edge_wrap_slow(stbir_edge edge, int n, int max) +{ + switch (edge) + { + case STBIR_EDGE_ZERO: + return 0; // we'll decode the wrong pixel here, and then overwrite with 0s later + + case STBIR_EDGE_CLAMP: + if (n < 0) + return 0; + + if (n >= max) + return max - 1; + + return n; // NOTREACHED + + case STBIR_EDGE_REFLECT: + { + if (n < 0) + { + if (n < max) + return -n; + else + return max - 1; + } + + if (n >= max) + { + int max2 = max * 2; + if (n >= max2) + return 0; + else + return max2 - n - 1; + } + + return n; // NOTREACHED + } + + case STBIR_EDGE_WRAP: + if (n >= 0) + return (n % max); + else + { + int m = (-n) % max; + + if (m != 0) + m = max - m; + + return (m); + } + // NOTREACHED + + default: + STBIR_ASSERT(!"Unimplemented edge type"); + return 0; + } +} + +stbir__inline static int stbir__edge_wrap(stbir_edge edge, int n, int max) +{ + // avoid per-pixel switch + if (n >= 0 && n < max) + return n; + return stbir__edge_wrap_slow(edge, n, max); +} + +// What input pixels contribute to this output pixel? +static void stbir__calculate_sample_range_upsample(int n, float out_filter_radius, float scale_ratio, float out_shift, int* in_first_pixel, int* in_last_pixel, float* in_center_of_out) +{ + float out_pixel_center = (float)n + 0.5f; + float out_pixel_influence_lowerbound = out_pixel_center - out_filter_radius; + float out_pixel_influence_upperbound = out_pixel_center + out_filter_radius; + + float in_pixel_influence_lowerbound = (out_pixel_influence_lowerbound + out_shift) / scale_ratio; + float in_pixel_influence_upperbound = (out_pixel_influence_upperbound + out_shift) / scale_ratio; + + *in_center_of_out = (out_pixel_center + out_shift) / scale_ratio; + *in_first_pixel = (int)(floor(in_pixel_influence_lowerbound + 0.5)); + *in_last_pixel = (int)(floor(in_pixel_influence_upperbound - 0.5)); +} + +// What output pixels does this input pixel contribute to? +static void stbir__calculate_sample_range_downsample(int n, float in_pixels_radius, float scale_ratio, float out_shift, int* out_first_pixel, int* out_last_pixel, float* out_center_of_in) +{ + float in_pixel_center = (float)n + 0.5f; + float in_pixel_influence_lowerbound = in_pixel_center - in_pixels_radius; + float in_pixel_influence_upperbound = in_pixel_center + in_pixels_radius; + + float out_pixel_influence_lowerbound = in_pixel_influence_lowerbound * scale_ratio - out_shift; + float out_pixel_influence_upperbound = in_pixel_influence_upperbound * scale_ratio - out_shift; + + *out_center_of_in = in_pixel_center * scale_ratio - out_shift; + *out_first_pixel = (int)(floor(out_pixel_influence_lowerbound + 0.5)); + *out_last_pixel = (int)(floor(out_pixel_influence_upperbound - 0.5)); +} + +static void stbir__calculate_coefficients_upsample(stbir_filter filter, float scale, int in_first_pixel, int in_last_pixel, float in_center_of_out, stbir__contributors* contributor, float* coefficient_group) +{ + int i; + float total_filter = 0; + float filter_scale; + + STBIR_ASSERT(in_last_pixel - in_first_pixel <= (int)ceil(stbir__filter_info_table[filter].support(1/scale) * 2)); // Taken directly from stbir__get_coefficient_width() which we can't call because we don't know if we're horizontal or vertical. + + contributor->n0 = in_first_pixel; + contributor->n1 = in_last_pixel; + + STBIR_ASSERT(contributor->n1 >= contributor->n0); + + for (i = 0; i <= in_last_pixel - in_first_pixel; i++) + { + float in_pixel_center = (float)(i + in_first_pixel) + 0.5f; + coefficient_group[i] = stbir__filter_info_table[filter].kernel(in_center_of_out - in_pixel_center, 1 / scale); + + // If the coefficient is zero, skip it. (Don't do the <0 check here, we want the influence of those outside pixels.) + if (i == 0 && !coefficient_group[i]) + { + contributor->n0 = ++in_first_pixel; + i--; + continue; + } + + total_filter += coefficient_group[i]; + } + + STBIR_ASSERT(stbir__filter_info_table[filter].kernel((float)(in_last_pixel + 1) + 0.5f - in_center_of_out, 1/scale) == 0); + + STBIR_ASSERT(total_filter > 0.9); + STBIR_ASSERT(total_filter < 1.1f); // Make sure it's not way off. + + // Make sure the sum of all coefficients is 1. + filter_scale = 1 / total_filter; + + for (i = 0; i <= in_last_pixel - in_first_pixel; i++) + coefficient_group[i] *= filter_scale; + + for (i = in_last_pixel - in_first_pixel; i >= 0; i--) + { + if (coefficient_group[i]) + break; + + // This line has no weight. We can skip it. + contributor->n1 = contributor->n0 + i - 1; + } +} + +static void stbir__calculate_coefficients_downsample(stbir_filter filter, float scale_ratio, int out_first_pixel, int out_last_pixel, float out_center_of_in, stbir__contributors* contributor, float* coefficient_group) +{ + int i; + + STBIR_ASSERT(out_last_pixel - out_first_pixel <= (int)ceil(stbir__filter_info_table[filter].support(scale_ratio) * 2)); // Taken directly from stbir__get_coefficient_width() which we can't call because we don't know if we're horizontal or vertical. + + contributor->n0 = out_first_pixel; + contributor->n1 = out_last_pixel; + + STBIR_ASSERT(contributor->n1 >= contributor->n0); + + for (i = 0; i <= out_last_pixel - out_first_pixel; i++) + { + float out_pixel_center = (float)(i + out_first_pixel) + 0.5f; + float x = out_pixel_center - out_center_of_in; + coefficient_group[i] = stbir__filter_info_table[filter].kernel(x, scale_ratio) * scale_ratio; + } + + STBIR_ASSERT(stbir__filter_info_table[filter].kernel((float)(out_last_pixel + 1) + 0.5f - out_center_of_in, scale_ratio) == 0); + + for (i = out_last_pixel - out_first_pixel; i >= 0; i--) + { + if (coefficient_group[i]) + break; + + // This line has no weight. We can skip it. + contributor->n1 = contributor->n0 + i - 1; + } +} + +static void stbir__normalize_downsample_coefficients(stbir__contributors* contributors, float* coefficients, stbir_filter filter, float scale_ratio, int input_size, int output_size) +{ + int num_contributors = stbir__get_contributors(scale_ratio, filter, input_size, output_size); + int num_coefficients = stbir__get_coefficient_width(filter, scale_ratio); + int i, j; + int skip; + + for (i = 0; i < output_size; i++) + { + float scale; + float total = 0; + + for (j = 0; j < num_contributors; j++) + { + if (i >= contributors[j].n0 && i <= contributors[j].n1) + { + float coefficient = *stbir__get_coefficient(coefficients, filter, scale_ratio, j, i - contributors[j].n0); + total += coefficient; + } + else if (i < contributors[j].n0) + break; + } + + STBIR_ASSERT(total > 0.9f); + STBIR_ASSERT(total < 1.1f); + + scale = 1 / total; + + for (j = 0; j < num_contributors; j++) + { + if (i >= contributors[j].n0 && i <= contributors[j].n1) + *stbir__get_coefficient(coefficients, filter, scale_ratio, j, i - contributors[j].n0) *= scale; + else if (i < contributors[j].n0) + break; + } + } + + // Optimize: Skip zero coefficients and contributions outside of image bounds. + // Do this after normalizing because normalization depends on the n0/n1 values. + for (j = 0; j < num_contributors; j++) + { + int range, max, width; + + skip = 0; + while (*stbir__get_coefficient(coefficients, filter, scale_ratio, j, skip) == 0) + skip++; + + contributors[j].n0 += skip; + + while (contributors[j].n0 < 0) + { + contributors[j].n0++; + skip++; + } + + range = contributors[j].n1 - contributors[j].n0 + 1; + max = stbir__min(num_coefficients, range); + + width = stbir__get_coefficient_width(filter, scale_ratio); + for (i = 0; i < max; i++) + { + if (i + skip >= width) + break; + + *stbir__get_coefficient(coefficients, filter, scale_ratio, j, i) = *stbir__get_coefficient(coefficients, filter, scale_ratio, j, i + skip); + } + + continue; + } + + // Using min to avoid writing into invalid pixels. + for (i = 0; i < num_contributors; i++) + contributors[i].n1 = stbir__min(contributors[i].n1, output_size - 1); +} + +// Each scan line uses the same kernel values so we should calculate the kernel +// values once and then we can use them for every scan line. +static void stbir__calculate_filters(stbir__contributors* contributors, float* coefficients, stbir_filter filter, float scale_ratio, float shift, int input_size, int output_size) +{ + int n; + int total_contributors = stbir__get_contributors(scale_ratio, filter, input_size, output_size); + + if (stbir__use_upsampling(scale_ratio)) + { + float out_pixels_radius = stbir__filter_info_table[filter].support(1 / scale_ratio) * scale_ratio; + + // Looping through out pixels + for (n = 0; n < total_contributors; n++) + { + float in_center_of_out; // Center of the current out pixel in the in pixel space + int in_first_pixel, in_last_pixel; + + stbir__calculate_sample_range_upsample(n, out_pixels_radius, scale_ratio, shift, &in_first_pixel, &in_last_pixel, &in_center_of_out); + + stbir__calculate_coefficients_upsample(filter, scale_ratio, in_first_pixel, in_last_pixel, in_center_of_out, stbir__get_contributor(contributors, n), stbir__get_coefficient(coefficients, filter, scale_ratio, n, 0)); + } + } + else + { + float in_pixels_radius = stbir__filter_info_table[filter].support(scale_ratio) / scale_ratio; + + // Looping through in pixels + for (n = 0; n < total_contributors; n++) + { + float out_center_of_in; // Center of the current out pixel in the in pixel space + int out_first_pixel, out_last_pixel; + int n_adjusted = n - stbir__get_filter_pixel_margin(filter, scale_ratio); + + stbir__calculate_sample_range_downsample(n_adjusted, in_pixels_radius, scale_ratio, shift, &out_first_pixel, &out_last_pixel, &out_center_of_in); + + stbir__calculate_coefficients_downsample(filter, scale_ratio, out_first_pixel, out_last_pixel, out_center_of_in, stbir__get_contributor(contributors, n), stbir__get_coefficient(coefficients, filter, scale_ratio, n, 0)); + } + + stbir__normalize_downsample_coefficients(contributors, coefficients, filter, scale_ratio, input_size, output_size); + } +} + +static float* stbir__get_decode_buffer(stbir__info* stbir_info) +{ + // The 0 index of the decode buffer starts after the margin. This makes + // it okay to use negative indexes on the decode buffer. + return &stbir_info->decode_buffer[stbir_info->horizontal_filter_pixel_margin * stbir_info->channels]; +} + +#define STBIR__DECODE(type, colorspace) ((type) * (STBIR_MAX_COLORSPACES) + (colorspace)) + +static void stbir__decode_scanline(stbir__info* stbir_info, int n) +{ + int c; + int channels = stbir_info->channels; + int alpha_channel = stbir_info->alpha_channel; + int type = stbir_info->type; + int colorspace = stbir_info->colorspace; + int input_w = stbir_info->input_w; + size_t input_stride_bytes = stbir_info->input_stride_bytes; + float* decode_buffer = stbir__get_decode_buffer(stbir_info); + stbir_edge edge_horizontal = stbir_info->edge_horizontal; + stbir_edge edge_vertical = stbir_info->edge_vertical; + size_t in_buffer_row_offset = stbir__edge_wrap(edge_vertical, n, stbir_info->input_h) * input_stride_bytes; + const void* input_data = (char *) stbir_info->input_data + in_buffer_row_offset; + int max_x = input_w + stbir_info->horizontal_filter_pixel_margin; + int decode = STBIR__DECODE(type, colorspace); + + int x = -stbir_info->horizontal_filter_pixel_margin; + + // special handling for STBIR_EDGE_ZERO because it needs to return an item that doesn't appear in the input, + // and we want to avoid paying overhead on every pixel if not STBIR_EDGE_ZERO + if (edge_vertical == STBIR_EDGE_ZERO && (n < 0 || n >= stbir_info->input_h)) + { + for (; x < max_x; x++) + for (c = 0; c < channels; c++) + decode_buffer[x*channels + c] = 0; + return; + } + + switch (decode) + { + case STBIR__DECODE(STBIR_TYPE_UINT8, STBIR_COLORSPACE_LINEAR): + for (; x < max_x; x++) + { + int decode_pixel_index = x * channels; + int input_pixel_index = stbir__edge_wrap(edge_horizontal, x, input_w) * channels; + for (c = 0; c < channels; c++) + decode_buffer[decode_pixel_index + c] = ((float)((const unsigned char*)input_data)[input_pixel_index + c]) / stbir__max_uint8_as_float; + } + break; + + case STBIR__DECODE(STBIR_TYPE_UINT8, STBIR_COLORSPACE_SRGB): + for (; x < max_x; x++) + { + int decode_pixel_index = x * channels; + int input_pixel_index = stbir__edge_wrap(edge_horizontal, x, input_w) * channels; + for (c = 0; c < channels; c++) + decode_buffer[decode_pixel_index + c] = stbir__srgb_uchar_to_linear_float[((const unsigned char*)input_data)[input_pixel_index + c]]; + + if (!(stbir_info->flags&STBIR_FLAG_ALPHA_USES_COLORSPACE)) + decode_buffer[decode_pixel_index + alpha_channel] = ((float)((const unsigned char*)input_data)[input_pixel_index + alpha_channel]) / stbir__max_uint8_as_float; + } + break; + + case STBIR__DECODE(STBIR_TYPE_UINT16, STBIR_COLORSPACE_LINEAR): + for (; x < max_x; x++) + { + int decode_pixel_index = x * channels; + int input_pixel_index = stbir__edge_wrap(edge_horizontal, x, input_w) * channels; + for (c = 0; c < channels; c++) + decode_buffer[decode_pixel_index + c] = ((float)((const unsigned short*)input_data)[input_pixel_index + c]) / stbir__max_uint16_as_float; + } + break; + + case STBIR__DECODE(STBIR_TYPE_UINT16, STBIR_COLORSPACE_SRGB): + for (; x < max_x; x++) + { + int decode_pixel_index = x * channels; + int input_pixel_index = stbir__edge_wrap(edge_horizontal, x, input_w) * channels; + for (c = 0; c < channels; c++) + decode_buffer[decode_pixel_index + c] = stbir__srgb_to_linear(((float)((const unsigned short*)input_data)[input_pixel_index + c]) / stbir__max_uint16_as_float); + + if (!(stbir_info->flags&STBIR_FLAG_ALPHA_USES_COLORSPACE)) + decode_buffer[decode_pixel_index + alpha_channel] = ((float)((const unsigned short*)input_data)[input_pixel_index + alpha_channel]) / stbir__max_uint16_as_float; + } + break; + + case STBIR__DECODE(STBIR_TYPE_UINT32, STBIR_COLORSPACE_LINEAR): + for (; x < max_x; x++) + { + int decode_pixel_index = x * channels; + int input_pixel_index = stbir__edge_wrap(edge_horizontal, x, input_w) * channels; + for (c = 0; c < channels; c++) + decode_buffer[decode_pixel_index + c] = (float)(((double)((const unsigned int*)input_data)[input_pixel_index + c]) / stbir__max_uint32_as_float); + } + break; + + case STBIR__DECODE(STBIR_TYPE_UINT32, STBIR_COLORSPACE_SRGB): + for (; x < max_x; x++) + { + int decode_pixel_index = x * channels; + int input_pixel_index = stbir__edge_wrap(edge_horizontal, x, input_w) * channels; + for (c = 0; c < channels; c++) + decode_buffer[decode_pixel_index + c] = stbir__srgb_to_linear((float)(((double)((const unsigned int*)input_data)[input_pixel_index + c]) / stbir__max_uint32_as_float)); + + if (!(stbir_info->flags&STBIR_FLAG_ALPHA_USES_COLORSPACE)) + decode_buffer[decode_pixel_index + alpha_channel] = (float)(((double)((const unsigned int*)input_data)[input_pixel_index + alpha_channel]) / stbir__max_uint32_as_float); + } + break; + + case STBIR__DECODE(STBIR_TYPE_FLOAT, STBIR_COLORSPACE_LINEAR): + for (; x < max_x; x++) + { + int decode_pixel_index = x * channels; + int input_pixel_index = stbir__edge_wrap(edge_horizontal, x, input_w) * channels; + for (c = 0; c < channels; c++) + decode_buffer[decode_pixel_index + c] = ((const float*)input_data)[input_pixel_index + c]; + } + break; + + case STBIR__DECODE(STBIR_TYPE_FLOAT, STBIR_COLORSPACE_SRGB): + for (; x < max_x; x++) + { + int decode_pixel_index = x * channels; + int input_pixel_index = stbir__edge_wrap(edge_horizontal, x, input_w) * channels; + for (c = 0; c < channels; c++) + decode_buffer[decode_pixel_index + c] = stbir__srgb_to_linear(((const float*)input_data)[input_pixel_index + c]); + + if (!(stbir_info->flags&STBIR_FLAG_ALPHA_USES_COLORSPACE)) + decode_buffer[decode_pixel_index + alpha_channel] = ((const float*)input_data)[input_pixel_index + alpha_channel]; + } + + break; + + default: + STBIR_ASSERT(!"Unknown type/colorspace/channels combination."); + break; + } + + if (!(stbir_info->flags & STBIR_FLAG_ALPHA_PREMULTIPLIED)) + { + for (x = -stbir_info->horizontal_filter_pixel_margin; x < max_x; x++) + { + int decode_pixel_index = x * channels; + + // If the alpha value is 0 it will clobber the color values. Make sure it's not. + float alpha = decode_buffer[decode_pixel_index + alpha_channel]; +#ifndef STBIR_NO_ALPHA_EPSILON + if (stbir_info->type != STBIR_TYPE_FLOAT) { + alpha += STBIR_ALPHA_EPSILON; + decode_buffer[decode_pixel_index + alpha_channel] = alpha; + } +#endif + for (c = 0; c < channels; c++) + { + if (c == alpha_channel) + continue; + + decode_buffer[decode_pixel_index + c] *= alpha; + } + } + } + + if (edge_horizontal == STBIR_EDGE_ZERO) + { + for (x = -stbir_info->horizontal_filter_pixel_margin; x < 0; x++) + { + for (c = 0; c < channels; c++) + decode_buffer[x*channels + c] = 0; + } + for (x = input_w; x < max_x; x++) + { + for (c = 0; c < channels; c++) + decode_buffer[x*channels + c] = 0; + } + } +} + +static float* stbir__get_ring_buffer_entry(float* ring_buffer, int index, int ring_buffer_length) +{ + return &ring_buffer[index * ring_buffer_length]; +} + +static float* stbir__add_empty_ring_buffer_entry(stbir__info* stbir_info, int n) +{ + int ring_buffer_index; + float* ring_buffer; + + stbir_info->ring_buffer_last_scanline = n; + + if (stbir_info->ring_buffer_begin_index < 0) + { + ring_buffer_index = stbir_info->ring_buffer_begin_index = 0; + stbir_info->ring_buffer_first_scanline = n; + } + else + { + ring_buffer_index = (stbir_info->ring_buffer_begin_index + (stbir_info->ring_buffer_last_scanline - stbir_info->ring_buffer_first_scanline)) % stbir_info->ring_buffer_num_entries; + STBIR_ASSERT(ring_buffer_index != stbir_info->ring_buffer_begin_index); + } + + ring_buffer = stbir__get_ring_buffer_entry(stbir_info->ring_buffer, ring_buffer_index, stbir_info->ring_buffer_length_bytes / sizeof(float)); + memset(ring_buffer, 0, stbir_info->ring_buffer_length_bytes); + + return ring_buffer; +} + + +static void stbir__resample_horizontal_upsample(stbir__info* stbir_info, float* output_buffer) +{ + int x, k; + int output_w = stbir_info->output_w; + int channels = stbir_info->channels; + float* decode_buffer = stbir__get_decode_buffer(stbir_info); + stbir__contributors* horizontal_contributors = stbir_info->horizontal_contributors; + float* horizontal_coefficients = stbir_info->horizontal_coefficients; + int coefficient_width = stbir_info->horizontal_coefficient_width; + + for (x = 0; x < output_w; x++) + { + int n0 = horizontal_contributors[x].n0; + int n1 = horizontal_contributors[x].n1; + + int out_pixel_index = x * channels; + int coefficient_group = coefficient_width * x; + int coefficient_counter = 0; + + STBIR_ASSERT(n1 >= n0); + STBIR_ASSERT(n0 >= -stbir_info->horizontal_filter_pixel_margin); + STBIR_ASSERT(n1 >= -stbir_info->horizontal_filter_pixel_margin); + STBIR_ASSERT(n0 < stbir_info->input_w + stbir_info->horizontal_filter_pixel_margin); + STBIR_ASSERT(n1 < stbir_info->input_w + stbir_info->horizontal_filter_pixel_margin); + + switch (channels) { + case 1: + for (k = n0; k <= n1; k++) + { + int in_pixel_index = k * 1; + float coefficient = horizontal_coefficients[coefficient_group + coefficient_counter++]; + STBIR_ASSERT(coefficient != 0); + output_buffer[out_pixel_index + 0] += decode_buffer[in_pixel_index + 0] * coefficient; + } + break; + case 2: + for (k = n0; k <= n1; k++) + { + int in_pixel_index = k * 2; + float coefficient = horizontal_coefficients[coefficient_group + coefficient_counter++]; + STBIR_ASSERT(coefficient != 0); + output_buffer[out_pixel_index + 0] += decode_buffer[in_pixel_index + 0] * coefficient; + output_buffer[out_pixel_index + 1] += decode_buffer[in_pixel_index + 1] * coefficient; + } + break; + case 3: + for (k = n0; k <= n1; k++) + { + int in_pixel_index = k * 3; + float coefficient = horizontal_coefficients[coefficient_group + coefficient_counter++]; + STBIR_ASSERT(coefficient != 0); + output_buffer[out_pixel_index + 0] += decode_buffer[in_pixel_index + 0] * coefficient; + output_buffer[out_pixel_index + 1] += decode_buffer[in_pixel_index + 1] * coefficient; + output_buffer[out_pixel_index + 2] += decode_buffer[in_pixel_index + 2] * coefficient; + } + break; + case 4: + for (k = n0; k <= n1; k++) + { + int in_pixel_index = k * 4; + float coefficient = horizontal_coefficients[coefficient_group + coefficient_counter++]; + STBIR_ASSERT(coefficient != 0); + output_buffer[out_pixel_index + 0] += decode_buffer[in_pixel_index + 0] * coefficient; + output_buffer[out_pixel_index + 1] += decode_buffer[in_pixel_index + 1] * coefficient; + output_buffer[out_pixel_index + 2] += decode_buffer[in_pixel_index + 2] * coefficient; + output_buffer[out_pixel_index + 3] += decode_buffer[in_pixel_index + 3] * coefficient; + } + break; + default: + for (k = n0; k <= n1; k++) + { + int in_pixel_index = k * channels; + float coefficient = horizontal_coefficients[coefficient_group + coefficient_counter++]; + int c; + STBIR_ASSERT(coefficient != 0); + for (c = 0; c < channels; c++) + output_buffer[out_pixel_index + c] += decode_buffer[in_pixel_index + c] * coefficient; + } + break; + } + } +} + +static void stbir__resample_horizontal_downsample(stbir__info* stbir_info, float* output_buffer) +{ + int x, k; + int input_w = stbir_info->input_w; + int channels = stbir_info->channels; + float* decode_buffer = stbir__get_decode_buffer(stbir_info); + stbir__contributors* horizontal_contributors = stbir_info->horizontal_contributors; + float* horizontal_coefficients = stbir_info->horizontal_coefficients; + int coefficient_width = stbir_info->horizontal_coefficient_width; + int filter_pixel_margin = stbir_info->horizontal_filter_pixel_margin; + int max_x = input_w + filter_pixel_margin * 2; + + STBIR_ASSERT(!stbir__use_width_upsampling(stbir_info)); + + switch (channels) { + case 1: + for (x = 0; x < max_x; x++) + { + int n0 = horizontal_contributors[x].n0; + int n1 = horizontal_contributors[x].n1; + + int in_x = x - filter_pixel_margin; + int in_pixel_index = in_x * 1; + int max_n = n1; + int coefficient_group = coefficient_width * x; + + for (k = n0; k <= max_n; k++) + { + int out_pixel_index = k * 1; + float coefficient = horizontal_coefficients[coefficient_group + k - n0]; + STBIR_ASSERT(coefficient != 0); + output_buffer[out_pixel_index + 0] += decode_buffer[in_pixel_index + 0] * coefficient; + } + } + break; + + case 2: + for (x = 0; x < max_x; x++) + { + int n0 = horizontal_contributors[x].n0; + int n1 = horizontal_contributors[x].n1; + + int in_x = x - filter_pixel_margin; + int in_pixel_index = in_x * 2; + int max_n = n1; + int coefficient_group = coefficient_width * x; + + for (k = n0; k <= max_n; k++) + { + int out_pixel_index = k * 2; + float coefficient = horizontal_coefficients[coefficient_group + k - n0]; + STBIR_ASSERT(coefficient != 0); + output_buffer[out_pixel_index + 0] += decode_buffer[in_pixel_index + 0] * coefficient; + output_buffer[out_pixel_index + 1] += decode_buffer[in_pixel_index + 1] * coefficient; + } + } + break; + + case 3: + for (x = 0; x < max_x; x++) + { + int n0 = horizontal_contributors[x].n0; + int n1 = horizontal_contributors[x].n1; + + int in_x = x - filter_pixel_margin; + int in_pixel_index = in_x * 3; + int max_n = n1; + int coefficient_group = coefficient_width * x; + + for (k = n0; k <= max_n; k++) + { + int out_pixel_index = k * 3; + float coefficient = horizontal_coefficients[coefficient_group + k - n0]; + STBIR_ASSERT(coefficient != 0); + output_buffer[out_pixel_index + 0] += decode_buffer[in_pixel_index + 0] * coefficient; + output_buffer[out_pixel_index + 1] += decode_buffer[in_pixel_index + 1] * coefficient; + output_buffer[out_pixel_index + 2] += decode_buffer[in_pixel_index + 2] * coefficient; + } + } + break; + + case 4: + for (x = 0; x < max_x; x++) + { + int n0 = horizontal_contributors[x].n0; + int n1 = horizontal_contributors[x].n1; + + int in_x = x - filter_pixel_margin; + int in_pixel_index = in_x * 4; + int max_n = n1; + int coefficient_group = coefficient_width * x; + + for (k = n0; k <= max_n; k++) + { + int out_pixel_index = k * 4; + float coefficient = horizontal_coefficients[coefficient_group + k - n0]; + STBIR_ASSERT(coefficient != 0); + output_buffer[out_pixel_index + 0] += decode_buffer[in_pixel_index + 0] * coefficient; + output_buffer[out_pixel_index + 1] += decode_buffer[in_pixel_index + 1] * coefficient; + output_buffer[out_pixel_index + 2] += decode_buffer[in_pixel_index + 2] * coefficient; + output_buffer[out_pixel_index + 3] += decode_buffer[in_pixel_index + 3] * coefficient; + } + } + break; + + default: + for (x = 0; x < max_x; x++) + { + int n0 = horizontal_contributors[x].n0; + int n1 = horizontal_contributors[x].n1; + + int in_x = x - filter_pixel_margin; + int in_pixel_index = in_x * channels; + int max_n = n1; + int coefficient_group = coefficient_width * x; + + for (k = n0; k <= max_n; k++) + { + int c; + int out_pixel_index = k * channels; + float coefficient = horizontal_coefficients[coefficient_group + k - n0]; + STBIR_ASSERT(coefficient != 0); + for (c = 0; c < channels; c++) + output_buffer[out_pixel_index + c] += decode_buffer[in_pixel_index + c] * coefficient; + } + } + break; + } +} + +static void stbir__decode_and_resample_upsample(stbir__info* stbir_info, int n) +{ + // Decode the nth scanline from the source image into the decode buffer. + stbir__decode_scanline(stbir_info, n); + + // Now resample it into the ring buffer. + if (stbir__use_width_upsampling(stbir_info)) + stbir__resample_horizontal_upsample(stbir_info, stbir__add_empty_ring_buffer_entry(stbir_info, n)); + else + stbir__resample_horizontal_downsample(stbir_info, stbir__add_empty_ring_buffer_entry(stbir_info, n)); + + // Now it's sitting in the ring buffer ready to be used as source for the vertical sampling. +} + +static void stbir__decode_and_resample_downsample(stbir__info* stbir_info, int n) +{ + // Decode the nth scanline from the source image into the decode buffer. + stbir__decode_scanline(stbir_info, n); + + memset(stbir_info->horizontal_buffer, 0, stbir_info->output_w * stbir_info->channels * sizeof(float)); + + // Now resample it into the horizontal buffer. + if (stbir__use_width_upsampling(stbir_info)) + stbir__resample_horizontal_upsample(stbir_info, stbir_info->horizontal_buffer); + else + stbir__resample_horizontal_downsample(stbir_info, stbir_info->horizontal_buffer); + + // Now it's sitting in the horizontal buffer ready to be distributed into the ring buffers. +} + +// Get the specified scan line from the ring buffer. +static float* stbir__get_ring_buffer_scanline(int get_scanline, float* ring_buffer, int begin_index, int first_scanline, int ring_buffer_num_entries, int ring_buffer_length) +{ + int ring_buffer_index = (begin_index + (get_scanline - first_scanline)) % ring_buffer_num_entries; + return stbir__get_ring_buffer_entry(ring_buffer, ring_buffer_index, ring_buffer_length); +} + + +static void stbir__encode_scanline(stbir__info* stbir_info, int num_pixels, void *output_buffer, float *encode_buffer, int channels, int alpha_channel, int decode) +{ + int x; + int n; + int num_nonalpha; + stbir_uint16 nonalpha[STBIR_MAX_CHANNELS]; + + if (!(stbir_info->flags&STBIR_FLAG_ALPHA_PREMULTIPLIED)) + { + for (x=0; x < num_pixels; ++x) + { + int pixel_index = x*channels; + + float alpha = encode_buffer[pixel_index + alpha_channel]; + float reciprocal_alpha = alpha ? 1.0f / alpha : 0; + + // unrolling this produced a 1% slowdown upscaling a large RGBA linear-space image on my machine - stb + for (n = 0; n < channels; n++) + if (n != alpha_channel) + encode_buffer[pixel_index + n] *= reciprocal_alpha; + + // We added in a small epsilon to prevent the color channel from being deleted with zero alpha. + // Because we only add it for integer types, it will automatically be discarded on integer + // conversion, so we don't need to subtract it back out (which would be problematic for + // numeric precision reasons). + } + } + + // build a table of all channels that need colorspace correction, so + // we don't perform colorspace correction on channels that don't need it. + for (x = 0, num_nonalpha = 0; x < channels; ++x) + { + if (x != alpha_channel || (stbir_info->flags & STBIR_FLAG_ALPHA_USES_COLORSPACE)) + { + nonalpha[num_nonalpha++] = (stbir_uint16)x; + } + } + + #define STBIR__ROUND_INT(f) ((int) ((f)+0.5)) + #define STBIR__ROUND_UINT(f) ((stbir_uint32) ((f)+0.5)) + + #ifdef STBIR__SATURATE_INT + #define STBIR__ENCODE_LINEAR8(f) stbir__saturate8 (STBIR__ROUND_INT((f) * stbir__max_uint8_as_float )) + #define STBIR__ENCODE_LINEAR16(f) stbir__saturate16(STBIR__ROUND_INT((f) * stbir__max_uint16_as_float)) + #else + #define STBIR__ENCODE_LINEAR8(f) (unsigned char ) STBIR__ROUND_INT(stbir__saturate(f) * stbir__max_uint8_as_float ) + #define STBIR__ENCODE_LINEAR16(f) (unsigned short) STBIR__ROUND_INT(stbir__saturate(f) * stbir__max_uint16_as_float) + #endif + + switch (decode) + { + case STBIR__DECODE(STBIR_TYPE_UINT8, STBIR_COLORSPACE_LINEAR): + for (x=0; x < num_pixels; ++x) + { + int pixel_index = x*channels; + + for (n = 0; n < channels; n++) + { + int index = pixel_index + n; + ((unsigned char*)output_buffer)[index] = STBIR__ENCODE_LINEAR8(encode_buffer[index]); + } + } + break; + + case STBIR__DECODE(STBIR_TYPE_UINT8, STBIR_COLORSPACE_SRGB): + for (x=0; x < num_pixels; ++x) + { + int pixel_index = x*channels; + + for (n = 0; n < num_nonalpha; n++) + { + int index = pixel_index + nonalpha[n]; + ((unsigned char*)output_buffer)[index] = stbir__linear_to_srgb_uchar(encode_buffer[index]); + } + + if (!(stbir_info->flags & STBIR_FLAG_ALPHA_USES_COLORSPACE)) + ((unsigned char *)output_buffer)[pixel_index + alpha_channel] = STBIR__ENCODE_LINEAR8(encode_buffer[pixel_index+alpha_channel]); + } + break; + + case STBIR__DECODE(STBIR_TYPE_UINT16, STBIR_COLORSPACE_LINEAR): + for (x=0; x < num_pixels; ++x) + { + int pixel_index = x*channels; + + for (n = 0; n < channels; n++) + { + int index = pixel_index + n; + ((unsigned short*)output_buffer)[index] = STBIR__ENCODE_LINEAR16(encode_buffer[index]); + } + } + break; + + case STBIR__DECODE(STBIR_TYPE_UINT16, STBIR_COLORSPACE_SRGB): + for (x=0; x < num_pixels; ++x) + { + int pixel_index = x*channels; + + for (n = 0; n < num_nonalpha; n++) + { + int index = pixel_index + nonalpha[n]; + ((unsigned short*)output_buffer)[index] = (unsigned short)STBIR__ROUND_INT(stbir__linear_to_srgb(stbir__saturate(encode_buffer[index])) * stbir__max_uint16_as_float); + } + + if (!(stbir_info->flags&STBIR_FLAG_ALPHA_USES_COLORSPACE)) + ((unsigned short*)output_buffer)[pixel_index + alpha_channel] = STBIR__ENCODE_LINEAR16(encode_buffer[pixel_index + alpha_channel]); + } + + break; + + case STBIR__DECODE(STBIR_TYPE_UINT32, STBIR_COLORSPACE_LINEAR): + for (x=0; x < num_pixels; ++x) + { + int pixel_index = x*channels; + + for (n = 0; n < channels; n++) + { + int index = pixel_index + n; + ((unsigned int*)output_buffer)[index] = (unsigned int)STBIR__ROUND_UINT(((double)stbir__saturate(encode_buffer[index])) * stbir__max_uint32_as_float); + } + } + break; + + case STBIR__DECODE(STBIR_TYPE_UINT32, STBIR_COLORSPACE_SRGB): + for (x=0; x < num_pixels; ++x) + { + int pixel_index = x*channels; + + for (n = 0; n < num_nonalpha; n++) + { + int index = pixel_index + nonalpha[n]; + ((unsigned int*)output_buffer)[index] = (unsigned int)STBIR__ROUND_UINT(((double)stbir__linear_to_srgb(stbir__saturate(encode_buffer[index]))) * stbir__max_uint32_as_float); + } + + if (!(stbir_info->flags&STBIR_FLAG_ALPHA_USES_COLORSPACE)) + ((unsigned int*)output_buffer)[pixel_index + alpha_channel] = (unsigned int)STBIR__ROUND_INT(((double)stbir__saturate(encode_buffer[pixel_index + alpha_channel])) * stbir__max_uint32_as_float); + } + break; + + case STBIR__DECODE(STBIR_TYPE_FLOAT, STBIR_COLORSPACE_LINEAR): + for (x=0; x < num_pixels; ++x) + { + int pixel_index = x*channels; + + for (n = 0; n < channels; n++) + { + int index = pixel_index + n; + ((float*)output_buffer)[index] = encode_buffer[index]; + } + } + break; + + case STBIR__DECODE(STBIR_TYPE_FLOAT, STBIR_COLORSPACE_SRGB): + for (x=0; x < num_pixels; ++x) + { + int pixel_index = x*channels; + + for (n = 0; n < num_nonalpha; n++) + { + int index = pixel_index + nonalpha[n]; + ((float*)output_buffer)[index] = stbir__linear_to_srgb(encode_buffer[index]); + } + + if (!(stbir_info->flags&STBIR_FLAG_ALPHA_USES_COLORSPACE)) + ((float*)output_buffer)[pixel_index + alpha_channel] = encode_buffer[pixel_index + alpha_channel]; + } + break; + + default: + STBIR_ASSERT(!"Unknown type/colorspace/channels combination."); + break; + } +} + +static void stbir__resample_vertical_upsample(stbir__info* stbir_info, int n) +{ + int x, k; + int output_w = stbir_info->output_w; + stbir__contributors* vertical_contributors = stbir_info->vertical_contributors; + float* vertical_coefficients = stbir_info->vertical_coefficients; + int channels = stbir_info->channels; + int alpha_channel = stbir_info->alpha_channel; + int type = stbir_info->type; + int colorspace = stbir_info->colorspace; + int ring_buffer_entries = stbir_info->ring_buffer_num_entries; + void* output_data = stbir_info->output_data; + float* encode_buffer = stbir_info->encode_buffer; + int decode = STBIR__DECODE(type, colorspace); + int coefficient_width = stbir_info->vertical_coefficient_width; + int coefficient_counter; + int contributor = n; + + float* ring_buffer = stbir_info->ring_buffer; + int ring_buffer_begin_index = stbir_info->ring_buffer_begin_index; + int ring_buffer_first_scanline = stbir_info->ring_buffer_first_scanline; + int ring_buffer_length = stbir_info->ring_buffer_length_bytes/sizeof(float); + + int n0,n1, output_row_start; + int coefficient_group = coefficient_width * contributor; + + n0 = vertical_contributors[contributor].n0; + n1 = vertical_contributors[contributor].n1; + + output_row_start = n * stbir_info->output_stride_bytes; + + STBIR_ASSERT(stbir__use_height_upsampling(stbir_info)); + + memset(encode_buffer, 0, output_w * sizeof(float) * channels); + + // I tried reblocking this for better cache usage of encode_buffer + // (using x_outer, k, x_inner), but it lost speed. -- stb + + coefficient_counter = 0; + switch (channels) { + case 1: + for (k = n0; k <= n1; k++) + { + int coefficient_index = coefficient_counter++; + float* ring_buffer_entry = stbir__get_ring_buffer_scanline(k, ring_buffer, ring_buffer_begin_index, ring_buffer_first_scanline, ring_buffer_entries, ring_buffer_length); + float coefficient = vertical_coefficients[coefficient_group + coefficient_index]; + for (x = 0; x < output_w; ++x) + { + int in_pixel_index = x * 1; + encode_buffer[in_pixel_index + 0] += ring_buffer_entry[in_pixel_index + 0] * coefficient; + } + } + break; + case 2: + for (k = n0; k <= n1; k++) + { + int coefficient_index = coefficient_counter++; + float* ring_buffer_entry = stbir__get_ring_buffer_scanline(k, ring_buffer, ring_buffer_begin_index, ring_buffer_first_scanline, ring_buffer_entries, ring_buffer_length); + float coefficient = vertical_coefficients[coefficient_group + coefficient_index]; + for (x = 0; x < output_w; ++x) + { + int in_pixel_index = x * 2; + encode_buffer[in_pixel_index + 0] += ring_buffer_entry[in_pixel_index + 0] * coefficient; + encode_buffer[in_pixel_index + 1] += ring_buffer_entry[in_pixel_index + 1] * coefficient; + } + } + break; + case 3: + for (k = n0; k <= n1; k++) + { + int coefficient_index = coefficient_counter++; + float* ring_buffer_entry = stbir__get_ring_buffer_scanline(k, ring_buffer, ring_buffer_begin_index, ring_buffer_first_scanline, ring_buffer_entries, ring_buffer_length); + float coefficient = vertical_coefficients[coefficient_group + coefficient_index]; + for (x = 0; x < output_w; ++x) + { + int in_pixel_index = x * 3; + encode_buffer[in_pixel_index + 0] += ring_buffer_entry[in_pixel_index + 0] * coefficient; + encode_buffer[in_pixel_index + 1] += ring_buffer_entry[in_pixel_index + 1] * coefficient; + encode_buffer[in_pixel_index + 2] += ring_buffer_entry[in_pixel_index + 2] * coefficient; + } + } + break; + case 4: + for (k = n0; k <= n1; k++) + { + int coefficient_index = coefficient_counter++; + float* ring_buffer_entry = stbir__get_ring_buffer_scanline(k, ring_buffer, ring_buffer_begin_index, ring_buffer_first_scanline, ring_buffer_entries, ring_buffer_length); + float coefficient = vertical_coefficients[coefficient_group + coefficient_index]; + for (x = 0; x < output_w; ++x) + { + int in_pixel_index = x * 4; + encode_buffer[in_pixel_index + 0] += ring_buffer_entry[in_pixel_index + 0] * coefficient; + encode_buffer[in_pixel_index + 1] += ring_buffer_entry[in_pixel_index + 1] * coefficient; + encode_buffer[in_pixel_index + 2] += ring_buffer_entry[in_pixel_index + 2] * coefficient; + encode_buffer[in_pixel_index + 3] += ring_buffer_entry[in_pixel_index + 3] * coefficient; + } + } + break; + default: + for (k = n0; k <= n1; k++) + { + int coefficient_index = coefficient_counter++; + float* ring_buffer_entry = stbir__get_ring_buffer_scanline(k, ring_buffer, ring_buffer_begin_index, ring_buffer_first_scanline, ring_buffer_entries, ring_buffer_length); + float coefficient = vertical_coefficients[coefficient_group + coefficient_index]; + for (x = 0; x < output_w; ++x) + { + int in_pixel_index = x * channels; + int c; + for (c = 0; c < channels; c++) + encode_buffer[in_pixel_index + c] += ring_buffer_entry[in_pixel_index + c] * coefficient; + } + } + break; + } + stbir__encode_scanline(stbir_info, output_w, (char *) output_data + output_row_start, encode_buffer, channels, alpha_channel, decode); +} + +static void stbir__resample_vertical_downsample(stbir__info* stbir_info, int n) +{ + int x, k; + int output_w = stbir_info->output_w; + stbir__contributors* vertical_contributors = stbir_info->vertical_contributors; + float* vertical_coefficients = stbir_info->vertical_coefficients; + int channels = stbir_info->channels; + int ring_buffer_entries = stbir_info->ring_buffer_num_entries; + float* horizontal_buffer = stbir_info->horizontal_buffer; + int coefficient_width = stbir_info->vertical_coefficient_width; + int contributor = n + stbir_info->vertical_filter_pixel_margin; + + float* ring_buffer = stbir_info->ring_buffer; + int ring_buffer_begin_index = stbir_info->ring_buffer_begin_index; + int ring_buffer_first_scanline = stbir_info->ring_buffer_first_scanline; + int ring_buffer_length = stbir_info->ring_buffer_length_bytes/sizeof(float); + int n0,n1; + + n0 = vertical_contributors[contributor].n0; + n1 = vertical_contributors[contributor].n1; + + STBIR_ASSERT(!stbir__use_height_upsampling(stbir_info)); + + for (k = n0; k <= n1; k++) + { + int coefficient_index = k - n0; + int coefficient_group = coefficient_width * contributor; + float coefficient = vertical_coefficients[coefficient_group + coefficient_index]; + + float* ring_buffer_entry = stbir__get_ring_buffer_scanline(k, ring_buffer, ring_buffer_begin_index, ring_buffer_first_scanline, ring_buffer_entries, ring_buffer_length); + + switch (channels) { + case 1: + for (x = 0; x < output_w; x++) + { + int in_pixel_index = x * 1; + ring_buffer_entry[in_pixel_index + 0] += horizontal_buffer[in_pixel_index + 0] * coefficient; + } + break; + case 2: + for (x = 0; x < output_w; x++) + { + int in_pixel_index = x * 2; + ring_buffer_entry[in_pixel_index + 0] += horizontal_buffer[in_pixel_index + 0] * coefficient; + ring_buffer_entry[in_pixel_index + 1] += horizontal_buffer[in_pixel_index + 1] * coefficient; + } + break; + case 3: + for (x = 0; x < output_w; x++) + { + int in_pixel_index = x * 3; + ring_buffer_entry[in_pixel_index + 0] += horizontal_buffer[in_pixel_index + 0] * coefficient; + ring_buffer_entry[in_pixel_index + 1] += horizontal_buffer[in_pixel_index + 1] * coefficient; + ring_buffer_entry[in_pixel_index + 2] += horizontal_buffer[in_pixel_index + 2] * coefficient; + } + break; + case 4: + for (x = 0; x < output_w; x++) + { + int in_pixel_index = x * 4; + ring_buffer_entry[in_pixel_index + 0] += horizontal_buffer[in_pixel_index + 0] * coefficient; + ring_buffer_entry[in_pixel_index + 1] += horizontal_buffer[in_pixel_index + 1] * coefficient; + ring_buffer_entry[in_pixel_index + 2] += horizontal_buffer[in_pixel_index + 2] * coefficient; + ring_buffer_entry[in_pixel_index + 3] += horizontal_buffer[in_pixel_index + 3] * coefficient; + } + break; + default: + for (x = 0; x < output_w; x++) + { + int in_pixel_index = x * channels; + + int c; + for (c = 0; c < channels; c++) + ring_buffer_entry[in_pixel_index + c] += horizontal_buffer[in_pixel_index + c] * coefficient; + } + break; + } + } +} + +static void stbir__buffer_loop_upsample(stbir__info* stbir_info) +{ + int y; + float scale_ratio = stbir_info->vertical_scale; + float out_scanlines_radius = stbir__filter_info_table[stbir_info->vertical_filter].support(1/scale_ratio) * scale_ratio; + + STBIR_ASSERT(stbir__use_height_upsampling(stbir_info)); + + for (y = 0; y < stbir_info->output_h; y++) + { + float in_center_of_out = 0; // Center of the current out scanline in the in scanline space + int in_first_scanline = 0, in_last_scanline = 0; + + stbir__calculate_sample_range_upsample(y, out_scanlines_radius, scale_ratio, stbir_info->vertical_shift, &in_first_scanline, &in_last_scanline, &in_center_of_out); + + STBIR_ASSERT(in_last_scanline - in_first_scanline + 1 <= stbir_info->ring_buffer_num_entries); + + if (stbir_info->ring_buffer_begin_index >= 0) + { + // Get rid of whatever we don't need anymore. + while (in_first_scanline > stbir_info->ring_buffer_first_scanline) + { + if (stbir_info->ring_buffer_first_scanline == stbir_info->ring_buffer_last_scanline) + { + // We just popped the last scanline off the ring buffer. + // Reset it to the empty state. + stbir_info->ring_buffer_begin_index = -1; + stbir_info->ring_buffer_first_scanline = 0; + stbir_info->ring_buffer_last_scanline = 0; + break; + } + else + { + stbir_info->ring_buffer_first_scanline++; + stbir_info->ring_buffer_begin_index = (stbir_info->ring_buffer_begin_index + 1) % stbir_info->ring_buffer_num_entries; + } + } + } + + // Load in new ones. + if (stbir_info->ring_buffer_begin_index < 0) + stbir__decode_and_resample_upsample(stbir_info, in_first_scanline); + + while (in_last_scanline > stbir_info->ring_buffer_last_scanline) + stbir__decode_and_resample_upsample(stbir_info, stbir_info->ring_buffer_last_scanline + 1); + + // Now all buffers should be ready to write a row of vertical sampling. + stbir__resample_vertical_upsample(stbir_info, y); + + STBIR_PROGRESS_REPORT((float)y / stbir_info->output_h); + } +} + +static void stbir__empty_ring_buffer(stbir__info* stbir_info, int first_necessary_scanline) +{ + int output_stride_bytes = stbir_info->output_stride_bytes; + int channels = stbir_info->channels; + int alpha_channel = stbir_info->alpha_channel; + int type = stbir_info->type; + int colorspace = stbir_info->colorspace; + int output_w = stbir_info->output_w; + void* output_data = stbir_info->output_data; + int decode = STBIR__DECODE(type, colorspace); + + float* ring_buffer = stbir_info->ring_buffer; + int ring_buffer_length = stbir_info->ring_buffer_length_bytes/sizeof(float); + + if (stbir_info->ring_buffer_begin_index >= 0) + { + // Get rid of whatever we don't need anymore. + while (first_necessary_scanline > stbir_info->ring_buffer_first_scanline) + { + if (stbir_info->ring_buffer_first_scanline >= 0 && stbir_info->ring_buffer_first_scanline < stbir_info->output_h) + { + int output_row_start = stbir_info->ring_buffer_first_scanline * output_stride_bytes; + float* ring_buffer_entry = stbir__get_ring_buffer_entry(ring_buffer, stbir_info->ring_buffer_begin_index, ring_buffer_length); + stbir__encode_scanline(stbir_info, output_w, (char *) output_data + output_row_start, ring_buffer_entry, channels, alpha_channel, decode); + STBIR_PROGRESS_REPORT((float)stbir_info->ring_buffer_first_scanline / stbir_info->output_h); + } + + if (stbir_info->ring_buffer_first_scanline == stbir_info->ring_buffer_last_scanline) + { + // We just popped the last scanline off the ring buffer. + // Reset it to the empty state. + stbir_info->ring_buffer_begin_index = -1; + stbir_info->ring_buffer_first_scanline = 0; + stbir_info->ring_buffer_last_scanline = 0; + break; + } + else + { + stbir_info->ring_buffer_first_scanline++; + stbir_info->ring_buffer_begin_index = (stbir_info->ring_buffer_begin_index + 1) % stbir_info->ring_buffer_num_entries; + } + } + } +} + +static void stbir__buffer_loop_downsample(stbir__info* stbir_info) +{ + int y; + float scale_ratio = stbir_info->vertical_scale; + int output_h = stbir_info->output_h; + float in_pixels_radius = stbir__filter_info_table[stbir_info->vertical_filter].support(scale_ratio) / scale_ratio; + int pixel_margin = stbir_info->vertical_filter_pixel_margin; + int max_y = stbir_info->input_h + pixel_margin; + + STBIR_ASSERT(!stbir__use_height_upsampling(stbir_info)); + + for (y = -pixel_margin; y < max_y; y++) + { + float out_center_of_in; // Center of the current out scanline in the in scanline space + int out_first_scanline, out_last_scanline; + + stbir__calculate_sample_range_downsample(y, in_pixels_radius, scale_ratio, stbir_info->vertical_shift, &out_first_scanline, &out_last_scanline, &out_center_of_in); + + STBIR_ASSERT(out_last_scanline - out_first_scanline + 1 <= stbir_info->ring_buffer_num_entries); + + if (out_last_scanline < 0 || out_first_scanline >= output_h) + continue; + + stbir__empty_ring_buffer(stbir_info, out_first_scanline); + + stbir__decode_and_resample_downsample(stbir_info, y); + + // Load in new ones. + if (stbir_info->ring_buffer_begin_index < 0) + stbir__add_empty_ring_buffer_entry(stbir_info, out_first_scanline); + + while (out_last_scanline > stbir_info->ring_buffer_last_scanline) + stbir__add_empty_ring_buffer_entry(stbir_info, stbir_info->ring_buffer_last_scanline + 1); + + // Now the horizontal buffer is ready to write to all ring buffer rows. + stbir__resample_vertical_downsample(stbir_info, y); + } + + stbir__empty_ring_buffer(stbir_info, stbir_info->output_h); +} + +static void stbir__setup(stbir__info *info, int input_w, int input_h, int output_w, int output_h, int channels) +{ + info->input_w = input_w; + info->input_h = input_h; + info->output_w = output_w; + info->output_h = output_h; + info->channels = channels; +} + +static void stbir__calculate_transform(stbir__info *info, float s0, float t0, float s1, float t1, float *transform) +{ + info->s0 = s0; + info->t0 = t0; + info->s1 = s1; + info->t1 = t1; + + if (transform) + { + info->horizontal_scale = transform[0]; + info->vertical_scale = transform[1]; + info->horizontal_shift = transform[2]; + info->vertical_shift = transform[3]; + } + else + { + info->horizontal_scale = ((float)info->output_w / info->input_w) / (s1 - s0); + info->vertical_scale = ((float)info->output_h / info->input_h) / (t1 - t0); + + info->horizontal_shift = s0 * info->output_w / (s1 - s0); + info->vertical_shift = t0 * info->output_h / (t1 - t0); + } +} + +static void stbir__choose_filter(stbir__info *info, stbir_filter h_filter, stbir_filter v_filter) +{ + if (h_filter == 0) + h_filter = stbir__use_upsampling(info->horizontal_scale) ? STBIR_DEFAULT_FILTER_UPSAMPLE : STBIR_DEFAULT_FILTER_DOWNSAMPLE; + if (v_filter == 0) + v_filter = stbir__use_upsampling(info->vertical_scale) ? STBIR_DEFAULT_FILTER_UPSAMPLE : STBIR_DEFAULT_FILTER_DOWNSAMPLE; + info->horizontal_filter = h_filter; + info->vertical_filter = v_filter; +} + +static stbir_uint32 stbir__calculate_memory(stbir__info *info) +{ + int pixel_margin = stbir__get_filter_pixel_margin(info->horizontal_filter, info->horizontal_scale); + int filter_height = stbir__get_filter_pixel_width(info->vertical_filter, info->vertical_scale); + + info->horizontal_num_contributors = stbir__get_contributors(info->horizontal_scale, info->horizontal_filter, info->input_w, info->output_w); + info->vertical_num_contributors = stbir__get_contributors(info->vertical_scale , info->vertical_filter , info->input_h, info->output_h); + + // One extra entry because floating point precision problems sometimes cause an extra to be necessary. + info->ring_buffer_num_entries = filter_height + 1; + + info->horizontal_contributors_size = info->horizontal_num_contributors * sizeof(stbir__contributors); + info->horizontal_coefficients_size = stbir__get_total_horizontal_coefficients(info) * sizeof(float); + info->vertical_contributors_size = info->vertical_num_contributors * sizeof(stbir__contributors); + info->vertical_coefficients_size = stbir__get_total_vertical_coefficients(info) * sizeof(float); + info->decode_buffer_size = (info->input_w + pixel_margin * 2) * info->channels * sizeof(float); + info->horizontal_buffer_size = info->output_w * info->channels * sizeof(float); + info->ring_buffer_size = info->output_w * info->channels * info->ring_buffer_num_entries * sizeof(float); + info->encode_buffer_size = info->output_w * info->channels * sizeof(float); + + STBIR_ASSERT(info->horizontal_filter != 0); + STBIR_ASSERT(info->horizontal_filter < STBIR__ARRAY_SIZE(stbir__filter_info_table)); // this now happens too late + STBIR_ASSERT(info->vertical_filter != 0); + STBIR_ASSERT(info->vertical_filter < STBIR__ARRAY_SIZE(stbir__filter_info_table)); // this now happens too late + + if (stbir__use_height_upsampling(info)) + // The horizontal buffer is for when we're downsampling the height and we + // can't output the result of sampling the decode buffer directly into the + // ring buffers. + info->horizontal_buffer_size = 0; + else + // The encode buffer is to retain precision in the height upsampling method + // and isn't used when height downsampling. + info->encode_buffer_size = 0; + + return info->horizontal_contributors_size + info->horizontal_coefficients_size + + info->vertical_contributors_size + info->vertical_coefficients_size + + info->decode_buffer_size + info->horizontal_buffer_size + + info->ring_buffer_size + info->encode_buffer_size; +} + +static int stbir__resize_allocated(stbir__info *info, + const void* input_data, int input_stride_in_bytes, + void* output_data, int output_stride_in_bytes, + int alpha_channel, stbir_uint32 flags, stbir_datatype type, + stbir_edge edge_horizontal, stbir_edge edge_vertical, stbir_colorspace colorspace, + void* tempmem, size_t tempmem_size_in_bytes) +{ + size_t memory_required = stbir__calculate_memory(info); + + int width_stride_input = input_stride_in_bytes ? input_stride_in_bytes : info->channels * info->input_w * stbir__type_size[type]; + int width_stride_output = output_stride_in_bytes ? output_stride_in_bytes : info->channels * info->output_w * stbir__type_size[type]; + +#ifdef STBIR_DEBUG_OVERWRITE_TEST +#define OVERWRITE_ARRAY_SIZE 8 + unsigned char overwrite_output_before_pre[OVERWRITE_ARRAY_SIZE]; + unsigned char overwrite_tempmem_before_pre[OVERWRITE_ARRAY_SIZE]; + unsigned char overwrite_output_after_pre[OVERWRITE_ARRAY_SIZE]; + unsigned char overwrite_tempmem_after_pre[OVERWRITE_ARRAY_SIZE]; + + size_t begin_forbidden = width_stride_output * (info->output_h - 1) + info->output_w * info->channels * stbir__type_size[type]; + memcpy(overwrite_output_before_pre, &((unsigned char*)output_data)[-OVERWRITE_ARRAY_SIZE], OVERWRITE_ARRAY_SIZE); + memcpy(overwrite_output_after_pre, &((unsigned char*)output_data)[begin_forbidden], OVERWRITE_ARRAY_SIZE); + memcpy(overwrite_tempmem_before_pre, &((unsigned char*)tempmem)[-OVERWRITE_ARRAY_SIZE], OVERWRITE_ARRAY_SIZE); + memcpy(overwrite_tempmem_after_pre, &((unsigned char*)tempmem)[tempmem_size_in_bytes], OVERWRITE_ARRAY_SIZE); +#endif + + STBIR_ASSERT(info->channels >= 0); + STBIR_ASSERT(info->channels <= STBIR_MAX_CHANNELS); + + if (info->channels < 0 || info->channels > STBIR_MAX_CHANNELS) + return 0; + + STBIR_ASSERT(info->horizontal_filter < STBIR__ARRAY_SIZE(stbir__filter_info_table)); + STBIR_ASSERT(info->vertical_filter < STBIR__ARRAY_SIZE(stbir__filter_info_table)); + + if (info->horizontal_filter >= STBIR__ARRAY_SIZE(stbir__filter_info_table)) + return 0; + if (info->vertical_filter >= STBIR__ARRAY_SIZE(stbir__filter_info_table)) + return 0; + + if (alpha_channel < 0) + flags |= STBIR_FLAG_ALPHA_USES_COLORSPACE | STBIR_FLAG_ALPHA_PREMULTIPLIED; + + if (!(flags&STBIR_FLAG_ALPHA_USES_COLORSPACE) || !(flags&STBIR_FLAG_ALPHA_PREMULTIPLIED)) { + STBIR_ASSERT(alpha_channel >= 0 && alpha_channel < info->channels); + } + + if (alpha_channel >= info->channels) + return 0; + + STBIR_ASSERT(tempmem); + + if (!tempmem) + return 0; + + STBIR_ASSERT(tempmem_size_in_bytes >= memory_required); + + if (tempmem_size_in_bytes < memory_required) + return 0; + + memset(tempmem, 0, tempmem_size_in_bytes); + + info->input_data = input_data; + info->input_stride_bytes = width_stride_input; + + info->output_data = output_data; + info->output_stride_bytes = width_stride_output; + + info->alpha_channel = alpha_channel; + info->flags = flags; + info->type = type; + info->edge_horizontal = edge_horizontal; + info->edge_vertical = edge_vertical; + info->colorspace = colorspace; + + info->horizontal_coefficient_width = stbir__get_coefficient_width (info->horizontal_filter, info->horizontal_scale); + info->vertical_coefficient_width = stbir__get_coefficient_width (info->vertical_filter , info->vertical_scale ); + info->horizontal_filter_pixel_width = stbir__get_filter_pixel_width (info->horizontal_filter, info->horizontal_scale); + info->vertical_filter_pixel_width = stbir__get_filter_pixel_width (info->vertical_filter , info->vertical_scale ); + info->horizontal_filter_pixel_margin = stbir__get_filter_pixel_margin(info->horizontal_filter, info->horizontal_scale); + info->vertical_filter_pixel_margin = stbir__get_filter_pixel_margin(info->vertical_filter , info->vertical_scale ); + + info->ring_buffer_length_bytes = info->output_w * info->channels * sizeof(float); + info->decode_buffer_pixels = info->input_w + info->horizontal_filter_pixel_margin * 2; + +#define STBIR__NEXT_MEMPTR(current, newtype) (newtype*)(((unsigned char*)current) + current##_size) + + info->horizontal_contributors = (stbir__contributors *) tempmem; + info->horizontal_coefficients = STBIR__NEXT_MEMPTR(info->horizontal_contributors, float); + info->vertical_contributors = STBIR__NEXT_MEMPTR(info->horizontal_coefficients, stbir__contributors); + info->vertical_coefficients = STBIR__NEXT_MEMPTR(info->vertical_contributors, float); + info->decode_buffer = STBIR__NEXT_MEMPTR(info->vertical_coefficients, float); + + if (stbir__use_height_upsampling(info)) + { + info->horizontal_buffer = NULL; + info->ring_buffer = STBIR__NEXT_MEMPTR(info->decode_buffer, float); + info->encode_buffer = STBIR__NEXT_MEMPTR(info->ring_buffer, float); + + STBIR_ASSERT((size_t)STBIR__NEXT_MEMPTR(info->encode_buffer, unsigned char) == (size_t)tempmem + tempmem_size_in_bytes); + } + else + { + info->horizontal_buffer = STBIR__NEXT_MEMPTR(info->decode_buffer, float); + info->ring_buffer = STBIR__NEXT_MEMPTR(info->horizontal_buffer, float); + info->encode_buffer = NULL; + + STBIR_ASSERT((size_t)STBIR__NEXT_MEMPTR(info->ring_buffer, unsigned char) == (size_t)tempmem + tempmem_size_in_bytes); + } + +#undef STBIR__NEXT_MEMPTR + + // This signals that the ring buffer is empty + info->ring_buffer_begin_index = -1; + + stbir__calculate_filters(info->horizontal_contributors, info->horizontal_coefficients, info->horizontal_filter, info->horizontal_scale, info->horizontal_shift, info->input_w, info->output_w); + stbir__calculate_filters(info->vertical_contributors, info->vertical_coefficients, info->vertical_filter, info->vertical_scale, info->vertical_shift, info->input_h, info->output_h); + + STBIR_PROGRESS_REPORT(0); + + if (stbir__use_height_upsampling(info)) + stbir__buffer_loop_upsample(info); + else + stbir__buffer_loop_downsample(info); + + STBIR_PROGRESS_REPORT(1); + +#ifdef STBIR_DEBUG_OVERWRITE_TEST + STBIR_ASSERT(memcmp(overwrite_output_before_pre, &((unsigned char*)output_data)[-OVERWRITE_ARRAY_SIZE], OVERWRITE_ARRAY_SIZE) == 0); + STBIR_ASSERT(memcmp(overwrite_output_after_pre, &((unsigned char*)output_data)[begin_forbidden], OVERWRITE_ARRAY_SIZE) == 0); + STBIR_ASSERT(memcmp(overwrite_tempmem_before_pre, &((unsigned char*)tempmem)[-OVERWRITE_ARRAY_SIZE], OVERWRITE_ARRAY_SIZE) == 0); + STBIR_ASSERT(memcmp(overwrite_tempmem_after_pre, &((unsigned char*)tempmem)[tempmem_size_in_bytes], OVERWRITE_ARRAY_SIZE) == 0); +#endif + + return 1; +} + + +static int stbir__resize_arbitrary( + void *alloc_context, + const void* input_data, int input_w, int input_h, int input_stride_in_bytes, + void* output_data, int output_w, int output_h, int output_stride_in_bytes, + float s0, float t0, float s1, float t1, float *transform, + int channels, int alpha_channel, stbir_uint32 flags, stbir_datatype type, + stbir_filter h_filter, stbir_filter v_filter, + stbir_edge edge_horizontal, stbir_edge edge_vertical, stbir_colorspace colorspace) +{ + stbir__info info; + int result; + size_t memory_required; + void* extra_memory; + + stbir__setup(&info, input_w, input_h, output_w, output_h, channels); + stbir__calculate_transform(&info, s0,t0,s1,t1,transform); + stbir__choose_filter(&info, h_filter, v_filter); + memory_required = stbir__calculate_memory(&info); + extra_memory = STBIR_MALLOC(memory_required, alloc_context); + + if (!extra_memory) + return 0; + + result = stbir__resize_allocated(&info, input_data, input_stride_in_bytes, + output_data, output_stride_in_bytes, + alpha_channel, flags, type, + edge_horizontal, edge_vertical, + colorspace, extra_memory, memory_required); + + STBIR_FREE(extra_memory, alloc_context); + + return result; +} + +STBIRDEF int stbir_resize_uint8( const unsigned char *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + unsigned char *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + int num_channels) +{ + return stbir__resize_arbitrary(NULL, input_pixels, input_w, input_h, input_stride_in_bytes, + output_pixels, output_w, output_h, output_stride_in_bytes, + 0,0,1,1,NULL,num_channels,-1,0, STBIR_TYPE_UINT8, STBIR_FILTER_DEFAULT, STBIR_FILTER_DEFAULT, + STBIR_EDGE_CLAMP, STBIR_EDGE_CLAMP, STBIR_COLORSPACE_LINEAR); +} + +STBIRDEF int stbir_resize_float( const float *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + float *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + int num_channels) +{ + return stbir__resize_arbitrary(NULL, input_pixels, input_w, input_h, input_stride_in_bytes, + output_pixels, output_w, output_h, output_stride_in_bytes, + 0,0,1,1,NULL,num_channels,-1,0, STBIR_TYPE_FLOAT, STBIR_FILTER_DEFAULT, STBIR_FILTER_DEFAULT, + STBIR_EDGE_CLAMP, STBIR_EDGE_CLAMP, STBIR_COLORSPACE_LINEAR); +} + +STBIRDEF int stbir_resize_uint8_srgb(const unsigned char *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + unsigned char *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + int num_channels, int alpha_channel, int flags) +{ + return stbir__resize_arbitrary(NULL, input_pixels, input_w, input_h, input_stride_in_bytes, + output_pixels, output_w, output_h, output_stride_in_bytes, + 0,0,1,1,NULL,num_channels,alpha_channel,flags, STBIR_TYPE_UINT8, STBIR_FILTER_DEFAULT, STBIR_FILTER_DEFAULT, + STBIR_EDGE_CLAMP, STBIR_EDGE_CLAMP, STBIR_COLORSPACE_SRGB); +} + +STBIRDEF int stbir_resize_uint8_srgb_edgemode(const unsigned char *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + unsigned char *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + int num_channels, int alpha_channel, int flags, + stbir_edge edge_wrap_mode) +{ + return stbir__resize_arbitrary(NULL, input_pixels, input_w, input_h, input_stride_in_bytes, + output_pixels, output_w, output_h, output_stride_in_bytes, + 0,0,1,1,NULL,num_channels,alpha_channel,flags, STBIR_TYPE_UINT8, STBIR_FILTER_DEFAULT, STBIR_FILTER_DEFAULT, + edge_wrap_mode, edge_wrap_mode, STBIR_COLORSPACE_SRGB); +} + +STBIRDEF int stbir_resize_uint8_generic( const unsigned char *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + unsigned char *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + int num_channels, int alpha_channel, int flags, + stbir_edge edge_wrap_mode, stbir_filter filter, stbir_colorspace space, + void *alloc_context) +{ + return stbir__resize_arbitrary(alloc_context, input_pixels, input_w, input_h, input_stride_in_bytes, + output_pixels, output_w, output_h, output_stride_in_bytes, + 0,0,1,1,NULL,num_channels,alpha_channel,flags, STBIR_TYPE_UINT8, filter, filter, + edge_wrap_mode, edge_wrap_mode, space); +} + +STBIRDEF int stbir_resize_uint16_generic(const stbir_uint16 *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + stbir_uint16 *output_pixels , int output_w, int output_h, int output_stride_in_bytes, + int num_channels, int alpha_channel, int flags, + stbir_edge edge_wrap_mode, stbir_filter filter, stbir_colorspace space, + void *alloc_context) +{ + return stbir__resize_arbitrary(alloc_context, input_pixels, input_w, input_h, input_stride_in_bytes, + output_pixels, output_w, output_h, output_stride_in_bytes, + 0,0,1,1,NULL,num_channels,alpha_channel,flags, STBIR_TYPE_UINT16, filter, filter, + edge_wrap_mode, edge_wrap_mode, space); +} + + +STBIRDEF int stbir_resize_float_generic( const float *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + float *output_pixels , int output_w, int output_h, int output_stride_in_bytes, + int num_channels, int alpha_channel, int flags, + stbir_edge edge_wrap_mode, stbir_filter filter, stbir_colorspace space, + void *alloc_context) +{ + return stbir__resize_arbitrary(alloc_context, input_pixels, input_w, input_h, input_stride_in_bytes, + output_pixels, output_w, output_h, output_stride_in_bytes, + 0,0,1,1,NULL,num_channels,alpha_channel,flags, STBIR_TYPE_FLOAT, filter, filter, + edge_wrap_mode, edge_wrap_mode, space); +} + + +STBIRDEF int stbir_resize( const void *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + void *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + stbir_datatype datatype, + int num_channels, int alpha_channel, int flags, + stbir_edge edge_mode_horizontal, stbir_edge edge_mode_vertical, + stbir_filter filter_horizontal, stbir_filter filter_vertical, + stbir_colorspace space, void *alloc_context) +{ + return stbir__resize_arbitrary(alloc_context, input_pixels, input_w, input_h, input_stride_in_bytes, + output_pixels, output_w, output_h, output_stride_in_bytes, + 0,0,1,1,NULL,num_channels,alpha_channel,flags, datatype, filter_horizontal, filter_vertical, + edge_mode_horizontal, edge_mode_vertical, space); +} + + +STBIRDEF int stbir_resize_subpixel(const void *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + void *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + stbir_datatype datatype, + int num_channels, int alpha_channel, int flags, + stbir_edge edge_mode_horizontal, stbir_edge edge_mode_vertical, + stbir_filter filter_horizontal, stbir_filter filter_vertical, + stbir_colorspace space, void *alloc_context, + float x_scale, float y_scale, + float x_offset, float y_offset) +{ + float transform[4]; + transform[0] = x_scale; + transform[1] = y_scale; + transform[2] = x_offset; + transform[3] = y_offset; + return stbir__resize_arbitrary(alloc_context, input_pixels, input_w, input_h, input_stride_in_bytes, + output_pixels, output_w, output_h, output_stride_in_bytes, + 0,0,1,1,transform,num_channels,alpha_channel,flags, datatype, filter_horizontal, filter_vertical, + edge_mode_horizontal, edge_mode_vertical, space); +} + +STBIRDEF int stbir_resize_region( const void *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + void *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + stbir_datatype datatype, + int num_channels, int alpha_channel, int flags, + stbir_edge edge_mode_horizontal, stbir_edge edge_mode_vertical, + stbir_filter filter_horizontal, stbir_filter filter_vertical, + stbir_colorspace space, void *alloc_context, + float s0, float t0, float s1, float t1) +{ + return stbir__resize_arbitrary(alloc_context, input_pixels, input_w, input_h, input_stride_in_bytes, + output_pixels, output_w, output_h, output_stride_in_bytes, + s0,t0,s1,t1,NULL,num_channels,alpha_channel,flags, datatype, filter_horizontal, filter_vertical, + edge_mode_horizontal, edge_mode_vertical, space); +} + +#endif // STB_IMAGE_RESIZE_IMPLEMENTATION + +/* +------------------------------------------------------------------------------ +This software is available under 2 licenses -- choose whichever you prefer. +------------------------------------------------------------------------------ +ALTERNATIVE A - MIT License +Copyright (c) 2017 Sean Barrett +Permission is hereby granted, free of charge, to any person obtaining a copy of +this software and associated documentation files (the "Software"), to deal in +the Software without restriction, including without limitation the rights to +use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies +of the Software, and to permit persons to whom the Software is furnished to do +so, subject to the following conditions: +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +------------------------------------------------------------------------------ +ALTERNATIVE B - Public Domain (www.unlicense.org) +This is free and unencumbered software released into the public domain. +Anyone is free to copy, modify, publish, use, compile, sell, or distribute this +software, either in source code form or as a compiled binary, for any purpose, +commercial or non-commercial, and by any means. +In jurisdictions that recognize copyright laws, the author or authors of this +software dedicate any and all copyright interest in the software to the public +domain. We make this dedication for the benefit of the public at large and to +the detriment of our heirs and successors. We intend this dedication to be an +overt act of relinquishment in perpetuity of all present and future rights to +this software under copyright law. +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN +ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION +WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +------------------------------------------------------------------------------ +*/ diff --git a/rknpu2/examples/3rdparty/stb/stb_image_write.h b/rknpu2/examples/3rdparty/stb/stb_image_write.h new file mode 100644 index 0000000000000000000000000000000000000000..c117344e7f7c2a112edcfe7abc217f0969e5baa7 --- /dev/null +++ b/rknpu2/examples/3rdparty/stb/stb_image_write.h @@ -0,0 +1,1619 @@ +/* stb_image_write - v1.13 - public domain - http://nothings.org/stb + writes out PNG/BMP/TGA/JPEG/HDR images to C stdio - Sean Barrett 2010-2015 + no warranty implied; use at your own risk + + Before #including, + + #define STB_IMAGE_WRITE_IMPLEMENTATION + + in the file that you want to have the implementation. + + Will probably not work correctly with strict-aliasing optimizations. + +ABOUT: + + This header file is a library for writing images to C stdio or a callback. + + The PNG output is not optimal; it is 20-50% larger than the file + written by a decent optimizing implementation; though providing a custom + zlib compress function (see STBIW_ZLIB_COMPRESS) can mitigate that. + This library is designed for source code compactness and simplicity, + not optimal image file size or run-time performance. + +BUILDING: + + You can #define STBIW_ASSERT(x) before the #include to avoid using assert.h. + You can #define STBIW_MALLOC(), STBIW_REALLOC(), and STBIW_FREE() to replace + malloc,realloc,free. + You can #define STBIW_MEMMOVE() to replace memmove() + You can #define STBIW_ZLIB_COMPRESS to use a custom zlib-style compress function + for PNG compression (instead of the builtin one), it must have the following signature: + unsigned char * my_compress(unsigned char *data, int data_len, int *out_len, int quality); + The returned data will be freed with STBIW_FREE() (free() by default), + so it must be heap allocated with STBIW_MALLOC() (malloc() by default), + +UNICODE: + + If compiling for Windows and you wish to use Unicode filenames, compile + with + #define STBIW_WINDOWS_UTF8 + and pass utf8-encoded filenames. Call stbiw_convert_wchar_to_utf8 to convert + Windows wchar_t filenames to utf8. + +USAGE: + + There are five functions, one for each image file format: + + int stbi_write_png(char const *filename, int w, int h, int comp, const void *data, int stride_in_bytes); + int stbi_write_bmp(char const *filename, int w, int h, int comp, const void *data); + int stbi_write_tga(char const *filename, int w, int h, int comp, const void *data); + int stbi_write_jpg(char const *filename, int w, int h, int comp, const void *data, int quality); + int stbi_write_hdr(char const *filename, int w, int h, int comp, const float *data); + + void stbi_flip_vertically_on_write(int flag); // flag is non-zero to flip data vertically + + There are also five equivalent functions that use an arbitrary write function. You are + expected to open/close your file-equivalent before and after calling these: + + int stbi_write_png_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void *data, int stride_in_bytes); + int stbi_write_bmp_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void *data); + int stbi_write_tga_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void *data); + int stbi_write_hdr_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const float *data); + int stbi_write_jpg_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data, int quality); + + where the callback is: + void stbi_write_func(void *context, void *data, int size); + + You can configure it with these global variables: + int stbi_write_tga_with_rle; // defaults to true; set to 0 to disable RLE + int stbi_write_png_compression_level; // defaults to 8; set to higher for more compression + int stbi_write_force_png_filter; // defaults to -1; set to 0..5 to force a filter mode + + + You can define STBI_WRITE_NO_STDIO to disable the file variant of these + functions, so the library will not use stdio.h at all. However, this will + also disable HDR writing, because it requires stdio for formatted output. + + Each function returns 0 on failure and non-0 on success. + + The functions create an image file defined by the parameters. The image + is a rectangle of pixels stored from left-to-right, top-to-bottom. + Each pixel contains 'comp' channels of data stored interleaved with 8-bits + per channel, in the following order: 1=Y, 2=YA, 3=RGB, 4=RGBA. (Y is + monochrome color.) The rectangle is 'w' pixels wide and 'h' pixels tall. + The *data pointer points to the first byte of the top-left-most pixel. + For PNG, "stride_in_bytes" is the distance in bytes from the first byte of + a row of pixels to the first byte of the next row of pixels. + + PNG creates output files with the same number of components as the input. + The BMP format expands Y to RGB in the file format and does not + output alpha. + + PNG supports writing rectangles of data even when the bytes storing rows of + data are not consecutive in memory (e.g. sub-rectangles of a larger image), + by supplying the stride between the beginning of adjacent rows. The other + formats do not. (Thus you cannot write a native-format BMP through the BMP + writer, both because it is in BGR order and because it may have padding + at the end of the line.) + + PNG allows you to set the deflate compression level by setting the global + variable 'stbi_write_png_compression_level' (it defaults to 8). + + HDR expects linear float data. Since the format is always 32-bit rgb(e) + data, alpha (if provided) is discarded, and for monochrome data it is + replicated across all three channels. + + TGA supports RLE or non-RLE compressed data. To use non-RLE-compressed + data, set the global variable 'stbi_write_tga_with_rle' to 0. + + JPEG does ignore alpha channels in input data; quality is between 1 and 100. + Higher quality looks better but results in a bigger image. + JPEG baseline (no JPEG progressive). + +CREDITS: + + + Sean Barrett - PNG/BMP/TGA + Baldur Karlsson - HDR + Jean-Sebastien Guay - TGA monochrome + Tim Kelsey - misc enhancements + Alan Hickman - TGA RLE + Emmanuel Julien - initial file IO callback implementation + Jon Olick - original jo_jpeg.cpp code + Daniel Gibson - integrate JPEG, allow external zlib + Aarni Koskela - allow choosing PNG filter + + bugfixes: + github:Chribba + Guillaume Chereau + github:jry2 + github:romigrou + Sergio Gonzalez + Jonas Karlsson + Filip Wasil + Thatcher Ulrich + github:poppolopoppo + Patrick Boettcher + github:xeekworx + Cap Petschulat + Simon Rodriguez + Ivan Tikhonov + github:ignotion + Adam Schackart + +LICENSE + + See end of file for license information. + +*/ + +#ifndef INCLUDE_STB_IMAGE_WRITE_H +#define INCLUDE_STB_IMAGE_WRITE_H + +#include + +// if STB_IMAGE_WRITE_STATIC causes problems, try defining STBIWDEF to 'inline' or 'static inline' +#ifndef STBIWDEF +#ifdef STB_IMAGE_WRITE_STATIC +#define STBIWDEF static +#else +#ifdef __cplusplus +#define STBIWDEF extern "C" +#else +#define STBIWDEF extern +#endif +#endif +#endif + +#ifndef STB_IMAGE_WRITE_STATIC // C++ forbids static forward declarations +extern int stbi_write_tga_with_rle; +extern int stbi_write_png_compression_level; +extern int stbi_write_force_png_filter; +#endif + +#ifndef STBI_WRITE_NO_STDIO +STBIWDEF int stbi_write_png(char const *filename, int w, int h, int comp, const void *data, int stride_in_bytes); +STBIWDEF int stbi_write_bmp(char const *filename, int w, int h, int comp, const void *data); +STBIWDEF int stbi_write_tga(char const *filename, int w, int h, int comp, const void *data); +STBIWDEF int stbi_write_hdr(char const *filename, int w, int h, int comp, const float *data); +STBIWDEF int stbi_write_jpg(char const *filename, int x, int y, int comp, const void *data, int quality); + +#ifdef STBI_WINDOWS_UTF8 +STBIWDEF int stbiw_convert_wchar_to_utf8(char *buffer, size_t bufferlen, const wchar_t* input); +#endif +#endif + +typedef void stbi_write_func(void *context, void *data, int size); + +STBIWDEF int stbi_write_png_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void *data, int stride_in_bytes); +STBIWDEF int stbi_write_bmp_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void *data); +STBIWDEF int stbi_write_tga_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void *data); +STBIWDEF int stbi_write_hdr_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const float *data); +STBIWDEF int stbi_write_jpg_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data, int quality); + +STBIWDEF void stbi_flip_vertically_on_write(int flip_boolean); + +#endif//INCLUDE_STB_IMAGE_WRITE_H + +#ifdef STB_IMAGE_WRITE_IMPLEMENTATION + +#ifdef _WIN32 + #ifndef _CRT_SECURE_NO_WARNINGS + #define _CRT_SECURE_NO_WARNINGS + #endif + #ifndef _CRT_NONSTDC_NO_DEPRECATE + #define _CRT_NONSTDC_NO_DEPRECATE + #endif +#endif + +#ifndef STBI_WRITE_NO_STDIO +#include +#endif // STBI_WRITE_NO_STDIO + +#include +#include +#include +#include + +#if defined(STBIW_MALLOC) && defined(STBIW_FREE) && (defined(STBIW_REALLOC) || defined(STBIW_REALLOC_SIZED)) +// ok +#elif !defined(STBIW_MALLOC) && !defined(STBIW_FREE) && !defined(STBIW_REALLOC) && !defined(STBIW_REALLOC_SIZED) +// ok +#else +#error "Must define all or none of STBIW_MALLOC, STBIW_FREE, and STBIW_REALLOC (or STBIW_REALLOC_SIZED)." +#endif + +#ifndef STBIW_MALLOC +#define STBIW_MALLOC(sz) malloc(sz) +#define STBIW_REALLOC(p,newsz) realloc(p,newsz) +#define STBIW_FREE(p) free(p) +#endif + +#ifndef STBIW_REALLOC_SIZED +#define STBIW_REALLOC_SIZED(p,oldsz,newsz) STBIW_REALLOC(p,newsz) +#endif + + +#ifndef STBIW_MEMMOVE +#define STBIW_MEMMOVE(a,b,sz) memmove(a,b,sz) +#endif + + +#ifndef STBIW_ASSERT +#include +#define STBIW_ASSERT(x) assert(x) +#endif + +#define STBIW_UCHAR(x) (unsigned char) ((x) & 0xff) + +#ifdef STB_IMAGE_WRITE_STATIC +static int stbi__flip_vertically_on_write=0; +static int stbi_write_png_compression_level = 8; +static int stbi_write_tga_with_rle = 1; +static int stbi_write_force_png_filter = -1; +#else +int stbi_write_png_compression_level = 8; +int stbi__flip_vertically_on_write=0; +int stbi_write_tga_with_rle = 1; +int stbi_write_force_png_filter = -1; +#endif + +STBIWDEF void stbi_flip_vertically_on_write(int flag) +{ + stbi__flip_vertically_on_write = flag; +} + +typedef struct +{ + stbi_write_func *func; + void *context; +} stbi__write_context; + +// initialize a callback-based context +static void stbi__start_write_callbacks(stbi__write_context *s, stbi_write_func *c, void *context) +{ + s->func = c; + s->context = context; +} + +#ifndef STBI_WRITE_NO_STDIO + +static void stbi__stdio_write(void *context, void *data, int size) +{ + fwrite(data,1,size,(FILE*) context); +} + +#if defined(_MSC_VER) && defined(STBI_WINDOWS_UTF8) +#ifdef __cplusplus +#define STBIW_EXTERN extern "C" +#else +#define STBIW_EXTERN extern +#endif +STBIW_EXTERN __declspec(dllimport) int __stdcall MultiByteToWideChar(unsigned int cp, unsigned long flags, const char *str, int cbmb, wchar_t *widestr, int cchwide); +STBIW_EXTERN __declspec(dllimport) int __stdcall WideCharToMultiByte(unsigned int cp, unsigned long flags, const wchar_t *widestr, int cchwide, char *str, int cbmb, const char *defchar, int *used_default); + +STBIWDEF int stbiw_convert_wchar_to_utf8(char *buffer, size_t bufferlen, const wchar_t* input) +{ + return WideCharToMultiByte(65001 /* UTF8 */, 0, input, -1, buffer, (int) bufferlen, NULL, NULL); +} +#endif + +static FILE *stbiw__fopen(char const *filename, char const *mode) +{ + FILE *f; +#if defined(_MSC_VER) && defined(STBI_WINDOWS_UTF8) + wchar_t wMode[64]; + wchar_t wFilename[1024]; + if (0 == MultiByteToWideChar(65001 /* UTF8 */, 0, filename, -1, wFilename, sizeof(wFilename))) + return 0; + + if (0 == MultiByteToWideChar(65001 /* UTF8 */, 0, mode, -1, wMode, sizeof(wMode))) + return 0; + +#if _MSC_VER >= 1400 + if (0 != _wfopen_s(&f, wFilename, wMode)) + f = 0; +#else + f = _wfopen(wFilename, wMode); +#endif + +#elif defined(_MSC_VER) && _MSC_VER >= 1400 + if (0 != fopen_s(&f, filename, mode)) + f=0; +#else + f = fopen(filename, mode); +#endif + return f; +} + +static int stbi__start_write_file(stbi__write_context *s, const char *filename) +{ + FILE *f = stbiw__fopen(filename, "wb"); + stbi__start_write_callbacks(s, stbi__stdio_write, (void *) f); + return f != NULL; +} + +static void stbi__end_write_file(stbi__write_context *s) +{ + fclose((FILE *)s->context); +} + +#endif // !STBI_WRITE_NO_STDIO + +typedef unsigned int stbiw_uint32; +typedef int stb_image_write_test[sizeof(stbiw_uint32)==4 ? 1 : -1]; + +static void stbiw__writefv(stbi__write_context *s, const char *fmt, va_list v) +{ + while (*fmt) { + switch (*fmt++) { + case ' ': break; + case '1': { unsigned char x = STBIW_UCHAR(va_arg(v, int)); + s->func(s->context,&x,1); + break; } + case '2': { int x = va_arg(v,int); + unsigned char b[2]; + b[0] = STBIW_UCHAR(x); + b[1] = STBIW_UCHAR(x>>8); + s->func(s->context,b,2); + break; } + case '4': { stbiw_uint32 x = va_arg(v,int); + unsigned char b[4]; + b[0]=STBIW_UCHAR(x); + b[1]=STBIW_UCHAR(x>>8); + b[2]=STBIW_UCHAR(x>>16); + b[3]=STBIW_UCHAR(x>>24); + s->func(s->context,b,4); + break; } + default: + STBIW_ASSERT(0); + return; + } + } +} + +static void stbiw__writef(stbi__write_context *s, const char *fmt, ...) +{ + va_list v; + va_start(v, fmt); + stbiw__writefv(s, fmt, v); + va_end(v); +} + +static void stbiw__putc(stbi__write_context *s, unsigned char c) +{ + s->func(s->context, &c, 1); +} + +static void stbiw__write3(stbi__write_context *s, unsigned char a, unsigned char b, unsigned char c) +{ + unsigned char arr[3]; + arr[0] = a; arr[1] = b; arr[2] = c; + s->func(s->context, arr, 3); +} + +static void stbiw__write_pixel(stbi__write_context *s, int rgb_dir, int comp, int write_alpha, int expand_mono, unsigned char *d) +{ + unsigned char bg[3] = { 255, 0, 255}, px[3]; + int k; + + if (write_alpha < 0) + s->func(s->context, &d[comp - 1], 1); + + switch (comp) { + case 2: // 2 pixels = mono + alpha, alpha is written separately, so same as 1-channel case + case 1: + if (expand_mono) + stbiw__write3(s, d[0], d[0], d[0]); // monochrome bmp + else + s->func(s->context, d, 1); // monochrome TGA + break; + case 4: + if (!write_alpha) { + // composite against pink background + for (k = 0; k < 3; ++k) + px[k] = bg[k] + ((d[k] - bg[k]) * d[3]) / 255; + stbiw__write3(s, px[1 - rgb_dir], px[1], px[1 + rgb_dir]); + break; + } + /* FALLTHROUGH */ + case 3: + stbiw__write3(s, d[1 - rgb_dir], d[1], d[1 + rgb_dir]); + break; + } + if (write_alpha > 0) + s->func(s->context, &d[comp - 1], 1); +} + +static void stbiw__write_pixels(stbi__write_context *s, int rgb_dir, int vdir, int x, int y, int comp, void *data, int write_alpha, int scanline_pad, int expand_mono) +{ + stbiw_uint32 zero = 0; + int i,j, j_end; + + if (y <= 0) + return; + + if (stbi__flip_vertically_on_write) + vdir *= -1; + + if (vdir < 0) { + j_end = -1; j = y-1; + } else { + j_end = y; j = 0; + } + + for (; j != j_end; j += vdir) { + for (i=0; i < x; ++i) { + unsigned char *d = (unsigned char *) data + (j*x+i)*comp; + stbiw__write_pixel(s, rgb_dir, comp, write_alpha, expand_mono, d); + } + s->func(s->context, &zero, scanline_pad); + } +} + +static int stbiw__outfile(stbi__write_context *s, int rgb_dir, int vdir, int x, int y, int comp, int expand_mono, void *data, int alpha, int pad, const char *fmt, ...) +{ + if (y < 0 || x < 0) { + return 0; + } else { + va_list v; + va_start(v, fmt); + stbiw__writefv(s, fmt, v); + va_end(v); + stbiw__write_pixels(s,rgb_dir,vdir,x,y,comp,data,alpha,pad, expand_mono); + return 1; + } +} + +static int stbi_write_bmp_core(stbi__write_context *s, int x, int y, int comp, const void *data) +{ + int pad = (-x*3) & 3; + return stbiw__outfile(s,-1,-1,x,y,comp,1,(void *) data,0,pad, + "11 4 22 4" "4 44 22 444444", + 'B', 'M', 14+40+(x*3+pad)*y, 0,0, 14+40, // file header + 40, x,y, 1,24, 0,0,0,0,0,0); // bitmap header +} + +STBIWDEF int stbi_write_bmp_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data) +{ + stbi__write_context s; + stbi__start_write_callbacks(&s, func, context); + return stbi_write_bmp_core(&s, x, y, comp, data); +} + +#ifndef STBI_WRITE_NO_STDIO +STBIWDEF int stbi_write_bmp(char const *filename, int x, int y, int comp, const void *data) +{ + stbi__write_context s; + if (stbi__start_write_file(&s,filename)) { + int r = stbi_write_bmp_core(&s, x, y, comp, data); + stbi__end_write_file(&s); + return r; + } else + return 0; +} +#endif //!STBI_WRITE_NO_STDIO + +static int stbi_write_tga_core(stbi__write_context *s, int x, int y, int comp, void *data) +{ + int has_alpha = (comp == 2 || comp == 4); + int colorbytes = has_alpha ? comp-1 : comp; + int format = colorbytes < 2 ? 3 : 2; // 3 color channels (RGB/RGBA) = 2, 1 color channel (Y/YA) = 3 + + if (y < 0 || x < 0) + return 0; + + if (!stbi_write_tga_with_rle) { + return stbiw__outfile(s, -1, -1, x, y, comp, 0, (void *) data, has_alpha, 0, + "111 221 2222 11", 0, 0, format, 0, 0, 0, 0, 0, x, y, (colorbytes + has_alpha) * 8, has_alpha * 8); + } else { + int i,j,k; + int jend, jdir; + + stbiw__writef(s, "111 221 2222 11", 0,0,format+8, 0,0,0, 0,0,x,y, (colorbytes + has_alpha) * 8, has_alpha * 8); + + if (stbi__flip_vertically_on_write) { + j = 0; + jend = y; + jdir = 1; + } else { + j = y-1; + jend = -1; + jdir = -1; + } + for (; j != jend; j += jdir) { + unsigned char *row = (unsigned char *) data + j * x * comp; + int len; + + for (i = 0; i < x; i += len) { + unsigned char *begin = row + i * comp; + int diff = 1; + len = 1; + + if (i < x - 1) { + ++len; + diff = memcmp(begin, row + (i + 1) * comp, comp); + if (diff) { + const unsigned char *prev = begin; + for (k = i + 2; k < x && len < 128; ++k) { + if (memcmp(prev, row + k * comp, comp)) { + prev += comp; + ++len; + } else { + --len; + break; + } + } + } else { + for (k = i + 2; k < x && len < 128; ++k) { + if (!memcmp(begin, row + k * comp, comp)) { + ++len; + } else { + break; + } + } + } + } + + if (diff) { + unsigned char header = STBIW_UCHAR(len - 1); + s->func(s->context, &header, 1); + for (k = 0; k < len; ++k) { + stbiw__write_pixel(s, -1, comp, has_alpha, 0, begin + k * comp); + } + } else { + unsigned char header = STBIW_UCHAR(len - 129); + s->func(s->context, &header, 1); + stbiw__write_pixel(s, -1, comp, has_alpha, 0, begin); + } + } + } + } + return 1; +} + +STBIWDEF int stbi_write_tga_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data) +{ + stbi__write_context s; + stbi__start_write_callbacks(&s, func, context); + return stbi_write_tga_core(&s, x, y, comp, (void *) data); +} + +#ifndef STBI_WRITE_NO_STDIO +STBIWDEF int stbi_write_tga(char const *filename, int x, int y, int comp, const void *data) +{ + stbi__write_context s; + if (stbi__start_write_file(&s,filename)) { + int r = stbi_write_tga_core(&s, x, y, comp, (void *) data); + stbi__end_write_file(&s); + return r; + } else + return 0; +} +#endif + +// ************************************************************************************************* +// Radiance RGBE HDR writer +// by Baldur Karlsson + +#define stbiw__max(a, b) ((a) > (b) ? (a) : (b)) + +static void stbiw__linear_to_rgbe(unsigned char *rgbe, float *linear) +{ + int exponent; + float maxcomp = stbiw__max(linear[0], stbiw__max(linear[1], linear[2])); + + if (maxcomp < 1e-32f) { + rgbe[0] = rgbe[1] = rgbe[2] = rgbe[3] = 0; + } else { + float normalize = (float) frexp(maxcomp, &exponent) * 256.0f/maxcomp; + + rgbe[0] = (unsigned char)(linear[0] * normalize); + rgbe[1] = (unsigned char)(linear[1] * normalize); + rgbe[2] = (unsigned char)(linear[2] * normalize); + rgbe[3] = (unsigned char)(exponent + 128); + } +} + +static void stbiw__write_run_data(stbi__write_context *s, int length, unsigned char databyte) +{ + unsigned char lengthbyte = STBIW_UCHAR(length+128); + STBIW_ASSERT(length+128 <= 255); + s->func(s->context, &lengthbyte, 1); + s->func(s->context, &databyte, 1); +} + +static void stbiw__write_dump_data(stbi__write_context *s, int length, unsigned char *data) +{ + unsigned char lengthbyte = STBIW_UCHAR(length); + STBIW_ASSERT(length <= 128); // inconsistent with spec but consistent with official code + s->func(s->context, &lengthbyte, 1); + s->func(s->context, data, length); +} + +static void stbiw__write_hdr_scanline(stbi__write_context *s, int width, int ncomp, unsigned char *scratch, float *scanline) +{ + unsigned char scanlineheader[4] = { 2, 2, 0, 0 }; + unsigned char rgbe[4]; + float linear[3]; + int x; + + scanlineheader[2] = (width&0xff00)>>8; + scanlineheader[3] = (width&0x00ff); + + /* skip RLE for images too small or large */ + if (width < 8 || width >= 32768) { + for (x=0; x < width; x++) { + switch (ncomp) { + case 4: /* fallthrough */ + case 3: linear[2] = scanline[x*ncomp + 2]; + linear[1] = scanline[x*ncomp + 1]; + linear[0] = scanline[x*ncomp + 0]; + break; + default: + linear[0] = linear[1] = linear[2] = scanline[x*ncomp + 0]; + break; + } + stbiw__linear_to_rgbe(rgbe, linear); + s->func(s->context, rgbe, 4); + } + } else { + int c,r; + /* encode into scratch buffer */ + for (x=0; x < width; x++) { + switch(ncomp) { + case 4: /* fallthrough */ + case 3: linear[2] = scanline[x*ncomp + 2]; + linear[1] = scanline[x*ncomp + 1]; + linear[0] = scanline[x*ncomp + 0]; + break; + default: + linear[0] = linear[1] = linear[2] = scanline[x*ncomp + 0]; + break; + } + stbiw__linear_to_rgbe(rgbe, linear); + scratch[x + width*0] = rgbe[0]; + scratch[x + width*1] = rgbe[1]; + scratch[x + width*2] = rgbe[2]; + scratch[x + width*3] = rgbe[3]; + } + + s->func(s->context, scanlineheader, 4); + + /* RLE each component separately */ + for (c=0; c < 4; c++) { + unsigned char *comp = &scratch[width*c]; + + x = 0; + while (x < width) { + // find first run + r = x; + while (r+2 < width) { + if (comp[r] == comp[r+1] && comp[r] == comp[r+2]) + break; + ++r; + } + if (r+2 >= width) + r = width; + // dump up to first run + while (x < r) { + int len = r-x; + if (len > 128) len = 128; + stbiw__write_dump_data(s, len, &comp[x]); + x += len; + } + // if there's a run, output it + if (r+2 < width) { // same test as what we break out of in search loop, so only true if we break'd + // find next byte after run + while (r < width && comp[r] == comp[x]) + ++r; + // output run up to r + while (x < r) { + int len = r-x; + if (len > 127) len = 127; + stbiw__write_run_data(s, len, comp[x]); + x += len; + } + } + } + } + } +} + +static int stbi_write_hdr_core(stbi__write_context *s, int x, int y, int comp, float *data) +{ + if (y <= 0 || x <= 0 || data == NULL) + return 0; + else { + // Each component is stored separately. Allocate scratch space for full output scanline. + unsigned char *scratch = (unsigned char *) STBIW_MALLOC(x*4); + int i, len; + char buffer[128]; + char header[] = "#?RADIANCE\n# Written by stb_image_write.h\nFORMAT=32-bit_rle_rgbe\n"; + s->func(s->context, header, sizeof(header)-1); + +#ifdef __STDC_WANT_SECURE_LIB__ + len = sprintf_s(buffer, sizeof(buffer), "EXPOSURE= 1.0000000000000\n\n-Y %d +X %d\n", y, x); +#else + len = snprintf(buffer, 128, "EXPOSURE= 1.0000000000000\n\n-Y %d +X %d\n", y, x); +#endif + s->func(s->context, buffer, len); + + for(i=0; i < y; i++) + stbiw__write_hdr_scanline(s, x, comp, scratch, data + comp*x*(stbi__flip_vertically_on_write ? y-1-i : i)); + STBIW_FREE(scratch); + return 1; + } +} + +STBIWDEF int stbi_write_hdr_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const float *data) +{ + stbi__write_context s; + stbi__start_write_callbacks(&s, func, context); + return stbi_write_hdr_core(&s, x, y, comp, (float *) data); +} + +#ifndef STBI_WRITE_NO_STDIO +STBIWDEF int stbi_write_hdr(char const *filename, int x, int y, int comp, const float *data) +{ + stbi__write_context s; + if (stbi__start_write_file(&s,filename)) { + int r = stbi_write_hdr_core(&s, x, y, comp, (float *) data); + stbi__end_write_file(&s); + return r; + } else + return 0; +} +#endif // STBI_WRITE_NO_STDIO + + +////////////////////////////////////////////////////////////////////////////// +// +// PNG writer +// + +#ifndef STBIW_ZLIB_COMPRESS +// stretchy buffer; stbiw__sbpush() == vector<>::push_back() -- stbiw__sbcount() == vector<>::size() +#define stbiw__sbraw(a) ((int *) (a) - 2) +#define stbiw__sbm(a) stbiw__sbraw(a)[0] +#define stbiw__sbn(a) stbiw__sbraw(a)[1] + +#define stbiw__sbneedgrow(a,n) ((a)==0 || stbiw__sbn(a)+n >= stbiw__sbm(a)) +#define stbiw__sbmaybegrow(a,n) (stbiw__sbneedgrow(a,(n)) ? stbiw__sbgrow(a,n) : 0) +#define stbiw__sbgrow(a,n) stbiw__sbgrowf((void **) &(a), (n), sizeof(*(a))) + +#define stbiw__sbpush(a, v) (stbiw__sbmaybegrow(a,1), (a)[stbiw__sbn(a)++] = (v)) +#define stbiw__sbcount(a) ((a) ? stbiw__sbn(a) : 0) +#define stbiw__sbfree(a) ((a) ? STBIW_FREE(stbiw__sbraw(a)),0 : 0) + +static void *stbiw__sbgrowf(void **arr, int increment, int itemsize) +{ + int m = *arr ? 2*stbiw__sbm(*arr)+increment : increment+1; + void *p = STBIW_REALLOC_SIZED(*arr ? stbiw__sbraw(*arr) : 0, *arr ? (stbiw__sbm(*arr)*itemsize + sizeof(int)*2) : 0, itemsize * m + sizeof(int)*2); + STBIW_ASSERT(p); + if (p) { + if (!*arr) ((int *) p)[1] = 0; + *arr = (void *) ((int *) p + 2); + stbiw__sbm(*arr) = m; + } + return *arr; +} + +static unsigned char *stbiw__zlib_flushf(unsigned char *data, unsigned int *bitbuffer, int *bitcount) +{ + while (*bitcount >= 8) { + stbiw__sbpush(data, STBIW_UCHAR(*bitbuffer)); + *bitbuffer >>= 8; + *bitcount -= 8; + } + return data; +} + +static int stbiw__zlib_bitrev(int code, int codebits) +{ + int res=0; + while (codebits--) { + res = (res << 1) | (code & 1); + code >>= 1; + } + return res; +} + +static unsigned int stbiw__zlib_countm(unsigned char *a, unsigned char *b, int limit) +{ + int i; + for (i=0; i < limit && i < 258; ++i) + if (a[i] != b[i]) break; + return i; +} + +static unsigned int stbiw__zhash(unsigned char *data) +{ + stbiw_uint32 hash = data[0] + (data[1] << 8) + (data[2] << 16); + hash ^= hash << 3; + hash += hash >> 5; + hash ^= hash << 4; + hash += hash >> 17; + hash ^= hash << 25; + hash += hash >> 6; + return hash; +} + +#define stbiw__zlib_flush() (out = stbiw__zlib_flushf(out, &bitbuf, &bitcount)) +#define stbiw__zlib_add(code,codebits) \ + (bitbuf |= (code) << bitcount, bitcount += (codebits), stbiw__zlib_flush()) +#define stbiw__zlib_huffa(b,c) stbiw__zlib_add(stbiw__zlib_bitrev(b,c),c) +// default huffman tables +#define stbiw__zlib_huff1(n) stbiw__zlib_huffa(0x30 + (n), 8) +#define stbiw__zlib_huff2(n) stbiw__zlib_huffa(0x190 + (n)-144, 9) +#define stbiw__zlib_huff3(n) stbiw__zlib_huffa(0 + (n)-256,7) +#define stbiw__zlib_huff4(n) stbiw__zlib_huffa(0xc0 + (n)-280,8) +#define stbiw__zlib_huff(n) ((n) <= 143 ? stbiw__zlib_huff1(n) : (n) <= 255 ? stbiw__zlib_huff2(n) : (n) <= 279 ? stbiw__zlib_huff3(n) : stbiw__zlib_huff4(n)) +#define stbiw__zlib_huffb(n) ((n) <= 143 ? stbiw__zlib_huff1(n) : stbiw__zlib_huff2(n)) + +#define stbiw__ZHASH 16384 + +#endif // STBIW_ZLIB_COMPRESS + +STBIWDEF unsigned char * stbi_zlib_compress(unsigned char *data, int data_len, int *out_len, int quality) +{ +#ifdef STBIW_ZLIB_COMPRESS + // user provided a zlib compress implementation, use that + return STBIW_ZLIB_COMPRESS(data, data_len, out_len, quality); +#else // use builtin + static unsigned short lengthc[] = { 3,4,5,6,7,8,9,10,11,13,15,17,19,23,27,31,35,43,51,59,67,83,99,115,131,163,195,227,258, 259 }; + static unsigned char lengtheb[]= { 0,0,0,0,0,0,0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 0 }; + static unsigned short distc[] = { 1,2,3,4,5,7,9,13,17,25,33,49,65,97,129,193,257,385,513,769,1025,1537,2049,3073,4097,6145,8193,12289,16385,24577, 32768 }; + static unsigned char disteb[] = { 0,0,0,0,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12,13,13 }; + unsigned int bitbuf=0; + int i,j, bitcount=0; + unsigned char *out = NULL; + unsigned char ***hash_table = (unsigned char***) STBIW_MALLOC(stbiw__ZHASH * sizeof(unsigned char**)); + if (hash_table == NULL) + return NULL; + if (quality < 5) quality = 5; + + stbiw__sbpush(out, 0x78); // DEFLATE 32K window + stbiw__sbpush(out, 0x5e); // FLEVEL = 1 + stbiw__zlib_add(1,1); // BFINAL = 1 + stbiw__zlib_add(1,2); // BTYPE = 1 -- fixed huffman + + for (i=0; i < stbiw__ZHASH; ++i) + hash_table[i] = NULL; + + i=0; + while (i < data_len-3) { + // hash next 3 bytes of data to be compressed + int h = stbiw__zhash(data+i)&(stbiw__ZHASH-1), best=3; + unsigned char *bestloc = 0; + unsigned char **hlist = hash_table[h]; + int n = stbiw__sbcount(hlist); + for (j=0; j < n; ++j) { + if (hlist[j]-data > i-32768) { // if entry lies within window + int d = stbiw__zlib_countm(hlist[j], data+i, data_len-i); + if (d >= best) { best=d; bestloc=hlist[j]; } + } + } + // when hash table entry is too long, delete half the entries + if (hash_table[h] && stbiw__sbn(hash_table[h]) == 2*quality) { + STBIW_MEMMOVE(hash_table[h], hash_table[h]+quality, sizeof(hash_table[h][0])*quality); + stbiw__sbn(hash_table[h]) = quality; + } + stbiw__sbpush(hash_table[h],data+i); + + if (bestloc) { + // "lazy matching" - check match at *next* byte, and if it's better, do cur byte as literal + h = stbiw__zhash(data+i+1)&(stbiw__ZHASH-1); + hlist = hash_table[h]; + n = stbiw__sbcount(hlist); + for (j=0; j < n; ++j) { + if (hlist[j]-data > i-32767) { + int e = stbiw__zlib_countm(hlist[j], data+i+1, data_len-i-1); + if (e > best) { // if next match is better, bail on current match + bestloc = NULL; + break; + } + } + } + } + + if (bestloc) { + int d = (int) (data+i - bestloc); // distance back + STBIW_ASSERT(d <= 32767 && best <= 258); + for (j=0; best > lengthc[j+1]-1; ++j); + stbiw__zlib_huff(j+257); + if (lengtheb[j]) stbiw__zlib_add(best - lengthc[j], lengtheb[j]); + for (j=0; d > distc[j+1]-1; ++j); + stbiw__zlib_add(stbiw__zlib_bitrev(j,5),5); + if (disteb[j]) stbiw__zlib_add(d - distc[j], disteb[j]); + i += best; + } else { + stbiw__zlib_huffb(data[i]); + ++i; + } + } + // write out final bytes + for (;i < data_len; ++i) + stbiw__zlib_huffb(data[i]); + stbiw__zlib_huff(256); // end of block + // pad with 0 bits to byte boundary + while (bitcount) + stbiw__zlib_add(0,1); + + for (i=0; i < stbiw__ZHASH; ++i) + (void) stbiw__sbfree(hash_table[i]); + STBIW_FREE(hash_table); + + { + // compute adler32 on input + unsigned int s1=1, s2=0; + int blocklen = (int) (data_len % 5552); + j=0; + while (j < data_len) { + for (i=0; i < blocklen; ++i) { s1 += data[j+i]; s2 += s1; } + s1 %= 65521; s2 %= 65521; + j += blocklen; + blocklen = 5552; + } + stbiw__sbpush(out, STBIW_UCHAR(s2 >> 8)); + stbiw__sbpush(out, STBIW_UCHAR(s2)); + stbiw__sbpush(out, STBIW_UCHAR(s1 >> 8)); + stbiw__sbpush(out, STBIW_UCHAR(s1)); + } + *out_len = stbiw__sbn(out); + // make returned pointer freeable + STBIW_MEMMOVE(stbiw__sbraw(out), out, *out_len); + return (unsigned char *) stbiw__sbraw(out); +#endif // STBIW_ZLIB_COMPRESS +} + +static unsigned int stbiw__crc32(unsigned char *buffer, int len) +{ +#ifdef STBIW_CRC32 + return STBIW_CRC32(buffer, len); +#else + static unsigned int crc_table[256] = + { + 0x00000000, 0x77073096, 0xEE0E612C, 0x990951BA, 0x076DC419, 0x706AF48F, 0xE963A535, 0x9E6495A3, + 0x0eDB8832, 0x79DCB8A4, 0xE0D5E91E, 0x97D2D988, 0x09B64C2B, 0x7EB17CBD, 0xE7B82D07, 0x90BF1D91, + 0x1DB71064, 0x6AB020F2, 0xF3B97148, 0x84BE41DE, 0x1ADAD47D, 0x6DDDE4EB, 0xF4D4B551, 0x83D385C7, + 0x136C9856, 0x646BA8C0, 0xFD62F97A, 0x8A65C9EC, 0x14015C4F, 0x63066CD9, 0xFA0F3D63, 0x8D080DF5, + 0x3B6E20C8, 0x4C69105E, 0xD56041E4, 0xA2677172, 0x3C03E4D1, 0x4B04D447, 0xD20D85FD, 0xA50AB56B, + 0x35B5A8FA, 0x42B2986C, 0xDBBBC9D6, 0xACBCF940, 0x32D86CE3, 0x45DF5C75, 0xDCD60DCF, 0xABD13D59, + 0x26D930AC, 0x51DE003A, 0xC8D75180, 0xBFD06116, 0x21B4F4B5, 0x56B3C423, 0xCFBA9599, 0xB8BDA50F, + 0x2802B89E, 0x5F058808, 0xC60CD9B2, 0xB10BE924, 0x2F6F7C87, 0x58684C11, 0xC1611DAB, 0xB6662D3D, + 0x76DC4190, 0x01DB7106, 0x98D220BC, 0xEFD5102A, 0x71B18589, 0x06B6B51F, 0x9FBFE4A5, 0xE8B8D433, + 0x7807C9A2, 0x0F00F934, 0x9609A88E, 0xE10E9818, 0x7F6A0DBB, 0x086D3D2D, 0x91646C97, 0xE6635C01, + 0x6B6B51F4, 0x1C6C6162, 0x856530D8, 0xF262004E, 0x6C0695ED, 0x1B01A57B, 0x8208F4C1, 0xF50FC457, + 0x65B0D9C6, 0x12B7E950, 0x8BBEB8EA, 0xFCB9887C, 0x62DD1DDF, 0x15DA2D49, 0x8CD37CF3, 0xFBD44C65, + 0x4DB26158, 0x3AB551CE, 0xA3BC0074, 0xD4BB30E2, 0x4ADFA541, 0x3DD895D7, 0xA4D1C46D, 0xD3D6F4FB, + 0x4369E96A, 0x346ED9FC, 0xAD678846, 0xDA60B8D0, 0x44042D73, 0x33031DE5, 0xAA0A4C5F, 0xDD0D7CC9, + 0x5005713C, 0x270241AA, 0xBE0B1010, 0xC90C2086, 0x5768B525, 0x206F85B3, 0xB966D409, 0xCE61E49F, + 0x5EDEF90E, 0x29D9C998, 0xB0D09822, 0xC7D7A8B4, 0x59B33D17, 0x2EB40D81, 0xB7BD5C3B, 0xC0BA6CAD, + 0xEDB88320, 0x9ABFB3B6, 0x03B6E20C, 0x74B1D29A, 0xEAD54739, 0x9DD277AF, 0x04DB2615, 0x73DC1683, + 0xE3630B12, 0x94643B84, 0x0D6D6A3E, 0x7A6A5AA8, 0xE40ECF0B, 0x9309FF9D, 0x0A00AE27, 0x7D079EB1, + 0xF00F9344, 0x8708A3D2, 0x1E01F268, 0x6906C2FE, 0xF762575D, 0x806567CB, 0x196C3671, 0x6E6B06E7, + 0xFED41B76, 0x89D32BE0, 0x10DA7A5A, 0x67DD4ACC, 0xF9B9DF6F, 0x8EBEEFF9, 0x17B7BE43, 0x60B08ED5, + 0xD6D6A3E8, 0xA1D1937E, 0x38D8C2C4, 0x4FDFF252, 0xD1BB67F1, 0xA6BC5767, 0x3FB506DD, 0x48B2364B, + 0xD80D2BDA, 0xAF0A1B4C, 0x36034AF6, 0x41047A60, 0xDF60EFC3, 0xA867DF55, 0x316E8EEF, 0x4669BE79, + 0xCB61B38C, 0xBC66831A, 0x256FD2A0, 0x5268E236, 0xCC0C7795, 0xBB0B4703, 0x220216B9, 0x5505262F, + 0xC5BA3BBE, 0xB2BD0B28, 0x2BB45A92, 0x5CB36A04, 0xC2D7FFA7, 0xB5D0CF31, 0x2CD99E8B, 0x5BDEAE1D, + 0x9B64C2B0, 0xEC63F226, 0x756AA39C, 0x026D930A, 0x9C0906A9, 0xEB0E363F, 0x72076785, 0x05005713, + 0x95BF4A82, 0xE2B87A14, 0x7BB12BAE, 0x0CB61B38, 0x92D28E9B, 0xE5D5BE0D, 0x7CDCEFB7, 0x0BDBDF21, + 0x86D3D2D4, 0xF1D4E242, 0x68DDB3F8, 0x1FDA836E, 0x81BE16CD, 0xF6B9265B, 0x6FB077E1, 0x18B74777, + 0x88085AE6, 0xFF0F6A70, 0x66063BCA, 0x11010B5C, 0x8F659EFF, 0xF862AE69, 0x616BFFD3, 0x166CCF45, + 0xA00AE278, 0xD70DD2EE, 0x4E048354, 0x3903B3C2, 0xA7672661, 0xD06016F7, 0x4969474D, 0x3E6E77DB, + 0xAED16A4A, 0xD9D65ADC, 0x40DF0B66, 0x37D83BF0, 0xA9BCAE53, 0xDEBB9EC5, 0x47B2CF7F, 0x30B5FFE9, + 0xBDBDF21C, 0xCABAC28A, 0x53B39330, 0x24B4A3A6, 0xBAD03605, 0xCDD70693, 0x54DE5729, 0x23D967BF, + 0xB3667A2E, 0xC4614AB8, 0x5D681B02, 0x2A6F2B94, 0xB40BBE37, 0xC30C8EA1, 0x5A05DF1B, 0x2D02EF8D + }; + + unsigned int crc = ~0u; + int i; + for (i=0; i < len; ++i) + crc = (crc >> 8) ^ crc_table[buffer[i] ^ (crc & 0xff)]; + return ~crc; +#endif +} + +#define stbiw__wpng4(o,a,b,c,d) ((o)[0]=STBIW_UCHAR(a),(o)[1]=STBIW_UCHAR(b),(o)[2]=STBIW_UCHAR(c),(o)[3]=STBIW_UCHAR(d),(o)+=4) +#define stbiw__wp32(data,v) stbiw__wpng4(data, (v)>>24,(v)>>16,(v)>>8,(v)); +#define stbiw__wptag(data,s) stbiw__wpng4(data, s[0],s[1],s[2],s[3]) + +static void stbiw__wpcrc(unsigned char **data, int len) +{ + unsigned int crc = stbiw__crc32(*data - len - 4, len+4); + stbiw__wp32(*data, crc); +} + +static unsigned char stbiw__paeth(int a, int b, int c) +{ + int p = a + b - c, pa = abs(p-a), pb = abs(p-b), pc = abs(p-c); + if (pa <= pb && pa <= pc) return STBIW_UCHAR(a); + if (pb <= pc) return STBIW_UCHAR(b); + return STBIW_UCHAR(c); +} + +// @OPTIMIZE: provide an option that always forces left-predict or paeth predict +static void stbiw__encode_png_line(unsigned char *pixels, int stride_bytes, int width, int height, int y, int n, int filter_type, signed char *line_buffer) +{ + static int mapping[] = { 0,1,2,3,4 }; + static int firstmap[] = { 0,1,0,5,6 }; + int *mymap = (y != 0) ? mapping : firstmap; + int i; + int type = mymap[filter_type]; + unsigned char *z = pixels + stride_bytes * (stbi__flip_vertically_on_write ? height-1-y : y); + int signed_stride = stbi__flip_vertically_on_write ? -stride_bytes : stride_bytes; + + if (type==0) { + memcpy(line_buffer, z, width*n); + return; + } + + // first loop isn't optimized since it's just one pixel + for (i = 0; i < n; ++i) { + switch (type) { + case 1: line_buffer[i] = z[i]; break; + case 2: line_buffer[i] = z[i] - z[i-signed_stride]; break; + case 3: line_buffer[i] = z[i] - (z[i-signed_stride]>>1); break; + case 4: line_buffer[i] = (signed char) (z[i] - stbiw__paeth(0,z[i-signed_stride],0)); break; + case 5: line_buffer[i] = z[i]; break; + case 6: line_buffer[i] = z[i]; break; + } + } + switch (type) { + case 1: for (i=n; i < width*n; ++i) line_buffer[i] = z[i] - z[i-n]; break; + case 2: for (i=n; i < width*n; ++i) line_buffer[i] = z[i] - z[i-signed_stride]; break; + case 3: for (i=n; i < width*n; ++i) line_buffer[i] = z[i] - ((z[i-n] + z[i-signed_stride])>>1); break; + case 4: for (i=n; i < width*n; ++i) line_buffer[i] = z[i] - stbiw__paeth(z[i-n], z[i-signed_stride], z[i-signed_stride-n]); break; + case 5: for (i=n; i < width*n; ++i) line_buffer[i] = z[i] - (z[i-n]>>1); break; + case 6: for (i=n; i < width*n; ++i) line_buffer[i] = z[i] - stbiw__paeth(z[i-n], 0,0); break; + } +} + +STBIWDEF unsigned char *stbi_write_png_to_mem(const unsigned char *pixels, int stride_bytes, int x, int y, int n, int *out_len) +{ + int force_filter = stbi_write_force_png_filter; + int ctype[5] = { -1, 0, 4, 2, 6 }; + unsigned char sig[8] = { 137,80,78,71,13,10,26,10 }; + unsigned char *out,*o, *filt, *zlib; + signed char *line_buffer; + int j,zlen; + + if (stride_bytes == 0) + stride_bytes = x * n; + + if (force_filter >= 5) { + force_filter = -1; + } + + filt = (unsigned char *) STBIW_MALLOC((x*n+1) * y); if (!filt) return 0; + line_buffer = (signed char *) STBIW_MALLOC(x * n); if (!line_buffer) { STBIW_FREE(filt); return 0; } + for (j=0; j < y; ++j) { + int filter_type; + if (force_filter > -1) { + filter_type = force_filter; + stbiw__encode_png_line((unsigned char*)(pixels), stride_bytes, x, y, j, n, force_filter, line_buffer); + } else { // Estimate the best filter by running through all of them: + int best_filter = 0, best_filter_val = 0x7fffffff, est, i; + for (filter_type = 0; filter_type < 5; filter_type++) { + stbiw__encode_png_line((unsigned char*)(pixels), stride_bytes, x, y, j, n, filter_type, line_buffer); + + // Estimate the entropy of the line using this filter; the less, the better. + est = 0; + for (i = 0; i < x*n; ++i) { + est += abs((signed char) line_buffer[i]); + } + if (est < best_filter_val) { + best_filter_val = est; + best_filter = filter_type; + } + } + if (filter_type != best_filter) { // If the last iteration already got us the best filter, don't redo it + stbiw__encode_png_line((unsigned char*)(pixels), stride_bytes, x, y, j, n, best_filter, line_buffer); + filter_type = best_filter; + } + } + // when we get here, filter_type contains the filter type, and line_buffer contains the data + filt[j*(x*n+1)] = (unsigned char) filter_type; + STBIW_MEMMOVE(filt+j*(x*n+1)+1, line_buffer, x*n); + } + STBIW_FREE(line_buffer); + zlib = stbi_zlib_compress(filt, y*( x*n+1), &zlen, stbi_write_png_compression_level); + STBIW_FREE(filt); + if (!zlib) return 0; + + // each tag requires 12 bytes of overhead + out = (unsigned char *) STBIW_MALLOC(8 + 12+13 + 12+zlen + 12); + if (!out) return 0; + *out_len = 8 + 12+13 + 12+zlen + 12; + + o=out; + STBIW_MEMMOVE(o,sig,8); o+= 8; + stbiw__wp32(o, 13); // header length + stbiw__wptag(o, "IHDR"); + stbiw__wp32(o, x); + stbiw__wp32(o, y); + *o++ = 8; + *o++ = STBIW_UCHAR(ctype[n]); + *o++ = 0; + *o++ = 0; + *o++ = 0; + stbiw__wpcrc(&o,13); + + stbiw__wp32(o, zlen); + stbiw__wptag(o, "IDAT"); + STBIW_MEMMOVE(o, zlib, zlen); + o += zlen; + STBIW_FREE(zlib); + stbiw__wpcrc(&o, zlen); + + stbiw__wp32(o,0); + stbiw__wptag(o, "IEND"); + stbiw__wpcrc(&o,0); + + STBIW_ASSERT(o == out + *out_len); + + return out; +} + +#ifndef STBI_WRITE_NO_STDIO +STBIWDEF int stbi_write_png(char const *filename, int x, int y, int comp, const void *data, int stride_bytes) +{ + FILE *f; + int len; + unsigned char *png = stbi_write_png_to_mem((const unsigned char *) data, stride_bytes, x, y, comp, &len); + if (png == NULL) return 0; + + f = stbiw__fopen(filename, "wb"); + if (!f) { STBIW_FREE(png); return 0; } + fwrite(png, 1, len, f); + fclose(f); + STBIW_FREE(png); + return 1; +} +#endif + +STBIWDEF int stbi_write_png_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data, int stride_bytes) +{ + int len; + unsigned char *png = stbi_write_png_to_mem((const unsigned char *) data, stride_bytes, x, y, comp, &len); + if (png == NULL) return 0; + func(context, png, len); + STBIW_FREE(png); + return 1; +} + + +/* *************************************************************************** + * + * JPEG writer + * + * This is based on Jon Olick's jo_jpeg.cpp: + * public domain Simple, Minimalistic JPEG writer - http://www.jonolick.com/code.html + */ + +static const unsigned char stbiw__jpg_ZigZag[] = { 0,1,5,6,14,15,27,28,2,4,7,13,16,26,29,42,3,8,12,17,25,30,41,43,9,11,18, + 24,31,40,44,53,10,19,23,32,39,45,52,54,20,22,33,38,46,51,55,60,21,34,37,47,50,56,59,61,35,36,48,49,57,58,62,63 }; + +static void stbiw__jpg_writeBits(stbi__write_context *s, int *bitBufP, int *bitCntP, const unsigned short *bs) { + int bitBuf = *bitBufP, bitCnt = *bitCntP; + bitCnt += bs[1]; + bitBuf |= bs[0] << (24 - bitCnt); + while(bitCnt >= 8) { + unsigned char c = (bitBuf >> 16) & 255; + stbiw__putc(s, c); + if(c == 255) { + stbiw__putc(s, 0); + } + bitBuf <<= 8; + bitCnt -= 8; + } + *bitBufP = bitBuf; + *bitCntP = bitCnt; +} + +static void stbiw__jpg_DCT(float *d0p, float *d1p, float *d2p, float *d3p, float *d4p, float *d5p, float *d6p, float *d7p) { + float d0 = *d0p, d1 = *d1p, d2 = *d2p, d3 = *d3p, d4 = *d4p, d5 = *d5p, d6 = *d6p, d7 = *d7p; + float z1, z2, z3, z4, z5, z11, z13; + + float tmp0 = d0 + d7; + float tmp7 = d0 - d7; + float tmp1 = d1 + d6; + float tmp6 = d1 - d6; + float tmp2 = d2 + d5; + float tmp5 = d2 - d5; + float tmp3 = d3 + d4; + float tmp4 = d3 - d4; + + // Even part + float tmp10 = tmp0 + tmp3; // phase 2 + float tmp13 = tmp0 - tmp3; + float tmp11 = tmp1 + tmp2; + float tmp12 = tmp1 - tmp2; + + d0 = tmp10 + tmp11; // phase 3 + d4 = tmp10 - tmp11; + + z1 = (tmp12 + tmp13) * 0.707106781f; // c4 + d2 = tmp13 + z1; // phase 5 + d6 = tmp13 - z1; + + // Odd part + tmp10 = tmp4 + tmp5; // phase 2 + tmp11 = tmp5 + tmp6; + tmp12 = tmp6 + tmp7; + + // The rotator is modified from fig 4-8 to avoid extra negations. + z5 = (tmp10 - tmp12) * 0.382683433f; // c6 + z2 = tmp10 * 0.541196100f + z5; // c2-c6 + z4 = tmp12 * 1.306562965f + z5; // c2+c6 + z3 = tmp11 * 0.707106781f; // c4 + + z11 = tmp7 + z3; // phase 5 + z13 = tmp7 - z3; + + *d5p = z13 + z2; // phase 6 + *d3p = z13 - z2; + *d1p = z11 + z4; + *d7p = z11 - z4; + + *d0p = d0; *d2p = d2; *d4p = d4; *d6p = d6; +} + +static void stbiw__jpg_calcBits(int val, unsigned short bits[2]) { + int tmp1 = val < 0 ? -val : val; + val = val < 0 ? val-1 : val; + bits[1] = 1; + while(tmp1 >>= 1) { + ++bits[1]; + } + bits[0] = val & ((1<0)&&(DU[end0pos]==0); --end0pos) { + } + // end0pos = first element in reverse order !=0 + if(end0pos == 0) { + stbiw__jpg_writeBits(s, bitBuf, bitCnt, EOB); + return DU[0]; + } + for(i = 1; i <= end0pos; ++i) { + int startpos = i; + int nrzeroes; + unsigned short bits[2]; + for (; DU[i]==0 && i<=end0pos; ++i) { + } + nrzeroes = i-startpos; + if ( nrzeroes >= 16 ) { + int lng = nrzeroes>>4; + int nrmarker; + for (nrmarker=1; nrmarker <= lng; ++nrmarker) + stbiw__jpg_writeBits(s, bitBuf, bitCnt, M16zeroes); + nrzeroes &= 15; + } + stbiw__jpg_calcBits(DU[i], bits); + stbiw__jpg_writeBits(s, bitBuf, bitCnt, HTAC[(nrzeroes<<4)+bits[1]]); + stbiw__jpg_writeBits(s, bitBuf, bitCnt, bits); + } + if(end0pos != 63) { + stbiw__jpg_writeBits(s, bitBuf, bitCnt, EOB); + } + return DU[0]; +} + +static int stbi_write_jpg_core(stbi__write_context *s, int width, int height, int comp, const void* data, int quality) { + // Constants that don't pollute global namespace + static const unsigned char std_dc_luminance_nrcodes[] = {0,0,1,5,1,1,1,1,1,1,0,0,0,0,0,0,0}; + static const unsigned char std_dc_luminance_values[] = {0,1,2,3,4,5,6,7,8,9,10,11}; + static const unsigned char std_ac_luminance_nrcodes[] = {0,0,2,1,3,3,2,4,3,5,5,4,4,0,0,1,0x7d}; + static const unsigned char std_ac_luminance_values[] = { + 0x01,0x02,0x03,0x00,0x04,0x11,0x05,0x12,0x21,0x31,0x41,0x06,0x13,0x51,0x61,0x07,0x22,0x71,0x14,0x32,0x81,0x91,0xa1,0x08, + 0x23,0x42,0xb1,0xc1,0x15,0x52,0xd1,0xf0,0x24,0x33,0x62,0x72,0x82,0x09,0x0a,0x16,0x17,0x18,0x19,0x1a,0x25,0x26,0x27,0x28, + 0x29,0x2a,0x34,0x35,0x36,0x37,0x38,0x39,0x3a,0x43,0x44,0x45,0x46,0x47,0x48,0x49,0x4a,0x53,0x54,0x55,0x56,0x57,0x58,0x59, + 0x5a,0x63,0x64,0x65,0x66,0x67,0x68,0x69,0x6a,0x73,0x74,0x75,0x76,0x77,0x78,0x79,0x7a,0x83,0x84,0x85,0x86,0x87,0x88,0x89, + 0x8a,0x92,0x93,0x94,0x95,0x96,0x97,0x98,0x99,0x9a,0xa2,0xa3,0xa4,0xa5,0xa6,0xa7,0xa8,0xa9,0xaa,0xb2,0xb3,0xb4,0xb5,0xb6, + 0xb7,0xb8,0xb9,0xba,0xc2,0xc3,0xc4,0xc5,0xc6,0xc7,0xc8,0xc9,0xca,0xd2,0xd3,0xd4,0xd5,0xd6,0xd7,0xd8,0xd9,0xda,0xe1,0xe2, + 0xe3,0xe4,0xe5,0xe6,0xe7,0xe8,0xe9,0xea,0xf1,0xf2,0xf3,0xf4,0xf5,0xf6,0xf7,0xf8,0xf9,0xfa + }; + static const unsigned char std_dc_chrominance_nrcodes[] = {0,0,3,1,1,1,1,1,1,1,1,1,0,0,0,0,0}; + static const unsigned char std_dc_chrominance_values[] = {0,1,2,3,4,5,6,7,8,9,10,11}; + static const unsigned char std_ac_chrominance_nrcodes[] = {0,0,2,1,2,4,4,3,4,7,5,4,4,0,1,2,0x77}; + static const unsigned char std_ac_chrominance_values[] = { + 0x00,0x01,0x02,0x03,0x11,0x04,0x05,0x21,0x31,0x06,0x12,0x41,0x51,0x07,0x61,0x71,0x13,0x22,0x32,0x81,0x08,0x14,0x42,0x91, + 0xa1,0xb1,0xc1,0x09,0x23,0x33,0x52,0xf0,0x15,0x62,0x72,0xd1,0x0a,0x16,0x24,0x34,0xe1,0x25,0xf1,0x17,0x18,0x19,0x1a,0x26, + 0x27,0x28,0x29,0x2a,0x35,0x36,0x37,0x38,0x39,0x3a,0x43,0x44,0x45,0x46,0x47,0x48,0x49,0x4a,0x53,0x54,0x55,0x56,0x57,0x58, + 0x59,0x5a,0x63,0x64,0x65,0x66,0x67,0x68,0x69,0x6a,0x73,0x74,0x75,0x76,0x77,0x78,0x79,0x7a,0x82,0x83,0x84,0x85,0x86,0x87, + 0x88,0x89,0x8a,0x92,0x93,0x94,0x95,0x96,0x97,0x98,0x99,0x9a,0xa2,0xa3,0xa4,0xa5,0xa6,0xa7,0xa8,0xa9,0xaa,0xb2,0xb3,0xb4, + 0xb5,0xb6,0xb7,0xb8,0xb9,0xba,0xc2,0xc3,0xc4,0xc5,0xc6,0xc7,0xc8,0xc9,0xca,0xd2,0xd3,0xd4,0xd5,0xd6,0xd7,0xd8,0xd9,0xda, + 0xe2,0xe3,0xe4,0xe5,0xe6,0xe7,0xe8,0xe9,0xea,0xf2,0xf3,0xf4,0xf5,0xf6,0xf7,0xf8,0xf9,0xfa + }; + // Huffman tables + static const unsigned short YDC_HT[256][2] = { {0,2},{2,3},{3,3},{4,3},{5,3},{6,3},{14,4},{30,5},{62,6},{126,7},{254,8},{510,9}}; + static const unsigned short UVDC_HT[256][2] = { {0,2},{1,2},{2,2},{6,3},{14,4},{30,5},{62,6},{126,7},{254,8},{510,9},{1022,10},{2046,11}}; + static const unsigned short YAC_HT[256][2] = { + {10,4},{0,2},{1,2},{4,3},{11,4},{26,5},{120,7},{248,8},{1014,10},{65410,16},{65411,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {12,4},{27,5},{121,7},{502,9},{2038,11},{65412,16},{65413,16},{65414,16},{65415,16},{65416,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {28,5},{249,8},{1015,10},{4084,12},{65417,16},{65418,16},{65419,16},{65420,16},{65421,16},{65422,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {58,6},{503,9},{4085,12},{65423,16},{65424,16},{65425,16},{65426,16},{65427,16},{65428,16},{65429,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {59,6},{1016,10},{65430,16},{65431,16},{65432,16},{65433,16},{65434,16},{65435,16},{65436,16},{65437,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {122,7},{2039,11},{65438,16},{65439,16},{65440,16},{65441,16},{65442,16},{65443,16},{65444,16},{65445,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {123,7},{4086,12},{65446,16},{65447,16},{65448,16},{65449,16},{65450,16},{65451,16},{65452,16},{65453,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {250,8},{4087,12},{65454,16},{65455,16},{65456,16},{65457,16},{65458,16},{65459,16},{65460,16},{65461,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {504,9},{32704,15},{65462,16},{65463,16},{65464,16},{65465,16},{65466,16},{65467,16},{65468,16},{65469,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {505,9},{65470,16},{65471,16},{65472,16},{65473,16},{65474,16},{65475,16},{65476,16},{65477,16},{65478,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {506,9},{65479,16},{65480,16},{65481,16},{65482,16},{65483,16},{65484,16},{65485,16},{65486,16},{65487,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {1017,10},{65488,16},{65489,16},{65490,16},{65491,16},{65492,16},{65493,16},{65494,16},{65495,16},{65496,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {1018,10},{65497,16},{65498,16},{65499,16},{65500,16},{65501,16},{65502,16},{65503,16},{65504,16},{65505,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {2040,11},{65506,16},{65507,16},{65508,16},{65509,16},{65510,16},{65511,16},{65512,16},{65513,16},{65514,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {65515,16},{65516,16},{65517,16},{65518,16},{65519,16},{65520,16},{65521,16},{65522,16},{65523,16},{65524,16},{0,0},{0,0},{0,0},{0,0},{0,0}, + {2041,11},{65525,16},{65526,16},{65527,16},{65528,16},{65529,16},{65530,16},{65531,16},{65532,16},{65533,16},{65534,16},{0,0},{0,0},{0,0},{0,0},{0,0} + }; + static const unsigned short UVAC_HT[256][2] = { + {0,2},{1,2},{4,3},{10,4},{24,5},{25,5},{56,6},{120,7},{500,9},{1014,10},{4084,12},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {11,4},{57,6},{246,8},{501,9},{2038,11},{4085,12},{65416,16},{65417,16},{65418,16},{65419,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {26,5},{247,8},{1015,10},{4086,12},{32706,15},{65420,16},{65421,16},{65422,16},{65423,16},{65424,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {27,5},{248,8},{1016,10},{4087,12},{65425,16},{65426,16},{65427,16},{65428,16},{65429,16},{65430,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {58,6},{502,9},{65431,16},{65432,16},{65433,16},{65434,16},{65435,16},{65436,16},{65437,16},{65438,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {59,6},{1017,10},{65439,16},{65440,16},{65441,16},{65442,16},{65443,16},{65444,16},{65445,16},{65446,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {121,7},{2039,11},{65447,16},{65448,16},{65449,16},{65450,16},{65451,16},{65452,16},{65453,16},{65454,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {122,7},{2040,11},{65455,16},{65456,16},{65457,16},{65458,16},{65459,16},{65460,16},{65461,16},{65462,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {249,8},{65463,16},{65464,16},{65465,16},{65466,16},{65467,16},{65468,16},{65469,16},{65470,16},{65471,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {503,9},{65472,16},{65473,16},{65474,16},{65475,16},{65476,16},{65477,16},{65478,16},{65479,16},{65480,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {504,9},{65481,16},{65482,16},{65483,16},{65484,16},{65485,16},{65486,16},{65487,16},{65488,16},{65489,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {505,9},{65490,16},{65491,16},{65492,16},{65493,16},{65494,16},{65495,16},{65496,16},{65497,16},{65498,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {506,9},{65499,16},{65500,16},{65501,16},{65502,16},{65503,16},{65504,16},{65505,16},{65506,16},{65507,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {2041,11},{65508,16},{65509,16},{65510,16},{65511,16},{65512,16},{65513,16},{65514,16},{65515,16},{65516,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {16352,14},{65517,16},{65518,16},{65519,16},{65520,16},{65521,16},{65522,16},{65523,16},{65524,16},{65525,16},{0,0},{0,0},{0,0},{0,0},{0,0}, + {1018,10},{32707,15},{65526,16},{65527,16},{65528,16},{65529,16},{65530,16},{65531,16},{65532,16},{65533,16},{65534,16},{0,0},{0,0},{0,0},{0,0},{0,0} + }; + static const int YQT[] = {16,11,10,16,24,40,51,61,12,12,14,19,26,58,60,55,14,13,16,24,40,57,69,56,14,17,22,29,51,87,80,62,18,22, + 37,56,68,109,103,77,24,35,55,64,81,104,113,92,49,64,78,87,103,121,120,101,72,92,95,98,112,100,103,99}; + static const int UVQT[] = {17,18,24,47,99,99,99,99,18,21,26,66,99,99,99,99,24,26,56,99,99,99,99,99,47,66,99,99,99,99,99,99, + 99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99}; + static const float aasf[] = { 1.0f * 2.828427125f, 1.387039845f * 2.828427125f, 1.306562965f * 2.828427125f, 1.175875602f * 2.828427125f, + 1.0f * 2.828427125f, 0.785694958f * 2.828427125f, 0.541196100f * 2.828427125f, 0.275899379f * 2.828427125f }; + + int row, col, i, k; + float fdtbl_Y[64], fdtbl_UV[64]; + unsigned char YTable[64], UVTable[64]; + + if(!data || !width || !height || comp > 4 || comp < 1) { + return 0; + } + + quality = quality ? quality : 90; + quality = quality < 1 ? 1 : quality > 100 ? 100 : quality; + quality = quality < 50 ? 5000 / quality : 200 - quality * 2; + + for(i = 0; i < 64; ++i) { + int uvti, yti = (YQT[i]*quality+50)/100; + YTable[stbiw__jpg_ZigZag[i]] = (unsigned char) (yti < 1 ? 1 : yti > 255 ? 255 : yti); + uvti = (UVQT[i]*quality+50)/100; + UVTable[stbiw__jpg_ZigZag[i]] = (unsigned char) (uvti < 1 ? 1 : uvti > 255 ? 255 : uvti); + } + + for(row = 0, k = 0; row < 8; ++row) { + for(col = 0; col < 8; ++col, ++k) { + fdtbl_Y[k] = 1 / (YTable [stbiw__jpg_ZigZag[k]] * aasf[row] * aasf[col]); + fdtbl_UV[k] = 1 / (UVTable[stbiw__jpg_ZigZag[k]] * aasf[row] * aasf[col]); + } + } + + // Write Headers + { + static const unsigned char head0[] = { 0xFF,0xD8,0xFF,0xE0,0,0x10,'J','F','I','F',0,1,1,0,0,1,0,1,0,0,0xFF,0xDB,0,0x84,0 }; + static const unsigned char head2[] = { 0xFF,0xDA,0,0xC,3,1,0,2,0x11,3,0x11,0,0x3F,0 }; + const unsigned char head1[] = { 0xFF,0xC0,0,0x11,8,(unsigned char)(height>>8),STBIW_UCHAR(height),(unsigned char)(width>>8),STBIW_UCHAR(width), + 3,1,0x11,0,2,0x11,1,3,0x11,1,0xFF,0xC4,0x01,0xA2,0 }; + s->func(s->context, (void*)head0, sizeof(head0)); + s->func(s->context, (void*)YTable, sizeof(YTable)); + stbiw__putc(s, 1); + s->func(s->context, UVTable, sizeof(UVTable)); + s->func(s->context, (void*)head1, sizeof(head1)); + s->func(s->context, (void*)(std_dc_luminance_nrcodes+1), sizeof(std_dc_luminance_nrcodes)-1); + s->func(s->context, (void*)std_dc_luminance_values, sizeof(std_dc_luminance_values)); + stbiw__putc(s, 0x10); // HTYACinfo + s->func(s->context, (void*)(std_ac_luminance_nrcodes+1), sizeof(std_ac_luminance_nrcodes)-1); + s->func(s->context, (void*)std_ac_luminance_values, sizeof(std_ac_luminance_values)); + stbiw__putc(s, 1); // HTUDCinfo + s->func(s->context, (void*)(std_dc_chrominance_nrcodes+1), sizeof(std_dc_chrominance_nrcodes)-1); + s->func(s->context, (void*)std_dc_chrominance_values, sizeof(std_dc_chrominance_values)); + stbiw__putc(s, 0x11); // HTUACinfo + s->func(s->context, (void*)(std_ac_chrominance_nrcodes+1), sizeof(std_ac_chrominance_nrcodes)-1); + s->func(s->context, (void*)std_ac_chrominance_values, sizeof(std_ac_chrominance_values)); + s->func(s->context, (void*)head2, sizeof(head2)); + } + + // Encode 8x8 macroblocks + { + static const unsigned short fillBits[] = {0x7F, 7}; + const unsigned char *imageData = (const unsigned char *)data; + int DCY=0, DCU=0, DCV=0; + int bitBuf=0, bitCnt=0; + // comp == 2 is grey+alpha (alpha is ignored) + int ofsG = comp > 2 ? 1 : 0, ofsB = comp > 2 ? 2 : 0; + int x, y, pos; + for(y = 0; y < height; y += 8) { + for(x = 0; x < width; x += 8) { + float YDU[64], UDU[64], VDU[64]; + for(row = y, pos = 0; row < y+8; ++row) { + // row >= height => use last input row + int clamped_row = (row < height) ? row : height - 1; + int base_p = (stbi__flip_vertically_on_write ? (height-1-clamped_row) : clamped_row)*width*comp; + for(col = x; col < x+8; ++col, ++pos) { + float r, g, b; + // if col >= width => use pixel from last input column + int p = base_p + ((col < width) ? col : (width-1))*comp; + + r = imageData[p+0]; + g = imageData[p+ofsG]; + b = imageData[p+ofsB]; + YDU[pos]=+0.29900f*r+0.58700f*g+0.11400f*b-128; + UDU[pos]=-0.16874f*r-0.33126f*g+0.50000f*b; + VDU[pos]=+0.50000f*r-0.41869f*g-0.08131f*b; + } + } + + DCY = stbiw__jpg_processDU(s, &bitBuf, &bitCnt, YDU, fdtbl_Y, DCY, YDC_HT, YAC_HT); + DCU = stbiw__jpg_processDU(s, &bitBuf, &bitCnt, UDU, fdtbl_UV, DCU, UVDC_HT, UVAC_HT); + DCV = stbiw__jpg_processDU(s, &bitBuf, &bitCnt, VDU, fdtbl_UV, DCV, UVDC_HT, UVAC_HT); + } + } + + // Do the bit alignment of the EOI marker + stbiw__jpg_writeBits(s, &bitBuf, &bitCnt, fillBits); + } + + // EOI + stbiw__putc(s, 0xFF); + stbiw__putc(s, 0xD9); + + return 1; +} + +STBIWDEF int stbi_write_jpg_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data, int quality) +{ + stbi__write_context s; + stbi__start_write_callbacks(&s, func, context); + return stbi_write_jpg_core(&s, x, y, comp, (void *) data, quality); +} + + +#ifndef STBI_WRITE_NO_STDIO +STBIWDEF int stbi_write_jpg(char const *filename, int x, int y, int comp, const void *data, int quality) +{ + stbi__write_context s; + if (stbi__start_write_file(&s,filename)) { + int r = stbi_write_jpg_core(&s, x, y, comp, data, quality); + stbi__end_write_file(&s); + return r; + } else + return 0; +} +#endif + +#endif // STB_IMAGE_WRITE_IMPLEMENTATION + +/* Revision history + 1.11 (2019-08-11) + + 1.10 (2019-02-07) + support utf8 filenames in Windows; fix warnings and platform ifdefs + 1.09 (2018-02-11) + fix typo in zlib quality API, improve STB_I_W_STATIC in C++ + 1.08 (2018-01-29) + add stbi__flip_vertically_on_write, external zlib, zlib quality, choose PNG filter + 1.07 (2017-07-24) + doc fix + 1.06 (2017-07-23) + writing JPEG (using Jon Olick's code) + 1.05 ??? + 1.04 (2017-03-03) + monochrome BMP expansion + 1.03 ??? + 1.02 (2016-04-02) + avoid allocating large structures on the stack + 1.01 (2016-01-16) + STBIW_REALLOC_SIZED: support allocators with no realloc support + avoid race-condition in crc initialization + minor compile issues + 1.00 (2015-09-14) + installable file IO function + 0.99 (2015-09-13) + warning fixes; TGA rle support + 0.98 (2015-04-08) + added STBIW_MALLOC, STBIW_ASSERT etc + 0.97 (2015-01-18) + fixed HDR asserts, rewrote HDR rle logic + 0.96 (2015-01-17) + add HDR output + fix monochrome BMP + 0.95 (2014-08-17) + add monochrome TGA output + 0.94 (2014-05-31) + rename private functions to avoid conflicts with stb_image.h + 0.93 (2014-05-27) + warning fixes + 0.92 (2010-08-01) + casts to unsigned char to fix warnings + 0.91 (2010-07-17) + first public release + 0.90 first internal release +*/ + +/* +------------------------------------------------------------------------------ +This software is available under 2 licenses -- choose whichever you prefer. +------------------------------------------------------------------------------ +ALTERNATIVE A - MIT License +Copyright (c) 2017 Sean Barrett +Permission is hereby granted, free of charge, to any person obtaining a copy of +this software and associated documentation files (the "Software"), to deal in +the Software without restriction, including without limitation the rights to +use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies +of the Software, and to permit persons to whom the Software is furnished to do +so, subject to the following conditions: +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +------------------------------------------------------------------------------ +ALTERNATIVE B - Public Domain (www.unlicense.org) +This is free and unencumbered software released into the public domain. +Anyone is free to copy, modify, publish, use, compile, sell, or distribute this +software, either in source code form or as a compiled binary, for any purpose, +commercial or non-commercial, and by any means. +In jurisdictions that recognize copyright laws, the author or authors of this +software dedicate any and all copyright interest in the software to the public +domain. We make this dedication for the benefit of the public at large and to +the detriment of our heirs and successors. We intend this dedication to be an +overt act of relinquishment in perpetuity of all present and future rights to +this software under copyright law. +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN +ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION +WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +------------------------------------------------------------------------------ +*/ diff --git a/rknpu2/examples/3rdparty/zlmediakit/LICENSE b/rknpu2/examples/3rdparty/zlmediakit/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..cdbc206afc2d63f0db7897fb11eca66c7423b52f --- /dev/null +++ b/rknpu2/examples/3rdparty/zlmediakit/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2016 The ZLMediaKit project authors. All Rights Reserved. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/rknpu2/examples/3rdparty/zlmediakit/aarch64/libmk_api.so b/rknpu2/examples/3rdparty/zlmediakit/aarch64/libmk_api.so new file mode 100644 index 0000000000000000000000000000000000000000..e1eb667f28aa67316c56ee3b1cb3e5f998407b34 --- /dev/null +++ b/rknpu2/examples/3rdparty/zlmediakit/aarch64/libmk_api.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0ee5b83c4c82b8cabe2f556da846aaedf9e3165fe233052404e57486edb79817 +size 4575376 diff --git a/rknpu2/examples/3rdparty/zlmediakit/include/mk_common.h b/rknpu2/examples/3rdparty/zlmediakit/include/mk_common.h new file mode 100644 index 0000000000000000000000000000000000000000..0ee50b66b11bef59a605b7cf5ee7f5baf0d37a01 --- /dev/null +++ b/rknpu2/examples/3rdparty/zlmediakit/include/mk_common.h @@ -0,0 +1,204 @@ +/* + * Copyright (c) 2016 The ZLMediaKit project authors. All Rights Reserved. + * + * This file is part of ZLMediaKit(https://github.com/xia-chu/ZLMediaKit). + * + * Use of this source code is governed by MIT license that can be found in the + * LICENSE file in the root of the source tree. All contributing project authors + * may be found in the AUTHORS file in the root of the source tree. + */ + +#ifndef MK_COMMON_H +#define MK_COMMON_H + +#include +#include + +#if defined(GENERATE_EXPORT) +#include "mk_export.h" +#endif + +#if defined(_WIN32) && defined(_MSC_VER) +# define API_CALL __cdecl +#else +# define API_CALL +#endif + +#if defined(_WIN32) && defined(_MSC_VER) +# if !defined(GENERATE_EXPORT) +# if defined(MediaKitApi_EXPORTS) +# define API_EXPORT __declspec(dllexport) +# else +# define API_EXPORT __declspec(dllimport) +# endif +# endif +#elif !defined(GENERATE_EXPORT) +# define API_EXPORT +#endif + +#ifdef __cplusplus +extern "C" { +#endif + +//输出日志到shell +#define LOG_CONSOLE (1 << 0) +//输出日志到文件 +#define LOG_FILE (1 << 1) +//输出日志到回调函数(mk_events::on_mk_log) +#define LOG_CALLBACK (1 << 2) + +typedef struct { + // 线程数 + int thread_num; + + // 日志级别,支持0~4 + int log_level; + //控制日志输出的掩模，请查看LOG_CONSOLE、LOG_FILE、LOG_CALLBACK等宏 + int log_mask; + //文件日志保存路径,路径可以不存在(内部可以创建文件夹)，设置为NULL关闭日志输出至文件 + const char *log_file_path; + //文件日志保存天数,设置为0关闭日志文件 + int log_file_days; + + // 配置文件是内容还是路径 + int ini_is_path; + // 配置文件内容或路径，可以为NULL,如果该文件不存在，那么将导出默认配置至该文件 + const char *ini; + + // ssl证书是内容还是路径 + int ssl_is_path; + // ssl证书内容或路径，可以为NULL + const char *ssl; + // 证书密码，可以为NULL + const char *ssl_pwd; +} mk_config; + +/** + * 初始化环境，调用该库前需要先调用此函数 + * @param cfg 库运行相关参数 + */ +API_EXPORT void API_CALL mk_env_init(const mk_config *cfg); + +/** + * 关闭所有服务器，请在main函数退出时调用 + */ +API_EXPORT void API_CALL mk_stop_all_server(); + +/** + * 基础类型参数版本的mk_env_init，为了方便其他语言调用 + * @param thread_num 线程数 + * @param log_level 日志级别,支持0~4 + * @param log_mask 日志输出方式掩模，请查看LOG_CONSOLE、LOG_FILE、LOG_CALLBACK等宏 + * @param log_file_path 文件日志保存路径,路径可以不存在(内部可以创建文件夹)，设置为NULL关闭日志输出至文件 + * @param log_file_days 文件日志保存天数,设置为0关闭日志文件 + * @param ini_is_path 配置文件是内容还是路径 + * @param ini 配置文件内容或路径，可以为NULL,如果该文件不存在，那么将导出默认配置至该文件 + * @param ssl_is_path ssl证书是内容还是路径 + * @param ssl ssl证书内容或路径，可以为NULL + * @param ssl_pwd 证书密码，可以为NULL + */ +API_EXPORT void API_CALL mk_env_init1(int thread_num, + int log_level, + int log_mask, + const char *log_file_path, + int log_file_days, + int ini_is_path, + const char *ini, + int ssl_is_path, + const char *ssl, + const char *ssl_pwd); + +/** +* 设置日志文件 +* @param file_max_size 单个切片文件大小(MB) +* @param file_max_count 切片文件个数 +*/ +API_EXPORT void API_CALL mk_set_log(int file_max_size, int file_max_count); + +/** + * 设置配置项 + * @param key 配置项名 + * @param val 配置项值 + */ +API_EXPORT void API_CALL mk_set_option(const char *key, const char *val); + +/** + * 获取配置项的值 + * @param key 配置项名 + */ +API_EXPORT const char * API_CALL mk_get_option(const char *key); + + +/** + * 创建http[s]服务器 + * @param port htt监听端口，推荐80，传入0则随机分配 + * @param ssl 是否为ssl类型服务器 + * @return 0:失败,非0:端口号 + */ +API_EXPORT uint16_t API_CALL mk_http_server_start(uint16_t port, int ssl); + +/** + * 创建rtsp[s]服务器 + * @param port rtsp监听端口，推荐554，传入0则随机分配 + * @param ssl 是否为ssl类型服务器 + * @return 0:失败,非0:端口号 + */ +API_EXPORT uint16_t API_CALL mk_rtsp_server_start(uint16_t port, int ssl); + +/** + * 创建rtmp[s]服务器 + * @param port rtmp监听端口，推荐1935，传入0则随机分配 + * @param ssl 是否为ssl类型服务器 + * @return 0:失败,非0:端口号 + */ +API_EXPORT uint16_t API_CALL mk_rtmp_server_start(uint16_t port, int ssl); + +/** + * 创建rtp服务器 + * @param port rtp监听端口(包括udp/tcp) + * @return 0:失败,非0:端口号 + */ +API_EXPORT uint16_t API_CALL mk_rtp_server_start(uint16_t port); + +/** + * 创建rtc服务器 + * @param port rtc监听端口 + * @return 0:失败,非0:端口号 + */ +API_EXPORT uint16_t API_CALL mk_rtc_server_start(uint16_t port); + +//获取webrtc answer sdp回调函数 +typedef void(API_CALL *on_mk_webrtc_get_answer_sdp)(void *user_data, const char *answer, const char *err); + +/** + * webrtc交换sdp，根据offer sdp生成answer sdp + * @param user_data 回调用户指针 + * @param cb 回调函数 + * @param type webrtc插件类型，支持echo,play,push + * @param offer webrtc offer sdp + * @param url rtc url, 例如 rtc://__defaultVhost/app/stream?key1=val1&key2=val2 + */ +API_EXPORT void API_CALL mk_webrtc_get_answer_sdp(void *user_data, on_mk_webrtc_get_answer_sdp cb, const char *type, + const char *offer, const char *url); + +/** + * 创建srt服务器 + * @param port srt监听端口 + * @return 0:失败,非0:端口号 + */ +API_EXPORT uint16_t API_CALL mk_srt_server_start(uint16_t port); + + +/** + * 创建shell服务器 + * @param port shell监听端口 + * @return 0:失败,非0:端口号 + */ +API_EXPORT uint16_t API_CALL mk_shell_server_start(uint16_t port); + +#ifdef __cplusplus +} +#endif + + +#endif /* MK_COMMON_H */ diff --git a/rknpu2/examples/3rdparty/zlmediakit/include/mk_events.h b/rknpu2/examples/3rdparty/zlmediakit/include/mk_events.h new file mode 100644 index 0000000000000000000000000000000000000000..c628a0ec21e3ce32c58445af0c2fc4b2512b4d24 --- /dev/null +++ b/rknpu2/examples/3rdparty/zlmediakit/include/mk_events.h @@ -0,0 +1,182 @@ +/* + * Copyright (c) 2016 The ZLMediaKit project authors. All Rights Reserved. + * + * This file is part of ZLMediaKit(https://github.com/xia-chu/ZLMediaKit). + * + * Use of this source code is governed by MIT license that can be found in the + * LICENSE file in the root of the source tree. All contributing project authors + * may be found in the AUTHORS file in the root of the source tree. + */ + +#ifndef MK_EVENTS_H +#define MK_EVENTS_H + +#include "mk_common.h" +#include "mk_events_objects.h" + +#ifdef __cplusplus +extern "C" { +#endif + +typedef struct { + /** + * 注册或反注册MediaSource事件广播 + * @param regist 注册为1，注销为0 + * @param sender 该MediaSource对象 + */ + void (API_CALL *on_mk_media_changed)(int regist, + const mk_media_source sender); + + /** + * 收到rtsp/rtmp推流事件广播，通过该事件控制推流鉴权 + * @see mk_publish_auth_invoker_do + * @param url_info 推流url相关信息 + * @param invoker 执行invoker返回鉴权结果 + * @param sender 该tcp客户端相关信息 + */ + void (API_CALL *on_mk_media_publish)(const mk_media_info url_info, + const mk_publish_auth_invoker invoker, + const mk_sock_info sender); + + /** + * 播放rtsp/rtmp/http-flv/hls事件广播，通过该事件控制播放鉴权 + * @see mk_auth_invoker_do + * @param url_info 播放url相关信息 + * @param invoker 执行invoker返回鉴权结果 + * @param sender 播放客户端相关信息 + */ + void (API_CALL *on_mk_media_play)(const mk_media_info url_info, + const mk_auth_invoker invoker, + const mk_sock_info sender); + + /** + * 未找到流后会广播该事件，请在监听该事件后去拉流或其他方式产生流，这样就能按需拉流了 + * @param url_info 播放url相关信息 + * @param sender 播放客户端相关信息 + * @return 1 直接关闭 + * 0 等待流注册 + */ + int (API_CALL *on_mk_media_not_found)(const mk_media_info url_info, + const mk_sock_info sender); + + /** + * 某个流无人消费时触发，目的为了实现无人观看时主动断开拉流等业务逻辑 + * @param sender 该MediaSource对象 + */ + void (API_CALL *on_mk_media_no_reader)(const mk_media_source sender); + + /** + * 收到http api请求广播(包括GET/POST) + * @param parser http请求内容对象 + * @param invoker 执行该invoker返回http回复 + * @param consumed 置1则说明我们要处理该事件 + * @param sender http客户端相关信息 + */ + void (API_CALL *on_mk_http_request)(const mk_parser parser, + const mk_http_response_invoker invoker, + int *consumed, + const mk_sock_info sender); + + /** + * 在http文件服务器中,收到http访问文件或目录的广播,通过该事件控制访问http目录的权限 + * @param parser http请求内容对象 + * @param path 文件绝对路径 + * @param is_dir path是否为文件夹 + * @param invoker 执行invoker返回本次访问文件的结果 + * @param sender http客户端相关信息 + */ + void (API_CALL *on_mk_http_access)(const mk_parser parser, + const char *path, + int is_dir, + const mk_http_access_path_invoker invoker, + const mk_sock_info sender); + + /** + * 在http文件服务器中,收到http访问文件或目录前的广播,通过该事件可以控制http url到文件路径的映射 + * 在该事件中通过自行覆盖path参数，可以做到譬如根据虚拟主机或者app选择不同http根目录的目的 + * @param parser http请求内容对象 + * @param path 文件绝对路径,覆盖之可以重定向到其他文件 + * @param sender http客户端相关信息 + */ + void (API_CALL *on_mk_http_before_access)(const mk_parser parser, + char *path, + const mk_sock_info sender); + + /** + * 该rtsp流是否需要认证？是的话调用invoker并传入realm,否则传入空的realm + * @param url_info 请求rtsp url相关信息 + * @param invoker 执行invoker返回是否需要rtsp专属认证 + * @param sender rtsp客户端相关信息 + */ + void (API_CALL *on_mk_rtsp_get_realm)(const mk_media_info url_info, + const mk_rtsp_get_realm_invoker invoker, + const mk_sock_info sender); + + /** + * 请求认证用户密码事件，user_name为用户名，must_no_encrypt如果为true，则必须提供明文密码(因为此时是base64认证方式),否则会导致认证失败 + * 获取到密码后请调用invoker并输入对应类型的密码和密码类型，invoker执行时会匹配密码 + * @param url_info 请求rtsp url相关信息 + * @param realm rtsp认证realm + * @param user_name rtsp认证用户名 + * @param must_no_encrypt 如果为true，则必须提供明文密码(因为此时是base64认证方式),否则会导致认证失败 + * @param invoker 执行invoker返回rtsp专属认证的密码 + * @param sender rtsp客户端信息 + */ + void (API_CALL *on_mk_rtsp_auth)(const mk_media_info url_info, + const char *realm, + const char *user_name, + int must_no_encrypt, + const mk_rtsp_auth_invoker invoker, + const mk_sock_info sender); + + /** + * 录制mp4分片文件成功后广播 + */ + void (API_CALL *on_mk_record_mp4)(const mk_mp4_info mp4); + + /** + * shell登录鉴权 + */ + void (API_CALL *on_mk_shell_login)(const char *user_name, + const char *passwd, + const mk_auth_invoker invoker, + const mk_sock_info sender); + + /** + * 停止rtsp/rtmp/http-flv会话后流量汇报事件广播 + * @param url_info 播放url相关信息 + * @param total_bytes 耗费上下行总流量，单位字节数 + * @param total_seconds 本次tcp会话时长，单位秒 + * @param is_player 客户端是否为播放器 + */ + void (API_CALL *on_mk_flow_report)(const mk_media_info url_info, + size_t total_bytes, + size_t total_seconds, + int is_player, + const mk_sock_info sender); + + + /** + * 日志输出广播 + * @param level 日志级别 + * @param file 源文件名 + * @param line 源文件行 + * @param function 源文件函数名 + * @param message 日志内容 + */ + void (API_CALL *on_mk_log)(int level, const char *file, int line, const char *function, const char *message); + +} mk_events; + + +/** + * 监听ZLMediaKit里面的事件 + * @param events 各个事件的结构体,这个对象在内部会再拷贝一次，可以设置为null以便取消监听 + */ +API_EXPORT void API_CALL mk_events_listen(const mk_events *events); + + +#ifdef __cplusplus +} +#endif +#endif //MK_EVENTS_H diff --git a/rknpu2/examples/3rdparty/zlmediakit/include/mk_events_objects.h b/rknpu2/examples/3rdparty/zlmediakit/include/mk_events_objects.h new file mode 100644 index 0000000000000000000000000000000000000000..d2fc2a57c1df9c5227f5c28e22b05dd64fa36806 --- /dev/null +++ b/rknpu2/examples/3rdparty/zlmediakit/include/mk_events_objects.h @@ -0,0 +1,332 @@ +/* + * Copyright (c) 2016 The ZLMediaKit project authors. All Rights Reserved. + * + * This file is part of ZLMediaKit(https://github.com/xia-chu/ZLMediaKit). + * + * Use of this source code is governed by MIT license that can be found in the + * LICENSE file in the root of the source tree. All contributing project authors + * may be found in the AUTHORS file in the root of the source tree. + */ + +#ifndef MK_EVENT_OBJECTS_H +#define MK_EVENT_OBJECTS_H +#include "mk_common.h" +#include "mk_tcp.h" +#ifdef __cplusplus +extern "C" { +#endif + +///////////////////////////////////////////MP4Info///////////////////////////////////////////// +//MP4Info对象的C映射 +typedef void* mk_mp4_info; +// GMT 标准时间，单位秒 +API_EXPORT uint64_t API_CALL mk_mp4_info_get_start_time(const mk_mp4_info ctx); +// 录像长度，单位秒 +API_EXPORT float API_CALL mk_mp4_info_get_time_len(const mk_mp4_info ctx); +// 文件大小，单位 BYTE +API_EXPORT size_t API_CALL mk_mp4_info_get_file_size(const mk_mp4_info ctx); +// 文件路径 +API_EXPORT const char* API_CALL mk_mp4_info_get_file_path(const mk_mp4_info ctx); +// 文件名称 +API_EXPORT const char* API_CALL mk_mp4_info_get_file_name(const mk_mp4_info ctx); +// 文件夹路径 +API_EXPORT const char* API_CALL mk_mp4_info_get_folder(const mk_mp4_info ctx); +// 播放路径 +API_EXPORT const char* API_CALL mk_mp4_info_get_url(const mk_mp4_info ctx); +// 应用名称 +API_EXPORT const char* API_CALL mk_mp4_info_get_vhost(const mk_mp4_info ctx); +// 流 ID +API_EXPORT const char* API_CALL mk_mp4_info_get_app(const mk_mp4_info ctx); +// 虚拟主机 +API_EXPORT const char* API_CALL mk_mp4_info_get_stream(const mk_mp4_info ctx); + +///////////////////////////////////////////Parser///////////////////////////////////////////// +//Parser对象的C映射 +typedef void* mk_parser; +//Parser::Method(),获取命令字，譬如GET/POST +API_EXPORT const char* API_CALL mk_parser_get_method(const mk_parser ctx); +//Parser::Url(),获取HTTP的访问url(不包括?后面的参数) +API_EXPORT const char* API_CALL mk_parser_get_url(const mk_parser ctx); +//Parser::Params(),?后面的参数字符串 +API_EXPORT const char* API_CALL mk_parser_get_url_params(const mk_parser ctx); +//Parser::getUrlArgs()["key"],获取?后面的参数中的特定参数 +API_EXPORT const char* API_CALL mk_parser_get_url_param(const mk_parser ctx,const char *key); +//Parser::Tail()，获取协议相关信息，譬如 HTTP/1.1 +API_EXPORT const char* API_CALL mk_parser_get_tail(const mk_parser ctx); +//Parser::getValues()["key"],获取HTTP头中特定字段 +API_EXPORT const char* API_CALL mk_parser_get_header(const mk_parser ctx,const char *key); +//Parser::Content(),获取HTTP body +API_EXPORT const char* API_CALL mk_parser_get_content(const mk_parser ctx, size_t *length); + +///////////////////////////////////////////MediaInfo///////////////////////////////////////////// +//MediaInfo对象的C映射 +typedef void* mk_media_info; +//MediaInfo::_param_strs +API_EXPORT const char* API_CALL mk_media_info_get_params(const mk_media_info ctx); +//MediaInfo::_schema +API_EXPORT const char* API_CALL mk_media_info_get_schema(const mk_media_info ctx); +//MediaInfo::_vhost +API_EXPORT const char* API_CALL mk_media_info_get_vhost(const mk_media_info ctx); +//MediaInfo::_app +API_EXPORT const char* API_CALL mk_media_info_get_app(const mk_media_info ctx); +//MediaInfo::_streamid +API_EXPORT const char* API_CALL mk_media_info_get_stream(const mk_media_info ctx); +//MediaInfo::_host +API_EXPORT const char* API_CALL mk_media_info_get_host(const mk_media_info ctx); +//MediaInfo::_port +API_EXPORT uint16_t API_CALL mk_media_info_get_port(const mk_media_info ctx); + + +///////////////////////////////////////////MediaSource///////////////////////////////////////////// +//MediaSource对象的C映射 +typedef void* mk_media_source; +//查找MediaSource的回调函数 +typedef void(API_CALL *on_mk_media_source_find_cb)(void *user_data, const mk_media_source ctx); + +//MediaSource::getSchema() +API_EXPORT const char* API_CALL mk_media_source_get_schema(const mk_media_source ctx); +//MediaSource::getVhost() +API_EXPORT const char* API_CALL mk_media_source_get_vhost(const mk_media_source ctx); +//MediaSource::getApp() +API_EXPORT const char* API_CALL mk_media_source_get_app(const mk_media_source ctx); +//MediaSource::getId() +API_EXPORT const char* API_CALL mk_media_source_get_stream(const mk_media_source ctx); +//MediaSource::readerCount() +API_EXPORT int API_CALL mk_media_source_get_reader_count(const mk_media_source ctx); +//MediaSource::totalReaderCount() +API_EXPORT int API_CALL mk_media_source_get_total_reader_count(const mk_media_source ctx); +/** + * 直播源在ZLMediaKit中被称作为MediaSource， + * 目前支持3种，分别是RtmpMediaSource、RtspMediaSource、HlsMediaSource + * 源的产生有被动和主动方式: + * 被动方式分别是rtsp/rtmp/rtp推流、mp4点播 + * 主动方式包括mk_media_create创建的对象(DevChannel)、mk_proxy_player_create创建的对象(PlayerProxy) + * 被动方式你不用做任何处理，ZLMediaKit已经默认适配了MediaSource::close()事件，都会关闭直播流 + * 主动方式你要设置这个事件的回调，你要自己选择删除对象 + * 通过mk_proxy_player_set_on_close、mk_media_set_on_close函数可以设置回调, + * 请在回调中删除对象来完成媒体的关闭，否则又为什么要调用mk_media_source_close函数？ + * @param ctx 对象 + * @param force 是否强制关闭，如果强制关闭，在有人观看的情况下也会关闭 + * @return 0代表失败，1代表成功 + */ +API_EXPORT int API_CALL mk_media_source_close(const mk_media_source ctx,int force); +//MediaSource::seekTo() +API_EXPORT int API_CALL mk_media_source_seek_to(const mk_media_source ctx,uint32_t stamp); + +/** + * rtp推流成功与否的回调(第一次成功后，后面将一直重试) + */ +typedef void(API_CALL *on_mk_media_source_send_rtp_result)(void *user_data, uint16_t local_port, int err, const char *msg); + +//MediaSource::startSendRtp,请参考mk_media_start_send_rtp,注意ctx参数类型不一样 +API_EXPORT void API_CALL mk_media_source_start_send_rtp(const mk_media_source ctx, const char *dst_url, uint16_t dst_port, const char *ssrc, int is_udp, on_mk_media_source_send_rtp_result cb, void *user_data); +//MediaSource::stopSendRtp，请参考mk_media_stop_send_rtp,注意ctx参数类型不一样 +API_EXPORT int API_CALL mk_media_source_stop_send_rtp(const mk_media_source ctx); + +//MediaSource::find() +API_EXPORT void API_CALL mk_media_source_find(const char *schema, + const char *vhost, + const char *app, + const char *stream, + int from_mp4, + void *user_data, + on_mk_media_source_find_cb cb); +//MediaSource::for_each_media() +API_EXPORT void API_CALL mk_media_source_for_each(void *user_data, on_mk_media_source_find_cb cb, const char *schema, + const char *vhost, const char *app, const char *stream); + +///////////////////////////////////////////HttpBody///////////////////////////////////////////// +//HttpBody对象的C映射 +typedef void* mk_http_body; +/** + * 生成HttpStringBody + * @param str 字符串指针 + * @param len 字符串长度，为0则用strlen获取 + */ +API_EXPORT mk_http_body API_CALL mk_http_body_from_string(const char *str,size_t len); + +/** + * 生成HttpFileBody + * @param file_path 文件完整路径 + */ +API_EXPORT mk_http_body API_CALL mk_http_body_from_file(const char *file_path); + +/** + * 生成HttpMultiFormBody + * @param key_val 参数key-value + * @param file_path 文件完整路径 + */ +API_EXPORT mk_http_body API_CALL mk_http_body_from_multi_form(const char *key_val[],const char *file_path); + +/** + * 销毁HttpBody + */ +API_EXPORT void API_CALL mk_http_body_release(mk_http_body ctx); + +///////////////////////////////////////////HttpResponseInvoker///////////////////////////////////////////// +//HttpSession::HttpResponseInvoker对象的C映射 +typedef void* mk_http_response_invoker; + +/** + * HttpSession::HttpResponseInvoker(const string &codeOut, const StrCaseMap &headerOut, const HttpBody::Ptr &body); + * @param response_code 譬如200 + * @param response_header 返回的http头，譬如 {"Content-Type","text/html",NULL} 必须以NULL结尾 + * @param response_body body对象 + */ +API_EXPORT void API_CALL mk_http_response_invoker_do(const mk_http_response_invoker ctx, + int response_code, + const char **response_header, + const mk_http_body response_body); + +/** + * HttpSession::HttpResponseInvoker(const string &codeOut, const StrCaseMap &headerOut, const string &body); + * @param response_code 譬如200 + * @param response_header 返回的http头，譬如 {"Content-Type","text/html",NULL} 必须以NULL结尾 + * @param response_content 返回的content部分，譬如一个网页内容 + */ +API_EXPORT void API_CALL mk_http_response_invoker_do_string(const mk_http_response_invoker ctx, + int response_code, + const char **response_header, + const char *response_content); +/** + * HttpSession::HttpResponseInvoker(const StrCaseMap &requestHeader,const StrCaseMap &responseHeader,const string &filePath); + * @param request_parser 请求事件中的mk_parser对象，用于提取其中http头中的Range字段，通过该字段先fseek然后再发送文件部分片段 + * @param response_header 返回的http头，譬如 {"Content-Type","text/html",NULL} 必须以NULL结尾 + * @param response_file_path 返回的content部分，譬如/path/to/html/file + */ +API_EXPORT void API_CALL mk_http_response_invoker_do_file(const mk_http_response_invoker ctx, + const mk_parser request_parser, + const char *response_header[], + const char *response_file_path); +/** +* 克隆mk_http_response_invoker对象，通过克隆对象为堆对象，可以实现跨线程异步执行mk_http_response_invoker_do +* 如果是同步执行mk_http_response_invoker_do，那么没必要克隆对象 +*/ +API_EXPORT mk_http_response_invoker API_CALL mk_http_response_invoker_clone(const mk_http_response_invoker ctx); + +/** + * 销毁堆上的克隆对象 + */ +API_EXPORT void API_CALL mk_http_response_invoker_clone_release(const mk_http_response_invoker ctx); + +///////////////////////////////////////////HttpAccessPathInvoker///////////////////////////////////////////// +//HttpSession::HttpAccessPathInvoker对象的C映射 +typedef void* mk_http_access_path_invoker; +/** + * HttpSession::HttpAccessPathInvoker(const string &errMsg,const string &accessPath, int cookieLifeSecond); + * @param err_msg 如果为空，则代表鉴权通过，否则为错误提示,可以为null + * @param access_path 运行或禁止访问的根目录,可以为null + * @param cookie_life_second 鉴权cookie有效期 + **/ +API_EXPORT void API_CALL mk_http_access_path_invoker_do(const mk_http_access_path_invoker ctx, + const char *err_msg, + const char *access_path, + int cookie_life_second); + +/** +* 克隆mk_http_access_path_invoker对象，通过克隆对象为堆对象，可以实现跨线程异步执行mk_http_access_path_invoker_do +* 如果是同步执行mk_http_access_path_invoker_do，那么没必要克隆对象 +*/ +API_EXPORT mk_http_access_path_invoker API_CALL mk_http_access_path_invoker_clone(const mk_http_access_path_invoker ctx); + +/** + * 销毁堆上的克隆对象 + */ +API_EXPORT void API_CALL mk_http_access_path_invoker_clone_release(const mk_http_access_path_invoker ctx); + +///////////////////////////////////////////RtspSession::onGetRealm///////////////////////////////////////////// +//RtspSession::onGetRealm对象的C映射 +typedef void* mk_rtsp_get_realm_invoker; +/** + * 执行RtspSession::onGetRealm + * @param realm 该rtsp流是否需要开启rtsp专属鉴权，至null或空字符串则不鉴权 + */ +API_EXPORT void API_CALL mk_rtsp_get_realm_invoker_do(const mk_rtsp_get_realm_invoker ctx, + const char *realm); + +/** +* 克隆mk_rtsp_get_realm_invoker对象，通过克隆对象为堆对象，可以实现跨线程异步执行mk_rtsp_get_realm_invoker_do +* 如果是同步执行mk_rtsp_get_realm_invoker_do，那么没必要克隆对象 +*/ +API_EXPORT mk_rtsp_get_realm_invoker API_CALL mk_rtsp_get_realm_invoker_clone(const mk_rtsp_get_realm_invoker ctx); + +/** + * 销毁堆上的克隆对象 + */ +API_EXPORT void API_CALL mk_rtsp_get_realm_invoker_clone_release(const mk_rtsp_get_realm_invoker ctx); + +///////////////////////////////////////////RtspSession::onAuth///////////////////////////////////////////// +//RtspSession::onAuth对象的C映射 +typedef void* mk_rtsp_auth_invoker; + +/** + * 执行RtspSession::onAuth + * @param encrypted 为true是则表明是md5加密的密码，否则是明文密码, 在请求明文密码时如果提供md5密码者则会导致认证失败 + * @param pwd_or_md5 明文密码或者md5加密的密码 + */ +API_EXPORT void API_CALL mk_rtsp_auth_invoker_do(const mk_rtsp_auth_invoker ctx, + int encrypted, + const char *pwd_or_md5); + +/** + * 克隆mk_rtsp_auth_invoker对象，通过克隆对象为堆对象，可以实现跨线程异步执行mk_rtsp_auth_invoker_do + * 如果是同步执行mk_rtsp_auth_invoker_do，那么没必要克隆对象 + */ +API_EXPORT mk_rtsp_auth_invoker API_CALL mk_rtsp_auth_invoker_clone(const mk_rtsp_auth_invoker ctx); + +/** + * 销毁堆上的克隆对象 + */ +API_EXPORT void API_CALL mk_rtsp_auth_invoker_clone_release(const mk_rtsp_auth_invoker ctx); + +///////////////////////////////////////////Broadcast::PublishAuthInvoker///////////////////////////////////////////// +//Broadcast::PublishAuthInvoker对象的C映射 +typedef void* mk_publish_auth_invoker; + +/** + * 执行Broadcast::PublishAuthInvoker + * @param err_msg 为空或null则代表鉴权成功 + * @param enable_hls 是否允许转换hls + * @param enable_mp4 是否运行MP4录制 + */ +API_EXPORT void API_CALL mk_publish_auth_invoker_do(const mk_publish_auth_invoker ctx, + const char *err_msg, + int enable_hls, + int enable_mp4); + +/** + * 克隆mk_publish_auth_invoker对象，通过克隆对象为堆对象，可以实现跨线程异步执行mk_publish_auth_invoker_do + * 如果是同步执行mk_publish_auth_invoker_do，那么没必要克隆对象 + */ +API_EXPORT mk_publish_auth_invoker API_CALL mk_publish_auth_invoker_clone(const mk_publish_auth_invoker ctx); + +/** + * 销毁堆上的克隆对象 + */ +API_EXPORT void API_CALL mk_publish_auth_invoker_clone_release(const mk_publish_auth_invoker ctx); + +///////////////////////////////////////////Broadcast::AuthInvoker///////////////////////////////////////////// +//Broadcast::AuthInvoker对象的C映射 +typedef void* mk_auth_invoker; + +/** + * 执行Broadcast::AuthInvoker + * @param err_msg 为空或null则代表鉴权成功 + */ +API_EXPORT void API_CALL mk_auth_invoker_do(const mk_auth_invoker ctx, const char *err_msg); + +/** + * 克隆mk_auth_invoker对象，通过克隆对象为堆对象，可以实现跨线程异步执行mk_auth_invoker_do + * 如果是同步执行mk_auth_invoker_do，那么没必要克隆对象 + */ +API_EXPORT mk_auth_invoker API_CALL mk_auth_invoker_clone(const mk_auth_invoker ctx); + +/** + * 销毁堆上的克隆对象 + */ +API_EXPORT void API_CALL mk_auth_invoker_clone_release(const mk_auth_invoker ctx); + +#ifdef __cplusplus +} +#endif +#endif //MK_EVENT_OBJECTS_H diff --git a/rknpu2/examples/3rdparty/zlmediakit/include/mk_export.h b/rknpu2/examples/3rdparty/zlmediakit/include/mk_export.h new file mode 100644 index 0000000000000000000000000000000000000000..421d858ea5cf9d142c426a8cbf4865870ac69a30 --- /dev/null +++ b/rknpu2/examples/3rdparty/zlmediakit/include/mk_export.h @@ -0,0 +1,42 @@ + +#ifndef API_EXPORT_H +#define API_EXPORT_H + +#ifdef MediaKitApi_STATIC +# define API_EXPORT +# define MK_API_NO_EXPORT +#else +# ifndef API_EXPORT +# ifdef mk_api_EXPORTS + /* We are building this library */ +# define API_EXPORT __attribute__((visibility("default"))) +# else + /* We are using this library */ +# define API_EXPORT __attribute__((visibility("default"))) +# endif +# endif + +# ifndef MK_API_NO_EXPORT +# define MK_API_NO_EXPORT __attribute__((visibility("hidden"))) +# endif +#endif + +#ifndef MK_API_DEPRECATED +# define MK_API_DEPRECATED __attribute__ ((__deprecated__)) +#endif + +#ifndef MK_API_DEPRECATED_EXPORT +# define MK_API_DEPRECATED_EXPORT API_EXPORT MK_API_DEPRECATED +#endif + +#ifndef MK_API_DEPRECATED_NO_EXPORT +# define MK_API_DEPRECATED_NO_EXPORT MK_API_NO_EXPORT MK_API_DEPRECATED +#endif + +#if 0 /* DEFINE_NO_DEPRECATED */ +# ifndef MK_API_NO_DEPRECATED +# define MK_API_NO_DEPRECATED +# endif +#endif + +#endif /* API_EXPORT_H */ diff --git a/rknpu2/examples/3rdparty/zlmediakit/include/mk_frame.h b/rknpu2/examples/3rdparty/zlmediakit/include/mk_frame.h new file mode 100644 index 0000000000000000000000000000000000000000..ec14a50823cd035ae17c7dc5a733647f22fb6488 --- /dev/null +++ b/rknpu2/examples/3rdparty/zlmediakit/include/mk_frame.h @@ -0,0 +1,123 @@ +/* + * Copyright (c) 2016 The ZLMediaKit project authors. All Rights Reserved. + * + * This file is part of ZLMediaKit(https://github.com/xia-chu/ZLMediaKit). + * + * Use of this source code is governed by MIT license that can be found in the + * LICENSE file in the root of the source tree. All contributing project authors + * may be found in the AUTHORS file in the root of the source tree. + */ + +#ifndef ZLMEDIAKIT_MK_FRAME_H +#define ZLMEDIAKIT_MK_FRAME_H + +#include "mk_common.h" + +#ifdef __cplusplus +extern "C" { +#endif + +//是否为关键帧 +#define MK_FRAME_FLAG_IS_KEY (1 << 0) +//是否为配置帧(sps/pps/vps等) +#define MK_FRAME_FLAG_IS_CONFIG (1 << 1) +//是否可丢弃的帧(sei/aud) +#define MK_FRAME_FLAG_DROP_ABLE (1 << 2) +//是否不可单独解码的帧(多slice的非vcl帧) +#define MK_FRAME_FLAG_NOT_DECODE_ABLE (1 << 3) + +//codec id常量定义 +API_EXPORT extern const int MKCodecH264; +API_EXPORT extern const int MKCodecH265; +API_EXPORT extern const int MKCodecAAC; +API_EXPORT extern const int MKCodecG711A; +API_EXPORT extern const int MKCodecG711U; +API_EXPORT extern const int MKCodecOpus; +API_EXPORT extern const int MKCodecL16; +API_EXPORT extern const int MKCodecVP8; +API_EXPORT extern const int MKCodecVP9; +API_EXPORT extern const int MKCodecAV1; +API_EXPORT extern const int MKCodecJPEG; + +typedef void *mk_frame; + +// 用户自定义free回调函数 +typedef void(API_CALL *on_mk_frame_data_release)(void *user_data, char *ptr); + +/** + * 创建frame对象，并返回其引用 + * @param codec_id 编解码类型，请参考MKCodecXXX定义 + * @param dts 解码时间戳，单位毫秒 + * @param pts 显示时间戳，单位毫秒 + * @param data 单帧数据 + * @param size 单帧数据长度 + * @param cb data指针free释放回调, 如果为空，内部会拷贝数据 + * @param user_data data指针free释放回调用户指针 + * @return frame对象引用 + */ +API_EXPORT mk_frame API_CALL mk_frame_create(int codec_id, uint64_t dts, uint64_t pts, const char *data, size_t size, + on_mk_frame_data_release cb, void *user_data); + +/** + * 减引用frame对象 + * @param frame 帧对象引用 + */ +API_EXPORT void API_CALL mk_frame_unref(mk_frame frame); + +/** + * 引用frame对象 + * @param frame 被引用的frame对象 + * @return 新的对象引用 + */ +API_EXPORT mk_frame API_CALL mk_frame_ref(mk_frame frame); + +/** + * 获取frame 编码codec类型，请参考MKCodecXXX定义 + */ +API_EXPORT int API_CALL mk_frame_codec_id(mk_frame frame); + +/** + * 获取帧编码codec名称 + */ +API_EXPORT const char* API_CALL mk_frame_codec_name(mk_frame frame); + +/** + * 帧是否为视频 + */ +API_EXPORT int API_CALL mk_frame_is_video(mk_frame frame); + +/** + * 获取帧数据指针 + */ +API_EXPORT const char* API_CALL mk_frame_get_data(mk_frame frame); + +/** + * 获取帧数据指针长度 + */ +API_EXPORT size_t API_CALL mk_frame_get_data_size(mk_frame frame); + +/** + * 返回帧数据前缀长度，譬如H264/H265前缀一般是0x00 00 00 01,那么本函数返回4 + */ +API_EXPORT size_t API_CALL mk_frame_get_data_prefix_size(mk_frame frame); + +/** + * 获取解码时间戳，单位毫秒 + */ +API_EXPORT uint64_t API_CALL mk_frame_get_dts(mk_frame frame); + +/** + * 获取显示时间戳，单位毫秒 + */ +API_EXPORT uint64_t API_CALL mk_frame_get_pts(mk_frame frame); + +/** + * 获取帧flag，请参考 MK_FRAME_FLAG + */ +API_EXPORT uint32_t API_CALL mk_frame_get_flags(mk_frame frame); + +#ifdef __cplusplus +} +#endif + +#endif //ZLMEDIAKIT_MK_FRAME_H diff --git a/rknpu2/examples/3rdparty/zlmediakit/include/mk_h264_splitter.h b/rknpu2/examples/3rdparty/zlmediakit/include/mk_h264_splitter.h new file mode 100644 index 0000000000000000000000000000000000000000..514992c952e2b3c2cf2204db06c2575650d92858 --- /dev/null +++ b/rknpu2/examples/3rdparty/zlmediakit/include/mk_h264_splitter.h @@ -0,0 +1,56 @@ +/* + * Copyright (c) 2016 The ZLMediaKit project authors. All Rights Reserved. + * + * This file is part of ZLMediaKit(https://github.com/xia-chu/ZLMediaKit). + * + * Use of this source code is governed by MIT license that can be found in the + * LICENSE file in the root of the source tree. All contributing project authors + * may be found in the AUTHORS file in the root of the source tree. + */ + +#ifndef ZLMEDIAKIT_MK_H264_SPLITTER_H +#define ZLMEDIAKIT_MK_H264_SPLITTER_H + +#include "mk_common.h" + +#ifdef __cplusplus +extern "C" { +#endif + +typedef void *mk_h264_splitter; + +/** + * h264 分帧器输出回调函数 + * @param user_data 设置回调时的用户数据指针 + * @param splitter 对象 + * @param frame 帧数据 + * @param size 帧数据长度 + */ +typedef void(API_CALL *on_mk_h264_splitter_frame)(void *user_data, mk_h264_splitter splitter, const char *frame, int size); + +/** + * 创建h264分帧器 + * @param cb 分帧回调函数 + * @param user_data 回调用户数据指针 + * @return 分帧器对象 + */ +API_EXPORT mk_h264_splitter API_CALL mk_h264_splitter_create(on_mk_h264_splitter_frame cb, void *user_data); + +/** + * 删除h264分帧器 + * @param ctx 分帧器 + */ +API_EXPORT void API_CALL mk_h264_splitter_release(mk_h264_splitter ctx); + +/** + * 输入数据并分帧 + * @param ctx 分帧器 + * @param data h264/h265数据 + * @param size 数据长度 + */ +API_EXPORT void API_CALL mk_h264_splitter_input_data(mk_h264_splitter ctx, const char *data, int size); + +#ifdef __cplusplus +} +#endif +#endif //ZLMEDIAKIT_MK_H264_SPLITTER_H diff --git a/rknpu2/examples/3rdparty/zlmediakit/include/mk_httpclient.h b/rknpu2/examples/3rdparty/zlmediakit/include/mk_httpclient.h new file mode 100644 index 0000000000000000000000000000000000000000..3aea1a357aca6dd46d73486dbda3e6aa1e2f47c4 --- /dev/null +++ b/rknpu2/examples/3rdparty/zlmediakit/include/mk_httpclient.h @@ -0,0 +1,158 @@ +/* + * Copyright (c) 2016 The ZLMediaKit project authors. All Rights Reserved. + * + * This file is part of ZLMediaKit(https://github.com/xia-chu/ZLMediaKit). + * + * Use of this source code is governed by MIT license that can be found in the + * LICENSE file in the root of the source tree. All contributing project authors + * may be found in the AUTHORS file in the root of the source tree. + */ + +#ifndef MK_HTTPCLIENT_H_ +#define MK_HTTPCLIENT_H_ + +#include "mk_common.h" +#include "mk_events_objects.h" + +#ifdef __cplusplus +extern "C" { +#endif + +///////////////////////////////////////////HttpDownloader///////////////////////////////////////////// + +typedef void *mk_http_downloader; + +/** + * @param user_data 用户数据指针 + * @param code 错误代码，0代表成功 + * @param err_msg 错误提示 + * @param file_path 文件保存路径 + */ +typedef void(API_CALL *on_mk_download_complete)(void *user_data, int code, const char *err_msg, const char *file_path); + +/** + * 创建http[s]下载器 + * @return 下载器指针 + */ +API_EXPORT mk_http_downloader API_CALL mk_http_downloader_create(); + +/** + * 销毁http[s]下载器 + * @param ctx 下载器指针 + */ +API_EXPORT void API_CALL mk_http_downloader_release(mk_http_downloader ctx); + +/** + * 开始http[s]下载 + * @param ctx 下载器指针 + * @param url http[s]下载url + * @param file 文件保存路径 + * @param cb 回调函数 + * @param user_data 用户数据指针 + */ +API_EXPORT void API_CALL mk_http_downloader_start(mk_http_downloader ctx, const char *url, const char *file, on_mk_download_complete cb, void *user_data); + + +///////////////////////////////////////////HttpRequester///////////////////////////////////////////// +typedef void *mk_http_requester; + +/** + * http请求结果回调 + * 在code == 0时代表本次http会话是完整的（收到了http回复） + * 用户应该通过user_data获取到mk_http_requester对象 + * 然后通过mk_http_requester_get_response等函数获取相关回复数据 + * 在回调结束时，应该通过mk_http_requester_release函数销毁该对象 + * 或者调用mk_http_requester_clear函数后再复用该对象 + * @param user_data 用户数据指针 + * @param code 错误代码，0代表成功 + * @param err_msg 错误提示 + */ +typedef void(API_CALL *on_mk_http_requester_complete)(void *user_data, int code, const char *err_msg); + +/** + * 创建HttpRequester + */ +API_EXPORT mk_http_requester API_CALL mk_http_requester_create(); + +/** + * 在复用mk_http_requester对象时才需要用到此方法 + */ +API_EXPORT void API_CALL mk_http_requester_clear(mk_http_requester ctx); + +/** + * 销毁HttpRequester + * 如果调用了mk_http_requester_start函数且正在等待http回复， + * 也可以调用mk_http_requester_release方法取消本次http请求 + */ +API_EXPORT void API_CALL mk_http_requester_release(mk_http_requester ctx); + +/** + * 设置HTTP方法，譬如GET/POST + */ +API_EXPORT void API_CALL mk_http_requester_set_method(mk_http_requester ctx,const char *method); + +/** + * 批量设置设置HTTP头 + * @param header 譬如 {"Content-Type","text/html",NULL} 必须以NULL结尾 + */ +API_EXPORT void API_CALL mk_http_requester_set_header(mk_http_requester ctx, const char *header[]); + +/** + * 添加HTTP头 + * @param key 譬如Content-Type + * @param value 譬如 text/html + * @param force 如果已经存在该key，是否强制替换 + */ +API_EXPORT void API_CALL mk_http_requester_add_header(mk_http_requester ctx,const char *key,const char *value,int force); + +/** + * 设置消息体， + * @param body mk_http_body对象，通过mk_http_body_from_string等函数生成，使用完毕后请调用mk_http_body_release释放之 + */ +API_EXPORT void API_CALL mk_http_requester_set_body(mk_http_requester ctx, mk_http_body body); + +/** + * 在收到HTTP回复后可调用该方法获取状态码 + * @return 譬如 200 OK + */ +API_EXPORT const char* API_CALL mk_http_requester_get_response_status(mk_http_requester ctx); + +/** + * 在收到HTTP回复后可调用该方法获取响应HTTP头 + * @param key HTTP头键名 + * @return HTTP头键值 + */ +API_EXPORT const char* API_CALL mk_http_requester_get_response_header(mk_http_requester ctx,const char *key); + +/** + * 在收到HTTP回复后可调用该方法获取响应HTTP body + * @param length 返回body长度,可以为null + * @return body指针 + */ +API_EXPORT const char* API_CALL mk_http_requester_get_response_body(mk_http_requester ctx, size_t *length); + +/** + * 在收到HTTP回复后可调用该方法获取响应 + * @return 响应对象 + */ +API_EXPORT mk_parser API_CALL mk_http_requester_get_response(mk_http_requester ctx); + +/** + * 设置回调函数 + * @param cb 回调函数，不能为空 + * @param user_data 用户数据指针 + */ +API_EXPORT void API_CALL mk_http_requester_set_cb(mk_http_requester ctx,on_mk_http_requester_complete cb, void *user_data); + +/** + * 开始url请求 + * @param url 请求url，支持http/https + * @param timeout_second 最大超时时间 + */ +API_EXPORT void API_CALL mk_http_requester_start(mk_http_requester ctx,const char *url, float timeout_second); + +#ifdef __cplusplus +} +#endif + +#endif /* MK_HTTPCLIENT_H_ */ diff --git a/rknpu2/examples/3rdparty/zlmediakit/include/mk_media.h b/rknpu2/examples/3rdparty/zlmediakit/include/mk_media.h new file mode 100644 index 0000000000000000000000000000000000000000..834a960e0f2ec8e93bb02e3ccba8df1032fc979b --- /dev/null +++ b/rknpu2/examples/3rdparty/zlmediakit/include/mk_media.h @@ -0,0 +1,279 @@ +/* + * Copyright (c) 2016 The ZLMediaKit project authors. All Rights Reserved. + * + * This file is part of ZLMediaKit(https://github.com/xia-chu/ZLMediaKit). + * + * Use of this source code is governed by MIT license that can be found in the + * LICENSE file in the root of the source tree. All contributing project authors + * may be found in the AUTHORS file in the root of the source tree. + */ + +#ifndef MK_MEDIA_H_ +#define MK_MEDIA_H_ + +#include "mk_common.h" +#include "mk_track.h" +#include "mk_frame.h" +#include "mk_events_objects.h" +#include "mk_thread.h" + +#ifdef __cplusplus +extern "C" { +#endif + +typedef void *mk_media; + +/** + * 创建一个媒体源 + * @param vhost 虚拟主机名，一般为__defaultVhost__ + * @param app 应用名，推荐为live + * @param stream 流id，例如camera + * @param duration 时长(单位秒)，直播则为0 + * @param hls_enabled 是否生成hls + * @param mp4_enabled 是否生成mp4 + * @return 对象指针 + */ +API_EXPORT mk_media API_CALL mk_media_create(const char *vhost, const char *app, const char *stream, + float duration, int hls_enabled, int mp4_enabled); + +/** + * 销毁媒体源 + * @param ctx 对象指针 + */ +API_EXPORT void API_CALL mk_media_release(mk_media ctx); + +/** + * 添加音视频track + * @param ctx mk_media对象 + * @param track mk_track对象，音视频轨道 + */ +API_EXPORT void API_CALL mk_media_init_track(mk_media ctx, mk_track track); + +/** + * 添加视频轨道，请改用mk_media_init_track方法 + * @param ctx 对象指针 + * @param codec_id 0:CodecH264/1:CodecH265 + * @param width 视频宽度; 在编码时才有效 + * @param height 视频高度; 在编码时才有效 + * @param fps 视频fps; 在编码时才有效 + * @param bit_rate 视频比特率,单位bps; 在编码时才有效 + * @param width 视频宽度 + * @param height 视频高度 + * @param fps 视频fps + * @return 1代表成功，0失败 + */ +API_EXPORT int API_CALL mk_media_init_video(mk_media ctx, int codec_id, int width, int height, float fps, int bit_rate); + +/** + * 添加音频轨道，请改用mk_media_init_track方法 + * @param ctx 对象指针 + * @param codec_id 2:CodecAAC/3:CodecG711A/4:CodecG711U/5:OPUS + * @param channel 通道数 + * @param sample_bit 采样位数，只支持16 + * @param sample_rate 采样率 + * @return 1代表成功，0失败 + */ +API_EXPORT int API_CALL mk_media_init_audio(mk_media ctx, int codec_id, int sample_rate, int channels, int sample_bit); + +/** + * 初始化h264/h265/aac完毕后调用此函数， + * 在单track(只有音频或视频)时，因为ZLMediaKit不知道后续是否还要添加track，所以会多等待3秒钟 + * 如果产生的流是单Track类型，请调用此函数以便加快流生成速度，当然不调用该函数，影响也不大(会多等待3秒) + * @param ctx 对象指针 + */ +API_EXPORT void API_CALL mk_media_init_complete(mk_media ctx); + +/** + * 输入frame对象 + * @param ctx mk_media对象 + * @param frame 帧对象 + * @return 1代表成功，0失败 + */ +API_EXPORT int API_CALL mk_media_input_frame(mk_media ctx, mk_frame frame); + +/** + * 输入单帧H264视频，帧起始字节00 00 01,00 00 00 01均可，请改用mk_media_input_frame方法 + * @param ctx 对象指针 + * @param data 单帧H264数据 + * @param len 单帧H264数据字节数 + * @param dts 解码时间戳，单位毫秒 + * @param pts 播放时间戳，单位毫秒 + * @return 1代表成功，0失败 + */ +API_EXPORT int API_CALL mk_media_input_h264(mk_media ctx, const void *data, int len, uint64_t dts, uint64_t pts); + +/** + * 输入单帧H265视频，帧起始字节00 00 01,00 00 00 01均可，请改用mk_media_input_frame方法 + * @param ctx 对象指针 + * @param data 单帧H265数据 + * @param len 单帧H265数据字节数 + * @param dts 解码时间戳，单位毫秒 + * @param pts 播放时间戳，单位毫秒 + * @return 1代表成功，0失败 + */ +API_EXPORT int API_CALL mk_media_input_h265(mk_media ctx, const void *data, int len, uint64_t dts, uint64_t pts); + +/** + * 输入YUV视频数据 + * @param ctx 对象指针 + * @param yuv yuv420p数据 + * @param linesize yuv420p linesize + * @param cts 视频采集时间戳，单位毫秒 + */ +API_EXPORT void API_CALL mk_media_input_yuv(mk_media ctx, const char *yuv[3], int linesize[3], uint64_t cts); + +/** + * 输入单帧AAC音频(单独指定adts头)，请改用mk_media_input_frame方法 + * @param ctx 对象指针 + * @param data 不包含adts头的单帧AAC数据，adts头7个字节 + * @param len 单帧AAC数据字节数 + * @param dts 时间戳，毫秒 + * @param adts adts头，可以为null + * @return 1代表成功，0失败 + */ +API_EXPORT int API_CALL mk_media_input_aac(mk_media ctx, const void *data, int len, uint64_t dts, void *adts); + +/** + * 输入单帧PCM音频,启用ENABLE_FAAC编译时，该函数才有效 + * @param ctx 对象指针 + * @param data 单帧PCM数据 + * @param len 单帧PCM数据字节数 + * @param dts 时间戳，毫秒 + * @return 1代表成功，0失败 + */ +API_EXPORT int API_CALL mk_media_input_pcm(mk_media ctx, void *data, int len, uint64_t pts); + +/** + * 输入单帧OPUS/G711音频帧，请改用mk_media_input_frame方法 + * @param ctx 对象指针 + * @param data 单帧音频数据 + * @param len 单帧音频数据字节数 + * @param dts 时间戳，毫秒 + * @return 1代表成功，0失败 + */ +API_EXPORT int API_CALL mk_media_input_audio(mk_media ctx, const void* data, int len, uint64_t dts); + +/** + * MediaSource.close()回调事件 + * 在选择关闭一个关联的MediaSource时，将会最终触发到该回调 + * 你应该通过该事件调用mk_media_release函数并且释放其他资源 + * 如果你不调用mk_media_release函数，那么MediaSource.close()操作将无效 + * @param user_data 用户数据指针，通过mk_media_set_on_close函数设置 + */ +typedef void(API_CALL *on_mk_media_close)(void *user_data); + +/** + * 监听MediaSource.close()事件 + * 在选择关闭一个关联的MediaSource时，将会最终触发到该回调 + * 你应该通过该事件调用mk_media_release函数并且释放其他资源 + * @param ctx 对象指针 + * @param cb 回调指针 + * @param user_data 用户数据指针 + */ +API_EXPORT void API_CALL mk_media_set_on_close(mk_media ctx, on_mk_media_close cb, void *user_data); + +/** + * 收到客户端的seek请求时触发该回调 + * @param user_data 用户数据指针,通过mk_media_set_on_seek设置 + * @param stamp_ms seek至的时间轴位置，单位毫秒 + * @return 1代表将处理seek请求，0代表忽略该请求 + */ +typedef int(API_CALL *on_mk_media_seek)(void *user_data,uint32_t stamp_ms); + +/** + * 收到客户端的pause或resume请求时触发该回调 + * @param user_data 用户数据指针,通过mk_media_set_on_pause设置 + * @param pause 1:暂停, 0: 恢复 + */ +typedef int(API_CALL* on_mk_media_pause)(void* user_data, int pause); + +/** + * 收到客户端的speed请求时触发该回调 + * @param user_data 用户数据指针,通过mk_media_set_on_pause设置 + * @param speed 0.5 1.0 2.0 + */ +typedef int(API_CALL* on_mk_media_speed)(void* user_data, float speed); + +/** + * 监听播放器seek请求事件 + * @param ctx 对象指针 + * @param cb 回调指针 + * @param user_data 用户数据指针 + */ +API_EXPORT void API_CALL mk_media_set_on_seek(mk_media ctx, on_mk_media_seek cb, void *user_data); + +/** + * 监听播放器pause请求事件 + * @param ctx 对象指针 + * @param cb 回调指针 + * @param user_data 用户数据指针 + */ +API_EXPORT void API_CALL mk_media_set_on_pause(mk_media ctx, on_mk_media_pause cb, void* user_data); + +/** + * 监听播放器pause请求事件 + * @param ctx 对象指针 + * @param cb 回调指针 + * @param user_data 用户数据指针 + */ +API_EXPORT void API_CALL mk_media_set_on_speed(mk_media ctx, on_mk_media_speed cb, void* user_data); + +/** + * 获取总的观看人数 + * @param ctx 对象指针 + * @return 观看人数 + */ +API_EXPORT int API_CALL mk_media_total_reader_count(mk_media ctx); + +/** + * 生成的MediaSource注册或注销事件 + * @param user_data 设置回调时的用户数据指针 + * @param sender 生成的MediaSource对象 + * @param regist 1为注册事件，0为注销事件 + */ +typedef void(API_CALL *on_mk_media_source_regist)(void *user_data, mk_media_source sender, int regist); + +/** + * 设置MediaSource注册或注销事件回调函数 + * @param ctx 对象指针 + * @param cb 回调指针 + * @param user_data 用户数据指针 + */ +API_EXPORT void API_CALL mk_media_set_on_regist(mk_media ctx, on_mk_media_source_regist cb, void *user_data); + +/** + * rtp推流成功与否的回调(第一次成功后，后面将一直重试) + */ +typedef on_mk_media_source_send_rtp_result on_mk_media_send_rtp_result; + +/** + * 开始发送一路ps-rtp流(通过ssrc区分多路)，此api线程安全 + * @param ctx 对象指针 + * @param dst_url 目标ip或域名 + * @param dst_port 目标端口 + * @param ssrc rtp的ssrc，10进制的字符串打印 + * @param is_udp 是否为udp + * @param cb 启动成功或失败回调 + * @param user_data 回调用户指针 + */ +API_EXPORT void API_CALL mk_media_start_send_rtp(mk_media ctx, const char *dst_url, uint16_t dst_port, const char *ssrc, int is_udp, on_mk_media_send_rtp_result cb, void *user_data); + +/** + * 停止某路或全部ps-rtp发送，此api线程安全 + * @param ctx 对象指针 + * @param ssrc rtp的ssrc，10进制的字符串打印，如果为null或空字符串，则停止所有rtp推流 + */ +API_EXPORT void API_CALL mk_media_stop_send_rtp(mk_media ctx, const char *ssrc); + +/** + * 获取所属线程 + * @param ctx 对象指针 + */ +API_EXPORT mk_thread API_CALL mk_media_get_owner_thread(mk_media ctx); + + +#ifdef __cplusplus +} +#endif + +#endif /* MK_MEDIA_H_ */ diff --git a/rknpu2/examples/3rdparty/zlmediakit/include/mk_mediakit.h b/rknpu2/examples/3rdparty/zlmediakit/include/mk_mediakit.h new file mode 100644 index 0000000000000000000000000000000000000000..42c1ddc0c9b043e9e8117c5d60fc79e1b28cf7f7 --- /dev/null +++ b/rknpu2/examples/3rdparty/zlmediakit/include/mk_mediakit.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2016 The ZLMediaKit project authors. All Rights Reserved. + * + * This file is part of ZLMediaKit(https://github.com/xia-chu/ZLMediaKit). + * + * Use of this source code is governed by MIT license that can be found in the + * LICENSE file in the root of the source tree. All contributing project authors + * may be found in the AUTHORS file in the root of the source tree. + */ + +#ifndef MK_API_H_ +#define MK_API_H_ + +#include "mk_common.h" +#include "mk_httpclient.h" +#include "mk_media.h" +#include "mk_proxyplayer.h" +#include "mk_recorder.h" +#include "mk_player.h" +#include "mk_pusher.h" +#include "mk_events.h" +#include "mk_tcp.h" +#include "mk_util.h" +#include "mk_thread.h" +#include "mk_rtp_server.h" +#include "mk_h264_splitter.h" +#include "mk_frame.h" +#include "mk_track.h" +#include "mk_transcode.h" + +#endif /* MK_API_H_ */ diff --git a/rknpu2/examples/3rdparty/zlmediakit/include/mk_player.h b/rknpu2/examples/3rdparty/zlmediakit/include/mk_player.h new file mode 100644 index 0000000000000000000000000000000000000000..c74d9739502b64c7133b8ed0d54e24ca0d16d472 --- /dev/null +++ b/rknpu2/examples/3rdparty/zlmediakit/include/mk_player.h @@ -0,0 +1,134 @@ +/* + * Copyright (c) 2016 The ZLMediaKit project authors. All Rights Reserved. + * + * This file is part of ZLMediaKit(https://github.com/xia-chu/ZLMediaKit). + * + * Use of this source code is governed by MIT license that can be found in the + * LICENSE file in the root of the source tree. All contributing project authors + * may be found in the AUTHORS file in the root of the source tree. + */ + +#ifndef MK_PLAYER_H_ +#define MK_PLAYER_H_ + +#include "mk_common.h" +#include "mk_frame.h" +#include "mk_track.h" + +#ifdef __cplusplus +extern "C" { +#endif + +typedef void* mk_player; + +/** + * 播放结果或播放中断事件的回调 + * @param user_data 用户数据指针 + * @param err_code 错误代码，0为成功 + * @param err_msg 错误提示 + * @param tracks track列表 + * @param track_count track个数 + */ +typedef void(API_CALL *on_mk_play_event)(void *user_data, int err_code, const char *err_msg, mk_track tracks[], + int track_count); + +/** + * 创建一个播放器,支持rtmp[s]/rtsp[s] + * @return 播放器指针 + */ +API_EXPORT mk_player API_CALL mk_player_create(); + +/** + * 销毁播放器 + * @param ctx 播放器指针 + */ +API_EXPORT void API_CALL mk_player_release(mk_player ctx); + +/** + * 设置播放器配置选项 + * @param ctx 播放器指针 + * @param key 配置项键,支持 net_adapter/rtp_type/rtsp_user/rtsp_pwd/protocol_timeout_ms/media_timeout_ms/beat_interval_ms/wait_track_ready + * @param val 配置项值,如果是整形，需要转换成统一转换成string + */ +API_EXPORT void API_CALL mk_player_set_option(mk_player ctx, const char *key, const char *val); + +/** + * 开始播放url + * @param ctx 播放器指针 + * @param url rtsp[s]/rtmp[s] url + */ +API_EXPORT void API_CALL mk_player_play(mk_player ctx, const char *url); + +/** + * 暂停或恢复播放，仅对点播有用 + * @param ctx 播放器指针 + * @param pause 1:暂停播放，0：恢复播放 + */ +API_EXPORT void API_CALL mk_player_pause(mk_player ctx, int pause); + +/** + * 倍数播放，仅对点播有用 + * @param ctx 播放器指针 + * @param speed 0.5 1.0 2.0 + */ +API_EXPORT void API_CALL mk_player_speed(mk_player ctx, float speed); + +/** + * 设置点播进度条 + * @param ctx 对象指针 + * @param progress 取值范围未 0.0～1.0 + */ +API_EXPORT void API_CALL mk_player_seekto(mk_player ctx, float progress); + +/** + * 设置点播进度条 + * @param ctx 对象指针 + * @param seek_pos 取值范围 相对于开始时间增量 单位秒 + */ +API_EXPORT void API_CALL mk_player_seekto_pos(mk_player ctx, int seek_pos); + +/** + * 设置播放器开启播放结果回调函数 + * @param ctx 播放器指针 + * @param cb 回调函数指针,设置null立即取消回调 + * @param user_data 用户数据指针 + */ +API_EXPORT void API_CALL mk_player_set_on_result(mk_player ctx, on_mk_play_event cb, void *user_data); + +/** + * 设置播放被异常中断的回调 + * @param ctx 播放器指针 + * @param cb 回调函数指针,设置null立即取消回调 + * @param user_data 用户数据指针 + */ +API_EXPORT void API_CALL mk_player_set_on_shutdown(mk_player ctx, on_mk_play_event cb, void *user_data); + +///////////////////////////获取音视频相关信息接口在播放成功回调触发后才有效/////////////////////////////// + +/** + * 获取点播节目时长，如果是直播返回0，否则返回秒数 + */ +API_EXPORT float API_CALL mk_player_duration(mk_player ctx); + +/** + * 获取点播播放进度，取值范围 0.0～1.0 + */ +API_EXPORT float API_CALL mk_player_progress(mk_player ctx); + +/** + * 获取点播播放进度位置，取值范围 相对于开始时间增量 单位秒 + */ +API_EXPORT int API_CALL mk_player_progress_pos(mk_player ctx); + +/** + * 获取丢包率，rtsp时有效 + * @param ctx 对象指针 + * @param track_type 0：视频，1：音频 + */ +API_EXPORT float API_CALL mk_player_loss_rate(mk_player ctx, int track_type); + +#ifdef __cplusplus +} +#endif + +#endif /* MK_PLAYER_H_ */ diff --git a/rknpu2/examples/3rdparty/zlmediakit/include/mk_proxyplayer.h b/rknpu2/examples/3rdparty/zlmediakit/include/mk_proxyplayer.h new file mode 100644 index 0000000000000000000000000000000000000000..537363971d0fa2694e2dd44d7de6ef81d2129544 --- /dev/null +++ b/rknpu2/examples/3rdparty/zlmediakit/include/mk_proxyplayer.h @@ -0,0 +1,85 @@ +/* + * Copyright (c) 2016 The ZLMediaKit project authors. All Rights Reserved. + * + * This file is part of ZLMediaKit(https://github.com/xia-chu/ZLMediaKit). + * + * Use of this source code is governed by MIT license that can be found in the + * LICENSE file in the root of the source tree. All contributing project authors + * may be found in the AUTHORS file in the root of the source tree. + */ + +#ifndef MK_PROXY_PLAYER_H_ +#define MK_PROXY_PLAYER_H_ + +#include "mk_common.h" + +#ifdef __cplusplus +extern "C" { +#endif + +typedef void *mk_proxy_player; + +/** + * 创建一个代理播放器 + * @param vhost 虚拟主机名，一般为__defaultVhost__ + * @param app 应用名 + * @param stream 流名 + * @param rtp_type rtsp播放方式:RTP_TCP = 0, RTP_UDP = 1, RTP_MULTICAST = 2 + * @param hls_enabled 是否生成hls + * @param mp4_enabled 是否生成mp4 + * @return 对象指针 + */ +API_EXPORT mk_proxy_player API_CALL mk_proxy_player_create(const char *vhost, const char *app, const char *stream, int hls_enabled, int mp4_enabled); + +/** + * 销毁代理播放器 + * @param ctx 对象指针 + */ +API_EXPORT void API_CALL mk_proxy_player_release(mk_proxy_player ctx); + +/** + * 设置代理播放器配置选项 + * @param ctx 代理播放器指针 + * @param key 配置项键,支持 net_adapter/rtp_type/rtsp_user/rtsp_pwd/protocol_timeout_ms/media_timeout_ms/beat_interval_ms + * @param val 配置项值,如果是整形，需要转换成统一转换成string + */ +API_EXPORT void API_CALL mk_proxy_player_set_option(mk_proxy_player ctx, const char *key, const char *val); + +/** + * 开始播放 + * @param ctx 对象指针 + * @param url 播放url,支持rtsp/rtmp + */ +API_EXPORT void API_CALL mk_proxy_player_play(mk_proxy_player ctx, const char *url); + +/** + * MediaSource.close()回调事件 + * 在选择关闭一个关联的MediaSource时，将会最终触发到该回调 + * 你应该通过该事件调用mk_proxy_player_release函数并且释放其他资源 + * 如果你不调用mk_proxy_player_release函数，那么MediaSource.close()操作将无效 + * @param user_data 用户数据指针，通过mk_proxy_player_set_on_close函数设置 + */ +typedef void(API_CALL *on_mk_proxy_player_close)(void *user_data, int err, const char *what, int sys_err); + +/** + * 监听MediaSource.close()事件 + * 在选择关闭一个关联的MediaSource时，将会最终触发到该回调 + * 你应该通过该事件调用mk_proxy_player_release函数并且释放其他资源 + * @param ctx 对象指针 + * @param cb 回调指针 + * @param user_data 用户数据指针 + */ +API_EXPORT void API_CALL mk_proxy_player_set_on_close(mk_proxy_player ctx, on_mk_proxy_player_close cb, void *user_data); + +/** + * 获取总的观看人数 + * @param ctx 对象指针 + * @return 观看人数 + */ +API_EXPORT int API_CALL mk_proxy_player_total_reader_count(mk_proxy_player ctx); + +#ifdef __cplusplus +} +#endif + +#endif /* MK_PROXY_PLAYER_H_ */ diff --git a/rknpu2/examples/3rdparty/zlmediakit/include/mk_pusher.h b/rknpu2/examples/3rdparty/zlmediakit/include/mk_pusher.h new file mode 100644 index 0000000000000000000000000000000000000000..2f2883ef3e84ed1a4c61fa70bbc850c054eb0214 --- /dev/null +++ b/rknpu2/examples/3rdparty/zlmediakit/include/mk_pusher.h @@ -0,0 +1,94 @@ +/* + * Copyright (c) 2016 The ZLMediaKit project authors. All Rights Reserved. + * + * This file is part of ZLMediaKit(https://github.com/xia-chu/ZLMediaKit). + * + * Use of this source code is governed by MIT license that can be found in the + * LICENSE file in the root of the source tree. All contributing project authors + * may be found in the AUTHORS file in the root of the source tree. + */ + +#ifndef MK_PUSHER_H +#define MK_PUSHER_H + +#include "mk_common.h" +#include "mk_events_objects.h" + +#ifdef __cplusplus +extern "C" { +#endif + +typedef void* mk_pusher; + +/** + * 推流结果或推流中断事件的回调 + * @param user_data 用户数据指针 + * @param err_code 错误代码，0为成功 + * @param err_msg 错误提示 + */ +typedef void(API_CALL *on_mk_push_event)(void *user_data,int err_code,const char *err_msg); + +/** + * 绑定的MediaSource对象并创建rtmp[s]/rtsp[s]推流器 + * MediaSource通过mk_media_create或mk_proxy_player_create或推流生成 + * 该MediaSource对象必须已注册 + * + * @param schema 绑定的MediaSource对象所属协议，支持rtsp/rtmp + * @param vhost 绑定的MediaSource对象的虚拟主机，一般为__defaultVhost__ + * @param app 绑定的MediaSource对象的应用名，一般为live + * @param stream 绑定的MediaSource对象的流id + * @return 对象指针 + */ +API_EXPORT mk_pusher API_CALL mk_pusher_create(const char *schema,const char *vhost,const char *app, const char *stream); + +/** + * 绑定的MediaSource对象并创建rtmp[s]/rtsp[s]推流器 + * MediaSource通过mk_media_create或mk_proxy_player_create或推流生成 + * 该MediaSource对象必须已注册 + * + * @param src MediaSource对象 + * @return 对象指针 + */ +API_EXPORT mk_pusher API_CALL mk_pusher_create_src(mk_media_source src); + +/** + * 释放推流器 + * @param ctx 推流器指针 + */ +API_EXPORT void API_CALL mk_pusher_release(mk_pusher ctx); + +/** + * 设置推流器配置选项 + * @param ctx 推流器指针 + * @param key 配置项键,支持 net_adapter/rtp_type/rtsp_user/rtsp_pwd/protocol_timeout_ms/media_timeout_ms/beat_interval_ms + * @param val 配置项值,如果是整形，需要转换成统一转换成string + */ +API_EXPORT void API_CALL mk_pusher_set_option(mk_pusher ctx, const char *key, const char *val); + +/** + * 开始推流 + * @param ctx 推流器指针 + * @param url 推流地址，支持rtsp[s]/rtmp[s] + */ +API_EXPORT void API_CALL mk_pusher_publish(mk_pusher ctx,const char *url); + +/** + * 设置推流器推流结果回调函数 + * @param ctx 推流器指针 + * @param cb 回调函数指针,不得为null + * @param user_data 用户数据指针 + */ +API_EXPORT void API_CALL mk_pusher_set_on_result(mk_pusher ctx, on_mk_push_event cb, void *user_data); + +/** + * 设置推流被异常中断的回调 + * @param ctx 推流器指针 + * @param cb 回调函数指针,不得为null + * @param user_data 用户数据指针 + */ +API_EXPORT void API_CALL mk_pusher_set_on_shutdown(mk_pusher ctx, on_mk_push_event cb, void *user_data); + +#ifdef __cplusplus +} +#endif +#endif //MK_PUSHER_H diff --git a/rknpu2/examples/3rdparty/zlmediakit/include/mk_recorder.h b/rknpu2/examples/3rdparty/zlmediakit/include/mk_recorder.h new file mode 100644 index 0000000000000000000000000000000000000000..2653fd50017fa3c3995b8cc6cbccbad0afc7b741 --- /dev/null +++ b/rknpu2/examples/3rdparty/zlmediakit/include/mk_recorder.h @@ -0,0 +1,85 @@ +/* + * Copyright (c) 2016 The ZLMediaKit project authors. All Rights Reserved. + * + * This file is part of ZLMediaKit(https://github.com/xia-chu/ZLMediaKit). + * + * Use of this source code is governed by MIT license that can be found in the + * LICENSE file in the root of the source tree. All contributing project authors + * may be found in the AUTHORS file in the root of the source tree. + */ + +#ifndef MK_RECORDER_API_H_ +#define MK_RECORDER_API_H_ + +#include "mk_common.h" + +#ifdef __cplusplus +extern "C" { +#endif + +///////////////////////////////////////////flv录制///////////////////////////////////////////// + +typedef void* mk_flv_recorder; + +/** + * 创建flv录制器 + * @return + */ +API_EXPORT mk_flv_recorder API_CALL mk_flv_recorder_create(); + +/** + * 释放flv录制器 + * @param ctx + */ +API_EXPORT void API_CALL mk_flv_recorder_release(mk_flv_recorder ctx); + +/** + * 开始录制flv + * @param ctx flv录制器 + * @param vhost 虚拟主机 + * @param app 绑定的RtmpMediaSource的 app名 + * @param stream 绑定的RtmpMediaSource的 stream名 + * @param file_path 文件存放地址 + * @return 0:开始超过，-1:失败,打开文件失败或该RtmpMediaSource不存在 + */ +API_EXPORT int API_CALL mk_flv_recorder_start(mk_flv_recorder ctx, const char *vhost, const char *app, const char *stream, const char *file_path); + +///////////////////////////////////////////hls/mp4录制///////////////////////////////////////////// + +/** + * 获取录制状态 + * @param type 0:hls,1:MP4 + * @param vhost 虚拟主机 + * @param app 应用名 + * @param stream 流id + * @return 录制状态,0:未录制, 1:正在录制 + */ +API_EXPORT int API_CALL mk_recorder_is_recording(int type, const char *vhost, const char *app, const char *stream); + +/** + * 开始录制 + * @param type 0:hls,1:MP4 + * @param vhost 虚拟主机 + * @param app 应用名 + * @param stream 流id + * @param customized_path 录像文件保存自定义目录，默认为空或null则自动生成 + * @param max_second mp4录制最大切片时间，单位秒，置0则采用配置文件配置 + * @return 1代表成功，0代表失败 + */ +API_EXPORT int API_CALL mk_recorder_start(int type, const char *vhost, const char *app, const char *stream, const char *customized_path, size_t max_second); + +/** + * 停止录制 + * @param type 0:hls,1:MP4 + * @param vhost 虚拟主机 + * @param app 应用名 + * @param stream 流id + * @return 1:成功，0：失败 + */ +API_EXPORT int API_CALL mk_recorder_stop(int type, const char *vhost, const char *app, const char *stream); + +#ifdef __cplusplus +} +#endif + +#endif /* MK_RECORDER_API_H_ */ diff --git a/rknpu2/examples/3rdparty/zlmediakit/include/mk_rtp_server.h b/rknpu2/examples/3rdparty/zlmediakit/include/mk_rtp_server.h new file mode 100644 index 0000000000000000000000000000000000000000..20a0c5efe92d86e6153441e86b75d5ba0bf59cb3 --- /dev/null +++ b/rknpu2/examples/3rdparty/zlmediakit/include/mk_rtp_server.h @@ -0,0 +1,74 @@ +/* + * Copyright (c) 2016 The ZLMediaKit project authors. All Rights Reserved. + * + * This file is part of ZLMediaKit(https://github.com/xia-chu/ZLMediaKit). + * + * Use of this source code is governed by MIT license that can be found in the + * LICENSE file in the root of the source tree. All contributing project authors + * may be found in the AUTHORS file in the root of the source tree. + */ + +#include "mk_common.h" + +#ifdef __cplusplus +extern "C" { +#endif + +typedef void* mk_rtp_server; + +/** + * 创建GB28181 RTP 服务器 + * @param port 监听端口，0则为随机 + * @param tcp_mode tcp模式(0: 不监听端口 1: 监听端口 2: 主动连接到服务端) + * @param stream_id 该端口绑定的流id + * @return + */ +API_EXPORT mk_rtp_server API_CALL mk_rtp_server_create(uint16_t port, int tcp_mode, const char *stream_id); + +/** + * TCP 主动模式时连接到服务器是否成功的回调 + */ +typedef void(API_CALL *on_mk_rtp_server_connected)(void *user_data, int err, const char *what, int sys_err); + +/** + * TCP 主动模式时连接到服务器 + * @param @param ctx 服务器对象 + * @param dst_url 服务端地址 + * @param dst_port 服务端端口 + * @param cb 连接到服务器是否成功的回调 + * @param user_data 用户数据指针 + * @return + */ +API_EXPORT void API_CALL mk_rtp_server_connect(mk_rtp_server ctx, const char *dst_url, uint16_t dst_port, on_mk_rtp_server_connected cb, void *user_data); + +/** + * 销毁GB28181 RTP 服务器 + * @param ctx 服务器对象 + */ +API_EXPORT void API_CALL mk_rtp_server_release(mk_rtp_server ctx); + +/** + * 获取本地监听的端口号 + * @param ctx 服务器对象 + * @return 端口号 + */ +API_EXPORT uint16_t API_CALL mk_rtp_server_port(mk_rtp_server ctx); + +/** + * GB28181 RTP 服务器接收流超时时触发 + * @param user_data 用户数据指针 + */ +typedef void(API_CALL *on_mk_rtp_server_detach)(void *user_data); + +/** + * 监听B28181 RTP 服务器接收流超时事件 + * @param ctx 服务器对象 + * @param cb 回调函数 + * @param user_data 回调函数用户数据指针 + */ +API_EXPORT void API_CALL mk_rtp_server_set_on_detach(mk_rtp_server ctx, on_mk_rtp_server_detach cb, void *user_data); + + +#ifdef __cplusplus +} +#endif \ No newline at end of file diff --git a/rknpu2/examples/3rdparty/zlmediakit/include/mk_tcp.h b/rknpu2/examples/3rdparty/zlmediakit/include/mk_tcp.h new file mode 100644 index 0000000000000000000000000000000000000000..910522f976f2ab4edcb0649b367bf088077f9490 --- /dev/null +++ b/rknpu2/examples/3rdparty/zlmediakit/include/mk_tcp.h @@ -0,0 +1,265 @@ +/* + * Copyright (c) 2016 The ZLMediaKit project authors. All Rights Reserved. + * + * This file is part of ZLMediaKit(https://github.com/xia-chu/ZLMediaKit). + * + * Use of this source code is governed by MIT license that can be found in the + * LICENSE file in the root of the source tree. All contributing project authors + * may be found in the AUTHORS file in the root of the source tree. + */ + +#ifndef MK_TCP_H +#define MK_TCP_H + +#include "mk_common.h" + +#ifdef __cplusplus +extern "C" { +#endif + +///////////////////////////////////////////Buffer::Ptr///////////////////////////////////////////// + +typedef void *mk_buffer; +typedef void(API_CALL *on_mk_buffer_free)(void *user_data, void *data); + +/** + * 创建buffer对象 + * @param data 数据指针 + * @param len 数据长度 + * @param cb 数据指针free回调函数，该参数置空时，内部会拷贝数据 + * @param user_data 数据指针free回调函数on_mk_buffer_free第一个参数 + * @return buffer对象 + */ +API_EXPORT mk_buffer API_CALL mk_buffer_from_char(const char *data, size_t len, on_mk_buffer_free cb, void *user_data); +API_EXPORT mk_buffer API_CALL mk_buffer_ref(mk_buffer buffer); +API_EXPORT void API_CALL mk_buffer_unref(mk_buffer buffer); +API_EXPORT const char* API_CALL mk_buffer_get_data(mk_buffer buffer); +API_EXPORT size_t API_CALL mk_buffer_get_size(mk_buffer buffer); + +///////////////////////////////////////////SockInfo///////////////////////////////////////////// +//SockInfo对象的C映射 +typedef void* mk_sock_info; + +//SockInfo::get_peer_ip() +API_EXPORT const char* API_CALL mk_sock_info_peer_ip(const mk_sock_info ctx, char *buf); +//SockInfo::get_local_ip() +API_EXPORT const char* API_CALL mk_sock_info_local_ip(const mk_sock_info ctx, char *buf); +//SockInfo::get_peer_port() +API_EXPORT uint16_t API_CALL mk_sock_info_peer_port(const mk_sock_info ctx); +//SockInfo::get_local_port() +API_EXPORT uint16_t API_CALL mk_sock_info_local_port(const mk_sock_info ctx); + +#ifndef SOCK_INFO_API_RENAME +#define SOCK_INFO_API_RENAME +//mk_tcp_session对象转换成mk_sock_info对象后再获取网络相关信息 +#define mk_tcp_session_peer_ip(x,buf) mk_sock_info_peer_ip(mk_tcp_session_get_sock_info(x),buf) +#define mk_tcp_session_local_ip(x,buf) mk_sock_info_local_ip(mk_tcp_session_get_sock_info(x),buf) +#define mk_tcp_session_peer_port(x) mk_sock_info_peer_port(mk_tcp_session_get_sock_info(x)) +#define mk_tcp_session_local_port(x) mk_sock_info_local_port(mk_tcp_session_get_sock_info(x)) + +//mk_tcp_client对象转换成mk_sock_info对象后再获取网络相关信息 +#define mk_tcp_client_peer_ip(x,buf) mk_sock_info_peer_ip(mk_tcp_client_get_sock_info(x),buf) +#define mk_tcp_client_local_ip(x,buf) mk_sock_info_local_ip(mk_tcp_client_get_sock_info(x),buf) +#define mk_tcp_client_peer_port(x) mk_sock_info_peer_port(mk_tcp_client_get_sock_info(x)) +#define mk_tcp_client_local_port(x) mk_sock_info_local_port(mk_tcp_client_get_sock_info(x)) +#endif +///////////////////////////////////////////TcpSession///////////////////////////////////////////// +//TcpSession对象的C映射 +typedef void* mk_tcp_session; +typedef void* mk_tcp_session_ref; + +//获取基类指针以便获取其网络相关信息 +API_EXPORT mk_sock_info API_CALL mk_tcp_session_get_sock_info(const mk_tcp_session ctx); + +//TcpSession::safeShutdown() +API_EXPORT void API_CALL mk_tcp_session_shutdown(const mk_tcp_session ctx,int err,const char *err_msg); +//TcpSession::send() +API_EXPORT void API_CALL mk_tcp_session_send(const mk_tcp_session ctx, const char *data, size_t len); +API_EXPORT void API_CALL mk_tcp_session_send_buffer(const mk_tcp_session ctx, mk_buffer buffer); + +//切换到该对象所在线程后再TcpSession::send() +API_EXPORT void API_CALL mk_tcp_session_send_safe(const mk_tcp_session ctx, const char *data, size_t len); +API_EXPORT void API_CALL mk_tcp_session_send_buffer_safe(const mk_tcp_session ctx, mk_buffer buffer); + +//创建mk_tcp_session的弱引用 +API_EXPORT mk_tcp_session_ref API_CALL mk_tcp_session_ref_from(const mk_tcp_session ctx); +//删除mk_tcp_session的弱引用 +API_EXPORT void mk_tcp_session_ref_release(const mk_tcp_session_ref ref); +//根据弱引用获取mk_tcp_session，如果mk_tcp_session已经销毁，那么返回NULL +API_EXPORT mk_tcp_session mk_tcp_session_from_ref(const mk_tcp_session_ref ref); + +///////////////////////////////////////////自定义tcp服务///////////////////////////////////////////// + +typedef struct { + /** + * 收到mk_tcp_session创建对象 + * @param server_port 服务器端口号 + * @param session 会话处理对象 + */ + void (API_CALL *on_mk_tcp_session_create)(uint16_t server_port,mk_tcp_session session); + + /** + * 收到客户端发过来的数据 + * @param server_port 服务器端口号 + * @param session 会话处理对象 + * @param buffer 数据 + */ + void (API_CALL *on_mk_tcp_session_data)(uint16_t server_port,mk_tcp_session session, mk_buffer buffer); + + /** + * 每隔2秒的定时器，用于管理超时等任务 + * @param server_port 服务器端口号 + * @param session 会话处理对象 + */ + void (API_CALL *on_mk_tcp_session_manager)(uint16_t server_port,mk_tcp_session session); + + /** + * 一般由于客户端断开tcp触发 + * @param server_port 服务器端口号 + * @param session 会话处理对象 + * @param code 错误代码 + * @param msg 错误提示 + */ + void (API_CALL *on_mk_tcp_session_disconnect)(uint16_t server_port,mk_tcp_session session,int code,const char *msg); +} mk_tcp_session_events; + + +typedef enum { + //普通的tcp + mk_type_tcp = 0, + //ssl类型的tcp + mk_type_ssl = 1, + //基于websocket的连接 + mk_type_ws = 2, + //基于ssl websocket的连接 + mk_type_wss = 3 +}mk_tcp_type; + +/** + * tcp会话对象附着用户数据 + * 该函数只对mk_tcp_server_server_start启动的服务类型有效 + * @param session 会话对象 + * @param user_data 用户数据指针 + */ +API_EXPORT void API_CALL mk_tcp_session_set_user_data(mk_tcp_session session,void *user_data); + +/** + * 获取tcp会话对象上附着的用户数据 + * 该函数只对mk_tcp_server_server_start启动的服务类型有效 + * @param session tcp会话对象 + * @return 用户数据指针 + */ +API_EXPORT void* API_CALL mk_tcp_session_get_user_data(mk_tcp_session session); + +/** + * 开启tcp服务器 + * @param port 监听端口号，0则为随机 + * @param type 服务器类型 + */ +API_EXPORT uint16_t API_CALL mk_tcp_server_start(uint16_t port, mk_tcp_type type); + +/** + * 监听tcp服务器事件 + */ +API_EXPORT void API_CALL mk_tcp_server_events_listen(const mk_tcp_session_events *events); + + +///////////////////////////////////////////自定义tcp客户端///////////////////////////////////////////// + +typedef void* mk_tcp_client; +//获取基类指针以便获取其网络相关信息 +API_EXPORT mk_sock_info API_CALL mk_tcp_client_get_sock_info(const mk_tcp_client ctx); + +typedef struct { + /** + * tcp客户端连接服务器成功或失败回调 + * @param client tcp客户端 + * @param code 0为连接成功，否则为失败原因 + * @param msg 连接失败错误提示 + */ + void (API_CALL *on_mk_tcp_client_connect)(mk_tcp_client client,int code,const char *msg); + + /** + * tcp客户端与tcp服务器之间断开回调 + * 一般是eof事件导致 + * @param client tcp客户端 + * @param code 错误代码 + * @param msg 错误提示 + */ + void (API_CALL *on_mk_tcp_client_disconnect)(mk_tcp_client client,int code,const char *msg); + + /** + * 收到tcp服务器发来的数据 + * @param client tcp客户端 + * @param buffer 数据 + */ + void (API_CALL *on_mk_tcp_client_data)(mk_tcp_client client, mk_buffer buffer); + + /** + * 每隔2秒的定时器，用于管理超时等任务 + * @param client tcp客户端 + */ + void (API_CALL *on_mk_tcp_client_manager)(mk_tcp_client client); +} mk_tcp_client_events; + +/** + * 创建tcp客户端 + * @param events 回调函数结构体 + * @param user_data 用户数据指针 + * @param type 客户端类型 + * @return 客户端对象 + */ +API_EXPORT mk_tcp_client API_CALL mk_tcp_client_create(mk_tcp_client_events *events, mk_tcp_type type); + +/** + * 释放tcp客户端 + * @param ctx 客户端对象 + */ +API_EXPORT void API_CALL mk_tcp_client_release(mk_tcp_client ctx); + +/** + * 发起连接 + * @param ctx 客户端对象 + * @param host 服务器ip或域名 + * @param port 服务器端口号 + * @param time_out_sec 超时时间 + */ +API_EXPORT void API_CALL mk_tcp_client_connect(mk_tcp_client ctx, const char *host, uint16_t port, float time_out_sec); + +/** + * 非线程安全的发送数据 + * 开发者如果能确保在本对象网络线程内，可以调用此此函数 + * @param ctx 客户端对象 + * @param data 数据指针 + * @param len 数据长度，等于0时，内部通过strlen获取 + */ +API_EXPORT void API_CALL mk_tcp_client_send(mk_tcp_client ctx, const char *data, int len); +API_EXPORT void API_CALL mk_tcp_client_send_buffer(mk_tcp_client ctx, mk_buffer buffer); + +/** + * 切换到本对象的网络线程后再发送数据 + * @param ctx 客户端对象 + * @param data 数据指针 + * @param len 数据长度，等于0时，内部通过strlen获取 + */ +API_EXPORT void API_CALL mk_tcp_client_send_safe(mk_tcp_client ctx, const char *data, int len); +API_EXPORT void API_CALL mk_tcp_client_send_buffer_safe(mk_tcp_client ctx, mk_buffer buffer); + +/** + * 客户端附着用户数据 + * @param ctx 客户端对象 + * @param user_data 用户数据指针 + */ +API_EXPORT void API_CALL mk_tcp_client_set_user_data(mk_tcp_client ctx,void *user_data); + +/** + * 获取客户端对象上附着的用户数据 + * @param ctx 客户端对象 + * @return 用户数据指针 + */ +API_EXPORT void* API_CALL mk_tcp_client_get_user_data(mk_tcp_client ctx); + +#ifdef __cplusplus +} +#endif +#endif //MK_TCP_H diff --git a/rknpu2/examples/3rdparty/zlmediakit/include/mk_thread.h b/rknpu2/examples/3rdparty/zlmediakit/include/mk_thread.h new file mode 100644 index 0000000000000000000000000000000000000000..dfd80edf83ff1f25b98001715138d12f6011fe57 --- /dev/null +++ b/rknpu2/examples/3rdparty/zlmediakit/include/mk_thread.h @@ -0,0 +1,161 @@ +/* + * Copyright (c) 2016 The ZLMediaKit project authors. All Rights Reserved. + * + * This file is part of ZLMediaKit(https://github.com/xia-chu/ZLMediaKit). + * + * Use of this source code is governed by MIT license that can be found in the + * LICENSE file in the root of the source tree. All contributing project authors + * may be found in the AUTHORS file in the root of the source tree. + */ + +#ifndef MK_THREAD_H +#define MK_THREAD_H + +#include +#include "mk_common.h" +#include "mk_tcp.h" + +#ifdef __cplusplus +extern "C" { +#endif + +///////////////////////////////////////////事件线程///////////////////////////////////////////// +typedef void* mk_thread; + +/** + * 获取tcp会话对象所在事件线程 + * @param ctx tcp会话对象 + * @return 对象所在事件线程 + */ +API_EXPORT mk_thread API_CALL mk_thread_from_tcp_session(mk_tcp_session ctx); + +/** + * 获取tcp客户端对象所在事件线程 + * @param ctx tcp客户端 + * @return 对象所在事件线程 + */ +API_EXPORT mk_thread API_CALL mk_thread_from_tcp_client(mk_tcp_client ctx); + +/** + * 根据负载均衡算法，从事件线程池中随机获取一个事件线程 + * 如果在事件线程内执行此函数将返回本事件线程 + * 事件线程指的是定时器、网络io事件线程 + * @return 事件线程 + */ +API_EXPORT mk_thread API_CALL mk_thread_from_pool(); + +/** + * 根据负载均衡算法，从后台线程池中随机获取一个线程 + * 后台线程本质与事件线程相同，只是优先级更低，同时可以执行短时间的阻塞任务 + * ZLMediaKit中后台线程用于dns解析、mp4点播时的文件解复用 + * @return 后台线程 + */ +API_EXPORT mk_thread API_CALL mk_thread_from_pool_work(); + +typedef void* mk_thread_pool; + +/** + * 创建线程池 + * @param name 线程池名称，方便调试 + * @param n_thread 线程个数，0时为cpu个数 + * @param priority 线程优先级，分为PRIORITY_LOWEST = 0,PRIORITY_LOW, PRIORITY_NORMAL, PRIORITY_HIGH, PRIORITY_HIGHEST + * @return 线程池 + */ +API_EXPORT mk_thread_pool API_CALL mk_thread_pool_create(const char *name, size_t n_thread, int priority); + +/** + * 销毁线程池 + * @param pool 线程池 + * @return 0:成功 + */ +API_EXPORT int API_CALL mk_thread_pool_release(mk_thread_pool pool); + +/** + * 从线程池获取一个线程 + * @param pool 线程池 + * @return 线程 + */ +API_EXPORT mk_thread API_CALL mk_thread_from_thread_pool(mk_thread_pool pool); + +///////////////////////////////////////////线程切换///////////////////////////////////////////// +typedef void (API_CALL *on_mk_async)(void *user_data); + +/** + * 切换到事件线程并异步执行 + * @param ctx 事件线程 + * @param cb 回调函数 + * @param user_data 用户数据指针 + */ +API_EXPORT void API_CALL mk_async_do(mk_thread ctx, on_mk_async cb, void *user_data); + +/** + * 切换到事件线程并延时执行 + * @param ctx 事件线程 + * @param ms 延时时间，单位毫秒 + * @param cb 回调函数 + * @param user_data 用户数据指针 + */ +API_EXPORT void API_CALL mk_async_do_delay(mk_thread ctx, size_t ms, on_mk_async cb, void *user_data); + +/** + * 切换到事件线程并同步执行 + * @param ctx 事件线程 + * @param cb 回调函数 + * @param user_data 用户数据指针 + */ +API_EXPORT void API_CALL mk_sync_do(mk_thread ctx, on_mk_async cb, void *user_data); + +///////////////////////////////////////////定时器///////////////////////////////////////////// +typedef void* mk_timer; + +/** + * 定时器触发事件 + * @return 下一次触发延时(单位毫秒)，返回0则不再重复 + */ +typedef uint64_t (API_CALL *on_mk_timer)(void *user_data); + +/** + * 创建定时器 + * @param ctx 线程对象 + * @param delay_ms 执行延时，单位毫秒 + * @param cb 回调函数 + * @param user_data 用户数据指针 + * @return 定时器对象 + */ +API_EXPORT mk_timer API_CALL mk_timer_create(mk_thread ctx, uint64_t delay_ms, on_mk_timer cb, void *user_data); + +/** + * 销毁和取消定时器 + * @param ctx 定时器对象 + */ +API_EXPORT void API_CALL mk_timer_release(mk_timer ctx); + +///////////////////////////////////////////信号量///////////////////////////////////////////// + +typedef void* mk_sem; + +/** + * 创建信号量 + */ +API_EXPORT mk_sem API_CALL mk_sem_create(); + +/** + * 销毁信号量 + */ +API_EXPORT void API_CALL mk_sem_release(mk_sem sem); + +/** + * 信号量加n + */ +API_EXPORT void API_CALL mk_sem_post(mk_sem sem, size_t n); + +/** + * 信号量减1 + * @param sem + */ +API_EXPORT void API_CALL mk_sem_wait(mk_sem sem); + +#ifdef __cplusplus +} +#endif +#endif //MK_THREAD_H diff --git a/rknpu2/examples/3rdparty/zlmediakit/include/mk_track.h b/rknpu2/examples/3rdparty/zlmediakit/include/mk_track.h new file mode 100644 index 0000000000000000000000000000000000000000..aa6b5f38606856f457a6f8f395a3b75c7f8d8eff --- /dev/null +++ b/rknpu2/examples/3rdparty/zlmediakit/include/mk_track.h @@ -0,0 +1,135 @@ +/* + * Copyright (c) 2016 The ZLMediaKit project authors. All Rights Reserved. + * + * This file is part of ZLMediaKit(https://github.com/xia-chu/ZLMediaKit). + * + * Use of this source code is governed by MIT license that can be found in the + * LICENSE file in the root of the source tree. All contributing project authors + * may be found in the AUTHORS file in the root of the source tree. + */ + +#ifndef ZLMEDIAKIT_MK_TRACK_H +#define ZLMEDIAKIT_MK_TRACK_H + +#include "mk_common.h" +#include "mk_frame.h" + +#ifdef __cplusplus +extern "C" { +#endif + +//音视频轨道 +typedef void* mk_track; +//输出frame回调 +typedef void(API_CALL *on_mk_frame_out)(void *user_data, mk_frame frame); + +//track创建参数 +typedef union { + struct { + int width; + int height; + int fps; + } video; + + struct { + int channels; + int sample_rate; + } audio; +} codec_args; + +/** + * 创建track对象引用 + * @param codec_id 请参考MKCodecXXX 常量定义 + * @param args 视频或音频参数 + * @return track对象引用 + */ +API_EXPORT mk_track API_CALL mk_track_create(int codec_id, codec_args *args); + +/** + * 减引用track对象 + * @param track track对象 + */ +API_EXPORT void API_CALL mk_track_unref(mk_track track); + +/** + * 引用track对象 + * @param track track对象 + * @return 新的track引用对象 + */ +API_EXPORT mk_track API_CALL mk_track_ref(mk_track track); + +/** + * 获取track 编码codec类型，请参考MKCodecXXX定义 + */ +API_EXPORT int API_CALL mk_track_codec_id(mk_track track); + +/** + * 获取编码codec名称 + */ +API_EXPORT const char* API_CALL mk_track_codec_name(mk_track track); + +/** + * 获取比特率信息 + */ +API_EXPORT int API_CALL mk_track_bit_rate(mk_track track); + +/** + * 监听frame输出事件 + * @param track track对象 + * @param cb frame输出回调 + * @param user_data frame输出回调用户指针参数 + */ +API_EXPORT void *API_CALL mk_track_add_delegate(mk_track track, on_mk_frame_out cb, void *user_data); + +/** + * 取消frame输出事件监听 + * @param track track对象 + * @param tag mk_track_add_delegate返回值 + */ +API_EXPORT void API_CALL mk_track_del_delegate(mk_track track, void *tag); + +/** + * 输入frame到track，通常你不需要调用此api + */ +API_EXPORT void API_CALL mk_track_input_frame(mk_track track, mk_frame frame); + +/** + * track是否为视频 + */ +API_EXPORT int API_CALL mk_track_is_video(mk_track track); + +/** + * 获取视频宽度 + */ +API_EXPORT int API_CALL mk_track_video_width(mk_track track); + +/** + * 获取视频高度 + */ +API_EXPORT int API_CALL mk_track_video_height(mk_track track); + +/** + * 获取视频帧率 + */ +API_EXPORT int API_CALL mk_track_video_fps(mk_track track); + +/** + * 获取音频采样率 + */ +API_EXPORT int API_CALL mk_track_audio_sample_rate(mk_track track); + +/** + * 获取音频通道数 + */ +API_EXPORT int API_CALL mk_track_audio_channel(mk_track track); + +/** + * 获取音频位数，一般为16bit + */ +API_EXPORT int API_CALL mk_track_audio_sample_bit(mk_track track); + +#ifdef __cplusplus +} +#endif + +#endif //ZLMEDIAKIT_MK_TRACK_H \ No newline at end of file diff --git a/rknpu2/examples/3rdparty/zlmediakit/include/mk_transcode.h b/rknpu2/examples/3rdparty/zlmediakit/include/mk_transcode.h new file mode 100644 index 0000000000000000000000000000000000000000..2fa71caea87ca886930588431f8fa1ddcc243877 --- /dev/null +++ b/rknpu2/examples/3rdparty/zlmediakit/include/mk_transcode.h @@ -0,0 +1,154 @@ +/* + * Copyright (c) 2016 The ZLMediaKit project authors. All Rights Reserved. + * + * This file is part of ZLMediaKit(https://github.com/xia-chu/ZLMediaKit). + * + * Use of this source code is governed by MIT license that can be found in the + * LICENSE file in the root of the source tree. All contributing project authors + * may be found in the AUTHORS file in the root of the source tree. + */ + +#ifndef ZLMEDIAKIT_MK_TRANSCODE_H +#define ZLMEDIAKIT_MK_TRANSCODE_H + +#include "mk_common.h" +#include "mk_track.h" + +#ifdef __cplusplus +extern "C" { +#endif + +//解码器对象 +typedef void *mk_decoder; +//解码后的frame +typedef void *mk_frame_pix; +//SwsContext的包装 +typedef void *mk_swscale; +//FFmpeg原始解码帧对象 +typedef struct AVFrame AVFrame; +//FFmpeg编解码器对象 +typedef struct AVCodecContext AVCodecContext; +//解码输出回调 +typedef void(API_CALL *on_mk_decode)(void *user_data, mk_frame_pix frame); + +/** + * 创建解码器 + * @param track track对象 + * @param thread_num 解码线程数，0时为自动 + * @return 返回解码器对象，NULL代表失败 + */ +API_EXPORT mk_decoder API_CALL mk_decoder_create(mk_track track, int thread_num); + +/** + * 销毁解码器 + * @param ctx 解码器对象 + * @param flush_frame 是否等待所有帧解码成功 + */ +API_EXPORT void API_CALL mk_decoder_release(mk_decoder ctx, int flush_frame); + +/** + * 解码音视频帧 + * @param ctx 解码器 + * @param frame 帧对象 + * @param async 是否异步解码 + * @param enable_merge 是否合并帧解码，有些情况下，需要把时间戳相同的slice合并输入到解码器才能解码 + */ +API_EXPORT void API_CALL mk_decoder_decode(mk_decoder ctx, mk_frame frame, int async, int enable_merge); + +/** + * 设置异步解码最大帧缓存积压数限制 + */ +API_EXPORT void API_CALL mk_decoder_set_max_async_frame_size(mk_decoder ctx, size_t size); + +/** + * 设置解码输出回调 + * @param ctx 解码器 + * @param cb 回调函数 + * @param user_data 回调函数用户指针参数 + */ +API_EXPORT void API_CALL mk_decoder_set_cb(mk_decoder ctx, on_mk_decode cb, void *user_data); + +/** + * 获取FFmpeg原始AVCodecContext对象 + * @param ctx 解码器 + */ +API_EXPORT const AVCodecContext* API_CALL mk_decoder_get_context(mk_decoder ctx); + +///////////////////////////////////////////////////////////////////////////////////////////// + +/** + * 创建解码帧mk_frame_pix新引用 + * @param frame 原始引用 + * @return 新引用 + */ +API_EXPORT mk_frame_pix API_CALL mk_frame_pix_ref(mk_frame_pix frame); + +/** + * 解码帧mk_frame_pix减引用 + * @param frame 原始引用 + */ +API_EXPORT void API_CALL mk_frame_pix_unref(mk_frame_pix frame); + +/** + * 从FFmpeg AVFrame转换为mk_frame_pix + * @param frame FFmpeg AVFrame + * @return mk_frame_pix对象 + */ +API_EXPORT mk_frame_pix API_CALL mk_frame_pix_from_av_frame(AVFrame *frame); + +/** + * 获取FFmpeg AVFrame对象 + * @param frame 解码帧mk_frame_pix + * @return FFmpeg AVFrame对象 + */ +API_EXPORT AVFrame* API_CALL mk_frame_pix_get_av_frame(mk_frame_pix frame); + +///////////////////////////////////////////////////////////////////////////////////////////// + +/** + * 创建ffmpeg SwsContext wrapper实例 + * @param output AVPixelFormat类型，AV_PIX_FMT_BGR24==3 + * @param width 目标宽度，置0时，则与输入时一致 + * @param height 目标高度，置0时，则与输入时一致 + * @return SwsContext wrapper 实例 + */ +API_EXPORT mk_swscale mk_swscale_create(int output, int width, int height); + +/** + * 释放ffmpeg SwsContext wrapper实例 + * @param ctx SwsContext wrapper实例 + */ +API_EXPORT void mk_swscale_release(mk_swscale ctx); + +/** + * 使用SwsContext转换pix format + * @param ctx SwsContext wrapper实例 + * @param frame pix frame + * @param out 转换后存放的数据指针，用户需要确保提前申请并大小足够 + * @return sws_scale()返回值：the height of the output slice + */ +API_EXPORT int mk_swscale_input_frame(mk_swscale ctx, mk_frame_pix frame, uint8_t *out); + +/** + * 使用SwsContext转换pix format + * @param ctx SwsContext wrapper实例 + * @param frame pix frame + * @return 新的pix frame对象，需要使用mk_frame_pix_unref销毁 + */ +API_EXPORT mk_frame_pix mk_swscale_input_frame2(mk_swscale ctx, mk_frame_pix frame); + +///////////////////////////////////////////////////////////////////////////////////////////// + +API_EXPORT uint8_t** API_CALL mk_get_av_frame_data(AVFrame *frame); +API_EXPORT int* API_CALL mk_get_av_frame_line_size(AVFrame *frame); +API_EXPORT int64_t API_CALL mk_get_av_frame_dts(AVFrame *frame); +API_EXPORT int64_t API_CALL mk_get_av_frame_pts(AVFrame *frame); +API_EXPORT int API_CALL mk_get_av_frame_width(AVFrame *frame); +API_EXPORT int API_CALL mk_get_av_frame_height(AVFrame *frame); +API_EXPORT int API_CALL mk_get_av_frame_format(AVFrame *frame); + +#ifdef __cplusplus +} +#endif + +#endif //ZLMEDIAKIT_MK_TRANSCODE_H \ No newline at end of file diff --git a/rknpu2/examples/3rdparty/zlmediakit/include/mk_util.h b/rknpu2/examples/3rdparty/zlmediakit/include/mk_util.h new file mode 100644 index 0000000000000000000000000000000000000000..f39768607a3ef983ae66772cca64153386ed248d --- /dev/null +++ b/rknpu2/examples/3rdparty/zlmediakit/include/mk_util.h @@ -0,0 +1,78 @@ +/* + * Copyright (c) 2016 The ZLMediaKit project authors. All Rights Reserved. + * + * This file is part of ZLMediaKit(https://github.com/xia-chu/ZLMediaKit). + * + * Use of this source code is governed by MIT license that can be found in the + * LICENSE file in the root of the source tree. All contributing project authors + * may be found in the AUTHORS file in the root of the source tree. + */ + +#ifndef MK_UTIL_H +#define MK_UTIL_H + +#include +#include "mk_common.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/** + * 获取本程序可执行文件路径 + * @return 文件路径，使用完后需要自己free + */ +API_EXPORT char* API_CALL mk_util_get_exe_path(); + +/** + * 获取本程序可执行文件相同目录下文件的绝对路径 + * @param relative_path 同目录下文件的路径相对,可以为null + * @return 文件路径，使用完后需要自己free + */ +API_EXPORT char* API_CALL mk_util_get_exe_dir(const char *relative_path); + +/** + * 获取unix标准的系统时间戳 + * @return 当前系统时间戳 + */ +API_EXPORT uint64_t API_CALL mk_util_get_current_millisecond(); + +/** + * 获取时间字符串 + * @param fmt 时间格式，譬如%Y-%m-%d %H:%M:%S + * @return 时间字符串，使用完后需要自己free + */ +API_EXPORT char* API_CALL mk_util_get_current_time_string(const char *fmt); + +/** + * 打印二进制为字符串 + * @param buf 二进制数据 + * @param len 数据长度 + * @return 可打印的调试信息，使用完后需要自己free + */ +API_EXPORT char* API_CALL mk_util_hex_dump(const void *buf, int len); +///////////////////////////////////////////日志///////////////////////////////////////////// + +/** + * 打印日志 + * @param level 日志级别,支持0~4 + * @param file __FILE__ + * @param function __FUNCTION__ + * @param line __LINE__ + * @param fmt printf类型的格式控制字符串 + * @param ... 不定长参数 + */ +API_EXPORT void API_CALL mk_log_printf(int level, const char *file, const char *function, int line, const char *fmt, ...); + +// 以下宏可以替换printf使用 +#define log_printf(lev, ...) mk_log_printf(lev, __FILE__, __FUNCTION__, __LINE__, ##__VA_ARGS__) +#define log_trace(...) log_printf(0, ##__VA_ARGS__) +#define log_debug(...) log_printf(1, ##__VA_ARGS__) +#define log_info(...) log_printf(2, ##__VA_ARGS__) +#define log_warn(...) log_printf(3, ##__VA_ARGS__) +#define log_error(...) log_printf(4, ##__VA_ARGS__) + +#ifdef __cplusplus +} +#endif +#endif //MK_UTIL_H diff --git a/rknpu2/examples/README.md b/rknpu2/examples/README.md new file mode 100644 index 0000000000000000000000000000000000000000..aeb4ca2aa948ca8f63a846f3db2890219888a040 --- /dev/null +++ b/rknpu2/examples/README.md @@ -0,0 +1,15 @@ + + +1. Currently, there are only two demo supported on the RV1106/1103 platform，located under the diretory of examples/RV1106_RV1103. **The RV1106 build script is referring to RV1106/RV1103 in the demo**. + +2. For all demos, except for the one under examples/RV1106_RV1103, they are all supported on the platform of RK356X/RK3588. + +3. **When setting LD_LIBRARY_PATH on the RV1106 or RV1103**, it must be the full path. For example, + + ```shell + export LD_LIBRARY_PATH=/userdata/lib + ``` + + For RK356X, RK3588, LD_LIBRARY_PATH can be set as either full path or relative path + + diff --git a/rknpu2/examples/README_CN.md b/rknpu2/examples/README_CN.md new file mode 100644 index 0000000000000000000000000000000000000000..09750cdd13e2faf4ffbbfcc73329dba3b09565d1 --- /dev/null +++ b/rknpu2/examples/README_CN.md @@ -0,0 +1,7 @@ +1、目前RV1106/RV1103仅支持rknn_mobilenet_demo 和 rknn_yolov5_demo两个demo，在examples/RV1106_RV1103目录下，demo中的**build脚本用RV1106指代RV1106/RV1103** + +2、RK356X和RK3588支持example目录下，除examples/RV1106_RV1103文件夹外的所有demo + +3、**RV1106/RV1103设置LD_LIBRARY_PATH必须为全路径**，例如：export LD_LIBRARY_PATH=/userdata/lib + +4、RK356X和RK3588设置LD_LIBRARY_PATH为全路径和相对路径均可 diff --git a/rknpu2/examples/RV1106_RV1103/rknn_mobilenet_demo/CMakeLists.txt b/rknpu2/examples/RV1106_RV1103/rknn_mobilenet_demo/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..0a0cbdbcdcc736d1f60a17159042ed72da83dd28 --- /dev/null +++ b/rknpu2/examples/RV1106_RV1103/rknn_mobilenet_demo/CMakeLists.txt @@ -0,0 +1,42 @@ +cmake_minimum_required(VERSION 3.6) + +project(rknn_mobilenet_demo) + +set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS}") +set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11") + +# rknn api + +set(RKNN_API_PATH ${CMAKE_SOURCE_DIR}/../../../runtime/${CMAKE_SYSTEM_NAME}/librknn_api) +set(RKNN_RT_LIB ${RKNN_API_PATH}/armhf-uclibc/librknnmrt.so) + +include_directories(${RKNN_API_PATH}/include) +include_directories(${CMAKE_SOURCE_DIR}/../../3rdparty) + + +set(CMAKE_INSTALL_RPATH "lib") + +add_executable(rknn_mobilenet_demo + src/main.cc +) + +target_link_libraries(rknn_mobilenet_demo + ${RKNN_RT_LIB} +) + +# install target and libraries +set(CMAKE_INSTALL_PREFIX ${CMAKE_SOURCE_DIR}/install/rknn_mobilenet_demo_${CMAKE_SYSTEM_NAME}) +install(TARGETS rknn_mobilenet_demo DESTINATION ./) + +add_executable(rknn_mobilenet_nhwc_demo + src/main_nhwc.cc +) + +target_link_libraries(rknn_mobilenet_nhwc_demo + ${RKNN_RT_LIB} +) + +install(TARGETS rknn_mobilenet_nhwc_demo DESTINATION ./) + +install(DIRECTORY model DESTINATION ./) +install(PROGRAMS ${RKNN_RT_LIB} DESTINATION lib) diff --git a/rknpu2/examples/RV1106_RV1103/rknn_mobilenet_demo/README.md b/rknpu2/examples/RV1106_RV1103/rknn_mobilenet_demo/README.md new file mode 100644 index 0000000000000000000000000000000000000000..55d94191d2323695f1437c471d474a136d94e0e9 --- /dev/null +++ b/rknpu2/examples/RV1106_RV1103/rknn_mobilenet_demo/README.md @@ -0,0 +1,34 @@ +The following is RV1106 or RV1103 + + +# Arm Linux Demo + +## build + +modify `GCC_COMPILER` on `build-linux_.sh` for target platform, + +then execute + +``` +./build-linux_.sh +``` + +## install + +connect device and push build output into `/userdata` + +``` +adb push install/rknn_mobilenet_demo_Linux /userdata/ +``` + +## run + +``` +adb shell +cd /userdata/rknn_mobilenet_demo_Linux/ +``` + +``` +export LD_LIBRARY_PATH=./lib +./rknn_mobilenet_demo model//mobilenet_v1.rknn model/dog_224x224.jpg +``` diff --git a/rknpu2/examples/RV1106_RV1103/rknn_mobilenet_demo/README_CN.md b/rknpu2/examples/RV1106_RV1103/rknn_mobilenet_demo/README_CN.md new file mode 100644 index 0000000000000000000000000000000000000000..8a3230e9259a745a711123a6a45f2f5a949a3bff --- /dev/null +++ b/rknpu2/examples/RV1106_RV1103/rknn_mobilenet_demo/README_CN.md @@ -0,0 +1,34 @@ +下述是RV1106或RV1103 + +# Arm Linux Demo + +## 编译 + +修改目标平台的`build-linux_.sh`上的`GCC_COMPILER`， + +然后执行 + +``` +./build-linux_.sh +``` + +## 安装 + +连接设备并将构建输出推送到“/userdata” + +``` +adb push install/rknn_mobilenet_demo_Linux /userdata/ +``` + +## 运行 + +``` +adb shell +cd /userdata/rknn_mobilenet_demo_Linux/ +``` + +``` +export LD_LIBRARY_PATH=./lib +./rknn_mobilenet_demo model//mobilenet_v1.rknn model/dog_224x224.jpg +``` + diff --git a/rknpu2/examples/RV1106_RV1103/rknn_mobilenet_demo/build-linux_RV1106.sh b/rknpu2/examples/RV1106_RV1103/rknn_mobilenet_demo/build-linux_RV1106.sh new file mode 100755 index 0000000000000000000000000000000000000000..fdb65e0dfbc1dea2fd2d82b8469f7b4958777971 --- /dev/null +++ b/rknpu2/examples/RV1106_RV1103/rknn_mobilenet_demo/build-linux_RV1106.sh @@ -0,0 +1,29 @@ +#!/bin/bash +set -e + +if [ -z $RK_RV1106_TOOLCHAIN ]; then + echo "Please set the RK_RV1106_TOOLCHAIN environment variable!" + echo "example:" + echo " export RK_RV1106_TOOLCHAIN=" + exit +fi + +# for arm +GCC_COMPILER=$RK_RV1106_TOOLCHAIN + +ROOT_PWD=$( cd "$( dirname $0 )" && cd -P "$( dirname "$SOURCE" )" && pwd ) + +# build +BUILD_DIR=${ROOT_PWD}/build/build_linux_arm + +if [[ ! -d "${BUILD_DIR}" ]]; then + mkdir -p ${BUILD_DIR} +fi + +cd ${BUILD_DIR} +cmake ../.. \ + -DCMAKE_C_COMPILER=${GCC_COMPILER}-gcc \ + -DCMAKE_CXX_COMPILER=${GCC_COMPILER}-g++ +make -j4 +make install +cd - diff --git a/rknpu2/examples/RV1106_RV1103/rknn_mobilenet_demo/model/RV1106/mobilenet_v1.rknn b/rknpu2/examples/RV1106_RV1103/rknn_mobilenet_demo/model/RV1106/mobilenet_v1.rknn new file mode 100644 index 0000000000000000000000000000000000000000..eb1b38368ebdc2a4868037e24721520ea5c51426 --- /dev/null +++ b/rknpu2/examples/RV1106_RV1103/rknn_mobilenet_demo/model/RV1106/mobilenet_v1.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f72440ca9838cc63ff9537d74056064b0a224fb3c76e775afe28bf69268a2cbc +size 4554392 diff --git a/rknpu2/examples/RV1106_RV1103/rknn_mobilenet_demo/model/cat_224x224.jpg b/rknpu2/examples/RV1106_RV1103/rknn_mobilenet_demo/model/cat_224x224.jpg new file mode 100644 index 0000000000000000000000000000000000000000..2159d5cfe3d6cbc01940a804cff0e3bc516af7fb --- /dev/null +++ b/rknpu2/examples/RV1106_RV1103/rknn_mobilenet_demo/model/cat_224x224.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0ce3ef6da3424c0744385935a210e4ec8ab3a6ddda205113ab07b77f9c0e812a +size 58701 diff --git a/rknpu2/examples/RV1106_RV1103/rknn_mobilenet_demo/model/dog_224x224.jpg b/rknpu2/examples/RV1106_RV1103/rknn_mobilenet_demo/model/dog_224x224.jpg new file mode 100644 index 0000000000000000000000000000000000000000..004b0416e23454a12eb06eaba238c7e2d5f2b0fc --- /dev/null +++ b/rknpu2/examples/RV1106_RV1103/rknn_mobilenet_demo/model/dog_224x224.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c350299c6283d5f62fecf1f845b6b3be9aafec8dff528ca09a129990f0a584b0 +size 18889 diff --git a/rknpu2/examples/RV1106_RV1103/rknn_mobilenet_demo/src/main.cc b/rknpu2/examples/RV1106_RV1103/rknn_mobilenet_demo/src/main.cc new file mode 100644 index 0000000000000000000000000000000000000000..5df35c7855edee87d5f31c9feed77bbce46187b3 --- /dev/null +++ b/rknpu2/examples/RV1106_RV1103/rknn_mobilenet_demo/src/main.cc @@ -0,0 +1,563 @@ +// Copyright (c) 2021 by Rockchip Electronics Co., Ltd. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +/*------------------------------------------- + Includes +-------------------------------------------*/ +#include "rknn_api.h" + +#include +#include +#include +#include +#include + +#include "fp16/Float16.h" + +#define STB_IMAGE_IMPLEMENTATION +#include "stb/stb_image.h" +#define STB_IMAGE_RESIZE_IMPLEMENTATION +#include + +using namespace rknpu2; + +/*------------------------------------------- + Functions +-------------------------------------------*/ +static inline int64_t getCurrentTimeUs() +{ + struct timeval tv; + gettimeofday(&tv, NULL); + return tv.tv_sec * 1000000 + tv.tv_usec; +} + +static int rknn_GetTopN(float *pfProb, float *pfMaxProb, uint32_t *pMaxClass, uint32_t outputCount, uint32_t topNum) +{ + uint32_t i, j; + uint32_t top_count = outputCount > topNum ? topNum : outputCount; + + for (i = 0; i < topNum; ++i) + { + pfMaxProb[i] = -FLT_MAX; + pMaxClass[i] = -1; + } + + for (j = 0; j < top_count; j++) + { + for (i = 0; i < outputCount; i++) + { + if ((i == *(pMaxClass + 0)) || (i == *(pMaxClass + 1)) || (i == *(pMaxClass + 2)) || (i == *(pMaxClass + 3)) || + (i == *(pMaxClass + 4))) + { + continue; + } + + float prob = pfProb[i]; + if (prob > *(pfMaxProb + j)) + { + *(pfMaxProb + j) = prob; + *(pMaxClass + j) = i; + } + } + } + + return 1; +} + +static int rknn_GetTopN_int8(int8_t *pProb, float scale, int zp, float *pfMaxProb, uint32_t *pMaxClass, + uint32_t outputCount, uint32_t topNum) +{ + uint32_t i, j; + uint32_t top_count = outputCount > topNum ? topNum : outputCount; + + for (i = 0; i < topNum; ++i) + { + pfMaxProb[i] = -FLT_MAX; + pMaxClass[i] = -1; + } + + for (j = 0; j < top_count; j++) + { + for (i = 0; i < outputCount; i++) + { + if ((i == *(pMaxClass + 0)) || (i == *(pMaxClass + 1)) || (i == *(pMaxClass + 2)) || (i == *(pMaxClass + 3)) || + (i == *(pMaxClass + 4))) + { + continue; + } + + float prob = (pProb[i] - zp) * scale; + if (prob > *(pfMaxProb + j)) + { + *(pfMaxProb + j) = prob; + *(pMaxClass + j) = i; + } + } + } + + return 1; +} + +static void dump_tensor_attr(rknn_tensor_attr *attr) +{ + char dims[128] = {0}; + for (int i = 0; i < attr->n_dims; ++i) + { + int idx = strlen(dims); + sprintf(&dims[idx], "%d%s", attr->dims[i], (i == attr->n_dims - 1) ? "" : ", "); + } + printf(" index=%d, name=%s, n_dims=%d, dims=[%s], n_elems=%d, size=%d, fmt=%s, type=%s, qnt_type=%s, " + "zp=%d, scale=%f\n", + attr->index, attr->name, attr->n_dims, dims, attr->n_elems, attr->size, get_format_string(attr->fmt), + get_type_string(attr->type), get_qnt_type_string(attr->qnt_type), attr->zp, attr->scale); +} + +static void *load_file(const char *file_path, size_t *file_size) +{ + FILE *fp = fopen(file_path, "r"); + if (fp == NULL) + { + printf("failed to open file: %s\n", file_path); + return NULL; + } + + fseek(fp, 0, SEEK_END); + size_t size = (size_t)ftell(fp); + fseek(fp, 0, SEEK_SET); + + void *file_data = malloc(size); + if (file_data == NULL) + { + fclose(fp); + printf("failed allocate file size: %zu\n", size); + return NULL; + } + + if (fread(file_data, 1, size, fp) != size) + { + fclose(fp); + free(file_data); + printf("failed to read file data!\n"); + return NULL; + } + + fclose(fp); + + *file_size = size; + + return file_data; +} + +static unsigned char *load_image(const char *image_path, rknn_tensor_attr *input_attr) +{ + int req_height = 0; + int req_width = 0; + int req_channel = 0; + + switch (input_attr->fmt) + { + case RKNN_TENSOR_NHWC: + req_height = input_attr->dims[1]; + req_width = input_attr->dims[2]; + req_channel = input_attr->dims[3]; + break; + case RKNN_TENSOR_NCHW: + req_height = input_attr->dims[2]; + req_width = input_attr->dims[3]; + req_channel = input_attr->dims[1]; + break; + default: + printf("meet unsupported layout\n"); + return NULL; + } + + int height = 0; + int width = 0; + int channel = 0; + + unsigned char *image_data = stbi_load(image_path, &width, &height, &channel, req_channel); + if (image_data == NULL) + { + printf("load image failed!\n"); + return NULL; + } + + if (width != req_width || height != req_height) + { + unsigned char *image_resized = (unsigned char *)STBI_MALLOC(req_width * req_height * req_channel); + if (!image_resized) + { + printf("malloc image failed!\n"); + STBI_FREE(image_data); + return NULL; + } + if (stbir_resize_uint8(image_data, width, height, 0, image_resized, req_width, req_height, 0, channel) != 1) + { + printf("resize image failed!\n"); + STBI_FREE(image_data); + return NULL; + } + STBI_FREE(image_data); + image_data = image_resized; + } + + return image_data; +} + +// 量化模型的npu输出结果为int8数据类型，后处理要按照int8数据类型处理 +// 如下提供了int8排布的NC1HWC2转换成float的nchw转换代码 +int NC1HWC2_int8_to_NCHW_float(const int8_t *src, float *dst, int *dims, int channel, int h, int w, int zp, float scale) +{ + int batch = dims[0]; + int C1 = dims[1]; + int C2 = dims[4]; + int hw_src = dims[2] * dims[3]; + int hw_dst = h * w; + for (int i = 0; i < batch; i++) + { + src = src + i * C1 * hw_src * C2; + dst = dst + i * channel * hw_dst; + for (int c = 0; c < channel; ++c) + { + int plane = c / C2; + const int8_t *src_c = plane * hw_src * C2 + src; + int offset = c % C2; + for (int cur_h = 0; cur_h < h; ++cur_h) + for (int cur_w = 0; cur_w < w; ++cur_w) + { + int cur_hw = cur_h * w + cur_w; + dst[c * hw_dst + cur_h * w + cur_w] = (src_c[C2 * cur_hw + offset] - zp) * scale; // int8-->float + } + } + } + + return 0; +} + +// 量化模型的npu输出结果为fp16数据类型，后处理要按照fp16数据类型处理 +// 如下提供了fp16排布的NC1HWC2转换成float的nchw转换代码 +int NC1HWC2_fp16_to_NCHW_fp32(const float16* src, float* dst, int* dims, int channel, int h, int w, int zp, float scale) +{ + int batch = dims[0]; + int C1 = dims[1]; + int C2 = dims[4]; + int hw_src = dims[2] * dims[3]; + int hw_dst = h * w; + for (int i = 0; i < batch; i++) { + const float16* src_b = src + i * C1 * hw_src * C2; + float* dst_b = dst + i * channel * hw_dst; + for (int c = 0; c < channel; ++c) { + int plane = c / C2; + const float16* src_bc = plane * hw_src * C2 + src_b; + int offset = c % C2; + for (int cur_h = 0; cur_h < h; ++cur_h) + for (int cur_w = 0; cur_w < w; ++cur_w) { + int cur_hw = cur_h * w + cur_w; + dst_b[c * hw_dst + cur_hw] = src_bc[C2 * cur_hw + offset]; // float16-->float + } + } + } + + return 0; +} + + + +/*------------------------------------------- + Main Functions +-------------------------------------------*/ +int main(int argc, char *argv[]) +{ + if (argc < 3) + { + printf("Usage:%s model_path input_path [loop_count]\n", argv[0]); + return -1; + } + + char *model_path = argv[1]; + char *input_path = argv[2]; + + int loop_count = 1; + if (argc > 3) + { + loop_count = atoi(argv[3]); + } + + rknn_context ctx = 0; + + // Load RKNN Model +#if 1 + // Init rknn from model path + int ret = rknn_init(&ctx, model_path, 0, 0, NULL); +#else + // Init rknn from model data + size_t model_size; + void *model_data = load_file(model_path, &model_size); + if (model_data == NULL) + { + return -1; + } + int ret = rknn_init(&ctx, model_data, model_size, 0, NULL); + free(model_data); +#endif + if (ret < 0) + { + printf("rknn_init fail! ret=%d\n", ret); + return -1; + } + + // Get sdk and driver version + rknn_sdk_version sdk_ver; + ret = rknn_query(ctx, RKNN_QUERY_SDK_VERSION, &sdk_ver, sizeof(sdk_ver)); + if (ret != RKNN_SUCC) + { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("rknn_api/rknnrt version: %s, driver version: %s\n", sdk_ver.api_version, sdk_ver.drv_version); + + // Get Model Input Output Info + rknn_input_output_num io_num; + ret = rknn_query(ctx, RKNN_QUERY_IN_OUT_NUM, &io_num, sizeof(io_num)); + if (ret != RKNN_SUCC) + { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("model input num: %d, output num: %d\n", io_num.n_input, io_num.n_output); + + printf("input tensors:\n"); + rknn_tensor_attr input_attrs[io_num.n_input]; + memset(input_attrs, 0, io_num.n_input * sizeof(rknn_tensor_attr)); + for (uint32_t i = 0; i < io_num.n_input; i++) + { + input_attrs[i].index = i; + // query info + ret = rknn_query(ctx, RKNN_QUERY_INPUT_ATTR, &(input_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret < 0) + { + printf("rknn_init error! ret=%d\n", ret); + return -1; + } + dump_tensor_attr(&input_attrs[i]); + } + + printf("output tensors:\n"); + rknn_tensor_attr output_attrs[io_num.n_output]; + memset(output_attrs, 0, io_num.n_output * sizeof(rknn_tensor_attr)); + for (uint32_t i = 0; i < io_num.n_output; i++) + { + output_attrs[i].index = i; + // query info + ret = rknn_query(ctx, RKNN_QUERY_NATIVE_OUTPUT_ATTR, &(output_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret != RKNN_SUCC) + { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + dump_tensor_attr(&output_attrs[i]); + } + + // Get custom string + rknn_custom_string custom_string; + ret = rknn_query(ctx, RKNN_QUERY_CUSTOM_STRING, &custom_string, sizeof(custom_string)); + if (ret != RKNN_SUCC) + { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("custom string: %s\n", custom_string.string); + + unsigned char *input_data = NULL; + rknn_tensor_type input_type = RKNN_TENSOR_UINT8; + rknn_tensor_format input_layout = RKNN_TENSOR_NHWC; + + // Load image + input_data = load_image(input_path, &input_attrs[0]); + + if (!input_data) + { + return -1; + } + + // Create input tensor memory + rknn_tensor_mem *input_mems[1]; + // default input type is int8 (normalize and quantize need compute in outside) + // if set uint8, will fuse normalize and quantize to npu + input_attrs[0].type = input_type; + // default fmt is NHWC, npu only support NHWC in zero copy mode + input_attrs[0].fmt = input_layout; + + input_mems[0] = rknn_create_mem(ctx, input_attrs[0].size_with_stride); + + // Copy input data to input tensor memory + int width = input_attrs[0].dims[2]; + int stride = input_attrs[0].w_stride; + + if (width == stride) + { + memcpy(input_mems[0]->virt_addr, input_data, width * input_attrs[0].dims[1] * input_attrs[0].dims[3]); + } + else + { + int height = input_attrs[0].dims[1]; + int channel = input_attrs[0].dims[3]; + // copy from src to dst with stride + uint8_t *src_ptr = input_data; + uint8_t *dst_ptr = (uint8_t *)input_mems[0]->virt_addr; + // width-channel elements + int src_wc_elems = width * channel; + int dst_wc_elems = stride * channel; + for (int h = 0; h < height; ++h) + { + memcpy(dst_ptr, src_ptr, src_wc_elems); + src_ptr += src_wc_elems; + dst_ptr += dst_wc_elems; + } + } + + // Create output tensor memory + rknn_tensor_mem *output_mems[io_num.n_output]; + for (uint32_t i = 0; i < io_num.n_output; ++i) + { + output_mems[i] = rknn_create_mem(ctx, output_attrs[i].size_with_stride); + } + + // Set input tensor memory + ret = rknn_set_io_mem(ctx, input_mems[0], &input_attrs[0]); + if (ret < 0) + { + printf("rknn_set_io_mem fail! ret=%d\n", ret); + return -1; + } + + // Set output tensor memory + for (uint32_t i = 0; i < io_num.n_output; ++i) + { + // set output memory and attribute + ret = rknn_set_io_mem(ctx, output_mems[i], &output_attrs[i]); + if (ret < 0) + { + printf("rknn_set_io_mem fail! ret=%d\n", ret); + return -1; + } + } + + // Run + printf("Begin perf ...\n"); + for (int i = 0; i < loop_count; ++i) + { + int64_t start_us = getCurrentTimeUs(); + ret = rknn_run(ctx, NULL); + int64_t elapse_us = getCurrentTimeUs() - start_us; + if (ret < 0) + { + printf("rknn run error %d\n", ret); + return -1; + } + printf("%4d: Elapse Time = %.2fms, FPS = %.2f\n", i, elapse_us / 1000.f, 1000.f * 1000.f / elapse_us); + } + + printf("output origin tensors:\n"); + rknn_tensor_attr orig_output_attrs[io_num.n_output]; + memset(orig_output_attrs, 0, io_num.n_output * sizeof(rknn_tensor_attr)); + for (uint32_t i = 0; i < io_num.n_output; i++) + { + orig_output_attrs[i].index = i; + // query info + ret = rknn_query(ctx, RKNN_QUERY_OUTPUT_ATTR, &(orig_output_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret != RKNN_SUCC) + { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + dump_tensor_attr(&orig_output_attrs[i]); + } + + float *output_mems_nchw[io_num.n_output]; + for (uint32_t i = 0; i < io_num.n_output; ++i) + { + int size = orig_output_attrs[i].size_with_stride * sizeof(float); + output_mems_nchw[i] = (float *)malloc(size); + } + + for (uint32_t i = 0; i < io_num.n_output; i++) + { + if (output_attrs[i].fmt == RKNN_TENSOR_NC1HWC2) + { + int channel = orig_output_attrs[i].dims[1]; + int h = orig_output_attrs[i].n_dims > 2 ? orig_output_attrs[i].dims[2] : 1; + int w = orig_output_attrs[i].n_dims > 3 ? orig_output_attrs[i].dims[3] : 1; + int zp = output_attrs[i].zp; + float scale = output_attrs[i].scale; + + if (orig_output_attrs[i].type == RKNN_TENSOR_INT8) { + NC1HWC2_int8_to_NCHW_float((int8_t *)output_mems[i]->virt_addr, (float *)output_mems_nchw[i], (int *)output_attrs[i].dims, + channel, h, w, zp, scale); + } + else if (orig_output_attrs[i].type == RKNN_TENSOR_FLOAT16) { + NC1HWC2_fp16_to_NCHW_fp32((float16*)output_mems[i]->virt_addr, (float*)output_mems_nchw[i], + (int*)output_attrs[i].dims, channel, h, w, zp, scale); + } else { + printf("dtype: %s cannot convert!", get_type_string(orig_output_attrs[i].type)); + } + } + else + { + int8_t *src = (int8_t *)output_mems[i]->virt_addr; + float *dst = output_mems_nchw[i]; + for (int index = 0; index < output_attrs[i].n_elems; index++) + { + dst[index] = (src[index] - output_attrs[i].zp) * output_attrs[i].scale; + } + } + } + + + // Get top 5 + uint32_t topNum = 5; + for (uint32_t i = 0; i < io_num.n_output; i++) + { + uint32_t MaxClass[topNum]; + float fMaxProb[topNum]; + + uint32_t sz = orig_output_attrs[i].n_elems; + int top_count = sz > topNum ? topNum : sz; + + float *buffer = (float *)output_mems_nchw[i]; + + rknn_GetTopN(buffer, fMaxProb, MaxClass, sz, topNum); + + printf("---- Top%d ----\n", top_count); + for (int j = 0; j < top_count; j++) + { + printf("%8.6f - %d\n", fMaxProb[j], MaxClass[j]); + } + } + + // Destroy rknn memory + rknn_destroy_mem(ctx, input_mems[0]); + for (uint32_t i = 0; i < io_num.n_output; ++i) + { + rknn_destroy_mem(ctx, output_mems[i]); + free(output_mems_nchw[i]); + } + + // destroy + rknn_destroy(ctx); + + free(input_data); + + return 0; +} diff --git a/rknpu2/examples/RV1106_RV1103/rknn_mobilenet_demo/src/main_nhwc.cc b/rknpu2/examples/RV1106_RV1103/rknn_mobilenet_demo/src/main_nhwc.cc new file mode 100755 index 0000000000000000000000000000000000000000..940a582b7baf3e4dc7b80905e805bc3db17cf760 --- /dev/null +++ b/rknpu2/examples/RV1106_RV1103/rknn_mobilenet_demo/src/main_nhwc.cc @@ -0,0 +1,444 @@ +// Copyright (c) 2021 by Rockchip Electronics Co., Ltd. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +/*------------------------------------------- + Includes +-------------------------------------------*/ +#include "rknn_api.h" + +#include +#include +#include +#include +#include + +#define STB_IMAGE_IMPLEMENTATION +#include "stb/stb_image.h" +#define STB_IMAGE_RESIZE_IMPLEMENTATION +#include + +/*------------------------------------------- + Functions +-------------------------------------------*/ +static inline int64_t getCurrentTimeUs() +{ + struct timeval tv; + gettimeofday(&tv, NULL); + return tv.tv_sec * 1000000 + tv.tv_usec; +} + +static int rknn_GetTopN(float *pfProb, float *pfMaxProb, uint32_t *pMaxClass, uint32_t outputCount, uint32_t topNum) +{ + uint32_t i, j; + uint32_t top_count = outputCount > topNum ? topNum : outputCount; + + for (i = 0; i < topNum; ++i) + { + pfMaxProb[i] = -FLT_MAX; + pMaxClass[i] = -1; + } + + for (j = 0; j < top_count; j++) + { + for (i = 0; i < outputCount; i++) + { + if ((i == *(pMaxClass + 0)) || (i == *(pMaxClass + 1)) || (i == *(pMaxClass + 2)) || (i == *(pMaxClass + 3)) || + (i == *(pMaxClass + 4))) + { + continue; + } + + float prob = pfProb[i]; + if (prob > *(pfMaxProb + j)) + { + *(pfMaxProb + j) = prob; + *(pMaxClass + j) = i; + } + } + } + + return 1; +} + +static int rknn_GetTopN_int8(int8_t *pProb, float scale, int zp, float *pfMaxProb, uint32_t *pMaxClass, + uint32_t outputCount, uint32_t topNum) +{ + uint32_t i, j; + uint32_t top_count = outputCount > topNum ? topNum : outputCount; + + for (i = 0; i < topNum; ++i) + { + pfMaxProb[i] = -FLT_MAX; + pMaxClass[i] = -1; + } + + for (j = 0; j < top_count; j++) + { + for (i = 0; i < outputCount; i++) + { + if ((i == *(pMaxClass + 0)) || (i == *(pMaxClass + 1)) || (i == *(pMaxClass + 2)) || (i == *(pMaxClass + 3)) || + (i == *(pMaxClass + 4))) + { + continue; + } + + float prob = (pProb[i] - zp) * scale; + if (prob > *(pfMaxProb + j)) + { + *(pfMaxProb + j) = prob; + *(pMaxClass + j) = i; + } + } + } + + return 1; +} + +static void dump_tensor_attr(rknn_tensor_attr *attr) +{ + char dims[128] = {0}; + for (int i = 0; i < attr->n_dims; ++i) + { + int idx = strlen(dims); + sprintf(&dims[idx], "%d%s", attr->dims[i], (i == attr->n_dims - 1) ? "" : ", "); + } + printf(" index=%d, name=%s, n_dims=%d, dims=[%s], n_elems=%d, size=%d, fmt=%s, type=%s, qnt_type=%s, " + "zp=%d, scale=%f\n", + attr->index, attr->name, attr->n_dims, dims, attr->n_elems, attr->size, get_format_string(attr->fmt), + get_type_string(attr->type), get_qnt_type_string(attr->qnt_type), attr->zp, attr->scale); +} + +static void *load_file(const char *file_path, size_t *file_size) +{ + FILE *fp = fopen(file_path, "r"); + if (fp == NULL) + { + printf("failed to open file: %s\n", file_path); + return NULL; + } + + fseek(fp, 0, SEEK_END); + size_t size = (size_t)ftell(fp); + fseek(fp, 0, SEEK_SET); + + void *file_data = malloc(size); + if (file_data == NULL) + { + fclose(fp); + printf("failed allocate file size: %zu\n", size); + return NULL; + } + + if (fread(file_data, 1, size, fp) != size) + { + fclose(fp); + free(file_data); + printf("failed to read file data!\n"); + return NULL; + } + + fclose(fp); + + *file_size = size; + + return file_data; +} + +static unsigned char *load_image(const char *image_path, rknn_tensor_attr *input_attr) +{ + int req_height = 0; + int req_width = 0; + int req_channel = 0; + + switch (input_attr->fmt) + { + case RKNN_TENSOR_NHWC: + req_height = input_attr->dims[1]; + req_width = input_attr->dims[2]; + req_channel = input_attr->dims[3]; + break; + case RKNN_TENSOR_NCHW: + req_height = input_attr->dims[2]; + req_width = input_attr->dims[3]; + req_channel = input_attr->dims[1]; + break; + default: + printf("meet unsupported layout\n"); + return NULL; + } + + int height = 0; + int width = 0; + int channel = 0; + + unsigned char *image_data = stbi_load(image_path, &width, &height, &channel, req_channel); + if (image_data == NULL) + { + printf("load image failed!\n"); + return NULL; + } + + if (width != req_width || height != req_height) + { + unsigned char *image_resized = (unsigned char *)STBI_MALLOC(req_width * req_height * req_channel); + if (!image_resized) + { + printf("malloc image failed!\n"); + STBI_FREE(image_data); + return NULL; + } + if (stbir_resize_uint8(image_data, width, height, 0, image_resized, req_width, req_height, 0, channel) != 1) + { + printf("resize image failed!\n"); + STBI_FREE(image_data); + return NULL; + } + STBI_FREE(image_data); + image_data = image_resized; + } + + return image_data; +} + +/*------------------------------------------- + Main Functions +-------------------------------------------*/ +int main(int argc, char *argv[]) +{ + if (argc < 3) + { + printf("Usage:%s model_path input_path [loop_count]\n", argv[0]); + return -1; + } + + char *model_path = argv[1]; + char *input_path = argv[2]; + + int loop_count = 1; + if (argc > 3) + { + loop_count = atoi(argv[3]); + } + + rknn_context ctx = 0; + + // Load RKNN Model +#if 1 + // Init rknn from model path + int ret = rknn_init(&ctx, model_path, 0, 0, NULL); +#else + // Init rknn from model data + size_t model_size; + void *model_data = load_file(model_path, &model_size); + if (model_data == NULL) + { + return -1; + } + int ret = rknn_init(&ctx, model_data, model_size, 0, NULL); + free(model_data); +#endif + if (ret < 0) + { + printf("rknn_init fail! ret=%d\n", ret); + return -1; + } + + // Get sdk and driver version + rknn_sdk_version sdk_ver; + ret = rknn_query(ctx, RKNN_QUERY_SDK_VERSION, &sdk_ver, sizeof(sdk_ver)); + if (ret != RKNN_SUCC) + { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("rknn_api/rknnrt version: %s, driver version: %s\n", sdk_ver.api_version, sdk_ver.drv_version); + + // Get Model Input Output Info + rknn_input_output_num io_num; + ret = rknn_query(ctx, RKNN_QUERY_IN_OUT_NUM, &io_num, sizeof(io_num)); + if (ret != RKNN_SUCC) + { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("model input num: %d, output num: %d\n", io_num.n_input, io_num.n_output); + + printf("input tensors:\n"); + rknn_tensor_attr input_attrs[io_num.n_input]; + memset(input_attrs, 0, io_num.n_input * sizeof(rknn_tensor_attr)); + for (uint32_t i = 0; i < io_num.n_input; i++) + { + input_attrs[i].index = i; + // query info + ret = rknn_query(ctx, RKNN_QUERY_INPUT_ATTR, &(input_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret < 0) + { + printf("rknn_init error! ret=%d\n", ret); + return -1; + } + dump_tensor_attr(&input_attrs[i]); + } + + printf("output tensors:\n"); + rknn_tensor_attr output_attrs[io_num.n_output]; + memset(output_attrs, 0, io_num.n_output * sizeof(rknn_tensor_attr)); + for (uint32_t i = 0; i < io_num.n_output; i++) + { + output_attrs[i].index = i; + // query info + ret = rknn_query(ctx, RKNN_QUERY_NATIVE_NHWC_OUTPUT_ATTR, &(output_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret != RKNN_SUCC) + { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + dump_tensor_attr(&output_attrs[i]); + } + + // Get custom string + rknn_custom_string custom_string; + ret = rknn_query(ctx, RKNN_QUERY_CUSTOM_STRING, &custom_string, sizeof(custom_string)); + if (ret != RKNN_SUCC) + { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("custom string: %s\n", custom_string.string); + + unsigned char *input_data = NULL; + rknn_tensor_type input_type = RKNN_TENSOR_UINT8; + rknn_tensor_format input_layout = RKNN_TENSOR_NHWC; + + // Load image + input_data = load_image(input_path, &input_attrs[0]); + + if (!input_data) + { + return -1; + } + + // Create input tensor memory + rknn_tensor_mem *input_mems[1]; + // default input type is int8 (normalize and quantize need compute in outside) + // if set uint8, will fuse normalize and quantize to npu + input_attrs[0].type = input_type; + // default fmt is NHWC, npu only support NHWC in zero copy mode + input_attrs[0].fmt = input_layout; + + input_mems[0] = rknn_create_mem(ctx, input_attrs[0].size_with_stride); + + // Copy input data to input tensor memory + int width = input_attrs[0].dims[2]; + int stride = input_attrs[0].w_stride; + + if (width == stride) + { + memcpy(input_mems[0]->virt_addr, input_data, width * input_attrs[0].dims[1] * input_attrs[0].dims[3]); + } + else + { + int height = input_attrs[0].dims[1]; + int channel = input_attrs[0].dims[3]; + // copy from src to dst with stride + uint8_t *src_ptr = input_data; + uint8_t *dst_ptr = (uint8_t *)input_mems[0]->virt_addr; + // width-channel elements + int src_wc_elems = width * channel; + int dst_wc_elems = stride * channel; + for (int h = 0; h < height; ++h) + { + memcpy(dst_ptr, src_ptr, src_wc_elems); + src_ptr += src_wc_elems; + dst_ptr += dst_wc_elems; + } + } + + // Create output tensor memory + rknn_tensor_mem *output_mems[io_num.n_output]; + for (uint32_t i = 0; i < io_num.n_output; ++i) + { + output_mems[i] = rknn_create_mem(ctx, output_attrs[i].n_elems * sizeof(float)); + } + + // Set input tensor memory + ret = rknn_set_io_mem(ctx, input_mems[0], &input_attrs[0]); + if (ret < 0) + { + printf("rknn_set_io_mem fail! ret=%d\n", ret); + return -1; + } + + // Set output tensor memory + for (uint32_t i = 0; i < io_num.n_output; ++i) + { + // set output memory and attribute + output_attrs[i].type = RKNN_TENSOR_FLOAT32; + ret = rknn_set_io_mem(ctx, output_mems[i], &output_attrs[i]); + if (ret < 0) + { + printf("rknn_set_io_mem fail! ret=%d\n", ret); + return -1; + } + } + + // Run + printf("Begin perf ...\n"); + for (int i = 0; i < loop_count; ++i) + { + int64_t start_us = getCurrentTimeUs(); + ret = rknn_run(ctx, NULL); + int64_t elapse_us = getCurrentTimeUs() - start_us; + if (ret < 0) + { + printf("rknn run error %d\n", ret); + return -1; + } + printf("%4d: Elapse Time = %.2fms, FPS = %.2f\n", i, elapse_us / 1000.f, 1000.f * 1000.f / elapse_us); + } + + // Get top 5 + uint32_t topNum = 5; + for (uint32_t i = 0; i < io_num.n_output; i++) + { + uint32_t MaxClass[topNum]; + float fMaxProb[topNum]; + + uint32_t sz = output_attrs[i].n_elems; + int top_count = sz > topNum ? topNum : sz; + + float *buffer = (float *)output_mems[i]->virt_addr; + + rknn_GetTopN(buffer, fMaxProb, MaxClass, sz, topNum); + + printf("---- Top%d ----\n", top_count); + for (int j = 0; j < top_count; j++) + { + printf("%8.6f - %d\n", fMaxProb[j], MaxClass[j]); + } + } + + // Destroy rknn memory + rknn_destroy_mem(ctx, input_mems[0]); + for (uint32_t i = 0; i < io_num.n_output; ++i) + { + rknn_destroy_mem(ctx, output_mems[i]); + } + + // destroy + rknn_destroy(ctx); + + free(input_data); + + return 0; +} \ No newline at end of file diff --git a/rknpu2/examples/RV1106_RV1103/rknn_yolov5_demo/CMakeLists.txt b/rknpu2/examples/RV1106_RV1103/rknn_yolov5_demo/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..381c0cf726cec288420ce627ae287f34dd3b9d36 --- /dev/null +++ b/rknpu2/examples/RV1106_RV1103/rknn_yolov5_demo/CMakeLists.txt @@ -0,0 +1,40 @@ +cmake_minimum_required(VERSION 3.6) + +project(rknn_yolov5_demo) + +set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -Wl,--allow-shlib-undefined") +set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -Wl,--allow-shlib-undefined") + +# install target and libraries +set(CMAKE_INSTALL_PREFIX ${CMAKE_SOURCE_DIR}/install/rknn_yolov5_demo_${CMAKE_SYSTEM_NAME}) + +set(CMAKE_SKIP_INSTALL_RPATH FALSE) +set(CMAKE_BUILD_WITH_INSTALL_RPATH TRUE) +set(CMAKE_INSTALL_RPATH "${CMAKE_INSTALL_PREFIX}/lib") + +# rknn api +set(RKNN_API_PATH ${CMAKE_SOURCE_DIR}/../../../runtime/${CMAKE_SYSTEM_NAME}/librknn_api) +set(RKNN_RT_LIB ${RKNN_API_PATH}/armhf-uclibc/librknnmrt.so) + +include_directories(${RKNN_API_PATH}/include) +include_directories(${CMAKE_SOURCE_DIR}/../../3rdparty) + +# rknn_yolov5_demo +include_directories(${CMAKE_SOURCE_DIR}/include) + +add_executable(rknn_yolov5_demo + src/main.cc + src/postprocess.cc +) + +target_link_libraries(rknn_yolov5_demo +${RKNN_RT_LIB} +) + +# install target and libraries +set(CMAKE_INSTALL_PREFIX ${CMAKE_SOURCE_DIR}/install/rknn_yolov5_demo_${CMAKE_SYSTEM_NAME}) +install(TARGETS rknn_yolov5_demo DESTINATION ./) + + +install(PROGRAMS ${RKNN_RT_LIB} DESTINATION lib) +install(DIRECTORY model DESTINATION ./) diff --git a/rknpu2/examples/RV1106_RV1103/rknn_yolov5_demo/README.md b/rknpu2/examples/RV1106_RV1103/rknn_yolov5_demo/README.md new file mode 100644 index 0000000000000000000000000000000000000000..9ff2e9e1dc1becba0c691c2d4b313d9d579b70a1 --- /dev/null +++ b/rknpu2/examples/RV1106_RV1103/rknn_yolov5_demo/README.md @@ -0,0 +1,51 @@ +# Yolo-v5 demo + +# Export RKNN Model + +Please refer https://github.com/airockchip/rknn_model_zoo/tree/main/models/CV/object_detection/yolo + + + +## Arm Linux Demo + +### Compiling and Building + +The 'build-linux_RV1106.sh' can be used for compiling demo for target including RV1106 and RV1103. + +Changing the cross compiler path via the setting the `RK_RV1106_TOOLCHAIN`, shown as below: + +```sh +export RK_RV1106_TOOLCHAIN=~/opts/toolchain/arm-rockchip830-linux-uclibcgnueabihf/bin/arm-rockchip830-linux-uclibcgnueabihf +``` + +then, run the script： + +```sh +./build-linux_RV1106.sh +``` + +Note: The RV1106 and RV1103 requires this 'arm-rockchip830-linux-uclibcgnueabihf' compiler to build the demo or another applications. + +### Push build output files to the board + +Connecting the usb port to the PC, and pushing all demo folder to the directory '/userdata': + +```sh +adb push install/rknn_yolov5_demo_Linux /userdata/ +``` + +### Running + +```sh +adb shell +cd /userdata/rknn_yolov5_demo_Linux/ + +export LD_LIBRARY_PATH=/userdata/rknn_yolov5_demo_Linux/lib +./rknn_yolov5_demo model/RV1106/yolov5s-640-640.rknn model/bus.jpg +``` + +Note: + +- LD_LIBRARY_PATH must use the full path +- For performance reasons, the output fmt of the RKNN model is set to **RKNN_QUERY_NATIVE_NHWC_OUTPUT_ATTR** in the demo to obtain better inference performance. At this time, the model output buf is arranged in the order of NHWC. For example, the original shape of the first output is **1,255,80,80**. At this case, the shape output by RKNN is 1,80,80,255. The post-processing in this demo is also optimized and adjusted according to this order. + diff --git a/rknpu2/examples/RV1106_RV1103/rknn_yolov5_demo/README_CN.md b/rknpu2/examples/RV1106_RV1103/rknn_yolov5_demo/README_CN.md new file mode 100644 index 0000000000000000000000000000000000000000..19d002a5c1271eb45259d8e8ee7e4bc70a7d9ec1 --- /dev/null +++ b/rknpu2/examples/RV1106_RV1103/rknn_yolov5_demo/README_CN.md @@ -0,0 +1,46 @@ +# Yolo-v5 demo + +# 导出rknn模型 + +请参考 https://github.com/airockchip/rknn_model_zoo/tree/main/models/CV/object_detection/yolo + + + +## Arm Linux Demo + +### 编译 + +RV1106/RV1103编译脚本均为 `build-linux_RV1106.sh`，设置交叉编译器所在目录的路径 `RK_RV1106_TOOLCHAIN`，例如修改成 + +```sh +export RK_RV1106_TOOLCHAIN=~/opts/toolchain/arm-rockchip830-linux-uclibcgnueabihf/bin/arm-rockchip830-linux-uclibcgnueabihf +``` + +然后执行： + +```sh +./build-linux_RV1106.sh +``` + +### 推送执行文件到板子 + +连接板子的usb口到PC,将整个demo目录到 `/userdata`: + +```sh +adb push install/rknn_yolov5_demo_Linux /userdata/ +``` + +### 运行 + +```sh +adb shell +cd /userdata/rknn_yolov5_demo_Linux/ + +export LD_LIBRARY_PATH=/userdata/rknn_yolov5_demo_Linux/lib +./rknn_yolov5_demo model/RV1106/yolov5s-640-640.rknn model/bus.jpg +``` + +Note: + +- LD_LIBRARY_PATH 必须采用全路径 +- 基于性能原因，demo中将 RKNN 模型的输出 fmt 设置为 RKNN_QUERY_NATIVE_NHWC_OUTPUT_ATTR，以获取更好的推理性能。此时模型输出 buf 是以 NHWC 顺序进行排布的，比如第一个输出的原始 shape 是 1,255,80,80，此时RKNN输出的 shape 是1,80,80,255，此demo中的后处理也根据这个顺序做了相应的优化调整。 diff --git a/rknpu2/examples/RV1106_RV1103/rknn_yolov5_demo/build-linux_RV1106.sh b/rknpu2/examples/RV1106_RV1103/rknn_yolov5_demo/build-linux_RV1106.sh new file mode 100755 index 0000000000000000000000000000000000000000..fdb65e0dfbc1dea2fd2d82b8469f7b4958777971 --- /dev/null +++ b/rknpu2/examples/RV1106_RV1103/rknn_yolov5_demo/build-linux_RV1106.sh @@ -0,0 +1,29 @@ +#!/bin/bash +set -e + +if [ -z $RK_RV1106_TOOLCHAIN ]; then + echo "Please set the RK_RV1106_TOOLCHAIN environment variable!" + echo "example:" + echo " export RK_RV1106_TOOLCHAIN=" + exit +fi + +# for arm +GCC_COMPILER=$RK_RV1106_TOOLCHAIN + +ROOT_PWD=$( cd "$( dirname $0 )" && cd -P "$( dirname "$SOURCE" )" && pwd ) + +# build +BUILD_DIR=${ROOT_PWD}/build/build_linux_arm + +if [[ ! -d "${BUILD_DIR}" ]]; then + mkdir -p ${BUILD_DIR} +fi + +cd ${BUILD_DIR} +cmake ../.. \ + -DCMAKE_C_COMPILER=${GCC_COMPILER}-gcc \ + -DCMAKE_CXX_COMPILER=${GCC_COMPILER}-g++ +make -j4 +make install +cd - diff --git a/rknpu2/examples/RV1106_RV1103/rknn_yolov5_demo/include/drm_func.h b/rknpu2/examples/RV1106_RV1103/rknn_yolov5_demo/include/drm_func.h new file mode 100644 index 0000000000000000000000000000000000000000..0a9e3b30058ff002a0f5c94e975f9c619b08597c --- /dev/null +++ b/rknpu2/examples/RV1106_RV1103/rknn_yolov5_demo/include/drm_func.h @@ -0,0 +1,53 @@ +#ifndef __DRM_FUNC_H__ +#define __DRM_FUNC_H__ +#include +#include +#include +#include +#include // open function +#include // close function +#include +#include + + +#include +#include "libdrm/drm_fourcc.h" +#include "xf86drm.h" + +#ifdef __cplusplus +extern "C" { +#endif + +typedef int (* FUNC_DRM_IOCTL)(int fd, unsigned long request, void *arg); + +typedef struct _drm_context{ + void *drm_handle; + FUNC_DRM_IOCTL io_func; +} drm_context; + +/* memory type definitions. */ +enum drm_rockchip_gem_mem_type +{ + /* Physically Continuous memory and used as default. */ + ROCKCHIP_BO_CONTIG = 1 << 0, + /* cachable mapping. */ + ROCKCHIP_BO_CACHABLE = 1 << 1, + /* write-combine mapping. */ + ROCKCHIP_BO_WC = 1 << 2, + ROCKCHIP_BO_SECURE = 1 << 3, + ROCKCHIP_BO_MASK = ROCKCHIP_BO_CONTIG | ROCKCHIP_BO_CACHABLE | + ROCKCHIP_BO_WC | ROCKCHIP_BO_SECURE +}; + +int drm_init(drm_context *drm_ctx); + +void* drm_buf_alloc(drm_context *drm_ctx,int drm_fd, int TexWidth, int TexHeight,int bpp,int *fd,unsigned int *handle,size_t *actual_size); + +int drm_buf_destroy(drm_context *drm_ctx,int drm_fd,int buf_fd, int handle,void *drm_buf,size_t size); + +void drm_deinit(drm_context *drm_ctx, int drm_fd); + +#ifdef __cplusplus +} +#endif +#endif /*__DRM_FUNC_H__*/ \ No newline at end of file diff --git a/rknpu2/examples/RV1106_RV1103/rknn_yolov5_demo/include/postprocess.h b/rknpu2/examples/RV1106_RV1103/rknn_yolov5_demo/include/postprocess.h new file mode 100755 index 0000000000000000000000000000000000000000..132807788f3be6ddc66fd657b844ce1fa23d164d --- /dev/null +++ b/rknpu2/examples/RV1106_RV1103/rknn_yolov5_demo/include/postprocess.h @@ -0,0 +1,40 @@ +#ifndef _RKNN_ZERO_COPY_DEMO_POSTPROCESS_H_ +#define _RKNN_ZERO_COPY_DEMO_POSTPROCESS_H_ + +#include + +#define OBJ_NAME_MAX_SIZE 16 +#define OBJ_NUMB_MAX_SIZE 64 +#define OBJ_CLASS_NUM 80 +#define NMS_THRESH 0.45 +#define BOX_THRESH 0.25 +#define PROP_BOX_SIZE (5+OBJ_CLASS_NUM) + +typedef struct _BOX_RECT +{ + int left; + int right; + int top; + int bottom; +} BOX_RECT; + +typedef struct __detect_result_t +{ + char name[OBJ_NAME_MAX_SIZE]; + BOX_RECT box; + float prop; +} detect_result_t; + +typedef struct _detect_result_group_t +{ + int id; + int count; + detect_result_t results[OBJ_NUMB_MAX_SIZE]; +} detect_result_group_t; + +int post_process(int8_t *input0, int8_t *input1, int8_t *input2, int model_in_h, int model_in_w, + float conf_threshold, float nms_threshold, float scale_w, float scale_h, + std::vector &qnt_zps, std::vector &qnt_scales, + detect_result_group_t *group); + +#endif //_RKNN_ZERO_COPY_DEMO_POSTPROCESS_H_ diff --git a/rknpu2/examples/RV1106_RV1103/rknn_yolov5_demo/model/RV1106/yolov5s-640-640.rknn b/rknpu2/examples/RV1106_RV1103/rknn_yolov5_demo/model/RV1106/yolov5s-640-640.rknn new file mode 100644 index 0000000000000000000000000000000000000000..9b36965199b223a61bfcdd910af8f5a5e975a662 --- /dev/null +++ b/rknpu2/examples/RV1106_RV1103/rknn_yolov5_demo/model/RV1106/yolov5s-640-640.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ba510cd89d89d7678648f664279c93f3086b358daa118b7b5700ae60f2b7dbb6 +size 7752683 diff --git a/rknpu2/examples/RV1106_RV1103/rknn_yolov5_demo/model/bus.jpg b/rknpu2/examples/RV1106_RV1103/rknn_yolov5_demo/model/bus.jpg new file mode 100644 index 0000000000000000000000000000000000000000..19172b78dbbcf34de2b875ac72eb0b4b0651ee66 --- /dev/null +++ b/rknpu2/examples/RV1106_RV1103/rknn_yolov5_demo/model/bus.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fb4914d123d97c440cd127ef0e98d4bdc68cd88e2683657528928b4a34014e16 +size 181374 diff --git a/rknpu2/examples/RV1106_RV1103/rknn_yolov5_demo/model/coco_80_labels_list.txt b/rknpu2/examples/RV1106_RV1103/rknn_yolov5_demo/model/coco_80_labels_list.txt new file mode 100644 index 0000000000000000000000000000000000000000..941cb4e1392266f6a6c09b1fdc5f79503b2e5df6 --- /dev/null +++ b/rknpu2/examples/RV1106_RV1103/rknn_yolov5_demo/model/coco_80_labels_list.txt @@ -0,0 +1,80 @@ +person +bicycle +car +motorcycle +airplane +bus +train +truck +boat +traffic light +fire hydrant +stop sign +parking meter +bench +bird +cat +dog +horse +sheep +cow +elephant +bear +zebra +giraffe +backpack +umbrella +handbag +tie +suitcase +frisbee +skis +snowboard +sports ball +kite +baseball bat +baseball glove +skateboard +surfboard +tennis racket +bottle +wine glass +cup +fork +knife +spoon +bowl +banana +apple +sandwich +orange +broccoli +carrot +hot dog +pizza +donut +cake +chair +couch +potted plant +bed +dining table +toilet +tv +laptop +mouse +remote +keyboard +cell phone +microwave +oven +toaster +sink +refrigerator +book +clock +vase +scissors +teddy bear +hair drier +toothbrush diff --git a/rknpu2/examples/RV1106_RV1103/rknn_yolov5_demo/src/main.cc b/rknpu2/examples/RV1106_RV1103/rknn_yolov5_demo/src/main.cc new file mode 100644 index 0000000000000000000000000000000000000000..fd769d010a7e99bd84df5317ff18713b75198317 --- /dev/null +++ b/rknpu2/examples/RV1106_RV1103/rknn_yolov5_demo/src/main.cc @@ -0,0 +1,406 @@ +// Copyright (c) 2021 by Rockchip Electronics Co., Ltd. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +/*------------------------------------------- + Includes +-------------------------------------------*/ +#include "rknn_api.h" + +#include +#include +#include +#include +#include +#include + +#define STB_IMAGE_IMPLEMENTATION +#include "stb/stb_image.h" +#define STB_IMAGE_RESIZE_IMPLEMENTATION +#include + +#include "postprocess.h" + +#define PERF_WITH_POST 1 + +/*------------------------------------------- + Functions +-------------------------------------------*/ +static inline int64_t getCurrentTimeUs() +{ + struct timeval tv; + gettimeofday(&tv, NULL); + return tv.tv_sec * 1000000 + tv.tv_usec; +} + +static void dump_tensor_attr(rknn_tensor_attr *attr) +{ + char dims[128] = {0}; + for (int i = 0; i < attr->n_dims; ++i) + { + int idx = strlen(dims); + sprintf(&dims[idx], "%d%s", attr->dims[i], (i == attr->n_dims - 1) ? "" : ", "); + } + printf(" index=%d, name=%s, n_dims=%d, dims=[%s], n_elems=%d, size=%d, fmt=%s, type=%s, qnt_type=%s, " + "zp=%d, scale=%f\n", + attr->index, attr->name, attr->n_dims, dims, attr->n_elems, attr->size, get_format_string(attr->fmt), + get_type_string(attr->type), get_qnt_type_string(attr->qnt_type), attr->zp, attr->scale); +} + +static void *load_file(const char *file_path, size_t *file_size) +{ + FILE *fp = fopen(file_path, "r"); + if (fp == NULL) + { + printf("failed to open file: %s\n", file_path); + return NULL; + } + + fseek(fp, 0, SEEK_END); + size_t size = (size_t)ftell(fp); + fseek(fp, 0, SEEK_SET); + + void *file_data = malloc(size); + if (file_data == NULL) + { + fclose(fp); + printf("failed allocate file size: %zu\n", size); + return NULL; + } + + if (fread(file_data, 1, size, fp) != size) + { + fclose(fp); + free(file_data); + printf("failed to read file data!\n"); + return NULL; + } + + fclose(fp); + + *file_size = size; + + return file_data; +} + +static unsigned char *load_image(const char *image_path, rknn_tensor_attr *input_attr, int *img_height, int *img_width) +{ + int req_height = 0; + int req_width = 0; + int req_channel = 0; + + switch (input_attr->fmt) + { + case RKNN_TENSOR_NHWC: + req_height = input_attr->dims[1]; + req_width = input_attr->dims[2]; + req_channel = input_attr->dims[3]; + break; + case RKNN_TENSOR_NCHW: + req_height = input_attr->dims[2]; + req_width = input_attr->dims[3]; + req_channel = input_attr->dims[1]; + break; + default: + printf("meet unsupported layout\n"); + return NULL; + } + + int channel = 0; + + unsigned char *image_data = stbi_load(image_path, img_width, img_height, &channel, req_channel); + if (image_data == NULL) + { + printf("load image failed!\n"); + return NULL; + } + + if (*img_width != req_width || *img_height != req_height) + { + unsigned char *image_resized = (unsigned char *)STBI_MALLOC(req_width * req_height * req_channel); + if (!image_resized) + { + printf("malloc image failed!\n"); + STBI_FREE(image_data); + return NULL; + } + if (stbir_resize_uint8(image_data, *img_width, *img_height, 0, image_resized, req_width, req_height, 0, channel) != 1) + { + printf("resize image failed!\n"); + STBI_FREE(image_data); + return NULL; + } + STBI_FREE(image_data); + image_data = image_resized; + } + + return image_data; +} + + +/*------------------------------------------- + Main Functions +-------------------------------------------*/ +int main(int argc, char *argv[]) +{ + if (argc < 3) + { + printf("Usage:%s model_path input_path [loop_count]\n", argv[0]); + return -1; + } + + char *model_path = argv[1]; + char *input_path = argv[2]; + + int loop_count = 1; + if (argc > 3) + { + loop_count = atoi(argv[3]); + } + + const float nms_threshold = NMS_THRESH; + const float box_conf_threshold = BOX_THRESH; + + int img_width = 0; + int img_height = 0; + + rknn_context ctx = 0; + + // Load RKNN Model +#if 1 + // Init rknn from model path + int ret = rknn_init(&ctx, model_path, 0, 0, NULL); +#else + // Init rknn from model data + size_t model_size; + void *model_data = load_file(model_path, &model_size); + if (model_data == NULL) + { + return -1; + } + int ret = rknn_init(&ctx, model_data, model_size, 0, NULL); + free(model_data); +#endif + if (ret < 0) + { + printf("rknn_init fail! ret=%d\n", ret); + return -1; + } + + // Get sdk and driver version + rknn_sdk_version sdk_ver; + ret = rknn_query(ctx, RKNN_QUERY_SDK_VERSION, &sdk_ver, sizeof(sdk_ver)); + if (ret != RKNN_SUCC) + { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("rknn_api/rknnrt version: %s, driver version: %s\n", sdk_ver.api_version, sdk_ver.drv_version); + + // Get Model Input Output Info + rknn_input_output_num io_num; + ret = rknn_query(ctx, RKNN_QUERY_IN_OUT_NUM, &io_num, sizeof(io_num)); + if (ret != RKNN_SUCC) + { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("model input num: %d, output num: %d\n", io_num.n_input, io_num.n_output); + + printf("input tensors:\n"); + rknn_tensor_attr input_attrs[io_num.n_input]; + memset(input_attrs, 0, io_num.n_input * sizeof(rknn_tensor_attr)); + for (uint32_t i = 0; i < io_num.n_input; i++) + { + input_attrs[i].index = i; + // query info + ret = rknn_query(ctx, RKNN_QUERY_INPUT_ATTR, &(input_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret < 0) + { + printf("rknn_init error! ret=%d\n", ret); + return -1; + } + dump_tensor_attr(&input_attrs[i]); + } + + printf("output tensors:\n"); + rknn_tensor_attr output_attrs[io_num.n_output]; + memset(output_attrs, 0, io_num.n_output * sizeof(rknn_tensor_attr)); + for (uint32_t i = 0; i < io_num.n_output; i++) + { + output_attrs[i].index = i; + // query info + ret = rknn_query(ctx, RKNN_QUERY_NATIVE_NHWC_OUTPUT_ATTR, &(output_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret != RKNN_SUCC) + { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + dump_tensor_attr(&output_attrs[i]); + } + + // Get custom string + rknn_custom_string custom_string; + ret = rknn_query(ctx, RKNN_QUERY_CUSTOM_STRING, &custom_string, sizeof(custom_string)); + if (ret != RKNN_SUCC) + { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("custom string: %s\n", custom_string.string); + + unsigned char *input_data = NULL; + rknn_tensor_type input_type = RKNN_TENSOR_UINT8; + rknn_tensor_format input_layout = RKNN_TENSOR_NHWC; + + // Load image + input_data = load_image(input_path, &input_attrs[0], &img_height, &img_width); + + if (!input_data) + { + return -1; + } + + // Create input tensor memory + rknn_tensor_mem *input_mems[1]; + // default input type is int8 (normalize and quantize need compute in outside) + // if set uint8, will fuse normalize and quantize to npu + input_attrs[0].type = input_type; + // default fmt is NHWC, npu only support NHWC in zero copy mode + input_attrs[0].fmt = input_layout; + + input_mems[0] = rknn_create_mem(ctx, input_attrs[0].size_with_stride); + + // Copy input data to input tensor memory + int width = input_attrs[0].dims[2]; + int stride = input_attrs[0].w_stride; + + if (width == stride) + { + memcpy(input_mems[0]->virt_addr, input_data, width * input_attrs[0].dims[1] * input_attrs[0].dims[3]); + } + else + { + int height = input_attrs[0].dims[1]; + int channel = input_attrs[0].dims[3]; + // copy from src to dst with stride + uint8_t *src_ptr = input_data; + uint8_t *dst_ptr = (uint8_t *)input_mems[0]->virt_addr; + // width-channel elements + int src_wc_elems = width * channel; + int dst_wc_elems = stride * channel; + for (int h = 0; h < height; ++h) + { + memcpy(dst_ptr, src_ptr, src_wc_elems); + src_ptr += src_wc_elems; + dst_ptr += dst_wc_elems; + } + } + + // Create output tensor memory + rknn_tensor_mem *output_mems[io_num.n_output]; + for (uint32_t i = 0; i < io_num.n_output; ++i) + { + output_mems[i] = rknn_create_mem(ctx, output_attrs[i].size_with_stride); + } + + // Set input tensor memory + ret = rknn_set_io_mem(ctx, input_mems[0], &input_attrs[0]); + if (ret < 0) + { + printf("rknn_set_io_mem fail! ret=%d\n", ret); + return -1; + } + + // Set output tensor memory + for (uint32_t i = 0; i < io_num.n_output; ++i) + { + // set output memory and attribute + ret = rknn_set_io_mem(ctx, output_mems[i], &output_attrs[i]); + if (ret < 0) + { + printf("rknn_set_io_mem fail! ret=%d\n", ret); + return -1; + } + } + + // Run + printf("Begin perf ...\n"); + for (int i = 0; i < loop_count; ++i) + { + int64_t start_us = getCurrentTimeUs(); + ret = rknn_run(ctx, NULL); + int64_t elapse_us = getCurrentTimeUs() - start_us; + if (ret < 0) + { + printf("rknn run error %d\n", ret); + return -1; + } + printf("%4d: Elapse Time = %.2fms, FPS = %.2f\n", i, elapse_us / 1000.f, 1000.f * 1000.f / elapse_us); + } + + int model_width = 0; + int model_height = 0; + if (input_attrs[0].fmt == RKNN_TENSOR_NCHW) + { + printf("model is NCHW input fmt\n"); + model_width = input_attrs[0].dims[2]; + model_height = input_attrs[0].dims[3]; + } + else + { + printf("model is NHWC input fmt\n"); + model_width = input_attrs[0].dims[1]; + model_height = input_attrs[0].dims[2]; + } + // post process + float scale_w = (float)model_width / img_width; + float scale_h = (float)model_height / img_height; + + detect_result_group_t detect_result_group; + std::vector out_scales; + std::vector out_zps; + for (int i = 0; i < io_num.n_output; ++i) + { + out_scales.push_back(output_attrs[i].scale); + out_zps.push_back(output_attrs[i].zp); + } + + post_process((int8_t *)output_mems[0]->virt_addr, (int8_t *)output_mems[1]->virt_addr, (int8_t *)output_mems[2]->virt_addr, 640, 640, + box_conf_threshold, nms_threshold, scale_w, scale_h, out_zps, out_scales, &detect_result_group); + + char text[256]; + for (int i = 0; i < detect_result_group.count; i++) + { + detect_result_t *det_result = &(detect_result_group.results[i]); + sprintf(text, "%s %.1f%%", det_result->name, det_result->prop * 100); + printf("%s @ (%d %d %d %d) %f\n", + det_result->name, + det_result->box.left, det_result->box.top, det_result->box.right, det_result->box.bottom, + det_result->prop); + } + + // Destroy rknn memory + rknn_destroy_mem(ctx, input_mems[0]); + for (uint32_t i = 0; i < io_num.n_output; ++i) + { + rknn_destroy_mem(ctx, output_mems[i]); + } + + // destroy + rknn_destroy(ctx); + + free(input_data); + + return 0; +} diff --git a/rknpu2/examples/RV1106_RV1103/rknn_yolov5_demo/src/postprocess.cc b/rknpu2/examples/RV1106_RV1103/rknn_yolov5_demo/src/postprocess.cc new file mode 100755 index 0000000000000000000000000000000000000000..bf76c15d94959f5af1a15108546bdf09405504bb --- /dev/null +++ b/rknpu2/examples/RV1106_RV1103/rknn_yolov5_demo/src/postprocess.cc @@ -0,0 +1,442 @@ +// Copyright (c) 2021 by Rockchip Electronics Co., Ltd. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include +#include +#include +#include +#include +#include +#include +#include "postprocess.h" +#include +#define LABEL_NALE_TXT_PATH "./model/coco_80_labels_list.txt" + +static char *labels[OBJ_CLASS_NUM]; + +const int anchor0[6] = {10, 13, 16, 30, 33, 23}; +const int anchor1[6] = {30, 61, 62, 45, 59, 119}; +const int anchor2[6] = {116, 90, 156, 198, 373, 326}; + +inline static int clamp(float val, int min, int max) +{ + return val > min ? (val < max ? val : max) : min; +} + +char *readLine(FILE *fp, char *buffer, int *len) +{ + int ch; + int i = 0; + size_t buff_len = 0; + + buffer = (char *)malloc(buff_len + 1); + if (!buffer) + return NULL; // Out of memory + + while ((ch = fgetc(fp)) != '\n' && ch != EOF) + { + buff_len++; + void *tmp = realloc(buffer, buff_len + 1); + if (tmp == NULL) + { + free(buffer); + return NULL; // Out of memory + } + buffer = (char *)tmp; + + buffer[i] = (char)ch; + i++; + } + buffer[i] = '\0'; + + *len = buff_len; + + // Detect end + if (ch == EOF && (i == 0 || ferror(fp))) + { + free(buffer); + return NULL; + } + return buffer; +} + +int readLines(const char *fileName, char *lines[], int max_line) +{ + FILE *file = fopen(fileName, "r"); + char *s; + int i = 0; + int n = 0; + while ((s = readLine(file, s, &n)) != NULL) + { + lines[i++] = s; + if (i >= max_line) + break; + } + return i; +} + +int loadLabelName(const char *locationFilename, char *label[]) +{ + printf("loadLabelName %s\n", locationFilename); + readLines(locationFilename, label, OBJ_CLASS_NUM); + return 0; +} + +static float CalculateOverlap(float xmin0, float ymin0, float xmax0, float ymax0, float xmin1, float ymin1, float xmax1, float ymax1) +{ + float w = fmax(0.f, fmin(xmax0, xmax1) - fmax(xmin0, xmin1) + 1.0); + float h = fmax(0.f, fmin(ymax0, ymax1) - fmax(ymin0, ymin1) + 1.0); + float i = w * h; + float u = (xmax0 - xmin0 + 1.0) * (ymax0 - ymin0 + 1.0) + (xmax1 - xmin1 + 1.0) * (ymax1 - ymin1 + 1.0) - i; + return u <= 0.f ? 0.f : (i / u); +} + +static int nms(int validCount, std::vector &outputLocations, std::vector classIds, std::vector &order,int filterId, float threshold) +{ + for (int i = 0; i < validCount; ++i) + { + int n = order[i]; + if (n == -1|| classIds[n] != filterId) + { + continue; + } + for (int j = i + 1; j < validCount; ++j) + { + int m = order[j]; + if (m == -1 || classIds[m] != filterId) + { + continue; + } + float xmin0 = outputLocations[n * 4 + 0]; + float ymin0 = outputLocations[n * 4 + 1]; + float xmax0 = outputLocations[n * 4 + 0] + outputLocations[n * 4 + 2]; + float ymax0 = outputLocations[n * 4 + 1] + outputLocations[n * 4 + 3]; + + float xmin1 = outputLocations[m * 4 + 0]; + float ymin1 = outputLocations[m * 4 + 1]; + float xmax1 = outputLocations[m * 4 + 0] + outputLocations[m * 4 + 2]; + float ymax1 = outputLocations[m * 4 + 1] + outputLocations[m * 4 + 3]; + + float iou = CalculateOverlap(xmin0, ymin0, xmax0, ymax0, xmin1, ymin1, xmax1, ymax1); + + if (iou > threshold) + { + order[j] = -1; + } + } + } + return 0; +} + +static int quick_sort_indice_inverse( + std::vector &input, + int left, + int right, + std::vector &indices) +{ + float key; + int key_index; + int low = left; + int high = right; + if (left < right) + { + key_index = indices[left]; + key = input[left]; + while (low < high) + { + while (low < high && input[high] <= key) + { + high--; + } + input[low] = input[high]; + indices[low] = indices[high]; + while (low < high && input[low] >= key) + { + low++; + } + input[high] = input[low]; + indices[high] = indices[low]; + } + input[low] = key; + indices[low] = key_index; + quick_sort_indice_inverse(input, left, low - 1, indices); + quick_sort_indice_inverse(input, low + 1, right, indices); + } + return low; +} + +static float sigmoid(float x) +{ + return 1.0 / (1.0 + expf(-x)); +} + +static float unsigmoid(float y) +{ + return -1.0 * logf((1.0 / y) - 1.0); +} + +inline static int32_t __clip(float val, float min, float max) +{ + float f = val <= min ? min : (val >= max ? max : val); + return f; +} + +static int8_t qnt_f32_to_affine(float f32, int32_t zp, float scale) +{ + float dst_val = (f32 / scale) + zp; + int8_t res = (int8_t)__clip(dst_val, -128, 127); + return res; +} + +static float deqnt_affine_to_f32(int8_t qnt, int32_t zp, float scale) +{ + return ((float)qnt - (float)zp) * scale; +} + +static int process(int8_t *input, int *anchor, int grid_h, int grid_w, int height, int width, int stride, + std::vector &boxes, std::vector &objProbs, std::vector &classId, + float threshold, int32_t zp, float scale) +{ + + int validCount = 0; + int grid_len = grid_h * grid_w; + int8_t thres_i8 = qnt_f32_to_affine(threshold, zp, scale); + for (int a = 0; a < 3; a++) + { + for (int i = 0; i < grid_h; i++) + { + for (int j = 0; j < grid_w; j++) + { + int8_t box_confidence = input[(PROP_BOX_SIZE * a + 4) * grid_len + i * grid_w + j]; + if (box_confidence >= thres_i8) + { + int offset = (PROP_BOX_SIZE * a) * grid_len + i * grid_w + j; + int8_t *in_ptr = input + offset; + float box_x = (deqnt_affine_to_f32(*in_ptr, zp, scale)) * 2.0 - 0.5; + float box_y = (deqnt_affine_to_f32(in_ptr[grid_len], zp, scale)) * 2.0 - 0.5; + float box_w = (deqnt_affine_to_f32(in_ptr[2 * grid_len], zp, scale)) * 2.0; + float box_h = (deqnt_affine_to_f32(in_ptr[3 * grid_len], zp, scale)) * 2.0; + box_x = (box_x + j) * (float)stride; + box_y = (box_y + i) * (float)stride; + box_w = box_w * box_w * (float)anchor[a * 2]; + box_h = box_h * box_h * (float)anchor[a * 2 + 1]; + box_x -= (box_w / 2.0); + box_y -= (box_h / 2.0); + + int8_t maxClassProbs = in_ptr[5 * grid_len]; + int maxClassId = 0; + for (int k = 1; k < OBJ_CLASS_NUM; ++k) + { + int8_t prob = in_ptr[(5 + k) * grid_len]; + if (prob > maxClassProbs) + { + maxClassId = k; + maxClassProbs = prob; + } + } + if (maxClassProbs>thres_i8){ + boxes.push_back(box_x); + boxes.push_back(box_y); + boxes.push_back(box_w); + boxes.push_back(box_h); + objProbs.push_back((deqnt_affine_to_f32(maxClassProbs, zp, scale))* (deqnt_affine_to_f32(box_confidence, zp, scale))); + classId.push_back(maxClassId); + validCount++; + } + } + } + } + } + return validCount; +} + + +static int process_native_nhwc(int8_t *input, int *anchor, int grid_h, int grid_w, int height, int width, int stride, + std::vector &boxes, std::vector &boxScores, std::vector &classId, + float threshold, int32_t zp, float scale) +{ + int validCount = 0; + int8_t thres_i8 = qnt_f32_to_affine(threshold, zp, scale); + + int anchor_per_branch = 3; + // 新驱动不再有对齐要求 + // int align_c = get_align(PROP_BOX_SIZE*anchor_per_branch, 16); + int align_c = PROP_BOX_SIZE*anchor_per_branch; + // printf("align_c %d\n", align_c); + + for (int h=0; h < grid_h; h++){ + for (int w=0; w < grid_w; w++){ + for (int a=0; a < anchor_per_branch; a++){ + int hw_offset = h*grid_w*align_c + w*align_c + a*PROP_BOX_SIZE; + // int hw_offset = h*grid_w*anchor_per_branch*PROP_BOX_SIZE + w*anchor_per_branch*PROP_BOX_SIZE + a*PROP_BOX_SIZE; + int8_t *hw_ptr = input + hw_offset; + int8_t box_confidence = hw_ptr[4]; + + if (box_confidence >= thres_i8){ + // printf("box_conf %d, thres_i8 %d\n", box_confidence, thres_i8); + + int8_t maxClassProbs = hw_ptr[5]; + int maxClassId = 0; + for (int k = 1; k < OBJ_CLASS_NUM; ++k) + { + int8_t prob = hw_ptr[5 + k]; + if (prob > maxClassProbs) + { + maxClassId = k; + maxClassProbs = prob; + } + } + + // printf("box_conf %d, thres_i8 %d, maxClassProbs %d\n", box_confidence, thres_i8, maxClassProbs); + float box_conf_f32 = deqnt_affine_to_f32(box_confidence, zp, scale); + float class_prob_f32 = deqnt_affine_to_f32(maxClassProbs, zp, scale); + float limit_score = box_conf_f32* class_prob_f32; + + if (limit_score > threshold){ + float box_x, box_y, box_w, box_h; + + box_x = deqnt_affine_to_f32(hw_ptr[0], zp, scale) * 2.0 - 0.5; + box_y = deqnt_affine_to_f32(hw_ptr[1], zp, scale) * 2.0 - 0.5; + box_w = deqnt_affine_to_f32(hw_ptr[2], zp, scale) * 2.0; + box_h = deqnt_affine_to_f32(hw_ptr[3], zp, scale) * 2.0; + box_w = box_w * box_w; + box_h = box_h * box_h; + + + box_x = (box_x + w) * (float)stride; + box_y = (box_y + h) * (float)stride; + box_w *= (float)anchor[a * 2]; + box_h *= (float)anchor[a * 2 + 1]; + + box_x -= (box_w / 2.0); + box_y -= (box_h / 2.0); + + boxes.push_back(box_x); + boxes.push_back(box_y); + boxes.push_back(box_w); + boxes.push_back(box_h); + boxScores.push_back(limit_score); + classId.push_back(maxClassId); + validCount++; + } + } + } + } + } + return validCount; +} + +int post_process(int8_t *input0, int8_t *input1, int8_t *input2, int model_in_h, int model_in_w, + float conf_threshold, float nms_threshold, float scale_w, float scale_h, + std::vector &qnt_zps, std::vector &qnt_scales, + detect_result_group_t *group) +{ + static int init = -1; + if (init == -1) + { + int ret = 0; + ret = loadLabelName(LABEL_NALE_TXT_PATH, labels); + if (ret < 0) + { + return -1; + } + + init = 0; + } + memset(group, 0, sizeof(detect_result_group_t)); + + std::vector filterBoxes; + std::vector objProbs; + std::vector classId; + + // stride 8 + int stride0 = 8; + int grid_h0 = model_in_h / stride0; + int grid_w0 = model_in_w / stride0; + int validCount0 = 0; + validCount0 = process_native_nhwc(input0, (int *)anchor0, grid_h0, grid_w0, model_in_h, model_in_w, + stride0, filterBoxes, objProbs, classId, conf_threshold, qnt_zps[0], qnt_scales[0]); + + // stride 16 + int stride1 = 16; + int grid_h1 = model_in_h / stride1; + int grid_w1 = model_in_w / stride1; + int validCount1 = 0; + validCount1 = process_native_nhwc(input1, (int *)anchor1, grid_h1, grid_w1, model_in_h, model_in_w, + stride1, filterBoxes, objProbs, classId, conf_threshold, qnt_zps[1], qnt_scales[1]); + + // stride 32 + int stride2 = 32; + int grid_h2 = model_in_h / stride2; + int grid_w2 = model_in_w / stride2; + int validCount2 = 0; + validCount2 = process_native_nhwc(input2, (int *)anchor2, grid_h2, grid_w2, model_in_h, model_in_w, + stride2, filterBoxes, objProbs, classId, conf_threshold, qnt_zps[2], qnt_scales[2]); + + int validCount = validCount0 + validCount1 + validCount2; + // no object detect + if (validCount <= 0) + { + return 0; + } + + std::vector indexArray; + for (int i = 0; i < validCount; ++i) + { + indexArray.push_back(i); + } + + quick_sort_indice_inverse(objProbs, 0, validCount - 1, indexArray); + + std::set class_set(std::begin(classId),std::end(classId)); + + for(auto c : class_set){ + nms(validCount, filterBoxes, classId, indexArray, c, nms_threshold); + } + + int last_count = 0; + group->count = 0; + /* box valid detect target */ + for (int i = 0; i < validCount; ++i) + { + + if (indexArray[i] == -1 || last_count >= OBJ_NUMB_MAX_SIZE) + { + continue; + } + int n = indexArray[i]; + + float x1 = filterBoxes[n * 4 + 0]; + float y1 = filterBoxes[n * 4 + 1]; + float x2 = x1 + filterBoxes[n * 4 + 2]; + float y2 = y1 + filterBoxes[n * 4 + 3]; + int id = classId[n]; + float obj_conf = objProbs[i]; + + group->results[last_count].box.left = (int)(clamp(x1, 0, model_in_w) / scale_w); + group->results[last_count].box.top = (int)(clamp(y1, 0, model_in_h) / scale_h); + group->results[last_count].box.right = (int)(clamp(x2, 0, model_in_w) / scale_w); + group->results[last_count].box.bottom = (int)(clamp(y2, 0, model_in_h) / scale_h); + group->results[last_count].prop = obj_conf; + char *label = labels[id]; + strncpy(group->results[last_count].name, label, OBJ_NAME_MAX_SIZE); + + // printf("result %2d: (%4d, %4d, %4d, %4d), %s\n", i, group->results[last_count].box.left, group->results[last_count].box.top, + // group->results[last_count].box.right, group->results[last_count].box.bottom, label); + last_count++; + } + group->count = last_count; + + return 0; +} diff --git a/rknpu2/examples/librknn_api_android_demo/.clang-format b/rknpu2/examples/librknn_api_android_demo/.clang-format new file mode 100644 index 0000000000000000000000000000000000000000..4531f095ab2a9824b7fa360485318f9c76b05860 --- /dev/null +++ b/rknpu2/examples/librknn_api_android_demo/.clang-format @@ -0,0 +1,128 @@ +--- +Language: Cpp +# BasedOnStyle: LLVM +AccessModifierOffset: -2 +AlignAfterOpenBracket: Align +AlignConsecutiveAssignments: true +AlignConsecutiveDeclarations: true +AlignEscapedNewlines: Left +AlignOperands: true +AlignTrailingComments: true +AllowAllParametersOfDeclarationOnNextLine: false +AllowShortBlocksOnASingleLine: false +AllowShortCaseLabelsOnASingleLine: false +AllowShortFunctionsOnASingleLine: All +AllowShortIfStatementsOnASingleLine: false +AllowShortLoopsOnASingleLine: false +AlwaysBreakAfterDefinitionReturnType: None +AlwaysBreakAfterReturnType: None +AlwaysBreakBeforeMultilineStrings: false +AlwaysBreakTemplateDeclarations: true +BinPackArguments: true +BinPackParameters: true +BraceWrapping: + AfterClass: true + AfterControlStatement: false + AfterEnum: true + AfterFunction: true + AfterNamespace: false + AfterObjCDeclaration: false + AfterStruct: true + AfterUnion: true + AfterExternBlock: false + BeforeCatch: false + BeforeElse: false + IndentBraces: false + SplitEmptyFunction: false + SplitEmptyRecord: false + SplitEmptyNamespace: false +BreakBeforeBinaryOperators: None +BreakBeforeBraces: Custom +BreakBeforeInheritanceComma: false +# BreakInheritanceList: BeforeColon +BreakBeforeTernaryOperators: true +BreakConstructorInitializersBeforeComma: true +BreakConstructorInitializers: BeforeComma +BreakAfterJavaFieldAnnotations: false +BreakStringLiterals: true +ColumnLimit: 120 +CommentPragmas: '^ IWYU pragma:' +CompactNamespaces: false +ConstructorInitializerAllOnOneLineOrOnePerLine: false +ConstructorInitializerIndentWidth: 2 +ContinuationIndentWidth: 2 +Cpp11BracedListStyle: true +DerivePointerAlignment: false +DisableFormat: false +ExperimentalAutoDetectBinPacking: false +FixNamespaceComments: true +ForEachMacros: + - foreach + - Q_FOREACH + - BOOST_FOREACH +IncludeBlocks: Regroup +# To obtain updated lists of system headers used in the below expressions, see: +# https://stackoverflow.com/a/2029106 +IncludeCategories: + # ONNC library headers + - Regex: '^[<|"]onnc/' + Priority: 2 + # ONNX library headers + - Regex: '^[<|"]onnx/' + Priority: 3 + # C system headers. + - Regex: '^[<"](aio|arpa/inet|assert|complex|cpio|ctype|curses|dirent|dlfcn|errno|fcntl|fenv|float|fmtmsg|fnmatch|ftw|glob|grp|iconv|inttypes|iso646|langinfo|libgen|limits|locale|math|monetary|mqueue|ndbm|netdb|net/if|netinet/in|netinet/tcp|nl_types|poll|pthread|pwd|regex|sched|search|semaphore|setjmp|signal|spawn|stdalign|stdarg|stdatomic|stdbool|stddef|stdint|stdio|stdlib|stdnoreturn|string|strings|stropts|sys/ipc|syslog|sys/mman|sys/msg|sys/resource|sys/select|sys/sem|sys/shm|sys/socket|sys/stat|sys/statvfs|sys/time|sys/times|sys/types|sys/uio|sys/un|sys/utsname|sys/wait|tar|term|termios|tgmath|threads|time|trace|uchar|ulimit|uncntrl|unistd|utime|utmpx|wchar|wctype|wordexp)\.h[">]$' + Priority: 4 + # C++ system headers (as of C++14). + - Regex: '^[<"](algorithm|array|atomic|bitset|cassert|ccomplex|cctype|cerrno|cfenv|cfloat|chrono|cinttypes|ciso646|climits|clocale|cmath|codecvt|complex|condition_variable|csetjmp|csignal|cstdalign|cstdarg|cstdbool|cstddef|cstdint|cstdio|cstdlib|cstring|ctgmath|ctime|cuchar|cwchar|cwctype|deque|exception|forward_list|fstream|functional|future|initializer_list|iomanip|ios|iosfwd|iostream|istream|iterator|limits|list|locale|map|memory|mutex|new|numeric|ostream|queue|random|ratio|regex|scoped_allocator|set|shared_mutex|sstream|stack|stdexcept|streambuf|string|strstream|system_error|thread|tuple|type_traits|typeindex|typeinfo|unordered_map|unordered_set|utility|valarray|vector)[">]$' + Priority: 5 + - Regex: '.*' + Priority: 1 +IncludeIsMainRegex: '(Test)?$' +IndentCaseLabels: false +IndentPPDirectives: AfterHash +IndentWidth: 2 +IndentWrappedFunctionNames: false +JavaScriptQuotes: Leave +JavaScriptWrapImports: true +KeepEmptyLinesAtTheStartOfBlocks: false +MacroBlockBegin: '' +MacroBlockEnd: '' +MaxEmptyLinesToKeep: 1 +NamespaceIndentation: None +# ObjCBinPackProtocolList: Auto +# ObjCBlockIndentWidth: 2 +# ObjCSpaceAfterProperty: false +# ObjCSpaceBeforeProtocolList: true +PenaltyBreakAssignment: 2 +PenaltyBreakBeforeFirstCallParameter: 19 +PenaltyBreakComment: 300 +PenaltyBreakFirstLessLess: 120 +PenaltyBreakString: 1000 +# PenaltyBreakTemplateDeclaration: 10 +PenaltyExcessCharacter: 1000000 +PenaltyReturnTypeOnItsOwnLine: 60 +PointerAlignment: Left +ReflowComments: true +SortIncludes: true +SortUsingDeclarations: true +SpaceAfterCStyleCast: false +SpaceAfterTemplateKeyword: true +SpaceBeforeAssignmentOperators: true +# SpaceBeforeCpp11BracedList: false +# SpaceBeforeCtorInitializerColon: true +# SpaceBeforeInheritanceColon: true +SpaceBeforeParens: ControlStatements +# SpaceBeforeRangeBasedForLoopColon: true +SpaceInEmptyParentheses: false +SpacesBeforeTrailingComments: 1 +SpacesInAngles: false +SpacesInContainerLiterals: false +SpacesInCStyleCastParentheses: false +SpacesInParentheses: false +SpacesInSquareBrackets: false +Standard: Cpp11 +TabWidth: 2 +UseTab: Never +... + diff --git a/rknpu2/examples/librknn_api_android_demo/Android.mk b/rknpu2/examples/librknn_api_android_demo/Android.mk new file mode 100755 index 0000000000000000000000000000000000000000..ae512c6e57330e23ecb360e8ac6713ac75ca85f7 --- /dev/null +++ b/rknpu2/examples/librknn_api_android_demo/Android.mk @@ -0,0 +1,28 @@ +LOCAL_PATH:= $(call my-dir) +#====================================================================== +# +# rknn_create_mem_demo +# +#====================================================================== +include $(CLEAR_VARS) +LOCAL_VENDOR_MODULE := true + +LOCAL_CFLAGS += -Wall -Werror -Wunreachable-code -Wno-implicit-fallthrough + +LOCAL_C_INCLUDES += \ + $(LOCAL_PATH)/include \ + +LOCAL_SHARED_LIBRARIES := \ + liblog \ + librknn_api_android + +LOCAL_HEADER_LIBRARIES += \ + librknn_api_android_headers \ + +LOCAL_SRC_FILES:= \ + rknn_create_mem_demo.cpp \ + +LOCAL_MODULE:= rknn_create_mem_demo + +include $(BUILD_EXECUTABLE) + diff --git a/rknpu2/examples/librknn_api_android_demo/README.md b/rknpu2/examples/librknn_api_android_demo/README.md new file mode 100644 index 0000000000000000000000000000000000000000..cde992194002d85094db90824b7ce2a2e026382f --- /dev/null +++ b/rknpu2/examples/librknn_api_android_demo/README.md @@ -0,0 +1,69 @@ +# Instructions + +There are two ways to use the RKNN API on the Android platform: + +1. Directly link to librknnrt.so. +2. Link to librknn_api_android.so, which is implemented based on Android platform HIDL. + +For Android devices that need to pass CTS/VTS testing, it is recommended to use the RKNN API implemented based on Android platform HIDL. If devices don't need to pass CTS/VTS testing, it is suggested to directly link and use librknnrt.so, which provides better performance due to shorter interface calling process. + +The code for using the RKNN API implemented with Android HIDL can be found in the vendor/rockchip/hardware/interfaces/neuralnetworks directory of the RK3566_RK3568/RK3588 Android system SDK. After completing the Android system compilation, some NPU-related libraries will be generated (for applications, only librknn_api_android.so needs to be linked). + +**This example is applicable to librknn_api_android.so.** + +## Compilation + +- Compile librknn_api_android.so + + First, download the RK3566_RK3568/RK3588 Android SDK, and in the root directory of the Android SDK, then execute following commands: + + ``` + source build/envsetup.sh + lunch your target ## Choose according to your actual situation + mmm vendor/rockchip/hardware/interfaces/neuralnetworks/ -j16 + ``` + + The following files will be generated: + + ``` + /vendor/lib/librknn_api_android.so + /vendor/lib/librknnhal_bridge.rockchip.so + /vendor/lib64/librknn_api_android.so + /vendor/lib64/librknnhal_bridge.rockchip.so + /vendor/lib64/rockchip.hardware.neuralnetworks@1.0.so + /vendor/lib64/rockchip.hardware.neuralnetworks@1.0-adapter-helper.so + /vendor/lib64/hw/rockchip.hardware.neuralnetworks@1.0-impl.so + /vendor/bin/hw/rockchip.hardware.neuralnetworks@1.0-service + ``` + +- Compile this demo + + Copy $RKNPU2_SDK to the root directory of the Android SDK, and execute: + + ``` + mmm rknpu2/examples/librknn_api_android_demo + ``` + + The following file will be generated: + + vendor/bin/rknn_create_mem_demo + +## Execution + +- Push rknn_create_mem_demo to the /vendor/bin/ directory of the target device. +- Push the model to the /data/ directory of the target device. +- Make sure that the [rockchip.hardware.neuralnetworks@1.0-service](mailto:rockchip.hardware.neuralnetworks@1.0-service) is running on the device. + +``` + rknn_create_mem_demo /data/model/RK3566_RK3568/mobilenet_v1.rknn /data/model/dog_224x224.jpg +``` + +## FAQ + +- What should I do if the [rockchip.hardware.neuralnetworks@1.0-service](mailto:rockchip.hardware.neuralnetworks@1.0-service) is not running? + + If the service is not running, make sure that the vendor/rockchip/hardware/interfaces/neuralnetworks/ directory exists in the Android SDK, recompile the system firmware, and burn it onto the device. Please refer to the instructions for compiling the SDK firmware for specific steps. + +- Encounter the error "sizeof(rknn_tensor_attr) != sizeof(::rockchip::hardware::neuralnetworks::V1_0::RKNNTensorAttr)" + + You need to update the vendor/rockchip/hardware/interfaces/neuralnetworks to the latest version. diff --git a/rknpu2/examples/librknn_api_android_demo/README_CN.md b/rknpu2/examples/librknn_api_android_demo/README_CN.md new file mode 100644 index 0000000000000000000000000000000000000000..09d7c2b39eac4ec5463ceb80e8ba235d4f2c80d2 --- /dev/null +++ b/rknpu2/examples/librknn_api_android_demo/README_CN.md @@ -0,0 +1,73 @@ +# 说明 +Android平台有两种方式来调用RKNN API +1）应用直接链接librknnrt.so +2）应用链接Android平台HIDL实现的librknn_api_android.so +对于需要通过CTS/VTS测试的Android设备可以使用基于Android平台HIDL实现的RKNN API。如果不需要通过CTS/VTS测试的设备建议直接链接使用librknnrt.so，对各个接口调用流程的链路更短，可以提供更好的性能。 + +对于使用Android HIDL实现的RKNN API的代码位于RK3566_RK3568/RK3588 Android系统SDK的vendor/rockchip/hardware/interfaces/neuralnetworks目录下。当完成Android系统编译后，将会生成一些NPU相关的库（对于应用只需要链接使用librknn_api_android.so即可） + +**本示例适用于librknn_api_android.so。** + + + +# 编译 + +- 编译librknn_api_android.so + + 需要先下载RK3566_RK3568/RK3588 Android SDK，在Android SDK根目录执行 + + ``` + source build/envsetup.sh + lunch your target ##需要根据自己的实际情况进行选择 + mmm vendor/rockchip/hardware/interfaces/neuralnetworks/ -j16 + + ``` + + 将生成 + + ``` + /vendor/lib/librknn_api_android.so + /vendor/lib/librknnhal_bridge.rockchip.so + /vendor/lib64/librknn_api_android.so + /vendor/lib64/librknnhal_bridge.rockchip.so + /vendor/lib64/rockchip.hardware.neuralnetworks@1.0.so + /vendor/lib64/rockchip.hardware.neuralnetworks@1.0-adapter-helper.so + /vendor/lib64/hw/rockchip.hardware.neuralnetworks@1.0-impl.so + /vendor/bin/hw/rockchip.hardware.neuralnetworks@1.0-service + ``` + + +- 编译本demo + + 将$RKNPU2_SDK拷贝到Android SDK根目录，并执行： + + ``` + mmm rknpu2/examples/librknn_api_android_demo + ``` + + 将生成的vendor/bin/rknn_create_mem_demo + +# 运行 + + +- 将rknn_create_mem_demo推到板子/vendor/bin/目录 + +- 将model推到板子/data/目录 + +- 确保板子的rockchip.hardware.neuralnetworks@1.0-service已经运行 + +``` + rknn_create_mem_demo /data/model/RK3566_RK3568/mobilenet_v1.rknn /data/model/dog_224x224.jpg +``` + + + +# FAQ + +- rockchip.hardware.neuralnetworks@1.0-service服务没有运行怎么办 + + 如果该服务没有运行，从Android SDK确保vendor/rockchip/hardware/interfaces/neuralnetworks/目录存在，并且重新编译系统固件，并重新烧写到板子上，具体步骤请参考SDK编译固件的说明。 + +- 遇到sizeof(rknn_tensor_attr) != sizeof(::rockchip::hardware::neuralnetworks::V1_0::RKNNTensorAttr)的错误 + + 需要更新vendor/rockchip/hardware/interfaces/neuralnetworks到最新代码 diff --git a/rknpu2/examples/librknn_api_android_demo/include/rknn_api.h b/rknpu2/examples/librknn_api_android_demo/include/rknn_api.h new file mode 100755 index 0000000000000000000000000000000000000000..a91fe3f06945a142d16afef75496f3e51ce80e78 --- /dev/null +++ b/rknpu2/examples/librknn_api_android_demo/include/rknn_api.h @@ -0,0 +1,668 @@ +/**************************************************************************** +* +* Copyright (c) 2017 - 2022 by Rockchip Corp. All rights reserved. +* +* The material in this file is confidential and contains trade secrets +* of Rockchip Corporation. This is proprietary information owned by +* Rockchip Corporation. No part of this work may be disclosed, +* reproduced, copied, transmitted, or used in any way for any purpose, +* without the express written permission of Rockchip Corporation. +* +*****************************************************************************/ + + +#ifndef _RKNN_API_H +#define _RKNN_API_H + +#ifdef __cplusplus +extern "C" { +#endif + +#include + +/* + Definition of extended flag for rknn_init. +*/ +/* set high priority context. */ +#define RKNN_FLAG_PRIOR_HIGH 0x00000000 + +/* set medium priority context */ +#define RKNN_FLAG_PRIOR_MEDIUM 0x00000001 + +/* set low priority context. */ +#define RKNN_FLAG_PRIOR_LOW 0x00000002 + +/* asynchronous mode. + when enable, rknn_outputs_get will not block for too long because it directly retrieves the result of + the previous frame which can increase the frame rate on single-threaded mode, but at the cost of + rknn_outputs_get not retrieves the result of the current frame. + in multi-threaded mode you do not need to turn this mode on. */ +#define RKNN_FLAG_ASYNC_MASK 0x00000004 + +/* collect performance mode. + when enable, you can get detailed performance reports via rknn_query(ctx, RKNN_QUERY_PERF_DETAIL, ...), + but it will reduce the frame rate. */ +#define RKNN_FLAG_COLLECT_PERF_MASK 0x00000008 + +/* allocate all memory in outside, includes weight/internal/inputs/outputs */ +#define RKNN_FLAG_MEM_ALLOC_OUTSIDE 0x00000010 + +/* weight sharing with the same network structure */ +#define RKNN_FLAG_SHARE_WEIGHT_MEM 0x00000020 + +/* send fence fd from outside */ +#define RKNN_FLAG_FENCE_IN_OUTSIDE 0x00000040 + +/* get fence fd from inside */ +#define RKNN_FLAG_FENCE_OUT_OUTSIDE 0x00000080 + +/* + Error code returned by the RKNN API. +*/ +#define RKNN_SUCC 0 /* execute succeed. */ +#define RKNN_ERR_FAIL -1 /* execute failed. */ +#define RKNN_ERR_TIMEOUT -2 /* execute timeout. */ +#define RKNN_ERR_DEVICE_UNAVAILABLE -3 /* device is unavailable. */ +#define RKNN_ERR_MALLOC_FAIL -4 /* memory malloc fail. */ +#define RKNN_ERR_PARAM_INVALID -5 /* parameter is invalid. */ +#define RKNN_ERR_MODEL_INVALID -6 /* model is invalid. */ +#define RKNN_ERR_CTX_INVALID -7 /* context is invalid. */ +#define RKNN_ERR_INPUT_INVALID -8 /* input is invalid. */ +#define RKNN_ERR_OUTPUT_INVALID -9 /* output is invalid. */ +#define RKNN_ERR_DEVICE_UNMATCH -10 /* the device is unmatch, please update rknn sdk + and npu driver/firmware. */ +#define RKNN_ERR_INCOMPATILE_PRE_COMPILE_MODEL -11 /* This RKNN model use pre_compile mode, but not compatible with current driver. */ +#define RKNN_ERR_INCOMPATILE_OPTIMIZATION_LEVEL_VERSION -12 /* This RKNN model set optimization level, but not compatible with current driver. */ +#define RKNN_ERR_TARGET_PLATFORM_UNMATCH -13 /* This RKNN model set target platform, but not compatible with current platform. */ + +/* + Definition for tensor +*/ +#define RKNN_MAX_DIMS 16 /* maximum dimension of tensor. */ +#define RKNN_MAX_NUM_CHANNEL 15 /* maximum channel number of input tensor. */ +#define RKNN_MAX_NAME_LEN 256 /* maximum name lenth of tensor. */ + + +#ifdef __arm__ +typedef uint32_t rknn_context; +#else +typedef uint64_t rknn_context; +#endif + + +/* + The query command for rknn_query +*/ +typedef enum _rknn_query_cmd { + RKNN_QUERY_IN_OUT_NUM = 0, /* query the number of input & output tensor. */ + RKNN_QUERY_INPUT_ATTR = 1, /* query the attribute of input tensor. */ + RKNN_QUERY_OUTPUT_ATTR = 2, /* query the attribute of output tensor. */ + RKNN_QUERY_PERF_DETAIL = 3, /* query the detail performance, need set + RKNN_FLAG_COLLECT_PERF_MASK when call rknn_init, + this query needs to be valid after rknn_outputs_get. */ + RKNN_QUERY_PERF_RUN = 4, /* query the time of run, + this query needs to be valid after rknn_outputs_get. */ + RKNN_QUERY_SDK_VERSION = 5, /* query the sdk & driver version */ + + RKNN_QUERY_MEM_SIZE = 6, /* query the weight & internal memory size */ + RKNN_QUERY_CUSTOM_STRING = 7, /* query the custom string */ + + RKNN_QUERY_NATIVE_INPUT_ATTR = 8, /* query the attribute of native input tensor. */ + RKNN_QUERY_NATIVE_OUTPUT_ATTR = 9, /* query the attribute of native output tensor. */ + + RKNN_QUERY_NATIVE_NC1HWC2_INPUT_ATTR = 8, /* query the attribute of native input tensor. */ + RKNN_QUERY_NATIVE_NC1HWC2_OUTPUT_ATTR = 9, /* query the attribute of native output tensor. */ + + RKNN_QUERY_NATIVE_NHWC_INPUT_ATTR = 10, /* query the attribute of native input tensor. */ + RKNN_QUERY_NATIVE_NHWC_OUTPUT_ATTR = 11, /* query the attribute of native output tensor. */ + + RKNN_QUERY_DEVICE_MEM_INFO = 12, /* query the attribute of rknn memory information. */ + + RKNN_QUERY_CMD_MAX +} rknn_query_cmd; + +/* + the tensor data type. +*/ +typedef enum _rknn_tensor_type { + RKNN_TENSOR_FLOAT32 = 0, /* data type is float32. */ + RKNN_TENSOR_FLOAT16, /* data type is float16. */ + RKNN_TENSOR_INT8, /* data type is int8. */ + RKNN_TENSOR_UINT8, /* data type is uint8. */ + RKNN_TENSOR_INT16, /* data type is int16. */ + RKNN_TENSOR_UINT16, /* data type is uint16. */ + RKNN_TENSOR_INT32, /* data type is int32. */ + RKNN_TENSOR_UINT32, /* data type is uint32. */ + RKNN_TENSOR_INT64, /* data type is int64. */ + RKNN_TENSOR_BOOL, + + RKNN_TENSOR_TYPE_MAX +} rknn_tensor_type; + +inline static const char* get_type_string(rknn_tensor_type type) +{ + switch(type) { + case RKNN_TENSOR_FLOAT32: return "FP32"; + case RKNN_TENSOR_FLOAT16: return "FP16"; + case RKNN_TENSOR_INT8: return "INT8"; + case RKNN_TENSOR_UINT8: return "UINT8"; + case RKNN_TENSOR_INT16: return "INT16"; + case RKNN_TENSOR_UINT16: return "UINT16"; + case RKNN_TENSOR_INT32: return "INT32"; + case RKNN_TENSOR_UINT32: return "UINT32"; + case RKNN_TENSOR_INT64: return "INT64"; + case RKNN_TENSOR_BOOL: return "BOOL"; + default: return "UNKNOW"; + } +} + +/* + the quantitative type. +*/ +typedef enum _rknn_tensor_qnt_type { + RKNN_TENSOR_QNT_NONE = 0, /* none. */ + RKNN_TENSOR_QNT_DFP, /* dynamic fixed point. */ + RKNN_TENSOR_QNT_AFFINE_ASYMMETRIC, /* asymmetric affine. */ + + RKNN_TENSOR_QNT_MAX +} rknn_tensor_qnt_type; + +inline static const char* get_qnt_type_string(rknn_tensor_qnt_type type) +{ + switch(type) { + case RKNN_TENSOR_QNT_NONE: return "NONE"; + case RKNN_TENSOR_QNT_DFP: return "DFP"; + case RKNN_TENSOR_QNT_AFFINE_ASYMMETRIC: return "AFFINE"; + default: return "UNKNOW"; + } +} + +/* + the tensor data format. +*/ +typedef enum _rknn_tensor_format { + RKNN_TENSOR_NCHW = 0, /* data format is NCHW. */ + RKNN_TENSOR_NHWC, /* data format is NHWC. */ + RKNN_TENSOR_NC1HWC2, /* data format is NC1HWC2. */ + RKNN_TENSOR_UNDEFINED, + + RKNN_TENSOR_FORMAT_MAX +} rknn_tensor_format; + +/* + the mode of running on target NPU core. +*/ +typedef enum _rknn_core_mask { + RKNN_NPU_CORE_AUTO = 0, /* default, run on NPU core randomly. */ + RKNN_NPU_CORE_0 = 1, /* run on NPU core 0. */ + RKNN_NPU_CORE_1 = 2, /* run on NPU core 1. */ + RKNN_NPU_CORE_2 = 4, /* run on NPU core 2. */ + RKNN_NPU_CORE_0_1 = 3, /* run on NPU core 1 and core 2. */ + RKNN_NPU_CORE_0_1_2 = 7, /* run on NPU core 1 and core 2 and core 3. */ + + RKNN_NPU_CORE_UNDEFINED, +} rknn_core_mask; + +inline static const char* get_format_string(rknn_tensor_format fmt) +{ + switch(fmt) { + case RKNN_TENSOR_NCHW: return "NCHW"; + case RKNN_TENSOR_NHWC: return "NHWC"; + case RKNN_TENSOR_NC1HWC2: return "NC1HWC2"; + case RKNN_TENSOR_UNDEFINED: return "UNDEFINED"; + default: return "UNKNOW"; + } +} + +/* + the information for RKNN_QUERY_IN_OUT_NUM. +*/ +typedef struct _rknn_input_output_num { + uint32_t n_input; /* the number of input. */ + uint32_t n_output; /* the number of output. */ +} rknn_input_output_num; + +/* + the information for RKNN_QUERY_INPUT_ATTR / RKNN_QUERY_OUTPUT_ATTR. +*/ +typedef struct _rknn_tensor_attr { + uint32_t index; /* input parameter, the index of input/output tensor, + need set before call rknn_query. */ + + uint32_t n_dims; /* the number of dimensions. */ + uint32_t dims[RKNN_MAX_DIMS]; /* the dimensions array. */ + char name[RKNN_MAX_NAME_LEN]; /* the name of tensor. */ + + uint32_t n_elems; /* the number of elements. */ + uint32_t size; /* the bytes size of tensor. */ + + rknn_tensor_format fmt; /* the data format of tensor. */ + rknn_tensor_type type; /* the data type of tensor. */ + rknn_tensor_qnt_type qnt_type; /* the quantitative type of tensor. */ + int8_t fl; /* fractional length for RKNN_TENSOR_QNT_DFP. */ + int32_t zp; /* zero point for RKNN_TENSOR_QNT_AFFINE_ASYMMETRIC. */ + float scale; /* scale for RKNN_TENSOR_QNT_AFFINE_ASYMMETRIC. */ + + uint32_t w_stride; /* the stride of tensor along the width dimention of input, + Note: it is read-only, 0 means equal to width. */ + uint32_t size_with_stride; /* the bytes size of tensor with stride. */ + + uint8_t pass_through; /* pass through mode, for rknn_set_io_mem interface. + if TRUE, the buf data is passed directly to the input node of the rknn model + without any conversion. the following variables do not need to be set. + if FALSE, the buf data is converted into an input consistent with the model + according to the following type and fmt. so the following variables + need to be set.*/ + uint32_t h_stride; /* the stride along the height dimention of input, + Note: it is write-only, if it was set to 0, h_stride = height. */ +} rknn_tensor_attr; + +/* + the information for RKNN_QUERY_PERF_DETAIL. +*/ +typedef struct _rknn_perf_detail { + char* perf_data; /* the string pointer of perf detail. don't need free it by user. */ + uint64_t data_len; /* the string length. */ +} rknn_perf_detail; + +/* + the information for RKNN_QUERY_PERF_RUN. +*/ +typedef struct _rknn_perf_run { + int64_t run_duration; /* real inference time (us) */ +} rknn_perf_run; + +/* + the information for RKNN_QUERY_SDK_VERSION. +*/ +typedef struct _rknn_sdk_version { + char api_version[256]; /* the version of rknn api. */ + char drv_version[256]; /* the version of rknn driver. */ +} rknn_sdk_version; + +/* + the information for RKNN_QUERY_MEM_SIZE. +*/ +typedef struct _rknn_mem_size { + uint32_t total_weight_size; /* the weight memory size */ + uint32_t total_internal_size; /* the internal memory size, exclude inputs/outputs */ + uint64_t total_dma_allocated_size; /* total dma memory allocated size */ + uint32_t total_sram_size; /* total system sram size reserved for rknn */ + uint32_t free_sram_size; /* free system sram size reserved for rknn */ + uint32_t reserved[10]; /* reserved */ +} rknn_mem_size; + +/* + the information for RKNN_QUERY_CUSTOM_STRING. +*/ +typedef struct _rknn_custom_string { + char string[1024]; /* the string of custom, lengths max to 1024 bytes */ +} rknn_custom_string; + +/* + The flags of rknn_tensor_mem. +*/ +typedef enum _rknn_tensor_mem_flags { + RKNN_TENSOR_MEMORY_FLAGS_ALLOC_INSIDE = 1, /*Used to mark in rknn_destroy_mem() whether it is necessary to release the "mem" pointer itself. + If the flag RKNN_TENSOR_MEMORY_FLAGS_ALLOC_INSIDE is set, rknn_destroy_mem() will call free(mem).*/ + RKNN_TENSOR_MEMORY_FLAGS_FROM_FD = 2, /*Used to mark in rknn_create_mem_from_fd() whether it is necessary to release the "mem" pointer itself. + If the flag RKNN_TENSOR_MEMORY_FLAGS_FROM_FD is set, rknn_destroy_mem() will call free(mem).*/ + RKNN_TENSOR_MEMORY_FLAGS_FROM_PHYS = 3, /*Used to mark in rknn_create_mem_from_phys() whether it is necessary to release the "mem" pointer itself. + If the flag RKNN_TENSOR_MEMORY_FLAGS_FROM_PHYS is set, rknn_destroy_mem() will call free(mem).*/ + RKNN_TENSOR_MEMORY_FLAGS_UNKNOWN +} rknn_tensor_mem_flags; + +/* + the memory information of tensor. +*/ +typedef struct _rknn_tensor_memory { + void* virt_addr; /* the virtual address of tensor buffer. */ + uint64_t phys_addr; /* the physical address of tensor buffer. */ + int32_t fd; /* the fd of tensor buffer. */ + int32_t offset; /* indicates the offset of the memory. */ + uint32_t size; /* the size of tensor buffer. */ + uint32_t flags; /* the flags of tensor buffer, reserved */ + void * priv_data; /* the private data of tensor buffer. */ +} rknn_tensor_mem; + +/* + the input information for rknn_input_set. +*/ +typedef struct _rknn_input { + uint32_t index; /* the input index. */ + void* buf; /* the input buf for index. */ + uint32_t size; /* the size of input buf. */ + uint8_t pass_through; /* pass through mode. + if TRUE, the buf data is passed directly to the input node of the rknn model + without any conversion. the following variables do not need to be set. + if FALSE, the buf data is converted into an input consistent with the model + according to the following type and fmt. so the following variables + need to be set.*/ + rknn_tensor_type type; /* the data type of input buf. */ + rknn_tensor_format fmt; /* the data format of input buf. + currently the internal input format of NPU is NCHW by default. + so entering NCHW data can avoid the format conversion in the driver. */ +} rknn_input; + +/* + the output information for rknn_outputs_get. +*/ +typedef struct _rknn_output { + uint8_t want_float; /* want transfer output data to float */ + uint8_t is_prealloc; /* whether buf is pre-allocated. + if TRUE, the following variables need to be set. + if FALSE, the following variables do not need to be set. */ + uint32_t index; /* the output index. */ + void* buf; /* the output buf for index. + when is_prealloc = FALSE and rknn_outputs_release called, + this buf pointer will be free and don't use it anymore. */ + uint32_t size; /* the size of output buf. */ +} rknn_output; + +/* + the extend information for rknn_init. +*/ +typedef struct _rknn_init_extend { + rknn_context ctx; /* rknn context */ + int32_t real_model_offset; /* real rknn model file size, only valid when init context with rknn file path */ + uint32_t real_model_size; /* real rknn model file offset, only valid when init context with rknn file path */ + uint8_t reserved[120]; /* reserved */ +} rknn_init_extend; + +/* + the extend information for rknn_run. +*/ +typedef struct _rknn_run_extend { + uint64_t frame_id; /* output parameter, indicate current frame id of run. */ + int32_t non_block; /* block flag of run, 0 is block else 1 is non block */ + int32_t timeout_ms; /* timeout for block mode, in milliseconds */ + int32_t fence_fd; /* fence fd from other unit */ +} rknn_run_extend; + +/* + the extend information for rknn_outputs_get. +*/ +typedef struct _rknn_output_extend { + uint64_t frame_id; /* output parameter, indicate the frame id of outputs, corresponds to + struct rknn_run_extend.frame_id.*/ +} rknn_output_extend; + + +/* rknn_init + + initial the context and load the rknn model. + + input: + rknn_context* context the pointer of context handle. + void* model if size > 0, pointer to the rknn model, if size = 0, filepath to the rknn model. + uint32_t size the size of rknn model. + uint32_t flag extend flag, see the define of RKNN_FLAG_XXX_XXX. + rknn_init_extend* extend the extend information of init. + return: + int error code. +*/ +int rknn_init(rknn_context* context, void* model, uint32_t size, uint32_t flag, rknn_init_extend* extend); + +/* rknn_dup_context + + initial the context and load the rknn model. + + input: + rknn_context* context_in the pointer of context in handle. + rknn_context* context_out the pointer of context out handle. + return: + int error code. +*/ +int rknn_dup_context(rknn_context* context_in, rknn_context* context_out); + +/* rknn_destroy + + unload the rknn model and destroy the context. + + input: + rknn_context context the handle of context. + return: + int error code. +*/ +int rknn_destroy(rknn_context context); + + +/* rknn_query + + query the information about model or others. see rknn_query_cmd. + + input: + rknn_context context the handle of context. + rknn_query_cmd cmd the command of query. + void* info the buffer point of information. + uint32_t size the size of information. + return: + int error code. +*/ +int rknn_query(rknn_context context, rknn_query_cmd cmd, void* info, uint32_t size); + + +/* rknn_inputs_set + + set inputs information by input index of rknn model. + inputs information see rknn_input. + + input: + rknn_context context the handle of context. + uint32_t n_inputs the number of inputs. + rknn_input inputs[] the arrays of inputs information, see rknn_input. + return: + int error code +*/ +int rknn_inputs_set(rknn_context context, uint32_t n_inputs, rknn_input inputs[]); + +/* + rknn_set_batch_core_num + + set rknn batch core_num. + + input: + rknn_context context the handle of context. + int core_num the core number. + return: + int error code. + +*/ +int rknn_set_batch_core_num(rknn_context context, int core_num); + +/* rknn_set_core_mask + + set rknn core mask.(only supported on RK3588 now) + + RKNN_NPU_CORE_AUTO: auto mode, default value + RKNN_NPU_CORE_0: core 0 mode + RKNN_NPU_CORE_1: core 1 mode + RKNN_NPU_CORE_2: core 2 mode + RKNN_NPU_CORE_0_1: combine core 0/1 mode + RKNN_NPU_CORE_0_1_2: combine core 0/1/2 mode + + input: + rknn_context context the handle of context. + rknn_core_mask core_mask the core mask. + return: + int error code. +*/ +int rknn_set_core_mask(rknn_context context, rknn_core_mask core_mask); + +/* rknn_run + + run the model to execute inference. + + input: + rknn_context context the handle of context. + rknn_run_extend* extend the extend information of run. + return: + int error code. +*/ +int rknn_run(rknn_context context, rknn_run_extend* extend); + + +/* rknn_wait + + wait the model after execute inference. + + input: + rknn_context context the handle of context. + rknn_run_extend* extend the extend information of run. + return: + int error code. +*/ +int rknn_wait(rknn_context context, rknn_run_extend* extend); + + +/* rknn_outputs_get + + wait the inference to finish and get the outputs. + this function will block until inference finish. + the results will set to outputs[]. + + input: + rknn_context context the handle of context. + uint32_t n_outputs the number of outputs. + rknn_output outputs[] the arrays of output, see rknn_output. + rknn_output_extend* the extend information of output. + return: + int error code. +*/ +int rknn_outputs_get(rknn_context context, uint32_t n_outputs, rknn_output outputs[], rknn_output_extend* extend); + + +/* rknn_outputs_release + + release the outputs that get by rknn_outputs_get. + after called, the rknn_output[x].buf get from rknn_outputs_get will + also be free when rknn_output[x].is_prealloc = FALSE. + + input: + rknn_context context the handle of context. + uint32_t n_ouputs the number of outputs. + rknn_output outputs[] the arrays of output. + return: + int error code +*/ +int rknn_outputs_release(rknn_context context, uint32_t n_ouputs, rknn_output outputs[]); + + +/* new api for zero copy */ + +/* rknn_create_mem_from_phys (memory allocated outside) + + initialize tensor memory from physical address. + + input: + rknn_context ctx the handle of context. + uint64_t phys_addr physical address. + void *virt_addr virtual address. + uint32_t size the size of tensor buffer. + return: + rknn_tensor_mem the pointer of tensor memory information. +*/ +rknn_tensor_mem* rknn_create_mem_from_phys(rknn_context ctx, uint64_t phys_addr, void *virt_addr, uint32_t size); + + +/* rknn_create_mem_from_fd (memory allocated outside) + + initialize tensor memory from file description. + + input: + rknn_context ctx the handle of context. + int32_t fd file description. + void *virt_addr virtual address. + uint32_t size the size of tensor buffer. + int32_t offset indicates the offset of the memory (virt_addr without offset). + return: + rknn_tensor_mem the pointer of tensor memory information. +*/ +rknn_tensor_mem* rknn_create_mem_from_fd(rknn_context ctx, int32_t fd, void *virt_addr, uint32_t size, int32_t offset); + + +/* rknn_create_mem_from_mb_blk (memory allocated outside) + + create tensor memory from mb_blk. + + input: + rknn_context ctx the handle of context. + void *mb_blk mb_blk allocate from system api. + int32_t offset indicates the offset of the memory. + return: + rknn_tensor_mem the pointer of tensor memory information. +*/ +rknn_tensor_mem* rknn_create_mem_from_mb_blk(rknn_context ctx, void *mb_blk, int32_t offset); + + +/* rknn_create_mem (memory allocated inside) + + create tensor memory. + + input: + rknn_context ctx the handle of context. + uint32_t size the size of tensor buffer. + return: + rknn_tensor_mem the pointer of tensor memory information. +*/ +rknn_tensor_mem* rknn_create_mem(rknn_context ctx, uint32_t size); + + +/* rknn_destroy_mem (support allocate inside and outside) + + destroy tensor memory. + + input: + rknn_context ctx the handle of context. + rknn_tensor_mem *mem the pointer of tensor memory information. + return: + int error code +*/ +int rknn_destroy_mem(rknn_context ctx, rknn_tensor_mem *mem); + + +/* rknn_set_weight_mem + + set the weight memory. + + input: + rknn_context ctx the handle of context. + rknn_tensor_mem *mem the array of tensor memory information + return: + int error code. +*/ +int rknn_set_weight_mem(rknn_context ctx, rknn_tensor_mem *mem); + + +/* rknn_set_internal_mem + + set the internal memory. + + input: + rknn_context ctx the handle of context. + rknn_tensor_mem *mem the array of tensor memory information + return: + int error code. +*/ +int rknn_set_internal_mem(rknn_context ctx, rknn_tensor_mem *mem); + + +/* rknn_set_io_mem + + set the input and output tensors buffer. + + input: + rknn_context ctx the handle of context. + rknn_tensor_mem *mem the array of tensor memory information. + rknn_tensor_attr *attr the attribute of input or output tensor buffer. + return: + int error code. +*/ +int rknn_set_io_mem(rknn_context ctx, rknn_tensor_mem *mem, rknn_tensor_attr *attr); + + +#ifdef __cplusplus +} //extern "C" +#endif + +#endif //_RKNN_API_H diff --git a/rknpu2/examples/librknn_api_android_demo/include/stb/stb_image.h b/rknpu2/examples/librknn_api_android_demo/include/stb/stb_image.h new file mode 100644 index 0000000000000000000000000000000000000000..196dfd5cc6ee3be167ce6280fb243327e77f4245 --- /dev/null +++ b/rknpu2/examples/librknn_api_android_demo/include/stb/stb_image.h @@ -0,0 +1,7559 @@ +/* stb_image - v2.23 - public domain image loader - http://nothings.org/stb + no warranty implied; use at your own risk + + Do this: + #define STB_IMAGE_IMPLEMENTATION + before you include this file in *one* C or C++ file to create the implementation. + + // i.e. it should look like this: + #include ... + #include ... + #include ... + #define STB_IMAGE_IMPLEMENTATION + #include "stb_image.h" + + You can #define STBI_ASSERT(x) before the #include to avoid using assert.h. + And #define STBI_MALLOC, STBI_REALLOC, and STBI_FREE to avoid using malloc,realloc,free + + + QUICK NOTES: + Primarily of interest to game developers and other people who can + avoid problematic images and only need the trivial interface + + JPEG baseline & progressive (12 bpc/arithmetic not supported, same as stock IJG lib) + PNG 1/2/4/8/16-bit-per-channel + + TGA (not sure what subset, if a subset) + BMP non-1bpp, non-RLE + PSD (composited view only, no extra channels, 8/16 bit-per-channel) + + GIF (*comp always reports as 4-channel) + HDR (radiance rgbE format) + PIC (Softimage PIC) + PNM (PPM and PGM binary only) + + Animated GIF still needs a proper API, but here's one way to do it: + http://gist.github.com/urraka/685d9a6340b26b830d49 + + - decode from memory or through FILE (define STBI_NO_STDIO to remove code) + - decode from arbitrary I/O callbacks + - SIMD acceleration on x86/x64 (SSE2) and ARM (NEON) + + Full documentation under "DOCUMENTATION" below. + + +LICENSE + + See end of file for license information. + +RECENT REVISION HISTORY: + + 2.23 (2019-08-11) fix clang static analysis warning + 2.22 (2019-03-04) gif fixes, fix warnings + 2.21 (2019-02-25) fix typo in comment + 2.20 (2019-02-07) support utf8 filenames in Windows; fix warnings and platform ifdefs + 2.19 (2018-02-11) fix warning + 2.18 (2018-01-30) fix warnings + 2.17 (2018-01-29) bugfix, 1-bit BMP, 16-bitness query, fix warnings + 2.16 (2017-07-23) all functions have 16-bit variants; optimizations; bugfixes + 2.15 (2017-03-18) fix png-1,2,4; all Imagenet JPGs; no runtime SSE detection on GCC + 2.14 (2017-03-03) remove deprecated STBI_JPEG_OLD; fixes for Imagenet JPGs + 2.13 (2016-12-04) experimental 16-bit API, only for PNG so far; fixes + 2.12 (2016-04-02) fix typo in 2.11 PSD fix that caused crashes + 2.11 (2016-04-02) 16-bit PNGS; enable SSE2 in non-gcc x64 + RGB-format JPEG; remove white matting in PSD; + allocate large structures on the stack; + correct channel count for PNG & BMP + 2.10 (2016-01-22) avoid warning introduced in 2.09 + 2.09 (2016-01-16) 16-bit TGA; comments in PNM files; STBI_REALLOC_SIZED + + See end of file for full revision history. + + + ============================ Contributors ========================= + + Image formats Extensions, features + Sean Barrett (jpeg, png, bmp) Jetro Lauha (stbi_info) + Nicolas Schulz (hdr, psd) Martin "SpartanJ" Golini (stbi_info) + Jonathan Dummer (tga) James "moose2000" Brown (iPhone PNG) + Jean-Marc Lienher (gif) Ben "Disch" Wenger (io callbacks) + Tom Seddon (pic) Omar Cornut (1/2/4-bit PNG) + Thatcher Ulrich (psd) Nicolas Guillemot (vertical flip) + Ken Miller (pgm, ppm) Richard Mitton (16-bit PSD) + github:urraka (animated gif) Junggon Kim (PNM comments) + Christopher Forseth (animated gif) Daniel Gibson (16-bit TGA) + socks-the-fox (16-bit PNG) + Jeremy Sawicki (handle all ImageNet JPGs) + Optimizations & bugfixes Mikhail Morozov (1-bit BMP) + Fabian "ryg" Giesen Anael Seghezzi (is-16-bit query) + Arseny Kapoulkine + John-Mark Allen + Carmelo J Fdez-Aguera + + Bug & warning fixes + Marc LeBlanc David Woo Guillaume George Martins Mozeiko + Christpher Lloyd Jerry Jansson Joseph Thomson Phil Jordan + Dave Moore Roy Eltham Hayaki Saito Nathan Reed + Won Chun Luke Graham Johan Duparc Nick Verigakis + the Horde3D community Thomas Ruf Ronny Chevalier github:rlyeh + Janez Zemva John Bartholomew Michal Cichon github:romigrou + Jonathan Blow Ken Hamada Tero Hanninen github:svdijk + Laurent Gomila Cort Stratton Sergio Gonzalez github:snagar + Aruelien Pocheville Thibault Reuille Cass Everitt github:Zelex + Ryamond Barbiero Paul Du Bois Engin Manap github:grim210 + Aldo Culquicondor Philipp Wiesemann Dale Weiler github:sammyhw + Oriol Ferrer Mesia Josh Tobin Matthew Gregan github:phprus + Julian Raschke Gregory Mullen Baldur Karlsson github:poppolopoppo + Christian Floisand Kevin Schmidt JR Smith github:darealshinji + Blazej Dariusz Roszkowski github:Michaelangel007 +*/ + +#ifndef STBI_INCLUDE_STB_IMAGE_H +#define STBI_INCLUDE_STB_IMAGE_H + +// DOCUMENTATION +// +// Limitations: +// - no 12-bit-per-channel JPEG +// - no JPEGs with arithmetic coding +// - GIF always returns *comp=4 +// +// Basic usage (see HDR discussion below for HDR usage): +// int x,y,n; +// unsigned char *data = stbi_load(filename, &x, &y, &n, 0); +// // ... process data if not NULL ... +// // ... x = width, y = height, n = # 8-bit components per pixel ... +// // ... replace '0' with '1'..'4' to force that many components per pixel +// // ... but 'n' will always be the number that it would have been if you said 0 +// stbi_image_free(data) +// +// Standard parameters: +// int *x -- outputs image width in pixels +// int *y -- outputs image height in pixels +// int *channels_in_file -- outputs # of image components in image file +// int desired_channels -- if non-zero, # of image components requested in result +// +// The return value from an image loader is an 'unsigned char *' which points +// to the pixel data, or NULL on an allocation failure or if the image is +// corrupt or invalid. The pixel data consists of *y scanlines of *x pixels, +// with each pixel consisting of N interleaved 8-bit components; the first +// pixel pointed to is top-left-most in the image. There is no padding between +// image scanlines or between pixels, regardless of format. The number of +// components N is 'desired_channels' if desired_channels is non-zero, or +// *channels_in_file otherwise. If desired_channels is non-zero, +// *channels_in_file has the number of components that _would_ have been +// output otherwise. E.g. if you set desired_channels to 4, you will always +// get RGBA output, but you can check *channels_in_file to see if it's trivially +// opaque because e.g. there were only 3 channels in the source image. +// +// An output image with N components has the following components interleaved +// in this order in each pixel: +// +// N=#comp components +// 1 grey +// 2 grey, alpha +// 3 red, green, blue +// 4 red, green, blue, alpha +// +// If image loading fails for any reason, the return value will be NULL, +// and *x, *y, *channels_in_file will be unchanged. The function +// stbi_failure_reason() can be queried for an extremely brief, end-user +// unfriendly explanation of why the load failed. Define STBI_NO_FAILURE_STRINGS +// to avoid compiling these strings at all, and STBI_FAILURE_USERMSG to get slightly +// more user-friendly ones. +// +// Paletted PNG, BMP, GIF, and PIC images are automatically depalettized. +// +// =========================================================================== +// +// UNICODE: +// +// If compiling for Windows and you wish to use Unicode filenames, compile +// with +// #define STBI_WINDOWS_UTF8 +// and pass utf8-encoded filenames. Call stbi_convert_wchar_to_utf8 to convert +// Windows wchar_t filenames to utf8. +// +// =========================================================================== +// +// Philosophy +// +// stb libraries are designed with the following priorities: +// +// 1. easy to use +// 2. easy to maintain +// 3. good performance +// +// Sometimes I let "good performance" creep up in priority over "easy to maintain", +// and for best performance I may provide less-easy-to-use APIs that give higher +// performance, in addition to the easy-to-use ones. Nevertheless, it's important +// to keep in mind that from the standpoint of you, a client of this library, +// all you care about is #1 and #3, and stb libraries DO NOT emphasize #3 above all. +// +// Some secondary priorities arise directly from the first two, some of which +// provide more explicit reasons why performance can't be emphasized. +// +// - Portable ("ease of use") +// - Small source code footprint ("easy to maintain") +// - No dependencies ("ease of use") +// +// =========================================================================== +// +// I/O callbacks +// +// I/O callbacks allow you to read from arbitrary sources, like packaged +// files or some other source. Data read from callbacks are processed +// through a small internal buffer (currently 128 bytes) to try to reduce +// overhead. +// +// The three functions you must define are "read" (reads some bytes of data), +// "skip" (skips some bytes of data), "eof" (reports if the stream is at the end). +// +// =========================================================================== +// +// SIMD support +// +// The JPEG decoder will try to automatically use SIMD kernels on x86 when +// supported by the compiler. For ARM Neon support, you must explicitly +// request it. +// +// (The old do-it-yourself SIMD API is no longer supported in the current +// code.) +// +// On x86, SSE2 will automatically be used when available based on a run-time +// test; if not, the generic C versions are used as a fall-back. On ARM targets, +// the typical path is to have separate builds for NEON and non-NEON devices +// (at least this is true for iOS and Android). Therefore, the NEON support is +// toggled by a build flag: define STBI_NEON to get NEON loops. +// +// If for some reason you do not want to use any of SIMD code, or if +// you have issues compiling it, you can disable it entirely by +// defining STBI_NO_SIMD. +// +// =========================================================================== +// +// HDR image support (disable by defining STBI_NO_HDR) +// +// stb_image supports loading HDR images in general, and currently the Radiance +// .HDR file format specifically. You can still load any file through the existing +// interface; if you attempt to load an HDR file, it will be automatically remapped +// to LDR, assuming gamma 2.2 and an arbitrary scale factor defaulting to 1; +// both of these constants can be reconfigured through this interface: +// +// stbi_hdr_to_ldr_gamma(2.2f); +// stbi_hdr_to_ldr_scale(1.0f); +// +// (note, do not use _inverse_ constants; stbi_image will invert them +// appropriately). +// +// Additionally, there is a new, parallel interface for loading files as +// (linear) floats to preserve the full dynamic range: +// +// float *data = stbi_loadf(filename, &x, &y, &n, 0); +// +// If you load LDR images through this interface, those images will +// be promoted to floating point values, run through the inverse of +// constants corresponding to the above: +// +// stbi_ldr_to_hdr_scale(1.0f); +// stbi_ldr_to_hdr_gamma(2.2f); +// +// Finally, given a filename (or an open file or memory block--see header +// file for details) containing image data, you can query for the "most +// appropriate" interface to use (that is, whether the image is HDR or +// not), using: +// +// stbi_is_hdr(char *filename); +// +// =========================================================================== +// +// iPhone PNG support: +// +// By default we convert iphone-formatted PNGs back to RGB, even though +// they are internally encoded differently. You can disable this conversion +// by calling stbi_convert_iphone_png_to_rgb(0), in which case +// you will always just get the native iphone "format" through (which +// is BGR stored in RGB). +// +// Call stbi_set_unpremultiply_on_load(1) as well to force a divide per +// pixel to remove any premultiplied alpha *only* if the image file explicitly +// says there's premultiplied data (currently only happens in iPhone images, +// and only if iPhone convert-to-rgb processing is on). +// +// =========================================================================== +// +// ADDITIONAL CONFIGURATION +// +// - You can suppress implementation of any of the decoders to reduce +// your code footprint by #defining one or more of the following +// symbols before creating the implementation. +// +// STBI_NO_JPEG +// STBI_NO_PNG +// STBI_NO_BMP +// STBI_NO_PSD +// STBI_NO_TGA +// STBI_NO_GIF +// STBI_NO_HDR +// STBI_NO_PIC +// STBI_NO_PNM (.ppm and .pgm) +// +// - You can request *only* certain decoders and suppress all other ones +// (this will be more forward-compatible, as addition of new decoders +// doesn't require you to disable them explicitly): +// +// STBI_ONLY_JPEG +// STBI_ONLY_PNG +// STBI_ONLY_BMP +// STBI_ONLY_PSD +// STBI_ONLY_TGA +// STBI_ONLY_GIF +// STBI_ONLY_HDR +// STBI_ONLY_PIC +// STBI_ONLY_PNM (.ppm and .pgm) +// +// - If you use STBI_NO_PNG (or _ONLY_ without PNG), and you still +// want the zlib decoder to be available, #define STBI_SUPPORT_ZLIB +// + + +#ifndef STBI_NO_STDIO +#include +#endif // STBI_NO_STDIO + +#define STBI_VERSION 1 + +enum +{ + STBI_default = 0, // only used for desired_channels + + STBI_grey = 1, + STBI_grey_alpha = 2, + STBI_rgb = 3, + STBI_rgb_alpha = 4 +}; + +#include +typedef unsigned char stbi_uc; +typedef unsigned short stbi_us; + +#ifdef __cplusplus +extern "C" { +#endif + +#ifndef STBIDEF +#ifdef STB_IMAGE_STATIC +#define STBIDEF static +#else +#define STBIDEF extern +#endif +#endif + +////////////////////////////////////////////////////////////////////////////// +// +// PRIMARY API - works on images of any type +// + +// +// load image by filename, open file, or memory buffer +// + +typedef struct +{ + int (*read) (void *user,char *data,int size); // fill 'data' with 'size' bytes. return number of bytes actually read + void (*skip) (void *user,int n); // skip the next 'n' bytes, or 'unget' the last -n bytes if negative + int (*eof) (void *user); // returns nonzero if we are at end of file/data +} stbi_io_callbacks; + +//////////////////////////////////// +// +// 8-bits-per-channel interface +// + +STBIDEF stbi_uc *stbi_load_from_memory (stbi_uc const *buffer, int len , int *x, int *y, int *channels_in_file, int desired_channels); +STBIDEF stbi_uc *stbi_load_from_callbacks(stbi_io_callbacks const *clbk , void *user, int *x, int *y, int *channels_in_file, int desired_channels); + +#ifndef STBI_NO_STDIO +STBIDEF stbi_uc *stbi_load (char const *filename, int *x, int *y, int *channels_in_file, int desired_channels); +STBIDEF stbi_uc *stbi_load_from_file (FILE *f, int *x, int *y, int *channels_in_file, int desired_channels); +// for stbi_load_from_file, file pointer is left pointing immediately after image +#endif + +#ifndef STBI_NO_GIF +STBIDEF stbi_uc *stbi_load_gif_from_memory(stbi_uc const *buffer, int len, int **delays, int *x, int *y, int *z, int *comp, int req_comp); +#endif + +#ifdef STBI_WINDOWS_UTF8 +STBIDEF int stbi_convert_wchar_to_utf8(char *buffer, size_t bufferlen, const wchar_t* input); +#endif + +//////////////////////////////////// +// +// 16-bits-per-channel interface +// + +STBIDEF stbi_us *stbi_load_16_from_memory (stbi_uc const *buffer, int len, int *x, int *y, int *channels_in_file, int desired_channels); +STBIDEF stbi_us *stbi_load_16_from_callbacks(stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *channels_in_file, int desired_channels); + +#ifndef STBI_NO_STDIO +STBIDEF stbi_us *stbi_load_16 (char const *filename, int *x, int *y, int *channels_in_file, int desired_channels); +STBIDEF stbi_us *stbi_load_from_file_16(FILE *f, int *x, int *y, int *channels_in_file, int desired_channels); +#endif + +//////////////////////////////////// +// +// float-per-channel interface +// +#ifndef STBI_NO_LINEAR + STBIDEF float *stbi_loadf_from_memory (stbi_uc const *buffer, int len, int *x, int *y, int *channels_in_file, int desired_channels); + STBIDEF float *stbi_loadf_from_callbacks (stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *channels_in_file, int desired_channels); + + #ifndef STBI_NO_STDIO + STBIDEF float *stbi_loadf (char const *filename, int *x, int *y, int *channels_in_file, int desired_channels); + STBIDEF float *stbi_loadf_from_file (FILE *f, int *x, int *y, int *channels_in_file, int desired_channels); + #endif +#endif + +#ifndef STBI_NO_HDR + STBIDEF void stbi_hdr_to_ldr_gamma(float gamma); + STBIDEF void stbi_hdr_to_ldr_scale(float scale); +#endif // STBI_NO_HDR + +#ifndef STBI_NO_LINEAR + STBIDEF void stbi_ldr_to_hdr_gamma(float gamma); + STBIDEF void stbi_ldr_to_hdr_scale(float scale); +#endif // STBI_NO_LINEAR + +// stbi_is_hdr is always defined, but always returns false if STBI_NO_HDR +STBIDEF int stbi_is_hdr_from_callbacks(stbi_io_callbacks const *clbk, void *user); +STBIDEF int stbi_is_hdr_from_memory(stbi_uc const *buffer, int len); +#ifndef STBI_NO_STDIO +STBIDEF int stbi_is_hdr (char const *filename); +STBIDEF int stbi_is_hdr_from_file(FILE *f); +#endif // STBI_NO_STDIO + + +// get a VERY brief reason for failure +// NOT THREADSAFE +STBIDEF const char *stbi_failure_reason (void); + +// free the loaded image -- this is just free() +STBIDEF void stbi_image_free (void *retval_from_stbi_load); + +// get image dimensions & components without fully decoding +STBIDEF int stbi_info_from_memory(stbi_uc const *buffer, int len, int *x, int *y, int *comp); +STBIDEF int stbi_info_from_callbacks(stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *comp); +STBIDEF int stbi_is_16_bit_from_memory(stbi_uc const *buffer, int len); +STBIDEF int stbi_is_16_bit_from_callbacks(stbi_io_callbacks const *clbk, void *user); + +#ifndef STBI_NO_STDIO +STBIDEF int stbi_info (char const *filename, int *x, int *y, int *comp); +STBIDEF int stbi_info_from_file (FILE *f, int *x, int *y, int *comp); +STBIDEF int stbi_is_16_bit (char const *filename); +STBIDEF int stbi_is_16_bit_from_file(FILE *f); +#endif + + + +// for image formats that explicitly notate that they have premultiplied alpha, +// we just return the colors as stored in the file. set this flag to force +// unpremultiplication. results are undefined if the unpremultiply overflow. +STBIDEF void stbi_set_unpremultiply_on_load(int flag_true_if_should_unpremultiply); + +// indicate whether we should process iphone images back to canonical format, +// or just pass them through "as-is" +STBIDEF void stbi_convert_iphone_png_to_rgb(int flag_true_if_should_convert); + +// flip the image vertically, so the first pixel in the output array is the bottom left +STBIDEF void stbi_set_flip_vertically_on_load(int flag_true_if_should_flip); + +// ZLIB client - used by PNG, available for other purposes + +STBIDEF char *stbi_zlib_decode_malloc_guesssize(const char *buffer, int len, int initial_size, int *outlen); +STBIDEF char *stbi_zlib_decode_malloc_guesssize_headerflag(const char *buffer, int len, int initial_size, int *outlen, int parse_header); +STBIDEF char *stbi_zlib_decode_malloc(const char *buffer, int len, int *outlen); +STBIDEF int stbi_zlib_decode_buffer(char *obuffer, int olen, const char *ibuffer, int ilen); + +STBIDEF char *stbi_zlib_decode_noheader_malloc(const char *buffer, int len, int *outlen); +STBIDEF int stbi_zlib_decode_noheader_buffer(char *obuffer, int olen, const char *ibuffer, int ilen); + + +#ifdef __cplusplus +} +#endif + +// +// +//// end header file ///////////////////////////////////////////////////// +#endif // STBI_INCLUDE_STB_IMAGE_H + +#ifdef STB_IMAGE_IMPLEMENTATION + +#if defined(STBI_ONLY_JPEG) || defined(STBI_ONLY_PNG) || defined(STBI_ONLY_BMP) \ + || defined(STBI_ONLY_TGA) || defined(STBI_ONLY_GIF) || defined(STBI_ONLY_PSD) \ + || defined(STBI_ONLY_HDR) || defined(STBI_ONLY_PIC) || defined(STBI_ONLY_PNM) \ + || defined(STBI_ONLY_ZLIB) + #ifndef STBI_ONLY_JPEG + #define STBI_NO_JPEG + #endif + #ifndef STBI_ONLY_PNG + #define STBI_NO_PNG + #endif + #ifndef STBI_ONLY_BMP + #define STBI_NO_BMP + #endif + #ifndef STBI_ONLY_PSD + #define STBI_NO_PSD + #endif + #ifndef STBI_ONLY_TGA + #define STBI_NO_TGA + #endif + #ifndef STBI_ONLY_GIF + #define STBI_NO_GIF + #endif + #ifndef STBI_ONLY_HDR + #define STBI_NO_HDR + #endif + #ifndef STBI_ONLY_PIC + #define STBI_NO_PIC + #endif + #ifndef STBI_ONLY_PNM + #define STBI_NO_PNM + #endif +#endif + +#if defined(STBI_NO_PNG) && !defined(STBI_SUPPORT_ZLIB) && !defined(STBI_NO_ZLIB) +#define STBI_NO_ZLIB +#endif + + +#include +#include // ptrdiff_t on osx +#include +#include +#include + +#if !defined(STBI_NO_LINEAR) || !defined(STBI_NO_HDR) +#include // ldexp, pow +#endif + +#ifndef STBI_NO_STDIO +#include +#endif + +#ifndef STBI_ASSERT +#include +#define STBI_ASSERT(x) assert(x) +#endif + +#ifdef __cplusplus +#define STBI_EXTERN extern "C" +#else +#define STBI_EXTERN extern +#endif + + +#ifndef _MSC_VER + #ifdef __cplusplus + #define stbi_inline inline + #else + #define stbi_inline + #endif +#else + #define stbi_inline __forceinline +#endif + + +#ifdef _MSC_VER +typedef unsigned short stbi__uint16; +typedef signed short stbi__int16; +typedef unsigned int stbi__uint32; +typedef signed int stbi__int32; +#else +#include +typedef uint16_t stbi__uint16; +typedef int16_t stbi__int16; +typedef uint32_t stbi__uint32; +typedef int32_t stbi__int32; +#endif + +// should produce compiler error if size is wrong +typedef unsigned char validate_uint32[sizeof(stbi__uint32)==4 ? 1 : -1]; + +#ifdef _MSC_VER +#define STBI_NOTUSED(v) (void)(v) +#else +#define STBI_NOTUSED(v) (void)sizeof(v) +#endif + +#ifdef _MSC_VER +#define STBI_HAS_LROTL +#endif + +#ifdef STBI_HAS_LROTL + #define stbi_lrot(x,y) _lrotl(x,y) +#else + #define stbi_lrot(x,y) (((x) << (y)) | ((x) >> (32 - (y)))) +#endif + +#if defined(STBI_MALLOC) && defined(STBI_FREE) && (defined(STBI_REALLOC) || defined(STBI_REALLOC_SIZED)) +// ok +#elif !defined(STBI_MALLOC) && !defined(STBI_FREE) && !defined(STBI_REALLOC) && !defined(STBI_REALLOC_SIZED) +// ok +#else +#error "Must define all or none of STBI_MALLOC, STBI_FREE, and STBI_REALLOC (or STBI_REALLOC_SIZED)." +#endif + +#ifndef STBI_MALLOC +#define STBI_MALLOC(sz) malloc(sz) +#define STBI_REALLOC(p,newsz) realloc(p,newsz) +#define STBI_FREE(p) free(p) +#endif + +#ifndef STBI_REALLOC_SIZED +#define STBI_REALLOC_SIZED(p,oldsz,newsz) STBI_REALLOC(p,newsz) +#endif + +// x86/x64 detection +#if defined(__x86_64__) || defined(_M_X64) +#define STBI__X64_TARGET +#elif defined(__i386) || defined(_M_IX86) +#define STBI__X86_TARGET +#endif + +#if defined(__GNUC__) && defined(STBI__X86_TARGET) && !defined(__SSE2__) && !defined(STBI_NO_SIMD) +// gcc doesn't support sse2 intrinsics unless you compile with -msse2, +// which in turn means it gets to use SSE2 everywhere. This is unfortunate, +// but previous attempts to provide the SSE2 functions with runtime +// detection caused numerous issues. The way architecture extensions are +// exposed in GCC/Clang is, sadly, not really suited for one-file libs. +// New behavior: if compiled with -msse2, we use SSE2 without any +// detection; if not, we don't use it at all. +#define STBI_NO_SIMD +#endif + +#if defined(__MINGW32__) && defined(STBI__X86_TARGET) && !defined(STBI_MINGW_ENABLE_SSE2) && !defined(STBI_NO_SIMD) +// Note that __MINGW32__ doesn't actually mean 32-bit, so we have to avoid STBI__X64_TARGET +// +// 32-bit MinGW wants ESP to be 16-byte aligned, but this is not in the +// Windows ABI and VC++ as well as Windows DLLs don't maintain that invariant. +// As a result, enabling SSE2 on 32-bit MinGW is dangerous when not +// simultaneously enabling "-mstackrealign". +// +// See https://github.com/nothings/stb/issues/81 for more information. +// +// So default to no SSE2 on 32-bit MinGW. If you've read this far and added +// -mstackrealign to your build settings, feel free to #define STBI_MINGW_ENABLE_SSE2. +#define STBI_NO_SIMD +#endif + +#if !defined(STBI_NO_SIMD) && (defined(STBI__X86_TARGET) || defined(STBI__X64_TARGET)) +#define STBI_SSE2 +#include + +#ifdef _MSC_VER + +#if _MSC_VER >= 1400 // not VC6 +#include // __cpuid +static int stbi__cpuid3(void) +{ + int info[4]; + __cpuid(info,1); + return info[3]; +} +#else +static int stbi__cpuid3(void) +{ + int res; + __asm { + mov eax,1 + cpuid + mov res,edx + } + return res; +} +#endif + +#define STBI_SIMD_ALIGN(type, name) __declspec(align(16)) type name + +#if !defined(STBI_NO_JPEG) && defined(STBI_SSE2) +static int stbi__sse2_available(void) +{ + int info3 = stbi__cpuid3(); + return ((info3 >> 26) & 1) != 0; +} +#endif + +#else // assume GCC-style if not VC++ +#define STBI_SIMD_ALIGN(type, name) type name __attribute__((aligned(16))) + +#if !defined(STBI_NO_JPEG) && defined(STBI_SSE2) +static int stbi__sse2_available(void) +{ + // If we're even attempting to compile this on GCC/Clang, that means + // -msse2 is on, which means the compiler is allowed to use SSE2 + // instructions at will, and so are we. + return 1; +} +#endif + +#endif +#endif + +// ARM NEON +#if defined(STBI_NO_SIMD) && defined(STBI_NEON) +#undef STBI_NEON +#endif + +#ifdef STBI_NEON +#include +// assume GCC or Clang on ARM targets +#define STBI_SIMD_ALIGN(type, name) type name __attribute__((aligned(16))) +#endif + +#ifndef STBI_SIMD_ALIGN +#define STBI_SIMD_ALIGN(type, name) type name +#endif + +/////////////////////////////////////////////// +// +// stbi__context struct and start_xxx functions + +// stbi__context structure is our basic context used by all images, so it +// contains all the IO context, plus some basic image information +typedef struct +{ + stbi__uint32 img_x, img_y; + int img_n, img_out_n; + + stbi_io_callbacks io; + void *io_user_data; + + int read_from_callbacks; + int buflen; + stbi_uc buffer_start[128]; + + stbi_uc *img_buffer, *img_buffer_end; + stbi_uc *img_buffer_original, *img_buffer_original_end; +} stbi__context; + + +static void stbi__refill_buffer(stbi__context *s); + +// initialize a memory-decode context +static void stbi__start_mem(stbi__context *s, stbi_uc const *buffer, int len) +{ + s->io.read = NULL; + s->read_from_callbacks = 0; + s->img_buffer = s->img_buffer_original = (stbi_uc *) buffer; + s->img_buffer_end = s->img_buffer_original_end = (stbi_uc *) buffer+len; +} + +// initialize a callback-based context +static void stbi__start_callbacks(stbi__context *s, stbi_io_callbacks *c, void *user) +{ + s->io = *c; + s->io_user_data = user; + s->buflen = sizeof(s->buffer_start); + s->read_from_callbacks = 1; + s->img_buffer_original = s->buffer_start; + stbi__refill_buffer(s); + s->img_buffer_original_end = s->img_buffer_end; +} + +#ifndef STBI_NO_STDIO + +static int stbi__stdio_read(void *user, char *data, int size) +{ + return (int) fread(data,1,size,(FILE*) user); +} + +static void stbi__stdio_skip(void *user, int n) +{ + fseek((FILE*) user, n, SEEK_CUR); +} + +static int stbi__stdio_eof(void *user) +{ + return feof((FILE*) user); +} + +static stbi_io_callbacks stbi__stdio_callbacks = +{ + stbi__stdio_read, + stbi__stdio_skip, + stbi__stdio_eof, +}; + +static void stbi__start_file(stbi__context *s, FILE *f) +{ + stbi__start_callbacks(s, &stbi__stdio_callbacks, (void *) f); +} + +//static void stop_file(stbi__context *s) { } + +#endif // !STBI_NO_STDIO + +static void stbi__rewind(stbi__context *s) +{ + // conceptually rewind SHOULD rewind to the beginning of the stream, + // but we just rewind to the beginning of the initial buffer, because + // we only use it after doing 'test', which only ever looks at at most 92 bytes + s->img_buffer = s->img_buffer_original; + s->img_buffer_end = s->img_buffer_original_end; +} + +enum +{ + STBI_ORDER_RGB, + STBI_ORDER_BGR +}; + +typedef struct +{ + int bits_per_channel; + int num_channels; + int channel_order; +} stbi__result_info; + +#ifndef STBI_NO_JPEG +static int stbi__jpeg_test(stbi__context *s); +static void *stbi__jpeg_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); +static int stbi__jpeg_info(stbi__context *s, int *x, int *y, int *comp); +#endif + +#ifndef STBI_NO_PNG +static int stbi__png_test(stbi__context *s); +static void *stbi__png_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); +static int stbi__png_info(stbi__context *s, int *x, int *y, int *comp); +static int stbi__png_is16(stbi__context *s); +#endif + +#ifndef STBI_NO_BMP +static int stbi__bmp_test(stbi__context *s); +static void *stbi__bmp_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); +static int stbi__bmp_info(stbi__context *s, int *x, int *y, int *comp); +#endif + +#ifndef STBI_NO_TGA +static int stbi__tga_test(stbi__context *s); +static void *stbi__tga_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); +static int stbi__tga_info(stbi__context *s, int *x, int *y, int *comp); +#endif + +#ifndef STBI_NO_PSD +static int stbi__psd_test(stbi__context *s); +static void *stbi__psd_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri, int bpc); +static int stbi__psd_info(stbi__context *s, int *x, int *y, int *comp); +static int stbi__psd_is16(stbi__context *s); +#endif + +#ifndef STBI_NO_HDR +static int stbi__hdr_test(stbi__context *s); +static float *stbi__hdr_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); +static int stbi__hdr_info(stbi__context *s, int *x, int *y, int *comp); +#endif + +#ifndef STBI_NO_PIC +static int stbi__pic_test(stbi__context *s); +static void *stbi__pic_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); +static int stbi__pic_info(stbi__context *s, int *x, int *y, int *comp); +#endif + +#ifndef STBI_NO_GIF +static int stbi__gif_test(stbi__context *s); +static void *stbi__gif_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); +static void *stbi__load_gif_main(stbi__context *s, int **delays, int *x, int *y, int *z, int *comp, int req_comp); +static int stbi__gif_info(stbi__context *s, int *x, int *y, int *comp); +#endif + +#ifndef STBI_NO_PNM +static int stbi__pnm_test(stbi__context *s); +static void *stbi__pnm_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); +static int stbi__pnm_info(stbi__context *s, int *x, int *y, int *comp); +#endif + +// this is not threadsafe +static const char *stbi__g_failure_reason; + +STBIDEF const char *stbi_failure_reason(void) +{ + return stbi__g_failure_reason; +} + +static int stbi__err(const char *str) +{ + stbi__g_failure_reason = str; + return 0; +} + +static void *stbi__malloc(size_t size) +{ + return STBI_MALLOC(size); +} + +// stb_image uses ints pervasively, including for offset calculations. +// therefore the largest decoded image size we can support with the +// current code, even on 64-bit targets, is INT_MAX. this is not a +// significant limitation for the intended use case. +// +// we do, however, need to make sure our size calculations don't +// overflow. hence a few helper functions for size calculations that +// multiply integers together, making sure that they're non-negative +// and no overflow occurs. + +// return 1 if the sum is valid, 0 on overflow. +// negative terms are considered invalid. +static int stbi__addsizes_valid(int a, int b) +{ + if (b < 0) return 0; + // now 0 <= b <= INT_MAX, hence also + // 0 <= INT_MAX - b <= INTMAX. + // And "a + b <= INT_MAX" (which might overflow) is the + // same as a <= INT_MAX - b (no overflow) + return a <= INT_MAX - b; +} + +// returns 1 if the product is valid, 0 on overflow. +// negative factors are considered invalid. +static int stbi__mul2sizes_valid(int a, int b) +{ + if (a < 0 || b < 0) return 0; + if (b == 0) return 1; // mul-by-0 is always safe + // portable way to check for no overflows in a*b + return a <= INT_MAX/b; +} + +// returns 1 if "a*b + add" has no negative terms/factors and doesn't overflow +static int stbi__mad2sizes_valid(int a, int b, int add) +{ + return stbi__mul2sizes_valid(a, b) && stbi__addsizes_valid(a*b, add); +} + +// returns 1 if "a*b*c + add" has no negative terms/factors and doesn't overflow +static int stbi__mad3sizes_valid(int a, int b, int c, int add) +{ + return stbi__mul2sizes_valid(a, b) && stbi__mul2sizes_valid(a*b, c) && + stbi__addsizes_valid(a*b*c, add); +} + +// returns 1 if "a*b*c*d + add" has no negative terms/factors and doesn't overflow +#if !defined(STBI_NO_LINEAR) || !defined(STBI_NO_HDR) +static int stbi__mad4sizes_valid(int a, int b, int c, int d, int add) +{ + return stbi__mul2sizes_valid(a, b) && stbi__mul2sizes_valid(a*b, c) && + stbi__mul2sizes_valid(a*b*c, d) && stbi__addsizes_valid(a*b*c*d, add); +} +#endif + +// mallocs with size overflow checking +static void *stbi__malloc_mad2(int a, int b, int add) +{ + if (!stbi__mad2sizes_valid(a, b, add)) return NULL; + return stbi__malloc(a*b + add); +} + +static void *stbi__malloc_mad3(int a, int b, int c, int add) +{ + if (!stbi__mad3sizes_valid(a, b, c, add)) return NULL; + return stbi__malloc(a*b*c + add); +} + +#if !defined(STBI_NO_LINEAR) || !defined(STBI_NO_HDR) +static void *stbi__malloc_mad4(int a, int b, int c, int d, int add) +{ + if (!stbi__mad4sizes_valid(a, b, c, d, add)) return NULL; + return stbi__malloc(a*b*c*d + add); +} +#endif + +// stbi__err - error +// stbi__errpf - error returning pointer to float +// stbi__errpuc - error returning pointer to unsigned char + +#ifdef STBI_NO_FAILURE_STRINGS + #define stbi__err(x,y) 0 +#elif defined(STBI_FAILURE_USERMSG) + #define stbi__err(x,y) stbi__err(y) +#else + #define stbi__err(x,y) stbi__err(x) +#endif + +#define stbi__errpf(x,y) ((float *)(size_t) (stbi__err(x,y)?NULL:NULL)) +#define stbi__errpuc(x,y) ((unsigned char *)(size_t) (stbi__err(x,y)?NULL:NULL)) + +STBIDEF void stbi_image_free(void *retval_from_stbi_load) +{ + STBI_FREE(retval_from_stbi_load); +} + +#ifndef STBI_NO_LINEAR +static float *stbi__ldr_to_hdr(stbi_uc *data, int x, int y, int comp); +#endif + +#ifndef STBI_NO_HDR +static stbi_uc *stbi__hdr_to_ldr(float *data, int x, int y, int comp); +#endif + +static int stbi__vertically_flip_on_load = 0; + +STBIDEF void stbi_set_flip_vertically_on_load(int flag_true_if_should_flip) +{ + stbi__vertically_flip_on_load = flag_true_if_should_flip; +} + +static void *stbi__load_main(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri, int bpc) +{ + memset(ri, 0, sizeof(*ri)); // make sure it's initialized if we add new fields + ri->bits_per_channel = 8; // default is 8 so most paths don't have to be changed + ri->channel_order = STBI_ORDER_RGB; // all current input & output are this, but this is here so we can add BGR order + ri->num_channels = 0; + + #ifndef STBI_NO_JPEG + if (stbi__jpeg_test(s)) return stbi__jpeg_load(s,x,y,comp,req_comp, ri); + #endif + #ifndef STBI_NO_PNG + if (stbi__png_test(s)) return stbi__png_load(s,x,y,comp,req_comp, ri); + #endif + #ifndef STBI_NO_BMP + if (stbi__bmp_test(s)) return stbi__bmp_load(s,x,y,comp,req_comp, ri); + #endif + #ifndef STBI_NO_GIF + if (stbi__gif_test(s)) return stbi__gif_load(s,x,y,comp,req_comp, ri); + #endif + #ifndef STBI_NO_PSD + if (stbi__psd_test(s)) return stbi__psd_load(s,x,y,comp,req_comp, ri, bpc); + #endif + #ifndef STBI_NO_PIC + if (stbi__pic_test(s)) return stbi__pic_load(s,x,y,comp,req_comp, ri); + #endif + #ifndef STBI_NO_PNM + if (stbi__pnm_test(s)) return stbi__pnm_load(s,x,y,comp,req_comp, ri); + #endif + + #ifndef STBI_NO_HDR + if (stbi__hdr_test(s)) { + float *hdr = stbi__hdr_load(s, x,y,comp,req_comp, ri); + return stbi__hdr_to_ldr(hdr, *x, *y, req_comp ? req_comp : *comp); + } + #endif + + #ifndef STBI_NO_TGA + // test tga last because it's a crappy test! + if (stbi__tga_test(s)) + return stbi__tga_load(s,x,y,comp,req_comp, ri); + #endif + + return stbi__errpuc("unknown image type", "Image not of any known type, or corrupt"); +} + +static stbi_uc *stbi__convert_16_to_8(stbi__uint16 *orig, int w, int h, int channels) +{ + int i; + int img_len = w * h * channels; + stbi_uc *reduced; + + reduced = (stbi_uc *) stbi__malloc(img_len); + if (reduced == NULL) return stbi__errpuc("outofmem", "Out of memory"); + + for (i = 0; i < img_len; ++i) + reduced[i] = (stbi_uc)((orig[i] >> 8) & 0xFF); // top half of each byte is sufficient approx of 16->8 bit scaling + + STBI_FREE(orig); + return reduced; +} + +static stbi__uint16 *stbi__convert_8_to_16(stbi_uc *orig, int w, int h, int channels) +{ + int i; + int img_len = w * h * channels; + stbi__uint16 *enlarged; + + enlarged = (stbi__uint16 *) stbi__malloc(img_len*2); + if (enlarged == NULL) return (stbi__uint16 *) stbi__errpuc("outofmem", "Out of memory"); + + for (i = 0; i < img_len; ++i) + enlarged[i] = (stbi__uint16)((orig[i] << 8) + orig[i]); // replicate to high and low byte, maps 0->0, 255->0xffff + + STBI_FREE(orig); + return enlarged; +} + +static void stbi__vertical_flip(void *image, int w, int h, int bytes_per_pixel) +{ + int row; + size_t bytes_per_row = (size_t)w * bytes_per_pixel; + stbi_uc temp[2048]; + stbi_uc *bytes = (stbi_uc *)image; + + for (row = 0; row < (h>>1); row++) { + stbi_uc *row0 = bytes + row*bytes_per_row; + stbi_uc *row1 = bytes + (h - row - 1)*bytes_per_row; + // swap row0 with row1 + size_t bytes_left = bytes_per_row; + while (bytes_left) { + size_t bytes_copy = (bytes_left < sizeof(temp)) ? bytes_left : sizeof(temp); + memcpy(temp, row0, bytes_copy); + memcpy(row0, row1, bytes_copy); + memcpy(row1, temp, bytes_copy); + row0 += bytes_copy; + row1 += bytes_copy; + bytes_left -= bytes_copy; + } + } +} + +#ifndef STBI_NO_GIF +static void stbi__vertical_flip_slices(void *image, int w, int h, int z, int bytes_per_pixel) +{ + int slice; + int slice_size = w * h * bytes_per_pixel; + + stbi_uc *bytes = (stbi_uc *)image; + for (slice = 0; slice < z; ++slice) { + stbi__vertical_flip(bytes, w, h, bytes_per_pixel); + bytes += slice_size; + } +} +#endif + +static unsigned char *stbi__load_and_postprocess_8bit(stbi__context *s, int *x, int *y, int *comp, int req_comp) +{ + stbi__result_info ri; + void *result = stbi__load_main(s, x, y, comp, req_comp, &ri, 8); + + if (result == NULL) + return NULL; + + if (ri.bits_per_channel != 8) { + STBI_ASSERT(ri.bits_per_channel == 16); + result = stbi__convert_16_to_8((stbi__uint16 *) result, *x, *y, req_comp == 0 ? *comp : req_comp); + ri.bits_per_channel = 8; + } + + // @TODO: move stbi__convert_format to here + + if (stbi__vertically_flip_on_load) { + int channels = req_comp ? req_comp : *comp; + stbi__vertical_flip(result, *x, *y, channels * sizeof(stbi_uc)); + } + + return (unsigned char *) result; +} + +static stbi__uint16 *stbi__load_and_postprocess_16bit(stbi__context *s, int *x, int *y, int *comp, int req_comp) +{ + stbi__result_info ri; + void *result = stbi__load_main(s, x, y, comp, req_comp, &ri, 16); + + if (result == NULL) + return NULL; + + if (ri.bits_per_channel != 16) { + STBI_ASSERT(ri.bits_per_channel == 8); + result = stbi__convert_8_to_16((stbi_uc *) result, *x, *y, req_comp == 0 ? *comp : req_comp); + ri.bits_per_channel = 16; + } + + // @TODO: move stbi__convert_format16 to here + // @TODO: special case RGB-to-Y (and RGBA-to-YA) for 8-bit-to-16-bit case to keep more precision + + if (stbi__vertically_flip_on_load) { + int channels = req_comp ? req_comp : *comp; + stbi__vertical_flip(result, *x, *y, channels * sizeof(stbi__uint16)); + } + + return (stbi__uint16 *) result; +} + +#if !defined(STBI_NO_HDR) && !defined(STBI_NO_LINEAR) +static void stbi__float_postprocess(float *result, int *x, int *y, int *comp, int req_comp) +{ + if (stbi__vertically_flip_on_load && result != NULL) { + int channels = req_comp ? req_comp : *comp; + stbi__vertical_flip(result, *x, *y, channels * sizeof(float)); + } +} +#endif + +#ifndef STBI_NO_STDIO + +#if defined(_MSC_VER) && defined(STBI_WINDOWS_UTF8) +STBI_EXTERN __declspec(dllimport) int __stdcall MultiByteToWideChar(unsigned int cp, unsigned long flags, const char *str, int cbmb, wchar_t *widestr, int cchwide); +STBI_EXTERN __declspec(dllimport) int __stdcall WideCharToMultiByte(unsigned int cp, unsigned long flags, const wchar_t *widestr, int cchwide, char *str, int cbmb, const char *defchar, int *used_default); +#endif + +#if defined(_MSC_VER) && defined(STBI_WINDOWS_UTF8) +STBIDEF int stbi_convert_wchar_to_utf8(char *buffer, size_t bufferlen, const wchar_t* input) +{ + return WideCharToMultiByte(65001 /* UTF8 */, 0, input, -1, buffer, (int) bufferlen, NULL, NULL); +} +#endif + +static FILE *stbi__fopen(char const *filename, char const *mode) +{ + FILE *f; +#if defined(_MSC_VER) && defined(STBI_WINDOWS_UTF8) + wchar_t wMode[64]; + wchar_t wFilename[1024]; + if (0 == MultiByteToWideChar(65001 /* UTF8 */, 0, filename, -1, wFilename, sizeof(wFilename))) + return 0; + + if (0 == MultiByteToWideChar(65001 /* UTF8 */, 0, mode, -1, wMode, sizeof(wMode))) + return 0; + +#if _MSC_VER >= 1400 + if (0 != _wfopen_s(&f, wFilename, wMode)) + f = 0; +#else + f = _wfopen(wFilename, wMode); +#endif + +#elif defined(_MSC_VER) && _MSC_VER >= 1400 + if (0 != fopen_s(&f, filename, mode)) + f=0; +#else + f = fopen(filename, mode); +#endif + return f; +} + + +STBIDEF stbi_uc *stbi_load(char const *filename, int *x, int *y, int *comp, int req_comp) +{ + FILE *f = stbi__fopen(filename, "rb"); + unsigned char *result; + if (!f) return stbi__errpuc("can't fopen", "Unable to open file"); + result = stbi_load_from_file(f,x,y,comp,req_comp); + fclose(f); + return result; +} + +STBIDEF stbi_uc *stbi_load_from_file(FILE *f, int *x, int *y, int *comp, int req_comp) +{ + unsigned char *result; + stbi__context s; + stbi__start_file(&s,f); + result = stbi__load_and_postprocess_8bit(&s,x,y,comp,req_comp); + if (result) { + // need to 'unget' all the characters in the IO buffer + fseek(f, - (int) (s.img_buffer_end - s.img_buffer), SEEK_CUR); + } + return result; +} + +STBIDEF stbi__uint16 *stbi_load_from_file_16(FILE *f, int *x, int *y, int *comp, int req_comp) +{ + stbi__uint16 *result; + stbi__context s; + stbi__start_file(&s,f); + result = stbi__load_and_postprocess_16bit(&s,x,y,comp,req_comp); + if (result) { + // need to 'unget' all the characters in the IO buffer + fseek(f, - (int) (s.img_buffer_end - s.img_buffer), SEEK_CUR); + } + return result; +} + +STBIDEF stbi_us *stbi_load_16(char const *filename, int *x, int *y, int *comp, int req_comp) +{ + FILE *f = stbi__fopen(filename, "rb"); + stbi__uint16 *result; + if (!f) return (stbi_us *) stbi__errpuc("can't fopen", "Unable to open file"); + result = stbi_load_from_file_16(f,x,y,comp,req_comp); + fclose(f); + return result; +} + + +#endif //!STBI_NO_STDIO + +STBIDEF stbi_us *stbi_load_16_from_memory(stbi_uc const *buffer, int len, int *x, int *y, int *channels_in_file, int desired_channels) +{ + stbi__context s; + stbi__start_mem(&s,buffer,len); + return stbi__load_and_postprocess_16bit(&s,x,y,channels_in_file,desired_channels); +} + +STBIDEF stbi_us *stbi_load_16_from_callbacks(stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *channels_in_file, int desired_channels) +{ + stbi__context s; + stbi__start_callbacks(&s, (stbi_io_callbacks *)clbk, user); + return stbi__load_and_postprocess_16bit(&s,x,y,channels_in_file,desired_channels); +} + +STBIDEF stbi_uc *stbi_load_from_memory(stbi_uc const *buffer, int len, int *x, int *y, int *comp, int req_comp) +{ + stbi__context s; + stbi__start_mem(&s,buffer,len); + return stbi__load_and_postprocess_8bit(&s,x,y,comp,req_comp); +} + +STBIDEF stbi_uc *stbi_load_from_callbacks(stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *comp, int req_comp) +{ + stbi__context s; + stbi__start_callbacks(&s, (stbi_io_callbacks *) clbk, user); + return stbi__load_and_postprocess_8bit(&s,x,y,comp,req_comp); +} + +#ifndef STBI_NO_GIF +STBIDEF stbi_uc *stbi_load_gif_from_memory(stbi_uc const *buffer, int len, int **delays, int *x, int *y, int *z, int *comp, int req_comp) +{ + unsigned char *result; + stbi__context s; + stbi__start_mem(&s,buffer,len); + + result = (unsigned char*) stbi__load_gif_main(&s, delays, x, y, z, comp, req_comp); + if (stbi__vertically_flip_on_load) { + stbi__vertical_flip_slices( result, *x, *y, *z, *comp ); + } + + return result; +} +#endif + +#ifndef STBI_NO_LINEAR +static float *stbi__loadf_main(stbi__context *s, int *x, int *y, int *comp, int req_comp) +{ + unsigned char *data; + #ifndef STBI_NO_HDR + if (stbi__hdr_test(s)) { + stbi__result_info ri; + float *hdr_data = stbi__hdr_load(s,x,y,comp,req_comp, &ri); + if (hdr_data) + stbi__float_postprocess(hdr_data,x,y,comp,req_comp); + return hdr_data; + } + #endif + data = stbi__load_and_postprocess_8bit(s, x, y, comp, req_comp); + if (data) + return stbi__ldr_to_hdr(data, *x, *y, req_comp ? req_comp : *comp); + return stbi__errpf("unknown image type", "Image not of any known type, or corrupt"); +} + +STBIDEF float *stbi_loadf_from_memory(stbi_uc const *buffer, int len, int *x, int *y, int *comp, int req_comp) +{ + stbi__context s; + stbi__start_mem(&s,buffer,len); + return stbi__loadf_main(&s,x,y,comp,req_comp); +} + +STBIDEF float *stbi_loadf_from_callbacks(stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *comp, int req_comp) +{ + stbi__context s; + stbi__start_callbacks(&s, (stbi_io_callbacks *) clbk, user); + return stbi__loadf_main(&s,x,y,comp,req_comp); +} + +#ifndef STBI_NO_STDIO +STBIDEF float *stbi_loadf(char const *filename, int *x, int *y, int *comp, int req_comp) +{ + float *result; + FILE *f = stbi__fopen(filename, "rb"); + if (!f) return stbi__errpf("can't fopen", "Unable to open file"); + result = stbi_loadf_from_file(f,x,y,comp,req_comp); + fclose(f); + return result; +} + +STBIDEF float *stbi_loadf_from_file(FILE *f, int *x, int *y, int *comp, int req_comp) +{ + stbi__context s; + stbi__start_file(&s,f); + return stbi__loadf_main(&s,x,y,comp,req_comp); +} +#endif // !STBI_NO_STDIO + +#endif // !STBI_NO_LINEAR + +// these is-hdr-or-not is defined independent of whether STBI_NO_LINEAR is +// defined, for API simplicity; if STBI_NO_LINEAR is defined, it always +// reports false! + +STBIDEF int stbi_is_hdr_from_memory(stbi_uc const *buffer, int len) +{ + #ifndef STBI_NO_HDR + stbi__context s; + stbi__start_mem(&s,buffer,len); + return stbi__hdr_test(&s); + #else + STBI_NOTUSED(buffer); + STBI_NOTUSED(len); + return 0; + #endif +} + +#ifndef STBI_NO_STDIO +STBIDEF int stbi_is_hdr (char const *filename) +{ + FILE *f = stbi__fopen(filename, "rb"); + int result=0; + if (f) { + result = stbi_is_hdr_from_file(f); + fclose(f); + } + return result; +} + +STBIDEF int stbi_is_hdr_from_file(FILE *f) +{ + #ifndef STBI_NO_HDR + long pos = ftell(f); + int res; + stbi__context s; + stbi__start_file(&s,f); + res = stbi__hdr_test(&s); + fseek(f, pos, SEEK_SET); + return res; + #else + STBI_NOTUSED(f); + return 0; + #endif +} +#endif // !STBI_NO_STDIO + +STBIDEF int stbi_is_hdr_from_callbacks(stbi_io_callbacks const *clbk, void *user) +{ + #ifndef STBI_NO_HDR + stbi__context s; + stbi__start_callbacks(&s, (stbi_io_callbacks *) clbk, user); + return stbi__hdr_test(&s); + #else + STBI_NOTUSED(clbk); + STBI_NOTUSED(user); + return 0; + #endif +} + +#ifndef STBI_NO_LINEAR +static float stbi__l2h_gamma=2.2f, stbi__l2h_scale=1.0f; + +STBIDEF void stbi_ldr_to_hdr_gamma(float gamma) { stbi__l2h_gamma = gamma; } +STBIDEF void stbi_ldr_to_hdr_scale(float scale) { stbi__l2h_scale = scale; } +#endif + +static float stbi__h2l_gamma_i=1.0f/2.2f, stbi__h2l_scale_i=1.0f; + +STBIDEF void stbi_hdr_to_ldr_gamma(float gamma) { stbi__h2l_gamma_i = 1/gamma; } +STBIDEF void stbi_hdr_to_ldr_scale(float scale) { stbi__h2l_scale_i = 1/scale; } + + +////////////////////////////////////////////////////////////////////////////// +// +// Common code used by all image loaders +// + +enum +{ + STBI__SCAN_load=0, + STBI__SCAN_type, + STBI__SCAN_header +}; + +static void stbi__refill_buffer(stbi__context *s) +{ + int n = (s->io.read)(s->io_user_data,(char*)s->buffer_start,s->buflen); + if (n == 0) { + // at end of file, treat same as if from memory, but need to handle case + // where s->img_buffer isn't pointing to safe memory, e.g. 0-byte file + s->read_from_callbacks = 0; + s->img_buffer = s->buffer_start; + s->img_buffer_end = s->buffer_start+1; + *s->img_buffer = 0; + } else { + s->img_buffer = s->buffer_start; + s->img_buffer_end = s->buffer_start + n; + } +} + +stbi_inline static stbi_uc stbi__get8(stbi__context *s) +{ + if (s->img_buffer < s->img_buffer_end) + return *s->img_buffer++; + if (s->read_from_callbacks) { + stbi__refill_buffer(s); + return *s->img_buffer++; + } + return 0; +} + +stbi_inline static int stbi__at_eof(stbi__context *s) +{ + if (s->io.read) { + if (!(s->io.eof)(s->io_user_data)) return 0; + // if feof() is true, check if buffer = end + // special case: we've only got the special 0 character at the end + if (s->read_from_callbacks == 0) return 1; + } + + return s->img_buffer >= s->img_buffer_end; +} + +static void stbi__skip(stbi__context *s, int n) +{ + if (n < 0) { + s->img_buffer = s->img_buffer_end; + return; + } + if (s->io.read) { + int blen = (int) (s->img_buffer_end - s->img_buffer); + if (blen < n) { + s->img_buffer = s->img_buffer_end; + (s->io.skip)(s->io_user_data, n - blen); + return; + } + } + s->img_buffer += n; +} + +static int stbi__getn(stbi__context *s, stbi_uc *buffer, int n) +{ + if (s->io.read) { + int blen = (int) (s->img_buffer_end - s->img_buffer); + if (blen < n) { + int res, count; + + memcpy(buffer, s->img_buffer, blen); + + count = (s->io.read)(s->io_user_data, (char*) buffer + blen, n - blen); + res = (count == (n-blen)); + s->img_buffer = s->img_buffer_end; + return res; + } + } + + if (s->img_buffer+n <= s->img_buffer_end) { + memcpy(buffer, s->img_buffer, n); + s->img_buffer += n; + return 1; + } else + return 0; +} + +static int stbi__get16be(stbi__context *s) +{ + int z = stbi__get8(s); + return (z << 8) + stbi__get8(s); +} + +static stbi__uint32 stbi__get32be(stbi__context *s) +{ + stbi__uint32 z = stbi__get16be(s); + return (z << 16) + stbi__get16be(s); +} + +#if defined(STBI_NO_BMP) && defined(STBI_NO_TGA) && defined(STBI_NO_GIF) +// nothing +#else +static int stbi__get16le(stbi__context *s) +{ + int z = stbi__get8(s); + return z + (stbi__get8(s) << 8); +} +#endif + +#ifndef STBI_NO_BMP +static stbi__uint32 stbi__get32le(stbi__context *s) +{ + stbi__uint32 z = stbi__get16le(s); + return z + (stbi__get16le(s) << 16); +} +#endif + +#define STBI__BYTECAST(x) ((stbi_uc) ((x) & 255)) // truncate int to byte without warnings + + +////////////////////////////////////////////////////////////////////////////// +// +// generic converter from built-in img_n to req_comp +// individual types do this automatically as much as possible (e.g. jpeg +// does all cases internally since it needs to colorspace convert anyway, +// and it never has alpha, so very few cases ). png can automatically +// interleave an alpha=255 channel, but falls back to this for other cases +// +// assume data buffer is malloced, so malloc a new one and free that one +// only failure mode is malloc failing + +static stbi_uc stbi__compute_y(int r, int g, int b) +{ + return (stbi_uc) (((r*77) + (g*150) + (29*b)) >> 8); +} + +static unsigned char *stbi__convert_format(unsigned char *data, int img_n, int req_comp, unsigned int x, unsigned int y) +{ + int i,j; + unsigned char *good; + + if (req_comp == img_n) return data; + STBI_ASSERT(req_comp >= 1 && req_comp <= 4); + + good = (unsigned char *) stbi__malloc_mad3(req_comp, x, y, 0); + if (good == NULL) { + STBI_FREE(data); + return stbi__errpuc("outofmem", "Out of memory"); + } + + for (j=0; j < (int) y; ++j) { + unsigned char *src = data + j * x * img_n ; + unsigned char *dest = good + j * x * req_comp; + + #define STBI__COMBO(a,b) ((a)*8+(b)) + #define STBI__CASE(a,b) case STBI__COMBO(a,b): for(i=x-1; i >= 0; --i, src += a, dest += b) + // convert source image with img_n components to one with req_comp components; + // avoid switch per pixel, so use switch per scanline and massive macros + switch (STBI__COMBO(img_n, req_comp)) { + STBI__CASE(1,2) { dest[0]=src[0]; dest[1]=255; } break; + STBI__CASE(1,3) { dest[0]=dest[1]=dest[2]=src[0]; } break; + STBI__CASE(1,4) { dest[0]=dest[1]=dest[2]=src[0]; dest[3]=255; } break; + STBI__CASE(2,1) { dest[0]=src[0]; } break; + STBI__CASE(2,3) { dest[0]=dest[1]=dest[2]=src[0]; } break; + STBI__CASE(2,4) { dest[0]=dest[1]=dest[2]=src[0]; dest[3]=src[1]; } break; + STBI__CASE(3,4) { dest[0]=src[0];dest[1]=src[1];dest[2]=src[2];dest[3]=255; } break; + STBI__CASE(3,1) { dest[0]=stbi__compute_y(src[0],src[1],src[2]); } break; + STBI__CASE(3,2) { dest[0]=stbi__compute_y(src[0],src[1],src[2]); dest[1] = 255; } break; + STBI__CASE(4,1) { dest[0]=stbi__compute_y(src[0],src[1],src[2]); } break; + STBI__CASE(4,2) { dest[0]=stbi__compute_y(src[0],src[1],src[2]); dest[1] = src[3]; } break; + STBI__CASE(4,3) { dest[0]=src[0];dest[1]=src[1];dest[2]=src[2]; } break; + default: STBI_ASSERT(0); + } + #undef STBI__CASE + } + + STBI_FREE(data); + return good; +} + +static stbi__uint16 stbi__compute_y_16(int r, int g, int b) +{ + return (stbi__uint16) (((r*77) + (g*150) + (29*b)) >> 8); +} + +static stbi__uint16 *stbi__convert_format16(stbi__uint16 *data, int img_n, int req_comp, unsigned int x, unsigned int y) +{ + int i,j; + stbi__uint16 *good; + + if (req_comp == img_n) return data; + STBI_ASSERT(req_comp >= 1 && req_comp <= 4); + + good = (stbi__uint16 *) stbi__malloc(req_comp * x * y * 2); + if (good == NULL) { + STBI_FREE(data); + return (stbi__uint16 *) stbi__errpuc("outofmem", "Out of memory"); + } + + for (j=0; j < (int) y; ++j) { + stbi__uint16 *src = data + j * x * img_n ; + stbi__uint16 *dest = good + j * x * req_comp; + + #define STBI__COMBO(a,b) ((a)*8+(b)) + #define STBI__CASE(a,b) case STBI__COMBO(a,b): for(i=x-1; i >= 0; --i, src += a, dest += b) + // convert source image with img_n components to one with req_comp components; + // avoid switch per pixel, so use switch per scanline and massive macros + switch (STBI__COMBO(img_n, req_comp)) { + STBI__CASE(1,2) { dest[0]=src[0]; dest[1]=0xffff; } break; + STBI__CASE(1,3) { dest[0]=dest[1]=dest[2]=src[0]; } break; + STBI__CASE(1,4) { dest[0]=dest[1]=dest[2]=src[0]; dest[3]=0xffff; } break; + STBI__CASE(2,1) { dest[0]=src[0]; } break; + STBI__CASE(2,3) { dest[0]=dest[1]=dest[2]=src[0]; } break; + STBI__CASE(2,4) { dest[0]=dest[1]=dest[2]=src[0]; dest[3]=src[1]; } break; + STBI__CASE(3,4) { dest[0]=src[0];dest[1]=src[1];dest[2]=src[2];dest[3]=0xffff; } break; + STBI__CASE(3,1) { dest[0]=stbi__compute_y_16(src[0],src[1],src[2]); } break; + STBI__CASE(3,2) { dest[0]=stbi__compute_y_16(src[0],src[1],src[2]); dest[1] = 0xffff; } break; + STBI__CASE(4,1) { dest[0]=stbi__compute_y_16(src[0],src[1],src[2]); } break; + STBI__CASE(4,2) { dest[0]=stbi__compute_y_16(src[0],src[1],src[2]); dest[1] = src[3]; } break; + STBI__CASE(4,3) { dest[0]=src[0];dest[1]=src[1];dest[2]=src[2]; } break; + default: STBI_ASSERT(0); + } + #undef STBI__CASE + } + + STBI_FREE(data); + return good; +} + +#ifndef STBI_NO_LINEAR +static float *stbi__ldr_to_hdr(stbi_uc *data, int x, int y, int comp) +{ + int i,k,n; + float *output; + if (!data) return NULL; + output = (float *) stbi__malloc_mad4(x, y, comp, sizeof(float), 0); + if (output == NULL) { STBI_FREE(data); return stbi__errpf("outofmem", "Out of memory"); } + // compute number of non-alpha components + if (comp & 1) n = comp; else n = comp-1; + for (i=0; i < x*y; ++i) { + for (k=0; k < n; ++k) { + output[i*comp + k] = (float) (pow(data[i*comp+k]/255.0f, stbi__l2h_gamma) * stbi__l2h_scale); + } + } + if (n < comp) { + for (i=0; i < x*y; ++i) { + output[i*comp + n] = data[i*comp + n]/255.0f; + } + } + STBI_FREE(data); + return output; +} +#endif + +#ifndef STBI_NO_HDR +#define stbi__float2int(x) ((int) (x)) +static stbi_uc *stbi__hdr_to_ldr(float *data, int x, int y, int comp) +{ + int i,k,n; + stbi_uc *output; + if (!data) return NULL; + output = (stbi_uc *) stbi__malloc_mad3(x, y, comp, 0); + if (output == NULL) { STBI_FREE(data); return stbi__errpuc("outofmem", "Out of memory"); } + // compute number of non-alpha components + if (comp & 1) n = comp; else n = comp-1; + for (i=0; i < x*y; ++i) { + for (k=0; k < n; ++k) { + float z = (float) pow(data[i*comp+k]*stbi__h2l_scale_i, stbi__h2l_gamma_i) * 255 + 0.5f; + if (z < 0) z = 0; + if (z > 255) z = 255; + output[i*comp + k] = (stbi_uc) stbi__float2int(z); + } + if (k < comp) { + float z = data[i*comp+k] * 255 + 0.5f; + if (z < 0) z = 0; + if (z > 255) z = 255; + output[i*comp + k] = (stbi_uc) stbi__float2int(z); + } + } + STBI_FREE(data); + return output; +} +#endif + +////////////////////////////////////////////////////////////////////////////// +// +// "baseline" JPEG/JFIF decoder +// +// simple implementation +// - doesn't support delayed output of y-dimension +// - simple interface (only one output format: 8-bit interleaved RGB) +// - doesn't try to recover corrupt jpegs +// - doesn't allow partial loading, loading multiple at once +// - still fast on x86 (copying globals into locals doesn't help x86) +// - allocates lots of intermediate memory (full size of all components) +// - non-interleaved case requires this anyway +// - allows good upsampling (see next) +// high-quality +// - upsampled channels are bilinearly interpolated, even across blocks +// - quality integer IDCT derived from IJG's 'slow' +// performance +// - fast huffman; reasonable integer IDCT +// - some SIMD kernels for common paths on targets with SSE2/NEON +// - uses a lot of intermediate memory, could cache poorly + +#ifndef STBI_NO_JPEG + +// huffman decoding acceleration +#define FAST_BITS 9 // larger handles more cases; smaller stomps less cache + +typedef struct +{ + stbi_uc fast[1 << FAST_BITS]; + // weirdly, repacking this into AoS is a 10% speed loss, instead of a win + stbi__uint16 code[256]; + stbi_uc values[256]; + stbi_uc size[257]; + unsigned int maxcode[18]; + int delta[17]; // old 'firstsymbol' - old 'firstcode' +} stbi__huffman; + +typedef struct +{ + stbi__context *s; + stbi__huffman huff_dc[4]; + stbi__huffman huff_ac[4]; + stbi__uint16 dequant[4][64]; + stbi__int16 fast_ac[4][1 << FAST_BITS]; + +// sizes for components, interleaved MCUs + int img_h_max, img_v_max; + int img_mcu_x, img_mcu_y; + int img_mcu_w, img_mcu_h; + +// definition of jpeg image component + struct + { + int id; + int h,v; + int tq; + int hd,ha; + int dc_pred; + + int x,y,w2,h2; + stbi_uc *data; + void *raw_data, *raw_coeff; + stbi_uc *linebuf; + short *coeff; // progressive only + int coeff_w, coeff_h; // number of 8x8 coefficient blocks + } img_comp[4]; + + stbi__uint32 code_buffer; // jpeg entropy-coded buffer + int code_bits; // number of valid bits + unsigned char marker; // marker seen while filling entropy buffer + int nomore; // flag if we saw a marker so must stop + + int progressive; + int spec_start; + int spec_end; + int succ_high; + int succ_low; + int eob_run; + int jfif; + int app14_color_transform; // Adobe APP14 tag + int rgb; + + int scan_n, order[4]; + int restart_interval, todo; + +// kernels + void (*idct_block_kernel)(stbi_uc *out, int out_stride, short data[64]); + void (*YCbCr_to_RGB_kernel)(stbi_uc *out, const stbi_uc *y, const stbi_uc *pcb, const stbi_uc *pcr, int count, int step); + stbi_uc *(*resample_row_hv_2_kernel)(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs); +} stbi__jpeg; + +static int stbi__build_huffman(stbi__huffman *h, int *count) +{ + int i,j,k=0; + unsigned int code; + // build size list for each symbol (from JPEG spec) + for (i=0; i < 16; ++i) + for (j=0; j < count[i]; ++j) + h->size[k++] = (stbi_uc) (i+1); + h->size[k] = 0; + + // compute actual symbols (from jpeg spec) + code = 0; + k = 0; + for(j=1; j <= 16; ++j) { + // compute delta to add to code to compute symbol id + h->delta[j] = k - code; + if (h->size[k] == j) { + while (h->size[k] == j) + h->code[k++] = (stbi__uint16) (code++); + if (code-1 >= (1u << j)) return stbi__err("bad code lengths","Corrupt JPEG"); + } + // compute largest code + 1 for this size, preshifted as needed later + h->maxcode[j] = code << (16-j); + code <<= 1; + } + h->maxcode[j] = 0xffffffff; + + // build non-spec acceleration table; 255 is flag for not-accelerated + memset(h->fast, 255, 1 << FAST_BITS); + for (i=0; i < k; ++i) { + int s = h->size[i]; + if (s <= FAST_BITS) { + int c = h->code[i] << (FAST_BITS-s); + int m = 1 << (FAST_BITS-s); + for (j=0; j < m; ++j) { + h->fast[c+j] = (stbi_uc) i; + } + } + } + return 1; +} + +// build a table that decodes both magnitude and value of small ACs in +// one go. +static void stbi__build_fast_ac(stbi__int16 *fast_ac, stbi__huffman *h) +{ + int i; + for (i=0; i < (1 << FAST_BITS); ++i) { + stbi_uc fast = h->fast[i]; + fast_ac[i] = 0; + if (fast < 255) { + int rs = h->values[fast]; + int run = (rs >> 4) & 15; + int magbits = rs & 15; + int len = h->size[fast]; + + if (magbits && len + magbits <= FAST_BITS) { + // magnitude code followed by receive_extend code + int k = ((i << len) & ((1 << FAST_BITS) - 1)) >> (FAST_BITS - magbits); + int m = 1 << (magbits - 1); + if (k < m) k += (~0U << magbits) + 1; + // if the result is small enough, we can fit it in fast_ac table + if (k >= -128 && k <= 127) + fast_ac[i] = (stbi__int16) ((k * 256) + (run * 16) + (len + magbits)); + } + } + } +} + +static void stbi__grow_buffer_unsafe(stbi__jpeg *j) +{ + do { + unsigned int b = j->nomore ? 0 : stbi__get8(j->s); + if (b == 0xff) { + int c = stbi__get8(j->s); + while (c == 0xff) c = stbi__get8(j->s); // consume fill bytes + if (c != 0) { + j->marker = (unsigned char) c; + j->nomore = 1; + return; + } + } + j->code_buffer |= b << (24 - j->code_bits); + j->code_bits += 8; + } while (j->code_bits <= 24); +} + +// (1 << n) - 1 +static const stbi__uint32 stbi__bmask[17]={0,1,3,7,15,31,63,127,255,511,1023,2047,4095,8191,16383,32767,65535}; + +// decode a jpeg huffman value from the bitstream +stbi_inline static int stbi__jpeg_huff_decode(stbi__jpeg *j, stbi__huffman *h) +{ + unsigned int temp; + int c,k; + + if (j->code_bits < 16) stbi__grow_buffer_unsafe(j); + + // look at the top FAST_BITS and determine what symbol ID it is, + // if the code is <= FAST_BITS + c = (j->code_buffer >> (32 - FAST_BITS)) & ((1 << FAST_BITS)-1); + k = h->fast[c]; + if (k < 255) { + int s = h->size[k]; + if (s > j->code_bits) + return -1; + j->code_buffer <<= s; + j->code_bits -= s; + return h->values[k]; + } + + // naive test is to shift the code_buffer down so k bits are + // valid, then test against maxcode. To speed this up, we've + // preshifted maxcode left so that it has (16-k) 0s at the + // end; in other words, regardless of the number of bits, it + // wants to be compared against something shifted to have 16; + // that way we don't need to shift inside the loop. + temp = j->code_buffer >> 16; + for (k=FAST_BITS+1 ; ; ++k) + if (temp < h->maxcode[k]) + break; + if (k == 17) { + // error! code not found + j->code_bits -= 16; + return -1; + } + + if (k > j->code_bits) + return -1; + + // convert the huffman code to the symbol id + c = ((j->code_buffer >> (32 - k)) & stbi__bmask[k]) + h->delta[k]; + STBI_ASSERT((((j->code_buffer) >> (32 - h->size[c])) & stbi__bmask[h->size[c]]) == h->code[c]); + + // convert the id to a symbol + j->code_bits -= k; + j->code_buffer <<= k; + return h->values[c]; +} + +// bias[n] = (-1<code_bits < n) stbi__grow_buffer_unsafe(j); + + sgn = (stbi__int32)j->code_buffer >> 31; // sign bit is always in MSB + k = stbi_lrot(j->code_buffer, n); + STBI_ASSERT(n >= 0 && n < (int) (sizeof(stbi__bmask)/sizeof(*stbi__bmask))); + j->code_buffer = k & ~stbi__bmask[n]; + k &= stbi__bmask[n]; + j->code_bits -= n; + return k + (stbi__jbias[n] & ~sgn); +} + +// get some unsigned bits +stbi_inline static int stbi__jpeg_get_bits(stbi__jpeg *j, int n) +{ + unsigned int k; + if (j->code_bits < n) stbi__grow_buffer_unsafe(j); + k = stbi_lrot(j->code_buffer, n); + j->code_buffer = k & ~stbi__bmask[n]; + k &= stbi__bmask[n]; + j->code_bits -= n; + return k; +} + +stbi_inline static int stbi__jpeg_get_bit(stbi__jpeg *j) +{ + unsigned int k; + if (j->code_bits < 1) stbi__grow_buffer_unsafe(j); + k = j->code_buffer; + j->code_buffer <<= 1; + --j->code_bits; + return k & 0x80000000; +} + +// given a value that's at position X in the zigzag stream, +// where does it appear in the 8x8 matrix coded as row-major? +static const stbi_uc stbi__jpeg_dezigzag[64+15] = +{ + 0, 1, 8, 16, 9, 2, 3, 10, + 17, 24, 32, 25, 18, 11, 4, 5, + 12, 19, 26, 33, 40, 48, 41, 34, + 27, 20, 13, 6, 7, 14, 21, 28, + 35, 42, 49, 56, 57, 50, 43, 36, + 29, 22, 15, 23, 30, 37, 44, 51, + 58, 59, 52, 45, 38, 31, 39, 46, + 53, 60, 61, 54, 47, 55, 62, 63, + // let corrupt input sample past end + 63, 63, 63, 63, 63, 63, 63, 63, + 63, 63, 63, 63, 63, 63, 63 +}; + +// decode one 64-entry block-- +static int stbi__jpeg_decode_block(stbi__jpeg *j, short data[64], stbi__huffman *hdc, stbi__huffman *hac, stbi__int16 *fac, int b, stbi__uint16 *dequant) +{ + int diff,dc,k; + int t; + + if (j->code_bits < 16) stbi__grow_buffer_unsafe(j); + t = stbi__jpeg_huff_decode(j, hdc); + if (t < 0) return stbi__err("bad huffman code","Corrupt JPEG"); + + // 0 all the ac values now so we can do it 32-bits at a time + memset(data,0,64*sizeof(data[0])); + + diff = t ? stbi__extend_receive(j, t) : 0; + dc = j->img_comp[b].dc_pred + diff; + j->img_comp[b].dc_pred = dc; + data[0] = (short) (dc * dequant[0]); + + // decode AC components, see JPEG spec + k = 1; + do { + unsigned int zig; + int c,r,s; + if (j->code_bits < 16) stbi__grow_buffer_unsafe(j); + c = (j->code_buffer >> (32 - FAST_BITS)) & ((1 << FAST_BITS)-1); + r = fac[c]; + if (r) { // fast-AC path + k += (r >> 4) & 15; // run + s = r & 15; // combined length + j->code_buffer <<= s; + j->code_bits -= s; + // decode into unzigzag'd location + zig = stbi__jpeg_dezigzag[k++]; + data[zig] = (short) ((r >> 8) * dequant[zig]); + } else { + int rs = stbi__jpeg_huff_decode(j, hac); + if (rs < 0) return stbi__err("bad huffman code","Corrupt JPEG"); + s = rs & 15; + r = rs >> 4; + if (s == 0) { + if (rs != 0xf0) break; // end block + k += 16; + } else { + k += r; + // decode into unzigzag'd location + zig = stbi__jpeg_dezigzag[k++]; + data[zig] = (short) (stbi__extend_receive(j,s) * dequant[zig]); + } + } + } while (k < 64); + return 1; +} + +static int stbi__jpeg_decode_block_prog_dc(stbi__jpeg *j, short data[64], stbi__huffman *hdc, int b) +{ + int diff,dc; + int t; + if (j->spec_end != 0) return stbi__err("can't merge dc and ac", "Corrupt JPEG"); + + if (j->code_bits < 16) stbi__grow_buffer_unsafe(j); + + if (j->succ_high == 0) { + // first scan for DC coefficient, must be first + memset(data,0,64*sizeof(data[0])); // 0 all the ac values now + t = stbi__jpeg_huff_decode(j, hdc); + diff = t ? stbi__extend_receive(j, t) : 0; + + dc = j->img_comp[b].dc_pred + diff; + j->img_comp[b].dc_pred = dc; + data[0] = (short) (dc << j->succ_low); + } else { + // refinement scan for DC coefficient + if (stbi__jpeg_get_bit(j)) + data[0] += (short) (1 << j->succ_low); + } + return 1; +} + +// @OPTIMIZE: store non-zigzagged during the decode passes, +// and only de-zigzag when dequantizing +static int stbi__jpeg_decode_block_prog_ac(stbi__jpeg *j, short data[64], stbi__huffman *hac, stbi__int16 *fac) +{ + int k; + if (j->spec_start == 0) return stbi__err("can't merge dc and ac", "Corrupt JPEG"); + + if (j->succ_high == 0) { + int shift = j->succ_low; + + if (j->eob_run) { + --j->eob_run; + return 1; + } + + k = j->spec_start; + do { + unsigned int zig; + int c,r,s; + if (j->code_bits < 16) stbi__grow_buffer_unsafe(j); + c = (j->code_buffer >> (32 - FAST_BITS)) & ((1 << FAST_BITS)-1); + r = fac[c]; + if (r) { // fast-AC path + k += (r >> 4) & 15; // run + s = r & 15; // combined length + j->code_buffer <<= s; + j->code_bits -= s; + zig = stbi__jpeg_dezigzag[k++]; + data[zig] = (short) ((r >> 8) << shift); + } else { + int rs = stbi__jpeg_huff_decode(j, hac); + if (rs < 0) return stbi__err("bad huffman code","Corrupt JPEG"); + s = rs & 15; + r = rs >> 4; + if (s == 0) { + if (r < 15) { + j->eob_run = (1 << r); + if (r) + j->eob_run += stbi__jpeg_get_bits(j, r); + --j->eob_run; + break; + } + k += 16; + } else { + k += r; + zig = stbi__jpeg_dezigzag[k++]; + data[zig] = (short) (stbi__extend_receive(j,s) << shift); + } + } + } while (k <= j->spec_end); + } else { + // refinement scan for these AC coefficients + + short bit = (short) (1 << j->succ_low); + + if (j->eob_run) { + --j->eob_run; + for (k = j->spec_start; k <= j->spec_end; ++k) { + short *p = &data[stbi__jpeg_dezigzag[k]]; + if (*p != 0) + if (stbi__jpeg_get_bit(j)) + if ((*p & bit)==0) { + if (*p > 0) + *p += bit; + else + *p -= bit; + } + } + } else { + k = j->spec_start; + do { + int r,s; + int rs = stbi__jpeg_huff_decode(j, hac); // @OPTIMIZE see if we can use the fast path here, advance-by-r is so slow, eh + if (rs < 0) return stbi__err("bad huffman code","Corrupt JPEG"); + s = rs & 15; + r = rs >> 4; + if (s == 0) { + if (r < 15) { + j->eob_run = (1 << r) - 1; + if (r) + j->eob_run += stbi__jpeg_get_bits(j, r); + r = 64; // force end of block + } else { + // r=15 s=0 should write 16 0s, so we just do + // a run of 15 0s and then write s (which is 0), + // so we don't have to do anything special here + } + } else { + if (s != 1) return stbi__err("bad huffman code", "Corrupt JPEG"); + // sign bit + if (stbi__jpeg_get_bit(j)) + s = bit; + else + s = -bit; + } + + // advance by r + while (k <= j->spec_end) { + short *p = &data[stbi__jpeg_dezigzag[k++]]; + if (*p != 0) { + if (stbi__jpeg_get_bit(j)) + if ((*p & bit)==0) { + if (*p > 0) + *p += bit; + else + *p -= bit; + } + } else { + if (r == 0) { + *p = (short) s; + break; + } + --r; + } + } + } while (k <= j->spec_end); + } + } + return 1; +} + +// take a -128..127 value and stbi__clamp it and convert to 0..255 +stbi_inline static stbi_uc stbi__clamp(int x) +{ + // trick to use a single test to catch both cases + if ((unsigned int) x > 255) { + if (x < 0) return 0; + if (x > 255) return 255; + } + return (stbi_uc) x; +} + +#define stbi__f2f(x) ((int) (((x) * 4096 + 0.5))) +#define stbi__fsh(x) ((x) * 4096) + +// derived from jidctint -- DCT_ISLOW +#define STBI__IDCT_1D(s0,s1,s2,s3,s4,s5,s6,s7) \ + int t0,t1,t2,t3,p1,p2,p3,p4,p5,x0,x1,x2,x3; \ + p2 = s2; \ + p3 = s6; \ + p1 = (p2+p3) * stbi__f2f(0.5411961f); \ + t2 = p1 + p3*stbi__f2f(-1.847759065f); \ + t3 = p1 + p2*stbi__f2f( 0.765366865f); \ + p2 = s0; \ + p3 = s4; \ + t0 = stbi__fsh(p2+p3); \ + t1 = stbi__fsh(p2-p3); \ + x0 = t0+t3; \ + x3 = t0-t3; \ + x1 = t1+t2; \ + x2 = t1-t2; \ + t0 = s7; \ + t1 = s5; \ + t2 = s3; \ + t3 = s1; \ + p3 = t0+t2; \ + p4 = t1+t3; \ + p1 = t0+t3; \ + p2 = t1+t2; \ + p5 = (p3+p4)*stbi__f2f( 1.175875602f); \ + t0 = t0*stbi__f2f( 0.298631336f); \ + t1 = t1*stbi__f2f( 2.053119869f); \ + t2 = t2*stbi__f2f( 3.072711026f); \ + t3 = t3*stbi__f2f( 1.501321110f); \ + p1 = p5 + p1*stbi__f2f(-0.899976223f); \ + p2 = p5 + p2*stbi__f2f(-2.562915447f); \ + p3 = p3*stbi__f2f(-1.961570560f); \ + p4 = p4*stbi__f2f(-0.390180644f); \ + t3 += p1+p4; \ + t2 += p2+p3; \ + t1 += p2+p4; \ + t0 += p1+p3; + +static void stbi__idct_block(stbi_uc *out, int out_stride, short data[64]) +{ + int i,val[64],*v=val; + stbi_uc *o; + short *d = data; + + // columns + for (i=0; i < 8; ++i,++d, ++v) { + // if all zeroes, shortcut -- this avoids dequantizing 0s and IDCTing + if (d[ 8]==0 && d[16]==0 && d[24]==0 && d[32]==0 + && d[40]==0 && d[48]==0 && d[56]==0) { + // no shortcut 0 seconds + // (1|2|3|4|5|6|7)==0 0 seconds + // all separate -0.047 seconds + // 1 && 2|3 && 4|5 && 6|7: -0.047 seconds + int dcterm = d[0]*4; + v[0] = v[8] = v[16] = v[24] = v[32] = v[40] = v[48] = v[56] = dcterm; + } else { + STBI__IDCT_1D(d[ 0],d[ 8],d[16],d[24],d[32],d[40],d[48],d[56]) + // constants scaled things up by 1<<12; let's bring them back + // down, but keep 2 extra bits of precision + x0 += 512; x1 += 512; x2 += 512; x3 += 512; + v[ 0] = (x0+t3) >> 10; + v[56] = (x0-t3) >> 10; + v[ 8] = (x1+t2) >> 10; + v[48] = (x1-t2) >> 10; + v[16] = (x2+t1) >> 10; + v[40] = (x2-t1) >> 10; + v[24] = (x3+t0) >> 10; + v[32] = (x3-t0) >> 10; + } + } + + for (i=0, v=val, o=out; i < 8; ++i,v+=8,o+=out_stride) { + // no fast case since the first 1D IDCT spread components out + STBI__IDCT_1D(v[0],v[1],v[2],v[3],v[4],v[5],v[6],v[7]) + // constants scaled things up by 1<<12, plus we had 1<<2 from first + // loop, plus horizontal and vertical each scale by sqrt(8) so together + // we've got an extra 1<<3, so 1<<17 total we need to remove. + // so we want to round that, which means adding 0.5 * 1<<17, + // aka 65536. Also, we'll end up with -128 to 127 that we want + // to encode as 0..255 by adding 128, so we'll add that before the shift + x0 += 65536 + (128<<17); + x1 += 65536 + (128<<17); + x2 += 65536 + (128<<17); + x3 += 65536 + (128<<17); + // tried computing the shifts into temps, or'ing the temps to see + // if any were out of range, but that was slower + o[0] = stbi__clamp((x0+t3) >> 17); + o[7] = stbi__clamp((x0-t3) >> 17); + o[1] = stbi__clamp((x1+t2) >> 17); + o[6] = stbi__clamp((x1-t2) >> 17); + o[2] = stbi__clamp((x2+t1) >> 17); + o[5] = stbi__clamp((x2-t1) >> 17); + o[3] = stbi__clamp((x3+t0) >> 17); + o[4] = stbi__clamp((x3-t0) >> 17); + } +} + +#ifdef STBI_SSE2 +// sse2 integer IDCT. not the fastest possible implementation but it +// produces bit-identical results to the generic C version so it's +// fully "transparent". +static void stbi__idct_simd(stbi_uc *out, int out_stride, short data[64]) +{ + // This is constructed to match our regular (generic) integer IDCT exactly. + __m128i row0, row1, row2, row3, row4, row5, row6, row7; + __m128i tmp; + + // dot product constant: even elems=x, odd elems=y + #define dct_const(x,y) _mm_setr_epi16((x),(y),(x),(y),(x),(y),(x),(y)) + + // out(0) = c0[even]*x + c0[odd]*y (c0, x, y 16-bit, out 32-bit) + // out(1) = c1[even]*x + c1[odd]*y + #define dct_rot(out0,out1, x,y,c0,c1) \ + __m128i c0##lo = _mm_unpacklo_epi16((x),(y)); \ + __m128i c0##hi = _mm_unpackhi_epi16((x),(y)); \ + __m128i out0##_l = _mm_madd_epi16(c0##lo, c0); \ + __m128i out0##_h = _mm_madd_epi16(c0##hi, c0); \ + __m128i out1##_l = _mm_madd_epi16(c0##lo, c1); \ + __m128i out1##_h = _mm_madd_epi16(c0##hi, c1) + + // out = in << 12 (in 16-bit, out 32-bit) + #define dct_widen(out, in) \ + __m128i out##_l = _mm_srai_epi32(_mm_unpacklo_epi16(_mm_setzero_si128(), (in)), 4); \ + __m128i out##_h = _mm_srai_epi32(_mm_unpackhi_epi16(_mm_setzero_si128(), (in)), 4) + + // wide add + #define dct_wadd(out, a, b) \ + __m128i out##_l = _mm_add_epi32(a##_l, b##_l); \ + __m128i out##_h = _mm_add_epi32(a##_h, b##_h) + + // wide sub + #define dct_wsub(out, a, b) \ + __m128i out##_l = _mm_sub_epi32(a##_l, b##_l); \ + __m128i out##_h = _mm_sub_epi32(a##_h, b##_h) + + // butterfly a/b, add bias, then shift by "s" and pack + #define dct_bfly32o(out0, out1, a,b,bias,s) \ + { \ + __m128i abiased_l = _mm_add_epi32(a##_l, bias); \ + __m128i abiased_h = _mm_add_epi32(a##_h, bias); \ + dct_wadd(sum, abiased, b); \ + dct_wsub(dif, abiased, b); \ + out0 = _mm_packs_epi32(_mm_srai_epi32(sum_l, s), _mm_srai_epi32(sum_h, s)); \ + out1 = _mm_packs_epi32(_mm_srai_epi32(dif_l, s), _mm_srai_epi32(dif_h, s)); \ + } + + // 8-bit interleave step (for transposes) + #define dct_interleave8(a, b) \ + tmp = a; \ + a = _mm_unpacklo_epi8(a, b); \ + b = _mm_unpackhi_epi8(tmp, b) + + // 16-bit interleave step (for transposes) + #define dct_interleave16(a, b) \ + tmp = a; \ + a = _mm_unpacklo_epi16(a, b); \ + b = _mm_unpackhi_epi16(tmp, b) + + #define dct_pass(bias,shift) \ + { \ + /* even part */ \ + dct_rot(t2e,t3e, row2,row6, rot0_0,rot0_1); \ + __m128i sum04 = _mm_add_epi16(row0, row4); \ + __m128i dif04 = _mm_sub_epi16(row0, row4); \ + dct_widen(t0e, sum04); \ + dct_widen(t1e, dif04); \ + dct_wadd(x0, t0e, t3e); \ + dct_wsub(x3, t0e, t3e); \ + dct_wadd(x1, t1e, t2e); \ + dct_wsub(x2, t1e, t2e); \ + /* odd part */ \ + dct_rot(y0o,y2o, row7,row3, rot2_0,rot2_1); \ + dct_rot(y1o,y3o, row5,row1, rot3_0,rot3_1); \ + __m128i sum17 = _mm_add_epi16(row1, row7); \ + __m128i sum35 = _mm_add_epi16(row3, row5); \ + dct_rot(y4o,y5o, sum17,sum35, rot1_0,rot1_1); \ + dct_wadd(x4, y0o, y4o); \ + dct_wadd(x5, y1o, y5o); \ + dct_wadd(x6, y2o, y5o); \ + dct_wadd(x7, y3o, y4o); \ + dct_bfly32o(row0,row7, x0,x7,bias,shift); \ + dct_bfly32o(row1,row6, x1,x6,bias,shift); \ + dct_bfly32o(row2,row5, x2,x5,bias,shift); \ + dct_bfly32o(row3,row4, x3,x4,bias,shift); \ + } + + __m128i rot0_0 = dct_const(stbi__f2f(0.5411961f), stbi__f2f(0.5411961f) + stbi__f2f(-1.847759065f)); + __m128i rot0_1 = dct_const(stbi__f2f(0.5411961f) + stbi__f2f( 0.765366865f), stbi__f2f(0.5411961f)); + __m128i rot1_0 = dct_const(stbi__f2f(1.175875602f) + stbi__f2f(-0.899976223f), stbi__f2f(1.175875602f)); + __m128i rot1_1 = dct_const(stbi__f2f(1.175875602f), stbi__f2f(1.175875602f) + stbi__f2f(-2.562915447f)); + __m128i rot2_0 = dct_const(stbi__f2f(-1.961570560f) + stbi__f2f( 0.298631336f), stbi__f2f(-1.961570560f)); + __m128i rot2_1 = dct_const(stbi__f2f(-1.961570560f), stbi__f2f(-1.961570560f) + stbi__f2f( 3.072711026f)); + __m128i rot3_0 = dct_const(stbi__f2f(-0.390180644f) + stbi__f2f( 2.053119869f), stbi__f2f(-0.390180644f)); + __m128i rot3_1 = dct_const(stbi__f2f(-0.390180644f), stbi__f2f(-0.390180644f) + stbi__f2f( 1.501321110f)); + + // rounding biases in column/row passes, see stbi__idct_block for explanation. + __m128i bias_0 = _mm_set1_epi32(512); + __m128i bias_1 = _mm_set1_epi32(65536 + (128<<17)); + + // load + row0 = _mm_load_si128((const __m128i *) (data + 0*8)); + row1 = _mm_load_si128((const __m128i *) (data + 1*8)); + row2 = _mm_load_si128((const __m128i *) (data + 2*8)); + row3 = _mm_load_si128((const __m128i *) (data + 3*8)); + row4 = _mm_load_si128((const __m128i *) (data + 4*8)); + row5 = _mm_load_si128((const __m128i *) (data + 5*8)); + row6 = _mm_load_si128((const __m128i *) (data + 6*8)); + row7 = _mm_load_si128((const __m128i *) (data + 7*8)); + + // column pass + dct_pass(bias_0, 10); + + { + // 16bit 8x8 transpose pass 1 + dct_interleave16(row0, row4); + dct_interleave16(row1, row5); + dct_interleave16(row2, row6); + dct_interleave16(row3, row7); + + // transpose pass 2 + dct_interleave16(row0, row2); + dct_interleave16(row1, row3); + dct_interleave16(row4, row6); + dct_interleave16(row5, row7); + + // transpose pass 3 + dct_interleave16(row0, row1); + dct_interleave16(row2, row3); + dct_interleave16(row4, row5); + dct_interleave16(row6, row7); + } + + // row pass + dct_pass(bias_1, 17); + + { + // pack + __m128i p0 = _mm_packus_epi16(row0, row1); // a0a1a2a3...a7b0b1b2b3...b7 + __m128i p1 = _mm_packus_epi16(row2, row3); + __m128i p2 = _mm_packus_epi16(row4, row5); + __m128i p3 = _mm_packus_epi16(row6, row7); + + // 8bit 8x8 transpose pass 1 + dct_interleave8(p0, p2); // a0e0a1e1... + dct_interleave8(p1, p3); // c0g0c1g1... + + // transpose pass 2 + dct_interleave8(p0, p1); // a0c0e0g0... + dct_interleave8(p2, p3); // b0d0f0h0... + + // transpose pass 3 + dct_interleave8(p0, p2); // a0b0c0d0... + dct_interleave8(p1, p3); // a4b4c4d4... + + // store + _mm_storel_epi64((__m128i *) out, p0); out += out_stride; + _mm_storel_epi64((__m128i *) out, _mm_shuffle_epi32(p0, 0x4e)); out += out_stride; + _mm_storel_epi64((__m128i *) out, p2); out += out_stride; + _mm_storel_epi64((__m128i *) out, _mm_shuffle_epi32(p2, 0x4e)); out += out_stride; + _mm_storel_epi64((__m128i *) out, p1); out += out_stride; + _mm_storel_epi64((__m128i *) out, _mm_shuffle_epi32(p1, 0x4e)); out += out_stride; + _mm_storel_epi64((__m128i *) out, p3); out += out_stride; + _mm_storel_epi64((__m128i *) out, _mm_shuffle_epi32(p3, 0x4e)); + } + +#undef dct_const +#undef dct_rot +#undef dct_widen +#undef dct_wadd +#undef dct_wsub +#undef dct_bfly32o +#undef dct_interleave8 +#undef dct_interleave16 +#undef dct_pass +} + +#endif // STBI_SSE2 + +#ifdef STBI_NEON + +// NEON integer IDCT. should produce bit-identical +// results to the generic C version. +static void stbi__idct_simd(stbi_uc *out, int out_stride, short data[64]) +{ + int16x8_t row0, row1, row2, row3, row4, row5, row6, row7; + + int16x4_t rot0_0 = vdup_n_s16(stbi__f2f(0.5411961f)); + int16x4_t rot0_1 = vdup_n_s16(stbi__f2f(-1.847759065f)); + int16x4_t rot0_2 = vdup_n_s16(stbi__f2f( 0.765366865f)); + int16x4_t rot1_0 = vdup_n_s16(stbi__f2f( 1.175875602f)); + int16x4_t rot1_1 = vdup_n_s16(stbi__f2f(-0.899976223f)); + int16x4_t rot1_2 = vdup_n_s16(stbi__f2f(-2.562915447f)); + int16x4_t rot2_0 = vdup_n_s16(stbi__f2f(-1.961570560f)); + int16x4_t rot2_1 = vdup_n_s16(stbi__f2f(-0.390180644f)); + int16x4_t rot3_0 = vdup_n_s16(stbi__f2f( 0.298631336f)); + int16x4_t rot3_1 = vdup_n_s16(stbi__f2f( 2.053119869f)); + int16x4_t rot3_2 = vdup_n_s16(stbi__f2f( 3.072711026f)); + int16x4_t rot3_3 = vdup_n_s16(stbi__f2f( 1.501321110f)); + +#define dct_long_mul(out, inq, coeff) \ + int32x4_t out##_l = vmull_s16(vget_low_s16(inq), coeff); \ + int32x4_t out##_h = vmull_s16(vget_high_s16(inq), coeff) + +#define dct_long_mac(out, acc, inq, coeff) \ + int32x4_t out##_l = vmlal_s16(acc##_l, vget_low_s16(inq), coeff); \ + int32x4_t out##_h = vmlal_s16(acc##_h, vget_high_s16(inq), coeff) + +#define dct_widen(out, inq) \ + int32x4_t out##_l = vshll_n_s16(vget_low_s16(inq), 12); \ + int32x4_t out##_h = vshll_n_s16(vget_high_s16(inq), 12) + +// wide add +#define dct_wadd(out, a, b) \ + int32x4_t out##_l = vaddq_s32(a##_l, b##_l); \ + int32x4_t out##_h = vaddq_s32(a##_h, b##_h) + +// wide sub +#define dct_wsub(out, a, b) \ + int32x4_t out##_l = vsubq_s32(a##_l, b##_l); \ + int32x4_t out##_h = vsubq_s32(a##_h, b##_h) + +// butterfly a/b, then shift using "shiftop" by "s" and pack +#define dct_bfly32o(out0,out1, a,b,shiftop,s) \ + { \ + dct_wadd(sum, a, b); \ + dct_wsub(dif, a, b); \ + out0 = vcombine_s16(shiftop(sum_l, s), shiftop(sum_h, s)); \ + out1 = vcombine_s16(shiftop(dif_l, s), shiftop(dif_h, s)); \ + } + +#define dct_pass(shiftop, shift) \ + { \ + /* even part */ \ + int16x8_t sum26 = vaddq_s16(row2, row6); \ + dct_long_mul(p1e, sum26, rot0_0); \ + dct_long_mac(t2e, p1e, row6, rot0_1); \ + dct_long_mac(t3e, p1e, row2, rot0_2); \ + int16x8_t sum04 = vaddq_s16(row0, row4); \ + int16x8_t dif04 = vsubq_s16(row0, row4); \ + dct_widen(t0e, sum04); \ + dct_widen(t1e, dif04); \ + dct_wadd(x0, t0e, t3e); \ + dct_wsub(x3, t0e, t3e); \ + dct_wadd(x1, t1e, t2e); \ + dct_wsub(x2, t1e, t2e); \ + /* odd part */ \ + int16x8_t sum15 = vaddq_s16(row1, row5); \ + int16x8_t sum17 = vaddq_s16(row1, row7); \ + int16x8_t sum35 = vaddq_s16(row3, row5); \ + int16x8_t sum37 = vaddq_s16(row3, row7); \ + int16x8_t sumodd = vaddq_s16(sum17, sum35); \ + dct_long_mul(p5o, sumodd, rot1_0); \ + dct_long_mac(p1o, p5o, sum17, rot1_1); \ + dct_long_mac(p2o, p5o, sum35, rot1_2); \ + dct_long_mul(p3o, sum37, rot2_0); \ + dct_long_mul(p4o, sum15, rot2_1); \ + dct_wadd(sump13o, p1o, p3o); \ + dct_wadd(sump24o, p2o, p4o); \ + dct_wadd(sump23o, p2o, p3o); \ + dct_wadd(sump14o, p1o, p4o); \ + dct_long_mac(x4, sump13o, row7, rot3_0); \ + dct_long_mac(x5, sump24o, row5, rot3_1); \ + dct_long_mac(x6, sump23o, row3, rot3_2); \ + dct_long_mac(x7, sump14o, row1, rot3_3); \ + dct_bfly32o(row0,row7, x0,x7,shiftop,shift); \ + dct_bfly32o(row1,row6, x1,x6,shiftop,shift); \ + dct_bfly32o(row2,row5, x2,x5,shiftop,shift); \ + dct_bfly32o(row3,row4, x3,x4,shiftop,shift); \ + } + + // load + row0 = vld1q_s16(data + 0*8); + row1 = vld1q_s16(data + 1*8); + row2 = vld1q_s16(data + 2*8); + row3 = vld1q_s16(data + 3*8); + row4 = vld1q_s16(data + 4*8); + row5 = vld1q_s16(data + 5*8); + row6 = vld1q_s16(data + 6*8); + row7 = vld1q_s16(data + 7*8); + + // add DC bias + row0 = vaddq_s16(row0, vsetq_lane_s16(1024, vdupq_n_s16(0), 0)); + + // column pass + dct_pass(vrshrn_n_s32, 10); + + // 16bit 8x8 transpose + { +// these three map to a single VTRN.16, VTRN.32, and VSWP, respectively. +// whether compilers actually get this is another story, sadly. +#define dct_trn16(x, y) { int16x8x2_t t = vtrnq_s16(x, y); x = t.val[0]; y = t.val[1]; } +#define dct_trn32(x, y) { int32x4x2_t t = vtrnq_s32(vreinterpretq_s32_s16(x), vreinterpretq_s32_s16(y)); x = vreinterpretq_s16_s32(t.val[0]); y = vreinterpretq_s16_s32(t.val[1]); } +#define dct_trn64(x, y) { int16x8_t x0 = x; int16x8_t y0 = y; x = vcombine_s16(vget_low_s16(x0), vget_low_s16(y0)); y = vcombine_s16(vget_high_s16(x0), vget_high_s16(y0)); } + + // pass 1 + dct_trn16(row0, row1); // a0b0a2b2a4b4a6b6 + dct_trn16(row2, row3); + dct_trn16(row4, row5); + dct_trn16(row6, row7); + + // pass 2 + dct_trn32(row0, row2); // a0b0c0d0a4b4c4d4 + dct_trn32(row1, row3); + dct_trn32(row4, row6); + dct_trn32(row5, row7); + + // pass 3 + dct_trn64(row0, row4); // a0b0c0d0e0f0g0h0 + dct_trn64(row1, row5); + dct_trn64(row2, row6); + dct_trn64(row3, row7); + +#undef dct_trn16 +#undef dct_trn32 +#undef dct_trn64 + } + + // row pass + // vrshrn_n_s32 only supports shifts up to 16, we need + // 17. so do a non-rounding shift of 16 first then follow + // up with a rounding shift by 1. + dct_pass(vshrn_n_s32, 16); + + { + // pack and round + uint8x8_t p0 = vqrshrun_n_s16(row0, 1); + uint8x8_t p1 = vqrshrun_n_s16(row1, 1); + uint8x8_t p2 = vqrshrun_n_s16(row2, 1); + uint8x8_t p3 = vqrshrun_n_s16(row3, 1); + uint8x8_t p4 = vqrshrun_n_s16(row4, 1); + uint8x8_t p5 = vqrshrun_n_s16(row5, 1); + uint8x8_t p6 = vqrshrun_n_s16(row6, 1); + uint8x8_t p7 = vqrshrun_n_s16(row7, 1); + + // again, these can translate into one instruction, but often don't. +#define dct_trn8_8(x, y) { uint8x8x2_t t = vtrn_u8(x, y); x = t.val[0]; y = t.val[1]; } +#define dct_trn8_16(x, y) { uint16x4x2_t t = vtrn_u16(vreinterpret_u16_u8(x), vreinterpret_u16_u8(y)); x = vreinterpret_u8_u16(t.val[0]); y = vreinterpret_u8_u16(t.val[1]); } +#define dct_trn8_32(x, y) { uint32x2x2_t t = vtrn_u32(vreinterpret_u32_u8(x), vreinterpret_u32_u8(y)); x = vreinterpret_u8_u32(t.val[0]); y = vreinterpret_u8_u32(t.val[1]); } + + // sadly can't use interleaved stores here since we only write + // 8 bytes to each scan line! + + // 8x8 8-bit transpose pass 1 + dct_trn8_8(p0, p1); + dct_trn8_8(p2, p3); + dct_trn8_8(p4, p5); + dct_trn8_8(p6, p7); + + // pass 2 + dct_trn8_16(p0, p2); + dct_trn8_16(p1, p3); + dct_trn8_16(p4, p6); + dct_trn8_16(p5, p7); + + // pass 3 + dct_trn8_32(p0, p4); + dct_trn8_32(p1, p5); + dct_trn8_32(p2, p6); + dct_trn8_32(p3, p7); + + // store + vst1_u8(out, p0); out += out_stride; + vst1_u8(out, p1); out += out_stride; + vst1_u8(out, p2); out += out_stride; + vst1_u8(out, p3); out += out_stride; + vst1_u8(out, p4); out += out_stride; + vst1_u8(out, p5); out += out_stride; + vst1_u8(out, p6); out += out_stride; + vst1_u8(out, p7); + +#undef dct_trn8_8 +#undef dct_trn8_16 +#undef dct_trn8_32 + } + +#undef dct_long_mul +#undef dct_long_mac +#undef dct_widen +#undef dct_wadd +#undef dct_wsub +#undef dct_bfly32o +#undef dct_pass +} + +#endif // STBI_NEON + +#define STBI__MARKER_none 0xff +// if there's a pending marker from the entropy stream, return that +// otherwise, fetch from the stream and get a marker. if there's no +// marker, return 0xff, which is never a valid marker value +static stbi_uc stbi__get_marker(stbi__jpeg *j) +{ + stbi_uc x; + if (j->marker != STBI__MARKER_none) { x = j->marker; j->marker = STBI__MARKER_none; return x; } + x = stbi__get8(j->s); + if (x != 0xff) return STBI__MARKER_none; + while (x == 0xff) + x = stbi__get8(j->s); // consume repeated 0xff fill bytes + return x; +} + +// in each scan, we'll have scan_n components, and the order +// of the components is specified by order[] +#define STBI__RESTART(x) ((x) >= 0xd0 && (x) <= 0xd7) + +// after a restart interval, stbi__jpeg_reset the entropy decoder and +// the dc prediction +static void stbi__jpeg_reset(stbi__jpeg *j) +{ + j->code_bits = 0; + j->code_buffer = 0; + j->nomore = 0; + j->img_comp[0].dc_pred = j->img_comp[1].dc_pred = j->img_comp[2].dc_pred = j->img_comp[3].dc_pred = 0; + j->marker = STBI__MARKER_none; + j->todo = j->restart_interval ? j->restart_interval : 0x7fffffff; + j->eob_run = 0; + // no more than 1<<31 MCUs if no restart_interal? that's plenty safe, + // since we don't even allow 1<<30 pixels +} + +static int stbi__parse_entropy_coded_data(stbi__jpeg *z) +{ + stbi__jpeg_reset(z); + if (!z->progressive) { + if (z->scan_n == 1) { + int i,j; + STBI_SIMD_ALIGN(short, data[64]); + int n = z->order[0]; + // non-interleaved data, we just need to process one block at a time, + // in trivial scanline order + // number of blocks to do just depends on how many actual "pixels" this + // component has, independent of interleaved MCU blocking and such + int w = (z->img_comp[n].x+7) >> 3; + int h = (z->img_comp[n].y+7) >> 3; + for (j=0; j < h; ++j) { + for (i=0; i < w; ++i) { + int ha = z->img_comp[n].ha; + if (!stbi__jpeg_decode_block(z, data, z->huff_dc+z->img_comp[n].hd, z->huff_ac+ha, z->fast_ac[ha], n, z->dequant[z->img_comp[n].tq])) return 0; + z->idct_block_kernel(z->img_comp[n].data+z->img_comp[n].w2*j*8+i*8, z->img_comp[n].w2, data); + // every data block is an MCU, so countdown the restart interval + if (--z->todo <= 0) { + if (z->code_bits < 24) stbi__grow_buffer_unsafe(z); + // if it's NOT a restart, then just bail, so we get corrupt data + // rather than no data + if (!STBI__RESTART(z->marker)) return 1; + stbi__jpeg_reset(z); + } + } + } + return 1; + } else { // interleaved + int i,j,k,x,y; + STBI_SIMD_ALIGN(short, data[64]); + for (j=0; j < z->img_mcu_y; ++j) { + for (i=0; i < z->img_mcu_x; ++i) { + // scan an interleaved mcu... process scan_n components in order + for (k=0; k < z->scan_n; ++k) { + int n = z->order[k]; + // scan out an mcu's worth of this component; that's just determined + // by the basic H and V specified for the component + for (y=0; y < z->img_comp[n].v; ++y) { + for (x=0; x < z->img_comp[n].h; ++x) { + int x2 = (i*z->img_comp[n].h + x)*8; + int y2 = (j*z->img_comp[n].v + y)*8; + int ha = z->img_comp[n].ha; + if (!stbi__jpeg_decode_block(z, data, z->huff_dc+z->img_comp[n].hd, z->huff_ac+ha, z->fast_ac[ha], n, z->dequant[z->img_comp[n].tq])) return 0; + z->idct_block_kernel(z->img_comp[n].data+z->img_comp[n].w2*y2+x2, z->img_comp[n].w2, data); + } + } + } + // after all interleaved components, that's an interleaved MCU, + // so now count down the restart interval + if (--z->todo <= 0) { + if (z->code_bits < 24) stbi__grow_buffer_unsafe(z); + if (!STBI__RESTART(z->marker)) return 1; + stbi__jpeg_reset(z); + } + } + } + return 1; + } + } else { + if (z->scan_n == 1) { + int i,j; + int n = z->order[0]; + // non-interleaved data, we just need to process one block at a time, + // in trivial scanline order + // number of blocks to do just depends on how many actual "pixels" this + // component has, independent of interleaved MCU blocking and such + int w = (z->img_comp[n].x+7) >> 3; + int h = (z->img_comp[n].y+7) >> 3; + for (j=0; j < h; ++j) { + for (i=0; i < w; ++i) { + short *data = z->img_comp[n].coeff + 64 * (i + j * z->img_comp[n].coeff_w); + if (z->spec_start == 0) { + if (!stbi__jpeg_decode_block_prog_dc(z, data, &z->huff_dc[z->img_comp[n].hd], n)) + return 0; + } else { + int ha = z->img_comp[n].ha; + if (!stbi__jpeg_decode_block_prog_ac(z, data, &z->huff_ac[ha], z->fast_ac[ha])) + return 0; + } + // every data block is an MCU, so countdown the restart interval + if (--z->todo <= 0) { + if (z->code_bits < 24) stbi__grow_buffer_unsafe(z); + if (!STBI__RESTART(z->marker)) return 1; + stbi__jpeg_reset(z); + } + } + } + return 1; + } else { // interleaved + int i,j,k,x,y; + for (j=0; j < z->img_mcu_y; ++j) { + for (i=0; i < z->img_mcu_x; ++i) { + // scan an interleaved mcu... process scan_n components in order + for (k=0; k < z->scan_n; ++k) { + int n = z->order[k]; + // scan out an mcu's worth of this component; that's just determined + // by the basic H and V specified for the component + for (y=0; y < z->img_comp[n].v; ++y) { + for (x=0; x < z->img_comp[n].h; ++x) { + int x2 = (i*z->img_comp[n].h + x); + int y2 = (j*z->img_comp[n].v + y); + short *data = z->img_comp[n].coeff + 64 * (x2 + y2 * z->img_comp[n].coeff_w); + if (!stbi__jpeg_decode_block_prog_dc(z, data, &z->huff_dc[z->img_comp[n].hd], n)) + return 0; + } + } + } + // after all interleaved components, that's an interleaved MCU, + // so now count down the restart interval + if (--z->todo <= 0) { + if (z->code_bits < 24) stbi__grow_buffer_unsafe(z); + if (!STBI__RESTART(z->marker)) return 1; + stbi__jpeg_reset(z); + } + } + } + return 1; + } + } +} + +static void stbi__jpeg_dequantize(short *data, stbi__uint16 *dequant) +{ + int i; + for (i=0; i < 64; ++i) + data[i] *= dequant[i]; +} + +static void stbi__jpeg_finish(stbi__jpeg *z) +{ + if (z->progressive) { + // dequantize and idct the data + int i,j,n; + for (n=0; n < z->s->img_n; ++n) { + int w = (z->img_comp[n].x+7) >> 3; + int h = (z->img_comp[n].y+7) >> 3; + for (j=0; j < h; ++j) { + for (i=0; i < w; ++i) { + short *data = z->img_comp[n].coeff + 64 * (i + j * z->img_comp[n].coeff_w); + stbi__jpeg_dequantize(data, z->dequant[z->img_comp[n].tq]); + z->idct_block_kernel(z->img_comp[n].data+z->img_comp[n].w2*j*8+i*8, z->img_comp[n].w2, data); + } + } + } + } +} + +static int stbi__process_marker(stbi__jpeg *z, int m) +{ + int L; + switch (m) { + case STBI__MARKER_none: // no marker found + return stbi__err("expected marker","Corrupt JPEG"); + + case 0xDD: // DRI - specify restart interval + if (stbi__get16be(z->s) != 4) return stbi__err("bad DRI len","Corrupt JPEG"); + z->restart_interval = stbi__get16be(z->s); + return 1; + + case 0xDB: // DQT - define quantization table + L = stbi__get16be(z->s)-2; + while (L > 0) { + int q = stbi__get8(z->s); + int p = q >> 4, sixteen = (p != 0); + int t = q & 15,i; + if (p != 0 && p != 1) return stbi__err("bad DQT type","Corrupt JPEG"); + if (t > 3) return stbi__err("bad DQT table","Corrupt JPEG"); + + for (i=0; i < 64; ++i) + z->dequant[t][stbi__jpeg_dezigzag[i]] = (stbi__uint16)(sixteen ? stbi__get16be(z->s) : stbi__get8(z->s)); + L -= (sixteen ? 129 : 65); + } + return L==0; + + case 0xC4: // DHT - define huffman table + L = stbi__get16be(z->s)-2; + while (L > 0) { + stbi_uc *v; + int sizes[16],i,n=0; + int q = stbi__get8(z->s); + int tc = q >> 4; + int th = q & 15; + if (tc > 1 || th > 3) return stbi__err("bad DHT header","Corrupt JPEG"); + for (i=0; i < 16; ++i) { + sizes[i] = stbi__get8(z->s); + n += sizes[i]; + } + L -= 17; + if (tc == 0) { + if (!stbi__build_huffman(z->huff_dc+th, sizes)) return 0; + v = z->huff_dc[th].values; + } else { + if (!stbi__build_huffman(z->huff_ac+th, sizes)) return 0; + v = z->huff_ac[th].values; + } + for (i=0; i < n; ++i) + v[i] = stbi__get8(z->s); + if (tc != 0) + stbi__build_fast_ac(z->fast_ac[th], z->huff_ac + th); + L -= n; + } + return L==0; + } + + // check for comment block or APP blocks + if ((m >= 0xE0 && m <= 0xEF) || m == 0xFE) { + L = stbi__get16be(z->s); + if (L < 2) { + if (m == 0xFE) + return stbi__err("bad COM len","Corrupt JPEG"); + else + return stbi__err("bad APP len","Corrupt JPEG"); + } + L -= 2; + + if (m == 0xE0 && L >= 5) { // JFIF APP0 segment + static const unsigned char tag[5] = {'J','F','I','F','\0'}; + int ok = 1; + int i; + for (i=0; i < 5; ++i) + if (stbi__get8(z->s) != tag[i]) + ok = 0; + L -= 5; + if (ok) + z->jfif = 1; + } else if (m == 0xEE && L >= 12) { // Adobe APP14 segment + static const unsigned char tag[6] = {'A','d','o','b','e','\0'}; + int ok = 1; + int i; + for (i=0; i < 6; ++i) + if (stbi__get8(z->s) != tag[i]) + ok = 0; + L -= 6; + if (ok) { + stbi__get8(z->s); // version + stbi__get16be(z->s); // flags0 + stbi__get16be(z->s); // flags1 + z->app14_color_transform = stbi__get8(z->s); // color transform + L -= 6; + } + } + + stbi__skip(z->s, L); + return 1; + } + + return stbi__err("unknown marker","Corrupt JPEG"); +} + +// after we see SOS +static int stbi__process_scan_header(stbi__jpeg *z) +{ + int i; + int Ls = stbi__get16be(z->s); + z->scan_n = stbi__get8(z->s); + if (z->scan_n < 1 || z->scan_n > 4 || z->scan_n > (int) z->s->img_n) return stbi__err("bad SOS component count","Corrupt JPEG"); + if (Ls != 6+2*z->scan_n) return stbi__err("bad SOS len","Corrupt JPEG"); + for (i=0; i < z->scan_n; ++i) { + int id = stbi__get8(z->s), which; + int q = stbi__get8(z->s); + for (which = 0; which < z->s->img_n; ++which) + if (z->img_comp[which].id == id) + break; + if (which == z->s->img_n) return 0; // no match + z->img_comp[which].hd = q >> 4; if (z->img_comp[which].hd > 3) return stbi__err("bad DC huff","Corrupt JPEG"); + z->img_comp[which].ha = q & 15; if (z->img_comp[which].ha > 3) return stbi__err("bad AC huff","Corrupt JPEG"); + z->order[i] = which; + } + + { + int aa; + z->spec_start = stbi__get8(z->s); + z->spec_end = stbi__get8(z->s); // should be 63, but might be 0 + aa = stbi__get8(z->s); + z->succ_high = (aa >> 4); + z->succ_low = (aa & 15); + if (z->progressive) { + if (z->spec_start > 63 || z->spec_end > 63 || z->spec_start > z->spec_end || z->succ_high > 13 || z->succ_low > 13) + return stbi__err("bad SOS", "Corrupt JPEG"); + } else { + if (z->spec_start != 0) return stbi__err("bad SOS","Corrupt JPEG"); + if (z->succ_high != 0 || z->succ_low != 0) return stbi__err("bad SOS","Corrupt JPEG"); + z->spec_end = 63; + } + } + + return 1; +} + +static int stbi__free_jpeg_components(stbi__jpeg *z, int ncomp, int why) +{ + int i; + for (i=0; i < ncomp; ++i) { + if (z->img_comp[i].raw_data) { + STBI_FREE(z->img_comp[i].raw_data); + z->img_comp[i].raw_data = NULL; + z->img_comp[i].data = NULL; + } + if (z->img_comp[i].raw_coeff) { + STBI_FREE(z->img_comp[i].raw_coeff); + z->img_comp[i].raw_coeff = 0; + z->img_comp[i].coeff = 0; + } + if (z->img_comp[i].linebuf) { + STBI_FREE(z->img_comp[i].linebuf); + z->img_comp[i].linebuf = NULL; + } + } + return why; +} + +static int stbi__process_frame_header(stbi__jpeg *z, int scan) +{ + stbi__context *s = z->s; + int Lf,p,i,q, h_max=1,v_max=1,c; + Lf = stbi__get16be(s); if (Lf < 11) return stbi__err("bad SOF len","Corrupt JPEG"); // JPEG + p = stbi__get8(s); if (p != 8) return stbi__err("only 8-bit","JPEG format not supported: 8-bit only"); // JPEG baseline + s->img_y = stbi__get16be(s); if (s->img_y == 0) return stbi__err("no header height", "JPEG format not supported: delayed height"); // Legal, but we don't handle it--but neither does IJG + s->img_x = stbi__get16be(s); if (s->img_x == 0) return stbi__err("0 width","Corrupt JPEG"); // JPEG requires + c = stbi__get8(s); + if (c != 3 && c != 1 && c != 4) return stbi__err("bad component count","Corrupt JPEG"); + s->img_n = c; + for (i=0; i < c; ++i) { + z->img_comp[i].data = NULL; + z->img_comp[i].linebuf = NULL; + } + + if (Lf != 8+3*s->img_n) return stbi__err("bad SOF len","Corrupt JPEG"); + + z->rgb = 0; + for (i=0; i < s->img_n; ++i) { + static const unsigned char rgb[3] = { 'R', 'G', 'B' }; + z->img_comp[i].id = stbi__get8(s); + if (s->img_n == 3 && z->img_comp[i].id == rgb[i]) + ++z->rgb; + q = stbi__get8(s); + z->img_comp[i].h = (q >> 4); if (!z->img_comp[i].h || z->img_comp[i].h > 4) return stbi__err("bad H","Corrupt JPEG"); + z->img_comp[i].v = q & 15; if (!z->img_comp[i].v || z->img_comp[i].v > 4) return stbi__err("bad V","Corrupt JPEG"); + z->img_comp[i].tq = stbi__get8(s); if (z->img_comp[i].tq > 3) return stbi__err("bad TQ","Corrupt JPEG"); + } + + if (scan != STBI__SCAN_load) return 1; + + if (!stbi__mad3sizes_valid(s->img_x, s->img_y, s->img_n, 0)) return stbi__err("too large", "Image too large to decode"); + + for (i=0; i < s->img_n; ++i) { + if (z->img_comp[i].h > h_max) h_max = z->img_comp[i].h; + if (z->img_comp[i].v > v_max) v_max = z->img_comp[i].v; + } + + // compute interleaved mcu info + z->img_h_max = h_max; + z->img_v_max = v_max; + z->img_mcu_w = h_max * 8; + z->img_mcu_h = v_max * 8; + // these sizes can't be more than 17 bits + z->img_mcu_x = (s->img_x + z->img_mcu_w-1) / z->img_mcu_w; + z->img_mcu_y = (s->img_y + z->img_mcu_h-1) / z->img_mcu_h; + + for (i=0; i < s->img_n; ++i) { + // number of effective pixels (e.g. for non-interleaved MCU) + z->img_comp[i].x = (s->img_x * z->img_comp[i].h + h_max-1) / h_max; + z->img_comp[i].y = (s->img_y * z->img_comp[i].v + v_max-1) / v_max; + // to simplify generation, we'll allocate enough memory to decode + // the bogus oversized data from using interleaved MCUs and their + // big blocks (e.g. a 16x16 iMCU on an image of width 33); we won't + // discard the extra data until colorspace conversion + // + // img_mcu_x, img_mcu_y: <=17 bits; comp[i].h and .v are <=4 (checked earlier) + // so these muls can't overflow with 32-bit ints (which we require) + z->img_comp[i].w2 = z->img_mcu_x * z->img_comp[i].h * 8; + z->img_comp[i].h2 = z->img_mcu_y * z->img_comp[i].v * 8; + z->img_comp[i].coeff = 0; + z->img_comp[i].raw_coeff = 0; + z->img_comp[i].linebuf = NULL; + z->img_comp[i].raw_data = stbi__malloc_mad2(z->img_comp[i].w2, z->img_comp[i].h2, 15); + if (z->img_comp[i].raw_data == NULL) + return stbi__free_jpeg_components(z, i+1, stbi__err("outofmem", "Out of memory")); + // align blocks for idct using mmx/sse + z->img_comp[i].data = (stbi_uc*) (((size_t) z->img_comp[i].raw_data + 15) & ~15); + if (z->progressive) { + // w2, h2 are multiples of 8 (see above) + z->img_comp[i].coeff_w = z->img_comp[i].w2 / 8; + z->img_comp[i].coeff_h = z->img_comp[i].h2 / 8; + z->img_comp[i].raw_coeff = stbi__malloc_mad3(z->img_comp[i].w2, z->img_comp[i].h2, sizeof(short), 15); + if (z->img_comp[i].raw_coeff == NULL) + return stbi__free_jpeg_components(z, i+1, stbi__err("outofmem", "Out of memory")); + z->img_comp[i].coeff = (short*) (((size_t) z->img_comp[i].raw_coeff + 15) & ~15); + } + } + + return 1; +} + +// use comparisons since in some cases we handle more than one case (e.g. SOF) +#define stbi__DNL(x) ((x) == 0xdc) +#define stbi__SOI(x) ((x) == 0xd8) +#define stbi__EOI(x) ((x) == 0xd9) +#define stbi__SOF(x) ((x) == 0xc0 || (x) == 0xc1 || (x) == 0xc2) +#define stbi__SOS(x) ((x) == 0xda) + +#define stbi__SOF_progressive(x) ((x) == 0xc2) + +static int stbi__decode_jpeg_header(stbi__jpeg *z, int scan) +{ + int m; + z->jfif = 0; + z->app14_color_transform = -1; // valid values are 0,1,2 + z->marker = STBI__MARKER_none; // initialize cached marker to empty + m = stbi__get_marker(z); + if (!stbi__SOI(m)) return stbi__err("no SOI","Corrupt JPEG"); + if (scan == STBI__SCAN_type) return 1; + m = stbi__get_marker(z); + while (!stbi__SOF(m)) { + if (!stbi__process_marker(z,m)) return 0; + m = stbi__get_marker(z); + while (m == STBI__MARKER_none) { + // some files have extra padding after their blocks, so ok, we'll scan + if (stbi__at_eof(z->s)) return stbi__err("no SOF", "Corrupt JPEG"); + m = stbi__get_marker(z); + } + } + z->progressive = stbi__SOF_progressive(m); + if (!stbi__process_frame_header(z, scan)) return 0; + return 1; +} + +// decode image to YCbCr format +static int stbi__decode_jpeg_image(stbi__jpeg *j) +{ + int m; + for (m = 0; m < 4; m++) { + j->img_comp[m].raw_data = NULL; + j->img_comp[m].raw_coeff = NULL; + } + j->restart_interval = 0; + if (!stbi__decode_jpeg_header(j, STBI__SCAN_load)) return 0; + m = stbi__get_marker(j); + while (!stbi__EOI(m)) { + if (stbi__SOS(m)) { + if (!stbi__process_scan_header(j)) return 0; + if (!stbi__parse_entropy_coded_data(j)) return 0; + if (j->marker == STBI__MARKER_none ) { + // handle 0s at the end of image data from IP Kamera 9060 + while (!stbi__at_eof(j->s)) { + int x = stbi__get8(j->s); + if (x == 255) { + j->marker = stbi__get8(j->s); + break; + } + } + // if we reach eof without hitting a marker, stbi__get_marker() below will fail and we'll eventually return 0 + } + } else if (stbi__DNL(m)) { + int Ld = stbi__get16be(j->s); + stbi__uint32 NL = stbi__get16be(j->s); + if (Ld != 4) return stbi__err("bad DNL len", "Corrupt JPEG"); + if (NL != j->s->img_y) return stbi__err("bad DNL height", "Corrupt JPEG"); + } else { + if (!stbi__process_marker(j, m)) return 0; + } + m = stbi__get_marker(j); + } + if (j->progressive) + stbi__jpeg_finish(j); + return 1; +} + +// static jfif-centered resampling (across block boundaries) + +typedef stbi_uc *(*resample_row_func)(stbi_uc *out, stbi_uc *in0, stbi_uc *in1, + int w, int hs); + +#define stbi__div4(x) ((stbi_uc) ((x) >> 2)) + +static stbi_uc *resample_row_1(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs) +{ + STBI_NOTUSED(out); + STBI_NOTUSED(in_far); + STBI_NOTUSED(w); + STBI_NOTUSED(hs); + return in_near; +} + +static stbi_uc* stbi__resample_row_v_2(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs) +{ + // need to generate two samples vertically for every one in input + int i; + STBI_NOTUSED(hs); + for (i=0; i < w; ++i) + out[i] = stbi__div4(3*in_near[i] + in_far[i] + 2); + return out; +} + +static stbi_uc* stbi__resample_row_h_2(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs) +{ + // need to generate two samples horizontally for every one in input + int i; + stbi_uc *input = in_near; + + if (w == 1) { + // if only one sample, can't do any interpolation + out[0] = out[1] = input[0]; + return out; + } + + out[0] = input[0]; + out[1] = stbi__div4(input[0]*3 + input[1] + 2); + for (i=1; i < w-1; ++i) { + int n = 3*input[i]+2; + out[i*2+0] = stbi__div4(n+input[i-1]); + out[i*2+1] = stbi__div4(n+input[i+1]); + } + out[i*2+0] = stbi__div4(input[w-2]*3 + input[w-1] + 2); + out[i*2+1] = input[w-1]; + + STBI_NOTUSED(in_far); + STBI_NOTUSED(hs); + + return out; +} + +#define stbi__div16(x) ((stbi_uc) ((x) >> 4)) + +static stbi_uc *stbi__resample_row_hv_2(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs) +{ + // need to generate 2x2 samples for every one in input + int i,t0,t1; + if (w == 1) { + out[0] = out[1] = stbi__div4(3*in_near[0] + in_far[0] + 2); + return out; + } + + t1 = 3*in_near[0] + in_far[0]; + out[0] = stbi__div4(t1+2); + for (i=1; i < w; ++i) { + t0 = t1; + t1 = 3*in_near[i]+in_far[i]; + out[i*2-1] = stbi__div16(3*t0 + t1 + 8); + out[i*2 ] = stbi__div16(3*t1 + t0 + 8); + } + out[w*2-1] = stbi__div4(t1+2); + + STBI_NOTUSED(hs); + + return out; +} + +#if defined(STBI_SSE2) || defined(STBI_NEON) +static stbi_uc *stbi__resample_row_hv_2_simd(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs) +{ + // need to generate 2x2 samples for every one in input + int i=0,t0,t1; + + if (w == 1) { + out[0] = out[1] = stbi__div4(3*in_near[0] + in_far[0] + 2); + return out; + } + + t1 = 3*in_near[0] + in_far[0]; + // process groups of 8 pixels for as long as we can. + // note we can't handle the last pixel in a row in this loop + // because we need to handle the filter boundary conditions. + for (; i < ((w-1) & ~7); i += 8) { +#if defined(STBI_SSE2) + // load and perform the vertical filtering pass + // this uses 3*x + y = 4*x + (y - x) + __m128i zero = _mm_setzero_si128(); + __m128i farb = _mm_loadl_epi64((__m128i *) (in_far + i)); + __m128i nearb = _mm_loadl_epi64((__m128i *) (in_near + i)); + __m128i farw = _mm_unpacklo_epi8(farb, zero); + __m128i nearw = _mm_unpacklo_epi8(nearb, zero); + __m128i diff = _mm_sub_epi16(farw, nearw); + __m128i nears = _mm_slli_epi16(nearw, 2); + __m128i curr = _mm_add_epi16(nears, diff); // current row + + // horizontal filter works the same based on shifted vers of current + // row. "prev" is current row shifted right by 1 pixel; we need to + // insert the previous pixel value (from t1). + // "next" is current row shifted left by 1 pixel, with first pixel + // of next block of 8 pixels added in. + __m128i prv0 = _mm_slli_si128(curr, 2); + __m128i nxt0 = _mm_srli_si128(curr, 2); + __m128i prev = _mm_insert_epi16(prv0, t1, 0); + __m128i next = _mm_insert_epi16(nxt0, 3*in_near[i+8] + in_far[i+8], 7); + + // horizontal filter, polyphase implementation since it's convenient: + // even pixels = 3*cur + prev = cur*4 + (prev - cur) + // odd pixels = 3*cur + next = cur*4 + (next - cur) + // note the shared term. + __m128i bias = _mm_set1_epi16(8); + __m128i curs = _mm_slli_epi16(curr, 2); + __m128i prvd = _mm_sub_epi16(prev, curr); + __m128i nxtd = _mm_sub_epi16(next, curr); + __m128i curb = _mm_add_epi16(curs, bias); + __m128i even = _mm_add_epi16(prvd, curb); + __m128i odd = _mm_add_epi16(nxtd, curb); + + // interleave even and odd pixels, then undo scaling. + __m128i int0 = _mm_unpacklo_epi16(even, odd); + __m128i int1 = _mm_unpackhi_epi16(even, odd); + __m128i de0 = _mm_srli_epi16(int0, 4); + __m128i de1 = _mm_srli_epi16(int1, 4); + + // pack and write output + __m128i outv = _mm_packus_epi16(de0, de1); + _mm_storeu_si128((__m128i *) (out + i*2), outv); +#elif defined(STBI_NEON) + // load and perform the vertical filtering pass + // this uses 3*x + y = 4*x + (y - x) + uint8x8_t farb = vld1_u8(in_far + i); + uint8x8_t nearb = vld1_u8(in_near + i); + int16x8_t diff = vreinterpretq_s16_u16(vsubl_u8(farb, nearb)); + int16x8_t nears = vreinterpretq_s16_u16(vshll_n_u8(nearb, 2)); + int16x8_t curr = vaddq_s16(nears, diff); // current row + + // horizontal filter works the same based on shifted vers of current + // row. "prev" is current row shifted right by 1 pixel; we need to + // insert the previous pixel value (from t1). + // "next" is current row shifted left by 1 pixel, with first pixel + // of next block of 8 pixels added in. + int16x8_t prv0 = vextq_s16(curr, curr, 7); + int16x8_t nxt0 = vextq_s16(curr, curr, 1); + int16x8_t prev = vsetq_lane_s16(t1, prv0, 0); + int16x8_t next = vsetq_lane_s16(3*in_near[i+8] + in_far[i+8], nxt0, 7); + + // horizontal filter, polyphase implementation since it's convenient: + // even pixels = 3*cur + prev = cur*4 + (prev - cur) + // odd pixels = 3*cur + next = cur*4 + (next - cur) + // note the shared term. + int16x8_t curs = vshlq_n_s16(curr, 2); + int16x8_t prvd = vsubq_s16(prev, curr); + int16x8_t nxtd = vsubq_s16(next, curr); + int16x8_t even = vaddq_s16(curs, prvd); + int16x8_t odd = vaddq_s16(curs, nxtd); + + // undo scaling and round, then store with even/odd phases interleaved + uint8x8x2_t o; + o.val[0] = vqrshrun_n_s16(even, 4); + o.val[1] = vqrshrun_n_s16(odd, 4); + vst2_u8(out + i*2, o); +#endif + + // "previous" value for next iter + t1 = 3*in_near[i+7] + in_far[i+7]; + } + + t0 = t1; + t1 = 3*in_near[i] + in_far[i]; + out[i*2] = stbi__div16(3*t1 + t0 + 8); + + for (++i; i < w; ++i) { + t0 = t1; + t1 = 3*in_near[i]+in_far[i]; + out[i*2-1] = stbi__div16(3*t0 + t1 + 8); + out[i*2 ] = stbi__div16(3*t1 + t0 + 8); + } + out[w*2-1] = stbi__div4(t1+2); + + STBI_NOTUSED(hs); + + return out; +} +#endif + +static stbi_uc *stbi__resample_row_generic(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs) +{ + // resample with nearest-neighbor + int i,j; + STBI_NOTUSED(in_far); + for (i=0; i < w; ++i) + for (j=0; j < hs; ++j) + out[i*hs+j] = in_near[i]; + return out; +} + +// this is a reduced-precision calculation of YCbCr-to-RGB introduced +// to make sure the code produces the same results in both SIMD and scalar +#define stbi__float2fixed(x) (((int) ((x) * 4096.0f + 0.5f)) << 8) +static void stbi__YCbCr_to_RGB_row(stbi_uc *out, const stbi_uc *y, const stbi_uc *pcb, const stbi_uc *pcr, int count, int step) +{ + int i; + for (i=0; i < count; ++i) { + int y_fixed = (y[i] << 20) + (1<<19); // rounding + int r,g,b; + int cr = pcr[i] - 128; + int cb = pcb[i] - 128; + r = y_fixed + cr* stbi__float2fixed(1.40200f); + g = y_fixed + (cr*-stbi__float2fixed(0.71414f)) + ((cb*-stbi__float2fixed(0.34414f)) & 0xffff0000); + b = y_fixed + cb* stbi__float2fixed(1.77200f); + r >>= 20; + g >>= 20; + b >>= 20; + if ((unsigned) r > 255) { if (r < 0) r = 0; else r = 255; } + if ((unsigned) g > 255) { if (g < 0) g = 0; else g = 255; } + if ((unsigned) b > 255) { if (b < 0) b = 0; else b = 255; } + out[0] = (stbi_uc)r; + out[1] = (stbi_uc)g; + out[2] = (stbi_uc)b; + out[3] = 255; + out += step; + } +} + +#if defined(STBI_SSE2) || defined(STBI_NEON) +static void stbi__YCbCr_to_RGB_simd(stbi_uc *out, stbi_uc const *y, stbi_uc const *pcb, stbi_uc const *pcr, int count, int step) +{ + int i = 0; + +#ifdef STBI_SSE2 + // step == 3 is pretty ugly on the final interleave, and i'm not convinced + // it's useful in practice (you wouldn't use it for textures, for example). + // so just accelerate step == 4 case. + if (step == 4) { + // this is a fairly straightforward implementation and not super-optimized. + __m128i signflip = _mm_set1_epi8(-0x80); + __m128i cr_const0 = _mm_set1_epi16( (short) ( 1.40200f*4096.0f+0.5f)); + __m128i cr_const1 = _mm_set1_epi16( - (short) ( 0.71414f*4096.0f+0.5f)); + __m128i cb_const0 = _mm_set1_epi16( - (short) ( 0.34414f*4096.0f+0.5f)); + __m128i cb_const1 = _mm_set1_epi16( (short) ( 1.77200f*4096.0f+0.5f)); + __m128i y_bias = _mm_set1_epi8((char) (unsigned char) 128); + __m128i xw = _mm_set1_epi16(255); // alpha channel + + for (; i+7 < count; i += 8) { + // load + __m128i y_bytes = _mm_loadl_epi64((__m128i *) (y+i)); + __m128i cr_bytes = _mm_loadl_epi64((__m128i *) (pcr+i)); + __m128i cb_bytes = _mm_loadl_epi64((__m128i *) (pcb+i)); + __m128i cr_biased = _mm_xor_si128(cr_bytes, signflip); // -128 + __m128i cb_biased = _mm_xor_si128(cb_bytes, signflip); // -128 + + // unpack to short (and left-shift cr, cb by 8) + __m128i yw = _mm_unpacklo_epi8(y_bias, y_bytes); + __m128i crw = _mm_unpacklo_epi8(_mm_setzero_si128(), cr_biased); + __m128i cbw = _mm_unpacklo_epi8(_mm_setzero_si128(), cb_biased); + + // color transform + __m128i yws = _mm_srli_epi16(yw, 4); + __m128i cr0 = _mm_mulhi_epi16(cr_const0, crw); + __m128i cb0 = _mm_mulhi_epi16(cb_const0, cbw); + __m128i cb1 = _mm_mulhi_epi16(cbw, cb_const1); + __m128i cr1 = _mm_mulhi_epi16(crw, cr_const1); + __m128i rws = _mm_add_epi16(cr0, yws); + __m128i gwt = _mm_add_epi16(cb0, yws); + __m128i bws = _mm_add_epi16(yws, cb1); + __m128i gws = _mm_add_epi16(gwt, cr1); + + // descale + __m128i rw = _mm_srai_epi16(rws, 4); + __m128i bw = _mm_srai_epi16(bws, 4); + __m128i gw = _mm_srai_epi16(gws, 4); + + // back to byte, set up for transpose + __m128i brb = _mm_packus_epi16(rw, bw); + __m128i gxb = _mm_packus_epi16(gw, xw); + + // transpose to interleave channels + __m128i t0 = _mm_unpacklo_epi8(brb, gxb); + __m128i t1 = _mm_unpackhi_epi8(brb, gxb); + __m128i o0 = _mm_unpacklo_epi16(t0, t1); + __m128i o1 = _mm_unpackhi_epi16(t0, t1); + + // store + _mm_storeu_si128((__m128i *) (out + 0), o0); + _mm_storeu_si128((__m128i *) (out + 16), o1); + out += 32; + } + } +#endif + +#ifdef STBI_NEON + // in this version, step=3 support would be easy to add. but is there demand? + if (step == 4) { + // this is a fairly straightforward implementation and not super-optimized. + uint8x8_t signflip = vdup_n_u8(0x80); + int16x8_t cr_const0 = vdupq_n_s16( (short) ( 1.40200f*4096.0f+0.5f)); + int16x8_t cr_const1 = vdupq_n_s16( - (short) ( 0.71414f*4096.0f+0.5f)); + int16x8_t cb_const0 = vdupq_n_s16( - (short) ( 0.34414f*4096.0f+0.5f)); + int16x8_t cb_const1 = vdupq_n_s16( (short) ( 1.77200f*4096.0f+0.5f)); + + for (; i+7 < count; i += 8) { + // load + uint8x8_t y_bytes = vld1_u8(y + i); + uint8x8_t cr_bytes = vld1_u8(pcr + i); + uint8x8_t cb_bytes = vld1_u8(pcb + i); + int8x8_t cr_biased = vreinterpret_s8_u8(vsub_u8(cr_bytes, signflip)); + int8x8_t cb_biased = vreinterpret_s8_u8(vsub_u8(cb_bytes, signflip)); + + // expand to s16 + int16x8_t yws = vreinterpretq_s16_u16(vshll_n_u8(y_bytes, 4)); + int16x8_t crw = vshll_n_s8(cr_biased, 7); + int16x8_t cbw = vshll_n_s8(cb_biased, 7); + + // color transform + int16x8_t cr0 = vqdmulhq_s16(crw, cr_const0); + int16x8_t cb0 = vqdmulhq_s16(cbw, cb_const0); + int16x8_t cr1 = vqdmulhq_s16(crw, cr_const1); + int16x8_t cb1 = vqdmulhq_s16(cbw, cb_const1); + int16x8_t rws = vaddq_s16(yws, cr0); + int16x8_t gws = vaddq_s16(vaddq_s16(yws, cb0), cr1); + int16x8_t bws = vaddq_s16(yws, cb1); + + // undo scaling, round, convert to byte + uint8x8x4_t o; + o.val[0] = vqrshrun_n_s16(rws, 4); + o.val[1] = vqrshrun_n_s16(gws, 4); + o.val[2] = vqrshrun_n_s16(bws, 4); + o.val[3] = vdup_n_u8(255); + + // store, interleaving r/g/b/a + vst4_u8(out, o); + out += 8*4; + } + } +#endif + + for (; i < count; ++i) { + int y_fixed = (y[i] << 20) + (1<<19); // rounding + int r,g,b; + int cr = pcr[i] - 128; + int cb = pcb[i] - 128; + r = y_fixed + cr* stbi__float2fixed(1.40200f); + g = y_fixed + cr*-stbi__float2fixed(0.71414f) + ((cb*-stbi__float2fixed(0.34414f)) & 0xffff0000); + b = y_fixed + cb* stbi__float2fixed(1.77200f); + r >>= 20; + g >>= 20; + b >>= 20; + if ((unsigned) r > 255) { if (r < 0) r = 0; else r = 255; } + if ((unsigned) g > 255) { if (g < 0) g = 0; else g = 255; } + if ((unsigned) b > 255) { if (b < 0) b = 0; else b = 255; } + out[0] = (stbi_uc)r; + out[1] = (stbi_uc)g; + out[2] = (stbi_uc)b; + out[3] = 255; + out += step; + } +} +#endif + +// set up the kernels +static void stbi__setup_jpeg(stbi__jpeg *j) +{ + j->idct_block_kernel = stbi__idct_block; + j->YCbCr_to_RGB_kernel = stbi__YCbCr_to_RGB_row; + j->resample_row_hv_2_kernel = stbi__resample_row_hv_2; + +#ifdef STBI_SSE2 + if (stbi__sse2_available()) { + j->idct_block_kernel = stbi__idct_simd; + j->YCbCr_to_RGB_kernel = stbi__YCbCr_to_RGB_simd; + j->resample_row_hv_2_kernel = stbi__resample_row_hv_2_simd; + } +#endif + +#ifdef STBI_NEON + j->idct_block_kernel = stbi__idct_simd; + j->YCbCr_to_RGB_kernel = stbi__YCbCr_to_RGB_simd; + j->resample_row_hv_2_kernel = stbi__resample_row_hv_2_simd; +#endif +} + +// clean up the temporary component buffers +static void stbi__cleanup_jpeg(stbi__jpeg *j) +{ + stbi__free_jpeg_components(j, j->s->img_n, 0); +} + +typedef struct +{ + resample_row_func resample; + stbi_uc *line0,*line1; + int hs,vs; // expansion factor in each axis + int w_lores; // horizontal pixels pre-expansion + int ystep; // how far through vertical expansion we are + int ypos; // which pre-expansion row we're on +} stbi__resample; + +// fast 0..255 * 0..255 => 0..255 rounded multiplication +static stbi_uc stbi__blinn_8x8(stbi_uc x, stbi_uc y) +{ + unsigned int t = x*y + 128; + return (stbi_uc) ((t + (t >>8)) >> 8); +} + +static stbi_uc *load_jpeg_image(stbi__jpeg *z, int *out_x, int *out_y, int *comp, int req_comp) +{ + int n, decode_n, is_rgb; + z->s->img_n = 0; // make stbi__cleanup_jpeg safe + + // validate req_comp + if (req_comp < 0 || req_comp > 4) return stbi__errpuc("bad req_comp", "Internal error"); + + // load a jpeg image from whichever source, but leave in YCbCr format + if (!stbi__decode_jpeg_image(z)) { stbi__cleanup_jpeg(z); return NULL; } + + // determine actual number of components to generate + n = req_comp ? req_comp : z->s->img_n >= 3 ? 3 : 1; + + is_rgb = z->s->img_n == 3 && (z->rgb == 3 || (z->app14_color_transform == 0 && !z->jfif)); + + if (z->s->img_n == 3 && n < 3 && !is_rgb) + decode_n = 1; + else + decode_n = z->s->img_n; + + // resample and color-convert + { + int k; + unsigned int i,j; + stbi_uc *output; + stbi_uc *coutput[4] = { NULL, NULL, NULL, NULL }; + + stbi__resample res_comp[4]; + + for (k=0; k < decode_n; ++k) { + stbi__resample *r = &res_comp[k]; + + // allocate line buffer big enough for upsampling off the edges + // with upsample factor of 4 + z->img_comp[k].linebuf = (stbi_uc *) stbi__malloc(z->s->img_x + 3); + if (!z->img_comp[k].linebuf) { stbi__cleanup_jpeg(z); return stbi__errpuc("outofmem", "Out of memory"); } + + r->hs = z->img_h_max / z->img_comp[k].h; + r->vs = z->img_v_max / z->img_comp[k].v; + r->ystep = r->vs >> 1; + r->w_lores = (z->s->img_x + r->hs-1) / r->hs; + r->ypos = 0; + r->line0 = r->line1 = z->img_comp[k].data; + + if (r->hs == 1 && r->vs == 1) r->resample = resample_row_1; + else if (r->hs == 1 && r->vs == 2) r->resample = stbi__resample_row_v_2; + else if (r->hs == 2 && r->vs == 1) r->resample = stbi__resample_row_h_2; + else if (r->hs == 2 && r->vs == 2) r->resample = z->resample_row_hv_2_kernel; + else r->resample = stbi__resample_row_generic; + } + + // can't error after this so, this is safe + output = (stbi_uc *) stbi__malloc_mad3(n, z->s->img_x, z->s->img_y, 1); + if (!output) { stbi__cleanup_jpeg(z); return stbi__errpuc("outofmem", "Out of memory"); } + + // now go ahead and resample + for (j=0; j < z->s->img_y; ++j) { + stbi_uc *out = output + n * z->s->img_x * j; + for (k=0; k < decode_n; ++k) { + stbi__resample *r = &res_comp[k]; + int y_bot = r->ystep >= (r->vs >> 1); + coutput[k] = r->resample(z->img_comp[k].linebuf, + y_bot ? r->line1 : r->line0, + y_bot ? r->line0 : r->line1, + r->w_lores, r->hs); + if (++r->ystep >= r->vs) { + r->ystep = 0; + r->line0 = r->line1; + if (++r->ypos < z->img_comp[k].y) + r->line1 += z->img_comp[k].w2; + } + } + if (n >= 3) { + stbi_uc *y = coutput[0]; + if (z->s->img_n == 3) { + if (is_rgb) { + for (i=0; i < z->s->img_x; ++i) { + out[0] = y[i]; + out[1] = coutput[1][i]; + out[2] = coutput[2][i]; + out[3] = 255; + out += n; + } + } else { + z->YCbCr_to_RGB_kernel(out, y, coutput[1], coutput[2], z->s->img_x, n); + } + } else if (z->s->img_n == 4) { + if (z->app14_color_transform == 0) { // CMYK + for (i=0; i < z->s->img_x; ++i) { + stbi_uc m = coutput[3][i]; + out[0] = stbi__blinn_8x8(coutput[0][i], m); + out[1] = stbi__blinn_8x8(coutput[1][i], m); + out[2] = stbi__blinn_8x8(coutput[2][i], m); + out[3] = 255; + out += n; + } + } else if (z->app14_color_transform == 2) { // YCCK + z->YCbCr_to_RGB_kernel(out, y, coutput[1], coutput[2], z->s->img_x, n); + for (i=0; i < z->s->img_x; ++i) { + stbi_uc m = coutput[3][i]; + out[0] = stbi__blinn_8x8(255 - out[0], m); + out[1] = stbi__blinn_8x8(255 - out[1], m); + out[2] = stbi__blinn_8x8(255 - out[2], m); + out += n; + } + } else { // YCbCr + alpha? Ignore the fourth channel for now + z->YCbCr_to_RGB_kernel(out, y, coutput[1], coutput[2], z->s->img_x, n); + } + } else + for (i=0; i < z->s->img_x; ++i) { + out[0] = out[1] = out[2] = y[i]; + out[3] = 255; // not used if n==3 + out += n; + } + } else { + if (is_rgb) { + if (n == 1) + for (i=0; i < z->s->img_x; ++i) + *out++ = stbi__compute_y(coutput[0][i], coutput[1][i], coutput[2][i]); + else { + for (i=0; i < z->s->img_x; ++i, out += 2) { + out[0] = stbi__compute_y(coutput[0][i], coutput[1][i], coutput[2][i]); + out[1] = 255; + } + } + } else if (z->s->img_n == 4 && z->app14_color_transform == 0) { + for (i=0; i < z->s->img_x; ++i) { + stbi_uc m = coutput[3][i]; + stbi_uc r = stbi__blinn_8x8(coutput[0][i], m); + stbi_uc g = stbi__blinn_8x8(coutput[1][i], m); + stbi_uc b = stbi__blinn_8x8(coutput[2][i], m); + out[0] = stbi__compute_y(r, g, b); + out[1] = 255; + out += n; + } + } else if (z->s->img_n == 4 && z->app14_color_transform == 2) { + for (i=0; i < z->s->img_x; ++i) { + out[0] = stbi__blinn_8x8(255 - coutput[0][i], coutput[3][i]); + out[1] = 255; + out += n; + } + } else { + stbi_uc *y = coutput[0]; + if (n == 1) + for (i=0; i < z->s->img_x; ++i) out[i] = y[i]; + else + for (i=0; i < z->s->img_x; ++i) { *out++ = y[i]; *out++ = 255; } + } + } + } + stbi__cleanup_jpeg(z); + *out_x = z->s->img_x; + *out_y = z->s->img_y; + if (comp) *comp = z->s->img_n >= 3 ? 3 : 1; // report original components, not output + return output; + } +} + +static void *stbi__jpeg_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) +{ + unsigned char* result; + stbi__jpeg* j = (stbi__jpeg*) stbi__malloc(sizeof(stbi__jpeg)); + STBI_NOTUSED(ri); + j->s = s; + stbi__setup_jpeg(j); + result = load_jpeg_image(j, x,y,comp,req_comp); + STBI_FREE(j); + return result; +} + +static int stbi__jpeg_test(stbi__context *s) +{ + int r; + stbi__jpeg* j = (stbi__jpeg*)stbi__malloc(sizeof(stbi__jpeg)); + j->s = s; + stbi__setup_jpeg(j); + r = stbi__decode_jpeg_header(j, STBI__SCAN_type); + stbi__rewind(s); + STBI_FREE(j); + return r; +} + +static int stbi__jpeg_info_raw(stbi__jpeg *j, int *x, int *y, int *comp) +{ + if (!stbi__decode_jpeg_header(j, STBI__SCAN_header)) { + stbi__rewind( j->s ); + return 0; + } + if (x) *x = j->s->img_x; + if (y) *y = j->s->img_y; + if (comp) *comp = j->s->img_n >= 3 ? 3 : 1; + return 1; +} + +static int stbi__jpeg_info(stbi__context *s, int *x, int *y, int *comp) +{ + int result; + stbi__jpeg* j = (stbi__jpeg*) (stbi__malloc(sizeof(stbi__jpeg))); + j->s = s; + result = stbi__jpeg_info_raw(j, x, y, comp); + STBI_FREE(j); + return result; +} +#endif + +// public domain zlib decode v0.2 Sean Barrett 2006-11-18 +// simple implementation +// - all input must be provided in an upfront buffer +// - all output is written to a single output buffer (can malloc/realloc) +// performance +// - fast huffman + +#ifndef STBI_NO_ZLIB + +// fast-way is faster to check than jpeg huffman, but slow way is slower +#define STBI__ZFAST_BITS 9 // accelerate all cases in default tables +#define STBI__ZFAST_MASK ((1 << STBI__ZFAST_BITS) - 1) + +// zlib-style huffman encoding +// (jpegs packs from left, zlib from right, so can't share code) +typedef struct +{ + stbi__uint16 fast[1 << STBI__ZFAST_BITS]; + stbi__uint16 firstcode[16]; + int maxcode[17]; + stbi__uint16 firstsymbol[16]; + stbi_uc size[288]; + stbi__uint16 value[288]; +} stbi__zhuffman; + +stbi_inline static int stbi__bitreverse16(int n) +{ + n = ((n & 0xAAAA) >> 1) | ((n & 0x5555) << 1); + n = ((n & 0xCCCC) >> 2) | ((n & 0x3333) << 2); + n = ((n & 0xF0F0) >> 4) | ((n & 0x0F0F) << 4); + n = ((n & 0xFF00) >> 8) | ((n & 0x00FF) << 8); + return n; +} + +stbi_inline static int stbi__bit_reverse(int v, int bits) +{ + STBI_ASSERT(bits <= 16); + // to bit reverse n bits, reverse 16 and shift + // e.g. 11 bits, bit reverse and shift away 5 + return stbi__bitreverse16(v) >> (16-bits); +} + +static int stbi__zbuild_huffman(stbi__zhuffman *z, const stbi_uc *sizelist, int num) +{ + int i,k=0; + int code, next_code[16], sizes[17]; + + // DEFLATE spec for generating codes + memset(sizes, 0, sizeof(sizes)); + memset(z->fast, 0, sizeof(z->fast)); + for (i=0; i < num; ++i) + ++sizes[sizelist[i]]; + sizes[0] = 0; + for (i=1; i < 16; ++i) + if (sizes[i] > (1 << i)) + return stbi__err("bad sizes", "Corrupt PNG"); + code = 0; + for (i=1; i < 16; ++i) { + next_code[i] = code; + z->firstcode[i] = (stbi__uint16) code; + z->firstsymbol[i] = (stbi__uint16) k; + code = (code + sizes[i]); + if (sizes[i]) + if (code-1 >= (1 << i)) return stbi__err("bad codelengths","Corrupt PNG"); + z->maxcode[i] = code << (16-i); // preshift for inner loop + code <<= 1; + k += sizes[i]; + } + z->maxcode[16] = 0x10000; // sentinel + for (i=0; i < num; ++i) { + int s = sizelist[i]; + if (s) { + int c = next_code[s] - z->firstcode[s] + z->firstsymbol[s]; + stbi__uint16 fastv = (stbi__uint16) ((s << 9) | i); + z->size [c] = (stbi_uc ) s; + z->value[c] = (stbi__uint16) i; + if (s <= STBI__ZFAST_BITS) { + int j = stbi__bit_reverse(next_code[s],s); + while (j < (1 << STBI__ZFAST_BITS)) { + z->fast[j] = fastv; + j += (1 << s); + } + } + ++next_code[s]; + } + } + return 1; +} + +// zlib-from-memory implementation for PNG reading +// because PNG allows splitting the zlib stream arbitrarily, +// and it's annoying structurally to have PNG call ZLIB call PNG, +// we require PNG read all the IDATs and combine them into a single +// memory buffer + +typedef struct +{ + stbi_uc *zbuffer, *zbuffer_end; + int num_bits; + stbi__uint32 code_buffer; + + char *zout; + char *zout_start; + char *zout_end; + int z_expandable; + + stbi__zhuffman z_length, z_distance; +} stbi__zbuf; + +stbi_inline static stbi_uc stbi__zget8(stbi__zbuf *z) +{ + if (z->zbuffer >= z->zbuffer_end) return 0; + return *z->zbuffer++; +} + +static void stbi__fill_bits(stbi__zbuf *z) +{ + do { + STBI_ASSERT(z->code_buffer < (1U << z->num_bits)); + z->code_buffer |= (unsigned int) stbi__zget8(z) << z->num_bits; + z->num_bits += 8; + } while (z->num_bits <= 24); +} + +stbi_inline static unsigned int stbi__zreceive(stbi__zbuf *z, int n) +{ + unsigned int k; + if (z->num_bits < n) stbi__fill_bits(z); + k = z->code_buffer & ((1 << n) - 1); + z->code_buffer >>= n; + z->num_bits -= n; + return k; +} + +static int stbi__zhuffman_decode_slowpath(stbi__zbuf *a, stbi__zhuffman *z) +{ + int b,s,k; + // not resolved by fast table, so compute it the slow way + // use jpeg approach, which requires MSbits at top + k = stbi__bit_reverse(a->code_buffer, 16); + for (s=STBI__ZFAST_BITS+1; ; ++s) + if (k < z->maxcode[s]) + break; + if (s == 16) return -1; // invalid code! + // code size is s, so: + b = (k >> (16-s)) - z->firstcode[s] + z->firstsymbol[s]; + STBI_ASSERT(z->size[b] == s); + a->code_buffer >>= s; + a->num_bits -= s; + return z->value[b]; +} + +stbi_inline static int stbi__zhuffman_decode(stbi__zbuf *a, stbi__zhuffman *z) +{ + int b,s; + if (a->num_bits < 16) stbi__fill_bits(a); + b = z->fast[a->code_buffer & STBI__ZFAST_MASK]; + if (b) { + s = b >> 9; + a->code_buffer >>= s; + a->num_bits -= s; + return b & 511; + } + return stbi__zhuffman_decode_slowpath(a, z); +} + +static int stbi__zexpand(stbi__zbuf *z, char *zout, int n) // need to make room for n bytes +{ + char *q; + int cur, limit, old_limit; + z->zout = zout; + if (!z->z_expandable) return stbi__err("output buffer limit","Corrupt PNG"); + cur = (int) (z->zout - z->zout_start); + limit = old_limit = (int) (z->zout_end - z->zout_start); + while (cur + n > limit) + limit *= 2; + q = (char *) STBI_REALLOC_SIZED(z->zout_start, old_limit, limit); + STBI_NOTUSED(old_limit); + if (q == NULL) return stbi__err("outofmem", "Out of memory"); + z->zout_start = q; + z->zout = q + cur; + z->zout_end = q + limit; + return 1; +} + +static const int stbi__zlength_base[31] = { + 3,4,5,6,7,8,9,10,11,13, + 15,17,19,23,27,31,35,43,51,59, + 67,83,99,115,131,163,195,227,258,0,0 }; + +static const int stbi__zlength_extra[31]= +{ 0,0,0,0,0,0,0,0,1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4,5,5,5,5,0,0,0 }; + +static const int stbi__zdist_base[32] = { 1,2,3,4,5,7,9,13,17,25,33,49,65,97,129,193, +257,385,513,769,1025,1537,2049,3073,4097,6145,8193,12289,16385,24577,0,0}; + +static const int stbi__zdist_extra[32] = +{ 0,0,0,0,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12,13,13}; + +static int stbi__parse_huffman_block(stbi__zbuf *a) +{ + char *zout = a->zout; + for(;;) { + int z = stbi__zhuffman_decode(a, &a->z_length); + if (z < 256) { + if (z < 0) return stbi__err("bad huffman code","Corrupt PNG"); // error in huffman codes + if (zout >= a->zout_end) { + if (!stbi__zexpand(a, zout, 1)) return 0; + zout = a->zout; + } + *zout++ = (char) z; + } else { + stbi_uc *p; + int len,dist; + if (z == 256) { + a->zout = zout; + return 1; + } + z -= 257; + len = stbi__zlength_base[z]; + if (stbi__zlength_extra[z]) len += stbi__zreceive(a, stbi__zlength_extra[z]); + z = stbi__zhuffman_decode(a, &a->z_distance); + if (z < 0) return stbi__err("bad huffman code","Corrupt PNG"); + dist = stbi__zdist_base[z]; + if (stbi__zdist_extra[z]) dist += stbi__zreceive(a, stbi__zdist_extra[z]); + if (zout - a->zout_start < dist) return stbi__err("bad dist","Corrupt PNG"); + if (zout + len > a->zout_end) { + if (!stbi__zexpand(a, zout, len)) return 0; + zout = a->zout; + } + p = (stbi_uc *) (zout - dist); + if (dist == 1) { // run of one byte; common in images. + stbi_uc v = *p; + if (len) { do *zout++ = v; while (--len); } + } else { + if (len) { do *zout++ = *p++; while (--len); } + } + } + } +} + +static int stbi__compute_huffman_codes(stbi__zbuf *a) +{ + static const stbi_uc length_dezigzag[19] = { 16,17,18,0,8,7,9,6,10,5,11,4,12,3,13,2,14,1,15 }; + stbi__zhuffman z_codelength; + stbi_uc lencodes[286+32+137];//padding for maximum single op + stbi_uc codelength_sizes[19]; + int i,n; + + int hlit = stbi__zreceive(a,5) + 257; + int hdist = stbi__zreceive(a,5) + 1; + int hclen = stbi__zreceive(a,4) + 4; + int ntot = hlit + hdist; + + memset(codelength_sizes, 0, sizeof(codelength_sizes)); + for (i=0; i < hclen; ++i) { + int s = stbi__zreceive(a,3); + codelength_sizes[length_dezigzag[i]] = (stbi_uc) s; + } + if (!stbi__zbuild_huffman(&z_codelength, codelength_sizes, 19)) return 0; + + n = 0; + while (n < ntot) { + int c = stbi__zhuffman_decode(a, &z_codelength); + if (c < 0 || c >= 19) return stbi__err("bad codelengths", "Corrupt PNG"); + if (c < 16) + lencodes[n++] = (stbi_uc) c; + else { + stbi_uc fill = 0; + if (c == 16) { + c = stbi__zreceive(a,2)+3; + if (n == 0) return stbi__err("bad codelengths", "Corrupt PNG"); + fill = lencodes[n-1]; + } else if (c == 17) + c = stbi__zreceive(a,3)+3; + else { + STBI_ASSERT(c == 18); + c = stbi__zreceive(a,7)+11; + } + if (ntot - n < c) return stbi__err("bad codelengths", "Corrupt PNG"); + memset(lencodes+n, fill, c); + n += c; + } + } + if (n != ntot) return stbi__err("bad codelengths","Corrupt PNG"); + if (!stbi__zbuild_huffman(&a->z_length, lencodes, hlit)) return 0; + if (!stbi__zbuild_huffman(&a->z_distance, lencodes+hlit, hdist)) return 0; + return 1; +} + +static int stbi__parse_uncompressed_block(stbi__zbuf *a) +{ + stbi_uc header[4]; + int len,nlen,k; + if (a->num_bits & 7) + stbi__zreceive(a, a->num_bits & 7); // discard + // drain the bit-packed data into header + k = 0; + while (a->num_bits > 0) { + header[k++] = (stbi_uc) (a->code_buffer & 255); // suppress MSVC run-time check + a->code_buffer >>= 8; + a->num_bits -= 8; + } + STBI_ASSERT(a->num_bits == 0); + // now fill header the normal way + while (k < 4) + header[k++] = stbi__zget8(a); + len = header[1] * 256 + header[0]; + nlen = header[3] * 256 + header[2]; + if (nlen != (len ^ 0xffff)) return stbi__err("zlib corrupt","Corrupt PNG"); + if (a->zbuffer + len > a->zbuffer_end) return stbi__err("read past buffer","Corrupt PNG"); + if (a->zout + len > a->zout_end) + if (!stbi__zexpand(a, a->zout, len)) return 0; + memcpy(a->zout, a->zbuffer, len); + a->zbuffer += len; + a->zout += len; + return 1; +} + +static int stbi__parse_zlib_header(stbi__zbuf *a) +{ + int cmf = stbi__zget8(a); + int cm = cmf & 15; + /* int cinfo = cmf >> 4; */ + int flg = stbi__zget8(a); + if ((cmf*256+flg) % 31 != 0) return stbi__err("bad zlib header","Corrupt PNG"); // zlib spec + if (flg & 32) return stbi__err("no preset dict","Corrupt PNG"); // preset dictionary not allowed in png + if (cm != 8) return stbi__err("bad compression","Corrupt PNG"); // DEFLATE required for png + // window = 1 << (8 + cinfo)... but who cares, we fully buffer output + return 1; +} + +static const stbi_uc stbi__zdefault_length[288] = +{ + 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, + 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, + 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, + 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, + 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, + 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, + 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, + 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, + 7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7, 7,7,7,7,7,7,7,7,8,8,8,8,8,8,8,8 +}; +static const stbi_uc stbi__zdefault_distance[32] = +{ + 5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5 +}; +/* +Init algorithm: +{ + int i; // use <= to match clearly with spec + for (i=0; i <= 143; ++i) stbi__zdefault_length[i] = 8; + for ( ; i <= 255; ++i) stbi__zdefault_length[i] = 9; + for ( ; i <= 279; ++i) stbi__zdefault_length[i] = 7; + for ( ; i <= 287; ++i) stbi__zdefault_length[i] = 8; + + for (i=0; i <= 31; ++i) stbi__zdefault_distance[i] = 5; +} +*/ + +static int stbi__parse_zlib(stbi__zbuf *a, int parse_header) +{ + int final, type; + if (parse_header) + if (!stbi__parse_zlib_header(a)) return 0; + a->num_bits = 0; + a->code_buffer = 0; + do { + final = stbi__zreceive(a,1); + type = stbi__zreceive(a,2); + if (type == 0) { + if (!stbi__parse_uncompressed_block(a)) return 0; + } else if (type == 3) { + return 0; + } else { + if (type == 1) { + // use fixed code lengths + if (!stbi__zbuild_huffman(&a->z_length , stbi__zdefault_length , 288)) return 0; + if (!stbi__zbuild_huffman(&a->z_distance, stbi__zdefault_distance, 32)) return 0; + } else { + if (!stbi__compute_huffman_codes(a)) return 0; + } + if (!stbi__parse_huffman_block(a)) return 0; + } + } while (!final); + return 1; +} + +static int stbi__do_zlib(stbi__zbuf *a, char *obuf, int olen, int exp, int parse_header) +{ + a->zout_start = obuf; + a->zout = obuf; + a->zout_end = obuf + olen; + a->z_expandable = exp; + + return stbi__parse_zlib(a, parse_header); +} + +STBIDEF char *stbi_zlib_decode_malloc_guesssize(const char *buffer, int len, int initial_size, int *outlen) +{ + stbi__zbuf a; + char *p = (char *) stbi__malloc(initial_size); + if (p == NULL) return NULL; + a.zbuffer = (stbi_uc *) buffer; + a.zbuffer_end = (stbi_uc *) buffer + len; + if (stbi__do_zlib(&a, p, initial_size, 1, 1)) { + if (outlen) *outlen = (int) (a.zout - a.zout_start); + return a.zout_start; + } else { + STBI_FREE(a.zout_start); + return NULL; + } +} + +STBIDEF char *stbi_zlib_decode_malloc(char const *buffer, int len, int *outlen) +{ + return stbi_zlib_decode_malloc_guesssize(buffer, len, 16384, outlen); +} + +STBIDEF char *stbi_zlib_decode_malloc_guesssize_headerflag(const char *buffer, int len, int initial_size, int *outlen, int parse_header) +{ + stbi__zbuf a; + char *p = (char *) stbi__malloc(initial_size); + if (p == NULL) return NULL; + a.zbuffer = (stbi_uc *) buffer; + a.zbuffer_end = (stbi_uc *) buffer + len; + if (stbi__do_zlib(&a, p, initial_size, 1, parse_header)) { + if (outlen) *outlen = (int) (a.zout - a.zout_start); + return a.zout_start; + } else { + STBI_FREE(a.zout_start); + return NULL; + } +} + +STBIDEF int stbi_zlib_decode_buffer(char *obuffer, int olen, char const *ibuffer, int ilen) +{ + stbi__zbuf a; + a.zbuffer = (stbi_uc *) ibuffer; + a.zbuffer_end = (stbi_uc *) ibuffer + ilen; + if (stbi__do_zlib(&a, obuffer, olen, 0, 1)) + return (int) (a.zout - a.zout_start); + else + return -1; +} + +STBIDEF char *stbi_zlib_decode_noheader_malloc(char const *buffer, int len, int *outlen) +{ + stbi__zbuf a; + char *p = (char *) stbi__malloc(16384); + if (p == NULL) return NULL; + a.zbuffer = (stbi_uc *) buffer; + a.zbuffer_end = (stbi_uc *) buffer+len; + if (stbi__do_zlib(&a, p, 16384, 1, 0)) { + if (outlen) *outlen = (int) (a.zout - a.zout_start); + return a.zout_start; + } else { + STBI_FREE(a.zout_start); + return NULL; + } +} + +STBIDEF int stbi_zlib_decode_noheader_buffer(char *obuffer, int olen, const char *ibuffer, int ilen) +{ + stbi__zbuf a; + a.zbuffer = (stbi_uc *) ibuffer; + a.zbuffer_end = (stbi_uc *) ibuffer + ilen; + if (stbi__do_zlib(&a, obuffer, olen, 0, 0)) + return (int) (a.zout - a.zout_start); + else + return -1; +} +#endif + +// public domain "baseline" PNG decoder v0.10 Sean Barrett 2006-11-18 +// simple implementation +// - only 8-bit samples +// - no CRC checking +// - allocates lots of intermediate memory +// - avoids problem of streaming data between subsystems +// - avoids explicit window management +// performance +// - uses stb_zlib, a PD zlib implementation with fast huffman decoding + +#ifndef STBI_NO_PNG +typedef struct +{ + stbi__uint32 length; + stbi__uint32 type; +} stbi__pngchunk; + +static stbi__pngchunk stbi__get_chunk_header(stbi__context *s) +{ + stbi__pngchunk c; + c.length = stbi__get32be(s); + c.type = stbi__get32be(s); + return c; +} + +static int stbi__check_png_header(stbi__context *s) +{ + static const stbi_uc png_sig[8] = { 137,80,78,71,13,10,26,10 }; + int i; + for (i=0; i < 8; ++i) + if (stbi__get8(s) != png_sig[i]) return stbi__err("bad png sig","Not a PNG"); + return 1; +} + +typedef struct +{ + stbi__context *s; + stbi_uc *idata, *expanded, *out; + int depth; +} stbi__png; + + +enum { + STBI__F_none=0, + STBI__F_sub=1, + STBI__F_up=2, + STBI__F_avg=3, + STBI__F_paeth=4, + // synthetic filters used for first scanline to avoid needing a dummy row of 0s + STBI__F_avg_first, + STBI__F_paeth_first +}; + +static stbi_uc first_row_filter[5] = +{ + STBI__F_none, + STBI__F_sub, + STBI__F_none, + STBI__F_avg_first, + STBI__F_paeth_first +}; + +static int stbi__paeth(int a, int b, int c) +{ + int p = a + b - c; + int pa = abs(p-a); + int pb = abs(p-b); + int pc = abs(p-c); + if (pa <= pb && pa <= pc) return a; + if (pb <= pc) return b; + return c; +} + +static const stbi_uc stbi__depth_scale_table[9] = { 0, 0xff, 0x55, 0, 0x11, 0,0,0, 0x01 }; + +// create the png data from post-deflated data +static int stbi__create_png_image_raw(stbi__png *a, stbi_uc *raw, stbi__uint32 raw_len, int out_n, stbi__uint32 x, stbi__uint32 y, int depth, int color) +{ + int bytes = (depth == 16? 2 : 1); + stbi__context *s = a->s; + stbi__uint32 i,j,stride = x*out_n*bytes; + stbi__uint32 img_len, img_width_bytes; + int k; + int img_n = s->img_n; // copy it into a local for later + + int output_bytes = out_n*bytes; + int filter_bytes = img_n*bytes; + int width = x; + + STBI_ASSERT(out_n == s->img_n || out_n == s->img_n+1); + a->out = (stbi_uc *) stbi__malloc_mad3(x, y, output_bytes, 0); // extra bytes to write off the end into + if (!a->out) return stbi__err("outofmem", "Out of memory"); + + if (!stbi__mad3sizes_valid(img_n, x, depth, 7)) return stbi__err("too large", "Corrupt PNG"); + img_width_bytes = (((img_n * x * depth) + 7) >> 3); + img_len = (img_width_bytes + 1) * y; + + // we used to check for exact match between raw_len and img_len on non-interlaced PNGs, + // but issue #276 reported a PNG in the wild that had extra data at the end (all zeros), + // so just check for raw_len < img_len always. + if (raw_len < img_len) return stbi__err("not enough pixels","Corrupt PNG"); + + for (j=0; j < y; ++j) { + stbi_uc *cur = a->out + stride*j; + stbi_uc *prior; + int filter = *raw++; + + if (filter > 4) + return stbi__err("invalid filter","Corrupt PNG"); + + if (depth < 8) { + STBI_ASSERT(img_width_bytes <= x); + cur += x*out_n - img_width_bytes; // store output to the rightmost img_len bytes, so we can decode in place + filter_bytes = 1; + width = img_width_bytes; + } + prior = cur - stride; // bugfix: need to compute this after 'cur +=' computation above + + // if first row, use special filter that doesn't sample previous row + if (j == 0) filter = first_row_filter[filter]; + + // handle first byte explicitly + for (k=0; k < filter_bytes; ++k) { + switch (filter) { + case STBI__F_none : cur[k] = raw[k]; break; + case STBI__F_sub : cur[k] = raw[k]; break; + case STBI__F_up : cur[k] = STBI__BYTECAST(raw[k] + prior[k]); break; + case STBI__F_avg : cur[k] = STBI__BYTECAST(raw[k] + (prior[k]>>1)); break; + case STBI__F_paeth : cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(0,prior[k],0)); break; + case STBI__F_avg_first : cur[k] = raw[k]; break; + case STBI__F_paeth_first: cur[k] = raw[k]; break; + } + } + + if (depth == 8) { + if (img_n != out_n) + cur[img_n] = 255; // first pixel + raw += img_n; + cur += out_n; + prior += out_n; + } else if (depth == 16) { + if (img_n != out_n) { + cur[filter_bytes] = 255; // first pixel top byte + cur[filter_bytes+1] = 255; // first pixel bottom byte + } + raw += filter_bytes; + cur += output_bytes; + prior += output_bytes; + } else { + raw += 1; + cur += 1; + prior += 1; + } + + // this is a little gross, so that we don't switch per-pixel or per-component + if (depth < 8 || img_n == out_n) { + int nk = (width - 1)*filter_bytes; + #define STBI__CASE(f) \ + case f: \ + for (k=0; k < nk; ++k) + switch (filter) { + // "none" filter turns into a memcpy here; make that explicit. + case STBI__F_none: memcpy(cur, raw, nk); break; + STBI__CASE(STBI__F_sub) { cur[k] = STBI__BYTECAST(raw[k] + cur[k-filter_bytes]); } break; + STBI__CASE(STBI__F_up) { cur[k] = STBI__BYTECAST(raw[k] + prior[k]); } break; + STBI__CASE(STBI__F_avg) { cur[k] = STBI__BYTECAST(raw[k] + ((prior[k] + cur[k-filter_bytes])>>1)); } break; + STBI__CASE(STBI__F_paeth) { cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k-filter_bytes],prior[k],prior[k-filter_bytes])); } break; + STBI__CASE(STBI__F_avg_first) { cur[k] = STBI__BYTECAST(raw[k] + (cur[k-filter_bytes] >> 1)); } break; + STBI__CASE(STBI__F_paeth_first) { cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k-filter_bytes],0,0)); } break; + } + #undef STBI__CASE + raw += nk; + } else { + STBI_ASSERT(img_n+1 == out_n); + #define STBI__CASE(f) \ + case f: \ + for (i=x-1; i >= 1; --i, cur[filter_bytes]=255,raw+=filter_bytes,cur+=output_bytes,prior+=output_bytes) \ + for (k=0; k < filter_bytes; ++k) + switch (filter) { + STBI__CASE(STBI__F_none) { cur[k] = raw[k]; } break; + STBI__CASE(STBI__F_sub) { cur[k] = STBI__BYTECAST(raw[k] + cur[k- output_bytes]); } break; + STBI__CASE(STBI__F_up) { cur[k] = STBI__BYTECAST(raw[k] + prior[k]); } break; + STBI__CASE(STBI__F_avg) { cur[k] = STBI__BYTECAST(raw[k] + ((prior[k] + cur[k- output_bytes])>>1)); } break; + STBI__CASE(STBI__F_paeth) { cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k- output_bytes],prior[k],prior[k- output_bytes])); } break; + STBI__CASE(STBI__F_avg_first) { cur[k] = STBI__BYTECAST(raw[k] + (cur[k- output_bytes] >> 1)); } break; + STBI__CASE(STBI__F_paeth_first) { cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k- output_bytes],0,0)); } break; + } + #undef STBI__CASE + + // the loop above sets the high byte of the pixels' alpha, but for + // 16 bit png files we also need the low byte set. we'll do that here. + if (depth == 16) { + cur = a->out + stride*j; // start at the beginning of the row again + for (i=0; i < x; ++i,cur+=output_bytes) { + cur[filter_bytes+1] = 255; + } + } + } + } + + // we make a separate pass to expand bits to pixels; for performance, + // this could run two scanlines behind the above code, so it won't + // intefere with filtering but will still be in the cache. + if (depth < 8) { + for (j=0; j < y; ++j) { + stbi_uc *cur = a->out + stride*j; + stbi_uc *in = a->out + stride*j + x*out_n - img_width_bytes; + // unpack 1/2/4-bit into a 8-bit buffer. allows us to keep the common 8-bit path optimal at minimal cost for 1/2/4-bit + // png guarante byte alignment, if width is not multiple of 8/4/2 we'll decode dummy trailing data that will be skipped in the later loop + stbi_uc scale = (color == 0) ? stbi__depth_scale_table[depth] : 1; // scale grayscale values to 0..255 range + + // note that the final byte might overshoot and write more data than desired. + // we can allocate enough data that this never writes out of memory, but it + // could also overwrite the next scanline. can it overwrite non-empty data + // on the next scanline? yes, consider 1-pixel-wide scanlines with 1-bit-per-pixel. + // so we need to explicitly clamp the final ones + + if (depth == 4) { + for (k=x*img_n; k >= 2; k-=2, ++in) { + *cur++ = scale * ((*in >> 4) ); + *cur++ = scale * ((*in ) & 0x0f); + } + if (k > 0) *cur++ = scale * ((*in >> 4) ); + } else if (depth == 2) { + for (k=x*img_n; k >= 4; k-=4, ++in) { + *cur++ = scale * ((*in >> 6) ); + *cur++ = scale * ((*in >> 4) & 0x03); + *cur++ = scale * ((*in >> 2) & 0x03); + *cur++ = scale * ((*in ) & 0x03); + } + if (k > 0) *cur++ = scale * ((*in >> 6) ); + if (k > 1) *cur++ = scale * ((*in >> 4) & 0x03); + if (k > 2) *cur++ = scale * ((*in >> 2) & 0x03); + } else if (depth == 1) { + for (k=x*img_n; k >= 8; k-=8, ++in) { + *cur++ = scale * ((*in >> 7) ); + *cur++ = scale * ((*in >> 6) & 0x01); + *cur++ = scale * ((*in >> 5) & 0x01); + *cur++ = scale * ((*in >> 4) & 0x01); + *cur++ = scale * ((*in >> 3) & 0x01); + *cur++ = scale * ((*in >> 2) & 0x01); + *cur++ = scale * ((*in >> 1) & 0x01); + *cur++ = scale * ((*in ) & 0x01); + } + if (k > 0) *cur++ = scale * ((*in >> 7) ); + if (k > 1) *cur++ = scale * ((*in >> 6) & 0x01); + if (k > 2) *cur++ = scale * ((*in >> 5) & 0x01); + if (k > 3) *cur++ = scale * ((*in >> 4) & 0x01); + if (k > 4) *cur++ = scale * ((*in >> 3) & 0x01); + if (k > 5) *cur++ = scale * ((*in >> 2) & 0x01); + if (k > 6) *cur++ = scale * ((*in >> 1) & 0x01); + } + if (img_n != out_n) { + int q; + // insert alpha = 255 + cur = a->out + stride*j; + if (img_n == 1) { + for (q=x-1; q >= 0; --q) { + cur[q*2+1] = 255; + cur[q*2+0] = cur[q]; + } + } else { + STBI_ASSERT(img_n == 3); + for (q=x-1; q >= 0; --q) { + cur[q*4+3] = 255; + cur[q*4+2] = cur[q*3+2]; + cur[q*4+1] = cur[q*3+1]; + cur[q*4+0] = cur[q*3+0]; + } + } + } + } + } else if (depth == 16) { + // force the image data from big-endian to platform-native. + // this is done in a separate pass due to the decoding relying + // on the data being untouched, but could probably be done + // per-line during decode if care is taken. + stbi_uc *cur = a->out; + stbi__uint16 *cur16 = (stbi__uint16*)cur; + + for(i=0; i < x*y*out_n; ++i,cur16++,cur+=2) { + *cur16 = (cur[0] << 8) | cur[1]; + } + } + + return 1; +} + +static int stbi__create_png_image(stbi__png *a, stbi_uc *image_data, stbi__uint32 image_data_len, int out_n, int depth, int color, int interlaced) +{ + int bytes = (depth == 16 ? 2 : 1); + int out_bytes = out_n * bytes; + stbi_uc *final; + int p; + if (!interlaced) + return stbi__create_png_image_raw(a, image_data, image_data_len, out_n, a->s->img_x, a->s->img_y, depth, color); + + // de-interlacing + final = (stbi_uc *) stbi__malloc_mad3(a->s->img_x, a->s->img_y, out_bytes, 0); + for (p=0; p < 7; ++p) { + int xorig[] = { 0,4,0,2,0,1,0 }; + int yorig[] = { 0,0,4,0,2,0,1 }; + int xspc[] = { 8,8,4,4,2,2,1 }; + int yspc[] = { 8,8,8,4,4,2,2 }; + int i,j,x,y; + // pass1_x[4] = 0, pass1_x[5] = 1, pass1_x[12] = 1 + x = (a->s->img_x - xorig[p] + xspc[p]-1) / xspc[p]; + y = (a->s->img_y - yorig[p] + yspc[p]-1) / yspc[p]; + if (x && y) { + stbi__uint32 img_len = ((((a->s->img_n * x * depth) + 7) >> 3) + 1) * y; + if (!stbi__create_png_image_raw(a, image_data, image_data_len, out_n, x, y, depth, color)) { + STBI_FREE(final); + return 0; + } + for (j=0; j < y; ++j) { + for (i=0; i < x; ++i) { + int out_y = j*yspc[p]+yorig[p]; + int out_x = i*xspc[p]+xorig[p]; + memcpy(final + out_y*a->s->img_x*out_bytes + out_x*out_bytes, + a->out + (j*x+i)*out_bytes, out_bytes); + } + } + STBI_FREE(a->out); + image_data += img_len; + image_data_len -= img_len; + } + } + a->out = final; + + return 1; +} + +static int stbi__compute_transparency(stbi__png *z, stbi_uc tc[3], int out_n) +{ + stbi__context *s = z->s; + stbi__uint32 i, pixel_count = s->img_x * s->img_y; + stbi_uc *p = z->out; + + // compute color-based transparency, assuming we've + // already got 255 as the alpha value in the output + STBI_ASSERT(out_n == 2 || out_n == 4); + + if (out_n == 2) { + for (i=0; i < pixel_count; ++i) { + p[1] = (p[0] == tc[0] ? 0 : 255); + p += 2; + } + } else { + for (i=0; i < pixel_count; ++i) { + if (p[0] == tc[0] && p[1] == tc[1] && p[2] == tc[2]) + p[3] = 0; + p += 4; + } + } + return 1; +} + +static int stbi__compute_transparency16(stbi__png *z, stbi__uint16 tc[3], int out_n) +{ + stbi__context *s = z->s; + stbi__uint32 i, pixel_count = s->img_x * s->img_y; + stbi__uint16 *p = (stbi__uint16*) z->out; + + // compute color-based transparency, assuming we've + // already got 65535 as the alpha value in the output + STBI_ASSERT(out_n == 2 || out_n == 4); + + if (out_n == 2) { + for (i = 0; i < pixel_count; ++i) { + p[1] = (p[0] == tc[0] ? 0 : 65535); + p += 2; + } + } else { + for (i = 0; i < pixel_count; ++i) { + if (p[0] == tc[0] && p[1] == tc[1] && p[2] == tc[2]) + p[3] = 0; + p += 4; + } + } + return 1; +} + +static int stbi__expand_png_palette(stbi__png *a, stbi_uc *palette, int len, int pal_img_n) +{ + stbi__uint32 i, pixel_count = a->s->img_x * a->s->img_y; + stbi_uc *p, *temp_out, *orig = a->out; + + p = (stbi_uc *) stbi__malloc_mad2(pixel_count, pal_img_n, 0); + if (p == NULL) return stbi__err("outofmem", "Out of memory"); + + // between here and free(out) below, exitting would leak + temp_out = p; + + if (pal_img_n == 3) { + for (i=0; i < pixel_count; ++i) { + int n = orig[i]*4; + p[0] = palette[n ]; + p[1] = palette[n+1]; + p[2] = palette[n+2]; + p += 3; + } + } else { + for (i=0; i < pixel_count; ++i) { + int n = orig[i]*4; + p[0] = palette[n ]; + p[1] = palette[n+1]; + p[2] = palette[n+2]; + p[3] = palette[n+3]; + p += 4; + } + } + STBI_FREE(a->out); + a->out = temp_out; + + STBI_NOTUSED(len); + + return 1; +} + +static int stbi__unpremultiply_on_load = 0; +static int stbi__de_iphone_flag = 0; + +STBIDEF void stbi_set_unpremultiply_on_load(int flag_true_if_should_unpremultiply) +{ + stbi__unpremultiply_on_load = flag_true_if_should_unpremultiply; +} + +STBIDEF void stbi_convert_iphone_png_to_rgb(int flag_true_if_should_convert) +{ + stbi__de_iphone_flag = flag_true_if_should_convert; +} + +static void stbi__de_iphone(stbi__png *z) +{ + stbi__context *s = z->s; + stbi__uint32 i, pixel_count = s->img_x * s->img_y; + stbi_uc *p = z->out; + + if (s->img_out_n == 3) { // convert bgr to rgb + for (i=0; i < pixel_count; ++i) { + stbi_uc t = p[0]; + p[0] = p[2]; + p[2] = t; + p += 3; + } + } else { + STBI_ASSERT(s->img_out_n == 4); + if (stbi__unpremultiply_on_load) { + // convert bgr to rgb and unpremultiply + for (i=0; i < pixel_count; ++i) { + stbi_uc a = p[3]; + stbi_uc t = p[0]; + if (a) { + stbi_uc half = a / 2; + p[0] = (p[2] * 255 + half) / a; + p[1] = (p[1] * 255 + half) / a; + p[2] = ( t * 255 + half) / a; + } else { + p[0] = p[2]; + p[2] = t; + } + p += 4; + } + } else { + // convert bgr to rgb + for (i=0; i < pixel_count; ++i) { + stbi_uc t = p[0]; + p[0] = p[2]; + p[2] = t; + p += 4; + } + } + } +} + +#define STBI__PNG_TYPE(a,b,c,d) (((unsigned) (a) << 24) + ((unsigned) (b) << 16) + ((unsigned) (c) << 8) + (unsigned) (d)) + +static int stbi__parse_png_file(stbi__png *z, int scan, int req_comp) +{ + stbi_uc palette[1024], pal_img_n=0; + stbi_uc has_trans=0, tc[3]={0}; + stbi__uint16 tc16[3]; + stbi__uint32 ioff=0, idata_limit=0, i, pal_len=0; + int first=1,k,interlace=0, color=0, is_iphone=0; + stbi__context *s = z->s; + + z->expanded = NULL; + z->idata = NULL; + z->out = NULL; + + if (!stbi__check_png_header(s)) return 0; + + if (scan == STBI__SCAN_type) return 1; + + for (;;) { + stbi__pngchunk c = stbi__get_chunk_header(s); + switch (c.type) { + case STBI__PNG_TYPE('C','g','B','I'): + is_iphone = 1; + stbi__skip(s, c.length); + break; + case STBI__PNG_TYPE('I','H','D','R'): { + int comp,filter; + if (!first) return stbi__err("multiple IHDR","Corrupt PNG"); + first = 0; + if (c.length != 13) return stbi__err("bad IHDR len","Corrupt PNG"); + s->img_x = stbi__get32be(s); if (s->img_x > (1 << 24)) return stbi__err("too large","Very large image (corrupt?)"); + s->img_y = stbi__get32be(s); if (s->img_y > (1 << 24)) return stbi__err("too large","Very large image (corrupt?)"); + z->depth = stbi__get8(s); if (z->depth != 1 && z->depth != 2 && z->depth != 4 && z->depth != 8 && z->depth != 16) return stbi__err("1/2/4/8/16-bit only","PNG not supported: 1/2/4/8/16-bit only"); + color = stbi__get8(s); if (color > 6) return stbi__err("bad ctype","Corrupt PNG"); + if (color == 3 && z->depth == 16) return stbi__err("bad ctype","Corrupt PNG"); + if (color == 3) pal_img_n = 3; else if (color & 1) return stbi__err("bad ctype","Corrupt PNG"); + comp = stbi__get8(s); if (comp) return stbi__err("bad comp method","Corrupt PNG"); + filter= stbi__get8(s); if (filter) return stbi__err("bad filter method","Corrupt PNG"); + interlace = stbi__get8(s); if (interlace>1) return stbi__err("bad interlace method","Corrupt PNG"); + if (!s->img_x || !s->img_y) return stbi__err("0-pixel image","Corrupt PNG"); + if (!pal_img_n) { + s->img_n = (color & 2 ? 3 : 1) + (color & 4 ? 1 : 0); + if ((1 << 30) / s->img_x / s->img_n < s->img_y) return stbi__err("too large", "Image too large to decode"); + if (scan == STBI__SCAN_header) return 1; + } else { + // if paletted, then pal_n is our final components, and + // img_n is # components to decompress/filter. + s->img_n = 1; + if ((1 << 30) / s->img_x / 4 < s->img_y) return stbi__err("too large","Corrupt PNG"); + // if SCAN_header, have to scan to see if we have a tRNS + } + break; + } + + case STBI__PNG_TYPE('P','L','T','E'): { + if (first) return stbi__err("first not IHDR", "Corrupt PNG"); + if (c.length > 256*3) return stbi__err("invalid PLTE","Corrupt PNG"); + pal_len = c.length / 3; + if (pal_len * 3 != c.length) return stbi__err("invalid PLTE","Corrupt PNG"); + for (i=0; i < pal_len; ++i) { + palette[i*4+0] = stbi__get8(s); + palette[i*4+1] = stbi__get8(s); + palette[i*4+2] = stbi__get8(s); + palette[i*4+3] = 255; + } + break; + } + + case STBI__PNG_TYPE('t','R','N','S'): { + if (first) return stbi__err("first not IHDR", "Corrupt PNG"); + if (z->idata) return stbi__err("tRNS after IDAT","Corrupt PNG"); + if (pal_img_n) { + if (scan == STBI__SCAN_header) { s->img_n = 4; return 1; } + if (pal_len == 0) return stbi__err("tRNS before PLTE","Corrupt PNG"); + if (c.length > pal_len) return stbi__err("bad tRNS len","Corrupt PNG"); + pal_img_n = 4; + for (i=0; i < c.length; ++i) + palette[i*4+3] = stbi__get8(s); + } else { + if (!(s->img_n & 1)) return stbi__err("tRNS with alpha","Corrupt PNG"); + if (c.length != (stbi__uint32) s->img_n*2) return stbi__err("bad tRNS len","Corrupt PNG"); + has_trans = 1; + if (z->depth == 16) { + for (k = 0; k < s->img_n; ++k) tc16[k] = (stbi__uint16)stbi__get16be(s); // copy the values as-is + } else { + for (k = 0; k < s->img_n; ++k) tc[k] = (stbi_uc)(stbi__get16be(s) & 255) * stbi__depth_scale_table[z->depth]; // non 8-bit images will be larger + } + } + break; + } + + case STBI__PNG_TYPE('I','D','A','T'): { + if (first) return stbi__err("first not IHDR", "Corrupt PNG"); + if (pal_img_n && !pal_len) return stbi__err("no PLTE","Corrupt PNG"); + if (scan == STBI__SCAN_header) { s->img_n = pal_img_n; return 1; } + if ((int)(ioff + c.length) < (int)ioff) return 0; + if (ioff + c.length > idata_limit) { + stbi__uint32 idata_limit_old = idata_limit; + stbi_uc *p; + if (idata_limit == 0) idata_limit = c.length > 4096 ? c.length : 4096; + while (ioff + c.length > idata_limit) + idata_limit *= 2; + STBI_NOTUSED(idata_limit_old); + p = (stbi_uc *) STBI_REALLOC_SIZED(z->idata, idata_limit_old, idata_limit); if (p == NULL) return stbi__err("outofmem", "Out of memory"); + z->idata = p; + } + if (!stbi__getn(s, z->idata+ioff,c.length)) return stbi__err("outofdata","Corrupt PNG"); + ioff += c.length; + break; + } + + case STBI__PNG_TYPE('I','E','N','D'): { + stbi__uint32 raw_len, bpl; + if (first) return stbi__err("first not IHDR", "Corrupt PNG"); + if (scan != STBI__SCAN_load) return 1; + if (z->idata == NULL) return stbi__err("no IDAT","Corrupt PNG"); + // initial guess for decoded data size to avoid unnecessary reallocs + bpl = (s->img_x * z->depth + 7) / 8; // bytes per line, per component + raw_len = bpl * s->img_y * s->img_n /* pixels */ + s->img_y /* filter mode per row */; + z->expanded = (stbi_uc *) stbi_zlib_decode_malloc_guesssize_headerflag((char *) z->idata, ioff, raw_len, (int *) &raw_len, !is_iphone); + if (z->expanded == NULL) return 0; // zlib should set error + STBI_FREE(z->idata); z->idata = NULL; + if ((req_comp == s->img_n+1 && req_comp != 3 && !pal_img_n) || has_trans) + s->img_out_n = s->img_n+1; + else + s->img_out_n = s->img_n; + if (!stbi__create_png_image(z, z->expanded, raw_len, s->img_out_n, z->depth, color, interlace)) return 0; + if (has_trans) { + if (z->depth == 16) { + if (!stbi__compute_transparency16(z, tc16, s->img_out_n)) return 0; + } else { + if (!stbi__compute_transparency(z, tc, s->img_out_n)) return 0; + } + } + if (is_iphone && stbi__de_iphone_flag && s->img_out_n > 2) + stbi__de_iphone(z); + if (pal_img_n) { + // pal_img_n == 3 or 4 + s->img_n = pal_img_n; // record the actual colors we had + s->img_out_n = pal_img_n; + if (req_comp >= 3) s->img_out_n = req_comp; + if (!stbi__expand_png_palette(z, palette, pal_len, s->img_out_n)) + return 0; + } else if (has_trans) { + // non-paletted image with tRNS -> source image has (constant) alpha + ++s->img_n; + } + STBI_FREE(z->expanded); z->expanded = NULL; + return 1; + } + + default: + // if critical, fail + if (first) return stbi__err("first not IHDR", "Corrupt PNG"); + if ((c.type & (1 << 29)) == 0) { + #ifndef STBI_NO_FAILURE_STRINGS + // not threadsafe + static char invalid_chunk[] = "XXXX PNG chunk not known"; + invalid_chunk[0] = STBI__BYTECAST(c.type >> 24); + invalid_chunk[1] = STBI__BYTECAST(c.type >> 16); + invalid_chunk[2] = STBI__BYTECAST(c.type >> 8); + invalid_chunk[3] = STBI__BYTECAST(c.type >> 0); + #endif + return stbi__err(invalid_chunk, "PNG not supported: unknown PNG chunk type"); + } + stbi__skip(s, c.length); + break; + } + // end of PNG chunk, read and skip CRC + stbi__get32be(s); + } +} + +static void *stbi__do_png(stbi__png *p, int *x, int *y, int *n, int req_comp, stbi__result_info *ri) +{ + void *result=NULL; + if (req_comp < 0 || req_comp > 4) return stbi__errpuc("bad req_comp", "Internal error"); + if (stbi__parse_png_file(p, STBI__SCAN_load, req_comp)) { + if (p->depth < 8) + ri->bits_per_channel = 8; + else + ri->bits_per_channel = p->depth; + result = p->out; + p->out = NULL; + if (req_comp && req_comp != p->s->img_out_n) { + if (ri->bits_per_channel == 8) + result = stbi__convert_format((unsigned char *) result, p->s->img_out_n, req_comp, p->s->img_x, p->s->img_y); + else + result = stbi__convert_format16((stbi__uint16 *) result, p->s->img_out_n, req_comp, p->s->img_x, p->s->img_y); + p->s->img_out_n = req_comp; + if (result == NULL) return result; + } + *x = p->s->img_x; + *y = p->s->img_y; + if (n) *n = p->s->img_n; + } + STBI_FREE(p->out); p->out = NULL; + STBI_FREE(p->expanded); p->expanded = NULL; + STBI_FREE(p->idata); p->idata = NULL; + + return result; +} + +static void *stbi__png_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) +{ + stbi__png p; + p.s = s; + return stbi__do_png(&p, x,y,comp,req_comp, ri); +} + +static int stbi__png_test(stbi__context *s) +{ + int r; + r = stbi__check_png_header(s); + stbi__rewind(s); + return r; +} + +static int stbi__png_info_raw(stbi__png *p, int *x, int *y, int *comp) +{ + if (!stbi__parse_png_file(p, STBI__SCAN_header, 0)) { + stbi__rewind( p->s ); + return 0; + } + if (x) *x = p->s->img_x; + if (y) *y = p->s->img_y; + if (comp) *comp = p->s->img_n; + return 1; +} + +static int stbi__png_info(stbi__context *s, int *x, int *y, int *comp) +{ + stbi__png p; + p.s = s; + return stbi__png_info_raw(&p, x, y, comp); +} + +static int stbi__png_is16(stbi__context *s) +{ + stbi__png p; + p.s = s; + if (!stbi__png_info_raw(&p, NULL, NULL, NULL)) + return 0; + if (p.depth != 16) { + stbi__rewind(p.s); + return 0; + } + return 1; +} +#endif + +// Microsoft/Windows BMP image + +#ifndef STBI_NO_BMP +static int stbi__bmp_test_raw(stbi__context *s) +{ + int r; + int sz; + if (stbi__get8(s) != 'B') return 0; + if (stbi__get8(s) != 'M') return 0; + stbi__get32le(s); // discard filesize + stbi__get16le(s); // discard reserved + stbi__get16le(s); // discard reserved + stbi__get32le(s); // discard data offset + sz = stbi__get32le(s); + r = (sz == 12 || sz == 40 || sz == 56 || sz == 108 || sz == 124); + return r; +} + +static int stbi__bmp_test(stbi__context *s) +{ + int r = stbi__bmp_test_raw(s); + stbi__rewind(s); + return r; +} + + +// returns 0..31 for the highest set bit +static int stbi__high_bit(unsigned int z) +{ + int n=0; + if (z == 0) return -1; + if (z >= 0x10000) { n += 16; z >>= 16; } + if (z >= 0x00100) { n += 8; z >>= 8; } + if (z >= 0x00010) { n += 4; z >>= 4; } + if (z >= 0x00004) { n += 2; z >>= 2; } + if (z >= 0x00002) { n += 1;/* >>= 1;*/ } + return n; +} + +static int stbi__bitcount(unsigned int a) +{ + a = (a & 0x55555555) + ((a >> 1) & 0x55555555); // max 2 + a = (a & 0x33333333) + ((a >> 2) & 0x33333333); // max 4 + a = (a + (a >> 4)) & 0x0f0f0f0f; // max 8 per 4, now 8 bits + a = (a + (a >> 8)); // max 16 per 8 bits + a = (a + (a >> 16)); // max 32 per 8 bits + return a & 0xff; +} + +// extract an arbitrarily-aligned N-bit value (N=bits) +// from v, and then make it 8-bits long and fractionally +// extend it to full full range. +static int stbi__shiftsigned(unsigned int v, int shift, int bits) +{ + static unsigned int mul_table[9] = { + 0, + 0xff/*0b11111111*/, 0x55/*0b01010101*/, 0x49/*0b01001001*/, 0x11/*0b00010001*/, + 0x21/*0b00100001*/, 0x41/*0b01000001*/, 0x81/*0b10000001*/, 0x01/*0b00000001*/, + }; + static unsigned int shift_table[9] = { + 0, 0,0,1,0,2,4,6,0, + }; + if (shift < 0) + v <<= -shift; + else + v >>= shift; + STBI_ASSERT(v >= 0 && v < 256); + v >>= (8-bits); + STBI_ASSERT(bits >= 0 && bits <= 8); + return (int) ((unsigned) v * mul_table[bits]) >> shift_table[bits]; +} + +typedef struct +{ + int bpp, offset, hsz; + unsigned int mr,mg,mb,ma, all_a; +} stbi__bmp_data; + +static void *stbi__bmp_parse_header(stbi__context *s, stbi__bmp_data *info) +{ + int hsz; + if (stbi__get8(s) != 'B' || stbi__get8(s) != 'M') return stbi__errpuc("not BMP", "Corrupt BMP"); + stbi__get32le(s); // discard filesize + stbi__get16le(s); // discard reserved + stbi__get16le(s); // discard reserved + info->offset = stbi__get32le(s); + info->hsz = hsz = stbi__get32le(s); + info->mr = info->mg = info->mb = info->ma = 0; + + if (hsz != 12 && hsz != 40 && hsz != 56 && hsz != 108 && hsz != 124) return stbi__errpuc("unknown BMP", "BMP type not supported: unknown"); + if (hsz == 12) { + s->img_x = stbi__get16le(s); + s->img_y = stbi__get16le(s); + } else { + s->img_x = stbi__get32le(s); + s->img_y = stbi__get32le(s); + } + if (stbi__get16le(s) != 1) return stbi__errpuc("bad BMP", "bad BMP"); + info->bpp = stbi__get16le(s); + if (hsz != 12) { + int compress = stbi__get32le(s); + if (compress == 1 || compress == 2) return stbi__errpuc("BMP RLE", "BMP type not supported: RLE"); + stbi__get32le(s); // discard sizeof + stbi__get32le(s); // discard hres + stbi__get32le(s); // discard vres + stbi__get32le(s); // discard colorsused + stbi__get32le(s); // discard max important + if (hsz == 40 || hsz == 56) { + if (hsz == 56) { + stbi__get32le(s); + stbi__get32le(s); + stbi__get32le(s); + stbi__get32le(s); + } + if (info->bpp == 16 || info->bpp == 32) { + if (compress == 0) { + if (info->bpp == 32) { + info->mr = 0xffu << 16; + info->mg = 0xffu << 8; + info->mb = 0xffu << 0; + info->ma = 0xffu << 24; + info->all_a = 0; // if all_a is 0 at end, then we loaded alpha channel but it was all 0 + } else { + info->mr = 31u << 10; + info->mg = 31u << 5; + info->mb = 31u << 0; + } + } else if (compress == 3) { + info->mr = stbi__get32le(s); + info->mg = stbi__get32le(s); + info->mb = stbi__get32le(s); + // not documented, but generated by photoshop and handled by mspaint + if (info->mr == info->mg && info->mg == info->mb) { + // ?!?!? + return stbi__errpuc("bad BMP", "bad BMP"); + } + } else + return stbi__errpuc("bad BMP", "bad BMP"); + } + } else { + int i; + if (hsz != 108 && hsz != 124) + return stbi__errpuc("bad BMP", "bad BMP"); + info->mr = stbi__get32le(s); + info->mg = stbi__get32le(s); + info->mb = stbi__get32le(s); + info->ma = stbi__get32le(s); + stbi__get32le(s); // discard color space + for (i=0; i < 12; ++i) + stbi__get32le(s); // discard color space parameters + if (hsz == 124) { + stbi__get32le(s); // discard rendering intent + stbi__get32le(s); // discard offset of profile data + stbi__get32le(s); // discard size of profile data + stbi__get32le(s); // discard reserved + } + } + } + return (void *) 1; +} + + +static void *stbi__bmp_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) +{ + stbi_uc *out; + unsigned int mr=0,mg=0,mb=0,ma=0, all_a; + stbi_uc pal[256][4]; + int psize=0,i,j,width; + int flip_vertically, pad, target; + stbi__bmp_data info; + STBI_NOTUSED(ri); + + info.all_a = 255; + if (stbi__bmp_parse_header(s, &info) == NULL) + return NULL; // error code already set + + flip_vertically = ((int) s->img_y) > 0; + s->img_y = abs((int) s->img_y); + + mr = info.mr; + mg = info.mg; + mb = info.mb; + ma = info.ma; + all_a = info.all_a; + + if (info.hsz == 12) { + if (info.bpp < 24) + psize = (info.offset - 14 - 24) / 3; + } else { + if (info.bpp < 16) + psize = (info.offset - 14 - info.hsz) >> 2; + } + + if (info.bpp == 24 && ma == 0xff000000) + s->img_n = 3; + else + s->img_n = ma ? 4 : 3; + if (req_comp && req_comp >= 3) // we can directly decode 3 or 4 + target = req_comp; + else + target = s->img_n; // if they want monochrome, we'll post-convert + + // sanity-check size + if (!stbi__mad3sizes_valid(target, s->img_x, s->img_y, 0)) + return stbi__errpuc("too large", "Corrupt BMP"); + + out = (stbi_uc *) stbi__malloc_mad3(target, s->img_x, s->img_y, 0); + if (!out) return stbi__errpuc("outofmem", "Out of memory"); + if (info.bpp < 16) { + int z=0; + if (psize == 0 || psize > 256) { STBI_FREE(out); return stbi__errpuc("invalid", "Corrupt BMP"); } + for (i=0; i < psize; ++i) { + pal[i][2] = stbi__get8(s); + pal[i][1] = stbi__get8(s); + pal[i][0] = stbi__get8(s); + if (info.hsz != 12) stbi__get8(s); + pal[i][3] = 255; + } + stbi__skip(s, info.offset - 14 - info.hsz - psize * (info.hsz == 12 ? 3 : 4)); + if (info.bpp == 1) width = (s->img_x + 7) >> 3; + else if (info.bpp == 4) width = (s->img_x + 1) >> 1; + else if (info.bpp == 8) width = s->img_x; + else { STBI_FREE(out); return stbi__errpuc("bad bpp", "Corrupt BMP"); } + pad = (-width)&3; + if (info.bpp == 1) { + for (j=0; j < (int) s->img_y; ++j) { + int bit_offset = 7, v = stbi__get8(s); + for (i=0; i < (int) s->img_x; ++i) { + int color = (v>>bit_offset)&0x1; + out[z++] = pal[color][0]; + out[z++] = pal[color][1]; + out[z++] = pal[color][2]; + if (target == 4) out[z++] = 255; + if (i+1 == (int) s->img_x) break; + if((--bit_offset) < 0) { + bit_offset = 7; + v = stbi__get8(s); + } + } + stbi__skip(s, pad); + } + } else { + for (j=0; j < (int) s->img_y; ++j) { + for (i=0; i < (int) s->img_x; i += 2) { + int v=stbi__get8(s),v2=0; + if (info.bpp == 4) { + v2 = v & 15; + v >>= 4; + } + out[z++] = pal[v][0]; + out[z++] = pal[v][1]; + out[z++] = pal[v][2]; + if (target == 4) out[z++] = 255; + if (i+1 == (int) s->img_x) break; + v = (info.bpp == 8) ? stbi__get8(s) : v2; + out[z++] = pal[v][0]; + out[z++] = pal[v][1]; + out[z++] = pal[v][2]; + if (target == 4) out[z++] = 255; + } + stbi__skip(s, pad); + } + } + } else { + int rshift=0,gshift=0,bshift=0,ashift=0,rcount=0,gcount=0,bcount=0,acount=0; + int z = 0; + int easy=0; + stbi__skip(s, info.offset - 14 - info.hsz); + if (info.bpp == 24) width = 3 * s->img_x; + else if (info.bpp == 16) width = 2*s->img_x; + else /* bpp = 32 and pad = 0 */ width=0; + pad = (-width) & 3; + if (info.bpp == 24) { + easy = 1; + } else if (info.bpp == 32) { + if (mb == 0xff && mg == 0xff00 && mr == 0x00ff0000 && ma == 0xff000000) + easy = 2; + } + if (!easy) { + if (!mr || !mg || !mb) { STBI_FREE(out); return stbi__errpuc("bad masks", "Corrupt BMP"); } + // right shift amt to put high bit in position #7 + rshift = stbi__high_bit(mr)-7; rcount = stbi__bitcount(mr); + gshift = stbi__high_bit(mg)-7; gcount = stbi__bitcount(mg); + bshift = stbi__high_bit(mb)-7; bcount = stbi__bitcount(mb); + ashift = stbi__high_bit(ma)-7; acount = stbi__bitcount(ma); + } + for (j=0; j < (int) s->img_y; ++j) { + if (easy) { + for (i=0; i < (int) s->img_x; ++i) { + unsigned char a; + out[z+2] = stbi__get8(s); + out[z+1] = stbi__get8(s); + out[z+0] = stbi__get8(s); + z += 3; + a = (easy == 2 ? stbi__get8(s) : 255); + all_a |= a; + if (target == 4) out[z++] = a; + } + } else { + int bpp = info.bpp; + for (i=0; i < (int) s->img_x; ++i) { + stbi__uint32 v = (bpp == 16 ? (stbi__uint32) stbi__get16le(s) : stbi__get32le(s)); + unsigned int a; + out[z++] = STBI__BYTECAST(stbi__shiftsigned(v & mr, rshift, rcount)); + out[z++] = STBI__BYTECAST(stbi__shiftsigned(v & mg, gshift, gcount)); + out[z++] = STBI__BYTECAST(stbi__shiftsigned(v & mb, bshift, bcount)); + a = (ma ? stbi__shiftsigned(v & ma, ashift, acount) : 255); + all_a |= a; + if (target == 4) out[z++] = STBI__BYTECAST(a); + } + } + stbi__skip(s, pad); + } + } + + // if alpha channel is all 0s, replace with all 255s + if (target == 4 && all_a == 0) + for (i=4*s->img_x*s->img_y-1; i >= 0; i -= 4) + out[i] = 255; + + if (flip_vertically) { + stbi_uc t; + for (j=0; j < (int) s->img_y>>1; ++j) { + stbi_uc *p1 = out + j *s->img_x*target; + stbi_uc *p2 = out + (s->img_y-1-j)*s->img_x*target; + for (i=0; i < (int) s->img_x*target; ++i) { + t = p1[i]; p1[i] = p2[i]; p2[i] = t; + } + } + } + + if (req_comp && req_comp != target) { + out = stbi__convert_format(out, target, req_comp, s->img_x, s->img_y); + if (out == NULL) return out; // stbi__convert_format frees input on failure + } + + *x = s->img_x; + *y = s->img_y; + if (comp) *comp = s->img_n; + return out; +} +#endif + +// Targa Truevision - TGA +// by Jonathan Dummer +#ifndef STBI_NO_TGA +// returns STBI_rgb or whatever, 0 on error +static int stbi__tga_get_comp(int bits_per_pixel, int is_grey, int* is_rgb16) +{ + // only RGB or RGBA (incl. 16bit) or grey allowed + if (is_rgb16) *is_rgb16 = 0; + switch(bits_per_pixel) { + case 8: return STBI_grey; + case 16: if(is_grey) return STBI_grey_alpha; + // fallthrough + case 15: if(is_rgb16) *is_rgb16 = 1; + return STBI_rgb; + case 24: // fallthrough + case 32: return bits_per_pixel/8; + default: return 0; + } +} + +static int stbi__tga_info(stbi__context *s, int *x, int *y, int *comp) +{ + int tga_w, tga_h, tga_comp, tga_image_type, tga_bits_per_pixel, tga_colormap_bpp; + int sz, tga_colormap_type; + stbi__get8(s); // discard Offset + tga_colormap_type = stbi__get8(s); // colormap type + if( tga_colormap_type > 1 ) { + stbi__rewind(s); + return 0; // only RGB or indexed allowed + } + tga_image_type = stbi__get8(s); // image type + if ( tga_colormap_type == 1 ) { // colormapped (paletted) image + if (tga_image_type != 1 && tga_image_type != 9) { + stbi__rewind(s); + return 0; + } + stbi__skip(s,4); // skip index of first colormap entry and number of entries + sz = stbi__get8(s); // check bits per palette color entry + if ( (sz != 8) && (sz != 15) && (sz != 16) && (sz != 24) && (sz != 32) ) { + stbi__rewind(s); + return 0; + } + stbi__skip(s,4); // skip image x and y origin + tga_colormap_bpp = sz; + } else { // "normal" image w/o colormap - only RGB or grey allowed, +/- RLE + if ( (tga_image_type != 2) && (tga_image_type != 3) && (tga_image_type != 10) && (tga_image_type != 11) ) { + stbi__rewind(s); + return 0; // only RGB or grey allowed, +/- RLE + } + stbi__skip(s,9); // skip colormap specification and image x/y origin + tga_colormap_bpp = 0; + } + tga_w = stbi__get16le(s); + if( tga_w < 1 ) { + stbi__rewind(s); + return 0; // test width + } + tga_h = stbi__get16le(s); + if( tga_h < 1 ) { + stbi__rewind(s); + return 0; // test height + } + tga_bits_per_pixel = stbi__get8(s); // bits per pixel + stbi__get8(s); // ignore alpha bits + if (tga_colormap_bpp != 0) { + if((tga_bits_per_pixel != 8) && (tga_bits_per_pixel != 16)) { + // when using a colormap, tga_bits_per_pixel is the size of the indexes + // I don't think anything but 8 or 16bit indexes makes sense + stbi__rewind(s); + return 0; + } + tga_comp = stbi__tga_get_comp(tga_colormap_bpp, 0, NULL); + } else { + tga_comp = stbi__tga_get_comp(tga_bits_per_pixel, (tga_image_type == 3) || (tga_image_type == 11), NULL); + } + if(!tga_comp) { + stbi__rewind(s); + return 0; + } + if (x) *x = tga_w; + if (y) *y = tga_h; + if (comp) *comp = tga_comp; + return 1; // seems to have passed everything +} + +static int stbi__tga_test(stbi__context *s) +{ + int res = 0; + int sz, tga_color_type; + stbi__get8(s); // discard Offset + tga_color_type = stbi__get8(s); // color type + if ( tga_color_type > 1 ) goto errorEnd; // only RGB or indexed allowed + sz = stbi__get8(s); // image type + if ( tga_color_type == 1 ) { // colormapped (paletted) image + if (sz != 1 && sz != 9) goto errorEnd; // colortype 1 demands image type 1 or 9 + stbi__skip(s,4); // skip index of first colormap entry and number of entries + sz = stbi__get8(s); // check bits per palette color entry + if ( (sz != 8) && (sz != 15) && (sz != 16) && (sz != 24) && (sz != 32) ) goto errorEnd; + stbi__skip(s,4); // skip image x and y origin + } else { // "normal" image w/o colormap + if ( (sz != 2) && (sz != 3) && (sz != 10) && (sz != 11) ) goto errorEnd; // only RGB or grey allowed, +/- RLE + stbi__skip(s,9); // skip colormap specification and image x/y origin + } + if ( stbi__get16le(s) < 1 ) goto errorEnd; // test width + if ( stbi__get16le(s) < 1 ) goto errorEnd; // test height + sz = stbi__get8(s); // bits per pixel + if ( (tga_color_type == 1) && (sz != 8) && (sz != 16) ) goto errorEnd; // for colormapped images, bpp is size of an index + if ( (sz != 8) && (sz != 15) && (sz != 16) && (sz != 24) && (sz != 32) ) goto errorEnd; + + res = 1; // if we got this far, everything's good and we can return 1 instead of 0 + +errorEnd: + stbi__rewind(s); + return res; +} + +// read 16bit value and convert to 24bit RGB +static void stbi__tga_read_rgb16(stbi__context *s, stbi_uc* out) +{ + stbi__uint16 px = (stbi__uint16)stbi__get16le(s); + stbi__uint16 fiveBitMask = 31; + // we have 3 channels with 5bits each + int r = (px >> 10) & fiveBitMask; + int g = (px >> 5) & fiveBitMask; + int b = px & fiveBitMask; + // Note that this saves the data in RGB(A) order, so it doesn't need to be swapped later + out[0] = (stbi_uc)((r * 255)/31); + out[1] = (stbi_uc)((g * 255)/31); + out[2] = (stbi_uc)((b * 255)/31); + + // some people claim that the most significant bit might be used for alpha + // (possibly if an alpha-bit is set in the "image descriptor byte") + // but that only made 16bit test images completely translucent.. + // so let's treat all 15 and 16bit TGAs as RGB with no alpha. +} + +static void *stbi__tga_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) +{ + // read in the TGA header stuff + int tga_offset = stbi__get8(s); + int tga_indexed = stbi__get8(s); + int tga_image_type = stbi__get8(s); + int tga_is_RLE = 0; + int tga_palette_start = stbi__get16le(s); + int tga_palette_len = stbi__get16le(s); + int tga_palette_bits = stbi__get8(s); + int tga_x_origin = stbi__get16le(s); + int tga_y_origin = stbi__get16le(s); + int tga_width = stbi__get16le(s); + int tga_height = stbi__get16le(s); + int tga_bits_per_pixel = stbi__get8(s); + int tga_comp, tga_rgb16=0; + int tga_inverted = stbi__get8(s); + // int tga_alpha_bits = tga_inverted & 15; // the 4 lowest bits - unused (useless?) + // image data + unsigned char *tga_data; + unsigned char *tga_palette = NULL; + int i, j; + unsigned char raw_data[4] = {0}; + int RLE_count = 0; + int RLE_repeating = 0; + int read_next_pixel = 1; + STBI_NOTUSED(ri); + STBI_NOTUSED(tga_x_origin); // @TODO + STBI_NOTUSED(tga_y_origin); // @TODO + + // do a tiny bit of precessing + if ( tga_image_type >= 8 ) + { + tga_image_type -= 8; + tga_is_RLE = 1; + } + tga_inverted = 1 - ((tga_inverted >> 5) & 1); + + // If I'm paletted, then I'll use the number of bits from the palette + if ( tga_indexed ) tga_comp = stbi__tga_get_comp(tga_palette_bits, 0, &tga_rgb16); + else tga_comp = stbi__tga_get_comp(tga_bits_per_pixel, (tga_image_type == 3), &tga_rgb16); + + if(!tga_comp) // shouldn't really happen, stbi__tga_test() should have ensured basic consistency + return stbi__errpuc("bad format", "Can't find out TGA pixelformat"); + + // tga info + *x = tga_width; + *y = tga_height; + if (comp) *comp = tga_comp; + + if (!stbi__mad3sizes_valid(tga_width, tga_height, tga_comp, 0)) + return stbi__errpuc("too large", "Corrupt TGA"); + + tga_data = (unsigned char*)stbi__malloc_mad3(tga_width, tga_height, tga_comp, 0); + if (!tga_data) return stbi__errpuc("outofmem", "Out of memory"); + + // skip to the data's starting position (offset usually = 0) + stbi__skip(s, tga_offset ); + + if ( !tga_indexed && !tga_is_RLE && !tga_rgb16 ) { + for (i=0; i < tga_height; ++i) { + int row = tga_inverted ? tga_height -i - 1 : i; + stbi_uc *tga_row = tga_data + row*tga_width*tga_comp; + stbi__getn(s, tga_row, tga_width * tga_comp); + } + } else { + // do I need to load a palette? + if ( tga_indexed) + { + // any data to skip? (offset usually = 0) + stbi__skip(s, tga_palette_start ); + // load the palette + tga_palette = (unsigned char*)stbi__malloc_mad2(tga_palette_len, tga_comp, 0); + if (!tga_palette) { + STBI_FREE(tga_data); + return stbi__errpuc("outofmem", "Out of memory"); + } + if (tga_rgb16) { + stbi_uc *pal_entry = tga_palette; + STBI_ASSERT(tga_comp == STBI_rgb); + for (i=0; i < tga_palette_len; ++i) { + stbi__tga_read_rgb16(s, pal_entry); + pal_entry += tga_comp; + } + } else if (!stbi__getn(s, tga_palette, tga_palette_len * tga_comp)) { + STBI_FREE(tga_data); + STBI_FREE(tga_palette); + return stbi__errpuc("bad palette", "Corrupt TGA"); + } + } + // load the data + for (i=0; i < tga_width * tga_height; ++i) + { + // if I'm in RLE mode, do I need to get a RLE stbi__pngchunk? + if ( tga_is_RLE ) + { + if ( RLE_count == 0 ) + { + // yep, get the next byte as a RLE command + int RLE_cmd = stbi__get8(s); + RLE_count = 1 + (RLE_cmd & 127); + RLE_repeating = RLE_cmd >> 7; + read_next_pixel = 1; + } else if ( !RLE_repeating ) + { + read_next_pixel = 1; + } + } else + { + read_next_pixel = 1; + } + // OK, if I need to read a pixel, do it now + if ( read_next_pixel ) + { + // load however much data we did have + if ( tga_indexed ) + { + // read in index, then perform the lookup + int pal_idx = (tga_bits_per_pixel == 8) ? stbi__get8(s) : stbi__get16le(s); + if ( pal_idx >= tga_palette_len ) { + // invalid index + pal_idx = 0; + } + pal_idx *= tga_comp; + for (j = 0; j < tga_comp; ++j) { + raw_data[j] = tga_palette[pal_idx+j]; + } + } else if(tga_rgb16) { + STBI_ASSERT(tga_comp == STBI_rgb); + stbi__tga_read_rgb16(s, raw_data); + } else { + // read in the data raw + for (j = 0; j < tga_comp; ++j) { + raw_data[j] = stbi__get8(s); + } + } + // clear the reading flag for the next pixel + read_next_pixel = 0; + } // end of reading a pixel + + // copy data + for (j = 0; j < tga_comp; ++j) + tga_data[i*tga_comp+j] = raw_data[j]; + + // in case we're in RLE mode, keep counting down + --RLE_count; + } + // do I need to invert the image? + if ( tga_inverted ) + { + for (j = 0; j*2 < tga_height; ++j) + { + int index1 = j * tga_width * tga_comp; + int index2 = (tga_height - 1 - j) * tga_width * tga_comp; + for (i = tga_width * tga_comp; i > 0; --i) + { + unsigned char temp = tga_data[index1]; + tga_data[index1] = tga_data[index2]; + tga_data[index2] = temp; + ++index1; + ++index2; + } + } + } + // clear my palette, if I had one + if ( tga_palette != NULL ) + { + STBI_FREE( tga_palette ); + } + } + + // swap RGB - if the source data was RGB16, it already is in the right order + if (tga_comp >= 3 && !tga_rgb16) + { + unsigned char* tga_pixel = tga_data; + for (i=0; i < tga_width * tga_height; ++i) + { + unsigned char temp = tga_pixel[0]; + tga_pixel[0] = tga_pixel[2]; + tga_pixel[2] = temp; + tga_pixel += tga_comp; + } + } + + // convert to target component count + if (req_comp && req_comp != tga_comp) + tga_data = stbi__convert_format(tga_data, tga_comp, req_comp, tga_width, tga_height); + + // the things I do to get rid of an error message, and yet keep + // Microsoft's C compilers happy... [8^( + tga_palette_start = tga_palette_len = tga_palette_bits = + tga_x_origin = tga_y_origin = 0; + STBI_NOTUSED(tga_palette_start); + // OK, done + return tga_data; +} +#endif + +// ************************************************************************************************* +// Photoshop PSD loader -- PD by Thatcher Ulrich, integration by Nicolas Schulz, tweaked by STB + +#ifndef STBI_NO_PSD +static int stbi__psd_test(stbi__context *s) +{ + int r = (stbi__get32be(s) == 0x38425053); + stbi__rewind(s); + return r; +} + +static int stbi__psd_decode_rle(stbi__context *s, stbi_uc *p, int pixelCount) +{ + int count, nleft, len; + + count = 0; + while ((nleft = pixelCount - count) > 0) { + len = stbi__get8(s); + if (len == 128) { + // No-op. + } else if (len < 128) { + // Copy next len+1 bytes literally. + len++; + if (len > nleft) return 0; // corrupt data + count += len; + while (len) { + *p = stbi__get8(s); + p += 4; + len--; + } + } else if (len > 128) { + stbi_uc val; + // Next -len+1 bytes in the dest are replicated from next source byte. + // (Interpret len as a negative 8-bit int.) + len = 257 - len; + if (len > nleft) return 0; // corrupt data + val = stbi__get8(s); + count += len; + while (len) { + *p = val; + p += 4; + len--; + } + } + } + + return 1; +} + +static void *stbi__psd_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri, int bpc) +{ + int pixelCount; + int channelCount, compression; + int channel, i; + int bitdepth; + int w,h; + stbi_uc *out; + STBI_NOTUSED(ri); + + // Check identifier + if (stbi__get32be(s) != 0x38425053) // "8BPS" + return stbi__errpuc("not PSD", "Corrupt PSD image"); + + // Check file type version. + if (stbi__get16be(s) != 1) + return stbi__errpuc("wrong version", "Unsupported version of PSD image"); + + // Skip 6 reserved bytes. + stbi__skip(s, 6 ); + + // Read the number of channels (R, G, B, A, etc). + channelCount = stbi__get16be(s); + if (channelCount < 0 || channelCount > 16) + return stbi__errpuc("wrong channel count", "Unsupported number of channels in PSD image"); + + // Read the rows and columns of the image. + h = stbi__get32be(s); + w = stbi__get32be(s); + + // Make sure the depth is 8 bits. + bitdepth = stbi__get16be(s); + if (bitdepth != 8 && bitdepth != 16) + return stbi__errpuc("unsupported bit depth", "PSD bit depth is not 8 or 16 bit"); + + // Make sure the color mode is RGB. + // Valid options are: + // 0: Bitmap + // 1: Grayscale + // 2: Indexed color + // 3: RGB color + // 4: CMYK color + // 7: Multichannel + // 8: Duotone + // 9: Lab color + if (stbi__get16be(s) != 3) + return stbi__errpuc("wrong color format", "PSD is not in RGB color format"); + + // Skip the Mode Data. (It's the palette for indexed color; other info for other modes.) + stbi__skip(s,stbi__get32be(s) ); + + // Skip the image resources. (resolution, pen tool paths, etc) + stbi__skip(s, stbi__get32be(s) ); + + // Skip the reserved data. + stbi__skip(s, stbi__get32be(s) ); + + // Find out if the data is compressed. + // Known values: + // 0: no compression + // 1: RLE compressed + compression = stbi__get16be(s); + if (compression > 1) + return stbi__errpuc("bad compression", "PSD has an unknown compression format"); + + // Check size + if (!stbi__mad3sizes_valid(4, w, h, 0)) + return stbi__errpuc("too large", "Corrupt PSD"); + + // Create the destination image. + + if (!compression && bitdepth == 16 && bpc == 16) { + out = (stbi_uc *) stbi__malloc_mad3(8, w, h, 0); + ri->bits_per_channel = 16; + } else + out = (stbi_uc *) stbi__malloc(4 * w*h); + + if (!out) return stbi__errpuc("outofmem", "Out of memory"); + pixelCount = w*h; + + // Initialize the data to zero. + //memset( out, 0, pixelCount * 4 ); + + // Finally, the image data. + if (compression) { + // RLE as used by .PSD and .TIFF + // Loop until you get the number of unpacked bytes you are expecting: + // Read the next source byte into n. + // If n is between 0 and 127 inclusive, copy the next n+1 bytes literally. + // Else if n is between -127 and -1 inclusive, copy the next byte -n+1 times. + // Else if n is 128, noop. + // Endloop + + // The RLE-compressed data is preceded by a 2-byte data count for each row in the data, + // which we're going to just skip. + stbi__skip(s, h * channelCount * 2 ); + + // Read the RLE data by channel. + for (channel = 0; channel < 4; channel++) { + stbi_uc *p; + + p = out+channel; + if (channel >= channelCount) { + // Fill this channel with default data. + for (i = 0; i < pixelCount; i++, p += 4) + *p = (channel == 3 ? 255 : 0); + } else { + // Read the RLE data. + if (!stbi__psd_decode_rle(s, p, pixelCount)) { + STBI_FREE(out); + return stbi__errpuc("corrupt", "bad RLE data"); + } + } + } + + } else { + // We're at the raw image data. It's each channel in order (Red, Green, Blue, Alpha, ...) + // where each channel consists of an 8-bit (or 16-bit) value for each pixel in the image. + + // Read the data by channel. + for (channel = 0; channel < 4; channel++) { + if (channel >= channelCount) { + // Fill this channel with default data. + if (bitdepth == 16 && bpc == 16) { + stbi__uint16 *q = ((stbi__uint16 *) out) + channel; + stbi__uint16 val = channel == 3 ? 65535 : 0; + for (i = 0; i < pixelCount; i++, q += 4) + *q = val; + } else { + stbi_uc *p = out+channel; + stbi_uc val = channel == 3 ? 255 : 0; + for (i = 0; i < pixelCount; i++, p += 4) + *p = val; + } + } else { + if (ri->bits_per_channel == 16) { // output bpc + stbi__uint16 *q = ((stbi__uint16 *) out) + channel; + for (i = 0; i < pixelCount; i++, q += 4) + *q = (stbi__uint16) stbi__get16be(s); + } else { + stbi_uc *p = out+channel; + if (bitdepth == 16) { // input bpc + for (i = 0; i < pixelCount; i++, p += 4) + *p = (stbi_uc) (stbi__get16be(s) >> 8); + } else { + for (i = 0; i < pixelCount; i++, p += 4) + *p = stbi__get8(s); + } + } + } + } + } + + // remove weird white matte from PSD + if (channelCount >= 4) { + if (ri->bits_per_channel == 16) { + for (i=0; i < w*h; ++i) { + stbi__uint16 *pixel = (stbi__uint16 *) out + 4*i; + if (pixel[3] != 0 && pixel[3] != 65535) { + float a = pixel[3] / 65535.0f; + float ra = 1.0f / a; + float inv_a = 65535.0f * (1 - ra); + pixel[0] = (stbi__uint16) (pixel[0]*ra + inv_a); + pixel[1] = (stbi__uint16) (pixel[1]*ra + inv_a); + pixel[2] = (stbi__uint16) (pixel[2]*ra + inv_a); + } + } + } else { + for (i=0; i < w*h; ++i) { + unsigned char *pixel = out + 4*i; + if (pixel[3] != 0 && pixel[3] != 255) { + float a = pixel[3] / 255.0f; + float ra = 1.0f / a; + float inv_a = 255.0f * (1 - ra); + pixel[0] = (unsigned char) (pixel[0]*ra + inv_a); + pixel[1] = (unsigned char) (pixel[1]*ra + inv_a); + pixel[2] = (unsigned char) (pixel[2]*ra + inv_a); + } + } + } + } + + // convert to desired output format + if (req_comp && req_comp != 4) { + if (ri->bits_per_channel == 16) + out = (stbi_uc *) stbi__convert_format16((stbi__uint16 *) out, 4, req_comp, w, h); + else + out = stbi__convert_format(out, 4, req_comp, w, h); + if (out == NULL) return out; // stbi__convert_format frees input on failure + } + + if (comp) *comp = 4; + *y = h; + *x = w; + + return out; +} +#endif + +// ************************************************************************************************* +// Softimage PIC loader +// by Tom Seddon +// +// See http://softimage.wiki.softimage.com/index.php/INFO:_PIC_file_format +// See http://ozviz.wasp.uwa.edu.au/~pbourke/dataformats/softimagepic/ + +#ifndef STBI_NO_PIC +static int stbi__pic_is4(stbi__context *s,const char *str) +{ + int i; + for (i=0; i<4; ++i) + if (stbi__get8(s) != (stbi_uc)str[i]) + return 0; + + return 1; +} + +static int stbi__pic_test_core(stbi__context *s) +{ + int i; + + if (!stbi__pic_is4(s,"\x53\x80\xF6\x34")) + return 0; + + for(i=0;i<84;++i) + stbi__get8(s); + + if (!stbi__pic_is4(s,"PICT")) + return 0; + + return 1; +} + +typedef struct +{ + stbi_uc size,type,channel; +} stbi__pic_packet; + +static stbi_uc *stbi__readval(stbi__context *s, int channel, stbi_uc *dest) +{ + int mask=0x80, i; + + for (i=0; i<4; ++i, mask>>=1) { + if (channel & mask) { + if (stbi__at_eof(s)) return stbi__errpuc("bad file","PIC file too short"); + dest[i]=stbi__get8(s); + } + } + + return dest; +} + +static void stbi__copyval(int channel,stbi_uc *dest,const stbi_uc *src) +{ + int mask=0x80,i; + + for (i=0;i<4; ++i, mask>>=1) + if (channel&mask) + dest[i]=src[i]; +} + +static stbi_uc *stbi__pic_load_core(stbi__context *s,int width,int height,int *comp, stbi_uc *result) +{ + int act_comp=0,num_packets=0,y,chained; + stbi__pic_packet packets[10]; + + // this will (should...) cater for even some bizarre stuff like having data + // for the same channel in multiple packets. + do { + stbi__pic_packet *packet; + + if (num_packets==sizeof(packets)/sizeof(packets[0])) + return stbi__errpuc("bad format","too many packets"); + + packet = &packets[num_packets++]; + + chained = stbi__get8(s); + packet->size = stbi__get8(s); + packet->type = stbi__get8(s); + packet->channel = stbi__get8(s); + + act_comp |= packet->channel; + + if (stbi__at_eof(s)) return stbi__errpuc("bad file","file too short (reading packets)"); + if (packet->size != 8) return stbi__errpuc("bad format","packet isn't 8bpp"); + } while (chained); + + *comp = (act_comp & 0x10 ? 4 : 3); // has alpha channel? + + for(y=0; ytype) { + default: + return stbi__errpuc("bad format","packet has bad compression type"); + + case 0: {//uncompressed + int x; + + for(x=0;xchannel,dest)) + return 0; + break; + } + + case 1://Pure RLE + { + int left=width, i; + + while (left>0) { + stbi_uc count,value[4]; + + count=stbi__get8(s); + if (stbi__at_eof(s)) return stbi__errpuc("bad file","file too short (pure read count)"); + + if (count > left) + count = (stbi_uc) left; + + if (!stbi__readval(s,packet->channel,value)) return 0; + + for(i=0; ichannel,dest,value); + left -= count; + } + } + break; + + case 2: {//Mixed RLE + int left=width; + while (left>0) { + int count = stbi__get8(s), i; + if (stbi__at_eof(s)) return stbi__errpuc("bad file","file too short (mixed read count)"); + + if (count >= 128) { // Repeated + stbi_uc value[4]; + + if (count==128) + count = stbi__get16be(s); + else + count -= 127; + if (count > left) + return stbi__errpuc("bad file","scanline overrun"); + + if (!stbi__readval(s,packet->channel,value)) + return 0; + + for(i=0;ichannel,dest,value); + } else { // Raw + ++count; + if (count>left) return stbi__errpuc("bad file","scanline overrun"); + + for(i=0;ichannel,dest)) + return 0; + } + left-=count; + } + break; + } + } + } + } + + return result; +} + +static void *stbi__pic_load(stbi__context *s,int *px,int *py,int *comp,int req_comp, stbi__result_info *ri) +{ + stbi_uc *result; + int i, x,y, internal_comp; + STBI_NOTUSED(ri); + + if (!comp) comp = &internal_comp; + + for (i=0; i<92; ++i) + stbi__get8(s); + + x = stbi__get16be(s); + y = stbi__get16be(s); + if (stbi__at_eof(s)) return stbi__errpuc("bad file","file too short (pic header)"); + if (!stbi__mad3sizes_valid(x, y, 4, 0)) return stbi__errpuc("too large", "PIC image too large to decode"); + + stbi__get32be(s); //skip `ratio' + stbi__get16be(s); //skip `fields' + stbi__get16be(s); //skip `pad' + + // intermediate buffer is RGBA + result = (stbi_uc *) stbi__malloc_mad3(x, y, 4, 0); + memset(result, 0xff, x*y*4); + + if (!stbi__pic_load_core(s,x,y,comp, result)) { + STBI_FREE(result); + result=0; + } + *px = x; + *py = y; + if (req_comp == 0) req_comp = *comp; + result=stbi__convert_format(result,4,req_comp,x,y); + + return result; +} + +static int stbi__pic_test(stbi__context *s) +{ + int r = stbi__pic_test_core(s); + stbi__rewind(s); + return r; +} +#endif + +// ************************************************************************************************* +// GIF loader -- public domain by Jean-Marc Lienher -- simplified/shrunk by stb + +#ifndef STBI_NO_GIF +typedef struct +{ + stbi__int16 prefix; + stbi_uc first; + stbi_uc suffix; +} stbi__gif_lzw; + +typedef struct +{ + int w,h; + stbi_uc *out; // output buffer (always 4 components) + stbi_uc *background; // The current "background" as far as a gif is concerned + stbi_uc *history; + int flags, bgindex, ratio, transparent, eflags; + stbi_uc pal[256][4]; + stbi_uc lpal[256][4]; + stbi__gif_lzw codes[8192]; + stbi_uc *color_table; + int parse, step; + int lflags; + int start_x, start_y; + int max_x, max_y; + int cur_x, cur_y; + int line_size; + int delay; +} stbi__gif; + +static int stbi__gif_test_raw(stbi__context *s) +{ + int sz; + if (stbi__get8(s) != 'G' || stbi__get8(s) != 'I' || stbi__get8(s) != 'F' || stbi__get8(s) != '8') return 0; + sz = stbi__get8(s); + if (sz != '9' && sz != '7') return 0; + if (stbi__get8(s) != 'a') return 0; + return 1; +} + +static int stbi__gif_test(stbi__context *s) +{ + int r = stbi__gif_test_raw(s); + stbi__rewind(s); + return r; +} + +static void stbi__gif_parse_colortable(stbi__context *s, stbi_uc pal[256][4], int num_entries, int transp) +{ + int i; + for (i=0; i < num_entries; ++i) { + pal[i][2] = stbi__get8(s); + pal[i][1] = stbi__get8(s); + pal[i][0] = stbi__get8(s); + pal[i][3] = transp == i ? 0 : 255; + } +} + +static int stbi__gif_header(stbi__context *s, stbi__gif *g, int *comp, int is_info) +{ + stbi_uc version; + if (stbi__get8(s) != 'G' || stbi__get8(s) != 'I' || stbi__get8(s) != 'F' || stbi__get8(s) != '8') + return stbi__err("not GIF", "Corrupt GIF"); + + version = stbi__get8(s); + if (version != '7' && version != '9') return stbi__err("not GIF", "Corrupt GIF"); + if (stbi__get8(s) != 'a') return stbi__err("not GIF", "Corrupt GIF"); + + stbi__g_failure_reason = ""; + g->w = stbi__get16le(s); + g->h = stbi__get16le(s); + g->flags = stbi__get8(s); + g->bgindex = stbi__get8(s); + g->ratio = stbi__get8(s); + g->transparent = -1; + + if (comp != 0) *comp = 4; // can't actually tell whether it's 3 or 4 until we parse the comments + + if (is_info) return 1; + + if (g->flags & 0x80) + stbi__gif_parse_colortable(s,g->pal, 2 << (g->flags & 7), -1); + + return 1; +} + +static int stbi__gif_info_raw(stbi__context *s, int *x, int *y, int *comp) +{ + stbi__gif* g = (stbi__gif*) stbi__malloc(sizeof(stbi__gif)); + if (!stbi__gif_header(s, g, comp, 1)) { + STBI_FREE(g); + stbi__rewind( s ); + return 0; + } + if (x) *x = g->w; + if (y) *y = g->h; + STBI_FREE(g); + return 1; +} + +static void stbi__out_gif_code(stbi__gif *g, stbi__uint16 code) +{ + stbi_uc *p, *c; + int idx; + + // recurse to decode the prefixes, since the linked-list is backwards, + // and working backwards through an interleaved image would be nasty + if (g->codes[code].prefix >= 0) + stbi__out_gif_code(g, g->codes[code].prefix); + + if (g->cur_y >= g->max_y) return; + + idx = g->cur_x + g->cur_y; + p = &g->out[idx]; + g->history[idx / 4] = 1; + + c = &g->color_table[g->codes[code].suffix * 4]; + if (c[3] > 128) { // don't render transparent pixels; + p[0] = c[2]; + p[1] = c[1]; + p[2] = c[0]; + p[3] = c[3]; + } + g->cur_x += 4; + + if (g->cur_x >= g->max_x) { + g->cur_x = g->start_x; + g->cur_y += g->step; + + while (g->cur_y >= g->max_y && g->parse > 0) { + g->step = (1 << g->parse) * g->line_size; + g->cur_y = g->start_y + (g->step >> 1); + --g->parse; + } + } +} + +static stbi_uc *stbi__process_gif_raster(stbi__context *s, stbi__gif *g) +{ + stbi_uc lzw_cs; + stbi__int32 len, init_code; + stbi__uint32 first; + stbi__int32 codesize, codemask, avail, oldcode, bits, valid_bits, clear; + stbi__gif_lzw *p; + + lzw_cs = stbi__get8(s); + if (lzw_cs > 12) return NULL; + clear = 1 << lzw_cs; + first = 1; + codesize = lzw_cs + 1; + codemask = (1 << codesize) - 1; + bits = 0; + valid_bits = 0; + for (init_code = 0; init_code < clear; init_code++) { + g->codes[init_code].prefix = -1; + g->codes[init_code].first = (stbi_uc) init_code; + g->codes[init_code].suffix = (stbi_uc) init_code; + } + + // support no starting clear code + avail = clear+2; + oldcode = -1; + + len = 0; + for(;;) { + if (valid_bits < codesize) { + if (len == 0) { + len = stbi__get8(s); // start new block + if (len == 0) + return g->out; + } + --len; + bits |= (stbi__int32) stbi__get8(s) << valid_bits; + valid_bits += 8; + } else { + stbi__int32 code = bits & codemask; + bits >>= codesize; + valid_bits -= codesize; + // @OPTIMIZE: is there some way we can accelerate the non-clear path? + if (code == clear) { // clear code + codesize = lzw_cs + 1; + codemask = (1 << codesize) - 1; + avail = clear + 2; + oldcode = -1; + first = 0; + } else if (code == clear + 1) { // end of stream code + stbi__skip(s, len); + while ((len = stbi__get8(s)) > 0) + stbi__skip(s,len); + return g->out; + } else if (code <= avail) { + if (first) { + return stbi__errpuc("no clear code", "Corrupt GIF"); + } + + if (oldcode >= 0) { + p = &g->codes[avail++]; + if (avail > 8192) { + return stbi__errpuc("too many codes", "Corrupt GIF"); + } + + p->prefix = (stbi__int16) oldcode; + p->first = g->codes[oldcode].first; + p->suffix = (code == avail) ? p->first : g->codes[code].first; + } else if (code == avail) + return stbi__errpuc("illegal code in raster", "Corrupt GIF"); + + stbi__out_gif_code(g, (stbi__uint16) code); + + if ((avail & codemask) == 0 && avail <= 0x0FFF) { + codesize++; + codemask = (1 << codesize) - 1; + } + + oldcode = code; + } else { + return stbi__errpuc("illegal code in raster", "Corrupt GIF"); + } + } + } +} + +// this function is designed to support animated gifs, although stb_image doesn't support it +// two back is the image from two frames ago, used for a very specific disposal format +static stbi_uc *stbi__gif_load_next(stbi__context *s, stbi__gif *g, int *comp, int req_comp, stbi_uc *two_back) +{ + int dispose; + int first_frame; + int pi; + int pcount; + STBI_NOTUSED(req_comp); + + // on first frame, any non-written pixels get the background colour (non-transparent) + first_frame = 0; + if (g->out == 0) { + if (!stbi__gif_header(s, g, comp,0)) return 0; // stbi__g_failure_reason set by stbi__gif_header + if (!stbi__mad3sizes_valid(4, g->w, g->h, 0)) + return stbi__errpuc("too large", "GIF image is too large"); + pcount = g->w * g->h; + g->out = (stbi_uc *) stbi__malloc(4 * pcount); + g->background = (stbi_uc *) stbi__malloc(4 * pcount); + g->history = (stbi_uc *) stbi__malloc(pcount); + if (!g->out || !g->background || !g->history) + return stbi__errpuc("outofmem", "Out of memory"); + + // image is treated as "transparent" at the start - ie, nothing overwrites the current background; + // background colour is only used for pixels that are not rendered first frame, after that "background" + // color refers to the color that was there the previous frame. + memset(g->out, 0x00, 4 * pcount); + memset(g->background, 0x00, 4 * pcount); // state of the background (starts transparent) + memset(g->history, 0x00, pcount); // pixels that were affected previous frame + first_frame = 1; + } else { + // second frame - how do we dispoase of the previous one? + dispose = (g->eflags & 0x1C) >> 2; + pcount = g->w * g->h; + + if ((dispose == 3) && (two_back == 0)) { + dispose = 2; // if I don't have an image to revert back to, default to the old background + } + + if (dispose == 3) { // use previous graphic + for (pi = 0; pi < pcount; ++pi) { + if (g->history[pi]) { + memcpy( &g->out[pi * 4], &two_back[pi * 4], 4 ); + } + } + } else if (dispose == 2) { + // restore what was changed last frame to background before that frame; + for (pi = 0; pi < pcount; ++pi) { + if (g->history[pi]) { + memcpy( &g->out[pi * 4], &g->background[pi * 4], 4 ); + } + } + } else { + // This is a non-disposal case eithe way, so just + // leave the pixels as is, and they will become the new background + // 1: do not dispose + // 0: not specified. + } + + // background is what out is after the undoing of the previou frame; + memcpy( g->background, g->out, 4 * g->w * g->h ); + } + + // clear my history; + memset( g->history, 0x00, g->w * g->h ); // pixels that were affected previous frame + + for (;;) { + int tag = stbi__get8(s); + switch (tag) { + case 0x2C: /* Image Descriptor */ + { + stbi__int32 x, y, w, h; + stbi_uc *o; + + x = stbi__get16le(s); + y = stbi__get16le(s); + w = stbi__get16le(s); + h = stbi__get16le(s); + if (((x + w) > (g->w)) || ((y + h) > (g->h))) + return stbi__errpuc("bad Image Descriptor", "Corrupt GIF"); + + g->line_size = g->w * 4; + g->start_x = x * 4; + g->start_y = y * g->line_size; + g->max_x = g->start_x + w * 4; + g->max_y = g->start_y + h * g->line_size; + g->cur_x = g->start_x; + g->cur_y = g->start_y; + + // if the width of the specified rectangle is 0, that means + // we may not see *any* pixels or the image is malformed; + // to make sure this is caught, move the current y down to + // max_y (which is what out_gif_code checks). + if (w == 0) + g->cur_y = g->max_y; + + g->lflags = stbi__get8(s); + + if (g->lflags & 0x40) { + g->step = 8 * g->line_size; // first interlaced spacing + g->parse = 3; + } else { + g->step = g->line_size; + g->parse = 0; + } + + if (g->lflags & 0x80) { + stbi__gif_parse_colortable(s,g->lpal, 2 << (g->lflags & 7), g->eflags & 0x01 ? g->transparent : -1); + g->color_table = (stbi_uc *) g->lpal; + } else if (g->flags & 0x80) { + g->color_table = (stbi_uc *) g->pal; + } else + return stbi__errpuc("missing color table", "Corrupt GIF"); + + o = stbi__process_gif_raster(s, g); + if (!o) return NULL; + + // if this was the first frame, + pcount = g->w * g->h; + if (first_frame && (g->bgindex > 0)) { + // if first frame, any pixel not drawn to gets the background color + for (pi = 0; pi < pcount; ++pi) { + if (g->history[pi] == 0) { + g->pal[g->bgindex][3] = 255; // just in case it was made transparent, undo that; It will be reset next frame if need be; + memcpy( &g->out[pi * 4], &g->pal[g->bgindex], 4 ); + } + } + } + + return o; + } + + case 0x21: // Comment Extension. + { + int len; + int ext = stbi__get8(s); + if (ext == 0xF9) { // Graphic Control Extension. + len = stbi__get8(s); + if (len == 4) { + g->eflags = stbi__get8(s); + g->delay = 10 * stbi__get16le(s); // delay - 1/100th of a second, saving as 1/1000ths. + + // unset old transparent + if (g->transparent >= 0) { + g->pal[g->transparent][3] = 255; + } + if (g->eflags & 0x01) { + g->transparent = stbi__get8(s); + if (g->transparent >= 0) { + g->pal[g->transparent][3] = 0; + } + } else { + // don't need transparent + stbi__skip(s, 1); + g->transparent = -1; + } + } else { + stbi__skip(s, len); + break; + } + } + while ((len = stbi__get8(s)) != 0) { + stbi__skip(s, len); + } + break; + } + + case 0x3B: // gif stream termination code + return (stbi_uc *) s; // using '1' causes warning on some compilers + + default: + return stbi__errpuc("unknown code", "Corrupt GIF"); + } + } +} + +static void *stbi__load_gif_main(stbi__context *s, int **delays, int *x, int *y, int *z, int *comp, int req_comp) +{ + if (stbi__gif_test(s)) { + int layers = 0; + stbi_uc *u = 0; + stbi_uc *out = 0; + stbi_uc *two_back = 0; + stbi__gif g; + int stride; + memset(&g, 0, sizeof(g)); + if (delays) { + *delays = 0; + } + + do { + u = stbi__gif_load_next(s, &g, comp, req_comp, two_back); + if (u == (stbi_uc *) s) u = 0; // end of animated gif marker + + if (u) { + *x = g.w; + *y = g.h; + ++layers; + stride = g.w * g.h * 4; + + if (out) { + out = (stbi_uc*) STBI_REALLOC( out, layers * stride ); + if (delays) { + *delays = (int*) STBI_REALLOC( *delays, sizeof(int) * layers ); + } + } else { + out = (stbi_uc*)stbi__malloc( layers * stride ); + if (delays) { + *delays = (int*) stbi__malloc( layers * sizeof(int) ); + } + } + memcpy( out + ((layers - 1) * stride), u, stride ); + if (layers >= 2) { + two_back = out - 2 * stride; + } + + if (delays) { + (*delays)[layers - 1U] = g.delay; + } + } + } while (u != 0); + + // free temp buffer; + STBI_FREE(g.out); + STBI_FREE(g.history); + STBI_FREE(g.background); + + // do the final conversion after loading everything; + if (req_comp && req_comp != 4) + out = stbi__convert_format(out, 4, req_comp, layers * g.w, g.h); + + *z = layers; + return out; + } else { + return stbi__errpuc("not GIF", "Image was not as a gif type."); + } +} + +static void *stbi__gif_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) +{ + stbi_uc *u = 0; + stbi__gif g; + memset(&g, 0, sizeof(g)); + STBI_NOTUSED(ri); + + u = stbi__gif_load_next(s, &g, comp, req_comp, 0); + if (u == (stbi_uc *) s) u = 0; // end of animated gif marker + if (u) { + *x = g.w; + *y = g.h; + + // moved conversion to after successful load so that the same + // can be done for multiple frames. + if (req_comp && req_comp != 4) + u = stbi__convert_format(u, 4, req_comp, g.w, g.h); + } else if (g.out) { + // if there was an error and we allocated an image buffer, free it! + STBI_FREE(g.out); + } + + // free buffers needed for multiple frame loading; + STBI_FREE(g.history); + STBI_FREE(g.background); + + return u; +} + +static int stbi__gif_info(stbi__context *s, int *x, int *y, int *comp) +{ + return stbi__gif_info_raw(s,x,y,comp); +} +#endif + +// ************************************************************************************************* +// Radiance RGBE HDR loader +// originally by Nicolas Schulz +#ifndef STBI_NO_HDR +static int stbi__hdr_test_core(stbi__context *s, const char *signature) +{ + int i; + for (i=0; signature[i]; ++i) + if (stbi__get8(s) != signature[i]) + return 0; + stbi__rewind(s); + return 1; +} + +static int stbi__hdr_test(stbi__context* s) +{ + int r = stbi__hdr_test_core(s, "#?RADIANCE\n"); + stbi__rewind(s); + if(!r) { + r = stbi__hdr_test_core(s, "#?RGBE\n"); + stbi__rewind(s); + } + return r; +} + +#define STBI__HDR_BUFLEN 1024 +static char *stbi__hdr_gettoken(stbi__context *z, char *buffer) +{ + int len=0; + char c = '\0'; + + c = (char) stbi__get8(z); + + while (!stbi__at_eof(z) && c != '\n') { + buffer[len++] = c; + if (len == STBI__HDR_BUFLEN-1) { + // flush to end of line + while (!stbi__at_eof(z) && stbi__get8(z) != '\n') + ; + break; + } + c = (char) stbi__get8(z); + } + + buffer[len] = 0; + return buffer; +} + +static void stbi__hdr_convert(float *output, stbi_uc *input, int req_comp) +{ + if ( input[3] != 0 ) { + float f1; + // Exponent + f1 = (float) ldexp(1.0f, input[3] - (int)(128 + 8)); + if (req_comp <= 2) + output[0] = (input[0] + input[1] + input[2]) * f1 / 3; + else { + output[0] = input[0] * f1; + output[1] = input[1] * f1; + output[2] = input[2] * f1; + } + if (req_comp == 2) output[1] = 1; + if (req_comp == 4) output[3] = 1; + } else { + switch (req_comp) { + case 4: output[3] = 1; /* fallthrough */ + case 3: output[0] = output[1] = output[2] = 0; + break; + case 2: output[1] = 1; /* fallthrough */ + case 1: output[0] = 0; + break; + } + } +} + +static float *stbi__hdr_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) +{ + char buffer[STBI__HDR_BUFLEN]; + char *token; + int valid = 0; + int width, height; + stbi_uc *scanline; + float *hdr_data; + int len; + unsigned char count, value; + int i, j, k, c1,c2, z; + const char *headerToken; + STBI_NOTUSED(ri); + + // Check identifier + headerToken = stbi__hdr_gettoken(s,buffer); + if (strcmp(headerToken, "#?RADIANCE") != 0 && strcmp(headerToken, "#?RGBE") != 0) + return stbi__errpf("not HDR", "Corrupt HDR image"); + + // Parse header + for(;;) { + token = stbi__hdr_gettoken(s,buffer); + if (token[0] == 0) break; + if (strcmp(token, "FORMAT=32-bit_rle_rgbe") == 0) valid = 1; + } + + if (!valid) return stbi__errpf("unsupported format", "Unsupported HDR format"); + + // Parse width and height + // can't use sscanf() if we're not using stdio! + token = stbi__hdr_gettoken(s,buffer); + if (strncmp(token, "-Y ", 3)) return stbi__errpf("unsupported data layout", "Unsupported HDR format"); + token += 3; + height = (int) strtol(token, &token, 10); + while (*token == ' ') ++token; + if (strncmp(token, "+X ", 3)) return stbi__errpf("unsupported data layout", "Unsupported HDR format"); + token += 3; + width = (int) strtol(token, NULL, 10); + + *x = width; + *y = height; + + if (comp) *comp = 3; + if (req_comp == 0) req_comp = 3; + + if (!stbi__mad4sizes_valid(width, height, req_comp, sizeof(float), 0)) + return stbi__errpf("too large", "HDR image is too large"); + + // Read data + hdr_data = (float *) stbi__malloc_mad4(width, height, req_comp, sizeof(float), 0); + if (!hdr_data) + return stbi__errpf("outofmem", "Out of memory"); + + // Load image data + // image data is stored as some number of sca + if ( width < 8 || width >= 32768) { + // Read flat data + for (j=0; j < height; ++j) { + for (i=0; i < width; ++i) { + stbi_uc rgbe[4]; + main_decode_loop: + stbi__getn(s, rgbe, 4); + stbi__hdr_convert(hdr_data + j * width * req_comp + i * req_comp, rgbe, req_comp); + } + } + } else { + // Read RLE-encoded data + scanline = NULL; + + for (j = 0; j < height; ++j) { + c1 = stbi__get8(s); + c2 = stbi__get8(s); + len = stbi__get8(s); + if (c1 != 2 || c2 != 2 || (len & 0x80)) { + // not run-length encoded, so we have to actually use THIS data as a decoded + // pixel (note this can't be a valid pixel--one of RGB must be >= 128) + stbi_uc rgbe[4]; + rgbe[0] = (stbi_uc) c1; + rgbe[1] = (stbi_uc) c2; + rgbe[2] = (stbi_uc) len; + rgbe[3] = (stbi_uc) stbi__get8(s); + stbi__hdr_convert(hdr_data, rgbe, req_comp); + i = 1; + j = 0; + STBI_FREE(scanline); + goto main_decode_loop; // yes, this makes no sense + } + len <<= 8; + len |= stbi__get8(s); + if (len != width) { STBI_FREE(hdr_data); STBI_FREE(scanline); return stbi__errpf("invalid decoded scanline length", "corrupt HDR"); } + if (scanline == NULL) { + scanline = (stbi_uc *) stbi__malloc_mad2(width, 4, 0); + if (!scanline) { + STBI_FREE(hdr_data); + return stbi__errpf("outofmem", "Out of memory"); + } + } + + for (k = 0; k < 4; ++k) { + int nleft; + i = 0; + while ((nleft = width - i) > 0) { + count = stbi__get8(s); + if (count > 128) { + // Run + value = stbi__get8(s); + count -= 128; + if (count > nleft) { STBI_FREE(hdr_data); STBI_FREE(scanline); return stbi__errpf("corrupt", "bad RLE data in HDR"); } + for (z = 0; z < count; ++z) + scanline[i++ * 4 + k] = value; + } else { + // Dump + if (count > nleft) { STBI_FREE(hdr_data); STBI_FREE(scanline); return stbi__errpf("corrupt", "bad RLE data in HDR"); } + for (z = 0; z < count; ++z) + scanline[i++ * 4 + k] = stbi__get8(s); + } + } + } + for (i=0; i < width; ++i) + stbi__hdr_convert(hdr_data+(j*width + i)*req_comp, scanline + i*4, req_comp); + } + if (scanline) + STBI_FREE(scanline); + } + + return hdr_data; +} + +static int stbi__hdr_info(stbi__context *s, int *x, int *y, int *comp) +{ + char buffer[STBI__HDR_BUFLEN]; + char *token; + int valid = 0; + int dummy; + + if (!x) x = &dummy; + if (!y) y = &dummy; + if (!comp) comp = &dummy; + + if (stbi__hdr_test(s) == 0) { + stbi__rewind( s ); + return 0; + } + + for(;;) { + token = stbi__hdr_gettoken(s,buffer); + if (token[0] == 0) break; + if (strcmp(token, "FORMAT=32-bit_rle_rgbe") == 0) valid = 1; + } + + if (!valid) { + stbi__rewind( s ); + return 0; + } + token = stbi__hdr_gettoken(s,buffer); + if (strncmp(token, "-Y ", 3)) { + stbi__rewind( s ); + return 0; + } + token += 3; + *y = (int) strtol(token, &token, 10); + while (*token == ' ') ++token; + if (strncmp(token, "+X ", 3)) { + stbi__rewind( s ); + return 0; + } + token += 3; + *x = (int) strtol(token, NULL, 10); + *comp = 3; + return 1; +} +#endif // STBI_NO_HDR + +#ifndef STBI_NO_BMP +static int stbi__bmp_info(stbi__context *s, int *x, int *y, int *comp) +{ + void *p; + stbi__bmp_data info; + + info.all_a = 255; + p = stbi__bmp_parse_header(s, &info); + stbi__rewind( s ); + if (p == NULL) + return 0; + if (x) *x = s->img_x; + if (y) *y = s->img_y; + if (comp) { + if (info.bpp == 24 && info.ma == 0xff000000) + *comp = 3; + else + *comp = info.ma ? 4 : 3; + } + return 1; +} +#endif + +#ifndef STBI_NO_PSD +static int stbi__psd_info(stbi__context *s, int *x, int *y, int *comp) +{ + int channelCount, dummy, depth; + if (!x) x = &dummy; + if (!y) y = &dummy; + if (!comp) comp = &dummy; + if (stbi__get32be(s) != 0x38425053) { + stbi__rewind( s ); + return 0; + } + if (stbi__get16be(s) != 1) { + stbi__rewind( s ); + return 0; + } + stbi__skip(s, 6); + channelCount = stbi__get16be(s); + if (channelCount < 0 || channelCount > 16) { + stbi__rewind( s ); + return 0; + } + *y = stbi__get32be(s); + *x = stbi__get32be(s); + depth = stbi__get16be(s); + if (depth != 8 && depth != 16) { + stbi__rewind( s ); + return 0; + } + if (stbi__get16be(s) != 3) { + stbi__rewind( s ); + return 0; + } + *comp = 4; + return 1; +} + +static int stbi__psd_is16(stbi__context *s) +{ + int channelCount, depth; + if (stbi__get32be(s) != 0x38425053) { + stbi__rewind( s ); + return 0; + } + if (stbi__get16be(s) != 1) { + stbi__rewind( s ); + return 0; + } + stbi__skip(s, 6); + channelCount = stbi__get16be(s); + if (channelCount < 0 || channelCount > 16) { + stbi__rewind( s ); + return 0; + } + (void) stbi__get32be(s); + (void) stbi__get32be(s); + depth = stbi__get16be(s); + if (depth != 16) { + stbi__rewind( s ); + return 0; + } + return 1; +} +#endif + +#ifndef STBI_NO_PIC +static int stbi__pic_info(stbi__context *s, int *x, int *y, int *comp) +{ + int act_comp=0,num_packets=0,chained,dummy; + stbi__pic_packet packets[10]; + + if (!x) x = &dummy; + if (!y) y = &dummy; + if (!comp) comp = &dummy; + + if (!stbi__pic_is4(s,"\x53\x80\xF6\x34")) { + stbi__rewind(s); + return 0; + } + + stbi__skip(s, 88); + + *x = stbi__get16be(s); + *y = stbi__get16be(s); + if (stbi__at_eof(s)) { + stbi__rewind( s); + return 0; + } + if ( (*x) != 0 && (1 << 28) / (*x) < (*y)) { + stbi__rewind( s ); + return 0; + } + + stbi__skip(s, 8); + + do { + stbi__pic_packet *packet; + + if (num_packets==sizeof(packets)/sizeof(packets[0])) + return 0; + + packet = &packets[num_packets++]; + chained = stbi__get8(s); + packet->size = stbi__get8(s); + packet->type = stbi__get8(s); + packet->channel = stbi__get8(s); + act_comp |= packet->channel; + + if (stbi__at_eof(s)) { + stbi__rewind( s ); + return 0; + } + if (packet->size != 8) { + stbi__rewind( s ); + return 0; + } + } while (chained); + + *comp = (act_comp & 0x10 ? 4 : 3); + + return 1; +} +#endif + +// ************************************************************************************************* +// Portable Gray Map and Portable Pixel Map loader +// by Ken Miller +// +// PGM: http://netpbm.sourceforge.net/doc/pgm.html +// PPM: http://netpbm.sourceforge.net/doc/ppm.html +// +// Known limitations: +// Does not support comments in the header section +// Does not support ASCII image data (formats P2 and P3) +// Does not support 16-bit-per-channel + +#ifndef STBI_NO_PNM + +static int stbi__pnm_test(stbi__context *s) +{ + char p, t; + p = (char) stbi__get8(s); + t = (char) stbi__get8(s); + if (p != 'P' || (t != '5' && t != '6')) { + stbi__rewind( s ); + return 0; + } + return 1; +} + +static void *stbi__pnm_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) +{ + stbi_uc *out; + STBI_NOTUSED(ri); + + if (!stbi__pnm_info(s, (int *)&s->img_x, (int *)&s->img_y, (int *)&s->img_n)) + return 0; + + *x = s->img_x; + *y = s->img_y; + if (comp) *comp = s->img_n; + + if (!stbi__mad3sizes_valid(s->img_n, s->img_x, s->img_y, 0)) + return stbi__errpuc("too large", "PNM too large"); + + out = (stbi_uc *) stbi__malloc_mad3(s->img_n, s->img_x, s->img_y, 0); + if (!out) return stbi__errpuc("outofmem", "Out of memory"); + stbi__getn(s, out, s->img_n * s->img_x * s->img_y); + + if (req_comp && req_comp != s->img_n) { + out = stbi__convert_format(out, s->img_n, req_comp, s->img_x, s->img_y); + if (out == NULL) return out; // stbi__convert_format frees input on failure + } + return out; +} + +static int stbi__pnm_isspace(char c) +{ + return c == ' ' || c == '\t' || c == '\n' || c == '\v' || c == '\f' || c == '\r'; +} + +static void stbi__pnm_skip_whitespace(stbi__context *s, char *c) +{ + for (;;) { + while (!stbi__at_eof(s) && stbi__pnm_isspace(*c)) + *c = (char) stbi__get8(s); + + if (stbi__at_eof(s) || *c != '#') + break; + + while (!stbi__at_eof(s) && *c != '\n' && *c != '\r' ) + *c = (char) stbi__get8(s); + } +} + +static int stbi__pnm_isdigit(char c) +{ + return c >= '0' && c <= '9'; +} + +static int stbi__pnm_getinteger(stbi__context *s, char *c) +{ + int value = 0; + + while (!stbi__at_eof(s) && stbi__pnm_isdigit(*c)) { + value = value*10 + (*c - '0'); + *c = (char) stbi__get8(s); + } + + return value; +} + +static int stbi__pnm_info(stbi__context *s, int *x, int *y, int *comp) +{ + int maxv, dummy; + char c, p, t; + + if (!x) x = &dummy; + if (!y) y = &dummy; + if (!comp) comp = &dummy; + + stbi__rewind(s); + + // Get identifier + p = (char) stbi__get8(s); + t = (char) stbi__get8(s); + if (p != 'P' || (t != '5' && t != '6')) { + stbi__rewind(s); + return 0; + } + + *comp = (t == '6') ? 3 : 1; // '5' is 1-component .pgm; '6' is 3-component .ppm + + c = (char) stbi__get8(s); + stbi__pnm_skip_whitespace(s, &c); + + *x = stbi__pnm_getinteger(s, &c); // read width + stbi__pnm_skip_whitespace(s, &c); + + *y = stbi__pnm_getinteger(s, &c); // read height + stbi__pnm_skip_whitespace(s, &c); + + maxv = stbi__pnm_getinteger(s, &c); // read max value + + if (maxv > 255) + return stbi__err("max value > 255", "PPM image not 8-bit"); + else + return 1; +} +#endif + +static int stbi__info_main(stbi__context *s, int *x, int *y, int *comp) +{ + #ifndef STBI_NO_JPEG + if (stbi__jpeg_info(s, x, y, comp)) return 1; + #endif + + #ifndef STBI_NO_PNG + if (stbi__png_info(s, x, y, comp)) return 1; + #endif + + #ifndef STBI_NO_GIF + if (stbi__gif_info(s, x, y, comp)) return 1; + #endif + + #ifndef STBI_NO_BMP + if (stbi__bmp_info(s, x, y, comp)) return 1; + #endif + + #ifndef STBI_NO_PSD + if (stbi__psd_info(s, x, y, comp)) return 1; + #endif + + #ifndef STBI_NO_PIC + if (stbi__pic_info(s, x, y, comp)) return 1; + #endif + + #ifndef STBI_NO_PNM + if (stbi__pnm_info(s, x, y, comp)) return 1; + #endif + + #ifndef STBI_NO_HDR + if (stbi__hdr_info(s, x, y, comp)) return 1; + #endif + + // test tga last because it's a crappy test! + #ifndef STBI_NO_TGA + if (stbi__tga_info(s, x, y, comp)) + return 1; + #endif + return stbi__err("unknown image type", "Image not of any known type, or corrupt"); +} + +static int stbi__is_16_main(stbi__context *s) +{ + #ifndef STBI_NO_PNG + if (stbi__png_is16(s)) return 1; + #endif + + #ifndef STBI_NO_PSD + if (stbi__psd_is16(s)) return 1; + #endif + + return 0; +} + +#ifndef STBI_NO_STDIO +STBIDEF int stbi_info(char const *filename, int *x, int *y, int *comp) +{ + FILE *f = stbi__fopen(filename, "rb"); + int result; + if (!f) return stbi__err("can't fopen", "Unable to open file"); + result = stbi_info_from_file(f, x, y, comp); + fclose(f); + return result; +} + +STBIDEF int stbi_info_from_file(FILE *f, int *x, int *y, int *comp) +{ + int r; + stbi__context s; + long pos = ftell(f); + stbi__start_file(&s, f); + r = stbi__info_main(&s,x,y,comp); + fseek(f,pos,SEEK_SET); + return r; +} + +STBIDEF int stbi_is_16_bit(char const *filename) +{ + FILE *f = stbi__fopen(filename, "rb"); + int result; + if (!f) return stbi__err("can't fopen", "Unable to open file"); + result = stbi_is_16_bit_from_file(f); + fclose(f); + return result; +} + +STBIDEF int stbi_is_16_bit_from_file(FILE *f) +{ + int r; + stbi__context s; + long pos = ftell(f); + stbi__start_file(&s, f); + r = stbi__is_16_main(&s); + fseek(f,pos,SEEK_SET); + return r; +} +#endif // !STBI_NO_STDIO + +STBIDEF int stbi_info_from_memory(stbi_uc const *buffer, int len, int *x, int *y, int *comp) +{ + stbi__context s; + stbi__start_mem(&s,buffer,len); + return stbi__info_main(&s,x,y,comp); +} + +STBIDEF int stbi_info_from_callbacks(stbi_io_callbacks const *c, void *user, int *x, int *y, int *comp) +{ + stbi__context s; + stbi__start_callbacks(&s, (stbi_io_callbacks *) c, user); + return stbi__info_main(&s,x,y,comp); +} + +STBIDEF int stbi_is_16_bit_from_memory(stbi_uc const *buffer, int len) +{ + stbi__context s; + stbi__start_mem(&s,buffer,len); + return stbi__is_16_main(&s); +} + +STBIDEF int stbi_is_16_bit_from_callbacks(stbi_io_callbacks const *c, void *user) +{ + stbi__context s; + stbi__start_callbacks(&s, (stbi_io_callbacks *) c, user); + return stbi__is_16_main(&s); +} + +#endif // STB_IMAGE_IMPLEMENTATION + +/* + revision history: + 2.20 (2019-02-07) support utf8 filenames in Windows; fix warnings and platform ifdefs + 2.19 (2018-02-11) fix warning + 2.18 (2018-01-30) fix warnings + 2.17 (2018-01-29) change sbti__shiftsigned to avoid clang -O2 bug + 1-bit BMP + *_is_16_bit api + avoid warnings + 2.16 (2017-07-23) all functions have 16-bit variants; + STBI_NO_STDIO works again; + compilation fixes; + fix rounding in unpremultiply; + optimize vertical flip; + disable raw_len validation; + documentation fixes + 2.15 (2017-03-18) fix png-1,2,4 bug; now all Imagenet JPGs decode; + warning fixes; disable run-time SSE detection on gcc; + uniform handling of optional "return" values; + thread-safe initialization of zlib tables + 2.14 (2017-03-03) remove deprecated STBI_JPEG_OLD; fixes for Imagenet JPGs + 2.13 (2016-11-29) add 16-bit API, only supported for PNG right now + 2.12 (2016-04-02) fix typo in 2.11 PSD fix that caused crashes + 2.11 (2016-04-02) allocate large structures on the stack + remove white matting for transparent PSD + fix reported channel count for PNG & BMP + re-enable SSE2 in non-gcc 64-bit + support RGB-formatted JPEG + read 16-bit PNGs (only as 8-bit) + 2.10 (2016-01-22) avoid warning introduced in 2.09 by STBI_REALLOC_SIZED + 2.09 (2016-01-16) allow comments in PNM files + 16-bit-per-pixel TGA (not bit-per-component) + info() for TGA could break due to .hdr handling + info() for BMP to shares code instead of sloppy parse + can use STBI_REALLOC_SIZED if allocator doesn't support realloc + code cleanup + 2.08 (2015-09-13) fix to 2.07 cleanup, reading RGB PSD as RGBA + 2.07 (2015-09-13) fix compiler warnings + partial animated GIF support + limited 16-bpc PSD support + #ifdef unused functions + bug with < 92 byte PIC,PNM,HDR,TGA + 2.06 (2015-04-19) fix bug where PSD returns wrong '*comp' value + 2.05 (2015-04-19) fix bug in progressive JPEG handling, fix warning + 2.04 (2015-04-15) try to re-enable SIMD on MinGW 64-bit + 2.03 (2015-04-12) extra corruption checking (mmozeiko) + stbi_set_flip_vertically_on_load (nguillemot) + fix NEON support; fix mingw support + 2.02 (2015-01-19) fix incorrect assert, fix warning + 2.01 (2015-01-17) fix various warnings; suppress SIMD on gcc 32-bit without -msse2 + 2.00b (2014-12-25) fix STBI_MALLOC in progressive JPEG + 2.00 (2014-12-25) optimize JPG, including x86 SSE2 & NEON SIMD (ryg) + progressive JPEG (stb) + PGM/PPM support (Ken Miller) + STBI_MALLOC,STBI_REALLOC,STBI_FREE + GIF bugfix -- seemingly never worked + STBI_NO_*, STBI_ONLY_* + 1.48 (2014-12-14) fix incorrectly-named assert() + 1.47 (2014-12-14) 1/2/4-bit PNG support, both direct and paletted (Omar Cornut & stb) + optimize PNG (ryg) + fix bug in interlaced PNG with user-specified channel count (stb) + 1.46 (2014-08-26) + fix broken tRNS chunk (colorkey-style transparency) in non-paletted PNG + 1.45 (2014-08-16) + fix MSVC-ARM internal compiler error by wrapping malloc + 1.44 (2014-08-07) + various warning fixes from Ronny Chevalier + 1.43 (2014-07-15) + fix MSVC-only compiler problem in code changed in 1.42 + 1.42 (2014-07-09) + don't define _CRT_SECURE_NO_WARNINGS (affects user code) + fixes to stbi__cleanup_jpeg path + added STBI_ASSERT to avoid requiring assert.h + 1.41 (2014-06-25) + fix search&replace from 1.36 that messed up comments/error messages + 1.40 (2014-06-22) + fix gcc struct-initialization warning + 1.39 (2014-06-15) + fix to TGA optimization when req_comp != number of components in TGA; + fix to GIF loading because BMP wasn't rewinding (whoops, no GIFs in my test suite) + add support for BMP version 5 (more ignored fields) + 1.38 (2014-06-06) + suppress MSVC warnings on integer casts truncating values + fix accidental rename of 'skip' field of I/O + 1.37 (2014-06-04) + remove duplicate typedef + 1.36 (2014-06-03) + convert to header file single-file library + if de-iphone isn't set, load iphone images color-swapped instead of returning NULL + 1.35 (2014-05-27) + various warnings + fix broken STBI_SIMD path + fix bug where stbi_load_from_file no longer left file pointer in correct place + fix broken non-easy path for 32-bit BMP (possibly never used) + TGA optimization by Arseny Kapoulkine + 1.34 (unknown) + use STBI_NOTUSED in stbi__resample_row_generic(), fix one more leak in tga failure case + 1.33 (2011-07-14) + make stbi_is_hdr work in STBI_NO_HDR (as specified), minor compiler-friendly improvements + 1.32 (2011-07-13) + support for "info" function for all supported filetypes (SpartanJ) + 1.31 (2011-06-20) + a few more leak fixes, bug in PNG handling (SpartanJ) + 1.30 (2011-06-11) + added ability to load files via callbacks to accomidate custom input streams (Ben Wenger) + removed deprecated format-specific test/load functions + removed support for installable file formats (stbi_loader) -- would have been broken for IO callbacks anyway + error cases in bmp and tga give messages and don't leak (Raymond Barbiero, grisha) + fix inefficiency in decoding 32-bit BMP (David Woo) + 1.29 (2010-08-16) + various warning fixes from Aurelien Pocheville + 1.28 (2010-08-01) + fix bug in GIF palette transparency (SpartanJ) + 1.27 (2010-08-01) + cast-to-stbi_uc to fix warnings + 1.26 (2010-07-24) + fix bug in file buffering for PNG reported by SpartanJ + 1.25 (2010-07-17) + refix trans_data warning (Won Chun) + 1.24 (2010-07-12) + perf improvements reading from files on platforms with lock-heavy fgetc() + minor perf improvements for jpeg + deprecated type-specific functions so we'll get feedback if they're needed + attempt to fix trans_data warning (Won Chun) + 1.23 fixed bug in iPhone support + 1.22 (2010-07-10) + removed image *writing* support + stbi_info support from Jetro Lauha + GIF support from Jean-Marc Lienher + iPhone PNG-extensions from James Brown + warning-fixes from Nicolas Schulz and Janez Zemva (i.stbi__err. Janez (U+017D)emva) + 1.21 fix use of 'stbi_uc' in header (reported by jon blow) + 1.20 added support for Softimage PIC, by Tom Seddon + 1.19 bug in interlaced PNG corruption check (found by ryg) + 1.18 (2008-08-02) + fix a threading bug (local mutable static) + 1.17 support interlaced PNG + 1.16 major bugfix - stbi__convert_format converted one too many pixels + 1.15 initialize some fields for thread safety + 1.14 fix threadsafe conversion bug + header-file-only version (#define STBI_HEADER_FILE_ONLY before including) + 1.13 threadsafe + 1.12 const qualifiers in the API + 1.11 Support installable IDCT, colorspace conversion routines + 1.10 Fixes for 64-bit (don't use "unsigned long") + optimized upsampling by Fabian "ryg" Giesen + 1.09 Fix format-conversion for PSD code (bad global variables!) + 1.08 Thatcher Ulrich's PSD code integrated by Nicolas Schulz + 1.07 attempt to fix C++ warning/errors again + 1.06 attempt to fix C++ warning/errors again + 1.05 fix TGA loading to return correct *comp and use good luminance calc + 1.04 default float alpha is 1, not 255; use 'void *' for stbi_image_free + 1.03 bugfixes to STBI_NO_STDIO, STBI_NO_HDR + 1.02 support for (subset of) HDR files, float interface for preferred access to them + 1.01 fix bug: possible bug in handling right-side up bmps... not sure + fix bug: the stbi__bmp_load() and stbi__tga_load() functions didn't work at all + 1.00 interface to zlib that skips zlib header + 0.99 correct handling of alpha in palette + 0.98 TGA loader by lonesock; dynamically add loaders (untested) + 0.97 jpeg errors on too large a file; also catch another malloc failure + 0.96 fix detection of invalid v value - particleman@mollyrocket forum + 0.95 during header scan, seek to markers in case of padding + 0.94 STBI_NO_STDIO to disable stdio usage; rename all #defines the same + 0.93 handle jpegtran output; verbose errors + 0.92 read 4,8,16,24,32-bit BMP files of several formats + 0.91 output 24-bit Windows 3.0 BMP files + 0.90 fix a few more warnings; bump version number to approach 1.0 + 0.61 bugfixes due to Marc LeBlanc, Christopher Lloyd + 0.60 fix compiling as c++ + 0.59 fix warnings: merge Dave Moore's -Wall fixes + 0.58 fix bug: zlib uncompressed mode len/nlen was wrong endian + 0.57 fix bug: jpg last huffman symbol before marker was >9 bits but less than 16 available + 0.56 fix bug: zlib uncompressed mode len vs. nlen + 0.55 fix bug: restart_interval not initialized to 0 + 0.54 allow NULL for 'int *comp' + 0.53 fix bug in png 3->4; speedup png decoding + 0.52 png handles req_comp=3,4 directly; minor cleanup; jpeg comments + 0.51 obey req_comp requests, 1-component jpegs return as 1-component, + on 'test' only check type, not whether we support this variant + 0.50 (2006-11-19) + first released version +*/ + + +/* +------------------------------------------------------------------------------ +This software is available under 2 licenses -- choose whichever you prefer. +------------------------------------------------------------------------------ +ALTERNATIVE A - MIT License +Copyright (c) 2017 Sean Barrett +Permission is hereby granted, free of charge, to any person obtaining a copy of +this software and associated documentation files (the "Software"), to deal in +the Software without restriction, including without limitation the rights to +use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies +of the Software, and to permit persons to whom the Software is furnished to do +so, subject to the following conditions: +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +------------------------------------------------------------------------------ +ALTERNATIVE B - Public Domain (www.unlicense.org) +This is free and unencumbered software released into the public domain. +Anyone is free to copy, modify, publish, use, compile, sell, or distribute this +software, either in source code form or as a compiled binary, for any purpose, +commercial or non-commercial, and by any means. +In jurisdictions that recognize copyright laws, the author or authors of this +software dedicate any and all copyright interest in the software to the public +domain. We make this dedication for the benefit of the public at large and to +the detriment of our heirs and successors. We intend this dedication to be an +overt act of relinquishment in perpetuity of all present and future rights to +this software under copyright law. +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN +ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION +WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +------------------------------------------------------------------------------ +*/ diff --git a/rknpu2/examples/librknn_api_android_demo/include/stb/stb_image_resize.h b/rknpu2/examples/librknn_api_android_demo/include/stb/stb_image_resize.h new file mode 100644 index 0000000000000000000000000000000000000000..4f6ad35e1cb51d5e48d79aa9eb73284a3fba58b7 --- /dev/null +++ b/rknpu2/examples/librknn_api_android_demo/include/stb/stb_image_resize.h @@ -0,0 +1,2630 @@ +/* stb_image_resize - v0.96 - public domain image resizing + by Jorge L Rodriguez (@VinoBS) - 2014 + http://github.com/nothings/stb + + Written with emphasis on usability, portability, and efficiency. (No + SIMD or threads, so it be easily outperformed by libs that use those.) + Only scaling and translation is supported, no rotations or shears. + Easy API downsamples w/Mitchell filter, upsamples w/cubic interpolation. + + COMPILING & LINKING + In one C/C++ file that #includes this file, do this: + #define STB_IMAGE_RESIZE_IMPLEMENTATION + before the #include. That will create the implementation in that file. + + QUICKSTART + stbir_resize_uint8( input_pixels , in_w , in_h , 0, + output_pixels, out_w, out_h, 0, num_channels) + stbir_resize_float(...) + stbir_resize_uint8_srgb( input_pixels , in_w , in_h , 0, + output_pixels, out_w, out_h, 0, + num_channels , alpha_chan , 0) + stbir_resize_uint8_srgb_edgemode( + input_pixels , in_w , in_h , 0, + output_pixels, out_w, out_h, 0, + num_channels , alpha_chan , 0, STBIR_EDGE_CLAMP) + // WRAP/REFLECT/ZERO + + FULL API + See the "header file" section of the source for API documentation. + + ADDITIONAL DOCUMENTATION + + SRGB & FLOATING POINT REPRESENTATION + The sRGB functions presume IEEE floating point. If you do not have + IEEE floating point, define STBIR_NON_IEEE_FLOAT. This will use + a slower implementation. + + MEMORY ALLOCATION + The resize functions here perform a single memory allocation using + malloc. To control the memory allocation, before the #include that + triggers the implementation, do: + + #define STBIR_MALLOC(size,context) ... + #define STBIR_FREE(ptr,context) ... + + Each resize function makes exactly one call to malloc/free, so to use + temp memory, store the temp memory in the context and return that. + + ASSERT + Define STBIR_ASSERT(boolval) to override assert() and not use assert.h + + OPTIMIZATION + Define STBIR_SATURATE_INT to compute clamp values in-range using + integer operations instead of float operations. This may be faster + on some platforms. + + DEFAULT FILTERS + For functions which don't provide explicit control over what filters + to use, you can change the compile-time defaults with + + #define STBIR_DEFAULT_FILTER_UPSAMPLE STBIR_FILTER_something + #define STBIR_DEFAULT_FILTER_DOWNSAMPLE STBIR_FILTER_something + + See stbir_filter in the header-file section for the list of filters. + + NEW FILTERS + A number of 1D filter kernels are used. For a list of + supported filters see the stbir_filter enum. To add a new filter, + write a filter function and add it to stbir__filter_info_table. + + PROGRESS + For interactive use with slow resize operations, you can install + a progress-report callback: + + #define STBIR_PROGRESS_REPORT(val) some_func(val) + + The parameter val is a float which goes from 0 to 1 as progress is made. + + For example: + + static void my_progress_report(float progress); + #define STBIR_PROGRESS_REPORT(val) my_progress_report(val) + + #define STB_IMAGE_RESIZE_IMPLEMENTATION + #include "stb_image_resize.h" + + static void my_progress_report(float progress) + { + printf("Progress: %f%%\n", progress*100); + } + + MAX CHANNELS + If your image has more than 64 channels, define STBIR_MAX_CHANNELS + to the max you'll have. + + ALPHA CHANNEL + Most of the resizing functions provide the ability to control how + the alpha channel of an image is processed. The important things + to know about this: + + 1. The best mathematically-behaved version of alpha to use is + called "premultiplied alpha", in which the other color channels + have had the alpha value multiplied in. If you use premultiplied + alpha, linear filtering (such as image resampling done by this + library, or performed in texture units on GPUs) does the "right + thing". While premultiplied alpha is standard in the movie CGI + industry, it is still uncommon in the videogame/real-time world. + + If you linearly filter non-premultiplied alpha, strange effects + occur. (For example, the 50/50 average of 99% transparent bright green + and 1% transparent black produces 50% transparent dark green when + non-premultiplied, whereas premultiplied it produces 50% + transparent near-black. The former introduces green energy + that doesn't exist in the source image.) + + 2. Artists should not edit premultiplied-alpha images; artists + want non-premultiplied alpha images. Thus, art tools generally output + non-premultiplied alpha images. + + 3. You will get best results in most cases by converting images + to premultiplied alpha before processing them mathematically. + + 4. If you pass the flag STBIR_FLAG_ALPHA_PREMULTIPLIED, the + resizer does not do anything special for the alpha channel; + it is resampled identically to other channels. This produces + the correct results for premultiplied-alpha images, but produces + less-than-ideal results for non-premultiplied-alpha images. + + 5. If you do not pass the flag STBIR_FLAG_ALPHA_PREMULTIPLIED, + then the resizer weights the contribution of input pixels + based on their alpha values, or, equivalently, it multiplies + the alpha value into the color channels, resamples, then divides + by the resultant alpha value. Input pixels which have alpha=0 do + not contribute at all to output pixels unless _all_ of the input + pixels affecting that output pixel have alpha=0, in which case + the result for that pixel is the same as it would be without + STBIR_FLAG_ALPHA_PREMULTIPLIED. However, this is only true for + input images in integer formats. For input images in float format, + input pixels with alpha=0 have no effect, and output pixels + which have alpha=0 will be 0 in all channels. (For float images, + you can manually achieve the same result by adding a tiny epsilon + value to the alpha channel of every image, and then subtracting + or clamping it at the end.) + + 6. You can suppress the behavior described in #5 and make + all-0-alpha pixels have 0 in all channels by #defining + STBIR_NO_ALPHA_EPSILON. + + 7. You can separately control whether the alpha channel is + interpreted as linear or affected by the colorspace. By default + it is linear; you almost never want to apply the colorspace. + (For example, graphics hardware does not apply sRGB conversion + to the alpha channel.) + + CONTRIBUTORS + Jorge L Rodriguez: Implementation + Sean Barrett: API design, optimizations + Aras Pranckevicius: bugfix + Nathan Reed: warning fixes + + REVISIONS + 0.96 (2019-03-04) fixed warnings + 0.95 (2017-07-23) fixed warnings + 0.94 (2017-03-18) fixed warnings + 0.93 (2017-03-03) fixed bug with certain combinations of heights + 0.92 (2017-01-02) fix integer overflow on large (>2GB) images + 0.91 (2016-04-02) fix warnings; fix handling of subpixel regions + 0.90 (2014-09-17) first released version + + LICENSE + See end of file for license information. + + TODO + Don't decode all of the image data when only processing a partial tile + Don't use full-width decode buffers when only processing a partial tile + When processing wide images, break processing into tiles so data fits in L1 cache + Installable filters? + Resize that respects alpha test coverage + (Reference code: FloatImage::alphaTestCoverage and FloatImage::scaleAlphaToCoverage: + https://code.google.com/p/nvidia-texture-tools/source/browse/trunk/src/nvimage/FloatImage.cpp ) +*/ + +#ifndef STBIR_INCLUDE_STB_IMAGE_RESIZE_H +#define STBIR_INCLUDE_STB_IMAGE_RESIZE_H + +#ifdef _MSC_VER +typedef unsigned char stbir_uint8; +typedef unsigned short stbir_uint16; +typedef unsigned int stbir_uint32; +#else +#include +typedef uint8_t stbir_uint8; +typedef uint16_t stbir_uint16; +typedef uint32_t stbir_uint32; +#endif + +#ifndef STBIRDEF +#ifdef STB_IMAGE_RESIZE_STATIC +#define STBIRDEF static +#else +#ifdef __cplusplus +#define STBIRDEF extern "C" +#else +#define STBIRDEF extern +#endif +#endif +#endif + +////////////////////////////////////////////////////////////////////////////// +// +// Easy-to-use API: +// +// * "input pixels" points to an array of image data with 'num_channels' channels (e.g. RGB=3, RGBA=4) +// * input_w is input image width (x-axis), input_h is input image height (y-axis) +// * stride is the offset between successive rows of image data in memory, in bytes. you can +// specify 0 to mean packed continuously in memory +// * alpha channel is treated identically to other channels. +// * colorspace is linear or sRGB as specified by function name +// * returned result is 1 for success or 0 in case of an error. +// #define STBIR_ASSERT() to trigger an assert on parameter validation errors. +// * Memory required grows approximately linearly with input and output size, but with +// discontinuities at input_w == output_w and input_h == output_h. +// * These functions use a "default" resampling filter defined at compile time. To change the filter, +// you can change the compile-time defaults by #defining STBIR_DEFAULT_FILTER_UPSAMPLE +// and STBIR_DEFAULT_FILTER_DOWNSAMPLE, or you can use the medium-complexity API. + +STBIRDEF int stbir_resize_uint8( const unsigned char *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + unsigned char *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + int num_channels); + +STBIRDEF int stbir_resize_float( const float *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + float *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + int num_channels); + + +// The following functions interpret image data as gamma-corrected sRGB. +// Specify STBIR_ALPHA_CHANNEL_NONE if you have no alpha channel, +// or otherwise provide the index of the alpha channel. Flags value +// of 0 will probably do the right thing if you're not sure what +// the flags mean. + +#define STBIR_ALPHA_CHANNEL_NONE -1 + +// Set this flag if your texture has premultiplied alpha. Otherwise, stbir will +// use alpha-weighted resampling (effectively premultiplying, resampling, +// then unpremultiplying). +#define STBIR_FLAG_ALPHA_PREMULTIPLIED (1 << 0) +// The specified alpha channel should be handled as gamma-corrected value even +// when doing sRGB operations. +#define STBIR_FLAG_ALPHA_USES_COLORSPACE (1 << 1) + +STBIRDEF int stbir_resize_uint8_srgb(const unsigned char *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + unsigned char *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + int num_channels, int alpha_channel, int flags); + + +typedef enum +{ + STBIR_EDGE_CLAMP = 1, + STBIR_EDGE_REFLECT = 2, + STBIR_EDGE_WRAP = 3, + STBIR_EDGE_ZERO = 4, +} stbir_edge; + +// This function adds the ability to specify how requests to sample off the edge of the image are handled. +STBIRDEF int stbir_resize_uint8_srgb_edgemode(const unsigned char *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + unsigned char *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + int num_channels, int alpha_channel, int flags, + stbir_edge edge_wrap_mode); + +////////////////////////////////////////////////////////////////////////////// +// +// Medium-complexity API +// +// This extends the easy-to-use API as follows: +// +// * Alpha-channel can be processed separately +// * If alpha_channel is not STBIR_ALPHA_CHANNEL_NONE +// * Alpha channel will not be gamma corrected (unless flags&STBIR_FLAG_GAMMA_CORRECT) +// * Filters will be weighted by alpha channel (unless flags&STBIR_FLAG_ALPHA_PREMULTIPLIED) +// * Filter can be selected explicitly +// * uint16 image type +// * sRGB colorspace available for all types +// * context parameter for passing to STBIR_MALLOC + +typedef enum +{ + STBIR_FILTER_DEFAULT = 0, // use same filter type that easy-to-use API chooses + STBIR_FILTER_BOX = 1, // A trapezoid w/1-pixel wide ramps, same result as box for integer scale ratios + STBIR_FILTER_TRIANGLE = 2, // On upsampling, produces same results as bilinear texture filtering + STBIR_FILTER_CUBICBSPLINE = 3, // The cubic b-spline (aka Mitchell-Netrevalli with B=1,C=0), gaussian-esque + STBIR_FILTER_CATMULLROM = 4, // An interpolating cubic spline + STBIR_FILTER_MITCHELL = 5, // Mitchell-Netrevalli filter with B=1/3, C=1/3 +} stbir_filter; + +typedef enum +{ + STBIR_COLORSPACE_LINEAR, + STBIR_COLORSPACE_SRGB, + + STBIR_MAX_COLORSPACES, +} stbir_colorspace; + +// The following functions are all identical except for the type of the image data + +STBIRDEF int stbir_resize_uint8_generic( const unsigned char *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + unsigned char *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + int num_channels, int alpha_channel, int flags, + stbir_edge edge_wrap_mode, stbir_filter filter, stbir_colorspace space, + void *alloc_context); + +STBIRDEF int stbir_resize_uint16_generic(const stbir_uint16 *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + stbir_uint16 *output_pixels , int output_w, int output_h, int output_stride_in_bytes, + int num_channels, int alpha_channel, int flags, + stbir_edge edge_wrap_mode, stbir_filter filter, stbir_colorspace space, + void *alloc_context); + +STBIRDEF int stbir_resize_float_generic( const float *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + float *output_pixels , int output_w, int output_h, int output_stride_in_bytes, + int num_channels, int alpha_channel, int flags, + stbir_edge edge_wrap_mode, stbir_filter filter, stbir_colorspace space, + void *alloc_context); + + + +////////////////////////////////////////////////////////////////////////////// +// +// Full-complexity API +// +// This extends the medium API as follows: +// +// * uint32 image type +// * not typesafe +// * separate filter types for each axis +// * separate edge modes for each axis +// * can specify scale explicitly for subpixel correctness +// * can specify image source tile using texture coordinates + +typedef enum +{ + STBIR_TYPE_UINT8 , + STBIR_TYPE_UINT16, + STBIR_TYPE_UINT32, + STBIR_TYPE_FLOAT , + + STBIR_MAX_TYPES +} stbir_datatype; + +STBIRDEF int stbir_resize( const void *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + void *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + stbir_datatype datatype, + int num_channels, int alpha_channel, int flags, + stbir_edge edge_mode_horizontal, stbir_edge edge_mode_vertical, + stbir_filter filter_horizontal, stbir_filter filter_vertical, + stbir_colorspace space, void *alloc_context); + +STBIRDEF int stbir_resize_subpixel(const void *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + void *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + stbir_datatype datatype, + int num_channels, int alpha_channel, int flags, + stbir_edge edge_mode_horizontal, stbir_edge edge_mode_vertical, + stbir_filter filter_horizontal, stbir_filter filter_vertical, + stbir_colorspace space, void *alloc_context, + float x_scale, float y_scale, + float x_offset, float y_offset); + +STBIRDEF int stbir_resize_region( const void *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + void *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + stbir_datatype datatype, + int num_channels, int alpha_channel, int flags, + stbir_edge edge_mode_horizontal, stbir_edge edge_mode_vertical, + stbir_filter filter_horizontal, stbir_filter filter_vertical, + stbir_colorspace space, void *alloc_context, + float s0, float t0, float s1, float t1); +// (s0, t0) & (s1, t1) are the top-left and bottom right corner (uv addressing style: [0, 1]x[0, 1]) of a region of the input image to use. + +// +// +//// end header file ///////////////////////////////////////////////////// +#endif // STBIR_INCLUDE_STB_IMAGE_RESIZE_H + + + + + +#ifdef STB_IMAGE_RESIZE_IMPLEMENTATION + +#ifndef STBIR_ASSERT +#include +#define STBIR_ASSERT(x) assert(x) +#endif + +// For memset +#include + +#include + +#ifndef STBIR_MALLOC +#include +// use comma operator to evaluate c, to avoid "unused parameter" warnings +#define STBIR_MALLOC(size,c) ((void)(c), malloc(size)) +#define STBIR_FREE(ptr,c) ((void)(c), free(ptr)) +#endif + +#ifndef _MSC_VER +#ifdef __cplusplus +#define stbir__inline inline +#else +#define stbir__inline +#endif +#else +#define stbir__inline __forceinline +#endif + + +// should produce compiler error if size is wrong +typedef unsigned char stbir__validate_uint32[sizeof(stbir_uint32) == 4 ? 1 : -1]; + +#ifdef _MSC_VER +#define STBIR__NOTUSED(v) (void)(v) +#else +#define STBIR__NOTUSED(v) (void)sizeof(v) +#endif + +#define STBIR__ARRAY_SIZE(a) (sizeof((a))/sizeof((a)[0])) + +#ifndef STBIR_DEFAULT_FILTER_UPSAMPLE +#define STBIR_DEFAULT_FILTER_UPSAMPLE STBIR_FILTER_CATMULLROM +#endif + +#ifndef STBIR_DEFAULT_FILTER_DOWNSAMPLE +#define STBIR_DEFAULT_FILTER_DOWNSAMPLE STBIR_FILTER_MITCHELL +#endif + +#ifndef STBIR_PROGRESS_REPORT +#define STBIR_PROGRESS_REPORT(float_0_to_1) +#endif + +#ifndef STBIR_MAX_CHANNELS +#define STBIR_MAX_CHANNELS 64 +#endif + +#if STBIR_MAX_CHANNELS > 65536 +#error "Too many channels; STBIR_MAX_CHANNELS must be no more than 65536." +// because we store the indices in 16-bit variables +#endif + +// This value is added to alpha just before premultiplication to avoid +// zeroing out color values. It is equivalent to 2^-80. If you don't want +// that behavior (it may interfere if you have floating point images with +// very small alpha values) then you can define STBIR_NO_ALPHA_EPSILON to +// disable it. +#ifndef STBIR_ALPHA_EPSILON +#define STBIR_ALPHA_EPSILON ((float)1 / (1 << 20) / (1 << 20) / (1 << 20) / (1 << 20)) +#endif + + + +#ifdef _MSC_VER +#define STBIR__UNUSED_PARAM(v) (void)(v) +#else +#define STBIR__UNUSED_PARAM(v) (void)sizeof(v) +#endif + +// must match stbir_datatype +static unsigned char stbir__type_size[] = { + 1, // STBIR_TYPE_UINT8 + 2, // STBIR_TYPE_UINT16 + 4, // STBIR_TYPE_UINT32 + 4, // STBIR_TYPE_FLOAT +}; + +// Kernel function centered at 0 +typedef float (stbir__kernel_fn)(float x, float scale); +typedef float (stbir__support_fn)(float scale); + +typedef struct +{ + stbir__kernel_fn* kernel; + stbir__support_fn* support; +} stbir__filter_info; + +// When upsampling, the contributors are which source pixels contribute. +// When downsampling, the contributors are which destination pixels are contributed to. +typedef struct +{ + int n0; // First contributing pixel + int n1; // Last contributing pixel +} stbir__contributors; + +typedef struct +{ + const void* input_data; + int input_w; + int input_h; + int input_stride_bytes; + + void* output_data; + int output_w; + int output_h; + int output_stride_bytes; + + float s0, t0, s1, t1; + + float horizontal_shift; // Units: output pixels + float vertical_shift; // Units: output pixels + float horizontal_scale; + float vertical_scale; + + int channels; + int alpha_channel; + stbir_uint32 flags; + stbir_datatype type; + stbir_filter horizontal_filter; + stbir_filter vertical_filter; + stbir_edge edge_horizontal; + stbir_edge edge_vertical; + stbir_colorspace colorspace; + + stbir__contributors* horizontal_contributors; + float* horizontal_coefficients; + + stbir__contributors* vertical_contributors; + float* vertical_coefficients; + + int decode_buffer_pixels; + float* decode_buffer; + + float* horizontal_buffer; + + // cache these because ceil/floor are inexplicably showing up in profile + int horizontal_coefficient_width; + int vertical_coefficient_width; + int horizontal_filter_pixel_width; + int vertical_filter_pixel_width; + int horizontal_filter_pixel_margin; + int vertical_filter_pixel_margin; + int horizontal_num_contributors; + int vertical_num_contributors; + + int ring_buffer_length_bytes; // The length of an individual entry in the ring buffer. The total number of ring buffers is stbir__get_filter_pixel_width(filter) + int ring_buffer_num_entries; // Total number of entries in the ring buffer. + int ring_buffer_first_scanline; + int ring_buffer_last_scanline; + int ring_buffer_begin_index; // first_scanline is at this index in the ring buffer + float* ring_buffer; + + float* encode_buffer; // A temporary buffer to store floats so we don't lose precision while we do multiply-adds. + + int horizontal_contributors_size; + int horizontal_coefficients_size; + int vertical_contributors_size; + int vertical_coefficients_size; + int decode_buffer_size; + int horizontal_buffer_size; + int ring_buffer_size; + int encode_buffer_size; +} stbir__info; + + +static const float stbir__max_uint8_as_float = 255.0f; +static const float stbir__max_uint16_as_float = 65535.0f; +static const double stbir__max_uint32_as_float = 4294967295.0; + + +static stbir__inline int stbir__min(int a, int b) +{ + return a < b ? a : b; +} + +static stbir__inline float stbir__saturate(float x) +{ + if (x < 0) + return 0; + + if (x > 1) + return 1; + + return x; +} + +#ifdef STBIR_SATURATE_INT +static stbir__inline stbir_uint8 stbir__saturate8(int x) +{ + if ((unsigned int) x <= 255) + return x; + + if (x < 0) + return 0; + + return 255; +} + +static stbir__inline stbir_uint16 stbir__saturate16(int x) +{ + if ((unsigned int) x <= 65535) + return x; + + if (x < 0) + return 0; + + return 65535; +} +#endif + +static float stbir__srgb_uchar_to_linear_float[256] = { + 0.000000f, 0.000304f, 0.000607f, 0.000911f, 0.001214f, 0.001518f, 0.001821f, 0.002125f, 0.002428f, 0.002732f, 0.003035f, + 0.003347f, 0.003677f, 0.004025f, 0.004391f, 0.004777f, 0.005182f, 0.005605f, 0.006049f, 0.006512f, 0.006995f, 0.007499f, + 0.008023f, 0.008568f, 0.009134f, 0.009721f, 0.010330f, 0.010960f, 0.011612f, 0.012286f, 0.012983f, 0.013702f, 0.014444f, + 0.015209f, 0.015996f, 0.016807f, 0.017642f, 0.018500f, 0.019382f, 0.020289f, 0.021219f, 0.022174f, 0.023153f, 0.024158f, + 0.025187f, 0.026241f, 0.027321f, 0.028426f, 0.029557f, 0.030713f, 0.031896f, 0.033105f, 0.034340f, 0.035601f, 0.036889f, + 0.038204f, 0.039546f, 0.040915f, 0.042311f, 0.043735f, 0.045186f, 0.046665f, 0.048172f, 0.049707f, 0.051269f, 0.052861f, + 0.054480f, 0.056128f, 0.057805f, 0.059511f, 0.061246f, 0.063010f, 0.064803f, 0.066626f, 0.068478f, 0.070360f, 0.072272f, + 0.074214f, 0.076185f, 0.078187f, 0.080220f, 0.082283f, 0.084376f, 0.086500f, 0.088656f, 0.090842f, 0.093059f, 0.095307f, + 0.097587f, 0.099899f, 0.102242f, 0.104616f, 0.107023f, 0.109462f, 0.111932f, 0.114435f, 0.116971f, 0.119538f, 0.122139f, + 0.124772f, 0.127438f, 0.130136f, 0.132868f, 0.135633f, 0.138432f, 0.141263f, 0.144128f, 0.147027f, 0.149960f, 0.152926f, + 0.155926f, 0.158961f, 0.162029f, 0.165132f, 0.168269f, 0.171441f, 0.174647f, 0.177888f, 0.181164f, 0.184475f, 0.187821f, + 0.191202f, 0.194618f, 0.198069f, 0.201556f, 0.205079f, 0.208637f, 0.212231f, 0.215861f, 0.219526f, 0.223228f, 0.226966f, + 0.230740f, 0.234551f, 0.238398f, 0.242281f, 0.246201f, 0.250158f, 0.254152f, 0.258183f, 0.262251f, 0.266356f, 0.270498f, + 0.274677f, 0.278894f, 0.283149f, 0.287441f, 0.291771f, 0.296138f, 0.300544f, 0.304987f, 0.309469f, 0.313989f, 0.318547f, + 0.323143f, 0.327778f, 0.332452f, 0.337164f, 0.341914f, 0.346704f, 0.351533f, 0.356400f, 0.361307f, 0.366253f, 0.371238f, + 0.376262f, 0.381326f, 0.386430f, 0.391573f, 0.396755f, 0.401978f, 0.407240f, 0.412543f, 0.417885f, 0.423268f, 0.428691f, + 0.434154f, 0.439657f, 0.445201f, 0.450786f, 0.456411f, 0.462077f, 0.467784f, 0.473532f, 0.479320f, 0.485150f, 0.491021f, + 0.496933f, 0.502887f, 0.508881f, 0.514918f, 0.520996f, 0.527115f, 0.533276f, 0.539480f, 0.545725f, 0.552011f, 0.558340f, + 0.564712f, 0.571125f, 0.577581f, 0.584078f, 0.590619f, 0.597202f, 0.603827f, 0.610496f, 0.617207f, 0.623960f, 0.630757f, + 0.637597f, 0.644480f, 0.651406f, 0.658375f, 0.665387f, 0.672443f, 0.679543f, 0.686685f, 0.693872f, 0.701102f, 0.708376f, + 0.715694f, 0.723055f, 0.730461f, 0.737911f, 0.745404f, 0.752942f, 0.760525f, 0.768151f, 0.775822f, 0.783538f, 0.791298f, + 0.799103f, 0.806952f, 0.814847f, 0.822786f, 0.830770f, 0.838799f, 0.846873f, 0.854993f, 0.863157f, 0.871367f, 0.879622f, + 0.887923f, 0.896269f, 0.904661f, 0.913099f, 0.921582f, 0.930111f, 0.938686f, 0.947307f, 0.955974f, 0.964686f, 0.973445f, + 0.982251f, 0.991102f, 1.0f +}; + +static float stbir__srgb_to_linear(float f) +{ + if (f <= 0.04045f) + return f / 12.92f; + else + return (float)pow((f + 0.055f) / 1.055f, 2.4f); +} + +static float stbir__linear_to_srgb(float f) +{ + if (f <= 0.0031308f) + return f * 12.92f; + else + return 1.055f * (float)pow(f, 1 / 2.4f) - 0.055f; +} + +#ifndef STBIR_NON_IEEE_FLOAT +// From https://gist.github.com/rygorous/2203834 + +typedef union +{ + stbir_uint32 u; + float f; +} stbir__FP32; + +static const stbir_uint32 fp32_to_srgb8_tab4[104] = { + 0x0073000d, 0x007a000d, 0x0080000d, 0x0087000d, 0x008d000d, 0x0094000d, 0x009a000d, 0x00a1000d, + 0x00a7001a, 0x00b4001a, 0x00c1001a, 0x00ce001a, 0x00da001a, 0x00e7001a, 0x00f4001a, 0x0101001a, + 0x010e0033, 0x01280033, 0x01410033, 0x015b0033, 0x01750033, 0x018f0033, 0x01a80033, 0x01c20033, + 0x01dc0067, 0x020f0067, 0x02430067, 0x02760067, 0x02aa0067, 0x02dd0067, 0x03110067, 0x03440067, + 0x037800ce, 0x03df00ce, 0x044600ce, 0x04ad00ce, 0x051400ce, 0x057b00c5, 0x05dd00bc, 0x063b00b5, + 0x06970158, 0x07420142, 0x07e30130, 0x087b0120, 0x090b0112, 0x09940106, 0x0a1700fc, 0x0a9500f2, + 0x0b0f01cb, 0x0bf401ae, 0x0ccb0195, 0x0d950180, 0x0e56016e, 0x0f0d015e, 0x0fbc0150, 0x10630143, + 0x11070264, 0x1238023e, 0x1357021d, 0x14660201, 0x156601e9, 0x165a01d3, 0x174401c0, 0x182401af, + 0x18fe0331, 0x1a9602fe, 0x1c1502d2, 0x1d7e02ad, 0x1ed4028d, 0x201a0270, 0x21520256, 0x227d0240, + 0x239f0443, 0x25c003fe, 0x27bf03c4, 0x29a10392, 0x2b6a0367, 0x2d1d0341, 0x2ebe031f, 0x304d0300, + 0x31d105b0, 0x34a80555, 0x37520507, 0x39d504c5, 0x3c37048b, 0x3e7c0458, 0x40a8042a, 0x42bd0401, + 0x44c20798, 0x488e071e, 0x4c1c06b6, 0x4f76065d, 0x52a50610, 0x55ac05cc, 0x5892058f, 0x5b590559, + 0x5e0c0a23, 0x631c0980, 0x67db08f6, 0x6c55087f, 0x70940818, 0x74a007bd, 0x787d076c, 0x7c330723, +}; + +static stbir_uint8 stbir__linear_to_srgb_uchar(float in) +{ + static const stbir__FP32 almostone = { 0x3f7fffff }; // 1-eps + static const stbir__FP32 minval = { (127-13) << 23 }; + stbir_uint32 tab,bias,scale,t; + stbir__FP32 f; + + // Clamp to [2^(-13), 1-eps]; these two values map to 0 and 1, respectively. + // The tests are carefully written so that NaNs map to 0, same as in the reference + // implementation. + if (!(in > minval.f)) // written this way to catch NaNs + in = minval.f; + if (in > almostone.f) + in = almostone.f; + + // Do the table lookup and unpack bias, scale + f.f = in; + tab = fp32_to_srgb8_tab4[(f.u - minval.u) >> 20]; + bias = (tab >> 16) << 9; + scale = tab & 0xffff; + + // Grab next-highest mantissa bits and perform linear interpolation + t = (f.u >> 12) & 0xff; + return (unsigned char) ((bias + scale*t) >> 16); +} + +#else +// sRGB transition values, scaled by 1<<28 +static int stbir__srgb_offset_to_linear_scaled[256] = +{ + 0, 40738, 122216, 203693, 285170, 366648, 448125, 529603, + 611080, 692557, 774035, 855852, 942009, 1033024, 1128971, 1229926, + 1335959, 1447142, 1563542, 1685229, 1812268, 1944725, 2082664, 2226148, + 2375238, 2529996, 2690481, 2856753, 3028870, 3206888, 3390865, 3580856, + 3776916, 3979100, 4187460, 4402049, 4622919, 4850123, 5083710, 5323731, + 5570236, 5823273, 6082892, 6349140, 6622065, 6901714, 7188133, 7481369, + 7781466, 8088471, 8402427, 8723380, 9051372, 9386448, 9728650, 10078021, + 10434603, 10798439, 11169569, 11548036, 11933879, 12327139, 12727857, 13136073, + 13551826, 13975156, 14406100, 14844697, 15290987, 15745007, 16206795, 16676389, + 17153826, 17639142, 18132374, 18633560, 19142734, 19659934, 20185196, 20718552, + 21260042, 21809696, 22367554, 22933648, 23508010, 24090680, 24681686, 25281066, + 25888850, 26505076, 27129772, 27762974, 28404716, 29055026, 29713942, 30381490, + 31057708, 31742624, 32436272, 33138682, 33849884, 34569912, 35298800, 36036568, + 36783260, 37538896, 38303512, 39077136, 39859796, 40651528, 41452360, 42262316, + 43081432, 43909732, 44747252, 45594016, 46450052, 47315392, 48190064, 49074096, + 49967516, 50870356, 51782636, 52704392, 53635648, 54576432, 55526772, 56486700, + 57456236, 58435408, 59424248, 60422780, 61431036, 62449032, 63476804, 64514376, + 65561776, 66619028, 67686160, 68763192, 69850160, 70947088, 72053992, 73170912, + 74297864, 75434880, 76581976, 77739184, 78906536, 80084040, 81271736, 82469648, + 83677792, 84896192, 86124888, 87363888, 88613232, 89872928, 91143016, 92423512, + 93714432, 95015816, 96327688, 97650056, 98982952, 100326408, 101680440, 103045072, + 104420320, 105806224, 107202800, 108610064, 110028048, 111456776, 112896264, 114346544, + 115807632, 117279552, 118762328, 120255976, 121760536, 123276016, 124802440, 126339832, + 127888216, 129447616, 131018048, 132599544, 134192112, 135795792, 137410592, 139036528, + 140673648, 142321952, 143981456, 145652208, 147334208, 149027488, 150732064, 152447968, + 154175200, 155913792, 157663776, 159425168, 161197984, 162982240, 164777968, 166585184, + 168403904, 170234160, 172075968, 173929344, 175794320, 177670896, 179559120, 181458992, + 183370528, 185293776, 187228736, 189175424, 191133888, 193104112, 195086128, 197079968, + 199085648, 201103184, 203132592, 205173888, 207227120, 209292272, 211369392, 213458480, + 215559568, 217672656, 219797792, 221934976, 224084240, 226245600, 228419056, 230604656, + 232802400, 235012320, 237234432, 239468736, 241715280, 243974080, 246245120, 248528464, + 250824112, 253132064, 255452368, 257785040, 260130080, 262487520, 264857376, 267239664, +}; + +static stbir_uint8 stbir__linear_to_srgb_uchar(float f) +{ + int x = (int) (f * (1 << 28)); // has headroom so you don't need to clamp + int v = 0; + int i; + + // Refine the guess with a short binary search. + i = v + 128; if (x >= stbir__srgb_offset_to_linear_scaled[i]) v = i; + i = v + 64; if (x >= stbir__srgb_offset_to_linear_scaled[i]) v = i; + i = v + 32; if (x >= stbir__srgb_offset_to_linear_scaled[i]) v = i; + i = v + 16; if (x >= stbir__srgb_offset_to_linear_scaled[i]) v = i; + i = v + 8; if (x >= stbir__srgb_offset_to_linear_scaled[i]) v = i; + i = v + 4; if (x >= stbir__srgb_offset_to_linear_scaled[i]) v = i; + i = v + 2; if (x >= stbir__srgb_offset_to_linear_scaled[i]) v = i; + i = v + 1; if (x >= stbir__srgb_offset_to_linear_scaled[i]) v = i; + + return (stbir_uint8) v; +} +#endif + +static float stbir__filter_trapezoid(float x, float scale) +{ + float halfscale = scale / 2; + float t = 0.5f + halfscale; + STBIR_ASSERT(scale <= 1); + + x = (float)fabs(x); + + if (x >= t) + return 0; + else + { + float r = 0.5f - halfscale; + if (x <= r) + return 1; + else + return (t - x) / scale; + } +} + +static float stbir__support_trapezoid(float scale) +{ + STBIR_ASSERT(scale <= 1); + return 0.5f + scale / 2; +} + +static float stbir__filter_triangle(float x, float s) +{ + STBIR__UNUSED_PARAM(s); + + x = (float)fabs(x); + + if (x <= 1.0f) + return 1 - x; + else + return 0; +} + +static float stbir__filter_cubic(float x, float s) +{ + STBIR__UNUSED_PARAM(s); + + x = (float)fabs(x); + + if (x < 1.0f) + return (4 + x*x*(3*x - 6))/6; + else if (x < 2.0f) + return (8 + x*(-12 + x*(6 - x)))/6; + + return (0.0f); +} + +static float stbir__filter_catmullrom(float x, float s) +{ + STBIR__UNUSED_PARAM(s); + + x = (float)fabs(x); + + if (x < 1.0f) + return 1 - x*x*(2.5f - 1.5f*x); + else if (x < 2.0f) + return 2 - x*(4 + x*(0.5f*x - 2.5f)); + + return (0.0f); +} + +static float stbir__filter_mitchell(float x, float s) +{ + STBIR__UNUSED_PARAM(s); + + x = (float)fabs(x); + + if (x < 1.0f) + return (16 + x*x*(21 * x - 36))/18; + else if (x < 2.0f) + return (32 + x*(-60 + x*(36 - 7*x)))/18; + + return (0.0f); +} + +static float stbir__support_zero(float s) +{ + STBIR__UNUSED_PARAM(s); + return 0; +} + +static float stbir__support_one(float s) +{ + STBIR__UNUSED_PARAM(s); + return 1; +} + +static float stbir__support_two(float s) +{ + STBIR__UNUSED_PARAM(s); + return 2; +} + +static stbir__filter_info stbir__filter_info_table[] = { + { NULL, stbir__support_zero }, + { stbir__filter_trapezoid, stbir__support_trapezoid }, + { stbir__filter_triangle, stbir__support_one }, + { stbir__filter_cubic, stbir__support_two }, + { stbir__filter_catmullrom, stbir__support_two }, + { stbir__filter_mitchell, stbir__support_two }, +}; + +stbir__inline static int stbir__use_upsampling(float ratio) +{ + return ratio > 1; +} + +stbir__inline static int stbir__use_width_upsampling(stbir__info* stbir_info) +{ + return stbir__use_upsampling(stbir_info->horizontal_scale); +} + +stbir__inline static int stbir__use_height_upsampling(stbir__info* stbir_info) +{ + return stbir__use_upsampling(stbir_info->vertical_scale); +} + +// This is the maximum number of input samples that can affect an output sample +// with the given filter +static int stbir__get_filter_pixel_width(stbir_filter filter, float scale) +{ + STBIR_ASSERT(filter != 0); + STBIR_ASSERT(filter < STBIR__ARRAY_SIZE(stbir__filter_info_table)); + + if (stbir__use_upsampling(scale)) + return (int)ceil(stbir__filter_info_table[filter].support(1/scale) * 2); + else + return (int)ceil(stbir__filter_info_table[filter].support(scale) * 2 / scale); +} + +// This is how much to expand buffers to account for filters seeking outside +// the image boundaries. +static int stbir__get_filter_pixel_margin(stbir_filter filter, float scale) +{ + return stbir__get_filter_pixel_width(filter, scale) / 2; +} + +static int stbir__get_coefficient_width(stbir_filter filter, float scale) +{ + if (stbir__use_upsampling(scale)) + return (int)ceil(stbir__filter_info_table[filter].support(1 / scale) * 2); + else + return (int)ceil(stbir__filter_info_table[filter].support(scale) * 2); +} + +static int stbir__get_contributors(float scale, stbir_filter filter, int input_size, int output_size) +{ + if (stbir__use_upsampling(scale)) + return output_size; + else + return (input_size + stbir__get_filter_pixel_margin(filter, scale) * 2); +} + +static int stbir__get_total_horizontal_coefficients(stbir__info* info) +{ + return info->horizontal_num_contributors + * stbir__get_coefficient_width (info->horizontal_filter, info->horizontal_scale); +} + +static int stbir__get_total_vertical_coefficients(stbir__info* info) +{ + return info->vertical_num_contributors + * stbir__get_coefficient_width (info->vertical_filter, info->vertical_scale); +} + +static stbir__contributors* stbir__get_contributor(stbir__contributors* contributors, int n) +{ + return &contributors[n]; +} + +// For perf reasons this code is duplicated in stbir__resample_horizontal_upsample/downsample, +// if you change it here change it there too. +static float* stbir__get_coefficient(float* coefficients, stbir_filter filter, float scale, int n, int c) +{ + int width = stbir__get_coefficient_width(filter, scale); + return &coefficients[width*n + c]; +} + +static int stbir__edge_wrap_slow(stbir_edge edge, int n, int max) +{ + switch (edge) + { + case STBIR_EDGE_ZERO: + return 0; // we'll decode the wrong pixel here, and then overwrite with 0s later + + case STBIR_EDGE_CLAMP: + if (n < 0) + return 0; + + if (n >= max) + return max - 1; + + return n; // NOTREACHED + + case STBIR_EDGE_REFLECT: + { + if (n < 0) + { + if (n < max) + return -n; + else + return max - 1; + } + + if (n >= max) + { + int max2 = max * 2; + if (n >= max2) + return 0; + else + return max2 - n - 1; + } + + return n; // NOTREACHED + } + + case STBIR_EDGE_WRAP: + if (n >= 0) + return (n % max); + else + { + int m = (-n) % max; + + if (m != 0) + m = max - m; + + return (m); + } + // NOTREACHED + + default: + STBIR_ASSERT(!"Unimplemented edge type"); + return 0; + } +} + +stbir__inline static int stbir__edge_wrap(stbir_edge edge, int n, int max) +{ + // avoid per-pixel switch + if (n >= 0 && n < max) + return n; + return stbir__edge_wrap_slow(edge, n, max); +} + +// What input pixels contribute to this output pixel? +static void stbir__calculate_sample_range_upsample(int n, float out_filter_radius, float scale_ratio, float out_shift, int* in_first_pixel, int* in_last_pixel, float* in_center_of_out) +{ + float out_pixel_center = (float)n + 0.5f; + float out_pixel_influence_lowerbound = out_pixel_center - out_filter_radius; + float out_pixel_influence_upperbound = out_pixel_center + out_filter_radius; + + float in_pixel_influence_lowerbound = (out_pixel_influence_lowerbound + out_shift) / scale_ratio; + float in_pixel_influence_upperbound = (out_pixel_influence_upperbound + out_shift) / scale_ratio; + + *in_center_of_out = (out_pixel_center + out_shift) / scale_ratio; + *in_first_pixel = (int)(floor(in_pixel_influence_lowerbound + 0.5)); + *in_last_pixel = (int)(floor(in_pixel_influence_upperbound - 0.5)); +} + +// What output pixels does this input pixel contribute to? +static void stbir__calculate_sample_range_downsample(int n, float in_pixels_radius, float scale_ratio, float out_shift, int* out_first_pixel, int* out_last_pixel, float* out_center_of_in) +{ + float in_pixel_center = (float)n + 0.5f; + float in_pixel_influence_lowerbound = in_pixel_center - in_pixels_radius; + float in_pixel_influence_upperbound = in_pixel_center + in_pixels_radius; + + float out_pixel_influence_lowerbound = in_pixel_influence_lowerbound * scale_ratio - out_shift; + float out_pixel_influence_upperbound = in_pixel_influence_upperbound * scale_ratio - out_shift; + + *out_center_of_in = in_pixel_center * scale_ratio - out_shift; + *out_first_pixel = (int)(floor(out_pixel_influence_lowerbound + 0.5)); + *out_last_pixel = (int)(floor(out_pixel_influence_upperbound - 0.5)); +} + +static void stbir__calculate_coefficients_upsample(stbir_filter filter, float scale, int in_first_pixel, int in_last_pixel, float in_center_of_out, stbir__contributors* contributor, float* coefficient_group) +{ + int i; + float total_filter = 0; + float filter_scale; + + STBIR_ASSERT(in_last_pixel - in_first_pixel <= (int)ceil(stbir__filter_info_table[filter].support(1/scale) * 2)); // Taken directly from stbir__get_coefficient_width() which we can't call because we don't know if we're horizontal or vertical. + + contributor->n0 = in_first_pixel; + contributor->n1 = in_last_pixel; + + STBIR_ASSERT(contributor->n1 >= contributor->n0); + + for (i = 0; i <= in_last_pixel - in_first_pixel; i++) + { + float in_pixel_center = (float)(i + in_first_pixel) + 0.5f; + coefficient_group[i] = stbir__filter_info_table[filter].kernel(in_center_of_out - in_pixel_center, 1 / scale); + + // If the coefficient is zero, skip it. (Don't do the <0 check here, we want the influence of those outside pixels.) + if (i == 0 && !coefficient_group[i]) + { + contributor->n0 = ++in_first_pixel; + i--; + continue; + } + + total_filter += coefficient_group[i]; + } + + STBIR_ASSERT(stbir__filter_info_table[filter].kernel((float)(in_last_pixel + 1) + 0.5f - in_center_of_out, 1/scale) == 0); + + STBIR_ASSERT(total_filter > 0.9); + STBIR_ASSERT(total_filter < 1.1f); // Make sure it's not way off. + + // Make sure the sum of all coefficients is 1. + filter_scale = 1 / total_filter; + + for (i = 0; i <= in_last_pixel - in_first_pixel; i++) + coefficient_group[i] *= filter_scale; + + for (i = in_last_pixel - in_first_pixel; i >= 0; i--) + { + if (coefficient_group[i]) + break; + + // This line has no weight. We can skip it. + contributor->n1 = contributor->n0 + i - 1; + } +} + +static void stbir__calculate_coefficients_downsample(stbir_filter filter, float scale_ratio, int out_first_pixel, int out_last_pixel, float out_center_of_in, stbir__contributors* contributor, float* coefficient_group) +{ + int i; + + STBIR_ASSERT(out_last_pixel - out_first_pixel <= (int)ceil(stbir__filter_info_table[filter].support(scale_ratio) * 2)); // Taken directly from stbir__get_coefficient_width() which we can't call because we don't know if we're horizontal or vertical. + + contributor->n0 = out_first_pixel; + contributor->n1 = out_last_pixel; + + STBIR_ASSERT(contributor->n1 >= contributor->n0); + + for (i = 0; i <= out_last_pixel - out_first_pixel; i++) + { + float out_pixel_center = (float)(i + out_first_pixel) + 0.5f; + float x = out_pixel_center - out_center_of_in; + coefficient_group[i] = stbir__filter_info_table[filter].kernel(x, scale_ratio) * scale_ratio; + } + + STBIR_ASSERT(stbir__filter_info_table[filter].kernel((float)(out_last_pixel + 1) + 0.5f - out_center_of_in, scale_ratio) == 0); + + for (i = out_last_pixel - out_first_pixel; i >= 0; i--) + { + if (coefficient_group[i]) + break; + + // This line has no weight. We can skip it. + contributor->n1 = contributor->n0 + i - 1; + } +} + +static void stbir__normalize_downsample_coefficients(stbir__contributors* contributors, float* coefficients, stbir_filter filter, float scale_ratio, int input_size, int output_size) +{ + int num_contributors = stbir__get_contributors(scale_ratio, filter, input_size, output_size); + int num_coefficients = stbir__get_coefficient_width(filter, scale_ratio); + int i, j; + int skip; + + for (i = 0; i < output_size; i++) + { + float scale; + float total = 0; + + for (j = 0; j < num_contributors; j++) + { + if (i >= contributors[j].n0 && i <= contributors[j].n1) + { + float coefficient = *stbir__get_coefficient(coefficients, filter, scale_ratio, j, i - contributors[j].n0); + total += coefficient; + } + else if (i < contributors[j].n0) + break; + } + + STBIR_ASSERT(total > 0.9f); + STBIR_ASSERT(total < 1.1f); + + scale = 1 / total; + + for (j = 0; j < num_contributors; j++) + { + if (i >= contributors[j].n0 && i <= contributors[j].n1) + *stbir__get_coefficient(coefficients, filter, scale_ratio, j, i - contributors[j].n0) *= scale; + else if (i < contributors[j].n0) + break; + } + } + + // Optimize: Skip zero coefficients and contributions outside of image bounds. + // Do this after normalizing because normalization depends on the n0/n1 values. + for (j = 0; j < num_contributors; j++) + { + int range, max, width; + + skip = 0; + while (*stbir__get_coefficient(coefficients, filter, scale_ratio, j, skip) == 0) + skip++; + + contributors[j].n0 += skip; + + while (contributors[j].n0 < 0) + { + contributors[j].n0++; + skip++; + } + + range = contributors[j].n1 - contributors[j].n0 + 1; + max = stbir__min(num_coefficients, range); + + width = stbir__get_coefficient_width(filter, scale_ratio); + for (i = 0; i < max; i++) + { + if (i + skip >= width) + break; + + *stbir__get_coefficient(coefficients, filter, scale_ratio, j, i) = *stbir__get_coefficient(coefficients, filter, scale_ratio, j, i + skip); + } + + continue; + } + + // Using min to avoid writing into invalid pixels. + for (i = 0; i < num_contributors; i++) + contributors[i].n1 = stbir__min(contributors[i].n1, output_size - 1); +} + +// Each scan line uses the same kernel values so we should calculate the kernel +// values once and then we can use them for every scan line. +static void stbir__calculate_filters(stbir__contributors* contributors, float* coefficients, stbir_filter filter, float scale_ratio, float shift, int input_size, int output_size) +{ + int n; + int total_contributors = stbir__get_contributors(scale_ratio, filter, input_size, output_size); + + if (stbir__use_upsampling(scale_ratio)) + { + float out_pixels_radius = stbir__filter_info_table[filter].support(1 / scale_ratio) * scale_ratio; + + // Looping through out pixels + for (n = 0; n < total_contributors; n++) + { + float in_center_of_out; // Center of the current out pixel in the in pixel space + int in_first_pixel, in_last_pixel; + + stbir__calculate_sample_range_upsample(n, out_pixels_radius, scale_ratio, shift, &in_first_pixel, &in_last_pixel, &in_center_of_out); + + stbir__calculate_coefficients_upsample(filter, scale_ratio, in_first_pixel, in_last_pixel, in_center_of_out, stbir__get_contributor(contributors, n), stbir__get_coefficient(coefficients, filter, scale_ratio, n, 0)); + } + } + else + { + float in_pixels_radius = stbir__filter_info_table[filter].support(scale_ratio) / scale_ratio; + + // Looping through in pixels + for (n = 0; n < total_contributors; n++) + { + float out_center_of_in; // Center of the current out pixel in the in pixel space + int out_first_pixel, out_last_pixel; + int n_adjusted = n - stbir__get_filter_pixel_margin(filter, scale_ratio); + + stbir__calculate_sample_range_downsample(n_adjusted, in_pixels_radius, scale_ratio, shift, &out_first_pixel, &out_last_pixel, &out_center_of_in); + + stbir__calculate_coefficients_downsample(filter, scale_ratio, out_first_pixel, out_last_pixel, out_center_of_in, stbir__get_contributor(contributors, n), stbir__get_coefficient(coefficients, filter, scale_ratio, n, 0)); + } + + stbir__normalize_downsample_coefficients(contributors, coefficients, filter, scale_ratio, input_size, output_size); + } +} + +static float* stbir__get_decode_buffer(stbir__info* stbir_info) +{ + // The 0 index of the decode buffer starts after the margin. This makes + // it okay to use negative indexes on the decode buffer. + return &stbir_info->decode_buffer[stbir_info->horizontal_filter_pixel_margin * stbir_info->channels]; +} + +#define STBIR__DECODE(type, colorspace) ((type) * (STBIR_MAX_COLORSPACES) + (colorspace)) + +static void stbir__decode_scanline(stbir__info* stbir_info, int n) +{ + int c; + int channels = stbir_info->channels; + int alpha_channel = stbir_info->alpha_channel; + int type = stbir_info->type; + int colorspace = stbir_info->colorspace; + int input_w = stbir_info->input_w; + size_t input_stride_bytes = stbir_info->input_stride_bytes; + float* decode_buffer = stbir__get_decode_buffer(stbir_info); + stbir_edge edge_horizontal = stbir_info->edge_horizontal; + stbir_edge edge_vertical = stbir_info->edge_vertical; + size_t in_buffer_row_offset = stbir__edge_wrap(edge_vertical, n, stbir_info->input_h) * input_stride_bytes; + const void* input_data = (char *) stbir_info->input_data + in_buffer_row_offset; + int max_x = input_w + stbir_info->horizontal_filter_pixel_margin; + int decode = STBIR__DECODE(type, colorspace); + + int x = -stbir_info->horizontal_filter_pixel_margin; + + // special handling for STBIR_EDGE_ZERO because it needs to return an item that doesn't appear in the input, + // and we want to avoid paying overhead on every pixel if not STBIR_EDGE_ZERO + if (edge_vertical == STBIR_EDGE_ZERO && (n < 0 || n >= stbir_info->input_h)) + { + for (; x < max_x; x++) + for (c = 0; c < channels; c++) + decode_buffer[x*channels + c] = 0; + return; + } + + switch (decode) + { + case STBIR__DECODE(STBIR_TYPE_UINT8, STBIR_COLORSPACE_LINEAR): + for (; x < max_x; x++) + { + int decode_pixel_index = x * channels; + int input_pixel_index = stbir__edge_wrap(edge_horizontal, x, input_w) * channels; + for (c = 0; c < channels; c++) + decode_buffer[decode_pixel_index + c] = ((float)((const unsigned char*)input_data)[input_pixel_index + c]) / stbir__max_uint8_as_float; + } + break; + + case STBIR__DECODE(STBIR_TYPE_UINT8, STBIR_COLORSPACE_SRGB): + for (; x < max_x; x++) + { + int decode_pixel_index = x * channels; + int input_pixel_index = stbir__edge_wrap(edge_horizontal, x, input_w) * channels; + for (c = 0; c < channels; c++) + decode_buffer[decode_pixel_index + c] = stbir__srgb_uchar_to_linear_float[((const unsigned char*)input_data)[input_pixel_index + c]]; + + if (!(stbir_info->flags&STBIR_FLAG_ALPHA_USES_COLORSPACE)) + decode_buffer[decode_pixel_index + alpha_channel] = ((float)((const unsigned char*)input_data)[input_pixel_index + alpha_channel]) / stbir__max_uint8_as_float; + } + break; + + case STBIR__DECODE(STBIR_TYPE_UINT16, STBIR_COLORSPACE_LINEAR): + for (; x < max_x; x++) + { + int decode_pixel_index = x * channels; + int input_pixel_index = stbir__edge_wrap(edge_horizontal, x, input_w) * channels; + for (c = 0; c < channels; c++) + decode_buffer[decode_pixel_index + c] = ((float)((const unsigned short*)input_data)[input_pixel_index + c]) / stbir__max_uint16_as_float; + } + break; + + case STBIR__DECODE(STBIR_TYPE_UINT16, STBIR_COLORSPACE_SRGB): + for (; x < max_x; x++) + { + int decode_pixel_index = x * channels; + int input_pixel_index = stbir__edge_wrap(edge_horizontal, x, input_w) * channels; + for (c = 0; c < channels; c++) + decode_buffer[decode_pixel_index + c] = stbir__srgb_to_linear(((float)((const unsigned short*)input_data)[input_pixel_index + c]) / stbir__max_uint16_as_float); + + if (!(stbir_info->flags&STBIR_FLAG_ALPHA_USES_COLORSPACE)) + decode_buffer[decode_pixel_index + alpha_channel] = ((float)((const unsigned short*)input_data)[input_pixel_index + alpha_channel]) / stbir__max_uint16_as_float; + } + break; + + case STBIR__DECODE(STBIR_TYPE_UINT32, STBIR_COLORSPACE_LINEAR): + for (; x < max_x; x++) + { + int decode_pixel_index = x * channels; + int input_pixel_index = stbir__edge_wrap(edge_horizontal, x, input_w) * channels; + for (c = 0; c < channels; c++) + decode_buffer[decode_pixel_index + c] = (float)(((double)((const unsigned int*)input_data)[input_pixel_index + c]) / stbir__max_uint32_as_float); + } + break; + + case STBIR__DECODE(STBIR_TYPE_UINT32, STBIR_COLORSPACE_SRGB): + for (; x < max_x; x++) + { + int decode_pixel_index = x * channels; + int input_pixel_index = stbir__edge_wrap(edge_horizontal, x, input_w) * channels; + for (c = 0; c < channels; c++) + decode_buffer[decode_pixel_index + c] = stbir__srgb_to_linear((float)(((double)((const unsigned int*)input_data)[input_pixel_index + c]) / stbir__max_uint32_as_float)); + + if (!(stbir_info->flags&STBIR_FLAG_ALPHA_USES_COLORSPACE)) + decode_buffer[decode_pixel_index + alpha_channel] = (float)(((double)((const unsigned int*)input_data)[input_pixel_index + alpha_channel]) / stbir__max_uint32_as_float); + } + break; + + case STBIR__DECODE(STBIR_TYPE_FLOAT, STBIR_COLORSPACE_LINEAR): + for (; x < max_x; x++) + { + int decode_pixel_index = x * channels; + int input_pixel_index = stbir__edge_wrap(edge_horizontal, x, input_w) * channels; + for (c = 0; c < channels; c++) + decode_buffer[decode_pixel_index + c] = ((const float*)input_data)[input_pixel_index + c]; + } + break; + + case STBIR__DECODE(STBIR_TYPE_FLOAT, STBIR_COLORSPACE_SRGB): + for (; x < max_x; x++) + { + int decode_pixel_index = x * channels; + int input_pixel_index = stbir__edge_wrap(edge_horizontal, x, input_w) * channels; + for (c = 0; c < channels; c++) + decode_buffer[decode_pixel_index + c] = stbir__srgb_to_linear(((const float*)input_data)[input_pixel_index + c]); + + if (!(stbir_info->flags&STBIR_FLAG_ALPHA_USES_COLORSPACE)) + decode_buffer[decode_pixel_index + alpha_channel] = ((const float*)input_data)[input_pixel_index + alpha_channel]; + } + + break; + + default: + STBIR_ASSERT(!"Unknown type/colorspace/channels combination."); + break; + } + + if (!(stbir_info->flags & STBIR_FLAG_ALPHA_PREMULTIPLIED)) + { + for (x = -stbir_info->horizontal_filter_pixel_margin; x < max_x; x++) + { + int decode_pixel_index = x * channels; + + // If the alpha value is 0 it will clobber the color values. Make sure it's not. + float alpha = decode_buffer[decode_pixel_index + alpha_channel]; +#ifndef STBIR_NO_ALPHA_EPSILON + if (stbir_info->type != STBIR_TYPE_FLOAT) { + alpha += STBIR_ALPHA_EPSILON; + decode_buffer[decode_pixel_index + alpha_channel] = alpha; + } +#endif + for (c = 0; c < channels; c++) + { + if (c == alpha_channel) + continue; + + decode_buffer[decode_pixel_index + c] *= alpha; + } + } + } + + if (edge_horizontal == STBIR_EDGE_ZERO) + { + for (x = -stbir_info->horizontal_filter_pixel_margin; x < 0; x++) + { + for (c = 0; c < channels; c++) + decode_buffer[x*channels + c] = 0; + } + for (x = input_w; x < max_x; x++) + { + for (c = 0; c < channels; c++) + decode_buffer[x*channels + c] = 0; + } + } +} + +static float* stbir__get_ring_buffer_entry(float* ring_buffer, int index, int ring_buffer_length) +{ + return &ring_buffer[index * ring_buffer_length]; +} + +static float* stbir__add_empty_ring_buffer_entry(stbir__info* stbir_info, int n) +{ + int ring_buffer_index; + float* ring_buffer; + + stbir_info->ring_buffer_last_scanline = n; + + if (stbir_info->ring_buffer_begin_index < 0) + { + ring_buffer_index = stbir_info->ring_buffer_begin_index = 0; + stbir_info->ring_buffer_first_scanline = n; + } + else + { + ring_buffer_index = (stbir_info->ring_buffer_begin_index + (stbir_info->ring_buffer_last_scanline - stbir_info->ring_buffer_first_scanline)) % stbir_info->ring_buffer_num_entries; + STBIR_ASSERT(ring_buffer_index != stbir_info->ring_buffer_begin_index); + } + + ring_buffer = stbir__get_ring_buffer_entry(stbir_info->ring_buffer, ring_buffer_index, stbir_info->ring_buffer_length_bytes / sizeof(float)); + memset(ring_buffer, 0, stbir_info->ring_buffer_length_bytes); + + return ring_buffer; +} + + +static void stbir__resample_horizontal_upsample(stbir__info* stbir_info, float* output_buffer) +{ + int x, k; + int output_w = stbir_info->output_w; + int channels = stbir_info->channels; + float* decode_buffer = stbir__get_decode_buffer(stbir_info); + stbir__contributors* horizontal_contributors = stbir_info->horizontal_contributors; + float* horizontal_coefficients = stbir_info->horizontal_coefficients; + int coefficient_width = stbir_info->horizontal_coefficient_width; + + for (x = 0; x < output_w; x++) + { + int n0 = horizontal_contributors[x].n0; + int n1 = horizontal_contributors[x].n1; + + int out_pixel_index = x * channels; + int coefficient_group = coefficient_width * x; + int coefficient_counter = 0; + + STBIR_ASSERT(n1 >= n0); + STBIR_ASSERT(n0 >= -stbir_info->horizontal_filter_pixel_margin); + STBIR_ASSERT(n1 >= -stbir_info->horizontal_filter_pixel_margin); + STBIR_ASSERT(n0 < stbir_info->input_w + stbir_info->horizontal_filter_pixel_margin); + STBIR_ASSERT(n1 < stbir_info->input_w + stbir_info->horizontal_filter_pixel_margin); + + switch (channels) { + case 1: + for (k = n0; k <= n1; k++) + { + int in_pixel_index = k * 1; + float coefficient = horizontal_coefficients[coefficient_group + coefficient_counter++]; + STBIR_ASSERT(coefficient != 0); + output_buffer[out_pixel_index + 0] += decode_buffer[in_pixel_index + 0] * coefficient; + } + break; + case 2: + for (k = n0; k <= n1; k++) + { + int in_pixel_index = k * 2; + float coefficient = horizontal_coefficients[coefficient_group + coefficient_counter++]; + STBIR_ASSERT(coefficient != 0); + output_buffer[out_pixel_index + 0] += decode_buffer[in_pixel_index + 0] * coefficient; + output_buffer[out_pixel_index + 1] += decode_buffer[in_pixel_index + 1] * coefficient; + } + break; + case 3: + for (k = n0; k <= n1; k++) + { + int in_pixel_index = k * 3; + float coefficient = horizontal_coefficients[coefficient_group + coefficient_counter++]; + STBIR_ASSERT(coefficient != 0); + output_buffer[out_pixel_index + 0] += decode_buffer[in_pixel_index + 0] * coefficient; + output_buffer[out_pixel_index + 1] += decode_buffer[in_pixel_index + 1] * coefficient; + output_buffer[out_pixel_index + 2] += decode_buffer[in_pixel_index + 2] * coefficient; + } + break; + case 4: + for (k = n0; k <= n1; k++) + { + int in_pixel_index = k * 4; + float coefficient = horizontal_coefficients[coefficient_group + coefficient_counter++]; + STBIR_ASSERT(coefficient != 0); + output_buffer[out_pixel_index + 0] += decode_buffer[in_pixel_index + 0] * coefficient; + output_buffer[out_pixel_index + 1] += decode_buffer[in_pixel_index + 1] * coefficient; + output_buffer[out_pixel_index + 2] += decode_buffer[in_pixel_index + 2] * coefficient; + output_buffer[out_pixel_index + 3] += decode_buffer[in_pixel_index + 3] * coefficient; + } + break; + default: + for (k = n0; k <= n1; k++) + { + int in_pixel_index = k * channels; + float coefficient = horizontal_coefficients[coefficient_group + coefficient_counter++]; + int c; + STBIR_ASSERT(coefficient != 0); + for (c = 0; c < channels; c++) + output_buffer[out_pixel_index + c] += decode_buffer[in_pixel_index + c] * coefficient; + } + break; + } + } +} + +static void stbir__resample_horizontal_downsample(stbir__info* stbir_info, float* output_buffer) +{ + int x, k; + int input_w = stbir_info->input_w; + int channels = stbir_info->channels; + float* decode_buffer = stbir__get_decode_buffer(stbir_info); + stbir__contributors* horizontal_contributors = stbir_info->horizontal_contributors; + float* horizontal_coefficients = stbir_info->horizontal_coefficients; + int coefficient_width = stbir_info->horizontal_coefficient_width; + int filter_pixel_margin = stbir_info->horizontal_filter_pixel_margin; + int max_x = input_w + filter_pixel_margin * 2; + + STBIR_ASSERT(!stbir__use_width_upsampling(stbir_info)); + + switch (channels) { + case 1: + for (x = 0; x < max_x; x++) + { + int n0 = horizontal_contributors[x].n0; + int n1 = horizontal_contributors[x].n1; + + int in_x = x - filter_pixel_margin; + int in_pixel_index = in_x * 1; + int max_n = n1; + int coefficient_group = coefficient_width * x; + + for (k = n0; k <= max_n; k++) + { + int out_pixel_index = k * 1; + float coefficient = horizontal_coefficients[coefficient_group + k - n0]; + STBIR_ASSERT(coefficient != 0); + output_buffer[out_pixel_index + 0] += decode_buffer[in_pixel_index + 0] * coefficient; + } + } + break; + + case 2: + for (x = 0; x < max_x; x++) + { + int n0 = horizontal_contributors[x].n0; + int n1 = horizontal_contributors[x].n1; + + int in_x = x - filter_pixel_margin; + int in_pixel_index = in_x * 2; + int max_n = n1; + int coefficient_group = coefficient_width * x; + + for (k = n0; k <= max_n; k++) + { + int out_pixel_index = k * 2; + float coefficient = horizontal_coefficients[coefficient_group + k - n0]; + STBIR_ASSERT(coefficient != 0); + output_buffer[out_pixel_index + 0] += decode_buffer[in_pixel_index + 0] * coefficient; + output_buffer[out_pixel_index + 1] += decode_buffer[in_pixel_index + 1] * coefficient; + } + } + break; + + case 3: + for (x = 0; x < max_x; x++) + { + int n0 = horizontal_contributors[x].n0; + int n1 = horizontal_contributors[x].n1; + + int in_x = x - filter_pixel_margin; + int in_pixel_index = in_x * 3; + int max_n = n1; + int coefficient_group = coefficient_width * x; + + for (k = n0; k <= max_n; k++) + { + int out_pixel_index = k * 3; + float coefficient = horizontal_coefficients[coefficient_group + k - n0]; + STBIR_ASSERT(coefficient != 0); + output_buffer[out_pixel_index + 0] += decode_buffer[in_pixel_index + 0] * coefficient; + output_buffer[out_pixel_index + 1] += decode_buffer[in_pixel_index + 1] * coefficient; + output_buffer[out_pixel_index + 2] += decode_buffer[in_pixel_index + 2] * coefficient; + } + } + break; + + case 4: + for (x = 0; x < max_x; x++) + { + int n0 = horizontal_contributors[x].n0; + int n1 = horizontal_contributors[x].n1; + + int in_x = x - filter_pixel_margin; + int in_pixel_index = in_x * 4; + int max_n = n1; + int coefficient_group = coefficient_width * x; + + for (k = n0; k <= max_n; k++) + { + int out_pixel_index = k * 4; + float coefficient = horizontal_coefficients[coefficient_group + k - n0]; + STBIR_ASSERT(coefficient != 0); + output_buffer[out_pixel_index + 0] += decode_buffer[in_pixel_index + 0] * coefficient; + output_buffer[out_pixel_index + 1] += decode_buffer[in_pixel_index + 1] * coefficient; + output_buffer[out_pixel_index + 2] += decode_buffer[in_pixel_index + 2] * coefficient; + output_buffer[out_pixel_index + 3] += decode_buffer[in_pixel_index + 3] * coefficient; + } + } + break; + + default: + for (x = 0; x < max_x; x++) + { + int n0 = horizontal_contributors[x].n0; + int n1 = horizontal_contributors[x].n1; + + int in_x = x - filter_pixel_margin; + int in_pixel_index = in_x * channels; + int max_n = n1; + int coefficient_group = coefficient_width * x; + + for (k = n0; k <= max_n; k++) + { + int c; + int out_pixel_index = k * channels; + float coefficient = horizontal_coefficients[coefficient_group + k - n0]; + STBIR_ASSERT(coefficient != 0); + for (c = 0; c < channels; c++) + output_buffer[out_pixel_index + c] += decode_buffer[in_pixel_index + c] * coefficient; + } + } + break; + } +} + +static void stbir__decode_and_resample_upsample(stbir__info* stbir_info, int n) +{ + // Decode the nth scanline from the source image into the decode buffer. + stbir__decode_scanline(stbir_info, n); + + // Now resample it into the ring buffer. + if (stbir__use_width_upsampling(stbir_info)) + stbir__resample_horizontal_upsample(stbir_info, stbir__add_empty_ring_buffer_entry(stbir_info, n)); + else + stbir__resample_horizontal_downsample(stbir_info, stbir__add_empty_ring_buffer_entry(stbir_info, n)); + + // Now it's sitting in the ring buffer ready to be used as source for the vertical sampling. +} + +static void stbir__decode_and_resample_downsample(stbir__info* stbir_info, int n) +{ + // Decode the nth scanline from the source image into the decode buffer. + stbir__decode_scanline(stbir_info, n); + + memset(stbir_info->horizontal_buffer, 0, stbir_info->output_w * stbir_info->channels * sizeof(float)); + + // Now resample it into the horizontal buffer. + if (stbir__use_width_upsampling(stbir_info)) + stbir__resample_horizontal_upsample(stbir_info, stbir_info->horizontal_buffer); + else + stbir__resample_horizontal_downsample(stbir_info, stbir_info->horizontal_buffer); + + // Now it's sitting in the horizontal buffer ready to be distributed into the ring buffers. +} + +// Get the specified scan line from the ring buffer. +static float* stbir__get_ring_buffer_scanline(int get_scanline, float* ring_buffer, int begin_index, int first_scanline, int ring_buffer_num_entries, int ring_buffer_length) +{ + int ring_buffer_index = (begin_index + (get_scanline - first_scanline)) % ring_buffer_num_entries; + return stbir__get_ring_buffer_entry(ring_buffer, ring_buffer_index, ring_buffer_length); +} + + +static void stbir__encode_scanline(stbir__info* stbir_info, int num_pixels, void *output_buffer, float *encode_buffer, int channels, int alpha_channel, int decode) +{ + int x; + int n; + int num_nonalpha; + stbir_uint16 nonalpha[STBIR_MAX_CHANNELS]; + + if (!(stbir_info->flags&STBIR_FLAG_ALPHA_PREMULTIPLIED)) + { + for (x=0; x < num_pixels; ++x) + { + int pixel_index = x*channels; + + float alpha = encode_buffer[pixel_index + alpha_channel]; + float reciprocal_alpha = alpha ? 1.0f / alpha : 0; + + // unrolling this produced a 1% slowdown upscaling a large RGBA linear-space image on my machine - stb + for (n = 0; n < channels; n++) + if (n != alpha_channel) + encode_buffer[pixel_index + n] *= reciprocal_alpha; + + // We added in a small epsilon to prevent the color channel from being deleted with zero alpha. + // Because we only add it for integer types, it will automatically be discarded on integer + // conversion, so we don't need to subtract it back out (which would be problematic for + // numeric precision reasons). + } + } + + // build a table of all channels that need colorspace correction, so + // we don't perform colorspace correction on channels that don't need it. + for (x = 0, num_nonalpha = 0; x < channels; ++x) + { + if (x != alpha_channel || (stbir_info->flags & STBIR_FLAG_ALPHA_USES_COLORSPACE)) + { + nonalpha[num_nonalpha++] = (stbir_uint16)x; + } + } + + #define STBIR__ROUND_INT(f) ((int) ((f)+0.5)) + #define STBIR__ROUND_UINT(f) ((stbir_uint32) ((f)+0.5)) + + #ifdef STBIR__SATURATE_INT + #define STBIR__ENCODE_LINEAR8(f) stbir__saturate8 (STBIR__ROUND_INT((f) * stbir__max_uint8_as_float )) + #define STBIR__ENCODE_LINEAR16(f) stbir__saturate16(STBIR__ROUND_INT((f) * stbir__max_uint16_as_float)) + #else + #define STBIR__ENCODE_LINEAR8(f) (unsigned char ) STBIR__ROUND_INT(stbir__saturate(f) * stbir__max_uint8_as_float ) + #define STBIR__ENCODE_LINEAR16(f) (unsigned short) STBIR__ROUND_INT(stbir__saturate(f) * stbir__max_uint16_as_float) + #endif + + switch (decode) + { + case STBIR__DECODE(STBIR_TYPE_UINT8, STBIR_COLORSPACE_LINEAR): + for (x=0; x < num_pixels; ++x) + { + int pixel_index = x*channels; + + for (n = 0; n < channels; n++) + { + int index = pixel_index + n; + ((unsigned char*)output_buffer)[index] = STBIR__ENCODE_LINEAR8(encode_buffer[index]); + } + } + break; + + case STBIR__DECODE(STBIR_TYPE_UINT8, STBIR_COLORSPACE_SRGB): + for (x=0; x < num_pixels; ++x) + { + int pixel_index = x*channels; + + for (n = 0; n < num_nonalpha; n++) + { + int index = pixel_index + nonalpha[n]; + ((unsigned char*)output_buffer)[index] = stbir__linear_to_srgb_uchar(encode_buffer[index]); + } + + if (!(stbir_info->flags & STBIR_FLAG_ALPHA_USES_COLORSPACE)) + ((unsigned char *)output_buffer)[pixel_index + alpha_channel] = STBIR__ENCODE_LINEAR8(encode_buffer[pixel_index+alpha_channel]); + } + break; + + case STBIR__DECODE(STBIR_TYPE_UINT16, STBIR_COLORSPACE_LINEAR): + for (x=0; x < num_pixels; ++x) + { + int pixel_index = x*channels; + + for (n = 0; n < channels; n++) + { + int index = pixel_index + n; + ((unsigned short*)output_buffer)[index] = STBIR__ENCODE_LINEAR16(encode_buffer[index]); + } + } + break; + + case STBIR__DECODE(STBIR_TYPE_UINT16, STBIR_COLORSPACE_SRGB): + for (x=0; x < num_pixels; ++x) + { + int pixel_index = x*channels; + + for (n = 0; n < num_nonalpha; n++) + { + int index = pixel_index + nonalpha[n]; + ((unsigned short*)output_buffer)[index] = (unsigned short)STBIR__ROUND_INT(stbir__linear_to_srgb(stbir__saturate(encode_buffer[index])) * stbir__max_uint16_as_float); + } + + if (!(stbir_info->flags&STBIR_FLAG_ALPHA_USES_COLORSPACE)) + ((unsigned short*)output_buffer)[pixel_index + alpha_channel] = STBIR__ENCODE_LINEAR16(encode_buffer[pixel_index + alpha_channel]); + } + + break; + + case STBIR__DECODE(STBIR_TYPE_UINT32, STBIR_COLORSPACE_LINEAR): + for (x=0; x < num_pixels; ++x) + { + int pixel_index = x*channels; + + for (n = 0; n < channels; n++) + { + int index = pixel_index + n; + ((unsigned int*)output_buffer)[index] = (unsigned int)STBIR__ROUND_UINT(((double)stbir__saturate(encode_buffer[index])) * stbir__max_uint32_as_float); + } + } + break; + + case STBIR__DECODE(STBIR_TYPE_UINT32, STBIR_COLORSPACE_SRGB): + for (x=0; x < num_pixels; ++x) + { + int pixel_index = x*channels; + + for (n = 0; n < num_nonalpha; n++) + { + int index = pixel_index + nonalpha[n]; + ((unsigned int*)output_buffer)[index] = (unsigned int)STBIR__ROUND_UINT(((double)stbir__linear_to_srgb(stbir__saturate(encode_buffer[index]))) * stbir__max_uint32_as_float); + } + + if (!(stbir_info->flags&STBIR_FLAG_ALPHA_USES_COLORSPACE)) + ((unsigned int*)output_buffer)[pixel_index + alpha_channel] = (unsigned int)STBIR__ROUND_INT(((double)stbir__saturate(encode_buffer[pixel_index + alpha_channel])) * stbir__max_uint32_as_float); + } + break; + + case STBIR__DECODE(STBIR_TYPE_FLOAT, STBIR_COLORSPACE_LINEAR): + for (x=0; x < num_pixels; ++x) + { + int pixel_index = x*channels; + + for (n = 0; n < channels; n++) + { + int index = pixel_index + n; + ((float*)output_buffer)[index] = encode_buffer[index]; + } + } + break; + + case STBIR__DECODE(STBIR_TYPE_FLOAT, STBIR_COLORSPACE_SRGB): + for (x=0; x < num_pixels; ++x) + { + int pixel_index = x*channels; + + for (n = 0; n < num_nonalpha; n++) + { + int index = pixel_index + nonalpha[n]; + ((float*)output_buffer)[index] = stbir__linear_to_srgb(encode_buffer[index]); + } + + if (!(stbir_info->flags&STBIR_FLAG_ALPHA_USES_COLORSPACE)) + ((float*)output_buffer)[pixel_index + alpha_channel] = encode_buffer[pixel_index + alpha_channel]; + } + break; + + default: + STBIR_ASSERT(!"Unknown type/colorspace/channels combination."); + break; + } +} + +static void stbir__resample_vertical_upsample(stbir__info* stbir_info, int n) +{ + int x, k; + int output_w = stbir_info->output_w; + stbir__contributors* vertical_contributors = stbir_info->vertical_contributors; + float* vertical_coefficients = stbir_info->vertical_coefficients; + int channels = stbir_info->channels; + int alpha_channel = stbir_info->alpha_channel; + int type = stbir_info->type; + int colorspace = stbir_info->colorspace; + int ring_buffer_entries = stbir_info->ring_buffer_num_entries; + void* output_data = stbir_info->output_data; + float* encode_buffer = stbir_info->encode_buffer; + int decode = STBIR__DECODE(type, colorspace); + int coefficient_width = stbir_info->vertical_coefficient_width; + int coefficient_counter; + int contributor = n; + + float* ring_buffer = stbir_info->ring_buffer; + int ring_buffer_begin_index = stbir_info->ring_buffer_begin_index; + int ring_buffer_first_scanline = stbir_info->ring_buffer_first_scanline; + int ring_buffer_length = stbir_info->ring_buffer_length_bytes/sizeof(float); + + int n0,n1, output_row_start; + int coefficient_group = coefficient_width * contributor; + + n0 = vertical_contributors[contributor].n0; + n1 = vertical_contributors[contributor].n1; + + output_row_start = n * stbir_info->output_stride_bytes; + + STBIR_ASSERT(stbir__use_height_upsampling(stbir_info)); + + memset(encode_buffer, 0, output_w * sizeof(float) * channels); + + // I tried reblocking this for better cache usage of encode_buffer + // (using x_outer, k, x_inner), but it lost speed. -- stb + + coefficient_counter = 0; + switch (channels) { + case 1: + for (k = n0; k <= n1; k++) + { + int coefficient_index = coefficient_counter++; + float* ring_buffer_entry = stbir__get_ring_buffer_scanline(k, ring_buffer, ring_buffer_begin_index, ring_buffer_first_scanline, ring_buffer_entries, ring_buffer_length); + float coefficient = vertical_coefficients[coefficient_group + coefficient_index]; + for (x = 0; x < output_w; ++x) + { + int in_pixel_index = x * 1; + encode_buffer[in_pixel_index + 0] += ring_buffer_entry[in_pixel_index + 0] * coefficient; + } + } + break; + case 2: + for (k = n0; k <= n1; k++) + { + int coefficient_index = coefficient_counter++; + float* ring_buffer_entry = stbir__get_ring_buffer_scanline(k, ring_buffer, ring_buffer_begin_index, ring_buffer_first_scanline, ring_buffer_entries, ring_buffer_length); + float coefficient = vertical_coefficients[coefficient_group + coefficient_index]; + for (x = 0; x < output_w; ++x) + { + int in_pixel_index = x * 2; + encode_buffer[in_pixel_index + 0] += ring_buffer_entry[in_pixel_index + 0] * coefficient; + encode_buffer[in_pixel_index + 1] += ring_buffer_entry[in_pixel_index + 1] * coefficient; + } + } + break; + case 3: + for (k = n0; k <= n1; k++) + { + int coefficient_index = coefficient_counter++; + float* ring_buffer_entry = stbir__get_ring_buffer_scanline(k, ring_buffer, ring_buffer_begin_index, ring_buffer_first_scanline, ring_buffer_entries, ring_buffer_length); + float coefficient = vertical_coefficients[coefficient_group + coefficient_index]; + for (x = 0; x < output_w; ++x) + { + int in_pixel_index = x * 3; + encode_buffer[in_pixel_index + 0] += ring_buffer_entry[in_pixel_index + 0] * coefficient; + encode_buffer[in_pixel_index + 1] += ring_buffer_entry[in_pixel_index + 1] * coefficient; + encode_buffer[in_pixel_index + 2] += ring_buffer_entry[in_pixel_index + 2] * coefficient; + } + } + break; + case 4: + for (k = n0; k <= n1; k++) + { + int coefficient_index = coefficient_counter++; + float* ring_buffer_entry = stbir__get_ring_buffer_scanline(k, ring_buffer, ring_buffer_begin_index, ring_buffer_first_scanline, ring_buffer_entries, ring_buffer_length); + float coefficient = vertical_coefficients[coefficient_group + coefficient_index]; + for (x = 0; x < output_w; ++x) + { + int in_pixel_index = x * 4; + encode_buffer[in_pixel_index + 0] += ring_buffer_entry[in_pixel_index + 0] * coefficient; + encode_buffer[in_pixel_index + 1] += ring_buffer_entry[in_pixel_index + 1] * coefficient; + encode_buffer[in_pixel_index + 2] += ring_buffer_entry[in_pixel_index + 2] * coefficient; + encode_buffer[in_pixel_index + 3] += ring_buffer_entry[in_pixel_index + 3] * coefficient; + } + } + break; + default: + for (k = n0; k <= n1; k++) + { + int coefficient_index = coefficient_counter++; + float* ring_buffer_entry = stbir__get_ring_buffer_scanline(k, ring_buffer, ring_buffer_begin_index, ring_buffer_first_scanline, ring_buffer_entries, ring_buffer_length); + float coefficient = vertical_coefficients[coefficient_group + coefficient_index]; + for (x = 0; x < output_w; ++x) + { + int in_pixel_index = x * channels; + int c; + for (c = 0; c < channels; c++) + encode_buffer[in_pixel_index + c] += ring_buffer_entry[in_pixel_index + c] * coefficient; + } + } + break; + } + stbir__encode_scanline(stbir_info, output_w, (char *) output_data + output_row_start, encode_buffer, channels, alpha_channel, decode); +} + +static void stbir__resample_vertical_downsample(stbir__info* stbir_info, int n) +{ + int x, k; + int output_w = stbir_info->output_w; + stbir__contributors* vertical_contributors = stbir_info->vertical_contributors; + float* vertical_coefficients = stbir_info->vertical_coefficients; + int channels = stbir_info->channels; + int ring_buffer_entries = stbir_info->ring_buffer_num_entries; + float* horizontal_buffer = stbir_info->horizontal_buffer; + int coefficient_width = stbir_info->vertical_coefficient_width; + int contributor = n + stbir_info->vertical_filter_pixel_margin; + + float* ring_buffer = stbir_info->ring_buffer; + int ring_buffer_begin_index = stbir_info->ring_buffer_begin_index; + int ring_buffer_first_scanline = stbir_info->ring_buffer_first_scanline; + int ring_buffer_length = stbir_info->ring_buffer_length_bytes/sizeof(float); + int n0,n1; + + n0 = vertical_contributors[contributor].n0; + n1 = vertical_contributors[contributor].n1; + + STBIR_ASSERT(!stbir__use_height_upsampling(stbir_info)); + + for (k = n0; k <= n1; k++) + { + int coefficient_index = k - n0; + int coefficient_group = coefficient_width * contributor; + float coefficient = vertical_coefficients[coefficient_group + coefficient_index]; + + float* ring_buffer_entry = stbir__get_ring_buffer_scanline(k, ring_buffer, ring_buffer_begin_index, ring_buffer_first_scanline, ring_buffer_entries, ring_buffer_length); + + switch (channels) { + case 1: + for (x = 0; x < output_w; x++) + { + int in_pixel_index = x * 1; + ring_buffer_entry[in_pixel_index + 0] += horizontal_buffer[in_pixel_index + 0] * coefficient; + } + break; + case 2: + for (x = 0; x < output_w; x++) + { + int in_pixel_index = x * 2; + ring_buffer_entry[in_pixel_index + 0] += horizontal_buffer[in_pixel_index + 0] * coefficient; + ring_buffer_entry[in_pixel_index + 1] += horizontal_buffer[in_pixel_index + 1] * coefficient; + } + break; + case 3: + for (x = 0; x < output_w; x++) + { + int in_pixel_index = x * 3; + ring_buffer_entry[in_pixel_index + 0] += horizontal_buffer[in_pixel_index + 0] * coefficient; + ring_buffer_entry[in_pixel_index + 1] += horizontal_buffer[in_pixel_index + 1] * coefficient; + ring_buffer_entry[in_pixel_index + 2] += horizontal_buffer[in_pixel_index + 2] * coefficient; + } + break; + case 4: + for (x = 0; x < output_w; x++) + { + int in_pixel_index = x * 4; + ring_buffer_entry[in_pixel_index + 0] += horizontal_buffer[in_pixel_index + 0] * coefficient; + ring_buffer_entry[in_pixel_index + 1] += horizontal_buffer[in_pixel_index + 1] * coefficient; + ring_buffer_entry[in_pixel_index + 2] += horizontal_buffer[in_pixel_index + 2] * coefficient; + ring_buffer_entry[in_pixel_index + 3] += horizontal_buffer[in_pixel_index + 3] * coefficient; + } + break; + default: + for (x = 0; x < output_w; x++) + { + int in_pixel_index = x * channels; + + int c; + for (c = 0; c < channels; c++) + ring_buffer_entry[in_pixel_index + c] += horizontal_buffer[in_pixel_index + c] * coefficient; + } + break; + } + } +} + +static void stbir__buffer_loop_upsample(stbir__info* stbir_info) +{ + int y; + float scale_ratio = stbir_info->vertical_scale; + float out_scanlines_radius = stbir__filter_info_table[stbir_info->vertical_filter].support(1/scale_ratio) * scale_ratio; + + STBIR_ASSERT(stbir__use_height_upsampling(stbir_info)); + + for (y = 0; y < stbir_info->output_h; y++) + { + float in_center_of_out = 0; // Center of the current out scanline in the in scanline space + int in_first_scanline = 0, in_last_scanline = 0; + + stbir__calculate_sample_range_upsample(y, out_scanlines_radius, scale_ratio, stbir_info->vertical_shift, &in_first_scanline, &in_last_scanline, &in_center_of_out); + + STBIR_ASSERT(in_last_scanline - in_first_scanline + 1 <= stbir_info->ring_buffer_num_entries); + + if (stbir_info->ring_buffer_begin_index >= 0) + { + // Get rid of whatever we don't need anymore. + while (in_first_scanline > stbir_info->ring_buffer_first_scanline) + { + if (stbir_info->ring_buffer_first_scanline == stbir_info->ring_buffer_last_scanline) + { + // We just popped the last scanline off the ring buffer. + // Reset it to the empty state. + stbir_info->ring_buffer_begin_index = -1; + stbir_info->ring_buffer_first_scanline = 0; + stbir_info->ring_buffer_last_scanline = 0; + break; + } + else + { + stbir_info->ring_buffer_first_scanline++; + stbir_info->ring_buffer_begin_index = (stbir_info->ring_buffer_begin_index + 1) % stbir_info->ring_buffer_num_entries; + } + } + } + + // Load in new ones. + if (stbir_info->ring_buffer_begin_index < 0) + stbir__decode_and_resample_upsample(stbir_info, in_first_scanline); + + while (in_last_scanline > stbir_info->ring_buffer_last_scanline) + stbir__decode_and_resample_upsample(stbir_info, stbir_info->ring_buffer_last_scanline + 1); + + // Now all buffers should be ready to write a row of vertical sampling. + stbir__resample_vertical_upsample(stbir_info, y); + + STBIR_PROGRESS_REPORT((float)y / stbir_info->output_h); + } +} + +static void stbir__empty_ring_buffer(stbir__info* stbir_info, int first_necessary_scanline) +{ + int output_stride_bytes = stbir_info->output_stride_bytes; + int channels = stbir_info->channels; + int alpha_channel = stbir_info->alpha_channel; + int type = stbir_info->type; + int colorspace = stbir_info->colorspace; + int output_w = stbir_info->output_w; + void* output_data = stbir_info->output_data; + int decode = STBIR__DECODE(type, colorspace); + + float* ring_buffer = stbir_info->ring_buffer; + int ring_buffer_length = stbir_info->ring_buffer_length_bytes/sizeof(float); + + if (stbir_info->ring_buffer_begin_index >= 0) + { + // Get rid of whatever we don't need anymore. + while (first_necessary_scanline > stbir_info->ring_buffer_first_scanline) + { + if (stbir_info->ring_buffer_first_scanline >= 0 && stbir_info->ring_buffer_first_scanline < stbir_info->output_h) + { + int output_row_start = stbir_info->ring_buffer_first_scanline * output_stride_bytes; + float* ring_buffer_entry = stbir__get_ring_buffer_entry(ring_buffer, stbir_info->ring_buffer_begin_index, ring_buffer_length); + stbir__encode_scanline(stbir_info, output_w, (char *) output_data + output_row_start, ring_buffer_entry, channels, alpha_channel, decode); + STBIR_PROGRESS_REPORT((float)stbir_info->ring_buffer_first_scanline / stbir_info->output_h); + } + + if (stbir_info->ring_buffer_first_scanline == stbir_info->ring_buffer_last_scanline) + { + // We just popped the last scanline off the ring buffer. + // Reset it to the empty state. + stbir_info->ring_buffer_begin_index = -1; + stbir_info->ring_buffer_first_scanline = 0; + stbir_info->ring_buffer_last_scanline = 0; + break; + } + else + { + stbir_info->ring_buffer_first_scanline++; + stbir_info->ring_buffer_begin_index = (stbir_info->ring_buffer_begin_index + 1) % stbir_info->ring_buffer_num_entries; + } + } + } +} + +static void stbir__buffer_loop_downsample(stbir__info* stbir_info) +{ + int y; + float scale_ratio = stbir_info->vertical_scale; + int output_h = stbir_info->output_h; + float in_pixels_radius = stbir__filter_info_table[stbir_info->vertical_filter].support(scale_ratio) / scale_ratio; + int pixel_margin = stbir_info->vertical_filter_pixel_margin; + int max_y = stbir_info->input_h + pixel_margin; + + STBIR_ASSERT(!stbir__use_height_upsampling(stbir_info)); + + for (y = -pixel_margin; y < max_y; y++) + { + float out_center_of_in; // Center of the current out scanline in the in scanline space + int out_first_scanline, out_last_scanline; + + stbir__calculate_sample_range_downsample(y, in_pixels_radius, scale_ratio, stbir_info->vertical_shift, &out_first_scanline, &out_last_scanline, &out_center_of_in); + + STBIR_ASSERT(out_last_scanline - out_first_scanline + 1 <= stbir_info->ring_buffer_num_entries); + + if (out_last_scanline < 0 || out_first_scanline >= output_h) + continue; + + stbir__empty_ring_buffer(stbir_info, out_first_scanline); + + stbir__decode_and_resample_downsample(stbir_info, y); + + // Load in new ones. + if (stbir_info->ring_buffer_begin_index < 0) + stbir__add_empty_ring_buffer_entry(stbir_info, out_first_scanline); + + while (out_last_scanline > stbir_info->ring_buffer_last_scanline) + stbir__add_empty_ring_buffer_entry(stbir_info, stbir_info->ring_buffer_last_scanline + 1); + + // Now the horizontal buffer is ready to write to all ring buffer rows. + stbir__resample_vertical_downsample(stbir_info, y); + } + + stbir__empty_ring_buffer(stbir_info, stbir_info->output_h); +} + +static void stbir__setup(stbir__info *info, int input_w, int input_h, int output_w, int output_h, int channels) +{ + info->input_w = input_w; + info->input_h = input_h; + info->output_w = output_w; + info->output_h = output_h; + info->channels = channels; +} + +static void stbir__calculate_transform(stbir__info *info, float s0, float t0, float s1, float t1, float *transform) +{ + info->s0 = s0; + info->t0 = t0; + info->s1 = s1; + info->t1 = t1; + + if (transform) + { + info->horizontal_scale = transform[0]; + info->vertical_scale = transform[1]; + info->horizontal_shift = transform[2]; + info->vertical_shift = transform[3]; + } + else + { + info->horizontal_scale = ((float)info->output_w / info->input_w) / (s1 - s0); + info->vertical_scale = ((float)info->output_h / info->input_h) / (t1 - t0); + + info->horizontal_shift = s0 * info->output_w / (s1 - s0); + info->vertical_shift = t0 * info->output_h / (t1 - t0); + } +} + +static void stbir__choose_filter(stbir__info *info, stbir_filter h_filter, stbir_filter v_filter) +{ + if (h_filter == 0) + h_filter = stbir__use_upsampling(info->horizontal_scale) ? STBIR_DEFAULT_FILTER_UPSAMPLE : STBIR_DEFAULT_FILTER_DOWNSAMPLE; + if (v_filter == 0) + v_filter = stbir__use_upsampling(info->vertical_scale) ? STBIR_DEFAULT_FILTER_UPSAMPLE : STBIR_DEFAULT_FILTER_DOWNSAMPLE; + info->horizontal_filter = h_filter; + info->vertical_filter = v_filter; +} + +static stbir_uint32 stbir__calculate_memory(stbir__info *info) +{ + int pixel_margin = stbir__get_filter_pixel_margin(info->horizontal_filter, info->horizontal_scale); + int filter_height = stbir__get_filter_pixel_width(info->vertical_filter, info->vertical_scale); + + info->horizontal_num_contributors = stbir__get_contributors(info->horizontal_scale, info->horizontal_filter, info->input_w, info->output_w); + info->vertical_num_contributors = stbir__get_contributors(info->vertical_scale , info->vertical_filter , info->input_h, info->output_h); + + // One extra entry because floating point precision problems sometimes cause an extra to be necessary. + info->ring_buffer_num_entries = filter_height + 1; + + info->horizontal_contributors_size = info->horizontal_num_contributors * sizeof(stbir__contributors); + info->horizontal_coefficients_size = stbir__get_total_horizontal_coefficients(info) * sizeof(float); + info->vertical_contributors_size = info->vertical_num_contributors * sizeof(stbir__contributors); + info->vertical_coefficients_size = stbir__get_total_vertical_coefficients(info) * sizeof(float); + info->decode_buffer_size = (info->input_w + pixel_margin * 2) * info->channels * sizeof(float); + info->horizontal_buffer_size = info->output_w * info->channels * sizeof(float); + info->ring_buffer_size = info->output_w * info->channels * info->ring_buffer_num_entries * sizeof(float); + info->encode_buffer_size = info->output_w * info->channels * sizeof(float); + + STBIR_ASSERT(info->horizontal_filter != 0); + STBIR_ASSERT(info->horizontal_filter < STBIR__ARRAY_SIZE(stbir__filter_info_table)); // this now happens too late + STBIR_ASSERT(info->vertical_filter != 0); + STBIR_ASSERT(info->vertical_filter < STBIR__ARRAY_SIZE(stbir__filter_info_table)); // this now happens too late + + if (stbir__use_height_upsampling(info)) + // The horizontal buffer is for when we're downsampling the height and we + // can't output the result of sampling the decode buffer directly into the + // ring buffers. + info->horizontal_buffer_size = 0; + else + // The encode buffer is to retain precision in the height upsampling method + // and isn't used when height downsampling. + info->encode_buffer_size = 0; + + return info->horizontal_contributors_size + info->horizontal_coefficients_size + + info->vertical_contributors_size + info->vertical_coefficients_size + + info->decode_buffer_size + info->horizontal_buffer_size + + info->ring_buffer_size + info->encode_buffer_size; +} + +static int stbir__resize_allocated(stbir__info *info, + const void* input_data, int input_stride_in_bytes, + void* output_data, int output_stride_in_bytes, + int alpha_channel, stbir_uint32 flags, stbir_datatype type, + stbir_edge edge_horizontal, stbir_edge edge_vertical, stbir_colorspace colorspace, + void* tempmem, size_t tempmem_size_in_bytes) +{ + size_t memory_required = stbir__calculate_memory(info); + + int width_stride_input = input_stride_in_bytes ? input_stride_in_bytes : info->channels * info->input_w * stbir__type_size[type]; + int width_stride_output = output_stride_in_bytes ? output_stride_in_bytes : info->channels * info->output_w * stbir__type_size[type]; + +#ifdef STBIR_DEBUG_OVERWRITE_TEST +#define OVERWRITE_ARRAY_SIZE 8 + unsigned char overwrite_output_before_pre[OVERWRITE_ARRAY_SIZE]; + unsigned char overwrite_tempmem_before_pre[OVERWRITE_ARRAY_SIZE]; + unsigned char overwrite_output_after_pre[OVERWRITE_ARRAY_SIZE]; + unsigned char overwrite_tempmem_after_pre[OVERWRITE_ARRAY_SIZE]; + + size_t begin_forbidden = width_stride_output * (info->output_h - 1) + info->output_w * info->channels * stbir__type_size[type]; + memcpy(overwrite_output_before_pre, &((unsigned char*)output_data)[-OVERWRITE_ARRAY_SIZE], OVERWRITE_ARRAY_SIZE); + memcpy(overwrite_output_after_pre, &((unsigned char*)output_data)[begin_forbidden], OVERWRITE_ARRAY_SIZE); + memcpy(overwrite_tempmem_before_pre, &((unsigned char*)tempmem)[-OVERWRITE_ARRAY_SIZE], OVERWRITE_ARRAY_SIZE); + memcpy(overwrite_tempmem_after_pre, &((unsigned char*)tempmem)[tempmem_size_in_bytes], OVERWRITE_ARRAY_SIZE); +#endif + + STBIR_ASSERT(info->channels >= 0); + STBIR_ASSERT(info->channels <= STBIR_MAX_CHANNELS); + + if (info->channels < 0 || info->channels > STBIR_MAX_CHANNELS) + return 0; + + STBIR_ASSERT(info->horizontal_filter < STBIR__ARRAY_SIZE(stbir__filter_info_table)); + STBIR_ASSERT(info->vertical_filter < STBIR__ARRAY_SIZE(stbir__filter_info_table)); + + if (info->horizontal_filter >= STBIR__ARRAY_SIZE(stbir__filter_info_table)) + return 0; + if (info->vertical_filter >= STBIR__ARRAY_SIZE(stbir__filter_info_table)) + return 0; + + if (alpha_channel < 0) + flags |= STBIR_FLAG_ALPHA_USES_COLORSPACE | STBIR_FLAG_ALPHA_PREMULTIPLIED; + + if (!(flags&STBIR_FLAG_ALPHA_USES_COLORSPACE) || !(flags&STBIR_FLAG_ALPHA_PREMULTIPLIED)) { + STBIR_ASSERT(alpha_channel >= 0 && alpha_channel < info->channels); + } + + if (alpha_channel >= info->channels) + return 0; + + STBIR_ASSERT(tempmem); + + if (!tempmem) + return 0; + + STBIR_ASSERT(tempmem_size_in_bytes >= memory_required); + + if (tempmem_size_in_bytes < memory_required) + return 0; + + memset(tempmem, 0, tempmem_size_in_bytes); + + info->input_data = input_data; + info->input_stride_bytes = width_stride_input; + + info->output_data = output_data; + info->output_stride_bytes = width_stride_output; + + info->alpha_channel = alpha_channel; + info->flags = flags; + info->type = type; + info->edge_horizontal = edge_horizontal; + info->edge_vertical = edge_vertical; + info->colorspace = colorspace; + + info->horizontal_coefficient_width = stbir__get_coefficient_width (info->horizontal_filter, info->horizontal_scale); + info->vertical_coefficient_width = stbir__get_coefficient_width (info->vertical_filter , info->vertical_scale ); + info->horizontal_filter_pixel_width = stbir__get_filter_pixel_width (info->horizontal_filter, info->horizontal_scale); + info->vertical_filter_pixel_width = stbir__get_filter_pixel_width (info->vertical_filter , info->vertical_scale ); + info->horizontal_filter_pixel_margin = stbir__get_filter_pixel_margin(info->horizontal_filter, info->horizontal_scale); + info->vertical_filter_pixel_margin = stbir__get_filter_pixel_margin(info->vertical_filter , info->vertical_scale ); + + info->ring_buffer_length_bytes = info->output_w * info->channels * sizeof(float); + info->decode_buffer_pixels = info->input_w + info->horizontal_filter_pixel_margin * 2; + +#define STBIR__NEXT_MEMPTR(current, newtype) (newtype*)(((unsigned char*)current) + current##_size) + + info->horizontal_contributors = (stbir__contributors *) tempmem; + info->horizontal_coefficients = STBIR__NEXT_MEMPTR(info->horizontal_contributors, float); + info->vertical_contributors = STBIR__NEXT_MEMPTR(info->horizontal_coefficients, stbir__contributors); + info->vertical_coefficients = STBIR__NEXT_MEMPTR(info->vertical_contributors, float); + info->decode_buffer = STBIR__NEXT_MEMPTR(info->vertical_coefficients, float); + + if (stbir__use_height_upsampling(info)) + { + info->horizontal_buffer = NULL; + info->ring_buffer = STBIR__NEXT_MEMPTR(info->decode_buffer, float); + info->encode_buffer = STBIR__NEXT_MEMPTR(info->ring_buffer, float); + + STBIR_ASSERT((size_t)STBIR__NEXT_MEMPTR(info->encode_buffer, unsigned char) == (size_t)tempmem + tempmem_size_in_bytes); + } + else + { + info->horizontal_buffer = STBIR__NEXT_MEMPTR(info->decode_buffer, float); + info->ring_buffer = STBIR__NEXT_MEMPTR(info->horizontal_buffer, float); + info->encode_buffer = NULL; + + STBIR_ASSERT((size_t)STBIR__NEXT_MEMPTR(info->ring_buffer, unsigned char) == (size_t)tempmem + tempmem_size_in_bytes); + } + +#undef STBIR__NEXT_MEMPTR + + // This signals that the ring buffer is empty + info->ring_buffer_begin_index = -1; + + stbir__calculate_filters(info->horizontal_contributors, info->horizontal_coefficients, info->horizontal_filter, info->horizontal_scale, info->horizontal_shift, info->input_w, info->output_w); + stbir__calculate_filters(info->vertical_contributors, info->vertical_coefficients, info->vertical_filter, info->vertical_scale, info->vertical_shift, info->input_h, info->output_h); + + STBIR_PROGRESS_REPORT(0); + + if (stbir__use_height_upsampling(info)) + stbir__buffer_loop_upsample(info); + else + stbir__buffer_loop_downsample(info); + + STBIR_PROGRESS_REPORT(1); + +#ifdef STBIR_DEBUG_OVERWRITE_TEST + STBIR_ASSERT(memcmp(overwrite_output_before_pre, &((unsigned char*)output_data)[-OVERWRITE_ARRAY_SIZE], OVERWRITE_ARRAY_SIZE) == 0); + STBIR_ASSERT(memcmp(overwrite_output_after_pre, &((unsigned char*)output_data)[begin_forbidden], OVERWRITE_ARRAY_SIZE) == 0); + STBIR_ASSERT(memcmp(overwrite_tempmem_before_pre, &((unsigned char*)tempmem)[-OVERWRITE_ARRAY_SIZE], OVERWRITE_ARRAY_SIZE) == 0); + STBIR_ASSERT(memcmp(overwrite_tempmem_after_pre, &((unsigned char*)tempmem)[tempmem_size_in_bytes], OVERWRITE_ARRAY_SIZE) == 0); +#endif + + return 1; +} + + +static int stbir__resize_arbitrary( + void *alloc_context, + const void* input_data, int input_w, int input_h, int input_stride_in_bytes, + void* output_data, int output_w, int output_h, int output_stride_in_bytes, + float s0, float t0, float s1, float t1, float *transform, + int channels, int alpha_channel, stbir_uint32 flags, stbir_datatype type, + stbir_filter h_filter, stbir_filter v_filter, + stbir_edge edge_horizontal, stbir_edge edge_vertical, stbir_colorspace colorspace) +{ + stbir__info info; + int result; + size_t memory_required; + void* extra_memory; + + stbir__setup(&info, input_w, input_h, output_w, output_h, channels); + stbir__calculate_transform(&info, s0,t0,s1,t1,transform); + stbir__choose_filter(&info, h_filter, v_filter); + memory_required = stbir__calculate_memory(&info); + extra_memory = STBIR_MALLOC(memory_required, alloc_context); + + if (!extra_memory) + return 0; + + result = stbir__resize_allocated(&info, input_data, input_stride_in_bytes, + output_data, output_stride_in_bytes, + alpha_channel, flags, type, + edge_horizontal, edge_vertical, + colorspace, extra_memory, memory_required); + + STBIR_FREE(extra_memory, alloc_context); + + return result; +} + +STBIRDEF int stbir_resize_uint8( const unsigned char *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + unsigned char *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + int num_channels) +{ + return stbir__resize_arbitrary(NULL, input_pixels, input_w, input_h, input_stride_in_bytes, + output_pixels, output_w, output_h, output_stride_in_bytes, + 0,0,1,1,NULL,num_channels,-1,0, STBIR_TYPE_UINT8, STBIR_FILTER_DEFAULT, STBIR_FILTER_DEFAULT, + STBIR_EDGE_CLAMP, STBIR_EDGE_CLAMP, STBIR_COLORSPACE_LINEAR); +} + +STBIRDEF int stbir_resize_float( const float *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + float *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + int num_channels) +{ + return stbir__resize_arbitrary(NULL, input_pixels, input_w, input_h, input_stride_in_bytes, + output_pixels, output_w, output_h, output_stride_in_bytes, + 0,0,1,1,NULL,num_channels,-1,0, STBIR_TYPE_FLOAT, STBIR_FILTER_DEFAULT, STBIR_FILTER_DEFAULT, + STBIR_EDGE_CLAMP, STBIR_EDGE_CLAMP, STBIR_COLORSPACE_LINEAR); +} + +STBIRDEF int stbir_resize_uint8_srgb(const unsigned char *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + unsigned char *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + int num_channels, int alpha_channel, int flags) +{ + return stbir__resize_arbitrary(NULL, input_pixels, input_w, input_h, input_stride_in_bytes, + output_pixels, output_w, output_h, output_stride_in_bytes, + 0,0,1,1,NULL,num_channels,alpha_channel,flags, STBIR_TYPE_UINT8, STBIR_FILTER_DEFAULT, STBIR_FILTER_DEFAULT, + STBIR_EDGE_CLAMP, STBIR_EDGE_CLAMP, STBIR_COLORSPACE_SRGB); +} + +STBIRDEF int stbir_resize_uint8_srgb_edgemode(const unsigned char *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + unsigned char *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + int num_channels, int alpha_channel, int flags, + stbir_edge edge_wrap_mode) +{ + return stbir__resize_arbitrary(NULL, input_pixels, input_w, input_h, input_stride_in_bytes, + output_pixels, output_w, output_h, output_stride_in_bytes, + 0,0,1,1,NULL,num_channels,alpha_channel,flags, STBIR_TYPE_UINT8, STBIR_FILTER_DEFAULT, STBIR_FILTER_DEFAULT, + edge_wrap_mode, edge_wrap_mode, STBIR_COLORSPACE_SRGB); +} + +STBIRDEF int stbir_resize_uint8_generic( const unsigned char *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + unsigned char *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + int num_channels, int alpha_channel, int flags, + stbir_edge edge_wrap_mode, stbir_filter filter, stbir_colorspace space, + void *alloc_context) +{ + return stbir__resize_arbitrary(alloc_context, input_pixels, input_w, input_h, input_stride_in_bytes, + output_pixels, output_w, output_h, output_stride_in_bytes, + 0,0,1,1,NULL,num_channels,alpha_channel,flags, STBIR_TYPE_UINT8, filter, filter, + edge_wrap_mode, edge_wrap_mode, space); +} + +STBIRDEF int stbir_resize_uint16_generic(const stbir_uint16 *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + stbir_uint16 *output_pixels , int output_w, int output_h, int output_stride_in_bytes, + int num_channels, int alpha_channel, int flags, + stbir_edge edge_wrap_mode, stbir_filter filter, stbir_colorspace space, + void *alloc_context) +{ + return stbir__resize_arbitrary(alloc_context, input_pixels, input_w, input_h, input_stride_in_bytes, + output_pixels, output_w, output_h, output_stride_in_bytes, + 0,0,1,1,NULL,num_channels,alpha_channel,flags, STBIR_TYPE_UINT16, filter, filter, + edge_wrap_mode, edge_wrap_mode, space); +} + + +STBIRDEF int stbir_resize_float_generic( const float *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + float *output_pixels , int output_w, int output_h, int output_stride_in_bytes, + int num_channels, int alpha_channel, int flags, + stbir_edge edge_wrap_mode, stbir_filter filter, stbir_colorspace space, + void *alloc_context) +{ + return stbir__resize_arbitrary(alloc_context, input_pixels, input_w, input_h, input_stride_in_bytes, + output_pixels, output_w, output_h, output_stride_in_bytes, + 0,0,1,1,NULL,num_channels,alpha_channel,flags, STBIR_TYPE_FLOAT, filter, filter, + edge_wrap_mode, edge_wrap_mode, space); +} + + +STBIRDEF int stbir_resize( const void *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + void *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + stbir_datatype datatype, + int num_channels, int alpha_channel, int flags, + stbir_edge edge_mode_horizontal, stbir_edge edge_mode_vertical, + stbir_filter filter_horizontal, stbir_filter filter_vertical, + stbir_colorspace space, void *alloc_context) +{ + return stbir__resize_arbitrary(alloc_context, input_pixels, input_w, input_h, input_stride_in_bytes, + output_pixels, output_w, output_h, output_stride_in_bytes, + 0,0,1,1,NULL,num_channels,alpha_channel,flags, datatype, filter_horizontal, filter_vertical, + edge_mode_horizontal, edge_mode_vertical, space); +} + + +STBIRDEF int stbir_resize_subpixel(const void *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + void *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + stbir_datatype datatype, + int num_channels, int alpha_channel, int flags, + stbir_edge edge_mode_horizontal, stbir_edge edge_mode_vertical, + stbir_filter filter_horizontal, stbir_filter filter_vertical, + stbir_colorspace space, void *alloc_context, + float x_scale, float y_scale, + float x_offset, float y_offset) +{ + float transform[4]; + transform[0] = x_scale; + transform[1] = y_scale; + transform[2] = x_offset; + transform[3] = y_offset; + return stbir__resize_arbitrary(alloc_context, input_pixels, input_w, input_h, input_stride_in_bytes, + output_pixels, output_w, output_h, output_stride_in_bytes, + 0,0,1,1,transform,num_channels,alpha_channel,flags, datatype, filter_horizontal, filter_vertical, + edge_mode_horizontal, edge_mode_vertical, space); +} + +STBIRDEF int stbir_resize_region( const void *input_pixels , int input_w , int input_h , int input_stride_in_bytes, + void *output_pixels, int output_w, int output_h, int output_stride_in_bytes, + stbir_datatype datatype, + int num_channels, int alpha_channel, int flags, + stbir_edge edge_mode_horizontal, stbir_edge edge_mode_vertical, + stbir_filter filter_horizontal, stbir_filter filter_vertical, + stbir_colorspace space, void *alloc_context, + float s0, float t0, float s1, float t1) +{ + return stbir__resize_arbitrary(alloc_context, input_pixels, input_w, input_h, input_stride_in_bytes, + output_pixels, output_w, output_h, output_stride_in_bytes, + s0,t0,s1,t1,NULL,num_channels,alpha_channel,flags, datatype, filter_horizontal, filter_vertical, + edge_mode_horizontal, edge_mode_vertical, space); +} + +#endif // STB_IMAGE_RESIZE_IMPLEMENTATION + +/* +------------------------------------------------------------------------------ +This software is available under 2 licenses -- choose whichever you prefer. +------------------------------------------------------------------------------ +ALTERNATIVE A - MIT License +Copyright (c) 2017 Sean Barrett +Permission is hereby granted, free of charge, to any person obtaining a copy of +this software and associated documentation files (the "Software"), to deal in +the Software without restriction, including without limitation the rights to +use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies +of the Software, and to permit persons to whom the Software is furnished to do +so, subject to the following conditions: +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +------------------------------------------------------------------------------ +ALTERNATIVE B - Public Domain (www.unlicense.org) +This is free and unencumbered software released into the public domain. +Anyone is free to copy, modify, publish, use, compile, sell, or distribute this +software, either in source code form or as a compiled binary, for any purpose, +commercial or non-commercial, and by any means. +In jurisdictions that recognize copyright laws, the author or authors of this +software dedicate any and all copyright interest in the software to the public +domain. We make this dedication for the benefit of the public at large and to +the detriment of our heirs and successors. We intend this dedication to be an +overt act of relinquishment in perpetuity of all present and future rights to +this software under copyright law. +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN +ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION +WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +------------------------------------------------------------------------------ +*/ diff --git a/rknpu2/examples/librknn_api_android_demo/include/stb/stb_image_write.h b/rknpu2/examples/librknn_api_android_demo/include/stb/stb_image_write.h new file mode 100644 index 0000000000000000000000000000000000000000..c117344e7f7c2a112edcfe7abc217f0969e5baa7 --- /dev/null +++ b/rknpu2/examples/librknn_api_android_demo/include/stb/stb_image_write.h @@ -0,0 +1,1619 @@ +/* stb_image_write - v1.13 - public domain - http://nothings.org/stb + writes out PNG/BMP/TGA/JPEG/HDR images to C stdio - Sean Barrett 2010-2015 + no warranty implied; use at your own risk + + Before #including, + + #define STB_IMAGE_WRITE_IMPLEMENTATION + + in the file that you want to have the implementation. + + Will probably not work correctly with strict-aliasing optimizations. + +ABOUT: + + This header file is a library for writing images to C stdio or a callback. + + The PNG output is not optimal; it is 20-50% larger than the file + written by a decent optimizing implementation; though providing a custom + zlib compress function (see STBIW_ZLIB_COMPRESS) can mitigate that. + This library is designed for source code compactness and simplicity, + not optimal image file size or run-time performance. + +BUILDING: + + You can #define STBIW_ASSERT(x) before the #include to avoid using assert.h. + You can #define STBIW_MALLOC(), STBIW_REALLOC(), and STBIW_FREE() to replace + malloc,realloc,free. + You can #define STBIW_MEMMOVE() to replace memmove() + You can #define STBIW_ZLIB_COMPRESS to use a custom zlib-style compress function + for PNG compression (instead of the builtin one), it must have the following signature: + unsigned char * my_compress(unsigned char *data, int data_len, int *out_len, int quality); + The returned data will be freed with STBIW_FREE() (free() by default), + so it must be heap allocated with STBIW_MALLOC() (malloc() by default), + +UNICODE: + + If compiling for Windows and you wish to use Unicode filenames, compile + with + #define STBIW_WINDOWS_UTF8 + and pass utf8-encoded filenames. Call stbiw_convert_wchar_to_utf8 to convert + Windows wchar_t filenames to utf8. + +USAGE: + + There are five functions, one for each image file format: + + int stbi_write_png(char const *filename, int w, int h, int comp, const void *data, int stride_in_bytes); + int stbi_write_bmp(char const *filename, int w, int h, int comp, const void *data); + int stbi_write_tga(char const *filename, int w, int h, int comp, const void *data); + int stbi_write_jpg(char const *filename, int w, int h, int comp, const void *data, int quality); + int stbi_write_hdr(char const *filename, int w, int h, int comp, const float *data); + + void stbi_flip_vertically_on_write(int flag); // flag is non-zero to flip data vertically + + There are also five equivalent functions that use an arbitrary write function. You are + expected to open/close your file-equivalent before and after calling these: + + int stbi_write_png_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void *data, int stride_in_bytes); + int stbi_write_bmp_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void *data); + int stbi_write_tga_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void *data); + int stbi_write_hdr_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const float *data); + int stbi_write_jpg_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data, int quality); + + where the callback is: + void stbi_write_func(void *context, void *data, int size); + + You can configure it with these global variables: + int stbi_write_tga_with_rle; // defaults to true; set to 0 to disable RLE + int stbi_write_png_compression_level; // defaults to 8; set to higher for more compression + int stbi_write_force_png_filter; // defaults to -1; set to 0..5 to force a filter mode + + + You can define STBI_WRITE_NO_STDIO to disable the file variant of these + functions, so the library will not use stdio.h at all. However, this will + also disable HDR writing, because it requires stdio for formatted output. + + Each function returns 0 on failure and non-0 on success. + + The functions create an image file defined by the parameters. The image + is a rectangle of pixels stored from left-to-right, top-to-bottom. + Each pixel contains 'comp' channels of data stored interleaved with 8-bits + per channel, in the following order: 1=Y, 2=YA, 3=RGB, 4=RGBA. (Y is + monochrome color.) The rectangle is 'w' pixels wide and 'h' pixels tall. + The *data pointer points to the first byte of the top-left-most pixel. + For PNG, "stride_in_bytes" is the distance in bytes from the first byte of + a row of pixels to the first byte of the next row of pixels. + + PNG creates output files with the same number of components as the input. + The BMP format expands Y to RGB in the file format and does not + output alpha. + + PNG supports writing rectangles of data even when the bytes storing rows of + data are not consecutive in memory (e.g. sub-rectangles of a larger image), + by supplying the stride between the beginning of adjacent rows. The other + formats do not. (Thus you cannot write a native-format BMP through the BMP + writer, both because it is in BGR order and because it may have padding + at the end of the line.) + + PNG allows you to set the deflate compression level by setting the global + variable 'stbi_write_png_compression_level' (it defaults to 8). + + HDR expects linear float data. Since the format is always 32-bit rgb(e) + data, alpha (if provided) is discarded, and for monochrome data it is + replicated across all three channels. + + TGA supports RLE or non-RLE compressed data. To use non-RLE-compressed + data, set the global variable 'stbi_write_tga_with_rle' to 0. + + JPEG does ignore alpha channels in input data; quality is between 1 and 100. + Higher quality looks better but results in a bigger image. + JPEG baseline (no JPEG progressive). + +CREDITS: + + + Sean Barrett - PNG/BMP/TGA + Baldur Karlsson - HDR + Jean-Sebastien Guay - TGA monochrome + Tim Kelsey - misc enhancements + Alan Hickman - TGA RLE + Emmanuel Julien - initial file IO callback implementation + Jon Olick - original jo_jpeg.cpp code + Daniel Gibson - integrate JPEG, allow external zlib + Aarni Koskela - allow choosing PNG filter + + bugfixes: + github:Chribba + Guillaume Chereau + github:jry2 + github:romigrou + Sergio Gonzalez + Jonas Karlsson + Filip Wasil + Thatcher Ulrich + github:poppolopoppo + Patrick Boettcher + github:xeekworx + Cap Petschulat + Simon Rodriguez + Ivan Tikhonov + github:ignotion + Adam Schackart + +LICENSE + + See end of file for license information. + +*/ + +#ifndef INCLUDE_STB_IMAGE_WRITE_H +#define INCLUDE_STB_IMAGE_WRITE_H + +#include + +// if STB_IMAGE_WRITE_STATIC causes problems, try defining STBIWDEF to 'inline' or 'static inline' +#ifndef STBIWDEF +#ifdef STB_IMAGE_WRITE_STATIC +#define STBIWDEF static +#else +#ifdef __cplusplus +#define STBIWDEF extern "C" +#else +#define STBIWDEF extern +#endif +#endif +#endif + +#ifndef STB_IMAGE_WRITE_STATIC // C++ forbids static forward declarations +extern int stbi_write_tga_with_rle; +extern int stbi_write_png_compression_level; +extern int stbi_write_force_png_filter; +#endif + +#ifndef STBI_WRITE_NO_STDIO +STBIWDEF int stbi_write_png(char const *filename, int w, int h, int comp, const void *data, int stride_in_bytes); +STBIWDEF int stbi_write_bmp(char const *filename, int w, int h, int comp, const void *data); +STBIWDEF int stbi_write_tga(char const *filename, int w, int h, int comp, const void *data); +STBIWDEF int stbi_write_hdr(char const *filename, int w, int h, int comp, const float *data); +STBIWDEF int stbi_write_jpg(char const *filename, int x, int y, int comp, const void *data, int quality); + +#ifdef STBI_WINDOWS_UTF8 +STBIWDEF int stbiw_convert_wchar_to_utf8(char *buffer, size_t bufferlen, const wchar_t* input); +#endif +#endif + +typedef void stbi_write_func(void *context, void *data, int size); + +STBIWDEF int stbi_write_png_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void *data, int stride_in_bytes); +STBIWDEF int stbi_write_bmp_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void *data); +STBIWDEF int stbi_write_tga_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void *data); +STBIWDEF int stbi_write_hdr_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const float *data); +STBIWDEF int stbi_write_jpg_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data, int quality); + +STBIWDEF void stbi_flip_vertically_on_write(int flip_boolean); + +#endif//INCLUDE_STB_IMAGE_WRITE_H + +#ifdef STB_IMAGE_WRITE_IMPLEMENTATION + +#ifdef _WIN32 + #ifndef _CRT_SECURE_NO_WARNINGS + #define _CRT_SECURE_NO_WARNINGS + #endif + #ifndef _CRT_NONSTDC_NO_DEPRECATE + #define _CRT_NONSTDC_NO_DEPRECATE + #endif +#endif + +#ifndef STBI_WRITE_NO_STDIO +#include +#endif // STBI_WRITE_NO_STDIO + +#include +#include +#include +#include + +#if defined(STBIW_MALLOC) && defined(STBIW_FREE) && (defined(STBIW_REALLOC) || defined(STBIW_REALLOC_SIZED)) +// ok +#elif !defined(STBIW_MALLOC) && !defined(STBIW_FREE) && !defined(STBIW_REALLOC) && !defined(STBIW_REALLOC_SIZED) +// ok +#else +#error "Must define all or none of STBIW_MALLOC, STBIW_FREE, and STBIW_REALLOC (or STBIW_REALLOC_SIZED)." +#endif + +#ifndef STBIW_MALLOC +#define STBIW_MALLOC(sz) malloc(sz) +#define STBIW_REALLOC(p,newsz) realloc(p,newsz) +#define STBIW_FREE(p) free(p) +#endif + +#ifndef STBIW_REALLOC_SIZED +#define STBIW_REALLOC_SIZED(p,oldsz,newsz) STBIW_REALLOC(p,newsz) +#endif + + +#ifndef STBIW_MEMMOVE +#define STBIW_MEMMOVE(a,b,sz) memmove(a,b,sz) +#endif + + +#ifndef STBIW_ASSERT +#include +#define STBIW_ASSERT(x) assert(x) +#endif + +#define STBIW_UCHAR(x) (unsigned char) ((x) & 0xff) + +#ifdef STB_IMAGE_WRITE_STATIC +static int stbi__flip_vertically_on_write=0; +static int stbi_write_png_compression_level = 8; +static int stbi_write_tga_with_rle = 1; +static int stbi_write_force_png_filter = -1; +#else +int stbi_write_png_compression_level = 8; +int stbi__flip_vertically_on_write=0; +int stbi_write_tga_with_rle = 1; +int stbi_write_force_png_filter = -1; +#endif + +STBIWDEF void stbi_flip_vertically_on_write(int flag) +{ + stbi__flip_vertically_on_write = flag; +} + +typedef struct +{ + stbi_write_func *func; + void *context; +} stbi__write_context; + +// initialize a callback-based context +static void stbi__start_write_callbacks(stbi__write_context *s, stbi_write_func *c, void *context) +{ + s->func = c; + s->context = context; +} + +#ifndef STBI_WRITE_NO_STDIO + +static void stbi__stdio_write(void *context, void *data, int size) +{ + fwrite(data,1,size,(FILE*) context); +} + +#if defined(_MSC_VER) && defined(STBI_WINDOWS_UTF8) +#ifdef __cplusplus +#define STBIW_EXTERN extern "C" +#else +#define STBIW_EXTERN extern +#endif +STBIW_EXTERN __declspec(dllimport) int __stdcall MultiByteToWideChar(unsigned int cp, unsigned long flags, const char *str, int cbmb, wchar_t *widestr, int cchwide); +STBIW_EXTERN __declspec(dllimport) int __stdcall WideCharToMultiByte(unsigned int cp, unsigned long flags, const wchar_t *widestr, int cchwide, char *str, int cbmb, const char *defchar, int *used_default); + +STBIWDEF int stbiw_convert_wchar_to_utf8(char *buffer, size_t bufferlen, const wchar_t* input) +{ + return WideCharToMultiByte(65001 /* UTF8 */, 0, input, -1, buffer, (int) bufferlen, NULL, NULL); +} +#endif + +static FILE *stbiw__fopen(char const *filename, char const *mode) +{ + FILE *f; +#if defined(_MSC_VER) && defined(STBI_WINDOWS_UTF8) + wchar_t wMode[64]; + wchar_t wFilename[1024]; + if (0 == MultiByteToWideChar(65001 /* UTF8 */, 0, filename, -1, wFilename, sizeof(wFilename))) + return 0; + + if (0 == MultiByteToWideChar(65001 /* UTF8 */, 0, mode, -1, wMode, sizeof(wMode))) + return 0; + +#if _MSC_VER >= 1400 + if (0 != _wfopen_s(&f, wFilename, wMode)) + f = 0; +#else + f = _wfopen(wFilename, wMode); +#endif + +#elif defined(_MSC_VER) && _MSC_VER >= 1400 + if (0 != fopen_s(&f, filename, mode)) + f=0; +#else + f = fopen(filename, mode); +#endif + return f; +} + +static int stbi__start_write_file(stbi__write_context *s, const char *filename) +{ + FILE *f = stbiw__fopen(filename, "wb"); + stbi__start_write_callbacks(s, stbi__stdio_write, (void *) f); + return f != NULL; +} + +static void stbi__end_write_file(stbi__write_context *s) +{ + fclose((FILE *)s->context); +} + +#endif // !STBI_WRITE_NO_STDIO + +typedef unsigned int stbiw_uint32; +typedef int stb_image_write_test[sizeof(stbiw_uint32)==4 ? 1 : -1]; + +static void stbiw__writefv(stbi__write_context *s, const char *fmt, va_list v) +{ + while (*fmt) { + switch (*fmt++) { + case ' ': break; + case '1': { unsigned char x = STBIW_UCHAR(va_arg(v, int)); + s->func(s->context,&x,1); + break; } + case '2': { int x = va_arg(v,int); + unsigned char b[2]; + b[0] = STBIW_UCHAR(x); + b[1] = STBIW_UCHAR(x>>8); + s->func(s->context,b,2); + break; } + case '4': { stbiw_uint32 x = va_arg(v,int); + unsigned char b[4]; + b[0]=STBIW_UCHAR(x); + b[1]=STBIW_UCHAR(x>>8); + b[2]=STBIW_UCHAR(x>>16); + b[3]=STBIW_UCHAR(x>>24); + s->func(s->context,b,4); + break; } + default: + STBIW_ASSERT(0); + return; + } + } +} + +static void stbiw__writef(stbi__write_context *s, const char *fmt, ...) +{ + va_list v; + va_start(v, fmt); + stbiw__writefv(s, fmt, v); + va_end(v); +} + +static void stbiw__putc(stbi__write_context *s, unsigned char c) +{ + s->func(s->context, &c, 1); +} + +static void stbiw__write3(stbi__write_context *s, unsigned char a, unsigned char b, unsigned char c) +{ + unsigned char arr[3]; + arr[0] = a; arr[1] = b; arr[2] = c; + s->func(s->context, arr, 3); +} + +static void stbiw__write_pixel(stbi__write_context *s, int rgb_dir, int comp, int write_alpha, int expand_mono, unsigned char *d) +{ + unsigned char bg[3] = { 255, 0, 255}, px[3]; + int k; + + if (write_alpha < 0) + s->func(s->context, &d[comp - 1], 1); + + switch (comp) { + case 2: // 2 pixels = mono + alpha, alpha is written separately, so same as 1-channel case + case 1: + if (expand_mono) + stbiw__write3(s, d[0], d[0], d[0]); // monochrome bmp + else + s->func(s->context, d, 1); // monochrome TGA + break; + case 4: + if (!write_alpha) { + // composite against pink background + for (k = 0; k < 3; ++k) + px[k] = bg[k] + ((d[k] - bg[k]) * d[3]) / 255; + stbiw__write3(s, px[1 - rgb_dir], px[1], px[1 + rgb_dir]); + break; + } + /* FALLTHROUGH */ + case 3: + stbiw__write3(s, d[1 - rgb_dir], d[1], d[1 + rgb_dir]); + break; + } + if (write_alpha > 0) + s->func(s->context, &d[comp - 1], 1); +} + +static void stbiw__write_pixels(stbi__write_context *s, int rgb_dir, int vdir, int x, int y, int comp, void *data, int write_alpha, int scanline_pad, int expand_mono) +{ + stbiw_uint32 zero = 0; + int i,j, j_end; + + if (y <= 0) + return; + + if (stbi__flip_vertically_on_write) + vdir *= -1; + + if (vdir < 0) { + j_end = -1; j = y-1; + } else { + j_end = y; j = 0; + } + + for (; j != j_end; j += vdir) { + for (i=0; i < x; ++i) { + unsigned char *d = (unsigned char *) data + (j*x+i)*comp; + stbiw__write_pixel(s, rgb_dir, comp, write_alpha, expand_mono, d); + } + s->func(s->context, &zero, scanline_pad); + } +} + +static int stbiw__outfile(stbi__write_context *s, int rgb_dir, int vdir, int x, int y, int comp, int expand_mono, void *data, int alpha, int pad, const char *fmt, ...) +{ + if (y < 0 || x < 0) { + return 0; + } else { + va_list v; + va_start(v, fmt); + stbiw__writefv(s, fmt, v); + va_end(v); + stbiw__write_pixels(s,rgb_dir,vdir,x,y,comp,data,alpha,pad, expand_mono); + return 1; + } +} + +static int stbi_write_bmp_core(stbi__write_context *s, int x, int y, int comp, const void *data) +{ + int pad = (-x*3) & 3; + return stbiw__outfile(s,-1,-1,x,y,comp,1,(void *) data,0,pad, + "11 4 22 4" "4 44 22 444444", + 'B', 'M', 14+40+(x*3+pad)*y, 0,0, 14+40, // file header + 40, x,y, 1,24, 0,0,0,0,0,0); // bitmap header +} + +STBIWDEF int stbi_write_bmp_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data) +{ + stbi__write_context s; + stbi__start_write_callbacks(&s, func, context); + return stbi_write_bmp_core(&s, x, y, comp, data); +} + +#ifndef STBI_WRITE_NO_STDIO +STBIWDEF int stbi_write_bmp(char const *filename, int x, int y, int comp, const void *data) +{ + stbi__write_context s; + if (stbi__start_write_file(&s,filename)) { + int r = stbi_write_bmp_core(&s, x, y, comp, data); + stbi__end_write_file(&s); + return r; + } else + return 0; +} +#endif //!STBI_WRITE_NO_STDIO + +static int stbi_write_tga_core(stbi__write_context *s, int x, int y, int comp, void *data) +{ + int has_alpha = (comp == 2 || comp == 4); + int colorbytes = has_alpha ? comp-1 : comp; + int format = colorbytes < 2 ? 3 : 2; // 3 color channels (RGB/RGBA) = 2, 1 color channel (Y/YA) = 3 + + if (y < 0 || x < 0) + return 0; + + if (!stbi_write_tga_with_rle) { + return stbiw__outfile(s, -1, -1, x, y, comp, 0, (void *) data, has_alpha, 0, + "111 221 2222 11", 0, 0, format, 0, 0, 0, 0, 0, x, y, (colorbytes + has_alpha) * 8, has_alpha * 8); + } else { + int i,j,k; + int jend, jdir; + + stbiw__writef(s, "111 221 2222 11", 0,0,format+8, 0,0,0, 0,0,x,y, (colorbytes + has_alpha) * 8, has_alpha * 8); + + if (stbi__flip_vertically_on_write) { + j = 0; + jend = y; + jdir = 1; + } else { + j = y-1; + jend = -1; + jdir = -1; + } + for (; j != jend; j += jdir) { + unsigned char *row = (unsigned char *) data + j * x * comp; + int len; + + for (i = 0; i < x; i += len) { + unsigned char *begin = row + i * comp; + int diff = 1; + len = 1; + + if (i < x - 1) { + ++len; + diff = memcmp(begin, row + (i + 1) * comp, comp); + if (diff) { + const unsigned char *prev = begin; + for (k = i + 2; k < x && len < 128; ++k) { + if (memcmp(prev, row + k * comp, comp)) { + prev += comp; + ++len; + } else { + --len; + break; + } + } + } else { + for (k = i + 2; k < x && len < 128; ++k) { + if (!memcmp(begin, row + k * comp, comp)) { + ++len; + } else { + break; + } + } + } + } + + if (diff) { + unsigned char header = STBIW_UCHAR(len - 1); + s->func(s->context, &header, 1); + for (k = 0; k < len; ++k) { + stbiw__write_pixel(s, -1, comp, has_alpha, 0, begin + k * comp); + } + } else { + unsigned char header = STBIW_UCHAR(len - 129); + s->func(s->context, &header, 1); + stbiw__write_pixel(s, -1, comp, has_alpha, 0, begin); + } + } + } + } + return 1; +} + +STBIWDEF int stbi_write_tga_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data) +{ + stbi__write_context s; + stbi__start_write_callbacks(&s, func, context); + return stbi_write_tga_core(&s, x, y, comp, (void *) data); +} + +#ifndef STBI_WRITE_NO_STDIO +STBIWDEF int stbi_write_tga(char const *filename, int x, int y, int comp, const void *data) +{ + stbi__write_context s; + if (stbi__start_write_file(&s,filename)) { + int r = stbi_write_tga_core(&s, x, y, comp, (void *) data); + stbi__end_write_file(&s); + return r; + } else + return 0; +} +#endif + +// ************************************************************************************************* +// Radiance RGBE HDR writer +// by Baldur Karlsson + +#define stbiw__max(a, b) ((a) > (b) ? (a) : (b)) + +static void stbiw__linear_to_rgbe(unsigned char *rgbe, float *linear) +{ + int exponent; + float maxcomp = stbiw__max(linear[0], stbiw__max(linear[1], linear[2])); + + if (maxcomp < 1e-32f) { + rgbe[0] = rgbe[1] = rgbe[2] = rgbe[3] = 0; + } else { + float normalize = (float) frexp(maxcomp, &exponent) * 256.0f/maxcomp; + + rgbe[0] = (unsigned char)(linear[0] * normalize); + rgbe[1] = (unsigned char)(linear[1] * normalize); + rgbe[2] = (unsigned char)(linear[2] * normalize); + rgbe[3] = (unsigned char)(exponent + 128); + } +} + +static void stbiw__write_run_data(stbi__write_context *s, int length, unsigned char databyte) +{ + unsigned char lengthbyte = STBIW_UCHAR(length+128); + STBIW_ASSERT(length+128 <= 255); + s->func(s->context, &lengthbyte, 1); + s->func(s->context, &databyte, 1); +} + +static void stbiw__write_dump_data(stbi__write_context *s, int length, unsigned char *data) +{ + unsigned char lengthbyte = STBIW_UCHAR(length); + STBIW_ASSERT(length <= 128); // inconsistent with spec but consistent with official code + s->func(s->context, &lengthbyte, 1); + s->func(s->context, data, length); +} + +static void stbiw__write_hdr_scanline(stbi__write_context *s, int width, int ncomp, unsigned char *scratch, float *scanline) +{ + unsigned char scanlineheader[4] = { 2, 2, 0, 0 }; + unsigned char rgbe[4]; + float linear[3]; + int x; + + scanlineheader[2] = (width&0xff00)>>8; + scanlineheader[3] = (width&0x00ff); + + /* skip RLE for images too small or large */ + if (width < 8 || width >= 32768) { + for (x=0; x < width; x++) { + switch (ncomp) { + case 4: /* fallthrough */ + case 3: linear[2] = scanline[x*ncomp + 2]; + linear[1] = scanline[x*ncomp + 1]; + linear[0] = scanline[x*ncomp + 0]; + break; + default: + linear[0] = linear[1] = linear[2] = scanline[x*ncomp + 0]; + break; + } + stbiw__linear_to_rgbe(rgbe, linear); + s->func(s->context, rgbe, 4); + } + } else { + int c,r; + /* encode into scratch buffer */ + for (x=0; x < width; x++) { + switch(ncomp) { + case 4: /* fallthrough */ + case 3: linear[2] = scanline[x*ncomp + 2]; + linear[1] = scanline[x*ncomp + 1]; + linear[0] = scanline[x*ncomp + 0]; + break; + default: + linear[0] = linear[1] = linear[2] = scanline[x*ncomp + 0]; + break; + } + stbiw__linear_to_rgbe(rgbe, linear); + scratch[x + width*0] = rgbe[0]; + scratch[x + width*1] = rgbe[1]; + scratch[x + width*2] = rgbe[2]; + scratch[x + width*3] = rgbe[3]; + } + + s->func(s->context, scanlineheader, 4); + + /* RLE each component separately */ + for (c=0; c < 4; c++) { + unsigned char *comp = &scratch[width*c]; + + x = 0; + while (x < width) { + // find first run + r = x; + while (r+2 < width) { + if (comp[r] == comp[r+1] && comp[r] == comp[r+2]) + break; + ++r; + } + if (r+2 >= width) + r = width; + // dump up to first run + while (x < r) { + int len = r-x; + if (len > 128) len = 128; + stbiw__write_dump_data(s, len, &comp[x]); + x += len; + } + // if there's a run, output it + if (r+2 < width) { // same test as what we break out of in search loop, so only true if we break'd + // find next byte after run + while (r < width && comp[r] == comp[x]) + ++r; + // output run up to r + while (x < r) { + int len = r-x; + if (len > 127) len = 127; + stbiw__write_run_data(s, len, comp[x]); + x += len; + } + } + } + } + } +} + +static int stbi_write_hdr_core(stbi__write_context *s, int x, int y, int comp, float *data) +{ + if (y <= 0 || x <= 0 || data == NULL) + return 0; + else { + // Each component is stored separately. Allocate scratch space for full output scanline. + unsigned char *scratch = (unsigned char *) STBIW_MALLOC(x*4); + int i, len; + char buffer[128]; + char header[] = "#?RADIANCE\n# Written by stb_image_write.h\nFORMAT=32-bit_rle_rgbe\n"; + s->func(s->context, header, sizeof(header)-1); + +#ifdef __STDC_WANT_SECURE_LIB__ + len = sprintf_s(buffer, sizeof(buffer), "EXPOSURE= 1.0000000000000\n\n-Y %d +X %d\n", y, x); +#else + len = snprintf(buffer, 128, "EXPOSURE= 1.0000000000000\n\n-Y %d +X %d\n", y, x); +#endif + s->func(s->context, buffer, len); + + for(i=0; i < y; i++) + stbiw__write_hdr_scanline(s, x, comp, scratch, data + comp*x*(stbi__flip_vertically_on_write ? y-1-i : i)); + STBIW_FREE(scratch); + return 1; + } +} + +STBIWDEF int stbi_write_hdr_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const float *data) +{ + stbi__write_context s; + stbi__start_write_callbacks(&s, func, context); + return stbi_write_hdr_core(&s, x, y, comp, (float *) data); +} + +#ifndef STBI_WRITE_NO_STDIO +STBIWDEF int stbi_write_hdr(char const *filename, int x, int y, int comp, const float *data) +{ + stbi__write_context s; + if (stbi__start_write_file(&s,filename)) { + int r = stbi_write_hdr_core(&s, x, y, comp, (float *) data); + stbi__end_write_file(&s); + return r; + } else + return 0; +} +#endif // STBI_WRITE_NO_STDIO + + +////////////////////////////////////////////////////////////////////////////// +// +// PNG writer +// + +#ifndef STBIW_ZLIB_COMPRESS +// stretchy buffer; stbiw__sbpush() == vector<>::push_back() -- stbiw__sbcount() == vector<>::size() +#define stbiw__sbraw(a) ((int *) (a) - 2) +#define stbiw__sbm(a) stbiw__sbraw(a)[0] +#define stbiw__sbn(a) stbiw__sbraw(a)[1] + +#define stbiw__sbneedgrow(a,n) ((a)==0 || stbiw__sbn(a)+n >= stbiw__sbm(a)) +#define stbiw__sbmaybegrow(a,n) (stbiw__sbneedgrow(a,(n)) ? stbiw__sbgrow(a,n) : 0) +#define stbiw__sbgrow(a,n) stbiw__sbgrowf((void **) &(a), (n), sizeof(*(a))) + +#define stbiw__sbpush(a, v) (stbiw__sbmaybegrow(a,1), (a)[stbiw__sbn(a)++] = (v)) +#define stbiw__sbcount(a) ((a) ? stbiw__sbn(a) : 0) +#define stbiw__sbfree(a) ((a) ? STBIW_FREE(stbiw__sbraw(a)),0 : 0) + +static void *stbiw__sbgrowf(void **arr, int increment, int itemsize) +{ + int m = *arr ? 2*stbiw__sbm(*arr)+increment : increment+1; + void *p = STBIW_REALLOC_SIZED(*arr ? stbiw__sbraw(*arr) : 0, *arr ? (stbiw__sbm(*arr)*itemsize + sizeof(int)*2) : 0, itemsize * m + sizeof(int)*2); + STBIW_ASSERT(p); + if (p) { + if (!*arr) ((int *) p)[1] = 0; + *arr = (void *) ((int *) p + 2); + stbiw__sbm(*arr) = m; + } + return *arr; +} + +static unsigned char *stbiw__zlib_flushf(unsigned char *data, unsigned int *bitbuffer, int *bitcount) +{ + while (*bitcount >= 8) { + stbiw__sbpush(data, STBIW_UCHAR(*bitbuffer)); + *bitbuffer >>= 8; + *bitcount -= 8; + } + return data; +} + +static int stbiw__zlib_bitrev(int code, int codebits) +{ + int res=0; + while (codebits--) { + res = (res << 1) | (code & 1); + code >>= 1; + } + return res; +} + +static unsigned int stbiw__zlib_countm(unsigned char *a, unsigned char *b, int limit) +{ + int i; + for (i=0; i < limit && i < 258; ++i) + if (a[i] != b[i]) break; + return i; +} + +static unsigned int stbiw__zhash(unsigned char *data) +{ + stbiw_uint32 hash = data[0] + (data[1] << 8) + (data[2] << 16); + hash ^= hash << 3; + hash += hash >> 5; + hash ^= hash << 4; + hash += hash >> 17; + hash ^= hash << 25; + hash += hash >> 6; + return hash; +} + +#define stbiw__zlib_flush() (out = stbiw__zlib_flushf(out, &bitbuf, &bitcount)) +#define stbiw__zlib_add(code,codebits) \ + (bitbuf |= (code) << bitcount, bitcount += (codebits), stbiw__zlib_flush()) +#define stbiw__zlib_huffa(b,c) stbiw__zlib_add(stbiw__zlib_bitrev(b,c),c) +// default huffman tables +#define stbiw__zlib_huff1(n) stbiw__zlib_huffa(0x30 + (n), 8) +#define stbiw__zlib_huff2(n) stbiw__zlib_huffa(0x190 + (n)-144, 9) +#define stbiw__zlib_huff3(n) stbiw__zlib_huffa(0 + (n)-256,7) +#define stbiw__zlib_huff4(n) stbiw__zlib_huffa(0xc0 + (n)-280,8) +#define stbiw__zlib_huff(n) ((n) <= 143 ? stbiw__zlib_huff1(n) : (n) <= 255 ? stbiw__zlib_huff2(n) : (n) <= 279 ? stbiw__zlib_huff3(n) : stbiw__zlib_huff4(n)) +#define stbiw__zlib_huffb(n) ((n) <= 143 ? stbiw__zlib_huff1(n) : stbiw__zlib_huff2(n)) + +#define stbiw__ZHASH 16384 + +#endif // STBIW_ZLIB_COMPRESS + +STBIWDEF unsigned char * stbi_zlib_compress(unsigned char *data, int data_len, int *out_len, int quality) +{ +#ifdef STBIW_ZLIB_COMPRESS + // user provided a zlib compress implementation, use that + return STBIW_ZLIB_COMPRESS(data, data_len, out_len, quality); +#else // use builtin + static unsigned short lengthc[] = { 3,4,5,6,7,8,9,10,11,13,15,17,19,23,27,31,35,43,51,59,67,83,99,115,131,163,195,227,258, 259 }; + static unsigned char lengtheb[]= { 0,0,0,0,0,0,0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 0 }; + static unsigned short distc[] = { 1,2,3,4,5,7,9,13,17,25,33,49,65,97,129,193,257,385,513,769,1025,1537,2049,3073,4097,6145,8193,12289,16385,24577, 32768 }; + static unsigned char disteb[] = { 0,0,0,0,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12,13,13 }; + unsigned int bitbuf=0; + int i,j, bitcount=0; + unsigned char *out = NULL; + unsigned char ***hash_table = (unsigned char***) STBIW_MALLOC(stbiw__ZHASH * sizeof(unsigned char**)); + if (hash_table == NULL) + return NULL; + if (quality < 5) quality = 5; + + stbiw__sbpush(out, 0x78); // DEFLATE 32K window + stbiw__sbpush(out, 0x5e); // FLEVEL = 1 + stbiw__zlib_add(1,1); // BFINAL = 1 + stbiw__zlib_add(1,2); // BTYPE = 1 -- fixed huffman + + for (i=0; i < stbiw__ZHASH; ++i) + hash_table[i] = NULL; + + i=0; + while (i < data_len-3) { + // hash next 3 bytes of data to be compressed + int h = stbiw__zhash(data+i)&(stbiw__ZHASH-1), best=3; + unsigned char *bestloc = 0; + unsigned char **hlist = hash_table[h]; + int n = stbiw__sbcount(hlist); + for (j=0; j < n; ++j) { + if (hlist[j]-data > i-32768) { // if entry lies within window + int d = stbiw__zlib_countm(hlist[j], data+i, data_len-i); + if (d >= best) { best=d; bestloc=hlist[j]; } + } + } + // when hash table entry is too long, delete half the entries + if (hash_table[h] && stbiw__sbn(hash_table[h]) == 2*quality) { + STBIW_MEMMOVE(hash_table[h], hash_table[h]+quality, sizeof(hash_table[h][0])*quality); + stbiw__sbn(hash_table[h]) = quality; + } + stbiw__sbpush(hash_table[h],data+i); + + if (bestloc) { + // "lazy matching" - check match at *next* byte, and if it's better, do cur byte as literal + h = stbiw__zhash(data+i+1)&(stbiw__ZHASH-1); + hlist = hash_table[h]; + n = stbiw__sbcount(hlist); + for (j=0; j < n; ++j) { + if (hlist[j]-data > i-32767) { + int e = stbiw__zlib_countm(hlist[j], data+i+1, data_len-i-1); + if (e > best) { // if next match is better, bail on current match + bestloc = NULL; + break; + } + } + } + } + + if (bestloc) { + int d = (int) (data+i - bestloc); // distance back + STBIW_ASSERT(d <= 32767 && best <= 258); + for (j=0; best > lengthc[j+1]-1; ++j); + stbiw__zlib_huff(j+257); + if (lengtheb[j]) stbiw__zlib_add(best - lengthc[j], lengtheb[j]); + for (j=0; d > distc[j+1]-1; ++j); + stbiw__zlib_add(stbiw__zlib_bitrev(j,5),5); + if (disteb[j]) stbiw__zlib_add(d - distc[j], disteb[j]); + i += best; + } else { + stbiw__zlib_huffb(data[i]); + ++i; + } + } + // write out final bytes + for (;i < data_len; ++i) + stbiw__zlib_huffb(data[i]); + stbiw__zlib_huff(256); // end of block + // pad with 0 bits to byte boundary + while (bitcount) + stbiw__zlib_add(0,1); + + for (i=0; i < stbiw__ZHASH; ++i) + (void) stbiw__sbfree(hash_table[i]); + STBIW_FREE(hash_table); + + { + // compute adler32 on input + unsigned int s1=1, s2=0; + int blocklen = (int) (data_len % 5552); + j=0; + while (j < data_len) { + for (i=0; i < blocklen; ++i) { s1 += data[j+i]; s2 += s1; } + s1 %= 65521; s2 %= 65521; + j += blocklen; + blocklen = 5552; + } + stbiw__sbpush(out, STBIW_UCHAR(s2 >> 8)); + stbiw__sbpush(out, STBIW_UCHAR(s2)); + stbiw__sbpush(out, STBIW_UCHAR(s1 >> 8)); + stbiw__sbpush(out, STBIW_UCHAR(s1)); + } + *out_len = stbiw__sbn(out); + // make returned pointer freeable + STBIW_MEMMOVE(stbiw__sbraw(out), out, *out_len); + return (unsigned char *) stbiw__sbraw(out); +#endif // STBIW_ZLIB_COMPRESS +} + +static unsigned int stbiw__crc32(unsigned char *buffer, int len) +{ +#ifdef STBIW_CRC32 + return STBIW_CRC32(buffer, len); +#else + static unsigned int crc_table[256] = + { + 0x00000000, 0x77073096, 0xEE0E612C, 0x990951BA, 0x076DC419, 0x706AF48F, 0xE963A535, 0x9E6495A3, + 0x0eDB8832, 0x79DCB8A4, 0xE0D5E91E, 0x97D2D988, 0x09B64C2B, 0x7EB17CBD, 0xE7B82D07, 0x90BF1D91, + 0x1DB71064, 0x6AB020F2, 0xF3B97148, 0x84BE41DE, 0x1ADAD47D, 0x6DDDE4EB, 0xF4D4B551, 0x83D385C7, + 0x136C9856, 0x646BA8C0, 0xFD62F97A, 0x8A65C9EC, 0x14015C4F, 0x63066CD9, 0xFA0F3D63, 0x8D080DF5, + 0x3B6E20C8, 0x4C69105E, 0xD56041E4, 0xA2677172, 0x3C03E4D1, 0x4B04D447, 0xD20D85FD, 0xA50AB56B, + 0x35B5A8FA, 0x42B2986C, 0xDBBBC9D6, 0xACBCF940, 0x32D86CE3, 0x45DF5C75, 0xDCD60DCF, 0xABD13D59, + 0x26D930AC, 0x51DE003A, 0xC8D75180, 0xBFD06116, 0x21B4F4B5, 0x56B3C423, 0xCFBA9599, 0xB8BDA50F, + 0x2802B89E, 0x5F058808, 0xC60CD9B2, 0xB10BE924, 0x2F6F7C87, 0x58684C11, 0xC1611DAB, 0xB6662D3D, + 0x76DC4190, 0x01DB7106, 0x98D220BC, 0xEFD5102A, 0x71B18589, 0x06B6B51F, 0x9FBFE4A5, 0xE8B8D433, + 0x7807C9A2, 0x0F00F934, 0x9609A88E, 0xE10E9818, 0x7F6A0DBB, 0x086D3D2D, 0x91646C97, 0xE6635C01, + 0x6B6B51F4, 0x1C6C6162, 0x856530D8, 0xF262004E, 0x6C0695ED, 0x1B01A57B, 0x8208F4C1, 0xF50FC457, + 0x65B0D9C6, 0x12B7E950, 0x8BBEB8EA, 0xFCB9887C, 0x62DD1DDF, 0x15DA2D49, 0x8CD37CF3, 0xFBD44C65, + 0x4DB26158, 0x3AB551CE, 0xA3BC0074, 0xD4BB30E2, 0x4ADFA541, 0x3DD895D7, 0xA4D1C46D, 0xD3D6F4FB, + 0x4369E96A, 0x346ED9FC, 0xAD678846, 0xDA60B8D0, 0x44042D73, 0x33031DE5, 0xAA0A4C5F, 0xDD0D7CC9, + 0x5005713C, 0x270241AA, 0xBE0B1010, 0xC90C2086, 0x5768B525, 0x206F85B3, 0xB966D409, 0xCE61E49F, + 0x5EDEF90E, 0x29D9C998, 0xB0D09822, 0xC7D7A8B4, 0x59B33D17, 0x2EB40D81, 0xB7BD5C3B, 0xC0BA6CAD, + 0xEDB88320, 0x9ABFB3B6, 0x03B6E20C, 0x74B1D29A, 0xEAD54739, 0x9DD277AF, 0x04DB2615, 0x73DC1683, + 0xE3630B12, 0x94643B84, 0x0D6D6A3E, 0x7A6A5AA8, 0xE40ECF0B, 0x9309FF9D, 0x0A00AE27, 0x7D079EB1, + 0xF00F9344, 0x8708A3D2, 0x1E01F268, 0x6906C2FE, 0xF762575D, 0x806567CB, 0x196C3671, 0x6E6B06E7, + 0xFED41B76, 0x89D32BE0, 0x10DA7A5A, 0x67DD4ACC, 0xF9B9DF6F, 0x8EBEEFF9, 0x17B7BE43, 0x60B08ED5, + 0xD6D6A3E8, 0xA1D1937E, 0x38D8C2C4, 0x4FDFF252, 0xD1BB67F1, 0xA6BC5767, 0x3FB506DD, 0x48B2364B, + 0xD80D2BDA, 0xAF0A1B4C, 0x36034AF6, 0x41047A60, 0xDF60EFC3, 0xA867DF55, 0x316E8EEF, 0x4669BE79, + 0xCB61B38C, 0xBC66831A, 0x256FD2A0, 0x5268E236, 0xCC0C7795, 0xBB0B4703, 0x220216B9, 0x5505262F, + 0xC5BA3BBE, 0xB2BD0B28, 0x2BB45A92, 0x5CB36A04, 0xC2D7FFA7, 0xB5D0CF31, 0x2CD99E8B, 0x5BDEAE1D, + 0x9B64C2B0, 0xEC63F226, 0x756AA39C, 0x026D930A, 0x9C0906A9, 0xEB0E363F, 0x72076785, 0x05005713, + 0x95BF4A82, 0xE2B87A14, 0x7BB12BAE, 0x0CB61B38, 0x92D28E9B, 0xE5D5BE0D, 0x7CDCEFB7, 0x0BDBDF21, + 0x86D3D2D4, 0xF1D4E242, 0x68DDB3F8, 0x1FDA836E, 0x81BE16CD, 0xF6B9265B, 0x6FB077E1, 0x18B74777, + 0x88085AE6, 0xFF0F6A70, 0x66063BCA, 0x11010B5C, 0x8F659EFF, 0xF862AE69, 0x616BFFD3, 0x166CCF45, + 0xA00AE278, 0xD70DD2EE, 0x4E048354, 0x3903B3C2, 0xA7672661, 0xD06016F7, 0x4969474D, 0x3E6E77DB, + 0xAED16A4A, 0xD9D65ADC, 0x40DF0B66, 0x37D83BF0, 0xA9BCAE53, 0xDEBB9EC5, 0x47B2CF7F, 0x30B5FFE9, + 0xBDBDF21C, 0xCABAC28A, 0x53B39330, 0x24B4A3A6, 0xBAD03605, 0xCDD70693, 0x54DE5729, 0x23D967BF, + 0xB3667A2E, 0xC4614AB8, 0x5D681B02, 0x2A6F2B94, 0xB40BBE37, 0xC30C8EA1, 0x5A05DF1B, 0x2D02EF8D + }; + + unsigned int crc = ~0u; + int i; + for (i=0; i < len; ++i) + crc = (crc >> 8) ^ crc_table[buffer[i] ^ (crc & 0xff)]; + return ~crc; +#endif +} + +#define stbiw__wpng4(o,a,b,c,d) ((o)[0]=STBIW_UCHAR(a),(o)[1]=STBIW_UCHAR(b),(o)[2]=STBIW_UCHAR(c),(o)[3]=STBIW_UCHAR(d),(o)+=4) +#define stbiw__wp32(data,v) stbiw__wpng4(data, (v)>>24,(v)>>16,(v)>>8,(v)); +#define stbiw__wptag(data,s) stbiw__wpng4(data, s[0],s[1],s[2],s[3]) + +static void stbiw__wpcrc(unsigned char **data, int len) +{ + unsigned int crc = stbiw__crc32(*data - len - 4, len+4); + stbiw__wp32(*data, crc); +} + +static unsigned char stbiw__paeth(int a, int b, int c) +{ + int p = a + b - c, pa = abs(p-a), pb = abs(p-b), pc = abs(p-c); + if (pa <= pb && pa <= pc) return STBIW_UCHAR(a); + if (pb <= pc) return STBIW_UCHAR(b); + return STBIW_UCHAR(c); +} + +// @OPTIMIZE: provide an option that always forces left-predict or paeth predict +static void stbiw__encode_png_line(unsigned char *pixels, int stride_bytes, int width, int height, int y, int n, int filter_type, signed char *line_buffer) +{ + static int mapping[] = { 0,1,2,3,4 }; + static int firstmap[] = { 0,1,0,5,6 }; + int *mymap = (y != 0) ? mapping : firstmap; + int i; + int type = mymap[filter_type]; + unsigned char *z = pixels + stride_bytes * (stbi__flip_vertically_on_write ? height-1-y : y); + int signed_stride = stbi__flip_vertically_on_write ? -stride_bytes : stride_bytes; + + if (type==0) { + memcpy(line_buffer, z, width*n); + return; + } + + // first loop isn't optimized since it's just one pixel + for (i = 0; i < n; ++i) { + switch (type) { + case 1: line_buffer[i] = z[i]; break; + case 2: line_buffer[i] = z[i] - z[i-signed_stride]; break; + case 3: line_buffer[i] = z[i] - (z[i-signed_stride]>>1); break; + case 4: line_buffer[i] = (signed char) (z[i] - stbiw__paeth(0,z[i-signed_stride],0)); break; + case 5: line_buffer[i] = z[i]; break; + case 6: line_buffer[i] = z[i]; break; + } + } + switch (type) { + case 1: for (i=n; i < width*n; ++i) line_buffer[i] = z[i] - z[i-n]; break; + case 2: for (i=n; i < width*n; ++i) line_buffer[i] = z[i] - z[i-signed_stride]; break; + case 3: for (i=n; i < width*n; ++i) line_buffer[i] = z[i] - ((z[i-n] + z[i-signed_stride])>>1); break; + case 4: for (i=n; i < width*n; ++i) line_buffer[i] = z[i] - stbiw__paeth(z[i-n], z[i-signed_stride], z[i-signed_stride-n]); break; + case 5: for (i=n; i < width*n; ++i) line_buffer[i] = z[i] - (z[i-n]>>1); break; + case 6: for (i=n; i < width*n; ++i) line_buffer[i] = z[i] - stbiw__paeth(z[i-n], 0,0); break; + } +} + +STBIWDEF unsigned char *stbi_write_png_to_mem(const unsigned char *pixels, int stride_bytes, int x, int y, int n, int *out_len) +{ + int force_filter = stbi_write_force_png_filter; + int ctype[5] = { -1, 0, 4, 2, 6 }; + unsigned char sig[8] = { 137,80,78,71,13,10,26,10 }; + unsigned char *out,*o, *filt, *zlib; + signed char *line_buffer; + int j,zlen; + + if (stride_bytes == 0) + stride_bytes = x * n; + + if (force_filter >= 5) { + force_filter = -1; + } + + filt = (unsigned char *) STBIW_MALLOC((x*n+1) * y); if (!filt) return 0; + line_buffer = (signed char *) STBIW_MALLOC(x * n); if (!line_buffer) { STBIW_FREE(filt); return 0; } + for (j=0; j < y; ++j) { + int filter_type; + if (force_filter > -1) { + filter_type = force_filter; + stbiw__encode_png_line((unsigned char*)(pixels), stride_bytes, x, y, j, n, force_filter, line_buffer); + } else { // Estimate the best filter by running through all of them: + int best_filter = 0, best_filter_val = 0x7fffffff, est, i; + for (filter_type = 0; filter_type < 5; filter_type++) { + stbiw__encode_png_line((unsigned char*)(pixels), stride_bytes, x, y, j, n, filter_type, line_buffer); + + // Estimate the entropy of the line using this filter; the less, the better. + est = 0; + for (i = 0; i < x*n; ++i) { + est += abs((signed char) line_buffer[i]); + } + if (est < best_filter_val) { + best_filter_val = est; + best_filter = filter_type; + } + } + if (filter_type != best_filter) { // If the last iteration already got us the best filter, don't redo it + stbiw__encode_png_line((unsigned char*)(pixels), stride_bytes, x, y, j, n, best_filter, line_buffer); + filter_type = best_filter; + } + } + // when we get here, filter_type contains the filter type, and line_buffer contains the data + filt[j*(x*n+1)] = (unsigned char) filter_type; + STBIW_MEMMOVE(filt+j*(x*n+1)+1, line_buffer, x*n); + } + STBIW_FREE(line_buffer); + zlib = stbi_zlib_compress(filt, y*( x*n+1), &zlen, stbi_write_png_compression_level); + STBIW_FREE(filt); + if (!zlib) return 0; + + // each tag requires 12 bytes of overhead + out = (unsigned char *) STBIW_MALLOC(8 + 12+13 + 12+zlen + 12); + if (!out) return 0; + *out_len = 8 + 12+13 + 12+zlen + 12; + + o=out; + STBIW_MEMMOVE(o,sig,8); o+= 8; + stbiw__wp32(o, 13); // header length + stbiw__wptag(o, "IHDR"); + stbiw__wp32(o, x); + stbiw__wp32(o, y); + *o++ = 8; + *o++ = STBIW_UCHAR(ctype[n]); + *o++ = 0; + *o++ = 0; + *o++ = 0; + stbiw__wpcrc(&o,13); + + stbiw__wp32(o, zlen); + stbiw__wptag(o, "IDAT"); + STBIW_MEMMOVE(o, zlib, zlen); + o += zlen; + STBIW_FREE(zlib); + stbiw__wpcrc(&o, zlen); + + stbiw__wp32(o,0); + stbiw__wptag(o, "IEND"); + stbiw__wpcrc(&o,0); + + STBIW_ASSERT(o == out + *out_len); + + return out; +} + +#ifndef STBI_WRITE_NO_STDIO +STBIWDEF int stbi_write_png(char const *filename, int x, int y, int comp, const void *data, int stride_bytes) +{ + FILE *f; + int len; + unsigned char *png = stbi_write_png_to_mem((const unsigned char *) data, stride_bytes, x, y, comp, &len); + if (png == NULL) return 0; + + f = stbiw__fopen(filename, "wb"); + if (!f) { STBIW_FREE(png); return 0; } + fwrite(png, 1, len, f); + fclose(f); + STBIW_FREE(png); + return 1; +} +#endif + +STBIWDEF int stbi_write_png_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data, int stride_bytes) +{ + int len; + unsigned char *png = stbi_write_png_to_mem((const unsigned char *) data, stride_bytes, x, y, comp, &len); + if (png == NULL) return 0; + func(context, png, len); + STBIW_FREE(png); + return 1; +} + + +/* *************************************************************************** + * + * JPEG writer + * + * This is based on Jon Olick's jo_jpeg.cpp: + * public domain Simple, Minimalistic JPEG writer - http://www.jonolick.com/code.html + */ + +static const unsigned char stbiw__jpg_ZigZag[] = { 0,1,5,6,14,15,27,28,2,4,7,13,16,26,29,42,3,8,12,17,25,30,41,43,9,11,18, + 24,31,40,44,53,10,19,23,32,39,45,52,54,20,22,33,38,46,51,55,60,21,34,37,47,50,56,59,61,35,36,48,49,57,58,62,63 }; + +static void stbiw__jpg_writeBits(stbi__write_context *s, int *bitBufP, int *bitCntP, const unsigned short *bs) { + int bitBuf = *bitBufP, bitCnt = *bitCntP; + bitCnt += bs[1]; + bitBuf |= bs[0] << (24 - bitCnt); + while(bitCnt >= 8) { + unsigned char c = (bitBuf >> 16) & 255; + stbiw__putc(s, c); + if(c == 255) { + stbiw__putc(s, 0); + } + bitBuf <<= 8; + bitCnt -= 8; + } + *bitBufP = bitBuf; + *bitCntP = bitCnt; +} + +static void stbiw__jpg_DCT(float *d0p, float *d1p, float *d2p, float *d3p, float *d4p, float *d5p, float *d6p, float *d7p) { + float d0 = *d0p, d1 = *d1p, d2 = *d2p, d3 = *d3p, d4 = *d4p, d5 = *d5p, d6 = *d6p, d7 = *d7p; + float z1, z2, z3, z4, z5, z11, z13; + + float tmp0 = d0 + d7; + float tmp7 = d0 - d7; + float tmp1 = d1 + d6; + float tmp6 = d1 - d6; + float tmp2 = d2 + d5; + float tmp5 = d2 - d5; + float tmp3 = d3 + d4; + float tmp4 = d3 - d4; + + // Even part + float tmp10 = tmp0 + tmp3; // phase 2 + float tmp13 = tmp0 - tmp3; + float tmp11 = tmp1 + tmp2; + float tmp12 = tmp1 - tmp2; + + d0 = tmp10 + tmp11; // phase 3 + d4 = tmp10 - tmp11; + + z1 = (tmp12 + tmp13) * 0.707106781f; // c4 + d2 = tmp13 + z1; // phase 5 + d6 = tmp13 - z1; + + // Odd part + tmp10 = tmp4 + tmp5; // phase 2 + tmp11 = tmp5 + tmp6; + tmp12 = tmp6 + tmp7; + + // The rotator is modified from fig 4-8 to avoid extra negations. + z5 = (tmp10 - tmp12) * 0.382683433f; // c6 + z2 = tmp10 * 0.541196100f + z5; // c2-c6 + z4 = tmp12 * 1.306562965f + z5; // c2+c6 + z3 = tmp11 * 0.707106781f; // c4 + + z11 = tmp7 + z3; // phase 5 + z13 = tmp7 - z3; + + *d5p = z13 + z2; // phase 6 + *d3p = z13 - z2; + *d1p = z11 + z4; + *d7p = z11 - z4; + + *d0p = d0; *d2p = d2; *d4p = d4; *d6p = d6; +} + +static void stbiw__jpg_calcBits(int val, unsigned short bits[2]) { + int tmp1 = val < 0 ? -val : val; + val = val < 0 ? val-1 : val; + bits[1] = 1; + while(tmp1 >>= 1) { + ++bits[1]; + } + bits[0] = val & ((1<0)&&(DU[end0pos]==0); --end0pos) { + } + // end0pos = first element in reverse order !=0 + if(end0pos == 0) { + stbiw__jpg_writeBits(s, bitBuf, bitCnt, EOB); + return DU[0]; + } + for(i = 1; i <= end0pos; ++i) { + int startpos = i; + int nrzeroes; + unsigned short bits[2]; + for (; DU[i]==0 && i<=end0pos; ++i) { + } + nrzeroes = i-startpos; + if ( nrzeroes >= 16 ) { + int lng = nrzeroes>>4; + int nrmarker; + for (nrmarker=1; nrmarker <= lng; ++nrmarker) + stbiw__jpg_writeBits(s, bitBuf, bitCnt, M16zeroes); + nrzeroes &= 15; + } + stbiw__jpg_calcBits(DU[i], bits); + stbiw__jpg_writeBits(s, bitBuf, bitCnt, HTAC[(nrzeroes<<4)+bits[1]]); + stbiw__jpg_writeBits(s, bitBuf, bitCnt, bits); + } + if(end0pos != 63) { + stbiw__jpg_writeBits(s, bitBuf, bitCnt, EOB); + } + return DU[0]; +} + +static int stbi_write_jpg_core(stbi__write_context *s, int width, int height, int comp, const void* data, int quality) { + // Constants that don't pollute global namespace + static const unsigned char std_dc_luminance_nrcodes[] = {0,0,1,5,1,1,1,1,1,1,0,0,0,0,0,0,0}; + static const unsigned char std_dc_luminance_values[] = {0,1,2,3,4,5,6,7,8,9,10,11}; + static const unsigned char std_ac_luminance_nrcodes[] = {0,0,2,1,3,3,2,4,3,5,5,4,4,0,0,1,0x7d}; + static const unsigned char std_ac_luminance_values[] = { + 0x01,0x02,0x03,0x00,0x04,0x11,0x05,0x12,0x21,0x31,0x41,0x06,0x13,0x51,0x61,0x07,0x22,0x71,0x14,0x32,0x81,0x91,0xa1,0x08, + 0x23,0x42,0xb1,0xc1,0x15,0x52,0xd1,0xf0,0x24,0x33,0x62,0x72,0x82,0x09,0x0a,0x16,0x17,0x18,0x19,0x1a,0x25,0x26,0x27,0x28, + 0x29,0x2a,0x34,0x35,0x36,0x37,0x38,0x39,0x3a,0x43,0x44,0x45,0x46,0x47,0x48,0x49,0x4a,0x53,0x54,0x55,0x56,0x57,0x58,0x59, + 0x5a,0x63,0x64,0x65,0x66,0x67,0x68,0x69,0x6a,0x73,0x74,0x75,0x76,0x77,0x78,0x79,0x7a,0x83,0x84,0x85,0x86,0x87,0x88,0x89, + 0x8a,0x92,0x93,0x94,0x95,0x96,0x97,0x98,0x99,0x9a,0xa2,0xa3,0xa4,0xa5,0xa6,0xa7,0xa8,0xa9,0xaa,0xb2,0xb3,0xb4,0xb5,0xb6, + 0xb7,0xb8,0xb9,0xba,0xc2,0xc3,0xc4,0xc5,0xc6,0xc7,0xc8,0xc9,0xca,0xd2,0xd3,0xd4,0xd5,0xd6,0xd7,0xd8,0xd9,0xda,0xe1,0xe2, + 0xe3,0xe4,0xe5,0xe6,0xe7,0xe8,0xe9,0xea,0xf1,0xf2,0xf3,0xf4,0xf5,0xf6,0xf7,0xf8,0xf9,0xfa + }; + static const unsigned char std_dc_chrominance_nrcodes[] = {0,0,3,1,1,1,1,1,1,1,1,1,0,0,0,0,0}; + static const unsigned char std_dc_chrominance_values[] = {0,1,2,3,4,5,6,7,8,9,10,11}; + static const unsigned char std_ac_chrominance_nrcodes[] = {0,0,2,1,2,4,4,3,4,7,5,4,4,0,1,2,0x77}; + static const unsigned char std_ac_chrominance_values[] = { + 0x00,0x01,0x02,0x03,0x11,0x04,0x05,0x21,0x31,0x06,0x12,0x41,0x51,0x07,0x61,0x71,0x13,0x22,0x32,0x81,0x08,0x14,0x42,0x91, + 0xa1,0xb1,0xc1,0x09,0x23,0x33,0x52,0xf0,0x15,0x62,0x72,0xd1,0x0a,0x16,0x24,0x34,0xe1,0x25,0xf1,0x17,0x18,0x19,0x1a,0x26, + 0x27,0x28,0x29,0x2a,0x35,0x36,0x37,0x38,0x39,0x3a,0x43,0x44,0x45,0x46,0x47,0x48,0x49,0x4a,0x53,0x54,0x55,0x56,0x57,0x58, + 0x59,0x5a,0x63,0x64,0x65,0x66,0x67,0x68,0x69,0x6a,0x73,0x74,0x75,0x76,0x77,0x78,0x79,0x7a,0x82,0x83,0x84,0x85,0x86,0x87, + 0x88,0x89,0x8a,0x92,0x93,0x94,0x95,0x96,0x97,0x98,0x99,0x9a,0xa2,0xa3,0xa4,0xa5,0xa6,0xa7,0xa8,0xa9,0xaa,0xb2,0xb3,0xb4, + 0xb5,0xb6,0xb7,0xb8,0xb9,0xba,0xc2,0xc3,0xc4,0xc5,0xc6,0xc7,0xc8,0xc9,0xca,0xd2,0xd3,0xd4,0xd5,0xd6,0xd7,0xd8,0xd9,0xda, + 0xe2,0xe3,0xe4,0xe5,0xe6,0xe7,0xe8,0xe9,0xea,0xf2,0xf3,0xf4,0xf5,0xf6,0xf7,0xf8,0xf9,0xfa + }; + // Huffman tables + static const unsigned short YDC_HT[256][2] = { {0,2},{2,3},{3,3},{4,3},{5,3},{6,3},{14,4},{30,5},{62,6},{126,7},{254,8},{510,9}}; + static const unsigned short UVDC_HT[256][2] = { {0,2},{1,2},{2,2},{6,3},{14,4},{30,5},{62,6},{126,7},{254,8},{510,9},{1022,10},{2046,11}}; + static const unsigned short YAC_HT[256][2] = { + {10,4},{0,2},{1,2},{4,3},{11,4},{26,5},{120,7},{248,8},{1014,10},{65410,16},{65411,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {12,4},{27,5},{121,7},{502,9},{2038,11},{65412,16},{65413,16},{65414,16},{65415,16},{65416,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {28,5},{249,8},{1015,10},{4084,12},{65417,16},{65418,16},{65419,16},{65420,16},{65421,16},{65422,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {58,6},{503,9},{4085,12},{65423,16},{65424,16},{65425,16},{65426,16},{65427,16},{65428,16},{65429,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {59,6},{1016,10},{65430,16},{65431,16},{65432,16},{65433,16},{65434,16},{65435,16},{65436,16},{65437,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {122,7},{2039,11},{65438,16},{65439,16},{65440,16},{65441,16},{65442,16},{65443,16},{65444,16},{65445,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {123,7},{4086,12},{65446,16},{65447,16},{65448,16},{65449,16},{65450,16},{65451,16},{65452,16},{65453,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {250,8},{4087,12},{65454,16},{65455,16},{65456,16},{65457,16},{65458,16},{65459,16},{65460,16},{65461,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {504,9},{32704,15},{65462,16},{65463,16},{65464,16},{65465,16},{65466,16},{65467,16},{65468,16},{65469,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {505,9},{65470,16},{65471,16},{65472,16},{65473,16},{65474,16},{65475,16},{65476,16},{65477,16},{65478,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {506,9},{65479,16},{65480,16},{65481,16},{65482,16},{65483,16},{65484,16},{65485,16},{65486,16},{65487,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {1017,10},{65488,16},{65489,16},{65490,16},{65491,16},{65492,16},{65493,16},{65494,16},{65495,16},{65496,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {1018,10},{65497,16},{65498,16},{65499,16},{65500,16},{65501,16},{65502,16},{65503,16},{65504,16},{65505,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {2040,11},{65506,16},{65507,16},{65508,16},{65509,16},{65510,16},{65511,16},{65512,16},{65513,16},{65514,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {65515,16},{65516,16},{65517,16},{65518,16},{65519,16},{65520,16},{65521,16},{65522,16},{65523,16},{65524,16},{0,0},{0,0},{0,0},{0,0},{0,0}, + {2041,11},{65525,16},{65526,16},{65527,16},{65528,16},{65529,16},{65530,16},{65531,16},{65532,16},{65533,16},{65534,16},{0,0},{0,0},{0,0},{0,0},{0,0} + }; + static const unsigned short UVAC_HT[256][2] = { + {0,2},{1,2},{4,3},{10,4},{24,5},{25,5},{56,6},{120,7},{500,9},{1014,10},{4084,12},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {11,4},{57,6},{246,8},{501,9},{2038,11},{4085,12},{65416,16},{65417,16},{65418,16},{65419,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {26,5},{247,8},{1015,10},{4086,12},{32706,15},{65420,16},{65421,16},{65422,16},{65423,16},{65424,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {27,5},{248,8},{1016,10},{4087,12},{65425,16},{65426,16},{65427,16},{65428,16},{65429,16},{65430,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {58,6},{502,9},{65431,16},{65432,16},{65433,16},{65434,16},{65435,16},{65436,16},{65437,16},{65438,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {59,6},{1017,10},{65439,16},{65440,16},{65441,16},{65442,16},{65443,16},{65444,16},{65445,16},{65446,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {121,7},{2039,11},{65447,16},{65448,16},{65449,16},{65450,16},{65451,16},{65452,16},{65453,16},{65454,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {122,7},{2040,11},{65455,16},{65456,16},{65457,16},{65458,16},{65459,16},{65460,16},{65461,16},{65462,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {249,8},{65463,16},{65464,16},{65465,16},{65466,16},{65467,16},{65468,16},{65469,16},{65470,16},{65471,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {503,9},{65472,16},{65473,16},{65474,16},{65475,16},{65476,16},{65477,16},{65478,16},{65479,16},{65480,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {504,9},{65481,16},{65482,16},{65483,16},{65484,16},{65485,16},{65486,16},{65487,16},{65488,16},{65489,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {505,9},{65490,16},{65491,16},{65492,16},{65493,16},{65494,16},{65495,16},{65496,16},{65497,16},{65498,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {506,9},{65499,16},{65500,16},{65501,16},{65502,16},{65503,16},{65504,16},{65505,16},{65506,16},{65507,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {2041,11},{65508,16},{65509,16},{65510,16},{65511,16},{65512,16},{65513,16},{65514,16},{65515,16},{65516,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {16352,14},{65517,16},{65518,16},{65519,16},{65520,16},{65521,16},{65522,16},{65523,16},{65524,16},{65525,16},{0,0},{0,0},{0,0},{0,0},{0,0}, + {1018,10},{32707,15},{65526,16},{65527,16},{65528,16},{65529,16},{65530,16},{65531,16},{65532,16},{65533,16},{65534,16},{0,0},{0,0},{0,0},{0,0},{0,0} + }; + static const int YQT[] = {16,11,10,16,24,40,51,61,12,12,14,19,26,58,60,55,14,13,16,24,40,57,69,56,14,17,22,29,51,87,80,62,18,22, + 37,56,68,109,103,77,24,35,55,64,81,104,113,92,49,64,78,87,103,121,120,101,72,92,95,98,112,100,103,99}; + static const int UVQT[] = {17,18,24,47,99,99,99,99,18,21,26,66,99,99,99,99,24,26,56,99,99,99,99,99,47,66,99,99,99,99,99,99, + 99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99}; + static const float aasf[] = { 1.0f * 2.828427125f, 1.387039845f * 2.828427125f, 1.306562965f * 2.828427125f, 1.175875602f * 2.828427125f, + 1.0f * 2.828427125f, 0.785694958f * 2.828427125f, 0.541196100f * 2.828427125f, 0.275899379f * 2.828427125f }; + + int row, col, i, k; + float fdtbl_Y[64], fdtbl_UV[64]; + unsigned char YTable[64], UVTable[64]; + + if(!data || !width || !height || comp > 4 || comp < 1) { + return 0; + } + + quality = quality ? quality : 90; + quality = quality < 1 ? 1 : quality > 100 ? 100 : quality; + quality = quality < 50 ? 5000 / quality : 200 - quality * 2; + + for(i = 0; i < 64; ++i) { + int uvti, yti = (YQT[i]*quality+50)/100; + YTable[stbiw__jpg_ZigZag[i]] = (unsigned char) (yti < 1 ? 1 : yti > 255 ? 255 : yti); + uvti = (UVQT[i]*quality+50)/100; + UVTable[stbiw__jpg_ZigZag[i]] = (unsigned char) (uvti < 1 ? 1 : uvti > 255 ? 255 : uvti); + } + + for(row = 0, k = 0; row < 8; ++row) { + for(col = 0; col < 8; ++col, ++k) { + fdtbl_Y[k] = 1 / (YTable [stbiw__jpg_ZigZag[k]] * aasf[row] * aasf[col]); + fdtbl_UV[k] = 1 / (UVTable[stbiw__jpg_ZigZag[k]] * aasf[row] * aasf[col]); + } + } + + // Write Headers + { + static const unsigned char head0[] = { 0xFF,0xD8,0xFF,0xE0,0,0x10,'J','F','I','F',0,1,1,0,0,1,0,1,0,0,0xFF,0xDB,0,0x84,0 }; + static const unsigned char head2[] = { 0xFF,0xDA,0,0xC,3,1,0,2,0x11,3,0x11,0,0x3F,0 }; + const unsigned char head1[] = { 0xFF,0xC0,0,0x11,8,(unsigned char)(height>>8),STBIW_UCHAR(height),(unsigned char)(width>>8),STBIW_UCHAR(width), + 3,1,0x11,0,2,0x11,1,3,0x11,1,0xFF,0xC4,0x01,0xA2,0 }; + s->func(s->context, (void*)head0, sizeof(head0)); + s->func(s->context, (void*)YTable, sizeof(YTable)); + stbiw__putc(s, 1); + s->func(s->context, UVTable, sizeof(UVTable)); + s->func(s->context, (void*)head1, sizeof(head1)); + s->func(s->context, (void*)(std_dc_luminance_nrcodes+1), sizeof(std_dc_luminance_nrcodes)-1); + s->func(s->context, (void*)std_dc_luminance_values, sizeof(std_dc_luminance_values)); + stbiw__putc(s, 0x10); // HTYACinfo + s->func(s->context, (void*)(std_ac_luminance_nrcodes+1), sizeof(std_ac_luminance_nrcodes)-1); + s->func(s->context, (void*)std_ac_luminance_values, sizeof(std_ac_luminance_values)); + stbiw__putc(s, 1); // HTUDCinfo + s->func(s->context, (void*)(std_dc_chrominance_nrcodes+1), sizeof(std_dc_chrominance_nrcodes)-1); + s->func(s->context, (void*)std_dc_chrominance_values, sizeof(std_dc_chrominance_values)); + stbiw__putc(s, 0x11); // HTUACinfo + s->func(s->context, (void*)(std_ac_chrominance_nrcodes+1), sizeof(std_ac_chrominance_nrcodes)-1); + s->func(s->context, (void*)std_ac_chrominance_values, sizeof(std_ac_chrominance_values)); + s->func(s->context, (void*)head2, sizeof(head2)); + } + + // Encode 8x8 macroblocks + { + static const unsigned short fillBits[] = {0x7F, 7}; + const unsigned char *imageData = (const unsigned char *)data; + int DCY=0, DCU=0, DCV=0; + int bitBuf=0, bitCnt=0; + // comp == 2 is grey+alpha (alpha is ignored) + int ofsG = comp > 2 ? 1 : 0, ofsB = comp > 2 ? 2 : 0; + int x, y, pos; + for(y = 0; y < height; y += 8) { + for(x = 0; x < width; x += 8) { + float YDU[64], UDU[64], VDU[64]; + for(row = y, pos = 0; row < y+8; ++row) { + // row >= height => use last input row + int clamped_row = (row < height) ? row : height - 1; + int base_p = (stbi__flip_vertically_on_write ? (height-1-clamped_row) : clamped_row)*width*comp; + for(col = x; col < x+8; ++col, ++pos) { + float r, g, b; + // if col >= width => use pixel from last input column + int p = base_p + ((col < width) ? col : (width-1))*comp; + + r = imageData[p+0]; + g = imageData[p+ofsG]; + b = imageData[p+ofsB]; + YDU[pos]=+0.29900f*r+0.58700f*g+0.11400f*b-128; + UDU[pos]=-0.16874f*r-0.33126f*g+0.50000f*b; + VDU[pos]=+0.50000f*r-0.41869f*g-0.08131f*b; + } + } + + DCY = stbiw__jpg_processDU(s, &bitBuf, &bitCnt, YDU, fdtbl_Y, DCY, YDC_HT, YAC_HT); + DCU = stbiw__jpg_processDU(s, &bitBuf, &bitCnt, UDU, fdtbl_UV, DCU, UVDC_HT, UVAC_HT); + DCV = stbiw__jpg_processDU(s, &bitBuf, &bitCnt, VDU, fdtbl_UV, DCV, UVDC_HT, UVAC_HT); + } + } + + // Do the bit alignment of the EOI marker + stbiw__jpg_writeBits(s, &bitBuf, &bitCnt, fillBits); + } + + // EOI + stbiw__putc(s, 0xFF); + stbiw__putc(s, 0xD9); + + return 1; +} + +STBIWDEF int stbi_write_jpg_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data, int quality) +{ + stbi__write_context s; + stbi__start_write_callbacks(&s, func, context); + return stbi_write_jpg_core(&s, x, y, comp, (void *) data, quality); +} + + +#ifndef STBI_WRITE_NO_STDIO +STBIWDEF int stbi_write_jpg(char const *filename, int x, int y, int comp, const void *data, int quality) +{ + stbi__write_context s; + if (stbi__start_write_file(&s,filename)) { + int r = stbi_write_jpg_core(&s, x, y, comp, data, quality); + stbi__end_write_file(&s); + return r; + } else + return 0; +} +#endif + +#endif // STB_IMAGE_WRITE_IMPLEMENTATION + +/* Revision history + 1.11 (2019-08-11) + + 1.10 (2019-02-07) + support utf8 filenames in Windows; fix warnings and platform ifdefs + 1.09 (2018-02-11) + fix typo in zlib quality API, improve STB_I_W_STATIC in C++ + 1.08 (2018-01-29) + add stbi__flip_vertically_on_write, external zlib, zlib quality, choose PNG filter + 1.07 (2017-07-24) + doc fix + 1.06 (2017-07-23) + writing JPEG (using Jon Olick's code) + 1.05 ??? + 1.04 (2017-03-03) + monochrome BMP expansion + 1.03 ??? + 1.02 (2016-04-02) + avoid allocating large structures on the stack + 1.01 (2016-01-16) + STBIW_REALLOC_SIZED: support allocators with no realloc support + avoid race-condition in crc initialization + minor compile issues + 1.00 (2015-09-14) + installable file IO function + 0.99 (2015-09-13) + warning fixes; TGA rle support + 0.98 (2015-04-08) + added STBIW_MALLOC, STBIW_ASSERT etc + 0.97 (2015-01-18) + fixed HDR asserts, rewrote HDR rle logic + 0.96 (2015-01-17) + add HDR output + fix monochrome BMP + 0.95 (2014-08-17) + add monochrome TGA output + 0.94 (2014-05-31) + rename private functions to avoid conflicts with stb_image.h + 0.93 (2014-05-27) + warning fixes + 0.92 (2010-08-01) + casts to unsigned char to fix warnings + 0.91 (2010-07-17) + first public release + 0.90 first internal release +*/ + +/* +------------------------------------------------------------------------------ +This software is available under 2 licenses -- choose whichever you prefer. +------------------------------------------------------------------------------ +ALTERNATIVE A - MIT License +Copyright (c) 2017 Sean Barrett +Permission is hereby granted, free of charge, to any person obtaining a copy of +this software and associated documentation files (the "Software"), to deal in +the Software without restriction, including without limitation the rights to +use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies +of the Software, and to permit persons to whom the Software is furnished to do +so, subject to the following conditions: +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +------------------------------------------------------------------------------ +ALTERNATIVE B - Public Domain (www.unlicense.org) +This is free and unencumbered software released into the public domain. +Anyone is free to copy, modify, publish, use, compile, sell, or distribute this +software, either in source code form or as a compiled binary, for any purpose, +commercial or non-commercial, and by any means. +In jurisdictions that recognize copyright laws, the author or authors of this +software dedicate any and all copyright interest in the software to the public +domain. We make this dedication for the benefit of the public at large and to +the detriment of our heirs and successors. We intend this dedication to be an +overt act of relinquishment in perpetuity of all present and future rights to +this software under copyright law. +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN +ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION +WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +------------------------------------------------------------------------------ +*/ diff --git a/rknpu2/examples/librknn_api_android_demo/model/RK3562/mobilenet_v1.rknn b/rknpu2/examples/librknn_api_android_demo/model/RK3562/mobilenet_v1.rknn new file mode 100644 index 0000000000000000000000000000000000000000..cece9720db616d18a6239a8881ab6eb0e3203bf1 --- /dev/null +++ b/rknpu2/examples/librknn_api_android_demo/model/RK3562/mobilenet_v1.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6a619867a76f6032e56fb143a99dfa71eede8e8df910fb903af1e8125ecb35bd +size 4484726 diff --git a/rknpu2/examples/librknn_api_android_demo/model/RK3566_RK3568/mobilenet_v1.rknn b/rknpu2/examples/librknn_api_android_demo/model/RK3566_RK3568/mobilenet_v1.rknn new file mode 100644 index 0000000000000000000000000000000000000000..e8c440aa2c176e6f0b56eb5393396336e7354dbe --- /dev/null +++ b/rknpu2/examples/librknn_api_android_demo/model/RK3566_RK3568/mobilenet_v1.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:95b9f69dbcbf30ea74b93d8e57366a84a00b4e9b219e3b7c6a0ee820a3613daa +size 4414774 diff --git a/rknpu2/examples/librknn_api_android_demo/model/RK3588/mobilenet_v1.rknn b/rknpu2/examples/librknn_api_android_demo/model/RK3588/mobilenet_v1.rknn new file mode 100644 index 0000000000000000000000000000000000000000..2d0ab57a29903722f8988a90412292832801a152 --- /dev/null +++ b/rknpu2/examples/librknn_api_android_demo/model/RK3588/mobilenet_v1.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9507a0373744647914edbfd238886a2a8c498a680c77f83892e95379881da643 +size 4594742 diff --git a/rknpu2/examples/librknn_api_android_demo/model/cat_224x224.jpg b/rknpu2/examples/librknn_api_android_demo/model/cat_224x224.jpg new file mode 100644 index 0000000000000000000000000000000000000000..2159d5cfe3d6cbc01940a804cff0e3bc516af7fb --- /dev/null +++ b/rknpu2/examples/librknn_api_android_demo/model/cat_224x224.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0ce3ef6da3424c0744385935a210e4ec8ab3a6ddda205113ab07b77f9c0e812a +size 58701 diff --git a/rknpu2/examples/librknn_api_android_demo/model/dog_224x224.jpg b/rknpu2/examples/librknn_api_android_demo/model/dog_224x224.jpg new file mode 100644 index 0000000000000000000000000000000000000000..004b0416e23454a12eb06eaba238c7e2d5f2b0fc --- /dev/null +++ b/rknpu2/examples/librknn_api_android_demo/model/dog_224x224.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c350299c6283d5f62fecf1f845b6b3be9aafec8dff528ca09a129990f0a584b0 +size 18889 diff --git a/rknpu2/examples/librknn_api_android_demo/rknn_create_mem_demo.cpp b/rknpu2/examples/librknn_api_android_demo/rknn_create_mem_demo.cpp new file mode 100644 index 0000000000000000000000000000000000000000..567d46a76975712c7b13812de198b0630773f468 --- /dev/null +++ b/rknpu2/examples/librknn_api_android_demo/rknn_create_mem_demo.cpp @@ -0,0 +1,355 @@ +// Copyright (c) 2021 by Rockchip Electronics Co., Ltd. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +/*------------------------------------------- + Includes +-------------------------------------------*/ +#include "rknn_api.h" + +#include +#include +#include +#include +#include + +#define STB_IMAGE_IMPLEMENTATION +#include "stb/stb_image.h" +#define STB_IMAGE_RESIZE_IMPLEMENTATION +#include + +/*------------------------------------------- + Functions +-------------------------------------------*/ +static inline int64_t getCurrentTimeUs() +{ + struct timeval tv; + gettimeofday(&tv, NULL); + return tv.tv_sec * 1000000 + tv.tv_usec; +} + +static int rknn_GetTopN(float* pfProb, float* pfMaxProb, uint32_t* pMaxClass, uint32_t outputCount, uint32_t topNum) +{ + uint32_t i, j; + uint32_t top_count = outputCount > topNum ? topNum : outputCount; + + for (i = 0; i < topNum; ++i) { + pfMaxProb[i] = -FLT_MAX; + pMaxClass[i] = -1; + } + + for (j = 0; j < top_count; j++) { + for (i = 0; i < outputCount; i++) { + if ((i == *(pMaxClass + 0)) || (i == *(pMaxClass + 1)) || (i == *(pMaxClass + 2)) || (i == *(pMaxClass + 3)) || + (i == *(pMaxClass + 4))) { + continue; + } + + if (pfProb[i] > *(pfMaxProb + j)) { + *(pfMaxProb + j) = pfProb[i]; + *(pMaxClass + j) = i; + } + } + } + + return 1; +} + +static void dump_tensor_attr(rknn_tensor_attr* attr) +{ + printf(" index=%d, name=%s, n_dims=%d, dims=[%d, %d, %d, %d], n_elems=%d, size=%d, fmt=%s, type=%s, qnt_type=%s, " + "zp=%d, scale=%f\n", + attr->index, attr->name, attr->n_dims, attr->dims[0], attr->dims[1], attr->dims[2], attr->dims[3], + attr->n_elems, attr->size, get_format_string(attr->fmt), get_type_string(attr->type), + get_qnt_type_string(attr->qnt_type), attr->zp, attr->scale); +} + +static unsigned char* load_image(const char* image_path, rknn_tensor_attr* input_attr) +{ + int req_height = 0; + int req_width = 0; + int req_channel = 0; + + switch (input_attr->fmt) { + case RKNN_TENSOR_NHWC: + req_height = input_attr->dims[1]; + req_width = input_attr->dims[2]; + req_channel = input_attr->dims[3]; + break; + case RKNN_TENSOR_NCHW: + req_height = input_attr->dims[2]; + req_width = input_attr->dims[3]; + req_channel = input_attr->dims[1]; + break; + default: + printf("meet unsupported layout\n"); + return NULL; + } + + int height = 0; + int width = 0; + int channel = 0; + + unsigned char* image_data = stbi_load(image_path, &width, &height, &channel, req_channel); + if (image_data == NULL) { + printf("load image failed!\n"); + return NULL; + } + + if (width != req_width || height != req_height) { + unsigned char* image_resized = (unsigned char*)STBI_MALLOC(req_width * req_height * req_channel); + if (!image_resized) { + printf("malloc image failed!\n"); + STBI_FREE(image_data); + return NULL; + } + if (stbir_resize_uint8(image_data, width, height, 0, image_resized, req_width, req_height, 0, channel) != 1) { + printf("resize image failed!\n"); + STBI_FREE(image_data); + return NULL; + } + STBI_FREE(image_data); + image_data = image_resized; + } + + return image_data; +} + +static unsigned char* load_model(const char* filename, int* model_size) +{ + FILE* fp = fopen(filename, "rb"); + if (fp == nullptr) { + printf("fopen %s fail!\n", filename); + return NULL; + } + fseek(fp, 0, SEEK_END); + int model_len = ftell(fp); + unsigned char* model = (unsigned char*)malloc(model_len); + fseek(fp, 0, SEEK_SET); + if (model_len != fread(model, 1, model_len, fp)) { + printf("fread %s fail!\n", filename); + free(model); + return NULL; + } + *model_size = model_len; + if (fp) { + fclose(fp); + } + return model; +} + +/*------------------------------------------- + Main Functions +-------------------------------------------*/ +int main(int argc, char* argv[]) +{ + if (argc < 3) { + printf("Usage:%s model_path input_path [loop_count]\n", argv[0]); + return -1; + } + + char* model_path = argv[1]; + char* input_path = argv[2]; + + int loop_count = 1; + if (argc > 3) { + loop_count = atoi(argv[3]); + } + + rknn_context ctx = 0; + + // Load RKNN Model + int model_len = 0; + unsigned char* model = load_model(model_path, &model_len); + int ret = rknn_init(&ctx, model, model_len, 0, NULL); + if (ret < 0) { + printf("rknn_init fail! ret=%d\n", ret); + return -1; + } + + // Get sdk and driver version + rknn_sdk_version sdk_ver; + ret = rknn_query(ctx, RKNN_QUERY_SDK_VERSION, &sdk_ver, sizeof(sdk_ver)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + + printf("rknn_api/rknnrt version: %s, driver version: %s\n", sdk_ver.api_version, sdk_ver.drv_version); + + // Get Model Input Output Info + rknn_input_output_num io_num; + ret = rknn_query(ctx, RKNN_QUERY_IN_OUT_NUM, &io_num, sizeof(io_num)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("model input num: %d, output num: %d\n", io_num.n_input, io_num.n_output); + + printf("input tensors:\n"); + rknn_tensor_attr input_attrs[io_num.n_input]; + memset(input_attrs, 0, io_num.n_input * sizeof(rknn_tensor_attr)); + for (uint32_t i = 0; i < io_num.n_input; i++) { + input_attrs[i].index = i; + // query info + ret = rknn_query(ctx, RKNN_QUERY_INPUT_ATTR, &(input_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret < 0) { + printf("rknn_init error! ret=%d\n", ret); + return -1; + } + dump_tensor_attr(&input_attrs[i]); + } + + printf("output tensors:\n"); + rknn_tensor_attr output_attrs[io_num.n_output]; + memset(output_attrs, 0, io_num.n_output * sizeof(rknn_tensor_attr)); + for (uint32_t i = 0; i < io_num.n_output; i++) { + output_attrs[i].index = i; + // query info + ret = rknn_query(ctx, RKNN_QUERY_OUTPUT_ATTR, &(output_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + dump_tensor_attr(&output_attrs[i]); + } + + // Get custom string + rknn_custom_string custom_string; + ret = rknn_query(ctx, RKNN_QUERY_CUSTOM_STRING, &custom_string, sizeof(custom_string)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("custom string: %s\n", custom_string.string); + + unsigned char* input_data = NULL; + rknn_tensor_type input_type = RKNN_TENSOR_UINT8; + rknn_tensor_format input_layout = RKNN_TENSOR_NHWC; + + // Load image + input_data = load_image(input_path, &input_attrs[0]); + + if (!input_data) { + return -1; + } + + // Create input tensor memory + rknn_tensor_mem* input_mems[1]; + // default input type is int8 (normalize and quantize need compute in outside) + // if set uint8, will fuse normalize and quantize to npu + input_attrs[0].type = input_type; + // default fmt is NHWC, npu only support NHWC in zero copy mode + input_attrs[0].fmt = input_layout; + + input_mems[0] = rknn_create_mem(ctx, input_attrs[0].size_with_stride); + + // Copy input data to input tensor memory + int width = input_attrs[0].dims[2]; + int stride = input_attrs[0].w_stride; + + if (width == stride) { + memcpy(input_mems[0]->virt_addr, input_data, width * input_attrs[0].dims[1] * input_attrs[0].dims[3]); + } else { + int height = input_attrs[0].dims[1]; + int channel = input_attrs[0].dims[3]; + // copy from src to dst with stride + uint8_t* src_ptr = input_data; + uint8_t* dst_ptr = (uint8_t*)input_mems[0]->virt_addr; + // width-channel elements + int src_wc_elems = width * channel; + int dst_wc_elems = stride * channel; + for (int h = 0; h < height; ++h) { + memcpy(dst_ptr, src_ptr, src_wc_elems); + src_ptr += src_wc_elems; + dst_ptr += dst_wc_elems; + } + } + + // Create output tensor memory + rknn_tensor_mem* output_mems[io_num.n_output]; + for (uint32_t i = 0; i < io_num.n_output; ++i) { + // default output type is depend on model, this require float32 to compute top5 + // allocate float32 output tensor + int output_size = output_attrs[i].n_elems * sizeof(float); + output_mems[i] = rknn_create_mem(ctx, output_size); + } + + // Set input tensor memory + ret = rknn_set_io_mem(ctx, input_mems[0], &input_attrs[0]); + if (ret < 0) { + printf("rknn_set_io_mem fail! ret=%d\n", ret); + return -1; + } + + // Set output tensor memory + for (uint32_t i = 0; i < io_num.n_output; ++i) { + // default output type is depend on model, this require float32 to compute top5 + output_attrs[i].type = RKNN_TENSOR_FLOAT32; + // set output memory and attribute + ret = rknn_set_io_mem(ctx, output_mems[i], &output_attrs[i]); + if (ret < 0) { + printf("rknn_set_io_mem fail! ret=%d\n", ret); + return -1; + } + } + + // Run + printf("Begin perf ...\n"); + for (int i = 0; i < loop_count; ++i) { + int64_t start_us = getCurrentTimeUs(); + ret = rknn_run(ctx, NULL); + int64_t elapse_us = getCurrentTimeUs() - start_us; + if (ret < 0) { + printf("rknn run error %d\n", ret); + return -1; + } + printf("%4d: Elapse Time = %.2fms, FPS = %.2f\n", i, elapse_us / 1000.f, 1000.f * 1000.f / elapse_us); + } + + // Get top 5 + uint32_t topNum = 5; + for (uint32_t i = 0; i < io_num.n_output; i++) { + uint32_t MaxClass[topNum]; + float fMaxProb[topNum]; + float* buffer = (float*)output_mems[i]->virt_addr; + uint32_t sz = output_attrs[i].n_elems; + int top_count = sz > topNum ? topNum : sz; + + rknn_GetTopN(buffer, fMaxProb, MaxClass, sz, topNum); + + printf("---- Top%d ----\n", top_count); + for (int j = 0; j < top_count; j++) { + printf("%8.6f - %d\n", fMaxProb[j], MaxClass[j]); + } + } + + // Destroy rknn memory + rknn_destroy_mem(ctx, input_mems[0]); + for (uint32_t i = 0; i < io_num.n_output; ++i) { + rknn_destroy_mem(ctx, output_mems[i]); + } + + // destroy + rknn_destroy(ctx); + + if (input_data != nullptr) { + free(input_data); + } + + if (model != nullptr) { + free(model); + } + + return 0; +} diff --git a/rknpu2/examples/rknn_api_demo/CMakeLists.txt b/rknpu2/examples/rknn_api_demo/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..f1ff2a19c59091d10f65964cff32d6ca91e092d8 --- /dev/null +++ b/rknpu2/examples/rknn_api_demo/CMakeLists.txt @@ -0,0 +1,129 @@ +cmake_minimum_required(VERSION 3.6) + +project(rknn_api_demo) + +set(CMAKE_CXX_STANDARD 11) +set(CMAKE_CXX_STANDARD_REQUIRED ON) + +# skip 3rd-party lib dependencies +set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} -Wl,--allow-shlib-undefined") + +# rknn api +set(RKNN_API_PATH ${CMAKE_SOURCE_DIR}/../../runtime//${CMAKE_SYSTEM_NAME}/librknn_api) +if(CMAKE_SYSTEM_NAME STREQUAL "Android") + set(RKNN_RT_LIB ${RKNN_API_PATH}/${CMAKE_ANDROID_ARCH_ABI}/librknnrt.so) +else() + if(CMAKE_C_COMPILER MATCHES "aarch64") + set(LIB_ARCH aarch64) + else() + set(LIB_ARCH armhf) + endif() + + set(RKNN_RT_LIB ${RKNN_API_PATH}/${LIB_ARCH}/librknnrt.so) +endif() + +include_directories(${RKNN_API_PATH}/include) + +# stb +include_directories(${CMAKE_SOURCE_DIR}/../3rdparty/) + +# rga +# comes from https://github.com/airockchip/librga +set(RGA_PATH ${CMAKE_SOURCE_DIR}/../3rdparty/rga/) +if(CMAKE_SYSTEM_NAME STREQUAL "Android") + set(RGA_LIB ${RGA_PATH}/libs/AndroidNdk/${CMAKE_ANDROID_ARCH_ABI}/librga.so) +else() + if(CMAKE_C_COMPILER MATCHES "aarch64") + set(LIB_ARCH aarch64) + else() + set(LIB_ARCH armhf) + endif() + + set(RGA_LIB ${RGA_PATH}/libs/Linux//gcc-${LIB_ARCH}/librga.so) +endif() + +include_directories(${RGA_PATH}/include) + +# mmz +set(MPI_MMZ_PATH ${CMAKE_SOURCE_DIR}/../3rdparty/rk_mpi_mmz) + +if(CMAKE_SYSTEM_NAME STREQUAL "Android") + set(MPI_MMZ_LIB ${MPI_MMZ_PATH}/lib/Android/${CMAKE_ANDROID_ARCH_ABI}/libmpimmz.so) +else() + if(CMAKE_C_COMPILER MATCHES "aarch64") + set(LIB_ARCH aarch64) + else() + set(LIB_ARCH armhf) + endif() + + set(MPI_MMZ_LIB ${MPI_MMZ_PATH}/lib/Linux//${LIB_ARCH}/libmpimmz.so) +endif() + +include_directories(${MPI_MMZ_PATH}/include) + +set(CMAKE_INSTALL_RPATH "lib") + +# rknn_create_mem_demo +add_executable(rknn_create_mem_demo + src/rknn_create_mem_demo.cpp +) + +target_link_libraries(rknn_create_mem_demo + ${RKNN_RT_LIB} +) + +# install target and libraries +set(CMAKE_INSTALL_PREFIX ${CMAKE_SOURCE_DIR}/install/rknn_api_demo_${CMAKE_SYSTEM_NAME}) +install(TARGETS rknn_create_mem_demo DESTINATION ./) + +# rknn_create_mem_with_rga_demo +add_executable(rknn_create_mem_with_rga_demo + src/rknn_create_mem_with_rga_demo.cpp +) + +target_link_libraries(rknn_create_mem_with_rga_demo + ${RKNN_RT_LIB} + ${RGA_LIB} +) + +# install target and libraries +set(CMAKE_INSTALL_PREFIX ${CMAKE_SOURCE_DIR}/install/rknn_api_demo_${CMAKE_SYSTEM_NAME}) +install(TARGETS rknn_create_mem_with_rga_demo DESTINATION ./) + +# At present, mmz　demo is only available under Android, but not for Linux temporarily, +# mainly because libmpimmz.so has no Linux implementation now. The API of the NPU itself supports Linux. +if(CMAKE_SYSTEM_NAME STREQUAL "Android") + # rknn_with_mmz_demo + add_executable(rknn_with_mmz_demo + src/rknn_with_mmz_demo.cpp + ) + + target_link_libraries(rknn_with_mmz_demo + ${RKNN_RT_LIB} + ${MPI_MMZ_LIB} + ) + + # install target and libraries + set(CMAKE_INSTALL_PREFIX ${CMAKE_SOURCE_DIR}/install/rknn_api_demo_${CMAKE_SYSTEM_NAME}) + install(TARGETS rknn_with_mmz_demo DESTINATION ./) + + # rknn_set_internal_mem_from_fd_demo + add_executable(rknn_set_internal_mem_from_fd_demo + src/rknn_set_internal_mem_from_fd_demo.cpp + ) + + target_link_libraries(rknn_set_internal_mem_from_fd_demo + ${RKNN_RT_LIB} + ${MPI_MMZ_LIB} + ) + + # install target and libraries + set(CMAKE_INSTALL_PREFIX ${CMAKE_SOURCE_DIR}/install/rknn_api_demo_${CMAKE_SYSTEM_NAME}) + install(TARGETS rknn_set_internal_mem_from_fd_demo DESTINATION ./) +endif() + +install(DIRECTORY model/${TARGET_SOC} DESTINATION ./model) +file(GLOB IMAGE_FILES "model/*.jpg") +install(FILES ${IMAGE_FILES} DESTINATION ./model/) +install(PROGRAMS ${RKNN_RT_LIB} DESTINATION lib) +install(PROGRAMS ${RGA_LIB} DESTINATION lib) diff --git a/rknpu2/examples/rknn_api_demo/README.md b/rknpu2/examples/rknn_api_demo/README.md new file mode 100644 index 0000000000000000000000000000000000000000..5640e48e1f48e05737732e4c5cb84bea1b7a638a --- /dev/null +++ b/rknpu2/examples/rknn_api_demo/README.md @@ -0,0 +1,81 @@ +The following represents RK3566_RK3568, RK3562, RK3576 or RK3588. +# Aarch64 Linux Demo +## Build + +First export `GCC_COMPILER`, for example `export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu`, then execute: + +``` +./build-linux.sh -t -a -b ] + +# such as: +./build-linux.sh -t rk3588 -a aarch64 -b Release +``` + +## Install + +Copy install/rknn_api_demo_Linux to the devices. + +- If you use rockchip's evb board, you can use the following way: + +Connect device and push the program and rknn model to `/userdata` + +``` +adb push install/rknn_api_demo_Linux /userdata/ +``` + +- If your board has sshd service, you can use scp or other methods to copy the program and rknn model to the board. + +## Run + +``` +adb shell +cd /userdata/rknn_api_demo_Linux/ +``` + +``` +export LD_LIBRARY_PATH=./lib +./rknn_create_mem_demo model//mobilenet_v1.rknn model/dog_224x224.jpg +./rknn_create_mem_with_rga_demo model//mobilenet_v1.rknn model/dog_224x224.jpg +``` + +# Android Demo +## Build + +First export `ANDROID_NDK_PATH`, for example `export ANDROID_NDK_PATH=~/opts/ndk/android-ndk-r18b`, then execute: + +``` +./build-android.sh -t -a [-b ] + +# sush as: +./build-android.sh -t rk3568 -a arm64-v8a -b Release +``` + +## Install + +connect device and push build output into `/data` + +``` +adb push install/rknn_api_demo_Android /data/ +``` + +## Run + +``` +adb shell +cd /data/rknn_api_demo_Android/ +``` + +``` +export LD_LIBRARY_PATH=./lib +./rknn_create_mem_demo model//mobilenet_v1.rknn model/dog_224x224.jpg +./rknn_create_mem_with_rga_demo model//mobilenet_v1.rknn model/dog_224x224.jpg +./rknn_with_mmz_demo model//mobilenet_v1.rknn model/dog_224x224.jpg +./rknn_set_internal_mem_from_fd_demo model//mobilenet_v1.rknn model/dog_224x224.jpg +``` + +# Note + - You may need to update libmpimmz.so and its header file of this project according to the implementation of MMZ in the system. + - You may need to update librga.so and its header file of this project according to the implementation of RGA in the system. https://github.com/airockchip/librga. + For rk3562, the librga version need to be 1.9.1 or higher. + - You may need to use r19c or older version of ndk for compiling with MMZ related demo. + - The test model comes from https://github.com/rockchip-linux/rknn-toolkit2/tree/master/rknn-toolkit2/examples/tflite/mobilenet_v1. \ No newline at end of file diff --git a/rknpu2/examples/rknn_api_demo/README_CN.md b/rknpu2/examples/rknn_api_demo/README_CN.md new file mode 100644 index 0000000000000000000000000000000000000000..0bfabb75b7319d488712129710b3e92a6bc3ab85 --- /dev/null +++ b/rknpu2/examples/rknn_api_demo/README_CN.md @@ -0,0 +1,78 @@ +以下 表示RK3566_RK3568、RK3562、RK3576或RK3588。 +# Aarch64 Linux 示例 +## 编译 +首先导入GCC_COMPILER，例如`export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu +`，然后执行如下命令： + +``` +./build-linux.sh -t -a -b ] + +# 例如: +./build-linux.sh -t rk3588 -a aarch64 -b Release +``` + +## 安装 + +将 install/rknn_api_demo_Linux 拷贝到设备上。 + +- 如果使用Rockchip的EVB板，可以使用以下命令： + +连接设备并将程序和模型传输到`/userdata` + +``` +adb push install/rknn_api_demo_Linux /userdata/ +``` + +- 如果你的板子有sshd服务，可以使用scp命令或者其他方式将程序和模型传输到板子上。 + +## 运行 + +``` +adb shell +cd /userdata/rknn_api_demo_Linux/ +``` + +``` +export LD_LIBRARY_PATH=./lib +./rknn_create_mem_demo model//mobilenet_v1.rknn model/dog_224x224.jpg +./rknn_create_mem_with_rga_demo model//mobilenet_v1.rknn model/dog_224x224.jpg +``` + +# Android 示例 +## 编译 +首先导入ANDROID_NDK_PATH，例如`export ANDROID_NDK_PATH=~/opts/ndk/android-ndk-r18b`，然后执行如下命令： +``` +./build-android.sh -t -a [-b ] + +# 例如: +./build-android.sh -t rk3568 -a arm64-v8a -b Release +``` + +## 安装 + +连接设备并将程序和模型传输到`/data` + +``` +adb push install/rknn_api_demo_Android /data/ +``` + +## 运行 + +``` +adb shell +cd /data/rknn_api_demo_Android/ +``` + +``` +export LD_LIBRARY_PATH=./lib +./rknn_create_mem_demo model//mobilenet_v1.rknn model/dog_224x224.jpg +./rknn_create_mem_with_rga_demo model//mobilenet_v1.rknn model/dog_224x224.jpg +./rknn_with_mmz_demo model//mobilenet_v1.rknn model/dog_224x224.jpg +./rknn_set_internal_mem_from_fd_demo model//mobilenet_v1.rknn model/dog_224x224.jpg +``` + +# 注意： +- 你可能需要依赖系统中的MMZ实现更新libmpimmz.so和头文件 +- 你可能需要依赖系统中的RGA实现更新librga.so和头文件。库地址：https://github.com/airockchip/librga。对于RK3562,librga库版本需要大于等于1.9.1 +- 你可能需要使用r19c或者更老的NDK版本编译MMZ相关的Demo +- 测试模型来源于：https://github.com/rockchip-linux/rknn-toolkit2/tree/master/rknn-toolkit2/examples/tflite/mobilenet_v1 diff --git a/rknpu2/examples/rknn_api_demo/build-android.sh b/rknpu2/examples/rknn_api_demo/build-android.sh new file mode 100755 index 0000000000000000000000000000000000000000..f256e08a7fb37b2645472d5fed717764527a32dd --- /dev/null +++ b/rknpu2/examples/rknn_api_demo/build-android.sh @@ -0,0 +1,112 @@ +#!/bin/bash + +set -e + +if [[ -z ${ANDROID_NDK_PATH} ]];then + echo "Please set ANDROID_NDK_PATH, such as export ANDROID_NDK_PATH=~/opts/ndk/android-ndk-r18b" + echo "NDK Version r18, r19 is recommanded. Other version may cause build failure." + exit +fi + +echo "$0 $@" +while getopts ":t:a:b" opt; do + case $opt in + t) + TARGET_SOC=$OPTARG + ;; + a) + TARGET_ARCH=$OPTARG + ;; + b) + BUILD_TYPE=$OPTARG + ;; + :) + echo "Option -$OPTARG requires an argument." + exit 1 + ;; + ?) + echo "Invalid option: -$OPTARG index:$OPTIND" + ;; + esac +done + +if [ -z ${TARGET_SOC} ] || [ -z ${TARGET_ARCH} ] ; then + echo "$0 -t -a [-b ]" + echo "" + echo " -t : target (rk3566/rk3568/rk3562/rk3576/rk3588)" + echo " -a : arch (arm64-v8a/armeabi-v7a)" + echo " -b : build_type (Debug/Release)" + echo "such as: $0 -t rk3588 -a arm64-v8a -b Release" + echo "" + exit -1 +fi + +# Debug / Release / RelWithDebInfo +if [[ -z ${BUILD_TYPE} ]];then + BUILD_TYPE=Release +fi + +case ${TARGET_SOC} in + rk356x) + TARGET_SOC="RK3566_RK3568" + ;; + rk3566_rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3588) + TARGET_SOC="RK3588" + ;; + rk3576) + TARGET_SOC="RK3576" + ;; + rk3566) + TARGET_SOC="RK3566_RK3568" + ;; + rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3562) + TARGET_SOC="RK3562" + ;; + *) + echo "Invalid target: ${TARGET_SOC}" + echo "Valid target: rk3562,rk3566,rk3568,rk3576,rk3588" + exit -1 + ;; +esac + +TARGET_PLATFORM=${TARGET_SOC}_android +if [[ -n ${TARGET_ARCH} ]];then +TARGET_PLATFORM=${TARGET_PLATFORM}_${TARGET_ARCH} +fi +ROOT_PWD=$( cd "$( dirname $0 )" && cd -P "$( dirname "$SOURCE" )" && pwd ) +BUILD_DIR=${ROOT_PWD}/build/build_${TARGET_PLATFORM}_${BUILD_TYPE} + +echo "===================================" +echo "TARGET_SOC=${TARGET_SOC}" +echo "TARGET_ARCH=${TARGET_ARCH}" +echo "BUILD_TYPE=${BUILD_TYPE}" +echo "BUILD_DIR=${BUILD_DIR}" +echo "ANDROID_NDK_PATH=${ANDROID_NDK_PATH}" +echo "===================================" + +if [[ ! -d "${BUILD_DIR}" ]]; then + mkdir -p ${BUILD_DIR} +fi + + +cd ${BUILD_DIR} +cmake ../.. \ + -DANDROID_TOOLCHAIN=clang \ + -DTARGET_SOC=${TARGET_SOC} \ + -DCMAKE_SYSTEM_NAME=Android \ + -DCMAKE_TOOLCHAIN_FILE=$ANDROID_NDK_PATH/build/cmake/android.toolchain.cmake \ + -DANDROID_ABI=${TARGET_ARCH} \ + -DANDROID_STL=c++_static \ + -DANDROID_PLATFORM=android-24 \ + -DCMAKE_BUILD_TYPE=${BUILD_TYPE} +# make VERBOSE=1 +make -j4 +make install +cd .. + diff --git a/rknpu2/examples/rknn_api_demo/build-linux.sh b/rknpu2/examples/rknn_api_demo/build-linux.sh new file mode 100755 index 0000000000000000000000000000000000000000..b205a1c203ccbb654d3dd130d67bc45cbd7dba72 --- /dev/null +++ b/rknpu2/examples/rknn_api_demo/build-linux.sh @@ -0,0 +1,121 @@ +#!/bin/bash + +set -e + +echo "$0 $@" +while getopts ":t:a:b" opt; do + case $opt in + t) + TARGET_SOC=$OPTARG + ;; + a) + TARGET_ARCH=$OPTARG + ;; + b) + BUILD_TYPE=$OPTARG + ;; + :) + echo "Option -$OPTARG requires an argument." + exit 1 + ;; + ?) + echo "Invalid option: -$OPTARG index:$OPTIND" + ;; + esac +done + +if [ -z ${TARGET_SOC} ];then + echo "$0 -t -a [-b ]" + echo "" + echo " -t : target (rk3566/rk3568/rk3562/rk3576/rk3588)" + echo " -a : arch (aarch64/armhf)" + echo " -b : build_type(Debug/Release)" + echo "such as: $0 -t rk3588 -a aarch64 -b Release" + echo "" + exit -1 +fi + +if [[ -z ${GCC_COMPILER} ]];then + echo "Please set GCC_COMPILER for $TARGET_SOC" + echo "such as export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu" + exit +fi +echo "$GCC_COMPILER" +export CC=${GCC_COMPILER}-gcc +export CXX=${GCC_COMPILER}-g++ + +if command -v ${CC} >/dev/null 2>&1; then + : +else + echo "${CC} is not available" + echo "Please set GCC_COMPILER for $TARGET_SOC" + echo "such as export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu" + exit +fi + +# Debug / Release +if [[ -z ${BUILD_TYPE} ]];then + BUILD_TYPE=Release +fi + + +case ${TARGET_SOC} in + rk356x) + TARGET_SOC="RK3566_RK3568" + ;; + rk3566_rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3588) + TARGET_SOC="RK3588" + ;; + rk3576) + TARGET_SOC="RK3576" + ;; + rk3566) + TARGET_SOC="RK3566_RK3568" + ;; + rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3562) + TARGET_SOC="RK3562" + ;; + *) + echo "Invalid target: ${TARGET_SOC}" + echo "Valid target: rk3562,rk3566,rk3568,rk3576,rk3588" + exit -1 + ;; +esac + +TARGET_PLATFORM=${TARGET_SOC}_linux +if [[ -n ${TARGET_ARCH} ]];then +TARGET_PLATFORM=${TARGET_PLATFORM}_${TARGET_ARCH} +fi +ROOT_PWD=$( cd "$( dirname $0 )" && cd -P "$( dirname "$SOURCE" )" && pwd ) +BUILD_DIR=${ROOT_PWD}/build/build_${TARGET_PLATFORM}_${BUILD_TYPE} + + +echo "===================================" +echo "TARGET_SOC=${TARGET_SOC}" +echo "TARGET_ARCH=${TARGET_ARCH}" +echo "BUILD_TYPE=${BUILD_TYPE}" +echo "BUILD_DIR=${BUILD_DIR}" +echo "CC=${CC}" +echo "CXX=${CXX}" +echo "===================================" + +if [[ ! -d "${BUILD_DIR}" ]]; then + mkdir -p ${BUILD_DIR} +fi + +cd ${BUILD_DIR} +cmake ../.. \ + -DTARGET_SOC=${TARGET_SOC} \ + -DCMAKE_SYSTEM_NAME=Linux \ + -DCMAKE_SYSTEM_PROCESSOR=${TARGET_ARCH} \ + -DCMAKE_BUILD_TYPE=${BUILD_TYPE} \ + -DCMAKE_C_COMPILER=${CC} \ + -DCMAKE_CXX_COMPILER=${CXX} +make -j4 +make install diff --git a/rknpu2/examples/rknn_api_demo/model/RK3562/mobilenet_v1.rknn b/rknpu2/examples/rknn_api_demo/model/RK3562/mobilenet_v1.rknn new file mode 100644 index 0000000000000000000000000000000000000000..52670451c3bd0c72733ead26be1cc69dba2db6ca --- /dev/null +++ b/rknpu2/examples/rknn_api_demo/model/RK3562/mobilenet_v1.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8a88faed454d7f33504079bef9ded09ba127b99e8bf3e28f2196d7fa1d16ae22 +size 4583849 diff --git a/rknpu2/examples/rknn_api_demo/model/RK3566_RK3568/mobilenet_v1.rknn b/rknpu2/examples/rknn_api_demo/model/RK3566_RK3568/mobilenet_v1.rknn new file mode 100644 index 0000000000000000000000000000000000000000..acfad8b2fdebd57c03fc742863516205d2ceccd7 --- /dev/null +++ b/rknpu2/examples/rknn_api_demo/model/RK3566_RK3568/mobilenet_v1.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:92b36515616fc568ddc573f07a345cf28441efe0d24ecc9b98051fe8bcfad980 +size 4470313 diff --git a/rknpu2/examples/rknn_api_demo/model/RK3576/mobilenet_v1.rknn b/rknpu2/examples/rknn_api_demo/model/RK3576/mobilenet_v1.rknn new file mode 100644 index 0000000000000000000000000000000000000000..cb229fdfe4eedddf6c82729471461b686a35e83a --- /dev/null +++ b/rknpu2/examples/rknn_api_demo/model/RK3576/mobilenet_v1.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bc66943ea85ec0dd8a04da22c4276bfc8a4c6fe24f5ea8be7a1e5c3c22c8259d +size 4679017 diff --git a/rknpu2/examples/rknn_api_demo/model/RK3588/mobilenet_v1.rknn b/rknpu2/examples/rknn_api_demo/model/RK3588/mobilenet_v1.rknn new file mode 100644 index 0000000000000000000000000000000000000000..e7c8380b1da6ac10257f0080304882c53791c340 --- /dev/null +++ b/rknpu2/examples/rknn_api_demo/model/RK3588/mobilenet_v1.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:381dae3b7038a98b10f6ec9dcdbb094a49247341856fb294e692ef518218fcfb +size 4693865 diff --git a/rknpu2/examples/rknn_api_demo/model/dog_224x224.jpg b/rknpu2/examples/rknn_api_demo/model/dog_224x224.jpg new file mode 100644 index 0000000000000000000000000000000000000000..004b0416e23454a12eb06eaba238c7e2d5f2b0fc --- /dev/null +++ b/rknpu2/examples/rknn_api_demo/model/dog_224x224.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c350299c6283d5f62fecf1f845b6b3be9aafec8dff528ca09a129990f0a584b0 +size 18889 diff --git a/rknpu2/examples/rknn_api_demo/src/rknn_create_mem_demo.cpp b/rknpu2/examples/rknn_api_demo/src/rknn_create_mem_demo.cpp new file mode 100644 index 0000000000000000000000000000000000000000..567d46a76975712c7b13812de198b0630773f468 --- /dev/null +++ b/rknpu2/examples/rknn_api_demo/src/rknn_create_mem_demo.cpp @@ -0,0 +1,355 @@ +// Copyright (c) 2021 by Rockchip Electronics Co., Ltd. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +/*------------------------------------------- + Includes +-------------------------------------------*/ +#include "rknn_api.h" + +#include +#include +#include +#include +#include + +#define STB_IMAGE_IMPLEMENTATION +#include "stb/stb_image.h" +#define STB_IMAGE_RESIZE_IMPLEMENTATION +#include + +/*------------------------------------------- + Functions +-------------------------------------------*/ +static inline int64_t getCurrentTimeUs() +{ + struct timeval tv; + gettimeofday(&tv, NULL); + return tv.tv_sec * 1000000 + tv.tv_usec; +} + +static int rknn_GetTopN(float* pfProb, float* pfMaxProb, uint32_t* pMaxClass, uint32_t outputCount, uint32_t topNum) +{ + uint32_t i, j; + uint32_t top_count = outputCount > topNum ? topNum : outputCount; + + for (i = 0; i < topNum; ++i) { + pfMaxProb[i] = -FLT_MAX; + pMaxClass[i] = -1; + } + + for (j = 0; j < top_count; j++) { + for (i = 0; i < outputCount; i++) { + if ((i == *(pMaxClass + 0)) || (i == *(pMaxClass + 1)) || (i == *(pMaxClass + 2)) || (i == *(pMaxClass + 3)) || + (i == *(pMaxClass + 4))) { + continue; + } + + if (pfProb[i] > *(pfMaxProb + j)) { + *(pfMaxProb + j) = pfProb[i]; + *(pMaxClass + j) = i; + } + } + } + + return 1; +} + +static void dump_tensor_attr(rknn_tensor_attr* attr) +{ + printf(" index=%d, name=%s, n_dims=%d, dims=[%d, %d, %d, %d], n_elems=%d, size=%d, fmt=%s, type=%s, qnt_type=%s, " + "zp=%d, scale=%f\n", + attr->index, attr->name, attr->n_dims, attr->dims[0], attr->dims[1], attr->dims[2], attr->dims[3], + attr->n_elems, attr->size, get_format_string(attr->fmt), get_type_string(attr->type), + get_qnt_type_string(attr->qnt_type), attr->zp, attr->scale); +} + +static unsigned char* load_image(const char* image_path, rknn_tensor_attr* input_attr) +{ + int req_height = 0; + int req_width = 0; + int req_channel = 0; + + switch (input_attr->fmt) { + case RKNN_TENSOR_NHWC: + req_height = input_attr->dims[1]; + req_width = input_attr->dims[2]; + req_channel = input_attr->dims[3]; + break; + case RKNN_TENSOR_NCHW: + req_height = input_attr->dims[2]; + req_width = input_attr->dims[3]; + req_channel = input_attr->dims[1]; + break; + default: + printf("meet unsupported layout\n"); + return NULL; + } + + int height = 0; + int width = 0; + int channel = 0; + + unsigned char* image_data = stbi_load(image_path, &width, &height, &channel, req_channel); + if (image_data == NULL) { + printf("load image failed!\n"); + return NULL; + } + + if (width != req_width || height != req_height) { + unsigned char* image_resized = (unsigned char*)STBI_MALLOC(req_width * req_height * req_channel); + if (!image_resized) { + printf("malloc image failed!\n"); + STBI_FREE(image_data); + return NULL; + } + if (stbir_resize_uint8(image_data, width, height, 0, image_resized, req_width, req_height, 0, channel) != 1) { + printf("resize image failed!\n"); + STBI_FREE(image_data); + return NULL; + } + STBI_FREE(image_data); + image_data = image_resized; + } + + return image_data; +} + +static unsigned char* load_model(const char* filename, int* model_size) +{ + FILE* fp = fopen(filename, "rb"); + if (fp == nullptr) { + printf("fopen %s fail!\n", filename); + return NULL; + } + fseek(fp, 0, SEEK_END); + int model_len = ftell(fp); + unsigned char* model = (unsigned char*)malloc(model_len); + fseek(fp, 0, SEEK_SET); + if (model_len != fread(model, 1, model_len, fp)) { + printf("fread %s fail!\n", filename); + free(model); + return NULL; + } + *model_size = model_len; + if (fp) { + fclose(fp); + } + return model; +} + +/*------------------------------------------- + Main Functions +-------------------------------------------*/ +int main(int argc, char* argv[]) +{ + if (argc < 3) { + printf("Usage:%s model_path input_path [loop_count]\n", argv[0]); + return -1; + } + + char* model_path = argv[1]; + char* input_path = argv[2]; + + int loop_count = 1; + if (argc > 3) { + loop_count = atoi(argv[3]); + } + + rknn_context ctx = 0; + + // Load RKNN Model + int model_len = 0; + unsigned char* model = load_model(model_path, &model_len); + int ret = rknn_init(&ctx, model, model_len, 0, NULL); + if (ret < 0) { + printf("rknn_init fail! ret=%d\n", ret); + return -1; + } + + // Get sdk and driver version + rknn_sdk_version sdk_ver; + ret = rknn_query(ctx, RKNN_QUERY_SDK_VERSION, &sdk_ver, sizeof(sdk_ver)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + + printf("rknn_api/rknnrt version: %s, driver version: %s\n", sdk_ver.api_version, sdk_ver.drv_version); + + // Get Model Input Output Info + rknn_input_output_num io_num; + ret = rknn_query(ctx, RKNN_QUERY_IN_OUT_NUM, &io_num, sizeof(io_num)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("model input num: %d, output num: %d\n", io_num.n_input, io_num.n_output); + + printf("input tensors:\n"); + rknn_tensor_attr input_attrs[io_num.n_input]; + memset(input_attrs, 0, io_num.n_input * sizeof(rknn_tensor_attr)); + for (uint32_t i = 0; i < io_num.n_input; i++) { + input_attrs[i].index = i; + // query info + ret = rknn_query(ctx, RKNN_QUERY_INPUT_ATTR, &(input_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret < 0) { + printf("rknn_init error! ret=%d\n", ret); + return -1; + } + dump_tensor_attr(&input_attrs[i]); + } + + printf("output tensors:\n"); + rknn_tensor_attr output_attrs[io_num.n_output]; + memset(output_attrs, 0, io_num.n_output * sizeof(rknn_tensor_attr)); + for (uint32_t i = 0; i < io_num.n_output; i++) { + output_attrs[i].index = i; + // query info + ret = rknn_query(ctx, RKNN_QUERY_OUTPUT_ATTR, &(output_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + dump_tensor_attr(&output_attrs[i]); + } + + // Get custom string + rknn_custom_string custom_string; + ret = rknn_query(ctx, RKNN_QUERY_CUSTOM_STRING, &custom_string, sizeof(custom_string)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("custom string: %s\n", custom_string.string); + + unsigned char* input_data = NULL; + rknn_tensor_type input_type = RKNN_TENSOR_UINT8; + rknn_tensor_format input_layout = RKNN_TENSOR_NHWC; + + // Load image + input_data = load_image(input_path, &input_attrs[0]); + + if (!input_data) { + return -1; + } + + // Create input tensor memory + rknn_tensor_mem* input_mems[1]; + // default input type is int8 (normalize and quantize need compute in outside) + // if set uint8, will fuse normalize and quantize to npu + input_attrs[0].type = input_type; + // default fmt is NHWC, npu only support NHWC in zero copy mode + input_attrs[0].fmt = input_layout; + + input_mems[0] = rknn_create_mem(ctx, input_attrs[0].size_with_stride); + + // Copy input data to input tensor memory + int width = input_attrs[0].dims[2]; + int stride = input_attrs[0].w_stride; + + if (width == stride) { + memcpy(input_mems[0]->virt_addr, input_data, width * input_attrs[0].dims[1] * input_attrs[0].dims[3]); + } else { + int height = input_attrs[0].dims[1]; + int channel = input_attrs[0].dims[3]; + // copy from src to dst with stride + uint8_t* src_ptr = input_data; + uint8_t* dst_ptr = (uint8_t*)input_mems[0]->virt_addr; + // width-channel elements + int src_wc_elems = width * channel; + int dst_wc_elems = stride * channel; + for (int h = 0; h < height; ++h) { + memcpy(dst_ptr, src_ptr, src_wc_elems); + src_ptr += src_wc_elems; + dst_ptr += dst_wc_elems; + } + } + + // Create output tensor memory + rknn_tensor_mem* output_mems[io_num.n_output]; + for (uint32_t i = 0; i < io_num.n_output; ++i) { + // default output type is depend on model, this require float32 to compute top5 + // allocate float32 output tensor + int output_size = output_attrs[i].n_elems * sizeof(float); + output_mems[i] = rknn_create_mem(ctx, output_size); + } + + // Set input tensor memory + ret = rknn_set_io_mem(ctx, input_mems[0], &input_attrs[0]); + if (ret < 0) { + printf("rknn_set_io_mem fail! ret=%d\n", ret); + return -1; + } + + // Set output tensor memory + for (uint32_t i = 0; i < io_num.n_output; ++i) { + // default output type is depend on model, this require float32 to compute top5 + output_attrs[i].type = RKNN_TENSOR_FLOAT32; + // set output memory and attribute + ret = rknn_set_io_mem(ctx, output_mems[i], &output_attrs[i]); + if (ret < 0) { + printf("rknn_set_io_mem fail! ret=%d\n", ret); + return -1; + } + } + + // Run + printf("Begin perf ...\n"); + for (int i = 0; i < loop_count; ++i) { + int64_t start_us = getCurrentTimeUs(); + ret = rknn_run(ctx, NULL); + int64_t elapse_us = getCurrentTimeUs() - start_us; + if (ret < 0) { + printf("rknn run error %d\n", ret); + return -1; + } + printf("%4d: Elapse Time = %.2fms, FPS = %.2f\n", i, elapse_us / 1000.f, 1000.f * 1000.f / elapse_us); + } + + // Get top 5 + uint32_t topNum = 5; + for (uint32_t i = 0; i < io_num.n_output; i++) { + uint32_t MaxClass[topNum]; + float fMaxProb[topNum]; + float* buffer = (float*)output_mems[i]->virt_addr; + uint32_t sz = output_attrs[i].n_elems; + int top_count = sz > topNum ? topNum : sz; + + rknn_GetTopN(buffer, fMaxProb, MaxClass, sz, topNum); + + printf("---- Top%d ----\n", top_count); + for (int j = 0; j < top_count; j++) { + printf("%8.6f - %d\n", fMaxProb[j], MaxClass[j]); + } + } + + // Destroy rknn memory + rknn_destroy_mem(ctx, input_mems[0]); + for (uint32_t i = 0; i < io_num.n_output; ++i) { + rknn_destroy_mem(ctx, output_mems[i]); + } + + // destroy + rknn_destroy(ctx); + + if (input_data != nullptr) { + free(input_data); + } + + if (model != nullptr) { + free(model); + } + + return 0; +} diff --git a/rknpu2/examples/rknn_api_demo/src/rknn_create_mem_with_rga_demo.cpp b/rknpu2/examples/rknn_api_demo/src/rknn_create_mem_with_rga_demo.cpp new file mode 100644 index 0000000000000000000000000000000000000000..4d8990c8f08fb767aec2fc67f2206e8f3afb6439 --- /dev/null +++ b/rknpu2/examples/rknn_api_demo/src/rknn_create_mem_with_rga_demo.cpp @@ -0,0 +1,352 @@ +// Copyright (c) 2021 by Rockchip Electronics Co., Ltd. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +/*------------------------------------------- + Includes +-------------------------------------------*/ + +#include +#include +#include +#include +#include + +#include "RgaUtils.h" +#include "im2d.h" +#include "rga.h" +#include "rknn_api.h" + +#define STB_IMAGE_IMPLEMENTATION +#include "stb/stb_image.h" +#define STB_IMAGE_RESIZE_IMPLEMENTATION +#include + +#define ALIGN 8 + +/*------------------------------------------- + Functions +-------------------------------------------*/ +static inline int64_t getCurrentTimeUs() +{ + struct timeval tv; + gettimeofday(&tv, NULL); + return tv.tv_sec * 1000000 + tv.tv_usec; +} + +static int rknn_GetTopN(float* pfProb, float* pfMaxProb, uint32_t* pMaxClass, uint32_t outputCount, uint32_t topNum) +{ + uint32_t i, j; + uint32_t top_count = outputCount > topNum ? topNum : outputCount; + + for (i = 0; i < topNum; ++i) { + pfMaxProb[i] = -FLT_MAX; + pMaxClass[i] = -1; + } + + for (j = 0; j < top_count; j++) { + for (i = 0; i < outputCount; i++) { + if ((i == *(pMaxClass + 0)) || (i == *(pMaxClass + 1)) || (i == *(pMaxClass + 2)) || (i == *(pMaxClass + 3)) || + (i == *(pMaxClass + 4))) { + continue; + } + + if (pfProb[i] > *(pfMaxProb + j)) { + *(pfMaxProb + j) = pfProb[i]; + *(pMaxClass + j) = i; + } + } + } + + return 1; +} + +static void dump_tensor_attr(rknn_tensor_attr* attr) +{ + printf(" index=%d, name=%s, n_dims=%d, dims=[%d, %d, %d, %d], n_elems=%d, size=%d, fmt=%s, type=%s, qnt_type=%s, " + "zp=%d, scale=%f\n", + attr->index, attr->name, attr->n_dims, attr->dims[0], attr->dims[1], attr->dims[2], attr->dims[3], + attr->n_elems, attr->size, get_format_string(attr->fmt), get_type_string(attr->type), + get_qnt_type_string(attr->qnt_type), attr->zp, attr->scale); +} + +unsigned char* load_image(const char* image_path, rknn_tensor_attr* input_attr) +{ + int req_height = 0; + int req_width = 0; + int req_channel = 0; + + switch (input_attr->fmt) { + case RKNN_TENSOR_NHWC: + req_height = input_attr->dims[1]; + req_width = input_attr->dims[2]; + req_channel = input_attr->dims[3]; + break; + case RKNN_TENSOR_NCHW: + req_height = input_attr->dims[2]; + req_width = input_attr->dims[3]; + req_channel = input_attr->dims[1]; + break; + default: + printf("meet unsupported layout\n"); + return NULL; + } + + int height = 0; + int width = 0; + int channel = 0; + + unsigned char* image_data = stbi_load(image_path, &width, &height, &channel, req_channel); + if (image_data == NULL) { + printf("load image failed!\n"); + return NULL; + } + + if (width != req_width || height != req_height) { + unsigned char* image_resized = (unsigned char*)STBI_MALLOC(req_width * req_height * req_channel); + if (!image_resized) { + printf("malloc image failed!\n"); + STBI_FREE(image_data); + return NULL; + } + if (stbir_resize_uint8(image_data, width, height, 0, image_resized, req_width, req_height, 0, channel) != 1) { + printf("resize image failed!\n"); + STBI_FREE(image_data); + return NULL; + } + STBI_FREE(image_data); + image_data = image_resized; + } + + return image_data; +} + +/*------------------------------------------- + Main Functions +-------------------------------------------*/ +int main(int argc, char* argv[]) +{ + if (argc < 3) { + printf("Usage:%s model_path input_path [loop_count]\n", argv[0]); + return -1; + } + + char* model_path = argv[1]; + char* input_path = argv[2]; + + int loop_count = 1; + if (argc > 3) { + loop_count = atoi(argv[3]); + } + + rknn_context ctx = 0; + + // init rga context + rga_buffer_t src; + rga_buffer_t dst; + im_rect src_rect; + im_rect dst_rect; + memset(&src_rect, 0, sizeof(src_rect)); + memset(&dst_rect, 0, sizeof(dst_rect)); + memset(&src, 0, sizeof(src)); + memset(&dst, 0, sizeof(dst)); + + // Load RKNN Model + int ret = rknn_init(&ctx, model_path, 0, 0, NULL); + if (ret < 0) { + printf("rknn_init fail! ret=%d\n", ret); + return -1; + } + + // Get sdk and driver version + rknn_sdk_version sdk_ver; + ret = rknn_query(ctx, RKNN_QUERY_SDK_VERSION, &sdk_ver, sizeof(sdk_ver)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("rknn_api/rknnrt version: %s, driver version: %s\n", sdk_ver.api_version, sdk_ver.drv_version); + + // Get Model Input Output Info + rknn_input_output_num io_num; + ret = rknn_query(ctx, RKNN_QUERY_IN_OUT_NUM, &io_num, sizeof(io_num)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("model input num: %d, output num: %d\n", io_num.n_input, io_num.n_output); + + printf("input tensors:\n"); + rknn_tensor_attr input_attrs[io_num.n_input]; + memset(input_attrs, 0, io_num.n_input * sizeof(rknn_tensor_attr)); + for (uint32_t i = 0; i < io_num.n_input; i++) { + input_attrs[i].index = i; + // query info + ret = rknn_query(ctx, RKNN_QUERY_INPUT_ATTR, &(input_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret < 0) { + printf("rknn_init error! ret=%d\n", ret); + return -1; + } + dump_tensor_attr(&input_attrs[i]); + } + + printf("output tensors:\n"); + rknn_tensor_attr output_attrs[io_num.n_output]; + memset(output_attrs, 0, io_num.n_output * sizeof(rknn_tensor_attr)); + for (uint32_t i = 0; i < io_num.n_output; i++) { + output_attrs[i].index = i; + // query info + ret = rknn_query(ctx, RKNN_QUERY_OUTPUT_ATTR, &(output_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + dump_tensor_attr(&output_attrs[i]); + } + + // Get custom string + rknn_custom_string custom_string; + ret = rknn_query(ctx, RKNN_QUERY_CUSTOM_STRING, &custom_string, sizeof(custom_string)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("custom string: %s\n", custom_string.string); + + unsigned char* input_data = NULL; + rknn_tensor_type input_type = RKNN_TENSOR_UINT8; + rknn_tensor_format input_layout = RKNN_TENSOR_NHWC; + + int img_input_height = 0; + int img_input_width = 0; + int channel = 0; + input_data = stbi_load(input_path, &img_input_height, &img_input_width, &channel, 3); + int model_in_height = 0; + int model_in_width = 0; + int req_channel = 0; + switch (input_attrs[0].fmt) { + case RKNN_TENSOR_NHWC: + model_in_height = input_attrs[0].dims[1]; + model_in_width = input_attrs[0].dims[2]; + req_channel = input_attrs[0].dims[3]; + break; + case RKNN_TENSOR_NCHW: + model_in_height = input_attrs[0].dims[2]; + model_in_width = input_attrs[0].dims[3]; + req_channel = input_attrs[0].dims[1]; + break; + default: + printf("meet unsupported layout\n"); + return -1; + } + + if (!input_data) { + printf("load image failed!\n"); + return -1; + } + + src = wrapbuffer_virtualaddr((void*)input_data, img_input_width, img_input_height, + RK_FORMAT_RGB_888); // wstride, hstride, + + // Create input tensor memory + rknn_tensor_mem* input_mems[1]; + // default input type is int8 (normalize and quantize need compute in outside) + // if set uint8, will fuse normalize and quantize to npu + input_attrs[0].type = input_type; + // default fmt is NHWC, npu only support NHWC in zero copy mode + input_attrs[0].fmt = input_layout; + input_mems[0] = rknn_create_mem(ctx, input_attrs[0].size_with_stride); + // Copy input data to input tensor memory + // memcpy(input_mems[0]->virt_addr, input_data, input_attrs[0].size); + + int wstride = model_in_width + (ALIGN - model_in_width % ALIGN) % ALIGN; + int hstride = model_in_height; + dst = wrapbuffer_fd_t(input_mems[0]->fd, model_in_width, model_in_height, wstride, hstride, + RK_FORMAT_RGB_888); // wstride, hstride, + + ret = imcheck(src, dst, src_rect, dst_rect); + if (IM_STATUS_NOERROR != ret) { + printf("%d, check error! %s", __LINE__, imStrError((IM_STATUS)ret)); + return -1; + } + + IM_STATUS STATUS = imresize(src, dst); + + // Create output tensor memory + rknn_tensor_mem* output_mems[io_num.n_output]; + for (uint32_t i = 0; i < io_num.n_output; ++i) { + int output_size = output_attrs[i].n_elems * sizeof(float); + // default output type is depend on model, this require float32 to compute top5 + output_attrs[i].type = RKNN_TENSOR_FLOAT32; + output_mems[i] = rknn_create_mem(ctx, output_size); + } + + // Set input tensor memory + ret = rknn_set_io_mem(ctx, input_mems[0], &input_attrs[0]); + if (ret < 0) { + printf("rknn_set_io_mem fail! ret=%d\n", ret); + return -1; + } + + // Set output tensor memory + for (uint32_t i = 0; i < io_num.n_output; ++i) { + ret = rknn_set_io_mem(ctx, output_mems[i], &output_attrs[i]); + if (ret < 0) { + printf("rknn_set_io_mem fail! ret=%d\n", ret); + return -1; + } + } + + // Run + printf("Begin perf ...\n"); + for (int i = 0; i < loop_count; ++i) { + int64_t start_us = getCurrentTimeUs(); + ret = rknn_run(ctx, NULL); + int64_t elapse_us = getCurrentTimeUs() - start_us; + if (ret < 0) { + printf("rknn run error %d\n", ret); + return -1; + } + printf("%4d: Elapse Time = %.2fms, FPS = %.2f\n", i, elapse_us / 1000.f, 1000.f * 1000.f / elapse_us); + } + + // Get top 5 + uint32_t topNum = 5; + for (uint32_t i = 0; i < io_num.n_output; i++) { + uint32_t MaxClass[topNum]; + float fMaxProb[topNum]; + float* buffer = (float*)output_mems[i]->virt_addr; + uint32_t sz = output_attrs[i].n_elems; + int top_count = sz > topNum ? topNum : sz; + + rknn_GetTopN(buffer, fMaxProb, MaxClass, sz, topNum); + + printf("---- Top%d ----\n", top_count); + for (int j = 0; j < top_count; j++) { + printf("%8.6f - %d\n", fMaxProb[j], MaxClass[j]); + } + } + + // Destroy rknn memory + rknn_destroy_mem(ctx, input_mems[0]); + for (uint32_t i = 0; i < io_num.n_output; ++i) { + rknn_destroy_mem(ctx, output_mems[i]); + } + + // destroy + rknn_destroy(ctx); + + free(input_data); + + return 0; +} diff --git a/rknpu2/examples/rknn_api_demo/src/rknn_set_internal_mem_from_fd_demo.cpp b/rknpu2/examples/rknn_api_demo/src/rknn_set_internal_mem_from_fd_demo.cpp new file mode 100755 index 0000000000000000000000000000000000000000..593ebcbd42c32feacdabf376f7f8be310081fad8 --- /dev/null +++ b/rknpu2/examples/rknn_api_demo/src/rknn_set_internal_mem_from_fd_demo.cpp @@ -0,0 +1,436 @@ +// Copyright (c) 2021 by Rockchip Electronics Co., Ltd. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +/*------------------------------------------- + Includes +-------------------------------------------*/ +#include "rk_mpi_mmz.h" +#include "rknn_api.h" + +#include +#include +#include +#include +#include + +#define STB_IMAGE_IMPLEMENTATION +#include "stb/stb_image.h" +#define STB_IMAGE_RESIZE_IMPLEMENTATION +#include + +/*------------------------------------------- + Functions +-------------------------------------------*/ +static inline int64_t getCurrentTimeUs() +{ + struct timeval tv; + gettimeofday(&tv, NULL); + return tv.tv_sec * 1000000 + tv.tv_usec; +} + +static int rknn_GetTopN(float* pfProb, float* pfMaxProb, uint32_t* pMaxClass, uint32_t outputCount, uint32_t topNum) +{ + uint32_t i, j; + uint32_t top_count = outputCount > topNum ? topNum : outputCount; + + for (i = 0; i < topNum; ++i) { + pfMaxProb[i] = -FLT_MAX; + pMaxClass[i] = -1; + } + + for (j = 0; j < top_count; j++) { + for (i = 0; i < outputCount; i++) { + if ((i == *(pMaxClass + 0)) || (i == *(pMaxClass + 1)) || (i == *(pMaxClass + 2)) || (i == *(pMaxClass + 3)) || + (i == *(pMaxClass + 4))) { + continue; + } + + if (pfProb[i] > *(pfMaxProb + j)) { + *(pfMaxProb + j) = pfProb[i]; + *(pMaxClass + j) = i; + } + } + } + + return 1; +} + +static void dump_tensor_attr(rknn_tensor_attr* attr) +{ + printf(" index=%d, name=%s, n_dims=%d, dims=[%d, %d, %d, %d], n_elems=%d, size=%d, fmt=%s, type=%s, qnt_type=%s, " + "zp=%d, scale=%f\n", + attr->index, attr->name, attr->n_dims, attr->dims[0], attr->dims[1], attr->dims[2], attr->dims[3], + attr->n_elems, attr->size, get_format_string(attr->fmt), get_type_string(attr->type), + get_qnt_type_string(attr->qnt_type), attr->zp, attr->scale); +} + +static unsigned char* load_image(const char* image_path, rknn_tensor_attr* input_attr) +{ + int req_height = 0; + int req_width = 0; + int req_channel = 0; + + switch (input_attr->fmt) { + case RKNN_TENSOR_NHWC: + req_height = input_attr->dims[1]; + req_width = input_attr->dims[2]; + req_channel = input_attr->dims[3]; + break; + case RKNN_TENSOR_NCHW: + req_height = input_attr->dims[2]; + req_width = input_attr->dims[3]; + req_channel = input_attr->dims[1]; + break; + default: + printf("meet unsupported layout\n"); + return NULL; + } + + int height = 0; + int width = 0; + int channel = 0; + + unsigned char* image_data = stbi_load(image_path, &width, &height, &channel, req_channel); + if (image_data == NULL) { + printf("load image failed!\n"); + return NULL; + } + + if (width != req_width || height != req_height) { + unsigned char* image_resized = (unsigned char*)STBI_MALLOC(req_width * req_height * req_channel); + if (!image_resized) { + printf("malloc image failed!\n"); + STBI_FREE(image_data); + return NULL; + } + if (stbir_resize_uint8(image_data, width, height, 0, image_resized, req_width, req_height, 0, channel) != 1) { + printf("resize image failed!\n"); + STBI_FREE(image_data); + return NULL; + } + STBI_FREE(image_data); + image_data = image_resized; + } + + return image_data; +} + +/*------------------------------------------- + Main Functions +-------------------------------------------*/ +int main(int argc, char* argv[]) +{ + if (argc < 3) { + printf("Usage:%s model_path input_path [loop_count]\n", argv[0]); + return -1; + } + + char* model_path = argv[1]; + char* input_path = argv[2]; + + int loop_count = 1; + if (argc > 3) { + loop_count = atoi(argv[3]); + } + + rknn_context ctx = 0; + + // Load RKNN Model + int ret = rknn_init(&ctx, model_path, 0, RKNN_FLAG_MEM_ALLOC_OUTSIDE, NULL); + if (ret < 0) { + printf("rknn_init fail! ret=%d\n", ret); + return -1; + } + + // Get sdk and driver version + rknn_sdk_version sdk_ver; + ret = rknn_query(ctx, RKNN_QUERY_SDK_VERSION, &sdk_ver, sizeof(sdk_ver)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("rknn_api/rknnrt version: %s, driver version: %s\n", sdk_ver.api_version, sdk_ver.drv_version); + + // Get weight and internal mem size + rknn_mem_size mem_size; + ret = rknn_query(ctx, RKNN_QUERY_MEM_SIZE, &mem_size, sizeof(mem_size)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("total weight size: %d, total internal size: %d\n", mem_size.total_weight_size, mem_size.total_internal_size); + + // Get Model Input Output Info + rknn_input_output_num io_num; + ret = rknn_query(ctx, RKNN_QUERY_IN_OUT_NUM, &io_num, sizeof(io_num)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("model input num: %d, output num: %d\n", io_num.n_input, io_num.n_output); + + printf("input tensors:\n"); + rknn_tensor_attr input_attrs[io_num.n_input]; + memset(input_attrs, 0, io_num.n_input * sizeof(rknn_tensor_attr)); + for (uint32_t i = 0; i < io_num.n_input; i++) { + input_attrs[i].index = i; + // query info + ret = rknn_query(ctx, RKNN_QUERY_INPUT_ATTR, &(input_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret < 0) { + printf("rknn_init error! ret=%d\n", ret); + return -1; + } + dump_tensor_attr(&input_attrs[i]); + } + + printf("output tensors:\n"); + rknn_tensor_attr output_attrs[io_num.n_output]; + memset(output_attrs, 0, io_num.n_output * sizeof(rknn_tensor_attr)); + for (uint32_t i = 0; i < io_num.n_output; i++) { + output_attrs[i].index = i; + // query info + ret = rknn_query(ctx, RKNN_QUERY_OUTPUT_ATTR, &(output_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + dump_tensor_attr(&output_attrs[i]); + } + + // Get custom string + rknn_custom_string custom_string; + ret = rknn_query(ctx, RKNN_QUERY_CUSTOM_STRING, &custom_string, sizeof(custom_string)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("custom string: %s\n", custom_string.string); + + unsigned char* input_data = NULL; + rknn_tensor_type input_type = RKNN_TENSOR_UINT8; + rknn_tensor_format input_layout = RKNN_TENSOR_NHWC; + + // Load image + input_data = load_image(input_path, &input_attrs[0]); + + if (!input_data) { + return -1; + } + + int mb_flags = RK_MMZ_ALLOC_TYPE_CMA | RK_MMZ_ALLOC_UNCACHEABLE; + + // Allocate weight memory in outside + MB_BLK weight_mb; + rknn_tensor_mem* weight_mem; + ret = RK_MPI_MMZ_Alloc(&weight_mb, mem_size.total_weight_size, mb_flags); + if (ret < 0) { + printf("RK_MPI_MMZ_Alloc failed, ret: %d\n", ret); + return ret; + } + void* weight_virt = RK_MPI_MMZ_Handle2VirAddr(weight_mb); + if (weight_virt == NULL) { + printf("RK_MPI_MMZ_Handle2VirAddr failed!\n"); + return -1; + } + int weight_fd = RK_MPI_MMZ_Handle2Fd(weight_mb); + if (weight_fd < 0) { + printf("RK_MPI_MMZ_Handle2Fd failed!\n"); + return -1; + } + weight_mem = rknn_create_mem_from_fd(ctx, weight_fd, weight_virt, mem_size.total_weight_size, 0); + ret = rknn_set_weight_mem(ctx, weight_mem); + if (ret < 0) { + printf("rknn_set_weight_mem fail! ret=%d\n", ret); + return -1; + } + printf("weight mb info: virt = %p, fd = %d, size: %d\n", weight_virt, weight_fd, mem_size.total_weight_size); + + // Allocate internal memory in outside + MB_BLK internal_mb; + rknn_tensor_mem* internal_mem; + ret = RK_MPI_MMZ_Alloc(&internal_mb, mem_size.total_internal_size, mb_flags); + if (ret < 0) { + printf("RK_MPI_MMZ_Alloc failed, ret: %d\n", ret); + return ret; + } + void* internal_virt = RK_MPI_MMZ_Handle2VirAddr(internal_mb); + if (internal_virt == NULL) { + printf("RK_MPI_MMZ_Handle2VirAddr failed!\n"); + return -1; + } + int internal_fd = RK_MPI_MMZ_Handle2Fd(internal_mb); + if (internal_fd < 0) { + printf("RK_MPI_MMZ_Handle2Fd failed!\n"); + return -1; + } + internal_mem = rknn_create_mem_from_fd(ctx, internal_fd, internal_virt, mem_size.total_internal_size, 0); + ret = rknn_set_internal_mem(ctx, internal_mem); + if (ret < 0) { + printf("rknn_set_internal_mem fail! ret=%d\n", ret); + return -1; + } + printf("internal mb info: virt = %p, fd = %d, size: %d\n", internal_virt, internal_fd, mem_size.total_internal_size); + + // Allocate input memory in outside + MB_BLK input_mb; + int input_size = input_attrs[0].size_with_stride; + ret = RK_MPI_MMZ_Alloc(&input_mb, input_size, mb_flags); + if (ret < 0) { + printf("RK_MPI_MMZ_Alloc failed, ret: %d\n", ret); + return ret; + } + void* input_virt = RK_MPI_MMZ_Handle2VirAddr(input_mb); + if (input_virt == NULL) { + printf("RK_MPI_MMZ_Handle2VirAddr failed!\n"); + return -1; + } + int input_fd = RK_MPI_MMZ_Handle2Fd(input_mb); + if (input_fd < 0) { + printf("RK_MPI_MMZ_Handle2Fd failed!\n"); + return -1; + } + printf("input mb info: virt = %p, fd = %d, size: %d\n", input_virt, input_fd, input_size); + + // Allocate outputs memory in outside + MB_BLK output_mbs[io_num.n_output]; + void* output_virts[io_num.n_output]; + int output_fds[io_num.n_output]; + for (uint32_t i = 0; i < io_num.n_output; ++i) { + // default output type is depend on model, this require float32 to compute top5 + output_attrs[i].type = RKNN_TENSOR_FLOAT32; + int output_size = output_attrs[i].n_elems * sizeof(float); + output_attrs[i].size = output_size; + ret = RK_MPI_MMZ_Alloc(&output_mbs[i], output_size, mb_flags); + if (ret < 0) { + printf("RK_MPI_MMZ_Alloc failed, ret: %d\n", ret); + return ret; + } + output_virts[i] = RK_MPI_MMZ_Handle2VirAddr(output_mbs[i]); + if (output_virts[i] == NULL) { + printf("RK_MPI_MMZ_Handle2VirAddr failed!\n"); + return -1; + } + output_fds[i] = RK_MPI_MMZ_Handle2Fd(output_mbs[i]); + if (output_fds[i] < 0) { + printf("RK_MPI_MMZ_Handle2Fd failed!\n"); + return -1; + } + printf("output%d mb info: virt = %p, fd = %d, size = %d\n", i, output_virts[i], output_fds[i], output_size); + } + + // Create input tensor memory + rknn_tensor_mem* input_mems[1]; + // default input type is int8 (normalize and quantize need compute in outside) + // if set uint8, will fuse normalize and quantize to npu + input_attrs[0].type = input_type; + // default fmt is NHWC, npu only support NHWC in zero copy mode + input_attrs[0].fmt = input_layout; + + input_mems[0] = rknn_create_mem_from_fd(ctx, input_fd, input_virt, input_attrs[0].size_with_stride, 0); + + // Copy input data to input tensor memory + int width = input_attrs[0].dims[2]; + int stride = input_attrs[0].w_stride; + if (width == stride) { + memcpy(input_mems[0]->virt_addr, input_data, width * input_attrs[0].dims[1] * input_attrs[0].dims[3]); + } else { + int height = input_attrs[0].dims[1]; + int channel = input_attrs[0].dims[3]; + // copy from src to dst with stride + uint8_t* src_ptr = input_data; + uint8_t* dst_ptr = (uint8_t*)input_mems[0]->virt_addr; + // width-channel elements + int src_wc_elems = width * channel; + int dst_wc_elems = stride * channel; + for (int h = 0; h < height; ++h) { + memcpy(dst_ptr, src_ptr, src_wc_elems); + src_ptr += src_wc_elems; + dst_ptr += dst_wc_elems; + } + } + + // Create output tensor memory + rknn_tensor_mem* output_mems[io_num.n_output]; + for (uint32_t i = 0; i < io_num.n_output; ++i) { + output_mems[i] = rknn_create_mem_from_fd(ctx, output_fds[i], output_virts[i], output_attrs[i].size, 0); + } + + // Set input tensor memory + ret = rknn_set_io_mem(ctx, input_mems[0], &input_attrs[0]); + if (ret < 0) { + printf("rknn_set_io_mem fail! ret=%d\n", ret); + return -1; + } + + // Set output tensor memory + for (uint32_t i = 0; i < io_num.n_output; ++i) { + // set output memory and attribute + ret = rknn_set_io_mem(ctx, output_mems[i], &output_attrs[i]); + if (ret < 0) { + printf("rknn_set_io_mem fail! ret=%d\n", ret); + return -1; + } + } + + // Run + printf("Begin perf ...\n"); + for (int i = 0; i < loop_count; ++i) { + int64_t start_us = getCurrentTimeUs(); + ret = rknn_run(ctx, NULL); + int64_t elapse_us = getCurrentTimeUs() - start_us; + if (ret < 0) { + printf("rknn run error %d\n", ret); + return -1; + } + printf("%4d: Elapse Time = %.2fms, FPS = %.2f\n", i, elapse_us / 1000.f, 1000.f * 1000.f / elapse_us); + } + + // Get top 5 + uint32_t topNum = 5; + for (uint32_t i = 0; i < io_num.n_output; i++) { + uint32_t MaxClass[topNum]; + float fMaxProb[topNum]; + float* buffer = (float*)output_mems[i]->virt_addr; + uint32_t sz = output_attrs[i].n_elems; + int top_count = sz > topNum ? topNum : sz; + + rknn_GetTopN(buffer, fMaxProb, MaxClass, sz, topNum); + + printf("---- Top%d ----\n", top_count); + for (int j = 0; j < top_count; j++) { + printf("%8.6f - %d\n", fMaxProb[j], MaxClass[j]); + } + } + + // free mb blk memory + RK_MPI_MMZ_Free(weight_mb); + RK_MPI_MMZ_Free(internal_mb); + RK_MPI_MMZ_Free(input_mb); + for (uint32_t i = 0; i < io_num.n_output; ++i) { + RK_MPI_MMZ_Free(output_mbs[i]); + } + + // Destroy rknn memory + rknn_destroy_mem(ctx, input_mems[0]); + for (uint32_t i = 0; i < io_num.n_output; ++i) { + rknn_destroy_mem(ctx, output_mems[i]); + } + + // destroy + rknn_destroy(ctx); + + free(input_data); + + return 0; +} diff --git a/rknpu2/examples/rknn_api_demo/src/rknn_with_mmz_demo.cpp b/rknpu2/examples/rknn_api_demo/src/rknn_with_mmz_demo.cpp new file mode 100755 index 0000000000000000000000000000000000000000..69a7d6e91a1e6f4f960e8b3ae732db8774738d7b --- /dev/null +++ b/rknpu2/examples/rknn_api_demo/src/rknn_with_mmz_demo.cpp @@ -0,0 +1,373 @@ +// Copyright (c) 2021 by Rockchip Electronics Co., Ltd. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +/*------------------------------------------- + Includes +-------------------------------------------*/ +#include "rk_mpi_mmz.h" +#include "rknn_api.h" + +#include +#include +#include +#include +#include + +#define STB_IMAGE_IMPLEMENTATION +#include "stb/stb_image.h" +#define STB_IMAGE_RESIZE_IMPLEMENTATION +#include + +/*------------------------------------------- + Functions +-------------------------------------------*/ +static inline int64_t getCurrentTimeUs() +{ + struct timeval tv; + gettimeofday(&tv, NULL); + return tv.tv_sec * 1000000 + tv.tv_usec; +} + +static int rknn_GetTopN(float* pfProb, float* pfMaxProb, uint32_t* pMaxClass, uint32_t outputCount, uint32_t topNum) +{ + uint32_t i, j; + uint32_t top_count = outputCount > topNum ? topNum : outputCount; + + for (i = 0; i < topNum; ++i) { + pfMaxProb[i] = -FLT_MAX; + pMaxClass[i] = -1; + } + + for (j = 0; j < top_count; j++) { + for (i = 0; i < outputCount; i++) { + if ((i == *(pMaxClass + 0)) || (i == *(pMaxClass + 1)) || (i == *(pMaxClass + 2)) || (i == *(pMaxClass + 3)) || + (i == *(pMaxClass + 4))) { + continue; + } + + if (pfProb[i] > *(pfMaxProb + j)) { + *(pfMaxProb + j) = pfProb[i]; + *(pMaxClass + j) = i; + } + } + } + + return 1; +} + +static void dump_tensor_attr(rknn_tensor_attr* attr) +{ + printf(" index=%d, name=%s, n_dims=%d, dims=[%d, %d, %d, %d], n_elems=%d, size=%d, fmt=%s, type=%s, qnt_type=%s, " + "zp=%d, scale=%f\n", + attr->index, attr->name, attr->n_dims, attr->dims[0], attr->dims[1], attr->dims[2], attr->dims[3], + attr->n_elems, attr->size, get_format_string(attr->fmt), get_type_string(attr->type), + get_qnt_type_string(attr->qnt_type), attr->zp, attr->scale); +} + +static unsigned char* load_image(const char* image_path, rknn_tensor_attr* input_attr) +{ + int req_height = 0; + int req_width = 0; + int req_channel = 0; + + switch (input_attr->fmt) { + case RKNN_TENSOR_NHWC: + req_height = input_attr->dims[1]; + req_width = input_attr->dims[2]; + req_channel = input_attr->dims[3]; + break; + case RKNN_TENSOR_NCHW: + req_height = input_attr->dims[2]; + req_width = input_attr->dims[3]; + req_channel = input_attr->dims[1]; + break; + default: + printf("meet unsupported layout\n"); + return NULL; + } + + int height = 0; + int width = 0; + int channel = 0; + + unsigned char* image_data = stbi_load(image_path, &width, &height, &channel, req_channel); + if (image_data == NULL) { + printf("load image failed!\n"); + return NULL; + } + + if (width != req_width || height != req_height) { + unsigned char* image_resized = (unsigned char*)STBI_MALLOC(req_width * req_height * req_channel); + if (!image_resized) { + printf("malloc image failed!\n"); + STBI_FREE(image_data); + return NULL; + } + if (stbir_resize_uint8(image_data, width, height, 0, image_resized, req_width, req_height, 0, channel) != 1) { + printf("resize image failed!\n"); + STBI_FREE(image_data); + return NULL; + } + STBI_FREE(image_data); + image_data = image_resized; + } + + return image_data; +} + +/*------------------------------------------- + Main Functions +-------------------------------------------*/ +int main(int argc, char* argv[]) +{ + if (argc < 3) { + printf("Usage:%s model_path input_path [loop_count]\n", argv[0]); + return -1; + } + + char* model_path = argv[1]; + char* input_path = argv[2]; + + int loop_count = 1; + if (argc > 3) { + loop_count = atoi(argv[3]); + } + + rknn_context ctx = 0; + + // Load RKNN Model + int ret = rknn_init(&ctx, model_path, 0, 0, NULL); + if (ret < 0) { + printf("rknn_init fail! ret=%d\n", ret); + return -1; + } + + // Get sdk and driver version + rknn_sdk_version sdk_ver; + ret = rknn_query(ctx, RKNN_QUERY_SDK_VERSION, &sdk_ver, sizeof(sdk_ver)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("rknn_api/rknnrt version: %s, driver version: %s\n", sdk_ver.api_version, sdk_ver.drv_version); + + // Get Model Input Output Info + rknn_input_output_num io_num; + ret = rknn_query(ctx, RKNN_QUERY_IN_OUT_NUM, &io_num, sizeof(io_num)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("model input num: %d, output num: %d\n", io_num.n_input, io_num.n_output); + + printf("input tensors:\n"); + rknn_tensor_attr input_attrs[io_num.n_input]; + memset(input_attrs, 0, io_num.n_input * sizeof(rknn_tensor_attr)); + for (uint32_t i = 0; i < io_num.n_input; i++) { + input_attrs[i].index = i; + // query info + ret = rknn_query(ctx, RKNN_QUERY_INPUT_ATTR, &(input_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret < 0) { + printf("rknn_init error! ret=%d\n", ret); + return -1; + } + dump_tensor_attr(&input_attrs[i]); + } + + printf("output tensors:\n"); + rknn_tensor_attr output_attrs[io_num.n_output]; + memset(output_attrs, 0, io_num.n_output * sizeof(rknn_tensor_attr)); + for (uint32_t i = 0; i < io_num.n_output; i++) { + output_attrs[i].index = i; + // query info + ret = rknn_query(ctx, RKNN_QUERY_OUTPUT_ATTR, &(output_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + dump_tensor_attr(&output_attrs[i]); + } + + // Get custom string + rknn_custom_string custom_string; + ret = rknn_query(ctx, RKNN_QUERY_CUSTOM_STRING, &custom_string, sizeof(custom_string)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("custom string: %s\n", custom_string.string); + + unsigned char* input_data = NULL; + rknn_tensor_type input_type = RKNN_TENSOR_UINT8; + rknn_tensor_format input_layout = RKNN_TENSOR_NHWC; + + // Load image + input_data = load_image(input_path, &input_attrs[0]); + + if (!input_data) { + return -1; + } + + int mb_flags = RK_MMZ_ALLOC_TYPE_CMA | RK_MMZ_ALLOC_UNCACHEABLE; + + // Allocate input memory in outside + MB_BLK input_mb; + int input_size = input_attrs[0].size_with_stride; + ret = RK_MPI_MMZ_Alloc(&input_mb, input_size, mb_flags); + if (ret < 0) { + printf("RK_MPI_MMZ_Alloc failed, ret: %d\n", ret); + return ret; + } + void* input_virt = RK_MPI_MMZ_Handle2VirAddr(input_mb); + if (input_virt == NULL) { + printf("RK_MPI_MMZ_Handle2VirAddr failed!\n"); + return -1; + } + uint64_t input_phys = RK_MPI_MMZ_Handle2PhysAddr(input_mb); + if (input_phys == 0) { + printf("RK_MPI_MMZ_Handle2PhysAddr failed!\n"); + return -1; + } + printf("input mb info: virt = %p, phys = %#lx, size: %d\n", input_virt, input_phys, input_size); + + // Allocate outputs memory in outside + MB_BLK output_mbs[io_num.n_output]; + void* output_virts[io_num.n_output]; + uint64_t output_physs[io_num.n_output]; + for (uint32_t i = 0; i < io_num.n_output; ++i) { + // default output type is depend on model, this require float32 to compute top5 + output_attrs[i].type = RKNN_TENSOR_FLOAT32; + int output_size = output_attrs[i].n_elems * sizeof(float); + output_attrs[i].size = output_size; + ret = RK_MPI_MMZ_Alloc(&output_mbs[i], output_size, mb_flags); + if (ret < 0) { + printf("RK_MPI_MMZ_Alloc failed, ret: %d\n", ret); + return ret; + } + output_virts[i] = RK_MPI_MMZ_Handle2VirAddr(output_mbs[i]); + if (output_virts[i] == NULL) { + printf("RK_MPI_MMZ_Handle2VirAddr failed!\n"); + return -1; + } + output_physs[i] = RK_MPI_MMZ_Handle2PhysAddr(output_mbs[i]); + if (output_physs[i] == 0) { + printf("RK_MPI_MMZ_Handle2PhysAddr failed!\n"); + return -1; + } + printf("output%d mb info: virt = %p, phys = %#lx, size = %d\n", i, output_virts[i], output_physs[i], output_size); + } + + // Create input tensor memory + rknn_tensor_mem* input_mems[1]; + // default input type is int8 (normalize and quantize need compute in outside) + // if set uint8, will fuse normalize and quantize to npu + input_attrs[0].type = input_type; + // default fmt is NHWC, npu only support NHWC in zero copy mode + input_attrs[0].fmt = input_layout; + + input_mems[0] = rknn_create_mem_from_phys(ctx, input_phys, input_virt, input_attrs[0].size_with_stride); + + // Copy input data to input tensor memory + int width = input_attrs[0].dims[2]; + int stride = input_attrs[0].w_stride; + if (width == stride) { + memcpy(input_mems[0]->virt_addr, input_data, width * input_attrs[0].dims[1] * input_attrs[0].dims[3]); + } else { + int height = input_attrs[0].dims[1]; + int channel = input_attrs[0].dims[3]; + // copy from src to dst with stride + uint8_t* src_ptr = input_data; + uint8_t* dst_ptr = (uint8_t*)input_mems[0]->virt_addr; + // width-channel elements + int src_wc_elems = width * channel; + int dst_wc_elems = stride * channel; + for (int h = 0; h < height; ++h) { + memcpy(dst_ptr, src_ptr, src_wc_elems); + src_ptr += src_wc_elems; + dst_ptr += dst_wc_elems; + } + } + + // Create output tensor memory + rknn_tensor_mem* output_mems[io_num.n_output]; + for (uint32_t i = 0; i < io_num.n_output; ++i) { + output_mems[i] = rknn_create_mem_from_phys(ctx, output_physs[i], output_virts[i], output_attrs[i].size); + } + + // Set input tensor memory + ret = rknn_set_io_mem(ctx, input_mems[0], &input_attrs[0]); + if (ret < 0) { + printf("rknn_set_io_mem fail! ret=%d\n", ret); + return -1; + } + + // Set output tensor memory + for (uint32_t i = 0; i < io_num.n_output; ++i) { + // set output memory and attribute + ret = rknn_set_io_mem(ctx, output_mems[i], &output_attrs[i]); + if (ret < 0) { + printf("rknn_set_io_mem fail! ret=%d\n", ret); + return -1; + } + } + + // Run + printf("Begin perf ...\n"); + for (int i = 0; i < loop_count; ++i) { + int64_t start_us = getCurrentTimeUs(); + ret = rknn_run(ctx, NULL); + int64_t elapse_us = getCurrentTimeUs() - start_us; + if (ret < 0) { + printf("rknn run error %d\n", ret); + return -1; + } + printf("%4d: Elapse Time = %.2fms, FPS = %.2f\n", i, elapse_us / 1000.f, 1000.f * 1000.f / elapse_us); + } + + // Get top 5 + uint32_t topNum = 5; + for (uint32_t i = 0; i < io_num.n_output; i++) { + uint32_t MaxClass[topNum]; + float fMaxProb[topNum]; + float* buffer = (float*)output_mems[i]->virt_addr; + uint32_t sz = output_attrs[i].n_elems; + int top_count = sz > topNum ? topNum : sz; + + rknn_GetTopN(buffer, fMaxProb, MaxClass, sz, topNum); + + printf("---- Top%d ----\n", top_count); + for (int j = 0; j < top_count; j++) { + printf("%8.6f - %d\n", fMaxProb[j], MaxClass[j]); + } + } + + // free mb blk memory + RK_MPI_MMZ_Free(input_mb); + for (uint32_t i = 0; i < io_num.n_output; ++i) { + RK_MPI_MMZ_Free(output_mbs[i]); + } + + // Destroy rknn memory + rknn_destroy_mem(ctx, input_mems[0]); + for (uint32_t i = 0; i < io_num.n_output; ++i) { + rknn_destroy_mem(ctx, output_mems[i]); + } + + // destroy + rknn_destroy(ctx); + + free(input_data); + + return 0; +} diff --git a/rknpu2/examples/rknn_benchmark/.clang-format b/rknpu2/examples/rknn_benchmark/.clang-format new file mode 100644 index 0000000000000000000000000000000000000000..4531f095ab2a9824b7fa360485318f9c76b05860 --- /dev/null +++ b/rknpu2/examples/rknn_benchmark/.clang-format @@ -0,0 +1,128 @@ +--- +Language: Cpp +# BasedOnStyle: LLVM +AccessModifierOffset: -2 +AlignAfterOpenBracket: Align +AlignConsecutiveAssignments: true +AlignConsecutiveDeclarations: true +AlignEscapedNewlines: Left +AlignOperands: true +AlignTrailingComments: true +AllowAllParametersOfDeclarationOnNextLine: false +AllowShortBlocksOnASingleLine: false +AllowShortCaseLabelsOnASingleLine: false +AllowShortFunctionsOnASingleLine: All +AllowShortIfStatementsOnASingleLine: false +AllowShortLoopsOnASingleLine: false +AlwaysBreakAfterDefinitionReturnType: None +AlwaysBreakAfterReturnType: None +AlwaysBreakBeforeMultilineStrings: false +AlwaysBreakTemplateDeclarations: true +BinPackArguments: true +BinPackParameters: true +BraceWrapping: + AfterClass: true + AfterControlStatement: false + AfterEnum: true + AfterFunction: true + AfterNamespace: false + AfterObjCDeclaration: false + AfterStruct: true + AfterUnion: true + AfterExternBlock: false + BeforeCatch: false + BeforeElse: false + IndentBraces: false + SplitEmptyFunction: false + SplitEmptyRecord: false + SplitEmptyNamespace: false +BreakBeforeBinaryOperators: None +BreakBeforeBraces: Custom +BreakBeforeInheritanceComma: false +# BreakInheritanceList: BeforeColon +BreakBeforeTernaryOperators: true +BreakConstructorInitializersBeforeComma: true +BreakConstructorInitializers: BeforeComma +BreakAfterJavaFieldAnnotations: false +BreakStringLiterals: true +ColumnLimit: 120 +CommentPragmas: '^ IWYU pragma:' +CompactNamespaces: false +ConstructorInitializerAllOnOneLineOrOnePerLine: false +ConstructorInitializerIndentWidth: 2 +ContinuationIndentWidth: 2 +Cpp11BracedListStyle: true +DerivePointerAlignment: false +DisableFormat: false +ExperimentalAutoDetectBinPacking: false +FixNamespaceComments: true +ForEachMacros: + - foreach + - Q_FOREACH + - BOOST_FOREACH +IncludeBlocks: Regroup +# To obtain updated lists of system headers used in the below expressions, see: +# https://stackoverflow.com/a/2029106 +IncludeCategories: + # ONNC library headers + - Regex: '^[<|"]onnc/' + Priority: 2 + # ONNX library headers + - Regex: '^[<|"]onnx/' + Priority: 3 + # C system headers. + - Regex: '^[<"](aio|arpa/inet|assert|complex|cpio|ctype|curses|dirent|dlfcn|errno|fcntl|fenv|float|fmtmsg|fnmatch|ftw|glob|grp|iconv|inttypes|iso646|langinfo|libgen|limits|locale|math|monetary|mqueue|ndbm|netdb|net/if|netinet/in|netinet/tcp|nl_types|poll|pthread|pwd|regex|sched|search|semaphore|setjmp|signal|spawn|stdalign|stdarg|stdatomic|stdbool|stddef|stdint|stdio|stdlib|stdnoreturn|string|strings|stropts|sys/ipc|syslog|sys/mman|sys/msg|sys/resource|sys/select|sys/sem|sys/shm|sys/socket|sys/stat|sys/statvfs|sys/time|sys/times|sys/types|sys/uio|sys/un|sys/utsname|sys/wait|tar|term|termios|tgmath|threads|time|trace|uchar|ulimit|uncntrl|unistd|utime|utmpx|wchar|wctype|wordexp)\.h[">]$' + Priority: 4 + # C++ system headers (as of C++14). + - Regex: '^[<"](algorithm|array|atomic|bitset|cassert|ccomplex|cctype|cerrno|cfenv|cfloat|chrono|cinttypes|ciso646|climits|clocale|cmath|codecvt|complex|condition_variable|csetjmp|csignal|cstdalign|cstdarg|cstdbool|cstddef|cstdint|cstdio|cstdlib|cstring|ctgmath|ctime|cuchar|cwchar|cwctype|deque|exception|forward_list|fstream|functional|future|initializer_list|iomanip|ios|iosfwd|iostream|istream|iterator|limits|list|locale|map|memory|mutex|new|numeric|ostream|queue|random|ratio|regex|scoped_allocator|set|shared_mutex|sstream|stack|stdexcept|streambuf|string|strstream|system_error|thread|tuple|type_traits|typeindex|typeinfo|unordered_map|unordered_set|utility|valarray|vector)[">]$' + Priority: 5 + - Regex: '.*' + Priority: 1 +IncludeIsMainRegex: '(Test)?$' +IndentCaseLabels: false +IndentPPDirectives: AfterHash +IndentWidth: 2 +IndentWrappedFunctionNames: false +JavaScriptQuotes: Leave +JavaScriptWrapImports: true +KeepEmptyLinesAtTheStartOfBlocks: false +MacroBlockBegin: '' +MacroBlockEnd: '' +MaxEmptyLinesToKeep: 1 +NamespaceIndentation: None +# ObjCBinPackProtocolList: Auto +# ObjCBlockIndentWidth: 2 +# ObjCSpaceAfterProperty: false +# ObjCSpaceBeforeProtocolList: true +PenaltyBreakAssignment: 2 +PenaltyBreakBeforeFirstCallParameter: 19 +PenaltyBreakComment: 300 +PenaltyBreakFirstLessLess: 120 +PenaltyBreakString: 1000 +# PenaltyBreakTemplateDeclaration: 10 +PenaltyExcessCharacter: 1000000 +PenaltyReturnTypeOnItsOwnLine: 60 +PointerAlignment: Left +ReflowComments: true +SortIncludes: true +SortUsingDeclarations: true +SpaceAfterCStyleCast: false +SpaceAfterTemplateKeyword: true +SpaceBeforeAssignmentOperators: true +# SpaceBeforeCpp11BracedList: false +# SpaceBeforeCtorInitializerColon: true +# SpaceBeforeInheritanceColon: true +SpaceBeforeParens: ControlStatements +# SpaceBeforeRangeBasedForLoopColon: true +SpaceInEmptyParentheses: false +SpacesBeforeTrailingComments: 1 +SpacesInAngles: false +SpacesInContainerLiterals: false +SpacesInCStyleCastParentheses: false +SpacesInParentheses: false +SpacesInSquareBrackets: false +Standard: Cpp11 +TabWidth: 2 +UseTab: Never +... + diff --git a/rknpu2/examples/rknn_benchmark/CMakeLists.txt b/rknpu2/examples/rknn_benchmark/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..e867d0412f547961ff6ed5775e824da1c6c1cecb --- /dev/null +++ b/rknpu2/examples/rknn_benchmark/CMakeLists.txt @@ -0,0 +1,40 @@ +cmake_minimum_required(VERSION 3.6) + +project(rknn_benchmark) + +set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS}") +set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++14") + +# rknn api +set(RKNN_API_PATH ${CMAKE_SOURCE_DIR}/../../runtime//${CMAKE_SYSTEM_NAME}/librknn_api) +if (CMAKE_SYSTEM_NAME STREQUAL "Android") + set(RKNN_RT_LIB ${RKNN_API_PATH}/${CMAKE_ANDROID_ARCH_ABI}/librknnrt.so) +else() + if (CMAKE_C_COMPILER MATCHES "aarch64") + set(LIB_ARCH aarch64) + else() + set(LIB_ARCH armhf) + endif() + set(RKNN_RT_LIB ${RKNN_API_PATH}/${LIB_ARCH}/librknnrt.so) +endif() +include_directories(${RKNN_API_PATH}/include) + +include_directories(${CMAKE_SOURCE_DIR}/../3rdparty) + +set(CMAKE_INSTALL_RPATH "lib") + +add_executable(rknn_benchmark + src/rknn_benchmark.cpp + src/cnpy/cnpy.cpp +) + +target_link_libraries(rknn_benchmark + ${RKNN_RT_LIB} + ${OpenCV_LIBS} +) + + +# install target and libraries +set(CMAKE_INSTALL_PREFIX ${CMAKE_SOURCE_DIR}/install/rknn_benchmark_${CMAKE_SYSTEM_NAME}) +install(TARGETS rknn_benchmark DESTINATION ./) +install(PROGRAMS ${RKNN_RT_LIB} DESTINATION lib) diff --git a/rknpu2/examples/rknn_benchmark/README.md b/rknpu2/examples/rknn_benchmark/README.md new file mode 100644 index 0000000000000000000000000000000000000000..c2f98a21020ee1b72407b978a25928865b7578fb --- /dev/null +++ b/rknpu2/examples/rknn_benchmark/README.md @@ -0,0 +1,95 @@ +rknn_benchmark is used to test the performance of the rknn model. Please make sure that the cpu/ddr/npu has been clocked to the highest frequency before testing. + +Usage: + +./rknn_benchmark xxx.rknn [input_data] [loop_count] [core_mask] + +core_mask: 0: auto, 1: npu core1, 2: npu core2, 4:npu core3, + +​ 3: npu core1&2, + +​ 7: npu core1&2&3 + +Only RK3588 support core mask. + +Such as: + +``` +./rknn_benchmark mobilenet_v1.rknn +./rknn_benchmark mobilenet_v1.rknn dog.jpg 10 3 +./rknn_benchmark mobilenet_v1.rknn dog.npy 10 7 +./rknn_benchmark xxx.rknn input1.npy#input2.npy +``` + + +The following represents RK3566_RK3568, RK3562 or RK3588 + +# Aarch64 Linux Demo +## Build + +First export `GCC_COMPILER`, for example `export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu`, then execute: + +``` +./build-linux.sh -t -a -b ] + +# such as: +./build-linux.sh -t rk3588 -a aarch64 -b Release +``` + +## Install + +Copy install/rknn_benchmark_Linux to the devices under /userdata/. + +- If you use rockchip's evb board, you can use the following way: + +Connect device and push the program and rknn model to `/userdata` + +``` +adb push install/rknn_benchmark_Linux /userdata/ +``` + +- If your board has sshd service, you can use scp or other methods to copy the program and rknn model to the board. + +## Run + +``` +adb shell +cd /userdata/rknn_benchmark_Linux/ +``` + +``` +export LD_LIBRARY_PATH=./lib +./rknn_benchmark xxx.rknn +``` + +# Android Demo +## Build + +First export `ANDROID_NDK_PATH`, for example `export ANDROID_NDK_PATH=~/opts/ndk/android-ndk-r18b`, then execute: + +``` +./build-android.sh -t -a [-b ] + +# sush as: +./build-android.sh -t rk3568 -a arm64-v8a -b Release +``` + +## Install + +connect device and push build output into `/data` + +``` +adb push install/rknn_benchmark_Android /data/ +``` + +## Run + +``` +adb shell +cd /data/rknn_benchmark_Android/ +``` + +``` +export LD_LIBRARY_PATH=./lib +./rknn_benchmark xxx.rknn +``` diff --git a/rknpu2/examples/rknn_benchmark/README_CN.md b/rknpu2/examples/rknn_benchmark/README_CN.md new file mode 100644 index 0000000000000000000000000000000000000000..3e316a17d78cedde7cb71625fb03f7c7fcc3cca3 --- /dev/null +++ b/rknpu2/examples/rknn_benchmark/README_CN.md @@ -0,0 +1,94 @@ +rknn_benchmark是用来测试rknn模型性能。请确保测试前将CPU/DDR/NPU频率锁定到最高频率。 +用法： + +./rknn_benchmark xxx.rknn [input_data] [loop_count] [core_mask] + +core_mask: 0: auto, 1: npu core1, 2: npu core2, 4:npu core3, + +​ 3: npu core1&2, + +​ 7: npu core1&2&3 + +仅RK3588支持 core mask。 + +例如: + +``` +./rknn_benchmark mobilenet_v1.rknn +./rknn_benchmark mobilenet_v1.rknn dog.jpg 10 3 +./rknn_benchmark mobilenet_v1.rknn dog.npy 10 7 +./rknn_benchmark xxx.rknn input1.npy#input2.npy +``` + + +以下 表示RK3566_RK3568、RK3562或RK3588。 + +# Aarch64 Linux 示例 +## 编译 + +首先导入GCC_COMPILER，例如`export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu +`，然后执行如下命令： + +``` +./build-linux.sh -t -a -b ] + +# 例如: +./build-linux.sh -t rk3588 -a aarch64 -b Release +``` + +## 安装 + +将 install/rknn_benchmark_Linux 拷贝到设备上. + +- 如果使用Rockchip的EVB板，可以使用以下命令： + +连接设备并将程序和模型传输到`/userdata` +``` +adb push install/rknn_benchmark_Linux /userdata/ +``` + +- 如果你的板子有sshd服务，可以使用scp命令或者其他方式将程序和模型传输到板子上。 + +## 运行 + +``` +adb shell +cd /userdata/rknn_benchmark_Linux/ +``` + +``` +export LD_LIBRARY_PATH=./lib +./rknn_benchmark xxx.rknn +``` + +# Android 示例 +## 编译 + +首先导入ANDROID_NDK_PATH，例如`export ANDROID_NDK_PATH=~/opts/ndk/android-ndk-r18b`，然后执行如下命令： + +``` +./build-android.sh -t -a [-b ] + +# 例如: +./build-android.sh -t rk3568 -a arm64-v8a -b Release +``` + +## 安装 + +connect device and push build output into `/data` + +``` +adb push install/rknn_benchmark_Android /data/ +``` + +## 运行 + +``` +adb shell +cd /data/rknn_benchmark_Android/ +``` + +``` +export LD_LIBRARY_PATH=./lib +./rknn_benchmark xxx.rknn +``` diff --git a/rknpu2/examples/rknn_benchmark/build-android.sh b/rknpu2/examples/rknn_benchmark/build-android.sh new file mode 100755 index 0000000000000000000000000000000000000000..67f922602dc373800acfd7c06752723b5990bb7d --- /dev/null +++ b/rknpu2/examples/rknn_benchmark/build-android.sh @@ -0,0 +1,111 @@ +#!/bin/bash + +set -e + +if [[ -z ${ANDROID_NDK_PATH} ]];then + echo "Please set ANDROID_NDK_PATH, such as export ANDROID_NDK_PATH=~/opts/ndk/android-ndk-r18b" + echo "NDK Version r18, r19 is recommanded. Other version may cause build failure." + exit +fi + +echo "$0 $@" +while getopts ":t:a:b" opt; do + case $opt in + t) + TARGET_SOC=$OPTARG + ;; + a) + TARGET_ARCH=$OPTARG + ;; + b) + BUILD_TYPE=$OPTARG + ;; + :) + echo "Option -$OPTARG requires an argument." + exit 1 + ;; + ?) + echo "Invalid option: -$OPTARG index:$OPTIND" + ;; + esac +done + +if [ -z ${TARGET_SOC} ] || [ -z ${TARGET_ARCH} ] ; then + echo "$0 -t -a [-b ]" + echo "" + echo " -t : target (rk3566/rk3568/rk3562/rk3576/rk3588)" + echo " -a : arch (arm64-v8a/armeabi-v7a)" + echo " -b : build_type (Debug/Release)" + echo "such as: $0 -t rk3588 -a arm64-v8a -b Release" + echo "" + exit -1 +fi + +# Debug / Release / RelWithDebInfo +if [[ -z ${BUILD_TYPE} ]];then + BUILD_TYPE=Release +fi + +case ${TARGET_SOC} in + rk356x) + TARGET_SOC="RK3566_RK3568" + ;; + rk3566_rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3588) + TARGET_SOC="RK3588" + ;; + rk3576) + TARGET_SOC="RK3576" + ;; + rk3566) + TARGET_SOC="RK3566_RK3568" + ;; + rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3562) + TARGET_SOC="RK3562" + ;; + *) + echo "Invalid target: ${TARGET_SOC}" + echo "Valid target: rk3562,rk3566,rk3568,rk3576,rk3588" + exit -1 + ;; +esac + +TARGET_PLATFORM=${TARGET_SOC}_android +if [[ -n ${TARGET_ARCH} ]];then +TARGET_PLATFORM=${TARGET_PLATFORM}_${TARGET_ARCH} +fi +ROOT_PWD=$( cd "$( dirname $0 )" && cd -P "$( dirname "$SOURCE" )" && pwd ) +BUILD_DIR=${ROOT_PWD}/build/build_${TARGET_PLATFORM}_${BUILD_TYPE} + +echo "===================================" +echo "TARGET_SOC=${TARGET_SOC}" +echo "TARGET_ARCH=${TARGET_ARCH}" +echo "BUILD_TYPE=${BUILD_TYPE}" +echo "BUILD_DIR=${BUILD_DIR}" +echo "ANDROID_NDK_PATH=${ANDROID_NDK_PATH}" +echo "===================================" + +if [[ ! -d "${BUILD_DIR}" ]]; then + mkdir -p ${BUILD_DIR} +fi + + +cd ${BUILD_DIR} +cmake ../.. \ + -DANDROID_TOOLCHAIN=clang \ + -DCMAKE_SYSTEM_NAME=Android \ + -DCMAKE_TOOLCHAIN_FILE=$ANDROID_NDK_PATH/build/cmake/android.toolchain.cmake \ + -DANDROID_ABI=${TARGET_ARCH} \ + -DANDROID_STL=c++_static \ + -DANDROID_PLATFORM=android-24 \ + -DCMAKE_BUILD_TYPE=${BUILD_TYPE} +# make VERBOSE=1 +make -j4 +make install +cd .. + diff --git a/rknpu2/examples/rknn_benchmark/build-linux.sh b/rknpu2/examples/rknn_benchmark/build-linux.sh new file mode 100755 index 0000000000000000000000000000000000000000..ca9714f09b9bf4f8c32f3fe20de38072c9ca6ecf --- /dev/null +++ b/rknpu2/examples/rknn_benchmark/build-linux.sh @@ -0,0 +1,120 @@ +#!/bin/bash + +set -e + +echo "$0 $@" +while getopts ":t:a:b" opt; do + case $opt in + t) + TARGET_SOC=$OPTARG + ;; + a) + TARGET_ARCH=$OPTARG + ;; + b) + BUILD_TYPE=$OPTARG + ;; + :) + echo "Option -$OPTARG requires an argument." + exit 1 + ;; + ?) + echo "Invalid option: -$OPTARG index:$OPTIND" + ;; + esac +done + +if [ -z ${TARGET_SOC} ];then + echo "$0 -t -a [-b ]" + echo "" + echo " -t : target (rk3566/rk3568/rk3562/rk3576/rk3588)" + echo " -a : arch (aarch64/armhf)" + echo " -b : build_type(Debug/Release)" + echo "such as: $0 -t rk3588 -a aarch64 -b Release" + echo "" + exit -1 +fi + +if [[ -z ${GCC_COMPILER} ]];then + echo "Please set GCC_COMPILER for $TARGET_SOC" + echo "such as export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu" + exit +fi +echo "$GCC_COMPILER" +export CC=${GCC_COMPILER}-gcc +export CXX=${GCC_COMPILER}-g++ + +if command -v ${CC} >/dev/null 2>&1; then + : +else + echo "${CC} is not available" + echo "Please set GCC_COMPILER for $TARGET_SOC" + echo "such as export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu" + exit +fi + +# Debug / Release +if [[ -z ${BUILD_TYPE} ]];then + BUILD_TYPE=Release +fi + + +case ${TARGET_SOC} in + rk356x) + TARGET_SOC="RK3566_RK3568" + ;; + rk3566_rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3588) + TARGET_SOC="RK3588" + ;; + rk3576) + TARGET_SOC="RK3576" + ;; + rk3566) + TARGET_SOC="RK3566_RK3568" + ;; + rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3562) + TARGET_SOC="RK3562" + ;; + *) + echo "Invalid target: ${TARGET_SOC}" + echo "Valid target: rk3562,rk3566,rk3568,rk3576,rk3588" + exit -1 + ;; +esac + +TARGET_PLATFORM=${TARGET_SOC}_linux +if [[ -n ${TARGET_ARCH} ]];then +TARGET_PLATFORM=${TARGET_PLATFORM}_${TARGET_ARCH} +fi +ROOT_PWD=$( cd "$( dirname $0 )" && cd -P "$( dirname "$SOURCE" )" && pwd ) +BUILD_DIR=${ROOT_PWD}/build/build_${TARGET_PLATFORM}_${BUILD_TYPE} + + +echo "===================================" +echo "TARGET_SOC=${TARGET_SOC}" +echo "TARGET_ARCH=${TARGET_ARCH}" +echo "BUILD_TYPE=${BUILD_TYPE}" +echo "BUILD_DIR=${BUILD_DIR}" +echo "CC=${CC}" +echo "CXX=${CXX}" +echo "===================================" + +if [[ ! -d "${BUILD_DIR}" ]]; then + mkdir -p ${BUILD_DIR} +fi + +cd ${BUILD_DIR} +cmake ../.. \ + -DCMAKE_SYSTEM_NAME=Linux \ + -DCMAKE_SYSTEM_PROCESSOR=${TARGET_ARCH} \ + -DCMAKE_BUILD_TYPE=${BUILD_TYPE} \ + -DCMAKE_C_COMPILER=${CC} \ + -DCMAKE_CXX_COMPILER=${CXX} +make -j4 +make install diff --git a/rknpu2/examples/rknn_benchmark/src/cnpy/cnpy.cpp b/rknpu2/examples/rknn_benchmark/src/cnpy/cnpy.cpp new file mode 100644 index 0000000000000000000000000000000000000000..5754a681e9df3e988f6d13792c43de111c8cf12f --- /dev/null +++ b/rknpu2/examples/rknn_benchmark/src/cnpy/cnpy.cpp @@ -0,0 +1,403 @@ +// Copyright (C) 2011 Carl Rogers +// Released under MIT License +// license available in LICENSE file, or at http://www.opensource.org/licenses/mit-license.php + +#include "cnpy.h" + +#include + +#include +#include +#include +#include +#include +#include +#include + +char cnpy::BigEndianTest(int size) +{ + if (size == 1) + return '|'; + int x = 1; + return (((char*)&x)[0]) ? '<' : '>'; +} + +char cnpy::map_type(const std::type_info& t) +{ + if (t == typeid(float)) + return 'f'; + if (t == typeid(double)) + return 'f'; + if (t == typeid(long double)) + return 'f'; + + if (t == typeid(int)) + return 'i'; + if (t == typeid(char)) + return 'i'; + if (t == typeid(signed char)) + return 'i'; + if (t == typeid(short)) + return 'i'; + if (t == typeid(long)) + return 'i'; + if (t == typeid(long long)) + return 'i'; + + if (t == typeid(unsigned char)) + return 'u'; + if (t == typeid(unsigned short)) + return 'u'; + if (t == typeid(unsigned long)) + return 'u'; + if (t == typeid(unsigned long long)) + return 'u'; + if (t == typeid(unsigned int)) + return 'u'; + + if (t == typeid(bool)) + return 'b'; + + if (t == typeid(std::complex)) + return 'c'; + if (t == typeid(std::complex)) + return 'c'; + if (t == typeid(std::complex)) + return 'c'; + + else + return '?'; +} + +template <> +std::vector& cnpy::operator+=(std::vector& lhs, const std::string rhs) +{ + lhs.insert(lhs.end(), rhs.begin(), rhs.end()); + return lhs; +} + +template <> +std::vector& cnpy::operator+=(std::vector& lhs, const char* rhs) +{ + // write in little endian + size_t len = strlen(rhs); + lhs.reserve(len); + for (size_t byte = 0; byte < len; byte++) { + lhs.push_back(rhs[byte]); + } + return lhs; +} + +void cnpy::parse_npy_header(unsigned char* buffer, size_t& word_size, std::vector& shape, bool& fortran_order, + std::string& typeName) +{ + // std::string magic_string(buffer,6); + uint8_t major_version = *reinterpret_cast(buffer + 6); + uint8_t minor_version = *reinterpret_cast(buffer + 7); + uint16_t header_len = *reinterpret_cast(buffer + 8); + std::string header(reinterpret_cast(buffer + 9), header_len); + + size_t loc1, loc2; + + // fortran order + loc1 = header.find("fortran_order") + 16; + fortran_order = (header.substr(loc1, 4) == "True" ? true : false); + if (fortran_order) + throw std::runtime_error("npy input file: 'fortran_order' must be false, use: arr2 = np.ascontiguousarray(arr1)"); + + // shape + loc1 = header.find("("); + loc2 = header.find(")"); + + std::regex num_regex("[0-9][0-9]*"); + std::smatch sm; + shape.clear(); + + std::string str_shape = header.substr(loc1 + 1, loc2 - loc1 - 1); + while (std::regex_search(str_shape, sm, num_regex)) { + shape.push_back(std::stoi(sm[0].str())); + str_shape = sm.suffix().str(); + } + + // endian, word size, data type + // byte order code | stands for not applicable. + // not sure when this applies except for byte array + loc1 = header.find("descr") + 9; + bool littleEndian = (header[loc1] == '<' || header[loc1] == '|' ? true : false); + assert(littleEndian); + + // char type = header[loc1+1]; + // assert(type == map_type(T)); + + std::string str_ws = header.substr(loc1 + 2); + loc2 = str_ws.find("'"); + word_size = atoi(str_ws.substr(0, loc2).c_str()); + if (header.substr(loc1 + 1, 1) == "i") { + typeName = "int"; + } else if (header.substr(loc1 + 1, 1) == "u") { + typeName = "uint"; + } else if (header.substr(loc1 + 1, 1) == "f") { + typeName = "float"; + } + typeName = typeName + std::to_string(word_size * 8); +} + +void cnpy::parse_npy_header(FILE* fp, size_t& word_size, std::vector& shape, bool& fortran_order, + std::string& typeName) +{ + char buffer[256]; + size_t res = fread(buffer, sizeof(char), 11, fp); + if (res != 11) + throw std::runtime_error("parse_npy_header: failed fread"); + std::string header = fgets(buffer, 256, fp); + assert(header[header.size() - 1] == '\n'); + + size_t loc1, loc2; + + // fortran order + loc1 = header.find("fortran_order"); + if (loc1 == std::string::npos) + throw std::runtime_error("parse_npy_header: failed to find header keyword: 'fortran_order'"); + loc1 += 16; + fortran_order = (header.substr(loc1, 4) == "True" ? true : false); + if (fortran_order) + throw std::runtime_error("npy input file: 'fortran_order' must be false, use: arr2 = np.ascontiguousarray(arr1)"); + + // shape + loc1 = header.find("("); + loc2 = header.find(")"); + if (loc1 == std::string::npos || loc2 == std::string::npos) + throw std::runtime_error("parse_npy_header: failed to find header keyword: '(' or ')'"); + + std::regex num_regex("[0-9][0-9]*"); + std::smatch sm; + shape.clear(); + + std::string str_shape = header.substr(loc1 + 1, loc2 - loc1 - 1); + while (std::regex_search(str_shape, sm, num_regex)) { + shape.push_back(std::stoi(sm[0].str())); + str_shape = sm.suffix().str(); + } + + // endian, word size, data type + // byte order code | stands for not applicable. + // not sure when this applies except for byte array + loc1 = header.find("descr"); + if (loc1 == std::string::npos) + throw std::runtime_error("parse_npy_header: failed to find header keyword: 'descr'"); + loc1 += 9; + bool littleEndian = (header[loc1] == '<' || header[loc1] == '|' ? true : false); + assert(littleEndian); + + // char type = header[loc1+1]; + // assert(type == map_type(T)); + + std::string str_ws = header.substr(loc1 + 2); + loc2 = str_ws.find("'"); + word_size = atoi(str_ws.substr(0, loc2).c_str()); + if (header.substr(loc1 + 1, 1) == "i") { + typeName = "int"; + } else if (header.substr(loc1 + 1, 1) == "u") { + typeName = "uint"; + } else if (header.substr(loc1 + 1, 1) == "f") { + typeName = "float"; + } + typeName = typeName + std::to_string(word_size * 8); +} + +void cnpy::parse_zip_footer(FILE* fp, uint16_t& nrecs, size_t& global_header_size, size_t& global_header_offset) +{ + std::vector footer(22); + fseek(fp, -22, SEEK_END); + size_t res = fread(&footer[0], sizeof(char), 22, fp); + if (res != 22) + throw std::runtime_error("parse_zip_footer: failed fread"); + + uint16_t disk_no, disk_start, nrecs_on_disk, comment_len; + disk_no = *(uint16_t*)&footer[4]; + disk_start = *(uint16_t*)&footer[6]; + nrecs_on_disk = *(uint16_t*)&footer[8]; + nrecs = *(uint16_t*)&footer[10]; + global_header_size = *(uint32_t*)&footer[12]; + global_header_offset = *(uint32_t*)&footer[16]; + comment_len = *(uint16_t*)&footer[20]; + + assert(disk_no == 0); + assert(disk_start == 0); + assert(nrecs_on_disk == nrecs); + assert(comment_len == 0); +} + +cnpy::NpyArray load_the_npy_file(FILE* fp) +{ + std::vector shape; + size_t word_size; + std::string typeName; + bool fortran_order; + cnpy::parse_npy_header(fp, word_size, shape, fortran_order, typeName); + + cnpy::NpyArray arr(shape, word_size, fortran_order, typeName); + size_t nread = fread(arr.data(), 1, arr.num_bytes(), fp); + if (nread != arr.num_bytes()) + throw std::runtime_error("load_the_npy_file: failed fread"); + return arr; +} + +cnpy::NpyArray load_the_npz_array(FILE* fp, uint32_t compr_bytes, uint32_t uncompr_bytes) +{ + std::vector buffer_compr(compr_bytes); + std::vector buffer_uncompr(uncompr_bytes); + size_t nread = fread(&buffer_compr[0], 1, compr_bytes, fp); + if (nread != compr_bytes) + throw std::runtime_error("load_the_npy_file: failed fread"); + +#if 0 + int err; + z_stream d_stream; + + d_stream.zalloc = Z_NULL; + d_stream.zfree = Z_NULL; + d_stream.opaque = Z_NULL; + d_stream.avail_in = 0; + d_stream.next_in = Z_NULL; + err = inflateInit2(&d_stream, -MAX_WBITS); + + d_stream.avail_in = compr_bytes; + d_stream.next_in = &buffer_compr[0]; + d_stream.avail_out = uncompr_bytes; + d_stream.next_out = &buffer_uncompr[0]; + + err = inflate(&d_stream, Z_FINISH); + err = inflateEnd(&d_stream); +#endif + + std::vector shape; + size_t word_size; + bool fortran_order; + std::string typeName; + cnpy::parse_npy_header(&buffer_uncompr[0], word_size, shape, fortran_order, typeName); + + cnpy::NpyArray array(shape, word_size, fortran_order, typeName); + + size_t offset = uncompr_bytes - array.num_bytes(); + memcpy(array.data(), &buffer_uncompr[0] + offset, array.num_bytes()); + + return array; +} + +cnpy::npz_t cnpy::npz_load(std::string fname) +{ + FILE* fp = fopen(fname.c_str(), "rb"); + + if (!fp) { + throw std::runtime_error("npz_load: Error! Unable to open file " + fname + "!"); + } + + cnpy::npz_t arrays; + + while (1) { + std::vector local_header(30); + size_t headerres = fread(&local_header[0], sizeof(char), 30, fp); + if (headerres != 30) + throw std::runtime_error("npz_load: failed fread"); + + // if we've reached the global header, stop reading + if (local_header[2] != 0x03 || local_header[3] != 0x04) + break; + + // read in the variable name + uint16_t name_len = *(uint16_t*)&local_header[26]; + std::string varname(name_len, ' '); + size_t vname_res = fread(&varname[0], sizeof(char), name_len, fp); + if (vname_res != name_len) + throw std::runtime_error("npz_load: failed fread"); + + // erase the lagging .npy + varname.erase(varname.end() - 4, varname.end()); + + // read in the extra field + uint16_t extra_field_len = *(uint16_t*)&local_header[28]; + if (extra_field_len > 0) { + std::vector buff(extra_field_len); + size_t efield_res = fread(&buff[0], sizeof(char), extra_field_len, fp); + if (efield_res != extra_field_len) + throw std::runtime_error("npz_load: failed fread"); + } + + uint16_t compr_method = *reinterpret_cast(&local_header[0] + 8); + uint32_t compr_bytes = *reinterpret_cast(&local_header[0] + 18); + uint32_t uncompr_bytes = *reinterpret_cast(&local_header[0] + 22); + + if (compr_method == 0) { + arrays[varname] = load_the_npy_file(fp); + } else { + arrays[varname] = load_the_npz_array(fp, compr_bytes, uncompr_bytes); + } + } + + fclose(fp); + return arrays; +} + +cnpy::NpyArray cnpy::npz_load(std::string fname, std::string varname) +{ + FILE* fp = fopen(fname.c_str(), "rb"); + + if (!fp) + throw std::runtime_error("npz_load: Unable to open file " + fname); + + while (1) { + std::vector local_header(30); + size_t header_res = fread(&local_header[0], sizeof(char), 30, fp); + if (header_res != 30) + throw std::runtime_error("npz_load: failed fread"); + + // if we've reached the global header, stop reading + if (local_header[2] != 0x03 || local_header[3] != 0x04) + break; + + // read in the variable name + uint16_t name_len = *(uint16_t*)&local_header[26]; + std::string vname(name_len, ' '); + size_t vname_res = fread(&vname[0], sizeof(char), name_len, fp); + if (vname_res != name_len) + throw std::runtime_error("npz_load: failed fread"); + vname.erase(vname.end() - 4, vname.end()); // erase the lagging .npy + + // read in the extra field + uint16_t extra_field_len = *(uint16_t*)&local_header[28]; + fseek(fp, extra_field_len, SEEK_CUR); // skip past the extra field + + uint16_t compr_method = *reinterpret_cast(&local_header[0] + 8); + uint32_t compr_bytes = *reinterpret_cast(&local_header[0] + 18); + uint32_t uncompr_bytes = *reinterpret_cast(&local_header[0] + 22); + + if (vname == varname) { + NpyArray array = (compr_method == 0) ? load_the_npy_file(fp) : load_the_npz_array(fp, compr_bytes, uncompr_bytes); + fclose(fp); + return array; + } else { + // skip past the data + uint32_t size = *(uint32_t*)&local_header[22]; + fseek(fp, size, SEEK_CUR); + } + } + + fclose(fp); + + // if we get here, we haven't found the variable in the file + throw std::runtime_error("npz_load: Variable name " + varname + " not found in " + fname); +} + +cnpy::NpyArray cnpy::npy_load(std::string fname) +{ + FILE* fp = fopen(fname.c_str(), "rb"); + + if (!fp) + throw std::runtime_error("npy_load: Unable to open file " + fname); + + NpyArray arr = load_the_npy_file(fp); + + fclose(fp); + return arr; +} diff --git a/rknpu2/examples/rknn_benchmark/src/cnpy/cnpy.h b/rknpu2/examples/rknn_benchmark/src/cnpy/cnpy.h new file mode 100644 index 0000000000000000000000000000000000000000..0252b4bf09a61472a37f9282f1c2b3c7efa72314 --- /dev/null +++ b/rknpu2/examples/rknn_benchmark/src/cnpy/cnpy.h @@ -0,0 +1,321 @@ +// Copyright (C) 2011 Carl Rogers +// Released under MIT License +// license available in LICENSE file, or at http://www.opensource.org/licenses/mit-license.php + +#ifndef LIBCNPY_H_ +#define LIBCNPY_H_ + +#if 0 +#include +#endif + +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace cnpy { + +struct NpyArray +{ + NpyArray(const std::vector& _shape, size_t _word_size, bool _fortran_order, std::string _typeName) + : shape(_shape) + , word_size(_word_size) + , fortran_order(_fortran_order) + , typeName(_typeName) + { + num_vals = 1; + for (size_t i = 0; i < shape.size(); i++) + num_vals *= shape[i]; + data_holder = std::shared_ptr>(new std::vector(num_vals * word_size)); + } + + NpyArray() + : shape(0) + , word_size(0) + , fortran_order(0) + , num_vals(0) + {} + + template + T* data() + { + return reinterpret_cast(&(*data_holder)[0]); + } + + template + const T* data() const + { + return reinterpret_cast(&(*data_holder)[0]); + } + + template + std::vector as_vec() const + { + const T* p = data(); + return std::vector(p, p + num_vals); + } + + size_t num_bytes() const { return data_holder->size(); } + + std::shared_ptr> data_holder; + std::vector shape; + size_t word_size; + bool fortran_order; + size_t num_vals; + std::string typeName; +}; + +using npz_t = std::map; + +char BigEndianTest(int size); +char map_type(const std::type_info& t); +template +std::vector create_npy_header(const std::vector& shape); +void parse_npy_header(FILE* fp, size_t& word_size, std::vector& shape, bool& fortran_order, + std::string& typeName); +void parse_npy_header(unsigned char* buffer, size_t& word_size, std::vector& shape, bool& fortran_order, + std::string& typeName); +void parse_zip_footer(FILE* fp, uint16_t& nrecs, size_t& global_header_size, size_t& global_header_offset); +npz_t npz_load(std::string fname); +NpyArray npz_load(std::string fname, std::string varname); +NpyArray npy_load(std::string fname); + +template +std::vector& operator+=(std::vector& lhs, const T rhs) +{ + // write in little endian + for (size_t byte = 0; byte < sizeof(T); byte++) { + char val = *((char*)&rhs + byte); + lhs.push_back(val); + } + return lhs; +} + +template <> +std::vector& operator+=(std::vector& lhs, const std::string rhs); +template <> +std::vector& operator+=(std::vector& lhs, const char* rhs); + +template +int npy_save(std::string fname, const T* data, const std::vector shape, std::string mode = "w") +{ + std::ofstream ofs(fname, std::ios::out); + if (!ofs.is_open()) { + return -1; + } + ofs.close(); + FILE* fp = NULL; + std::vector true_data_shape; // if appending, the shape of existing + new data + + if (mode == "a") + fp = fopen(fname.c_str(), "r+b"); + + if (fp) { + // file exists. we need to append to it. read the header, modify the array size + size_t word_size; + bool fortran_order; + std::string typeName; + parse_npy_header(fp, word_size, true_data_shape, fortran_order, typeName); + assert(!fortran_order); + + if (word_size != sizeof(T)) { + std::cout << "libnpy error: " << fname << " has word size " << word_size << " but npy_save appending data sized " + << sizeof(T) << "\n"; + assert(word_size == sizeof(T)); + } + if (true_data_shape.size() != shape.size()) { + std::cout << "libnpy error: npy_save attempting to append misdimensioned data to " << fname << "\n"; + assert(true_data_shape.size() != shape.size()); + } + + for (size_t i = 1; i < shape.size(); i++) { + if (shape[i] != true_data_shape[i]) { + std::cout << "libnpy error: npy_save attempting to append misshaped data to " << fname << "\n"; + assert(shape[i] == true_data_shape[i]); + } + } + true_data_shape[0] += shape[0]; + } else { + fp = fopen(fname.c_str(), "wb"); + true_data_shape = shape; + } + + std::vector header = create_npy_header(true_data_shape); + size_t nels = std::accumulate(shape.begin(), shape.end(), 1, std::multiplies()); + + fseek(fp, 0, SEEK_SET); + fwrite(&header[0], sizeof(char), header.size(), fp); + fseek(fp, 0, SEEK_END); + fwrite(data, sizeof(T), nels, fp); + fclose(fp); + return 0; +} + +template +void npz_save(std::string zipname, std::string fname, const T* data, const std::vector& shape, + std::string mode = "w") +{ + // first, append a .npy to the fname + fname += ".npy"; + + // now, on with the show + FILE* fp = NULL; + uint16_t nrecs = 0; + size_t global_header_offset = 0; + std::vector global_header; + + if (mode == "a") + fp = fopen(zipname.c_str(), "r+b"); + + if (fp) { + // zip file exists. we need to add a new npy file to it. + // first read the footer. this gives us the offset and size of the global header + // then read and store the global header. + // below, we will write the the new data at the start of the global header then append the global header and footer + // below it + size_t global_header_size; + parse_zip_footer(fp, nrecs, global_header_size, global_header_offset); + fseek(fp, global_header_offset, SEEK_SET); + global_header.resize(global_header_size); + size_t res = fread(&global_header[0], sizeof(char), global_header_size, fp); + if (res != global_header_size) { + throw std::runtime_error("npz_save: header read error while adding to existing zip"); + } + fseek(fp, global_header_offset, SEEK_SET); + } else { + fp = fopen(zipname.c_str(), "wb"); + } + + std::vector npy_header = create_npy_header(shape); + + size_t nels = std::accumulate(shape.begin(), shape.end(), 1, std::multiplies()); + size_t nbytes = nels * sizeof(T) + npy_header.size(); + +#if 0 + // get the CRC of the data to be added + uint32_t crc = crc32(0L, (uint8_t*)&npy_header[0], npy_header.size()); + crc = crc32(crc, (uint8_t*)data, nels * sizeof(T)); +#else + uint32_t crc = 0; +#endif + + // build the local header + std::vector local_header; + local_header += "PK"; // first part of sig + local_header += (uint16_t)0x0403; // second part of sig + local_header += (uint16_t)20; // min version to extract + local_header += (uint16_t)0; // general purpose bit flag + local_header += (uint16_t)0; // compression method + local_header += (uint16_t)0; // file last mod time + local_header += (uint16_t)0; // file last mod date + local_header += (uint32_t)crc; // crc + local_header += (uint32_t)nbytes; // compressed size + local_header += (uint32_t)nbytes; // uncompressed size + local_header += (uint16_t)fname.size(); // fname length + local_header += (uint16_t)0; // extra field length + local_header += fname; + + // build global header + global_header += "PK"; // first part of sig + global_header += (uint16_t)0x0201; // second part of sig + global_header += (uint16_t)20; // version made by + global_header.insert(global_header.end(), local_header.begin() + 4, local_header.begin() + 30); + global_header += (uint16_t)0; // file comment length + global_header += (uint16_t)0; // disk number where file starts + global_header += (uint16_t)0; // internal file attributes + global_header += (uint32_t)0; // external file attributes + global_header += (uint32_t) + global_header_offset; // relative offset of local file header, since it begins where the global header used to begin + global_header += fname; + + // build footer + std::vector footer; + footer += "PK"; // first part of sig + footer += (uint16_t)0x0605; // second part of sig + footer += (uint16_t)0; // number of this disk + footer += (uint16_t)0; // disk where footer starts + footer += (uint16_t)(nrecs + 1); // number of records on this disk + footer += (uint16_t)(nrecs + 1); // total number of records + footer += (uint32_t)global_header.size(); // nbytes of global headers + footer += + (uint32_t)(global_header_offset + nbytes + local_header.size()); // offset of start of global headers, since global + // header now starts after newly written array + footer += (uint16_t)0; // zip file comment length + + // write everything + fwrite(&local_header[0], sizeof(char), local_header.size(), fp); + fwrite(&npy_header[0], sizeof(char), npy_header.size(), fp); + fwrite(data, sizeof(T), nels, fp); + fwrite(&global_header[0], sizeof(char), global_header.size(), fp); + fwrite(&footer[0], sizeof(char), footer.size(), fp); + fclose(fp); +} + +template +void npy_save(std::string fname, const std::vector data, std::string mode = "w") +{ + std::vector shape; + shape.push_back(data.size()); + npy_save(fname, &data[0], shape, mode); +} + +template +void npz_save(std::string zipname, std::string fname, const std::vector data, std::string mode = "w") +{ + std::vector shape; + shape.push_back(data.size()); + npz_save(zipname, fname, &data[0], shape, mode); +} + +template +std::vector create_npy_header(const std::vector& shape) +{ + const char* tpye_name = typeid(T).name(); + std::vector dict; + dict += "{'descr': '"; + dict += BigEndianTest(sizeof(T)); + if (std::string(tpye_name) == "N4rknn7float16E") { + dict += "f"; + } else { + dict += map_type(typeid(T)); + } + dict += std::to_string(sizeof(T)); + dict += "', 'fortran_order': False, 'shape': ("; + dict += std::to_string(shape[0]); + for (size_t i = 1; i < shape.size(); i++) { + dict += ", "; + dict += std::to_string(shape[i]); + } + if (shape.size() == 1) + dict += ","; + dict += "), }"; + // pad with spaces so that preamble+dict is modulo 16 bytes. preamble is 10 bytes. dict needs to end with \n + int remainder = 16 - (10 + dict.size()) % 16; + dict.insert(dict.end(), remainder, ' '); + dict.back() = '\n'; + + std::vector header; + header += (char)0x93; + header += "NUMPY"; + header += (char)0x01; // major version of numpy format + header += (char)0x00; // minor version of numpy format + header += (uint16_t)dict.size(); + header.insert(header.end(), dict.begin(), dict.end()); + + return header; +} + +} // namespace cnpy + +#endif diff --git a/rknpu2/examples/rknn_benchmark/src/rknn_benchmark.cpp b/rknpu2/examples/rknn_benchmark/src/rknn_benchmark.cpp new file mode 100755 index 0000000000000000000000000000000000000000..6d7e59ad5a4b9be4a2465a574add04ecccbcd400 --- /dev/null +++ b/rknpu2/examples/rknn_benchmark/src/rknn_benchmark.cpp @@ -0,0 +1,497 @@ +// Copyright (c) 2022 by Rockchip Electronics Co., Ltd. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +/*------------------------------------------- + Includes +-------------------------------------------*/ +#include "rknn_api.h" + +#include +#include +#include +#include +#include + +#define STB_IMAGE_IMPLEMENTATION +#include "stb/stb_image.h" +#define STB_IMAGE_RESIZE_IMPLEMENTATION +#include + +#include "cnpy/cnpy.h" +using namespace cnpy; + + +/*------------------------------------------- + Functions +-------------------------------------------*/ +static inline int64_t getCurrentTimeUs() +{ + struct timeval tv; + gettimeofday(&tv, NULL); + return tv.tv_sec * 1000000 + tv.tv_usec; +} + +static int rknn_GetTopN(float* pfProb, float* pfMaxProb, uint32_t* pMaxClass, uint32_t outputCount, uint32_t topNum) +{ + uint32_t i, j; + uint32_t top_count = outputCount > topNum ? topNum : outputCount; + + for (i = 0; i < topNum; ++i) { + pfMaxProb[i] = -FLT_MAX; + pMaxClass[i] = -1; + } + + for (j = 0; j < top_count; j++) { + for (i = 0; i < outputCount; i++) { + if ((i == *(pMaxClass + 0)) || (i == *(pMaxClass + 1)) || (i == *(pMaxClass + 2)) || (i == *(pMaxClass + 3)) || + (i == *(pMaxClass + 4))) { + continue; + } + + if (pfProb[i] > *(pfMaxProb + j)) { + *(pfMaxProb + j) = pfProb[i]; + *(pMaxClass + j) = i; + } + } + } + + return 1; +} + +static void dump_tensor_attr(rknn_tensor_attr* attr) +{ + std::string shape_str = attr->n_dims < 1 ? "" : std::to_string(attr->dims[0]); + for (int i = 1; i < attr->n_dims; ++i) { + shape_str += ", " + std::to_string(attr->dims[i]); + } + + printf(" index=%d, name=%s, n_dims=%d, dims=[%s], n_elems=%d, size=%d, w_stride = %d, size_with_stride=%d, fmt=%s, " + "type=%s, qnt_type=%s, " + "zp=%d, scale=%f\n", + attr->index, attr->name, attr->n_dims, shape_str.c_str(), attr->n_elems, attr->size, attr->w_stride, + attr->size_with_stride, get_format_string(attr->fmt), get_type_string(attr->type), + get_qnt_type_string(attr->qnt_type), attr->zp, attr->scale); +} + +static unsigned char* load_npy(const char* input_path, rknn_tensor_attr* input_attr, int* input_type, int* input_size) +{ + int req_height = 0; + int req_width = 0; + int req_channel = 0; + + printf("Loading %s\n", input_path); + + switch (input_attr->fmt) { + case RKNN_TENSOR_NHWC: + req_height = input_attr->dims[1]; + req_width = input_attr->dims[2]; + req_channel = input_attr->dims[3]; + break; + case RKNN_TENSOR_NCHW: + req_height = input_attr->dims[2]; + req_width = input_attr->dims[3]; + req_channel = input_attr->dims[1]; + break; + case RKNN_TENSOR_UNDEFINED: + break; + default: + printf("meet unsupported layout\n"); + return NULL; + } + + NpyArray npy_data = npy_load(input_path); + + int type_bytes = npy_data.word_size; + std::string typeName = npy_data.typeName; + + printf("npy data type:%s\n", typeName.c_str()); + + if (typeName == "int8") { + *input_type = RKNN_TENSOR_INT8; + } else if (typeName == "uint8") { + *input_type = RKNN_TENSOR_UINT8; + } else if (typeName == "float16") { + *input_type = RKNN_TENSOR_FLOAT16; + } else if (typeName == "float32") { + *input_type = RKNN_TENSOR_FLOAT32; + } else if (typeName == "8") { + *input_type = RKNN_TENSOR_BOOL; + } else if (typeName == "int64") { + *input_type = RKNN_TENSOR_INT64; + } + + // npy shape = NHWC + int npy_shape[4] = {1, 1, 1, 1}; + + int start = npy_data.shape.size() == 4 ? 0 : 1; + for (size_t i = 0; i < npy_data.shape.size() && i < 4; ++i) { + npy_shape[start + i] = npy_data.shape[i]; + } + + int height = npy_shape[1]; + int width = npy_shape[2]; + int channel = npy_shape[3]; + + if ((input_attr->fmt != RKNN_TENSOR_UNDEFINED) && + (width != req_width || height != req_height || channel != req_channel)) { + printf("npy shape match failed!, (%d, %d, %d) != (%d, %d, %d)\n", height, width, channel, req_height, req_width, + req_channel); + return NULL; + } + + unsigned char* data = (unsigned char*)malloc(npy_data.num_bytes()); + if (!data) { + return NULL; + } + + // TODO: copy + memcpy(data, npy_data.data(), npy_data.num_bytes()); + + *input_size = npy_data.num_bytes(); + + return data; +} + +static void save_npy(const char* output_path, float* output_data, rknn_tensor_attr* output_attr) +{ + std::vector output_shape; + + for (uint32_t i = 0; i < output_attr->n_dims; ++i) { + output_shape.push_back(output_attr->dims[i]); + } + + npy_save(output_path, output_data, output_shape); +} + + +static unsigned char* load_image(const char* image_path, rknn_tensor_attr* input_attr) +{ + int req_height = 0; + int req_width = 0; + int req_channel = 0; + + switch (input_attr->fmt) { + case RKNN_TENSOR_NHWC: + req_height = input_attr->dims[1]; + req_width = input_attr->dims[2]; + req_channel = input_attr->dims[3]; + break; + case RKNN_TENSOR_NCHW: + req_height = input_attr->dims[2]; + req_width = input_attr->dims[3]; + req_channel = input_attr->dims[1]; + break; + default: + printf("meet unsupported layout\n"); + return NULL; + } + + int height = 0; + int width = 0; + int channel = 0; + + unsigned char* image_data = stbi_load(image_path, &width, &height, &channel, req_channel); + if (image_data == NULL) { + printf("load image failed!\n"); + return NULL; + } + + if (width != req_width || height != req_height) { + unsigned char* image_resized = (unsigned char*)STBI_MALLOC(req_width * req_height * req_channel); + if (!image_resized) { + printf("malloc image failed!\n"); + STBI_FREE(image_data); + return NULL; + } + if (stbir_resize_uint8(image_data, width, height, 0, image_resized, req_width, req_height, 0, channel) != 1) { + printf("resize image failed!\n"); + STBI_FREE(image_data); + return NULL; + } + STBI_FREE(image_data); + image_data = image_resized; + } + + return image_data; +} + +static std::vector split(const std::string& str, const std::string& pattern) +{ + std::vector res; + if (str == "") + return res; + std::string strs = str + pattern; + size_t pos = strs.find(pattern); + while (pos != strs.npos) { + std::string temp = strs.substr(0, pos); + res.push_back(temp); + strs = strs.substr(pos + 1, strs.size()); + pos = strs.find(pattern); + } + return res; +} + +/*------------------------------------------- + Main Functions +-------------------------------------------*/ +int main(int argc, char* argv[]) +{ + if (argc < 2) { + printf("Usage:%s model_path [input_path] [loop_count] [core_mask]\n", argv[0]); + return -1; + } + + char* model_path = argv[1]; + std::vector input_paths_split; + int loop_count = 10; + uint32_t core_mask = 1; + rknn_context ctx = 0; + uint32_t topNum = 5; + double total_time = 0; + + if (argc > 2) { + char* input_paths = argv[2]; + input_paths_split = split(input_paths, "#"); + } + + if (argc > 3) { + loop_count = atoi(argv[3]); + } + + if (argc > 4) { + core_mask = strtoul(argv[4], NULL, 10); + } + + + // Init rknn from model path + int ret = rknn_init(&ctx, model_path, 0, 0, NULL); + + if (ret < 0) { + printf("rknn_init fail! ret=%d\n", ret); + return -1; + } + + // Get sdk and driver version + rknn_sdk_version sdk_ver; + ret = rknn_query(ctx, RKNN_QUERY_SDK_VERSION, &sdk_ver, sizeof(sdk_ver)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + rknn_destroy(ctx); + return -1; + } + printf("rknn_api/rknnrt version: %s, driver version: %s\n", sdk_ver.api_version, sdk_ver.drv_version); + + // Get weight and internal mem size, dma used size + rknn_mem_size mem_size; + ret = rknn_query(ctx, RKNN_QUERY_MEM_SIZE, &mem_size, sizeof(mem_size)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + rknn_destroy(ctx); + return -1; + } + printf("total weight size: %d, total internal size: %d\n", mem_size.total_weight_size, mem_size.total_internal_size); + printf("total dma used size: %zu\n", (size_t)mem_size.total_dma_allocated_size); + + // Get Model Input Output Info + rknn_input_output_num io_num; + ret = rknn_query(ctx, RKNN_QUERY_IN_OUT_NUM, &io_num, sizeof(io_num)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + rknn_destroy(ctx); + return -1; + } + printf("model input num: %d, output num: %d\n", io_num.n_input, io_num.n_output); + + printf("input tensors:\n"); + rknn_tensor_attr input_attrs[io_num.n_input]; + memset(input_attrs, 0, io_num.n_input * sizeof(rknn_tensor_attr)); + for (uint32_t i = 0; i < io_num.n_input; i++) { + input_attrs[i].index = i; + // query info + ret = rknn_query(ctx, RKNN_QUERY_INPUT_ATTR, &(input_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret < 0) { + printf("rknn_init error! ret=%d\n", ret); + rknn_destroy(ctx); + return -1; + } + dump_tensor_attr(&input_attrs[i]); + } + + printf("output tensors:\n"); + rknn_tensor_attr output_attrs[io_num.n_output]; + memset(output_attrs, 0, io_num.n_output * sizeof(rknn_tensor_attr)); + for (uint32_t i = 0; i < io_num.n_output; i++) { + output_attrs[i].index = i; + // query info + ret = rknn_query(ctx, RKNN_QUERY_OUTPUT_ATTR, &(output_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + rknn_destroy(ctx); + return -1; + } + dump_tensor_attr(&output_attrs[i]); + } + + // Get custom string + rknn_custom_string custom_string; + ret = rknn_query(ctx, RKNN_QUERY_CUSTOM_STRING, &custom_string, sizeof(custom_string)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + rknn_destroy(ctx); + return -1; + } + printf("custom string: %s\n", custom_string.string); + + unsigned char* input_data[io_num.n_input]; + int input_type[io_num.n_input]; + int input_layout[io_num.n_input]; + int input_size[io_num.n_input]; + rknn_input inputs[io_num.n_input]; + rknn_output outputs[io_num.n_output]; + + for (int i = 0; i < io_num.n_input; i++) { + input_data[i] = NULL; + input_type[i] = RKNN_TENSOR_UINT8; + input_layout[i] = RKNN_TENSOR_NHWC; + input_size[i] = input_attrs[i].n_elems * sizeof(uint8_t); + } + + if (input_paths_split.size() > 0) { + // Load input + if (io_num.n_input != input_paths_split.size()) { + printf("input missing!, need input number: %d, only get %zu inputs\n", io_num.n_input, input_paths_split.size()); + goto out; + } + for (int i = 0; i < io_num.n_input; i++) { + if (strstr(input_paths_split[i].c_str(), ".npy")) { + input_data[i] = load_npy(input_paths_split[i].c_str(), &input_attrs[i], &input_type[i], &input_size[i]); + } else { + // Load image + input_data[i] = load_image(input_paths_split[i].c_str(), &input_attrs[i]); + } + + if (!input_data[i]) { + goto out; + } + } + } else { + for (int i = 0; i < io_num.n_input; i++) { + input_data[i] = (unsigned char*)malloc(input_size[i]); + memset(input_data[i], 0x00, input_size[i]); + } + } + + + memset(inputs, 0, io_num.n_input * sizeof(rknn_input)); + for (int i = 0; i < io_num.n_input; i++) { + inputs[i].index = i; + inputs[i].pass_through = 0; + inputs[i].type = (rknn_tensor_type)input_type[i]; + inputs[i].fmt = (rknn_tensor_format)input_layout[i]; + inputs[i].buf = input_data[i]; + inputs[i].size = input_size[i]; + } + + // Set input + ret = rknn_inputs_set(ctx, io_num.n_input, inputs); + if (ret < 0) { + printf("rknn_input_set fail! ret=%d\n", ret); + goto out; + } + + rknn_set_core_mask(ctx, (rknn_core_mask)core_mask); + + // Warmup + printf("Warmup ...\n"); + for (int i = 0; i < 5; ++i) { + int64_t start_us = getCurrentTimeUs(); + ret = rknn_run(ctx, NULL); + int64_t elapse_us = getCurrentTimeUs() - start_us; + if (ret < 0) { + printf("rknn run error %d\n", ret); + goto out; + } + printf("%4d: Elapse Time = %.2fms, FPS = %.2f\n", i, elapse_us / 1000.f, 1000.f * 1000.f / elapse_us); + } + + + // Run + printf("Begin perf ...\n"); + for (int i = 0; i < loop_count; ++i) { + int64_t start_us = getCurrentTimeUs(); + ret = rknn_run(ctx, NULL); + int64_t elapse_us = getCurrentTimeUs() - start_us; + if (ret < 0) { + printf("rknn run error %d\n", ret); + return -1; + } + total_time += elapse_us / 1000.f; + printf("%4d: Elapse Time = %.2fms, FPS = %.2f\n", i, elapse_us / 1000.f, 1000.f * 1000.f / elapse_us); + } + printf("\nAvg Time %.2fms, Avg FPS = %.3f\n\n", total_time/loop_count, loop_count * 1000.f / total_time); + + // Get output + memset(outputs, 0, io_num.n_output * sizeof(rknn_output)); + for (uint32_t i = 0; i < io_num.n_output; ++i) { + outputs[i].want_float = 1; + outputs[i].index = i; + outputs[i].is_prealloc = 0; + } + + ret = rknn_outputs_get(ctx, io_num.n_output, outputs, NULL); + if (ret < 0) { + printf("rknn_outputs_get fail! ret=%d\n", ret); + goto out; + } + + // save output + for (uint32_t i = 0; i < io_num.n_output; i++) { + char output_path[PATH_MAX]; + memset(output_path, 0x00, sizeof(output_path)); + sprintf(output_path, "rt_output%d.npy", i); + printf("Save output to %s\n", output_path); + save_npy(output_path, (float*)outputs[i].buf, &output_attrs[i]); + } + + // Get top 5 + for (uint32_t i = 0; i < io_num.n_output; i++) { + uint32_t MaxClass[topNum]; + float fMaxProb[topNum]; + float* buffer = (float*)outputs[i].buf; + uint32_t sz = outputs[i].size / sizeof(float); + int top_count = sz > topNum ? topNum : sz; + + rknn_GetTopN(buffer, fMaxProb, MaxClass, sz, topNum); + + printf("---- Top%d ----\n", top_count); + for (int j = 0; j < top_count; j++) { + printf("%8.6f - %d\n", fMaxProb[j], MaxClass[j]); + } + } + + // release outputs + ret = rknn_outputs_release(ctx, io_num.n_output, outputs); + +out: + // destroy + rknn_destroy(ctx); + + for (int i = 0; i < io_num.n_input; i++) { + if (input_data[i] != NULL) { + free(input_data[i]); + } + } + + return 0; +} diff --git a/rknpu2/examples/rknn_common_test/CMakeLists.txt b/rknpu2/examples/rknn_common_test/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..23e4f6fcc81f065e634094370ba34f83882b0cd2 --- /dev/null +++ b/rknpu2/examples/rknn_common_test/CMakeLists.txt @@ -0,0 +1,52 @@ +cmake_minimum_required(VERSION 3.6) + +project(rknn_common_test) + +set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS}") +set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11") + +# rknn api +set(RKNN_API_PATH ${CMAKE_SOURCE_DIR}/../../runtime//${CMAKE_SYSTEM_NAME}/librknn_api) +if (CMAKE_SYSTEM_NAME STREQUAL "Android") + set(RKNN_RT_LIB ${RKNN_API_PATH}/${CMAKE_ANDROID_ARCH_ABI}/librknnrt.so) +else() + if (CMAKE_C_COMPILER MATCHES "aarch64") + set(LIB_ARCH aarch64) + else() + set(LIB_ARCH armhf) + endif() + set(RKNN_RT_LIB ${RKNN_API_PATH}/${LIB_ARCH}/librknnrt.so) +endif() +include_directories(${RKNN_API_PATH}/include) + +# opencv +if (CMAKE_SYSTEM_NAME STREQUAL "Android") + set(OpenCV_DIR ${CMAKE_SOURCE_DIR}/../3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/abi-${CMAKE_ANDROID_ARCH_ABI}) +else() + if(LIB_ARCH STREQUAL "armhf") + set(OpenCV_DIR ${CMAKE_SOURCE_DIR}/../3rdparty/opencv/opencv-linux-armhf/share/OpenCV) + else() + set(OpenCV_DIR ${CMAKE_SOURCE_DIR}/../3rdparty/opencv/opencv-linux-aarch64/share/OpenCV) + endif() +endif() +find_package(OpenCV REQUIRED) + +set(CMAKE_INSTALL_RPATH "lib") + +add_executable(rknn_common_test + src/main.cc +) + +target_link_libraries(rknn_common_test + ${RKNN_RT_LIB} + ${OpenCV_LIBS} +) + + +# install target and libraries +set(CMAKE_INSTALL_PREFIX ${CMAKE_SOURCE_DIR}/install/rknn_common_test_${CMAKE_SYSTEM_NAME}) +install(TARGETS rknn_common_test DESTINATION ./) +install(DIRECTORY model/${TARGET_SOC} DESTINATION ./model) +file(GLOB IMAGE_FILES "model/*.jpg") +install(FILES ${IMAGE_FILES} DESTINATION ./model/) +install(PROGRAMS ${RKNN_RT_LIB} DESTINATION lib) diff --git a/rknpu2/examples/rknn_common_test/README.md b/rknpu2/examples/rknn_common_test/README.md new file mode 100644 index 0000000000000000000000000000000000000000..5c733ff541a3414411a75e164434e854ba7ead26 --- /dev/null +++ b/rknpu2/examples/rknn_common_test/README.md @@ -0,0 +1,70 @@ +The following represents RK3566_RK3568, RK3562, RK3576 or RK3588 +# Aarch64 Linux Demo +## Build + +First export `GCC_COMPILER`, for example `export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu`, then execute: + +``` +./build-linux.sh -t -a -b ] + +# such as: +./build-linux.sh -t rk3588 -a aarch64 -b Release +``` + +## Install + +Copy install/rknn_common_test_Linux to the devices under /userdata/. + +- If you use rockchip's evb board, you can use the following way: + +Connect device and push the program and rknn model to `/userdata` + +``` +adb push install/rknn_common_test_Linux /userdata/ +``` + +- If your board has sshd service, you can use scp or other methods to copy the program and rknn model to the board. + +## Run + +``` +adb shell +cd /userdata/rknn_common_test_Linux/ +``` + +``` +export LD_LIBRARY_PATH=./lib +./rknn_common_test model//mobilenet_v1.rknn model/dog_224x224.jpg +``` + +# Android Demo +## Build + +First export `ANDROID_NDK_PATH`, for example `export ANDROID_NDK_PATH=~/opts/ndk/android-ndk-r18b`, then execute: + +``` +./build-android.sh -t -a [-b ] + +# sush as: +./build-android.sh -t rk3568 -a arm64-v8a -b Release +``` + +## Install + +connect device and push build output into `/data` + +``` +adb push install/rknn_common_test_Android /data/ +``` + +## Run + +``` +adb shell +cd /data/rknn_common_test_Android/ +``` + +``` +export LD_LIBRARY_PATH=./lib +./rknn_common_test model//mobilenet_v1.rknn model/dog_224x224.jpg +``` diff --git a/rknpu2/examples/rknn_common_test/README_CN.md b/rknpu2/examples/rknn_common_test/README_CN.md new file mode 100644 index 0000000000000000000000000000000000000000..ffc24efc7682fd4c7f6d0137bf01348a83b3b9f1 --- /dev/null +++ b/rknpu2/examples/rknn_common_test/README_CN.md @@ -0,0 +1,71 @@ +以下 表示RK3566_RK3568、RK3562、RK3576或RK3588。 +# Aarch64 Linux 示例 +## 编译 + +首先导入GCC_COMPILER，例如`export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu +`，然后执行如下命令： + +``` +./build-linux.sh -t -a -b ] + +# 例如: +./build-linux.sh -t rk3588 -a aarch64 -b Release +``` + +## 安装 + +将 install/rknn_common_test_Android 拷贝到设备上。 + +- 如果使用Rockchip的EVB板，可以使用以下命令： + +连接设备并将程序和模型传输到`/userdata` + +``` +adb push install/rknn_common_test_Linux /userdata/ +``` + +- 如果你的板子有sshd服务，可以使用scp命令或者其他方式将程序和模型传输到板子上。 + +## 运行 + +``` +adb shell +cd /userdata/rknn_common_test_Linux/ +``` + +``` +export LD_LIBRARY_PATH=./lib +./rknn_common_test model//mobilenet_v1.rknn model/dog_224x224.jpg +``` + +# Android 示例 +## 编译 + +首先导入ANDROID_NDK_PATH，例如`export ANDROID_NDK_PATH=~/opts/ndk/android-ndk-r18b`，然后执行如下命令： + +``` +./build-android.sh -t -a [-b ] + +# 例如: +./build-android.sh -t rk3568 -a arm64-v8a -b Release +``` + +## 安装 + +连接设备并将程序和模型传输到`/data` + +``` +adb push install/rknn_common_test_Android /data/ +``` + +## 运行 + +``` +adb shell +cd /data/rknn_common_test_Android/ +``` + +``` +export LD_LIBRARY_PATH=./lib +./rknn_common_test model//mobilenet_v1.rknn model/dog_224x224.jpg +``` diff --git a/rknpu2/examples/rknn_common_test/build-android.sh b/rknpu2/examples/rknn_common_test/build-android.sh new file mode 100755 index 0000000000000000000000000000000000000000..4778e831eaff14905900f6fea7b8e3ee44147549 --- /dev/null +++ b/rknpu2/examples/rknn_common_test/build-android.sh @@ -0,0 +1,112 @@ +#!/bin/bash + +set -e + +if [[ -z ${ANDROID_NDK_PATH} ]];then + echo "Please set ANDROID_NDK_PATH, such as export ANDROID_NDK_PATH=~/opts/ndk/android-ndk-r18b" + echo "NDK Version r18, r19 is recommanded. Other version may cause build failure." + exit +fi + +echo "$0 $@" +while getopts ":t:a:b" opt; do + case $opt in + t) + TARGET_SOC=$OPTARG + ;; + a) + TARGET_ARCH=$OPTARG + ;; + b) + BUILD_TYPE=$OPTARG + ;; + :) + echo "Option -$OPTARG requires an argument." + exit 1 + ;; + ?) + echo "Invalid option: -$OPTARG index:$OPTIND" + ;; + esac +done + +if [ -z ${TARGET_SOC} ] || [ -z ${TARGET_ARCH} ] ; then + echo "$0 -t -a [-b ]" + echo "" + echo " -t : target (rk3566/rk3568/rk3562/rk3576/rk3588)" + echo " -a : arch (arm64-v8a/armeabi-v7a)" + echo " -b : build_type (Debug/Release)" + echo "such as: $0 -t rk3588 -a arm64-v8a -b Release" + echo "" + exit -1 +fi + +# Debug / Release / RelWithDebInfo +if [[ -z ${BUILD_TYPE} ]];then + BUILD_TYPE=Release +fi + +case ${TARGET_SOC} in + rk356x) + TARGET_SOC="RK3566_RK3568" + ;; + rk3566_rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3588) + TARGET_SOC="RK3588" + ;; + rk3576) + TARGET_SOC="RK3576" + ;; + rk3566) + TARGET_SOC="RK3566_RK3568" + ;; + rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3562) + TARGET_SOC="RK3562" + ;; + *) + echo "Invalid target: ${TARGET_SOC}" + echo "Valid target: rk3562,rk3566,rk3568,rk3576,rk3588" + exit -1 + ;; +esac + +TARGET_PLATFORM=${TARGET_SOC}_android +if [[ -n ${TARGET_ARCH} ]];then +TARGET_PLATFORM=${TARGET_PLATFORM}_${TARGET_ARCH} +fi +ROOT_PWD=$( cd "$( dirname $0 )" && cd -P "$( dirname "$SOURCE" )" && pwd ) +BUILD_DIR=${ROOT_PWD}/build/build_${TARGET_PLATFORM}_${BUILD_TYPE} + +echo "===================================" +echo "TARGET_SOC=${TARGET_SOC}" +echo "TARGET_ARCH=${TARGET_ARCH}" +echo "BUILD_TYPE=${BUILD_TYPE}" +echo "BUILD_DIR=${BUILD_DIR}" +echo "ANDROID_NDK_PATH=${ANDROID_NDK_PATH}" +echo "===================================" + +if [[ ! -d "${BUILD_DIR}" ]]; then + mkdir -p ${BUILD_DIR} +fi + + +cd ${BUILD_DIR} +cmake ../.. \ + -DANDROID_TOOLCHAIN=clang \ + -DTARGET_SOC=${TARGET_SOC} \ + -DCMAKE_SYSTEM_NAME=Android \ + -DCMAKE_TOOLCHAIN_FILE=$ANDROID_NDK_PATH/build/cmake/android.toolchain.cmake \ + -DANDROID_ABI=${TARGET_ARCH} \ + -DANDROID_STL=c++_static \ + -DANDROID_PLATFORM=android-24 \ + -DCMAKE_BUILD_TYPE=${BUILD_TYPE} +# make VERBOSE=1 +make -j4 +make install +cd .. + diff --git a/rknpu2/examples/rknn_common_test/build-linux.sh b/rknpu2/examples/rknn_common_test/build-linux.sh new file mode 100755 index 0000000000000000000000000000000000000000..ae972803b8bda834c3a95588e513ec7af832cbeb --- /dev/null +++ b/rknpu2/examples/rknn_common_test/build-linux.sh @@ -0,0 +1,122 @@ +#!/bin/bash + +set -e + +echo "$0 $@" +while getopts ":t:a:b" opt; do + case $opt in + t) + TARGET_SOC=$OPTARG + ;; + a) + TARGET_ARCH=$OPTARG + ;; + b) + BUILD_TYPE=$OPTARG + ;; + :) + echo "Option -$OPTARG requires an argument." + exit 1 + ;; + ?) + echo "Invalid option: -$OPTARG index:$OPTIND" + ;; + esac +done + +if [ -z ${TARGET_SOC} ];then + echo "$0 -t -a [-b ]" + echo "" + echo " -t : target (rk3566/rk3568/rk3562/rk3576/rk3588)" + echo " -a : arch (aarch64/armhf)" + echo " -b : build_type(Debug/Release)" + echo "such as: $0 -t rk3588 -a aarch64 -b Release" + echo "" + exit -1 +fi + +if [[ -z ${GCC_COMPILER} ]];then + echo "Please set GCC_COMPILER for $TARGET_SOC" + echo "such as export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu" + exit +fi +echo "$GCC_COMPILER" +export CC=${GCC_COMPILER}-gcc +export CXX=${GCC_COMPILER}-g++ + +if command -v ${CC} >/dev/null 2>&1; then + : +else + echo "${CC} is not available" + echo "Please set GCC_COMPILER for $TARGET_SOC" + echo "such as export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu" + exit +fi + +# Debug / Release +if [[ -z ${BUILD_TYPE} ]];then + BUILD_TYPE=Release +fi + + +case ${TARGET_SOC} in + rk356x) + TARGET_SOC="RK3566_RK3568" + ;; + rk3566_rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3588) + TARGET_SOC="RK3588" + ;; + rk3576) + TARGET_SOC="RK3576" + ;; + rk3566) + TARGET_SOC="RK3566_RK3568" + ;; + rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3562) + TARGET_SOC="RK3562" + ;; + *) + echo "Invalid target: ${TARGET_SOC}" + echo "Valid target: rk3562,rk3566,rk3568,rk3576,rk3588" + exit -1 + ;; +esac + +TARGET_PLATFORM=${TARGET_SOC}_linux +if [[ -n ${TARGET_ARCH} ]];then +TARGET_PLATFORM=${TARGET_PLATFORM}_${TARGET_ARCH} +fi +ROOT_PWD=$( cd "$( dirname $0 )" && cd -P "$( dirname "$SOURCE" )" && pwd ) +BUILD_DIR=${ROOT_PWD}/build/build_${TARGET_PLATFORM}_${BUILD_TYPE} + + +echo "===================================" +echo "TARGET_SOC=${TARGET_SOC}" +echo "TARGET_ARCH=${TARGET_ARCH}" +echo "BUILD_TYPE=${BUILD_TYPE}" +echo "BUILD_DIR=${BUILD_DIR}" +echo "CC=${CC}" +echo "CXX=${CXX}" +echo "===================================" + +if [[ ! -d "${BUILD_DIR}" ]]; then + mkdir -p ${BUILD_DIR} +fi + + +cd ${BUILD_DIR} +cmake ../.. \ + -DTARGET_SOC=${TARGET_SOC} \ + -DCMAKE_SYSTEM_NAME=Linux \ + -DCMAKE_SYSTEM_PROCESSOR=${TARGET_ARCH} \ + -DCMAKE_BUILD_TYPE=${BUILD_TYPE} \ + -DCMAKE_C_COMPILER=${CC} \ + -DCMAKE_CXX_COMPILER=${CXX} +make -j4 +make install diff --git a/rknpu2/examples/rknn_common_test/model/RK3562/mobilenet_v1.rknn b/rknpu2/examples/rknn_common_test/model/RK3562/mobilenet_v1.rknn new file mode 100644 index 0000000000000000000000000000000000000000..52670451c3bd0c72733ead26be1cc69dba2db6ca --- /dev/null +++ b/rknpu2/examples/rknn_common_test/model/RK3562/mobilenet_v1.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8a88faed454d7f33504079bef9ded09ba127b99e8bf3e28f2196d7fa1d16ae22 +size 4583849 diff --git a/rknpu2/examples/rknn_common_test/model/RK3566_RK3568/mobilenet_v1.rknn b/rknpu2/examples/rknn_common_test/model/RK3566_RK3568/mobilenet_v1.rknn new file mode 100644 index 0000000000000000000000000000000000000000..acfad8b2fdebd57c03fc742863516205d2ceccd7 --- /dev/null +++ b/rknpu2/examples/rknn_common_test/model/RK3566_RK3568/mobilenet_v1.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:92b36515616fc568ddc573f07a345cf28441efe0d24ecc9b98051fe8bcfad980 +size 4470313 diff --git a/rknpu2/examples/rknn_common_test/model/RK3576/mobilenet_v1.rknn b/rknpu2/examples/rknn_common_test/model/RK3576/mobilenet_v1.rknn new file mode 100644 index 0000000000000000000000000000000000000000..cb229fdfe4eedddf6c82729471461b686a35e83a --- /dev/null +++ b/rknpu2/examples/rknn_common_test/model/RK3576/mobilenet_v1.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bc66943ea85ec0dd8a04da22c4276bfc8a4c6fe24f5ea8be7a1e5c3c22c8259d +size 4679017 diff --git a/rknpu2/examples/rknn_common_test/model/RK3588/mobilenet_v1.rknn b/rknpu2/examples/rknn_common_test/model/RK3588/mobilenet_v1.rknn new file mode 100644 index 0000000000000000000000000000000000000000..e7c8380b1da6ac10257f0080304882c53791c340 --- /dev/null +++ b/rknpu2/examples/rknn_common_test/model/RK3588/mobilenet_v1.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:381dae3b7038a98b10f6ec9dcdbb094a49247341856fb294e692ef518218fcfb +size 4693865 diff --git a/rknpu2/examples/rknn_common_test/model/cat_224x224.jpg b/rknpu2/examples/rknn_common_test/model/cat_224x224.jpg new file mode 100644 index 0000000000000000000000000000000000000000..2159d5cfe3d6cbc01940a804cff0e3bc516af7fb --- /dev/null +++ b/rknpu2/examples/rknn_common_test/model/cat_224x224.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0ce3ef6da3424c0744385935a210e4ec8ab3a6ddda205113ab07b77f9c0e812a +size 58701 diff --git a/rknpu2/examples/rknn_common_test/model/dog_224x224.jpg b/rknpu2/examples/rknn_common_test/model/dog_224x224.jpg new file mode 100644 index 0000000000000000000000000000000000000000..004b0416e23454a12eb06eaba238c7e2d5f2b0fc --- /dev/null +++ b/rknpu2/examples/rknn_common_test/model/dog_224x224.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c350299c6283d5f62fecf1f845b6b3be9aafec8dff528ca09a129990f0a584b0 +size 18889 diff --git a/rknpu2/examples/rknn_common_test/src/main.cc b/rknpu2/examples/rknn_common_test/src/main.cc new file mode 100644 index 0000000000000000000000000000000000000000..e5dc8621bb2a8c514f800b4d4cb4c1900035b67a --- /dev/null +++ b/rknpu2/examples/rknn_common_test/src/main.cc @@ -0,0 +1,308 @@ +// Copyright (c) 2021 by Rockchip Electronics Co., Ltd. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +/*------------------------------------------- + Includes +-------------------------------------------*/ +#include "opencv2/core/core.hpp" +#include "opencv2/imgcodecs.hpp" +#include "opencv2/imgproc.hpp" +#include "rknn_api.h" + +#include +#include +#include +#include +#include + +using namespace std; +using namespace cv; + +/*------------------------------------------- + Functions +-------------------------------------------*/ +static inline int64_t getCurrentTimeUs() +{ + struct timeval tv; + gettimeofday(&tv, NULL); + return tv.tv_sec * 1000000 + tv.tv_usec; +} + +static int rknn_GetTopN(float* pfProb, float* pfMaxProb, uint32_t* pMaxClass, uint32_t outputCount, uint32_t topNum) +{ + uint32_t i, j; + uint32_t top_count = outputCount > topNum ? topNum : outputCount; + + for (i = 0; i < topNum; ++i) { + pfMaxProb[i] = -FLT_MAX; + pMaxClass[i] = -1; + } + + for (j = 0; j < top_count; j++) { + for (i = 0; i < outputCount; i++) { + if ((i == *(pMaxClass + 0)) || (i == *(pMaxClass + 1)) || (i == *(pMaxClass + 2)) || (i == *(pMaxClass + 3)) || + (i == *(pMaxClass + 4))) { + continue; + } + + if (pfProb[i] > *(pfMaxProb + j)) { + *(pfMaxProb + j) = pfProb[i]; + *(pMaxClass + j) = i; + } + } + } + + return 1; +} + +static void dump_tensor_attr(rknn_tensor_attr* attr) +{ + printf(" index=%d, name=%s, n_dims=%d, dims=[%d, %d, %d, %d], n_elems=%d, size=%d, fmt=%s, type=%s, qnt_type=%s, " + "zp=%d, scale=%f\n", + attr->index, attr->name, attr->n_dims, attr->dims[0], attr->dims[1], attr->dims[2], attr->dims[3], + attr->n_elems, attr->size, get_format_string(attr->fmt), get_type_string(attr->type), + get_qnt_type_string(attr->qnt_type), attr->zp, attr->scale); +} + +/*------------------------------------------- + Main Functions +-------------------------------------------*/ +int main(int argc, char* argv[]) +{ + if (argc < 3) { + printf("Usage:%s model_path input_path [loop_count]\n", argv[0]); + return -1; + } + + char* model_path = argv[1]; + char* input_path = argv[2]; + + int loop_count = 1; + if (argc > 3) { + loop_count = atoi(argv[3]); + } + + rknn_context ctx = 0; + + // Load RKNN Model + int ret = rknn_init(&ctx, model_path, 0, 0, NULL); + if (ret < 0) { + printf("rknn_init fail! ret=%d\n", ret); + return -1; + } + + // Get sdk and driver version + rknn_sdk_version sdk_ver; + ret = rknn_query(ctx, RKNN_QUERY_SDK_VERSION, &sdk_ver, sizeof(sdk_ver)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("rknn_api/rknnrt version: %s, driver version: %s\n", sdk_ver.api_version, sdk_ver.drv_version); + + // Get Model Input Output Info + rknn_input_output_num io_num; + ret = rknn_query(ctx, RKNN_QUERY_IN_OUT_NUM, &io_num, sizeof(io_num)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("model input num: %d, output num: %d\n", io_num.n_input, io_num.n_output); + + printf("input tensors:\n"); + rknn_tensor_attr input_attrs[io_num.n_input]; + memset(input_attrs, 0, io_num.n_input * sizeof(rknn_tensor_attr)); + for (uint32_t i = 0; i < io_num.n_input; i++) { + input_attrs[i].index = i; + // query info + ret = rknn_query(ctx, RKNN_QUERY_INPUT_ATTR, &(input_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret < 0) { + printf("rknn_init error! ret=%d\n", ret); + return -1; + } + dump_tensor_attr(&input_attrs[i]); + } + + printf("output tensors:\n"); + rknn_tensor_attr output_attrs[io_num.n_output]; + memset(output_attrs, 0, io_num.n_output * sizeof(rknn_tensor_attr)); + for (uint32_t i = 0; i < io_num.n_output; i++) { + output_attrs[i].index = i; + // query info + ret = rknn_query(ctx, RKNN_QUERY_OUTPUT_ATTR, &(output_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + dump_tensor_attr(&output_attrs[i]); + } + + // Get custom string + rknn_custom_string custom_string; + ret = rknn_query(ctx, RKNN_QUERY_CUSTOM_STRING, &custom_string, sizeof(custom_string)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("custom string: %s\n", custom_string.string); + + unsigned char* input_data = NULL; + rknn_tensor_type input_type = RKNN_TENSOR_UINT8; + rknn_tensor_format input_layout = RKNN_TENSOR_NHWC; + + // Load image + int req_height = 0; + int req_width = 0; + int req_channel = 0; + + switch (input_attrs[0].fmt) { + case RKNN_TENSOR_NHWC: + req_height = input_attrs[0].dims[1]; + req_width = input_attrs[0].dims[2]; + req_channel = input_attrs[0].dims[3]; + break; + case RKNN_TENSOR_NCHW: + req_height = input_attrs[0].dims[2]; + req_width = input_attrs[0].dims[3]; + req_channel = input_attrs[0].dims[1]; + break; + default: + printf("meet unsupported layout\n"); + return -1; + } + + int height = 0; + int width = 0; + int channel = 0; + + cv::Mat orig_img = imread(input_path, cv::IMREAD_COLOR); + if (!orig_img.data) { + printf("cv::imread %s fail!\n", input_path); + return -1; + } + + // if origin model is from Caffe, you maybe not need do BGR2RGB. + cv::Mat orig_img_rgb; + cv::cvtColor(orig_img, orig_img_rgb, cv::COLOR_BGR2RGB); + + cv::Mat img = orig_img_rgb.clone(); + if (orig_img.cols != req_width || orig_img.rows != req_height) { + printf("resize %d %d to %d %d\n", orig_img.cols, orig_img.rows, req_width, req_height); + cv::resize(orig_img_rgb, img, cv::Size(req_width, req_height), 0, 0, cv::INTER_LINEAR); + } + input_data = img.data; + if (!input_data) { + return -1; + } + + // Create input tensor memory + rknn_tensor_mem* input_mems[1]; + // default input type is int8 (normalize and quantize need compute in outside) + // if set uint8, will fuse normalize and quantize to npu + input_attrs[0].type = input_type; + // default fmt is NHWC, npu only support NHWC in zero copy mode + input_attrs[0].fmt = input_layout; + + input_mems[0] = rknn_create_mem(ctx, input_attrs[0].size_with_stride); + + // Copy input data to input tensor memory + width = input_attrs[0].dims[2]; + int stride = input_attrs[0].w_stride; + + if (width == stride) { + memcpy(input_mems[0]->virt_addr, input_data, width * input_attrs[0].dims[1] * input_attrs[0].dims[3]); + } else { + int height = input_attrs[0].dims[1]; + int channel = input_attrs[0].dims[3]; + // copy from src to dst with stride + uint8_t* src_ptr = input_data; + uint8_t* dst_ptr = (uint8_t*)input_mems[0]->virt_addr; + // width-channel elements + int src_wc_elems = width * channel; + int dst_wc_elems = stride * channel; + for (int h = 0; h < height; ++h) { + memcpy(dst_ptr, src_ptr, src_wc_elems); + src_ptr += src_wc_elems; + dst_ptr += dst_wc_elems; + } + } + + // Create output tensor memory + rknn_tensor_mem* output_mems[io_num.n_output]; + for (uint32_t i = 0; i < io_num.n_output; ++i) { + // default output type is depend on model, this require float32 to compute top5 + // allocate float32 output tensor + int output_size = output_attrs[i].n_elems * sizeof(float); + output_mems[i] = rknn_create_mem(ctx, output_size); + } + + // Set input tensor memory + ret = rknn_set_io_mem(ctx, input_mems[0], &input_attrs[0]); + if (ret < 0) { + printf("rknn_set_io_mem fail! ret=%d\n", ret); + return -1; + } + + // Set output tensor memory + for (uint32_t i = 0; i < io_num.n_output; ++i) { + // default output type is depend on model, this require float32 to compute top5 + output_attrs[i].type = RKNN_TENSOR_FLOAT32; + // set output memory and attribute + ret = rknn_set_io_mem(ctx, output_mems[i], &output_attrs[i]); + if (ret < 0) { + printf("rknn_set_io_mem fail! ret=%d\n", ret); + return -1; + } + } + + // Run + printf("Begin perf ...\n"); + for (int i = 0; i < loop_count; ++i) { + int64_t start_us = getCurrentTimeUs(); + ret = rknn_run(ctx, NULL); + int64_t elapse_us = getCurrentTimeUs() - start_us; + if (ret < 0) { + printf("rknn run error %d\n", ret); + return -1; + } + printf("%4d: Elapse Time = %.2fms, FPS = %.2f\n", i, elapse_us / 1000.f, 1000.f * 1000.f / elapse_us); + } + + // Get top 5 + uint32_t topNum = 5; + for (uint32_t i = 0; i < io_num.n_output; i++) { + uint32_t MaxClass[topNum]; + float fMaxProb[topNum]; + float* buffer = (float*)output_mems[i]->virt_addr; + uint32_t sz = output_attrs[i].n_elems; + int top_count = sz > topNum ? topNum : sz; + + rknn_GetTopN(buffer, fMaxProb, MaxClass, sz, topNum); + + printf("---- Top%d ----\n", top_count); + for (int j = 0; j < top_count; j++) { + printf("%8.6f - %d\n", fMaxProb[j], MaxClass[j]); + } + } + + // Destroy rknn memory + rknn_destroy_mem(ctx, input_mems[0]); + for (uint32_t i = 0; i < io_num.n_output; ++i) { + rknn_destroy_mem(ctx, output_mems[i]); + } + + // destroy + rknn_destroy(ctx); + return 0; +} diff --git a/rknpu2/examples/rknn_custom_op_demo/rknn_custom_cpu_op_demo/CMakeLists.txt b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_cpu_op_demo/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..d0bb899af6c5d42d3b0d80f6393233f9ef548d82 --- /dev/null +++ b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_cpu_op_demo/CMakeLists.txt @@ -0,0 +1,50 @@ +cmake_minimum_required(VERSION 3.6) + +project(rknn_custom_cpu_op_demo) + +set(CMAKE_CXX_STANDARD 11) +set(CMAKE_CXX_STANDARD_REQUIRED ON) + +# rknn api +set(RKNN_API_PATH ${CMAKE_SOURCE_DIR}/../../../runtime/${CMAKE_SYSTEM_NAME}/librknn_api) +if(CMAKE_SYSTEM_NAME STREQUAL "Android") + set(RKNN_RT_LIB ${RKNN_API_PATH}/${CMAKE_ANDROID_ARCH_ABI}/librknnrt.so) +else() + if(CMAKE_C_COMPILER MATCHES "aarch64") + set(LIB_ARCH aarch64) + else() + set(LIB_ARCH armhf) + endif() + + set(RKNN_RT_LIB ${RKNN_API_PATH}/${LIB_ARCH}/librknnrt.so) +endif() + +include_directories(${RKNN_API_PATH}/include) +include_directories(${CMAKE_SOURCE_DIR}/../../3rdparty) + +set(CMAKE_INSTALL_RPATH "lib") + + +add_executable(rknn_custom_cpu_op_demo + src/rknn_api_test_custom_cpu_op.cpp +) + +target_link_libraries(rknn_custom_cpu_op_demo + ${RKNN_RT_LIB} +) + +if(${CMAKE_SYSTEM_NAME} STREQUAL "Android") + target_link_libraries(rknn_custom_cpu_op_demo log) +endif() + +# install target and libraries +set(CMAKE_INSTALL_PREFIX ${CMAKE_SOURCE_DIR}/install/rknn_custom_cpu_op_demo_${CMAKE_SYSTEM_NAME}) + +install(TARGETS rknn_custom_cpu_op_demo DESTINATION ./) +install(PROGRAMS ${RKNN_RT_LIB} DESTINATION lib) +file(GLOB IMAGE_FILES "model/*.jpg") +install(FILES ${IMAGE_FILES} DESTINATION ./model/) +file(GLOB LABELS_FILES "model/*.txt") +install(FILES ${LABELS_FILES} DESTINATION ./model/) +message(STATUS "target_soc = ${TARGET_SOC}") +install(DIRECTORY model/${TARGET_SOC} DESTINATION ./model) diff --git a/rknpu2/examples/rknn_custom_op_demo/rknn_custom_cpu_op_demo/README.md b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_cpu_op_demo/README.md new file mode 100644 index 0000000000000000000000000000000000000000..b4a24a9c4d9059dfb196ca72a4d4973c3798a8fa --- /dev/null +++ b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_cpu_op_demo/README.md @@ -0,0 +1,99 @@ +# RKNN Custom CPU OP Demo +This demo is an example of showing how to implement custom cpu OP on C demo for RKNN model, taking 'Sigmoid' as an example. Replace ONNX OP Sigmoid with custom OP cstSigmoid. Users can refer to [RKNN-Toolkit2 example](https://github.com/airockchip/rknn-toolkit2/tree/master/rknn-toolkit2/examples/functions/custom_op/non-onnx_standard) for how to replace ONNX OP and convert ONNX model to RKNN model with registered custom op. + +## Model Source +The model and YOLOX version used in this example come from the following open source project: +https://github.com/airockchip/YOLOX + +Download link: + +[yolox_s.onnx](https://ftrg.zbox.filez.com/v2/delivery/data/95f00b0fc900458ba134f8b180b3f7a1/examples/yolox/yolox_s.onnx) + +The converted RKNN models of platforms RK3562, RK356X, RK3576 and RK3588 are located in model folder. + +# Aarch64 Linux Demo + +## Build + +First export `GCC_COMPILER`, for example `export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu`, then execute: + +``` +./build-linux.sh -t -a -b ] + +# such as: +./build-linux.sh -t rk3588 -a aarch64 -b Release +``` + +## *Install* + +Copy install/rknn_custom_cpu_op_demo_Linux to the devices under /userdata/. + +- If you use rockchip's evb board, you can use the following way: + +Connect device and push the program and RKNN model to `/userdata` + +``` +adb push install/rknn_custom_cpu_op_demo_Linux/ /data/ +``` + +- If board has sshd service, users can use scp or other methods to copy the program and RKNN model to the board. + +## Run + +``` +adb shell +cd /data/rknn_custom_cpu_op_demo_Linux +``` + +```shell +export LD_LIBRARY_PATH=./lib +./rknn_custom_cpu_op_demo model//yolox_s_custom_sigmoid.rknn model/test_image.jpg 1 +``` + +# Android Demo + +## Build + +First export `ANDROID_NDK_PATH`, for example `export ANDROID_NDK_PATH=~/opts/ndk/android-ndk-r18b`, then execute: + +``` +./build-android.sh -t -a [-b ] + +# sush as: +./build-android.sh -t rk3568 -a arm64-v8a -b Release +``` + +## Install + +connect device and push build output into `/data` + +``` + adb push install/rknn_custom_cpu_op_demo_Android/ /data/ +``` + +## Run + +``` +adb shell +cd /data/rknn_custom_cpu_op_demo_Android/ +``` + +then exporting LD_LIBRARY_PATH to the local path of lib. + +``` +export LD_LIBRARY_PATH=./lib +./rknn_custom_cpu_op_demo model//yolox_s_custom_sigmoid.rknn model/test_image.jpg 1 +``` + +Note: Different platforms, different versions of tools and drivers may have slightly different results. + +## Results + +Take RKN3588 platform as an example, its results are as shown: + +bus @ (92 132 558 438) 0.933 +person @ (104 237 221 534) 0.901 +person @ (211 240 285 504) 0.901 +person @ (472 248 559 525) 0.774 +person @ (79 329 118 512) 0.347 +bicycle @ (80 420 96 515) 0.253 \ No newline at end of file diff --git a/rknpu2/examples/rknn_custom_op_demo/rknn_custom_cpu_op_demo/build-android.sh b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_cpu_op_demo/build-android.sh new file mode 100755 index 0000000000000000000000000000000000000000..4778e831eaff14905900f6fea7b8e3ee44147549 --- /dev/null +++ b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_cpu_op_demo/build-android.sh @@ -0,0 +1,112 @@ +#!/bin/bash + +set -e + +if [[ -z ${ANDROID_NDK_PATH} ]];then + echo "Please set ANDROID_NDK_PATH, such as export ANDROID_NDK_PATH=~/opts/ndk/android-ndk-r18b" + echo "NDK Version r18, r19 is recommanded. Other version may cause build failure." + exit +fi + +echo "$0 $@" +while getopts ":t:a:b" opt; do + case $opt in + t) + TARGET_SOC=$OPTARG + ;; + a) + TARGET_ARCH=$OPTARG + ;; + b) + BUILD_TYPE=$OPTARG + ;; + :) + echo "Option -$OPTARG requires an argument." + exit 1 + ;; + ?) + echo "Invalid option: -$OPTARG index:$OPTIND" + ;; + esac +done + +if [ -z ${TARGET_SOC} ] || [ -z ${TARGET_ARCH} ] ; then + echo "$0 -t -a [-b ]" + echo "" + echo " -t : target (rk3566/rk3568/rk3562/rk3576/rk3588)" + echo " -a : arch (arm64-v8a/armeabi-v7a)" + echo " -b : build_type (Debug/Release)" + echo "such as: $0 -t rk3588 -a arm64-v8a -b Release" + echo "" + exit -1 +fi + +# Debug / Release / RelWithDebInfo +if [[ -z ${BUILD_TYPE} ]];then + BUILD_TYPE=Release +fi + +case ${TARGET_SOC} in + rk356x) + TARGET_SOC="RK3566_RK3568" + ;; + rk3566_rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3588) + TARGET_SOC="RK3588" + ;; + rk3576) + TARGET_SOC="RK3576" + ;; + rk3566) + TARGET_SOC="RK3566_RK3568" + ;; + rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3562) + TARGET_SOC="RK3562" + ;; + *) + echo "Invalid target: ${TARGET_SOC}" + echo "Valid target: rk3562,rk3566,rk3568,rk3576,rk3588" + exit -1 + ;; +esac + +TARGET_PLATFORM=${TARGET_SOC}_android +if [[ -n ${TARGET_ARCH} ]];then +TARGET_PLATFORM=${TARGET_PLATFORM}_${TARGET_ARCH} +fi +ROOT_PWD=$( cd "$( dirname $0 )" && cd -P "$( dirname "$SOURCE" )" && pwd ) +BUILD_DIR=${ROOT_PWD}/build/build_${TARGET_PLATFORM}_${BUILD_TYPE} + +echo "===================================" +echo "TARGET_SOC=${TARGET_SOC}" +echo "TARGET_ARCH=${TARGET_ARCH}" +echo "BUILD_TYPE=${BUILD_TYPE}" +echo "BUILD_DIR=${BUILD_DIR}" +echo "ANDROID_NDK_PATH=${ANDROID_NDK_PATH}" +echo "===================================" + +if [[ ! -d "${BUILD_DIR}" ]]; then + mkdir -p ${BUILD_DIR} +fi + + +cd ${BUILD_DIR} +cmake ../.. \ + -DANDROID_TOOLCHAIN=clang \ + -DTARGET_SOC=${TARGET_SOC} \ + -DCMAKE_SYSTEM_NAME=Android \ + -DCMAKE_TOOLCHAIN_FILE=$ANDROID_NDK_PATH/build/cmake/android.toolchain.cmake \ + -DANDROID_ABI=${TARGET_ARCH} \ + -DANDROID_STL=c++_static \ + -DANDROID_PLATFORM=android-24 \ + -DCMAKE_BUILD_TYPE=${BUILD_TYPE} +# make VERBOSE=1 +make -j4 +make install +cd .. + diff --git a/rknpu2/examples/rknn_custom_op_demo/rknn_custom_cpu_op_demo/build-linux.sh b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_cpu_op_demo/build-linux.sh new file mode 100755 index 0000000000000000000000000000000000000000..ae972803b8bda834c3a95588e513ec7af832cbeb --- /dev/null +++ b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_cpu_op_demo/build-linux.sh @@ -0,0 +1,122 @@ +#!/bin/bash + +set -e + +echo "$0 $@" +while getopts ":t:a:b" opt; do + case $opt in + t) + TARGET_SOC=$OPTARG + ;; + a) + TARGET_ARCH=$OPTARG + ;; + b) + BUILD_TYPE=$OPTARG + ;; + :) + echo "Option -$OPTARG requires an argument." + exit 1 + ;; + ?) + echo "Invalid option: -$OPTARG index:$OPTIND" + ;; + esac +done + +if [ -z ${TARGET_SOC} ];then + echo "$0 -t -a [-b ]" + echo "" + echo " -t : target (rk3566/rk3568/rk3562/rk3576/rk3588)" + echo " -a : arch (aarch64/armhf)" + echo " -b : build_type(Debug/Release)" + echo "such as: $0 -t rk3588 -a aarch64 -b Release" + echo "" + exit -1 +fi + +if [[ -z ${GCC_COMPILER} ]];then + echo "Please set GCC_COMPILER for $TARGET_SOC" + echo "such as export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu" + exit +fi +echo "$GCC_COMPILER" +export CC=${GCC_COMPILER}-gcc +export CXX=${GCC_COMPILER}-g++ + +if command -v ${CC} >/dev/null 2>&1; then + : +else + echo "${CC} is not available" + echo "Please set GCC_COMPILER for $TARGET_SOC" + echo "such as export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu" + exit +fi + +# Debug / Release +if [[ -z ${BUILD_TYPE} ]];then + BUILD_TYPE=Release +fi + + +case ${TARGET_SOC} in + rk356x) + TARGET_SOC="RK3566_RK3568" + ;; + rk3566_rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3588) + TARGET_SOC="RK3588" + ;; + rk3576) + TARGET_SOC="RK3576" + ;; + rk3566) + TARGET_SOC="RK3566_RK3568" + ;; + rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3562) + TARGET_SOC="RK3562" + ;; + *) + echo "Invalid target: ${TARGET_SOC}" + echo "Valid target: rk3562,rk3566,rk3568,rk3576,rk3588" + exit -1 + ;; +esac + +TARGET_PLATFORM=${TARGET_SOC}_linux +if [[ -n ${TARGET_ARCH} ]];then +TARGET_PLATFORM=${TARGET_PLATFORM}_${TARGET_ARCH} +fi +ROOT_PWD=$( cd "$( dirname $0 )" && cd -P "$( dirname "$SOURCE" )" && pwd ) +BUILD_DIR=${ROOT_PWD}/build/build_${TARGET_PLATFORM}_${BUILD_TYPE} + + +echo "===================================" +echo "TARGET_SOC=${TARGET_SOC}" +echo "TARGET_ARCH=${TARGET_ARCH}" +echo "BUILD_TYPE=${BUILD_TYPE}" +echo "BUILD_DIR=${BUILD_DIR}" +echo "CC=${CC}" +echo "CXX=${CXX}" +echo "===================================" + +if [[ ! -d "${BUILD_DIR}" ]]; then + mkdir -p ${BUILD_DIR} +fi + + +cd ${BUILD_DIR} +cmake ../.. \ + -DTARGET_SOC=${TARGET_SOC} \ + -DCMAKE_SYSTEM_NAME=Linux \ + -DCMAKE_SYSTEM_PROCESSOR=${TARGET_ARCH} \ + -DCMAKE_BUILD_TYPE=${BUILD_TYPE} \ + -DCMAKE_C_COMPILER=${CC} \ + -DCMAKE_CXX_COMPILER=${CXX} +make -j4 +make install diff --git a/rknpu2/examples/rknn_custom_op_demo/rknn_custom_cpu_op_demo/model/RK3562/yolox_s_custom_sigmoid.rknn b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_cpu_op_demo/model/RK3562/yolox_s_custom_sigmoid.rknn new file mode 100644 index 0000000000000000000000000000000000000000..f2ff9dc4b815a2295a39b77c9d49650408afda86 --- /dev/null +++ b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_cpu_op_demo/model/RK3562/yolox_s_custom_sigmoid.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1fa735651ab4cff19255b05edaa11b30b6441bfe5ccd4511bdc493b7f9bc25ae +size 9646528 diff --git a/rknpu2/examples/rknn_custom_op_demo/rknn_custom_cpu_op_demo/model/RK3566_RK3568/yolox_s_custom_sigmoid.rknn b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_cpu_op_demo/model/RK3566_RK3568/yolox_s_custom_sigmoid.rknn new file mode 100644 index 0000000000000000000000000000000000000000..e59215bc91dd56fe441426ff166fecab3817c810 --- /dev/null +++ b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_cpu_op_demo/model/RK3566_RK3568/yolox_s_custom_sigmoid.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8b21dfca21cc368c1e5527dd7b122cfbf0f54f57f18b10ad51128cc8c29cb173 +size 9861760 diff --git a/rknpu2/examples/rknn_custom_op_demo/rknn_custom_cpu_op_demo/model/RK3576/yolox_s_custom_sigmoid.rknn b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_cpu_op_demo/model/RK3576/yolox_s_custom_sigmoid.rknn new file mode 100644 index 0000000000000000000000000000000000000000..eb2723055b8a4210a35bfbb765978e053ca90cc7 --- /dev/null +++ b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_cpu_op_demo/model/RK3576/yolox_s_custom_sigmoid.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b7c20dc8638560133acbdf41d84b024ef33541bf744330f29adab8c6a6300b6d +size 10558144 diff --git a/rknpu2/examples/rknn_custom_op_demo/rknn_custom_cpu_op_demo/model/RK3588/yolox_s_custom_sigmoid.rknn b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_cpu_op_demo/model/RK3588/yolox_s_custom_sigmoid.rknn new file mode 100644 index 0000000000000000000000000000000000000000..ef4de25b2540da65c7e80b2e90a8d1184b1de0d4 --- /dev/null +++ b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_cpu_op_demo/model/RK3588/yolox_s_custom_sigmoid.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e7539f8f57b59da7cb043231be12cf49e82862136b05044b1c07f82047c40e79 +size 10768064 diff --git a/rknpu2/examples/rknn_custom_op_demo/rknn_custom_cpu_op_demo/model/coco_80_labels_list.txt b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_cpu_op_demo/model/coco_80_labels_list.txt new file mode 100644 index 0000000000000000000000000000000000000000..941cb4e1392266f6a6c09b1fdc5f79503b2e5df6 --- /dev/null +++ b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_cpu_op_demo/model/coco_80_labels_list.txt @@ -0,0 +1,80 @@ +person +bicycle +car +motorcycle +airplane +bus +train +truck +boat +traffic light +fire hydrant +stop sign +parking meter +bench +bird +cat +dog +horse +sheep +cow +elephant +bear +zebra +giraffe +backpack +umbrella +handbag +tie +suitcase +frisbee +skis +snowboard +sports ball +kite +baseball bat +baseball glove +skateboard +surfboard +tennis racket +bottle +wine glass +cup +fork +knife +spoon +bowl +banana +apple +sandwich +orange +broccoli +carrot +hot dog +pizza +donut +cake +chair +couch +potted plant +bed +dining table +toilet +tv +laptop +mouse +remote +keyboard +cell phone +microwave +oven +toaster +sink +refrigerator +book +clock +vase +scissors +teddy bear +hair drier +toothbrush diff --git a/rknpu2/examples/rknn_custom_op_demo/rknn_custom_cpu_op_demo/model/test_image.jpg b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_cpu_op_demo/model/test_image.jpg new file mode 100644 index 0000000000000000000000000000000000000000..19172b78dbbcf34de2b875ac72eb0b4b0651ee66 --- /dev/null +++ b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_cpu_op_demo/model/test_image.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fb4914d123d97c440cd127ef0e98d4bdc68cd88e2683657528928b4a34014e16 +size 181374 diff --git a/rknpu2/examples/rknn_custom_op_demo/rknn_custom_cpu_op_demo/src/rknn_api_test_custom_cpu_op.cpp b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_cpu_op_demo/src/rknn_api_test_custom_cpu_op.cpp new file mode 100755 index 0000000000000000000000000000000000000000..95310aa73eb97f6aca441b2ada8174c33a769b56 --- /dev/null +++ b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_cpu_op_demo/src/rknn_api_test_custom_cpu_op.cpp @@ -0,0 +1,817 @@ +// Copyright (c) 2023 by Rockchip Electronics Co., Ltd. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +/*------------------------------------------- + Includes +-------------------------------------------*/ + +#include +#include +#include +#include + +#include "rknn_api.h" +#include "rknn_custom_op.h" + +#define STB_IMAGE_IMPLEMENTATION +#include "stb/stb_image.h" +#define STB_IMAGE_RESIZE_IMPLEMENTATION +#include +#define STB_IMAGE_WRITE_IMPLEMENTATION +#include + +#define OBJ_NAME_MAX_SIZE 64 +#define OBJ_NUMB_MAX_SIZE 128 +#define OBJ_CLASS_NUM 80 +#define NMS_THRESH 0.45 +#define BOX_THRESH 0.25 +#define LABEL_NALE_TXT_PATH "./model/coco_80_labels_list.txt" + +static char *labels[OBJ_CLASS_NUM]; + +typedef enum { + IMAGE_FORMAT_GRAY8, + IMAGE_FORMAT_RGB888, + IMAGE_FORMAT_RGBA8888, + IMAGE_FORMAT_YUV420SP_NV21, + IMAGE_FORMAT_YUV420SP_NV12, +} image_format_t; + +typedef struct { + int width; + int height; + int width_stride; + int height_stride; + image_format_t format; + unsigned char* virt_addr; + int size; + int fd; +} image_buffer_t; + +typedef struct { + int left; + int top; + int right; + int bottom; +} image_rect_t; + +typedef struct { + int x_pad; + int y_pad; + float scale; +} letterbox_t; + +typedef struct { + image_rect_t box; + float prop; + int cls_id; +} object_detect_result; + +typedef struct { + int id; + int count; + object_detect_result results[OBJ_NUMB_MAX_SIZE]; +} object_detect_result_list; + +/*------------------------------------------- + Functions +-------------------------------------------*/ +static inline int64_t getCurrentTimeUs() +{ + struct timeval tv; + gettimeofday(&tv, NULL); + return tv.tv_sec * 1000000 + tv.tv_usec; +} + +static void dump_tensor_attr(rknn_tensor_attr* attr) +{ + std::string shape_str = attr->n_dims < 1 ? "" : std::to_string(attr->dims[0]); + for (int i = 1; i < attr->n_dims; ++i) { + shape_str += ", " + std::to_string(attr->dims[i]); + } + + printf(" index=%d, name=%s, n_dims=%d, dims=[%s], n_elems=%d, size=%d, w_stride = %d, size_with_stride=%d, fmt=%s, " + "type=%s, qnt_type=%s, " + "zp=%d, scale=%f\n", + attr->index, attr->name, attr->n_dims, shape_str.c_str(), attr->n_elems, attr->size, attr->w_stride, + attr->size_with_stride, get_format_string(attr->fmt), get_type_string(attr->type), + get_qnt_type_string(attr->qnt_type), attr->zp, attr->scale); +} + +static unsigned char* load_image(const char* image_path, rknn_tensor_attr* input_attr) +{ + int req_height = 0; + int req_width = 0; + int req_channel = 0; + + switch (input_attr->fmt) { + case RKNN_TENSOR_NHWC: + req_height = input_attr->dims[1]; + req_width = input_attr->dims[2]; + req_channel = input_attr->dims[3]; + break; + case RKNN_TENSOR_NCHW: + req_height = input_attr->dims[2]; + req_width = input_attr->dims[3]; + req_channel = input_attr->dims[1]; + break; + default: + printf("meet unsupported layout\n"); + return NULL; + } + + int height = 0; + int width = 0; + int channel = 0; + + unsigned char* image_data = stbi_load(image_path, &width, &height, &channel, req_channel); + if (image_data == NULL) { + printf("load image failed!\n"); + return NULL; + } + + if (width != req_width || height != req_height) { + unsigned char* image_resized = (unsigned char*)STBI_MALLOC(req_width * req_height * req_channel); + if (!image_resized) { + printf("malloc image failed!\n"); + STBI_FREE(image_data); + return NULL; + } + if (stbir_resize_uint8(image_data, width, height, 0, image_resized, req_width, req_height, 0, channel) != 1) { + printf("resize image failed!\n"); + STBI_FREE(image_data); + return NULL; + } + STBI_FREE(image_data); + image_data = image_resized; + } + + return image_data; +} + +static std::vector split(const std::string& str, const std::string& pattern) +{ + std::vector res; + if (str == "") + return res; + std::string strs = str + pattern; + size_t pos = strs.find(pattern); + while (pos != strs.npos) { + std::string temp = strs.substr(0, pos); + res.push_back(temp); + strs = strs.substr(pos + 1, strs.size()); + pos = strs.find(pattern); + } + return res; +} + +static void* load_file(const char* file_path, size_t* file_size) +{ + FILE* fp = fopen(file_path, "r"); + if (fp == NULL) { + printf("failed to open file: %s\n", file_path); + return NULL; + } + + fseek(fp, 0, SEEK_END); + size_t size = (size_t)ftell(fp); + fseek(fp, 0, SEEK_SET); + + void* file_data = malloc(size); + if (file_data == NULL) { + fclose(fp); + printf("failed allocate file size: %zu\n", size); + return NULL; + } + + if (fread(file_data, 1, size, fp) != size) { + fclose(fp); + free(file_data); + printf("failed to read file data!\n"); + return NULL; + } + + fclose(fp); + + *file_size = size; + + return file_data; +} + +inline static int clamp(float val, int min, int max) { return val > min ? (val < max ? val : max) : min; } + +static char *readLine(FILE *fp, char *buffer, int *len) +{ + int ch; + int i = 0; + size_t buff_len = 0; + + buffer = (char *)malloc(buff_len + 1); + if (!buffer) + return NULL; // Out of memory + + while ((ch = fgetc(fp)) != '\n' && ch != EOF) + { + buff_len++; + void *tmp = realloc(buffer, buff_len + 1); + if (tmp == NULL) + { + free(buffer); + return NULL; // Out of memory + } + buffer = (char *)tmp; + + buffer[i] = (char)ch; + i++; + } + buffer[i] = '\0'; + + *len = buff_len; + + // Detect end + if (ch == EOF && (i == 0 || ferror(fp))) + { + free(buffer); + return NULL; + } + return buffer; +} + +static int readLines(const char *fileName, char *lines[], int max_line) +{ + FILE *file = fopen(fileName, "r"); + char *s; + int i = 0; + int n = 0; + + if (file == NULL) + { + printf("Open %s fail!\n", fileName); + return -1; + } + + while ((s = readLine(file, s, &n)) != NULL) + { + lines[i++] = s; + if (i >= max_line) + break; + } + fclose(file); + return i; +} + +static int loadLabelName(const char *locationFilename, char *label[]) +{ + printf("load lable %s\n", locationFilename); + readLines(locationFilename, label, OBJ_CLASS_NUM); + return 0; +} + +static float CalculateOverlap(float xmin0, float ymin0, float xmax0, float ymax0, float xmin1, float ymin1, float xmax1, + float ymax1) +{ + float w = fmax(0.f, fmin(xmax0, xmax1) - fmax(xmin0, xmin1) + 1.0); + float h = fmax(0.f, fmin(ymax0, ymax1) - fmax(ymin0, ymin1) + 1.0); + float i = w * h; + float u = (xmax0 - xmin0 + 1.0) * (ymax0 - ymin0 + 1.0) + (xmax1 - xmin1 + 1.0) * (ymax1 - ymin1 + 1.0) - i; + return u <= 0.f ? 0.f : (i / u); +} + +static int nms(int validCount, std::vector &outputLocations, std::vector classIds, std::vector &order, + int filterId, float threshold) +{ + for (int i = 0; i < validCount; ++i) + { + int n = order[i]; + if (n == -1 || classIds[n] != filterId) + { + continue; + } + for (int j = i + 1; j < validCount; ++j) + { + int m = order[j]; + if (m == -1 || classIds[m] != filterId) + { + continue; + } + float xmin0 = outputLocations[n * 4 + 0]; + float ymin0 = outputLocations[n * 4 + 1]; + float xmax0 = outputLocations[n * 4 + 0] + outputLocations[n * 4 + 2]; + float ymax0 = outputLocations[n * 4 + 1] + outputLocations[n * 4 + 3]; + + float xmin1 = outputLocations[m * 4 + 0]; + float ymin1 = outputLocations[m * 4 + 1]; + float xmax1 = outputLocations[m * 4 + 0] + outputLocations[m * 4 + 2]; + float ymax1 = outputLocations[m * 4 + 1] + outputLocations[m * 4 + 3]; + + float iou = CalculateOverlap(xmin0, ymin0, xmax0, ymax0, xmin1, ymin1, xmax1, ymax1); + + if (iou > threshold) + { + order[j] = -1; + } + } + } + return 0; +} + +static int quick_sort_indice_inverse(std::vector &input, int left, int right, std::vector &indices) +{ + float key; + int key_index; + int low = left; + int high = right; + if (left < right) + { + key_index = indices[left]; + key = input[left]; + while (low < high) + { + while (low < high && input[high] <= key) + { + high--; + } + input[low] = input[high]; + indices[low] = indices[high]; + while (low < high && input[low] >= key) + { + low++; + } + input[high] = input[low]; + indices[high] = indices[low]; + } + input[low] = key; + indices[low] = key_index; + quick_sort_indice_inverse(input, left, low - 1, indices); + quick_sort_indice_inverse(input, low + 1, right, indices); + } + return low; +} + +inline static int32_t __clip(float val, float min, float max) +{ + float f = val <= min ? min : (val >= max ? max : val); + return f; +} + +static int8_t qnt_f32_to_affine(float f32, int32_t zp, float scale) +{ + float dst_val = (f32 / scale) + zp; + int8_t res = (int8_t)__clip(dst_val, -128, 127); + return res; +} + +static float deqnt_affine_to_f32(int8_t qnt, int32_t zp, float scale) { return ((float)qnt - (float)zp) * scale; } + +static int process_i8(int8_t *input, int grid_h, int grid_w, int height, int width, int stride, + std::vector &boxes, std::vector &objProbs, std::vector &classId, float threshold, + int32_t zp, float scale) +{ + int validCount = 0; + int grid_len = grid_h * grid_w; + int8_t thres_i8 = qnt_f32_to_affine(threshold, zp, scale); + + for (int i = 0; i < grid_h; ++i) { + for (int j = 0; j < grid_w; ++j) { + int8_t box_confidence = input[4 * grid_len + i * grid_w + j]; + if (box_confidence >= thres_i8) { + int offset = i * grid_w + j; + int8_t *in_ptr = input + offset; + + int8_t maxClassProbs = in_ptr[5 * grid_len]; + int maxClassId = 0; + for (int k = 1; k < OBJ_CLASS_NUM; ++k) + { + int8_t prob = in_ptr[(5 + k) * grid_len]; + if (prob > maxClassProbs) + { + maxClassId = k; + maxClassProbs = prob; + } + } + + if (maxClassProbs > thres_i8) + { + float box_x = (deqnt_affine_to_f32(*in_ptr, zp, scale)); + float box_y = (deqnt_affine_to_f32(in_ptr[grid_len], zp, scale)); + float box_w = (deqnt_affine_to_f32(in_ptr[2 * grid_len], zp, scale)); + float box_h = (deqnt_affine_to_f32(in_ptr[3 * grid_len], zp, scale)); + box_x = (box_x + j) * (float)stride; + box_y = (box_y + i) * (float)stride; + box_w = exp(box_w) * stride; + box_h = exp(box_h) * stride; + box_x -= (box_w / 2.0); + box_y -= (box_h / 2.0); + + objProbs.push_back((deqnt_affine_to_f32(maxClassProbs, zp, scale)) * (deqnt_affine_to_f32(box_confidence, zp, scale))); + classId.push_back(maxClassId); + validCount++; + boxes.push_back(box_x); + boxes.push_back(box_y); + boxes.push_back(box_w); + boxes.push_back(box_h); + } + } + } + } + return validCount; +} + +int post_process(rknn_tensor_attr *input_attrs, rknn_tensor_attr *output_attrs, rknn_output *outputs, letterbox_t *letter_box, float conf_threshold, float nms_threshold, object_detect_result_list *od_results) +{ + std::vector filterBoxes; + std::vector objProbs; + std::vector classId; + int validCount = 0; + int stride = 0; + int grid_h = 0; + int grid_w = 0; + + int model_in_w = 0; + int model_in_h = 0; + if (input_attrs[0].fmt == RKNN_TENSOR_NCHW) + { + printf("model is NCHW input fmt\n"); + model_in_h = input_attrs[0].dims[2]; + model_in_w = input_attrs[0].dims[3]; + } + else + { + printf("model is NHWC input fmt\n"); + model_in_h = input_attrs[0].dims[1]; + model_in_w = input_attrs[0].dims[2]; + } + + memset(od_results, 0, sizeof(object_detect_result_list)); + + for (int i = 0; i < 3; i++) + { + grid_h = output_attrs[i].dims[2]; + grid_w = output_attrs[i].dims[3]; + stride = model_in_h / grid_h; + + validCount += process_i8((int8_t *)outputs[i].buf, grid_h, grid_w, model_in_h, model_in_w, stride, filterBoxes, objProbs, + classId, conf_threshold, output_attrs[i].zp, output_attrs[i].scale); + } + + // no object detect + if (validCount <= 0) + { + return 0; + } + std::vector indexArray; + for (int i = 0; i < validCount; ++i) + { + indexArray.push_back(i); + } + quick_sort_indice_inverse(objProbs, 0, validCount - 1, indexArray); + + std::set class_set(std::begin(classId), std::end(classId)); + + for (auto c : class_set) + { + nms(validCount, filterBoxes, classId, indexArray, c, nms_threshold); + } + + int last_count = 0; + od_results->count = 0; + + /* box valid detect target */ + for (int i = 0; i < validCount; ++i) + { + if (indexArray[i] == -1 || last_count >= OBJ_NUMB_MAX_SIZE) + { + continue; + } + int n = indexArray[i]; + + float x1 = filterBoxes[n * 4 + 0] - letter_box->x_pad; + float y1 = filterBoxes[n * 4 + 1] - letter_box->y_pad; + float x2 = x1 + filterBoxes[n * 4 + 2]; + float y2 = y1 + filterBoxes[n * 4 + 3]; + int id = classId[n]; + float obj_conf = objProbs[i]; + + od_results->results[last_count].box.left = (int)(clamp(x1, 0, model_in_w) / letter_box->scale); + od_results->results[last_count].box.top = (int)(clamp(y1, 0, model_in_h) / letter_box->scale); + od_results->results[last_count].box.right = (int)(clamp(x2, 0, model_in_w) / letter_box->scale); + od_results->results[last_count].box.bottom = (int)(clamp(y2, 0, model_in_h) / letter_box->scale); + od_results->results[last_count].prop = obj_conf; + od_results->results[last_count].cls_id = id; + last_count++; + } + od_results->count = last_count; + return 0; +} + +char *coco_cls_to_name(int cls_id) +{ + + if (cls_id >= OBJ_CLASS_NUM) + { + return (char *)"null"; + } + + if (labels[cls_id]) + { + return labels[cls_id]; + } + + return (char *)"null"; +} + +/** + * float32 kernel implemetation sample for custom cpu op + * */ +int compute_custom_sigmoid_float32(rknn_custom_op_context* op_ctx, rknn_custom_op_tensor* inputs, uint32_t n_inputs, + rknn_custom_op_tensor* outputs, uint32_t n_outputs) +{ + unsigned char* in_ptr = (unsigned char*)inputs[0].mem.virt_addr + inputs[0].mem.offset; + unsigned char* out_ptr = (unsigned char*)outputs[0].mem.virt_addr + outputs[0].mem.offset; + const float* in_data = (const float*)in_ptr; + float* out_data = (float*)out_ptr; + + // kernel implemetation for custom sigmoid cpu op + { + int inside = 1; + + for (int i = 0; i < inputs[0].attr.n_dims; i++) { + inside *= inputs[0].attr.dims[i]; + } + + for (int y = 0; y < inside; y++) { + const float* src_y = in_data + y; + float* dst_y = out_data + y; + dst_y[0] = 1 / (1 + exp(-src_y[0])); + } + } + return 0; +} + +/*------------------------------------------- + Main Functions +-------------------------------------------*/ +int main(int argc, char* argv[]) +{ + if (argc < 2) { + printf("Usage:%s model_path [input_path] [loop_count] \n", argv[0]); + return -1; + } + + char* model_path = argv[1]; + + std::vector input_paths_split; + if (argc > 2) { + char* input_paths = argv[2]; + input_paths_split = split(input_paths, "#"); + } + + int loop_count = 1; + if (argc > 3) { + loop_count = atoi(argv[3]); + } + + char* output_dir = NULL; + if (argc > 6) { + output_dir = argv[6]; + } + + rknn_context ctx = 0; + + // Load RKNN Model + size_t model_size; + void* model_data = load_file(model_path, &model_size); + if (model_data == NULL) { + return -1; + } + int ret = rknn_init(&ctx, model_data, model_size, 0, NULL); + free(model_data); + if (ret < 0) { + printf("rknn_init fail! ret=%d\n", ret); + return -1; + } + + // register a custom op + rknn_custom_op user_op[1]; + memset(user_op, 0, sizeof(rknn_custom_op)); + strncpy(user_op[0].op_type, "cstSigmoid", RKNN_MAX_NAME_LEN - 1); + user_op[0].version = 1; + user_op[0].target = RKNN_TARGET_TYPE_CPU; + user_op[0].compute = compute_custom_sigmoid_float32; + ret = rknn_register_custom_ops(ctx, user_op, 1); + if (ret < 0) { + printf("rknn_register_custom_op fail! ret = %d\n", ret); + return -1; + } + + // Get sdk and driver version + rknn_sdk_version sdk_ver; + ret = rknn_query(ctx, RKNN_QUERY_SDK_VERSION, &sdk_ver, sizeof(sdk_ver)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("rknn_api/rknnrt version: %s, driver version: %s\n", sdk_ver.api_version, sdk_ver.drv_version); + rknn_mem_size mem_size; + ret = rknn_query(ctx, RKNN_QUERY_MEM_SIZE, &mem_size, sizeof(mem_size)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("total weight size: %u, total internal size: %u\n", mem_size.total_weight_size, mem_size.total_internal_size); + printf("total dma used size: %zu\n", (size_t)mem_size.total_dma_allocated_size); + + // Get Model Input Output Info + rknn_input_output_num io_num; + ret = rknn_query(ctx, RKNN_QUERY_IN_OUT_NUM, &io_num, sizeof(io_num)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("model input num: %d, output num: %d\n", io_num.n_input, io_num.n_output); + + printf("input tensors:\n"); + rknn_tensor_attr input_attrs[io_num.n_input]; + memset(input_attrs, 0, io_num.n_input * sizeof(rknn_tensor_attr)); + for (uint32_t i = 0; i < io_num.n_input; i++) { + input_attrs[i].index = i; + // query info + ret = rknn_query(ctx, RKNN_QUERY_INPUT_ATTR, &(input_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret < 0) { + printf("rknn_query error! ret=%d\n", ret); + return -1; + } + dump_tensor_attr(&input_attrs[i]); + } + + printf("output tensors:\n"); + rknn_tensor_attr output_attrs[io_num.n_output]; + memset(output_attrs, 0, io_num.n_output * sizeof(rknn_tensor_attr)); + for (uint32_t i = 0; i < io_num.n_output; i++) { + output_attrs[i].index = i; + // query info + ret = rknn_query(ctx, RKNN_QUERY_OUTPUT_ATTR, &(output_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + dump_tensor_attr(&output_attrs[i]); + } + + // Get custom string + rknn_custom_string custom_string; + ret = rknn_query(ctx, RKNN_QUERY_CUSTOM_STRING, &custom_string, sizeof(custom_string)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("custom string: %s\n", custom_string.string); + + unsigned char* input_data[io_num.n_input]; + int input_type[io_num.n_input]; + int input_layout[io_num.n_input]; + int input_size[io_num.n_input]; + for (int i = 0; i < io_num.n_input; i++) { + input_data[i] = NULL; + input_type[i] = RKNN_TENSOR_UINT8; + input_layout[i] = input_attrs[i].fmt; + input_size[i] = input_attrs[i].n_elems * sizeof(uint8_t); + } + + if (input_paths_split.size() > 0) { + // Load input + if (io_num.n_input != input_paths_split.size()) { + printf("input missing!, need input number: %d\n", io_num.n_input); + return -1; + } + for (int i = 0; i < io_num.n_input; i++) { + // Load image + input_data[i] = load_image(input_paths_split[i].c_str(), &input_attrs[i]); + + if (!input_data[i]) { + return -1; + } + } + } else { + for (int i = 0; i < io_num.n_input; i++) { + input_data[i] = (unsigned char*)malloc(input_size[i]); + } + } + + rknn_input inputs[io_num.n_input]; + memset(inputs, 0, io_num.n_input * sizeof(rknn_input)); + for (int i = 0; i < io_num.n_input; i++) { + inputs[i].index = i; + inputs[i].pass_through = 0; + inputs[i].type = (rknn_tensor_type)input_type[i]; + inputs[i].fmt = (rknn_tensor_format)input_layout[i]; + inputs[i].buf = input_data[i]; + inputs[i].size = input_size[i]; + } + + // Set input + ret = rknn_inputs_set(ctx, io_num.n_input, inputs); + if (ret < 0) { + printf("rknn_input_set fail! ret=%d\n", ret); + return -1; + } + + // Run + printf("Begin perf ...\n"); + double total_time = 0; + for (int i = 0; i < loop_count; ++i) { + int64_t start_us = getCurrentTimeUs(); + ret = rknn_run(ctx, NULL); + int64_t elapse_us = getCurrentTimeUs() - start_us; if (ret < 0) { + printf("rknn run error %d\n", ret); + return -1; + } + total_time += elapse_us / 1000.f; + printf("%4d: Elapse Time = %.2fms, FPS = %.2f\n", i, elapse_us / 1000.f, 1000.f * 1000.f / elapse_us); + } + printf("Avg elapse Time = %.3fms\n", total_time / loop_count); + printf("Avg FPS = %.3f\n", loop_count * 1000.f / total_time); + + // Get perf detail + rknn_perf_detail perf_detail; + ret = rknn_query(ctx, RKNN_QUERY_PERF_DETAIL, &perf_detail, sizeof(perf_detail)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("rknn run perf detail is:\n%s", perf_detail.perf_data); + + // Get run duration time + rknn_perf_run perf_run; + ret = rknn_query(ctx, RKNN_QUERY_PERF_RUN, &perf_run, sizeof(perf_run)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("rknn run perf time is %ldus\n", perf_run.run_duration); + + // Get output + rknn_output outputs[io_num.n_output]; + memset(outputs, 0, io_num.n_output * sizeof(rknn_output)); + for (uint32_t i = 0; i < io_num.n_output; ++i) { + outputs[i].want_float = output_attrs[i].qnt_type != RKNN_TENSOR_QNT_NONE ? 0 : 1; + outputs[i].index = i; + outputs[i].is_prealloc = 0; + } + + ret = rknn_outputs_get(ctx, io_num.n_output, outputs, NULL); + if (ret < 0) { + printf("rknn_outputs_get fail! ret=%d\n", ret); + return ret; + } + + const float nms_threshold = NMS_THRESH; + const float box_conf_threshold = BOX_THRESH; + object_detect_result_list od_results; + memset(&od_results, 0, sizeof(object_detect_result_list)); + + letterbox_t letter_box; + memset(&letter_box, 0, sizeof(letterbox_t)); + letter_box.scale = 1; + + post_process(input_attrs, output_attrs, outputs, &letter_box, box_conf_threshold, nms_threshold, &od_results); + + // release outputs + ret = rknn_outputs_release(ctx, io_num.n_output, outputs); + + // destroy + rknn_destroy(ctx); + + for (int i = 0; i < io_num.n_input; i++) { + free(input_data[i]); + } + + // print results + ret = loadLabelName(LABEL_NALE_TXT_PATH, labels); + if (ret < 0) + { + printf("Load %s failed!\n", LABEL_NALE_TXT_PATH); + return -1; + } + + for (int i = 0; i < od_results.count; i++) + { + object_detect_result *det_result = &(od_results.results[i]); + printf("%s @ (%d %d %d %d) %.3f\n", coco_cls_to_name(det_result->cls_id), + det_result->box.left, det_result->box.top, + det_result->box.right, det_result->box.bottom, + det_result->prop); + } + + return 0; +} diff --git a/rknpu2/examples/rknn_custom_op_demo/rknn_custom_gpu_op_demo/CMakeLists.txt b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_gpu_op_demo/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..4200297c1af4ce6e75b3b32b2b8495ee17c8d9f5 --- /dev/null +++ b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_gpu_op_demo/CMakeLists.txt @@ -0,0 +1,85 @@ +cmake_minimum_required(VERSION 3.6) + +project(rknn_custom_gpu_op_demo) + +set(CMAKE_CXX_STANDARD 11) +set(CMAKE_CXX_STANDARD_REQUIRED ON) + +# rknn api +set(RKNN_API_PATH ${CMAKE_SOURCE_DIR}/../../../runtime/${CMAKE_SYSTEM_NAME}/librknn_api) +if(CMAKE_SYSTEM_NAME STREQUAL "Android") + set(RKNN_RT_LIB ${RKNN_API_PATH}/${CMAKE_ANDROID_ARCH_ABI}/librknnrt.so) +else() + if(CMAKE_C_COMPILER MATCHES "aarch64") + set(LIB_ARCH aarch64) + else() + set(LIB_ARCH armhf) + endif() + + set(RKNN_RT_LIB ${RKNN_API_PATH}/${LIB_ARCH}/librknnrt.so) +endif() + +set(OPENCL_PATH ${CMAKE_SOURCE_DIR}/../../3rdparty/opencl/libopencl-stub/) + +include_directories(${RKNN_API_PATH}/include) +include_directories(${CMAKE_SOURCE_DIR}/../../3rdparty) +include_directories(${CMAKE_SOURCE_DIR}/../../3rdparty/opencl/libopencl-stub/include/) + +set(CMAKE_INSTALL_RPATH "lib") + +### opencl wrapper +set(libopencl_SRCS + ${OPENCL_PATH}/src/libopencl.cc +) + +add_library(OpenCL SHARED ${libopencl_SRCS}) + + +add_library(rkcst_argmax SHARED + src/rknn_custom_op_opencl_plugin_lib.cpp +) + +target_link_libraries(rkcst_argmax + ${RKNN_RT_LIB} +) + + +add_executable(rknn_custom_gpu_op_demo + src/rknn_api_test_custom_op_opencl.cpp + src/rknn_custom_op_opencl_plugin_lib.cpp +) + +target_link_libraries(rknn_custom_gpu_op_demo + ${RKNN_RT_LIB} +) + +if(${CMAKE_SYSTEM_NAME} STREQUAL "Linux") + target_link_libraries(OpenCL "-Wl,--allow-shlib-undefined" dl) +endif() + +#link cl library +if(${CMAKE_SYSTEM_NAME} STREQUAL "Android") + target_link_libraries(rknn_custom_gpu_op_demo + OpenCL log dl) +else() + target_link_libraries(rknn_custom_gpu_op_demo + OpenCL dl) +endif() + + +target_link_libraries(rkcst_argmax + OpenCL dl +) + +# install target and libraries +set(CMAKE_INSTALL_PREFIX ${CMAKE_SOURCE_DIR}/install/rknn_custom_gpu_op_demo_${CMAKE_SYSTEM_NAME}) + +install(TARGETS rknn_custom_gpu_op_demo DESTINATION ./) +install(PROGRAMS ${RKNN_RT_LIB} DESTINATION lib) +file(GLOB IMAGE_FILES "model/*.jpg") +install(FILES ${IMAGE_FILES} DESTINATION ./model/) +message(STATUS "target_soc = ${TARGET_SOC}") +install(DIRECTORY model/${TARGET_SOC} DESTINATION ./model) +file(GLOB CL_CERNEL_FILES "cl_kernel/*.cl ") +install(FILES ${CL_CERNEL_FILES} DESTINATION ./model/) +install(TARGETS rkcst_argmax DESTINATION lib) diff --git a/rknpu2/examples/rknn_custom_op_demo/rknn_custom_gpu_op_demo/README.md b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_gpu_op_demo/README.md new file mode 100644 index 0000000000000000000000000000000000000000..28bfe29f73e861896eba79efbef8027f910bdf3c --- /dev/null +++ b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_gpu_op_demo/README.md @@ -0,0 +1,118 @@ +# RKNN Custom GPU op Demo +This demo is an example of showing how to implement custom gpu op on c demo for rknn model, using 'ArgMax' as an example. + +The example model is the pp-human-seg-v2_mobile from PaddleSeg. It is recommended referring to RKNN-Toolkit2 for how to replace existed onnx op, referring to [README.md](model/convert/README.md) for more details. + +## Model Source + +The example demo comes from open source project of PaddleSeg. The specfication can be seen with the following link: [PP-HumanSeg](https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.9/contrib/PP-HumanSeg) + +The example model is the PP-HumanSegV2-Mobile, which can be downloaded with this link: [PP-HumanSegV2-Mobile](https://paddleseg.bj.bcebos.com/dygraph/pp_humanseg_v2/human_pp_humansegv1_mobile_192x192_inference_model.zip). + +Note: Referring to Paddle2onnx for converting paddle model to onnx model. + +The converted RKNN models of platforms RK3562, RK3566, RK3568,RK3576, RK3588 are located in model folder. + +# Aarch64 Linux Demo + +## Build + +First export `GCC_COMPILER`, for example `export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu`, then execute: + +``` +./build-linux.sh -t -a -b ] + +# such as: +./build-linux.sh -t rk3588 -a aarch64 -b Release +``` + +## *Install* + +Copy install/rknn_custom_gpu_op_demo_Linux to the devices under /userdata/. + +- If you use rockchip's evb board, you can use the following way: + +Connect device and push the program and rknn model to `/userdata` + +``` +adb push install/rknn_custom_gpu_op_demo_Linux/ /data/ +``` + +- If your board has sshd service, you can use scp or other methods to copy the program and rknn model to the board. + +## Run + +``` +adb shell +cd /data/rknn_custom_gpu_op_demo_Linux +``` + +```shell +export LD_LIBRARY_PATH=./lib +./rknn_custom_gpu_op_demo model//pp_human_segv2_custom_argmax.rknn model/test_image.jpg 1 +``` + +# Android Demo + +## Build + +First export `ANDROID_NDK_PATH`, for example `export ANDROID_NDK_PATH=~/opts/ndk/android-ndk-r18b`, then execute: + +``` +./build-android.sh -t -a [-b ] + +# sush as: +./build-android.sh -t rk3568 -a arm64-v8a -b Release +``` + +## Install + +connect device and push build output into `/data` + +``` + adb push install/rknn_custom_gpu_op_demo_Android/ /data/ +``` + +## Run + +``` +adb shell +cd /data/rknn_custom_gpu_op_demo_Android/ +``` + +If got this type of error library "libOpenCL.so" not found: needed by main executable, by copying the 'libOpenCL.so' to local path to solve it, for instance, + +```shell +cp /vendor/lib64/libOpenCL.so ./lib/ +``` + +then exporting LD_LIBRARY_PATH to the local path of lib. + +``` +export LD_LIBRARY_PATH=./lib +./rknn_custom_gpu_op_demo model//pp_human_segv2_custom_argmax.rknn model/test_image.jpg 1 +``` + +Note: Different platforms, different versions of tools and drivers may have slightly different results. + +## Plug-in Librarys + +If users want to verify the accuracy of their own implemented custom op, the accuracy analysis from the rknn-toolkit2 should be used for this purpose. To do this, the plug-in library need to be compiled for single custom op implmentation for accuracy analysis. + +Compile plug-in library, the source file is located at the **./src/rknn_custom_op_opencl_plugin_lib.cpp** + +```sh +./build-linux_.sh +or +./build-android_.sh +``` + +then push plug-in library to the path for Linux **/usr/lib/rknpu/op_plugins**, for android, **/vendor/lib64/（Android arm64-v8a）**, for example, for Linux system, + +```sh +adb push install/rknn_custom_gpu_op_demo_Linux/lib/librkcst_argmax.so /usr/lib/rknpu/op_plugins +``` + +Note: if there is no such directory on your board, users should create one instead. + +At last, users can run the accuracy analysis from toolkit2 for veirying whether it is accurate for their custom op. diff --git a/rknpu2/examples/rknn_custom_op_demo/rknn_custom_gpu_op_demo/build-android.sh b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_gpu_op_demo/build-android.sh new file mode 100755 index 0000000000000000000000000000000000000000..4778e831eaff14905900f6fea7b8e3ee44147549 --- /dev/null +++ b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_gpu_op_demo/build-android.sh @@ -0,0 +1,112 @@ +#!/bin/bash + +set -e + +if [[ -z ${ANDROID_NDK_PATH} ]];then + echo "Please set ANDROID_NDK_PATH, such as export ANDROID_NDK_PATH=~/opts/ndk/android-ndk-r18b" + echo "NDK Version r18, r19 is recommanded. Other version may cause build failure." + exit +fi + +echo "$0 $@" +while getopts ":t:a:b" opt; do + case $opt in + t) + TARGET_SOC=$OPTARG + ;; + a) + TARGET_ARCH=$OPTARG + ;; + b) + BUILD_TYPE=$OPTARG + ;; + :) + echo "Option -$OPTARG requires an argument." + exit 1 + ;; + ?) + echo "Invalid option: -$OPTARG index:$OPTIND" + ;; + esac +done + +if [ -z ${TARGET_SOC} ] || [ -z ${TARGET_ARCH} ] ; then + echo "$0 -t -a [-b ]" + echo "" + echo " -t : target (rk3566/rk3568/rk3562/rk3576/rk3588)" + echo " -a : arch (arm64-v8a/armeabi-v7a)" + echo " -b : build_type (Debug/Release)" + echo "such as: $0 -t rk3588 -a arm64-v8a -b Release" + echo "" + exit -1 +fi + +# Debug / Release / RelWithDebInfo +if [[ -z ${BUILD_TYPE} ]];then + BUILD_TYPE=Release +fi + +case ${TARGET_SOC} in + rk356x) + TARGET_SOC="RK3566_RK3568" + ;; + rk3566_rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3588) + TARGET_SOC="RK3588" + ;; + rk3576) + TARGET_SOC="RK3576" + ;; + rk3566) + TARGET_SOC="RK3566_RK3568" + ;; + rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3562) + TARGET_SOC="RK3562" + ;; + *) + echo "Invalid target: ${TARGET_SOC}" + echo "Valid target: rk3562,rk3566,rk3568,rk3576,rk3588" + exit -1 + ;; +esac + +TARGET_PLATFORM=${TARGET_SOC}_android +if [[ -n ${TARGET_ARCH} ]];then +TARGET_PLATFORM=${TARGET_PLATFORM}_${TARGET_ARCH} +fi +ROOT_PWD=$( cd "$( dirname $0 )" && cd -P "$( dirname "$SOURCE" )" && pwd ) +BUILD_DIR=${ROOT_PWD}/build/build_${TARGET_PLATFORM}_${BUILD_TYPE} + +echo "===================================" +echo "TARGET_SOC=${TARGET_SOC}" +echo "TARGET_ARCH=${TARGET_ARCH}" +echo "BUILD_TYPE=${BUILD_TYPE}" +echo "BUILD_DIR=${BUILD_DIR}" +echo "ANDROID_NDK_PATH=${ANDROID_NDK_PATH}" +echo "===================================" + +if [[ ! -d "${BUILD_DIR}" ]]; then + mkdir -p ${BUILD_DIR} +fi + + +cd ${BUILD_DIR} +cmake ../.. \ + -DANDROID_TOOLCHAIN=clang \ + -DTARGET_SOC=${TARGET_SOC} \ + -DCMAKE_SYSTEM_NAME=Android \ + -DCMAKE_TOOLCHAIN_FILE=$ANDROID_NDK_PATH/build/cmake/android.toolchain.cmake \ + -DANDROID_ABI=${TARGET_ARCH} \ + -DANDROID_STL=c++_static \ + -DANDROID_PLATFORM=android-24 \ + -DCMAKE_BUILD_TYPE=${BUILD_TYPE} +# make VERBOSE=1 +make -j4 +make install +cd .. + diff --git a/rknpu2/examples/rknn_custom_op_demo/rknn_custom_gpu_op_demo/build-linux.sh b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_gpu_op_demo/build-linux.sh new file mode 100755 index 0000000000000000000000000000000000000000..ae972803b8bda834c3a95588e513ec7af832cbeb --- /dev/null +++ b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_gpu_op_demo/build-linux.sh @@ -0,0 +1,122 @@ +#!/bin/bash + +set -e + +echo "$0 $@" +while getopts ":t:a:b" opt; do + case $opt in + t) + TARGET_SOC=$OPTARG + ;; + a) + TARGET_ARCH=$OPTARG + ;; + b) + BUILD_TYPE=$OPTARG + ;; + :) + echo "Option -$OPTARG requires an argument." + exit 1 + ;; + ?) + echo "Invalid option: -$OPTARG index:$OPTIND" + ;; + esac +done + +if [ -z ${TARGET_SOC} ];then + echo "$0 -t -a [-b ]" + echo "" + echo " -t : target (rk3566/rk3568/rk3562/rk3576/rk3588)" + echo " -a : arch (aarch64/armhf)" + echo " -b : build_type(Debug/Release)" + echo "such as: $0 -t rk3588 -a aarch64 -b Release" + echo "" + exit -1 +fi + +if [[ -z ${GCC_COMPILER} ]];then + echo "Please set GCC_COMPILER for $TARGET_SOC" + echo "such as export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu" + exit +fi +echo "$GCC_COMPILER" +export CC=${GCC_COMPILER}-gcc +export CXX=${GCC_COMPILER}-g++ + +if command -v ${CC} >/dev/null 2>&1; then + : +else + echo "${CC} is not available" + echo "Please set GCC_COMPILER for $TARGET_SOC" + echo "such as export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu" + exit +fi + +# Debug / Release +if [[ -z ${BUILD_TYPE} ]];then + BUILD_TYPE=Release +fi + + +case ${TARGET_SOC} in + rk356x) + TARGET_SOC="RK3566_RK3568" + ;; + rk3566_rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3588) + TARGET_SOC="RK3588" + ;; + rk3576) + TARGET_SOC="RK3576" + ;; + rk3566) + TARGET_SOC="RK3566_RK3568" + ;; + rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3562) + TARGET_SOC="RK3562" + ;; + *) + echo "Invalid target: ${TARGET_SOC}" + echo "Valid target: rk3562,rk3566,rk3568,rk3576,rk3588" + exit -1 + ;; +esac + +TARGET_PLATFORM=${TARGET_SOC}_linux +if [[ -n ${TARGET_ARCH} ]];then +TARGET_PLATFORM=${TARGET_PLATFORM}_${TARGET_ARCH} +fi +ROOT_PWD=$( cd "$( dirname $0 )" && cd -P "$( dirname "$SOURCE" )" && pwd ) +BUILD_DIR=${ROOT_PWD}/build/build_${TARGET_PLATFORM}_${BUILD_TYPE} + + +echo "===================================" +echo "TARGET_SOC=${TARGET_SOC}" +echo "TARGET_ARCH=${TARGET_ARCH}" +echo "BUILD_TYPE=${BUILD_TYPE}" +echo "BUILD_DIR=${BUILD_DIR}" +echo "CC=${CC}" +echo "CXX=${CXX}" +echo "===================================" + +if [[ ! -d "${BUILD_DIR}" ]]; then + mkdir -p ${BUILD_DIR} +fi + + +cd ${BUILD_DIR} +cmake ../.. \ + -DTARGET_SOC=${TARGET_SOC} \ + -DCMAKE_SYSTEM_NAME=Linux \ + -DCMAKE_SYSTEM_PROCESSOR=${TARGET_ARCH} \ + -DCMAKE_BUILD_TYPE=${BUILD_TYPE} \ + -DCMAKE_C_COMPILER=${CC} \ + -DCMAKE_CXX_COMPILER=${CXX} +make -j4 +make install diff --git a/rknpu2/examples/rknn_custom_op_demo/rknn_custom_gpu_op_demo/model/RK3562/pp_human_segv2_custom_argmax.rknn b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_gpu_op_demo/model/RK3562/pp_human_segv2_custom_argmax.rknn new file mode 100644 index 0000000000000000000000000000000000000000..f2515f34e450777fdba4933717584ea9d51003a3 --- /dev/null +++ b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_gpu_op_demo/model/RK3562/pp_human_segv2_custom_argmax.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:416bf8d1f5c856af326b9998678a11e81c85a9fd0459bd952afafde01ee51cf0 +size 8049487 diff --git a/rknpu2/examples/rknn_custom_op_demo/rknn_custom_gpu_op_demo/model/RK3566_RK3568/pp_human_segv2_custom_argmax.rknn b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_gpu_op_demo/model/RK3566_RK3568/pp_human_segv2_custom_argmax.rknn new file mode 100644 index 0000000000000000000000000000000000000000..67f27cccb0e7841e4d6f0ca5a35bc76508dd71ab --- /dev/null +++ b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_gpu_op_demo/model/RK3566_RK3568/pp_human_segv2_custom_argmax.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5267622c1995dc8faaf5734f32d313bbdc649d11f54a026ae167191923b1e5e5 +size 7999823 diff --git a/rknpu2/examples/rknn_custom_op_demo/rknn_custom_gpu_op_demo/model/RK3576/pp_human_segv2_custom_argmax.rknn b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_gpu_op_demo/model/RK3576/pp_human_segv2_custom_argmax.rknn new file mode 100644 index 0000000000000000000000000000000000000000..cce0035bb33a498f038cac5a5b436015a3112357 --- /dev/null +++ b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_gpu_op_demo/model/RK3576/pp_human_segv2_custom_argmax.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b6f5dda8dbfa2db4f7a843dd865502850c9dcaec5d5ea40ac229492cd9fc40ce +size 8504911 diff --git a/rknpu2/examples/rknn_custom_op_demo/rknn_custom_gpu_op_demo/model/RK3588/pp_human_segv2_custom_argmax.rknn b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_gpu_op_demo/model/RK3588/pp_human_segv2_custom_argmax.rknn new file mode 100644 index 0000000000000000000000000000000000000000..0e285fdecfc333bb22ebc5e0270ee66c63537876 --- /dev/null +++ b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_gpu_op_demo/model/RK3588/pp_human_segv2_custom_argmax.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e0206da4750914ffddb2efcafdb95a8474f406f6d9e0dc79fd17885e24218566 +size 8700047 diff --git a/rknpu2/examples/rknn_custom_op_demo/rknn_custom_gpu_op_demo/model/convert/README.md b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_gpu_op_demo/model/convert/README.md new file mode 100644 index 0000000000000000000000000000000000000000..b3e00c94cc3c16492ce4f08eebf72c8696273402 --- /dev/null +++ b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_gpu_op_demo/model/convert/README.md @@ -0,0 +1,35 @@ +# How to use custom OP function + +## Model Source + +The example demo is from PaddleSeg under the project of PaddlePaddle. The specfication can be seen with the following link: [link_PP-HumanSeg](https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.9/contrib/PP-HumanSeg) + +The example model is the PP-HumanSegV2-Mobile, which can be downloaded with this link: [link_PP-HumanSegV2-Mobile](https://paddleseg.bj.bcebos.com/dygraph/pp_humanseg_v2/human_pp_humansegv1_mobile_192x192_inference_model.zip). + +Note: Referring to Paddle2onnx for converting paddle model to onnx model. + +## Script Usage +*Usage:* + +``` +python test.py +``` +*Description:* + +- The default target platform in script is 'RK3588', please modify the 'target_platform' parameter of 'rknn.config()' according to the actual platform. + +## Processes +The process of this example is as follows: +1. Init RKNN. +2. Register custom OP ArgMax. +3. Convert ONNX model to RKNN model. +4. Infer RKNN model in simulator. +5. Save the picture of results. +6. Release RKNN resource. + +## Expected Results +1. Got a rknn model with registered ArgMax op. +3. Get a picture of mask and composed image results named result.jpg. +![Results](result_truth.jpg) + +- Note: Different platforms, different versions of tools and drivers may have slightly different results. diff --git a/rknpu2/examples/rknn_custom_op_demo/rknn_custom_gpu_op_demo/model/convert/get_dataset_colormap.py b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_gpu_op_demo/model/convert/get_dataset_colormap.py new file mode 100644 index 0000000000000000000000000000000000000000..37e232e7892eef6cff377826a927a6048e0cc3a0 --- /dev/null +++ b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_gpu_op_demo/model/convert/get_dataset_colormap.py @@ -0,0 +1,149 @@ +# Copyright 2018 The TensorFlow Authors All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Visualizes the segmentation results via specified color map. + +Visualizes the semantic segmentation results by the color map +defined by the different datasets. Supported colormaps are: + +1. PASCAL VOC semantic segmentation benchmark. +Website: http://host.robots.ox.ac.uk/pascal/VOC/ +""" + +import numpy as np + +# Dataset names. +_CITYSCAPES = 'cityscapes' +_PASCAL = 'pascal' + +# Max number of entries in the colormap for each dataset. +_DATASET_MAX_ENTRIES = { + _CITYSCAPES: 19, + _PASCAL: 256, +} + + +def create_cityscapes_label_colormap(): + """Creates a label colormap used in CITYSCAPES segmentation benchmark. + + Returns: + A Colormap for visualizing segmentation results. + """ + colormap = np.asarray([ + [128, 64, 128], + [244, 35, 232], + [70, 70, 70], + [102, 102, 156], + [190, 153, 153], + [153, 153, 153], + [250, 170, 30], + [220, 220, 0], + [107, 142, 35], + [152, 251, 152], + [70, 130, 180], + [220, 20, 60], + [255, 0, 0], + [0, 0, 142], + [0, 0, 70], + [0, 60, 100], + [0, 80, 100], + [0, 0, 230], + [119, 11, 32], + ]) + return colormap + + +def get_pascal_name(): + return _PASCAL + + +def get_cityscapes_name(): + return _CITYSCAPES + + +def bit_get(val, idx): + """Gets the bit value. + + Args: + val: Input value, int or numpy int array. + idx: Which bit of the input val. + + Returns: + The "idx"-th bit of input val. + """ + return (val >> idx) & 1 + + +def create_pascal_label_colormap(): + """Creates a label colormap used in PASCAL VOC segmentation benchmark. + + Returns: + A Colormap for visualizing segmentation results. + """ + colormap = np.zeros((_DATASET_MAX_ENTRIES[_PASCAL], 3), dtype=int) + ind = np.arange(_DATASET_MAX_ENTRIES[_PASCAL], dtype=int) + + for shift in reversed(range(8)): + for channel in range(3): + colormap[:, channel] |= bit_get(ind, channel) << shift + ind >>= 3 + + return colormap + + +def create_label_colormap(dataset=_PASCAL): + """Creates a label colormap for the specified dataset. + + Args: + dataset: The colormap used in the dataset. + + Returns: + A numpy array of the dataset colormap. + + Raises: + ValueError: If the dataset is not supported. + """ + if dataset == _PASCAL: + return create_pascal_label_colormap() + elif dataset == _CITYSCAPES: + return create_cityscapes_label_colormap() + else: + raise ValueError('Unsupported dataset.') + + +def label_to_color_image(label, dataset=_PASCAL): + """Adds color defined by the dataset colormap to the label. + + Args: + label: A 2D array with integer type, storing the segmentation label. + dataset: The colormap used in the dataset. + + Returns: + result: A 2D array with floating type. The element of the array + is the color indexed by the corresponding element in the input label + to the PASCAL color map. + + Raises: + ValueError: If label is not of rank 2 or its value is larger than color + map maximum entry. + """ + if label.ndim != 2: + raise ValueError('Expect 2-D input label') + + if np.max(label) >= _DATASET_MAX_ENTRIES[dataset]: + raise ValueError('label value too large.') + + colormap = create_label_colormap(dataset) + + return colormap[label] diff --git a/rknpu2/examples/rknn_custom_op_demo/rknn_custom_gpu_op_demo/model/convert/result_truth.jpg b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_gpu_op_demo/model/convert/result_truth.jpg new file mode 100644 index 0000000000000000000000000000000000000000..dac1b539e769d86d50b8e4a41e096cbab1f01b63 --- /dev/null +++ b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_gpu_op_demo/model/convert/result_truth.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3b0f54fefc6e8fca8e0ca0ef8b6e0b1f9732a97e69c7c2eb71c458e7cf39a4cf +size 66480 diff --git a/rknpu2/examples/rknn_custom_op_demo/rknn_custom_gpu_op_demo/model/convert/test.py b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_gpu_op_demo/model/convert/test.py new file mode 100644 index 0000000000000000000000000000000000000000..21b13a3feccfaec220b21224aa344563064b0e40 --- /dev/null +++ b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_gpu_op_demo/model/convert/test.py @@ -0,0 +1,131 @@ +import os +import urllib +import traceback +import time +import sys +import numpy as np +import cv2 as cv +from rknn.api import RKNN +from matplotlib import pyplot as plt + +from rknn.api.custom_op import get_node_attr + +from matplotlib import gridspec + +import get_dataset_colormap + +OUT_SIZE = 192 + +LABEL_NAMES = np.asarray([ + 'background', 'person' +]) + +FULL_LABEL_MAP = np.arange(len(LABEL_NAMES)).reshape(len(LABEL_NAMES), 1) +FULL_COLOR_MAP = get_dataset_colormap.label_to_color_image(FULL_LABEL_MAP) + +def vis_segmentation(image, seg_map): + plt.figure(figsize=(15, 5)) + grid_spec = gridspec.GridSpec(1, 4, width_ratios=[6, 6, 6, 1]) + + plt.subplot(grid_spec[0]) + plt.imshow(image) + plt.axis('off') + plt.title('input image') + + plt.subplot(grid_spec[1]) + seg_image = get_dataset_colormap.label_to_color_image( + seg_map, get_dataset_colormap.get_pascal_name()).astype(np.uint8) + plt.imshow(seg_image) + plt.axis('off') + plt.title('segmentation map') + + plt.subplot(grid_spec[2]) + plt.imshow(image) + plt.imshow(seg_image, alpha=0.7) + plt.axis('off') + plt.title('segmentation overlay') + + unique_labels = np.unique(seg_map) + + ax = plt.subplot(grid_spec[3]) + plt.imshow(FULL_COLOR_MAP[unique_labels].astype(np.uint8), interpolation='nearest') + ax.yaxis.tick_right() + plt.yticks(range(len(unique_labels)), LABEL_NAMES[unique_labels]) + plt.xticks([], []) + ax.tick_params(width=0) + + # plt.show() + # Save the figure to a file + plt.savefig('result.jpg') + + + +class ArgMax: + op_type = "ArgMax" + + + + +if __name__ == '__main__': + + # Create RKNN object + rknn = RKNN(verbose=True) + + onnx_model = 'pp_humansegv2_mobile.onnx' + rknn_model = 'pp_human_segv2_custom_argmax.rknn' + DATASET = './dataset.txt' + QUANTIZE_ON = True + + rknn.config(mean_values=[127.5, 127.5, 127.5], std_values=[127.5, 127.5, 127.5], + quant_img_RGB2BGR=False, + target_platform="rk3576") + + # customize argmax op + ret = rknn.reg_custom_op(ArgMax) + + # Load ONNX model + print('--> Loading model') + ret = rknn.load_onnx(model=onnx_model, outputs=['p2o.ArgMax.1']) + if ret != 0: + print('Load model failed!') + exit(ret) + print('done') + + # Build model + print('--> Building model') + ret = rknn.build(do_quantization=QUANTIZE_ON, dataset=DATASET) + if ret != 0: + print('Build model failed!') + exit(ret) + print('done') + + # Export RKNN model + print('--> Export rknn model') + ret = rknn.export_rknn(rknn_model) + if ret != 0: + print('Export rknn model failed!') + exit(ret) + print('done') + + # init runtime environment + print('--> Init runtime environment') + ret = rknn.init_runtime() + if ret != 0: + print('Init runtime environment failed') + exit(ret) + print('done') + + img = cv.imread('../test_image.jpg') + img_rgb = cv.cvtColor(img, cv.COLOR_BGR2RGB) + img_rgb = cv.resize(img_rgb, (OUT_SIZE, OUT_SIZE)) + + # Inference + print('--> Running model') + outputs = rknn.inference(inputs=[img_rgb]) + np.save('./functions_custom_op_replace_onnx_0.npy', outputs[0]) + print('--> done') + + seg_mask = outputs[0].reshape(OUT_SIZE, OUT_SIZE) + vis_segmentation(img_rgb, seg_mask) + + rknn.release() diff --git a/rknpu2/examples/rknn_custom_op_demo/rknn_custom_gpu_op_demo/model/test_image.jpg b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_gpu_op_demo/model/test_image.jpg new file mode 100644 index 0000000000000000000000000000000000000000..93d32ef0fe4f6c3e34d796d66398fac548fe66c1 --- /dev/null +++ b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_gpu_op_demo/model/test_image.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3645e76b38eef46f59a9633e5e9a86c772a0a710089acd7cb8e49363567ab62b +size 149615 diff --git a/rknpu2/examples/rknn_custom_op_demo/rknn_custom_gpu_op_demo/src/rknn_api_test_custom_op_opencl.cpp b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_gpu_op_demo/src/rknn_api_test_custom_op_opencl.cpp new file mode 100644 index 0000000000000000000000000000000000000000..80204469d2430de4703a49263ade27c4e4266a57 --- /dev/null +++ b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_gpu_op_demo/src/rknn_api_test_custom_op_opencl.cpp @@ -0,0 +1,473 @@ +// Copyright (c) 2023 by Rockchip Electronics Co., Ltd. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +/*------------------------------------------- + Includes +-------------------------------------------*/ + +#include +#include + +#include "CL/cl.h" +#include "CL/cl_ext.h" +#include "fp16/Float16.h" +#include "rknn_api.h" +#include "rknn_custom_op.h" + +#include +#include +#include +#include +#include + +#define STB_IMAGE_IMPLEMENTATION +#include "stb/stb_image.h" +#define STB_IMAGE_RESIZE_IMPLEMENTATION +#include +#define STB_IMAGE_WRITE_IMPLEMENTATION +#include + +#define LOAD_FROM_PATH + +extern "C" { +RKNN_CUSTOM_OP_EXPORT rknn_custom_op* get_rknn_custom_op(); +} + + +/*------------------------------------------- + Functions +-------------------------------------------*/ +static inline int64_t getCurrentTimeUs() +{ + struct timeval tv; + gettimeofday(&tv, NULL); + return tv.tv_sec * 1000000 + tv.tv_usec; +} + + +static void compose_img(float *res_buf, unsigned char *img_buf, const int height, const int width) +{ + for (int h = 0; h < height; ++h) + { + for (int w = 0; w < width; ++w) + { + auto pixel = res_buf[h * width + w]; + if (pixel > 0.f) { + img_buf[h * width * 3 + w * 3] = 0; + img_buf[h * width * 3 + w * 3 + 1] = 0; + img_buf[h * width * 3 + w * 3 + 2] = 0; + } + } + } + + constexpr char out_path[] = "test_compose_img.png"; + // write out image using stb + printf("Writing out composed image with: %dx%d\n", width, height); + stbi_write_png(out_path, width, height, 3, img_buf, 0); +} + +static void dump_tensor_attr(rknn_tensor_attr* attr) +{ + std::string shape_str = attr->n_dims < 1 ? "" : std::to_string(attr->dims[0]); + for (int i = 1; i < attr->n_dims; ++i) { + shape_str += ", " + std::to_string(attr->dims[i]); + } + + printf(" index=%d, name=%s, n_dims=%d, dims=[%s], n_elems=%d, size=%d, w_stride = %d, size_with_stride=%d, fmt=%s, " + "type=%s, qnt_type=%s, " + "zp=%d, scale=%f\n", + attr->index, attr->name, attr->n_dims, shape_str.c_str(), attr->n_elems, attr->size, attr->w_stride, + attr->size_with_stride, get_format_string(attr->fmt), get_type_string(attr->type), + get_qnt_type_string(attr->qnt_type), attr->zp, attr->scale); +} + +static int get_type_bytes(rknn_tensor_type type) +{ + switch (type) { + case RKNN_TENSOR_INT64: + return 8; + case RKNN_TENSOR_UINT32:; + case RKNN_TENSOR_INT32: + case RKNN_TENSOR_FLOAT32: + return 4; + case RKNN_TENSOR_INT16: + case RKNN_TENSOR_UINT16:; + case RKNN_TENSOR_FLOAT16: + return 2; + case RKNN_TENSOR_BOOL: + case RKNN_TENSOR_INT8: + case RKNN_TENSOR_UINT8: + return 1; + default: + return 0; + } +} + +static unsigned char* load_image(const char* image_path, rknn_tensor_attr* input_attr) +{ + int req_height = 0; + int req_width = 0; + int req_channel = 0; + + switch (input_attr->fmt) { + case RKNN_TENSOR_NHWC: + req_height = input_attr->dims[1]; + req_width = input_attr->dims[2]; + req_channel = input_attr->dims[3]; + break; + case RKNN_TENSOR_NCHW: + req_height = input_attr->dims[2]; + req_width = input_attr->dims[3]; + req_channel = input_attr->dims[1]; + break; + default: + printf("meet unsupported layout\n"); + return NULL; + } + + int height = 0; + int width = 0; + int channel = 0; + + unsigned char* image_data = stbi_load(image_path, &width, &height, &channel, req_channel); + if (image_data == NULL) { + printf("load image failed!\n"); + return NULL; + } + + if (width != req_width || height != req_height) { + unsigned char* image_resized = (unsigned char*)STBI_MALLOC(req_width * req_height * req_channel); + if (!image_resized) { + printf("malloc image failed!\n"); + STBI_FREE(image_data); + return NULL; + } + if (stbir_resize_uint8(image_data, width, height, 0, image_resized, req_width, req_height, 0, channel) != 1) { + printf("resize image failed!\n"); + STBI_FREE(image_data); + return NULL; + } + STBI_FREE(image_data); + image_data = image_resized; + } + + return image_data; +} + +static std::vector split(const std::string& str, const std::string& pattern) +{ + std::vector res; + if (str == "") + return res; + std::string strs = str + pattern; + size_t pos = strs.find(pattern); + while (pos != strs.npos) { + std::string temp = strs.substr(0, pos); + res.push_back(temp); + strs = strs.substr(pos + 1, strs.size()); + pos = strs.find(pattern); + } + return res; +} + +static int write_data_to_file(char* path, char* data, unsigned int size) +{ + FILE* fp; + + fp = fopen(path, "w"); + if (fp == NULL) { + printf("open error: %s", path); + return -1; + } + + fwrite(data, 1, size, fp); + fflush(fp); + + fclose(fp); + return 0; +} + +static void* load_file(const char* file_path, size_t* file_size) +{ + FILE* fp = fopen(file_path, "r"); + if (fp == NULL) { + printf("failed to open file: %s\n", file_path); + return NULL; + } + + fseek(fp, 0, SEEK_END); + size_t size = (size_t)ftell(fp); + fseek(fp, 0, SEEK_SET); + + void* file_data = malloc(size); + if (file_data == NULL) { + fclose(fp); + printf("failed allocate file size: %zu\n", size); + return NULL; + } + + if (fread(file_data, 1, size, fp) != size) { + fclose(fp); + free(file_data); + printf("failed to read file data!\n"); + return NULL; + } + + fclose(fp); + + *file_size = size; + + return file_data; +} + + +/*------------------------------------------- + Main Functions +-------------------------------------------*/ +int main(int argc, char* argv[]) +{ + if (argc < 2) { + printf("Usage:%s model_path [input_path] [loop_count] \n", argv[0]); + return -1; + } + + char* model_path = argv[1]; + + std::vector input_paths_split; + if (argc > 2) { + char* input_paths = argv[2]; + input_paths_split = split(input_paths, "#"); + } + + int loop_count = 1; + if (argc > 3) { + loop_count = atoi(argv[3]); + } + + char* output_dir = NULL; + if (argc > 6) { + output_dir = argv[6]; + } + + rknn_context ctx = 0; + + // Load RKNN Model + uint32_t flag = 0; + flag = flag | RKNN_FLAG_EXECUTE_FALLBACK_PRIOR_DEVICE_GPU; +#ifdef LOAD_FROM_PATH + // Init rknn from model path + int ret = rknn_init(&ctx, model_path, 0, flag, NULL); +#else + // Init rknn from model data + size_t model_size; + printf("load model from buffer.\n"); + void* model_data = load_file(model_path, &model_size); + if (model_data == NULL) { + return -1; + } + int ret = rknn_init(&ctx, model_data, model_size, 0, NULL); + free(model_data); +#endif + if (ret < 0) { + printf("rknn_init fail! ret=%d\n", ret); + return -1; + } + + //user op already delcared on other files. + auto gpu_user_op = get_rknn_custom_op(); + ret = rknn_register_custom_ops(ctx, gpu_user_op, 1); + if (ret < 0) { + printf("rknn_register_custom_ops fail! ret = %d\n", ret); + return -1; + } + + // Get sdk and driver version + rknn_sdk_version sdk_ver; + ret = rknn_query(ctx, RKNN_QUERY_SDK_VERSION, &sdk_ver, sizeof(sdk_ver)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("rknn_api/rknnrt version: %s, driver version: %s\n", sdk_ver.api_version, sdk_ver.drv_version); + rknn_mem_size mem_size; + ret = rknn_query(ctx, RKNN_QUERY_MEM_SIZE, &mem_size, sizeof(mem_size)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("total weight size: %u, total internal size: %u\n", mem_size.total_weight_size, mem_size.total_internal_size); + printf("total dma used size: %zu\n", (size_t)mem_size.total_dma_allocated_size); + + // Get Model Input Output Info + rknn_input_output_num io_num; + ret = rknn_query(ctx, RKNN_QUERY_IN_OUT_NUM, &io_num, sizeof(io_num)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("model input num: %d, output num: %d\n", io_num.n_input, io_num.n_output); + + printf("input tensors:\n"); + rknn_tensor_attr input_attrs[io_num.n_input]; + memset(input_attrs, 0, io_num.n_input * sizeof(rknn_tensor_attr)); + for (uint32_t i = 0; i < io_num.n_input; i++) { + input_attrs[i].index = i; + // query info + ret = rknn_query(ctx, RKNN_QUERY_INPUT_ATTR, &(input_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret < 0) { + printf("rknn_query error! ret=%d\n", ret); + return -1; + } + dump_tensor_attr(&input_attrs[i]); + } + + printf("output tensors:\n"); + rknn_tensor_attr output_attrs[io_num.n_output]; + memset(output_attrs, 0, io_num.n_output * sizeof(rknn_tensor_attr)); + for (uint32_t i = 0; i < io_num.n_output; i++) { + output_attrs[i].index = i; + // query info + ret = rknn_query(ctx, RKNN_QUERY_OUTPUT_ATTR, &(output_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + dump_tensor_attr(&output_attrs[i]); + } + + // Get custom string + rknn_custom_string custom_string; + ret = rknn_query(ctx, RKNN_QUERY_CUSTOM_STRING, &custom_string, sizeof(custom_string)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("custom string: %s\n", custom_string.string); + + unsigned char* input_data[io_num.n_input]; + int input_type[io_num.n_input]; + int input_layout[io_num.n_input]; + int input_size[io_num.n_input]; + for (int i = 0; i < io_num.n_input; i++) { + input_data[i] = NULL; + input_type[i] = RKNN_TENSOR_UINT8; + input_layout[i] = input_attrs[i].fmt; + input_size[i] = input_attrs[i].n_elems * sizeof(uint8_t); + } + + if (input_paths_split.size() > 0) { + // Load input + if (io_num.n_input != input_paths_split.size()) { + printf("input missing!, need input number: %d\n", io_num.n_input); + return -1; + } + for (int i = 0; i < io_num.n_input; i++) { + // Load image + input_data[i] = load_image(input_paths_split[i].c_str(), &input_attrs[i]); + + if (!input_data[i]) { + return -1; + } + } + } else { + for (int i = 0; i < io_num.n_input; i++) { + input_data[i] = (unsigned char*)malloc(input_size[i]); + } + } + + rknn_input inputs[io_num.n_input]; + memset(inputs, 0, io_num.n_input * sizeof(rknn_input)); + for (int i = 0; i < io_num.n_input; i++) { + inputs[i].index = i; + inputs[i].pass_through = 0; + inputs[i].type = (rknn_tensor_type)input_type[i]; + inputs[i].fmt = (rknn_tensor_format)input_layout[i]; + inputs[i].buf = input_data[i]; + inputs[i].size = input_size[i]; + } + + // Set input + ret = rknn_inputs_set(ctx, io_num.n_input, inputs); + if (ret < 0) { + printf("rknn_input_set fail! ret=%d\n", ret); + return -1; + } + + auto core_mask = RKNN_NPU_CORE_ALL; + rknn_set_core_mask(ctx, (rknn_core_mask)core_mask); + + // Run + printf("Begin perf ...\n"); + double total_time = 0; + for (int i = 0; i < loop_count; ++i) { + int64_t start_us = getCurrentTimeUs(); + ret = rknn_run(ctx, NULL); + int64_t elapse_us = getCurrentTimeUs() - start_us; if (ret < 0) { + printf("rknn run error %d\n", ret); + return -1; + } + total_time += elapse_us / 1000.f; + printf("%4d: Elapse Time = %.2fms, FPS = %.2f\n", i, elapse_us / 1000.f, 1000.f * 1000.f / elapse_us); + } + printf("Avg elapse Time = %.3fms\n", total_time / loop_count); + printf("Avg FPS = %.3f\n", loop_count * 1000.f / total_time); + + // Get perf detail + rknn_perf_detail perf_detail; + ret = rknn_query(ctx, RKNN_QUERY_PERF_DETAIL, &perf_detail, sizeof(perf_detail)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("rknn run perf detail is:\n%s", perf_detail.perf_data); + + // Get run duration time + rknn_perf_run perf_run; + ret = rknn_query(ctx, RKNN_QUERY_PERF_RUN, &perf_run, sizeof(perf_run)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("rknn run perf time is %ldus\n", perf_run.run_duration); + + // Get output + rknn_output outputs[io_num.n_output]; + memset(outputs, 0, io_num.n_output * sizeof(rknn_output)); + for (uint32_t i = 0; i < io_num.n_output; ++i) { + outputs[i].want_float = 1; + outputs[i].index = i; + outputs[i].is_prealloc = 0; + } + + ret = rknn_outputs_get(ctx, io_num.n_output, outputs, NULL); + if (ret < 0) { + printf("rknn_outputs_get fail! ret=%d\n", ret); + return ret; + } + + auto out_width = output_attrs[0].dims[2]; + auto out_height = output_attrs[0].dims[1]; + compose_img((float* )outputs[0].buf, input_data[0], out_height, out_width); + + // release outputs + ret = rknn_outputs_release(ctx, io_num.n_output, outputs); + + // destroy + rknn_destroy(ctx); + + for (int i = 0; i < io_num.n_input; i++) { + free(input_data[i]); + } + + return 0; +} diff --git a/rknpu2/examples/rknn_custom_op_demo/rknn_custom_gpu_op_demo/src/rknn_custom_op_opencl_plugin_lib.cpp b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_gpu_op_demo/src/rknn_custom_op_opencl_plugin_lib.cpp new file mode 100644 index 0000000000000000000000000000000000000000..81523318ac282a038a5cdbb433ca958e71fc298f --- /dev/null +++ b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_gpu_op_demo/src/rknn_custom_op_opencl_plugin_lib.cpp @@ -0,0 +1,183 @@ +// Copyright (c) 2023 by Rockchip Electronics Co., Ltd. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "rknn_custom_op.h" +#include "CL/cl.h" +#include "CL/cl_ext.h" + +#include +#include +#include +#include + +#ifdef __cplusplus +extern "C" { +#endif + + +static rknn_custom_op user_op{}; + +static char* cl_kernel_source = "#pragma OPENCL EXTENSION cl_arm_printf : enable \n" +" __kernel void Argmax_channel_float(__global const float* src_buf, const int inputChannel, __global float* dst_buf) \n" +"{\n" +" unsigned int i = get_global_id(0);\n" +" unsigned int j = get_global_id(1); \n" +" unsigned int width = get_global_size(0);\n" +" unsigned int height = get_global_size(1);\n" +" int max_index = 0;\n" +" float max_value = src_buf[i*width+j];\n" +" for (int c=1; c < inputChannel; c++) {\n" +" float value = src_buf[c*height*width + i*width+j];\n" +" if (value > max_value) {\n" +" max_value = value;\n" +" max_index = c;\n" +" }\n" +" }\n" +" dst_buf[i*width+j] = max_index;\n" +"}\n"; + + +static int get_type_bytes(rknn_tensor_type type) +{ + switch (type) { + case RKNN_TENSOR_INT64: + return 8; + case RKNN_TENSOR_UINT32:; + case RKNN_TENSOR_INT32: + case RKNN_TENSOR_FLOAT32: + return 4; + case RKNN_TENSOR_INT16: + case RKNN_TENSOR_UINT16:; + case RKNN_TENSOR_FLOAT16: + return 2; + case RKNN_TENSOR_BOOL: + case RKNN_TENSOR_INT8: + case RKNN_TENSOR_UINT8: + return 1; + default: + return 0; + } +} + +/** + * opencl kernel init callback for custom op + * */ +int custom_init_callback_gpu(rknn_custom_op_context* op_ctx, rknn_custom_op_tensor* inputs, uint32_t n_inputs, + rknn_custom_op_tensor* outputs, uint32_t n_outputs) +{ + printf("argmax_init_callback_gpu\n"); + // 获取opencl context + cl_context cl_ctx = (cl_context)op_ctx->gpu_ctx.cl_context; + + return 0; +} + +/** + * opencl kernel init callback for custom op + * */ +int compute_custom_gpu_op_float32(rknn_custom_op_context* op_ctx, rknn_custom_op_tensor* inputs, uint32_t num_inputs, + rknn_custom_op_tensor* outputs, uint32_t num_outputs) +{ + + // get context + cl_context cl_ctx = (cl_context)op_ctx->gpu_ctx.cl_context; + + // get command queue + cl_command_queue queue = (cl_command_queue)op_ctx->gpu_ctx.cl_command_queue; + + // get kernel + cl_kernel kernel = (cl_kernel)op_ctx->gpu_ctx.cl_kernel; + + // import input/output buffer + const cl_import_properties_arm props[3] = { + CL_IMPORT_TYPE_ARM, + CL_IMPORT_TYPE_DMA_BUF_ARM, + 0, + }; + + cl_int status; + cl_mem inObject = clImportMemoryARM(cl_ctx, CL_MEM_READ_WRITE, props, &inputs[0].mem.fd, + inputs[0].mem.offset + inputs[0].mem.size, &status); + if (status != CL_SUCCESS) { + printf("Tensor: %s clImportMemoryARM failed\n", inputs[0].attr.name); + } + cl_mem outObject = clImportMemoryARM(cl_ctx, CL_MEM_READ_WRITE, props, &outputs[0].mem.fd, + outputs[0].mem.offset + outputs[0].mem.size, &status); + if (status != CL_SUCCESS) { + printf("Tensor: %s clImportMemoryARM failed\n", outputs[0].attr.name); + } + + int in_type_bytes = get_type_bytes(inputs[0].attr.type); + int out_type_bytes = get_type_bytes(outputs[0].attr.type); + int in_offset = inputs[0].mem.offset / in_type_bytes; + int out_offset = outputs[0].mem.offset / out_type_bytes; + unsigned int elems = inputs[0].attr.n_elems; + + auto in_channel = inputs[0].attr.dims[1]; + auto dst_width = outputs[0].attr.dims[3]; + auto dst_height = outputs[0].attr.dims[2]; + + // set kernel args + int argIndex = 0; + clSetKernelArg(kernel, argIndex++, sizeof(cl_mem), &inObject); + clSetKernelArg(kernel, argIndex++, sizeof(int), &in_channel); + clSetKernelArg(kernel, argIndex++, sizeof(cl_mem), &outObject); + + size_t global_work_size[] = { (size_t)dst_width , (size_t)dst_height}; + auto workItems_dims = 2; + + // enqueueNDRangeKernel + clEnqueueNDRangeKernel(queue, kernel, workItems_dims, NULL, global_work_size, NULL, 0, NULL, NULL); + + // finish command queue + clFinish(queue); + + return 0; +} + +/** + * opencl kernel destroy callback for custom op + */ +int destroy_callback_gpu(rknn_custom_op_context* op_ctx) +{ + // clear tmp buffer + printf("Argmax_destroy_callback_gpu\n"); + return 0; +} + +//code +RKNN_CUSTOM_OP_EXPORT rknn_custom_op* get_rknn_custom_op() +{ + // register a custom op + memset(&user_op, 0, sizeof(rknn_custom_op)); + char op_type[] = "ArgMax"; + strcpy(user_op.op_type, op_type); + user_op.version = 1; + user_op.target = RKNN_TARGET_TYPE_GPU; + user_op.init = custom_init_callback_gpu; + user_op.compute = compute_custom_gpu_op_float32; + user_op.destroy = destroy_callback_gpu; + + char kernel_name[] = "Argmax_channel_float"; + strcpy(user_op.cl_kernel_name, kernel_name); + + user_op.cl_kernel_source = cl_kernel_source; + user_op.cl_source_size = strlen(cl_kernel_source); + + return &user_op; +} + +#ifdef __cplusplus +} // extern "C" +#endif diff --git a/rknpu2/examples/rknn_custom_op_demo/rknn_custom_pytorch_op_demo/CMakeLists.txt b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_pytorch_op_demo/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..d4c3212808f8c8b10b8237e5bb3c07b76d41cb70 --- /dev/null +++ b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_pytorch_op_demo/CMakeLists.txt @@ -0,0 +1,49 @@ +cmake_minimum_required(VERSION 3.6) + +project(rknn_custom_pytorch_op_demo) + +set(CMAKE_CXX_STANDARD 11) +set(CMAKE_CXX_STANDARD_REQUIRED ON) + +# rknn api +set(RKNN_API_PATH ${CMAKE_SOURCE_DIR}/../../../runtime/${CMAKE_SYSTEM_NAME}/librknn_api) +if(CMAKE_SYSTEM_NAME STREQUAL "Android") + set(RKNN_RT_LIB ${RKNN_API_PATH}/${CMAKE_ANDROID_ARCH_ABI}/librknnrt.so) +else() + if(CMAKE_C_COMPILER MATCHES "aarch64") + set(LIB_ARCH aarch64) + else() + set(LIB_ARCH armhf) + endif() + + set(RKNN_RT_LIB ${RKNN_API_PATH}/${LIB_ARCH}/librknnrt.so) +endif() + +include_directories(${RKNN_API_PATH}/include) +include_directories(${CMAKE_SOURCE_DIR}/../../3rdparty) + +set(CNPY_ROOT ${CMAKE_SOURCE_DIR}/../../3rdparty/cnpy) +include_directories(${CNPY_ROOT}) + +set(CMAKE_INSTALL_RPATH "lib") + +add_executable(rknn_custom_pytorch_op_demo + src/rknn_api_test_custom_pytorch_op.cpp + ${CNPY_ROOT}/cnpy.cpp +) + +target_link_libraries(rknn_custom_pytorch_op_demo + ${RKNN_RT_LIB} +) + +# install target and libraries +set(CMAKE_INSTALL_PREFIX ${CMAKE_SOURCE_DIR}/install/rknn_custom_pytorch_op_demo_${CMAKE_SYSTEM_NAME}) + +install(TARGETS rknn_custom_pytorch_op_demo DESTINATION ./) +install(PROGRAMS ${RKNN_RT_LIB} DESTINATION lib) +file(GLOB NPY_FILES "model/*.npy") +install(FILES ${NPY_FILES} DESTINATION ./model) +file(GLOB BIN_FILES "model/*.bin") +install(FILES ${BIN_FILES} DESTINATION ./model) +message(STATUS "target_soc = ${TARGET_SOC}") +install(DIRECTORY model/${TARGET_SOC} DESTINATION ./model) diff --git a/rknpu2/examples/rknn_custom_op_demo/rknn_custom_pytorch_op_demo/README.md b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_pytorch_op_demo/README.md new file mode 100644 index 0000000000000000000000000000000000000000..e47e22b42f9c46506a24fc8e3a3f70b14b5218c5 --- /dev/null +++ b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_pytorch_op_demo/README.md @@ -0,0 +1,114 @@ +# RKNN Custom Pytorch OP Demo +This demo is an example of showing how to create, convert and run model with pytorch custom op. + +## Model Source +The pytorch custom op model can be created in pytorch model and export to ONNX by following scripts under the folder + +**convert_rknn_demo**. The specific steps are shown below: + +```shell +cd /path_to_rknn_custom_pytorch_op_demo/convert_rknn_demo/ +``` + +then, export a pytorch custom op model with single custom op only to ONNX, + +Note: The details can be reffered to the example of pytorch custom op from rknn-toolkit2. + +```shell +python3 generate_pytorch_custom_op_model.py +``` + +Finally, convert to rknn model, + +Note: Remeber to change the platform according to yours. + +```shell +python3 test.py +``` + +At this step, you should have a .rknn model on the current path. + +# Aarch64 Linux Demo + +## Build + +First export `GCC_COMPILER`, for example `export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu`, then execute: + +```shell +./build-linux.sh -t -a -b ] + +# such as: +./build-linux.sh -t rk3588 -a aarch64 -b Release +``` + +## *Install* + +Copy install/rknn_custom_cpu_op_demo_Linux to the devices under /userdata/. + +- If you use rockchip's evb board, you can use the following way: + +Connect device and push the program and RKNN model to `/userdata` + +```shell +adb push install/rknn_custom_pytorch_op_demo_Linux/ /data/ +``` + +- If board has sshd service, users can use scp or other methods to copy the program and RKNN model to the board. + +## Run + +```shell +adb shell +cd /data/rknn_custom_pytorch_op_demo_Linux +``` + +```shell +export LD_LIBRARY_PATH=./lib +./rknn_custom_pytorch_op_demo model//dual_residual_custom.rknn +model/dual_residual_input_0_nhwc.npy#model/dual_residual_input_1_nhwc.npy 1 +``` + +# Android Demo + +## Build + +First export `ANDROID_NDK_PATH`, for example `export ANDROID_NDK_PATH=~/opts/ndk/android-ndk-r18b`, then execute: + +```shell +./build-android.sh -t -a [-b ] + +# sush as: +./build-android.sh -t rk3568 -a arm64-v8a -b Release +``` + +## Install + +connect device and push build output into `/data` + +```shell + adb push install/rknn_custom_pytorch_op_demo_Android/ /data/ +``` + +## Run + +```shell +adb shell +cd /data/rknn_custom_pytorch_op_demo_Android/ +``` + +then exporting LD_LIBRARY_PATH to the local path of lib. + +```shell +export LD_LIBRARY_PATH=./lib +./rknn_custom_pytorch_op_demo model//dual_residual_custom.rknn +model/dual_residual_input_0_nhwc.npy#model/dual_residual_input_1_nhwc.npy 1 +``` + +Note_1: Different platforms, different versions of tools and drivers may have slightly different results. + +Note_2: This demo model has two inputs and outputs and they have been provided under folder **model** for better verifying. Users can have their own inputs for their own models. + +Note_3: Becareful for input layout for your input own data that should be matched from rknn_query . + +Note_4: The input npy for current rknn_custom_cpu_op_demo only supports NHWC or Undefined layout ! + diff --git a/rknpu2/examples/rknn_custom_op_demo/rknn_custom_pytorch_op_demo/build-android.sh b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_pytorch_op_demo/build-android.sh new file mode 100755 index 0000000000000000000000000000000000000000..4778e831eaff14905900f6fea7b8e3ee44147549 --- /dev/null +++ b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_pytorch_op_demo/build-android.sh @@ -0,0 +1,112 @@ +#!/bin/bash + +set -e + +if [[ -z ${ANDROID_NDK_PATH} ]];then + echo "Please set ANDROID_NDK_PATH, such as export ANDROID_NDK_PATH=~/opts/ndk/android-ndk-r18b" + echo "NDK Version r18, r19 is recommanded. Other version may cause build failure." + exit +fi + +echo "$0 $@" +while getopts ":t:a:b" opt; do + case $opt in + t) + TARGET_SOC=$OPTARG + ;; + a) + TARGET_ARCH=$OPTARG + ;; + b) + BUILD_TYPE=$OPTARG + ;; + :) + echo "Option -$OPTARG requires an argument." + exit 1 + ;; + ?) + echo "Invalid option: -$OPTARG index:$OPTIND" + ;; + esac +done + +if [ -z ${TARGET_SOC} ] || [ -z ${TARGET_ARCH} ] ; then + echo "$0 -t -a [-b ]" + echo "" + echo " -t : target (rk3566/rk3568/rk3562/rk3576/rk3588)" + echo " -a : arch (arm64-v8a/armeabi-v7a)" + echo " -b : build_type (Debug/Release)" + echo "such as: $0 -t rk3588 -a arm64-v8a -b Release" + echo "" + exit -1 +fi + +# Debug / Release / RelWithDebInfo +if [[ -z ${BUILD_TYPE} ]];then + BUILD_TYPE=Release +fi + +case ${TARGET_SOC} in + rk356x) + TARGET_SOC="RK3566_RK3568" + ;; + rk3566_rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3588) + TARGET_SOC="RK3588" + ;; + rk3576) + TARGET_SOC="RK3576" + ;; + rk3566) + TARGET_SOC="RK3566_RK3568" + ;; + rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3562) + TARGET_SOC="RK3562" + ;; + *) + echo "Invalid target: ${TARGET_SOC}" + echo "Valid target: rk3562,rk3566,rk3568,rk3576,rk3588" + exit -1 + ;; +esac + +TARGET_PLATFORM=${TARGET_SOC}_android +if [[ -n ${TARGET_ARCH} ]];then +TARGET_PLATFORM=${TARGET_PLATFORM}_${TARGET_ARCH} +fi +ROOT_PWD=$( cd "$( dirname $0 )" && cd -P "$( dirname "$SOURCE" )" && pwd ) +BUILD_DIR=${ROOT_PWD}/build/build_${TARGET_PLATFORM}_${BUILD_TYPE} + +echo "===================================" +echo "TARGET_SOC=${TARGET_SOC}" +echo "TARGET_ARCH=${TARGET_ARCH}" +echo "BUILD_TYPE=${BUILD_TYPE}" +echo "BUILD_DIR=${BUILD_DIR}" +echo "ANDROID_NDK_PATH=${ANDROID_NDK_PATH}" +echo "===================================" + +if [[ ! -d "${BUILD_DIR}" ]]; then + mkdir -p ${BUILD_DIR} +fi + + +cd ${BUILD_DIR} +cmake ../.. \ + -DANDROID_TOOLCHAIN=clang \ + -DTARGET_SOC=${TARGET_SOC} \ + -DCMAKE_SYSTEM_NAME=Android \ + -DCMAKE_TOOLCHAIN_FILE=$ANDROID_NDK_PATH/build/cmake/android.toolchain.cmake \ + -DANDROID_ABI=${TARGET_ARCH} \ + -DANDROID_STL=c++_static \ + -DANDROID_PLATFORM=android-24 \ + -DCMAKE_BUILD_TYPE=${BUILD_TYPE} +# make VERBOSE=1 +make -j4 +make install +cd .. + diff --git a/rknpu2/examples/rknn_custom_op_demo/rknn_custom_pytorch_op_demo/build-linux.sh b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_pytorch_op_demo/build-linux.sh new file mode 100755 index 0000000000000000000000000000000000000000..ae972803b8bda834c3a95588e513ec7af832cbeb --- /dev/null +++ b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_pytorch_op_demo/build-linux.sh @@ -0,0 +1,122 @@ +#!/bin/bash + +set -e + +echo "$0 $@" +while getopts ":t:a:b" opt; do + case $opt in + t) + TARGET_SOC=$OPTARG + ;; + a) + TARGET_ARCH=$OPTARG + ;; + b) + BUILD_TYPE=$OPTARG + ;; + :) + echo "Option -$OPTARG requires an argument." + exit 1 + ;; + ?) + echo "Invalid option: -$OPTARG index:$OPTIND" + ;; + esac +done + +if [ -z ${TARGET_SOC} ];then + echo "$0 -t -a [-b ]" + echo "" + echo " -t : target (rk3566/rk3568/rk3562/rk3576/rk3588)" + echo " -a : arch (aarch64/armhf)" + echo " -b : build_type(Debug/Release)" + echo "such as: $0 -t rk3588 -a aarch64 -b Release" + echo "" + exit -1 +fi + +if [[ -z ${GCC_COMPILER} ]];then + echo "Please set GCC_COMPILER for $TARGET_SOC" + echo "such as export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu" + exit +fi +echo "$GCC_COMPILER" +export CC=${GCC_COMPILER}-gcc +export CXX=${GCC_COMPILER}-g++ + +if command -v ${CC} >/dev/null 2>&1; then + : +else + echo "${CC} is not available" + echo "Please set GCC_COMPILER for $TARGET_SOC" + echo "such as export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu" + exit +fi + +# Debug / Release +if [[ -z ${BUILD_TYPE} ]];then + BUILD_TYPE=Release +fi + + +case ${TARGET_SOC} in + rk356x) + TARGET_SOC="RK3566_RK3568" + ;; + rk3566_rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3588) + TARGET_SOC="RK3588" + ;; + rk3576) + TARGET_SOC="RK3576" + ;; + rk3566) + TARGET_SOC="RK3566_RK3568" + ;; + rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3562) + TARGET_SOC="RK3562" + ;; + *) + echo "Invalid target: ${TARGET_SOC}" + echo "Valid target: rk3562,rk3566,rk3568,rk3576,rk3588" + exit -1 + ;; +esac + +TARGET_PLATFORM=${TARGET_SOC}_linux +if [[ -n ${TARGET_ARCH} ]];then +TARGET_PLATFORM=${TARGET_PLATFORM}_${TARGET_ARCH} +fi +ROOT_PWD=$( cd "$( dirname $0 )" && cd -P "$( dirname "$SOURCE" )" && pwd ) +BUILD_DIR=${ROOT_PWD}/build/build_${TARGET_PLATFORM}_${BUILD_TYPE} + + +echo "===================================" +echo "TARGET_SOC=${TARGET_SOC}" +echo "TARGET_ARCH=${TARGET_ARCH}" +echo "BUILD_TYPE=${BUILD_TYPE}" +echo "BUILD_DIR=${BUILD_DIR}" +echo "CC=${CC}" +echo "CXX=${CXX}" +echo "===================================" + +if [[ ! -d "${BUILD_DIR}" ]]; then + mkdir -p ${BUILD_DIR} +fi + + +cd ${BUILD_DIR} +cmake ../.. \ + -DTARGET_SOC=${TARGET_SOC} \ + -DCMAKE_SYSTEM_NAME=Linux \ + -DCMAKE_SYSTEM_PROCESSOR=${TARGET_ARCH} \ + -DCMAKE_BUILD_TYPE=${BUILD_TYPE} \ + -DCMAKE_C_COMPILER=${CC} \ + -DCMAKE_CXX_COMPILER=${CXX} +make -j4 +make install diff --git a/rknpu2/examples/rknn_custom_op_demo/rknn_custom_pytorch_op_demo/convert_rknn_demo/generate_pytorch_custom_op_model.py b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_pytorch_op_demo/convert_rknn_demo/generate_pytorch_custom_op_model.py new file mode 100644 index 0000000000000000000000000000000000000000..e1904782694881983db94ad093f25cf895ba2776 --- /dev/null +++ b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_pytorch_op_demo/convert_rknn_demo/generate_pytorch_custom_op_model.py @@ -0,0 +1,60 @@ +import torch +import torch.utils.cpp_extension +import numpy as np + +cpp_source = """ +#include +std::tuple dual_residual(torch::Tensor x, torch::Tensor y, double_t alpha) { + torch::Tensor tmp1 = x* alpha - y; + torch::Tensor tmp2 = y* alpha - x; + return std::tuple(tmp1, tmp2);} +static auto registry = torch::RegisterOperators() + .op("cst::dual_residual", &dual_residual); +""" + +torch.utils.cpp_extension.load_inline( + name="test", + cpp_sources=cpp_source, + functions="dual_residual", + verbose=True, + is_python_module=True, +) + +class Model(torch.nn.Module): + def __init__(self, *args, **kwargs) -> None: + super().__init__(*args, **kwargs) + self.const = .5 + + def forward(self, x, y): + r1, r2 = torch.ops.cst.dual_residual(x, y, self.const) + return r1, r2 + +x = torch.randn(1, 3, 10, 10) +y = torch.randn(1, 3, 10, 10) +m = Model() +z = m(x, y) + +# target_result1 = x * .5 - y +# target_result2 = y * .5 - x +# print('result1 is equal:', torch.equal(z[0], target_result1)) +# print('result2 is equal:', torch.equal(z[1], target_result2)) + +# jit_m = torch.jit.trace(m, (x, y)) +# jit_m.save("dual_residual.pt") + +from torch.onnx import register_custom_op_symbolic +from torch.onnx.symbolic_helper import parse_args + +@parse_args('v', 'v', 'f') +def dual_residual_symbolic(g, x, y, alpha): + output = g.op("rknn_cst::cstDualResidual", x, y, alpha_f=alpha, + outputs=2) + return output + +register_custom_op_symbolic("cst::dual_residual", dual_residual_symbolic, 12) +torch.onnx.export(m, (x, y), "dual_residual.onnx", opset_version=12) + +# np.save("dual_residual_input_0.npy", x.numpy()) +# np.save("dual_residual_input_1.npy", y.numpy()) +# np.save("dual_residual_output_0.npy", z[0].numpy()) +# np.save("dual_residual_output_1.npy", z[1].numpy()) \ No newline at end of file diff --git a/rknpu2/examples/rknn_custom_op_demo/rknn_custom_pytorch_op_demo/convert_rknn_demo/test.py b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_pytorch_op_demo/convert_rknn_demo/test.py new file mode 100644 index 0000000000000000000000000000000000000000..e77532648ce581f1e3dfd447bf9df5368f8149cb --- /dev/null +++ b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_pytorch_op_demo/convert_rknn_demo/test.py @@ -0,0 +1,67 @@ +import numpy as np +from rknn.api import RKNN +from rknn.api.custom_op import get_node_attr +from scipy.spatial import distance + + +class cstDualResidual: + # custom operator cstDualResidual + op_type = 'cstDualResidual' + def shape_infer(self, node, in_shapes, in_dtypes): + return in_shapes.copy(), in_dtypes.copy() + def compute(self, node, inputs): + x = inputs[0] + y = inputs[1] + alpha = get_node_attr(node, 'alpha') + tmp_1 = x*alpha - y + tmp_2 = y* alpha - x + return [tmp_1, tmp_2] + + + +if __name__ == '__main__': + + custom_model_path = 'dual_residual.onnx' + + # Create RKNN object + rknn = RKNN() + + # Pre-process config + print('--> Config model') + rknn.config(mean_values=[[0, 0, 0]], std_values=[[1, 1, 1]], target_platform='rk3566') + print('done') + + # Register cstSigmoid op + print('--> Register cstDualResidual op') + ret = rknn.reg_custom_op(cstDualResidual()) + if ret != 0: + print('Register cstDualResidual op failed!') + exit(ret) + print('done') + + # Load model + print('--> Loading model') + ret = rknn.load_onnx(model=custom_model_path, input_size_list=[[1, 3, 10, 10]]) + if ret != 0: + print('Load model failed!') + exit(ret) + print('done') + + # Build model + print('--> Building model') + ret = rknn.build(do_quantization=False, dataset=None) + if ret != 0: + print('Build model failed!') + exit(ret) + print('done') + + # Export rknn model + print('--> Export rknn model') + ret = rknn.export_rknn('dual_residual_custom.rknn') + if ret != 0: + print('Export rknn model failed!') + exit(ret) + print('done') + + # Release + rknn.release() diff --git a/rknpu2/examples/rknn_custom_op_demo/rknn_custom_pytorch_op_demo/model/RK3562/dual_residual_custom.rknn b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_pytorch_op_demo/model/RK3562/dual_residual_custom.rknn new file mode 100644 index 0000000000000000000000000000000000000000..1c365dc12ea9ce742ac3f87f7350ff89e8088c55 --- /dev/null +++ b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_pytorch_op_demo/model/RK3562/dual_residual_custom.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c951c50c9143702ec05577977c0b3082eebf4ccc98aa436975b37fb3ab95bfa7 +size 10807 diff --git a/rknpu2/examples/rknn_custom_op_demo/rknn_custom_pytorch_op_demo/model/RK3566_RK3568/dual_residual_custom.rknn b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_pytorch_op_demo/model/RK3566_RK3568/dual_residual_custom.rknn new file mode 100644 index 0000000000000000000000000000000000000000..a8892a611d97d09b2aedc41d6863ca0d873ab829 --- /dev/null +++ b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_pytorch_op_demo/model/RK3566_RK3568/dual_residual_custom.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:76ab92789d4dc9f427c6c7be282856309302e404cfc517b1e6d23874361545d9 +size 10807 diff --git a/rknpu2/examples/rknn_custom_op_demo/rknn_custom_pytorch_op_demo/model/RK3576/dual_residual_custom.rknn 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0000000000000000000000000000000000000000..0fff13beda5d1e769b3cee69d27b3f93ca082b72 --- /dev/null +++ b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_pytorch_op_demo/model/dual_residual_input_1_nhwc.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b7f445fac02a80c09f79ac26a2039c48ad847a4ee89202060da582cef73ae713 +size 1328 diff --git a/rknpu2/examples/rknn_custom_op_demo/rknn_custom_pytorch_op_demo/model/dual_residual_output_0.bin b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_pytorch_op_demo/model/dual_residual_output_0.bin new file mode 100644 index 0000000000000000000000000000000000000000..595f0c155c0e60500e73854c1c4001715b97998b --- /dev/null +++ b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_pytorch_op_demo/model/dual_residual_output_0.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e01256c7e9ea1dd26f4bea4891ea8185cf7d1d9e8833bc1746a0ec1d5c58e367 +size 1200 diff --git a/rknpu2/examples/rknn_custom_op_demo/rknn_custom_pytorch_op_demo/model/dual_residual_output_1.bin b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_pytorch_op_demo/model/dual_residual_output_1.bin new file mode 100644 index 0000000000000000000000000000000000000000..47d8194e1eab160fd67c10dfa0cae2ee578d2cda --- /dev/null +++ b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_pytorch_op_demo/model/dual_residual_output_1.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8a1d39e02c34b07e977ff376e2fe7716624ebd9a713758e55b6209a49d69b7cc +size 1200 diff --git a/rknpu2/examples/rknn_custom_op_demo/rknn_custom_pytorch_op_demo/src/rknn_api_test_custom_pytorch_op.cpp b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_pytorch_op_demo/src/rknn_api_test_custom_pytorch_op.cpp new file mode 100755 index 0000000000000000000000000000000000000000..c546696a8859e8243214cc58cdc6a318449aadba --- /dev/null +++ b/rknpu2/examples/rknn_custom_op_demo/rknn_custom_pytorch_op_demo/src/rknn_api_test_custom_pytorch_op.cpp @@ -0,0 +1,535 @@ +// Copyright (c) 2023 by Rockchip Electronics Co., Ltd. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +/*------------------------------------------- + Includes +-------------------------------------------*/ + +#include +#include +#include +#include +#include +#include + +#include "rknn_api.h" +#include "rknn_custom_op.h" + + +#include "cnpy/cnpy.h" +using namespace cnpy; + +constexpr const char* golden_out_file_0 = "./model/dual_residual_output_0.bin"; +constexpr const char* golden_out_file_1 = "./model/dual_residual_output_1.bin"; + +constexpr const size_t _PATH_MAX = 4096; + +using namespace std; + + +/*------------------------------------------- + Functions +-------------------------------------------*/ + + +static int load_bin_file(void *pBuffer, const char *fileName, size_t sizeFile) +{ + FILE *pFile = fopen(fileName, "rb"); + if (pFile == NULL) { + puts("Error in reading files."); + return -1; + } + + fread(pBuffer, 1, sizeFile, pFile); + + if (fclose(pFile) != 0) { + puts("Error in closing files."); + return -1; + } + return 1; +} + + + +static inline int64_t getCurrentTimeUs() +{ + struct timeval tv; + gettimeofday(&tv, NULL); + return tv.tv_sec * 1000000 + tv.tv_usec; +} + +static void dump_tensor_attr(rknn_tensor_attr* attr) +{ + std::string shape_str = attr->n_dims < 1 ? "" : std::to_string(attr->dims[0]); + for (int i = 1; i < attr->n_dims; ++i) { + shape_str += ", " + std::to_string(attr->dims[i]); + } + + printf(" index=%d, name=%s, n_dims=%d, dims=[%s], n_elems=%d, size=%d, w_stride = %d, size_with_stride=%d, fmt=%s, " + "type=%s, qnt_type=%s, " + "zp=%d, scale=%f\n", + attr->index, attr->name, attr->n_dims, shape_str.c_str(), attr->n_elems, attr->size, attr->w_stride, + attr->size_with_stride, get_format_string(attr->fmt), get_type_string(attr->type), + get_qnt_type_string(attr->qnt_type), attr->zp, attr->scale); +} + + +static std::vector split(const std::string& str, const std::string& pattern) +{ + std::vector res; + if (str == "") + return res; + std::string strs = str + pattern; + size_t pos = strs.find(pattern); + while (pos != strs.npos) { + std::string temp = strs.substr(0, pos); + res.push_back(temp); + strs = strs.substr(pos + 1, strs.size()); + pos = strs.find(pattern); + } + return res; +} + +static void* load_file(const char* file_path, size_t* file_size) +{ + FILE* fp = fopen(file_path, "rb"); + if (fp == NULL) { + printf("failed to open file: %s\n", file_path); + return NULL; + } + + fseek(fp, 0, SEEK_END); + size_t size = (size_t)ftell(fp); + fseek(fp, 0, SEEK_SET); + + void* file_data = malloc(size); + if (file_data == NULL) { + fclose(fp); + printf("failed allocate file size: %zu\n", size); + return NULL; + } + + if (fread(file_data, 1, size, fp) != size) { + fclose(fp); + free(file_data); + printf("failed to read file data!\n"); + return NULL; + } + + fclose(fp); + + *file_size = size; + + return file_data; +} + + +static unsigned char *load_npy(const char *input_path, rknn_tensor_attr *input_attr, int *input_type, int *input_size, + int *type_bytes) +{ + printf("Loading %s\n", input_path); + + NpyArray npy_data = npy_load(input_path); + + *type_bytes = npy_data.word_size; + std::string typeName = npy_data.typeName; + + printf("npy data type:%s\n", typeName.c_str()); + + if (typeName == "int8") + { + *input_type = RKNN_TENSOR_INT8; + } + else if (typeName == "uint8") + { + *input_type = RKNN_TENSOR_UINT8; + } + else if (typeName == "float16") + { + *input_type = RKNN_TENSOR_FLOAT16; + } + else if (typeName == "float32") + { + *input_type = RKNN_TENSOR_FLOAT32; + } + else if (typeName == "8") + { + *input_type = RKNN_TENSOR_BOOL; + } + else if (typeName == "int64") + { + *input_type = RKNN_TENSOR_INT64; + } + + // npy shape = NHWC + std::vector npy_shape; + for (size_t i = 0; i < npy_data.shape.size(); ++i) + { + npy_shape.emplace_back(npy_data.shape[i]); + } + + int height = npy_shape.size() > 1 ? npy_shape[1] : 1; + int width = npy_shape.size() > 2 ? npy_shape[2] : 1; + int channel = npy_shape.size() > 3 ? npy_shape[3] : 1; + + switch (input_attr->fmt) + { + case RKNN_TENSOR_NHWC: + input_attr->dims[0] = npy_shape[0]; + input_attr->dims[1] = height; + input_attr->dims[2] = width; + input_attr->dims[3] = channel; + break; + case RKNN_TENSOR_UNDEFINED: + for (int idx = 0; idx < input_attr->n_dims; ++idx) + { + input_attr->dims[idx] = npy_shape[idx]; + } + break; + default: + fprintf(stderr, "load_npy error, unsupport model input layout: %s\n", get_format_string(input_attr->fmt)); + break; + } + + unsigned char *data = (unsigned char *)malloc(npy_data.num_bytes()); + if (!data) + { + return NULL; + } + + // TODO: copy + memcpy(data, npy_data.data(), npy_data.num_bytes()); + + *input_size = npy_data.num_bytes(); + + return data; +} + + +double cosine(float *u, float *v, int num_elems) +{ + double uv = 0, uu = 0, vv = 0; + for (int i = 0; i < num_elems; i++) { + uv += u[i] * v[i]; + uu += u[i] * u[i]; + vv += v[i] * v[i]; + } + double cos = -1; + if (uu != 0 && vv != 0) { + cos = uv / sqrt(uu * vv); + } + return cos; +} + + + +static void save_npy(const char* output_path, float* output_data, rknn_tensor_attr* output_attr) +{ + std::vector output_shape; + + for (uint32_t i = 0; i < output_attr->n_dims; ++i) { + output_shape.push_back(output_attr->dims[i]); + } + + npy_save(output_path, output_data, output_shape); +} + + +/** + * float32 kernel implemetation sample for custom cpu op + * */ +int compute_custom_dual_residual_float32(rknn_custom_op_context* op_ctx, rknn_custom_op_tensor* inputs, uint32_t n_inputs, + rknn_custom_op_tensor* outputs, uint32_t n_outputs) +{ + unsigned char* in_ptr_0 = (unsigned char*)inputs[0].mem.virt_addr + inputs[0].mem.offset; + unsigned char* in_ptr_1 = (unsigned char*)inputs[1].mem.virt_addr + inputs[1].mem.offset; + unsigned char* out_ptr_0 = (unsigned char*)outputs[0].mem.virt_addr + outputs[0].mem.offset; + unsigned char* out_ptr_1 = (unsigned char*)outputs[1].mem.virt_addr + outputs[1].mem.offset; + const float* in_data_0 = (const float*)in_ptr_0; + const float* in_data_1 = (const float*)in_ptr_1; + float* out_data_0 = (float*)out_ptr_0; + float* out_data_1 = (float*)out_ptr_1; + + rknn_custom_op_attr op_attr; + float alpha = 0.f; + rknn_custom_op_get_op_attr(op_ctx, "alpha", &op_attr); + if (op_attr.n_elems == 1 && op_attr.dtype == RKNN_TENSOR_FLOAT32) { + alpha = ((float*)op_attr.data)[0]; + } + + const auto out_elems = outputs[0].attr.n_elems; + for (size_t idx=0; idx input_paths_split; + if (argc > 2) { + char* input_paths = argv[2]; + input_paths_split = split(input_paths, "#"); + } + + int loop_count = 1; + if (argc > 3) { + loop_count = atoi(argv[3]); + } + + char* output_dir = NULL; + if (argc > 6) { + output_dir = argv[6]; + } + + rknn_context ctx = 0; + + // Load RKNN Model + size_t model_size; + void* model_data = load_file(model_path, &model_size); + if (model_data == NULL) { + return -1; + } + int ret = rknn_init(&ctx, model_data, model_size, 0, NULL); + free(model_data); + if (ret < 0) { + printf("rknn_init fail! ret=%d\n", ret); + return -1; + } + + // register a custom op + rknn_custom_op user_op[1]; + memset(user_op, 0, sizeof(rknn_custom_op)); + strncpy(user_op[0].op_type, "cstDualResidual", RKNN_MAX_NAME_LEN - 1); + user_op[0].version = 1; + user_op[0].target = RKNN_TARGET_TYPE_CPU; + user_op[0].compute = compute_custom_dual_residual_float32; + ret = rknn_register_custom_ops(ctx, user_op, 1); + if (ret < 0) { + printf("rknn_register_custom_op fail! ret = %d\n", ret); + return -1; + } + + // Get sdk and driver version + rknn_sdk_version sdk_ver; + ret = rknn_query(ctx, RKNN_QUERY_SDK_VERSION, &sdk_ver, sizeof(sdk_ver)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("rknn_api/rknnrt version: %s, driver version: %s\n", sdk_ver.api_version, sdk_ver.drv_version); + rknn_mem_size mem_size; + ret = rknn_query(ctx, RKNN_QUERY_MEM_SIZE, &mem_size, sizeof(mem_size)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("total weight size: %u, total internal size: %u\n", mem_size.total_weight_size, mem_size.total_internal_size); + printf("total dma used size: %zu\n", (size_t)mem_size.total_dma_allocated_size); + + // Get Model Input Output Info + rknn_input_output_num io_num; + ret = rknn_query(ctx, RKNN_QUERY_IN_OUT_NUM, &io_num, sizeof(io_num)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("model input num: %d, output num: %d\n", io_num.n_input, io_num.n_output); + + printf("input tensors:\n"); + rknn_tensor_attr input_attrs[io_num.n_input]; + memset(input_attrs, 0, io_num.n_input * sizeof(rknn_tensor_attr)); + for (uint32_t i = 0; i < io_num.n_input; i++) { + input_attrs[i].index = i; + // query info + ret = rknn_query(ctx, RKNN_QUERY_INPUT_ATTR, &(input_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret < 0) { + printf("rknn_query error! ret=%d\n", ret); + return -1; + } + dump_tensor_attr(&input_attrs[i]); + } + + printf("output tensors:\n"); + rknn_tensor_attr output_attrs[io_num.n_output]; + memset(output_attrs, 0, io_num.n_output * sizeof(rknn_tensor_attr)); + for (uint32_t i = 0; i < io_num.n_output; i++) { + output_attrs[i].index = i; + // query info + ret = rknn_query(ctx, RKNN_QUERY_OUTPUT_ATTR, &(output_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + dump_tensor_attr(&output_attrs[i]); + } + + // Get custom string + rknn_custom_string custom_string; + ret = rknn_query(ctx, RKNN_QUERY_CUSTOM_STRING, &custom_string, sizeof(custom_string)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("custom string: %s\n", custom_string.string); + + unsigned char* input_data[io_num.n_input]; + int input_type[io_num.n_input]; + int input_layout[io_num.n_input]; + int input_size[io_num.n_input]; + int type_bytes[io_num.n_input]; + for (int i = 0; i < io_num.n_input; i++) { + input_data[i] = NULL; + input_type[i] = RKNN_TENSOR_FLOAT32; + input_layout[i] = input_attrs[i].fmt; + input_size[i] = input_attrs[i].n_elems * sizeof(float); + type_bytes[i] = 4; + } + + if (input_paths_split.size() > 0) { + // Load input + if (io_num.n_input != input_paths_split.size()) { + printf("input missing!, need input number: %d\n", io_num.n_input); + return -1; + } + for (int i = 0; i < io_num.n_input; i++) { + // Load npy file + input_data[i] = load_npy(input_paths_split[i].c_str(), &input_attrs[i], &input_type[i], + &input_size[i], &type_bytes[i]); + + if (!input_data[i]) { + return -1; + } + } + } else { + for (int i = 0; i < io_num.n_input; i++) { + input_data[i] = (unsigned char*)malloc(input_size[i]); + } + } + + rknn_input inputs[io_num.n_input]; + memset(inputs, 0, io_num.n_input * sizeof(rknn_input)); + for (int i = 0; i < io_num.n_input; i++) { + inputs[i].index = i; + inputs[i].pass_through = 0; + inputs[i].type = (rknn_tensor_type)input_type[i]; + inputs[i].fmt = (rknn_tensor_format)input_layout[i]; + inputs[i].buf = input_data[i]; + inputs[i].size = input_size[i]; + } + + // Set input + ret = rknn_inputs_set(ctx, io_num.n_input, inputs); + if (ret < 0) { + printf("rknn_input_set fail! ret=%d\n", ret); + return -1; + } + + // Run + printf("Begin perf ...\n"); + double total_time = 0; + for (int i = 0; i < loop_count; ++i) { + int64_t start_us = getCurrentTimeUs(); + ret = rknn_run(ctx, NULL); + int64_t elapse_us = getCurrentTimeUs() - start_us; if (ret < 0) { + printf("rknn run error %d\n", ret); + return -1; + } + total_time += elapse_us / 1000.f; + printf("%4d: Elapse Time = %.2fms, FPS = %.2f\n", i, elapse_us / 1000.f, 1000.f * 1000.f / elapse_us); + } + printf("Avg elapse Time = %.3fms\n", total_time / loop_count); + printf("Avg FPS = %.3f\n", loop_count * 1000.f / total_time); + + // Get perf detail + rknn_perf_detail perf_detail; + ret = rknn_query(ctx, RKNN_QUERY_PERF_DETAIL, &perf_detail, sizeof(perf_detail)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("rknn run perf detail is:\n%s", perf_detail.perf_data); + + // Get run duration time + rknn_perf_run perf_run; + ret = rknn_query(ctx, RKNN_QUERY_PERF_RUN, &perf_run, sizeof(perf_run)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("rknn run perf time is %ldus\n", perf_run.run_duration); + + // Get output + rknn_output outputs[io_num.n_output]; + memset(outputs, 0, io_num.n_output * sizeof(rknn_output)); + for (uint32_t i = 0; i < io_num.n_output; ++i) { + outputs[i].want_float = 1; + outputs[i].index = i; + outputs[i].is_prealloc = 0; + } + + ret = rknn_outputs_get(ctx, io_num.n_output, outputs, NULL); + if (ret < 0) { + printf("rknn_outputs_get fail! ret=%d\n", ret); + return ret; + } + + //save output in npy + for (uint32_t i = 0; i < io_num.n_output; i++) { + char output_path[_PATH_MAX]; + sprintf(output_path, "%s/rt_output%d.npy", output_dir ? output_dir : ".", i); + save_npy(output_path, (float*)outputs[i].buf, &output_attrs[i]); + } + + // verify + const size_t out_sz = 1*3*10*10*sizeof(float); + float *p_out_data_0 = (float*)malloc(out_sz); + float *p_out_data_1 = (float*)malloc(out_sz); + load_bin_file((void*)p_out_data_0, golden_out_file_0, out_sz); + load_bin_file((void*)p_out_data_1, golden_out_file_1, out_sz); + auto cos_out_0 = cosine( (float*)outputs[0].buf, p_out_data_0, output_attrs[0].n_elems); + auto cos_out_1 = cosine( (float*)outputs[1].buf, p_out_data_1, output_attrs[1].n_elems); + if (cos_out_0 > 0.999 && cos_out_1 > 0.999) { + printf("Results are correct\n"); + } + else{ + printf("Results are failed\n"); + } + + // release outputs + ret = rknn_outputs_release(ctx, io_num.n_output, outputs); + + // destroy + rknn_destroy(ctx); + + for (int i = 0; i < io_num.n_input; i++) { + free(input_data[i]); + } + + return 0; +} diff --git a/rknpu2/examples/rknn_dynamic_shape_input_demo/CMakeLists.txt b/rknpu2/examples/rknn_dynamic_shape_input_demo/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..8ddaeb39004d69443daca9274cc8b742f696f5a9 --- /dev/null +++ b/rknpu2/examples/rknn_dynamic_shape_input_demo/CMakeLists.txt @@ -0,0 +1,107 @@ +cmake_minimum_required(VERSION 3.6) + +project(rknn_dynshape_demo) + +set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS}") +set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11") + +# skip 3rd-party lib dependencies +set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} -Wl,--allow-shlib-undefined") + +# rknn api +set(RKNN_API_PATH ${CMAKE_SOURCE_DIR}/../../runtime//${CMAKE_SYSTEM_NAME}/librknn_api) +if (CMAKE_SYSTEM_NAME STREQUAL "Android") + set(RKNN_RT_LIB ${RKNN_API_PATH}/${CMAKE_ANDROID_ARCH_ABI}/librknnrt.so) +else() + if (CMAKE_C_COMPILER MATCHES "aarch64") + set(LIB_ARCH aarch64) + else() + set(LIB_ARCH armhf) + endif() + set(RKNN_RT_LIB ${RKNN_API_PATH}/${LIB_ARCH}/librknnrt.so) +endif() +include_directories(${RKNN_API_PATH}/include) +include_directories(${CMAKE_SOURCE_DIR}/../3rdparty) + +# opencv +if (CMAKE_SYSTEM_NAME STREQUAL "Android") + set(OpenCV_DIR ${CMAKE_SOURCE_DIR}/../3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/abi-${CMAKE_ANDROID_ARCH_ABI}) +else() + if(LIB_ARCH STREQUAL "armhf") + set(OpenCV_DIR ${CMAKE_SOURCE_DIR}/../3rdparty/opencv/opencv-linux-armhf/share/OpenCV) + else() + set(OpenCV_DIR ${CMAKE_SOURCE_DIR}/../3rdparty/opencv/opencv-linux-aarch64/share/OpenCV) + endif() +endif() +find_package(OpenCV REQUIRED) + +# mmz +set(MPI_MMZ_PATH ${CMAKE_SOURCE_DIR}/../3rdparty/rk_mpi_mmz) + +if(CMAKE_SYSTEM_NAME STREQUAL "Android") + set(MPI_MMZ_LIB ${MPI_MMZ_PATH}/lib/Android/${CMAKE_ANDROID_ARCH_ABI}/libmpimmz.so) +else() + if(CMAKE_C_COMPILER MATCHES "aarch64") + set(LIB_ARCH aarch64) + else() + set(LIB_ARCH armhf) + endif() + + set(MPI_MMZ_LIB ${MPI_MMZ_PATH}/lib/Linux//${LIB_ARCH}/libmpimmz.so) +endif() +include_directories(${MPI_MMZ_PATH}/include) + +#### cnpy +set(CNPY_ROOT ${CMAKE_SOURCE_DIR}/../3rdparty/cnpy) +include_directories(${CNPY_ROOT}) + +set(CMAKE_INSTALL_RPATH "lib") + +### 普通API Demo +add_executable(rknn_dynshape_inference + src/rknn_dynshape_inference.cc + ${CNPY_ROOT}/cnpy.cpp +) +target_link_libraries(rknn_dynshape_inference + ${RKNN_RT_LIB} + ${OpenCV_LIBS} +) +# install target and libraries +set(CMAKE_INSTALL_PREFIX ${CMAKE_SOURCE_DIR}/install/rknn_dynshape_demo_${CMAKE_SYSTEM_NAME}) +install(TARGETS rknn_dynshape_inference DESTINATION ./) + +## 零拷贝API Demo +add_executable(rknn_dynshape_inference_zero_copy + src/rknn_dynshape_inference_zero_copy.cc + ${CNPY_ROOT}/cnpy.cpp +) +target_link_libraries(rknn_dynshape_inference_zero_copy + ${RKNN_RT_LIB} + ${OpenCV_LIBS} +) +# install target and libraries +install(TARGETS rknn_dynshape_inference_zero_copy DESTINATION ./) + +install(DIRECTORY model/${TARGET_SOC} DESTINATION ./model) +install(DIRECTORY images DESTINATION ./) +install(PROGRAMS ${RKNN_RT_LIB} DESTINATION lib) + + + +# At present, mmz　demo is only available under Android, but not for Linux temporarily, +# mainly because libmpimmz.so has no Linux implementation now. The API of the NPU itself supports Linux. +if(CMAKE_SYSTEM_NAME STREQUAL "Android") + # rknn_dynshape_inference_zero_copy_alloc_outside + add_executable(rknn_dynshape_inference_zero_copy_alloc_outside + src/rknn_dynshape_inference_zero_copy_alloc_outside.cc + ${CNPY_ROOT}/cnpy.cpp + ) + + target_link_libraries(rknn_dynshape_inference_zero_copy_alloc_outside + ${RKNN_RT_LIB} + ${MPI_MMZ_LIB} + ) + + # install target and libraries + install(TARGETS rknn_dynshape_inference_zero_copy_alloc_outside DESTINATION ./) +endif() diff --git a/rknpu2/examples/rknn_dynamic_shape_input_demo/README.md b/rknpu2/examples/rknn_dynamic_shape_input_demo/README.md new file mode 100644 index 0000000000000000000000000000000000000000..1e8e7fd2df658c6370b05ed21c9f5a9a99576be4 --- /dev/null +++ b/rknpu2/examples/rknn_dynamic_shape_input_demo/README.md @@ -0,0 +1,79 @@ +# RKNN C API Dynamic Shape Input Demo + +This is a demo that uses the RKNN C API for dynamic shape input inference. In this demo, you can see how to use the RKNN dynamic shape C API to perform image classification. + +## How to Use + +1. Clone or download this code repository: https://github.com/rockchip-linux/rknn-toolkit2/tree/master/rknpu2. +2. Navigate to the dynamic shape inference demo directory in your terminal. + +```shell +cd examples/rknn_dynamic_shape_input_demo +``` + +3. Compile the application by running the shell script based on the chip platform. For example, for the RK3562 Android system, run the following command: + +```shell +./build-android_RK3562.sh +``` + +4. Push the demo program directory to the target board's system using the adb command. For example: + +```shell +#If using Android system, make sure to run adb root & adb remount first. +adb push ./install/rknn_dynshape_demo_Android/ /data +``` + +5. Set the runtime library path. + +``` +export LD_LIBRARY_PATH=./lib +``` + +6. Run the program. For example, on the RK3562 platform, use the command + + ```shell + ./rknn_dynshape_inference model/RK3562/mobilenet_v2.rknn images/dog_224x224.jpg + ``` + + ,where `mobilenet_v2.rknn` is the name of the neural network model file, and `dog_224x224.jpg` is the name of the image file to classify. + +## Compilation Instructions + +### Arm Linux + +First export `GCC_COMPILER`, for example `export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu`, then execute: + +``` +./build-linux.sh -t -a -b ] + +# such as: +./build-linux.sh -t rk3588 -a aarch64 -b Release +``` + +### Android + +First export `ANDROID_NDK_PATH`, for example `export ANDROID_NDK_PATH=~/opts/ndk/android-ndk-r18b`, then execute: + +``` +./build-android.sh -t -a [-b ] + +# sush as: +./build-android.sh -t rk3568 -a arm64-v8a -b Release +``` + +## Included Features + +This demonstration application includes the following features: + +- Creating a neural network model with dynamic shape inputs. Please refer to the examples/functions/dynamic_input directory in the https://github.com/rockchip-linux/rknn-toolkit2 repository for more information. +- Reading an image from a file and performing classification using the neural network model. The program follows these steps: + +1. Initialize the RKNN context using the `rknn_init()` function. +2. Set the shape information of all the model inputs using the `rknn_set_input_shapes()` function, including shape and layout. +3. Query the current model input and output information, including shape, data type, and size, using the `rknn_query()` function. +4. Set the input data of the model using the `rknn_inputs_set()` function, including data pointer and size. +5. Run the model using the `rknn_run()` function. +6. Retrieve the output data by using the `rknn_outputs_get()` function, specifying the need for float-type results. +7. Process the output data to obtain the classification results and probabilities. +8. Release the RKNN context using the `rknn_release()` function. \ No newline at end of file diff --git a/rknpu2/examples/rknn_dynamic_shape_input_demo/README_CN.md b/rknpu2/examples/rknn_dynamic_shape_input_demo/README_CN.md new file mode 100644 index 0000000000000000000000000000000000000000..f6af9a0ce592bcda308db7b742089c1d7eafb2b5 --- /dev/null +++ b/rknpu2/examples/rknn_dynamic_shape_input_demo/README_CN.md @@ -0,0 +1,63 @@ +# RKNN C API 动态形状输入Demo +这是一个使用RKNN C API进行动态形状输入推理的演示应用。您可以在这个应用中看到如何使用RKNN 动态形状 C API对图像进行分类。 + +# 如何使用 +1. 克隆或下载此代码库https://github.com/rockchip-linux/rknn-toolkit2/tree/master/rknpu2。 +2. 在终端中进入动态形状推理Demo目录。 +``` +cd examples/rknn_dynamic_shape_input_demo +``` +3. 根据芯片平台，运行shell脚本编译应用程序,以RK3562 Android系统为例，命令如下: +``` +./build-android_RK3562.sh +``` +4. 将Demo程序目录通过adb命令推送到开发板系统中，命令如下: +``` +adb push ./install/rknn_dynshape_demo_Android/ /data +注意：如果是安卓系统，需要adb root & adb remount +``` +5. 设置runtime库链接路径 +``` +export LD_LIBRARY_PATH=./lib +``` +6. 运行程序，以rk3562平台为例，./rknn_dynshape_inference model/RK3562/mobilenet_v2.rknn images/dog_224x224.jpg 命令对图像进行分类，其中 mobilenet_v2.rknn 是神经网络模型文件的名称，dog_224x224.jpg 是要分类的图像文件的名称。 + + +# 编译说明 +## Arm Linux系统 + +首先导入GCC_COMPILER，例如`export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu +`，然后执行如下命令： + +``` +./build-linux.sh -t -a -b ] + +# 例如: +./build-linux.sh -t rk3588 -a aarch64 -b Release +``` +## Android系统 + +首先导入ANDROID_NDK_PATH，例如`export ANDROID_NDK_PATH=~/opts/ndk/android-ndk-r18b`，然后执行如下命令： + +``` +./build-android.sh -t -a [-b ] + +# 例如: +./build-android.sh -t rk3568 -a arm64-v8a -b Release +``` + +# 包含的功能 +此演示应用程序包含以下功能： + +- 创建一个包含动态形状的神经网络模型。 +参考https://github.com/rockchip-linux/rknn-toolkit2仓库下的examples/functions/dynamic_input + +- 从文件中读取一张图像，并使用神经网络模型对其进行分类。程序步骤如下： +1. 使用 rknn_init() 函数初始化 RKNN 上下文。 +2. 使用 rknn_set_input_shapes() 函数设置模型所有的输入的形状信息，包括形状、布局等。 +3. 使用 rknn_query() 函数查询当前设置的模型输入和输出的信息，包括形状、数据类型和大小等。 +4. 使用 rknn_inputs_set() 函数设置模型输入的数据，包括数据指针和数据大小等。 +5. 使用 rknn_run() 函数运行模型。 +6. 使用 rknn_outputs_get() 函数设置是否需要float类型结果并获取输出数据。 +7. 处理输出数据，得到分类结果和概率。 +8. 使用 rknn_release() 函数释放RKNN上下文。 \ No newline at end of file diff --git a/rknpu2/examples/rknn_dynamic_shape_input_demo/build-android.sh b/rknpu2/examples/rknn_dynamic_shape_input_demo/build-android.sh new file mode 100755 index 0000000000000000000000000000000000000000..4778e831eaff14905900f6fea7b8e3ee44147549 --- /dev/null +++ b/rknpu2/examples/rknn_dynamic_shape_input_demo/build-android.sh @@ -0,0 +1,112 @@ +#!/bin/bash + +set -e + +if [[ -z ${ANDROID_NDK_PATH} ]];then + echo "Please set ANDROID_NDK_PATH, such as export ANDROID_NDK_PATH=~/opts/ndk/android-ndk-r18b" + echo "NDK Version r18, r19 is recommanded. Other version may cause build failure." + exit +fi + +echo "$0 $@" +while getopts ":t:a:b" opt; do + case $opt in + t) + TARGET_SOC=$OPTARG + ;; + a) + TARGET_ARCH=$OPTARG + ;; + b) + BUILD_TYPE=$OPTARG + ;; + :) + echo "Option -$OPTARG requires an argument." + exit 1 + ;; + ?) + echo "Invalid option: -$OPTARG index:$OPTIND" + ;; + esac +done + +if [ -z ${TARGET_SOC} ] || [ -z ${TARGET_ARCH} ] ; then + echo "$0 -t -a [-b ]" + echo "" + echo " -t : target (rk3566/rk3568/rk3562/rk3576/rk3588)" + echo " -a : arch (arm64-v8a/armeabi-v7a)" + echo " -b : build_type (Debug/Release)" + echo "such as: $0 -t rk3588 -a arm64-v8a -b Release" + echo "" + exit -1 +fi + +# Debug / Release / RelWithDebInfo +if [[ -z ${BUILD_TYPE} ]];then + BUILD_TYPE=Release +fi + +case ${TARGET_SOC} in + rk356x) + TARGET_SOC="RK3566_RK3568" + ;; + rk3566_rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3588) + TARGET_SOC="RK3588" + ;; + rk3576) + TARGET_SOC="RK3576" + ;; + rk3566) + TARGET_SOC="RK3566_RK3568" + ;; + rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3562) + TARGET_SOC="RK3562" + ;; + *) + echo "Invalid target: ${TARGET_SOC}" + echo "Valid target: rk3562,rk3566,rk3568,rk3576,rk3588" + exit -1 + ;; +esac + +TARGET_PLATFORM=${TARGET_SOC}_android +if [[ -n ${TARGET_ARCH} ]];then +TARGET_PLATFORM=${TARGET_PLATFORM}_${TARGET_ARCH} +fi +ROOT_PWD=$( cd "$( dirname $0 )" && cd -P "$( dirname "$SOURCE" )" && pwd ) +BUILD_DIR=${ROOT_PWD}/build/build_${TARGET_PLATFORM}_${BUILD_TYPE} + +echo "===================================" +echo "TARGET_SOC=${TARGET_SOC}" +echo "TARGET_ARCH=${TARGET_ARCH}" +echo "BUILD_TYPE=${BUILD_TYPE}" +echo "BUILD_DIR=${BUILD_DIR}" +echo "ANDROID_NDK_PATH=${ANDROID_NDK_PATH}" +echo "===================================" + +if [[ ! -d "${BUILD_DIR}" ]]; then + mkdir -p ${BUILD_DIR} +fi + + +cd ${BUILD_DIR} +cmake ../.. \ + -DANDROID_TOOLCHAIN=clang \ + -DTARGET_SOC=${TARGET_SOC} \ + -DCMAKE_SYSTEM_NAME=Android \ + -DCMAKE_TOOLCHAIN_FILE=$ANDROID_NDK_PATH/build/cmake/android.toolchain.cmake \ + -DANDROID_ABI=${TARGET_ARCH} \ + -DANDROID_STL=c++_static \ + -DANDROID_PLATFORM=android-24 \ + -DCMAKE_BUILD_TYPE=${BUILD_TYPE} +# make VERBOSE=1 +make -j4 +make install +cd .. + diff --git a/rknpu2/examples/rknn_dynamic_shape_input_demo/build-linux.sh b/rknpu2/examples/rknn_dynamic_shape_input_demo/build-linux.sh new file mode 100755 index 0000000000000000000000000000000000000000..ae972803b8bda834c3a95588e513ec7af832cbeb --- /dev/null +++ b/rknpu2/examples/rknn_dynamic_shape_input_demo/build-linux.sh @@ -0,0 +1,122 @@ +#!/bin/bash + +set -e + +echo "$0 $@" +while getopts ":t:a:b" opt; do + case $opt in + t) + TARGET_SOC=$OPTARG + ;; + a) + TARGET_ARCH=$OPTARG + ;; + b) + BUILD_TYPE=$OPTARG + ;; + :) + echo "Option -$OPTARG requires an argument." + exit 1 + ;; + ?) + echo "Invalid option: -$OPTARG index:$OPTIND" + ;; + esac +done + +if [ -z ${TARGET_SOC} ];then + echo "$0 -t -a [-b ]" + echo "" + echo " -t : target (rk3566/rk3568/rk3562/rk3576/rk3588)" + echo " -a : arch (aarch64/armhf)" + echo " -b : build_type(Debug/Release)" + echo "such as: $0 -t rk3588 -a aarch64 -b Release" + echo "" + exit -1 +fi + +if [[ -z ${GCC_COMPILER} ]];then + echo "Please set GCC_COMPILER for $TARGET_SOC" + echo "such as export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu" + exit +fi +echo "$GCC_COMPILER" +export CC=${GCC_COMPILER}-gcc +export CXX=${GCC_COMPILER}-g++ + +if command -v ${CC} >/dev/null 2>&1; then + : +else + echo "${CC} is not available" + echo "Please set GCC_COMPILER for $TARGET_SOC" + echo "such as export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu" + exit +fi + +# Debug / Release +if [[ -z ${BUILD_TYPE} ]];then + BUILD_TYPE=Release +fi + + +case ${TARGET_SOC} in + rk356x) + TARGET_SOC="RK3566_RK3568" + ;; + rk3566_rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3588) + TARGET_SOC="RK3588" + ;; + rk3576) + TARGET_SOC="RK3576" + ;; + rk3566) + TARGET_SOC="RK3566_RK3568" + ;; + rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3562) + TARGET_SOC="RK3562" + ;; + *) + echo "Invalid target: ${TARGET_SOC}" + echo "Valid target: rk3562,rk3566,rk3568,rk3576,rk3588" + exit -1 + ;; +esac + +TARGET_PLATFORM=${TARGET_SOC}_linux +if [[ -n ${TARGET_ARCH} ]];then +TARGET_PLATFORM=${TARGET_PLATFORM}_${TARGET_ARCH} +fi +ROOT_PWD=$( cd "$( dirname $0 )" && cd -P "$( dirname "$SOURCE" )" && pwd ) +BUILD_DIR=${ROOT_PWD}/build/build_${TARGET_PLATFORM}_${BUILD_TYPE} + + +echo "===================================" +echo "TARGET_SOC=${TARGET_SOC}" +echo "TARGET_ARCH=${TARGET_ARCH}" +echo "BUILD_TYPE=${BUILD_TYPE}" +echo "BUILD_DIR=${BUILD_DIR}" +echo "CC=${CC}" +echo "CXX=${CXX}" +echo "===================================" + +if [[ ! -d "${BUILD_DIR}" ]]; then + mkdir -p ${BUILD_DIR} +fi + + +cd ${BUILD_DIR} +cmake ../.. \ + -DTARGET_SOC=${TARGET_SOC} \ + -DCMAKE_SYSTEM_NAME=Linux \ + -DCMAKE_SYSTEM_PROCESSOR=${TARGET_ARCH} \ + -DCMAKE_BUILD_TYPE=${BUILD_TYPE} \ + -DCMAKE_C_COMPILER=${CC} \ + -DCMAKE_CXX_COMPILER=${CXX} +make -j4 +make install diff --git a/rknpu2/examples/rknn_dynamic_shape_input_demo/images/cat_224x224.jpg b/rknpu2/examples/rknn_dynamic_shape_input_demo/images/cat_224x224.jpg new file mode 100644 index 0000000000000000000000000000000000000000..2159d5cfe3d6cbc01940a804cff0e3bc516af7fb --- /dev/null +++ b/rknpu2/examples/rknn_dynamic_shape_input_demo/images/cat_224x224.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0ce3ef6da3424c0744385935a210e4ec8ab3a6ddda205113ab07b77f9c0e812a +size 58701 diff --git a/rknpu2/examples/rknn_dynamic_shape_input_demo/images/dog_224x224.jpg b/rknpu2/examples/rknn_dynamic_shape_input_demo/images/dog_224x224.jpg new file mode 100644 index 0000000000000000000000000000000000000000..004b0416e23454a12eb06eaba238c7e2d5f2b0fc --- /dev/null +++ b/rknpu2/examples/rknn_dynamic_shape_input_demo/images/dog_224x224.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c350299c6283d5f62fecf1f845b6b3be9aafec8dff528ca09a129990f0a584b0 +size 18889 diff --git a/rknpu2/examples/rknn_dynamic_shape_input_demo/model/RK3562/mobilenet_v2.rknn b/rknpu2/examples/rknn_dynamic_shape_input_demo/model/RK3562/mobilenet_v2.rknn new file mode 100644 index 0000000000000000000000000000000000000000..3112213b1572d4fd1d9ee29c396a4c74f0a75c29 --- /dev/null +++ b/rknpu2/examples/rknn_dynamic_shape_input_demo/model/RK3562/mobilenet_v2.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:87658fdd5fba7c72ba6a959cb0604b49b2f68bd1d5ee070a88e897880c962c68 +size 4898701 diff --git a/rknpu2/examples/rknn_dynamic_shape_input_demo/model/RK3566_RK3568/mobilenet_v2.rknn b/rknpu2/examples/rknn_dynamic_shape_input_demo/model/RK3566_RK3568/mobilenet_v2.rknn new file mode 100644 index 0000000000000000000000000000000000000000..131dce1d8a6d16d97cf1ffa59ac28411311e2162 --- /dev/null +++ b/rknpu2/examples/rknn_dynamic_shape_input_demo/model/RK3566_RK3568/mobilenet_v2.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:74bfaa550257b9bb7607b86044dde92e7a675c9f28c0b4fdcd8b8a6447b4d4ad +size 4616525 diff --git a/rknpu2/examples/rknn_dynamic_shape_input_demo/model/RK3576/mobilenet_v2.rknn b/rknpu2/examples/rknn_dynamic_shape_input_demo/model/RK3576/mobilenet_v2.rknn new file mode 100644 index 0000000000000000000000000000000000000000..a63599058b66a08e59dc64ba4a93e14ef2033df4 --- /dev/null +++ b/rknpu2/examples/rknn_dynamic_shape_input_demo/model/RK3576/mobilenet_v2.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d692f749ae2451b0684b175a6bf7db49b9055c58715f3ba0897d95010218a069 +size 5539725 diff --git a/rknpu2/examples/rknn_dynamic_shape_input_demo/model/RK3588/mobilenet_v2.rknn b/rknpu2/examples/rknn_dynamic_shape_input_demo/model/RK3588/mobilenet_v2.rknn new file mode 100644 index 0000000000000000000000000000000000000000..7447fe0f58e95badfcb50e7af67c6a3a77249d54 --- /dev/null +++ b/rknpu2/examples/rknn_dynamic_shape_input_demo/model/RK3588/mobilenet_v2.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:863c638d1a4bfab6b06492ad62a2f801f32fb342de38a2471274348e610e7200 +size 5719053 diff --git a/rknpu2/examples/rknn_dynamic_shape_input_demo/src/rknn_dynshape_inference.cc b/rknpu2/examples/rknn_dynamic_shape_input_demo/src/rknn_dynshape_inference.cc new file mode 100644 index 0000000000000000000000000000000000000000..e1510067dc000c15a577f973388d3c87aabc27e8 --- /dev/null +++ b/rknpu2/examples/rknn_dynamic_shape_input_demo/src/rknn_dynshape_inference.cc @@ -0,0 +1,610 @@ +/**************************************************************************** + * + * Copyright (c) 2017 - 2023 by Rockchip Corp. All rights reserved. + * + * The material in this file is confidential and contains trade secrets + * of Rockchip Corporation. This is proprietary information owned by + * Rockchip Corporation. No part of this work may be disclosed, + * reproduced, copied, transmitted, or used in any way for any purpose, + * without the express written permission of Rockchip Corporation. + * + *****************************************************************************/ +#include +#include +#include +#include +#include "rknn_api.h" +#include + +#define STB_IMAGE_IMPLEMENTATION +#include "stb/stb_image.h" +#define STB_IMAGE_RESIZE_IMPLEMENTATION +#include + +#define NPY_SUPPORT 1 +#if NPY_SUPPORT +#include "cnpy/cnpy.h" +using namespace cnpy; +#endif + +/*------------------------------------------- + Functions +-------------------------------------------*/ +static inline int64_t getCurrentTimeUs() +{ + struct timeval tv; + gettimeofday(&tv, NULL); + return tv.tv_sec * 1000000 + tv.tv_usec; +} + +static int rknn_GetTopN(float *pfProb, float *pfMaxProb, uint32_t *pMaxClass, uint32_t outputCount, uint32_t topNum) +{ + uint32_t i, j; + uint32_t top_count = outputCount > topNum ? topNum : outputCount; + + for (i = 0; i < topNum; ++i) + { + pfMaxProb[i] = -FLT_MAX; + pMaxClass[i] = -1; + } + + for (j = 0; j < top_count; j++) + { + for (i = 0; i < outputCount; i++) + { + if ((i == *(pMaxClass + 0)) || (i == *(pMaxClass + 1)) || (i == *(pMaxClass + 2)) || (i == *(pMaxClass + 3)) || + (i == *(pMaxClass + 4))) + { + continue; + } + + if (pfProb[i] > *(pfMaxProb + j)) + { + *(pfMaxProb + j) = pfProb[i]; + *(pMaxClass + j) = i; + } + } + } + + return 1; +} + +static void dump_tensor_attr(rknn_tensor_attr *attr) +{ + std::string shape_str = attr->n_dims < 1 ? "" : std::to_string(attr->dims[0]); + for (int i = 1; i < attr->n_dims; ++i) + { + shape_str += ", " + std::to_string(attr->dims[i]); + } + + printf(" index=%d, name=%s, n_dims=%d, dims=[%s], n_elems=%d, size=%d, w_stride = %d, size_with_stride=%d, fmt=%s, " + "type=%s, qnt_type=%s, " + "zp=%d, scale=%f\n", + attr->index, attr->name, attr->n_dims, shape_str.c_str(), attr->n_elems, attr->size, attr->w_stride, + attr->size_with_stride, get_format_string(attr->fmt), get_type_string(attr->type), + get_qnt_type_string(attr->qnt_type), attr->zp, attr->scale); +} + +static void dump_input_dynamic_range(rknn_input_range *dyn_range) +{ + std::string range_str = ""; + for (int n = 0; n < dyn_range->shape_number; ++n) + { + range_str += n == 0 ? "[" : ",["; + range_str += dyn_range->n_dims < 1 ? "" : std::to_string(dyn_range->dyn_range[n][0]); + for (int i = 1; i < dyn_range->n_dims; ++i) + { + range_str += ", " + std::to_string(dyn_range->dyn_range[n][i]); + } + range_str += "]"; + } + + printf(" index=%d, name=%s, shape_number=%d, range=[%s], fmt = %s\n", dyn_range->index, dyn_range->name, + dyn_range->shape_number, range_str.c_str(), get_format_string(dyn_range->fmt)); +} + +#if NPY_SUPPORT +static unsigned char *load_npy(const char *input_path, rknn_tensor_attr *input_attr, int *input_size) +{ + if (input_attr->fmt != RKNN_TENSOR_NHWC) + { + fprintf(stderr, "Only support input fmt = RKNN_TENSOR_NHWC\n"); + return NULL; + } + int req_height = input_attr->dims[1]; + int req_width = input_attr->dims[2]; + int req_channel = input_attr->dims[3]; + + printf("Loading %s\n", input_path); + + NpyArray npy_data = npy_load(input_path); + + int type_bytes = npy_data.word_size; + std::string typeName = npy_data.typeName; + + printf("npy data type:%s\n", typeName.c_str()); + + if (typeName != "uint8") + { + fprintf(stderr, "Model required npy data type = uint8, but get %s\n", typeName.c_str()); + return NULL; + } + + // npy shape = NHWC + int npy_shape[4] = {1, 1, 1, 1}; + + int start = npy_data.shape.size() == 4 ? 0 : 1; + for (size_t i = 0; i < npy_data.shape.size() && i < 4; ++i) + { + npy_shape[start + i] = npy_data.shape[i]; + } + + int height = npy_shape[1]; + int width = npy_shape[2]; + int channel = npy_shape[3]; + + if ((input_attr->fmt != RKNN_TENSOR_UNDEFINED) && + (width != req_width || height != req_height || channel != req_channel)) + { + printf("npy shape match failed!, npy shape =(%d, %d, %d) , expect input shape = (%d, %d, %d)\n", + height, width, channel, req_height, req_width, req_channel); + return NULL; + } + + unsigned char *data = (unsigned char *)malloc(npy_data.num_bytes()); + if (!data) + { + return NULL; + } + + // TODO: copy + memcpy(data, npy_data.data(), npy_data.num_bytes()); + + *input_size = npy_data.num_bytes(); + + return data; +} + +template +static void save_npy(const char *output_path, T *output_data, rknn_tensor_attr *output_attr) +{ + std::vector output_shape; + + for (uint32_t i = 0; i < output_attr->n_dims; ++i) + { + output_shape.push_back(output_attr->dims[i]); + } + + npy_save(output_path, output_data, output_shape); +} +#endif + +static unsigned char *load_image(const char *image_path, rknn_tensor_attr *input_attr) +{ + int req_channel = input_attr->dims[3]; + int height = 0; + int width = 0; + int channel = 0; + + printf("Loading %s\n", image_path); + + unsigned char *image_data = stbi_load(image_path, &width, &height, &channel, req_channel); + if (image_data == NULL) + { + printf("load image failed!\n"); + return NULL; + } + + switch (input_attr->fmt) + { + case RKNN_TENSOR_NHWC: + input_attr->dims[0] = 1; + input_attr->dims[1] = height; + input_attr->dims[2] = width; + input_attr->dims[3] = channel; + break; + + default: + fprintf(stderr, "load_image error, unsupport model input layout: %s\n", get_format_string(input_attr->fmt)); + break; + } + + return image_data; +} + +static std::vector split(const std::string &str, const std::string &pattern) +{ + std::vector res; + if (str == "") + return res; + std::string strs = str + pattern; + size_t pos = strs.find(pattern); + while (pos != strs.npos) + { + std::string temp = strs.substr(0, pos); + res.push_back(temp); + strs = strs.substr(pos + 1, strs.size()); + pos = strs.find(pattern); + } + return res; +} + +static int write_data_to_file(char *path, char *data, unsigned int size) +{ + FILE *fp; + + fp = fopen(path, "w"); + if (fp == NULL) + { + printf("open error: %s", path); + return -1; + } + + fwrite(data, 1, size, fp); + fflush(fp); + + fclose(fp); + return 0; +} + +static void *load_file(const char *file_path, size_t *file_size) +{ + FILE *fp = fopen(file_path, "r"); + if (fp == NULL) + { + printf("failed to open file: %s\n", file_path); + return NULL; + } + + fseek(fp, 0, SEEK_END); + size_t size = (size_t)ftell(fp); + fseek(fp, 0, SEEK_SET); + + void *file_data = malloc(size); + if (file_data == NULL) + { + fclose(fp); + printf("failed allocate file size: %zu\n", size); + return NULL; + } + + if (fread(file_data, 1, size, fp) != size) + { + fclose(fp); + free(file_data); + printf("failed to read file data!\n"); + return NULL; + } + + fclose(fp); + + *file_size = size; + + return file_data; +} + +int main(int argc, char **argv) +{ + if (argc < 2) + { + printf("Usage:%s model_path [input_path] [loop_count] [core_mask] [output_dir]\n", argv[0]); + return -1; + } + // 程序参数 + char *model_path = argv[1]; + std::vector input_paths_split; + if (argc > 2) + { + char *input_paths = argv[2]; + input_paths_split = split(input_paths, "#"); + } + + int loop_count = 1; + if (argc > 3) + { + loop_count = atoi(argv[3]); + } + + uint32_t core_mask = 1; + if (argc > 4) + { + core_mask = strtoul(argv[4], NULL, 10); + } + + char *output_dir = NULL; + if (argc > 5) + { + output_dir = argv[4]; + } + + // 加载模型 + rknn_context ctx; + int ret = rknn_init(&ctx, model_path, 0, 0, NULL); + if (ret < 0) + { + fprintf(stderr, "rknn_init error! ret=%d\n", ret); + return -1; + } + printf("Load model sucess\n"); + + // 获取模型SDK版本 + rknn_sdk_version sdk_ver; + ret = rknn_query(ctx, RKNN_QUERY_SDK_VERSION, &sdk_ver, sizeof(sdk_ver)); + if (ret != RKNN_SUCC) + { + fprintf(stderr, "rknn_query error! ret=%d\n", ret); + return -1; + } + printf("rknn_api/rknnrt version: %s, driver version: %s\n", sdk_ver.api_version, sdk_ver.drv_version); + + // 获取模型输入输出个数 + rknn_input_output_num io_num; + ret = rknn_query(ctx, RKNN_QUERY_IN_OUT_NUM, &io_num, sizeof(io_num)); + if (ret != RKNN_SUCC) + { + fprintf(stderr, "rknn_query error! ret=%d\n", ret); + return -1; + } + if (io_num.n_input != input_paths_split.size()) + { + return -1; + } + + printf("model input num: %d, output num: %d\n", io_num.n_input, io_num.n_output); + + // 获取模型输入输出信息 + printf("input tensors:\n"); + rknn_tensor_attr input_attrs[io_num.n_input]; + memset(input_attrs, 0, io_num.n_input * sizeof(rknn_tensor_attr)); + for (uint32_t i = 0; i < io_num.n_input; i++) + { + input_attrs[i].index = i; + // query info + ret = rknn_query(ctx, RKNN_QUERY_INPUT_ATTR, &(input_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret < 0) + { + fprintf(stderr, "rknn_query error! ret=%d\n", ret); + return -1; + } + dump_tensor_attr(&input_attrs[i]); + } + + printf("output tensors:\n"); + rknn_tensor_attr output_attrs[io_num.n_output]; + memset(output_attrs, 0, io_num.n_output * sizeof(rknn_tensor_attr)); + for (uint32_t i = 0; i < io_num.n_output; i++) + { + output_attrs[i].index = i; + // query info + ret = rknn_query(ctx, RKNN_QUERY_OUTPUT_ATTR, &(output_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret != RKNN_SUCC) + { + fprintf(stderr, "rknn_query error! ret=%d\n", ret); + return -1; + } + dump_tensor_attr(&output_attrs[i]); + } + + // 查询模型支持的输入形状 + printf("dynamic inputs shape range:\n"); + rknn_input_range shape_range[io_num.n_input]; + memset(shape_range, 0, io_num.n_input * sizeof(rknn_input_range)); + for (uint32_t i = 0; i < io_num.n_input; i++) + { + shape_range[i].index = i; + ret = rknn_query(ctx, RKNN_QUERY_INPUT_DYNAMIC_RANGE, &shape_range[i], sizeof(rknn_input_range)); + if (ret != RKNN_SUCC) + { + fprintf(stderr, "rknn_query error! ret=%d\n", ret); + return -1; + } + dump_input_dynamic_range(&shape_range[i]); + } + + // 加载测试图片或npy文件 + std::vector imgs; + unsigned char *input_data[io_num.n_input]; + int input_size[io_num.n_input]; + for (int i = 0; i < io_num.n_input; i++) + { + input_data[i] = NULL; + input_size[i] = input_attrs[i].n_elems * sizeof(uint8_t); + } + int shape_num = shape_range[0].shape_number; + if (input_paths_split.size() > 0) + { + // Load input + if (io_num.n_input != input_paths_split.size()) + { + printf("input missing!, need input number: %d\n", io_num.n_input); + return -1; + } + for (int i = 0; i < io_num.n_input; i++) + { + if (strstr(input_paths_split[i].c_str(), ".npy")) + { +#if NPY_SUPPORT + // 读取NHWC uint8格式npy + input_data[i] = load_npy(input_paths_split[i].c_str(), &input_attrs[i], &input_size[i]); + if (input_size[i] != input_attrs[i].n_elems * sizeof(uint8_t)) + { + fprintf(stderr, "Read input[%d] npy size error, read: %d, but requried: %lu\n", i, input_size[i], input_attrs[i].n_elems * sizeof(uint8_t)); + return -1; + } + if (input_data[i] == NULL) + { + return -1; + } + int height = input_attrs[i].fmt == RKNN_TENSOR_NHWC ? input_attrs[i].dims[1] : input_attrs[i].dims[2]; + int width = input_attrs[i].fmt == RKNN_TENSOR_NHWC ? input_attrs[i].dims[2] : input_attrs[i].dims[3]; + cv::Mat image(height, width, CV_8UC3, input_data[i]); + imgs.emplace_back(image); +#else + return -1; +#endif + } + else + { + cv::Mat img = cv::imread(input_paths_split[i]); + imgs.emplace_back(img); + } + } + } + else + { + for (int i = 0; i < io_num.n_input; i++) + { + input_data[i] = (unsigned char *)malloc(input_size[i]); + } + } + + // 加载输入并设置模型输入形状，每次切换输入形状要调用一次 + for (int s = 0; s < shape_num; ++s) + { + for (int i = 0; i < io_num.n_input; i++) + { + for (int j = 0; j < input_attrs[i].n_dims; ++j) + { + input_attrs[i].dims[j] = shape_range[i].dyn_range[s][j]; + } + } + ret = rknn_set_input_shapes(ctx, io_num.n_input, input_attrs); + if (ret < 0) + { + fprintf(stderr, "rknn_set_input_shapes error! ret=%d\n", ret); + return -1; + } + + // 获取当前次推理的输入和输出形状 + printf("current input tensors:\n"); + rknn_tensor_attr cur_input_attrs[io_num.n_input]; + memset(cur_input_attrs, 0, io_num.n_input * sizeof(rknn_tensor_attr)); + for (uint32_t i = 0; i < io_num.n_input; i++) + { + cur_input_attrs[i].index = i; + // query info + ret = rknn_query(ctx, RKNN_QUERY_CURRENT_INPUT_ATTR, &(cur_input_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret < 0) + { + printf("rknn_init error! ret=%d\n", ret); + return -1; + } + dump_tensor_attr(&cur_input_attrs[i]); + } + + printf("current output tensors:\n"); + rknn_tensor_attr cur_output_attrs[io_num.n_output]; + memset(cur_output_attrs, 0, io_num.n_output * sizeof(rknn_tensor_attr)); + for (uint32_t i = 0; i < io_num.n_output; i++) + { + cur_output_attrs[i].index = i; + // query info + ret = rknn_query(ctx, RKNN_QUERY_CURRENT_OUTPUT_ATTR, &(cur_output_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret != RKNN_SUCC) + { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + dump_tensor_attr(&cur_output_attrs[i]); + } + + // 指定NPU核心数量，仅3588支持 + rknn_set_core_mask(ctx, (rknn_core_mask)core_mask); + + // 设置输入信息 + rknn_input inputs[io_num.n_input]; + memset(inputs, 0, io_num.n_input * sizeof(rknn_input)); + std::vector resize_imgs; + resize_imgs.resize(io_num.n_input); + for (int i = 0; i < io_num.n_input; i++) + { + int height = cur_input_attrs[i].fmt == RKNN_TENSOR_NHWC ? cur_input_attrs[i].dims[1] : cur_input_attrs[i].dims[2]; + int width = cur_input_attrs[i].fmt == RKNN_TENSOR_NHWC ? cur_input_attrs[i].dims[2] : cur_input_attrs[i].dims[3]; + cv::resize(imgs[i], resize_imgs[i], cv::Size(width, height)); + inputs[i].index = i; + inputs[i].pass_through = 0; + inputs[i].type = RKNN_TENSOR_UINT8; + inputs[i].fmt = RKNN_TENSOR_NHWC; + inputs[i].buf = resize_imgs[i].data; + inputs[i].size = resize_imgs[i].total() * resize_imgs[i].channels(); + } + + // 将输入数据转换成正确的格式后，放到输入缓冲区 + ret = rknn_inputs_set(ctx, io_num.n_input, inputs); + if (ret < 0) + { + printf("rknn_input_set fail! ret=%d\n", ret); + return -1; + } + + // 进行推理 + printf("Begin perf ...\n"); + double total_time = 0; + for (int i = 0; i < loop_count; ++i) + { + int64_t start_us = getCurrentTimeUs(); + ret = rknn_run(ctx, NULL); + int64_t elapse_us = getCurrentTimeUs() - start_us; + if (ret < 0) + { + printf("rknn run error %d\n", ret); + return -1; + } + total_time += elapse_us / 1000.f; + printf("%4d: Elapse Time = %.2fms, FPS = %.2f\n", i, elapse_us / 1000.f, 1000.f * 1000.f / elapse_us); + } + printf("Avg FPS = %.3f\n", loop_count * 1000.f / total_time); + + // 获取输出结果 + rknn_output outputs[io_num.n_output]; + memset(outputs, 0, io_num.n_output * sizeof(rknn_output)); + for (uint32_t i = 0; i < io_num.n_output; ++i) + { + outputs[i].want_float = 1; + outputs[i].index = i; + outputs[i].is_prealloc = 0; + } + + ret = rknn_outputs_get(ctx, io_num.n_output, outputs, NULL); + if (ret < 0) + { + printf("rknn_outputs_get fail! ret=%d\n", ret); + return ret; + } + +#if NPY_SUPPORT + // save output + for (uint32_t i = 0; i < io_num.n_output; i++) + { + char output_path[PATH_MAX]; + sprintf(output_path, "%s/rt_output%d.npy", output_dir ? output_dir : ".", i); + save_npy(output_path, (float *)outputs[i].buf, &cur_output_attrs[i]); + } +#endif + + // 处理输出结果 + float *output = (float *)outputs[0].buf; + int out_elems = outputs[0].size / sizeof(float); + std::vector> results; + for (int i = 0; i < out_elems; i++) + { + results.emplace_back(output[i], i); + } + std::partial_sort(results.begin(), results.begin() + 5, results.end(), std::greater>()); + printf(" --- Top5 ---\n"); + for (int i = 0; i < 5; i++) + { + printf("%-3d: %.2f%%\n", results[i].second, results[i].first * 100); + } + + // 释放输出缓冲区数据，调用一次rknn_outputs_get后，必须调用一次rknn_outputs_release + ret = rknn_outputs_release(ctx, io_num.n_output, outputs); + } + // 释放资源 + rknn_destroy(ctx); + for (int i = 0; i < io_num.n_input; i++) + { + free(input_data[i]); + } + return 0; +} \ No newline at end of file diff --git a/rknpu2/examples/rknn_dynamic_shape_input_demo/src/rknn_dynshape_inference_zero_copy.cc b/rknpu2/examples/rknn_dynamic_shape_input_demo/src/rknn_dynshape_inference_zero_copy.cc new file mode 100644 index 0000000000000000000000000000000000000000..862d4bdb642c8bcd6e4f7193d45fe0dab27904a2 --- /dev/null +++ b/rknpu2/examples/rknn_dynamic_shape_input_demo/src/rknn_dynshape_inference_zero_copy.cc @@ -0,0 +1,665 @@ +/**************************************************************************** + * + * Copyright (c) 2017 - 2023 by Rockchip Corp. All rights reserved. + * + * The material in this file is confidential and contains trade secrets + * of Rockchip Corporation. This is proprietary information owned by + * Rockchip Corporation. No part of this work may be disclosed, + * reproduced, copied, transmitted, or used in any way for any purpose, + * without the express written permission of Rockchip Corporation. + * + *****************************************************************************/ +#include +#include +#include +#include +#include "rknn_api.h" +#include + +#define STB_IMAGE_IMPLEMENTATION +#include "stb/stb_image.h" +#define STB_IMAGE_RESIZE_IMPLEMENTATION +#include + +#define NPY_SUPPORT 1 +#if NPY_SUPPORT +#include "cnpy/cnpy.h" +using namespace cnpy; +#endif + +/*------------------------------------------- + Functions +-------------------------------------------*/ +static inline int64_t getCurrentTimeUs() +{ + struct timeval tv; + gettimeofday(&tv, NULL); + return tv.tv_sec * 1000000 + tv.tv_usec; +} + +static int rknn_GetTopN(float *pfProb, float *pfMaxProb, uint32_t *pMaxClass, uint32_t outputCount, uint32_t topNum) +{ + uint32_t i, j; + uint32_t top_count = outputCount > topNum ? topNum : outputCount; + + for (i = 0; i < topNum; ++i) + { + pfMaxProb[i] = -FLT_MAX; + pMaxClass[i] = -1; + } + + for (j = 0; j < top_count; j++) + { + for (i = 0; i < outputCount; i++) + { + if ((i == *(pMaxClass + 0)) || (i == *(pMaxClass + 1)) || (i == *(pMaxClass + 2)) || (i == *(pMaxClass + 3)) || + (i == *(pMaxClass + 4))) + { + continue; + } + + if (pfProb[i] > *(pfMaxProb + j)) + { + *(pfMaxProb + j) = pfProb[i]; + *(pMaxClass + j) = i; + } + } + } + + return 1; +} + +static void dump_tensor_attr(rknn_tensor_attr *attr) +{ + std::string shape_str = attr->n_dims < 1 ? "" : std::to_string(attr->dims[0]); + for (int i = 1; i < attr->n_dims; ++i) + { + shape_str += ", " + std::to_string(attr->dims[i]); + } + + printf(" index=%d, name=%s, n_dims=%d, dims=[%s], n_elems=%d, size=%d, w_stride = %d, size_with_stride=%d, fmt=%s, " + "type=%s, qnt_type=%s, " + "zp=%d, scale=%f\n", + attr->index, attr->name, attr->n_dims, shape_str.c_str(), attr->n_elems, attr->size, attr->w_stride, + attr->size_with_stride, get_format_string(attr->fmt), get_type_string(attr->type), + get_qnt_type_string(attr->qnt_type), attr->zp, attr->scale); +} + +static void dump_input_dynamic_range(rknn_input_range *dyn_range) +{ + std::string range_str = ""; + for (int n = 0; n < dyn_range->shape_number; ++n) + { + range_str += n == 0 ? "[" : ",["; + range_str += dyn_range->n_dims < 1 ? "" : std::to_string(dyn_range->dyn_range[n][0]); + for (int i = 1; i < dyn_range->n_dims; ++i) + { + range_str += ", " + std::to_string(dyn_range->dyn_range[n][i]); + } + range_str += "]"; + } + + printf(" index=%d, name=%s, shape_number=%d, range=[%s], fmt = %s\n", dyn_range->index, dyn_range->name, + dyn_range->shape_number, range_str.c_str(), get_format_string(dyn_range->fmt)); +} + +#if NPY_SUPPORT +static unsigned char *load_npy(const char *input_path, rknn_tensor_attr *input_attr, int *input_size) +{ + if (input_attr->fmt != RKNN_TENSOR_NHWC) + { + fprintf(stderr, "Only support input fmt = RKNN_TENSOR_NHWC\n"); + return NULL; + } + int req_height = input_attr->dims[1]; + int req_width = input_attr->dims[2]; + int req_channel = input_attr->dims[3]; + + printf("Loading %s\n", input_path); + + NpyArray npy_data = npy_load(input_path); + + int type_bytes = npy_data.word_size; + std::string typeName = npy_data.typeName; + + printf("npy data type:%s\n", typeName.c_str()); + + if (typeName != "uint8") + { + fprintf(stderr, "Model required npy data type = uint8, but get %s\n", typeName.c_str()); + return NULL; + } + + // npy shape = NHWC + int npy_shape[4] = {1, 1, 1, 1}; + + int start = npy_data.shape.size() == 4 ? 0 : 1; + for (size_t i = 0; i < npy_data.shape.size() && i < 4; ++i) + { + npy_shape[start + i] = npy_data.shape[i]; + } + + int height = npy_shape[1]; + int width = npy_shape[2]; + int channel = npy_shape[3]; + + if ((input_attr->fmt != RKNN_TENSOR_UNDEFINED) && + (width != req_width || height != req_height || channel != req_channel)) + { + printf("npy shape match failed!, npy shape =(%d, %d, %d) , expect input shape = (%d, %d, %d)\n", + height, width, channel, req_height, req_width, req_channel); + return NULL; + } + + unsigned char *data = (unsigned char *)malloc(npy_data.num_bytes()); + if (!data) + { + return NULL; + } + + // TODO: copy + memcpy(data, npy_data.data(), npy_data.num_bytes()); + + *input_size = npy_data.num_bytes(); + + return data; +} + +template +static void save_npy(const char *output_path, T *output_data, rknn_tensor_attr *output_attr) +{ + std::vector output_shape; + + for (uint32_t i = 0; i < output_attr->n_dims; ++i) + { + output_shape.push_back(output_attr->dims[i]); + } + + npy_save(output_path, output_data, output_shape); +} +#endif + +static unsigned char *load_image(const char *image_path, rknn_tensor_attr *input_attr) +{ + int req_channel = input_attr->dims[3]; + int height = 0; + int width = 0; + int channel = 0; + + printf("Loading %s\n", image_path); + + unsigned char *image_data = stbi_load(image_path, &width, &height, &channel, req_channel); + if (image_data == NULL) + { + printf("load image failed!\n"); + return NULL; + } + + switch (input_attr->fmt) + { + case RKNN_TENSOR_NHWC: + input_attr->dims[0] = 1; + input_attr->dims[1] = height; + input_attr->dims[2] = width; + input_attr->dims[3] = channel; + break; + + default: + fprintf(stderr, "load_image error, unsupport model input layout: %s\n", get_format_string(input_attr->fmt)); + break; + } + + return image_data; +} + +static std::vector split(const std::string &str, const std::string &pattern) +{ + std::vector res; + if (str == "") + return res; + std::string strs = str + pattern; + size_t pos = strs.find(pattern); + while (pos != strs.npos) + { + std::string temp = strs.substr(0, pos); + res.push_back(temp); + strs = strs.substr(pos + 1, strs.size()); + pos = strs.find(pattern); + } + return res; +} + +static int write_data_to_file(char *path, char *data, unsigned int size) +{ + FILE *fp; + + fp = fopen(path, "w"); + if (fp == NULL) + { + printf("open error: %s", path); + return -1; + } + + fwrite(data, 1, size, fp); + fflush(fp); + + fclose(fp); + return 0; +} + +static void *load_file(const char *file_path, size_t *file_size) +{ + FILE *fp = fopen(file_path, "r"); + if (fp == NULL) + { + printf("failed to open file: %s\n", file_path); + return NULL; + } + + fseek(fp, 0, SEEK_END); + size_t size = (size_t)ftell(fp); + fseek(fp, 0, SEEK_SET); + + void *file_data = malloc(size); + if (file_data == NULL) + { + fclose(fp); + printf("failed allocate file size: %zu\n", size); + return NULL; + } + + if (fread(file_data, 1, size, fp) != size) + { + fclose(fp); + free(file_data); + printf("failed to read file data!\n"); + return NULL; + } + + fclose(fp); + + *file_size = size; + + return file_data; +} + +int main(int argc, char **argv) +{ + if (argc < 2) + { + printf("Usage:%s model_path [input_path] [loop_count] [core_mask] [output_dir]\n", argv[0]); + return -1; + } + // 程序参数 + char *model_path = argv[1]; + std::vector input_paths_split; + if (argc > 2) + { + char *input_paths = argv[2]; + input_paths_split = split(input_paths, "#"); + } + + int loop_count = 1; + if (argc > 3) + { + loop_count = atoi(argv[3]); + } + + uint32_t core_mask = 1; + if (argc > 4) + { + core_mask = strtoul(argv[4], NULL, 10); + } + + char *output_dir = NULL; + if (argc > 5) + { + output_dir = argv[4]; + } + + // 加载模型 + rknn_context ctx; + int ret = rknn_init(&ctx, model_path, 0, 0, NULL); + if (ret < 0) + { + fprintf(stderr, "rknn_init error! ret=%d\n", ret); + return -1; + } + printf("Load model sucess\n"); + + // 获取模型SDK版本 + rknn_sdk_version sdk_ver; + ret = rknn_query(ctx, RKNN_QUERY_SDK_VERSION, &sdk_ver, sizeof(sdk_ver)); + if (ret != RKNN_SUCC) + { + fprintf(stderr, "rknn_query error! ret=%d\n", ret); + return -1; + } + printf("rknn_api/rknnrt version: %s, driver version: %s\n", sdk_ver.api_version, sdk_ver.drv_version); + + // 获取模型输入输出个数 + rknn_input_output_num io_num; + ret = rknn_query(ctx, RKNN_QUERY_IN_OUT_NUM, &io_num, sizeof(io_num)); + if (ret != RKNN_SUCC) + { + fprintf(stderr, "rknn_query error! ret=%d\n", ret); + return -1; + } + if (io_num.n_input != input_paths_split.size()) + { + return -1; + } + + printf("model input num: %d, output num: %d\n", io_num.n_input, io_num.n_output); + + // 获取模型输入输出信息 + printf("input tensors:\n"); + rknn_tensor_attr input_attrs[io_num.n_input]; + memset(input_attrs, 0, io_num.n_input * sizeof(rknn_tensor_attr)); + for (uint32_t i = 0; i < io_num.n_input; i++) + { + input_attrs[i].index = i; + // query info + ret = rknn_query(ctx, RKNN_QUERY_INPUT_ATTR, &(input_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret < 0) + { + fprintf(stderr, "rknn_query error! ret=%d\n", ret); + return -1; + } + dump_tensor_attr(&input_attrs[i]); + } + + printf("output tensors:\n"); + rknn_tensor_attr output_attrs[io_num.n_output]; + memset(output_attrs, 0, io_num.n_output * sizeof(rknn_tensor_attr)); + for (uint32_t i = 0; i < io_num.n_output; i++) + { + output_attrs[i].index = i; + // query info + ret = rknn_query(ctx, RKNN_QUERY_OUTPUT_ATTR, &(output_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret != RKNN_SUCC) + { + fprintf(stderr, "rknn_query error! ret=%d\n", ret); + return -1; + } + dump_tensor_attr(&output_attrs[i]); + } + + // 查询模型支持的输入形状 + printf("dynamic inputs shape range:\n"); + rknn_input_range shape_range[io_num.n_input]; + memset(shape_range, 0, io_num.n_input * sizeof(rknn_input_range)); + for (uint32_t i = 0; i < io_num.n_input; i++) + { + shape_range[i].index = i; + ret = rknn_query(ctx, RKNN_QUERY_INPUT_DYNAMIC_RANGE, &shape_range[i], sizeof(rknn_input_range)); + if (ret != RKNN_SUCC) + { + fprintf(stderr, "rknn_query error! ret=%d\n", ret); + return -1; + } + dump_input_dynamic_range(&shape_range[i]); + } + + // 加载测试图片或npy文件 + std::vector imgs; + unsigned char *input_data[io_num.n_input]; + int input_size[io_num.n_input]; + for (int i = 0; i < io_num.n_input; i++) + { + input_data[i] = NULL; + input_size[i] = input_attrs[i].n_elems * sizeof(uint8_t); + } + int shape_num = shape_range[0].shape_number; + if (input_paths_split.size() > 0) + { + // Load input + if (io_num.n_input != input_paths_split.size()) + { + printf("input missing!, need input number: %d\n", io_num.n_input); + return -1; + } + for (int i = 0; i < io_num.n_input; i++) + { + if (strstr(input_paths_split[i].c_str(), ".npy")) + { +#if NPY_SUPPORT + // 读取NHWC uint8格式npy + input_data[i] = load_npy(input_paths_split[i].c_str(), &input_attrs[i], &input_size[i]); + if (input_size[i] != input_attrs[i].n_elems * sizeof(uint8_t)) + { + fprintf(stderr, "Read input[%d] npy size error, read: %d, but requried: %lu\n", i, input_size[i], input_attrs[i].n_elems * sizeof(uint8_t)); + return -1; + } + if (input_data[i] == NULL) + { + return -1; + } + int height = input_attrs[i].fmt == RKNN_TENSOR_NHWC ? input_attrs[i].dims[1] : input_attrs[i].dims[2]; + int width = input_attrs[i].fmt == RKNN_TENSOR_NHWC ? input_attrs[i].dims[2] : input_attrs[i].dims[3]; + cv::Mat image(height, width, CV_8UC3, input_data[i]); + imgs.emplace_back(image); +#else + return -1; +#endif + } + else + { + cv::Mat img = cv::imread(input_paths_split[i]); + imgs.emplace_back(img); + } + } + } + else + { + for (int i = 0; i < io_num.n_input; i++) + { + input_data[i] = (unsigned char *)malloc(input_size[i]); + } + } + + // 创建最大的输入tensor内存 + rknn_tensor_mem *input_mems[io_num.n_input]; + for (int i = 0; i < io_num.n_input; i++) + { + // default input type is int8 (normalize and quantize need compute in outside) + // if set uint8, will fuse normalize and quantize to npu + input_attrs[i].type = RKNN_TENSOR_UINT8; + // default fmt is NHWC, npu only support NHWC in zero copy mode + input_attrs[i].fmt = RKNN_TENSOR_NHWC; + + input_mems[i] = rknn_create_mem(ctx, input_attrs[i].size_with_stride); + } + + // 创建最大的输出tensor内存 + rknn_tensor_mem *output_mems[io_num.n_output]; + for (uint32_t i = 0; i < io_num.n_output; ++i) + { + // default output type is depend on model, this require float32 to compute top5 + // allocate float32 output tensor + int output_size = output_attrs[i].size * sizeof(float); + output_mems[i] = rknn_create_mem(ctx, output_size); + } + + // 加载输入并设置模型输入形状，每次切换输入形状要调用一次 + for (int s = 0; s < shape_num; ++s) + { + for (int i = 0; i < io_num.n_input; i++) + { + for (int j = 0; j < input_attrs[i].n_dims; ++j) + { + input_attrs[i].dims[j] = shape_range[i].dyn_range[s][j]; + } + } + ret = rknn_set_input_shapes(ctx, io_num.n_input, input_attrs); + if (ret < 0) + { + fprintf(stderr, "rknn_set_input_shape error! ret=%d\n", ret); + return -1; + } + + // 获取当前次推理的输入和输出形状 + printf("current input tensors:\n"); + rknn_tensor_attr cur_input_attrs[io_num.n_input]; + memset(cur_input_attrs, 0, io_num.n_input * sizeof(rknn_tensor_attr)); + for (uint32_t i = 0; i < io_num.n_input; i++) + { + cur_input_attrs[i].index = i; + // query info + ret = rknn_query(ctx, RKNN_QUERY_CURRENT_INPUT_ATTR, &(cur_input_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret < 0) + { + printf("rknn_init error! ret=%d\n", ret); + return -1; + } + dump_tensor_attr(&cur_input_attrs[i]); + } + + printf("current output tensors:\n"); + rknn_tensor_attr cur_output_attrs[io_num.n_output]; + memset(cur_output_attrs, 0, io_num.n_output * sizeof(rknn_tensor_attr)); + for (uint32_t i = 0; i < io_num.n_output; i++) + { + cur_output_attrs[i].index = i; + // query info + ret = rknn_query(ctx, RKNN_QUERY_CURRENT_OUTPUT_ATTR, &(cur_output_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret != RKNN_SUCC) + { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + dump_tensor_attr(&cur_output_attrs[i]); + } + + // 指定NPU核心数量，仅3588支持 + rknn_set_core_mask(ctx, (rknn_core_mask)core_mask); + + // 设置输入信息 + rknn_input inputs[io_num.n_input]; + memset(inputs, 0, io_num.n_input * sizeof(rknn_input)); + std::vector resize_imgs; + resize_imgs.resize(io_num.n_input); + for (int i = 0; i < io_num.n_input; i++) + { + int height = cur_input_attrs[i].fmt == RKNN_TENSOR_NHWC ? cur_input_attrs[i].dims[1] : cur_input_attrs[i].dims[2]; + int width = cur_input_attrs[i].fmt == RKNN_TENSOR_NHWC ? cur_input_attrs[i].dims[2] : cur_input_attrs[i].dims[3]; + int stride = cur_input_attrs[i].w_stride; + cv::resize(imgs[i], resize_imgs[i], cv::Size(width, height)); + int input_size = resize_imgs[i].total() * resize_imgs[i].channels(); + // 拷贝外部数据到零拷贝输入缓冲区 + if (width == stride) + { + memcpy(input_mems[i]->virt_addr, resize_imgs[i].data, input_size); + } + else + { + int height = cur_input_attrs[i].dims[1]; + int channel = cur_input_attrs[i].dims[3]; + // copy from src to dst with stride + uint8_t *src_ptr = resize_imgs[i].data; + uint8_t *dst_ptr = (uint8_t *)input_mems[i]->virt_addr; + // width-channel elements + int src_wc_elems = width * channel; + int dst_wc_elems = stride * channel; + for (int b = 0; b < cur_input_attrs[i].dims[0]; b++) + { + for (int h = 0; h < height; ++h) + { + memcpy(dst_ptr, src_ptr, src_wc_elems); + src_ptr += src_wc_elems; + dst_ptr += dst_wc_elems; + } + } + } + } + + // 更新输入零拷贝缓冲区内存 + for (int i = 0; i < io_num.n_input; i++) + { + cur_input_attrs[i].type = RKNN_TENSOR_UINT8; + ret = rknn_set_io_mem(ctx, input_mems[i], &cur_input_attrs[i]); + if (ret < 0) + { + printf("rknn_set_io_mem fail! ret=%d\n", ret); + return -1; + } + } + + // 更新输出零拷贝缓冲区内存 + for (uint32_t i = 0; i < io_num.n_output; ++i) + { + // default output type is depend on model, this require float32 to compute top5 + cur_output_attrs[i].type = RKNN_TENSOR_FLOAT32; + cur_output_attrs[i].fmt = RKNN_TENSOR_NCHW; + // set output memory and attribute + ret = rknn_set_io_mem(ctx, output_mems[i], &cur_output_attrs[i]); + if (ret < 0) + { + printf("rknn_set_io_mem fail! ret=%d\n", ret); + return -1; + } + } + + // 进行推理 + printf("Begin perf ...\n"); + double total_time = 0; + for (int i = 0; i < loop_count; ++i) + { + int64_t start_us = getCurrentTimeUs(); + ret = rknn_run(ctx, NULL); + int64_t elapse_us = getCurrentTimeUs() - start_us; + if (ret < 0) + { + printf("rknn run error %d\n", ret); + return -1; + } + total_time += elapse_us / 1000.f; + printf("%4d: Elapse Time = %.2fms, FPS = %.2f\n", i, elapse_us / 1000.f, 1000.f * 1000.f / elapse_us); + } + printf("Avg FPS = %.3f\n", loop_count * 1000.f / total_time); + +#if NPY_SUPPORT + // save output + for (uint32_t i = 0; i < io_num.n_output; i++) + { + char output_path[PATH_MAX]; + sprintf(output_path, "%s/rt_output%d.npy", output_dir ? output_dir : ".", i); + save_npy(output_path, (float *)output_mems[i]->virt_addr, &cur_output_attrs[i]); + } +#endif + + // 处理输出结果 + for (uint32_t i = 0; i < io_num.n_output; i++) + { + float *output = (float *)output_mems[i]->virt_addr; + int out_elems = cur_output_attrs[i].n_elems; + std::vector> results; + for (int i = 0; i < out_elems; i++) + { + results.emplace_back(output[i], i); + } + std::partial_sort(results.begin(), results.begin() + 5, results.end(), std::greater>()); + printf(" --- Top5 ---\n"); + for (int i = 0; i < 5; i++) + { + printf("%-3d: %.2f%%\n", results[i].second, results[i].first * 100); + } + } + } + // 释放资源 + for (uint32_t i = 0; i < io_num.n_input; ++i) + { + rknn_destroy_mem(ctx, input_mems[i]); + } + for (uint32_t i = 0; i < io_num.n_output; ++i) + { + rknn_destroy_mem(ctx, output_mems[i]); + } + + rknn_destroy(ctx); + for (int i = 0; i < io_num.n_input; i++) + { + free(input_data[i]); + } + return 0; +} \ No newline at end of file diff --git a/rknpu2/examples/rknn_dynamic_shape_input_demo/src/rknn_dynshape_inference_zero_copy_alloc_outside.cc b/rknpu2/examples/rknn_dynamic_shape_input_demo/src/rknn_dynshape_inference_zero_copy_alloc_outside.cc new file mode 100644 index 0000000000000000000000000000000000000000..d2be50d32289e8a6097579e21087f491341d4d8a --- /dev/null +++ b/rknpu2/examples/rknn_dynamic_shape_input_demo/src/rknn_dynshape_inference_zero_copy_alloc_outside.cc @@ -0,0 +1,904 @@ +/**************************************************************************** + * + * Copyright (c) 2017 - 2023 by Rockchip Corp. All rights reserved. + * + * The material in this file is confidential and contains trade secrets + * of Rockchip Corporation. This is proprietary information owned by + * Rockchip Corporation. No part of this work may be disclosed, + * reproduced, copied, transmitted, or used in any way for any purpose, + * without the express written permission of Rockchip Corporation. + * + *****************************************************************************/ + +/*------------------------------------------- + Includes +-------------------------------------------*/ +#include "rk_mpi_mmz.h" +#include "rknn_api.h" + +#include +#include +#include +#include +#include + +#include +#include + +#define STB_IMAGE_IMPLEMENTATION +#include "stb/stb_image.h" +#define STB_IMAGE_RESIZE_IMPLEMENTATION +#include + +#define NPY_SUPPORT 1 +#if NPY_SUPPORT +#include "cnpy/cnpy.h" +using namespace cnpy; +#endif + +#define TIME_BEGIN(name) \ + struct timeval tv##name; \ + gettimeofday(&tv##name, NULL); \ + long val##name = tv##name.tv_usec; \ + long min##name = tv##name.tv_sec; + +#define TIME_END(name) \ + gettimeofday(&tv##name, NULL); \ + val##name = tv##name.tv_usec - val##name; \ + val##name += 1000000 * (tv##name.tv_sec - min##name); \ + printf("[%s]exectime is %ld us, %ld ms\n", #name, val##name, val##name / 1000); + +static int mb_flags = RK_MMZ_ALLOC_TYPE_IOMMU | RK_MMZ_ALLOC_CACHEABLE; + +#define SIZE_ALIGN(size, align) (((size) + ((align)-1)) & (~((align)-1))) +#define SIZE_ALIGN_128(size) SIZE_ALIGN(size, 128) + +/*------------------------------------------- + Functions +-------------------------------------------*/ +static inline int64_t getCurrentTimeUs() +{ + struct timeval tv; + gettimeofday(&tv, NULL); + return tv.tv_sec * 1000000 + tv.tv_usec; +} + +static int rknn_GetTopN(float *pfProb, float *pfMaxProb, uint32_t *pMaxClass, uint32_t outputCount, uint32_t topNum) +{ + uint32_t i, j; + uint32_t top_count = outputCount > topNum ? topNum : outputCount; + + for (i = 0; i < topNum; ++i) + { + pfMaxProb[i] = -FLT_MAX; + pMaxClass[i] = -1; + } + + for (j = 0; j < top_count; j++) + { + for (i = 0; i < outputCount; i++) + { + if ((i == *(pMaxClass + 0)) || (i == *(pMaxClass + 1)) || (i == *(pMaxClass + 2)) || (i == *(pMaxClass + 3)) || + (i == *(pMaxClass + 4))) + { + continue; + } + + if (pfProb[i] > *(pfMaxProb + j)) + { + *(pfMaxProb + j) = pfProb[i]; + *(pMaxClass + j) = i; + } + } + } + + return 1; +} + +static void dump_tensor_attr(rknn_tensor_attr *attr) +{ + printf(" index=%d, name=%s, n_dims=%d, dims=[%d, %d, %d, %d], n_elems=%d, size=%d, fmt=%s, type=%s, qnt_type=%s, " + "zp=%d, scale=%f\n", + attr->index, attr->name, attr->n_dims, attr->dims[0], attr->dims[1], attr->dims[2], attr->dims[3], + attr->n_elems, attr->size, get_format_string(attr->fmt), get_type_string(attr->type), + get_qnt_type_string(attr->qnt_type), attr->zp, attr->scale); +} + +static void dump_input_dynamic_range(rknn_input_range *dyn_range) +{ + std::string range_str = ""; + for (int n = 0; n < dyn_range->shape_number; ++n) + { + range_str += n == 0 ? "[" : ",["; + range_str += dyn_range->n_dims < 1 ? "" : std::to_string(dyn_range->dyn_range[n][0]); + for (int i = 1; i < dyn_range->n_dims; ++i) + { + range_str += ", " + std::to_string(dyn_range->dyn_range[n][i]); + } + range_str += "]"; + } + + printf(" index=%d, name=%s, shape_number=%d, range=[%s], fmt = %s\n", dyn_range->index, dyn_range->name, + dyn_range->shape_number, range_str.c_str(), get_format_string(dyn_range->fmt)); +} + +static unsigned char *load_image(const char *image_path, rknn_tensor_attr *input_attr) +{ + int req_height = 0; + int req_width = 0; + int req_channel = 0; + + switch (input_attr->fmt) + { + case RKNN_TENSOR_NHWC: + req_height = input_attr->dims[1]; + req_width = input_attr->dims[2]; + req_channel = input_attr->dims[3]; + break; + case RKNN_TENSOR_NCHW: + req_height = input_attr->dims[2]; + req_width = input_attr->dims[3]; + req_channel = input_attr->dims[1]; + break; + default: + printf("meet unsupported layout\n"); + return NULL; + } + + int height = 0; + int width = 0; + int channel = 0; + + unsigned char *image_data = stbi_load(image_path, &width, &height, &channel, req_channel); + if (image_data == NULL) + { + printf("load image failed!\n"); + return NULL; + } + + if (width != req_width || height != req_height) + { + unsigned char *image_resized = (unsigned char *)STBI_MALLOC(req_width * req_height * req_channel); + if (!image_resized) + { + printf("malloc image failed!\n"); + STBI_FREE(image_data); + return NULL; + } + if (stbir_resize_uint8(image_data, width, height, 0, image_resized, req_width, req_height, 0, channel) != 1) + { + printf("resize image failed!\n"); + STBI_FREE(image_data); + return NULL; + } + STBI_FREE(image_data); + image_data = image_resized; + } + + return image_data; +} + +#if NPY_SUPPORT +static unsigned char *load_npy(const char *input_path, rknn_tensor_attr *input_attr, int *input_type, int *input_size, + int *type_bytes) +{ + printf("Loading %s\n", input_path); + + NpyArray npy_data = npy_load(input_path); + + *type_bytes = npy_data.word_size; + std::string typeName = npy_data.typeName; + + printf("npy data type:%s\n", typeName.c_str()); + + if (typeName == "int8") + { + *input_type = RKNN_TENSOR_INT8; + } + else if (typeName == "uint8") + { + *input_type = RKNN_TENSOR_UINT8; + } + else if (typeName == "float16") + { + *input_type = RKNN_TENSOR_FLOAT16; + } + else if (typeName == "float32") + { + *input_type = RKNN_TENSOR_FLOAT32; + } + else if (typeName == "8") + { + *input_type = RKNN_TENSOR_BOOL; + } + else if (typeName == "int64") + { + *input_type = RKNN_TENSOR_INT64; + } + + // npy shape = NHWC + std::vector npy_shape; + for (size_t i = 0; i < npy_data.shape.size(); ++i) + { + npy_shape.emplace_back(npy_data.shape[i]); + } + + int height = npy_shape.size() > 1 ? npy_shape[1] : 1; + int width = npy_shape.size() > 2 ? npy_shape[2] : 1; + int channel = npy_shape.size() > 3 ? npy_shape[3] : 1; + + switch (input_attr->fmt) + { + case RKNN_TENSOR_NHWC: + input_attr->dims[0] = npy_shape[0]; + input_attr->dims[1] = height; + input_attr->dims[2] = width; + input_attr->dims[3] = channel; + break; + case RKNN_TENSOR_UNDEFINED: + for (int idx = 0; idx < input_attr->n_dims; ++idx) + { + input_attr->dims[idx] = npy_shape[idx]; + } + break; + default: + fprintf(stderr, "load_npy error, unsupport model input layout: %s\n", get_format_string(input_attr->fmt)); + break; + } + + unsigned char *data = (unsigned char *)malloc(npy_data.num_bytes()); + if (!data) + { + return NULL; + } + + // TODO: copy + memcpy(data, npy_data.data(), npy_data.num_bytes()); + + *input_size = npy_data.num_bytes(); + + return data; +} + +static void save_npy(const char *output_path, float *output_data, rknn_tensor_attr *output_attr) +{ + std::vector output_shape; + + for (uint32_t i = 0; i < output_attr->n_dims; ++i) + { + output_shape.push_back(output_attr->dims[i]); + } + + npy_save(output_path, output_data, output_shape); +} +#endif + +static std::vector split(const std::string &str, const std::string &pattern) +{ + std::vector res; + if (str == "") + return res; + std::string strs = str + pattern; + size_t pos = strs.find(pattern); + while (pos != strs.npos) + { + std::string temp = strs.substr(0, pos); + res.push_back(temp); + strs = strs.substr(pos + 1, strs.size()); + pos = strs.find(pattern); + } + return res; +} + +uint32_t get_file_size(char *file_name) +{ + FILE *fid = fopen(file_name, "rb"); + if (fid == NULL) + { + printf("open file error\n"); + fclose(fid); + return -1; + } + + fseek(fid, 0, SEEK_END); + uint32_t size = ftell(fid); + + fclose(fid); + return size; +} + +int read_bin_file(char *file_name, void *out_ptr, unsigned int size) +{ + FILE *fid = fopen(file_name, "rb"); + if (fid == NULL) + { + printf("open file error\n"); + fclose(fid); + return -1; + } + fread(out_ptr, 1, size, fid); + + fclose(fid); + return 0; +} + +/*------------------------------------------- + Main Functions +-------------------------------------------*/ +int main(int argc, char *argv[]) +{ + if (argc < 3) + { + printf("Usage:%s model_path input_path [loop_count] [core_mask] [output_dir]\n", argv[0]); + return -1; + } + + char *model_path = argv[1]; + char *input_paths = argv[2]; + std::vector input_paths_split = split(input_paths, "#"); + + int loop_count = 1; + if (argc > 3) + { + loop_count = atoi(argv[3]); + } + + uint32_t core_mask = 1; + if (argc > 4) + { + // core_mask = atoi(argv[4]); + core_mask = strtoul(argv[4], NULL, 10); + } + + char *output_dir = NULL; + if (argc > 5) + { + output_dir = argv[5]; + } + + rknn_context ctx = 0; + + // Allocate model memory in outside + MB_BLK model_mb; + uint32_t model_size = get_file_size(model_path); + int ret = RK_MPI_MMZ_Alloc(&model_mb, model_size, mb_flags); + if (ret < 0) + { + printf("RK_MPI_MMZ_Alloc failed, ret: %d\n", ret); + return ret; + } + void *model_virt = RK_MPI_MMZ_Handle2VirAddr(model_mb); + if (model_virt == NULL) + { + printf("RK_MPI_MMZ_Handle2VirAddr failed!\n"); + return -1; + } + ret = read_bin_file(model_path, model_virt, model_size); + if (ret < 0) + { + printf("read_bin_file failed, ret: %d\n", ret); + return ret; + } + + TIME_BEGIN(dummpy_rknn_init); + ret = rknn_init(&ctx, model_virt, model_size, RKNN_FLAG_COLLECT_MODEL_INFO_ONLY | RKNN_FLAG_MEM_ALLOC_OUTSIDE, NULL); + TIME_END(dummpy_rknn_init); + if (ret < 0) + { + printf("rknn_init with RKNN_FLAG_COLLECT_MODEL_INFO_ONLY fail! ret=%d\n", ret); + return -1; + } + + // [dummpy_rknn_init] Get weight and internal mem size + rknn_mem_size mem_size; + ret = rknn_query(ctx, RKNN_QUERY_MEM_SIZE, &mem_size, sizeof(mem_size)); + if (ret != RKNN_SUCC) + { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("[dummpy init] total weight size: %d, total internal size: %d\n", mem_size.total_weight_size, + mem_size.total_internal_size); + + // Load RKNN Model + TIME_BEGIN(rknn_init); + ret = rknn_init(&ctx, model_virt, model_size, RKNN_FLAG_MEM_ALLOC_OUTSIDE, NULL); + TIME_END(rknn_init); + if (ret < 0) + { + printf("rknn_init fail! ret=%d\n", ret); + return -1; + } + + // Get sdk and driver version + rknn_sdk_version sdk_ver; + ret = rknn_query(ctx, RKNN_QUERY_SDK_VERSION, &sdk_ver, sizeof(sdk_ver)); + if (ret != RKNN_SUCC) + { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("rknn_api/rknnrt version: %s, driver version: %s\n", sdk_ver.api_version, sdk_ver.drv_version); + + // Get weight and internal mem size + ret = rknn_query(ctx, RKNN_QUERY_MEM_SIZE, &mem_size, sizeof(mem_size)); + if (ret != RKNN_SUCC) + { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("total weight size: %u, total internal size: %u\n", mem_size.total_weight_size, mem_size.total_internal_size); + + // Get Model Input Output Info + rknn_input_output_num io_num; + ret = rknn_query(ctx, RKNN_QUERY_IN_OUT_NUM, &io_num, sizeof(io_num)); + if (ret != RKNN_SUCC) + { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("model input num: %d, output num: %d\n", io_num.n_input, io_num.n_output); + + printf("default input tensors:\n"); + rknn_tensor_attr input_attrs[io_num.n_input]; + memset(input_attrs, 0, io_num.n_input * sizeof(rknn_tensor_attr)); + for (uint32_t i = 0; i < io_num.n_input; i++) + { + input_attrs[i].index = i; + // query info + ret = rknn_query(ctx, RKNN_QUERY_INPUT_ATTR, &(input_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret < 0) + { + printf("rknn_init error! ret=%d\n", ret); + return -1; + } + dump_tensor_attr(&input_attrs[i]); + } + + printf("default output tensors:\n"); + rknn_tensor_attr output_attrs[io_num.n_output]; + memset(output_attrs, 0, io_num.n_output * sizeof(rknn_tensor_attr)); + for (uint32_t i = 0; i < io_num.n_output; i++) + { + output_attrs[i].index = i; + // query info + ret = rknn_query(ctx, RKNN_QUERY_OUTPUT_ATTR, &(output_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret != RKNN_SUCC) + { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + dump_tensor_attr(&output_attrs[i]); + } + + // Get custom string + rknn_custom_string custom_string; + ret = rknn_query(ctx, RKNN_QUERY_CUSTOM_STRING, &custom_string, sizeof(custom_string)); + if (ret != RKNN_SUCC) + { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("custom string: %s\n", custom_string.string); + + printf("dynamic inputs shape range:\n"); + rknn_input_range dyn_range[io_num.n_input]; + memset(dyn_range, 0, io_num.n_input * sizeof(rknn_input_range)); + for (uint32_t i = 0; i < io_num.n_input; i++) + { + dyn_range[i].index = i; + ret = rknn_query(ctx, RKNN_QUERY_INPUT_DYNAMIC_RANGE, &dyn_range[i], sizeof(rknn_input_range)); + if (ret != RKNN_SUCC) + { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + dump_input_dynamic_range(&dyn_range[i]); + } + + unsigned char *input_data[io_num.n_input]; + int input_type[io_num.n_input]; + int input_layout[io_num.n_input]; + int input_size[io_num.n_input]; + int type_bytes[io_num.n_input]; + for (int i = 0; i < io_num.n_input; i++) + { + input_data[i] = NULL; + input_type[i] = RKNN_TENSOR_UINT8; + input_layout[i] = RKNN_TENSOR_NHWC; + input_size[i] = input_attrs[i].size; + type_bytes[i] = 1; + } + + // Load input + if (io_num.n_input != input_paths_split.size()) + { + return -1; + } + for (int i = 0; i < io_num.n_input; i++) + { + if (strstr(input_paths_split[i].c_str(), ".npy")) + { +// Load npy +#if NPY_SUPPORT + input_data[i] = + load_npy(input_paths_split[i].c_str(), &input_attrs[i], &input_type[i], &input_size[i], &type_bytes[i]); +#else + return -1; +#endif + } + else + { + // Load image + for (int i = 0; i < io_num.n_input; i++) + { + input_data[i] = load_image(input_paths_split[i].c_str(), &input_attrs[i]); + } + } + + if (!input_data[i]) + { + return -1; + } + } + + ret = rknn_set_input_shapes(ctx, io_num.n_input, input_attrs); + if (ret < 0) + { + fprintf(stderr, "rknn_set_input_shapes error! ret=%d\n", ret); + return -1; + } + + // Allocate weight memory in outside + MB_BLK weight_mb; + rknn_tensor_mem *weight_mem; + ret = RK_MPI_MMZ_Alloc(&weight_mb, SIZE_ALIGN_128(mem_size.total_weight_size), mb_flags); + if (ret < 0) + { + printf("RK_MPI_MMZ_Alloc failed, ret: %d\n", ret); + return ret; + } + void *weight_virt = RK_MPI_MMZ_Handle2VirAddr(weight_mb); + if (weight_virt == NULL) + { + printf("RK_MPI_MMZ_Handle2VirAddr failed!\n"); + return -1; + } + int weight_fd = RK_MPI_MMZ_Handle2Fd(weight_mb); + if (weight_fd < 0) + { + printf("RK_MPI_MMZ_Handle2Fd failed!\n"); + return -1; + } + weight_mem = rknn_create_mem_from_fd(ctx, weight_fd, weight_virt, mem_size.total_weight_size, 0); + ret = rknn_set_weight_mem(ctx, weight_mem); + if (ret < 0) + { + printf("rknn_set_weight_mem fail! ret=%d\n", ret); + return -1; + } + printf("weight mb info: virt = %p, fd = %d, size: %d\n", weight_virt, weight_fd, mem_size.total_weight_size); + + // Allocate internal memory in outside + MB_BLK internal_mb; + rknn_tensor_mem *internal_mem; + ret = RK_MPI_MMZ_Alloc(&internal_mb, SIZE_ALIGN_128(mem_size.total_internal_size), mb_flags); + if (ret < 0) + { + printf("RK_MPI_MMZ_Alloc failed, ret: %d\n", ret); + return ret; + } + void *internal_virt = RK_MPI_MMZ_Handle2VirAddr(internal_mb); + if (internal_virt == NULL) + { + printf("RK_MPI_MMZ_Handle2VirAddr failed!\n"); + return -1; + } + int internal_fd = RK_MPI_MMZ_Handle2Fd(internal_mb); + if (internal_fd < 0) + { + printf("RK_MPI_MMZ_Handle2Fd failed!\n"); + return -1; + } + internal_mem = rknn_create_mem_from_fd(ctx, internal_fd, internal_virt, mem_size.total_internal_size, 0); + ret = rknn_set_internal_mem(ctx, internal_mem); + if (ret < 0) + { + printf("rknn_set_internal_mem fail! ret=%d\n", ret); + return -1; + } + printf("internal mb info: virt = %p, fd = %d, size: %d\n", internal_virt, internal_fd, mem_size.total_internal_size); + + printf("current input tensors:\n"); + rknn_tensor_attr cur_input_attrs[io_num.n_input]; + memset(cur_input_attrs, 0, io_num.n_input * sizeof(rknn_tensor_attr)); + for (uint32_t i = 0; i < io_num.n_input; i++) + { + cur_input_attrs[i].index = i; + // query info + ret = rknn_query(ctx, RKNN_QUERY_CURRENT_INPUT_ATTR, &(cur_input_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret < 0) + { + printf("rknn_init error! ret=%d\n", ret); + return -1; + } + dump_tensor_attr(&cur_input_attrs[i]); + } + + printf("current output tensors:\n"); + rknn_tensor_attr cur_output_attrs[io_num.n_output]; + memset(cur_output_attrs, 0, io_num.n_output * sizeof(rknn_tensor_attr)); + for (uint32_t i = 0; i < io_num.n_output; i++) + { + cur_output_attrs[i].index = i; + // query info + ret = rknn_query(ctx, RKNN_QUERY_CURRENT_OUTPUT_ATTR, &(cur_output_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret != RKNN_SUCC) + { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + dump_tensor_attr(&cur_output_attrs[i]); + } + + // Allocate inputs memory in outside + MB_BLK input_mbs[io_num.n_input]; + void *input_virts[io_num.n_input]; + int input_fds[io_num.n_input]; + for (uint32_t i = 0; i < io_num.n_input; ++i) + { + int input_size = cur_input_attrs[i].size_with_stride; + + ret = RK_MPI_MMZ_Alloc(&input_mbs[i], SIZE_ALIGN_128(input_size), mb_flags); + if (ret < 0) + { + printf("RK_MPI_MMZ_Alloc failed, ret: %d\n", ret); + return ret; + } + input_virts[i] = RK_MPI_MMZ_Handle2VirAddr(input_mbs[i]); + if (input_virts[i] == NULL) + { + printf("RK_MPI_MMZ_Handle2VirAddr failed!\n"); + return -1; + } + input_fds[i] = RK_MPI_MMZ_Handle2Fd(input_mbs[i]); + if (input_fds[i] < 0) + { + printf("RK_MPI_MMZ_Handle2Fd failed!\n"); + return -1; + } + printf("input%d mb info: virt = %p, fd = %d, size = %d\n", i, input_virts[i], input_fds[i], input_size); + } + + // Allocate outputs memory in outside + MB_BLK output_mbs[io_num.n_output]; + void *output_virts[io_num.n_output]; + int output_fds[io_num.n_output]; + for (uint32_t i = 0; i < io_num.n_output; ++i) + { + // default output type is depend on model, this require float32 to compute top5 + cur_output_attrs[i].type = RKNN_TENSOR_FLOAT32; + int output_size = cur_output_attrs[i].n_elems * sizeof(float); + cur_output_attrs[i].size = output_size; + ret = RK_MPI_MMZ_Alloc(&output_mbs[i], SIZE_ALIGN_128(output_size), mb_flags); + if (ret < 0) + { + printf("RK_MPI_MMZ_Alloc failed, ret: %d\n", ret); + return ret; + } + output_virts[i] = RK_MPI_MMZ_Handle2VirAddr(output_mbs[i]); + if (output_virts[i] == NULL) + { + printf("RK_MPI_MMZ_Handle2VirAddr failed!\n"); + return -1; + } + output_fds[i] = RK_MPI_MMZ_Handle2Fd(output_mbs[i]); + if (output_fds[i] < 0) + { + printf("RK_MPI_MMZ_Handle2Fd failed!\n"); + return -1; + } + printf("output%d mb info: virt = %p, fd = %d, size = %d\n", i, output_virts[i], output_fds[i], output_size); + } + + // Create input tensor memory + rknn_tensor_mem *input_mems[io_num.n_input]; + for (int i = 0; i < io_num.n_input; i++) + { + // default input type is int8 (normalize and quantize need compute in outside) + // if set uint8, will fuse normalize and quantize to npu + cur_input_attrs[i].type = RKNN_TENSOR_UINT8; + // default fmt is NHWC, npu only support NHWC in zero copy mode + cur_input_attrs[i].fmt = RKNN_TENSOR_NHWC; + + input_mems[i] = rknn_create_mem_from_fd(ctx, input_fds[i], input_virts[i], cur_input_attrs[i].size_with_stride, 0); + + // Copy input data to input tensor memory + int width = cur_input_attrs[i].dims[2]; + int stride = cur_input_attrs[i].w_stride; + + if (width == stride) + { + memcpy(input_mems[i]->virt_addr, input_data[i], input_size[i]); + } + else + { + int height = cur_input_attrs[i].dims[1]; + int channel = cur_input_attrs[i].dims[3]; + // copy from src to dst with stride + uint8_t *src_ptr = input_data[i]; + uint8_t *dst_ptr = (uint8_t *)input_mems[i]->virt_addr; + // width-channel elements + int src_wc_elems = width * channel; + int dst_wc_elems = stride * channel; + for (int b = 0; b < cur_input_attrs[i].dims[0]; b++) + { + for (int h = 0; h < height; ++h) + { + memcpy(dst_ptr, src_ptr, src_wc_elems); + src_ptr += src_wc_elems; + dst_ptr += dst_wc_elems; + } + } + } + } + + // Create output tensor memory + rknn_tensor_mem *output_mems[io_num.n_output]; + for (uint32_t i = 0; i < io_num.n_output; ++i) + { + output_mems[i] = rknn_create_mem_from_fd(ctx, output_fds[i], output_virts[i], cur_output_attrs[i].size, 0); + } + + // Set input tensor memory + for (uint32_t i = 0; i < io_num.n_input; ++i) + { + ret = rknn_set_io_mem(ctx, input_mems[i], &cur_input_attrs[i]); + if (ret < 0) + { + printf("rknn_set_io_mem fail! ret=%d\n", ret); + return -1; + } + } + + // Set output tensor memory + for (uint32_t i = 0; i < io_num.n_output; ++i) + { + // set output memory and attribute + ret = rknn_set_io_mem(ctx, output_mems[i], &cur_output_attrs[i]); + if (ret < 0) + { + printf("rknn_set_io_mem fail! ret=%d\n", ret); + return -1; + } + } + + // weight flush cache + ret = RK_MPI_MMZ_FlushCacheEnd(weight_mb, 0, SIZE_ALIGN_128(mem_size.total_weight_size), RK_MMZ_SYNC_RW); + if (ret < 0) + { + printf("weight_mb FlushCacheEnd fail! ret=%d\n", ret); + return -1; + } + + // Run + printf("Begin perf ...\n"); + for (int i = 0; i < loop_count; ++i) + { + ret = RK_MPI_MMZ_FlushCacheEnd(internal_mb, 0, SIZE_ALIGN_128(mem_size.total_internal_size), RK_MMZ_SYNC_RW); + if (ret < 0) + { + printf("internal_mb FlushCacheEnd fail! ret=%d\n", ret); + return -1; + } + + for (uint32_t i = 0; i < io_num.n_input; ++i) + { + ret = + RK_MPI_MMZ_FlushCacheEnd(input_mbs[i], 0, SIZE_ALIGN_128(cur_input_attrs[i].size_with_stride), RK_MMZ_SYNC_RW); + if (ret < 0) + { + printf("input_mbs FlushCacheEnd fail! ret=%d\n", ret); + return -1; + } + } + + for (uint32_t i = 0; i < io_num.n_output; ++i) + { + ret = RK_MPI_MMZ_FlushCacheEnd(output_mbs[i], 0, SIZE_ALIGN_128(cur_output_attrs[i].n_elems * sizeof(float)), + RK_MMZ_SYNC_RW); + if (ret < 0) + { + printf("output_mbs FlushCacheEnd fail! ret=%d\n", ret); + return -1; + } + } + + int64_t start_us = getCurrentTimeUs(); + ret = rknn_run(ctx, NULL); + int64_t elapse_us = getCurrentTimeUs() - start_us; + if (ret < 0) + { + printf("rknn run error %d\n", ret); + return -1; + } + printf("%4d: Elapse Time = %.2fms, FPS = %.2f\n", i, elapse_us / 1000.f, 1000.f * 1000.f / elapse_us); + + for (uint32_t i = 0; i < io_num.n_output; ++i) + { + ret = RK_MPI_MMZ_FlushCacheStart(output_mbs[i], 0, SIZE_ALIGN_128(cur_output_attrs[i].n_elems * sizeof(float)), + RK_MMZ_SYNC_RW); + if (ret < 0) + { + printf("output_mbs FlushCacheStart fail! ret=%d\n", ret); + return -1; + } + } + } + + // 处理输出结果 + for (uint32_t i = 0; i < io_num.n_output; i++) + { + float *output = (float *)output_mems[i]->virt_addr; + int out_elems = cur_output_attrs[i].n_elems; + std::vector> results; + for (int i = 0; i < out_elems; i++) + { + results.emplace_back(output[i], i); + } + std::partial_sort(results.begin(), results.begin() + 5, results.end(), std::greater>()); + printf(" --- Top5 ---\n"); + for (int i = 0; i < 5; i++) + { + printf("%-3d: %.2f%%\n", results[i].second, results[i].first * 100); + } + } + + // save output + for (uint32_t i = 0; i < io_num.n_output; i++) + { + char output_path[PATH_MAX]; + sprintf(output_path, "%s/rt_output%d.npy", output_dir ? output_dir : ".", i); + rknn_tensor_attr cur_output_attrs_npy = cur_output_attrs[i]; + if (cur_output_attrs[i].fmt == RKNN_TENSOR_NHWC && cur_output_attrs[i].n_dims == 4) + { + std::vector axis = {0, 2, 3, 1}; + for (int j = 0; j < axis.size(); j++) + { + cur_output_attrs_npy.dims[j] = cur_output_attrs[i].dims[axis[j]]; + } + } + save_npy(output_path, (float *)output_mems[i]->virt_addr, &cur_output_attrs_npy); + } + + // free mb blk memory + RK_MPI_MMZ_Free(model_mb); + RK_MPI_MMZ_Free(weight_mb); + RK_MPI_MMZ_Free(internal_mb); + for (uint32_t i = 0; i < io_num.n_input; ++i) + { + RK_MPI_MMZ_Free(input_mbs[i]); + } + for (uint32_t i = 0; i < io_num.n_output; ++i) + { + RK_MPI_MMZ_Free(output_mbs[i]); + } + + // Destroy rknn memory + for (uint32_t i = 0; i < io_num.n_input; ++i) + { + rknn_destroy_mem(ctx, input_mems[i]); + free(input_data[i]); + } + for (uint32_t i = 0; i < io_num.n_output; ++i) + { + rknn_destroy_mem(ctx, output_mems[i]); + } + rknn_destroy_mem(ctx, weight_mem); + rknn_destroy_mem(ctx, internal_mem); + + // destroy + rknn_destroy(ctx); + + return 0; +} diff --git a/rknpu2/examples/rknn_internal_mem_reuse_demo/CMakeLists.txt b/rknpu2/examples/rknn_internal_mem_reuse_demo/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..4aed8a9e4a776c84a908313d1ee4178bdc2f65b2 --- /dev/null +++ b/rknpu2/examples/rknn_internal_mem_reuse_demo/CMakeLists.txt @@ -0,0 +1,86 @@ +cmake_minimum_required(VERSION 3.6) + +project(rknn_internal_mem_reuse_demo) + +set(CMAKE_CXX_STANDARD 11) +set(CMAKE_CXX_STANDARD_REQUIRED ON) + +# skip 3rd-party lib dependencies +set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} -Wl,--allow-shlib-undefined") + +# install target and libraries +set(CMAKE_INSTALL_PREFIX ${CMAKE_SOURCE_DIR}/install/rknn_internal_mem_reuse_demo_${CMAKE_SYSTEM_NAME}) + +set(CMAKE_SKIP_INSTALL_RPATH FALSE) +set(CMAKE_BUILD_WITH_INSTALL_RPATH TRUE) +set(CMAKE_INSTALL_RPATH "${CMAKE_INSTALL_PREFIX}/lib") + +# rknn api +set(RKNN_API_PATH ${CMAKE_SOURCE_DIR}/../../runtime//${CMAKE_SYSTEM_NAME}/librknn_api) +if (CMAKE_SYSTEM_NAME STREQUAL "Android") + set(RKNN_RT_LIB ${RKNN_API_PATH}/${CMAKE_ANDROID_ARCH_ABI}/librknnrt.so) +else() + if (CMAKE_C_COMPILER MATCHES "aarch64") + set(LIB_ARCH aarch64) + else() + set(LIB_ARCH armhf) + endif() + set(RKNN_RT_LIB ${RKNN_API_PATH}/${LIB_ARCH}/librknnrt.so) +endif() +include_directories(${RKNN_API_PATH}/include) +include_directories(${CMAKE_SOURCE_DIR}/../3rdparty) + +# opencv +if (CMAKE_SYSTEM_NAME STREQUAL "Android") + set(OpenCV_DIR ${CMAKE_SOURCE_DIR}/../3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/abi-${CMAKE_ANDROID_ARCH_ABI}) +else() + if(LIB_ARCH STREQUAL "armhf") + set(OpenCV_DIR ${CMAKE_SOURCE_DIR}/../3rdparty/opencv/opencv-linux-armhf/share/OpenCV) + else() + set(OpenCV_DIR ${CMAKE_SOURCE_DIR}/../3rdparty/opencv/opencv-linux-aarch64/share/OpenCV) + endif() +endif() +find_package(OpenCV REQUIRED) + +# rga +# comes from https://github.com/airockchip/librga +set(RGA_PATH ${CMAKE_SOURCE_DIR}/../3rdparty/rga/) +if(CMAKE_SYSTEM_NAME STREQUAL "Android") + set(RGA_LIB ${RGA_PATH}/libs/AndroidNdk/${CMAKE_ANDROID_ARCH_ABI}/librga.so) +else() + if(CMAKE_C_COMPILER MATCHES "aarch64") + set(LIB_ARCH aarch64) + else() + set(LIB_ARCH armhf) + endif() + set(RGA_LIB ${RGA_PATH}/libs/Linux//gcc-${LIB_ARCH}/librga.so) +endif() +include_directories( ${RGA_PATH}/include) + +set(CMAKE_INSTALL_RPATH "lib") + +# rknn_internal_mem_reuse_demo +include_directories( ${CMAKE_SOURCE_DIR}/include) + +add_executable(rknn_internal_mem_reuse_demo + src/rknn_internal_mem_reuse_demo.cc +) + +target_link_libraries(rknn_internal_mem_reuse_demo + ${RKNN_RT_LIB} + ${RGA_LIB} + ${OpenCV_LIBS} +) + + +# install target and libraries +set(CMAKE_INSTALL_PREFIX ${CMAKE_SOURCE_DIR}/install/rknn_internal_mem_reuse_demo_${CMAKE_SYSTEM_NAME}) +install(TARGETS rknn_internal_mem_reuse_demo DESTINATION ./) + +install(PROGRAMS ${RKNN_RT_LIB} DESTINATION lib) +install(PROGRAMS ${RGA_LIB} DESTINATION lib) +install(DIRECTORY ../rknn_yolov5_demo/model/${TARGET_SOC} DESTINATION ./model) +install(DIRECTORY ../rknn_mobilenet_demo/model/${TARGET_SOC} DESTINATION ./model) +file(GLOB YOLOV5_IMAGE_FILES "../rknn_yolov5_demo/model/*.jpg") +file(GLOB MOBILENET_IMAGE_FILES "../rknn_mobilenet_demo/model/*.jpg") +install(FILES ${YOLOV5_IMAGE_FILES} ${MOBILENET_IMAGE_FILES} DESTINATION ./model/) diff --git a/rknpu2/examples/rknn_internal_mem_reuse_demo/README.md b/rknpu2/examples/rknn_internal_mem_reuse_demo/README.md new file mode 100644 index 0000000000000000000000000000000000000000..b1731a76b8c5aab8f612841587a25761bfe85700 --- /dev/null +++ b/rknpu2/examples/rknn_internal_mem_reuse_demo/README.md @@ -0,0 +1,102 @@ +# rknn_internal_mem_reuse_demo + +## Description + +This project is mainly used to demonstrate the usage of **RKNN_FLAG_MEM_ALLOC_OUTSIDE** and **rknn_set_internal_mem**. + +RKNN_FLAG_MEM_ALLOC_OUTSIDE has two main purposes: + +- All memory is allocated by the user, allowing for better overall memory management. +- It enables memory reuse, especially for memory-constrained chips like RV1103/RV1106. + +Assuming there are two models, Model A and Model B, designed to run sequentially, the intermediate tensor memory between these two models can be reused. The example code is as follows: + +``` +rknn_context ctx_a, ctx_b; + +rknn_init(&ctx_a, model_path_a, 0, RKNN_FLAG_MEM_ALLOC_OUTSIDE, NULL); +rknn_query(ctx_a, RKNN_QUERY_MEM_SIZE, &mem_size_a, sizeof(mem_size_a)); + +rknn_init(&ctx_b, model_path_b, 0, RKNN_FLAG_MEM_ALLOC_OUTSIDE, NULL); +rknn_query(ctx_b, RKNN_QUERY_MEM_SIZE, &mem_size_b, sizeof(mem_size_b)); + +max_internal_size = MAX(mem_size_a.total_internal_size, mem_size_b.total_internal_size); +internal_mem_max = rknn_create_mem(ctx_a, max_internal_size); + +internal_mem_a = rknn_create_mem_from_fd(ctx_a, internal_mem_max->fd, + internal_mem_max->virt_addr, mem_size_a.total_internal_size, 0); +rknn_set_internal_mem(ctx_a, internal_mem_a); + +internal_mem_b = rknn_create_mem_from_fd(ctx_b, internal_mem_max->fd, + internal_mem_max->virt_addr, mem_size_a.total_internal_size, 0); +rknn_set_internal_mem(ctx_b, internal_mem_b); +``` + +Note: This demo uses the RKNN models from the rknn_mobilenet_demo and rknn_yolov5_demo example in the parent directory. Make sure they exist before compiling. + +## Android Demo + +### Compilation + +First export `ANDROID_NDK_PATH`, for example `export ANDROID_NDK_PATH=~/opts/ndk/android-ndk-r18b`, then execute: + +``` +./build-android.sh -t -a [-b ] + +# sush as: +./build-android.sh -t rk3568 -a arm64-v8a -b Release +``` + +### Pushing the Executable to the Device + +Connect the device to the PC via USB and push the entire demo directory to `/data`: + +``` +adb remount +adb push install/rknn_internal_mem_reuse_demo_Android /data/ +``` + +### Execution + +``` +cd /data/rknn_internal_mem_reuse_demo_Android/ + +export LD_LIBRARY_PATH=./lib +./rknn_internal_mem_reuse_demo model//yolov5s-640-640.rknn model/bus.jpg model//mobilenet_v1.rknn model/cat_224x224.jpg +``` + +## Aarch64 Linux Demo + +### Compilation + +First export `GCC_COMPILER`, for example `export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu`, then execute: + +``` +./build-linux.sh -t -a -b ] + +# such as: +./build-linux.sh -t rk3588 -a aarch64 -b Release +``` + +### Pushing the Executable to the Device + +Copy the `install/rknn_internal_mem_reuse_demo_Linux` to the `/userdata/` directory on the target device. + +- If using a Rockchip EVB board, use adb to push the file to the device: + +``` +bashCopy code +adb push install/rknn_internal_mem_reuse_demo_Linux /userdata/ +``` + +- If using other board, use scp or other methods to copy `install/rknn_internal_mem_reuse_demo_Linux` to the `/userdata/` directory on the device. + +### Execution + +``` +adb shell +cd /userdata/rknn_internal_mem_reuse_demo_Linux/ + +export LD_LIBRARY_PATH=./lib +./rknn_internal_mem_reuse_demo model//yolov5s-640-640.rknn model/bus.jpg model//mobilenet_v1.rknn model/cat_224x224.jpg +``` diff --git a/rknpu2/examples/rknn_internal_mem_reuse_demo/README_CN.md b/rknpu2/examples/rknn_internal_mem_reuse_demo/README_CN.md new file mode 100644 index 0000000000000000000000000000000000000000..2359aac11c71fa698d88299297620604b77bdf3f --- /dev/null +++ b/rknpu2/examples/rknn_internal_mem_reuse_demo/README_CN.md @@ -0,0 +1,107 @@ +# rknn_internal_mem_reuse_demo + +## 说明 + +本工程主要用于**RKNN_FLAG_MEM_ALLOC_OUTSIDE** 及 **rknn_set_internal_mem**的使用演示。 + +RKNN_FLAG_MEM_ALLOC_OUTSIDE：主要有两方面的作用: +- 所有内存均是用户自行分配，便于对整个系统内存进行统筹安排 +- 用于内存复用，特别是针对RV1103/RV1106这种内存极为紧张的情况。 + + +假设有模型A、B 两个模型，这两个模型在设计上串行运行的，那么这两个模型的中间tensor的内存就可以复用。示例代码如下： +``` +rknn_context ctx_a, ctx_b; + +rknn_init(&ctx_a, model_path_a, 0, RKNN_FLAG_MEM_ALLOC_OUTSIDE, NULL); +rknn_query(ctx_a, RKNN_QUERY_MEM_SIZE, &mem_size_a, sizeof(mem_size_a)); + +rknn_init(&ctx_b, model_path_b, 0, RKNN_FLAG_MEM_ALLOC_OUTSIDE, NULL); +rknn_query(ctx_b, RKNN_QUERY_MEM_SIZE, &mem_size_b, sizeof(mem_size_b)); + +max_internal_size = MAX(mem_size_a.total_internal_size, mem_size_b.total_internal_size); +internal_mem_max = rknn_create_mem(ctx_a, max_internal_size); + +internal_mem_a = rknn_create_mem_from_fd(ctx_a, internal_mem_max->fd, + internal_mem_max->virt_addr, mem_size_a.total_internal_size, 0); +rknn_set_internal_mem(ctx_a, internal_mem_a); + +internal_mem_b = rknn_create_mem_from_fd(ctx_b, internal_mem_max->fd, + internal_mem_max->virt_addr, mem_size_a.total_internal_size, 0); +rknn_set_internal_mem(ctx_b, internal_mem_b); +``` + + + +注意：本工程使用了上级目录的rknn_mobilenet_demo及rknn_yolov5_demo两个工程的rknn模型，编译之前请确保其存在。 + +## Android Demo + +### 编译 + +首先导入ANDROID_NDK_PATH，例如`export ANDROID_NDK_PATH=~/opts/ndk/android-ndk-r18b`，然后执行如下命令： + +``` +./build-android.sh -t -a [-b ] + +# 例如: +./build-android.sh -t rk3568 -a arm64-v8a -b Release +``` + +### 推送执行文件到板子 + +连接板子的usb口到PC,将整个demo目录到 `/data`: + +```sh +adb root +adb remount +adb push install/rknn_internal_mem_reuse_demo_Android /data/ +``` + +### 运行 + +```sh +adb shell +cd /data/rknn_internal_mem_reuse_demo_Android/ + +export LD_LIBRARY_PATH=./lib +./rknn_internal_mem_reuse_demo model//yolov5s-640-640.rknn model/bus.jpg model//mobilenet_v1.rknn model/cat_224x224.jpg +``` + +## Aarch64 Linux Demo + +### 编译 + +首先导入GCC_COMPILER，例如`export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu +`，然后执行如下命令： + +``` +./build-linux.sh -t -a -b ] + +# 例如: +./build-linux.sh -t rk3588 -a aarch64 -b Release +``` + +### 推送执行文件到板子 + + +将 install/rknn_internal_mem_reuse_demo_Linux 拷贝到板子的/userdata/目录. + +- 如果使用rockchip的EVB板子，可以使用adb将文件推到板子上： + +``` +adb push install/rknn_internal_mem_reuse_demo_Linux /userdata/ +``` + +- 如果使用其他板子，可以使用scp等方式将install/rknn_internal_mem_reuse_demo_Linux拷贝到板子的/userdata/目录 + +### 运行 + +```sh +adb shell +cd /userdata/rknn_internal_mem_reuse_demo_Linux/ + +export LD_LIBRARY_PATH=./lib +./rknn_internal_mem_reuse_demo model//yolov5s-640-640.rknn model/bus.jpg model//mobilenet_v1.rknn model/cat_224x224.jpg +``` + diff --git a/rknpu2/examples/rknn_internal_mem_reuse_demo/build-android.sh b/rknpu2/examples/rknn_internal_mem_reuse_demo/build-android.sh new file mode 100755 index 0000000000000000000000000000000000000000..4778e831eaff14905900f6fea7b8e3ee44147549 --- /dev/null +++ b/rknpu2/examples/rknn_internal_mem_reuse_demo/build-android.sh @@ -0,0 +1,112 @@ +#!/bin/bash + +set -e + +if [[ -z ${ANDROID_NDK_PATH} ]];then + echo "Please set ANDROID_NDK_PATH, such as export ANDROID_NDK_PATH=~/opts/ndk/android-ndk-r18b" + echo "NDK Version r18, r19 is recommanded. Other version may cause build failure." + exit +fi + +echo "$0 $@" +while getopts ":t:a:b" opt; do + case $opt in + t) + TARGET_SOC=$OPTARG + ;; + a) + TARGET_ARCH=$OPTARG + ;; + b) + BUILD_TYPE=$OPTARG + ;; + :) + echo "Option -$OPTARG requires an argument." + exit 1 + ;; + ?) + echo "Invalid option: -$OPTARG index:$OPTIND" + ;; + esac +done + +if [ -z ${TARGET_SOC} ] || [ -z ${TARGET_ARCH} ] ; then + echo "$0 -t -a [-b ]" + echo "" + echo " -t : target (rk3566/rk3568/rk3562/rk3576/rk3588)" + echo " -a : arch (arm64-v8a/armeabi-v7a)" + echo " -b : build_type (Debug/Release)" + echo "such as: $0 -t rk3588 -a arm64-v8a -b Release" + echo "" + exit -1 +fi + +# Debug / Release / RelWithDebInfo +if [[ -z ${BUILD_TYPE} ]];then + BUILD_TYPE=Release +fi + +case ${TARGET_SOC} in + rk356x) + TARGET_SOC="RK3566_RK3568" + ;; + rk3566_rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3588) + TARGET_SOC="RK3588" + ;; + rk3576) + TARGET_SOC="RK3576" + ;; + rk3566) + TARGET_SOC="RK3566_RK3568" + ;; + rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3562) + TARGET_SOC="RK3562" + ;; + *) + echo "Invalid target: ${TARGET_SOC}" + echo "Valid target: rk3562,rk3566,rk3568,rk3576,rk3588" + exit -1 + ;; +esac + +TARGET_PLATFORM=${TARGET_SOC}_android +if [[ -n ${TARGET_ARCH} ]];then +TARGET_PLATFORM=${TARGET_PLATFORM}_${TARGET_ARCH} +fi +ROOT_PWD=$( cd "$( dirname $0 )" && cd -P "$( dirname "$SOURCE" )" && pwd ) +BUILD_DIR=${ROOT_PWD}/build/build_${TARGET_PLATFORM}_${BUILD_TYPE} + +echo "===================================" +echo "TARGET_SOC=${TARGET_SOC}" +echo "TARGET_ARCH=${TARGET_ARCH}" +echo "BUILD_TYPE=${BUILD_TYPE}" +echo "BUILD_DIR=${BUILD_DIR}" +echo "ANDROID_NDK_PATH=${ANDROID_NDK_PATH}" +echo "===================================" + +if [[ ! -d "${BUILD_DIR}" ]]; then + mkdir -p ${BUILD_DIR} +fi + + +cd ${BUILD_DIR} +cmake ../.. \ + -DANDROID_TOOLCHAIN=clang \ + -DTARGET_SOC=${TARGET_SOC} \ + -DCMAKE_SYSTEM_NAME=Android \ + -DCMAKE_TOOLCHAIN_FILE=$ANDROID_NDK_PATH/build/cmake/android.toolchain.cmake \ + -DANDROID_ABI=${TARGET_ARCH} \ + -DANDROID_STL=c++_static \ + -DANDROID_PLATFORM=android-24 \ + -DCMAKE_BUILD_TYPE=${BUILD_TYPE} +# make VERBOSE=1 +make -j4 +make install +cd .. + diff --git a/rknpu2/examples/rknn_internal_mem_reuse_demo/build-linux.sh b/rknpu2/examples/rknn_internal_mem_reuse_demo/build-linux.sh new file mode 100755 index 0000000000000000000000000000000000000000..ae972803b8bda834c3a95588e513ec7af832cbeb --- /dev/null +++ b/rknpu2/examples/rknn_internal_mem_reuse_demo/build-linux.sh @@ -0,0 +1,122 @@ +#!/bin/bash + +set -e + +echo "$0 $@" +while getopts ":t:a:b" opt; do + case $opt in + t) + TARGET_SOC=$OPTARG + ;; + a) + TARGET_ARCH=$OPTARG + ;; + b) + BUILD_TYPE=$OPTARG + ;; + :) + echo "Option -$OPTARG requires an argument." + exit 1 + ;; + ?) + echo "Invalid option: -$OPTARG index:$OPTIND" + ;; + esac +done + +if [ -z ${TARGET_SOC} ];then + echo "$0 -t -a [-b ]" + echo "" + echo " -t : target (rk3566/rk3568/rk3562/rk3576/rk3588)" + echo " -a : arch (aarch64/armhf)" + echo " -b : build_type(Debug/Release)" + echo "such as: $0 -t rk3588 -a aarch64 -b Release" + echo "" + exit -1 +fi + +if [[ -z ${GCC_COMPILER} ]];then + echo "Please set GCC_COMPILER for $TARGET_SOC" + echo "such as export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu" + exit +fi +echo "$GCC_COMPILER" +export CC=${GCC_COMPILER}-gcc +export CXX=${GCC_COMPILER}-g++ + +if command -v ${CC} >/dev/null 2>&1; then + : +else + echo "${CC} is not available" + echo "Please set GCC_COMPILER for $TARGET_SOC" + echo "such as export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu" + exit +fi + +# Debug / Release +if [[ -z ${BUILD_TYPE} ]];then + BUILD_TYPE=Release +fi + + +case ${TARGET_SOC} in + rk356x) + TARGET_SOC="RK3566_RK3568" + ;; + rk3566_rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3588) + TARGET_SOC="RK3588" + ;; + rk3576) + TARGET_SOC="RK3576" + ;; + rk3566) + TARGET_SOC="RK3566_RK3568" + ;; + rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3562) + TARGET_SOC="RK3562" + ;; + *) + echo "Invalid target: ${TARGET_SOC}" + echo "Valid target: rk3562,rk3566,rk3568,rk3576,rk3588" + exit -1 + ;; +esac + +TARGET_PLATFORM=${TARGET_SOC}_linux +if [[ -n ${TARGET_ARCH} ]];then +TARGET_PLATFORM=${TARGET_PLATFORM}_${TARGET_ARCH} +fi +ROOT_PWD=$( cd "$( dirname $0 )" && cd -P "$( dirname "$SOURCE" )" && pwd ) +BUILD_DIR=${ROOT_PWD}/build/build_${TARGET_PLATFORM}_${BUILD_TYPE} + + +echo "===================================" +echo "TARGET_SOC=${TARGET_SOC}" +echo "TARGET_ARCH=${TARGET_ARCH}" +echo "BUILD_TYPE=${BUILD_TYPE}" +echo "BUILD_DIR=${BUILD_DIR}" +echo "CC=${CC}" +echo "CXX=${CXX}" +echo "===================================" + +if [[ ! -d "${BUILD_DIR}" ]]; then + mkdir -p ${BUILD_DIR} +fi + + +cd ${BUILD_DIR} +cmake ../.. \ + -DTARGET_SOC=${TARGET_SOC} \ + -DCMAKE_SYSTEM_NAME=Linux \ + -DCMAKE_SYSTEM_PROCESSOR=${TARGET_ARCH} \ + -DCMAKE_BUILD_TYPE=${BUILD_TYPE} \ + -DCMAKE_C_COMPILER=${CC} \ + -DCMAKE_CXX_COMPILER=${CXX} +make -j4 +make install diff --git a/rknpu2/examples/rknn_internal_mem_reuse_demo/src/rknn_internal_mem_reuse_demo.cc b/rknpu2/examples/rknn_internal_mem_reuse_demo/src/rknn_internal_mem_reuse_demo.cc new file mode 100644 index 0000000000000000000000000000000000000000..5c99365a9dad2f022b26b15dd02316b8f41d5f45 --- /dev/null +++ b/rknpu2/examples/rknn_internal_mem_reuse_demo/src/rknn_internal_mem_reuse_demo.cc @@ -0,0 +1,633 @@ +// Copyright (c) 2021 by Rockchip Electronics Co., Ltd. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + + +/*------------------------------------------- + Includes +-------------------------------------------*/ +#include "rknn_api.h" + +#include +#include +#include +#include +#include +#include +#include + +#define STB_IMAGE_IMPLEMENTATION +#include "stb/stb_image.h" +#define STB_IMAGE_RESIZE_IMPLEMENTATION +#include + +#define NPY_SUPPORT 0 + +#if NPY_SUPPORT +# include "cnpy/cnpy.h" +#endif + +/*------------------------------------------- + Functions +-------------------------------------------*/ +static inline int64_t getCurrentTimeUs() +{ + struct timeval tv; + gettimeofday(&tv, NULL); + return tv.tv_sec * 1000000 + tv.tv_usec; +} + +static int rknn_GetTopN(float* pfProb, float* pfMaxProb, uint32_t* pMaxClass, uint32_t outputCount, uint32_t topNum) +{ + uint32_t i, j; + uint32_t top_count = outputCount > topNum ? topNum : outputCount; + + for (i = 0; i < topNum; ++i) { + pfMaxProb[i] = -FLT_MAX; + pMaxClass[i] = -1; + } + + for (j = 0; j < top_count; j++) { + for (i = 0; i < outputCount; i++) { + if ((i == *(pMaxClass + 0)) || (i == *(pMaxClass + 1)) || (i == *(pMaxClass + 2)) || (i == *(pMaxClass + 3)) || + (i == *(pMaxClass + 4))) { + continue; + } + + if (pfProb[i] > *(pfMaxProb + j)) { + *(pfMaxProb + j) = pfProb[i]; + *(pMaxClass + j) = i; + } + } + } + + return 1; +} + + +static void dump_tensor_attr(rknn_tensor_attr* attr) +{ + char dims[128] = {0}; + for (int i = 0; i < attr->n_dims; ++i) { + int idx = strlen(dims); + sprintf(&dims[idx], "%d%s", attr->dims[i], (i == attr->n_dims - 1) ? "" : ", "); + } + printf(" index=%d, name=%s, n_dims=%d, dims=[%s], n_elems=%d, size=%d, fmt=%s, type=%s, qnt_type=%s, " + "zp=%d, scale=%f\n", + attr->index, attr->name, attr->n_dims, dims, attr->n_elems, attr->size, get_format_string(attr->fmt), + get_type_string(attr->type), get_qnt_type_string(attr->qnt_type), attr->zp, attr->scale); +} + +static void* load_file(const char* file_path, size_t* file_size) +{ + FILE* fp = fopen(file_path, "r"); + if (fp == NULL) { + printf("failed to open file: %s\n", file_path); + return NULL; + } + + fseek(fp, 0, SEEK_END); + size_t size = (size_t)ftell(fp); + fseek(fp, 0, SEEK_SET); + + void* file_data = malloc(size); + if (file_data == NULL) { + fclose(fp); + printf("failed allocate file size: %zu\n", size); + return NULL; + } + + if (fread(file_data, 1, size, fp) != size) { + fclose(fp); + free(file_data); + printf("failed to read file data!\n"); + return NULL; + } + + fclose(fp); + + *file_size = size; + + return file_data; +} + +static int load_bin(const char *filename, void *data, int max_size) +{ + + FILE *fp; + int ret = 0; + + fp = fopen(filename, "rb"); + if (NULL == fp) + { + printf("Open file %s failed.\n", filename); + return -1; + } + + fseek(fp, 0, SEEK_END); + int size = ftell(fp); + + if (size != max_size) { + printf("file size not match: %d vs %d!\n", size, max_size); + fclose(fp); + return -1; + } + + ret = fseek(fp, 0, SEEK_SET); + ret = fread(data, 1, size, fp); + + fclose(fp); + + return ret; +} + + +static unsigned char* load_image(const char* image_path, rknn_tensor_attr* input_attr) +{ + int req_height = 0; + int req_width = 0; + int req_channel = 0; + + switch (input_attr->fmt) { + case RKNN_TENSOR_NHWC: + req_height = input_attr->dims[1]; + req_width = input_attr->dims[2]; + req_channel = input_attr->dims[3]; + break; + case RKNN_TENSOR_NCHW: + req_height = input_attr->dims[2]; + req_width = input_attr->dims[3]; + req_channel = input_attr->dims[1]; + break; + default: + printf("meet unsupported layout\n"); + return NULL; + } + + int height = 0; + int width = 0; + int channel = 0; + + unsigned char* image_data = stbi_load(image_path, &width, &height, &channel, req_channel); + if (image_data == NULL) { + printf("load image failed!\n"); + return NULL; + } + + if (width != req_width || height != req_height) { + unsigned char* image_resized = (unsigned char*)STBI_MALLOC(req_width * req_height * req_channel); + if (!image_resized) { + printf("malloc image failed!\n"); + STBI_FREE(image_data); + return NULL; + } + if (stbir_resize_uint8(image_data, width, height, 0, image_resized, req_width, req_height, 0, channel) != 1) { + printf("resize image failed!\n"); + STBI_FREE(image_data); + return NULL; + } + STBI_FREE(image_data); + image_data = image_resized; + } + + return image_data; +} + +#if NPY_SUPPORT +static unsigned char* load_npy(const char* input_path, rknn_tensor_attr* input_attr, int* input_type, int* input_size) +{ + int req_height = 0; + int req_width = 0; + int req_channel = 0; + + printf("Loading %s\n", input_path); + + switch (input_attr->fmt) { + case RKNN_TENSOR_NHWC: + req_height = input_attr->dims[1]; + req_width = input_attr->dims[2]; + req_channel = input_attr->dims[3]; + break; + case RKNN_TENSOR_NCHW: + req_height = input_attr->dims[2]; + req_width = input_attr->dims[3]; + req_channel = input_attr->dims[1]; + break; + case RKNN_TENSOR_UNDEFINED: + break; + default: + printf("meet unsupported layout\n"); + return NULL; + } + + cnpy_array npy_data; + + bool writable = false; + if (cnpy_open(input_path, writable, &npy_data) != CNPY_SUCCESS) { + printf("Unable to load file %s\n", input_path); + return NULL; + } + + int data_bytes = npy_data.raw_data_size - npy_data.data_begin; + cnpy_dtype dtype = npy_data.dtype; + + if (dtype == CNPY_I8) { + *input_type = RKNN_TENSOR_INT8; + } else if (dtype == CNPY_U8) { + *input_type = RKNN_TENSOR_UINT8; + } else if (dtype == CNPY_F4) { + *input_type = RKNN_TENSOR_FLOAT32; + } + + // npy shape = NHWC + int npy_shape[4] = {1, 1, 1, 1}; + + int start = npy_data.n_dim == 4 ? 0 : 1; + for (size_t i = 0; i < npy_data.n_dim && i < 4; ++i) { + npy_shape[start + i] = npy_data.dims[i]; + } + + int height = npy_shape[1]; + int width = npy_shape[2]; + int channel = npy_shape[3]; + + if ((input_attr->fmt != RKNN_TENSOR_UNDEFINED) && + (width != req_width || height != req_height || channel != req_channel)) { + printf("npy shape match failed!, (%d, %d, %d) != (%d, %d, %d)\n", height, width, channel, req_height, req_width, + req_channel); + return NULL; + } + + unsigned char* data = (unsigned char*)malloc(data_bytes); + if (!data) { + return NULL; + } + + // TODO: copy + memcpy(data, npy_data.raw_data + npy_data.data_begin, data_bytes); + + *input_size = data_bytes; + + return data; +} + +static int save_npy(const char* output_path, float* output_data, rknn_tensor_attr* output_attr) +{ + int size = 1; + + for (uint32_t i = 0; i < output_attr->n_dims; ++i) { + size *= output_attr->dims[i]; + } + + cnpy_array npy_data; + cnpy_byte_order byte_order = CNPY_LE; /* little endian */ + cnpy_dtype dtype = CNPY_F4; /* float */ + cnpy_flat_order order = CNPY_C_ORDER; /* Fortran (row major) order */ + + if (cnpy_create(output_path, byte_order, dtype, order, output_attr->n_dims, (const size_t*)output_attr->dims, + &npy_data) != CNPY_SUCCESS) { + cnpy_perror("Unable to create file: "); + return -1; + } + + memcpy(npy_data.raw_data + npy_data.data_begin, (uint8_t*)output_data, sizeof(float) * size); + + /* optional: */ + if (cnpy_close(&npy_data) != CNPY_SUCCESS) { + cnpy_perror("Unable to close file: "); + return -1; + } + return 0; +} +#endif + +#define MAX_OUTPUT_NUM 4 +#define TOTAL_RKNN_MODEL_NUM 2 + +/*------------------------------------------- + Main Functions +-------------------------------------------*/ +int main(int argc, char* argv[]) +{ + if (argc < 5) { + printf("Usage:%s model_path_a input_path_a model_path_b input_path_b [loop_count] \n", argv[0]); + return -1; + } + + char* model_path_a = argv[1]; + char* input_path_a = argv[2]; + char* model_path_b = argv[3]; + char* input_path_b = argv[4]; + + int loop_count = 1; + if (argc > 5) { + loop_count = atoi(argv[5]); + } + + char *model_path[TOTAL_RKNN_MODEL_NUM]; + char *input_path[TOTAL_RKNN_MODEL_NUM]; + rknn_context ctx[TOTAL_RKNN_MODEL_NUM]; + rknn_mem_size mem_size[TOTAL_RKNN_MODEL_NUM]; + rknn_input_output_num io_num[TOTAL_RKNN_MODEL_NUM]; + rknn_tensor_mem* internal_mem[TOTAL_RKNN_MODEL_NUM]; + rknn_tensor_mem *weight_mems[TOTAL_RKNN_MODEL_NUM]; + rknn_tensor_attr input_attrs[TOTAL_RKNN_MODEL_NUM][1]; // this demo only support one input + rknn_tensor_attr output_attrs[TOTAL_RKNN_MODEL_NUM][MAX_OUTPUT_NUM]; + rknn_tensor_mem* input_mems[TOTAL_RKNN_MODEL_NUM][1]; // this demo only support one input + rknn_tensor_mem* output_mems[TOTAL_RKNN_MODEL_NUM][MAX_OUTPUT_NUM]; + rknn_tensor_mem* internal_mem_max = NULL; + uint32_t max_internal_size = 0; + unsigned char* input_data = NULL; + rknn_tensor_type input_type = RKNN_TENSOR_UINT8; + rknn_tensor_format input_layout = RKNN_TENSOR_NHWC; + int ret = 0; + + memset(ctx, 0x00, sizeof(ctx)); + memset(internal_mem, 0x00, sizeof(internal_mem)); + memset(input_mems, 0x00, sizeof(input_mems)); + memset(output_mems, 0x00, sizeof(output_mems)); + + model_path[0] = model_path_a; + model_path[1] = model_path_b; + input_path[0] = input_path_a; + input_path[1] = input_path_b; + + for (int n=0; n MAX_OUTPUT_NUM) { + printf("Please adjust the value of MAX_OUTPUT_NUM, it is too small for this model\n"); + return -1; + }; + + printf("input tensors:\n"); + memset(input_attrs[n], 0, io_num[n].n_input * sizeof(rknn_tensor_attr)); + for (uint32_t i = 0; i < io_num[n].n_input; i++) { + input_attrs[n][i].index = i; + // query info + ret = rknn_query(ctx[n], RKNN_QUERY_INPUT_ATTR, &(input_attrs[n][i]), sizeof(rknn_tensor_attr)); + if (ret < 0) { + printf("rknn_init error! ret=%d\n", ret); + goto out; + } + dump_tensor_attr(&input_attrs[n][i]); + } + + printf("output tensors:\n"); + memset(output_attrs[n], 0, io_num[n].n_output * sizeof(rknn_tensor_attr)); + for (uint32_t i = 0; i < io_num[n].n_output; i++) { + output_attrs[n][i].index = i; + // query info + ret = rknn_query(ctx[n], RKNN_QUERY_OUTPUT_ATTR, &(output_attrs[n][i]), sizeof(rknn_tensor_attr)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + goto out; + } + dump_tensor_attr(&output_attrs[n][i]); + } + + // Get custom string + rknn_custom_string custom_string; + ret = rknn_query(ctx[n], RKNN_QUERY_CUSTOM_STRING, &custom_string, sizeof(custom_string)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + goto out; + } + printf("custom string: %s\n", custom_string.string); + + // check max max_internal_size + + if (max_internal_size < mem_size[n].total_internal_size) { + max_internal_size = mem_size[n].total_internal_size; + } + } + + printf("\033[0;32mMax internal size %d \033[0;0m\n", max_internal_size); + + // Allocate internal memory in outside + internal_mem_max = rknn_create_mem(ctx[0], max_internal_size); + + for (int n=0; nfd, + internal_mem_max->virt_addr, mem_size[n].total_internal_size, 0); + ret = rknn_set_internal_mem(ctx[n], internal_mem[n]); + if (ret < 0) + { + printf("rknn_set_internal_mem fail! ret=%d\n", ret); + goto out; + } + + printf("internal cma info: virt = %p, phy=0x%lx, fd =%d, size=%d\n", internal_mem[n]->virt_addr, internal_mem[n]->phys_addr, internal_mem[n]->fd, internal_mem[n]->size); + + // 使用rknn_create_mem作为分配器， 分配和设置每个模型的外部weight内存 + weight_mems[n] = rknn_create_mem(ctx[n], mem_size[n].total_weight_size); + rknn_set_weight_mem(ctx[n], weight_mems[n]); + } + + for (int n = 0; n < TOTAL_RKNN_MODEL_NUM; n++) + { + // Create input tensor memory + // default input type is int8 (normalize and quantize need compute in outside) + // if set uint8, will fuse normalize and quantize to npu + input_attrs[n][0].type = input_type; + // default fmt is NHWC, npu only support NHWC in zero copy mode + input_attrs[n][0].fmt = input_layout; + + input_mems[n][0] = rknn_create_mem(ctx[n], input_attrs[n][0].size_with_stride); + + // Set input tensor memory + ret = rknn_set_io_mem(ctx[n], input_mems[n][0], &input_attrs[n][0]); + if (ret < 0) + { + printf("rknn_set_io_mem fail! ret=%d\n", ret); + goto out; + } + + // Create output tensor memory + for (uint32_t i = 0; i < io_num[n].n_output; ++i) { + output_mems[n][i] = rknn_create_mem(ctx[n], output_attrs[n][i].n_elems * sizeof(float)); + } + + // Set output tensor memory + for (uint32_t i = 0; i < io_num[n].n_output; ++i) { + // set output memory and attribute + output_attrs[n][i].type = RKNN_TENSOR_FLOAT32; + output_attrs[n][i].fmt = RKNN_TENSOR_NCHW; + ret = rknn_set_io_mem(ctx[n], output_mems[n][i], &output_attrs[n][i]); + if (ret < 0) { + printf("rknn_set_io_mem fail! ret=%d\n", ret); + goto out; + } + } + } + + // Copy input data to input tensor memory + for (int n=0; nvirt_addr) + input_mems[n][0]->offset, input_data, input_attrs[n][0].dims[2] * input_attrs[n][0].dims[1] * input_attrs[n][0].dims[3]); + } + else + { + // copy from src to dst with stride + uint8_t* src_ptr = input_data; + uint8_t* dst_ptr = (uint8_t*)input_mems[n][0]->virt_addr+input_mems[n][0]->offset; + // width-channel elements + int src_wc_elems = width * channel; + int dst_wc_elems = stride * channel; + for (int h = 0; h < height; ++h) { + memcpy(dst_ptr, src_ptr, src_wc_elems); + src_ptr += src_wc_elems; + dst_ptr += dst_wc_elems; + } + } + + STBI_FREE(input_data); + } + + // Run + printf("Begin perf ...\n"); + for (int n=0; nvirt_addr; + uint32_t sz = output_attrs[n][i].n_elems; + int top_count = sz > topNum ? topNum : sz; + + rknn_GetTopN(buffer, fMaxProb, MaxClass, sz, topNum); + + printf("---- Top%d ----\n", top_count); + for (int j = 0; j < top_count; j++) { + printf("%8.6f - %d\n", fMaxProb[j], MaxClass[j]); + } + } + } + + +out: + + // free all objects + if (internal_mem_max) { + rknn_destroy_mem(ctx[0], internal_mem_max); + } + + for (int n=0; n + matmul_type = 1: RKNN_FLOAT16_MM_FLOAT16_TO_FLOAT32 + matmul_type = 2: RKNN_INT8_MM_INT8_TO_INT32 + matmul_type = 4: RKNN_FLOAT16_MM_FLOAT16_TO_FLOAT16 + matmul_type = 7: RKNN_FLOAT16_MM_INT4_TO_FLOAT32 + matmul_type = 10: RKNN_INT4_MM_INT4_TO_INT16 +Example: A = [4,64], B = [64,32], int8 matmul test command as followed: +./rknn_matmul_api_demo 2 4,64,32 +``` + + + +2. dynamic shape mode + + ```sh + ./rknn_matmul_api_dynshape_demo + M_shapes: M shape array, which separeted by ',' + matmul_type = 1: RKNN_FLOAT16_MM_FLOAT16_TO_FLOAT32 + matmul_type = 2: RKNN_INT8_MM_INT8_TO_INT32 + matmul_type = 4: RKNN_FLOAT16_MM_FLOAT16_TO_FLOAT16 + matmul_type = 7: RKNN_FLOAT16_MM_INT4_TO_FLOAT32 + matmul_type = 10: RKNN_INT4_MM_INT4_TO_INT16 + Example: A = [1,64]#[4,64]#[8,64], B = [64,32], int8 matmul test command as followed: + feature+const: ./rknn_matmul_api_dynshape_demo 2 1,64,32#4,64,32#8,64,32 1 1 + two feature: ./rknn_matmul_api_dynshape_demo 2 1,64,32#4,64,32#8,64,32 2 1 + ``` + + + +# Aarch64 Linux Demo +## Build + +First export `GCC_COMPILER`, for example `export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu`, then execute: + +``` +./build-linux.sh -t -a -b ] + +# such as: +./build-linux.sh -t rk3588 -a aarch64 -b Release +``` + +## Install + +Copy install/rknn_matmul_api_demo_Linux to the devices under /userdata/. + +- If you use rockchip's evb board, you can use the following way: + +Connect device and push the program to `/userdata` + +``` +adb push install/rknn_matmul_api_demo_Linux /userdata/ +``` + +- If your board has sshd service, you can use scp or other methods to copy the program to the board. + +## Run + +``` +adb shell +cd /userdata/rknn_matmul_api_demo_Linux/ +``` + +``` +export LD_LIBRARY_PATH=./lib +./rknn_matmul_api_demo 2 4 64 32 +``` + +or + +```sh +export LD_LIBRARY_PATH=./lib +./rknn_matmul_api_dynshape_demo 2 1,64,32#4,64,32#8,64,32 1 1 +``` + + + +# Android Demo + +## Build + +First export `ANDROID_NDK_PATH`, for example `export ANDROID_NDK_PATH=~/opts/ndk/android-ndk-r18b`, then execute: + +```sh +./build-android.sh -t -a [-b ] + +# sush as: +./build-android.sh -t rk3568 -a arm64-v8a -b Release +``` + +## Install + +connect device and push build output into `/data` + +```sh +adb push install/rknn_matmul_api_demo_Android /data/ +``` + +## Run + +``` +adb shell +cd /data/rknn_matmul_api_demo_Android/ +``` + +```sh +export LD_LIBRARY_PATH=./lib +./rknn_matmul_api_demo 2 4 64 32 +``` + +or + +```sh +export LD_LIBRARY_PATH=./lib +./rknn_matmul_api_dynshape_demo 2 1,64,32#4,64,32#8,64,32 1 1 +``` + diff --git a/rknpu2/examples/rknn_matmul_api_demo/README_CN.md b/rknpu2/examples/rknn_matmul_api_demo/README_CN.md new file mode 100644 index 0000000000000000000000000000000000000000..f57041ad926377935a21767400faf646002f1da3 --- /dev/null +++ b/rknpu2/examples/rknn_matmul_api_demo/README_CN.md @@ -0,0 +1,119 @@ +rknn_matmul_api_demo是一个使用matmul C API在NPU上执行矩阵乘法的示例。包含静态shape和动态shape两种模式。 + +用法: + +1. 静态shape模式 + + ``` + Usage: + ./rknn_matmul_api_demo + matmul_type = 1: RKNN_FLOAT16_MM_FLOAT16_TO_FLOAT32 + matmul_type = 2: RKNN_INT8_MM_INT8_TO_INT32 + matmul_type = 4: RKNN_FLOAT16_MM_FLOAT16_TO_FLOAT16 + matmul_type = 7: RKNN_FLOAT16_MM_INT4_TO_FLOAT32 + matmul_type = 10: RKNN_INT4_MM_INT4_TO_INT16 + Example: A = [4,64], B = [64,32], int8 matmul test command as followed: + ./rknn_matmul_api_demo 2 4,64,32 + ``` +2. 动态shape模式 + + ```sh + ./rknn_matmul_api_dynshape_demo + M_shapes: M shape array, which separeted by ',' + matmul_type = 1: RKNN_FLOAT16_MM_FLOAT16_TO_FLOAT32 + matmul_type = 2: RKNN_INT8_MM_INT8_TO_INT32 + matmul_type = 4: RKNN_FLOAT16_MM_FLOAT16_TO_FLOAT16 + matmul_type = 7: RKNN_FLOAT16_MM_INT4_TO_FLOAT32 + matmul_type = 10: RKNN_INT4_MM_INT4_TO_INT16 + Example: A = [1,64]#[4,64]#[8,64], B = [64,32], int8 matmul test command as followed: + feature+const: ./rknn_matmul_api_dynshape_demo 2 1,64,32#4,64,32#8,64,32 1 1 + two feature: ./rknn_matmul_api_dynshape_demo 2 1,64,32#4,64,32#8,64,32 2 1 + ``` + +# Aarch64 Linux 示例 + +## 编译 + +首先导入GCC_COMPILER，例如`export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu +`，然后执行如下命令： + +``` +./build-linux.sh -t -a -b ] + +# 例如: +./build-linux.sh -t rk3588 -a aarch64 -b Release +``` +## 安装 + +将 install/rknn_matmul_api_demo_Linux 拷贝到设备上。 + +- 如果使用Rockchip的EVB板，可以使用以下命令： + +连接设备并将程序传输到`/userdata` + +``` +adb push install/rknn_matmul_api_demo_Linux /userdata/ +``` + +- 如果你的板子有sshd服务，可以使用scp命令或者其他方式将程序和模型传输到板子上。 + +## 运行 + + +``` +adb shell +cd /userdata/rknn_matmul_api_demo_Linux/ +``` + +``` +export LD_LIBRARY_PATH=./lib +./rknn_matmul_api_demo 2 4 64 32 +``` + +或者 + +```sh +export LD_LIBRARY_PATH=./lib +./rknn_matmul_api_dynshape_demo 2 1,64,32#4,64,32#8,64,32 1 1 +``` + + + +# Android 示例 + +## 编译 + +首先导入ANDROID_NDK_PATH，例如`export ANDROID_NDK_PATH=~/opts/ndk/android-ndk-r18b`，然后执行如下命令： + +``` +./build-android.sh -t -a [-b ] + +# 例如: +./build-android.sh -t rk3568 -a arm64-v8a -b Release +``` + +## 安装 + +连接设备并将程序传输到`/data` + +``` +adb push install/rknn_matmul_api_demo_Android /data/ +``` + +## 运行 + +``` +adb shell +cd /data/rknn_matmul_api_demo_Android/ +``` + +``` +export LD_LIBRARY_PATH=./lib +./rknn_matmul_api_demo 2 4 64 32 +``` +或者 +```sh +export LD_LIBRARY_PATH=./lib +./rknn_matmul_api_dynshape_demo 2 1,64,32#4,64,32#8,64,32 1 1 +``` + diff --git a/rknpu2/examples/rknn_matmul_api_demo/build-android.sh b/rknpu2/examples/rknn_matmul_api_demo/build-android.sh new file mode 100755 index 0000000000000000000000000000000000000000..f809dc843a0c5fcf03cca4e5e897ffecd2e607a1 --- /dev/null +++ b/rknpu2/examples/rknn_matmul_api_demo/build-android.sh @@ -0,0 +1,111 @@ +#!/bin/bash + +set -e + +if [[ -z ${ANDROID_NDK_PATH} ]];then + echo "Please set ANDROID_NDK_PATH, such as export ANDROID_NDK_PATH=~/opts/ndk/android-ndk-r18b" + echo "NDK Version r18, r19 is recommanded. Other version may cause build failure." + exit +fi + +echo "$0 $@" +while getopts ":t:a:b" opt; do + case $opt in + t) + TARGET_SOC=$OPTARG + ;; + a) + TARGET_ARCH=$OPTARG + ;; + b) + BUILD_TYPE=$OPTARG + ;; + :) + echo "Option -$OPTARG requires an argument." + exit 1 + ;; + ?) + echo "Invalid option: -$OPTARG index:$OPTIND" + ;; + esac +done + +if [ -z ${TARGET_SOC} ] || [ -z ${TARGET_ARCH} ] ; then + echo "$0 -t -a [-b ]" + echo "" + echo " -t : target (rk3566/rk3568/rk3562/rk3576/rk3588)" + echo " -a : arch (arm64-v8a/armeabi-v7a)" + echo " -b : build_type (Debug/Release)" + echo "such as: $0 -t rk3588 -a arm64-v8a -b Release" + echo "" + exit -1 +fi + +# Debug / Release / RelWithDebInfo +if [[ -z ${BUILD_TYPE} ]];then + BUILD_TYPE=Release +fi + +case ${TARGET_SOC} in + rk356x) + TARGET_SOC="RK3566_RK3568" + ;; + rk3566_rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3588) + TARGET_SOC="RK3588" + ;; + rk3576) + TARGET_SOC="RK3576" + ;; + rk3566) + TARGET_SOC="RK3566_RK3568" + ;; + rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3562) + TARGET_SOC="RK3562" + ;; + *) + echo "Invalid target: ${TARGET_SOC}" + echo "Valid target: rk3562,rk3566,rk3568,rk3576,rk3588" + exit -1 + ;; +esac + +TARGET_PLATFORM=${TARGET_SOC}_android +if [[ -n ${TARGET_ARCH} ]];then +TARGET_PLATFORM=${TARGET_PLATFORM}_${TARGET_ARCH} +fi +ROOT_PWD=$( cd "$( dirname $0 )" && cd -P "$( dirname "$SOURCE" )" && pwd ) +BUILD_DIR=${ROOT_PWD}/build/build_${TARGET_PLATFORM}_${BUILD_TYPE} + +echo "===================================" +echo "TARGET_SOC=${TARGET_SOC}" +echo "TARGET_ARCH=${TARGET_ARCH}" +echo "BUILD_TYPE=${BUILD_TYPE}" +echo "BUILD_DIR=${BUILD_DIR}" +echo "ANDROID_NDK_PATH=${ANDROID_NDK_PATH}" +echo "===================================" + +if [[ ! -d "${BUILD_DIR}" ]]; then + mkdir -p ${BUILD_DIR} +fi + + +cd ${BUILD_DIR} +cmake ../.. \ + -DANDROID_TOOLCHAIN=clang \ + -DCMAKE_SYSTEM_NAME=Android \ + -DCMAKE_TOOLCHAIN_FILE=$ANDROID_NDK_PATH/build/cmake/android.toolchain.cmake \ + -DANDROID_ABI=${TARGET_ARCH} \ + -DANDROID_STL=c++_static \ + -DANDROID_PLATFORM=android-24 \ + -DCMAKE_BUILD_TYPE=${BUILD_TYPE} +# make VERBOSE=1 +make -j4 +make install +cd .. + diff --git a/rknpu2/examples/rknn_matmul_api_demo/build-linux.sh b/rknpu2/examples/rknn_matmul_api_demo/build-linux.sh new file mode 100755 index 0000000000000000000000000000000000000000..c3bf62205d7ab90ea41c504e67842c9a3c3cf104 --- /dev/null +++ b/rknpu2/examples/rknn_matmul_api_demo/build-linux.sh @@ -0,0 +1,121 @@ +#!/bin/bash + +set -e + +echo "$0 $@" +while getopts ":t:a:b" opt; do + case $opt in + t) + TARGET_SOC=$OPTARG + ;; + a) + TARGET_ARCH=$OPTARG + ;; + b) + BUILD_TYPE=$OPTARG + ;; + :) + echo "Option -$OPTARG requires an argument." + exit 1 + ;; + ?) + echo "Invalid option: -$OPTARG index:$OPTIND" + ;; + esac +done + +if [ -z ${TARGET_SOC} ];then + echo "$0 -t -a [-b ]" + echo "" + echo " -t : target (rk3566/rk3568/rk3562/rk3576/rk3588)" + echo " -a : arch (aarch64/armhf)" + echo " -b : build_type(Debug/Release)" + echo "such as: $0 -t rk3588 -a aarch64 -b Release" + echo "" + exit -1 +fi + +if [[ -z ${GCC_COMPILER} ]];then + echo "Please set GCC_COMPILER for $TARGET_SOC" + echo "such as export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu" + exit +fi +echo "$GCC_COMPILER" +export CC=${GCC_COMPILER}-gcc +export CXX=${GCC_COMPILER}-g++ + +if command -v ${CC} >/dev/null 2>&1; then + : +else + echo "${CC} is not available" + echo "Please set GCC_COMPILER for $TARGET_SOC" + echo "such as export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu" + exit +fi + +# Debug / Release +if [[ -z ${BUILD_TYPE} ]];then + BUILD_TYPE=Release +fi + + +case ${TARGET_SOC} in + rk356x) + TARGET_SOC="RK3566_RK3568" + ;; + rk3566_rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3588) + TARGET_SOC="RK3588" + ;; + rk3576) + TARGET_SOC="RK3576" + ;; + rk3566) + TARGET_SOC="RK3566_RK3568" + ;; + rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3562) + TARGET_SOC="RK3562" + ;; + *) + echo "Invalid target: ${TARGET_SOC}" + echo "Valid target: rk3562,rk3566,rk3568,rk3576,rk3588" + exit -1 + ;; +esac + +TARGET_PLATFORM=${TARGET_SOC}_linux +if [[ -n ${TARGET_ARCH} ]];then +TARGET_PLATFORM=${TARGET_PLATFORM}_${TARGET_ARCH} +fi +ROOT_PWD=$( cd "$( dirname $0 )" && cd -P "$( dirname "$SOURCE" )" && pwd ) +BUILD_DIR=${ROOT_PWD}/build/build_${TARGET_PLATFORM}_${BUILD_TYPE} + + +echo "===================================" +echo "TARGET_SOC=${TARGET_SOC}" +echo "TARGET_ARCH=${TARGET_ARCH}" +echo "BUILD_TYPE=${BUILD_TYPE}" +echo "BUILD_DIR=${BUILD_DIR}" +echo "CC=${CC}" +echo "CXX=${CXX}" +echo "===================================" + +if [[ ! -d "${BUILD_DIR}" ]]; then + mkdir -p ${BUILD_DIR} +fi + + +cd ${BUILD_DIR} +cmake ../.. \ + -DCMAKE_SYSTEM_NAME=Linux \ + -DCMAKE_SYSTEM_PROCESSOR=${TARGET_ARCH} \ + -DCMAKE_BUILD_TYPE=${BUILD_TYPE} \ + -DCMAKE_C_COMPILER=${CC} \ + -DCMAKE_CXX_COMPILER=${CXX} +make -j4 +make install diff --git a/rknpu2/examples/rknn_matmul_api_demo/src/matmul_utils.cpp b/rknpu2/examples/rknn_matmul_api_demo/src/matmul_utils.cpp new file mode 100644 index 0000000000000000000000000000000000000000..6a32ffd32ee087245c11fd3593c6e82e64b1802f --- /dev/null +++ b/rknpu2/examples/rknn_matmul_api_demo/src/matmul_utils.cpp @@ -0,0 +1,311 @@ + +#include +#include +#include +#include +#include +#include +#include "fp16/Float16.h" + +namespace rknpu2 +{ + + /** + * @brief generate random buffer + * + */ + template + void generate_random_buffer(T *buffer, size_t size, std::vector range) + { + if (buffer == nullptr || size == 0) + { + return; + } + // 设置随机种子 + srand((unsigned)time(NULL)); + + float min = range[0], max = range[1]; + for (size_t i = 0; i < size; ++i) + { + buffer[i] = static_cast(min + (max - min) * (static_cast(rand()) / RAND_MAX)); + } + } + + template void generate_random_buffer(int8_t *buffer, size_t size, std::vector range); + template void generate_random_buffer(float16 *buffer, size_t size, std::vector range); + + /** + * @brief convert norm layout to perf layout + * norm layout: [M,K] + * perf layout: [K/subK, M, subK] + */ + template + void norm_layout_to_perf_layout(Ti *src, To *dst, int32_t M, int32_t K, int32_t subK, bool isInt4Type) + { + int outter_size = (int)std::ceil(K * 1.0f / subK); + for (int i = 0; i < outter_size; i++) + { + for (int m = 0; m < M; m++) + { + for (int j = 0; j < subK; j++) + { + int ki = i * subK + j; + if (isInt4Type) + { + int input_index = m * K + ki; + int output_index = i * M * subK + m * subK + j; + int8_t int4 = src[input_index]; + if (ki >= K) + { + int4 = 0; + } + else + { + int4 = int4 & 0xf; + } + if (output_index % 2 == 0) + { + dst[output_index / 2] = int4; + } + else + { + int8_t temp = dst[output_index / 2]; + int8_t result = temp | (int4 << 4); + dst[output_index / 2] = result; + } + } + else + { + if (ki >= K) + { + dst[i * M * subK + m * subK + j] = 0; + } + else + { + dst[i * M * subK + m * subK + j] = src[m * K + ki]; + } + } + } + } + } + } + + template void norm_layout_to_perf_layout(int8_t *src, int8_t *dst, int32_t M, int32_t K, int32_t subK, + bool isInt4Type); + template void norm_layout_to_perf_layout(float16 *src, float16 *dst, int32_t M, int32_t K, + int32_t subK, bool isInt4Type); + + /** + * @brief convert norm layout to native layout + * norm layout: [K,N] + * native layout: [N1, K1, subN, subK] + * + */ + template + void norm_layout_to_native_layout(Ti *src, To *dst, int32_t K, int32_t N, int32_t subN, int32_t subK, bool isInt4Type) + { + int N_remain = (int)std::ceil(N * 1.0f / subN); + int K_remain = (int)std::ceil(K * 1.0f / subK); + for (int i = 0; i < N_remain; i++) + { + for (int j = 0; j < K_remain; j++) + { + for (int n = 0; n < subN; n++) + { + int ni = i * subN + n; + for (int k = 0; k < subK; k++) + { + int ki = j * subK + k; + if (isInt4Type) + { + int input_index = ki * N + ni; + int output_index = i * (K_remain * subN * subK) + j * (subN * subK) + n * subK + k; + int8_t int4 = src[input_index]; + if (ki < K && ni < N) + { + int4 = int4 & 0xf; + } + else + { + int4 = 0; + } + if (output_index % 2 == 0) + { + dst[output_index / 2] = int4 << 4; + } + else + { + int8_t temp = dst[output_index / 2]; + int8_t result = temp | int4; + dst[output_index / 2] = result; + } + } + else + { + if (ki < K && ni < N) + { + dst[((i * K_remain + j) * subN + n) * subK + k] = src[ki * N + ni]; + } + else + { + dst[((i * K_remain + j) * subN + n) * subK + k] = 0; + } + } + } + } + } + } + } + + template void norm_layout_to_native_layout(int8_t *src, int8_t *dst, int32_t K, int32_t N, int32_t subN, + int32_t subK, bool isInt4Type); + template void norm_layout_to_native_layout(float16 *src, float16 *dst, int32_t K, int32_t N, + int32_t subN, int32_t subK, bool isInt4Type); + + /** + * @brief convert perf to norm layout + * perf layout: [K1, M, subK] + * norm layout: [M,K] + * + */ + template + void perf_layout_to_norm_layout(Ti *src, To *dst, int32_t M, int32_t K, int32_t K_remain, int32_t subK) + { + for (int i = 0; i < K_remain; i++) + { + for (int j = 0; j < subK; j++) + { + for (int m = 0; m < M; m++) + { + int ki = i * subK + j; + if (ki < K) + { + dst[m * K + ki] = src[i * M * subK + m * subK + j]; + } + } + } + } + } + + template void perf_layout_to_norm_layout(int8_t *src, int8_t *dst, int32_t M, int32_t K, + int32_t K_remain, int32_t subK); + template void perf_layout_to_norm_layout(int16_t *src, int16_t *dst, int32_t M, int32_t K, + int32_t K_remain, int32_t subK); + template void perf_layout_to_norm_layout(int32_t *src, int32_t *dst, int32_t M, int32_t K, + int32_t K_remain, int32_t subK); + template void perf_layout_to_norm_layout(float *src, float *dst, int32_t M, int32_t K, int32_t K_remain, + int32_t subK); + template void perf_layout_to_norm_layout(float16 *src, float16 *dst, int32_t M, int32_t K, int32_t K_remain, + int32_t subK); + + template + bool arraysEqual(const std::vector &arr1, const std::vector &arr2, float eps) + { + if (arr1.size() != arr2.size()) + { + return false; + } + + for (size_t i = 0; i < arr1.size(); ++i) + { + if (std::abs(arr1[i] - arr2[i]) > eps) + { + return false; + } + } + + return true; + } + + template bool arraysEqual(const std::vector &arr1, const std::vector &arr2, float eps); + template bool arraysEqual(const std::vector &arr1, const std::vector &arr2, float eps); + template bool arraysEqual(const std::vector &arr1, const std::vector &arr2, float eps); + template bool arraysEqual(const std::vector &arr1, const std::vector &arr2, float eps); + + template + bool arraysCosineSimilarity(const std::vector &arr1, const std::vector &arr2, float eps) + { + if (arr1.size() != arr2.size()) + { + return false; + } + + // 计算点积 +#pragma omp parallel for reduction(+ : dotProduct) + double dotProduct = 0.0; + for (size_t i = 0; i < arr1.size(); ++i) + { + dotProduct += arr1[i] * arr2[i]; + } + +// 计算向量范数 +#pragma omp parallel for reduction(+ : normA, normB) + double normA = 0.0, normB = 0.0; + for (size_t i = 0; i < arr1.size(); ++i) + { + normA += std::pow(arr1[i], 2); + normB += std::pow(arr2[i], 2); + } + + // 避免除以零 + if (normA == 0.0 || normB == 0.0) + { + return false; + } + + if ((dotProduct / (std::sqrt(normA) * std::sqrt(normB))) < eps) + { + return false; + } + + return true; + } + template bool arraysCosineSimilarity(const std::vector &arr1, const std::vector &arr2, float eps); + template bool arraysCosineSimilarity(const std::vector &arr1, const std::vector &arr2, + float eps); + template bool arraysCosineSimilarity(const std::vector &arr1, const std::vector &arr2, + float eps); + + // 转置模板函数 + template + void transposeB(const T *input, T *output, int32_t K, int32_t N) + { + for (int32_t k = 0; k < K; ++k) + { + for (int32_t n = 0; n < N; ++n) + { + output[n * K + k] = input[k * N + n]; + } + } + } + + template void transposeB(const int8_t *input, int8_t *output, int32_t K, int32_t N); + template void transposeB(const float16 *input, float16 *output, int32_t K, int32_t N); + + // 4bit数据类型的特殊处理函数 + void transpose4bit(const int8_t *input, int8_t *output, int32_t K, int32_t N) + { + for (int32_t k = 0; k < K; ++k) + { + for (int32_t n = 0; n < N; ++n) + { + int32_t input_idx = (k * N + n) / 2; + int32_t input_offset = (k * N + n) % 2; + int32_t output_idx = (n * K + k) / 2; + int32_t output_offset = (n * K + k) % 2; + + uint8_t value = (input[input_idx] >> (4 * input_offset)) & 0xF; + + if (output_offset == 0) + { + output[output_idx] = (output[output_idx] & 0xF0) | value; + } + else + { + output[output_idx] = (output[output_idx] & 0x0F) | (value << 4); + } + } + } + } + +} // namespace rknn \ No newline at end of file diff --git a/rknpu2/examples/rknn_matmul_api_demo/src/matmul_utils.h b/rknpu2/examples/rknn_matmul_api_demo/src/matmul_utils.h new file mode 100644 index 0000000000000000000000000000000000000000..7cc63d0184bad721bf0d4fa24e19ec059fb9a01c --- /dev/null +++ b/rknpu2/examples/rknn_matmul_api_demo/src/matmul_utils.h @@ -0,0 +1,35 @@ +#ifndef _RKNN_MATMUL_API_DEMO_UTILS_H_ +#define _RKNN_MATMUL_API_DEMO_UTILS_H_ +#include +#include + +#include + +namespace rknpu2 +{ + + template + void generate_random_buffer(T *buffer, size_t size, std::vector range = {0.0f, 1.0f}); + + template + void norm_layout_to_perf_layout(Ti *src, To *dst, int32_t M, int32_t K, int32_t subK, bool isInt4Type); + + template + void norm_layout_to_native_layout(Ti *src, To *dst, int32_t K, int32_t N, int32_t subN, int32_t subK, bool isInt4Type); + + template + void perf_layout_to_norm_layout(Ti *src, To *dst, int32_t M, int32_t K, int32_t K_remain, int32_t subK); + + template + bool arraysEqual(const std::vector &arr1, const std::vector &arr2, float eps = 0.0001f); + + template + bool arraysCosineSimilarity(const std::vector &arr1, const std::vector &arr2, float eps = 0.9999f); + + template + void transposeB(const T *input, T *output, int32_t K, int32_t N); + + void transpose4bit(const int8_t *input, int8_t *output, int32_t K, int32_t N); + +} // namespace rknpu2 +#endif \ No newline at end of file diff --git a/rknpu2/examples/rknn_matmul_api_demo/src/rknn_matmul_api_demo.cpp b/rknpu2/examples/rknn_matmul_api_demo/src/rknn_matmul_api_demo.cpp new file mode 100644 index 0000000000000000000000000000000000000000..bf05bbe8b49f51f34709292feed7d28796df4046 --- /dev/null +++ b/rknpu2/examples/rknn_matmul_api_demo/src/rknn_matmul_api_demo.cpp @@ -0,0 +1,1338 @@ +/**************************************************************************** + * + * Copyright (c) 2017 - 2024 by Rockchip Corp. All rights reserved. + * + * The material in this file is confidential and contains trade secrets + * of Rockchip Corporation. This is proprietary information owned by + * Rockchip Corporation. No part of this work may be disclosed, + * reproduced, copied, transmitted, or used in any way for any purpose, + * without the express written permission of Rockchip Corporation. + * + *****************************************************************************/ + +/*------------------------------------------- + Includes +-------------------------------------------*/ +#include "fp16/Float16.h" +#include "matmul_utils.h" +#include "rknn_api.h" +#include "rknn_matmul_api.h" + +#include +#include +#include +#include + +#include +#include +using namespace rknpu2; + +// #define DEBUG_PRINT + +/*------------------------------------------- + Functions +-------------------------------------------*/ +static inline int64_t getCurrentTimeUs() +{ + struct timeval tv; + gettimeofday(&tv, NULL); + return tv.tv_sec * 1000000 + tv.tv_usec; +} + +static void set_mem_from_int8_to_int4(int8_t *dst, int8_t *src, int size) +{ + for (int idx = 0; idx < size; idx += 2) + { + dst[idx / 2] = ((src[idx]) & 0xf) | ((src[idx + 1] << 4) & 0xf0); + } +} + +// 一维矩阵乘法函数 +template +std::vector matrixMultiply(const Ti *A, const Ti *B, int M, int K, int N) +{ + std::vector result(M * N, 0); + + for (int i = 0; i < M; ++i) + { + for (int j = 0; j < N; ++j) + { + float sum = 0; + for (int k = 0; k < K; ++k) + { +#if DEBUG_PRINT + printf("A[%d][%d] = %d, B[%d][%d] = %d, A*B = %6d\n", i, k, A[i * K + k], k, j, B[k * N + j], + A[i * K + k] * B[k * N + j]); +#endif + sum += (float)A[i * K + k] * (float)B[k * N + j]; + } + result[i * N + j] = sum; + } + } + + return result; +} + +// 一维矩阵混合量化乘法函数 +template +std::vector matrixMultiplyWithHybridQuant(const Ta *A, const Tb *B, int M, int K, int N, float input_scale, + float weight_scale, float output_scale, float weight_zp) +{ + std::vector result(M * N, 0); + + for (int i = 0; i < M; ++i) + { + for (int j = 0; j < N; ++j) + { + float sum = 0; + for (int k = 0; k < K; ++k) + { + sum += (float)A[i * K + k] * ((float)B[k * N + j] - weight_zp); + } + result[i * N + j] = sum * input_scale * weight_scale / output_scale; + } + } + + return result; +} + +// 一维矩阵乘法函数 +std::vector matrixMultiplyWithQuant(const int8_t *A, const int8_t *B, int M, int K, int N, float input_scale, + float weight_scale, float output_scale, int input_zp = 0) +{ + std::vector result(M * N, 0); + + for (int i = 0; i < M; ++i) + { + for (int j = 0; j < N; ++j) + { + float sum = 0; + for (int k = 0; k < K; ++k) + { + sum += (float)(A[i * K + k] - input_zp) * (float)B[k * N + j]; + } + result[i * N + j] = sum * input_scale * weight_scale / output_scale; + } + } + + return result; +} + +static const char *get_dims_string(rknn_matmul_tensor_attr *attr) +{ + if (!attr->n_dims) + { + return "()"; + } + static char dims_str[128]; + memset(&dims_str[0], 0, sizeof(dims_str)); + sprintf(&dims_str[0], "(%d", attr->dims[0]); + for (uint32_t i = 1; i < attr->n_dims; ++i) + { + int idx = strlen(dims_str); + sprintf(&dims_str[idx], ", %d", attr->dims[i]); + } + strcat(&dims_str[0], ")"); + return dims_str; +} + +static void dump_matmul_tensor_attr(rknn_matmul_tensor_attr *attr) +{ + printf(" name=%s, dims=%s, size=%d, type=%s\n", attr->name, get_dims_string(attr), attr->size, + get_type_string(attr->type)); +} + +static int8_t get_virt_addr_int4(void *virt_addr, int index) +{ + int8_t int4 = 0; + if (index % 2 == 0) + { + int4 = (((int8_t *)virt_addr)[index / 2] >> 4) & 0xf; + } + else + { + int4 = (((int8_t *)virt_addr)[index / 2]) & 0xf; + } + if (int4 & 0x8) + { + int4 = int4 | 0xf0; + } + return int4; +} + +static void dump_matmul_tensor_reverse(rknn_tensor_mem *tensor, rknn_matmul_tensor_attr *attr) +{ + printf(" %s%s:\n", attr->name, get_dims_string(attr)); + // normal layout + if (attr->n_dims == 2) + { + for (uint32_t j = 0; j < attr->dims[0]; ++j) + { + for (uint32_t i = 0; i < attr->dims[1]; ++i) + { + void *virt_addr = (void *)((size_t)tensor->virt_addr + tensor->offset); + if (attr->type == RKNN_TENSOR_INT8) + { + printf(" %4d", ((int8_t *)virt_addr)[i * attr->dims[1] + j]); + } + else if (attr->type == RKNN_TENSOR_INT32) + { + printf(" %6d", ((int32_t *)virt_addr)[i * attr->dims[1] + j]); + } + else if (attr->type == RKNN_TENSOR_FLOAT16) + { + printf(" %5.2f", (float)(((float16 *)virt_addr)[i * attr->dims[1] + j])); + } + else if (attr->type == RKNN_TENSOR_FLOAT32) + { + printf(" %5.2f", ((float *)virt_addr)[i * attr->dims[1] + j]); + } + else if (attr->type == RKNN_TENSOR_INT16) + { + printf(" %d", ((int16_t *)virt_addr)[i * attr->dims[1] + j]); + } + else if (attr->type == RKNN_TENSOR_INT4) + { + int index = i * attr->dims[1] + j; + int8_t int4 = get_virt_addr_int4(virt_addr, index); + printf("%d ", int4); + } + } + printf("\n"); + } + printf("\n"); + } + // perf layout + else if (attr->n_dims == 3) + { + for (uint32_t i = 0; i < attr->dims[0]; ++i) + { + for (uint32_t j = 0; j < attr->dims[1]; ++j) + { + for (uint32_t k = 0; k < attr->dims[2]; ++k) + { + void *virt_addr = (void *)((size_t)tensor->virt_addr + tensor->offset); + if (attr->type == RKNN_TENSOR_INT4) + { + int index = (i * attr->dims[1] + j) * attr->dims[2] + k; + int8_t int4 = get_virt_addr_int4(virt_addr, index); + printf("%d ", int4); + } + else if (attr->type == RKNN_TENSOR_INT8) + { + printf(" %4d ", ((int8_t *)virt_addr)[(i * attr->dims[1] + j) * attr->dims[2] + k]); + } + else if (attr->type == RKNN_TENSOR_INT16) + { + printf(" %6d ", ((int16_t *)virt_addr)[(i * attr->dims[1] + j) * attr->dims[2] + k]); + } + else if (attr->type == RKNN_TENSOR_INT32) + { + printf(" %6d ", ((int32_t *)virt_addr)[(i * attr->dims[1] + j) * attr->dims[2] + k]); + } + else if (attr->type == RKNN_TENSOR_FLOAT16) + { + printf(" %5.2f ", (float)(((float16 *)virt_addr)[(i * attr->dims[1] + j) * attr->dims[2] + k])); + } + else if (attr->type == RKNN_TENSOR_FLOAT32) + { + printf(" %5.2f ", ((float *)virt_addr)[(i * attr->dims[1] + j) * attr->dims[2] + k]); + } + } + printf("\n"); + } + printf("\n"); + } + } + // native layout + else if (attr->n_dims == 4) + { + // N / 16 + for (uint32_t n = 0; n < attr->dims[0]; ++n) + { + // K / 32 + for (uint32_t k = 0; k < attr->dims[1]; ++k) + { + // 16 + for (uint32_t nn = 0; nn < attr->dims[2]; ++nn) + { + // 32 + for (uint32_t kk = 0; kk < attr->dims[3]; kk++) + { + void *virt_addr = (void *)((size_t)tensor->virt_addr + tensor->offset); + if (attr->type == RKNN_TENSOR_INT4) + { + int index = ((n * attr->dims[1] + k) * attr->dims[2] + nn) * attr->dims[3] + kk; + int8_t int4 = get_virt_addr_int4(virt_addr, index); + printf("%d ", int4); + } + else if (attr->type == RKNN_TENSOR_INT8) + { + printf(" %4d ", + ((int8_t *)virt_addr)[((n * attr->dims[1] + k) * attr->dims[2] + nn) * attr->dims[3] + kk]); + } + else if (attr->type == RKNN_TENSOR_INT32) + { + printf(" %6d ", + ((int32_t *)virt_addr)[((n * attr->dims[1] + k) * attr->dims[2] + nn) * attr->dims[3] + kk]); + } + else if (attr->type == RKNN_TENSOR_FLOAT16) + { + printf( + " %5.2f ", + (float)(((float16 *)virt_addr)[((n * attr->dims[1] + k) * attr->dims[2] + nn) * attr->dims[3] + kk])); + } + else if (attr->type == RKNN_TENSOR_FLOAT32) + { + printf(" %5.2f ", + ((float *)virt_addr)[((n * attr->dims[1] + k) * attr->dims[2] + nn) * attr->dims[3] + kk]); + } + } + printf("\n"); + } + printf("\n"); + } + printf("\n"); + } + } +} + +static void dump_matmul_tensor(rknn_tensor_mem *tensor, rknn_matmul_tensor_attr *attr) +{ + printf(" %s%s:\n", attr->name, get_dims_string(attr)); + // normal layout + if (attr->n_dims == 2) + { + for (uint32_t i = 0; i < attr->dims[0]; ++i) + { + for (uint32_t j = 0; j < attr->dims[1]; ++j) + { + void *virt_addr = (void *)((size_t)tensor->virt_addr + tensor->offset); + if (attr->type == RKNN_TENSOR_INT8) + { + printf(" %4d", ((int8_t *)virt_addr)[i * attr->dims[1] + j]); + } + else if (attr->type == RKNN_TENSOR_INT32) + { + printf(" %6d", ((int32_t *)virt_addr)[i * attr->dims[1] + j]); + } + else if (attr->type == RKNN_TENSOR_FLOAT16) + { + printf(" %5.2f", (float)(((float16 *)virt_addr)[i * attr->dims[1] + j])); + } + else if (attr->type == RKNN_TENSOR_FLOAT32) + { + printf(" %5.2f", ((float *)virt_addr)[i * attr->dims[1] + j]); + } + else if (attr->type == RKNN_TENSOR_INT16) + { + printf(" %d", ((int16_t *)virt_addr)[i * attr->dims[1] + j]); + } + else if (attr->type == RKNN_TENSOR_INT4) + { + int index = i * attr->dims[1] + j; + int8_t int4 = get_virt_addr_int4(virt_addr, index); + printf("%d ", int4); + } + } + printf("\n"); + } + printf("\n"); + } + // perf layout + else if (attr->n_dims == 3) + { + for (uint32_t i = 0; i < attr->dims[0]; ++i) + { + for (uint32_t j = 0; j < attr->dims[1]; ++j) + { + for (uint32_t k = 0; k < attr->dims[2]; ++k) + { + void *virt_addr = (void *)((size_t)tensor->virt_addr + tensor->offset); + if (attr->type == RKNN_TENSOR_INT4) + { + int index = (i * attr->dims[1] + j) * attr->dims[2] + k; + int8_t int4 = get_virt_addr_int4(virt_addr, index); + printf("%d ", int4); + } + else if (attr->type == RKNN_TENSOR_INT8) + { + printf(" %4d ", ((int8_t *)virt_addr)[(i * attr->dims[1] + j) * attr->dims[2] + k]); + } + else if (attr->type == RKNN_TENSOR_INT16) + { + printf(" %6d ", ((int16_t *)virt_addr)[(i * attr->dims[1] + j) * attr->dims[2] + k]); + } + else if (attr->type == RKNN_TENSOR_INT32) + { + printf(" %6d ", ((int32_t *)virt_addr)[(i * attr->dims[1] + j) * attr->dims[2] + k]); + } + else if (attr->type == RKNN_TENSOR_FLOAT16) + { + printf(" %5.2f ", (float)(((float16 *)virt_addr)[(i * attr->dims[1] + j) * attr->dims[2] + k])); + } + else if (attr->type == RKNN_TENSOR_FLOAT32) + { + printf(" %5.2f ", ((float *)virt_addr)[(i * attr->dims[1] + j) * attr->dims[2] + k]); + } + } + printf("\n"); + } + printf("\n"); + } + } + // native layout + else if (attr->n_dims == 4) + { + // N / 16 + for (uint32_t n = 0; n < attr->dims[0]; ++n) + { + // K / 32 + for (uint32_t k = 0; k < attr->dims[1]; ++k) + { + // 16 + for (uint32_t nn = 0; nn < attr->dims[2]; ++nn) + { + // 32 + for (uint32_t kk = 0; kk < attr->dims[3]; kk++) + { + void *virt_addr = (void *)((size_t)tensor->virt_addr + tensor->offset); + if (attr->type == RKNN_TENSOR_INT4) + { + int index = ((n * attr->dims[1] + k) * attr->dims[2] + nn) * attr->dims[3] + kk; + int8_t int4 = get_virt_addr_int4(virt_addr, index); + printf("%d ", int4); + } + else if (attr->type == RKNN_TENSOR_INT8) + { + printf(" %4d ", + ((int8_t *)virt_addr)[((n * attr->dims[1] + k) * attr->dims[2] + nn) * attr->dims[3] + kk]); + } + else if (attr->type == RKNN_TENSOR_INT32) + { + printf(" %6d ", + ((int32_t *)virt_addr)[((n * attr->dims[1] + k) * attr->dims[2] + nn) * attr->dims[3] + kk]); + } + else if (attr->type == RKNN_TENSOR_FLOAT16) + { + printf( + " %5.2f ", + (float)(((float16 *)virt_addr)[((n * attr->dims[1] + k) * attr->dims[2] + nn) * attr->dims[3] + kk])); + } + else if (attr->type == RKNN_TENSOR_FLOAT32) + { + printf(" %5.2f ", + ((float *)virt_addr)[((n * attr->dims[1] + k) * attr->dims[2] + nn) * attr->dims[3] + kk]); + } + } + printf("\n"); + } + printf("\n"); + } + printf("\n"); + } + } +} + +static std::vector split(const std::string &str, const std::string &pattern) +{ + std::vector res; + if (str == "") + return res; + std::string strs = str + pattern; + size_t pos = strs.find(pattern); + while (pos != strs.npos) + { + std::string temp = strs.substr(0, pos); + res.push_back(temp); + strs = strs.substr(pos + 1, strs.size()); + pos = strs.find(pattern); + } + return res; +} + +static void print_usage(char *argv[]) +{ + printf("Usage:\n%s " + "\n", + argv[0]); + printf("\tmatmul_type = 1: RKNN_FLOAT16_MM_FLOAT16_TO_FLOAT32\n"); + printf("\tmatmul_type = 2: RKNN_INT8_MM_INT8_TO_INT32\n"); + printf("\tmatmul_type = 4: RKNN_FLOAT16_MM_FLOAT16_TO_FLOAT16\n"); + printf("\tmatmul_type = 7: RKNN_FLOAT16_MM_INT4_TO_FLOAT32\n"); + printf("\tmatmul_type = 10: RKNN_INT4_MM_INT4_TO_INT16\n"); + printf("Example: A = [4,64], B = [64,32], int8 matmul test command as followed:\n"); + printf("%s 2 4,64,32\n", argv[0]); +} + +int main(int argc, char *argv[]) +{ + if (argc < 3) + { + print_usage(argv); + return -1; + } + // The quantization parameters used in this demo are set for test and the actual scale and zp are based on customer usage scenarios. + // all scales and zp are the same as those in B per-channel quantization, and the result is the same as the result of per-layer quantization. + const float A_SCALE = 0.2f; + const float B_SCALE = 0.1f; + const float C_SCALE = 0.8f; + const int A_ZP = 0; + const int B_ZP = 0; + const int C_ZP = 0; + + int loop_count = 10; + int print_tensor = 1; + int iommu_domain_id = 0; + + rknn_matmul_type matmul_type = (rknn_matmul_type)atoi(argv[1]); + // check matmul_type value range + if (matmul_type < 1 || matmul_type > 11) + { + fprintf(stderr, "invalid matmul_type: %d, required matmul_type =1~11!\n", matmul_type); + print_usage(argv); + return -1; + } + + std::vector MKN_strs = split(argv[2], ","); + if (MKN_strs.size() != 3) + { + fprintf(stderr, "MKN splited by # must be 3 number!\n"); + print_usage(argv); + return -1; + } + int32_t M = std::atoi(MKN_strs[0].c_str()); + int32_t K = std::atoi(MKN_strs[1].c_str()); + int32_t N = std::atoi(MKN_strs[2].c_str()); + + // request normal or native layout for B + int B_layout = 0; + if (argc > 3) + { + B_layout = atoi(argv[3]); + } + + // request normal or perf layout for A and C + int AC_layout = 0; + if (argc > 4) + { + AC_layout = atoi(argv[4]); + } + + if (argc > 5) + { + loop_count = atoi(argv[5]); + } + + int core_mask = 0; + if (argc > 6) + { + core_mask = atoi(argv[6]); + } + + if (argc > 7) + { + print_tensor = atoi(argv[7]); + } + + if (argc > 8) + { + iommu_domain_id = atoi(argv[8]); + } + + printf("MatMul matmul_type = %s, M = %d, K = %d, N = %d, B_layout = %d, AC_layout = %d, loop_count = %d, core_mask = " + "%d, iommu_domain_id = %u\n", + get_matmul_type_string(matmul_type), M, K, N, B_layout, AC_layout, loop_count, core_mask, iommu_domain_id); + + rknn_matmul_ctx ctx; + + rknn_matmul_info info; + memset(&info, 0, sizeof(rknn_matmul_info)); + info.M = M; + info.K = K; + info.N = N; + info.type = matmul_type; + info.B_layout = B_layout; + info.AC_layout = AC_layout; + info.iommu_domain_id = iommu_domain_id; + + if (matmul_type == RKNN_FLOAT16_MM_INT8_TO_FLOAT32 || matmul_type == RKNN_FLOAT16_MM_INT4_TO_FLOAT32 || + matmul_type == RKNN_INT8_MM_INT4_TO_INT32) + { + info.B_quant_type = RKNN_QUANT_TYPE_PER_CHANNEL_SYM; + } + + if (matmul_type == RKNN_FLOAT16_MM_INT4_TO_FLOAT16 || matmul_type == RKNN_INT8_MM_INT8_TO_FLOAT32) + { + info.B_quant_type = RKNN_QUANT_TYPE_PER_GROUP_SYM; + info.group_size = 32; + } + + rknn_matmul_io_attr io_attr; + memset(&io_attr, 0, sizeof(rknn_matmul_io_attr)); + + int ret = rknn_matmul_create(&ctx, &info, &io_attr); + if (ret < 0) + { + fprintf(stderr, "rknn_matmul_create fail! ret=%d\n", ret); + return -1; + } + + ret = rknn_matmul_set_core_mask(ctx, (rknn_core_mask)core_mask); + if (ret < 0) + { + fprintf(stderr, "rknn_matmul_set_core_mask fail! ret=%d\n", ret); + return -1; + } + + if (matmul_type == RKNN_INT8_MM_INT8_TO_INT8) + { + rknn_quant_params params_a; + memcpy(params_a.name, io_attr.A.name, RKNN_MAX_NAME_LEN); + params_a.scale_len = 1; + params_a.scale = (float *)malloc(params_a.scale_len * sizeof(float)); + params_a.scale[0] = A_SCALE; + params_a.zp_len = 1; + params_a.zp = (int32_t *)malloc(params_a.zp_len * sizeof(int32_t)); + params_a.zp[0] = A_ZP; + rknn_matmul_set_quant_params(ctx, ¶ms_a); + free(params_a.scale); + free(params_a.zp); + + if (info.B_quant_type == RKNN_QUANT_TYPE_PER_CHANNEL_SYM) + { + rknn_quant_params params_b; + memcpy(params_b.name, io_attr.B.name, RKNN_MAX_NAME_LEN); + params_b.scale_len = N; + params_b.scale = (float *)malloc(params_b.scale_len * sizeof(float)); + for (int i = 0; i < params_b.scale_len; i++) + params_b.scale[i] = B_SCALE; + params_b.zp_len = N; + params_b.zp = (int32_t *)malloc(params_b.zp_len * sizeof(int32_t)); + memset(params_b.zp, 0, sizeof(int32_t) * params_b.zp_len); + for (int i = 0; i < params_b.scale_len; i++) + params_b.zp[i] = B_ZP; + rknn_matmul_set_quant_params(ctx, ¶ms_b); + free(params_b.scale); + free(params_b.zp); + } + else + { + rknn_quant_params params_b; + memcpy(params_b.name, io_attr.B.name, RKNN_MAX_NAME_LEN); + params_b.scale_len = 1; + params_b.scale = (float *)malloc(params_b.scale_len * sizeof(float)); + params_b.scale[0] = B_SCALE; + params_b.zp_len = 1; + params_b.zp = (int32_t *)malloc(params_b.zp_len * sizeof(int32_t)); + params_b.zp[0] = B_ZP; + rknn_matmul_set_quant_params(ctx, ¶ms_b); + free(params_b.scale); + free(params_b.zp); + } + + rknn_quant_params params_c; + memcpy(params_c.name, io_attr.C.name, RKNN_MAX_NAME_LEN); + params_c.scale_len = 1; + params_c.scale = (float *)malloc(params_c.scale_len * sizeof(float)); + params_c.scale[0] = C_SCALE; + params_c.zp_len = 1; + params_c.zp = (int32_t *)malloc(params_c.zp_len * sizeof(int32_t)); + params_c.zp[0] = C_ZP; + rknn_matmul_set_quant_params(ctx, ¶ms_c); + free(params_c.scale); + free(params_c.zp); + } + + if (matmul_type == RKNN_FLOAT16_MM_INT8_TO_FLOAT32 || matmul_type == RKNN_FLOAT16_MM_INT4_TO_FLOAT32 || + matmul_type == RKNN_FLOAT16_MM_INT4_TO_FLOAT16 || matmul_type == RKNN_INT8_MM_INT8_TO_FLOAT32) + { + if (info.B_quant_type == RKNN_QUANT_TYPE_PER_CHANNEL_SYM) + { + rknn_quant_params params_b; + memcpy(params_b.name, io_attr.B.name, RKNN_MAX_NAME_LEN); + params_b.scale_len = N; + params_b.scale = (float *)malloc(params_b.scale_len * sizeof(float)); + for (int i = 0; i < params_b.scale_len; i++) + params_b.scale[i] = B_SCALE; + params_b.zp_len = N; + params_b.zp = (int32_t *)malloc(params_b.zp_len * sizeof(int32_t)); + memset(params_b.zp, 0, sizeof(int32_t) * params_b.zp_len); + if (matmul_type == RKNN_FLOAT16_MM_INT4_TO_FLOAT32) + { + for (int i = 0; i < N; i++) + { + params_b.zp[i] = B_ZP; + } + } + rknn_matmul_set_quant_params(ctx, ¶ms_b); + free(params_b.scale); + free(params_b.zp); + } + else if (info.B_quant_type == RKNN_QUANT_TYPE_PER_GROUP_SYM) + { + rknn_quant_params params_b; + memset(¶ms_b, 0, sizeof(rknn_quant_params)); + memcpy(params_b.name, io_attr.B.name, RKNN_MAX_NAME_LEN); + params_b.scale_len = N * K / info.group_size; + params_b.scale = (float *)malloc(params_b.scale_len * sizeof(float)); + for (int i = 0; i < params_b.scale_len; i++) + params_b.scale[i] = 0.0001; + rknn_matmul_set_quant_params(ctx, ¶ms_b); + free(params_b.scale); + } + else + { + rknn_quant_params params_b; + memcpy(params_b.name, io_attr.B.name, RKNN_MAX_NAME_LEN); + params_b.scale_len = 1; + params_b.scale = (float *)malloc(params_b.scale_len * sizeof(float)); + params_b.scale[0] = B_SCALE; + params_b.zp_len = 1; + params_b.zp = (int32_t *)malloc(params_b.zp_len * sizeof(int32_t)); + params_b.zp[0] = B_ZP; + rknn_matmul_set_quant_params(ctx, ¶ms_b); + free(params_b.scale); + free(params_b.zp); + } + } + + void *A_Matrix = nullptr; + void *B_Matrix = nullptr; + void *B_Matrix_ = nullptr; + void *C_Matrix = nullptr; + int A_type_bytes = 1; + int B_type_bytes = 1; + if (info.type == RKNN_INT4_MM_INT4_TO_INT16) + { + A_type_bytes = 1; + B_type_bytes = 1; + A_Matrix = malloc(M * K); + B_Matrix = malloc(K * N); + B_Matrix_ = malloc(K * N); + C_Matrix = malloc(M * N * sizeof(int16_t)); + + // generate int4 A buffer + int8_t *A_int8_Matrix = (int8_t *)A_Matrix; + int8_t *B_int8_Matrix = (int8_t *)B_Matrix; + generate_random_buffer(A_int8_Matrix, M * K, {-8, 7}); + generate_random_buffer(B_int8_Matrix, K * N, {-8, 7}); + } + else if (info.type == RKNN_INT8_MM_INT8_TO_INT32 || info.type == RKNN_INT8_MM_INT8_TO_FLOAT32) + { + A_type_bytes = 1; + B_type_bytes = 1; + A_Matrix = malloc(M * K); + B_Matrix = malloc(K * N); + C_Matrix = malloc(M * N * sizeof(int32_t)); + + // generate int8 A buffer + int8_t *A_int8_Matrix = (int8_t *)A_Matrix; + int8_t *B_int8_Matrix = (int8_t *)B_Matrix; + generate_random_buffer(A_int8_Matrix, M * K, {-128, 127}); + generate_random_buffer(B_int8_Matrix, K * N, {-128, 127}); + } + else if (info.type == RKNN_INT8_MM_INT8_TO_INT8) + { + A_type_bytes = 1; + B_type_bytes = 1; + A_Matrix = malloc(M * K); + B_Matrix = malloc(K * N); + C_Matrix = malloc(M * N * sizeof(int8_t)); + + // generate int8 A buffer + int8_t *A_int8_Matrix = (int8_t *)A_Matrix; + int8_t *B_int8_Matrix = (int8_t *)B_Matrix; + generate_random_buffer(A_int8_Matrix, M * K, {7, 8}); + generate_random_buffer(B_int8_Matrix, K * N, {10, 11}); + } + else if (info.type == RKNN_FLOAT16_MM_FLOAT16_TO_FLOAT16 || info.type == RKNN_FLOAT16_MM_FLOAT16_TO_FLOAT32) + { + A_type_bytes = 2; + B_type_bytes = 2; + A_Matrix = malloc(M * K * A_type_bytes); + B_Matrix = malloc(K * N * B_type_bytes); + C_Matrix = malloc(M * N * sizeof(float)); + + // generate int16 A buffer + float16 *A_float16_Matrix = (float16 *)A_Matrix; + float16 *B_float16_Matrix = (float16 *)B_Matrix; + generate_random_buffer(A_float16_Matrix, M * K, {-1.f, 1.f}); + generate_random_buffer(B_float16_Matrix, K * N, {-1.f, 1.f}); + } + else if (info.type == RKNN_FLOAT16_MM_INT8_TO_FLOAT32 || info.type == RKNN_FLOAT16_MM_INT4_TO_FLOAT32 || + info.type == RKNN_FLOAT16_MM_INT4_TO_FLOAT16) + { + A_type_bytes = 2; + B_type_bytes = 1; + A_Matrix = malloc(M * K * A_type_bytes); + B_Matrix = malloc(K * N); + B_Matrix_ = malloc(K * N); + C_Matrix = malloc(M * N * sizeof(float)); + + float16 *A_float16_Matrix = (float16 *)A_Matrix; + int8_t *B_int8_Matrix = (int8_t *)B_Matrix; + generate_random_buffer(A_float16_Matrix, M * K, {-1.f, 1.f}); + generate_random_buffer(B_int8_Matrix, K * N, {-8, 7}); + } + else if (info.type == RKNN_INT8_MM_INT4_TO_INT32) + { + A_type_bytes = 1; + B_type_bytes = 1; + A_Matrix = malloc(M * K * A_type_bytes); + B_Matrix = malloc(K * N); + B_Matrix_ = malloc(K * N); + C_Matrix = malloc(M * N * sizeof(int32_t)); + + int8_t *A_int8_Matrix = (int8_t *)A_Matrix; + int8_t *B_int8_Matrix = (int8_t *)B_Matrix; + generate_random_buffer(A_int8_Matrix, M * K, {-128, 127}); + generate_random_buffer(B_int8_Matrix, K * N, {-8, 7}); + } + else + { + fprintf(stderr, "unsupported data type: %d\n", info.type); + return -1; + } + printf("input/output matmul tensor attribute:\n"); + dump_matmul_tensor_attr(&io_attr.A); + dump_matmul_tensor_attr(&io_attr.B); + dump_matmul_tensor_attr(&io_attr.C); + + // Create A + rknn_tensor_mem *A = rknn_create_mem(ctx, io_attr.A.size); + if (A == NULL) + { + fprintf(stderr, "rknn_create_mem fail!\n"); + return -1; + } + + // Create B + rknn_tensor_mem *B = rknn_create_mem(ctx, io_attr.B.size); + if (B == NULL) + { + fprintf(stderr, "rknn_create_mem fail!\n"); + return -1; + } + + // A matrix + // normal layout + uint32_t aii = 0; + if (info.AC_layout == 0) + { + if (info.type == RKNN_INT4_MM_INT4_TO_INT16) + { + int size = io_attr.A.dims[1] * io_attr.A.dims[0]; + set_mem_from_int8_to_int4((int8_t *)A->virt_addr, (int8_t *)A_Matrix, size); + } + else + { + memcpy(A->virt_addr, A_Matrix, M * K * A_type_bytes); + } + } + else + { + // perf layout: [K1, M, subK] + int32_t subK = io_attr.A.dims[2]; + if (info.type == RKNN_INT4_MM_INT4_TO_INT16) + { + norm_layout_to_perf_layout(static_cast(A_Matrix), static_cast(A->virt_addr), M, + K, subK, true); + } + else if ((info.type == RKNN_INT8_MM_INT8_TO_INT32) || (info.type == RKNN_INT8_MM_INT8_TO_INT8) || + (info.type == RKNN_INT8_MM_INT4_TO_INT32) || info.type == RKNN_INT8_MM_INT8_TO_FLOAT32) + { + norm_layout_to_perf_layout(static_cast(A_Matrix), static_cast(A->virt_addr), M, + K, subK, false); + } + else + { + norm_layout_to_perf_layout(static_cast(A_Matrix), static_cast(A->virt_addr), + M, K, subK, false); + } + } + + // B matrix + // normal layout + uint32_t bii = 0; + if (info.B_layout == 0) + { + if (info.type == RKNN_FLOAT16_MM_INT4_TO_FLOAT32 || info.type == RKNN_INT8_MM_INT4_TO_INT32 || + info.type == RKNN_INT4_MM_INT4_TO_INT16 || info.type == RKNN_FLOAT16_MM_INT4_TO_FLOAT16) + { + int size = io_attr.B.dims[1] * io_attr.B.dims[0]; + set_mem_from_int8_to_int4((int8_t *)B->virt_addr, (int8_t *)B_Matrix, size); + } + else + { + memcpy(B->virt_addr, B_Matrix, K * N * B_type_bytes); + } + } + else if (info.B_layout == 1) + { + // native layout: [N1, K1, subN, subK] + int32_t subN = io_attr.B.dims[2]; + int32_t subK = io_attr.B.dims[3]; + std::vector input_shape = {(int)(K / subK), subK, int(N / subN), subN}; + std::vector output_shape = {int(N / subN), (int)(K / subK), subN, subK}; + if (info.type == RKNN_FLOAT16_MM_INT4_TO_FLOAT32 || info.type == RKNN_INT8_MM_INT4_TO_INT32 || + info.type == RKNN_INT4_MM_INT4_TO_INT16 || info.type == RKNN_FLOAT16_MM_INT4_TO_FLOAT16) + { + int size = io_attr.B.dims[1] * io_attr.B.dims[0]; + memcpy(B_Matrix_, B_Matrix, K * N * B_type_bytes); + set_mem_from_int8_to_int4((int8_t *)B_Matrix_, (int8_t *)B_Matrix, K * N); + rknn_B_normal_layout_to_native_layout(B_Matrix_, B->virt_addr, K, N, &info); + } + else + { + rknn_B_normal_layout_to_native_layout(B_Matrix, B->virt_addr, K, N, &info); + } + } + else if (info.B_layout == 2) + { + // B_Matrix is [K, N], which needs to be converted to [N, K] + if (info.type == RKNN_FLOAT16_MM_INT4_TO_FLOAT32 || info.type == RKNN_INT8_MM_INT4_TO_INT32 || + info.type == RKNN_INT4_MM_INT4_TO_INT16 || info.type == RKNN_FLOAT16_MM_INT4_TO_FLOAT16) + { + // transpose int4 B + transpose4bit((const int8_t *)B_Matrix, (int8_t *)B->virt_addr, K, N); + } + else if ((info.type == RKNN_INT8_MM_INT8_TO_INT32) || (info.type == RKNN_INT8_MM_INT8_TO_INT8) || + (info.type == RKNN_INT8_MM_INT4_TO_INT32) || info.type == RKNN_INT8_MM_INT8_TO_FLOAT32) + { + transposeB(static_cast(B_Matrix), static_cast(B->virt_addr), (int32_t)K, + (int32_t)N); + } + else + { + transposeB(static_cast(B_Matrix), static_cast(B->virt_addr), K, N); + } + } + + // Create C + rknn_tensor_mem *C = rknn_create_mem(ctx, io_attr.C.size); + if (C == NULL) + { + fprintf(stderr, "rknn_create_mem fail!\n"); + return -1; + } + + // Set A + ret = rknn_matmul_set_io_mem(ctx, A, &io_attr.A); + if (ret < 0) + { + fprintf(stderr, "rknn_matmul_set_io_mem fail! ret=%d\n", ret); + return -1; + } + + // Set B + ret = rknn_matmul_set_io_mem(ctx, B, &io_attr.B); + if (ret < 0) + { + fprintf(stderr, "rknn_matmul_set_io_mem fail! ret=%d\n", ret); + return -1; + } + + // Set C + ret = rknn_matmul_set_io_mem(ctx, C, &io_attr.C); + if (ret < 0) + { + fprintf(stderr, "rknn_matmul_set_io_mem fail! ret=%d\n", ret); + return -1; + } + + // Run + printf("Begin perf ...\n"); + int64_t total_us = 0; + for (int i = 0; i < loop_count; ++i) + { + int64_t start_us = getCurrentTimeUs(); + ret = rknn_matmul_run(ctx); + int64_t elapse_us = getCurrentTimeUs() - start_us; + total_us += elapse_us; + if (ret < 0) + { + printf("rknn_matmul_run error %d\n", ret); + return -1; + } + printf("%4d: Elapse Time = %.2fms, FPS = %.2f\n", i, elapse_us / 1000.f, 1000.f * 1000.f / elapse_us); + } + int64_t average_us = total_us / loop_count; + printf("Average Time = %.2fms, Average FPS = %.2f\n", average_us / 1000.f, 1000.f * 1000.f / average_us); + + // Dump A/B/C tensors + if (print_tensor != 0) + { + printf("matmul tensors:\n"); + dump_matmul_tensor(A, &io_attr.A); + dump_matmul_tensor(B, &io_attr.B); +#if DUMP_REVERSE_WEIGHT + // Dump the two axes of weight in reverse so that it is easy to multiply them by the input one-to-one + dump_matmul_tensor_reverse(B, &io_attr.B); +#endif + dump_matmul_tensor(C, &io_attr.C); + } + + // compare NPU res vs CPU res + + if (info.type == RKNN_INT4_MM_INT4_TO_INT16) + { + int16_t *npu_res_ptr = (int16_t *)C->virt_addr; + if (info.AC_layout == 1) + { + int32_t N_remain = io_attr.C.dims[0]; + int32_t subN = io_attr.C.dims[2]; + perf_layout_to_norm_layout(npu_res_ptr, (int16_t *)C_Matrix, M, N, N_remain, subN); + npu_res_ptr = (int16_t *)C_Matrix; + } + std::vector npu_res(npu_res_ptr, npu_res_ptr + M * N); + + std::vector cpu_res; + cpu_res.reserve(M * N); + cpu_res = matrixMultiply((const int8_t *)A_Matrix, (const int8_t *)B_Matrix, M, K, N); + + if (arraysEqual(cpu_res, npu_res)) + { + printf("INT4_MM_INT4_TO_INT16 matmul result is correct M x K x N is %d %d %d AC_layout is %d B_layout is %d\n", M, + K, N, AC_layout, B_layout); + ret = 0; + } + else + { + printf("INT4_MM_INT4_TO_INT16 matmul result is wrong M x K x N is %d %d %d AC_layout is %d B_layout is %d\n", M, + K, N, AC_layout, B_layout); + ret = -1; + } + } + else if (info.type == RKNN_INT8_MM_INT8_TO_INT32) + { + int32_t *npu_res_ptr = (int32_t *)C->virt_addr; + if (info.AC_layout == 1) + { + int32_t N_remain = io_attr.C.dims[0]; + int32_t subN = io_attr.C.dims[2]; + perf_layout_to_norm_layout(npu_res_ptr, (int32_t *)C_Matrix, M, N, N_remain, subN); + npu_res_ptr = (int32_t *)C_Matrix; + } + std::vector npu_res(npu_res_ptr, npu_res_ptr + M * N); + + std::vector cpu_res; + cpu_res.reserve(M * N); + cpu_res = matrixMultiply((const int8_t *)A_Matrix, (const int8_t *)B_Matrix, M, K, N); + +#if DEBUG_PRINT + // print cpu_res + printf("cpu_res:\n"); + for (uint32_t i = 0; i < M; ++i) + { + for (uint32_t j = 0; j < N; ++j) + { + printf(" %6d", cpu_res[i * N + j]); + } + printf("\n"); + } +#endif + + if (arraysEqual(cpu_res, npu_res)) + { + printf("INT8_MM_INT8_TO_INT32 matmul result is correct M x K x N is %d %d %d AC_layout is %d B_layout is %d\n", M, + K, N, AC_layout, B_layout); + ret = 0; + } + else + { + printf("INT8_MM_INT8_TO_INT32 matmul result is wrong M x K x N is %d %d %d AC_layout is %d B_layout is %d\n", M, + K, N, AC_layout, B_layout); + ret = -1; + } + } + else if (info.type == RKNN_INT8_MM_INT8_TO_FLOAT32) + { + float *npu_res_ptr = (float *)C->virt_addr; + if (info.AC_layout == 1) + { + int32_t N_remain = io_attr.C.dims[0]; + int32_t subN = io_attr.C.dims[2]; + perf_layout_to_norm_layout(npu_res_ptr, (float *)C_Matrix, M, N, N_remain, subN); + npu_res_ptr = (float *)C_Matrix; + } + std::vector npu_res(npu_res_ptr, npu_res_ptr + M * N); + + float input_scale = 1.0; + float weight_scale = 0.0001; + float output_scale = 1.0; + float weight_zp = 0; + std::vector cpu_res; + cpu_res.reserve(M * N); + cpu_res = matrixMultiplyWithHybridQuant( + (const int8_t *)A_Matrix, (const int8_t *)B_Matrix, M, K, N, input_scale, weight_scale, output_scale, weight_zp); + + if (arraysCosineSimilarity(cpu_res, npu_res)) + { + printf("INT8_MM_INT8_TO_FLOAT32 matmul result is correct M x K x N is %d %d %d AC_layout is %d B_layout is %d\n", + M, K, N, AC_layout, B_layout); + ret = 0; + } + else + { + printf("INT8_MM_INT8_TO_FLOAT32 matmul result is wrong M x K x N is %d %d %d AC_layout is %d B_layout is %d\n", M, + K, N, AC_layout, B_layout); + ret = -1; + } + } + else if (info.type == RKNN_FLOAT16_MM_FLOAT16_TO_FLOAT32) + { + float *npu_res_ptr = (float *)C->virt_addr; + if (info.AC_layout == 1) + { + int32_t N_remain = io_attr.C.dims[0]; + int32_t subN = io_attr.C.dims[2]; + perf_layout_to_norm_layout(npu_res_ptr, (float *)C_Matrix, M, N, N_remain, subN); + npu_res_ptr = (float *)C_Matrix; + } + std::vector npu_res(npu_res_ptr, npu_res_ptr + M * N); + + // calculate cpu res + std::vector cpu_res; + cpu_res.reserve(M * N); + cpu_res = matrixMultiply((const float16 *)A_Matrix, (const float16 *)B_Matrix, M, K, N); + + if (arraysCosineSimilarity(cpu_res, npu_res)) + { + printf( + "FLOAT16_MM_FLOAT16_TO_FLOAT32 matmul result is correct M x K x N is %d %d %d AC_layout is %d B_layout is %d\n", + M, K, N, AC_layout, B_layout); + ret = 0; + } + else + { + printf( + "FLOAT16_MM_FLOAT16_TO_FLOAT32 matmul result is wrong M x K x N is %d %d %d AC_layout is %d B_layout is %d\n", + M, K, N, AC_layout, B_layout); + ret = -1; + } + } + else if (info.type == RKNN_FLOAT16_MM_FLOAT16_TO_FLOAT16) + { + float16 *npu_res_ptr = (float16 *)C->virt_addr; + if (info.AC_layout == 1) + { + int32_t N_remain = io_attr.C.dims[0]; + int32_t subN = io_attr.C.dims[2]; + perf_layout_to_norm_layout(npu_res_ptr, (float16 *)C_Matrix, M, N, N_remain, subN); + npu_res_ptr = (float16 *)C_Matrix; + } + std::vector npu_res(npu_res_ptr, npu_res_ptr + M * N); + + // calculate cpu res + std::vector cpu_res; + cpu_res.reserve(M * N); + cpu_res = matrixMultiply((const float16 *)A_Matrix, (const float16 *)B_Matrix, M, K, N); + + if (arraysCosineSimilarity(cpu_res, npu_res)) + { + printf( + "FLOAT16_MM_FLOAT16_TO_FLOAT16 matmul result is correct M x K x N is %d %d %d AC_layout is %d B_layout is %d\n", + M, K, N, AC_layout, B_layout); + ret = 0; + } + else + { + printf( + "FLOAT16_MM_FLOAT16_TO_FLOAT16 matmul result is wrong M x K x N is %d %d %d AC_layout is %d B_layout is %d\n", + M, K, N, AC_layout, B_layout); + ret = -1; + } + } + else if (info.type == RKNN_INT8_MM_INT8_TO_INT8) + { + int8_t *npu_res_ptr = (int8_t *)C->virt_addr; + if (info.AC_layout == 1) + { + int32_t N_remain = io_attr.C.dims[0]; + int32_t subN = io_attr.C.dims[2]; + perf_layout_to_norm_layout(npu_res_ptr, (int8_t *)C_Matrix, M, N, N_remain, subN); + npu_res_ptr = (int8_t *)C_Matrix; + } + std::vector npu_res(npu_res_ptr, npu_res_ptr + M * N); + + float input_scale = A_SCALE; + float weight_scale = B_SCALE; + float output_scale = C_SCALE; + float input_zp = A_ZP; + std::vector cpu_res; + cpu_res.reserve(M * N); + cpu_res = matrixMultiplyWithQuant((const int8_t *)A_Matrix, (const int8_t *)B_Matrix, M, K, N, input_scale, + weight_scale, output_scale, input_zp); + + if (arraysEqual(cpu_res, npu_res)) + { + printf("INT8_MM_INT8_TO_INT8 matmul result is correct M x K x N is %d %d %d AC_layout is %d B_layout is %d\n", M, + K, N, AC_layout, B_layout); + ret = 0; + } + else + { + printf("INT8_MM_INT8_TO_INT8 matmul result is wrong M x K x N is %d %d %d AC_layout is %d B_layout is %d\n", M, K, + N, AC_layout, B_layout); + ret = -1; + } + } + else if (matmul_type == RKNN_FLOAT16_MM_INT8_TO_FLOAT32) + { + float *npu_res_ptr = (float *)C->virt_addr; + if (info.AC_layout == 1) + { + int32_t N_remain = io_attr.C.dims[0]; + int32_t subN = io_attr.C.dims[2]; + perf_layout_to_norm_layout(npu_res_ptr, (float *)C_Matrix, M, N, N_remain, subN); + npu_res_ptr = (float *)C_Matrix; + } + std::vector npu_res(npu_res_ptr, npu_res_ptr + M * N); + + float input_scale = 1; + float weight_scale = B_SCALE; + float output_scale = 1; + float weight_zp = B_ZP; + std::vector cpu_res; + cpu_res.reserve(M * N); + cpu_res = matrixMultiplyWithHybridQuant( + (const float16 *)A_Matrix, (const int8_t *)B_Matrix, M, K, N, input_scale, weight_scale, output_scale, weight_zp); + + if (arraysCosineSimilarity(cpu_res, npu_res)) + { + printf( + "FLOAT16_MM_INT8_TO_FLOAT32 matmul result is correct M x K x N is %d %d %d AC_layout is %d B_layout is %d\n", M, + K, N, AC_layout, B_layout); + ret = 0; + } + else + { + printf("FLOAT16_MM_INT8_TO_FLOAT32 matmul result is wrong M x K x N is %d %d %d AC_layout is %d B_layout is %d\n", + M, K, N, AC_layout, B_layout); + ret = -1; + } + } + else if (matmul_type == RKNN_FLOAT16_MM_INT4_TO_FLOAT16) + { + float16 *npu_res_ptr = (float16 *)C->virt_addr; + if (info.AC_layout == 1) + { + int32_t N_remain = io_attr.C.dims[0]; + int32_t subN = io_attr.C.dims[2]; + perf_layout_to_norm_layout(npu_res_ptr, (float16 *)C_Matrix, M, N, N_remain, subN); + npu_res_ptr = (float16 *)C_Matrix; + } + std::vector npu_res(npu_res_ptr, npu_res_ptr + M * N); + + float input_scale = 1; + float weight_scale = 0.1; + float output_scale = 1; + float weight_zp = 0; + std::vector cpu_res; + cpu_res.reserve(M * N); + cpu_res = matrixMultiplyWithHybridQuant( + (const float16 *)A_Matrix, (const int8_t *)B_Matrix, M, K, N, input_scale, weight_scale, output_scale, weight_zp); + + if (arraysCosineSimilarity(cpu_res, npu_res)) + { + printf( + "FLOAT16_MM_INT4_TO_FLOAT16 matmul result is correct M x K x N is %d %d %d AC_layout is %d B_layout is %d\n", M, + K, N, AC_layout, B_layout); + ret = 0; + } + else + { + printf("FLOAT16_MM_INT4_TO_FLOAT16 matmul result is wrong M x K x N is %d %d %d AC_layout is %d B_layout is %d\n", + M, K, N, AC_layout, B_layout); + ret = -1; + } + } + else if (matmul_type == RKNN_FLOAT16_MM_INT4_TO_FLOAT32) + { + float *npu_res_ptr = (float *)C->virt_addr; + if (info.AC_layout == 1) + { + int32_t N_remain = io_attr.C.dims[0]; + int32_t subN = io_attr.C.dims[2]; + perf_layout_to_norm_layout(npu_res_ptr, (float *)C_Matrix, M, N, N_remain, subN); + npu_res_ptr = (float *)C_Matrix; + } + std::vector npu_res(npu_res_ptr, npu_res_ptr + M * N); + + float input_scale = 1; + float weight_scale = B_SCALE; + float output_scale = 1; + float weight_zp = B_ZP; + std::vector cpu_res; + cpu_res.reserve(M * N); + cpu_res = matrixMultiplyWithHybridQuant( + (const float16 *)A_Matrix, (const int8_t *)B_Matrix, M, K, N, input_scale, weight_scale, output_scale, weight_zp); + + if (arraysCosineSimilarity(cpu_res, npu_res)) + { + printf( + "FLOAT16_MM_INT4_TO_FLOAT32 matmul result is correct M x K x N is %d %d %d AC_layout is %d B_layout is %d\n", M, + K, N, AC_layout, B_layout); + ret = 0; + } + else + { + printf("FLOAT16_MM_INT4_TO_FLOAT32 matmul result is wrong M x K x N is %d %d %d AC_layout is %d B_layout is %d\n", + M, K, N, AC_layout, B_layout); + ret = -1; + } + } + else if (matmul_type == RKNN_INT8_MM_INT4_TO_INT32) + { + int32_t *npu_res_ptr = (int32_t *)C->virt_addr; + if (info.AC_layout == 1) + { + int32_t N_remain = io_attr.C.dims[0]; + int32_t subN = io_attr.C.dims[2]; + perf_layout_to_norm_layout(npu_res_ptr, (int32_t *)C_Matrix, M, N, N_remain, subN); + npu_res_ptr = (int32_t *)C_Matrix; + } + std::vector npu_res(npu_res_ptr, npu_res_ptr + M * N); + + std::vector cpu_res; + cpu_res.reserve(M * N); + cpu_res = matrixMultiply((const int8_t *)A_Matrix, (const int8_t *)B_Matrix, M, K, N); + + if (arraysEqual(cpu_res, npu_res)) + { + printf("INT8_MM_INT4_TO_INT32 matmul result is correct M x K x N is %d %d %d AC_layout is %d B_layout is %d\n", M, + K, N, AC_layout, B_layout); + ret = 0; + } + else + { + printf("INT8_MM_INT4_TO_INT32 matmul result is wrong M x K x N is %d %d %d AC_layout is %d B_layout is %d\n", M, + K, N, AC_layout, B_layout); + ret = -1; + } + } + + // destroy + rknn_destroy_mem(ctx, A); + rknn_destroy_mem(ctx, B); + rknn_destroy_mem(ctx, C); + + rknn_matmul_destroy(ctx); + + // clean data + if (A_Matrix) + { + free(A_Matrix); + } + if (B_Matrix) + { + free(B_Matrix); + } + if (C_Matrix) + { + free(C_Matrix); + } + + return ret; +} \ No newline at end of file diff --git a/rknpu2/examples/rknn_matmul_api_demo/src/rknn_matmul_api_dynshape_demo.cpp b/rknpu2/examples/rknn_matmul_api_demo/src/rknn_matmul_api_dynshape_demo.cpp new file mode 100644 index 0000000000000000000000000000000000000000..d826fa74c1f221a6f297783943a70a83afb35dbd --- /dev/null +++ b/rknpu2/examples/rknn_matmul_api_demo/src/rknn_matmul_api_dynshape_demo.cpp @@ -0,0 +1,1357 @@ +/**************************************************************************** + * + * Copyright (c) 2017 - 2024 by Rockchip Corp. All rights reserved. + * + * The material in this file is confidential and contains trade secrets + * of Rockchip Corporation. This is proprietary information owned by + * Rockchip Corporation. No part of this work may be disclosed, + * reproduced, copied, transmitted, or used in any way for any purpose, + * without the express written permission of Rockchip Corporation. + * + *****************************************************************************/ + +/*------------------------------------------- + Includes +-------------------------------------------*/ +#include "fp16/Float16.h" +#include "matmul_utils.h" +#include "rknn_api.h" +#include "rknn_matmul_api.h" + +#include +#include +#include +#include + +#include +#include +#include +using namespace rknpu2; + +/*------------------------------------------- + Functions +-------------------------------------------*/ +static inline int64_t getCurrentTimeUs() +{ + struct timeval tv; + gettimeofday(&tv, NULL); + return tv.tv_sec * 1000000 + tv.tv_usec; +} + +static void set_mem_from_int8_to_int4(int8_t *dst, int8_t *src, int size) +{ + for (int idx = 0; idx < size; idx += 2) + { + dst[idx / 2] = ((src[idx]) & 0xf) | ((src[idx + 1] << 4) & 0xf0); + } +} + +// 一维矩阵乘法函数 +template +std::vector matrixMultiply(const Ti *A, const Ti *B, int M, int K, int N) +{ + std::vector result(M * N, 0); + + for (int i = 0; i < M; ++i) + { + for (int j = 0; j < N; ++j) + { + float sum = 0; + for (int k = 0; k < K; ++k) + { + sum += (float)A[i * K + k] * (float)B[k * N + j]; + } + result[i * N + j] = sum; + } + } + + return result; +} + +// 一维矩阵混合量化乘法函数 +template +std::vector matrixMultiplyWithHybridQuant(const Ta *A, const Tb *B, int M, int K, int N, float input_scale, + float weight_scale, float output_scale) +{ + std::vector result(M * N, 0); + + for (int i = 0; i < M; ++i) + { + for (int j = 0; j < N; ++j) + { + float sum = 0; + for (int k = 0; k < K; ++k) + { + sum += (float)A[i * K + k] * (float)B[k * N + j]; + } + result[i * N + j] = sum * input_scale * weight_scale / output_scale; + } + } + + return result; +} + +// 一维矩阵乘法函数 +std::vector matrixMultiplyWithQuant(const int8_t *A, const int8_t *B, int M, int K, int N, float input_scale, + float weight_scale, float output_scale) +{ + std::vector result(M * N, 0); + + for (int i = 0; i < M; ++i) + { + for (int j = 0; j < N; ++j) + { + float sum = 0; + for (int k = 0; k < K; ++k) + { + sum += (float)A[i * K + k] * (float)B[k * N + j]; + } + result[i * N + j] = sum * input_scale * weight_scale / output_scale; + } + } + + return result; +} + +static const char *get_dims_string(rknn_matmul_tensor_attr *attr) +{ + if (!attr->n_dims) + { + return "()"; + } + static char dims_str[128]; + memset(&dims_str[0], 0, sizeof(dims_str)); + sprintf(&dims_str[0], "(%d", attr->dims[0]); + for (uint32_t i = 1; i < attr->n_dims; ++i) + { + int idx = strlen(dims_str); + sprintf(&dims_str[idx], ", %d", attr->dims[i]); + } + strcat(&dims_str[0], ")"); + return dims_str; +} + +static void dump_matmul_tensor_attr(rknn_matmul_tensor_attr *attr) +{ + printf(" name=%s, dims=%s, size=%d, type=%s\n", attr->name, get_dims_string(attr), attr->size, + get_type_string(attr->type)); +} + +static int8_t get_virt_addr_int4(void *virt_addr, int index) +{ + int8_t int4 = 0; + if (index % 2 == 0) + { + int4 = (((int8_t *)virt_addr)[index / 2] >> 4) & 0xf; + } + else + { + int4 = (((int8_t *)virt_addr)[index / 2]) & 0xf; + } + if (int4 & 0x8) + { + int4 = int4 | 0xf0; + } + return int4; +} + +static void dump_matmul_tensor(rknn_tensor_mem *tensor, rknn_matmul_tensor_attr *attr) +{ + printf(" %s%s:\n", attr->name, get_dims_string(attr)); + // normal layout + if (attr->n_dims == 2) + { + for (uint32_t i = 0; i < attr->dims[0]; ++i) + { + for (uint32_t j = 0; j < attr->dims[1]; ++j) + { + void *virt_addr = (void *)((size_t)tensor->virt_addr + tensor->offset); + if (attr->type == RKNN_TENSOR_INT8) + { + printf(" %4d", ((int8_t *)virt_addr)[i * attr->dims[1] + j]); + } + else if (attr->type == RKNN_TENSOR_INT32) + { + printf(" %6d", ((int32_t *)virt_addr)[i * attr->dims[1] + j]); + } + else if (attr->type == RKNN_TENSOR_FLOAT16) + { + printf(" %5.2f", (float)(((float16 *)virt_addr)[i * attr->dims[1] + j])); + } + else if (attr->type == RKNN_TENSOR_FLOAT32) + { + printf(" %5.2f", ((float *)virt_addr)[i * attr->dims[1] + j]); + } + else if (attr->type == RKNN_TENSOR_INT16) + { + printf(" %d", ((int16_t *)virt_addr)[i * attr->dims[1] + j]); + } + else if (attr->type == RKNN_TENSOR_INT4) + { + int index = i * attr->dims[1] + j; + int8_t int4 = get_virt_addr_int4(virt_addr, index); + printf("%d ", int4); + } + } + printf("\n"); + } + printf("\n"); + } + // perf layout + else if (attr->n_dims == 3) + { + for (uint32_t i = 0; i < attr->dims[0]; ++i) + { + for (uint32_t j = 0; j < attr->dims[1]; ++j) + { + for (uint32_t k = 0; k < attr->dims[2]; ++k) + { + void *virt_addr = (void *)((size_t)tensor->virt_addr + tensor->offset); + if (attr->type == RKNN_TENSOR_INT4) + { + int index = (i * attr->dims[1] + j) * attr->dims[2] + k; + int8_t int4 = get_virt_addr_int4(virt_addr, index); + printf("%d ", int4); + } + else if (attr->type == RKNN_TENSOR_INT8) + { + printf(" %4d ", ((int8_t *)virt_addr)[(i * attr->dims[1] + j) * attr->dims[2] + k]); + } + else if (attr->type == RKNN_TENSOR_INT16) + { + printf(" %6d ", ((int16_t *)virt_addr)[(i * attr->dims[1] + j) * attr->dims[2] + k]); + } + else if (attr->type == RKNN_TENSOR_INT32) + { + printf(" %6d ", ((int32_t *)virt_addr)[(i * attr->dims[1] + j) * attr->dims[2] + k]); + } + else if (attr->type == RKNN_TENSOR_FLOAT16) + { + printf(" %5.2f ", (float)(((float16 *)virt_addr)[(i * attr->dims[1] + j) * attr->dims[2] + k])); + } + else if (attr->type == RKNN_TENSOR_FLOAT32) + { + printf(" %5.2f ", ((float *)virt_addr)[(i * attr->dims[1] + j) * attr->dims[2] + k]); + } + } + printf("\n"); + } + printf("\n"); + } + } + // native layout + else if (attr->n_dims == 4) + { + // N / 16 + for (uint32_t n = 0; n < attr->dims[0]; ++n) + { + // K / 32 + for (uint32_t k = 0; k < attr->dims[1]; ++k) + { + // 16 + for (uint32_t nn = 0; nn < attr->dims[2]; ++nn) + { + // 32 + for (uint32_t kk = 0; kk < attr->dims[3]; kk++) + { + void *virt_addr = (void *)((size_t)tensor->virt_addr + tensor->offset); + if (attr->type == RKNN_TENSOR_INT4) + { + int index = ((n * attr->dims[1] + k) * attr->dims[2] + nn) * attr->dims[3] + kk; + int8_t int4 = get_virt_addr_int4(virt_addr, index); + printf("%d ", int4); + } + else if (attr->type == RKNN_TENSOR_INT8) + { + printf(" %4d ", + ((int8_t *)virt_addr)[((n * attr->dims[1] + k) * attr->dims[2] + nn) * attr->dims[3] + kk]); + } + else if (attr->type == RKNN_TENSOR_INT32) + { + printf(" %6d ", + ((int32_t *)virt_addr)[((n * attr->dims[1] + k) * attr->dims[2] + nn) * attr->dims[3] + kk]); + } + else if (attr->type == RKNN_TENSOR_FLOAT16) + { + printf( + " %5.2f ", + (float)(((float16 *)virt_addr)[((n * attr->dims[1] + k) * attr->dims[2] + nn) * attr->dims[3] + kk])); + } + else if (attr->type == RKNN_TENSOR_FLOAT32) + { + printf(" %5.2f ", + ((float *)virt_addr)[((n * attr->dims[1] + k) * attr->dims[2] + nn) * attr->dims[3] + kk]); + } + } + printf("\n"); + } + printf("\n"); + } + printf("\n"); + } + } +} + +static std::vector split(const std::string &str, const std::string &pattern) +{ + std::vector res; + if (str == "") + return res; + std::string strs = str + pattern; + size_t pos = strs.find(pattern); + while (pos != strs.npos) + { + std::string temp = strs.substr(0, pos); + res.push_back(temp); + strs = strs.substr(pos + 1, strs.size()); + pos = strs.find(pattern); + } + return res; +} + +static void print_usage(char *argv[]) +{ + printf("Usage:\n%s \n", argv[0]); + printf("\tM_shapes: M shape array, which separeted by ',' \n"); + printf("\tmatmul_type = 1: RKNN_FLOAT16_MM_FLOAT16_TO_FLOAT32\n"); + printf("\tmatmul_type = 2: RKNN_INT8_MM_INT8_TO_INT32\n"); + printf("\tmatmul_type = 4: RKNN_FLOAT16_MM_FLOAT16_TO_FLOAT16\n"); + printf("\tmatmul_type = 7: RKNN_FLOAT16_MM_INT4_TO_FLOAT32\n"); + printf("\tmatmul_type = 10: RKNN_INT4_MM_INT4_TO_INT16\n"); + printf("Example: A = [1,64]#[4,64]#[8,64], B = [64,32], int8 matmul test command as followed:\n"); + printf(" feature+const: %s 2 1,64,32#4,64,32#8,64,32 1 1\n", argv[0]); + printf(" two feature: %s 2 1,64,32#4,64,32#8,64,32 2 1\n", argv[0]); +} + +int main(int argc, char *argv[]) +{ + if (argc < 3) + { + print_usage(argv); + return -1; + } + int loop_count = 10; + int print_tensor = 1; + rknn_matmul_type matmul_type = (rknn_matmul_type)atoi(argv[1]); + // check matmul_type value range + if (matmul_type < 1 || matmul_type > 11) + { + fprintf(stderr, "invalid matmul_type: %d, required matmul_type =1~11!\n", matmul_type); + print_usage(argv); + return -1; + } + + // Parse matmul_shapes, as "M1,K1,N1#M2,K2,N2#..." + std::vector shapes; + std::vector MKNs = split(argv[2], "#"); + + shapes.resize(MKNs.size()); + for (int i = 0; i < MKNs.size(); ++i) + { + std::vector MKN_strs = split(MKNs[i], ","); + if (MKN_strs.size() != 3) + { + fprintf(stderr, "MKN splited by # must be 3 number!\n"); + print_usage(argv); + return -1; + } + shapes[i].M = std::atoi(MKN_strs[0].c_str()); + shapes[i].K = std::atoi(MKN_strs[1].c_str()); + shapes[i].N = std::atoi(MKN_strs[2].c_str()); + } + const int shape_num = shapes.size(); + int maxM = std::max_element(shapes.begin(), shapes.end(), [](const rknn_matmul_shape &a, const rknn_matmul_shape &b) + { return a.M < b.M; }) + ->M; + int maxK = std::max_element(shapes.begin(), shapes.end(), [](const rknn_matmul_shape &a, const rknn_matmul_shape &b) + { return a.K < b.K; }) + ->K; + int maxN = std::max_element(shapes.begin(), shapes.end(), [](const rknn_matmul_shape &a, const rknn_matmul_shape &b) + { return a.N < b.N; }) + ->N; + + // request normal or native layout for B + int B_layout = 0; + if (argc > 3) + { + B_layout = atoi(argv[3]); + } + if (B_layout != 2) + { + // Check B_layout = 0 or 1 when all dynamic_shapes. K and N must be the same + for (int i = 0; i < shape_num; ++i) + { + if (shapes[i].K != shapes[0].K || shapes[i].N != shapes[0].N) + { + fprintf(stderr, "B_layout = 0 or 1, all shapes.K and N must be same!\n"); + print_usage(argv); + return -1; + } + } + } + + // request normal or perf layout for A and C + int AC_layout = 0; + if (argc > 4) + { + AC_layout = atoi(argv[4]); + } + + if (argc > 5) + { + loop_count = atoi(argv[5]); + } + + int core_mask = 0; + if (argc > 6) + { + core_mask = atoi(argv[6]); + } + + if (argc > 7) + { + print_tensor = atoi(argv[7]); + } + + printf("MatMul matmul_type = %s, MKNs = %s, B_layout = %d, AC_layout = %d, loop_count = %d, core_mask = %d\n", + get_matmul_type_string(matmul_type), argv[2], B_layout, AC_layout, loop_count, core_mask); + + rknn_matmul_ctx ctx; + + rknn_matmul_info info; + memset(&info, 0, sizeof(rknn_matmul_info)); + info.M = maxM; // malloc data by max M shape + info.K = maxK; + info.N = maxN; + info.type = matmul_type; + info.B_layout = B_layout; + info.AC_layout = AC_layout; + + if (matmul_type == RKNN_FLOAT16_MM_INT8_TO_FLOAT32 || matmul_type == RKNN_FLOAT16_MM_INT4_TO_FLOAT32 || + matmul_type == RKNN_INT8_MM_INT4_TO_INT32) + { + info.B_quant_type = RKNN_QUANT_TYPE_PER_CHANNEL_SYM; + } + + if (matmul_type == RKNN_FLOAT16_MM_INT4_TO_FLOAT16 || matmul_type == RKNN_FLOAT16_MM_INT4_TO_FLOAT32 || + matmul_type == RKNN_INT8_MM_INT8_TO_FLOAT32) + { + info.B_quant_type = RKNN_QUANT_TYPE_PER_GROUP_SYM; + info.group_size = 32; + } + + rknn_matmul_io_attr io_attr[shape_num]; + memset(io_attr, 0, sizeof(rknn_matmul_io_attr) * shape_num); + + int ret = rknn_matmul_create_dynamic_shape(&ctx, &info, shape_num, shapes.data(), io_attr); + if (ret < 0) + { + fprintf(stderr, "rknn_matmul_create fail! ret=%d\n", ret); + return -1; + } + + ret = rknn_matmul_set_core_mask(ctx, (rknn_core_mask)core_mask); + + printf("support input/output attribute:\n"); + for (int i = 0; i < shape_num; i++) + { + printf("------------------------------- shape[%d] -------------------------------\n", i); + dump_matmul_tensor_attr(&io_attr[i].A); + dump_matmul_tensor_attr(&io_attr[i].B); + dump_matmul_tensor_attr(&io_attr[i].C); + } + printf("------------------------------------------------------------------------\n"); + + // set A/B/C quant params + if (matmul_type == RKNN_INT8_MM_INT8_TO_INT8) + { + rknn_quant_params params_a; + memcpy(params_a.name, io_attr[0].A.name, RKNN_MAX_NAME_LEN); + params_a.scale_len = 1; + params_a.scale = (float *)malloc(params_a.scale_len * sizeof(float)); + params_a.scale[0] = 0.2; + params_a.zp_len = 1; + params_a.zp = (int32_t *)malloc(params_a.zp_len * sizeof(int32_t)); + params_a.zp[0] = 0; + rknn_matmul_set_quant_params(ctx, ¶ms_a); + + rknn_quant_params params_b; + memcpy(params_b.name, io_attr[0].B.name, RKNN_MAX_NAME_LEN); + params_b.scale_len = 1; + params_b.scale = (float *)malloc(params_b.scale_len * sizeof(float)); + params_b.scale[0] = 0.1; + params_b.zp_len = 1; + params_b.zp = (int32_t *)malloc(params_b.zp_len * sizeof(int32_t)); + params_b.zp[0] = 0; + rknn_matmul_set_quant_params(ctx, ¶ms_b); + + rknn_quant_params params_c; + memcpy(params_c.name, io_attr[0].C.name, RKNN_MAX_NAME_LEN); + params_c.scale_len = 1; + params_c.scale = (float *)malloc(params_c.scale_len * sizeof(float)); + params_c.scale[0] = 0.8; + params_c.zp_len = 1; + params_c.zp = (int32_t *)malloc(params_c.zp_len * sizeof(int32_t)); + params_c.zp[0] = 0; + rknn_matmul_set_quant_params(ctx, ¶ms_c); + } + + if (matmul_type == RKNN_INT8_MM_INT4_TO_INT32) + { + rknn_quant_params params_a; + memcpy(params_a.name, io_attr[0].A.name, RKNN_MAX_NAME_LEN); + params_a.scale_len = 1; + params_a.scale = (float *)malloc(params_a.scale_len * sizeof(float)); + params_a.scale[0] = 0.2; + params_a.zp_len = 1; + params_a.zp = (int32_t *)malloc(params_a.zp_len * sizeof(int32_t)); + params_a.zp[0] = 0; + rknn_matmul_set_quant_params(ctx, ¶ms_a); + free(params_a.scale); + free(params_a.zp); + + if (info.B_quant_type == 1) + { + rknn_quant_params params_b; + memcpy(params_b.name, io_attr[0].B.name, RKNN_MAX_NAME_LEN); + params_b.scale_len = maxN; + params_b.scale = (float *)malloc(params_b.scale_len * sizeof(float)); + for (int i = 0; i < params_b.scale_len; i++) + params_b.scale[i] = 0.1; + params_b.zp_len = maxN; + params_b.zp = (int32_t *)malloc(params_b.zp_len * sizeof(int32_t)); + memset(params_b.zp, 0, sizeof(int32_t) * params_b.zp_len); + rknn_matmul_set_quant_params(ctx, ¶ms_b); + free(params_b.scale); + free(params_b.zp); + } + else + { + rknn_quant_params params_b; + memcpy(params_b.name, io_attr[0].B.name, RKNN_MAX_NAME_LEN); + params_b.scale_len = 1; + params_b.scale = (float *)malloc(params_b.scale_len * sizeof(float)); + params_b.scale[0] = 0.1; + params_b.zp_len = 1; + params_b.zp = (int32_t *)malloc(params_b.zp_len * sizeof(int32_t)); + params_b.zp[0] = 0; + rknn_matmul_set_quant_params(ctx, ¶ms_b); + free(params_b.scale); + free(params_b.zp); + } + } + + if (matmul_type == RKNN_FLOAT16_MM_INT8_TO_FLOAT32 || matmul_type == RKNN_FLOAT16_MM_INT4_TO_FLOAT16 || + matmul_type == RKNN_FLOAT16_MM_INT4_TO_FLOAT32 || matmul_type == RKNN_INT8_MM_INT8_TO_FLOAT32) + { + if (info.B_quant_type == RKNN_QUANT_TYPE_PER_CHANNEL_SYM) + { + rknn_quant_params params_b; + memcpy(params_b.name, io_attr[0].B.name, RKNN_MAX_NAME_LEN); + params_b.scale_len = maxN; + params_b.scale = (float *)malloc(params_b.scale_len * sizeof(float)); + for (int i = 0; i < params_b.scale_len; i++) + params_b.scale[i] = 0.1; + params_b.zp_len = maxN; + params_b.zp = (int32_t *)malloc(params_b.zp_len * sizeof(int32_t)); + memset(params_b.zp, 0, sizeof(int32_t) * params_b.zp_len); + rknn_matmul_set_quant_params(ctx, ¶ms_b); + free(params_b.scale); + free(params_b.zp); + } + else if (info.B_quant_type == RKNN_QUANT_TYPE_PER_GROUP_SYM) + { + rknn_quant_params params_b; + memset(¶ms_b, 0, sizeof(rknn_quant_params)); + memcpy(params_b.name, io_attr[0].B.name, RKNN_MAX_NAME_LEN); + params_b.scale_len = maxN * maxK / info.group_size; + params_b.scale = (float *)malloc(params_b.scale_len * sizeof(float)); + for (int i = 0; i < maxK / info.group_size; i++) + for (int j = 0; j < maxN; j++) + params_b.scale[i * maxN + j] = 0.1; + rknn_matmul_set_quant_params(ctx, ¶ms_b); + free(params_b.scale); + } + else + { + rknn_quant_params params_b; + memcpy(params_b.name, io_attr[0].B.name, RKNN_MAX_NAME_LEN); + params_b.scale_len = 1; + params_b.scale = (float *)malloc(params_b.scale_len * sizeof(float)); + params_b.scale[0] = 0.1; + params_b.zp_len = 1; + params_b.zp = (int32_t *)malloc(params_b.zp_len * sizeof(int32_t)); + params_b.zp[0] = 0; + rknn_matmul_set_quant_params(ctx, ¶ms_b); + free(params_b.scale); + free(params_b.zp); + } + } + + // malloc A/B/C buffer + void *A_Matrix = nullptr; + void *B_Matrix = nullptr; + void *B_Matrix_ = nullptr; + void *C_Matrix = nullptr; + int A_type_bytes = 1; + int B_type_bytes = 1; + if (info.type == RKNN_INT4_MM_INT4_TO_INT16) + { + A_type_bytes = 1; + B_type_bytes = 1; + A_Matrix = malloc(maxM * maxK); + B_Matrix = malloc(maxK * maxN); + B_Matrix_ = malloc(maxK * maxN); + C_Matrix = malloc(maxM * maxN * sizeof(int16_t)); + + // generate int4 A buffer + int8_t *A_int8_Matrix = (int8_t *)A_Matrix; + int8_t *B_int8_Matrix = (int8_t *)B_Matrix; + generate_random_buffer(A_int8_Matrix, maxM * maxK, {-8, 7}); + generate_random_buffer(B_int8_Matrix, maxK * maxN, {-8, 7}); + } + else if (info.type == RKNN_INT8_MM_INT8_TO_INT32 || info.type == RKNN_INT8_MM_INT8_TO_FLOAT32) + { + A_type_bytes = 1; + B_type_bytes = 1; + A_Matrix = malloc(maxM * maxK); + B_Matrix = malloc(maxK * maxN); + C_Matrix = malloc(maxM * maxN * sizeof(int32_t)); + + // generate int8 A buffer + int8_t *A_int8_Matrix = (int8_t *)A_Matrix; + int8_t *B_int8_Matrix = (int8_t *)B_Matrix; + generate_random_buffer(A_int8_Matrix, maxM * maxK, {-128, 127}); + generate_random_buffer(B_int8_Matrix, maxK * maxN, {-128, 127}); + } + else if (info.type == RKNN_INT8_MM_INT8_TO_INT8) + { + A_type_bytes = 1; + B_type_bytes = 1; + A_Matrix = malloc(maxM * maxK); + B_Matrix = malloc(maxK * maxN); + C_Matrix = malloc(maxM * maxN * sizeof(int8_t)); + + // generate int8 A buffer + int8_t *A_int8_Matrix = (int8_t *)A_Matrix; + int8_t *B_int8_Matrix = (int8_t *)B_Matrix; + generate_random_buffer(A_int8_Matrix, maxM * maxK, {5, 6}); + generate_random_buffer(B_int8_Matrix, maxK * maxN, {10, 11}); + } + else if (info.type == RKNN_FLOAT16_MM_FLOAT16_TO_FLOAT16 || info.type == RKNN_FLOAT16_MM_FLOAT16_TO_FLOAT32) + { + A_type_bytes = 2; + B_type_bytes = 2; + A_Matrix = malloc(maxM * maxK * A_type_bytes); + B_Matrix = malloc(maxK * maxN * B_type_bytes); + C_Matrix = malloc(maxM * maxN * sizeof(float)); + + // generate int16 A buffer + float16 *A_float16_Matrix = (float16 *)A_Matrix; + float16 *B_float16_Matrix = (float16 *)B_Matrix; + generate_random_buffer(A_float16_Matrix, maxM * maxK, {-1.f, 1.f}); + generate_random_buffer(B_float16_Matrix, maxK * maxN, {-1.f, 1.f}); + } + else if (info.type == RKNN_FLOAT16_MM_INT8_TO_FLOAT32 || info.type == RKNN_FLOAT16_MM_INT4_TO_FLOAT16 || + info.type == RKNN_FLOAT16_MM_INT4_TO_FLOAT32) + { + A_type_bytes = 2; + B_type_bytes = 1; + A_Matrix = malloc(maxM * maxK * A_type_bytes); + B_Matrix = malloc(maxK * maxN); + B_Matrix_ = malloc(maxK * maxN); + C_Matrix = malloc(maxM * maxN * sizeof(float)); + + float16 *A_float16_Matrix = (float16 *)A_Matrix; + int8_t *B_int8_Matrix = (int8_t *)B_Matrix; + generate_random_buffer(A_float16_Matrix, maxM * maxK, {-1.f, 1.f}); + generate_random_buffer(B_int8_Matrix, maxK * maxN, {-8, 7}); + } + else if (info.type == RKNN_INT8_MM_INT4_TO_INT32) + { + A_type_bytes = 1; + B_type_bytes = 1; + A_Matrix = malloc(maxM * maxK * A_type_bytes); + B_Matrix = malloc(maxK * maxN); + B_Matrix_ = malloc(maxK * maxN); + C_Matrix = malloc(maxM * maxN * sizeof(int32_t)); + + int8_t *A_int8_Matrix = (int8_t *)A_Matrix; + int8_t *B_int8_Matrix = (int8_t *)B_Matrix; + generate_random_buffer(A_int8_Matrix, maxM * maxK, {-128, 127}); + generate_random_buffer(B_int8_Matrix, maxK * maxN, {-8, 7}); + } + else + { + fprintf(stderr, "unsupported data type: %d\n", info.type); + return -1; + } + + // Create A + int max_a_size = io_attr[0].A.size; + for (int i = 1; i < shapes.size(); i++) + { + if (io_attr[i].A.size > max_a_size) + { + max_a_size = io_attr[i].A.size; + } + } + rknn_tensor_mem *A = rknn_create_mem(ctx, max_a_size); + if (A == NULL) + { + fprintf(stderr, "rknn_create_mem fail!\n"); + return -1; + } + + // Create B + int max_b_size = io_attr[0].B.size; + for (int i = 1; i < shapes.size(); i++) + { + if (io_attr[i].B.size > max_b_size) + { + max_b_size = io_attr[i].B.size; + } + } + rknn_tensor_mem *B = rknn_create_mem(ctx, max_b_size); + if (B == NULL) + { + fprintf(stderr, "rknn_create_mem fail!\n"); + return -1; + } + + // Create C + int max_c_size = io_attr[0].C.size; + for (int i = 1; i < shapes.size(); i++) + { + if (io_attr[i].C.size > max_c_size) + { + max_c_size = io_attr[i].C.size; + } + } + rknn_tensor_mem *C = rknn_create_mem(ctx, max_c_size); + if (C == NULL) + { + fprintf(stderr, "rknn_create_mem fail!\n"); + return -1; + } + + // random B data + if (info.type == RKNN_INT4_MM_INT4_TO_INT16) + { + // generate int4 A buffer + int8_t *B_int8_Matrix = (int8_t *)B_Matrix; + generate_random_buffer(B_int8_Matrix, maxK * maxN, {-8, 7}); + } + else if (info.type == RKNN_INT8_MM_INT8_TO_INT32 || info.type == RKNN_INT8_MM_INT8_TO_FLOAT32) + { + // generate int8 A buffer + int8_t *B_int8_Matrix = (int8_t *)B_Matrix; + generate_random_buffer(B_int8_Matrix, maxK * maxN, {-128, 127}); + } + else if (info.type == RKNN_INT8_MM_INT8_TO_INT8) + { + // generate int8 A buffer + int8_t *B_int8_Matrix = (int8_t *)B_Matrix; + generate_random_buffer(B_int8_Matrix, maxK * maxN, {10, 11}); + } + else if (info.type == RKNN_FLOAT16_MM_FLOAT16_TO_FLOAT32 || info.type == RKNN_FLOAT16_MM_FLOAT16_TO_FLOAT16) + { + // generate int16 A buffer + float16 *B_float16_Matrix = (float16 *)B_Matrix; + generate_random_buffer(B_float16_Matrix, maxK * maxN, {-1.f, 1.f}); + } + else if (info.type == RKNN_FLOAT16_MM_INT8_TO_FLOAT32 || info.type == RKNN_FLOAT16_MM_INT4_TO_FLOAT16 || + info.type == RKNN_FLOAT16_MM_INT4_TO_FLOAT32) + { + int8_t *B_int8_Matrix = (int8_t *)B_Matrix; + generate_random_buffer(B_int8_Matrix, maxK * maxN, {-8, 7}); + } + else if (info.type == RKNN_INT8_MM_INT4_TO_INT32) + { + int8_t *B_int8_Matrix = (int8_t *)B_Matrix; + generate_random_buffer(B_int8_Matrix, maxK * maxN, {-8, 7}); + } + else + { + fprintf(stderr, "unsupported data type: %d\n", info.type); + return -1; + } + + // B matrix + // normal layout + uint32_t bii = 0; + if (info.B_layout == 0) + { + if (info.type == RKNN_FLOAT16_MM_INT4_TO_FLOAT16 || info.type == RKNN_FLOAT16_MM_INT4_TO_FLOAT32 || + info.type == RKNN_INT8_MM_INT4_TO_INT32 || info.type == RKNN_INT4_MM_INT4_TO_INT16) + { + int size = io_attr[0].B.dims[1] * io_attr[0].B.dims[0]; + set_mem_from_int8_to_int4((int8_t *)B->virt_addr, (int8_t *)B_Matrix, size); + } + else + { + memcpy(B->virt_addr, B_Matrix, maxK * maxN * B_type_bytes); + } + } + else if (info.B_layout == 1) + { + // native layout: [N1, K1, subN, subK] + int32_t subN = io_attr[0].B.dims[2]; + int32_t subK = io_attr[0].B.dims[3]; + std::vector input_shape = {(int)(maxK / subK), subK, int(maxN / subN), subN}; + std::vector output_shape = {int(maxN / subN), (int)(maxK / subK), subN, subK}; + if (info.type == RKNN_FLOAT16_MM_INT4_TO_FLOAT16 || info.type == RKNN_FLOAT16_MM_INT4_TO_FLOAT32 || + info.type == RKNN_INT8_MM_INT4_TO_INT32 || info.type == RKNN_INT4_MM_INT4_TO_INT16) + { + int size = io_attr[0].B.dims[1] * io_attr[0].B.dims[0]; + memcpy(B_Matrix_, B_Matrix, maxK * maxN * B_type_bytes); + set_mem_from_int8_to_int4((int8_t *)B_Matrix_, (int8_t *)B_Matrix, maxK * maxN); + rknn_B_normal_layout_to_native_layout(B_Matrix_, B->virt_addr, maxK, maxN, &info); + } + else + { + rknn_B_normal_layout_to_native_layout(B_Matrix, B->virt_addr, maxK, maxN, &info); + } + } + // rknn_matmul_set_io_mem must call after rknn_matmul_set_dynamic_shape + ret = rknn_matmul_set_dynamic_shape(ctx, &shapes.at(0)); + if (ret != 0) + { + fprintf(stderr, "rknn_matmul_set_dynamic_shapes fail!\n"); + return -1; + } + + // B can not be dynamic, only call set_io_mem once. + ret = rknn_matmul_set_io_mem(ctx, B, &io_attr[0].B); + if (ret < 0) + { + fprintf(stderr, "rknn_matmul_set_io_mem fail! ret=%d\n", ret); + return -1; + } + + for (int s = 0; s < shapes.size(); s++) + { + // set A shape + int M = shapes[s].M; + int K = shapes[s].K; + int N = shapes[s].N; + + ret = rknn_matmul_set_dynamic_shape(ctx, &shapes.at(s)); + if (ret != 0) + { + fprintf(stderr, "rknn_matmul_set_dynamic_shapes fail!\n"); + return -1; + } + + printf("input/output matmul current tensor attribute:\n"); + dump_matmul_tensor_attr(&io_attr[s].A); + dump_matmul_tensor_attr(&io_attr[s].B); + dump_matmul_tensor_attr(&io_attr[s].C); + + // random A data + if (info.type == RKNN_INT4_MM_INT4_TO_INT16) + { + // generate int4 A buffer + int8_t *A_int8_Matrix = (int8_t *)A_Matrix; + generate_random_buffer(A_int8_Matrix, M * K, {-8, 7}); + } + else if (info.type == RKNN_INT8_MM_INT8_TO_INT32 || info.type == RKNN_INT8_MM_INT8_TO_FLOAT32) + { + // generate int8 A buffer + int8_t *A_int8_Matrix = (int8_t *)A_Matrix; + generate_random_buffer(A_int8_Matrix, M * K, {-128, 127}); + } + else if (info.type == RKNN_INT8_MM_INT8_TO_INT8) + { + // generate int8 A buffer + int8_t *A_int8_Matrix = (int8_t *)A_Matrix; + generate_random_buffer(A_int8_Matrix, M * K, {5, 6}); + } + else if (info.type == RKNN_FLOAT16_MM_FLOAT16_TO_FLOAT32 || info.type == RKNN_FLOAT16_MM_FLOAT16_TO_FLOAT16) + { + // generate int16 A buffer + float16 *A_float16_Matrix = (float16 *)A_Matrix; + generate_random_buffer(A_float16_Matrix, M * K, {-1.f, 1.f}); + } + else if (info.type == RKNN_FLOAT16_MM_INT8_TO_FLOAT32 || info.type == RKNN_FLOAT16_MM_INT4_TO_FLOAT16 || + info.type == RKNN_FLOAT16_MM_INT4_TO_FLOAT32) + { + float16 *A_float16_Matrix = (float16 *)A_Matrix; + generate_random_buffer(A_float16_Matrix, M * K, {-1.f, 1.f}); + } + else if (info.type == RKNN_INT8_MM_INT4_TO_INT32) + { + int8_t *A_int8_Matrix = (int8_t *)A_Matrix; + generate_random_buffer(A_int8_Matrix, M * K, {-128, 127}); + } + else + { + fprintf(stderr, "unsupported data type: %d\n", info.type); + return -1; + } + + // A matrix + // normal layout + uint32_t aii = 0; + if (info.AC_layout == 0) + { + if (info.type == RKNN_INT4_MM_INT4_TO_INT16) + { + int size = io_attr[s].A.dims[1] * io_attr[s].A.dims[0]; + set_mem_from_int8_to_int4((int8_t *)A->virt_addr, (int8_t *)A_Matrix, size); + } + else + { + memcpy(A->virt_addr, A_Matrix, M * K * A_type_bytes); + } + } + else + { + // perf layout: [K1, M, subK] + int32_t subK = io_attr[s].A.dims[2]; + if (info.type == RKNN_INT4_MM_INT4_TO_INT16) + { + norm_layout_to_perf_layout(static_cast(A_Matrix), static_cast(A->virt_addr), + M, K, subK, true); + } + else if (info.type == RKNN_INT8_MM_INT8_TO_INT32 || info.type == RKNN_INT8_MM_INT8_TO_FLOAT32) + { + norm_layout_to_perf_layout(static_cast(A_Matrix), static_cast(A->virt_addr), + M, K, subK, false); + } + else + { + norm_layout_to_perf_layout(static_cast(A_Matrix), + static_cast(A->virt_addr), M, K, subK, false); + } + } + + // Set A + ret = rknn_matmul_set_io_mem(ctx, A, &io_attr[s].A); + if (ret < 0) + { + fprintf(stderr, "rknn_matmul_set_io_mem fail! ret=%d\n", ret); + return -1; + } + + if (info.B_layout == RKNN_MM_LAYOUT_TP_NORM) + { + // B_Matrix is [K, maxN], which needs to be converted to [N, K] + if (info.type == RKNN_FLOAT16_MM_INT4_TO_FLOAT16 || info.type == RKNN_FLOAT16_MM_INT4_TO_FLOAT32 || + info.type == RKNN_INT8_MM_INT4_TO_INT32 || info.type == RKNN_INT4_MM_INT4_TO_INT16) + { + // transpose int4 B + transpose4bit((const int8_t *)B_Matrix, (int8_t *)B->virt_addr, K, N); + } + else if ((info.type == RKNN_INT8_MM_INT8_TO_INT32) || (info.type == RKNN_INT8_MM_INT8_TO_INT8) || + (info.type == RKNN_INT8_MM_INT4_TO_INT32) || info.type == RKNN_INT8_MM_INT8_TO_FLOAT32) + { + transposeB(static_cast(B_Matrix), static_cast(B->virt_addr), K, N); + } + else + { + transposeB(static_cast(B_Matrix), static_cast(B->virt_addr), K, N); + } + + // Set B + ret = rknn_matmul_set_io_mem(ctx, B, &io_attr[s].B); + if (ret < 0) + { + fprintf(stderr, "rknn_matmul_set_io_mem fail! ret=%d\n", ret); + return -1; + } + } + + // Set C + ret = rknn_matmul_set_io_mem(ctx, C, &io_attr[s].C); + if (ret < 0) + { + fprintf(stderr, "rknn_matmul_set_io_mem fail! ret=%d\n", ret); + return -1; + } + + // Run + printf("Begin perf ...\n"); + for (int i = 0; i < loop_count; ++i) + { + int64_t start_us = getCurrentTimeUs(); + ret = rknn_matmul_run(ctx); + int64_t elapse_us = getCurrentTimeUs() - start_us; + if (ret < 0) + { + printf("rknn_matmul_run error %d\n", ret); + return -1; + } + printf("%4d: Elapse Time = %.2fms, FPS = %.2f\n", i, elapse_us / 1000.f, 1000.f * 1000.f / elapse_us); + } + + // Dump A/B/C tensors + if (print_tensor != 0) + { + printf("matmul tensors:\n"); + dump_matmul_tensor(A, &io_attr[s].A); + dump_matmul_tensor(B, &io_attr[s].B); +#if DUMP_REVERSE_WEIGHT + // Dump the two axes of weight in reverse so that it is easy to multiply them by the input one-to-one + dump_matmul_tensor_reverse(B, &io_attr.B); +#endif + dump_matmul_tensor(C, &io_attr[s].C); + } + + // compare NPU res vs CPU res + if (info.type == RKNN_INT4_MM_INT4_TO_INT16) + { + int16_t *npu_res_ptr = (int16_t *)C->virt_addr; + if (info.AC_layout == 1) + { + int32_t N_remain = io_attr[s].C.dims[0]; + int32_t subN = io_attr[s].C.dims[2]; + perf_layout_to_norm_layout(npu_res_ptr, (int16_t *)C_Matrix, M, N, N_remain, subN); + npu_res_ptr = (int16_t *)C_Matrix; + } + std::vector npu_res(npu_res_ptr, npu_res_ptr + M * N); + + std::vector cpu_res; + cpu_res.reserve(M * N); + cpu_res = matrixMultiply((const int8_t *)A_Matrix, (const int8_t *)B_Matrix, M, K, N); + + if (arraysEqual(cpu_res, npu_res)) + { + printf("INT4_MM_INT4_TO_INT16 matmul result is correct M x K x N is %d %d %d AC_layout is %d B_layout is %d\n", + M, K, N, AC_layout, B_layout); + ret = 0; + } + else + { + printf("INT4_MM_INT4_TO_INT16 matmul result is wrong M x K x N is %d %d %d AC_layout is %d B_layout is %d\n", M, + K, N, AC_layout, B_layout); + ret = -1; + } + } + else if (info.type == RKNN_INT8_MM_INT8_TO_INT32) + { + int32_t *npu_res_ptr = (int32_t *)C->virt_addr; + // rknn_mem_sync(0, C, RKNN_MEMORY_SYNC_FROM_DEVICE); + if (info.AC_layout == 1) + { + int32_t N_remain = io_attr[s].C.dims[0]; + int32_t subN = io_attr[s].C.dims[2]; + perf_layout_to_norm_layout(npu_res_ptr, (int32_t *)C_Matrix, M, N, N_remain, subN); + npu_res_ptr = (int32_t *)C_Matrix; + } + std::vector npu_res(npu_res_ptr, npu_res_ptr + M * N); + + std::vector cpu_res; + cpu_res.reserve(M * N); + cpu_res = matrixMultiply((const int8_t *)A_Matrix, (const int8_t *)B_Matrix, M, K, N); + + if (arraysEqual(cpu_res, npu_res)) + { + printf("INT8_MM_INT8_TO_INT32 matmul result is correct M x K x N is %d %d %d AC_layout is %d B_layout is %d\n", + M, K, N, AC_layout, B_layout); + ret = 0; + ret = 0; + } + else + { + printf("INT8_MM_INT8_TO_INT32 matmul result is wrong M x K x N is %d %d %d AC_layout is %d B_layout is %d\n", M, + K, N, AC_layout, B_layout); + ret = -1; + } + } + else if (info.type == RKNN_INT8_MM_INT8_TO_FLOAT32) + { + float *npu_res_ptr = (float *)C->virt_addr; + // rknn_mem_sync(0, C, RKNN_MEMORY_SYNC_FROM_DEVICE); + if (info.AC_layout == 1) + { + int32_t N_remain = io_attr[s].C.dims[0]; + int32_t subN = io_attr[s].C.dims[2]; + perf_layout_to_norm_layout(npu_res_ptr, (float *)C_Matrix, M, N, N_remain, subN); + npu_res_ptr = (float *)C_Matrix; + } + std::vector npu_res(npu_res_ptr, npu_res_ptr + M * N); + + float input_scale = 1.0; + float weight_scale = 0.0001; + float output_scale = 1.0; + std::vector cpu_res; + cpu_res.reserve(M * N); + cpu_res = matrixMultiplyWithHybridQuant((const int8_t *)A_Matrix, (const int8_t *)B_Matrix, + M, K, N, input_scale, weight_scale, output_scale); + + if (arraysCosineSimilarity(cpu_res, npu_res)) + { + printf("INT8_MM_INT8_TO_FLOAT32 matmul result is correct M x K x N is %d %d %d AC_layout is %d B_layout is %d\n", + M, K, N, AC_layout, B_layout); + ret = 0; + ret = 0; + } + else + { + printf("INT8_MM_INT8_TO_FLOAT32 matmul result is wrong M x K x N is %d %d %d AC_layout is %d B_layout is %d\n", M, + K, N, AC_layout, B_layout); + ret = -1; + } + } + else if (info.type == RKNN_FLOAT16_MM_FLOAT16_TO_FLOAT32) + { + float *npu_res_ptr = (float *)C->virt_addr; + if (info.AC_layout == 1) + { + int32_t N_remain = io_attr[s].C.dims[0]; + int32_t subN = io_attr[s].C.dims[2]; + perf_layout_to_norm_layout(npu_res_ptr, (float *)C_Matrix, M, N, N_remain, subN); + npu_res_ptr = (float *)C_Matrix; + } + std::vector npu_res(npu_res_ptr, npu_res_ptr + M * N); + + // calculate cpu res + std::vector cpu_res; + cpu_res.reserve(M * N); + cpu_res = matrixMultiply((const float16 *)A_Matrix, (const float16 *)B_Matrix, M, K, N); + + if (arraysCosineSimilarity(cpu_res, npu_res)) + { + printf("FLOAT16_MM_FLOAT16_TO_FLOAT32 matmul result is correct M x K x N is %d %d %d AC_layout is %d B_layout " + "is %d\n", + M, K, N, AC_layout, B_layout); + ret = 0; + ret = 0; + } + else + { + printf("FLOAT16_MM_FLOAT16_TO_FLOAT32 matmul result is wrong M x K x N is %d %d %d AC_layout is %d B_layout " + "is %d\n", + M, K, N, AC_layout, B_layout); + ret = -1; + } + } + else if (info.type == RKNN_FLOAT16_MM_FLOAT16_TO_FLOAT16) + { + float16 *npu_res_ptr = (float16 *)C->virt_addr; + if (info.AC_layout == 1) + { + int32_t N_remain = io_attr[s].C.dims[0]; + int32_t subN = io_attr[s].C.dims[2]; + perf_layout_to_norm_layout(npu_res_ptr, (float16 *)C_Matrix, M, N, N_remain, subN); + npu_res_ptr = (float16 *)C_Matrix; + } + std::vector npu_res(npu_res_ptr, npu_res_ptr + M * N); + + // calculate cpu res + std::vector cpu_res; + cpu_res.reserve(M * N); + cpu_res = matrixMultiply((const float16 *)A_Matrix, (const float16 *)B_Matrix, M, K, N); + + if (arraysCosineSimilarity(cpu_res, npu_res)) + { + printf("FLOAT16_MM_FLOAT16_TO_FLOAT16 matmul result is correct M x K x N is %d %d %d AC_layout is %d B_layout " + "is %d\n", + M, K, N, AC_layout, B_layout); + ret = 0; + } + else + { + printf("FLOAT16_MM_FLOAT16_TO_FLOAT16 matmul result is wrong M x K x N is %d %d %d AC_layout is %d B_layout " + "is %d\n", + M, K, N, AC_layout, B_layout); + ret = -1; + } + } + else if (info.type == RKNN_INT8_MM_INT8_TO_INT8) + { + int8_t *npu_res_ptr = (int8_t *)C->virt_addr; + if (info.AC_layout == 1) + { + int32_t N_remain = io_attr[s].C.dims[0]; + int32_t subN = io_attr[s].C.dims[2]; + perf_layout_to_norm_layout(npu_res_ptr, (int8_t *)C_Matrix, M, N, N_remain, subN); + npu_res_ptr = (int8_t *)C_Matrix; + } + std::vector npu_res(npu_res_ptr, npu_res_ptr + M * N); + + float input_scale = 0.2; + float weight_scale = 0.1; + float output_scale = 0.8; + std::vector cpu_res; + cpu_res.reserve(M * N); + cpu_res = matrixMultiplyWithQuant((const int8_t *)A_Matrix, (const int8_t *)B_Matrix, M, K, N, input_scale, + weight_scale, output_scale); + + if (arraysEqual(cpu_res, npu_res)) + { + printf("INT8_MM_INT8_TO_INT8 matmul result is correct M x K x N is %d %d %d AC_layout is %d B_layout is %d\n", + M, K, N, AC_layout, B_layout); + ret = 0; + } + else + { + printf("INT8_MM_INT8_TO_INT8 matmul result is wrong M x K x N is %d %d %d AC_layout is %d B_layout is %d\n", M, + K, N, AC_layout, B_layout); + ret = -1; + } + } + else if (matmul_type == RKNN_FLOAT16_MM_INT8_TO_FLOAT32) + { + float *npu_res_ptr = (float *)C->virt_addr; + if (info.AC_layout == 1) + { + int32_t N_remain = io_attr[s].C.dims[0]; + int32_t subN = io_attr[s].C.dims[2]; + perf_layout_to_norm_layout(npu_res_ptr, (float *)C_Matrix, M, N, N_remain, subN); + npu_res_ptr = (float *)C_Matrix; + } + std::vector npu_res(npu_res_ptr, npu_res_ptr + M * N); + + float input_scale = 1; + float weight_scale = 0.1; + float output_scale = 1; + std::vector cpu_res; + cpu_res.reserve(M * N); + cpu_res = matrixMultiplyWithHybridQuant((const float16 *)A_Matrix, (const int8_t *)B_Matrix, + M, K, N, input_scale, weight_scale, output_scale); + + if (arraysCosineSimilarity(cpu_res, npu_res)) + { + printf("FLOAT16_MM_INT8_TO_FLOAT32 matmul result is correct M x K x N is %d %d %d AC_layout is %d B_layout " + "is %d\n", + M, K, N, AC_layout, B_layout); + ret = 0; + } + else + { + printf( + "FLOAT16_MM_INT8_TO_FLOAT32 matmul result is wrong M x K x N is %d %d %d AC_layout is %d B_layout is %d\n", M, + K, N, AC_layout, B_layout); + ret = -1; + } + } + else if (matmul_type == RKNN_FLOAT16_MM_INT4_TO_FLOAT16) + { + float16 *npu_res_ptr = (float16 *)C->virt_addr; + if (info.AC_layout == 1) + { + int32_t N_remain = io_attr[s].C.dims[0]; + int32_t subN = io_attr[s].C.dims[2]; + perf_layout_to_norm_layout(npu_res_ptr, (float16 *)C_Matrix, M, N, N_remain, subN); + npu_res_ptr = (float16 *)C_Matrix; + } + std::vector npu_res(npu_res_ptr, npu_res_ptr + M * N); + + float input_scale = 1; + float weight_scale = 0.1; + float output_scale = 1; + std::vector cpu_res; + cpu_res.reserve(M * N); + cpu_res = matrixMultiplyWithHybridQuant((const float16 *)A_Matrix, (const int8_t *)B_Matrix, + M, K, N, input_scale, weight_scale, output_scale); + + if (arraysCosineSimilarity(cpu_res, npu_res)) + { + printf("FLOAT16_MM_INT4_TO_FLOAT16 matmul result is correct M x K x N is %d %d %d AC_layout is %d B_layout " + "is %d\n", + M, K, N, AC_layout, B_layout); + ret = 0; + } + else + { + printf( + "FLOAT16_MM_INT4_TO_FLOAT16 matmul result is wrong M x K x N is %d %d %d AC_layout is %d B_layout is %d\n", M, + K, N, AC_layout, B_layout); + ret = -1; + } + } + else if (matmul_type == RKNN_FLOAT16_MM_INT4_TO_FLOAT32) + { + float *npu_res_ptr = (float *)C->virt_addr; + if (info.AC_layout == 1) + { + int32_t N_remain = io_attr[s].C.dims[0]; + int32_t subN = io_attr[s].C.dims[2]; + perf_layout_to_norm_layout(npu_res_ptr, (float *)C_Matrix, M, N, N_remain, subN); + npu_res_ptr = (float *)C_Matrix; + } + std::vector npu_res(npu_res_ptr, npu_res_ptr + M * N); + + float input_scale = 1; + float weight_scale = 0.1; + float output_scale = 1; + std::vector cpu_res; + cpu_res.reserve(M * N); + cpu_res = matrixMultiplyWithHybridQuant((const float16 *)A_Matrix, (const int8_t *)B_Matrix, + M, K, N, input_scale, weight_scale, output_scale); + + if (arraysCosineSimilarity(cpu_res, npu_res)) + { + printf("FLOAT16_MM_INT4_TO_FLOAT32 matmul result is correct M x K x N is %d %d %d AC_layout is %d B_layout " + "is %d\n", + M, K, N, AC_layout, B_layout); + ret = 0; + } + else + { + printf( + "FLOAT16_MM_INT4_TO_FLOAT32 matmul result is wrong M x K x N is %d %d %d AC_layout is %d B_layout is %d\n", M, + K, N, AC_layout, B_layout); + ret = -1; + } + } + else if (matmul_type == RKNN_INT8_MM_INT4_TO_INT32) + { + int32_t *npu_res_ptr = (int32_t *)C->virt_addr; + if (info.AC_layout == 1) + { + int32_t N_remain = io_attr[s].C.dims[0]; + int32_t subN = io_attr[s].C.dims[2]; + perf_layout_to_norm_layout(npu_res_ptr, (int32_t *)C_Matrix, M, N, N_remain, subN); + npu_res_ptr = (int32_t *)C_Matrix; + } + std::vector npu_res(npu_res_ptr, npu_res_ptr + M * N); + + float input_scale = 0.2; + float weight_scale = 0.1; + float output_scale = 1; + std::vector cpu_res; + cpu_res.reserve(M * N); + cpu_res = matrixMultiplyWithHybridQuant( + (const int8_t *)A_Matrix, (const int8_t *)B_Matrix, M, K, N, input_scale, weight_scale, output_scale); + + if (arraysCosineSimilarity(cpu_res, npu_res)) + { + printf("INT8_MM_INT4_TO_INT32 matmul result is correct M x K x N is %d %d %d AC_layout is %d B_layout is %d\n", + M, K, N, AC_layout, B_layout); + ret = 0; + } + else + { + printf("INT8_MM_INT4_TO_INT32 matmul result is wrong M x K x N is %d %d %d AC_layout is %d B_layout is %d\n", M, + K, N, AC_layout, B_layout); + ret = -1; + } + } + } + + // destroy + rknn_destroy_mem(ctx, A); + rknn_destroy_mem(ctx, B); + rknn_destroy_mem(ctx, C); + + rknn_matmul_destroy(ctx); + + // clean data + if (A_Matrix) + { + free(A_Matrix); + } + if (B_Matrix) + { + free(B_Matrix); + } + if (C_Matrix) + { + free(C_Matrix); + } + + return ret; +} \ No newline at end of file diff --git a/rknpu2/examples/rknn_mobilenet_demo/CMakeLists.txt b/rknpu2/examples/rknn_mobilenet_demo/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..e89e1bc15915fdcfc6c1d88eea228e99b10ac74e --- /dev/null +++ b/rknpu2/examples/rknn_mobilenet_demo/CMakeLists.txt @@ -0,0 +1,55 @@ +cmake_minimum_required(VERSION 3.6) + +project(rknn_mobilenet_demo) + +set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS}") +set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11") + +# rknn api +set(RKNN_API_PATH ${CMAKE_SOURCE_DIR}/../../runtime//${CMAKE_SYSTEM_NAME}/librknn_api) +if (CMAKE_SYSTEM_NAME STREQUAL "Android") + set(RKNN_RT_LIB ${RKNN_API_PATH}/${CMAKE_ANDROID_ARCH_ABI}/librknnrt.so) +else() + if (CMAKE_C_COMPILER MATCHES "aarch64") + set(LIB_ARCH aarch64) + else() + set(LIB_ARCH armhf) + endif() + set(RKNN_RT_LIB ${RKNN_API_PATH}/${LIB_ARCH}/librknnrt.so) +endif() +include_directories(${RKNN_API_PATH}/include) +include_directories(${CMAKE_SOURCE_DIR}/../3rdparty) + +# opencv +if (CMAKE_SYSTEM_NAME STREQUAL "Android") + set(OpenCV_DIR ${CMAKE_SOURCE_DIR}/../3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/abi-${CMAKE_ANDROID_ARCH_ABI}) +else() + if(LIB_ARCH STREQUAL "armhf") + set(OpenCV_DIR ${CMAKE_SOURCE_DIR}/../3rdparty/opencv/opencv-linux-armhf/share/OpenCV) + else() + set(OpenCV_DIR ${CMAKE_SOURCE_DIR}/../3rdparty/opencv/opencv-linux-aarch64/share/OpenCV) + endif() +endif() +find_package(OpenCV REQUIRED) + +set(CMAKE_INSTALL_RPATH "lib") + +add_executable(rknn_mobilenet_demo + src/main.cc +) + +target_link_libraries(rknn_mobilenet_demo + ${RKNN_RT_LIB} + ${OpenCV_LIBS} +) + +# install target and libraries +set(CMAKE_INSTALL_PREFIX ${CMAKE_SOURCE_DIR}/install/rknn_mobilenet_demo_${CMAKE_SYSTEM_NAME}) +install(TARGETS rknn_mobilenet_demo DESTINATION ./) + + +message(STATUS "target_soc = ${TARGET_SOC}") +install(DIRECTORY model/${TARGET_SOC} DESTINATION ./model) +file(GLOB IMAGE_FILES "model/*.jpg") +install(FILES ${IMAGE_FILES} DESTINATION ./model/) +install(PROGRAMS ${RKNN_RT_LIB} DESTINATION lib) diff --git a/rknpu2/examples/rknn_mobilenet_demo/README.md b/rknpu2/examples/rknn_mobilenet_demo/README.md new file mode 100644 index 0000000000000000000000000000000000000000..ce86701ad49e211fdc42bb3dd20fcfc9ad486477 --- /dev/null +++ b/rknpu2/examples/rknn_mobilenet_demo/README.md @@ -0,0 +1,76 @@ +The following represents RK3566_RK3568, RK3562, RK3576 or RK3588. + +# rknn model source description +The rknn model of each platform comes from the examples/tflite/mobilenet_v1 example of RKNN-Toolkit2. + +# Aarch64 Linux Demo + +## Build + +First export `GCC_COMPILER`, for example `export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu`, then execute: + +``` +./build-linux.sh -t -a -b ] + +# such as: +./build-linux.sh -t rk3588 -a aarch64 -b Release +``` + +## *Install* + +Copy install/rknn_mobilenet_demo_Linux to the devices under /userdata/. + +- If you use rockchip's evb board, you can use the following way: + +Connect device and push the program and rknn model to `/userdata` + +``` +adb push install/rknn_mobilenet_demo_Linux /userdata/ +``` + +- If your board has sshd service, you can use scp or other methods to copy the program and rknn model to the board. + +## Run + +``` +adb shell +cd /userdata/rknn_mobilenet_demo_Linux/ +``` + +``` +export LD_LIBRARY_PATH=./lib +./rknn_mobilenet_demo model//mobilenet_v1.rknn model/dog_224x224.jpg +``` + +# Android Demo + +## Build + +First export `ANDROID_NDK_PATH`, for example `export ANDROID_NDK_PATH=~/opts/ndk/android-ndk-r18b`, then execute: + +``` +./build-android.sh -t -a [-b ] + +# sush as: +./build-android.sh -t rk3568 -a arm64-v8a -b Release +``` + +## Install + +connect device and push build output into `/data` + +``` +adb push install/rknn_mobilenet_demo_Android /data/ +``` + +## Run + +``` +adb shell +cd /data/rknn_mobilenet_demo_Android/ +``` + +``` +export LD_LIBRARY_PATH=./lib +./rknn_mobilenet_demo model//mobilenet_v1.rknn model/dog_224x224.jpg +``` diff --git a/rknpu2/examples/rknn_mobilenet_demo/README_CN.md b/rknpu2/examples/rknn_mobilenet_demo/README_CN.md new file mode 100644 index 0000000000000000000000000000000000000000..e58f5736669c505480785bf367a5d8690f6aab87 --- /dev/null +++ b/rknpu2/examples/rknn_mobilenet_demo/README_CN.md @@ -0,0 +1,76 @@ +以下 表示RK3566_RK3568、RK3562、RK3576或RK3588。 + +# rknn模型来源说明 +各个平台的rknn模型来源于RKNN-Toolkit2的的examples/tflite/mobilenet_v1示例。 + +# Aarch64 Linux 示例 +## 编译 + +首先导入GCC_COMPILER，例如`export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu +`，然后执行如下命令： + +``` +./build-linux.sh -t -a -b ] + +# 例如: +./build-linux.sh -t rk3588 -a aarch64 -b Release +``` + +## 安装 + +将 install/rknn_api_demo_Linux 拷贝到设备上。 + +- 如果使用Rockchip的EVB板，可以使用以下命令： + +连接设备并将程序和模型传输到`/userdata` + +``` +adb push install/rknn_mobilenet_demo_Linux /userdata/ +``` + +- 如果你的板子有sshd服务，可以使用scp命令或者其他方式将程序和模型传输到板子上。 + +## 运行 + + +``` +adb shell +cd /userdata/rknn_mobilenet_demo_Linux/ +``` + +``` +export LD_LIBRARY_PATH=./lib +./rknn_mobilenet_demo model//mobilenet_v1.rknn model/dog_224x224.jpg +``` + +# Android 示例 +## 编译 + +首先导入ANDROID_NDK_PATH，例如`export ANDROID_NDK_PATH=~/opts/ndk/android-ndk-r18b`，然后执行如下命令： + +``` +./build-android.sh -t -a [-b ] + +# 例如: +./build-android.sh -t rk3568 -a arm64-v8a -b Release +``` + +## 安装 + +连接设备并将程序和模型传输到`/data` + +``` +adb push install/rknn_mobilenet_demo_Android /data/ +``` + +## 运行 + +``` +adb shell +cd /data/rknn_mobilenet_demo_Android/ +``` + +``` +export LD_LIBRARY_PATH=./lib +./rknn_mobilenet_demo model//mobilenet_v1.rknn model/dog_224x224.jpg +``` diff --git a/rknpu2/examples/rknn_mobilenet_demo/build-android.sh b/rknpu2/examples/rknn_mobilenet_demo/build-android.sh new file mode 100755 index 0000000000000000000000000000000000000000..4778e831eaff14905900f6fea7b8e3ee44147549 --- /dev/null +++ b/rknpu2/examples/rknn_mobilenet_demo/build-android.sh @@ -0,0 +1,112 @@ +#!/bin/bash + +set -e + +if [[ -z ${ANDROID_NDK_PATH} ]];then + echo "Please set ANDROID_NDK_PATH, such as export ANDROID_NDK_PATH=~/opts/ndk/android-ndk-r18b" + echo "NDK Version r18, r19 is recommanded. Other version may cause build failure." + exit +fi + +echo "$0 $@" +while getopts ":t:a:b" opt; do + case $opt in + t) + TARGET_SOC=$OPTARG + ;; + a) + TARGET_ARCH=$OPTARG + ;; + b) + BUILD_TYPE=$OPTARG + ;; + :) + echo "Option -$OPTARG requires an argument." + exit 1 + ;; + ?) + echo "Invalid option: -$OPTARG index:$OPTIND" + ;; + esac +done + +if [ -z ${TARGET_SOC} ] || [ -z ${TARGET_ARCH} ] ; then + echo "$0 -t -a [-b ]" + echo "" + echo " -t : target (rk3566/rk3568/rk3562/rk3576/rk3588)" + echo " -a : arch (arm64-v8a/armeabi-v7a)" + echo " -b : build_type (Debug/Release)" + echo "such as: $0 -t rk3588 -a arm64-v8a -b Release" + echo "" + exit -1 +fi + +# Debug / Release / RelWithDebInfo +if [[ -z ${BUILD_TYPE} ]];then + BUILD_TYPE=Release +fi + +case ${TARGET_SOC} in + rk356x) + TARGET_SOC="RK3566_RK3568" + ;; + rk3566_rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3588) + TARGET_SOC="RK3588" + ;; + rk3576) + TARGET_SOC="RK3576" + ;; + rk3566) + TARGET_SOC="RK3566_RK3568" + ;; + rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3562) + TARGET_SOC="RK3562" + ;; + *) + echo "Invalid target: ${TARGET_SOC}" + echo "Valid target: rk3562,rk3566,rk3568,rk3576,rk3588" + exit -1 + ;; +esac + +TARGET_PLATFORM=${TARGET_SOC}_android +if [[ -n ${TARGET_ARCH} ]];then +TARGET_PLATFORM=${TARGET_PLATFORM}_${TARGET_ARCH} +fi +ROOT_PWD=$( cd "$( dirname $0 )" && cd -P "$( dirname "$SOURCE" )" && pwd ) +BUILD_DIR=${ROOT_PWD}/build/build_${TARGET_PLATFORM}_${BUILD_TYPE} + +echo "===================================" +echo "TARGET_SOC=${TARGET_SOC}" +echo "TARGET_ARCH=${TARGET_ARCH}" +echo "BUILD_TYPE=${BUILD_TYPE}" +echo "BUILD_DIR=${BUILD_DIR}" +echo "ANDROID_NDK_PATH=${ANDROID_NDK_PATH}" +echo "===================================" + +if [[ ! -d "${BUILD_DIR}" ]]; then + mkdir -p ${BUILD_DIR} +fi + + +cd ${BUILD_DIR} +cmake ../.. \ + -DANDROID_TOOLCHAIN=clang \ + -DTARGET_SOC=${TARGET_SOC} \ + -DCMAKE_SYSTEM_NAME=Android \ + -DCMAKE_TOOLCHAIN_FILE=$ANDROID_NDK_PATH/build/cmake/android.toolchain.cmake \ + -DANDROID_ABI=${TARGET_ARCH} \ + -DANDROID_STL=c++_static \ + -DANDROID_PLATFORM=android-24 \ + -DCMAKE_BUILD_TYPE=${BUILD_TYPE} +# make VERBOSE=1 +make -j4 +make install +cd .. + diff --git a/rknpu2/examples/rknn_mobilenet_demo/build-linux.sh b/rknpu2/examples/rknn_mobilenet_demo/build-linux.sh new file mode 100755 index 0000000000000000000000000000000000000000..ae972803b8bda834c3a95588e513ec7af832cbeb --- /dev/null +++ b/rknpu2/examples/rknn_mobilenet_demo/build-linux.sh @@ -0,0 +1,122 @@ +#!/bin/bash + +set -e + +echo "$0 $@" +while getopts ":t:a:b" opt; do + case $opt in + t) + TARGET_SOC=$OPTARG + ;; + a) + TARGET_ARCH=$OPTARG + ;; + b) + BUILD_TYPE=$OPTARG + ;; + :) + echo "Option -$OPTARG requires an argument." + exit 1 + ;; + ?) + echo "Invalid option: -$OPTARG index:$OPTIND" + ;; + esac +done + +if [ -z ${TARGET_SOC} ];then + echo "$0 -t -a [-b ]" + echo "" + echo " -t : target (rk3566/rk3568/rk3562/rk3576/rk3588)" + echo " -a : arch (aarch64/armhf)" + echo " -b : build_type(Debug/Release)" + echo "such as: $0 -t rk3588 -a aarch64 -b Release" + echo "" + exit -1 +fi + +if [[ -z ${GCC_COMPILER} ]];then + echo "Please set GCC_COMPILER for $TARGET_SOC" + echo "such as export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu" + exit +fi +echo "$GCC_COMPILER" +export CC=${GCC_COMPILER}-gcc +export CXX=${GCC_COMPILER}-g++ + +if command -v ${CC} >/dev/null 2>&1; then + : +else + echo "${CC} is not available" + echo "Please set GCC_COMPILER for $TARGET_SOC" + echo "such as export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu" + exit +fi + +# Debug / Release +if [[ -z ${BUILD_TYPE} ]];then + BUILD_TYPE=Release +fi + + +case ${TARGET_SOC} in + rk356x) + TARGET_SOC="RK3566_RK3568" + ;; + rk3566_rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3588) + TARGET_SOC="RK3588" + ;; + rk3576) + TARGET_SOC="RK3576" + ;; + rk3566) + TARGET_SOC="RK3566_RK3568" + ;; + rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3562) + TARGET_SOC="RK3562" + ;; + *) + echo "Invalid target: ${TARGET_SOC}" + echo "Valid target: rk3562,rk3566,rk3568,rk3576,rk3588" + exit -1 + ;; +esac + +TARGET_PLATFORM=${TARGET_SOC}_linux +if [[ -n ${TARGET_ARCH} ]];then +TARGET_PLATFORM=${TARGET_PLATFORM}_${TARGET_ARCH} +fi +ROOT_PWD=$( cd "$( dirname $0 )" && cd -P "$( dirname "$SOURCE" )" && pwd ) +BUILD_DIR=${ROOT_PWD}/build/build_${TARGET_PLATFORM}_${BUILD_TYPE} + + +echo "===================================" +echo "TARGET_SOC=${TARGET_SOC}" +echo "TARGET_ARCH=${TARGET_ARCH}" +echo "BUILD_TYPE=${BUILD_TYPE}" +echo "BUILD_DIR=${BUILD_DIR}" +echo "CC=${CC}" +echo "CXX=${CXX}" +echo "===================================" + +if [[ ! -d "${BUILD_DIR}" ]]; then + mkdir -p ${BUILD_DIR} +fi + + +cd ${BUILD_DIR} +cmake ../.. \ + -DTARGET_SOC=${TARGET_SOC} \ + -DCMAKE_SYSTEM_NAME=Linux \ + -DCMAKE_SYSTEM_PROCESSOR=${TARGET_ARCH} \ + -DCMAKE_BUILD_TYPE=${BUILD_TYPE} \ + -DCMAKE_C_COMPILER=${CC} \ + -DCMAKE_CXX_COMPILER=${CXX} +make -j4 +make install diff --git a/rknpu2/examples/rknn_mobilenet_demo/model/RK3562/mobilenet_v1.rknn b/rknpu2/examples/rknn_mobilenet_demo/model/RK3562/mobilenet_v1.rknn new file mode 100644 index 0000000000000000000000000000000000000000..52670451c3bd0c72733ead26be1cc69dba2db6ca --- /dev/null +++ b/rknpu2/examples/rknn_mobilenet_demo/model/RK3562/mobilenet_v1.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8a88faed454d7f33504079bef9ded09ba127b99e8bf3e28f2196d7fa1d16ae22 +size 4583849 diff --git a/rknpu2/examples/rknn_mobilenet_demo/model/RK3566_RK3568/mobilenet_v1.rknn b/rknpu2/examples/rknn_mobilenet_demo/model/RK3566_RK3568/mobilenet_v1.rknn new file mode 100644 index 0000000000000000000000000000000000000000..acfad8b2fdebd57c03fc742863516205d2ceccd7 --- /dev/null +++ b/rknpu2/examples/rknn_mobilenet_demo/model/RK3566_RK3568/mobilenet_v1.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:92b36515616fc568ddc573f07a345cf28441efe0d24ecc9b98051fe8bcfad980 +size 4470313 diff --git a/rknpu2/examples/rknn_mobilenet_demo/model/RK3576/mobilenet_v1.rknn b/rknpu2/examples/rknn_mobilenet_demo/model/RK3576/mobilenet_v1.rknn new file mode 100644 index 0000000000000000000000000000000000000000..cb229fdfe4eedddf6c82729471461b686a35e83a --- /dev/null +++ b/rknpu2/examples/rknn_mobilenet_demo/model/RK3576/mobilenet_v1.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bc66943ea85ec0dd8a04da22c4276bfc8a4c6fe24f5ea8be7a1e5c3c22c8259d +size 4679017 diff --git a/rknpu2/examples/rknn_mobilenet_demo/model/RK3588/mobilenet_v1.rknn b/rknpu2/examples/rknn_mobilenet_demo/model/RK3588/mobilenet_v1.rknn new file mode 100644 index 0000000000000000000000000000000000000000..e7c8380b1da6ac10257f0080304882c53791c340 --- /dev/null +++ b/rknpu2/examples/rknn_mobilenet_demo/model/RK3588/mobilenet_v1.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:381dae3b7038a98b10f6ec9dcdbb094a49247341856fb294e692ef518218fcfb +size 4693865 diff --git a/rknpu2/examples/rknn_mobilenet_demo/model/cat_224x224.jpg b/rknpu2/examples/rknn_mobilenet_demo/model/cat_224x224.jpg new file mode 100644 index 0000000000000000000000000000000000000000..2159d5cfe3d6cbc01940a804cff0e3bc516af7fb --- /dev/null +++ b/rknpu2/examples/rknn_mobilenet_demo/model/cat_224x224.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0ce3ef6da3424c0744385935a210e4ec8ab3a6ddda205113ab07b77f9c0e812a +size 58701 diff --git a/rknpu2/examples/rknn_mobilenet_demo/model/dog_224x224.jpg b/rknpu2/examples/rknn_mobilenet_demo/model/dog_224x224.jpg new file mode 100644 index 0000000000000000000000000000000000000000..004b0416e23454a12eb06eaba238c7e2d5f2b0fc --- /dev/null +++ b/rknpu2/examples/rknn_mobilenet_demo/model/dog_224x224.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c350299c6283d5f62fecf1f845b6b3be9aafec8dff528ca09a129990f0a584b0 +size 18889 diff --git a/rknpu2/examples/rknn_mobilenet_demo/src/main.cc b/rknpu2/examples/rknn_mobilenet_demo/src/main.cc new file mode 100644 index 0000000000000000000000000000000000000000..e6bc13b88a5f3ff64456f1759865ea95b0cc55b6 --- /dev/null +++ b/rknpu2/examples/rknn_mobilenet_demo/src/main.cc @@ -0,0 +1,240 @@ +// Copyright (c) 2021 by Rockchip Electronics Co., Ltd. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +/*------------------------------------------- + Includes +-------------------------------------------*/ +#include "opencv2/core/core.hpp" +#include "opencv2/imgcodecs.hpp" +#include "opencv2/imgproc.hpp" +#include "rknn_api.h" + +#include +#include +#include +#include + +#include +#include + +using namespace std; +using namespace cv; + +/*------------------------------------------- + Functions +-------------------------------------------*/ + +static void dump_tensor_attr(rknn_tensor_attr* attr) +{ + printf(" index=%d, name=%s, n_dims=%d, dims=[%d, %d, %d, %d], n_elems=%d, size=%d, fmt=%s, type=%s, qnt_type=%s, " + "zp=%d, scale=%f\n", + attr->index, attr->name, attr->n_dims, attr->dims[0], attr->dims[1], attr->dims[2], attr->dims[3], + attr->n_elems, attr->size, get_format_string(attr->fmt), get_type_string(attr->type), + get_qnt_type_string(attr->qnt_type), attr->zp, attr->scale); +} + +static unsigned char* load_model(const char* filename, int* model_size) +{ + FILE* fp = fopen(filename, "rb"); + if (fp == nullptr) { + printf("fopen %s fail!\n", filename); + return NULL; + } + fseek(fp, 0, SEEK_END); + int model_len = ftell(fp); + unsigned char* model = (unsigned char*)malloc(model_len); + fseek(fp, 0, SEEK_SET); + if (model_len != fread(model, 1, model_len, fp)) { + printf("fread %s fail!\n", filename); + free(model); + return NULL; + } + *model_size = model_len; + if (fp) { + fclose(fp); + } + return model; +} + +static int rknn_GetTop(float* pfProb, float* pfMaxProb, uint32_t* pMaxClass, uint32_t outputCount, uint32_t topNum) +{ + uint32_t i, j; + +#define MAX_TOP_NUM 20 + if (topNum > MAX_TOP_NUM) + return 0; + + memset(pfMaxProb, 0, sizeof(float) * topNum); + memset(pMaxClass, 0xff, sizeof(float) * topNum); + + for (j = 0; j < topNum; j++) { + for (i = 0; i < outputCount; i++) { + if ((i == *(pMaxClass + 0)) || (i == *(pMaxClass + 1)) || (i == *(pMaxClass + 2)) || (i == *(pMaxClass + 3)) || + (i == *(pMaxClass + 4))) { + continue; + } + + if (pfProb[i] > *(pfMaxProb + j)) { + *(pfMaxProb + j) = pfProb[i]; + *(pMaxClass + j) = i; + } + } + } + + return 1; +} + +/*------------------------------------------- + Main Function +-------------------------------------------*/ +int main(int argc, char** argv) +{ + const int MODEL_IN_WIDTH = 224; + const int MODEL_IN_HEIGHT = 224; + const int MODEL_IN_CHANNELS = 3; + + rknn_context ctx = 0; + int ret; + int model_len = 0; + unsigned char* model; + + const char* model_path = argv[1]; + const char* img_path = argv[2]; + + if (argc != 3) { + printf("Usage: %s \n", argv[0]); + return -1; + } + + // Load image + cv::Mat orig_img = imread(img_path, cv::IMREAD_COLOR); + if (!orig_img.data) { + printf("cv::imread %s fail!\n", img_path); + return -1; + } + + cv::Mat orig_img_rgb; + //rknn模型说明来源于RKNN-Toolkit2的的examples/tflite/mobilenet_v1示例，输入通道顺序与python代码保持一致 + cv::cvtColor(orig_img, orig_img_rgb, cv::COLOR_BGR2RGB); + + cv::Mat img = orig_img_rgb.clone(); + if (orig_img.cols != MODEL_IN_WIDTH || orig_img.rows != MODEL_IN_HEIGHT) { + printf("resize %d %d to %d %d\n", orig_img.cols, orig_img.rows, MODEL_IN_WIDTH, MODEL_IN_HEIGHT); + cv::resize(orig_img_rgb, img, cv::Size(MODEL_IN_WIDTH, MODEL_IN_HEIGHT), 0, 0, cv::INTER_LINEAR); + } + + // Load RKNN Model + model = load_model(model_path, &model_len); + ret = rknn_init(&ctx, model, model_len, 0, NULL); + if (ret < 0) { + printf("rknn_init fail! ret=%d\n", ret); + return -1; + } + + // Get Model Input Output Info + rknn_input_output_num io_num; + ret = rknn_query(ctx, RKNN_QUERY_IN_OUT_NUM, &io_num, sizeof(io_num)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("model input num: %d, output num: %d\n", io_num.n_input, io_num.n_output); + + printf("input tensors:\n"); + rknn_tensor_attr input_attrs[io_num.n_input]; + memset(input_attrs, 0, sizeof(input_attrs)); + for (int i = 0; i < io_num.n_input; i++) { + input_attrs[i].index = i; + ret = rknn_query(ctx, RKNN_QUERY_INPUT_ATTR, &(input_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + dump_tensor_attr(&(input_attrs[i])); + } + + printf("output tensors:\n"); + rknn_tensor_attr output_attrs[io_num.n_output]; + memset(output_attrs, 0, sizeof(output_attrs)); + for (int i = 0; i < io_num.n_output; i++) { + output_attrs[i].index = i; + ret = rknn_query(ctx, RKNN_QUERY_OUTPUT_ATTR, &(output_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + dump_tensor_attr(&(output_attrs[i])); + } + + // Set Input Data + rknn_input inputs[1]; + memset(inputs, 0, sizeof(inputs)); + inputs[0].index = 0; + inputs[0].type = RKNN_TENSOR_UINT8; + inputs[0].size = img.cols * img.rows * img.channels() * sizeof(uint8_t); + inputs[0].fmt = RKNN_TENSOR_NHWC; + inputs[0].buf = img.data; + + ret = rknn_inputs_set(ctx, io_num.n_input, inputs); + if (ret < 0) { + printf("rknn_input_set fail! ret=%d\n", ret); + return -1; + } + + // Run + printf("rknn_run\n"); + ret = rknn_run(ctx, nullptr); + if (ret < 0) { + printf("rknn_run fail! ret=%d\n", ret); + return -1; + } + + // Get Output + rknn_output outputs[1]; + memset(outputs, 0, sizeof(outputs)); + outputs[0].want_float = 1; + ret = rknn_outputs_get(ctx, 1, outputs, NULL); + if (ret < 0) { + printf("rknn_outputs_get fail! ret=%d\n", ret); + return -1; + } + + // Post Process + for (int i = 0; i < io_num.n_output; i++) { + uint32_t MaxClass[5]; + float fMaxProb[5]; + float* buffer = (float*)outputs[i].buf; + uint32_t sz = outputs[i].size / 4; + + rknn_GetTop(buffer, fMaxProb, MaxClass, sz, 5); + + printf(" --- Top5 ---\n"); + for (int i = 0; i < 5; i++) { + printf("%3d: %8.6f\n", MaxClass[i], fMaxProb[i]); + } + } + + // Release rknn_outputs + rknn_outputs_release(ctx, 1, outputs); + + // Release + if (ctx > 0) + { + rknn_destroy(ctx); + } + if (model) { + free(model); + } + return 0; +} diff --git a/rknpu2/examples/rknn_multiple_input_demo/CMakeLists.txt b/rknpu2/examples/rknn_multiple_input_demo/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..29a1c1568b5954642392650153814f301d98c437 --- /dev/null +++ b/rknpu2/examples/rknn_multiple_input_demo/CMakeLists.txt @@ -0,0 +1,42 @@ +cmake_minimum_required(VERSION 3.6) + +project(rknn_multiple_input_demo) + +set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS}") +set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11") + +# rknn api +set(RKNN_API_PATH ${CMAKE_SOURCE_DIR}/../../runtime//${CMAKE_SYSTEM_NAME}/librknn_api) +if (CMAKE_SYSTEM_NAME STREQUAL "Android") + set(RKNN_RT_LIB ${RKNN_API_PATH}/${CMAKE_ANDROID_ARCH_ABI}/librknnrt.so) +else() + if (CMAKE_C_COMPILER MATCHES "aarch64") + set(LIB_ARCH aarch64) + else() + set(LIB_ARCH armhf) + endif() + set(RKNN_RT_LIB ${RKNN_API_PATH}/${LIB_ARCH}/librknnrt.so) +endif() +include_directories(${RKNN_API_PATH}/include) + +set(CMAKE_INSTALL_RPATH "lib") + +add_executable(rknn_multiple_input_demo + src/main.cc +) + +target_link_libraries(rknn_multiple_input_demo + ${RKNN_RT_LIB} +) + +if(${CMAKE_SYSTEM_NAME} STREQUAL "Android") + target_link_libraries(rknn_multiple_input_demo log) +endif() + +# install target and libraries +set(CMAKE_INSTALL_PREFIX ${CMAKE_SOURCE_DIR}/install/rknn_multiple_input_demo_${CMAKE_SYSTEM_NAME}) +install(TARGETS rknn_multiple_input_demo DESTINATION ./) +install(DIRECTORY model/${TARGET_SOC} DESTINATION ./model) +install(PROGRAMS ${RKNN_RT_LIB} DESTINATION lib) +file(GLOB DATA_FILES "model/*.bin") +install(FILES ${DATA_FILES} DESTINATION ./model/) diff --git a/rknpu2/examples/rknn_multiple_input_demo/README.md b/rknpu2/examples/rknn_multiple_input_demo/README.md new file mode 100644 index 0000000000000000000000000000000000000000..9c51c0015e69af914f5e31a8cb1dd1cc54119033 --- /dev/null +++ b/rknpu2/examples/rknn_multiple_input_demo/README.md @@ -0,0 +1,74 @@ +The following represents RK3566_RK3568,RK3562,RK3576 or RK3588. +# Aarch64 Linux Demo +## Build + +First export `GCC_COMPILER`, for example `export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu`, then execute: + +``` +./build-linux.sh -t -a -b ] + +# such as: +./build-linux.sh -t rk3588 -a aarch64 -b Release +``` + +## Install + +Copy install/rknn_multiple_input_demo_Linux to the devices under `/userdata`. + +- If you use rockchip's evb board, you can use the following way: + +Connect device and push the program and rknn model to `/userdata` + +``` +adb push install/rknn_multiple_input_demo_Linux /userdata/ +``` + +- If your board has sshd service, you can use scp or other methods to copy the program and rknn model to the board. + +## Run + +``` +adb shell +cd /userdata/rknn_multiple_input_demo_Linux/ +``` + + + +``` +export LD_LIBRARY_PATH=./lib +./rknn_multiple_input_demo model//multiple_input_demo.rknn model/input1.bin#model/input2.bin +``` + +# Android Demo +## Build + +First export `ANDROID_NDK_PATH`, for example `export ANDROID_NDK_PATH=~/opts/ndk/android-ndk-r18b`, then execute: + +``` +./build-android.sh -t -a [-b ] + +# sush as: +./build-android.sh -t rk3568 -a arm64-v8a -b Release +``` + +## Install + +connect device and push build output into `/data` + +``` +adb push install/rknn_multiple_input_demo_Android /data/ +``` + +## Run + +``` +adb shell +cd /data/rknn_multiple_input_demo_Android/ +``` + + + +``` +export LD_LIBRARY_PATH=./lib +./rknn_multiple_input_demo model//multiple_input_demo.rknn model/input1.bin#model/input2.bin +``` \ No newline at end of file diff --git a/rknpu2/examples/rknn_multiple_input_demo/README_CN.md b/rknpu2/examples/rknn_multiple_input_demo/README_CN.md new file mode 100644 index 0000000000000000000000000000000000000000..010041dfe478a85f841e8bda8e121f5aa26ab814 --- /dev/null +++ b/rknpu2/examples/rknn_multiple_input_demo/README_CN.md @@ -0,0 +1,76 @@ + +以下 表示RK3566_RK3568、RK3562、RK3576或RK3588。 +# Aarch64 Linux 示例 +## 编译 + +首先导入GCC_COMPILER，例如`export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu +`，然后执行如下命令： + +``` +./build-linux.sh -t -a -b ] + +# 例如: +./build-linux.sh -t rk3588 -a aarch64 -b Release +``` + +## 安装 + +将 install/rknn_api_demo_Linux 拷贝到设备上。 + +- 如果使用Rockchip的EVB板，可以使用以下命令： + +连接设备并将程序和模型传输到`/userdata` + +``` +adb push install/rknn_multiple_input_demo_Linux /userdata/ +``` + +- 如果你的板子有sshd服务，可以使用scp命令或者其他方式将程序和模型传输到板子上。 + +## 运行 + + +``` +adb shell +cd /userdata/rknn_multiple_input_demo_Linux/ +``` + + + +``` +export LD_LIBRARY_PATH=./lib +./rknn_multiple_input_demo model//multiple_input_demo.rknn model/input1.bin#model/input2.bin +``` + +# Android 示例 +## 编译 + +首先导入ANDROID_NDK_PATH，例如`export ANDROID_NDK_PATH=~/opts/ndk/android-ndk-r18b`，然后执行如下命令： + +``` +./build-android.sh -t -a [-b ] + +# 例如: +./build-android.sh -t rk3568 -a arm64-v8a -b Release +``` + +## 安装 + +connect device and push build output into `/data` + +``` +adb push install/rknn_multiple_input_demo_Android /data/ +``` + +## 运行 +``` +adb shell +cd /data/rknn_multiple_input_demo_Android/ +``` + + + +``` +export LD_LIBRARY_PATH=./lib +./rknn_multiple_input_demo model//multiple_input_demo.rknn model/input1.bin#model/input2.bin +``` \ No newline at end of file diff --git a/rknpu2/examples/rknn_multiple_input_demo/build-android.sh b/rknpu2/examples/rknn_multiple_input_demo/build-android.sh new file mode 100755 index 0000000000000000000000000000000000000000..4778e831eaff14905900f6fea7b8e3ee44147549 --- /dev/null +++ b/rknpu2/examples/rknn_multiple_input_demo/build-android.sh @@ -0,0 +1,112 @@ +#!/bin/bash + +set -e + +if [[ -z ${ANDROID_NDK_PATH} ]];then + echo "Please set ANDROID_NDK_PATH, such as export ANDROID_NDK_PATH=~/opts/ndk/android-ndk-r18b" + echo "NDK Version r18, r19 is recommanded. Other version may cause build failure." + exit +fi + +echo "$0 $@" +while getopts ":t:a:b" opt; do + case $opt in + t) + TARGET_SOC=$OPTARG + ;; + a) + TARGET_ARCH=$OPTARG + ;; + b) + BUILD_TYPE=$OPTARG + ;; + :) + echo "Option -$OPTARG requires an argument." + exit 1 + ;; + ?) + echo "Invalid option: -$OPTARG index:$OPTIND" + ;; + esac +done + +if [ -z ${TARGET_SOC} ] || [ -z ${TARGET_ARCH} ] ; then + echo "$0 -t -a [-b ]" + echo "" + echo " -t : target (rk3566/rk3568/rk3562/rk3576/rk3588)" + echo " -a : arch (arm64-v8a/armeabi-v7a)" + echo " -b : build_type (Debug/Release)" + echo "such as: $0 -t rk3588 -a arm64-v8a -b Release" + echo "" + exit -1 +fi + +# Debug / Release / RelWithDebInfo +if [[ -z ${BUILD_TYPE} ]];then + BUILD_TYPE=Release +fi + +case ${TARGET_SOC} in + rk356x) + TARGET_SOC="RK3566_RK3568" + ;; + rk3566_rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3588) + TARGET_SOC="RK3588" + ;; + rk3576) + TARGET_SOC="RK3576" + ;; + rk3566) + TARGET_SOC="RK3566_RK3568" + ;; + rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3562) + TARGET_SOC="RK3562" + ;; + *) + echo "Invalid target: ${TARGET_SOC}" + echo "Valid target: rk3562,rk3566,rk3568,rk3576,rk3588" + exit -1 + ;; +esac + +TARGET_PLATFORM=${TARGET_SOC}_android +if [[ -n ${TARGET_ARCH} ]];then +TARGET_PLATFORM=${TARGET_PLATFORM}_${TARGET_ARCH} +fi +ROOT_PWD=$( cd "$( dirname $0 )" && cd -P "$( dirname "$SOURCE" )" && pwd ) +BUILD_DIR=${ROOT_PWD}/build/build_${TARGET_PLATFORM}_${BUILD_TYPE} + +echo "===================================" +echo "TARGET_SOC=${TARGET_SOC}" +echo "TARGET_ARCH=${TARGET_ARCH}" +echo "BUILD_TYPE=${BUILD_TYPE}" +echo "BUILD_DIR=${BUILD_DIR}" +echo "ANDROID_NDK_PATH=${ANDROID_NDK_PATH}" +echo "===================================" + +if [[ ! -d "${BUILD_DIR}" ]]; then + mkdir -p ${BUILD_DIR} +fi + + +cd ${BUILD_DIR} +cmake ../.. \ + -DANDROID_TOOLCHAIN=clang \ + -DTARGET_SOC=${TARGET_SOC} \ + -DCMAKE_SYSTEM_NAME=Android \ + -DCMAKE_TOOLCHAIN_FILE=$ANDROID_NDK_PATH/build/cmake/android.toolchain.cmake \ + -DANDROID_ABI=${TARGET_ARCH} \ + -DANDROID_STL=c++_static \ + -DANDROID_PLATFORM=android-24 \ + -DCMAKE_BUILD_TYPE=${BUILD_TYPE} +# make VERBOSE=1 +make -j4 +make install +cd .. + diff --git a/rknpu2/examples/rknn_multiple_input_demo/build-linux.sh b/rknpu2/examples/rknn_multiple_input_demo/build-linux.sh new file mode 100755 index 0000000000000000000000000000000000000000..ae972803b8bda834c3a95588e513ec7af832cbeb --- /dev/null +++ b/rknpu2/examples/rknn_multiple_input_demo/build-linux.sh @@ -0,0 +1,122 @@ +#!/bin/bash + +set -e + +echo "$0 $@" +while getopts ":t:a:b" opt; do + case $opt in + t) + TARGET_SOC=$OPTARG + ;; + a) + TARGET_ARCH=$OPTARG + ;; + b) + BUILD_TYPE=$OPTARG + ;; + :) + echo "Option -$OPTARG requires an argument." + exit 1 + ;; + ?) + echo "Invalid option: -$OPTARG index:$OPTIND" + ;; + esac +done + +if [ -z ${TARGET_SOC} ];then + echo "$0 -t -a [-b ]" + echo "" + echo " -t : target (rk3566/rk3568/rk3562/rk3576/rk3588)" + echo " -a : arch (aarch64/armhf)" + echo " -b : build_type(Debug/Release)" + echo "such as: $0 -t rk3588 -a aarch64 -b Release" + echo "" + exit -1 +fi + +if [[ -z ${GCC_COMPILER} ]];then + echo "Please set GCC_COMPILER for $TARGET_SOC" + echo "such as export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu" + exit +fi +echo "$GCC_COMPILER" +export CC=${GCC_COMPILER}-gcc +export CXX=${GCC_COMPILER}-g++ + +if command -v ${CC} >/dev/null 2>&1; then + : +else + echo "${CC} is not available" + echo "Please set GCC_COMPILER for $TARGET_SOC" + echo "such as export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu" + exit +fi + +# Debug / Release +if [[ -z ${BUILD_TYPE} ]];then + BUILD_TYPE=Release +fi + + +case ${TARGET_SOC} in + rk356x) + TARGET_SOC="RK3566_RK3568" + ;; + rk3566_rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3588) + TARGET_SOC="RK3588" + ;; + rk3576) + TARGET_SOC="RK3576" + ;; + rk3566) + TARGET_SOC="RK3566_RK3568" + ;; + rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3562) + TARGET_SOC="RK3562" + ;; + *) + echo "Invalid target: ${TARGET_SOC}" + echo "Valid target: rk3562,rk3566,rk3568,rk3576,rk3588" + exit -1 + ;; +esac + +TARGET_PLATFORM=${TARGET_SOC}_linux +if [[ -n ${TARGET_ARCH} ]];then +TARGET_PLATFORM=${TARGET_PLATFORM}_${TARGET_ARCH} +fi +ROOT_PWD=$( cd "$( dirname $0 )" && cd -P "$( dirname "$SOURCE" )" && pwd ) +BUILD_DIR=${ROOT_PWD}/build/build_${TARGET_PLATFORM}_${BUILD_TYPE} + + +echo "===================================" +echo "TARGET_SOC=${TARGET_SOC}" +echo "TARGET_ARCH=${TARGET_ARCH}" +echo "BUILD_TYPE=${BUILD_TYPE}" +echo "BUILD_DIR=${BUILD_DIR}" +echo "CC=${CC}" +echo "CXX=${CXX}" +echo "===================================" + +if [[ ! -d "${BUILD_DIR}" ]]; then + mkdir -p ${BUILD_DIR} +fi + + +cd ${BUILD_DIR} +cmake ../.. \ + -DTARGET_SOC=${TARGET_SOC} \ + -DCMAKE_SYSTEM_NAME=Linux \ + -DCMAKE_SYSTEM_PROCESSOR=${TARGET_ARCH} \ + -DCMAKE_BUILD_TYPE=${BUILD_TYPE} \ + -DCMAKE_C_COMPILER=${CC} \ + -DCMAKE_CXX_COMPILER=${CXX} +make -j4 +make install diff --git a/rknpu2/examples/rknn_multiple_input_demo/model/RK3562/multiple_input_demo.rknn b/rknpu2/examples/rknn_multiple_input_demo/model/RK3562/multiple_input_demo.rknn new file mode 100644 index 0000000000000000000000000000000000000000..f257cca59cf1492d465457c74fe74b687c502181 --- /dev/null +++ b/rknpu2/examples/rknn_multiple_input_demo/model/RK3562/multiple_input_demo.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8af21e6c468f243252d340741d6a86f982e07c337fc19ddc0eb2c285b43eda5f +size 23500 diff --git a/rknpu2/examples/rknn_multiple_input_demo/model/RK3566_RK3568/multiple_input_demo.rknn b/rknpu2/examples/rknn_multiple_input_demo/model/RK3566_RK3568/multiple_input_demo.rknn new file mode 100644 index 0000000000000000000000000000000000000000..7d273c7356f2f418c6addd81493a79cc0c3f8fa8 --- /dev/null +++ b/rknpu2/examples/rknn_multiple_input_demo/model/RK3566_RK3568/multiple_input_demo.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b1640bd8283573ae564a19bae584578d768482f2cfc54f7ee875983f995451f9 +size 24012 diff --git a/rknpu2/examples/rknn_multiple_input_demo/model/RK3576/multiple_input_demo.rknn b/rknpu2/examples/rknn_multiple_input_demo/model/RK3576/multiple_input_demo.rknn new file mode 100644 index 0000000000000000000000000000000000000000..38050d086c1a9cbbc472ab89bcd69543e394851b --- /dev/null +++ b/rknpu2/examples/rknn_multiple_input_demo/model/RK3576/multiple_input_demo.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7c7b2daa6574582fa52e9c7f86674bd620a4d319a5569a29dc7660c9b61835e8 +size 23372 diff --git a/rknpu2/examples/rknn_multiple_input_demo/model/RK3588/multiple_input_demo.rknn b/rknpu2/examples/rknn_multiple_input_demo/model/RK3588/multiple_input_demo.rknn new file mode 100644 index 0000000000000000000000000000000000000000..f3b053dff7dc9bb14fffeb5e5dee151e7076a3b9 --- /dev/null +++ b/rknpu2/examples/rknn_multiple_input_demo/model/RK3588/multiple_input_demo.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:914429a397514182f8ba6e07323b89143c46bb3b2fbfef6706ad079c526d18e3 +size 31628 diff --git a/rknpu2/examples/rknn_multiple_input_demo/model/input1.bin b/rknpu2/examples/rknn_multiple_input_demo/model/input1.bin new file mode 100644 index 0000000000000000000000000000000000000000..b1357219b40856cb91634473df139a3f23a8271c --- /dev/null +++ b/rknpu2/examples/rknn_multiple_input_demo/model/input1.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0c7992f6e407174bd5a2a7e3fc6107b2de36c7935ab7f95186a240308e03431f +size 16384 diff --git a/rknpu2/examples/rknn_multiple_input_demo/model/input2.bin b/rknpu2/examples/rknn_multiple_input_demo/model/input2.bin new file mode 100644 index 0000000000000000000000000000000000000000..eb34e08ca6e01d0c9c82bee3e5bd02c4eeea388f --- /dev/null +++ b/rknpu2/examples/rknn_multiple_input_demo/model/input2.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:65c4164e1ac2ff985cb9ba2cb186709fe5c114069031767e4b199bce6fd00c05 +size 49152 diff --git a/rknpu2/examples/rknn_multiple_input_demo/onnx_model/image1_nchw.npy b/rknpu2/examples/rknn_multiple_input_demo/onnx_model/image1_nchw.npy new file mode 100755 index 0000000000000000000000000000000000000000..71f4fd2dd295176c0c1ac339793db5e075518482 --- /dev/null +++ b/rknpu2/examples/rknn_multiple_input_demo/onnx_model/image1_nchw.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dd1518a7fa4dfe1c29ef06e7db0cd7a99bb6fcdf677838c10aca65df4bfab446 +size 16512 diff --git a/rknpu2/examples/rknn_multiple_input_demo/onnx_model/image2_nchw.npy b/rknpu2/examples/rknn_multiple_input_demo/onnx_model/image2_nchw.npy new file mode 100755 index 0000000000000000000000000000000000000000..40584c15ca08c2558d71155024f1d434bcf07e9a --- /dev/null +++ b/rknpu2/examples/rknn_multiple_input_demo/onnx_model/image2_nchw.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a35d106d4db6f8af5aff21d397b8cc7386b63a8a216b0d3471cdd92355c55c9b +size 49280 diff --git a/rknpu2/examples/rknn_multiple_input_demo/onnx_model/test.onnx b/rknpu2/examples/rknn_multiple_input_demo/onnx_model/test.onnx new file mode 100644 index 0000000000000000000000000000000000000000..1dc8401298feb8abb7404a588a70d5073002f067 --- /dev/null +++ b/rknpu2/examples/rknn_multiple_input_demo/onnx_model/test.onnx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9ebf3a035d36346ec43a5d9c867d1760eb06ae2bc2dfd62db9117a5a3f77f658 +size 1779 diff --git a/rknpu2/examples/rknn_multiple_input_demo/src/main.cc b/rknpu2/examples/rknn_multiple_input_demo/src/main.cc new file mode 100644 index 0000000000000000000000000000000000000000..d0b52e72c95828a2378c905890274cc199691d53 --- /dev/null +++ b/rknpu2/examples/rknn_multiple_input_demo/src/main.cc @@ -0,0 +1,258 @@ +// Copyright (c) 2021 by Rockchip Electronics Co., Ltd. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +/*------------------------------------------- + Includes +-------------------------------------------*/ +#include "rknn_api.h" + +#include +#include +#include +#include +#include + +#include +#include + +/*------------------------------------------- + Functions +-------------------------------------------*/ + +static int rknn_GetTopN(float* pfProb, float* pfMaxProb, uint32_t* pMaxClass, uint32_t outputCount, uint32_t topNum) +{ + uint32_t i, j; + uint32_t top_count = outputCount > topNum ? topNum : outputCount; + + for (i = 0; i < topNum; ++i) { + pfMaxProb[i] = -FLT_MAX; + pMaxClass[i] = -1; + } + + for (j = 0; j < top_count; j++) { + for (i = 0; i < outputCount; i++) { + if ((i == *(pMaxClass + 0)) || (i == *(pMaxClass + 1)) || (i == *(pMaxClass + 2)) || (i == *(pMaxClass + 3)) || + (i == *(pMaxClass + 4))) { + continue; + } + + if (pfProb[i] > *(pfMaxProb + j)) { + *(pfMaxProb + j) = pfProb[i]; + *(pMaxClass + j) = i; + } + } + } + + return 1; +} + +static void dump_tensor_attr(rknn_tensor_attr* attr) +{ + printf(" index=%d, name=%s, n_dims=%d, dims=[%d, %d, %d, %d], n_elems=%d, size=%d, fmt=%s, type=%s, qnt_type=%s, " + "zp=%d, scale=%f\n", + attr->index, attr->name, attr->n_dims, attr->dims[0], attr->dims[1], attr->dims[2], attr->dims[3], + attr->n_elems, attr->size, get_format_string(attr->fmt), get_type_string(attr->type), + get_qnt_type_string(attr->qnt_type), attr->zp, attr->scale); +} + +static std::vector split(const std::string& str, const std::string& pattern) +{ + std::vector res; + if (str == "") + return res; + std::string strs = str + pattern; + size_t pos = strs.find(pattern); + while (pos != strs.npos) { + std::string temp = strs.substr(0, pos); + res.push_back(temp); + strs = strs.substr(pos + 1, strs.size()); + pos = strs.find(pattern); + } + return res; +} + +/*------------------------------------------- + Main Functions +-------------------------------------------*/ +int main(int argc, char* argv[]) +{ + char* model_path = argv[1]; + char* input_paths = argv[2]; + std::vector input_paths_split = split(input_paths, "#"); + + rknn_context ctx = 0; + + // Load RKNN Model + int ret = rknn_init(&ctx, model_path, 0, 0, NULL); + if (ret < 0) { + printf("rknn_init fail! ret=%d\n", ret); + return -1; + } + + // Get sdk and driver version + rknn_sdk_version sdk_ver; + ret = rknn_query(ctx, RKNN_QUERY_SDK_VERSION, &sdk_ver, sizeof(sdk_ver)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("rknn_api/rknnrt version: %s, driver version: %s\n", sdk_ver.api_version, sdk_ver.drv_version); + + // Get Model Input Output Info + rknn_input_output_num io_num; + ret = rknn_query(ctx, RKNN_QUERY_IN_OUT_NUM, &io_num, sizeof(io_num)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("model input num: %d, output num: %d\n", io_num.n_input, io_num.n_output); + + printf("input tensors:\n"); + rknn_tensor_attr input_attrs[io_num.n_input]; + memset(input_attrs, 0, io_num.n_input * sizeof(rknn_tensor_attr)); + for (uint32_t i = 0; i < io_num.n_input; i++) { + input_attrs[i].index = i; + // query info + ret = rknn_query(ctx, RKNN_QUERY_INPUT_ATTR, &(input_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret < 0) { + printf("rknn_init error! ret=%d\n", ret); + return -1; + } + dump_tensor_attr(&input_attrs[i]); + } + + printf("output tensors:\n"); + rknn_tensor_attr output_attrs[io_num.n_output]; + memset(output_attrs, 0, io_num.n_output * sizeof(rknn_tensor_attr)); + for (uint32_t i = 0; i < io_num.n_output; i++) { + output_attrs[i].index = i; + // query info + ret = rknn_query(ctx, RKNN_QUERY_OUTPUT_ATTR, &(output_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + dump_tensor_attr(&output_attrs[i]); + } + + // Get custom string + rknn_custom_string custom_string; + ret = rknn_query(ctx, RKNN_QUERY_CUSTOM_STRING, &custom_string, sizeof(custom_string)); + if (ret != RKNN_SUCC) { + printf("rknn_query fail! ret=%d\n", ret); + return -1; + } + printf("custom string: %s\n", custom_string.string); + + unsigned char* input_data[io_num.n_input]; + int input_type[io_num.n_input]; + int input_layout[io_num.n_input]; + int input_size[io_num.n_input]; + for (int i = 0; i < io_num.n_input; i++) { + input_data[i] = NULL; + input_type[i] = RKNN_TENSOR_UINT8; + input_layout[i] = RKNN_TENSOR_NHWC; + input_size[i] = input_attrs[i].n_elems * sizeof(uint8_t); + } + + // Load input + if (io_num.n_input != input_paths_split.size()) { + return -1; + } + for (int i = 0; i < io_num.n_input; i++) { + input_data[i] = new unsigned char[input_attrs[i].size]; + + printf("%s\n", input_paths_split[i].c_str()); + + FILE* fp = fopen(input_paths_split[i].c_str(), "rb"); + if (fp == NULL) { + perror("open failed!"); + return -1; + } + + fread(input_data[i], input_attrs[i].size, 1, fp); + fclose(fp); + if (!input_data[i]) { + return -1; + } + } + + rknn_input inputs[io_num.n_input]; + memset(inputs, 0, io_num.n_input * sizeof(rknn_input)); + for (int i = 0; i < io_num.n_input; i++) { + inputs[i].index = i; + inputs[i].pass_through = 0; + inputs[i].type = (rknn_tensor_type)input_type[i]; + inputs[i].fmt = (rknn_tensor_format)input_layout[i]; + inputs[i].buf = input_data[i]; + inputs[i].size = input_size[i]; + } + + // Set input + ret = rknn_inputs_set(ctx, io_num.n_input, inputs); + if (ret < 0) { + printf("rknn_input_set fail! ret=%d\n", ret); + return -1; + } + + ret = rknn_run(ctx, NULL); + if (ret < 0) { + printf("rknn_run fail! ret=%d\n", ret); + return -1; + } + + // Get output + rknn_output outputs[io_num.n_output]; + memset(outputs, 0, io_num.n_output * sizeof(rknn_output)); + for (uint32_t i = 0; i < io_num.n_output; ++i) { + outputs[i].want_float = 1; + outputs[i].index = i; + outputs[i].is_prealloc = 0; + } + + ret = rknn_outputs_get(ctx, io_num.n_output, outputs, NULL); + if (ret < 0) { + printf("rknn_outputs_get fail! ret=%d\n", ret); + return ret; + } + + // Get top 5 + uint32_t topNum = 5; + for (uint32_t i = 0; i < io_num.n_output; i++) { + uint32_t MaxClass[topNum]; + float fMaxProb[topNum]; + float* buffer = (float*)outputs[i].buf; + uint32_t sz = outputs[i].size / sizeof(float); + int top_count = sz > topNum ? topNum : sz; + + rknn_GetTopN(buffer, fMaxProb, MaxClass, sz, topNum); + + printf("---- Top%d ----\n", top_count); + for (int j = 0; j < top_count; j++) { + printf("%8.6f - %d\n", fMaxProb[j], MaxClass[j]); + } + } + + // release outputs + ret = rknn_outputs_release(ctx, io_num.n_output, outputs); + + // destroy + rknn_destroy(ctx); + + for (int i = 0; i < io_num.n_input; i++) { + free(input_data[i]); + } + + return 0; +} diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/.gitignore b/rknpu2/examples/rknn_yolov5_android_apk_demo/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..1fa5b4b2c4e720c769c6191be208a3102ecdb17c --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/.gitignore @@ -0,0 +1,14 @@ +build +~* +*.DS_Store +.gradle +.idea +.externalNativeBuild +gradle.properties +glhelper/glhelper.iml +local.properties +app/.cxx +app/.externalNativeBuild +app/build +app/app.iml +app/release \ No newline at end of file diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/README.md b/rknpu2/examples/rknn_yolov5_android_apk_demo/README.md new file mode 100644 index 0000000000000000000000000000000000000000..8f5da047ee789d86e997482531dfe6a2790dbd69 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/README.md @@ -0,0 +1,79 @@ +# Introduction + - rknn_yolov5_android_apk_demo is the one showing how to deploy yolov5s model on the android apk on RK3566_RK3568, RK3562 or RK3588 + + + +# Pre-requisite + + - Using the andorid studio 2021 1.2.1 or 1.3.1, higher version + + - Yolov5s.rknn is required, which can be converted from yolov5s.onnx, which SiLU activation layer was replace with ReLU. The detail should refer to this link: https://github.com/airockchip/rknn_model_zoo/blob/main/examples/yolov5/README.md + + +# Structure + +## The demo can be split into two parts: + - JAVA: com.rockchip.gpadc.demo: Reading camera inputs, and invoking jni interface to inferece and show the result + + - JNI: The interface for invoking rknnrt to do the inference of model. + + + +# Permission + This demo requires the permission of Camera and Read/Write permission for EXTERNAL_STORAGE + + +# FAQ + +## Compiling error like“files found with path 'lib/arm64-v8a/xxx.so' from inputs:” + +``` +Execution failed for task ':app:mergeDebugNativeLibs'. +> A failure occurred while executing com.android.build.gradle.internal.tasks.MergeNativeLibsTask$MergeNativeLibsTaskWorkAction + > 2 files found with path 'lib/arm64-v8a/librga.so' from inputs: + - rknpu2/examples/rknn_yolov5_android_apk_demo/app/build/intermediates/merged_jni_libs/debug/out/arm64-v8a/librga.so + - rknpu2/examples/rknn_yolov5_android_apk_demo/app/build/intermediates/cxx/Debug/3f40b265/obj/arm64-v8a/librga.so + If you are using jniLibs and CMake IMPORTED targets, see + https://developer.android.com/r/tools/jniLibs-vs-imported-targets + +* Try: +> Run with --info or --debug option to get more log output. +> Run with --scan to get full insights. +``` +Alternatively, +``` +2 files found with path 'lib/arm64-v8a/xxx.so' from inputs: + - rknn_yolov5_android_apk_demo/app/build/intermediates/merged_jni_libs/debug/out/arm64-v8a/librga.so + - rknn_yolov5_android_apk_demo/app/build/intermediates/cxx/Debug/3f40b265/obj/arm64-v8a/librga.so +If you are using jniLibs and CMake IMPORTED targets, see +https://developer.android.com/r/tools/jniLibs-vs-imported-targets +``` +This requires add the "jniLibs.srcDirs = ['libs']" on app/build.gradle file “https://developer.android.com/r/tools/jniLibs-vs-imported-targets” + + + +## During launch app，errors like "E/SurfaceView: Exception configuring surface" + +``` +D/rkyolo: camera facing: 1 +V/rkyolo: Camera Supported Preview Size = 160x90 +V/rkyolo: Camera Supported Preview Size = 320x180 +V/rkyolo: Camera Supported Preview Size = 432x240 +V/rkyolo: Camera Supported Preview Size = 640x360 +V/rkyolo: Camera Supported Preview Size = 800x448 +V/rkyolo: Camera Supported Preview Size = 864x480 +V/rkyolo: Camera Supported Preview Size = 1024x576 +V/rkyolo: Camera Supported Preview Size = 1280x720 +V/rkyolo: Camera Supported Preview Size = 1600x896 +V/rkyolo: Camera Supported Preview Size = 1920x1080 +E/SurfaceView: Exception configuring surface + java.lang.RuntimeException: setParameters failed + at android.hardware.Camera.native_setParameters(Native Method) + at android.hardware.Camera.setParameters(Camera.java:2068) + at com.rockchip.gpadc.demo.MainActivity.setCameraParameters(MainActivity.java:295) + at com.rockchip.gpadc.demo.MainActivity.startCamera(MainActivity.java:245) + at com.rockchip.gpadc.demo.MainActivity.access$300(MainActivity.java:47) + at com.rockchip.gpadc.demo.MainActivity$TSurfaceHolderCallback.surfaceCreated(MainActivity.java:199) +``` + +This is casued by the unsupported camera resolution , modifying CAMERA_PREVIEW_WIDTH in "app/src/main/java/com/rockchip/gpadc/demo/rga/HALDefine.java" for supported resolution. \ No newline at end of file diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/README_CN.md b/rknpu2/examples/rknn_yolov5_android_apk_demo/README_CN.md new file mode 100644 index 0000000000000000000000000000000000000000..37945e451ec8211703d7bba60dee26bd3a33fb19 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/README_CN.md @@ -0,0 +1,76 @@ +# 简介 + - rknn_yolov5_android_apk_demo 是RK3566_RK3568, RK3562或RK3588上如何调用NPU的demo，该demo的基础模型是yolov5s + + + +# 使用说明 + + - 可以使用android studio 2021.2.1 （Windows）或者android studio 2021.3.1（Linux/macOS） 编译该工程 + - yolov5s.rknn是使用rknn toolkit2将yolov5s.onnx转换而来，其中激活函数用relu替换silu，具体转换方法参考: https://github.com/airockchip/rknn_model_zoo/blob/main/examples/yolov5/README.md + +# 代码说明 + +## 代码分为两大部分： + - JAVA: com.rockchip.gpadc.demo: 读取camera输入，并调用jni进行inference，并将结果显示出来 + + - JNI: 调用rknnrt进行实际inference + + + +# 权限 + 运行本程序需要Camera及EXTERNAL_STORAGE读写权限 + + +# FAQ + +## 编译时出现“files found with path 'lib/arm64-v8a/xxx.so' from inputs:”类似错误 +``` +Execution failed for task ':app:mergeDebugNativeLibs'. +> A failure occurred while executing com.android.build.gradle.internal.tasks.MergeNativeLibsTask$MergeNativeLibsTaskWorkAction + > 2 files found with path 'lib/arm64-v8a/librga.so' from inputs: + - rknpu2/examples/rknn_yolov5_android_apk_demo/app/build/intermediates/merged_jni_libs/debug/out/arm64-v8a/librga.so + - rknpu2/examples/rknn_yolov5_android_apk_demo/app/build/intermediates/cxx/Debug/3f40b265/obj/arm64-v8a/librga.so + If you are using jniLibs and CMake IMPORTED targets, see + https://developer.android.com/r/tools/jniLibs-vs-imported-targets + +* Try: +> Run with --info or --debug option to get more log output. +> Run with --scan to get full insights. +``` +或者 +``` +2 files found with path 'lib/arm64-v8a/xxx.so' from inputs: + - rknn_yolov5_android_apk_demo/app/build/intermediates/merged_jni_libs/debug/out/arm64-v8a/librga.so + - rknn_yolov5_android_apk_demo/app/build/intermediates/cxx/Debug/3f40b265/obj/arm64-v8a/librga.so +If you are using jniLibs and CMake IMPORTED targets, see +https://developer.android.com/r/tools/jniLibs-vs-imported-targets +``` +则需要添加 app/build.gradle中的"jniLibs.srcDirs = ['libs']"，具体原因参考“https://developer.android.com/r/tools/jniLibs-vs-imported-targets” + + + +## app启动时，出现"E/SurfaceView: Exception configuring surface"的错误 + +``` +D/rkyolo: camera facing: 1 +V/rkyolo: Camera Supported Preview Size = 160x90 +V/rkyolo: Camera Supported Preview Size = 320x180 +V/rkyolo: Camera Supported Preview Size = 432x240 +V/rkyolo: Camera Supported Preview Size = 640x360 +V/rkyolo: Camera Supported Preview Size = 800x448 +V/rkyolo: Camera Supported Preview Size = 864x480 +V/rkyolo: Camera Supported Preview Size = 1024x576 +V/rkyolo: Camera Supported Preview Size = 1280x720 +V/rkyolo: Camera Supported Preview Size = 1600x896 +V/rkyolo: Camera Supported Preview Size = 1920x1080 +E/SurfaceView: Exception configuring surface + java.lang.RuntimeException: setParameters failed + at android.hardware.Camera.native_setParameters(Native Method) + at android.hardware.Camera.setParameters(Camera.java:2068) + at com.rockchip.gpadc.demo.MainActivity.setCameraParameters(MainActivity.java:295) + at com.rockchip.gpadc.demo.MainActivity.startCamera(MainActivity.java:245) + at com.rockchip.gpadc.demo.MainActivity.access$300(MainActivity.java:47) + at com.rockchip.gpadc.demo.MainActivity$TSurfaceHolderCallback.surfaceCreated(MainActivity.java:199) +``` + +是因为app设置的分辨率该camera不支持，需要修改"app/src/main/java/com/rockchip/gpadc/demo/rga/HALDefine.java"中CAMERA_PREVIEW_WIDTH及CAMERA_PREVIEW_HEIGHT为摄像头支持的分辨率。 \ No newline at end of file diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/CMakeLists.txt b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..eaaa6fa08203158748aa5e41e4ca04685a5fb59c --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/CMakeLists.txt @@ -0,0 +1,56 @@ +# For more information about using CMake with Android Studio, read the +# documentation: https://d.android.com/studio/projects/add-native-code.html + +# Sets the minimum version of CMake required to build the native library. + +cmake_minimum_required(VERSION 3.4.1) + +# Creates and names a library, sets it as either STATIC +# or SHARED, and provides the relative paths to its source code. +# You can define multiple libraries, and CMake builds them for you. +# Gradle automatically packages shared libraries with your APK. + +# include rga. +include_directories(src/main/cpp/rga) + +add_library( # Sets the name of the library. + rknn4j + + # Sets the library as a shared library. + SHARED + + # Provides a relative path to your source file(s). + src/main/cpp/native-lib.cc + src/main/cpp/post_process.cc + src/main/cpp/yolo_image.cc + src/main/cpp/rknn_api.h + src/main/cpp/object_tracker/track_link.cc + src/main/cpp/object_tracker/objects_tracker.cc + src/main/cpp/object_tracker/objects_update.cc + ) + +# Searches for a specified prebuilt library and stores the path as a +# variable. Because CMake includes system libraries in the search path by +# default, you only need to specify the name of the public NDK library +# you want to add. CMake verifies that the library exists before +# completing its build. + +find_library( # Sets the name of the path variable. + log-lib + + # Specifies the name of the NDK library that + # you want CMake to locate. + log ) + +# Specifies libraries CMake should link to your target library. You +# can link multiple libraries, such as libraries you define in this +# build script, prebuilt third-party libraries, or system libraries. + +target_link_libraries( # Specifies the target library. + rknn4j + + # Links the target library to the log library + # included in the NDK. + ${CMAKE_SOURCE_DIR}/src/main/jniLibs/${ANDROID_ABI}/librknnrt.so + ${CMAKE_SOURCE_DIR}/src/main/jniLibs/${ANDROID_ABI}/librga.so + ${log-lib} ) \ No newline at end of file diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/build.gradle b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/build.gradle new file mode 100644 index 0000000000000000000000000000000000000000..0b1a98d4169c3711d409ba0ac610b4a3c5de3462 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/build.gradle @@ -0,0 +1,56 @@ +apply plugin: 'com.android.application' + +android { + compileSdk 28 + defaultConfig { + applicationId "com.rockchip.gpadc.yolodemo" + minSdkVersion 25 + targetSdkVersion 32 + versionCode 1 + versionName "1.0" + testInstrumentationRunner "android.support.test.runner.AndroidJUnitRunner" + externalNativeBuild { + cmake { + cppFlags "-std=c++11 -fexceptions" + arguments "-DANDROID_ARM_NEON=TRUE" + } + } + + ndk { + abiFilters "arm64-v8a" + } + } + buildTypes { + release { + minifyEnabled false + proguardFiles getDefaultProguardFile('proguard-android.txt'), 'proguard-rules.pro' + } + } + externalNativeBuild { + cmake { + path "CMakeLists.txt" + } + } + compileOptions { + sourceCompatibility JavaVersion.VERSION_1_7 + targetCompatibility JavaVersion.VERSION_1_7 + } + + // https://developer.android.com/studio/releases/gradle-plugin#groovy + sourceSets { + main { + // The libs directory contains prebuilt libraries that are used by the + // app's library defined in CMakeLists.txt via an IMPORTED target. + // jniLibs.srcDirs = ['libs'] + } + } +} + +dependencies { + implementation fileTree(dir: 'libs', include: ['*.jar']) + implementation 'com.android.support:appcompat-v7:28.0.0' + implementation 'com.android.support.constraint:constraint-layout:2.0.4' + testImplementation 'junit:junit:4.12' + androidTestImplementation 'com.android.support.test:runner:1.0.2' + androidTestImplementation 'com.android.support.test.espresso:espresso-core:3.0.2' +} diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/proguard-rules.pro b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/proguard-rules.pro new file mode 100644 index 0000000000000000000000000000000000000000..f1b424510da51fd82143bc74a0a801ae5a1e2fcd --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/proguard-rules.pro @@ -0,0 +1,21 @@ +# Add project specific ProGuard rules here. +# You can control the set of applied configuration files using the +# proguardFiles setting in build.gradle. +# +# For more details, see +# http://developer.android.com/guide/developing/tools/proguard.html + +# If your project uses WebView with JS, uncomment the following +# and specify the fully qualified class name to the JavaScript interface +# class: +#-keepclassmembers class fqcn.of.javascript.interface.for.webview { +# public *; +#} + +# Uncomment this to preserve the line number information for +# debugging stack traces. +#-keepattributes SourceFile,LineNumberTable + +# If you keep the line number information, uncomment this to +# hide the original source file name. +#-renamesourcefileattribute SourceFile diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/AndroidManifest.xml b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/AndroidManifest.xml new file mode 100644 index 0000000000000000000000000000000000000000..ab9fdbbc821fb585bf1d1115865ce5d6f453e60e --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/AndroidManifest.xml @@ -0,0 +1,40 @@ + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/assets/coco_80_labels_list.txt b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/assets/coco_80_labels_list.txt new file mode 100644 index 0000000000000000000000000000000000000000..941cb4e1392266f6a6c09b1fdc5f79503b2e5df6 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/assets/coco_80_labels_list.txt @@ -0,0 +1,80 @@ +person +bicycle +car +motorcycle +airplane +bus +train +truck +boat +traffic light +fire hydrant +stop sign +parking meter +bench +bird +cat +dog +horse +sheep +cow +elephant +bear +zebra +giraffe +backpack +umbrella +handbag +tie +suitcase +frisbee +skis +snowboard +sports ball +kite +baseball bat +baseball glove +skateboard +surfboard +tennis racket +bottle +wine glass +cup +fork +knife +spoon +bowl +banana +apple +sandwich +orange +broccoli +carrot +hot dog +pizza +donut +cake +chair +couch +potted plant +bed +dining table +toilet +tv +laptop +mouse +remote +keyboard +cell phone +microwave +oven +toaster +sink +refrigerator +book +clock +vase +scissors +teddy bear +hair drier +toothbrush diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/native-lib.cc b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/native-lib.cc new file mode 100644 index 0000000000000000000000000000000000000000..879b893eb893df04d7b85f25be5ccc6148d8c582 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/native-lib.cc @@ -0,0 +1,185 @@ + +#include +#include +#include +#include +#include +#include + +#include + +#include "yolo_image.h" +#include "rga/rga.h" +#include "object_tracker/track_link.h" + +static char* jstringToChar(JNIEnv* env, jstring jstr) { + char* rtn = NULL; + jclass clsstring = env->FindClass("java/lang/String"); + jstring strencode = env->NewStringUTF("utf-8"); + jmethodID mid = env->GetMethodID(clsstring, "getBytes", "(Ljava/lang/String;)[B"); + jbyteArray barr = (jbyteArray) env->CallObjectMethod(jstr, mid, strencode); + jsize alen = env->GetArrayLength(barr); + jbyte* ba = env->GetByteArrayElements(barr, JNI_FALSE); + + if (alen > 0) { + rtn = new char[alen + 1]; + memcpy(rtn, ba, alen); + rtn[alen] = 0; + } + env->ReleaseByteArrayElements(barr, ba, 0); + return rtn; +} + +extern "C" +JNIEXPORT jint JNICALL Java_com_rockchip_gpadc_demo_yolo_InferenceWrapper_navite_1init + (JNIEnv *env, jobject obj, jint im_height, jint im_width, jint im_channel, + jstring model_path) +{ + char *model_path_p = jstringToChar(env, model_path); + return create(im_height, im_width, im_channel, model_path_p); +} + +extern "C" +JNIEXPORT void JNICALL Java_com_rockchip_gpadc_demo_yolo_InferenceWrapper_native_1deinit + (JNIEnv *env, jobject obj) { + destroy(); + +} + +extern "C" +JNIEXPORT jint JNICALL Java_com_rockchip_gpadc_demo_yolo_InferenceWrapper_native_1run + (JNIEnv *env, jobject obj, jbyteArray in, + jbyteArray grid0Out, jbyteArray grid1Out, jbyteArray grid2Out) { + + + jboolean inputCopy = JNI_FALSE; + jbyte* const inData = env->GetByteArrayElements(in, &inputCopy); + + jboolean outputCopy = JNI_FALSE; + + jbyte* const y0 = env->GetByteArrayElements(grid0Out, &outputCopy); + jbyte* const y1 = env->GetByteArrayElements(grid1Out, &outputCopy); + jbyte* const y2 = env->GetByteArrayElements(grid2Out, &outputCopy); + + run_yolo((char *)inData, (char *)y0, (char *)y1, (char *)y2); + + env->ReleaseByteArrayElements(in, inData, JNI_ABORT); + env->ReleaseByteArrayElements(grid0Out, y0, 0); + env->ReleaseByteArrayElements(grid1Out, y1, 0); + env->ReleaseByteArrayElements(grid2Out, y2, 0); + + return 0; +} + +extern "C" +JNIEXPORT jint JNICALL +Java_com_rockchip_gpadc_demo_yolo_InferenceWrapper_native_1post_1process(JNIEnv *env, jobject thiz, + jbyteArray grid0_out, + jbyteArray grid1_out, + jbyteArray grid2_out, + jintArray ids, + jfloatArray scores, + jfloatArray boxes) { + jint detect_counts; + jboolean inputCopy = JNI_FALSE; + jbyte* const grid0_buf = env->GetByteArrayElements(grid0_out, &inputCopy); + jbyte* const grid1_buf = env->GetByteArrayElements(grid1_out, &inputCopy); + jbyte* const grid2_buf = env->GetByteArrayElements(grid2_out, &inputCopy); + + jboolean outputCopy = JNI_FALSE; + + jint* const y0 = env->GetIntArrayElements(ids, &outputCopy); + jfloat* const y1 = env->GetFloatArrayElements(scores, &outputCopy); + jfloat* const y2 = env->GetFloatArrayElements(boxes, &outputCopy); + + detect_counts = yolo_post_process((char *)grid0_buf, (char *)grid1_buf, (char *)grid2_buf, + (int *)y0, (float *)y1, (float *)y2); + + env->ReleaseByteArrayElements(grid0_out, grid0_buf, JNI_ABORT); + env->ReleaseByteArrayElements(grid1_out, grid1_buf, JNI_ABORT); + env->ReleaseByteArrayElements(grid2_out, grid2_buf, JNI_ABORT); + env->ReleaseIntArrayElements(ids, y0, 0); + env->ReleaseFloatArrayElements(scores, y1, 0); + env->ReleaseFloatArrayElements(boxes, y2, 0); + + return detect_counts; +} + +extern "C" +JNIEXPORT jlong JNICALL Java_com_rockchip_gpadc_demo_tracker_ObjectTracker_native_1create + (JNIEnv * env, jobject obj) { + return create_tracker(); +} + +extern "C" +JNIEXPORT void JNICALL Java_com_rockchip_gpadc_demo_tracker_ObjectTracker_native_1destroy + (JNIEnv * env, jobject obj, + jlong handle) { + destroy_tracker(handle); +} + + +extern "C" +JNIEXPORT void JNICALL Java_com_rockchip_gpadc_demo_tracker_ObjectTracker_native_1track + (JNIEnv * env, jobject obj, + jlong handle, + jint maxTrackLifetime, + jint track_input_num, + jfloatArray track_input_locations, + jintArray track_input_class, + jfloatArray track_input_score, + jintArray track_output_num, + jfloatArray track_output_locations, + jintArray track_output_class, + jfloatArray track_output_score, + jintArray track_output_id, + jint width, + jint height) { + + jboolean inputCopy = JNI_FALSE; + jfloat *const c_track_input_locations = env->GetFloatArrayElements(track_input_locations, + &inputCopy); + jint *const c_track_input_class = env->GetIntArrayElements(track_input_class, &inputCopy); + jfloat *const c_track_input_score = env->GetFloatArrayElements(track_input_score, &inputCopy); + jboolean outputCopy = JNI_FALSE; + + jint *const c_track_output_num = env->GetIntArrayElements(track_output_num, &outputCopy); + jfloat *const c_track_output_locations = env->GetFloatArrayElements(track_output_locations, + &outputCopy); + jint *const c_track_output_class = env->GetIntArrayElements(track_output_class, &outputCopy); + jfloat *const c_track_output_score = env->GetFloatArrayElements(track_output_score, &inputCopy); + jint *const c_track_output_id = env->GetIntArrayElements(track_output_id, &outputCopy); + + + track(handle, (int)maxTrackLifetime, + (int) track_input_num, (float *) c_track_input_locations, (int *) c_track_input_class, (float *)c_track_input_score, + (int *) c_track_output_num, (float *) c_track_output_locations, (int *) c_track_output_class, (float *)c_track_output_score, + (int *) c_track_output_id, (int) width, (int)height); + + env->ReleaseFloatArrayElements(track_input_locations, c_track_input_locations, JNI_ABORT); + env->ReleaseIntArrayElements(track_input_class, c_track_input_class, JNI_ABORT); + env->ReleaseFloatArrayElements(track_input_score, c_track_input_score, JNI_ABORT); + + env->ReleaseIntArrayElements(track_output_num, c_track_output_num, 0); + env->ReleaseFloatArrayElements(track_output_locations, c_track_output_locations, 0); + env->ReleaseIntArrayElements(track_output_class, c_track_output_class, 0); + env->ReleaseFloatArrayElements(track_output_score, c_track_output_score, 0); + env->ReleaseIntArrayElements(track_output_id, c_track_output_id, 0); +} +extern "C" +JNIEXPORT jint JNICALL +Java_com_rockchip_gpadc_demo_rga_RGA_color_1convert_1and_1flip(JNIEnv *env, jclass clazz, + jbyteArray src, jint src_fmt, + jbyteArray dst, jint dst_fmt, + jint width, jint height, jint flip) { + jboolean copy = JNI_FALSE; + jbyte* src_buf = env->GetByteArrayElements(src, ©); + jbyte* dst_buf = env->GetByteArrayElements(dst, ©); + + jint ret = colorConvertAndFlip(src_buf, src_fmt, dst_buf, dst_fmt, width, height, flip); + + env->ReleaseByteArrayElements(src, src_buf, 0); + env->ReleaseByteArrayElements(dst, dst_buf, 0); + + return ret; +} \ No newline at end of file diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/object_tracker/objects_tracker.cc b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/object_tracker/objects_tracker.cc new file mode 100644 index 0000000000000000000000000000000000000000..4e6360ab9548d9658034073a6982549ff9245c7d --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/object_tracker/objects_tracker.cc @@ -0,0 +1,288 @@ +#include "objects_tracker.h" +#include "track_link.h" + +#define max(a,b) (((a) > (b)) ? (a) : (b)) +#define min(a,b) (((a) < (b)) ? (a) : (b)) + +ObjectsTracker::ObjectsTracker() : + parameters(), + numTrackedSteps(0) +{ + +} + +ObjectsTracker::~ObjectsTracker() +{ + +} + +ObjectsTracker::Parameters::Parameters() +{ + maxTrackLifetime=6; + numLastPositionsToTrack=4; + numDetectedToWaitBeforeFirstShow=0; + numStepsToWaitBeforeFirstShow=6; + numStepsToTrackWithoutDetectingIfObjectHasNotBeenShown=4; + numStepsToShowWithoutDetecting=5; +} + +float CalculateIOU(Rect_T r1, Rect_T r2) { + int xmin0 = r1.x; + int ymin0 = r1.y; + int xmax0 = r1.x +r1.width; + int ymax0 = r1.y +r1.height; + int xmin1 = r2.x; + int ymin1 = r2.y; + int xmax1 = r2.x +r2.width; + int ymax1 = r2.y +r2.height; + float w = max(0.f, min(xmax0, xmax1) - max(xmin0, xmin1)); + float h = max(0.f, min(ymax0, ymax1) - max(ymin0, ymin1)); + float i = w * h; + float u = (xmax0 - xmin0) * (ymax0 - ymin0) + (xmax1 - xmin1) * (ymax1 - ymin1) - i; + return u <= 0.f ? 0.f : (i / u); +} + +float CalculateArea(Rect_T r) { + float i = r.width * r.height; + return i; +} + +void ObjectsTracker::predict_loctation(TrackedObject& curObject, int image_width,int image_height, float& pre_x, float& pre_y, float& v_x, float& v_y){ + int numpositions = (int)curObject.lastPositions.size(); + Rect_T prevRect = curObject.lastPositions[numpositions-1]; + Rect_T tmpRect=curObject.lastPositions[numpositions-2]; + float vx_1 = (prevRect.x -tmpRect.x)/(1.0f +curObject.preNumFramesNotDetected); + float vx_2 = (prevRect.x +prevRect.width -tmpRect.x -tmpRect.width)/(1.0f +curObject.preNumFramesNotDetected); + float vx_ = min(vx_1, vx_2); + float vy_1 = (prevRect.y -tmpRect.y)/(1.0f +curObject.preNumFramesNotDetected); + float vy_2 = (prevRect.y +prevRect.height -tmpRect.y -tmpRect.height)/(1.0f +curObject.preNumFramesNotDetected); + float vy_ = min(vy_1, vy_2); + v_x = 0.5f*vx_ + 0.5f*curObject.vx; + v_y = 0.5f*vy_ + 0.5f*curObject.vy; + int x = (int)(prevRect.x + v_x*(1 ) *0.7f +0.5f); + int y = (int)(prevRect.y + v_y*(1 ) *0.7f +0.5f); + x = x >= 0 ? x : 0; + y = y >= 0 ? y : 0; + if(x + prevRect.width >= image_width) + x -= (x + prevRect.width -image_width +1); + if(y + prevRect.height >= image_height) + y -= (y + prevRect.height -image_height +1); + x = x >= 0 ? x : 0; + y = y >= 0 ? y : 0; + pre_x = x; + pre_y = y; + +} + +void ObjectsTracker::getObjects(std::vector& result) +{ + result.clear(); + for(size_t i=0; i < trackedObjects.size(); i++) { + ObjectStatus status; + Rect_T r=calcTrackedObjectPositionToShow((int)i, status); + + if (CalculateArea(r)==0.f || trackedObjects[i].numFramesDetected < 2){ + continue; + } + + result.push_back(ExtObject(trackedObjects[i].id, r, trackedObjects[i].predict_loc_when_miss, trackedObjects[i].smooth_Positionn, status, + trackedObjects[i].numFramesDetected, trackedObjects[i].numFramesNotDetected, trackedObjects[i].obj_class, trackedObjects[i].score, trackedObjects[i].miss)); + + } +} + +void ObjectsTracker::updateTrackedObjects(const std::vector& detectedObjects, const std::vector objects_class + , const std::vector objects_score, int maxTrackLifetime, int image_width,int image_height) +{ + enum { + NEW_RECTANGLE=-1, + INTERSECTED_RECTANGLE=-2 + }; + + int N1=(int)trackedObjects.size(); + int N2=(int)detectedObjects.size(); + + for(int i=0; i < N1; i++) { + trackedObjects[i].numDetectedFrames++; + } + + std::vector correspondence(detectedObjects.size(), NEW_RECTANGLE); + std::vector correspondenceScore(detectedObjects.size(), 0); + + for(int i=0; i < N1; i++) { + TrackedObject& curObject=trackedObjects[i]; + int bestIndex=-1; + float bestArea=-1; + int numpositions=(int)curObject.lastPositions.size(); + Rect_T prevRect=curObject.lastPositions[numpositions-1]; + + //save predict loctation + if(numpositions>1) + { + float pre_x, pre_y, vx, vy; + predict_loctation(curObject, image_width, image_height, pre_x, pre_y, vx, vy); + curObject.vx = vx; + curObject.vy = vy; + prevRect.x = (int)pre_x; + prevRect.y = (int)pre_y; + curObject.predict_loc_when_miss = prevRect; + } + + //search track loaction + for(int j=0; j < N2; j++) { + + float percentage_IOU = CalculateIOU(prevRect, detectedObjects[j]); + if ( percentage_IOU > 0.1f ) {//&& objects_class[j] == curObject.obj_class + + float trackScore = percentage_IOU *1.f / (curObject.numFramesNotDetected + 1); + if ( percentage_IOU > bestArea && correspondenceScore[j] < trackScore) { + bestIndex = j; + bestArea = percentage_IOU; + correspondenceScore[j] = trackScore; + } + } + + } + if (bestIndex >= 0) { + correspondence[bestIndex] = i; + } + } + + //select track loaction + for (int i = 0; i < N1; i++) { + TrackedObject& curObject = trackedObjects[i]; + int bestIndex = -1; + for (int j = 0; j < N2; j++) { + if (correspondence[j] == i){ + bestIndex = j; + break; + } + } + if (bestIndex >= 0) { + correspondence[bestIndex] = i; + for (int j = 0; j < N2; j++) { + if (correspondence[j] >= 0) + continue; + + float percentage_IOU = CalculateIOU(detectedObjects[j], detectedObjects[bestIndex]); + if (percentage_IOU > 0.45f ){//&& objects_class[j] == curObject.obj_class + correspondence[j] = INTERSECTED_RECTANGLE; + } + } + curObject.numFramesDetected++; + } + else { + + curObject.numFramesNotDetected++; + curObject.miss = 1; + } + + } + + //allocate new detected location + for(int j=0; j < N2; j++) { + int i = correspondence[j]; + if (i >= 0) {//add position + trackedObjects[i].lastPositions.push_back(detectedObjects[j]); + while ((int)trackedObjects[i].lastPositions.size() > (int) parameters.numLastPositionsToTrack) { + trackedObjects[i].lastPositions.erase(trackedObjects[i].lastPositions.begin()); + } + trackedObjects[i].preNumFramesNotDetected = trackedObjects[i].numFramesNotDetected; + trackedObjects[i].numFramesNotDetected = 0; + trackedObjects[i].score = objects_score[j]; + trackedObjects[i].obj_class = objects_class[j]; + trackedObjects[i].miss = 0; + //smooth rect + trackedObjects[i].smooth_Positionn.width = (trackedObjects[i].smooth_Positionn.width *1 +detectedObjects[j].width )/(2); + trackedObjects[i].smooth_Positionn.height = (trackedObjects[i].smooth_Positionn.height *1 +detectedObjects[j].height)/(2); + Rect_T r_smooth = trackedObjects[i].smooth_Positionn; + float weight_p = 0.5f, weight_n = 1.f; + trackedObjects[i].smooth_Positionn.x = (int)(((r_smooth.x +r_smooth.width*0.5f) *weight_p +(detectedObjects[j].x +detectedObjects[j].width*0.5f)*weight_n)/(weight_p +weight_n) + +trackedObjects[i].vx *weight_p /(weight_p +weight_n) -r_smooth.width *0.5f); + + trackedObjects[i].smooth_Positionn.y = (int)(((r_smooth.y +r_smooth.height*0.5f) *weight_p +(detectedObjects[j].y +detectedObjects[j].height*0.5f)*weight_n)/(weight_p +weight_n) + +trackedObjects[i].vy *weight_p /(weight_p +weight_n)-r_smooth.height *0.5f); + } else if (i==NEW_RECTANGLE){ //new object + + trackedObjects.push_back(detectedObjects[j]); + int _N2 = (int)trackedObjects.size(); + trackedObjects[_N2-1].obj_class = objects_class[j]; + trackedObjects[_N2-1].score = objects_score[j]; + correspondence[j] = _N2 -1; + trackedObjects[_N2-1].smooth_Positionn.width = detectedObjects[j].width; + trackedObjects[_N2-1].smooth_Positionn.height = detectedObjects[j].height; + trackedObjects[_N2-1].smooth_Positionn.x = (detectedObjects[j].x); + trackedObjects[_N2-1].smooth_Positionn.y = (detectedObjects[j].y); + } + } + + std::vector::iterator it=trackedObjects.begin(); + while( it != trackedObjects.end() ) { + if ( (it->numFramesNotDetected > maxTrackLifetime) +#if 0 + || + ( + (it->numDetectedFrames <= parameters.numStepsToWaitBeforeFirstShow) + && + (it->numFramesNotDetected > parameters.numStepsToTrackWithoutDetectingIfObjectHasNotBeenShown) + ) +#endif + ) + { + it=trackedObjects.erase(it); + + } else { + it++; + } + + } + +} + +Rect_T ObjectsTracker::calcTrackedObjectPositionToShow(int i, ObjectStatus& status) const +{ + Rect_T r; + r.x = 0; + r.y = 0; + r.width = 0; + r.height = 0; + + if ( (i < 0) || (i >= (int)trackedObjects.size()) ) { + status = WRONG_OBJECT; + + return r; + } + +#if 0 + if (trackedObjects[i].numDetectedFrames <= parameters.numStepsToWaitBeforeFirstShow) { + status = DETECTED_NOT_SHOWN_YET; + return r; + } + + + if (trackedObjects[i].numFramesDetected <= parameters.numDetectedToWaitBeforeFirstShow) { + status = DETECTED_NOT_SHOWN_YET; + return r; + } + + + if (trackedObjects[i].numFramesNotDetected > parameters.numStepsToShowWithoutDetecting) { + status = DETECTED_TEMPORARY_LOST; + + return r; + } +#endif + + const TrackedObject::PositionsVector& lastPositions=trackedObjects[i].lastPositions; + + int N=(int)lastPositions.size(); + if (N<=0) { + status = WRONG_OBJECT; + return r; + } + + return lastPositions[N-1]; + +} + + diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/object_tracker/objects_tracker.h b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/object_tracker/objects_tracker.h new file mode 100644 index 0000000000000000000000000000000000000000..d3cb7107ab94e6b0286896ec4cbbe499277bfb61 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/object_tracker/objects_tracker.h @@ -0,0 +1,110 @@ +#ifndef __OBJECTS_TRACKER_H_ +#define __OBJECTS_TRACKER_H_ + +#include "stdio.h" +#include +#include "math.h" +#include "track_link.h" + +class ObjectsTracker { + +public: + + struct Parameters + { + int maxTrackLifetime; + + int numLastPositionsToTrack; + int numDetectedToWaitBeforeFirstShow; + int numStepsToWaitBeforeFirstShow; + int numStepsToTrackWithoutDetectingIfObjectHasNotBeenShown; + int numStepsToShowWithoutDetecting; + + Parameters(); + }; + + enum ObjectStatus + { + DETECTED_NOT_SHOWN_YET, + DETECTED, + DETECTED_TEMPORARY_LOST, + WRONG_OBJECT + }; + + struct ExtObject + { + int id; + Rect_T location; + Rect_T predict_loc_when_miss; + Rect_T smooth_rect; + ObjectStatus status; + int detectedNum; + int noDetectedNum; + int obj_class; + float score; + int miss; + + ExtObject() {} + ExtObject(int _id, Rect_T _location, Rect_T _predict_loc_when_miss, Rect_T _smooth_rect, ObjectStatus _status, + int _detectedNum, int _noDetectedNum, int _obj_class, float _score, int _miss) + :id(_id), location(_location), predict_loc_when_miss(_predict_loc_when_miss), smooth_rect(_smooth_rect), status(_status), + detectedNum(_detectedNum), noDetectedNum(_noDetectedNum), obj_class(_obj_class), score(_score), miss(_miss) + { + + } + + }; + + ObjectsTracker(); + virtual ~ObjectsTracker(); + + /* bool setParameters(const Parameters& params); + const Parameters& getParameters() const;*/ + + virtual void getObjects(std::vector& result); + + //virtual int addObject(const Rect_T& location); //returns id of the new object + + void updateTrackedObjects(const std::vector& detectedObjects, const std::vector objects_class, const std::vector objects_score, int maxTrackLifetime, int image_width, int image_height); + Rect_T calcTrackedObjectPositionToShow(int i, ObjectStatus& status) const; + Parameters parameters; + + struct TrackedObject + { + typedef std::vector PositionsVector; + PositionsVector lastPositions; + Rect_T predict_loc_when_miss; + Rect_T smooth_Positionn; + float vx; + float vy; + int numDetectedFrames; + int numFramesDetected; + int numFramesNotDetected; + int preNumFramesNotDetected; + int id; + int obj_class; + float score; + int miss; + //int flag_unshift;*/ + TrackedObject(const Rect_T& Rect_T):vx(0), vy(0), numDetectedFrames(1), numFramesDetected(1), numFramesNotDetected(0) + , preNumFramesNotDetected(0), score(0), miss(0) + + { + lastPositions.push_back(Rect_T); + id=getNextId(); + }; + + static int getNextId() + { + static int _id=0; + return _id++; + } + }; + + int numTrackedSteps; + std::vector trackedObjects; + void predict_loctation(TrackedObject& curObject, int image_width,int image_height, float& pre_x, float& pre_y, float& v_x, float& v_y); + +}; + +#endif /* end of __OBJECTS_TRACKER_H_ */ diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/object_tracker/objects_update.cc b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/object_tracker/objects_update.cc new file mode 100644 index 0000000000000000000000000000000000000000..ab3c65f82d72d905f7f1c97b179c194f9c567b50 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/object_tracker/objects_update.cc @@ -0,0 +1,59 @@ +#include "stdio.h" +#include "objects_update.h" +#include "track_link.h" + +OdtDetector::OdtDetector() +{ + printf("success build\n"); +} + +OdtDetector::~OdtDetector() +{ +} + +int OdtDetector::update(int maxTrackLifetime, int track_num_input, object_T* object_input, + int* track_num_output, object_T* object_output, int width, int height) +{ + + m_width = width; + m_height = height; + + std::vector objects_Rect_T; + std::vector objects_class; + std::vector object_score; + for (int i = 0; i < track_num_input; i++) { + objects_Rect_T.push_back(object_input[i].r); + objects_class.push_back(object_input[i].obj_class); + object_score.push_back(object_input[i].score); + } + + m_objects_tracker.updateTrackedObjects(objects_Rect_T, objects_class, object_score, maxTrackLifetime, m_width, m_height); + objects_Rect_T.clear(); + objects_class.clear(); + object_score.clear(); + + std::vector extObjects; + m_objects_tracker.getObjects(extObjects); + + int nobjects = (int)extObjects.size(); + track_num_output[0] = nobjects; + + int i = 0; + for (; i < nobjects && i < 100; i++) + { + if(extObjects[i].miss == 0){ + //object_output[i].r = extObjects[i].location; + object_output[i].r = extObjects[i].smooth_rect; + + } + else{ + object_output[i].r = extObjects[i].predict_loc_when_miss; + //object_output[i].score = -10000; + } + object_output[i].obj_class = extObjects[i].obj_class; + object_output[i].score = extObjects[i].score; + object_output[i].id = extObjects[i].id; + + } + return nobjects; +} diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/object_tracker/objects_update.h b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/object_tracker/objects_update.h new file mode 100644 index 0000000000000000000000000000000000000000..ebbf1ce3f6918cfdf651395f6ef9e11b374648c8 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/object_tracker/objects_update.h @@ -0,0 +1,26 @@ +#ifndef __OBJECTS_UPDATE_H_ +#define __OBJECTS_UPDATE_H_ + +#include "objects_tracker.h" + +class OdtDetector +{ +public: + + OdtDetector(); + virtual ~OdtDetector(); + int update(int maxTrackLifetime, int track_num_input, object_T* object_input, + int* track_num_output, object_T* object_output, int width, int height); + int getWidth() { return m_width; } + int getHeight() { return m_height; } + +private: + + int m_width; + int m_height; + + ObjectsTracker m_objects_tracker; + +}; + +#endif /* end of __ODT_DETECTOR_H_ */ diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/object_tracker/track_link.cc b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/object_tracker/track_link.cc new file mode 100644 index 0000000000000000000000000000000000000000..65295d0a438ae4c13b57ba22b60e788e36538591 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/object_tracker/track_link.cc @@ -0,0 +1,84 @@ +#include "track_link.h" +#include "objects_update.h" +#include "objects_tracker.h" +#define MAX_OUTPUT 100 + +object_T object_input[MAX_OUTPUT]; +object_T object_output[MAX_OUTPUT]; + +long create_tracker() { + OdtDetector *object = new OdtDetector(); + return (long)object; +} + +void destroy_tracker(long handle) { + delete((OdtDetector*)handle); +} + +void track(long handle, int maxTrackLifetime, + int track_input_num, float * c_track_input_locations, int * c_track_input_class, float * c_track_input_score, + int * c_track_output_num, float * c_track_output_locations, int * c_track_output_class, float * c_track_output_score, + int * c_track_output_id, int width, int height){ + + OdtDetector *object = (OdtDetector*)handle; + + for (int i = 0; i < track_input_num; i++) { + object_input[i].r.x = (int)c_track_input_locations[i*4 +0]; + object_input[i].r.y = (int)c_track_input_locations[i*4 +1]; + object_input[i].r.width = (int)(c_track_input_locations[i*4 +2] -c_track_input_locations[i*4 +0]); + object_input[i].r.height = (int)(c_track_input_locations[i*4 +3] -c_track_input_locations[i*4 +1]); + object_input[i].score = c_track_input_score[i]; + object_input[i].obj_class = c_track_input_class[i]; + //LOGI("%d input P: %f\n", i, object_input[i].score); + } + + object->update(maxTrackLifetime, track_input_num, object_input, c_track_output_num, object_output, width, height); + + for (int i = 0; i < *c_track_output_num; i++) { + c_track_output_locations[i*4 +0] = (float)object_output[i].r.x; + c_track_output_locations[i*4 +1] = (float)object_output[i].r.y; + c_track_output_locations[i*4 +2] = (float)(object_output[i].r.x +object_output[i].r.width); + c_track_output_locations[i*4 +3] = (float)(object_output[i].r.y +object_output[i].r.height); + c_track_output_class[i] = object_output[i].obj_class; + c_track_output_score[i] = object_output[i].score; +// LOGI("output P: %f\n", c_track_output_score[i]); + c_track_output_id[i] = object_output[i].id; + } + + //LOGI("handle=%ld tracker input_num=%d output_num=%d maxTrackLifetime=%d width=%d height=%d\n", + // handle, track_input_num, *c_track_output_num, maxTrackLifetime, width, height); +} + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/object_tracker/track_link.h b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/object_tracker/track_link.h new file mode 100644 index 0000000000000000000000000000000000000000..b6313336253b40c742b64e16ff695314063204a7 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/object_tracker/track_link.h @@ -0,0 +1,44 @@ +#ifndef __TRACK_C_LINK_C_H +#define __TRACK_C_LINK_C_H +#include "stdio.h" +#include +#include +#include + +#ifdef __cplusplus +extern "C"{ +#endif +#include +#define LOG_TAG "hpc -- JNILOG" // 这个是自定义的LOG的标识 +#define LOGI(...) __android_log_print(ANDROID_LOG_INFO,LOG_TAG, __VA_ARGS__) + +typedef struct +{ + int x; + int y; + int width; + int height; +} +Rect_T; + +typedef struct +{ + Rect_T r; + int obj_class; + float score; + int id; + int reserve[1]; +} +object_T; + +long create_tracker(); + +void destroy_tracker(long handle); + +void track(long handle, int maxTrackLifetime, int track_input_num, float * c_track_input_locations, int * c_track_input_class, float * c_track_input_score, + int * c_track_output_num, float * c_track_output_locations, int * c_track_output_class, float * c_track_output_score, int * c_track_output_id, + int width, int height); +#ifdef __cplusplus +} +#endif +#endif \ No newline at end of file diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/post_process.cc b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/post_process.cc new file mode 100644 index 0000000000000000000000000000000000000000..bee6743b6c83d6503ff65b201a88b0c4b9b5f8c5 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/post_process.cc @@ -0,0 +1,308 @@ +/* + * Copyright (C) 2022 Rockchip Electronics Co., Ltd. + * Authors: + * raul.rao + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include +#include +#include +#include +#include +#include +#include +#include "post_process.h" + +inline static int clamp(float val, int min, int max) +{ + return val > min ? (val < max ? val : max) : min; +} + +static float CalculateOverlap(float xmin0, float ymin0, float xmax0, float ymax0, float xmin1, float ymin1, float xmax1, float ymax1) +{ + float w = fmax(0.f, fmin(xmax0, xmax1) - fmax(xmin0, xmin1) + 1.0); + float h = fmax(0.f, fmin(ymax0, ymax1) - fmax(ymin0, ymin1) + 1.0); + float i = w * h; + float u = (xmax0 - xmin0 + 1.0) * (ymax0 - ymin0 + 1.0) + (xmax1 - xmin1 + 1.0) * (ymax1 - ymin1 + 1.0) - i; + return u <= 0.f ? 0.f : (i / u); +} + +static int nms(int validCount, std::vector &outputLocations, std::vector classIds, std::vector &order,int filterId, float threshold) +{ + for (int i = 0; i < validCount; ++i) + { + int n = order[i]; + if (n == -1|| classIds[n] != filterId) + { + continue; + } + for (int j = i + 1; j < validCount; ++j) + { + int m = order[j]; + if (m == -1 || classIds[m] != filterId) + { + continue; + } + float xmin0 = outputLocations[n * 4 + 0]; + float ymin0 = outputLocations[n * 4 + 1]; + float xmax0 = outputLocations[n * 4 + 0] + outputLocations[n * 4 + 2]; + float ymax0 = outputLocations[n * 4 + 1] + outputLocations[n * 4 + 3]; + + float xmin1 = outputLocations[m * 4 + 0]; + float ymin1 = outputLocations[m * 4 + 1]; + float xmax1 = outputLocations[m * 4 + 0] + outputLocations[m * 4 + 2]; + float ymax1 = outputLocations[m * 4 + 1] + outputLocations[m * 4 + 3]; + + float iou = CalculateOverlap(xmin0, ymin0, xmax0, ymax0, xmin1, ymin1, xmax1, ymax1); + + if (iou > threshold) + { + order[j] = -1; + } + } + } + return 0; +} + +static int quick_sort_indice_inverse( + std::vector &input, + int left, + int right, + std::vector &indices) +{ + float key; + int key_index; + int low = left; + int high = right; + if (left < right) + { + key_index = indices[left]; + key = input[left]; + while (low < high) + { + while (low < high && input[high] <= key) + { + high--; + } + input[low] = input[high]; + indices[low] = indices[high]; + while (low < high && input[low] >= key) + { + low++; + } + input[high] = input[low]; + indices[high] = indices[low]; + } + input[low] = key; + indices[low] = key_index; + quick_sort_indice_inverse(input, left, low - 1, indices); + quick_sort_indice_inverse(input, low + 1, right, indices); + } + return low; +} + +static float sigmoid(float x) +{ + return 1.0 / (1.0 + expf(-x)); +} + +static float unsigmoid(float y) +{ + return -1.0 * logf((1.0 / y) - 1.0); +} + +inline static int32_t __clip(float val, float min, float max) +{ + float f = val <= min ? min : (val >= max ? max : val); + return f; +} + +static int8_t qnt_f32_to_affine(float f32, int32_t zp, float scale) +{ + float dst_val = (f32 / scale) + zp; + int8_t res = (int8_t)__clip(dst_val, -128, 127); + return res; +} + +static float deqnt_affine_to_f32(int8_t qnt, int32_t zp, float scale) +{ + return ((float)qnt - (float)zp) * scale; +} + +static int process(int8_t *input, int *anchor, int grid_h, int grid_w, int height, int width, int stride, + std::vector &boxes, std::vector &objProbs, std::vector &classId, + float threshold, int32_t zp, float scale) +{ + + int validCount = 0; + int grid_len = grid_h * grid_w; + int8_t thres_i8 = qnt_f32_to_affine(threshold, zp, scale); + for (int a = 0; a < 3; a++) + { + for (int i = 0; i < grid_h; i++) + { + for (int j = 0; j < grid_w; j++) + { + int8_t box_confidence = input[(PROP_BOX_SIZE * a + 4) * grid_len + i * grid_w + j]; + if (box_confidence >= thres_i8) + { + int offset = (PROP_BOX_SIZE * a) * grid_len + i * grid_w + j; + int8_t *in_ptr = input + offset; + float box_x = (deqnt_affine_to_f32(*in_ptr, zp, scale)) * 2.0 - 0.5; + float box_y = (deqnt_affine_to_f32(in_ptr[grid_len], zp, scale)) * 2.0 - 0.5; + float box_w = (deqnt_affine_to_f32(in_ptr[2 * grid_len], zp, scale)) * 2.0; + float box_h = (deqnt_affine_to_f32(in_ptr[3 * grid_len], zp, scale)) * 2.0; + box_x = (box_x + j) * (float)stride; + box_y = (box_y + i) * (float)stride; + box_w = box_w * box_w * (float)anchor[a * 2]; + box_h = box_h * box_h * (float)anchor[a * 2 + 1]; + box_x -= (box_w / 2.0); + box_y -= (box_h / 2.0); + + int8_t maxClassProbs = in_ptr[5 * grid_len]; + int maxClassId = 0; + for (int k = 1; k < OBJ_CLASS_NUM; ++k) + { + int8_t prob = in_ptr[(5 + k) * grid_len]; + if (prob > maxClassProbs) + { + maxClassId = k; + maxClassProbs = prob; + } + } + float max_class_prob = deqnt_affine_to_f32(maxClassProbs, zp, scale); + float box_prob = deqnt_affine_to_f32(box_confidence, zp, scale); + if (max_class_prob * box_prob > threshold){ + boxes.push_back(box_x); + boxes.push_back(box_y); + boxes.push_back(box_w); + boxes.push_back(box_h); + objProbs.push_back((max_class_prob * box_prob)); + classId.push_back(maxClassId); + validCount++; + } + } + } + } + } + return validCount; +} + +int post_process(int8_t *input0, int8_t *input1, int8_t *input2, int model_in_h, int model_in_w, + float conf_threshold, float nms_threshold, float scale_w, float scale_h, + std::vector &qnt_zps, std::vector &qnt_scales, + detect_result_group_t *group) +{ +// LOGI("post process start."); +// LOGI("qunt_zps: [%d, %d, %d]", qnt_zps[0], qnt_zps[1], qnt_zps[2]); +// LOGI("qnt_scales: [%f, %f, %f]", qnt_scales[0], qnt_scales[1], qnt_scales[2]); + + int anchor0[6] = {10, 13, 16, 30, 33, 23}; + int anchor1[6] = {30, 61, 62, 45, 59, 119}; + int anchor2[6] = {116, 90, 156, 198, 373, 326}; + + memset(group, 0, sizeof(detect_result_group_t)); + + std::vector filterBoxes; + std::vector objProbs; + std::vector classId; + + // stride 8 + int stride0 = 8; + int grid_h0 = model_in_h / stride0; + int grid_w0 = model_in_w / stride0; + int validCount0 = 0; + validCount0 = process(input0, (int *)anchor0, grid_h0, grid_w0, model_in_h, model_in_w, + stride0, filterBoxes, objProbs, classId, conf_threshold, qnt_zps[0], qnt_scales[0]); + + // stride 16 + int stride1 = 16; + int grid_h1 = model_in_h / stride1; + int grid_w1 = model_in_w / stride1; + int validCount1 = 0; + validCount1 = process(input1, (int *)anchor1, grid_h1, grid_w1, model_in_h, model_in_w, + stride1, filterBoxes, objProbs, classId, conf_threshold, qnt_zps[1], qnt_scales[1]); + + // stride 32 + int stride2 = 32; + int grid_h2 = model_in_h / stride2; + int grid_w2 = model_in_w / stride2; + int validCount2 = 0; + validCount2 = process(input2, (int *)anchor2, grid_h2, grid_w2, model_in_h, model_in_w, + stride2, filterBoxes, objProbs, classId, conf_threshold, qnt_zps[2], qnt_scales[2]); + + int validCount = validCount0 + validCount1 + validCount2; +// LOGI("vc0: %d, vc1: %d, vc2: %d\n", validCount0, validCount1, validCount2); + // no object detect + if (validCount <= 0) + { +// LOGI("post process end."); + return 0; + } + + std::vector indexArray; + for (int i = 0; i < validCount; ++i) + { + indexArray.push_back(i); + } + + quick_sort_indice_inverse(objProbs, 0, validCount - 1, indexArray); + + std::set class_set(std::begin(classId),std::end(classId)); + + for(auto c : class_set){ + nms(validCount, filterBoxes, classId, indexArray, c, nms_threshold); + } + + int last_count = 0; + group->count = 0; + /* box valid detect target */ + for (int i = 0; i < validCount; ++i) + { + + if (indexArray[i] == -1 || last_count >= OBJ_NUMB_MAX_SIZE) + { + continue; + } + int n = indexArray[i]; + + float x1 = filterBoxes[n * 4 + 0]; + float y1 = filterBoxes[n * 4 + 1]; + float x2 = x1 + filterBoxes[n * 4 + 2]; + float y2 = y1 + filterBoxes[n * 4 + 3]; + int id = classId[n]; + float obj_conf = objProbs[i]; + + group->results[last_count].box.left = (int)(clamp(x1, 0, model_in_w) / scale_w); + group->results[last_count].box.top = (int)(clamp(y1, 0, model_in_h) / scale_h); + group->results[last_count].box.right = (int)(clamp(x2, 0, model_in_w) / scale_w); + group->results[last_count].box.bottom = (int)(clamp(y2, 0, model_in_h) / scale_h); + group->results[last_count].prop = obj_conf; + group->results[last_count].class_id = id; +// char *label = labels[id]; +// strncpy(group->results[last_count].name, label, OBJ_NAME_MAX_SIZE); + +// LOGI("result %2d: (%4d, %4d, %4d, %4d), %d\n", i, group->results[last_count].box.left, group->results[last_count].box.top, +// group->results[last_count].box.right, group->results[last_count].box.bottom, id); + last_count++; + } + group->count = last_count; +// LOGI("post process end."); + + return 0; +} + +void deinitPostProcess() { + return; +} \ No newline at end of file diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/post_process.h b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/post_process.h new file mode 100644 index 0000000000000000000000000000000000000000..6172f84a9f4742feae095cbee21109e657a1ecf1 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/post_process.h @@ -0,0 +1,65 @@ +/* + * Copyright (C) 2022 Rockchip Electronics Co., Ltd. + * Authors: + * raul.rao + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef RK_YOLOV5_DEMO_POST_PROCESS_H +#define RK_YOLOV5_DEMO_POST_PROCESS_H + +#include +#include + +#define LOGI(...) __android_log_print(ANDROID_LOG_INFO, "rkyolo4j", ##__VA_ARGS__); +#define LOGE(...) __android_log_print(ANDROID_LOG_ERROR, "rkyolo4j", ##__VA_ARGS__); + +#define OBJ_NAME_MAX_SIZE 16 +#define OBJ_NUMB_MAX_SIZE 64 +#define OBJ_CLASS_NUM 80 +#define NMS_THRESH 0.6 +#define BOX_THRESH 0.5 +#define PROP_BOX_SIZE (5+OBJ_CLASS_NUM) +#define BOX_LEN 4 + +typedef struct _BOX_RECT +{ + int left; + int right; + int top; + int bottom; +} BOX_RECT; + +typedef struct __detect_result_t +{ + char name[OBJ_NAME_MAX_SIZE]; + int class_id; + BOX_RECT box; + float prop; +} detect_result_t; + +typedef struct _detect_result_group_t +{ + int id; + int count; + detect_result_t results[OBJ_NUMB_MAX_SIZE]; +} detect_result_group_t; + +int post_process(int8_t *input0, int8_t *input1, int8_t *input2, int model_in_h, int model_in_w, + float conf_threshold, float nms_threshold, float scale_w, float scale_h, + std::vector &qnt_zps, std::vector &qnt_scales, + detect_result_group_t *group); + +void deinitPostProcess(); + +#endif //RK_YOLOV5_DEMO_POST_PROCESS_H diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/GrallocOps.h b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/GrallocOps.h new file mode 100644 index 0000000000000000000000000000000000000000..97e92d427b37e01990fc6a5f33f15461147bf7ca --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/GrallocOps.h @@ -0,0 +1,59 @@ +/* + * Copyright (C) 2016 Rockchip Electronics Co., Ltd. + * Authors: + * Zhiqin Wei + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef _rk_graphic_buffer_h_ +#define _rk_graphic_buffer_h_ + +#ifdef ANDROID + +#include +#include +#include + +#include + +#include + +#include +#include +#include +#include +#include +#include + +#include +#include + +#include +#include +#include +#include +#include + +#include "drmrga.h" +#include "rga.h" + +// ------------------------------------------------------------------------------- +int RkRgaGetHandleFd(buffer_handle_t handle, int *fd); +int RkRgaGetHandleAttributes(buffer_handle_t handle, + std::vector *attrs); +int RkRgaGetHandleMapAddress(buffer_handle_t handle, + void **buf); +#endif //Android + +#endif //_rk_graphic_buffer_h_ diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/RgaApi.h b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/RgaApi.h new file mode 100644 index 0000000000000000000000000000000000000000..b0049f72b71f2bbfea749f3e921db26dca388919 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/RgaApi.h @@ -0,0 +1,81 @@ +/* + * Copyright (C) 2016 Rockchip Electronics Co., Ltd. + * Authors: + * Zhiqin Wei + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef _rockchip_rga_c_h_ +#define _rockchip_rga_c_h_ + +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include + +#include "drmrga.h" +#include "rga.h" + +#ifdef __cplusplus +extern "C"{ +#endif + +/* + * Compatible with the old version of C interface.The new + * version of the C interface no longer requires users to + * initialize rga, so RgaInit and RgaDeInit are just for + * compatibility with the old C interface, so please do + * not use ctx, because it is usually a NULL. + */ +#define RgaInit(ctx) ({ \ + int ret = 0; \ + ret = c_RkRgaInit(); \ + c_RkRgaGetContext(ctx); \ + ret;\ +}) +#define RgaDeInit(ctx) { \ + (void)ctx; /* unused */ \ + c_RkRgaDeInit(); \ +} +#define RgaBlit(...) c_RkRgaBlit(__VA_ARGS__) +#define RgaCollorFill(...) c_RkRgaColorFill(__VA_ARGS__) +#define RgaFlush() c_RkRgaFlush() + +int c_RkRgaInit(); +void c_RkRgaDeInit(); +void c_RkRgaGetContext(void **ctx); +int c_RkRgaBlit(rga_info_t *src, rga_info_t *dst, rga_info_t *src1); +int c_RkRgaColorFill(rga_info_t *dst); +int c_RkRgaFlush(); + +#ifndef ANDROID /* linux */ +int c_RkRgaGetAllocBuffer(bo_t *bo_info, int width, int height, int bpp); +int c_RkRgaGetAllocBufferCache(bo_t *bo_info, int width, int height, int bpp); +int c_RkRgaGetMmap(bo_t *bo_info); +int c_RkRgaUnmap(bo_t *bo_info); +int c_RkRgaFree(bo_t *bo_info); +int c_RkRgaGetBufferFd(bo_t *bo_info, int *fd); +#endif /* #ifndef ANDROID */ + +#ifdef __cplusplus +} +#endif + +#endif /* #ifndef _rockchip_rga_c_h_ */ diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/RgaMutex.h b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/RgaMutex.h new file mode 100644 index 0000000000000000000000000000000000000000..61909e2c809e3b33cda6054ebfc9b397c0cd88cb --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/RgaMutex.h @@ -0,0 +1,193 @@ +/* + * Copyright (C) 2020 Rockchip Electronics Co., Ltd. + * Authors: + * PutinLee + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + + +#ifndef _LIBS_RGA_MUTEX_H +#define _LIBS_RGA_MUTEX_H + +#ifndef ANDROID +#include +#include +#include + +#include + + +// Enable thread safety attributes only with clang. +// The attributes can be safely erased when compiling with other compilers. +#if defined(__clang__) && (!defined(SWIG)) +#define THREAD_ANNOTATION_ATTRIBUTE__(x) __attribute__((x)) +#else +#define THREAD_ANNOTATION_ATTRIBUTE__(x) // no-op +#endif + +#define CAPABILITY(x) THREAD_ANNOTATION_ATTRIBUTE__(capability(x)) + +#define SCOPED_CAPABILITY THREAD_ANNOTATION_ATTRIBUTE__(scoped_lockable) + +#define GUARDED_BY(x) THREAD_ANNOTATION_ATTRIBUTE__(guarded_by(x)) + +#define PT_GUARDED_BY(x) THREAD_ANNOTATION_ATTRIBUTE__(pt_guarded_by(x)) + +#define ACQUIRED_BEFORE(...) THREAD_ANNOTATION_ATTRIBUTE__(acquired_before(__VA_ARGS__)) + +#define ACQUIRED_AFTER(...) THREAD_ANNOTATION_ATTRIBUTE__(acquired_after(__VA_ARGS__)) + +#define REQUIRES(...) THREAD_ANNOTATION_ATTRIBUTE__(requires_capability(__VA_ARGS__)) + +#define REQUIRES_SHARED(...) THREAD_ANNOTATION_ATTRIBUTE__(requires_shared_capability(__VA_ARGS__)) + +#define ACQUIRE(...) THREAD_ANNOTATION_ATTRIBUTE__(acquire_capability(__VA_ARGS__)) + +#define ACQUIRE_SHARED(...) THREAD_ANNOTATION_ATTRIBUTE__(acquire_shared_capability(__VA_ARGS__)) + +#define RELEASE(...) THREAD_ANNOTATION_ATTRIBUTE__(release_capability(__VA_ARGS__)) + +#define RELEASE_SHARED(...) THREAD_ANNOTATION_ATTRIBUTE__(release_shared_capability(__VA_ARGS__)) + +#define TRY_ACQUIRE(...) THREAD_ANNOTATION_ATTRIBUTE__(try_acquire_capability(__VA_ARGS__)) + +#define TRY_ACQUIRE_SHARED(...) \ + THREAD_ANNOTATION_ATTRIBUTE__(try_acquire_shared_capability(__VA_ARGS__)) + +#define EXCLUDES(...) THREAD_ANNOTATION_ATTRIBUTE__(locks_excluded(__VA_ARGS__)) + +#define ASSERT_CAPABILITY(x) THREAD_ANNOTATION_ATTRIBUTE__(assert_capability(x)) + +#define ASSERT_SHARED_CAPABILITY(x) THREAD_ANNOTATION_ATTRIBUTE__(assert_shared_capability(x)) + +#define RETURN_CAPABILITY(x) THREAD_ANNOTATION_ATTRIBUTE__(lock_returned(x)) + +#define NO_THREAD_SAFETY_ANALYSIS THREAD_ANNOTATION_ATTRIBUTE__(no_thread_safety_analysis) + +class Condition; + +/* + * NOTE: This class is for code that builds on Win32. Its usage is + * deprecated for code which doesn't build for Win32. New code which + * doesn't build for Win32 should use std::mutex and std::lock_guard instead. + * + * Simple mutex class. The implementation is system-dependent. + * + * The mutex must be unlocked by the thread that locked it. They are not + * recursive, i.e. the same thread can't lock it multiple times. + */ +class CAPABILITY("mutex") Mutex { + public: + enum { + PRIVATE = 0, + SHARED = 1 + }; + + Mutex(); + explicit Mutex(const char* name); + explicit Mutex(int type, const char* name = nullptr); + ~Mutex(); + + // lock or unlock the mutex + int32_t lock() ACQUIRE(); + void unlock() RELEASE(); + + // lock if possible; returns 0 on success, error otherwise + int32_t tryLock() TRY_ACQUIRE(0); + + int32_t timedLock(int64_t timeoutNs) TRY_ACQUIRE(0); + + // Manages the mutex automatically. It'll be locked when Autolock is + // constructed and released when Autolock goes out of scope. + class SCOPED_CAPABILITY Autolock { + public: + inline explicit Autolock(Mutex& mutex) ACQUIRE(mutex) : mLock(mutex) { + mLock.lock(); + } + inline explicit Autolock(Mutex* mutex) ACQUIRE(mutex) : mLock(*mutex) { + mLock.lock(); + } + inline ~Autolock() RELEASE() { + mLock.unlock(); + } + + private: + Mutex& mLock; + // Cannot be copied or moved - declarations only + Autolock(const Autolock&); + Autolock& operator=(const Autolock&); + }; + + private: + friend class Condition; + + // A mutex cannot be copied + Mutex(const Mutex&); + Mutex& operator=(const Mutex&); + + pthread_mutex_t mMutex; +}; + +// --------------------------------------------------------------------------- +inline Mutex::Mutex() { + pthread_mutex_init(&mMutex, nullptr); +} +inline Mutex::Mutex(__attribute__((unused)) const char* name) { + pthread_mutex_init(&mMutex, nullptr); +} +inline Mutex::Mutex(int type, __attribute__((unused)) const char* name) { + if (type == SHARED) { + pthread_mutexattr_t attr; + pthread_mutexattr_init(&attr); + pthread_mutexattr_setpshared(&attr, PTHREAD_PROCESS_SHARED); + pthread_mutex_init(&mMutex, &attr); + pthread_mutexattr_destroy(&attr); + } else { + pthread_mutex_init(&mMutex, nullptr); + } +} +inline Mutex::~Mutex() { + pthread_mutex_destroy(&mMutex); +} +inline int32_t Mutex::lock() { + return -pthread_mutex_lock(&mMutex); +} +inline void Mutex::unlock() { + pthread_mutex_unlock(&mMutex); +} +inline int32_t Mutex::tryLock() { + return -pthread_mutex_trylock(&mMutex); +} +inline int32_t Mutex::timedLock(int64_t timeoutNs) { + timespec now; + clock_gettime(CLOCK_REALTIME, &now); + timeoutNs += now.tv_sec*1000000000 + now.tv_nsec; + const struct timespec ts = { + /* .tv_sec = */ static_cast(timeoutNs / 1000000000), + /* .tv_nsec = */ static_cast(timeoutNs % 1000000000), + }; + return -pthread_mutex_timedlock(&mMutex, &ts); +} + +// --------------------------------------------------------------------------- + +/* + * Automatic mutex. Declare one of these at the top of a function. + * When the function returns, it will go out of scope, and release the + * mutex. + */ + +typedef Mutex::Autolock AutoMutex; +#endif // __ANDROID_VNDK__ +#endif // _LIBS_RGA_MUTEX_H diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/RgaSingleton.h b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/RgaSingleton.h new file mode 100644 index 0000000000000000000000000000000000000000..e87cf1c385533d009360f66e95d3badba0a66be7 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/RgaSingleton.h @@ -0,0 +1,70 @@ +/* + * Copyright (C) 2016 Rockchip Electronics Co., Ltd. + * Authors: + * Zhiqin Wei + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef _LIBS_RGA_SINGLETON_H +#define _LIBS_RGA_SINGLETON_H + +#ifndef ANDROID +#include "RgaMutex.h" + +#if defined(__clang__) +#pragma clang diagnostic push +#pragma clang diagnostic ignored "-Wundefined-var-template" +#endif + +template +class Singleton { + public: + static TYPE& getInstance() { + Mutex::Autolock _l(sLock); + TYPE* instance = sInstance; + if (instance == nullptr) { + instance = new TYPE(); + sInstance = instance; + } + return *instance; + } + + static bool hasInstance() { + Mutex::Autolock _l(sLock); + return sInstance != nullptr; + } + + protected: + ~Singleton() { } + Singleton() { } + + private: + Singleton(const Singleton&); + Singleton& operator = (const Singleton&); + static Mutex sLock; + static TYPE* sInstance; +}; + +#if defined(__clang__) +#pragma clang diagnostic pop +#endif + +#define RGA_SINGLETON_STATIC_INSTANCE(TYPE) \ + template<> ::Mutex \ + (::Singleton< TYPE >::sLock)(::Mutex::PRIVATE); \ + template<> TYPE* ::Singleton< TYPE >::sInstance(nullptr); /* NOLINT */ \ + template class ::Singleton< TYPE >; + +#endif //ANDROID +#endif //_LIBS_RGA_SINGLETON_H diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/RgaUtils.h b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/RgaUtils.h new file mode 100644 index 0000000000000000000000000000000000000000..217ecea0ae4db29b7afaa179351a19b4e8ac1e62 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/RgaUtils.h @@ -0,0 +1,31 @@ +/* + * Copyright (C) 2016 Rockchip Electronics Co., Ltd. + * Authors: + * Zhiqin Wei + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef _rga_utils_h_ +#define _rga_utils_h_ + +// ------------------------------------------------------------------------------- +float get_bpp_from_format(int format); +int get_perPixel_stride_from_format(int format); +int get_buf_from_file(void *buf, int f, int sw, int sh, int index); +int output_buf_data_to_file(void *buf, int f, int sw, int sh, int index); +const char *translate_format_str(int format); +int get_buf_from_file_FBC(void *buf, int f, int sw, int sh, int index); +int output_buf_data_to_file_FBC(void *buf, int f, int sw, int sh, int index); +#endif + diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/RockchipRga.h b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/RockchipRga.h new file mode 100644 index 0000000000000000000000000000000000000000..082f9e1e874d61d8063d971e276acca9e989a157 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/RockchipRga.h @@ -0,0 +1,110 @@ +/* + * Copyright (C) 2016 Rockchip Electronics Co., Ltd. + * Authors: + * Zhiqin Wei + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef _rockchip_rga_h_ +#define _rockchip_rga_h_ + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include "drmrga.h" +#include "GrallocOps.h" +#include "RgaUtils.h" +#include "rga.h" + +////////////////////////////////////////////////////////////////////////////////// +#ifndef ANDROID +#include "RgaSingleton.h" +#endif + +#ifdef ANDROID +#include +#include +#include + +namespace android { +#endif + + class RockchipRga :public Singleton { + public: + + static inline RockchipRga& get() { + return getInstance(); + } + + int RkRgaInit(); + void RkRgaDeInit(); + void RkRgaGetContext(void **ctx); +#ifndef ANDROID /* LINUX */ + int RkRgaAllocBuffer(int drm_fd /* input */, bo_t *bo_info, + int width, int height, int bpp, int flags); + int RkRgaFreeBuffer(int drm_fd /* input */, bo_t *bo_info); + int RkRgaGetAllocBuffer(bo_t *bo_info, int width, int height, int bpp); + int RkRgaGetAllocBufferExt(bo_t *bo_info, int width, int height, int bpp, int flags); + int RkRgaGetAllocBufferCache(bo_t *bo_info, int width, int height, int bpp); + int RkRgaGetMmap(bo_t *bo_info); + int RkRgaUnmap(bo_t *bo_info); + int RkRgaFree(bo_t *bo_info); + int RkRgaGetBufferFd(bo_t *bo_info, int *fd); +#else + int RkRgaGetBufferFd(buffer_handle_t handle, int *fd); + int RkRgaGetHandleMapCpuAddress(buffer_handle_t handle, void **buf); +#endif + int RkRgaBlit(rga_info *src, rga_info *dst, rga_info *src1); + int RkRgaCollorFill(rga_info *dst); + int RkRgaCollorPalette(rga_info *src, rga_info *dst, rga_info *lut); + int RkRgaFlush(); + + + void RkRgaSetLogOnceFlag(int log) { + mLogOnce = log; + } + void RkRgaSetAlwaysLogFlag(bool log) { + mLogAlways = log; + } + void RkRgaLogOutRgaReq(struct rga_req rgaReg); + int RkRgaLogOutUserPara(rga_info *rgaInfo); + inline bool RkRgaIsReady() { + return mSupportRga; + } + + RockchipRga(); + ~RockchipRga(); + private: + bool mSupportRga; + int mLogOnce; + int mLogAlways; + void * mContext; + + friend class Singleton; + }; + +#ifdef ANDROID +}; // namespace android +#endif + +#endif + diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/drmrga.h b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/drmrga.h new file mode 100644 index 0000000000000000000000000000000000000000..71c50feceb66b6d134630d217fff0efa828aae3f --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/drmrga.h @@ -0,0 +1,362 @@ +/* + * Copyright (C) 2016 Rockchip Electronics Co., Ltd. + * Authors: + * Zhiqin Wei + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef _rk_drm_rga_ +#define _rk_drm_rga_ + +#include +#include +#include + +#include "rga.h" + +#ifdef ANDROID +#define DRMRGA_HARDWARE_MODULE_ID "librga" + +#include +#include +#include +#include + +#if defined(ANDROID_12) || defined(USE_HARDWARE_ROCKCHIP) +#include +#endif + +#endif + +#define RGA_BLIT_SYNC 0x5017 +#define RGA_BLIT_ASYNC 0x5018 + +#ifndef ANDROID /* LINUX */ +/* flip source image horizontally (around the vertical axis) */ +#define HAL_TRANSFORM_FLIP_H 0x01 +/* flip source image vertically (around the horizontal axis)*/ +#define HAL_TRANSFORM_FLIP_V 0x02 +/* rotate source image 90 degrees clockwise */ +#define HAL_TRANSFORM_ROT_90 0x04 +/* rotate source image 180 degrees */ +#define HAL_TRANSFORM_ROT_180 0x03 +/* rotate source image 270 degrees clockwise */ +#define HAL_TRANSFORM_ROT_270 0x07 +#endif + +#define HAL_TRANSFORM_FLIP_H_V 0x08 + +/*****************************************************************************/ + +/* for compatibility */ +#define DRM_RGA_MODULE_API_VERSION HWC_MODULE_API_VERSION_0_1 +#define DRM_RGA_DEVICE_API_VERSION HWC_DEVICE_API_VERSION_0_1 +#define DRM_RGA_API_VERSION HWC_DEVICE_API_VERSION + +#define DRM_RGA_TRANSFORM_ROT_MASK 0x0000000F +#define DRM_RGA_TRANSFORM_ROT_0 0x00000000 +#define DRM_RGA_TRANSFORM_ROT_90 HAL_TRANSFORM_ROT_90 +#define DRM_RGA_TRANSFORM_ROT_180 HAL_TRANSFORM_ROT_180 +#define DRM_RGA_TRANSFORM_ROT_270 HAL_TRANSFORM_ROT_270 + +#define DRM_RGA_TRANSFORM_FLIP_MASK 0x00000003 +#define DRM_RGA_TRANSFORM_FLIP_H HAL_TRANSFORM_FLIP_H +#define DRM_RGA_TRANSFORM_FLIP_V HAL_TRANSFORM_FLIP_V + +enum { + AWIDTH = 0, + AHEIGHT, + ASTRIDE, + AFORMAT, + ASIZE, + ATYPE, +}; +/*****************************************************************************/ + +#ifndef ANDROID /* LINUX */ +/* memory type definitions. */ +enum drm_rockchip_gem_mem_type { + /* Physically Continuous memory and used as default. */ + ROCKCHIP_BO_CONTIG = 1 << 0, + /* cachable mapping. */ + ROCKCHIP_BO_CACHABLE = 1 << 1, + /* write-combine mapping. */ + ROCKCHIP_BO_WC = 1 << 2, + ROCKCHIP_BO_SECURE = 1 << 3, + ROCKCHIP_BO_MASK = ROCKCHIP_BO_CONTIG | ROCKCHIP_BO_CACHABLE | + ROCKCHIP_BO_WC | ROCKCHIP_BO_SECURE +}; + +typedef struct bo { + int fd; + void *ptr; + size_t size; + size_t offset; + size_t pitch; + unsigned handle; +} bo_t; +#endif + +/* + @value size: user not need care about.For avoid read/write out of memory + */ +typedef struct rga_rect { + int xoffset; + int yoffset; + int width; + int height; + int wstride; + int hstride; + int format; + int size; +} rga_rect_t; + +typedef struct rga_nn { + int nn_flag; + int scale_r; + int scale_g; + int scale_b; + int offset_r; + int offset_g; + int offset_b; +} rga_nn_t; + +typedef struct rga_dither { + int enable; + int mode; + int lut0_l; + int lut0_h; + int lut1_l; + int lut1_h; +} rga_dither_t; + +struct rga_mosaic_info { + uint8_t enable; + uint8_t mode; +}; + +struct rga_pre_intr_info { + uint8_t enable; + + uint8_t read_intr_en; + uint8_t write_intr_en; + uint8_t read_hold_en; + uint32_t read_threshold; + uint32_t write_start; + uint32_t write_step; +}; + +/* MAX(min, (max - channel_value)) */ +struct rga_osd_invert_factor { + uint8_t alpha_max; + uint8_t alpha_min; + uint8_t yg_max; + uint8_t yg_min; + uint8_t crb_max; + uint8_t crb_min; +}; + +struct rga_color { + union { + struct { + uint8_t red; + uint8_t green; + uint8_t blue; + uint8_t alpha; + }; + uint32_t value; + }; +}; + +struct rga_osd_bpp2 { + uint8_t ac_swap; // ac swap flag + // 0: CA + // 1: AC + uint8_t endian_swap; // rgba2bpp endian swap + // 0: Big endian + // 1: Little endian + struct rga_color color0; + struct rga_color color1; +}; + +struct rga_osd_mode_ctrl { + uint8_t mode; // OSD cal mode: + // 0b'1: statistics mode + // 1b'1: auto inversion overlap mode + uint8_t direction_mode; // horizontal or vertical + // 0: horizontal + // 1: vertical + uint8_t width_mode; // using @fix_width or LUT width + // 0: fix width + // 1: LUT width + uint16_t block_fix_width; // OSD block fixed width + // real width = (fix_width + 1) * 2 + uint8_t block_num; // OSD block num + uint16_t flags_index; // auto invert flags index + + /* invertion config */ + uint8_t color_mode; // selete color + // 0: src1 color + // 1: config data color + uint8_t invert_flags_mode; // invert flag selete + // 0: use RAM flag + // 1: usr last result + uint8_t default_color_sel; // default color mode + // 0: default is bright + // 1: default is dark + uint8_t invert_enable; // invert channel enable + // 1 << 0: aplha enable + // 1 << 1: Y/G disable + // 1 << 2: C/RB disable + uint8_t invert_mode; // invert cal mode + // 0: normal(max-data) + // 1: swap + uint8_t invert_thresh; // if luma > thresh, osd_flag to be 1 + uint8_t unfix_index; // OSD width config index +}; + +struct rga_osd_info { + uint8_t enable; + + struct rga_osd_mode_ctrl mode_ctrl; + struct rga_osd_invert_factor cal_factor; + struct rga_osd_bpp2 bpp2_info; + + union { + struct { + uint32_t last_flags1; + uint32_t last_flags0; + }; + uint64_t last_flags; + }; + + union { + struct { + uint32_t cur_flags1; + uint32_t cur_flags0; + }; + uint64_t cur_flags; + }; +}; + +/* + @value fd: use fd to share memory, it can be ion shard fd,and dma fd. + @value virAddr:userspace address + @value phyAddr:use phy address + @value hnd: use buffer_handle_t + */ +typedef struct rga_info { + int fd; + void *virAddr; + void *phyAddr; +#ifndef ANDROID /* LINUX */ + unsigned hnd; +#else /* Android */ + buffer_handle_t hnd; +#endif + int format; + rga_rect_t rect; + unsigned int blend; + int bufferSize; + int rotation; + int color; + int testLog; + int mmuFlag; + int colorkey_en; + int colorkey_mode; + int colorkey_max; + int colorkey_min; + int scale_mode; + int color_space_mode; + int sync_mode; + rga_nn_t nn; + rga_dither_t dither; + int rop_code; + int rd_mode; + unsigned short is_10b_compact; + unsigned short is_10b_endian; + + int in_fence_fd; + int out_fence_fd; + + int core; + int priority; + + unsigned short enable; + + int handle; + + struct rga_mosaic_info mosaic_info; + + struct rga_osd_info osd_info; + + struct rga_pre_intr_info pre_intr; + + int mpi_mode; + + union { + int ctx_id; + int job_handle; + }; + + uint16_t rgba5551_flags; + uint8_t rgba5551_alpha0; + uint8_t rgba5551_alpha1; + + char reserve[398]; +} rga_info_t; + + +typedef struct drm_rga { + rga_rect_t src; + rga_rect_t dst; +} drm_rga_t; + +/* + @fun rga_set_rect:For use to set the rects esayly + + @param rect:The rect user want to set,like setting the src rect: + drm_rga_t rects; + rga_set_rect(rects.src,0,0,1920,1080,1920,NV12); + mean to set the src rect to the value. + */ +static inline int rga_set_rect(rga_rect_t *rect, + int x, int y, int w, int h, int sw, int sh, int f) { + if (!rect) + return -EINVAL; + + rect->xoffset = x; + rect->yoffset = y; + rect->width = w; + rect->height = h; + rect->wstride = sw; + rect->hstride = sh; + rect->format = f; + + return 0; +} + +#ifndef ANDROID /* LINUX */ +static inline void rga_set_rotation(rga_info_t *info, int angle) { + if (angle == 90) + info->rotation = HAL_TRANSFORM_ROT_90; + else if (angle == 180) + info->rotation = HAL_TRANSFORM_ROT_180; + else if (angle == 270) + info->rotation = HAL_TRANSFORM_ROT_270; +} +#endif +/*****************************************************************************/ + +#endif diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/im2d.h b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/im2d.h new file mode 100644 index 0000000000000000000000000000000000000000..187867035ff2048a679d95a26d0890b8783f1dee --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/im2d.h @@ -0,0 +1,32 @@ +/* + * Copyright (C) 2020 Rockchip Electronics Co., Ltd. + * Authors: + * PutinLee + * Cerf Yu + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef _im2d_h_ +#define _im2d_h_ + +#include "im2d_version.h" +#include "im2d_type.h" + +#include "im2d_common.h" +#include "im2d_buffer.h" +#include "im2d_single.h" +#include "im2d_task.h" +#include "im2d_mpi.h" + +#endif /* #ifndef _im2d_h_ */ diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/im2d.hpp b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/im2d.hpp new file mode 100644 index 0000000000000000000000000000000000000000..2d5ac83d33bb7cdb6fcc1f1119979fc881624a44 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/im2d.hpp @@ -0,0 +1,27 @@ +/* + * Copyright (C) 2020 Rockchip Electronics Co., Ltd. + * Authors: + * PutinLee + * Cerf Yu + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef _im2d_hpp_ +#define _im2d_hpp_ + +#include "im2d.h" +#include "im2d_expand.h" + +#endif /* #ifndef _im2d_hpp_ */ + + diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/im2d_buffer.h b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/im2d_buffer.h new file mode 100644 index 0000000000000000000000000000000000000000..023aa2f9a1261fe8860fe3c49a992b3c65794918 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/im2d_buffer.h @@ -0,0 +1,183 @@ +/* + * Copyright (C) 2022 Rockchip Electronics Co., Ltd. + * Authors: + * Cerf Yu + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef _im2d_buffer_h_ +#define _im2d_buffer_h_ + +#include "im2d_type.h" + +/** + * Import external buffers into RGA driver. + * + * @param fd/va/pa + * Select dma_fd/virtual_address/physical_address by buffer type + * @param size + * Describes the size of the image buffer + * + * @return rga_buffer_handle_t + */ +#ifdef __cplusplus +IM_API rga_buffer_handle_t importbuffer_fd(int fd, int size); +IM_API rga_buffer_handle_t importbuffer_virtualaddr(void *va, int size); +IM_API rga_buffer_handle_t importbuffer_physicaladdr(uint64_t pa, int size); +#endif + +/** + * Import external buffers into RGA driver. + * + * @param fd/va/pa + * Select dma_fd/virtual_address/physical_address by buffer type + * @param width + * Describes the pixel width stride of the image buffer + * @param height + * Describes the pixel height stride of the image buffer + * @param format + * Describes the pixel format of the image buffer + * + * @return rga_buffer_handle_t + */ +#ifdef __cplusplus +IM_API rga_buffer_handle_t importbuffer_fd(int fd, int width, int height, int format); +IM_API rga_buffer_handle_t importbuffer_virtualaddr(void *va, int width, int height, int format); +IM_API rga_buffer_handle_t importbuffer_physicaladdr(uint64_t pa, int width, int height, int format); +#endif + +/** + * Import external buffers into RGA driver. + * + * @param fd/va/pa + * Select dma_fd/virtual_address/physical_address by buffer type + * @param param + * Configure buffer parameters + * + * @return rga_buffer_handle_t + */ +IM_EXPORT_API rga_buffer_handle_t importbuffer_fd(int fd, im_handle_param_t *param); +IM_EXPORT_API rga_buffer_handle_t importbuffer_virtualaddr(void *va, im_handle_param_t *param); +IM_EXPORT_API rga_buffer_handle_t importbuffer_physicaladdr(uint64_t pa, im_handle_param_t *param); + +/** + * Import external buffers into RGA driver. + * + * @param handle + * rga buffer handle + * + * @return success or else negative error code. + */ +IM_EXPORT_API IM_STATUS releasebuffer_handle(rga_buffer_handle_t handle); + +/** + * Wrap image Parameters. + * + * @param handle/virtualaddr/physicaladdr/fd + * RGA buffer handle/virtualaddr/physicaladdr/fd. + * @param width + * Width of image manipulation area. + * @param height + * Height of image manipulation area. + * @param wstride + * Width pixel stride, default (width = wstride). + * @param hstride + * Height pixel stride, default (height = hstride). + * @param format + * Image format. + * + * @return rga_buffer_t + */ +#define wrapbuffer_handle(handle, width, height, format, ...) \ + ({ \ + rga_buffer_t im2d_api_buffer; \ + int __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(int); \ + if (__argc == 0) { \ + im2d_api_buffer = wrapbuffer_handle_t(handle, width, height, width, height, format); \ + } else if (__argc == 2){ \ + im2d_api_buffer = wrapbuffer_handle_t(handle, width, height, __args[0], __args[1], format); \ + } else { \ + memset(&im2d_api_buffer, 0x0, sizeof(im2d_api_buffer)); \ + printf("invalid parameter\n"); \ + } \ + im2d_api_buffer; \ + }) + +#define wrapbuffer_virtualaddr(vir_addr, width, height, format, ...) \ + ({ \ + rga_buffer_t im2d_api_buffer; \ + int __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(int); \ + if (__argc == 0) { \ + im2d_api_buffer = wrapbuffer_virtualaddr_t(vir_addr, width, height, width, height, format); \ + } else if (__argc == 2){ \ + im2d_api_buffer = wrapbuffer_virtualaddr_t(vir_addr, width, height, __args[0], __args[1], format); \ + } else { \ + memset(&im2d_api_buffer, 0x0, sizeof(im2d_api_buffer)); \ + printf("invalid parameter\n"); \ + } \ + im2d_api_buffer; \ + }) + +#define wrapbuffer_physicaladdr(phy_addr, width, height, format, ...) \ + ({ \ + rga_buffer_t im2d_api_buffer; \ + int __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(int); \ + if (__argc == 0) { \ + im2d_api_buffer = wrapbuffer_physicaladdr_t(phy_addr, width, height, width, height, format); \ + } else if (__argc == 2){ \ + im2d_api_buffer = wrapbuffer_physicaladdr_t(phy_addr, width, height, __args[0], __args[1], format); \ + } else { \ + memset(&im2d_api_buffer, 0x0, sizeof(im2d_api_buffer)); \ + printf("invalid parameter\n"); \ + } \ + im2d_api_buffer; \ + }) + +#define wrapbuffer_fd(fd, width, height, format, ...) \ + ({ \ + rga_buffer_t im2d_api_buffer; \ + int __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(int); \ + if (__argc == 0) { \ + im2d_api_buffer = wrapbuffer_fd_t(fd, width, height, width, height, format); \ + } else if (__argc == 2){ \ + im2d_api_buffer = wrapbuffer_fd_t(fd, width, height, __args[0], __args[1], format); \ + } else { \ + memset(&im2d_api_buffer, 0x0, sizeof(im2d_api_buffer)); \ + printf("invalid parameter\n"); \ + } \ + im2d_api_buffer; \ + }) +/* Symbols for define *_t functions */ +IM_C_API rga_buffer_t wrapbuffer_handle_t(rga_buffer_handle_t handle, int width, int height, int wstride, int hstride, int format); +IM_C_API rga_buffer_t wrapbuffer_virtualaddr_t(void* vir_addr, int width, int height, int wstride, int hstride, int format); +IM_C_API rga_buffer_t wrapbuffer_physicaladdr_t(void* phy_addr, int width, int height, int wstride, int hstride, int format); +IM_C_API rga_buffer_t wrapbuffer_fd_t(int fd, int width, int height, int wstride, int hstride, int format); + +#ifdef __cplusplus +#undef wrapbuffer_handle +IM_API rga_buffer_t wrapbuffer_handle(rga_buffer_handle_t handle, + int width, int height, int format); +IM_API rga_buffer_t wrapbuffer_handle(rga_buffer_handle_t handle, + int width, int height, int format, + int wstride, int hstride); +#endif + +void imsetAlphaBit(rga_buffer_t *buf, uint8_t alpha0, uint8_t alpha1); +void imsetOpacity(rga_buffer_t *buf, uint8_t alpha); +void imsetColorSpace(rga_buffer_t *buf, IM_COLOR_SPACE_MODE mode); + +#endif /* #ifndef _im2d_buffer_h_ */ diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/im2d_common.h b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/im2d_common.h new file mode 100644 index 0000000000000000000000000000000000000000..9030efe29ff3ac5a2dc3ab2a8c73fb9bf40dd5dc --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/im2d_common.h @@ -0,0 +1,151 @@ +/* + * Copyright (C) 2022 Rockchip Electronics Co., Ltd. + * Authors: + * Cerf Yu + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef _im2d_common_h_ +#define _im2d_common_h_ + +#include "im2d_type.h" + +/** + * Query RGA basic information, supported resolution, supported format, etc. + * + * @param name + * RGA_VENDOR + * RGA_VERSION + * RGA_MAX_INPUT + * RGA_MAX_OUTPUT + * RGA_INPUT_FORMAT + * RGA_OUTPUT_FORMAT + * RGA_EXPECTED + * RGA_ALL + * + * @returns a string describing properties of RGA. + */ +IM_EXPORT_API const char* querystring(int name); + +/** + * String to output the error message + * + * @param status + * process result value. + * + * @returns error message. + */ +#define imStrError(...) \ + ({ \ + const char* im2d_api_err; \ + int __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(int); \ + if (__argc == 0) { \ + im2d_api_err = imStrError_t(IM_STATUS_INVALID_PARAM); \ + } else if (__argc == 1){ \ + im2d_api_err = imStrError_t((IM_STATUS)__args[0]); \ + } else { \ + im2d_api_err = ("Fatal error, imStrError() too many parameters\n"); \ + printf("Fatal error, imStrError() too many parameters\n"); \ + } \ + im2d_api_err; \ + }) +IM_C_API const char* imStrError_t(IM_STATUS status); + +/** + * check im2d api header file + * + * @param header_version + * Default is RGA_CURRENT_API_HEADER_VERSION, no need to change if there are no special cases. + * + * @returns no error or else negative error code. + */ +#ifdef __cplusplus +IM_API IM_STATUS imcheckHeader(im_api_version_t header_version = RGA_CURRENT_API_HEADER_VERSION); +#endif + +/** + * check RGA basic information, supported resolution, supported format, etc. + * + * @param src + * @param dst + * @param pat + * @param src_rect + * @param dst_rect + * @param pat_rect + * @param mode_usage + * + * @returns no error or else negative error code. + */ +#define imcheck(src, dst, src_rect, dst_rect, ...) \ + ({ \ + IM_STATUS __ret = IM_STATUS_NOERROR; \ + rga_buffer_t __pat; \ + im_rect __pat_rect; \ + memset(&__pat, 0, sizeof(rga_buffer_t)); \ + memset(&__pat_rect, 0, sizeof(im_rect)); \ + int __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(int); \ + if (__argc == 0) { \ + __ret = imcheck_t(src, dst, __pat, src_rect, dst_rect, __pat_rect, 0); \ + } else if (__argc == 1){ \ + __ret = imcheck_t(src, dst, __pat, src_rect, dst_rect, __pat_rect, __args[0]); \ + } else { \ + __ret = IM_STATUS_FAILED; \ + printf("check failed\n"); \ + } \ + __ret; \ + }) +#define imcheck_composite(src, dst, pat, src_rect, dst_rect, pat_rect, ...) \ + ({ \ + IM_STATUS __ret = IM_STATUS_NOERROR; \ + int __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(int); \ + if (__argc == 0) { \ + __ret = imcheck_t(src, dst, pat, src_rect, dst_rect, pat_rect, 0); \ + } else if (__argc == 1){ \ + __ret = imcheck_t(src, dst, pat, src_rect, dst_rect, pat_rect, __args[0]); \ + } else { \ + __ret = IM_STATUS_FAILED; \ + printf("check failed\n"); \ + } \ + __ret; \ + }) +IM_C_API IM_STATUS imcheck_t(const rga_buffer_t src, const rga_buffer_t dst, const rga_buffer_t pat, + const im_rect src_rect, const im_rect dst_rect, const im_rect pat_rect, const int mode_usage); +/* Compatible with the legacy symbol */ +IM_C_API void rga_check_perpare(rga_buffer_t *src, rga_buffer_t *dst, rga_buffer_t *pat, + im_rect *src_rect, im_rect *dst_rect, im_rect *pat_rect, int mode_usage); + +/** + * block until all execution is complete + * + * @param release_fence_fd + * RGA job release fence fd + * + * @returns success or else negative error code. + */ +IM_EXPORT_API IM_STATUS imsync(int release_fence_fd); + +/** + * config + * + * @param name + * enum IM_CONFIG_NAME + * @param value + * + * @returns success or else negative error code. + */ +IM_EXPORT_API IM_STATUS imconfig(IM_CONFIG_NAME name, uint64_t value); + +#endif /* #ifndef _im2d_common_h_ */ diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/im2d_expand.h b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/im2d_expand.h new file mode 100644 index 0000000000000000000000000000000000000000..c213bda0808ea5e76a4e01c369bb99ed8c59ca96 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/im2d_expand.h @@ -0,0 +1,47 @@ +/* + * Copyright (C) 2022 Rockchip Electronics Co., Ltd. + * Authors: + * Cerf Yu + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef _im2d_expand_h_ +#define _im2d_expand_h_ + +#ifdef __cplusplus + +#include "im2d_type.h" + +#if ANDROID + +#include + +using namespace android; + +IM_API rga_buffer_handle_t importbuffer_GraphicBuffer_handle(buffer_handle_t hnd); +IM_API rga_buffer_handle_t importbuffer_GraphicBuffer(sp buf); + +IM_API rga_buffer_t wrapbuffer_handle(buffer_handle_t hnd); +IM_API rga_buffer_t wrapbuffer_GraphicBuffer(sp buf); + +#if USE_AHARDWAREBUFFER +#include +IM_API rga_buffer_handle_t importbuffer_AHardwareBuffer(AHardwareBuffer *buf); +IM_API rga_buffer_t wrapbuffer_AHardwareBuffer(AHardwareBuffer *buf); + +#endif /* #if USE_AHARDWAREBUFFER */ +#endif /* #if ANDROID */ + +#endif /* #ifdef __cplusplus */ + +#endif diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/im2d_mpi.h b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/im2d_mpi.h new file mode 100644 index 0000000000000000000000000000000000000000..f984d15daf9ec173be781aaf13f40ea73c57ecb1 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/im2d_mpi.h @@ -0,0 +1,80 @@ +/* + * Copyright (C) 2022 Rockchip Electronics Co., Ltd. + * Authors: + * Cerf Yu + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef _im2d_mpi_hpp_ +#define _im2d_mpi_hpp_ + +#include "im2d_type.h" + +/** + * Create and config an rga ctx for rockit-ko + * + * @param flags + * Some configuration flags for this job + * + * @returns job id. + */ +IM_EXPORT_API im_ctx_id_t imbegin(uint32_t flags); + +/** + * Cancel and delete an rga ctx for rockit-ko + * + * @param flags + * Some configuration flags for this job + * + * @returns success or else negative error code. + */ +IM_EXPORT_API IM_STATUS imcancel(im_ctx_id_t id); + +/** + * process for rockit-ko + * + * @param src + * The input source image and is also the foreground image in blend. + * @param dst + * The output destination image and is also the foreground image in blend. + * @param pat + * The foreground image, or a LUT table. + * @param srect + * The rectangle on the src channel image that needs to be processed. + * @param drect + * The rectangle on the dst channel image that needs to be processed. + * @param prect + * The rectangle on the pat channel image that needs to be processed. + * @param acquire_fence_fd + * @param release_fence_fd + * @param opt + * The image processing options configuration. + * @param usage + * The image processing usage. + * @param ctx_id + * ctx id + * + * @returns success or else negative error code. + */ +#ifdef __cplusplus +IM_API IM_STATUS improcess(rga_buffer_t src, rga_buffer_t dst, rga_buffer_t pat, + im_rect srect, im_rect drect, im_rect prect, + int acquire_fence_fd, int *release_fence_fd, + im_opt_t *opt, int usage, im_ctx_id_t ctx_id); +#endif +IM_EXPORT_API IM_STATUS improcess_ctx(rga_buffer_t src, rga_buffer_t dst, rga_buffer_t pat, + im_rect srect, im_rect drect, im_rect prect, + int acquire_fence_fd, int *release_fence_fd, + im_opt_t *opt, int usage, im_ctx_id_t ctx_id); + +#endif /* #ifndef _im2d_mpi_hpp_ */ \ No newline at end of file diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/im2d_single.h b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/im2d_single.h new file mode 100644 index 0000000000000000000000000000000000000000..fb96a9afadd2564550895b594140e9ec926500dd --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/im2d_single.h @@ -0,0 +1,940 @@ +/* + * Copyright (C) 2022 Rockchip Electronics Co., Ltd. + * Authors: + * Cerf Yu + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef _im2d_single_h_ +#define _im2d_single_h_ + +#include "im2d_type.h" + +#ifdef __cplusplus + +/** + * copy + * + * @param src + * The input source image. + * @param dst + * The output destination image. + * @param sync + * When 'sync == 1', wait for the operation to complete and return, otherwise return directly. + * @param release_fence_fd + * When 'sync == 0', the fence_fd used to identify the current job state + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imcopy(const rga_buffer_t src, rga_buffer_t dst, int sync = 1, int *release_fence_fd = NULL); + +/** + * Resize + * + * @param src + * The input source image. + * @param dst + * The output destination image. + * @param fx + * X-direction resize factor. + * @param fy + * X-direction resize factor. + * @param interpolation + * Interpolation formula(Only RGA1 support). + * @param sync + * When 'sync == 1', wait for the operation to complete and return, otherwise return directly. + * @param release_fence_fd + * When 'sync == 0', the fence_fd used to identify the current job state + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imresize(const rga_buffer_t src, rga_buffer_t dst, double fx = 0, double fy = 0, int interpolation = 0, int sync = 1, int *release_fence_fd = NULL); + +/** + * Crop + * + * @param src + * The input source image. + * @param dst + * The output destination image. + * @param rect + * The rectangle on the source image that needs to be cropped. + * @param sync + * When 'sync == 1', wait for the operation to complete and return, otherwise return directly. + * @param release_fence_fd + * When 'sync == 0', the fence_fd used to identify the current job state + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imcrop(const rga_buffer_t src, rga_buffer_t dst, im_rect rect, int sync = 1, int *release_fence_fd = NULL); + +/** + * translate + * + * @param src + * The input source image. + * @param dst + * The output destination image. + * @param x + * Output the coordinates of the starting point in the X-direction of the destination image. + * @param y + * Output the coordinates of the starting point in the Y-direction of the destination image. + * @param sync + * When 'sync == 1', wait for the operation to complete and return, otherwise return directly. + * @param release_fence_fd + * When 'sync == 0', the fence_fd used to identify the current job state + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imtranslate(const rga_buffer_t src, rga_buffer_t dst, int x, int y, int sync = 1, int *release_fence_fd = NULL); + +/** + * format convert + * + * @param src + * The input source image. + * @param dst + * The output destination image. + * @param sfmt + * The source image format. + * @param dfmt + * The destination image format. + * @param mode + * color space mode: + * IM_YUV_TO_RGB_BT601_LIMIT + * IM_YUV_TO_RGB_BT601_FULL + * IM_YUV_TO_RGB_BT709_LIMIT + * IM_RGB_TO_YUV_BT601_FULL + * IM_RGB_TO_YUV_BT601_LIMIT + * IM_RGB_TO_YUV_BT709_LIMIT + * @param sync + * When 'sync == 1', wait for the operation to complete and return, otherwise return directly. + * @param release_fence_fd + * When 'sync == 0', the fence_fd used to identify the current job state + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imcvtcolor(rga_buffer_t src, rga_buffer_t dst, int sfmt, int dfmt, int mode = IM_COLOR_SPACE_DEFAULT, int sync = 1, int *release_fence_fd = NULL); + +/** + * rotation + * + * @param src + * The input source image. + * @param dst + * The output destination image. + * @param rotation + * IM_HAL_TRANSFORM_ROT_90 + * IM_HAL_TRANSFORM_ROT_180 + * IM_HAL_TRANSFORM_ROT_270 + * @param sync + * When 'sync == 1', wait for the operation to complete and return, otherwise return directly. + * @param release_fence_fd + * When 'sync == 0', the fence_fd used to identify the current job state + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imrotate(const rga_buffer_t src, rga_buffer_t dst, int rotation, int sync = 1, int *release_fence_fd = NULL); + +/** + * flip + * + * @param src + * The input source image. + * @param dst + * The output destination image. + * @param mode + * IM_HAL_TRANSFORM_FLIP_H + * IM_HAL_TRANSFORM_FLIP_V + * @param sync + * When 'sync == 1', wait for the operation to complete and return, otherwise return directly. + * @param release_fence_fd + * When 'sync == 0', the fence_fd used to identify the current job state + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imflip(const rga_buffer_t src, rga_buffer_t dst, int mode, int sync = 1, int *release_fence_fd = NULL); + +/** + * 2-channel blend (SRC + DST -> DST or SRCA + SRCB -> DST) + * + * @param fg_image + * The foreground image. + * @param bg_image + * The background image, which is also the output destination image. + * @param mode + * Port-Duff mode: + * IM_ALPHA_BLEND_SRC + * IM_ALPHA_BLEND_DST + * IM_ALPHA_BLEND_SRC_OVER + * IM_ALPHA_BLEND_DST_OVER + * @param sync + * When 'sync == 1', wait for the operation to complete and return, otherwise return directly. + * @param release_fence_fd + * When 'sync == 0', the fence_fd used to identify the current job state + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imblend(const rga_buffer_t fd_image, rga_buffer_t bg_image, int mode = IM_ALPHA_BLEND_SRC_OVER, int sync = 1, int *release_fence_fd = NULL); + +/** + * 3-channel blend (SRC + DST -> DST or SRCA + SRCB -> DST) + * + * @param fg_image + * The foreground image. + * @param bg_image + * The background image. + * @param output_image + * The output destination image. + * @param mode + * Port-Duff mode: + * IM_ALPHA_BLEND_SRC + * IM_ALPHA_BLEND_DST + * IM_ALPHA_BLEND_SRC_OVER + * IM_ALPHA_BLEND_DST_OVER + * @param sync + * When 'sync == 1', wait for the operation to complete and return, otherwise return directly. + * @param release_fence_fd + * When 'sync == 0', the fence_fd used to identify the current job state + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imcomposite(const rga_buffer_t srcA, const rga_buffer_t srcB, rga_buffer_t dst, int mode = IM_ALPHA_BLEND_SRC_OVER, int sync = 1, int *release_fence_fd = NULL); + +/** + * color key + * + * @param fg_image + * The foreground image. + * @param bg_image + * The background image, which is also the output destination image. + * @param colorkey_range + * The range of color key. + * @param sync + * When 'sync == 1', wait for the operation to complete and return, otherwise return directly. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imcolorkey(const rga_buffer_t src, rga_buffer_t dst, im_colorkey_range range, int mode = IM_ALPHA_COLORKEY_NORMAL, int sync = 1, int *release_fence_fd = NULL); + +/** + * OSD + * + * @param osd + * The osd text block. + * @param dst + * The background image. + * @param osd_rect + * The rectangle on the source image that needs to be OSD. + * @param osd_config + * osd mode configuration. + * @param sync + * When 'sync == 1', wait for the operation to complete and return, otherwise return directly. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imosd(const rga_buffer_t osd,const rga_buffer_t dst, + const im_rect osd_rect, im_osd_t *osd_config, + int sync = 1, int *release_fence_fd = NULL); + +/** + * nn quantize + * + * @param src + * The input source image. + * @param dst + * The output destination image. + * @param nninfo + * nn configuration + * @param sync + * When 'sync == 1', wait for the operation to complete and return, otherwise return directly. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imquantize(const rga_buffer_t src, rga_buffer_t dst, im_nn_t nn_info, int sync = 1, int *release_fence_fd = NULL); + +/** + * ROP + * + * @param src + * The input source image. + * @param dst + * The output destination image. + * @param rop_code + * The ROP opcode. + * @param sync + * When 'sync == 1', wait for the operation to complete and return, otherwise return directly. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imrop(const rga_buffer_t src, rga_buffer_t dst, int rop_code, int sync = 1, int *release_fence_fd = NULL); + +/** + * fill/reset/draw + * + * @param dst + * The output destination image. + * @param rect + * The rectangle on the source image that needs to be filled with color. + * @param color + * The fill color value. + * @param sync + * When 'sync == 1', wait for the operation to complete and return, otherwise return directly. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imfill(rga_buffer_t dst, im_rect rect, int color, int sync = 1, int *release_fence_fd = NULL); + +/** + * fill array + * + * @param dst + * The output destination image. + * @param rect_array + * The rectangle arrays on the source image that needs to be filled with color. + * @param array_size + * The size of rectangular area arrays. + * @param color + * The fill color value. + * @param sync + * When 'sync == 1', wait for the operation to complete and return, otherwise return directly. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imfillArray(rga_buffer_t dst, im_rect *rect_array, int array_size, uint32_t color, int sync = 1, int *release_fence_fd = NULL); + +/** + * fill rectangle + * + * @param dst + * The output destination image. + * @param rect + * The rectangle on the source image that needs to be filled with color. + * @param color + * The fill color value. + * @param thickness + * Thickness of lines that make up the rectangle. Negative values, like -1, + * mean that the function has to draw a filled rectangle. + * @param sync + * When 'sync == 1', wait for the operation to complete and return, otherwise return directly. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imrectangle(rga_buffer_t dst, im_rect rect, + uint32_t color, int thickness, + int sync = 1, int *release_fence_fd = NULL); + +/** + * fill rectangle array + * + * @param dst + * The output destination image. + * @param rect_array + * The rectangle arrays on the source image that needs to be filled with color. + * @param array_size + * The size of rectangular area arrays. + * @param color + * The fill color value. + * @param thickness + * Thickness of lines that make up the rectangle. Negative values, like -1, + * mean that the function has to draw a filled rectangle. + * @param sync + * When 'sync == 1', wait for the operation to complete and return, otherwise return directly. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imrectangleArray(rga_buffer_t dst, im_rect *rect_array, int array_size, + uint32_t color, int thickness, + int sync = 1, int *release_fence_fd = NULL); + +/** + * MOSAIC + * + * @param image + * The output destination image. + * @param rect + * The rectangle on the source image that needs to be mosaicked. + * @param mosaic_mode + * mosaic block width configuration: + * IM_MOSAIC_8 + * IM_MOSAIC_16 + * IM_MOSAIC_32 + * IM_MOSAIC_64 + * IM_MOSAIC_128 + * @param sync + * When 'sync == 1', wait for the operation to complete and return, otherwise return directly. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS immosaic(const rga_buffer_t image, im_rect rect, int mosaic_mode, int sync = 1, int *release_fence_fd = NULL); + +/** + * MOSAIC array + * + * @param image + * The output destination image. + * @param rect_array + * The rectangle arrays on the source image that needs to be filled with color. + * @param array_size + * The size of rectangular area arrays. + * @param mosaic_mode + * mosaic block width configuration: + * IM_MOSAIC_8 + * IM_MOSAIC_16 + * IM_MOSAIC_32 + * IM_MOSAIC_64 + * IM_MOSAIC_128 + * @param sync + * When 'sync == 1', wait for the operation to complete and return, otherwise return directly. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS immosaicArray(const rga_buffer_t image, im_rect *rect_array, int array_size, int mosaic_mode, int sync = 1, int *release_fence_fd = NULL); + +/** + * palette + * + * @param src + * The input source image. + * @param dst + * The output destination image. + * @param lut + * The LUT table. + * @param sync + * When 'sync == 1', wait for the operation to complete and return, otherwise return directly. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS impalette(rga_buffer_t src, rga_buffer_t dst, rga_buffer_t lut, int sync = 1, int *release_fence_fd = NULL); + +/** + * process for single task mode + * + * @param src + * The input source image and is also the foreground image in blend. + * @param dst + * The output destination image and is also the foreground image in blend. + * @param pat + * The foreground image, or a LUT table. + * @param srect + * The rectangle on the src channel image that needs to be processed. + * @param drect + * The rectangle on the dst channel image that needs to be processed. + * @param prect + * The rectangle on the pat channel image that needs to be processed. + * @param opt + * The image processing options configuration. + * @param usage + * The image processing usage. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS improcess(rga_buffer_t src, rga_buffer_t dst, rga_buffer_t pat, + im_rect srect, im_rect drect, im_rect prect, + int acquire_fence_fd, int *release_fence_fd, + im_opt_t *opt_ptr, int usage); + +/** + * make border + * + * @param src + * The input source image. + * @param dst + * The output destination image. + * @param top + * the top pixels + * @param bottom + * the bottom pixels + * @param left + * the left pixels + * @param right + * the right pixels + * @param border_type + * Border type. + * @param value + * The pixel value at which the border is filled. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS immakeBorder(rga_buffer_t src, rga_buffer_t dst, + int top, int bottom, int left, int right, + int border_type, int value = 0, + int sync = 1, int acquir_fence_fd = -1, int *release_fence_fd = NULL); + +#endif /* #ifdef __cplusplus */ + +IM_C_API IM_STATUS immosaic(const rga_buffer_t image, im_rect rect, int mosaic_mode, int sync); +IM_C_API IM_STATUS imosd(const rga_buffer_t osd,const rga_buffer_t dst, + const im_rect osd_rect, im_osd_t *osd_config, int sync); +IM_C_API IM_STATUS improcess(rga_buffer_t src, rga_buffer_t dst, rga_buffer_t pat, + im_rect srect, im_rect drect, im_rect prect, int usage); + +/* Start: Symbols reserved for compatibility with macro functions */ +IM_C_API IM_STATUS imcopy_t(const rga_buffer_t src, rga_buffer_t dst, int sync); +IM_C_API IM_STATUS imresize_t(const rga_buffer_t src, rga_buffer_t dst, double fx, double fy, int interpolation, int sync); +IM_C_API IM_STATUS imcrop_t(const rga_buffer_t src, rga_buffer_t dst, im_rect rect, int sync); +IM_C_API IM_STATUS imtranslate_t(const rga_buffer_t src, rga_buffer_t dst, int x, int y, int sync); +IM_C_API IM_STATUS imcvtcolor_t(rga_buffer_t src, rga_buffer_t dst, int sfmt, int dfmt, int mode, int sync); +IM_C_API IM_STATUS imrotate_t(const rga_buffer_t src, rga_buffer_t dst, int rotation, int sync); +IM_C_API IM_STATUS imflip_t (const rga_buffer_t src, rga_buffer_t dst, int mode, int sync); +IM_C_API IM_STATUS imblend_t(const rga_buffer_t srcA, const rga_buffer_t srcB, rga_buffer_t dst, int mode, int sync); +IM_C_API IM_STATUS imcolorkey_t(const rga_buffer_t src, rga_buffer_t dst, im_colorkey_range range, int mode, int sync); +IM_C_API IM_STATUS imquantize_t(const rga_buffer_t src, rga_buffer_t dst, im_nn_t nn_info, int sync); +IM_C_API IM_STATUS imrop_t(const rga_buffer_t src, rga_buffer_t dst, int rop_code, int sync); +IM_C_API IM_STATUS imfill_t(rga_buffer_t dst, im_rect rect, int color, int sync); +IM_C_API IM_STATUS impalette_t(rga_buffer_t src, rga_buffer_t dst, rga_buffer_t lut, int sync); +/* End: Symbols reserved for compatibility with macro functions */ + +#ifndef __cplusplus + +#define RGA_GET_MIN(n1, n2) ((n1) < (n2) ? (n1) : (n2)) + +/** + * copy + * + * @param src + * @param dst + * @param sync + * wait until operation complete + * + * @returns success or else negative error code. + */ +#define imcopy(src, dst, ...) \ + ({ \ + IM_STATUS __ret = IM_STATUS_SUCCESS; \ + int __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(int); \ + if (__argc == 0) { \ + __ret = imcopy_t(src, dst, 1); \ + } else if (__argc == 1){ \ + __ret = imcopy_t(src, dst, (int)__args[RGA_GET_MIN(__argc, 0)]); \ + } else { \ + __ret = IM_STATUS_INVALID_PARAM; \ + printf("invalid parameter\n"); \ + } \ + __ret; \ + }) + +/** + * Resize + * + * @param src + * @param dst + * @param fx + * @param fy + * @param interpolation + * @param sync + * wait until operation complete + * + * @returns success or else negative error code. + */ +#define imresize(src, dst, ...) \ + ({ \ + IM_STATUS __ret = IM_STATUS_SUCCESS; \ + double __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(double); \ + if (__argc == 0) { \ + __ret = imresize_t(src, dst, 0, 0, INTER_LINEAR, 1); \ + } else if (__argc == 2){ \ + __ret = imresize_t(src, dst, __args[RGA_GET_MIN(__argc, 0)], __args[RGA_GET_MIN(__argc, 1)], INTER_LINEAR, 1); \ + } else if (__argc == 3){ \ + __ret = imresize_t(src, dst, __args[RGA_GET_MIN(__argc, 0)], __args[RGA_GET_MIN(__argc, 1)], (int)__args[RGA_GET_MIN(__argc, 2)], 1); \ + } else if (__argc == 4){ \ + __ret = imresize_t(src, dst, __args[RGA_GET_MIN(__argc, 0)], __args[RGA_GET_MIN(__argc, 1)], (int)__args[RGA_GET_MIN(__argc, 2)], (int)__args[RGA_GET_MIN(__argc, 3)]); \ + } else { \ + __ret = IM_STATUS_INVALID_PARAM; \ + printf("invalid parameter\n"); \ + } \ + __ret; \ + }) + +#define impyramid(src, dst, direction) \ + imresize_t(src, \ + dst, \ + direction == IM_UP_SCALE ? 0.5 : 2, \ + direction == IM_UP_SCALE ? 0.5 : 2, \ + INTER_LINEAR, 1) + +/** + * format convert + * + * @param src + * @param dst + * @param sfmt + * @param dfmt + * @param mode + * color space mode: IM_COLOR_SPACE_MODE + * @param sync + * wait until operation complete + * + * @returns success or else negative error code. + */ +#define imcvtcolor(src, dst, sfmt, dfmt, ...) \ + ({ \ + IM_STATUS __ret = IM_STATUS_SUCCESS; \ + int __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(int); \ + if (__argc == 0) { \ + __ret = imcvtcolor_t(src, dst, sfmt, dfmt, IM_COLOR_SPACE_DEFAULT, 1); \ + } else if (__argc == 1){ \ + __ret = imcvtcolor_t(src, dst, sfmt, dfmt, (int)__args[RGA_GET_MIN(__argc, 0)], 1); \ + } else if (__argc == 2){ \ + __ret = imcvtcolor_t(src, dst, sfmt, dfmt, (int)__args[RGA_GET_MIN(__argc, 0)], (int)__args[RGA_GET_MIN(__argc, 1)]); \ + } else { \ + __ret = IM_STATUS_INVALID_PARAM; \ + printf("invalid parameter\n"); \ + } \ + __ret; \ + }) + +/** + * Crop + * + * @param src + * @param dst + * @param rect + * @param sync + * wait until operation complete + * + * @returns success or else negative error code. + */ +#define imcrop(src, dst, rect, ...) \ + ({ \ + IM_STATUS __ret = IM_STATUS_SUCCESS; \ + int __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(int); \ + if (__argc == 0) { \ + __ret = imcrop_t(src, dst, rect, 1); \ + } else if (__argc == 1){ \ + __ret = imcrop_t(src, dst, rect, (int)__args[RGA_GET_MIN(__argc, 0)]); \ + } else { \ + __ret = IM_STATUS_INVALID_PARAM; \ + printf("invalid parameter\n"); \ + } \ + __ret; \ + }) + +/** + * translate + * + * @param src + * @param dst + * @param x + * @param y + * @param sync + * wait until operation complete + * + * @returns success or else negative error code. + */ +#define imtranslate(src, dst, x, y, ...) \ + ({ \ + IM_STATUS __ret = IM_STATUS_SUCCESS; \ + int __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(int); \ + if (__argc == 0) { \ + __ret = imtranslate_t(src, dst, x, y, 1); \ + } else if (__argc == 1){ \ + __ret = imtranslate_t(src, dst, x, y, (int)__args[RGA_GET_MIN(__argc, 0)]); \ + } else { \ + __ret = IM_STATUS_INVALID_PARAM; \ + printf("invalid parameter\n"); \ + } \ + __ret; \ + }) + +/** + * rotation + * + * @param src + * @param dst + * @param rotation + * IM_HAL_TRANSFORM_ROT_90 + * IM_HAL_TRANSFORM_ROT_180 + * IM_HAL_TRANSFORM_ROT_270 + * @param sync + * wait until operation complete + * + * @returns success or else negative error code. + */ +#define imrotate(src, dst, rotation, ...) \ + ({ \ + IM_STATUS __ret = IM_STATUS_SUCCESS; \ + int __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(int); \ + if (__argc == 0) { \ + __ret = imrotate_t(src, dst, rotation, 1); \ + } else if (__argc == 1){ \ + __ret = imrotate_t(src, dst, rotation, (int)__args[RGA_GET_MIN(__argc, 0)]); \ + } else { \ + __ret = IM_STATUS_INVALID_PARAM; \ + printf("invalid parameter\n"); \ + } \ + __ret; \ + }) + + +/** + * flip + * + * @param src + * @param dst + * @param mode + * IM_HAL_TRANSFORM_FLIP_H + * IM_HAL_TRANSFORM_FLIP_V + * @param sync + * wait until operation complete + * + * @returns success or else negative error code. + */ +#define imflip(src, dst, mode, ...) \ + ({ \ + IM_STATUS __ret = IM_STATUS_SUCCESS; \ + int __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(int); \ + if (__argc == 0) { \ + __ret = imflip_t(src, dst, mode, 1); \ + } else if (__argc == 1){ \ + __ret = imflip_t(src, dst, mode, (int)__args[RGA_GET_MIN(__argc, 0)]); \ + } else { \ + __ret = IM_STATUS_INVALID_PARAM; \ + printf("invalid parameter\n"); \ + } \ + __ret; \ + }) + +/** + * blend (SRC + DST -> DST or SRCA + SRCB -> DST) + * + * @param srcA + * @param srcB can be NULL. + * @param dst + * @param mode + * IM_ALPHA_BLEND_MODE + * @param sync + * wait until operation complete + * + * @returns success or else negative error code. + */ +#define imblend(srcA, dst, ...) \ + ({ \ + IM_STATUS __ret = IM_STATUS_SUCCESS; \ + rga_buffer_t srcB; \ + memset(&srcB, 0x00, sizeof(rga_buffer_t)); \ + int __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(int); \ + if (__argc == 0) { \ + __ret = imblend_t(srcA, srcB, dst, IM_ALPHA_BLEND_SRC_OVER, 1); \ + } else if (__argc == 1){ \ + __ret = imblend_t(srcA, srcB, dst, (int)__args[RGA_GET_MIN(__argc, 0)], 1); \ + } else if (__argc == 2){ \ + __ret = imblend_t(srcA, srcB, dst, (int)__args[RGA_GET_MIN(__argc, 0)], (int)__args[RGA_GET_MIN(__argc, 1)]); \ + } else { \ + __ret = IM_STATUS_INVALID_PARAM; \ + printf("invalid parameter\n"); \ + } \ + __ret; \ + }) +#define imcomposite(srcA, srcB, dst, ...) \ + ({ \ + IM_STATUS __ret = IM_STATUS_SUCCESS; \ + int __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(int); \ + if (__argc == 0) { \ + __ret = imblend_t(srcA, srcB, dst, IM_ALPHA_BLEND_SRC_OVER, 1); \ + } else if (__argc == 1){ \ + __ret = imblend_t(srcA, srcB, dst, (int)__args[RGA_GET_MIN(__argc, 0)], 1); \ + } else if (__argc == 2){ \ + __ret = imblend_t(srcA, srcB, dst, (int)__args[RGA_GET_MIN(__argc, 0)], (int)__args[RGA_GET_MIN(__argc, 1)]); \ + } else { \ + __ret = IM_STATUS_INVALID_PARAM; \ + printf("invalid parameter\n"); \ + } \ + __ret; \ + }) + +/** + * color key + * + * @param src + * @param dst + * @param colorkey_range + * max color + * min color + * @param sync + * wait until operation complete + * + * @returns success or else negative error code. + */ +#define imcolorkey(src, dst, range, ...) \ + ({ \ + IM_STATUS __ret = IM_STATUS_SUCCESS; \ + int __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(int); \ + if (__argc == 0) { \ + __ret = imcolorkey_t(src, dst, range, IM_ALPHA_COLORKEY_NORMAL, 1); \ + } else if (__argc == 1){ \ + __ret = imcolorkey_t(src, dst, range, (int)__args[RGA_GET_MIN(__argc, 0)], 1); \ + } else if (__argc == 2){ \ + __ret = imcolorkey_t(src, dst, range, (int)__args[RGA_GET_MIN(__argc, 0)], (int)__args[RGA_GET_MIN(__argc, 1)]); \ + } else { \ + __ret = IM_STATUS_INVALID_PARAM; \ + printf("invalid parameter\n"); \ + } \ + __ret; \ + }) + +/** + * nn quantize + * + * @param src + * @param dst + * @param nninfo + * @param sync + * wait until operation complete + * + * @returns success or else negative error code. + */ +#define imquantize(src, dst, nn_info, ...) \ + ({ \ + IM_STATUS __ret = IM_STATUS_SUCCESS; \ + int __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(int); \ + if (__argc == 0) { \ + __ret = imquantize_t(src, dst, nn_info, 1); \ + } else if (__argc == 1){ \ + __ret = imquantize_t(src, dst, nn_info, (int)__args[RGA_GET_MIN(__argc, 0)]); \ + } else { \ + __ret = IM_STATUS_INVALID_PARAM; \ + printf("invalid parameter\n"); \ + } \ + __ret; \ + }) + + +/** + * ROP + * + * @param src + * @param dst + * @param rop_code + * @param sync + * wait until operation complete + * + * @returns success or else negative error code. + */ +#define imrop(src, dst, rop_code, ...) \ + ({ \ + IM_STATUS __ret = IM_STATUS_SUCCESS; \ + int __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(int); \ + if (__argc == 0) { \ + __ret = imrop_t(src, dst, rop_code, 1); \ + } else if (__argc == 1){ \ + __ret = imrop_t(src, dst, rop_code, (int)__args[RGA_GET_MIN(__argc, 0)]); \ + } else { \ + __ret = IM_STATUS_INVALID_PARAM; \ + printf("invalid parameter\n"); \ + } \ + __ret; \ + }) + +/** + * fill/reset/draw + * + * @param src + * @param dst + * @param rect + * @param color + * @param sync + * wait until operation complete + * + * @returns success or else negative error code. + */ +#define imfill(buf, rect, color, ...) \ + ({ \ + IM_STATUS __ret = IM_STATUS_SUCCESS; \ + int __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(int); \ + if (__argc == 0) { \ + __ret = imfill_t(buf, rect, color, 1); \ + } else if (__argc == 1){ \ + __ret = imfill_t(buf, rect, color, (int)__args[RGA_GET_MIN(__argc, 0)]); \ + } else { \ + __ret = IM_STATUS_INVALID_PARAM; \ + printf("invalid parameter\n"); \ + } \ + __ret; \ + }) + +#define imreset(buf, rect, color, ...) \ + ({ \ + IM_STATUS __ret = IM_STATUS_SUCCESS; \ + int __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(int); \ + if (__argc == 0) { \ + __ret = imfill_t(buf, rect, color, 1); \ + } else if (__argc == 1){ \ + __ret = imfill_t(buf, rect, color, (int)__args[RGA_GET_MIN(__argc, 0)]); \ + } else { \ + __ret = IM_STATUS_INVALID_PARAM; \ + printf("invalid parameter\n"); \ + } \ + __ret; \ + }) + +#define imdraw(buf, rect, color, ...) \ + ({ \ + IM_STATUS __ret = IM_STATUS_SUCCESS; \ + int __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(int); \ + if (__argc == 0) { \ + __ret = imfill_t(buf, rect, color, 1); \ + } else if (__argc == 1){ \ + __ret = imfill_t(buf, rect, color, (int)__args[RGA_GET_MIN(__argc, 0)]); \ + } else { \ + __ret = IM_STATUS_INVALID_PARAM; \ + printf("invalid parameter\n"); \ + } \ + __ret; \ + }) + +/** + * palette + * + * @param src + * @param dst + * @param lut + * @param sync + * wait until operation complete + * + * @returns success or else negative error code. + */ +#define impalette(src, dst, lut, ...) \ + ({ \ + IM_STATUS __ret = IM_STATUS_SUCCESS; \ + int __args[] = {__VA_ARGS__}; \ + int __argc = sizeof(__args)/sizeof(int); \ + if (__argc == 0) { \ + __ret = impalette_t(src, dst, lut, 1); \ + } else if (__argc == 1){ \ + __ret = impalette_t(src, dst, lut, (int)__args[RGA_GET_MIN(__argc, 0)]); \ + } else { \ + __ret = IM_STATUS_INVALID_PARAM; \ + printf("invalid parameter\n"); \ + } \ + __ret; \ + }) +/* End define IM2D macro API */ +#endif + +#endif /* #ifndef _im2d_single_h_ */ \ No newline at end of file diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/im2d_task.h b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/im2d_task.h new file mode 100644 index 0000000000000000000000000000000000000000..9f8db59a3b0fbb1d3b77cd13db8a23f3beae9a26 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/im2d_task.h @@ -0,0 +1,497 @@ +/* + * Copyright (C) 2022 Rockchip Electronics Co., Ltd. + * Authors: + * Cerf Yu + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifndef _im2d_task_h_ +#define _im2d_task_h_ + +#include "im2d_type.h" + +#ifdef __cplusplus + +/** + * Create an rga job + * + * @param flags + * Some configuration flags for this job + * + * @returns job handle. + */ +IM_API im_job_handle_t imbeginJob(uint64_t flags = 0); + +/** + * Submit and run an rga job + * + * @param job_handle + * This is the job handle that will be submitted. + * @param sync_mode + * run mode: + * IM_SYNC + * IM_ASYNC + * @param acquire_fence_fd + * @param release_fence_fd + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imendJob(im_job_handle_t job_handle, + int sync_mode = IM_SYNC, + int acquire_fence_fd = 0, int *release_fence_fd = NULL); + +/** + * Cancel and delete an rga job + * + * @param job_handle + * This is the job handle that will be cancelled. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imcancelJob(im_job_handle_t job_handle); + +/** + * Add copy task + * + * @param job_handle + * Insert the task into the job handle. + * @param src + * The input source image. + * @param dst + * The output destination image. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imcopyTask(im_job_handle_t job_handle, const rga_buffer_t src, rga_buffer_t dst); + +/** + * Add resize task + * + * @param job_handle + * Insert the task into the job handle. + * @param src + * The input source image. + * @param dst + * The output destination image. + * @param fx + * X-direction resize factor. + * @param fy + * X-direction resize factor. + * @param interpolation + * Interpolation formula(Only RGA1 support). + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imresizeTask(im_job_handle_t job_handle, + const rga_buffer_t src, rga_buffer_t dst, + double fx = 0, double fy = 0, + int interpolation = 0); + +/** + * Add crop task + * + * @param job_handle + * Insert the task into the job handle. + * @param src + * The input source image. + * @param dst + * The output destination image. + * @param rect + * The rectangle on the source image that needs to be cropped. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imcropTask(im_job_handle_t job_handle, + const rga_buffer_t src, rga_buffer_t dst, im_rect rect); + +/** + * Add translate task + * + * @param job_handle + * Insert the task into the job handle. + * @param src + * The input source image. + * @param dst + * The output destination image. + * @param x + * Output the coordinates of the starting point in the X-direction of the destination image. + * @param y + * Output the coordinates of the starting point in the Y-direction of the destination image. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imtranslateTask(im_job_handle_t job_handle, + const rga_buffer_t src, rga_buffer_t dst, int x, int y); + +/** + * Add format convert task + * + * @param job_handle + * Insert the task into the job handle. + * @param src + * The input source image. + * @param dst + * The output destination image. + * @param sfmt + * The source image format. + * @param dfmt + * The destination image format. + * @param mode + * color space mode: + * IM_YUV_TO_RGB_BT601_LIMIT + * IM_YUV_TO_RGB_BT601_FULL + * IM_YUV_TO_RGB_BT709_LIMIT + * IM_RGB_TO_YUV_BT601_FULL + * IM_RGB_TO_YUV_BT601_LIMIT + * IM_RGB_TO_YUV_BT709_LIMIT + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imcvtcolorTask(im_job_handle_t job_handle, + rga_buffer_t src, rga_buffer_t dst, + int sfmt, int dfmt, int mode = IM_COLOR_SPACE_DEFAULT); + +/** + * Add rotation task + * + * @param job_handle + * Insert the task into the job handle. + * @param src + * The input source image. + * @param dst + * The output destination image. + * @param rotation + * IM_HAL_TRANSFORM_ROT_90 + * IM_HAL_TRANSFORM_ROT_180 + * IM_HAL_TRANSFORM_ROT_270 + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imrotateTask(im_job_handle_t job_handle, + const rga_buffer_t src, rga_buffer_t dst, int rotation); + +/** + * Add flip task + * + * @param job_handle + * Insert the task into the job handle. + * @param src + * The input source image. + * @param dst + * The output destination image. + * @param mode + * IM_HAL_TRANSFORM_FLIP_H + * IM_HAL_TRANSFORM_FLIP_V + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imflipTask(im_job_handle_t job_handle, + const rga_buffer_t src, rga_buffer_t dst, int mode); + +/** + * Add blend(SRC + DST -> DST) task + * + * @param job_handle + * Insert the task into the job handle. + * @param fg_image + * The foreground image. + * @param bg_image + * The background image, which is also the output destination image. + * @param mode + * Port-Duff mode: + * IM_ALPHA_BLEND_SRC + * IM_ALPHA_BLEND_DST + * IM_ALPHA_BLEND_SRC_OVER + * IM_ALPHA_BLEND_DST_OVER + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imblendTask(im_job_handle_t job_handle, + const rga_buffer_t fg_image, rga_buffer_t bg_image, + int mode = IM_ALPHA_BLEND_SRC_OVER); + +/** + * Add composite(SRCA + SRCB -> DST) task + * + * @param job_handle + * Insert the task into the job handle. + * @param fg_image + * The foreground image. + * @param bg_image + * The background image. + * @param output_image + * The output destination image. + * @param mode + * Port-Duff mode: + * IM_ALPHA_BLEND_SRC + * IM_ALPHA_BLEND_DST + * IM_ALPHA_BLEND_SRC_OVER + * IM_ALPHA_BLEND_DST_OVER + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imcompositeTask(im_job_handle_t job_handle, + const rga_buffer_t fg_image, const rga_buffer_t bg_image, + rga_buffer_t output_image, + int mode = IM_ALPHA_BLEND_SRC_OVER); + +/** + * Add color key task + * + * @param job_handle + * Insert the task into the job handle. + * @param fg_image + * The foreground image. + * @param bg_image + * The background image, which is also the output destination image. + * @param colorkey_range + * The range of color key. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imcolorkeyTask(im_job_handle_t job_handle, + const rga_buffer_t fg_image, rga_buffer_t bg_image, + im_colorkey_range range, int mode = IM_ALPHA_COLORKEY_NORMAL); + +/** + * Add OSD task + * + * @param job_handle + * Insert the task into the job handle. + * @param osd + * The osd text block. + * @param dst + * The background image. + * @param osd_rect + * The rectangle on the source image that needs to be OSD. + * @param osd_config + * osd mode configuration. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imosdTask(im_job_handle_t job_handle, + const rga_buffer_t osd,const rga_buffer_t bg_image, + const im_rect osd_rect, im_osd_t *osd_config); + +/** + * Add nn quantize task + * + * @param job_handle + * Insert the task into the job handle. + * @param src + * The input source image. + * @param dst + * The output destination image. + * @param nninfo + * nn configuration + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imquantizeTask(im_job_handle_t job_handle, + const rga_buffer_t src, rga_buffer_t dst, im_nn_t nn_info); + +/** + * Add ROP task + * + * @param job_handle + * Insert the task into the job handle. + * @param src + * The input source image. + * @param dst + * The output destination image. + * @param rop_code + * The ROP opcode. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imropTask(im_job_handle_t job_handle, + const rga_buffer_t src, rga_buffer_t dst, int rop_code); + +/** + * Add color fill task + * + * @param job_handle + * Insert the task into the job handle. + * @param dst + * The output destination image. + * @param rect + * The rectangle on the source image that needs to be filled with color. + * @param color + * The fill color value. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imfillTask(im_job_handle_t job_handle, rga_buffer_t dst, im_rect rect, uint32_t color); + +/** + * Add color fill task array + * + * @param job_handle + * Insert the task into the job handle. + * @param dst + * The output destination image. + * @param rect_array + * The rectangle arrays on the source image that needs to be filled with color. + * @param array_size + * The size of rectangular area arrays. + * @param color + * The fill color value. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imfillTaskArray(im_job_handle_t job_handle, + rga_buffer_t dst, + im_rect *rect_array, int array_size, uint32_t color); + +/** + * Add fill rectangle task + * + * @param job_handle + * Insert the task into the job handle. + * @param dst + * The output destination image. + * @param rect + * The rectangle on the source image that needs to be filled with color. + * @param color + * The fill color value. + * @param thickness + * Thickness of lines that make up the rectangle. Negative values, like -1, + * mean that the function has to draw a filled rectangle. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imrectangleTask(im_job_handle_t job_handle, + rga_buffer_t dst, + im_rect rect, + uint32_t color, int thickness); + +/** + * Add fill rectangle task array + * + * @param job_handle + * Insert the task into the job handle. + * @param dst + * The output destination image. + * @param rect_array + * The rectangle arrays on the source image that needs to be filled with color. + * @param array_size + * The size of rectangular area arrays. + * @param color + * The fill color value. + * @param thickness + * Thickness of lines that make up the rectangle. Negative values, like -1, + * mean that the function has to draw a filled rectangle. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS imrectangleTaskArray(im_job_handle_t job_handle, + rga_buffer_t dst, + im_rect *rect_array, int array_size, + uint32_t color, int thickness); + +/** + * Add mosaic task + * + * @param job_handle + * Insert the task into the job handle. + * @param image + * The output destination image. + * @param rect + * The rectangle on the source image that needs to be mosaicked. + * @param mosaic_mode + * mosaic block width configuration: + * IM_MOSAIC_8 + * IM_MOSAIC_16 + * IM_MOSAIC_32 + * IM_MOSAIC_64 + * IM_MOSAIC_128 + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS immosaicTask(im_job_handle_t job_handle, + const rga_buffer_t image, im_rect rect, int mosaic_mode); + +/** + * Add mosaic task + * + * @param job_handle + * Insert the task into the job handle. + * @param image + * The output destination image. + * @param rect_array + * The rectangle arrays on the source image that needs to be filled with color. + * @param array_size + * The size of rectangular area arrays. + * @param mosaic_mode + * mosaic block width configuration: + * IM_MOSAIC_8 + * IM_MOSAIC_16 + * IM_MOSAIC_32 + * IM_MOSAIC_64 + * IM_MOSAIC_128 + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS immosaicTaskArray(im_job_handle_t job_handle, + const rga_buffer_t image, + im_rect *rect_array, int array_size, int mosaic_mode); + +/** + * Add palette task + * + * @param job_handle + * Insert the task into the job handle. + * @param src + * The input source image. + * @param dst + * The output destination image. + * @param lut + * The LUT table. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS impaletteTask(im_job_handle_t job_handle, + rga_buffer_t src, rga_buffer_t dst, rga_buffer_t lut); + +/** + * Add process task + * + * @param job_handle + * Insert the task into the job handle. + * @param src + * The input source image and is also the foreground image in blend. + * @param dst + * The output destination image and is also the foreground image in blend. + * @param pat + * The foreground image, or a LUT table. + * @param srect + * The rectangle on the src channel image that needs to be processed. + * @param drect + * The rectangle on the dst channel image that needs to be processed. + * @param prect + * The rectangle on the pat channel image that needs to be processed. + * @param opt + * The image processing options configuration. + * @param usage + * The image processing usage. + * + * @returns success or else negative error code. + */ +IM_API IM_STATUS improcessTask(im_job_handle_t job_handle, + rga_buffer_t src, rga_buffer_t dst, rga_buffer_t pat, + im_rect srect, im_rect drect, im_rect prect, + im_opt_t *opt_ptr, int usage); + +#endif /* #ifdef __cplusplus */ + +#endif /* #ifndef _im2d_task_h_ */ diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/im2d_type.h b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/im2d_type.h new file mode 100644 index 0000000000000000000000000000000000000000..7ca8a2192ea119da9dfdf0f27844f81f273c127a --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/im2d_type.h @@ -0,0 +1,470 @@ +/* + * Copyright (C) 2022 Rockchip Electronics Co., Ltd. + * Authors: + * Cerf Yu + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef _RGA_IM2D_TYPE_H_ +#define _RGA_IM2D_TYPE_H_ + +#include + +#include "rga.h" + +#define IM_API /* define API export as needed */ + +#ifdef __cplusplus +#define IM_C_API extern "C" +#define IM_EXPORT_API extern "C" +#else +#define IM_C_API +#define IM_EXPORT_API +#endif + +#ifdef __cplusplus +#define DEFAULT_INITIALIZER(x) = x +#else +#define DEFAULT_INITIALIZER(x) +#endif + +typedef uint32_t im_api_version_t; +typedef uint32_t im_job_handle_t; +typedef uint32_t im_ctx_id_t; +typedef uint32_t rga_buffer_handle_t; + +typedef enum { + /* Rotation */ + IM_HAL_TRANSFORM_ROT_90 = 1 << 0, + IM_HAL_TRANSFORM_ROT_180 = 1 << 1, + IM_HAL_TRANSFORM_ROT_270 = 1 << 2, + IM_HAL_TRANSFORM_FLIP_H = 1 << 3, + IM_HAL_TRANSFORM_FLIP_V = 1 << 4, + IM_HAL_TRANSFORM_FLIP_H_V = 1 << 5, + IM_HAL_TRANSFORM_MASK = 0x3f, + + /* + * Blend + * Additional blend usage, can be used with both source and target configs. + * If none of the below is set, the default "SRC over DST" is applied. + */ + IM_ALPHA_BLEND_SRC_OVER = 1 << 6, /* Default, Porter-Duff "SRC over DST" */ + IM_ALPHA_BLEND_SRC = 1 << 7, /* Porter-Duff "SRC" */ + IM_ALPHA_BLEND_DST = 1 << 8, /* Porter-Duff "DST" */ + IM_ALPHA_BLEND_SRC_IN = 1 << 9, /* Porter-Duff "SRC in DST" */ + IM_ALPHA_BLEND_DST_IN = 1 << 10, /* Porter-Duff "DST in SRC" */ + IM_ALPHA_BLEND_SRC_OUT = 1 << 11, /* Porter-Duff "SRC out DST" */ + IM_ALPHA_BLEND_DST_OUT = 1 << 12, /* Porter-Duff "DST out SRC" */ + IM_ALPHA_BLEND_DST_OVER = 1 << 13, /* Porter-Duff "DST over SRC" */ + IM_ALPHA_BLEND_SRC_ATOP = 1 << 14, /* Porter-Duff "SRC ATOP" */ + IM_ALPHA_BLEND_DST_ATOP = 1 << 15, /* Porter-Duff "DST ATOP" */ + IM_ALPHA_BLEND_XOR = 1 << 16, /* Xor */ + IM_ALPHA_BLEND_MASK = 0x1ffc0, + + IM_ALPHA_COLORKEY_NORMAL = 1 << 17, + IM_ALPHA_COLORKEY_INVERTED = 1 << 18, + IM_ALPHA_COLORKEY_MASK = 0x60000, + + IM_SYNC = 1 << 19, + IM_CROP = 1 << 20, /* Unused */ + IM_COLOR_FILL = 1 << 21, + IM_COLOR_PALETTE = 1 << 22, + IM_NN_QUANTIZE = 1 << 23, + IM_ROP = 1 << 24, + IM_ALPHA_BLEND_PRE_MUL = 1 << 25, + IM_ASYNC = 1 << 26, + IM_MOSAIC = 1 << 27, + IM_OSD = 1 << 28, + IM_PRE_INTR = 1 << 29, + IM_ALPHA_BIT_MAP = 1 << 30, +} IM_USAGE; + +typedef enum { + IM_RASTER_MODE = 1 << 0, + IM_AFBC16x16_MODE = 1 << 1, + IM_TILE8x8_MODE = 1 << 2, + IM_TILE4x4_MODE = 1 << 3, + IM_RKFBC64x4_MODE = 1 << 4, + IM_AFBC32x8_MODE = 1 << 5, + /* legacy */ + IM_FBC_MODE = IM_AFBC16x16_MODE, + IM_TILE_MODE = IM_TILE8x8_MODE, +} IM_RD_MODE; + +typedef enum { + IM_SCHEDULER_RGA3_CORE0 = 1 << 0, + IM_SCHEDULER_RGA3_CORE1 = 1 << 1, + IM_SCHEDULER_RGA2_CORE0 = 1 << 2, + IM_SCHEDULER_RGA2_CORE1 = 1 << 3, + IM_SCHEDULER_RGA3_DEFAULT = IM_SCHEDULER_RGA3_CORE0, + IM_SCHEDULER_RGA2_DEFAULT = IM_SCHEDULER_RGA2_CORE0, + IM_SCHEDULER_MASK = 0xf, + IM_SCHEDULER_DEFAULT = 0, +} IM_SCHEDULER_CORE; + +typedef enum { + IM_ROP_AND = 0x88, + IM_ROP_OR = 0xee, + IM_ROP_NOT_DST = 0x55, + IM_ROP_NOT_SRC = 0x33, + IM_ROP_XOR = 0xf6, + IM_ROP_NOT_XOR = 0xf9, +} IM_ROP_CODE; + +typedef enum { + IM_MOSAIC_8 = 0x0, + IM_MOSAIC_16 = 0x1, + IM_MOSAIC_32 = 0x2, + IM_MOSAIC_64 = 0x3, + IM_MOSAIC_128 = 0x4, +} IM_MOSAIC_MODE; + +typedef enum { + IM_BORDER_CONSTANT = 0, /* iiiiii|abcdefgh|iiiiiii with some specified value 'i' */ + IM_BORDER_REFLECT = 2, /* fedcba|abcdefgh|hgfedcb */ + IM_BORDER_WRAP = 3, /* cdefgh|abcdefgh|abcdefg */ +} IM_BORDER_TYPE; + +/* Status codes, returned by any blit function */ +typedef enum { + IM_YUV_TO_RGB_BT601_LIMIT = 1 << 0, + IM_YUV_TO_RGB_BT601_FULL = 2 << 0, + IM_YUV_TO_RGB_BT709_LIMIT = 3 << 0, + IM_YUV_TO_RGB_MASK = 3 << 0, + IM_RGB_TO_YUV_BT601_FULL = 1 << 2, + IM_RGB_TO_YUV_BT601_LIMIT = 2 << 2, + IM_RGB_TO_YUV_BT709_LIMIT = 3 << 2, + IM_RGB_TO_YUV_MASK = 3 << 2, + IM_RGB_TO_Y4 = 1 << 4, + IM_RGB_TO_Y4_DITHER = 2 << 4, + IM_RGB_TO_Y1_DITHER = 3 << 4, + IM_Y4_MASK = 3 << 4, + IM_RGB_FULL = 1 << 8, + IM_RGB_CLIP = 2 << 8, + IM_YUV_BT601_LIMIT_RANGE = 3 << 8, + IM_YUV_BT601_FULL_RANGE = 4 << 8, + IM_YUV_BT709_LIMIT_RANGE = 5 << 8, + IM_YUV_BT709_FULL_RANGE = 6 << 8, + IM_FULL_CSC_MASK = 0xf << 8, + IM_COLOR_SPACE_DEFAULT = 0, +} IM_COLOR_SPACE_MODE; + +typedef enum { + IM_UP_SCALE, + IM_DOWN_SCALE, +} IM_SCALE; + +/* legacy */ +typedef enum { + INTER_NEAREST, + INTER_LINEAR, + INTER_CUBIC, +} IM_SCALE_MODE; + +typedef enum { + IM_INTERP_DEFAULT = 0, + IM_INTERP_LINEAR = 1, + IM_INTERP_CUBIC = 2, + IM_INTERP_AVERAGE = 3, + + IM_INTERP_MASK = 0xf, + IM_INTERP_HORIZ_SHIFT = 0, + IM_INTERP_VERTI_SHIFT = 4, + IM_INTERP_HORIZ_FLAG = 0x1 << 8, + IM_INTERP_VERTI_FLAG = 0x1 << 9, +} IM_INTER_MODE; + +typedef enum { + IM_CONFIG_SCHEDULER_CORE, + IM_CONFIG_PRIORITY, + IM_CONFIG_CHECK, +} IM_CONFIG_NAME; + +typedef enum { + IM_OSD_MODE_STATISTICS = 0x1 << 0, + IM_OSD_MODE_AUTO_INVERT = 0x1 << 1, +} IM_OSD_MODE; + +typedef enum { + IM_OSD_INVERT_CHANNEL_NONE = 0x0, + IM_OSD_INVERT_CHANNEL_Y_G = 0x1 << 0, + IM_OSD_INVERT_CHANNEL_C_RB = 0x1 << 1, + IM_OSD_INVERT_CHANNEL_ALPHA = 0x1 << 2, + IM_OSD_INVERT_CHANNEL_COLOR = IM_OSD_INVERT_CHANNEL_Y_G | + IM_OSD_INVERT_CHANNEL_C_RB, + IM_OSD_INVERT_CHANNEL_BOTH = IM_OSD_INVERT_CHANNEL_COLOR | + IM_OSD_INVERT_CHANNEL_ALPHA, +} IM_OSD_INVERT_CHANNEL; + +typedef enum { + IM_OSD_FLAGS_INTERNAL = 0, + IM_OSD_FLAGS_EXTERNAL, +} IM_OSD_FLAGS_MODE; + +typedef enum { + IM_OSD_INVERT_USE_FACTOR, + IM_OSD_INVERT_USE_SWAP, +} IM_OSD_INVERT_MODE; + +typedef enum { + IM_OSD_BACKGROUND_DEFAULT_BRIGHT = 0, + IM_OSD_BACKGROUND_DEFAULT_DARK, +} IM_OSD_BACKGROUND_DEFAULT; + +typedef enum { + IM_OSD_BLOCK_MODE_NORMAL = 0, + IM_OSD_BLOCK_MODE_DIFFERENT, +} IM_OSD_BLOCK_WIDTH_MODE; + +typedef enum { + IM_OSD_MODE_HORIZONTAL, + IM_OSD_MODE_VERTICAL, +} IM_OSD_DIRECTION; + +typedef enum { + IM_OSD_COLOR_PIXEL, + IM_OSD_COLOR_EXTERNAL, +} IM_OSD_COLOR_MODE; + +typedef enum { + IM_INTR_READ_INTR = 1 << 0, + IM_INTR_READ_HOLD = 1 << 1, + IM_INTR_WRITE_INTR = 1 << 2, +} IM_PRE_INTR_FLAGS; + +typedef enum { + IM_CONTEXT_NONE = 0x0, + IM_CONTEXT_SRC_FIX_ENABLE = 0x1 << 0, // Enable kernel to modify the image parameters of the channel. + IM_CONTEXT_SRC_CACHE_INFO = 0x1 << 1, // It will replace the parameters in ctx with the modified parameters. + IM_CONTEXT_SRC1_FIX_ENABLE = 0x1 << 2, + IM_CONTEXT_SRC1_CACHE_INFO = 0x1 << 3, + IM_CONTEXT_DST_FIX_ENABLE = 0x1 << 4, + IM_CONTEXT_DST_CACHE_INFO = 0x1 << 5, +} IM_CONTEXT_FLAGS; + +/* Get RGA basic information index */ +typedef enum { + RGA_VENDOR = 0, + RGA_VERSION, + RGA_MAX_INPUT, + RGA_MAX_OUTPUT, + RGA_BYTE_STRIDE, + RGA_SCALE_LIMIT, + RGA_INPUT_FORMAT, + RGA_OUTPUT_FORMAT, + RGA_FEATURE, + RGA_EXPECTED, + RGA_ALL, +} IM_INFORMATION; + +/* Status codes, returned by any blit function */ +typedef enum { + IM_STATUS_NOERROR = 2, + IM_STATUS_SUCCESS = 1, + IM_STATUS_NOT_SUPPORTED = -1, + IM_STATUS_OUT_OF_MEMORY = -2, + IM_STATUS_INVALID_PARAM = -3, + IM_STATUS_ILLEGAL_PARAM = -4, + IM_STATUS_ERROR_VERSION = -5, + IM_STATUS_FAILED = 0, +} IM_STATUS; + +/* Rectangle definition */ +typedef struct { + int x; /* upper-left x */ + int y; /* upper-left y */ + int width; /* width */ + int height; /* height */ +} im_rect; + +typedef struct { + int max; /* The Maximum value of the color key */ + int min; /* The minimum value of the color key */ +} im_colorkey_range; + + +typedef struct im_nn { + int scale_r; /* scaling factor on R channal */ + int scale_g; /* scaling factor on G channal */ + int scale_b; /* scaling factor on B channal */ + int offset_r; /* offset on R channal */ + int offset_g; /* offset on G channal */ + int offset_b; /* offset on B channal */ +} im_nn_t; + +/* im_info definition */ +typedef struct { + void* vir_addr; /* virtual address */ + void* phy_addr; /* physical address */ + int fd; /* shared fd */ + + int width; /* width */ + int height; /* height */ + int wstride; /* wstride */ + int hstride; /* hstride */ + int format; /* format */ + + int color_space_mode; /* color_space_mode */ + union { + int global_alpha; /* global_alpha, the default should be 0xff */ + struct { + uint16_t alpha0; + uint16_t alpha1; + } alpha_bit; /* alpha bit(e.g. RGBA5551), 0: alpha0, 1: alpha1 */ + }; + int rd_mode; + + /* legacy */ + int color; /* color, used by color fill */ + im_colorkey_range colorkey_range; /* range value of color key */ + im_nn_t nn; + int rop_code; + + rga_buffer_handle_t handle; /* buffer handle */ +} rga_buffer_t; + +typedef struct im_color { + union { + struct { + uint8_t red; + uint8_t green; + uint8_t blue; + uint8_t alpha; + }; + uint32_t value; + }; +} im_color_t; + +typedef struct im_osd_invert_factor { + uint8_t alpha_max; + uint8_t alpha_min; + uint8_t yg_max; + uint8_t yg_min; + uint8_t crb_max; + uint8_t crb_min; +} im_osd_invert_factor_t; + +typedef struct im_osd_bpp2 { + uint8_t ac_swap; // ac swap flag + // 0: CA + // 1: AC + uint8_t endian_swap; // rgba2bpp endian swap + // 0: Big endian + // 1: Little endian + im_color_t color0; + im_color_t color1; +} im_osd_bpp2_t; + +typedef struct im_osd_block { + int width_mode; // normal or different + // IM_OSD_BLOCK_MODE_NORMAL + // IM_OSD_BLOCK_MODE_DIFFERENT + union { + int width; // normal_mode block width + int width_index; // different_mode block width index in RAM + }; + + int block_count; // block count + + int background_config; // background config is bright or dark + // IM_OSD_BACKGROUND_DEFAULT_BRIGHT + // IM_OSD_BACKGROUND_DEFAULT_DARK + + int direction; // osd block direction + // IM_OSD_MODE_HORIZONTAL + // IM_OSD_MODE_VERTICAL + + int color_mode; // using src1 color or config color + // IM_OSD_COLOR_PIXEL + // IM_OSD_COLOR_EXTERNAL + im_color_t normal_color; // config color: normal + im_color_t invert_color; // config color: invert +} im_osd_block_t; + +typedef struct im_osd_invert { + int invert_channel; // invert channel config: + // IM_OSD_INVERT_CHANNEL_NONE + // IM_OSD_INVERT_CHANNEL_Y_G + // IM_OSD_INVERT_CHANNEL_C_RB + // IM_OSD_INVERT_CHANNEL_ALPHA + // IM_OSD_INVERT_CHANNEL_COLOR + // IM_OSD_INVERT_CHANNEL_BOTH + int flags_mode; // use external or inertnal RAM invert flags + // IM_OSD_FLAGS_EXTERNAL + // IM_OSD_FLAGS_INTERNAL + int flags_index; // flags index when using internal RAM invert flags + + uint64_t invert_flags; // external invert flags + uint64_t current_flags; // current flags + + int invert_mode; // invert use swap or factor + // IM_OSD_INVERT_USE_FACTOR + // IM_OSD_INVERT_USE_SWAP + im_osd_invert_factor_t factor; + + int threash; +} im_osd_invert_t; + +typedef struct im_osd { + int osd_mode; // osd mode: statistics or auto_invert + // IM_OSD_MODE_STATISTICS + // IM_OSD_MODE_AUTO_INVERT + im_osd_block_t block_parm; // osd block info config + + im_osd_invert_t invert_config; + + im_osd_bpp2_t bpp2_info; +} im_osd_t; + +typedef struct im_intr_config { + uint32_t flags; + + int read_threshold; + int write_start; + int write_step; +} im_intr_config_t; + +typedef struct im_opt { + im_api_version_t version DEFAULT_INITIALIZER(RGA_CURRENT_API_HEADER_VERSION); + + int color; /* color, used by color fill */ + im_colorkey_range colorkey_range; /* range value of color key */ + im_nn_t nn; + int rop_code; + + int priority; + int core; + + int mosaic_mode; + + im_osd_t osd_config; + + im_intr_config_t intr_config; + + int interp; + + char reserve[124]; +} im_opt_t; + +typedef struct im_handle_param { + uint32_t width; + uint32_t height; + uint32_t format; +} im_handle_param_t; + +#define IM_INTERP_HORIZ(x) ( ((x) & IM_INTERP_MASK) << IM_INTERP_HORIZ_SHIFT | IM_INTERP_HORIZ_FLAG ) +#define IM_INTERP_VERTI(x) ( ((x) & IM_INTERP_MASK) << IM_INTERP_VERTI_SHIFT | IM_INTERP_VERTI_FLAG ) +#define IM_INTERP(h,v) ( IM_INTERP_HORIZ(h) | IM_INTERP_VERTI(v) ) + +#endif /* _RGA_IM2D_TYPE_H_ */ diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/im2d_version.h b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/im2d_version.h new file mode 100644 index 0000000000000000000000000000000000000000..370cb0d9c4ce1d09bfa74cd5a5d040e7006a7877 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/im2d_version.h @@ -0,0 +1,49 @@ +/* + * Copyright (C) 2022 Rockchip Electronics Co., Ltd. + * Authors: + * Cerf Yu + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef _RGA_IM2D_VERSION_H_ +#define _RGA_IM2D_VERSION_H_ + +#define RGA_VERSION_STR_HELPER(x) #x +#define RGA_VERSION_STR(x) RGA_VERSION_STR_HELPER(x) + +/* RGA im2d api verison */ +#define RGA_API_MAJOR_VERSION 1 +#define RGA_API_MINOR_VERSION 10 +#define RGA_API_REVISION_VERSION 1 +#define RGA_API_BUILD_VERSION 0 + +#define RGA_API_SUFFIX + +#define RGA_API_VERSION \ + RGA_VERSION_STR(RGA_API_MAJOR_VERSION) "." \ + RGA_VERSION_STR(RGA_API_MINOR_VERSION) "." \ + RGA_VERSION_STR(RGA_API_REVISION_VERSION) "_[" \ + RGA_VERSION_STR(RGA_API_BUILD_VERSION) "]" +#define RGA_API_FULL_VERSION "rga_api version " RGA_API_VERSION RGA_API_SUFFIX + +/* For header file version verification */ +#define RGA_CURRENT_API_VERSION (\ + (RGA_API_MAJOR_VERSION & 0xff) << 24 | \ + (RGA_API_MINOR_VERSION & 0xff) << 16 | \ + (RGA_API_REVISION_VERSION & 0xff) << 8 | \ + (RGA_API_BUILD_VERSION & 0xff)\ + ) +#define RGA_CURRENT_API_HEADER_VERSION RGA_CURRENT_API_VERSION + +#endif /* _RGA_IM2D_VERSION_H_ */ diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/rga.h b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/rga.h new file mode 100644 index 0000000000000000000000000000000000000000..efba2b0c6880d6998a41b76a03b82502c56674c8 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rga/rga.h @@ -0,0 +1,197 @@ +/* + * Copyright (C) 2016 Rockchip Electronics Co., Ltd. + * Authors: + * Zhiqin Wei + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef _RGA_DRIVER_H_ +#define _RGA_DRIVER_H_ + + +#ifndef ENABLE +#define ENABLE 1 +#endif + +#ifndef DISABLE +#define DISABLE 0 +#endif + +#ifdef __cplusplus +extern "C" +{ +#endif + +/* In order to be compatible with RK_FORMAT_XX and HAL_PIXEL_FORMAT_XX, + * RK_FORMAT_XX is shifted to the left by 8 bits to distinguish. */ +typedef enum _Rga_SURF_FORMAT { + RK_FORMAT_RGBA_8888 = 0x0 << 8, /* [0:31] R:G:B:A 8:8:8:8 little endian */ + RK_FORMAT_RGBX_8888 = 0x1 << 8, /* [0:31] R:G:B:X 8:8:8:8 little endian */ + RK_FORMAT_RGB_888 = 0x2 << 8, /* [0:23] R:G:B 8:8:8 little endian */ + RK_FORMAT_BGRA_8888 = 0x3 << 8, /* [0:31] B:G:R:A 8:8:8:8 little endian */ + RK_FORMAT_RGB_565 = 0x4 << 8, /* [0:15] R:G:B 5:6:5 little endian */ + RK_FORMAT_RGBA_5551 = 0x5 << 8, /* [0:15] R:G:B:A 5:5:5:1 little endian */ + RK_FORMAT_RGBA_4444 = 0x6 << 8, /* [0:15] R:G:B:A 4:4:4:4 little endian */ + RK_FORMAT_BGR_888 = 0x7 << 8, /* [0:23] B:G:R 8:8:8 little endian */ + + RK_FORMAT_YCbCr_422_SP = 0x8 << 8, /* 2 plane YCbCr little endian + * plane 0: [0:7] Y + * plane 1: 2x1 subsampled [0:15] Cb:Cr 8:8 */ + RK_FORMAT_YCbCr_422_P = 0x9 << 8, /* 3 plane YCbCr little endian + * plane 0: [0:7] Y + * plane 1: 2x1 subsampled [0:7] Cb + * plane 2: 2x1 subsampled [0:7] Cr */ + RK_FORMAT_YCbCr_420_SP = 0xa << 8, /* 2 plane YCbCr little endian + * plane 0: [0:7] Y + * plane 1: 2x2 subsampled [0:15] Cr:Cb 8:8 */ + RK_FORMAT_YCbCr_420_P = 0xb << 8, /* 3 plane YCbCr little endian + * plane 0: [0:7] Y + * plane 1: 2x2 subsampled [0:7] Cb + * plane 2: 2x2 subsampled [0:7] Cr */ + + RK_FORMAT_YCrCb_422_SP = 0xc << 8, /* 2 plane YCbCr little endian + * plane 0: [0:7] Y + * plane 1: 2x1 subsampled [0:15] Cb:Cr 8:8 */ + RK_FORMAT_YCrCb_422_P = 0xd << 8, /* 3 plane YCbCr little endian + * plane 0: [0:7] Y + * plane 1: 2x1 subsampled [0:7] Cr + * plane 2: 2x1 subsampled [0:7] Cb */ + RK_FORMAT_YCrCb_420_SP = 0xe << 8, /* 2 plane YCbCr little endian + * plane 0: [0:7] Y + * plane 1: 2x2 subsampled [0:15] Cb:Cr 8:8 */ + RK_FORMAT_YCrCb_420_P = 0xf << 8, /* 3 plane YCbCr little endian + * plane 0: [0:7] Y + * plane 1: 2x2 subsampled [0:7] Cr + * plane 2: 2x2 subsampled [0:7] Cb */ + + RK_FORMAT_BPP1 = 0x10 << 8, /* [0] little endian */ + RK_FORMAT_BPP2 = 0x11 << 8, /* [0:1] little endian */ + RK_FORMAT_BPP4 = 0x12 << 8, /* [0:3] little endian */ + RK_FORMAT_BPP8 = 0x13 << 8, /* [0:7] little endian */ + + RK_FORMAT_Y4 = 0x14 << 8, /* [0:3] Y little endian */ + RK_FORMAT_YCbCr_400 = 0x15 << 8, /* [0:7] Y little endian */ + + RK_FORMAT_BGRX_8888 = 0x16 << 8, /* [0:31] B:G:R:X 8:8:8:8 little endian */ + + RK_FORMAT_YVYU_422 = 0x18 << 8, /* [0:31] Y0:Cr0:Y1:cb0 8:8:8:8 little endian */ + RK_FORMAT_YVYU_420 = 0x19 << 8, /* ODD : [0:31] Y0:Cr0:Y1:cb0 8:8:8:8 little endian + * EVEN: [0:31] Y2:Y3:X:X 8:8:8:8 little endian */ + RK_FORMAT_VYUY_422 = 0x1a << 8, /* [0:31] Cr0:Y0:Cb0:Y1 8:8:8:8 little endian */ + RK_FORMAT_VYUY_420 = 0x1b << 8, /* ODD : [0:31] Cr0:Y0:Cb0:Y1 8:8:8:8 little endian + * EVEN: [0:31] Y2:Y3:X:X 8:8:8:8 little endian */ + RK_FORMAT_YUYV_422 = 0x1c << 8, /* [0:31] Y0:Cb0:Y1:cr0 8:8:8:8 little endian */ + RK_FORMAT_YUYV_420 = 0x1d << 8, /* ODD : [0:31] Y0:Cb0:Y1:cr0 8:8:8:8 little endian + * EVEN: [0:31] Y2:Y3:X:X 8:8:8:8 little endian */ + RK_FORMAT_UYVY_422 = 0x1e << 8, /* [0:31] Cb0:Y0:Cr0:Y1 8:8:8:8 little endian */ + RK_FORMAT_UYVY_420 = 0x1f << 8, /* ODD : [0:31] Cb0:Y0:Cr0:Y1 8:8:8:8 little endian + * EVEN: [0:31] Y2:Y3:X:X 8:8:8:8 little endian */ + + RK_FORMAT_YCbCr_420_SP_10B = 0x20 << 8, /* 2 plane YCbCr little endian + * plane 0: [0:9] Y + * plane 1: 2x2 subsampled [0:19] Cb:Cr 10: 10 (default) + * or + * plane 1: 2x2 subsampled [0:23] Cb:Cr 16: 16 */ + RK_FORMAT_YCrCb_420_SP_10B = 0x21 << 8, /* 2 plane YCbCr little endian + * plane 0: [0:9] Y + * plane 1: 2x2 subsampled [0:19] Cr:Cb 10: 10 (default) + * or + * plane 1: 2x2 subsampled [0:23] Cr:Cb 16: 16 */ + RK_FORMAT_YCbCr_422_SP_10B = 0x22 << 8, /* 2 plane YCbCr little endian + * plane 0: [0:9] Y + * plane 1: 2x1 subsampled [0:19] Cb:Cr 10:10 (default) + * or + * plane 1: 2x1 subsampled [0:23] Cb:Cr 16: 16 */ + RK_FORMAT_YCrCb_422_SP_10B = 0x23 << 8, /* 2 plane YCbCr little endian + * plane 0: [0:9] Y + * plane 1: 2x1 subsampled [0:19] Cr:Cb 10:10 (default) + * or + * plane 1: 2x1 subsampled [0:23] Cr:Cb 16: 16 */ + /* For compatibility with misspellings */ + RK_FORMAT_YCbCr_422_10b_SP = RK_FORMAT_YCbCr_422_SP_10B << 8, + RK_FORMAT_YCrCb_422_10b_SP = RK_FORMAT_YCrCb_422_SP_10B << 8, + + RK_FORMAT_BGR_565 = 0x24 << 8, /* [0:16] B:G:R 5:6:5 little endian */ + RK_FORMAT_BGRA_5551 = 0x25 << 8, /* [0:16] B:G:R:A 5:5:5:1 little endian */ + RK_FORMAT_BGRA_4444 = 0x26 << 8, /* [0:16] B:G:R:A 4:4:4:4 little endian */ + + RK_FORMAT_ARGB_8888 = 0x28 << 8, /* [0:31] A:R:G:B 8:8:8:8 little endian */ + RK_FORMAT_XRGB_8888 = 0x29 << 8, /* [0:31] X:R:G:B 8:8:8:8 little endian */ + RK_FORMAT_ARGB_5551 = 0x2a << 8, /* [0:16] A:R:G:B 5:5:5:1 little endian */ + RK_FORMAT_ARGB_4444 = 0x2b << 8, /* [0:16] A:R:G:B 4:4:4:4 little endian */ + RK_FORMAT_ABGR_8888 = 0x2c << 8, /* [0:31] A:B:G:R 8:8:8:8 little endian */ + RK_FORMAT_XBGR_8888 = 0x2d << 8, /* [0:31] X:B:G:R 8:8:8:8 little endian */ + RK_FORMAT_ABGR_5551 = 0x2e << 8, /* [0:16] A:B:G:R 5:5:5:1 little endian */ + RK_FORMAT_ABGR_4444 = 0x2f << 8, /* [0:16] A:B:G:R 4:4:4:4 little endian */ + + RK_FORMAT_RGBA2BPP = 0x30 << 8, /* [0:1] Color:Alpha 1:1 little endian */ + RK_FORMAT_A8 = 0x31 << 8, /* [0:7] Alpha */ + + RK_FORMAT_YCbCr_444_SP = 0x32 << 8, /* 2 plane YCbCr little endian + * plane 0: [0:7] Y + * plane 1: non-subsampled [0:15] Cb:Cr 8:8 */ + RK_FORMAT_YCrCb_444_SP = 0x33 << 8, /* 2 plane YCrCb little endian + * plane 0: [0:7] Y + * plane 1: non-subsampled [0:15] Cr:Cb 8:8 */ + RK_FORMAT_Y8 = 0x34 << 8, /* [0:7] zero:Y 4:4 little endian */ + + RK_FORMAT_UNKNOWN = 0x100 << 8, +} RgaSURF_FORMAT; + +enum { + yuv2rgb_mode0 = 0x0, /* BT.601 MPEG */ + yuv2rgb_mode1 = 0x1, /* BT.601 JPEG */ + yuv2rgb_mode2 = 0x2, /* BT.709 */ + + rgb2yuv_601_full = 0x1 << 8, + rgb2yuv_709_full = 0x2 << 8, + yuv2yuv_601_limit_2_709_limit = 0x3 << 8, + yuv2yuv_601_limit_2_709_full = 0x4 << 8, + yuv2yuv_709_limit_2_601_limit = 0x5 << 8, + yuv2yuv_709_limit_2_601_full = 0x6 << 8, //not support + yuv2yuv_601_full_2_709_limit = 0x7 << 8, + yuv2yuv_601_full_2_709_full = 0x8 << 8, //not support + yuv2yuv_709_full_2_601_limit = 0x9 << 8, //not support + yuv2yuv_709_full_2_601_full = 0xa << 8, //not support + rgb2yuv_709_limit = 0xb << 8, + yuv2rgb_709_full = 0xc << 8, //not support + yuv2yuv_601_limit_2_601_full = 0xd << 8, //not support + yuv2yuv_601_full_2_601_limit = 0xe << 8, //not support + yuv2yuv_709_limit_2_709_full = 0xf << 8, //not support + yuv2yuv_709_full_2_709_limit = 0x10 << 8, //not support + full_csc_mask = 0xf00, +}; + +enum { + RGA3_SCHEDULER_CORE0 = 1 << 0, + RGA3_SCHEDULER_CORE1 = 1 << 1, + RGA2_SCHEDULER_CORE0 = 1 << 2, + RGA2_SCHEDULER_CORE1 = 1 << 3, + RGA_CORE_MASK = 0xf, + RGA_NONE_CORE = 0x0, +}; + +/* RGA3 rd_mode */ +enum +{ + raster_mode = 0x1 << 0, + fbc_mode = 0x1 << 1, + tile_mode = 0x1 << 2, +}; + +#ifdef __cplusplus +} +#endif + +#endif /*_RK29_IPP_DRIVER_H_*/ diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rknn_api.h b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rknn_api.h new file mode 100755 index 0000000000000000000000000000000000000000..8007931f966fc8fb72e30dd96afc8027c25e0f9d --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/rknn_api.h @@ -0,0 +1,697 @@ +/**************************************************************************** +* +* Copyright (c) 2017 - 2022 by Rockchip Corp. All rights reserved. +* +* The material in this file is confidential and contains trade secrets +* of Rockchip Corporation. This is proprietary information owned by +* Rockchip Corporation. No part of this work may be disclosed, +* reproduced, copied, transmitted, or used in any way for any purpose, +* without the express written permission of Rockchip Corporation. +* +*****************************************************************************/ + + +#ifndef _RKNN_API_H +#define _RKNN_API_H + +#ifdef __cplusplus +extern "C" { +#endif + +#include + +/* + Definition of extended flag for rknn_init. +*/ +/* set high priority context. */ +#define RKNN_FLAG_PRIOR_HIGH 0x00000000 + +/* set medium priority context */ +#define RKNN_FLAG_PRIOR_MEDIUM 0x00000001 + +/* set low priority context. */ +#define RKNN_FLAG_PRIOR_LOW 0x00000002 + +/* asynchronous mode. + when enable, rknn_outputs_get will not block for too long because it directly retrieves the result of + the previous frame which can increase the frame rate on single-threaded mode, but at the cost of + rknn_outputs_get not retrieves the result of the current frame. + in multi-threaded mode you do not need to turn this mode on. */ +#define RKNN_FLAG_ASYNC_MASK 0x00000004 + +/* collect performance mode. + when enable, you can get detailed performance reports via rknn_query(ctx, RKNN_QUERY_PERF_DETAIL, ...), + but it will reduce the frame rate. */ +#define RKNN_FLAG_COLLECT_PERF_MASK 0x00000008 + +/* allocate all memory in outside, includes weight/internal/inputs/outputs */ +#define RKNN_FLAG_MEM_ALLOC_OUTSIDE 0x00000010 + +/* weight sharing with the same network structure */ +#define RKNN_FLAG_SHARE_WEIGHT_MEM 0x00000020 + +/* send fence fd from outside */ +#define RKNN_FLAG_FENCE_IN_OUTSIDE 0x00000040 + +/* get fence fd from inside */ +#define RKNN_FLAG_FENCE_OUT_OUTSIDE 0x00000080 + +/* dummy init flag: could only get total_weight_size and total_internal_size by rknn_query*/ +#define RKNN_FLAG_COLLECT_MODEL_INFO_ONLY 0x00000100 + +/* + Error code returned by the RKNN API. +*/ +#define RKNN_SUCC 0 /* execute succeed. */ +#define RKNN_ERR_FAIL -1 /* execute failed. */ +#define RKNN_ERR_TIMEOUT -2 /* execute timeout. */ +#define RKNN_ERR_DEVICE_UNAVAILABLE -3 /* device is unavailable. */ +#define RKNN_ERR_MALLOC_FAIL -4 /* memory malloc fail. */ +#define RKNN_ERR_PARAM_INVALID -5 /* parameter is invalid. */ +#define RKNN_ERR_MODEL_INVALID -6 /* model is invalid. */ +#define RKNN_ERR_CTX_INVALID -7 /* context is invalid. */ +#define RKNN_ERR_INPUT_INVALID -8 /* input is invalid. */ +#define RKNN_ERR_OUTPUT_INVALID -9 /* output is invalid. */ +#define RKNN_ERR_DEVICE_UNMATCH -10 /* the device is unmatch, please update rknn sdk + and npu driver/firmware. */ +#define RKNN_ERR_INCOMPATILE_PRE_COMPILE_MODEL -11 /* This RKNN model use pre_compile mode, but not compatible with current driver. */ +#define RKNN_ERR_INCOMPATILE_OPTIMIZATION_LEVEL_VERSION -12 /* This RKNN model set optimization level, but not compatible with current driver. */ +#define RKNN_ERR_TARGET_PLATFORM_UNMATCH -13 /* This RKNN model set target platform, but not compatible with current platform. */ + +/* + Definition for tensor +*/ +#define RKNN_MAX_DIMS 16 /* maximum dimension of tensor. */ +#define RKNN_MAX_NUM_CHANNEL 15 /* maximum channel number of input tensor. */ +#define RKNN_MAX_NAME_LEN 256 /* maximum name lenth of tensor. */ +#define RKNN_MAX_DYNAMIC_SHAPE_NUM 512 /* maximum number of dynamic shape for each input. */ + +#ifdef __arm__ +typedef uint32_t rknn_context; +#else +typedef uint64_t rknn_context; +#endif + + +/* + The query command for rknn_query +*/ +typedef enum _rknn_query_cmd { + RKNN_QUERY_IN_OUT_NUM = 0, /* query the number of input & output tensor. */ + RKNN_QUERY_INPUT_ATTR = 1, /* query the attribute of input tensor. */ + RKNN_QUERY_OUTPUT_ATTR = 2, /* query the attribute of output tensor. */ + RKNN_QUERY_PERF_DETAIL = 3, /* query the detail performance, need set + RKNN_FLAG_COLLECT_PERF_MASK when call rknn_init, + this query needs to be valid after rknn_outputs_get. */ + RKNN_QUERY_PERF_RUN = 4, /* query the time of run, + this query needs to be valid after rknn_outputs_get. */ + RKNN_QUERY_SDK_VERSION = 5, /* query the sdk & driver version */ + + RKNN_QUERY_MEM_SIZE = 6, /* query the weight & internal memory size */ + RKNN_QUERY_CUSTOM_STRING = 7, /* query the custom string */ + + RKNN_QUERY_NATIVE_INPUT_ATTR = 8, /* query the attribute of native input tensor. */ + RKNN_QUERY_NATIVE_OUTPUT_ATTR = 9, /* query the attribute of native output tensor. */ + + RKNN_QUERY_NATIVE_NC1HWC2_INPUT_ATTR = 8, /* query the attribute of native input tensor. */ + RKNN_QUERY_NATIVE_NC1HWC2_OUTPUT_ATTR = 9, /* query the attribute of native output tensor. */ + + RKNN_QUERY_NATIVE_NHWC_INPUT_ATTR = 10, /* query the attribute of native input tensor. */ + RKNN_QUERY_NATIVE_NHWC_OUTPUT_ATTR = 11, /* query the attribute of native output tensor. */ + + RKNN_QUERY_DEVICE_MEM_INFO = 12, /* query the attribute of rknn memory information. */ + + RKNN_QUERY_INPUT_DYNAMIC_RANGE = 13, /* query the dynamic shape range of rknn input tensor. */ + RKNN_QUERY_CURRENT_INPUT_ATTR = 14, /* query the current shape of rknn input tensor, only valid for dynamic rknn model*/ + RKNN_QUERY_CURRENT_OUTPUT_ATTR = 15, /* query the current shape of rknn output tensor, only valid for dynamic rknn model*/ + + RKNN_QUERY_CMD_MAX +} rknn_query_cmd; + +/* + the tensor data type. +*/ +typedef enum _rknn_tensor_type { + RKNN_TENSOR_FLOAT32 = 0, /* data type is float32. */ + RKNN_TENSOR_FLOAT16, /* data type is float16. */ + RKNN_TENSOR_INT8, /* data type is int8. */ + RKNN_TENSOR_UINT8, /* data type is uint8. */ + RKNN_TENSOR_INT16, /* data type is int16. */ + RKNN_TENSOR_UINT16, /* data type is uint16. */ + RKNN_TENSOR_INT32, /* data type is int32. */ + RKNN_TENSOR_UINT32, /* data type is uint32. */ + RKNN_TENSOR_INT64, /* data type is int64. */ + RKNN_TENSOR_BOOL, + + RKNN_TENSOR_TYPE_MAX +} rknn_tensor_type; + +inline static const char* get_type_string(rknn_tensor_type type) +{ + switch(type) { + case RKNN_TENSOR_FLOAT32: return "FP32"; + case RKNN_TENSOR_FLOAT16: return "FP16"; + case RKNN_TENSOR_INT8: return "INT8"; + case RKNN_TENSOR_UINT8: return "UINT8"; + case RKNN_TENSOR_INT16: return "INT16"; + case RKNN_TENSOR_UINT16: return "UINT16"; + case RKNN_TENSOR_INT32: return "INT32"; + case RKNN_TENSOR_UINT32: return "UINT32"; + case RKNN_TENSOR_INT64: return "INT64"; + case RKNN_TENSOR_BOOL: return "BOOL"; + default: return "UNKNOW"; + } +} + +/* + the quantitative type. +*/ +typedef enum _rknn_tensor_qnt_type { + RKNN_TENSOR_QNT_NONE = 0, /* none. */ + RKNN_TENSOR_QNT_DFP, /* dynamic fixed point. */ + RKNN_TENSOR_QNT_AFFINE_ASYMMETRIC, /* asymmetric affine. */ + + RKNN_TENSOR_QNT_MAX +} rknn_tensor_qnt_type; + +inline static const char* get_qnt_type_string(rknn_tensor_qnt_type type) +{ + switch(type) { + case RKNN_TENSOR_QNT_NONE: return "NONE"; + case RKNN_TENSOR_QNT_DFP: return "DFP"; + case RKNN_TENSOR_QNT_AFFINE_ASYMMETRIC: return "AFFINE"; + default: return "UNKNOW"; + } +} + +/* + the tensor data format. +*/ +typedef enum _rknn_tensor_format { + RKNN_TENSOR_NCHW = 0, /* data format is NCHW. */ + RKNN_TENSOR_NHWC, /* data format is NHWC. */ + RKNN_TENSOR_NC1HWC2, /* data format is NC1HWC2. */ + RKNN_TENSOR_UNDEFINED, + + RKNN_TENSOR_FORMAT_MAX +} rknn_tensor_format; + +/* + the mode of running on target NPU core. +*/ +typedef enum _rknn_core_mask { + RKNN_NPU_CORE_AUTO = 0, /* default, run on NPU core randomly. */ + RKNN_NPU_CORE_0 = 1, /* run on NPU core 0. */ + RKNN_NPU_CORE_1 = 2, /* run on NPU core 1. */ + RKNN_NPU_CORE_2 = 4, /* run on NPU core 2. */ + RKNN_NPU_CORE_0_1 = RKNN_NPU_CORE_0 | RKNN_NPU_CORE_1, /* run on NPU core 1 and core 2. */ + RKNN_NPU_CORE_0_1_2 = RKNN_NPU_CORE_0_1 | RKNN_NPU_CORE_2, /* run on NPU core 1 and core 2 and core 3. */ + + RKNN_NPU_CORE_UNDEFINED, +} rknn_core_mask; + +inline static const char* get_format_string(rknn_tensor_format fmt) +{ + switch(fmt) { + case RKNN_TENSOR_NCHW: return "NCHW"; + case RKNN_TENSOR_NHWC: return "NHWC"; + case RKNN_TENSOR_NC1HWC2: return "NC1HWC2"; + case RKNN_TENSOR_UNDEFINED: return "UNDEFINED"; + default: return "UNKNOW"; + } +} + +/* + the information for RKNN_QUERY_IN_OUT_NUM. +*/ +typedef struct _rknn_input_output_num { + uint32_t n_input; /* the number of input. */ + uint32_t n_output; /* the number of output. */ +} rknn_input_output_num; + +/* + the information for RKNN_QUERY_INPUT_ATTR / RKNN_QUERY_OUTPUT_ATTR. +*/ +typedef struct _rknn_tensor_attr { + uint32_t index; /* input parameter, the index of input/output tensor, + need set before call rknn_query. */ + + uint32_t n_dims; /* the number of dimensions. */ + uint32_t dims[RKNN_MAX_DIMS]; /* the dimensions array. */ + char name[RKNN_MAX_NAME_LEN]; /* the name of tensor. */ + + uint32_t n_elems; /* the number of elements. */ + uint32_t size; /* the bytes size of tensor. */ + + rknn_tensor_format fmt; /* the data format of tensor. */ + rknn_tensor_type type; /* the data type of tensor. */ + rknn_tensor_qnt_type qnt_type; /* the quantitative type of tensor. */ + int8_t fl; /* fractional length for RKNN_TENSOR_QNT_DFP. */ + int32_t zp; /* zero point for RKNN_TENSOR_QNT_AFFINE_ASYMMETRIC. */ + float scale; /* scale for RKNN_TENSOR_QNT_AFFINE_ASYMMETRIC. */ + + uint32_t w_stride; /* the stride of tensor along the width dimention of input, + Note: it is read-only, 0 means equal to width. */ + uint32_t size_with_stride; /* the bytes size of tensor with stride. */ + + uint8_t pass_through; /* pass through mode, for rknn_set_io_mem interface. + if TRUE, the buf data is passed directly to the input node of the rknn model + without any conversion. the following variables do not need to be set. + if FALSE, the buf data is converted into an input consistent with the model + according to the following type and fmt. so the following variables + need to be set.*/ + uint32_t h_stride; /* the stride along the height dimention of input, + Note: it is write-only, if it was set to 0, h_stride = height. */ +} rknn_tensor_attr; + +typedef struct _rknn_input_range { + uint32_t index; /* input parameter, the index of input/output tensor, + need set before call rknn_query. */ + uint32_t shape_number; /* the number of shape. */ + rknn_tensor_format fmt; /* the data format of tensor. */ + char name[RKNN_MAX_NAME_LEN]; /* the name of tensor. */ + uint32_t dyn_range[RKNN_MAX_DYNAMIC_SHAPE_NUM][RKNN_MAX_DIMS]; /* the dynamic input dimensions range. */ + uint32_t n_dims; /* the number of dimensions. */ + +} rknn_input_range; + +/* + the information for RKNN_QUERY_PERF_DETAIL. +*/ +typedef struct _rknn_perf_detail { + char* perf_data; /* the string pointer of perf detail. don't need free it by user. */ + uint64_t data_len; /* the string length. */ +} rknn_perf_detail; + +/* + the information for RKNN_QUERY_PERF_RUN. +*/ +typedef struct _rknn_perf_run { + int64_t run_duration; /* real inference time (us) */ +} rknn_perf_run; + +/* + the information for RKNN_QUERY_SDK_VERSION. +*/ +typedef struct _rknn_sdk_version { + char api_version[256]; /* the version of rknn api. */ + char drv_version[256]; /* the version of rknn driver. */ +} rknn_sdk_version; + +/* + the information for RKNN_QUERY_MEM_SIZE. +*/ +typedef struct _rknn_mem_size { + uint32_t total_weight_size; /* the weight memory size */ + uint32_t total_internal_size; /* the internal memory size, exclude inputs/outputs */ + uint64_t total_dma_allocated_size; /* total dma memory allocated size */ + uint32_t total_sram_size; /* total system sram size reserved for rknn */ + uint32_t free_sram_size; /* free system sram size reserved for rknn */ + uint32_t reserved[10]; /* reserved */ +} rknn_mem_size; + +/* + the information for RKNN_QUERY_CUSTOM_STRING. +*/ +typedef struct _rknn_custom_string { + char string[1024]; /* the string of custom, lengths max to 1024 bytes */ +} rknn_custom_string; + +/* + The flags of rknn_tensor_mem. +*/ +typedef enum _rknn_tensor_mem_flags { + RKNN_TENSOR_MEMORY_FLAGS_ALLOC_INSIDE = 1, /*Used to mark in rknn_destroy_mem() whether it is necessary to release the "mem" pointer itself. + If the flag RKNN_TENSOR_MEMORY_FLAGS_ALLOC_INSIDE is set, rknn_destroy_mem() will call free(mem).*/ + RKNN_TENSOR_MEMORY_FLAGS_FROM_FD = 2, /*Used to mark in rknn_create_mem_from_fd() whether it is necessary to release the "mem" pointer itself. + If the flag RKNN_TENSOR_MEMORY_FLAGS_FROM_FD is set, rknn_destroy_mem() will call free(mem).*/ + RKNN_TENSOR_MEMORY_FLAGS_FROM_PHYS = 3, /*Used to mark in rknn_create_mem_from_phys() whether it is necessary to release the "mem" pointer itself. + If the flag RKNN_TENSOR_MEMORY_FLAGS_FROM_PHYS is set, rknn_destroy_mem() will call free(mem).*/ + RKNN_TENSOR_MEMORY_FLAGS_UNKNOWN +} rknn_tensor_mem_flags; + +/* + the memory information of tensor. +*/ +typedef struct _rknn_tensor_memory { + void* virt_addr; /* the virtual address of tensor buffer. */ + uint64_t phys_addr; /* the physical address of tensor buffer. */ + int32_t fd; /* the fd of tensor buffer. */ + int32_t offset; /* indicates the offset of the memory. */ + uint32_t size; /* the size of tensor buffer. */ + uint32_t flags; /* the flags of tensor buffer, reserved */ + void * priv_data; /* the private data of tensor buffer. */ +} rknn_tensor_mem; + +/* + the input information for rknn_input_set. +*/ +typedef struct _rknn_input { + uint32_t index; /* the input index. */ + void* buf; /* the input buf for index. */ + uint32_t size; /* the size of input buf. */ + uint8_t pass_through; /* pass through mode. + if TRUE, the buf data is passed directly to the input node of the rknn model + without any conversion. the following variables do not need to be set. + if FALSE, the buf data is converted into an input consistent with the model + according to the following type and fmt. so the following variables + need to be set.*/ + rknn_tensor_type type; /* the data type of input buf. */ + rknn_tensor_format fmt; /* the data format of input buf. + currently the internal input format of NPU is NCHW by default. + so entering NCHW data can avoid the format conversion in the driver. */ +} rknn_input; + +/* + the output information for rknn_outputs_get. +*/ +typedef struct _rknn_output { + uint8_t want_float; /* want transfer output data to float */ + uint8_t is_prealloc; /* whether buf is pre-allocated. + if TRUE, the following variables need to be set. + if FALSE, the following variables do not need to be set. */ + uint32_t index; /* the output index. */ + void* buf; /* the output buf for index. + when is_prealloc = FALSE and rknn_outputs_release called, + this buf pointer will be free and don't use it anymore. */ + uint32_t size; /* the size of output buf. */ +} rknn_output; + +/* + the extend information for rknn_init. +*/ +typedef struct _rknn_init_extend { + rknn_context ctx; /* rknn context */ + int32_t real_model_offset; /* real rknn model file offset, only valid when init context with rknn file path */ + uint32_t real_model_size; /* real rknn model file size, only valid when init context with rknn file path */ + uint8_t reserved[120]; /* reserved */ +} rknn_init_extend; + +/* + the extend information for rknn_run. +*/ +typedef struct _rknn_run_extend { + uint64_t frame_id; /* output parameter, indicate current frame id of run. */ + int32_t non_block; /* block flag of run, 0 is block else 1 is non block */ + int32_t timeout_ms; /* timeout for block mode, in milliseconds */ + int32_t fence_fd; /* fence fd from other unit */ +} rknn_run_extend; + +/* + the extend information for rknn_outputs_get. +*/ +typedef struct _rknn_output_extend { + uint64_t frame_id; /* output parameter, indicate the frame id of outputs, corresponds to + struct rknn_run_extend.frame_id.*/ +} rknn_output_extend; + + +/* rknn_init + + initial the context and load the rknn model. + + input: + rknn_context* context the pointer of context handle. + void* model if size > 0, pointer to the rknn model, if size = 0, filepath to the rknn model. + uint32_t size the size of rknn model. + uint32_t flag extend flag, see the define of RKNN_FLAG_XXX_XXX. + rknn_init_extend* extend the extend information of init. + return: + int error code. +*/ +int rknn_init(rknn_context* context, void* model, uint32_t size, uint32_t flag, rknn_init_extend* extend); + +/* rknn_dup_context + + initial the context and load the rknn model. + + input: + rknn_context* context_in the pointer of context in handle. + rknn_context* context_out the pointer of context out handle. + return: + int error code. +*/ +int rknn_dup_context(rknn_context* context_in, rknn_context* context_out); + +/* rknn_destroy + + unload the rknn model and destroy the context. + + input: + rknn_context context the handle of context. + return: + int error code. +*/ +int rknn_destroy(rknn_context context); + + +/* rknn_query + + query the information about model or others. see rknn_query_cmd. + + input: + rknn_context context the handle of context. + rknn_query_cmd cmd the command of query. + void* info the buffer point of information. + uint32_t size the size of information. + return: + int error code. +*/ +int rknn_query(rknn_context context, rknn_query_cmd cmd, void* info, uint32_t size); + + +/* rknn_inputs_set + + set inputs information by input index of rknn model. + inputs information see rknn_input. + + input: + rknn_context context the handle of context. + uint32_t n_inputs the number of inputs. + rknn_input inputs[] the arrays of inputs information, see rknn_input. + return: + int error code +*/ +int rknn_inputs_set(rknn_context context, uint32_t n_inputs, rknn_input inputs[]); + +/* + rknn_set_batch_core_num + + set rknn batch core_num. + + input: + rknn_context context the handle of context. + int core_num the core number. + return: + int error code. + +*/ +int rknn_set_batch_core_num(rknn_context context, int core_num); + +/* rknn_set_core_mask + + set rknn core mask.(only supported on RK3588 now) + + RKNN_NPU_CORE_AUTO: auto mode, default value + RKNN_NPU_CORE_0: core 0 mode + RKNN_NPU_CORE_1: core 1 mode + RKNN_NPU_CORE_2: core 2 mode + RKNN_NPU_CORE_0_1: combine core 0/1 mode + RKNN_NPU_CORE_0_1_2: combine core 0/1/2 mode + + input: + rknn_context context the handle of context. + rknn_core_mask core_mask the core mask. + return: + int error code. +*/ +int rknn_set_core_mask(rknn_context context, rknn_core_mask core_mask); + +/* rknn_run + + run the model to execute inference. + + input: + rknn_context context the handle of context. + rknn_run_extend* extend the extend information of run. + return: + int error code. +*/ +int rknn_run(rknn_context context, rknn_run_extend* extend); + + +/* rknn_wait + + wait the model after execute inference. + + input: + rknn_context context the handle of context. + rknn_run_extend* extend the extend information of run. + return: + int error code. +*/ +int rknn_wait(rknn_context context, rknn_run_extend* extend); + + +/* rknn_outputs_get + + wait the inference to finish and get the outputs. + this function will block until inference finish. + the results will set to outputs[]. + + input: + rknn_context context the handle of context. + uint32_t n_outputs the number of outputs. + rknn_output outputs[] the arrays of output, see rknn_output. + rknn_output_extend* the extend information of output. + return: + int error code. +*/ +int rknn_outputs_get(rknn_context context, uint32_t n_outputs, rknn_output outputs[], rknn_output_extend* extend); + + +/* rknn_outputs_release + + release the outputs that get by rknn_outputs_get. + after called, the rknn_output[x].buf get from rknn_outputs_get will + also be free when rknn_output[x].is_prealloc = FALSE. + + input: + rknn_context context the handle of context. + uint32_t n_ouputs the number of outputs. + rknn_output outputs[] the arrays of output. + return: + int error code +*/ +int rknn_outputs_release(rknn_context context, uint32_t n_ouputs, rknn_output outputs[]); + + +/* new api for zero copy */ + +/* rknn_create_mem_from_phys (memory allocated outside) + + initialize tensor memory from physical address. + + input: + rknn_context ctx the handle of context. + uint64_t phys_addr physical address. + void *virt_addr virtual address. + uint32_t size the size of tensor buffer. + return: + rknn_tensor_mem the pointer of tensor memory information. +*/ +rknn_tensor_mem* rknn_create_mem_from_phys(rknn_context ctx, uint64_t phys_addr, void *virt_addr, uint32_t size); + + +/* rknn_create_mem_from_fd (memory allocated outside) + + initialize tensor memory from file description. + + input: + rknn_context ctx the handle of context. + int32_t fd file description. + void *virt_addr virtual address. + uint32_t size the size of tensor buffer. + int32_t offset indicates the offset of the memory (virt_addr without offset). + return: + rknn_tensor_mem the pointer of tensor memory information. +*/ +rknn_tensor_mem* rknn_create_mem_from_fd(rknn_context ctx, int32_t fd, void *virt_addr, uint32_t size, int32_t offset); + + +/* rknn_create_mem_from_mb_blk (memory allocated outside) + + create tensor memory from mb_blk. + + input: + rknn_context ctx the handle of context. + void *mb_blk mb_blk allocate from system api. + int32_t offset indicates the offset of the memory. + return: + rknn_tensor_mem the pointer of tensor memory information. +*/ +rknn_tensor_mem* rknn_create_mem_from_mb_blk(rknn_context ctx, void *mb_blk, int32_t offset); + + +/* rknn_create_mem (memory allocated inside) + + create tensor memory. + + input: + rknn_context ctx the handle of context. + uint32_t size the size of tensor buffer. + return: + rknn_tensor_mem the pointer of tensor memory information. +*/ +rknn_tensor_mem* rknn_create_mem(rknn_context ctx, uint32_t size); + + +/* rknn_destroy_mem (support allocate inside and outside) + + destroy tensor memory. + + input: + rknn_context ctx the handle of context. + rknn_tensor_mem *mem the pointer of tensor memory information. + return: + int error code +*/ +int rknn_destroy_mem(rknn_context ctx, rknn_tensor_mem *mem); + + +/* rknn_set_weight_mem + + set the weight memory. + + input: + rknn_context ctx the handle of context. + rknn_tensor_mem *mem the array of tensor memory information + return: + int error code. +*/ +int rknn_set_weight_mem(rknn_context ctx, rknn_tensor_mem *mem); + + +/* rknn_set_internal_mem + + set the internal memory. + + input: + rknn_context ctx the handle of context. + rknn_tensor_mem *mem the array of tensor memory information + return: + int error code. +*/ +int rknn_set_internal_mem(rknn_context ctx, rknn_tensor_mem *mem); + + +/* rknn_set_io_mem + + set the input and output tensors buffer. + + input: + rknn_context ctx the handle of context. + rknn_tensor_mem *mem the array of tensor memory information. + rknn_tensor_attr *attr the attribute of input or output tensor buffer. + return: + int error code. +*/ +int rknn_set_io_mem(rknn_context ctx, rknn_tensor_mem *mem, rknn_tensor_attr *attr); + +/* rknn_set_input_shape + + set the input tensor shape (only valid for dynamic shape rknn model). + + input: + rknn_context ctx the handle of context. + rknn_tensor_attr *attr the attribute of input or output tensor buffer. + return: + int error code. +*/ +int rknn_set_input_shape(rknn_context ctx, rknn_tensor_attr* attr); + +#ifdef __cplusplus +} //extern "C" +#endif + +#endif //_RKNN_API_H diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/yolo_image.cc b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/yolo_image.cc new file mode 100644 index 0000000000000000000000000000000000000000..7fff5a09af024ab8befe6bdeca28c4236d9a40b7 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/yolo_image.cc @@ -0,0 +1,431 @@ +/** + * @ClassName yolo_image + * @Description inference code for yolo + * @Author raul.rao + * @Date 2022/5/23 11:10 + * @Version 1.0 + */ + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include + +#include "rknn_api.h" + +#include "yolo_image.h" +#include "rga/rga.h" +#include "rga/im2d.h" +#include "rga/im2d_version.h" +#include "post_process.h" + +//#define DEBUG_DUMP +//#define EVAL_TIME +#define ZERO_COPY 1 +#define DO_NOT_FLIP -1 + +int g_inf_count = 0; + +int g_post_count = 0; + +rknn_context ctx = 0; + +bool created = false; + +int img_width = 0; // the width of the actual input image +int img_height = 0; // the height of the actual input image + +int m_in_width = 0; // the width of the RKNN model input +int m_in_height = 0; // the height of the RKNN model input +int m_in_channel = 0; // the channel of the RKNN model input + +float scale_w = 0.0; +float scale_h = 0.0; + +uint32_t n_input = 1; +uint32_t n_output = 3; + +rknn_tensor_attr input_attrs[1]; +rknn_tensor_attr output_attrs[3]; + +rknn_tensor_mem *input_mems[1]; +rknn_tensor_mem *output_mems[3]; + +rga_buffer_t g_rga_src; +rga_buffer_t g_rga_dst; + +std::vector out_scales; +std::vector out_zps; + +double __get_us(struct timeval t) { return (t.tv_sec * 1000000 + t.tv_usec); } + + +int create(int im_height, int im_width, int im_channel, char *model_path) +{ + img_height = im_height; + img_width = im_width; + + LOGI("try rknn_init!") + + // 0. RGA version check + LOGI("RGA API Version: %s", RGA_API_VERSION) + // Please refer to the link to confirm the RGA driver version, make sure it is higher than 1.2.4 + // https://github.com/airockchip/librga/blob/main/docs/Rockchip_FAQ_RGA_CN.md#rga-driver + + // 1. Load model + FILE *fp = fopen(model_path, "rb"); + if(fp == NULL) { + LOGE("fopen %s fail!\n", model_path); + return -1; + } + fseek(fp, 0, SEEK_END); + uint32_t model_len = ftell(fp); + void *model = malloc(model_len); + fseek(fp, 0, SEEK_SET); + if(model_len != fread(model, 1, model_len, fp)) { + LOGE("fread %s fail!\n", model_path); + free(model); + fclose(fp); + return -1; + } + + fclose(fp); + + // 2. Init RKNN model + int ret = rknn_init(&ctx, model, model_len, 0, nullptr); + free(model); + + if(ret < 0) { + LOGE("rknn_init fail! ret=%d\n", ret); + return -1; + } + + // 3. Query input/output attr. + rknn_input_output_num io_num; + rknn_query_cmd cmd = RKNN_QUERY_IN_OUT_NUM; + // 3.1 Query input/output num. + ret = rknn_query(ctx, cmd, &io_num, sizeof(io_num)); + if (ret != RKNN_SUCC) { + LOGE("rknn_query io_num fail!ret=%d\n", ret); + return -1; + } + n_input = io_num.n_input; + n_output = io_num.n_output; + + // 3.2 Query input attributes + memset(input_attrs, 0, n_input * sizeof(rknn_tensor_attr)); + for (int i = 0; i < n_input; ++i) { + input_attrs[i].index = i; + cmd = RKNN_QUERY_INPUT_ATTR; + ret = rknn_query(ctx, cmd, &(input_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret < 0) { + LOGE("rknn_query input_attrs[%d] fail!ret=%d\n", i, ret); + return -1; + } + } + // 3.2.0 Update global model input shape. + if (RKNN_TENSOR_NHWC == input_attrs[0].fmt) { + m_in_height = input_attrs[0].dims[1]; + m_in_width = input_attrs[0].dims[2]; + m_in_channel = input_attrs[0].dims[3]; + } else if (RKNN_TENSOR_NCHW == input_attrs[0].fmt) { + m_in_height = input_attrs[0].dims[2]; + m_in_width = input_attrs[0].dims[3]; + m_in_channel = input_attrs[0].dims[1]; + } else { + LOGE("Unsupported model input layout: %d!\n", input_attrs[0].fmt); + return -1; + } + + // set scale_w, scale_h for post process + scale_w = (float)m_in_width / img_width; + scale_h = (float)m_in_height / img_height; + + // 3.3 Query output attributes + memset(output_attrs, 0, n_output * sizeof(rknn_tensor_attr)); + for (int i = 0; i < n_output; ++i) { + output_attrs[i].index = i; + cmd = RKNN_QUERY_OUTPUT_ATTR; + ret = rknn_query(ctx, cmd, &(output_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret < 0) { + LOGE("rknn_query output_attrs[%d] fail!ret=%d\n", i, ret); + return -1; + } + // set out_scales/out_zps for post_process + out_scales.push_back(output_attrs[i].scale); + out_zps.push_back(output_attrs[i].zp); + } + +#if ZERO_COPY + // 4. Set input/output buffer + // 4.1 Set inputs memory + // 4.1.1 Create input tensor memory, input data type is INT8, yolo has only 1 input. + input_mems[0] = rknn_create_mem(ctx, input_attrs[0].size_with_stride * sizeof(char)); + memset(input_mems[0]->virt_addr, 0, input_attrs[0].size_with_stride * sizeof(char)); + // 4.1.2 Update input attrs + input_attrs[0].index = 0; + input_attrs[0].type = RKNN_TENSOR_UINT8; + input_attrs[0].size = m_in_height * m_in_width * m_in_channel * sizeof(char); + input_attrs[0].fmt = RKNN_TENSOR_NHWC; + // TODO -- The efficiency of pass through will be higher, we need adjust the layout of input to + // meet the use condition of pass through. + input_attrs[0].pass_through = 0; + // 4.1.3 Set input buffer + rknn_set_io_mem(ctx, input_mems[0], &(input_attrs[0])); + // 4.1.4 bind virtual address to rga virtual address + g_rga_dst = wrapbuffer_virtualaddr((void *)input_mems[0]->virt_addr, m_in_width, m_in_height, + RK_FORMAT_RGB_888); + + // 4.2 Set outputs memory + for (int i = 0; i < n_output; ++i) { + // 4.2.1 Create output tensor memory, output data type is int8, post_process need int8 data. + output_mems[i] = rknn_create_mem(ctx, output_attrs[i].n_elems * sizeof(unsigned char)); + memset(output_mems[i]->virt_addr, 0, output_attrs[i].n_elems * sizeof(unsigned char)); + // 4.2.2 Update input attrs + output_attrs[i].type = RKNN_TENSOR_INT8; + // 4.1.3 Set output buffer + rknn_set_io_mem(ctx, output_mems[i], &(output_attrs[i])); + } +#else + void *in_data = malloc(m_in_width * m_in_height * m_in_channel); + memset(in_data, 0, m_in_width * m_in_height * m_in_channel); + g_rga_dst = wrapbuffer_virtualaddr(in_data, m_in_width, m_in_height, RK_FORMAT_RGB_888); +#endif + + created = true; + + LOGI("rknn_init success!"); + + return 0; +} + +void destroy() { +// LOGI("rknn_destroy!"); + // release io_mem resource + for (int i = 0; i < n_input; ++i) { + rknn_destroy_mem(ctx, input_mems[i]); + } + for (int i = 0; i < n_output; ++i) { + rknn_destroy_mem(ctx, output_mems[i]); + } + rknn_destroy(ctx); +} + +bool run_yolo(char *inDataRaw, char *y0, char *y1, char *y2) +{ + int ret; + bool status = false; + if(!created) { + LOGE("run_yolo: init yolo hasn't successful!"); + return false; + } + +#ifdef EVAL_TIME + struct timeval start_time, stop_time; + + gettimeofday(&start_time, NULL); +#endif + g_rga_src = wrapbuffer_virtualaddr((void *)inDataRaw, img_width, img_height, + RK_FORMAT_RGBA_8888); + + // convert color format and resize. RGA8888 -> RGB888 + ret = imresize(g_rga_src, g_rga_dst); + if (IM_STATUS_SUCCESS != ret) { + LOGE("run_yolo: resize image with rga failed: %s\n", imStrError((IM_STATUS)ret)); + return false; + } +#ifdef EVAL_TIME + gettimeofday(&stop_time, NULL); + LOGI("imresize use %f ms\n", (__get_us(stop_time) - __get_us(start_time)) / 1000); +#endif + +#ifdef DEBUG_DUMP + // save resized image + if (g_inf_count == 5) { + char out_img_name[1024]; + memset(out_img_name, 0, sizeof(out_img_name)); + sprintf(out_img_name, "/data/user/0/com.rockchip.gpadc.yolodemo/cache/resized_img_%d.rgb", g_inf_count); + FILE *fp = fopen(out_img_name, "w"); +// LOGI("n_elems: %d", input_attrs[0].n_elems); +// fwrite(input_mems[0]->virt_addr, 1, input_attrs[0].n_elems * sizeof(unsigned char), fp); +// fflush(fp); + for (int i = 0; i < input_attrs[0].n_elems; ++i) { + fprintf(fp, "%d\n", *((uint8_t *)(g_rga_dst.vir_addr) + i)); + } + fclose(fp); + } + +#endif + +#if ZERO_COPY +#else + rknn_input inputs[1]; + inputs[0].index = 0; + inputs[0].type = RKNN_TENSOR_UINT8; + inputs[0].size = m_in_width * m_in_height * m_in_channel; + inputs[0].fmt = RKNN_TENSOR_NHWC; + inputs[0].pass_through = 0; + inputs[0].buf = g_rga_dst.vir_addr; +#ifdef EVAL_TIME + gettimeofday(&start_time, NULL); +#endif + rknn_inputs_set(ctx, 1, inputs); +#ifdef EVAL_TIME + gettimeofday(&stop_time, NULL); + LOGI("rknn_inputs_set use %f ms\n", (__get_us(stop_time) - __get_us(start_time)) / 1000); +#endif +#endif + +#ifdef EVAL_TIME + gettimeofday(&start_time, NULL); +#endif + ret = rknn_run(ctx, nullptr); + if(ret < 0) { + LOGE("rknn_run fail! ret=%d\n", ret); + return false; + } +#ifdef EVAL_TIME + gettimeofday(&stop_time, NULL); + LOGI("inference use %f ms\n", (__get_us(stop_time) - __get_us(start_time)) / 1000); + + // outputs format are all NCHW. + gettimeofday(&start_time, NULL); +#endif + +#if ZERO_COPY + memcpy(y0, output_mems[0]->virt_addr, output_attrs[0].n_elems * sizeof(char)); + memcpy(y1, output_mems[1]->virt_addr, output_attrs[1].n_elems * sizeof(char)); + memcpy(y2, output_mems[2]->virt_addr, output_attrs[2].n_elems * sizeof(char)); +#else + rknn_output outputs[3]; + memset(outputs, 0, sizeof(outputs)); + for (int i = 0; i < 3; ++i) { + outputs[i].want_float = 0; + } + rknn_outputs_get(ctx, 3, outputs, NULL); + memcpy(y0, outputs[0].buf, output_attrs[0].n_elems * sizeof(char)); + memcpy(y1, outputs[1].buf, output_attrs[1].n_elems * sizeof(char)); + memcpy(y2, outputs[2].buf, output_attrs[2].n_elems * sizeof(char)); + rknn_outputs_release(ctx, 3, outputs); +#endif + +#ifdef EVAL_TIME + gettimeofday(&stop_time, NULL); + LOGI("copy output use %f ms\n", (__get_us(stop_time) - __get_us(start_time)) / 1000); +#endif + +#ifdef DEBUG_DUMP + if (g_inf_count == 5) { + for (int i = 0; i < n_output; ++i) { + char out_path[1024]; + memset(out_path, 0, sizeof(out_path)); + sprintf(out_path, "/data/user/0/com.rockchip.gpadc.yolodemo/cache/out_%d.tensor", i); + FILE *fp = fopen(out_path, "w"); + for (int j = 0; j < output_attrs[i].n_elems; ++j) { +#if ZERO_COPY + fprintf(fp, "%d\n", *((int8_t *)(output_mems[i]->virt_addr) + i)); +#else + fprintf(fp, "%d\n", *((int8_t *)(outputs[i].buf) + i)); +#endif + } + fclose(fp); + } + } + if (g_inf_count < 10) { + g_inf_count++; + } +#endif + + status = true; + +// LOGI("run_yolo: end\n"); + + return status; +} + +int yolo_post_process(char *grid0_buf, char *grid1_buf, char *grid2_buf, + int *ids, float *scores, float *boxes) { + int ret; + if(!created) { + LOGE("yolo_post_process: init yolo hasn't successful!"); + return false; + } + + detect_result_group_t detect_result_group; +// LOGI("start yolo post."); + ret = post_process((int8_t *)grid0_buf, (int8_t *)grid1_buf, (int8_t *)grid2_buf, + m_in_height, m_in_width, BOX_THRESH, NMS_THRESH, scale_w, scale_h, + out_zps, out_scales, &detect_result_group); + if (ret < 0) { + LOGE("yolo_post_process: post process failed!"); + return -1; + } +// LOGI("deteced %d objects.\n", detect_result_group.count); + + memset(ids, 0, sizeof(int) * OBJ_NUMB_MAX_SIZE); + memset(scores, 0, sizeof(float) * OBJ_NUMB_MAX_SIZE); + memset(boxes, 0, sizeof(float) * OBJ_NUMB_MAX_SIZE * BOX_LEN); + + int count = detect_result_group.count; + for (int i = 0; i < count; ++i) { + ids[i] = detect_result_group.results[i].class_id; + scores[i] = detect_result_group.results[i].prop; + *(boxes+4*i+0) = detect_result_group.results[i].box.left; + *(boxes+4*i+1) = detect_result_group.results[i].box.top; + *(boxes+4*i+2) = detect_result_group.results[i].box.right; + *(boxes+4*i+3) = detect_result_group.results[i].box.bottom; +#ifdef DEBUG_DUMP + if (g_post_count == 5) { + LOGI("result %2d: (%4d, %4d, %4d, %4d), %d\n", i, + detect_result_group.results[i].box.left, + detect_result_group.results[i].box.top, + detect_result_group.results[i].box.right, + detect_result_group.results[i].box.bottom, + detect_result_group.results->class_id) + } + if (g_post_count < 10) { + g_post_count++; + } +#endif + } + + return count; +} + +int colorConvertAndFlip(void *src, int srcFmt, void *dst, int dstFmt, int width, int height, int flip) { + int ret; + + rga_buffer_t rga_src = wrapbuffer_virtualaddr((void *)src, width, height, srcFmt); + rga_buffer_t rga_dst = wrapbuffer_virtualaddr((void *)dst, width, height, dstFmt); + + if (DO_NOT_FLIP == flip) + { + // convert color format + ret = imcvtcolor(rga_src, rga_dst, rga_src.format, rga_dst.format); + } else { + // convert color format and flip. + ret = imflip(rga_src, rga_dst, flip); + } + + if (IM_STATUS_SUCCESS != ret) { + LOGE("colorConvertAndFlip failed. Ret: %s\n", imStrError((IM_STATUS)ret)); + } + + return ret; +} + +void rknn_app_destory() { + LOGI("rknn app destroy.\n"); + if (g_rga_dst.vir_addr) { + free(g_rga_dst.vir_addr); + } + rknn_destroy(ctx); +} diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/yolo_image.h b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/yolo_image.h new file mode 100644 index 0000000000000000000000000000000000000000..fa5dccfaadb6959e22a58fae9da4e973e316c3eb --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/cpp/yolo_image.h @@ -0,0 +1,23 @@ +/** + * @ClassName yolo_image + * @Description TODO + * @Author raul.rao + * @Date 2022/5/23 11:43 + * @Version 1.0 + */ +#ifndef RK_YOLOV5_DEMO_YOLO_IMAGE_H +#define RK_YOLOV5_DEMO_YOLO_IMAGE_H + +#include + +#define LOGI(...) __android_log_print(ANDROID_LOG_INFO, "rkyolo4j", ##__VA_ARGS__); +#define LOGE(...) __android_log_print(ANDROID_LOG_ERROR, "rkyolo4j", ##__VA_ARGS__); + +int create(int im_height, int im_width, int im_channel, char *model_path); +void destroy(); +bool run_yolo(char *inDataRaw, char *y0, char *y1, char *y2); +int yolo_post_process(char *grid0_buf, char *grid1_buf, char *grid2_buf, + int *ids, float *scores, float *boxes); +int colorConvertAndFlip(void *src, int srcFmt, void *dst, int dstFmt, int width, int height, int flip); + +#endif //RK_YOLOV5_DEMO_YOLO_IMAGE_H diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/java/com/rockchip/gpadc/demo/CameraPreviewActivity.java b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/java/com/rockchip/gpadc/demo/CameraPreviewActivity.java new file mode 100644 index 0000000000000000000000000000000000000000..19afae42ef8e794c4a51f96b41d90d3835410e6a --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/java/com/rockchip/gpadc/demo/CameraPreviewActivity.java @@ -0,0 +1,586 @@ +package com.rockchip.gpadc.demo; + +import android.app.Activity; +import android.content.SharedPreferences; +import android.content.res.Resources; +import android.graphics.Bitmap; +import android.graphics.Canvas; +import android.graphics.Paint; +import android.graphics.PorterDuff; +import android.graphics.PorterDuffXfermode; +import android.graphics.RectF; +import android.graphics.SurfaceTexture; +import android.graphics.Typeface; +import android.hardware.Camera; +import android.os.Build; +import android.os.Bundle; +import android.os.Handler; +import android.os.Message; +import android.util.Log; +import android.view.Gravity; +import android.view.SurfaceHolder; +import android.view.SurfaceView; +import android.view.WindowManager; +import android.widget.ImageView; +import android.widget.TextView; +import android.widget.Toast; + +import com.rockchip.gpadc.demo.rga.RGA; +import com.rockchip.gpadc.demo.yolo.InferenceWrapper; + +import java.io.File; +import java.io.FileOutputStream; +import java.io.IOException; +import java.io.InputStream; +import java.lang.reflect.Method; +import java.text.DecimalFormat; +import java.util.ArrayList; +import java.util.List; + +import static com.rockchip.gpadc.demo.rga.HALDefine.CAMERA_PREVIEW_HEIGHT; +import static com.rockchip.gpadc.demo.rga.HALDefine.CAMERA_PREVIEW_WIDTH; +import static com.rockchip.gpadc.demo.rga.HALDefine.IM_HAL_TRANSFORM_FLIP_H; +import static com.rockchip.gpadc.demo.rga.HALDefine.RK_FORMAT_RGBA_8888; +import static com.rockchip.gpadc.demo.rga.HALDefine.RK_FORMAT_YCrCb_420_SP; +import static com.rockchip.gpadc.demo.yolo.PostProcess.INPUT_CHANNEL; +import static java.lang.Thread.sleep; + + +public class CameraPreviewActivity extends Activity implements Camera.PreviewCallback { + + private final String TAG = "rkyolo"; + private static final int MAGIC_TEXTURE_ID = 10; + + TSurfaceHolderCallback mSurfaceHolderCallback = null; + + private Camera mCamera0 = null; + private SurfaceView mSurfaceView = null; + public SurfaceTexture mSurfaceTexture = null; + private SurfaceHolder mSurfaceHolder = null; + public int flip = -1; // for CAMERA_FACING_BACK(camera comes with RK3588 using this mode), + // we do not need flip, using -1, or we need using + // IM_HAL_TRANSFORM_FLIP_H + + private boolean mIsCameraOpened = false; + private int mCameraId = -1; + public byte textureBuffer[]; + + // for inference + private String mModelName = "yolov5s.rknn"; + private String platform = "rk3588"; + private InferenceWrapper mInferenceWrapper; + private String fileDirPath; // file dir to store model cache + private ImageBufferQueue mImageBufferQueue; // intermedia between camera thread and inference thread + private InferenceResult mInferenceResult = new InferenceResult(); // detection result + private int mWidth; //surface width + private int mHeight; //surface height + private volatile boolean mStopInference = false; + + //draw result + private TextView mFpsNum1; + private TextView mFpsNum2; + private TextView mFpsNum3; + private TextView mFpsNum4; + private ImageView mTrackResultView; + private Bitmap mTrackResultBitmap = null; + private Canvas mTrackResultCanvas = null; + private Paint mTrackResultPaint = null; + private Paint mTrackResultTextPaint = null; + + private PorterDuffXfermode mPorterDuffXfermodeClear; + private PorterDuffXfermode mPorterDuffXfermodeSRC; + + @Override + protected void onCreate(Bundle savedInstanceState) { + super.onCreate(savedInstanceState); + setContentView(R.layout.activity_main); + + getWindow().addFlags(WindowManager.LayoutParams.FLAG_KEEP_SCREEN_ON); + + mFpsNum1 = (TextView) findViewById(R.id.fps_num1); + mFpsNum2 = (TextView) findViewById(R.id.fps_num2); + mFpsNum3 = (TextView) findViewById(R.id.fps_num3); + mFpsNum4 = (TextView) findViewById(R.id.fps_num4); + mTrackResultView = (ImageView) findViewById(R.id.canvasView); + + fileDirPath = getCacheDir().getAbsolutePath(); + + platform = getPlatform(); + Log.d(TAG, "get soc platform:" + platform); + + if (platform.equals("rk3588")) { + createFile(mModelName, R.raw.yolov5s_rk3588); + } else if (platform.equals("rk356x")) { + createFile(mModelName, R.raw.yolov5s_rk3566); + } else if (platform.equals("rk3562")) { + createFile(mModelName, R.raw.yolov5s_rk3562); + } else if (platform.equals("rk3576")) { + createFile(mModelName, R.raw.yolov5s_rk3576); + } else { + Toast toast = Toast.makeText(this, "Can not get platform use RK3588 instead.", Toast.LENGTH_LONG); + toast.setGravity(Gravity.CENTER, 0, 0); + toast.show(); + createFile(mModelName, R.raw.yolov5s_rk3588); + } + + try { + mInferenceResult.init(getAssets()); + } catch (IOException e) { + e.printStackTrace(); + } + + mInferenceWrapper = new InferenceWrapper(); + + } + + @Override + protected void onDestroy() { + Log.d(TAG, "onDestroy"); + + destroyPreviewView(); + super.onDestroy(); + } + + @Override + protected void onPause() { + Log.d(TAG, "onPause"); + stopTrack(); + stopCamera(); + destroyPreviewView(); + super.onPause(); + + } + + @Override + protected void onResume() { + Log.d(TAG, "onResume"); + + createPreviewView(); + super.onResume(); + + } + + private boolean createPreviewView() { + mSurfaceView = findViewById(R.id.surfaceViewCamera1); + mSurfaceHolder = mSurfaceView.getHolder(); +// mSurfaceView.setZOrderMediaOverlay(true); + + mSurfaceTexture = new SurfaceTexture(MAGIC_TEXTURE_ID); + + mSurfaceHolderCallback = new TSurfaceHolderCallback(); + mSurfaceHolder.addCallback(mSurfaceHolderCallback); + + return true; + } + + private void destroyPreviewView() { + if (mSurfaceHolder != null) { + mSurfaceHolder.removeCallback(mSurfaceHolderCallback); + mSurfaceHolderCallback = null; + mSurfaceHolder = null; + } + + } + + @Override + public void onPreviewFrame(byte[] data, Camera camera) { + mCamera0.addCallbackBuffer(data); + ImageBufferQueue.ImageBuffer imageBuffer = mImageBufferQueue.getFreeBuffer(); + + if (imageBuffer != null) { + // RK_FORMAT_YCrCb_420_SP -> RK_FORMAT_RGBA_8888 + // flip for CAMERA_FACING_FRONT + RGA.colorConvertAndFlip(data, RK_FORMAT_YCrCb_420_SP, + imageBuffer.mImage, RK_FORMAT_RGBA_8888, + CAMERA_PREVIEW_WIDTH, CAMERA_PREVIEW_HEIGHT, this.flip); + + mImageBufferQueue.postBuffer(imageBuffer); + } + } + + private class TSurfaceHolderCallback implements SurfaceHolder.Callback { + + @Override + public void surfaceChanged(SurfaceHolder holder, int format, int width, int height) { + Log.d(TAG, "surfaceChanged"); + mWidth = width; + mHeight = height; + + textureBuffer=new byte[CAMERA_PREVIEW_WIDTH * CAMERA_PREVIEW_HEIGHT * 4]; + } + + @Override + public void surfaceCreated(SurfaceHolder holder) { + Log.d(TAG, "surfaceCreated"); + + startCamera(); + startTrack(); + + } + + @Override + public void surfaceDestroyed(SurfaceHolder holder) { + Log.d(TAG, "surfaceDestroyed"); + + stopTrack(); + stopCamera(); + } + } + + + private boolean startCamera() { + if (mIsCameraOpened) { + return true; + } + + //(Camera.CameraInfo.CAMERA_FACING_BACK); + int num = Camera.getNumberOfCameras(); + if (num > 2) + mCameraId = 2; + else + mCameraId = 0; + Log.d(TAG, "mCameraId = " + mCameraId); + Camera.CameraInfo camInfo = new Camera.CameraInfo(); + try { + Camera.getCameraInfo(mCameraId, camInfo); + if (mCameraId != -1) { + mCamera0 = Camera.open(mCameraId); + } else { + mCamera0 = Camera.open(); + } + Log.d(TAG, "mCamera0 = " + mCamera0); + Log.d(TAG, "camera facing: " + camInfo.facing); + if (Camera.CameraInfo.CAMERA_FACING_FRONT == camInfo.facing) { + this.flip = IM_HAL_TRANSFORM_FLIP_H; + } + + } catch (RuntimeException e) { + Log.w(TAG, "Unable to open camera!"); + Toast toast = Toast.makeText(this, "Unable to open camera!", Toast.LENGTH_LONG); + toast.setGravity(Gravity.CENTER, 0, 0); + toast.show(); + return false; + } + + setCameraParameters(); + + try { + mCamera0.setPreviewDisplay(mSurfaceHolder); + mCamera0.setDisplayOrientation(0); + int BUFFER_SIZE0 = CAMERA_PREVIEW_WIDTH * CAMERA_PREVIEW_HEIGHT * 3 / 2; // NV21 + byte[][] mPreviewData0 = new byte[][]{new byte[BUFFER_SIZE0], new byte[BUFFER_SIZE0],new byte[BUFFER_SIZE0]}; + //================================ + for (byte[] buffer : mPreviewData0) + mCamera0.addCallbackBuffer(buffer); + mCamera0.setPreviewCallbackWithBuffer(this); + //================================== + mCamera0.startPreview(); + } catch (Exception e) { + mCamera0.release(); + return false; + } + + mIsCameraOpened = true; + + return true; + } + + private void stopCamera() { + if (mIsCameraOpened) { + mCamera0.setPreviewCallback(null); + mCamera0.stopPreview(); + mCamera0.release(); + mCamera0 = null; + mIsCameraOpened = false; + } + + } + + private void setCameraParameters() { + Camera.Parameters parameters; + boolean checkWH = false; + parameters = mCamera0.getParameters(); + int nearest_width_index = 0; + int nearest_width_value = 1920; + + // TODO, android sdk > android 14 + // mCamera0.setParameters(parameters); maybe fail under android 14 + if (Build.VERSION.SDK_INT >=34) { + Camera.Size size = parameters.getPreviewSize(); + CAMERA_PREVIEW_WIDTH = size.width; + CAMERA_PREVIEW_HEIGHT = size.height; + Log.i(TAG, "Use preview Size = " + CAMERA_PREVIEW_WIDTH + "x" + CAMERA_PREVIEW_HEIGHT); + return; + } + + List sizes = parameters.getSupportedPreviewSizes(); + for (int i = 0; i < sizes.size(); i++) { + Camera.Size size = sizes.get(i); + + if (Math.abs(size.width-CAMERA_PREVIEW_WIDTH) < nearest_width_value ) { + nearest_width_value = Math.abs(size.width-CAMERA_PREVIEW_WIDTH); + nearest_width_index = i; + } + + if ( (size.width == CAMERA_PREVIEW_WIDTH) && (size.height == CAMERA_PREVIEW_HEIGHT)) { + checkWH = true; + } + + Log.v(TAG, "Camera Supported Preview Size = " + size.width + "x" + size.height); + } + if (!checkWH) { + Log.e(TAG, "Camera don't support this preview Size = " + CAMERA_PREVIEW_WIDTH + "x" + CAMERA_PREVIEW_HEIGHT); + CAMERA_PREVIEW_WIDTH = sizes.get(nearest_width_index).width; + CAMERA_PREVIEW_HEIGHT = sizes.get(nearest_width_index).height; + } + + Log.i(TAG, "Use preview Size = " + CAMERA_PREVIEW_WIDTH + "x" + CAMERA_PREVIEW_HEIGHT); + + parameters.setPreviewSize(CAMERA_PREVIEW_WIDTH, CAMERA_PREVIEW_HEIGHT); + + if (parameters.isZoomSupported()) { + parameters.setZoom(0); + } + mCamera0.setParameters(parameters); + Log.i(TAG, "mCamera0 set parameters success."); + } + + private void startTrack() { + mInferenceResult.reset(); + mImageBufferQueue = new ImageBufferQueue(3, CAMERA_PREVIEW_WIDTH, CAMERA_PREVIEW_HEIGHT); + mStopInference = false; + mInferenceThread = new Thread(mInferenceRunnable); + mInferenceThread.start(); + } + + private void stopTrack() { + + mStopInference = true; + try { + if (mInferenceThread != null) { + mInferenceThread.join(); + } + } catch (InterruptedException e) { + e.printStackTrace(); + } + + if (mImageBufferQueue != null) { + mImageBufferQueue.release(); + mImageBufferQueue = null; + } + } + + private Thread mInferenceThread; + private Runnable mInferenceRunnable = new Runnable() { + public void run() { + + int count = 0; + long oldTime = System.currentTimeMillis(); + long currentTime; + + updateMainUI(1, 0); + + String paramPath = fileDirPath + "/" + mModelName; + + try { + mInferenceWrapper.initModel(CAMERA_PREVIEW_HEIGHT, CAMERA_PREVIEW_WIDTH, INPUT_CHANNEL, paramPath); + } catch (Exception e) { + e.printStackTrace(); + System.exit(1); + } + + + while (!mStopInference) { + ImageBufferQueue.ImageBuffer buffer = mImageBufferQueue.getReadyBuffer(); + + if (buffer == null) { + try { +// Log.w(TAG, "buffer is null."); + sleep(10); + } catch (InterruptedException e) { + e.printStackTrace(); + } + continue; + } + + InferenceResult.OutputBuffer outputs = mInferenceWrapper.run(buffer.mImage); + + mInferenceResult.setResult(outputs); + + mImageBufferQueue.releaseBuffer(buffer); + + if (++count >= 30) { + currentTime = System.currentTimeMillis(); + + float fps = count * 1000.f / (currentTime - oldTime); + +// Log.d(TAG, "current fps = " + fps); + + oldTime = currentTime; + count = 0; + updateMainUI(0, fps); + + } + + updateMainUI(1, 0); + } + + mInferenceWrapper.deinit(); + } + }; + + private void createFile(String fileName, int id) { + String filePath = fileDirPath + "/" + fileName; + try { + File dir = new File(fileDirPath); + + if (!dir.exists()) { + dir.mkdirs(); + } + + // 目录存在，则将apk中raw中的需要的文档复制到该目录下 + File file = new File(filePath); + + if (!file.exists() || isFirstRun()) { + + InputStream ins = getResources().openRawResource(id);// 通过raw得到数据资源 + FileOutputStream fos = new FileOutputStream(file); + byte[] buffer = new byte[8192]; + int count = 0; + + while ((count = ins.read(buffer)) > 0) { + fos.write(buffer, 0, count); + } + + fos.close(); + ins.close(); + + Log.d(TAG, "Create " + filePath); + } + } catch (Exception e) { + e.printStackTrace(); + } + } + + private boolean isFirstRun() { + SharedPreferences sharedPreferences = getSharedPreferences("setting", MODE_PRIVATE); + boolean isFirstRun = sharedPreferences.getBoolean("isFirstRun", true); + SharedPreferences.Editor editor = sharedPreferences.edit(); + if (isFirstRun) { + editor.putBoolean("isFirstRun", false); + editor.commit(); + } + + return isFirstRun; + } + + // UI线程，用于更新处理结果 + private Handler mHandler = new Handler() + { + public void handleMessage(Message msg) + { + if (msg.what == 0) { + float fps = (float) msg.obj; + + DecimalFormat decimalFormat = new DecimalFormat("00.00"); + String fpsStr = decimalFormat.format(fps); + mFpsNum1.setText(String.valueOf(fpsStr.charAt(0))); + mFpsNum2.setText(String.valueOf(fpsStr.charAt(1))); + mFpsNum3.setText(String.valueOf(fpsStr.charAt(3))); + mFpsNum4.setText(String.valueOf(fpsStr.charAt(4))); + } else { + showTrackSelectResults(); + } + } + }; + + private void updateMainUI(int type, Object data) { + Message msg = mHandler.obtainMessage(); + msg.what = type; + msg.obj = data; + mHandler.sendMessage(msg); + } + + public static int sp2px(float spValue) { + Resources r = Resources.getSystem(); + final float scale = r.getDisplayMetrics().scaledDensity; + return (int) (spValue * scale + 0.5f); + } + + private void showTrackSelectResults() { + + int width = CAMERA_PREVIEW_WIDTH; + int height = CAMERA_PREVIEW_HEIGHT; + + if (mTrackResultBitmap == null) { + + mTrackResultBitmap = Bitmap.createBitmap(width, height, Bitmap.Config.ARGB_8888); + mTrackResultCanvas = new Canvas(mTrackResultBitmap); + + //用于画线 + mTrackResultPaint = new Paint(); + mTrackResultPaint.setColor(0xff06ebff); + mTrackResultPaint.setStrokeJoin(Paint.Join.ROUND); + mTrackResultPaint.setStrokeCap(Paint.Cap.ROUND); + mTrackResultPaint.setStrokeWidth(4); + mTrackResultPaint.setStyle(Paint.Style.STROKE); + mTrackResultPaint.setTextAlign(Paint.Align.LEFT); + mTrackResultPaint.setTextSize(sp2px(10)); + mTrackResultPaint.setTypeface(Typeface.SANS_SERIF); + mTrackResultPaint.setFakeBoldText(false); + + //用于文字 + mTrackResultTextPaint = new Paint(); + mTrackResultTextPaint.setColor(0xff06ebff); + mTrackResultTextPaint.setStrokeWidth(2); + mTrackResultTextPaint.setTextAlign(Paint.Align.LEFT); + mTrackResultTextPaint.setTextSize(sp2px(12)); + mTrackResultTextPaint.setTypeface(Typeface.SANS_SERIF); + mTrackResultTextPaint.setFakeBoldText(false); + + + mPorterDuffXfermodeClear = new PorterDuffXfermode(PorterDuff.Mode.CLEAR); + mPorterDuffXfermodeSRC = new PorterDuffXfermode(PorterDuff.Mode.SRC); + } + + // clear canvas + mTrackResultPaint.setXfermode(mPorterDuffXfermodeClear); + mTrackResultCanvas.drawPaint(mTrackResultPaint); + mTrackResultPaint.setXfermode(mPorterDuffXfermodeSRC); + + //detect result + ArrayList recognitions = mInferenceResult.getResult(mInferenceWrapper); + for (int i=0; i classType = Class.forName("android.os.SystemProperties"); + Method getMethod = classType.getDeclaredMethod("get", new Class[]{String.class}); + platform = (String) getMethod.invoke(classType, new Object[]{"ro.board.platform"}); + } catch (Exception e) { + e.printStackTrace(); + } + return platform; + } +} diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/java/com/rockchip/gpadc/demo/ImageBufferQueue.java b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/java/com/rockchip/gpadc/demo/ImageBufferQueue.java new file mode 100644 index 0000000000000000000000000000000000000000..d949b59795c2e46251a4644cc6e5737c04f13eed --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/java/com/rockchip/gpadc/demo/ImageBufferQueue.java @@ -0,0 +1,123 @@ +package com.rockchip.gpadc.demo; + + + + +/** + * Created by randall on 18-4-27. + */ + + +/* +* 一个简单的bufferqueue实现 +* +* */ +public class ImageBufferQueue { + private ImageBuffer[] mQueueBuffer; + private int mQueueBufferSize; + private int mCurrentFreeBufferIndex; + private int mCurrentUsingBufferIndex; + + public ImageBufferQueue(int bufferSize, int width, int height) { + mCurrentFreeBufferIndex = -1; + mCurrentUsingBufferIndex = -1; + mQueueBufferSize = bufferSize; + mQueueBuffer = new ImageBuffer[mQueueBufferSize]; + + for (int i=0; i= mQueueBufferSize) { + index = 0; + } + + if (mQueueBuffer[index].mStatus == ImageBuffer.STATUS_READY) { + break; + } + } + + if ((index != mCurrentUsingBufferIndex) && (mQueueBuffer[index].mStatus == ImageBuffer.STATUS_READY)) { + mCurrentUsingBufferIndex = index; + mQueueBuffer[index].mStatus = ImageBuffer.STATUS_USING; + + return mQueueBuffer[index]; + } + + return null; + } + + public synchronized void releaseBuffer(ImageBuffer buffer) { + buffer.mStatus = ImageBuffer.STATUS_INVAILD; + } + + public synchronized ImageBuffer getFreeBuffer() { + + int index = mCurrentFreeBufferIndex; + + for (int i=0; i= mQueueBufferSize) { + index = 0; + } + + if (mQueueBuffer[index].mStatus != ImageBuffer.STATUS_USING) { + break; + } + } + + mCurrentFreeBufferIndex = index; + + mQueueBuffer[index].mStatus = ImageBuffer.STATUS_INVAILD; + return mQueueBuffer[index]; + } + public synchronized void postBuffer(ImageBuffer buffer) { + buffer.mStatus = ImageBuffer.STATUS_READY; + } + + public class ImageBuffer { + static public final int STATUS_INVAILD = 0; + static public final int STATUS_READY = 1; + static public final int STATUS_USING = 2; + + + public int mStatus; + public int mWidth; + public int mHeight; + public int mBufferSize; //nv21 + public byte[] mImage; + + + public ImageBuffer(int width, int height) { + mStatus = STATUS_INVAILD; + mWidth = width; + mHeight = height; + mBufferSize = mWidth * mHeight * 4; + mImage = new byte[mBufferSize]; + + } + + public void finalize() { + + } + + } +} diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/java/com/rockchip/gpadc/demo/InferenceResult.java b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/java/com/rockchip/gpadc/demo/InferenceResult.java new file mode 100644 index 0000000000000000000000000000000000000000..e7ed66b50e05c6e3193ff94d7c63f938361b2014 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/java/com/rockchip/gpadc/demo/InferenceResult.java @@ -0,0 +1,212 @@ +package com.rockchip.gpadc.demo; + +import android.content.res.AssetManager; +import android.graphics.RectF; + +import com.rockchip.gpadc.demo.yolo.InferenceWrapper; +import com.rockchip.gpadc.demo.yolo.PostProcess; +import com.rockchip.gpadc.demo.tracker.ObjectTracker; + +import java.io.IOException; +import java.util.ArrayList; + +import static com.rockchip.gpadc.demo.yolo.PostProcess.INPUT_SIZE; +import static com.rockchip.gpadc.demo.rga.HALDefine.CAMERA_PREVIEW_WIDTH; +import static com.rockchip.gpadc.demo.rga.HALDefine.CAMERA_PREVIEW_HEIGHT; +import static java.lang.System.arraycopy; + +public class InferenceResult { + + OutputBuffer mOutputBuffer; + ArrayList recognitions = null; + private boolean mIsVaild = false; //是否需要重新计算 + PostProcess mPostProcess = new PostProcess(); + private ObjectTracker mSSDObjectTracker; + + public void init(AssetManager assetManager) throws IOException { + mOutputBuffer = new OutputBuffer(); + + mPostProcess.init(assetManager); + +// mSSDObjectTracker = new ObjectTracker(CAMERA_PREVIEW_WIDTH, CAMERA_PREVIEW_HEIGHT, 3); + } + + public void reset() { + if (recognitions != null) { + recognitions.clear(); + mIsVaild = true; + } + mSSDObjectTracker = new ObjectTracker(CAMERA_PREVIEW_WIDTH, CAMERA_PREVIEW_HEIGHT, 3); + } + public synchronized void setResult(OutputBuffer outputs) { + + if (mOutputBuffer.mGrid0Out == null) { + mOutputBuffer.mGrid0Out = outputs.mGrid0Out.clone(); + mOutputBuffer.mGrid1Out = outputs.mGrid1Out.clone(); + mOutputBuffer.mGrid2Out = outputs.mGrid2Out.clone(); + } else { + arraycopy(outputs.mGrid0Out, 0, mOutputBuffer.mGrid0Out, 0, + outputs.mGrid0Out.length); + arraycopy(outputs.mGrid1Out, 0, mOutputBuffer.mGrid1Out, 0, + outputs.mGrid1Out.length); + arraycopy(outputs.mGrid2Out, 0, mOutputBuffer.mGrid2Out, 0, + outputs.mGrid2Out.length); + } + mIsVaild = false; + } + + public synchronized ArrayList getResult(InferenceWrapper mInferenceWrapper) { + if (!mIsVaild) { + mIsVaild = true; + + recognitions = mInferenceWrapper.postProcess(mOutputBuffer); + + recognitions = mSSDObjectTracker.tracker(recognitions); + } + + return recognitions; + } + + public static class OutputBuffer { + public byte[] mGrid0Out; + public byte[] mGrid1Out; + public byte[] mGrid2Out; + } + + /** + * An immutable result returned by a Classifier describing what was recognized. + */ + public static class Recognition { + + private int trackId = 0; + + /** + * A unique identifier for what has been recognized. Specific to the class, not the instance of + * the object. + */ + private final int id; + + /** + * A sortable score for how good the recognition is relative to others. Higher should be better. + */ + private final Float confidence; + + /** Optional location within the source image for the location of the recognized object. */ + private RectF location; + + public Recognition( + final int id, final Float confidence, final RectF location) { + this.id = id; + this.confidence = confidence; + this.location = location; + // TODO -- add name field, and show it. + } + + public int getId() { + return id; + } + + public Float getConfidence() { + return confidence; + } + + public RectF getLocation() { + return new RectF(location); + } + + public void setLocation(RectF location) { + this.location = location; + } + + public void setTrackId(int trackId) { + this.trackId = trackId; + } + + public int getTrackId() { + return this.trackId; + } + + @Override + public String toString() { + String resultString = ""; + + resultString += "[" + id + "] "; + + if (confidence != null) { + resultString += String.format("(%.1f%%) ", confidence * 100.0f); + } + + if (location != null) { + resultString += location + " "; + } + + return resultString.trim(); + } + } + + /** + * Detected objects, returned from native yolo_post_process + */ + public static class DetectResultGroup { + /** + * detected objects count. + */ + public int count = 0; + + /** + * id for each detected object. + */ + public int[] ids; + + /** + * score for each detected object. + */ + public float[] scores; + + /** + * box for each detected object. + */ + public float[] boxes; + +// public DetectResultGroup( +// int count, int[] ids, float[] scores, float[] boxes +// ) { +// this.count = count; +// this.ids = ids; +// this.scores = scores; +// this.boxes = boxes; +// } +// +// public int getCount() { +// return count; +// } +// +// public void setCount(int count) { +// this.count = count; +// } +// +// public int[] getIds() { +// return ids; +// } +// +// public void setIds(int[] ids) { +// this.ids = ids; +// } +// +// public float[] getScores() { +// return scores; +// } +// +// public void setScores(float[] scores) { +// this.scores = scores; +// } +// +// public float[] getBoxes() { +// return boxes; +// } +// +// public void setBoxes(float[] boxes) { +// this.boxes = boxes; +// } + } +} diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/java/com/rockchip/gpadc/demo/MainActivity.java b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/java/com/rockchip/gpadc/demo/MainActivity.java new file mode 100644 index 0000000000000000000000000000000000000000..243c5c7245d630266ec0565944e3aceba3613e01 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/java/com/rockchip/gpadc/demo/MainActivity.java @@ -0,0 +1,88 @@ +package com.rockchip.gpadc.demo; + +import android.Manifest; +import android.app.Activity; +import android.content.Intent; +import android.content.pm.PackageManager; +import android.os.Build; +import android.os.Bundle; +import android.support.annotation.NonNull; +import android.support.v4.app.ActivityCompat; +import android.support.v4.content.ContextCompat; +import android.util.Log; +import android.view.Gravity; +import android.widget.Toast; + +import java.lang.reflect.Method; + +public class MainActivity extends Activity + implements ActivityCompat.OnRequestPermissionsResultCallback { + + String[] permissions = new String[]{ + Manifest.permission.CAMERA, + Manifest.permission.READ_EXTERNAL_STORAGE, + Manifest.permission.WRITE_EXTERNAL_STORAGE + }; + + + @Override + protected void onCreate(Bundle savedInstanceState) { + super.onCreate(savedInstanceState); + checkPermissions(); + } + + /** + * 申请权限 + */ + private void checkPermissions(){ + //如果系统大于android6.0，进行动态权限申请 + if (Build.VERSION.SDK_INT >= Build.VERSION_CODES.M) { + int i = ContextCompat.checkSelfPermission(this, permissions[0]); + int l = ContextCompat.checkSelfPermission(this, permissions[1]); + int m = ContextCompat.checkSelfPermission(this, permissions[2]); + if (i != PackageManager.PERMISSION_GRANTED || + l != PackageManager.PERMISSION_GRANTED || + m != PackageManager.PERMISSION_GRANTED) { + // 如果有权限没有授予，就去提示用户请求 + startRequestPermission(); + } else { + startCamera(); + } + + } else { + startCamera(); + } + + } + + /** + * 通过权限列表，提示用户赋予或禁止当前还未拥有的权限 + */ + private void startRequestPermission() { + ActivityCompat.requestPermissions(this, permissions, 321); + } + @Override + public void onRequestPermissionsResult(int requestCode, @NonNull String[] permissions, @NonNull int[] grantResults) { + super.onRequestPermissionsResult(requestCode, permissions, grantResults); + Log.d("yolo", "onRequestPermissionsResult:"+requestCode); + if (requestCode == 321) { + Log.d("yolo", "onRequestPermissionsResult"); + if (Build.VERSION.SDK_INT >= Build.VERSION_CODES.M) { + if (grantResults[0] != PackageManager.PERMISSION_GRANTED) { + //如果没有获取权限，那么可以提示用户去设置界面--->应用权限开启权限 + Toast toast = Toast.makeText(this, "请从设置界面开启权限", Toast.LENGTH_LONG); + toast.setGravity(Gravity.CENTER, 0, 0); + toast.show(); + finish(); + } else { + startCamera(); + } + } + } + } + + private void startCamera() { + Intent intent = new Intent(this, CameraPreviewActivity.class); + startActivity(intent); + } +} diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/java/com/rockchip/gpadc/demo/rga/HALDefine.java b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/java/com/rockchip/gpadc/demo/rga/HALDefine.java new file mode 100644 index 0000000000000000000000000000000000000000..7b92fa0154670268e9bea0268816b4f256fa5b09 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/java/com/rockchip/gpadc/demo/rga/HALDefine.java @@ -0,0 +1,15 @@ +package com.rockchip.gpadc.demo.rga; + +public class HALDefine { + // for RGA + public static final int RK_FORMAT_RGBA_8888 = (0x0 << 8); + public static final int RK_FORMAT_RGB_888 = (0x2 << 8); + public static final int RK_FORMAT_YCrCb_420_SP = (0xe << 8); + + // for RGA + public static final int IM_HAL_TRANSFORM_FLIP_H = (1 << 3); // 0x08 + + // for camera comes with RK3588 + public static int CAMERA_PREVIEW_WIDTH = 1280; + public static int CAMERA_PREVIEW_HEIGHT = 720; +} diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/java/com/rockchip/gpadc/demo/rga/RGA.java b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/java/com/rockchip/gpadc/demo/rga/RGA.java new file mode 100644 index 0000000000000000000000000000000000000000..eeb07d708b29d187e1351544bce31b03434cc98c --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/java/com/rockchip/gpadc/demo/rga/RGA.java @@ -0,0 +1,36 @@ +package com.rockchip.gpadc.demo.rga; + +import android.util.Log; + +public class RGA { + static { + System.loadLibrary("rknn4j"); + } + + /** + * Convert color format and flip image if flip is set true. + * Please refer to the link to confirm the RGA driver version, make sure it is higher than 1.2.4 + * https://github.com/airockchip/librga/blob/main/docs/Rockchip_FAQ_RGA_CN.md#rga-driver + * @param src: image raw data, which need be processed. + * @param srcFmt: source color format, use the color format defined in HALDefine. + * @param dst: destination memory address. + * @param dstFmt: destination color format, use the color format defined in HALDefine. + * @param width: image width. + * @param height: image height. + * @param flip: flip type, use the flip defined in HALDefine, if not flip, set -1. + * @return: -1 means failure, + */ + public static int colorConvertAndFlip(byte[] src, int srcFmt, byte[] dst, int dstFmt, + int width, int height, int flip) { + + if (src == null || dst == null) { + Log.w("rkyolo.RGA", "src or dst is null"); + return -1; + } + + return color_convert_and_flip(src, srcFmt, dst, dstFmt, width, height, flip); + } + + private static native int color_convert_and_flip(byte[] src, int srcFmt, byte[] dst, int dstFmt, + int width, int height, int flip); +} diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/java/com/rockchip/gpadc/demo/tracker/ObjectTracker.java b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/java/com/rockchip/gpadc/demo/tracker/ObjectTracker.java new file mode 100644 index 0000000000000000000000000000000000000000..d5762ee78c29b451261890e336d12863d542d425 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/java/com/rockchip/gpadc/demo/tracker/ObjectTracker.java @@ -0,0 +1,115 @@ +package com.rockchip.gpadc.demo.tracker; + +import android.graphics.RectF; +import android.util.Log; + +import com.rockchip.gpadc.demo.InferenceResult; + +import java.util.ArrayList; + +public class ObjectTracker { + + private final String TAG = "rkyolo.ObjectTracker"; + + private long mHandle; + + private int mMaxTrackLifetime = 3; + + private int mWidth; + + private int mHeight; + + private static int MAX_TRACKED_NUM = 64; + + private static float[] track_input_location = new float[MAX_TRACKED_NUM *4]; + private static int[] track_input_class = new int[MAX_TRACKED_NUM]; + private static float[] track_input_score = new float[MAX_TRACKED_NUM]; + private static float[] track_output_location = new float[MAX_TRACKED_NUM *4]; + private static int[] track_output_class = new int[MAX_TRACKED_NUM]; + private static int[] track_output_id = new int[MAX_TRACKED_NUM]; + private static float[] track_output_score = new float[MAX_TRACKED_NUM]; + +// public int track_count = 0; +// public long track_time = 0; + + public ObjectTracker(int width, int height, int maxTrackLifetime) { + mWidth = width; + mHeight = height; + mMaxTrackLifetime = maxTrackLifetime; + mHandle = native_create(); + } + + protected void finalize() { + native_destroy(mHandle); + } + + public ArrayList tracker(ArrayList recognitions) { +// long startTime = System.currentTimeMillis(); +// long endTime; + int track_input_num = 0; + ArrayList tracked_recognitions = new ArrayList<>(); + + for (int i = 0; i < recognitions.size(); ++i) { + + track_input_location[4*track_input_num +0] = recognitions.get(i).getLocation().left; + track_input_location[4*track_input_num +1] = recognitions.get(i).getLocation().top; + track_input_location[4*track_input_num +2] = recognitions.get(i).getLocation().right; + track_input_location[4*track_input_num +3] = recognitions.get(i).getLocation().bottom; + track_input_class[track_input_num] = recognitions.get(i).getId(); + track_input_score[track_input_num] = recognitions.get(i).getConfidence(); + //Log.i(TAG, track_input_num +" javain class:" +topClassScoreIndex +" P:" +track_input_score[track_input_num] +" score:" +expit(track_input_score[track_input_num])); + track_input_num++; + if (track_input_num >= MAX_TRACKED_NUM){ + break; + } + } + + int[] track_output_num = new int[1]; + + native_track(mHandle, mMaxTrackLifetime, + track_input_num, track_input_location, track_input_class, track_input_score, + track_output_num, track_output_location, track_output_class, track_output_score, + track_output_id, mWidth, mHeight); + + for (int i = 0; i < track_output_num[0]; ++i) { + + RectF detection = new RectF( + track_output_location[i * 4 + 0]/mWidth, + track_output_location[i * 4 + 1]/mHeight, + track_output_location[i * 4 + 2]/mWidth, + track_output_location[i * 4 + 3]/mHeight); + float exp_score = track_output_score[i]; + if (track_output_score[i] == -10000){ + exp_score = 0; + } + InferenceResult.Recognition recog = new InferenceResult.Recognition( + track_output_class[i], + exp_score, + detection); + recog.setTrackId(track_output_id[i]); + //Log.i(TAG, "javaout"+i +" class:" +topClassScoreIndex +" P:" +track_output_score[i] +" score:" +exp_score); + tracked_recognitions.add(recog); + } +// endTime = System.currentTimeMillis(); +// this.track_count += 1; +// this.track_time += (endTime - startTime); +// if (this.track_count >= 100) { +// float track_avg = this.track_time * 1.0f / this.track_count; +// Log.i(TAG, String.format("track cost time avg: %.5f", track_avg)); +// this.track_count = 0; +// this.track_time = 0; +// } + return tracked_recognitions; + } + + private native long native_create(); + private native void native_destroy(long handle); + private native void native_track(long hanle, int maxTrackLifetime, + int track_input_num, float[] track_input_locations, int[] track_input_class, float[] track_input_score, + int[] track_output_num, float[] track_output_locations, int[] track_output_class, float[] track_output_score, + int[] track_output_id, int width, int height); + + static { + System.loadLibrary("rknn4j"); + } +} diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/java/com/rockchip/gpadc/demo/yolo/InferenceWrapper.java b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/java/com/rockchip/gpadc/demo/yolo/InferenceWrapper.java new file mode 100644 index 0000000000000000000000000000000000000000..d363ee700f1e12351426ee2861a0f1054df2b471 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/java/com/rockchip/gpadc/demo/yolo/InferenceWrapper.java @@ -0,0 +1,135 @@ +package com.rockchip.gpadc.demo.yolo; + +import android.graphics.RectF; +import android.util.Log; + +import com.rockchip.gpadc.demo.InferenceResult; +import com.rockchip.gpadc.demo.InferenceResult.OutputBuffer; +import com.rockchip.gpadc.demo.InferenceResult.Recognition; +import com.rockchip.gpadc.demo.InferenceResult.DetectResultGroup; + +import java.io.IOException; +import java.util.ArrayList; + +/** + * Created by randall on 18-4-18. + */ + +public class InferenceWrapper { + private final String TAG = "rkyolo.InferenceWrapper"; + + static { + System.loadLibrary("rknn4j"); + } + + OutputBuffer mOutputs; + ArrayList mRecognitions = new ArrayList(); + DetectResultGroup mDetectResults; + + public int OBJ_NUMB_MAX_SIZE = 64; +// public int inf_count = 0; +// public int post_count = 0; +// public long inf_time = 0; +// public long post_time = 0; + + + public InferenceWrapper() { + + } + + public int initModel(int im_height, int im_width, int im_channel, String modelPath) throws Exception { + mOutputs = new InferenceResult.OutputBuffer(); + mOutputs.mGrid0Out = new byte[255 * 80 * 80]; + mOutputs.mGrid1Out = new byte[255 * 40 * 40]; + mOutputs.mGrid2Out = new byte[255 * 20 * 20]; + if (navite_init(im_height, im_width, im_channel, modelPath) != 0) { + throw new IOException("rknn init fail!"); + } + return 0; + } + + + public void deinit() { + native_deinit(); + mOutputs.mGrid0Out = null; + mOutputs.mGrid1Out = null; + mOutputs.mGrid2Out = null; + mOutputs = null; + + } + + public InferenceResult.OutputBuffer run(byte[] inData) { +// long startTime = System.currentTimeMillis(); +// long endTime; + native_run(inData, mOutputs.mGrid0Out, mOutputs.mGrid1Out, mOutputs.mGrid2Out); +// this.inf_count += 1; +// endTime = System.currentTimeMillis(); +// this.inf_time += (endTime - startTime); +// if (this.inf_count >= 100) { +// float inf_avg = this.inf_time * 1.0f / this.inf_count; +// Log.w(TAG, String.format("inference avg cost: %.5f ms", inf_avg)); +// this.inf_count = 0; +// this.inf_time = 0; +// } +// Log.i(TAG, String.format("inference count: %d", this.inf_count)); + return mOutputs; + } + + public ArrayList postProcess(InferenceResult.OutputBuffer outputs) { + ArrayList recognitions = new ArrayList(); + + mDetectResults = new DetectResultGroup(); + mDetectResults.count = 0; + mDetectResults.ids = new int[OBJ_NUMB_MAX_SIZE]; + mDetectResults.scores = new float[OBJ_NUMB_MAX_SIZE]; + mDetectResults.boxes = new float[4 * OBJ_NUMB_MAX_SIZE]; + + if (null == outputs || null == outputs.mGrid0Out || null == outputs.mGrid1Out + || null == outputs.mGrid2Out) { + return recognitions; + } + +// long startTime = System.currentTimeMillis(); +// long endTime; + int count = native_post_process(outputs.mGrid0Out, outputs.mGrid1Out, outputs.mGrid2Out, + mDetectResults.ids, mDetectResults.scores, mDetectResults.boxes); + if (count < 0) { + Log.w(TAG, "post_process may fail."); + mDetectResults.count = 0; + } else { + mDetectResults.count = count; + } +// Log.i(TAG, String.format("Detected %d objects", count)); +// this.post_count += 1; +// Log.i(TAG, String.format("post count: %d", this.post_count)); + + for (int i = 0; i < count; ++i) { + RectF rect = new RectF(); + rect.left = mDetectResults.boxes[i*4+0]; + rect.top = mDetectResults.boxes[i*4+1]; + rect.right = mDetectResults.boxes[i*4+2]; + rect.bottom = mDetectResults.boxes[i*4+3]; + + Recognition recog = new InferenceResult.Recognition(mDetectResults.ids[i], + mDetectResults.scores[i], rect); + recognitions.add(recog); + } +// endTime = System.currentTimeMillis(); +// this.post_time += (endTime - startTime); +// if (this.post_count >= 100) { +// float post_avg = this.post_time * 1.0f / this.post_count; +// Log.w(TAG, String.format("post process avg cost: %.5f ms", post_avg)); +// this.post_time = 0; +// this.post_count = 0; +// } + + return recognitions; + } + + private native int navite_init(int im_height, int im_width, int im_channel, String modelPath); + private native void native_deinit(); + private native int native_run(byte[] inData, byte[] grid0Out, byte[] grid1Out, byte[] grid2Out); + private native int native_post_process(byte[] grid0Out, byte[] grid1Out, byte[] grid2Out, + int[] ids, float[] scores, float[] boxes); + +} \ No newline at end of file diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/java/com/rockchip/gpadc/demo/yolo/PostProcess.java b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/java/com/rockchip/gpadc/demo/yolo/PostProcess.java new file mode 100644 index 0000000000000000000000000000000000000000..1e3a2d987b31d8b5fd27250b0fbb1ed80b194de5 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/java/com/rockchip/gpadc/demo/yolo/PostProcess.java @@ -0,0 +1,67 @@ +package com.rockchip.gpadc.demo.yolo;/* + * Copyright (C) 2022 Rockchip Electronics Co., Ltd. + * Authors: + * raul.rao + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +import android.content.res.AssetManager; +import android.util.Log; + +import java.io.BufferedReader; +import java.io.FileInputStream; +import java.io.IOException; +import java.io.InputStream; +import java.io.InputStreamReader; +import java.util.Vector; + +public class PostProcess { + public static final int INPUT_SIZE = 640; + public static final int INPUT_CHANNEL = 3; + + public static final String TAG = "rkyolo.PostProcess"; + + // Pre-allocated buffers. + private Vector labels = new Vector(); + + public void init(AssetManager assetManager) throws IOException { + loadLabelName(assetManager, + "file:///android_asset/coco_80_labels_list.txt", labels); + } + + public String getLabelTitle(int id) { + return labels.get(id); + } + + private void loadLabelName(final AssetManager assetManager, final String locationFilename, + Vector labels) throws IOException { + // Try to be intelligent about opening from assets or sdcard depending on prefix. + final String assetPrefix = "file:///android_asset/"; + InputStream is; + if (locationFilename.startsWith(assetPrefix)) { + is = assetManager.open(locationFilename.split(assetPrefix, -1)[1]); + } else { + is = new FileInputStream(locationFilename); + } + + final BufferedReader reader = new BufferedReader(new InputStreamReader(is)); + String line; + while ((line = reader.readLine()) != null) { + labels.add(line); + } + reader.close(); + + Log.d(TAG, "Loaded label!"); + } +} diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/jniLibs/arm64-v8a/libopencv_java3.so b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/jniLibs/arm64-v8a/libopencv_java3.so new file mode 100644 index 0000000000000000000000000000000000000000..ffedf3a48e055ed58aeef34fa83b31ec987e48a1 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/jniLibs/arm64-v8a/libopencv_java3.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:61d1729dfe564afb0094b192b11637fb8b4072407cdd885e4df2473764c75029 +size 17889264 diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/jniLibs/arm64-v8a/librga.so b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/jniLibs/arm64-v8a/librga.so new file mode 100644 index 0000000000000000000000000000000000000000..40a20e5fd22bf0d7651ad666ba61c539668558b8 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/jniLibs/arm64-v8a/librga.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:68c90c40c5f77ca9f0c106afb69b306c2199e7d81bdebf8742d7e810c93fd9d9 +size 1124552 diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/jniLibs/arm64-v8a/librknnrt.so b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/jniLibs/arm64-v8a/librknnrt.so new file mode 100644 index 0000000000000000000000000000000000000000..db1dcb5c16c7b08cd33afed74e5019f109be0738 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/jniLibs/arm64-v8a/librknnrt.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7b66eb2e68923d7422fc648f68c429af2eab2a95d1c0d5903edddb0915116acf +size 7724384 diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/drawable-hdpi/fps_bg.9.png b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/drawable-hdpi/fps_bg.9.png new file mode 100644 index 0000000000000000000000000000000000000000..b722dc1147061365d7c051143bd7eb0118651646 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/drawable-hdpi/fps_bg.9.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1f15a4fc4a940702c97396a6163fe4f5805946b25f1cdf3b607ac599986cf7a5 +size 310 diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/drawable-hdpi/ic_launcher.png b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/drawable-hdpi/ic_launcher.png new file mode 100644 index 0000000000000000000000000000000000000000..6cde17ab076d13f98b25ef10c1e7230aa0adeca2 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/drawable-hdpi/ic_launcher.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ded7aabf6a56b694e486e096efd89e2f0c9067d292b634663898e352c9491f10 +size 3056 diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/drawable-hdpi/ic_launcher_round.png b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/drawable-hdpi/ic_launcher_round.png new file mode 100644 index 0000000000000000000000000000000000000000..08863165f67863c1639df6eec2b355264a077f38 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/drawable-hdpi/ic_launcher_round.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:21304a0c9b00da6a72cfa31c7229c9528fc17b6e5eb4e68a969bce08c01a2fee +size 5024 diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/drawable-hdpi/img_logo.png b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/drawable-hdpi/img_logo.png new file mode 100644 index 0000000000000000000000000000000000000000..dc7d74cf0ef2a2ad2ef35658ee7096369c0ba286 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/drawable-hdpi/img_logo.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:58d25d2ef571d99ffe38ead9f4ebc78c74b7ab3bf90e7aa02002323ca2c94cf4 +size 5025 diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/drawable-hdpi/num_bg.png b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/drawable-hdpi/num_bg.png new file mode 100644 index 0000000000000000000000000000000000000000..5cd622313a3c21231564f0b34fd9787d85612e3f --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/drawable-hdpi/num_bg.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:98a8600f2aba64d56f8d3db5500a03cef719f66ff20d43287ea92c061184e082 +size 960 diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/drawable-hdpi/rockchip.png b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/drawable-hdpi/rockchip.png new file mode 100644 index 0000000000000000000000000000000000000000..f0d7229b1f70cdbb66865c14b1402367613ad5ad --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/drawable-hdpi/rockchip.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ddf9fe3abdab1aefeeb8c98120c57f13918305f2771210bc3016464fd6393f0d +size 6445 diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/layout/activity_main.xml b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/layout/activity_main.xml new file mode 100644 index 0000000000000000000000000000000000000000..5bddf9a425ee411b6d84f8d75b6a46662c468aa5 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/layout/activity_main.xml @@ -0,0 +1,135 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/layout/main2.xml b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/layout/main2.xml new file mode 100644 index 0000000000000000000000000000000000000000..f8d91ca818e596f8b7c7299ba78d236969270b6e --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/layout/main2.xml @@ -0,0 +1,11 @@ + + + + + + diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/raw/yolov5s_rk3562.rknn b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/raw/yolov5s_rk3562.rknn new file mode 100644 index 0000000000000000000000000000000000000000..f28aa03c66192103a2a46ec9b711cc9a9cdac059 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/raw/yolov5s_rk3562.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5afce0e7bcad185ea360a262c8ba68000d6e8004960b37623c5dc15d2d115926 +size 7638339 diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/raw/yolov5s_rk3566.rknn b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/raw/yolov5s_rk3566.rknn new file mode 100644 index 0000000000000000000000000000000000000000..b93a13381971147f3c0c9cb7bce81c635304d7e7 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/raw/yolov5s_rk3566.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2377d12e7a590f457fd33c767ae3c7f9f224eb115aed7acdf9b76a10597e19dc +size 7925699 diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/raw/yolov5s_rk3576.rknn b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/raw/yolov5s_rk3576.rknn new file mode 100644 index 0000000000000000000000000000000000000000..384b863a51d901ace41101620ce03a658a21b1a6 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/raw/yolov5s_rk3576.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:21679c019ae66965962b7de7690776f16026841121844d959af9099e4f1e9f1b +size 8178307 diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/raw/yolov5s_rk3588.rknn b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/raw/yolov5s_rk3588.rknn new file mode 100644 index 0000000000000000000000000000000000000000..1a8b5886f2457e8fbcf9428be1705bb50ded7747 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/raw/yolov5s_rk3588.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:de9b43193d9df1fc0694de76f65fd8dbc8dd95a1562a28c6eee0ff056bda7328 +size 8413699 diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/values/colors.xml b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/values/colors.xml new file mode 100644 index 0000000000000000000000000000000000000000..80523799c14825bd4a01b240d29357f0c5151170 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/values/colors.xml @@ -0,0 +1,6 @@ + + + #50001631 + #06ebff + #001631 + diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/values/dimens.xml b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/values/dimens.xml new file mode 100644 index 0000000000000000000000000000000000000000..55c1e5908c7e0f157fe815acd6d5cd7358463390 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/values/dimens.xml @@ -0,0 +1,7 @@ + + + + 16dp + 16dp + + diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/values/strings.xml b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/values/strings.xml new file mode 100644 index 0000000000000000000000000000000000000000..a5e47ec134b62761c4ba7979ed22069bd5192e11 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/values/strings.xml @@ -0,0 +1,10 @@ + + + + RKNN YOLO Demo + RKNN YOLO Demo + Powered by Rockchip GPADC. + RKNN YOLO Demo + YOLO Camera Demo + YOLO Image Demo + diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/values/styles.xml b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/values/styles.xml new file mode 100644 index 0000000000000000000000000000000000000000..6ce89c7ba4394d8cab27953d41456ce234ca37c3 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/app/src/main/res/values/styles.xml @@ -0,0 +1,20 @@ + + + + + + + + + diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/build.gradle b/rknpu2/examples/rknn_yolov5_android_apk_demo/build.gradle new file mode 100644 index 0000000000000000000000000000000000000000..c0e9e69fe33706e6bcb93cce3bb1d7389f01296e --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/build.gradle @@ -0,0 +1,27 @@ +// Top-level build file where you can add configuration options common to all sub-projects/modules. + +buildscript { + + repositories { + google() + jcenter() + } + dependencies { + classpath 'com.android.tools.build:gradle:7.2.0' + + + // NOTE: Do not place your application dependencies here; they belong + // in the individual module build.gradle files + } +} + +allprojects { + repositories { + google() + jcenter() + } +} + +task clean(type: Delete) { + delete rootProject.buildDir +} diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/gradle/wrapper/gradle-wrapper.jar b/rknpu2/examples/rknn_yolov5_android_apk_demo/gradle/wrapper/gradle-wrapper.jar new file mode 100644 index 0000000000000000000000000000000000000000..7a3265ee94c0ab25cf079ac8ccdf87f41d455d42 Binary files /dev/null and b/rknpu2/examples/rknn_yolov5_android_apk_demo/gradle/wrapper/gradle-wrapper.jar differ diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/gradle/wrapper/gradle-wrapper.properties b/rknpu2/examples/rknn_yolov5_android_apk_demo/gradle/wrapper/gradle-wrapper.properties new file mode 100644 index 0000000000000000000000000000000000000000..2e6e5897b5285c749d75662c65ac5d2904c37bc6 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/gradle/wrapper/gradle-wrapper.properties @@ -0,0 +1,5 @@ +distributionBase=GRADLE_USER_HOME +distributionPath=wrapper/dists +distributionUrl=https\://services.gradle.org/distributions/gradle-7.3.3-bin.zip +zipStoreBase=GRADLE_USER_HOME +zipStorePath=wrapper/dists diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/gradlew b/rknpu2/examples/rknn_yolov5_android_apk_demo/gradlew new file mode 100644 index 0000000000000000000000000000000000000000..cccdd3d517fc5249beaefa600691cf150f2fa3e6 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/gradlew @@ -0,0 +1,172 @@ +#!/usr/bin/env sh + +############################################################################## +## +## Gradle start up script for UN*X +## +############################################################################## + +# Attempt to set APP_HOME +# Resolve links: $0 may be a link +PRG="$0" +# Need this for relative symlinks. +while [ -h "$PRG" ] ; do + ls=`ls -ld "$PRG"` + link=`expr "$ls" : '.*-> \(.*\)$'` + if expr "$link" : '/.*' > /dev/null; then + PRG="$link" + else + PRG=`dirname "$PRG"`"/$link" + fi +done +SAVED="`pwd`" +cd "`dirname \"$PRG\"`/" >/dev/null +APP_HOME="`pwd -P`" +cd "$SAVED" >/dev/null + +APP_NAME="Gradle" +APP_BASE_NAME=`basename "$0"` + +# Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script. +DEFAULT_JVM_OPTS="" + +# Use the maximum available, or set MAX_FD != -1 to use that value. +MAX_FD="maximum" + +warn () { + echo "$*" +} + +die () { + echo + echo "$*" + echo + exit 1 +} + +# OS specific support (must be 'true' or 'false'). +cygwin=false +msys=false +darwin=false +nonstop=false +case "`uname`" in + CYGWIN* ) + cygwin=true + ;; + Darwin* ) + darwin=true + ;; + MINGW* ) + msys=true + ;; + NONSTOP* ) + nonstop=true + ;; +esac + +CLASSPATH=$APP_HOME/gradle/wrapper/gradle-wrapper.jar + +# Determine the Java command to use to start the JVM. +if [ -n "$JAVA_HOME" ] ; then + if [ -x "$JAVA_HOME/jre/sh/java" ] ; then + # IBM's JDK on AIX uses strange locations for the executables + JAVACMD="$JAVA_HOME/jre/sh/java" + else + JAVACMD="$JAVA_HOME/bin/java" + fi + if [ ! -x "$JAVACMD" ] ; then + die "ERROR: JAVA_HOME is set to an invalid directory: $JAVA_HOME + +Please set the JAVA_HOME variable in your environment to match the +location of your Java installation." + fi +else + JAVACMD="java" + which java >/dev/null 2>&1 || die "ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH. + +Please set the JAVA_HOME variable in your environment to match the +location of your Java installation." +fi + +# Increase the maximum file descriptors if we can. +if [ "$cygwin" = "false" -a "$darwin" = "false" -a "$nonstop" = "false" ] ; then + MAX_FD_LIMIT=`ulimit -H -n` + if [ $? -eq 0 ] ; then + if [ "$MAX_FD" = "maximum" -o "$MAX_FD" = "max" ] ; then + MAX_FD="$MAX_FD_LIMIT" + fi + ulimit -n $MAX_FD + if [ $? -ne 0 ] ; then + warn "Could not set maximum file descriptor limit: $MAX_FD" + fi + else + warn "Could not query maximum file descriptor limit: $MAX_FD_LIMIT" + fi +fi + +# For Darwin, add options to specify how the application appears in the dock +if $darwin; then + GRADLE_OPTS="$GRADLE_OPTS \"-Xdock:name=$APP_NAME\" \"-Xdock:icon=$APP_HOME/media/gradle.icns\"" +fi + +# For Cygwin, switch paths to Windows format before running java +if $cygwin ; then + APP_HOME=`cygpath --path --mixed "$APP_HOME"` + CLASSPATH=`cygpath --path --mixed "$CLASSPATH"` + JAVACMD=`cygpath --unix "$JAVACMD"` + + # We build the pattern for arguments to be converted via cygpath + ROOTDIRSRAW=`find -L / -maxdepth 1 -mindepth 1 -type d 2>/dev/null` + SEP="" + for dir in $ROOTDIRSRAW ; do + ROOTDIRS="$ROOTDIRS$SEP$dir" + SEP="|" + done + OURCYGPATTERN="(^($ROOTDIRS))" + # Add a user-defined pattern to the cygpath arguments + if [ "$GRADLE_CYGPATTERN" != "" ] ; then + OURCYGPATTERN="$OURCYGPATTERN|($GRADLE_CYGPATTERN)" + fi + # Now convert the arguments - kludge to limit ourselves to /bin/sh + i=0 + for arg in "$@" ; do + CHECK=`echo "$arg"|egrep -c "$OURCYGPATTERN" -` + CHECK2=`echo "$arg"|egrep -c "^-"` ### Determine if an option + + if [ $CHECK -ne 0 ] && [ $CHECK2 -eq 0 ] ; then ### Added a condition + eval `echo args$i`=`cygpath --path --ignore --mixed "$arg"` + else + eval `echo args$i`="\"$arg\"" + fi + i=$((i+1)) + done + case $i in + (0) set -- ;; + (1) set -- "$args0" ;; + (2) set -- "$args0" "$args1" ;; + (3) set -- "$args0" "$args1" "$args2" ;; + (4) set -- "$args0" "$args1" "$args2" "$args3" ;; + (5) set -- "$args0" "$args1" "$args2" "$args3" "$args4" ;; + (6) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" ;; + (7) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" ;; + (8) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" ;; + (9) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" "$args8" ;; + esac +fi + +# Escape application args +save () { + for i do printf %s\\n "$i" | sed "s/'/'\\\\''/g;1s/^/'/;\$s/\$/' \\\\/" ; done + echo " " +} +APP_ARGS=$(save "$@") + +# Collect all arguments for the java command, following the shell quoting and substitution rules +eval set -- $DEFAULT_JVM_OPTS $JAVA_OPTS $GRADLE_OPTS "\"-Dorg.gradle.appname=$APP_BASE_NAME\"" -classpath "\"$CLASSPATH\"" org.gradle.wrapper.GradleWrapperMain "$APP_ARGS" + +# by default we should be in the correct project dir, but when run from Finder on Mac, the cwd is wrong +if [ "$(uname)" = "Darwin" ] && [ "$HOME" = "$PWD" ]; then + cd "$(dirname "$0")" +fi + +exec "$JAVACMD" "$@" diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/gradlew.bat b/rknpu2/examples/rknn_yolov5_android_apk_demo/gradlew.bat new file mode 100644 index 0000000000000000000000000000000000000000..f9553162f122c71b34635112e717c3e733b5b212 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/gradlew.bat @@ -0,0 +1,84 @@ +@if "%DEBUG%" == "" @echo off +@rem ########################################################################## +@rem +@rem Gradle startup script for Windows +@rem +@rem ########################################################################## + +@rem Set local scope for the variables with windows NT shell +if "%OS%"=="Windows_NT" setlocal + +set DIRNAME=%~dp0 +if "%DIRNAME%" == "" set DIRNAME=. +set APP_BASE_NAME=%~n0 +set APP_HOME=%DIRNAME% + +@rem Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script. +set DEFAULT_JVM_OPTS= + +@rem Find java.exe +if defined JAVA_HOME goto findJavaFromJavaHome + +set JAVA_EXE=java.exe +%JAVA_EXE% -version >NUL 2>&1 +if "%ERRORLEVEL%" == "0" goto init + +echo. +echo ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH. +echo. +echo Please set the JAVA_HOME variable in your environment to match the +echo location of your Java installation. + +goto fail + +:findJavaFromJavaHome +set JAVA_HOME=%JAVA_HOME:"=% +set JAVA_EXE=%JAVA_HOME%/bin/java.exe + +if exist "%JAVA_EXE%" goto init + +echo. +echo ERROR: JAVA_HOME is set to an invalid directory: %JAVA_HOME% +echo. +echo Please set the JAVA_HOME variable in your environment to match the +echo location of your Java installation. + +goto fail + +:init +@rem Get command-line arguments, handling Windows variants + +if not "%OS%" == "Windows_NT" goto win9xME_args + +:win9xME_args +@rem Slurp the command line arguments. +set CMD_LINE_ARGS= +set _SKIP=2 + +:win9xME_args_slurp +if "x%~1" == "x" goto execute + +set CMD_LINE_ARGS=%* + +:execute +@rem Setup the command line + +set CLASSPATH=%APP_HOME%\gradle\wrapper\gradle-wrapper.jar + +@rem Execute Gradle +"%JAVA_EXE%" %DEFAULT_JVM_OPTS% %JAVA_OPTS% %GRADLE_OPTS% "-Dorg.gradle.appname=%APP_BASE_NAME%" -classpath "%CLASSPATH%" org.gradle.wrapper.GradleWrapperMain %CMD_LINE_ARGS% + +:end +@rem End local scope for the variables with windows NT shell +if "%ERRORLEVEL%"=="0" goto mainEnd + +:fail +rem Set variable GRADLE_EXIT_CONSOLE if you need the _script_ return code instead of +rem the _cmd.exe /c_ return code! +if not "" == "%GRADLE_EXIT_CONSOLE%" exit 1 +exit /b 1 + +:mainEnd +if "%OS%"=="Windows_NT" endlocal + +:omega diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/settings.gradle b/rknpu2/examples/rknn_yolov5_android_apk_demo/settings.gradle new file mode 100644 index 0000000000000000000000000000000000000000..e7b4def49cb53d9aa04228dd3edb14c9e635e003 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/settings.gradle @@ -0,0 +1 @@ +include ':app' diff --git a/rknpu2/examples/rknn_yolov5_android_apk_demo/tools/save_capture_img.py b/rknpu2/examples/rknn_yolov5_android_apk_demo/tools/save_capture_img.py new file mode 100644 index 0000000000000000000000000000000000000000..392d986e81f52d903775affa3d2c5441a16819c7 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_android_apk_demo/tools/save_capture_img.py @@ -0,0 +1,19 @@ +# Copyright 2021 The Rockchip Electronics Co., Ltd +# @File : save_capture_img.py +# @Author : raul.rao +# @Date : 06/07/22 +# @Desc : read rgb raw data and save it to jpg file. +# ===================================================== + +import cv2 +from cv2 import cvtColor +import numpy as np + +if __name__ == '__main__': + data = np.loadtxt('resized_img_5.rgb') + data = np.reshape(data, (640, 640, 3)).astype(np.uint8) + img = cv2.cvtColor(data, cv2.COLOR_RGB2BGR) + cv2.imshow("resized img", img) + cv2.waitKeyEx(0) + + cv2.imwrite('test.jpg', img) \ No newline at end of file diff --git a/rknpu2/examples/rknn_yolov5_demo/CMakeLists.txt b/rknpu2/examples/rknn_yolov5_demo/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..dea2e7fda36b96e65022b90916c936089ea303cc --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_demo/CMakeLists.txt @@ -0,0 +1,143 @@ +cmake_minimum_required(VERSION 3.6) + +project(rknn_yolov5_demo) + +set(CMAKE_CXX_STANDARD 11) +set(CMAKE_CXX_STANDARD_REQUIRED ON) + +# skip 3rd-party lib dependencies +set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} -Wl,--allow-shlib-undefined") + +# install target and libraries +set(CMAKE_INSTALL_PREFIX ${CMAKE_SOURCE_DIR}/install/rknn_yolov5_demo_${CMAKE_SYSTEM_NAME}) + +set(CMAKE_SKIP_INSTALL_RPATH FALSE) +set(CMAKE_BUILD_WITH_INSTALL_RPATH TRUE) +set(CMAKE_INSTALL_RPATH "${CMAKE_INSTALL_PREFIX}/lib") + +if(CMAKE_C_COMPILER MATCHES "aarch64") + set(LIB_ARCH aarch64) +else() + set(LIB_ARCH armhf) +endif() + +include_directories(${CMAKE_SOURCE_DIR}) + +# rknn api +set(RKNN_API_PATH ${CMAKE_SOURCE_DIR}/../../runtime//${CMAKE_SYSTEM_NAME}/librknn_api) + +if(CMAKE_SYSTEM_NAME STREQUAL "Android") + set(RKNN_RT_LIB ${RKNN_API_PATH}/${CMAKE_ANDROID_ARCH_ABI}/librknnrt.so) +else() + set(RKNN_RT_LIB ${RKNN_API_PATH}/${LIB_ARCH}/librknnrt.so) +endif() + +include_directories(${RKNN_API_PATH}/include) +include_directories(${CMAKE_SOURCE_DIR}/../3rdparty) + +# opencv +if(CMAKE_SYSTEM_NAME STREQUAL "Android") + set(OpenCV_DIR ${CMAKE_SOURCE_DIR}/../3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/abi-${CMAKE_ANDROID_ARCH_ABI}) +else() + if(LIB_ARCH STREQUAL "armhf") + set(OpenCV_DIR ${CMAKE_SOURCE_DIR}/../3rdparty/opencv/opencv-linux-armhf/share/OpenCV) + else() + set(OpenCV_DIR ${CMAKE_SOURCE_DIR}/../3rdparty/opencv/opencv-linux-aarch64/share/OpenCV) + endif() +endif() + +find_package(OpenCV REQUIRED) + +# rga +# comes from https://github.com/airockchip/librga +set(RGA_PATH ${CMAKE_SOURCE_DIR}/../3rdparty/rga/) +if(CMAKE_SYSTEM_NAME STREQUAL "Android") + set(RGA_LIB ${RGA_PATH}/libs/AndroidNdk/${CMAKE_ANDROID_ARCH_ABI}/librga.so) +else() + if(CMAKE_C_COMPILER MATCHES "aarch64") + set(LIB_ARCH aarch64) + else() + set(LIB_ARCH armhf) + endif() + + set(RGA_LIB ${RGA_PATH}/libs/Linux//gcc-${LIB_ARCH}/librga.so) +endif() +include_directories( ${RGA_PATH}/include) + +# mpp +set(MPP_PATH ${CMAKE_CURRENT_SOURCE_DIR}/../3rdparty/mpp) + +if(CMAKE_SYSTEM_NAME STREQUAL "Linux") + set(MPP_LIBS ${MPP_PATH}/${CMAKE_SYSTEM_NAME}/${LIB_ARCH}/librockchip_mpp.so) +elseif(CMAKE_SYSTEM_NAME STREQUAL "Android") + set(MPP_LIBS ${MPP_PATH}/${CMAKE_SYSTEM_NAME}/${CMAKE_ANDROID_ARCH_ABI}/libmpp.so) +endif() + +include_directories(${MPP_PATH}/include) + +# zlmediakit +set(ZLMEDIAKIT_PATH ${CMAKE_SOURCE_DIR}/../3rdparty/zlmediakit) + +if(CMAKE_SYSTEM_NAME STREQUAL "Linux") + include_directories(${ZLMEDIAKIT_PATH}/include) + set(ZLMEDIAKIT_LIBS ${ZLMEDIAKIT_PATH}/${LIB_ARCH}/libmk_api.so) +endif() + +if(ZLMEDIAKIT_LIBS) + add_definitions(-DBUILD_VIDEO_RTSP) +endif() + +set(CMAKE_INSTALL_RPATH "lib") + +# rknn_yolov5_demo +include_directories(${CMAKE_SOURCE_DIR}/include) + +add_executable(rknn_yolov5_demo + src/main.cc + src/preprocess.cc + src/postprocess.cc +) + +target_link_libraries(rknn_yolov5_demo + ${RKNN_RT_LIB} + ${RGA_LIB} + ${OpenCV_LIBS} +) + +if(MPP_LIBS) + add_executable(rknn_yolov5_video_demo + src/main_video.cc + src/postprocess.cc + utils/mpp_decoder.cpp + utils/mpp_encoder.cpp + utils/drawing.cpp + ) + target_link_libraries(rknn_yolov5_video_demo + ${RKNN_RT_LIB} + ${RGA_LIB} + ${OpenCV_LIBS} + ${MPP_LIBS} + ${ZLMEDIAKIT_LIBS} + ) +endif() + +# install target and libraries +set(CMAKE_INSTALL_PREFIX ${CMAKE_SOURCE_DIR}/install/rknn_yolov5_demo_${CMAKE_SYSTEM_NAME}) +install(TARGETS rknn_yolov5_demo DESTINATION ./) + +install(PROGRAMS ${RKNN_RT_LIB} DESTINATION lib) +install(PROGRAMS ${RGA_LIB} DESTINATION lib) +install(DIRECTORY model/${TARGET_SOC} DESTINATION ./model) +file(GLOB IMAGE_FILES "model/*.jpg") +file(GLOB LABEL_FILE "model/*.txt") +install(FILES ${IMAGE_FILES} DESTINATION ./model/) +install(FILES ${LABEL_FILE} DESTINATION ./model/) + +if(MPP_LIBS) + install(TARGETS rknn_yolov5_video_demo DESTINATION ./) + install(PROGRAMS ${MPP_LIBS} DESTINATION lib) +endif() + +if(ZLMEDIAKIT_LIBS) + install(PROGRAMS ${ZLMEDIAKIT_LIBS} DESTINATION lib) +endif() diff --git a/rknpu2/examples/rknn_yolov5_demo/README.md b/rknpu2/examples/rknn_yolov5_demo/README.md new file mode 100644 index 0000000000000000000000000000000000000000..eb2c36106a547c4c04f4004e7341f7f93256b54b --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_demo/README.md @@ -0,0 +1,119 @@ +# Yolo-v5 demo + +## Guide for exporting rknn model + +Please refer to this link: https://github.com/airockchip/rknn_model_zoo/tree/main/models/CV/object_detection/yolo + +## Precautions + +1. Use rknn-toolkit2 version greater than or equal to **1.4.0**. +2. When using the model trained by yourself, please pay attention to aligning post-processing parameters such as anchor, otherwise it will cause post-processing analysis errors. +3. The official website and rk pre-training models both detect 80 types of targets. If you train your own model, you need to change the OBJ_CLASS_NUM and NMS_THRESH, BOX_THRESH post-processing parameters in include/postprocess.h. +4. The demo needs the support of librga.so, please refer to https://github.com/airockchip/librga for compiling and using +5. Due to hardware limitations, the demo model moves the post-processing part of the yolov5 model to the cpu implementation by default. The models attached to this demo all use relu as the activation function. Compared with the silu activation function, the accuracy is slightly lower, and the performance is greatly improved. + + +## Android Demo + +### Compiling && Building + +First export `ANDROID_NDK_PATH`, for example `export ANDROID_NDK_PATH=~/opts/ndk/android-ndk-r18b`, then execute: + +``` +./build-android.sh -t -a [-b ] + +# sush as: +./build-android.sh -t rk3568 -a arm64-v8a -b Release +``` + +### Push all build output file to the board + +Connecting the usb port to PC, then pushing all demo files to the board, + +```sh +adb root +adb remount +adb push install/rknn_yolov5_demo /data/ +``` + +### Running + +```sh +adb shell +cd /data/rknn_yolov5_demo/ + +export LD_LIBRARY_PATH=./lib +./rknn_yolov5_demo model//yolov5s-640-640.rknn model/bus.jpg +``` + +## Aarch64 Linux Demo + +### Compiling && Building + +First export `GCC_COMPILER`, for example `export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu`, then execute: + +``` +./build-linux.sh -t -a -b ] + +# such as: +./build-linux.sh -t rk3588 -a aarch64 -b Release +``` + +### Push all build output file to the board + + +Push install/rknn_yolov5_demo_Linux to the board, + +- If using adb via the EVB board： + +``` +adb push install/rknn_yolov5_demo_Linux /userdata/ +``` + +- For other boards, using the scp or other different approaches to push all files under install/rknn_yolov5_demo_Linux to '/userdata' + +### Running + +```sh +adb shell +cd /userdata/rknn_yolov5_demo_Linux/ + +export LD_LIBRARY_PATH=./lib +./rknn_yolov5_demo model//yolov5s-640-640.rknn model/bus.jpg +``` + +Note: Try searching the location of librga.so and add it to LD_LIBRARY_PATH if the librga.so is not found on the lib folder. +Using the following commands to add to LD_LIBRARY_PATH. + +```sh +export LD_LIBRARY_PATH=./lib: +``` + +## Guide for Video Demo： +- H264 +``` +./rknn_yolov5_video_demo model//yolov5s-640-640.rknn xxx.h264 264 +``` +For converting to h264 via the ffmpeg ： +``` +ffmpeg -i xxx.mp4 -vcodec h264 xxx.h264 +``` + +- H265 +``` +./rknn_yolov5_video_demo model//yolov5s-640-640.rknn xxx.hevc 265 +``` +For converting to h265 via the ffmpeg ： +``` +ffmpeg -i xxx.mp4 -vcodec hevc xxx.hevc +``` +- RTSP +``` +./rknn_yolov5_video_demo model//yolov5s-640-640.rknn 265 +``` + +### Remark + +- **RK3562 only supports h264 video stream ** +- **rtsp video stream only available on the Linux system ** +- **The h264 name of the video stream input cannot be "out.h264", it will be overwritten.** \ No newline at end of file diff --git a/rknpu2/examples/rknn_yolov5_demo/README_CN.md b/rknpu2/examples/rknn_yolov5_demo/README_CN.md new file mode 100644 index 0000000000000000000000000000000000000000..197b0a1baac454aaf2ef2c40869ce260dc5748bf --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_demo/README_CN.md @@ -0,0 +1,123 @@ +# Yolo-v5 demo + +## 导出rknn模型步骤 + +请参考 https://github.com/airockchip/rknn_model_zoo/tree/main/models/CV/object_detection/yolo + + +## 注意事项 + +1. 使用rknn-toolkit2版本大于等于1.4.0。 +2. 切换成自己训练的模型时，请注意对齐anchor等后处理参数，否则会导致后处理解析出错。 +3. 官网和rk预训练模型都是检测80类的目标，如果自己训练的模型,需要更改include/postprocess.h中的OBJ_CLASS_NUM以及NMS_THRESH,BOX_THRESH后处理参数。 +4. demo需要librga.so的支持,编译使用请参考 https://github.com/airockchip/librga +5. 由于硬件限制，该demo的模型默认把 yolov5 模型的后处理部分，移至cpu实现。本demo附带的模型均使用relu为激活函数，相比silu激活函数精度略微下降，性能大幅上升。 + + + +## Android Demo + +### 编译 + +首先导入ANDROID_NDK_PATH，例如`export ANDROID_NDK_PATH=~/opts/ndk/android-ndk-r18b`，然后执行如下命令： + +``` +./build-android.sh -t -a [-b ] + +# 例如: +./build-android.sh -t rk3568 -a arm64-v8a -b Release +``` + +### 推送执行文件到板子 + +连接板子的usb口到PC,将整个demo目录到 `/data`: + +```sh +adb root +adb remount +adb push install/rknn_yolov5_demo /data/ +``` + +### 运行 + +```sh +adb shell +cd /data/rknn_yolov5_demo/ + +export LD_LIBRARY_PATH=./lib +./rknn_yolov5_demo model//yolov5s-640-640.rknn model/bus.jpg +``` + +## Aarch64 Linux Demo + +### 编译 + +首先导入GCC_COMPILER，例如`export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu +`，然后执行如下命令： + +``` +./build-linux.sh -t -a -b ] + +# 例如: +./build-linux.sh -t rk3588 -a aarch64 -b Release +``` + +### 推送执行文件到板子 + + +将 install/rknn_yolov5_demo_Linux 拷贝到板子的/userdata/目录. + +- 如果使用rockchip的EVB板子，可以使用adb将文件推到板子上： + +``` +adb push install/rknn_yolov5_demo_Linux /userdata/ +``` + +- 如果使用其他板子，可以使用scp等方式将install/rknn_yolov5_demo_Linux拷贝到板子的/userdata/目录 + +### 运行 + +```sh +adb shell +cd /userdata/rknn_yolov5_demo_Linux/ + +export LD_LIBRARY_PATH=./lib +./rknn_yolov5_demo model//yolov5s-640-640.rknn model/bus.jpg +``` + +Note: Try searching the location of librga.so and add it to LD_LIBRARY_PATH if the librga.so is not found on the lib folder. +Using the following commands to add to LD_LIBRARY_PATH. + +```sh +export LD_LIBRARY_PATH=./lib: +``` + +## 视频流Demo运行命令参考如下： +- h264视频 +``` +./rknn_yolov5_video_demo model//yolov5s-640-640.rknn xxx.h264 264 +``` +注意需要使用h264码流视频，可以使用如下命令转换得到： +``` +ffmpeg -i xxx.mp4 -vcodec h264 xxx.h264 +``` + +- h265视频 +``` +./rknn_yolov5_video_demo model//yolov5s-640-640.rknn xxx.hevc 265 +``` +注意需要使用h265码流视频，可以使用如下命令转换得到： +``` +ffmpeg -i xxx.mp4 -vcodec hevc xxx.hevc +``` +- rtsp视频流 +``` +./rknn_yolov5_video_demo model//yolov5s-640-640.rknn 265 +``` + +### 注意 + +- 需要根据系统的rga驱动选择正确的librga库，具体依赖请参考： https://github.com/airockchip/librga +- **rk3562 目前仅支持h264视频流** +- **rtsp 视频流Demo仅在Linux系统上支持，Android上目前还不支持** +- 视频流输入的h264名称不能为"out.h264"，会被覆盖 \ No newline at end of file diff --git a/rknpu2/examples/rknn_yolov5_demo/build-android.sh b/rknpu2/examples/rknn_yolov5_demo/build-android.sh new file mode 100755 index 0000000000000000000000000000000000000000..4778e831eaff14905900f6fea7b8e3ee44147549 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_demo/build-android.sh @@ -0,0 +1,112 @@ +#!/bin/bash + +set -e + +if [[ -z ${ANDROID_NDK_PATH} ]];then + echo "Please set ANDROID_NDK_PATH, such as export ANDROID_NDK_PATH=~/opts/ndk/android-ndk-r18b" + echo "NDK Version r18, r19 is recommanded. Other version may cause build failure." + exit +fi + +echo "$0 $@" +while getopts ":t:a:b" opt; do + case $opt in + t) + TARGET_SOC=$OPTARG + ;; + a) + TARGET_ARCH=$OPTARG + ;; + b) + BUILD_TYPE=$OPTARG + ;; + :) + echo "Option -$OPTARG requires an argument." + exit 1 + ;; + ?) + echo "Invalid option: -$OPTARG index:$OPTIND" + ;; + esac +done + +if [ -z ${TARGET_SOC} ] || [ -z ${TARGET_ARCH} ] ; then + echo "$0 -t -a [-b ]" + echo "" + echo " -t : target (rk3566/rk3568/rk3562/rk3576/rk3588)" + echo " -a : arch (arm64-v8a/armeabi-v7a)" + echo " -b : build_type (Debug/Release)" + echo "such as: $0 -t rk3588 -a arm64-v8a -b Release" + echo "" + exit -1 +fi + +# Debug / Release / RelWithDebInfo +if [[ -z ${BUILD_TYPE} ]];then + BUILD_TYPE=Release +fi + +case ${TARGET_SOC} in + rk356x) + TARGET_SOC="RK3566_RK3568" + ;; + rk3566_rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3588) + TARGET_SOC="RK3588" + ;; + rk3576) + TARGET_SOC="RK3576" + ;; + rk3566) + TARGET_SOC="RK3566_RK3568" + ;; + rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3562) + TARGET_SOC="RK3562" + ;; + *) + echo "Invalid target: ${TARGET_SOC}" + echo "Valid target: rk3562,rk3566,rk3568,rk3576,rk3588" + exit -1 + ;; +esac + +TARGET_PLATFORM=${TARGET_SOC}_android +if [[ -n ${TARGET_ARCH} ]];then +TARGET_PLATFORM=${TARGET_PLATFORM}_${TARGET_ARCH} +fi +ROOT_PWD=$( cd "$( dirname $0 )" && cd -P "$( dirname "$SOURCE" )" && pwd ) +BUILD_DIR=${ROOT_PWD}/build/build_${TARGET_PLATFORM}_${BUILD_TYPE} + +echo "===================================" +echo "TARGET_SOC=${TARGET_SOC}" +echo "TARGET_ARCH=${TARGET_ARCH}" +echo "BUILD_TYPE=${BUILD_TYPE}" +echo "BUILD_DIR=${BUILD_DIR}" +echo "ANDROID_NDK_PATH=${ANDROID_NDK_PATH}" +echo "===================================" + +if [[ ! -d "${BUILD_DIR}" ]]; then + mkdir -p ${BUILD_DIR} +fi + + +cd ${BUILD_DIR} +cmake ../.. \ + -DANDROID_TOOLCHAIN=clang \ + -DTARGET_SOC=${TARGET_SOC} \ + -DCMAKE_SYSTEM_NAME=Android \ + -DCMAKE_TOOLCHAIN_FILE=$ANDROID_NDK_PATH/build/cmake/android.toolchain.cmake \ + -DANDROID_ABI=${TARGET_ARCH} \ + -DANDROID_STL=c++_static \ + -DANDROID_PLATFORM=android-24 \ + -DCMAKE_BUILD_TYPE=${BUILD_TYPE} +# make VERBOSE=1 +make -j4 +make install +cd .. + diff --git a/rknpu2/examples/rknn_yolov5_demo/build-linux.sh b/rknpu2/examples/rknn_yolov5_demo/build-linux.sh new file mode 100755 index 0000000000000000000000000000000000000000..ae972803b8bda834c3a95588e513ec7af832cbeb --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_demo/build-linux.sh @@ -0,0 +1,122 @@ +#!/bin/bash + +set -e + +echo "$0 $@" +while getopts ":t:a:b" opt; do + case $opt in + t) + TARGET_SOC=$OPTARG + ;; + a) + TARGET_ARCH=$OPTARG + ;; + b) + BUILD_TYPE=$OPTARG + ;; + :) + echo "Option -$OPTARG requires an argument." + exit 1 + ;; + ?) + echo "Invalid option: -$OPTARG index:$OPTIND" + ;; + esac +done + +if [ -z ${TARGET_SOC} ];then + echo "$0 -t -a [-b ]" + echo "" + echo " -t : target (rk3566/rk3568/rk3562/rk3576/rk3588)" + echo " -a : arch (aarch64/armhf)" + echo " -b : build_type(Debug/Release)" + echo "such as: $0 -t rk3588 -a aarch64 -b Release" + echo "" + exit -1 +fi + +if [[ -z ${GCC_COMPILER} ]];then + echo "Please set GCC_COMPILER for $TARGET_SOC" + echo "such as export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu" + exit +fi +echo "$GCC_COMPILER" +export CC=${GCC_COMPILER}-gcc +export CXX=${GCC_COMPILER}-g++ + +if command -v ${CC} >/dev/null 2>&1; then + : +else + echo "${CC} is not available" + echo "Please set GCC_COMPILER for $TARGET_SOC" + echo "such as export GCC_COMPILER=~/opt/gcc-linaro-7.5.0-2019.12-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu" + exit +fi + +# Debug / Release +if [[ -z ${BUILD_TYPE} ]];then + BUILD_TYPE=Release +fi + + +case ${TARGET_SOC} in + rk356x) + TARGET_SOC="RK3566_RK3568" + ;; + rk3566_rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3588) + TARGET_SOC="RK3588" + ;; + rk3576) + TARGET_SOC="RK3576" + ;; + rk3566) + TARGET_SOC="RK3566_RK3568" + ;; + rk3568) + TARGET_SOC="RK3566_RK3568" + ;; + rk3562) + TARGET_SOC="RK3562" + ;; + *) + echo "Invalid target: ${TARGET_SOC}" + echo "Valid target: rk3562,rk3566,rk3568,rk3576,rk3588" + exit -1 + ;; +esac + +TARGET_PLATFORM=${TARGET_SOC}_linux +if [[ -n ${TARGET_ARCH} ]];then +TARGET_PLATFORM=${TARGET_PLATFORM}_${TARGET_ARCH} +fi +ROOT_PWD=$( cd "$( dirname $0 )" && cd -P "$( dirname "$SOURCE" )" && pwd ) +BUILD_DIR=${ROOT_PWD}/build/build_${TARGET_PLATFORM}_${BUILD_TYPE} + + +echo "===================================" +echo "TARGET_SOC=${TARGET_SOC}" +echo "TARGET_ARCH=${TARGET_ARCH}" +echo "BUILD_TYPE=${BUILD_TYPE}" +echo "BUILD_DIR=${BUILD_DIR}" +echo "CC=${CC}" +echo "CXX=${CXX}" +echo "===================================" + +if [[ ! -d "${BUILD_DIR}" ]]; then + mkdir -p ${BUILD_DIR} +fi + + +cd ${BUILD_DIR} +cmake ../.. \ + -DTARGET_SOC=${TARGET_SOC} \ + -DCMAKE_SYSTEM_NAME=Linux \ + -DCMAKE_SYSTEM_PROCESSOR=${TARGET_ARCH} \ + -DCMAKE_BUILD_TYPE=${BUILD_TYPE} \ + -DCMAKE_C_COMPILER=${CC} \ + -DCMAKE_CXX_COMPILER=${CXX} +make -j4 +make install diff --git a/rknpu2/examples/rknn_yolov5_demo/convert_rknn_demo/yolov5/README.md b/rknpu2/examples/rknn_yolov5_demo/convert_rknn_demo/yolov5/README.md new file mode 100644 index 0000000000000000000000000000000000000000..41c354bebdd427711ef6e1609ada68145728fccb --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_demo/convert_rknn_demo/yolov5/README.md @@ -0,0 +1,17 @@ +# onnx model + +* onnx_models/yolov5s_relu.onnx + +The model comes from https://github.com/airockchip/rknn_model_zoo + +# convert rknn model + +1. Modify the following parameters of onnx2rknn.py to the corresponding platform, such as the RK3566_RK3568 model, modify as follows: + +```python +platform="rk3566" +``` + +2. Execute python onnx2rknn.py + +3. The rknn model is generated in the rknn_models directory \ No newline at end of file diff --git a/rknpu2/examples/rknn_yolov5_demo/convert_rknn_demo/yolov5/README_CN.md b/rknpu2/examples/rknn_yolov5_demo/convert_rknn_demo/yolov5/README_CN.md new file mode 100644 index 0000000000000000000000000000000000000000..65e0f3da4bd0fc6ff4298ba949afccaf914597f2 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_demo/convert_rknn_demo/yolov5/README_CN.md @@ -0,0 +1,18 @@ +# onnx模型 + +* onnx_models/yolov5s_relu.onnx + + +模型来源：https://github.com/airockchip/rknn_model_zoo + +# 转换rknn模型 + +1. 将onnx2rknn.py以下的参数修改成对应的平台，例如RK3566_RK3568模型，修改为： + +```python +platform="rk3566" +``` + +2. 执行python onnx2rknn.py + +3. rknn模型生成在rknn_models目录 diff --git a/rknpu2/examples/rknn_yolov5_demo/convert_rknn_demo/yolov5/bus.jpg b/rknpu2/examples/rknn_yolov5_demo/convert_rknn_demo/yolov5/bus.jpg new file mode 100644 index 0000000000000000000000000000000000000000..19172b78dbbcf34de2b875ac72eb0b4b0651ee66 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_demo/convert_rknn_demo/yolov5/bus.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fb4914d123d97c440cd127ef0e98d4bdc68cd88e2683657528928b4a34014e16 +size 181374 diff --git a/rknpu2/examples/rknn_yolov5_demo/convert_rknn_demo/yolov5/dataset.txt b/rknpu2/examples/rknn_yolov5_demo/convert_rknn_demo/yolov5/dataset.txt new file mode 100644 index 0000000000000000000000000000000000000000..a81b1f855159be64b372f8927d6733de2a6e595c --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_demo/convert_rknn_demo/yolov5/dataset.txt @@ -0,0 +1 @@ +bus.jpg diff --git a/rknpu2/examples/rknn_yolov5_demo/convert_rknn_demo/yolov5/onnx2rknn.py b/rknpu2/examples/rknn_yolov5_demo/convert_rknn_demo/yolov5/onnx2rknn.py new file mode 100644 index 0000000000000000000000000000000000000000..a5306b4489ef71b636022f8649c563b2627c04b1 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_demo/convert_rknn_demo/yolov5/onnx2rknn.py @@ -0,0 +1,58 @@ + +import cv2 +import numpy as np + +from rknn.api import RKNN +import os + +if __name__ == '__main__': + + platform = 'rk3566' + exp = 'yolov5s' + Width = 640 + Height = 640 + # Model from https://github.com/airockchip/rknn_model_zoo + MODEL_PATH = './onnx_models/yolov5s_relu.onnx' + NEED_BUILD_MODEL = True + # NEED_BUILD_MODEL = False + im_file = './dog_bike_car_640x640.jpg' + + # Create RKNN object + rknn = RKNN() + + OUT_DIR = "rknn_models" + RKNN_MODEL_PATH = './{}/{}_{}.rknn'.format( + OUT_DIR, exp+'-'+str(Width)+'-'+str(Height), platform) + if NEED_BUILD_MODEL: + DATASET = './dataset.txt' + rknn.config(mean_values=[[0, 0, 0]], std_values=[ + [255, 255, 255]], target_platform=platform) + # Load model + print('--> Loading model') + ret = rknn.load_onnx(MODEL_PATH) + if ret != 0: + print('load model failed!') + exit(ret) + print('done') + + # Build model + print('--> Building model') + ret = rknn.build(do_quantization=True, dataset=DATASET) + if ret != 0: + print('build model failed.') + exit(ret) + print('done') + + # Export rknn model + if not os.path.exists(OUT_DIR): + os.mkdir(OUT_DIR) + print('--> Export RKNN model: {}'.format(RKNN_MODEL_PATH)) + ret = rknn.export_rknn(RKNN_MODEL_PATH) + if ret != 0: + print('Export rknn model failed.') + exit(ret) + print('done') + else: + ret = rknn.load_rknn(RKNN_MODEL_PATH) + + rknn.release() diff --git a/rknpu2/examples/rknn_yolov5_demo/convert_rknn_demo/yolov5/onnx_models/yolov5s_for_apk_demo.onnx b/rknpu2/examples/rknn_yolov5_demo/convert_rknn_demo/yolov5/onnx_models/yolov5s_for_apk_demo.onnx new file mode 100644 index 0000000000000000000000000000000000000000..478af55d5b6baa42cc25218d9641cc739bda369d --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_demo/convert_rknn_demo/yolov5/onnx_models/yolov5s_for_apk_demo.onnx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:362d65f0e4d803683802a90767a62ba9435e18688a3c525b5e25b1fb33f38351 +size 28923110 diff --git a/rknpu2/examples/rknn_yolov5_demo/convert_rknn_demo/yolov5/onnx_models/yolov5s_relu.onnx b/rknpu2/examples/rknn_yolov5_demo/convert_rknn_demo/yolov5/onnx_models/yolov5s_relu.onnx new file mode 100644 index 0000000000000000000000000000000000000000..7c0fea5bd06cf3940948870c11fd86763dfedea2 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_demo/convert_rknn_demo/yolov5/onnx_models/yolov5s_relu.onnx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b532116d87b115490b2c5ff309ef5441e372877122eee7ef7f8c50079509beba +size 28924147 diff --git a/rknpu2/examples/rknn_yolov5_demo/include/drm_func.h b/rknpu2/examples/rknn_yolov5_demo/include/drm_func.h new file mode 100644 index 0000000000000000000000000000000000000000..0a9e3b30058ff002a0f5c94e975f9c619b08597c --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_demo/include/drm_func.h @@ -0,0 +1,53 @@ +#ifndef __DRM_FUNC_H__ +#define __DRM_FUNC_H__ +#include +#include +#include +#include +#include // open function +#include // close function +#include +#include + + +#include +#include "libdrm/drm_fourcc.h" +#include "xf86drm.h" + +#ifdef __cplusplus +extern "C" { +#endif + +typedef int (* FUNC_DRM_IOCTL)(int fd, unsigned long request, void *arg); + +typedef struct _drm_context{ + void *drm_handle; + FUNC_DRM_IOCTL io_func; +} drm_context; + +/* memory type definitions. */ +enum drm_rockchip_gem_mem_type +{ + /* Physically Continuous memory and used as default. */ + ROCKCHIP_BO_CONTIG = 1 << 0, + /* cachable mapping. */ + ROCKCHIP_BO_CACHABLE = 1 << 1, + /* write-combine mapping. */ + ROCKCHIP_BO_WC = 1 << 2, + ROCKCHIP_BO_SECURE = 1 << 3, + ROCKCHIP_BO_MASK = ROCKCHIP_BO_CONTIG | ROCKCHIP_BO_CACHABLE | + ROCKCHIP_BO_WC | ROCKCHIP_BO_SECURE +}; + +int drm_init(drm_context *drm_ctx); + +void* drm_buf_alloc(drm_context *drm_ctx,int drm_fd, int TexWidth, int TexHeight,int bpp,int *fd,unsigned int *handle,size_t *actual_size); + +int drm_buf_destroy(drm_context *drm_ctx,int drm_fd,int buf_fd, int handle,void *drm_buf,size_t size); + +void drm_deinit(drm_context *drm_ctx, int drm_fd); + +#ifdef __cplusplus +} +#endif +#endif /*__DRM_FUNC_H__*/ \ No newline at end of file diff --git a/rknpu2/examples/rknn_yolov5_demo/include/postprocess.h b/rknpu2/examples/rknn_yolov5_demo/include/postprocess.h new file mode 100755 index 0000000000000000000000000000000000000000..de3c2887bbf2dfa3afb379586a5f9fc4605daac2 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_demo/include/postprocess.h @@ -0,0 +1,42 @@ +#ifndef _RKNN_YOLOV5_DEMO_POSTPROCESS_H_ +#define _RKNN_YOLOV5_DEMO_POSTPROCESS_H_ + +#include +#include + +#define OBJ_NAME_MAX_SIZE 16 +#define OBJ_NUMB_MAX_SIZE 64 +#define OBJ_CLASS_NUM 80 +#define NMS_THRESH 0.45 +#define BOX_THRESH 0.25 +#define PROP_BOX_SIZE (5 + OBJ_CLASS_NUM) + +typedef struct _BOX_RECT +{ + int left; + int right; + int top; + int bottom; +} BOX_RECT; + +typedef struct __detect_result_t +{ + char name[OBJ_NAME_MAX_SIZE]; + BOX_RECT box; + float prop; +} detect_result_t; + +typedef struct _detect_result_group_t +{ + int id; + int count; + detect_result_t results[OBJ_NUMB_MAX_SIZE]; +} detect_result_group_t; + +int post_process(int8_t *input0, int8_t *input1, int8_t *input2, int model_in_h, int model_in_w, + float conf_threshold, float nms_threshold, BOX_RECT pads, float scale_w, float scale_h, + std::vector &qnt_zps, std::vector &qnt_scales, + detect_result_group_t *group); + +void deinitPostProcess(); +#endif //_RKNN_YOLOV5_DEMO_POSTPROCESS_H_ diff --git a/rknpu2/examples/rknn_yolov5_demo/include/preprocess.h b/rknpu2/examples/rknn_yolov5_demo/include/preprocess.h new file mode 100644 index 0000000000000000000000000000000000000000..dc8319f3252e80d41de2f8088aa518b8e45ad694 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_demo/include/preprocess.h @@ -0,0 +1,16 @@ +#ifndef _RKNN_YOLOV5_DEMO_PREPROCESS_H_ +#define _RKNN_YOLOV5_DEMO_PREPROCESS_H_ + +#include +#include "im2d.h" +#include "rga.h" +#include "opencv2/core/core.hpp" +#include "opencv2/imgcodecs.hpp" +#include "opencv2/imgproc.hpp" +#include "postprocess.h" + +void letterbox(const cv::Mat &image, cv::Mat &padded_image, BOX_RECT &pads, const float scale, const cv::Size &target_size, const cv::Scalar &pad_color = cv::Scalar(128, 128, 128)); + +int resize_rga(rga_buffer_t &src, rga_buffer_t &dst, const cv::Mat &image, cv::Mat &resized_image, const cv::Size &target_size); + +#endif //_RKNN_YOLOV5_DEMO_PREPROCESS_H_ diff --git a/rknpu2/examples/rknn_yolov5_demo/include/rga_func.h b/rknpu2/examples/rknn_yolov5_demo/include/rga_func.h new file mode 100644 index 0000000000000000000000000000000000000000..beeb44100395f4f255ca93c44dea6cc2a2b485e7 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_demo/include/rga_func.h @@ -0,0 +1,33 @@ +#ifndef __RGA_FUNC_H__ +#define __RGA_FUNC_H__ + +#include +#include "RgaApi.h" + +#ifdef __cplusplus +extern "C" { +#endif + +typedef int(* FUNC_RGA_INIT)(); +typedef void(* FUNC_RGA_DEINIT)(); +typedef int(* FUNC_RGA_BLIT)(rga_info_t *, rga_info_t *, rga_info_t *); + +typedef struct _rga_context{ + void *rga_handle; + FUNC_RGA_INIT init_func; + FUNC_RGA_DEINIT deinit_func; + FUNC_RGA_BLIT blit_func; +} rga_context; + +int RGA_init(rga_context* rga_ctx); + +void img_resize_fast(rga_context *rga_ctx, int src_fd, int src_w, int src_h, uint64_t dst_phys, int dst_w, int dst_h); + +void img_resize_slow(rga_context *rga_ctx, void *src_virt, int src_w, int src_h, void *dst_virt, int dst_w, int dst_h); + +int RGA_deinit(rga_context* rga_ctx); + +#ifdef __cplusplus +} +#endif +#endif/*__RGA_FUNC_H__*/ diff --git a/rknpu2/examples/rknn_yolov5_demo/model/RK3562/yolov5s-640-640.rknn b/rknpu2/examples/rknn_yolov5_demo/model/RK3562/yolov5s-640-640.rknn new file mode 100644 index 0000000000000000000000000000000000000000..d3f7fbfbe8122176468b07cec9dd90f3e2a4128f --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_demo/model/RK3562/yolov5s-640-640.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:32ca301e2c17bd48dd96ef2c8d598c05ac83ab5c23ff319e2b130b1775d12793 +size 7668932 diff --git a/rknpu2/examples/rknn_yolov5_demo/model/RK3566_RK3568/yolov5s-640-640.rknn b/rknpu2/examples/rknn_yolov5_demo/model/RK3566_RK3568/yolov5s-640-640.rknn new file mode 100644 index 0000000000000000000000000000000000000000..665e6f64affd3211db27fecff1c69235d91d5be8 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_demo/model/RK3566_RK3568/yolov5s-640-640.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fb5c4b7ee6ff7fbaa4c3e0433d2a71bbfee95272eba55d081da390f3c46570e1 +size 7932548 diff --git a/rknpu2/examples/rknn_yolov5_demo/model/RK3576/yolov5s-640-640.rknn b/rknpu2/examples/rknn_yolov5_demo/model/RK3576/yolov5s-640-640.rknn new file mode 100644 index 0000000000000000000000000000000000000000..cef2d5d9f66cd5e55b02a490a81fcaebefc1e1f6 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_demo/model/RK3576/yolov5s-640-640.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a17a20294af6821d43b7c6fe9624c633c803c19e8e1e86f920b88f02348bdff3 +size 8197828 diff --git a/rknpu2/examples/rknn_yolov5_demo/model/RK3588/yolov5s-640-640.rknn b/rknpu2/examples/rknn_yolov5_demo/model/RK3588/yolov5s-640-640.rknn new file mode 100644 index 0000000000000000000000000000000000000000..a57c5e924623597a5fc6000bace028c05e39751e --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_demo/model/RK3588/yolov5s-640-640.rknn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7c6b801de602b8aaa72269fab8daab21810418a4b01c8e8995313916e278fe71 +size 8357380 diff --git a/rknpu2/examples/rknn_yolov5_demo/model/bus.jpg b/rknpu2/examples/rknn_yolov5_demo/model/bus.jpg new file mode 100644 index 0000000000000000000000000000000000000000..19172b78dbbcf34de2b875ac72eb0b4b0651ee66 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_demo/model/bus.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fb4914d123d97c440cd127ef0e98d4bdc68cd88e2683657528928b4a34014e16 +size 181374 diff --git a/rknpu2/examples/rknn_yolov5_demo/model/coco_80_labels_list.txt b/rknpu2/examples/rknn_yolov5_demo/model/coco_80_labels_list.txt new file mode 100644 index 0000000000000000000000000000000000000000..941cb4e1392266f6a6c09b1fdc5f79503b2e5df6 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_demo/model/coco_80_labels_list.txt @@ -0,0 +1,80 @@ +person +bicycle +car +motorcycle +airplane +bus +train +truck +boat +traffic light +fire hydrant +stop sign +parking meter +bench +bird +cat +dog +horse +sheep +cow +elephant +bear +zebra +giraffe +backpack +umbrella +handbag +tie +suitcase +frisbee +skis +snowboard +sports ball +kite +baseball bat +baseball glove +skateboard +surfboard +tennis racket +bottle +wine glass +cup +fork +knife +spoon +bowl +banana +apple +sandwich +orange +broccoli +carrot +hot dog +pizza +donut +cake +chair +couch +potted plant +bed +dining table +toilet +tv +laptop +mouse +remote +keyboard +cell phone +microwave +oven +toaster +sink +refrigerator +book +clock +vase +scissors +teddy bear +hair drier +toothbrush diff --git a/rknpu2/examples/rknn_yolov5_demo/src/main.cc b/rknpu2/examples/rknn_yolov5_demo/src/main.cc new file mode 100755 index 0000000000000000000000000000000000000000..ef9c1abebfb30fa118b7cc4709d8ce2c76303632 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_demo/src/main.cc @@ -0,0 +1,378 @@ +// Copyright (c) 2021 by Rockchip Electronics Co., Ltd. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +/*------------------------------------------- + Includes +-------------------------------------------*/ +#include +#include +#include +#include +#include + +#define _BASETSD_H + +#include "RgaUtils.h" + +#include "postprocess.h" + +#include "rknn_api.h" +#include "preprocess.h" + +#define PERF_WITH_POST 1 +/*------------------------------------------- + Functions +-------------------------------------------*/ + +static void dump_tensor_attr(rknn_tensor_attr *attr) +{ + std::string shape_str = attr->n_dims < 1 ? "" : std::to_string(attr->dims[0]); + for (int i = 1; i < attr->n_dims; ++i) + { + shape_str += ", " + std::to_string(attr->dims[i]); + } + + printf(" index=%d, name=%s, n_dims=%d, dims=[%s], n_elems=%d, size=%d, w_stride = %d, size_with_stride=%d, fmt=%s, " + "type=%s, qnt_type=%s, " + "zp=%d, scale=%f\n", + attr->index, attr->name, attr->n_dims, shape_str.c_str(), attr->n_elems, attr->size, attr->w_stride, + attr->size_with_stride, get_format_string(attr->fmt), get_type_string(attr->type), + get_qnt_type_string(attr->qnt_type), attr->zp, attr->scale); +} + +double __get_us(struct timeval t) { return (t.tv_sec * 1000000 + t.tv_usec); } + +static unsigned char *load_data(FILE *fp, size_t ofst, size_t sz) +{ + unsigned char *data; + int ret; + + data = NULL; + + if (NULL == fp) + { + return NULL; + } + + ret = fseek(fp, ofst, SEEK_SET); + if (ret != 0) + { + printf("blob seek failure.\n"); + return NULL; + } + + data = (unsigned char *)malloc(sz); + if (data == NULL) + { + printf("buffer malloc failure.\n"); + return NULL; + } + ret = fread(data, 1, sz, fp); + return data; +} + +static unsigned char *load_model(const char *filename, int *model_size) +{ + FILE *fp; + unsigned char *data; + + fp = fopen(filename, "rb"); + if (NULL == fp) + { + printf("Open file %s failed.\n", filename); + return NULL; + } + + fseek(fp, 0, SEEK_END); + int size = ftell(fp); + + data = load_data(fp, 0, size); + + fclose(fp); + + *model_size = size; + return data; +} + +static int saveFloat(const char *file_name, float *output, int element_size) +{ + FILE *fp; + fp = fopen(file_name, "w"); + for (int i = 0; i < element_size; i++) + { + fprintf(fp, "%.6f\n", output[i]); + } + fclose(fp); + return 0; +} + +/*------------------------------------------- + Main Functions +-------------------------------------------*/ +int main(int argc, char **argv) +{ + if (argc < 3) + { + printf("Usage: %s \n", argv[0]); + return -1; + } + int ret; + rknn_context ctx; + size_t actual_size = 0; + int img_width = 0; + int img_height = 0; + int img_channel = 0; + const float nms_threshold = NMS_THRESH; // 默认的NMS阈值 + const float box_conf_threshold = BOX_THRESH; // 默认的置信度阈值 + struct timeval start_time, stop_time; + char *model_name = (char *)argv[1]; + char *input_path = argv[2]; + std::string option = "letterbox"; + std::string out_path = "./out.jpg"; + if (argc >= 4) + { + option = argv[3]; + } + if (argc >= 5) + { + out_path = argv[4]; + } + + // init rga context + rga_buffer_t src; + rga_buffer_t dst; + memset(&src, 0, sizeof(src)); + memset(&dst, 0, sizeof(dst)); + + printf("post process config: box_conf_threshold = %.2f, nms_threshold = %.2f\n", box_conf_threshold, nms_threshold); + + /* Create the neural network */ + printf("Loading mode...\n"); + int model_data_size = 0; + unsigned char *model_data = load_model(model_name, &model_data_size); + ret = rknn_init(&ctx, model_data, model_data_size, 0, NULL); + if (ret < 0) + { + printf("rknn_init error ret=%d\n", ret); + return -1; + } + + rknn_sdk_version version; + ret = rknn_query(ctx, RKNN_QUERY_SDK_VERSION, &version, sizeof(rknn_sdk_version)); + if (ret < 0) + { + printf("rknn_init error ret=%d\n", ret); + return -1; + } + printf("sdk version: %s driver version: %s\n", version.api_version, version.drv_version); + + rknn_input_output_num io_num; + ret = rknn_query(ctx, RKNN_QUERY_IN_OUT_NUM, &io_num, sizeof(io_num)); + if (ret < 0) + { + printf("rknn_init error ret=%d\n", ret); + return -1; + } + printf("model input num: %d, output num: %d\n", io_num.n_input, io_num.n_output); + + rknn_tensor_attr input_attrs[io_num.n_input]; + memset(input_attrs, 0, sizeof(input_attrs)); + for (int i = 0; i < io_num.n_input; i++) + { + input_attrs[i].index = i; + ret = rknn_query(ctx, RKNN_QUERY_INPUT_ATTR, &(input_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret < 0) + { + printf("rknn_init error ret=%d\n", ret); + return -1; + } + dump_tensor_attr(&(input_attrs[i])); + } + + rknn_tensor_attr output_attrs[io_num.n_output]; + memset(output_attrs, 0, sizeof(output_attrs)); + for (int i = 0; i < io_num.n_output; i++) + { + output_attrs[i].index = i; + ret = rknn_query(ctx, RKNN_QUERY_OUTPUT_ATTR, &(output_attrs[i]), sizeof(rknn_tensor_attr)); + dump_tensor_attr(&(output_attrs[i])); + } + + int channel = 3; + int width = 0; + int height = 0; + if (input_attrs[0].fmt == RKNN_TENSOR_NCHW) + { + printf("model is NCHW input fmt\n"); + channel = input_attrs[0].dims[1]; + height = input_attrs[0].dims[2]; + width = input_attrs[0].dims[3]; + } + else + { + printf("model is NHWC input fmt\n"); + height = input_attrs[0].dims[1]; + width = input_attrs[0].dims[2]; + channel = input_attrs[0].dims[3]; + } + + printf("model input height=%d, width=%d, channel=%d\n", height, width, channel); + + rknn_input inputs[1]; + memset(inputs, 0, sizeof(inputs)); + inputs[0].index = 0; + inputs[0].type = RKNN_TENSOR_UINT8; + inputs[0].size = width * height * channel; + inputs[0].fmt = RKNN_TENSOR_NHWC; + inputs[0].pass_through = 0; + + // 读取图片 + printf("Read %s ...\n", input_path); + cv::Mat orig_img = cv::imread(input_path, 1); + if (!orig_img.data) + { + printf("cv::imread %s fail!\n", input_path); + return -1; + } + cv::Mat img; + cv::cvtColor(orig_img, img, cv::COLOR_BGR2RGB); + img_width = img.cols; + img_height = img.rows; + printf("img width = %d, img height = %d\n", img_width, img_height); + + // 指定目标大小和预处理方式,默认使用LetterBox的预处理 + BOX_RECT pads; + memset(&pads, 0, sizeof(BOX_RECT)); + cv::Size target_size(width, height); + cv::Mat resized_img(target_size.height, target_size.width, CV_8UC3); + // 计算缩放比例 + float scale_w = (float)target_size.width / img.cols; + float scale_h = (float)target_size.height / img.rows; + + if (img_width != width || img_height != height) + { + // 直接缩放采用RGA加速 + if (option == "resize") + { + printf("resize image by rga\n"); + ret = resize_rga(src, dst, img, resized_img, target_size); + if (ret != 0) + { + fprintf(stderr, "resize with rga error\n"); + return -1; + } + // 保存预处理图片 + cv::imwrite("resize_input.jpg", resized_img); + } + else if (option == "letterbox") + { + printf("resize image with letterbox\n"); + float min_scale = std::min(scale_w, scale_h); + scale_w = min_scale; + scale_h = min_scale; + letterbox(img, resized_img, pads, min_scale, target_size); + // 保存预处理图片 + cv::imwrite("letterbox_input.jpg", resized_img); + } + else + { + fprintf(stderr, "Invalid resize option. Use 'resize' or 'letterbox'.\n"); + return -1; + } + inputs[0].buf = resized_img.data; + } + else + { + inputs[0].buf = img.data; + } + + gettimeofday(&start_time, NULL); + rknn_inputs_set(ctx, io_num.n_input, inputs); + + rknn_output outputs[io_num.n_output]; + memset(outputs, 0, sizeof(outputs)); + for (int i = 0; i < io_num.n_output; i++) + { + outputs[i].index = i; + outputs[i].want_float = 0; + } + + // 执行推理 + ret = rknn_run(ctx, NULL); + ret = rknn_outputs_get(ctx, io_num.n_output, outputs, NULL); + gettimeofday(&stop_time, NULL); + printf("once run use %f ms\n", (__get_us(stop_time) - __get_us(start_time)) / 1000); + + // 后处理 + detect_result_group_t detect_result_group; + std::vector out_scales; + std::vector out_zps; + for (int i = 0; i < io_num.n_output; ++i) + { + out_scales.push_back(output_attrs[i].scale); + out_zps.push_back(output_attrs[i].zp); + } + post_process((int8_t *)outputs[0].buf, (int8_t *)outputs[1].buf, (int8_t *)outputs[2].buf, height, width, + box_conf_threshold, nms_threshold, pads, scale_w, scale_h, out_zps, out_scales, &detect_result_group); + + // 画框和概率 + char text[256]; + for (int i = 0; i < detect_result_group.count; i++) + { + detect_result_t *det_result = &(detect_result_group.results[i]); + sprintf(text, "%s %.1f%%", det_result->name, det_result->prop * 100); + printf("%s @ (%d %d %d %d) %f\n", det_result->name, det_result->box.left, det_result->box.top, + det_result->box.right, det_result->box.bottom, det_result->prop); + int x1 = det_result->box.left; + int y1 = det_result->box.top; + int x2 = det_result->box.right; + int y2 = det_result->box.bottom; + rectangle(orig_img, cv::Point(x1, y1), cv::Point(x2, y2), cv::Scalar(256, 0, 0, 256), 3); + putText(orig_img, text, cv::Point(x1, y1 + 12), cv::FONT_HERSHEY_SIMPLEX, 0.4, cv::Scalar(255, 255, 255)); + } + + printf("save detect result to %s\n", out_path.c_str()); + imwrite(out_path, orig_img); + ret = rknn_outputs_release(ctx, io_num.n_output, outputs); + + // 耗时统计 + int test_count = 10; + gettimeofday(&start_time, NULL); + for (int i = 0; i < test_count; ++i) + { + rknn_inputs_set(ctx, io_num.n_input, inputs); + ret = rknn_run(ctx, NULL); + ret = rknn_outputs_get(ctx, io_num.n_output, outputs, NULL); +#if PERF_WITH_POST + post_process((int8_t *)outputs[0].buf, (int8_t *)outputs[1].buf, (int8_t *)outputs[2].buf, height, width, + box_conf_threshold, nms_threshold, pads, scale_w, scale_h, out_zps, out_scales, &detect_result_group); +#endif + ret = rknn_outputs_release(ctx, io_num.n_output, outputs); + } + gettimeofday(&stop_time, NULL); + printf("loop count = %d , average run %f ms\n", test_count, + (__get_us(stop_time) - __get_us(start_time)) / 1000.0 / test_count); + + deinitPostProcess(); + + // release + ret = rknn_destroy(ctx); + + if (model_data) + { + free(model_data); + } + + return 0; +} diff --git a/rknpu2/examples/rknn_yolov5_demo/src/main_video.cc b/rknpu2/examples/rknn_yolov5_demo/src/main_video.cc new file mode 100755 index 0000000000000000000000000000000000000000..6f4f4e3003bb38d79a91d0344099e0bc8a06bda8 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_demo/src/main_video.cc @@ -0,0 +1,627 @@ +// Copyright (c) 2023 by Rockchip Electronics Co., Ltd. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +/*------------------------------------------- + Includes +-------------------------------------------*/ +#include +#include +#include +#include +#include +#include + +#include "im2d.h" +#include "rga.h" +#include "RgaUtils.h" + +#include "rknn_api.h" +#include "postprocess.h" + +#include "utils/mpp_decoder.h" +#include "utils/mpp_encoder.h" +#include "utils/drawing.h" +#if defined(BUILD_VIDEO_RTSP) +#include "mk_mediakit.h" +#endif + +#define OUT_VIDEO_PATH "out.h264" + +typedef struct +{ + rknn_context rknn_ctx; + rknn_input_output_num io_num; + rknn_tensor_attr *input_attrs; + rknn_tensor_attr *output_attrs; + int model_channel; + int model_width; + int model_height; + FILE *out_fp; + MppDecoder *decoder; + MppEncoder *encoder; +} rknn_app_context_t; + +typedef struct +{ + int width; + int height; + int width_stride; + int height_stride; + int format; + char *virt_addr; + int fd; +} image_frame_t; + +/*------------------------------------------- + Functions +-------------------------------------------*/ + +static void dump_tensor_attr(rknn_tensor_attr *attr) +{ + printf(" index=%d, name=%s, n_dims=%d, dims=[%d, %d, %d, %d], n_elems=%d, size=%d, fmt=%s, type=%s, qnt_type=%s, " + "zp=%d, scale=%f\n", + attr->index, attr->name, attr->n_dims, attr->dims[0], attr->dims[1], attr->dims[2], attr->dims[3], + attr->n_elems, attr->size, get_format_string(attr->fmt), get_type_string(attr->type), + get_qnt_type_string(attr->qnt_type), attr->zp, attr->scale); +} + +double __get_us(struct timeval t) { return (t.tv_sec * 1000000 + t.tv_usec); } + +static unsigned char *load_data(FILE *fp, size_t ofst, size_t sz) +{ + unsigned char *data; + int ret; + + data = NULL; + + if (NULL == fp) + { + return NULL; + } + + ret = fseek(fp, ofst, SEEK_SET); + if (ret != 0) + { + printf("blob seek failure.\n"); + return NULL; + } + + data = (unsigned char *)malloc(sz); + if (data == NULL) + { + printf("buffer malloc failure.\n"); + return NULL; + } + ret = fread(data, 1, sz, fp); + return data; +} + +static unsigned char *read_file_data(const char *filename, int *model_size) +{ + FILE *fp; + unsigned char *data; + + fp = fopen(filename, "rb"); + if (NULL == fp) + { + printf("Open file %s failed.\n", filename); + return NULL; + } + + fseek(fp, 0, SEEK_END); + int size = ftell(fp); + + data = load_data(fp, 0, size); + + fclose(fp); + + *model_size = size; + return data; +} + +static int write_data_to_file(const char *path, char *data, unsigned int size) +{ + FILE *fp; + + fp = fopen(path, "w"); + if (fp == NULL) + { + printf("open error: %s", path); + return -1; + } + + fwrite(data, 1, size, fp); + fflush(fp); + + fclose(fp); + return 0; +} + +static int init_model(const char *model_path, rknn_app_context_t *app_ctx) +{ + int ret; + rknn_context ctx; + + /* Create the neural network */ + printf("Loading mode...\n"); + int model_data_size = 0; + unsigned char *model_data = read_file_data(model_path, &model_data_size); + if (model_data == NULL) + { + return -1; + } + + ret = rknn_init(&ctx, model_data, model_data_size, 0, NULL); + if (ret < 0) + { + printf("rknn_init error ret=%d\n", ret); + return -1; + } + + if (model_data) + { + free(model_data); + } + + rknn_sdk_version version; + ret = rknn_query(ctx, RKNN_QUERY_SDK_VERSION, &version, sizeof(rknn_sdk_version)); + if (ret < 0) + { + printf("rknn_query RKNN_QUERY_SDK_VERSION error ret=%d\n", ret); + return -1; + } + printf("sdk version: %s driver version: %s\n", version.api_version, version.drv_version); + + ret = rknn_query(ctx, RKNN_QUERY_IN_OUT_NUM, &app_ctx->io_num, sizeof(rknn_input_output_num)); + if (ret < 0) + { + printf("rknn_query RKNN_QUERY_IN_OUT_NUM error ret=%d\n", ret); + return -1; + } + printf("model input num: %d, output num: %d\n", app_ctx->io_num.n_input, app_ctx->io_num.n_output); + + rknn_tensor_attr *input_attrs = (rknn_tensor_attr *)malloc(app_ctx->io_num.n_input * sizeof(rknn_tensor_attr)); + memset(input_attrs, 0, app_ctx->io_num.n_input * sizeof(rknn_tensor_attr)); + for (int i = 0; i < app_ctx->io_num.n_input; i++) + { + input_attrs[i].index = i; + ret = rknn_query(ctx, RKNN_QUERY_INPUT_ATTR, &(input_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret < 0) + { + printf("rknn_query RKNN_QUERY_INPUT_ATTR error ret=%d\n", ret); + return -1; + } + dump_tensor_attr(&(input_attrs[i])); + } + + rknn_tensor_attr *output_attrs = (rknn_tensor_attr *)malloc(app_ctx->io_num.n_output * sizeof(rknn_tensor_attr)); + memset(output_attrs, 0, app_ctx->io_num.n_output * sizeof(rknn_tensor_attr)); + for (int i = 0; i < app_ctx->io_num.n_output; i++) + { + output_attrs[i].index = i; + ret = rknn_query(ctx, RKNN_QUERY_OUTPUT_ATTR, &(output_attrs[i]), sizeof(rknn_tensor_attr)); + if (ret < 0) + { + printf("rknn_query RKNN_QUERY_OUTPUT_ATTR error ret=%d\n", ret); + return -1; + } + dump_tensor_attr(&(output_attrs[i])); + } + + app_ctx->input_attrs = input_attrs; + app_ctx->output_attrs = output_attrs; + app_ctx->rknn_ctx = ctx; + + if (input_attrs[0].fmt == RKNN_TENSOR_NCHW) + { + printf("model is NCHW input fmt\n"); + app_ctx->model_channel = input_attrs[0].dims[1]; + app_ctx->model_height = input_attrs[0].dims[2]; + app_ctx->model_width = input_attrs[0].dims[3]; + } + else + { + printf("model is NHWC input fmt\n"); + app_ctx->model_height = input_attrs[0].dims[1]; + app_ctx->model_width = input_attrs[0].dims[2]; + app_ctx->model_channel = input_attrs[0].dims[3]; + } + printf("model input height=%d, width=%d, channel=%d\n", app_ctx->model_height, app_ctx->model_width, app_ctx->model_channel); + + return 0; +} + +static int release_model(rknn_app_context_t *app_ctx) +{ + if (app_ctx->rknn_ctx != 0) + { + rknn_destroy(app_ctx->rknn_ctx); + } + free(app_ctx->input_attrs); + free(app_ctx->output_attrs); + deinitPostProcess(); + return 0; +} + +static int inference_model(rknn_app_context_t *app_ctx, image_frame_t *img, detect_result_group_t *detect_result) +{ + int ret; + rknn_context ctx = app_ctx->rknn_ctx; + int model_width = app_ctx->model_width; + int model_height = app_ctx->model_height; + int model_channel = app_ctx->model_channel; + + struct timeval start_time, stop_time; + const float nms_threshold = NMS_THRESH; + const float box_conf_threshold = BOX_THRESH; + // You may not need resize when src resulotion equals to dst resulotion + void *resize_buf = nullptr; + // init rga context + rga_buffer_t src; + rga_buffer_t dst; + im_rect src_rect; + im_rect dst_rect; + memset(&src_rect, 0, sizeof(src_rect)); + memset(&dst_rect, 0, sizeof(dst_rect)); + memset(&src, 0, sizeof(src)); + memset(&dst, 0, sizeof(dst)); + + printf("input image %dx%d stride %dx%d format=%d\n", img->width, img->height, img->width_stride, img->height_stride, img->format); + + float scale_w = (float)model_width / img->width; + float scale_h = (float)model_height / img->height; + + rknn_input inputs[1]; + memset(inputs, 0, sizeof(inputs)); + inputs[0].index = 0; + inputs[0].type = RKNN_TENSOR_UINT8; + inputs[0].size = model_width * model_height * model_channel; + inputs[0].fmt = RKNN_TENSOR_NHWC; + inputs[0].pass_through = 0; + + printf("resize with RGA!\n"); + resize_buf = malloc(model_width * model_height * model_channel); + memset(resize_buf, 0, model_width * model_height * model_channel); + + src = wrapbuffer_virtualaddr((void *)img->virt_addr, img->width, img->height, img->format, img->width_stride, img->height_stride); + dst = wrapbuffer_virtualaddr((void *)resize_buf, model_width, model_height, RK_FORMAT_RGB_888); + ret = imcheck(src, dst, src_rect, dst_rect); + if (IM_STATUS_NOERROR != ret) + { + printf("%d, check error! %s", __LINE__, imStrError((IM_STATUS)ret)); + return -1; + } + IM_STATUS STATUS = imresize(src, dst); + + inputs[0].buf = resize_buf; + + gettimeofday(&start_time, NULL); + rknn_inputs_set(ctx, app_ctx->io_num.n_input, inputs); + + rknn_output outputs[app_ctx->io_num.n_output]; + memset(outputs, 0, sizeof(outputs)); + for (int i = 0; i < app_ctx->io_num.n_output; i++) + { + outputs[i].index = i; + outputs[i].want_float = 0; + } + + ret = rknn_run(ctx, NULL); + ret = rknn_outputs_get(ctx, app_ctx->io_num.n_output, outputs, NULL); + gettimeofday(&stop_time, NULL); + printf("once run use %f ms\n", (__get_us(stop_time) - __get_us(start_time)) / 1000); + + printf("post process config: box_conf_threshold = %.2f, nms_threshold = %.2f\n", box_conf_threshold, nms_threshold); + + std::vector out_scales; + std::vector out_zps; + for (int i = 0; i < app_ctx->io_num.n_output; ++i) + { + out_scales.push_back(app_ctx->output_attrs[i].scale); + out_zps.push_back(app_ctx->output_attrs[i].zp); + } + BOX_RECT pads; + memset(&pads, 0, sizeof(BOX_RECT)); + + post_process((int8_t *)outputs[0].buf, (int8_t *)outputs[1].buf, (int8_t *)outputs[2].buf, model_height, model_width, + box_conf_threshold, nms_threshold, pads, scale_w, scale_h, out_zps, out_scales, detect_result); + ret = rknn_outputs_release(ctx, app_ctx->io_num.n_output, outputs); + + if (resize_buf) + { + free(resize_buf); + } + return 0; +} + +void mpp_decoder_frame_callback(void *userdata, int width_stride, int height_stride, int width, int height, int format, int fd, void *data) +{ + + rknn_app_context_t *ctx = (rknn_app_context_t *)userdata; + + int ret = 0; + static int frame_index = 0; + frame_index++; + + void *mpp_frame = NULL; + int mpp_frame_fd = 0; + void *mpp_frame_addr = NULL; + int enc_data_size; + + rga_buffer_t origin; + rga_buffer_t src; + + if (ctx->encoder == NULL) + { + MppEncoder *mpp_encoder = new MppEncoder(); + MppEncoderParams enc_params; + memset(&enc_params, 0, sizeof(MppEncoderParams)); + enc_params.width = width; + enc_params.height = height; + enc_params.hor_stride = width_stride; + enc_params.ver_stride = height_stride; + enc_params.fmt = MPP_FMT_YUV420SP; + // enc_params.type = MPP_VIDEO_CodingHEVC; + // Note: rk3562只能支持h264格式的视频流 + enc_params.type = MPP_VIDEO_CodingAVC; + mpp_encoder->Init(enc_params, NULL); + + ctx->encoder = mpp_encoder; + } + + int enc_buf_size = ctx->encoder->GetFrameSize(); + char *enc_data = (char *)malloc(enc_buf_size); + + image_frame_t img; + img.width = width; + img.height = height; + img.width_stride = width_stride; + img.height_stride = height_stride; + img.fd = fd; + img.virt_addr = (char *)data; + img.format = RK_FORMAT_YCbCr_420_SP; + detect_result_group_t detect_result; + memset(&detect_result, 0, sizeof(detect_result_group_t)); + + ret = inference_model(ctx, &img, &detect_result); + if (ret != 0) + { + printf("inference model fail\n"); + goto RET; + } + + mpp_frame = ctx->encoder->GetInputFrameBuffer(); + mpp_frame_fd = ctx->encoder->GetInputFrameBufferFd(mpp_frame); + mpp_frame_addr = ctx->encoder->GetInputFrameBufferAddr(mpp_frame); + + // Copy To another buffer avoid to modify mpp decoder buffer + origin = wrapbuffer_fd(fd, width, height, RK_FORMAT_YCbCr_420_SP, width_stride, height_stride); + src = wrapbuffer_fd(mpp_frame_fd, width, height, RK_FORMAT_YCbCr_420_SP, width_stride, height_stride); + imcopy(origin, src); + + // Draw objects + for (int i = 0; i < detect_result.count; i++) + { + detect_result_t *det_result = &(detect_result.results[i]); + printf("%s @ (%d %d %d %d) %f\n", det_result->name, det_result->box.left, det_result->box.top, + det_result->box.right, det_result->box.bottom, det_result->prop); + int x1 = det_result->box.left; + int y1 = det_result->box.top; + int x2 = det_result->box.right; + int y2 = det_result->box.bottom; + draw_rectangle_yuv420sp((unsigned char *)mpp_frame_addr, width_stride, height_stride, x1, y1, x2 - x1 + 1, y2 - y1 + 1, 0x00FF0000, 4); + } + + // Encode to file + // Write header on first frame + if (frame_index == 1) + { + enc_data_size = ctx->encoder->GetHeader(enc_data, enc_buf_size); + fwrite(enc_data, 1, enc_data_size, ctx->out_fp); + } + memset(enc_data, 0, enc_buf_size); + enc_data_size = ctx->encoder->Encode(mpp_frame, enc_data, enc_buf_size); + fwrite(enc_data, 1, enc_data_size, ctx->out_fp); + +RET: + if (enc_data != nullptr) + { + free(enc_data); + } +} + +int process_video_file(rknn_app_context_t *ctx, const char *path) +{ + int video_size; + char *video_data = (char *)read_file_data(path, &video_size); + char *video_data_end = video_data + video_size; + printf("read video size=%d\n", video_size); + + const int SIZE = 8192; + char *video_data_ptr = video_data; + + do + { + int pkt_eos = 0; + int size = SIZE; + if (video_data_ptr + size >= video_data_end) + { + pkt_eos = 1; + size = video_data_end - video_data_ptr; + } + + ctx->decoder->Decode((uint8_t *)video_data_ptr, size, pkt_eos); + + video_data_ptr += size; + + if (video_data_ptr >= video_data_end) + { + printf("reset decoder\n"); + break; + } + + // LOGD("video_data_ptr=%p video_data_end=%p", video_data_ptr, video_data_end); + // usleep(10*1000); + } while (1); + + return 0; +} + +#if defined(BUILD_VIDEO_RTSP) +void API_CALL on_track_frame_out(void *user_data, mk_frame frame) +{ + rknn_app_context_t *ctx = (rknn_app_context_t *)user_data; + printf("on_track_frame_out ctx=%p\n", ctx); + const char *data = mk_frame_get_data(frame); + size_t size = mk_frame_get_data_size(frame); + printf("decoder=%p\n", ctx->decoder); + ctx->decoder->Decode((uint8_t *)data, size, 0); +} + +void API_CALL on_mk_play_event_func(void *user_data, int err_code, const char *err_msg, mk_track tracks[], + int track_count) +{ + rknn_app_context_t *ctx = (rknn_app_context_t *)user_data; + if (err_code == 0) + { + // success + printf("play success!"); + int i; + for (i = 0; i < track_count; ++i) + { + if (mk_track_is_video(tracks[i])) + { + log_info("got video track: %s", mk_track_codec_name(tracks[i])); + // 监听track数据回调 + mk_track_add_delegate(tracks[i], on_track_frame_out, user_data); + } + } + } + else + { + printf("play failed: %d %s", err_code, err_msg); + } +} + +void API_CALL on_mk_shutdown_func(void *user_data, int err_code, const char *err_msg, mk_track tracks[], int track_count) +{ + printf("play interrupted: %d %s", err_code, err_msg); +} + +int process_video_rtsp(rknn_app_context_t *ctx, const char *url) +{ + mk_config config; + memset(&config, 0, sizeof(mk_config)); + config.log_mask = LOG_CONSOLE; + mk_env_init(&config); + mk_player player = mk_player_create(); + mk_player_set_on_result(player, on_mk_play_event_func, ctx); + mk_player_set_on_shutdown(player, on_mk_shutdown_func, ctx); + mk_player_play(player, url); + + printf("enter any key to exit\n"); + getchar(); + + if (player) + { + mk_player_release(player); + } + return 0; +} +#endif + +/*------------------------------------------- + Main Functions +-------------------------------------------*/ +int main(int argc, char **argv) +{ + int status = 0; + int ret; + + if (argc != 4) + { + printf("Usage: %s \n", argv[0]); + return -1; + } + + char *model_name = (char *)argv[1]; + char *video_name = argv[2]; + int video_type = atoi(argv[3]); + + rknn_app_context_t app_ctx; + memset(&app_ctx, 0, sizeof(rknn_app_context_t)); + + ret = init_model(model_name, &app_ctx); + if (ret != 0) + { + printf("init model fail\n"); + return -1; + } + + if (app_ctx.decoder == NULL) + { + MppDecoder *decoder = new MppDecoder(); + decoder->Init(video_type, 30, &app_ctx); + decoder->SetCallback(mpp_decoder_frame_callback); + app_ctx.decoder = decoder; + } + + if (app_ctx.out_fp == NULL) + { + FILE *fp = fopen(OUT_VIDEO_PATH, "w"); + if (fp == NULL) + { + printf("open %s error\n", OUT_VIDEO_PATH); + return -1; + } + app_ctx.out_fp = fp; + } + + printf("app_ctx=%p decoder=%p\n", &app_ctx, app_ctx.decoder); + + if (strncmp(video_name, "rtsp", 4) == 0) + { +#if defined(BUILD_VIDEO_RTSP) + process_video_rtsp(&app_ctx, video_name); +#else + printf("rtsp no support\n"); +#endif + } + else + { + process_video_file(&app_ctx, video_name); + } + + printf("waiting finish\n"); + usleep(3 * 1000 * 1000); + + // release + fflush(app_ctx.out_fp); + fclose(app_ctx.out_fp); + + if (app_ctx.decoder != nullptr) + { + delete (app_ctx.decoder); + app_ctx.decoder = nullptr; + } + if (app_ctx.encoder != nullptr) + { + delete (app_ctx.encoder); + app_ctx.encoder = nullptr; + } + + release_model(&app_ctx); + + return 0; +} diff --git a/rknpu2/examples/rknn_yolov5_demo/src/postprocess.cc b/rknpu2/examples/rknn_yolov5_demo/src/postprocess.cc new file mode 100755 index 0000000000000000000000000000000000000000..40047031f3de78c66020639227833f4580f68170 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_demo/src/postprocess.cc @@ -0,0 +1,374 @@ +// Copyright (c) 2021 by Rockchip Electronics Co., Ltd. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "postprocess.h" + +#include +#include +#include +#include +#include +#include + +#include +#include +#define LABEL_NALE_TXT_PATH "./model/coco_80_labels_list.txt" + +static char *labels[OBJ_CLASS_NUM]; + +const int anchor0[6] = {10, 13, 16, 30, 33, 23}; +const int anchor1[6] = {30, 61, 62, 45, 59, 119}; +const int anchor2[6] = {116, 90, 156, 198, 373, 326}; + +inline static int clamp(float val, int min, int max) { return val > min ? (val < max ? val : max) : min; } + +char *readLine(FILE *fp, char *buffer, int *len) +{ + int ch; + int i = 0; + size_t buff_len = 0; + + buffer = (char *)malloc(buff_len + 1); + if (!buffer) + return NULL; // Out of memory + + while ((ch = fgetc(fp)) != '\n' && ch != EOF) + { + buff_len++; + void *tmp = realloc(buffer, buff_len + 1); + if (tmp == NULL) + { + free(buffer); + return NULL; // Out of memory + } + buffer = (char *)tmp; + + buffer[i] = (char)ch; + i++; + } + buffer[i] = '\0'; + + *len = buff_len; + + // Detect end + if (ch == EOF && (i == 0 || ferror(fp))) + { + free(buffer); + return NULL; + } + return buffer; +} + +int readLines(const char *fileName, char *lines[], int max_line) +{ + FILE *file = fopen(fileName, "r"); + char *s; + int i = 0; + int n = 0; + + if (file == NULL) + { + printf("Open %s fail!\n", fileName); + return -1; + } + + while ((s = readLine(file, s, &n)) != NULL) + { + lines[i++] = s; + if (i >= max_line) + break; + } + fclose(file); + return i; +} + +int loadLabelName(const char *locationFilename, char *label[]) +{ + printf("loadLabelName %s\n", locationFilename); + readLines(locationFilename, label, OBJ_CLASS_NUM); + return 0; +} + +static float CalculateOverlap(float xmin0, float ymin0, float xmax0, float ymax0, float xmin1, float ymin1, float xmax1, + float ymax1) +{ + float w = fmax(0.f, fmin(xmax0, xmax1) - fmax(xmin0, xmin1) + 1.0); + float h = fmax(0.f, fmin(ymax0, ymax1) - fmax(ymin0, ymin1) + 1.0); + float i = w * h; + float u = (xmax0 - xmin0 + 1.0) * (ymax0 - ymin0 + 1.0) + (xmax1 - xmin1 + 1.0) * (ymax1 - ymin1 + 1.0) - i; + return u <= 0.f ? 0.f : (i / u); +} + +static int nms(int validCount, std::vector &outputLocations, std::vector classIds, std::vector &order, + int filterId, float threshold) +{ + for (int i = 0; i < validCount; ++i) + { + int n = order[i]; + if (n == -1 || classIds[n] != filterId) + { + continue; + } + for (int j = i + 1; j < validCount; ++j) + { + int m = order[j]; + if (m == -1 || classIds[m] != filterId) + { + continue; + } + float xmin0 = outputLocations[n * 4 + 0]; + float ymin0 = outputLocations[n * 4 + 1]; + float xmax0 = outputLocations[n * 4 + 0] + outputLocations[n * 4 + 2]; + float ymax0 = outputLocations[n * 4 + 1] + outputLocations[n * 4 + 3]; + + float xmin1 = outputLocations[m * 4 + 0]; + float ymin1 = outputLocations[m * 4 + 1]; + float xmax1 = outputLocations[m * 4 + 0] + outputLocations[m * 4 + 2]; + float ymax1 = outputLocations[m * 4 + 1] + outputLocations[m * 4 + 3]; + + float iou = CalculateOverlap(xmin0, ymin0, xmax0, ymax0, xmin1, ymin1, xmax1, ymax1); + + if (iou > threshold) + { + order[j] = -1; + } + } + } + return 0; +} + +static int quick_sort_indice_inverse(std::vector &input, int left, int right, std::vector &indices) +{ + float key; + int key_index; + int low = left; + int high = right; + if (left < right) + { + key_index = indices[left]; + key = input[left]; + while (low < high) + { + while (low < high && input[high] <= key) + { + high--; + } + input[low] = input[high]; + indices[low] = indices[high]; + while (low < high && input[low] >= key) + { + low++; + } + input[high] = input[low]; + indices[high] = indices[low]; + } + input[low] = key; + indices[low] = key_index; + quick_sort_indice_inverse(input, left, low - 1, indices); + quick_sort_indice_inverse(input, low + 1, right, indices); + } + return low; +} + +static float sigmoid(float x) { return 1.0 / (1.0 + expf(-x)); } + +static float unsigmoid(float y) { return -1.0 * logf((1.0 / y) - 1.0); } + +inline static int32_t __clip(float val, float min, float max) +{ + float f = val <= min ? min : (val >= max ? max : val); + return f; +} + +static int8_t qnt_f32_to_affine(float f32, int32_t zp, float scale) +{ + float dst_val = (f32 / scale) + zp; + int8_t res = (int8_t)__clip(dst_val, -128, 127); + return res; +} + +static float deqnt_affine_to_f32(int8_t qnt, int32_t zp, float scale) { return ((float)qnt - (float)zp) * scale; } + +static int process(int8_t *input, int *anchor, int grid_h, int grid_w, int height, int width, int stride, + std::vector &boxes, std::vector &objProbs, std::vector &classId, float threshold, + int32_t zp, float scale) +{ + int validCount = 0; + int grid_len = grid_h * grid_w; + int8_t thres_i8 = qnt_f32_to_affine(threshold, zp, scale); + for (int a = 0; a < 3; a++) + { + for (int i = 0; i < grid_h; i++) + { + for (int j = 0; j < grid_w; j++) + { + int8_t box_confidence = input[(PROP_BOX_SIZE * a + 4) * grid_len + i * grid_w + j]; + if (box_confidence >= thres_i8) + { + int offset = (PROP_BOX_SIZE * a) * grid_len + i * grid_w + j; + int8_t *in_ptr = input + offset; + float box_x = (deqnt_affine_to_f32(*in_ptr, zp, scale)) * 2.0 - 0.5; + float box_y = (deqnt_affine_to_f32(in_ptr[grid_len], zp, scale)) * 2.0 - 0.5; + float box_w = (deqnt_affine_to_f32(in_ptr[2 * grid_len], zp, scale)) * 2.0; + float box_h = (deqnt_affine_to_f32(in_ptr[3 * grid_len], zp, scale)) * 2.0; + box_x = (box_x + j) * (float)stride; + box_y = (box_y + i) * (float)stride; + box_w = box_w * box_w * (float)anchor[a * 2]; + box_h = box_h * box_h * (float)anchor[a * 2 + 1]; + box_x -= (box_w / 2.0); + box_y -= (box_h / 2.0); + + int8_t maxClassProbs = in_ptr[5 * grid_len]; + int maxClassId = 0; + for (int k = 1; k < OBJ_CLASS_NUM; ++k) + { + int8_t prob = in_ptr[(5 + k) * grid_len]; + if (prob > maxClassProbs) + { + maxClassId = k; + maxClassProbs = prob; + } + } + if (maxClassProbs > thres_i8) + { + objProbs.push_back((deqnt_affine_to_f32(maxClassProbs, zp, scale)) * (deqnt_affine_to_f32(box_confidence, zp, scale))); + classId.push_back(maxClassId); + validCount++; + boxes.push_back(box_x); + boxes.push_back(box_y); + boxes.push_back(box_w); + boxes.push_back(box_h); + } + } + } + } + } + return validCount; +} + +int post_process(int8_t *input0, int8_t *input1, int8_t *input2, int model_in_h, int model_in_w, float conf_threshold, + float nms_threshold, BOX_RECT pads, float scale_w, float scale_h, std::vector &qnt_zps, + std::vector &qnt_scales, detect_result_group_t *group) +{ + static int init = -1; + if (init == -1) + { + int ret = 0; + ret = loadLabelName(LABEL_NALE_TXT_PATH, labels); + if (ret < 0) + { + return -1; + } + + init = 0; + } + memset(group, 0, sizeof(detect_result_group_t)); + + std::vector filterBoxes; + std::vector objProbs; + std::vector classId; + + // stride 8 + int stride0 = 8; + int grid_h0 = model_in_h / stride0; + int grid_w0 = model_in_w / stride0; + int validCount0 = 0; + validCount0 = process(input0, (int *)anchor0, grid_h0, grid_w0, model_in_h, model_in_w, stride0, filterBoxes, objProbs, + classId, conf_threshold, qnt_zps[0], qnt_scales[0]); + + // stride 16 + int stride1 = 16; + int grid_h1 = model_in_h / stride1; + int grid_w1 = model_in_w / stride1; + int validCount1 = 0; + validCount1 = process(input1, (int *)anchor1, grid_h1, grid_w1, model_in_h, model_in_w, stride1, filterBoxes, objProbs, + classId, conf_threshold, qnt_zps[1], qnt_scales[1]); + + // stride 32 + int stride2 = 32; + int grid_h2 = model_in_h / stride2; + int grid_w2 = model_in_w / stride2; + int validCount2 = 0; + validCount2 = process(input2, (int *)anchor2, grid_h2, grid_w2, model_in_h, model_in_w, stride2, filterBoxes, objProbs, + classId, conf_threshold, qnt_zps[2], qnt_scales[2]); + + int validCount = validCount0 + validCount1 + validCount2; + // no object detect + if (validCount <= 0) + { + return 0; + } + + std::vector indexArray; + for (int i = 0; i < validCount; ++i) + { + indexArray.push_back(i); + } + + quick_sort_indice_inverse(objProbs, 0, validCount - 1, indexArray); + + std::set class_set(std::begin(classId), std::end(classId)); + + for (auto c : class_set) + { + nms(validCount, filterBoxes, classId, indexArray, c, nms_threshold); + } + + int last_count = 0; + group->count = 0; + /* box valid detect target */ + for (int i = 0; i < validCount; ++i) + { + if (indexArray[i] == -1 || last_count >= OBJ_NUMB_MAX_SIZE) + { + continue; + } + int n = indexArray[i]; + + float x1 = filterBoxes[n * 4 + 0] - pads.left; + float y1 = filterBoxes[n * 4 + 1] - pads.top; + float x2 = x1 + filterBoxes[n * 4 + 2]; + float y2 = y1 + filterBoxes[n * 4 + 3]; + int id = classId[n]; + float obj_conf = objProbs[i]; + + group->results[last_count].box.left = (int)(clamp(x1, 0, model_in_w) / scale_w); + group->results[last_count].box.top = (int)(clamp(y1, 0, model_in_h) / scale_h); + group->results[last_count].box.right = (int)(clamp(x2, 0, model_in_w) / scale_w); + group->results[last_count].box.bottom = (int)(clamp(y2, 0, model_in_h) / scale_h); + group->results[last_count].prop = obj_conf; + char *label = labels[id]; + strncpy(group->results[last_count].name, label, OBJ_NAME_MAX_SIZE); + + // printf("result %2d: (%4d, %4d, %4d, %4d), %s\n", i, group->results[last_count].box.left, + // group->results[last_count].box.top, + // group->results[last_count].box.right, group->results[last_count].box.bottom, label); + last_count++; + } + group->count = last_count; + + return 0; +} + +void deinitPostProcess() +{ + for (int i = 0; i < OBJ_CLASS_NUM; i++) + { + if (labels[i] != nullptr) + { + free(labels[i]); + labels[i] = nullptr; + } + } +} diff --git a/rknpu2/examples/rknn_yolov5_demo/src/preprocess.cc b/rknpu2/examples/rknn_yolov5_demo/src/preprocess.cc new file mode 100644 index 0000000000000000000000000000000000000000..4fc1d7b98d7c5f2dd66b339ac0e117c122ccf8c5 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_demo/src/preprocess.cc @@ -0,0 +1,61 @@ +// Copyright (c) 2023 by Rockchip Electronics Co., Ltd. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "preprocess.h" + +void letterbox(const cv::Mat &image, cv::Mat &padded_image, BOX_RECT &pads, const float scale, const cv::Size &target_size, const cv::Scalar &pad_color) +{ + // 调整图像大小 + cv::Mat resized_image; + cv::resize(image, resized_image, cv::Size(), scale, scale); + + // 计算填充大小 + int pad_width = target_size.width - resized_image.cols; + int pad_height = target_size.height - resized_image.rows; + + pads.left = pad_width / 2; + pads.right = pad_width - pads.left; + pads.top = pad_height / 2; + pads.bottom = pad_height - pads.top; + + // 在图像周围添加填充 + cv::copyMakeBorder(resized_image, padded_image, pads.top, pads.bottom, pads.left, pads.right, cv::BORDER_CONSTANT, pad_color); +} + +int resize_rga(rga_buffer_t &src, rga_buffer_t &dst, const cv::Mat &image, cv::Mat &resized_image, const cv::Size &target_size) +{ + im_rect src_rect; + im_rect dst_rect; + memset(&src_rect, 0, sizeof(src_rect)); + memset(&dst_rect, 0, sizeof(dst_rect)); + size_t img_width = image.cols; + size_t img_height = image.rows; + if (image.type() != CV_8UC3) + { + printf("source image type is %d!\n", image.type()); + return -1; + } + size_t target_width = target_size.width; + size_t target_height = target_size.height; + src = wrapbuffer_virtualaddr((void *)image.data, img_width, img_height, RK_FORMAT_RGB_888); + dst = wrapbuffer_virtualaddr((void *)resized_image.data, target_width, target_height, RK_FORMAT_RGB_888); + int ret = imcheck(src, dst, src_rect, dst_rect); + if (IM_STATUS_NOERROR != ret) + { + fprintf(stderr, "rga check error! %s", imStrError((IM_STATUS)ret)); + return -1; + } + IM_STATUS STATUS = imresize(src, dst); + return 0; +} diff --git a/rknpu2/examples/rknn_yolov5_demo/utils/drawing.cpp b/rknpu2/examples/rknn_yolov5_demo/utils/drawing.cpp new file mode 100644 index 0000000000000000000000000000000000000000..e761793adc355ace2302be303d95dca49459f67f --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_demo/utils/drawing.cpp @@ -0,0 +1,601 @@ +#include "drawing.h" +#include "string.h" + +#define max(a, b) (((a) > (b)) ? (a) : (b)) +#define min(a, b) (((a) < (b)) ? (a) : (b)) + +void draw_rectangle_c1(unsigned char* pixels, int w, int h, int stride, int rx, int ry, int rw, int rh, unsigned int color, int thickness) +{ + const unsigned char* pen_color = (const unsigned char*)&color; + + if (thickness == -1) + { + // filled + for (int y = ry; y < ry + rh; y++) + { + if (y < 0) + continue; + + if (y >= h) + break; + + unsigned char* p = pixels + stride * y; + + for (int x = rx; x < rx + rw; x++) + { + if (x < 0) + continue; + + if (x >= w) + break; + + p[x] = pen_color[0]; + } + } + + return; + } + + const int t0 = thickness / 2; + const int t1 = thickness - t0; + + // draw top + { + for (int y = ry - t0; y < ry + t1; y++) + { + if (y < 0) + continue; + + if (y >= h) + break; + + unsigned char* p = pixels + stride * y; + + for (int x = rx - t0; x < rx + rw + t1; x++) + { + if (x < 0) + continue; + + if (x >= w) + break; + + p[x] = pen_color[0]; + } + } + } + + // draw bottom + { + for (int y = ry + rh - t0; y < ry + rh + t1; y++) + { + if (y < 0) + continue; + + if (y >= h) + break; + + unsigned char* p = pixels + stride * y; + + for (int x = rx - t0; x < rx + rw + t1; x++) + { + if (x < 0) + continue; + + if (x >= w) + break; + + p[x] = pen_color[0]; + } + } + } + + // draw left + for (int x = rx - t0; x < rx + t1; x++) + { + if (x < 0) + continue; + + if (x >= w) + break; + + for (int y = ry + t1; y < ry + rh - t0; y++) + { + if (y < 0) + continue; + + if (y >= h) + break; + + unsigned char* p = pixels + stride * y; + + p[x] = pen_color[0]; + } + } + + // draw right + for (int x = rx + rw - t0; x < rx + rw + t1; x++) + { + if (x < 0) + continue; + + if (x >= w) + break; + + for (int y = ry + t1; y < ry + rh - t0; y++) + { + if (y < 0) + continue; + + if (y >= h) + break; + + unsigned char* p = pixels + stride * y; + + p[x] = pen_color[0]; + } + } +} + +void draw_rectangle_c2(unsigned char* pixels, int w, int h, int stride, int rx, int ry, int rw, int rh, unsigned int color, int thickness) +{ + const unsigned char* pen_color = (const unsigned char*)&color; + + if (thickness == -1) + { + // filled + for (int y = ry; y < ry + rh; y++) + { + if (y < 0) + continue; + + if (y >= h) + break; + + unsigned char* p = pixels + stride * y; + + for (int x = rx; x < rx + rw; x++) + { + if (x < 0) + continue; + + if (x >= w) + break; + + p[x * 2 + 0] = pen_color[0]; + p[x * 2 + 1] = pen_color[1]; + } + } + + return; + } + + const int t0 = thickness / 2; + const int t1 = thickness - t0; + + // draw top + { + for (int y = ry - t0; y < ry + t1; y++) + { + if (y < 0) + continue; + + if (y >= h) + break; + + unsigned char* p = pixels + stride * y; + + for (int x = rx - t0; x < rx + rw + t1; x++) + { + if (x < 0) + continue; + + if (x >= w) + break; + + p[x * 2 + 0] = pen_color[0]; + p[x * 2 + 1] = pen_color[1]; + } + } + } + + // draw bottom + { + for (int y = ry + rh - t0; y < ry + rh + t1; y++) + { + if (y < 0) + continue; + + if (y >= h) + break; + + unsigned char* p = pixels + stride * y; + + for (int x = rx - t0; x < rx + rw + t1; x++) + { + if (x < 0) + continue; + + if (x >= w) + break; + + p[x * 2 + 0] = pen_color[0]; + p[x * 2 + 1] = pen_color[1]; + } + } + } + + // draw left + for (int x = rx - t0; x < rx + t1; x++) + { + if (x < 0) + continue; + + if (x >= w) + break; + + for (int y = ry + t1; y < ry + rh - t0; y++) + { + if (y < 0) + continue; + + if (y >= h) + break; + + unsigned char* p = pixels + stride * y; + + p[x * 2 + 0] = pen_color[0]; + p[x * 2 + 1] = pen_color[1]; + } + } + + // draw right + for (int x = rx + rw - t0; x < rx + rw + t1; x++) + { + if (x < 0) + continue; + + if (x >= w) + break; + + for (int y = ry + t1; y < ry + rh - t0; y++) + { + if (y < 0) + continue; + + if (y >= h) + break; + + unsigned char* p = pixels + stride * y; + + p[x * 2 + 0] = pen_color[0]; + p[x * 2 + 1] = pen_color[1]; + } + } +} + +void draw_rectangle_c3(unsigned char* pixels, int w, int h, int stride, int rx, int ry, int rw, int rh, unsigned int color, int thickness) +{ + const unsigned char* pen_color = (const unsigned char*)&color; + + if (thickness == -1) + { + // filled + for (int y = ry; y < ry + rh; y++) + { + if (y < 0) + continue; + + if (y >= h) + break; + + unsigned char* p = pixels + stride * y; + + for (int x = rx; x < rx + rw; x++) + { + if (x < 0) + continue; + + if (x >= w) + break; + + p[x * 3 + 0] = pen_color[0]; + p[x * 3 + 1] = pen_color[1]; + p[x * 3 + 2] = pen_color[2]; + } + } + + return; + } + + const int t0 = thickness / 2; + const int t1 = thickness - t0; + + // draw top + { + for (int y = ry - t0; y < ry + t1; y++) + { + if (y < 0) + continue; + + if (y >= h) + break; + + unsigned char* p = pixels + stride * y; + + for (int x = rx - t0; x < rx + rw + t1; x++) + { + if (x < 0) + continue; + + if (x >= w) + break; + + p[x * 3 + 0] = pen_color[0]; + p[x * 3 + 1] = pen_color[1]; + p[x * 3 + 2] = pen_color[2]; + } + } + } + + // draw bottom + { + for (int y = ry + rh - t0; y < ry + rh + t1; y++) + { + if (y < 0) + continue; + + if (y >= h) + break; + + unsigned char* p = pixels + stride * y; + + for (int x = rx - t0; x < rx + rw + t1; x++) + { + if (x < 0) + continue; + + if (x >= w) + break; + + p[x * 3 + 0] = pen_color[0]; + p[x * 3 + 1] = pen_color[1]; + p[x * 3 + 2] = pen_color[2]; + } + } + } + + // draw left + for (int x = rx - t0; x < rx + t1; x++) + { + if (x < 0) + continue; + + if (x >= w) + break; + + for (int y = ry + t1; y < ry + rh - t0; y++) + { + if (y < 0) + continue; + + if (y >= h) + break; + + unsigned char* p = pixels + stride * y; + + p[x * 3 + 0] = pen_color[0]; + p[x * 3 + 1] = pen_color[1]; + p[x * 3 + 2] = pen_color[2]; + } + } + + // draw right + for (int x = rx + rw - t0; x < rx + rw + t1; x++) + { + if (x < 0) + continue; + + if (x >= w) + break; + + for (int y = ry + t1; y < ry + rh - t0; y++) + { + if (y < 0) + continue; + + if (y >= h) + break; + + unsigned char* p = pixels + stride * y; + + p[x * 3 + 0] = pen_color[0]; + p[x * 3 + 1] = pen_color[1]; + p[x * 3 + 2] = pen_color[2]; + } + } +} + +void draw_rectangle_c4(unsigned char* pixels, int w, int h, int stride, int rx, int ry, int rw, int rh, unsigned int color, int thickness) +{ + const unsigned char* pen_color = (const unsigned char*)&color; + + if (thickness == -1) + { + // filled + for (int y = ry; y < ry + rh; y++) + { + if (y < 0) + continue; + + if (y >= h) + break; + + unsigned char* p = pixels + stride * y; + + for (int x = rx; x < rx + rw; x++) + { + if (x < 0) + continue; + + if (x >= w) + break; + + p[x * 4 + 0] = pen_color[0]; + p[x * 4 + 1] = pen_color[1]; + p[x * 4 + 2] = pen_color[2]; + p[x * 4 + 3] = pen_color[3]; + } + } + + return; + } + + const int t0 = thickness / 2; + const int t1 = thickness - t0; + + // draw top + { + for (int y = ry - t0; y < ry + t1; y++) + { + if (y < 0) + continue; + + if (y >= h) + break; + + unsigned char* p = pixels + stride * y; + + for (int x = rx - t0; x < rx + rw + t1; x++) + { + if (x < 0) + continue; + + if (x >= w) + break; + + p[x * 4 + 0] = pen_color[0]; + p[x * 4 + 1] = pen_color[1]; + p[x * 4 + 2] = pen_color[2]; + p[x * 4 + 3] = pen_color[3]; + } + } + } + + // draw bottom + { + for (int y = ry + rh - t0; y < ry + rh + t1; y++) + { + if (y < 0) + continue; + + if (y >= h) + break; + + unsigned char* p = pixels + stride * y; + + for (int x = rx - t0; x < rx + rw + t1; x++) + { + if (x < 0) + continue; + + if (x >= w) + break; + + p[x * 4 + 0] = pen_color[0]; + p[x * 4 + 1] = pen_color[1]; + p[x * 4 + 2] = pen_color[2]; + p[x * 4 + 3] = pen_color[3]; + } + } + } + + // draw left + for (int x = rx - t0; x < rx + t1; x++) + { + if (x < 0) + continue; + + if (x >= w) + break; + + for (int y = ry + t1; y < ry + rh - t0; y++) + { + if (y < 0) + continue; + + if (y >= h) + break; + + unsigned char* p = pixels + stride * y; + + p[x * 4 + 0] = pen_color[0]; + p[x * 4 + 1] = pen_color[1]; + p[x * 4 + 2] = pen_color[2]; + p[x * 4 + 3] = pen_color[3]; + } + } + + // draw right + for (int x = rx + rw - t0; x < rx + rw + t1; x++) + { + if (x < 0) + continue; + + if (x >= w) + break; + + for (int y = ry + t1; y < ry + rh - t0; y++) + { + if (y < 0) + continue; + + if (y >= h) + break; + + unsigned char* p = pixels + stride * y; + + p[x * 4 + 0] = pen_color[0]; + p[x * 4 + 1] = pen_color[1]; + p[x * 4 + 2] = pen_color[2]; + p[x * 4 + 3] = pen_color[3]; + } + } +} + +void draw_rectangle_yuv420sp(unsigned char* yuv420sp, int w, int h, int rx, int ry, int rw, int rh, unsigned int color, int thickness) +{ + // assert w % 2 == 0 + // assert h % 2 == 0 + // assert rx % 2 == 0 + // assert ry % 2 == 0 + // assert rw % 2 == 0 + // assert rh % 2 == 0 + // assert thickness % 2 == 0 + + const unsigned char* pen_color = (const unsigned char*)&color; + + unsigned int v_y; + unsigned int v_uv; + unsigned char* pen_color_y = (unsigned char*)&v_y; + unsigned char* pen_color_uv = (unsigned char*)&v_uv; + pen_color_y[0] = pen_color[0]; + pen_color_uv[0] = pen_color[1]; + pen_color_uv[1] = pen_color[2]; + + unsigned char* Y = yuv420sp; + draw_rectangle_c1(Y, w, h, w, rx, ry, rw, rh, v_y, thickness); + + unsigned char* UV = yuv420sp + w * h; + int thickness_uv = thickness == -1 ? thickness : max(thickness / 2, 1); + draw_rectangle_c2(UV, w / 2, h / 2, w, rx / 2, ry / 2, rw / 2, rh / 2, v_uv, thickness_uv); +} + + +void draw_image_yuv420sp(unsigned char* yuv420sp, int w, int h, unsigned char* draw_img, int rx, int ry, int rw, int rh) { + for (int i = 0; i < rh; i++) { + memcpy(yuv420sp + (ry+i) * w + rx, draw_img + i * rw, rw); + } + for (int i = 0; i < rh/2; i++) { + memcpy(yuv420sp + w*h + (ry/2+i) * w+ rx, draw_img + rw*rh + i*rw, rw); + } +} \ No newline at end of file diff --git a/rknpu2/examples/rknn_yolov5_demo/utils/drawing.h b/rknpu2/examples/rknn_yolov5_demo/utils/drawing.h new file mode 100644 index 0000000000000000000000000000000000000000..f33de506b9ab3ef7149aa6b9607822b410fbaa33 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_demo/utils/drawing.h @@ -0,0 +1,8 @@ +#ifndef DRAWING_MODULE_ +#define DRAWING_MODULE_ + +void draw_rectangle_yuv420sp(unsigned char* yuv420sp, int w, int h, int rx, int ry, int rw, int rh, unsigned int color, int thickness); + +void draw_image_yuv420sp(unsigned char* yuv420sp, int w, int h, unsigned char* draw_img, int rx, int ry, int rw, int rh); + +#endif diff --git a/rknpu2/examples/rknn_yolov5_demo/utils/mpp_decoder.cpp b/rknpu2/examples/rknn_yolov5_demo/utils/mpp_decoder.cpp new file mode 100755 index 0000000000000000000000000000000000000000..70637b630f3e623d2516ef0e2b1d9a07e8695b29 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_demo/utils/mpp_decoder.cpp @@ -0,0 +1,334 @@ +#include +#include + +#include "mpp_decoder.h" +#include +#include +#include + +#define LOGD printf +// #define LOGD + +static unsigned long GetCurrentTimeMS() { + struct timeval tv; + gettimeofday(&tv, NULL); + return tv.tv_sec*1000+tv.tv_usec/1000; +} + +MppDecoder::MppDecoder() +{ + +} + +MppDecoder::~MppDecoder() { + if (loop_data.packet) { + mpp_packet_deinit(&loop_data.packet); + loop_data.packet = NULL; + } + if (frame) { + mpp_frame_deinit(&frame); + frame = NULL; + } + if (mpp_ctx) { + mpp_destroy(mpp_ctx); + mpp_ctx = NULL; + } + + if (loop_data.frm_grp) { + mpp_buffer_group_put(loop_data.frm_grp); + loop_data.frm_grp = NULL; + } +} + +int MppDecoder::Init(int video_type, int fps, void* userdata) +{ + MPP_RET ret = MPP_OK; + this->userdata = userdata; + this->fps = fps; + this->last_frame_time_ms = 0; + if(video_type == 264) { + mpp_type = MPP_VIDEO_CodingAVC; + } else if (video_type == 265) { + mpp_type =MPP_VIDEO_CodingHEVC; + } else { + LOGD("unsupport video_type %d", video_type); + return -1; + } + LOGD("mpi_dec_test start "); + memset(&loop_data, 0, sizeof(loop_data)); + LOGD("mpi_dec_test decoder test start mpp_type %d ", mpp_type); + + MppDecCfg cfg = NULL; + + MppCtx mpp_ctx = NULL; + ret = mpp_create(&mpp_ctx, &mpp_mpi); + if (MPP_OK != ret) { + LOGD("mpp_create failed "); + return 0; + } + + ret = mpp_init(mpp_ctx, MPP_CTX_DEC, mpp_type); + if (ret) { + LOGD("%p mpp_init failed ", mpp_ctx); + return -1; + } + + mpp_dec_cfg_init(&cfg); + + /* get default config from decoder context */ + ret = mpp_mpi->control(mpp_ctx, MPP_DEC_GET_CFG, cfg); + if (ret) { + LOGD("%p failed to get decoder cfg ret %d ", mpp_ctx, ret); + return -1; + } + + /* + * split_parse is to enable mpp internal frame spliter when the input + * packet is not aplited into frames. + */ + ret = mpp_dec_cfg_set_u32(cfg, "base:split_parse", need_split); + if (ret) { + LOGD("%p failed to set split_parse ret %d ", mpp_ctx, ret); + return -1; + } + + ret = mpp_mpi->control(mpp_ctx, MPP_DEC_SET_CFG, cfg); + if (ret) { + LOGD("%p failed to set cfg %p ret %d ", mpp_ctx, cfg, ret); + return -1; + } + + mpp_dec_cfg_deinit(cfg); + + loop_data.ctx = mpp_ctx; + loop_data.mpi = mpp_mpi; + loop_data.eos = 0; + loop_data.packet_size = packet_size; + loop_data.frame = 0; + loop_data.frame_count = 0; + return 1; +} + +int MppDecoder::Reset() { + if (mpp_mpi != NULL) { + mpp_mpi->reset(mpp_ctx); + } + return 0; +} + +int MppDecoder::Decode(uint8_t* pkt_data, int pkt_size, int pkt_eos) +{ + MpiDecLoopData *data=&loop_data; + RK_U32 pkt_done = 0; + RK_U32 err_info = 0; + MPP_RET ret = MPP_OK; + MppCtx ctx = data->ctx; + MppApi *mpi = data->mpi; + + size_t read_size = 0; + size_t packet_size = data->packet_size; + + LOGD("receive packet size=%d ", pkt_size); + + if (packet == NULL) { + ret = mpp_packet_init(&packet, NULL, 0); + } + + /////////////////////////////////////////////// + // ret = mpp_packet_init(&packet, frame_data, frame_size); + mpp_packet_set_data(packet, pkt_data); + mpp_packet_set_size(packet, pkt_size); + mpp_packet_set_pos(packet, pkt_data); + mpp_packet_set_length(packet, pkt_size); + // setup eos flag + if (pkt_eos) + mpp_packet_set_eos(packet); + do { + + RK_S32 times = 5; + // send the packet first if packet is not done + if (!pkt_done) { + ret = mpi->decode_put_packet(ctx, packet); + if (MPP_OK == ret) + pkt_done = 1; + } + // then get all available frame and release + do { + RK_S32 get_frm = 0; + RK_U32 frm_eos = 0; + + try_again: + ret = mpi->decode_get_frame(ctx, &frame); + if (MPP_ERR_TIMEOUT == ret) { + if (times > 0) { + times--; + usleep(2000); + goto try_again; + } + LOGD("decode_get_frame failed too much time "); + } + + if (MPP_OK != ret) { + LOGD("decode_get_frame failed ret %d ", ret); + break; + } + + if (frame) { + RK_U32 hor_stride = mpp_frame_get_hor_stride(frame); + RK_U32 ver_stride = mpp_frame_get_ver_stride(frame); + RK_U32 hor_width = mpp_frame_get_width(frame); + RK_U32 ver_height = mpp_frame_get_height(frame); + RK_U32 buf_size = mpp_frame_get_buf_size(frame); + RK_S64 pts = mpp_frame_get_pts(frame); + RK_S64 dts = mpp_frame_get_dts(frame); + + LOGD("decoder require buffer w:h [%d:%d] stride [%d:%d] buf_size %d pts=%lld dts=%lld ", + hor_width, ver_height, hor_stride, ver_stride, buf_size, pts, dts); + + if (mpp_frame_get_info_change(frame)) { + + LOGD("decode_get_frame get info changed found "); + // ret = mpp_buffer_group_get_internal(&data->frm_grp, MPP_BUFFER_TYPE_DRM); + // if (ret) { + // LOGD("get mpp buffer group failed ret %d ", ret); + // break; + // } + // mpi->control(ctx, MPP_DEC_SET_EXT_BUF_GROUP, data->frm_grp); + // mpi->control(ctx, MPP_DEC_SET_INFO_CHANGE_READY, NULL); + if (NULL == data->frm_grp) { + /* If buffer group is not set create one and limit it */ + ret = mpp_buffer_group_get_internal(&data->frm_grp, MPP_BUFFER_TYPE_DRM); + if (ret) { + LOGD("%p get mpp buffer group failed ret %d ", ctx, ret); + break; + } + + /* Set buffer to mpp decoder */ + ret = mpi->control(ctx, MPP_DEC_SET_EXT_BUF_GROUP, data->frm_grp); + if (ret) { + LOGD("%p set buffer group failed ret %d ", ctx, ret); + break; + } + } else { + /* If old buffer group exist clear it */ + ret = mpp_buffer_group_clear(data->frm_grp); + if (ret) { + LOGD("%p clear buffer group failed ret %d ", ctx, ret); + break; + } + } + + /* Use limit config to limit buffer count to 24 with buf_size */ + ret = mpp_buffer_group_limit_config(data->frm_grp, buf_size, 24); + if (ret) { + LOGD("%p limit buffer group failed ret %d ", ctx, ret); + break; + } + + /* + * All buffer group config done. Set info change ready to let + * decoder continue decoding + */ + ret = mpi->control(ctx, MPP_DEC_SET_INFO_CHANGE_READY, NULL); + if (ret) { + LOGD("%p info change ready failed ret %d ", ctx, ret); + break; + } + + this->last_frame_time_ms = GetCurrentTimeMS(); + } else { + err_info = mpp_frame_get_errinfo(frame) | mpp_frame_get_discard(frame); + if (err_info) { + LOGD("decoder_get_frame get err info:%d discard:%d. ", + mpp_frame_get_errinfo(frame), mpp_frame_get_discard(frame)); + } + data->frame_count++; + struct timeval tv; + gettimeofday(&tv, NULL); + LOGD("get one frame %ld ", (tv.tv_sec * 1000 + tv.tv_usec/1000)); + // mpp_frame_get_width(frame); + // char *input_data =(char *) mpp_buffer_get_ptr(mpp_frame_get_buffer(frame)); + if (callback != nullptr) { + MppFrameFormat format = mpp_frame_get_fmt(frame); + char *data_vir =(char *) mpp_buffer_get_ptr(mpp_frame_get_buffer(frame)); + int fd = mpp_buffer_get_fd(mpp_frame_get_buffer(frame)); + LOGD("data_vir=%p fd=%d ", data_vir, fd); + callback(this->userdata, hor_stride, ver_stride, hor_width, ver_height, format, fd, data_vir); + } + unsigned long cur_time_ms = GetCurrentTimeMS(); + long time_gap = 1000/this->fps - (cur_time_ms - this->last_frame_time_ms); + LOGD("time_gap=%ld", time_gap); + if (time_gap > 0) { + usleep(time_gap * 1000); + } + this->last_frame_time_ms = GetCurrentTimeMS(); + } + frm_eos = mpp_frame_get_eos(frame); + + ret = mpp_frame_deinit(&frame); + frame = NULL; + + // if(frame_pre!=NULL) + // { + // mpp_frame_deinit(&frame_pre); + // } + // &frame_pre=&frame; + + get_frm = 1; + } + + // try get runtime frame memory usage + if (data->frm_grp) { + size_t usage = mpp_buffer_group_usage(data->frm_grp); + if (usage > data->max_usage) + data->max_usage = usage; + } + + // if last packet is send but last frame is not found continue + if (pkt_eos && pkt_done && !frm_eos) { + usleep(1*1000); + continue; + } + + if (frm_eos) { + LOGD("found last frame "); + break; + } + + if (data->frame_num > 0 && data->frame_count >= data->frame_num) { + data->eos = 1; + break; + } + + if (get_frm) + continue; + break; + } while (1); + + if (data->frame_num > 0 && data->frame_count >= data->frame_num) { + data->eos = 1; + LOGD("reach max frame number %d ", data->frame_count); + break; + } + + if (pkt_done) + break; + + /* + * why sleep here: + * mpi->decode_put_packet will failed when packet in internal queue is + * full,waiting the package is consumed .Usually hardware decode one + * frame which resolution is 1080p needs 2 ms,so here we sleep 3ms + * * is enough. + */ + usleep(3*1000); + } while (1); + mpp_packet_deinit(&packet); + + return ret; +} + +int MppDecoder::SetCallback(MppDecoderFrameCallback callback) { + this->callback = callback; + return 0; +} \ No newline at end of file diff --git a/rknpu2/examples/rknn_yolov5_demo/utils/mpp_decoder.h b/rknpu2/examples/rknn_yolov5_demo/utils/mpp_decoder.h new file mode 100755 index 0000000000000000000000000000000000000000..d434587abf91eda0a95b62123962a0d562cb0b77 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_demo/utils/mpp_decoder.h @@ -0,0 +1,69 @@ +#ifndef __MPP_DECODER_H__ +#define __MPP_DECODER_H__ + +#include +#include "rockchip/rk_mpi.h" +#include "rockchip/mpp_frame.h" +#include +#include + +#define MPI_DEC_STREAM_SIZE (SZ_4K) +#define MPI_DEC_LOOP_COUNT 4 +#define MAX_FILE_NAME_LENGTH 256 + +typedef void (*MppDecoderFrameCallback)(void* userdata, int width_stride, int height_stride, int width, int height, int format, int fd, void* data); + +typedef struct +{ + MppCtx ctx; + MppApi *mpi; + RK_U32 eos; + char *buf; + + MppBufferGroup frm_grp; + MppBufferGroup pkt_grp; + MppPacket packet; + size_t packet_size; + MppFrame frame; + + RK_S32 frame_count; + RK_S32 frame_num; + size_t max_usage; +} MpiDecLoopData; + +class MppDecoder +{ +public: + MppCtx mpp_ctx = NULL; + MppApi *mpp_mpi = NULL; + MppDecoder(); + ~MppDecoder(); + int Init(int video_type, int fps, void* userdata); + int SetCallback(MppDecoderFrameCallback callback); + int Decode(uint8_t* pkt_data, int pkt_size, int pkt_eos); + int Reset(); +private: + // base flow context + MpiCmd mpi_cmd = MPP_CMD_BASE; + MppParam mpp_param1 = NULL; + RK_U32 need_split = 1; + RK_U32 width_mpp ; + RK_U32 height_mpp ; + MppCodingType mpp_type; + size_t packet_size = 2400*1300*3/2; + MpiDecLoopData loop_data; + // bool vedio_type;//判断vedio是h264/h265 + MppPacket packet = NULL; + MppFrame frame = NULL; + pthread_t th; + MppDecoderFrameCallback callback; + int fps = -1; + unsigned long last_frame_time_ms = 0; + + void* userdata = NULL; +}; + +size_t mpp_frame_get_buf_size(const MppFrame s); +size_t mpp_buffer_group_usage(MppBufferGroup group); + +#endif //__MPP_DECODER_H__ diff --git a/rknpu2/examples/rknn_yolov5_demo/utils/mpp_encoder.cpp b/rknpu2/examples/rknn_yolov5_demo/utils/mpp_encoder.cpp new file mode 100644 index 0000000000000000000000000000000000000000..4830f5dbcbccae891406919d57bb2ed99eebf397 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_demo/utils/mpp_encoder.cpp @@ -0,0 +1,762 @@ +#include +#include + +#include "mpp_encoder.h" +#include "rockchip/mpp_buffer.h" + +#define MPP_ALIGN(x, a) (((x)+(a)-1)&~((a)-1)) +#define SZ_4K 4096 +#define LOGD printf +// #define LOGD +#define LOGE printf + +int MppEncoder::InitParams(MppEncoderParams& params) +{ + memcpy(&enc_params, ¶ms, sizeof(MppEncoderParams)); + + // get paramter from cmd + if (enc_params.hor_stride == 0) { + enc_params.hor_stride = MPP_ALIGN(enc_params.width, 16); + } + if (enc_params.ver_stride == 0) { + enc_params.ver_stride = (MPP_ALIGN(enc_params.height, 16)); + } + + if (enc_params.fps_in_den == 0) + enc_params.fps_in_den = 1; + if (enc_params.fps_in_num == 0) + enc_params.fps_in_num = 30; + if (enc_params.fps_out_den == 0) + enc_params.fps_out_den = 1; + if (enc_params.fps_out_num == 0) + enc_params.fps_out_num = 30; + + if (!enc_params.bps) + enc_params.bps = enc_params.width * enc_params.height / 8 * (enc_params.fps_out_num / enc_params.fps_out_den); + + this->mdinfo_size = (MPP_VIDEO_CodingHEVC == enc_params.type) ? + (MPP_ALIGN(enc_params.hor_stride, 32) >> 5) * + (MPP_ALIGN(enc_params.ver_stride, 32) >> 5) * 16 : + (MPP_ALIGN(enc_params.hor_stride, 64) >> 6) * + (MPP_ALIGN(enc_params.ver_stride, 16) >> 4) * 16; + + // update resource parameter + switch (enc_params.fmt & MPP_FRAME_FMT_MASK) { + case MPP_FMT_YUV420SP: + case MPP_FMT_YUV420P: { + this->frame_size = MPP_ALIGN(enc_params.hor_stride, 64) * MPP_ALIGN(enc_params.ver_stride, 64) * 3 / 2; + } break; + + case MPP_FMT_YUV422_YUYV : + case MPP_FMT_YUV422_YVYU : + case MPP_FMT_YUV422_UYVY : + case MPP_FMT_YUV422_VYUY : + case MPP_FMT_YUV422P : + case MPP_FMT_YUV422SP : { + this->frame_size = MPP_ALIGN(enc_params.hor_stride, 64) * MPP_ALIGN(enc_params.ver_stride, 64) * 2; + } break; + case MPP_FMT_RGB444 : + case MPP_FMT_BGR444 : + case MPP_FMT_RGB555 : + case MPP_FMT_BGR555 : + case MPP_FMT_RGB565 : + case MPP_FMT_BGR565 : + case MPP_FMT_RGB888 : + case MPP_FMT_BGR888 : + case MPP_FMT_RGB101010 : + case MPP_FMT_BGR101010 : + case MPP_FMT_ARGB8888 : + case MPP_FMT_ABGR8888 : + case MPP_FMT_BGRA8888 : + case MPP_FMT_RGBA8888 : { + this->frame_size = MPP_ALIGN(enc_params.hor_stride, 64) * MPP_ALIGN(enc_params.ver_stride, 64); + } break; + + default: { + this->frame_size = MPP_ALIGN(enc_params.hor_stride, 64) * MPP_ALIGN(enc_params.ver_stride, 64) * 4; + } break; + } + + if (MPP_FRAME_FMT_IS_FBC(enc_params.fmt)) { + if ((enc_params.fmt & MPP_FRAME_FBC_MASK) == MPP_FRAME_FBC_AFBC_V1) + this->header_size = MPP_ALIGN(MPP_ALIGN(enc_params.width, 16) * MPP_ALIGN(enc_params.height, 16) / 16, SZ_4K); + else + this->header_size = MPP_ALIGN(enc_params.width, 16) * MPP_ALIGN(enc_params.height, 16) / 16; + } else { + this->header_size = 0; + } + + return 0; +} + +int MppEncoder::SetupEncCfg() +{ + MPP_RET ret; + + ret = mpp_enc_cfg_init(&cfg); + if (ret) { + LOGE("mpp_enc_cfg_init failed ret %d\n", ret); + return -1; + } + + /* setup default parameter */ + if (enc_params.fps_in_den == 0) + enc_params.fps_in_den = 1; + if (enc_params.fps_in_num == 0) + enc_params.fps_in_num = 30; + if (enc_params.fps_out_den == 0) + enc_params.fps_out_den = 1; + if (enc_params.fps_out_num == 0) + enc_params.fps_out_num = 30; + + if (!enc_params.bps) + enc_params.bps = enc_params.width * enc_params.height / 8 * (enc_params.fps_out_num / enc_params.fps_out_den); + + mpp_enc_cfg_set_s32(cfg, "prep:width", enc_params.width); + mpp_enc_cfg_set_s32(cfg, "prep:height", enc_params.height); + mpp_enc_cfg_set_s32(cfg, "prep:hor_stride", enc_params.hor_stride); + mpp_enc_cfg_set_s32(cfg, "prep:ver_stride", enc_params.ver_stride); + mpp_enc_cfg_set_s32(cfg, "prep:format", enc_params.fmt); + + mpp_enc_cfg_set_s32(cfg, "rc:mode", enc_params.rc_mode); + + /* fix input / output frame rate */ + mpp_enc_cfg_set_s32(cfg, "rc:fps_in_flex", enc_params.fps_in_flex); + mpp_enc_cfg_set_s32(cfg, "rc:fps_in_num", enc_params.fps_in_num); + mpp_enc_cfg_set_s32(cfg, "rc:fps_in_denorm", enc_params.fps_in_den); + mpp_enc_cfg_set_s32(cfg, "rc:fps_out_flex", enc_params.fps_out_flex); + mpp_enc_cfg_set_s32(cfg, "rc:fps_out_num", enc_params.fps_out_num); + mpp_enc_cfg_set_s32(cfg, "rc:fps_out_denorm", enc_params.fps_out_den); + mpp_enc_cfg_set_s32(cfg, "rc:gop", enc_params.gop_len ? enc_params.gop_len : enc_params.fps_out_num * 2); + + /* drop frame or not when bitrate overflow */ + mpp_enc_cfg_set_u32(cfg, "rc:drop_mode", MPP_ENC_RC_DROP_FRM_DISABLED); + mpp_enc_cfg_set_u32(cfg, "rc:drop_thd", 20); /* 20% of max bps */ + mpp_enc_cfg_set_u32(cfg, "rc:drop_gap", 1); /* Do not continuous drop frame */ + + /* setup bitrate for different rc_mode */ + mpp_enc_cfg_set_s32(cfg, "rc:bps_target", enc_params.bps); + switch (enc_params.rc_mode) { + case MPP_ENC_RC_MODE_FIXQP : { + /* do not setup bitrate on FIXQP mode */ + } break; + case MPP_ENC_RC_MODE_CBR : { + /* CBR mode has narrow bound */ + mpp_enc_cfg_set_s32(cfg, "rc:bps_max", enc_params.bps_max ? enc_params.bps_max : enc_params.bps * 17 / 16); + mpp_enc_cfg_set_s32(cfg, "rc:bps_min", enc_params.bps_min ? enc_params.bps_min : enc_params.bps * 15 / 16); + } break; + case MPP_ENC_RC_MODE_VBR : + case MPP_ENC_RC_MODE_AVBR : { + /* VBR mode has wide bound */ + mpp_enc_cfg_set_s32(cfg, "rc:bps_max", enc_params.bps_max ? enc_params.bps_max : enc_params.bps * 17 / 16); + mpp_enc_cfg_set_s32(cfg, "rc:bps_min", enc_params.bps_min ? enc_params.bps_min : enc_params.bps * 1 / 16); + } break; + default : { + /* default use CBR mode */ + mpp_enc_cfg_set_s32(cfg, "rc:bps_max", enc_params.bps_max ? enc_params.bps_max : enc_params.bps * 17 / 16); + mpp_enc_cfg_set_s32(cfg, "rc:bps_min", enc_params.bps_min ? enc_params.bps_min : enc_params.bps * 15 / 16); + } break; + } + + /* setup qp for different codec and rc_mode */ + switch (enc_params.type) { + case MPP_VIDEO_CodingAVC : + case MPP_VIDEO_CodingHEVC : { + switch (enc_params.rc_mode) { + case MPP_ENC_RC_MODE_FIXQP : { + RK_S32 fix_qp = enc_params.qp_init; + + mpp_enc_cfg_set_s32(cfg, "rc:qp_init", fix_qp); + mpp_enc_cfg_set_s32(cfg, "rc:qp_max", fix_qp); + mpp_enc_cfg_set_s32(cfg, "rc:qp_min", fix_qp); + mpp_enc_cfg_set_s32(cfg, "rc:qp_max_i", fix_qp); + mpp_enc_cfg_set_s32(cfg, "rc:qp_min_i", fix_qp); + mpp_enc_cfg_set_s32(cfg, "rc:qp_ip", 0); + } break; + case MPP_ENC_RC_MODE_CBR : + case MPP_ENC_RC_MODE_VBR : + case MPP_ENC_RC_MODE_AVBR : { + mpp_enc_cfg_set_s32(cfg, "rc:qp_init", -1); + mpp_enc_cfg_set_s32(cfg, "rc:qp_max", 51); + mpp_enc_cfg_set_s32(cfg, "rc:qp_min", 10); + mpp_enc_cfg_set_s32(cfg, "rc:qp_max_i", 51); + mpp_enc_cfg_set_s32(cfg, "rc:qp_min_i", 10); + mpp_enc_cfg_set_s32(cfg, "rc:qp_ip", 2); + } break; + default : { + LOGE("unsupport encoder rc mode %d\n", enc_params.rc_mode); + } break; + } + } break; + case MPP_VIDEO_CodingVP8 : { + /* vp8 only setup base qp range */ + mpp_enc_cfg_set_s32(cfg, "rc:qp_init", 40); + mpp_enc_cfg_set_s32(cfg, "rc:qp_max", 127); + mpp_enc_cfg_set_s32(cfg, "rc:qp_min", 0); + mpp_enc_cfg_set_s32(cfg, "rc:qp_max_i", 127); + mpp_enc_cfg_set_s32(cfg, "rc:qp_min_i", 0); + mpp_enc_cfg_set_s32(cfg, "rc:qp_ip", 6); + } break; + case MPP_VIDEO_CodingMJPEG : { + /* jpeg use special codec config to control qtable */ + mpp_enc_cfg_set_s32(cfg, "jpeg:q_factor", 80); + mpp_enc_cfg_set_s32(cfg, "jpeg:qf_max", 99); + mpp_enc_cfg_set_s32(cfg, "jpeg:qf_min", 1); + } break; + default : { + } break; + } + + /* setup codec */ + mpp_enc_cfg_set_s32(cfg, "codec:type", enc_params.type); + switch (enc_params.type) { + case MPP_VIDEO_CodingAVC : { + RK_U32 constraint_set = enc_params.constraint_set; + + /* + * H.264 profile_idc parameter + * 66 - Baseline profile + * 77 - Main profile + * 100 - High profile + */ + mpp_enc_cfg_set_s32(cfg, "h264:profile", 100); + /* + * H.264 level_idc parameter + * 10 / 11 / 12 / 13 - qcif@15fps / cif@7.5fps / cif@15fps / cif@30fps + * 20 / 21 / 22 - cif@30fps / half-D1@@25fps / D1@12.5fps + * 30 / 31 / 32 - D1@25fps / 720p@30fps / 720p@60fps + * 40 / 41 / 42 - 1080p@30fps / 1080p@30fps / 1080p@60fps + * 50 / 51 / 52 - 4K@30fps + */ + mpp_enc_cfg_set_s32(cfg, "h264:level", 40); + mpp_enc_cfg_set_s32(cfg, "h264:cabac_en", 1); + mpp_enc_cfg_set_s32(cfg, "h264:cabac_idc", 0); + mpp_enc_cfg_set_s32(cfg, "h264:trans8x8", 1); + + if (constraint_set & 0x3f0000) + mpp_enc_cfg_set_s32(cfg, "h264:constraint_set", constraint_set); + } break; + case MPP_VIDEO_CodingHEVC : + case MPP_VIDEO_CodingMJPEG : + case MPP_VIDEO_CodingVP8 : { + } break; + default : { + LOGE("unsupport encoder coding type %d\n", enc_params.type); + } break; + } + + if (enc_params.split_mode) { + LOGD("%p split mode %d arg %d out %d\n", mpp_ctx, + enc_params.split_mode, enc_params.split_arg, enc_params.split_out); + mpp_enc_cfg_set_s32(cfg, "split:mode", enc_params.split_mode); + mpp_enc_cfg_set_s32(cfg, "split:arg", enc_params.split_arg); + mpp_enc_cfg_set_s32(cfg, "split:out", enc_params.split_out); + } + + mpp_enc_cfg_set_s32(cfg, "prep:mirroring", enc_params.mirroring); + mpp_enc_cfg_set_s32(cfg, "prep:rotation", enc_params.rotation); + mpp_enc_cfg_set_s32(cfg, "prep:flip", enc_params.flip); + + ret = mpp_mpi->control(mpp_ctx, MPP_ENC_SET_CFG, cfg); + if (ret) { + LOGE("mpi control enc set cfg failed ret %d\n", ret); + goto RET; + } + +#if 0 + /* optional */ + { + ret = mpp_mpi->control(mpp_ctx, MPP_ENC_SET_SEI_CFG, &enc_params.sei_mode); + if (ret) { + LOGE("mpi control enc set sei cfg failed ret %d\n", ret); + goto RET; + } + } +#endif + + if (enc_params.type == MPP_VIDEO_CodingAVC || enc_params.type == MPP_VIDEO_CodingHEVC) { + enc_params.header_mode = MPP_ENC_HEADER_MODE_EACH_IDR; + ret = mpp_mpi->control(mpp_ctx, MPP_ENC_SET_HEADER_MODE, &enc_params.header_mode); + if (ret) { + LOGE("mpi control enc set header mode failed ret %d\n", ret); + goto RET; + } + } + +#if 0 + RK_U32 gop_mode = enc_params.gop_mode; + if (gop_mode) { + MppEncRefCfg ref; + + mpp_enc_ref_cfg_init(&ref); + + if (enc_params.gop_mode < 4) + mpi_enc_gen_ref_cfg(ref, gop_mode); + else + mpi_enc_gen_smart_gop_ref_cfg(ref, enc_params.gop_len, enc_params.vi_len); + + ret = mpp_mpi->control(mpp_ctx, MPP_ENC_SET_REF_CFG, ref); + if (ret) { + LOGE("mpi control enc set ref cfg failed ret %d\n", ret); + goto RET; + } + mpp_enc_ref_cfg_deinit(&ref); + } + + if (enc_params.roi_enable) { + mpp_enc_roi_init(&enc_params.roi_mpp_ctx, enc_params.width, enc_params.height, enc_params.type, 4); + mpp_assert(enc_params.roi_mpp_ctx); + } +#endif + +RET: + return ret; +} + +MppEncoder::MppEncoder() { + memset(&enc_params, 0, sizeof(MppEncoderParams)); + this->mpp_ctx = NULL; + this->mpp_mpi = NULL; + memset(&osd_data, 0, sizeof(MppEncOSDData)); +} + +MppEncoder::~MppEncoder() { + if (this->mpp_ctx) { + mpp_destroy(this->mpp_ctx); + this->mpp_ctx = NULL; + } + + if (this->cfg) { + mpp_enc_cfg_deinit(this->cfg); + this->cfg = NULL; + } + + if (this->frm_buf) { + mpp_buffer_put(this->frm_buf); + this->frm_buf = NULL; + } + + if (this->pkt_buf) { + mpp_buffer_put(this->pkt_buf); + this->pkt_buf = NULL; + } + + if (this->md_info) { + mpp_buffer_put(this->md_info); + this->md_info = NULL; + } + + if (this->osd_data.buf) { + mpp_buffer_put(this->osd_data.buf); + this->osd_data.buf = NULL; + } + + if (this->buf_grp) { + mpp_buffer_group_put(this->buf_grp); + this->buf_grp = NULL; + } +} + +int MppEncoder::Init(MppEncoderParams& params, void* userdata) { + int ret; + MppPollType timeout = MPP_POLL_BLOCK; + + this->userdata = userdata; + + this->InitParams(params); + + ret = mpp_buffer_group_get_internal(&this->buf_grp, MPP_BUFFER_TYPE_DRM); + if (ret) { + LOGD("failed to get mpp buffer group ret %d\n", ret); + goto MPP_TEST_OUT; + } + + ret = mpp_buffer_get(this->buf_grp, &this->pkt_buf, this->frame_size); + if (ret) { + LOGD("failed to get buffer for output packet ret %d\n", ret); + goto MPP_TEST_OUT; + } + + ret = mpp_buffer_get(this->buf_grp, &this->md_info, this->mdinfo_size); + if (ret) { + LOGD("failed to get buffer for motion info output packet ret %d\n", ret); + goto MPP_TEST_OUT; + } + + // encoder demo + ret = mpp_create(&this->mpp_ctx, &this->mpp_mpi); + if (ret) { + LOGE("mpp_create failed ret %d\n", ret); + goto MPP_TEST_OUT; + } + + LOGD("%p encoder test start w %d h %d type %d\n", + this->mpp_ctx, enc_params.width, enc_params.height, enc_params.type); + + ret = mpp_mpi->control(mpp_ctx, MPP_SET_OUTPUT_TIMEOUT, &timeout); + if (MPP_OK != ret) { + LOGE("mpi control set output timeout %d ret %d\n", timeout, ret); + goto MPP_TEST_OUT; + } + + ret = mpp_init(mpp_ctx, MPP_CTX_ENC, enc_params.type); + if (ret) { + LOGE("mpp_init failed ret %d\n", ret); + goto MPP_TEST_OUT; + } + + this->SetupEncCfg(); + +MPP_TEST_OUT: + if (ret) { + if (this->mpp_ctx) { + mpp_destroy(this->mpp_ctx); + this->mpp_ctx = NULL; + } + + if (this->cfg) { + mpp_enc_cfg_deinit(this->cfg); + this->cfg = NULL; + } + + if (this->frm_buf) { + mpp_buffer_put(this->frm_buf); + this->frm_buf = NULL; + } + + if (this->pkt_buf) { + mpp_buffer_put(this->pkt_buf); + this->pkt_buf = NULL; + } + + if (this->md_info) { + mpp_buffer_put(this->md_info); + this->md_info = NULL; + } + + if (this->osd_data.buf) { + mpp_buffer_put(this->osd_data.buf); + this->osd_data.buf = NULL; + } + + if (this->buf_grp) { + mpp_buffer_group_put(this->buf_grp); + this->buf_grp = NULL; + } + + // if (this->roi_ctx) { + // mpp_enc_roi_deinit(this->roi_ctx); + // this->roi_ctx = NULL; + // } + } + + return ret; +} + +int MppEncoder::SetCallback(MppEncoderFrameCallback callback) { + this->callback = callback; + return 0; +} + +int MppEncoder::GetHeader(char* enc_buf, int max_size) { + int ret; + void* out_ptr = enc_buf; + size_t out_len = 0; + + if (enc_params.type == MPP_VIDEO_CodingAVC || enc_params.type == MPP_VIDEO_CodingHEVC) { + MppPacket packet = NULL; + + /* + * Can use packet with normal malloc buffer as input not pkt_buf. + * Please refer to vpu_api_legacy.cpp for normal buffer case. + * Using pkt_buf buffer here is just for simplifing demo. + */ + mpp_packet_init_with_buffer(&packet, this->pkt_buf); + /* NOTE: It is important to clear output packet length!! */ + mpp_packet_set_length(packet, 0); + + ret = mpp_mpi->control(mpp_ctx, MPP_ENC_GET_HDR_SYNC, packet); + if (ret) { + LOGD("mpi control enc get extra info failed\n"); + return -1; + } else { + /* get and write sps/pps for H.264 */ + + void *ptr = mpp_packet_get_pos(packet); + size_t len = mpp_packet_get_length(packet); + + memcpy(out_ptr, ptr, len); + out_ptr = (char*)(out_ptr) + len; + out_len += len; + } + + mpp_packet_deinit(&packet); + } + return out_len; +} + +int MppEncoder::Encode(void* mpp_buf, char* enc_buf, int max_size) { + MPP_RET ret; + void* out_ptr = enc_buf; + size_t out_len = 0; + + MppMeta meta = NULL; + MppFrame frame = NULL; + MppPacket packet = NULL; + // void *buf = mpp_buffer_get_ptr(this->frm_buf); + // RK_S32 cam_frm_idx = -1; + // MppBuffer cam_buf = NULL; + RK_U32 eoi = 1; + RK_U32 frm_eos = 0; + + ret = mpp_frame_init(&frame); + if (ret) { + LOGD("mpp_frame_init failed\n"); + return -1; + } + + mpp_frame_set_width(frame, enc_params.width); + mpp_frame_set_height(frame, enc_params.height); + mpp_frame_set_hor_stride(frame, enc_params.hor_stride); + mpp_frame_set_ver_stride(frame, enc_params.ver_stride); + mpp_frame_set_fmt(frame, enc_params.fmt); + mpp_frame_set_eos(frame, frm_eos); + mpp_frame_set_buffer(frame, mpp_buf); + + meta = mpp_frame_get_meta(frame); + mpp_packet_init_with_buffer(&packet, pkt_buf); + /* NOTE: It is important to clear output packet length!! */ + mpp_packet_set_length(packet, 0); + mpp_meta_set_packet(meta, KEY_OUTPUT_PACKET, packet); + mpp_meta_set_buffer(meta, KEY_MOTION_INFO, this->md_info); + +#if 0 + if (enc_params.osd_enable || enc_params.user_data_enable || enc_params.roi_enable) { + if (enc_params.user_data_enable) { + MppEncUserData user_data; + char *str = "this is user data\n"; + + if ((enc_params.frame_count & 10) == 0) { + user_data.pdata = str; + user_data.len = strlen(str) + 1; + mpp_meta_set_ptr(meta, KEY_USER_DATA, &user_data); + } + static RK_U8 uuid_debug_info[16] = { + 0x57, 0x68, 0x97, 0x80, 0xe7, 0x0c, 0x4b, 0x65, + 0xa9, 0x06, 0xae, 0x29, 0x94, 0x11, 0xcd, 0x9a + }; + + MppEncUserDataSet data_group; + MppEncUserDataFull datas[2]; + char *str1 = "this is user data 1\n"; + char *str2 = "this is user data 2\n"; + data_group.count = 2; + datas[0].len = strlen(str1) + 1; + datas[0].pdata = str1; + datas[0].uuid = uuid_debug_info; + + datas[1].len = strlen(str2) + 1; + datas[1].pdata = str2; + datas[1].uuid = uuid_debug_info; + + data_group.datas = datas; + + mpp_meta_set_ptr(meta, KEY_USER_DATAS, &data_group); + } + + if (enc_params.osd_enable) { + /* gen and cfg osd plt */ + mpi_enc_gen_osd_plt(&enc_params.osd_plt, enc_params.frame_count); + + enc_params.osd_plt_cfg.change = MPP_ENC_OSD_PLT_CFG_CHANGE_ALL; + enc_params.osd_plt_cfg.type = MPP_ENC_OSD_PLT_TYPE_USERDEF; + enc_params.osd_plt_cfg.plt = &enc_params.osd_plt; + + ret = mpp_mpi->control(mpp_ctx, MPP_ENC_SET_OSD_PLT_CFG, &enc_params.osd_plt_cfg); + if (ret) { + LOGD("mpi control enc set osd plt failed ret %d\n", ret); + goto RET; + } + + /* gen and cfg osd plt */ + mpi_enc_gen_osd_data(&enc_params.osd_data, enc_params.buf_grp, enc_params.width, + enc_params.height, enc_params.frame_count); + mpp_meta_set_ptr(meta, KEY_OSD_DATA, (void*)&enc_params.osd_data); + } + + if (enc_params.roi_enable) { + RoiRegionCfg *region = &enc_params.roi_region; + + /* calculated in pixels */ + region->x = MPP_ALIGN(enc_params.width / 8, 16); + region->y = MPP_ALIGN(enc_params.height / 8, 16); + region->w = 128; + region->h = 256; + region->force_intra = 0; + region->qp_mode = 1; + region->qp_val = 24; + + mpp_enc_roi_add_region(enc_params.roi_mpp_ctx, region); + + region->x = MPP_ALIGN(enc_params.width / 2, 16); + region->y = MPP_ALIGN(enc_params.height / 4, 16); + region->w = 256; + region->h = 128; + region->force_intra = 1; + region->qp_mode = 1; + region->qp_val = 10; + + mpp_enc_roi_add_region(enc_params.roi_mpp_ctx, region); + + /* send roi info by metadata */ + mpp_enc_roi_setup_meta(enc_params.roi_mpp_ctx, meta); + } + } +#endif + + /* + * NOTE: in non-block mode the frame can be resent. + * The default input timeout mode is block. + * + * User should release the input frame to meet the requirements of + * resource creator must be the resource destroyer. + */ + ret = mpp_mpi->encode_put_frame(mpp_ctx, frame); + mpp_frame_deinit(&frame); + + if (ret) { + LOGD("chn %d encode put frame failed\n", chn); + return -1; + } + + do { + ret = mpp_mpi->encode_get_packet(mpp_ctx, &packet); + if (ret) { + LOGD("chn %d encode get packet failed\n", chn); + return -1; + } + + // mpp_assert(packet); + + if (packet) { + // write packet to file here + void *ptr = mpp_packet_get_pos(packet); + size_t len = mpp_packet_get_length(packet); + char log_buf[256]; + RK_S32 log_size = sizeof(log_buf) - 1; + RK_S32 log_len = 0; + + // if (!enc_params.first_pkt) + // enc_params.first_pkt = mpp_time(); + + RK_U32 pkt_eos = mpp_packet_get_eos(packet); + + /* set encode result */ + if (this->callback != nullptr) { + this->callback(this->userdata, (const char*)ptr, len); + } + if (enc_buf != nullptr && max_size > 0) { + if (out_len + log_len < max_size) { + memcpy(out_ptr, ptr, len); + out_len += len; + out_ptr = (char*)out_ptr + len; + } else { + LOGE("error enc_buf no enought"); + } + } + + // log_len += snprintf(log_buf + log_len, log_size - log_len, + // "encoded frame %-4d", enc_params.frame_count); + + /* for low delay partition encoding */ + if (mpp_packet_is_partition(packet)) { + eoi = mpp_packet_is_eoi(packet); + + // log_len += snprintf(log_buf + log_len, log_size - log_len, + // " pkt %d", enc_params.frm_pkt_cnt); + // enc_params.frm_pkt_cnt = (eoi) ? (0) : (enc_params.frm_pkt_cnt + 1); + } + + log_len += snprintf(log_buf + log_len, log_size - log_len, + " size %-7zu", len); + + if (mpp_packet_has_meta(packet)) { + meta = mpp_packet_get_meta(packet); + RK_S32 temporal_id = 0; + RK_S32 lt_idx = -1; + RK_S32 avg_qp = -1; + + if (MPP_OK == mpp_meta_get_s32(meta, KEY_TEMPORAL_ID, &temporal_id)) + log_len += snprintf(log_buf + log_len, log_size - log_len, + " tid %d", temporal_id); + + if (MPP_OK == mpp_meta_get_s32(meta, KEY_LONG_REF_IDX, <_idx)) + log_len += snprintf(log_buf + log_len, log_size - log_len, + " lt %d", lt_idx); + + if (MPP_OK == mpp_meta_get_s32(meta, KEY_ENC_AVERAGE_QP, &avg_qp)) + log_len += snprintf(log_buf + log_len, log_size - log_len, + " qp %d", avg_qp); + } + + LOGD("chn %d %s\n", chn, log_buf); + + mpp_packet_deinit(&packet); + + // enc_params.stream_size += len; + // enc_params.frame_count += eoi; + + // if (enc_params.pkt_eos) { + // LOGD("chn %d found last packet\n", chn); + // mpp_assert(enc_params.frm_eos); + // } + } + } while (!eoi); + + // if (enc_params.frm_eos && enc_params.pkt_eos) + // break; + return out_len; +} + +int MppEncoder::Reset() { + if (mpp_mpi != NULL) { + mpp_mpi->reset(mpp_ctx); + } + return 0; +} + +size_t MppEncoder::GetFrameSize() { + return this->frame_size; +} + +void* MppEncoder::ImportBuffer(int index, size_t size, int fd, int type) { + MppBuffer buf; + MppBufferInfo info; + memset(&info, 0, sizeof(MppBufferInfo)); + info.type = (MppBufferType)type; // MPP_BUFFER_TYPE_EXT_DMA + info.fd = fd; + info.size = size; + info.index = index; + mpp_buffer_import(&buf, &info); + return buf; +} + +void* MppEncoder::GetInputFrameBuffer() { + int ret; + if (this->frm_buf == nullptr) { + ret = mpp_buffer_get(this->buf_grp, &this->frm_buf, this->frame_size); + if (ret) { + LOGD("failed to get buffer for input frame ret %d\n", ret); + return NULL; + } + } + return this->frm_buf; +} + +int MppEncoder::GetInputFrameBufferFd(void* mpp_buffer) { + return mpp_buffer_get_fd(mpp_buffer); +} + +void* MppEncoder::GetInputFrameBufferAddr(void* mpp_buffer) { + return mpp_buffer_get_ptr(mpp_buffer); +} \ No newline at end of file diff --git a/rknpu2/examples/rknn_yolov5_demo/utils/mpp_encoder.h b/rknpu2/examples/rknn_yolov5_demo/utils/mpp_encoder.h new file mode 100644 index 0000000000000000000000000000000000000000..3a3a0fef3e226a96ad3870113e9d6fee363663a5 --- /dev/null +++ b/rknpu2/examples/rknn_yolov5_demo/utils/mpp_encoder.h @@ -0,0 +1,127 @@ +#ifndef __MPP_ENCODER_H__ +#define __MPP_ENCODER_H__ + +#include "rockchip/mpp_frame.h" +#include "rockchip/rk_mpi.h" +#include +#include + +typedef void (*MppEncoderFrameCallback)(void* userdata, const char* data, int size); + +typedef struct +{ + RK_U32 width; + RK_U32 height; + RK_U32 hor_stride; + RK_U32 ver_stride; + MppFrameFormat fmt; + MppCodingType type; + + RK_U32 osd_enable; + RK_U32 osd_mode; + RK_U32 split_mode; + RK_U32 split_arg; + RK_U32 split_out; + + RK_U32 user_data_enable; + RK_U32 roi_enable; + + // rate control runtime parameter + RK_S32 fps_in_flex; + RK_S32 fps_in_den; + RK_S32 fps_in_num; + RK_S32 fps_out_flex; + RK_S32 fps_out_den; + RK_S32 fps_out_num; + RK_S32 bps; + RK_S32 bps_max; + RK_S32 bps_min; + RK_S32 rc_mode; + RK_S32 gop_mode; + RK_S32 gop_len; + RK_S32 vi_len; + + /* general qp control */ + RK_S32 qp_init; + RK_S32 qp_max; + RK_S32 qp_max_i; + RK_S32 qp_min; + RK_S32 qp_min_i; + RK_S32 qp_max_step; /* delta qp between each two P frame */ + RK_S32 qp_delta_ip; /* delta qp between I and P */ + RK_S32 qp_delta_vi; /* delta qp between vi and P */ + + RK_U32 constraint_set; + RK_U32 rotation; + RK_U32 mirroring; + RK_U32 flip; + + MppEncHeaderMode header_mode; + MppEncSeiMode sei_mode; +} MppEncoderParams; + +class MppEncoder { + public: + MppEncoder(); + ~MppEncoder(); + int Init(MppEncoderParams& params, void* userdata); + int SetCallback(MppEncoderFrameCallback callback); + int Encode(void* mpp_buf, char* enc_buf, int max_size); + int GetHeader(char* enc_buf, int max_size); + int Reset(); + void* ImportBuffer(int index, size_t size, int fd, int type); + size_t GetFrameSize(); + void* GetInputFrameBuffer(); + int GetInputFrameBufferFd(void* mpp_buffer); + void* GetInputFrameBufferAddr(void* mpp_buffer); + private: + int InitParams(MppEncoderParams& params); + int SetupEncCfg(); + + MppCtx mpp_ctx = NULL; + MppApi* mpp_mpi = NULL; + RK_S32 chn = 0; + + MppEncoderFrameCallback callback = NULL; + + // global flow control flag + // RK_U32 frm_eos = 0; + // RK_U32 pkt_eos = 0; + // RK_U32 frm_pkt_cnt = 0; + // RK_S32 frame_num = 0; + // RK_S32 frame_count = 0; + // RK_U64 stream_size = 0; + + /* encoder config set */ + MppEncCfg cfg = NULL; + MppEncPrepCfg prep_cfg; + MppEncRcCfg rc_cfg; + MppEncCodecCfg codec_cfg; + MppEncSliceSplit split_cfg; + MppEncOSDPltCfg osd_plt_cfg; + MppEncOSDPlt osd_plt; + MppEncOSDData osd_data; + // RoiRegionCfg roi_region; + MppEncROICfg roi_cfg; + + // input / output + MppBufferGroup buf_grp = NULL; + MppBuffer frm_buf = NULL; + MppBuffer pkt_buf = NULL; + MppBuffer md_info = NULL; + + // MppEncRoiCtx roi_ctx; + + // resources + size_t header_size; + size_t frame_size; + size_t mdinfo_size; + /* NOTE: packet buffer may overflow */ + size_t packet_size; + + MppEncoderParams enc_params; + + void* userdata = NULL; +}; + +#endif //__MPP_ENCODER_H__ diff --git a/rknpu2/rknpu.mk b/rknpu2/rknpu.mk new file mode 100644 index 0000000000000000000000000000000000000000..bf63639f0116b57fd15450a4c4cad426eab07714 --- /dev/null +++ b/rknpu2/rknpu.mk @@ -0,0 +1,4 @@ +LOCAL_PATH := $(call my-dir) + +PRODUCT_PACKAGES += \ + librknnrt diff --git a/rknpu2/runtime/Android.bp b/rknpu2/runtime/Android.bp new file mode 100644 index 0000000000000000000000000000000000000000..ee87d065742aa706b45db9f1cd76e06059e6aed9 --- /dev/null +++ b/rknpu2/runtime/Android.bp @@ -0,0 +1,38 @@ +cc_prebuilt_library_shared { + name: "librknnrt", + compile_multilib: "both", + + target: { + android_arm: { + srcs: ["Android/librknn_api/armeabi-v7a/librknnrt.so"], + }, + android_arm64: { + srcs: ["Android/librknn_api/arm64-v8a/librknnrt.so"], + }, + }, + + strip: { + none: true, + }, + check_elf_files: false, + vendor: true, +} + +cc_prebuilt_binary { + name: "rknn_server", + target: { + android_arm: { + srcs: ["Android/rknn_server/arm/rknn_server"], + }, + android_arm64: { + srcs: ["Android/rknn_server/arm64/rknn_server"], + }, + }, + + init_rc: ["init.rknn_server.rc"], + strip: { + none: true, + }, + check_elf_files: false, + vendor: true, +} diff --git a/rknpu2/runtime/Android/librknn_api/arm64-v8a/librknnrt.so b/rknpu2/runtime/Android/librknn_api/arm64-v8a/librknnrt.so new file mode 100644 index 0000000000000000000000000000000000000000..e202a59548f5b5b7f80e92b0f56ad552e3ae0543 --- /dev/null +++ b/rknpu2/runtime/Android/librknn_api/arm64-v8a/librknnrt.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:47b9591c349a2acd80cc253fe669a202b7c1ce0ca9efd634447c4168a6d25761 +size 7724432 diff --git a/rknpu2/runtime/Android/librknn_api/armeabi-v7a/librknnrt.so b/rknpu2/runtime/Android/librknn_api/armeabi-v7a/librknnrt.so new file mode 100644 index 0000000000000000000000000000000000000000..a105e36539475119c167f2fa1160081cf255ba33 --- /dev/null +++ b/rknpu2/runtime/Android/librknn_api/armeabi-v7a/librknnrt.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:84823bf76d999fa77d0a381444b7958b0a1653ea4b0e6698dba7ff6f70cb1936 +size 4936772 diff --git a/rknpu2/runtime/Android/librknn_api/include/rknn_api.h b/rknpu2/runtime/Android/librknn_api/include/rknn_api.h new file mode 100755 index 0000000000000000000000000000000000000000..2e5e9f2e5c18f28217229d26e12f8cb1f8b5fb9f --- /dev/null +++ b/rknpu2/runtime/Android/librknn_api/include/rknn_api.h @@ -0,0 +1,801 @@ +/**************************************************************************** +* +* Copyright (c) 2017 - 2022 by Rockchip Corp. All rights reserved. +* +* The material in this file is confidential and contains trade secrets +* of Rockchip Corporation. This is proprietary information owned by +* Rockchip Corporation. No part of this work may be disclosed, +* reproduced, copied, transmitted, or used in any way for any purpose, +* without the express written permission of Rockchip Corporation. +* +*****************************************************************************/ + + +#ifndef _RKNN_API_H +#define _RKNN_API_H + +#ifdef __cplusplus +extern "C" { +#endif + +#include + +/* + Definition of extended flag for rknn_init. +*/ +/* set high priority context. */ +#define RKNN_FLAG_PRIOR_HIGH 0x00000000 + +/* set medium priority context */ +#define RKNN_FLAG_PRIOR_MEDIUM 0x00000001 + +/* set low priority context. */ +#define RKNN_FLAG_PRIOR_LOW 0x00000002 + +/* asynchronous mode. + when enable, rknn_outputs_get will not block for too long because it directly retrieves the result of + the previous frame which can increase the frame rate on single-threaded mode, but at the cost of + rknn_outputs_get not retrieves the result of the current frame. + in multi-threaded mode you do not need to turn this mode on. */ +#define RKNN_FLAG_ASYNC_MASK 0x00000004 + +/* collect performance mode. + when enable, you can get detailed performance reports via rknn_query(ctx, RKNN_QUERY_PERF_DETAIL, ...), + but it will reduce the frame rate. */ +#define RKNN_FLAG_COLLECT_PERF_MASK 0x00000008 + +/* allocate all memory in outside, includes weight/internal/inputs/outputs */ +#define RKNN_FLAG_MEM_ALLOC_OUTSIDE 0x00000010 + +/* weight sharing with the same network structure */ +#define RKNN_FLAG_SHARE_WEIGHT_MEM 0x00000020 + +/* send fence fd from outside */ +#define RKNN_FLAG_FENCE_IN_OUTSIDE 0x00000040 + +/* get fence fd from inside */ +#define RKNN_FLAG_FENCE_OUT_OUTSIDE 0x00000080 + +/* dummy init flag: could only get total_weight_size and total_internal_size by rknn_query*/ +#define RKNN_FLAG_COLLECT_MODEL_INFO_ONLY 0x00000100 + +/* allocate internal memory in outside */ +#define RKNN_FLAG_INTERNAL_ALLOC_OUTSIDE 0x00000200 + +/* set GPU as the preferred execution backend When the operator is not supported by the NPU */ +#define RKNN_FLAG_EXECUTE_FALLBACK_PRIOR_DEVICE_GPU 0x00000400 + +/* enable allocate sram type buffers */ +#define RKNN_FLAG_ENABLE_SRAM 0x00000800 + +/* sram type buffers are shared among different contexts */ +#define RKNN_FLAG_SHARE_SRAM 0x00001000 + +/* default nice -19, this flag can disable default priority */ +#define RKNN_FLAG_DISABLE_PROC_HIGH_PRIORITY 0x00002000 + +/* don't flush input buffer cache, the user must ensure that the input tensor has flushed the cache before calling rknn_run. +!!! Don't use this flags when you call rknn_inputs_set() to set input data. */ +#define RKNN_FLAG_DISABLE_FLUSH_INPUT_MEM_CACHE 0x00004000 + +/* Don't invalid output buffer cache. + Users cannot directly access output_mem->virt_addr, + which will cause cache consistency problems. + If you want to use output_mem->virt_addr, + you must use rknn_mem_sync (ctx, mem, RKNN_MEMORY_SYNC_FROM_DEVICE) to flush the cache. + This flags is generally used when the output data of the NPU is not accessed by the CPU, + but is accessed by the GPU or RGA to reduce the time required to flush the cache. + !!! Don't use this flags when you call rknn_outputs_get() to get output data.*/ +#define RKNN_FLAG_DISABLE_FLUSH_OUTPUT_MEM_CACHE 0x00008000 + +/* This flag is used when the model data buffer is allocated by NPU, and can be accessed by NPU directly. */ +#define RKNN_FLAG_MODEL_BUFFER_ZERO_COPY 0x00010000 + +/* This flag is a memory allocation flag, which is used in rknn_create_mem2() when no context is available. */ +#define RKNN_MEM_FLAG_ALLOC_NO_CONTEXT 0x00020000 + + +/* + Error code returned by the RKNN API. +*/ +#define RKNN_SUCC 0 /* execute succeed. */ +#define RKNN_ERR_FAIL -1 /* execute failed. */ +#define RKNN_ERR_TIMEOUT -2 /* execute timeout. */ +#define RKNN_ERR_DEVICE_UNAVAILABLE -3 /* device is unavailable. */ +#define RKNN_ERR_MALLOC_FAIL -4 /* memory malloc fail. */ +#define RKNN_ERR_PARAM_INVALID -5 /* parameter is invalid. */ +#define RKNN_ERR_MODEL_INVALID -6 /* model is invalid. */ +#define RKNN_ERR_CTX_INVALID -7 /* context is invalid. */ +#define RKNN_ERR_INPUT_INVALID -8 /* input is invalid. */ +#define RKNN_ERR_OUTPUT_INVALID -9 /* output is invalid. */ +#define RKNN_ERR_DEVICE_UNMATCH -10 /* the device is unmatch, please update rknn sdk + and npu driver/firmware. */ +#define RKNN_ERR_INCOMPATILE_PRE_COMPILE_MODEL -11 /* This RKNN model use pre_compile mode, but not compatible with current driver. */ +#define RKNN_ERR_INCOMPATILE_OPTIMIZATION_LEVEL_VERSION -12 /* This RKNN model set optimization level, but not compatible with current driver. */ +#define RKNN_ERR_TARGET_PLATFORM_UNMATCH -13 /* This RKNN model set target platform, but not compatible with current platform. */ + +/* + Definition for tensor +*/ +#define RKNN_MAX_DIMS 16 /* maximum dimension of tensor. */ +#define RKNN_MAX_NUM_CHANNEL 15 /* maximum channel number of input tensor. */ +#define RKNN_MAX_NAME_LEN 256 /* maximum name lenth of tensor. */ +#define RKNN_MAX_DYNAMIC_SHAPE_NUM 512 /* maximum number of dynamic shape for each input. */ + +#ifdef __arm__ +typedef uint32_t rknn_context; +#else +typedef uint64_t rknn_context; +#endif + + +/* + The query command for rknn_query +*/ +typedef enum _rknn_query_cmd { + RKNN_QUERY_IN_OUT_NUM = 0, /* query the number of input & output tensor. */ + RKNN_QUERY_INPUT_ATTR = 1, /* query the attribute of input tensor. */ + RKNN_QUERY_OUTPUT_ATTR = 2, /* query the attribute of output tensor. */ + RKNN_QUERY_PERF_DETAIL = 3, /* query the detail performance, need set + RKNN_FLAG_COLLECT_PERF_MASK when call rknn_init, + this query needs to be valid after rknn_outputs_get. */ + RKNN_QUERY_PERF_RUN = 4, /* query the time of run, + this query needs to be valid after rknn_outputs_get. */ + RKNN_QUERY_SDK_VERSION = 5, /* query the sdk & driver version */ + + RKNN_QUERY_MEM_SIZE = 6, /* query the weight & internal memory size */ + RKNN_QUERY_CUSTOM_STRING = 7, /* query the custom string */ + + RKNN_QUERY_NATIVE_INPUT_ATTR = 8, /* query the attribute of native input tensor. */ + RKNN_QUERY_NATIVE_OUTPUT_ATTR = 9, /* query the attribute of native output tensor. */ + + RKNN_QUERY_NATIVE_NC1HWC2_INPUT_ATTR = 8, /* query the attribute of native input tensor. */ + RKNN_QUERY_NATIVE_NC1HWC2_OUTPUT_ATTR = 9, /* query the attribute of native output tensor. */ + + RKNN_QUERY_NATIVE_NHWC_INPUT_ATTR = 10, /* query the attribute of native input tensor. */ + RKNN_QUERY_NATIVE_NHWC_OUTPUT_ATTR = 11, /* query the attribute of native output tensor. */ + + RKNN_QUERY_DEVICE_MEM_INFO = 12, /* query the attribute of rknn memory information. */ + + RKNN_QUERY_INPUT_DYNAMIC_RANGE = 13, /* query the dynamic shape range of rknn input tensor. */ + RKNN_QUERY_CURRENT_INPUT_ATTR = 14, /* query the current shape of rknn input tensor, only valid for dynamic rknn model*/ + RKNN_QUERY_CURRENT_OUTPUT_ATTR = 15, /* query the current shape of rknn output tensor, only valid for dynamic rknn model*/ + + RKNN_QUERY_CURRENT_NATIVE_INPUT_ATTR = 16, /* query the current native shape of rknn input tensor, only valid for dynamic rknn model*/ + RKNN_QUERY_CURRENT_NATIVE_OUTPUT_ATTR = 17, /* query the current native shape of rknn output tensor, only valid for dynamic rknn model*/ + + + RKNN_QUERY_CMD_MAX +} rknn_query_cmd; + +/* + the tensor data type. +*/ +typedef enum _rknn_tensor_type { + RKNN_TENSOR_FLOAT32 = 0, /* data type is float32. */ + RKNN_TENSOR_FLOAT16, /* data type is float16. */ + RKNN_TENSOR_INT8, /* data type is int8. */ + RKNN_TENSOR_UINT8, /* data type is uint8. */ + RKNN_TENSOR_INT16, /* data type is int16. */ + RKNN_TENSOR_UINT16, /* data type is uint16. */ + RKNN_TENSOR_INT32, /* data type is int32. */ + RKNN_TENSOR_UINT32, /* data type is uint32. */ + RKNN_TENSOR_INT64, /* data type is int64. */ + RKNN_TENSOR_BOOL, + RKNN_TENSOR_INT4, + + RKNN_TENSOR_TYPE_MAX +} rknn_tensor_type; + +inline static const char* get_type_string(rknn_tensor_type type) +{ + switch(type) { + case RKNN_TENSOR_FLOAT32: return "FP32"; + case RKNN_TENSOR_FLOAT16: return "FP16"; + case RKNN_TENSOR_INT8: return "INT8"; + case RKNN_TENSOR_UINT8: return "UINT8"; + case RKNN_TENSOR_INT16: return "INT16"; + case RKNN_TENSOR_UINT16: return "UINT16"; + case RKNN_TENSOR_INT32: return "INT32"; + case RKNN_TENSOR_UINT32: return "UINT32"; + case RKNN_TENSOR_INT64: return "INT64"; + case RKNN_TENSOR_BOOL: return "BOOL"; + case RKNN_TENSOR_INT4: return "INT4"; + default: return "UNKNOW"; + } +} + +/* + the quantitative type. +*/ +typedef enum _rknn_tensor_qnt_type { + RKNN_TENSOR_QNT_NONE = 0, /* none. */ + RKNN_TENSOR_QNT_DFP, /* dynamic fixed point. */ + RKNN_TENSOR_QNT_AFFINE_ASYMMETRIC, /* asymmetric affine. */ + + RKNN_TENSOR_QNT_MAX +} rknn_tensor_qnt_type; + +inline static const char* get_qnt_type_string(rknn_tensor_qnt_type type) +{ + switch(type) { + case RKNN_TENSOR_QNT_NONE: return "NONE"; + case RKNN_TENSOR_QNT_DFP: return "DFP"; + case RKNN_TENSOR_QNT_AFFINE_ASYMMETRIC: return "AFFINE"; + default: return "UNKNOW"; + } +} + +/* + the tensor data format. +*/ +typedef enum _rknn_tensor_format { + RKNN_TENSOR_NCHW = 0, /* data format is NCHW. */ + RKNN_TENSOR_NHWC, /* data format is NHWC. */ + RKNN_TENSOR_NC1HWC2, /* data format is NC1HWC2. */ + RKNN_TENSOR_UNDEFINED, + + RKNN_TENSOR_FORMAT_MAX +} rknn_tensor_format; + +/* + the mode of running on target NPU core. +*/ +typedef enum _rknn_core_mask { + RKNN_NPU_CORE_AUTO = 0, /* default, run on NPU core randomly. */ + RKNN_NPU_CORE_0 = 1, /* run on NPU core 0. */ + RKNN_NPU_CORE_1 = 2, /* run on NPU core 1. */ + RKNN_NPU_CORE_2 = 4, /* run on NPU core 2. */ + RKNN_NPU_CORE_0_1 = RKNN_NPU_CORE_0 | RKNN_NPU_CORE_1, /* run on NPU core 0 and core 1. */ + RKNN_NPU_CORE_0_1_2 = RKNN_NPU_CORE_0_1 | RKNN_NPU_CORE_2, /* run on NPU core 0 and core 1 and core 2. */ + RKNN_NPU_CORE_ALL = 0xffff, /* auto choice, run on NPU cores depending on platform */ + + RKNN_NPU_CORE_UNDEFINED, +} rknn_core_mask; + +inline static const char* get_format_string(rknn_tensor_format fmt) +{ + switch(fmt) { + case RKNN_TENSOR_NCHW: return "NCHW"; + case RKNN_TENSOR_NHWC: return "NHWC"; + case RKNN_TENSOR_NC1HWC2: return "NC1HWC2"; + case RKNN_TENSOR_UNDEFINED: return "UNDEFINED"; + default: return "UNKNOW"; + } +} + +/* + the information for RKNN_QUERY_IN_OUT_NUM. +*/ +typedef struct _rknn_input_output_num { + uint32_t n_input; /* the number of input. */ + uint32_t n_output; /* the number of output. */ +} rknn_input_output_num; + +/* + the information for RKNN_QUERY_INPUT_ATTR / RKNN_QUERY_OUTPUT_ATTR. +*/ +typedef struct _rknn_tensor_attr { + uint32_t index; /* input parameter, the index of input/output tensor, + need set before call rknn_query. */ + + uint32_t n_dims; /* the number of dimensions. */ + uint32_t dims[RKNN_MAX_DIMS]; /* the dimensions array. */ + char name[RKNN_MAX_NAME_LEN]; /* the name of tensor. */ + + uint32_t n_elems; /* the number of elements. */ + uint32_t size; /* the bytes size of tensor. */ + + rknn_tensor_format fmt; /* the data format of tensor. */ + rknn_tensor_type type; /* the data type of tensor. */ + rknn_tensor_qnt_type qnt_type; /* the quantitative type of tensor. */ + int8_t fl; /* fractional length for RKNN_TENSOR_QNT_DFP. */ + int32_t zp; /* zero point for RKNN_TENSOR_QNT_AFFINE_ASYMMETRIC. */ + float scale; /* scale for RKNN_TENSOR_QNT_AFFINE_ASYMMETRIC. */ + + uint32_t w_stride; /* the stride of tensor along the width dimention of input, + Note: it is read-only, 0 means equal to width. */ + uint32_t size_with_stride; /* the bytes size of tensor with stride. */ + + uint8_t pass_through; /* pass through mode, for rknn_set_io_mem interface. + if TRUE, the buf data is passed directly to the input node of the rknn model + without any conversion. the following variables do not need to be set. + if FALSE, the buf data is converted into an input consistent with the model + according to the following type and fmt. so the following variables + need to be set.*/ + uint32_t h_stride; /* the stride along the height dimention of input, + Note: it is write-only, if it was set to 0, h_stride = height. */ +} rknn_tensor_attr; + +typedef struct _rknn_input_range { + uint32_t index; /* input parameter, the index of input/output tensor, + need set before call rknn_query. */ + uint32_t shape_number; /* the number of shape. */ + rknn_tensor_format fmt; /* the data format of tensor. */ + char name[RKNN_MAX_NAME_LEN]; /* the name of tensor. */ + uint32_t dyn_range[RKNN_MAX_DYNAMIC_SHAPE_NUM][RKNN_MAX_DIMS]; /* the dynamic input dimensions range. */ + uint32_t n_dims; /* the number of dimensions. */ + +} rknn_input_range; + +/* + the information for RKNN_QUERY_PERF_DETAIL. +*/ +typedef struct _rknn_perf_detail { + char* perf_data; /* the string pointer of perf detail. don't need free it by user. */ + uint64_t data_len; /* the string length. */ +} rknn_perf_detail; + +/* + the information for RKNN_QUERY_PERF_RUN. +*/ +typedef struct _rknn_perf_run { + int64_t run_duration; /* real inference time (us) */ +} rknn_perf_run; + +/* + the information for RKNN_QUERY_SDK_VERSION. +*/ +typedef struct _rknn_sdk_version { + char api_version[256]; /* the version of rknn api. */ + char drv_version[256]; /* the version of rknn driver. */ +} rknn_sdk_version; + +/* + the information for RKNN_QUERY_MEM_SIZE. +*/ +typedef struct _rknn_mem_size { + uint32_t total_weight_size; /* the weight memory size */ + uint32_t total_internal_size; /* the internal memory size, exclude inputs/outputs */ + uint64_t total_dma_allocated_size; /* total dma memory allocated size */ + uint32_t total_sram_size; /* total system sram size reserved for rknn */ + uint32_t free_sram_size; /* free system sram size reserved for rknn */ + uint32_t reserved[10]; /* reserved */ +} rknn_mem_size; + +/* + the information for RKNN_QUERY_CUSTOM_STRING. +*/ +typedef struct _rknn_custom_string { + char string[1024]; /* the string of custom, lengths max to 1024 bytes */ +} rknn_custom_string; + +/* + The flags of rknn_tensor_mem. +*/ +typedef enum _rknn_tensor_mem_flags { + RKNN_TENSOR_MEMORY_FLAGS_ALLOC_INSIDE = 1, /*Used to mark in rknn_destroy_mem() whether it is necessary to release the "mem" pointer itself. + If the flag RKNN_TENSOR_MEMORY_FLAGS_ALLOC_INSIDE is set, rknn_destroy_mem() will call free(mem).*/ + RKNN_TENSOR_MEMORY_FLAGS_FROM_FD = 2, /*Used to mark in rknn_create_mem_from_fd() whether it is necessary to release the "mem" pointer itself. + If the flag RKNN_TENSOR_MEMORY_FLAGS_FROM_FD is set, rknn_destroy_mem() will call free(mem).*/ + RKNN_TENSOR_MEMORY_FLAGS_FROM_PHYS = 3, /*Used to mark in rknn_create_mem_from_phys() whether it is necessary to release the "mem" pointer itself. + If the flag RKNN_TENSOR_MEMORY_FLAGS_FROM_PHYS is set, rknn_destroy_mem() will call free(mem).*/ + RKNN_TENSOR_MEMORY_FLAGS_UNKNOWN +} rknn_tensor_mem_flags; + +/* + The mode to sync cacheable rknn memory. +*/ +typedef enum _rknn_mem_alloc_flags { + RKNN_FLAG_MEMORY_FLAGS_DEFAULT = 0 << 0, /* Same with RKNN_FLAG_MEMORY_CACHEABLE */ + RKNN_FLAG_MEMORY_CACHEABLE = 1 << 0, /* Create Cacheable memory. */ + RKNN_FLAG_MEMORY_NON_CACHEABLE = 1 << 1, /* Create NON-Cacheable memory. */ +} rknn_mem_alloc_flags; + +/* + The mode to sync cacheable rknn memory. +*/ +typedef enum _rknn_mem_sync_mode { + RKNN_MEMORY_SYNC_TO_DEVICE = 0x1, /* the mode used for consistency of device access after CPU accesses data. */ + RKNN_MEMORY_SYNC_FROM_DEVICE = 0x2, /* the mode used for consistency of CPU access after device accesses data. */ + RKNN_MEMORY_SYNC_BIDIRECTIONAL = RKNN_MEMORY_SYNC_TO_DEVICE | RKNN_MEMORY_SYNC_FROM_DEVICE, /* the mode used for consistency of data access + between device and CPU in both directions. */ +} rknn_mem_sync_mode; + +/* + the memory information of tensor. +*/ +typedef struct _rknn_tensor_memory { + void* virt_addr; /* the virtual address of tensor buffer. */ + uint64_t phys_addr; /* the physical address of tensor buffer. */ + int32_t fd; /* the fd of tensor buffer. */ + int32_t offset; /* indicates the offset of the memory. */ + uint32_t size; /* the size of tensor buffer. */ + uint32_t flags; /* the flags of tensor buffer, reserved */ + void * priv_data; /* the private data of tensor buffer. */ +} rknn_tensor_mem; + +/* + the input information for rknn_input_set. +*/ +typedef struct _rknn_input { + uint32_t index; /* the input index. */ + void* buf; /* the input buf for index. */ + uint32_t size; /* the size of input buf. */ + uint8_t pass_through; /* pass through mode. + if TRUE, the buf data is passed directly to the input node of the rknn model + without any conversion. the following variables do not need to be set. + if FALSE, the buf data is converted into an input consistent with the model + according to the following type and fmt. so the following variables + need to be set.*/ + rknn_tensor_type type; /* the data type of input buf. */ + rknn_tensor_format fmt; /* the data format of input buf. + currently the internal input format of NPU is NCHW by default. + so entering NCHW data can avoid the format conversion in the driver. */ +} rknn_input; + +/* + the output information for rknn_outputs_get. +*/ +typedef struct _rknn_output { + uint8_t want_float; /* want transfer output data to float */ + uint8_t is_prealloc; /* whether buf is pre-allocated. + if TRUE, the following variables need to be set. + if FALSE, the following variables do not need to be set. */ + uint32_t index; /* the output index. */ + void* buf; /* the output buf for index. + when is_prealloc = FALSE and rknn_outputs_release called, + this buf pointer will be free and don't use it anymore. */ + uint32_t size; /* the size of output buf. */ +} rknn_output; + +/* + the extend information for rknn_init. +*/ +typedef struct _rknn_init_extend { + rknn_context ctx; /* rknn context */ + int32_t real_model_offset; /* real rknn model file offset, only valid when init context with rknn file path and zero-copy model model */ + uint32_t real_model_size; /* real rknn model file size, only valid when init context with rknn file path and zero-copy model model */ + int32_t model_buffer_fd; /* the fd of model buffer. */ + uint32_t model_buffer_flags; /* the flags of model_buffer */ + uint8_t reserved[112]; /* reserved */ +} rknn_init_extend; + +/* + the extend information for rknn_run. +*/ +typedef struct _rknn_run_extend { + uint64_t frame_id; /* output parameter, indicate current frame id of run. */ + int32_t non_block; /* block flag of run, 0 is block else 1 is non block */ + int32_t timeout_ms; /* timeout for block mode, in milliseconds */ + int32_t fence_fd; /* fence fd from other unit */ +} rknn_run_extend; + +/* + the extend information for rknn_outputs_get. +*/ +typedef struct _rknn_output_extend { + uint64_t frame_id; /* output parameter, indicate the frame id of outputs, corresponds to + struct rknn_run_extend.frame_id.*/ +} rknn_output_extend; + + +/* rknn_init + + initial the context and load the rknn model. + + input: + rknn_context* context the pointer of context handle. + void* model if size > 0, pointer to the rknn model, if size = 0, filepath to the rknn model. + uint32_t size the size of rknn model. + uint32_t flag extend flag, see the define of RKNN_FLAG_XXX_XXX. + rknn_init_extend* extend the extend information of init. + return: + int error code. +*/ +int rknn_init(rknn_context* context, void* model, uint32_t size, uint32_t flag, rknn_init_extend* extend); + +/* rknn_dup_context + + initial the context and load the rknn model. + + input: + rknn_context* context_in the pointer of context in handle. + rknn_context* context_out the pointer of context out handle. + return: + int error code. +*/ +int rknn_dup_context(rknn_context* context_in, rknn_context* context_out); + +/* rknn_destroy + + unload the rknn model and destroy the context. + + input: + rknn_context context the handle of context. + return: + int error code. +*/ +int rknn_destroy(rknn_context context); + + +/* rknn_query + + query the information about model or others. see rknn_query_cmd. + + input: + rknn_context context the handle of context. + rknn_query_cmd cmd the command of query. + void* info the buffer point of information. + uint32_t size the size of information. + return: + int error code. +*/ +int rknn_query(rknn_context context, rknn_query_cmd cmd, void* info, uint32_t size); + + +/* rknn_inputs_set + + set inputs information by input index of rknn model. + inputs information see rknn_input. + + input: + rknn_context context the handle of context. + uint32_t n_inputs the number of inputs. + rknn_input inputs[] the arrays of inputs information, see rknn_input. + return: + int error code +*/ +int rknn_inputs_set(rknn_context context, uint32_t n_inputs, rknn_input inputs[]); + +/* + rknn_set_batch_core_num + + set rknn batch core_num. + + input: + rknn_context context the handle of context. + int core_num the core number. + return: + int error code. + +*/ +int rknn_set_batch_core_num(rknn_context context, int core_num); + +/* rknn_set_core_mask + + set rknn core mask.(only supported on RK3588 now) + + RKNN_NPU_CORE_AUTO: auto mode, default value + RKNN_NPU_CORE_0: core 0 mode + RKNN_NPU_CORE_1: core 1 mode + RKNN_NPU_CORE_2: core 2 mode + RKNN_NPU_CORE_0_1: combine core 0/1 mode + RKNN_NPU_CORE_0_1_2: combine core 0/1/2 mode + RKNN_NPU_CORE_ALL: auto mode, select multiple npu cores to run depending on platform + + + input: + rknn_context context the handle of context. + rknn_core_mask core_mask the core mask. + return: + int error code. +*/ +int rknn_set_core_mask(rknn_context context, rknn_core_mask core_mask); + +/* rknn_run + + run the model to execute inference. + + input: + rknn_context context the handle of context. + rknn_run_extend* extend the extend information of run. + return: + int error code. +*/ +int rknn_run(rknn_context context, rknn_run_extend* extend); + + +/* rknn_wait + + wait the model after execute inference. + + input: + rknn_context context the handle of context. + rknn_run_extend* extend the extend information of run. + return: + int error code. +*/ +int rknn_wait(rknn_context context, rknn_run_extend* extend); + + +/* rknn_outputs_get + + wait the inference to finish and get the outputs. + this function will block until inference finish. + the results will set to outputs[]. + + input: + rknn_context context the handle of context. + uint32_t n_outputs the number of outputs. + rknn_output outputs[] the arrays of output, see rknn_output. + rknn_output_extend* the extend information of output. + return: + int error code. +*/ +int rknn_outputs_get(rknn_context context, uint32_t n_outputs, rknn_output outputs[], rknn_output_extend* extend); + + +/* rknn_outputs_release + + release the outputs that get by rknn_outputs_get. + after called, the rknn_output[x].buf get from rknn_outputs_get will + also be free when rknn_output[x].is_prealloc = FALSE. + + input: + rknn_context context the handle of context. + uint32_t n_ouputs the number of outputs. + rknn_output outputs[] the arrays of output. + return: + int error code +*/ +int rknn_outputs_release(rknn_context context, uint32_t n_ouputs, rknn_output outputs[]); + + +/* new api for zero copy */ + +/* rknn_create_mem_from_phys (memory allocated outside) + + initialize tensor memory from physical address. + + input: + rknn_context ctx the handle of context. + uint64_t phys_addr physical address. + void *virt_addr virtual address. + uint32_t size the size of tensor buffer. + return: + rknn_tensor_mem the pointer of tensor memory information. +*/ +rknn_tensor_mem* rknn_create_mem_from_phys(rknn_context ctx, uint64_t phys_addr, void *virt_addr, uint32_t size); + + +/* rknn_create_mem_from_fd (memory allocated outside) + + initialize tensor memory from file description. + + input: + rknn_context ctx the handle of context. + int32_t fd file description. + void *virt_addr virtual address. + uint32_t size the size of tensor buffer. + int32_t offset indicates the offset of the memory (virt_addr without offset). + return: + rknn_tensor_mem the pointer of tensor memory information. +*/ +rknn_tensor_mem* rknn_create_mem_from_fd(rknn_context ctx, int32_t fd, void *virt_addr, uint32_t size, int32_t offset); + + +/* rknn_create_mem_from_mb_blk (memory allocated outside) + + create tensor memory from mb_blk. + + input: + rknn_context ctx the handle of context. + void *mb_blk mb_blk allocate from system api. + int32_t offset indicates the offset of the memory. + return: + rknn_tensor_mem the pointer of tensor memory information. +*/ +rknn_tensor_mem* rknn_create_mem_from_mb_blk(rknn_context ctx, void *mb_blk, int32_t offset); + + +/* rknn_create_mem (memory allocated inside) + + create tensor memory. + + input: + rknn_context ctx the handle of context. + uint32_t size the size of tensor buffer. + return: + rknn_tensor_mem the pointer of tensor memory information. +*/ +rknn_tensor_mem* rknn_create_mem(rknn_context ctx, uint32_t size); + +/* rknn_create_mem2 (memory allocated inside) + + create tensor memory. + + input: + rknn_context ctx the handle of context. + uint64_t size the size of tensor buffer. + uint64_t alloc_flags control the memory is cacheable + return: + rknn_tensor_mem the pointer of tensor memory information. +*/ +rknn_tensor_mem* rknn_create_mem2(rknn_context ctx, uint64_t size, uint64_t alloc_flags); + +/* rknn_destroy_mem (support allocate inside and outside) + + destroy tensor memory. + + input: + rknn_context ctx the handle of context. + rknn_tensor_mem *mem the pointer of tensor memory information. + return: + int error code +*/ +int rknn_destroy_mem(rknn_context ctx, rknn_tensor_mem *mem); + + +/* rknn_set_weight_mem + + set the weight memory. + + input: + rknn_context ctx the handle of context. + rknn_tensor_mem *mem the array of tensor memory information + return: + int error code. +*/ +int rknn_set_weight_mem(rknn_context ctx, rknn_tensor_mem *mem); + + +/* rknn_set_internal_mem + + set the internal memory. + + input: + rknn_context ctx the handle of context. + rknn_tensor_mem *mem the array of tensor memory information + return: + int error code. +*/ +int rknn_set_internal_mem(rknn_context ctx, rknn_tensor_mem *mem); + + +/* rknn_set_io_mem + + set the input and output tensors buffer. + + input: + rknn_context ctx the handle of context. + rknn_tensor_mem *mem the array of tensor memory information. + rknn_tensor_attr *attr the attribute of input or output tensor buffer. + return: + int error code. +*/ +int rknn_set_io_mem(rknn_context ctx, rknn_tensor_mem *mem, rknn_tensor_attr *attr); + +/* rknn_set_input_shape(deprecated) + + set the input tensor shape (only valid for dynamic shape rknn model). + + input: + rknn_context ctx the handle of context. + rknn_tensor_attr *attr the attribute of input or output tensor buffer. + return: + int error code. +*/ +int rknn_set_input_shape(rknn_context ctx, rknn_tensor_attr* attr); + +/* rknn_set_input_shapes + + set all the input tensor shapes. graph will run under current set of input shapes after rknn_set_input_shapes.(only valid for dynamic shape rknn model). + + input: + rknn_context ctx the handle of context. + uint32_t n_inputs the number of inputs. + rknn_tensor_attr attr[] the attribute array of all input tensors. + return: + int error code. +*/ +int rknn_set_input_shapes(rknn_context ctx, uint32_t n_inputs, rknn_tensor_attr attr[]); + +/* rknn_mem_sync + + sync cacheable rknn memory when both cpu and device access data. + + input: + rknn_context context the handle of context. + rknn_tensor_mem *mem the pointer of tensor memory information. + rknn_mem_sync_mode mode the mode of sync cache. + return: + int error code. +*/ +int rknn_mem_sync(rknn_context context, rknn_tensor_mem* mem, rknn_mem_sync_mode mode); + +#ifdef __cplusplus +} //extern "C" +#endif + +#endif //_RKNN_API_H diff --git a/rknpu2/runtime/Android/librknn_api/include/rknn_custom_op.h b/rknpu2/runtime/Android/librknn_api/include/rknn_custom_op.h new file mode 100755 index 0000000000000000000000000000000000000000..253263f77958ad0f97b10a323e14f032ef51eca9 --- /dev/null +++ b/rknpu2/runtime/Android/librknn_api/include/rknn_custom_op.h @@ -0,0 +1,144 @@ +/**************************************************************************** + * + * Copyright (c) 2017 - 2023 by Rockchip Corp. All rights reserved. + * + * The material in this file is confidential and contains trade secrets + * of Rockchip Corporation. This is proprietary information owned by + * Rockchip Corporation. No part of this work may be disclosed, + * reproduced, copied, transmitted, or used in any way for any purpose, + * without the express written permission of Rockchip Corporation. + * + *****************************************************************************/ + +#ifndef _RKNN_CUSTOM_OP_H +#define _RKNN_CUSTOM_OP_H + +#ifdef __cplusplus +extern "C" { +#endif + +#include "rknn_api.h" + +#include + +/* + Error code returned by the RKNN Custom Operator API. +*/ +#define RKNN_WARNING_SKIP_CUSTOM_OP_COMPUTE -14 /* if custom op init callback funtion return this code and op type is supported by RKNN, it will use RKNN implementation. */ + +#define RKNN_CUSTOM_OP_MAX_STR_LEN 64 +#define RKNN_CUSTOM_OP_MAX_VALUE_LEN 32 +#define RKNN_CUSTOM_OP_EXPORT __attribute__((visibility("default"))) + +#ifdef __arm__ +typedef uint32_t rknn_custom_op_interal_context; +#else +typedef uint64_t rknn_custom_op_interal_context; +#endif +/* + the backend execution device of custom operator. +*/ +typedef enum _rknn_target_type +{ + RKNN_TARGET_TYPE_CPU = 1, /* backend device is cpu */ + RKNN_TARGET_TYPE_GPU = 2, /* backend device is gpu */ + RKNN_TARGET_TYPE_MAX +} rknn_target_type; + +typedef struct _rknn_gpu_op_context +{ + void* cl_context; + void* cl_command_queue; + void* cl_kernel; + +} rknn_gpu_op_context; + +typedef struct _rknn_custom_op_context +{ + rknn_target_type target; /* custom op backend target */ + rknn_custom_op_interal_context internal_ctx; /* the context of custom op*/ + rknn_gpu_op_context gpu_ctx; /* the gpu context of custom op */ + void* priv_data; /* the private data managed by user */ +} rknn_custom_op_context; + +typedef struct _rknn_custom_op_tensor +{ + rknn_tensor_attr attr; /* the attribute of tensor buffer. */ + rknn_tensor_mem mem; /* the memory information of tensor. */ +} rknn_custom_op_tensor; + +typedef struct _rknn_custom_op_attr +{ + char name[RKNN_MAX_NAME_LEN]; /* the name of operator atrributes. */ + rknn_tensor_type dtype; /* the data type of operator attributes, indicate the 'array' type. */ + uint32_t n_elems; /* the number of 'array'. */ + void* data; /* the array pointer of operator attributes, the data type of each element is determined by type. */ +} rknn_custom_op_attr; + +/* + the information of custom operator to add to the rknn_context. +*/ +typedef struct _rknn_custom_op +{ + uint32_t version; /* custom op version */ + rknn_target_type target; /* custom op backend target */ + char op_type[RKNN_MAX_NAME_LEN]; /* custom op type */ + + char cl_kernel_name[RKNN_MAX_NAME_LEN]; /* the opencl kernel name used by custom op */ + char* cl_kernel_source; /* if cl_source_size > 0, pointer to the cl kernel source string, if cl_source_size = 0, + filepath to the cl kernel file. */ + uint64_t cl_source_size; /* the size of cl_kernel_source */ + char cl_build_options[RKNN_MAX_NAME_LEN]; /* the options for opencl to build clProgram used by custom op */ + + /** + * The callback function sets that the users need to code + */ + int (*init)(rknn_custom_op_context* op_ctx, rknn_custom_op_tensor* inputs, uint32_t n_inputs, + rknn_custom_op_tensor* outputs, uint32_t n_outputs); /* [optional] custom op kernel init falllback function*/ + int (*prepare)(rknn_custom_op_context* op_ctx, rknn_custom_op_tensor* inputs, uint32_t n_inputs, + rknn_custom_op_tensor* outputs, uint32_t n_outputs); /* [optional] custom op kernel prepare falllback function*/ + int (*compute)(rknn_custom_op_context* op_ctx, rknn_custom_op_tensor* inputs, uint32_t n_inputs, + rknn_custom_op_tensor* outputs, uint32_t n_outputs); /* [required] custom op kernel compute falllback function */ + int (*compute_native)(rknn_custom_op_context* op_ctx, rknn_custom_op_tensor* inputs, uint32_t n_inputs, + rknn_custom_op_tensor* outputs, uint32_t n_outputs); /* [optional] custom op kernel compute with native attribute falllback function */ + int (*destroy)(rknn_custom_op_context* op_ctx); /* [optional] custom op kernel compute falllback function */ + +} rknn_custom_op; + +/** + * dlopen custom op with so required this function + */ +typedef rknn_custom_op* (*get_custom_op_func)(); + +/* rknn_register_custom_ops + + Register custom operators to rknn_context. + Steps to use a custom op: + 1. Create a rknn_custom_op structure array and fill in it. + 2. Setup prepare/compute/compute_native/destroy callback function and add them to the + rknn_custom_op.(compute is required and other function is optional, compute_native is not supported now, set it + to nullptr) + 3. Call rknn_register_custom_ops to register the op type after rknn_init. + input: + rknn_context ctx the handle of context. + rknn_custom_op* op the custom operator array, each of which contains operator information and calllback function. + uint32_t custom_op_num the length of rknn_custom_op array. + return: + int error code. +*/ +int rknn_register_custom_ops(rknn_context ctx, rknn_custom_op* op, uint32_t custom_op_num); + +/* rknn_custom_op_get_op_attr + + input: + rknn_custom_op_context* op_ctx the handle of custom op context. + const char* attr_name the attribute name of operator. + rknn_custom_op_attr* op_attr the data and information of operator attributes. +*/ +void rknn_custom_op_get_op_attr(rknn_custom_op_context* op_ctx, const char* attr_name, rknn_custom_op_attr* op_attr); + +#ifdef __cplusplus +} // extern "C" +#endif + +#endif //_RKNN_CUSTOM_OP_H diff --git a/rknpu2/runtime/Android/librknn_api/include/rknn_matmul_api.h b/rknpu2/runtime/Android/librknn_api/include/rknn_matmul_api.h new file mode 100644 index 0000000000000000000000000000000000000000..0ae78798c3945042cc8079c351919166cb05571f --- /dev/null +++ b/rknpu2/runtime/Android/librknn_api/include/rknn_matmul_api.h @@ -0,0 +1,540 @@ +/**************************************************************************** + * + * Copyright (c) 2017 - 2018 by Rockchip Corp. All rights reserved. + * + * The material in this file is confidential and contains trade secrets + * of Rockchip Corporation. This is proprietary information owned by + * Rockchip Corporation. No part of this work may be disclosed, + * reproduced, copied, transmitted, or used in any way for any purpose, + * without the express written permission of Rockchip Corporation. + * + *****************************************************************************/ + +#ifndef _RKNN_MATMUL_API_H +#define _RKNN_MATMUL_API_H + +#ifdef __cplusplus +extern "C" { +#endif + +#include "rknn_api.h" + +typedef rknn_context rknn_matmul_ctx; + +typedef enum _rknn_matmul_quant_type +{ + RKNN_QUANT_TYPE_PER_LAYER_SYM = 0, + RKNN_QUANT_TYPE_PER_LAYER_ASYM = 1, + RKNN_QUANT_TYPE_PER_CHANNEL_SYM = 2, + RKNN_QUANT_TYPE_PER_CHANNEL_ASYM = 3, + RKNN_QUANT_TYPE_PER_GROUP_SYM = 4, + RKNN_QUANT_TYPE_PER_GROUP_ASYM = 5, +} rknn_matmul_quant_type; + +typedef struct _rknn_quant_params +{ + char name[RKNN_MAX_NAME_LEN]; + + // matmul tensor scale + float* scale; + int32_t scale_len; + + // matmul tensor zero point + int32_t* zp; + int32_t zp_len; + +} rknn_quant_params; + +typedef enum _rknn_matmul_type +{ + RKNN_FLOAT16_MM_FLOAT16_TO_FLOAT32 = 1, + RKNN_INT8_MM_INT8_TO_INT32 = 2, + RKNN_INT8_MM_INT8_TO_INT8 = 3, + RKNN_FLOAT16_MM_FLOAT16_TO_FLOAT16 = 4, + RKNN_FLOAT16_MM_INT8_TO_FLOAT32 = 5, + RKNN_FLOAT16_MM_INT8_TO_FLOAT16 = 6, + RKNN_FLOAT16_MM_INT4_TO_FLOAT32 = 7, + RKNN_FLOAT16_MM_INT4_TO_FLOAT16 = 8, + RKNN_INT8_MM_INT8_TO_FLOAT32 = 9, + RKNN_INT4_MM_INT4_TO_INT16 = 10, + RKNN_INT8_MM_INT4_TO_INT32 = 11, +} rknn_matmul_type; + +inline static const char* get_matmul_type_string(rknn_matmul_type type) +{ + switch (type) { + case RKNN_FLOAT16_MM_FLOAT16_TO_FLOAT32: + return "RKNN_FLOAT16_MM_FLOAT16_TO_FLOAT32"; + case RKNN_INT8_MM_INT8_TO_INT32: + return "RKNN_INT8_MM_INT8_TO_INT32"; + case RKNN_INT8_MM_INT8_TO_INT8: + return "RKNN_INT8_MM_INT8_TO_INT8"; + case RKNN_FLOAT16_MM_FLOAT16_TO_FLOAT16: + return "RKNN_FLOAT16_MM_FLOAT16_TO_FLOAT16"; + case RKNN_FLOAT16_MM_INT8_TO_FLOAT32: + return "RKNN_FLOAT16_MM_INT8_TO_FLOAT32"; + case RKNN_FLOAT16_MM_INT8_TO_FLOAT16: + return "RKNN_FLOAT16_MM_INT8_TO_FLOAT16"; + case RKNN_INT4_MM_INT4_TO_INT16: + return "RKNN_INT4_MM_INT4_TO_INT16"; + case RKNN_FLOAT16_MM_INT4_TO_FLOAT32: + return "RKNN_FLOAT16_MM_INT4_TO_FLOAT32"; + case RKNN_FLOAT16_MM_INT4_TO_FLOAT16: + return "RKNN_FLOAT16_MM_INT4_TO_FLOAT16"; + case RKNN_INT8_MM_INT4_TO_INT32: + return "RKNN_INT8_MM_INT4_TO_INT32"; + case RKNN_INT8_MM_INT8_TO_FLOAT32: + return "RKNN_INT8_MM_INT8_TO_FLOAT32"; + default: + return "UNKNOW"; + } +} + +typedef struct _rknn_matmul_tensor_attr +{ + char name[RKNN_MAX_NAME_LEN]; + + // indicate A(M, K) or B(K, N) or C(M, N) + uint32_t n_dims; + uint32_t dims[RKNN_MAX_DIMS]; + + // matmul tensor size + uint32_t size; + + // matmul tensor data type + // int8 : A, B + // int32: C + rknn_tensor_type type; + +} rknn_matmul_tensor_attr; + +typedef struct _rknn_matmul_io_attr +{ + // indicate A(M, K) or B(K, N) or C(M, N) + rknn_matmul_tensor_attr A; + rknn_matmul_tensor_attr B; + rknn_matmul_tensor_attr C; +} rknn_matmul_io_attr; + +/* + matmul dynamic shape struct +*/ +typedef struct _rknn_matmul_shape +{ + int32_t M; + int32_t K; + int32_t N; +} rknn_matmul_shape; + +/* + the layout of matmul input/output tensor. +*/ +typedef enum +{ + RKNN_MM_LAYOUT_NORM = 0, + RKNN_MM_LAYOUT_NATIVE = 1, + RKNN_MM_LAYOUT_TP_NORM = 2, +} rknn_matmul_layout; + +/* + matmul information struct + */ +typedef struct rknn_matmul_info_t +{ + int32_t M; + int32_t K; // limit: RK3566/3568: int8 type must be aligned with 32byte, float16 type must be aligned with 16byte; + // RK3562: int8 type must be aligned with 32byte, float16 type must be aligned with 32byte; + // RK3588/3576: int8 type must be aligned with 32byte, float16 type must be aligned with 32byte, + // int4 type must be aligned with 32byte; + int32_t N; // limit: RK3566/3568: int8 type must be aligned with 16byte, float16 type must be aligned with 8byte; + // RK3562: int8 type must be aligned with 16byte, float16 type must be aligned with 8byte; + // RK3588/3576: int8 type must be aligned with 32byte, float16 type must be aligned with 16byte, + // int4 type must be aligned with 64byte; + // matmul data type + // int4: int4(A) x int4(B) -> int16(C) + // int8: int8(A) x int8(B) -> int32(C) + // float16: float16(A) x float16(B) -> float32(C) + rknn_matmul_type type; + + // matmul native layout for B + // 0: normal layout + // 1: native layout + int16_t B_layout; + + // matmul quant type for B + // A and C only support per layer + // 0: per layer + // 1: per channel + // 2: per group + int16_t B_quant_type; + + // matmul native layout for A and C + // 0: normal layout + // 1: native layout + int16_t AC_layout; + + // matmul quant type for A and C, only support 0 + int16_t AC_quant_type; + + // iommu domain id, each domain has 4GB of space + int32_t iommu_domain_id; + + // B_quant_type set 2, group size is enable + int16_t group_size; + + // reserved field + int8_t reserved[34]; +} rknn_matmul_info; + +/* rknn_matmul_create + + params: + rknn_matmul_ctx *ctx the handle of context. + rknn_matmul_info *info the matmal information. + rknn_matmul_io_attr *io_attr inputs/output attribute + return: + int error code +*/ +int rknn_matmul_create(rknn_matmul_ctx* ctx, rknn_matmul_info* info, rknn_matmul_io_attr* io_attr); + +/* rknn_matmul_create_dynamic_shape + + params: + rknn_matmul_ctx *ctx the handle of context. + rknn_matmul_info *info the matmal information. + int shape_num the supported shape number of matmul. + rknn_matmul_shape dynamic_shapes[] the supported M,K,N shape struct array. + rknn_matmul_io_attr *io_attr the array of inputs and output attribute + return: + int error code +*/ +/* + 原来的info.M, K, N无效 +*/ +int rknn_matmul_create_dynamic_shape(rknn_matmul_ctx* ctx, rknn_matmul_info* info, int shape_num, + rknn_matmul_shape dynamic_shapes[], rknn_matmul_io_attr io_attrs[]); + +/* rknn_matmul_set_io_mem + + params: + rknn_matmul_ctx ctx the handle of context. + rknn_tensor_mem *mem the pointer of tensor memory information. + rknn_matmul_tensor_attr *attr the attribute of input or output tensor buffer. + return: + int error code. + + formula: + C = A * B, + + limit: + K max: k <= 10240 + K limit: RK3566/3568: int8 type must be aligned with 32byte, float16 type must be aligned with 16byte; + RK3562: int8 type must be aligned with 32byte, float16 type must be aligned with 32byte; + RK3588/3576: int8 type must be aligned with 32byte, float16 type must be aligned with 32byte, + int4 type must be aligned with 32byte; + N limit: RK3566/3568: int8 type must be aligned with 16byte, float16 type must be aligned with 8byte; + RK3562: int8 type must be aligned with 16byte, float16 type must be aligned with 8byte; + RK3588/3576: int8 type must be aligned with 32byte, float16 type must be aligned with 16byte, + int4 type must be aligned with 64byte; + A shape: M x K + normal layout: (M, K) + [M1K1, M1K2, ..., M1Kk, + M2K1, M2K2, ..., M2Kk, + ... + MmK1, MmK2, ..., MmKk] + for RK3566/3568： + int8: + native layout: (K / 8, M, 8) + [K1M1, K2M1, ..., K8M1, + K9M2, K10M2, ..., K16M2, + ... + K(k-7)Mm, K(k-6)Mm, ..., KkMm] + float16: + native layout: (K / 4, M, 4) + [K1M1, K2M1, ..., K4M1, + K9M2, K10M2, ..., K8M2, + ... + K(k-3)Mm, K(k-2)Mm, ..., KkMm] + for RK3562： + int8: + native layout: (K / 16, M, 16) + [K1M1, K2M1, ..., K16M1, + K17M2, K18M2, ..., K32M2, + ... + K(k-15)Mm, K(k-14)Mm, ..., KkMm] + float16: + native layout: (K / 8, M, 8) + [K1M1, K2M1, ..., K8M1, + K9M2, K10M2, ..., K16M2, + ... + K(k-7)Mm, K(k-6)Mm, ..., KkMm] + for RK3588/3576： + int4: + native layout: (K / 32, M, 32) + [K1M1, K2M1, ..., K32M1, + K33M2, K10M2, ..., K64M2, + ... + K(k-31)Mm, K(k-30)Mm, ..., KkMm] + int8: + native layout: (K / 16, M, 16) + [K1M1, K2M1, ..., K16M1, + K17M2, K18M2, ..., K32M2, + ... + K(k-15)Mm, K(k-14)Mm, ..., KkMm] + float16: + native layout: (K / 8, M, 8) + [K1M1, K2M1, ..., K8M1, + K9M2, K10M2, ..., K16M2, + ... + K(k-7)Mm, K(k-6)Mm, ..., KkMm] + B shape: K x N + normal layout: (K, N) + [K1N1, K1N2, ..., K1Nn, + K2N1, K2N2, ..., K2Nn, + ... + KkN1, KkN2, ..., KkNn] + for RK3566/3568： + int8: + native layout: (N / 16, K / 32, 16, 32) + [K1N1, K2N1, ..., K32N1, + K1N2, K2N2, ..., K32N2, + ... + K1N16, K2N16, ..., K32N16, + K33N1, K34N1, ..., K64N1, + K33N2, K34N2, ..., K64N2, + ... + K(k-31)N16, K(k-30)N16, ..., KkN16, + K1N17, K2N17, ..., K32N17, + K1N18, K2N18, ..., K32N18, + ... + K(k-31)Nn, K(k-30)Nn, ..., KkNn] + float16: + native layout: (N / 8, K / 16, 8, 16) + [K1N1, K2N1, ..., K16N1, + K1N2, K2N2, ..., K16N2, + ... + K1N8, K2N8, ..., K16N8, + K17N1, K18N1, ..., K32N1, + K17N2, K18N2, ..., K32N2, + ... + K(k-15)N8, K(k-30)N8, ..., KkN8, + K1N9, K2N9, ..., K16N9, + K1N10, K2N10, ..., K16N10, + ... + K(k-15)Nn, K(k-14)Nn, ..., KkNn] + for RK3562： + int8: + native layout: (N / 16, K / 32, 16, 32) + [K1N1, K2N1, ..., K32N1, + K1N2, K2N2, ..., K32N2, + ... + K1N16, K2N16, ..., K32N16, + K33N1, K34N1, ..., K64N1, + K33N2, K34N2, ..., K64N2, + ... + K(k-31)N16, K(k-30)N16, ..., KkN16, + K1N17, K2N17, ..., K32N17, + K1N18, K2N18, ..., K32N18, + ... + K(k-31)Nn, K(k-30)Nn, ..., KkNn] + float16: + native layout: (N / 8, K / 32, 8, 32) + [K1N1, K2N1, ..., K32N1, + K1N2, K2N2, ..., K32N2, + ... + K1N8, K2N8, ..., K32N8, + K33N1, K34N1, ..., K64N1, + K33N2, K34N2, ..., K64N2, + ... + K(k-31)N8, K(k-30)N8, ..., KkN8, + K1N9, K2N9, ..., K16N9, + K1N10, K2N10, ..., K16N10, + ... + K(k-31)Nn, K(k-30)Nn, ..., KkNn] + for RK3588： + when K > 8192, the B data will be split into T segments. + int T = std::ceil(K / 8192); + For example: normal layout -> native layout + K = 20488, N = 4096, T = 3, the data will be split into 3 segments. + subN = rknn_matmul_io_attr.B.dims[2]; + subK = rknn_matmul_io_attr.B.dims[3]; + (8196, 4096) (4096 / subN, 8196 / subK, subN, subK) + (K, N) = (20488, 4096) -> (8196, 4096) -> (4096 / subN, 8196 / subK, subN, subK) + normal layout (4096, 4096) (4096 / subN, 4096 / subK, subN, subK) + T normal layout T native layout + It is recommended to use the rknn_B_normal_layout_to_native_layout interface for direct data conversion. + for RK3576： + when K > 4096, the B data will be split into T segments. + int T = std::ceil(K / 4096); + For example: normal layout -> native layout + K = 10240, N = 2048, T = 3, the data will be split into 3 segments. + subN = rknn_matmul_io_attr.B.dims[2]; + subK = rknn_matmul_io_attr.B.dims[3]; + (4096, 2048) (2048 / subN, 4096 / subK, subN, subK) + (K, N) = (10240, 2048) -> (4096, 2048) -> (2048 / subN, 4096 / subK, subN, subK) + normal layout (2048, 2048) (2048 / subN, 2048 / subK, subN, subK) + T normal layout T native layout + It is recommended to use the rknn_B_normal_layout_to_native_layout interface for direct data conversion. + for RK3588/3576： + int4: + native layout: (N / 64, K / 32, 64, 32) + [K1N1, K2N1, ..., K32N1, + K1N2, K2N2, ..., K32N2, + ... + K1N64, K2N64, ..., K32N64, + K33N1, K34N1, ..., K64N1, + K33N2, K34N2, ..., K64N2, + ... + K(k-31)N64, K(k-30)N64, ..., KkN64, + K1N65, K2N65, ..., K32N65, + K1N66, K2N66, ..., K32N66, + ... + K(k-31)Nn, K(k-30)Nn, ..., KkNn] + int8: + native layout: (N / 32, K / 32, 32, 32) + [K1N1, K2N1, ..., K32N1, + K1N2, K2N2, ..., K32N2, + ... + K1N32, K2N32, ..., K32N32, + K33N1, K34N1, ..., K64N1, + K33N2, K34N2, ..., K64N2, + ... + K(k-31)N32, K(k-30)N32, ..., KkN32, + K1N33, K2N33, ..., K32N33, + K1N34, K2N34, ..., K32N34, + ... + K(k-31)Nn, K(k-30)Nn, ..., KkNn] + float16: + native layout: (N / 16, K / 32, 16, 32) + [K1N1, K2N1, ..., K32N1, + K1N2, K2N2, ..., K32N2, + ... + K1N16, K2N16, ..., K32N16, + K33N1, K34N1, ..., K64N1, + K33N2, K34N2, ..., K64N2, + ... + K(k-31)N16, K(k-30)N16, ..., KkN16, + K1N17, K2N17, ..., K32N17, + K1N18, K2N18, ..., K32N18, + ... + K(k-31)Nn, K(k-30)Nn, ..., KkNn] + C shape: M x N + normal layout: (M, N) + [M1N1, M1N2, ..., M1Nn, + M2N1, M2N2, ..., M2Nn, + ... + MmN1, MmN2, ..., MmNn] + native layout: (N / 4, M, 4) + [N1M1, N2M1, ..., N4M1, + N5M2, N6M2, ..., N8M2, + ... + N(n-3)Mm, N(n-2)Mm, ..., NnMm] + for RK3588： + int4: + native layout: (N / 8, M, 8) + [N1M1, N2M1, ..., N8M1, + N9M2, N10M2, ..., N16M2, + ... + N(n-7)Mm, N(n-6)Mm, ..., NnMm] + */ +int rknn_matmul_set_io_mem(rknn_matmul_ctx ctx, rknn_tensor_mem* mem, rknn_matmul_tensor_attr* attr); + +/* rknn_matmul_set_core_mask + + set rknn core mask.(only support RK3588 in current) + + RKNN_NPU_CORE_AUTO: auto mode, default value + RKNN_NPU_CORE_0: core 0 mode + RKNN_NPU_CORE_1: core 1 mode + RKNN_NPU_CORE_2: core 2 mode + RKNN_NPU_CORE_0_1: combine core 0/1 mode + RKNN_NPU_CORE_0_1_2: combine core 0/1/2 mode + + input: + rknn_matmul_ctx context the handle of context. + rknn_core_mask core_mask the core mask. + return: + int error code. +*/ +int rknn_matmul_set_core_mask(rknn_matmul_ctx context, rknn_core_mask core_mask); + +/* rknn_matmul_set_quant_params + + set quant params.(only support matmul type RKNN_INT8_MM_INT8_TO_INT8, RKNN_INT8_MM_INT8_TO_INT32) + + input: + rknn_matmul_ctx context the handle of context. + rknn_quant_params params quant params. + return: + int error code. +*/ +int rknn_matmul_set_quant_params(rknn_matmul_ctx context, rknn_quant_params* params); + +/* rknn_matmul_get_quant_params + + get per channel quant params.(only support matmul type RKNN_INT8_MM_INT8_TO_INT32) + + input: + rknn_matmul_ctx context the handle of context. + rknn_quant_params params quant params. + float scale get scale for user. + return: + int error code. +*/ +int rknn_matmul_get_quant_params(rknn_matmul_ctx ctx, rknn_quant_params* params, float* scale); + +/* rknn_matmul_set_dynamic_shape + + set the matmul input/output shape. matmul will run under current input shape after rknn_matmul_set_dynamic_shape, + only support M dynamicly now. + + input: + rknn_matmul_ctx ctx the handle of context. + rknn_matmul_shape* shape the M,K,N shape of matmul currently + return: + int error code. +*/ +int rknn_matmul_set_dynamic_shape(rknn_matmul_ctx ctx, rknn_matmul_shape* shape); + +/* rknn_matmul_run + + run the matmul in blocking mode + + params: + rknn_matmul_ctx ctx the handle of context. + return: + int error code. + */ +int rknn_matmul_run(rknn_matmul_ctx ctx); + +/* rknn_matmul_destroy + + destroy the matmul context + + params: + rknn_matmul_ctx ctx the handle of context. + return: + int error code. + */ +int rknn_matmul_destroy(rknn_matmul_ctx ctx); + +/* rknn_B_normal_layout_to_native_layout + + change the B normal layout buffer to native layout buffer + + params: + void* B_input B normal layout buffer. + void* B_output B native layout buffer. + int K K + int N N + rknn_matmul_info info matmul info + return: + int error code. + */ +int rknn_B_normal_layout_to_native_layout(void* B_input, void* B_output, int K, int N, rknn_matmul_info* info); + +#ifdef __cplusplus +} // extern "C" +#endif + +#endif // _RKNN_MATMUL_API_H \ No newline at end of file diff --git a/rknpu2/runtime/Android/rknn_server/arm/rknn_server b/rknpu2/runtime/Android/rknn_server/arm/rknn_server new file mode 100755 index 0000000000000000000000000000000000000000..89faf328fd587dd5e5d18f97c6623415759f91d5 Binary files /dev/null and b/rknpu2/runtime/Android/rknn_server/arm/rknn_server differ diff --git a/rknpu2/runtime/Android/rknn_server/arm64/rknn_server b/rknpu2/runtime/Android/rknn_server/arm64/rknn_server new file mode 100755 index 0000000000000000000000000000000000000000..f9007c987f84a4c5a43f87de76ca99beaa1d4ad6 Binary files /dev/null and b/rknpu2/runtime/Android/rknn_server/arm64/rknn_server differ diff --git a/rknpu2/runtime/Linux/librknn_api/aarch64/librknnrt.so b/rknpu2/runtime/Linux/librknn_api/aarch64/librknnrt.so new file mode 100644 index 0000000000000000000000000000000000000000..1a8352368843803f211c33b0a4760baf8db59368 --- /dev/null +++ b/rknpu2/runtime/Linux/librknn_api/aarch64/librknnrt.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0f1c5db6c649c8705759308aa56ecffc3295c7eb465f9ec9170882f44004aa8d +size 7155496 diff --git a/rknpu2/runtime/Linux/librknn_api/armhf-uclibc/librknnmrt.a b/rknpu2/runtime/Linux/librknn_api/armhf-uclibc/librknnmrt.a new file mode 100644 index 0000000000000000000000000000000000000000..023a05d63a3a3b83880e3aaddf2da60cc644b52e --- /dev/null +++ b/rknpu2/runtime/Linux/librknn_api/armhf-uclibc/librknnmrt.a @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:23195d744c3311c24afab92f7f317b862ddc9fe4e78b8981673e49590f0db9b7 +size 294382 diff --git a/rknpu2/runtime/Linux/librknn_api/armhf-uclibc/librknnmrt.so b/rknpu2/runtime/Linux/librknn_api/armhf-uclibc/librknnmrt.so new file mode 100755 index 0000000000000000000000000000000000000000..03e57fe02429f2134672dbe51e4fde219d9cdccc --- /dev/null +++ b/rknpu2/runtime/Linux/librknn_api/armhf-uclibc/librknnmrt.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b5e5211f6442dc159c246b96306c135786c72c7ce8b5dea8ac792830a4d69657 +size 206892 diff --git a/rknpu2/runtime/Linux/librknn_api/armhf/librknnrt.so b/rknpu2/runtime/Linux/librknn_api/armhf/librknnrt.so new file mode 100644 index 0000000000000000000000000000000000000000..ccb63785c63933cb956efcb72b83cd97f95a86f5 --- /dev/null +++ b/rknpu2/runtime/Linux/librknn_api/armhf/librknnrt.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:23f6691a195e2c559ab35b597b5d9cbcee7d6106625884a2fdc53254b78dd0df +size 4514080 diff --git a/rknpu2/runtime/Linux/librknn_api/include/rknn_api.h b/rknpu2/runtime/Linux/librknn_api/include/rknn_api.h new file mode 100755 index 0000000000000000000000000000000000000000..2e5e9f2e5c18f28217229d26e12f8cb1f8b5fb9f --- /dev/null +++ b/rknpu2/runtime/Linux/librknn_api/include/rknn_api.h @@ -0,0 +1,801 @@ +/**************************************************************************** +* +* Copyright (c) 2017 - 2022 by Rockchip Corp. All rights reserved. +* +* The material in this file is confidential and contains trade secrets +* of Rockchip Corporation. This is proprietary information owned by +* Rockchip Corporation. No part of this work may be disclosed, +* reproduced, copied, transmitted, or used in any way for any purpose, +* without the express written permission of Rockchip Corporation. +* +*****************************************************************************/ + + +#ifndef _RKNN_API_H +#define _RKNN_API_H + +#ifdef __cplusplus +extern "C" { +#endif + +#include + +/* + Definition of extended flag for rknn_init. +*/ +/* set high priority context. */ +#define RKNN_FLAG_PRIOR_HIGH 0x00000000 + +/* set medium priority context */ +#define RKNN_FLAG_PRIOR_MEDIUM 0x00000001 + +/* set low priority context. */ +#define RKNN_FLAG_PRIOR_LOW 0x00000002 + +/* asynchronous mode. + when enable, rknn_outputs_get will not block for too long because it directly retrieves the result of + the previous frame which can increase the frame rate on single-threaded mode, but at the cost of + rknn_outputs_get not retrieves the result of the current frame. + in multi-threaded mode you do not need to turn this mode on. */ +#define RKNN_FLAG_ASYNC_MASK 0x00000004 + +/* collect performance mode. + when enable, you can get detailed performance reports via rknn_query(ctx, RKNN_QUERY_PERF_DETAIL, ...), + but it will reduce the frame rate. */ +#define RKNN_FLAG_COLLECT_PERF_MASK 0x00000008 + +/* allocate all memory in outside, includes weight/internal/inputs/outputs */ +#define RKNN_FLAG_MEM_ALLOC_OUTSIDE 0x00000010 + +/* weight sharing with the same network structure */ +#define RKNN_FLAG_SHARE_WEIGHT_MEM 0x00000020 + +/* send fence fd from outside */ +#define RKNN_FLAG_FENCE_IN_OUTSIDE 0x00000040 + +/* get fence fd from inside */ +#define RKNN_FLAG_FENCE_OUT_OUTSIDE 0x00000080 + +/* dummy init flag: could only get total_weight_size and total_internal_size by rknn_query*/ +#define RKNN_FLAG_COLLECT_MODEL_INFO_ONLY 0x00000100 + +/* allocate internal memory in outside */ +#define RKNN_FLAG_INTERNAL_ALLOC_OUTSIDE 0x00000200 + +/* set GPU as the preferred execution backend When the operator is not supported by the NPU */ +#define RKNN_FLAG_EXECUTE_FALLBACK_PRIOR_DEVICE_GPU 0x00000400 + +/* enable allocate sram type buffers */ +#define RKNN_FLAG_ENABLE_SRAM 0x00000800 + +/* sram type buffers are shared among different contexts */ +#define RKNN_FLAG_SHARE_SRAM 0x00001000 + +/* default nice -19, this flag can disable default priority */ +#define RKNN_FLAG_DISABLE_PROC_HIGH_PRIORITY 0x00002000 + +/* don't flush input buffer cache, the user must ensure that the input tensor has flushed the cache before calling rknn_run. +!!! Don't use this flags when you call rknn_inputs_set() to set input data. */ +#define RKNN_FLAG_DISABLE_FLUSH_INPUT_MEM_CACHE 0x00004000 + +/* Don't invalid output buffer cache. + Users cannot directly access output_mem->virt_addr, + which will cause cache consistency problems. + If you want to use output_mem->virt_addr, + you must use rknn_mem_sync (ctx, mem, RKNN_MEMORY_SYNC_FROM_DEVICE) to flush the cache. + This flags is generally used when the output data of the NPU is not accessed by the CPU, + but is accessed by the GPU or RGA to reduce the time required to flush the cache. + !!! Don't use this flags when you call rknn_outputs_get() to get output data.*/ +#define RKNN_FLAG_DISABLE_FLUSH_OUTPUT_MEM_CACHE 0x00008000 + +/* This flag is used when the model data buffer is allocated by NPU, and can be accessed by NPU directly. */ +#define RKNN_FLAG_MODEL_BUFFER_ZERO_COPY 0x00010000 + +/* This flag is a memory allocation flag, which is used in rknn_create_mem2() when no context is available. */ +#define RKNN_MEM_FLAG_ALLOC_NO_CONTEXT 0x00020000 + + +/* + Error code returned by the RKNN API. +*/ +#define RKNN_SUCC 0 /* execute succeed. */ +#define RKNN_ERR_FAIL -1 /* execute failed. */ +#define RKNN_ERR_TIMEOUT -2 /* execute timeout. */ +#define RKNN_ERR_DEVICE_UNAVAILABLE -3 /* device is unavailable. */ +#define RKNN_ERR_MALLOC_FAIL -4 /* memory malloc fail. */ +#define RKNN_ERR_PARAM_INVALID -5 /* parameter is invalid. */ +#define RKNN_ERR_MODEL_INVALID -6 /* model is invalid. */ +#define RKNN_ERR_CTX_INVALID -7 /* context is invalid. */ +#define RKNN_ERR_INPUT_INVALID -8 /* input is invalid. */ +#define RKNN_ERR_OUTPUT_INVALID -9 /* output is invalid. */ +#define RKNN_ERR_DEVICE_UNMATCH -10 /* the device is unmatch, please update rknn sdk + and npu driver/firmware. */ +#define RKNN_ERR_INCOMPATILE_PRE_COMPILE_MODEL -11 /* This RKNN model use pre_compile mode, but not compatible with current driver. */ +#define RKNN_ERR_INCOMPATILE_OPTIMIZATION_LEVEL_VERSION -12 /* This RKNN model set optimization level, but not compatible with current driver. */ +#define RKNN_ERR_TARGET_PLATFORM_UNMATCH -13 /* This RKNN model set target platform, but not compatible with current platform. */ + +/* + Definition for tensor +*/ +#define RKNN_MAX_DIMS 16 /* maximum dimension of tensor. */ +#define RKNN_MAX_NUM_CHANNEL 15 /* maximum channel number of input tensor. */ +#define RKNN_MAX_NAME_LEN 256 /* maximum name lenth of tensor. */ +#define RKNN_MAX_DYNAMIC_SHAPE_NUM 512 /* maximum number of dynamic shape for each input. */ + +#ifdef __arm__ +typedef uint32_t rknn_context; +#else +typedef uint64_t rknn_context; +#endif + + +/* + The query command for rknn_query +*/ +typedef enum _rknn_query_cmd { + RKNN_QUERY_IN_OUT_NUM = 0, /* query the number of input & output tensor. */ + RKNN_QUERY_INPUT_ATTR = 1, /* query the attribute of input tensor. */ + RKNN_QUERY_OUTPUT_ATTR = 2, /* query the attribute of output tensor. */ + RKNN_QUERY_PERF_DETAIL = 3, /* query the detail performance, need set + RKNN_FLAG_COLLECT_PERF_MASK when call rknn_init, + this query needs to be valid after rknn_outputs_get. */ + RKNN_QUERY_PERF_RUN = 4, /* query the time of run, + this query needs to be valid after rknn_outputs_get. */ + RKNN_QUERY_SDK_VERSION = 5, /* query the sdk & driver version */ + + RKNN_QUERY_MEM_SIZE = 6, /* query the weight & internal memory size */ + RKNN_QUERY_CUSTOM_STRING = 7, /* query the custom string */ + + RKNN_QUERY_NATIVE_INPUT_ATTR = 8, /* query the attribute of native input tensor. */ + RKNN_QUERY_NATIVE_OUTPUT_ATTR = 9, /* query the attribute of native output tensor. */ + + RKNN_QUERY_NATIVE_NC1HWC2_INPUT_ATTR = 8, /* query the attribute of native input tensor. */ + RKNN_QUERY_NATIVE_NC1HWC2_OUTPUT_ATTR = 9, /* query the attribute of native output tensor. */ + + RKNN_QUERY_NATIVE_NHWC_INPUT_ATTR = 10, /* query the attribute of native input tensor. */ + RKNN_QUERY_NATIVE_NHWC_OUTPUT_ATTR = 11, /* query the attribute of native output tensor. */ + + RKNN_QUERY_DEVICE_MEM_INFO = 12, /* query the attribute of rknn memory information. */ + + RKNN_QUERY_INPUT_DYNAMIC_RANGE = 13, /* query the dynamic shape range of rknn input tensor. */ + RKNN_QUERY_CURRENT_INPUT_ATTR = 14, /* query the current shape of rknn input tensor, only valid for dynamic rknn model*/ + RKNN_QUERY_CURRENT_OUTPUT_ATTR = 15, /* query the current shape of rknn output tensor, only valid for dynamic rknn model*/ + + RKNN_QUERY_CURRENT_NATIVE_INPUT_ATTR = 16, /* query the current native shape of rknn input tensor, only valid for dynamic rknn model*/ + RKNN_QUERY_CURRENT_NATIVE_OUTPUT_ATTR = 17, /* query the current native shape of rknn output tensor, only valid for dynamic rknn model*/ + + + RKNN_QUERY_CMD_MAX +} rknn_query_cmd; + +/* + the tensor data type. +*/ +typedef enum _rknn_tensor_type { + RKNN_TENSOR_FLOAT32 = 0, /* data type is float32. */ + RKNN_TENSOR_FLOAT16, /* data type is float16. */ + RKNN_TENSOR_INT8, /* data type is int8. */ + RKNN_TENSOR_UINT8, /* data type is uint8. */ + RKNN_TENSOR_INT16, /* data type is int16. */ + RKNN_TENSOR_UINT16, /* data type is uint16. */ + RKNN_TENSOR_INT32, /* data type is int32. */ + RKNN_TENSOR_UINT32, /* data type is uint32. */ + RKNN_TENSOR_INT64, /* data type is int64. */ + RKNN_TENSOR_BOOL, + RKNN_TENSOR_INT4, + + RKNN_TENSOR_TYPE_MAX +} rknn_tensor_type; + +inline static const char* get_type_string(rknn_tensor_type type) +{ + switch(type) { + case RKNN_TENSOR_FLOAT32: return "FP32"; + case RKNN_TENSOR_FLOAT16: return "FP16"; + case RKNN_TENSOR_INT8: return "INT8"; + case RKNN_TENSOR_UINT8: return "UINT8"; + case RKNN_TENSOR_INT16: return "INT16"; + case RKNN_TENSOR_UINT16: return "UINT16"; + case RKNN_TENSOR_INT32: return "INT32"; + case RKNN_TENSOR_UINT32: return "UINT32"; + case RKNN_TENSOR_INT64: return "INT64"; + case RKNN_TENSOR_BOOL: return "BOOL"; + case RKNN_TENSOR_INT4: return "INT4"; + default: return "UNKNOW"; + } +} + +/* + the quantitative type. +*/ +typedef enum _rknn_tensor_qnt_type { + RKNN_TENSOR_QNT_NONE = 0, /* none. */ + RKNN_TENSOR_QNT_DFP, /* dynamic fixed point. */ + RKNN_TENSOR_QNT_AFFINE_ASYMMETRIC, /* asymmetric affine. */ + + RKNN_TENSOR_QNT_MAX +} rknn_tensor_qnt_type; + +inline static const char* get_qnt_type_string(rknn_tensor_qnt_type type) +{ + switch(type) { + case RKNN_TENSOR_QNT_NONE: return "NONE"; + case RKNN_TENSOR_QNT_DFP: return "DFP"; + case RKNN_TENSOR_QNT_AFFINE_ASYMMETRIC: return "AFFINE"; + default: return "UNKNOW"; + } +} + +/* + the tensor data format. +*/ +typedef enum _rknn_tensor_format { + RKNN_TENSOR_NCHW = 0, /* data format is NCHW. */ + RKNN_TENSOR_NHWC, /* data format is NHWC. */ + RKNN_TENSOR_NC1HWC2, /* data format is NC1HWC2. */ + RKNN_TENSOR_UNDEFINED, + + RKNN_TENSOR_FORMAT_MAX +} rknn_tensor_format; + +/* + the mode of running on target NPU core. +*/ +typedef enum _rknn_core_mask { + RKNN_NPU_CORE_AUTO = 0, /* default, run on NPU core randomly. */ + RKNN_NPU_CORE_0 = 1, /* run on NPU core 0. */ + RKNN_NPU_CORE_1 = 2, /* run on NPU core 1. */ + RKNN_NPU_CORE_2 = 4, /* run on NPU core 2. */ + RKNN_NPU_CORE_0_1 = RKNN_NPU_CORE_0 | RKNN_NPU_CORE_1, /* run on NPU core 0 and core 1. */ + RKNN_NPU_CORE_0_1_2 = RKNN_NPU_CORE_0_1 | RKNN_NPU_CORE_2, /* run on NPU core 0 and core 1 and core 2. */ + RKNN_NPU_CORE_ALL = 0xffff, /* auto choice, run on NPU cores depending on platform */ + + RKNN_NPU_CORE_UNDEFINED, +} rknn_core_mask; + +inline static const char* get_format_string(rknn_tensor_format fmt) +{ + switch(fmt) { + case RKNN_TENSOR_NCHW: return "NCHW"; + case RKNN_TENSOR_NHWC: return "NHWC"; + case RKNN_TENSOR_NC1HWC2: return "NC1HWC2"; + case RKNN_TENSOR_UNDEFINED: return "UNDEFINED"; + default: return "UNKNOW"; + } +} + +/* + the information for RKNN_QUERY_IN_OUT_NUM. +*/ +typedef struct _rknn_input_output_num { + uint32_t n_input; /* the number of input. */ + uint32_t n_output; /* the number of output. */ +} rknn_input_output_num; + +/* + the information for RKNN_QUERY_INPUT_ATTR / RKNN_QUERY_OUTPUT_ATTR. +*/ +typedef struct _rknn_tensor_attr { + uint32_t index; /* input parameter, the index of input/output tensor, + need set before call rknn_query. */ + + uint32_t n_dims; /* the number of dimensions. */ + uint32_t dims[RKNN_MAX_DIMS]; /* the dimensions array. */ + char name[RKNN_MAX_NAME_LEN]; /* the name of tensor. */ + + uint32_t n_elems; /* the number of elements. */ + uint32_t size; /* the bytes size of tensor. */ + + rknn_tensor_format fmt; /* the data format of tensor. */ + rknn_tensor_type type; /* the data type of tensor. */ + rknn_tensor_qnt_type qnt_type; /* the quantitative type of tensor. */ + int8_t fl; /* fractional length for RKNN_TENSOR_QNT_DFP. */ + int32_t zp; /* zero point for RKNN_TENSOR_QNT_AFFINE_ASYMMETRIC. */ + float scale; /* scale for RKNN_TENSOR_QNT_AFFINE_ASYMMETRIC. */ + + uint32_t w_stride; /* the stride of tensor along the width dimention of input, + Note: it is read-only, 0 means equal to width. */ + uint32_t size_with_stride; /* the bytes size of tensor with stride. */ + + uint8_t pass_through; /* pass through mode, for rknn_set_io_mem interface. + if TRUE, the buf data is passed directly to the input node of the rknn model + without any conversion. the following variables do not need to be set. + if FALSE, the buf data is converted into an input consistent with the model + according to the following type and fmt. so the following variables + need to be set.*/ + uint32_t h_stride; /* the stride along the height dimention of input, + Note: it is write-only, if it was set to 0, h_stride = height. */ +} rknn_tensor_attr; + +typedef struct _rknn_input_range { + uint32_t index; /* input parameter, the index of input/output tensor, + need set before call rknn_query. */ + uint32_t shape_number; /* the number of shape. */ + rknn_tensor_format fmt; /* the data format of tensor. */ + char name[RKNN_MAX_NAME_LEN]; /* the name of tensor. */ + uint32_t dyn_range[RKNN_MAX_DYNAMIC_SHAPE_NUM][RKNN_MAX_DIMS]; /* the dynamic input dimensions range. */ + uint32_t n_dims; /* the number of dimensions. */ + +} rknn_input_range; + +/* + the information for RKNN_QUERY_PERF_DETAIL. +*/ +typedef struct _rknn_perf_detail { + char* perf_data; /* the string pointer of perf detail. don't need free it by user. */ + uint64_t data_len; /* the string length. */ +} rknn_perf_detail; + +/* + the information for RKNN_QUERY_PERF_RUN. +*/ +typedef struct _rknn_perf_run { + int64_t run_duration; /* real inference time (us) */ +} rknn_perf_run; + +/* + the information for RKNN_QUERY_SDK_VERSION. +*/ +typedef struct _rknn_sdk_version { + char api_version[256]; /* the version of rknn api. */ + char drv_version[256]; /* the version of rknn driver. */ +} rknn_sdk_version; + +/* + the information for RKNN_QUERY_MEM_SIZE. +*/ +typedef struct _rknn_mem_size { + uint32_t total_weight_size; /* the weight memory size */ + uint32_t total_internal_size; /* the internal memory size, exclude inputs/outputs */ + uint64_t total_dma_allocated_size; /* total dma memory allocated size */ + uint32_t total_sram_size; /* total system sram size reserved for rknn */ + uint32_t free_sram_size; /* free system sram size reserved for rknn */ + uint32_t reserved[10]; /* reserved */ +} rknn_mem_size; + +/* + the information for RKNN_QUERY_CUSTOM_STRING. +*/ +typedef struct _rknn_custom_string { + char string[1024]; /* the string of custom, lengths max to 1024 bytes */ +} rknn_custom_string; + +/* + The flags of rknn_tensor_mem. +*/ +typedef enum _rknn_tensor_mem_flags { + RKNN_TENSOR_MEMORY_FLAGS_ALLOC_INSIDE = 1, /*Used to mark in rknn_destroy_mem() whether it is necessary to release the "mem" pointer itself. + If the flag RKNN_TENSOR_MEMORY_FLAGS_ALLOC_INSIDE is set, rknn_destroy_mem() will call free(mem).*/ + RKNN_TENSOR_MEMORY_FLAGS_FROM_FD = 2, /*Used to mark in rknn_create_mem_from_fd() whether it is necessary to release the "mem" pointer itself. + If the flag RKNN_TENSOR_MEMORY_FLAGS_FROM_FD is set, rknn_destroy_mem() will call free(mem).*/ + RKNN_TENSOR_MEMORY_FLAGS_FROM_PHYS = 3, /*Used to mark in rknn_create_mem_from_phys() whether it is necessary to release the "mem" pointer itself. + If the flag RKNN_TENSOR_MEMORY_FLAGS_FROM_PHYS is set, rknn_destroy_mem() will call free(mem).*/ + RKNN_TENSOR_MEMORY_FLAGS_UNKNOWN +} rknn_tensor_mem_flags; + +/* + The mode to sync cacheable rknn memory. +*/ +typedef enum _rknn_mem_alloc_flags { + RKNN_FLAG_MEMORY_FLAGS_DEFAULT = 0 << 0, /* Same with RKNN_FLAG_MEMORY_CACHEABLE */ + RKNN_FLAG_MEMORY_CACHEABLE = 1 << 0, /* Create Cacheable memory. */ + RKNN_FLAG_MEMORY_NON_CACHEABLE = 1 << 1, /* Create NON-Cacheable memory. */ +} rknn_mem_alloc_flags; + +/* + The mode to sync cacheable rknn memory. +*/ +typedef enum _rknn_mem_sync_mode { + RKNN_MEMORY_SYNC_TO_DEVICE = 0x1, /* the mode used for consistency of device access after CPU accesses data. */ + RKNN_MEMORY_SYNC_FROM_DEVICE = 0x2, /* the mode used for consistency of CPU access after device accesses data. */ + RKNN_MEMORY_SYNC_BIDIRECTIONAL = RKNN_MEMORY_SYNC_TO_DEVICE | RKNN_MEMORY_SYNC_FROM_DEVICE, /* the mode used for consistency of data access + between device and CPU in both directions. */ +} rknn_mem_sync_mode; + +/* + the memory information of tensor. +*/ +typedef struct _rknn_tensor_memory { + void* virt_addr; /* the virtual address of tensor buffer. */ + uint64_t phys_addr; /* the physical address of tensor buffer. */ + int32_t fd; /* the fd of tensor buffer. */ + int32_t offset; /* indicates the offset of the memory. */ + uint32_t size; /* the size of tensor buffer. */ + uint32_t flags; /* the flags of tensor buffer, reserved */ + void * priv_data; /* the private data of tensor buffer. */ +} rknn_tensor_mem; + +/* + the input information for rknn_input_set. +*/ +typedef struct _rknn_input { + uint32_t index; /* the input index. */ + void* buf; /* the input buf for index. */ + uint32_t size; /* the size of input buf. */ + uint8_t pass_through; /* pass through mode. + if TRUE, the buf data is passed directly to the input node of the rknn model + without any conversion. the following variables do not need to be set. + if FALSE, the buf data is converted into an input consistent with the model + according to the following type and fmt. so the following variables + need to be set.*/ + rknn_tensor_type type; /* the data type of input buf. */ + rknn_tensor_format fmt; /* the data format of input buf. + currently the internal input format of NPU is NCHW by default. + so entering NCHW data can avoid the format conversion in the driver. */ +} rknn_input; + +/* + the output information for rknn_outputs_get. +*/ +typedef struct _rknn_output { + uint8_t want_float; /* want transfer output data to float */ + uint8_t is_prealloc; /* whether buf is pre-allocated. + if TRUE, the following variables need to be set. + if FALSE, the following variables do not need to be set. */ + uint32_t index; /* the output index. */ + void* buf; /* the output buf for index. + when is_prealloc = FALSE and rknn_outputs_release called, + this buf pointer will be free and don't use it anymore. */ + uint32_t size; /* the size of output buf. */ +} rknn_output; + +/* + the extend information for rknn_init. +*/ +typedef struct _rknn_init_extend { + rknn_context ctx; /* rknn context */ + int32_t real_model_offset; /* real rknn model file offset, only valid when init context with rknn file path and zero-copy model model */ + uint32_t real_model_size; /* real rknn model file size, only valid when init context with rknn file path and zero-copy model model */ + int32_t model_buffer_fd; /* the fd of model buffer. */ + uint32_t model_buffer_flags; /* the flags of model_buffer */ + uint8_t reserved[112]; /* reserved */ +} rknn_init_extend; + +/* + the extend information for rknn_run. +*/ +typedef struct _rknn_run_extend { + uint64_t frame_id; /* output parameter, indicate current frame id of run. */ + int32_t non_block; /* block flag of run, 0 is block else 1 is non block */ + int32_t timeout_ms; /* timeout for block mode, in milliseconds */ + int32_t fence_fd; /* fence fd from other unit */ +} rknn_run_extend; + +/* + the extend information for rknn_outputs_get. +*/ +typedef struct _rknn_output_extend { + uint64_t frame_id; /* output parameter, indicate the frame id of outputs, corresponds to + struct rknn_run_extend.frame_id.*/ +} rknn_output_extend; + + +/* rknn_init + + initial the context and load the rknn model. + + input: + rknn_context* context the pointer of context handle. + void* model if size > 0, pointer to the rknn model, if size = 0, filepath to the rknn model. + uint32_t size the size of rknn model. + uint32_t flag extend flag, see the define of RKNN_FLAG_XXX_XXX. + rknn_init_extend* extend the extend information of init. + return: + int error code. +*/ +int rknn_init(rknn_context* context, void* model, uint32_t size, uint32_t flag, rknn_init_extend* extend); + +/* rknn_dup_context + + initial the context and load the rknn model. + + input: + rknn_context* context_in the pointer of context in handle. + rknn_context* context_out the pointer of context out handle. + return: + int error code. +*/ +int rknn_dup_context(rknn_context* context_in, rknn_context* context_out); + +/* rknn_destroy + + unload the rknn model and destroy the context. + + input: + rknn_context context the handle of context. + return: + int error code. +*/ +int rknn_destroy(rknn_context context); + + +/* rknn_query + + query the information about model or others. see rknn_query_cmd. + + input: + rknn_context context the handle of context. + rknn_query_cmd cmd the command of query. + void* info the buffer point of information. + uint32_t size the size of information. + return: + int error code. +*/ +int rknn_query(rknn_context context, rknn_query_cmd cmd, void* info, uint32_t size); + + +/* rknn_inputs_set + + set inputs information by input index of rknn model. + inputs information see rknn_input. + + input: + rknn_context context the handle of context. + uint32_t n_inputs the number of inputs. + rknn_input inputs[] the arrays of inputs information, see rknn_input. + return: + int error code +*/ +int rknn_inputs_set(rknn_context context, uint32_t n_inputs, rknn_input inputs[]); + +/* + rknn_set_batch_core_num + + set rknn batch core_num. + + input: + rknn_context context the handle of context. + int core_num the core number. + return: + int error code. + +*/ +int rknn_set_batch_core_num(rknn_context context, int core_num); + +/* rknn_set_core_mask + + set rknn core mask.(only supported on RK3588 now) + + RKNN_NPU_CORE_AUTO: auto mode, default value + RKNN_NPU_CORE_0: core 0 mode + RKNN_NPU_CORE_1: core 1 mode + RKNN_NPU_CORE_2: core 2 mode + RKNN_NPU_CORE_0_1: combine core 0/1 mode + RKNN_NPU_CORE_0_1_2: combine core 0/1/2 mode + RKNN_NPU_CORE_ALL: auto mode, select multiple npu cores to run depending on platform + + + input: + rknn_context context the handle of context. + rknn_core_mask core_mask the core mask. + return: + int error code. +*/ +int rknn_set_core_mask(rknn_context context, rknn_core_mask core_mask); + +/* rknn_run + + run the model to execute inference. + + input: + rknn_context context the handle of context. + rknn_run_extend* extend the extend information of run. + return: + int error code. +*/ +int rknn_run(rknn_context context, rknn_run_extend* extend); + + +/* rknn_wait + + wait the model after execute inference. + + input: + rknn_context context the handle of context. + rknn_run_extend* extend the extend information of run. + return: + int error code. +*/ +int rknn_wait(rknn_context context, rknn_run_extend* extend); + + +/* rknn_outputs_get + + wait the inference to finish and get the outputs. + this function will block until inference finish. + the results will set to outputs[]. + + input: + rknn_context context the handle of context. + uint32_t n_outputs the number of outputs. + rknn_output outputs[] the arrays of output, see rknn_output. + rknn_output_extend* the extend information of output. + return: + int error code. +*/ +int rknn_outputs_get(rknn_context context, uint32_t n_outputs, rknn_output outputs[], rknn_output_extend* extend); + + +/* rknn_outputs_release + + release the outputs that get by rknn_outputs_get. + after called, the rknn_output[x].buf get from rknn_outputs_get will + also be free when rknn_output[x].is_prealloc = FALSE. + + input: + rknn_context context the handle of context. + uint32_t n_ouputs the number of outputs. + rknn_output outputs[] the arrays of output. + return: + int error code +*/ +int rknn_outputs_release(rknn_context context, uint32_t n_ouputs, rknn_output outputs[]); + + +/* new api for zero copy */ + +/* rknn_create_mem_from_phys (memory allocated outside) + + initialize tensor memory from physical address. + + input: + rknn_context ctx the handle of context. + uint64_t phys_addr physical address. + void *virt_addr virtual address. + uint32_t size the size of tensor buffer. + return: + rknn_tensor_mem the pointer of tensor memory information. +*/ +rknn_tensor_mem* rknn_create_mem_from_phys(rknn_context ctx, uint64_t phys_addr, void *virt_addr, uint32_t size); + + +/* rknn_create_mem_from_fd (memory allocated outside) + + initialize tensor memory from file description. + + input: + rknn_context ctx the handle of context. + int32_t fd file description. + void *virt_addr virtual address. + uint32_t size the size of tensor buffer. + int32_t offset indicates the offset of the memory (virt_addr without offset). + return: + rknn_tensor_mem the pointer of tensor memory information. +*/ +rknn_tensor_mem* rknn_create_mem_from_fd(rknn_context ctx, int32_t fd, void *virt_addr, uint32_t size, int32_t offset); + + +/* rknn_create_mem_from_mb_blk (memory allocated outside) + + create tensor memory from mb_blk. + + input: + rknn_context ctx the handle of context. + void *mb_blk mb_blk allocate from system api. + int32_t offset indicates the offset of the memory. + return: + rknn_tensor_mem the pointer of tensor memory information. +*/ +rknn_tensor_mem* rknn_create_mem_from_mb_blk(rknn_context ctx, void *mb_blk, int32_t offset); + + +/* rknn_create_mem (memory allocated inside) + + create tensor memory. + + input: + rknn_context ctx the handle of context. + uint32_t size the size of tensor buffer. + return: + rknn_tensor_mem the pointer of tensor memory information. +*/ +rknn_tensor_mem* rknn_create_mem(rknn_context ctx, uint32_t size); + +/* rknn_create_mem2 (memory allocated inside) + + create tensor memory. + + input: + rknn_context ctx the handle of context. + uint64_t size the size of tensor buffer. + uint64_t alloc_flags control the memory is cacheable + return: + rknn_tensor_mem the pointer of tensor memory information. +*/ +rknn_tensor_mem* rknn_create_mem2(rknn_context ctx, uint64_t size, uint64_t alloc_flags); + +/* rknn_destroy_mem (support allocate inside and outside) + + destroy tensor memory. + + input: + rknn_context ctx the handle of context. + rknn_tensor_mem *mem the pointer of tensor memory information. + return: + int error code +*/ +int rknn_destroy_mem(rknn_context ctx, rknn_tensor_mem *mem); + + +/* rknn_set_weight_mem + + set the weight memory. + + input: + rknn_context ctx the handle of context. + rknn_tensor_mem *mem the array of tensor memory information + return: + int error code. +*/ +int rknn_set_weight_mem(rknn_context ctx, rknn_tensor_mem *mem); + + +/* rknn_set_internal_mem + + set the internal memory. + + input: + rknn_context ctx the handle of context. + rknn_tensor_mem *mem the array of tensor memory information + return: + int error code. +*/ +int rknn_set_internal_mem(rknn_context ctx, rknn_tensor_mem *mem); + + +/* rknn_set_io_mem + + set the input and output tensors buffer. + + input: + rknn_context ctx the handle of context. + rknn_tensor_mem *mem the array of tensor memory information. + rknn_tensor_attr *attr the attribute of input or output tensor buffer. + return: + int error code. +*/ +int rknn_set_io_mem(rknn_context ctx, rknn_tensor_mem *mem, rknn_tensor_attr *attr); + +/* rknn_set_input_shape(deprecated) + + set the input tensor shape (only valid for dynamic shape rknn model). + + input: + rknn_context ctx the handle of context. + rknn_tensor_attr *attr the attribute of input or output tensor buffer. + return: + int error code. +*/ +int rknn_set_input_shape(rknn_context ctx, rknn_tensor_attr* attr); + +/* rknn_set_input_shapes + + set all the input tensor shapes. graph will run under current set of input shapes after rknn_set_input_shapes.(only valid for dynamic shape rknn model). + + input: + rknn_context ctx the handle of context. + uint32_t n_inputs the number of inputs. + rknn_tensor_attr attr[] the attribute array of all input tensors. + return: + int error code. +*/ +int rknn_set_input_shapes(rknn_context ctx, uint32_t n_inputs, rknn_tensor_attr attr[]); + +/* rknn_mem_sync + + sync cacheable rknn memory when both cpu and device access data. + + input: + rknn_context context the handle of context. + rknn_tensor_mem *mem the pointer of tensor memory information. + rknn_mem_sync_mode mode the mode of sync cache. + return: + int error code. +*/ +int rknn_mem_sync(rknn_context context, rknn_tensor_mem* mem, rknn_mem_sync_mode mode); + +#ifdef __cplusplus +} //extern "C" +#endif + +#endif //_RKNN_API_H diff --git a/rknpu2/runtime/Linux/librknn_api/include/rknn_custom_op.h b/rknpu2/runtime/Linux/librknn_api/include/rknn_custom_op.h new file mode 100755 index 0000000000000000000000000000000000000000..253263f77958ad0f97b10a323e14f032ef51eca9 --- /dev/null +++ b/rknpu2/runtime/Linux/librknn_api/include/rknn_custom_op.h @@ -0,0 +1,144 @@ +/**************************************************************************** + * + * Copyright (c) 2017 - 2023 by Rockchip Corp. All rights reserved. + * + * The material in this file is confidential and contains trade secrets + * of Rockchip Corporation. This is proprietary information owned by + * Rockchip Corporation. No part of this work may be disclosed, + * reproduced, copied, transmitted, or used in any way for any purpose, + * without the express written permission of Rockchip Corporation. + * + *****************************************************************************/ + +#ifndef _RKNN_CUSTOM_OP_H +#define _RKNN_CUSTOM_OP_H + +#ifdef __cplusplus +extern "C" { +#endif + +#include "rknn_api.h" + +#include + +/* + Error code returned by the RKNN Custom Operator API. +*/ +#define RKNN_WARNING_SKIP_CUSTOM_OP_COMPUTE -14 /* if custom op init callback funtion return this code and op type is supported by RKNN, it will use RKNN implementation. */ + +#define RKNN_CUSTOM_OP_MAX_STR_LEN 64 +#define RKNN_CUSTOM_OP_MAX_VALUE_LEN 32 +#define RKNN_CUSTOM_OP_EXPORT __attribute__((visibility("default"))) + +#ifdef __arm__ +typedef uint32_t rknn_custom_op_interal_context; +#else +typedef uint64_t rknn_custom_op_interal_context; +#endif +/* + the backend execution device of custom operator. +*/ +typedef enum _rknn_target_type +{ + RKNN_TARGET_TYPE_CPU = 1, /* backend device is cpu */ + RKNN_TARGET_TYPE_GPU = 2, /* backend device is gpu */ + RKNN_TARGET_TYPE_MAX +} rknn_target_type; + +typedef struct _rknn_gpu_op_context +{ + void* cl_context; + void* cl_command_queue; + void* cl_kernel; + +} rknn_gpu_op_context; + +typedef struct _rknn_custom_op_context +{ + rknn_target_type target; /* custom op backend target */ + rknn_custom_op_interal_context internal_ctx; /* the context of custom op*/ + rknn_gpu_op_context gpu_ctx; /* the gpu context of custom op */ + void* priv_data; /* the private data managed by user */ +} rknn_custom_op_context; + +typedef struct _rknn_custom_op_tensor +{ + rknn_tensor_attr attr; /* the attribute of tensor buffer. */ + rknn_tensor_mem mem; /* the memory information of tensor. */ +} rknn_custom_op_tensor; + +typedef struct _rknn_custom_op_attr +{ + char name[RKNN_MAX_NAME_LEN]; /* the name of operator atrributes. */ + rknn_tensor_type dtype; /* the data type of operator attributes, indicate the 'array' type. */ + uint32_t n_elems; /* the number of 'array'. */ + void* data; /* the array pointer of operator attributes, the data type of each element is determined by type. */ +} rknn_custom_op_attr; + +/* + the information of custom operator to add to the rknn_context. +*/ +typedef struct _rknn_custom_op +{ + uint32_t version; /* custom op version */ + rknn_target_type target; /* custom op backend target */ + char op_type[RKNN_MAX_NAME_LEN]; /* custom op type */ + + char cl_kernel_name[RKNN_MAX_NAME_LEN]; /* the opencl kernel name used by custom op */ + char* cl_kernel_source; /* if cl_source_size > 0, pointer to the cl kernel source string, if cl_source_size = 0, + filepath to the cl kernel file. */ + uint64_t cl_source_size; /* the size of cl_kernel_source */ + char cl_build_options[RKNN_MAX_NAME_LEN]; /* the options for opencl to build clProgram used by custom op */ + + /** + * The callback function sets that the users need to code + */ + int (*init)(rknn_custom_op_context* op_ctx, rknn_custom_op_tensor* inputs, uint32_t n_inputs, + rknn_custom_op_tensor* outputs, uint32_t n_outputs); /* [optional] custom op kernel init falllback function*/ + int (*prepare)(rknn_custom_op_context* op_ctx, rknn_custom_op_tensor* inputs, uint32_t n_inputs, + rknn_custom_op_tensor* outputs, uint32_t n_outputs); /* [optional] custom op kernel prepare falllback function*/ + int (*compute)(rknn_custom_op_context* op_ctx, rknn_custom_op_tensor* inputs, uint32_t n_inputs, + rknn_custom_op_tensor* outputs, uint32_t n_outputs); /* [required] custom op kernel compute falllback function */ + int (*compute_native)(rknn_custom_op_context* op_ctx, rknn_custom_op_tensor* inputs, uint32_t n_inputs, + rknn_custom_op_tensor* outputs, uint32_t n_outputs); /* [optional] custom op kernel compute with native attribute falllback function */ + int (*destroy)(rknn_custom_op_context* op_ctx); /* [optional] custom op kernel compute falllback function */ + +} rknn_custom_op; + +/** + * dlopen custom op with so required this function + */ +typedef rknn_custom_op* (*get_custom_op_func)(); + +/* rknn_register_custom_ops + + Register custom operators to rknn_context. + Steps to use a custom op: + 1. Create a rknn_custom_op structure array and fill in it. + 2. Setup prepare/compute/compute_native/destroy callback function and add them to the + rknn_custom_op.(compute is required and other function is optional, compute_native is not supported now, set it + to nullptr) + 3. Call rknn_register_custom_ops to register the op type after rknn_init. + input: + rknn_context ctx the handle of context. + rknn_custom_op* op the custom operator array, each of which contains operator information and calllback function. + uint32_t custom_op_num the length of rknn_custom_op array. + return: + int error code. +*/ +int rknn_register_custom_ops(rknn_context ctx, rknn_custom_op* op, uint32_t custom_op_num); + +/* rknn_custom_op_get_op_attr + + input: + rknn_custom_op_context* op_ctx the handle of custom op context. + const char* attr_name the attribute name of operator. + rknn_custom_op_attr* op_attr the data and information of operator attributes. +*/ +void rknn_custom_op_get_op_attr(rknn_custom_op_context* op_ctx, const char* attr_name, rknn_custom_op_attr* op_attr); + +#ifdef __cplusplus +} // extern "C" +#endif + +#endif //_RKNN_CUSTOM_OP_H diff --git a/rknpu2/runtime/Linux/librknn_api/include/rknn_matmul_api.h b/rknpu2/runtime/Linux/librknn_api/include/rknn_matmul_api.h new file mode 100644 index 0000000000000000000000000000000000000000..0ae78798c3945042cc8079c351919166cb05571f --- /dev/null +++ b/rknpu2/runtime/Linux/librknn_api/include/rknn_matmul_api.h @@ -0,0 +1,540 @@ +/**************************************************************************** + * + * Copyright (c) 2017 - 2018 by Rockchip Corp. All rights reserved. + * + * The material in this file is confidential and contains trade secrets + * of Rockchip Corporation. This is proprietary information owned by + * Rockchip Corporation. No part of this work may be disclosed, + * reproduced, copied, transmitted, or used in any way for any purpose, + * without the express written permission of Rockchip Corporation. + * + *****************************************************************************/ + +#ifndef _RKNN_MATMUL_API_H +#define _RKNN_MATMUL_API_H + +#ifdef __cplusplus +extern "C" { +#endif + +#include "rknn_api.h" + +typedef rknn_context rknn_matmul_ctx; + +typedef enum _rknn_matmul_quant_type +{ + RKNN_QUANT_TYPE_PER_LAYER_SYM = 0, + RKNN_QUANT_TYPE_PER_LAYER_ASYM = 1, + RKNN_QUANT_TYPE_PER_CHANNEL_SYM = 2, + RKNN_QUANT_TYPE_PER_CHANNEL_ASYM = 3, + RKNN_QUANT_TYPE_PER_GROUP_SYM = 4, + RKNN_QUANT_TYPE_PER_GROUP_ASYM = 5, +} rknn_matmul_quant_type; + +typedef struct _rknn_quant_params +{ + char name[RKNN_MAX_NAME_LEN]; + + // matmul tensor scale + float* scale; + int32_t scale_len; + + // matmul tensor zero point + int32_t* zp; + int32_t zp_len; + +} rknn_quant_params; + +typedef enum _rknn_matmul_type +{ + RKNN_FLOAT16_MM_FLOAT16_TO_FLOAT32 = 1, + RKNN_INT8_MM_INT8_TO_INT32 = 2, + RKNN_INT8_MM_INT8_TO_INT8 = 3, + RKNN_FLOAT16_MM_FLOAT16_TO_FLOAT16 = 4, + RKNN_FLOAT16_MM_INT8_TO_FLOAT32 = 5, + RKNN_FLOAT16_MM_INT8_TO_FLOAT16 = 6, + RKNN_FLOAT16_MM_INT4_TO_FLOAT32 = 7, + RKNN_FLOAT16_MM_INT4_TO_FLOAT16 = 8, + RKNN_INT8_MM_INT8_TO_FLOAT32 = 9, + RKNN_INT4_MM_INT4_TO_INT16 = 10, + RKNN_INT8_MM_INT4_TO_INT32 = 11, +} rknn_matmul_type; + +inline static const char* get_matmul_type_string(rknn_matmul_type type) +{ + switch (type) { + case RKNN_FLOAT16_MM_FLOAT16_TO_FLOAT32: + return "RKNN_FLOAT16_MM_FLOAT16_TO_FLOAT32"; + case RKNN_INT8_MM_INT8_TO_INT32: + return "RKNN_INT8_MM_INT8_TO_INT32"; + case RKNN_INT8_MM_INT8_TO_INT8: + return "RKNN_INT8_MM_INT8_TO_INT8"; + case RKNN_FLOAT16_MM_FLOAT16_TO_FLOAT16: + return "RKNN_FLOAT16_MM_FLOAT16_TO_FLOAT16"; + case RKNN_FLOAT16_MM_INT8_TO_FLOAT32: + return "RKNN_FLOAT16_MM_INT8_TO_FLOAT32"; + case RKNN_FLOAT16_MM_INT8_TO_FLOAT16: + return "RKNN_FLOAT16_MM_INT8_TO_FLOAT16"; + case RKNN_INT4_MM_INT4_TO_INT16: + return "RKNN_INT4_MM_INT4_TO_INT16"; + case RKNN_FLOAT16_MM_INT4_TO_FLOAT32: + return "RKNN_FLOAT16_MM_INT4_TO_FLOAT32"; + case RKNN_FLOAT16_MM_INT4_TO_FLOAT16: + return "RKNN_FLOAT16_MM_INT4_TO_FLOAT16"; + case RKNN_INT8_MM_INT4_TO_INT32: + return "RKNN_INT8_MM_INT4_TO_INT32"; + case RKNN_INT8_MM_INT8_TO_FLOAT32: + return "RKNN_INT8_MM_INT8_TO_FLOAT32"; + default: + return "UNKNOW"; + } +} + +typedef struct _rknn_matmul_tensor_attr +{ + char name[RKNN_MAX_NAME_LEN]; + + // indicate A(M, K) or B(K, N) or C(M, N) + uint32_t n_dims; + uint32_t dims[RKNN_MAX_DIMS]; + + // matmul tensor size + uint32_t size; + + // matmul tensor data type + // int8 : A, B + // int32: C + rknn_tensor_type type; + +} rknn_matmul_tensor_attr; + +typedef struct _rknn_matmul_io_attr +{ + // indicate A(M, K) or B(K, N) or C(M, N) + rknn_matmul_tensor_attr A; + rknn_matmul_tensor_attr B; + rknn_matmul_tensor_attr C; +} rknn_matmul_io_attr; + +/* + matmul dynamic shape struct +*/ +typedef struct _rknn_matmul_shape +{ + int32_t M; + int32_t K; + int32_t N; +} rknn_matmul_shape; + +/* + the layout of matmul input/output tensor. +*/ +typedef enum +{ + RKNN_MM_LAYOUT_NORM = 0, + RKNN_MM_LAYOUT_NATIVE = 1, + RKNN_MM_LAYOUT_TP_NORM = 2, +} rknn_matmul_layout; + +/* + matmul information struct + */ +typedef struct rknn_matmul_info_t +{ + int32_t M; + int32_t K; // limit: RK3566/3568: int8 type must be aligned with 32byte, float16 type must be aligned with 16byte; + // RK3562: int8 type must be aligned with 32byte, float16 type must be aligned with 32byte; + // RK3588/3576: int8 type must be aligned with 32byte, float16 type must be aligned with 32byte, + // int4 type must be aligned with 32byte; + int32_t N; // limit: RK3566/3568: int8 type must be aligned with 16byte, float16 type must be aligned with 8byte; + // RK3562: int8 type must be aligned with 16byte, float16 type must be aligned with 8byte; + // RK3588/3576: int8 type must be aligned with 32byte, float16 type must be aligned with 16byte, + // int4 type must be aligned with 64byte; + // matmul data type + // int4: int4(A) x int4(B) -> int16(C) + // int8: int8(A) x int8(B) -> int32(C) + // float16: float16(A) x float16(B) -> float32(C) + rknn_matmul_type type; + + // matmul native layout for B + // 0: normal layout + // 1: native layout + int16_t B_layout; + + // matmul quant type for B + // A and C only support per layer + // 0: per layer + // 1: per channel + // 2: per group + int16_t B_quant_type; + + // matmul native layout for A and C + // 0: normal layout + // 1: native layout + int16_t AC_layout; + + // matmul quant type for A and C, only support 0 + int16_t AC_quant_type; + + // iommu domain id, each domain has 4GB of space + int32_t iommu_domain_id; + + // B_quant_type set 2, group size is enable + int16_t group_size; + + // reserved field + int8_t reserved[34]; +} rknn_matmul_info; + +/* rknn_matmul_create + + params: + rknn_matmul_ctx *ctx the handle of context. + rknn_matmul_info *info the matmal information. + rknn_matmul_io_attr *io_attr inputs/output attribute + return: + int error code +*/ +int rknn_matmul_create(rknn_matmul_ctx* ctx, rknn_matmul_info* info, rknn_matmul_io_attr* io_attr); + +/* rknn_matmul_create_dynamic_shape + + params: + rknn_matmul_ctx *ctx the handle of context. + rknn_matmul_info *info the matmal information. + int shape_num the supported shape number of matmul. + rknn_matmul_shape dynamic_shapes[] the supported M,K,N shape struct array. + rknn_matmul_io_attr *io_attr the array of inputs and output attribute + return: + int error code +*/ +/* + 原来的info.M, K, N无效 +*/ +int rknn_matmul_create_dynamic_shape(rknn_matmul_ctx* ctx, rknn_matmul_info* info, int shape_num, + rknn_matmul_shape dynamic_shapes[], rknn_matmul_io_attr io_attrs[]); + +/* rknn_matmul_set_io_mem + + params: + rknn_matmul_ctx ctx the handle of context. + rknn_tensor_mem *mem the pointer of tensor memory information. + rknn_matmul_tensor_attr *attr the attribute of input or output tensor buffer. + return: + int error code. + + formula: + C = A * B, + + limit: + K max: k <= 10240 + K limit: RK3566/3568: int8 type must be aligned with 32byte, float16 type must be aligned with 16byte; + RK3562: int8 type must be aligned with 32byte, float16 type must be aligned with 32byte; + RK3588/3576: int8 type must be aligned with 32byte, float16 type must be aligned with 32byte, + int4 type must be aligned with 32byte; + N limit: RK3566/3568: int8 type must be aligned with 16byte, float16 type must be aligned with 8byte; + RK3562: int8 type must be aligned with 16byte, float16 type must be aligned with 8byte; + RK3588/3576: int8 type must be aligned with 32byte, float16 type must be aligned with 16byte, + int4 type must be aligned with 64byte; + A shape: M x K + normal layout: (M, K) + [M1K1, M1K2, ..., M1Kk, + M2K1, M2K2, ..., M2Kk, + ... + MmK1, MmK2, ..., MmKk] + for RK3566/3568： + int8: + native layout: (K / 8, M, 8) + [K1M1, K2M1, ..., K8M1, + K9M2, K10M2, ..., K16M2, + ... + K(k-7)Mm, K(k-6)Mm, ..., KkMm] + float16: + native layout: (K / 4, M, 4) + [K1M1, K2M1, ..., K4M1, + K9M2, K10M2, ..., K8M2, + ... + K(k-3)Mm, K(k-2)Mm, ..., KkMm] + for RK3562： + int8: + native layout: (K / 16, M, 16) + [K1M1, K2M1, ..., K16M1, + K17M2, K18M2, ..., K32M2, + ... + K(k-15)Mm, K(k-14)Mm, ..., KkMm] + float16: + native layout: (K / 8, M, 8) + [K1M1, K2M1, ..., K8M1, + K9M2, K10M2, ..., K16M2, + ... + K(k-7)Mm, K(k-6)Mm, ..., KkMm] + for RK3588/3576： + int4: + native layout: (K / 32, M, 32) + [K1M1, K2M1, ..., K32M1, + K33M2, K10M2, ..., K64M2, + ... + K(k-31)Mm, K(k-30)Mm, ..., KkMm] + int8: + native layout: (K / 16, M, 16) + [K1M1, K2M1, ..., K16M1, + K17M2, K18M2, ..., K32M2, + ... + K(k-15)Mm, K(k-14)Mm, ..., KkMm] + float16: + native layout: (K / 8, M, 8) + [K1M1, K2M1, ..., K8M1, + K9M2, K10M2, ..., K16M2, + ... + K(k-7)Mm, K(k-6)Mm, ..., KkMm] + B shape: K x N + normal layout: (K, N) + [K1N1, K1N2, ..., K1Nn, + K2N1, K2N2, ..., K2Nn, + ... + KkN1, KkN2, ..., KkNn] + for RK3566/3568： + int8: + native layout: (N / 16, K / 32, 16, 32) + [K1N1, K2N1, ..., K32N1, + K1N2, K2N2, ..., K32N2, + ... + K1N16, K2N16, ..., K32N16, + K33N1, K34N1, ..., K64N1, + K33N2, K34N2, ..., K64N2, + ... + K(k-31)N16, K(k-30)N16, ..., KkN16, + K1N17, K2N17, ..., K32N17, + K1N18, K2N18, ..., K32N18, + ... + K(k-31)Nn, K(k-30)Nn, ..., KkNn] + float16: + native layout: (N / 8, K / 16, 8, 16) + [K1N1, K2N1, ..., K16N1, + K1N2, K2N2, ..., K16N2, + ... + K1N8, K2N8, ..., K16N8, + K17N1, K18N1, ..., K32N1, + K17N2, K18N2, ..., K32N2, + ... + K(k-15)N8, K(k-30)N8, ..., KkN8, + K1N9, K2N9, ..., K16N9, + K1N10, K2N10, ..., K16N10, + ... + K(k-15)Nn, K(k-14)Nn, ..., KkNn] + for RK3562： + int8: + native layout: (N / 16, K / 32, 16, 32) + [K1N1, K2N1, ..., K32N1, + K1N2, K2N2, ..., K32N2, + ... + K1N16, K2N16, ..., K32N16, + K33N1, K34N1, ..., K64N1, + K33N2, K34N2, ..., K64N2, + ... + K(k-31)N16, K(k-30)N16, ..., KkN16, + K1N17, K2N17, ..., K32N17, + K1N18, K2N18, ..., K32N18, + ... + K(k-31)Nn, K(k-30)Nn, ..., KkNn] + float16: + native layout: (N / 8, K / 32, 8, 32) + [K1N1, K2N1, ..., K32N1, + K1N2, K2N2, ..., K32N2, + ... + K1N8, K2N8, ..., K32N8, + K33N1, K34N1, ..., K64N1, + K33N2, K34N2, ..., K64N2, + ... + K(k-31)N8, K(k-30)N8, ..., KkN8, + K1N9, K2N9, ..., K16N9, + K1N10, K2N10, ..., K16N10, + ... + K(k-31)Nn, K(k-30)Nn, ..., KkNn] + for RK3588： + when K > 8192, the B data will be split into T segments. + int T = std::ceil(K / 8192); + For example: normal layout -> native layout + K = 20488, N = 4096, T = 3, the data will be split into 3 segments. + subN = rknn_matmul_io_attr.B.dims[2]; + subK = rknn_matmul_io_attr.B.dims[3]; + (8196, 4096) (4096 / subN, 8196 / subK, subN, subK) + (K, N) = (20488, 4096) -> (8196, 4096) -> (4096 / subN, 8196 / subK, subN, subK) + normal layout (4096, 4096) (4096 / subN, 4096 / subK, subN, subK) + T normal layout T native layout + It is recommended to use the rknn_B_normal_layout_to_native_layout interface for direct data conversion. + for RK3576： + when K > 4096, the B data will be split into T segments. + int T = std::ceil(K / 4096); + For example: normal layout -> native layout + K = 10240, N = 2048, T = 3, the data will be split into 3 segments. + subN = rknn_matmul_io_attr.B.dims[2]; + subK = rknn_matmul_io_attr.B.dims[3]; + (4096, 2048) (2048 / subN, 4096 / subK, subN, subK) + (K, N) = (10240, 2048) -> (4096, 2048) -> (2048 / subN, 4096 / subK, subN, subK) + normal layout (2048, 2048) (2048 / subN, 2048 / subK, subN, subK) + T normal layout T native layout + It is recommended to use the rknn_B_normal_layout_to_native_layout interface for direct data conversion. + for RK3588/3576： + int4: + native layout: (N / 64, K / 32, 64, 32) + [K1N1, K2N1, ..., K32N1, + K1N2, K2N2, ..., K32N2, + ... + K1N64, K2N64, ..., K32N64, + K33N1, K34N1, ..., K64N1, + K33N2, K34N2, ..., K64N2, + ... + K(k-31)N64, K(k-30)N64, ..., KkN64, + K1N65, K2N65, ..., K32N65, + K1N66, K2N66, ..., K32N66, + ... + K(k-31)Nn, K(k-30)Nn, ..., KkNn] + int8: + native layout: (N / 32, K / 32, 32, 32) + [K1N1, K2N1, ..., K32N1, + K1N2, K2N2, ..., K32N2, + ... + K1N32, K2N32, ..., K32N32, + K33N1, K34N1, ..., K64N1, + K33N2, K34N2, ..., K64N2, + ... + K(k-31)N32, K(k-30)N32, ..., KkN32, + K1N33, K2N33, ..., K32N33, + K1N34, K2N34, ..., K32N34, + ... + K(k-31)Nn, K(k-30)Nn, ..., KkNn] + float16: + native layout: (N / 16, K / 32, 16, 32) + [K1N1, K2N1, ..., K32N1, + K1N2, K2N2, ..., K32N2, + ... + K1N16, K2N16, ..., K32N16, + K33N1, K34N1, ..., K64N1, + K33N2, K34N2, ..., K64N2, + ... + K(k-31)N16, K(k-30)N16, ..., KkN16, + K1N17, K2N17, ..., K32N17, + K1N18, K2N18, ..., K32N18, + ... + K(k-31)Nn, K(k-30)Nn, ..., KkNn] + C shape: M x N + normal layout: (M, N) + [M1N1, M1N2, ..., M1Nn, + M2N1, M2N2, ..., M2Nn, + ... + MmN1, MmN2, ..., MmNn] + native layout: (N / 4, M, 4) + [N1M1, N2M1, ..., N4M1, + N5M2, N6M2, ..., N8M2, + ... + N(n-3)Mm, N(n-2)Mm, ..., NnMm] + for RK3588： + int4: + native layout: (N / 8, M, 8) + [N1M1, N2M1, ..., N8M1, + N9M2, N10M2, ..., N16M2, + ... + N(n-7)Mm, N(n-6)Mm, ..., NnMm] + */ +int rknn_matmul_set_io_mem(rknn_matmul_ctx ctx, rknn_tensor_mem* mem, rknn_matmul_tensor_attr* attr); + +/* rknn_matmul_set_core_mask + + set rknn core mask.(only support RK3588 in current) + + RKNN_NPU_CORE_AUTO: auto mode, default value + RKNN_NPU_CORE_0: core 0 mode + RKNN_NPU_CORE_1: core 1 mode + RKNN_NPU_CORE_2: core 2 mode + RKNN_NPU_CORE_0_1: combine core 0/1 mode + RKNN_NPU_CORE_0_1_2: combine core 0/1/2 mode + + input: + rknn_matmul_ctx context the handle of context. + rknn_core_mask core_mask the core mask. + return: + int error code. +*/ +int rknn_matmul_set_core_mask(rknn_matmul_ctx context, rknn_core_mask core_mask); + +/* rknn_matmul_set_quant_params + + set quant params.(only support matmul type RKNN_INT8_MM_INT8_TO_INT8, RKNN_INT8_MM_INT8_TO_INT32) + + input: + rknn_matmul_ctx context the handle of context. + rknn_quant_params params quant params. + return: + int error code. +*/ +int rknn_matmul_set_quant_params(rknn_matmul_ctx context, rknn_quant_params* params); + +/* rknn_matmul_get_quant_params + + get per channel quant params.(only support matmul type RKNN_INT8_MM_INT8_TO_INT32) + + input: + rknn_matmul_ctx context the handle of context. + rknn_quant_params params quant params. + float scale get scale for user. + return: + int error code. +*/ +int rknn_matmul_get_quant_params(rknn_matmul_ctx ctx, rknn_quant_params* params, float* scale); + +/* rknn_matmul_set_dynamic_shape + + set the matmul input/output shape. matmul will run under current input shape after rknn_matmul_set_dynamic_shape, + only support M dynamicly now. + + input: + rknn_matmul_ctx ctx the handle of context. + rknn_matmul_shape* shape the M,K,N shape of matmul currently + return: + int error code. +*/ +int rknn_matmul_set_dynamic_shape(rknn_matmul_ctx ctx, rknn_matmul_shape* shape); + +/* rknn_matmul_run + + run the matmul in blocking mode + + params: + rknn_matmul_ctx ctx the handle of context. + return: + int error code. + */ +int rknn_matmul_run(rknn_matmul_ctx ctx); + +/* rknn_matmul_destroy + + destroy the matmul context + + params: + rknn_matmul_ctx ctx the handle of context. + return: + int error code. + */ +int rknn_matmul_destroy(rknn_matmul_ctx ctx); + +/* rknn_B_normal_layout_to_native_layout + + change the B normal layout buffer to native layout buffer + + params: + void* B_input B normal layout buffer. + void* B_output B native layout buffer. + int K K + int N N + rknn_matmul_info info matmul info + return: + int error code. + */ +int rknn_B_normal_layout_to_native_layout(void* B_input, void* B_output, int K, int N, rknn_matmul_info* info); + +#ifdef __cplusplus +} // extern "C" +#endif + +#endif // _RKNN_MATMUL_API_H \ No newline at end of file diff --git a/rknpu2/runtime/Linux/rknn_server/aarch64/usr/bin/restart_rknn.sh b/rknpu2/runtime/Linux/rknn_server/aarch64/usr/bin/restart_rknn.sh new file mode 100755 index 0000000000000000000000000000000000000000..6c443a171cdc65d3028624cef3548a0a9e0815dc --- /dev/null +++ b/rknpu2/runtime/Linux/rknn_server/aarch64/usr/bin/restart_rknn.sh @@ -0,0 +1,5 @@ +#!/bin/sh + +killall start_rknn.sh > /dev/null 2>&1 +killall rknn_server > /dev/null 2>&1 +start_rknn.sh & \ No newline at end of file diff --git a/rknpu2/runtime/Linux/rknn_server/aarch64/usr/bin/rknn_server b/rknpu2/runtime/Linux/rknn_server/aarch64/usr/bin/rknn_server new file mode 100755 index 0000000000000000000000000000000000000000..2c85fb22799135d1c70de4d529d938044d484156 Binary files /dev/null and b/rknpu2/runtime/Linux/rknn_server/aarch64/usr/bin/rknn_server differ diff --git a/rknpu2/runtime/Linux/rknn_server/aarch64/usr/bin/start_rknn.sh b/rknpu2/runtime/Linux/rknn_server/aarch64/usr/bin/start_rknn.sh new file mode 100755 index 0000000000000000000000000000000000000000..01843ffb7c42a98d46d1d31fa01ef7c7d2873b00 --- /dev/null +++ b/rknpu2/runtime/Linux/rknn_server/aarch64/usr/bin/start_rknn.sh @@ -0,0 +1,7 @@ +#!/bin/sh + +while true +do + sleep 1 + rknn_server #>/dev/null 2>&1 +done diff --git a/rknpu2/runtime/Linux/rknn_server/armhf-uclibc/usr/bin/restart_rknn.sh b/rknpu2/runtime/Linux/rknn_server/armhf-uclibc/usr/bin/restart_rknn.sh new file mode 100755 index 0000000000000000000000000000000000000000..6c443a171cdc65d3028624cef3548a0a9e0815dc --- /dev/null +++ b/rknpu2/runtime/Linux/rknn_server/armhf-uclibc/usr/bin/restart_rknn.sh @@ -0,0 +1,5 @@ +#!/bin/sh + +killall start_rknn.sh > /dev/null 2>&1 +killall rknn_server > /dev/null 2>&1 +start_rknn.sh & \ No newline at end of file diff --git a/rknpu2/runtime/Linux/rknn_server/armhf-uclibc/usr/bin/rknn_server b/rknpu2/runtime/Linux/rknn_server/armhf-uclibc/usr/bin/rknn_server new file mode 100755 index 0000000000000000000000000000000000000000..0379b27921734126a4594ea0b6563ba322441c40 Binary files /dev/null and b/rknpu2/runtime/Linux/rknn_server/armhf-uclibc/usr/bin/rknn_server differ diff --git a/rknpu2/runtime/Linux/rknn_server/armhf-uclibc/usr/bin/start_rknn.sh b/rknpu2/runtime/Linux/rknn_server/armhf-uclibc/usr/bin/start_rknn.sh new file mode 100755 index 0000000000000000000000000000000000000000..01843ffb7c42a98d46d1d31fa01ef7c7d2873b00 --- /dev/null +++ b/rknpu2/runtime/Linux/rknn_server/armhf-uclibc/usr/bin/start_rknn.sh @@ -0,0 +1,7 @@ +#!/bin/sh + +while true +do + sleep 1 + rknn_server #>/dev/null 2>&1 +done diff --git a/rknpu2/runtime/Linux/rknn_server/armhf/usr/bin/restart_rknn.sh b/rknpu2/runtime/Linux/rknn_server/armhf/usr/bin/restart_rknn.sh new file mode 100755 index 0000000000000000000000000000000000000000..6c443a171cdc65d3028624cef3548a0a9e0815dc --- /dev/null +++ b/rknpu2/runtime/Linux/rknn_server/armhf/usr/bin/restart_rknn.sh @@ -0,0 +1,5 @@ +#!/bin/sh + +killall start_rknn.sh > /dev/null 2>&1 +killall rknn_server > /dev/null 2>&1 +start_rknn.sh & \ No newline at end of file diff --git a/rknpu2/runtime/Linux/rknn_server/armhf/usr/bin/rknn_server b/rknpu2/runtime/Linux/rknn_server/armhf/usr/bin/rknn_server new file mode 100755 index 0000000000000000000000000000000000000000..feae9c41aa6c6d4e5d5e88d9b070a29cea3220cb Binary files /dev/null and b/rknpu2/runtime/Linux/rknn_server/armhf/usr/bin/rknn_server differ diff --git a/rknpu2/runtime/Linux/rknn_server/armhf/usr/bin/start_rknn.sh b/rknpu2/runtime/Linux/rknn_server/armhf/usr/bin/start_rknn.sh new file mode 100755 index 0000000000000000000000000000000000000000..01843ffb7c42a98d46d1d31fa01ef7c7d2873b00 --- /dev/null +++ b/rknpu2/runtime/Linux/rknn_server/armhf/usr/bin/start_rknn.sh @@ -0,0 +1,7 @@ +#!/bin/sh + +while true +do + sleep 1 + rknn_server #>/dev/null 2>&1 +done diff --git a/rknpu2/runtime/init.rknn_server.rc b/rknpu2/runtime/init.rknn_server.rc new file mode 100644 index 0000000000000000000000000000000000000000..731f0bb7e50f0033b74cac2c684c634d14c6f68c --- /dev/null +++ b/rknpu2/runtime/init.rknn_server.rc @@ -0,0 +1,9 @@ +#on early-boot +on init + start rknn_server + + +service rknn_server /vendor/bin/rknn_server + class core + seclabel u:r:rknn_server:s0 + disabled